From fab33881252ca9155bfbe98f97ca0e2ab4c43305 Mon Sep 17 00:00:00 2001 From: Matt Hall <5151457+mattEhall@users.noreply.github.com> Date: Wed, 23 Jul 2025 14:10:40 -0600 Subject: [PATCH 01/15] Squashed commit of the following: commit 0c3ef44e36fecdedcd7392f4f602e5f1661eb4ef Author: Matt Hall <5151457+mattEhall@users.noreply.github.com> Date: Wed Jul 23 11:41:24 2025 -0600 Removing HAMS BEM files and what causes them: - removing examples 03 BEM folder created by RAFT runs. - changing RAFT YAML PotMods from True to False to avoid issue in future. commit 7828a9f2d2b9f4636e621801a82f08c7f3596a27 Author: Moreno Date: Fri Jul 18 09:50:56 2025 -0600 Depreciated function trapz needs to be reused due to uncompatibility with CI tests commit 33edd4bef917996665b4053f212b1dcb31b19838 Author: Moreno Date: Fri Jul 18 09:40:11 2025 -0600 support_soils linked connected commit 09418638bbec7f0d88626e0d5942c0eca8dd382c Author: Moreno Date: Fri Jul 18 09:36:12 2025 -0600 Depreciated function trapz needs to be reused due to uncompatibility with CI tests commit b4dea97c68b6ed0921332f3c64a87b381e45a358 Author: Moreno Date: Thu Jul 17 16:16:45 2025 -0600 Change order of the CI tests: pytest + example_driver.py commit 30b2ca5a54806d026839123176fea9b442b98062 Author: Moreno Date: Thu Jul 17 15:37:25 2025 -0600 Changes on the yaml file to include new soil structure commit 4e0866e5da1ced91c5c1568e4a54dc9bd11d3dc7 Author: Moreno Date: Thu Jul 17 11:42:37 2025 -0600 Removed anchor tension limit exception commit 7de4650acf3acd79c666b5b05cd5a8930ff7592d Author: Moreno Date: Wed Jul 16 11:26:31 2025 -0600 Update FAModel_anchors: new anchor scripts, updated RAFT BEM inputs, suction pile improvements commit 0d7599338472b2021f4b7f647ffea0f458077fac Author: Moreno Date: Tue Jul 8 13:05:36 2025 -0600 Remove outdated 04_Anchors examples after migrating to 05_Anchors commit b8dbd3f10a106b7ee3526198bc4b0986effc7cc3 Merge: 4383c4c 831d651 Author: Moreno Date: Tue Jul 8 13:04:43 2025 -0600 Merge upstream changes; keep local anchor.py, store upstream version as backup commit 4383c4c4526ebca4df14493f882f54be4a51b46d Author: Moreno Date: Tue Jul 8 12:16:53 2025 -0600 WIP: Local development progress before merging upstream FAModel commit 834999b927d71967089cc2eed91bf4ef6e1ad489 Author: Moreno Date: Mon Jun 23 11:15:04 2025 -0600 Merge upstream/main into FAModel_anchors with local enhancements, including update to capacity_suction.py commit f149d9637aa39ae31cce667a358d2023c29f596d Merge: 6030d1c 12ce2a3 Author: Moreno Date: Mon Jun 23 11:09:12 2025 -0600 Merge remote-tracking branch 'upstream/main' into sync/upstream-2025-06-23 commit 6030d1c78771b7e09fe5b7448669a43dac10906f Author: Moreno Date: Mon Jun 23 10:59:03 2025 -0600 WIP: custom changes before upstream sync commit 883b024798e71a493ecc9f8d0da3ca420a229ed5 Author: Moreno Date: Wed Jun 18 16:30:20 2025 -0600 Major refactor of anchor module: removed legacy map files, added new anchor types and updated capacities commit 66e4bc04586e45ef39428a30c0ad6f794352f87a Author: Moreno Date: Fri Jun 13 16:00:51 2025 -0600 feat: update anchor capacity scripts and add suction example notebook --- .github/workflows/CI_FAModel.yml | 14 +- .../05_visual_lease_boundaries.py | 2 +- .../01_Visualization/07_3D-visual_platform.py | 2 +- .../07_3D-visual_platform.yaml | 4 +- .../08_3D-visual_turbine.yaml | 4 +- .../01_platform.yaml | 4 +- .../02_FOWT.yaml | 4 +- examples/05_Anchors/anchor_dandg.py | 65 + examples/05_Anchors/anchor_driven_rock.py | 65 + .../05_Anchors/anchor_driven_soil.py | 37 +- .../05_Anchors/anchor_helical.py | 28 +- .../05_Anchors/anchor_plate.py | 27 +- examples/05_Anchors/anchor_soil.py | 67 + .../05_Anchors/anchor_suction.py | 93 +- .../05_Anchors/anchor_torpedo.py | 14 +- examples/05_Anchors/example_suction.ipynb | 3725 +++++++++++++++++ .../inputs/GulfOfMaine_bathymetry_100x100.txt | 104 + .../GulfOfMaine_soil_layered_100x100.txt | 112 + .../inputs/GulfOfMaine_soil_profiles.yaml | 116 + .../Inputs/GulfOfMaine_bathymetry_100x100.txt | 104 + .../GulfOfMaine_soil_layered_100x100.txt | 112 + .../Inputs/GulfOfMaine_soil_profiles.yaml | 67 + .../GulfOfMaine_soil_uniform_100x100.txt | 112 + examples/Inputs/OntologySample200m.yaml | 64 +- examples/Inputs/OntologySample200m_1turb.yaml | 6 +- .../OntologySample200m_uniformArray.yaml | 4 +- .../Inputs/OntologySample200mbis_1turb.yaml | 1323 ++++++ .../Inputs/OntologySample600m_shared.yaml | 12 +- examples/Inputs/checkyaml.yaml | 1331 ++++++ examples/Inputs/output_MD.dat | 20 +- examples/duplicate_platform.py | 6 + examples/example_anchors.py | 148 - examples/example_driver.py | 32 +- famodel/anchors/README.md | 831 +++- famodel/anchors/README_FMO.md | 267 -- famodel/anchors/anchor.py | 2219 +++++----- famodel/anchors/anchor_capacity.py | 153 - famodel/anchors/anchor_conflict_backup.py | 1153 +++++ famodel/anchors/anchor_map.py | 889 ---- famodel/anchors/anchor_profile.py | 915 ---- famodel/anchors/anchors_famodel/__init__.py | 0 .../anchors/anchors_famodel/capacity_dandg.py | 477 +-- .../capacity_driven.py} | 78 +- .../anchors_famodel/capacity_drivenrock.py | 373 -- .../anchors_famodel/capacity_drivensoil.py | 628 --- .../anchors_famodel/capacity_helical.py | 234 +- .../anchors/anchors_famodel/capacity_load.py | 393 +- .../anchors/anchors_famodel/capacity_plate.py | 227 +- .../anchors_famodel/capacity_suction.py | 740 ++-- .../anchors_famodel/capacity_torpedo.py | 326 +- .../anchors_famodel/installatioin_torque.py | 176 + .../anchors_famodel/installation_buckling.py | 164 + .../anchors_famodel/installation_buckling2.py | 157 + ...{capacity_drag.py => installation_drag.py} | 17 +- .../anchors_famodel/installation_driven.py | 186 + .../anchors_famodel/installation_driven2.py | 167 + .../anchors_famodel/installation_dynamic.py | 164 + .../anchors_famodel/installation_dynamic2.py | 152 + .../anchors_famodel/installation_dynamic3.py | 126 + .../anchors_famodel/installation_suction.py | 215 + .../support_plots.py} | 77 +- .../support_pycurves.py} | 44 +- .../support_soils.py} | 0 .../support_solvers.py} | 9 +- .../anchors_famodel_map/capacity_dandg_map.py | 233 -- .../capacity_helical_map.py | 172 - .../anchors_famodel_map/capacity_load_map.py | 212 - .../anchors_famodel_map/capacity_plate_map.py | 177 - .../capacity_suction_map.py | 401 -- .../capacity_torpedo_map.py | 272 -- .../anchors_famodel_profile/capacity_dandg.py | 272 -- .../capacity_driven.py | 418 -- .../capacity_helical.py | 152 - .../anchors_famodel_profile/capacity_load.py | 189 - .../anchors_famodel_profile/capacity_plate.py | 143 - .../anchors_famodel_profile/capacity_plots.py | 435 -- .../capacity_pycurves.py | 298 -- .../anchors_famodel_profile/capacity_soils.py | 176 - .../capacity_suction.py | 293 -- .../capacity_torpedo.py | 159 - famodel/anchors/getCapacityAnchor_profile.py | 94 - famodel/anchors/getCapacityHelical_map.py | 70 - famodel/anchors/getCapacityHelical_sand.py | 70 - .../images/Drilledandgroutedpiles/Drilled.png | Bin 0 -> 42987 bytes .../Rock - deformed dandg.png | Bin 0 -> 32453 bytes .../Drivenpiles/Clay - deformed pile.png | Bin 0 -> 33204 bytes famodel/anchors/images/Drivenpiles/Driven.png | Bin 0 -> 40353 bytes .../Drivenpiles/Rock - deformed pile.png | Bin 0 -> 37773 bytes .../images/Drivenpiles/Rock - driven pile.png | Bin 0 -> 39337 bytes .../Drivenpiles/Sand - deformed pile.png | Bin 0 -> 34428 bytes .../images/Drivenpiles/Sand - driven pile.png | Bin 0 -> 43778 bytes .../images/Drivenpiles/pycurves - API.png | Bin 0 -> 31690 bytes .../images/Drivenpiles/pycurves - Lovera.png | Bin 0 -> 51770 bytes .../images/Drivenpiles/pycurves - Matlock.png | Bin 0 -> 58159 bytes .../images/Drivenpiles/pycurves - Reese.png | Bin 0 -> 57701 bytes .../Helicalpiles/Clay - deformed pile.png | Bin 0 -> 34846 bytes .../anchors/images/Helicalpiles/Helical.png | Bin 0 -> 30831 bytes .../Helicalpiles/Sand - deformed pile.png | Bin 0 -> 33291 bytes .../Helicalpiles/Sand - helical pile.png | Bin 0 -> 31726 bytes .../images/Helicalpiles/pycurves - API.png | Bin 0 -> 41702 bytes .../Helicalpiles/pycurves - Matlock.png | Bin 0 -> 67994 bytes famodel/anchors/images/Plateanchors/Plate.png | Bin 0 -> 29892 bytes .../Suctionpiles/Clay - suction envelope.png | Bin 0 -> 27551 bytes .../Suctionpiles/Sand - suction envelope.png | Bin 0 -> 26736 bytes .../Suctionpiles/Sand - suction pile.png | Bin 0 -> 28937 bytes .../anchors/images/Suctionpiles/Suction.png | Bin 0 -> 30493 bytes .../Torpedopiles/Clay - torpedo envelope.png | Bin 0 -> 27723 bytes .../anchors/images/Torpedopiles/Torpedo.png | Bin 0 -> 35131 bytes famodel/geography.py | 132 +- famodel/mooring/mooringOntology.yaml | 4 +- famodel/project.py | 323 +- famodel/seabed/seabed_tools.py | 127 +- famodel/seabed/test output.txt | 55 + tests/simple_farm.yaml | 4 +- tests/testOntology.yaml | 76 +- tests/test_anchors.py | 200 +- 116 files changed, 14104 insertions(+), 10438 deletions(-) create mode 100644 examples/05_Anchors/anchor_dandg.py create mode 100644 examples/05_Anchors/anchor_driven_rock.py rename famodel/anchors/getCapacityPile_map.py => examples/05_Anchors/anchor_driven_soil.py (55%) rename famodel/anchors/getCapacityHelical_clay.py => examples/05_Anchors/anchor_helical.py (63%) rename famodel/anchors/getCapacityPlate_map.py => examples/05_Anchors/anchor_plate.py (71%) create mode 100644 examples/05_Anchors/anchor_soil.py rename famodel/anchors/getCapacitySuction_map.py => examples/05_Anchors/anchor_suction.py (59%) rename famodel/anchors/getCapacityTorpedo_map.py => examples/05_Anchors/anchor_torpedo.py (85%) create mode 100644 examples/05_Anchors/example_suction.ipynb create mode 100644 examples/05_Anchors/inputs/GulfOfMaine_bathymetry_100x100.txt create mode 100644 examples/05_Anchors/inputs/GulfOfMaine_soil_layered_100x100.txt create mode 100644 examples/05_Anchors/inputs/GulfOfMaine_soil_profiles.yaml create mode 100644 examples/Inputs/GulfOfMaine_bathymetry_100x100.txt create mode 100644 examples/Inputs/GulfOfMaine_soil_layered_100x100.txt create mode 100644 examples/Inputs/GulfOfMaine_soil_profiles.yaml create mode 100644 examples/Inputs/GulfOfMaine_soil_uniform_100x100.txt create mode 100644 examples/Inputs/OntologySample200mbis_1turb.yaml create mode 100644 examples/Inputs/checkyaml.yaml delete mode 100644 examples/example_anchors.py delete mode 100644 famodel/anchors/README_FMO.md delete mode 100644 famodel/anchors/anchor_capacity.py create mode 100644 famodel/anchors/anchor_conflict_backup.py delete mode 100644 famodel/anchors/anchor_map.py delete mode 100644 famodel/anchors/anchor_profile.py create mode 100644 famodel/anchors/anchors_famodel/__init__.py rename famodel/anchors/{anchors_famodel_map/capacity_driven_map.py => anchors_famodel/capacity_driven.py} (81%) delete mode 100644 famodel/anchors/anchors_famodel/capacity_drivenrock.py delete mode 100644 famodel/anchors/anchors_famodel/capacity_drivensoil.py create mode 100644 famodel/anchors/anchors_famodel/installatioin_torque.py create mode 100644 famodel/anchors/anchors_famodel/installation_buckling.py create mode 100644 famodel/anchors/anchors_famodel/installation_buckling2.py rename famodel/anchors/anchors_famodel/{capacity_drag.py => installation_drag.py} (95%) create mode 100644 famodel/anchors/anchors_famodel/installation_driven.py create mode 100644 famodel/anchors/anchors_famodel/installation_driven2.py create mode 100644 famodel/anchors/anchors_famodel/installation_dynamic.py create mode 100644 famodel/anchors/anchors_famodel/installation_dynamic2.py create mode 100644 famodel/anchors/anchors_famodel/installation_dynamic3.py create mode 100644 famodel/anchors/anchors_famodel/installation_suction.py rename famodel/anchors/{anchors_famodel_map/capacity_plots_map.py => anchors_famodel/support_plots.py} (89%) rename famodel/anchors/{anchors_famodel_map/capacity_pycurves_map.py => anchors_famodel/support_pycurves.py} (86%) rename famodel/anchors/{anchors_famodel_map/capacity_soils_map.py => anchors_famodel/support_soils.py} (100%) rename famodel/anchors/{anchors_famodel_map/capacity_solvers.py => anchors_famodel/support_solvers.py} (97%) delete mode 100644 famodel/anchors/anchors_famodel_map/capacity_dandg_map.py delete mode 100644 famodel/anchors/anchors_famodel_map/capacity_helical_map.py delete mode 100644 famodel/anchors/anchors_famodel_map/capacity_load_map.py delete mode 100644 famodel/anchors/anchors_famodel_map/capacity_plate_map.py delete mode 100644 famodel/anchors/anchors_famodel_map/capacity_suction_map.py delete mode 100644 famodel/anchors/anchors_famodel_map/capacity_torpedo_map.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_dandg.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_driven.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_helical.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_load.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_plate.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_plots.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_pycurves.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_soils.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_suction.py delete mode 100644 famodel/anchors/anchors_famodel_profile/capacity_torpedo.py delete mode 100644 famodel/anchors/getCapacityAnchor_profile.py delete mode 100644 famodel/anchors/getCapacityHelical_map.py delete mode 100644 famodel/anchors/getCapacityHelical_sand.py create mode 100644 famodel/anchors/images/Drilledandgroutedpiles/Drilled.png create mode 100644 famodel/anchors/images/Drilledandgroutedpiles/Rock - deformed dandg.png create mode 100644 famodel/anchors/images/Drivenpiles/Clay - deformed pile.png create mode 100644 famodel/anchors/images/Drivenpiles/Driven.png create mode 100644 famodel/anchors/images/Drivenpiles/Rock - deformed pile.png create mode 100644 famodel/anchors/images/Drivenpiles/Rock - driven pile.png create mode 100644 famodel/anchors/images/Drivenpiles/Sand - deformed pile.png create mode 100644 famodel/anchors/images/Drivenpiles/Sand - driven pile.png create mode 100644 famodel/anchors/images/Drivenpiles/pycurves - API.png create mode 100644 famodel/anchors/images/Drivenpiles/pycurves - Lovera.png create mode 100644 famodel/anchors/images/Drivenpiles/pycurves - Matlock.png create mode 100644 famodel/anchors/images/Drivenpiles/pycurves - Reese.png create mode 100644 famodel/anchors/images/Helicalpiles/Clay - deformed pile.png create mode 100644 famodel/anchors/images/Helicalpiles/Helical.png create mode 100644 famodel/anchors/images/Helicalpiles/Sand - deformed pile.png create mode 100644 famodel/anchors/images/Helicalpiles/Sand - helical pile.png create mode 100644 famodel/anchors/images/Helicalpiles/pycurves - API.png create mode 100644 famodel/anchors/images/Helicalpiles/pycurves - Matlock.png create mode 100644 famodel/anchors/images/Plateanchors/Plate.png create mode 100644 famodel/anchors/images/Suctionpiles/Clay - suction envelope.png create mode 100644 famodel/anchors/images/Suctionpiles/Sand - suction envelope.png create mode 100644 famodel/anchors/images/Suctionpiles/Sand - suction pile.png create mode 100644 famodel/anchors/images/Suctionpiles/Suction.png create mode 100644 famodel/anchors/images/Torpedopiles/Clay - torpedo envelope.png create mode 100644 famodel/anchors/images/Torpedopiles/Torpedo.png create mode 100644 famodel/seabed/test output.txt diff --git a/.github/workflows/CI_FAModel.yml b/.github/workflows/CI_FAModel.yml index fcacaf5a..4a8aa2f7 100644 --- a/.github/workflows/CI_FAModel.yml +++ b/.github/workflows/CI_FAModel.yml @@ -49,14 +49,16 @@ jobs: - name: Overwrite MoorPy run: | pip install git+https://github.com/NREL/MoorPy@dev - - - name: Example run - run: | - cd examples - python example_driver.py false - + - name: Test run run: | cd tests pytest . + # - name: Example run + # run: | + # cd examples + # python example_driver.py false + + + diff --git a/examples/01_Visualization/05_visual_lease_boundaries.py b/examples/01_Visualization/05_visual_lease_boundaries.py index 7b052544..95e15551 100644 --- a/examples/01_Visualization/05_visual_lease_boundaries.py +++ b/examples/01_Visualization/05_visual_lease_boundaries.py @@ -11,7 +11,7 @@ import matplotlib.pyplot as plt # define name of ontology input file -input_file = '06_visual_lease_boundaries.yaml' +input_file = '05_visual_lease_boundaries.yaml' # initialize Project class with input file, we don't need RAFT for this so mark False project = Project(file=input_file,raft=False) diff --git a/examples/01_Visualization/07_3D-visual_platform.py b/examples/01_Visualization/07_3D-visual_platform.py index e868c26b..655e867d 100644 --- a/examples/01_Visualization/07_3D-visual_platform.py +++ b/examples/01_Visualization/07_3D-visual_platform.py @@ -9,7 +9,7 @@ import matplotlib.pyplot as plt # define name of ontology input file -input_file = '07_3D-visual_platform.yaml' +input_file = 'examples/01_Visualization/07_3D-visual_platform.yaml' # initialize Project class with input file, we don't need RAFT for this so mark False project = Project(file=input_file,raft=True) diff --git a/examples/01_Visualization/07_3D-visual_platform.yaml b/examples/01_Visualization/07_3D-visual_platform.yaml index a6dcf3c2..602f73bd 100644 --- a/examples/01_Visualization/07_3D-visual_platform.yaml +++ b/examples/01_Visualization/07_3D-visual_platform.yaml @@ -39,7 +39,7 @@ platform: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -62,7 +62,7 @@ platform: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) diff --git a/examples/01_Visualization/08_3D-visual_turbine.yaml b/examples/01_Visualization/08_3D-visual_turbine.yaml index f13dfa81..a27a5e4f 100644 --- a/examples/01_Visualization/08_3D-visual_turbine.yaml +++ b/examples/01_Visualization/08_3D-visual_turbine.yaml @@ -1090,7 +1090,7 @@ platform: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1113,7 +1113,7 @@ platform: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) diff --git a/examples/03_Frequency_Domain_Analysis_RAFT/01_platform.yaml b/examples/03_Frequency_Domain_Analysis_RAFT/01_platform.yaml index 1586deb1..56f523a3 100644 --- a/examples/03_Frequency_Domain_Analysis_RAFT/01_platform.yaml +++ b/examples/03_Frequency_Domain_Analysis_RAFT/01_platform.yaml @@ -27,7 +27,7 @@ platform: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -50,7 +50,7 @@ platform: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) diff --git a/examples/03_Frequency_Domain_Analysis_RAFT/02_FOWT.yaml b/examples/03_Frequency_Domain_Analysis_RAFT/02_FOWT.yaml index 78f800d8..a05ac7ef 100644 --- a/examples/03_Frequency_Domain_Analysis_RAFT/02_FOWT.yaml +++ b/examples/03_Frequency_Domain_Analysis_RAFT/02_FOWT.yaml @@ -1082,7 +1082,7 @@ platform: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1105,7 +1105,7 @@ platform: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) diff --git a/examples/05_Anchors/anchor_dandg.py b/examples/05_Anchors/anchor_dandg.py new file mode 100644 index 00000000..a5fef24d --- /dev/null +++ b/examples/05_Anchors/anchor_dandg.py @@ -0,0 +1,65 @@ + +from famodel.anchors.anchor import Anchor + +# --- Define soil profile --- +profile_map = [ + { + 'name': 'CPT_D1', + 'x': 0.0, 'y': 0.0, + 'layers': [ + {'top': 1.5, 'bottom': 5.0, 'soil_type': 'rock', 'UCS_top': 6.0, 'UCS_bot': 8.0, 'Em_top': 175, 'Em_bot': 290}, + {'top': 5.0, 'bottom': 9.0, 'soil_type': 'rock', 'UCS_top': 8.0, 'UCS_bot': 10.7, 'Em_top': 277, 'Em_bot': 297}, + {'top': 9.0, 'bottom': 30.0, 'soil_type': 'rock', 'UCS_top': 8.0, 'UCS_bot': 10.5, 'Em_top': 280, 'Em_bot': 305} + ] + } +] + +# --- Create driven pile anchor --- +anchor = Anchor( + dd = { + 'type': 'dandg', + 'design': { + 'L': 10.0, # Embedded length + 'D': 2.85, # Diameter + 'zlug': 1.0 # Padeye depth + } + }, + r = [0.0, 0.0, 0.0] +) + +# Assign mooring loads +anchor.loads = { + 'Hm': 5.0e6, + 'Vm': 2.5e5 +} +anchor.line_type = 'chain' +anchor.d = 0.16 +anchor.w = 5000.0 + +# Assign local soil +anchor.setSoilProfile(profile_map) + +# --- Step 1: Capacity --- +anchor.getCapacityAnchor( + Hm = anchor.loads['Hm'], + Vm = anchor.loads['Vm'], + zlug = anchor.dd['design']['zlug'], + line_type = anchor.line_type, + d = anchor.d, + w = anchor.w, + plot = True) + +print('\nCapacity Results:') +for key, val in anchor.anchorCapacity.items(): + print(f'{key}: {val:.2f}') + +# --- Step 2: Optimize Anchor Geometry --- +anchor.getSizeAnchor( + geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], + geomKeys = ['L', 'D'], + geomBounds = [(2.0, 70.0), (0.25, 3.0)], + loads = None, + lambdap_con = [4, 50], + zlug_fix = True, + safety_factor = {'SF_horizontal': 1, 'SF_vertical': 1}, + plot = True) \ No newline at end of file diff --git a/examples/05_Anchors/anchor_driven_rock.py b/examples/05_Anchors/anchor_driven_rock.py new file mode 100644 index 00000000..8564bea6 --- /dev/null +++ b/examples/05_Anchors/anchor_driven_rock.py @@ -0,0 +1,65 @@ + +from famodel.anchors.anchor import Anchor + +# --- Define soil profile --- +profile_map = [ + { + 'name': 'CPT_D1', + 'x': 0.0, 'y': 0.0, + 'layers': [ + {'top': 1.5, 'bottom': 6.0, 'soil_type': 'rock', 'UCS_top': 5.0, 'UCS_bot': 5.0, 'Em_top': 7, 'Em_bot': 7}, + {'top': 6.0, 'bottom': 15.0, 'soil_type': 'rock', 'UCS_top': 6.0, 'UCS_bot': 6.7, 'Em_top': 7, 'Em_bot': 7}, + {'top': 15.0, 'bottom': 35.0, 'soil_type': 'rock', 'UCS_top': 10.0, 'UCS_bot': 10.5, 'Em_top': 7, 'Em_bot': 7} + ] + } +] + +# --- Create driven pile anchor --- +anchor = Anchor( + dd = { + 'type': 'driven', + 'design': { + 'L': 15.0, # Embedded length + 'D': 1.85, # Diameter + 'zlug': 1.5 # Padeye depth + } + }, + r = [0.0, 0.0, 0.0] +) + +# Assign mooring loads +anchor.loads = { + 'Hm': 2.5e6, + 'Vm': 2.5e5} + +anchor.line_type = 'chain' +anchor.d = 0.16 +anchor.w = 5000.0 + +# Assign local soil +anchor.setSoilProfile(profile_map) + +# --- Step 1: Capacity --- +anchor.getCapacityAnchor( + Hm = anchor.loads['Hm'], + Vm = anchor.loads['Vm'], + zlug = anchor.dd['design']['zlug'], + line_type = anchor.line_type, + d = anchor.d, + w = anchor.w, + plot = True) + +print('\nCapacity Results:') +for key, val in anchor.anchorCapacity.items(): + print(f'{key}: {val:.2f}') + +# --- Step 2: Optimize Anchor Geometry --- +anchor.getSizeAnchor( + geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], + geomKeys = ['L', 'D'], + geomBounds = [(2.0, 70.0), (0.25, 3.0)], + loads = None, + lambdap_con = [4, 50], + zlug_fix = True, + safety_factor = {'SF_horizontal': 1, 'SF_vertical': 1}, + plot = True) \ No newline at end of file diff --git a/famodel/anchors/getCapacityPile_map.py b/examples/05_Anchors/anchor_driven_soil.py similarity index 55% rename from famodel/anchors/getCapacityPile_map.py rename to examples/05_Anchors/anchor_driven_soil.py index 38998786..f12bc5ab 100644 --- a/famodel/anchors/getCapacityPile_map.py +++ b/examples/05_Anchors/anchor_driven_soil.py @@ -1,5 +1,5 @@ -from anchor_map import Anchor +from famodel.anchors.anchor import Anchor # --- Define soil profile --- profile_map = [ @@ -7,9 +7,9 @@ 'name': 'CPT_D1', 'x': 0.0, 'y': 0.0, 'layers': [ - {'top': 1.0, 'bottom': 6.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 10.0, 'Su_top': 45, 'Su_bot': 60}, - {'top': 6.0, 'bottom': 15.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.0, 'Su_top': 60, 'Su_bot': 80}, - {'top': 15.0, 'bottom': 35.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.5, 'Su_top': 80, 'Su_bot': 100} + {'top': 1.5, 'bottom': 6.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 10.0, 'Su_top': 25, 'Su_bot': 200}, + {'top': 6.0, 'bottom': 15.0, 'soil_type': 'sand', 'gamma_top': 10.0, 'gamma_bot': 10.0, 'phi_top': 28, 'phi_bot': 32, 'Dr_top': 80, 'Dr_bot': 85}, + {'top': 15.0, 'bottom': 35.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.5, 'Su_top': 100, 'Su_bot': 100} ] } ] @@ -20,8 +20,8 @@ 'type': 'driven', 'design': { 'L': 25.0, # Embedded length - 'D': 2.0, # Diameter - 'zlug': 10.0 # Padeye depth + 'D': 4.25, # Diameter + 'zlug': 3.0 # Padeye depth } }, r = [0.0, 0.0, 0.0] @@ -29,9 +29,9 @@ # Assign mooring loads anchor.loads = { - 'Hm': 4.0e6, - 'Vm': 2.5e6 -} + 'Hm': 2.0e6, + 'Vm': 2.5e5} + anchor.line_type = 'chain' anchor.d = 0.16 anchor.w = 5000.0 @@ -47,8 +47,7 @@ line_type = anchor.line_type, d = anchor.d, w = anchor.w, - plot = True -) + plot = True) print('\nLug Forces Computed:') print(f'Ha = {Ha:.2f} N') @@ -62,9 +61,19 @@ line_type = anchor.line_type, d = anchor.d, w = anchor.w, - plot = True -) + plot = True) print('\nCapacity Results:') -for key, val in anchor.capacity_results.items(): +for key, val in anchor.anchorCapacity.items(): print(f'{key}: {val:.2f}') + +# --- Step 3: Optimize Anchor Geometry --- +anchor.getSizeAnchor( + geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], + geomKeys = ['L', 'D'], + geomBounds = [(2.0, 70.0), (0.25, 3.0)], + loads = None, + lambdap_con = [4, 50], + zlug_fix = True, + safety_factor = {'SF_horizontal': 1, 'SF_vertical': 1}, + plot = True) \ No newline at end of file diff --git a/famodel/anchors/getCapacityHelical_clay.py b/examples/05_Anchors/anchor_helical.py similarity index 63% rename from famodel/anchors/getCapacityHelical_clay.py rename to examples/05_Anchors/anchor_helical.py index f1367126..7adbb02d 100644 --- a/famodel/anchors/getCapacityHelical_clay.py +++ b/examples/05_Anchors/anchor_helical.py @@ -1,5 +1,5 @@ -from anchor_map import Anchor +from famodel.anchors.anchor import Anchor # --- Soil profile for helical pile in clay --- profile_map = [ @@ -7,9 +7,9 @@ 'name': 'CPT_H1', 'x': 0.0, 'y': 0.0, 'layers': [ - {'top': 1.0, 'bottom': 3.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 9.0, 'Su_top': 60, 'Su_bot': 50}, - {'top': 3.0, 'bottom': 7.0, 'soil_type': 'clay', 'gamma_top': 15.0, 'gamma_bot': 25.0, 'Su_top': 100, 'Su_bot': 150}, - {'top': 7.0, 'bottom': 15.0, 'soil_type': 'clay', 'gamma_top': 25.0, 'gamma_bot': 50.0, 'Su_top': 200, 'Su_bot': 400} + {'top': 1.0, 'bottom': 3.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 9.0, 'Su_top': 60, 'Su_bot': 50}, + {'top': 3.0, 'bottom': 7.0, 'soil_type': 'clay', 'gamma_top': 15.0, 'gamma_bot': 25.0, 'Su_top': 100, 'Su_bot': 150}, + {'top': 7.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 25.0, 'gamma_bot': 50.0, 'Su_top': 200, 'Su_bot': 400} ] } ] @@ -31,8 +31,8 @@ # --- Assign mooring loads and properties --- anchor.loads = { 'Hm': 80e4, - 'Vm': 50e3 -} + 'Vm': 50e5} + anchor.line_type = 'chain' anchor.d = 0.16 anchor.w = 5000.0 @@ -63,9 +63,21 @@ line_type = anchor.line_type, d = anchor.d, w = anchor.w, - plot = True + plot = False ) print('\nCapacity Results:') -for key, val in anchor.capacity_results.items(): +for key, val in anchor.anchorCapacity.items(): print(f'{key}: {val:.2f}') + +# --- Step 3: Optimize Anchor Geometry --- +anchor.getSizeAnchor( + geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], + geomKeys = ['L', 'D'], + geomBounds = [(6.0, 25.0), (0.5, 2.0)], + loads = None, + lambdap_con = [6, 15], + zlug_fix = True, + safety_factor = {'SF_horizontal': 1, 'SF_vertical': 1}, + plot = False +) diff --git a/famodel/anchors/getCapacityPlate_map.py b/examples/05_Anchors/anchor_plate.py similarity index 71% rename from famodel/anchors/getCapacityPlate_map.py rename to examples/05_Anchors/anchor_plate.py index 088cd40b..aa841f73 100644 --- a/famodel/anchors/getCapacityPlate_map.py +++ b/examples/05_Anchors/anchor_plate.py @@ -1,7 +1,6 @@ -from anchor_map import Anchor -import numpy as np -from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_load +from famodel.anchors.anchor import Anchor +from famodel.anchors.anchors_famodel.support_plots import plot_load # --- Define soil profile --- profile_map = [ @@ -10,7 +9,7 @@ 'x': 0.0, 'y': 0.0, 'layers': [ {'top': 2.0, 'bottom': 4.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top': 10, 'Su_bot': 25}, - {'top': 4.0, 'bottom': 6.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 25, 'Su_bot': 50}, + {'top': 4.0, 'bottom': 6.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 15, 'Su_bot': 40}, {'top': 6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.5, 'Su_top': 50, 'Su_bot': 100}, {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 9.5, 'gamma_bot': 9.5, 'Su_top': 100, 'Su_bot': 100} ] @@ -19,14 +18,14 @@ # --- Create plate anchor --- anchor = Anchor( - dd = {'type': 'plate', 'design': {'B': 3.0, 'L': 6.0, 'zlug': 5.0, 'beta': 30.0}}, + dd = {'type': 'plate', 'design': {'B': 3.0, 'L': 6.0, 'zlug': 14.0, 'beta': 30.0}}, r = [100.0, 100.0, 0.0] ) # --- Assign load and mooring properties --- anchor.loads = { - 'Hm': 2.5e6, - 'Vm': 1.5e6 + 'Hm': 3.5e6, + 'Vm': 2.5e6 } anchor.line_type = 'chain' anchor.d = 0.16 @@ -62,5 +61,17 @@ ) print('\nCapacity Results:') -for key, value in anchor.capacity_results.items(): +for key, value in anchor.anchorCapacity.items(): print(f'{key}: {value:.2f}') + +# --- Step 3: Optimize Anchor Geometry --- +anchor.getSizeAnchor( + geom = [anchor.dd['design']['B'], anchor.dd['design']['L']], + geomKeys = ['B', 'L'], + geomBounds = [(0.5, 6.0), (2.0, 12.0)], + loads = None, + lambdap_con = [2, 4], # less critical for plates + zlug_fix = True, + safety_factor = {'SF_combined': 3}, + plot = True +) diff --git a/examples/05_Anchors/anchor_soil.py b/examples/05_Anchors/anchor_soil.py new file mode 100644 index 00000000..5d76b294 --- /dev/null +++ b/examples/05_Anchors/anchor_soil.py @@ -0,0 +1,67 @@ + +import sys +sys.path.append(r'C:\Code\FAModel_anchors\famodel') + +from project import Project +from anchors.anchor import Anchor + +# Step 1: Initialize and load soil +proj = Project() +proj.loadSoil( + filename='inputs/GulfOfMaine_soil_layered_100x100.txt', + soil_mode='layered', + profile_source='inputs/GulfOfMaine_soil_profiles.yaml') + +for label, props in proj.soilProps.items(): + print(f"{label}: {props}") + +# Step 2: Create and register an anchor at a known position in the grid +anchor = Anchor( + dd = {'type': 'suction', 'design': {'D': 3.5, 'L': 12.0, 'zlug': 9.67}}, + r = [54.0, -4450.0, 0.0]) + +# Step 3: Assign local soil profile from project (nearest neighbor lookup) +soil_id, soil_profile = proj.getSoilAtLocation(anchor.r[0], anchor.r[1]) +anchor.soilProps = {soil_id: soil_profile} +anchor.setSoilProfile([{ 'name': soil_id, 'layers': soil_profile }]) # ensures `anchor.soil_profile` is set + +# Step 4: Assign loads and line +anchor.loads = {'Hm': 2e6, 'Vm': 1.5e6} +anchor.line_type = 'chain' +anchor.d = 0.16 +anchor.w = 5000.0 + +# Step 5: Run capacity check and optimization +anchor.getLugForces( + Hm=anchor.loads['Hm'], Vm=anchor.loads['Vm'], + zlug = anchor.dd['design']['zlug'], + d=anchor.d, w=anchor.w, + plot=True) + +anchor.getCapacityAnchor( + Hm=anchor.loads['Hm'], Vm=anchor.loads['Vm'], + zlug = anchor.dd['design']['zlug'], + line_type=anchor.line_type, d=anchor.d, w=anchor.w, + mass_update=True, + plot=True) +anchor.getCostAnchor() +print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') + +results = anchor.getSizeAnchor( + geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], + geomKeys = ['L', 'D'], + geomBounds = [(8.0, 15.0), (2.0, 4.0)], + loads = None, + lambdap_con = [3, 6], + zlug_fix = False, + safety_factor = {'SF_combined': 1}, + plot = True) + +# Step 6: Report +print('\nFinal Optimized Anchor:') +print('Design:', anchor.dd['design']) +print('Capacity Results:', anchor.anchorCapacity) +anchor.getCostAnchor() +print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') + + diff --git a/famodel/anchors/getCapacitySuction_map.py b/examples/05_Anchors/anchor_suction.py similarity index 59% rename from famodel/anchors/getCapacitySuction_map.py rename to examples/05_Anchors/anchor_suction.py index 1c41ac6e..12a10b05 100644 --- a/famodel/anchors/getCapacitySuction_map.py +++ b/examples/05_Anchors/anchor_suction.py @@ -1,7 +1,6 @@ -from anchor_map import Anchor -import numpy as np -from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_load +from famodel.anchors.anchor import Anchor +from famodel.anchors.anchors_famodel.support_plots import plot_suction # --- Define soil profile --- profile_map = [ @@ -9,10 +8,10 @@ 'name': 'CPT_A1', 'x': 0.0, 'y': 0.0, 'layers': [ - {'top': 2.0, 'bottom': 4.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top': 10, 'Su_bot': 25}, - {'top': 4.0, 'bottom': 6.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 25, 'Su_bot': 50}, - {'top': 6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.5, 'Su_top': 50, 'Su_bot': 100}, - {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 9.5, 'gamma_bot': 9.5, 'Su_top': 100, 'Su_bot': 100} + {'top': 2.0, 'bottom': 4.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top': 10, 'Su_bot': 25}, + {'top': 4.0, 'bottom': 6.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 25, 'Su_bot': 50}, + {'top': 6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.5, 'Su_top': 50, 'Su_bot': 100}, + {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 9.5, 'gamma_bot': 9.5, 'Su_top': 100, 'Su_bot': 100} ] }, { @@ -29,10 +28,10 @@ 'name': 'CPT_A2', 'x': 0.0, 'y': 500.0, 'layers': [ - {'top': 2.0, 'bottom': 4.0, 'soil_type': 'clay', 'gamma_top': 7.5, 'gamma_bot': 8.0, 'Su_top': 5, 'Su_bot': 20}, - {'top': 4.0, 'bottom': 8.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top': 20, 'Su_bot': 45}, - {'top': 8.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 45, 'Su_bot': 95}, - {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.0, 'Su_top': 95, 'Su_bot': 95} + {'top': 2.0, 'bottom': 4.0, 'soil_type': 'clay', 'gamma_top': 7.5, 'gamma_bot': 8.0, 'Su_top': 5, 'Su_bot': 20}, + {'top': 4.0, 'bottom': 8.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top': 20, 'Su_bot': 45}, + {'top': 8.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 45, 'Su_bot': 95}, + {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.0, 'Su_top': 95, 'Su_bot': 95} ] }, { @@ -49,14 +48,27 @@ anchor = Anchor( - dd = {'type': 'suction', 'design': {'D': 2.5, 'L': 12.0, 'zlug': 9.0}}, - r = [250.0, 000.0, 000.0] -) + dd = {'type': 'suction', 'design': {'D': 2.5, 'L': 12.0, 'zlug': 8.67}}, + r = [250.0, 250.0, 000.0]) + +# --- Step 0: Create anchor based grid CPTs --- +anchor.interpolateSoilProfile(profile_map) + +# --- Step 1: Plot suction pile and soil profile --- +# Access anchor geometrical properties +L = anchor.dd['design']['L'] +D = anchor.dd['design']['D'] +zlug = anchor.dd['design']['zlug'] +# Access matched profile +layers = anchor.soil_profile +z0 = layers[0]['top'] + +plot_suction(layers, L=L, D=D, z0=z0, zlug=zlug) # Assign loads manually anchor.loads = { - 'Hm': 3e6, # Horizontal mudline load (N) - 'Vm': 2e6 # Vertical mudline load (N) + 'Hm': 3.0e6, # Horizontal mudline load (N) + 'Vm': 1.0e6 # Vertical mudline load (N) } # Assign line properties manually @@ -64,11 +76,8 @@ anchor.d = 0.16 # Chain diameter (m) anchor.w = 5000.0 # Nominal submerged weight (N/m) -# --- Step 0: Create anchor based grid CPTs --- -anchor.setSoilProfile(profile_map) - -# --- Step 1: Compute Lug Forces --- +# --- Step 2: Compute Lug Forces --- layers, Ha, Va = anchor.getLugForces( Hm = anchor.loads['Hm'], Vm = anchor.loads['Vm'], @@ -76,43 +85,55 @@ line_type = anchor.line_type, d = anchor.d, w = anchor.w, - plot = True -) + plot = True) print('\nLug Forces Computed:') print(f'Ha = {Ha:.2f} N') print(f'Va = {Va:.2f} N') -# --- Step 2: Compute Capacity --- +# --- Step 3: Compute Capacity --- anchor.getCapacityAnchor( Hm = anchor.loads['Hm'], Vm = anchor.loads['Vm'], zlug = anchor.dd['design']['zlug'], line_type = anchor.line_type, - d = anchor.d, - w = anchor.w, - plot = True -) + d = anchor.d, w = anchor.w, + mass_update=False, + plot = True) print('\nCapacity Results:') -for key, value in anchor.capacity_results.items(): +for key, value in anchor.anchorCapacity.items(): print(f'{key}: {value:.2f}') + +# --- Step 4: Compute Costs --- +anchor.getCostAnchor() + +print(f"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg") +print(f"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg") +print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') -# --- Step 3: Optimize Anchor Geometry --- +#%% +# --- Step 5: Optimize Anchor Geometry --- anchor.getSizeAnchor( geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], geomKeys = ['L', 'D'], - geomBounds = [(5.0, 15.0), (2.0, 6.0)], + geomBounds = [(5.0, 15.0), (1.0, 4.0)], loads = None, - minfs = {'Ha': 1.0, 'Va': 1.0}, lambdap_con = [3, 6], zlug_fix = False, - plot = True -) + safety_factor = {'SF_combined': 1}, + plot = True) print('\nFinal Optimized Anchor:') print('Design:', anchor.dd['design']) -print('Capacity Results:', anchor.capacity_results) +print('Capacity Results:', anchor.anchorCapacity) + +# # --- Step 6: Compute Costs --- +anchor.getCostAnchor() + +print(f"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg") +print(f"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg") +print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') -# --- Step 4: Visualize Anchor Geometry --- -anchor.getCombinedPlot() +# --- Step 7: Visualize Anchor Geometry --- +# anchor.getCombinedPlot() diff --git a/famodel/anchors/getCapacityTorpedo_map.py b/examples/05_Anchors/anchor_torpedo.py similarity index 85% rename from famodel/anchors/getCapacityTorpedo_map.py rename to examples/05_Anchors/anchor_torpedo.py index 6ecb01cb..28cdc25c 100644 --- a/famodel/anchors/getCapacityTorpedo_map.py +++ b/examples/05_Anchors/anchor_torpedo.py @@ -1,6 +1,6 @@ -from anchor_map import Anchor -from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_load +from famodel.anchors.anchor import Anchor +from famodel.anchors.anchors_famodel.support_plots import plot_load # --- Define soil profile --- profile_map = [ @@ -10,7 +10,7 @@ 'layers': [ {'top': 0.0, 'bottom': 20.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top': 50, 'Su_bot': 70}, {'top': 20.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 8.5, 'Su_top': 80, 'Su_bot': 100}, - {'top': 25.0, 'bottom': 50.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 125, 'Su_bot': 150} + {'top': 25.0, 'bottom': 50.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 100, 'Su_bot': 150} ] } ] @@ -24,7 +24,7 @@ 'D2': 1.5, # Shaft diameter 'L1': 11.0, # Winged section length 'L2': 5.0, # Shaft section length - 'zlug': 20.0, # Padeye depth + 'zlug': 15.0, # Padeye depth 'ballast': 10000 } }, @@ -71,7 +71,7 @@ ) print('\nCapacity Results:') -for key, value in anchor.capacity_results.items(): +for key, value in anchor.anchorCapacity.items(): print(f'{key}: {value:.2f}') @@ -84,15 +84,15 @@ geomKeys = ['L1', 'D1'], geomBounds = [(7.0, 25.0), (2.5, 4.5)], loads = None, - minfs = {'Ha': 1.0, 'Va': 1.0}, lambdap_con = [2, 8], zlug_fix = True, + safety_factor = {'SF_combined': 1}, plot = True ) print('\nFinal Optimized Anchor:') print('Design:', anchor.dd['design']) -print('Capacity Results:', anchor.capacity_results) +print('Capacity Results:', anchor.anchorCapacity) # --- Step 4: Visualize Anchor Geometry --- # anchor.getCombinedPlot() diff --git a/examples/05_Anchors/example_suction.ipynb b/examples/05_Anchors/example_suction.ipynb new file mode 100644 index 00000000..8df5784c --- /dev/null +++ b/examples/05_Anchors/example_suction.ipynb @@ -0,0 +1,3725 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ef2db749", + "metadata": {}, + "source": [ + "# Suction Anchor Capacity – Jupyter Notebook" + ] + }, + { + "cell_type": "markdown", + "id": "cf0c1c98", + "metadata": {}, + "source": [ + "### Step 1: Import required libraries\n", + "\n", + "We begin by importing the essential modules:\n", + "\n", + "- `Anchor` from `famodel.anchors.anchor`: the main class that encapsulates the suction anchor's capacity methods - soil properties, anchor geometry and extreme loads.\n", + "- `plot_suction` from `famodel.anchors.anchors_famodel.capacity_plots`: a custom plotting utility that visualizes anchor geometry and soil properties.\n", + "\n", + "These imports set up the environment to define, simulate, and visualize the anchor system." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "9f2d8d4b", + "metadata": {}, + "outputs": [], + "source": [ + "from famodel.anchors.anchor import Anchor\n", + "from famodel.anchors.anchors_famodel.support_plots import plot_suction" + ] + }, + { + "cell_type": "markdown", + "id": "b84ceffb", + "metadata": {}, + "source": [ + "### Step 2: Define the layered soil profile map\n", + "We create a list of CPT locations in the vertices of a 500x500 m square within the Lease Area, each with a set of layered clay soil parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "935551c4", + "metadata": {}, + "outputs": [], + "source": [ + "profile_map = [\n", + " {\n", + " 'name': 'CPT_A1',\n", + " 'x': 0.0, 'y': 0.0,\n", + " 'layers': [\n", + " {'top': 2.0, 'bottom': 4.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top': 10, 'Su_bot': 25},\n", + " {'top': 4.0, 'bottom': 6.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 25, 'Su_bot': 50},\n", + " {'top': 6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.5, 'Su_top': 50, 'Su_bot': 100},\n", + " {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 9.5, 'gamma_bot': 9.5, 'Su_top': 100, 'Su_bot': 100}\n", + " ]\n", + " },\n", + " {\n", + " 'name': 'CPT_B1',\n", + " 'x': 500.0, 'y': 0.0,\n", + " 'layers': [\n", + " {'top': 2.0, 'bottom': 4.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 15, 'Su_bot': 30},\n", + " {'top': 4.0, 'bottom': 6.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.5, 'Su_top': 30, 'Su_bot': 55},\n", + " {'top': 6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 9.5, 'gamma_bot': 10.0, 'Su_top': 55, 'Su_bot': 105},\n", + " {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.0, 'Su_top': 105, 'Su_bot': 105}\n", + " ]\n", + " },\n", + " {\n", + " 'name': 'CPT_A2',\n", + " 'x': 0.0, 'y': 500.0,\n", + " 'layers': [\n", + " {'top': 2.0, 'bottom': 4.0, 'soil_type': 'clay', 'gamma_top': 7.5, 'gamma_bot': 8.0, 'Su_top': 5, 'Su_bot': 20},\n", + " {'top': 4.0, 'bottom': 8.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top': 20, 'Su_bot': 45},\n", + " {'top': 8.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 45, 'Su_bot': 95},\n", + " {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.0, 'Su_top': 95, 'Su_bot': 95}\n", + " ]\n", + " },\n", + " {\n", + " 'name': 'CPT_B2',\n", + " 'x': 500.0, 'y': 500.0,\n", + " 'layers': [\n", + " {'top': 1.0, 'bottom': 2.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.5, 'Su_top': 20, 'Su_bot': 35},\n", + " {'top': 2.0, 'bottom': 8.0, 'soil_type': 'clay', 'gamma_top': 9.5, 'gamma_bot': 10.0, 'Su_top': 35, 'Su_bot': 60},\n", + " {'top': 8.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.5, 'Su_top': 60, 'Su_bot': 110},\n", + " {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 10.5, 'gamma_bot': 10.5, 'Su_top': 110, 'Su_bot': 110}\n", + " ]\n", + " }\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "6d32c699", + "metadata": {}, + "source": [ + "### Step 3: Initialize the anchor object\n", + "We define a suction anchor with its type, initial geometry and anchor location within the defined area." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "3aab0b15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zlug: 8.67\n", + "L: 12.0\n" + ] + } + ], + "source": [ + "anchor = Anchor(\n", + " dd = {'type': 'suction', 'design': {'D': 2.5, 'L': 12.0, 'zlug': 8.67}},\n", + " r = [250.0, 250.0, 0.0]\n", + ")\n", + "print('zlug:', anchor.dd['design']['zlug'])\n", + "print('L:', anchor.dd['design']['L'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "c26832ae", + "metadata": {}, + "source": [ + "### Step 4: Assign soil profile to anchor location\n", + "This connects the anchor object to the appropriate CPT soil data based on proximity." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "368fac90", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Anchor] Interpolated soil profile: Interpolated_2D with soil types ['clay']\n", + "zlug: 8.67\n", + "L: 12.0\n" + ] + } + ], + "source": [ + "anchor.interpolateSoilProfile(profile_map)\n", + "print('zlug:', anchor.dd['design']['zlug'])\n", + "print('L:', anchor.dd['design']['L'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "b7ca698d", + "metadata": {}, + "source": [ + "### Step 5: Plot suction anchor and soil profile\n", + "We represent a suction anchor embedded in the soil." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "71419ebe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zlug: 7.3994720299213235\n", + "L: 11.099208044881985\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Access anchor geometrical properties\n", + "L = anchor.dd['design']['L']\n", + "D = anchor.dd['design']['D']\n", + "zlug = anchor.dd['design']['zlug']\n", + "print('zlug:', anchor.dd['design']['zlug'])\n", + "print('L:', anchor.dd['design']['L'])\n", + "# Access matched profile\n", + "layers = anchor.soil_profile\n", + "z0 = layers[0]['top'] \n", + "\n", + "plot_suction(layers, L=L, D=D, z0=z0, zlug=zlug)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d5ee57b", + "metadata": {}, + "source": [ + "### Step 6: Assign external loads and line properties\n", + "We assign horizontal and vertical loads and specify the mooring line type and its physical properties (nominal diameter and weight (N/m))." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38df38f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zlug: 8.67\n", + "L: 12.0\n" + ] + } + ], + "source": [ + "anchor.loads = {\n", + " 'Hm': 3e6,\n", + " 'Vm': 2e6\n", + "}\n", + "anchor.line_type = 'chain'\n", + "anchor.d = 0.16\n", + "anchor.w = 5000.0\n" + ] + }, + { + "cell_type": "markdown", + "id": "b70c8102", + "metadata": {}, + "source": [ + "### Step 7: Compute lug forces\n", + "We compute the forces acting at the lug using load, geometry, and soil interaction. " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "4ae865bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.67\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3375980.0732258284, thetaa = 55.648978744279006\n", + "Output Ha = 1904935.434154513, Va = 2787196.1621888806\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Lug Forces Computed:\n", + "Ha = 1904935.43 N\n", + "Va = 2787196.16 N\n" + ] + } + ], + "source": [ + "layers, Ha, Va = anchor.getLugForces(\n", + " Hm = anchor.loads['Hm'],\n", + " Vm = anchor.loads['Vm'],\n", + " zlug = anchor.dd['design']['zlug'],\n", + " line_type = anchor.line_type,\n", + " d = anchor.d,\n", + " w = anchor.w,\n", + " plot = True\n", + ")\n", + "\n", + "print('\\nLug Forces Computed:')\n", + "print(f'Ha = {Ha:.2f} N')\n", + "print(f'Va = {Va:.2f} N')" + ] + }, + { + "cell_type": "markdown", + "id": "97f25452", + "metadata": {}, + "source": [ + "### Step 8: Compute the anchor capacity\n", + "This checks whether the current anchor design meets load requirements. Results and plots are printed for reference." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "aea072d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Debug] mass_update = False\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.67\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3375980.0732258284, thetaa = 55.648978744279006\n", + "Output Ha = 1904935.434154513, Va = 2787196.1621888806\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.67\n", + "Output Ha = 1904935.4341545128, Va = 2787196.162188881, zlug = 8.67\n", + "Output Ta = 3375980.073225829, thetaa = 55.648978744279006\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.382\n", + "Vmax_layer = 280523.02 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 280523.02 N\n", + "Vmax3 = 241917.02 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.388\n", + "Vmax_layer = 977721.41 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 977721.41 N\n", + "Vmax3 = 697709.86 N\n", + "dz_clip = 5.00 m\n", + "ez_layer = 9.68 m\n", + "Su_av_z (at ez_layer) = 67381.35 Pa\n", + "alphastar = 0.381\n", + "Vmax_layer = 4714446.61 N\n", + "Vmax1 = 4714446.61 N\n", + "Vmax2 = 2131059.03 N\n", + "Vmax3 = 1378013.04 N\n", + "dz_clip = -4.00 m\n", + "Hmax_layer = 1068338.04 m\n", + "Hmax_layer = 4213508.43 m\n", + "ez_global = 7.51 m\n", + "Hmax_final = 11659911.93 m\n", + "rlug_eff = 0.49 m\n", + "zlug_eff = 8.75 m\n", + "M = -3719492.55 Nm\n", + "delta_phi = 1.23 deg\n", + "phi_MH = -37.45 deg\n", + "a_MH = 14.68\n", + "b_MH = 2.13\n", + "a_VH = 4.60\n", + "b_VH = 5.87\n", + "pile_head = 65180.03 N\n", + "Vmax_final = 6037871.08 N\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Capacity Results:\n", + "Hmax: 8496895.31\n", + "Vmax: 6037871.08\n", + "Ha: 1904935.43\n", + "Va: 2787196.16\n", + "zlug: 8.67\n", + "z0: 1.75\n", + "UC: 0.01\n", + "Weight pile: 457496.77\n", + "Initial mass from dd: Not defined\n" + ] + } + ], + "source": [ + "anchor.getCapacityAnchor(\n", + " Hm = anchor.loads['Hm'],\n", + " Vm = anchor.loads['Vm'],\n", + " zlug = anchor.dd['design']['zlug'],\n", + " line_type = anchor.line_type,\n", + " d = anchor.d,\n", + " w = anchor.w,\n", + " plot = True\n", + ")\n", + "\n", + "print('\\nCapacity Results:')\n", + "for key, value in anchor.anchorCapacity.items():\n", + " print(f'{key}: {value:.2f}')\n", + "print('Initial mass from dd:', anchor.dd['design'].get('mass', 'Not defined'))" + ] + }, + { + "cell_type": "markdown", + "id": "052f68ee", + "metadata": {}, + "source": [ + "### Step 9: Anchor material costs\n", + "We assess the cost of the suction pile defined by the manufacturing cost (USD/kg)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2858630b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mass: 46635.76 kg\n", + "Material unit cost: 10.25 USD/kg\n", + "Material cost: 478016.50 USD [2024]\n" + ] + } + ], + "source": [ + "anchor.getCostAnchor()\n", + "\n", + "print(f\"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg\")\n", + "print(f\"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg\")\n", + "print(f'Material cost: {anchor.cost[\"Material cost\"]:.2f} USD [2024]')\n" + ] + }, + { + "cell_type": "markdown", + "id": "ec72f15a", + "metadata": {}, + "source": [ + "### Step 10: Optimize anchor geometry\n", + "We optimize anchor length and diameter to ensure capacity requirements are met efficiently within given bounds. Note that a safety factor (SF_combined) = 2 is used in this optimization process. This means that the unity check (UC = 1/SF) equals 0.5. This way the design can accept some extra capacity based on input preference." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "304da340", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.0\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.0\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.0\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.382\n", + "Vmax_layer = 280523.02 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 280523.02 N\n", + "Vmax3 = 241917.02 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.388\n", + "Vmax_layer = 977721.41 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 977721.41 N\n", + "Vmax3 = 697709.86 N\n", + "dz_clip = 5.00 m\n", + "ez_layer = 9.68 m\n", + "Su_av_z (at ez_layer) = 67381.35 Pa\n", + "alphastar = 0.381\n", + "Vmax_layer = 4714446.61 N\n", + "Vmax1 = 4714446.61 N\n", + "Vmax2 = 2131059.03 N\n", + "Vmax3 = 1378013.04 N\n", + "dz_clip = -4.00 m\n", + "Hmax_layer = 1068338.04 m\n", + "Hmax_layer = 4213508.43 m\n", + "ez_global = 7.51 m\n", + "Hmax_final = 11659911.93 m\n", + "rlug_eff = 0.55 m\n", + "zlug_eff = 8.08 m\n", + "M = -2654716.69 Nm\n", + "delta_phi = 1.23 deg\n", + "phi_MH = -37.45 deg\n", + "a_MH = 14.68\n", + "b_MH = 2.13\n", + "a_VH = 4.60\n", + "b_VH = 5.87\n", + "pile_head = 65180.03 N\n", + "Vmax_final = 6037871.08 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.066666666666666\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.066666666666666\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.066666666666666\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.382\n", + "Vmax_layer = 280523.02 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 280523.02 N\n", + "Vmax3 = 241917.02 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.388\n", + "Vmax_layer = 977721.41 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 977721.41 N\n", + "Vmax3 = 697709.86 N\n", + "dz_clip = 5.10 m\n", + "ez_layer = 9.74 m\n", + "Su_av_z (at ez_layer) = 67694.92 Pa\n", + "alphastar = 0.383\n", + "Vmax_layer = 4770807.37 N\n", + "Vmax1 = 4770807.37 N\n", + "Vmax2 = 2190495.24 N\n", + "Vmax3 = 1414096.12 N\n", + "dz_clip = -3.90 m\n", + "Hmax_layer = 1068338.04 m\n", + "Hmax_layer = 4213508.43 m\n", + "ez_global = 7.58 m\n", + "Hmax_final = 11886754.80 m\n", + "rlug_eff = 0.54 m\n", + "zlug_eff = 8.14 m\n", + "M = -2625933.52 Nm\n", + "delta_phi = 1.22 deg\n", + "phi_MH = -37.44 deg\n", + "a_MH = 14.67\n", + "b_MH = 2.13\n", + "a_VH = 4.64\n", + "b_VH = 5.88\n", + "pile_head = 65180.03 N\n", + "Vmax_final = 6094231.84 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.0\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.0\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.0\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.382\n", + "Vmax_layer = 294553.72 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 294553.72 N\n", + "Vmax3 = 257302.57 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.388\n", + "Vmax_layer = 1020831.37 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 1020831.37 N\n", + "Vmax3 = 735946.66 N\n", + "dz_clip = 5.00 m\n", + "ez_layer = 9.68 m\n", + "Su_av_z (at ez_layer) = 67381.35 Pa\n", + "alphastar = 0.381\n", + "Vmax_layer = 5051461.65 N\n", + "Vmax1 = 5051461.65 N\n", + "Vmax2 = 2222396.23 N\n", + "Vmax3 = 1448165.42 N\n", + "dz_clip = -4.00 m\n", + "Hmax_layer = 1111071.56 m\n", + "Hmax_layer = 4382048.77 m\n", + "ez_global = 7.51 m\n", + "Hmax_final = 12126308.41 m\n", + "rlug_eff = 0.60 m\n", + "zlug_eff = 8.08 m\n", + "M = -2798831.67 Nm\n", + "delta_phi = 1.26 deg\n", + "phi_MH = -37.49 deg\n", + "a_MH = 14.68\n", + "b_MH = 2.13\n", + "a_VH = 4.44\n", + "b_VH = 5.81\n", + "pile_head = 70824.69 N\n", + "Vmax_final = 6437671.43 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.982130175536096\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.982130175536096\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.982130175536096\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.383\n", + "Vmax_layer = 267237.19 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 267237.19 N\n", + "Vmax3 = 227542.82 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.388\n", + "Vmax_layer = 936475.62 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 936475.62 N\n", + "Vmax3 = 661643.15 N\n", + "dz_clip = 4.97 m\n", + "ez_layer = 9.66 m\n", + "Su_av_z (at ez_layer) = 67297.38 Pa\n", + "alphastar = 0.381\n", + "Vmax_layer = 4381689.40 N\n", + "Vmax1 = 4381689.40 N\n", + "Vmax2 = 2028361.30 N\n", + "Vmax3 = 1302390.78 N\n", + "dz_clip = -4.03 m\n", + "Hmax_layer = 1027168.32 m\n", + "Hmax_layer = 4051135.70 m\n", + "ez_global = 7.49 m\n", + "Hmax_final = 11152427.49 m\n", + "rlug_eff = 0.50 m\n", + "zlug_eff = 8.06 m\n", + "M = -2533815.29 Nm\n", + "delta_phi = 1.19 deg\n", + "phi_MH = -37.42 deg\n", + "a_MH = 14.67\n", + "b_MH = 2.13\n", + "a_VH = 4.75\n", + "b_VH = 5.92\n", + "pile_head = 60000.40 N\n", + "Vmax_final = 5645402.62 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.968928702525485\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.968928702525485\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.968928702525485\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.383\n", + "Vmax_layer = 253949.36 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 253949.36 N\n", + "Vmax3 = 213365.55 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.389\n", + "Vmax_layer = 894799.42 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 894799.42 N\n", + "Vmax3 = 625721.83 N\n", + "dz_clip = 4.95 m\n", + "ez_layer = 9.65 m\n", + "Su_av_z (at ez_layer) = 67235.37 Pa\n", + "alphastar = 0.381\n", + "Vmax_layer = 4059873.87 N\n", + "Vmax1 = 4059873.87 N\n", + "Vmax2 = 1929735.84 N\n", + "Vmax3 = 1229931.07 N\n", + "dz_clip = -4.05 m\n", + "Hmax_layer = 985281.02 m\n", + "Hmax_layer = 3885932.88 m\n", + "ez_global = 7.48 m\n", + "Hmax_final = 10656507.21 m\n", + "rlug_eff = 0.45 m\n", + "zlug_eff = 8.04 m\n", + "M = -2399145.75 Nm\n", + "delta_phi = 1.14 deg\n", + "phi_MH = -37.37 deg\n", + "a_MH = 14.66\n", + "b_MH = 2.12\n", + "a_VH = 4.93\n", + "b_VH = 5.98\n", + "pile_head = 54986.19 N\n", + "Vmax_final = 5263608.84 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.98319669640548\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.98319669640548\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.98319669640548\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.384\n", + "Vmax_layer = 240934.05 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 240934.05 N\n", + "Vmax3 = 199681.97 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.389\n", + "Vmax_layer = 853555.73 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 853555.73 N\n", + "Vmax3 = 590698.27 N\n", + "dz_clip = 4.97 m\n", + "ez_layer = 9.66 m\n", + "Su_av_z (at ez_layer) = 67302.39 Pa\n", + "alphastar = 0.381\n", + "Vmax_layer = 3770930.94 N\n", + "Vmax1 = 3770930.94 N\n", + "Vmax2 = 1854188.89 N\n", + "Vmax3 = 1172231.33 N\n", + "dz_clip = -4.03 m\n", + "Hmax_layer = 943537.67 m\n", + "Hmax_layer = 3721297.74 m\n", + "ez_global = 7.49 m\n", + "Hmax_final = 10247596.94 m\n", + "rlug_eff = 0.40 m\n", + "zlug_eff = 8.05 m\n", + "M = -2252875.71 Nm\n", + "delta_phi = 1.09 deg\n", + "phi_MH = -37.32 deg\n", + "a_MH = 14.65\n", + "b_MH = 2.12\n", + "a_VH = 5.13\n", + "b_VH = 6.04\n", + "pile_head = 50241.16 N\n", + "Vmax_final = 4915661.89 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.0260784418972\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.0260784418972\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.0260784418972\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.384\n", + "Vmax_layer = 230888.20 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 230888.20 N\n", + "Vmax3 = 189264.17 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.389\n", + "Vmax_layer = 821429.12 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 821429.12 N\n", + "Vmax3 = 563784.35 N\n", + "dz_clip = 5.04 m\n", + "ez_layer = 9.70 m\n", + "Su_av_z (at ez_layer) = 67503.95 Pa\n", + "alphastar = 0.382\n", + "Vmax_layer = 3572663.63 N\n", + "Vmax1 = 3572663.63 N\n", + "Vmax2 = 1818508.67 N\n", + "Vmax3 = 1141524.26 N\n", + "dz_clip = -3.96 m\n", + "Hmax_layer = 910817.63 m\n", + "Hmax_layer = 3592250.41 m\n", + "ez_global = 7.54 m\n", + "Hmax_final = 10016185.59 m\n", + "rlug_eff = 0.36 m\n", + "zlug_eff = 8.09 m\n", + "M = -2112176.19 Nm\n", + "delta_phi = 1.04 deg\n", + "phi_MH = -37.27 deg\n", + "a_MH = 14.64\n", + "b_MH = 2.12\n", + "a_VH = 5.33\n", + "b_VH = 6.11\n", + "pile_head = 46694.67 N\n", + "Vmax_final = 4671675.62 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.071808646017427\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.071808646017427\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.071808646017427\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.384\n", + "Vmax_layer = 221466.71 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 221466.71 N\n", + "Vmax3 = 179612.64 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 791062.42 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 791062.42 N\n", + "Vmax3 = 538644.96 N\n", + "dz_clip = 5.11 m\n", + "ez_layer = 9.74 m\n", + "Su_av_z (at ez_layer) = 67719.12 Pa\n", + "alphastar = 0.383\n", + "Vmax_layer = 3391245.16 N\n", + "Vmax1 = 3391245.16 N\n", + "Vmax2 = 1786501.75 N\n", + "Vmax3 = 1113768.92 N\n", + "dz_clip = -3.89 m\n", + "Hmax_layer = 879722.76 m\n", + "Hmax_layer = 3469612.74 m\n", + "ez_global = 7.59 m\n", + "Hmax_final = 9802617.52 m\n", + "rlug_eff = 0.32 m\n", + "zlug_eff = 8.13 m\n", + "M = -1987645.24 Nm\n", + "delta_phi = 0.99 deg\n", + "phi_MH = -37.22 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.53\n", + "b_VH = 6.18\n", + "pile_head = 43462.90 N\n", + "Vmax_final = 4447237.19 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.134836617745083\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.134836617745083\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.134836617745083\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.384\n", + "Vmax_layer = 217286.79 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 217286.79 N\n", + "Vmax3 = 175368.64 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 777515.41 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 777515.41 N\n", + "Vmax3 = 527525.03 N\n", + "dz_clip = 5.20 m\n", + "ez_layer = 9.79 m\n", + "Su_av_z (at ez_layer) = 68016.06 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 3337860.11 N\n", + "Vmax1 = 3337860.11 N\n", + "Vmax2 = 1802474.45 N\n", + "Vmax3 = 1119287.48 N\n", + "dz_clip = -3.80 m\n", + "Hmax_layer = 865797.80 m\n", + "Hmax_layer = 3414692.94 m\n", + "ez_global = 7.66 m\n", + "Hmax_final = 9822765.87 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 8.19 m\n", + "M = -1913941.07 Nm\n", + "delta_phi = 0.96 deg\n", + "phi_MH = -37.18 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.12\n", + "a_VH = 5.66\n", + "b_VH = 6.22\n", + "pile_head = 42058.90 N\n", + "Vmax_final = 4374721.22 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.128098362499056\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.128098362499056\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.128098362499056\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.384\n", + "Vmax_layer = 204673.39 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 204673.39 N\n", + "Vmax3 = 162708.37 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 736353.01 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 736353.01 N\n", + "Vmax3 = 494101.60 N\n", + "dz_clip = 5.19 m\n", + "ez_layer = 9.79 m\n", + "Su_av_z (at ez_layer) = 67984.30 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 3055268.25 N\n", + "Vmax1 = 3055268.25 N\n", + "Vmax2 = 1704894.70 N\n", + "Vmax3 = 1050360.20 N\n", + "dz_clip = -3.81 m\n", + "Hmax_layer = 823283.12 m\n", + "Hmax_layer = 3247015.71 m\n", + "ez_global = 7.65 m\n", + "Hmax_final = 9322538.75 m\n", + "rlug_eff = 0.25 m\n", + "zlug_eff = 8.18 m\n", + "M = -1773403.69 Nm\n", + "delta_phi = 0.89 deg\n", + "phi_MH = -37.12 deg\n", + "a_MH = 14.61\n", + "b_MH = 2.11\n", + "a_VH = 5.92\n", + "b_VH = 6.31\n", + "pile_head = 37935.66 N\n", + "Vmax_final = 4034230.31 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.064733812581633\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.064733812581633\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.064733812581633\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200778.12 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200778.12 N\n", + "Vmax3 = 158844.34 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 723554.22 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 723554.22 N\n", + "Vmax3 = 483821.94 N\n", + "dz_clip = 5.10 m\n", + "ez_layer = 9.73 m\n", + "Su_av_z (at ez_layer) = 67685.82 Pa\n", + "alphastar = 0.383\n", + "Vmax_layer = 2934560.60 N\n", + "Vmax1 = 2934560.60 N\n", + "Vmax2 = 1633123.89 N\n", + "Vmax3 = 1004880.85 N\n", + "dz_clip = -3.90 m\n", + "Hmax_layer = 810000.62 m\n", + "Hmax_layer = 3194629.72 m\n", + "ez_global = 7.58 m\n", + "Hmax_final = 9007383.29 m\n", + "rlug_eff = 0.24 m\n", + "zlug_eff = 8.12 m\n", + "M = -1755541.43 Nm\n", + "delta_phi = 0.89 deg\n", + "phi_MH = -37.11 deg\n", + "a_MH = 14.61\n", + "b_MH = 2.11\n", + "a_VH = 5.96\n", + "b_VH = 6.32\n", + "pile_head = 36697.38 N\n", + "Vmax_final = 3895590.32 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.998215467623575\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.998215467623575\n", + "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 7.998215467623575\n", + "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 199945.41 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 199945.41 N\n", + "Vmax3 = 158021.18 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 720812.78 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 720812.78 N\n", + "Vmax3 = 481627.11 N\n", + "dz_clip = 5.00 m\n", + "ez_layer = 9.68 m\n", + "Su_av_z (at ez_layer) = 67372.96 Pa\n", + "alphastar = 0.382\n", + "Vmax_layer = 2879193.91 N\n", + "Vmax1 = 2879193.91 N\n", + "Vmax2 = 1582650.96 N\n", + "Vmax3 = 974616.00 N\n", + "dz_clip = -4.00 m\n", + "Hmax_layer = 807151.64 m\n", + "Hmax_layer = 3183393.38 m\n", + "ez_global = 7.51 m\n", + "Hmax_final = 8804738.14 m\n", + "rlug_eff = 0.24 m\n", + "zlug_eff = 8.05 m\n", + "M = -1774666.08 Nm\n", + "delta_phi = 0.89 deg\n", + "phi_MH = -37.12 deg\n", + "a_MH = 14.61\n", + "b_MH = 2.11\n", + "a_VH = 5.93\n", + "b_VH = 6.31\n", + "pile_head = 36434.85 N\n", + "Vmax_final = 3836386.95 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.932052105212464\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.932052105212464\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.932052105212464\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 198416.10 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 198416.10 N\n", + "Vmax3 = 156512.04 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 715773.02 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 715773.02 N\n", + "Vmax3 = 477598.68 N\n", + "dz_clip = 4.90 m\n", + "ez_layer = 9.62 m\n", + "Su_av_z (at ez_layer) = 67062.26 Pa\n", + "alphastar = 0.381\n", + "Vmax_layer = 2810131.86 N\n", + "Vmax1 = 2810131.86 N\n", + "Vmax2 = 1528398.93 N\n", + "Vmax3 = 941545.42 N\n", + "dz_clip = -4.10 m\n", + "Hmax_layer = 801910.52 m\n", + "Hmax_layer = 3162722.52 m\n", + "ez_global = 7.44 m\n", + "Hmax_final = 8580006.44 m\n", + "rlug_eff = 0.24 m\n", + "zlug_eff = 7.98 m\n", + "M = -1800507.88 Nm\n", + "delta_phi = 0.90 deg\n", + "phi_MH = -37.13 deg\n", + "a_MH = 14.61\n", + "b_MH = 2.11\n", + "a_VH = 5.91\n", + "b_VH = 6.30\n", + "pile_head = 35954.70 N\n", + "Vmax_final = 3760275.68 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.865586696499585\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.865586696499585\n", + "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.865586696499585\n", + "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 197449.65 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 197449.65 N\n", + "Vmax3 = 155560.13 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 712584.83 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 712584.83 N\n", + "Vmax3 = 475054.60 N\n", + "dz_clip = 4.80 m\n", + "ez_layer = 9.57 m\n", + "Su_av_z (at ez_layer) = 66750.64 Pa\n", + "alphastar = 0.379\n", + "Vmax_layer = 2753492.76 N\n", + "Vmax1 = 2753492.76 N\n", + "Vmax2 = 1478731.12 N\n", + "Vmax3 = 911638.29 N\n", + "dz_clip = -4.20 m\n", + "Hmax_layer = 798592.51 m\n", + "Hmax_layer = 3149636.33 m\n", + "ez_global = 7.36 m\n", + "Hmax_final = 8378262.49 m\n", + "rlug_eff = 0.25 m\n", + "zlug_eff = 7.92 m\n", + "M = -1819224.36 Nm\n", + "delta_phi = 0.91 deg\n", + "phi_MH = -37.14 deg\n", + "a_MH = 14.61\n", + "b_MH = 2.11\n", + "a_VH = 5.88\n", + "b_VH = 6.29\n", + "pile_head = 35652.62 N\n", + "Vmax_final = 3699179.85 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.798991463084559\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3414465.46817813, thetaa = 51.581346423704765\n", + "Output Ha = 2121758.7150329417, Va = 2675203.616280948\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.798991463084559\n", + "Output Ha = 2121758.715032941, Va = 2675203.616280948, zlug = 7.798991463084559\n", + "Output Ta = 3414465.46817813, thetaa = 51.58134642370477\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 196874.23 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 196874.23 N\n", + "Vmax3 = 154994.01 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 710685.36 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 710685.36 N\n", + "Vmax3 = 473540.48 N\n", + "dz_clip = 4.70 m\n", + "ez_layer = 9.51 m\n", + "Su_av_z (at ez_layer) = 66438.92 Pa\n", + "alphastar = 0.377\n", + "Vmax_layer = 2705430.48 N\n", + "Vmax1 = 2705430.48 N\n", + "Vmax2 = 1432335.01 N\n", + "Vmax3 = 883958.63 N\n", + "dz_clip = -4.30 m\n", + "Hmax_layer = 796614.80 m\n", + "Hmax_layer = 3141836.27 m\n", + "ez_global = 7.29 m\n", + "Hmax_final = 8192744.37 m\n", + "rlug_eff = 0.25 m\n", + "zlug_eff = 7.85 m\n", + "M = -1857294.69 Nm\n", + "delta_phi = 0.92 deg\n", + "phi_MH = -37.14 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.11\n", + "a_VH = 5.84\n", + "b_VH = 6.28\n", + "pile_head = 35473.25 N\n", + "Vmax_final = 3648463.32 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.7325628950142224\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3414465.46817813, thetaa = 51.581346423704765\n", + "Output Ha = 2121758.7150329417, Va = 2675203.616280948\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.7325628950142224\n", + "Output Ha = 2121758.715032941, Va = 2675203.616280948, zlug = 7.7325628950142224\n", + "Output Ta = 3414465.46817813, thetaa = 51.58134642370477\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 195825.56 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 195825.56 N\n", + "Vmax3 = 153963.57 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 707221.33 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 707221.33 N\n", + "Vmax3 = 470782.30 N\n", + "dz_clip = 4.60 m\n", + "ez_layer = 9.45 m\n", + "Su_av_z (at ez_layer) = 66128.49 Pa\n", + "alphastar = 0.376\n", + "Vmax_layer = 2648458.05 N\n", + "Vmax1 = 2648458.05 N\n", + "Vmax2 = 1383719.44 N\n", + "Vmax3 = 854590.09 N\n", + "dz_clip = -4.40 m\n", + "Hmax_layer = 793006.36 m\n", + "Hmax_layer = 3127604.63 m\n", + "ez_global = 7.22 m\n", + "Hmax_final = 7993393.93 m\n", + "rlug_eff = 0.25 m\n", + "zlug_eff = 7.78 m\n", + "M = -1876357.33 Nm\n", + "delta_phi = 0.92 deg\n", + "phi_MH = -37.15 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.11\n", + "a_VH = 5.81\n", + "b_VH = 6.27\n", + "pile_head = 35147.33 N\n", + "Vmax_final = 3586652.27 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.677288410606607\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3414465.46817813, thetaa = 51.581346423704765\n", + "Output Ha = 2121758.7150329417, Va = 2675203.616280948\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.677288410606607\n", + "Output Ha = 2121758.715032941, Va = 2675203.616280948, zlug = 7.677288410606607\n", + "Output Ta = 3414465.46817813, thetaa = 51.58134642370477\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 202798.06 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 202798.06 N\n", + "Vmax3 = 160845.34 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 730196.38 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 730196.38 N\n", + "Vmax3 = 489150.03 N\n", + "dz_clip = 4.52 m\n", + "ez_layer = 9.41 m\n", + "Su_av_z (at ez_layer) = 65870.59 Pa\n", + "alphastar = 0.374\n", + "Vmax_layer = 2758674.00 N\n", + "Vmax1 = 2758674.00 N\n", + "Vmax2 = 1392151.06 N\n", + "Vmax3 = 864978.49 N\n", + "dz_clip = -4.48 m\n", + "Hmax_layer = 816897.59 m\n", + "Hmax_layer = 3221831.27 m\n", + "ez_global = 7.15 m\n", + "Hmax_final = 8096223.75 m\n", + "rlug_eff = 0.28 m\n", + "zlug_eff = 7.73 m\n", + "M = -1982366.87 Nm\n", + "delta_phi = 0.97 deg\n", + "phi_MH = -37.20 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.61\n", + "b_VH = 6.20\n", + "pile_head = 37337.42 N\n", + "Vmax_final = 3729005.87 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.665922524227427\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n", + "Output Ha = 2154823.310507873, Va = 2655538.689355862\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.665922524227427\n", + "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.665922524227427\n", + "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 196174.16 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 196174.16 N\n", + "Vmax3 = 154305.93 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 708373.20 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 708373.20 N\n", + "Vmax3 = 471699.01 N\n", + "dz_clip = 4.50 m\n", + "ez_layer = 9.40 m\n", + "Su_av_z (at ez_layer) = 65817.60 Pa\n", + "alphastar = 0.374\n", + "Vmax_layer = 2619818.77 N\n", + "Vmax1 = 2619818.77 N\n", + "Vmax2 = 1344383.57 N\n", + "Vmax3 = 831736.86 N\n", + "dz_clip = -4.50 m\n", + "Hmax_layer = 794206.50 m\n", + "Hmax_layer = 3132337.95 m\n", + "ez_global = 7.14 m\n", + "Hmax_final = 7843864.78 m\n", + "rlug_eff = 0.26 m\n", + "zlug_eff = 7.72 m\n", + "M = -1926167.79 Nm\n", + "delta_phi = 0.94 deg\n", + "phi_MH = -37.16 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.11\n", + "a_VH = 5.75\n", + "b_VH = 6.25\n", + "pile_head = 35255.54 N\n", + "Vmax_final = 3559621.67 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.599299557791959\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n", + "Output Ha = 2154823.310507873, Va = 2655538.689355862\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.599299557791959\n", + "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.599299557791959\n", + "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 195724.83 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 195724.83 N\n", + "Vmax3 = 153864.68 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 706888.43 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 706888.43 N\n", + "Vmax3 = 470517.44 N\n", + "dz_clip = 4.40 m\n", + "ez_layer = 9.34 m\n", + "Su_av_z (at ez_layer) = 65507.31 Pa\n", + "alphastar = 0.372\n", + "Vmax_layer = 2575602.34 N\n", + "Vmax1 = 2575602.34 N\n", + "Vmax2 = 1300634.14 N\n", + "Vmax3 = 805649.13 N\n", + "dz_clip = -4.60 m\n", + "Hmax_layer = 792659.46 m\n", + "Hmax_layer = 3126236.47 m\n", + "ez_global = 7.07 m\n", + "Hmax_final = 7668689.19 m\n", + "rlug_eff = 0.26 m\n", + "zlug_eff = 7.65 m\n", + "M = -1953647.14 Nm\n", + "delta_phi = 0.95 deg\n", + "phi_MH = -37.17 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.11\n", + "a_VH = 5.70\n", + "b_VH = 6.23\n", + "pile_head = 35116.08 N\n", + "Vmax_final = 3513331.68 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.53340544846019\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n", + "Output Ha = 2154823.310507873, Va = 2655538.689355862\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.53340544846019\n", + "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.53340544846019\n", + "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 197610.86 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 197610.86 N\n", + "Vmax3 = 155718.82 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 713116.82 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 713116.82 N\n", + "Vmax3 = 475478.87 N\n", + "dz_clip = 4.30 m\n", + "ez_layer = 9.29 m\n", + "Su_av_z (at ez_layer) = 65200.95 Pa\n", + "alphastar = 0.371\n", + "Vmax_layer = 2577607.38 N\n", + "Vmax1 = 2577607.38 N\n", + "Vmax2 = 1271420.39 N\n", + "Vmax3 = 789811.18 N\n", + "dz_clip = -4.70 m\n", + "Hmax_layer = 799146.30 m\n", + "Hmax_layer = 3151820.44 m\n", + "ez_global = 6.99 m\n", + "Hmax_final = 7573363.72 m\n", + "rlug_eff = 0.27 m\n", + "zlug_eff = 7.59 m\n", + "M = -2008087.24 Nm\n", + "delta_phi = 0.97 deg\n", + "phi_MH = -37.19 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.12\n", + "a_VH = 5.61\n", + "b_VH = 6.20\n", + "pile_head = 35702.93 N\n", + "Vmax_final = 3524037.99 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.466940379053499\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.466940379053499\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.466940379053499\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 196644.71 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 196644.71 N\n", + "Vmax3 = 154768.34 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 709927.45 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 709927.45 N\n", + "Vmax3 = 472936.67 N\n", + "dz_clip = 4.20 m\n", + "ez_layer = 9.23 m\n", + "Su_av_z (at ez_layer) = 64892.47 Pa\n", + "alphastar = 0.369\n", + "Vmax_layer = 2523844.77 N\n", + "Vmax1 = 2523844.77 N\n", + "Vmax2 = 1225690.08 N\n", + "Vmax3 = 762107.62 N\n", + "dz_clip = -4.80 m\n", + "Hmax_layer = 795825.49 m\n", + "Hmax_layer = 3138723.25 m\n", + "ez_global = 6.92 m\n", + "Hmax_final = 7384462.35 m\n", + "rlug_eff = 0.28 m\n", + "zlug_eff = 7.52 m\n", + "M = -2044839.88 Nm\n", + "delta_phi = 0.98 deg\n", + "phi_MH = -37.19 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.12\n", + "a_VH = 5.57\n", + "b_VH = 6.19\n", + "pile_head = 35401.81 N\n", + "Vmax_final = 3465818.74 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.528345537398288\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n", + "Output Ha = 2154823.310507873, Va = 2655538.689355862\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.528345537398288\n", + "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.528345537398288\n", + "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 191489.13 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 191489.13 N\n", + "Vmax3 = 149720.01 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.391\n", + "Vmax_layer = 692864.61 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 692864.61 N\n", + "Vmax3 = 459393.52 N\n", + "dz_clip = 4.29 m\n", + "ez_layer = 9.28 m\n", + "Su_av_z (at ez_layer) = 65177.44 Pa\n", + "alphastar = 0.371\n", + "Vmax_layer = 2456531.86 N\n", + "Vmax1 = 2456531.86 N\n", + "Vmax2 = 1233020.90 N\n", + "Vmax3 = 762760.60 N\n", + "dz_clip = -4.71 m\n", + "Hmax_layer = 778027.14 m\n", + "Hmax_layer = 3068526.82 m\n", + "ez_global = 6.99 m\n", + "Hmax_final = 7361457.45 m\n", + "rlug_eff = 0.25 m\n", + "zlug_eff = 7.58 m\n", + "M = -1940635.38 Nm\n", + "delta_phi = 0.94 deg\n", + "phi_MH = -37.15 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.11\n", + "a_VH = 5.74\n", + "b_VH = 6.25\n", + "pile_head = 33812.71 N\n", + "Vmax_final = 3374698.32 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.467198257566559\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.467198257566559\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.467198257566559\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 199070.56 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 199070.56 N\n", + "Vmax3 = 157157.45 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 717930.56 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 717930.56 N\n", + "Vmax3 = 479322.23 N\n", + "dz_clip = 4.20 m\n", + "ez_layer = 9.23 m\n", + "Su_av_z (at ez_layer) = 64893.66 Pa\n", + "alphastar = 0.369\n", + "Vmax_layer = 2570776.56 N\n", + "Vmax1 = 2570776.56 N\n", + "Vmax2 = 1239396.50 N\n", + "Vmax3 = 771938.93 N\n", + "dz_clip = -4.80 m\n", + "Hmax_layer = 804154.83 m\n", + "Hmax_layer = 3171574.04 m\n", + "ez_global = 6.92 m\n", + "Hmax_final = 7462364.83 m\n", + "rlug_eff = 0.29 m\n", + "zlug_eff = 7.52 m\n", + "M = -2072151.45 Nm\n", + "delta_phi = 0.99 deg\n", + "phi_MH = -37.20 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.52\n", + "b_VH = 6.17\n", + "pile_head = 36159.86 N\n", + "Vmax_final = 3523937.54 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.400809287203985\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.400809287203985\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.400809287203985\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200208.23 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200208.23 N\n", + "Vmax3 = 158280.88 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721678.26 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721678.26 N\n", + "Vmax3 = 482319.75 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.18 m\n", + "Su_av_z (at ez_layer) = 64586.08 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557372.63 N\n", + "Vmax1 = 2557372.63 N\n", + "Vmax2 = 1205455.44 N\n", + "Vmax3 = 752641.00 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808051.22 m\n", + "Hmax_layer = 3186941.31 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7340231.71 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.46 m\n", + "M = -2119835.45 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.45\n", + "b_VH = 6.15\n", + "pile_head = 36517.63 N\n", + "Vmax_final = 3515776.75 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.334659934620789\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n", + "Output Ha = 2218182.0096819005, Va = 2615301.443540927\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.334659934620789\n", + "Output Ha = 2218182.0096819005, Va = 2615301.443540927, zlug = 7.334659934620789\n", + "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 201762.59 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 201762.59 N\n", + "Vmax3 = 159818.83 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 726792.83 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 726792.83 N\n", + "Vmax3 = 486418.02 N\n", + "dz_clip = 4.00 m\n", + "ez_layer = 9.12 m\n", + "Su_av_z (at ez_layer) = 64280.15 Pa\n", + "alphastar = 0.365\n", + "Vmax_layer = 2551829.96 N\n", + "Vmax1 = 2551829.96 N\n", + "Vmax2 = 1173891.23 N\n", + "Vmax3 = 734980.53 N\n", + "dz_clip = -5.00 m\n", + "Hmax_layer = 813364.49 m\n", + "Hmax_layer = 3207896.79 m\n", + "ez_global = 6.77 m\n", + "Hmax_final = 7231118.91 m\n", + "rlug_eff = 0.31 m\n", + "zlug_eff = 7.39 m\n", + "M = -2186006.01 Nm\n", + "delta_phi = 1.03 deg\n", + "phi_MH = -37.22 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.36\n", + "b_VH = 6.12\n", + "pile_head = 37008.76 N\n", + "Vmax_final = 3517394.13 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.367735235084056\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n", + "Output Ha = 2218182.0096819005, Va = 2615301.443540927\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.367735235084056\n", + "Output Ha = 2218182.0096819005, Va = 2615301.443540927, zlug = 7.367735235084056\n", + "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200985.91 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200985.91 N\n", + "Vmax3 = 159049.91 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 724238.04 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 724238.04 N\n", + "Vmax3 = 484369.80 N\n", + "dz_clip = 4.05 m\n", + "ez_layer = 9.15 m\n", + "Su_av_z (at ez_layer) = 64433.05 Pa\n", + "alphastar = 0.366\n", + "Vmax_layer = 2554651.42 N\n", + "Vmax1 = 2554651.42 N\n", + "Vmax2 = 1189687.39 N\n", + "Vmax3 = 743827.80 N\n", + "dz_clip = -4.95 m\n", + "Hmax_layer = 810711.05 m\n", + "Hmax_layer = 3197431.63 m\n", + "ez_global = 6.81 m\n", + "Hmax_final = 7285791.25 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.42 m\n", + "M = -2159357.06 Nm\n", + "delta_phi = 1.02 deg\n", + "phi_MH = -37.22 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.40\n", + "b_VH = 6.13\n", + "pile_head = 36763.02 N\n", + "Vmax_final = 3516638.39 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.402328930637527\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.402328930637527\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.402328930637527\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 203323.99 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 203323.99 N\n", + "Vmax3 = 161367.30 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 731923.95 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 731923.95 N\n", + "Vmax3 = 490538.20 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.18 m\n", + "Su_av_z (at ez_layer) = 64593.11 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2618168.22 N\n", + "Vmax1 = 2618168.22 N\n", + "Vmax2 = 1223180.09 N\n", + "Vmax3 = 765347.70 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 818690.10 m\n", + "Hmax_layer = 3228900.88 m\n", + "ez_global = 6.85 m\n", + "Hmax_final = 7440529.26 m\n", + "rlug_eff = 0.31 m\n", + "zlug_eff = 7.46 m\n", + "M = -2154084.22 Nm\n", + "delta_phi = 1.03 deg\n", + "phi_MH = -37.23 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.38\n", + "b_VH = 6.13\n", + "pile_head = 37504.80 N\n", + "Vmax_final = 3590920.97 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.387262874558214\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.387262874558214\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.387262874558214\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 194526.39 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 194526.39 N\n", + "Vmax3 = 152689.26 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 702925.62 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 702925.62 N\n", + "Vmax3 = 467367.43 N\n", + "dz_clip = 4.08 m\n", + "ez_layer = 9.16 m\n", + "Su_av_z (at ez_layer) = 64523.38 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2442151.12 N\n", + "Vmax1 = 2442151.12 N\n", + "Vmax2 = 1166741.18 N\n", + "Vmax3 = 725788.37 N\n", + "dz_clip = -4.92 m\n", + "Hmax_layer = 788528.44 m\n", + "Hmax_layer = 3109943.77 m\n", + "ez_global = 6.83 m\n", + "Hmax_final = 7131536.23 m\n", + "rlug_eff = 0.28 m\n", + "zlug_eff = 7.44 m\n", + "M = -2062716.68 Nm\n", + "delta_phi = 0.99 deg\n", + "phi_MH = -37.18 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.12\n", + "a_VH = 5.56\n", + "b_VH = 6.19\n", + "pile_head = 34745.26 N\n", + "Vmax_final = 3374348.39 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399868809039603\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399868809039603\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399868809039603\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 197098.22 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 197098.22 N\n", + "Vmax3 = 155214.33 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 711424.87 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 711424.87 N\n", + "Vmax3 = 474129.82 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64581.72 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2497443.65 N\n", + "Vmax1 = 2497443.65 N\n", + "Vmax2 = 1188082.69 N\n", + "Vmax3 = 740184.52 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 797384.86 m\n", + "Hmax_layer = 3144873.36 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7241136.90 m\n", + "rlug_eff = 0.28 m\n", + "zlug_eff = 7.45 m\n", + "M = -2085188.94 Nm\n", + "delta_phi = 1.00 deg\n", + "phi_MH = -37.19 deg\n", + "a_MH = 14.62\n", + "b_MH = 2.12\n", + "a_VH = 5.51\n", + "b_VH = 6.17\n", + "pile_head = 35543.03 N\n", + "Vmax_final = 3441509.77 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.401280820159469\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.401280820159469\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.401280820159469\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 198651.52 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 198651.52 N\n", + "Vmax3 = 156744.14 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 716549.26 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 716549.26 N\n", + "Vmax3 = 478218.60 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.18 m\n", + "Su_av_z (at ez_layer) = 64588.26 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2527816.21 N\n", + "Vmax1 = 2527816.21 N\n", + "Vmax2 = 1197328.35 N\n", + "Vmax3 = 746738.23 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 802718.09 m\n", + "Hmax_layer = 3165907.52 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7292898.26 m\n", + "rlug_eff = 0.29 m\n", + "zlug_eff = 7.45 m\n", + "M = -2102012.31 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.20 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.48\n", + "b_VH = 6.16\n", + "pile_head = 36028.45 N\n", + "Vmax_final = 3479045.44 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.404969278223096\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.404969278223096\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.404969278223096\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200252.40 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200252.40 N\n", + "Vmax3 = 158324.53 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721823.67 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721823.67 N\n", + "Vmax3 = 482436.15 N\n", + "dz_clip = 4.11 m\n", + "ez_layer = 9.18 m\n", + "Su_av_z (at ez_layer) = 64605.33 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2560434.10 N\n", + "Vmax1 = 2560434.10 N\n", + "Vmax2 = 1208205.08 N\n", + "Vmax3 = 754302.10 N\n", + "dz_clip = -4.89 m\n", + "Hmax_layer = 808202.34 m\n", + "Hmax_layer = 3187537.35 m\n", + "ez_global = 6.85 m\n", + "Hmax_final = 7351484.53 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.46 m\n", + "M = -2118126.36 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.45\n", + "b_VH = 6.15\n", + "pile_head = 36531.55 N\n", + "Vmax_final = 3519041.72 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.392551909093574\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.392551909093574\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.392551909093574\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200418.54 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200418.54 N\n", + "Vmax3 = 158488.76 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 722370.64 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 722370.64 N\n", + "Vmax3 = 482874.05 N\n", + "dz_clip = 4.09 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64547.86 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557010.14 N\n", + "Vmax1 = 2557010.14 N\n", + "Vmax2 = 1201607.66 N\n", + "Vmax3 = 750506.23 N\n", + "dz_clip = -4.91 m\n", + "Hmax_layer = 808770.78 m\n", + "Hmax_layer = 3189779.27 m\n", + "ez_global = 6.83 m\n", + "Hmax_final = 7327159.07 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2126596.87 Nm\n", + "delta_phi = 1.02 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.43\n", + "b_VH = 6.14\n", + "pile_head = 36583.92 N\n", + "Vmax_final = 3516383.25 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.39671089582197\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.39671089582197\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.39671089582197\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200348.96 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200348.96 N\n", + "Vmax3 = 158419.97 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 722141.58 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 722141.58 N\n", + "Vmax3 = 482690.65 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64567.11 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557890.21 N\n", + "Vmax1 = 2557890.21 N\n", + "Vmax2 = 1203741.72 N\n", + "Vmax3 = 751723.22 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808532.74 m\n", + "Hmax_layer = 3188840.42 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7334873.38 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2123599.58 Nm\n", + "delta_phi = 1.02 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36561.98 N\n", + "Vmax_final = 3516942.73 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.398765463164171\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.398765463164171\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.398765463164171\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200283.88 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200283.88 N\n", + "Vmax3 = 158355.64 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721927.32 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721927.32 N\n", + "Vmax3 = 482519.12 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64576.62 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557735.79 N\n", + "Vmax1 = 2557735.79 N\n", + "Vmax2 = 1204630.47 N\n", + "Vmax3 = 752204.75 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808310.06 m\n", + "Hmax_layer = 3187962.18 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7337730.23 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2121773.85 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36541.47 N\n", + "Vmax_final = 3516488.45 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.398967367270157\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.398967367270157\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.398967367270157\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200475.34 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200475.34 N\n", + "Vmax3 = 158544.92 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 722557.64 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 722557.64 N\n", + "Vmax3 = 483023.78 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64577.55 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2561515.84 N\n", + "Vmax1 = 2561515.84 N\n", + "Vmax2 = 1205784.76 N\n", + "Vmax3 = 753023.08 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808965.11 m\n", + "Hmax_layer = 3190545.68 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7344156.40 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2123824.98 Nm\n", + "delta_phi = 1.02 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36601.84 N\n", + "Vmax_final = 3521150.66 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.398361136666139\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.398361136666139\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.398361136666139\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 199901.12 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 199901.12 N\n", + "Vmax3 = 157977.43 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 720666.94 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 720666.94 N\n", + "Vmax3 = 481510.41 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64574.74 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2550184.33 N\n", + "Vmax1 = 2550184.33 N\n", + "Vmax2 = 1202322.59 N\n", + "Vmax3 = 750569.08 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 807000.03 m\n", + "Hmax_layer = 3182795.44 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7324879.64 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2117672.10 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.45\n", + "b_VH = 6.15\n", + "pile_head = 36420.91 N\n", + "Vmax_final = 3507173.30 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3997901611151775\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3997901611151775\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3997901611151775\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200248.80 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200248.80 N\n", + "Vmax3 = 158320.97 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721811.82 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721811.82 N\n", + "Vmax3 = 482426.67 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64581.36 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557608.37 N\n", + "Vmax1 = 2557608.37 N\n", + "Vmax2 = 1205059.48 N\n", + "Vmax3 = 752434.61 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808190.03 m\n", + "Hmax_layer = 3187488.79 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7339073.09 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2120834.02 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36530.41 N\n", + "Vmax_final = 3516199.41 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399278551221133\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399278551221133\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399278551221133\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200267.08 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200267.08 N\n", + "Vmax3 = 158339.04 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721872.01 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721872.01 N\n", + "Vmax3 = 482474.85 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64578.99 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557686.71 N\n", + "Vmax1 = 2557686.71 N\n", + "Vmax2 = 1204849.43 N\n", + "Vmax3 = 752322.84 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808252.58 m\n", + "Hmax_layer = 3187735.49 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7338426.50 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2121311.88 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36536.17 N\n", + "Vmax_final = 3516361.97 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399327343324559\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399327343324559\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399327343324559\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200315.00 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200315.00 N\n", + "Vmax3 = 158386.41 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 722029.79 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 722029.79 N\n", + "Vmax3 = 482601.15 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64579.22 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2558631.75 N\n", + "Vmax1 = 2558631.75 N\n", + "Vmax2 = 1205137.35 N\n", + "Vmax3 = 752527.06 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808416.55 m\n", + "Hmax_layer = 3188382.19 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7340031.12 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2121826.25 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36551.28 N\n", + "Vmax_final = 3517527.81 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399180964245797\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399180964245797\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399180964245797\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200171.25 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200171.25 N\n", + "Vmax3 = 158244.33 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721556.47 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721556.47 N\n", + "Vmax3 = 482222.27 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64578.54 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2555797.15 N\n", + "Vmax1 = 2555797.15 N\n", + "Vmax2 = 1204273.64 N\n", + "Vmax3 = 751914.46 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 807924.64 m\n", + "Hmax_layer = 3186442.09 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7335217.41 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2120283.14 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36505.98 N\n", + "Vmax_final = 3514030.84 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399534460150541\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399534460150541\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399534460150541\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200258.04 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200258.04 N\n", + "Vmax3 = 158330.11 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721842.25 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721842.25 N\n", + "Vmax3 = 482451.03 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64580.17 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557649.57 N\n", + "Vmax1 = 2557649.57 N\n", + "Vmax2 = 1204955.08 N\n", + "Vmax3 = 752379.16 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808221.66 m\n", + "Hmax_layer = 3187613.52 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7338753.25 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2121074.04 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36533.33 N\n", + "Vmax_final = 3516283.19 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.39940652842244\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.39940652842244\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.39940652842244\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200262.58 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200262.58 N\n", + "Vmax3 = 158334.60 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721857.21 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721857.21 N\n", + "Vmax3 = 482462.99 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64579.58 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557668.58 N\n", + "Vmax1 = 2557668.58 N\n", + "Vmax2 = 1204902.39 N\n", + "Vmax3 = 752351.10 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808237.20 m\n", + "Hmax_layer = 3187674.81 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7338590.63 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2121193.20 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36534.76 N\n", + "Vmax_final = 3516323.13 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399418649509588\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399418649509588\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399418649509588\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200274.56 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200274.56 N\n", + "Vmax3 = 158346.44 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721896.66 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721896.66 N\n", + "Vmax3 = 482494.58 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64579.64 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557904.84 N\n", + "Vmax1 = 2557904.84 N\n", + "Vmax2 = 1204974.34 N\n", + "Vmax3 = 752402.14 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808278.20 m\n", + "Hmax_layer = 3187836.52 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7338991.70 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2121321.87 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36538.53 N\n", + "Vmax_final = 3516614.60 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3993822862479135\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3993822862479135\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3993822862479135\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200238.62 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200238.62 N\n", + "Vmax3 = 158310.91 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721778.30 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721778.30 N\n", + "Vmax3 = 482399.83 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64579.47 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557196.10 N\n", + "Vmax1 = 2557196.10 N\n", + "Vmax2 = 1204758.49 N\n", + "Vmax3 = 752249.02 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808155.19 m\n", + "Hmax_layer = 3187351.39 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7337788.51 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2120935.87 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36527.21 N\n", + "Vmax_final = 3515740.23 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3994720299213235\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3994720299213235\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3994720299213235\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200260.26 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200260.26 N\n", + "Vmax3 = 158332.30 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721849.55 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721849.55 N\n", + "Vmax3 = 482456.87 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64579.89 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557658.86 N\n", + "Vmax1 = 2557658.86 N\n", + "Vmax2 = 1204929.37 N\n", + "Vmax3 = 752365.47 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808229.24 m\n", + "Hmax_layer = 3187643.44 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7338673.91 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2121132.20 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36534.03 N\n", + "Vmax_final = 3516302.69 N\n", + "[Debug] mass_update = True\n", + "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3994720299213235\n", + "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", + "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", + "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3994720299213235\n", + "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3994720299213235\n", + "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", + "[Branch Check] Entered zlug>z0 for anchor suction\n", + "dz_clip = 1.75 m\n", + "ez_layer = 2.74 m\n", + "Su_av_z (at ez_layer) = 20960.65 Pa\n", + "alphastar = 0.385\n", + "Vmax_layer = 200260.26 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 200260.26 N\n", + "Vmax3 = 158332.30 N\n", + "dz_clip = 3.50 m\n", + "ez_layer = 5.44 m\n", + "Su_av_z (at ez_layer) = 41334.23 Pa\n", + "alphastar = 0.390\n", + "Vmax_layer = 721849.55 N\n", + "Vmax1 = not applicable\n", + "Vmax2 = 721849.55 N\n", + "Vmax3 = 482456.87 N\n", + "dz_clip = 4.10 m\n", + "ez_layer = 9.17 m\n", + "Su_av_z (at ez_layer) = 64579.89 Pa\n", + "alphastar = 0.367\n", + "Vmax_layer = 2557658.86 N\n", + "Vmax1 = 2557658.86 N\n", + "Vmax2 = 1204929.37 N\n", + "Vmax3 = 752365.47 N\n", + "dz_clip = -4.90 m\n", + "Hmax_layer = 808229.24 m\n", + "Hmax_layer = 3187643.44 m\n", + "ez_global = 6.84 m\n", + "Hmax_final = 7338673.91 m\n", + "rlug_eff = 0.30 m\n", + "zlug_eff = 7.45 m\n", + "M = -2121132.20 Nm\n", + "delta_phi = 1.01 deg\n", + "phi_MH = -37.21 deg\n", + "a_MH = 14.63\n", + "b_MH = 2.12\n", + "a_VH = 5.44\n", + "b_VH = 6.15\n", + "pile_head = 36534.03 N\n", + "Vmax_final = 3516302.69 N\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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Zi+JJRWJ9rDpEzuoQOStPZCy4C9GX1SFyVofIWXlazlgUTyoqLS11dRNmBJGzOkTOyhMZC+5C9GV1iJzVIXJWnpYzFsWTIAiCIAiCIAjCRRBblau4Vfng4CBms1m155upRM7qEDkrT2SsvJGRET755BOysrLEuZ4UJPqyOkTO6hA5K0/tjMVW5Rp1+vRpVzdhRhA5q0PkrDyRsfK8vb3R6XSicFKY6MvqEDmrQ+SsPC1nLIonFXV1dbm6CTOCyFkdImfliYyVV1dXx09+8hPq6upc3RS3JvqyOkTO6hA5K0/LGYviSUU+Pj6ubsKMIHJWh8hZeSJj5XV1dbFt2zZN/6F2B6Ivq0PkrA6Rs/K0nLE45knFY57sdjsGg0G155upRM7qEDkrT2SsvIKCAnJzc8nPz2fBggWubo7bEn1ZHSJndYiclad2xuKYJ43auXOnq5swI4ic1SFyVp7IWHAXoi+rQ+SsDpGz8rScsSieBEEQBEEQBEEQLoIonlSUlJTk6ibMCCJndYiclScyVl5ERAT33HMPERERrm6KWxN9WR0iZ3WInJWn5YyNrm7ATCLOCaAOkbM6RM7KExkrLyYmhp/+9KdER0e7uiluTfRldYic1SFyVp6WMxYzTyoqLi52dRNmBJGzOkTOyhMZK29gYICXX36ZgYEBVzfFrYm+rA6RszpEzsrTcsaieBIEQRBmrMrKSv7zP/+TyspKVzdFEARBmAZE8aSixYsXu7oJM4LIWR0iZ+WJjAV3IfqyOkTO6hA5K0/LGYviSUXV1dWubsKMIHJWh8hZeSJjwV2IvqwOkbM6RM7K03LGonhSUXt7u6ubMCOInNUhclaeyFhwF6Ivq0PkrA6Rs/K0nLEonlTk6enp6ibMCCJndYiclScyVp7JZCI0NBSTyeTqprg10ZfVIXJWh8hZeVrOWCdJkuTqRqitv7+fgIAA+vr68Pf3d3VzBEEQBEEQBEFwkUupDcTMk4q2bdvm6ibMCCJndYiclScyVofIWXkiY3WInNUhclaeljMWxZMgCIIwYxUXF/ONb3xD0+cUEQRBELRDFE8qio+Pd3UTZgSRszpEzsoTGSvParXS2dmJ1Wp1dVPcmujL6hA5q0PkrDwtZyyKJxUFBwe7ugkzgshZHSJn5YmMBXch+rI6RM7qEDkrT8sZi+JJRUVFRa5uwowgclaHyFl5ImPBXYi+rA6RszpEzsrTcsaieBIEQRAEQRAEQbgIYqtyFbcq7+7u1vQ0pLsQOatD5Kw8kbHyBgYG2LNnD2vWrMHPz8/VzXFboi+rQ+SsDpGz8tTOWFNblT/11FMkJSXh5eVFbm4uBw4cuOB9v/nNb6LT6SZdMjMz5fts2bLlvPcZHR1V+qVctsbGRlc3YUYQOatD5Kw8kbHy/Pz8SExMFIWTwkRfVofIWR0iZ+VpOWNFi6fXXnuN73//+/z4xz+msLCQFStWsGnTJurr6897/yeeeIKWlhb50tDQQHBwMLfccsuE+/n7+0+4X0tLC15eXkq+lCnR0tLi6ibMCCJndYiclScyVl5TUxO/+MUvaGpqcnVT3Jroy+oQOatD5Kw8LWesaPH0m9/8hrvvvptvfetbpKen8/jjjxMXF8fTTz993vsHBAQQGRkpX06cOEFPTw933XXXhPvpdLoJ94uMjFTyZUwZo9Ho6ibMCCJndYiclScyVl5bWxuvv/46bW1trm6KWxN9WR0iZ3WInJWn5YwVK54sFgv5+fls2LBhwvUbNmzg8OHDF/UYzz//POvWrSMhIWHC9YODgyQkJBAbG8u1115LYWHh5z7O2NgY/f39Ey6usHbtWpc870wjclaHyFl5ImPBXYi+rA6RszpEzsrTcsaKlXWdnZ3Y7XYiIiImXB8REUFra+sX/nxLSwsfffQRf/vb3yZcP2fOHLZs2UJ2djb9/f088cQTLFu2jJMnT5Kamnrex3rsscd45JFHJl2/c+dOfH19ufLKKzl+/DiDg4MEBQWRmZnJwYMH5edzOBxUVFQAsGrVKoqKiuQDyhYsWMDevXsBSE1NxWg0UlZWBsDy5cspLS2lu7sbX19fhoaG5OdOTk7Gx8eHTz75BIAlS5ZQVVVFR0cHXl5erFy5ku3btwOQkJBAYGAgJ0+eBGDRokXU19fT2tqKyWTiyiuvZPv27UiSRGxsLOHh4RQUFACQm5tLa2srTU1N6PV61q9fz65du7DZbERFRREbG8vHH38MwLx58+ju7paXVW7cuJG9e/cyNjZGeHg4ycnJHD16FIDs7GwGBwepqakBYN26dRw+fJjh4WFCQkKYM2cOhw4dAiAjIwOLxUJVVRUAa9as4cSJEwwMDBAYGMjcuXPZv38/ALNnzwagvLwcgJUrV3Lq1Cl6e3vx8/Nj4cKF7NmzB4CUlBQ8PDwoLS0FYNmyZZw+fZqCggLS09NZunQpO3fuBCApKQmz2UxxcTEAixcvprq6mvb2djw9PVm9ejXbtm0DnCdmCw4OlrfJvOKKK2hsbKSlpQWj0cjatWvZsWMHDoeDmJgYIiMjyc/PB2DBggW0t7fT2NiITqdjw4YN7N69G6vVSmRkJPHx8Rw/fhyAnJwcent7qaurA5wfLOzfv5/R0VHCwsJISUnhyJEjAGRlZTE8PEx1dTXgHFSOHj3K0NAQwcHBZGRkyH02PT0dm81GZWUlAKtXr6agoEA+GHLevHns27cPgLS0NPR6PadPn5b7bElJCT09PZjNZhYtWsTu3bsBmDVrFl5eXpSUlABgs9mIjIyks7MTHx8fli1bxo4dOwBITEzE39+fU6dOAZCXl0dtbS1tbW14eHiwZs0aOe+4uDhCQ0PlD0EWLlxIc3Mzzc3NGAwG1q1bx86dO7Hb7URHRxMdHc2JEycAmD9/Pp2dnTQ0NMh9ds+ePVgsFiIiIkhMTOTYsWMAzJ07l/7+fmprawFYv349hw4dYnh4mNDQUNLS0uQPdjIzMxkdHeXMmTMAqo0RixcvZteuXYBzjCguLsbX1xcQY8RUjhFdXV34+PiwdOlS+XdeV1dHZGSkGCOmcIxYunQpFRUVdHZ20tzczJ133inGCKZ2jDj3fcR7771HWFiYGCOYujHifO8j6urq2Lx5sxgjmLox4tz3Eb29vfLvDpQfI8bbfzEU222vubmZmJgYDh8+zJIlS+Trf/GLX/CXv/zlCxv52GOP8etf/5rm5mY8PDwueD+Hw8GCBQtYuXIlTz755HnvMzY2xtjYmPzv/v5+4uLiVN9tb9u2bWzcuFG155upRM7qEDkrT2SsvIKCAnJzc8nPz2fBggWubo7bEn1ZHSJndYiclad2xpey255iM0+hoaEYDIZJs0zt7e2TZqPOJUkSL7zwArfffvvnFk4Aer2eK664Qq6Oz8fT0xNPT8+Lb7xCYmJiXN2EGUHkrA6Rs/JExsoLCQnhpptuIiQkxNVNcWuiL6tD5KwOkbPytJyxYsc8eXh4kJubK0+/jduxYwdLly793J/dt28fVVVV3H333V/4PJIkUVRURFRU1GW1Vw3TZWOL6U7krA6Rs/JExspLSEjgj3/846Rja4WpJfqyOkTO6hA5K0/LGSu6296DDz7In/70J1544QXKysp44IEHqK+v59577wXg4Ycf5o477pj0c88//zx5eXlkZWVNuu2RRx5h27ZtVFdXU1RUxN13301RUZH8mFo2vp5VUJbIWR0iZ+WJjJU3MjLCm2++ycjIiKub4tZEX1aHyFkdImflaTljRfcB3Lx5M11dXTz66KO0tLSQlZXFhx9+KH/C19LSMumcT319fbz55ps88cQT533M3t5e7rnnHlpbWwkICGD+/Pns37+fRYsWKflSBEEQBDdUVlbGvffeyxVXXCGOeRIEQRC+kGIbRmjZpRwUNpU6OjoICwtT7flmKpGzOkTOyhMZK09sGKEO0ZfVIXJWh8hZeWpnfCm1gaLL9oSJ2tvbXd2EGUHkrA6Rs/JExoK7EH1ZHSJndYiclafljEXxpKLGxkZXN2FGEDmrQ+SsPJGx4C5EX1aHyFkdImflaTljUTypSKfTuboJM4LIWR0iZ+WJjJWn0+kwmUwia4WJfNUhclaHyFl5Ws5YHPOk4jFPgiAIgiAIgiBoizjmSaN2797t6ibMCCJndYiclScyVofIWXkiY3WInNUhclaeljMWxZOKrFarq5swI4ic1SFyVp7IWHllZWXcc889lJWVubopbk30ZXWInNUhclaeljMWxZOKtHy2ZHciclaHyFl5ImPljYyMcObMGXGSXIWJvqwOkbM6RM7K03LGonhSUXx8vKubMCOInNUhclaeyFhwF6Ivq0PkrA6Rs/K0nLEonlR0/PhxVzdhRhA5q0PkrDyRseAuRF9Wh8hZHSJn5Wk5Y1E8CYIgCIIgCIIgXARRPKkoJyfH1U2YEUTO6hA5K09krLykpCSeffZZkpKSXN0Utyb6sjpEzuoQOStPyxmL4klFvb29rm7CjCByVofIWXkiY+UFBQWxYsUKgoKCXN0Utyb6sjpEzuoQOStPyxmL4klFdXV1rm7CjCByVofIWXkiY+W1tbXxm9/8hra2Nlc3xa2JvqwOkbM6RM7K03LGongSBEEQZqympiaee+45mpqaXN0UQRAEYRrQSZIkuboRauvv7ycgIIC+vj78/f1Ve15JktDpdKo930wlcnZmYLFYGB0dZWRkhNHRUcbGxrDZbFit1vN+tdls2O12JElifFgY//7c6wwGAzqdDoPBgMFgQK/XT/pqNBoxmUx4eHhM+nru93q9+BznfERfVl5BQQG5ubnk5+ezYMECVzfHbYm+rA6RszpEzspTO+NLqQ2MKrVJAPbv38+qVatc3Qy3564522w2BgcHGRwcZGBgYMJlcHCQkZERuVAaHR3F4XAo2p7a2loSExOn5LE8PDzw9vbGy8sLLy8v+ftzr/Px8cHX11e+GAyGKXl+rXLXvizMPKIvq0PkrA6Rs/K0nLEonlQ0Ojrq6ibMCNM1Z0mSGB4epru7m56eHrq7u+VLT08PQ0NDl/yYBoNBLkA8PT0xmUzyjJDRaJzwvclkQq/Xy7NAOp1uwmX8OgCHw8GRI0dYuHAhDocDu90+6ev4rJbFYpn0dfz78QJv/Lq+vr5Len1eXl4TiqmzL35+fvj7++Pn54fZbJ6Ws1vTtS8LwrlEX1aHyFkdImflaTljUTypKCwszNVNmBGmQ85jY2O0tbXR2toqf+3s7GRsbOxzf85gMODn5ydfzGaz/L23t/ekGRuj0ajYtLeXl9dlLXOSJAm73Y7FYpkwY3a+78e/Dg8PMzQ0xNDQEA6HQ75fV1fX5z6XTqfDbDbLxdT41/HvAwICCAgIwGjU1pA4HfrydBcQEMDKlSsJCAhwdVPcmujL6hA5q0PkrDwtZyyOeVLxmKf+/n5Vn2+m0lrOFouFpqYmGhoaaG5upq2tjZ6envPeV6fT4e/vT3BwMMHBwQQFBcnf+/v74+3trZl11q7MWZIkRkdH5ULq3Mv40sb+/n4GBwcvagnjeIEVGBh43osriiut9WV3JXJWnshYHSJndYiclad2xuKYJ406cuQIGzdudHUz3J6rcx4ZGaG2tpaamhrq6+tpa2vjfJ9R+Pv7ExkZSUREBJGRkYSHhxMUFKS52Y8LcWXOOp1OnmkLDQ393Ps6HA6GhobkYurcr/39/fT19WG1WuVjyBoaGs77WGazeUJBGxISIn/v5eU15a/T1X15JrBarXz00UfcdNNNmEwmVzfHbYm+rA6RszpEzsrTcsbT412aIGiYw+GgsbGRiooKqquraWlpmVQsBQQEEBcXR0xMjFww+fj4uKjFM4ter5eX6EVHR5/3PuPHm/X29tLX10dvb++ki8VikTfsOF9x5ePjIxdSZ19CQ0MVKayEqVFcXMxtt90mdtsTBEEQLooonlSUlZXl6ibMCGrkbLPZqKqq4vTp01RUVDA8PDzh9tDQUJKTk0lMTCQ2NtYtp/fdqT/rdDp5o4mYmJhJt0uSxMjICL29vZM28+ju7mZgYIDh4WGGh4dpbGyc9PNms5nQ0FDCwsIIDQ2VL/7+/p+7DNOdMhZmNtGX1SFyVofIWXlazlgUTyo69w22oAylcnY4HNTW1lJcXExZWdmEnWC8vLxITU0lJSWFpKQktyyWzjWT+rNOp8PHxwcfH5/zzl5ZLBZ6enro6uqaUFR1dXXJW8kPDg5SW1s74ec8PDwmFFOhoaGEh4cTHByMXq+fURkL7k30ZXWInNUhclaeljMWxZOKqqurSU1NdXUz3N5U59zf309hYSH5+fn09/fL1/v7+5ORkcHs2bOJj493+3MOnUv05894eHgQERFBRETEpNvGxsbo7OyULx0dHXR2dtLd3Y3FYqG5uZnm5uYJP2M0GgkLC6OpqYmrrrqK8PBwwsPD8fPz08yGIYJwKcR4oQ6RszpEzsrTcsaieBKEC6ivr+fIkSOUl5fLu7V5e3uTmZlJdnY28fHx4o2s8IU8PT2JiYmZtBzQbrfT09MjF1PjhVVHRwdWq5WWlhaqq6vZvn27/DPe3t5yIRURESF/9fT0VPtlCYIgCMKMJLYqV3F5lc1mmzY7qU1nl5OzJElUVVVx4MAB6uvr5esTEhJYuHAh6enp4nf4KdGfleFwOOjp6aG9vZ2WlhY6Oztpb2+nq6vrvLs2AgQHBxMVFUVkZCSRkZFERUVhNptVbvn0ZLfb6evrIyAgYMbNHqtJjBfqEDmrQ+SsPLUzFluVa9TRo0dZvny5q5vh9r5szrW1tezYsYOmpibAeULaefPmkZeXR3h4+FQ3c9oT/VkZer2ekJAQQkJC6Orq4tZbbwWcf0g6Ojpob2+nvb2dtrY22tvb6e/vl4+xKikpkR/HbDbLhdR4URUcHCxmS89hMBgoLS0VfVlhYrxQh8hZHSJn5Wk5Y1E8qWhoaMjVTZgRLjXnzs5Otm/fTkVFBeA8fmXhwoUsWbIEPz8/JZroFkR/Vt7ZGRuNRqKiooiKipp0n9bWVvnS0tJCV1cXg4ODVFVVUVVVJd/Xw8ODqKgooqOjiY6OJiYmhqCgoBldUFVWVnL//ffz6quvanZ9vTsQ44U6RM7qEDkrT8sZi+JJRcHBwa5uwoxwsTnbbDYOHjzIgQMHsNvt6PV6Fi5cyMqVK8WSp4sg+rPyLiZjX19fZs2axaxZs+TrLBYLbW1tEwqq9vZ2LBYLdXV11NXVyff18vKSC6nxouqLtlB3JwMDAxQUFDAwMODqprg1MV6oQ+SsDpGz8rScsSieVJSRkeHqJswIF5NzS0sLb731Fh0dHQCkpqZy1VVXERISonTz3Iboz8r7shl7eHgQFxdHXFycfJ3dbqezs5OWlhaamppobm6mtbWV0dFRqqurqa6ulu9rNpsnzE7FxMSIkzoLl0WMF+oQOatD5Kw8LWcsiicVHTx4kI0bN7q6GW7v83KWJIljx46xY8cO7HY7ZrOZTZs2kZGRMWM+aZ8qoj8rbyozNhgM8nbq8+bNA5wFVVtbm7xdelNTEx0dHQwODlJRUSEvZQUICQkhNjaWuLg4YmNjCQ8PR6/XT0nbBPcnxgt1iJzVIXJWnpYzFsWTMGNYrVbefvttSktLAZgzZw433HAD3t7eLm6ZILiGwWCQZ5jGWa1WWltb5WKqqamJrq4u+XLy5EnAObsVHR09oaDy9fV11UsRBEEQBFWI4klF6enprm7CjHC+nIeGhnjllVdobGzEYDCwceNGrrjiCjHbdBlEf1aeKzI2mUyTlvwNDw/T1NREY2OjfBkbG6O2tpba2lr5fkFBQXIxFR8fPy1mp+Li4nj00UcnvF5h6onxQh0iZ3WInJWn5YwVL56eeuop/vd//5eWlhYyMzN5/PHHWbFixXnvu3fvXtasWTPp+rKyMubMmSP/+8033+S//uu/OHPmDLNmzeIXv/gFN954o2KvYarYbDZXN2FGODfngYEBtmzZQldXF97e3tx2220kJCS4qHXuQ/Rn5WklYx8fH1JTU+Xd6BwOB52dnXIh1dDQQEdHBz09PfT09FBcXAw4N6MYL6Ti4+OJjo7GZDK58qVMEhYWxte//nXCwsJc3RS3ppW+7O5EzuoQOStPyxkrWjy99tprfP/73+epp55i2bJl/PGPf2TTpk2UlpYSHx9/wZ8rLy+fcIKqs/+oHTlyhM2bN/Ozn/2MG2+8kbfffptbb72VgwcPkpeXp+TLuWyVlZUkJye7uhlu7+ych4aGeOmll+jq6iIwMJBvfOMbhIaGuriF7kH0Z+VpNWO9Xk94eDjh4eEsWLAAgNHRUXl2qr6+noaGBkZHR6msrKSyshL4bJngeDEVHx/v8mWz3d3dPPPMM/zwhz/U9O5O051W+7K7ETmrQ+SsPC1nrGjx9Jvf/Ia7776bb33rWwA8/vjjbNu2jaeffprHHnvsgj8XHh5OYGDgeW97/PHHWb9+PQ8//DAADz/8MPv27ePxxx/nlVdemfLXIExfVquVl19+mY6ODvz9/bnzzjsJCgpydbMEwS15eXlN2DLd4XDQ1tZGfX099fX11NXVMTg4SENDAw0NDRw6dAhwjvfx8fEkJCSQmJio+rnVamtr+d///V9uu+02UTwJgiAIX0ix4slisZCfn88Pf/jDCddv2LCBw4cPf+7Pzp8/n9HRUTIyMvjJT34yYSnfkSNHeOCBBybcf+PGjTz++ONT1nalrF692tVNmBFWr16NJEn84x//oKWlBV9fX1E4KUD0Z+VN54z1er18Ut+8vDwkSaK3t5e6ujq5oOrs7KS9vZ329nZOnDgBOHf1S0xMlC/iRNXuYTr35elE5KwOkbPytJyxYsVTZ2cndrudiIiICddHRETQ2tp63p+Jiori2WefJTc3l7GxMf7yl7+wdu1a9u7dy8qVKwFobW29pMcEGBsbY2xsTP53f3//l31Zl6WgoIAlS5a45LlnkoKCAoxGI6dOnUKv13PLLbeI8zcpQPRn5blTxjqdjqCgIIKCguSt0oeGhmhoaJBP3NvS0iLv6pefnw+IYspduFNf1jKRszpEzsrTcsaKbxhx7m5mkiRdcIez2bNnM3v2bPnfS5YsoaGhgf/7v/+Ti6dLfUyAxx57jEceeWTS9Tt37sTX15crr7yS48ePMzg4SFBQEJmZmRw8eBBwbmftcDjk852sWrWKoqIi+vr68Pf3Z8GCBezduxdwnmjVaDRSVlYGwPLlyyktLaW7uxtfX18GBgbYtm0bAMnJyfj4+PDJJ5/Ir7WqqoqOjg68vLxYuXIl27dvByAhIYHAwEB5i+BFixZRX19Pa2srJpOJK6+8ku3btyNJknz+lYKCAgByc3NpbW2lqakJvV7P+vXr2bVrFzabjaioKGJjY/n4448BmDdvHt3d3dTX1wPOGb29e/cyNjZGeHg4ycnJHD16FIDs7GwGBwepqakBYN26dRw+fJjh4WFCQkKYM2eOvCwnIyMDi8VCVVUVAGvWrOHEiRMMDAwQGBjI3Llz2b9/v9wHwHncG8DKlSs5deoUvb29+Pn5sXDhQvbs2QNASkoKHh4e8tbjy5Yt4/Tp0xw7dozS0lIiIyMJCAigvLwci8WC2WyWD2RfvHgx1dXVtLe34+npyerVq+XfTXx8PMHBwRQVFQFwxRVX0NjYSEtLC0ajkbVr17Jjxw4cDgcxMTFERkbKb/QWLFhAe3s7jY2N6HQ6NmzYwO7du7FarURGRhIfH8/x48cByMnJkT+JB+es7P79+xkdHSUsLIyUlBSOHDkCQFZWFsPDw/KJTNeuXcvRo0cZGhoiODiYjIwMuc+mp6djs9nk40xWr15NQUEB/f39BAQEMG/ePPbt2wdAWloaer2e06dPy322pKSEnp4ezGYzixYtYvfu3QDMmjULLy8vSkpKgM9mlzs7O/Hx8WHZsmXs2LEDgMTERPz9/Tl16hQAeXl51NbW0tbWhoeHB2vWrJHzjouLIzQ0lMLCQgAWLlwon3fIYDCwbt06du7cid1ul7fVHp+lmD9/Pp2dnTQ0NMh9ds+ePVgsFiIiIkhMTOTYsWMAzJ07l/7+fnl3uPXr13Po0CGGh4cJDQ0lLS1NnhXPzMxkdHSUM2fOAKg2RixevJhdu3YBzjGiublZzsndx4iUlBRKS0tpb28nMjKS/fv309zcTGNjI62trbz22mvy//vY2FhsNhuRkZFs3LjxkseIrq4ufHx8WLp0qfw7r6urIzIyUowRUzhGLF26lIqKCjo7O2lqaiIvL0+MEUztGHG+9xH9/f1uOUao+T7i7DFi586dACQlJcnvI2pra8nIyBBjBFM3Rpz7PqK7u5vExETVxojx9l8MnSRJ0kXf+xJYLBZ8fHx44403JuyEd//991NUVCSH/kV+8Ytf8PLLL8sDSXx8PA888MCEpXu//e1vefzxx+XOc67zzTzFxcXJA5dajh49yuLFi1V7vpnqkUceQZIk4uPjueuuu8R25AoR/Vl5Mz3j0dFR6uvr5S3RW1paOPdP1vgbsuTkZBISEvD09Lyk5ygvL+fmm2/m73//+4QP74SpNdP7slpEzuoQOStP7YzHC8OLqQ0UK57AWSXm5uby1FNPyddlZGRwww03fO6GEWe7+eab6e7ulqvWzZs3MzAwwIcffijfZ9OmTQQGBl70hhGXEtBUGh0dxcvLS7Xnm4nq6+t59tln8fDw4F//9V/F9sMKEv1ZeSLjic4upmpqamhtbZ1QTOn1emJiYuRiKjY2FoPBcFGPK3JWlshYHSJndYiclad2xpdSGyi6bO/BBx/k9ttvZ+HChSxZsoRnn32W+vp67r33XsC5U15TUxMvvfQS4NxJLzExkczMTCwWCy+//DJvvvkmb775pvyY999/PytXruRXv/oVN9xwA++++y47d+6Upxq1bN++fWzcuNHVzXBre/fupba2ls2bN4vCSWGiPytPZDyRl5cXaWlppKWlAc6T99bW1lJdXU11dTXd3d3ybn779u3DZDKRkJAgF1MRERHnnYkWOStPZKwOkbM6RM7K03LGihZPmzdvpquri0cffZSWlhaysrL48MMP5ROUtrS0yOtiwbnU76GHHqKpqQlvb28yMzP54IMPuPrqq+X7LF26lFdffZWf/OQn/Nd//RezZs3itdde0/w5ngTldXR0UF1djV6vv+CJmAVtkyQJi8WCxWJhbGxswler1YrNZptwsdvt2Gw2JEmacHE4HPL3Op3ugheDwYDBYMBoNKLX6yd8NRqNmEymSRcPDw/5e7Ek1LV8fHzIyMggIyMDgN7eXqqrq6mpqaG6upqhoSGqqqrk4yR8fX1JTk4mJSWFWbNmYTabKSgo4KqrriI/P18+Z5UgCIIgXIiiy/a0ylXL9mpqakhKSlLt+Waa8RnIoKAg7r//flc3x+19mf5st9sZGBhgcHCQ4eHhCZeRkRFGRkZwOBwKtXhq6fV6PDw88PLywtPTU754eXlNuN7Lywtvb29MJtMlP4cYM748SZJob2+XZ6Xq6uqwWCwT7hMZGYkkSXznO9/h+PHjXHHFFS5qrfsTfVkdImd1iJyVp3bGmlm2J0yk1+td3QS3JUmSvEtWVlaWi1szM3xef7bb7fT399Pd3U1/fz/9/f0MDAwwNDQ06YD/8zEajXIx4uHhgaenJyaTCaPRKM8UjV8MBgN6vV6eTTr7e51ON2lW6uzL+MyVw+GY8HX8+vEZr3Mvdrsdh8PB6Ogoo6OjF5WXyWTCx8cHb2/vC3718PCYMJslxowvT6fTERERQUREBEuWLMFut9PQ0MCZM2eoqqqipaWF1tZWWlpaANiyZQtVVVXyrJQ4Ye7UEn1ZHSJndYiclafljEXxpKLTp0/LSxaFqdXT00NfXx8Gg+Gi38wKl+fs/jw0NER7eztdXV309PTQ29uL3W4/78+ZTCb8/Pzw9fXF29tb/urj44OPjw+enp4XdZC/K9ntdnkXz7GxMUZHRyf8++zrRkdH5aKrr6+Pvr6+Cz6u0WjE19cXX19fzGYzn3zyCWvXrpX/bTSKIfvLMhgM8rmi1q5dy+DgINXV1fJWzjabjfLycnl74+DgYFJSUkhJSSEpKelLzRwKnxF//9QhclaHyFl5Ws5Y/CUW3ML4NvUxMTHiDaYKRkZG6Orq4vjx47S1tTE0NDTpPh4eHgQFBREYGIifnx/+/v74+fnh5eU17Y8VMhgMcrF3MaxWKyMjI/LyxHO/joyMMDo6is1mm1BgNTQ0cODAAflxvLy8MJvNmM1muaDy9/fH399fvLm/RGazmblz52Kz2QC46aab8PHxoaqqioaGBrq7uzl+/DjHjx/HaDSSlJREamoqaWlpBAYGurbxgiAIgsuIY55UPOZpaGgIX19f1Z5vJvnoo484duwYS5cuZdmyZSJnBQwMDNDY2EhTUxNdXV1YLBb5Dbteryc4OJjQ0FCCgoIIDg7GbDZP+yJJTXa7neHhYYaGhhgaGmJgYIDu7m6sVitDQ0OTjtc5l7e3t1ygnv3Vx8dH/B4+x+joKBUVFaSlpcnb4o6NjVFTU0NVVRWVlZWTZgvDwsJIS0sjNTWVuLg4zc+UaoH4+6cOkbM6RM7KUztjccyTRpWUlLBo0SJXN8Mt9fb2As6lNiLnqTM2NiafoLSnp2fCbSMjI2RnZxMREUFoaKiY+bhMBoMBPz8//Pz85OuOHz8u9+WxsTGGhoYYHByUvw4MDNDf38/o6Kg8g9XW1jbhcY1Go/y4AQEBBAQEEBgYiK+vryiqcM7mnXs+EU9PT+bMmcOcOXOQJImOjg4qKiqorKykoaGBjo4OOjo6OHToEF5eXsyaNYu0tDRSUlLEG6oLEOOyOkTO6hA5K0/LGYviSUXnvvkUps74J8OBgYGcOXPGxa2Z3sbfLJ45c4bGxkb52CW9Xk9YWBixsbHExMRw4MAB5s2b59rGurmzx4zxDTTOt5GBxWKRC6mzvw4MDGCz2ejp6Zk0/hiNxgnFVGBgIAEBAXh6eir+urSkpqaGH/7whzz//PPn3dlJp9MRHh5OeHg4y5cvZ2RkhDNnzlBRUUFVVRXDw8OUlJRQUlKCTqcjOjqa2bNnM3v2bMLDw0WB+inx908dImd1iJyVp+WMRfGkIrPZ7OomuK3xJU0eHh4i5y9JkiRaW1spLS2lo6NDvj4oKIjk5GTi4+MnvLEWOSvvYjP28PAgJCSEkJCQCdc7HA6GhobkHQ/Hj6fq6+vDZrPR1dVFV1fXhJ/x9vaWC6nAwECCgoLw8/PT9M5Hl6Onp4c9e/bQ09NzUdvient7k5WVRVZWFg6Hg+bmZnlWqqWlhaamJpqamti9ezdBQUFyIZWQkOC2GV4MMV6oQ+SsDpGz8rScsTjmScVjnqxWq1japJDHH3+c3t5evv3tbxMeHi5yvkStra0UFxfLb6THdyabNWsWQUFB5/30XPRn5SmVscPhYGBggN7eXvr7++nt7aW3t/e8G3+Ac5YqKChowsXf398tioGCggJyc3On5CS5AwMDVFRUUF5eTnV1tbwZBTiLrtTUVGbPnk1KSsqMm+ET44U6RM7qEDkrT+2MxTFPGrV79242btzo6ma4pfE393a7XeR8CYaGhigsLKSxsRFwvkmeNWsWc+bMwdvb+3N/VuSsPKUy1uv18pK9s41vpz5eTI1fbDabfJzPOKPRKM9MjW8S4i4F1Zfl5+dHbm4uubm5WCwWzpw5Q3l5ORUVFQwPD3Pq1ClOnTqFwWAgKSlJnpVS80M8VxHjhTpEzuoQOStPyxmL4klwCz4+PvT09DA8POzqpkwLkiRRXV1NYWEhNpsNvV5PSkoKGRkZEw6cF2YWk8lEaGgooaGh8nUOh4P+/n75uKnxi81mo7Ozk87OTvm+BoOB4OBgeQlhSEjIRW/n7m48PDxIT08nPT0dh8NBQ0ODfB6prq4uqqqqqKqq4oMPPiA6Olq+79nZC4IgCNojiicVzZo1y9VNcFvjb9CGhoZEzl/AYrFw/PhxebYpLCyM3NzcSz53jchZeVrIWK/XyxtKjB8T5HA4GBwcpKenh+7ubrmgslqtk2aofHx8JhRTQUFBmjoXW1RUFN/73veIiopS7Dn0ej0JCQkkJCSwfv16Ojs75UKqsbGR5uZmmpub2bVrF2FhYXIhFRkZ6TYbTmihL88EImd1iJyVp+WMtfMXbAYQn+grJygoCICuri4iIiJc3BrtGhgY4MCBA/T396PX68nJySEtLe1LvUET/Vl5Ws1Yr9fLJ+cdPwO8JEkMDAzIm1B0dXXR19fH8PAww8PDNDQ0yD8bEBAgF1OhoaEuPSdYVFQU//Ef/6Fo8XQ2nU5HWFgYYWFhLF++nMHBQcrLyykrK6OmpkYuPvfv309QUJBcSMXGxk7rQkqrfdndiJzVIXJWnpYzFsWTikpKSoiNjXV1M9zSeMHU1taGJEki5/Po7e1l7969jI6O4uPjw7JlyybtznYpRH9W3nTKWKfTyQXV+AyV1Wqlp6dnQkE1MjIiz1RVVVUBzs0UwsLCCA0NJSwsjICAANWOnerv7+fPf/4z3/ve91xy7JHZbJaPkxo/YW9ZWRlVVVX09PRw+PBhDh8+jJ+fH3PmzCE9PZ3ExMRpd2zZdOrL05nIWR0iZ+VpOWNRPAluYbx4amlpISwszMWt0Z6BgQH27NnD2NgYgYGBrFq16gs3hBCEy2UymeRzJIFzdmp4eHhCMdXd3c3IyAj19fXU19fLPzdeSIWGhhISEoLBYFCkjVVVVfzkJz9h06ZNl73b3uXy8vJi7ty5zJ07F4vFQlVVFWVlZVRUVDAwMMDHH3/Mxx9/jLe3N3PmzCEzM5OkpCTFshEEQRAmE1uVq/hJ48DAAH5+fqo930xit9v51a9+hcVi4Rvf+AYpKSmubpJmjI2NsWPHDgYHBwkODmbVqlVTsk2y6M/KmwkZ22w2uru76ejokDegsFqtE+6j1+vlJX7jS96magvbqdyqXCk2m42amhrKyso4ffr0hI1xvL29SU9Plwsprc5IzYS+rAUiZ3WInJWndsZiq3KNqqioIDc319XNcEvj5yWqqKhg3759onj6lCRJHDt2jMHBQcxmMytWrJiy88uI/qy8mZCx0WicMDvlcDjo7e2ls7NTPv5ndHRU/r6srAy9Xk9QUBARERGEh4cTGhqqqU0opprRaCQ1NZXU1FSuvfZa6urqKC0tpbS0lKGhIQoKCigoKMDHx4f09HSysrI0d1LemdCXtUDkrA6Rs/K0nLH7/rXRoLO39BWm3qxZs6ioqOCTTz5xdVM0o7q6mubmZgwGA8uXL5/SpXqiPytvJmas1+sJDg4mODiYtLQ0JElicHBQnpnq6OiYsDFFaWmpPDM1XkwpuczP1fR6PUlJSSQlJbFp0ybq6uooKSmhtLSU4eFh8vPzyc/Px9fXl4yMDDIzM4mPj3d5ITUT+7IriJzVIXJWnpYzFsWTimbq+U7UkpGRwdatW+nt7aWnp0fegW+mslgsnDp1CoDs7OxL3or8i4j+rDyRsXMjCj8/P/z8/EhOTgacpyRob2+nra2N9vZ2hoeHJ2yRbjQaCQ0NlYupoKCgCxYPnp6exMTETNmMrJrOLqSuvvpqamtr5UJqaGhIPkbKbDaTkZFBVlYWcXFxLtm1T/RldYic1SFyVp6WMxbHPKl4zJPD4XD5p3/u7qWXXuLMmTOsXbuWlStXuro5LlVSUkJxcTH+/v5s3Lhxyj+JF/1ZeSLjLzY+M3V2MTU6OjrhPiaTiYiICCIjI4mMjMRsNk+43d1yttvt1NTUUFJSwunTpxkZGZFvCwgIIDs7m+zsbMLDw1UrpNwtY60SOatD5Kw8tTO+lNpA/OZVtGPHDlc3we3l5ORw5swZ8vPzcTgcrm6Oy9jtdiorKwHIzMxUZAmT6M/KExl/sfGZqVmzZrF06VJuuOEGrrrqKhYsWEBsbCweHh5YrVYaGxs5ceIE77//Ph988AH5+fk0NTVhtVrdLmeDwUBKSgo33HADDz30EF//+tfJycnB09OTvr4+Dh48yNNPP83TTz/NgQMH6OnpUbxN7paxVomc1SFyVp6WMxbL9gS3kpmZiZeXF319fZSWlpKVleXqJrlEW1sbo6OjeHl5afY8CYKgBJ1OR2BgIIGBgaSlpeFwOOjp6aG1tZXW1la6uroYGBhgYGCAyspKGhoaeOSRR/jLX/7CqlWrCAoKmtYnoz2XwWCQN5uwWq1UVlZSXFxMRUUF7e3t7Nq1i127dhEXF0d2djYZGRmTZuYEQRCEz4jiSUWJiYmuboLbMxqNrFy5kurqao4cOUJmZqZbvRG6WE1NTQDExsYqduC86M/KExlfvvHNJEJCQsjMzMRqtdLW1iYXUzabjaGhIU6fPs3Y2BheXl7yEr+oqChNn+X+UplMJjIyMsjIyGB0dJSysjKKi4upqamhoaGBhoYGtm7dSlJSEtnZ2aSnp0/ZsWCiL6tD5KwOkbPytJyxKJ5U5Iqz189ES5cupb6+nqamJqqqqkhNTXV1k1Q3vktNVFSUYs8h+rPyRMZTz2QyERsbK8/IBgQEABAWFobRaGR0dJS6ujrq6urQ6XQEBwcTHR1NdHQ0gYGBbvNhjJeXF/Pnz2f+/PkMDAzIx0g2NTVx5swZzpw5w/vvv096ejo5OTkkJydf1vEHoi+rQ+SsDpGz8rScsSieVHTq1ClF38wKTmfOnCEvL49Dhw6xa9cuUlJS3OYNz8Ww2+0MDAwAKLrjoOjPYLNBSws0NX126eqC3l7o63Nexr8fGQGrdfLF4QCDAYzGyRebzUxkJJjNky/+/hAS4ryEhk78OoU70rs9X19fABYsWEBOTg5dXV20trbS0tJCT0+PvCV6cXExPj4+REVFER0dTUREhNucW8rPz4/FixezePFiuru7KS4upri4mM7OTvl7s9lMdnY2OTk5REZGXvJziPFCHSJndYiclafljN1j5BeEcyxbtowTJ07Q2tpKSUnJjDr2aXR0FIfDgcFgmNLzOs1UkgQNDVBWNvFSVQVtbc7iRzl+1NZe+k/5+EBkJERHQ0yM8+v4Zfzf8fEwDXfnVpTBYJBP2Dt37lyGh4dpaWmhubmZtrY2hoeH5VmZ8fuOz0qNF2HTXXBwMKtWrWLlypW0tLRw8uRJiouLGRwc5MiRIxw5coSIiAhycnLIzs7Gz8/P1U0WBEFQldiqXMVpwd7e3ik/144w2XjO+/btY8+ePfj7+/Nv//ZveHh4uLppquju7mb79u14e3tzww03KPY87tqfh4fh4EE4cgSOHXNeursvfH+j8bOiJCYGwsIgIAACAyd+9fYGkwk8PJxfxy96PdjtYLVK8sVmA5tNR3f3EGBmaEjH4CATLn19zlmuzk7n1/HvbbaLe506nbPdiYmQlDT5a3y8c0bM3Q0ODnL48GGWLl36uRsl2O122tvbaW5uprm5maGhoQm3BwQEEBMTQ0xMDMHBwW41222326mqquLkyZOUl5djt9sB5+Ycs2bNIicnhzlz5mAymS74GO46XmiNyFkdImflqZ3xpdQGYuZJRbW1tcybN8/VzXB74zkvXbqUwsJCent72b9/P+vWrXN101Sl9Js3d+rPlZXw9tuwfTscOAAWy8TbjUZITYX09M8us2dDXJyzWBo/FESSJGw2GxaLBavVisViwWKxYLPZsNls2O12+WKx2Bgbk+Qt9c/eWn982Z5e30pkZCQ6nU6+6PV6DAYDBoNB/v6z64yMjpro7TXR1WWivd1Ee7uB1lYDbW16mpp0NDdDY6OzSBxfanjo0ORMPD0hJQXS0pyX2bM/+z401Fl8uQOz2Ux4ePgX7jBnMBiIiooiKiqKBQsW0NfXJ89KdXZ20tfXJ+/y6ePjQ3R0NLGxsYSFhSm2aYtaDAYDs2fPZvbs2YyMjFBSUsLJkydpaGigqqqKqqoqPD09ycjIYP78+ec9Ea87jRdaJnJWh8hZeVrOWBRPKmpra3N1E2aE8ZxNJhObNm3ilVde4ciRI+Tk5BAWFubi1ilv/KBu28VOQXxJ070/t7bCSy/Bq69CYeHE2+LiYOVKWLwY8vIgJ8c5YzROkiTGxsYYHh6mtXWUkZERRkedX6fy/GLjsxuSJDG+SMBut2O1Wr/wZ8ePh0pPd/5br9djMpkwmUwYjSYGB71obfWmudmDpiYPmppM1Nfrqa3VUVMDY2NQUuK8nCsoCDIyIDsbsrKcX7OznddPN42NjTz66KM8+eSTF72t/9nboaenpzM2NkZraytNTU00NzczPDwsFxUeHh5ER0cTExNDZGTk587OTAfe3t4sXLiQhQsX0tXVxalTpzh58iS9vb0UFhZSWFhISEgI8+fPJycnR17WN93Hi+lC5KwOkbPytJyxKJ5UNFOWjbna2TmnpaWRlpZGRUUF77zzDnfffbfbnxV8/Din8RkPpQ5qn679+ehRePJJ+PvfnRs2gHN52tq1cO21sGGDc3bl7A/OJUlicHCI/v5+BgcHGRoa+twCxmQy4eHhgYeHx1kFi1GeMTr7As7C5uzZpfHnHBgYYP78+XLxJEnOmSq73T7pq91ul2e4zndxOByMjY0xNjYmtzM01HmZOxe5HR4eHhiNnnR3+9LQ4E1dnRe1tSbOnDFSWamnvh56epyzVefOWEVHf1ZIjRdV6ena3sCivb2dt99+m5/85Cdf+pxonp6eJCQkkJCQgN1up62tjcbGRpqbmxkdHaW2tpba2loMBgORkZHExMQQHR097bdBDwkJYc2aNaxevZr6+nqKioooKSmhq6uLnTt3smvXLlJTU5k/f/60n32bLqbruDzdiJyVp+WMxTFPGt4KUZga/f39PPXUU4yOjrJu3TqWL1/u6iYpSpIk3nrrLaxWK5s2bZK3Yp7pCgrgxz+GrVs/u27JErjzTvjqV51FxNnsdjt9fX309PTQ19c3aSZPr9fj7e2Nl5fXhK+enp6aK9AdDgdWq1VeSnj292dfvmjWzDlr4k1rqx81Nb5UVXlRXm6ipERPXd351/EZDM5ZqoULITfXecnJ0U5BVVBQQG5uLvn5+SxYsGBKH9vhcNDV1UVjYyNNTU0MDg7Kt+l0OkJDQ4mLiyMuLs5tNnexWCyUlJRQWFhIfX29fL2Pjw85OTnMmzePiIgIF7ZQEARhskupDUTxpGLxtG3bNjZu3Kja881U58u5qKiId955B4PBwD333OP2f7x37dpFR0cHeXl5JCUlKfIc06U/9/bCQw/B8887/200wte/DvfdB+e+V5YkiaGhIdrb2+np6ZEPjAfncR/+/v74+flhNpvx8fFRvEhSM2NJkrBYLIyNjclfR0dHJ1x3IXq9HpvNh4YGf6qrfamu9ub0aQ9KSnR0dU0uqgwGyMx0FlLjRdXcua4pqJQsns4mSRJ9fX00NTXR2NhIT0+PfNt4IRUfH09sbKzbFFJdXV0UFhZy8uRJCgsLSUlJASA6Opr58+eTnZ097WfftGa6jMvTnchZeWpnLDaMEIRz5OTkUFZWRnl5OX//+9/59re/rekp4csVEhJCR0cHnZ2dihVP08GuXXDHHdDc7Pz3174Gjzzi3AjhbJIk0dPTQ0tLy4Rd1Dw9PQkODiYwMBCz2exWO6idS6fT4enpiecF9i+32+2Mjo7Kx3aNfz++Nb5eP0hCwiAJCbBmjfNnDAYjg4P+VFUFUFrqQ0mJJ4WFBtrbdZw6BadOwYsvjt/XucxvyRJYutR5SUpyn40pzj5OKjMzk6GhIRobG2loaKCzs5OOjg46OjooKChwm0IqJCSEdevWceWVV/Liiy9iNpspLy+Xdyzctm0bmZmZ5ObmnneTCUEQBC0SxZOK4uLiXN2EGeF8Oet0Oq6//nqeeeYZOjo6+PDDD/nKV76ifuNUEh4ezunTp2lubkaSJEXelGi9P//hD3D//c5twFNS4IUXYMWKyffr7++noaFBLpr0ej3BwcGEhYW5vGDSUsYGgwFfX99J5zMa3zzj7E0zhoeHGRkZwW634e3dTXZ2N9nZ4z+ho7/fj6qqACoq/Cgp8aKw0EBHh46iIigqgqefdt4zImJiMZWbC1M9UREaGsptt91G6LnrNhXm6+sr72A3XkjV19fT1dXldoWUXq9nyZIlZGRkMDQ0xKlTpygsLKS9vZ2TJ09y8uRJwsLCyM3NJScnZ9q+Ti3Q0pjhzkTOytNyxmLZnorL9trb2wkPD1ft+Waqz8u5rq6OLVu2IEkSX/nKVzS7DeblstvtvPPOO1itVtatW6fIG0Mt9+ef/Qz++7+d399+OzzzjPPEsWez2Ww0NDTQ0dEBIB/MHx4erpkd0bSc8RdxOByMjo4yPDw84XL+XSB19PcHUFERSHGxHwUFnhQW6rBaJxauJpNzqeV4MbVqlXOr+MulpZzPLaTG6XQ6wsLC5GOkpttyt3MzliSJpqYm8vPz+eSTT+QNWIxGIxkZGeTm5hIfHy9moy6RlvqyOxM5K0/tjC+lNlD8qOannnqKpKQkvLy8yM3N5cCBAxe871tvvcX69esJCwvD39+fJUuWsG3btgn32bJly4RdqcYvo6OjSr+Uy1Z47n7IgiI+L+eEhATWfLqm6P3336epqUmtZqnKYDAQExMDQHV1tSLPodX+/MwznxVOv/gF/PnPkwun4eFhSkpK5MIpPDycuXPnEhMTo5nCCbSb8cXQ6/X4+PjIMydz5syRt69OS0sjJiaGwMDAT/OW8PfvZeHCWu66q5jf/e4Ee/cW8te/1vGjH/Vy7bUWIiIkrFbnSYt/+1u45RYID3fu6vdv/+bcPbG9/dLbOTw8zOuvv87w8PCUZ/BljM9IrV+/nuuuu4758+cTEhKCJEm0t7eTn5/Pe++9x759+6itrb2obeu14Ny+rNPpiI2N5YYbbuDf//3fueaaa4iMjMRms3Hq1ClefPFF/vCHP3DkyBHN/G6mg+k8ZkwnImflaTljRZftvfbaa3z/+9/nqaeeYtmyZfzxj39k06ZNlJaWEh8fP+n++/fvZ/369fzyl78kMDCQF198keuuu45jx44xf/58+X7+/v6Ul5dP+Nnp9imc4DorVqygqamJ8vJyXn31Ve655x75XCTuJDk5mdraWurr65k3b55bH+M1rqgIvvc95/c//Sn86EeT79PX10dVVRV2ux1PT0+Sk5Pd8vevRWcfVzV+5vjxjSqGhoYYHBxkeHj40yWUNlJS2khJaeOGG0CSoLvbn9Ongyku9uPjjz355BO9fC6qP/zB+RyZmbB6tfOycqWzwDqfykoYGICysjq+970XCApaS3p6On5+zhMia8G5S/saGhqor6+nu7ublpYWWlpaMBqNxMTEkJCQQERExLTcEtzLy4srrriChQsX0tzcLM9GdXZ2sm3bNnbu3CnPRiUkJIjZKEEQXErRZXt5eXksWLCAp8cXsAPp6el85Stf4bHHHruox8jMzGTz5s3896cfJW/ZsoXvf//79Pb2ful2uWrZXldXFyEhIao930x1MTmPjY3xpz/9iY6ODmJjY/nmN7+p2PmQXEWSJLZu3UpfXx85OTmkj58tdYporT/b7c4lXadOwY03wptvTt5sYHBwkPLycux2O/7+/syaNUtTM03n0lrGapEkiZGREYaGhiYUVecaGvKmtDSUwsIAjh71oqRk8mKKjAxnIbVhg3MjC39/Z+GUlnbh56+o0E4BdT79/f3U1dVRX1/PwMCAfL2npydxcXEkJCQQGhqqqSLjUvvy2NgYxcXF5Ofn09LSIl8fFhbGFVdcQU5OzgU3N5nJZuqYoTaRs/LUzlgTu+1ZLBby8/P54Q9/OOH6DRs2cPjw4Yt6DIfDwcDAAMHBwROuHxwclE9GOG/ePH72s59NmJk617knhuzv77+EVzJ1mpubxX82FVxMzp6entx2220899xzNDY28vbbb3PzzTdr6s3G5dLpdMyZM4djx45x+vRpUlJSprRQ0Fp/fvNNZ+EUFORcunfur9JqtVJZWYndbicgIIDU1FTNnY/pXFrLWC06nQ4fHx98fHwI+/SgJpvNxuDgIAMDAwwMDDA8PIyv7whXXNHAFVc0cM89MDDgQVlZOEVFARw75s0nn+gpLYXSUnjqKeeOfkuWOJf6Abz8svMkvuPKyuAb33DOSGmZv78/2dnZZGVl0d3dLRdSo6OjVFVVUVVVha+vLwkJCcTHx8uzfK50qX3Z09OThQsXTpiNKi4uljf82blzJzk5OVxxxRXi2JOzzNQxQ20iZ+VpOWPFiqfOzk7sdvuk8+lERETQ2tp6UY/x61//mqGhIW699Vb5ujlz5rBlyxays7Pp7+/niSeeYNmyZZw8eZLUC3xU+Nhjj/HII49Mun7nzp34+vpy5ZVXcvz4cQYHBwkKCiIzM5ODBw/Kz+dwOKioqABg1apVFBUVyZXpggUL2Lt3LwCpqakYjUbKysoAWL58OaWlpXR3d+Pr68vAwADNn+6ZnJycjI+PD5988gkAS5Ysoaqqio6ODry8vFi5ciXbt28HnMfpBAYGcvLkSQAWLVpEfX09ra2tmEwmrrzySrZv344kScTGxhIeHk5BQQEAubm5tLa20tTUhF6vZ/369ezatQubzUZUVBSxsbF8/PHHAMybN4/u7m75xIYbN25k7969jI2NER4eTnJyMkePHgUgOzubwcFBampqAFi3bh2HDx9meHiYkJAQ5syZw6FDhwDIyMjAYrFQVVUFwJo1azhx4gQDAwMEBgYyd+5c9u/fD8Ds2bMB5GWZK1eu5NSpU/T29uLn58fChQvZs2cPACkpKXh4eFBaWgrAsmXLOH36NPn5+fT29rJ06VJ27twJQFJSEmazmeLiYgAWL15MfX09UVFR7Nu3D4DKykr5IOXg4GCKiooAuOKKK2hsbJSXyKxdu5YdO3bgcDiIiYkhMjKS/Px8ABYsWEB7ezuNjY3odDo2bNjA7t27sVqtREZGEh8fz/HjxwHn9um9vb3U1dUBzg8W9u/fz+joKGFhYaSkpHDkyBEAsrKyGB4elo9fWrt2LUePHmVoaIjg4GAyMjLkPpueno7NZqOyshKHw4GXlxdVVVW8/PLLpKenM2/ePPk1p6WlodfrOX36tNxnS0pK6OnpwWw2s2jRInbv3g3ArFmz8PLyoqSkBEA+sWpnZyc+Pj4sW7aMHTt2AJCYmIi/vz+nTp0CnLPQtbW1tLW14eHhwZo1a+TjGePi4ggNDZXXN4+/WWpubsZgMLBu3Tp27tyJ3W4nOjqa6OhoTpw4AcD8+fPp7OykoaGB//7vPCCQ66+vobCwgoiICBITEzl27BgAwcHBdHZ2MjAwgL+/P6mpqRw4cIDh4WFCQ0NJS0uTP9jJzMxkdHSUM2fOAKg2RixevJhdu3YBzjGioqJCHjPEGPHZGOHj40N9fT02m43MzEzKysro7+/HZDKRmztCSEgta9eCh0c0VVVR7N9vpKgoguZmXw4ehE9/daSnTz7XF8Dhw4eZNSuT6upq2tvb8fT0ZPXq1XKf1dIYMf5/ITc3l6amJoqKiujt7SU+Pp6tW7ditVoJCwsjLy+PhoYGTCbThDECYPXq1RQUFMifvE7VGLF06VIqKiro7OykqamJzMzMLz1G5OXlAVBTU8Po6Cg1NTWUlpby2muvsWbNGmw2GzExMcTFxV1wjBjvs3v27MFisUwaI+bOnUt/fz+1tbUArF+/nkOHDml6jDj3fURRURHNzc0zfoyAy3sf0dXVhY+PzwXfR9TW1hIfHz8txojLfR8B6owR576P6O7uJjQ0VLX3EePtvxiKLdtrbm4mJiaGw4cPs2TJEvn6X/ziF/zlL3/5wka+8sorfOtb3+Ldd99l3bp1F7yfw+FgwYIFrFy5kieffPK89znfzFNcXJzqy/Z27tz5ua9FmBqXmvOpU6d46623ANi0aZP8R9pd1NXVceTIEYxGI9dcc82UbQOspf7c0gLR0c7ZppYW5xbXZxsYGKCsrAydTkdmZiY+5+4goVFayljrJElieHiY/v5+eXbq7JMcA3R0mCkqCmPbtgAOHPAgP39i8VRQ4NwO/ckn4dvfnvpt0dVis9lobm6mvr6e5uZmHA4H4NzEIzo6mqSkJCIjI1U9Pmoq+7IkSdTW1vLxxx9z+vRp+fWZzWZyc3PJzc1V9W+7logxQx0iZ+WpnfGlLNtTrHiyWCz4+PjwxhtvcOONN8rX33///RQVFckV6/m89tpr3HXXXbzxxhtcc801X/hc3/72t2lsbOSjjz66qLa56pgnQbsOHjzIzp070el03HjjjcydO9fVTZoykiSxc+dOurq6SEhImPBhhrv429/g6193vvH99MOkCSorK+np6SEsLGxGnzR4JnE4HAwNDcnF1ODgoPwm+/RpH+68M+uCxRM4d2hcvx6uvRauuQaiolzwIqbA2NgY9fX11NTU0N3dLV/v5eVFQkICiYmJBAUFubCFl6e/v5/8/Hzy8/MZHBwEnEXi7NmzWbRoEYmJiW61HFsQBGVoYqtyDw8PcnNz5em3cTt27GDp0qUX/LlXXnmFb37zm/ztb3+7qMJJkiSKioqImgZ/2canfgVlfZmcly1bxqJFi5AkiXfeeeeSpm+1TqfTkZubi06no66ubsq2Z9dSf/505QznO/TRbrfT19cHMGkZsdZpKePpRq/X4+fnR0xMjLxN+uzZs4mOjpZnHsvKnAXT+OXTlVKEhUkMD8O77zpnoKKjYeFCeOQRyM+HT2uwacHT05PU1FQ2bNjAVVddxZw5c/Dy8mJ0dJTy8nK2bdvGtm3bKC8vV/SUH0r1ZX9/f9asWcMDDzzALbfcQmJiIg6Hg7KyMv785z/zzDPPUFBQMG22dL9cYsxQh8hZeVrOWNHtxR588EFuv/12Fi5cyJIlS3j22Wepr6/n3nvvBeDhhx+mqamJl156CXAWTnfccQdPPPEEixcvlo+N8vb2JiAgAIBHHnmExYsXk5qaSn9/P08++SRFRUX8YXyfWg07dwmJoIwvk7NOp2PTpk1YLBaKiop44403+NrXvsasWbMUaKH6goODmT17NqdPn+bEiROEhYVd9tblWurP45txRUZOvm18xsHT03PKliyqRUsZT3cGg4GAgAACAgLIyXFe941vnP++f/jDKcCHo0fD2LfPTH6+kfx8Z+H0P//jnIW65hrnrNT69ZPPI6ZVgYGBzJs3j+zsbNra2qipqaGpqYmenh56eno4efKkvKwvKipqSjdUUbovGwwGMjMzyczMpL29nY8//piTJ0/S1tbGe++9x86dO8nNzeWKK65w6xUnYsxQh8hZeVrOWNHiafPmzXR1dfHoo4/S0tJCVlYWH374IQkJCQC0tLTIBxUC/PGPf8Rms/Hd736X7373u/L1d955J1u2bAGgt7eXe+65h9bWVgICApg/fz779+9n0aJFSr6UKREdHe3qJswIXzZnnU7H9ddfj8ViobS0lFdffZWvf/3rJCYmTm0DXSQrK4umpiYGBgYoKChg8eLFl/V4WurP44dunG8R8vin6T4+PtNu+Y6WMnYnqanO7cid53kq4xvf+DrPP/8CMTExOBx9hIZakKQxEhJ62LwZeno8KSyM5ODBQPbu9aClRcef/gR/+hN4e8OmTXDTTc5i6tPP+TTNYDDIB02fu6yvsbGRxsZGvL29SU5OJjk5GV9f38t+TjX7cnh4ONdccw1r166loKCA48eP09vby4EDBzh06BAZGRnk5eURGxs77caELyLGDHWInJWn5YwVPc+TVonzPLm3y83Zbrfz6quvUllZiclk4mtf+5rbHCfT0dHB7t27kSSJRYsWkZyc/KUfS0v9+cc/hl/+Er77Xfj97yfe1tjYSHNzMxEREfIHN9OFljJ2VwUFBeTm5pKfn8+CTw+Astls9Pf309fXR19fHxaLRb6/1arn9OlwjhwJYccOH+rrP3vzbTLBunXOQuqGG+DTXdanjd7eXmpra6mpqZE3WdLpdERGRpKcnEx0dPSX3mTClX3Z4XBQXl7OsWPH5J30AGJiYsjLyyMzM3Nanlz4fMSYoQ6Rs/K0fJ4nbZ/kxM2cON+R7MKUu9ycDQYDmzdvJjU1FavVyl//+ld5O9rpLiwsjOzsbADy8/Pp6en50o+lpf48XgN+utvsBOOfLE/Hz4m0lPFMYjQaCQ4OJikpiZycHDIyMuRjpUwmB9nZrdxzTwmvv/4xr79exfe+18/s2Q6sVvjoI+dxUpGRzpPy/u530Njo6ld0ccaX9V1//fUsWbKEiIgIJEmipaWFQ4cO8Y9//IOTJ09OODHvxXJlX9br9aSnp/PNb36Te++9l/nz52M0GmlqauKtt97i8ccfZ9++fec9EfN0I8YMdYiclafljBVdticI05XRaGTz5s28/vrrVFRU8Morr8gF1XSXnp5OR0cHLS0tHD58mPXr11/28U+uNr5DWkEB2O2fLeMD5OM2tLx+WtAunU6H2WzGbDYTGxvL6Ogovb299Pb2MjAwQEJCNwkJ3XzjG9DY6MfRo5Hs2OFPUZGBvXth71647z7Iy4Obb4bNmyEuztWv6vMZDAYSEhJISEhgYGCAmpoaqqurGR0dpaysjLKyMiIiIkhOTiY2NnZazdpERkZyww03sG7dOvLz8/n4448ZGBhgz549HDx4kHnz5rF48WIxqyAIwgWJZXsqLttrb28XZ0JXwVTmbLPZeOONNygvL0ev1/OVr3zFLbYxHxsbY/v27QwNDREVFcXy5csv+Q2Qlvqz1Qrh4dDb63yzumrVZ7f19PRQWVmJj48PWVlZrmril6KljN1Vb28v7733Htdffz2BgYGX9LM2m00upPr6+iYU6B0dvhw9GsWuXf4cPWpAkj5b3rdiBdx2G9xyy/RZ2me322lpaeHMmTO0trbKM7menp4kJSWRkpKC2Wy+4M9rtS/b7XZKS0s5cuSIfEJqnU7H7NmzWbp0KXFxcdPquCit5uxuRM7KUztjTZznSctcVTyVlpaSkZGh2vPNVFOds91u55133qG4uBhwnxPpdnd3s3v3bmw2G7NmzWLhwoWX9CZBa/35n/8ZXnwR/vVf4amnPrt+fAdFnU4nL9eZLrSWsbuaipwdDgf9/f10d3fT29uLzWaTb+vr8+bYsWi2bg3g8OHPCimDwXmM1G23wY03To/NJgCGhoaorq6mpqZGXuqm0+mIiooiJSWFqKioSWOJ1vuyJEnU19dz+PBhysvL5etjY2NZunQpc+bMmdLdB5Wi9ZzdhchZeWpnLI550qiGhgZXN2FGmOqcDQYDN910k1wwffTRR/KmC9NZcHAwixcvRqfTcebMmUs+t5XW+vNttzm/vvwyfHpaJ8B5zjlvb28kSaK3t9clbfuytJaxO2ptbeV///d/5VNjfFl6vZ7AwECSk5OZN28eaWlphIWFYTQaCQgYYcOGM/zmNwV8+GEJP/5xN/Pn27HbYds2uOsuiIhwbjTx+uug9UNvfH19yc7O5tprr2XFihVERUUhSRLNzc3s37+fDz/8kPLycnnTCdB+X9bpdCQkJPBP//RP/Nu//Ru5ubkYjUYaGxt5/fXX+d3vfsexY8cmbB6iRVrP2V2InJWn5YxF8SQIF0Gn03HVVVdx5ZVXArB//34++OADHNPpbJnnERsby7x58wA4efIkdXV1rm3QZVi3DjIynNtPP/vsxNuCg4MB5+49gnC25uZmtmzZIi/ZmgrjhVRSUhLz5s1j9uzZciEVHDzM9ddX8cwz+bz7bhkPPdRHerqDsTF4+23nMVHh4XDHHbBrl7ZPyKvX64mJiWHVqlVcc801pKWl4eHhwcDAAIWFhfzjH//g+PHjdHd3u7qplyQ0NJTrrruO73//+6xatQofHx96enr46KOP+O1vf8uuXbsYHBx0dTMFQXARsWzPjU+WJyjjxIkTfPDBB0iSREZGBjfeeCMmk8nVzbosBQUFVFRUoNfrWbp0KbGxsa5u0peyZYvzU/ywMOd5fMYPYRkdHaW4uBhJksjOzp52J8sVlHO+rcqV4nA4GBgYoLu7m56eHnlpnyRBY2MQe/dG8Y9/+FJX99mSt7g4ZyF1xx2QlqZo86aE1Wqlvr6eysrKCTO9oaGhpKSkEBcXN602mADnayoqKuLIkSNyIWg0Gpk/fz5Lly4lKCjIxS0UBOFyiWOevoCriqc9e/awZs0a1Z5vplIj59LSUt58803sdjuxsbHcdtttn3uwtNY5HA6OHz9ObW0ter2e5cuXf+EJ6rTYn61WmDsXTp+GBx+EX//6s9sqKyvp6ekhNDT0ss5vpSYtZuxu1CyezuZwOOjt7aWrq4u+vj55FluSoLo6nK1bI3j3XS/6+j4rpJYsgTvvdM5OXeLeFqqTJInOzk4qKytpbGykurqaxMREvLy8SElJISUlBS8vL1c385KMny/q0KFDNH66/7xerycrK4vly5drYgMBMWaoQ+SsPLUzFsc8aZTW10q7CzVyzsjI4Pbbb8fb25vGxkaee+452traFH9epej1ehYtWkR8fDwOh4NDhw594evRYn82meC3v3V+/+ST8OkeHwBERUUBzqV7Q0NDLmjdpdNixuB8Y+xwOLDb7dhsNqxW64SLzWbDZrNht9txOBzT/vhAJej1eoKDg0lNTWXevHkkJSXh7++PTgezZrXz3e8W88EHBTz+eAvr11vR6yWOHIF773WeQ2rzZuc5pc7al0JTdDodYWFhLF26lOuuu46IiAi8vb0ZHR3lk08+kZf0TafjEMfPF3X33XfzzW9+k1mzZuFwODh16hRPPfUUf/vb31x+nIZWxwx3I3JWnpYzFjNPKs48FRUVyceXCMpRM+euri7+9re/0dXVhYeHB7fccsu0PheU3W7n8OHDNDU1YTQaWb58OZGRkee9r5b78403wjvvQE4OHDsGnp7O68+cOUNXVxd+fn7MmTNH81sQuzJjSZImFUGXUwzpdDr0er389XwXg8Gg+u+kurqaf/mXf+GPf/yjJmYkx8bG6O7upqura8JJW3t7vdm/P4533vGnpOSzzz0jI51LVb/1rc9OFq1FRUVFZGdn09jYSEVFxYTjDyMjI0lLSzvvLn1a19LSwsGDByktLZX/XyQkJLB8+XJSUlJUfz1aHpfdichZeWpnLJbtfQFXFU+9vb2XfB4R4dKpnfPIyAivv/46NTU18sYSixYtmnZvAsbZ7XYOHjxIS0vL5x4DpeX+3NYGWVnQ2Qn/+Z/w//6f83qLxcKpU6dwOBwkJCQQERHh2oZ+AbUzttlsWCwWefboi/486HQ6+XIuSZLky6UYL6LOvYwXXkrQal8eHh6ms7OTrq4urFYrMH58VBjbtkXw9tvedHZ+lsn69XDPPXD99aC1816fnbEkSXR1dVFeXk5jY6PcR/z9/Zk9ezYJCQnT6pQC4Pwg7dChQ5w8eVI+31dkZCTLly8nIyNDtW3OtdqX3Y3IWXlqZyyKpy/gquJp27ZtbNy4UbXnm6lckbPdbueDDz6goKAAgIULF7Jp06Zpd2D0OLvdztGjR2loaJCX9CUmJk64j9b789tvO7d+Bnj3XecbSoC2tjbq6urQ6/VkZmZqevMINTKWJAmLxcLIyMiEcxOBs5AxGo0YjUa5qBkvYi5UNJ3v8ccv47NWDodD/n58Nmv8ciE6nU4upMbbNN6ey2GxWHj99de59dZb8dBaxfGp8eOjOjs76evrk4sNh8NIQUEMb70Vwu7dn50/Kjz8s9molBRXtvwzF+rLg4ODVFZWUl1dLReInp6e8nFRWv7/eT79/f0cOXKE/Px8edlRaGgoK1euJCsrS/EiSuvjsrsQOStP7YwvpTaYXh/tCIJGGQwGrrvuOkJDQ9mxYwcnTpygra2NW2+9FT8/P1c375IZDAaWLFmC0WikpqaGo0ePYrVap9WSxBtvhO99D373O/jGN+D4cZgzB8LDw+nt7aWvr48zZ86Qnp4+bYvcy2Wz2RgcHJSLJp1Oh8lkwsPDA5PJNCW5nF1kfdHjjRdQdrt90uXsZYRnnz/o7GJq/HIpM1SffPIJt99+OxkZGapuGHEpxo+PCg4OxmKx0NXVRUdHB6OjoyxcWMfChXV0dvqxfXscr73mS2urjl/9Cn71K1i7Fv7lX+CGG7Q3GwVgNpuZP38+WVlZVFdXU1FRwdDQECUlJZSVlZGYmMicOXOmzc64/v7+bNy4kZUrV3Ls2DGOHTtGZ2cnb731Fvv27WPFihXMnTt3WpxwVxCE8xMzTyoOyC0tLfJB64JyXJ1zRUUFb731FqOjo5jNZm655RYSEhJc1p7LIUkShYWFVFRUAM6NMrKzs9HpdC7P+WJYrc43jwcOQGoqHD4MoaHO2YaSkhKsVishISEkJydrcpmlkhmPjY0xODiIJEno9Xq8vLzw8vLS5Ju6czeoOPvrucZnqEwmE0ajEZPJ9LmvyVW77V0uSZIYHByks7OT7u5uOQuHw0BRUQxvvx3Kjh2fzUaFhcE//zN85zsQH69+ey+2LzscDpqamigvL6ezsxNw/k5jYmJIT08nJCRE6aZOqbGxMY4fP86RI0fkY9iCgoJYsWIFOTk5U/7BzXQYl92ByFl5amcslu19AVcVT+Xl5cyePVu155uptJBzd3c3r776Ku3t7ej1ejZs2EBeXp4m36B/EUmSKCkp4ZNPPgGcB0MvWrSIqqoql+d8MdraYNEiqK+HvDzniUd9fWFgYIDTp08jSRKxsbFfuDW7KyjVly0WCwMDA0iShIeHB2azWZNF0xdxOBwTNrW4UEF1bjF19hvW6Vo8nc1ut9PV1UV7e/uETSb6+gLZujWGV1/1obnZOfbo9fCVrzhnZVetArWGpC/Tlzs7Ozl9+rS8LThAREQE6enpRERETKvx1GKx8PHHH3P48GF5t8/AwECWL1/OvHnzpuwYLy38/ZsJRM7KUztjsVW5RtXW1rq6CTOCFnIODg7mW9/6FtnZ2TgcDrZu3cpbb72l6a03L0Sn05GVlUVeXh56vZ66ujr27t1LVVWVq5t2USIiYNs2CA527rx3663OGSk/Pz/iP/0IvrGxUf6UW0uU6MsOh0OecfLy8sLPz29aFk7gXM7m4eGBj48Pfn5+BAUFERQUhJ+fH15eXvISPrvdzujoKIODg/T09NDT08Pg4CAWi+Vzj7OaLgwGA+Hh4WRmZpKRkUFoaCh6vZ6AgF42by7hrbcK+OMf21m92o7DAW+9BWvWOHej/NOf4Kx6SzFfpi+HhoayfPlyNm3aRFJSEnq9nra2Nvbu3cv27dupr6+fNr8/Dw8Pli1bxv3338/GjRsxm8309vby/vvv8+STT3L8+PFJxxx+GVr4+zcTiJyVp+WMp+dfTEGYBjw8PLjpppu46qqr0Ov1FBcX8/zzz0/Yonc6SUpKYuXKlZhMJjo6Ojh9+jQDAwOubtZFmTMH3n8fvL3hww+d58ixWp2fYo9vxV5TUzOtzjnzZY2NjeFwODAYDPj6+k6rT+8vhsFgwNPTE7PZTGBgIEFBQfj7++Pj44PJZJpQTPX399Pf3w84d820Wq3T+pxUOp0Os9lMcnIyOTk5xMXF4eXlhU5nZ968Wn71q3zefbeab35zDB8fieJi+Pa3ITYW/uM/QKvvVQICAsjLy+Oaa64hLS0No9FIT08Phw8f5qOPPuLMmTPnnXHUIg8PD5YsWcL999/Ppk2b8PPzo7+/nw8//JAnn3yS/Pz8afNaBGGmEsv2VFy253A4pu0nvNOJFnOuq6vjjTfeYHBwEA8PD6677jqys7Nd3awvpbe3lwMHDjA4OIinpydLliyZNmu/t21zHjg/Nubcie/VV8FolKiurqarqwu9Xk9qaioBAQGubiqgTF/u7+/HYrHg6+s77XYymwoOh2PSSX0tFgseHh7y+aY8PDzkTTOme3EpSRL9/f20t7fT29srF4cWiy+7dsXz5z+bqan5bEnf9dfD/fdP/ZK+qezLY2NjVFZWUlFRIc/me3l5kZ6ezqxZs6bVNuc2m43CwkIOHDggF/JBQUGsWrXqS20socW/f+5I5Kw8tTMWxzx9AVcVTwcOHGDFihWqPd9MpdWc+/v7efPNN6mrqwNg/vz5bNq0SbPbI3+ekZERtmzZQlBQEDqdjrlz506Lk84CbN3qLKAsFufXV18FDw8HZ86coaenR1MFlBJ9uaenB7vdTkBAACaTaUofezqy2+0cP36crKwsrFbrhGVgZ+8+OF5cTWcWi4W2tjY6OjrOWiJmoLg4nr/9LYTduz97fQsXwkMPwVe/ClNRiyjRl61WK9XV1ZSXl8vHenl5eTFnzhxmzZo1rfq3zWYjPz9f/mAKnMsWV69eTWZm5kWPrVr9++duRM7KUztjccyTRg2rsbBc0GzO/v7+3HnnnaxevRqdTkdhYSHPPvssbW1trm7aJfP29iYxMZHk5GQkSeLkyZMcOXJEPk+Lll11FbzzDnh6Os//dNVVMDCgZ9asWQQGBuJwOKisrKSnp8fVTVWkL4+/CZuBn5ud15kzZ/iXf/kXWlpa5CV+Xl5eGAwG+RxY48dK9fX1MTo6Om2OszmXh4cHcXFx5OTkkJiY+OnMo53s7Br+3//L58MP67j7bgteXhInTsBttzl3qXzySfj0/fyXpkRfNplMzJ49m2uuuYYrrrgCX19fRkdHKSoq4oMPPqCsrGxajEkARqORvLw87rvvPtavX4+Pjw+dnZ38/e9/55lnnpE3t/kiWv37525EzsrTcsaieFJRaGioq5swI2g5Z71ez+rVq7nzzjvx8/Ojs7OT5557jhMnTky7N7Ph4eFcccUVLFy4EL1eT319Pbt3754Wx0Ft2uScgfLzg337YPVqaG/Xk5KSQlBQEA6Hg6qqKjo6OlzaTiX68vhOc1NxcLo7GBwcpLi4mMHBQXQ6nbz7YGBgIIGBgfj4+GA0GpEkCavV6haF1PgGE1lZWaSlpREQEIAkSYSEtHHPPUVs317Of/7nMKGhErW1zmV88fHw4x9Da+uXe04lx2WDwcCsWbO4+uqrWbRoEWazmdHRUU6ePMn7778/rYqoszeWWLNmDZ6enrS1tfHqq6/y3HPPUVVV9bl/K7T898+diJyVp+WMxbI9FZftDQwMTMsTpk430yXnoaEh3nnnHSorKwHnOZSuv/56vLy8XNyyi3N2zu3t7Rw+fJjR0VFMJhNXXHGFvJOdlhUWOmee2tudbw7/8Q/IynJQW1sr774XGxtLVFSUS5YkKtGXx3edMxgMBAYGToullkq62K3K7XY7FouFsbGxCYXn2Uv7PD09p22ew8PDtLW10dXVJReEkuTN/v2JPPusmaoq5+vy8IA774Qf/hCSky/+8dUcl+12O/X19ZSWlsof5nh6ejJ79mxSU1On1XK+kZERjhw5wtGjR+XjuxISEli3bh1xcXGT7j9d/v5NdyJn5amdsVi2p1GHDx92dRNmhOmSs6+vL1/72tfYuHEjBoOB0tJSnnrqKaqrq13dtItyds7h4eFs2LCBsLAwrFYrhw8fnha7Rs2fD4cOOZcm1dfDsmXwwQd6kpKS5E0wGhsbqa+vd8nMoBJ9efzYHbvdPm0+jdcCg8GAt7e3vIOfr6+vPCM1vrSvu7ubwcHBablrn4+PD0lJSeTk5BATE/PpNu8jrFpVxquvnuT553tYvFjCYoHnnoO0NLj9digtvbjHV3NcNhgMJCUlsWnTJvLy8vDz82NsbIxTp07xj3/8g9LS0mnT9729vbnyyiu5//77Wbp0KUajkbq6Op5//nlee+21SadYmC5//6Y7kbPytJyxKJ4EwYV0Oh1Llizhn//5nwkODqa/v5+XXnqJrVu3Tps/7uN8fHxYs2YN6enpAFRWVrJr1y7NL+NLSYGjR+HKK53HddxwA/zf/+mIjY2TZ8/a2tooLy+fdr+T89Hr9fLs5tDQ0LR7k68F5xZSPj4+8jFSo6Oj9PX10dvby/DwsOY/QDiXyWQiJiZG3urcw8MDu91CVlYlf/hDIW+91cHGjQ7sdnj5ZcjKgptvds7iao1er5eLqMWLF+Pv74/FYuHUqVN88MEHVFZWTpvfj6+vLxs2bOC+++5jwYIF6HQ6ysrK+MMf/sB7770n79QnCILyxLI9FZftNTY2Ehsbq9rzzVTTNWeLxcL27ds5ceIEAGFhYdx4441ER0e7uGXn93k5Nzc3c+zYMcbGxjCZTCxcuJCEhASVW3hprFa47z545hnnv++4A55+GkZHu6mpqcFut+Pp6Ulqaio+Pj6qtEmpvixJEr29vdjtdry8vDCbzVP+HNNFZ2cnL774InfddddlrbGXJAmbzcbY2Nikk++aTCa8vLzw8PCYdsv6HA4HXV1dtLS0MDo6CvDpyWpjeO65CN5997PPYDdtgp/8BJYunfw4WhiXHQ4H9fX1fPLJJ/KOdr6+vmRlZZGQkDCtdlPs6Ohg165dnD59GnBuOLF48WISExNJSUlxcevcnxb6s7tTO2OxVfkXcFXxVFVVJQY1FUz3nCsrK3n33XcZHBxEr9ezatUqVqxYobk/7F+U89DQEEeOHJGXlSQmJrJgwQJNb80uSfCHPzgPkHc4IDsb3ngD4uKGqaysZGxsDIPBQHJyMkFBQYq3R8m+bLFYGBgYQJIkzGbztDnWTglTnfP4Ur6xsbEJS/jGZ/08PT3ljTumC0mS6OnpoaWlhaGhIcD5enp6YnjhhQhef13PeL24cSM8+igsWvTZz2tpXLbb7VRXV1NSUiIXhAEBAWRnZxMTEzOtCtyGhgZ27NhBfX094Bx3b7zxRhYtWjStznc13WipP7srtTMWxzxp1JkzZ1zdhBlhuuecmprKd77zHTIzM3E4HOzZs4cXXniBrq4uVzdtgi/K2dfXlzVr1pCVlYVOp6O2tpZt27bR3t6uUgsvnU4H//ZvsHMnRERAcbHzfDf/+IcPGRkZ+Pv7Y7fbqayspL6+XvGd1pTsyx4eHvJJcoeGhuSD0Weazs5Ofv/73086duRy6HQ6PD098ff3l3fs0+v1OBwOhoeH6e3tlU9WPF0+v9TpdAQHB5ORkcHs2bMxm804HA4CAhr4938vYM+eZu66y4HR6DwZdV4eXHfdZ8v5tDQuGwwGUlNTufbaa8nJycHDw4O+vj4OHjzIzp07af2yWwq6QFxcHHfddRf/9E//RFhYGC0tLWzfvp3f/e53nDp1atr0r+lGS/3ZXWk5Y1E8CYIG+fj4cPPNN3PTTTfh5eVFY2MjzzzzDEeOHJlWWyMbDAaysrJYu3YtZrOZoaEh9uzZw6lTpzR9rMGaNc43fStXOo+Duu02eOABEwkJaURGRgLQ2trK6dOnGRsbc3Frvzxvb2+8vLyQJImBgYEZWUDV19fzxBNPyJ/cTzWDwYCPjw9BQUH4+flhMpnkman+/n55y/Pp8iZXp9MREBBAenr6hCLKy6uR73yngD17mrjjDgd6Pbz/PixY4DzRbm2t9paGGo1G0tPTufbaa8nMzMRoNNLV1cXevXvZs2cP3d3drm7iRdHpdMyePZt//dd/ZenSpfj7+9PX18dbb73Fn/70J8X6tiDMVGLZnorL9qxW67TaInW6crec+/r6ePfdd+Vd+GJjY7nhhhsICwtzabsuNWer1UphYaH8OoKCgsjLyyMwMFChFl4+mw3++7/hscec/87Jgb/+FaKje6ipqcFms2E0GklMTCQ4OHjKn1+Nvnx24aTT6fDz89P00sqpdrFblU8lu93O6OgoY2Nj8och40v6vLy8NLdE9/NIkkRfXx/Nzc3ycUQGg4Hh4ViefjqMV1/VI0mg00ncequOn//cuUmLFo2MjFBWVkZVVZX8e0lMTCQ7OxtfX18Xt+7ijG9qc/ToUQ4cOCB/IJKZmcm6detUWW48E7jb+wwtUjtjsWxPo44fP+7qJswI7pZzQEAAt99+O9dddx2enp7yLNT+/ftdOntzqTmbTCYWLVrEsmXL8PDwoKenh+3bt1NSUqLZWSijEX75S+cn6CEhcPIk5ObCSy8FkZ6eidlsxmazUVVVRW1t7ZS/DjX68njB5OnpKRdS48eBCMowGAz4+voSGBiIr68vBoNBXtLX09PD4OCgZv9PnEun0xEYGEh6ejppaWn4+vp+urlKHT/4wUn27Onk5pslJEnHa69Bejp873vOc6tpjbe3NwsWLOCaa64hMTERgNraWj788EOKi4unxW6bx48fx2QysWLFCu677z5yc3PR6XSUlJTwhz/8gZ07d07r2XKtcLf3GVqk5YxF8aSi8U/lBGW5Y846nY7c3Fy++93vkpaWht1uZ/fu3Tz33HO0tLS4pE1fNue4uDg2bdpETEwMDoeD4uJidu7cSW9v79Q2cApdc43z+KerroKxMfj+9+H66z3x85sjnw+qvb2dkpKSKd2aXa2+rNPp5E0jJElicHCQwcHBabOUbLrS6/Xylud+fn7yeaNGR0fp7e2dlkVURkYGKSkpeHl5YbVa8fau5ic/OcXjj+9l0yYJmw1+/3uYNcu5qYQWh2tfX18WL14sn7vObrdTUlLCBx98wJkzZzS9dPrsMcNsNnPddddx7733kpycjM1m4+DBgzz55JOcOHFC069D69zxfYbWaDljUTypSEyXq8Odc/b39+ef/umfuOmmm/D29qa1tZXnnnuOXbt2YbPZVG3L5eTs7e3N8uXLWbx48bSZhYqKgg8/dO7G5+3t3FQiJ0fP4cNxpKXNxsPDg9HRUU6fPk1jY+OUvDFRsy/rdDp8fX3lbdhHR0fp7+93+zdYZrOZBQsWuHS79vENJgICAggICMDDw2NCETUwMKDZ/xfnGt9YIjs7m8TERDw8PBgbGyM+vof/7/8r4b33Blm40Fk0/fSnziV8zzzjPFWA1gQHB3PllVeyfPly/Pz8GB0d5eOPP2b79u2a3VTifGNGREQEt99+O1/72tcIDQ1laGiI999/n2eeeWbanJRda9z5fYZWaDljccyTisc8DQ0NTZt109PZTMl5cHCQjz76iJKSEgBCQ0O59tpr5eUmSpuqnEdGRsjPz6exsRFwDpgLFy4kJCTksh9bKeXl8I1vwKen5OKGG+DJJ23YbPXyrm0+Pj4kJydf1jmhXNWXz97GXK/XYzab3fo4KC2OGVarlZGREfmYlfECy9vbe1ptc26322lvb6eurk4+lsvPL4ATJ5L4n//xYPy9+5w58NvfOmd3tchut1NVVUVJSYn8O4mKimLevHkEBAS4uHWf+aK+bLfbOXHiBHv37mVkZASAjIwMNm7cqKnXoXVaHDPcjdoZa+qYp6eeeoqkpCS8vLzIzc3lwIEDn3v/ffv2kZubi5eXF8nJyTwzfsbKs7z55ptkZGTg6elJRkYGb7/9tlLNn1IHDx50dRNmhJmSs9ls5pZbbmHz5s2YzWY6OzvZsmUL77zzjnweFiVNVc7e3t4sW7aMJUuWyLNQO3fupKCgQLPHGMyeDYcPw//8D5hM8O67MHeukV27kpk1KwWTycTw8DAlJSU0NjZ+6VkDV/VlDw8PAgMDMRqNOBwO+vv7GRoacstlfA6Hg927d2tuhs1kMslbnZ87EzU0NKS59l6IwWAgKiqKnp4eIiMj0ev1DAz0MWfOSbZureO3v7UTGgqnTztPsnvttVBR4epWT2YwGJg9ezbXXHMNaWlp6PV6Wlpa2LZtG4WFhZrZqfKLxgyDwUBeXh733XcfeXl56PV6SktL+f3vf8/+/ftVX8EwXc2U9xmupOWMFS2eXnvtNb7//e/z4x//mMLCQlasWMGmTZsuuG1mTU0NV199NStWrKCwsJAf/ehH3Hfffbz55pvyfY4cOcLmzZu5/fbbOXnyJLfffju33norx44dU/KlCIJmpaen893vfpeFCxei0+koKiri97//PQUFBdPmza5OpyMhIYGrr76axMREJEmioqKCjz76SJ6R0hqTybnsqKDAeTLQvj645x645ZZgvLyyCAoKQpIkmpubKSkpob+/39VNviQGg4GAgAD55LkjIyP09vZqtqD9soqKirj++uspKipydVPOy2g04u/vT0BAgLzN+fjvYmRkZNr8H9fr9cTHx5OV9dn/jZ6eNlauPMnBg2088ICE0QgffACZmfDQQ87/W+e7VFa67nV4enqyYMECNm3aRGxsLA6Hg/Lycj788ENqamqmze/D29ubTZs28S//8i8kJCRgtVrZvXs3f/jDH6jQYvUqCBqi6LK9vLw8FixYwNNPPy1fl56ezle+8hUeG9/79yz/+Z//yXvvvUdZWZl83b333svJkyc5cuQIAJs3b6a/v5+PPvpIvs9VV11FUFAQr7zyykW1y1XL9urq6khISFDt+WaqmZxzY2Mj77//vrwePz4+nmuvvZbw8PApfy4lc25tbeXEiRPyAaOxsbEsWLDgspbAKcluhyefhJ/8BIaHncdE/fd/w1139dDSUid/Kh0WFkZcXBxGo/GiHlcrfdlisTA4OIjD4UCn0+Hl5YWPjw86nc7VTbtsrtiq/MuSJAmr1crw8LA8QzC+c5/JZNL07+Pcvtzf3099fT3Dw8OA8828xZLET39q5oMPvvjxKiogNVWp1l68lpYWCgoK5I1iQkNDWbBggSKnLrgYX2bMkCSJTz75hO3bt8uvIzU1lauuukrTy6ddSStjsztTO2NNLNuzWCzk5+ezYcOGCddv2LCBw4cPn/dnjhw5Mun+Gzdu5MSJE/KnnRe6z4UeU0umyzKL6W4m5xwbG8s999zDhg0b8PDwoL6+nmeeeYadO3dO+YyBkjlHRkZy1VVXkZ6ejl6vp7GxkQ8//JDy8nJNHjhvMMADDzh35Fu7FkZG4OGHYdWqINrbswkPD0en09HR0UFxcTGdnZ0X9Qm1Vvry+DK+8d34xmc+LBbLtPmk3R3odDo8PDwICAjAz88PvV6P3W6nv79f85tKnNuX/f39yczMJCkpCZPJxMjICHZ7KU88cYb33rMx/p7p5ZchP/+zy8svO6+fwk0tL0tUVBRXXXUVOTk5GI1GOjs72bFjBydOnHDJluBfZszQ6XRkZ2fzve99j+XLl2MwGKisrOSpp55i165dmlmSqCVaGZvdmZYzvriPP7+Ezs5O7HY7ERERE66PiIi44C41ra2t572/zWajs7OTqKioC97n83a+GRsbmzCIjS+fKSoqmrDDUlBQEElJSYyOjlJaWjrpccY/lSwvL590TMn4STI7OjpoaGiYcJufnx+pqamUlZXR09Mz6XGzs7MxmUycOXOGvr6+CbfFxMQQERFBT4/zpJxn8/b2Jj09HYDCwsJJb2LS09Px9vamrq6Orq6uCbdFREQQExPDwMAAleesgTCZTGRnZwOc99wWqamp+Pn50dTURFtb24TbQkJCSEhIkE82eDadTsf8+fMBKCsrkw9WHZeUlERQUBBtbW00NTVNuC0gIIBZs2ZhtVopLi7mXDk5OfKAv23bNpYuXSrfFhcXR1hYGN3d3dTW1k74OV9fX2bPng04P4E+V0ZGBl5eXtTU1Ez63UVFRREVFUV/fz9VVVUTbvP09CQzMxOAU6dOTVpHnpaWhtlsprGxkfZzTngSGhpKfHw8w8PDnD59esJter2eefPmAVBaWjrpfDzJyckEBgaSnJyM1Wrl8OHD1NbW8sYbb7B3717uuOMOEhMT+eSTTya91nnz5qHX66moqJi0RWh8fDyhoaF0dnbKy24PHz7M0qVLMZvNpKWl4XA4zrv0KSsrCw8PD6qrqydtRx4dHU1kZCS9vb2Tdn3y8vIiJyeHhIQEXn31VXp6eqisrMRsNpOens7SpUvx8fGhvv6zTRrGhYeHExsby+Dg4KQlKEajkblz5wJQUlIy6Q1OSkoK/v7+tLS0TNoG/mLGiB074H//t5z/9/+GKC+HjRth/Xr42c8i8PFxnky0uLgYHx8foqKi8Pb2lscIu93OyZMn5ccbz1hLY4TVasVmszF79mz6+/uprKzEaDRO2MRA62PE2dvJn92G6TZGWK1WTp48ydjYGJIkodPp8Pb2ZvHixeh0us8dI1pbW2lubp5w2/jYYbFYpmSMGGc2m6mpqSEhIeG8Y8ScOXPo6OigsLCQ06dPYzAc5d//fRb33beY9HQ434RgWVkZXl4SGRkZgPPv+blvtubMmaPqGDH+HsVqtVJaWsq+ffuYPXs2UVFR8qzg5b6POHeMGDc+Ruzdu3dSP7zYMaKkpITg4GCWLl3K4cOHaWxsZPfu3RQXFzNv3rxJn8a78/uIc085ce77iPGxGbQ9RlzO+wi1x4hz30ecnfHlvI+42DHi3LH/c0kKaWpqkgDp8OHDE67/+c9/Ls2ePfu8P5Oamir98pe/nHDdwYMHJUBqaWmRJEmSTCaT9Le//W3CfV5++WXJ09Pzgm356U9/KgFfeFmzZo107Ngx6eTJk+e9fevWrdLIyIiUlZU16bYf/OAH0pkzZ6RHH3100m0LFiyQDhw4IP39738/7+O++uqrUl9fn7Ry5cpJt33729+WysrKpGeffXbSbbNmzZJ27dol53Lu7c8884zU0dEh3XTTTZNuu/XWW6WTJ09K77777qTbQkNDpa1bt0qSJEmhoaGTbv/Vr34lNTU1Sffcc8+k2zZu3Ch9/PHH0vHjxyfdZjKZpK1bt0pjY2NSWlrapNt/9KMfSTU1NdKPf/zjSbfl5eVJhw4dkhobG8+b4ZtvvikNDAxIixcvnnTbd77zHam8vFx68sknJ902Z84cac+ePZIkSed93BdeeEHq6uqSrr766km3ff3rX5eKi4ul1157bdJtUVFR0rZt2yRJkqSAgIBJt//mN7+RWlpapDvvvHPSbddee62Un58v7du3b9JtPj4+0tatWyWr1SolJiZOuv2nP/2pVFdXJz300EOTbouPj5fuvvtu6f/+7//O+1rfe+89aXBwUFqwYMGk2+6//36psrJS+v/+v/9v0m3Z2dnS/v37peHh4fM+7l/+8hepp6dHWrdu3aTbvvnNb0olJSXSn//85/O2d8eOHZIkSZKPj8+k23/4wx9KtbW10m233TbpthtvvFEqLCyUtm3bNum2gIAAaevWrZLdbpdiYmIm3f7zn/9camhokL73ve9Nuu1yxgiTaYv0wx92SD/4wQ8n3TY+RnR1dZ33cbU4Ruzdu1fq7OyUwsPDJ90+3cYIQHrrrbfcZow4dOiQNDIycsljxPLly6UjR45IlZWV532tlzNGPP/88184Rlx55ZVnXT9fAknKz5/49zw/X5JAkmD+F44Rv/vd76S2tjbVx4jt27dLTz/99Hlf6+W+j/iiMWL+/PmTbrucMeLmm2+WfvCDH0hz5syZdJt4HzF9x4gv+z5CyTHCFe8jzjdG9PX1XbCeGKfYMU8WiwUfHx/eeOMNbrzxRvn6+++/n6KiIvbt2zfpZ1auXMn8+fN54okn5Ovefvttbr31VoaHhzGZTMTHx/PAAw/wwAMPyPf57W9/y+OPP05dXd1523K+mae4uDj27dun6szT0NAQ5eXlkx5XS58qw/T/xKizsxNPT0/5tpk483TuJ0ZWq5WKigpqa2uxWCx0dnaSnZ3NggUL5C2oL/UTo7GxMTw9PRWdeTr3EyOLxUJVVRWNjY1ERUXh6+tLcHAwfn5+E2Y+XDnzBBPHiPJyeOwxKC5OBIKZNauDe++tITW1Rf7/7u/vz5IlSwgJCeHUqVPy441nrNUxwm63c/z4cfm4Fb1ej6enJ1lZWfj7+2t2jDj7U2Wr1YpOp2PevHkMDg5O2zFC+vR4qLGxMTIzM9HpdNTU1KDT6SYcC+WqT5Xj4+Px8PD4wjGip6eHnp4ejh618F//dTX5+RNnngoKIDcX5s8v49e/llizRjszT/DZGDE0NMTWrVupqqrC4XCg1+tJTk7mpptuwmAwKDbzdL6/C5czRsyaNYtjx46xdetWhoaGMJlM5OXlkZ6eTlRUlNu+j/iimafxsRmmzxgxbrrMPJ2dsVozT6tWrbqoY54U3zAiNzeXp556Sr4uIyODG2644YIbRvzjH/+Y8KbkX//1XykqKpqwYcTAwAAffvihfJ9NmzYRGBio+Q0jjh49yuLFi1V7vplK5HxhXV1dbN26Vf5D5+fnx/r168nOzr7kg81dmXN3dzcFBQXyGyJ/f3/mzZs3YXmMljgc8Oc/w3/+J3R0OK9btw4efXQIP78aufjw8fEhNjaWgIAAdDrdtOjL42/ah4aG5GNuDAYDPj4+eHh4aPL3ca7pkPPFcjgcDA0NyW/4jUYjZrP5ojcpUcqlZnz8uJW8PBMvvwyfvrcHoKzMeY41gIAAePxxuPNO0Go3GxwcJD8/Xy6y/P39WbhwoSKb+IByfbm1tZV//OMfckESGxvLtddeS2Rk5JQ/13TgTmOGVqmdsSY2jAB48MEH+dOf/sQLL7xAWVkZDzzwAPX19dx7770APPzww9xxxx3y/e+9917q6up48MEHKSsr44UXXuD555/noYceku9z//33s337dn71q19x+vRpfvWrX7Fz506+//3vK/lSpsS5nxgLyhA5X1hISAhf//rX+drXvkZwcDADAwO89dZbvPDCC5M+Qf0irsw5ODiYtWvXkpeXh5eXF/39/ezfv599+/ZN+lRKC/R6uOsu5xbL//Ef4OEBO3fC8uW+PPFEJt7eSRiNRoaHh6moqKC8vJzh4eFp0ZfHNzEIDAzEbDbLmxgMDAzQ19cnH4+jVdXV1fzgBz+Y9InldKXX6/Hz88Pf3x+9Xo/NZqOvr4/R0VGX/h4utS8HBZkAZ6GUm/vZZbxwmjvXeXqAu+5ynhvqnEkGzTCbzaxcuZIlS5bIY9Xu3bvJz89XZNt/pcaMyMhI7r77bq6++mo8PT1pbGzk2WefZfv27TNyQ4npMDZPd1rOWNHiafPmzTz++OM8+uijzJs3j/379/Phhx/KWw+2tLRMmLpLSkriww8/ZO/evcybN4+f/exnPPnkk3z1q1+V77N06VJeffVVXnzxRebOncuWLVt47bXXyMvLU/KlTAk1Z7lmsv+fvfMOb+JK+/YtWXLvvRdc6b330AMEQhqhJKRCet1kU3aT7Ldpm3d3E5LspocUSCOwIZDQa+hgMAYXbGxj3Lvc1DXfH4omCBuwwZJlM/d1zWVpdDQ6+vnozDxzniLpfHmSkpJ48MEHmTRpEkqlknPnzvHRRx+xfv16cRXkcnS2zjKZjLi4OGbOnClm5SsrK2PTpk0cOXKkhSuCI+DjA2++aS4Ieuut5hWpTz6RMWpUEL/+2h8fnzDkcjn19fWcOnVKdFXsClhSmPv5+eHh4SFeuDu6EVVXV8dvv/3mkEb31WAxaC0FdhsbG2lsbOy0/0F754vERHM68qNH4eBBA+vWFfPFFyf54ouT/PRTJjt3NvDmm+DiAr/8Yq4N9fnn4IBDTKxjN2PGDHr06AFATk4OGzduvGSyqyvBlvOyXC5n2LBhPPzww/Tq1QuTycS+ffv4z3/+021uPrSVzj7/XQs4ssY2ddtzVDrLbe98/00J2yHp3D7q6+vZsmWL6APu6urK2LFjGT58+CVdfRxN58bGRtLS0sRYAaVSSe/evUlMTLSKh3Ik9u41pzg/fNj8PCwMnn9ez+TJhdTXV2M0GlEqlYSGhhIaGtrprlftwWQyodFo0Gg0op+5I7rzdaU6T1eCIAhoNBqam5sRBAGFQtEiRtAedMR8oVKpxHgTmUxGeHg4dXVh3H23nEOHzG1mzoTPPgMbecV1CGVlZRw+fFiMeYqPj2fAgAEolcqrPrY95+XTp0+zYcMGcYVg8ODBTJ061aHOC7bC0c5/3RF7a+wwbnsS1uzcubOzu3BNIOncPry9vbnpppu46667CA0NRaPRsGXLFt577z1Onjx50TvVjqazp6cno0eP5rrrrsPPzw+9Xs/x48f55ZdfKCgocMhVj9Gj4cABc+2a2FgoLYVHHlEye3Y8p071o7CwCJPJRElJCSdOnKCkpMSha/mcj1wux93dHV9fX9zd3a3c+erq6lCr1Q5dx6O7YElffqEb34XB57amI+YLHx8fevfuTUBAAIIgUFxcjEyWxbZtGv7xD/Mq1IYNZpe+zZuvvs+2wlLHLvH3Kr9nzpzh119/bbfrdGvYc162eDAMHToUgKNHj/Kf//ynReKD7oijnf+6I46ssWQ8SUhIABATE8P999/P3Llz8fLyoq6ujtWrV/PZZ5+1yPzkyAQHBzN16lSGDx+Om5sbTU1NHDhwgM2bN1NaWupwRpRcDgsXmrPyvfuu+Y55bi7cfbcrzz8/jYKCnri5uWMwGCgqKuLEiROUl5d3GcPjYkZUU1MTdXV1VokmJGyHUqnEx8cHhUKByWSivr7eJjE3tkahUBAfH098fDwKhYLGxkays0+xZEklhw+b3ffKy8311Z5+GjqhTm2bUCqVDB48mOuuuw4vLy+am5vZtWsXR48etbthezW4uLgwc+ZMlixZgp+fHyqViq+//pqffvqpRSY8CYnuguS2Z0e3vby8PNHfWcJ2SDpfPTqdjv379/Pbb7+JF1h9+vRh0qRJ+Pn5AV1DZ4PBwOnTp8nKyhJjh0JCQujXrx8BAQGd3LvWaWw0ZxB76y34vZ43Y8cKPPlkPXFxZ9FqzbFcLi4uhIeHExAQgFzede6DCYKAVqtFrVaLRpMl6YSrqysKhcKuLn1lZWX885//5KmnnromMoeZTCYaGhrEFO3e3t4d4i52OWwxX2i1WvLz88XC90FBQQQFRfPnPzvx/vvmNgMHwjffwO+ZpB0SS+psS8p0b29vRowYgb+/f7uP1Znzsk6nY/v27Rw8eBBBEPDy8mLWrFliGu/uRFc4/3V17K1xe2wDyXiyo/FUWFhIdHS03T7vWkXSueNoaGhg+/btHD9+HEEQcHJyYsSIEYwdO5aKioouo7NWqyUzM5PTp0+LKzbR0dH07dsXLy+vTu5d61RVwfPP1/Pll97i3fNx4wQee6yOuLiz6PVmY9DFxYWwsDACAwO7nBGl1+tRq9VWKyAKhQJXV1dcXFzsZkRda3OGIAg0NDSg0+mQy+V4e3vbPJ7OVhoLgkBpaSnFxcUIgoC7uzsJCQls3uzK3XdDdTV4esKKFXBe7imHpKysjIMHD6JWq5HL5fTu3VtMhtNWHGEsFxYW8tNPP4m14/r378+MGTNwdXXt1H51JI6gc3fH3hpLMU8OyoXF3iRsg6Rzx+Hl5cWcOXNYunQpPXr0wGg0snfvXpYvX86PP/7YZdxLXFxcGDBgADNnziQuLg6ZTEZhYSG//vorR44ccUj3ksBAuOmm/eTmwkMPmdOb794t46ab/Hj88f6cO5eAQqFEq9VSUFBAenp6l3Lns6w2+fj44Ovri6urKzKZDIPBQGNjI7W1tTQ2Ntp8jNXV1fHZZ591u2x7l0Imk+Hl5YVSqRRd+GztOmmredmSOCIpKQmlUklzczMZGRmMHVvLiRMwYYJ5Nffmm8111hx5yrLEQkVHR2MymUhPT2f79u0tio1eCkc4/0VHR7Ns2TJGjRqFTCYjLS2N//73vy0KUHdlHEHn7o4jayytPNlx5WnTpk1Mmzat9RcfeMBxC1V0MSoqKmxWgPBaRgCaGhuprKxEp9PR1NSEj68vgQEBeHt7O0z2tLag1+upr68X05nLZDI8PDzw9PLCyYFWb84fy2q1ORbq7Fkw/T5rB/hDXA89Hu7NmH6fyuUyGS6urrg4UEa7tiIIAiaTCaPJZBWbJpfJkDs5IZfJOvw71alU7N69m3HjxuHr49Ohx3Z0BEHAYDBgEgTkMhkKpRJbjRh7zMuWAsGG3w1BV1dXXJxdycyEM79n0g4MNNeLcnG2aVeuCgHEOm+CyYRcLsfX1xc3N7fLvtfRzn9qtZrS0lJxddnf35+AwEDkXWxuuhBH07lLExEB//1vi92XvGa2AZLb3mXoLOOpqakJDw8Pu33etYqks20xmUwcP36czZs3i8ZHUFAQkyZNIjk5uUtdsFdUVJCWlia6lygUCpKSkkhOTnaINLStjeWiInjjDfj4Y7CUgBo5UmDZMhV9+hRgMJh3KpVKQkJCCA4O7lIpzuEPlz6NRoNerxcNKctqlYuLC0qlskPGWndPVX45jEYjKpUKk8mEu7s77u7uNvkce83LJpOJoqIisX5SQEAAcXFxrF4t5+67oakJoqLgp5/M8VCOTFNTE/v376eqqgqAxMREBgwYcMk08454/tNqtWzatInU1FTAvMI2b968Lm18OKLO3Q17ayy57TkoGRkZnd2FawJJZ9sil8sZNGgQ48ePZ+rUqbi5uVFZWcm3337LZ599xtmzZzu7i20mODiYyZMnM27cOPz8/DAYDGRkZLB+/XrS09PRdnKqrtbGcmQkvPcenDljdudzcYH9+2Xceacvd9zRnyNHkpHLXdDr9RQVFZGWlkZhYWGnf5f2YDGSvL298fX1xcPDAycnJzHZRH19PXV1dTQ3N0uZ+q4SJycn8QLl/CQeHY295mW5XE50dDSxsbHIZDKqq6vJysrixhv1HDxoLr577hyMHWsuruvIeHh4MHHiRFJSUgBzYd2tW7fS0NBw0fc44vnPxcWFG264gfnz5+Pu7k5ZWRkfffQR+/fvd7jsp23FEXXubjiyxpLxZEdqamo6uwvXBJLO9qG+vp5Ro0bx2GOPMXbsWJRKJefOnePzzz9n1apVlJeXd3YX24QlZmLq1KmMGTNGrBF16tQpNmzYwKlTp8RMffbmUmPZYkQVFJhjOby84NQpGQ895MMtt/Rjx45eyOUeGI1GysrKOHHiBHl5eTQ3N9vvC3QATk5OuLm54evri4+PD66urmK68+bmZmprayVD6ipxdnZGqVQiCILN4v/sPS8HBweTlJQkpjPPyMggPl7D4cMwebJ5BWr2bPjgA7t2q904OTkxYMAAxo0bh4uLC7W1tWzevJnCwsJW2zvy+S8lJYUHH3yQxMREDAYDmzZt4ssvv7ykMeioOLLO3QVH1lgynuyItMRrHySd7YNFZ1dXVyZNmsSjjz7KkCFDkMvlnD59mg8++IAff/xRdIlzdGQyGZGRkUydOpXRo0fj4+ODTqcjPT2d9evXk5GRYfe6OG0Zy6GhZje+wkJ47TVznaiCAhl//rMn11/fi3Xr+mE0+iIIAlVVVZw8eZLTp09TX1/fpe76ymQylEolnp6e+Pn54eXlhfPvcV0Gg8HKkGrPCoqrqyuxsbHdKhNYe5HJZKK7nlartcm46Ix52cfHh549e+Lq6ipm3FQqm/nlF1iyBEwmc7jxM8+YHzsy4eHhTJs2jaCgIPR6Pfv27eP48eMtEsQ4+vnP09OTBQsWMGvWLJRKJfn5+fz3v//tcoV1HV3n7oAjayzFPNkx5slgMHS52IOuiKSzfbiYztXV1Wzfvp1Tp04BZjeafv36MX78eLFGVFdAEATOnTvHqVOnUKlUgNn9JCkpicTERJydbR9xfiVjWa2Gzz8314myJLdyc4OFC/XcemsJvr4V4sWxp6cnISEh+Pn5dak05+djMpnQ6XTodDqr+Cgwx7A5Ozvj7OyMk5PTRWOkpDnDPN7r6uowGo14e3t3+PjuTI31ej3Z2dk0NzeLcY0eHp78/e/w17+a2yxcaE5n7ujDwGQyceLECbKysgBz3bpRo0aJMZpdaSxXVVWxevVqMT5tzJgxTJw48ZIxXY5CV9K5q2JvjaWEEZfBIbPtSXQYks724XI6l5aWsmPHDrHwo1wuZ+DAgYwbNw6fLpTVzGQyce7cOU6ePCm6lyiVShITE0lKSrLpisXVjGW9Hr7/Hv75Tzh27I/9M2caWbSokvj4IgTBfNfa2dmZoKAggoOD7VIw1VZYDCmtVovBYLAypJycnERD6sJCvNKcYaaxsRGNRmOTxBGdrbGlYHZjYyNOTk4kJibi7e3NV1/B3XebU5jPm2cuqGuH+yJXTWFhIYcOHcJgMODh4cHo0aPx9/fvdJ3bi8V97/DhwwBERUVx8803O/w5oqvp3BVx5Gx7XfNWo4SEhMMTFhbGggULuPfee4mPj8dkMnH06FGWL1/OL7/80mX83OVyOTExMcyYMYORI0fi4+ODXq8XE0ukpqbS1NTU2d1sgVJpvpt+9Cjs2GGO7wDYsMGJ228P5f77B3PgQALgjE6no7i4mLS0NPLy8hzy+7QFuVyOq6srPj4++Pn54enpKbr2GY1G1Go1KpWK2tpaGhoa0Gq1pKamMm/ePI4fP97Z3e90LAZld7ynqlAoSE5OxtvbG6PRyOnTp2loaGDxYvjxR7PBtGYNzJ0LvycRdWiio6OZPHkyXl5eNDU1sW3bti6VrMeCQqFg5syZ3Hrrrbi4uHDu3Dk++OADsrOzO7trEhIXRVp5suPKU05ODomJiXb7vGsVSWf70F6dCwsL2b59u1goUaFQMHToUEaPHo2np6eNetnxCIJAcXExmZmZYjyXXC4nNjaWnj174uXl1WGf1dFjOTsb3nnH7J5kyQsQGiqwaJGa668vwsOjTmzr5eVFSEgIvr6+Xdalz4Il9bnFve/8OJG0tDQmT57Mb7/9xtChQzssBXpXpL6+Hp1Oh4eHR5tqCrUHR5mXTSYTOTk5qFQq0aDy8PBgyxaYM8f8u5g922xQdYVFWK1Wy4EDBygtLQXMdZSmTJnSJcdwbW0tP/zwAyUlJQCMGDGCKVOmOKQbn6OM5+6MvTWW3PYuQ2cZT8XFxURERNjt865VJJ3tw5XqnJ+fz44dO8RsUUqlkmHDhjFy5MguZ0SVl5eTkZFBRUUFYL5zHxUVRa9evfD19b3qz7DVWK6uhg8/hHffhd/DDXBygtmzDdx8czlJSaUtXPqCgoLsEudlayyFYS3GVGpqKpMmTWLr1q30798fmUyGQqFAqVSiVCpbuPh1V0wmE7W1tQiCgK+vb4fHGjjSvHz+ypNSqRSTSuzcCTNmmFeebrsNVq40/y4cHUvtPct36tevH0OGDHFIo+NyGI1GtmzZwoEDBwDzCtutt97qcOcGRxrP3RV7ayy57TkoJ0+e7OwuXBNIOtuHK9U5Li6Ou+66i8WLFxMREYFer2fv3r288847bNy4scu488lkMkJDQ7nuuuuYPHky4eHhCIJAYWEhGzduZPfu3VRUVFyVC5StxnJAADz/PJw9C99+a655YzTC//6nYNGiCJYsGcTWrUnodC5WLn2WO/Zd+Z6bJWufu7s7vr6+4knSxcVFrCWl1+tpbm5GpVJRU1ODSqWiqampxapVd0EQBBobGxEEAYVCYZOLbkealy0xTx4eHuj1enJycjAYDEyYYHbdUyrhu+/MddS6wlC31N4bPHgwFRUV5Ofns3v37i5V282Ck5MT06dPZ/78+bi4uFBYWMiHH35IUVFRZ3fNCkcaz90VR9ZYMp4kJCTsjkwmIz4+nnvvvZcFCxYQGRmJXq/nwIEDvPPOO2zYsEHMcNcVCAwMZNy4cUybNo3o6GhkMhklJSVs376dLVu2cPbsWYe86HZ2Nt9h370b0tJg6VJwd4eMDDkvvODL9df346OP+lJREYAgCNTW1pKdnc2JEycoKSnptPpXHYnFJdFiTPn5+eHh4YGLiwtyuVw0ptRqNfX19WI6dEtyhQsTU3Q1jEYj1dXV6HQ6ZDIZnp6e18RKm0KhELNmqtVqcnNzEQSBGTNg1SqQy82rs//6V2f3tO0kJiaSmJiIQqGgvLyc7du3d7m6bhZSUlK47777CAoKoqGhgc8//5zU1NTO7paEBCC57dnVba++vt6un3etIulsHzpSZ0EQyMvLY9euXaI7n5OTE/3792fs2LFdKsU5QENDA9nZ2eTn54v1hjw8PEhKSqJHjx5tzmjXGWNZpYIvvoD334ffEyUCMG6ckZtuqmXo0HM4OZnrXclkMnx9fQkODsbb27tLXnQ3Nzdz5MgRhgwZ0iLDnCAImEwm9Hq96OrXWv0omUyGk5MTCoVCXLlxdHc/S0Hc8y+ubZGi3IKjzsvNzc1kZmZiNBoJDw8nMjISgLffhieeAJkM1q41x0N1Berr6zGZTOzevZvm5mY8PDyYMGFCh8Zi2hOtVsv//vc/MjMzARg8eDAzZszo9DThjjqeuxP21liKeboMnWU8paamMmjQILt93rWKpLN9sIXOgiBw9uxZdu3aRX5+PmBeGejbty9jx44lMDCwQz/P1mg0GnJzc8nJyRFdaJydnYmPjycxMfGy6aA7cyybTLBtm9mI+vnnP4qI+vkJ3HSThuuvLyEi4o8CyC4uLgQFBREQECDWnOkqtEdnizFlNBoxGAwYDIaLrio6OTm1uslksk4xrC6VOMMWSSLOx5Hn5ZqaGnJzc5HJZCQlJeHj44MgwIMPwgcfgKcnpKZCV8gPYNG5qamJnTt30tDQgKura5ers3c+giDw22+/sX37dgRBIDIykttuu61TDUJHHs/dBXtrLMU8OSiVlZWd3YVrAkln+2ALnWUyGbGxsdx5553cfffdJCQkYDKZSEtL4/3332f16tWUl5d3+OfaCldXV/r06cPs2bMZOnQoXl5e6HQ6MjMzWb9+PQcOHKCuru6i7+/MsSyXw5Qp8L//mYvtvvIKREdDba2MTz5xY968eB58cDDbtsWj0Tij1WopKioSC3hWVVW1ukrjaBQWFvLSSy+JK56XQy6X4+Ligru7O97e3vj5+eHn54eXlxdubm4olUrRFdBoNKLT6VCr1TQ2NooxVBbXv4aGBpqamlCr1Wi1WtEoM5lMV+UKaFkxMxgMaLVampqaqK+vp6amhvr6ejQaDSaTCScnJzw8PPD397ep4QSOPS/7+/sTHBwsroAbDAZkMli+HMaNg8ZGuPXWrpHC3KKzh4cHkyZNws/PD41Gw44dOxz6f3ApZDIZY8eOZcGCBbi6ulJUVMTHH38sFtftDLqqll0JR9ZYWnmy48rTrl27GD9+vN0+71pF0tk+2Evn4uJidu/ebVX3IzExkTFjxhATE2Pzz+9IBEGgpKSErKwsqxNDaGgoSUlJhIWFWa1IONpYNhrNq1GffGI2qvRm7z3c3QXmzNEye3Y5PXqUY/kKTk5O+Pv7ExgY6LCxNKmpqQwePJijR4926F1Ok8mE0WgUN4PBIBpGbUEmk4lGmGWl6mIrVoIgWG2XMr7kcrlYLNieadkdbSxfiNFoJCMjA7VaTVBQEHFxcQAUF8OAAVBVBY89bOTtm/ZAaSmEhZkzrThYRrsLddbpdOzZs4fKykoUCgXjxo0jODi4E3t4ddTU1PDNN99QWVmJs7MzN998M0lJSXbvh6OP5+6AvTWW3PYuQ2cZT4IgOOTFQ3dD0tk+2Fvn0tJSfvvtNzIyMsQLw6ioKMaMGUNSUlKX+59XV1eTnZ3NuXPnxO/j5eVFQkICcXFxODs7O/RYrqyEr76Cjz+GrKw/9qekmLjxxgYmTCjG17dR3O/i4kJgYCCBgYEO5dZnK+PpYgiCIBpRlr8Xbh11WpbL5aKr4PnxWJ3lMuioY9lCQ0ODGFtzfs22X3+Fj69fwzs8RhTnZX2LjDQXTps3rzO62yqt6WwwGNi7dy+lpaUoFAomTJjQ5Vygz0ej0fD999+Tl5eHTCZj+vTpDB8+3K596Arjuatjb40l4+kydJbxtGnTJqZNm2a3z7tWkXS2D52lc3V1Nfv27eP48eOiW1hwcDCjR4+mT58+Xa62SVNTEzk5OeTl5YnZ6xQKBbGxsRQVFTF37tzO7eBlEATYt8+8GvX992DJPyCTCYwbZ2TmzFqGDy/C1VUvvsfLy4uAgAD8/PzanDzDVtjbeGoL568etbZdyIUrU3K5HLlc7lAXd11lXs7Pz6eyshIvLy9SUlLMGq5Zg3DTzQgI1rEOFn1Xr3YYA+piOhsMBvbs2UN5eTlKpZLx48d3aQPKaDSyYcMGMQPf8OHDmTZtmt0KeneV8dyVsbfGUsyThIREtyUgIIDZs2fz+OOPM2bMGFxcXKioqGDt2rUsX76cgwcPotfrL38gB8HDw4MBAwaIcVE+Pj4YDAZyc3M5deoUO3bsoLi42CFTnYP5+nH0aPj8c7M308cfm+NEBEHGrl0KnnkmiOnTB/CPf/QjLS0Mo1FGQ0MDBQUFYmHPrhIfZS/Oz96nVCpxdnbGxcUFV1dX3NzcWmyurq64uLiIrnidtbrUHYiIiEAul9PQ0EB9fb3ZV/Wxx+BCwwn+KAL1+OPmdg6MQqFg7NixBAcHo9fr2b17NzU1NZ3drSvGycmJ2bNnM3nyZAAOHjzIt99+2y3KJ0g4PtLKkx1XnrKyskhJSWn5QnOztd+LxFWRn58v+qtL2A5H0Vmr1ZKRkUF6ejpqtRr4I1FDr169bB4I39EIgkBNTQ2FhYWczsnB18cHADc3N6KiooiIiLBZOumOpKTE7O60fj2cPS8XQ2CAwPjxakaMqCY4WCXevJfL5Xh5eeHj44Onp6fd7iCXl5fzn//8hwcffJCQkBC7fOa1iKPMF22htLSU6upqPD09ia2qMhdAuxwffghDhti+c5fhcjrrDQaOpaZSW1uLs7Mzw4cPv2zWT0cnLy+P7du3YzQaCQwK4voZM2w+73el8dwlSEkxFxk8j4teM9sIyW3vMnSW8VRWVkZoaGjLF1JTYfBgu/VDQkJCQkJCQkJCwiE4ehQucJu+6DWzjWiPbdC5VcYcFKPRaBO3n5MnT+Lr69vyhbg4OHKkwz/vWuXYsWMMHDiws7vR7bkSnZWAk43diUwmE3l5eaSdOEHVeRntYmJi6NevX4uMdo7Mvn37GDVqFAaDgdLSUs6dO0dDQ4P4ure3N5GRkYSGhaHs5KKRbUGvN8dHbdgAu3aB3vDHa4kJRkaPbmbAgEr8/f8o3Ork5IS3tzfe3t54eHh0+IpUc3Mza9eu5cYbb+zyd+AdGctY7irk5+fT1NREZFkZvs8+e/k3OMjKU1t11mg0HDp0CLVaja+vL4MHD+70wrNXS21tLRs2bKCpqQlPT09mzpzZ+jVXB9DVxrPD08oKU1paml2Np/YgrTxdYF02NjZSVFTUYdmOzketVnc5F6KuiKSzfbgSnWUyGZGRkXh6etqoV38gCAKFhYXs27eP06dPi7/p8PBwRo4cSa9evRw+ucSFAbOCIFBZWcmZM2c4d+6cGAelVCqJiYkhISHBZhcLHU1dHaxdC999B1u3WoeMDBxoZPp0FWPGlBAYaG1I+fr64ufnh4+PT4f8/xwxYUR3pKsF2JeXl3P27Fm8PTxImT7dnLO8tesCmcycdS8/3yHSlrdHZ5VKxbZt29DpdERFRTFq1Kguc2PpYtTV1fHVV19RXV2Nh4cHixcvtskFeFcbz10RR04YIRlP5wlkNBrJycnB3d2doKCgDp9EDAZDl7+z0xWQdLYP7dXZcuHf3NxMYmKiXQ2X6upq9u/fz/HjxzEYzMsdPj4+DB8+nEGDBuHq6mq3vrSH2tpa/Pz8Wn1No9GQn59PXl6e1WpUYGAg8fHxREVFdZnfQVXVH4bUjh1wfm6MYcMMTJtWz5gxpfj6Non75XK5aEj5+vpe8XiSjCf7cKmx7Iio1WrS09ORy+UMPnsW2S23mF8475LJhAwZIPvRcbLttVfniooKdu7ciclkon///vTs2dOGvbMPTU1NfPXVV5SVleHq6sqCBQuIjo7u0M/oauO5K2JvjSXj6TJcTCDLxUhsbKxNVi6am5sltxA7IOlsH65EZ7VaTUFBAXFxcZ1isDQ3N3P48GEOHTpEU5P5QtzFxYVBgwYxbNgwhzsZpqWl0b9//0u2EQSB8vJyzpw5Y5WVz9nZmdjYWOLj4/H5PelEV6C8HNasMRtSu3db3+wfMsTIpEkNjBxZRlhYvbjfkmzC19cXX1/fdtWRkown+9CWsexImEwmjh49iiAIDBgwAOf1681Z94r+qPNUqoziIf3bTPtgXptyStiDK9E5NzeXI0eOIJPJmDBhQrdInKLRaFi1ahWFhYUolUoWLlxIbGxshx2/q43nroi9NZZSlV8ltlq27krpk7syks724Up07myXEHd3d8aPH88TTzzBDTfcQFBQEFqtlv3797N8+XK+/fZb8vPzbeK2eyWUlZVdto1MJiM0NJTRo0cze/Zs+vXrh4eHBzqdjtOnT/Prr7+ybds28vLyusRvIyQEHngAdu40X6cuX25OhQ5w5IgTb77py9y5KSxePIQvv0whN9cfg8GESqXi7NmzpKWlcfLkSYqKimhsbHSY/+W1TlvGsiMhl8vFGmQ6nc68slRQwLondnA7q3hn7g5CmvKJeHgey5aZQ54cgSvROT4+ntjYWARBYP/+/TQ3N1/+TQ6Oq6srixcvJiEhAb1ez8qVKykoKOiw43e18dwVcWSNu4ZPRzehsy8c28vLL79MWVkZH3zwATt37mTZsmVk/Z5S3dPTk7y8PIKDgzu5ly3pajp3VbqyzgqFgkGDBjFw4EByc3M5ePAgubm5ZGVlkZWVRXBwMMOHD6dfv36dWsS1vZ/t5uZGr169SElJoaysjDNnzlBSUkJlZSWVlZWkpqYSHR1NXFwcgYGBDv8/DA+HRx4xb6WlsG6d2b1v+3Y4fVrO6dPevP++N+HhAlOmNDN2bBW9epldQ5ubmykpKUGpVOLr64uPj0+rcVIKhQIfH58u4+LYVensYshXg+V38uEnTiz79wQeeQQefccc7rR8ufnvsmXmtp29AnUlOstkMoYMGYJKpaK2tpaDBw8yYcIEh58fLodSqWT+/Pl8++235ObmsnLlShYtWkRMTEyHHFvCtjiyxtLKkx25mrTosbGxeHt7i3VswLzE6ObmZpUHPzY2lgMHDli9d9myZbz88stX/Nmt0djY6JCG08MPP8zatWut9t133308/PDDLdouX76c8ePHi8+PHDnCxIkTSUpKYvXq1S3az5s3j5deeqnjO21Dzpw5w+jRo3F3d2fQoEGkpaVdtO2ECRNwdXXF09MTT09PZsyYIb62YsUKBgwYgJeXFz169OCDDz6wGs/19fXcc889+Pv74+vry4IFC2z6vToKmUxGYmIiixYt4uGHH2bo0KE4OztTUVHBzz//zL/+9S+2bNlCXV1dp/Tvuuuuu6L3yeVywsPDGTt2LDfccAP9+/fHy8sLg8FAXl4e27Zt45dffiEzM9NqTnFkwsLMF6YbN0JlJaxaBbfeCp6eUFIi44svPLj33hiuv34wb73Vj717o2lsdEav11NZWUlubi7Hjh0jOzubsrIy1Go1giDQr18/6urq6NevX2d/xW7NlY7lzkIQBLFws1wu58MPzQbSI4/AO78bTmD++8475v2OsAJ1pTorFApGjRqFQqGgvLyc3NzcDu5Z56BQKJg/fz7x8fHiCtS5c+eu+rhdbTx3RRxZY8l4siMqleqq3h8aGsq6devE52vWrCEqKupqu9Wt2LRpEyNGjLDat2jRIr7//nsxUYCFVatWsXDhQvH5xo0bmTZtGgsXLmTlypVWbVUqFb/++muXMQos3H777UydOpWamhruvvtubrzxxhY6nM+KFStobGyksbGRX3/9Vdyv1Wr54IMPqK2t5eeff+all17il19+EV+/66678PT0JD8/n8rKSv70pz/Z9HvZgsDAQGbOnMmTTz7JtGnT8PPzQ61Ws3fvXt555x2+++47CgoK7OoGtnnz5qs+hpubGz179uT6669n0qRJ9OjRA4VCQUNDA2lpaaxbt47du3dTVFQkXiw6Oj4+cPvt5rioykpz6vN774WgIKirk7F6tStPPhnKlCn9eeyxQaxenUhBgQ9Go9m9r7CwkPT0dE6cOEF+fj4///zzJX8XEldPR4xle6LVajEajb+nxnfhkUegXz94++0/DCcLMpl5f79+ZiNKq+2EDv/O1ejs5eUl3kRIS0ujsbGxo7rVqZxvQOl0OlauXEl5eflVHbOrjeeuiCNrbFPjqba2lsWLF4vuEosXL77kHVy9Xs+zzz5L37598fDwIDw8nDvuuIOSkhKrdpbl5PO3+fPn2/KrOAS333671UX9ypUrr/piXq1W8/DDDxMeHk5kZCRvvvlmm94nk8lEf9TY2FjefPNNEhISCAoKslrlWr9+PcnJyXh5eREVFcU333wDmDMbvvTSS8TExBAaGspTTz3V6sXL5s2bGW0JeADi4uJ46KGHAHNKUm9vb/F9Z86cwd3dvUVa0nHjxuHm5saWLVvEfXl5eRw7doybb75Z3GdJi7lo0SJ+/fVXq7H6448/0qdPH5KTk9m5cycpKSn85S9/wdfXl+TkZDIyMvj73/+Ov78/PXv25NSpU+J7H3zwQcLDw/H19WXq1KkUFhYCkJ2dTWBgoHiH78CBA4SGhlJRUdGm/8HlyM7OJjs7m+eeew5XV1cefvhhjEYj+/bta/exli5dyogRI1AoFPTu3ZvJkyeTmpoKwKlTpzh69Cj/+te/8PHxQalUduk6W66urowcOZJHHnmE22+/nR49eiAIApmZmaxYsYIPPviAo0ePmuMgbExHGmoymYygoCCGDRvGnDlzGDZsGEFBQQiCQElJCb/99hvr1q0jNTWV2traLhMr5OoK118PH39sdu3bsweeeQZ69waTScaBAwreesuP225LZt68oSxf3ofDhyNQqxVotVoOHjzIfffdx+rVq8nIyKC4uJiGhoYu8/27Cl1NT8vNTnd3d9zc5Lz7Lpw4AY8/3jJjuSCY9584Ae++C+3IV9LhXK3OiYmJBAcHYzAYOHToUJf7v10MiwtfdHQ0Go1GTGd+pXQXXRwZR9bYpsbTggULOH78OBs3bmTjxo0cP36cxYsXX7R9c3Mzqamp/OUvfyE1NZU1a9Zw+vRpbrjhhhZt77vvPkpLS8XtQxuslQsCNDV13KbXO7e6v63jY8qUKaSmplJTU0NZWRk5OTmMGzfuqr7j008/jUql4vTp0xw6dIgvv/ySn3/+ud3H+fHHH9m/fz8HDx7k008/Zf369QDce++9fPbZZzQ0NHD48GExc8q//vUv9u3bx9GjR8nKyiI1NZX//ve/LY47cuRIjh07hlqtpri4GIDffvsNgL179zJ06FAxVsGycuTs7Gx1DJlMxu23386qVavEfatWrWLGjBn4+/sD5hNlfn4+AwYMID4+ngEDBvDjjz9atT9/lSo3N5egoCCqqqqYOnUq119/PW5ublRUVDBr1ixefPFFse2YMWPIzMykrKyMyMhIHn30UQCSk5N5/vnnWbJkCU1NTSxZsoTly5e36g7522+/iZnEWttaIyMjg+TkZCs9+vXrZ2XYXcgjjzxCUFAQU6ZM4cSJE622MRqNHDp0iL59+wJmd8ekpCQWLVpEQEAAw4YNY8+ePRf9jK6CXC4nOTmZO+64gwcffJAhQ4agVCopLy8XXfp+/fVXqqqqbNaHyMhImxxXqVTSo0cPJk2axMyZM+nZsydubm5otVpOnz7Npk2b2LhxI5mZmV0qeNzJCcaMgTffhJMnoaAA/vtfmD0b3N3N7n0rV7rz8MMRTJkykGeeGcS2baGUl5ej1epobGykuLiYzMxMUlNTycnJoaysjObmZoc+kXcFbDWWbYEgCOLv2nKOWLoUPvjAbBw9+ugf521BMCfhe/dd8+udHfN0tTrLZDKGDRuGQqGgoqJCvNnXHVAqlSxYsIDQ0FAaGxv56quvrMo8tIeuNJ67Ko6ssc0iZDMzM9m4cSMHDhxg+PDhAHz88ceMHDmS7OxskpOTW7zHx8fHanUA4N1332XYsGEUFhZa5elvbYWho2luNvvTdxytpz9vbAQPj8u/W6FQMHfuXH744QfUajW33HLL7y4F1kyZMsUqKFqtVvPcc8+1aCcIAp9//jkFBQVinMsDDzzA6tWrmT17dtu/FvD4448TFBREUFAQS5cu5ccff2TWrFkolUpOnjxJ//79CQ0NFf9nn376KV9++SWBgYEAPPXUU7z11ls88sgjVsf18vKiZ8+eHDp0iNLSUubOncvGjRupra1lz549jBkzRmy7ceNGHn/88VYDvxctWsTIkSPF9NqrVq3ilVdeEV/funUrEydOFANkFy1axMqVK7nnnnsoLS1l9+7dfP3112J7X19fHnnkEWQyGfPmzWPFihU88cQTyOVy5s2bx6JFi8S2568OPvvss1YraY8//jhr165l2LBh9O3bl1tvvbVVfceMGdPuuJvGxsYWcXbe3t4XdcX4xz/+IRaOfffdd7n++uvJyspqUdD2xRdfJCIiQoyJKi4uZsuWLXzyySei8T137lxycnLEC4+uTnBwMLNmzWLSpEkcO3aMI0eOUFNTw8GDBzl48CBxcXEMHTqU5OTkDq1fZY+4Qi8vL/r370/fvn0pKysjPz+fkpISVCoVaWlpnDhxguDgYGJiYoiKinLoIN4LiYkxx6EsWwYaDezaBb/8YnbzO3NGxq5dCnbtMv9GnnqqNxMn9mbYMBV9+1bg76+mtraW2tpawHzh5enpibe3N15eXri5uXX5gHp74ogxshejtraWpqYm5HI5AQEB4n6LYWRJDvHOO+YVJ0cxnKBjdPb09KRnz56kp6eTlpZGREREt0mo4urqyqJFi/j888+prq5m1apV3HXXXS1uul6OrjSeuyqOrLHNVp72798vFqG0MGLECHx8fNrlNqRSqZDJZC3urq9cuZLAwEB69+7N008/fcm7B1qtlvr6equtq7Jw4UJWrVrVYiXkfCwB7pbtrrvuarVdZWUlarWapKQkcQXj+eefvyK3sfPvEERFRVFaWgrA6tWrWbduHREREUydOlXM1ldYWMiUKVPEz124cCGVlZWtHnvs2LHs2bOHPXv2MHbsWEaNGsXevXutjCedTsfhw4cZO3Zsq3fK+/TpQ48ePVi3bh3Hjh2juLjYykDctGkT06dPF5/fdttt7Nu3j5KSEr799lsmTJhgZayfn6nMzc2NgIAA0ZB1c3MTawgBvPrqqyQkJODt7c2wYcOsXAXkcjlLliwhIyODxx57rI1qtw1PT88WY72+vr6FMWRh2LBheHp64ubmxjPPPIOnpyeHDh2yavPBBx+wZs0aVq9eLSYacHNzIy4ujnvuuQelUsm8efOIj49n//79Hfp9HAE3NzdGjRrFI488wqJFi0hOTkYmk5Gfn8/333/P22+/zc6dOztsjrG4RtoDS5KJ0aNHM2fOHIYOHSq69ZWXl3Po0CH+97//ib+LrhIfZcHVFaZNM1/w5uRAdjb8+99gCZGsrjbHSj3zTAgzZvTljjuG8OGHvTl6NBy1Woler6e2tpazZ89y8uRJjh8/Tm5uLuXl5WLyCYmLY8+xfDXodDrOnj0LQFhYWIubBUuXQt++8N57MHCgYxlO0HE6Jycn4+HhQXNzc7dJHmHB09OTRYsW4eHhQWlpKatXrxbr47WVrjKeuzKOrLHNbiWUlZW1ajUGBwe3OXe7RqPhz3/+MwsWLLC6g75w4ULi4uIIDQ3l5MmTPPfcc6SlpbVYtbLw+uuvW60yWNi6dSseHh5cd911HDp0CLVaTWBgIEajEZVKhSBAdbWr2Bcw36Vtbm7GaDTi5OSEu7u7aLhZin5a2np6eqLRaDAYDMjlcqsfp4uLC3K5HLVajV4PRqN12/MvfC13RBobGxk6dCjFxcUolUri4uJEFzaLf7YgCBgMBit/bYPBgEajob6+Hm9vb+rr6xEEAXd3d1xdXTlx4gQ+Pj5iW51OJ75fr9ejUqnE73R+0guNRoNKpcJkMnHu3DkaGhowmUycOXOGkJAQVCoVKSkprF27FrVazUsvvcTSpUvZsWMH4eHhfPHFF/Tv399KQ+3vkbbn6z1kyBBWrFhBRUUFf/nLXygrK2PDhg0cP36cQYMGoVKp2LVrF4MHD0av16PT6WhoaLDS0MXFhfnz5/PFF1+QmJjI3LlzMZnMweNyuZwtW7bwzDPPoFKpcHZ2xtfXl/Hjx/P555+zdu1a7r//ftGQB8T3Ojs7YzAYxOfu7u5oNBoEQUClUnH8+HE++OADfvrpJ5KTkykoKKBPnz5i2/Lycl588UVuu+02/vSnP/Hrr78ik8lQKBS4urqKq0RHjx5t1X3VgkqlEttaxmFUVBTZ2dlotVr0ej1Go5G0tDSeeuop8f94qTF7/vcEWLduHa+++iobNmxAqVQiCAJNTU3ExcUhCAKCIIh6G41GcewAYt2hpqYm8bM2bdok9jMwMJBjx44BMGTIEEpKSigpKcHJyYnJkyezdetWjEYj4eHhhIeHc+TIEQAGDhxIVVWVmD1p2rRp7NixA51OR0hICLGxsRw8eBAwuyzW19eLtT6mTJnC3r17aW5uJjAwkKSkJPHGTu/evdFoNJw5cwZAnCMaGxvx8/Ojd+/enDlzBn9/f+bNm8eJEyfYsWMHGo2G2tpaVq5cSVBQEIMGDWLWrFns2rULMMcTKBQKMjMzAfOKYkZGBjU1NXh4eDBixAi2bdsGQI8ePWhsbBR1GjlyJLm5uVRWVuLq6sq4cePEgNqYmBh8fX3FbIqW1fqysjKUSiXXXXcdmzdvRhAEIiMjCQ4OFk9MgwcPpqysjOLiYuRyOVOmTGHPnj0YDAbCwsJITk5m69atVFdX4+vrS3l5OTt27EChUDBlyhRKSkpQKBSEhITQo0cPMdtn3759aWxsJD8/H4DJkyezb98+mpubCQgIICUlhb179wLQq1cvdDqdeJE2ceJEjhw5QkNDA76+vvTr14/du3cDiF4L2dnZgDmm8cSJE9TV1YnzxY4dOwBISEjA2dmZjIwMAEaPHk1WVhbV1dW4u7vzyCOjUCj2ceAAvPJKGZWVDWzebCInx5vsbDnZ2R589pkHcnk4Q4cKxMTk0KtXOf37a/DxcRZvBoWHh9PU1ERzczMuLi6MHz+eAwcOIAgCERERhIaGcvToUQAGDRpERUUFRUVFyGQypk6dyvbt29Hr9YSGhhIdHS3etOjfvz91dXXixfzUqVPZvXs3Go2GoKAgEhISxJsUffr0obm5mby8PAAmTZrEgQMHaGpqwt/fn169eonni549e2IwGMjJyQHMccSpqalikcgBAwaIYzYpKQm5XC5+1zFjxnDq1Clqa2vx9PRk2LBhbN++HTDXC3J1dRVdg0eNGsXp06epqqoSCzhbztOWDLIW9+Dhw4dTUFBAeXk5zs7OTJw40e5zRO/evTl9+jTl5eU4OTkxcOBA9uzZYzVHfP75KdLTzRZ3RobAI49kEBtbhF7fco6w6J2SkoLJZOL06dMAjB8/nuPHj4vFOAcNGsTOnTuvaI5wd3fn5MmT4hxRUVHBpk2bOmSOsMyXRqMRjUZDeXm5OEds27ZNnCMiIyM5fPgwAAMGDKCmpkZ095s2bRo7d+5Eq9USHBzsMHPEoUOHCAsLIzU1lcOHD5Obm8uwYcMYM2aM1RwxatQotm7dCphjrj09PUlPT6egoACVSkVeXh4VFRW4uLgwYcIEccxGR0fj7+/P8ePHARg6dChFRUWUlpaiUCiYNGkSW7ZswWQySXPE73OEu7s7o0ePFueImpoaSktL7TZHWPrfJoR28tJLLwnAJbfDhw8Lr776qpCUlNTi/QkJCcLrr79+2c/R6XTCnDlzhIEDBwoqleqSbY8cOSIAwtGjR1t9XaPRCCqVStzOnTsnAC2Oq1arhYyMDEGtVl+2f1eCTqe74vfGxMQI+/fvFwRBENLS0oT09HRBEARhx44dQnJycqvtLCxdulR46aWXWj3u0qVLhXvvvVeora0VjEajkJGRIRw8eFAQBPP/eunSpa1+DiCUlpaKnzl8+HChsrJSyMvLE6KiooR169YJWq1WWLlypaBSqQSj0Sj87W9/E6ZMmSIIgiC8/vrrwqxZs4TS0lLBZDIJ+fn5ws6dO1vtY1lZmeDh4SH06tVLEARByM3NFby8vIRBgwaJbZ555hlh+fLlgiBcXOeioiLB1dVVCA0NFbZs2SLuz8jIsDqWhZUrVwrR0dGCm5ubUF9fL+6/UIv9+/cLMTEx4vNjx44JISEhgiAIws8//yzExcUJ1dXVQkNDg7Bo0SLh/J/drbfeKjz66KOCyWQSxo0bJ7z55put9v1KGTp0qPC3v/1N0Gg0wvvvvy/ExcUJer2+Rbva2lph8+bNgkajEbRarfCvf/1LCAkJEX8jmzZtEoKCgoS0tDTxPRaddTqdEBsbK6xYsUIwGAzCTz/9JAQEBAjV1dUtPsfWv7HOxGAwCOnp6cJnn30mvPTSS+K2fPlyYe/evUJjY2O7j1lZWWmDnl4ZJpNJqK6uFo4ePSqsXbtW+Oabb8Rt/fr1wokTJ4Ta2trO7ma7UalUwnfffWd1PqiuFoQffxSEZcsEISFBEMyRLX9szs4mYeRIvfDII/XCxx8XCjt3HhUOHjxotR06dEg4deqUcPbsWaG6ulrQarWd+C07H0cay62h0+mEU6dOCQcPHhSOHj3a6hyl0QjC4MHmMXDnnebnjkZH6mwwGISff/5Z+Oabb4Ts7OwOO64jkZGRIbz88svCSy+9JBw+fLjN73P08dwdsLfGKpWqVdugNdq98vTwww9fNrNdbGwsJ06caDUVZGVlJSEhIZd8v16v59ZbbyU/P5/t27dftj7SoEGDUCqV5OTkMGjQoBavu7i44NKZ6W9+x2AwdEi8QEfWI/nXv/7F888/T9++fWloaCAxMZG///3v7T7OjTfeyIgRI6irq+PBBx9k9uzZ6HQ6vvjiCx566CFMJhP9+/cXE3s8/fTT6PV6Ro0aRVVVFTExMTz77LOtHjskJER0JwLznQtPT88W8U4//PADcHGdIyIiGDlyJFlZWVb1AyyJJi5k7ty5LF26lNmzZ+Pl5dVuTQCmT5/OyJEjiYmJITAwkGeeeUaMnfrhhx9ITU0lLS0NmUzGZ599xrBhw5g9ezY9e/a8os+7kFWrVnHnnXfy2muvkZKSwpo1a0Tf9ddee409e/bw66+/otfree6558jKysLZ2ZkBAwbwyy+/iL+9119/ndraWkaNGiUee/78+XzyyScolUp++ukn7rnnHh566CESExNZs2ZNt4l3aitOTk706dOHPn36UFFRweHDhzlx4gTV1dVs3ryZbdu20bNnTwYNGkRcXFyb4mXKysrEuMDORiaT4e/vj7+/PwMGDKC8vJyCggIxO92pU6c4deoUPj4+REdHEx0dfcW/G3vi7e1Nr169rM4z/v4wb555A3PiiW3bYOtWc9xUaamM/fsV7N/vBXghl0cyYICJoUM1DBhQT0pKFe7uajHtvwUXFxc8PT1xd3fHw8MDDw+PDo2Rc2QcaSxfSEVFBcXFxej1epRKJUlJSeKqvAVBgIcegqNHwdcXXnutc7PqXYyO1NnJyYnk5GSOHj1Kbm4uiYmJ3S7Or2fPnkyaNImtW7fy66+/EhwcbBVffzEceTx3FxxZY5kg2MZROzMzk169enHw4EGGDRsGwMGDBxkxYgRZWVmtJoyAPwynnJwcduzYQVBQ0GU/6+TJk/Tt25ddu3a1KfucZdnRsmRuQaPRkJ+fT1xcXIuJsyNQqVT4+Ph0+HE7m9jYWL799tsW9ZXsRWlpKaNHjxaXotur87Rp03jhhReuOnPhtcaVjGdb/8YcDa1WS3p6OqmpqVYlF/z9/Rk0aBADBgy4aAwa/JE+35HR6/WUlJRw7tw5SkpKrNyT/fz8iI6OJioq6pLfszMpLS3lT3/6E2+99RZhYWGXbS8IkJcHu3ebt1274HevIyt69zYxZIiOPn0aSUqqISREhUzW8nTr5uZmZUy5u7t3S4PKEceywWDg+PHj4ph1dXUlMTERNzfr5E6CAE88YY6Xk8vNSUcc7KuIdLTOer2edevWodfrue666xw6iP9KEQSBH374gYyMDDw9PVm6dOllb/w44njubthb44vZBq1hs5innj17Mn36dO677z5xteH+++9n1qxZVoZTSkoKr7/+uli88+abbyY1NZX169djNBrF+Ch/f3+cnZ05c+YMK1eu5PrrrycwMJCMjAyeeuopBg4caJXFzBHpbndsHIX6+nr+8Y9/iM/bq/OkSZMYOXJkR3er2yON58vj4uLCkCFDGDJkCKWlpaSmpnLixAlqamrYunUr27dvJzk5mcGDB9OjR48W2TNby6bpaCiVSmJiYoiJiUGn01FcXExhYSHl5eVitrq0tDQCAgJEQ8rd3b2zuy1SWlrKypUrefLJJ9tkPMlkEB9v3iy5eM6dM9eXshhUmZlw6pScU6dcAVcgEF9fgcGDDfTvr6FXr3oSE2txdW1GrVajVqvFRDIymUw0qMw1hsyPu1KWw9ZwpLFsNBqprKyktLTUytjv3bt3C8O1sdGcDMJS6eK99xzXcIKO11mpVBIVFUVeXh6FhYXd0niSyWTMnTuXqqoqKioqWL16NXfeeecltXSk8dxdcWSNbbbyBOZgr0cffZR169YBcMMNN/Dee+9ZZc6TyWR8/vnnLFmyhIKCAuLi4lo91o4dO5gwYQLnzp1j0aJFnDx5ksbGRqKiopg5cyYvvfRSm92EOmvlqbvS2StPEl0H6TdmzuZ16tQpUlNTxSB2MJdqGDRoEAMHDrzsXa+ugFarpaioiMLCQioqKqyy0QUFBREZGUlkZCQebanTYENSU1MZPHgwR48ebdXt+0qoqIDffoP9++HAAbOr1+/JKa1ITBQYPFhPv37NJCfXExFRi0ymbfWYSqVSNKTc3NzErTuuUtkKjUZDZWUllZWVYlIcNzc3/Pz8iIiIaHFD6LPP4J57zI/lcvPzO++0d687n9LSUnbt2oWrqytz5szptjfOqqur+eijj9BqtUyYMIEJEyZ0dpck7Eh7Vp5sajw5Kp1lPFmy3UnYFkln+3AlOkvGkzUVFRUcPXqUtLQ0MQuhTCYjISGBAQMGUFxczNSpUzu5l1ePWq0WDakLSxIEBASIhlRnxEjZwni6EL0e0tPNhpRl+z2JlRVOTtCzp4k+ffSkpKhJTGwkNrYOpbL1VOgymQwXFxdcXV1bbEql0qEucrdt28akSZPs/rk6nY7a2lqqq6ut4s9cXV0JDQ0lKCiohU4lJfDCC7BixR/7du+GsWPt1OmrwBY6G41G1q5di8FgYPr06RctzN4dOHHiBGvWrEEmk7FkyRJiYmJabddZ4/lawt4aO4TbnkRLrkE7tVOQdLYPks5XT3BwMDNmzGDy5MlkZmZy9OhRzp49S05ODjk5OZw7dw6j0ciAAQPa5FLmqLi5uZGYmEhiYiLNzc2cO3eOoqIiqqqqqK6uprq6mrS0NHx8fERDytfX16Eu/q8GpRIGDTJvDz5o3lddDYcO/WFMpaZCVRWcPCnn5EkXwAXwBSJJSBDo29dIr14akpKaiYmpx8urAYNBj0ajEQ3v83FycmphUDk7O+Pi4tIphpVlpcfW6PV6mpqaaGhoQKVSWdX9k8lkeHl5ERIS0ur4OnvWXLfpv/+F88sFnjoFvXrZpftXjS10dnJyIjAwkLKyMiorK7u18dSvXz/OnDlDWloaa9as4cEHH2w14Zi9xvO1jCNrLBlPdqSr+6x3FSSd7YOkc8ehVCrp168f/fr1o7q6muPHj5OWloazszMHDx7k4MGDhIaGMmDAAPr16+dQMUPtxd3dneTkZJKTk1Gr1ZSUlFBUVER5eTkqlQqVSsWpU6fw8vIiIiKCyMhIAgICbHax7+fnx/XXX4+fn59Njn8xAgJgxgzzBuakBEVFcOyYeUtNNf89dw5yc2Xk5ipYu9YT8ASC8fU1r1IlJRmIj9fSo4eaqKh6vL2b0Ot1GI1GmpqarAp2W5DL5aIh5eLigrOzM87OziiVSpRKJc7Ozjg5OXWo5pcy/rXa9meuEwQBrVYrxoyp1Wqam5vF4t0WZDIZHh4eYqZIS91ECzodbN5sXmVauxYsIVAjR8Jbb4GDh1K3wFY3WQICAigrK7Oq9dhduf766yksLKS2tpatW7cyc+bMFm268s2sroIjayy57dnRbc9gMIhpoiVsh6SzfbgSnSW3vbZjMplITU0lPz+frKwsjEYj8Ef64AEDBpCQkODQQbXtQavVUlJSQnFxMaWlpeL3BbPBFRERQXh4OMHBwR0e51NTU+OwqfWrqv4wqCxbbi6cJ48Vvr7Qq5dAcrKR+HgdkZEaIiKaCQpqBLTodLo2rRrL5XLRmFIqlSgUCqvNyclJfCyXy3FyckIulyOXy1s1ui6m8YcfwiOPmFd87r9fwGQyYTQaMRgM4l+9Xi8WQLcU/dZqtVbJHs7Hzc0NT09PvL298fb2bnGjp7ERduyA9evhxx/Nq4AWJk+Gxx+H6683JwfpathqLBcUFHDgwAGCg4OtSn10V/Ly8vjyyy8BWLJkCbGxsVavO/Kc0V2wt8aS256D0tTU1C1TlTsaks72QdLZtsjlcqqrq7nllltQq9Wkp6dz7NgxSktLycjIICMjAy8vL/r168eAAQPaVNbBkXFxcSEuLo64uDj0ej1lZWUUFRVRUlJCc3Oz6MqoUCgICwsjPDycsLCwqzbCNRoNP/30E7fffrtDGvSBgTBlinmzoNVCdjZkZJhdyk6dMj/OzYW6Oti3T8a+fQrMp3h3wB+ZDKKjISFBIC7OREyMnuhoHSEhGgIDm3F312A0GtDpdBgMBkwmE1qtFq229QQWl8JiTMlkMtGQysrKotfvvm8ymQyTycQPP/jx979H0a+fwLJlMgoKznLjjRXt+hxXV1cxeYYl5fuFxpJKBQcPmhN47N5tTuah0/3xekgIzJ9vTg7Rt2+7v65DcfjwYZukd7asdrfmItod6dGjhxgLuW7dOh588EGrm4W20lniDxxZY8l4kpCQkHBw3NzcGDZsGMOGDaO8vJxjx45x4sQJGhoa2Lt3L3v37iUsLIz+/fvTp08fh62p1FYs6ZGjoqIwGo2Ul5dTXFxMSUkJarWac+fOce7cOWQyGYGBgYSHhxMeHo63t3e7Xc0yMjK4++676d+/v80SRnQ0Li7Qr595Ox+tFk6f/sOgys42G1Q5OebVlrNn4exZGeD0++YKmO+wurubjauYGIiKEoiIMBIaqicw0IC/vw5/fz0+PjoEwYDB8MdmNBoxGo1Wq0Amk6nFqpDJZLJyqVuzJog334zi4YcF3nlHxmOPCbzxRiyCIHDTTVUtVrcsroXnby4uLlb/74YGyMoyf+f0dDh50vw3O9vsFnk+cXFml8m5c2HiRJCcFS6NxXDQ6/Wd3BP7MWXKFE6fPk1NTQ0HDx50+HI4EvZDmi7sSFeOU+hIzk9tvmzZMpKSknjyySc77PiSzvZB0tn2DBgwoMW+kJAQpk+fLp7Yjx8/Tk5ODqWlpZSWlrJ582bi4+Pp378/ycnJXT42zcnJSTSOBEGgtrZWNKRqa2vF1NNpaWl4eXmJbQMDA6+5NN4uLuaVkwtXTwTBnD49N9d6O3MGCguhvNycICEry7yBDPPlgfUlglxuXgkLDTVvgYFmN0E/P/D1FfD1FfDxMeHtbcLLy4irqyBubm7BREYG4OQk8NlnSt58042HHxZYvlyGTAbLl8sAgTffjCMmJpa775ahViNuKpXZva6qyvpvUREUFJi3893vLqRHDxg1yhzLNHkyJCZ2Tbe8y9HanNGRdJckLm3B1dWVyZMns3btWnbt2kW/fv3EjKC21lnCsTWWjCc7YjAYrvhCJjY2lpqaGsrLy8Xq5/X19YSEhBATE0OW+YzX6RQUFJCSktLmpf0PPvigw/twNTpLtB1JZ9tTU1NDSEhIq685OTnRs2dPevbsSVNTE6dOnSItLY3i4mLRxc3FxYVevXrRv39/YmJiuvyFj0wmEwP/+/btS1NTEyUlJZSUlFBeXk5DQwPZ2dlkZ2fj7OxMaGgoYWFhhIaGivPmtYhMZnZNCwlpPQGCRmM2QgoLzdvZs+a/586ZDavycrPxZTKZ/1ZUwIkTLT7l9+1iMXjm1VC53Hychx9GNJwsfTQbUPDggzIxK2F78fc3ryr17v2HIdm/v9nYuxa41JxxNVgyn11r8cT9+vXj0KFDFBcXs337dubMmQPYTmeJP3Bkja+tX0Eno9PpruoEHhoayrp167jtttsAWLNmDVFRUR3VvW7D1eos0TYknW1PYWEhPXv2vGw7Dw8P0a2vqqqKEydOcOLECerq6jh27BjHjh3Dx8dHzOjX1eOjLHh4eIgp0PV6vZV7n1arpbCwkMLCQsCcVc9iSAUEBFxzq1KXwtUVEhLM28UwGMyrPeXlUFZm3qqrobbWHGdVW2v9WKX6Y9Xo/HtpJpPZ3fCdd1qu/Mhk5v27d/9hnMlk4OYGnp7mla6AAOu/oaFmYyk21rxd6yX+2jpntBdLjaxrbc6XyWRMnz6dTz/9lLS0NMaOHYu/v7/NdJb4A0fWWDKeuhC33347K1euFI2nlStXsmDBAr799luxTXp6OsuWLePUqVMkJCTw3nvvMWLECMC8evXII4/w4YcfUlZWxt/+9jdGjRrFkiVLKCsr4y9/+QtPPPEEYC5q+ac//Yk1a9Ygl8t55JFHePbZZwFz5hl/f3/S0tI4dOgQo0aN4ttvv8XPz4+pU6ei1WrFmIvTp08THh5+0e+0ZMkSUlJS+POf/8zLL79MXl4earWajRs30rt3b7777juxSF16ejoPPfQQ6enp9OjRgw8//JAhQ4Z0vNASEl2YwMBArrvuOiZOnEhhYSFpaWmcOnUKlUrFnj172LNnD2FhYfTt25c+ffp0m4LSSqVSrBFlMpmoqakRXRlramqora2ltraWjIwMlEolISEhhIWFtUhtLdE6CsUf7nr9+7fvvYIAP/+8hTFjpvDxx/DnP8Njj8Hy5dYGlCCY9584Af/+NzzwADg7d0/3uq5GXV0dwDWZJCgqKoqEhARyc3PZs2ePuPokce3SPXLcdhGudtKZMmUKqamp1NTUUFZWRk5ODuPGjRNf1+l0zJ49mwULFlBZWcnTTz/NrFmzrOoy/PLLLxw+fJitW7fy7LPP8tZbb7F371527NjB888/T2VlJQBPP/00KpWK06dPc+jQIb788kt+/vln8Tjfffcd77zzDpWVlRgMBt577z0ANm/ejIuLC42NjTQ2Nl7ScGqNNWvW8Oijj1JbW0tSUhJ/+9vfAGhoaGDGjBk88cQTVFVV8Ze//IUbb7yxVffAa3Fy7wwknW3P1WQakslkxMTEcMMNN/D0009zyy23kJSUhFwuF2Oj/v3vf/P5559z5MgRq2KiXR25XE5gYCB9+/Zl6tSpzJ07lxEjRhATE4OLiwt6vZ6ioiIOHz7MuXPn+OWXX5DL5ZSXl1ulSJfoGGQyuOGGKfj7w7PPwgcfwHvvmQ0lSyIHi+H03nvm1x9/3BzDJRlO7cNW2cnKy8sBc72na5EJEyYAkJaWRl1dncNmgetOOLLG0srT5bBE0XYATU1NeHh4tHwhJcWc6ugyKBQK5s6dyw8//IBareaWW26xqvFy4MABnJyceOihhwCYP38+77zzDps3b+aWW24B4LHHHsPHx4dhw4YRGhrKrbfeip+fH35+fkRHR5OVlUVgYCCff/45BQUFeHp64unpyQMPPMDq1auZPXs2ALfddht9+vQB4KabbmL79u1XKw8AU6dOZezYsWL///rXvwKwYcMG+vXrx4033gjA3Llz+fvf/87+/fuZOHGi1TEaGhrEoE4J2yHpbHt27twpnrSvBqVSSe/evenduzdNTU1kZGRw8uRJzp49K26//PIL8fHx9O3bl+TkZFzaW7XUgXF1dSU2NpbY2FhMJhO1tbWUlZVRUlJCTU0NaWlpqFQqsrKyUCgUBAYGEhISQkhICH5+fl0+VswROH8sL11q3rdsmfnv22+bjaV33zUbTpbXJdpPR80Z59PQ0IBKpUImkxF6rQSPXUBkZCQ9evQgLy+PI0eOoFAoOlxnCWtsMZY7Csl4uhxZWTB4cIccqhWzyczRo9DGFLkLFy7kz3/+M2q1mo8++khcSgcoKSkhOjraqn1MTAwlJSXi8+DgYPGxm5ubVeyDm5sbTU1NVFZWolarSUpKEl8zmUxWaTrPP467u7voD321XOy4hYWFbNu2DV9fX/F1vV5PaWlpi2NcrHCiRMci6Wx7rqTGzuXw8PBg6NChDB06FJVKxcmTJ0lPTxdXs3NyclAqlSQlJdG3b18SEhK6VZC4XC4nICCAgIAAevfuTXp6Oi+++CKvvvoqzs7OaDQaysrKKCsrA8z1p0JCQggODiYkJARPT0/JmLoCLhzL5xtQu3aZXfUkw+nqscWckZubC0BYWFi3uqnSXoYPH05eXh6pqan07t27s7vT7bHFWO4ous8Z0VakpJiNmw5ArVa3HmyZktLmY4wcOZLi4mKcnZ0ZMGAAO3fuFF8LDw/n3LlzVu0LCwu56aab2tXPwMBAXF1dOXv2bLtds2x1UREREcHMmTNZs2bNZdtKGeDsg6Sz7Tn/ZoIt8PHxYfTo0YwePZqqqirS09NJT0+npqaGU6dOcerUKVxdXenZsye9e/cmLi6u2yVa0Ov15ObmkpCQwMCBA1GpVJSXl1NRUUFFRUWLxBMeHh7iqlRISIhDFtZ1RFobyxZD6ZFHJMOpo+joOUOtVnPmzBkAEi6VUeQaIDExEV9fX+rq6jrshrHExbH1+e9qkIyny+Hu3uZVocuhNBg6pBKfJYnDhYwYMQK9Xs9///tf7rvvPtauXUt2djZTp05t1/Hlcjl33nknTz/9NG+99Rbe3t5kZ2fT0NDAsGHDLvnewMBAcUUoLCysXZ97KWbNmsVzzz3HunXrmDlzJjqdjl27djFy5MgWBp6zs3OHfa7ExZF0tj09evSw22cFBgYyceJEJkyYQGlpKenp6Zw8eZKGhgYxY5+bmxspKSnd1pCSyWT4+vri6+tLcnIyRqOR2tpaysvLKSsro7q6mqamJvLy8sjLywPA19eX4OBggoKCCAoKkoypi3Cxsbx0KSxZYo5vkrh6OnrOSE9Px2AwEBAQ0KHn9K6IXC4Xb1o3NDR0dne6PfY8/7UXKWGEHWlqauqQ4/Tr10+MNzofZ2dnfvrpJ7766isCAgJ44403WLdu3RUF9v/rX//Cw8ODvn374u/vzx133EFtbe1l3+fh4cGzzz5L37598fX1tXIZvBp8fHxYv34977zzDkFBQcTGxvLRRx+12rajdJa4NJLOtufAgQN2/0yZTEZ4eDjTpk3jiSeeYMmSJQwdOhQPDw/UajXHjh3j66+/5v/+7/9Yt24dubm53TbJgpOTE4GBgfTu3ZtJkyZx4403Mn78eFJSUvDz8wPMWchOnz7N3r17+d///sevv/7KkSNHOHv2rJTJ7zwuNZYlw6nj6Mg5o6SkhLy8PGQyGQMGDJDcVUF019u9e3eb61lKXBmdcf5rKzJBsOS6uXaor6/Hx8cHlUpllaZXo9GQn59PXFycTe4eqlQqKUOZHZB0tg9XorOtf2PdjU2bNjlMxiGTycTZs2c5deoUmZmZVsazm5sbPXv2pFevXl1uRSo1NZXBgwdz9OhRBrXTy0Cj0VBRUUFlZSWVlZVWMagWvLy8xFWp4ODg1pMGXQM40ljuznSUzg0NDWzZsgWdTkdycjIDBw7sgN51D/7zn/+wb98+8UaxhG2w95xxMdugNSS3PTtyrRWX6ywkne2DpLPtcaQTs1wuJy4ujri4OK6//voWhlRqaiqpqaldzpCKjY1l+fLlxMbGtvu9rq6uREdHi4l6tFqtaEhVVFRQV1dHQ0MDDQ0Nopufh4cHQUFBBAQEEBgYiI+PT6tu2N0NRxrL3ZmO0Lm5uZndu3ej0+kICAigX79+HdCz7kNCQgJnzpyhoKBAGtc2xJG1lYwnOyJlJ7MPks72QdLZ9jhqUHJbDSlXV1eSkpLo2bMn8fHxDhkn5+/vz7Rp0/D397/qY7m4uIiFesFce6+qqkpcnaqtraWpqYmmpiYKCgoAcwkKiyEVGBhIQECAQ+p0tTjqWO5uXK3OTU1NYkyPh4cHY8aMcfgbIPYmJiYGnU4n/oYlbIMjzxmS8WRHtFqt5KpkBySd7YOks+3Jz8+3KhngiFzMkMrKyqKxsZETJ05w4sQJlEol8fHx9OzZk6SkJIdZuaysrOSdd97h5Zdftird0BE4OzsTHh4uFgvX6/VUV1dTVVVFVVUV1dXV6PV6ysvLxSKkYI7xPN+g8vLy6vLxJl1hLHcHrkbnqqoqfvvtNzQaDR4eHlx33XUO8zt1JKKjo6mtraW6uvriWZQlrhpHnjMk40lCQkJCokM435CaOXMmRUVFZGZmkpmZSW1tLVlZWWRlZYntevbsSUpKCp6enp3W53PnzvGf//yHe+65p8ONpwtRKpWEhoaKhUZNJhP19fVWxpSlIKlKpRJd/ZydnQkICMDf31/cpAs2iY5CEAROnjxJZmYmJpMJX19fxo4de83G510ONzc33N3dAaiurhZXmiWuHaSEEXZMGCEIQpe/e9gVkHS2D1eis5Qwon0YjcZu4TIjCALl5eWiIVVRUSG+JpPJiIqKIiUlhZSUlA5xn2sPV5MwwhZoNBqr1amamppWsxm6ublZGVP+/v4OXcC0u4xlR6e9OldWVrJt2zbxeWRkJMOHD5fq+F2Gzz//nLNnzzJ37lwGDBjQ2d3plth7zpASRjgojY2NeHl5dXY3uj2SzvZB0tn27Nu3j7Fjx3Z2N64amUwmrrhMnDiR6upqMjMzycrKoqioSCxCu3nzZoKCgkhOTiY5OZmIiIhrIpnC+bi6uhIREUFERARgvoBQqVRUV1dTW1tLTU0NKpUKtVpNcXExxcXF4ns9PT3x9/fHz88Pf39/fH19Hcag6i5j2dFpq856vZ7MzEwyMjLEfQkJCQwePFi6+dgGSktLAceOy+nqOPKcIRlPdkQKsLcPks72QdLZ9jQ3N3d2F2xCQEAAY8aMYcyYMdTX15OVlUVmZiZnz54Vs9X99ttveHh4kJSURHJyMj169OiWiRQuh5OTk7iyZEGv11NXV0dNTY24NTQ00NjYSGNjI4WFhWJbd3d3fH198fPzw8fHBz8/Pzw9Pe1+gdxdx7KjcTmddTodubm5nD592qpO0bhx48TYPInLY3Hakmo92Q5HnjMk48mOKBSS3PZA0tk+SDrbnoCAgM7ugs3x9vZm2LBhDBs2DI1GQ25uLtnZ2eTk5NDU1MSxY8c4duwYCoWCHj16kJycTFJSUoetenp5eTFixIgutYqqVCrF2lEWdDodNTU1YiB7XV0djY2NNDc309zcbFWwXKFQ4Ovra7X5+PjY1FXrWhjLjsDFdFar1Zw+fZrc3Fz0ej1gHvv9+/cnIiJCWm1qJ/7+/pSXl6PT6Tq7K90WR54zpKsfO+KoMR4rV65k9erVrF279oqPsWTJElJSUvjzn//cgT27Mmyt8/nftSO066o46njuTqSkpHR2F+yKq6srffr0oU+fPhiNRgoLC8nOziYrK4u6ujpOnz7N6dOnAYiIiCApKYmkpCRCQ0Ov+OIvMTGRLVu2dGrSio7A2dnZKhkF/LFCVVdXR21tLSqVirq6OgwGgxhTZUEmk+Hh4YGXlxc+Pj54e3vj7e3dYUbVtTaWO4vzdTYajZSUlJCXl0dZWZm4WuLj40PPnj2JioqS4tCukIiICMrLyyX9bIgjzxmS8WRHGhsb8fHxaff7pkyZwrRp03j66aet9j/55JNUV1fzxRdftOt4MpmM0tJS8SS7cOFCFi5c2O5+OSpXqrOF2NhYvv32W0aMGHHZtt1Nu/ZwtTpLXJ69e/fatcK6I+Hk5CRm7ps2bRqVlZVkZ2eTnZ1NUVGRGO+zY8cOvLy8SEhIIDExkfj4+HbF+RiNRjZv3sycOXO63YVQaytUJpOJhoYG0aiybGq1WnT7s8RzWHB3d7cypiyP26PztTyW7cmePXvo378/xcXFnDt3zmplJCgoiJSUFMLDw6WVpqvk+PHjANekK7G9cOQ5QzKeugCLFi3i7bfftjKeTCYT3333HZ9//nmbj6PX66UMOhISEl0OmUxGcHAwwcHBjB07loaGBk6fPk1OTg55eXk0NDSI7n1yuZyYmBgSExNJTEwkMDDwkheKaWlp3HTTTQ6Tbc/WyOVyfHx88PHxISYmRtyv1WpRqVTU19dTX18vPlar1aLrX1lZmdWxnJ2d8fLywtPTU9wsz11cXKQLdDsgCAJNTU1UVFRQUlLC8ePHUalU4utubm7iTYiu5Jrq6Gg0GpydnaWSAdco11Yao07mSn9k8+bNIzs7m8zMTHHfzp07MRqNTJo0icLCQmbOnElAQAA9e/Zk48aNYrvY2Fj+8Y9/kJycTK9evZg6dSoA8fHxeHp6sn//flasWMH06dPF92zfvp0hQ4bg7e1NYmIie/bsAeDjjz8mMTERLy8v+vXrx86dO9vU/9jYWP75z3+SlJSEt7c3b7/9NocOHaJXr174+/vz73//W2xbU1PD/PnzCQwMJCEhgU8++UR8bcmSJTz++OOMHz8eT09PFixYQFlZGZMnT8bHx4eFCxdiNBpFnd9//33x4unOO++kqakJgBUrVjB16lQeeOABvL296d27t3gX6d5776WwsJDrrrsOT09Pvvvuu0t+t/O127lzJykpKbzyyiv4+/sTFxfHli1brL7bggULCA4OpkePHu1eMXQ0pJOG7enVq1dnd8Eh8fLyYvDgwcyfP59nnnmGxYsXM2LECAICAjCZTOTn57N582bef/993nnnHTZs2EBOTo4Y6yHREhcXF4KDg0lISGDQoEFMnDiROXPmMG/ePCZPnszQoUNJTk4mLCxMrP+j0+morq4WCyMfPHiQrVu38r///Y+1a9eyefNm9u3bR3p6Ot7e3pSVldHQ0IDBYOjkb9t1MZlM1NXVcebMGQ4cOMD69etZv349hw4doqioiICAANzd3UlISGDChAnMnj2bfv36SYZTB2Op8xQYGNjJPem+OPL5T1p5siNXmp3My8uLG264gVWrVvH//t//A2DVqlXMnz8fmUzG7Nmzuf/++/npp584fPgws2fP5uTJk6Jb3v/+9z/27NmDt7c3rq6uyGQyzpw5I76enZ0tflZeXh433ngjK1euZMaMGRQXF4vL/uHh4Wzbto3IyEg+/fRT5s+fz9mzZ9vkuvHLL79w+PBhsrOzGTt2LDfccAN79+6lsLCQESNGsGjRIoKCgnjooYdQKBQUFhaSm5vL5MmTSUlJYcyYMQD88MMPbNu2jaCgIAYNGsSsWbP48ssvCQ8PZ8iQIaxfv55p06bxww8/8NFHH7F161aCg4O55557+Otf/8o///lPAHbs2MH999/Pe++9x0svvcRTTz3Ftm3b+OSTT9i6dWub3fYuJDc3Fy8vLyoqKvjss89YtmwZZ86cAWDx4sX06dOHc+fOkZ+fz3XXXceAAQPo379/uz/HEZCy7dkeKRj58igUCuLj44mPj2f69OnU1NSQk5NDTk4OBQUF1NXVcfjwYQ4fPoxCoSA2NpaEhATi4+OlC5824OzsTGBgYAut9Ho9TU1NNDY2WmX6a2xspKmpSUxgUVNTA0BtbS1nz54V3+/q6oq7u7u4eXh44OHhgbu7O25ubri4uFxzaeovRKvVikWTa2trqa2tpa6urkXdL7lcjr+/PyEhIej1egYOHCit+tkQg8FAdXU1Pj4+0hxiQxz5/CcZT3ZEq9VecZD9okWLeOyxx/h//+//odVq+fHHH9m8eTOHDh1Cr9fz0EMPATBy5EgmTJjAr7/+yl133QXAE088QXBwcJs+55tvvmHOnDnMmjULgOjoaPG1mTNnio/vu+8+/vrXv5KTk0OfPn0ue9zHHnsMHx8fhg0bRmhoKLfeeit+fn74+fkRHR1NVlYW/v7+/Pjjj5w5cwZ3d3f69evHPffcwzfffCMaT7fddpsYRDhhwgQ8PT3FuxOTJk3ixIkTTJgwgU8//ZQXXnhBdEt5/vnnmTlzpmg89e3bl5tvvhmABQsW8MEHH7RJn8vh4+PDE088gUwmY9GiRSxdulS8oNizZw/r1q3DycmJlJQUFixYwJo1a7qs8XQ141mibeTm5hIfH9/Z3ehS+Pv7M3z4cIYPH45OpyM/P180plQqFbm5ueTm5gJYxexJKYfbh1KpFDP1XYjBYKCpqcnKqNq/fz8+Pj40NTVhMBjQaDRoNBrRuLoQmUyGi4sLbm5uuLq64uLigqurq/j8/H1KpbJLxqvp9Xo0Gg3Nzc2o1WorzRoaGtBqta2+T6FQ4OfnR3BwMEFBQQQEBIgu+Zs2bZIMJxtjKakQFRUlxf3aEEc+/0nGU1t44AE4rxDhleKu18OFMUcREfDf/172vdOmTaO+vp4DBw5QWlpKUFAQQ4cO5fvvvycnJ8fqBGYwGBg8eLD4PDIyss19LCoqokePHq2+9r///Y+//e1v5OXlAdDQ0EB1dXWbjnu+8ebm5mYVwOzm5kZTUxOVlZUYjUar/sbExLBp06Z2HQegsLCQe+65h/vvv198/XyXnfOP4+7u3mGF7oKCgsQTl2VZ31J3pampySr1ptFovGaTTUhI2ANnZ2ex4K4gCFRWVpKbm8uZM2c4e/YsKpVKTI7wxRdfkJGRIa5iRUZGXvMrH1eKQqEQ46osVFdXM23aNARBQKfTiXFUzc3NNDU1Wf3VaDQIgiAaWG39TGdnZ5ydnVEqlVaPXVxcUCqVKBQKnJycWv0rl8uRy+XIZDJkMpnVY5lMhiAI4gZ/rLybTCaMRiMGgwGj0Sg+NhgM6PV6dDqd1abVatFqtajV6jbdWXdzcxPrc/n6+uLv798pdbok/sBy8yU+Pl76P1yjSMZTW2iDcdMWnEwmuMKTsVKp5NZbb2XVqlWUlpaKF90RERH07duX1NTUi763PT/uqKgoKzc+C1qtlttvv52ffvqJSZMm4eTkRFhYmHgi6QiCgoKQy+UUFRURFRUFmI2g9hbu8/LyIiIigjfeeIMbbrih3f2wxWQYERGBr69vm43NroDkQ297Jk6c2Nld6Dacn3Ri1KhR6PV6zp49S3Z2NjExMajVaoqKiigqKmLXrl24uroSFxcnGlN+fn6d/RW6NJaxbFlRcnFxuaimJpMJrVYrGk+WTa1WW+0/3wCxGCyOXFizNRQKheiq6O7uLibdsCTeaG+SJ2nOsC1Go5H09HTi4uJISkrq7O50axx5LEvGkx1pbm6+qloiCxcuZO7cuTQ2NvLaa68BMHz4cPR6PR999BFLliwB4ODBg8TExFi53J1PcHAwBQUFVvVALNx+++0MGDCAX375henTp4sxT0FBQeJfgHfeeYfKysor/i6t4eTkxLx583jhhRf48MMPOXPmDJ9++imrV69u13Gam5u55557ePXVV+nTpw89evSgtLSUtLQ0q8QYF8Oiz5XEPF2MiIgIhg4dyl//+lf+/Oc/4+zszIkTJ3B1dXXooMhLcbXjWeLyHDlyhFGjRnV2N7olSqWShIQEEhIS8PPzo0+fPpw5c4bc3Fzy8vJQq9VkZmaKiXp8fX3p0aOHmLlMGvvtoz1jWS6X4+bm1qakNCaTyWqF51KPLatDF64SWf5ajtceZDJZq6tZlscuLi44OzuLfy2PLcZSR2fAleYM23L69GkaGxupqamRjCcb48hj2abGU21tLY8++ijr1q0D4IYbbuDdd99t1UfawpIlS1pkIRs+fDgHDhwQn2u1Wp5++mm++eYb1Go1kyZN4j//+U+73NM6gwuDPNvLqFGj8PLyIi4ujsTERMB812r9+vU89thjvPDCCwiCwJAhQy4Zw/PXv/6VOXPmoNVqrTLzAcTFxfHjjz/ypz/9idtuu42wsDA+++wz4uPjeeutt5gyZQoymYwHHniAhISEq/o+rfH+++/z4IMPEhkZiY+PD3/7298YO3Zsu45hNBqZP38+tbW1XH/99RQXFxMWFsayZcvaZDw9++yzPProoyxbtoyPPvqIW2+99Uq/jhUrV67kySefpEePHuh0Ovr06WOVabCrcbXjWeLyNDQ0dHYXuj1nzpzhySefZOXKlQwcOJCBAwdiMpkoLS0VXfyKioqoq6sjNTVVXOW3ZM2Mi4sjNja2XTWPrkVsNZblcrm4ktURnO+eZzKZxMfnu/BZvBMsbp2O5LolzRm2QxAEfvvtN8AcDtEV4+y6Eo48lmVCR/pdXcCMGTMoKirio48+AuD+++8nNjaWn3/++aLvWbJkCeXl5Vb1i5ydnfH39xefP/DAA/z888+sWLGCgIAAnnrqKWpqajh69GibBnN9fT0+Pj6oVCq8vb3F/RqNhvz8fOLi4mwSCN/Y2CjdrbQDks724Up0tvVvrLtx8OBBhg8f3tnd6NakpqYyePDgS9Z50ul0nD17lvz8fPLy8lrUO5LL5YSHh4vGVFRUFAqF5NhxPtJYtg+SzrYjIyOD77//HmdnZ0aNGsWECRM6u0vdGnuP5YvZBq1hs9k9MzOTjRs3cuDAAfHLf/zxx4wcOZLs7GySk5Mv+l4XF5dWXcoAVCoVn376KV999RWTJ08G4OuvvyYqKoqtW7c6bDVi+COBgIRtkXS2D5LOtqdfv36d3QUJzDfwLEV3AZqamigoKCAvL4/8/HxqamrEeKndu3ejUCiIiooiNjaWmJgYIiMjr3ljShrL9kHS2TZotVo2b94MmL2AJAPV9jjyWLbZbG5JS3r+ABsxYgQ+Pj7s27fvksbTzp07CQ4OxtfXl/Hjx/Pqq6+K2dGOHj2KXq8Xi72Cuf5Qnz592Ldvn0MbTw0NDVJaSzsg6WwfJJ1tz+7dux16TrtW8fDwoHfv3vTu3RuAuro6cVUqPz+fxsZG8vPzyc/PB8zu1ZGRkcTExBAbG0tkZGSHx7o4OtJYtg+SzrZh06ZN1NXV4efnx6hRo9ixY4eks41x5LFsM+OprKys1dpCwcHBLVwezmfGjBnccsstxMTEkJ+fz1/+8heuu+46jh49iouLC2VlZTg7O7fI0hMSEnLR41pSg1qor6+/wm8lISEhISFhja+vrxgvJQgCVVVVFBQUcPbsWQoKCmhsbKSgoICCggJ27dqFk5MTERER4spUVFQUzs7Onf01JCQkWiE9PZ3U1FRkMhlz5syRfqsS7TeeXn75ZV555ZVLtjl8+DDQehClJfDyYtx2223i4z59+jBkyBBiYmLYsGED8+bNu+j7LnXc119/vdU+b926FQ8PD6677joOHTqEWq0mMDAQo9GISqUCEOMyLLUmvLy8aG5uxmg04uTkhLu7uxjUdmFbT09PNBoNBoNBDGq1HNdSPV2tVrfa1tPTUzTynJ2dUSgUYgpWDw8PMXuQTCbD29tbPO6Fbd3d3TEYDOh0OrFtfX09giCItTAstZHObwvmApINDQ2YTKYWbd3c3MR0sgDe3t40NjZiMplQKBS4urqKtZMubNseDS/V9mIaGo1GGhoarDS8sO35Gsrlcry8vC6qYWt6WzS8lN4WDduqd3s0vFTbjhqzl9Pb8l1aG7MX07upqUn8LEv9rqioKAIDAzl27BgAQ4YMoaSkhJKSEpycnJg8eTJbt27FaDQSHh5OeHg4R44cAWDgwIFUVVVx7tw5wFwPbceOHeh0OkJCQoiNjeXgwYOA2QWgvr6egoICAKZMmcLevXtpbm4mMDCQpKQk9u3bB0Dv3r3RaDScOXMGQJwjGhsb8fPzo3fv3mLgcEpKCiaTidOnTwMwfvx4jh8/LvpNDxo0iJ07dwKQmJiIQqEQM7iNGTOGjIwMampq8PDwYMSIEWzbtg2AHj164O/vL+o0cuRIcnNzqaysxNXVlXHjxoluJDExMfj6+pKWlgbAsGHDKCwspKysDKVSyXXXXcfmzZsRBIHIyEiCg4PFxAeDBw+mrKyM4uJi5HI5U6ZMYdu2bRgMBsLCwoiMjBTn9AEDBlBTU0NhYaGo986dO9FqtWICBUtyn759+4orMACTJ09m3759NDc3ExAQQEpKCnv37gWgV69e6HQ6sX7KxIkTOXLkCA0NDfj6+tKvXz92794NIHotWEoqjBs3jhMnTlBXV4eXlxdDhgxhx44dACQkJODs7ExGRgYAo0ePJisri+rqatzd3Rk1ahSnT5/mjjvuQKPRUFJSQnp6OmD2lMjLy6OiogIXFxcmTJgg/i+io6Px9/fn+PHjAAwdOpSioiJKS0tRKBRMmjSJ48eP4+XlxbRp03B2dmbLli2Ul5ejUCgoLS0lOzsbmUxGfHw8BQUF+Pr6kpyczKBBgygrK8PV1ZX+/ftTV1fH2bNnAZg6dSq7d+9Go9EQFBREQkIC+/fvB8zny+bmZrEO36RJkzhw4ABNTU34+/vTq1cvccz27NkTg8FATk4OYC44npqaKvr8DxgwgF27dgGQlJSEXC4nKytLHLOnTp2itrYWT09Phg0bxvbt2wFz3RtXV1dOnToFIOpbVVWFVqvFZDKxZcsWAGJjY/H29ubEiROAOTFUQUEB5eXlODs7M3HiRGmO4PJzhLu7OydPngTMc4RcLmfTpk3SHEHHzBHR0dH83//9H0ajkcmTJ+Ps7MymTZuor69HpVJd1RyxZcsWTCYTERERhIaGcvToUQAGDRpERUUFRUVFyGQypk6dyvbt29Hr9YSGhhIdHc2hQ4cAutUc4e7uzujRo8U5ws3NjdLSUrvNEZb+t4V2J4yoqqqiqqrqkm1iY2NZtWoVTz75JHV1dVav+fr68u9//5u77rqrzZ+ZmJjIvffey7PPPsv27duZNGkSNTU1VqtP/fv3Z+7cua0aSa2tPEVFRdk9YYRWq5UyMtkBSWf7cCU6Swkj2kdBQQGxsbGd3Y1ujz11FgSB2tpaq5Upy42b8wkMDCQ6Opro6GiioqLw9/d3qKxu7UUay/ZB0rnjKC8v54svvqC5uZmUlBRuu+028Tco6Wx77K2xTRNGBAYGEhgYeNl2I0eORKVScejQIYYNGwaYM2eoVKp25W2vrq7m3LlzhIWFAeY7IEqlki1btogppEtLSzl58iT/+Mc/Wj1GR6YxvRo0Go1D9KO7I+lsHySdbU92drZ0grYxtbW1fPzxxzz99NN2KYQrk8nw9/fH39+fQYMGIQgCdXV1FBYWiltlZaV4o9Jy99/Dw0M0pqKjowkNDe1SqZKlsWwfJJ07hrKyMr788kuam5sJDw9n3rx5VjcvJJ1tjyNrbLOYp549ezJ9+nTuu+8+PvzwQ8CcqnzWrFlWySJSUlJ4/fXXufHGG2lsbOTll1/mpptuIiwsjIKCAp5//nkCAwO58cYbAbMb1D333MNTTz1FQEAA/v7+PP300/Tt21fMvichISEhIdEW8vPzee2117jpppvsYjxdiEwmw8/PDz8/P/r37w+YC1CfO3eOc+fOUVhYSHFxMU1NTVZFe5VKJREREURGRhIZGUlERAReXl5277+ERHcjNzeXH374Aa1WS0REBIsXL5binCSssGnu1JUrV/Loo4+KmfFuuOEG3nvvPas22dnZosuCk5MT6enpfPnll9TV1REWFsbEiRP57rvvrE4K//73v1EoFNx6661ikdwVK1Y4/F046cRmHySd7YOks+0ZN25cZ3dBohNwd3cnOTlZvNFoMBgoKSmhsLBQNKjUarWYhMKCj4+PaExFRkYSFhbmMCnSpbFsHySdrxxBEDh48CCbNm1CEARiYmK4/fbbW3Uxl3S2PY6ssU2L5DoqUpHc7o2ks32QiuTaHqngpe1pS5FcR8OS0c9SW6qoqIiKigouPJ07OTkRGhpqtTrl5+fXKbFT0li2D5LOV0ZTUxM///yzmDRg4MCBzJw586I3HySdbY8jF8mV26lP3R+jEXbuhG++Mf81Gltp0nJfW4mNjRUz1FhYtmwZL7/88hUf09H56aefSElJwcfHh7CwMJ588smLamgymXj++eeJiIggKiqKp59+usWFhIUVK1Ygk8n4+9//brX/+eefRyaT8e2331q1s7idgtkPuisHbXckVzOeJdrGhQl3JCTA7OoXFBTEwIEDmT17Ng888AB//vOfufPOO5k0aRLJycl4eHhgNBopLi7m4MGD/Pjjjyxfvpx//OMffPXVV2zbto3MzExUKtVF58qORBrL9kHSuf1s3LiRt956i6ysLJycnJg+fTo33HDDJVdtJZ1tjyNr7Bjr+V2dNWvgscegqOiPfZGR8M47cF56dUd3K3Q0hgwZwp49ewgKCqK2tpZbbrmFjz76iAceeKBF208//ZQNGzaQmpqKVqtl/vz5fPTRRyxdurTVYyckJLBq1SpefPFFwHwn97vvviM+Pt6qnZ+fH6+99hp33333NVfU8nJI49n2SK6RtsfNzY2kpCTc3Nw6uytXhYuLC3FxccTFxQHmOU2lUlmtTpWWlqJWqzlz5oyYYhvMySgs6XstW0ePPWks2wdJ57ZTV1fHpk2bxDhCgPvuu4/Q0NDLvlfS2fY4ssaS8XS1rFkDN98MF965Ky4271+9WjSg3N3dbdaNFStWsGrVKmJiYvj2229JTk7mp59+4rXXXuPrr78mJSWFtWvXEh4ejslk4uabb+a3337DYDAwadIkPvzwQ/z9/dm5cycLFy4kPT0df39/fvjhB1588UWOHz9udXGhVqsJCQkhPT2dmJgYwFw36/HHHxdrTlwtERERLfZZ6kFcyMaNG1m2bBkhISGYTCYeeeQR3nvvvYsaT/Hx8dTW1pKamsqgQYPYt28fUVFRLdoNGzaMxsZGPv/8c+6///6r+0LdDFuOZwkzQ4YM6ewudHt69uxJenp6twsIl8lk+Pr64uvrS58+fQDzanFFRYVY/6SkpITy8nKamprIyckRa7qA+cIlPDycsLAwQkNDCQ0NxcfH54pX3vPMm34AAEy5SURBVKWxbB8knS+PVqtlz549HDhwAIPBIO5/5pln2nxek3S2PY6sseS2dzUYjeYVp9ZcHiz7Hn9cdOGzFBu1FTt27OD666+npqaGyMhIRo8ezfjx46muriY2Npa33npLbDtv3jzy8/PJz8+noaGBv/3tb4C5ENpNN93Eww8/TGVlJY888ggrVqxocVfWzc2NWbNm8cMPP4j7vv/+e6six+cza9Ys8UR+4fbGG29c9Dv99ttv+Pj44O/vT3p6OnffffdF21pcTxoaGhAEQSzCdjEWLlzIqlWrAFi1ahULFy5std1LL73Ea6+9hl6vv+TxrjVsPZ4lEAs5StiWa0VnJycnwsLCGDx4MLNnz2bp0qU8//zz3HfffcycOZOBAwcSEhKCTCajoaGB7Oxsdu7cybfffsvbb7/Nm2++yYoVK9i4cSPHjx+nrKysze6714rGnY2k88XRaDTs3r2bt99+W7x5HBcXJ4ZAtOeGoKSz7XFkjaWVp6thzx5rV70LEQQ4d87cbsKEq/64KVOmWLlKqdVqnnvuOfF53759xZTuc+bMIScnR6yFNXfuXD755BMA5HI5ixYtEt/3xBNP8MILL4jP33jjDfr378+ECRNYvHgxI0eObLU/t912G6+++ipPP/00BoOBtWvXitXAL2T9+vVX9J3HjBmDSqUiPz+fFStWXDSV8NSpU/nPf/7DjTfeiFqt5v3336e5ufmSx77tttsYNmwYr732Gj/99BN///vfWblyZYt2U6ZMISIighUrVjB79uwr+h4SEhKOybFjx5g9ezYHDx5k4MCBnd0du6NQKIiIiLBa6dfr9ZSVlVFSUkJZWRllZWVUVFSg0WhaZPhzcnIiKChIXJ0KCQkhODgYDw+PTvg2EhItqa+v5/Dhwxw+fBiNRgOYa5ZOmTKFpKQkKY5Zot1IxtPVUFrarnZXW1B0y5YtjBgxQny+bNkyq9eDg4PFx25ubgQFBVk9b2pqAsxpb59++mnWrl1LbW0tgiBYFT52d3dn/vz5vPrqq2zcuPGi/Zk+fTp33nknBQUFZGdnExkZSVJS0lV9x4sRFxdH3759efzxx/nmm29avH7vvfeSn5/PsGHDcHJy4r777qOkpOSSxwwJCSElJYXnn3+eIUOGXLLGy0svvcTSpUuZPn36VX+X7oJUINf2JCQkdHYXuj2CIKDX6+2SNKGroFQqiYqKsnJlNhqNVFVVUVpaKhpUZWVlaDQa8fH5eHp6EhwcLG4eHh5otVpp3rAx0pxhRhAECgsLOXToEJmZmZhMJgCCgoIYN24cvXv3Ri6/cucrSWfb48gaS8bT1RAW1q52V/ND7UhWrlzJnj172L9/P+Hh4WzatMkqNignJ4f//ve/3HLLLTz11FN8//33rR7HxcWFOXPm8MMPP5CVlXVRlz2AGTNmsGfPnlZfe/7553n++ecv22+TyWQV5Hw+Tk5OvPHGG7zxxhvodDq+/PLLNvnLLliwgLvuukvMsHcxpk6dSlhYGF988cVlj3mt4CjjuTvT3eJwJLouTk5OhISEEBISIu6zJKU435gqLy+ntraWxsZGGhsbycvLA0ClUrF161Z8fX2tjKqgoCACAgKksd5BXOs61tbWcuLECdLS0qipqRH3x8TEMHz4cHr27NkhK03Xus72wJE1loynq2HsWHNWveLi1uOeZDLz62PHAmY3O0cYDA0NDbi4uODr60tVVRX/93//J75mMpm48847eeGFF1i2bBn9+/fn+++/F93/YmNjefnll1myZAlgdn174YUXKCws5PDhwxf9zF9//bXd/fzhhx8YPnw40dHR5Obm8sYbbzBlypRW21ZXV9PQ0EBMTAz79u3j1VdfZfXq1Zf9jFtuuYWQkBAmtMGt8qWXXmLBggXt/RrdFkcZz92ZjIyMVhOZSEg4AucnpUhJSRH363Q6KisrqaioELc9e/bg4+NDXV0ddXV1nD592upYPj4+BAYGWm0BAQF4eXlJblXt4FqcM2pra8nMzCQzM5Nz586J+52dnenbty/Dhg2zMvo7gmtRZ3vjyBpLxtPV4ORkTkd+881mQ+l8A8oy2b/9trmdA3HHHXewYcMGgoODiYqK4t577xWzLP3f//0fTk5OPPbYY8jlcj7//HPmzZvHhAkT8PPzo7q62sp1cMqUKSxevJgePXrQo0ePDu1nTk4OTzzxBLW1tQQEBHDLLbfwyiuviK97enry66+/MnbsWCoqKpg9ezYlJSVERkbyz3/+k8GDB1/2M9zd3dvsijdt2jSSkpJa1NuSkJCQkPgDZ2fnFnFUwcHBjBs3zsqgqqiooLKykubmZlQqFSqVqoV3gbOzcwuDyt/fHz8/P6nQ9jWK0WikqKiIM2fOkJ2dTXl5ufiaTCajR48e9O/fn5SUFOkGn4RNkAnXoKP3xaoIazQa8vPziYuLa9+k3Fqdp6gos+F0Xp0no9HYpWvj7N+/n+XLl7cac+RIdHWduwpXovMV/8auURobG/H09OzsbnRr1Go1J0+epE+fPl2+1pMjc6mx3NzcTHV1NVVVVVZbbW2tGKvSGu7u7vj7+4ubn5+f+Njd3f2aXLHqjnOGyWSivLycs2fPilmCdTqd+LpcLicmJoaePXuSkpJidV1nK7qjzo6GvTW+mG3QGtLKU0cwbx7MmWPOqldaao5xGju2xYqTRqPp0hmIRo4cedHMe45EV9e5qyDpbHuysrIcutZFd8DNzQ2ZTCYZTjbmUmPZ3d0dd3f3Fi46RqORmpoaK4OqpqaGmpoampqaaG5uprm5maJWst66uLjg5+cnuhX6+PhY/bX837sbXX3OEASBhoYGysrKKC0tpbCwkHPnzlkZS2Au7NyjRw8SEhJITEy0e93Brq5zV8CRNZaMp47Cyemy6cjPL8YmYTskne2DpLPtqa6u7uwudHvOnj3Liy++yIcffigW/JboeK5kLFvSoJ+fOdaCVqultrZWNKZqamrE5/X19Wi12lazAFpQKpUtjCofHx+8vLzErStmBuxKc4bRaKS2tlY0lCz/L0tm4PNxdXUlKiqKmJgY4uPjCQ0N7VTjtyvp3FVxZI0l48mOSNnJ7IOks32QdLY99r6bei1SXV3Npk2bqK6ulownG9LRY9nFxUWsLXUhBoOB2tpaamtrqaurQ6VSWf1tbGxEr9dTWVlJZWXlRT/D2dnZypjy8vLC29sbLy8vPD09cXd3x8PDw6FWsRxtzjAajdTX11NdXU1NTQ3V1dXiY0uplAuRy+UEBgYSFhZGZGQk0dHRBAUFOdQ5x9F07o44ssaS8WRHJP9Y+yDpbB8knW3PqFGjOrsLEhIdgj3HskKhuOiKFZiNK0uCivONqoaGBurr62loaECr1aLT6cSL/Ushk8lEQ6q1v25ubri6urbYFApFhxtd9tDZUhtNrVajVqtpamqy0u78v01NTZesoebs7ExwcDChoaGEhYURGhpKcHAwSqXS5t/japDmZtvjyBpLxpMdsQSjSdgWSWf7IOlse7Zu3cq0adM6uxsSEleNI41lhUJBQEAAAQEBF22j0+loaGhodbMYBU1NTWg0GgRBEJ+3BycnJ9GQcnFxwcXFBWdnZ5RK5SU3hUKBXC5vdduzZw8TJ04UV2kEQcBkMln9Pf+x0WhEr9dfctNoNKKhZNmMRmO79Pbz8xM1t2RMDAgIwNPT02FW7dqDI43n7oojaywZTxISEhISEhIS5+Hs7HxZAwvMbmmWxBUWA8ry2PJXo9G02CyGy5UYXZciNzdXLExsa5ycnMSVtfNdGs//6+3tfc1mPpTovkjGkx3pisGnXRFJZ/sg6Wx74uLiOrsL3Z6QkBDuv//+Di+iKWFNdx3LTk5OYjxUWxEEAZ1OZ2VMabVaNBoNBoPhsitBer3eajXpws3f3x+j0YhMJhM3uVxu9dfyWC6XX3a1y8XFBTc3txabUqm8Zo2i7jqeHQlH1lgynuyIIwU7dmckne2DpLPtkeLKbE9ERAQvvfQS4eHhnd2Vbo00lv9AJpOJbnod7fpcUlIijWU7II1n2+PIGktXP3ZErVZf8XtjY2M5cOCA1b5ly5bx8ssvX2Wv2kZ2djazZs0iMDCQoKAgFi1aRG1t7UXbb9++nf79++Pp6cn48eMpKCi4aFuZTEZCQoLVvpycHGQyGdOnT7dqd2EA4fTp01mxYoXVvqvRWaLtSDrbnvT09M7uQrenoaGBr7/+moaGhs7uSrdGGsv2QdLZPkg62x5H1lgyniTahEql4tZbb+XMmTMUFBSg0+l4+umnW21bVVXFzTffzOuvv45KpWLWrFncfvvtlzy+XC7n4MGD4vOVK1eSmJjYol1WVhabN2++ui8jISEh8Ts5OTk8++yz5OTkdHZXJCQkJCS6AJLx1AHk5EBqasvtwnOxh4eHTfvx7rvv0qNHD4KCgrjjjjuor69v9zEullJ02LBh3HHHHfj4+ODh4cF9993HoUOHWm27f/9+EhMTuf7663FycuKpp57i+PHjl7w4uf3221m5cqX4/JtvvmnV4HriiSd45ZVXLvkdbK2zhBlJZ9szYsSIzu6ChESHII1l+yDpbB8knW2PI2ssGU9XSU4OJCXB4MEtt6QkawNKp9PZrB+bNm3ijTfeYMOGDRQUFNDU1MSTTz7Zatvy8nLuu+8+YmJiGDRoEP/v//0/9u/fz5o1a7jjjjva9Hn79u2jd+/eF329NSPs1KlTF21/6623snbtWoxGI4cPHyYwMLDVYMElS5ZQXFzMli1bLnosW+os8QeSzrbHXlmzJCRsjTSW7YOks32QdLY9jqyxZDxdJRY3+a+/hqNH/9i+/tr6dQC9Xn9VnzVlyhR8fX3F7fPPPxdf++6771i2bBk9e/bEw8OD1157jW+//bbV4xw4cIAZM2Zw8uRJvvjiC5qbm3nhhRf45Zdf+Mtf/nLZfhw/fpzly5dftO3IkSM5ffo0GzZsQK/X89Zbb6HVamlubr7oMQMCAujfvz9bt25l5cqVLFiwoNV2SqWS559//pKrT1ers0TbkHS2PRUVFZ3dBQmJDkEay/ZB0tk+SDrbHkfWWDKeOoiePWHQoD+2nj1btrna7GRbtmyhrq5O3O666y7xtZKSEqKjo8XnMTExNDU1oVKpWhxn5syZVFRUcO+99/L+++8zefJktmzZwquvvspPP/10yT7k5+cze/ZsPv3004uuPAUGBvLDDz/w4osvEhoaSlFREb179yYiIuKSx164cCFfffUVa9as4dZbb71ou7vuuouioiK2bt3a6utSFjj7IOlse6R08LZHqVQSGBiIUqns7K50a6SxbB8kne2DpLPtcWSNpVTldqQ9dSDaS3h4OIWFheLzwsJC3N3dW02D+vXXX5OTk8OSJUsoKCjgtdde46abbiI8PPySK09lZWVMmTKFv/zlL8ydO/eS/ZkyZQrHjh0DzMkm4uPj6dOnzyXfM2fOHJYuXcqYMWMICgq6aDulUslzzz3HK6+80mrcjS11lvgDSWfbM2HChM7uQrenb9++VFZWdnY3uj3SWLYPks72QdLZ9jiyxtKtYzvS2ipQR3HLLbfw4YcfkpWVRVNTEy+88ALz589vte3ixYv55z//yYwZM3jggQfYtm0bdXV1ZGRkXDQrnkqlYtq0adxxxx3cf//9l+3P8ePHMRgM1NTU8MADD7Bo0aLLVmp3d3dny5YtvPvuu5c9/l133UVhYSGHDx9uta8StkfS2fZs2rSps7twTSDpbHskje2DpLN9kHS2PY6ssWQ8dRCZmdaZ9jIz7fv5M2bM4E9/+hMzZswgJiYGFxcX/vnPf7ba1snJqd3H/9///seJEyf4xz/+gaenp7hZWLZsGcuWLROf//3vf8ff35/ExEQCAwN588032/Q5w4cPJz4+/rLtnJ2dee6556ipqWn3d5GQkJCwkJ6ezqJFixy6poiEhISEhOMgEy6Wm7obU19fj4+PDyqVCm9vb3G/RqMhPz+fuLg4XF1d23QsS7a9i3H6NFjKFanVatzc3K6m6xJtQNLZPlyJzlfyG7uWyczMpGdrAZQSHUZqaiqDBw/m6NGjDBo0qLO7022RxrJ9kHS2D5LOtsfeGl/MNmgNKebpKklMNBtIrRWn9/L6w3ACUCgkue2BpLN9kHS2Pf7+/p3dBQmJDkEay/ZB0tk+SDrbHkfWWHLb6wASE60z7Vm28w0n4JKpuiU6Dkln+yDpbHuOHz/e2V2QkOgQpLFsHySd7YOks+1xZI0l40lCQkJCQkJCQkJCQqINSMaTHWktrbZExyPpbB8knW3P0KFDO7sL3Z7ExER++uknEi90FZDoUKSxbB8kne2DpLPtcWSNJePJjuh0us7uwjWBpLN9kHS2PUVFRZ3dhW6Pl5cXsbGxUt0yGyONZfsg6WwfJJ1tjyNrLBlPdkSv13d2F64JJJ3tg6Sz7SktLe3sLnR7iouLefXVVykuLu7srnRrpLFsHySd7YOks+1xZI1tajzV1tayePFifHx88PHxYfHixdTV1V3yPTKZrNXtrbfeEttMmDChxesXKwjrSMhkss7uwjWBpLN9kHS2PVJGQ9tTXl7O999/T3l5eWd3pVsjjWX7IOlsHySdbY8ja2zTni1YsICioiI2btwIwP3338/ixYv5+eefL/qeCy3NX3/9lXvuuYebbrrJav99993H3/72N/F5V6jrc7m88RIdg6SzfZB0tj2TJk3q7C5ISHQI0li2D5LO9kHS2fY4ssY2W3nKzMxk48aNfPLJJ4wcOZKRI0fy8ccfs379erKzsy/6vtDQUKvtp59+YuLEifTo0cOqnbu7u1U7Hx8fW32VDqO+vr6zu3BNIOlsHySdbc+WLVs6uwsSEh2CNJbtg6SzfZB0tj2OrLHNjKf9+/fj4+PD8OHDxX0jRozAx8eHffv2tekY5eXlbNiwgXvuuafFaytXriQwMJDevXvz9NNP09Baldrf0Wq11NfXW22dgSAIV/ze2NhYvL29UavV4r76+nrc3NxISUnpiO61iSeffJIePXrg5eXFkCFD2L1792Xfo1KpCA0NZfr06Rdts2TJEmQyGb/99pvV/lGjRiGTySgrKxPbOTk5kZmZKbb59ttvmTBhgvj8anSWaDuSzrbHZDJ1dhckJDoEaSzbB0ln+yDpbHscWWObue2VlZURHBzcYn9wcLB4IXw5vvjiC7y8vJg3b57V/oULFxIXF0doaCgnT57kueeeIy0t7aJW6uuvv84rr7zSYv/WrVvx8PDguuuu49ChQ6jVagIDAzEajahUKgBcXV0B0Gg0gDkzU3NzM0ajEScnJ9zd3UXD7cK2np6eaDQaDAYDcrkcpVIpHtfFxQW5XC4aQxe29fT0FI08Z2dnUbtvv/2WxYsXo9Pp+Prrr4mIiAAQj+vs7IxCoRALmLq7u2MwGNDpdMhkMry9vamvr0cQBJRKJc7OzjQ1NbVoC+Dj40NDQwMmk0ls6+Liwo8//kjPnj358ccfmTNnDunp6URERNDY2IjJZEKhUODq6kpjYyMAL774Ij169MBgMKBSqVrVUKfTER8fz5dffsnQoUPRaDQUFBRQXV0NmA1FT09PBEHA29ubv/71r3z11VfI5XKam5sxGAwYjUZRw4aGBisNL9Tbw8MDnU6HXq9HLpfj5eV1UQ3Pb3uhhpfS26JhW/X29va+qIZubm6YTCa0Wu1l23bUmL2w7YUaKhQKmpqaWh2zF9O7qalJ/KxNmzYBEBUVRWBgIMeOHQNgyJAhlJSUUFJSgpOTE5MnT2br1q0YjUbCw8MJDw/nyJEjAAwcOJCqqirOnTsHwLRp09ixYwc6nY6QkBBiY2M5ePAgAP369aO+vp6CggIApkyZwt69e2lubiYwMJCkpCTxxk7v3r3RaDScOXMGQJwjGhsb8fPzo3fv3qKhn5KSgslk4vTp0wCMHz+e48ePo1Kp8Pb2ZtCgQezcuRMwp8VWKBSi8T9mzBgyMjKoqanBw8ODESNG8P/bu/OwJq69D+DfAAkQliACBkSQooAIqOCGKyqi3tpFe7UqdavX1roUW1tr9WnVVi293fT2tm614lvX16q1tbYVFNQquIBVFFRUcGEREQl7COS8f/AyNSRAgMwQ4Pd5njw0k5PJmW8PI7/MzJnjx48DAJ555hlYWlpyOQUHB+PWrVt49OgRLCwsMGzYMBw7dgwA4O7uDjs7O1y+fBkA0L9/f9y7dw85OTkQi8UYOXIkjh07BsYYXF1d4eTkhKSkJABAUFAQcnJykJmZCRMTE4wePRrHjx9HZWUlnJ2d4erqigsXLgAAevfujfz8fNy7d4/LOy4uDkqlEk5OTnjmmWeQkJAAAPD390dxcTHS09MBAKGhoTh79ixKS0vRsWNH+Pj44MyZMwAAX19fVFRU4NatWwCAESNG4OLFiygqKoKdnR0CAgK4L2q8vb0BgDuDYdiwYbhy5QoKCgq4L3ViY2MBAN26dYNEIkFKSgoAYPDgwbh+/ToeP34MqVSKQYMG4erVqxg+fDgUCgWysrKQnJwMoPrLvjt37iA3Nxfm5uYICQnh/l+4ubnB3t6eu4ljv3798ODBA2RnZ8PMzAyjRo1CdHQ01Go1OnfuDLlcjsTERABAYGAgcnNz8eDBA4hEIoSFheHEiRNQqVSQy+Vwc3PD+fPnAQC9evVCQUEB7t69CwAICwvDqVOnUF5eDkdHR3Tr1g3x8fEAAD8/P5SWluLOnTsAqk95SUhIQElJCezt7eHr68uN2R49eqCyshJpaWkAqq8jTkpKQmFhIWQyGXr37o2TJ08CALy8vGBiYoLr169zY/batWt48uQJrK2t0b9/f5w4cQIA4OnpCQsLC1y7dg1A9ZdfN2/eRF5eHvdvSc2/0zVfCF65cgUAMGDAAGRkZODhw4eQSCQYMWIE7SPQ8D5CKpXi6tWrAKr3ERUVFfjjjz9oHwHD7SNiYmIAAB4eHrC2tkZycjLy8vKgUChoHwHD7SOkUikGDx7M7SNEIhGys7MF20fU9F8vrJFWrlzJANT7uHDhAlu7di3z8vLSen+3bt3YJ598otdneXt7s4ULFzbY7uLFiwwAS0xM1Pl6eXk5UygU3OP+/fsMAFMoFBrtysrKWEpKCisrK9OrfzVu3mQsMVH7cfOmZruKiopGrfdp7u7u7MMPP2TPPfcctyw0NJStXLmSeXt7c8uuXLnCBg0axGQyGQsKCmLx8fFN+jy1Wq1XOxcXF3bx4sU6X09OTmb9+vVj27ZtY2PGjKmz3cyZM9l7773HXFxcuJzWrFnDjbfs7Gyu3bvvvsvs7OxYamoqY4yxPXv2sOHDhzPGGEtPT2fm5ubs22+/ZY6OjszV1ZXFxsay7777jsnlctalSxcWFxen17aR+jVlPDf1d6y9evToUUt3oV2gnPlHGQuDchYG5cw/oTNWKBQ6awNdGn3a3sKFC5Gamlrvw8/PD3K5XOfsRY8ePUKnTp0a/JzTp0/jxo0b+Ne//tVg28DAQIjFYq5Crs3c3By2trYaD0NJSwO8vICgIO2Hl1f16zVqjk401ejRo5GUlIT8/Hzk5OQgLS0Nw4YN416vqKjAc889h2nTpuHRo0d45513MH78eO6ISm0bN25E79694ebmhjlz5uDIkSM4deoUFixYwFXk9cnIyEB+fj66detWZ5s333wTX3zxBUxMGh5qdnZ2GDBgAPdNwp49ezBt2jStdvb29pg/fz4+/vhjneupqKhARkYGMjMzERERgVdeeQUpKSm4e/culi5disWLFzfYF9Kw5o5n0rCabyIJf8rKynDgwAGNU6KJ4dFYFgblLAzKmX/GnHGjT9tzcHCAg4NDg+2Cg4OhUChw/vx59O/fHwBw7tw5KBQKDBo0qMH3b9u2DUFBQejVq1eDba9duwaVSgVnZ+eGN8DAai612rkT6NHj7+WpqcArr/z9uiGYmZnhxRdfxP79+1FWVoZJkyZpFCUJCQkwNTXFggULAABTpkzBhg0bcOzYMUyaNEljXUqlEhkZGThy5AjMzc1x+PBhbNmyBUD1LIkN3dlZpVJh5syZePfdd+ucrGPv3r1wcnLC0KFDuVMbGjJt2jTs2rULrq6usLS0hJeXl852b7/9Njw9PXUeZmWMYcWKFRCLxXjppZfw7rvvYtmyZZBIJHjppZfw9ttvQ61W61XQEULattTUVMybNw/9+vVDYGBgS3eHEEKIkePtr8cePXpg7NixmDt3LhISEpCQkIC5c+di/Pjx3PmoQPW5wIcOHdJ4b2FhIfbv36/zqNPt27fx0Ucf4eLFi8jIyMDRo0cxadIk9OnTB4MHD+ZrcxrUowcQGPj34+lCqoZUKm3254SHh2P37t3YvXs3wsPDNV7LysqCm5ubxjJ3d3dkZWVprcfc3BwTJkzAmjVrsGDBAqjVauzYsQM//vgj1Go1dz6qLowxzJo1C05OTli1apXONiUlJVi5cqXG/bn0MX78eJw8eRJbtmzR2r6ndezYEfPnz8eaNWt0blvN0cWaKewdHR255yqVirvOiDSdIcYzqR/9MU/aChrLwqCchUE588+YM+b1q/ddu3bB398fYWFhCAsLQ0BAAH744QeNNjdu3NA6rWzv3r1gjGHq1Kla65RIJDh+/DjGjBkDb29vvPnmmwgLC0NMTAxMTU353Jxmq6ysbPY6goODkZmZieLiYvTu3VvjNRcXF+6i2Br37t2Di4uL1nqUSiWWL1+OkJAQTJ06FefOnUOPHj3g7u6OM2fOaBVhT1u0aBGysrKwc+fOOo/epKWlIT09Hf369YNcLkdERARiY2PRs2fPerfPwsICY8aMwdatW/Hyyy/X23bJkiUNTn1P+GOI8Uzql5ub29JdIMQgaCwLg3IWBuXMP2POmNeb5Nrb22Pnzp31tmE6pjt+7bXX8Nprr+ls36VLF26Gj9amoqLCIDfzPXjwoM6iZeDAgVCpVNi4cSPmzp2LQ4cO4caNGwgLC9NqK5FIEBMTw61nwoQJen32ypUrcebMGZw8eRLm5uZ1tvPz8+Nm2wGAffv24fDhw9i7d2+Dn/Hxxx9j9uzZDZ6G2bFjR7zxxhv4z3/+A39/f736TwzHUOOZ1O3BgwcNfuFASGtAY1kYlLMwKGf+GXPGdNGHgaSmAklJfz+eug2RwQUEBMDPz09ruUQiweHDh/HDDz+gY8eOiIyMxM8//6zzmiSRSNSka34++ugjpKamwsXFBdbW1rC2tsauXbsAVE/yYW1tDaD6+qzaNzGWSCQ6p6+vzdXVVWMijPosWbKETsEjbZZIJGrpLrR5IpEIYrGYsuYZ5SsMylkYlDP/jDljEdN16KeNq5mnvuYeCzXKy8uRnp4ODw8P7v43DamZba8uN28C3bs3t8eEtA1N+R0jhBBCCOFTXbWBLnTkqZm6d68ukBITtR+1C6eaG4gSflHOwqCc+VdzY0HCL8qZf5SxMChnYVDO/DPmjHm95qm90PfIUjs8yNciKGdhUM78U6lULd2FNi81NRWvvfYafvnlF/TQNU0qMQgay8KgnIVBOfPPmDOmI08CEovFLd2FdoFyFgblzD+5XN7SXWjzysrKcPv2bbpJLs9oLAuDchYG5cw/Y86YiicBSSSSlu5Cu0A5C4Ny5l99twwgpDWhsSwMylkYlDP/jDljKp4EVFJS0tJdaBcoZ2FQzvw7f/58S3eBEIOgsSwMylkYlDP/jDljKp4IIYQQQgghRA9UPAlIKpW2dBfaBcpZGJQz/3r16tXSXWjzPDw8sGXLFnh4eLR0V9o0GsvCoJyFQTnzz5gzpuJJQJWVlS3dhXaBchYG5cy/goKClu5Cm9ehQwcMHToUHTp0aOmutGk0loVBOQuDcuafMWdMxZOAKioqWroL7QLlLAzKmX93795t6S60eQ8fPsSXX36Jhw8ftnRX2jQay8KgnIVBOfPPmDOm4snAlEp+1tu1a1ckJCRoLJs3bx5WrVrFzwcKIDIyEiKRSGu7nnb79m0MHjwYUqkUgYGBuHz5cp1tRSIRunXrprEsLS0NIpEIY8eO1Wg3aNAgjXZjx45FVFRU0zaEENJqZWZmYuvWrcjMzGzprhBCCGkFqHgyoM2bARub6p+62NraCtshI5aZmYndu3c3OI//1KlTERYWhvz8fLz66quYMGFCvaeLmZiYIDU1lXu+a9cudNdxF+Pr16/j2LFjTd8AQuNZAGFhYS3dBUIMgsayMChnYVDO/DPmjKl4MpDNm4F584AePap/6iqgiouLefv8qKgohIWFYe7cubCxsUHfvn2RmZmJBQsWQCaTYcCAAcjKygIAqNVqTJw4EU5OTrC3t8ekSZOQn58PAIiLi0Pnzp255/v374e3t3ejbyDJGKv39SVLlmD16tUwNzevs82NGzdw48YNvP/++7CwsMDChQtRVVWFs2fP1vmeqVOnahxB2rNnD6ZOnarV7q233sLq1asb3hBSJz7HM6l26tSplu4CIQZBY1kYlLMwKGf+GXPGVDwZQE3htGgRcOlS9U9dBZRarea1H7GxsfjHP/6B/Px8uLq6YvDgwRg+fDgeP36Mrl274rPPPuPaTpw4Eenp6UhPT0dRURE++ugjAEBISAheeuklLFy4EI8ePcKiRYsQFRUFS0tLrc97+PAh5s6dC3d3dwQGBuLjjz9GfHw8Dh48iBkzZtTZz7i4OOTl5WHChAn1bk9KSgq8vb01bsYaEBCAa9eu1fmeyZMn48iRI6iqqsKFCxfg4OCgcxatWbNmITMzE9HR0fX2gdSN7/FMgPLy8pbuAiEGQWNZGJSzMChn/hlzxlQ8NdPThdOGDYCJSfVPXQWUmZlZsz5r9OjRsLOz4x7bt2/XeN3f3x8TJkyAWCzGCy+8ACsrK0yePBlmZmZ48cUXceXKFQDVp7a98sorsLKygkwmw1tvvYU///yTW09kZCQuXLiAkJAQTJ8+HcHBwTr7k5CQgHHjxuHq1avYsWMHSktLsWLFChw9ehQffPCBzvdUVlbirbfewvr16xvc3uLiYq1Tw2xtbes94tGxY0f4+/sjJiYGu3btwrRp03S2E4vFWL58OR19aobmjmfSMEdHx5buQpsnk8kwbNgwyGSylu5Km0ZjWRiUszAoZ/4Zc8ZUPDVD7cJJJKpeLhLpLqAsLCya9XnR0dEoKCjgHrNnz9Z43cnJiftvS0tLjYFnaWmJkpISANUFzOLFi+Hu7g5bW1v885//xOPHj7m2UqkUU6ZMQWpqKt588806+/Pss88iNzcX//rXv/DNN98gNDQU0dHRWLt2LQ4fPqzzPd988w2GDBkCPz+/BrfX2toahYWFGssKCwthbW1d7/teeeUV/PDDDzh48CAmT55cZ7vZs2fjwYMHiImJabAvRFtzxzNpWO0JUIjheXp64pdffoGnp2dLd6VNo7EsDMpZGJQz/4w5YyqemkiprC6OAgKA9ev/LpxqiETVywMCqtsplcZzjciuXbtw+vRpxMfHo7CwED/++KPGNUppaWnYuHEjJk2ahCVLltS5np07dyItLQ2zZs1Cr169sG7dOnTs2BEjRoyAq6urzvfExsZi165dkMvlkMvluH//Pp599lmto2gA4Ovrixs3bkClUnHLrly5gp49e9a7fSNHjsTPP/8MPz+/er+5EIvFeP/99+noUxMZy3huy+Lj41u6C22eSqXCb7/9prGfIYZHY1kYlLMwKGf+GXPGdN5NE5mbA19/XX1kafFizSNPAMBY9fIrV4BNm6rbG8vpm0VFRTA3N4ednR3y8vLw+eefc6+p1WrMnDkTK1aswLx589CrVy/87//+r84jONOnT4epqSn3/I033mjws6OiojTOY+3Xrx82b96MkJAQrbbe3t7w9vZGZGQkli5dim3btsHU1FRrmvHapFIpoqOj4eDg0GB/Zs+ejXXr1qG4uBhTpkxpsD0hpG1JTk7GlClTkJiYiMDAwJbuDiGEECNHR56a4fXXqwujr78GIiKqCyag+mdERPXyTZuq2wHQOelCS5gxYwZkMhmcnJwwdOhQjXsgff755zA1NUVERAQsLS2xfft2LFq0CLm5uVrrebpw0pednR131Ekul8PU1BT29vaQSqUAqu9dNW/ePK797t278fvvv8POzg5bt27FwYMHG7zWxtLSEgMGDNDrNByJRIL333+fm12Q6M9YxnNbps/prYS0BjSWhUE5C4Ny5p8xZyxiDc0p3QYVFhZCJpNBoVBoTEhQXl6O9PR0eHh4NOp6jqevfVq/vvqIU+3CqWb9dJ0I/yhnYTQl56b+jrVXaWlpOu9TRgwnKSkJQUFBdOSJZzSWhUE5C4Ny5p/QGddVG+hCR54M4OkjUH366C6cAECpVLZMB9sZylkYlDP/7ty509JdIMQgaCwLg3IWBuXMP2POmK55MpCaQmnRIt2FEyGEEEIIIaR1o9P2DHDa3tOUyurJIXRhjEFUe1o+YnCUszCakjOdttc4lZWVdD8tnlVVVUGhUEAmkzXpOk6iHxrLwqCchUE580/ojOm0vRZUV+EE0NTOQqGchUE58y8hIaGlu9DmmZqaIiUlhQonntFYFgblLAzKmX/GnDEVTwJSq9Ut3YV2gXIWBuXMv5obWxP+pKWlISIiAmlpaS3dlTaNxrIwKGdhUM78M+aMqXgSEB3iFQblLAzKmX/29vYt3YU2r6ioCElJSSgqKmrprrRpNJaFQTkLg3LmnzFnTMWTgOgaD2FQzsKgnPnn6+vb0l0gxCBoLAuDchYG5cw/Y86YiicB0TUiwqCchUE58+/PP/9s6S4QYhA0loVBOQuDcuafMWdMxRMhhBBCCCGE6IGKJwOr776hzTnNqWvXrrC1tUVZWRm3rLCwEJaWlvDx8Wnyeo3F4cOH4ePjA5lMBmdnZ7z99tuoqqrS2TYhIQEBAQGws7ODk5MTZs6cqXEU5Omco6KiIBKJsGbNGo11LF++HCKRCHv37tVot3nzZq5NTk4OTXleDzptj389evRo6S60eV26dMFHH32ELl26tHRX2jQay8KgnIVBOfPPmDOm4smANm8GbGyqf/JBLpfj559/5p4fPHiwzfyD37dvX5w+fRoKhQIpKSm4cuUKtmzZorNtt27d8Ntvv6GgoAAZGRlQq9X4+OOP61x3t27dsHv3bu45Ywz79u2Dp6enRrsOHTpg3bp1UKlUhtkoQpqpsrKypbvQ5jk6OiI8PByOjo4t3ZU2jcayMChnYVDO/DPmjKl4MpDNm4F584AePap/6iqgysvLm/UZU6dOxa5du7jnu3btwrRp0zTaiEQibNy4EW5ubnBwcMC+fftw5MgRPPPMM3BycsK+ffu4tlu3bkX37t1hY2ODgIAAxMXFcf309fXFnj17AAAFBQVwdXXFiRMnGt1nfe/B3LlzZ60/XtLT03W2dXBwQOfOnbn1i0Qijba1c/b09ISNjQ2SkpIAAGfPnkWXLl3g6uqq0a5///7o0qULtm/frvNzu3btii+++AJeXl6wtbXF+vXrcf78efj6+sLe3h5fffWVXtvaVjR3PJOG0fTZ/MvPz8emTZuQn5/f0l1p02gsC4NyFgblzD9jzpjX4mnt2rUYNGgQpFIp7Ozs9HoPYwyrVq2Ci4sLLC0tERISgmvXrmm0USqVWLRoERwcHGBlZYXnn38eDx484GEL9FNTOC1aBFy6VP2zrgKqOUaPHo2kpCTk5+cjJycHaWlpGDZsmFa7M2fO4ObNm9i4cSPmz5+PAwcO4OrVq9i2bRsWLlzInQ7n4uKC48ePQ6FQYNGiRZgyZQqUSiUsLCywY8cOLF68GNnZ2YiIiMDzzz+PkSNH6uzXxo0b0bt3b7i5uWHOnDk4cuQITp06hQULFuDixYt6b9+ff/4JmUwGe3t7JCcn49VXX62z7b1792BnZwdra2scOnQICxcurHfd4eHh3NGn3bt3Izw8XGe7lStX1nv06ejRo7hw4QJiYmLw3nvv4bPPPsOZM2cQGxuL5cuX49GjR3puLSHEGGRkZOCzzz5DRkZGS3eFEEJIK8Br8VRRUYFJkybhjTfe0Ps9//73v/Hll1/iv//9Ly5cuAC5XI7Ro0dr3INj8eLFOHToEPbu3Ys///wTxcXFGD9+fJ3XyPDp6cJpwwbAxKT6p64CysbGplmfZWZmhhdffBH79+/H3r17MWnSJJiYaP8vXLp0KSwsLDBx4kQUFBRg/vz5kEqleO6551BUVISsrCwAwLPPPgs3NzeYmJhg7ty5EIlEXKXfr18/zJkzB6GhoTh9+jT+/e9/6+yTUqlERkYGjhw5gsTERAQHB2PLli34/PPPMXToUPTr10/v7RsyZAgUCgXu3LmDefPmoUOHDnW2dXNzQ0FBAR4+fIilS5fC2dmZe01Xzi+//DL279+PiooKHD58GP/85z91rnf06NHo3LkzoqKidL4eEREBmUyG/v37Qy6XY/LkyejQoQN69eoFNzc3XL9+Xe/tbe2aO55Jw0JCQlq6C4QYBI1lYVDOwqCc+WfMGfNaPK1evRpvvfUW/P399WrPGMP69euxYsUKTJw4EX5+ftixYwdKS0u5owYKhQLbtm3DF198gdDQUPTp0wc7d+5EcnIyYmJi+NwcLbULp5q5BUQi3QVUaWlpsz+z5ghKfUdPnJycAACmpqYQi8Uap8NZWFhwd23+6aefEBgYCDs7O9jZ2SE3NxePHz/m2r766qtISUnBq6++Cmtra52fZW5ujgkTJmDNmjVYsGAB1Go1duzYgR9//BFqtVrrqCEAnD59GtbW1rC2tsa4ceO0Xvfw8IC/vz8WL17cYB5OTk4YN24cZsyYwS3TlXOnTp3g4+OD5cuXo2/fvvUWZvUdfarJFgAsLS01srW0tDTqO2IbmiHGM6lfzammhLR2NJaFQTkLg3LmnzFnbFTXPKWnpyMnJwdhYWHcMnNzcwwfPhxnz54FACQmJkKlUmm0cXFxgZ+fH9dGCEpldXEUEACsX/934VRDJKpeHhBQ3U6phEGOjAUHByMzMxPFxcXo3bt3k9ejVCoxdepUREZG4vHjxygoKICTkxN3jRJjDG+88QbCw8OxYcMGZGZm1rme5cuXIyQkBFOnTsW5c+fQo0cPuLu748yZM3Bzc9N6z9ChQ1FcXIzi4mL89ttvOterVqtx+/Ztvbaldtu6cp42bRq+/PJLrevEagsLC4OzszN27Nih1+e3Vy1xpLe9KSwsbOkuEGIQNJaFQTkLg3LmnzFnbNbSHXhaTk4OgOqjBE/r1KkT7t69y7WRSCRaRw46derEvb82pVIJ5VNziBvif4i5OfD119VHlhYv1jzyBACMVS+/cgXYtKm6vUpl2uzPBapn2dN1ul5jKJVKVFRUcEdONmzYoHG9zqZNm/DkyRP89ttvWLVqFebOnYujR49qrUcikSAmJobrz4QJE5rUn/3792PAgAFwc3PDrVu3EBkZidGjR+tse/ToUXh6esLLyws5OTn44IMPMGLECO51U1PdOU+aNAmdOnXS61DwypUrGyyy2ru6ciaGI5PJWroLbZ6VlRX8/PxgZWXV0l1p02gsC4NyFgblzD9jzrjRxdOqVauwevXqettcuHABffv2bXKnat9bp2ZGtfrU1+aTTz7R2eeYmBhYWVlh5MiROH/+PMrKyuDg4ICqqiooFAoAf9/LpmZmMRsbG5SWlqKqqgrh4aZQq6WYP98EjDH85z8iiETVhdObbzL8978ibNigxJQp5SgqMoFUKuXWa25uDhMTE+6+TdbW1igvL0dlZSVMTExgbW3NFXkSiQQAUFxcDIVCAV9fX1RUVEChUHCnidWsFwBUKpXG8/LycigUCi6f4uJiMMawdu1arkB59dVX4enpCaVSicuXL2PFihU4deoUysrKEBERgREjRmDr1q2YPHkygOpT1NRqNVeU2traori4GGq1GmZmZrCwsODuvVS77dMZmpqaQiqVIjk5GYsXL0ZBQQHs7e3xwgsv4O2334ZarUZpaSnkcjkOHDiA0aNHIz09HfPnz8ejR48gk8kwatQofPzxx6iqquIyLCoqgrW1NUpLS1FZWYny8nKYmZkhODgYSqUSZmZm3LprrqerrKyEQqGARCLByJEj4enpifz8fFRWVqKiogJqtZrLu7CwkNsmlUrFbU9lZSXKyspQUVEBoPqXv7CwEIwxiMViSCQSbh1SqZRbd2MzrK9tfWO2Ju+aba7d9ulxWLtt7TErlUpRUlKic8zWbmtlZYWKigqUlJRwn/XHH38AqL7PjoODAy5dugSgetr6rKwsZGVlwdTUFKGhoYiJiUFVVRVcXFzg4uLCTUTSp08f5OXl4f79+wCAMWPGIDY2FhUVFejUqRO6du2Kc+fOAQACAgJQWFjITQwwevRonDlzBqWlpXBwcICXlxd39Lpnz54oLy/njmjW7COKi4vRoUMH9OzZk7v7uY+PD9RqNW7evAkAGD58OP766y8oFArY2toiMDCQm8mye/fuMDMzQ2pqKoDq6/xSUlKQn58PKysrDBw4EMePHwcAbobMmpyCg4Nx69YtPHr0CBYWFhg2bBiOHTsGAHB3d4ednR0uX74MoHrmyHv37iEnJwdisRgjR47EsWPHwBiDq6srnJycuFMigoKCkJOTg8zMTJiYmGD06NE4fvw4Kisr4ezsDFdXV1y4cAEA0Lt3b+Tn5+PevXtc3nFxcVAqlXBycsIzzzyDhIQEAIC/vz+Ki4u5mTBDQ0Nx9uxZlJaWomPHjvDx8cGZM2cAgNun3bp1CwAwYsQIXLx4EUVFRbCzs0NAQABOnToFAPD29gYA3LhxAwAwbNgwXLlyBQUFBbCxsUHfvn0RGxsLoPo2BRKJBCkpKQCAwYMH4/r163j8+DGkUikGDRqEjIwMREZGQiQSISsrC8nJyQCAgQMH4s6dO8jNzYW5uTlCQkK4/xdubm6wt7fHX3/9BaD6GtEHDx4gOzsbZmZmGDVqFKKjo6FWq9G5c2fI5XIkJiYCAAIDA5Gbm4sHDx5AJBIhLCwMJ06cgEqlglwuh5ubG86fPw8A6NWrFwoKCrgvD8PCwnDq1CmUl5fD0dER3bp1Q3x8PADAz88PpaWluHPnDgBg1KhRSEhIQElJCezt7eHr68uN2R49eqCyspK7xjUkJARJSUkoLCyETCZD7969cfLkSQCAl5cXTExMuOs5hwwZgmvXruHJkyewtrZG//79uZlYPT09YWFhwZ2uPWjQINy8eRN5eXmQSCRQq9WIjo4G8Pe9C69cuQIAGDBgADIyMvDw4UNIJBKMGDGC9hFoeB8hlUpx9epVANX7CJFIhD/++IP2ETDcPqLmkhAPDw9YW1sjOTmZ+3uB9hGG20dIpVIMHjyY20e4uLggOztbsH1EY65ZFzF955L+f3l5ecjLy6u3TdeuXbVuVFrzh3F97ty5A09PTyQlJaFPnz7c8hdeeAF2dnbYsWMHTpw4gVGjRiE/P1/j6FOvXr3w4osv6iySdB156tKlC7fjqlFeXo709HR4eHg06gagT1/7tH599RGnr7+uPuL0+ut/t1MoFEZdSbcVlLMwmpJzU3/H2qs//vgDY8aMaelutHmUM/8oY2FQzsKgnPkndMY1hWHt2kCXRh95cnBwgIODQ5M7Vx8PDw/I5XJER0dzxVNFRQVOnjyJTz/9FED1NyBisRjR0dHcUZDs7GxcvXq1zhnhzM3NYW5uzkufgb8LpHnzgJMn/z5V7+nCiRBCiPFJSkrC2LFjkZiYiMDAwJbuDiGEECPH6zVP9+7d4w7fVlVVcYcvu3Xrxs3e5uPjg08++QQTJkyASCTC4sWLsW7dOnTv3h3du3fHunXrIJVKuetPZDIZ5syZgyVLlqBjx46wt7fHO++8A39/f4SGhvK5OfWqKZQWLaq7cKJv2oVBOQuDcuafl5dXS3eBEIOgsSwMylkYlDP/jDljXounDz/8UGPGspqjSbGxsdxF+zdu3NC4Pmfp0qUoKyvD/Pnz8eTJEwwYMADHjh3TuKfMV199BTMzM0yePBllZWUYNWoUoqKiWvwC9tdfB2bNqp4cghBCmqu5E8MQYixoLAuDchYG5cw/Y86Y155FRUWBMab1eHq2M8YYZs2axT0XiURYtWoVsrOzUV5ejpMnT8LPz09jvRYWFvj666/x+PFjlJaW4pdffkGXLl343BS91Vc41VwoT/hFOQuDcuZfe7rpMmnbaCwLg3IWBuXMP2PO2HjLuhbUyDk0CCF6ot8tQgghhLRmjZ5try2oa0aNqqoqpKWlQSqVwtHRscHp0RurZqpowi/KWRiNzZkxhkePHqG0tBTdu3en/0d6KCkpofsP8ay8vBw3b96El5cXXcfHIxrLwqCchUE580/ojHmdba8tMzU1haurKx48eMDd58GQlEolr7P+kWqUszCakrNIJIKrqysVTnq6du0a+vfv39LdaNMsLCxQXl5OhRPPaCwLg3IWBuXMP2POmIqnWqytrdG9e3eoVCqDr/vPP//EkCFDDL5eoolyFkZTchaLxVQ4NcKTJ09augttXnp6OpYtW4Zt27bBw8OjpbvTZtFYFgblLAzKmX/GnDEVTzqYmpry8geepaUlfbspAMpZGJQz/2pu6UD48+TJE8TGxuLJkydUPPGIxrIwKGdhUM78M+aM6ZqnBs5rNCSVSgWxWCzY57VXlLMwKGf+Ucb8S0pKQlBQEN0kl2c0loVBOQuDcuaf0Bk3pjag2fYEdOLEiZbuQrtAOQuDcuYfZUzaChrLwqCchUE588+YM26Xp+3VHGwrLCwU9HNLSkoE/8z2iHIWBuXMP8qYf8XFxdxPypo/NJaFQTkLg3Lmn9AZ13yWPifktcvT9h48eGA0N9UlhBBCCCGEtLz79+/D1dW13jbtsnhSq9XIysqCjY2Nwe/lVJfCwkJ06dIF9+/fF/Q6q/aGchYG5cw/ylgYlDP/KGNhUM7CoJz51xIZM8ZQVFQEFxcXmJjUf1VTuzxtz8TEpMGqki+2trb0yyYAylkYlDP/KGNhUM78o4yFQTkLg3Lmn9AZy2QyvdrRhBGEEEIIIYQQogcqngghhBBCCCFED1Q8CcTc3BwrV66Eubl5S3elTaOchUE5848yFgblzD/KWBiUszAoZ/4Ze8btcsIIQgghhBBCCGksOvJECCGEEEIIIXqg4okQQgghhBBC9EDFEyGEEEIIIYTogYonQgghhBBCCNEDFU8GtHbtWgwaNAhSqRR2dnZ6vYcxhlWrVsHFxQWWlpYICQnBtWvXNNoolUosWrQIDg4OsLKywvPPP48HDx7wsAXG78mTJ5g+fTpkMhlkMhmmT5+OgoKCet8jEol0Pj777DOuTUhIiNbrU6ZM4XlrjFdTcp41a5ZWhgMHDtRoQ2P5b43NWKVS4b333oO/vz+srKzg4uKCGTNmICsrS6Ndex/L3377LTw8PGBhYYGgoCCcPn263vYnT55EUFAQLCws8Mwzz2DTpk1abQ4cOABfX1+Ym5vD19cXhw4d4qv7rUZjcj548CBGjx4NR0dH2NraIjg4GH/88YdGm6ioKJ376fLycr43xWg1JuO4uDid+V2/fl2jHY1lbY3JWde/cyKRCD179uTa0FjWdOrUKTz33HNwcXGBSCTCTz/91OB7jH6/zIjBfPjhh+zLL79kb7/9NpPJZHq9JzIyktnY2LADBw6w5ORk9vLLLzNnZ2dWWFjItZk3bx7r3Lkzi46OZklJSWzEiBGsV69erLKykqctMV5jx45lfn5+7OzZs+zs2bPMz8+PjR8/vt73ZGdnazy+//57JhKJ2O3bt7k2w4cPZ3PnztVoV1BQwPfmGK2m5Dxz5kw2duxYjQwfP36s0YbG8t8am3FBQQELDQ1l+/btY9evX2fx8fFswIABLCgoSKNdex7Le/fuZWKxmG3dupWlpKSwiIgIZmVlxe7evauz/Z07d5hUKmUREREsJSWFbd26lYnFYvbjjz9ybc6ePctMTU3ZunXrWGpqKlu3bh0zMzNjCQkJQm2W0WlszhEREezTTz9l58+fZzdv3mTvv/8+E4vFLCkpiWuzfft2Zmtrq7W/bq8am3FsbCwDwG7cuKGR39P7VhrL2hqbc0FBgUa+9+/fZ/b29mzlypVcGxrLmo4ePcpWrFjBDhw4wACwQ4cO1du+NeyXqXjiwfbt2/UqntRqNZPL5SwyMpJbVl5ezmQyGdu0aRNjrPoXVSwWs71793JtMjMzmYmJCfv9998N3ndjlpKSwgBo/HLEx8czAOz69et6r+eFF15gI0eO1Fg2fPhwFhERYaiutmpNzXnmzJnshRdeqPN1Gst/M9RYPn/+PAOg8Q99ex7L/fv3Z/PmzdNY5uPjw5YtW6az/dKlS5mPj4/Gstdff50NHDiQez558mQ2duxYjTZjxoxhU6ZMMVCvW5/G5qyLr68vW716Nfdc338324vGZlxTPD158qTOddJY1tbcsXzo0CEmEolYRkYGt4zGct30KZ5aw36ZTttrQenp6cjJyUFYWBi3zNzcHMOHD8fZs2cBAImJiVCpVBptXFxc4Ofnx7VpL+Lj4yGTyTBgwABu2cCBAyGTyfTO4uHDh/j1118xZ84crdd27doFBwcH9OzZE++88w6KiooM1vfWpDk5x8XFwcnJCV5eXpg7dy5yc3O512gs/80QYxkAFAoFRCKR1mnC7XEsV1RUIDExUWN8AUBYWFidmcbHx2u1HzNmDC5evAiVSlVvm/Y2Zms0Jefa1Go1ioqKYG9vr7G8uLgY7u7ucHV1xfjx43Hp0iWD9bs1aU7Gffr0gbOzM0aNGoXY2FiN12gsazLEWN62bRtCQ0Ph7u6usZzGctO1hv2ymSCfQnTKyckBAHTq1EljeadOnXD37l2ujUQiQYcOHbTa1Ly/vcjJyYGTk5PWcicnJ72z2LFjB2xsbDBx4kSN5eHh4fDw8IBcLsfVq1fx/vvv4/Lly4iOjjZI31uTpuY8btw4TJo0Ce7u7khPT8cHH3yAkSNHIjExEebm5jSWn2KIsVxeXo5ly5Zh2rRpsLW15Za317Gcl5eHqqoqnfvTujLNycnR2b6yshJ5eXlwdnaus017G7M1mpJzbV988QVKSkowefJkbpmPjw+ioqLg7++PwsJCbNiwAYMHD8bly5fRvXt3g26DsWtKxs7OztiyZQuCgoKgVCrxww8/YNSoUYiLi8OwYcMA1D3eaSw3LZPs7Gz89ttv2L17t8ZyGsvN0xr2y1Q8NWDVqlVYvXp1vW0uXLiAvn37NvkzRCKRxnPGmNay2vRp01romzGgnRXQuCy+//57hIeHw8LCQmP53Llzuf/28/ND9+7d0bdvXyQlJSEwMFCvdRs7vnN++eWXuf/28/ND37594e7ujl9//VWrWG3MelsTocaySqXClClToFar8e2332q81h7Gcn0auz/V1b728qbso9u6pmayZ88erFq1CocPH9b4AmHgwIEaE8wMHjwYgYGB+Prrr/Gf//zHcB1vRRqTsbe3N7y9vbnnwcHBuH//Pj7//HOueGrsOtuLpmYSFRUFOzs7vPjiixrLaSw3n7Hvl6l4asDChQsbnKmqa9euTVq3XC4HUF1lOzs7c8tzc3O5iloul6OiogJPnjzR+MY+NzcXgwYNatLnGht9M75y5QoePnyo9dqjR4+0voHQ5fTp07hx4wb27dvXYNvAwECIxWKkpaW1mT84hcq5hrOzM9zd3ZGWlgaAxnKN5masUqkwefJkpKen48SJExpHnXRpi2NZFwcHB5iammp98/j0/rQ2uVyus72ZmRk6duxYb5vG/C60JU3Juca+ffswZ84c7N+/H6GhofW2NTExQb9+/bj9R3vSnIyfNnDgQOzcuZN7TmNZU3NyZozh+++/x/Tp0yGRSOpt257HclO0hv0yXfPUAAcHB/j4+NT7qH0UQ181p9Y8fTpNRUUFTp48yf0xGRQUBLFYrNEmOzsbV69ebTN/cOqbcXBwMBQKBc6fP8+999y5c1AoFHplsW3bNgQFBaFXr14Ntr127RpUKpVGUdvaCZVzjcePH+P+/ftchjSWm59xTeGUlpaGmJgY7h+S+rTFsayLRCJBUFCQ1umJ0dHRdWYaHBys1f7YsWPo27cvxGJxvW3ayphtrKbkDFQfcZo1axZ2796NZ599tsHPYYzhr7/+avPjVpemZlzbpUuXNPKjsaypOTmfPHkSt27d0nn9dG3teSw3RavYLwsyLUU7cffuXXbp0iW2evVqZm1tzS5dusQuXbrEioqKuDbe3t7s4MGD3PPIyEgmk8nYwYMHWXJyMps6darOqcpdXV1ZTEwMS0pKYiNHjmzX0zsHBASw+Ph4Fh8fz/z9/bWmd66dMWOMKRQKJpVK2caNG7XWeevWLbZ69Wp24cIFlp6ezn799Vfm4+PD+vTp0y4zZqzxORcVFbElS5aws2fPsvT0dBYbG8uCg4NZ586daSzXobEZq1Qq9vzzzzNXV1f2119/aUyBq1QqGWM0lmumHd62bRtLSUlhixcvZlZWVtxMWMuWLWPTp0/n2tdMifvWW2+xlJQUtm3bNq0pcc+cOcNMTU1ZZGQkS01NZZGRkTS9cyNz3r17NzMzM2PffPNNnVPor1q1iv3+++/s9u3b7NKlS2z27NnMzMyMnTt3TvDtMwaNzfirr75ihw4dYjdv3mRXr15ly5YtYwDYgQMHuDY0lrU1Nucar7zyChswYIDOddJY1lRUVMT9PQyAffnll+zSpUvcLLGtcb9MxZMBzZw5kwHQesTGxnJtALDt27dzz9VqNVu5ciWTy+XM3NycDRs2jCUnJ2ust6ysjC1cuJDZ29szS0tLNn78eHbv3j2Btsq4PH78mIWHhzMbGxtmY2PDwsPDtaZmrZ0xY4xt3ryZWVpa6rzfzb1799iwYcOYvb09k0gkzNPTk7355pta9yhqTxqbc2lpKQsLC2OOjo5MLBYzNzc3NnPmTK1xSmP5b43NOD09Xef+5el9DI1lxr755hvm7u7OJBIJCwwMZCdPnuRemzlzJhs+fLhG+7i4ONanTx8mkUhY165ddX7Bsn//fubt7c3EYjHz8fHR+IO0vWpMzsOHD9c5bmfOnMm1Wbx4MXNzc2MSiYQ5OjqysLAwdvbsWQG3yPg0JuNPP/2UeXp6MgsLC9ahQwc2ZMgQ9uuvv2qtk8aytsbuMwoKCpilpSXbsmWLzvXRWNZUM41+Xb//rXG/LGLs/6/CIoQQQgghhBBSJ7rmiRBCCCGEEEL0QMUTIYQQQgghhOiBiidCCCGEEEII0QMVT4QQQgghhBCiByqeCCGEEEIIIUQPVDwRQgghhBBCiB6oeCKEEEIIIYQQPVDxRAghhBBCCDFqp06dwnPPPQcXFxeIRCL89NNPjXr/qlWrIBKJtB5WVlaNWg8VT4QQQgghhBCjVlJSgl69euG///1vk97/zjvvIDs7W+Ph6+uLSZMmNWo9VDwRQgghhBBCjNq4ceOwZs0aTJw4UefrFRUVWLp0KTp37gwrKysMGDAAcXFx3OvW1taQy+Xc4+HDh0hJScGcOXMa1Q+z5mwEIYQQQgghhLS02bNnIyMjA3v37oWLiwsOHTqEsWPHIjk5Gd27d9dq/91338HLywtDhw5t1OfQkSdCCCGEEEJIq3X79m3s2bMH+/fvx9ChQ+Hp6Yl33nkHQ4YMwfbt27XaK5VK7Nq1q9FHnQA68kQIIYQQQghpxZKSksAYg5eXl8ZypVKJjh07arU/ePAgioqKMGPGjEZ/FhVPhBBCCCGEkFZLrVbD1NQUiYmJMDU11XjN2tpaq/13332H8ePHQy6XN/qzqHgihBBCCCGEtFp9+vRBVVUVcnNzG7yGKT09HbGxsfj555+b9FlUPBFCCCGEEEKMWnFxMW7dusU9T09Px19//QV7e3t4eXkhPDwcM2bMwBdffIE+ffogLy8PJ06cgL+/P/7xj39w7/v+++/h7OyMcePGNakfIsYYa/bWEEIIIYQQQghP4uLiMGLECK3lM2fORFRUFFQqFdasWYP/+Z//QWZmJjp27Ijg4GCsXr0a/v7+AKpP73N3d8eMGTOwdu3aJvWDiidCCCGEEEII0QNNVU4IIYQQQggheqDiiRBCCCGEEEL0QMUTIYQQQgghhOiBiidCCCGEEEII0QMVT4QQQgghhBCiByqeCCGEEEIIIUQPVDwRQgghhBBCiB6oeCKEEEIIIYQQPVDxRAghhBBCCCF6oOKJEEIIIYQQQvRAxRMhhBBCCCGE6IGKJ0IIIYQQQgjRw/8BTAs1xQ/7buAAAAAASUVORK5CYII=", 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", 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dLI0+zoqLi5UugscS2StD5K4MkbvzeXl5oVKpREdKAaK+K0PkrgyRuzKckbvoTDmgtbVV6SJ4LJG9MkTuyhC5O19VVRVPPvkkVVVVShfF44j6rgyRuzJE7spwRu6iM+UAb29vpYvgsUT2yhC5K0Pk7nytra1s2rRJHOgoQNR3ZYjclSFyV4YzchfXTDkwL9Jms6HRaJxQMuF8IntliNyVIXJ3vry8PHJycsjNzSU7O1vp4ngUUd+VIXJXhshdGY7mLq6ZGmdbt25VuggeS2SvDJG7MkTugicR9V0ZIndliNyV4YzcRWdKEARBEARBEAThCojOlAMSExOVLoLHEtkrQ+SuDJG780VERPDggw8SERGhdFE8jqjvyhC5K0Pkrgxn5K4d958wAYh9AZQjsleGyF0ZInfni4mJ4ac//SnR0dFKF8XjiPquDJG7MkTuynBG7mJkygHHjx9XuggeS2SvDJG7MkTuztfd3c369evp7u5WuigeR9R3ZYjclSFyV4YzchedKUEQBMFjnTlzhh/84AecOXNG6aIIgiAIbkh0phwwb948pYvgsUT2yhC5K0PkLngSUd+VIXJXhshdGc7IXXSmHFBeXq50ETyWyF4ZIndliNwFTyLquzJE7soQuSvDGbmLzpQDmpqalC6CxxLZK0PkrgyRu+BJRH1XhshdGSJ3ZTgjd9GZcoDBYFC6CB5LZK8MkbsyRO7Op9PpCA0NRafTKV0UjyPquzJE7soQuSvDGbmrJEmSxv2nuJiuri4CAgLo7OzE399f6eIIgiAIgiAIgqCQq+kbjPvI1AsvvEBiYiJGo5GcnBz27Nlz0efee++9qFSqEbfMzEz5Oa+99toFn9Pf3z9un2HTpk3j9t7CpYnslSFyV4bIXRkid2WI3JUhcleGyF0Zzsh9XDtT7733Ho899hg//vGPyc/PZ/Hixaxdu5bq6uoLPv+5556jvr5evp09e5bg4GC+9rWvDXuev7//sOfV19djNBrH86MIgiAIE9Dx48e58847xR4wgiAIwhUZ187U73//e+6//36+8Y1vkJ6ezrPPPktcXBwvvvjiBZ8fEBBAZGSkfDt69Cjt7e3cd999w56nUqmGPS8yMnI8Pwbx8fHj+v7CxYnslSFyV4bI3fksFgstLS1YLBali+JxRH1XhshdGSJ3ZTgj93HrTA0MDJCbm8vq1auH3b969Wr279/v0Hu88sorrFy5kkmTJg27v6enh0mTJhEbG8v1119Pfn7+Jd/HbDbT1dU17DYawcHBo3q+MHZE9soQuStD5C54ElHflSFyV4bIXRnOyF07Xm/c0tKCzWYjIiJi2P0RERE0NDRc9vX19fV8+eWXvP3228PuT0tL47XXXmPq1Kl0dXXx3HPPsXDhQgoLC0lJSbngez3zzDM89dRTI+7funUrPj4+rFixgsOHD9PT00NQUBCZmZns3btX/nmHDh0iICAAgKVLl1JQUCBfoJadnc3OnTsBSElJQavVUlRUBMCiRYs4deoUbW1t+Pj4MG/ePLZt2wZAUlIS3t7enDhxAoD58+dTWlpKc3MzRqORJUuWsHnzZgAmTZpEYGAghYWFAMyZM4fq6moaGhrQ6XSsWLGCzZs3I0kSsbGxhIeHk5eXB0BOTg4NDQ3U1taiVqtZtWoV27Ztw2q1EhUVRWxsLEeOHAFgxowZtLW1ydMw16xZw86dOzGbzYSHh5OUlMTBgwcBmDp1Kj09PVRUVACwcuVK9u/fT19fHyEhIaSlpbFv3z4AMjIyGBgYoLS0FIDly5dz9OhRuru7CQwMZNq0aezevRuAKVOmAHD69GkArFYroaGhdHR04Ofnx6xZs9ixYwcAkydPRq/Xc+rUKQAWLlxIcXExra2teHt7s2DBArZu3QpAYmIivr6+8lSeefPmUV5eTlNTEwaDgWXLlsnzauPj4wkODqagoACA2bNnU1NTQ319PVqtlmuuuYYtW7Zgt9uJiYkhMjKS3NxcALKzs2lqaqKmpgaVSsXq1avZvn07FouFyMhI4uPjOXz4MADTp0+no6ODqqoqYPBEw+7du+nv7ycsLIzJkydz4MABALKysujr65P3S7jmmms4ePAgvb29BAcHk5GRIdfZ9PR0rFYrZ86cAWDZsmXk5eXJF1fOmDGDXbt2AZCamoparaa4uFiusydPniQ/P5+srCzmzJnD9u3bAUhOTsZoNHLy5EkAFixYQElJCS0tLXh7e7Nw4UK2bNkCQEJCAv7+/hw7dgyAuXPnUllZSWNjI3q9nuXLl8t5x8XFERoaKp8UmTVrFnV1ddTV1aHRaFi5ciVbt27FZrMRHR1NdHQ0R48eBWDmzJm0tLRw9uxZuc7u2LGDgYEBIiIiSEhI4NChQwBMmzaNrq4uKisrAVi1ahX79u2jr6+P0NBQUlNT5RM9mZmZ9Pf3U1ZWBnDZNsJut1NSUgJcXRvR3d2NWj14jku0EY61EUuWLOHYsWNX3EYM/Z9XVVURGRkp2ggH24j29nZ8fX2vqo04efIkPj4+gGgjnHkcUVlZybp160QbgXOPI7Zs2UJYWJhoI5x8HFFdXc0DDzxw2TZiqPxXYtxW86urqyMmJob9+/czf/58+f5f/vKXvPnmm5ct9DPPPMPvfvc76urq0Ov1F32e3W4nOzubJUuW8Pzzz1/wOWazGbPZLP+7q6uLuLg4h1fs2LRpE2vWrLns84SxJ7JXhshdGSJ358vLyyMnJ4fc3Fyys7OVLo5HEfVdGSJ3ZYjcleFo7lezmt+4jUyFhoai0WhGjEI1NTWNGK06nyRJvPrqq9x1112X7EgBqNVqZs+eLfeeL8RgMFzVOvOzZ8++4tcKV0dkrwyRuzJE7s6XkpLCJ598ctGZDcL4EfVdGSJ3ZYjcleGM3Mftmim9Xk9OTo48XDdky5YtLFiw4JKv3bVrF6Wlpdx///2X/TmSJFFQUEBUVNRVlfdSampqxu29hUsT2StD5K4Mkbvz+fn5kZCQgJ+fn9JF8TiivitD5K4MkbsynJH7uK7m98QTT/Dyyy/z6quvUlRUxOOPP051dTUPPfQQAD/60Y+4++67R7zulVdeYe7cuWRlZY147KmnnmLTpk2Ul5dTUFDA/fffT0FBgfye46G+vn7c3lu4NJG9MkTuyhC5O19tbS2//OUvqa2tVbooHkfUd2WI3JUhcleGM3Ift2l+ALfffjutra08/fTT1NfXk5WVxYYNG+TV+err60fsOdXZ2clHH33Ec889d8H37Ojo4MEHH6ShoYGAgABmzpzJ7t27mTNnzrh9Dq12XGMSLkFkrwyRuzJE7s7X2NjI+++/zw9+8ANiYmKULo5HEfVdGSJ3ZYjcleGM3MdtAQpXdjUXmQmCIAgTh1iAQhAEQbiavsG4TvObKM6/7ktwHpG9MkTuyhC5C55E1HdliNyVIXJXhjNyF50pB9jtdqWL4LFE9soQuStD5C54ElHflSFyV4bIXRnOyF10phwg5tErR2SvDJG7MkTuzhcSEsItt9xCSEiI0kXxOKK+K0PkrgyRuzKckbu4Gs4BkZGRShfBY4nslSFyV4bI3fkmTZrEX//6V0JDQ5UuiscR9V0ZIndliNyV4YzcxciUA3Jzc5UugscS2StD5K4MkbvzmUwmPvroI0wmk9JF8TiivitD5K4MkbsynJG76EwJgiAIHquoqIiHHnqIoqIipYsiCIIguCHRmXKAWC5XOSJ7ZYjclSFyFzyJqO/KELkrQ+SuDGfkLjpTDmhqalK6CB5LZK8MkbsyRO6CJxH1XRkid2WI3JXhjNxFZ8oBNTU1ShfBY4nslSFyV4bIXfAkor4rQ+SuDJG7MpyRu+hMOUClUildBI8lsleGyF0ZInfnU6lU6HQ6kb0CRObKELkrQ+SuDGfkrpIkSRr3n+Jiurq6CAgIoLOzE39/f6WLIwiCIAiCIAiCQq6mbyBGphywfft2pYvgsUT2yhC5K0PkrgyRuzJE7soQuStD5K4MZ+QuOlMOsFgsShfBY4nslSFyV4bI3fmKiop48MEHxdLoChD1XRkid2WI3JXhjNxFZ8oBYtdq5YjslSFyV4bI3flMJhNlZWVi014FiPquDJG7MkTuynBG7qIz5YD4+Hili+CxRPbKELkrQ+QueBJR35UhcleGyF0ZzshddKYccPjwYaWL4LFE9soQuStD5C54ElHflSFyV4bIXRnOyF10pgRBEARBEARBEK6A6Ew5YPr06UoXwWOJ7JUhcleGyN35EhMTeemll0hMTFS6KB5H1HdliNyVIXJXhjNyF50pB3R0dChdBI8lsleGyF0ZInfnCwoKYvHixQQFBSldFI8j6rsyRO7KELkrwxm5i86UA6qqqpQugscS2StD5K4MkbvzNTY28vvf/57Gxkali+JxRH1XhshdGSJ3ZTgjd9GZEgThkiRJwm63Y7PZsFqtDAwMYLFYsNlsSJKkdPEE4arU1tbyt7/9jdraWqWLIgiCILghleSBR0NdXV0EBATQ2dmJv7//ZZ8vSRIqlcoJJRPOJ7J3jCRJmM1mTCYTJpOJ/v5++e/n/ttisVzwZrVa5b/b7Xbsdrv8vpdrIlQqFRqNBrVaPeKm0+kueNPr9cP+bTAYMBqNF7wZDAaPqQOivjtfXl4eOTk55Obmkp2drXRxPIqo78oQuStD5K4MR3Mfbd/gXNorLZwn2b17N0uXLlW6GB5JZA92u52uri46Ojro7u6mq6uL7u7uETer1TpmP7OyspKEhASHnitJ0pj+7POpVKphnS0fHx+8vb2H/Xn+3921Aybqu+BJRH1XhshdGSJ3ZTgjd9GZckB/f7/SRfBYnpK9JEl0dHTQ1tZGW1sbra2t8t/b29ux2WwOvY9Op8PLywsvLy+MRqP896F/nz8ipNVqR/xbrVazfft2Vq1ahUqlGnZTq9WoVCp56t+lbkPTAodGvIamB557G7rPbDbT398/4ma1WpEkSf63ozQaDT4+Pvj5+Y24+fv7y383Go0u1enylPouCCDqu1JE7soQuSvDGbmLzpQDwsLClC6Cx5qI2VssFpqbm2loaJBvjY2NmM3mi75Go9EQGBg4rCNwfsfA19cXrXZsfqUTEhLw8/Mbk/e6GlardVjnymQy0dfXR29vr/znuX/v6+vDbDZjs9no6uqiq6vrku+v1WrlHAMCAggICCAwMHDYnzqdzkmfdmLWd1cXEBDAkiVLCAgIULooHkfUd2WI3JUhcleGM3IX10w5MC+yq6tr1PMnhbHh7tlLkkRraytnz56lurqa2tpaWlpa5GuSzqXRaAgODiYkJITg4OBhN39/f9Rq560X4865W61WuZM1NAXy/KmRXV1dmEwmh97Px8dnRAcrKCiI4OBgAgMDx6wDC+6duzsTuStD5K4MkbsyRO7KcDR3cc3UODtw4ABr1qxRuhgeyd2yH+o8lZeXU15eTlVV1QUP2r29vYmMjBx2Cw0NdWqH6VLcLfdzabVaeZTpUqxW67DOVWdnJx0dHXR0dMh/HxgYkDtmF1rtTaVSERAQQHBwsNzBGroFBQWh1+tHVXZ3zt1dWSwWvvzyS2655RanjkIKor4rReSuDJG7MpyRu+hMCcJVGhgYoLy8nNOnT1NWVjZiaplWqyUmJoa4uDji4uKIjo7G19fXpa7V8URarZagoKCLbtY6dK3WuZ2roT/b29tpa2tjYGBA7oBdiJ+fnzzaGBoaKt8CAwNdpuPs6Y4fP87Xv/51sZqfIAiCcEVEZ8oBWVlZShfBY7lq9v39/RQVFVFcXExZWdmw1ey0Wi3x8fEkJSWRkJBAVFQUGo1GwdKOnqvm7kwqlUpevCMqKmrE45Ik0dvbKy8SMrRgyNDNZDLJI1/nbxqo0WhGdLBCQ0NJSUlx1scTBMWJdkYZIndliNyV4YzcRWfKAX19fUoXwWO5UvZWq5UzZ85w/PhxSkpKhnWgAgMDSUtLIyUlhfj4eLefLuRKubsqlUqFr68vvr6+xMfHj3jcZDLR3t5Oa2srra2ttLS0yDer1UpTUxNNTU3DXtPa2kpiYiJhYWGEh4cTERFBeHg4YWFhbl+nBOF8op1RhshdGSJ3ZTgjd9GZckB5ebk4Y6wQV8i+vb2do0ePkp+fP+yXMiwsjKysLNLS0ggPD59Q0/ZcIXd3NzSqFR0dPex+SZLo7Owc1rkaupWWlhISEkJXVxdlZWXya1QqFUFBQYSHhw+7hYSEuN2opyAMEe2MMkTuyhC5K8MZuYvOlCBcgCRJVFZWsn//fkpLSxla9NLPz4+pU6cybdo0IiIiJlQHSnAOlUpFYGAggYGBTJ48edhjn376KTNnzpRHrZqammhsbKSvr0+ePlhcXCw/f2i64PmLmXh7ezv7YwmCIAiCRxJLozuw/KHVah3T5Y8Fxzk7e0mSOH36NHv27Bm2etvkyZOZNWsWqampHrFwgKjzyrhY7r29vSM6WE1NTQwMDFzwfQICAkZ0sAIDA0Xn/wJsNhudnZ0EBASIUT4nE+2MMkTuyhC5K8PR3MXS6OPs4MGDLFq0SOlieCRnZl9RUcGWLVuoq6sDBheSyM7OZt68eQQHBzulDK5C1HllXCx3Hx8fEhMTSUxMlO8bmi7Y1NQ0bAPotrY2Ojs76ezs5PTp0/LzjUYjERERREZGEhUVRXR0tEstx68UjUbDqVOnRH1XgGhnlCFyV4bIXRnOyF10phzQ29urdBE8ljOyb2lpYdOmTZw5cwYAg8HAnDlzmDt3Lr6+vuP+812RqPPKGE3u504XTE1Nle83m800NjZSX18vd7Campro7++nqqpq2MqCer1e7lgN3YKDgz1qBOvMmTM8+uijvPvuu+J6BicT7YwyRO7KELkrwxm5i86UAzxtVMKVjGf2VquVvXv3smfPHmw2G2q1mtmzZ7NkyRJ8fHzG7ee6A1HnlTEWuRsMBuLj44etMGiz2WhpaaGhoYH6+nr5NjAwMKKDZTQaR3SwJvIUwe7ubvLy8uju7la6KB5HtDPKELkrQ+SuDGfkPu7XTL3wwgv89re/pb6+nszMTJ599lkWL158wefu3LmT5cuXj7i/qKiItLQ0+d8fffQRP/nJTygrKyM5OZlf/vKX3HzzzQ6XabTzInt7ez3+4Fop45V9fX09H330ES0tLQCkpKSwdu1a0dj9H1HnleHM3O12O62trdTV1cm3+vr6YUv+D/H29iYmJobY2FhiY2OJiYnBaDQ6pZzjLS8vj5ycHLFprwJEO6MMkbsyRO7KcDR3l71m6r333uOxxx7jhRdeYOHChfz1r39l7dq1nDp16oL7sgw5ffr0sA8SFhYm//3AgQPcfvvt/PznP+fmm2/m448/Zt26dezdu5e5c+eOy+fYu3cva9asGZf3Fi5trLOXJInDhw+zefNmbDYbvr6+rF27loyMjAl75v1KiDqvDGfmrlarCQsLIywsjOnTpwODI1jNzc3DOlhDqwmeOXNGngqrUqkIDQ0lNjaWuLg4YmNjxfVXwqiJdkYZIndliNyV4Yzcx7Uz9fvf/57777+fb3zjGwA8++yzbNq0iRdffJFnnnnmoq8LDw8nMDDwgo89++yzrFq1ih/96EcA/OhHP2LXrl08++yzvPPOO2P+GYSJw2Kx8PHHH3Pq1CkA0tLSuOmmm/Dy8lK4ZILgGjQajbz639AojdVqpbGxkZqaGvnW3t5Oc3Mzzc3N5OfnA4PTC88dvYqNjRVLtAuCIAgT3rh1pgYGBsjNzeWHP/zhsPtXr17N/v37L/namTNn0t/fT0ZGBk8++eSwqX8HDhzg8ccfH/b8NWvW8Oyzz170/cxmM2azWf53V1fXKD4JpKenj+r5wtgZq+x7e3t55513qKmpQaPRsHr1aubMmSNGoy5C1HlluGLuWq2WmJgYYmJi5NH/3t7eYZ2r2tpazGYz5eXllJeXy68NDQ2Vr9+Kj48nKCjI5X7n4uLiePrpp4mLi1O6KB7HFeu7JxC5K0Pkrgxn5D5unamWlhZsNhsRERHD7o+IiKChoeGCr4mKiuKll14iJycHs9nMm2++yTXXXMPOnTtZsmQJAA0NDaN6T4BnnnmGp556asT9W7duxcfHhxUrVnD48GF6enoICgoiMzOTvXv3AoOjFzU1NRQVFQGwdOlSCgoK5DmV2dnZ7Ny5Exi87kar1crPXbRoEadOnaKtrQ0fHx/mzZvHtm3bAEhKSsLb25sTJ04AMH/+fEpLS2lubsZoNLJkyRI2b94MwKRJkwgMDKSwsBCAOXPmUF1dTUNDAzqdjhUrVrB582YkSSI2Npbw8HDy8vIAyMnJoaGhgdraWtRqNatWrWLbtm1YrVaioqKIjY3lyJEjAMyYMYO2tjaqq6uBwU7qzp07MZvNhIeHk5SUxMGDBwGYOnUqPT09VFRUALBy5Ur2799PX18fISEhpKWlsW/fPgAyMjIYGBigtLQUgOXLl3P06FG6u7sJDAxk2rRp7N69G4ApU6YAyEs6x8fHc+jQITo6OvDz82PWrFns2LEDGNz7Sa/XyyNNCxcupLi4mNbWVry9vVmwYAFbt26lt7eXvLw8TCYTHR0dLF26lLS0NAoKCmhqasJgMLBs2TI2bdok/8zg4GAKCgoAmD17NjU1NdTX16PVarnmmmvYsmULdrudmJgYIiMjyc3NBSA7O5umpiZqampQqVSsXr2a7du3Y7FYiIyMJD4+nsOHDwMwffp0Ojo65Iv/V69eze7du+nv7ycsLIzJkydz4MABALKysujr65MPVK+55hoOHjxIb28vwcHBZGRkyHU2PT0dq9UqT8latmwZeXl58nzgGTNmsGvXLgB536yhjWAXLVrEyZMnqays5OzZs8yZM4ft27cDkJycjNFo5OTJkwAsWLCAkpISWlpa8Pb2ZuHChWzZsgWAhIQE/P39OXbsGABz586lsrKSxsZG9Ho9y5cvl/OOi4sjNDRUHuGYNWuWPMVMo9GwcuVKtm7dis1mkxdEOHr0KDB44qWlpYWzZ8/KdXbHjh0MDAwQERFBQkIChw4dAmDatGl0dXVRWVkJwKpVq9i3bx99fX2EhoaSmpoqn+jJzMykv7+fsrIygMu2EXa7nZKSEuDq2oiIiAg5F3dqI8LDw9HpdOh0Onbs2CGXsbW1VW4zkpKSaGlpISgoiMmTJ7No0SJKS0tRq9VX1UYsWbKEY8eOXXEbkZeXx5QpU2hvb8disXD8+HEA5s2bR3l5uWgjLtJGtLe34+vre1VtRFdXl/y7INqInYBzjiM6Ozvx8/PzmOOIq20jtm7dCkBiYiK+vr5X3EaUlJRQVFQk2ggnH0d0d3cTHx9/2TZiqPxXYtwWoKirqyMmJob9+/czf/58+f5f/vKXvPnmmw4X+oYbbkClUvHpp58Cg0v5vv766/zbv/2b/Jy33nqL+++/n/7+/gu+x4VGpuLi4hy+yGzTpk1inqtCrjb77u5uXnvtNVpbWwkMDOTOO+8kNDR0DEs4MYk6r4yJlHtfXx9nz56lurqa6upq6urqsNlsw56j1+uJiYmRR67i4uLQ6/VOLWdbWxv/+7//yw9/+EOxAI2TTaT67k5E7soQuSvD0dxdcgGK0NBQNBrNiBGjpqamESNLlzJv3jzWr18v/zsyMnLU72kwGDAYDA7/TGFiMJvNrF+/ntbWVgICArj33nsvei2eIAhjy9vbmylTpshnia1WK3V1dXLnqrq6mv7+fioqKuQz02q1mujoaBISEkhISCAuLm7c2+7Kykp++9vf8vWvf110pgRBEIRRG9el0efOnUtOTg4vvPCCfF9GRgY33XTTJRegONdtt91GW1ubPER4++23093dzYYNG+TnrF27lsDAQIcXoBht79NsNovOmEKuNHtJknj//fcpKirC19eX+++/n6CgoHEo4cQk6rwyPCl3SZJobm6WO1ZVVVV0dnYOe45arSYqKkruXMXHx495PmJpdOV4Un13JSJ3ZYjcleFo7i45MgXwxBNPcNdddzFr1izmz5/PSy+9RHV1NQ899BAwuBJfbW0tb7zxBjC4Ul9CQgKZmZkMDAywfv16PvroIz766CP5PR999FGWLFnCr3/9a2666SY++eQTtm7dKs/zHA95eXnDpioKznOl2e/bt4+ioiI0Gg1f//rXRUdqlESdV4Yn5a5SqQgPDyc8PJxZs2YB0NHRQWVlJZWVlVRVVdHe3k5tbS21tbXs27cPlUpFdHQ0kyZNkjtXE2W/K0/kSfXdlYjclSFyV4Yzch/XztTtt99Oa2srTz/9NPX19WRlZbFhwwYmTZoEDG6cOnSRIgyuAPjd736X2tpavLy8yMzM5IsvvuC6666Tn7NgwQLeffddnnzySX7yk5+QnJzMe++9N257TMHoV/8Txs6VZN/c3CxfXHrdddcRGxs71sWa8ESdV4an5x4YGMiMGTOYMWMGgHxh9VAH69zO1f79++XOVVJSEklJScTFxaHVjuvXmjCGPL2+K0XkrgyRuzKckfu4f+s8/PDDPPzwwxd87LXXXhv27+9///t8//vfv+x73nbbbdx2221jUTyHBAQEOO1nCcONNntJkvjkk0+w2WykpqaKaTtXSNR5ZYjchwsMDCQwMFDeVLizs3NY56qtrU3uXO3ZswetVkt8fDxJSUkkJiYSFRV12Y2EfXx8yMrKwsfHxxkfSTiHqO/KELkrQ+SuDGfkPq7XTLmq0c6L7O/vF1NJFDLa7E+ePMkHH3yAwWDg29/+9qjnvQqDRJ1Xhsh9dDo7O6moqKC8vJyKigq6u7uHPW40GklMTCQxMZGkpCRCQkIuuM+VyF0ZIndliNyVIXJXhqO5X801U5c+ZScAyGvpC843muztdru8V8f8+fNFR+oquFOdlyQJm83GwMAA/f39mEwmTCYTfX199Pb20tvbS3d3Nz09PfT29mIymejv72dgYACLxYLNZsNutyv9MQD3yt0VDO13csstt/DEE0/w7W9/m7Vr15KWlobRaKS/v5+ioiI2bNjAn/70J/7whz/w8ccfc/z4cXp7e+X3EbkrQ+SuDJG7MkTuynBG7mJyuTBhDG1W6OXlxbx585QujjBKFotF7giZTCbMZrPc4RkYGJD/bbVasVqt2Gw2+c+xGGDXaDRoNBq0Wu2wPzUaDTqdDr1eP+zPc/9uMBgwGo3o9foLjnwI40+lUhEWFkZYWBhz587FbrdTV1cnj1ydPXuWrq4uCgsLKSwsRKVSERUVhd1u51vf+haHDx9m9uzZSn8MQRAEwc2IzpQDUlNTlS6CxxpN9kM7X8+YMUMMpV+l8ajzNpuNnp4e+TY0WtTX14fJZMJisYzZz1KpVMNuQyRJQpKkC45E2Ww2eYTrSqnVavR6PUajUb4NdbSG/jQajfj4+Fyw4yXamrGjVquJjY0lNjaWxYsXY7FYOHv2LGVlZZSVldHQ0EBdXR319fUAvP7665SVlZGcnMzkyZPFCqBOIOq7MkTuyhC5K8MZuYvOlAMudwGzMH4czd5kMnH69GkAZs6cOZ5F8ghXU+clScJkMtHR0UF7ezsdHR10dHTQ09Nz2REkrVaLt7c3Xl5eGAwG9Hr9iJtOpxsxgqRWq+U/HRkZGupQndu5On+069w/LRaLPEJ2/t/PHTWz2+309/fT399/2TJoNBq8vb3lz+vj40NHRwcGg0G+T4x0jR2dTiev+rdq1Sq6u7spLy9n8+bNwODIaHFxMcXFxQAEBwczefJkkpOTSUxMRK/XK1n8CUl8typD5K4MkbsynJG76Ew5oLi4WF7OXXAuR7OvrKzEbrcTFhZGeHi4E0o2sY2mztvtdtrb22lpaaG5uZmWlpaLdia0Wi1+fn74+fnh6+uLr68vPj4+eHl54eXlhU6nG8uPcVEqlQqNRjPsvqvdTNFms2E2m+nv77/on0PXdPX392Oz2eju7h62aEJZWRm1tbXyv3U63bCc/Pz85D+9vLzEl/NV8PPzY/r06dhsNgBuueUWvLy8KCsr4+zZs7S1tXH48GEOHz6MRqMhISGBlJQUUlJSCAkJUbj0E4P4blWGyF0ZIndlOCN30ZkSJoSKigoAkpKSFC6JZ+jp6ZGnSDU3N2O1Woc9rlar8fPzIygoSF7eOiAgAKPROGFHWs4dabocm80mL5IxNM2xt7eX1tZWgoKC6Ovrw2w2Y7FYaG9vp729fcR7qNVqfHx85M6Wr68vfn5++Pv74+PjM2FzHi9hYWFkZ2ezZMkSzGYzFRUVlJWVUVpaSnt7uzw9cOPGjYSEhJCSkkJqairx8fFibytBEAQPJpZGd2DFt97eXrEHiUIczf5vf/sbtbW1fO1rXyMzM9MJJZvYzs9dkiTa29s5e/YsdXV1dHZ2Dnu+Xq8nNDRUXgAgKChoxMiPcHnn5m61WoetRnjunz09PZdcgVCj0eDv7y93roZufn5+4v/lPP39/ZSUlJCamnrBay0lSaK1tZUzZ85QUlJCVVXVsOz1ej1JSUmkpqaSkpKCn5+fM4vv1sR3qzJE7soQuSvD0dyvZml0cTrNASdPnmTOnDlKF8MjOZp9W1sbMHh2Wbh6Q7mbTCZ5g9RzO1BqtZrQ0FCio6OJjIwkICBAjISMgXPru1arJSAg4IIbDtrtdnk069wOVldXF93d3dhstguOaKlUqmEjWAEBAQQGBuLv7++xnayhJdQvtmiNSqUiNDSU0NBQ5s+fj9lspqysjDNnznDmzBl6enqGXWsVFRUlj1rFxMSI34tLEN+tyhC5K0Pkrgxn5C46Uw640BQbwTkcyX5oKW1ArMA1BiRJorq6moGBAWpra+VFIzQaDTExMcTExBAZGXnV1xgJIzna1gxN8fPx8RlxjaDdbpc7WZ2dnXR3d9PV1UVXVxcDAwPydVp1dXXD3m9oWuZQByswMHBCT8scUlFRwQ9/+ENeeeUVEhMTL/t8g8FARkYGGRkZSJJEfX29PGo1NPW1vr6e3bt34+vry5QpU5gyZQqJiYlOuybQXYjvVmWI3JUhcleGM3IXnSkH+Pr6Kl0Ej+VI9kOLHQzt/SNcGUmSqKmpobi4mIqKCvk6qLCwMBISEoiLixMrmo2zsWhrhjpGfn5+REdHy/dLkkR/f7/cyerq6qKzs5OOjg4GBgbo7OwcMX3TYDDI17sFBgYSFBQ04Uax2tvb2bFjB+3t7Q51ps6lUqmIjo4mOjqapUuX0tvbS2lpKSUlJZSWltLT00Nubi65ubnodDqSk5OZMmUKqampYroP4rtVKSJ3ZYjcleGM3MU1Uw7Mi7RYLOIgXSGOZN/U1MQLL7yAt7c33//+951UsomloaGBwsLCYWdwkpOTSU1NveA0M2F8KNHWSJJEX1+f3LHq6OiQO1sX+nrQaDRyxyo4ONjtO1h5eXnk5OSQm5tLdnb2mL2vzWajsrKS06dPc/r06WEdVZVKRVxcnDxqFRoaOmY/152I71ZliNyVIXJXhqO5i2umxtn27dtZs2aN0sXwSI5kP3QxuFgmevR6e3vJy8uTl+PW6XSkpqZSVVXF7NmzFS6d51GirVGpVPKUwXNHsqxWK11dXcM6WO3t7QwMDNDa2kpra6v83KEO1lDnKjg4GH9/f4/+ndRoNCQnJ5OcnMzatWtpaGiQO1b19fVUV1dTXV3Nli1bCA0NZcqUKaSlpREbGzvhp1YOEd+tyhC5K0Pkrgxn5C46U4LbG7pw3JGNUoVBkiRRWlpKYWEhVqsVtVrN5MmTyczMxGAwDLueRvBMWq2W4OBggoOD5fskSaKnp4e2tjZ5gYu2tjYsFsuIDpZWqyUwMJCQkBBCQkIIDQ11aNn4iUilUhEVFUVUVBTLli2js7NT7lhVVlbS0tJCS0sL+/btw8/Pj/T0dNLT05k0aZJHd0gFQRDcgehMOSA5OVnpIngsR7If6kxZrVYxjO4As9nMkSNHqKmpAQaviZo1a9aw6XyizivD1XNXqVTy9VhDmyCe38Ea+tNiscidhCHe3t6EhobKHSxXWEI/KiqKRx55hKioKKf9zICAAObMmcOcOXMwm82UlpZSXFxMSUkJ3d3d8mbB3t7eTJkyhfT0dJKSkibcflauXt8nKpG7MkTuynBG7hOrZR4nF1syVxh/jmRvMBjQ6/XyRfSeev2BI3p6eti9ezddXV2o1WqmT59OamrqiGlFos4rwx1zv1gHq7u7Wx6tam1tpbOzk76+Pnl6GwxOzQ0KCpJHrkJCQvD29nbqNLeoqCi+//3vO7UzdS6DwUBmZiaZmZlYrVYqKiooKiqiuLiYvr4+8vPzyc/Px2AwkJKSQnp6OikpKRNiMRh3rO8TgchdGSJ3ZTgjd9GZcsDJkyeJjY1VuhgeyZHsh/aBqauro7m5WXSmLqKjo4OdO3fS39+Pj48PCxcuHDaF61yizitjouSuUqnkjYKHVsizWCy0t7fT2tpKS0sLra2t9Pf3y52tkpISYHD0amjz57CwMPz9/ce1c9XV1cXrr7/OI488MuqLjseaVqslJSWFlJQUrr/+eqqrqzl16hTFxcV0dXVx4sQJTpw4gVarJTk5mYyMDKZMmeK2B2kTpb67G5G7MkTuynBG7qIzJUwIYWFh1NXV0dTURHp6utLFcTmdnZ3s2LEDs9lMUFAQS5YswcvLS+liCR5Ep9MRHh4u74slSRK9vb1yx6q1tZWOjg76+vqoqqqiqqoKGBy5ObdzFRgYOKbXEZWWlvLkk0+ydu3aMV3N72qp1WoSEhJISEhg7dq11NbWUlRURFFREW1tbfI1VxqNRr7eccqUKWL/N0EQBCcTS6M7cCayu7sbPz8/J5RMOJ+j2R8+fJgNGzaQlJTE3Xff7YSSuY/+/n62bNlCb28vwcHBLFu27LJThESdV4an526xWGhra6O5uZnm5mZaW1vl/c6G6HQ6QkND5c5VcHDwVV13NV5Lo48XSZJoamri1KlTnDp1iubmZvkxrVYrd6xSU1NdvmPl6fVdKSJ3ZYjcleFo7mJp9HFWUlJCTk6O0sXwSI5mPzSVqLq6GqvVOuEu1L5SdrudAwcO0Nvbi5+fH0uXLnXoWgtR55Xh6bnrdDoiIiKIiIgABvdqam9vp7m5maamJlpaWrBYLNTX11NfXw8MLkEeGhpKeHg4ERERBAcHT+gV8FQqlZzR8uXLaWpq4uTJk5w8eZKWlhaKi4spLi6WpwwOdaxc8RorT6/vShG5K0Pkrgxn5C6OOB1w7mpUgnM5mn1oaCi+vr709PRQXV1NUlLSOJfMPZw5c4bGxka0Wi2LFi1y+Ey1qPPKELkPN9RRCg0NJT09HbvdTmdnpzxy1dzcTH9/P42NjTQ2NnL8+PFh0wkjIiIICAiY0Ps2DX3WZcuWDetYtba2ytMCdTrdsI6Vq6x4Kuq7MkTuyhC5K8MZuYvOlAM8dW8UV+Bo9iqViilTppCbm8vJkydFZwowmUwcP34cgBkzZgxb+vxyRJ1Xhsj90oZW/wsKCiI1NRVJkujq6qKpqYnGxkaampoYGBigtrZW3ojaaDTKHauIiAh8fX2HvafBYCAmJsblp8RdzvkjVo2NjXLHqq2tTZ4WqNfrSUtLY+rUqSQlJSm6NL2o78oQuStD5K4MZ+QurplyYF6k3W6f0NNGXNlosq+oqOD111/Hy8uL7373u4rvX6O0vLw8SkpKCAkJYeXKlaM6Oy/qvDJE7lfHbrfT0dEhj1S1tLSMuObKx8eHiIgIIiMjiYiIwGAwTOjcJUmioaFB7li1t7fLj3l7e5OZmcnUqVOJi4tz+gjeRM7dlYnclSFyV4ajuV/NNVPif9UBW7ZsUboIHms02U+aNAlfX19MJhOnT58ex1K5PrPZTFlZGQBTp04d9UGSqPPKELlfHbVaTXBwMOnp6Sxbtoybb76ZFStWkJWVRVhYGGq1mt7eXsrLy9m/fz///Oc/2bp1K6+//jotLS3Y7XalP8KYU6lUREVFsXLlSv7zP/+Tb3zjG8ydOxcfHx/6+vo4cuQIr776Ks8++yxbt26lsbERZ51jFfVdGSJ3ZYjcleGM3MU0P2HCUKvVZGdns3v3bg4fPkxGRobSRVJMdXU1NpuNwMBA+WJ+QfA0Go1GvqYoKysLi8VCS0sLDQ0NNDQ00NnZSW5uLk8//TRNTU2kpKQQGRlJVFQUkZGRE277AJVKRWxsLLGxsaxZs4aKigqOHz9OUVERnZ2d7N27l7179xIeHs7UqVPJysoiKChI6WILgiC4NNGZckBCQoLSRfBYo81+1qxZ7N27l8rKShoaGoiMjByfgrm4oetFEhISrmjqjqjzyhC5jy+dTkdUVBRRUVEA9Pb2snXrVvr6+tBoNAwMDFBdXU11dTUAQUFBREZGEhkZSWho6ISaOqxWq0lOTiY5OZmvfOUrnDlzhuPHj1NSUkJTUxPbtm1j27ZtxMbGMn36dLKyssa8cynquzJE7soQuSvDGbmLzpQDRjt3Uhg7o83e39+f9PR0Tp48yb59+7j11lvHqWSuy2azyavXXGlnUtR5ZYjcncvHx4e4uDgAli1bxqRJk+RRq7a2Ntrb22lvb6eoqAitVktkZCQxMTFERUVhNBoVLv3Y0el0ZGRkkJGRQX9/P0VFRRw/fpyKigpqamqoqalh48aNpKamMn36dFJSUsakYynquzJE7soQuSvDGbmLzpQDjh07Jp/JFJzrSrJftGgRJ0+e5MSJEyxevJjw8PBxKp1r6u7uxmq1otfrR7WC37lEnVeGyF05arVa3gh46tSp8pLr9fX1NDQ00N/fL3csVCoVwcHBxMTEEB0dPaGWXzcajcycOZOZM2fS3d3NiRMnKCwspKGhQV5q3dvbm6ysLKZPn050dPQVf3ZR35UhcleGyF0ZzshddKaECScqKor09HSKiorYuXMn69atU7pITtXT0wOAn5/fhDnAEwRnMxqNTJo0iUmTJiFJEu3t7dTV1VFbW0t7ezutra20trZy7NgxfHx8iI6OJjo6mvDw8AkzHdDPz4/58+czf/58GhsbKSws5NixY/T09HD48GEOHz5MaGgo06dPZ9q0aVd88kYQBMGdiaXRHRj+6+joIDAwcPwLJoxwpdk3Njbyl7/8BUmSuP/+++WpPJ6grKyMI0eOEBMTw+LFi6/oPUSdH1+SJGG32+WbxWKnt9dOR0cXvr5+2O0gSWC3S4AKvV6F0ajCy0uNWq1CrVajVqtRqVRiqd2r1NPTw/79+1mwYMGIPagupq+vj/r6empra2lsbMRms8mPDU0HHOpcTaTpgDC4zHB5eTmFhYUUFxdjsViAwcUtEhISmD59OhkZGej1+su+l2hnlCFyV4bIXRmO5n41S6OLkSkHVFZWMmPGDKWL4ZGuNPuIiAhmzJhBfn4+X375JQ888IDHjNIM7atzNWfHRZ2/Mjabjb4+M+XlFs6csXH2rERzMzQ3q2hpUdPSoqatTUNfnxqTSY3JpKG/X8fAwFCHyOeyP0Ons6PTSej1dgwGOz4+dvz9bfj52fHzk/D3H7wFBUmEh6sID4fISDWRkWqio7X4+WnRarUe8/twOb6+voSHhzvckYLB/ZmGFm+wWq00NTVRV1dHXV0dfX19w6YDhoaGyivo+fhc/v/X1anVaiZPnszkyZMxm82cOnWKY8eOUVFRId82bNhAZmYmM2fOvOT+VaKdUYbIXRkid2U4I3fRmXJAY2Oj0kXwWFeT/TXXXMOpU6eoq6sjPz+f7OzsMSyZ6xoaqbiaPXNEnb80SZLo7jZRUGDm6FEbBQVqysq0nD2rp77eiNU6fjuuWyxqLBbo67uyzrK3t43gYDMREVaiomxER9uJjZWYNElNfLyKxEQtUVF6dDrP6HDV1NTw9NNP8/zzzxMbGzvq12u1WnkUamg64NCoVVtbG83NzTQ3N5Ofn09QUJDcsfL393f7fA0Gg3x9VUdHB8ePH6egoIDW1lby8/PJz88nJCSEmTNnMn36dPz8/Ia9XrQzyhC5K0Pkrgxn5C46Uw5wZLqCMD6uJntfX1+WLVvGpk2b2LJlC6mpqaM6++yuhjIbGBi46vcQBtlsNurre9m6dYAdO1Tk5hooLfXGbL5wp0mrtRMXZyUmxkZ4uERYGEREQESEmvBwNQEBanx9Vfj5Df7p4wPe3rBnzy6WL1+KSgUqFQzN4LNaob8fzObBm8kk0d8v0dtrp7198NbRIdHRIdHZCZ2dEm1tKlpa+L8RMQ2trRosFjV9fRr6+jTU1Fz88xqNNmJj+5k0yUJCgo3JkyElRcWUKVqSk/UYjTq37wgMaWpq4uOPP+bJJ5+8os7UuYYWpggODiYzM5Pe3l5qa2upqamhublZXh3w+PHj+Pn5yR2r4OBgt88zMDCQxYsXs2jRIs6ePUt+fj4nT56ktbWVrVu3sn37diZPnszMmTNJTU1Fo9GIdkYhIndliNyV4YzcxTVTYqnKCc1ms/Hyyy9TX19Peno669atc/uDlstpbm5m27Zt+Pj4cMMNNyhdHLdltVo5fryLDz+08eWXRo4d88VmG153fHxsZGZamDFDYupUFWlpOlJTNcTEgKutQSBJ0NUFDQ12amqsVFUNTkM8e1aipkZFXZ2aujotra2XPsem1dqJjh5g0iQLkyfbyMiQmDpVzfTpOsLDjW53DVdeXh45OTnk5uaO6+h1f38/dXV11NTU0NDQMGzk2Nvbm5iYGGJjYwkLC3O7DC9maBpgfn6+vHcXDC5JP23aNGbOnOlxq60KguCarqZvIDpTDgS2adMm1qxZ44SSCecbi+wbGhp46aWXsNvt3HbbbWRlZY1R6VyT2Wzm448/BuDWW29Fp9ON+j08tc5LkkRTUw+vv27irbe8OXZs+EjmpEkDLFliY8UKDQsW6Jg8WcVYHve6Qu5mM1RU2CgutlJSYuPMGYnychWVlRrOntVhsVz8A0dEmJk8eYApU6xkZMDUqWpmzNATFmZ02ZMYzupMnctiscgrA9bV1cnXOcLgKoKxsbHEx8cTGho6YTpWLS0tFBQUUFBQIK84WlpaytKlS8nOziYrKwuDwaBwKT2DK7QznkjkrgxHc3fpBSheeOEFfvvb31JfX09mZibPPvvsRVcY+8c//sGLL75IQUEBZrOZzMxMfvaznw0L4bXXXuO+++4b8VqTyTThVk0SxkZkZCSLFy9m165dfPHFF8TFxU3oJXwNBgM+Pj709vbS2tp6xRv3ehJJkigt7eA3v7Hy7rtB9PQMXtuhVkvMmTPALbfA176mJyFh4k/TMBggLU1DWtrIoTWbDc6etVNcbKW42MbJk3aKitScOaOlqUlHY6OBxkYD+/YNf110tJnMTDPTptnIyVExd66ehAT3G8UaKzqdTl523Waz0djYKC9a0d/fT2lpKaWlpXh5eREXF0dcXByhoaEu2yF1RGhoKCtXrmTFihWUlpaSn59PeXk5tbW11NbWsmnTJqZNm0ZOTo7Yi0cQBLcyrp2p9957j8cee4wXXniBhQsX8te//pW1a9dy6tQp4uPjRzx/9+7drFq1il/96lcEBgby97//nRtuuIFDhw4xc+ZM+Xn+/v6cPn162GvHsyPlSctqu5qxyn7JkiWcOXOGuro6/vGPf3DPPfdM6AO5sLAwent7aWxsvKLOlCfV+cbGbn76UxNvvBGCyTTYgYiPt3DffXYefFBPdLTzzpa7eu4aDSQkqElI0HPttcMfa2uTKCy0UFho5cQJiaIiNSUlWlpadNTVGairM7Bly7+eHxo6QHq6mWnTrGRnq5gzR0tamhdarXPnR4aGhvL1r3+d0NBQp/7cIRqNRl7AIicnh6amJs6ePUtNTQ0mk4mSkhJKSkrw9vaWO1YhISFu27FSq9WkpqaSmppKcnIyAwMD5Obm0traytGjRzl69KichRitGh+u3s5MVCJ3ZTgj93Gd5jd37lyys7N58cUX5fvS09P56le/yjPPPOPQe2RmZnL77bfzP//zP8DgyNRjjz1GR0fHFZdrtEN5TU1NYl63QsYy+7a2Nv7yl78wMDDA8uXLWbp06Zi8ryuqrKzk4MGD+Pv7s3bt2lEfeHlCnbdaraxf38x//3cw9fWDB2xTpw7ws59p+OpXNWM6fc9REzH3lhaJo0ctHDpkoaAAjh3TUlGhR5JG1kl/fyvTp5uYNcvK/PkqFi82EBEx/lMEXTH3oRGr6upqamtr5f2cYPCao7i4OOLj4wkKCnLbjtVQ7pIkUVVVxdGjRykqKpL37dLr9UydOpWcnByio6MVLu3E4Yr13ROI3JXhaO5XM81v3A4Xhs42rV69etj9q1evZv/+/Q69h91up7u7m+Dg4GH39/T0MGnSJGJjY7n++uvJz88fs3JfyHi/v3BxY5l9cHAw119/PQA7d+6krKxszN7b1cTExKDRaOjq6qK9vX3Ur5/odb6rq49vfrON++6Lor7eQHS0lffes1FYqOeWW5TpSMHEzD00VMW11+r56U99+PhjH8rKDHR2wo4dA/zmN33cfnsv6en9aLV2urq07Nnjxx/+EMS6dYFERxuZPLmf227r4De/aWffvi7MZuvlf+go9PX18f7779PX1zem73u1hkas5s2bx1e/+lUWLVrEpEmT0Gq19Pb2UlxczObNm9mwYQMnTpygu7tb6SKP2lB9H9rw97bbbuOJJ55g9erVhISEyMcRL730Ei+99BK5ubmYzWaFS+3+JmI74w5E7spwRu7jNs2vpaUFm81GRETEsPsjIiJoaGhw6D1+97vf0dvby7p16+T70tLSeO2115g6dSpdXV0899xzLFy4kMLCQlJSUi74PmazeVgD3NXVdQWfSJgIpk2bRmVlJXl5eXz44Yc8+OCDBAUFKV2sMafT6YiOjubs2bOUl5ePOCHhyZqaurjtNjt79gyeqXrggQF+/3s9HrBqvsvw81OxbJmeZcv+dQ2a2Qy5uRZ27x7g0CE4elRHTY2e8nIvysu9+Oijwed5e9vIyupm7lwry5erWbbMSFDQlU8FKy4u5pFHHmHBggUuuxedRqORl1G3Wq3U19dz9uxZ6urq6O7u5sSJE5w4cYKQkBASEhKIi4tz22uIfXx8WLBgAfPnz6eqqorc3Fx5v8C6ujr52qo5c+aIs/yCILiEcV+A4vzpB5IkOTQl4Z133uFnP/sZn3zyybAGc968ecybN0/+98KFC8nOzuaPf/wjzz///AXf65lnnuGpp54acf/WrVvx8fFhxYoVHD58mJ6eHoKCgsjMzGTv3r3AYOctMjKSTZs2AbB06VIKCgrkYcDs7Gx27twJQEpKClqtlqKiIgAWLVrEqVOnaGtrw8fHh3nz5rFt2zYAkpKS8Pb25sSJEwDMnz+f0tJSmpubMRqNLFmyhM2bNwMwadIkAgMDKSwsBGDOnDlUV1fT0NCATqdjxYoVbN68GUmSiI2NJTw8nLy8PABycnJoaGigtrYWtVrNqlWr2LZtG1arlaioKGJjYzly5AgAM2bMoK2tTV7Cds2aNezcuROz2Ux4eDhJSUkcPHgQgKlTp9LT00NFRQUAK1euZP/+/fT19RESEkJaWhr7/u8q9IyMDAYGBigtLQVg+fLlHD16lO7ubgIDA5k2bRq7d+8GYMqUKQDyNXFZWVkcOnSIjo4O/Pz8mDVrFjt27ABg8uTJ6PV6Tp06JdeF4uJiWltb8fb2ZsGCBWzduhWAxMREfH19OX78OFqtluDgYE6ePMn//M//cOONN7Jq1Sr5/zg+Pp7g4GAKCgoAmD17NjU1NdTX16PVarnmmmvYsmULdrudmJgYIiMjyc3NBSA7O5umpiZqampQqVSsXr2a7du3Y7FYiIyMJD4+nsOHDwMwffp0Ojo6qKqqAgZHbXfv3k1/fz9hYWFMnjyZAwcOyDn09fVRXl4ODG5IfPDgQXp7ewkODiYjI0Ous+np6VitVurq6igrK0OlUmEymejr6yMgIIAZM2awa9cuAFJTU1Gr1RQXF8t19uTJk5hMJvbt28ecOXPYvn07AMnJyRiNRk6ePAnAggULKCkpoaWlBW9vbxYuXMiW/7soJiEhAX9/f44dOwYMTvmtrKyksbERvV7P8uXL5byHLq4fOns0a9Ys+cBJo9GwcuVKtm7dis1mk68tOXr0KAAzZ86kpaWFs2fPynV2x44dDAwMEBERQUJCAocOHQIGO9I1NU088EAYhYXRGI12vv/9YhYsOMvp06GkpqbKo+aZmZn09/fLo5eXayPsdjslJSVX3UbMmDFDzsUT24ju7lJmzoQnnhhsI6qq+qmqiqSmJoZdu8ycORNEX5+Ow4f9OHwY/vjHwaXaJ09uIz29idmzTdx1VzInTuxHpVI51EYM/Z9XVVURGRnJ8ePHgcHvmvLycpqamjAYDPKedUq3Efv27ZPbiBUrVrBx40ba2towGo20tbVx+PBhVCoVCxYsoL29HYPBQFhY2AXbiDNnzgCwbNky8vLy5GkujrQR7e3t+Pr6XlUbER8fL2d6oTbi9OnT+Pr6ct1119HQ0MCGDRvo7Oyko6ODL774Am9vb9LS0viP//gPduzYMSZtRFdXF5WVlQCsWrWKffv20dfXR2ioa7QRY3EcYTKZaG9vd8s24kqOI5YsWcKxY8fG9DjiStoIPz8/Nm3a5FbHEUq3EWNxHDG05+bljiOGyn8lxu2aqYGBAby9vfnggw+4+eab5fsfffRRCgoK5P+EC3nvvfe47777+OCDD/jKV75y2Z/1wAMPUFNTw5dffnnBxy80MhUXF+fwvMjjx48zderUyz5PGHvjlX1XVxcvvfQSPT09E3b/KUmS2LhxI52dnUydOpXMzEyHXzsR6/zAwAB3393Je++F4eVl5/PPYcUK11qEZCLmPpZsNigosLBrl4U9eyQOHNDT2Dh86X+1WiItrY958wZYskTFNdfoiYnxGvH7feYMdHdDUVERd955B+vXv0V6ejp+fnCRSQ4uzWQycfbsWaqqqmhtbZXv12q1xMbGkpCQQHh4uEstvDPa+i5JEpWVlRw5coTi4mJ5ry5fX19ycnLIyckRe0c6QLQzyhC5K8PR3F3ymim9Xk9OTo7cwxyyZcsWFixYcNHXvfPOO9x77728/fbbDnWkJEmioKDgkkupGgwG/P39h91Go66ublTPF8bOeGXv7+/PunXr0Gg0FBUVjainE4FKpSI9PR0YPEM3dHbGEROtzkuSxCuvNPHee2EAvPOO5HIdKZh4uY81jQZycnQ88YQ3H3/sQ329jpISG3/+cx9f+1ovcXED2O0qTp3y4dVXg7j33kDi473IyurjW99q5+OPO+nqGuDMGUhNhZwcuPPOdCCPO+9MJydn8P7/OxnrVry8vEhNTWXVqlVcd911ZGZm4uvri9VqpbKykp07d/LZZ5+Rn59/VQs4jaXR1neVSkViYiLr1q3jscceY+nSpfj6+tLT08OuXbt49tlnee+996ioqMADt9B0mGhnlCFyV4Yzch/XaX5PPPEEd911F7NmzWL+/Pm89NJLVFdX89BDDwHwox/9iNraWt544w1gsCN1991389xzzzFv3jz52iovLy95X6CnnnqKefPmkZKSQldXF88//zwFBQX8+c9/HrfPodE4d6le4V/GM/v4+Hi++tWv8tFHH7F//34CAwOZM2fOuP08JcTHx3Pq1Cm6uro4ffq0w2fFJlqdb23t4plnBpe+fuwxKzfdNO4znK/IRMt9vKlUkJKiISXFm4cfHryvutrO1q1mduyws3+/lvJyA6dO+XDqlA9/+QsYDDamTOkFfFi/Hv7vfAMARUVw552DI1buzN/fn6lTp5KVlUVrayuVlZVUV1djMpk4ffo0p0+fJigoiKSkJOLj4xVbfvxq6ru/vz/Lly9nyZIlFBcXc/jwYaqqqigqKqKoqIjQ0FBmz57N9OnT3fb6sfEi2hlliNyV4Yzcx3VpdBjctPc3v/kN9fX1ZGVl8Yc//IElS5YAcO+998pnzGBwTuaFpv/dc889vPbaawA8/vjj/OMf/6ChoYGAgABmzpzJz372M+bPn+9wma5mKE+YePbs2cO2bdtQqVTcfvvtpKWlKV2kMVVTU8PevXvRaDSsXbsWXw9caeG3v63j+9+PJiTERkWFBj8/pUskOEttrZ0vvjCzebOd3bv1NDf/a1pgbi6cu+ZEXt7gaNX5908ENpuNhoYGKisrqa2tlafIqdVqYmNjSUxMJCIiwqWmAY5WU1MTR44cobCwUB6J1+v1zJgxg7lz5xISEqJwCQVBcFVX0zcY986UKxptYFu3bmXlypVOKJlwPmdkL0kSn332GXl5eWi1Wu644w4SExPH9Wc6kyRJ7Ny5k8bGRqKjo1m8ePFlrw+bSHV+cF+xPvbvD+THP7byi1+45qgUTKzcXZEkwbFjVv72Nwt//rPXRTtTt93WxX33SaxY4YPR6Lr15UqZzWaqqqqoqKgYtnWCt7c3CQkJJCYm4ueEMw7jVd/NZjOFhYUcOXKE5uZm+f7U1FTmzp1LUlLShLtGdjREO6MMkbsyHM3dJa+ZmkiGNhAUnM8Z2atUKq6//nrS0tKwWq2888471NbWjvvPdRaVSkV2djZqtZq6ujp5papLmUh1vqWli8OHBxvGO+5w7QPjiZS7K1KpYPp0Lf/xH16XfN6HH/rzla8EEBEB11/fyZ//3M7Zs30T5jocg8FAamoqa9asYfXq1aSmpqLX6+nr6+PUqVN88cUXbNu2jfLy8mGbBY+18arvBoOBOXPm8PDDD3PXXXeRmpoKQElJCW+++SYvvPACR48eHdfP5spEO6MMkbsynJG7ax9ZuAix87pynJW9Wq3mtttu46233qKiooL169dz3333TZh9TAICAsjKyuLYsWPk5+cTERGBt7f3RZ8/ker8yZMWrFY1fn520tJc+/zRRMrdHfzf6tMj/r1kiYmCAh1dXVq++CKAL76A//xPialT+1i5coCbbtIyf743Wq37XwMRHBxMcHAw06dPp7a2loqKChoaGmhubqa5uZm8vDzi4+OZPHnymO9XN971XaVSkZycTHJyMq2trRw+fJj8/Hyam5v5/PPP2bZtGzk5OcyePVu+LtsTiHZGGSJ3ZTgjdzHNz4GhvNbWVjHXWiHOzt5sNvPmm29SU1ODj48P99xzz4TpUNntdrZt20ZrayuRkZEsXbr0olNdJlKdf/nleh54IIqpUy0cO6a7/AsUNJFyd2VDq/ldTEkJJCbCrl1mPvnEyubNWk6fHr5IQ1SUmTVrTNx8M6xZ44vBMHHOTfb19VFVVUV5eTnd56zGERQURHJyMpMmTUKnu/rfJSXqe39/P/n5+Rw+fFie4qhWq0lPT2fu3LnExcVN+CmAop1RhshdGY7mLqb5jbOhjb0E53N29gaDgTvuuIOoqCh6e3t5/fXXaWpqcmoZxotarWbu3LloNBoaGhouuUHdRKrzVuvgn3q96583mki5u7KUlMEOU24urF9fBGSzfn0RubmD96ekgFYL11xj4PnnfSguNlBRYeP3v+9j5co+jEY79fUGXnstkJtuCiQqSuJrX+tg/foOuroc34LAVXl7e5Oens51113HihUrSEhIQKPR0N7eztGjR/nkk084cuQIbW1tV/VzlKjvRqOR+fPn88gjj/D1r3+dxMRE7HY7J0+e5NVXX+WVV17h1KlT8gIdE5FoZ5QhcleGM3KfOKfSBGGMeHl5cffdd/PGG29QX1/P66+/PmFGqPz9/cnOzubIkSMcO3aMoKAgIiMjlS7WuBpa9bmvb2KfbRZG518b85qAfNLTTZdcwS8hQcPjj3vz+OPQ2yvx2Wf9fPSRjc2bDbS36/jww0A+/BC8vW0sXtzJjTfaufVWLyIi3HdZbpVKRXh4OOHh4cycOZPKykrKysro6uqirKyMsrIygoODSU5OJj4+fkxGq5xFrVaTlpZGWloajY2NHDx4kOPHj1NTU8P7779PUFAQ8+fPZ8aMGej1eqWLKwiCCxPT/BwYymtqapoQB9LuSMnsTSaT3KHy9vbmzjvvnBBzniVJ4siRI5SXl2MwGFi9ejU+Pj7DnjOR6vyePY0sWRKBl5ed3l41rjyDZyLl7i46Ojr49NNPufHGGwkMDBz16y0W2LLFzAcfWPnySz2Njf/qUGi1dubP7+XWW618/evu3bEaIkkSzc3NlJaWUlNTI4/g6HQ6Jk2aRHJyMkFBQQ69l6vV956eHo4cOcLhw4cxmUzA4Mm12bNnM2fOnAmzrYSr5e4pRO7KcDR3sTT6KI02sFOnTpGRkeGEkgnnUzp7k8nEm2++SV1dHQaDgX/7t38jISFBsfKMFavVyrZt22hvbyc4OJjly5cPO6usdO5jqaGhjfj4QCwWNUVF4MrbiE2k3N3JWOVut8OBAxbee2+Azz/XUlHxr+us9Ho7ixb1cNttNm6/3ZvgYGU2yh1L/f398mjVuddWhYaGkpKSQmxs7CU3zHTV+j4wMEBhYSEHDhyQpzJqtVqmTZvG/PnzCQsLU7iEV8dVc5/oRO7KcDR3cc3UODt79qzSRfBYSmfv5eXFPffcQ2JiImazmfXr13P69GlFyzQWtFotixYtwmAw0NbWxoEDB4ZdI6B07mMpJMSPGTMGD/S++MKqcGkubSLl7i4aGhr47W9/S0NDw1W/l1oNCxfqeP55H8rLDRw7ZuV73+slKcnMwICa7dv9efjhIGJitKxd28mrr3bQ2em+11gZjUbS0tK47rrrWL58OfHx8ajValpaWjhw4ACff/45J0+elEd5zueq9V2v1zN79my+853vcPvttxMbG4vVaiUvL48///nPvP3221RWVrrtUvmumvtEJ3JXhjNyF50pQbiMoUUphvaheu+99ygsLFS6WFfNx8eHxYsXo9FoqKurIzc3120PDi5Fp9OxbNngwdy7707ci8qFK1NXV8drr71GXV3dmL/31KlafvMbH0pLDRw9auHRR3uIixugv1/Dxo0B3H9/IDExGm68sZO33uqgr8899z1SqVRERESwYMECbrjhBrKysjAajZhMJo4fP85nn33GwYMHaWlpUbqoozK0yt83vvEN/uM//oO0tDRUKhUlJSW89tprvPLKKxQXF0/IdlMQBMeJaX6jHMoTPJfdbufTTz+loKAAgGuvvZZ58+YpW6gxUFNTw759+5AkialTp5KZmal0kcbc8eMtzJwZgs2morBQYto0F75wSnCqvLw8cnJyyM3NJftSK1CMEUmCgwctvPnmAB9/rKeh4V/Ta4OCLNxwQw/33qthyRJfNBr3Pd9ps9moqamhpKSE1tZW+f6QkBAmT55MfHz8JacAuqrW1lYOHDhAQUEB1v9bKjQ8PJyFCxeSlZXllp9JEAQxzW/c7dixQ+kieCxXyl6tVnPTTTfJHaiNGzeyefNmtz8rGRsbKx9EHj9+nDNnzrhU7mMhIyOIpUs7APjf/3XdaVUTLXdhJJUK5s/X8cILPtTW6tixY4D77+8lONhKe7uON94IYsUKf1JTzfzwh+2cPt3rlm2MRqNh0qRJrFq1itWrV5OQkIBaraa1tZVDhw7x2Wef8cYbb9DX16d0UUclJCSE66+/nscee0yeKt3U1MTHH3/MH//4R44cOYLF4tojjKKdUYbIXRnOyF10phwwMOC6B18Tnatlr1KpWLNmDStWrABg//79vP/++y7/5Xk5KSkp8ohUbm4utbW1CpdobGk0Gh57bLAuvfeenpMnXfPg1NXq+4VIkoTdbsdms2GxWBgYGLjozWKxYLVasdls2O12t+wUjCe1GpYt0/Pyyz40Nmr56KN+brihD4PBTnm5F7/+dRDp6d7Mn9/Dc8+109pqVrrIVyQ4OJh58+Zx4403Mm3aNLy9venv7+fs2bN8/vnnHDx4UN5A1134+vqycuVKHn/8ca655hp8fHzo6Ojgiy++4Nlnn2Xv3r309/crXcwLcod2ZiISuSvDGbmLaX4ODOUVFBQwY8aM8S+YMIIrZ3/8+HH++c9/YrPZiImJ4d/+7d/ceulcSZIoLCykuLiYhoYGbrjhBpKTk5Uu1pixWq2sWtXDzp2BrFxpZvNmg8stk+5K9d1ut8sdIavVKnegJEm6qk6RWq1GpVKhVqtH3DQajfx3ZykvL+eb3/wmf/3rX0lKSnLaz72Ujg6JN9808eabKo4c8ZLvNxptrF7dw333wfXX+6LVuueUMrvdTm1tLdu2bcNo/Ndy8REREaSlpREZGYnK1X45L8NisZCfn8/+/fvp6OgABq+3nTNnDvPmzRux/YSSXKmd8SQid2U4mrtYGn2URhtYR0fHFe0/Ilw9V8++urqad999l76+PgICArjjjjvceh8JSZLIz8/n2LFj8v4qrnKAORb27GlmxYoQrFY1b71l5d//3bX2LVe6vlutVnlUaajjdCEqlWrE7VxDrxvqeI22A3Zu5+r823gcZCud+6WUltp45ZV+3n5bR3X1vzaPjYvr59//vY9vftNIYqK3giW8ch0dHdhsNk6fPj1sz6qAgACmTJnCpEmT3O4aJJvNxokTJ9i7dy/Nzc3A4Oqps2bNYuHChfj5+SlcQteu7xOZyF0ZjuYuOlOjNNrANm3axJo1a5xQMuF87pB9W1sbb731Fq2trRgMBr72ta8xefJkpYt1xSRJ4uWXX5a/9LOzs0lNTVW4VGNDkiQeeaSZP/85nJAQK6dOaQgPd50z4ErUd0mSGBgYoL+/f8R0VY1Gg1arHdaZGRpZGm2n5txOld1uv+BtaDrgxahUKrkcWq1WLtvVjGQNDAzw/vvvs27dOvR6/eVfoBBJgj17BnjpJQsff2ykr2+wk6HR2Fm+vIf777dzyy1+6PXu0/k4t7739vZSUlJCWVmZvLCD0WgkJSWFyZMnYzC4175ckiRx+vRp9uzZI0+b1mq15OTksHDhQkUXv3KH79WJSOSuDEdzv5rOlGudlhUENxQcHMw3vvEN3nvvPSorK3n77bdZtWoV8+bNc7upKjB4wBoXF0d4eDglJSXk5eUxMDBAZmamW36ec6lUKn7+c1++/NJEebkXX/+6ma1bDThxVplLsVqt9PT0yAevKpUKvV6PXq9Hp9ON6XS7cztglxptkCRJ7lTZbDZ5muHQSJnVasVqtWI2/+v6oXM7V0M3R+vqiRMnuOuuu8jIyHDKan5XSqWCJUv0LFmip7tb4rXX+njlFTWFhUa2bvVn61aIjDRz++1dPPSQnilTvN3q99XHx4eZM2eSmZlJWVkZZ86coa+vj+PHj1NUVERCQgJTpkxxiZEdR6hUKtLS0pgyZQrl5eXs2rWL6upqDh06xNGjR8nOzmbRokUEBAQoXVRBEK6SGJlyoPdZX19PVFSUE0omnM+dsrfZbHz++efk5+cDMHXqVG688UZ0Ot1lXul66uvriYyM5NSpUxw/fhyA1NRUZs6c6VYHaBezc2cL114bhNms4ac/NfOzn7nGWW9n1nez2UxPTw+SJKFWqzEajRiNRqderzQa5y58ce61XDabbcRzVSqV3KnS6XRotdqLfi5nL40+1vLyLLzwwgAffGCgq2vw/KhaLbFwYQ8PPmjl9tv90elcc7TqUvXdZrNx9uxZTp8+LS9OMXSiJyMjw+2mS0mSRGVlJTt37qSqqgoYPAkwc+ZMFi1a5NTP407fqxOJyF0ZjuYulkYfZ11dXUoXwWO5U/YajYYbb7yR6667DrVazfHjx3n55ZfdbpUqGMxdpVKRmZlJTk4OACUlJRw8ePCCB6/uZunSEP7nfwY3EH36aT3/+IdrrMborPo+MDAgd6T0ej2BgYF4e3u7bEcK/jXFT6/X4+3tjZ+fH0FBQQQHB+Pv74+Pjw8GgwG1Wo0kSVgsFkwmE11dXbS1tdHe3k5PTw9ms/mSUwndTXa2jpdf9qG+XsPf/mZi7lwTdruKPXv8uOuuIBITrTz5ZBu1tSalizrCpeq7RqMhISGB1atXs3z5cqKiopAkierqajZu3Mju3bvdahNglUpFYmIi9913H/feey+JiYnYbDaOHj3K888/z6effuq07wp3+l6dSETuynBG7q77zelCKisrlS6Cx3K37FUqFXPmzOGee+7Bx8eHxsZGXnrpJUpLS5Uu2qicm3tKSgrz5s1DrVZTVVXFzp07h02xckcqlYrvfS+EW29tQ5JU3Hmnmtxc5TuJzqjvdrtd7kgZjUb8/PxcuhN1OWq1Gr1ej5eXl9zBCgoKws/PD6PRiFY7OFpjs9no7++nu7ubtrY2Ojo66O3tdfttDYZ4e6v4xje8OHjQi5MnrXzzm734+VmprTXwy18Gk5ysZ926Dvbs6XKZJeodqe8qlYqIiAiWLl3KmjVriI+PR6VSUVdXx9atW9m+fTsNDQ0u85kckZCQwD333MN9991HUlISdrudvLw8/vjHP/Lpp5/S2dk5rj/f3b5XJwqRuzKckbv7foMKggubNGkS3/zmN4mNjcVkMvHWW2+xZ88et/rCP1dCQgJLlixBp9PR3NzM1q1b6e7uVrpYV0Wn0/Laaz7MnduFyaThuuvslJVNnBGLixkamdFqtfj4+EyIaZvnGhrBMhgM+Pr6EhgYKI9eeXl5yZ0rq9WKyWSip6cHgO7ubkwmE1ar1W1/T4dkZGj5y18GR6uef76PKVPMmM0aPvggkCVL/MnJ6eUvf2mnr8+9OpJBQUEsWLCA6667jqSkJNRqNU1NTezcuZMtW7ZQU1PjVv93kyZN4u677+Y//uM/SE5OljtVzz//PBs2bHD7NlYQPIW4ZsqBeZF2u92tz9y6M3fP3mq18uWXX5KbmwtAWloaN910E15eXpd5pbIulntHRwd79uyht7cXvV7PokWL3HopeICamh6WLtVQXu5FbKyFAwe0xMYq08FwRn3v7OzEYrHg6+s7bI8fT2K327FYLFgsFsxmMyaTCb1eL2c/NJ1Qr9ePajELVyVJsH27mWeftfHll17YbIOfJzR0gDvv7OWxxwxMmuT85dWvtr739vZy+vRpysvL5UVUAgICSEtLIz4+3u2WVT979izbt2+noqICAJ1Ox5w5c1i4cCHe3mP3/+Pu36vuSuSuDEdzF9dMjbN9+/YpXQSP5e7Za7VabrjhBm644QY0Gg3FxcX89a9/paamRumiXdLFcg8MDGTlypWEhIQwMDDAzp07KS8vd3LpxlZsrC9ffGEhNrafmhody5ZZaGhQ5hyTM+r70DVvQyM0nkitVssjV8HBwVRWVuLn54der0elUmGz2TCZTHR2dtLe3k53d7dbX2ulUsE11xj47DNvysvt/Nd/9RISYqGlRc+zzwYxZYqRf//3DvLze5w6snO19d3Hx4fs7Gyuv/56MjMz0ev1dHZ2cujQIb788ksqKirc6v8sLi6Oe+65h3vuuYfY2FgsFgv79u3jueeeY8eOHfT394/Jz3H371V3JXJXhjNyF50pB/T19SldBI81UbLPycnh/vvvJzg4mI6ODl599VX27dvnslNSLpW7l5cXy5cvJy4uDrvdzuHDhzl69KhbL0yRlubP55+bCQ8foKxMz4IFFiornX8QNlHquzs5c+YMDz/8MGfPnsXf35/g4GD5eiu1Wo3dbsdsNtPd3U17eztdXV1u3bGKj9fw//6fD7W1Wl5+2URWVj9ms5p33gkkJ8eHVau6+fLLTqd8vrGq70ajkalTp3L99dczffp0jEYjPT09btupSkxM5P777+ff//3fiYyMxGw2s2vXLp577jn27t3LwMDAVb2/aGeUIXJXhjNyF50pB4SGhipdBI81kbKPjo7mm9/8JllZWdjtdrZs2cLbb7/tkg3s5XLXarUsWLCAqVOnolKpKC0tZceOHZhMrrdimKOmTw/giy/6iIoyU1GhZ8ECG0VFzu0gOqO+D019GpoW5el6eno4fvy4fO2USqWSR62CgoIICAjA29sbjUYjb3A81LEaGrFy1ZMil2IwqLj/fi+OHTOyadMAK1b0IUkqtm3z57rrApg508Qrr7QzMDB+vwNjXd/1ej3p6el85StfYfr06RgMBrq7u+VOVVVVldt0qlQqFampqXzzm99k3bp1hIWFYTKZ2Lp1K8899xyHDh264hNYE+l71Z2I3JXhjNzFNVMOzIvs7u52m40CJ5qJmL0kSeTl5fHll19itVrx8/PjtttuY9KkSUoXTTaa3Ovq6jh48CADAwMYjUYWLlxIWFjYOJdw/Jw82cV11+mprjYSEmJl82bIznbOlDhn1Pe+vj76+vrQ6/Wjnhc+ETm6z9TQZsIDAwOYzeZhB7JDmx0bDAZ0Op3bXmNVWGjh178e4MMPvbBYBs+1xsb289BDJr7zHR8CAvRj+vPGu75bLBZKS0spKiqSR3MCAgLIyMiQVwV0F3a7nRMnTrBjxw55CfWgoCCuueaaUW+oPhG/V92ByF0ZjuYurpkaZ/v371e6CB5rImavUqnIycnhgQceIDQ0lO7ubl577TV27tzpMmdNR5N7dHQ0q1atIiAggP7+fnbs2MHp06fd8mw9QGamPzt2WElN7aO1VcvSpSo+//zqptU4yhn13WAY3KDYYrG49dRMZxvaCNjb25vAwEB5b66hESuz2UxXVxft7e309va65cjf9Ok63n7bh8pKePzxXvz9rdTUGHnyySCSk+G//7uN1tax2xZhvOu7TqcjPT2dG264galTp8rXVB04cICNGzdSXV3tNu2UWq1m2rRpfOc73+H666/H19eX9vZ2PvzwQ/72t7+N6trVifi96g5E7spwRu6iMyUIComIiODBBx9k+vTpSJLEzp07efXVV2ltbVW6aKPm5+fHypUriY+Px263k5+fz969e912P6qkJF+2b5fIzu6mp0fDV7+q4w9/GJuLv5U2tFKdJEn09va6zcGkK7lQx8rLy0u+xspkMtHR0UFHRwcmk8llTpI4Kjpaze9/70NtrYZf/7qPqCgLra16nnkmmKQkDf/1X+00NLjP74NOpyMzM5Prr79+WKdq//79bNq0ibq6Orf5PdBoNMyaNYv//M//ZMWKFRgMBurq6njjjTdYv349DQ0NShdREDyOmObnwFBeTU0NsbGxTiiZcD5Pyf748eN88cUX9Pf3o9PpuPbaa8nOzlZsGsqV5i5JEqWlpRQUFGCz2fD29mb+/PluO+2vq8vMXXf18emnQQA88ICJF17wYrwWwnNWfbfZbHR0dCBJkkcvkQ7Q0tLC3//+d+67776rnls/dE2V2WzGYrHIB+hD12EZDAa3XGrdbJZ45ZV+fv1rDdXVg1P9fHys3H13Nz/4wZUvq65U+z4wMEBJSQmnT5+WN20ODw9n2rRpbnddS29vL7t375YXAVKpVEydOpUVK1YQGBh4wdd4yveqqxG5K8PR3K9mmp/oTDkQWGlpKZMnT3ZCyYTzeVL2nZ2d/POf/5T3GElNTeXGG2/E19fX6WW52tzb29vZv38/3d3dqNVqsrKySE9Pd7uDSACr1cYPf9jO7343eJC1ZEk/H35oICxs7D+LM+u7yWSit7cXlUpFQECARy+VPh65D60CaDabh03502q1GI1GDAaD2/0+2Gzwxhv9PPOMijNnBqeLGo027r67i5/8xEhs7Oj2z1O6fTebzRQVFXHmzBl5ymtsbCzTpk1zu+sJ29ra2LFjB8ePHwcGR7DmzJnDkiVLRuxrqHTunkrkrgxHcxfXTI2zsrIypYvgsTwp+4CAAO6++27WrFmDRqOhpKSEF198kdOnTzu9LFebe1BQEKtXr2bSpEnY7XaOHTvGzp076e3tHaMSOo9Wq+G3vw3hxRebMRhs7N5tZPp0K/v3j/01Mc6s70ajUZ7u19XV5bHXT7W0tPCnP/2JlpaWMX1ftVqNl5cXAQEBBAQEyJ0nq9VKT0+PfG2VO+Wu0cB99xkpLjbwzjv9ZGX109+v4aWXgkhL0/PII22jmv6ndPtuMBiYMWMG1113HUlJSahUKmpqati4cSNHjhxxyZVWLyY4OJhbb72VBx98kKSkJGw2GwcOHOD5558fsfKf0rl7KpG7MpyRu+hMCYILUalUzJ8/nwcffJCIiAh6e3t55513+PTTT93u+iOdTse8efOYPXs2Wq2WxsZGNm3aRFVVldJFGzWVSsVDD4WxeXM3cXH91NfrWLpUze9+14+7ju2rVCp8fX3RarXY7Xa6urrc7tqesVBdXc1zzz1HdXX1uLy/SqVCp9Ph5+dHUFAQPj4+aDSaYddWdXV1DZsW6OrUavj6140cO2bkgw/MpKf309ur4U9/CiY1Vct3vzu2C1WMNx8fH+bMmcOaNWuIiYnBbrdTVlbGhg0bKCwsvOp9nZwpOjqau+66izvvvJPw8HBMJhNffvklL774IiUlJW5TxwTBnYhpfg4M5VksFnQ6nRNKJpzPk7O3Wq1s376dAwcOIEkSAQEB3HDDDU6ZJjDWuXd3d3Pw4EF5cY34+HhycnLkleXcSUODiTvvHGDbtgAAbrrJxBtvGPH3v/opW0rUd5vNJo9MaTQa/P395b2oPIGjS6OPJUmSsFgs9Pf3DztQ12q1eHl5odfr3WoKoN0O77zTz89+pqK0dPB3OjDQwsMPd/ODH/ji73/hJdVdtX1vbm7m2LFjNDc3A4P7V2VmZjJ58mS3+t2w2+3k5eWxY8cOeVZAcnIyK1asICYmRuHSeR5Xre8TnaO5i2l+4+zw4cNKF8FjeXL2Wq2W1atXc8899xAUFERnZyfr16/nk08+GffNccc6dz8/P6655hqysrJQq9VUV1ezadMmt1x5KjLSi40bffnv/25Bo7HzySdeZGVZ2LXLctXvrUR9P7cDNdSxcsdlvd3J0L5U/v7+BAUFYTQa5SmA3d3ddHR00N/f7zajCGo13HHH4PS/l1/uJz5+gI4OHb/6VTBTpkg8/3wbFsvI6Yyu2r6HhYWxYsUKFi9eTEBAAAMDA+Tn57Nx40Zqamrc6P9FzaxZs3jkkUdYuHAhGo2GsrIy/ud//ofPP//cLadduzNXre8TnTNyF50pB/T09ChdBI8lsoeEhAS+9a1vMW/ePFQqFfn5+bzwwgvjei3VeOQ+tBDFihUr8PPzo6+vj507d3L06FF5RS13odVq+MUvQvjnPzuJijJz9qyeFSu0fP/7Jq7moyhV38/vUHV2drrV1CZ3ptFo8PX1JSgoCG9vb9RqNTabjZ6eHnlpdXc5eNdo4P77jZSV6fnTn0xERFhoaDDw6KPBTJ1q5v3324dNJXXl9l2lUhETE8OaNWuYPXs2RqOR7u5u9u7dy86dO+no6FC6iA4zGo2sWrWKb3/722RkZGA2mzl69CjPP/88+/btEydPnMSV6/tE5ozcRWfKAUFBQUoXwWOJ7Afp9XquvfZa7rvvPkJCQuju7uadd97ho48+GpeLpMcz99DQUFavXi1PVywtLWXjxo3U19eP288cDyqViuuvDyIvz87113dgt6v47W+9mDOnn+LiK1tUQMn6rtFoCAgIQKfTIUkS3d3d9PX1uc2B/JXy9fUlOztbkVUzz6VWq/H29pavqxrqVPX29tLe3u5WnSqtFr79bS/KyrT85Cd9+PraOH3am9tvD2LRol527epEkiS3aN/VajXJycl85StfISMjA41GI1//eeTIkXGfJTCWgoODWbduHbfddhtRUVGYzWa2bNnCiy++SGlpqdLFm/Dcob5PRM7IXVwz5cC8yN7eXnx8fJxQMuF8IvuRLBYLO3fuZP/+/UiShI+Pj/xFP1aclXtjYyNHjhyRzxwlJSUxY8YM9PoLX2Phqux2Oy++2MaPfhRId7cWLy8bTz89wBNPeKEexSkrV6jvQ5v59vcPrsqm1+vx9fVFPZoP4mZcIffzSZKE2WzGZDLJK7FpNBq8vLzcbln1piY7Tz5p4u9/98JqVaNSSdx8cydPP20lM9P99nUqLCyUFyzR6XRkZGSQmprqNtdT9fb24u3tTWFhIVu3bpXb37S0NK699tqL7k8lXB1XbGc8gaO5u/Q1Uy+88AKJiYkYjUZycnLYs2fPJZ+/a9cucnJyMBqNJCUl8Ze//GXEcz766CMyMjIwGAxkZGTw8ccfj1fxAdi7d++4vr9wcSL7kXQ6HatWreIb3/gGYWFh9Pb28v777/POO+/Q2dk5Jj/DWblHRESwZs0aUlNTUalUlJeX8+WXX1JbW+uUnz9W1Go13/52KAcPmpg1qweTScP3vufF3Ln9nDrl+BQaV6jvKpUKHx8ffH19UalUDAwM0NHRMWGn/dntdrZv3+5yKxmqVCqMRiOBgYH4+vrKUzB7enrkaZjuci40PFzNSy/5cOqUxI039iFJKv7xj0Bmz/bnxz9uo7fXfab5+vj4sGDBAq655hpCQkKwWCwUFha61TWge/fuRaVSMWPGDB555BHmz5+PWq2muLiYP/3pT+zatUtM/RsHrtC+eyJn5D6unan33nuPxx57jB//+Mfk5+ezePFi1q5de9ElaCsqKrjuuutYvHgx+fn5/Pd//zf/+Z//yUcffSQ/58CBA9x+++3cddddFBYWctddd7Fu3ToOHTo0nh9FEFxOTEwM3/zmN1myZAlqtZrTp0/z5z//mQMHDrjcgeGl6HQ6srOz5WupTCYTe/bsYd++fW41hQYgI8OP/fu9eOqpVry9bRw9amTmTDU/+5kJdzo2GTqQH9rMd2jp9J6eHreqW44oKCjgxhtvpKCgQOmiXNC5naqh6X9Wq5Wuri63WywkJUXDJ594s2PHAFlZ/ZhMen71q2AyM2387ncdHD1qJy+PYbczZ5Qu9YWFhYWxcuVK5s6di9FopKuri507d7Jv3z63WtjBYDCwZs0aHnroIRISErBarezYsYMXXniBkpISpYsnCG5hXKf5zZ07l+zsbF588UX5vvT0dL761a/yzDPPjHj+D37wAz799FOKiork+x566CEKCws5cOAAALfffjtdXV18+eWX8nOuvfZagoKCeOeddxwq12iH8qqqqpg0aZJD7y2MLZG9Y5qamvj888/lExWRkZHccMMNV7z8rVK5W61WTpw4QUlJCXa7HZ1Ox7Rp00hOTna7aWYnTnTz4INw4IAfAFlZ/bz6qobZsy++RKsr1vfzp/1pNBp8fHzcbirmxSixNPrVsNvt9Pf3y9dQqVQqDAaDvHiFu7Db4Ve/qucPfwijrU17yeeWlEBKipMKdgUGBgY4ceIEZ86cQZIktFotGRkZTJkyxSWn/l2snZEkiZMnT7Jp0ya6u7sBmDJlinyMJVwdV2zfPYGjubvkNL+BgQFyc3NZvXr1sPtXr17N/v37L/iaAwcOjHj+mjVrhq32dbHnXOw9x8JEOxPrTkT2jgkPD+e+++7jxhtvxMvLi4aGBl5++WU2bNggHwSPhlK5a7VaZsyYwapVq+QpNLm5uWzbto329nZFynSlsrL82L3bm//3/1rx87Ny4oSRefO0PPhgH52dFz6H5Yr1fWhz34CAgGHLp0/EUSp3MLRQRWBgIAaDAUmS6O/vd7uV/waXU++nrEzDrbcOjkCvXw+5uf+6rV8/+Nz/O653WXq9nuzsbNasWUNYWBhWq5Vjx46xceNGl5z6d7HfW5VKRVZWFt/5zndYuHDhsBkPu3fvlq/dE66MaC+V4YzcL3066Cq0tLRgs9mIiIgYdn9ERMRFG5eGhoYLPt9qtdLS0kJUVNRFn3OpBstsNmM2/2s39q6uLmBwese5KzgFBQWRmJhIf38/p06dku/fv38/CxYskM9anj59esQwfkJCAsHBwTQ3N3P27Nlhj/n5+ZGSkoLNZqOwsHBE+aZOnYpOp6OsrGzENS8xMTFERETQ3t5ORUXFsMe8vLxIT08HID8/f8SXaHp6Ol5eXlRVVcmbpQ6JiIggJiaG7u5uzpw3j0Kn0zF16lQAjh8/PmLZ6pSUFPz8/KitraWxsXHYYyEhIUyaNAmTyTRshBEGG+qZM2cCUFRUNGIKV2JiIkFBQTQ2NsrXzAxlHxAQQHJyMhaLhePHj4/IcPr06Wg0Gs6cOSOfURsSFxdHWFgYbW1tVFZWDnvMx8eHKVOmAINnqM+XkZGB0WikoqJixMF8VFQUUVFRdHV1jVgJyWAwkJmZCcCxY8dGTMVJTU3F19eXmpoampqahj0WGhpKfHw8fX19FBcXD3tMrVYzY8YMAE6dOjWio5SUlMR3vvMd3n33XQ4fPswnn3zCli1bWLhwITNnziQ5OVk+i3q+GTNmoFarKSkpYfPmzSxYsEB+LD4+ntDQUFpaWkZM0/X19SU1NRW73X7BqVJZWVno9XrKy8tHLCccHR1NZGQkHR0dlJeXD3ssKiqKhIQEjh07Rm5uLrm5ucTHxzN58mS0Wi1paWl4e3tTXV1NS0vLsNeGh4cTGxtLT0/PiKkqWq2WadOmAXDy5MlhbQPA5MmT8ff3p76+fsQKgxdrI4ZcqI1YvhwmTzbx3HMx7NiRwN/+1ssHH5zg0Udt3HCDAZXqX21EUVHRBTuNrtJGSJKEyWTCbreTkZHBwMAAZWVlqNXqYQsiOLONGHKlbcS5ZXC3NkKj0VBeXo7JZJJfr9frmT9/Plqt9qJtRGBgIA0NDdTV1Q17LDAwkKSkJIfaiPOXG76SNmKoff/e96by0UeQng4XGxy8UBthNBrlhXcKCgpGHDQp0UYkJiaSnJzM4cOHOXbsGMeOHSM6OprU1FQMBoNLHEcM5Q4XbyNCQkJYuHAhFRUV1NTU8I9//IMNGzawePFioqKiAPc5jhii9HHE7t273eY4wlXaiCFXehxhNBo5e/YsiYmJl20jzv9/HRVpnNTW1kqAtH///mH3/+IXv5CmTJlywdekpKRIv/rVr4bdt3fvXgmQ6uvrJUmSJJ1OJ7399tvDnrN+/XrJYDBctCw//elPJeCyt+XLl0uHDh2SCgsLL/j4xo0bJZPJJGVlZY147Hvf+55UVlYmPf300yMey87Olvbs2SO1trZe8H3fffddqbOzU1qyZMmIxx544AGpqKhIeumll0Y8lpycLG3btk3O5fzH//KXv0jNzc3SLbfcMuKxdevWSYWFhdInn3wy4rHQ0FBp48aNkiRJUmho6IjHf/3rX0u1tbXSgw8+OOKxNWvWSEeOHJEOHz484jGdTidt3LhRMpvNUmpq6ojH//u//1uqqKiQfvzjH494bO7cudK+ffukmpqaC2b40UcfSd3d3dK8efNGPPbwww9Lp0+flp5//vkRj6WlpUk7duyQJEm64Pu++uqrUmtrq3TdddeNeOyOO+6Qjh8/Lr333nsjHouKipI2bdokSZIkBQQEjHj897//vVRfXy/dc889Ix67/vrrpdzcXGnXrl0jHvP29pY2btwoWSwWKSEhYcTjP/3pT6Wqqirpu9/97ojHUlNTpY0bN0pnzpy54Gf99NNPpZ6eHik7O3vEY48++qh05swZ6Te/+c2Ix6ZOnSrt3r1b6uvru+D7vvnmm1J7e7u0cuXKEY/de++90smTJ6XXX399xGPx8fHSli1bpL6+PsloNI54/I9//KPU2Ngoff3rXx/x2M033yzl5+dLmzZtGvFYQECAtHHjRslms0kxMTEjHv/FL34hnT17VnrkkUdGPHa1bcSf/1wiBQT8fsRjQ23Ehx9+eMH3dcU2Yt++fVJzc7MUHh4+4nF3ayMA6R//+MeEaSMOHjwodXV1jbqNWLRokXTgwAGntxE///kXEkhSbu7w7+7cXEkCSXrppZJLthGSJEne3t4jHleyjcjNzb3gZ/3yyy/d7jhi165dFyyvOI4YXRuxYMGCEY+523GEUm3E1RxHvPTSS6NqIzo7Oy/an7iYcbtmamBgAG9vbz744ANuvvlm+f5HH32UgoICdu3aNeI1S5YsYebMmTz33HPyfR9//DHr1q2jr68PnU5HfHw8jz/+OI8//rj8nD/84Q88++yzVFVVXbAsFxqZiouLY9euXQ6NTJnNZpc5o3QuTxiZGspe6TNK7jQyde4ZJavVSn5+PgUFBej1ekJDQ5k1axbBwcHodMOv3Tn3jFJraysGg0F+TKkzSueedW5sbBxWb6ZNm8bChQvp6upy6ZGpIUNtREVFLU8+eZoPPvDHYlGj0dhZt07Pn/88Fb2+74KbMbtiG5GVlSVv/jmUoU6nw8vLi7S0NLc562yxWOSVzXp6ety6jRi6niotLQ0Y3MNNrVaj1f5rEoornXUeat8tlqnMm6cjN3f4yFReHuTkgL+/hZ///CwLFrQNuy7MFUemzm8j2tvbKSoqkuvcvHnzmDVrFnV1dYodRwzlDo63EadPn2bDhg1yffPy8uKGG25gxYoV9PT0uPRxxBCljyOKi4tH7AvpDscR53LHkamkpCSMRqNDI1NLly69omumxn0BipycHF544QX5voyMDG666aaLLkDx2WefDTtI+da3vkVBQcGwBSi6u7vZsGGD/Jy1a9cSGBg4bgtQHDx4kHnz5jn03sLYEtmPjdbWVjZu3Ch/4fn7+7N69WoyMzMvuF+Nq+Zus9koLi6mqKgIq9WKSqVi8uTJZGVlDev8uYNjx7p55BGJ3bsH26CgICv33lvOb36TinbcJmCPPbvdTl9fH2az2W0XRHDV+n4lJEliYGCA3t5e7HY7KpUKb29vjEajy+1NNZT7UKdp/frBqX5Diorgzjv/9e+vfKWDF1/UExfn7fzCXgWbzUZJSQknT57EarWi0WjIzMwkLS1Nkd+Rq6nvVVVVfPbZZ3LnNCUlha985StibyoHTKR2xp04mrtLLkAB8MQTT/Dyyy/z6quvUlRUxOOPP051dTUPPfQQAD/60Y+4++675ec/9NBDVFVV8cQTT1BUVMSrr77KK6+8wne/+135OY8++iibN2/m17/+NcXFxfz6179m69atPPbYY+P2OcZq7x5h9ET2YyMkJIR///d/59/+7d8ICgqiq6uLDz/8kNdff33EWUFw3dyHDkLWrl1LfHw8kiRx5swZNmzYQFlZmVtd4Dttmh87dviyfn0biYkm2tu1/OEPqWRkmPnsM/fZ00mtVssLVOj1enlBhPb2dvmA3pWVl5fzve99b8QZTXc11JkNDAyU/z96e3vp7u52uf+LoXbGb3DBS+68c7BTNXQb6kjdc48JtVriiy8CmTlTy2uvtbrNQhsw2G6lp6dz7bXXEhUVhc1m49ixY2zdunXEWXZnuJr2fdKkSTz00EMsX75cHsVxxy05lOCq36sTnTNyH9fO1O23386zzz7L008/zYwZM9i9ezcbNmyQlyisr68fNtSXmJjIhg0b2LlzJzNmzODnP/85zz//PLfeeqv8nAULFvDuu+/y97//nWnTpvHaa6/x3nvvMXfu3HH7HKPtoQpjR2Q/dlQqFVOmTOHb3/42K1asQKfTUVlZyV/+8he+/PLLYUP9rp770MaZy5cvJyAgALPZzJEjR9i8ebNLrp51MWq1mjvuCObkSR1PP92Kn5+ZM2cM3HijnhUrTBQWus9mplqtFn9/fwICAtDpdPJiFa7eqero6GDv3r2KHNSOJ7VajZ+fHz4+PvLmy52dnS61L9VQO5OSMrj8+bkr+Q3dSkrgtde82LPHRlLSAK2teu67L4SvfrWThgb32ofO19eXJUuWMHfuXPR6PW1tbWzevJmTJ086daW8q23ftVotS5cu5Vvf+hYJCQlYLBY2bdrEq6++OmI6pfAvrv69OlE5I/dxnebnqkY7lHfu/GLBuUT246ezs5NNmzbJ02q9vb255pprmDlzJhaLxW1yt9lslJaWcvLkSQYGBkd0YmJimD59utt9eVVVdfKzn9l5880AbDY1arXEbbeZ+N//NZCY6Hr71VyMJElYLBb6+vrkg/ehzWe9vLxcavqfu+0zdSWsVivd3d3YbDZUKhV+fn4usU/YaNt3k0niBz8w8ec/e2G3q4iONvPyy31ce22gy01hvByTycSRI0fka1KCgoKYO3euU6bLjeX3qiRJ5Ofns2nTJsxmM1qtlhUrVjBv3jyX+j13BeJ4RhmO5u6y0/wmip07dypdBI8lsh8/AQEBrFu3jrvvvpuwsDD6+vr47LPP+Otf/8rbb7+tdPEcptFomDJlCl/5yldITU1FrVZTW1vLxo0bycvLG3HxuCsrLj7I3/8exOHDvaxc2YndruL9972ZMkXFQw/10tTkmqM751OpVOj1egICAvD390er1Q4bqerp6RF71jiRVqsdNmLY3d19RfvPjbXRtu9eXiqef96bHTusxMQMUFdn4IYbAvne99oYGHCdETdHeHl5sXjxYubNm4der6e9vZ3Nmzdz6tSpcR/FHcvvVZVKRXZ2Ng8//DCTJ0/GarWyefNmXn31VZqbm8fs50wE4nhGGc7IXXSmBMHDJSUl8dBDD3Httdfi5eVFY2Mj27Zt46233nKrL8OhFTevvfZaYmJisNvtlJSU8MUXX3D69Gm3OnjPzvZj82Z/Nm3qZNasHiwWNX/9qw9JSRI/+lEf3d3uMaHg/E7V0MH80DVVXV1dWCwWt7r+xV2p1Wr8/f0xGo1IkkRPT8+IldDcxZIlOk6c0HHjjSZsNhW/+10IS5aYKCnpufyLXYhKpSIhIYG1a9fKbdaxY8fYsWPHiJX+XF1AQAB33HEHN910EwaDgZqaGv7617+yb98+l53iKwhjRUzzc2Aor7y8nKSkJCeUTDifyN65TCYTu3fvZuPGjQQEBKBWq5k1axZLly7Fx8dH6eKNSkNDAwUFBfK1MD4+PmRlZTFp0iSXnX5yofpus9n54INOnnrKQHHx4CpmwcFWHn10gP/6Ly98fNxnepMkSVitVkwmkzwlEwaXUTYajej1eqdP12poaOB3v/sd//Vf/0VkZKRTf7YSJEmir69P7kj5+Pjg5eWlSFmutn2XJPjLX/p54gk9/f1qgoIsvPxyNzffHOR20/4kSaKysvL/s3fe8VGV2f9/T8lkMum9V5KQkNB7lY4FqaJUxVURV10s+FPRXd11Lbuu37XtKnZ3AStYQKWD9BZaKAmBNNL7JJPJZOrvj+xcE5LABJKZlPt+ve4rmXufuXPuJ0+eueee85yHlJQUjEYjTk5ODB48mKioqHb/rI7+Xq2urmbjxo1C9djQ0FBmzZqFv79/h31mV0C8n3EMtup+I2l+ojNlg2C5ublERETYwTKRKxG1dwynTp3i/PnzwvoUSqWScePGMWzYsCZr1nR2zGYzWVlZnD17Vljfw9PTk379+hESEtLpbriu1t8NBhMffaTm1VdVXL6sBBqcqscf1/P4413LqYKGuW51dXVCSXVoiJ4olUqcnZ2Ryew3R6wnjjNarVb4n3Bzc0OpVNrdhvbS/dw5E3fcYeT8eWdkMgvPPFPOiy96I5d3nXmGVmpqajh8+LBQyCEyMpLBgwe36xw3e/R3i8XCqVOn2Lx5MzqdDrlcztSpUxk6dGinG3ftRU8cZzoDtuouzpnqYK5cNE7EfojaO4aioiLmz5/P0qVLCQ4ORqfTsXXrVv71r39x9uzZLpOWJZVK6dWrF7feeiv9+/dHoVCgVqvZu3cvO3bsaLbQoaO5Wn93cpLx0EM+pKXJeP31CkJD66mokPPHP6qIjDTy8staamu7xt8FGua6ubm54e3tLaxJZV2zqqqqiurqavR6fYf3taqqKj755JNuV83vWqhUKiEiVVtb22xRVXvQXuN7nz4yjh5VcMcdDWl/L7/sx8yZNVRUOH5eWFtxd3dn4sSJ9O3bF6lUSk5ODlu2bGnXKnn2+F61LoTdeC7Vzz//zNq1a5st5tpTEO9nHIM9dBedKRERkVaJiopi2bJlzJo1C3d3dyorK/nmm2/46KOPyMrKcrR5NiOXy0lMTOS2224jMTERuVxOWVkZO3fu5Ndff+1SN9IqlRMrV/pw4YJUcKrKy514/nkVUVENTpVG03WcKqlUikqlwtvbG3d3d2FelV6vp7q6mqqqKrRabYfNecvMzOTPf/5zt1lnqi2oVCqcnZ2FohRdeW6Lq6uEr7924W9/0yGTWfj5Zy9GjTJz5ky1o01rM1KplKSkJCZOnIi7uzu1tbXs3LmTtLS0LvMgy4qHhweLFi3i1ltvRS6Xc/HiRf79738LWQ8iIt0BMc3PhlBebW1tx88XeeghyM/v2M/oghhNJuR2TPcRaaAl3c1mMxWVlVRUVGD5302Xq6srfv7+KLtYuVeTyURNTQ21Wm3DxAuJBBcXFzzc3R2axng9/d1oMpOdbSLzkgxdfcPzMSe5hcgoM716SXFWdL2UGrPFgtlkwmyxCDePEkAilSL939ZeV1WlVrNnzx7GjRuHl6dnO52162ABjAYDZosFmVRq1/7fUeN7aamZYykSDAYJCoWZwYNN+Ps5tfvn2AOz2UxVVZUwx02pVOLl7Y3sBuZ9Oup7tb6+nsLCQqHCqqeXFwH+/p12Dmt7I97PtEJoKLz3Xoed3tZ7eHHOVBtpq2BHjx5l6NChdrBM5EpE7R3D1XSvra1lz549HDt2TIgW9O3bl4kTJ+Lt7W1PM2+YmpoaUlNThcXDpVIpUVFR9OnTBzc3N7vbcyP9vbbWwL//XcM77/w2p0qlMnH33TpWrVISHt71vsStESqdTtckDU0qlaJQKHB2dkYul9/QHIyesM7UtTAajajVaiwWCx4eHnZbg6ojx/fsbDPTphm4cMEZlcrEBx9UsnChb5ecr2OxWLh06RInTpzAZDLh6urKyJEj8fPzu67zOfJ71Wg0smvXLg4cOIDFYsHX15c5c+YQGhrqEHvsiXg/4xhs1V2cM9XBVFRUONqEHouovWO4mu6urq7ccsstPPzww/Tt2xeA1NRU3n33XX755ZcuVdLX3d2dUaNGMXXqVIKCgjCbzWRmZvLzzz9z5MgRu1/LjfR3V1cnnnrKh4wMOe+9V0l8vBatVsb777vSq5eExYtrSUvrWmvxSCQSnJ2d8fT0FOZWyWQyzGYzOp0OtVpNVVUVtbW1GI3GLpcC1VmQy+VCAQp7lkvvyPE9KkrKoUMKRo3SodXKuOceX/7+97IumcookUiIjY1l0qRJTdL+MjIyrqvPO/J7VS6XM2XKFO6++248PDwoLy/n448/Fpyr7ox4P+MY7KG76EzZQFcrCd2dELV3DLbo7uPjw9y5c3nwwQfp1asXJpOJw4cP89Zbb7F79+4utViuj48P48ePZ9KkSU2cqp9++omjR4/azalqj/7u7Cxn+XJvzp51Zu3aSgYNalinau1aV/r0kXHzzVq2b6+nq923yGQyVCoVXl5eeHp6olQqkUqlQlXAqqoqYX6V0Wi706hUKomKinJINbvOhFKpRCKRYDAY7LYmW0eP797eEnbtUnLHHTpMJgnPPOPPqlXlXdKhgoZxaurUqURERGA2m0lJSeHo0aNt/nt1hu/V6OhoHnroIZKSkjCbzWzdupUvv/yyy659ZgudQfeeiD10F9P8bAjlGY3GLlUOujshau8Yrkf3zMxMtm/fTkFBAdAwuX3MmDEMHToUJ6euNV+htLSUM2fOUFxcDDSklUVHR9OnT58OHZg7or+bzWZ+/rmav/9dxt697sL+vn11rFhhYckSJYouOK8KGtKfDAYD9fX1zSr/yWQyFAoFCoXimqmA4jjTgFqtxmAw2K1Uur10t1jg8cd1vPVWwzU99lgp//iHj13L77cnFouF9PR0Tp06JaTKjR49GpVKZdP7O1N/t1gspKSksHnzZoxGI56ensybN4+wsDBHm9budCbdexK26i6m+XUwO3bscLQJPRZRe8dwPbrHxMTwwAMPMG/ePHx9fdFqtWzdupW33nqLw4cPtyla4Gj8/f2ZMGECkyZNIjAwELPZzKVLl/jpp584duxYh0WqOqK/S6VSpk/34tdf3Th8uIY776zC2dlMaqqS++93ISLCyAsv1FJR0fWeq0kkEhQKBe7u7vj4+ODu7i4s/GuNWKnVaiorK9FoNK2WWhfHmQasNxz2ikzZS3eJBN58U8lzzzWUSn/zTX8ee6zCbtfZ3kgkEhISErjppptQKBSUl5ezdetWm8und6b+LpFIGDJkCPfffz8+Pj6o1Wo++eSTbpn215l070nYQ3fRmRIREWk3JBIJSUlJPPzww8yaNQsvLy80Gg2//PILb7/9dpOiFV0Bq1M1ceJEwam6ePEiP/30E0eOHKGmpsbRJtqMRCJh2DB3vvrKi/R0HY8/XomPj4HiYif+8hdXwsLM/O53XW9elRXr/CoPDw+hzLqzs7OwfpVOp6O6upqKigqqq6vR6XSYTCZOnjzJnDlzOHnypKMvweFYo3fd7SbWyl//quQvf2lIP373XX+ef77rpvwBBAUFMWXKFLy8vNDpdOzcuZPs7GxHm3VdBAUF8eCDD5KcnNxj0v5Eug9imp8NobyMjAzi4uLsYJnIlYjaO4b20t1kMnHixAn27NlDdXXDei9eXl6MHz+efv36dbmSuCUlJZw9e1ZI/5NIJISFhZGYmIiPj88Nn9/e/V2jMfDhhxr+/W8lFy82LOAqkVgYP76ORx6RMnOmki6aCSVgTQXU6/Utzgc6c+YMEyZM4NChQwwdOrTL9cn2pKamhvr6elQqlc0pYzeCo8b3P/2pnpdealjO4dVXS3j6af8uWeXPisFg4PDhw+Tl5QEN1VX79OnT6jV15u/VltL+5s+fT3BwsKNNu2E6s+7dGVt1F0ujt5G2Cpafn98jynZ2RkTtHUN76240GklJSWHv3r1oNBoAfH19GT9+PElJSV3uBrasrIzz58+T32htuKCgIPr06YO///XfmDmqv5tMZr7/vpq33pKyd+9vY2JoqJ777jPw+9+7EBjYtf5GLWGxWDCZTIJjZTQaOXnyJJMnT2b79u0MGDAAuVyOk5MTTk5ON1x2vSthXc/IbDbj6elpl3mOjurvFgv84Q863n1XiVRq4dNPS7n77gC729GeWCwWTp06JSyGGxMTw+DBg1ucF9YVvlcLCwv55ptvqKioQC6XM2PGDPr16+dos26IrqB7d8RW3cU5Ux3MmTNnHG1Cj0XU3jG0t+5yuZzhw4ezYsUKpkyZgkqlory8nPXr1/Pee++RmprapdJt/Pz8GDt2LDfffDNRUVFIpVKKiorYuXMnO3bsID8//7pSpRzV32UyKXPnevHrr+4cP67hd7+rxMPDSH6+gr/8xZXwcJg7t5Zff9V3uSqAjZFIJMjlclQqlVBu3bqemEwmE6JYWq0WtVpNRUUFarWa2tpa9Hp9l+qjbcFisaDRaDCbzcjlcrtNkndUf5dI4O23lcyfr8NslvD73/uwf3/XLlstkUgYMGAAgwcPRiKRkJmZyZ49e9Dr9c3adoXv1eDgYJYtW0ZcXBxGo5ENGzawdevWLv0/2BV0747YQ3fRmRIREbEbTk5OjB49mhUrVjBx4kSUSiWlpaWsX7+ef/3rX5w8ebJLfVl6eXkxYsQIbr31VmJjY5HJZJSVlbF37142b95MdnZ2l7oeiUTCwIFufPyxN7m5Zv7v/ypJSqrFYJCyYYMr48crSErS8fbbWjSaLuxV/Q+pVCpEYKxzrdzc3HB2dm7iXNXV1VFdXU1lZSVVVVVoNBp0Ol23WNvKZDJRXl6OXq9HIpHg5ubWI6JxEgl89pmSYcPqqa2VM3++iry8rjMHsjXi4uIYO3Yscrmc4uJidu3ahU6nc7RZ14VSqWTBggWMGzcOgAMHDrBmzRq0Wq2DLRMRaYqY5mdDKK+6urrNIT+R9kHU3jHYS3edTseRI0c4ePCgMNHY29ubsWPH0r9//y5Xuriuro709HQuXbqEwWAAGta4iI+PJyYm5pqpU52xv5vNZnburOG99yz89JMH9fUNz+BcXU3MmqXjoYcUjBrlRFe9/9ZqtRw7dowhQ4Y0mSdksVgwm80YjUYMBkOr6y9JJBJkMpkQ0ZHL5chksk7vkFgsFurq6prcmHp4eKBQKOxmQ2fo7yUlFgYNMpKf78S0aZVs3OiOk1PXL19dUVHBnj170Ol0eHh4MH78eKF/dwbd28q5c+f4/vvv0ev1eHt7M3/+fAIDAx1tVpvoirp3B2zVXZwz1UbaKtjx48cZNGiQHSwTuRJRe8dgb93r6+s5evQoBw8eFMqOe3p6MnbsWGEeS1dCr9eTkZHBhQsXhMWLnZyc6NWrF3Fxca2uVdXZ+3t+fh3vv1/H55+ruHz5t3WI4uPruftuIw884EJAQNdLeLBVd6tz1XhrKfIokUiQSqWCkyWTyYTNkU6WxWLBaDQK63I1tt1ea0s1prP09wMHTNx0kxSjUcLLLxfz7LMBnd4ZtoXq6mp2796NVqvF1dWV8ePH4+7u3ml0byvFxcV8+eWXVFZW4uTkxJw5c0hMTHS0WTbTVXXv6tiquzhnqoMpLS11tAk9FlF7x2Bv3Z2dnRkzZgwrVqxg2rRpuLm5oVar2bRpE2+//TaHDx8WIj1dAYVCQVJSErfffjtDhgzBw8MDg8FAWloaP/30EwcPHqSiovkcjc7e30NDXXjpJR8uXXLi++/V3H67GmdnMxcuOPP8866EhsJtt2n57jsdXaUCfm5uLi+88AK5ubnXbCuVSlEoFKhUKiEt0FqG3cXFBScnJ6RSaZNCF1qtlpqaGqqqqigvLxfmYdXU1KDVatHpdOj1esExa6/nm1Yb6uvr0Wq1QpqiWq1Gp9NhNpuRyWS4urri4+Njd0cKOk9/HzVKxgsvNIwvL7/sR2pqpYMtah88PDyYNGkS7u7u1NbWsnPnTtRqdafRva0EBgaybNkyevXqhcFg4Ouvv+bgwYNdJtW2q+re1bGH7mJkygbv89dff+Wmm26yg2UiVyJq7xgcrbvBYOD48ePs379fKKnu5ubGyJEjGTJkCM7Ozg6z7XqwWCwUFhaSnp4ulFWHhnWsEhISCAkJQSKROFz366GoSMenn9axZo0z5879liYXFGRgwQI9Dz2kJC6u86ZrHj9+nMGDB5OSktJuT43NZjMmkwmj0YjJZBI2W+bPSSQSIbJl/d26WY83xmKxCDeT1tRE69YS1nlizs7OODk5OTQC05n6u9kMw4bpSUlRMHFiFVu2uHW5iHhr1NXVsXv3btRqNUqlEqVSyc033+xos64bs9nML7/8wtGjRwEYNmwYN998c6evCtuZ+ntPwlbdxTS/NtJWwSwWS7cI+XdFRO0dQ2fR3Vq6eu/evajVaqBhUvLQoUMZPny4UImtK1FRUcGFCxfIzc0Vbnjd3d2Jj48nMjLSrnNW2hOLxcL+/TV89JGJ775zp7r6txvRwYPrWLjQwpIlSvz9O9cNT0c4U61hdXKsjpX1p3Vr7Bi1B43nc8lkMpycnByeatiYzjLOWDl50syQIRJMJglr1hSzaFHXmpNzNerr69m1axdVVVW4uLgwadKkLjl+WrFYLBw6dIitW7disVjo3bs3c+fO7dTjZ2fr7z0FW3UXnak20lbBtmzZwrRp0+xgmciViNo7hs6mu8lkIjU1lf379wshe7lczsCBAxk1ahTe3t4OtrDtaLVaMjIyuHTpklC+OCcnh2nTphEbG9ulb3Q0GgPr1mn4/HM5hw65YTY3fJHJ5WYmTNCxZImEuXOVqFSOv7GwpzN1LazOVGPHqvHrK9sCzaJX1qhW48hWZ6WzjTMADz1Uz/vvO9M/Wc2RNw6jKC+H4GAYO5auvoK1Tqdj165dHD9+nP79+zNx4kS7LM7ckZw7d44NGzZgNBoJCQlhwYIFuLu7O9qsFumM/b0nYKvu4pwpERGRbo1MJmPAgAH8/ve/Z/78+YSGhmI0Gjl69CjvvPMO69evb5I+1xVQqVT079+f22+/nUGDBuHm5obRaBTmVe3du5eioqIuMx+gMW5uTixb5s2+fW6kp9fx/POVJCRoMRqlbNum4u67XQgMNDN/fi1btujpQtXjOxSrI2RdOFihUODs7IyLiwsqlarJ5urqiqurKyqVChcXF5RKJc7OzigUCuRyueBMibSNF15w5i6nb9l4JhnFtGmwcCFMmABRUbBhg6PNuyGUSiXjx4/H2dkZjUbTpcumW+nTpw9Lly5FpVJRUFDARx99JM5NErE7YmTKBu8zLS2NhIQEO1gmciWi9o6hs+tusVjIzs5m3759XLp0SdgfHx/PmDFjiIiIcKB114fZbGb//v2YTCaKioqE/e7u7sTFxREdHX3N0uqdGYvFwuHDGj7/3Mj336soKvpt3ltQkIG5c/UsWaJg2DD7llnPy8vj+eef569//SthYWH2+2CRzjnObNiAZe4dWLA0fdps7ZTffgtz5jjCsnbj5MmTXL58mdraWvz8/Bg/fnyXnx9WUVHB2rVrKS8vR6VSsWTJEoKDgx1tVhM6ZX/vAdiqu5jm10baKlhRURFBQUF2sEzkSkTtHUNX0r2wsJB9+/Zx7tw5IYoTERHBqFGjiI+P7/STkhtj1b26upqLFy+SlZUlVDGUy+VERUURFxeHp6engy29MQwGE5s3a1izxsLPP7uh0fx2IxcermfWLAOLFysYOtQ+jlVX6u/diU6nu8kEUVFY8vJosdtJJBAWBllZXTrlr6ioCJVKxfbt29Hr9URERDBy5MguH8nUarWsWbOGgoICnJ2dWbRoUad6sNbp+nsPwVbdRWeqjVxLMJPJ1KQM8759+xgzZkzHGVRXB5mZHXf+LsyJEycYOHCgo83ocXSU7k6ArIO+sKuqqjh9+jTp6elCYQcPDw/69etHfO/eOHWBJ68HDhxg1KhRwmuD0UhhQQGXL19Go9EI+318fIiIiMDf379LOYstUVtrZPv2OrZskXD8uAt6w283qUGBBsaPN3LrrU4kJck7xLHSarV89913zJ49u8vPH+lqXNnfHc6xY/Dgg9dut3o1DBnS8fZ0EFbdKyoqOHbsGBaLhaioKHr37u1o024YvV7P5s2bKSwsRC6XM3XqVMLDwx1tFtAJ+3tnIiEBOmj8tcecKdGZukIwjUZDXl5ek3kKdXV1uLi4dJxBej0UFnbc+UVEOgMWCxK1mrCXX8btzBlHWyMiIiIiIiLSGUhJgQ4qAGQPZ6rzP6q1IyaTiby8PFQqFf7+/kLI22g0dmw+sckEneTJSWfDZLF0WCRDpHU6QneLxUJpVRV5//kPcWZzh/9drYvkpqamUlNTAzSssRMXF0e/fv3w8fHp0M+/Hqqrq685iNfV1ZGXl8fly5eFCLpEIsHPz4+wsDD8/Py6fLQKoKpKz46d9ezY3jxiFRhgYNQoI5Mnyxk61IkbGZ7Pnz/PosWLWbtmDYmJie1guYit2NLf7UoPiUxdqXtGRgaZmZnIZDKGDx/eaavhtQWTycTOnTvJ/F/Wz4QJE4iPj3eoTZ2uv3cmOnAu2bBhwzrs3FbEyFSjjq3T6cjKyiIqKqpJJEqr1YrpHw5C1N4xdJTudXV1ZGdnEx0djVKpbPfzt4TZbCYtLY0DBw6Ql5cn7I+Li2PkyJFER0d3mrkCp06don///ja1tT78yczMbFLJUKVSER0dTUxMDK6urh1lql0pL6/n22+1rF8vY88eV+rrf3OsPD2NTJ5cz+zZUmbMUOLu3ra/ZWcqjd7TaEt/twsmExq/KFRV+UhpfmtkRoLWOwy30q49Z+pK3S0WC3v27KGwsBB3d3emTp3apYvdWDGbzWzcuJETJ04gkUiYPXs2/fr1c5g9na6/9xBs1V0sjd7OXHlj1Xj+lIh9EbV3DB2luyOcFqlUSp8+fbj//vu57777SExMRCKRkJGRwX/+8x9Wr17NiRMnMBqNdrftShpX8bsWMpmMyMhIJkyYwG233UZCQgLOzs5otVrOnj3Lpk2b+PXXX8nLyxPmkHVVfH2defBBb7Zu9aCgwMinn1Yxa5YaT08DarWc9etdWbzYBT8/CxMnannzTS0FBV37mnsCbenv9mD1RzLurnoLCWC5YqyySCRIgLsr32T1R13XkYLmukskEkaMGIGrqys1NTUcPXq0Sy7JcCVSqZQZM2YwZMgQLBYL3333HWccmGLe2fp7T8EeuovOlA10lqfW9ubFF19k+fLlAOzevbtJaUk3NzdKSko63Iaeqr2j6a66h4eHc9ddd/Hoo48ybNgwnJycKCoq4ocffuD//u//2LlzJ9XV1Q6z73qfBru7uzNgwABmzJjByJEjCQwMxGKxCJUON27cSGpqapMiFl0VHx9nli714rvvPCkqkvDjj2p+97tKwsJ06PVSdu1S8fjjKkJDpQwYoOOZZ2o5eNDQ6lpWcrkcT0/PLl8auivSmaIf9fXw6KNwqd8cLN98iyQ0tMlxSVgYlm++5VK/OTz6aEP7rkpLujs7OzNy5EikUim5ubnk5uY6wLL2RyKRcNtttzFo0CAsFgsbNmzg7NmzDrGlM/X3noQ9dBedKRvoDDmuUVFReHh4UFdXJ+yrrq7GxcWliZMTFRXFoUOHmrx3+fLlvPjii+1qj0ajISAgoF3P2RJt1f6RRx7h888/b7LvgQce4JFHHmnW9u233+amm24SXh87dkzIq/7222+btZ8zZw4vvPBCm+xxNJcuXWL06NGoVCoGDRrEqVOnrvmegwcP4uXlxWuvvSbs02q1PPjggwQEBBAYGMg//vGPJu/56quviIuLw83NjRkzZlBRUdHu19Ke+Pj4cOutt/LEE08wZcoUPD090Wq17NmzhzfffJNvv/2Wy5cv2/3p7MSJE2/o/S1Fq5RKJXV1dUK0aufOnU1KrndllEo5t9/uyccfe5OdreDgwRpWrqwkObkWgFOnlPztb66MGuWEv7+RuXNr+fjjOkpLf/Os+vXrR1VVlUPTf3oqN9rf2xNnZ3jnHTh9Gh7bMwdLVjbs2gXr1sGuXVgys3hszxxOn25o5+x8zVN2WlrT3c/Pj6SkJABSUlKa3G90ZSQSCbfffjsDBgzAbDazfv16Ll68aHc7OlN/70nYQ3fRmbIBtVrtaBMACAoK4scffxReb9iwodOU/Owo2qr9li1bmDp1apN9ixcv5uuvv26WxrVu3ToWLVokvN68eTPTpk1j0aJFrF27tpkdv/zyCwsXLmzjFTiWBQsWMHXqVCoqKvjd737H7Nmzr5rOZjabefzxx5vNHXn55ZfJysri4sWLHDt2jNWrV7NlyxagYQL/gw8+yBdffEFlZSWRkZE8/PDDHXpd7YWLiwujR49mxYoV3HnnnURGRmI2mzlz5gwff/wxH330EadPn8ZkMtnFnq1bt7bbuazRqttvv51Ro0YRGBiIRCKhpKSEw4cP88MPP3DkyBFKS0u7RUqPTCZlxAh3Xn/dm9OnVWRkaPnb3yqZPLkaV1cTFRVyNmxw5f77XQgMlDBggI7/9/9q2b/fwObN7ae7iO20Z39vDx58EN5/v8FZ+sPjUiw3jYcFC7DcNJ4VT8h4552G47bUqOjMXE33hIQEvL290ev1HD9+3I5WdSwSiYQZM2aQnJyM2Wzmq6++4vLly3a1obP1956CPXQXnakuxIIFC5rc5K9du/aGb+7r6up45JFHCAkJISwsjL/97W82vU8ikQh5qFFRUfztb38jNjYWf3//JlGwTZs20bt3b9zd3QkPD+eLL74AGibPv/DCC0RGRhIUFMSTTz7Z4k3+zp07GT16tPA6OjpauFGvqqrCw8NDeN+lS5dQqVTNVj0fN24cLi4ubNu2TdiXmZnJiRMnuOOOO4R91vKZixcv5pdffqGqqko4tn79epKTk+ndu7eQ8vjHP/4RLy8vevfuzblz5/jrX/+Kj48PiYmJTdIIfv/73xMSEoKXlxdTp04V0ifS09Px8/MTnpAdOnSIoKCgdkufTE9PJz09nWeffRalUskjjzyCyWTiwIEDrb7ngw8+YPjw4c2qHm3evJnHH38cDw8PwsPD+d3vfsd//vMfALZv3860adMYMmQITk5OrFq1ivXr11NbW9su12EPrPOq7r33Xh588EEGDhyIXC4nPz+fDRs28M9//pPdu3d3eJpcRzg1MpmMiIgIJkyYwPTp0+nbty/u7u4YjUYyMzPZsWMHP//8M2fPnu1Sf7OrIZFIiI1V8f/+nzfbtnlQXGzm++/VLFtWSWxsHRaLhFOnlLz+uitjxlzg1lsfYtKko7z7rpZLl+zjOIt0TH+/UawO1bvvSvjDH8BshhUr6DaOFFxdd5lMxtChQ5FKpVy+fJn8/Hw7WtaxSKVSZs+eTWxsLAaDgXXr1tlluoKVztjfewL20F10pq6CxQK1tWAwKKitpcM2W//OU6ZM4fjx41RUVFBUVERGRgbjxo27oWtcuXIlarWaCxcucOTIEf7zn/+wcePGNp9n/fr1HDx4kMOHD/Pxxx+zadMmAO6//34++eQTYVKrtaLK//3f/3HgwAFSUlJIS0vj+PHjvPfee83OO3r0aE6cOEFdXZ0wqO/btw+A/fv3M3ToUGGugzWydCUSiYQFCxawbt06Yd+6deu45ZZbhPLYarWarKwsBgwYQK9evRgwYADr169v0r5xFOvixYv4+/tTVlbG1KlTufXWW3FxcaGkpITp06fz/PPPC23HjBnD+fPnKSoqIiwsjD/84Q8A9O7dm1WrVrF06VJqa2tZunQpb7/9dovpk/v27cPLy6vVrSXOnTtH7969USgUwr5+/fq1mi9eUVHBm2++yYsvvoishUpVjQcki8XS5DxXHjMYDGRkZLT4OZ2d4OBgZs6cyeOPP87EiRNxd3dHo9Gwe/du/vnPf7Jhw4YOSwEMCwtr93M2xtXVlaSkJG699VYmTpxITEwMcrmcmpoaUlNT2bRpE7t37yYnJ6dTFORoL1xdnZg505PVq725cEFJWlotf/97Q9RKqazFYslk504Zjz6qIjZWRkSEnkWLavnsszqKisRCFh1FR/f362XcOAtg4d13YeBAS7dypODauvv4+AgL+J48edJukXl7IJPJuPPOOwkLC6Ouro41a9bYbZ5sZ+3v3R176N6hzlRlZSVLlizB09MTT09PlixZ0uRp/5UYDAaefvpp+vbti6urKyEhIdx9990UFBQ0aTd+/HgkEkmTbf78+e1uv1YLbm7g7++Cmxsdtmm1ttkjl8uZNWsW33zzDV9++SXz5s1rcT2ZKVOmNLnR/vTTT1s8n8Vi4dNPP+WNN97Azc2NkJAQHnrooRbnC12Lxx57DH9/f2JiYnjwwQcFR8TJyYkzZ86g0WgICgqiT58+AHz88ce8/PLL+Pn54eXlxZNPPtni53p7e5OYmMiRI0fYu3cvs2bNQq/XU1lZyd69exkzZozQtjVnChpS/b7//nu0/xP7Sudo+/btTJgwQSi8sHjxYiEKWFhYyJ49e5r0MS8vLx599FHkcjlz5syhvLycxx9/XHh9+vRpoe3ChQvx9PREqVTy9NNPC86gVTeJRMKwYcPo27cvd955Z4v2jxkzhqqqqla3ltBoNM3mnHl4eLQaXVm1ahWPPfYY3t7ezQpQTJ06lTfffJOqqiqys7P57LPPBC0nTZrEli1bOHLkCPX19bz66qtIJBLheFfF1dWVcePG8dhjj3HHHXcQHh6OyWTi9OnTfPzxx6xevZqUlBT0en27faY95iFCwwOGgIAAhg0bxsyZMxk+fDgBAQFYLBaKioo4ePAgP/74I0ePHqWkpKRbPVGVSCT07u3KU081RK22b2/Yf+ed1fTrV4tMZuHyZQXr1rly770uBAdL6d27nmXLNKxfr0Ot7j5aOBp79fe2YLHAo48aAQmxsbWcP9+9HCmwTfc+ffqgVCqpqanh0qVLdrDKfigUChYuXIi/vz/V1dV88cUX7TqOt0Zn7O89AXvo3qHO1MKFCzl58iSbN29m8+bNnDx5kiVLlrTaXqvVcvz4cf74xz9y/PhxNmzYwIULF5gxY0aztg888ACFhYXCtnr16o68lE7DokWLWLduXTNnoDHbtm1rcqN97733ttiutLSUuro64uPjBcdr1apV1xX2buz5h4eHU1hYCMC3337Ljz/+SGhoKFOnTiUtLQ2A3NzcJk7fokWLKC0tbXZerVbL2LFj2bt3L3v37mXs2LGMGjWK/fv3N3Gm9Ho9R48eZezYsS3al5ycTExMDD/++CMnTpwgPz+f22+/XTi+ZcsWbr75ZuH1XXfdxYEDBygoKODLL79k/PjxBAUFCcf9/PwEh8PFxQVfX1/BsXVxcWmSLvXyyy8TGxuLh4cHw4YNo7y8XDgmlUpZunQp586dY8WKFTaqbRtubm7NnrhVV1fj5ubWrO2JEyc4cuQIDzzwAECzqMTzzz9PVFQUiYmJ3HLLLcybN4/Q/1W76tOnD++99x733HMP4eHhBAUF4e7uLhzv6shkMpKTk7nvvvtYtmyZkAJYVFTExo0beeONN/j5559b7L9txRFzFJycnIiOjmbixIlMnz6dpKQkXF1d0ev1XLp0iZ07d7Jp0yZOnTp11YdhXRUXl4bI9tNPe3DypIrCwnrWrKni3nuriI9veCBw4YIzH37oxh13KPH1hX79dDz4YC3r1tVRXCw6V9dLZ5yT89ZbJnbscEIuN/Pxx9XU1Ei6lSMFtunu5OREcnIy0JDl0J2iU9CwHt/ChQtxdXWlsLCQ9evXd/gSEp2xv/cE7KF7h9WCPX/+PJs3b+bQoUMMHz4cgA8//JCRI0eSnp4uhJAb4+np2WReC8A777zDsGHDyM3NJSIiQtivUqma3Nx2BCoVaDQNKWCenp4d+jm2MnLkSPLz81EoFAwYMIDdu3df9+f6+fmhVCrJycm54etrvBjq5cuXhb/N8OHD+emnn6ivr+dPf/oTDz/8MDt27CA0NJT169fbVEFrzJgxfPTRRxQVFfHCCy9QXV3N9u3bOXnyJCNGjAAa0uCGDBnSJKXtShYvXsy6devo3bs3c+bMabJo7LZt2/jrX/8qvPb392fSpEl8+eWXfPHFFy1WA7SFX3/9ldWrV7Njxw5iY2O5cOFCk+qL5eXl/PGPf2TJkiU89dRT7Nu3r8UUu71793LLLbe0+jktRZv69OlDeno6BoNBKA16+vRpnnrqqRbtvHDhguAAqdVqNm7cyKVLl/jwww9xdXXlgw8+ENo///zzDBkyRHi9cOFCYf7exYsXeeedd7plSkNISAgzZ85k6tSpnDp1iqNHj1JeXs6RI0c4cuQIUVFRDB06lISEhBb/jp0dNzc3+vbtS3JyMiUlJeTk5HD58mVqa2s5f/4858+fx8vLi8jISCIiIrrNosBWJBIJ/v5KFi1SsmhRQ/Q+N1fLli16du6EAweUXL6sJDVVSWoqWP8loqPrGT7cyNixEiZPdiYuTkY3XV2gW/P99yZWrmx4KPbYY8WMHh3QldfmvWGio6M5f/48tbW1ZGZmEhcX52iT2hVvb2/mz5/P559/Tnp6Otu3b29WwEpExBY6LDJ18OBBPD09BUcKYMSIEXh6el51AvyVqNVqJBJJs3kha9euFcp4rly5kpqamlbPUV9fT3V1dZPNFiQScHUFf38Vrq502NbWL90NGzbw9ddft+1NLSCVSrnnnntYuXIlVVVVmM1mzp8/z5EjR9p8rrfffpuysjKysrL44IMPmDt3Lnq9nnXr1lFdXY2TkxNubm7CDeZ9993Hc889R1FRERaLhezsbH799ddm51WpVIwdO5Z9+/ZhMBgICAhg7NixfPLJJyQkJODu7g78VjziaixcuJBt27axZs2aJlG98+fP4+Pj0ywUvGjRIt566y3Onj3LnDlz2qwJQE1NDXK5HF9fX2pra5s4bNBQnGLevHl8/vnnKBQK3njjjRbPM3bsWDQaTatbS/Tu3ZvevXvz2muvUV9fz7///W9kMhmjRo1q1nbZsmVcvHiRkydPcvLkSaZPn86KFSt4/fXXgQZnuaioCJPJxLZt2/j00095/PHHhfcfP34cs9lMfn4+Dz74IM8880yXdCZsxcXFhREjRvDII49w9913CwsBZ2dn88033/Dmm2+ya9euNufiDx48uIMsbhsSiYTAwECGDRvGrFmzGD16NGFhYUilUqqqqjh16hQbN25k586dXLp0ifouvOhObGwsX331FbGxsc2OSSQSIiNVLFvmxZdfepGdreDcOQ3vvlvJ/PlV9OrVUDo6K8uZL7905eGHVfTuLSMw0MD06bW8+mote/ca0OnsfVVdg87S3wE+/dTIHXdIMZkk3HZbOX/5i3e3HcNs1V0mkwkP/y5cuNCt0n2thIeHM3PmTAAOHDjQoWtQdab+3pOwh+4dFpkqKipqMU8xICDA5tWIdTodzzzzDAsXLmwy92PRokVER0cTFBTEmTNnePbZZzl16lSzqJaVV199lT//+c/N9m/fvh1XV1cmTpzIkSNHqKurw8/PD5PJJJTkViqV1NfXC/M/3N3d0Wq1mEwmZDIZKpVKcOSskQ7d/7453dzc0Ol0GI1GpFJpk7QrZ2dnpFKpsI7D1dpaoy0ajQa1Wk2fPn3Q6/Wo1Wohncxqr8ViwWg0Cq9VKhVGoxGdTkd1dTUeHh5UV1djsVhwcnLi73//O08//TTJycloNBp69erFc889J0QODQYDarVauKbGpcp1Oh1qtRqz2cysWbMYNmwYarWaBx54gFtvvZWKigo+/vhjHn74YcxmM0lJSbz55puYzWaWL19OTU0NI0eOpLy8nPDwcFasWCFEmqyfJ5fLcXV1JSgoiGHDhmE2m/Hz88PV1ZURI0ag1+upq6vjp59+4ptvvqG2trZVvf39/Rk6dKhQuEOr1WIwGPj++++ZNm2acG0KhQK5XM6ECROoqKhg+vTpyGQywbGHhhLiarUahUKB0WgUXqtUKnQ6HRaLBbVazc0338yQIUOIjIzE19eXlStXsmbNGtRqNT///DMpKSns3buX6upqPv74Y4YNG8b48eNJSkpCqVQKjpKLiwtms1m4abXOfTKbzcjl8iZtG/fD1atX88gjj/DKK68QFxfHmjVrkEqlqNVq3njjDQ4fPsyPP/6IwWDAxcVF6IdyuRwnJyc8PDxQq9WcOHGChx56iIqKCuLi4vjoo48IDAwU9F62bBlpaWm4ubkJFfGsfxtASBurra0V/rbW0urh4eH4+flx4sQJAIYMGUJBQQEFBQXIZDImT57M9u3bMZlMhISEEBISwrFjxwAYOHAgZWVlQonbadOmsWvXLvR6PYGBgURFRXH48GGgofhGdXU12dnZQMPcwv3796PVavHz8yM+Pl540JOUlIROpxPmCljHCI1Gg7e3N0lJScLct4SEBGEB4IsXL2IymTh//jwnTpxg7dq1jBs3DoPBQEhICL1790Yul3P+/HmgIep67tw5KioqcHV1xd3dnZSUFABiYmJQqVScOXMGaIhKX7x4kdLSUpRKJePGjRNKvkZGRuLl5SWsI2aN5hcVFeHk5MTEiRPZunUrFouFsLAwAgIChNSHwYMHU1RURH5+PlKplClTprBjxw6MRiPBwcGEhYVx7tw5wYb09HTOnTsnjHtHjhzBbDYTHh7O4MGDyc7ORiaT0bdvXzQaDVlZWQBMnjyZAwcOoNVq8fX1JSEhgf379wMIY5q1suWECRM4duwYNTU1eHl50a9fP/bs2QMgjE3p6elAQ7XO06dPU1VVhbu7O0OGDGHXrl1Ag6OkUCgE+0ePHk1aWhrl5eWoVCpGjRrFwYMHMRgMFBUVodFoSE1NBRoe/mVmZlJSUoKzszPjx48XvmMmTozgjjt8OHHiEFVVUqqrk9m508CJE65cuuRNaakTP/3kxE8/8b9xzExkZBVxcVVMnuxOXFwlTk5ZyGQSpk6dys6dOzEYDAQFBRERESE80Orfvz9VVVXk5OQADXMX9+zZg06nw9/fn9jYWA4ePAg0pDJrtVoyMzOBhrmMhw4dora2Fh8fH/r06SP02cTERIxGo1AkZvz48Rw/fpzq6mo8PT0ZMGCA8HArPj4eqVQqpGiPGTOGs2fPUllZiZubG8OGDWPnzp0A9OrVC6VSKdyQjho1igsXLlBWVoZKpWL06NGChlFRUZSUlAjfrcOHDyc7O5vi4mIUCgUTJkywyxjh7R3N4MEKystd/tdPC1i27CR79sjadYwwm81cuHABgJtuuomTJ0+iVqvx8PBg0KBBQpZJXFzcVceIESNGsGPHjhsaI8rKyrj11lttGiNMJhMGg4FLly7x7bff4uXl1eIYcfToUQAGDBhARUWFULV22rRp7N69m/r6egICAoiJiRHWwuwsY4SXlxfOzs6cPXuWr7/+mjFjxmA2m4UxYvv/JlZGR0fj5uZ21THC2mcjIiLw8fHh5MmTAAwdOpQjR44gk8mQy+VMmjSJbdu2YTabCQ0NJSgoSBj7Bw0aRElJCXl5eUgkPXuM8PDwEOagX+8YUVlZyfz58685Rljtvx4kljY+anjxxRdbdEwac/ToUbZu3SqEThsTFxfHfffdxzPPPHPVcxgMBubNm0dubi67d+++6uKtKSkpDBkyhJSUlGbr40BDZKrxk9Pq6mrCw8OFgcyKTqcjKyuL6OjoJilgHZ3m19WJioriyy+/FByh9sQW7QsLCxk9erQwQLSVadOm8dxzz91wZcTuREf1+db+x7oTJpOJtLQ0jh49Kjhu0OAADxw4kEGDBrWqrS0R1s5CbW0tubm55OTkNJlLJZfLCQkJISIigqCgIKHaZmelsLCQp556itdff73ZsgrXQ1VVPXv36vj1VzNHjshJTVVSVeXUrJ2bm4l+/eoZMsTCyJEyRoxQEBkp7VHpgY7s7xYLfPWVmQULfkvQuf/+Yt580wNXVxeH2GQv2qp7SkoKGRkZREZGMnLkyA60zHGYzWb++9//kpWVhZ+fHw888ADO7bwyc1ca37sTtupudRSv9A1soc3fco888sg1K+dFRUVx+vRpiouLmx0rLS0lMDDwqu83GAzceeedZGVlsXPnzmte1KBBg3ByciIjI6NFZ8rZ2fmG/imurGwmYj9s0b66upq///3v1/0ZkyZN6rZfENeL2OevH5lMRlJSEklJSZSVlXH8+HFOnjxJdXU1v/76K3v27CE2NpbBgwcTFxfXJJWopeqcnRVXV1cSExNJTEwUnozm5uYKTlZubi5yuZzQ0FDCw8MJDg7ulGlThYWFrF27lieeeKJdnCkvL2duv90Za30bo9FEWpqG/fsNHDkCJ044ce6cCxqNjAMHVBw4AG+/3dDWw8NIYqKBfv3MDBwoZcgQOf37O3GVqaBdGkf0d4sF9uwx8fzzRvbt++2+YOHCQt5/P6BT9tH2pq26R0ZGkpGRQUFBgZCV092QSqXccccdrF69mrKyMn755RdmzZrV7p8hYn/soXubI1O2cv78efr06cPhw4cZNmwYAIcPH2bEiBGkpaW1WIACfnOkMjIy2LVrF/7+/tf8rDNnztC3b19+/fVXm6ILrXmfPeGpeUfQkZEpke5FT/0fMxqNpKWlkZKSIqS0QEN6rzVa5e3t7UAL2weLxUJFRQWXL18WCldYcXJyEhyroKCgTnNDdvz4cQYPHtxqZkNHUFdn4NixOg4eNHHsmISTJxVkZioxmZp/6cvlFnr10pOcbGTAAAn9+8vo319BeLiky0Wx6uuhnR/220xtLXzzjZF//tPM6dMN3qmTk5nZsytZvlzP+PFB4kOkVrBYLPz444/U1dU1q2zb3cjJyeGzzz7DYrEwb948kpKSHG2SiJ24kchUhzlTALfccgsFBQVC2fJly5YRGRnZZFHYhIQEXn31VWbPno3RaGTu3LkcP36cTZs2NYlg+fj4oFAouHTpEmvXruXWW2/Fz8+Pc+fO8eSTT+Li4sLRo0dt+oJuqzNlnWskYn9E7R1DR+neU52pxlRUVHD8+HFOnDjRxNno1asXer2ee+65p9OnxtmCxWKhvLxccKwarztmdawiIiIIDAx0qGPlCGfqSiwWCzU1ek6cqOf4cROnTkk4e1ZOerqSmpqW+4JKZSImxkB8vImEBEhOltGvnxPx8TKcmmcVOpzVq+HRR+Gdd35bs2nHjh1MmjSpwz5Tq4WtW42sXWvk558VaLUNzqpCYea22ypZtcrC4MG+Pc6Juh7dDx06RHZ2NklJSfTt27eDLOsc7Ny5kz179qBUKnnooYfaLeW9o/u7SMvYqrtd0/zawtq1a/nDH/4glJqcMWMG7777bpM26enpwsT/vLw8fvzxR6BhEmNjdu3axfjx41EoFOzYsYO33noLjUZDeHg4t912Gy+88EKHfSF3xwo2XQVRe8cg6t5x+Pj4MHnyZCZMmEB6ejopKSlkZmZy6dIlLl68SFlZGf369WPAgAHtknbmKCQSCX5+fvj5+TFgwADKy8vJzc3l8uXL1NXVkZ2dTXZ2Nk5OTsIk9uDgYKGMf09CIpHg4eHMTTc5c9NNv+03Gk1cuKDh2DEjJ09aSE2VkZGhIC/PGa1WxpkzMv5Xe0BALrcQEaEnJsZEVJSFmBiIjZUTHy8nNlaGI6rZr14Ny5dDv34NP6HBobpyPbsbRaeDo0dN7N1rYOtWOHhQgV4vx3qrExqqY+7cah5+2Im4OJ8e50RZuR7d/fz8yM7OpqysrAMs6lzcdNNNZGZmkpeXx8aNG1m0aFG79JX27u8itmEP3TvUmfLx8WHNmjVXbdP4pi0qKuqaN3Hh4eEtltDuSHril3tnQdTeMYi6dzwymYw+ffrQp08fKisrOXHiBBqNhrq6Og4fPszhw4cJCgpi4MCB9O3bF1VbFqTrZDR2rBpXV7M6VtY5VjKZjMDAQEJDQwkNDbVL9NLb25tbb721U6ZZyuUy+vRxo08fuPvuhn0WiwWNRs/583pSU42cPw8XLki5eNGJ7Gxn6upkZGY601o9Hh8fI+HhRiIjTURFQXi4hPBwGeHhMiIiZAQFSWjPwKjVkXr0UXjzTXjssd8cqpEjr+9hgcUCpaVw9qyR1FQjqakWjh6VcvasE0ajDPjtwWpwcD3jx2uYP9/M1KkeKJXNqwz3NK7nIY11eZqrLUPTXZDJZMyePZv33nuPixcvkpqaatOamNeiKz8c68rYQ/cOTfPrrLQ1zc9oNHaLtJuuiKi9Y+go3cU0v6tTVlZGVVUVJ06cIC0tDZPJBDR8uffu3ZuBAwfSq1evbjOR2ZoKmJeXR35+fpMbNasDFhYWRlhYWIcuEFxRUYGPj0+Hnd9e6PVGLl3SkZpq5OJFM1lZEnJyZFy+LKegQEF19bX/p6VSC76+RgIDTfj7m/HxAT8/C76+Evz9Jfj5SQgIkOLnJ8PDQ4q7uwQ3t4bF5698eN/YkXrrrYbjFgusWNGQ7vfGG7U88cRvf1eTCaqrG7bKSjOlpWYuXzZx+bKZggIL+fkSCgokZGY6oVa3nIni7W2gf38tI0YYuO02KUOHuuHs3E0reFwn19Pf6+vr+e677wCYN29ep5nz2JHs3buXHTt2oFKpePjhh294DOou40xXw1bdO22aX3ehtrZWLI3uIETtHYOou2NISUlh2rRpxMbGUldXR2pqKidOnKCwsJBz585x7tw53N3d6d+/PwMHDsTX19fRJt8QjSNW/fv3R61Wk5+fT15eHpWVlZSWllJaWsqJEyfw9vYmLCyM0NBQPD092y1FS6fT8cMPP7BgwYIu7+ArFHISE91ITGx+zGQyUVysJSPDSEaGiawsyMmRUFQkpbhYRkmJnPJyBSaThNJSJ0pL2xadlkgsqFRmVCozCoWFujopFRVyHnnEwltv/VYsQyJpcKwsFgtPPunKSy8ZUSgsaDRStNrGN+jS/20t36ZIJBaCg+uJjtYTG2tk8GAzY8fKSUhwwdnZo8em8NnC0aNH21yiW9GopKTBYOgRztSoUaM4c+YMxcXF7NixgxkzZtzQ+a5Hd5Ebxx66i86UiIiISCfExcWFYcOGMWzYMIqLizlx4gSnT5+mpqaGffv2sW/fPsLDw+nfvz9JSUm4uHTttXEkEgleXl54eXmRlJREbW2tELEqLS2lsrKSyspKUlNTcXV1FRZeDAi4sXLW586d43e/+x39+/d3WAEKeyCTyQgJURESQpN5WVbMZjM6nZ7CQiO5uUby8swUF1soL4fycgkVFRIqKqRUVkqprJSh0cioq5MJDpDFIqG2VkZt7W9/i379aOJIWWlwqCTs2QOnTze/DVEozLi6mvDwMBIQ0BAlCw42ExxsISQEEhKk9OnjhLe3Erm8azvAXQWJRIJcLsdoNPaYuT8ymYzbbruNTz75hBMnTjB8+PBrLu0j0jMRnSkb6MpzFTojjUupL1++nPj4eJ544okW24raOwZRd8dwZeEdK4GBgdx8881MmTKFCxcucOLECTIyMoR5R7/88gu9e/emX79+zdau6qq4urrSu3dvevfujU6no6CggLy8PIqLi6mtrSUjI4OMjAzkcjlBQUEEBwcTEhLS5Z1KRyGVSlGpnOnVy5leva7e1mKxYDabMZvNGAwGNBoz1dVmqqstVFebqa+38OOPCv79bxUrVlh4++2mDlVDqp+F06clPPqollmz9Hh6SvDxkeHlJcXFRYpcLkcmU4oRpg6gtXHmalgsliZpxz2FiIgIkpKSOHv2LFu2bGHJkiXX3SevR3eRG8ceuovOlA0YjUaHT8iPioqioqKC4uJi4WahurqawMBAIiMjSUtLc6h9VrKzs0lISECn09nU/v3337/q8c6gfU9E1N0xVFRUXPXJp0wmExbKrampITU1lVOnTlFcXCykAapUKpKTk+nXrx+hoaHd4mZUqVQSExNDTEwMBoOBkpISCgoKKCgooK6ujry8PPLy8oCGwkfWqJW3t3e3uP7OhkQiQSaTIZPJcHJyQqWCgCvqOkybZq3eJxFS+xrPmXr3XQkvvFDIiy8GA+LDG3tyrXGmJQwGg1AgrKd9N0yePJm0tDQyMzPJzMyk17WeNrTC9egucuPYQ3fRmbIBvV7fKZ52BgUF8eOPP3LXXXcBsGHDBsLDwx1sVcfSWbTvaYi6O4bc3FwSW5rw0gLu7u6MGjWKUaNGUVxczKlTp0hNTaWmpoYjR45w5MgRfH196devH/369euU1equB+saVaGhoVgsFiorKyksLKSgoIDy8nIqKiqoqKjgzJkzuLi4CBGrwMDAHncT6Gis60lZq/dZq/m98w68/z5ERZ0GxApn9qYt44yV6upqoCH9uKcVhfL29mbIkCEcPnyYPXv2XLczdT26i9w49tC9e5SE6iEsWLCAtWvXCq/Xrl3LwoULm7RJTU1l9OjReHl5MWTIEA4dOiQci4qK4o033iA+Ph4PDw/efPNNjhw5Qp8+ffDx8eGf//yn0Lauro5HHnmEkJAQwsLC+Nvf/iYcW7p0KU888QSTJk3C3d2dadOmUVlZCcDUqVOpr6/Hzc0NNzc3CgoKrnpNS5cu5bXXXgPgxRdf5O6772bevHm4u7szYsQIcnNzm1zbuHHj8Pb2ZvDgwRw7duw6VBQR6X4EBgYydepUHn/8cZYsWUK/fv1wcnKivLycXbt28dZbb/Hpp5+SkpJCXV2do81tNyQSCT4+PiQlJTFlyhRmzpzJsGHDCAsLQy6XU1dXR2ZmJvv27eO7775jx44dnD17loqKCnEtNTvx4IMNjtM778DAgb85UlZHS6RrYP2O76mFiUaPHo1MJiMnJ4ecnBxHmyPSyRCdKRvoLIPHlClTOH78OBUVFRQVFZGRkcG4ceOE43q9nttvv52FCxdSWlrKypUrmT59urAoMsDPP//M0aNH2b59O08//TSvv/46+/fvZ9euXaxatYrS0lIAVq5ciVqt5sKFCxw5coT//Oc/bNy4UTjPV199xVtvvUVpaSlGo1FYjHnr1q04Ozuj0WjQaDSEhIS06Ro3bNjAH/7wByorK4mPj+fNN98EGta2uOWWW3j88ccpKyvjj3/8I7Nnz7Y5nVCkbXSWPt/TuNGKQ1KplF69ejFnzhyeeuopZs+eTUxMDBKJhJycHDZu3Mg//vEPvvjiC86cOYNer28nyzsHLi4uxMTEMGbMGGbPns348eOJj4/H3d0ds9lMaWkpqampbN26le+//56DBw/i7e2NVqvt1sUnHI3VoTp/vqkjJVY2cwzXo3tRUREAAVfmc/YQPDw8GDhwIAD79++/rnOI/d0x2EP3nhWrvR60WmpTUjp0jRMSEhoW6bgGcrmcWbNm8c0331BXV8e8efOarDdz6NAhZDIZDz/8MADz58/nrbfeYuvWrcybNw+AFStW4OnpybBhwwgKCuLOO+/E29sbb29vIiIiSEtLw8/Pj08//ZTs7GwhwvTQQw/x7bffcvvttwNw1113kZycDMDcuXPZuXNnu0gxdepUxo4dK9j//PPPA/DTTz/Rr18/Zs+eDcCsWbP461//ysGDB5kwYUK7fLbIb9TU1ODu7u5oM3ocu3fvZvz48e1yLoVCQf/+/enfvz/V1dWkpqZy+vRpiouLSU9PJz09HYVCQe/evenbty+9evXqVhPLZTIZQUFBBAUFAaDRaCgqKqKoqIji4mLq6+uFp8zZ2dkMGDBAKGTh5+fXrbToDDz4ICxdCs7Ov+1rz/4uYjtt1b2+vl5wpnrywrMjRozg2LFjZGRkUFVVJSxkbCtif3cM9tBddKauRVoaro2iPx1CSgrY+FR00aJFPPPMM9TV1fHBBx9QVVUlHCsoKCAiIqJJ+8jIyCapdo2fKrm4uODv79/kdW1tLaWlpdTV1REfHy8cM5vNjB49usXzqFQqNBqNTfZfi9bOm5uby44dO5oMXgaDgcLCwnb5XJGmmM1mR5vQI6mvr++Q83p4eDB69GhGjx5NSUkJZ86cITU1VSg1npqaiouLC3369KFv375ERER0m4WBrbi5uREbG0tsbCwmk4ny8nKKi4s5cuQI//73v3nssceoqqoiLS0NuVyOv78/gYGBBAQEiIUs2onGjhR0XH8XuTpt1T0zMxOTyYS3t3ebHYjuhJ+fH9HR0WRlZXH8+HEmTpzYpveL/d0x2EN30Zm6FgkJ1O3b17GT8RMSbG46cuRI8vPzUSgUDBgwgN27dwvHQkJCuHz5cpP2ubm5zJ07t03m+Pn5oVQqycnJaXO6V3vfcFjPFxoaym233caGDRva9fwiLSNO1HcM9kihCQgIYOLEiUyYMIH8/HzOnDnDmTNn0Gg0pKSkkJKSgru7O8nJyfTt25fg4OBu50jIZDICAgIICAjAYDBw+fJlevfuja+vL0VFReh0OgoLC4WHNQqFgoCAAMG58vAQF4VtD3pqypijaYvuer1eqBYcFxfX4/v9kCFDyMrK4uTJk0yYMKFNeoj93THYQ3fRmboWKhVOw4dDJ6pes2HDhhafGo8YMQKDwcB7773HAw88wHfffUd6ejpTp05t0/mlUin33HMPK1eu5PXXX8fDw4P09HRqamoYNmzYVd/r5+cnRIzaIx3AOlBNnz6dZ599lh9//JHbbrsNvV7Pr7/+ysiRI8X5PR1A49XuRexHTEyM3T5LIpEQFhZGWFgYU6dOJTs7mzNnznDu3Dlqamo4ePAgBw8eFAo89OnTh6CgoG57MxUcHMygQYOwWCxUVVVRUlJCcXExpaWl6PX6JuXXlUql4FgFBgbi5ubmYOu7Jvbs7yK/0Rbdz5w5Q319PR4eHkRFRXWcUV2E3r17o1AoqK6upqCggNDQUJvfK/Z3x2AP3btXHkcHUVtb62gTmtCvXz9hvlJjFAoFP/zwA//973/x9fXltdde48cff7wuZ+P//u//cHV1pW/fvvj4+HD33XcL1XyuhqurK08//TR9+/bFy8vrmtX8roU13czT05NNmzbx1ltv4e/vT1RUFB988MENnVukdTpbn+8pNK6+aU+kUikxMTHMmDGDlStXMn/+fJKTk3FycqKiooK9e/eyevVq3nnnHXbs2EFhYWG3rYYnkUjw9vamd+/ejBs3jtmzZzN58mT69etHYGAgMpkMnU5HTk4OR48eZdOmTWzcuJEjR46QlZWFRqPpttq0N47q7z0dW3UvLi7mwoULAAwcOLDbpf5eD3K5nLi4OADOnz/fpveK/d0x2EN3iaUHjvrV1dV4enqiVqvx8PAQ9ut0OrKysoiOjkapVAr71Wq1GP1wEKL2jqGjdG/tf0ykgS1btnSqik96vZ4LFy5w9uxZMjIyMBqNwjFrxCopKYnAwMAuG7E6fvw4gwcPJiUlxaaKfo3nW5WUlFBeXt5sjqFKpcLf3x8/Pz/8/f3x9PTssvp0JJ2tv/cUbNFdo9Gwbds26uvr6dWrF0OHDrWTdZ2fM2fO8O233xIYGMhDDz1k8/vE/u4YbNW9Nd/AFjpP7lonRly81HGI2jsGUXfH0LdvX0eb0ASFQkFycjLJycnNHCtrxGrv3r1d2rGKiori7bfftjmFqfF8K2gohFNWViakBFZWVqLVapusR6NQKJo4V97e3mK1QDpff+8pXEv3uro6fv31V+rr6/Hx8WHAgAH2MayLYB0rSkpK0Ol0Nj8YFPu7Y7CH7qIzZQNiZTPHIWrvGETdHUN7VcXsCGx1rHx9fUlMTCQxMZGQkJBO71j5+Pgwbdo0fHx8ruv9Tk5OBAcHC3NEDQYDFRUVlJaWUlpaSnl5OXq9nvz8fPLz84GGVCFfX1/8/f3x9fXFx8cH5ytL3fUAOnN/785cTffa2lp2795NTU0Nrq6ujB07VixIdAVubm74+PhQUVFBXl4esbGxNr1P7O+OwR66i86UDdTX14spSQ5C1N4xiLo7hqysrCZLEnRWruZYlZeXs2/fPvbt24eHhwcJCQkkJiYSGRnZKedclJaW8tZbb/Hiiy82WSrienFyciIwMJDAwECgIS2wqqpKcK6sBS2Ki4spLi4W3ufh4YGvr6+weXp6dkq92pOu0t+7G63pXlZWxr59+9DpdLi6ujJhwgQxS6EVQkNDqaiooLi42GZnSuzvjsEeuovOlIiIiIjIddOSY3X+/HkyMjKorq7myJEjHDlyBJVKRe/evUlMTCQmJgZ5J6mQevnyZf79739z3333tYszdSUymUxwkBISErBYLKjVasrKyigrK6O8vJyamhqqq6uprq4mKysLaIhe+fj4NHGwxBtbkY7AYrFw7tw5zp49i9lsxsvLi7Fjx+Lq6upo0zot1kh2RUWFgy0R6Qx0jm+zTk5bJ6KJtB+i9o5B1N0xTJ482dEm3BCNHSuj0UhmZibnz58nPT0drVbLiRMnOHHiBAqFgri4OBITE4mLi+tRKW4SiQQvLy+8vLyEJ9o6nY6KigrKy8spKyujoqICg8FASUkJJSUlwntdXV3x9fXF29tb2Lqydl29v3dVGuteWlrKjh07hNdhYWEMHz5cTO27BtfjTIn93THYQ3fRmbIBjUaDu7u7o83okYjaOwZRd8dw4MABxo4d62gz2gW5XE58fDzx8fGYzWZyc3M5f/4858+fp7q6mrNnz3L27FlkMhkxMTEkJCQQHx/fI/udUqkkJCSEkJAQoGHOYnV1NeXl5cJWXV1NbW0ttbW15ObmCu+1zt/oig5Wd+rvXYkDBw4wYsQIzp8/z7lz54T98fHxDBw4sNPPc+wMWKN2dXV1Nr9H7O+OwR66i86UDYiT8R2HqL1jEHV3DFqt1tEmdAhSqZSoqCiioqK4+eabKSgoEByr8vJyMjIyyMjIABrmIsTHx9O7d+8uVxmwvZBKpUL0qlevXkBDYYvy8nIqKyupqKigsrISjUYjbI0dLFdXV8HB8vHxwdPTs1OmCHbX/t6ZMRgMZGZmUl5ejk6nE/aPGzdOcOZFro01cmcwGGx+j9jfHYM9dBedKRvoLLn9PRFRe8cg6u4YfH19HW1ChyORSAgNDSU0NJRJkyZRVlZGWloa6enpQsW7/Px8du3ahaenJ71796Z3795ERUV1SDlxd3d3RowY0ekjYk5OTgQFBREUFCTsq6+vp7KysslWU1MjRLAuX74stFUqlXh6euLp6Sk4ah4eHg79X+8J/b2zoNPphIcW5eXlODs74+7uTv/+/QkNDe2RDy1uBOv/TeO1966F2N8dgz10F++YbKC7VzVbu3Yt3377Ld999911n2Pp0qUkJCTwzDPPtKNlnVP7xtfaHtp1Rjqj7j2BhIQER5tgVyQSCf7+/vj7+zN27Fg0Gg0XLlwgPT2dzMxM1Gq1UMDC2dmZ2NhYevfuTVxcXLtFWuLi4ti2bRtubm7tcj574uzs3KKDVVVVJUSvrBEsnU6HTqdrUkFQIpHg7u4uOFjWn66urna5ue5p/d3emM1mioqKyMzMpKCgQMg4iI6Opn///kRGRorrnV0ner0eaJgnaitif3cM9tBddKZsQKPR4Onp6bDPnzJlCtOmTWPlypVN9j/xxBOUl5fz+eeft+l8EomEwsJC4Qt40aJFLFq0qN3sbU8coX1UVBRffvklI0aMuGbbzqzdjeDoPt9T2b9/v00rtXdX3NzcGDRoEIMGDRLSkdLT07lw4QIajUaYZyWVSgkPDycuLo64uDgCAgKu++bfZDKxdetWZs6c2S1uLJ2dnZuUZoeGp+fV1dVUVVVRVVWFWq2mqqqK+vp6oYpg4yiWTCbDw8MDd3f3Zj/bM5LV0/t7R2A2mykvLyc/P5+cnJwmc3qsFSXPnj1LTEyMA63s+lhTJNvy4FHs747BHrqLzlQXYPHixbz55ptNnCmz2cxXX33Fp59+avN5DAaDWKFHRESkS+Dk5CSk+FksFvLz84WoVXFxMTk5OeTk5LB9+3Y8PT0Fxyo6OrpNT4tPnTrF3LlzSUlJYdCgQR14RY7DWma98cLEFosFnU4nOFZWJ0utVmMymYSo1pW4uro2c7Dc3NxwcXHp9utidVb0ej0lJSUUFBRQUFDQZC6Us7MzkZGRxMTE4OXlBdCk6ITI9VFVVQXQ6dODReyDOPLZgKMn7s6ZM4f09HTOnz8v7Nu9ezcmk4lJkyaRm5vLbbfdhq+vL4mJiWzevFloFxUVxd///nd69+5Nnz59mDp1KgC9evXCzc2NgwcP8tlnn3HzzTcL79m5cydDhgzBw8ODuLg49u7dC8CHH35IXFwc7u7u9OvXj927d9tkf1RUFG+88Qbx8fF4eHjw5ptvcuTIEfr06YOPjw///Oc/hbYVFRXMnz8fPz8/YmNj+eKLL4RjS5cu5bHHHuOmm27Czc2NhQsXUlRUxOTJk/H09GTRokWYTCah/b/+9S/i4uLw8/Pjnnvuoba2FoDPPvuMqVOn8tBDD+Hh4UFSUhInT54E4P777yc3N5eJEyfi5ubGV199ddVra6zd7t27SUhI4M9//jM+Pj5ER0ezbdu2Jte2cOFCAgICiImJaXNE0Z44us/3VPr06eNoEzolEomEsLAwJk6cyEMPPcSKFSu49dZbiYuLQy6Xo1arOXbsGF988QV/+9vf+O9//8uhQ4coLy93tOmdFolEgouLC0FBQSQkJDBixAimTZvGHXfcwW233ca4ceMYMGAAMTEx+Pv7Cw5qbW0thYWFXLhwgWPHjrFr1y42btzIt99+y08//cSvv/7KsWPHSEtLIy8vj8rKylYn6Yv9/fqor68nLy+PEydOsGXLFr777jv27dtHZmYmOp0OhUJBZGQkY8aMYcaMGQwaNEhwpEDUvT2wLlkQEBBg83tE3R2DPXQXI1M24OjKZu7u7syYMYN169bx0ksvAbBu3Trmz5+PRCLh9ttvZ9myZfzwww8cPXqU22+/nTNnzghpfN9//z179+7Fw8MDpVKJRCLh0qVLwvH09HThszIzM5k9ezZr167llltuIT8/X8gNDgkJYceOHYSFhfHxxx8zf/58cnJybCrD+/PPP3P06FHS09MZO3YsM2bMYP/+/eTm5jJixAgWL16Mv78/Dz/8MHK5nNzcXC5evMjkyZNJTk5mzJgxAHzzzTfs2LEDf39/Bg0axPTp0/nPf/5DSEgIQ4YMYdOmTcycOZNvvvmGDz74gO3btxMQEMB9993Hn/70J9544w0Adu3axbJly3j33Xd54YUXePLJJ9mxYwcfffQR27dvtznN70ouXryIu7s7JSUlfPLJJyxfvpxLly4BsGTJEpKTk7l8+TJZWVlMnDiRAQMG0L9//zZ/Tkfj6D7fU7H+r4lcHW9vb4YNG8awYcMwGAxkZ2cLk+srKyu5dOkSly5dYvPmzfj4+AhRq6ioKLG4yjWQSqW4u7vj7u7erLpb47RA60LD1oIXZrOZmpoaampqWjyvUqnE1dUVV1dXVCoVLi4ulJeX4+LigkqlQqlUipGtK7BGD6uqqpoUGdFoNM3auru7ExwcTEhICP7+/ldNWRXHmRunsLAQoE0LfYu6OwZ76C5+q9hAfX29wyfkL168mBUrVvDSSy9RX1/P+vXr2bp1K0eOHMFgMPDwww8DMHLkSMaPH88vv/zCvffeC8Djjz9u89OTL774gpkzZzJ9+nQAIiIihGO33Xab8PsDDzzAn/70JzIyMkhOTr7meVesWIGnpyfDhg0jKCiIO++8U1gTJSIigrS0NHx8fFi/fj2XLl1CpVLRr18/Fi9ezBdffCE4U3fddZcwmXD8+PG4ubkJTx0mTZrE6dOnmTlzJh9//DHPPfcckZGRAKxatYrbbrtNcKb69u3LHXfcAcDChQt5//33bdLnWnh6evL4448jkUhYvHgxDz74oFC6eO/evfz444/IZDISEhJYuHAhGzZs6JTOVGfo8z2RixcvCqWwRWzDyclJcJYsFkuTUus5OTlUVFRw+PBhDh8+jFwuJyoqil69etGrV6823QiJNKSMWQuGNMZsNlNXVyeMdbW1tU1+1tfXCwUwGkcLL126JJR0t0bKrM6V9XelUomzs7Pw09nZuVs5xBaLhfr6erRaLRqNRnBIrVtrN4IeHh4EBATg7+9PQEBAm7IJxHHmxqipqaGoqAhoyLyxFVF3x2AP3bvPiNSBKJ94AkpLO+bkoaHw3nvXbDZt2jSqq6s5dOgQhYWF+Pv7M3ToUL7++msyMjKahPCNRiODBw8WXoeFhdlsTl5eXqsTU7///nv+8pe/kJmZCTQMKLam0TR25lxcXJp8Gbu4uFBbW0tpaSkmk6mJveHh4ezZs6dN5wHIzc3lvvvuY9myZcLxxqkmjc+jUqlafNJ3Pfj7+wsT4VUqFYCwBkxtbW2TEp0mk6lbFq8QEXEUEokEPz8//Pz8GDlyJPX19WRlZQnOVXV1NRcvXuTixYtAww2plbYsvinSFKlUKkSdGhe+sKLX64Vy7bW1tWi1Wurq6iguLsbV1ZW6ujrMZjNarRatVnvN7xW5XN7EuVIqlSgUCpycnFAoFMjlcuF1400mkyGTyTq0UqHJZMJkMmE0GtHr9dTX16PX65v8XldXJ2ig1WqvmglgrbjYeFFmLy+vLrMwc3ek8Zp41sV7RXo2ojNlA04ffggOTj9wcnLizjvvZN26dRQWFgo34aGhofTt25fjx4+3+t62fHGEh4c3SfuzUl9fz4IFC/jhhx+YNGkSMpmM4OBgLBZL2y+mFfz9/ZFKpeTl5REeHg405CVfz0KCoaGhvPbaa8yYMaPN7+2IL9rQ0FC8vLy6zBwOcVKtY5gwYYKjTehWODs7k5CQQEJCAhaLhdLSUiEFMDs7m+rqakwmE48//jhbtmzh7Nmz9OrVi9jYWMLCwrpFdb/OgEKhQKFQ4O3t3WT/kCFDUCgUmM1mITpjdTC0Wq0QzaqvrxeiW2azGaPRKETBrgerUyWXy5s4WNY0wyt/QkMEqfFmdYCMRqPgPBmNxutKkZZIJDg7Ozcp7mHd3Nzc2j0SJ44zN4b1fqutJbdF3R2DPXQXnSkb0Gq1nWINkkWLFjFr1iw0Gg2vvPIKAMOHD8dgMPDBBx+wdOlSAA4fPkxkZGSTFL3GBAQEkJ2d3WRtEisLFixgwIAB/Pzzz9x8883CnCl/f3/hJ8Bbb71FaTtH62QyGXPmzOG5555j9erVXLp0iY8//pj169e3+Vz33XcfL7/8MsnJycTExFBYWMipU6eaFNpoDas+1zNnqjVCQ0MZOnQof/rTn3jmmWdQKBScPn0apVLZKSeldpY+39M4duwYo0aNcrQZ3RKJREJAQAABAQGMHDkSo9EozM3ctm0bUqlUqIa2d+9eFAoF0dHRxMTEEB0d3STqLNI+WPu7VCoV0vquhsViwWAwNHGurL/X19djMBiabHq9Xvi9cXEia/SoI+dSSKVSnJyccHZ2RqFQCFE0hUKBUqkU0hmt88Xs6biL48z1U1hYSF5eHjKZjIEDB7bpvaLujsEeuovOlA00HoQdyahRo3B3dyc6Opq4uDigId1h06ZNrFixgueeew6LxcKQIUOuOgfoT3/6EzNnzqS+vr5J5T9oWMxv/fr1PPXUU9x1110EBwfzySef0KtXL15//XWmTJmCRCLhoYceIjY2tt2v8V//+he///3vCQsLw9PTk1WrVjF27Ng2n2f+/PlUVlZy6623kp+fT3BwMMuXL7fJmXr66af5wx/+wPLly/nggw+48847r+dSmrF27VqeeOIJYmJi0Ov1JCcnN6lk2JnoLH2+p9Ha5H2R9kculxMTE4PFYuFPf/oTq1evBhry6zMzM6mtrSU9PV2I1Lu5uTVxrhqnVotcH23t7xKJRIhytTV6bjabBSfKGklq/NoaUWr80xqFsn62VCptFsGSSqXI5XIhymX93dq2MyKOM9ePtYpxYmJimx84iro7BnvoLrG0Z55WF6G6uhpPT0/UanWTnHmdTkdWVhbR0dFNJt9rNBrxKb2DELV3DB2le2v/YyINHD58mOHDhzvajB7F8ePHGTx4cJN1piwWC0VFRVy6dImsrCxyc3Oblff29vYWHKvo6Ghx7sR1IPZ3xyDqfn3k5OTw6aefIpVK+f3vf4+fn1+b3i/q7hhs1b0138AWxMiUDVgLCYjYH1F7xyDq7hj69evnaBNEaIhCBAcHExwczJgxYzAajeTl5ZGVlUVmZib5+flUVlaSkpJCSkoKAIGBgYJjFRkZKT4ssAGxvzsGUfe2YzQa2bRpEwCDBg1qsyMFou6Owh66i86UDdTU1ODp6eloM3okovaOQdTdMezZs4dp06Y52gyRK7CWVI+KimLChAnU19eTk5NDVlYWWVlZFBUVUVxcTHFxMYcOHUIikRAUFERkZKSwiQ8omiP2d8cg6t52du7cSWlpKa6urtdd0EDU3THYQ3fRmRIREREREWkDzs7OxMfHEx8fD0BtbS3Z2dmCc1VeXk5hYSGFhYUcOnQIaChsExUVJThXYvqyiEjX4Pz58xw8eBCAmTNniim9Is3oUGeqsrKSP/zhD/z4448AzJgxg3feeeeqE3eXLl3K559/3mTf8OHDhS8kaCjTvXLlSr744gvq6uqYNGkS//73v9u0nlJbENM1HIeovWMQdXcMvXv3drQJPY7Q0FCee+45QkNDr/scrq6uJCUlkZSUBDREdnNycsjOziYnJ4fS0lJKSkooKSnhyJEjAPj5+TVxrtqao98dEPu7YxB1t52CggI2bNiAxWJh+PDhwgOU60HU3THYQ/cOdaYWLlxIXl6eUDFu2bJlLFmyhI0bN171fTfffDOffvqp8FqhUDQ5/thjj7Fx40a+/PJLfH19efLJJ5k+fTopKSniuiAiIiIiIjYTGBjI/fff3+Jis9eLu7s7ycnJJCcnAw2Rq8bOVXFxMWVlZZSVlXHs2DEAvLy8iIiIIDw8nIiICGHdPREREcdQUlLC2rVrMRgMxMbGiil6Iq3SYc7U+fPn2bx5M4cOHRKqaHz44YeMHDmS9PT0q3qKzs7OLa6BBKBWq/n444/573//y+TJkwFYs2YN4eHhbN++vUM6u06nE1cbdxCi9o5B1N0xpKenExUV5WgzehSVlZV8+OGHrFy5stmisu2Fq6srffr0EdaUq6urIycnR3CwioqKqKqqoqqqitOnTwMN0eGwsDDBwQoNDW32YLGrI/Z3xyDqfm2Ki4v5/PPP0Wq1BAUFcccdd9zwww1Rd8dgD907zJk6ePAgnp6eTcoRjhgxAk9PTw4cOHBVZ2r37t0EBATg5eXFTTfdxMsvv0xAQAAAKSkpGAwGpk6dKrQPCQkhOTmZAwcOtOhMWRf0s1JdXd0elygiIiIi0sXJysrilVdeYe7cuR3mTF2Ji4sLCQkJJCQkAA3fUXl5eVy+fJnc3Fzy8vLQ6XRcvHiRixcvAg1rGgUHBwuRq/Dw8DavtSQiInJtLl68yLfffotOpyMkJIQlS5aIqe8iV6XDnKmioiLBAWpMQEAARUVFrb7vlltuYd68eURGRpKVlcUf//hHJk6cSEpKCs7OzhQVFaFQKJp96QUGBrZ63ldffZU///nPzfZv374dV1dXJk6cyJEjR6irq8PPzw+TyYRarQYang4qFArhtbu7O1qtFpPJhEwmQ6VSCQuCWf/ZdDod0LDIo06nw2g0IpVKcXNzExw5Z2dnpFIpdXV112yrUCiQy+VotVqg4SmndWV3iUSCh4eHYN+VbVUqFUajEb1eL7Strq7GYrHg5OSEQqGgtra2WVsAT09PampqMJvNzdq6uLhgNpsFJ9XDwwONRoPZbEYul6NUKtFoNC22bYuGrq6uaDSaFtu2RcMr2zbWUCqV4u7u3qqGLelt1fBqels1tFXvtmh4tbZXang9fdZisaDRaNpd79raWuGztmzZAkB4eDh+fn6cOHECgCFDhlBQUEBBQQEymYzJkyezfft2TCYTISEhhISECKlRAwcOpKysjMuXLwMwbdo0du3ahV6vJzAwkKioKA4fPgw0lEetrq4mOzsbgClTprB//360Wi1+fn7Ex8dz4MABAJKSktDpdFy6dAlAGCM0Gg3e3t4kJSWxb98+ABISEjCbzVy4cAGAm266iZMnTwprVQwaNEhY6DEuLg65XM758+cBGDNmDOfOnaOiogJXV1dGjRol6BITE4NKpeLMmTMAjBw5kosXL1JaWopSqWTcuHFs3boVgMjISLy8vDh16hQAw4YNIzc3l6KiIpycnJg4cSJbt27FYrEQFhZGQEAAx48fB2Dw4MEUFRWRn5+PVCplypQp7NixA6PRSHBwMGFhYRw9ehSAAQMGUFFRQW5urqD37t27qa+vJyAggJiYGGF+a9++fdFoNGRlZQEwefJkDhw4gFarxdfXl4SEBPbv3w9Anz590Ov1guMwYcIEjh07Rk1NDV5eXvTr1489e/YAv+W/WxfTHTduHKdPn6aqqgp3d3eGDBnCrl27AIiNjUWhUHDu3DkARo8eTVpaGuXl5ahUKkaNGiX8zXNycggKCiI1NRVoePiXmZlJSUkJzs7OjB8/XvjbRERE4OPjw8mTJwEYOnQoeXl5FBYWIpfLmTRpEtu2bcNsNhMaGkpQUJBQQn3QoEGUlJSQl5eHRCJh6tSp7N+/H4PBQFBQEP379+fQoUNUVlbi5eVFRkYGZ8+eFf6H9+/fj9FoxNXVlaioKDQaDX5+fowePRo3Nzfhf2HSpEkcOnSI2tpafHx86NOnj9BnExMTMRqNZGRkADB+/HiOHz8urLMyYMAAfv31VwDi4+ORSqWkpaUJffbs2bNUVlbi5ubGsGHD2LlzJwC9evVCqVRy9uxZoGGh+QsXLlBWVoZKpWL06NFs27YNgKioKOLj4wVNhw8fTnZ2NsXFxSgUCiZMmCCOETQfI0aMGMGOHTuA6x8jjEYjlZWV4hhxxRhh/U7/6quvqK2tJSwsjEWLFgnnjY6Oxs3N7brHCF9fX7Zs2XJdY8TOnTuFMSIiIkKYe9m/f3+qqqrIyckBYOrUqezZswedToe/vz+xsbFC8Yzk5GS0Wi2ZmZlA1xgjPDw8hGj99Y4R1uV0rzVGWO2/Htq8aO+LL77YomPSmKNHj7J161Y+//xzoTNbiYuL47777uOZZ56x6fMKCwuJjIzkyy+/ZM6cOaxbt4577723SaQJGga9Xr168f777zc7R0uRqfDwcHHR3i6AqL1jEBftdQzioo72p6VFezsbFosFtVotRK4uX75McXExV359S6VSAgMDCQsLIywsjNDQUHx9fZFIJA6y/OqI/d0xiLo3p7a2lo0bNwo31AMHDuS2225DLm+/mIOou2PolIv2PvLII8yfP/+qbaKiojh9+jTFxcXNjpWWlrZpom9wcDCRkZGCdxwUFIRer6eysrJJdKqkpIRRo0a1eA5nZ+cbmv9hMplsaQR790JhIQQHw9ix0I7FMKKiovjyyy8ZMWKEsG/58uUEBQXx4osvttvndCZ++OEHnnrqKYqLi1GpVCxYsIDXX3+91SIjaWlpPProoxw6dAiVSsWqVatYsWJFs3afffYZ9957Ly+99BLPP/+8sH/VqlW8+uqrfPHFF8yfP19o9/777/Pggw8CDRHX4ODgZjcx3Q2b+rxIu1NVVeVoE0Q6IRKJBC8vL7y8vOjbty8Aer2egoIC8vLyyMvLIz8/n5qaGqEkuzVS4OLiQmhoKKGhocIT2c6SHij2d8cg6t4U6/x+AJlMxpQpUxg+fHi7P4QQdXcM9tC9zc6Un5+fTSs/jxw5ErVazZEjRxg2bBjQ4B2q1epWnZ6WKC8v5/LlywQHBwMN4WYnJye2bdvGnXfeCTREr86cOcPf//73tl6OTVyzQuCGDbBiBeTl/bYvLAzeegvmzOkQm3oCQ4YMYcuWLURHR1NZWcm8efP44IMPeOihh5q1raur45ZbbuEvf/kLmzZtQq/Xk9f473EFsbGxrFu3TnCmLBYLX331Fb169WrSztvbm1deeYXf/e53ODk5te8FdmLEqpiOobPc5PYkXFxciI+Px8XFxdGmtAmFQiEsJAwNY1h1dbXgWOXl5VFQUEBdXV2TuVfQ0M9CQkIIDg4WHCxHZACI/d0xiLo3UFVVxebNm5ukdz3wwAOtFkC7UUTdHYM9dO+wOVOJiYncfPPNPPDAA6xevRpoKI0+ffr0JsUnEhISePXVV5k9ezYajYYXX3yRuXPnEhwcTHZ2NqtWrcLPz4/Zs2cDDXNQ7rvvPp588kl8fX3x8fFh5cqV9O3bV6ju195cdeX6DRvgjjvgykhFfn7D/m+/tYtD9dlnn7Fu3TohJbJ379788MMPvPLKK6xZs4aEhAS+++47QkJCMJvN3HHHHezbtw+j0cikSZNYvXo1Pj4+7N69m0WLFpGamoqPjw/ffPMNzz//PCdPnmxys1FXV0dgYCCpqalERkYCDfmojz32mJC/faOEhoZiNpub7LPmVl/Jp59+yqhRo1iyZAnQEI1MTExs9dy9evWisrKS48ePM2jQIA4cOEB4eHizdsOGDUOj0fDpp5+ybNmyG7iarsVV+7xIhzFkyBBHm9DjSExMJDU1tctXypNIJHh6euLp6Smsd2UymSguLhacq8LCQkpLS6mpqSE9Pb1JGr6Hh0cT5yo4OLjDHSyxvzuGnq57fX09+/bt4+DBgxiNRmH///t//69Dv/t6uu6Owh66d+giFmvXrqVv375MnTqVqVOn0q9fP/773/82aZOeni5M/JfJZKSmpjJz5kzi4+O55557iI+P5+DBg008y3/+85/MmjWLO++8k9GjR6NSqdi4cWOHPU23TsBvhsnUEJFqKeXLuu+xxxra2YFdu3Zx6623UlFRQVhYGKNHj+amm26ivLycqKgoXn/9daHtnDlzyMrKIisri5qaGv7yl78ADZMM586dyyOPPEJpaSmPPvoon332WbOnti4uLkyfPp1vvvlG2Pf1119z1113tWjb9OnThTSVK7fXXnut1WvaunUrnp6e+Pj4kJqayu9+97sW2x09ehQfHx+GDx+Ov78/M2bMECYct8aiRYtYt24dAOvWrWPRokUttnvhhRd45ZVXMBgMVz1fd6LVPi/SoVgnRYvYl+6qu0wmIyQkhKFDhzJ79mx+//vf8+yzz/K73/2OW265hf79++Pv749EIqG6upr09HR27drF2rVr+cc//sEbb7zB2rVr2bFjB2fPnqW8vLxdU5y7q+6dnZ6qu9WJevvtt9m7dy9Go5GoqCiWL1/Oiy++2OEPEXuq7o7GHrp36KK9Pj4+rFmz5qptGg/MLi4uQmWOq6FUKnnnnXd45513btjGG2Lv3qapfVdiscDlyw3txo+/4Y+bMmVKE4exrq6OZ599Vnjdt29fIYI3c+ZMMjIyhFTIWbNm8dFHHwENk5QXL14svO/xxx/nueeeE16/9tpr9O/fn/Hjx7NkyRJGjhzZoj133XUXL7/8MitXrsRoNPLdd98JlXeuZNOmTdd1zdZ00aysLD777LNWSxfn5+dz5MgRtm3bRr9+/Xj++edZvHixUG2mNfuHDRvGK6+8wg8//MBf//pX1q5d26zdlClTCA0N5bPPPuP222+/rusQERHpnJw4cYLbb7+dw4cPM3DgQEeb0+EoFAoiIiKIiIgQ9un1emGulbUCVnl5OTU1NdTU1Ahzlq3vDwgIICgoSNgCAgK6fGRPpPtSW1vLsWPHOHTokFBh1sfHh6lTp9K7d+9OW6BFpOvQoc5Ud6HV4hWFhbadwNZ212Dbtm3NClA0pnEpehcXF/z9/Zu8tpbkNhqNrFy5ku+++47KykosFkuTeXAqlYr58+fz8ssvs3nz5lbtufnmm7nnnnvIzs4mPT2dsLAw4uPjb/g6G2PVPjo6mr59+/LYY4/xxRdfNGvn4uLCnDlzGDp0KNBQddLd3f2qVekCAwNJSEhg1apVDBky5KprzLzwwgs8+OCD3Hzzze1wVZ0fccFexxAbG+toE3ocFosFg8HQ7YvKXA2FQkFkZKSQsg0NT/GLi4spLi6mqKiIoqIiiouLhfmojeekSiQSfHx8CAoKwt/fn4CAAAICAvDx8bnqQqdif3cMPUV360PWM2fOCEWVfH19GTduHH379r3hRXjbSk/RvbNhD91FZ8oGWv2H+19RjGtiazs7sXbtWvbu3cvBgwcJCQlhy5YtQrU6gIyMDN577z3mzZvHk08+yddff93ieZydnZk5cybffPMNaWlprab4QcP6YXv37m3x2KpVq1i1alWLxxprbzabhTU9riQ5OZnCFpzWa90gLVy4kHvvvZcvv/zyqu2mTp1KcHAwn3/++VXbdRfs/SUj0oD4dF+ks+Ds7NwsgmU2mykvL2/iYBUVFaHRaCgvL6e8vLzJOWQyGX5+foJzZd28vLyQSCRif3cQ3Vl3jUZDamoqp06darL2aGhoKCNGjCApKclh32/dWffOjD10F50pG6irq2v5jzF2bEPVvvz8ludNSSQNx8eO7Xgj20BNTQ3Ozs54eXlRVlbGP/7xD+GY2Wzmnnvu4bnnnmP58uX079+fr7/+WkgXjIqK4sUXX2Tp0qVAQ6rcc889R25urlCKtyV++eWXNtv5zTffkJiYSHJyMhcvXuS1115jypQpLbZdvHgxY8eO5cSJEyQnJ/PSSy8xbty4a1ZxmTdvHoGBgYy3IQ3zhRdeYOHChW2+jq5Iq31epEM5d+5ci4VQREQ6A1KpFH9/f/z9/UlOThb2azQaIYpVUlJCaWkpJSUlGAwGYX9jnJyc8Pf3Jy8vjylTpuDn5ycUlGrPdX1EWqa7jTNarZb09HTOnTvHpUuXhMJVMpmMpKQkhg8fTmhoqIOt7H66dxXsobs4at0IMllD+fM77mhwnBo7VNYc3DffbNf1ptqDu+++m59++omAgADCw8O5//77hZz4f/zjH8hkMlasWIFUKuXTTz9lzpw5jB8/Hm9vb8rLy5ukGk6ZMoUlS5YQExNDTExMu9qZkZHBY489RlVVFb6+vsybN6/JgtFubm788ssvjB07lsTERP71r38xe/ZsqqqqGD16dLNiJy2hUqlsTt2bNm0a8fHxwnoUIiIiIiINY7Gbm1uTpSUsFgtVVVVNnKuSkhLKysowGAwUFBSQmZnJjh07hPdIJBK8vb3x9fUVHCzrciyurq7i3BYRoKFvVVRUcPHiRdLS0sjJyWlS+TcsLIz+/fuTnJzc5ZY8EOmaSCw9MFG8tVWOdTodWVlZREdHo1Qqhf0mk+nqlQJbWmcqPLzBkepG60wdPHiQt99+u8U5Sx3FNbUX6RA6SvfW/sdEGrjaHD+RjqGuro4zZ86IN152wmw2U1lZSUlJCTk5OWi1WsrLyykrK6O+vr7V9zk7OwvRK29v7yY/3d3dRUerDXTFcaa2tpacnBwuXbrEpUuXmi3EGhQURGJiIklJSTatheoIuqLu3QFbdW/NN7AFMTJlAzqdDldX19YbzJkDM2c2VO0rLGyYIzV2bKeLSN0oI0eObLWyX0dxTe1FOgRRd8eQlpYmrkViZ1xcXJBIJKIjZSekUim+vr74+vpSW1sr9HeLxSLMvSorKxN+lpWVUVVVRX19vVBp8Erkcjne3t7CZnW0rMtviCnLTens44zZbKaiooL8/HxycnLIzc2lrKysSRuZTEZERARxcXEkJiZetYBUZ6Gz695dsYfuojNlA40XdWsVmaxdyp+LNMUm7UXaHVF3x3DlBH6RjicnJ4fnn3+e1atXN6lmJ9LxNO7vEokEd3d33N3diYqKatLOaDRSUVFBeXk5lZWVVFZWUlFRQWVlJVVVVRiNRkpLSyktLW3xc1xcXIQFjT09PfHy8mry2s3NrUdFtjrTOGMymSgtLaWwsJCioiLhp16vb9Y2ICCA6OhoevXqRVRUVJdzkjuT7j0Je+guOlM2IFY2cxyi9o5B1N0xdPSikSLNKS8vZ8uWLZSXl4vOlJ2xtb/L5XKhEuCVmM1m1Gq14Fxd6WjpdDrq6uqoq6trUt2tMTKZDA8PD9zd3XFzcxOcuitfK5XKbuF02XucsVgs1NXVCRHHKzdr2fLGODk5ERQUJFSUjIiI6PLRY3F8dwz20F10pmxAzHF1HKL2jkHU3TGMGjXK0SaIiNiN9ujvUqlUSO9rifr6etRqdZOtqqpK+L26uhqTySQ4YhGpiDQAAEPCSURBVFdDLpcLzpVKpUKlUuHq6ir8fuVrhULRKZ2v9h5njEYjNTU1VFdXN9vUajWVlZXCYrkt4ezsTHBwcJPN19e32z3UE8d3x2AP3UVnygask9JE7I+ovWMQdXcM27dvZ9q0aY42Q0TELtijvzs7O7ca1YKGyFZNTQ1qtRqNRkNNTQ01NTVNfq+pqaGurg6j0UhVVVWz4getIZfLcXFxwdnZGaVSec2fTk5OyOVynJycWvy9vYoCNdbdYrFgMpnQ6/UYDIYWf9bV1aHVaq/60xY8PT2F+XKNKzZa1x3r7ojju2Owh+6iMyUiIiIiIiLSI5FKpcLcqathNBoFB0uj0aDVaqmtrUWr1Qpb49cGg0GI2NTU1LSbrU5OTkilUiQSCVKptMnvjfdBg6NkNpuFzfo6LS2NEydOYDabMRgM11zc3hbkcjkeHh54enri4eHRZPPy8sLHxwcnJ6cb/hwRkc6I6EzZgLOzs6NN6LGI2jsGUXfHEB0d7WgTehyBgYEsW7aMwMBAR5vS4+hK/V0ulwvVAW3BYDBQW1tLXV0d9fX16HS6Vn9af7c6YI1/GgwG4Zxms/mq5eNtxcXFBZ1O12y/TCZDoVDg5OTU5KeLiwsuLi6oVKoWf7q5uQlVMUVapyv19+6EPXQXnSkb6G55u10JUXvHIOruGMS5avYnNDSUF154gZCQEEeb0uPozv3dycmpTc5Xa1jT8KyOldFobBZpavx745/WKNWVUauSkhKCg4OFSJfVaRLH/Y6lO/f3zow9dBf/c2zA1nzgjiQqKopDhw412bd8+XJefPFFu3x+eno606dPx8/PD39/fxYvXtzqZN3c3Fzc3NyEzbpyfUpKSovtJRIJsbGxTfZlZGQgkUi49dZbm7S7ciLhzTffzGeffXZjFyfSjM7Q53siqampjjahx1FTU8OaNWvaLRVLxHbE/n5tJBKJMPfKw8MDHx8f/Pz8CAgIIDAwkODgYEJDQwkLCyMiIoLIyEiio6OJiYkhKiqKiIgIwsLCCA0NJTg4mMDAQAoKCvDz8xMWPFYqlaIjZQfE/u4Y7KG7+N8jYhNqtZo777yTS5cukZ2djV6vZ+XKlS22jYiIQKPRCNsXX3xBREQEgwYNavX8UqmUw4cPC6/Xrl1LXFxcs3ZpaWls3br1xi9IREREhIYHN08//TQZGRmONkVEREREpAsiOlM24OrqetXjGRlw/Hjzzd7fze+88w4xMTH4+/tz9913U11d3eZztDYRddiwYdx99914enri6urKAw88wJEjR2w659q1a1m4cOFV86kXLFjA2rVrhddffPEFCxYsaFa96PHHH+fPf/6zTZ8rcv1cq8+LdAwjRoxwtAkiInZD7O+OQdTdMYi6OwZ76C46UzbQ0krcVjIyID4eBg9uvsXH28+h2rJlC6+99ho//fQT2dnZ1NbW8sQTT7TYtri4mAceeIDIyEgGDRrESy+9xMGDB9mwYQN33323TZ934MABkpKSrtmupqaGjRs3smjRoqu2u/POO/nuu+8wmUwcPXoUPz8/oqOjMZvNTdotXbqU/Px8tm3bZpOdItfH1fq8SMeRmZnpaBNEROyG2N8dg6i7YxB1dwz20F10pmygcTWdK7Gm2a9ZAykpv21r1jQ93h5MmTJFmNDq5eXFp59+Khz76quvWL58OYmJibi6uvLKK6/w5ZdftnieQ4cOccstt3DmzBk+//xztFotzz33HD///DN//OMfr2nHyZMnefvtt21qu2HDBuLi4khOTr5qO19fX/r378/27duFSBY0j5Q5OTmxatUqMTrVwVytz4t0HCUlJY42QUTEboj93TGIujsGUXfHYA/dRWfKBmyZmJmYCIMG/bYlJra/Hdu2bRMWDKyqquLee+8VjhUUFBARESG8joyMpLa2FrVa3ew8t912GyUlJdx///3861//YvLkyWzbto2XX36ZH3744ao2ZGVlcfvtt/Pxxx/bFJlau3btNaNSVhYtWsR///tfNmzYwJ133tlqu3vvvZe8vDy2b99u03lF2o44GdkxiCXp7Y+TkxN+fn7iGjgOQOzvjkHU3TGIujsGe+gu3jHZgLu7u6NNuCYhISHk5uYKr3Nzc1GpVC0uRLhmzRoyMjJYunQp/fv355VXXsHX15cJEyYQFhbW6mcUFRUxZcoU/vjHPzJr1qxr2lRUVMTu3btZsGCBTdcwc+ZMfvzxR5KTk/H39wca1va4EicnJ5599lkxOtWBdIU+3x0ZP368o03ocfTt25fS0lL69u3raFN6HGJ/dwyi7o5B1N0x2EN30ZmygZaiO52NefPmsXr1atLS0qitreW5555j/vz5LbZdsmQJb7zxBrfccgsPPfQQO3bsoKqqinPnzrXq+KjVaqZNm8bdd9/NsmXLbLLpyy+/ZNSoUYSHh9vUXqVSsW3bNt555x1hn9FobLHtvffeS25uLkePHrXp3CJtoyv0+e7Ili1bHG1Cj0TU3TGIujsGUXfHIOruGOyhu+hMtRPnzzet5Hf+vH0//5ZbbuGpp57illtuITIyEmdnZ954440W215ZIc8Wvv/+e06fPs3f//73JmtIWVm+fDnLly9v8p62pPhZGT58OL169bpmO4VCwbPPPktFRUWbzi8iIiLSmNTUVBYvXiyuASMiIiIicl1ILK3Vwu7GVFdX4+npiVqtxsPDQ9iv0+nIysoiOjoapVIp7K+rq8PFxaXFc1mr+bXGhQvQwnJJIjZyNe1FOo6O0r21/zGRBs6fP09iR0y4FGmV48ePM3jwYFJSUq66Fp5I+yP2d8cg6u4YRN0dg626t+Yb2ELzCSkizWhp3o6VuLgGh6mlqn3u7qIjdaNcTXuRjkPU3TH4+Pg42gQREbsh9nfHIOruGETdHYM9dBfT/GxAq9Ve9XhcXNNKftZNdKRunGtpL9IxiLo7hpMnTzraBBERuyH2d8cg6u4YRN0dgz10F50pERERERERERERERGR60B0pmzA1dXV0Sb0WETtHYOou2MYOnSoo03occTFxfHDDz8QJ6YS2B2xvzsGUXfHIOruGOyhu+hM2YBer3e0CT0WUXvHIOruGPLy8hxtQo/D3d2dqKgocW01ByD2d8cg6u4YRN0dgz10F50pGzAYDI42occiau8YRN0dQ2FhoaNN6HHk5+fz8ssvk5+f72hTehxif3cMou6OQdTdMdhDd9GZsgGJROJoE3osovaOQdTdMYhVFO1PcXExX3/9NcXFxY42pcch9nfHIOruGETdHYM9dBedKRtoa715kfZD1N4xiLo7hkmTJjnaBBERuyH2d8cg6u4YRN0dgz10F50pG6iurna0CT0WUXvHIOruGLZt2+ZoE0RE7IbY3x2DqLtjEHV3DPbQXXSmbMBisTjaBKKiovDw8KCurk7YV11djYuLCwkJCXaz44knniAmJgZ3d3eGDBnCnj17Wm37t7/9jYSEBNzd3enTpw8bNmxote3SpUuRSCTs27evyf4pU6YgkUgoKioS2slkMs6fPy+0+fLLLxk/fvyNXZhIEzpDn++JmM1mR5sgImI3xP7uGETdHYOou2Owh+6iM2UDCoXC0SYAEBQUxI8//ii83rBhA+Hh4Xa1wdPTk61bt6JWq3n66aeZNWsWNTU1LbaVyWR88803qNVq3n//fe69914uXbrU6rnj4uJYu3at8DorK4vKysoWbXjppZdu/GJEWqWz9PmeRmhoqKNN6HH4+voyZ84cfH19HW1Kj0Ps745B1N0xiLo7BnvoLjpTNnCtyWsZGXD8ePMtI6N97ViwYEETZ2Pt2rUsXLiwSZvU1FRGjx6Nl5cXQ4YM4dChQ9f1Wa1FJl544QViY2ORSqXMmzcPFxcXLly40GLblStX0rdvX6RSKePGjSM5OfmqK1HPmTOHH3/8Uagkt27dOu66665m7e6//35++eUX0tLSmh3Lzs5GqVTy3nvvERAQQHh4OLt37+bjjz8mODiYiIgIfv31VxsU6NmIE2UdQ1BQkKNN6HFERkayevVqIiMjHW1Kj0Ps745B1N0xiLo7Bnvo3qHOVGVlJUuWLMHT0xNPT0+WLFlCVVXVVd8jkUha3F5//XWhzfjx45sdnz9/foddh1arbfVYRgbEx8Pgwc23+Pj2daimTJnC8ePHqaiooKioiIyMDMaNGycc1+v13H777SxcuJDS0lJWrlzJ9OnTUavVLZ7vvffeY8CAAURERHDfffexadMm9uzZw8MPP8yxY8euaU92djYVFRXExsZes21NTQ1nz56lT58+rbbx8vJi+PDhbNmyBYAvvviCWbNmNWvn4+PD73//+1ajU3q9nuzsbPLz81mxYgWLFy/m3Llz5OTk8P/+3//jscceu6a9PZ2r9XmRjiMlJcXRJvQ46urqWL9+fZMUahH7IPZ3xyDq7hhE3R2DPXTvUGdq4cKFnDx5ks2bN7N582ZOnjzJkiVLrvqewsLCJtsnn3yCRCJh7ty5Tdo98MADTdqtXr26Iy+lVawZbmvWQErKb9uaNU2PtwdyuZxZs2bxzTff8OWXXzJv3jyk0t/+hIcOHUImk/Hwww/j5OTE/PnziYuLY+vWrc3OVV9fT3Z2Nps2bSIlJYWRI0fywQcf8I9//IOxY8dec8Vog8HAPffcw1NPPYWnp+c1bX/wwQeZMWMGiYmJV223cOFC1q5dy8mTJ3FxcWnVUXviiSf46aefWoxOWSwWnnvuOZycnJg7dy75+fk888wzKBQK5s6dy9mzZ8XcZREREQDOnz/P8uXLm8zDFBERERERsZUOy+U5f/48mzdv5tChQwwfPhyADz/8kJEjR5Kenk7v3r1bfN+V4bgffviBCRMmEBMT02S/SqWyW8hUpVJds01iIgwa1PG2LFq0iGeeeYa6ujo++OCDJpG+goICIiIimrSPjIykoKCg2XmcnZ2ZPXs2f/3rX6moqGDy5Ml8/vnnuLq68u2333L27FmSkpJatMFisbB06VICAgJ48cUXr2nzM888Q25urk0VVaZPn84f/vAHvL29WbRoUava+/r68vvf/56//vWvTJ8+vdm1WUt7u7i4AODv7y+8NhgM6PV6lErlNe3pqdjS50Xan0H2GERERDoJYn93DKLujkHU3THYQ/cOi0wdPHgQT09PwZECGDFiBJ6enhw4cMCmcxQXF/PTTz9x3333NTu2du1a/Pz8SEpKYuXKla0WQWgPjEZjh527rYwcOZL8/Hw0Gg0DBgxociwkJITLly832Zebm0tISEiz89TX17Nq1SrGjx/PggULOHz4MImJiURGRrJ///5mTlljHn30UQoKClizZk2TyFhLvP7662zcuJGNGzcKjs3VUCqVTJs2jQ8//JC77rrrqto/+eSTbNq0ifT09GueV6RtdKY+35MoKSlxtAkiInZD7O+OQdTdMYi6OwZ76N5hkamioiICAgKa7Q8ICBDKXF+Lzz//HHd3d+bMmdNk/6JFi4iOjiYoKIgzZ87w7LPPcurUqVYjH/X19dTX1wuv27qGjl6vt8kRsBcbNmxo0YkZMWIEBoOB9957jwceeIDvvvuO9PR0pk6d2qytQqFg+/btwnlmz55t02e/8MIL7N+/n19//RVnZ+ertv3kk09499132b9/P97e3jadH+Cll17i3nvvJTg4uNX5XtAQnXrooYd4++236du3r83nF7k2na3P9xTy8vJajQiLiHQ3xP7uGETdHYOou2Owh+5tdqZefPFF/vznP1+1zdGjR4GGYhJXYrFYWtzfEp988gmLFi1qlo71wAMPCL8nJycTFxfHkCFDOH78eIvhvFdffbVFm7dv346rqysTJ07kyJEj1NXV4efnh8lkEm7ilUplk9fu7u5otVpMJhMymQyzWQVIuTLd3vq6rq4OtVqPVCrFzc1NcOScnZ2RSqXCpGc3Nzd0Oh1Go7FZW2uZao1Gg1qtpk+fPuj1etRqNbW1tQCCfV999RWPP/44zzzzDNHR0WzYsAGFQoFarUYikeDh4UF1dTUWiwUnJycUCoVwDpVKhdFoRK/XAw0lyGtqajCbzULbv/zlLzg7OzeJdv3zn//k/vvvZ+vWrcydO5fi4mKUSiV//vOfKSwsbLIO1hNPPMGLL77YREOVSoVer0en01FfX4+/vz/u7u6o1WqhqmB1dTVubm5YLBZ0Oh1qtRpnZ2ceffRR3n33XYxGIyaTSSicUFNT00RDnU6HVCoVdDIajWi1WgwGA1KpVPg8q95yuVw4l6urK3q9HoPB0EzDK9u2pKGtent4eKDRaDCbzcjlcpRKJRqNBmhITzSbzcJDgau1tf6/6HS6FvusSqUSIrlXtrX2Q71ej0ajadK2LX32yrZWDWtra4XPshYaCQ8Px8/PjxMnTgAwZMgQCgoKKCgoQCaTMXnyZLZv347JZCIkJISQkBChQMrAgQMpKysTIrLTpk1j165d6PV6AgMDiYqK4vDhwwD069eP6upqsrOzgYaCLvv370er1eLn50d8fLwQNU9KSkKn0wml/K1jhEajwdvbm6SkJGFNtISEBMxms1DV8qabbuLkyZOo1Wo8PDwYNGgQu3fvBhrK/8vlcmF+zpgxYzh37hwVFRW4urpisVgEXWJiYlCpVJw5cwZoiEpfvHiR0tJSlEol48aNE+ZDRkZG4uXlxalTpwAYNmwYubm5FBUV4eTkxMSJE9m6dSsWi4WwsDACAgI4fvw4AIMHD6aoqIj8/HykUilTpkxhx44dGI1GgoODCQsLE8b0AQMGUFFRQW5urqD37t27qa+vJyAggJiYGKGCaN++fdFoNGRlZQEwefJkDhw4gFarxdfXl4SEBPbv3w8gjGkXL14EYMKECRw7doyamhq8vLzo16+fsKadNUXcGpEeN24cp0+fpqqqSlj/bteuXQDExsaiUCg4d+4cAKNHjyYtLY3y8nJUKhWjRo3i4MGDyOVycnNzCQoKIjU1FWh4OJWZmUlJSQnOzs6MHz9e+NtERETg4+MjVCcdOnQoeXl5FBYWIpfLmTRpEtu2bcNsNhMaGkpQUJAwCXrQoEGUlJSQl5eHRCJh6tSp7Ny5E4PBQFBQEBERERw5cgSA/v37U1VVRU5ODgBTp05lz5496HQ6/P39iY2N5eDBg0DDd6FWqyUzMxOASZMmcejQIWpra/Hx8aFPnz5Cn01MTMRoNJLxvwpJ48eP5/jx41RXV+Pp6cmAAQOEqqfx8fFIpVJhfuqYMWM4e/YslZWVuLm5MWzYMHbu3AlAr169UCqVnD17FoBRo0Zx4cIFysrKUKlUjB49WnjoGRUVRW1traDp8OHDyc7Opri4GIVCwYQJE8QxguZjxIgRI9ixYwdw/WNETk4OlZWV4hiBbWPE9u3bAYiOjsbNze26x4iysjK2bNkijhFtGCM8PDw4ffo0cP1jhLUvXmuMaGkOvq1ILG1cnbOsrIyysrKrtomKimLdunU88cQTzar3eXl58c9//pN77733qufYu3cv48aN4+TJk/Tv3/+qbS0WC87O/7+9O4+Lqtz/AP4ZYNhhAAEBWcQVkCUWF1xxQy3L8nc1l9TMa1pp2PJq0Vvq/aXZYtmvW5ldFX8u6c/S6rbcAhU3UElwRRMVN0RRZFVZlOf3x1xOjgwwjMw8Ip/368WL5swzZ57z6Zkj3znnPMcGq1ev1juVtr4jU35+fsqOrEZ5eTlycnIQGBho8PU0NbP51eXECaBjR4NWRfTAM+YzRkRERGRKNYXi3bWBIRp9ZMrd3R3u7u4NtouNjUVxcTH27duHbt26AQD27t2L4uJi9OzZs8HXL1++HNHR0Q0WUgBw9OhRVFVVwdvbW+/zNjY2DZ6SVp+SkpI6g+3YUVsw6btky8mJhdS9qi97Mh3mLsfWrVsxYMAA2d1ocZi7HMxdDuYuB3OXwxy5m+yaqeDgYAwdOhRTp05Vpi1/9tlnMXz4cJ2Z/IKCgvDuu+/qXLNTUlKCjRs3YvHixbXWe+rUKaxduxYPP/ww3N3dkZWVhVdeeQWRkZHo1auXSbaloYN3LJhMp5EHTqmJMHc5am5YTeZz7NgxPPvss/jXv/7V4K0bqGlxvMvB3OVg7nKYI3eT3mdq7dq1CAsLQ3x8POLj4xEeHo7Vq1frtPnjjz9qTTKwfv16CCEwduzYWuu0trbGli1bMGTIEHTu3Bkvvvgi4uPjkZycDEtLS5Nsh1qtNsl6qWHMXg7mLoe5bvdAf7p58yZOnTrFm/ZKwPEuB3OXg7nLYY7cTXZkCgDc3NywpubutXXQ9w34s88+i2effVZvez8/P+WiN3OpmQCCzI/Zy8Hc5ajvlgREDxqOdzmYuxzMXQ5z5G7SI1MPiprZ18j8mL0czF2OmhmaiFoCjnc5mLsczF0Oc+TOYoqIiIiIiMgILKYMYG9vL7sLLRazl4O5y2HI7KXUtAIDA7Fs2TIEBgbK7kqLw/EuB3OXg7nLYY7cWUwZ4NatW7K70GIxezmYuxx335ePTM/V1RV9+vSBq6ur7K60OBzvcjB3OZi7HObIncWUASorK2V3ocVi9nIwdzlq7mJP5nP58mV89NFHuHz5suyutDgc73IwdzmYuxzmyJ3FVBOrqDDNetu2bYs9e/boLJs+fTrmzZtnmjc0kbKyMvTu3RutWrWCq6srBg4ciOPHj9fZvqqqCq+++ipat24NZ2dn9OnTp862KpUKHTp00FmWnZ0NlUqFoUOH6rS7+8bRQ4cORWJionEbRUTNVm5uLr766ivk5ubK7goRETVDLKYM4OzsbFC7L78EnJy0v0k/GxsbfPXVV7hy5QoKCgowcuRITJo0qc72CxcuxLlz53DkyBEUFhZiyZIl9a7fwsICe/fuVR6vXbsWHfXcVfn48eP47bffjN6OB52hY56aVnx8vOwuEJkNx7sczF0O5i6HOXJnMWWAsrKyBtt8+SUwfToQHKz9be6CKjExEfHx8Zg6dSqcnJwQExOD3NxcvPDCC9BoNOjevTsuXrwIAKiursbIkSPh6ekJNzc3jBo1CteuXQMApKSkoE2bNsrjjRs3onPnzo2+oaW++4cB2pvBBgcHw8LCAkIIWFhYICcnR2/bgoICrFq1CsuWLYOHhwcsLS0RHR1d7/uOHTsWa9euVR5//fXXem/+/NJLL2H+/PmN2KKWxZAxT01vx44dsrtAZDYc73IwdzmYuxzmyJ3FlAGqq6vrfb6mkJo5E8jM1P6WUVBt27YNDz/8MK5duwZfX1/06tUL/fr1Q0FBAdq2bYsPPvhAaTty5Ejk5OQgJycHpaWl+Pvf/w4AiIuLw3/9139hxowZuHLlCmbOnInExETY2dnVer/Lly9j6tSpCAgIQFRUFP77v/8baWlp2LRpEyZOnFhvX8PDw2Fra4sZM2bg9ddf19vmyJEj8PLywty5c+Hu7o6wsDBs3ry53vWOHj0amzdvxu3bt5Geng53d3e9s3Q9/fTTyM3NRVJSUr3ra6kaGvNkGuXl5bK7QGQ2HO9yMHc5mLsc5sidxZQBrKys6nzuzkLqk08ACwvtb1MUVIMHD4aLi4vys3LlSp3nw8LC8MQTT0CtVmPEiBFwcHDA6NGjYWVlhccffxyHDh0CoD0V7qmnnoKDgwM0Gg1eeukl7Nq1S1nPokWLkJ6ejri4OEyYMAGxsbF6+7Nnzx4MGzYMR44cwapVq3Djxg3MmTMHP//8M9566616t+XQoUMoKSnB0qVLERwcrLdNbm4usrKy4OrqitzcXCxduhSTJ0/GiRMn6lxvq1atEBERgeTkZKxduxbjxo3T206tVmP27Nk8OlWH+sY8mY6Hh4fsLrQ4Go0Gffv2hUajkd2VFofjXQ7mLgdzl8McubOYMoCtra3e5XcXUiqVdrlKZZqCKikpCUVFRcrP5MmTdZ739PRU/tvOzk5nANnZ2eH69esAtNNez5o1CwEBAXB2dsZf/vIXFBQUKG3t7e0xZswYHDt2DC+++GKd/XnkkUeQn5+Pv/71r/jss88waNAgJCUlYcGCBfj+++8b3B47Ozv89a9/xTPPPIPCwkK9z6vVavztb3+DjY0NevXqhfj4+AaPJo0fPx6rV6/Gpk2bMHr06DrbTZ48GRcuXEBycnKDfW1p6hrzZFp3T6BCpte+fXv861//Qvv27WV3pcXheJeDucvB3OUwR+4spgyg7/qRigptsRQeDixZ8mchVUOl0i4PD9e2M9Usf8ZYu3Ytdu7cibS0NJSUlOCbb77RucYpOzsbX3zxBUaNGoVXXnmlzvWsWbMG2dnZePrppxEREYGFCxeiVatW6N+/P3x9fQ3qixACZWVlyMvLq/VcaGio3vYNGTFiBH744QeEhobW+42EWq3Gm2++yaNTevCaKTnS0tJkd6HFqaqqwi+//IKqqirZXWlxON7lYO5yMHc5zJE7iykj2dgAn34KHDoEzJoF3P03vhDa5YcOadvZ2MjopX6lpaWwsbGBi4sLrl69ig8//FB5rrq6GpMmTcKcOXOQmJiIAwcO4P/+7//0rmfChAlYvHgxhg0bhueeew5btmxBUVERsrKy9E76AAAHDx7Ejh07UFlZievXr2P27NlwcXHRO+Nex44dERUVhXfffRe3bt3C3r17kZSUhEGDBtW7ffb29khKSsKnn37aYBaTJ0/GuXPnkJ6e3mBbInrwHD58GGPGjMHhw4dld4WIiJohFlMG0Df5AgBMmwYsXaotlhIS/iyohNA+/vRT7fPTppmxswaYOHEiNBoNPD090adPH517MH344YewtLREQkIC7OzssHLlSsycORP5+fm11mNpadno966qqkJCQgJatWoFf39/HDhwAD///DPUajUA7b2zpk+frrRfs2YNUlJS4OLigkmTJmHFihXo3Llzg+/TvXt3g07bsba2xptvvqnMXkhadY15Mi19R2OJHlQc73IwdzmYuxzmyF0lDDlv6gFTUlICjUaD4uJinfvplJeXIycnB4GBgTrXjJSXl9d7Dcmd104tWaI9InW/FlLNTUPZk2mYKve6PmOklZ2drfcoLZlORkYGoqOjsX//fkRFRcnuTovC8S4Hc5eDucthaO511QaG4JEpA1Q0cMHTnUeoIiNZSDWlhrIn02Ducpw+fVp2F4jMhuNdDuYuB3OXwxy5c/7jJlJTOM2cyUKKiIiIiKgl4Gl+BpzmJ4SA6u7p+upQUXF/TTbR3DUme2o6psqdp/nV79atW7zHl5ndvn0bxcXF0Gg0Rl0HSsbjeJeDucvB3OUwNHee5mdijZkmmoVU0+IU3XIwdzn27NkjuwstjqWlJbKyslhIScDxLgdzl4O5y2GO3FlMGaC6ulp2F1osZi8Hc5ej5sbaZD7Z2dlISEhAdna27K60OBzvcjB3OZi7HObIncWUAXhYVh5mLwdzl8PNzU12F1qc0tJSZGRkoLS0VHZXWhyOdzmYuxzMXQ5z5M5iygC8tkMeZi8Hc5cjJCREdheIzIbjXQ7mLgdzl8McubOYMgCvH5GH2cvB3OXYtWuX7C4QmQ3HuxzMXQ7mLoc5cmcxRUREREREZAQWUwZozClPprrXadu2beHs7IybN28qy0pKSmBnZ4egoCDTvKkZff/99wgKCoJGo4G3tzdefvll3L59u87sExMT4evrC2dnZ0yePBmVlZV1tlOpVHjnnXd0ls+ePRsqlQrr16/Xaffll18qbS5dutRip2XnaX5yBAcHy+5Ci+Pn54e///3v8PPzk92VFofjXQ7mLgdzl8McubOYakJffgk4OWl/m4KXlxd++OEH5fGmTZsemD8AYmJisHPnThQXFyMrKwuHDh3CsmXL9LY9fPgwXn75ZXz33Xc4f/48zpw5U6tYulOHDh2wbt065bEQAhs2bED79u112rm6umLhwoWoqqpqmo0iaqRbt27J7kKL4+HhgfHjx8PDw0N2V1ocjnc5mLsczF0Oc+TOYsoA5eXlDbb58ktg+nQgOFj72xQF1dixY7F27Vrl8dq1azFu3DidNiqVCl988QX8/f3h7u6ODRs24Mcff0S7du3g6emJDRs2KG2/+uordOzYEU5OTggPD0dKSgoA7faGhITg66+/BgAUFRXB19cXW7dubXSfDb0ndJs2bWr9MZOTk6M3+3Xr1uHJJ59ETEwMNBoN3nrrLaxZs6bOdbdv3x5OTk7IyMgAAKSmpsLPzw++vr467bp16wY/Pz+sXLlS73ratm2LxYsXo1OnTnB2dsaSJUuwb98+hISEwM3NDR9//LFB29ocGDLmqelxem7zu3btGpYuXYpr167J7kqLw/EuB3OXg7nLYY7cWUw1gZpCauZMIDNT+9sUBdXgwYORkZGBa9eu4dKlS8jOzkbfvn1rtdu9ezdOnDiBL774As8//zy+/fZbHDlyBMuXL8eMGTNw+/ZtAICPjw+2bNmC4uJizJw5E2PGjEFFRQVsbW2xatUqzJo1C3l5eUhISMBjjz2GAQMG6O3XF198gYceegj+/v6YMmUKfvzxR+zYsQMvvPACfv/9d4O3b9euXdBoNHBzc8Phw4fxzDPP6G2XlZWFsLAw5XFERARycnJ0ToG82/jx45WjU+vWrcP48eP1tps7d269R6d+/vlnpKenIzk5Ga+//jo++OAD7N69G9u2bcPs2bNx5coVQzeXiO4DZ86cwQcffIAzZ87I7goRETVDLKYM4OTkVOdzdxZSn3wCWFhof5uioLKyssLjjz+OjRs3Yv369Rg1ahQsLGr/L3zttddga2uLkSNHoqioCM8//zzs7e3x6KOPorS0FBcvXgQAPPLII/D394eFhQWmTp0KlUqlVPBdu3bFlClTMGjQIOzcuRPvv/++3j5VVFTgzJkz+PHHH7F//37ExsZi2bJl+PDDD9GnTx907drV4O3r3bs3iouLcfr0aUyfPh2urq56sy8rK4Ozs7PyuOa/65uB7sknn8TGjRtRWVmJ77//Hn/5y1/0ths8eDDatGmDxMREvc8nJCRAo9GgW7du8PLywujRo+Hq6oqIiAj4+/vj+PHjBm/v/ay+MU+mExcXJ7sLRGbD8S4Hc5eDucthjtxZTBngxo0bepffXUjVzFWgUpmuoKo5wlLf0RVPT08AgKWlJdRqtc7pc7a2tsrdoL/77jtERUXBxcUFLi4uyM/PR0FBgdL2mWeeQVZWFp555hk4OjrqfS8bGxs88cQTeOedd/DCCy+guroaq1atwjfffIPq6mocPXq01mt27twJR0dHODo6YtiwYbWeDwwMRFhYGGbNmqU3e0dHR5SUlCiPa/67rj4CQOvWrREUFITZs2cjJiYGrq6udbat7+hUTbYAYGdnp5OtnZ3dA3OH87rGPJlWzamoRC0Bx7sczF0O5i6HOXJnMWWAmtPi7lRRoS2WwsOBJUv+LKRqqFTa5eHh2nZNNctfbGwscnNzUVZWhoceesjo9VRUVGDs2LFYtGgRCgoKUFRUBE9PT+UaJyEEnnvuOYwfPx6ffPIJcnNz61zP7NmzERcXh7Fjx2Lv3r0IDg5GQEAAdu/eDX9//1qv6dOnD8rKylBWVoZffvlF73qrq6tx6tQpvdmHhITg8OHDyuODBw8iMDAQdnZ29W7zuHHj8NFHH9W6zuxu8fHx8Pb2xqpVq+pt9yDTlzuZ3p1fEhA96Dje5WDucjB3OcyRu5XJ3+EBYGlpWWuZjQ3w6afaI0+zZukemQIAIbTLDx0Cli7Vtm8qmzZt0nt6X2NUVFSgsrJSObLyySef6Fzvs3TpUhQWFuKXX37BvHnzMHXqVPz888+11mNtbY3k5GSlP0888YRR/dm4cSO6d+8Of39/nDx5EosWLcLgwYP1Zj9u3DjExcVh6tSpaN++PRYsWICnnnqqwfcYNWoUWrdubdAh37lz5zZYdD3I9OVOpqfRaGR3ocVxcHBAaGgoHBwcZHelxeF4l4O5y8Hc5TBH7jwyZQB7e3u9y6dN0xZKn34KJCRoCyhA+zshQbt86VJtu6YUHh6O0NDQe1qHs7MzPvjgAwwePBheXl4oKChAhw4dAGhn0fvb3/6GxMREWFlZ4e2338aFCxewYsWKWutRqVT3XNgB2tlWevbsCQcHBwwYMAD9+/fH/PnzlewdHR2xc+dOAEBYWBgWL16MRx99FL6+vvDz88OcOXMafA97e3sMHTrUoHsoDRkyBJ06dbq3jWrG6hrzZFr3crSZjNO5c2ekp6ejc+fOsrvS4nC8y8Hc5WDucpgjd5UwdO7qB0hJSQk0Gg2Ki4t1JjIoLy9HTk4OAgMDdf7gLi4urreyvfPaqSVLtEekTFVItTQNZU+mYarc6/qMkdavv/6KIUOGyO5Gi8Pc5WDucjB3OZi7HIbmXldtYAie5tcEagqm6dOB7dv/PLWPhRQR0f0tIyMDQ4cOxf79+xEVFSW7O0RE1MywmDKAId+g1xROM2eykGpKPHohB3OXoyWfWkotD8e7HMxdDuYuhzlyN+k1UwsWLEDPnj1hb28PFxcXg14jhMC8efPg4+MDOzs7xMXF1Zpeu6KiAjNnzoS7uzscHBzw2GOP4cKFCybYgsaZNg0oLWUhRUTGaYrrD4maC453OZi7HMxdDnPkbtJ3qKysxKhRo/Dcc88Z/Jr3338fH330Ef7xj38gPT0dXl5eGDx4MEpLS5U2s2bNwubNm7F+/Xrs2rULZWVlGD58uMmmcy4vLze4bVPO2keNy56aDnOX40G56TORITje5WDucjB3OcyRu0lP85s/fz4AIDEx0aD2QggsWbIEc+bMwciRIwEAq1atQuvWrbFu3TpMmzYNxcXFWL58OVavXo1BgwYBANasWQM/Pz8kJyc3ycV9LXBODiKz4GeLiIiIHiT31TVTOTk5uHTpEuLj45VlNjY26NevH1JTUzFt2jTs378fVVVVOm18fHwQGhqK1NTUeyqm1Go1VCoVrly5Ag8PD6j+c+MoKysrflMvCbOXwxS5CyFw5coVqFQqqNXqJl33g6J3796yu9DihISE4ODBg7yeQQKOdzmYuxzMXQ5z5H5fFVOXLl0CALRu3VpneevWrXH27FmljbW1NVxdXWu1qXn93SoqKlBRUaE8rutuyJaWlvD19cWFCxdw5swZndfb8Pw9KZi9HKbKXaVSwdfXlzcFrsPRo0fRrVs32d1oUWxtbVFeXs5JVyTgeJeDucvB3OUwR+6NLqbmzZunnL5Xl/T0dMTExBjdqZojQjWEELWW3a2+Nu+++67ePicnJys3id23bx/Kysrg6uqK4OBg7NmzByqVCoGBgTh+/DicnJwAAF27dsWxY8dQVlYGBwcHdOnSBfv27QMABAQEwNLSEqdPnwYAREVF4dSpUyguLoadnR0iIiKwZ88eAICvry/s7OyQnZ0NQHtTsbNnz6KwsBDW1tbo2rUrdu/eDUB75M3JyQl//PEHAO1Na/Py8nD16lVYWVmhR48e2LVrFwBtUdmqVStkZWUBALp06YIrV64gPz8fKpUKvXr1QlpaGm7fvg0PDw+0bt0aR44cAQAEBQWhuLgYeXl5ALTV/L59+1BZWQk3Nzf4+fnh4MGDALSzo1y/fh25ubkAgNjYWGRmZqK8vBwuLi4IDAxEZmYmAKB9+/aoqqrCuXPnAADdunXD0aNHcf36dTg5OaFz5874/fffAQCBgYEAtEcpAeDWrVtwdXVFaWlprbz9/f2hVqtx6tQpAEBkZCRycnJQVFQEW1tbREZGIi0tDQDQpk0bODg44MSJEwCAiIgInD9/HteuXYO1tTW6deumZOjt7Q2NRqOcZxsaGorLly/jypUrsLS0RGxsLHbv3g0hBDw9PeHh4aFMkhISEoKCggJcvnxZyXDPnj24desW3N3d4e3tjcOHDwPQ3iy0tLQUFy9eBAD06tUL6enpqKyshKurKwICAnDgwAEAQMeOHXHz5k1lopUePXrg4MGDuHnzJjQaDdq3b4+MjAwAQLt27XD79m3lC4g783Z0dERwcDDS09MBAG3btoVKpVLyjoqKwsmTJ3Hs2DF07NgRYWFh2Lt3LwDAz88PNjY2OHnypJL3mTNnUFhYCFtbW0RFRSE1NVVv3uHh4cjNzUV+fj7y8vLQv39//Prrr8p63d3dlfESExODixcv4uLFi7C0tMSgQYOQnJyM27dvw8fHBz4+Psp4iYyMxNWrV3H+/HkA2pstb9u2DZWVlWjdujXatm2r9D88PBwlJSXKFyWDBw/G7t27cePGDbi7u6NTp05K/7t06YLy8nJlbN29j+jSpYsyXoKCglBdXa1sa79+/XDgwAHlXhVRUVFISUlR/j9aWVnh2LFjyvjIysrCtWvX4ODggNLSUiWXdu3awd7eXvl8xsbG4uTJk7hy5QpsbW3Rt29f/PbbbwC0+x4XFxfl89mtWzecO3cOly5dglqtxoABA/Dbb79BCAFfX194enoq4yU6OhqXLl1Cbm4uLCwsMHjwYGzZsgW3bt2Ct7c3fH19lfHy0EMP4dq1a8pneciQIUhJSUFFRQU8PT3Rrl07ZR8XFhaGsrIyZWwNGjQIqampuHHjBlq1aoWgoCBlHxcSEoLKykplbPXv3x+///47SktL4eLigvDwcOzYsUP53ABQ9od9+/bFoUOHUFRUBCcnJ8TExGDbtm0AgA4dOsDa2lrZH/bq1QvHjx9HQUEB7O3t0bNnT6xZswaff/45Fi5ciODgYOXz2aNHD5w+fRr5+fmwsbFBXFyc8v/G398fbm5uyueza9euuHDhAvLy8mBlZYWBAwciKSkJ1dXVaNOmDby8vLB//37lM5afn48LFy5ApVIhPj4eW7duRVVVFby8vODv76/s4yIiIlBUVKR8luPj47Fjxw6Ul5fDw8MDHTp0UPZxoaGhuHHjhvLvz8CBA7Fnzx5cv34dbm5uCAkJUcZscHAwbt26pfz7ExcXh4yMDOU+Kw899BC2b98OQLuvt7CwUPaHvXv3xtGjR1FYWAhHR0d069YNW7duBaDd19va2ir7w549e+LEiRO4evUq7O3t0atXLyQlJQHQ7nsuXLiAwsJCAED37t1x5swZXL58GdbW1txH1LGP6NGjB7Zs2QLA+H3EmTNn0LFjR+4jYNg+Ijk5GYD27xNHR0ej9xEnTpxAYWEh9xGN2Ec4Ozvj0KFDAIzfR5w7dw7dunVrcB9xL9dWNfqmvVevXsXVq1frbdO2bVudb/kSExMxa9YsFBUV1fu606dPK38URkZGKstHjBgBFxcXrFq1Clu3bsXAgQNx7do1naNTERERePzxx/UWTfqOTPn5+Rl8Y67du3ejV69eDbajpsfs5WDucjB388vIyEB0dDTvMyUBx7sczF0O5i6Hobmb9aa97u7ucHd3b+zLDBIYGAgvLy8kJSUpxVRlZSW2b9+O9957D4D2GxK1Wo2kpCSMHj0aAJCXl4cjR47g/fff17teGxubezpliYdl5WH2cjB3OZg7tSQc73IwdzmYuxzmyN2kU6OfO3cOBw4cwLlz53D79m0cOHAABw4cQFlZmdImKCgImzdvBqA9vW/WrFlYuHAhNm/ejCNHjuDpp5+Gvb09xo0bBwDQaDSYMmUKXnnlFWzZsgWZmZl46qmnEBYWpszu19RqDk+S+TF7OZi7HMydWhKOdzmYuxzMXQ5z5G7SCSjefvttrFq1Snlcc7Rp27ZtiIuLA6A9p7W4uFhp89prr+HmzZt4/vnnUVhYiO7du+O3335TrlkCgI8//hhWVlYYPXo0bt68iYEDByIxMdHgi9przmysayKKu12/ft3gttS0mL0czF0O5m5+NV/ulZWVMXsz43iXg7nLwdzlMDT3mjbG3MKl0ddMPQguXLgAPz8/2d0gIiIiIqL7xPnz5+Hr69uo17TIYqq6uhoXL16Ek5NTg7ME1kxWcf78+UZfkEb3htnLwdzlYO5yMHc5mLsczF0O5i5HY3IXQqC0tBQ+Pj6wsGjcVVD31X2mzMXCwqLRVaezszM/AJIwezmYuxzMXQ7mLgdzl4O5y8Hc5TA0d41GY9T6TToBBRERERER0YOKxRQREREREZERWEw1wMbGBnPnzr2n+1SRcZi9HMxdDuYuB3OXg7nLwdzlYO5ymCv3FjkBBRERERER0b3ikSkiIiIiIiIjsJgiIiIiIiIyAospIiIiIiIiI7CYIiIiIiIiMgKLKQALFixAz549YW9vDxcXF4NeI4TAvHnz4OPjAzs7O8TFxeHo0aM6bSoqKjBz5ky4u7vDwcEBjz32GC5cuGCCLWieCgsLMWHCBGg0Gmg0GkyYMAFFRUX1vkalUun9+eCDD5Q2cXFxtZ4fM2aMibem+TAm96effrpWpj169NBpw/Fev8bmXlVVhddffx1hYWFwcHCAj48PJk6ciIsXL+q043jX9fnnnyMwMBC2traIjo7Gzp07622/fft2REdHw9bWFu3atcPSpUtrtfn2228REhICGxsbhISEYPPmzabqfrPVmNw3bdqEwYMHw8PDA87OzoiNjcWvv/6q0yYxMVHvvr68vNzUm9KsNCb3lJQUvZkeP35cpx3He8Mak7u+fz9VKhW6dOmitOF4b9iOHTvw6KOPwsfHByqVCt99912DrzHb/l2QePvtt8VHH30kXn75ZaHRaAx6zaJFi4STk5P49ttvxeHDh8WTTz4pvL29RUlJidJm+vTpok2bNiIpKUlkZGSI/v37i4iICHHr1i0TbUnzMnToUBEaGipSU1NFamqqCA0NFcOHD6/3NXl5eTo/K1asECqVSpw6dUpp069fPzF16lSddkVFRabenGbDmNwnTZokhg4dqpNpQUGBThuO9/o1NveioiIxaNAgsWHDBnH8+HGRlpYmunfvLqKjo3Xacbz/af369UKtVouvvvpKZGVliYSEBOHg4CDOnj2rt/3p06eFvb29SEhIEFlZWeKrr74SarVafPPNN0qb1NRUYWlpKRYuXCiOHTsmFi5cKKysrMSePXvMtVn3vcbmnpCQIN577z2xb98+ceLECfHmm28KtVotMjIylDYrV64Uzs7Otfb59KfG5r5t2zYBQPzxxx86md65j+Z4b1hjcy8qKtLJ+/z588LNzU3MnTtXacPx3rCff/5ZzJkzR3z77bcCgNi8eXO97c25f2cxdYeVK1caVExVV1cLLy8vsWjRImVZeXm50Gg0YunSpUII7YdHrVaL9evXK21yc3OFhYWF+Pe//93kfW9usrKyBACdAZuWliYAiOPHjxu8nhEjRogBAwboLOvXr59ISEhoqq4+UIzNfdKkSWLEiBF1Ps/xXr+mGu/79u0TAHT+0eZ4/1O3bt3E9OnTdZYFBQWJN954Q2/71157TQQFBeksmzZtmujRo4fyePTo0WLo0KE6bYYMGSLGjBnTRL1u/hqbuz4hISFi/vz5ymND/z1uyRqbe00xVVhYWOc6Od4bdq/jffPmzUKlUokzZ84oyzjeG8eQYsqc+3ee5meEnJwcXLp0CfHx8coyGxsb9OvXD6mpqQCA/fv3o6qqSqeNj48PQkNDlTYtWVpaGjQaDbp3764s69GjBzQajcH5XL58GT/99BOmTJlS67m1a9fC3d0dXbp0wauvvorS0tIm63tzdi+5p6SkwNPTE506dcLUqVORn5+vPMfxXr+mGO8AUFxcDJVKVet0ZI53oLKyEvv379cZgwAQHx9fZ8ZpaWm12g8ZMgS///47qqqq6m3Dca1lTO53q66uRmlpKdzc3HSWl5WVISAgAL6+vhg+fDgyMzObrN/N3b3kHhkZCW9vbwwcOBDbtm3TeY7jvX5NMd6XL1+OQYMGISAgQGc5x3vTMuf+3ereutoyXbp0CQDQunVrneWtW7fG2bNnlTbW1tZwdXWt1abm9S3ZpUuX4OnpWWu5p6enwfmsWrUKTk5OGDlypM7y8ePHIzAwEF5eXjhy5AjefPNNHDx4EElJSU3S9+bM2NyHDRuGUaNGISAgADk5OXjrrbcwYMAA7N+/HzY2NhzvDWiK8V5eXo433ngD48aNg7Ozs7Kc413r6tWruH37tt79cl0ZX7p0SW/7W7du4erVq/D29q6zDce1ljG5323x4sW4fv06Ro8erSwLCgpCYmIiwsLCUFJSgk8++QS9evXCwYMH0bFjxybdhubImNy9vb2xbNkyREdHo6KiAqtXr8bAgQORkpKCvn37Aqj7M8HxrnWv4z0vLw+//PIL1q1bp7Oc473pmXP//sAWU/PmzcP8+fPrbZOeno6YmBij30OlUuk8FkLUWnY3Q9o0Z4bmDtTOD2hcPitWrMD48eNha2urs3zq1KnKf4eGhqJjx46IiYlBRkYGoqKiDFp3c2Pq3J988knlv0NDQxETE4OAgAD89NNPtYrZxqy3uTPXeK+qqsKYMWNQXV2Nzz//XOe5ljje69PY/bK+9ncvN2Zf39IYm9HXX3+NefPm4fvvv9f5wqFHjx46k9z06tULUVFR+PTTT/E///M/TdfxZq4xuXfu3BmdO3dWHsfGxuL8+fP48MMPlWKqsetsqYzNKDExES4uLnj88cd1lnO8m4a59u8PbDE1Y8aMBme0atu2rVHr9vLyAqCter29vZXl+fn5SoXr5eWFyspKFBYW6nxbn5+fj549exr1vs2BobkfOnQIly9frvXclStXan1LoM/OnTvxxx9/YMOGDQ22jYqKglqtRnZ29gP7x6W5cq/h7e2NgIAAZGdnA+B4r8+95l5VVYXRo0cjJycHW7du1TkqpU9LGO/6uLu7w9LSstY3inful+/m5eWlt72VlRVatWpVb5vGfF4eZMbkXmPDhg2YMmUKNm7ciEGDBtXb1sLCAl27dlX2OS3dveR+px49emDNmjXKY473+t1L7kIIrFixAhMmTIC1tXW9bTne75059+8P7DVT7u7uCAoKqvfn7iMahqo5pebO02gqKyuxfft25Q/H6OhoqNVqnTZ5eXk4cuTIA/3HpaG5x8bGori4GPv27VNeu3fvXhQXFxuUz/LlyxEdHY2IiIgG2x49ehRVVVU6he+Dxly51ygoKMD58+eVTDneTZN7TSGVnZ2N5ORk5R+A+rSE8a6PtbU1oqOja53emJSUVGfGsbGxtdr/9ttviImJgVqtrrfNgzyuG8OY3AHtEamnn34a69atwyOPPNLg+wghcODAgRY3rutibO53y8zM1MmU471+95L79u3bcfLkSb3Xed+N4/3emXX/3qjpKh5QZ8+eFZmZmWL+/PnC0dFRZGZmiszMTFFaWqq06dy5s9i0aZPyeNGiRUKj0YhNmzaJw4cPi7Fjx+qdGt3X11ckJyeLjIwMMWDAAE4VfYehQ4eK8PBwkZaWJtLS0kRYWFitqaLvzl0IIYqLi4W9vb344osvaq3z5MmTYv78+SI9PV3k5OSIn376SQQFBYnIyEjm/h+Nzb20tFS88sorIjU1VeTk5Iht27aJ2NhY0aZNG473Rmhs7lVVVeKxxx4Tvr6+4sCBAzrT5VZUVAghON7vVjNl8fLly0VWVpaYNWuWcHBwUGbNeuONN8SECROU9jVT57700ksiKytLLF++vNbUubt37xaWlpZi0aJF4tixY2LRokWcKvoujc193bp1wsrKSnz22Wd1Tuk/b9488e9//1ucOnVKZGZmismTJwsrKyuxd+9es2/f/aqxuX/88cdi8+bN4sSJE+LIkSPijTfeEADEt99+q7TheG9YY3Ov8dRTT4nu3bvrXSfHe8NKS0uVv88BiI8++khkZmYqs9vK3L+zmBLaaZ8B1PrZtm2b0gaAWLlypfK4urpazJ07V3h5eQkbGxvRt29fcfjwYZ313rx5U8yYMUO4ubkJOzs7MXz4cHHu3DkzbdX9r6CgQIwfP144OTkJJycnMX78+FpTtt6duxBCfPnll8LOzk7vvXTOnTsn+vbtK9zc3IS1tbVo3769ePHFF2vdE6kla2zuN27cEPHx8cLDw0Oo1Wrh7+8vJk2aVGssc7zXr7G55+Tk6N0v3blv4niv7bPPPhMBAQHC2tpaREVFie3btyvPTZo0SfTr10+nfUpKioiMjBTW1taibdu2er+k2bhxo+jcubNQq9UiKChI549P0mpM7v369dM7ridNmqS0mTVrlvD39xfW1tbCw8NDxMfHi9TUVDNuUfPQmNzfe+890b59e2FraytcXV1F7969xU8//VRrnRzvDWvsfqaoqEjY2dmJZcuW6V0fx3vDaqb2r2u/IXP/rhLiP1djERERERERkcEe2GumiIiIiIiITInFFBERERERkRFYTBERERERERmBxRQREREREZERWEwREREREREZgcUUERERERGREVhMERERERERGYHFFBERERER3dd27NiBRx99FD4+PlCpVPjuu+8a9fp58+ZBpVLV+nFwcLinfrGYIiIiIiKi+9r169cRERGBf/zjH0a9/tVXX0VeXp7OT0hICEaNGnVP/WIxRURERERE97Vhw4bhnXfewciRI/U+X1lZiddeew1t2rSBg4MDunfvjpSUFOV5R0dHeHl5KT+XL19GVlYWpkyZck/9srqnVxMREREREUk2efJknDlzBuvXr4ePjw82b96MoUOH4vDhw+jYsWOt9v/85z/RqVMn9OnT557el0emiIiIiIio2Tp16hS+/vprbNy4EX369EH79u3x6quvonfv3li5cmWt9hUVFVi7du09H5UCeGSKiIiIiIiasYyMDAgh0KlTJ53lFRUVaNWqVa32mzZtQmlpKSZOnHjP781iioiIiIiImq3q6mpYWlpi//79sLS01HnO0dGxVvt//vOfGD58OLy8vO75vVlMERERERFRsxUZGYnbt28jPz+/wWugcnJysG3bNvzwww9N8t4spoiIiIiI6L5WVlaGkydPKo9zcnJw4MABuLm5oVOnThg/fjwmTpyIxYsXIzIyElevXsXWrVsRFhaGhx9+WHndihUr4O3tjWHDhjVJv1RCCNEkayIiIiIiIjKBlJQU9O/fv9bySZMmITExEVVVVXjnnXfwv//7v8jNzUWrVq0QGxuL+fPnIywsDID2dMCAgABMnDgRCxYsaJJ+sZgiIiIiIiIyAqdGJyIiIiIiMgKLKSIiIiIiIiOwmCIiIiIiIjICiykiIiIiIiIjsJgiIiIiIiIyAospIiIiIiIiI7CYIiIiIiIiMgKLKSIiIiIiIiOwmCIiIiIiIjICiykiIiIiIiIjsJgiIiIiIiIyAospIiIiIiIiI/w/6IB8v1wSRSoAAAAASUVORK5CYII=", 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", 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", 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", 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", 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hhBB+QV9j5PV6efrpp3nggQcwGgN/3OOPP05HRwe/+MUvAPje975HQUEB5eXlarGG559/nueffz7YzRR/tXr16lA3QVyDxEa7JDZCTJ30G+2S2GiXxCa4gj5itGfPHlpbW3nooYcmnOvq6lIX8YGvct3nP/95Fi9ezIYNGzh8+DCvvPIK7373u4PdTPFX/oV7QnskNtolsRFi6qTfaJfERrskNsEV9BGjbdu2ca36Dj//+c8D/v3FL36RL37xi8FukrgOi8US6iaIa5DYaJfERoipk36jXRIb7ZLYBNdt28dIhIfIyMhQN0Fcg8RGuyQ22mQymUhNTcVkMoW6KWIS0m+0S2KjXRKb4Lpt5bpvl6mU5BNCCCGEEELMXJor1y3Ch39DLaE9Ehvtkthol8RGuyQ22iWx0S6JTXBJYiSEEGJGunjxIvfff7/s+yGEEOKGSGIkAuTn54e6CeIaJDbaJbHRJpfLRV9fHy6XK9RNEZOQfqNdEhvtktgElyRGIkBycnKomyCuQWKjXRIbIaZO+o12SWy0S2ITXJIYiQDnzp0LdRPENUhstEtiI8TUSb/RLomNdklsgksSIyGEEEIIIcSsJ4mRCHDHHXeEugniGiQ22iWx0aZ58+bx5z//mXnz5oW6KWIS0m+0S2KjXRKb4JLESARob28PdRPENUhstEtio01xcXEUFBQQFxcX6qaISUi/0S6JjXZJbIJLEiMRoKurK9RNENcgsdEuiY02dXR08I1vfIOOjo5QN0VMQvqNdklstEtiE1ySGIkARqMx1E0Q1yCx0S6JjTb19PTwu9/9jp6enlA3RUxC+o12SWy0S2ITXDpFUZRQN2I6jYyMkJCQwPDwMPHx8aFujhBCiBCprKxk+fLlnDlzhmXLloW6OUIIIUJgKrmBjBiJAK+99lqomyCuQWKjXRIbIaZO+o12SWy0S2ITXJIYiQBerzfUTRDXILHRLomNEFMn/Ua7JDbaJbEJLkmMRICcnJxQN0Fcg8RGuyQ22pSSksK73/1uUlJSQt0UMQnpN9olsdEuiU1wyQouESAzMzPUTRDXILHRLomNNs2ZM4cf//jHpKamhropYhLSb7RLYqNdEpvgkhEjEeDMmTOhboK4BomNdklstGl0dJTnn3+e0dHRUDdFTEL6jXZJbLRLYhNckhgJIYSYkWpqanj44YepqakJdVOEEEKEAUmMRAApaatdEhvtktgIMXXSb7RLYqNdEpvgksRIBLBYLKFugrgGiY12SWyEmDrpN9olsdEuiU1wSWIkArS3t4e6CeIaJDbaJbERYuqk32iXxEa7JDbBJYmRCKDT6ULdBHENEhvtkthok06nw2QySXw0SuKiXRIb7ZLYBJdOURQl1I2YTiMjIyQkJDA8PEx8fHyomyOEEEIIIYQIkankBjJiJALs27cv1E0Q1yCx0S6JjXZJbLRLYqNdEhvtktgElyRGIoDL5Qp1E8Q1SGy0S2KjTTU1NXziE5+Qct0aJf1GuyQ22iWxCS5JjEQA2VFZuyQ22iWx0abR0VGuXLkiG7xqlPQb7ZLYaJfEJrgkMRIB8vPzQ90EcQ0SG+2S2AgxddJvtEtio10Sm+CSxEgEOHnyZKibIK5BYqNdEhshpk76jXZJbLRLYhNckhgJIYQQQgghZj1JjESAJUuWhLoJ4hokNtolsdGmwsJCnnrqKQoLC0PdFDEJ6TfaJbHRLolNcEliJAIMDQ2FugniGiQ22iWx0aakpCQ2bNhAUlJSqJsiJiH9RrskNtolsQkuSYxEgJaWllA3QVxDsGKjKAperxePx4Pb7cblcuF0OhkfH2dsbAyHw8Ho6Cjj4+O4XC48Hg8zbF/oWyb9Rpt6enr47ne/S09PT6ibIiYh/Ua7JDbaJbEJLmOoGyCEmBqv16smK2NjY9f86E9k/MmO/+ObH7tZer1+wmE0GjGZTOoRERER8O+rHzebzdc8TCYTOp1uGv/XxGzU0dHB//7v//Lwww+TkZER6uYIIYTQOJ0yw976HRkZISEhgeHhYeLj40PdnLCjKIrckIbQ+Pg4g4ODDA8PY7VasdlsWK1WrFYrIyMj2O12bDbbjB+x0ev1AYlSTEwMMTExREdHX/PziIiIkLVX+o02VVZWsnz5cs6cOcOyZctC3RzxJtJvtEtio10Sm6mbSm4gI0YiwKFDh9i0aVOomzGjjY6O0tfXx8DAAIODgwEf7Xb7Nb+uubmZgoICAHQ6HZGRkURFRWE2myf9GBkZiclkChjFefPnRqMRvV6PTqe77gG+kSr/lDv/51cfHo8Hj8eD0+lUR6b8x5sfczqdjI2NTXr4v5/D4cDhcNzw/6vJZCImJoa4uDji4uKIj49XP7/6iIyMvKX4TUb6jRBTJ/1GuyQ22iWxCS5JjESAsbGxUDdhxvB4PPT399PT0xNwjIyMXPfroqOjSUhIID4+ntjYWPWG/ty5c9x7773ExsYSExODXn97lwjejp+nKAoulysgURodHcVut+NwOAI+Xv25f2rg0NDQWy5MjYiIUP9PExISSExMVD8mJiYSHx+P0Ti1S6P0GyGmTvqNdklstEtiE1ySGIkAaWlpoW5CWFIUhYGBAdrb29XDYrHg8XgmfX58fDzJycnqkZSUpH40m82Tfo1OpyMrKyuYLyPkdDodERERRERE3PBUWEVRcDqdOByOgKmHVx8jIyNYrVbGx8dxOp309/fT399/ze95raQpOTmZxMREDAZDwPOl32hTQkICGzduJCEhIdRNEZOQfqNdEhvtktgEl6wxEgFGRkbk/+0GKIpCT08Pzc3NNDU10dbWNum0r4iICDIyMgKO9PT0ayY/1yOxuXVOpzMgWRoeHmZ4eFgdaRoeHsblcl33e+h0OhISEgIS24iICPLy8khOTsZkMt2mVyNuhPQb7ZLYaJfERrskNlMna4zETTt27Bjbt28PdTM0yWazUV9fT319Pc3NzRMSIaPRSFZWFrm5ueTm5pKdnU1iYuK0LZKU2Ny6iIgIUlJSSElJmfS8oig4HA41Wbo6aRoaGmJgYCBgyl5jYyMADQ0NzJ07F/CNNvkTptTUVPVISkq67dMfZzuXy8Vf/vIX3v3ud0vCqkFyTdMuiY12SWyCK6iJ0b/+67/yta99LeCxjIwMuru7r/k1Bw8e5LHHHqOqqors7Gy++MUv8vDDDwezmUJck8Vioaamhrq6Ojo6OgLORUREkJ+fT2FhIXPmzCErK2vCFCsRXnQ6nVrpLjs7e8J5RVGw2WxqsQz/MTQ0RFRUFKOjo+qI1Jv3mjAYDBOSJf8RjIIQAi5evMgHPvABqUonhBDihgR9xKi8vJw9e/ao/77ejWNTUxP33nsvH//4x3nuuec4cuQI//AP/0BaWhrvec97gt1UASxcuDDUTQi5/v5+Ll26RFVVFRaLJeBcdnY2JSUlFBcXk52dfVsTIYlN6Ol0OrVwQ35+vvr4mjVryMnJYXR0NCBh6uvro7e3l/7+flwuF729vfT29k74vnFxcaSlpZGenq4eaWlpkjCJGU2uadolsdEuiU1wBT0xMhqNZGZm3tBzn3zySfLz8/ne974HwIIFCzh9+jT/+Z//KYnRbTKV8sgzidPp5NKlS5w9e5a2tjb1cYPBQHFxMQsWLGDu3LnExcWFrI2zNTbhwB+bqKgocnJyyMnJCTivKArDw8P09fVNOK4uGOGfmueXmJgYkCxlZGSQkpIy5ap5QmiRXNO0S2KjXRKb4Ar6X9f6+nqys7OJjIxk1apVfPOb36SoqGjS5x47doxt27YFPLZ9+3Z++tOf4nK5Jp0jPj4+zvj4uPrvtyqFLK6vsbGRefPmhboZt01vby/Hjx/n4sWLOJ1OwFeWuri4mPLyckpLS2+qUEIwzLbYhJO3io1Op1Mr2/nXIvmNjY2pI0sWi0U9rFarupaprq5Ofb5eryclJYX09HQyMzPVIzY2Vjb9E2FFrmnaJbHRLolNcAU1MVq1ahW/+MUvKCkpoaenh69//eusXbuWqqqqSRc/d3d3k5GREfBYRkYGbrebvr6+SUsVf+tb35qwjglgz549xMTEcNddd3Hy5ElsNhtJSUmUl5dz+PBhAEpLS/F6vepNx6ZNmzh37pxatWLZsmUcOHAAgHnz5mE0GqmpqQFg/fr1VFdXMzAwQExMDKtXr2bv3r0AFBUVER0dzaVLlwDfNJuGhgZ6e3sxm81s3LiR3bt3AzBnzhwSExM5f/48ACtXrqS1tZXu7m5MJhN33XUXu3fvRlEUcnNzSU9Pp7KyEoDly5fT3d1NR0cHer2erVu3snfvXtxut1oE4NSpUwBUVFQwMDBAa2sr4Es4Dxw4wPj4OOnp6RQVFXH8+HGam5vp7OzEZrPR1NQEwJYtWzh69CgOh4OUlBRKS0s5cuQIAGVlZTidThoaGgC48847OX36NFarlcTERBYvXsyhQ4cAmD9/PgC1tbUAbNy4kQsXLjA0NERcXBwrVqxg//79AMydO5eIiAiqq6sBWLduHZcvX6a/v5/o6GjWrl2rTtEsLCwkNjaWixcvArB69WoaGxuxWCxERkayefNmdu3aBUB+fj5JSUns3LmT6upqdDqdWso5MTGRD37wg/T39xMZGYnRaMRms3Hw4EEAli1bhsViob29HZ1Ox7Zt29i3bx8ul4vMzEzy8/M5efIkAEuWLGFoaEhdZ7Jt2zYOHTrE2NgYaWlpzJ07l2PHjgG+YXGHw6GOFtx9990cP34cu91OcnIyZWVlHD58mObmZlpbW3G73dTX1wOwefNmKisr1YorFRUVantLSkrQ6/VcvnxZ/Z2tqqpicHCQ2NhYVq5cyb59+wAoLi7GbDZTVVUFwNq1a6mrq6Ovr4/o6GjWrVvHa6+9BkBBQQHx8fFcuHAB8PXz5uZmenp6iIiI4M4771T/v/Py8khNTeXs2bMArFixgs7OTjo7OzEYDGzZsoU9e/bg8XjIzs4mOzub06dPA7B06VL6+vrUEbzt27ezf/9+nE4nGRkZFBQUcOLECQAWL17MyMgIzc3NAGzdupUjR47gcDhITU2lpKSEo0ePAr7pvWNjY1y5cgVgWq4Rzc3NNDY23tI1wj9tMz8/n/vvv58XX3yR4eFhoqKiGBsb4+zZswwNDZGWlqb2UYPBQGFhIVeuXCEyMpLi4mLmzZtHf38/SUlJbN68GYvFMq3XCIBFixaFxTXCH/OWlhYyMzNv+BqRnJzMuXPnALjjjjtob2+nq6sLo9HI3XffzWuvvYbX6yUnJ4fMzEzOnDmjiWsE+GZahMs1orm5merq6llxjYDwuo9obm7mwoULM/4acbP3EaG8RjQ3N1NZWTkrrhEwPfcR/vbfiNtarttut1NcXMwXv/hFHnvssQnnS0pKePDBB3n88cfVx44cOcL69evp6uqadEreZCNGeXl5Uq77Jrnd7hk7TUdRFK5cucL+/fvVQgo6nY7S0lJWrVrFnDlzNP2O+0yOTbi7XbFRFAWr1YrFYqGnp4fu7m66u7vp6+tjsku5yWQiIyODzMxMsrKyyMrKIj09fdb8Hnk8HoaHh0lISJDCKBok1zTtkthol8Rm6jRbrjsmJoZFixapWeqbZWZmTqhYZ7FYMBqN1yyvGxkZKQuUp9Hx48dZv359qJsx7VpbW9mzZ4/6TpfJZGLZsmWsWrWK5OTkELfuxszU2MwEtys2Op2O+Ph44uPjA6bkuVwuLBYL3d3ddHV10d3dTU9PDy6XS91w2M9gMJCRkaG+s5adnU16evqMLCVuMBiorq6WfqNRck3TLomNdklsguu2Jkbj4+PU1NSwYcOGSc+vWbOGl156KeCx3bt3s2LFCtmD4jax2+2hbsK0GhkZYc+ePepwrdFo5I477mDdunXExsaGuHVTM9NiM5OEOjYmk2lC0Qev18vAwIA6qtTV1UVXVxcOh0OdhuDn34Pr6mQpJSUl7JOl+vp6Hn30UX7zm9/InHwNCnW/EdcmsdEuiU1wBTUx+vznP8+OHTvIz8/HYrHw9a9/nZGRER544AEAHn/8cTo6OvjFL34BwMMPP8wPf/hDHnvsMT7+8Y9z7NgxfvrTn/LrX/86mM0UVwmX0ZO3oigKJ06cYN++fTidTnQ6HcuWLWPz5s0hrSx3K2ZKbGYiLcZGr9er+yT5y7sqisLQ0JCaGPmP8fFx2traAioyRkREkJ2dTU5ODnl5eeTm5obdmwlWq5XKykqsVmuomyImocV+I3wkNtolsQmuoCZG7e3tfPCDH6Svr4+0tDRWr17N8ePHmTNnDgBdXV3q1CbwLX579dVX+dznPsf//M//kJ2dzfe//30p1X0blZWVhboJt2xwcJAXXnhBXbCYl5fHPffcM+mGneFkJsRmpgqX2Oh0OpKSktQF5OBLlvr7+wMSpa6uLpxOJ83NzepidfCVD8/NzVWPzMxMmesublq49JvZSGKjXRKb4LqtxRduh6kssBIT7dq1i+3bt4e6GTft4sWLvPTSSzidTiIiIti2bRvLly/XdFGFGxXusZnJZlpsvF4vfX19tLe309HRQXt7OxaLZUKBB4PBQFZWljqilJubS3x8vGb6W2VlJcuXL+fMmTMsW7Ys1M0RbzLT+s1MIrHRLonN1Gm2+IIQweLxeNizZ49aunLOnDm8853vJCkpKcQtEyL86PV6dVNZf0IxPj6uJkn+w+FwTCjuEBcXR35+vnpkZGSE/VolIYQQs4MkRiLAggULQt2EKRsfH+e3v/2tWr9/w4YN3HnnnTPuZiwcYzNbzIbYREZGUlRUpG7QrSgKg4ODAYlSd3c3VquVqqoqdS+LyMhI8vLy1EQpJyfnthXTycvL49/+7d/Iy8u7LT9PTM1s6DfhSmKjXRKb4JLESARwu92hbsKUOBwOfvnLX9LR0UFERATvete7ZuxFI9xiM5vMxtjodDqSk5NJTk5m8eLFgK9seEdHB62trbS2ttLW1sb4+DgNDQ3q5o16vZ7s7OyAUaXo6OigtDEtLY0PfehDpKWlBeX7i1szG/tNuJDYaJfEJrgkMRIB6uvr1XeEtc7hcPDzn/8ci8VCdHQ0H/rQhwLKFc804RSb2UZi42MymSgoKKCgoADwrVWyWCxqotTS0oLValVHmI4ePQr4Ehj/1xUUFBATEzMt7RkYGODJJ5/kn/7pn6SSkwZJv9EuiY12SWyCSxIjEZacTie/+tWvsFgsxMXF8ZGPfETeFRZCY/R6PZmZmWRmZrJy5UoURWF4eDggUert7VWPU6dOAdOXKDU3N/Mf//EffOADH5DESAghxFuSqnQiwPj4OJGRkaFuxnUpisJvfvMbamtriYqK4qGHHpoVSVE4xGa2ktjcPIfDQWtrq1oavLu7e8JzbjZRkqp02ib9RrskNtolsZk6qUonblplZSVr1qwJdTOu6/Dhw9TW1mI0Gvm7v/u7WZEUQXjEZraS2Ny86OhoSktLKS0tBSZPlCYbUSosLKSoqIiCggLMZnMoX4K4SdJvtEtio10Sm+CSxEgEGBkZCXUTrqu1tZV9+/YB8Pa3v31WVZvSemxmM4nN9JlKonTy5El0Oh05OTlqopSXlyebzoYJ6TfaJbHRLolNcMlfDxEgISEh1E24JrfbzZ///GcURWHJkiVUVFSEukm3lZZjM9tJbIJnskSppaWFpqYmGhsb1Y1o29vbef311zGZTOTn51NUVITX66W8vHzaijmI6SX9RrskNtolsQkuWWMkAoyNjWl2Wsrrr7/O3r17iY2N5dOf/rRm2xksWo7NbCexCZ2RkREaGxvVw2azBZw3mUyUlJRQWFhIcXGxbPqsIdJvtEtio10Sm6mTNUbiph08eJDt27eHuhkTOBwODh06BMC2bdtm5UVBq7G5WR6PB6fTyejoKOPj44yPj+NyuXC73bhcroDD4/Hg8Xjwer3q4f/39d7b0el06PV69TAYDBP+bTKZMBqNkx6RkZGYTCYiIiLUQ6fTTfg5My024SQ+Pp6KigoqKipQFIXe3l4aGxtpamqiubmZqqoqXC6XuuFsSkoKc+fOZe7cuRQUFNy2zWbFRNJvtEtio10Sm+CSxEiEhRMnTuByucjOzmbRokWhbo54C16vl9HRUex2u3o4HA7sdjujo6OMjY3hdDpD3cwp0+l0mEwmIiMj1UQpKiqK9vZ26urqMJvNmM1moqKiMJvNGI3GSRMpERw6nY709HTS09NZvXo1p06d4stf/jJPPfUUBoOB9vZ2+vv76e/v58SJExiNRubMmUNxcTFz584lLS1N4iWEELOYJEYiQElJSaibMIHT6eTEiRMArF+/ftbeuGgxNh6PB5vNxvDwMCMjI4yMjDA8PIzVasXr9b7l1+v1eiIiIjCbzerozGSHf6TnzR/1ev11fx8URQkYZXrzaJPb7VYP/2jV1f92uVw4nU6cTidutxtFUdR/X83lclFZWTnh5xuNxoBkKTo6mujoaGJiYtTPIyMjZ+3vdLAZDAYAli9fzrJlyxgbG6OpqYmGhgYaGhoYHh7mypUrXLlyhd27dxMfH6+OJhUVFc3KkenbSYvXNOEjsdEuiU1wSWIkAuj1+lA3YYK6ujrGxsZISkpSF2DPRqGOjaIo2Gw2BgYG6O/vZ2BggMHBQTwez6TP1+v1agIQExMzIRmIioq65tQ0LfJP/bv6GBsbY2xsjNbWVuLj49V/j42NqYmWzWabsO7lakajUf1/uTppio2NJTY2FrPZHDb/R1pnNptZsGABCxYsQFEU+vr6uHLlCg0NDTQ3NzMyMkJlZSWVlZXodDpyc3MpKSmhpKSE9PR0icM0C/U1TVybxEa7JDbBJYmRCHD58mXmzJkT6mYEuHjxIgCLFi2a1ReEUMTGbrfT3d1NT08PFouFsbGxCc8xmUzEx8eTkJBAfHy8+nl0dPSMupE0GAxERUURFRU14Vx7ezvr1q0LeMzlcjE+Pq4mSg6HQz38UwtHR0dxu93qaNtkjEYjcXFxaqIUGxtLXFwccXFxkjTdAp1OR1paGmlpaaxevRqXy0VLSwsNDQ1cuXKF3t5e2traaGtrY+/evcTHx6tJUmFhoaxNmgZa/HsjfCQ22iWxCS5JjISmud1uGhoaAFi4cGGIWzPzeb1e9Yawp6cHq9UacF6v15OUlERKSgrJycmkpKQQGxsrN+eT8E8DjI2NveZzPB5PQMLkT5rsdjs2mw2Hw4Hb7WZwcJDBwcEJX280GtVk6erENC4uTvbymSKTyaROowMYHh6mvr6euro6mpqaGBkZ4fTp05w+fRqj0UhhYSElJSXMmzePxMTE0DZeCCHEtJBy3SKA3W7X1J4fra2t/OxnPyM2NpZ//Md/nNU34MGKjdfrxWKx0NbWRnt7O+Pj4+o5vV5PcnIyGRkZZGRkkJKSoq7bEG8IVmw8Ho+aJFmtVnVantVqxW63X7Min06nIzY2Vk2U/KN4cXFxs2qkY2xsjLq6OkpKSm5pvZDL5aK5uZm6ujrq6+sZGhoKOJ+enq6OJuXm5s7qke2p0NrfG/EGiY12SWymTsp1i5tWVVXFypUrQ90MVWtrKwB5eXmzOimC6Y+N3W5Xyxo7HA718cjISHJycsjJySE9PX1W3UjfrGD1G4PBoCY2b+YfbbJarVit1oDiF06nU328o6Mj4OtiYmKIj48nMTGRxMREkpKSiI2NnZE382azeVr2/DCZTMybN4958+apJcHr6uqoq6ujra0Ni8WCxWLh8OHDREVFUVJSQmlpKcXFxUREREzTq5l5tPb3RrxBYqNdEpvgksRIBJhsuk4o9fX1AZCVlRXiloTedMRGURQsFgu1tbV0dXWpIw6RkZHk5eWRm5tLenr6jLxJDqZQ9BuDwaCuNbqaoiiMjY0FJEr+z8fGxtSpel1dXerXGI1GEhISApKlhISEsE+Km5qa+Kd/+id++tOfUlhYOC3f8+qS4OvXr2d0dJSGhgbq6upoaGhgdHSU8+fPc/78eYxGI0VFRZSWllJSUnLdaZWzkdb+3og3SGy0S2ITXJIYiQBa+8Ptn7Iic/hvLTaKotDT00NVVRW9vb3q4+np6RQXF5ObmytT5G6BlvqNTqdTi0RkZGQEnPMnTMPDwwwPDzM4OMjw8DBut1vd3+fq7xMbG6smSUlJSSQnJ4dVCevBwUH279/P4ODgtCVGbxYVFcWiRYtYtGgRXq+XtrY2Ll++zOXLlxkcHFRHlvxV7kpLS5k/fz6pqalBaU840VK/EYEkNtolsQkuWWMkArhcLk29S/z973+fgYEBHnzwwVlfheVmY9PX18f58+fVhEiv11NUVERJSYn0kWmitX4zFV6vF5vNpiZJg4ODDA0NMTo6OunzY2JiSE5OVo+kpCTNTherrKxk+fLlnDlzhmXLlt3Wn+2fcudPkjo7OwPOp6amqklSbm7urJwqHM79ZqaT2GiXxGbqZI2RuGn79u1j+/btoW6Gyr9HjlwEph6bsbExzp8/T1NTE+CbelVcXExpaSnR0dHBauaspLV+MxV6vX7SdUxjY2MMDQ2px8DAgFr0wW6309bWpj43Pj4+IFlKTEyc9VXxrp5yt3HjRkZGRqitreXy5cs0NzfT19fH4cOHOXz4MLGxsSxYsICysjLmzJkza6ayhnO/mekkNtolsQmu2f2XS2ie1+sFZEOzqWpvb+f06dPqvkOFhYUsXrx40j14hJiM2WwmMzOTzMxM9TGXy8XAwEDAYbfb1TVMzc3NgK+/JiQkkJKSQkpKCmlpacTExMzKURG/+Ph47rjjDu644w7GxsZoaGigtraWuro6bDYbp06d4tSpU0RHR1NaWsqCBQsoKiqSKa5CCHEbSWIkAhQXF4e6CZOaYTM+b8qNxMbj8XD27Fl176eEhATuuOMOWc8QZFrtN9PNZDKppdv9xsbGJiRLY2Nj6t5L/t9Fs9lMamqqeiQlJQX9pj8rK4vPfOYzmiveYjabWbhwIQsXLsTj8dDY2EhNTQ2XL1/G4XBQWVlJZWUlZrOZ+fPns2DBAoqLi2fcyPls6TfhSGKjXRKb4JLESATQ2sLq6OhorFZrQDnp2eqtYjM6Osrhw4fp7+9Hp9NRWlrKwoUL5R3n20Br/eZ2MpvNZGdnk52dDfjexHA4HAwMDNDf309vby+Dg4OMjY3R3t5Oe3s78MYeWf5EKSUlZdpHNLOysvjiF7+oucToagaDQS0F/o53vIOWlhaqq6upqanBZrOpFe4iIiKYN28eZWVlzJs3T7PruqZiNvcbrZPYaJfEJrgkMRIBqqqqyM3NDXUzVP61MHa7PcQtCb3rxcZms3Hw4EGsVisRERGsXr1avVEVwae1fhNKOp2OmJgYYmJiyMvLA3wjmQMDA/T19dHf309fXx9jY2P09fWpJfkB4uLiSEtLIy0tjfT09FvexHBkZIRnnnmGz3zmM2FRaESv11NYWEhhYSH33HMP7e3tapI0PDxMVVUVVVVVGI1G5s2bx8KFC8M6SZJ+o10SG+2S2ASXJEZC0xISEgCp2389drudffv24XA4iI2NZdOmTRP2thEilAwGg5rwgG9UyWazqYlSb28vIyMj6qa0jY2NgK8CXnp6ekCiNJV1Sg0NDXz1q1/lnnvuue1V6W6VXq8nPz+f/Px8tm/fTmdnp5okDQwMUFNTQ01NDSaTifnz51NeXs68efNmfdELIYS4FXIFFQHWrl0b6iYESE9PB8BisYS4JaE3WWzGx8c5cOAADoeDhIQENm/eLAUWQkBr/UbrdDqdujmtf38hp9NJX18fvb29WCwWBgcHsdvtNDU1qZUVY2JiAkaUYmNjZ0VBB51OR05ODjk5OWzZsoWenh4uXbrEpUuXGBoaUj+PjIyktLSU8vJyiouLNT+NVvqNdklstEtiE1ySGIkAdXV1LF++PNTNUPkTo56enhC3JPTeHBtFUTh27BhWq5WYmBg2bdokSVGIaK3fhKOIiIiAtUoul4v+/n4sFgsWi0WtgGe329Xqd1FRUaSnp6sFIW516l040Ol0arXAu+++m87OTi5dukRVVRUjIyPqmqSoqCh1nWFhYaEmK3tKv9EuiY12SWyCSxIjEeDq+f5akJ2djU6no6+vD5vNNqt3fH5zbC5fvkx3dzdGo5ENGzbI3kQhpLV+MxOYTKaAcuFutztgRKm/v5/R0VFaWlpoaWkBfCWx/UmS/02VmezqkaRt27bR1tamrkOy2WycPXuWs2fPEh0dTVlZGYsXLyYvL08zo2zSb7RLYqNdEpvgksRIBNDazXV0dDSZmZl0dXXR1NTEokWLQt2kkLk6NlarlUuXLgGwbNkyEhMTQ9QqAdrrNzOR0WickCj5R5S6u7sZGBhQ91Oqr69Hr9czMjJCRkYGNpsNj8ej+allt0Kn0wWsSWptbeXSpUtUV1fjcDg4ffo0p0+fJjExkUWLFrFo0aKQJ4/Sb7RLYqNdEpvg0ikzbIOYkZEREhISGB4eDosqRFrj9Xo1N+Vi9+7dHD16lCVLlvCud70r1M0Jmatjc/jwYdrb28nIyGDz5s2aeQd4ttJiv5ltnE4nFouFnp4euru7sVqtgG/KqU6nw2g0kpaWRmZmJhkZGSQkJMyKfuP1emlqauLixYvU1NQwPj6unsvMzGTx4sUsXLgwJH8vpd9ol8RGuyQ2UzeV3EASIxFg165dbN++PdTNCNDS0sLTTz+N2Wzm85///KytuuSPzdDQEDt37kSn0/G2t71NrdwnQkeL/Wa2s9vt9PT0sGvXLrKyshgbGws4HxMTQ1ZWlpoozbTNUyfjcrmoq6vjwoUL1NfX4/V6Ad9oU0FBAYsXL2bBggW3bZ8U6TfaJbHRLonN1E0lN5idd5girOTn5xMfH69OkVmwYEGomxRSdXV1AOTm5kpSJMQ1xMTEYLPZePzxxzl48CD5+fnqaFJvby92u52GhgYaGhrQ6/WkpaWRnZ1NZmYm8fHxM3I0yWQyUV5eTnl5OQ6Hg+rqai5cuEBra6ta/e+VV16hpKSERYsWUVJSMqOnHwohxJtJYiQCFBQUhLoJE+h0OhYuXMjRo0c5f/78rE2MCgoK8Hg8tLe3AzB37twQt0j4abHfCN86pOHhYTweD0lJSSQlJVFaWorb7cZisdDV1UVXVxc2m42enh61+qV/NCkrK4v09PQZOZoUHR3NihUrWLFiBUNDQ1y8eJELFy7Q29tLdXU11dXVREdHs3DhQioqKsjKypr2ZFH6jXZJbLRLYhNckhiJAFqdfrh06VKOHj1KbW0tQ0NDs7LYQHx8PL29vTidTqKiotTNMkXoabXfiMkZjUa1NLh/s9nOzk66urqwWCwTRpPS09PJysoiJydnRlbGTExMZMOGDaxfv56enh4uXLjAxYsXsVqtnDx5kpMnT5KWlkZFRQWLFy+etg2kpd9ol8RGuyQ2wSWJkQhw4cIFsrKyQt2MCdLS0igqKqKxsZFTp06xdevWUDfptrtw4YK6x0tGRoYsvtQQrfYb8db8m83Onz+f+fPn43K56O3tVRMlu91Od3c33d3dnD17lsTERLVEdlJS0oyacnf1HklbtmyhsbGRc+fOcfnyZXp7e3nttdfYs2cPxcXFVFRUMH/+/FsaTZN+o10SG+2S2ASXJEYibKxatYrGxkbOnDnDhg0bbtsCYS0ZHBwEICUlJcQtEWJmMplMAaNJIyMjdHV10dnZSW9vL0NDQwwNDVFVVUVUVBQ5OTlkZ2eTkZExo9bj6PV65s6dy9y5cxkbG6Oqqopz587R1tamjqaZzWbKy8upqKggNzd3RiWJQojZSarSiQBanqbm9Xp54okn6O3t5c4772TTpk2hbtJtNTQ0xLFjxxgeHmbTpk3yjpFGeDweent7iYmJwePx4Ha7cbvdeDwePB4PXq8Xr9eLoigBn/u9+WZSr9cHHDqdDoPBoP7baDRiNBoxGAwBH+WmdCKbzcbRo0dZu3bttEyBGx8fp7OzUx1Ncrvd6jmj0UhWVpaaVEVGRt7yz9Oi/v5+zp8/z/nz5xkeHlYfT0lJoaKigoqKihueaqflvzezncRGuyQ2UyfluiUxumnnzp2joqIi1M24pqqqKn7/+99jNpt59NFHiYqKCnWTbptz587R1NTE+Pg4b3vb2+TCeJsoioLL5WJ0dBSHw4HT6cTpdDI+Po7T6cTtdtPd3a1uPBoqRqMRk8l0zSMiIoKIiIgZNapxI4J1TfN4PPT09NDZ2UlHRwejo6PqOb1eT2pqKrm5ueTm5s7IDRkVRaG5uZlz585RXV2Ny+UCfK993rx5LFu2jHnz5l13yq/W/97MZhIb7ZLYTJ1mynV/61vf4o9//COXL18mKiqKtWvX8u1vf5v58+df82sOHDjAnXfeOeHxmpoaSktLg9lcAWpVJq0qKysjIyODnp4eDh8+PKvWGvX09Kj7jsy2m9vbRVEUnE4nNpsNm82Gw+FgdHQ0YGRgMg6Hg8jISHU05+pRHZ1OFzD64/949c+8mn9UyT+ydPWok38Uyj8i5f8IqCNVV9+gT8ZoNBIZGakmShEREURGRmI2m4mMjJxRv1vt7e3827/9G9///vfJzc2d1u9tMBjU0aHly5czMDBAZ2cn7e3tDA8PY7FYsFgsVFZWkpqaSl5eHrm5ucTExExrO0JFp9NRWFhIYWEh9957L9XV1Zw9e5bW1lZqa2upra0lLi6OiooKli5dSnJy8oTvofW/N7OZxEa7JDbBFdTE6ODBgzzyyCPccccduN1uvvKVr7Bt2zaqq6vf8o9DbW1tQFYnFbhuj4iIiFA34bp0Oh133303v/rVrzh+/DjLly+f9A/uTHR1bGbYQG9IuVwuRkZGGB4eZmRkBKfTOeE5Op0Os9lMVFQUZrN5QlJht9tZsmRJCFrv+13wJ0Uul+uah390y3/Y7fZJv5/JZMJsNquJkv9zs9kcdgU/LBYLf/rTn/jqV7867YnR1XQ6HSkpKaSkpLBo0SJsNhsdHR20t7fT29tLX18ffX19nD17luTkZDVJmq7qbqEWGRnJ0qVLWbp0Kb29vZw9e5bz589jtVp5/fXXef311ykoKGDZsmUsWLBALdig9b83s5nERrskNsF1W6fS9fb2kp6ezsGDB9m4ceOkz/GPGA0ODt7UVCGZSjfzKYrCL3/5SxoaGpg/fz4f/OAHQ92k2+bll1/GZrOxZcsWUlNTQ92csOV2uxkYGGBgYACr1TphzU9MTAyxsbFER0cTHR0dlKRAUWBsDKxWsNnA6QSP543D7QavF4xGiIgAk8l3+D+PifEdU2mW2+2eMBXQ//n4+Lg6HWoyOp2OyMhIoqKiAg4tJ0yVlZUsX76cM2fOsGzZspC0weFw0NHRQVtbG729vQG/a0lJSeTm5pKXlzfj/l55PB5qa2s5e/YsDQ0N6us2m80sXryYZcuWhXz6qRBidtDMVLo38y/UvJF3+JcuXcrY2BhlZWV89atfnXR6HaD+QfcbGRmZnsbOUrt27WL79u2hbsZ16XQ63va2t/GjH/2I2tpa6urqKCkpCXWzgm7Xrl1ERkaqU7zE1Pj3q7FYLAwODqrTEsG32WVCQgLx8fHExsZOeTrZ1f1mdBSuXIH6emhrg+7uwKO/H0ZGfAnRX2fB3TSdzpccxcdDXJzvSEyEtLTAIz3d/7mR9HQjiYnRTFarwe12Mz4+ztjYGGNjYwGfu91u9XN/dURfG95ImPyJZHR0NBEREVIQAt/v1rx585g3bx6jo6MBSdLg4CCDg4NcvHiRhIQE8vLyyM/PnxFJksFgoKysjLKyMoaHhzl37hxnz55laGhI3RtpcHCQj370o5SXl8u74BoTDvcCs5XEJrhuW2KkKAqPPfYY69evZ+HChdd8XlZWFk899RTLly9nfHycZ599lrvvvpsDBw5MOsr0rW99i6997WsTHt+zZw8xMTHcddddnDx5EpvNRlJSEuXl5Rw+fBiA0tJSvF4vdXV1AGzatIlz586pGeWyZcs4cOAAAPPmzcNoNFJTUwPA+vXrqa6uZmBggJiYGFavXs3evXsBKCoqIjo6mkuXLgGwZs0aGhoa6O3txWw2s3HjRnbv3g3AnDlzSExM5Pz58wCsXLmS1tZWuru7MZlM3HXXXezevRtFUcjNzSU9PZ3KykoAli9fTnd3Nx0dHej1erZu3crevXtxu91kZWWRm5vLqVOnAKioqGBgYIDW1lYAtm/fzoEDBxgfHyc9PZ2ioiKOHz9Oc3MznZ2d2Gw2mpqaANiyZQtHjx7F4XCQkpJCaWkpR44cAXxrfpxOJw0NDQDceeednD59GqvVSmJiIosXL+bQoUMA6tqy2tpaADZu3MiFCxcYGhoiLi6OFStWsH//fgDmzp1LREQE1dXVAKxbt47Lly/T399PdHQ0a9euxWQyUVVVxdNPP83HP/5x6uvrAVi9ejWNjY1YLBYiIyPZvHkzu3btAiA/P5/k5GTOnTsHwB133EF7eztdXV0YjUbuvvtuXnvtNbxeLzk5OWRmZnLmzBkAli1bhsViob29HZ1Ox7Zt29i3bx8ul4vMzEzy8/M5efIkAEuWLGFoaIiWlhYAtm3bxqFDhxgbGyMtLY25c+dy7NgxABYuXIjD4aCxsRGAu+++m+PHj2O320lOTqasrIzDhw/T3NxMbm4ug4OD7N27l+zsbDZv3kxlZaX6bkhFRQUHDx4EoKSkBL1ez+XLl9Xf2aqqKgYHB4mNjWXlypXs27cPgOLiYsxmM1VVVQCsXbuWuro6+vr6iI6OZt26dbz22muAb9ft+Ph4Lly4APjKqDc3N9PT00NERAR33nmn+v+dl5dHamoqZ8+eBWDFihVqVS+DwcCWLVvYs2cPHo9HXa9x+vRpwPfmSF9fH21tberv7P79+3E6nWRkZFBQUMCJEycAWLx4MSMjIzQ3NwOwdetWjhw5ov7OZmVlcfToUdxuN2lpabjdbkZGRoiIiGDLli2cP3+ejo6OKV0jYmPjiYtbznPPXebMmWz+9V9HaWkx0dU19ctqdLSCweBBp/NiMEBUlAm3ewydTkGnM+Hx6Bkd9eLx6PB4DLhcoCg6FMU32mSzTe3nRUZ6yMpyMWeOgcjIHtLSxli+PI34+GEUpZW0tDH+5m+2qNcI/z5ix48fx+PxkJuby8jICD09PSiKQlFREXV1dbhcLqKiokhNTaW9vR2j0UhRURF6vZ7Ozk70ej133XXXbbtGHD16FICWlhYyMzO5ePEiENprxJUrV9Q21NfXU11djdVqpaioiAsXLuB2u0lPT2fZsmV0dHQQGRl5w9cIgAULFuB2u9XroVauEQkJCZSWltLX14fZbObFF19kYGCAn/zkJyQlJWE2m5k3bx5btmy57deI1NRUSkpK1N+X8vJyxsbG1FjNxvuI5uZmLly4cFP3EYA6pVTr9xF79uwBoLCwkNjYWE1cI97qPqK5uZnKysqbuo8A7V4jgnkf4W//jbhtU+keeeQRXnnlFQ4fPjzlud47duxAp9Px4osvTjg32YhRXl6eTKW7SdXV1ZSVlYW6GTfE5XLxxBNPMDAwwLJly7jvvvtC3aSgqq6uxmAwcPbsWXJyctiwYUOom6R5drud1tZWrFYr4KuYlZKSQnp6+pQXwXu9cO4c7N4Nr70GJ09eOyFJSIB586CgALKyIDPzjSM19Y0RnthY3zHVegeK4huZslrfOPyjUEND0NsLFovv45s/v9FB9aws32t48zF3LviLQV5dsc9/2O12RkdHJ10HZzAY1BEl/3TFyMjIoI0stba28qUvfYlvf/vb5OfnB+VnTAd/GfDW1taAIivgW1+bn59PXl7ejNq7zW6386c//Umd0uo3Z84cVqxYwYIFCzAaZavFUAmne4HZRmIzdZor1/2Zz3yGF154gUOHDlFYWDjlr//GN77Bc889p77Lcj2yxujWWCwW0tPTQ92MG9bS0sLTTz8NwP3338/cuXND3KLgsVgsGI1Gdu/ejclk4l3vepdm13aEmtfrpaOjg+7ubhRFQa/Xk56eTlZWlrrw+0a4XL4k6De/gZ07fYnF1aKjYelSKCtzsHZtNPPn+5KHlBQmnaqmBaOj0NHhm+bX2ur7ePXnra2+BOt68vJ8r7OsDBYuhEWLoLzclxCC7//fX97cbrfjcDhwOBwBN/x+JpOJ2NhYNVGKiYmZ1sp44XZNGx8fp62tjZaWFvr6+tQEU6/Xk5GRQX5+Prm5uVP6PdYqi8VCWloajY2NnD59mtraWvV3JDo6mqVLl86qAjtaEm79ZjaR2EydZhIjRVH4zGc+w5/+9CcOHDjAvHnzbur7vPe972VgYEAdrrseSYxuTTjOXX311Vc5efIksbGxPPzww9OykaMW7dq1i61bt/LCCy/gdDq5++67pVrjJMbHx2loaFCrrqWkpJCXlzelNQxXrsAPfwjPPutbE+QXGwt33QXbtsHGjbBgga84Qjj2m+sZHISGBt86qbo630f/MTR07a/Ly3sjUVq40HcsWABms+/vgT9Zcjgc6lq5NydLOp2OqKiogGTJbDbf1KiSw+HgZz/7GQ899FBY7iXkcDhobW2ltbU1YFTFXyo8Pz+f7OzssC2x/uZ+Y7Vaqays5MyZMwHrhYuLi1mxYgXz58+XN4Nuk5l2TZtJJDZTp5niC4888gi/+tWv+POf/0xcXBzd3d0AJCQkqBtzPv7443R0dPCLX/wCgO9973sUFBRQXl6O0+nkueee4/nnn+f5558PZlNFGNu6dSvNzc1YLBZeeOEFPvShD83YRd96vZ7s7Gyam5tpa2uTxOhN7Ha7utbFZDJRUFBAUlLSDX/9uXPwL/8CL73km64GvsIFf/u38J73wJo1vqpwM11SEtxxh++4mqL4EkV/wlRVBZcu+Q7/yFNbG/zlL298jdHoG01avlzH8uXRLF8ezeLFkJ/vG1nyJ0l2ux2bzcb4+LiaPPmZTCbi4uLUIyoq6ob6+OXLl/nMZz7D2rVrQ1aV7lZER0dTWlpKaWkpVquV1tZWWlpaGBkZoa2tjba2NiIiIsjLy6OwsJCUlJSwvvbFxcWxadMmNmzYQH19PadPn6ahoYErV65w5coV4uPjWbFiBcuXL58x+0EJIbQlqCNG17pAP/3003z0ox8F4KMf/SjNzc3q4sTvfOc7PPXUU3R0dBAVFUV5eTmPP/4499577w39TBkxujX9/f2kpKSEuhlTZrFYeOqpp3C73Wzbto21a9eGuknTzh+bzs5ODh06hNlsZseOHWH7bvF0czgcXL58GbfbrVYCi4yMvKGv7eiAL3wBfv3rNx5729vg05+G7dt9N/fXE679ZjoNDQUmShcv+o6rBjpUBoM/WXrjqKjwjSz5N9j1J0p2u33CqJLRaAxIlKKjoyf9e6OFct3TTVEUdUF2W1tbwH5UcXFxFBQUUFBQEBaJw430m8HBQSorK6msrFRfq8FgYOHChaxatYrs7Ozb0dRZR65p2iWxmTrNTKULBUmMbs3FixdZtGhRqJtxU06fPs3LL7+MXq/nIx/5CAUFBaFu0rTyx8bj8fDSSy8xNjbGmjVrmDNnTqibFnIul4uqqiqcTiexsbHMnz//hhJGRYGnn4bHHoO/7ibABz7gGzUqLb3xnx/O/SaYFAXa2+HMmcDDYpn4XJPJlyCtWQNr1/o+5uT4RpXsdjtWq1U93pwoGQwGNUnyz0jQ6XQzMjG6mqIoWCwWdQTZ7Xar59LT0ykoKCAvL0+z65Gm0m/cbjdVVVWcPHmSjo4O9fHc3FxWrlxJeXm5vEk0jeSapl0Sm6mTxEgSo5sWznNXFUXhT3/6ExcuXCA6OppPfvKTJPhXg88AV8emqqqKixcvkpKSwpYtW8J6+sytUhSFhoYGBgcHiYqKuuFqVmNj8PDD8Mwzvn+vXAlPPukrpjBV4dxvbjdF8Y3QnTkDlZW+j6dOTZ4s5ecHJkoVFWAw+KbfXZ0oed60IZTJZCI+Pp7m5ma2bNkyYxOjq7lcLtrb29Vpxf4/7UajkdzcXAoKCkhPT9fUGp2b7Tft7e2cPHmSqqoqNfaxsbEsX76cFStWEBcXN91NnXXkmqZdEpup08waIxF+wvkdN51Ox44dO+jt7aWrq4vf/OY3PPTQQ5p9t3Sqro5NUVER1dXV9Pf309PTM6t3kB8aGmJwcBC9Xk9xcfENJUV2O9xzD7z+um9a1ze+AZ///NTLZvuFc7+53XQ6yM31HX/zN77HFAWam+HoUd9x7BicP++rkNfaCr/9re95UVGwdq2ezZtj2bw5lpUrszCZFDVRGhkZYWRkhCtX9Dgco7S2jhMZuYbXXuunq6uLzMwYKiqmt+qdVphMJgoLCyksLMRut9PS0kJTUxNWq5Xm5maam5uJjo6moKCAwsJCTSQPNxuH3NxccnNz2bZtG2fOnFH3vDl48CCvv/46ZWVlrFq1itzc3Fn9ptGtmIl9ZKaQ2ASXjBiJGWdoaIinnnoKh8NBeXk5733ve2fkH8fKykrq6upITU3l7rvvnpGv8a0oikJ1dTV2u52srCzy8vLe8mvGx+Htb4e9e33lpf/wB9iy5TY0VkyJzeYbSbo6WRocDHyOL1GCzZt9x8qV0NTkpbT02qMizz9/kQULjCQmJgZMu5uJFEWhv7+f5uZmWltbcTqd6rmMjAyKiorIzc0N+xstj8fD5cuXOXHihLr5KEBOTg5r1qyhrKxMUyNlQojbS6bSSWJ00/bs2cOWGXCX2NzczLPPPovH42H9+vUz4jW9OTajo6O8/PLL6muc6sbJM4Hdbqeqqgq9Xs+SJUtuaHTwH/8RvvtdiImBPXtg9epbb8dM6Tda5vVCTQ0cPAgHDviON+8rFRXlKxF+6hQ895yvVLhfTQ3cfz8888wlSkvfqHgXGRlJQkICiYmJxMXFhX2ScC0ej4fOzk4aGxvV/b0AIiIiKCgooKioiMTExNvapmD0m+7ubk6cOMHFixfVNVcJCQmsWrWKZcuWzahNcoNJrmnaJbGZOplKJ27am+fqh6uCggJ27NjBCy+8wOHDh0lKSmL58uWhbtYteXNsoqKiKC0tpaqqirNnz5KZmTnrdoof/OsQQmJi4g0lRYcO+ZIigF/9anqSIpg5/UbL9HpfJbvycviHf/BNv6up8SVI/mTJYvElReBLiiZbVjRv3jzmzBlieHiYkZERxsfHsVgsWCwW9Ho98fHx6mjSjVY1DAcGg4G8vDzy8vKw2+00NTXR1NSklrivq6sjJSWFoqIi8vPzb8sU5GD0m8zMTP7mb/6GLVu2cOrUKU6dOsXw8DC7d+/mwIEDLF26lNWrV0+pjP9sJNc07ZLYBNfsuosSb2kmlT6tqKhgaGiIAwcO8MorrxAXF0dJSUmom3XTJotNaWmpenNTU1Mz6yrV2Gw2gBsqsqEo8OUv+z7/2Mfgvvumrx0zqd+EC50Oysp8hz9RunzZV0zj29++9tf94AeRfPjDGWzcmEFxsQer1crQ0BBDQ0M4nU71c/DtI5SYmEhSUtI1S4KHo5iYGBYuXEhZWRk9PT1cuXKFzs5O+vv76e/v5+zZs+Tn51NcXExycnLQXncw+01MTAybN29m/fr1XLx4kWPHjmGxWDhx4gQnT56ktLSUNWvWkJeXN2PiOp3kmqZdEpvgkql0IsBMq4+vKAovvPAC58+fx2g08uEPfzhsy1tfKzZtbW0cOXIEvV7Pli1bSE5ODkHrQuPcuXM4nU7KysqIjY297nOPH/dVNjOb4coVmM6/LTOt34Szykpf2e8zZwJHjPyP+0VEwMaN8I53wI4dUFioMDo6qiZGdrudq/88RkZGkpSURFJSErGxsTPuZnp0dJTm5mYaGxuxWq3q44mJicydO5c5c+ZM+yjS7ew3iqLQ2NjIsWPHaGhoUB/Pzs5mzZo1lJeXyzqkq8g1TbskNlM3ldxArgIiwOnTp0PdhGml0+m47777mD9/Pm63m1/96ld0dXWFulk35VqxycvLIz8/H6/Xy4kTJ2bVMLt/DcGN3LA9/7zv47veNb1JEcy8fjMT1NT4kiH/UVPje/xd7/KVAXc6fWvMPvtZKC6G8nIdX/taNI2N2cyfX0ZFRQVFRUUkJyej1+sZHx+nu7ubmpoazp07R3NzM8PDwxP2VApX/lL39957L3fddRcFBQUYDAaGhoY4ffo0L774IpWVlQz7N/yaBrez3+h0OoqLi7n//vt55JFHWL58OUajkc7OTp5//nm+//3vc/LkSVwu121rk5bJNU27JDbBJVPpxIxnMBh473vfyy9/+Uuam5t57rnnePDBB0lNTQ1106bN8uXLsVgsDA8Pc/HiRSoqKkLdJM05eND3cceO0LZDBJe/CvX9909+/tvfhrlzoa4OXnkFXnrJV7a9psZ3fOc7kJwM995rYseOVLZvT6Ww0MPIyAiDg4MMDQ3hcrnUdUlGo5GEhASSkpJISEgI++INOp2O9PR00tPTWbp0Kc3NzTQ0NGC1WtW1SBkZGcydO5fs7OywfL1paWns2LGDu+66i9OnT3Py5EmGhoZ49dVXOXDgACtXrmTlypVER0eHuqlCiNtMptKJABaLhfT09FA3IyjGx8d55pln6OzsJDY2lo9+9KNhlRy9VWza29s5fPgwOp2ODRs2zIp5yGfPnsXlclFeXk5MTMw1n6cokJQEw8Nw6ZJvAf90msn9JhzV14PVirq3zaZNm4iLiyMuDubNm/j8oSHYudOXJP3lL4FlwY1GXynw974X3vlOSEvzYrVaGRwcZHBwMGCEwWAwkJiYSHJyMgkJCTNmapaiKPT09NDQ0EBHR4c6xTAqKoq5c+dSVFREVFTUlL+vVvqNy+Xi3LlzHD16VC3oYjKZWLZsGWvWrLnt1fq0QCuxERNJbKZOynVLYnTTqqurKSsrC3UzgsbhcPDMM8/Q09NDXFwcDzzwQNgkRzcSm9OnT9PQ0EBERATbtm17y3U34a6mpgar1UpxcfF151y7XL41JQB9fTDd07Nner8JZ1ONjdvt2zfp5Zd9idLly2+c0+lgwwZ4z3vg3e+GnBwFm82mJknj4+Pqc41GI0lJSSQnJxMfHz9j1iTZ7XYaGxu5cuUKY2NjAOj1enJzc5k7dy5paWk3/Fq11m+8Xi81NTUcPnxYnXKt1+spLy9n3bp1s2ojba3FRrxBYjN1ssZI3LS2trZQNyGooqOjeeCBB8jIyMBqtfLMM8/Q398f6mbdkBuJzdKlS0lJScHpdHLkyBF1Dc5M5X+X2l+d7lqufuM+GG8FzfR+E666u7v5j//4D7q7u2/4a4xGX1GG73zHN7Wurg7+/d/hjjt8vzuHDsGjj0JeHqxdq+PHP47D48ln8eLFlJWVkZmZSUREBG63m97eXmprazl37hwtLS1YrVbC/b3ImJgYFi1axI4dO1izZg1paWl4vV5aW1vZt28fu3btorGx8YbWOmqt3/iToE984hN85CMfobi4GK/Xy8WLF3nyySd57rnnaGpqCvsY3gitxUa8QWITXJIYiVknOjqaj3zkI6Snp2O1Wnn66afp6ekJdbOmhcFgYN26dZjNZgYHBzl9+vSM/iPuf+fnrRaE6/Xgf5NohoRa3IDOzk5+/vOf09nZedPfY948+NKX4ORJaG727YO1bp1v9Oj4cfjCF6CoCJYv1/GjH8UC+SxZsoTS0lLS09MxmUy4XC56enqoqanhwoULtLW14XA43upHa5rBYGDOnDncfffdbN++nblz52I0GhkaGuLkyZO89NJLXLp0idHR0VA3dcp0Oh1FRUV8+MMf5pOf/CQLFy5Ep9PR0NDAM888w89+9jPq6+tn9LVViNlKptKJWctut/Pss8/S3d2N2WzmQx/6EHl5eaFu1rTo6enh4MGDeL1eysvLZ+z+Rh6Ph7Nnz6qv83rrjFau9G3++fvf+9aLiJmvsrKS5cuXc+bMGZZNttvrLejqgj/9yVft8MABuLo43fr18Hd/B+97HyQnexkZGWFgYIDBwcGAkZSYmBhSU1NJTk6+LRuqBtv4+DiNjY3U19eriZ9er6egoICSkpKwXqszODjI0aNHOXv2rDoSn5WVxcaNGyktLZ0xUyWFmIlkKp24afv37w91E26bmJgYPvrRj5Kfn8/Y2Bi/+MUvAva30JqpxCYjI4Plf920paqqiitXrgSrWSFlMBjUHex7e3uv+1z/HjYHDkx/O2ZTvxE+WVm+jWX37oXubvjxj31FGnQ6OHzYdy4rC3bs0PPKK4mkpxdRUVHB3LlzSUpKQqfTYbfbaWlp4fz589TX1zM4OBjW5b8jIyNZsGABb3/721mzZg0pKSl4vV4aGxvZuXMn+/fvp7OzUx1pCad+k5SUxNvf/nYeffRR1q5di8lkoquri9/+9rc88cQTXLp0Kaxj92bhFJvZRmITXJIYiQBOpzPUTbitzGYz999/P3PnzsXlcvHrX/+aqqqqUDdrUlONTXFxMeV/Lb925syZW5pOpGVpaWkA9PX1XXcPkre9zffxlVemf53RTOg3iqLgdrtxuVyMj48zOjqKw+HA4XBgt9ux2+3YbDZsNpv6b//50dFRxsfHcTqduFwuPB4PXq931kw1SkuDT3wC9u+H1lb4z//0bS7rdsOrr/pKh6enw4c+ZODIkWQKCuZRUVFBfn4+MTExeL1eBgcHqa+v5/z587S2tob1VDv/NLutW7eyZcsW8vPz0ev19PT0cOjQIV599VXq6+vDcppdXFwc27Zt43Of+xwbN24kMjISi8XCH/7wB/7nf/6Hc+fOzYi95GbCNW2mktgEl0ylEwHOnTs3K/fA8Xg8/PGPf6SqqgqdTseOHTumferNrbqZ2CiKwsmTJ2lqasJoNLJp0yY1kZgpFEWhpqYGm81GRkYGc+bMmfR5drvvHXyr1bex5913T18bwq3feDwe3G63eni93qDczOl0OvR6fcBhMBgCPgZzClJjYyOf/OQn+fGPf0xRUVHQfs611NbCr38Nv/qVr4S4X0aGL1l68EFf6XiHw0FfXx/9/f0ByX10dDRpaWmkpKRgNIb3toN2u536+noaGxvVG7v+/n42btxISUkJkZGRIW7hzRkbG+PEiRMcP35cTfQSExNZv349FRUVYRu3cLumzSYSm6mTct2SGN20oaGhsJ4Hfiu8Xi+vvPIKZ86cAWDz5s1s2rRJM3PHbzY2Ho9HLT9rNBrZvHlz2JQov1HDw8PU1tai1+tZuHAhZrN50uf9wz/AE0/49qP505+m7+eHQ79xu904nU6cTuc1qxX6ExV/EuP/3dfpdAH9wP9n4+qPiqKoo0T+z9+K/2cZDAYMBgNGo1H9fLr6nRZioyhw5gz88pe+JMlieePcihW+BOmDH4TERIXh4WH6+voYGhpS/w/1ej0pKSmkpaURExOjmWvSzXC5XDQ3N1NXV0dvby9msxmj0UhRURHz58+/7jpBLRsfH+f06dMcPXoUu90O+IrDbNy4kaVLl4bdRrha6DdichKbqZPESBKjm7Zr1y62b98e6maEjKIo7N27l8OHDwOwePFi7rvvPk2863crsXG73bz++uv09PRgMpnYvHnzdff9CUe1tbVqv58/f/6kN4/V1bBw4Rs3qtM1KKjVfqMoCi6Xi9HR0YCRCJ1OpyYiVycj07khqT85evPh8Xjecqqdv31Xt9FoNE45IXA6nfzud7/j/e9/PxH+jaxCzOXybSL79NO+vZL8OWpkpC9h/+hHYetWUBQ3/f399Pb2BkyrmymjSF6vl9/+9rekpKQwMDAA+BLA/Px8SktLw/bGz+VyUVlZyZEjRxgZGQF8I0gbN25kyZIlYZMgafWaJiQ2N0OKLwhxk3Q6HVu2bGHHjh3o9XouXLjAs88+G5Zz4a9mNBpZv349aWlpuFwuDh48qN6MzBRz5sxBr9czMjJCX1/fpM8pK/NVCwNfCeaZ9bZQII/Hg9VqZWRkBJfLhU6nIyIigri4OJKSkkhMTCQ2Nhaz2YzJZJrWpAjeSG5MJhORkZFERUURExNDfHy8uvFpUlIS8fHxxMTEBLTDv95pfHwcu93O8PAwAwMDDA0NYbPZGBsbw+12v+UapkuXLvHhD3+YS5cuTetruxUmE9x3n2/EsqPDV/570SIYH4ff/hbuuQfmzIGvf92Iy5VBeXk5CxYsIDU1Fb1ej8PhoKWlhXPnztHU1ITNZgvLtVx6vZ7k5GS2bt3K5s2bycjIwOv10tzczM6dOzl06NBbFlTRIpPJxKpVq/g//+f/cM899xAXF8fQ0BAvvvgiP/zhDzl37tyMKtIgxEwjI0YiQFdXF1lZWaFuhiZcuXKF3/3ud4yPj5OSksKHPvQhkpOTQ9ae6YiNPynq6+sjIiKCDRs2zKg1R11dXbS1tWEwGCgvL590Sl1jIyxYAE6nb1rTBz84PT9XS/3G6XRis9nwer3odDrMZjNRUVHTnvwEg3+kyb8O6uqPb6bT6TCZTBiNRvXj1aNKwSzXPZ0UBc6e9Y0i/epX4H/PQq+HHTvg4Ydh2zbwet309fXR29sb8GZNdHQ0GRkZJCcnh82IBEzsNwMDA1y+fJm2tjY12UtLS6O0tJTs7OywnELocrk4c+YMr7/+ujrFLjk5mc2bN7Nw4ULN9kmtXdPEGyQ2UydT6SQxumm1tbXMnz8/1M3QDIvFwi9/+UuGh4eJjo7mAx/4APn5+SFpy3TFxuVyqe/GGo1G1q1bN2MusoqiUFtby8jICNHR0ZSVlU164/F//y/88z/7qolVV8OtLrnSUr9xuVyMjIygKAomk4nY2Niwulm+Fq/XG1Awwl804mo6nU5NkkwmExcuXGDFihWaT4yuNj7uG0168kk4ePCNxwsLfZXvHnwQ0tMVbDYbvb29DAwMqP8PJpOJtLQ00tPTNTN18Hqu1W+sViuXL1+mqalJfW2JiYmUlZWRl5cXlgmS0+nk9OnTHD58WJ0amZqayubNm695nQolLV3TRCCJzdTJVDpx05qbm0PdBE1JT0/n4x//ONnZ2TgcDp555hnOnj0bkrZMV2xMJhObNm0iKytLXXvU1tY2Ld871Pw71ptMJhwOB83NzZNOM/rSl3zVwHp7fWs6bnVmi1b6jaIo6tSqiIgI4uPjZ0RSBL6pVxEREURHR6vT8RITE4mJiSEyMlKdgudyuXA4HAwPDzM8PAz4Kod5PJ6wmHIWGQkf+IBvv63qanj0UUhMhKYmePxxyMuDD35Qx5kzcRQW+vZGysvLIzIyEpfLRWdnJ+fPn6ehoQGr1arp13ytfhMXF8cdd9zBjh07WLBgASaTiaGhIY4ePcrOnTtpaWkJu+loERERrF27ls9+9rNs2bKFqKgo+vr6+MMf/sCTTz5JbW2tpmKllWuamEhiE1ySGAnxFmJjY3nwwQdZsGABHo+HP//5z7z66qthvVeFf81Rfn4+Xq+Xo0ePzphNYCMiIigqKkKn09HX10dXV9ckz/FVCIuM9O1r9F//FYKGBoE/ATAYDMTFxYXlO+s3yj86FBUVpa6bSkpKIjY2NiBRAhgdHWVwcFBdo+R0OsPixnrBAvje93xrkZ5+Glau9BVv+O1v4c47YckS+MUvjCQlZbF48WLmzp1LfHw8iqIwMDBATU0N1dXV9PX1hcXrfbOoqCiWLFnCO97xDhYuXEhERATDw8McO3aMXbt2hW2CtH79ej772c9y1113YTabsVgs/PrXv+ZnP/sZLS0toW6iELOaTKUTAbxer+aG9LVCURQOHTqk7jqdn5/P+9//fmJjY2/Lzw9GbLxeL2fOnFGTorKyMhYtWjQjbqh7enrUm4zi4uJJq/D9+Me+9RsGgy9ButlCP1rpN0NDQ7jdbrWowmzmHz2y2WzodLoJVfD865MiIiKIiIjQRPxuRGWlb5rdL38J/mJ1aWm+3+NPfcq3V5fD4aCnp4f+/v6AaXbp6emkp6djMplC+AreMNV+43Q6qauro66uTt0LKT4+nrKyMnUT2XAzNjbGkSNHOH78uFo5ct68edx9991kZmaGrF1auaaJiSQ2UydrjCQxummvv/46GzZsCHUzNK2uro7nn3+e8fFx4uLi+Nu//Vtyc3OD/nODFRtFUbh06RJVVVWAL+FbtWrVjJiC1dLSQk9PD3q9nrlz504oAawo8NBD8POfQ3w8HD3qm2I3VVroN/5RAkVRSEpKmhHxmw7+2PgTJafTicvlChjx9Y8++ZOkcPi/GxyEn/wEfvAD8M+ENZl80/A++1lfKXqXy0Vvby8Wi0VNJPR6PWlpaWRkZIQ8eb7ZfuN0Oqmvr6e2tlZ9XXFxcZSVlanVKcON1Wrl0KFDnDlzRk1mFy1axJ133hmSoj9auKaJyUlspk7WGImbdvV+GWJyJSUlfOITnyAtLQ2r1crTTz9NZWVl0H9usGKj0+lYtGgRq1atQq/X09rayv79+xkbGwvKz7ud8vPzSUlJwev10tDQoO4r4qfT+UaNNm6EkRFfqeSbmcmilX7jf59rJoz4TYe6ujoeeeQR6urq1HLlsbGxJCYmkpiYSHR0NEajUU2a7Ha7OuXO4XBoerpsUhJ84Qu+Kou/+x2sW+ebZvfss7B8ue93+uWXTWRmZrN48WKKi4uJiYnB6/XS09PDxYsXuXLlilopLRRutt9ERERQXl7Ojh07WLRoEREREVitVk6cOMFf/vIXWlpaNLVe50bExcXx9re/nU9/+tMsWrQIgIsXL/LDH/6QV155BavVelvbo5VrmphIYhNckhiJAKm3Wp5rlkhJSeFjH/uYuu7oxRdf5KWXXgrYRHO6BTs2hYWFbNq0iYiICPr6+tizZ4+6eD1c6XQ6CgsLSUxMxOv1Ul9fPyE5ioiAP/4R5s/3vfN+112+NR1ToYV+o9Pp1HfKw23dRbDYbDYuXryIzWYLeNw/QhQdHU1iYiJJSUnExMRgMpnQ6XS43W4cDgeDg4MMDw8zOjqq2f9ToxHe9z44fBhOnoQPfcj32Ouvw7vf7du765ln9MTFpVBWVkZpaSkJCQkoikJ/fz9VVVVcvnyZ4eHh255M3Gq/MZlMaoK0ePFiIiMjsVqtHDt2jN27d9PZ2Rl2CVJycjLvec97ePjhh5k3bx5er5dTp07x/e9/n7179zI+Pn5b2qGFa5qYnMQmuGQqnQhgtVqJi4sLdTPChqIoHD58mH379qEoChkZGbzvfe8LyoXrdsVmZGSEQ4cOYbPZMBqNrF69+rZMFQwmf1I0PDyMXq9n3rx5JCQkBDyno8P3LntjI5SW+kolp6ff2PfXSr8ZGRnB6XQSExNDVFRUqJsTcjezj5HX68XpdKpT7q4ehfNvVhsREaHpUbnOTvif/4Ef/QiGhnyP5eTAP/4jfPzjEBvre9e5q6tLnX4JvkIz2dnZJCQk3JbXN939xuVyUVdXx+XLl9U3qdLS0li8eHHY7tfW3NzM3r171cqhMTEx3HnnnSxbtiyoUwa1ck0TE0lspk6m0ombdvTo0VA3IazodDo2bNjA/fffT0xMDD09PTz11FNcuHBh2n/W7YpNfHw8W7ZsIT09HbfbzeHDh7l48WLYvfN6NX8ydPXI0YB/F82/ysmBvXt95ZAvX57ayJFW+o1/Uf3Y2FhYxyuU9Ho9ZrNZLQkeExOjTrdzOp1YrVYGBgaw2WwBiZOWZGfDN77hmxb6H//hK8jQ0QGPPQZz5sC//iuMjkZTXFzM4sWLycjIQK/XY7PZqKuro7q6OiBhCpbp7jf+EaR3vOMdlJaWYjAY6O3tZe/evRw8eJDBwcFp/Xm3Q0FBAQ899BAf+MAHSElJwW638/LLL/PEE09QX18ftBhp5ZomJpLYBJckRkJMg+LiYh5++GEKCwtxOp388Y9/5M9//rO6MDjcmM1mNm/erG4iV1VVxaFDh8L29QBqAYbk5GS8Xi9Xrlyhu7s74DkFBb7kKDsbqqp86zbq60PT3pvhL1Pt8XjCOlZaodfriYqKUqfbRUdHYzAYUBSFsbExda8krU61i4+Hz3/etwfSU0/B3LkwMABf+xrk58PnPgcDA5HMmTOHJUuWkJWVhcFgwG6309DQQFVVFf39/ZpM/q4nMjKSiooK3v72t1NcXIxer6erq4tdu3Zx9OjR275e51bpdDpKS0v5h3/4B+655x6io6Pp7e3ll7/8Jc8+++yE65gQ4ubJVDoRoL29PeynTYWS1+vl0KFDHDx4EEVRSEtL433vex/pNzon6zpCFZvm5mZOnTqFx+MhLi6OdevWTajuFk4URaGlpQWLxQJARkYG+fn5AVOHmpth2zZfUpSWBjt3+qp8XYuW+o3D4cDhcGAwGEhISAjLCl3Tpa+vj6effpoHH3xw2qa3KoqC2+1mfHyc8fHxgKl2ERERmM1mjEajJqfaeTzw/PPw7/8O/n2qzWZfme8vfQkyMnzT0Xp6eujp6VGLT0RFRZGdnU1ycvK0vq7b1W+sVisXL16ktbUV8CW8RUVFlJeXh+WU07GxMQ4dOsSJEyfweDzodDoqKiq46667pm2KlZauaSKQxGbqpFy3JEY3raGhgblz54a6GWGvqamJ559/HpvNhslk4p577mHp0qW3dFMRytgMDAxw5MgR7HY7BoOBpUuXUlxcrMmbvxuhKArd3d3qvP2kpCSKiooCyjRbLPC2t/luIOPifJtq3nPP5N9PS/1GURSGhobweDxERkYSGxsbtnGaDsGMjX890vj4eEDhFaPRiNlsJjIyUpP/94oCu3fDv/2br0Q9QFQUfPrTvkp3aWngdrvVBMntdgMQHR1NTk4OiYmJ0/K6bne/GRwc5MKFC+qmz0ajkQULFjB//nyMRuNta8d0GRwcZO/evVy6dAnwTSVct24d69atu+W9qrR0TROBJDZTJ2uMxE3zb/Qpbk1hYSEPP/wwxcXFuFwuXnzxRX73u9/dUpnNUMYmOTmZbdu2kZ2djcfj4fTp0xw7diyoVfiCSafTkZWVxdy5c9Hr9QwODlJTUxNQojw9HQ4cgM2bwWqFd7wDvvtd303lm2mp3+h0OjUZGh8fnxFl129WX18fP/zhD+nr6wvK9/evR0pISCAxMRGz2axWtbPZbAwODmK32zVX9lun821mfPiwbzR01SoYHfWtRyoshC9/GYaHjeTk5LBkyRJycnIwGAw4HA7q6+upqamZUN3xZtzufpOUlMSmTZu46667SElJwe12c/HiRV599VWamprCbspgUlIS733ve/nYxz5GXl4eLpeLAwcO8MMf/pCqqqpbej1auqaJQBKb4JLESIggiY2N5f7772fLli0YDAZqamr40Y9+RH04LVq5SmRkJBs2bKCiokLd72j37t0TihiEk+TkZObPn4/JZMLhcFBdXR1Qojw+Hnbtgr//e/B6fVW9/v7v4TZVzL1pJpOJ6OhoAOx2+20r8as1ra2t/Pd//7c6hSqYjEYjsbGxasEGg8GA1+tldHSUoaEhrFarOvKiFf4E6dgxeOUV3/5Hdjt861u+BOn/+//AZjOQk5PD4sWLycrKUos0XL58mcuXL08ohR4O0tPT2bJlC2vWrCEmJgaHw8GJEyfYvXs3PT09oW7elOXm5vLQQw/xvve9T31X/Pe//z3PPPNMWL4eIUJJptKJAC6X65aH4MVEXV1d/PGPf6S3txeAO+64g23btk3p/1pLsenr6+PYsWPY7Xb0ej1LliyhpKREk9OGbsT4+DhXrlzBZrOh0+nIyckhKytLfT2KAt//vq+ql9frK8rwu9/5ijSAtmLjpygKDoeD0dFRdRQpMjIy1M26rW6mXPd08W8aOzY2FlAIw78Oyb9nkpYoCrz0EvzzP8P5877HUlN9CdLDD/v2/HK5XHR2dtLb26sWnEhJSSEnJwez2Tyln6eFfuPxeNRKfP4R8JycHCoqKsKyJLLL5eLIkSMcPnwYt9uNTqfjjjvu4M4775zSeiotxEZMTmIzdTKVTty0kydPhroJM1JWVhaf+MQnWLVqFQCnTp3ixz/+MZ2dnTf8PbQUm9TUVLZt20Zubi5er5ezZ89y8ODBsN2ROzIyktLSUtLT01EUhfb2dhoaGtR3+HU6ePRRePVVSEiAI0egogJee8339VqKjZ9OpyM6OprIyEgURcFms83akaNQ8BdjiI+PJzExUV1v5HQ6GRkZYXh4GKfTqanpWzod3HcfVFbCH/7g2/S4r8/3u79ggW+dncFgYs6cOSxatIi0tDR0Oh39/f1cunSJ1tbWKY2KaaHfGAwGFixYwNvf/nbmzZuHXq+no6ODv/zlL5w/fz7spgubTCY2b97Mpz/9acrKylAUhZMnT/KDH/yA06dP33D1RC3ERkxOYhNckhiJAOE4LSJc+IswfPjDHyYuLo6+vj5+8pOfcOjQoRv6Y6W12ERGRrJu3TqWL1+O0Wiku7ubnTt33pZpS8Gg1+spKCigsLBQXXd06dKlgLUU27fDqVOwZAn09vr+/c//DMPD2oqN39UjRf7kaHR0VFM347OB0WgkLi6OxMREoqKi1HVIWk2Q9Hp4z3vg0iV48knIzPRtfPyBD/jWIx044Ov/hYWFlJeXk5CQgNfrpbu7mwsXLtDd3R121zSz2czy5ct529veRlZWFl6vl5qaGl599VWam5s1FZ8bkZiYyPvf/34eeOAB0tPTcTgcvPzyyzz11FM3dI3WUmxEIIlNcMlUOhHg5MmTrFy5MtTNmPFGR0d5+eWXqaqqAnxTN/7mb/7mumW9tRybkZERjh8/rq43KigoYNmyZURERIS4ZTfHbrdz5coVxsbG1EIN2dnZaunr0VH47Gd9e8MALFs2wksvxatT67RGURTsdrtaiMFsNhMTE6O5qVzTra6ujg9+8IP8+te/pqSkJNTNUfnXHl29Ea/RaCQ6OlqTU+xsNl/hkf/4D9/nAO9+N/znf/rWIgEMDw/T1tamjhpHRkaSn59/3Qp2Wr2mKYpCZ2cnZ8+eVW9C09LSWLp0KcnJySFu3dR5vV5OnTrF/v371WtARUUFW7duJSYmZtKv0WpshMTmZki5bkmMbprdbr/mhVJML0VR1IpIY2NjGAwGNm3axLp16wLKRvtpPTYej4fq6mqqq6tRFIWYmBjuuOMOMjMzQ920m+LxeGhtbVXXhcXGxlJUVBSwjuJXv4JPfMK3YD0pCZ54Av72b0PV4uvzb0rqcDhQFAWTyURcXNyM3+dIy/1msgQpIiKC6OhoTZaP7unxlfh+8knfWrvISF9573/6J4iJ8f2O9fX10dHRoa6rSkhIID8/f9L1LVqODfiuAbW1tVRXV6vrdYqLi1m0aFFYrtez2+3s27ePyspKFEUhKiqKrVu3TrqVhNZjM5tJbKZOU2uMfvSjH1FYWKgOU7/++uvXff7BgwdZvnw5ZrOZoqIinnzyyWA3UVzl8OHDoW7CrKHT6Vi8eDGPPPII8+fPx+PxsG/fPn7yk59MupO51mNjMBhYtGgRd999N7Gxsdjtdg4cOMCpU6fCbp4++F5PYWEhxcXFGI1GbDYbVVVVWCwW9Sb27/4OzpyBefOGGRz0TTX6u7+DwcEQN34SOp2OqKgoNRlyuVwMDQ0FFAaYabxeL/v27bvhdRW3m16vJyYmhqSkJHWKndPpZHh4GKvVqrky3xkZ8D//A+fOwV13+aozfv3rUFoKv/41gI60tDQWLVqkjrAODw9TVVVFW1vbhNcTDte0srIy7r33XubMmYOiKDQ0NPCXv/wlLMt7x8TEsGPHDv7+7/+ezMxMRkdHefHFF3n66acnVK/TemxmM4lNcAU1Mfrtb3/LZz/7Wb7yla9w9uxZNmzYwD333HPN+a1NTU3ce++9bNiwgbNnz/LlL3+Z//N//g/PP/98MJspREjFxcXxgQ98gHe/+91ERUXR1dXFU089xf79+zV3Y3QjUlNT2b59u7oB3ZUrV/jLX/6ibqoYblJSUigvLycuLg6Px0NzczO1tbVqIYP58+H//b8T/PM/g8Hgu0FctOiNwgxaExERQUJCAkajEa/Xy8jICHa7Pexu8m7EuXPnuO+++zh37lyom3Jd/gTJX6RBURTGx8cZGhpSR/i0ZNEi2LMHnn8eCgqgvd33hsCdd/r2RTp/3oDFkovLtYiOjgyqq82cODHAxYsX6e/v19zreSvR0dGsWbOGu+66i4SEBMbGxjhx4gQHDhyYlv2cbrfc3Fw+8YlPsH37diIiImhtbeXHP/4xr7322ox+o0SIGxHUqXSrVq1i2bJlPPHEE+pjCxYs4J3vfCff+ta3Jjz/S1/6Ei+++CI1NTXqYw8//DDnz5/n2LFjN/QzZSrdrWlpaWHOnDmhbsasZbPZePXVV6murgZ8+228853vJDs7OyxjY7FYOHnypDpPv7CwkIqKirCchqIoCj09PbS3t+P1ejEYDOTl5ZGWlkZraytz5szhxAn48IfBv1XVQw/51mEkJYW27ZN587ojo9FITEzMjCoDG8py3bfC5XLhcDjUkVaDwUBMTIwm1+yNjsJ//Rd885u+z6/n978/T37+OAkJCcyZM4eenp6wu6b5y3tXVVXhdrvR6/UsWLCABQsWaHL641sZHh5m586d6n1XQkIC9957L2azOexiM1uE471AqGliKp3T6eTMmTNs27Yt4PFt27Zx9OjRSb/m2LFjE56/fft2Tp8+HZZTccKRVqeczBaxsbG8//3v533vex8xMTFYLBb+93//l927d6s3sOEkPT2d7du3q3scNTU1sXPnTtrb28PuXWOdTkdmZiYLFy6cMHrkj82qVXD2LDzyiO9rfvYzX5njP/zBt0eMlvgr1sXHx6PX69UqaTN19CicmEwm4uPjiYuLw2Aw4PF4GBkZ0eT0uqgo+OpXoboa1q/3Pfbcc74ppv7jued8j8fGZqnT6y5duhSWo0f+8t5XV6+rqqpi586dk06B1rqEhAT+9m//lg9+8IPqjeOvf/1rXnzxRaxWa6ibJyYh92nBFbS3N/r6+vB4PGRkZAQ8npGRcc2LR3d396TPd7vd9PX1kZWVNeFrxsfHA/bm8A9rnzt3jtjYWPXxpKQkCgsLGRsbU9+Nv5r/3cTa2lrsdnvAuYKCApKTk+nt7aWtrS3gXFxcHPPmzcPj8XDevyPeVRYtWoTJZOLKlSsMDw8HnMvJySEjI4PBwUGampoCzkVFRbFgwQIAzp49O+GPx4IFC4iKiqKlpYX+/v6AcxkZGeTk5GC1Wqn3v3X9VyaTiUWLFgFw8eLFCQlnS0sLhYWFdHR0TJhznJKSwpw5cxgdHQ0Y1QPfTdbSpUsBqKmpYfRNbx0WFhaSlJRET08PHR0dAecSEhIoLi7G5XJx8eJF3mzJkiUYDAbq6+snXKj979gPDAzQ3NwccC4mJob58+cDvneO36ysrAyz2UxTUxODb1oUkpWVRVZWFiMjIzQ0NASci4yMpLy8HIALFy5M2LejpKSE2NhY2tvbsVgsAedSU1PJz8/H4XBw+fLlgHN6vZ6KigrA9/+5du1ajhw5wpUrV3j++edxOp08/vjjxMfHT9j/KDExkaKiIpxOJ5cuXZrwWisqKtDr9dTV1U0o9Zmfn09qaip9fX0TprnGxsZSUlKC1+uddDrSwoULiYiIoLGxkaGhoYBz2dnZZGZmqv0pNTWVS5cu4XA4qK+vV0eU6+vrJ1zoS0tLiY6OprW1lb6+voBz6enp5ObmYrPZqKurCzhnNBpZvHgxAFVVVRP27Zk7dy7x8fF0dXVNmNp3o9eIlpYWHA4Hw8PD9PT04PV6cTqdxMbGYjQa6ejo4KGHYOlS3/qL5uY43ve+edx3n4dPfeo8by48qIVrhL8IgNPpVK8RMTEx1NbWTrhGzJs3j7i4uLC4RlzdhnC7RoyOjnL58mW8Xq/6d86/LjEmJoYrV65M+P0uKioiMTGR7u7u236NyM/38vDDtRw+vIAFC2CyAbr+/n4SEnwbxNrtdux2O5GRkSQmJk74XTKbzZSVlQG+v+dau0bU1dWpbyxcvnyZ8fFxbDYbRUVFREVFTeg3Wr+PsNvtrF27ljNnznDhwgUuXLiA3W5n/fr16vo3v7e6jwinawSE331EVVUVhYWFb3kfUV1dPeEN1VBeI27lPmJoaIjGxsaAc1O5Rrw5rtelBElHR4cCKEePHg14/Otf/7oyf/78Sb9m3rx5yje/+c2Axw4fPqwASldX16Rf8y//8i8K8JbHnXfeqZw4cUI5f/78pOd37typjI6OKgsXLpxw7gtf+IJy5coV5d/+7d8mnFu2bJny+uuvK/39/ZN+39/85jfK8PCwsnHjxgnnPv7xjys1NTXKU089NeFccXGxsnfvXkVRFMVkMk04/+STTyq9vb3Ku9/97gnn3v/+9yvnz59X/vznP084l5qaquzcuVNRFEVJTU2dcP5zn/uc0tHRoXziE5+YcG779u3KqVOnlJMnT044ZzKZlJ07dyrj4+NKSUnJhPNf/vKXlaamJuUrX/nKhHOrVq1Sjhw5orS3t0/6f/j8888rVqtVWb169YRz//AP/6DU1tYq3//+9yecKy0tVfbv368oijLp9/3Zz36m9Pf3K/fee++Ecx/60IeUixcvKr/97W8nnMvKylJ27dqlKIqiJCQkTDj/3e9+V+nq6lIeeOCBCefe8Y53KGfOnFEOHjw44Vx0dLSyc+dOxeVyKQUFBRPOr1ixQvnsZz+rvOMd75hwbv369cqxY8eU+vr6SV/riy++qNhsNmXZsmUTzj366KNKfX298p3vfGfCuUWLFimHDh1SHA7HpN/32WefVQYHB5UtW7ZMOPfRj35UqaqqUp555pkJ59LT05VvfOMbyu9//3vFbDZPOP+DH/xA6enpUT7wgQ9MOPeud71LOXv2rLJr164J5xISEpSdO3cqHo9HycnJmXD+61//utLW1qZ85jOfmXDuVq4RDzzwgLJz507l0UcfnXAuM3O9YjR6FBie9Ptq7RqRlpamHD9+XOnt7VXS0tImnP/2t78dVtcIQPnjH/84Y64Rx48fV3p6epTCwsIJ5//lX/5FaWlpUT7/+c9POHd7rhFLFVCUM2cC/06fOaMooPz1/Btfe9999ykvv/yy8vWvf33C983Pz1dee+01RVEUJTo6esJ5LV4jvv71ryvPPvvspNfvcLuPKCsrUx5++OFJ/3/f6j4i3K4R4XYfsWnTppu+jwj9NWLi973Z+4ibuUYMDw9PmktcLWhrjJxOJ9HR0fz+97/nXe96l/r4o48+yrlz5zh48OCEr9m4cSNLly7lv//7v9XH/vSnP/H+978fh8Mx6dz3yUaM8vLyOHjwoIwY3cSIkf+dE3mnJ7QjRm9+p8flcqnTtkZGRhgbG+OOO+6gvLwcvV4fdu/0uFwubDYbvb29NDc3ExsbS1lZGYmJiUDo3w2eyjVC+WvZ2/HxcSwWCz09PSQkJJCVlaVu7Dk+Po+//3sPJ0/6rhHFxfClL8Hy5dq7RhgMBubOncvY2Ji6jiIyMhKz2YxOpwurd4NdLhc6nY6KigpsNlvYXyN0Oh2lpaXqOafTSVRUlLr2KNTvBv/617Xcf/8CzpwJHDGqrPT9rm/YUMP//b+jxMX5Ho+Pj0en09Hc3ExHRwdms5mcnByioqI0P2I02TUiNzeXU6dOqcVZsrKyKC0tJSIiIuzuI+Lj4+nu7mbXrl1YLBaMRiOrVq2ivLyciIgIGTH6q1BcI/x9TkaMpjZitGnTptDvY7Rq1SqWL1/Oj370I/WxsrIy/uZv/uaaxRdeeumlgAvOpz71Kc6dOyfFF26T48ePs3r16lA3Q0zi+PHjFBQU8PLLL9Pe3g74Lho7duyYdJqp1imKQlNTE+fOncPpdIb1HiHHjx9nxYoVtLe309vbi6IoGI1GsrOzSU9PR6/X4/XCT38Kjz8O/nuQD37QV5xBixvDut1u7Ha7etOj1+uJjo4mMjJScxuQXs9MvKa53W5sNpt6M6WVDXv9CdBzz/nW1vnV1MD99/s+z8nxrb3btu2N2AwODtLc3Kwmsjk5OWRlZYX89dwMt9vNpUuXqK2tRVEUdauSvLy8UDdtSvyx6evr48UXX1RvdufMmcN9991HSkpKiFs4e83Ea1qwaaL4AsBjjz3GT37yE372s59RU1PD5z73OVpbW3n44YcBePzxx/nIRz6iPv/hhx+mpaWFxx57jJqaGn72s5/x05/+lM9//vPBbKa4ypvfjRLaMTw8TGZmJg899BBvf/vbMZvNdHZ28tRTT7Fz584J73xqnU6no6ioiHvvvZeCggJ1j5BXX32VhoaGsFpgOjw8jNFopKCggAULFhATE4Pb7aa1tZWqqiqGhobQ6+HjH4e6OvjUp0Cn85X2nj8fvv1t0FptDaPRGFAAwOv1YrPZGBoaYnx8PCwWzTc2NvKFL3xhwjuN4c5oNJKQkKBumjo2Nsbw8HDICzP4R4Luv9+XIPkPf1I0Zw50dMD27b6NYfv6fGuCk5KSWLhwIUlJSSiKQnt7OzU1NWFZcMZoNFJRUcGWLVvU0t5Hjhzh+PHjYVUK238vkJqayoMPPsi9995LREQELS0tPPHEExw5ciSsrtEzidynBVdQR4zAt8Hrd77zHbq6uli4cCH/7//9PzZu3AjARz/6UZqbmzlw4ID6/IMHD/K5z32OqqoqsrOz+dKXvqQmUjdCRoxuzbFjx1izZk2omyEm8ebYWK1Wdu3apQ55x8bGsnXrVhYvXhyW77RaLBbOnDmjXvSTkpJYvnw5qampIW7ZW3tzbBRFobe3l46ODnXEJTExkby8PPVmtrLSV73u+HHf1+Tnw7e+5dskVh/0rbenRlEUxsbGGB0dVW+GTCYTUVFRmEwmzf6+hWu57qlwOp3YbDa8Xi96vZ64uLiQllyvr4fJipnFxflGi77wBfBPIikttfHSS7H8dcszFEWhv7+f1tZW3G43BoOBOXPmhMU1YDIej4fq6mqqq6tRFIWYmBhWrlw5ociUFk12LzA0NMSLL76ovtGQn5/Pu971LpK0uB/BDCb3aVM3ldwg6InR7SaJ0a0ZHx8Pu2lMs8W1YuPfid0/RzwvL4977703LKfXeb1eGhoauHTpkvruamFhIYsXL1YTCi26VmzcbjednZ1YLBa8Xi86nY6MjAyysrIwmUx4vfDLX8KXv+zbJBPgjjvgu999o/Sxlni9XjVB8v/pMBqNREdHazJBmg2JEfhuwK1WK263Wy3DruXr+Asv+Pb4GhyE2Fh48kn40IfeOD8+Pk5TU5NaZTY1NZU5c+ZgMBhC0+Bb1NfXx4kTJ9S1LSUlJSxevFjT+x5d65qmKApnz55l165djI+PExERwdve9jaWLl2quf4/U8l92tRpZiqdCD9Xj94JbblWbObOncunPvUptmzZQkREBG1tbTz11FO8/PLLOByO29vIW6TX6ykpKeHee++lqKgIgKamJl599VVqampCPlXoWq4VG6PRSH5+PgsXLiQxMRFFUeju7ubixYt0dXWhKB4+/GGorfWV9o6NhVOnYMMGeM97fI9riX+dUVJSklrC17//0fDwME6nMyym2M00BoOB+Ph4IiIiUBQFm82m6Wlo73wnnD8PixYNYLP5pto98gj4Z5pFRkYyf/58cnJy0Ol09PX1UV1dHXbXM7/U1FS2bdvG3L8OjdXV1fHaa69NWKyvJde6pul0OpYtW8bDDz/MnDlzcDqdvPjii/zmN7+ZsCBfBIfcpwWXJEZCzABGo5H169fz6U9/mkWLFqEoCqdPn+YHP/gBp0+fDru54GazmZUrV7JlyxZSUlJwuVycP3+ev/zlL7S1tYXdzbfZbKakpIT58+cTHR2N2+2mra2Nixcv0tvbS1SUwle+Ag0N8MlP+qbS/fGPUFYGf//30NIS6lcQSK/XExMTM2mCNDQ0xNjYWNjFKNz5p9GZzWYURcFut2s6OcrLg3//91P8y7/41tv96Edw113g3+bQX4Rh/vz5REREMDo6SnV19YQKdOHCZDKxYsUKNm7ciNlsZnh4mD179lBfXx+WfSUpKYkHHniArVu3YjAYqK2t5YknnphQJU2IcCNT6USAxsZG9Z16oS1TiU1zczN/+ctf1FKpWVlZ3HPPPeTn5weziUGhKArNzc1cuHBBLd+alpZGRUWFZiojTSU2/nUUHR0dasGMqKgocnNzSUxMRKfTUVXlm1734ou+r4mIgIcf9j2mxeUJ/il2Y2NjahKu1+sxm82YzWb0IVo01d3dzX/913/xj//4j2RmZoakDbfbm5OiuLg4zU678febl1/2TaUbGfFVaHz+ebi66JbL5aKxsVFdf5iZmUleXl7YTt0aHx/nxIkTarnkvLw8VqxYoak4TeWa1tPTwx//+Ef1701FRQX33HOPpl7PTCL3aVMna4wkMbppra2tYXnzPBtMNTZer5dTp06xf/9+9SZp4cKFbNmyRd0vKJy4XC5qa2u5fPmyWqZ4zpw5LF68mJiYmJC27Wb6jdfrxWKx0NnZqb6e2NhYcnJy1P1djh+Hr3wF9u3zfU10NDz6KHz+85CcPN2v4tZ5vV7Gx8cZGxtTpz3qdDp1H6RQrKmYjde0q5MjnU5HfHx8SAsyXMvVsamrg3e9C6qrwWSCn/8c/u7v3niuoih0dHSoyYR/3xotr9O5HkVRqKur4/z583i9XmJiYlizZo1mCk1Mtd+43W4OHDjAkSNHUBSFpKQk3vve95KTkxPEVs5Os/GadqtkjZG4aW/ecE1ox1Rjo9frWbVqFZ/5zGdYtmwZOp2OS5cu8cMf/pA9e/ZoeprNZEwmEwsXLuTee++lsLAQnU5HS0sLr776KufPnw9pufKb6Td6vZ7MzEwWL15MdnY2er0em82mJn/Dw8OsWqWwdy/s2QMrV4LD4atcV1Dg2w+pt3f6X8ut0Ov1REVFkZiYSFxcHEajUa1oNzQ0xPDw8G0t9T00NMTPfvazCZsGznQ6nY6YmBh1zZHVatXkdNqr+01Jia9C47veBS6XbwTp3/8d/L8qOp2O3Nxc5s6di16vZ3h4mOrq6gmbgIYLnU7H/Pnz2bJlC3Fxcdjtdvbt20dNTY0mptZN9ZpmNBrZsmULDz74IImJiQwODvLTn/6Uo0ePauL1zCRynxZckhgJMcPFxMRw33338clPfpLCwkLcbjeHDx8O2/VH0dHRrFq1iq1bt5Keno7H46GmpoZXXnmFmpqaCTuIa53RaCQ3N5clS5aQmZmJXq/HarWqCdLIyAh33+27aXzhBVi82FcO+d//3Zcgff7zb6zL0Ar/KFFCQgIJCQnqprAulwur1crg4CB2uz3oxTQaGxv52te+NuP2MboROp1OTU79e1Bp/QY1Lg7+8Ad47DHfvx9/3FeU4epfk+TkZMrKyoiMjGRsbIyamhq12ls4Sk5OZtu2beTn5+P1ejl//jxHjx5Vy/yHm/z8fB5++GHKysrwer3s3r2bX/3qV9jt9lA3TYgbIlPpRAC73R7yaUlictMRG0VRqK+vZ/fu3eoi5rS0NLZv365WTAoniqLQ2dnJhQsX1PUH0dHRlJeXU1hYeNvWtkxnv3E6nXR1ddHb26smrfHx8WRnZxMXF4ei6HjpJfi//xfOnPF9jdns2zz2i1+E3Nxpaca0869DGh8fD0iITCYTkZGRRERETHu8Zku57utxu90MDw+jKIpauU4rrtdv/vu/4XOf840YvfOd8Nvf+tba+blcLurr67HZbOj1eoqLi8N6Px3/Btdnz57F6/WSkJDAunXrQnYfc6vXNEVROHPmDDt37sTtdhMXF8d73vMeCgoKpq+Rs5Tcp02dTKUTN626ujrUTRDXMB2x0el0lJSU8KlPfYp7772XqKgoent7ee6553juueewWCzT0NLbx1+5avv27axatYqYmBgcDgenTp1i586dtLe335Z3yaez30RERDBnzhwWLVpEeno6er2ekZERLl++TE1NDSMjQ9x3n8KpU/Dqq7BmDYyNwQ9+AMXFviINTU3T1pxp4y/1nZiYqN6g+0eRbDYbg4OD2Gw2XC6X5kc2wonRaMRsNgO+Gyot/d9er988+ij8/vcQGekbKX3Pe+Dq2bImk4n58+eTmJio7n/Wq7W5pVOg0+mYN28ed911F1FRUQwPD/Paa6/R7t/g7Da71WuaTqdjxYoVfPzjHyctLQ2r1cozzzzD/v37w26WgtbIfVpwyYhRsH3qU9DREepW3DCLxUJ6enqomyEmEYzYeDwe+gcGGBocVG+YEhISSElNxRSGi5r9i86vXlMRERFBXHy8bzpXkH5uMPuNx+tlfGzMt0fQXx8zGAyYIyMx/fUt9L4+3+L1v+7xiw7IyoLiuZCUGJRmTQtFUfB4vXi93oAbdp1Oh16vR6/Xo9PpbjpuQ8PDHDp0iI0bN5KYkDA9jQ5DiqLgcrtRFAWT0RiyKoFvdiP9prcXTpwErxcy0mHFHWC4qvkK4HA41A2ho/5aCTGceTweBgYHcf41E4yLiyMuPj5o16/JTOc1zev10mOxMPLXUf2o6Giys7LCtnBGqIXdfVpODjzxREibMJXcQH4rgy3EvwxTVXf4MOnr14e6GWISwYiNAUgHjAMD7NmzR30nymg0smrVKtavX09UVNS0/sxg0gGxQITTSW1tLbW1teqao7S0NBYuXEh6evq0l/kNZr8xANGA0emkp6cHi8WiTkUzm81kZWWRkpLCOr2e11+Hb3wDdu0CunzHhg2+dUjveIdvfyQt0eH7I6QoCm63m/HxcZxOZ8A7ygaDQZ1qN9Ubqc7q6v+fvfMOj6LcGvhvW8qm994LIRB67wihXEGaoBQVO1as18K192u56nevXlGvXBUuqKAiFjrSBamBkJCQHtL7Jpts/f5Yd8zCJgRIsptkfs8zz+7OvjtzZs6+78yZc95zWH7ttfz44Yd4Jia2r/BdCAmg+aPoq5OTE66urrYWCWhbv/EDnLfDzJmgLoXpBvhuw59hdRJAaTRS1SxjXVhYGEFBQR0rfAciA7z1ek6cOMHZs2cB09ydYcOGdZox0Z5jmhQIAspTUti0aRNNTU24ublxww03EGqvsb92jHif1rGIHiMRC3Q6nfgUx07pDN3k5+ezbds2cv+oKOrk5MSYMWMYPny4Xab7vRRqtZq0tDQyMzMFY8Lf318wkNqLzuw3Op2OkpISSkpKBKPPwcGBgIAA/Pz8kMvlpKTAO+/A6tWmDF9gyvr16KNw001gz7au0WhEo9EIS/NLlFwux8HBAQcHB2QyWZsMXHFMM6HRaKitrUUul9tNuv7L0c2OHSbjXq02Zaz7/POLDf3CwkIK/4jQ6OrGkZmsrCwhSY6Pj0+nPazqqH5TXl7OunXrKCsrQyaTMX36dAYPHtxla1LZAnFMu3zEOkaiYXTFbN68malTp9paDBErdJZuzAkatm3bJsw5cnNzY8KECQwcONBuwnAuB7VazZkzZ8jMzBS8EQEBAfTt2xc/P7+r3r4t+o1er6e0tJSSkhIhjEgmk+Hr60tAQABOTk4UFprmHv373/BHFAt+fqZMX3ffDfZe89RgMKDVamlqarpo7pFMJhOMJLlc3uKNlTimmdDr9VRVVSGVSvG2kyJYl6ubX34xeY50Onj8cfj73y9u09w4ioyM7FohRy1QWlrK3r170Wg0uLi4MHbs2A43bjuy3zQ1NfH9998LEQoDBw7kL3/5S5d8+GYLxDHt8hGTL4iIiFwx5gQNy5YtY86cOXh6elJXV8cPP/zABx98QGpqql1N4G4Lzs7ODBo0iGuvvVaog1JSUsL27dv59ddfhQx9XQmZTEZQUBD9+vUjOjoapVKJXq+npKSElJQUMjMz8fBQ8frrkJ8P//gHhIeb5mw8/7zp/eLFcODAn7Vi7A2pVIqjoyPu7u54eXnh6uoqJG3Q6/Wo1Wpqamqoqqqirq6OpqYmizC848ePM3fuXI4fP267g7ATulqftca0afDpp6b3b75pKgJ7ISEhIQQHBwOQm5tLVVVV5wnYQfj7+5OcnCzUO9q+fTvF9paj/zJwdHRk/vz5JCcnI5FIOHbsWI+sNyZin4geIxELMjIyiIuLs7UYIlawlW50Oh2///47u3fvpqGhATDdfFxzzTVER0d3yRCI+vp6UlNTyc7OFm6kg4KC6NOnzxVVnreHfmM0GqmtraW4uFhIXQ7g6upKQEAAXl5eGAxSvvkG3n/fZBCZGTQI7r8fbrzRvsPszJjD7bRa7UVzkiQSCXK5HIVCwalTpxg+fHiPTtdtprGxEZVKZVehdFfab154wWTcOzrCnj0wdKjl90ajkZycHMrKypBKpfTq1Qs3N7f2EdqGNDU1sW/fPkpLS5FKpYwcOZKwsLAO2VdnjWlZWVl88803NDQ0oFQqmT9/PlFRUR2+366MPVxvuhpiKJ1oGF0xhYWFhISE2FoMESvYWjdNTU3s37+fAwcOCKFbERERTJw4scvWplCpVKSmppKTkyPcXPv7+5OYmEhAQECbjT5b6+ZCGhoaKCkpoaKiQjguhUKBv78/fn5+ODg4cOQI/OtfsGbNn2mQvb3hjjtMyTS7ikrNiRvMc5Ka10g6ceIEkydP5tdff2XYsGEoFAoh011Pwmg0UlNTg06nQ6lUolQqbS0ScOX9xmCAOXNg40aT5/PkSbgw6aC5LlBVVRUODg4kJibaVQ2nK0Wv13Pw4EHy8/OFlNgxMTHtvp/OHNOqq6v56quvOH/+PFKplBkzZvT4BxmtYW/Xm66AGEoncsWcOnXK1iKItICtdePo6MjEiRN58MEHGTFiBHK5nNzcXFatWsXnn39Ofn6+TeW7ElxdXRk2bBjTp08nOjoaqVRKaWkpu3btYtu2bW2ug2Rr3VyIUqkkKiqK/v37ExISgoODA1qtlsLCQk6cOEFmZibx8XV8+qmRggJ44w2IiIDKStO8jehouO46+OEH03wOe0YikaBQKHBxccHLy0sIuXN0dBTmwzWvlWQOu1Or1ej+SGHdnTEajTQ0NKDT6ZBKpXaVyvpK+41Uakq+EB0NeXnw4IMXt5FIJEKIqUajsZhf2JWRyWSMHDmSmJgYjEYjhw8f7pDw5s4c0zw9PbnttttISkrCYDCwceNGtmzZ0i301RHY2/WmuyEaRiIiIpeFq6sr06ZN48EHH2To0KHIZDKysrL49NNP+fLLL4WUuV0JNzc3hg0bxrXXXkt8fDxyuZyKigr27t3LL7/8YuFR6kooFApCQkLo168fsbGxuLm5YTQaqays5MyZM5w+fRqDoZRHH9Vz7hx8/z0kJ5vmHP3wg8k4Cg+HFSsgK8vWR9M2ZDIZTk5OuLm5CU8GnZycUCgUSCQSDAYDTU1N1NfXU11dTVVVFbW1tUItnK6o55YwGAxUVlaiVqsBcHFx6ZLJU6zh4fFnZrrPPzd5jy5EJpMRGxuLXC5HpVLZrFhqeyOVShkyZAiJf6SgP3nyJCdPnuzSRr5cLmfu3LlMnDgRgP3797Nu3TohOkFEpLMQQ+lELKitrRXPm51ir7qprq5m9+7dHD9+XLip7NWrFxMnTiTQ3tOetUBjYyMZGRmcPXsW7R/5rl1dXUlISCAqKgqZTGbR3l51Y42GhgZKS0spLy8X9CWXy/Hx8cHPzw+lUkl6Onz8Mfz3v6bisWYmTTKF2s2ZY5rfYe80NDTw+++/M2TIEJRKpRB2p9Vq0el06HQ6q4aQTCZDLpcjl8uF913JoDAajTQ1NaFSqYR1rq6uduUtgvbpN088YfJyRkZCaqr1OXJVVVVkZGQgkUjo1atXl+mrbSE9PZ1jx44B0Lt3b/r169cuoaK2HNNOnTrFd999h06nIzAwkEWLFnUrnV0tXel6Yy+Ic4xEw+iKOXr0qBjba6fYu24qKyv59ddfLZ5cJiYmMn78eAICAmws3ZVhDsE5e/YsjY2NgCnDXXx8PDExMcKcBXvXjTV0Oh3l5eWUlpYKxwamG2g/Pz+8vb3R62Vs3AiffAJbtvyZvc7bG26+GW6/Hfr2tdEBtJHWdGM0GtHr9RaGUvM5Ss2RyWQXLa2lCbcFer2epqYmmpqaLI7D2dkZFxcXG0pmnfboN/X10KsXFBbCyy+bvJvWyM7OpqysDEdHR/r27XvRw42uzNmzZzl69ChgGnOTkpKu+n9p6zEtPz+ftWvXUl9fj5ubG4sWLeoWdanaA1vrpisiGkaiYXTFiPnx7Zeuopvy8nJ27drF6dOnBQOpd+/ejBs3rste2HQ6HVlZWaSlpQmZ+RQKBdHR0cTHx7N3794uoRtrmLPZlZWVUVVVJehMJpPh7e2Nn58fLi4u5OZK+Owz+M9/oHlE0qBBpqKxCxeCvdm/eXl53HffffzrX/8iPDy8Tb8xGAyCgXQpYwlM50kqlV70al46ynAyG3Vmw06r1VrIaZ5P5OjoaLdGQHuNaWvWmFLPe3lBbi5YS0Cn1+s5deoUTU1NhISEdLvJ682Noz59+tC3b9+r+u/Zw/WmurqaNWvWUFpaioODAwsXLhQz1mEfuulqiIaRaBhdMb/++ivjx4+3tRgiVuhquikpKWH37t0WE4Pj4+MZN24coaGhNpbuytDr9eTm5pKeni6kxJZKpdTW1jJ37ly7KZx5pWi1WsrLyykrK7PwIimVSvz8/PDx8UEikbN5synUbtOmP5MzyGQwZYrJkzRrln2k/T569CiDBw++6nTdBoNBMELMhpJer7/kfCSJRIJEIrEwksyvzRdz2+aY+4zRaBQWvV4vvBoMhovmlJgTUTg6Ogr1nuyZ9hrT9HpITISzZ+Htt+GRR6y3q6ysJDMzE6lUSt++fe0utPBqaR5W179/f3r37n3F27KX601jYyPr1q0jOzsbmUzGvHnzhLlVPRV70U1XQjSMRMPoijEajXZ/Me2pdFXdlJWVsWfPHlJSUoQbuZiYGMaNG0dERISNpbsyjEYjRUVFpKenU1JSIqz38/MjISGB4ODgLqkrM0ajEZVKRVlZGZWVlYIBIJVK8fLywsfHB3d3dyorpaxbB198Ab/99ufv3dzg+utNRtK4caYJ8ragvQyjljAbTNZerRku7Y3ZQ2Wu29QV50K1Vz/5+GO46y5ISIBjx8CazWM0GklPT6e2thY/P79u6X1IS0sTChoPHz78io/Rnq43Op2O9evXc+bMGSQSCTNmzGDw4MG2Fstm2JNuugqiYSQaRleM6KK1X7q6bsxZ3k6cOCHcaEdGRjJu3DiioqK67EBfVVXF2rVr8fHxEY7Lzc2NXr16ERkZiVwut7GEV4dOp6OiooKysjIhjBBMoYQ+Pj74+PigVCrJyJDwxRfw5ZeQk/Pn78PDTYVjFywwhd11ppo72jBqDbOXx2wkmQ0l87rmnqALfwdc5FFqHp7XGaF6nUF7jmm1teDvb6rJpVDA//0f3H33xe3MtcukUin9+vXrFrWNLuT48eOkpaUhlUoZO3bsFYUw29v1xmAw8OOPP3LkyBEAJk2axJgxY7r0//9KsTfddAUuxzbo2ldsERGRLoOPjw+zZs1i/Pjx7N27l2PHjpGTk0NOTg5hYWGMGzeO2NjYLneh8/LyIjo6mrFjx3L27FmysrKoq6vj999/JyUlhdjYWGJjY3G2h9iyK0AulxMQEIC/vz8NDQ1UVFRQUVGBVquluLiY4uJinJ2d8fHx4W9/8+GFFxzZt8/kRfrqK1Odmb//3bTExJgMpAULoH//zjWSOpvmYXQiHY+7u8lbdOIE9O4Ny5aZ1l9oHLm6uuLu7k5tbS2lpaVdNqy3Nfr3749arSY3N5d9+/ZxzTXXdPkwX3PhV6VSyZ49e9i+fTsNDQ1MmTKly10zROwb0WMkYkFaWhoJCQm2FkPECt1NNzU1Nezbt4+jR4+i+2OiSmBgIKNHj6ZPnz5d6oayuW60Wi1ZWVmcPXuW+vp6wHRRDw0NJS4uDl9f3y5/ITcYDNTW1lJRUUFVVZXgKZNIJLi5ueHj44O3tzdarYwff4R160zzkf4opwNAfPyfRlLfvh1jJBUUFPC3v/2Nl19+uVveAHd12nNM++gjkzF0//3w3nvw0EMmr9G//32xcWSea+To6Nhu6a3tDb1ez+7duykpKcHZ2ZkpU6Zc1sMZe77eHDhwgM2bNwMwePBgZsyY0S112BL2rBt7RQylEw2jK6a4uLjL1p7p7nRX3dTV1XHgwAF+//13oZifl5cXI0eOZODAgSgUChtLeGms6cZgMFBQUEBGRgZlZWXCei8vL+Li4ggPD+/yYXZgCrWrqqqioqKC2tpaYb1UKsXDwwNvb288PT1pbJSxaZPJi/TTT9AstwO9e5sMpDlzoF+/9jWSumu/6Q60l26aG0Xvv2/6/xiNsHy5deNIr9dz/Phx9Ho9iYmJuLq6XrUM9ohWq2Xbtm3U1NTg5+fHhAkT2pyh0N77zfHjx/n+++8xGo0MGjSImTNn9hjjyN51Y4+IhlErB28u8Nda+tWezN69exkzZoytxRCxgr3rRqFQXFVaYLVazeHDh/ntt98ET4uLiwvDhw9n6NChdh2KdqmYb/MT6tzcXGHscXR0JDo6mtjYWLusMXMlNDU1CaF26mbuIalUiqenJ97e3nh4eNDQIOOHH0yepF9+gebF7SMjYfZs0zJ6NFyN7ahSqfj444+58847u+3Nb1emPeZKmI2iBx4weYqa3xu3ZhxlZGRQVVVFWFhYly0j0Bbq6urYsmULWq2WuLi4Nict6ArzWFJSUtiwYQNGo5GBAwdy3XXX9QjjqCvoxt4QDaMWDl6j0VBUVGQxgVjEErVabdc3oD0Ze9eNRCIhNDT0qm9AtVotx44dY//+/VRXVwPg4ODA4MGDGTlypF16gtt6oWpqaiIrK4vMzEzB+JNIJISEhBAXF4e/v3+3uLAbjUbUajWVlZVUVlZapP42Z7bz8vLCw8MDlUrG99/Dhg2webOlJ8nHB2bONBlJycmgVF6eHLZMviByaa72Bq+pyZQBsXdvUyY6a9G3BgMMHAhnzkBdHTg6mtYXFRWRn58veHC7M+fPn2f37t1A2zPVdZWb71OnTrF+/foeZRx1Fd3YE6JhZOXgDQYDGRkZyGQy/Pz8ukSNB1ug0+m6RXhPd8SedWM0GoWsZXFxce1SUNJgMHD69Gn27t0rpMSWyWT069ePUaNG4efnd9X7aC+qqqrw8vJqc3uDwcD58+fJyMiwSPft4eFBbGwsERER3SZbltFopKGhQTCSmpqahO9kMhmenp6CkdTYKGPrVvjuO/jhB6is/HM7zs4wdSpcdx1Mnw5tiSQRDSP75nL7jTU++gjuXabnH3P28MD1RUiCg2DsWJDJWvUY1dTUkJ6ejlKppG/fvld5JPbPqVOnOHXqFHK5nGnTpl3yAVZ76KazOH36NOvXr8dgMDB8+HCmTZvWre/vupJu7AXRMLJy8I2NjWRnZxMREYHych879iAaGhrE82On2Ltu1Go1OTk5REVFtWvhRKPRSGZmJvv27SOnWR7ouLg4Ro0aRWRkpM0vgidOnKB///5X9NuamhoyMjLIyckRklDI5XLCw8OJjo7+o6hq97jIm40kc9KG5kaSVCrF3d0dLy8vPD09kUgU7N1rMpK++w5ycy23NWiQyUD6y19g+HBTgdkLEQ0j++Zq+o3Ahg2obl+Oa3XBn+tCQzG++x7Lf53bYgKG+vp6Tp8+jYODAwMGDLg6GboABoOBnTt3UlZWhp+fHxMnTmw1wU276KYTOXnyJN9++y1Go5Fx48ZxzTXX2FqkDqOr6cYeENN1t0JXynRlC7Rara1FEGkBe9dNR928SyQS4uLiiIuLo6CggL1795Kenk5GRgYZGRkEBgYycuRI+vbt2y6eqiuhuLj4ii9UHh4eDBkyhH79+pGTk8O5c+eoqakhKyuLrKwsPD09iYmJ6RZeJIlEgouLCy4uLoSFhVFfX09lZSXV1dU0NjZSXV1NdXU1EokEV1dXevXy5PXXvfjHP5w4ccJkIP34I/z+Oxw9alpeeQW8vGDKFJORNG2aqZ6NiP1zNf0GMMVfXn89rhfWgioshOuvp4Bv+Pe/51qtZ2Qer7rZs+EWkUqlDB8+nF9++YWysjLS09Pp3bt3i+2vWjedTL9+/dBoNGzatIndu3fj4OBg13Nyr4auppuuhmgl9FCef/55lv1R6GHXrl1C6kfzDUlpaaktxROxQnfxGlwNoaGh3Hjjjdx///0MGzYMhUJBcXEx3377Le+++y579+61mPTfWbRH5jwHBwfi4+OZNm0akyZNIjIyEplMRnV1NUeOHGHjxo0cOnSIioqKbnEzZx5rwsPDSUpKom/fvoSGhuLi4oLRaKSuro78/HxOnjzJqVMp+PoW8NhjKg4dMlJcDP/9r6lwrJcXVFWZEjnccgsEBMDQobBiBRw9Ksfd3cNuQ1B7OlfVb/R6U5yclb4gMRoxAp97PcTdd1hPtGROgmKrhym2wNXVVfCcpqSkWGSRvJCukA30QoYMGUJycjIA27Zt48SJEzaWqGPoirrpSvS4ULr2DvNpFb3eciZxC0QmJlJZVUVJVpYwub62tpaA6GgiwsJIO3ZMaLd21SpGDBsm/HbZgw8SGBDA8ytWXJZoz7/yCsUlJfz7/ffZtXs3y5YvF/Zjr9z/yCMMHTyYWxYvFtbdef/9ODo48M933rFo+/4HH7D+++/59Y9aB78fPcrjK1ZQWFTEq889x/Vz5li0n7twIUl9+vDC3/7W8QfSTpzLyuLmu+7i2IkTJMTH89m//03/pKRWf3Pgt98YPXkyrz7/PE8++qiw/uChQzz0xBOcSk3F08ODd157jQXz5gFw+MgR7rjvPjLOnWPooEF8/vHHRISHX7TtxqYmsvPzidLpcOokI66xsZHU1FROnz4tJFWRy+X06tWLpKQkPDw8OkWOjkKj0XD+/HkKCwtRqVTCejc3N0JDQwkKDkbRDW/6NRoNdXV11NbW0tDQYGEIyuVyXFxccHNzw8XVFQlyTp+GvXth/344k2a5LUcHGDAAhg0zLQkJ1sPuRLoYv/9+cXycNT76CIYMuWh1ZWUl58+fx8XFpU3JCLoLRqORo0ePUl5ejre3N0OGDOl2D90OHjzIiRMnkEgkTJ8+nbCwMFuLJJKQcPnZc9oRMZTOXmhsNKXCuRRaLYFeXmxcuZIbpkwBYMMPPxDm72/KY2vehlYLOTmmNDxmqqtNV/m27Kc55eWm3545YypN33w/dsrmn39mxZw5FnIuGTGC+U89xbu33mrxVHjN559z28yZQttf1qxhar9+NPXuzepPP+X6ZsXRalQqft6yhdduvtnuz0FzFt5yC9eOGcP2t97ik++/Z868eZxdv77Fp+MGg4GHly9naGIilJYKx1pUXs68m29m5dNPM3XECGpUKqpVKjhzhiaNhrnz5/PC3XezaOpUnlu5kpsWL2b3ypXWhSovN+XOvXBCSAfhBAz6Y+mOOACRfyw9CQfA54+lLfT/Y7nP2pca4NAfi0jPowXjyfuPpachAdqWsLvrMuKPBTAZxiK258gR08TQLoBoGHUkTk6mPKKXQqFg4aJFrN63jxuWLwdg9V//yqLFi1n7zTd/bkOhMBX5aL5NT0/w9bW6H7VazeMrVrBh40akUikPLFvGE488YvrS19fk0erdG8rKwMEBevdGVV+PW0AARefOERgQQGRiIvfccQcfr1pFTU0N9911l+Cd2vTzzzz69NOcLyrC08ODv7/0EgsXLECv1/Pia6+xavVqmpqaWHzDDbzx0ksX3bBv2b6dF157jX3btgEQ1acPf5kyhX/94x9UV1cT3rs3lfn5yOVyzmVlofTwIGjsWIttjEtIwPmVV9haVMT0P4zKrOxsjp09y/X33APepkvf5hMneP+tt3B3c+O1oUOpDgrC09MTgPWff07fPn3oNX264D2bP2cO//fRRwT4+/PtmjVs2LiRd/75TwL8/Pjmyy/pk5gIwL0PPcR3mzbRoFYzbPBgPvnXvwgPCyP97FlGT57MwZ07iY2J4eChQ8y+8UZOHjyI/xVOgFDV1+P6R72b9LNnSS8oYO/rr+Pg4MD9Awbw5tq17K+pYVwLcdUrP/mE4WPHUlNba5qE8cd/5h9/+xtLb7mFa++8E7C8Id21bRuunp7c9sQTADz75pv4RUaS6+Jysdeoqcn0H12/vn0rdF4GRqORwsJCTp48SX5+vrDex8eHvn37EhMb2yEelv379zNq1Kh23641WvIiOTs7ExISQnBwsF2ndb8aDAYDarWauro6VCqVRRpwMHmTXF1dcXV1xcXVldycXO6++z7mzPkXWVkxHDkCdSrLbfp4mzxKAwealvh40aPUWVxVv7kKj5FOp+NsRgYGvZ6oqKhuU0fscsjMzOTcuXMolUpGjRp1UUhhZ45pHYFOp+Onn36iqKgIFxcX5s6da9fJiy6HLqmbZg+j7Z0eaxgZjdDx5YxkKJUul75HlEhIvvZaPv3iCyqbmtBoNGRkZfHU3/7G2g0bwDxoSySmnLXNB3GFwmTUWBnYH/vrX6ltaOBsRga1tbUkJyeTOGAAM2fONP1GoTD9ztnZVADCxQWZOTmFUmn6TiJh/Q8/cODgQerq6hg/fjxDRo1ixowZ3HH//axfv57Ro0dTXFxMZWUluLjwzptvsv/33zly9ChyuZw5c+bw4eef88ADD1jIN3LiRI7deCNqqdT0W4mEvb/9Bi4u7Nu1i6FDhyL/IxTql927mTp9+kXHKQEWLlrEmg0bmP5HeNya775j+vTpeP/hPq+pqSE7L48BI0cikUgYMGAA63/5hdtvv93Ufv16Ft90k3AuMrOy8AsJoby8nIcffpi/XH89DzzwAKWlpTz11FP87dVX+fbbbwEYM3Eir731Fo6Ojtx77708+OSTfPfdd/QaOJCnV6xg6b33snnzZpbecw/v/9//4W8lZGPv3r3MmDGjxb+HuZaPTCo16QpIzcmhV69eODRL2dmvf39OZ2Uxzkp9g8rKSt798EMOHDjAww8/bPGfOXz8OKNHj6bPsGFUVlYyefJk3n//fby8vEjNyiKpf3+hrYuLCzExMaTm5BBxoTEuk5m226uX6aGADZAAoYMHE3rddZSVlQkhFUU6HafOnkVZUMCgQYMYOnRou4bZeTg6Qp8+7ba91jB7kSKMRioqKsjKyiI/P58qrZbzej2SggL8/f2JiooiNDS0W82vkQIufyxgMhJramqorq42hd3p9ZhnTUgkErIxsqO6jGdv8eeeMf0xGmUcPQrbtsH27bBvHzRVwrYdwA7T79zcYORIU8bnsWNN4Xfd1M60OVfVb/r3R/vcS8iKC5FiZUaARAKhoXD77RdZumVFRagApVKJsk8fmz3IsSURSUmc+vFHqhobyfbyIjY21uL7zhzTOgI5MKVfPz799FMyy8r4X3o6t9xyS7cYD7u6buydHpt8oaEBXF07fmmr8SWXy5k9ezZff/01a9euZf78+VYz6CUnJ+Pp6Sksn332mdXtGY1GPvvsM95++21cXV0JDg7mnnvu4ZtvvrmkHBfy0EMP4efnR3R0NHfffTfr168HTBMAT506hUqlIjAwkMQ/vCiffvopr7zyCr6+vnh6evLoo49a3a+bmxu9e/fm0KFD7Nmzh9mzZ6PRaKiqqmLPnj0WGWV++eWXFguaLVmyhO+++06YY7JmzRoWN5uHtG3bNiZOnCjEUS9ZsoTVq1cDpiJ/u3fv5sYbbxTae3p68sADDyCXy5k7dy4VFRU8/PDDwueTJ08KbRctWoSHhwdOTk488cQT7N271+K8SSQShg0bRlJSEgsWLLAq/5gxY4RsXNYWa7pRqVQXxcm6u7tbeBCa8/TTT/PQQw9ZrX1QWFjI6tWr+fbbb8nMzESn0/HQQw9d0X7sCT8/P2bOnMkjjzxCcnIyHh4eNDQ0sHfvXt577z2++uorcnNz2yWRwZV6Aa8GiUSCr68vw4YN47rrrmP48OEEBARgNBopKSnh4MGDfP/99xw6dIjy8vJukbDhQhwcHPDz8yMuLo6BAweSkJBAUFAQSqUSo9EoeJRyc3M5duwYGRlnCAkp5IEH6ti61UB1NezZA6++akr97e5uKgK6ZQs88wxMmAAeHjBqFDzyCKxdC1lZVuf7i1wBV9NvPl0l4w7VewAYuMCwMRs67757kVFk9rgCBAYGdrv5NW1FoVAI1+wzZ85gMBgsvrfFmNbeODk5ceONN+Lk5ER+fj4//fRTtxgHu4Nu7Jmubzp3IxYvXsyTTz6JWq1m5cqVFjfFZrZu3cqIEUL0rJBZ7kLKyspQq9XEx8cL6wwGA6NHj25VhgYrllxoaKjwPiwsTLj5/+abb3jxxRf561//yvDhw3n//fdJSEggLy+P5ORki3SoISEhVvc3duxY9uzZQ1FREZMmTaK2tpZ9+/axZ88enn/+ecB0ITt8+DBjLwijM9O3b1+io6PZuHEjvXr1orCw0OQV+4PNmzczbdo04fMNN9zAY489xvnz51m3bh0TJkwgsFm1SF9fX0F2Z2dnfHx8BCPV2dmZ+vp6oe0rr7zCZ599RmlpKRKJxCLLj1QqZenSpdxxxx181A5xzg0NDYKXw9XV9aKMQrW1tVaL9h07doxDhw7xr3/9y+p2nZ2dWbJkifBfeeaZZxg/fvxl78deUSqVjB49mpEjR5Kens6hQ4fIzs4mNTWV1NRUAgICGD58OElJSVec7efo0aM2rUSuUCiIiooiKiqK+vp6srOzyc7Opr6+Xkj77ebmRlRUFBEREd0ydMhcB8nd3Z2wsDC0Wq1gwDs4OGAwGKirq6Ouro7CwkJkMhlubm7ExrozYIArTz7pgsEg4dQpk7FkXoqK4MAB02LG19fkSRo+3PQ6dCj4tHVClIjAlfSb4mIICjJ/mktAzDe81rAciizrGPHuuzB3rsVvDQYDmZmZ6PV6XF1d8enhSouJiSE1NZX6+nry8/OJiIgQvrP1mNZe+Pj4cP3117N69WqOHj1KYGAgw5olsOqKdBfd2Cs91jBSKqEzHnpfTkjryJEjKSwsFArO7dq164r36+vri5OTE7m5uVcdMlRQ8OcFJz8/XzAihg8fzo8//khTUxPPPvss9913H9u3byckJIT169fTr1+/S257zJgxfPLJJxQXF/Pcc89RW1vLtm3bOH78uGAA7t27lyFDhrRaw2XJkiWsWbOGXr16MXfuXIvMg1u3buXll18WPvv5+TFp0iTWrl3L//73P+6///7LPicAv/76Kx999BHbt28nNjaWs2fPCmnPASoqKnjmmWe46aabePzxx9m7d6/V1LB79uxh+vTpLe7HmncmMTGR9PR0tFqtcDN/8uRJHn/8catynj17VjBOa2pqTPO2zp3j448/vqjqe/MnaomJiaxslmihvr6ec+fOCU8auxJSqZTevXvTu3dvSkpKOHToECdPnqSkpISNGzeydetWIczOPP+sK+Li4kLfvn3p06cPZWVlZGVlUVBQQF1dHSdPniQlJQU/Pz8iIiIICwvr8rWRWkKhUAh6jIuLo0+fPtTW1lJbW0tdXR1ardbCKyuTyXBxccHX142bb3bjnntckEplZGWZQu4OHTItx4+bcoz89JNpMRMba5rKMmDAn0tAQOcec3emoQE++ABefPHPdVIpvHR6LjL5rD+t2KAgUwzkBWOt0WgkOzsblUqFXC4nOjq6x3qLzMhkMmJjYzl16hQZGRkWhlF3IjY2luTkZLZs2cLmzZsJCwsj6E/rWkTEgh5rGEkkVqfl2JwNGza0SxFaqVTKLbfcwmOPPcabb76Ju7s76enp1NXVtfq0xNrkxPfff58pU6ZQV1fHypUr+de//oVGo+Gbb75hxowZwmRn803/7bffzooVK/j4448JCAggNzeX3NxcwQvRnLFjx7J06VIiIiLw9/dn7NixPPjggyQkJOD2R/a9zZs3X/LpyKJFi3j22Wc5fPgwX3zxhbD+zJkzeHt7X+R6Xrx4MU899RRlZWXMveCpYlupq6tDLpfj4+NDfX29hfEFcO+99zJ//nzeffddJkyYwNtvv81f//pXq+egLaFpzXXTq1cvevXqxeuvv85f//pXPv30U2QymdUJmXfddZdFqODy5cuJi4vjscceA2Dp0qXcddddLFmyhKCgIF577TWuvfZaACZMmIBKpWLVqlUsXLiQl19+mSFDhnT5C2hAQAAzZ85k8uTJgketurqaffv2sX//fmJjYxk6dCixsbFt6o+DB9tfnieJRIK/vz/+/v5otVry8/PJzs6mrKyM0tJSSktLOXLkCMHBwURERBAcHNztarrExsaybt064uLicHJywsnJCX9/f4xGI2q1mpqaGiGRg06nEwwn+LMYraurKzNnurFwoSsKhYKmJpNxZDaUfvsNMjIgM9O0rF375/4DAv40kvr3N712xeQOTU3g6Nj+221LvykshA8/NC2VlaZ1Xl6mqUNvvmluJTPFPbaAXq8nKyuLqqoqJBIJMTExnVe2w84xe43Ky8upq6sTrrv2OKZdDSNHjiQvL4+0tDS+/vpr7r77bhw74k/dCXQ33dgbHTLHKCcnh9tvv52oqCicnZ2JiYnhueeeQ6PRtPq7pUuXIpFILJbmYWM9gX79+l30BP9Keeedd3BxcSEpKQlvb29uvvlmqqqqWv2NTqe7aN2cOXMYMWIEQ4cOZenSpUKY2n//+18iIiLw8vJi69atvPeeKd77scceY9iwYYwaNQoPDw9mzpxpkSGsOQEBAQQHBwshfjExMbi6urZ5fpGZkJAQRv6RXOGaa6655G9nz55NZWUlM2fOFC4El8u0adMYOXIkERERJCUlWRglX3/9NUePHuW1115DIpHwn//8hzfeeIMzV5EO/ELdrFmzhl9++QVPT08+/vhjNmzYIMxDevXVVwUvlFKpJDAwUFicnZ1xdXUVnqYnJyfz8MMPM3r0aEJDQzEYDPzjH/8AwNHRkQ0bNvDOO+/g6enJvn37LAzPro6zszOjRo3iwQcfZOHChURHR2M0GsnIyGDNmjW8//777Nmz55KGa3FxcSdJfGUoFAqio6OZNGkSM2fOpH///nh4eGAwGCgoKGDfvn3CfKSSkpJuEYcPpvlwiYmJF82Tk0gkKJVKgoKCiI+PZ+DAgfTt25eIiAh8fHxwcHDAaDSiUqkoLi4mIyODY8eOkZKSQmFhFpGRJdx2Wz3//a+Bs2ehogJ++cU0V+mGG0z5RyQSKCmBzZvhjTdg0SJITDTNPR040PT5pZfg66/h1CmT8WGPfPSRKSFFR2Q9bqnfVFXB559DcjKEhcErr5iMoqgo+OwzUyLVP42i1qmpqeHUqVNUVVUhlUqJjY3t8vXN2hNnZ2cC/nBt5uTkCOvtfUy7XCQSCbNmzcLDw4PKyko2btzYZce57qYbe6NDCrz+8ssvrFu3joULFwpu2jvvvJObbrqJt956q8XfLV26lJKSEouEAg4ODnh7t73agF0VeO2C1NTUWFw0IiMjWbt2rc0M1KKiIkaPHk1WVtYV/X7q1KmsWLGCcePGtbNknc+FurE3uksfq6io4MiRIxw7dgy1Wg2YQk569+4teMsuDMFpi1fTHqmuriYnJ4e8vDyL+YVKpZLw8HAiIiLw9PTssiFHRUVFPP7447z55puXFTpjNBqFIrMqlYq6ujrhv9AcqVSKUqnExcVF8C45OjoikUiorzcZPMePw4kTpteTJ6HZFMULtgXR0aYs+gkJJs9SVJRpCQszJRHtbD76yFSWrF8/k+z//nfbMmS3FXO/aWqCY8dg92748UdT6KJe/2e7sWPhoYdg1qy2e9sMBgOnT58W9KZQKIiNjb3iB2HdmZycHA4ePIiXl5cwjnXVMe1SFBQU8J///AeDwcC8efNIukRRdHuku+qmI7F5gddp06ZZTHaPjo4mPT2dDz/8sFXDCExPqJtPhBfpXOztBqi2tpa///3vV/z7SZMmMXLkyHaUyHbYm266Kz4+PkyZMoWJEyeSmprK4cOHKSgo4NSpU5w6dQo/Pz+GDBlC//79BQOwPcJfbYGnpycDBgygf//+lJWVkZOTQ0FBAQ0NDaSlpZGWloaHhwdhYWGEhYXZtWFujaKiIlavXs0jjzxyWYaRRCLB0dERR0dHfH19AdBqtdTX11NfX49KpaK+vh6dTodKpbLwKMrlclxcXFAqlcTGKunXT4mTkxMSiQSDAc6dg7Q0U33l5ktt7Z/heD/8YCmPTGYyjqKiTMaT2WAKD4fgYNO0mvZOKW42ih54wJTH4KGHTJ/h6oyjxkbT8aemwvffx/Pyy3D48MUes8REk/dtyRLTMbcVo9FIZWUlhYWFFnWu+vbte8XJVbo75ux8VVVVqNVqnJ2du+yYdilCQ0MZP348O3fu5KeffiIyMrLLGcvdVTf2QqfNMaqpqWmT52fXrl34+/vj6enJ+PHjeeWVV8TUhJ3IpSzpzsY8l+ZKsTanp6tib7rp7igUCvr370///v0pLi7m8OHDpKSkUFZWxs8//8y2bdtISkpi8ODBTJ482dbiXhXN5yMNHjyYoqIicnNzOX/+PDU1NUI4koeHB+Hh4YSFhfW4/6M5mYM5BNVoNNLU1GRhKDU0NKDT6YRzZsbsWVIqlbi7OzNhgpK//EUpzOkyGk3Z1s6c+dNoOnfOlBo8J8dkNOTkmJadO63L5+lpMpDMhpL51cfHVOe6+eLlBa2Vc2luFL33niks8I9I6VaNI60WqqtNoXDnz0N+vmkpKIC8PEhPNx3Tn5mh/6zr5utrSouenAzXXmsy/C4HvV5PRUUFxcXFgkHk4OCAu7s7UVFR4oOlVnBycsLT05OqqirKy8sJCwsjOTnZ1mJ1GGPGjCE9PZ3z58/zww8/sHDhwi71/+jOurEHOiSU7kLOnTvHoEGDePvtt7njjjtabLdu3TpcXV2JiIggOzubZ555Bp1Ox5EjR1qcJNfU1ERTs0dNtbW1hIWFiaF0V0htbW2Pu+HpKti7bnpCH2tsbOTkyZP8/vvvlJaWCuurqqpYtGgR/fr1w7kbVQPVaDQUFhaSn59PcXGxRa0TT09PwZNkr//Lo0ePMnjwYI4cOcKgQYM6fH8GgwG1Wo1KpUKtVtPQ0EBDQ8NFNWLgT6+Us7Mzzs7OODk5Ca/Nk2AYDCajKSsLsrNNi/l9QYEpEZuVKL9L4uJiWpTKP+t5K5Wm+TupqXD//fD++5a1T41GePBB+Oc/TVn4fHxM+66qMi1tzfTq5WWqT+npWcC8eaGMGgVxcZdfZ9VoNFJXV0dFRQWVlZXo/4i/UygUBAQEEBAQ0O0SinQUhw8fFjKO9uvXj+3btzNp0iRbi9VhlJaW8tFHH6HX65k/fz59ulDB1O6um47gckLpLsswev7553nhhRdabXP48GGGDBkifD5//jzjx49n/PjxfPLJJ23dFWAKg4iIiGDt2rUtZg5rSab169fj4uLCNddcw6FDh1Cr1fj6+hIXFydMYjffvJmfLrm5udHQ0IBer0cmk6FUKqmrq7Pa1tXVlcbGRnQ6HVKp1KLei6OjI1KpVIhtbq2tg4MDcrlciO93cXFBo9Gg1WqRSCS4u7sLTx4vbKtUKtHpdGg0GqFtbW0tRqMRhUKBg4ODUHOneVsADw8P6urqMBgMFm01Go0wKdtscJoLehoMBuRyOU5OTkLoiLOzs0XbyzmHrbW9nHN4Ydvm51AqleLm5tbiObR2vs3nsLXzbT6HbT3fl3MOW2qr0WiEDn21/9mOON/19fUUFBTQu3dvfv31V8BU98rX15djx44BMGTIEM6fP8/58+eRyWRMnjyZbdu2odfrCQ4OJjg4mN9//x2AgQMHUl5eLiTumDp1Kjt37kSj0RAQEEBkZCS//fYbYEpaUltbK0weTk5OZt++fTQ0NODr60t8fDz79+8HoE+fPjQ2NnLu3DkAYYxQqVR4eXnRp08foVZXQkICBoOBs2fPAjB+/HiOHz9OdXW14B348ccfqaioICYmBoVCgbOzM7GxscybN48zZ85QWVmJi4sLI0aMYPv27YApvFipVHLq1CnAlDEpMzOTsrIynJycGDduHFu2bAEQ5vmcOHECgGHDhpGXl0dxcTEKhYJrrrmGLVu2YDQaCQ0Nxd/fn6NHjwKm7EXFxcUUFhYilUpJTk5m+/bt6HQ6goKCCA0N5fDhwwAMGDCAyspK8vLyhPO9a9cumpqa8Pf3JywsjM2bN1NVVYWzszONjY1CquuBAwdSU1ODUqkkJCSEhIQE9u3bB5hSvms0GjIzMwGYOHEiv//+O3V1dXh6etKvXz92794NIHiH09PTARg3bhwnT56kuroaNzc3hgwZws4/XCaxsbE4ODiQmpoKwOjRo0lLS6OiogKlUsmoUaP48MMPeeCBB9iwYQPDhw8nJSUFgBEjRpCVlUVpaSmOjo5MmDCBzZs3AxAeHo63tzfHjx8HYOjQoRQUFFBUVIRcLmfSpEls3boVg8FASEgIgYGBHDlyBIBBgwZRWlpKQUEBEomEKVOmsH37dpqamvD09MTLy4vTp0+j1+vx8/OjsbFRGJtiYmLIzc1Fp9Ph7u5OSEgIWVlZyGQyEhMT0el05ObmIpFImDRpEgcPHqS+vh4vL29CQhL56adjVFY64ugYRXGxhLNn66isdEQuD6CwsIGaGikqlQN1dZcOFOnXzzTnx1rEjsFgSh7RrM71Rbi66vH3N+LuXoOfXyP9+/vg5FSGm1sxMTFNzJ07hi1bNpOTk8PYsWMva4zw9/fn0KFDaLVa/Pz8LDIJ9u3bl/z8fKRSKYGBgTYdI8w3YIMGDRJKb8TFxSGXy4UkPGPGjCE1NdUuxohjx46Rn5/PuHHjaGpqIjMzk5EjR17WGBEdHc3BgwcBSEpKQqVSkZ2dDcDkyZPZv38/DQ0N+Pj42MUYodFo+Oyzz1AqlTz44IN4e3vbZIzYsWMHWq2WwMBAwsPDOXToEAD9+/enurqa3NxcAKZMmcLu3btJS0sTsqYe+KPAWt++fWloaBDmYzcfI7y9vUlMTBT+s71790an05GRkQGYss8ePXpUMB4GDBggXMPj4+ORSqWkpaUJ/9nTp09TVVWFq6srw4YNY8eOHQBCtsfTp08DMGrUKM6ePUt5eblQT3Dr1q2Aaf66u7s7J/8YSIYPH05OTg4lJSU4ODgwceJE4Xy3x31EWloa8+bNa3/DqLy8nPLy8lbbREZGCjdk58+fZ+LEiQwfPpxVq1ZdUVxkXFwcd9xxB0888YTV70WPUfvS0NBgNWW3iO2xd9301D6mVqvZsGEDdXV1FtmCvL29GTRoEP379+9yMeyXoqmpifPnz5OXl0dJSYmFR8TLy4vQ0FBCQkLw8PCwaYhKdnY2999/P//85z+JutzYrA5Gq9XS0NCAWq2msbGRxsZG1Go1Wq22xd9IpVJh7pO1pS3eEb3eFO5WXW3y9tTXm2oENTSY3v/4oykj3KU8RnfeCTNmgJOTKYzPy8sUpufh0XqYXnNOnjzZar07c1r1hoYG6uvrqauru6gIuTnE0dfXF1dX1y4VEmVPFBQUsHfvXnx8fEhOTr6kbroDWq2WDz74gKqqKkaPHt1lQtR6gm7amw5LvuDr6ytMRL0UhYWFTJw4kcGDB/PZZ59dkVFUUVFBfn5+q5NmzRcEkfahuxZ77A6IurFPnJ2dmT59Ol5eXhQVFXH06FFSUlKorKxk27Zt7Nixg/j4eAYNGtTmukj2jqOjI1FRUURFRdHU1CSE25WUlFBVVUVVVRUpKSm4ubkREhJCSEgIPj4+nX7sUVFRfPHFF5eV2bSzUCgUeHh4XJTQQqfTCcZS81eNRiOE6lnLkAem7IkODg4Wi9mbrVAokMvlf9Rek+HjY12uBQtMc32WLftzbpFEYjKKli83GUXtlZ0uNDRUOGbzQ87GxkaampoEg8haGKJprpY7np6euLm5icZQO3BhhIFZN90ZhULB9OnTWbNmDQcOHGDw4MF2OVZcSE/QjS3pkOQL58+fZ8KECYSHh/PWW29RVlYmfNc841xCQgKvvfYac+bMQaVS8fzzzzNv3jyCgoLIycnh6aefxtfXlzlz5nSEmCJWqK+v73KZp3oKom7sl8OHDzN16lTBjT9lyhROnz7N0aNHyc/PFzK8ubu7Cwkd2vqQyd5xdHQkOjqa6OhowUgqLCykuLiYuro64didnJwEI6mz5n40Njby/fffs3Dhwi7jxZTL5bi5uV3kZTQYDGg0GsGAuHDR6XTo9fpWDSczMpkMuVwuGEsymcximT1bhkrlwmOPuWI0GnnvPQnLlxv55z8lvP++hltuMdDUZDJGmgedmN8bDAYMBgN6vd7ivU6nQ6fTodVq0Wq1pKSkEBsba7V+XnNZm6dEd3d3F7PLdQAXPrQwj2ndnfj4eGJjY8nMzGTnzp3MmzfP1iJdkp6iG1vRIYbRli1byMzMJDMz8yLLtvkgmp6eLsRXy2QyUlJS+Pzzz6muriYoKIiJEyeybt26bheGIiIi0r1xcHBg4MCBDBw4kLKyMo4ePcqJEyeora1lz5497Nmzh7CwMAYMGECfPn26zE37pWhuJGm1WmF+0/nz54U5G+fOnUOhUBAUFERISAjBwcEddqObmprKbbfdRv/+/Tsl+UJHIpVKcXJyavG/otfr0Wg0wpxJ83vzYjZKzEaKXq+3CEO/kLFj4Ykn/HjjjSh27zZy8qSEJ57IZvjwslbnF10OZmMJTE/vHR0dcXJyEl5dXFyEulAiHYs1z1xPYdKkSWRmZnLq1CnGjBkjFLwV6Zl0Sla6zkQs8Hp1aLXaq7pJaV4QdtmyZcTHx/PII4+0o4Q9l6vVTUfTk/tYSUnJJS+mOp2Os2fPcvz4cTIyMoSHRHK5nN69ezNgwACioqK6Rajdhej1esrKyigoKKCwsNDCoyGVSgkICBC8bS4uLu22387OSmfvGI1GDAaD4LExG0sXGkzNl7VrPXj99WCeeKKAefMqMBqNwnbgz/pqzV8lEglSqRSZTHbRq0KhEDxVNTU1BAYGtnl+lEjHceEco7aMad2Jb775hlOnTtG7d29uuOEGW4vTKj1NN+2BzQu8ilwekZGRVFZWUlJSIqT6ra2tJSAggIiICCEbSGeg0+lavPnOyckhISHBomhea/z73/9uT9F6PK3pRsS2VFZWXvJCJZfLSUxMJDExkbq6OlJSUjh27BhlZWWkpKSQkpIihNoNGDAAn5YmgXRBZDIZgYGBBAYGMnjwYCoqKigsLKSgoIC6ujqKioooKiriyJEjeHh4EBwcTFBQEL6+vt3SULQVEolECJdr68OLF1+EFSvA0TEMCGtXeUpLS+06oUxPwpxR1XwP0pYxrTsxfvx4Tp06JWSts+fxt6fpprMRDSM7ITAwkI0bNwpPKjZs2EBYWPtehNqCRqPpVnVYuhOibuyXvLw8evfu3eb2bm5ujBo1ipEjR1JUVMSxY8c4depUtw+1A9PNuTmRT//+/ampqRFSr5aXlwvFUc+cOYODgwNBQUEEBwcLngWRzqejTvvl9huRjqOqqgowZZWEnqcbPz8/4uPjOXv2LAcPHuTaa6+1tUgt0tN009mIj+LshIULF7J69Wrh8+rVq1m0aJFFm5SUFEaPHo2npydDhgwR6gWAyev09ttvEx8fj7u7O++++y6HDh0iMTERb29v/vGPfwht1Wo1999/P8HBwYSGhvLGG28I391zzz088sgjTJo0CTc3N6ZOnSoMmFOmTKGpqQlXV1dcXV05f/58q8e0dOlSXn/9dcBUb+rmm29m/vz5uLm5MWLECCE3v/nYxo0bh5eXF4MHDxZy0IuIdGckEgnBwcFce+21PProo8yfP5+4uDgkEgn5+fn88MMPvPXWW3z11VekpaW1Okm9q+Lh4UHv3r2ZNGkSs2fPZuTIkURGRuLg4IBGoyE3N5cDBw7w3XffsX37ds6cOUN1dTXdLApcRMRmGI1GSkpKAOzaU9LRjBw5EoDjx4+3OTJGpPshGkZ2QnJyMkePHqWyspLi4mIyMjIYN26c8L1Go2HmzJksWrSIsrIyHnvsMWbMmCEkrwD46aefOHz4MNu2beOJJ57gzTffZN++fezcuZOnn35ayA742GOPUVNTw9mzZzl06BCff/45P/zwA2CaNL5u3Tree+89ysrK0Ol0/POf/wRMSTUcHR1RqVSoVCqCg4Mv6xg3bNjAgw8+SFVVFfHx8bz44osA1NXVMX36dB5++GHKy8t55plnmDNnjjgwXYCYkc5+aY8MQXK5nD59+rB48WIeeeQRkpOT8ff3R6fTkZqaytq1a3n77bf54YcfyM3N7ZaGgaOjIxEREYwYMYLZs2czadIkevfujaenJ0ajkbKyMk6cOMEvv/zCpk2bOHToEHl5eS0mERg0aBBGo1GcX2SniJm17IOKigrUajUKhQI/Pz+gZ+omMjISPz8/tFqtUBTWHumJuulMenYoXUMDdPT8nYQEaEMMtVwuZ/bs2Xz99deo1Wrmz59vEVt/8OBBZDIZ9913HwA33ngj7733Hlu2bGH+/PkALF++HA8PD4YNG0ZgYCALFizAy8sLLy8vwsPDSUtLw9fXl88++4ycnBzB83PPPffwzTffMHPmTLRaLTfccAN9+/YFYN68eUJV46tlypQpjB07VpD/2WefBeDHH3+kX79+Qlr22bNn8/LLL3PgwAEmTpzYLvvuDtTV1YkZGu2UXbt2MWHChHbbnpubG6NHj2bUqFGUlJRw8uRJUlJSqKur48iRIxw5cgRPT0+SkpLo16+fcDPTnZBKpfj5+eHn50f//v2pr6/n/PnzFBUVUVJSQn19PVlZWWRlZSGRSPD29hbmMXl7ewuT+dtbNyLth6gb+yAzMxOAkJCQHt1vJBIJ/fv3Z9u2bRw/ftxuH6j0RN10Jj3bMEpLg8GDO3YfR45AGzvX4sWLefLJJ1Gr1axcuZLq6mrhu/PnzxMeHm7RPiIiwiKczd/fX3jv7OxscbPk7OxMfX09ZWVlqNVq4uPjhe8MBgOjR48GTC715ttRKpWoVKq2HeslaGm7eXl5bN++HU9PT+F7rVZLUVFRu+y3u9CT06naO62lPb4aJBKJcLM/efJkcnJyOHnypBBOZp6PFBQURL9+/ejbt2+3NZ5dXFyIi4sjLi4OnU5HWVkZxcXFFBcXU1NTQ0VFBRUVFZw+fRqFQkFAQAD19fUsX76c77//nl69etn6EEQuoKP6jUjbqa+vJy8vD4C4uDhhfU/VTb9+/di+fTt5eXlUV1db3JfYCz1VN51FzzaMEhJMhktH76ONjBw5ksLCQhwcHBgwYAC7du0SvgsODiY/P9+ifV5e3mUXI/P19cXJyYnc3FyroVmtZYDqqFoSISEhXHvttWzYsKFDtt9dEDPS2S/Njf6OQiqVCjWCrr32Ws6ePcvJkyfJyMgQsrpt2bKFqKgo+vbtS+/evbttsg65XE5QUBBBQUEANDQ0CEZSSUkJTU1NFBQUkJ2dTXp6Oj///DN1dXUEBATg7+8vJnGwEzqj34i0zokTJzAYDAQEBFjML+qpunF3dycsLIy8vDwyMjIYOnSorUW6iJ6qm86iZxtGSmWbvTmdxYYNG6waJyNGjECr1fLhhx9y55138u2335Kens6UKVMua/tSqZRbbrmFxx57jDfffBN3d3fS09Opq6tj2LBhrRpGvr6+gifHfEPSHsyYMYOnnnqKjRs3cu2116LRaPj1118ZOXKkOK+mGQ4ODrYWQaQFoqOjO3V/CoWCPn360KdPHxoaGjh9+jQnT54kPz9fCC/btGkTsbGx9O3bl169enVrY0CpVApGo9FopKqqiuLiYsHrrlarhaLjEokET09P/Pz8CAgIwM/PT+xbNqKz+42IJefPnycvLw+JRMKAAQMsvuvJuomLiyMvL4/MzEy7NIx6sm46AzH5gp1hDoe5EAcHB77//nu++OILfHx8eP3119m4ceMVGQ7vvPMOLi4uJCUl4e3tzc033yxknmst65WLiwtPPPEESUlJeHp6XjIrXVvx8PBg06ZNvPfee/j5+REZGcnKlSvbZdvdCXOdCRH7o3mGyM5GqVQydOhQbr/9dpYvX86kSZMICAjAYDBw9uxZNmzYwJtvvslXX33F6dOn0Wq1NpO1MzDPN0pMTGTYsGEADBw4kLi4ODw8PATD6ezZs+zZs4dvv/2WLVu2cPz4cYqKirr9+bEnbNlvejpqtZpDhw4BJkPAnKbbTE/WTWxsLADZ2dl2GcLek3XTGUiM3Sy1UUvVbRsbG8nOziYqKqpb1QRpb2pqakQvjZ1i77rpyX1s8+bNdpcpqKysjFOnTnHq1CkqKiqE9Q4ODvTq1Yu+ffsSExODXN59AweOHj3K4MGDOXLkiDCRWq1WU1paKix1dXUWv5FKpXh7e+Pv74+/vz++vr7d+hzZEnvsNz0BrVbLjh07qKqqwtPTk+TkZCHpgpmerBuDwcDrr7+ORqPhvvvus7vkNj1ZN1dKS7aBNcTRXsSC7jonoTsg6sZ+SUpKsrUIF+Hn58fEiROZMGECJSUlgpFUXV1NSkoKKSkpODk50bt3bxITE4mOjr7o5qirExkZyfvvv09kZKSwztnZmYiICCIiIgDT/CSzkWTOdldeXk55eTmpqalIpVK8vLzw8/MTCtP2NMO/o7DHftPd0Wq17N27l6qqKpycnBgzZozVft+TdSOVSgkMDCQvL4/z58/bnWHUk3XTGYiGkYgF9ug2FjEh6sZ+aa/MjR1B88x2kyZNorCwkFOnTnH69Gnq6uo4duwYx44dw8nJiV69epGYmNhtPEne3t5MnToVb2/vFtsolUoiIyMF46m+vp6SkhLBWGpoaBAy3plxd3fH19dXMJZcXV07LDlNd8ae+013RKPRsGfPHsrKypDL5YwZMwZXV1erbXu6bsyGUWlpqa1FuYierpuOputf+UTalaamJvFpqJ0i6sZ+yc7OtkiBb69IJBJCQ0MJDQ1lypQp5OXlcfr0ac6cOYNKpeLEiROcOHFCCLdLTEwkNja2y2ZELCsr47333uP5559v81NfFxcXi0QODQ0NlJWVUV5eTllZGTU1NdTW1lJbW0tWVhYATk5OgpHk5+eHh4dHt/O+dQRdpd90B2pra9mzZw91dXUoFArGjx+Pr69vi+17um7Mabpra2ttK4gVerpuOhrRMBIRERHpgUilUsFTMn36dAoKCkhNTSU1NZXa2loh3E6hUBAfH09iYiJxcXFdKoNbfn4+H3zwAbfffvsVhcNIJBJcXFxwcXERPEpNTU1UVFQIxlJFRQWNjY3k5+cLJRXkcjleXl54e3vj4+ODj48PSqVS9CqJdDpGo5Hs7GyOHTuGVqvFxcWFMWPGXJRsQcQScz04ezSMRDoW0TASseBSk9JEbIeoG/tl8uTJthbhqpBKpYSHhxMeHs7UqVMpLCwUjKTq6mpOnz7N6dOnkcvlxMbGCkZST5z35ujoSHBwMMHBwQDo9XoqKioEj1JFRQUajYaysjLKysqE3zk5OQlGktlg6qqeuPaiq/cbe6euro4ff/xR+Ozn58eoUaPa1G97um5cXFwAU7IWe6On66ajEQ0jEQtUKpXwpETEvhB1Y7/s37+fsWPH2lqMdqF5uF1ycjJFRUWCkVRZWUlaWhppaWmCxykhIYFevXrZdcbEjkQmkwkZ7MD0hL6urk6Yl1RZWUl1dTWNjY0UFhZSWFgImM6zu7s7Pj4+gnfJw8OjW8ztaivdqd/YE3q9nszMTI4dOyasCw0NZdSoUa3WKmxOT9eN+TzZY+Lmnq6bjqbnjMAibUKc4G+/iLqxXxoaGmwtQocgkUgE78ikSZMoKSkhNTWVtLQ0SktLhWKyP/30E0FBQSQkJJCQkIC/v3+PDRszGzzu7u5ERUUBpvpw1dXVFsaSSqWipqaGmpoa4bdSqRQ3Nze8vLwslu7qWequ/cZW6PV68vLySE1NtUhDP3jwYOLi4i5rW6JuTNijYSTqpmMRDSMRC3rS08quhqgb+8XHx8fWInQ4zbPbXXPNNRbeo/z8fIqKiigqKmLnzp14eXkJRlJYWFibn1K3N25ubowYMcLmnla5XC6k+jbT2NhIZWWlYChVVVXR2NgoGEs5OTlC2wuNJU9Pz26RiKUn9JvOQK/Xk52dzZkzZ4RC4E5OTiQlJREVFXVF/a+n60aj0QDY5UOJnq6bjkYs8NrNWb16Nd988w3ffvttm9rr9fqLsiktXbqUhIQEnnzyyY4Q0a5ofqyXe+46Gmu6sSd6ah8DU5hjS2lvewL19fWkp6eTlpZGVlYWOp1O+E6pVNKrVy969epFdHR0pydv6Cq6MRqNNDY2UlVVZbGYb3QvxNnZGQ8PD4vF3d3dLm/kWqKr6MZeMWdGzMnJobGxETAZRPHx8cTFxV3Vf6Gn6+bo0aNs3LiR+Ph4Fi1aZGtxLOjpurkSxAKvXYjk5GSmTp3KY489ZrH+kUceoaKigv/+97+XtT2JREJRURGBgYEALF68mMWLF7f59yqVqlvPFYiMjGTt2rWMGDHikm0v99x1NN1dN12Zffv29ehK5C4uLgwaNIhBgwah0WjIzMwkLS2Ns2fP0tDQINRKksvlREZGEh8fT3x8vJASt6PQ6/Vs2bKFWbNm2fVDBTCN3c7Ozjg7OwuJHcCUBe9CY6murg61Wo1araa4uNhiO66urhcZTG5ubnZ5/D2931wJTU1NFBYWkp2dbZHcw8XFhYSEBKKiotoluqCn68Yc4mqPBkhP101HIxpGNmbJkiW8++67FoaRwWBg3bp1fPbZZ23ejlar7VJPCkVERLonDg4OJCYmkpiYKMx5MBtJVVVVZGZmkpmZyU8//YS/v79gJIWGhrZ7yN2JEyeYN28eR44cYdCgQe267c7C0dFRCGE0o9VqhZC75ktjYyMqlQqVSiUkeYA/5y65ubnh6uqKm5sb7u7uuLm54ejo2GPng3UVGhoaKCwspKCggLKyMmG+qVQqJSgoiOjoaIKCgmwWstodMT9wMCdVEek5iL3IxsydO5f09HTOnDkjrNu1axd6vZ5JkyaRl5fHtddei4+PD7179+aXX34R2kVGRvL3v/9dKMQ4ZcoUAGJiYnB1deXAgQOsWrWKadOmCb/ZsWMHQ4YMwd3dnbi4OPbs2QPAxx9/TFxcHKGhofTr149du3a1Sf7IyEjefvtt4uPjcXd359133+XQoUMkJibi7e3NP/7xD6FtZWUlN954I76+vsTGxvLJJ58I3y1dupSHHnqI8ePH4+rqyqJFiyguLmby5Ml4eHiwePFi9Hq90P5f//oXcXFx+Pr6cssttwjhJqtWrWLKlCncc889uLu706dPH44fPw7AHXfcQV5eHtdccw2urq6sW7eu1WNrfu527dpFQkICL7zwAt7e3kRFRbF161aLY1u0aBH+/v5ER0dftqevLfTE1MhdhcTERFuLYJfIZDKioqKYPn06Dz74IPfddx/JyclEREQgkUgoLS1l7969/Oc//+Gtt95iw4YNnDp1SggLErGOQqHA19eXmJgYBg0axMSJE5k9ezazZ8/mmmuuYfDgwcTExODr64tCocBgMFBTU0NBQQFpaWkcPnyY7du389133/Htt9+ydetWDh48yOnTp8nLy6OqqgqtVtvhxyH2G+toNBoKCws5evQomzdvZuPGjRw5coSSkhIMBgOenp7069ePmTNnMnbsWEJCQtrdKOrJujEajcKDhZCQEBtLczE9WTedgegxsjFubm5cd911rFmzhpdeegmANWvWcOONNyKRSJg5cyZ33XUX33//PYcPH2bmzJmcOnVKeHr43XffsWfPHtzd3XFyckIikXDu3Dnh+/T0dGFfWVlZzJkzh9WrVzN9+nQKCwuFCYbBwcFs374dX19fVq9ezY033khubi6Ojo6XPIaffvqJw4cPk56eztixY7nuuuvYt28feXl5jBgxgiVLluDn58d9992HXC4nLy+PzMxMJk+eTEJCAmPGjAHg66+/Zvv27fj5+TFo0CBmzJjB559/TnBwMEOGDGHTpk3MmjWLr7/+mpUrV7Jt2zb8/f25/fbbefbZZ3n77bcB2LlzJ3fddRf//Oc/ee6553j00UfZvn07n3zyCdu2bWtzKN2FZGZm4ubmRmlpKf/5z39YtmwZ586dA+Cmm26ib9++5Ofnk52dzTXXXMOAAQPo37//Ze+nJcSsdPaLuR+JtIxEIsHPzw8/Pz9Gjx6NWq0mMzOTs2fPkpGRQUNDAydPnuTkyZNCXaW4uDhiY2N7dJa7y8HJyQknJyeLp9xGo5GGhgbq6uqEpba2lrq6OhoaGtBoNEK2PGvbMxe4VSqVuLq6Cp9dXFyuOjxP7Demcb2urk5IwFFeXk5VVZVFNjSJRIKvry8hISGEhIR0SjKRnqyb0tJSVCoVcrncwlNrL/Rk3XQGomFkByxZsoTly5fz0ksv0dTUxPr169myZQuHDh1Cq9Vy3333ATBy5EgmTJjAzz//zK233grAww8/3GZX7//+9z9mzZrFjBkzAAgPDxe+u/baawFTXO2dd97Js88+S0ZGBn379r3kdpcvX46HhwfDhg0jMDCQBQsWCNmTwsPDSUtLw9vbm/Xr13Pu3DmUSiX9+vXj9ttv53//+59gGN1www0kJCQAMGHCBFxdXYUnI5MmTeLkyZPMmjWLTz/9lBUrVhAREQHA008/zbXXXisYRklJSVx//fUALFq0iH//+99tOj+XwsPDg4cffhiJRMKSJUu4++67hbCVPXv2sHHjRmQyGQkJCSxatIgNGza0q2HU1NTU45IadBUyMzOJiYmxtRhdCmdnZ5KSkkhKSsJgMJCfn8/Zs2dJT0+nvLycnJwccnJy2Lp1K25ubsTGxhIbG0tMTIzYDy4DiUQiGDIX3uTpdDpUKpWF0WQ2nDQaDY2NjTQ2Nlo1msCkQ/O2zfOjnJ2dUSqVODs74+Tk1Krx1JP6jdFopKmpidraWmExzxlrnqzEjJubGwEBAfj5+REQENDp//mepJsLSUtLA0zRN/Y4RaEn66YzEA2je+6BZrHY7UpICHz44SWbTZ06ldraWg4ePEhRURF+fn4MHTqUr776ioyMDIsJyjqdjsGDBwufQ0ND2yxOQUEB0dHRVr/77rvvePHFFzl37hwSiUQoUNgWmhtmzs7O+Pn5WXyur6+nrKwMvV5vIW9ERASbN2++rO0A5OXlcfvtt3PXXXcJ3zcP+2i+HaVSiUqlatNxXAo/Pz/hqbVSqQRMCRHy8vKor6+3SKGp1+vtKnGDiIg9I5VKiYiIICIiguTkZCorK8nIyCAzM5OcnBzq6uqEBA5SqZTQ0FDBUAoKChK9SVeIXC7H09PTahIMjUZDfX291UWlUqHT6YQEEOXl5S3uw9HR0cJoMhtMDg4OgnHg6OiIg4NDly9JoNVqaWhosFjM58tsbFpDLpcLDxO9vb3x9/cXrjEinYvRaCQlJQVAeFAr0rPo2qNQe9AGw6WjUSgULFiwgDVr1lBUVCTcUIeEhJCUlMTRo0db/O3l3BCEhYVZhNaZaWpqYuHChXz//fdMnDgRhUJBUFBQuxY28/PzQyqVUlBQQFhYGGAycJpnX2orISEhvP7661x33XWX/duOuIEKCQnB09OzzYbklWLrWiwiLTNx4kRbi9Ct8Pb2Zvjw4QwfPhytViuE32ZmZlJWVkZeXh55eXns2LEDFxcXYmJiBG+Si4uLsJ2kpCQKCgrECdRXgIODAw4ODnh5eV30ndFoFAwnlUpFfX09arWaxsZGwVhSq9Xo9Xqamppoamqiurr6ou3o9XqLh2NyuVwwkhwcHCwMJoVCYfVVJpMhk8mQSqUW783LpcZ8o9GI0WjEYDAIi9FoRKfTodVq0el0Fu+1Wq1wTE1NTTQ2NgretUvNyzJ778zJLzw9PfHy8sLd3d3uEif01DHt3LlzlJeX4+joaLdzeXqqbjoL0TCyExYvXszs2bNRqVS8+uqrAMJNwcqVK1m6dCkAv/32GxERERZhcM3x9/cnJyfHalzswoULGTBgAD/99BPTpk0T5hj5+fkJrw0NDaxatcoiDWh7IJPJmDt3LitWrOCjjz7i3LlzfPrpp3zzzTeXva3bb7+dV155hb59+xIdHU1RUREnTpywSDLREubzcyVzjFoiJCSEoUOH8uyzz/Lkk0/i4ODAyZMncXJyateBtaGhwS5Th4rA77//zqhRo2wtRrdEoVAQExNDTEwMU6dOpbq6mnPnzpGZmUlWVhb19fXC3CRAyNIVHR1NeHg4ubm5djmBuisjkUhwdHTE0dERb29vq23MxlNzQ8m8mI2KM2fOEBISgkajwWAwCEZIS7WbrkTO5q8XrjMbRO35EFChUAhzspRKpfDenAWwq3jFeuKYZjQa2bdvHwCDBg1q0xxrW9ATddOZdI0e2gMYNWoUbm5uREVFERcXB5ienm3atInly5ezYsUKjEYjQ4YMaXXOzLPPPsusWbNoamqyyGAHEBUVxfr163n88ce54YYbCAoK4j//+Q8xMTG8+eabJCcnA3DvvfcSGxvb7sf4r3/9i3vvvZfQ0FA8PDx48cUXGTt27GVv58Ybb6Sqqoq//OUvFBYWEhQUxLJly9pkGD3xxBM8+OCDLFu2jJUrV7JgwYIrOZSLWL16NY888gjR0dFoNBr69u1rkZGvPWielU/Evqirq7O1CD0GT09PBg8ezODBg9Hr9eTn5wvepOLiYoqKiigqKmLfvn3U1tayceNGXnnlFcaOHUtgYKDdPZnvrjQ3nlqqV6XVapk6dargoWnuidFoNDQ1NaHVai28NRd6cnQ6HQaDAb1eL3h8zJgNnss1fMzep5Y8VWZvlqOjI05OThbvO7uAcUfRE8e0jIwMsrOzkclkDB8+3NbitEhP1E1nIjG256MSO6Cl6raNjY1kZ2cTFRUlTtxtBbGisv1i77rpyX3st99+s+sLaU9BpVKRnZ1NVlYW586dIz09nZUrV3LXXXcRFBSEs7MzUVFRxMTEEB0dbTVMTKTz6Ih+YzaOzIYSWDeQjEYjEonEIuyureF3PYGeNqbpdDo+/PBDKioqGD16tPCg2B7pabppD1qyDawheoxELBAnfNovom7sl379+tlaBBFMVerNme6MRiM7duxg5cqVREZGAqBWq0lNTSU1NRUALy8voqOjiYyMJDIyUpzH18l0RL8xGzddJWTNXulpY9qOHTuoqKjAxcWFcePG2VqcVulpuulsxJFDxIK6ujo8PDxsLYaIFUTd2C+7d+9m6tSpthZDpBkSiUTwCE2dOpUBAwZQWFhIVlYWWVlZ5OfnU1VVxZEjRzhy5AgAvr6+REZGEhUVRUREhF17aLsDYr+xX3qSbs6dO8f+/fsBuO666+x2bpGZnqQbWyAaRiIiIiIi3R6pVEpYWBhhYWGMHz8ejUZDTk4O2dnZ5OTkUFxcTHl5OeXl5fz++++AKZtmVFSU4FESvbYiIt2LiooKIQnUkCFD6NWrl40lErE1omEkYkFPmxvSlRB1Y7+IF1P7JCQkhBUrVljNSufg4EB8fDzx8fGAKcwuNzdXKCxbXFxMWVkZZWVlHDp0CICAgADBSAoPD7dIDS5y+Yj9xn7pCbqpr69n9erVqNVqQkJCuowXpifoxpaIhpGIiIiISLckICCAO+64g4CAgEu2dXZ2JiEhQSjq2NDQIBhK2dnZlJaWUlJSQklJCb/99hsAPj4+QvmE8PBwvLy8xIn7IiJdgPr6ej7//HMqKyvx8vJi4cKFKBQKW4slYgeIhpGIBY2NjXYfX9tTEXVjv6SnpwsT/EXsh6qqKj7++GMee+yxy85Ap1Qq6d27N7179wZMN1K5ublkZ2eTm5tLaWkpFRUVVFRUCEW43dzcCA8PF4wlf39/MT14K4j9xn7pzrqpq6vjv//9L+Xl5bi6urJ48eIuNZ+wO+vGHhANIxERERGRbkl2djavvvoq8+bNu+rU3C4uLiQmJgpFm9VqNfn5+eTm5pKXl8f58+epq6vj9OnTnD59GjCFv4aFhREeHk5YWBjBwcHdps6NiEhXpKioiLVr1wppm2+55RZ8fHxsLZaIHSEaRiIWiOlq7RdRN/aLvad3FWl/nJ2dLeYoabVaCgsLycvLIy8vj/z8fBobG8nIyCAjIwMwJYAICAggLCyM0NBQwsLC8PT07LHhd2K/sV+6o25Onz7Nd999h1arxcfHhyVLlnTJWmbdUTf2hGgYiVjQ0NDQpVzKPQlRN/bLyZMnxYJ7PRyFQiEkZgBTodGSkhLBo1RQUEBtbS1FRUUUFRUJCR1cXFwEIyk0NJSQkJAeM9dB7Df2S3fSjUajYfPmzUJa/tjYWK6//voum9CoO+nGHhGDn9uCXg+7dsH//md61evbdfORkZEcPHjQYt2yZct4/vnn23U/bUHfzsfWEt9//z0JCQl4eHgQFBTEI4880uq+V61aRWhoKO7u7tx6661oNJoW20kkEl5++WWL9U8//TQSiYS1a9datPvoo4+ENsXFxXb95LazdCNy+VRXV9taBBE7QyqVEhQUxIgRI1iwYAGPPPIIDz/8MPPnz2fEiBGEhoYik8mor68nPT2dbdu2sWrVKl577TU++ugjfvrpJ44fP05ZWRlGo9HWh9MhiP3Gfukuujl06BCvvvoqR44cQSKRMHr0aBYtWtRljSLoPrqxV0SP0aXYsAGWL4eCgj/XhYbCe+/B3Lm2k6uDkMlknbKfIUOGsGfPHvz8/KiqqmL+/PmsXLmSe+6556K2KSkpPPLII2zZsoW4uDhmz57Nyy+/zIsvvmh127GxsaxZs4a//e1vABiNRtatW0dMTIxFOy8vL1599VVuu+22LvGEtrN0I3L5iGGO9ok53M3Z2dnWogDg4eGBh4cHffr0AUCn01FUVER+fj4FBQXk5+dTV1cneJXMODg4EBwcTHBwMCEhIYSEhODh4WHXD3Lagthv7Jeurhu1Ws327duFmmQAN998M1FRUTaUqn3o6rqxdzrMYxQZGYlEIrFYnnzyyVZ/YzQaef755wkODsbZ2ZkJEyYIk1htwoYNcP31lkYRQGGhaf2GDZ0ixqpVq5gyZQp33nknbm5uDBkyhMLCQu677z48PDwYPnw458+fB0zhG3PnzsXf3x9vb2/mz59PZWUlALt27SIkJET4/PXXX9OrVy/UarWwL6VSiVqtxt3dndzcXGH9tm3b6Nu3b7sdU0hICH5+fhbrsrOzrbZds2YNN9xwA0OGDMHDw4NnnnmGL7/8ssVtx8TE4ObmJmSK2r9/vxCm0pxhw4YRFhbGZ599dpVH0zmIxSXtlyFDhthaBBEr9O7dm5SUFCGznL0hl8sJCwtj1KhRFl6l66+/nhEjRhAeHo5CoRCK0e7fv5+vv/6ad999lzfffJPVq1ezc+dOzp49i0qlsvXhXDZiv7Ffuqpu9Ho9v/32G++//76FUfTAAw90C6MIuq5uugodGkr34osvCk++ioqKhCf4LfH3v/+dd955h3/+858cPnyYwMBAkpOTqaur60gxraPXmzxF1kIYzOseeqjdw+paYufOnfzlL3+hsrKS0NBQRo8ezfjx46moqCAyMpI333xTaDt37lyys7PJzs6mrq5O8KxMmDCBefPmcf/991NWVsYDDzzAqlWrLJ6m1tXV4ezszIwZM/j666+F9V999RU33HCDVdlmzJiBp6en1eX1119v8Zj27t2Lh4cH3t7epKSkcNttt1ltl5qaSlJSkvC5f//+ZGdnWxh0F7J48WLWrFkDmAyrxYsXW2333HPP8eqrr6LValvclr1gk34g0iZ27txpaxFEWqAr6UYikeDh4UHfvn2ZNm0at912G0899RT33HMPs2bNYsiQIQQHByOTyWhoaCAjI4Nff/2VNWvW8NZbb/HOO++wZs0adu7cSVpaGtXV1XYdhteVdNPT6Gq6MRgMnDx5kg8++ICff/4ZtVpNQEAAt9xyC88//3y3yjzX1XTT1ejQUDo3NzcCAwPb1NZoNPLuu++yYsUK5v4Rovbf//6XgIAA1qxZw913392Rol7Mnj0Xe4qaYzRCfr6p3YQJV7275ORki1AptVrNU089JXxOSkpizpw5AMyaNYuMjAwWLFgAwOzZs/nkk08AU1z7kiVLhN89/PDDrFixQvj8+uuv079/fyZMmMBNN93EyJEjrcpzww038Morr/DYY4+h0+n49ttv2bdvn9W2mzZtuqJjHjNmDDU1NWRnZ7Nq1aoWs8OoVCrc3d2Fz+b3KpWqxRCZG264gWHDhvHqq6/y/fff8/LLL7N69eqL2iUnJxMSEsKqVauYOXPmFR2HiIiIfXLs2DFmzpzJb7/9xsCBA20tzhVhzmQXEBAgHINOp6OkpITCwkLOnz9PYWEh5eXl1NbWUltby9mzZ4XfOzs7ExgYSFBQEIGBgQQGBuLr6yvWVxLpFuj1elJSUti9e7cQDePi4sI111zDwIEDxf+5yGXToYbRG2+8wUsvvURYWBjz58/n8ccfb7GGQ3Z2NsXFxUyZMkVY5+joyPjx49m/f3/nG0bN4rvbpd0l2Lp1KyNGjBA+L1u2zOJ7f39/4b2zs7NFGJqzszP19fWA6YL52GOP8e2331JVVYXRaMTX11doq1QqufHGG3nllVf45ZdfLpLDXEB02rRp3HLLLeTk5JCenk5oaKiQlra9iYqKIikpiYceeoj//e9/F33v6upKbW2t8Nn8vrUMbQEBASQkJPD0008zZMiQVlNyPvfcc9x9991MmzbtKo6i4xGLu9ovsbGxthZBxApGoxGtVmvXXpMrQS6XC3ONzDQ1NVFSUkJRURHFxcUUFRVRWlqKWq0WIgjMKBQK/P39CQoKIiAgAH9/fwICAjp9QrrYb+wXe9eNSqXiyJEjHD58WAgjVSqVjBo1iqFDh3br66W966ar02GG0fLlyxk0aBBeXl4cOnSIp556iuzsbMGzcSHFxcWA6Ya2OQEBARZzXS6kqamJpqYm4XPzG+irIiiofdt1EqtXr2bPnj0cOHCA4OBgNm/ebGFUZmRk8OGHHzJ//nweffRRvvrqK4vfm5+uODo6MmvWLL7++mvS0tJaDKMDmD59Onv27LH63dNPP83TTz99SbkNBgPnzp2z+l1iYiIpKSnC5xMnThAVFXXJCdWLFi3i1ltvFTLRtcSUKVMICgriv//97yXltCXiky/7RSzaKWJrHB0dCQ8PJzw8XFin0+koKyuzMJZKSkrQaDQUFhZSWFhosQ13d3cLQ8nf3x9fX1/k8o65VRD7jf1ij7oxGAzk5ORw7NgxUlNThUytbm5ujBgxgqFDh9ql3O1NTzhGW3JZo93zzz/PCy+80Gqbw4cPM2TIEB5++GFhXb9+/fDy8uL666/njTfeaDXW88IsO0ajsdXMO6+99ppVmbZt2ya4Uw8dOoRarcbX1xe9Xk9NTQ2A8HSssbERMHWuhoYG9Ho9soEDcQkNhcJCJFaeNholEowhIdT164e0rs7Cq+Ho6IhUKhXmwLi6utLY2IhOp0MqlVq0Nf/BVSoVNTU1uLi4oNFo0Gg0gsHX0NCATqdDrVYjl8uFz1qtFp1OR319vTBAlJWVIZfLcXBwoLi4mNdffx2DwYBWq0Wj0bBkyRJhgm9SUhKrVq1iwYIFODg4EB0dzeOPP87dd9+NwWBgxowZvPTSSxQWFrJz505qamqQy+U4OTkJT2icnZ359ttvBVktzqFMhlKptHq+v/vuO8aPH4+vry8ZGRm8+uqrTJkyRWjb/BzOnDmTmTNnsmTJEsLCwnjxxRdZvHix1bYNDQ0YjUYaGhqYMmUK33zzDX/5y1+oqalBp9Oh0WjQarXCOTSve+yxx7jjjjsAk3FtNBpxcHAQzjeYnkaZ24Mpw5S5rUKhwMHBQfDcXdjW3d0dlUqFwWCweg4NBoNwDltqq9FohDBCq//ZP863eS7Shf/v5v/DC9tezn/2wrbm/2x9fb2wr82bNwMQFhaGr68vx44dA0yTRs+fP8/58+eRyWRMnjyZbdu2odfrhaxb5gmzAwcOpLy8nPz8fACmTp3Kzp070Wg0BAQEEBkZyW+//QaYxpja2lpycnIAU4jkvn37aGhowNfXl/j4ePbv3w9Anz59aGxsFAxx8xihUqnw8vKiT58+7N27F4CEhAQMBoMQmjR+/HiOHz8uVE0fNGgQu3btIicnh+TkZORyOWfOnAFMoaKpqalUVlbi4uLCiBEj2L59OwDR0dEolUpOnToFwMiRI8nMzKSsrAwnJyfGjRvHli1bAIiIiMDT05MTJ04ApqQheXl5FBcXo1AouOaaa9iyZQtGo5HQ0FD8/f2FxCODBw+muLiYwsJCpFIpycnJbN++HZ1OR1BQEKGhoRw+fBiAAQMGUFlZSV5ennC+d+3aRVNTE/7+/kRHRwtlBZKSklCpVIIXYvLkyezfv5+GhgZ8fHxISEgQQm8TExPRaDRkZmYCMHHiRH7//Xfq6urw9PSkX79+7N69G4BevXoBkJ6eDpgKGZ48eZLq6moh8Yw5vj42NhYHBwdSU1MBGD16NGlpaVRUVAhPjs06z83NJTAwUHjAMmLECLKysigtLcXR0ZEJEyYI/9nw8HC8vb05fvw4AEOHDqWgoICioiLkcjmTJk1i69atGAwGQkJCCAwMFOqjDBo0iNLSUgoKCpBIJEyZMoUdO3ag1WoJDAwkPDxcqF/Uv39/qqurhQd/U6ZMYffu3TQ2NuLn50dsbCwHDhwAoG/fvjQ0NJCVlQXApEmTOHjwIPX19Xh7e5OYmCj8Z3v37o1OpxOKyk6YMIGcnBxkMhl9+vRh4cKFbNq0iaqqKpRKJZWVlaSmplJfX094eDhnzpxBrVbj4OBASEgIubm5uLm5kZCQQFBQEFVVVbi7uzN16lTOnTtHeXk5SqWS0aNHs3XrVsCUfMnd3Z2TJ08CMHz4cHJycigpKcHBwYGJEyeyefNmcnJyGDt2bI8YIwDi4uK6zBiRmZnJyJEj7WKMCAoK4n//+x9ZWVnCA9Hy8nJ8fX1ZvHgxWq2Wuro6fv/998seI7Zt2waYIldcXV27xBiRlpbG0KFD23WMOHr0KLW1tXh4eDBgwAB+/fVXAOLj45FKpaSlpQn/2dOnT1NVVYWrqyvDhg1jx44dgCnxlZOTk5A0bdSoUZw9e/aqxghon/sIs/xtQWK8jBiD8vJyysvLW20TGRlp1R1fWFhIaGgoBw8etFqYKisri5iYGI4ePWoRCz5r1iw8PT1bfJpvzWMUFhYmDEpmGhsbyc7OJioqqu3hAuasdGCZhMFsqH3zTbuk7I6MjGTt2rUXhdIFBgby/PPPs2rVKtauXSuEvq1du5Z///vfwmD73Xff8frrr3Pw4EFqa2u54YYb2LNnD2FhYdxxxx383//9Hzk5Ofz973/nhx9+4Ndff0UqlbJv3z7mzp1LSkoKXl5eeHt7s2PHDoYOHQogdNTo6GhhYGwvXn31VT744AOqqqrw8fFh/vz5vPLKK4JuXF1d+fnnnxk7dixgysy3YsUKamtrmTdvHh999JFVV/mF56o5EyZMYNmyZdx4441W240cOZKDBw/abdhNTU0NHh4ethajRa6oj3UTNm/ezNSpU20thsgFHD16lMGDB3PkyBEGDRpka3HsmsbGRkpLSyktLaWkpER4NT/suBCpVIq3tze+vr74+fnh6+srLG0NYxL7jf1iS90YjUZKS0tJTU0lNTWVsrIy4TsnJyeSkpIYMGCARShpT0LsN5eP2ei70DawxmUZRlfDpk2bmDlzJrm5uRaufjNGo5Hg4GAefvhh/vrXvwKmasX+/v688cYbbZ5j1NLBX/FNm7U6RmFh8O673aqO0YEDB3j//ff58ssvxXo5dorZK2Sv9GTDSKVStTrnTcQ2qNVqTp06Rd++fe2mllFXwmg0UldXJxhKpaWllJWVUV5e3mKRbTB5vc0Gk4+PD97e3vj4+ODh4WEREiz2G/uls3XT1NREdnY2mZmZZGRkCBEhYKrhFxMTQ//+/enVq1eHhXZ2FcR+c/lcjmHUIf+uAwcOcPDgQSZOnIiHhweHDx/m4Ycf5rrrrrMwihISEnjttdeYM2cOEomEhx56iFdffZW4uDji4uJ49dVXUSqVLFq0qCPEbBtz58KsWabsc0VFpjlFY8eCHd+gXgkjR45k5MiR1NfX4+LiYmtxRKzQ2Ngo6sZOSUtLE2tL2CHOzs5IJBLRKLpCJBIJ7u7uuLu7ExcXJ6w3Go3U1tYKUSRmY6msrIz6+nohO545rMeMVCoVohO8vb0pKSlhzJgxeHt74+HhYdcPfnoaHT2maTQaCgoKyMnJITc3l4KCAmFKAJgSjMTGxpKYmEh8fHyPe9jWGuL1pmPpEMPI0dGRdevW8cILL9DU1ERERAR33nmn4Akyk56ebvFU4K9//StqtZp7772Xqqoqhg8fzpYtW2xf5Vcma5eU3F0BnU5naxFEWkDUjf1SUVFhaxFErJCbm8vf/vY3PvroIyIiImwtTrfBXG/Jw8ODmJgYi+/UarWFsVRRUUFlZSVVVVXodDoqKiqE/pKZmSnM+ZFKpXh6euLl5WW1Jp6rq2ur841F2pf2HNOMRiPV1dVCwo+CggIKCwsxGAwW7by9vYmNjSUuLo7IyEgUCkW7ydCdEK83HUuHGEaDBg0SJuC1xoVRfBKJhOeff57nn3++I8QSaQNi5jP7RdSN/aJUKm0tgogVKioq2Lx5MxUVFaJh1Ek4OzsTFhZGWFiYxXqzl6myspLKykrh5s7f35/Kykp0Op3wnTVkMplgJHl4eAjv3d3dcXNzw93dXbyRbkeudEzTaDSUlZUJoZelpaUUFRUJSYya4+7uTmRkJBEREURFReHt7X21YvcIxOtNx9KzAzVFLkKMW7VfRN3YL6NGjbK1CCIidk1zL1NUVBRgypwlk8mEuUwVFRVUV1dTU1NDdXW1sNTU1KDX6y28TdZwdnYWjCRrr25ubiiVSvEhUxtobUwzGAzU1dVRVVVFVVUVlZWVgjFkrp94ITKZjMDAQKH+Vnh4OJ6enqIX8AoQrzcdi2gYiVhgnqAmYn+IurFftm3bJmYJEhG5TMz9pvlcJmvo9Xrq6uosjCXzUldXR21tLVqtFrVajVqtprS0tNX9KpVKXFxcWl2USiVOTk44Ozv3qLlP5nIXP/zwA0OHDkWlUgnn3mwImQ3VlnB1dcXPzw9/f3/8/f0JDAwkICCgxydNaC/E603HIv5LRUREREREROyW5mF01jAajTQ1NVFbWysYStZe6+vrhRv/hoYGizTQraFQKHB2dhYMpeavTk5OODo64uDgINSza+m9QqHocA+JwWAQavSZ6x02NTXR2NjY4qJWq1GpVKhUKurr6zEYDGRmZrZa+6W5Try8vCwMITFJkEhXRjSMRCxoa/0Jkc5H1I39Yg4NErEvAgICuOuuuwgICLC1KCJWaK9+I5FIBCPF39+/xXYGgwG1Wk19fX2ri0qlQq1WCzWctFotWq1WKHJ9NUilUqRSKTKZDJlMZvW9OdTPHJLW0qter7cwgnQ63UUJDa6U4OBgAgICcHV1xdXV1cII8vLyws3NTQxJtBHi9aZjEQ0jEQvEgc5+EXVjv4jzv+yTkJAQnnvuOYKDg20tiogVOrvfSKVSIVSuLRgMBpqamgQjyfza/L1arUaj0aDRaNBqtS2+b75Ns1eno5HJZMjlcsFoNHu3mn82L25uboIRpFQqKSkpEfuNnSJebzoW0TCyAyIjI1m7di0jRowQ1i1btozAwMBOydCXnp7Oo48+KmQSnDZtGv/3f/+Hl5eX1fbXXnsthw8fpqmpiYSEBN59911Gjhxpta1EIiEmJobMzExhXUZGBvHx8UydOpVffvlFaDdy5Ej2798vtJs2bRo33ngjS5cubacj7dqo1WocHBxsLYaIFVJSUsSbCDukrq6OL7/8knvuucf2ZR9ELsLe+41UKsXZ2fmq62AZjUbB62QwGNDr9ej1+lbfm0PuWnuVSqXI5XJhUSgUwvvmnqcrwd5105MRddOxiIaRCDU1NSxYsIDVq1dTX1/PQw89xGOPPcann35qtf3f//53ofr0Dz/8wJw5cygqKmoxdloqlfLbb78xfPhwAFavXm1RLNBMWloaW7ZsYcqUKe13cCIiIj2WjIwMnnjiCSZPnsygQYNsLY5ID0UikQjzjUREROwbMTbnEmRkwNGjFy8ZGZ0rx//93/8RHR2Nn58fN9988xXFOltLoQkwbNgwbr75Zjw8PPD39+fOO+/k0KFDLW6nT58+yOVyjEYjUqmUkpISqzUKzCxcuJDVq1cLn//3v/+xcOHCi9o9/PDDvPDCC5dxRD0LcUKr/dLc2ysiItI2xH5jv4i6sV9E3XQsomHUChkZEB8PgwdfvMTHd55xtHnzZl5//XV+/PFHcnJyqK+v55FHHrHatqSkhDvvvJOIiAgGDRrESy+9xIEDB9iwYQM333zzJfel0WjYv38/ffr0abXdjBkzcHJyYsaMGTz44IOt3rQvWLCAb7/9Fr1ez+HDh/H19bU6eXDp0qUUFhaydevWS8rZE9FoNLYWQaQFsrKybC2CiEiXQ+w39ouoG/tF1E3HIobStUJdnen1yy+hd+8/1585A0uW/Pl9e5CcnGxRK0GtVvPUU08BsG7dOpYtW0bvP4R49dVXGTx4MJ988slF2zl48CDTp0/nnXfeIScnhzVr1rBixQqio6N55plnLinHkSNHeP/999m9e3er7TZt2oRGo+GHH35ApVK12tbHx4f+/fuzbds2fv75ZxYtWmS1nUKh4Omnn+aFF14gOTn5krL2NJpP4BWxLy5VN0VERORixH5jv4i6sV9E3XQsoseoDVoSddwAADJfSURBVPTuDYMG/bk0N5Lai61bt1oUrbv11luF786fP094eLjwOSIigvr6empqai7azrXXXktpaSl33HEH//rXv5g8eTJbt27llVde4fvvv29VhuzsbBYuXMinn356SY8RgIODA/PmzePtt9/mzJkzrbZdvHgxX3zxBRs2bGDBggUttrv11lspKChg27Ztl9x/T0PMSme/iKnU7ROFQoGvry8KhcLWoohYQew39ouoG/tF1E3HIt5pdQGCg4PJy8sTPufl5aFUKvHw8Lio7ZdffklGRgZLly6lf//+vPrqq/j4+DBx4kRCQ0Nb3EdxcTHJyck8++yzzJ49+7Lk0+l0ZGdnt9pm1qxZbNy4kb59++Ln59diO4VCwVNPPSXONbKCmFXLfpkwYYKtRRCxQlJSEmVlZSQlJdlaFBEriP3GfhF1Y7+IuulYRMOoCzB//nw++ugj0tLSqK+vZ8WKFdx4441W29500028/fbbTJ8+nXvuuYft27dTXV1Namqq1YQHYMpKN3XqVG6++WZuuOGGVmXJzc1l06ZNNDY20tTUxD//+U8KCgoYPHhwq79TKpVs3bqV//u//7vk8d56663k5eVx+PDhS7btSVjzEIrYB5s3b7a1CCItIOrGfhF1Y7+IurFfRN10LKJh1AbOnLHMSHeJqLF2Z/r06Tz++ONMnz6diIgIHB0defvtt622bT5Pqa189913nDx5kr///e+EhIQIRd7MLFu2jGXLlgmfX3nlFfz9/QkMDGTdunX88MMPbaosP3z4cGJiYi7ZzsHBgaeeeorKysrLPhYRERERMykpKSxZsoSUlBRbiyIiIiIi0gWQGFvK4dxFqa2txcPDg5qaGtzd3YX1jY2NZGdnExUVhZOTU5u2Zc5K1xJnz4KVcjxdGrVafdXF7EQ6BnvXzZX0se7CmTNnhOQoIvbD0aNHGTx4MEeOHBHrGNkhYr+xX0Td2C+ibi6flmwDa4hZ6VohLs5k/FjLPufm1v2MIgC5XPxL2CuibuwXb29vW4sgItLlEPuN/SLqxn4RddOxiKF0lyAuzjIjnXnpjkYR0GqhVhHbIurGfjl+/LitRRAR6XKI/cZ+EXVjv4i66VhEw0hEREREREREREREpMcjGkYiFri4uNhaBJEWEHVjvwwdOtTWIohYIS4uju+//5647uri7+KI/cZ+EXVjv4i66VhEw0jEAo1GY2sRRFpA1I39UlBQYGsRRKzg5uZGZGSkWAPMThH7jf0i6sZ+EXXTsYiGkYgFWq3W1iKItICoG/ulqKjI1iKIWKGwsJBXXnmFwsJCW4siYgWx39gvom7sF1E3HYtoGIlYIJFIbC2CSAuIurFfxIyB9klJSQlfffUVJSUlthZFxApiv7FfRN3YL6JuOhbRMBKx4FL53UVsh6gb+2XSpEm2FkFEpMsh9hv7RdSN/SLqpmMRDSMRC2pra20tgkgLiLqxX7Zu3WprEUREuhxiv7FfRN3YL6JuOhbRMLIDIiMjcXd3R61WC+tqa2txdnYmISGh0+T44IMPGD16NHK5nNdff73VtuXl5SxYsABvb2/Cw8NZvXp1i22XLl2KRCJh7969FutHjRqFRCKhuLhYaCeTyThz5ozQZu3atUyYMOHKD6obYTQabS2CSAsYDAZbiyAi0uUQ+439IurGfhF107GIhpGdEBgYyMaNG4XPGzZsICwsrFNlCA4O5vnnn+e66667ZNvly5fj7OxMUVERP//8M4888gipqaktto+Li7MwnrKzs6moqLionYeHBy+99NKVHUA3x8HBwdYiiLRASEiIrUUQsYKPjw9z587Fx8fH1qKIWEHsN/aLqBv7RdRNxyIaRpcgIwOOHr14ycho3/0sXLjQwnBYvXo1ixYtsmiTkpLC6NGj8fT0ZMiQIRw8ePCK9tWS52H27Nlcd911bZrL8ssvv/Dkk0/i6OhInz59mD17dqteo7lz57Jx40Yhs9qaNWtYuHDhRe3uuOMOfv75Z9LS0i76LicnBycnJz788EP8/f0JCwtj165dfPrppwQFBREeHs6vv/56Sdm7KuKES/slMDDQ1iKIWCEiIoKPPvqIiIgIW4siYgWx39gvom7sF1E3HYtoGLVCRgbEx8PgwRcv8fHtaxwlJydz9OhRKisrKS4uJiMjg3HjxgnfazQaZs6cyaJFiygrK+Oxxx5jxowZ1NTUWN3ehx9+yIABAwgPD+f2229n06ZN7N69m/vuu4/ff/+9RTkaGhraLHNzA8toNHL69OkW23p6ejJ8+HA2b94MwP/+97+LDD8Ab29v7r333ha9RhqNhpycHAoLC1m+fDlLliwhNTWV3Nxc/vrXv/LQQw+1Wf6uxuXoRqRzOXLkiK1FELGCWq1m/fr1FmHKIvaD2G/sF1E39ouom45FNIxaoa7O9Prll3DkyJ/Ll19aft8eyOVyZs+ezddff83atWuZP38+Uumf6jl48CAymYz77rsPhULBjTfeSFxcHFu2bLloW01NTeTk5LBp0yaOHDnCyJEjWblyJW+99RZjx45tl6rJU6ZM4Y033kCtVpOSksKGDRsueeO+aNEiVq9ezfHjx3F2diY+Pt5qu0ceeYQff/zRqtfIaDSyYsUKFAoF8+bNo7CwkCeffBIHBwfmzZvH6dOnxfhbERERAM6cOcOyZcss5i2KiIiIiIi0hBib0wZ694ZBgzp+P4sXL+bJJ59ErVazcuVKqqurhe/Onz9PeHi4RfuIiAjOnz9/0XYcHR2ZM2cOL7/8MpWVlUyePJn//ve/uLi48M0333D69Gn69OljVQalUtkmWd9//33uvfdeIiIiiIiIYOHChahUqlZ/M2PGDB588EG8vLxYvHhxi+18fHy49957efnll5kxY8ZFx2YO9XN2dgbAz89P+KzVatFoNDg5ObXpOLoSbdWNSOczqDMGCBGRbobYb+wXUTf2i6ibjkX0GNkRI0eOpLCwEJVKxYABAyy+Cw4OJj8/32JdXl4ewcHBF22nqamJp59+mgkTJrBw4UJ+++03evfuTUREBPv27bvIwGqOTqdrk6x+fn58/fXXlJaWcvjwYaqqqhgyZEirv3FycmLq1Kl8/PHH3HDDDa22ffTRR9m0aRPp6eltkqcn0FbdiHQ+paWlthZBRKTLIfYb+0XUjf0i6qZjET1GdsaGDRssQujMjBgxAq1Wy4cffsidd97Jt99+S3p6OlOmTLmorYODA9u2bRO2M2fOnDbtW6fTUVtbi16vR6fT0djYiEKhQCaTXdT23LlzeHt74+rqyvr169mzZw8rV6685D5eeuklbr31VoKCglpt5+Pjwz333MP7779PUlJSm+Tv7mg0GsFLJmJfFBQUtOiFFRERsY7Yb+wXUTf2i6ibjkX0GLWBM2csM9J1ZLh6v3796Nu370XrHRwc+P777/niiy/w8fHh9ddfZ+PGjXh4eFzUViKRWDWuLsXLL79MYGAgX375Jc888wzOzs588cUXAOzZswdXV1eh7W+//UZCQgKenp58+OGH/Pjjj20K9QoNDbVIKtEajz76KBqN5rKPQ0Sks5FIJLYWQcQKEokEhUIh6sdOEfViv4i6sV9E3XQsEmM3qxpZW1uLh4cHNTU1FmmnGxsbyc7OJioqqs3zT8xZ6Vri7FmIi7taiUVEugdX0sdERERERERERDqSlmwDa4geo1aIizMZP80z0pmX7moU1dbW2loEkRYQdWO/7Nixw9YiiLSAqBv7RdSN/SLqxn4RddOxiHOMLkF3NH5ao5s5ELsVom7sF3PhYhH74syZM9x111388MMP9O7d29biiFyA2G/sF1E39ouom45F9BiJWKBQKGwtgkgLiLqxX8RK5PaJWq3m3LlzYoFXO0XsN/aLqBv7RdRNxyIaRiIWODg42FoEkRYQdWO/tJYCX0RExDpiv7FfRN3YL6JuOhbRMBKxoL6+3tYiiLSAqBv75dChQ7YWQUSkyyH2G/tF1I39IuqmYxENIxERERERERERERGRHo9oGIlY0JZaRCK2QdSN/dK/f39biyBihaioKFauXElUVJStRRGxgthv7BdRN/aLqJuOpUMMo127diGRSKwuhw8fbvF3S5cuvaj9iBEjOkJEkRbQ6XS2FkGkBUTd2C/V1dW2FkHECl5eXowdOxYvLy9biyJiBbHf2C+ibuwXUTcdS4cYRqNGjaKoqMhiueOOO4iMjGTIkCGt/nbatGkWv/vpp586QkSRFtBoNLYWQaQFRN3YL7m5ubYWQcQKJSUlvPPOO5SUlNhaFBEriP3GfhF1Y7+IuulYOsQwcnBwIDAwUFh8fHzYuHEjt912GxKJpNXfOjo6WvzW29u7I0S8IpqaOma7kZGRHDx40GLdsmXLeP755ztmhx2ESqVizJgx+Pj44OXlxaRJk0hLS2ux/blz5xg9ejRKpZJBgwZx4sSJFttKJBJiY2Mt1mVkZCCRSJg2bZpFu1GjRlm0mzZtGqtWrbqygxIREemyFBYW8vHHH1NYWGhrUUREREREugCdMsdo48aNlJeXs3Tp0ku23bVrF/7+/sTHx3PnnXdSWlraavumpiZqa2stlo7go4/Azc302p1xd3e/4t86Ojry8ccfU1ZWRkVFBXPnzuWWW25psf3ChQuZMmUKlZWV3HbbbcyZM6fVcDGpVMpvv/0mfF69ejVxVirwpqWlsWXLlis+DnvlanQj0rFMmTLF1iKIiHQ5xH5jv4i6sV9E3XQs8s7YyaeffsrUqVMJCwtrtd306dOZP38+ERERZGdn88wzz3DNNddw5MgRHB0drf7mtdde44UXXrho/bZt23BxceGaa67h0KFDqNVqfH190ev11NTUAODk5ARAY2MjAG5ubjQ0NKDX65HJZCiVSurq6vjsMwceftiZfv2MLFsmQa1W88ADDjQ2NqLT6ZBKpbi6ugpGmaOjI1KpVCgq6Orq2mJbc20alUpFTU0NLi4uaDQaNBoNTX+4qD744AO++eYbIiIi+Oabb4iNjWXNmjW8//77rFmzhri4OFavXk2vXr2orq7mpptu4uDBg+j1esaNG8e7775LSEgIO3bs4NZbb2X//v1ERkby+eef89JLL3Hw4EE8PDyor69Ho9Hg4eGBwWAQ9u/u7o5KpcJgMCCXy3F0dBRSRzs7O1u07dWrFw0NDWg0GrRaLdnZ2VbPd0ZGBunp6fzyyy80NTWxdOlS3nzzTbZs2cLo0aMvOocA8+bN4z//+Q+JiYm4urqyevVq5s6dy7Fjx9BoNELb5cuX89xzzzF8+HCkUpPt39DQQE1NDQ4ODsjlchoaGgCE863VapFIJLi7u1NbW4vRaLyorVKpRKfTCSFtHh4eQluFQoGDg4NwXi5se+E5dHJyQqVSWT2HLbXVaDSCcXSp/6y1/3fz/+GFbS/nP3thW/M5rK+vF/a1efNmAMLCwvD19eXYsWMADBkyhPPnz3P+/HlkMhmTJ09m27Zt6PV6goODCQ4O5vfffwdg4MCBlJeXk5+fD8DUqVPZuXMnGo2GgIAAIiMjBUO5X79+1NbWkpOTA0BycjL79u2joaEBX19f4uPj2b9/PwB9+vShsbGRc+fOAQhjhEqlwsvLiz59+rB3714AEhISMBgMnD17FoDx48dz/PhxampqcHd3Z9CgQezatYuCggImTpyIXC7nzJkzAIwZM4bU1FQqKytxcXFhxIgRbN++HYDo6GiUSiWnTp0CYOTIkWRmZlJWVoaTkxPjxo0TjPuIiAg8PT0Fj+qwYcPIy8ujuLgYhULBNddcw5YtWzAajYSGhuLv78/Ro0cBGDx4MMXFxRQWFiKVSklOTmb79u3odDqCgoIIDQ0V5n0OGDCAyspK8vLyhPO9a9cumpqa8Pf3Jzo6WvBsJyUloVKpyM7OBmDy5Mns37+fhoYGfHx8SEhIYN++fQAkJiai0WjIzMwEYOLEifz+++/U1dXh6elJv3792L17N2AaPwDS09MBGDduHCdPnqS6uho3NzeGDBnCzp07AYiNjcXBwYHU1FQARo8eTVpaGhUVFSiVSkaNGiXoPDc3l8DAQFJSUgAYMWIEWVlZlJaW4ujoyIQJE4T/bHh4ON7e3hw/fhyAoUOHUlBQQFFREXK5nEmTJrF161YMBgMhISEEBgZy5MgRAAYNGkRpaSkFBQVIJBKmTJnCjh070Gq1BAYGEh4eLqTa7d+/P9XV1UJYzJQpU9i9ezeNjY34+fkRGxvLgQMHAOjbty8NDQ1kZWUBMGnSJA4ePEh9fT3e3t4kJiYK/9nevXuj0+nIyMgAYMKECRw9epTa2lo8PDwYMGAAv/76KwDx8fFIpVLBsz9mzBhOnz5NVVUVrq6uDBs2jB07dgAQExODk5MTp0+fBkwh82fPnqW8vBylUsno0aPZunUrYIqCcHd35+TJkwAMHz6cnJwcSkpKcHBwYOLEiWzevJmCggJGjhzZI8YIgLi4uC4zRuTk5DB06NBuP0Zs27YNMCVqcXV17RJjRGZmJgMHDuwRYwS0z31Ea9FLF2G8DJ577jkj0Opy+PBhi9/k5+cbpVKp8ZtvvrmcXRmNRqPx/PnzRoVCYVy/fn2LbRobG401NTXCkp+fbwSMNTU1Fu3UarUxNTXVqFarL0uGf//baASj8YEHjEa93vQKpvXtRUREhPHAgQMW6+6++27jc889ZzQajcbPPvvMKJfLjRs2bDBqNBrjrFmzjBEREcZ169YZtVqtccGCBcaHHnrIaDQajXq93vjFF18YVSqVsbq62jh16lTj8uXLhe0+8MADxoULFxpLS0uNAQEBxv3791vst7q62mg0Go3FxcXGO+64wxgeHm4cOHCg8cUXXzTu37/fuH79euOSJUtaPZ6kpCSjXC43SqVS41tvvWW1zYYNG4xDhw61WDdjxgzjBx98YLU9YDx16pQxNDTUqNPpjIcOHTKOGjXK+NlnnxmnTp1q0S4vL88YERFh3LJli9FoNBqnTp1q/Oyzz1qVuStg1o29cqV9rDvwyy+/2FoEESscOXLECBiPHDlia1FErCD2G/tF1I39Iurm8qmpqbFqG1jjsjxG999/PzfeeGOrbSIjIy0+f/bZZ/j4+HDdddddzq4ACAoKIiIiQrBqreHo6NiiN+lq+egjWLYMHngA3nsPJBLTK5jWA9x9d/vsKzk5GZlMJnxWq9U89dRTwuekpCTmzJkDwKxZs8jIyGDBggUAzJ49m08++QQwhZstWbJE+N3DDz/MihUrhM+vv/46/fv3Z8KECdx0002MHDnSQg653PSXOHjwINOnT+edd94hJyeHNWvWsGLFCqKjo3nmmWdaPZaTJ0+iVqv58ssvCQkJsdpGpVJdFBpm9pS0hI+PD/3792fbtm38/PPPLFq0yGo7hULB008/zQsvvEBycnKrsnYlzLoRsT/8/PxsLYKIFTw8PBg3bhweHh62FkXECmK/sV9E3dgvom46lsu60/L19cXX17fN7Y1GI5999hk333wzCoXisoWrqKggPz+foKCgy/7t/7d351FRnecfwL8DssnmioCAIAq47wtGEzURNe4mNhqT4HpcOdhojTG2WIvGBZfU04imFLVqJCoa16pUEEXTELB1QREVEhU4BkUQLcMy7+8PfjNlZBgZZZhX5vs5h4Nz73tnnpmHR324733vq9LVFAHGa45Onz6ttTT5bPWT/z8XFxfNn+3s7LQKw87OTjOFq6ysDIsWLcLBgweRn58PIYRWzho2bIiJEydi5cqV+Mc//lElDvX0qxEjRuCvf/0rZsyYgcaNG2PChAkIDw9HXl4edu7cid/97nd634+dnR1mzJgBNzc3XL9+vcpyuZWnZqkVFhbCwcFB7/NOnjwZf//735GYmIiUlBQcO3ZM57ipU6di1apVmtPk9YE6NySf5xcGITn4+vriyJEjvD5PUqwbeTE38mJujMuoiy+cOXMGmZmZmD59us79AQEBOHjwIICKMwiLFi3CxYsXkZWVhYSEBIwaNQrNmjXTnCmpK0plRUPUuTOwadP/miI1haJie+fOFeOMtVrdy9i9ezfOnTuHixcvorCwEPv374cQQrM/IyMDW7ZswYQJE7Bw4cIqx6vP2OzatQsZGRmYMmUKunTpglWrVqFp06YYNGgQPDw8ahSLEAJFRUXIycmpsq99+/ZIT09HaWmpZtvly5fRoUMHvc85ZswYHD58GB07dtT7WxMrKyt8/vnnOq8/e13pO5tGpqWe501yKS0txYkTJ7T+niF5sG7kxdzIi7kxLqPOzYmKikK/fv3Qrl07nfvT09M1F+ZbWlriypUr2LlzJx4/fgw3NzcMGjQIMTExcHR0NGaYVdjYAJs3V5wRWrBA+4wRAAhRsf3yZSAysmK8LJ48eQIbGxs0atQIeXl5iIiI0OxTqVQIDg7GF198gdmzZ6NLly747rvvNFPyKvv444+1pvbNmTPnha/9n//8BwUFBejbty9KS0vxpz/9CY0aNdK5cpy/vz/8/f2xevVqLF68GFFRUbC0tKyy1PbzGjZsiNOnT9fozKX6rFFRUdELp4ASUf1z5coVTJw4ESkpKejevbupwyEiIskZtTHas2eP3v2Vz2TY2dlpVqCQgXp6nHpGm7o5EgIIDa1onCIja+8ao9ryySef4NixY3BxcYGnpydmzJihuUYrIiIClpaWCA0NhYWFBaKjozF+/HgMHDhQM1XPzs4OALSaopoqLS1FaGgobt26BWtra/Tq1QvHjx/XTKNUTw+MjIwEUPHzERwcjFWrViEgIACxsbE1uo6mT58+NYrH2toan3/+eY2auteBOjckn44dO5o6BKLXDutGXsyNvJgb41KIyt1JPaBeblC9VKZacXExMjMz4ePjY9C1GpWvNdq0qeJMkaxNUW0oLi7mtSySkj03L1tj9UFGRobOM6NkWqmpqejRowfPGEmKdSMv5kZezI3hqusNdKmTG7y+zmbNqmiCNm8GunWr300RAM29dEg+zI281PeOIKKaY93Ii7mRF3NjXFz/twbUTVBISP1uioiIiIiIzBWn0hlAqZRroQVjEEJA8fwyfCQF2XNjzlPpysrKeJ8pCZWXl6OgoADOzs4vdd0kGRfrRl7MjbyYG8NxKp2R1PemCOCS0DJjbuT1ww8/mDoE0sHS0hJpaWlsiiTFupEXcyMv5sa42BiRFpVKZeoQqBrMjbzUN1gmuWRkZCA0NFSzMifJhXUjL+ZGXsyNcbExIi08PSsv5kZeTZo0MXUIpMOTJ0+QmpqKJ0+emDoU0oF1Iy/mRl7MjXGxMSIt5nZtyOuEuZFX+/btTR0C0WuHdSMv5kZezI1xsTEiLbyORV7MjbzOnz9v6hCIXjusG3kxN/JiboyLjREREREREZk9NkYGMNb9Nb29veHk5IT//ve/mm2FhYWws7NDQECAcV60GsaYrrV9+3Z07doVjo6OaN26NSIjI6sdm5CQAAsLCzg4OGi+zp07V+3zKhQKhIeHa21funQpFAoF9u7dqzVu69atmjG5ublSL32tC6fSyatdu3amDoF08PT0xIoVK+Dp6WnqUEgH1o28mBt5MTfGxcaohrZuBRwdK74bg6urKw4fPqx5HBsbW2/+MVcqlYiMjER+fj6OHDmCsLAwJCYmVjvez88PRUVFmq8BAwZUO7ZNmzbYs2eP5rEQAjExMfD19dUa17hxY6xatQqlpaWv/oaInlNWVmbqEEiH5s2bY/LkyWjevLmpQyEdWDfyYm7kxdwYFxujGti6FZg9G2jXruK7MZqjSZMmYffu3ZrHu3fvxocffqg1RqFQYMuWLfDy8kKzZs0QExODo0ePonXr1nBxcUFMTIxm7DfffIO2bdvC0dERnTt3RkJCAoCKm3C2b98e3377LQDg8ePH8PDwwJkzZzT7a6qm9waeNWsW+vbtiwYNGqBDhw545513kJycXOPX0cfX1xeOjo5ITU0FAFy4cAGenp7w8PDQGte7d294enoiOjpa5/N4e3tj/fr18PPzg5OTEzZt2oQff/wR7du3R5MmTbBx48ZaifdVGJIbqltcDlpOjx49QmRkJB49emTqUEgH1o28mBt5MTfGxcboBdRNUUgIcOlSxXdjNEdDhgxBamoqHj16hNzcXGRkZODNN9+sMi4pKQk3b97Eli1bMHfuXBw4cABXr15FVFQU5s+fj/LycgCAu7s7/vnPf6KgoAAhISGYOHEilEolbG1tsWPHDixYsAA5OTkIDQ3F6NGjMXjwYJ1xbdmyBV27doWXlxemT5+Oo0ePIjExEfPmzcNPP/1k8PssLy/Hjz/+iA4dOlQ7JisrCy4uLmjbti1WrFiheU/VmTx5suas0Z49ezB58mSd48LCwvSeNTp+/DiSk5MRFxeHzz77DOvWrUNSUhLi4+OxdOlS/PrrrzV8l0Qkg6ysLKxbtw5ZWVmmDoWIiF4DbIz0qNwUffUVYGFR8d0YzVGDBg0wduxY7Nu3D3v37sWECRNgYVE1PYsXL4atrS3Gjx+Px48fY+7cuWjYsCFGjRqFJ0+eIDs7GwAwYsQIeHl5wcLCAjNnzoRCodD8lqFXr16YPn063nnnHZw7dw5r167VPL+jo6Pmz0qlEllZWTh69ChSUlIQGBiIbdu2ISIiAgMGDECvXr0Mfp/Lli1Dy5YtMXToUJ37AwIC8O9//xu5ubn4/vvv8d133+HPf/6z3uf84IMPsG/fPpSUlOD777/H+++/r3PckCFD0LJlS2zfvl3n/tDQUDg7O6N3795wdXXFb37zGzRu3BhdunSBl5cXbty4YdB7rW2Vc0NyGThwoKlDIHrtsG7kxdzIi7kxLjZG1Xi+KVJfp69QGK85Up/50HfWw8XFBQBgaWkJKysrrbnztra2mjsiHzp0CN27d0ejRo3QqFEjPHjwAA8fPtSMnTZtGtLS0jBt2jQ4ODhotj979kzzZxsbG4wbNw7h4eGYN28eVCoVduzYgf3790OlUuHatWtV4jt37pxm0YThw4dr7YuMjERsbCz2799f7cIHrq6uCAgIgIWFBdq3b49ly5bh4MGDej+3Fi1aICAgAEuXLkXPnj3RuHHjasfqO2uk/mwBwM7OTuuztbOzM/ndpivnhuSinspJRDXHupEXcyMv5sa42BjpoFRWND6dOwObNv2vKVJTKCq2d+5cMa62VqsLDAzE/fv3UVRUhK5du7708yiVSkyaNAmrV6/Gw4cP8fjxY7i4uGiuCRJCYM6cOZg8eTK++uor3L9/X3Ns5WlrSqUSS5cuxcCBAzFp0iT861//Qrt27dCqVSskJSXBy8urymsPGDBAs2jCiRMnNNtjYmKwcuVKnDx5Es2aNavxe9F11kyXDz/8EBs2bKhyXdbzgoKC4Obmhh07dtQ4Blm8aEohmU5hYaGpQyB67bBu5MXcyIu5Ma4Gpg5ARjY2wObNFWeEFizQPmMEAEJUbL98GYiMrBhfW2JjY2vcDFRHqVSipKREc8bjq6++0ro+Rr1C3IkTJ7B8+XLMnDkTx48fB1BxJkrN2toacXFxmnjGjRv3UvGcOnUKISEhiIuLg7e3t96xCQkJ8PX1haenJzIyMhAeHo6PPvroha8xYcIEtGjRokanmMPCwl7YQMmocm5ILs7OzqYOgXSwt7dHx44dYW9vb+pQSAfWjbyYG3kxN8bFM0bVmDWrounZvBkIDa1ohoCK76GhFdsjIyvG1abOnTujY8eOr/QcTk5OWLduHYYMGQJXV1c8fPgQbdq0AQBkZmZi2bJl2L59Oxo0aIA//OEPuHfvHv72t78BABo2bKh5HoVC8cpNGgB8+eWXyM/PR79+/TTT7GbPnq3ZX/leRSkpKejbty/s7e0RFBSEsWPH4tNPP33hazRs2BDDhg2r0b1+hg4dCj8/v5d/QyZSOTckl1c5w0vG4+/vj+TkZPj7+5s6FNKBdSMv5kZezI1xKURN11x+TRQWFsLZ2RkFBQVwcnLSbC8uLkZmZiZ8fHwMulFm5WuNNm2qOFNkrKZIBgUFBfxthKRkz83L1lh9cPLkyWoXFCHTYm7kxdzIi7mRF3NjuOp6A104le4F1M3P7NnA2bP/mz5XH5siIqL6JDU1FcOGDUNKSgq6d+9u6nCIiEhybIxqQN0EhYTU/6bI3H7T/zphbuT1Ok7NJDI11o28mBt5MTfGxcaohmbNAqZMqd2FFoiofqiNa/GIzA3rRl7MjbyYG+Pip2sAc2iKiouLTR0CVYO5kZepb/5L9Dpi3ciLuZEXc2NcZtcYqVQqU4dAVC/Vs3VciIiIyMyYzap0KpUKGRkZsLS0RPPmzWFtbQ3F83duJZSXl/N+OZKSOTdCCPz666949uwZ2rZtK22cxvL06VPeK0dCxcXFuHnzJvz8/HiNnoRYN/JibuTF3BiOq9LpYGFhAR8fH+Tk5CA7O9vU4UhLqVTCxhzmDL6GZM+NQqGAh4eH2TVFAHDt2jX07t3b1GHQc2xtbVFcXMymSFKsG3kxN/JibozLbBojALC2toaXlxfKyspQXl5u6nCkdP78efTv39/UYZAOsufGysrKLJsiAMjPzzd1CKRDZmYmlixZgqioKPj4+Jg6HHoO60ZezI28mBvjMqvGCKj4rbaVlRWsrKxMHYqU7Ozs+NtVSTE38nJwcDB1CKRDfn4+4uPjkZ+fz8ZIQqwbeTE38mJujMvsFl8g/Xh6Vl7MjbyYGyLDsW7kxdzIi7kxLjZGpOXMmTOmDoGqwdzIi7khMhzrRl7MjbyYG+Oqd1Pp1IvsFRYWmjiS19PTp0/52UmKuZEXcyOnoqIizXfmRz6sG3kxN/Jibgyn/rxqshB3vVuu+969e/D09DR1GEREREREJIm7d+/Cw8ND75h61xipVCpkZ2fD0dGR9ykyUGFhITw9PXH37t0XrvNOdYu5kRdzIy/mRl7MjbyYG3kxNy9HCIEnT57A3d0dFhb6ryKqd1PpLCwsXtgNkn5OTk4sOEkxN/JibuTF3MiLuZEXcyMv5sZwzs7ONRrHxReIiIiIiMjssTEiIiIiIiKzx8aINGxsbBAWFgYbGxtTh0LPYW7kxdzIi7mRF3MjL+ZGXsyN8dW7xReIiIiIiIgMxTNGRERERERk9tgYERERERGR2WNjREREREREZo+NERERERERmT02RmYsISEBCoVC51dycnK1x02ZMqXK+L59+9Zh5ObB29u7yue8ZMkSvccIIbB8+XK4u7vDzs4OAwcOxLVr1+ooYvOQlZWF6dOnw8fHB3Z2dvD19UVYWBhKSkr0Hse6MY6vv/4aPj4+sLW1RY8ePXDu3Dm948+ePYsePXrA1tYWrVu3RmRkZB1Faj6+/PJL9OrVC46OjnBxccHYsWORnp6u95jq/j26ceNGHUVtHpYvX17lM3Z1ddV7DGumbuj6N1+hUGDevHk6x7NmjKOBqQMg0+nXrx9ycnK0tv3+979HXFwcevbsqffYYcOGITo6WvPY2traKDGauxUrVmDmzJmaxw4ODnrHr127Fhs2bMD27dvh5+eH8PBwDBkyBOnp6XB0dDR2uGbhxo0bUKlU2Lp1K9q0aYOrV69i5syZePr0KSIiIvQey7qpXTExMViwYAG+/vprvPHGG9i6dSuGDx+OtLQ0eHl5VRmfmZmJd999FzNnzsSuXbuQlJSEuXPnonnz5njvvfdM8A7qp7Nnz2LevHno1asXysrK8MUXXyAoKAhpaWmwt7fXe2x6ejqcnJw0j5s3b27scM1Ohw4dEBcXp3lsaWlZ7VjWTN1JTk5GeXm55vHVq1cxZMgQTJgwQe9xrJlaJoj+X0lJiXBxcRErVqzQOy44OFiMGTOmboIyY61atRIbN26s8XiVSiVcXV3F6tWrNduKi4uFs7OziIyMNEKEpLZ27Vrh4+Ojdwzrpvb17t1bzJ49W2tbQECAWLJkic7xixcvFgEBAVrbZs2aJfr27Wu0GEmIBw8eCADi7Nmz1Y6Jj48XAER+fn7dBWaGwsLCRJcuXWo8njVjOqGhocLX11eoVCqd+1kzxsGpdKRx+PBh5OXlYcqUKS8cm5CQABcXF/j5+WHmzJl48OCB8QM0Q2vWrEHTpk3RtWtXrFy5Uu90rczMTOTm5iIoKEizzcbGBm+99RYuXLhQF+GarYKCAjRp0uSF41g3taekpAQpKSlaP+8AEBQUVO3P+8WLF6uMHzp0KH766SeUlpYaLVZzV1BQAAA1qpFu3brBzc0Nb7/9NuLj440dmlnKyMiAu7s7fHx8MHHiRNy5c6fasawZ0ygpKcGuXbswbdo0KBQKvWNZM7WLjRFpREVFYejQofD09NQ7bvjw4di9ezfOnDmD9evXIzk5GYMHD4ZSqayjSM1DaGgo9u7di/j4eMyfPx+bNm3C3Llzqx2fm5sLAGjRooXW9hYtWmj2Ue27ffs2Nm/ejNmzZ+sdx7qpXXl5eSgvLzfo5z03N1fn+LKyMuTl5RktVnMmhMCnn36K/v37o2PHjtWOc3Nzw7Zt23DgwAHExsbC398fb7/9NhITE+sw2vqvT58+2LlzJ06ePIlvvvkGubm56NevHx4+fKhzPGvGNA4dOoTHjx/r/UU1a8ZITH3KimpfWFiYAKD3Kzk5WeuYu3fvCgsLC7F//36DXy87O1tYWVmJAwcO1NZbqLdeJjdq+/fvFwBEXl6ezv1JSUkCgMjOztbaPmPGDDF06NBafy/1zcvk5v79+6JNmzZi+vTpBr8e6+bV3L9/XwAQFy5c0NoeHh4u/P39dR7Ttm1bsWrVKq1t58+fFwBETk6O0WI1Z3PnzhWtWrUSd+/eNfjYkSNHilGjRhkhKlIrKioSLVq0EOvXr9e5nzVjGkFBQWLkyJEGH8eaeXVcfKEemj9/PiZOnKh3jLe3t9bj6OhoNG3aFKNHjzb49dzc3NCqVStkZGQYfKy5eZncqKlXMLt16xaaNm1aZb96ZaHc3Fy4ublptj948KDKb/yoKkNzk52djUGDBiEwMBDbtm0z+PVYN6+mWbNmsLS0rHJ2SN/Pu6urq87xDRo00FlT9GpCQkJw+PBhJCYmwsPDw+Dj+/bti127dhkhMlKzt7dHp06dqv17iDVT937++WfExcUhNjbW4GNZM6+OjVE91KxZMzRr1qzG44UQiI6OxieffAIrKyuDX+/hw4e4e/eu1n/GSTdDc1PZpUuXAKDaz9nHxweurq44ffo0unXrBqBinvLZs2exZs2alwvYjBiSm/v372PQoEHo0aMHoqOjYWFh+Kxk1s2rsba2Ro8ePXD69GmMGzdOs/306dMYM2aMzmMCAwNx5MgRrW2nTp1Cz549X+rvPtJNCIGQkBAcPHgQCQkJ8PHxeannuXTpEuvDyJRKJa5fv44BAwbo3M+aqXvR0dFwcXHBiBEjDD6WNVMLTH3KikwvLi5OABBpaWk69/v7+4vY2FghhBBPnjwRCxcuFBcuXBCZmZkiPj5eBAYGipYtW4rCwsK6DLteu3DhgtiwYYO4dOmSuHPnjoiJiRHu7u5i9OjRWuMq50YIIVavXi2cnZ1FbGysuHLlipg0aZJwc3NjbmqRevrc4MGDxb1790ROTo7mqzLWjfHt3btXWFlZiaioKJGWliYWLFgg7O3tRVZWlhBCiCVLloiPP/5YM/7OnTuiYcOG4re//a1IS0sTUVFRwsrK6qWmEFP15syZI5ydnUVCQoJWfTx79kwz5vncbNy4URw8eFDcvHlTXL16VSxZskQA4FTTWrZw4UKRkJAg7ty5I3744QcxcuRI4ejoyJqRRHl5ufDy8hKfffZZlX2smbrBxojEpEmTRL9+/ardD0BER0cLIYR49uyZCAoKEs2bNxdWVlbCy8tLBAcHi19++aWOojUPKSkpok+fPsLZ2VnY2toKf39/ERYWJp4+fao1rnJuhKhYsjssLEy4uroKGxsb8eabb4orV67UcfT1W3R0dLXXIFXGuqkbf/nLX0SrVq2EtbW16N69u9aS0MHBweKtt97SGp+QkCC6desmrK2thbe3t9iyZUsdR1z/VVcflf+uej43a9asEb6+vsLW1lY0btxY9O/fXxw7dqzug6/nPvjgA+Hm5iasrKyEu7u7GD9+vLh27ZpmP2vGtE6ePCkAiPT09Cr7WDN1QyGEEHV8koqIiIiIiEgqXK6biIiIiIjMHhsjIiIiIiIye2yMiIiIiIjI7LExIiIiIiIis8fGiIiIiIiIzB4bIyIiIiIiMntsjIiIiIiIyOyxMSIiIiIiIpNJTEzEqFGj4O7uDoVCgUOHDhn8HEIIREREwM/PDzY2NvD09MSqVasMeo4GBr8qERERERFRLXn69Cm6dOmCqVOn4r333nup5wgNDcWpU6cQERGBTp06oaCgAHl5eQY9h0IIIV7q1YmIiIiIiGqRQqHAwYMHMXbsWM22kpISLFu2DLt378bjx4/RsWNHrFmzBgMHDgQAXL9+HZ07d8bVq1fh7+//0q/NqXRERERERCStqVOnIikpCXv37sXly5cxYcIEDBs2DBkZGQCAI0eOoHXr1jh69Ch8fHzg7e2NGTNm4NGjRwa9DhsjIiIiIiKS0u3bt/Htt99i3759GDBgAHx9fbFo0SL0798f0dHRAIA7d+7g559/xr59+7Bz505s374dKSkpeP/99w16LV5jREREREREUkpNTYUQAn5+flrblUolmjZtCgBQqVRQKpXYuXOnZlxUVBR69OiB9PT0Gk+vY2NERERERERSUqlUsLS0REpKCiwtLbX2OTg4AADc3NzQoEEDreapXbt2AIBffvmFjREREREREb3eunXrhvLycjx48AADBgzQOeaNN95AWVkZbt++DV9fXwDAzZs3AQCtWrWq8WtxVToiIiIiIjKZoqIi3Lp1C0BFI7RhwwYMGjQITZo0gZeXFz766CMkJSVh/fr16NatG/Ly8nDmzBl06tQJ7777LlQqFXr16gUHBwds2rQJKpUK8+bNg5OTE06dOlXjONgYERERERGRySQkJGDQoEFVtgcHB2P79u0oLS1FeHg4du7cifv376Np06YIDAzEH//4R3Tq1AkAkJ2djZCQEJw6dQr29vYYPnw41q9fjyZNmtQ4DjZGRERERERk9rhcNxERERERmT02RkREREREZPbYGBERERERkdljY0RERERERGaPjREREREREZk9NkZERERERGT22BgREREREZHZY2NERERERERmj40RERERERGZPTZGRERERERk9tgYERERERGR2WNjREREREREZu//ACWY0T0fa2dxAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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l8lWWyFc5ZWVlLF++nNLSUpYtW+br5gQk0X+VJfJVlshXWf6Sr1iTo6A9e/b4ugkBTeSrLJGvskS+gpqJ/qsska+yRL7KUmO+osgRBEEQBEEQBCGgiCJnljIzM33dhIAm8lWWyFdZIl/lxMfH8/jjjxMfH+/rpgQs0X+VJfJVlshXWWrMV+/rBqiNP+wRHshEvsoS+SpL5Kuc5ORkvvnNb5KUlOTrpgQs0X+VJfJVlshXWWrMV4zkzFJVVZWvmxDQRL7KEvkqS+SrnLGxMV555RXGxsZ83ZSAJfqvskS+yhL5KkuN+YoiRxAEQfB79fX1fPWrX6W+vt7XTREEQRBUQBQ5s7RmzRpfNyGgiXyVJfJVlshXUDPRf5Ul8lWWyFdZasxXFDmz1NjY6OsmBDSRr7JEvsoS+QpqJvqvskS+yhL5KkuN+YoiZ5Z6e3t93YSAJvJVlshXWSJfQc1E/1WWyFdZIl9lqTFfUeTMUlBQkK+bENBEvsoS+SpL5Kscg8FATEwMBoPB100JWKL/KkvkqyyRr7LUmK9GkiTJ1424ktHRUcLDwxkZGSEsLMzXzREEQRAEQRAEwUdmUxuIkZxZ2rlzp6+bENBEvsoS+SpL5Ksska+yRL7KEvkqS+SrLDXmK4ocQRAEwe9VVVXx0EMPqfKsBkEQBGH+iSJnltLS0nzdhIAm8lWWyFdZIl/lOBwO+vv7cTgcvm5KwBL9V1kiX2WJfJWlxnxFkTNLUVFRvm5CQBP5KkvkqyyRr6Bmov8qS+SrLJGvstSYryhyZqmiosLXTQhoIl9liXyVJfIV1Ez0X2WJfJUl8lWWGvMVRY4gCIIgCIIgCAFFbCE9S4ODg6ocslMLka+yRL7KEvkqZ2xsjP3797N582YsFouvmxOQRP9VlshXWSJfZflLvmILaQW1t7f7ugkBTeSrLJGvskS+yrFYLGRkZIgCR0Gi/ypL5Ksska+y1JivKHJmqaury9dNCGgiX2WJfJUl8lVOR0cH3/3ud+no6PB1UwKW6L/KEvkqS+SrLDXmq3iR09HRwUMPPUR0dDQmk4klS5ZQWlqq9NMqRq/X+7oJAU3kqyyRr7JEvsrp6enhD3/4Az09Pb5uSsAS/VdZIl9liXyVpcZ8FV2TMzQ0xNKlS9m8eTOf/exniYuL48KFC2RkZJCdnX3Nv++Pa3IEQRCE+VdWVsby5cspLS1l2bJlvm6OIAiC4AN+sybn+9//PqmpqfzqV79i1apVZGRksHXr1usqcPzV7t27fd2EgCbyVZbIV1kiX0HNRP9VlshXWSJfZakxX0WLnDfffJMVK1Zw3333ERcXx9KlS/n5z39+xcfbbDZGR0en3fyN2+32dRMCmshXWSJfZYl8BTUT/VdZIl9liXyVpcZ8FZ1g19jYyE9+8hO+9KUv8S//8i+cPHmSf/zHfyQoKIhPf/rTMx7/7LPP8swzz8z4+p49ezCbzWzZsoWTJ08yPj5OZGQkhYWFHDlyBID8/Hzcbjd1dXUAbNy4kYqKCnk4a9myZRw4cACA3Nxc9Ho9NTU1ANx6662cO3eOwcFBzGYza9asYe/evQBkZWVhMpk4e/YsAJGRkZSVldHX10dwcDAbNmxg165dAKSnpxMREcGZM2cAWLVqFa2trXR3d2MwGNiyZQu7du1CkiRSUlKIi4ujrKwMgOXLl9Pd3U1HRwdarZbbbruNvXv34nQ6SUxMJCUlhVOnTgGwZMkSBgcHaW1tBWDHjh0cOHAAm81GXFwcWVlZHD9+HICioiLGx8dpamoCYNu2bZSUlDAxMUF0dDT5+fkcPXoUgIKCAux2Ow0NDQBs3ryZ06dPMzY2RkREBIsXL+bQoUMALFiwAIDz588DsGHDBiorKxkeHsZisbBixQr2798PQE5ODkajkXPnzgGwbt06amtrGRgYwGQysXbtWvbs2QOAVquls7OTqqoqANasWUNjYyO9vb0EBQWxadMmdu7cCUBaWhpRUVHyAVUrV66kvb2drq4u9Ho9W7duZffu3bjdbpKTk0lISJDXgy1btoze3l7a29vRaDRs376dffv24XA4SEhIIC0tjZMnTwJQXFzM8PAwLS0tAGzfvp1Dhw4xNTVFbGwsOTk5HDt2DIBFixYxMTFBY2MjAFu3buX48eNYrVaioqIoKCiQ++zChQtxOp3U19cDsGnTJsrKyuSh2CVLlnDw4EEA8vLy0Gq11NbWyn22urqaoaEhQkNDWbVqFfv27QMgOzub4OBgqqurAVi7di11dXX09/czNjaG2+2W35HJyMggLCyMyspKAFavXk1zczM9PT0YjUY2b94s552amkpMTAzl5eUArFixgs7OTjo7O9HpdGzbto09e/bgcrlISkoiKSmJ06dPA7B06VL6+/tpa2uT++z+/fux2+3Ex8eTkZHBiRMnAFi8eDGjo6M0NzcDcNttt3H06FEmJiaIiYkhLy+PkpISAAoLC5mamuLChQsAPr9G9Pf3Mzo6SkNDg7hGzPE14uzZs2zcuJGRkRFxjVDwGnH48GHWrVsnrhHM/TWiv7+fnTt3csstt4hrBHN/jfDmm5mZSWhoqLhGzPE1wm63s3PnTkwmk0+vEd72Xw9F1+QYjUZWrFghX2wA/vEf/5FTp07JP8yL2Ww2bDab/OfR0VFSU1P9ak1Of38/MTExvm5GwBL5KkvkqyyRr7JEvsoS+SpL5Ksska+y/CVfv1mTk5iYSEFBwbSvLVy4UH7n4FJBQUGEhYVNu/kbNe8MpwYiX2WJfJUl8lXO5OQkf/rTn5icnPR1UwKW6L/KEvkqS+SrLDXmq2iRs27dOnkY0quuro709HQln1YQBEEIMDU1NTz55JPy9CBBEARBuBpFi5wvfvGLHD9+nO9973s0NDTw29/+lhdffJGnnnpKyadVlNi6VFkiX2WJfJUl8hXUTPRfZYl8lSXyVZYa81W0yFm5ciWvv/46v/vd71i0aBHf/va3ef7553nwwQeVfFpF9fb2+roJAU3kqyyRr7JEvoKaif6rLJGvskS+ylJjvooWOQB33303VVVVTE1NUVNTwz/8wz8o/ZSKam9v93UTAprIV1kiX2WJfAU1E/1XWSJfZYl8laXGfBUvcgKNRqPxdRMCmshXWSJfZYl8laPRaDAYDCJjBYlslSXyVZbIV1lqzFfRLaRv1Gy2iRMEQRAEQRAEIXD5zRbSgch7UJKgDJGvskS+yhL5KkvkqyyRr7JEvsoS+SpLjfmKImeWHA6Hr5sQ0ES+yhL5Kkvkq5yamhoef/xxsYW0gkT/VZbIV1kiX2WpMV9R5MxSQkKCr5sQ0ES+yhL5Kkvkq5zJyUkuXLggDgNVkOi/yhL5Kkvkqyw15iuKnFlKS0vzdRMCmshXWSJfZYl8BTUT/VdZIl9liXyVpcZ8RZEzSydPnvR1EwKayFdZIl9liXwFNRP9V1kiX2WJfJWlxnxFkSMIgiAIgiAIQkARRc4sFRcX+7oJAU3kqyyRr7JEvsrJzMzkxRdfJDMz09dNCVii/ypL5Ksska+y1Jiv3tcNUJvh4WFVLr5SC5Hv5UmShN1uZ2pqCpvNNu3j1NQUdrsdp9N5xZvL5cLpdNLe3k5iYiLe47Gu9BFAp9Oh1WrR6XRX/dxgMGAwGDAajRiNRvnzSz8ajUaCg4PR6/WqPFTseoj+q5zIyEjWr19PZGSkr5sSsET/VZbIV1kiX2WpMV9R5MxSS0sL+fn5vm5GwLqZ8nU6nYyNjTE+Ps74+DhWq3XaR+/nExMT2Gw25uLc3oaGBux2+xy0/oPT6XSEhIQQHBxMcHDwZT8PCQnBZDJhNpvlmxpOu7+Z+u986+np4bnnnuPb3/428fHxvm5OQBL9V1kiX2WJfJWlxnxFkSMICnG5XIyMjDA8PMzw8DBDQ0Py58PDw4yPj8+6cNHpdAQFBREcHDztY1BQEAaDAb1eL990Ot20P+v1ekpKSli/fj2AXDBc+vHi9rvdblwu17TPL/6ay+XC4XDgcDiw2+3yx4s/v/hrkiThcrnkIm42DAbDtKLn0ltYWBgWi4WwsDAMBsOs/m3B/3V0dPDzn/+cJ598UhQ5giAIwjVppLl4e1gho6OjhIeHMzIyQlhYmK+bA3im8/j7u8lqpsZ8HQ4HAwMD9PX1TbsNDg7idruv+nf1ej2hoaHyzWw2z/hoNpvlguZGp3r5Ml/vlLvJyUl5mt3VPp+YmJBHtZxO56yeKzg4eFrRY7FYpn0eHh6OyWSa8yzU2H/VoqysjOXLl1NaWsqyZct83ZyAJPqvskS+yhL5Kstf8p1NbSBGcmbp0KFDbNy40dfNCFj+nq/VaqWrq4vOzk46Ozvp7e1laGjoiiMyBoOBiIiIK96UeKF9Nb7MV6PRyKNOsyFJEg6HA6vVesXb+Pg4o6OjjI2NyWuXpqam6O3tveK/e+nPJjw8fNqfzWbzrH82/t5/BeFqRP9VlshXWSJfZakxX1HkzNLU1JSvmxDQ/Clfl8tFd3c3bW1ttLa20tHRwcjIyGUfGxISQmxs7IybxWLxi3c+vPwp3+ul0WjkjQuutehckiRsNhtjY2Ny0XO5j+Pj4zgcDnnU7XL0er1c/ERFRU27RUZGotfPvHyqMV9B8BL9V1kiX2WJfJWlxnxFkTNLsbGxvm5CQPNlvi6Xi87OThobG2lubqa9vR2HwzHjcdHR0SQlJZGUlERCQgKxsbEf6F1/Xwj0/qvRaORNDK72vTqdTnm91MXrpry3sbExnE4n/f399Pf3c+HChRnPExYWNqP40el0OBwOsSZIAeHh4WzYsIHw8HBfNyVgBfr1wddEvsoS+SpLjfmKNTmzNDo66jdtCUTzne/w8DB1dXVcuHCB5uZmbDbbtPtDQkJITU0lLS2NlJQUEhMTZz3dyp+I/nt9XC4Xo6OjctEzODg47XZpP/Gy2WwEBQURHh5OTEwMMTExxMbGyp+rpRj2V6L/KkvkqyyRr7JEvsryl3zFmhwFHTt2jB07dvi6GQFL6XwlSaKrq4va2lrOnz9PT0/PtPtDQkLIysoiMzOT9PR0YmJiAupFqei/10en0xEZGXnZ6XGSJDExMTGj8BkYGOD48eOkpaUxMjLCyMjIjBGgkJAQueC5uACKiIhAqxVnM1+Nw+Hg3Xff5d577xUjZQoR1wdliXyVJfJVlhrzFUWOcFPo6emhqqqKs2fPMjw8LH9do9GQnp5OTk4OWVlZJCYmBlRRI8w9jUYj73qXmpo67b60tDQ2bNggT3Pr7++nr6+P/v5+hoeHmZycpK2tjba2tml/T6/XExsbS3x8PHFxccTFxREfH09oaKjoj++rqqriE5/4hNhdTRAEQbguosiZpUWLFvm6CQFtLvO1Wq1UVFRw5syZabtsGY1GcnJyWLBgAbm5uZhMpjl7Tn8n+q+yioqKMJlMpKWlkZaWNu0+71bjlxY/AwMDOJ1Ourq66OrqmvZ3QkJC5MLn4gJIzVMmBf8lrg/KEvkqS+SrLDXmK4qcWZqYmPB1EwLajeYrSRJNTU2UlpZSW1uLy+UCPNOP8vLyWLRoEXl5eTftdBfRf5V1tXwNBgMJCQkkJCRM+7rb7WZoaIje3l56enro7e2lt7eXgYEBJicnaW5uprm5edrfiYiIkP+txMREEhISCAsLE6M+wg0R1wdliXyVJfJVlhrzFUXOLDU2NpKbm+vrZgSsD5qv0+mksrKSkpIS+vv75a+npKSwbNkyCgoKCA4OnsumqpLov8r6IPlqtVqio6OJjo5m4cKF8tcdDgf9/f3TCp+enh7GxsbkDRFqa2vlx5tMJrno8RY+0dHRovARrpu4PihL5Ksska+y1JivKHIEVbPZbJw4cYKTJ08yPj4OQFBQEIsXL2b58uUz3jUXBLUwGAxywXKxyclJenp66O7upquri+7ubvr6+piYmKCxsZHGxkb5sUajkfj4eLn4SU5OJjY2VmxyIAiCIAQ8sYX0LDmdzsseAijMjevN1263c+rUKY4cOcLk5CTgOUdjzZo1LFu2TKxZuALRf5Xlq3ydTie9vb1y0dPV1UVPT89lz3nyFk9JSUkkJyeTnJxMZGSk34/4uFwuRkZGCA8PR6fT+bo5AUlcH5Ql8lWWyFdZ/pKv2EJaQcePH+fWW2/1dTMC1rXydbvdlJeXs3//fnnkJiYmhg0bNlBYWChe/FyD6L/K8lW+er1ePqDWy+12MzAwIBc9XV1ddHZ2YrPZaG1tpbW1VX5sSEiIXPR4P1oslnn/Pq5Gp9Nx7tw50X8VJK4PyhL5Kkvkqyw15iuKnFmyWq2+bkJAu1q+bW1tvPPOO/IOVJGRkWzatImioiIx/eY6if6rLH/KV6vVEhsbS2xsLEVFRYBnY47+/n46Ozvp6Oigs7OT7u5uJicnuXDhwrRzfSwWC8nJyaSmppKamkpiYqJPN+yor6/nC1/4Aq+++qrq5oWrhT/130Ak8lWWyFdZasxXFDmzFBUV5esmBLTL5Wuz2di5cydlZWWAZ83N5s2bWblypRi5mSXRf5Xl7/lqNBq58CkuLgY808B6enqmFT69vb2MjY1RW1srb26g1WpJTEwkJSVFLnzmc0e3sbExysrKGBsbm5fnuxn5e/9VO5GvskS+ylJjvmJNzixZrVbMZrOvmxGwLs23sbGRN954g5GREQCWLl3Ktm3bxM/gAxL9V1mBkq/dbqerq4uOjg758FLv9NCLWSwWUlNT5cInMTFRsTnbZWVlLF++XBwGqqBA6b/+SuSrLJGvsvwlX7EmR0FHjhxhx44dvm5GwPLm63a72bNnDyUlJYBnato999xDRkaGbxuocqL/KitQ8jUajaSnp5Oeng54prmNjIzIBU97ezvd3d2MjY1x7tw5zp07B3jWzSQlJZGWlkZ6ejppaWli63YVCZT+669EvsoS+SpLjfmKIkfwO+Pj4/zxj3+UD0BcsWIF27dvx2g0+rZhgnCT0mg0REREEBERIa/vcTgcdHZ2Tit8rFar/OejR4+i0WiIi4uTC6a0tDS/29BAEARBCEyiyJmliw/rE+ZeTEwML774IqOjoxiNRj7ykY9QUFDg62YFDNF/lXUz5WswGGaM9gwNDdHa2kpLSwutra0MDAzQ09NDT08PJ0+eBDzzur0jPenp6de9fXVqairf+ta3SE1NVfT7upndTP3XF0S+yhL5KkuN+YoiZ5acTqevmxCwWlpa+P3vf4/JZCImJoYHHniA2NhYXzcroIj+q6ybOV+NRkNUVBRRUVEsWbIE8IzKeguelpYWenp6GBwcZHBwkIqKCgBCQ0PJyMggIyODzMxMoqKiLlv0xMbG8uCDD4prgoJu5v47H0S+yhL5KkuN+YoiZ5bq6+vJysrydTMCTn19Pb///e/p7Oxky5YtfPKTnyQkJMTXzQo4ov8qS+Q7XWhoKIWFhRQWFgIwNTVFW1ubXPh0dHQwPj7O2bNnOXv2LABhYWFywZORkUFkZCQAg4OD/PSnP+VrX/uaKnf5UQPRf5Ul8lWWyFdZasx33oqcZ599ln/5l3/hC1/4As8///x8Pa2gAs3Nzfz+97/H6XSSkpLCpz71KZ+exyEIgjKCg4PJzc2Vz7lxOBx0dHTQ3NxMU1MT7e3tjI6OUllZSWVlJQARERFkZmYyOTnJD37wAz7xiU+IIkcQBEG4pnnZQvrUqVPcf//9hIWFsXnz5usucvxxC2mbzUZQUJCvmxEwurq6+PWvf43NZmPBggXcc889mEwmXzcrYIn+qyyR741xOBy0tbXJRU9HRwdutxvwXCtefPFFvvzlL7NhwwaysrLIzMwU14s5JPqvskS+yhL5Kstf8p1NbaD4MfHj4+M8+OCD/PznP5enHaiZ90BK4cZZrVZ+97vfYbPZyMzM5L777uPMmTO+blZAE/1XWSLfG2MwGMjKymLLli38/d//PV/72td46KGHWLdunbwWZ3R0lNLSUl577TV+8IMf8OKLL7Jnzx6amppUOWfcn4j+qyyRr7JEvspSY76KT1d76qmnuOuuu9i2bRvf+c53rvpYm82GzWaT/zw6Oqp082bNH9ukRm63mz/+8Y+Mjo4SExPDJz7xCfR6vchXYSJfZYl855bRaCQnJ4ecnByio6P57ne/y44dOzCZTDQ2NtLb20tnZyednZ0cOXJE3vEtOzubrKws4uLirmvnNsFD9F9liXyVJfJVlhrzVbTIefXVVykrK+PUqVPX9fhnn32WZ555ZsbX9+zZg9lsZsuWLZw8eZLx8XEiIyMpLCzkyJEjAOTn5+N2u6mrqwNg48aNVFRUyMNZy5Yt48CBAwDk5uai1+upqakB4NZbb+XcuXMMDg5iNptZs2YNe/fuBSArKwuTySQvig0KCqKsrIy+vj6Cg4PZsGEDu3btAiA9PZ2IiAh5NGLVqlW0trbS3d2NwWBgy5Yt7Nq1C0mSSElJIS4uTq6Mly9fTnd3Nx0dHWi1Wm677Tb27t2L0+kkMTGRlJQUOcclS5YwODhIa2srADt27ODAgQPYbDbi4uLIysri+PHjABQVFTE+Pk5TUxMA27Zto6SkhImJCaKjo8nPz+fo0aMAFBQUYLfbaWhoAGDz5s2cPn2asbExIiIiWLx4MYcOHQJgwYIFAJw/fx6ADRs2UFlZyfDwMBaLhRUrVrB//34AcnJyMBqN8oGB69at47e//S2HDh3CZDLx5JNPyj8bu91OZ2cnVVVVAKxZs0Z+MRMUFMSmTZvYuXMnAGlpaURFRcm7NK1cuZL29na6urrQ6/Vs3bqV3bt343a7SU5OJiEhgdLSUgCWLVtGb28v7e3taDQatm/fzr59+3A4HCQkJJCWliZveVtcXMzw8DAtLS0AbN++nUOHDjE1NUVsbCw5OTkcO3YMgEWLFjExMUFjYyMAW7du5fjx41itVqKioigoKJD77MKFC3E6ndTX1wOwadMmysrK5KHYJUuWcPDgQQDy8vLQarXU1tbKfba6upqhoSFCQ0NZtWoV+/btAyA7O5vg4GCqq6sBWLt2LXV1dfT39zMwMIDb7Wb37t0AZGRkEBYWJq9/WL16Nc3NzfT09GA0Gtm8ebOcd2pqKjExMZSXlwOe84u8LzB1Oh3btm1jz549uFwukpKSSEpK4vTp0wAsXbqU/v5+2tra5D67f/9+7HY78fHxpKenc+zYMSRJoqCggJGREfmcpA0bNnDy5EkmJyeJjIwkOztb/r+Qn5+P3W6npaUFjUYjZ+jNu6ioSO7f83GN6OrqYnR0lIaGBnGNuMFrRG1tLQMDA5hMJtauXUtZWRnZ2dmYTCYWL16MRqMhMTGR2NhYjh8/zvnz53E4HDgcDt577z0AEhMTWbhwITabjYSEBDZu3CiuEVe5RnR1dXH48GHWrVvnd9eIjIwMTpw4AcDixYsZHR2VrxG33XYbR48eZWJigpiYGPLy8uSDpAsLC5mamuLChQsAPn0d0dXVxc6dO7nlllvENUKBa4Q338zMTEJDQ8XriDm+RoyOjrJz505MJpNPrxHe9l8PxdbktLW1sWLFCnbt2kVxcTHgCX/JkiVXXJNzuZGc1NRUv1qTMzU1JU7wvkF9fX389Kc/xeVyce+997J48WL5PpGvspTMV5Ik7HY7U1NT2Gw2JicnsdlsTE1NYbfb5Regl96cTqe87mKuaTQadDoder1+2k2n02EwGORbUFDQFT8PCgpCq72+mb2i/yrravlKkkRvby+NjY1cuHCBlpYWHA7HtMfEx8eTm5tLTk4Oqamp6HS6+Wi2aoj+qyyRr7JEvsryl3xnsyZHsSLnL3/5Cx/96Een/RJxuVxoNBq0Wi02m+2av2D8ceOBnTt3smPHDl83Q7UkSeJXv/oVra2t5OXl8clPfnLadBKRr7JuJF9JkpicnGR8fByr1crExARWq1W+TUxMzEmx4i1MtFrtjOLC21e8H91uN263G0mSpn0+lzQaDUajkeDgYIKCgmZ8NJlMBAcHYzKZOHDgALfffvucPr/wN7Ppv06nk7a2Ni5cuEBjYyNdXV3T+kZQUBDZ2dnydDh/+R3jS+L6qyyRr7JEvsryl3xnUxsoNl1t69at8lCh1yOPPEJ+fj5f/epXxTtoN6nz58/T2tqKwWDgrrvuEvPl/ZC3mBkeHmZ0dJTR0VFGRkYYHR2d8c745RiNRrkICAkJISgoCKPRiNFoRK/XYzQap42ieEdWvEWNVqu9oX5xccHjdDrlm8vlmvZnp9OJw+HAZrPJo0qX+1ySpBmjzFfiXfweEhKCyWQiJCRk2ufej6Lfz15ZWRm33347paWlLFu27JqP1+v1ZGZmkpmZCcDExAQNDQ3ybWJignPnzsnTX7yjPLm5uaSkpIjfUYIgCCqnWJFjsVhYtGjRtK+ZzWaio6NnfF1N8vLyfN0E1ZIkSZ5fu2bNGsLDw2c8RuSrrMvla7PZ5FPoBwYGGBwcZGpq6rJ/X6vVYjKZMJvNM27eEQ1fvzj0jgR5p6TdCLfbLRc43ml4F/95amqKiYkJJicnmZqaIjIykvHxccbHx6/4b+p0Ojmz0NBQQkND5c/NZrM4I0oh3rU8ixcvxu1209XVRX19PfX19XR2dtLT00NPTw9HjhyZNsqTl5dHaGior5s/L8T1V1kiX2WJfJWlxnzn7TDQQHG9c/OFmerr6+np6SE4OJi1a9de9jEiX2V5p4r29fXR09NDb28vIyMjl32cxWIhPDycsLAw+WaxWHxexMwnrVYrj8Zci8vlor6+nujoaLnw8d4u/rPL5ZJHyC4nODh4WgFksVjk7EUBNDe0Wi3JyckkJyezadMmrFYrFy5cuOIoT3JyMgsWLCAvL4/4+PiAHYkT119liXyVJfJVlhrzndcix7sriZrV1taSnp7u62aokndHkqVLl17xRaPId+5JksTQ0BDt7e3s3buXpKSkGetWLBYL0dHRREZGEh0dTUREBHq9eA9kNnQ6HS0tLeTn51/xMW63m4mJCXld08Ufx8fH5Y0bpqamGBgYmPH3TSaTXPR4C5+wsDAxBe4Gmc3maaM8nZ2dNDQ0UF9fT0dHh3zbt28f4eHhcsGTkZERUP9PxPVXWSJfZYl8laXGfAPn6iz4tbGxMXlbzuuZTy/cGLfbTX9/P+3t7XR0dGC1WgHPugRJkggPDycuLo74+HhiY2P94hTjm4FWq5VHaC7HbrfPKHxGR0cZGxuTp8ZNTEzQ09Mz7e/p9Xq58AkPD5dvJpNJFD+zpNVqSUlJISUlhU2bNsnXrrq6OhobGxkZGeHkyZOcPHkSo9FIdnY2CxYsIDc3F7PZ7OvmC4IgCO9TbHe1ueCPu6tZrVbxi+wDKC0t5a233iIlJYXHHnvsio8T+d4Y71kGTU1NTExMyF/X6/UkJiYSFRVFRkbGdU2/EmZPyf5rs9kYGxtjbGxMLnxGR0cZHx+/4q52RqNRLngiIiKIiIggPDxcldPepqamqKurIy8vz2fbmDocDpqamjh//jx1dXWMjY3J92k0GlJSUliwYAH5+fnExMT4pI03Qlx/lSXyVZbIV1n+kq9f7K4WqKqrq1m1apWvm6E63oPBrrVwTeQ7e263m7a2NhobG6e9w280GklJSSE5OZn4+Hj0ej0nT54UBY6ClOy/3jN7Ln3x7HK5sFqt03bCGx4eZmxsDLvdTl9fH319fdP+Tmho6LTCJzIyErPZ7NejPsHBwT4/p8FgMJCXl0deXh6SJNHV1SUXPF1dXbS1tdHW1saePXuIjY0lPz+fhQsXkpiY6NfZeonrr7JEvsoS+SpLjfmKImeWhoaGfN0E1ZEkST65Nzs7+6qPFfleP6fTSVNTE7W1tfJ0NI1GQ0JCApmZmSQnJ8/YJEDkqyxf5KvT6eSpahfzbnDgLXq8H71nHY2Pj9PR0SE/3mg0EhkZSVRUlPzRnwqfpqYmvva1r/HLX/5S3hbalzQajXwi9+bNmxkdHaWuro7a2lqamprk4vLw4cOEh4fLBU9aWprfLuAV1wdliXyVJfJVlhrzFUXOLN0sW4nOpeHhYWw2mzxl6mpEvtfmdDqpr6/n/Pnz8lbPwcHB5OTkkJmZedXhZJGvsvwpX51OR2RkJJGRkdO+brPZphU9Q0NDjIyMYLfb5W2Uvfyp8BkaGmL//v0MDQ35RZFzqbCwMFasWMGKFSuYmpqivr6empoa6uvrGRkZ4cSJE5w4cQKTycSCBQtYuHAhWVlZfrVxgT/130Ak8lWWyFdZasxXrMmZJYfDocr57L5UW1vLq6++SkJCAk8++eRVHyvyvTJJkmhubqaqqkpeb2M2m8nPzyczM/O6XiyJfJWl1nxdLhcjIyMMDQ0xODjI0NAQw8PDl13rYzQaiYqKIjo6mpiYGKKiouZl44qysjKWL19+3YeB+guHw0FjYyM1NTWcP3+eyclJ+T6j0Uhubi4LFy4kLy8Po9How5aqt/+qhchXWSJfZflLvmJNjoL27dvHjh07fN0MVfFuhRsbG3vNx4p8L294eJhTp07JWZrNZhYtWkR6evqspr6IfJWl1nx1Oh1RUVFERUXJU0qvVPjY7Xa6u7vp7u6W/35YWBgxMTFER0cTHR1NWFiY307Jmm8Gg4EFCxawYMEC3G43LS0t1NTUUFtby+joKNXV1VRXV2MwGMjNzaWgoMBnBY9a+69aiHyVJfJVlhrzFUWOoDjvO5f+sCuH2rjdbvlQQrfbjcFgkF8E3UyHcgrz72qFz8DAgHzz7vI2Ojoqr70zGAxERUVNK3zENuWe7akzMzPJzMzkjjvuoLOzk5qaGs6dO8fg4KD8f90fCh5BEAS1E0XOLF1r4bwwk7fIuZ5dvUS+f2O1Wjl+/Li8M1ZKSgrLli3DZDJ94H9T5KusQM/34sInNzcX8KzxGRgYoL+/Xy58HA7HjPU94eHhxMbGyrfZ9uPExESefvrpa67rUwuNRkNycjLJycls3bqV7u5uzp07R3V1tc8KnkDvv74m8lWWyFdZasxXFDmz5MvtS9XKbrcDXNcvZ5GvR39/P0eOHGFqagqDwcDy5cvJyMi44X9X5KusmzHfoKAgeZcx8Iw+jo6OTit6vLu8jYyMyNvJh4aGTit6QkNDr7qhQWJiIv/8z/8cMEXOxTQaDYmJiSQmJrJlyxa6u7uprq6e9xGem7H/zieRr7JEvspSY76iyJml6upqUlJSfN0MVfFOq3K5XNd8rMgX2traOH78OC6Xi8jISNauXYvFYpmTf1vkqyyRr2dKlvf8nZycHMAz2tPf3y9vqzw0NCRvY93U1AR4foHGxcXJRU94ePi0omd0dJSXX36Zp59+2m82olHCxQWPd4TnSgVPfn4+RUVFZGdnz8n0VdF/lSXyVZbIV1lqzFcUOYLivLt+OZ1OH7fE/7W0tHD8+HEkSSIlJYXVq1f7xW4mgnAjgoKC5KlZ4NmlZ2BggL6+Pnp7exkYGGBqaorW1lZaW1sBz8hvXFwc8fHxxMfHU19fzze+8Q3uuOMOVe2udiOuVPBUV1czNDREVVUVVVVVhISEUFhYKG9G4i9nGwmCIPiS2EJ6lsbGxubsXfWbxZ49ezhy5AirV6/mjjvuuOpjb+Z829vbOXr0KJIkkZWVxYoVK+Z8h6qbOd/5IPL9YFwul1z09PX10d/fP+NNka6uLr70pS/x5ptvsnXr1htam6Z2kiTR0dFBVVUV1dXVjI+Py/eFhYWxaNEiioqKSEhImFXBI/qvskS+yhL5Kstf8hVbSCuorq6O5cuX+7oZqhIeHg7AyMjINR97s+Y7ODgoj+BkZWWxcuVKRd6NvVnznS8i3w9Gp9MRFxdHXFwc4Cl6hoaG6O3tpaenh/7+fvng27Nnz2K1WgkLC5NHeWJjY2+q3ds0Gg0pKSmkpKSwY8cO+fysmpoaRkdHKSkpoaSkhJiYGIqKili0aBHR0dHX/HdF/1WWyFdZIl9lqTFfUeTMUn9/v6+boDreImd4ePiaj70Z87Xb7Rw9ehSn00liYiIrVqxQbLrJzZjvfBL5zg2dTkdMTAwxMTEUFBTgdDrZt28fgLxWx7ttdX19PRqNhqioKOLi4khMTCQ6Ovqm2WJdq9WSlZVFVlYWd911F/X19VRVVVFXV0d/fz/79+9n//79JCcnU1xczKJFi644Cib6r7JEvsoS+SpLjfmKImeWbuYpEh+U9x3E/v5+XC7XVV983Iz5nj59GqvVSmhoKGvXrlX0EMWbMd/5JPJVhl6vl9f0bNy4kZycHPr6+uRtqkdHR+Wd3GpqajAYDMTHx5OYmEhCQsJNc0aXXq9n4cKFLFy4EJvNRm1tLVVVVTQ2NtLR0UFHRwfvvfceeXl5FBcXk5ubK6+ZBNF/lSbyVZbIV1lqzFesyZklt9stTvKeJUmS+P73v8/U1BRPPPHEVbeAvdny7e7u5sCBA2i1WrZs2UJMTIyiz3ez5TvfRL7KulK+ExMTcsHT3d0tT23zCg8PJyEhgcTERGJjY2+aUR4vq9XK2bNnOXPmDJ2dnfLXQ0JCWLRoEcXFxSQnJyNJkui/ChLXB2WJfJXlL/nOpjbwfWtVZvfu3b5ugupoNBr5DI2Ojo6rPvZmytftdlNeXg5ATk6O4gUO3Fz5+oLIV1lXytdkMpGZmcmaNWu455572L59O0VFRcTGxqLRaBgZGeH8+fMcOHCA119/nUOHDlFfX8/Y2Ng8fwe+YTabWb16NY8//jif+9znuPXWWwkLC2NycpJTp07xi1/8gv/6r//iueeeu65pxcIHI64PyhL5KkuN+YrpasK8SEtLo7GxkcbGRlasWOHr5viFjo4ORkZGMBqNFBYW+ro5guDXKisreeCBBzh06BCLFy++4uO863OioqIoLCzEZrPJIzxdXV1MTk7S2dkpj2hYLBaSkpJITk4mJibGL96pVFJcXBzbtm1jy5YtNDc3c+bMGc6dO8fAwAANDQ08//zzpKenU1xcTGFh4U21oYMgCIFFFDmzNBenzt+McnNzOXDgABcuXLjqupybKd/z588Dnmzm64XEzZSvL4h8leN0OhkZGZn1eVtBQUGkpaWRlpaGJEmMjIzQ1dVFV1cX/f39jI2Ncf78ec6fP4/RaCQpKYmkpCQSExMD+oyqSzcsqKmp4b333mNqaoqWlhZaWlp49913KSwsZNmyZaSmporzd26QuD4oS+SrLDXmK4qcWfKXtUFqk5SUhNlsxmq10traSmZm5mUfd7PkOzY2Rn9/PxqNRj4Vfj7cLPn6isjXv2k0GiIiIoiIiGDhwoU4HA66u7vp6Oigq6sLm81Gc3Mzzc3NaLVa4uLiSE5Olq9fgcpoNFJcXExcXBxms5nKykoqKiro7++noqKCiooKoqOjWbp0KcXFxX5xVoYaieuDskS+ylJjvqLImaXKysqrLpwXLk+j0ZCXl0d5eTlVVVVXLHJulnzb29sBz9SRkJCQeXvemyVfXxH5qovBYCA1NZXU1FTcbjf9/f10dnbS0dHB2NgY3d3ddHd3U1paSmRkpDytLTIyMiBHNSorK9mxYwe33nor69ato62tjfLycqqrqxkYGGDPnj3s27eP3Nxcli5dSm5u7k23icONENcHZYl8laXGfEWRI8ybJUuWyL8w77jjjoCeCnItvb29ACQnJ/u4JcLVSJKE0+nE5XLhcDhwuVw4nU7cbrd8kyQJl8uFJElYrVaam5sv+29ptVo0Gg1arXbG51qtFr1ej06nQ6fTyZ8H4gtpf+UduYmLi2PJkiWMjo7KBU9/fz9DQ0MMDQ1RXV2NyWSSD+MM1HU8Go1GnuZ3++23c+7cOcrKymhra5On95nNZoqLi1m6dCmxsbG+brIgCMI0YgvpWRoeHiYiIsLXzVAlSZJ44YUXGBoa4t57773s4uGbIV9Jknj99dex2+1s376dqKioeXvumyHf2XC5XExOTmK327HZbNjt9mmfe4uX6zU1NUVwcPCctc9b8BgMBvnjpZ8bDAaMRmPAv6M+Pj5OSUkJa9euJTQ0dF6f22azyZsVdHV1TVsXFBwcLBc8at+e+nquD/39/ZSXl3PmzBnGx8flr6emprJ8+XIKCwtv6jewrkZcf5Ul8lWWv+Q7m9pAjOTMUnNzM0uWLPF1M1RJo9GwZMkS9u/fz4kTJygqKprxTvXNkO/U1BR2ux2NRkN4ePi8PvfNkO/lSJKEzWbDarUyOTnJ5OQkExMT2Gy26/r73mLDe7t4BMY7KqPT6WhoaLji6JwkSTNGgLyfu1wu+eYdKQLkr11PO70FT1BQkPzRaDRiNBoJDg5Gr9eremQoNDSUuLi4eS9wwLN5QWZmJpmZmbhcLnp6emhra6Ojo4OpqSkaGhpoaGjAaDTKBU98fLzqCp7ruT7ExMRw2223sWXLFhoaGigrK6O+vp62tjba2tp47733KC4uZvny5cTFxc1Pw1XiZr3+zheRr7LUmK8ocmapp6fH101QtRUrVnD48GE6OjpobW0lPT192v03Q75WqxXwHMQ33y+CboZ8wVNQTE5OMjIywvj4OOPj4zgcjss+1mAwEBwcjNFonFYYGI1GDAYDOp3uuqcjnT17dk6mIF5c+DidThwOBw6H47Kfe0ecvF+fmJi47L+p0+kIDg4mKCiIoKAg+fPg4GAMBoPfF0Dt7e1861vf4oUXXiAlJcVn7dDpdPIObC6Xi76+vmkFj3erfIPBQHJyMikpKSQkJKDX+/+v29lcH3Q6HQsWLGDBggWMj49TUVFBaWkpQ0NDnDhxghMnTpCWlsaKFSsoKChQxfevtJvl+usrIl9lqTFfcdWZJaPR6OsmqJp3DndpaSklJSUzipybIV/vi21fnD8RyPm63W5GR0cZGhpiZGQEu90+7X6tVovJZMJkMhESEiLfZju1RpJgeBi6ujy3gQEYG4PRUThzJo/duz2fT02B0+m5ORx/+1ySwGAAvd5z835uMIDZDBYLhIZq378ZCA2FsDCIjv7bzWIBb03iXRPknWp36c1ms2Gz2XC5XFitVrnIvpheryc4OJiQkJBpH4OCgvym+Ont7eX111/nG9/4hk+LnIvpdDoSEhJISEhg+fLl9Pf309bWRnt7O5OTk/JObXq9nqSkJNLS0khMTPTbEZ4Pen0IDQ2VNytobGzk9OnTnD9/ntbWVlpbW3n33XdZsmQJy5cvn5dDj/1VIF9//YHIV1lqzFesyRHmXX9/Pz/+8Y+RJIknnnhCdbt13KiOjg4OHz5MdHQ0t912m6+bo2qSJDE+Pk5fXx/Dw8PT1kpotVosFgthYWGEhoZiNpuve0RmZATq66ffLlyAzk7o7obrnOWmGIPhbwVPbCwkJXluycl/+9x7Cw72FIA2m42pqSm56Ln48yv9GtBqtXIx6C0QTSaTT96VLysrY/ny5ZSWlrJs2bJ5f/7ZkCSJgYEBueC5uLD0TmlLS0sjLi4uIDctAM82+eXl5ZSWljIyMiJ/PSMjg+XLl7Nw4UIxuiMIwqyJNTkK2rlzJzt27PB1M1QtJiaGoqIiKisr2bt3Lw899JB8382Qr/edce+6i/kUKPm6XC76+/vp6+ubNj3LaDQSGRlJREQEFovlul5ADg/D6dNQWvq3j01N125DZCQkJnoKjfBwz2jL8HAbhYWpWCwQEjJztMb7ms47qnPxSI/dDlYrjI9Pv42NeYqugQHo7/cUWA6Hp9jq7r52O+PjIStLS2ZmyPs35Ft2Nmi1bqamppiampLXK3n/7Ha7Lzv6YzQapxU9JpPJr0Z9fE2j0RATE0NMTAxLlixhcHCQ1tZW2tramJiYkKe0BQcHk5qaSnp6OtHR0T7Pby6vDxaLhQ0bNnDrrbdy4cIFTp8+TV1dnTy6ZTKZWL58OStWrJj3tYm+EijXX38l8lWWGvMVRY7gE5s2beLs2bM0NDTQ0tIyY9paIPMO+V46nUq4Nu8aiK6uLnnan1arJTo6mujoaCwWyzVfKNpsUFICu3fDnj2ewuZyAxnx8ZCbO/2WkuIpbOLjPSMkl9q58xw7dqTOxbd6WZIEExOegsdb9PT2eqbNdXbOvE1OQk+P53bs2Mx/T6eD1FQteXkm8vNNLFwICxdCfj7ExkrY7TYmJibkjRq8mzV4p8INDw/L/5bBYMBkMhEaGip/FLtseQoeb/9csmQJfX19csEzNTVFfX099fX1mM1mecvmiIgInxc8c0Wr1ZKbm0tubi6jo6OUlZVRVlbG6Ogohw8f5siRI+Tn57Nq1SoyMjIC5vsWBMH3RJEzS6mpyr2AuZlERUWxbNkyTp8+zc6dO3nsscfQarU3Rb7eLYa975TP53QVteYrSRJDQ0O0trbKxWFQUBAJCQlER0dfc9rL5CS8+y78/vfw1796CoWLZWXB8uWwYoXn47JlnpGa2VI6X43Gs27HbIa0tKs/VpJgaAiam6Gx0TM6denNbvfc39wMu3ZN//uRkRry84NZuDCY/HwoKIDFiyEhwcnU1N+KHm8R5HA4GBkZmTY1KSgoSC54zGYzZrP5A69HiYmJ4ROf+ISq13RoNBr5LJ6lS5fS09NDa2urPKWtpqaGmpoawsPDSUtLIyMjA7PZPG/tU7r/hoWFsWnTJjZs2EBdXR0nT56ksbFR/r5jY2NZuXIlxcXFPlmzqDS1Xn/VQuSrLDXmK9bkzFJvb6/YFnOOjI+P81//9V9MTU1x9913s2LFipsiX7fbzZ/+9CdcLhd33XUXFotl3p5bjfna7Xaam5vlUYOgoCCSkpKIjo6+ZoF49iz85Cfwv//rmfblFR8P27bBbbfB1q2eEZq5oKZ83W7PCFBjI5w/D7W1UFPj+djUdPnRLYCoKFiyBIqLPR+XLIG8PDdO54Q8tc1qtTI1NTVjrY9GoyEkJASLxUJoaCgWi2VWi1nVlO9sOJ1Ourq6aGlpoaurC5fLJd8XFxdHRkYGqampio+M+SLfvr4+Tp06RUVFxbQ3MIqLi1m1apWqi9pLBWr/9RciX2X5S76zqQ0ULXKeffZZ/vznP1NbW0tISAhr167l+9//PgsWLLiuv++PRY4a5yT6sxMnTvDuu+8SEhLC008/zeHDh2+KfHfu3MnQ0BDr1q2b13dH1NZ/R0ZGaGxsxOFwoNVqSUxMJCEh4ZqjAYcOwTPPwL59f/taWhrcf7/ntmLF33Ynm0tqy/dKJic9my3U1Pyt8Kmu9nx+0etvmcEAhYWegmfpUli1CoqKXLhc1mmFz+XO+wkKCpILntDQUEJCQi47ZWliYoKXXnqJRx99FJPJpMB37R/sdjsdHR00NzfT29srF4p6vZ6UlBQyMjIU27DAl/3XZrNx5swZTp48SX9/v/z1rKwsVq1aRV5enuo3aQiU64O/Evkqy1/y9ZuNBw4ePMhTTz3FypUrcTqdfP3rX2f79u2cO3duXofgBf+1cuVKysvL6e7uZufOnQH94uVi0dHRDA0N0dfXp8oh4PnQ19dHc3MzkiRhMpnIzs4mJCTkqn/nzBn40pf+VtzodHDPPfC5z8GWLcoUNoEoJMQzNW3x4ulfn5qCc+egosKTdUWF5zY6+rfPf/1rz2MNBh3FxWGsWhXG6tWewmfBAjsTE+OMjY0xPj4ur/Gx2WwMDAwAnhfz3qInLCwMk8mERqOhtraWp59+mrVr1/r97mo3wmg0ygePWq1WWlpaaG5uZnR0dNqi/fT0dDIyMgJm0X5QUBCrVq1i5cqVNDU1cfLkSc6fPy9v0hAREcHq1atZtmxZQE5lEwRh7s3rdLW+vj7i4uI4ePAgGzZsuObj/XEkZ2BggOjoaF83I6C0tbXx0ksvIUkSt99+O2vWrPF1kxTX2tpKSUkJ4eHh3HHHHfP2vGrpvz09PbS0tACetRjp6elXHb2xWuFf/xVeeMEz0mAwwGOPwde+du21K3NJLfnOJUmClpa/FTmlpXDiBPT1zXxseDisXAmrV8OaNbBmjQuDwXNYq7fwuXTXQb1ej8Viobm5me3bt3P69GmWL18+L9+bv/BuSd3c3DxtXRp43jDJyMggLS3thl/8+1v/HR4e5vTp05SWljI5OQl4iqGlS5eyevVqIj/Iwjkf8rd8A43IV1n+kq/fjORcyrsgNSoqaj6fdk51dnb6xQ85kKSmpnLLLbdQUlLCH//4R4qLi6/5jr3axcfHo9VqGRkZYXR0dN6KeDX038HBQbnASUxMJCUl5ao7Lp0965mCVlPj+fN998EPfzi/xY2XGvKdaxoNZGR4bh/5iOdr3sLn5ElPwXPypKf4GRnx7Gi3Z4/37+pYtCic9evDWb8e1q1zExU1ydjYGGNjY4yOjtLYqGNiwkZzsw1Yys6dfbS3t5GQYGbJEvNN8a7+xVtSL126lM7OTpqbm+nq6mJgYICBgQHKy8tJTk4mOzub+Pj4D7RLmb/134iICLZt28bGjRuprKzk+PHj9PX1cfz4cU6cOEF+fj633HILqampqtiVzd/yDTQiX2WpMd95G8mRJIl77rmHoaEhDh8+fNnHeKcteI2OjpKamupXIzn+Micx0DgcDn72s59x/PhxPvrRj3Lvvfeq4pfWjTh48CBdXV0UFRVRWFg4L8/p7/13cnKS6upq3G438fHxpKWlXbUfvP22p6iZnPQcfPnSS+DLb8/f8/Ulh8NTkHoLn5ISz4YHl8rKgvXrPbeUFInbb7/yz/+1186Ql6chLCxMPhvpg+7epkZTU1PydLahoSH562azmaysLDIzM2c1Bdjf+68kSVy4cIFjx45x4cIF+etJSUnccsstFBQU+PXP39/zVTuRr7L8JV+/HMn5/Oc/T2VlJUeOHLniY5599lmeeeaZGV/fs2cPZrOZLVu2cPLkScbHx4mMjKSwsFD+9/Lz83G73dTV1QGwceNGKioq5BCWLVvGgQMHAMjNzUWv11Pz/lu/t956K+fOnWNwcBCz2cyaNWvYu3cv4Fn0aDKZOHv2LODZBaesrIy+vj6Cg4PZsGEDu97fezU9PZ2IiAjOnDkDwKpVq2htbaW7uxuDwcCWLVvYtWsXkiSRkpJCXFwcZWVlACxfvpzu7m46OjrQarXcdttt7N27F6fTKb+bferUKYBph8sB7NixgwMHDmCz2YiLiyMrK4vjx48DUFRUxPj4OE3vn264bds2SkpKmJiYIDo6mvz8fI4ePQpAQUEBdrudhoYGADZv3szp06cZGxsjIiKCxYsXc+jQIQB584jz779K2bBhA5WVlQwPD2OxWFixYgX79+8HICcnB6PRyLlz5wBYt24dtbW1DAwMYDKZWLt2Lfv27SMiIgK73U5JSQnd3d3k5OSwZs0aGhsb6e3tJSgoiE2bNrFz504A0tLSiIqKoqKiAvCs72lvb6erqwu9Xs/WrVvZvXs3breb5ORkEhISKC0tBWDZsmX09vbS3t6ORqNh+/bt7Nu3D4fDQUJCAmlpaZw8eRKA4uJihoeH5dGF7du3c+jQIaampoiNjSUnJ4dj7x9CsmjRIvmwP4CtW7dy/PhxrFYrUVFRFBQUyH02LCyMoaEh3nnnHdra2ti8ebN8fkR4eDhLlizh4MGDAPKi29raWrnPVldXMzQ0RGhoKKtWrWLf+wtRsrOzCQ4Oprq6GoC1a9dSV1dHf38/nZ2duN1udu/eDXhOHw8LC6OyshKA1atX09zcTE9PD0ajkc2bN8t5p6amEhMTQ3l5OQArVqygs7OTzs5OdDod27ZtY8+ePbhcLpKSkkhKSuL06dMALF26lP7+ftra2uQ+u3//fux2O/Hx8WRkZHD8+HHGxsbkXdNqamqora3ltttu4+jRo0xMTBATE0NeXh4lJSUcOhTPv/1bMU6nhmXL+vnnf65iy5ZbOXrUd9eI1tZWRkdHaWhoENeIy1wj7PZKMjKGKSqy8NOfruDPfz5KdXUk7e0ZnD5t4tw5A42NGhob4eWXATwFziuveM7v8aqpgYcegrq6LuLjzTQ3N2O1WtHr9SxevJja2loMBgMZGRlER0er9hqxcOFCnE4n9fX1gOd8sUuvEc3NzfJjOzs7OXPmDC6Xi+HhYXbu3InRaCQ9PZ3t27fL/ftK14jW1lYOHz7MunXr/PIaceLECbmPZmdns2vXLpqampAkieeeew6DwcDq1av50Ic+JLehsLCQqakpuSjy5euI1tZWdu7cyS233CKuEVe4RtzI6whvvpmZmYSGhlJVVQUQ0K8jrucaMVevI/r6+uS10768Rnjbfz3mZSTn6aef5i9/+QuHDh0iMzPzio9Tw0iOoKzDhw+zd+9eDAYDjz/+OLGxsb5ukmKcTidvvvkmdrudDRs2kJSU5Osm+dTAwAAXLlxAq9VSVFR01WlIhw55tn52OuHv/s6z2F2cO6l+IyOeQ0sPHYLDh+H4cc/PuLTUc3aRV1mZ5zyjvXtdLF06Kp/Pc+nubUFBQYSHh99UozxOp5O2tjYaGxvpu2hhVEhIiDy6Exoa6sMWzi2r1crp06c5deoU4+PjgGfzhmXLlrFmzRoiIiJ820BBEOaU32whLUkSTz/9NK+//joHDhwgNzd3Vn/fHzce2LNnD9u2bfN1MwLW7t276e7u5sKFC8TFxfEP//APAX1qenl5OefPnyc+Pp7Nmzcr/nz+2n8lSeLcuXNYrVaSk5NJTk6+4mO7ujy7fvX3e6aqvfoq+MvOsv6ar1odOwZr1165yNFoPLu2bdsG27ZJLFs2xcTECMPDwzM2MdBqtVgsFiIiIoiMjJzV+Txq5VnT1EhTU9O0AjA+Pp7s7GySk5OnFX5q7r9Op5OzZ89y/Phxuru7Ac/PfNGiRaxbt474+Hgft1Dd+aqByFdZ/pKv30xXe+qpp/jtb3/LG2+8gcVikS884eHhql1Y7rrcIRHCnHG73Xz0ox/lpz/9Kb29vbz55psBvT4nLy+P+vp6enp66OvrU3zkyl/778SE5yBJrVZ7zcPGvvhFT4GzZIlnBMdfChzw33zV6lp7CkiSZ33PiRPw3e9qCAsLYfv2EO66K4HbbnNhMk0f5fF+3tLSQmhoqFzwBAcHB+Q1JiwsjCVLllBUVERHRweNjY10d3fT09NDT08PwcHBZGdnk52djclkUnX/1ev1LFmyhOLiYhobGzl69CiNjY1UVlZSWVlJTk4Ot956K+np6T77Was5XzUQ+SpLjfkqWuT85Cc/ATxzBC/2q1/9iocffljJp1bMzT6lSGlJSUmEhoZy33338fLLL1NVVUVCQgLr1q3zddMUYTabycjIoLGxkbNnz7Jp0yZFfwH7a//17rwYHh5+1ZG7khL4/e89hc1LL4G/Havkr/mqnXfnvEv//Pbb0NsLu3fDrl2e4vePf/TcQMeKFZHceWckd9whUVQ0xejoMENDQ1itVsbHPVtXt7e3ExwcTGRkJBEREYSGhgZcwaPT6UhLSyMtLU1eW9HY2Chv9FFTU0NycjIhISFIkqTq71+j0ciFW2dnJ0ePHuXcuXM0NDTQ0NBAcnIy69atIz8/f94PFxXXB2WJfJWlxnzn9Zyc2fLH6Wr+sk94oLo435MnT/LOO++g0Wh48MEHycnJ8XHrlGG1Wnn77bdxu92Kr83x1/57/vx5RkZGSE9Pv+q0kvvvh9deg0cfhV/+ch4beJ38NV+1qq+HvLwr319XB95Z0C4XnD4N77zjKX7eXxssi42F22+Hu++GbdscOJ1DDA8PMzo6Om1am8FgIDIyksjISCwWy7y/EJ4vLpeLjo4O6uvr5bU7k5OTJCQkkJOTQ0ZGRsBMFR4cHOTYsWOUl5fjdDoBz1EWa9euZcmSJej187MHk7g+KEvkqyx/yXc2tUFgXr0V5N3lQVDGxfmuXLmSZcuWIUkSf/zjH6ctog0kZrOZvPdfyVVUVCg6JOyv/de7XuBq292OjMDrr3s+/8d/nI9WzZ6/5qtWubmeQqa0FF55pQZYxiuv1FBaOr3AAdDpPIeMPvOMp9jp6vKM9n3sY2CxeA4n/d//hQcegKQkA48+Gsfhw3mkpCwlJyeH6Oho9Ho9DoeD3t5ezp8/T0VFBU1NTYyMjODH7wd+IN7Rna1bt3L77beTk5NDd3c3IyMjlJaW8uabb1JaWiqPsqpZVFQUd911F1/84hfZuHEjISEhDA4O8te//pXnn3+eo0ePTjtgVSni+qAska+y1JjvvB4GKgizodFouPPOO+nv76e1tZXf/OY3PPbYYwG1M5BXQUEBzc3NjI6Ocv78eQoKCnzdpHnlcDgArvrO8eHDnp22cnKguHi+Wib42t8KmUmgnIULJ6dtQnAlCQnwyCOem90OR496RnjeeAMaGjyfv/02aLU61q+P4qMfjeKee9xERo4xNDTE0NAQDoeDvr4++vr65BGeqKgoLBaLqqd0XSoiIoIVK1bQ09NDbm4uDQ0NjI6OUl9fT319PfHx8eTm5pKUlKTqkS2z2czmzZtZt24d5eXllJSUMDIywu7duzly5Ai33HILq1atIjg42NdNFQRhDojparPU29t7zYXRwgd3uXwnJib45S9/ycDAAImJiTzyyCMBuTNSc3Mzx48fR6fTcfvtt2OxWOb8Ofy1/54+fRq3283ixYuv+ALjm9+Eb33L86L1pZfmuYHXyV/zDQTDw8O8+eabfPjDH76hbYElCaqrPaOCr78O7x/XIFu2DO69F+69VyI5eYzBwUG54PEyGAxERUURFRUVUGt4vP1XkiR6enpoaGigo6NDHsUKDQ0lLy+PzMzMgJjK5nK5qKqq4vDhwwwMDACebcdXr17NmjVrZnWQ6vUQ1wdliXyV5S/5iulqCurv7/d1EwLa5fI1mUw8+OCDmM1murq6eO2111S5y8e1pKenk5iYiMvl4sSJE9PWCcwVf+2/3m1sr/Y9d3V5PmZkzEODPiB/zTcQeEcbbvTcE40GFi2Cf/1Xz1bUjY3w3HOwfr3nvrIy+MY3oKBAw4YNYbz6agZhYcUsWLCA2NhYeUpbT08PNTU1nDlzhra2NiYnJ+fmG/Uhb//VaDQkJCRw6623cvfdd1NQUIDRaGR8fJyysjLeeustKioqsFqtPm7xjdHpdCxZsoSnnnqKj33sY8TFxWGz2Th06BD/8R//wa5duxgbG5uz5xPXB2WJfJWlxnxFkTNL3tOYBWVcKd+oqCg++clPYjAYqK+v5y9/+UvAzZHXaDSsWLECg8FAf3+/fNLwXPLX/ut9V/jSwxwvNjzs+RgZOQ8N+oD8Nd9A0N3dzQ9+8AP5KIK5kpnp2Zb80CHo7oaf/xzuuAP0ejhzBv7v/4WcHC233x7Om29mEhe3hLy8PGJiYtDpdNjtdrq6uqiqqqK6upqenp5poz5qcrn+azabWbx4MR/60IdYvnw5FosFu91ObW0tb7/9NseOHWNwcNAHrZ073gOIP/vZz/LAAw+QmJiIw+GgpKSE//zP/+Sdd96Zk7VJ4vqgLJGvstSYryhyBNVISUnhvvvuQ6vVUlVVxTvvvBNwhY7ZbGbFihUAnDt3jt7eXh+3aH54p4VMTExc5TGejyp/81j4gDo7O/n1r39NZ2enYs8RFwePPebZoa27G158EbZs8YzwHD8O//RPkJam5aMfjWD37ixSUz2bFkRGRqLRaLBarbS0tFBRUUF9fT2Dg4OKjMj6gsFgIDc3lzvvvJP169cTHx+P2+2mpaWFXbt2sXfvXtrb21X9/Wo0GhYuXMjjjz/Ogw8+SGpqKk6nk5MnT/LCCy/w1ltvBcRGDIJwsxBrcgTVOXv2LH/605+QJIlbb73VL07gnWsnTpygqakJs9nM9u3bCbrWqYgq193dTWtrK2FhYeTn51/2MV/6EvzHf3heaP7Hf8xv+wTfKysrY/ny5ZSWlrLsenYemENdXZ6zd1591XNWk5de79mW+uGHYccOB1brIAMDA4yPj1/0GD1RUVFER0cH1Pod8GzNXFdXR2trq1zcWCwWed3OfG3NrBRJkmhububQoUM0NTUBniluy5YtY/369eJ1iSD4gFiTo6D9+/f7ugkB7XryXbRoEXfffTcAR44c4cCBAwq3av4tW7YMi8WC1WqlpKRkzt4d9df+611nMTY2Jp9jcanCQs/Hysp5atQH4K/5XokkSQE3GqqExER4+mnPDm3NzfD978PSpZ7d/v76V/j4xyE93cC3vx2P1VrAokVFJCUlYTQacTqd9Pb2UlNTw9mzZ+nq6vLb6Wyz7b9RUVGsWbNm2rqdsbExSktLeeutt6iurp6XrZmVotFoyMzM5DOf+QyPPvoomZmZuFwuTp06xQsvvMC77747qzU7ars+qI3IV1lqzFfdb7P4gJov2GpwvfkuX74cm83Grl275CJn06ZNyjVsnhkMBtatW8eePXvo6emhoqJiTt699tf+GxwcjMlkYmJigsHBwcvu4OL99k+d8mwJ7I8b7Pk6X0mScLvduFwuXC4XbrdbvnkLmosLG+/Hi0cXvJ9rtVo0Gs20m1arnXHz3nezSE+Hf/5nz622Fl5+2XP+TkcH/PjHnltBQQif+UwKDz6YjMUyRn9/P0NDQ0xOTtLW1kZHRwcRERHExsYSFhbmN/l90P5rMplYvHgxCxcupLm5mfPnzzM+Pk5VVRW1tbVkZ2ezYMECQkJC5rjF8yctLY3PfOYzNDc3s3//flpaWjhx4gSlpaWsXLmSW2+9FbPZfNV/w9fXh0An8lWWGvMVRc4sXe00duHGzSbftWvXAgRsoRMREcGaNWs4cuQIdXV1hIeHk52dfUP/pj/33+joaCYmJujt7SU2NnbGC7/iYs/ZJ93dsH8/7Njho4ZexXznK0kSDocDh8OB0+nE6XR+oJGZi/+O9/PrHT3UaDTodDq0Wu20j97P5+oFfEREBNu2bbvh3dXmUn4+PPssfOc7sHevp+D585/h3Dn46lfh//5fDdu3h/Hww2F86EMuxscH6O/vZ3x8nMHBQQYHBwkKCiI2NpaYmBifb41/o/3Xu24nOzubtrY2zp07x8jICLW1tdTX15OZmUl+fr6qzzrLyMjg4Ycfpqmpif3799PW1saxY8c4ffo0q1evZu3atVfcetqfr7+BQOSrLDXmK9bkzNLw8LBf/ZINNB8k35KSEnbt2gXAhg0b2Lx5s9+8MzoXqqurqaqqQqvVsmnTphvap96f+6/T6aSiogK3282CBQsIDw+f8Zgnn4Sf/Qw+/WnPC0p/Mx/5SpKE3W6Xb5dewr1Fh7fIuHTE5eKRl4v/n1xa6HhvF48CXTwy5L1djbcter1ebtONFD/+3H+9Rkbgtdfg17/2TG/zio31nPH0D/8ASUkT9PX1MTAwIE/P1Gg0REREEBcX57PRnbnOV5IkOjs7qampkbef1Wq1pKamsnDhQr//WV6LJElcuHCB/fv309HRAYDRaGTNmjWsXbt2xplfaui/aibyVZa/5Dub2kAUObO0c+dOdvjjW8gB4oPme3Ghs3r1am6//faAKXQkSeLYsWO0trZiMBjYsmULkR9wH2V/778tLS309PRgNpspKCiY8TM8fhxuucUzVa2tzbMblj9RMl9JkpiammJqamraOVE6nQ6DwYBer5eLifnq+xdPj7t0mpzL5briqJJWq5Xb671ptVdfImq32/nDH/7A/fff7/MRj+vV0OApxl96CS7eFG7bNnjiCbj7bhfj40P09fVNW9sREhJCXFycvE31fFGq/0qSRF9fHzU1NXR5D7wCkpOTWbhwITExMXP+nPNJkiTq6+vZv3+//P2FhISwfv16Vq1aJW/A4O/XX7UT+SrLX/IVGw8IN521a9dy5513Ap6dyd544w1Vb2V6MY1Gw6pVq4iNjcXhcHDw4ME5PaDOnyQlJaHT6bBarZc9e2PNGli1yrMm54UXfNBAH3E4HAwPD2O1WnG5XGi1WkJCQoiIiCAiIoLQ0FCCg4PR6/XzWtx7R2qMRiPBwcGYzWbCwsKIiIggKiqKyMhILBYLJpOJoKAguQBzu93Y7XYmJiYYHR1lcHCQoaEhxsbGmJycvOy0u7Nnz/KpT32Ks2fPztv3d6NycuDb34aWFvjLXzzn72g0sGcP3HcfZGToeO65GIKDF1JUVER8fDw6nY7JyUl5K+rm5uarbq2uBhqNhri4ODZu3Mj27dtJS0tDo9HQ0dHBnj17OHjwoCoPGvTSaDTk5eXx+OOP88ADDxAbG8vk5CS7du3ihRdeoLy8PGB+HwmCmoiRnFnq6uoiMTHR180IWDea75kzZ+QCp6CggHvvvVf125h62e129u/fz9DQEGazmW3bts16Ia8a+m9nZyft7e0YjUYWLVo04+f35z/Dxz7mOTenocGz85W/mOt8JUlicnKSyclJJElCq9XKBYNaRyolSZLXD3lv3ilxF7t4tMdgMFBZWcmKFSt8soX0XGpuhl/8An75S8/6MvAUPtu3e3Zw277dxeBgP729vUxOTsp/LywsjPj4eCIiIhT72c/n9WFsbIyamhqam5vlAiAxMZHCwkLVj+y43W7OnDnDgQMH5HN1YmNjKSoqYv369ar9v+vv1PD7Tc38JV8xkqOg0dFRXzchoN1ovsXFxdx///3odDrOnTvHb37zG6ampuaodb5lNBrZsGGDvLX0gQMHZv29qaH/xsfHExwcjN1uv+wJyx/9qGdEZ2ICvvENHzTwKuY638nJSSYmJpAkieDgYCIjIwkODlb1iySNRoPBYCAkJASLxUJkZCSRkZGEh4djMpkwGo1otdppoz0jIyPyi8WpqakPvMGCP8jI8GxU0NoKf/qTp7iRJNi5E+6+GwoLdbz2Wjzp6YvIz88nKioKjUbD6Ogo9fX1VFVV0dPTM23K4lyZz+uDxWJh1apV3HnnnWRlZaHVaunq6gqIkR2tVsvSpUt5+umn2bFjByEhIfT19fGHP/yBX/7ylzQ3N/u6iQFJDb/f1EyN+YoiZ5bExUlZc5Fvfn4+Dz74IEFBQTQ1NfHSSy8FzCnVISEhbNy4kZCQEEZGRti/f/+0d3uvRQ39V6fTkZmZiUajoa+vj6GhoWn3azTw7//u+fyll2DfPh808grmMl/vC3wAs9kccAdJXkyr1WIwGDCZTISFhREZGUlERARms5mgoCC0Wq1c1ExOTjI8PCxPb7PZbKqcCmQwwL33eoqbhgb4P/8HwsOhvt4zopOaquH/+//C0OlyKC4uJjExEb1ez9TUFC0tLZw5c4a2trY53dbVF9eH0NDQgC129Ho9t9xyC1/4whfYsGED4+PjtLe38+tf/5pXXnmFbu9QnjAn1PD7Tc3UmK8ocoSAlJWVxSOPPILFYqG3t5df/OIXAfMLJTQ0lM2bN8uFzoEDB2ZV6KiBxWKRt6tsamrCZrNNu3/tWvjsZz2fP/YYXHTAfECQJAmr1Qp4zhBS8/kiH4RGo0Gv108b7bFYLIBnm2Lvuh6bzcbY2BhDQ0OMjIxccT2Pv8vOhh/+ENrb4b/+C/LyYHQU/uM/POt6HnjAyIULqSxeXEx6ejrBwcE4nU66uro4c+YMFy5cUP26nasVO4cPH2Z4eNjXTfzAgoOD2bJlCx/5yEdYuXIlWq2WhoYGfvazn/HGG28E7BpLQfA1sSZnltxu9zV3ARI+uLnOd2RkhN/85jf09vZiNBq5//77ycnJmbN/35fGxsbYv38/ExMTWCwWNm/efMXzGbzU1H/dbje1tbWMj48TGhpKfn7+tLaPjsKiRZ5d1h580HMgo68HOuYqX4fDwcjICFqtloiICNX8zJTkLWq865GcTicOhwO73S5vw+zl3QzBaDTO+2YMc8Ht9ozwPP88vL9pJABLlsBXvgL33ScxPj5MT0/PtCkkERERJCYmygXh7J/Xf64P4+PjnDt3jqamJiRJQqPRkJ6eTmFh4Qf+/nzNm+/g4CD79u2TN9HwHv68du1a1ewc6I/8qf8GIn/JV6zJUdDRiw8+EObcXOcbHh7Oo48+SmZmJna7nd/+9reUlZXN6XP4irewMZvNcsEzfo0hDTX1X61WS3Z2Nnq9nvHxcfnFjldYGPzmN6DTeT7+4hc+bOz75ipfh8MBeF78+MMvFX+g1Wo5ffq0fMaOd3pbREQEkZGRhIaGYjQa0Wg0uFwuJicnGRkZYWhoCKvVisPhUM0Ij1br2Ylt507PwaJPPunZaKOiwlPQ5+Vp+M1vIklLy6ewsJDo6Gg0Gg3Dw8PU1NRQU1PD8PDwrL9ff7o+eEd27rjjDtLS0pAkiebmZt59911Onz6typErb75RUVF8/OMf57HHHiM1NRWHw8GBAwf40Y9+REVFhWr6qb/xp/4biNSYr/jtOUtqvLCqiRL5BgcH89BDD1FcXIzb7ebNN99kz549qpzHfymLxcKWLVvkQmfPnj0z1rBcTG39NygoiOzsbDQaDQMDA3RefNgIsH49fO97ns8//3k4csQHjbzIXOXr7ZvzeUaKv6urq+Opp56irq5uxn06nY7g4GDCwsKIiooiLCxMXsvjdrunFTzj4+OqKngWLoSf/MQzYvmd73gOFW1u9qzbSU+H554zExmZTVFREXFxcWi1WsbGxqirq6O6upqBgYHr/l798foQFhbG2rVr2b59O4mJibjdbhoaGnj77bepqKiYMZXVn12ab0pKCo8++ij33XcfERERjI2N8Ze//IUXX3yRpqYmH7VSvfyx/wYSNeYripxZUvvWlv5OqXx1Oh0f+chH2LBhAwBHjhzhd7/7XUDsvObdTjoiIoKpqSn27dtHT0/PZR+rxv4bHh5ORkYGAB0dHfT19U27/8tf9uy4ZrfDRz7iWcTtK2rMVy3Gx8epqqq65milRqPBaDTKa3nCwsIIDg6WC56pqSlGRkYYHh5mYmJCkV3KlBAVBV//uufMnR//2LNLW38//L//B2lp8PWvBxMcnMHixYtJTExEp9MxMTHBhQsXqKqqor+//5rFjj/336ioKDZu3MiWLVuIjY3F5XJRW1vL22+/TW1trSp+jpfLV6PRUFhYyOc//3luu+02goKC6Orq4uWXX+bVV19lYGDABy1VJ3/uv4FAjfmKNTmzNDY2ptr5wGowH/lWVVXxxhtv4HQ6iYmJ4ROf+IQq//Neym63c+TIEXp7e9FqtaxZs4a0tLRpj1Fz/21ra6OrqwuNRkNWVhbR0dHyfRMTsHEjnD4Nublw+DC8v2/BvJqrfCcmJpiYmCAoKEi1P6+5VlZWxvLlyz/wOTmSJOFwOLDZbNjt9mkv+A0GA0FBQfL21WrgdMJrr8H3vw9nzni+FhwMTzwBX/0qxMY66e3tpaenR57+GBwcTFJSkjy97VJquT5IkkRXVxeVlZXyhgRms5nFixfLB436o+vJ12q1cvDgQU6fPi2vgVi9ejWbNm0iKChonlqqTmrpv2rlL/mKNTkKKikp8XUTAtp85FtUVMSjjz5KWFgY/f39/PznP6e+vl7x51Wa0Whk48aNpKam4na7OXbsGOfPn5/2Yk7N/TclJYW4uDgkSaKxsXHatDyTCd56yzN9p77ec/bI4OD8t3Gu8vUegKqmaVX+7uIRnqioKCwWi7yGx+FwMD4+rqrpbHo9fPKTUF4O77zjOTtqagr+8z8hMxO+9CU9kpTE4sWLSU1NxWAwMDU1RWNjI2fPnr3sNDa1XB80Gg1JSUls376d1atXExISgtVq5dixY+zevZve3l5fN/Gyridfs9nMnXfeyWc/+1ny8vLka/mPfvQjzpw54/f90pfU0n/VSo35iiJHuCklJSXx+OOPk5aWhs1m47e//S1HjhxR/S8QnU7HLbfcQk5ODpIkUV5ezqlTp1QxleNavLsrxcTEIEkSDQ0NDF5UySQkwO7dno+VlZ6F2yo8uwz424YD3gMxhbml0WgICgqSz+Qxm83o9XokSZKns3m3pPb3tXsajaevl5R4NipYuxZsNvjRjzxbU3/hCzokKZHFixeTkpKCXq9ncnKSCxcucPbsWQYHB1V73dNqtWRmZnLXXXdRVFSEXq+Xdy47fPiwKg8v9IqNjeXv/u7veOihh4iOjmZ8fJzXX3+dl156ia6uLl83TxBUQUxXm6X29nZSUlJ83YyANd/5ulwu3nnnHUpLSwFYtGgRH/7wh1W/jackSdTV1ck79cTGxrJu3Tr6+/tV33+9IzkDAwNoNBoyMzOnTTc8exY2bYKBAVi+HN57D+ZrNuJc9l/vlDW9Xk94eLjfTsGZL/39/fzqV7/ikUceUWR6qSRJOJ1ObDYbNptNfuHvLYiCg4PlETZ/Jkmwdy8888zfNuIICYF//EfPNLawMBc9PT10d3fLW2+bzWZSUlIYGxtT9fVhcnKS6upqGhsb5aleubm5FBYW+sU1/YNeH5xOJ8ePH+fQoUPY7XY0Gg3Lli1j69at1zw24GYiXp8py1/ynU1tIIqcWWpoaAiYc1b8ka/yPX36NO+88w5ut5uYmBjuv/9+4uLi5r0dc62rq4uSkhIcDgdms5nU1FSWLFni62bdMEmSaGlpkaelpKWlkZCQIN9fUQG33eZZmF1Q4DlrJDlZ+XbNZf91u90MDw/jdrsxm8033YGglzNf1wfvmTw2m23aGTxGo5Hg4GD5QFJ/Jkmwfz/86796RnkAwsM9hc4//iMEBTnlYsc70js1NcXy5csxm80+bPmNGx0dpaKiQt6NMTg4mKKiIjIzM3265upG++/o6Ci7d++mqqoKgJCQELZs2cLy5ctVs5ZMSeL1mbL8JV+xJkdBFy5c8HUTApqv8l2xYgUPP/wwFotFXqdTUVHhk7bMpcTERLZt24bFYsFqtfLuu+/S1tbm62bdMO/UNW9h09raSktLi/zu+5Ilns0HkpM954ysXw/z0bXmsv9qtVr5XdqJiYkZB17ebPr7+/mv//ov+vv7FX8urVZLSEgI4eHhhIeHyweQ2u12RkdHGR4eZmpqyq+neWk0sGWLZzTnrbegqAhGRuBf/sUzje1nP9MTG5vM4sWLiY+PR6vV0tHRQXV1NRcuXFD1zpNhYWFs2LCBjRs3EhYWxtTUFKdOnWL37t0zdmecTzd6fQgLC+NjH/sYjzzyCPHx8UxOTvL222/z4osvBsR1/UaJ12fKUmO+osgRhPelpaXx5JNPkp2djcPh4C9/+QtvvPGGvDORWoWHh7Nt2zbi4+Nxu90cPXqUiooKv19rcC0ajYbU1FRSU1MB6OnpoaGhQX5XOj/f8wIvJweamjxrFY4f92WLZ8+745ckSYyNjan+Z3YjWltb+c///E9aW1vn7Tm9h45aLBYiIiIICQlBq9XicrnkjQr8fd2ORgN33+3ZoOCVVzybEvT0eM6VWrQI3nnHQFpaOosWLZKndA0MDHD27FlaW1tVXVwnJiayY8cOli5disFgYGhoiL1793Ls2DFVnvnhlZ6ezhNPPMGdd95JcHAw3d3dvPTSS/z1r39lcnLS180TBL8hpqvNksPhwGAw+LoZAcsf8nW73Rw+fJgDBw4gSRLx8fHcd999qt9m2uVyUV5eTsP7B8nExsaydu3agJgGNTg4KM/DN5vN5Obmyi/Yurs9C7MrKiAoCH71K8+uVEpQov+63W5GRkZwuVwYDAbCwsL8fqqUEm50C+m54p3KNjU1JRfUWq2W4OBg+Twef2a3wy9+Ad/6lqfYAdi8GZ57DgoLHdjtdtrb2xkZGQE8m2AkJycTGxur6n43NTVFVVUVjY2NSJKEXq+nqKiI3NzcefuZKXF9sFqt7Nmzh/LycgBCQ0O5/fbbKSwsVPXP64Pwh9cPgcxf8hXT1RR08uRJXzchoPlDvlqtlo0bN/LpT3+a0NBQenp6ePHFFzl79qyvm3ZDdDodNpuNdevWYTAY6OvrY+fOnX673epsREVFsWDBAgwGA1arlXPnzjE2NgZ4dls7fBg+/GHPrlN/93fwzW961izMNSX6r1arxWKxoNVqcTgcjI2N+fU0qUDnncoWERFBaGgoOp0Ot9vNxMQEQ0NDTExM+PXIjtEIn/ucZ6v1//t/PYX//v2wbBnce+8go6NmFixYwIIFCwgJCcHhcNDc3Ex1dbWqdysLDg5m5cqV3HbbbcTExOB0OikvL2fXrl3zMgUSlLk+mM1m7rnnHh5++GFiYmIYHx/nj3/8I7/5zW+mbbN/M/CH1w+BTI35iiJnlq512rZwY/wp38zMTJ544gkyMjKw2+388Y9/5I033sBms/m6aR/Y+Pg4qamp3HbbbYSHhzM1NcWBAweoqalR/Qtni8XCwoULCQkJwW63c/78eXp6epAkidBQ+POf4Stf8Tz2W9+C++6b+y2mleq/er0ei8UirwsRhY7vaTQagoODiYiIwGKxyFtQT0xMMDw8zOTkpF//jCwW+N734Px5+MQnPEX/X/8aT24u/Nu/QUhIOIWFhaSnp6PX65mYmKC2tpaGhgZVXwOjoqLYunUrK1euxGg0Mjw8zJ49ezh58qTi35eSv98yMjJ48skn2bx5M3q9noaGBn784x9z5MiRgDhC4Hr40+uHQKTGfEWRM0uRkZG+bkJA87d8LRYLn/70p9mwYQMajYby8nJ+9rOf0d7e7uumfSDefMPCwti2bRvp6em43W7OnDnDgQMHVD+fOzg4mIKCAqKionC73bS0tNDY2IjL5UKn87x4+8UvPAcp/ulPsGIFvL9R0ZxQsv9614ZcvADen0cM5lpoaCjLli0jNDTU102ZxrvFdHh4uFzsuN1urFarKjYoSE+H3/3OswNbYeE4VqtnB7YlS+DQIS3x8fEUFRURFxeHRqNhcHCQqqoqurq6VNv/NBoN2dnZ3HnnnWRlZQHQ2NjIO++8Q1NTk2I/L6V/v+n1ejZu3MhnP/tZMjMzcTqd7Nmzh5/97GfzupbNV/zt9UOgUWO+Yk3OLFmtVtVvr+nP/Dnf5uZmXn/9dUZGRuQpbevXr/f7OfgXuzRf75kz5eXlOJ1OgoKCWL16NUlJST5s5Y2TJImenh7a2tqQJAmTyUR2dra8/ujYMbj/fmhv95wh8pOfwGc+c+PPOx/91ztlze12yyM8Op1O0ef0F/58ffCSJAmbzcbk5KT8Drper8dsNvvFfParGRuz8uc/m/nKV8C7CdmDD8IPf+iZ9jkxMUFLS4s8FdRkMpGeno7FYvFhq29cX18fp0+fltchxcfHs3LlyjkvqOez/0qSRGVlJTt37mRiYgKNRsOqVavYunWrX5wZpAQ1XB/UzF/y9bs1Of/93/9NZmYmwcHBLF++nMOHD8/H0yriiPd0NUER/pxvRkYGn/3sZykqKsLtdrN//35+9atfqWre86X5et/R3L59O5GRkdhsNg4dOkRZWZmqpzhoNBoSEhLkdToTExNUV1fT19eHJEnccotnt6nt22FyEh5+GB57DKzWG3ve+ei/3s0HtFotTqeTkZER7Ha74s/ra263m3379vn96MHF09jMZvO0n9PY2Jhf/78qKTnCZz7jmcL22c96dmb7zW9gwQL40Y8gKMhEfn4+WVlZ8v+rmpoampqaVL0LZWxsLNu3b6e4uBidTkdPTw/vvfce58+fn9P+Np+/3zQaDcXFxXz+859n6dKlSJLEiRMn+MlPfkJTU9O8tWM++fPrh0CgxnwVL3J+//vf80//9E98/etfp7y8nPXr13PHHXfcFEOnQuAJDg7mYx/7GPfeey9BQUG0tbXx05/+lDNnzvj1lJRr8U5fy8vLA6Curo49e/YwPDzs24bdoLCwMAoLCwkLC8PtdtPU1ERDQwNOp5OYGHjnHc/J8BoN/PKXnsXXp075utXXptfriYiIwGAw4Ha7GRsbY2JiQtV98FoqKir48Ic/rJrzqzQajbxBQXBwMBqNBpvNxvDwsN//rCIj4b//G06c8EzpHB31HCC6YQPs3KmhtTUGh2MRPT3J1NaaOHzYyjvv1DMwMODX39fV6HQ6Fi5cyO23305cXJy8McHevXtVfR00mUzcc889fOpTnyI8PJyhoSFefvll/vrXv6p6bZUgXA/Fp6utXr2aZcuW8ZOf/ET+2sKFC/nIRz7Cs88+e9W/64/T1VpaWkhPT/d1MwKWmvIdHh7mz3/+s1ywFxYWctddd8kHOPqj68m3s7OTEydOYLPZ0Gq1FBUVsWDBAlVNy7uUJEl0d3fT0dGB2+3GaDSSlZUlX1f27vVMV+vo8KzX+eY34Wtf83w+G/PdfyVJwmq1ygc3GgwGecevQOMvW0h/UE6nE6vVKo94+OMUtsv1X5cLfvpTz/+Ha607fu21MxQXm8jIyPCr72u2vNN4KyoqcDgcaLVaCgoKWLhw4Q393/L17zebzcbu3bs5ffo04DlD7cMf/jDZ2dk+a9Nc8nW+gc5f8vWb6Wp2u53S0lK2b98+7evbt2+npKREyadWjL9PlVA7NeUbERHBww8/zJYtW9BqtVRXV/PjH/+YmpoaXzftiq4n36SkJG6//XaSk5PlTQn27t0rz8NXI41GQ2JiIgsXLiQ4OFjefa21tRWXy8XWrVBZ6dlxzemEf/1Xz7vWsz3geb77r0ajITQ0VN6QwOFwMDIygs1mU+076oFKr9cTFhYmbwfudDoZHR3FarX6zc/qcv1Xp4OnnoLqarjlFs/XXnkFSkv/dnvlFc/XJyf1DA0NcfbsWVWP6nin8d5xxx3ydfDs2bM3PLrt699vQUFB3H333XzmM58hMjKSkZER/vd//5c333xTfqNEzXydb6BTY76zfJ9ydvr7+3G5XMTHx0/7enx8PN3d3TMeb7PZpg2fevfkr6iomLYAMDIykszMTKampjh37tyMf8f7Lt/58+exXjLJPiMjg6ioKPr6+mhra5t2n8ViITc3F5fLxZkzZ2b8u0VFRdTV1cmH810sOTmZ+Ph4hoaGZsx3DQkJYeHChQCUl5fPuPB7t71taWlhYGBg2n3x8fEkJyczNjZGfX39tPsMBgNFRUUAVFVVzZgTnZubi8VioaOjgx7vqW/vi46OJj09ncnJyRkvyjUaDUuXLgWgpqZmxo5bmZmZREZG0tPTQ0dHx7T7wsPDyc7OxuFwUHWZbau8c57r6+tnvGhOTU2lrq6O8PBwmpubp91nNnvObgDPO7qXKigoIDg4mKamphlrZBITE0lMTGR0dFQ+CNMrKCiIwsJCACorK2ec7p2Xl0doaCjt7e0zzpOJiYkhLS2NFStWMDk5yf79++nq6uL5558nJyeHp556CpPJxLlz52b8AsnKyiIiIoLu7m46Ozun3RcREUFWVhZ2u/2yZ/MsWbIErVZLXV3djC0d09LSiImJob+/f8aU0NDQUJqamkhPT7/slB/vieeNjY0MDw9jMpkwm83U1tYyPDzMyMgIGRkZaDSaaYfMeXc0A8//1UsvhPn5+ZhMJlpbW2ecRxEXF0dKSgrj4+PU1dVNu0+v17N48WIAqqurZ0ytyMnJISwsjK6uLrq6uqbdd7VrhMvlIjY2lr6+Pk6cOIHb7SYxMZHQ0FC++lXYvDmDr341imPH+li0qI2nnoIHHvC82LvWNaKrq4vMzEwuXLgw79eIyclJzpw5I/dhg8FAeHg4xcXFgPqvERe3YXBwUFXXCO/2y15utxu73U5eXh6Tk5NUVlai0+nQXzR06ItrRHl5OX//93+P2+2+7DXiueeKuOUWAwsXeqZ2XiorKwuns5za2lqqqqoICwsjKSmJ0NBQVV0jwPM6wmQyERsby+joqLz2qLy8nE2bNrFq1SoGBgZm9TqipKSEJ554AoPB4JNrxMWvI9asWcPJkyc5d+4cZWVlNDQ0kJ+fT0JCwrS/q6ZrRElJCQ888ACxsbGqv0aA50yuJUuWAPjF64iSkhK2b99OXl7eFa8Rl76OuFhSUhIJCQkMDw/T2Ng47b7ZvI649Od6VZKCOjo6JEAqKSmZ9vXvfOc70oIFC2Y8/pvf/KYEXPO2efNm6cSJE9KZM2cue/97770nTU5OSosWLZpx31e+8hXpwoUL0re+9a0Z9y1btkw6fPiwNDAwcNl/99VXX5XeeOMNacOGDTPu+4d/+AeppqZGevHFF2fcl52dLe3du1eSJEkyGAwz7v/pT38q9fX1Sffee++M++6//37pzJkz0htvvDHjvpiYGOm9996TJEmSYmJiZtz//e9/X+ro6JAef/zxGfft2LFDOnXqlHTy5MkZ9xkMBum9996TbDablJeXN+P+f/mXf5Gampqkr3/96zPuW716tXT06FGpvb39shn+6U9/ksbGxqQ1a9bMuO9zn/uc9Lvf/U564YUXZtyXn58v7d+/X5I8V/YZt5deekkaGBiQ7rzzzhn3Pfjgg1JVVZX0+9//fsZ9iYmJ0s6dOyVJkqTw8PAZ9z/33HNSV1eX9JnPfGbGfXfffbdUWloqHTx4cMZ9er1e+vu//3upsrJSysjImHH/N7/5TamlpUX68pe/POO+W2+9VTp27JhUX19/2e/1zTfflMbHx6Vly5bNuO8LX/iCVF9fL/3bv/3bjPuKioqkX/7yl9LExMRl/93//d//lYaGhqRt27Zd9nt94YUXpM9//vMz7ktLS5N2794tSZIkmUymGff/6Ec/knp6eqRPfOITM+776Ec/KpWXl0s7d+6ccV94eLj03nvvSS6XS0pOTp5x/3e+8x2pra1Nevrpp2fcdz3XiJ6eHmnBggUz7vvKV74iHTzYImVk/MeM+651jfjBD34gjYyM+M01IjY2Vtq3b5/kdrsD4hoBSH/+858D4hphMpmkPXv2SH19fVJWVtaM+31xjcjNzZUOHTp0xWvEt7/9tgSSVFo6/Xd3aakkgSS99toF6eWXX57x91JTU1V5jbjS64iPf/zj0u9+9zvpG9/4xoz7rud1hD9dIyIjI6Unn3xS+uY3v3nZn43arhGf+9znpPPnzwfMNeK9996THA6HX72OuNo14mqvIx5++GGpurr6steID/I6YmRk5Jp1iKJrcux2OyaTiddee42PfvSj8te/8IUvUFFRwcGDB6c9/nIjOampqRw8eNBvRnJcLhcdHR1iJEehkRyLxcLExISq34Hp6+vjwIEDBAUFyZmsWrVK3r4YfDeSk5aWhtFonPU7MImJiYyPj1NSUkJnZydarZacnBzS09MxmUyqfJfW++82NTUxODgIgNFoZM2aNWRkZNDT08cLL7Tx/PMwMeFZn/PUUxb+7d9y0ekuf43w/p/z9bu0TqeTyclJNBoNBQUF6PX6Ge+cXdxeNVwjHA4HGo2GJUuWMD4+ruprBPztXVq3201paak8c8FgMGAymcjJyZn3a4T3d8qV3qV1OIpYs8ZAaen0kZyyMli+HJ59Fp580vMu7eTkJB0dHUxNTWE0Glm7di0pKSlUVlaq6hpx8esISZJob29ncHCQkJAQrFYrMTExpKSkyKPbV3sdYbPZWLFihV+M5HgZDAby8/PZtWsXf/3rX3G73URGRrJlyxZiYmJUdY2w2Wzk5OSIkRyUuUbYbDaio6P9YiRn48aN17UmZ142Hli+fDn//d//LX+toKCAe+65R5UbDxw/fpw1a9b4uhkBK1DydTqdHDx4kKNHj+J2uzGbzdx1113yf2JfudF8x8bGOH36tPzLLjIykpUrVxIVFTVXTfSJkZERmpub5RdJ0dHRpKamYjQaaW+HJ5+Et9/2PHbRIs/OU+vXz/x3/Kn/SpLE1NQUk5OT8i+M4OBgTCaTajeR8Kd855L0/tk63vU5Op1OPlh0Pl0rX28x88or8P7rbQBqauChhzyfP/ww/PjHYDJ5puW1tbXJ1wuz2UxWVta0N3zUaHx8nBMnTtD3/mFCqamprFixQn5j60r8vf/W1dXxxhtvYLVa0el0bN26lVtuuWXa9GR/5u/5qp2/5Os3Gw8AfOlLX+IXv/gFL730EjU1NXzxi1+ktbWVJ598UumnVsSl77wIcytQ8tXr9WzdupXHHnuMuLg4rFYrf/jDH3j11Vd9+j3e6HNbLBZ5PrrRaGRoaIg9e/ZQUVEx490rNQkPD2fRokXEx8ej0WgYGBigqqqK7u5ukpMl3noLfvtbiImBs2c9mxJ8+tNw6dJCf+q/F29h7H3xNTU1xfDwMJOTk6pbFN7Y2MhXvvKVy45IqZ33bJ3w8HB0Oh0ul8sn5x9dq/96z/x86CFPseO9eQscjQZ+/WvPBgUNDZ53otPT08nNzcVgMGC1Wqmurp4xWqM2oaGhbN68meLiYrRaLW1tbezatUsueq7En64Pl5OXl8fnPvc58vPzcblc7Nq1i5dfftnv2+2llnaqlRrzVbzIeeCBB3j++ef51re+xZIlSzh06BDvvPOOX2xD90H4y4hSoAq0fJOSknj88cfZsGEDWq2W2tpafvzjH8sL3ufbXOSr0WjIysrijjvuIC0tDbfbTW1tLTt37rzshiJqodPpSE9Pp6CggNDQUFwuF62trZw7dw6rdZxPfhJqa+Hxxz0v5v73fz2HJL7wgmdHNvDP/qvVarFYLISHh6PX63G73VitVoaHh5mamlJNsTM8PMyRI0dUfWbJtej1esLDwzEajUiSxNjY2LzuenWt/pubC3V103dW897q6jxbscfFeXYqXL4c3njD8/ciIyOnnVfV2NhIc3OzKndr8tJqtSxcuJBt27ZhsViwWq3s37+f6urqK35f/nh9uJTZbOaBBx7gwx/+MEajkebmZn7yk59cdtqYv1FDvmqmxnwVn652I/xxuprNZrvmkLTwwQVyvr29vbz11lvyWrDk5GQ+9KEPzdjNRklK5NvR0UFpaSkTExOAZz7vkiVL/Pq8oGuRJIm+vj7a29txOp1oNBp57r3BYODkSc+2uu8fN8HixZ4T4Vev9u/+650WNTk5icvlAjzFnclkwmg0+vW0FLWfkzMb0iXnH5nN5nmZ4jUX14eODrj/fvCeEvG1r8F3vuPZnVCSJDo7O+ns7ESSJCwWC9nZ2RiNxjlove84HA5Onz5NS0sL4FkDs2bNmhk/M7X9fhscHOTPf/4z7e3tgGed0h133OG3ZyCpLV+18Zd8/Wq6WqA5cOCAr5sQ0AI537i4OB599FHuvvtugoOD6ejo4MUXX2T37t3zNi1FiXyTk5O54447yMvLQ6PR0NrayjvvvMP58+flF9Jqo9FoiIuLo6ioiJiYGLnoqayspKurixUr3Bw/7jkkMTLS8871xo2wZcswl6xJ9SveaVERERGYzWa0Wi0ul4uxsTFxvo4f0Wg0mM1m+Y0Cq9U6Y+G2Eubi+pCcDAcOwD/9k+fP/7//H9x7r+cgUY1GQ3JyMrm5uej1esbGxjh37pyqz+ACz+L9NWvWsHr1avR6PT09PezcuXPGInO1/X6Liori0UcfZePGjWg0GsrKyvjFL37ht9MN1Zav2qgxX1HkCMI80mg0rFixgqeeeorCwkLcbjdHjx7lv//7v2fs2KImBoOBZcuWsX37dmJiYnA6nZSXl7Nr164Zv+jVxGAwkJWVxcKFC+UpbG1tbVRVVTEyMsjjj0vU1Xk2JtBqoaQknoIC+OIX4f0N2/zSxet1TCYTGo0Gp9PJ2NgYw8PDotjxAxqNBpPJNK3QUcuBjQYD/Md/wO9+B0FB8OabnnVs3k20IiIiKCgowGQyyQfz+usL5+ul0WjIzMxk+/btREREMDU1xYEDBzh//ryq/y9ptVo2b97Mpz71KUJDQ+np6eHFF1+ksrLS100ThGsS09VmqbGxkaysLF83I2DdbPnW1dXx9ttvywv6CgoK2LFjB+Hh4Yo833zkK0kSTU1NnDlzRt6tLCMjg+LiYlXvqiRJEgMDA7S3t8sjbxaLhbS0NMxmM9XV8NRTExw86HlRGhkJ/+//wec+B/4+G8ftdjM1NcXU1JS8nkCn0xESEkJQUJBfTGPr7u7m3//93/k//+f/zOsUT1+TJImJiQl5S/CwsDDFpgspcX04fhw+/GHo64OkJPjrX+H9nYVxuVzTtnBPTk4mKSnJL/rbjXA6nZw+fVrewjgtLY2VK1fS1tam6t9v4+Pj/OlPf5K3t/a36Ws32+uH+eYv+c6mNhBFziy1traSlpbm62YErJsxX7vdzv79+zl+/DiSJGEwGNiwYQO33HLLnG8hO5/52mw2KisraWxsRJIk9Ho9BQUFLFiwAJ1ONy9tUILL5aK7u5uuri65IIiOjiY5OZne3l5qatL48pc9u7ABZGbCM8/A3/2dZ12CP3O73fKaHe/3ptVqCQoKIjg42Oc/t5vx+gCeQmd8fBybzYZWqyUiIkKRbcCVyrepCe6+G86dA7MZ3noLNm/23Oc9e8Z7jk1sbCzp6emq3ebcS5IkGhoaKC8vx+12Ex4eLm9somZut5tDhw5x8OBBJEkiLi6O+++/n5iYGF837aa9PswXf8lXrMlR0KUHXglz62bM12g0smPHDp544gnS0tJwOBzs3buXn/zkJ3M+hW0+8w0KCmLlypVs27aN6OhonE4nlZWVvPvuu7S1tal2CodOpyM5OZmioiKio6OnbTl9+vRpNm+2U14OL74ICQmeF3if/rRnc4LXXwd//ra1Wi0hISFERkZiNpvR6XS43W4mJycZHh5mbGzMZ1uFDw8P89JLLwX07mpXotFoCA0NlXfHGx8fV+T/j1LXh8xMz0YEW7eC1Qp33gnvvOO5T6PRkJqaSkZGBhqNhr6+Purr61W7ns9Lo9GQm5vLli1bCAkJYWRkhDfffHPGoaRqo9Vq2bRpE5/+9KcJDQ2lt7eXF1988bIHqs63m/H1w3xSY76iyBEEP5GQkMAjjzzCvffeS2hoKAMDA7zyyiu8+uqrqn5hFx0dzbZt2+TdhsbHxzl69Cj79++fcaq0mgQFBZGdnU1hYSERERHyzmWVlZV0d7fzyCNOLlzwLLyOjPS8i33vvbB6NezZ4+vWX93Fa3a806O839/w8LBPtp9ubGzkmWeeCchzcq6Ht9DRaDTY7fZ5P0PnRoWHe6aqffjDMDUFH/kI/OlPf7s/Li6O3NxctFotIyMjnD9/XtVnb3nFxMSwfft2YmNjcblcHD58mPr6el8364ZlZmby5JNPkpmZid1u5w9/+AN79+5V9bbgQuAR09VmyWq1Yjabfd2MgCXy9bDZbBw4cEA+T0ev17N+/XrWrVt3Q1PYfJ2vw+GgtraW2tpaXC6XfObOokWLVL1eB2BsbIwLFy7ILz71ej0JCQnEx8czNqbjhz+E55/3vJMNnt3YvvlN2LTJc+6Ov3M6nUxOTmK32+XiRqvVYjQaCQ4OnvOplZe6mbaQvpqJiQkmJibkM3Xmcv3KfFwfHA741Kfg97/3bNbxm9/AJz7xt/vHx8epq6vD6XRiNptZsGCB4n3r/8/eeYfHUV19+N3VFvXeey9uMrbce5MxGAw2BjeICQFMKIbgFCDEEGpCqEk+SkKoBozBYDAY94Z7r5IsySpWtXpdrbbM98d6B8kqlmyNpJXmfZ77bJm7M3d+e/funLnnntMdmEwmfv75Z3EmJzY2lqFDh9q8W57ZbGbLli3svRQzPCYmhrlz5/bIeN7T/299nd6ir+yuJiG9YUq2LyPra0Gr1TJz5kzxTpnRaGT79u38+9//JiUl5arvoPe0vmq1msGDB3PDDTcQGhqKIAhkZmbyww8/cOrUKQwGQ4+271pwcXGhsbGRmJgYHBwcMBqN5OXlceLECerrC3j2WROZmbB8uSUQwc6dMHWqJerU5s29240NLEabi4tLC1e2hoYGKisrqaqqaha4QEYa7O3tUSqVGI3GLv+9dMf4oFZbDJu77waz2WLwrF//y3ZnZ2fi4+NRq9XU1dWRmpraJ2Z0rIE8EhMTAUvQmd27d9vcjNzlKJVKkpOTmTdvHmq1mvT0dP7zn//0SFTNnv5/6+vYor6ykdNJyntzXNg+gKxvc3x9fbnrrru47bbbcHV1paKigtWrV/PRRx9dlW93b9HXycmJsWPHMnXqVHG9zpkzZ/jhhx/IyMiwWX/8iooKPDw8GDRoEJGRkdjb2zczdkymAl591URGBjz0kCW87s8/Q3IyjB0LGzb0fmPHum7H6spmjb5mMBiora2loqKCmpqaZjM+Ml2HNRAEIEYv7Cq6a3yws4P//heWLAGjEebPtxj9VhwdHYmLi0OtVlNfX8+5c+dsdkxoSkVFBQkJCYwfPx6VSkVhYSHbtm3rlhxIUjN48GDuuece3N3dKS8v57///W+3r+HoLf9vfRVb1Fc2cjpJb5iq68vI+rZEoVAwaNAgHnroISZOnIhKpSI7O5v33nuPdevWdSqRXm/T19fXl+nTpzNu3DhcXFxoaGjg8OHDbNy4kby8PJu7SLbqq1Ao8Pb2ZvDgwa0aO3Z2Bbzxhonz5y1JE+3tLaF2b7gBRo60rFXo7dd0CoUCjUbTbHZHpVKJa3eqq6upqKigrq4Og8Fwzd+lvb094eHh2Nvbd9EZ2C6aSzHJu3qGozvHB6US/ve/X9bozJkDqam/bLcaOiqVitraWtLT021+ltCqb3BwMFOnTsXe3p7Kykq2bt1q8wlRwbKu9L777hPX6axevZrt27d32zje2/7f+hq2qK+8JqeTGI3GPuEf3FuR9b0yVVVVbNmyhVOnTgGWC57x48czZsyYK+Yr6M36mkwmzp8/z+nTp8U71D4+PgwePBhfX98ebl3HaEtfa46dgoICMaGjSqXC19cXPz8/ysrUvPoq/N//QX295TMxMbBihSUym61c1wuCgMlkQq/Xo9frm12U2tnZodFo0Gg0qFSqq1pL0pv7b3diNpvFu6rWCH9dQU/o29AA06fDnj0QFQUHDoCX1y/ba2trSUtLw2Qy4eXlRWRkpM3m0blc39raWnbu3ElNTQ329vZMnDgRT0/PHmxh12A2m9m8eTP79u0DLLM8c+bMkbxvyeODtPQWfeU8OV3JAw/8kqYZuHjxos1ccNkisr4dR6fTcbGkhIZLrg4qtRofb29cXF1p6xLAFvS1hshtGiZXa2+Pq6srml6SdK4trqSvgCUvkr6hAdMlA0CBxVDV2ttjNCrJOg9Z2ZYF2gBaDUREQng4aHr36TdDAASzGfOl0vSPRqFQoFQqUSoUKJTKNvvr5dhC/+0OrP0IQKNWd9lFf0/pq2+E3bugXgfe3jBmdPNgHAaDgdpLETvs7e1xsBWr/zJa09dkMlFWVobBYEChVOLl6Sm6I9o6VVVVFBUXgyDg4OhIYGAgKglzbcnjg7Q00zcoCN5+u0fa0RnboOdNst7OZV/isY0bmTlzZg81pu8j69txHIBQQeDMmTNs3ryZqqoqwJI1/PrrryckJKTFZ2xBXyXgCqjq6zl79iznz58XZwSCg4MZNGgQ7u7uPdnENrmSvgpAC2gEgcrKSgoLC6mtrbVsUyjw9PQkNCCAYLMj778Pr70GublAKjjnwb33Wtby9IKk01dEcakosczwGAwG9Hp9i7U6SqUStVqNRqNBrVa3GW3q+PHjjBs3jj179jB06NDuOIVei9lkoqaiQuwzXRWer6fGBy3gfxpGj4a6UvjrSHj66V+2qwHDxYtkZ2cDEB0dbZMzHq3pawd4GAz8/PPPFBcXY2dnx/jx4wkICOiZRnYhbkDZ+fOsXr0avV6Pl5cXixcvluy7s4X/N1vGFvWV1+R0kkhbuLqwYWR9O0fT9TrTpk1Do9GQn5/P+++/zxdffEFJSUmz+rakr6OjI0lJScyaNUtMFJiXl8fGjRvZv3+/aBz0Jjqqr0KhwMPDg4SEBOLj43FzcxNd2k6fPk1+fhpLl1aRni7w6aeWRKK1tfD66xAdbckxsm1b7w9SYKXp+h1PT08xYIFSqcRsNqPX66mpqaGiooLq6mp0Oh0mk6mZMWQ2m6mvr7f5dRldQdMw5V3putWT48OgQRZ3TYBnnoHdu5tv9/X1xd/fH4CsrCzR7dOWaEtftVrNxIkTCQ4ObhFq2taJjIzknnvuwc3NjbKyMv773/9y4cIFyY4lIx22qK9s5HQSR0fHnm5Cn0bW9+pQq9VMmDCBRx55hGHDhqFQKEhNTeX//u//+O6776iurgZsU18XFxdGjx7NzJkzCQ4ORhAEsrOz+fHHHzl06BB11sQzvYDO6qtQKHB1dSUuLo6BAweK6yusyRDT0k4zY8ZFjhwxsWEDzJxpMWzWrbNkj09MtESpsq7jsQUuD1jg5uaGg4MDdnZ2CIJAY2MjdXV1VFRUUFlZSW1tbYv1Pf0Zs9ksRuPqaremnh4f7rrLUsxm+PWv4fKgYyEhIbi4uGAymcjMzLS5PtGevnZ2dowZM6ZPGjq+vr785je/ITAwkPr6ej766CPOnDnT5cfp6f7b17FFfWUjp5OcPn26p5vQp5H1vTacnZ25+eab+e1vf0tCQgKCIHD06FHeeusttmzZwpEjR3q6iVeNu7s748ePJzk5mYCAAMxms5hj5+DBg71iZuda+q+TkxNRUVEMHjwYf39/7Ozs0Ol0ZGdnc+rUSQYNyuO77xpJSYHf/hacnODUKYsLW0gIPPEESHSDVDIUCgVqtRonJyc8PDzEKG0ajQaFQoHJZKKhoYGamhrRHVOn0/Vbo0cQBGpqasQEwV1t5PSG8fettyAwEDIy4Pnnm29TKBRERUWJOXTym6yXtQWupG9fNnRcXFxYunQpcXFxGI1GvvrqKw4dOtSlx+gN/bcvY4v6ykaOjEwfxMfHhzvuuIN77rmHsLAwjEYjP//8M+vWrWPPnj02nXTT09OTSZMmMW3aNPz8/DCbzZw/f54ff/yx1xg714K9vT2hoaEkJiYSGhqKVqvFYDBQUFDAyZMn0WjO87e/1XLhgsCrr0JEBJSXw8svW57fdpsluagt2gDWhImurq6iW5uDg0OziD5Wo6e8vJyKigpxpudy97a+hsFgEBeoK5VKnJ2dbTbKWHu4ucG//mV5/ve/w/nzzbdrNBrCw8MBKCoqot6WpjE7QGuGTk8k1pQCjUbDHXfcwYgRIxAEgR9++IFdu3b16d+tTM8iR1frJNXV1b2mLX0RWd+uRxAE0tPT2bJlCxcuXECr1eLq6sqUKVNITExsc6G3rVBaWsrp06cpKioCLAvZw8LCGDBgAC4uLt3aFin6ryAIVFRUUFxc3CyXhpOTE76+vri5ebJhgx1vvWVZp2MlMtIyy7N0KVxaymDT1NbWcvDgQQYNGoRGo2k1R4xSqUSlUjUrtt6/BUEQDTkrbm5uVwwXfzX0pvF35kzYtAnuvBM+/rjl9oyMDMrLy3FycmLAgAE2YfB1Rl+TycTevXvJz89Ho9EwderUXhtwpbMIgsCOHTvYeSkDrNUd+Vq/w97Uf/sivUVfOYS0hBw9epRhw4b1dDP6LLK+0mE2m1m9ejVFRUWi64+3tzdTpkyxmYuE9igtLeXMmTOie4dSqSQ0NJT4+PhuuziQuv/W1tZy8eJFysvLRXctlUqFt7c3vr6+ZGTY8+678MkncOkrRqWyJFq87z5LPhJbvuZvqq/ZbMZoNGIwGDAYDG3O5NjZ2YlFpVKJz3t7f7e66l3umufq6iomA+1qetP4e/gwjBhhCRyXkgJxcc23NzY2cvr0aYxGI5GRkXh7e/dMQztBZ/U1Go3s3LmTkpISHBwcmD59uk0mZGyL/fv389NPPwEwdOhQbr755mu6KdGb+m9fpLfo2xnbwIb/7nqGy6NVyXQtsr7SoVQq8fT05OGHH2bmzJk4ODhQWlrKmjVreOedd0hNTbVptwFvb28mTZrE9OnTxTU72dnZ/PTTT+zatYvS0lLJ2yB1/3V2diYyMpLExERCQkLQarUYjUaKioo4efIkanUaf/1rBXl5Zj78EMaMAaMRvv7acmc8OhpeegkuTXrZFLm5uaxcuZLc3FzA0p81Gg1OTk64u7vj6emJm5sbTk5O2Nvbi5HHTCYTjY2N6HQ6ampqqKyspLy8nMrKSmpqaqirq6OhoaFdQ6k7MJvNzYIuVFRUoNPpMJvN2NnZ4eTkhJeXl2QGDvSu8TcpCW680RJo4513Wm7XaDRitLX8/HybWKPVWX1VKhXjx4/Hzc0NnU7Hzp07m83o2TqjR4/m1ltvRalUcvz4cb799ttr+h57U//ti9iivrKR00nsbTQJma0g6yst1ou/MWPG8OijjzJlyhS0Wi3FxcV88cUX/Oc//yE9Pb1PGDvJycmEhoaiUCgoKChgy5YtbN26lYKCAsnOr7v6r1qtJiAggCFDhhAbG4u7u7sYlS09PZ309BNMnXqBbdsaOHnSklvHzQ2ysuDJJyE4GGbPhjVrLBnnbYHS0lLWr1/fprFqDWLg4OCAs7Mz7u7uYvQ2Z2dn7O3txTw8giBgNBrR6/XodDpqa2upqqqioqJCXOtTVVUlGkHWYAcGgwGj0YjJZLIkOO1EPxIEQZx9amxspKGhgbq6Oqqrq8XjNg2fbY1C5+rqiru7Ow4ODpLPPvW28ffBBy2PH3zQMtIagJ+fH2q1Gr1eT1lZWfc27iq4Gn21Wi2TJk3CycmJ6upq9uzZg8lkkqB1PUNiYiK33XYbSqWSkydPsnbt2qs2dHpb/+1r2KK+srtaJxEEode7Odgysr7S0pq+Op2OvXv3cuDAATH/RkhICFOmTCEiIsLmv4+amhpSUlLIzs4W/zzd3d1JSEggJCSkS9ds9GT/bWhooKSkhNLS0maBJVxdXfH29kar9WDtWjvefRf27fvlc+7usGCBJXTv6NFdlleyyzl69CjDhw/nyJEj1+QyYTU2TCaTaKw0fd6Zv0SFQtHs+778u7fuSxCEDu3X6lJ3pcSoUtHbxl+zGUJDIT/fEjb95ptb1iksLOTChQs4OTkxcODA7m9kJ7gWfauqqtiyZQsGg4GYmBiGDx/exa3rWVJTU1mzZg0mk4kBAwYwb9487OzsOrWP3tZ/+xq9RV/ZXU1CNm3a1NNN6NPI+kpLa/o6ODgwbdo0li9fztixY1Gr1Vy4cIGPP/6Yjz76iJycnB5oadfh4uLCyJEjmT17NvHx8ahUKiorK9m3bx8//vgj6enprS5ivxp6sv/a29sTEhJCYmIi0dHR4uxOdXU158+fJz39BJMn57B5cx1nzwo88YRlRqey0uIONHYsxMfDCy+AjX/l7aJQKLCzs0Oj0eDg4ICTkxOurq54eHjg6ekpzv64uLjg5OSEg4MDWq0WtVotBjKw/tFbDSZrsRpLTY2mpoaTQqEQgyNcfnzrsV1cXMREqd1Nbxt/lUqLyxrA3Lnw7rst63h7e6NUKqmrq+tVObNa41r0dXNzY9SoUQCkp6dz/vKwczZOfHw8d9xxB3Z2dpw9e/aqZnR6W//ta9iivqorV5GRkekPODk5kZyczJgxY/j55585fPgw2dnZfPDBB0RGRjJp0iTCwsJ6uplXjaOjI0OHDiUhIYGMjAzOnTtHbW0tR44c4cyZM8TExBAVFWWTU/JNsa698vT0RK/XU1paSmlpKXq9nuLiYoqLi3F0dOThh714+mkv9uzR8PHHlnU7587Bn/9sKVOmwJIlcOut4OHR02fVPVgNoI7cQbbOzjSdpbl8tqbpTI/1eW+4E2pLWCUdOBCWLbM8v//+X7ar1Wrc3d1FN8O+tDD/coKDgxk8eDCnTp3i8OHD4ixtXyE2NpY77riD1atXc+bMGdRqNXPmzJF/MzJXjTyT00ls+SLPFpD1lZaO6Ovi4sKsWbN45JFHSEpKQqlUcv78eT744AM+/PBDzp8/b9NrdrRaLQMHDuSmm25i+PDhODk50dDQwKlTp/j+++85ePAglZWVV7Xv3tZ/tVotQUFBDBkyhLi4ODw9PVEqldTX13PhwgVOnz5BcHAqr71WSn6+iQ8+sBg3ANu3wz33gJ8f3HQTfPopNIlg3e34+vryq1/9Cl9f355rRBOsszJWFzOVSoVarW5WmkZzazoD1Fvpbf333XfhP/+xrCk7dszyuGxZyxkda/REa9TI3kpX6DtgwACCg4Mxm83s37/fpnOetUZsbCzz5s1DoVBw/PhxNmzY0OH/m97Wf/satqivvCankxQVFYkRXWS6HllfabkafSsrK/n55585duyYuOA1JCSEiRMnEh0d3esv3K6EyWQiLy+PtLQ0ysvLxff9/f2Ji4vD39+/w+doC/3XaDRSXl5OWVlZs7w7SqUSDw8PvL29qahwZdUqBatXw6lTv3zW3t7iPnTHHZZHR8fubbst6GvL9CZ9333XYtA89BC89ZZlrZggwPLl8M9/WlwsrTM6BoOBY8eOATB8+PBOr+XoLrpKX4PBwE8//URdXR0RERGiG1tf4uTJk3zzzTcIgsCECROYNm3aFT/Tm/pvX6S36NsZ28Cm3dVMJlO338U4ffp0n0nI1RuR9ZWWjuhrvTNtvbB3d3dn9uzZTJgwgT179nD06FEuXLjAqlWrCAwMZOLEicTFxdmssWNnZ0dYWBihoaGUlpaSlpZGfn4+RUVFFBUV4ebmRmxsLGFhYahU7Q+ZJ06c6BV/Au2hUqnw9fXF19eXhoYGysvLKS0tpaGhgbKyMsrKylCr1dx5pxfLl3uSk+PEl18q+OILizvb119bipOTZSH4HXdYwlNL7eVXW1vL559/zr333ouzs7O0B+un9Jb+azVwHn4Y3nzzl2AYCoXlNTR3XbPOnBkMBnQ6Xa/tH12lr1qtZvTo0Wzbto2srCwCAwMJCQnpghb2HoYMGUJjYyPr169n9+7duLq6MmLEiHY/01v6b1/FFvW12Zmc2tpa8vLyut1tRqfT4eDg0K3H7E/I+kpLR/V1dHQkICCg1ZwcNTU17N27l8OHD4s3Gfz8/Jg4cWKfSCoKlvHl3LlzZGVlieeo1WqJjo4mOjq6TQ03btzIzJkzu7OpXYIgCNTV1VFWVkZ5eXmzm0darfbSonhP0tMdRYMnO/uXzzs5waxZcMstlhkeKe5TdFV0NZm26Q39V68HFxdISLC4qLUWf8FshuuusyQJrakBrRZSUlKoqakhKioKLy+v7m94B+hqfU+dOsWZM2fQaDTMmjWrT/537ty5k+3bt6NQKLjjjjuIj49vs25v6L99md6ib2dmcmzSyDGZTKSnp+Po6IiPj0+3XlQZjcYr3s2VuXpkfaXlSvoKgkBjYyMlJSWYTCZiYmLajPJUV1fHvn37OHjwoBh62sfHh/HjxzNo0KBe6zLSGQwGA+fPn+fcuXNi5CalUkloaCjR0dF4eXk1G38qKirwsPFV+mazmaqqKjFhZtOcHE0NnjNnHFm9WsFXX8GFC798XqWyrOu59VaYMwcCA7umXbKRIz29pf+2NZMDbbusnTt3jsrKSiIiIvDx8emZhl+BrtbXZDKxdetWysvLCQ0NZezYsV22796CIAisX7+eI0eOoFKp+NWvftXmrFVv6b99ld6ib583choaGsjKyiI8PLzb71zU19fj2N2O6P0IWV9p6ai+9fX15OTkEBERccVoYzqdjv3793PgwAEaLmWWdHNzY8yYMQwbNkzSDO3dhdlsJj8/n3PnzjXL+uzh4UF0dDShoaGo1WpOnDhBYmJiD7a0azGZTM0MnqYhXe3t7UWDJyXFgW+/VfDNN3D2bPN9jBplmeG55RaIi7v6PDyykSM9van/dmZNDljCKldUVBAeHt5rglNcjhT6lpeXs3nzZgRBYNKkSQQEBHTp/nsDZrOZ1atXk5aWhqOjI/fdd1+rbte9qf/2RXqLvv0mT05PuMX0tUgmvQ1ZX2npqL6dydHh4ODAlClTePTRR5k2bRrOzs5UVVXx008/8frrr7N9+3bq6+uvtsm9AqVSSUhICNOmTSM5OZmIiAjs7OyoqKjg0KFDfP/99xw9epTspj5cfQA7Ozs8PT2Jjo7muuuuIzo6WozQ1tDQQEFBAWfOnEatPsl99+Wyb181qakCf/sbjBlj2ceBA/DEExb3o5gYeOQR+Omn1jPYy/QsRUVFPd0Ekfvvtxgy//qXpc+YzW0bOPDL2NabPQGk0NfT05OYmBjAciOg6cxrX0GpVDJv3jwCAgKor6/n888/F70HmtKb+m9fxBb1tWkjpyfojesNnnnmGZZdWoW5Y8eOZj6rzs7OXLx4saea1ml6o759CSn1tbe3Z8KECTz66KPMnj0bT09PdDodO3fu5PXXX2fDhg1XHZq5N+Hp6cmoUaO4+eabGTp0KM7OzjQ2NnLu3DnOnj3Ljh07yMvL63Qiu97O5QZPVFQUHh4eKJVK9Ho9RUVFpKamUl9/nNtuO8/69eVcuGDi7bctgQnUasjMtFykzpoFXl4wezb83/9BVtaVj69SqXBzc+vVF7G2jlqt7ukmNOOmmyxrcv71L8sanLYMHEEQ0Ov1gMWlsrcilb6DBw9Gq9VSU1NDVkd+TDaIRqNhwYIFODs7U1xczNq1a1usye5t/bevYYv6ykZOJ7nS1Fh4eDiurq7omtymrK6uxsHBoZnxER4ezv79+5t9dtmyZTzzzDNd2t7a2tpeOXX/0EMP8dFHHzV779577+XJJ59sUfett95i0qRJ4uvDhw8zZcoUYmNj+eqrr1rUnzt3LitXruz6RktIZmYm48aNw9HRkWHDhnHixIkrfmbfvn0olUpefvnlZu/v37+f0aNH4+zsTHBwMF9++WWz7ffccw+enp64u7uzaNGiLj0PsFyMJiUl8dBDDzF//nwCAgIwGAwcOHCAt956i7Vr11JcXNzlx+1utFot8fHx3HjjjUyaNInAwEAiIyMpKiri559/Zv369Zw5c6bZWNBXsLOzw8vLi5iYGHGGx9vbG5VKhcFgoLS0lIyMDIqKjjF16jk++aSEoiID334L990HwcGWmZwffoAHH4TISMtMz+OPw5YtcMnrsRlDhgyhsrKSIUOGdPv59hemTp3a001oxnPPWWZwoqIsQQZaM3DA4jJrMBhQKpW9evG9VPqq1WoGDRoEWCJoGo1GSY7T07i5uXHHHXdgZ2dHamoqO3fubLa9t/XfvoYt6iuZkZOdnc0999xDREQEDg4OREVFsXLlylanGG2JjiQb8/f357vvvhNfr127ts+Fd7xWNm7cSHJycrP3lixZwurVq1sM0J999hmLFy8WX//000/MnDmTxYsXs2rVqmZ1q6qq2LBhgyQX71KycOFCkpOTKS8v59e//jW33npru39UZrOZxx57rEVIzcLCQubNm8fTTz9NZWUlJ06cYPjw4eL2JUuW4OzsTFZWFiUlJfz+97+X7JyUSiUDBw7kvvvu46677iIyMhKz2czJkyd5++23WbVqFdnZ2TadWBQss2MBAQFMnDgRR0dHEhIS0Gq11NfXiwlG9+zZQ2Fhoc2fa2tYZ3giIyO57rrriI+Px9/fH61Wi9lsprKykqysLDIzjxMTc5aVKwtISanjxAmBl1+GSZPAzg5SU+G112DGDEt0tunT4aWX4OBBsHrgbNq0qUfPta/Tm/TdudNi1IAlIWhNTesGDlgWRIPlJmRnXG27Gyn1jYyMxNnZWVyz3FcJCQnhpptuAiyR186fPy9u6039ty9ii/pKNhqkpqZiNpt59913OXPmDK+//jrvvPNOq3fq+xoLFy5sdvG9atWqa77o1ul0PPTQQwQGBhIcHMzf/va3Dn1OoVCIfpTh4eH87W9/Izo6Gh8fn2azRuvXrycuLg4XFxdCQkL4/PPPAcvC45UrVxIWFoa/vz+PP/54qxffmzZtYty4ceLriIgIHnzwQcCSTNLV1VX8XGZmphiiuCkTJ07E3t6ezZs3i++dP3+eY8eOcdttt4nvWcMYLlmypIUL1Ndff82gQYOIi4sTXfeefvpp3N3diYuL4+zZszz//PN4enqSkJDAmTNnxM/+9re/JTAwEHd3d5KTk8nNzQUgLS0Nb29vMjIyAMtMib+/f5e5AaalpZGWlsYTTzyBvb09Dz30ECaTib1797b5mffee49Ro0aRkJDQ7P3XX3+dpUuXcuONN6JSqfDy8iIqKgqAM2fOcPz4cV577TXc3NxQq9Vcd911XXIO7aFQKIiMjOSuu+7ivvvuY+DAgSgUCtLT0/nwww957733OHHiRJ/wJddoNCQmJnLzzTczevRovL29MZvNXLhwgZ07d7J+/XpOnz4tRmrraygUClxdXQkNDWXIkCEMGjSI4OBgnJycEARBDP1/9uwZTKbjzJ9/nq+/LqOw0MCaNXD33ZZobHo9bN0KTz5pCVzg5QVTp55h/vzf8N13Z+iDtmKvoLcY4VlZMH++5flvfmOJ1teWF5rJZBLH4t4aOtqKlPra2dkRFxcHWK6/+sJ42hZDhw5l2LBhCILA119/LSY17i39t69ii/pKZuRcf/31fPDBByQnJxMZGcnNN9/MihUrWLt2bZcfSxCgrk76Igh0KFLUjBkzOHr0KOXl5RQVFZGens7EiROv6RxXrFhBVVUV586d4+DBg3z88cd8//33nd7P119/zb59+zhw4ADvv/8+69evB+A3v/kN//vf/6ipqeHQoUNiBI3XXnuNvXv3cuTIEVJTUzl69Chvv/12i/2OGTOGY8eOodPpyM/PB+Dnn38GYM+ePYwYMUL0pbfOxFyONQ7+Z599Jr732WefMWvWLDw9PQHLTE1WVhZDhw4lKiqKoUOH8vXXXzer33TWJyMjAx8fH0pLS0lOTuaGG27AwcGBixcvMnv2bP785z+LdcePH09KSgpFRUUEBwfzyCOPABAXF8eTTz7J0qVLqaurY+nSpbz11lutugH+/PPPuLu7t1la4+zZs8TFxTXrW0OGDGlmgDWlvLycN954o1XXxkOHDqFQKBg4cCABAQHceeed4l3Ow4cPExsby5IlS/Dy8mLkyJHs3r271WNIRWBgIPPnz+fhhx8mKSkJtVpNYWEh33zzDW+88Qa7d++2afeu4OBgwHLBER4ezvTp05k5cyaxsbFoNBrq6uo4ffo069evZ+fOnVy4cKHPXowoFAocHR0JDAxk4MCBDB06lPDwcDw8PLCzsxPd2jIzM8nOPs6AAWd57rl8UlNrOXtW4F//soShdneHqirYvl1PdfUF5szRExQEd94J771ncWOywf/eXom1/3YbJhPs2AGff255NJnIzras4SopsazDef319ndRXFyMwWAQw5v3ZqTW1xoNs66ujsLCQkmP1dPMmjULPz8/6urq+OqrrzCbzd3ff/sZtqhvt87rVlVVtTsI6fV6qqurm5WOUF8Pzs7Sl/r6jkVuUalU3HLLLaxZs4YvvviC+fPntzqFPmPGjGYXwB988EGr+xMEgQ8++IBXX30VZ2dnAgMDeeCBB1pdj3IlHn30UXx8fIiMjOT+++8XDQS1Ws3p06epra3F39+fAQMGAPD+++/zwgsv4O3tjbu7O48//nirx3VxcSEhIYGDBw+ye/dubrnlFhobG6moqGD37t2MHz9erNuWkQOwePFivv32WzEa1+VGy5YtW5gyZYq4gH7JkiXirFlhYSG7du1iwYIFYn13d3cefvhhVCoVc+fOpaysjMcee0x8ffLkSbHuokWLcHNzw97enj/+8Y+ikWbVTaFQMHLkSAYPHsztt9/eavvHjx9PZWVlm6U1amtrW6z1cnV1pba2ttX6Tz75JI8++mir8erz8/NZtWoV33zzDRkZGRiNRh599FFx29atW5k+fTpFRUX86U9/4pZbbqG8vLzV40iJp6cns2fP5rHHHmPatGm4uLhQU1PD1q1bee211/jhhx8oKyvr9nZdK60Zvh4eHgwbNoybb76ZMWPG4OfnhyAIFBYWsmfPHr7//nuOHz/e4fHOVtFoNPj6+orreOLj4wkICMDR0VGc5cnPzycl5Sw63TFmzszgnXcukpfXwIEDAg89ZN0PFBbCp59a3JcGDAAfH0uI6ldftURzk4M0Xh3dun5z7VoID7dM0yxaBFOm0BgYznPXrSU9HcLCYP16y39vW9TX11NQUABYLsB6e+AaqfVVqVREREQAFo+Jvoxareb2229Hq9WSk5PD3r17e+X6476ELerbbWFqMjMz+ec//8mrr77aZp2XXnqJZ599tsX7W7ZswcnJialTp3Lw4EF0Oh3e3t5iDgeL54ebdI2/RFVVFY6OAmq1GqPRiFKpxNnZWbw4sd6Jr62tZc6cOTz33HPU19fz+uuvi3Wsa3oEQWDDhg0MHjwYsGSYf+CBB2hoaKC6uhpXV1eqq6sRBIGKigp0Op0YJlKhUGA2mxk1apS4P4PBQFVVlZinpOnaoYaGBqqqqjCbzQQFBVFTU4PZbMbX15fdu3dTVVXFhx9+yD/+8Q/+8Ic/MHz4cP72t78xfPhwcnNzmTFjhvjnIQgCAQEBYiQb6/FcXFwYPXo0mzdv5uLFiyQnJ1NaWsqmTZvYuXMnTz/9NFVVVTQ2NnLo0CGGDRtGVVVVCw3DwsKIiIjgiy++IDo6mvz8fKZNmybW3bhxIxMnTqSqqgqNRsPcuXNZsWIFqamprF+/ngkTJuDg4CDuz9PTk+rqajQaDWq1Gg8PD2pqanB0dEShUFBbW0tVVRVubm48/fTTrFq1itLSUhQKBdXV1Ze+c0eMRiO33347jzzyCO+8846ooUqlwt7eXjRIHBwcMJvNoj5WY6W1utb8MwqFgoqKCsxmM/X19ZhMJsrLy3F0dBS/R2vdAwcOsG/fPt566y3q6upobGxEr9djNpupqakRI9CEh4ej0+nESGdW96iwsDB+/etfU11dzbRp04iIiGDXrl1MmTIFACcnJxobG6mrqxO/240bNwIWX2hvb2+OHTsGQFJSEgUFBRQUFGBnZ8f06dPZsmULJpOJwMBAAgMDOXz4MADXXXcdpaWlXLiUNXLmzJls376dxsZG/Pz8WLp0KV9++SUpKSmo1Wo2bNjAF198QVBQEPfddx95eXni7z42NlZ05Rs4cCANDQ3iH7p1jKitrcXDw4OBAweKxmp8fDxms5lz584BMGnSJI4fPy7G2h82bBg7duwAICYmBpVKRUpKCmAxXs+ePUt5eTlOTk6MHj2arVu3AhZfeEdHR06fPk12djYLFy4kIyODkpIS7O3tmThxoujLHBYWRkJCAtXV1ZSWluLo6EhOTg5nzpxBqVQycuRIiouLcXd3JywsDF9fX44ePQrA8OHDKSoqIj8/H6VSyYwZM9i6dStGo5GAgACCg4M5dOgQYHHnKC8vF10uZ86cyY4dO9Dr9fj6+hIZGSkGPhk8eDC1tbWiH//06dPZu3cv9fX1eHl5ER8fz549ewAYMGAAjY2NouvmlClTOHz4MDU1Nbi7uzNkyBB27doFILrOpKWlARZ31JMnT1JZWYmLiwtJSUns27cPsLjSGgwG0tLSMBgMBAcHk5ubi06nQ61WExUVRWioRe8PPshCpQpl3boqzpxx59w5T8rKFKxbB+vWWcY8rdZEfHwlY8eamTxZhZ3dUZydjYwYMYK8vDwKCwtRqVRMmzaNzZs3i2Ojv78/R44cAWDYsGFcvHiRvLw8FAoFycnJbNu2DYPBgL+/P6GhoRw8eBCAxMREKisrycnJASA5OZldu3bR0NCAj48P0dHR4rkOGjSI+vp6cS3BtGnT2L9/P3V1dXh6ejJgwACxzyYkJGA0GklPTwdg8uTJHD16VMwTMXToUHHxdWxsLEqlktTUVLHPnjlzhoqKCpydnRk5ciTbtm0DICoqCnt7e3G2eOzYsZw7d47Dhw8zYMAAxo0bJ7oNW4PpWG8IjRo1iuzsbIqLi9FoNEyZMqXTY4TXzp0MfeEFEASamiWqi/n8h9tQ+3zC03sWk5a2nVOnLGNEeHg4Bw4cACwz3RUVFZw6dQqz2cywYcPEIB+9eYxYv3494eHhjBkzpt0xwt3dXQw+M3LkSHJzcykqKkKtVjN16lQ2bdqEIAgEBwe3GCP0ej2ZmZmcP3+ekSNHsnfvXpseI7Zv3w5AdHQ0Go2Gs5cScY0bN46srCzc3Nw4dOgQW7duZe/evSQmJhIREYGzszOnTp0CYPTo0Zw/f56LFy+i1WqZPHmy2GdDQ0Px9PTk+PHjAPIY0c4YsWHDBvz8/HB0dJR8jGjvOsLa/g4hdJKVK1cKQLvl0KFDzT6Tn58vREdHC/fcc0+7+25oaBCqqqrEcuHCBQEQqqqqmtXT6XTC2bNnBZ1OJwiCIJjNglBbK30xmwWhsrKy3XMICwsT9u3bJwiCIERFRQkJCQmCIAjC9u3bhbi4uFbrWbn//vuFlStXttinyWQS7O3t2zz2ypUrhfvvv7/V4wBCYWGheMxVq1aJ25577jnhV7/6VbN9NTQ0CH/4wx+EqVOnCoIgCNHR0cKJEyfaPWcra9asEWbOnCkkJiYKxcXFwgcffCAsX75ccHR0FKqrqwVBEIStW7cKN954Y5v7qKysFP7+978LN910k7BixQph6dKlzbaHh4cLxcXFzd674YYbhFdffVVISkoSPvzwQ/H9y7XYt2+fEBYWJr4+duyY4OfnJwiCIOzYsUMICQkRzp07J5jNZiE1NVVo+vMoLS0VAgIChDvvvFMYPXq0YDQaW23/rl27BCcnpzZLa6Smpgqurq5CY2Oj+F5oaKiwc+fOFnVff/11wcnJSfDz8xP8/PwEe3t7wdnZWfjNb34jCIIgLFq0SHj22WfF+qdPnxa8vb0FQRCETZs2NTt/QRCEpKQkYf369S2Oc/lvrLswm83C+fPnhVWrVgkrV64UyzvvvCMcP35cMBgM3dqezvLTTz91qr7JZBLy8vKEXbt2CV988YXw+eefC59//rmwZs0aYd++fUJhYaFgMpkkam3vxGw2C9XV1UJeXp5w9uxZ4dChQ8KBAweEjz76SACEjz76SDhx4oSQlZUllJaWCrW1jcK+fYLwyiuCcPPNguDpKQgWB7bmJTZWEJYsEYS33hKE/fsFoZu7tk3Q2f57VRiNghAc3PqXBIIJhWAKCrHUa4PGxkbhzJkzwoEDB4Tjx483Gzt7M92ir2AZ6z///HMhPT29W47Xk5jNZuHzzz8XVq5cKTz00EO9/j/Clumu/nslqqqqWrUNWqPTMzkPPfRQM3eg1ggPDxefFxQUMGXKFMaMGcN7773X7ue0Wu1VxbhXKMDJqdMfuzImU/NYpvXgpFBAe4uGBcESG7WujrWrVqG01tfpLLEwrZ9tUk/EYIDGxhb7VwK/WrSIFY8+yivPP4+rqytp585RU1vLyKQky2cMhtaPAxY/u0uLit564w2Sx42jpraW9959l3+/9hqNFRV89e23zL7+epydnXHWaLADqKvjnjvv5Kk//Yn//Otf+Pn6kpObS05uLpMmTGhx6hOGDWPpzz8TFhKCr5MTE4YP55FHHiE+NhYXpRLq6ti4fj0zJ09uU0MnhYJFt9zCX/7yFw4dPMgn//2vWDclNRVPd3d8nZyafX7xbbfxxMqVlJSWMnfmzF+2Xa6FTvfLAq7LXteUlKCys8PL3p66ixd53rre5VLd3953H/NvuYU3/v53Jl9/Pa++9BJ/eOyxVjWobS88civnHRccTFxMDC//9a/84bHHeP+jj7BTKhmbmNii/n2LF7Pg5pvF18t//3tioqJYsXw51NWxdMEC7nv4YZbMnUuAvz8vPfccN17SZPKIESgEgY/ee48lCxbww08/kXX+PGOGDGnZLr3e0q/OnLn6FPVXgQKIACLi46nw8+PUqVOkp6djLCxk/9GjHLe3JyEhgQEDBuDcnh9LDzFSpYJLd1U7ghIIAoKcnNAFB1NYWEh+fj719fVUpadzAsssXkBAAAEBAbi4uEjV9F6DAnC5VIIAkyBQr9Ph4+TEZytWEK3ToTx+nFrA6tDppdWyMM6Re65zxP4PjhQUaDh+QsGxo3DyJOTlA+fgzDk48yl8AKhUluSkgwbCwIEWt7fwcMv7/ZXO9t+r4vBhyMtrc7MSAfIvwPvvQ1JSi+2NjY3k5ORg1utxsbMjIiIC9aU79r2dbtEXiK6uxpyVRVVNDfRxN1gFMCckBP2+fXhUV3Ps/fdbRB2V6Rpa9N/4eHB07LkGdYBOD+fe3t54e3t3qG5+fj5Tpkxh+PDhfPDBB706tGOrNDRYVrU24YqCGQyQnQ0uLgyxs7O8l5ICubmWi0br/prUE6mstMRSveyYAK/dfTdP/vvfDB42jJr6emJCQnj+gQcs1l1pqeWzrR0H4Nw5KC8Hg4FbR41i9PjxVNbU8NvbbuOmiAgaU1P56L33eHD5csxmM4mxsbz7xBOQksKK5GQMBQWMnTCB0qoqwvz9+eNdd0ErfcAPCPTyYlx8PKSkEAU4a7WMj4sT2/PT+vWseemlVs/Rqm8QMGbQIFKzs5nq6/vLZz/7jJlDh7b47C0xMdxfVsZN48bh0vTP83ItsrMtultfnz8PRiOkpHB9cDBj4uIIi4/H292dP9x5J59e+u7WbNnC0UOHOPHZZyhSU/nfihWMXLqUm+LiSLjk/3ytfPbUU/zqmWd48ZVXiA8LY+3zz6O6NP384gcfsPvYMTa89RaOQNMhxUGvx7m+HvfCQigsZEZAAI/ddhvjJk+m0Whk5ujRvP6HP0BKCmpg3csvc89zz/Hgo48SExLC2pdewrO4GFozzEpLYdkyuDS93t14ABMvFVuh5SqpjuMARF4qMr9gxy9GT0d/bVGXyrz2KhmBlEul88sb+yTX0n+7nDbiRWuAmO5tSZfRXfpG0PHfSl/AAfiV9cXHH/dgS/o2LfrvkSMwbFhPNKXDKARBmrg0BQUFTJo0idDQUD7++GPsrBf8WPLIdASrT6HVH9aKNQ68NZKIZFw+kwPU1tXhLMm0kfSEDxjAFx9+yOiRI3vk+IVFRYybPp3zp0+3Wac9fWfOmcNTv/89E5sEMZDpHB3tvw16PVkXLhBhNGLfSxbzms1msrOzOX36dLPIQV5eXgwcOJDomBjUPXwbfu/evYwdO7bL9mcymSgpKaGgoIDS0lIxhKdCocDHx4fAwEC8vb2bja99lZKSEt58802WL1+Oj4+P+L7BaERXX099fT06nQ6dTofZbG72WWuSSGuxt3egtFTNmTMKzpyxTFimpICulSSkAP5+EBtrmfmJibHM+ISGgpR/P12NvlGBVtP+331X91+wTJZnZFhCgv/wA/gXHOY/tJHwpinvvivO5FjXa1lxcHAgJCSkQ9FOexNS6NsagiCwfft2DAYDo0ePxs1N+jXLPY0gCLz11lvY29sTGBjI7Nmze30gClujRf/toZmctmyD1pDsimDTpk1kZGSQkZHRIuycRHZV12Nn18IPzmwySeQb1w0oFODg0GPtrzYa+fsrr7R7/Pb0nZaczJipU0GtlqqJfZ4O9187O0sYq7i4XnMlpwQik5KIvO02iouLOXjwICdPnqTQYOB0ejoOeXkMGzaMESNGtBmuW2rqysq69M6WHeB/qTQ0NJCbm0t2djbl5eWUA2lVVWh0OkJCQggNDcXHx8f2Zsw7yIWjR3lhwwbmPv88Pk00Vl8q1r86s9lMXV0dtbW1YjEYDKJrm/i5YCXhcU4MvNMJJycnHBycuHBBzYkTNCs5OUAxbCgGmkRcVygsEcDi41sWX99u9fK8Iu++Cw8/DP/8Z9sJNaHr+m9dHezaBVu2WCKkXVrHD4CHayJ/Nz+He10+itauBRQKCA6Ge+7BBFy8eJHCwkKM8fGXNisYMHy4Tfbzrh4f2kIBKGtqqCgqojQ0FLdLudL6MgogZM4czp49S6HRSLhGIwZ2kukauqv/diWSzeR0BT0+k9PHCA8P54svvmD06NE93RSZXo6t/MZ0Oh3Hjh3j4MGDYohuhUJBbGwsI0aMICoqqk/ezauqqiI7O5ucnBwx3Dr8coc7NDQULy+vPnXuR48eZfjw4Rw5coRhnfijFQQBvV5PTU0NtbW11NXVodPpWr3ZptVqcXR0xMnJYvg4OjpSX6/m5MlfjJ6zZy2zPpfST7WKuztERlpmfCIiLI/W52Fh7YdF7mrefdfidTpkiGV90jvvtG/odBbrTM3Bg3DokKUcPmzxFLai1cL118Ntt8HcueD401rLC+sOrFzqr8YvvqBkwgSKioowXIoHrtFo8PHxISgoqOsa34c5cuQI6enpJCQkiHnv+gM7d+5k+/btuLu789BDD3Uo7YeMbdErZnL6KtbwzrZIdnZ2TzfhitiyvrZAX9PXwcGBsWPHMnr0aNLT0zl48CCZmZmkpaWRlpaGh4cHw4cPZ+jQod0SqGDr1q1MmzZN8uO4ubmRmJjI4MGDuXjxIrm5uWKY7XPnznHu3DmcnJwIDQ0lNDQUd3f3PmXwdAaFQoG9vT329vaim5vJZEKn01FXVyeWhoYG9Ho9er1eTKALlnwcPj4O3HyzIwsWOOLo6IhWa095uZLUVFqUrCzLEsmjR9teY+7tbTF4goMhMBACApqXwEBLnWudrLAaOA8/DG+8AY8+ankNrRs6bfVfQbCc04ULllkZa0lLsxh8TTIWiISFwYwZljJrVvPlp8ydC199BcuXNwtCYA4MpOSpp7gQGYn5Urh5rVZLUFBQnzDau2t8AMTxrq6NID99ka1btzJhwgQOHz5MZWUlhw8flm/qdiHd2X+7CtnI6SS9eOKrTyDrKy19VV+lUklcXBxxcXGUlpZy+PBhjh8/TkVFBVu2bGH79u0kJCSQlJREWFiYZBdLRqNRkv22hVKpxN/fH39/f4YPH05xcbFo8NTV1ZGSkkJKSgqurq6iwdOXjNyrxc7OzhJJsonhazKZmhk99fX16PV6DAYDBoOhWbJWhUKBg4MDAQH2REY6MHeuPQ4ODmi1WgwGOzIyLMZOdralWJ9bDaDSUku5lLKkVRQK8PAALy/w9Pzl0dPTMlPk5GSZEXJyal40GkuEuG++gRdfhIcegjfftOzvzTctBsuyZZbZl1mzLIEUq6os5dChYLZtszwvKYGCgl9KQxvrlcAyU3PddTByJIwYAaNHQ1TUFVz25s5FuPlmGjZvpj4zkwp7e8oHDrS4yprNODo64ufnh5eXl026prVGd44P6ktu3YZ+lBnXaDSKeVm+++47du7cydChQ3u1N4It0d3/b12BbOR0ErW8HkRSZH2lpT/o6+3tzfXXX8+0adM4c+YMhw8fJi8vj9OnT3P69Gm8vb3F2R0HB4cuPXZAQECX7q8z2NnZicnTjEYjhYWF5OTkUFhYSHV1tXj+Hh4ehIaGEhwcbFMhqT08PLjhhhvw8JAmRpWdnR2urq7NjEDrjI9OpxMDG9TX12M0Gqm/FOygKQqFAo1Gg4ODA4mJ9owcqcXe3h6tVotGo0GpVFJZaVnnk5UF+flQWGgxIi4FSKSgwGJgCIIlKGZ5+dWf00MPwVtv/WJsKBSW1wD/+IelNCeu3f15eFgCL8TFWQIxxMVZSkJCx5ZKWl0HrUmYq6urMXh5WSw4LN+Bh4cHPj4+ODs72/zMzeV05/hgddOyxQvTq8Wq79ChQ9m7d694w2u8HKyoS+jJ/7erRV6T00mMRqPs4ykhsr7S0lF9bWVNTkcpLCzkyJEjnDx5ksZLiwVUKhUDBw4kKSmJ4ODgLrmgKi8vx9PT85r305UYDAby8vLIzc2luLi4WeQxd3d3QkJCCAoKws3NrddfVPYGfQVBoLGxUTR+GhoaaGhoQKfTtXtBaTWAtNrmho+1qNVqccbCYLDM9FiNnLKy5o9VVZbF/bW1lsempbHRYkQNHgzHjrXu8mY2W2ZeTp2CxETLzJC7O9jb6/H11eLmZpkxCgqyuM4FBoK/vyVuTWd00uv1ok7WYBCXzyyoVCpcXV3x9PTEzc2tT0cK7M7+m5WVxYEDB/D392fy5Mndcsyepqm+J06c4JtvvsHZ2Znly5f3ixt8UtMbxl+Q1+RISl1dXb8Ix9hTyPpKS3/VNyAggNmzZzNjxgxOnTrF4cOHKSoq4sSJE5w4cQI/Pz+GDRvG4MGDcbyGkJiHDh1i5syZXdjya0etVhMREUFERAR6vV40eEpKSqisrKSyspJTp07h4uJCSEgIwcHBeHh49DqDp6GhgXXr1rFw4cIeNbwVCoWYuLppFD9BEDAajaLBY13jY300m83imp/qVhI0KhQK1Gp1M8PH01ONr68KtVqNSqVCpbI8v5IhYF2L8+ijv7iq/dJOy/utBSHYuHFHp/qv1eDT6/Xio/WcdTodJpOpxWeUSiVOTk64uLjg5uaGk5NTn3FHuxLdOT5YDe7+dNOwqb6DBg1i27ZtVFVVcfz4cTlBaBfQG//frkT/6f0yMjL9Hq1WS1JSEsOHDyc/P5/Dhw9z+vRpiouL2bBhA5s2bSIhIYFhw4YRERHR6y70rxWtVktUVBRRUVHo9XoKCgq4cOECRUVF1NTUcPbsWc6ePYuTkxPBwcEEBwfj7e3dK3Q4e/Ysv/71r0lMTOxUdLXuwmqkqNXqFm6AgiBgMBhEI8BqCDQ2NtLY2IjBYMBsNouvr4RSqRSNndbKTTfZUVXlxB//6HYpf4gChcJi4DzyiMC//qXg9dd1LFjQKAYNsBos5eXlmM1mTCYTZrNZLEajUSwGgwGj0YjJZGp3nZ9SqcTe3h5HR0ccHBxwdnbuV0ZNT2INOHAtN21sGTs7O8aOHcuGDRs4dOgQSUlJvWIck+leZCOnk/SnAaNpyOlly5YRGxvL7373O0mP2Z/07QlkfS0oFArxIn7mzJmcOnWKo0ePUlRUJK5dcXd357rrrmPo0KEdnv0aOnSotA3vQrRarTjDYzAYKCgoIC8vj8LCQurq6sQIdfb29qJWPj4+fdqdSCqsrmoajabVdVDWWSCrkWMtTQ0KawAEq9Gh1+vbPebkyfDHP/rwt79FAAJvvqlg+XKLgfPHP2YxdmwJTfJrApaIXBkZGZ06N6VSKbrhWV3wtFrtpaSr9rJB04TuHB+ss4W2tO7uWrlc38TERLZs2cLFixfJz89vkbNRpnPY0v+bFdnI6SRGo7Fd387w8HDKy8spLi4WFzVXV1fj5+dHWFgYqamp3dXUdsnOziY+Pp6G9kLmNOGdd96RuEUWrqSvzLUh69sSBwcHRo4cyciRIyksLOTo0aOcOnWKyspKtm/fzo4dO4iKimLYsGHExcW1e5FfXl6On59fN7a+a1Cr1YSFhREWFobRaKSoqIi8vDwKCgpoaGgQEzur1Wr8/f3FAAdarbanm94naDoL5NROsl5BEDCbzc1mUtoqZrOZe+4x4uhYzMqVfuzaJXDypIKnn87n9tvrAEfx2FZqampwdXVFqVS2KFZ3uabF6kYn3yHvGN01PgiCQGlpKYBkgTp6I5fra29vz8CBAzl+/DhHjhyRjZxrxBb/32Qjp5M0NjZeMSKTv78/3333HXfccQcAa9euJSQkpDuaZ/N0RF+Zq0fWt30CAgK48cYbSU5OJiUlhaNHj5KdnS1e5Ds6OpKYmMh1112Hr69vi8/n5uaSkJDQAy3vOlQqlThzYzKZuHjxIhcuXBANngsXLnDhwgUUCgXe3t6iwePq6ipf7EqMQqEQXdI6yl/+An5+8PDDiktrcIKA1hNq5uXlER8f30Wtlbmc7hofysvLaWxsRKVS9YqF4t1Fa/oOGzaM48ePc/bsWW688cZ+tUapq7HF/zd5HlkCFi5cyKpVq8TXq1atYtGiRc3qnDp1inHjxuHu7k5SUhL79+8Xt4WHh/Pqq68SGxuLq6srb7zxBgcPHmTAgAF4enry+uuvi3V1Oh0PPfQQgYGBBAcH87e//U3ctnTpUn73u98xbdo0XFxcmDlzppjkLjk5Gb1eL+aKKCgoaPecli5dyssvvwzAM888w1133cX8+fNxcXFh9OjR5OTkNDu3iRMniokYDx8+fBUqysj0HGq1miFDhrB06VIeeeQRJkyYgIuLC/X19ezbt4//+7//47333uPAgQMtwgj3Jezs7AgICGDkyJHMmTOHGTNmMHDgQDw8PBAEgZKSEk6cOMGGDRv48ccfOXbsGMXFxa0uOJfpOe6/H2pqWk8AKtP3sP4fBwYG9nt3wZCQEFxcXNDr9WRlZfV0c2S6mf7d+6+Cjvjmz5gxg6NHj1JeXk5RURHp6elMnDhR3N7Y2MhNN93EokWLKCkpYcWKFcyePZuqJmmjf/zxRw4dOsSWLVv44x//yCuvvMKePXvYvn07Tz75JCUlJQCsWLGCqqoqzp07x8GDB/n444/5/vvvxf2sXr2aN998k5KSEoxGI//6178A2LRpE1qtltraWmprawkMDOyUDmvXruWRRx6hoqKC2NhY/vrXvwIWd4dZs2bx2GOPUVpaytNPP82tt97aYbe4/hj5qzuR9e08np6eTJs2jccee4xFixYRHx+PUqmkoKCADRs28Oqrr/LFF1+QkpLC9OnTe7q5kqFQKPDy8mLw4MHMnDmTm266ieHDhxMQEIBSqaSmpoa0tDS2b9/OunXr2Lt3L9nZ2R3+7V+JYcOGIQhCrww6YAt0xLPQ1iIn2Rrdoa/BYBCNnPDwcMmP15toTV+FQkFcnCX/U29ZLmCr2OL40Hfm7errQeoOHB9Pjcl0xYV8KpWKW265hTVr1qDT6Zg/f36zuyn79+/Hzs6OBx98EIAFCxbw5ptvsmnTJubPnw/A8uXLcXNzY+TIkfj7+3P77bfj4eEhJvJLTU3F29ubDz74gOzsbHFG5oEHHuCrr77ipptuAuCOO+5g0KBBAMybN49t27Z1iRTJyclMmDBBbP9f/vIXAH744QeGDBnCrbfeCsAtt9zC888/z759+5gyZcoV91tTU9OvFkp2N7K+V49SqSQ2NpbY2Fjq6+s5deoUJ06coKCggNTUVFJTUyksLGTOnDkkJiYSGBjYp923nJyciImJISYmBoPBQFFREQUFBRQUFKDX68nNzSU3NxewGIoBAQH4+/tfUwb7HTt29JucHz2BrK+0dIe+58+fF700/P39JT1Wb6MtfePj4zl8+DDp6end36g+hC2OD33HyElNheHDpT3GkSOYo6I6VHXx4sX86U9/QqfT8d5771FZWSluKygoIDQ0tFn9sLCwZi5jTf39HRwc8PHxafa6rq6OkpISdDodsbGx4jaz2cy4ceNa3Y+joyO1tbUdav+VaGu/ubm5bN26tVn+CIPBQGFhYYf22zRRoUzXI+vbNTg6OjJq1ChGjRrFxYsXOXHiBCdPniQjI4ODBw9y8OBBfHx8SExMZMiQIVdMWGbrqNVqQkJCCAkJwWw2U15eTn5+PkVFRVRUVFBeXk55eTlnzpxBo9Hg6+srGj3tLbRvSlpaGg888ADffvuteGdWpmu5UsQ2mWtDan31ej1nz54FICEhod+5qrWlb2hoKEqlkurqaqqqqmSPhqvEFseHvmPkxMfDkSOSH6OjcanGjBlDfn4+Go2GoUOHsmPHDnFbYGAgFy5caFY/NzeXefPmdao53t7e2Nvbk5OT0+kfrVR3mIOCgrjxxhtZu3btVX1ejvwlLbK+XY+vry8zZsxg2rRpfP/99xiNRlJSUigpKWHLli1s3bqVyMhIBg8eTEJCQp+PSKZUKvH29sbb25vExER0Oh1FRUVisSYkzcvLAywulP7+/gQEBLQborquro7U1FQx/4dM19NaMA2ZrkNqfU+ePIler8fNza3fuapB2/pqNBr8/f3FvGCykXN12OL40HeMHEdH6AZfbc2lLMIdYe3ata3eSRk9ejQGg4G3336be++9l2+++Ya0tDSSk5M71RalUsmvfvUrVqxYwSuvvIKrqytpaWnU1NQwcuTIdj/r7e0tzrAEBAR06rjtMXv2bJ544gm+++47brzxRhobG9m5cydjxozp0MCi0Wi6rC0yLZH1lQ6lUsnkyZNxc3NDr9dz5swZTpw4QU5ODpmZmWRmZrJ+/Xri4uIYPHgw0dHR/SLSj4ODg5iPx2w2U1FRQVFREYWFhZSVlVFVVUVVVRVpaWmoVCq8vb3x8/PDz88Pd3f3fnc3uieJjIzs6Sb0aaTUNy8vj8zMTACGDx/eL/NZtadvSEgIBQUF5Ofniy78Mp3DFscH+d+jk3TmLuKQIUNa/TFpNBrWrVvHJ598gpeXFy+//DLffffdVd1deO2113BycmLw4MF4enpy1113iRHU2sPJyYk//vGPDB48GHd39ytGV+sobm5urF+/njfffBMfHx/Cw8N57733Ovx5+S6ttMj6Sos1SqJWq2XYsGHcfffdLF++nClTpuDt7Y3RaOTMmTN88cUX/OMf/+C7774jOzu73azxfQmlUomXlxcDBw5k+vTp3HLLLYwdO5bIyEgcHBzEHD0nTpxg06ZNfPvtt/z888+cO3eOmpqanm5+n6dplE+Zrkcqfaurqzl48CBgWX9ii3fcu4L29PX29gYs4bVlrg5bHB8UQi/+d62ursbNzY2qqqpmPu0NDQ1kZWURERGBvb19t7ZJ9ueUFllfaemovj35G7NlNm7c2GYEGkEQKCoq4tSpU5w6darZRburqyuDBg1i8ODB+Pv79+mABW0hCAJVVVUUFxdz8eJFLl68iMFgELdnZWXx5JNP8sEHHzBhwoROreeR6Rjt9V+Za0cKfXU6HVu2bKGurg5vb2+mTJnSL2dxoH19MzMz+eSTT/Dx8RGDPsl0jt4yPrRlG7RG3/eV6GLkRIrSIusrLbK+0jJ48OA2tykUCgICAggICGD69Onk5ORw6tQpzp49S3V1NXv37mXv3r14e3szePBgBg0ahJeXVze2vmdRKBS4u7vj7u5OXFyc6NpWXFxMcXExOp2OX//61xiNRg4dOgSAs7Mzvr6++Pj44OvrKxs910h7/Vfm2ulqfevr69mxYwd1dXW4uLgwfvz4fmvgQPv6enh4ADQLAiXTOWxxfJCNnE4iR6eSFllfaZH1lZaORi9UKpXiOpUbbriBjIwMTp06RVpaGqWlpWzfvp3t27cTEBDAwIEDxQSc/Qmra5uXlxcDBgzAZDKRkJCAk5MTxcXFlJeXi3m+zp8/D1jccL29vfHx8cHb2xs3N7d+OSt2tXRV9E2Z1ulKfaurq9m5cyd1dXU4OTkxadKkfj/r3p6+Vm0MBgNms1le63cV2OL4IBs5nUSv1/f7gURKZH2lRdZXWrKyspqFdO8IKpWK+Ph44uPj0ev1pKamcurUKc6fP09hYSGFhYVs2bKFwMBA0eBpGqK9v1BeXs4nn3zCM888w+DBgzEYDJSUlHDx4kVKSkqoqKigrq6Ouro6MRmiVqsVDR4fHx/c3d379Z3uK3E1/Vem43SVvgUFBezbtw+DwYCLiwuTJ0+WZzFpX9+mQXcaGxvl/8GrwBbHB9nIkZGRkeklaLVaEhMTSUxMpL6+npSUFM6cOUNWVpaYaHPz5s0EBQWJBk9/WcN24cIF/u///o977rkHHx8f1Go1gYGBBAYGApY7tGVlZZSUlFBaWkpZWVmLcNUqlQovLy98fX3x8vLC09NTjjgoYzOYTCZxxlcQBHx8fBg3bpx8wd4Bmt7cMJlMPdgSme5ENnI6SV9P6tfTyPpKi6yvtEyfPr3L9uXo6Mjw4cMZPnw4dXV1osGTnZ1Nfn4++fn5bNq0ieDgYAYOHMiAAQP6jcHTGmq1Gn9/fzHLu8lkoqKigpKSEtHwaWxsFNf4gGUdkKurq+gW5+Xlhaura791ZenK/ivTkmvRNycnh3379omvo6Ojue666+SZySa0p2/TICZyvrirwxbHB9nI6SS1tbW4uLj0dDP6LLK+0iLrKy179+5lwoQJXb5fJycnkpKSSEpKora2VjR4cnJyxJmKjRs3EhwcTEJCAgkJCXh6enZ5O2wJOzs7MSlpQkKCGL2t6UxPbW2tmKfHuq7HOtvTtPSXO+VS9V8ZC1ejrzXn1rlz58T3xo8fT3BwcFc3z+ZpT9/GxkbAcmNDNnKuDlscH2Qjp5PIC7elRdZXWmR9paW+vl7yYzg7OzNixAhGjBhBTU2NaPDk5uaKBs/mzZvx8/MTDR5fX99+vwC/afS2mJgYwBIqvaysrFkxGo3NZnvAormnpyeenp54eHjg4eHRJ93cuqP/9mc6o6/RaCQzM5MzZ86IF+gAycnJ/f4GRlu0p6910byDg0O/HwuvFlscH2Qjp5P0hwzlPYmsr7TI+kpLd4d8dnFxYeTIkYwcOZKamhrS0tJISUkhKytLvFDfsWMHnp6eosETFBRkk3/yLi4ujB49uktnIu3t7QkKCiIoKAiw3ASorq5uZvRUV1eLUdxyc3PFz1oNH6vR4+HhgVar7bK29QT9KWR5T9ARfQ0GAxkZGaSlpdHQ0ABYkmxfd911oiumTOu0p29ZWdkV68i0jy1qJycD7SQmk6nHfGBXrVrFV199xTfffHPV+1i6dCnx8fH86U9/6sKWdR1dqW/Tc+0K7foCHdVXTgZ6ddTW1uLs7NzTzUCn03Hu3DlSUlLIyMjAaDSK21xcXESDJywszKbWn/SEvo2NjZSXl1NeXk5FRQUVFRVthlJ1cnJqZvR4eHhgb29vM0Zlb+m/fZX29LWGQs/IyBBnbpycnBgwYAARERE29TvtKdrTd+fOnWzfvp3ExERuvfXWbm5Z36C3jA9yMlAJqa2tbXNx74wZM5g5cyYrVqxo9v7vfvc7ysrK+Oijjzp1LIVCQWFhoXj3ZvHixSxevPjqGm4jtKfv5YSHh/PFF18wevToK9btD9p1hM7oK9N59uzZ0ysyQjs4OIhR2hobG8nIyCAlJYVz585RU1PDwYMHOXjwIA4ODsTExBAXF0d0dHSvnokwmUxs2rSJOXPmdOuNJo1G0yygAVjWSVgNHmupqakRQ1hbo7mBJWKem5sb7u7uuLm5iaU3rgvoLf23r3K5viaTicLCQjIzMykqKsJ6z9nV1ZWEhARCQ0PlwAKdoL3+a52FtUZjlOk8tjg+yEZOF7JkyRLeeOONZkaO2Wxm9erVfPDBBx3ej8Fg6JV/gDIyMraHRqNhwIABDBgwAKPRSFZWFikpKaSmplJfX8/Jkyc5efIkdnZ2hIeHExcXR2xsbK/LxXPixAnmzZvHkSNHGDZsWI+2RavVtjB8GhsbqaysFI2e8vJyampq0Ov1XLx4kYsXLzbbh5OTUwvDx8XFRb6o7eOYzWYuXrworp+zuqQB+Pv7ExUVRVBQkDxz04WYTCbRyAkPD+/Zxsh0K/KvqJM4ODi0uW3u3LmiT7yVHTt2YDKZmDZtGrm5udx44414eXmRkJDATz/9JNYLDw/n73//O3FxcQwYMIDk5GQAoqKicHZ2Zt++fXz44Ydcf/314me2bdtGUlISrq6uxMTEsHv3bgD+85//EBMTg4uLC0OGDGHHjh0dOrfw8HBeffVVYmNjcXV15Y033uDgwYMMGDAAT09PXn/9dbFueXk5CxYswNvbm+joaP773/+K25YuXcqjjz7KpEmTcHZ2ZtGiRRQVFTF9+nTc3NxYvHhxszj1//73v4mJicHb25uHHnqIuro6AD788EOSk5N54IEHcHV1ZeDAgRw/fhyA3/zmN+Tm5jJ16lScnZ1ZvXp1u+fWVLsdO3YQHx/Ps88+i6enJxEREWzevLnZuS1atAhfX18iIyM7PQPXm2mv/8pcOwMGDOjpJrSLSqUiJiaGm2++mRUrVvDrX/+acePG4e3tjclkIjMzkx9//JE33niDd955h+3bt5Ofn08v9mruNWg0Gnx9fYmLi2P06NHccMMNzJs3j+TkZEaNGkV8fDz+/v7ib7Curo78/HzOnj3Lvn37+Omnn/j666/ZsGEDP//8MydOnOD8+fOUlpai1+u75Rx6e/+1VQwGA/n5+ZhMJtatW8eOHTvIyMigoaEBe3t7BgwYwOzZs5k8eTIhISGygXOVtNV/s7OzMRgMODo64uvr282t6jvY4vggz+R0kvaiU7m4uHDzzTfz2Wef8dxzzwHw2WefsWDBAhQKBTfddBP33Xcf69at49ChQ9x0002cPn1avBv47bffsnv3blxdXUU/7szMTHF7WlqaeKzz589z6623smrVKmbNmkV+fr7oxxsYGMjWrVsJDg7m/fffZ8GCBeTk5HTIFeXHH3/k0KFDpKWlMWHCBG6++Wb27NlDbm4uo0ePZsmSJfj4+PDggw+iUqnIzc0lIyOD6dOnEx8fz/jx4wFYs2YNW7duxcfHh2HDhjF79mw+/vhjAgMDSUpKYv369cyZM4c1a9bw3nvvsWXLFnx9fVm6dCl/+ctfePXVVwHYvn079913H//6179YuXIljz/+OFu3buW///0vW7Zs6bC72uVkZGTg4uLCxYsX+d///seyZcvIzMwE4M4772TQoEFcuHCBrKwspk6dytChQ0lMTOz0cXobcnQ1aWkaBam3o1QqCQ0NJTQ0lBkzZlBaWsq5c+dIS0sjNzeXoqIiioqK2LlzJy4uLsTGxhIXF0dERIQ809xBVCqVGJWtKXq9XgxdXVlZKT43GAzi88vRarW4urri4uIiFldXV5ycnLps9seW+m9vxmQyUV5eTnFxMUVFRZSXl2M2m6moqBADVAQHBxMSEoKPj488e9dFtNV/T548CVgu0m1lfVxvxBbHB9nI6SR6vb7dhdhLlixh+fLlPPfcc+j1er7++ms2bdrEwYMHMRgMPPjggwCMGTOGyZMns2HDBu6++24AHnvssQ7fZfj888+ZM2cOs2fPBiA0NFTcduONN4rP7733Xv7yl7+Qnp7OoEGDrrjf5cuX4+bmxsiRI/H39+f2228XF9CGhoaSmpqKp6cnX3/9NZmZmTg6OjJkyBDuuecePv/8c9HIueOOO4iPjwdg8uTJODs7i3cBpk2bxsmTJ5kzZw7vv/8+Tz31FGFhYQA8+uijLFiwQDRyBg8ezG233QbAokWLeOeddzqkz5Vwc3PjscceQ6FQsGTJEu6//34xgtLu3bv57rvvsLOzIz4+nkWLFrF27do+YeRcqf/KXBsZGRlERUX1dDOuCmtOmbFjx1JfX096ejppaWlkZGRQU1PDkSNHOHLkCGq1mvDwcGJiYoiJicHDw6Onm25zaLVafH19m433giBQX19PTU0N1dXV4mNtbS11dXXo9XoxsWlTFAoFDg4OODk54eTkhLOzs/jcyckJBweHDs8M2HL/7Sms31tZWRnl5eXiY1NvBbDcBK2pqWHKlCn4+PjIszUS0Fr/1ev1ondNX/gP70lscXzoW0bOAw9Afr40+w4KgrffvmK1mTNnUl1dzf79+yksLMTHx4cRI0bw5Zdfkp6e3szP3Wg0Mnz4cPF1Z5J75eXlERkZ2eq2b7/9lr/+9a9icruamhoxfOKVaPqn6+DggI+PT7PXdXV1lJSUYDKZmrU3LCyMjRs3dmo/YFkMeM8993DfffcBlj+MppGgmu7H0dGxzahGncXHx0e8o+Po6Agghoitq6trFirRZDLJQQtk+hWOjo5i4AKj0Uh2djZpaWmcO3eOqqoq0tPTSU9PByzGkdXgCQ0NlcOUXyUKhUI0TC4PFWwwGKipqWlWrIaQ0Wikvr6e+vr6FgYQWGbsmhpADg4OODo64uDgIBa1Wi3f4e4A1u/BOttmnYnT6XQt6mq1Wvz8/PDz88Pf3x8nJyc2btyIn59fD7S8/3Lo0CEaGxvx8fGRE6j2Q/rWv1EHjJBr5Uo5GtRqNbfffjufffYZhYWF4sVxUFAQgwcP5ujRo21+tjN/MiEhIc3c16zo9XoWLlzIunXrmDZtGnZ2dgQEBHSpT731LlReXh4hISGAxVi5mqglQUFBvPzyy9x8882AxZ2qo3e4pPhTDgoKwt3dvcNGoa3RlTlGZFoyZcqUnm5Cl6NSqYiOjiY6OpobbriBkpIS0cjJzc2ltLSU0tJS9u3bh0ajITIykpiYGKKjo7s0kt/gwYPJy8vrlz71arW6Vbc3QRDQ6/XibE9dXV2z5/X19ZjNZtEwaguVSiUaO/v378fe3l40hLRaLVqtFnt7ezQaTZ82hgRBoLGxsYWO1ln+urq6Vv9LlUolbm5ueHl5icXFxaWFVn1xfOhNXK5vY2Mje/fuBWDChAl9uu92B7bYf7vFyNHr9YwaNYoTJ05w7Ngxhg4d2h2HlYT6+vorxglfvHgxt9xyC7W1tbz44osAjBo1CoPBwHvvvcfSpUsBOHDgAGFhYc1czZri6+tLdnZ2qwnAFi5cyNChQ/nxxx+5/vrrxTU5Pj4+4iPAm2++2erdvWvBzs6OuXPn8tRTT/Huu++SmZnJ+++/z1dffdXpfd1zzz288MILDBo0iMjISDFPQNMAC21h1edq1uS0RVBQECNGjOAvf/kLf/rTn9BoNJw8eVJcHGrrdKT/ylw9hw8fZuzYsT3dDMlQKBSim9W4ceNoaGjg/PnzotFTW1tLamoqqampgOU3ajV4QkJCrmmWR61Wk5OTIybulLF8H/b29tjb2+Pt7d1iu9lsRqfTNbtg1+l01NfXo9Pp0Ol0NDY2YjQaqamp4cKFC+KNq7aOZzV6mho+arVafGztuZ2dnVi6E7PZjMFgoLGxUXy0Pm9oaECn09HQ0NDs+eVuZpdjb2/fLCKeNTx4R/p2Xx8feprL9d27dy/19fV4eHh0yF1fpn1ssf92i5Hzhz/8gcDAQE6cONEdh5OUKw2AAGPHjsXFxYWIiAhiYmIAy52y9evXs3z5cp566ikEQSApKandNSZ/+ctfmDNnDnq9vlkkNoCIiAi+/vprfv/733PHHXcQEBDA//73P6KionjllVeYMWMGCoWCBx54gOjo6Gs76Vb497//zW9/+1uCg4Nxc3Pjr3/9KxMmTOj0fhYsWEBFRQU33HAD+fn5+Pn58dvf/rZDRs4f//hHHnnkEZYtW8Z7773H7bfffjWn0oJVq1bxu9/9jsjISBobGxk0aFCzyHK2TEf6r8zV097d8r6I1fgfMGAAgiBQVFQkGjx5eXli6OQ9e/aIa3mioqKIiorC29u7U3dWMzMz+d3vfseqVatszi+8p2jqqtYWTS/4t2zZQmJiomgA6XQ69Ho9er2exsZGBEEQjYKrbY/V2FGpVKhUKuzs7FAoFCgUCpRKJUqlUnxufV8QBHEGxfrc+tpkMrVZmro+dwbrGifr+ibrozUo0NXS38aH7qapvmVlZWLE2RkzZshroLoAW+y/CkHi2KAbNmzgd7/7HV9//TUDBw7s1ExOW1lNezIbe2/J+NpXkfWVlo7q25O/MVvmwIEDjBo1qqeb0Suor68nMzOT9PR0zp8/32I9naurq2jwREREtHshDnD06FGGDx/eK/Lk9FXa678mk4nGxkb0ej0NDQ3NjB/r7Ii1NJ05MRqNPR7VUaVSNZtx0mg0aLVaHBwcsLe3Fx+tRap1ZfL4IC1Wfc1mMx9//DHZ2dlER0ezePFi2VWtC+gt/bct26A1JJ3JKS4u5t577+Xbb78VF3e3h3XQtFJdXS1l866KjpyHzNUj6ystsr7SMmTIkJ5uQq/B0dGRwYMHM3jwYARB4OLFi2RmZpKZmUlOTg7V1dUcO3aMY8eOARAQECAaPdfq2iZzdbTXf+3s7MRABZ1BEATMZnOz2RWj0Sg+N5lMYp3WHgVBEC9QrXfjrTM81vesM0LWYn1PrVaL23oD8vggLVZ9d+7cSXZ2NhqNhhtuuEE2cLoIW+y/kv2LCILA0qVLWbZsGUlJSWRnZ1/xMy+99BLPPvtsi/e3bNmCk5MTU6dO5eDBg+h0OjF5nTWfgPVus3Ua3cXFhfr6ekwmE3Z2djg6OopTbZfXdXZ2pqGhAaPRiFKpxNnZWTSwtFotSqVSjJ4iCAJqtbrVuhqNBpVKRX19PWDJaG29o6VQKHB1dRXbe3ldR0dHjEYjjY2NYt3q6mrxeBqNRoxI1rQuWMIh19TUYDabW9R1cHDAbDaLxqOrqyu1tbWYzWZUKhX29vbiHdbL63ZGw/bqXq5he3qbTCacnZ3Fuk01VCqVuLi4tKlha3pbNWxPb6uGHdW7Mxq2V7er+mxn9DYYDHh5ebXZv60a1tXViceyRs0LCQnB29tbvChNSkqioKCAgoIC7OzsmD59Olu2bMFkMhEYGEhgYCCHDx8G4LrrrqO0tJQLFy4AliiE27dvp7GxET8/P8LDwzlw4ABgGUirq6vFMWPGjBns2bOH+vp6vL29iY2NFReTDhw4kIaGBjHHkXWMqK2txcPDg4EDB/Lzzz8DEB8fj9ls5ty5cwBMmjSJ48ePi3eDhg0bJibOjYmJQaVSiaFHx48fz9mzZykvL8fJyYnRo0ezdetWACIjI3F0dOT06dNkZ2ezcOFCMjIyKCkpwd7enokTJ7Jp0ybAEoXQ3d1ddN0dOXKkmJNGrVYzdepUNm3ahCAIBAcH4+vrKwYrGT58OEVFReTn56NUKpkxYwZbt27FaDQSEBBAcHAwhw4dAmDo0KGUl5eLWb5nzpzJjh070Ov1YpLb/fv3A5YF/bW1tWRlZQEwffp00Zfdy8uL+Ph49uzZA1jyTDQ2NpKRkQFYFqIePnyYmpoa3N3dGTJkCLt27QIgLi4O+CW/18SJE7Gzs8PX15ewsDB8fHz45ptvKCwsRKFQUFVVJbqXREVFYWdnh7OzMxEREcyePVv8znNycvD39+fUqVMAjB49mvPnz3Px4kW0Wi2TJ08W+2xoaCienp5iEuERI0aQl5dHYWEhKpWKadOmsXnzZsxmM0FBQfj7+3PkyBEAhg0bJmamVygUJCcns23bNgwGA/7+/oSGhnLw4EHAEpq2srKSnJwcAJKTk9m1axcNDQ34+PgQHR3Nvn37ABg0aBD19fVi9Mtp06axf/9+6urq8PT0ZMCAAWKfTUhIwGg0ipHsJk+ezNGjR8U7mUOHDmXnzp0AxMbGolQqxfVQ48eP58yZM1RUVODs7MzIkSPZtm2bqK+9vT1nzpwBLC7W586d4/DhwwwYMIBx48aJCZLDw8NxdXUVc42MGjWK7OxsiouL0Wg0TJky5ZrGCOtvoavHCGuf7U1jxPr16wkPD2fMmDHyGEHrY8TJkyeprKzExcWFpKQktm/fDkB0dDQajYazZ88CMG7cOFJTUykrK8PR0ZGxY8fy8ccfo9FoOHbsGGq1mujoaA4dOiSPEV00RnzzzTf4+fnh6OjYrWPE5dcR1vZ3hE67qz3zzDOtGiJNOXToEHv37mX16tXs2rULOzs7srOziYiIaNddrbWZnJCQkF7lrlZVVdWlEYNkmiPrKy0d1Vd2V7s6Nm7cyMyZM3u6GTZHTU0N58+fJzMzs1XXNnt7ewRB4Mknn2Tz5s1MmzZNvjsrAXL/lRZZX2n57LPPyM7OprGxkeHDh3PTTTf1dJP6FL2l/0rqrvbQQw+xYMGCduuEh4fz/PPPs3//frRabbNtSUlJLF68mI8++qjF56wRW3oz8gWftMj6Sousr7RY70zKdA4XFxcxL4/VtS0rK4usrCyys7NpaGigtraWCRMm8NNPP3Hs2DEiIiIIDw8nIiICT09P2ejpAuT+Ky2yvtJRXl7OqVOn0Gq1REREMGvWrJ5uUp/DFvtvp40ca1bsK/HWW2/x/PPPi68LCgqYOXMmq1ev7hULl2RkZGRkeh8KhUJMojh69GjMZjOFhYVkZWURGBiITqejrq6O06dPc/r0acDiFmo1esLCwvDw8JCNHhmZfsLFixf55JNP0Ol0hIWFsWDBAnlNnwwg4Zqcy3O/WCM6RUVF2XTW2YaGhl4/22TLyPpKi6yvtKSlpREeHt7TzehTKJVKgoKCcHR0ZMOGDTz22GPU19eLMz0XLlygurqaEydOiOsYXFxcCAsLE/OQ+fr6ykZPB5D7r7TI+nY9BQUFfPrpp9TX12M0GlmyZIn8HycRtth/ZVNXRkZGRqbXk5WVxYsvvsi8efMYNmwYoaGhTJo0CYPBwIULF8jKyiI3N5e8vDxqamqazfQ4ODgQGhoqGj4BAQFy3gwZGRvn9OnTrFu3DoPBQFBQEMOGDZNTUMg0o9uMnPDwcCROydMtuLi49HQT+jSyvtIi6ystEydO7Okm9DvUajWRkZFERkYCYDQayc/PJycnh5ycHC5cuIBOpyMtLU2M4qTRaAgJCSEsLIyQkBCCgoLQaDQ9eRq9Arn/Sousb9dgNpvZtm2bGGEsOjqa+fPn93g+pr6OLfZfeSank9TX18t3CiRE1ldaZH2l5eTJk/Kawx5GpVKJMzZgCUtfVFQkGj25ubnodDoxZw9YXOL8/PwICQkRi5ubW79zcZP7r7TI+l47FRUVfPPNN2Lo63HjxjFt2jSUSmWvSVbZV7HF/tv/5utNJtixAz7/3PJoMnXy4+3XDw8PF2PLW1m2bBnPPPNM59ppQ3z44YcMHToUFxcXIiMjeeedd9qs++KLL+Ls7CwWrVbL4MGDxe1N9f3www9RKBTNAlgAPPnkkygUCr744otm9d59912xTlFRUb+7QOkIV+q/MtdGZWVlTzdB5jLs7OwICgpi7NixLFy4kD/84Q888MAD3HDDDQwaNAg3NzcxuMHBgwf5+uuveeONN3jttdf48ssv2bdvH3l5eRiNxp4+FcmR+6+0yPpePYIg8PHHH/Pmm2+Sm5uLVqtl3rx5zJgxQ3Q9lfWVFlvUt3/N5KxdC8uXQ17eL+8FB8Obb8LcuR3aRW/JnNyb0Ov1vPPOOyQlJZGWlsbUqVMZMGBAq1ObTz75JE8++aT4eu7cuQwcOFB8fbm+0dHRfPbZZ/z5z38GLAPd6tWriYqKalbPw8ODF198kV//+teo1equPL0+hdx/pUV2B5QOBwcHYmNjcXBwuKb9NI3eNnLkSMCSd+HChQtiKSwspKamhrNnz4rJB1UqFYGBgQQHBxMUFERQUFCfm+2R+6+0yPpeHSUlJfz73/8WX3t6enLXXXfh7u7erJ6sr7TYor79ZyZn7Vq47bbmBg5Afr7l/bVrO7QbR0fHa2rGhx9+SHJyMvfee6+Y0Tc/P58HH3wQNzc3Ro0aRUFBAWDxO507dy6+vr54enoyf/58ysvLAdixYwdBQUHi6zVr1hAXFydmrrei0+lwdXUVs+wCbNmyhUGDBl3TeTTl/vvvZ/To0ahUKgYOHMj06dPFrMrtUVlZyY8//sjixYvF9y7XNyoqChcXFzGj8969ewkJCWkRoW/kyJGEhITwwQcfdMEZ9V2utf/KtE9SUlJPN6HPkpCQwKlTp0hISOjyfbu6ujJw4ECuv/567r33Xp544gnuvvtupk+fTlxcHI6OjhiNRnJzc9m7dy9r1qzhjTfe4B//+AefffYZO3fuJCMjg/r6+i5vW3ci919pkfXtHA0NDWzZsqWZd4hWq+WBBx5oYeCArK/U2KK+/cPIMZksMzitBT6wvvfoox1yXaupqbnm5mzfvp0bbriB8vJygoODGTduHJMmTaKsrIzw8HBeeeUVse7cuXPFUKk1NTX89a9/BWDy5MnMmzePhx56iJKSEh5++GE+/PDDFnc5HRwcmD17NmvWrBHf+/LLL7njjjtabdvs2bNxd3dvtbz88stXPDeTycTBgwebzc60xVdffcWgQYOIj48X32tN38WLF/PZZ58BlozGTY2ipqxcuZIXX3wRg8FwxWP3V7qi/8q0zfbt23u6CX2a7tJXrVYTFhbG+PHjWbhwIb///e95+OGHufXWW0lKSiIwMBClUkldXR3nzp1j+/btfPrpp/z973/nrbfe4uuvv2b//v1cuHDBpsYjuf9Ki6xvxzAajezbt4+33nqLn3/+GZPJRFxcHMuXL+eJJ55o01tD1ldabFHf/uGutnt3yxmcpggCXLhgqTd58jUfbsaMGc3cgnQ6HU888YT4evDgwdx6660AzJkzh/T0dG6//XYAbrnlFv773/8ClsWwS5YsET/32GOP8dRTT4mvX375ZRITE5k8eTJ33nknY8aMabU9d9xxBy+88AIrVqzAaDTyzTffsGfPnlbrrl+//irP2sKf//xngoKCmDlz5hXrrlq1qk2DpSl33HEHI0eO5MUXX2TdunU8//zzrFq1qkW9GTNmEBQUxIcffshNN910Ve2XkZHpnRw7doybbrqJAwcOcN1113XrsRUKBV5eXnh5eZGYmAhYLsSKiorIz88XS1lZGeXl5WL2dbCM476+vgQEBODv7y8+ytHcZGSaYzAYOH78OD///DNVVVWAJQH9jBkziIuL6+HWydgi/cPIKSzssnodSTK1efNmRo8eLb5etmxZs+2+vr7icwcHB3x8fJq9rqurAyx/oitWrOCbb76hoqICQRDw9vYW6zo6OrJgwQJeeOEFfvrppzbbc/311/OrX/2K7Oxs0tLSCA4OJjY29orn0Vneeecd1q5dy549e67op56Xl8fPP/8sztBYaU1fPz8/4uPjefLJJ0lKSsLDw6PN/a5cuZL777+f66+//upOoo8jJ0mTlujo6J5uQp9FEAQMBkOvSUWgUqkIDg5u5jqr0+koKCggPz+fgoIC8vLyqK2tpaioiKKiIrGe1WgKCAgQi7+//zWvN7pW5P4rLbK+rdPQ0MChQ4fYv3+/eP3j6urK5MmTGTp0aIdzWsn6Sost6ts/jJyAgC6r150J5FatWsXu3bvZt28fgYGBbNy4kfvvv1/cnp6ezttvv838+fN5/PHH+fLLL1vdj1arZc6cOaxZs4bU1NQ2XdUAZs2axe7du1vddnnQgKasXr2aF154gd27dzczxNri888/Z/LkyQRcpnlb+i5atIi7775bjKjWFsnJyQQEBPDRRx9dsQ39ETkBorTId+f7Nw4ODkRFRYmBUQRBoKamhoKCAgoLCykqKqKwsJDq6mpKS0spLS0VZ3wA3N3dmxk9vr6+3RrcQO6/0iLr25yioiIOHTrEyZMnRbdOd3d3xo4dy3XXXdfpIEKyvtJii/r2DyNnwgRLFLX8/NbX5SgUlu0TJlxxVzqdrtu+6JqaGrRaLe7u7pSWlvKPf/xD3GY2m/nVr37FU089xbJly0hMTOTLL78U3d7Cw8N55plnWLp0KWBx+XrqqafIzc1tNyjAhg0bOt3OTZs28fDDD7NlyxbCw8M79JlVq1bx6KOPtni/LX3nz5+Pn58fkzvgTrhy5UoWLVrUoXb0N7qz//ZHzp49S0hISE83Q6aXoFAocHV1xdXVtdnaQ+vsTmFhoVgqKiqorKyksrKSlJQUsa5WqxWjwfn5+eHr64ufn58ks7Jy/5UWWV9LNNazZ89y5MgR8posI/Dz82PcuHEMHDjwqqOAyvpKiy3q2z+MHDs7S5jo226zGDRNDR3rHbI33rDU60Xcdddd/PDDD/j6+hISEsJvfvMb0tPTAfjHP/6BnZ0dy5cvR6lU8sEHHzB37lwmT56Mh4cHZWVlzVzmZsyYwZ133tksM3hX8dJLL1FRUcHYsWPF95YsWSJGRHF2dmbDhg1MuGREnj17lrS0NOZ2MGw3WFzzOuqCNnPmTGJjY1vkK5KRkZHpDTg7OxMdHd3M/UOn0zUzfC5evEhJSQl6vZ7c3Fwx+aEVd3f3FoaPp6enHCZeptdhMpnIzMzkxIkTpKWliTmn7OzsSEhIYMSIEYSGhvapcOwyvQOF0FscnFuhuroaNzc3qqqqcHV1Fd9vaGggKyuLiIgI7O3tO77D1vLkhIRYDJwOXnCbTKZe/ydijUry+eef93RTOo0t6GvLdFTfq/6N9XNqa2txdnbu6Wb0SXQ6HadPn2bQoEE9vnaluzCZTJSWllJcXExxcTEXL16kuLiY6urqVusrlUo8PT3x8fHB29sbHx8f8XlHXH/k/ist/UnfxsZGMjMzSUlJ4dy5czQ0NIjbfHx8SExMZOjQoV2qR3/StyfoLfq2ZRu0Rv+YybEydy7MmWOJolZYaFmDM2FCp2ZwGhoacHJykrCR186YMWPajLTW27EFfW0ZWV9pSU1NtclcAraAg4MDCoWi3xg4YLnTbZ2taYpOp2tm9FifNzY2imt9Lsfd3b2F8ePl5SXqCnL/lZq+rm95eTmZmZlkZGRw/vz5ZuHTnZ2dGTRoEImJifj7+0sya9PX9e1pbFHf/mXkgMWguYYw0dZpVhlpkPWVFllfaSkrK+vpJvRZcnJy+POf/8y7775LWFhYTzenR3FwcCA8PLzZGkhrkIOSkhJKSkooLS0Vn9fX14vrfawuz1bs7e3x8vLC09OTzMxMNBoNnp6eogEk03X0tfGhtraW3NxcsrKyyMzMFJOTW3F3dychIYGEhASCg4MlD3zT1/Ttbdiivv3PyLlG5OhU0iLrKy2yvtLi6OjY003os5SVlbFx40bKysr6vZHTGk2DHFiju1mpq6sTjZ6mxk91dTUNDQ1inp+cnBzq6+vFzzk4OIgGkJeXFx4eHmJyaBcXF3kNRSex5fHBbDZTVlZGfn4+ubm55OTktLjoVSqVhIaGEhUVRUxMDH5+ft3aR2xZX1vAFvWVjZxO0hv8Efsysr7SIusrLU2Db8jI9BacnJxwcnJqYRwaDAYxeWl5eTklJSVUVlZSVlZGTU0NOp2OvLy8ZlGwrNjZ2YkGj7U0NYKcnJxkI+gybGV8sBo01tDnBQUFFBUV0djY2KyeQqHA19eXsLAwoqKiCA8P79FcbLair61ii/rKRk4nsS54kpEGWV9pkfWVli1btjBz5syeboaMTIdQq9XN1vxs3LiRW265BbAsHLcaP2VlZZSXl1NZWUlFRQXV1dWYTCbKysradGFRq9XizJK1uLi4NHvd3wyh3jY+mM1mysvLxXVc1mJd33U5arWagIAAQkJCCAsLIyQkpFe5NPY2ffsatqivbOTIyMjIyMjINEOj0eDv74+/v3+LbWazmerqanGdT9McP5WVlVRXV2MwGNo1gsDi3tTU8HF2dhZnnS5/3tnEkDKWdVoNDQ1UVVWJxfp9lZaWUl5ejslkavWzVoMmICCAwMBAAgMD8fLykl2eZWwK2cjpJD05FdsfkPWVFllfaYmIiOjpJvRZ/Pz8uO+++1pEGpPpOjraf5VKpeiW1homk4mqqiqqq6upqamhurq6RamtrcVsNosX31dCo9E0M34cHR1xcHBoUezt7cXnGo2mV80UddX4YDab0el01Oz/4mcAAD0KSURBVNfXU1dX16I0NWr0en27+1Kr1Xh7e4tR96yPtmjQyOOvtNiivrKR00ls7Udva8j6Sousr7TIa56kIygoiJUrVxIYGNjTTemzdFX/tbOzw9PTE09PzzbrmEwmamtrmxlBdXV11NbWihfr1udGo5HGxkYaGxupqKjocDuUSqVo+Gg0mg4VtVqNnZ1ds6JSqVq8Z2dn12EDymw2YzQa0ev1FBUVYTQaMZlMYjEajeI56vX6dkt9fT319fV0JsWhk5MTbm5uYvHw8BANG1dX115lCF4L8vgrLbaor2zkdBKdTodGo2lze3h4OF988QWjR48W31u2bBn+/v4888wzkrcvLS2Nxx9/nP3796NQKJg5cyb//Oc/8fDwaLX+jTfeyKFDh9Dr9cTHx/PGG2+0mWNHoVAQFRVFRkaG+F56ejqxsbHMnDmTn376Saw3ZswY9u7dK9a7/vrrWbBgAUuXLm23/VfSV+bakPWVllOnTskX4RJRU1PDp59+ygMPPICLi0tPN6dP0p39187OTrzobg9BEGhsbBQNHutjfX09DQ0N6HS6ZsX6ntFoxGw2iwZTbyAjI4Po6Ogu25+Dg4Po0mctjo6OuLq64u7uLurbX1z95PFXWmxRX9nI6WNUVVVx++23s2rVKlQqFXfffTcrVqzg/fffb7X+3//+d+Li4lCpVHz//ffceuutFBYWtnlnR6lUcuDAAUaNGgXAqlWriImJaVEvNTWVTZs2kZyc3HUnJyMj029JT0/nj3/8I9OnT2fYsGE93RyZbkKhUKDVatFqtXh5eXX4cwaDQTR89Hq9OBN0ebl8m3WW5fLZltbe6wiCIIgzP/b29ri4uLQ6Q6RSqcTz1Gg04vPLi6Ojo1jsOpHIXEamP9KvjJz0dKipafm+iwu0cp3eKl2RLf6f//wnr7/+OjU1NcyaNYt//etfuLq6dmofgiC0aoiMHDmSkSNHiq/vvfdefve737W5n4EDB4r7UyqVFBcXU19f3+Z5Lly4kFWrVolGzueff87ChQs5cOBAs3qPPfYYzz77bKeNnK7QV6ZtZH2lpekMroyMrdGX+q9arRYjvPUWqqqq5OiWEtKX+m9vxBb17TcO+unpEBsLw4e3LLGxlu0dobWwip1h48aNvPzyy/zwww9kZ2dTV1fXphFSXFzMvffeS1hYGMOGDeO5555j3759rF27lrvuuqtDx9u7d69oyLTF7Nmzsbe3Z/bs2TzyyCPtXgjffvvtfPPNN5hMJg4dOoS3t3eri9GWLl1Kfn4+mzdv7lA7rVyrvjLtI+srLefPn+/pJsjIXDVy/5UWWV9pkfWVFlvUt9/M5FhncD79FBISfnk/JQWWLGl9hqc1DAbDFevMmDGj2TSyTqfjiSeeAGD16tUsW7aMhEuNePHFFxk+fDj//e9/W+xn//79zJo1i9dee43s7Gw+++wznnrqKSIjI3n66aev2I7jx4/z1ltvsWvXrnbrrV+/nsbGRr7//ntqa2vbrevl5UViYiJbtmxhw4YNLFq0qNV6arWaJ598kmeffZYZM2Zcsa1WOqKvzNUj6ystFy9e7OkmyMhcNXL/lRZZX2mR9ZUWW9S338zkWElIgGHDfilNDZ6O0JHoVJs3b26WM+Duu+8WtxUUFBAaGiq+DgsLE0M+Xs6NN97IxYsX+c1vfsO///1vpk+fzubNm3nhhRdYt25du23Iysripptu4v3337/iTA5YwnPOmzePV199lZSUlHbrLl68mE8++YS1a9dy++23t1nv7rvvJi8vjy1btlzx+Fbk6F/SIusrLXKIbumwhrrtL4uoewK5/0qLrK+0yPpKiy3qK1/xdJJrjeoTGBhIbm6u+Do3NxdHR8dW/XQ//fRT0tPTWbp0KYmJibz44ot4eXkxZcoUgoOD2zxGUVERM2bM4OmnnxazV3cUo9FIVlZWu3XmzJnDd999x6BBg/Dx8Wmznlqt5oknnuDZZ5/t8PHlqEnSIusrLZMnT+7pJvRZBg8eTElJCYMHD+7ppvRZ5P4rLbK+0iLrKy22qK9s5HSSjiQta4/58+fz7rvvkpqaSl1dHU899RQLFixote6dd97Jq6++yqxZs3jggQfYunUrlZWVnD17loULF7bZvpkzZ3LXXXdx3333tduWnJwc1q9fT0NDA3q9nn/961/k5eUxfPjwdj/n6OjI5s2b+ec//3nF87377rvJzc3l0KFDV6xrbb+MdMj6SsvGjRt7ugl9GllfaZH1lRZZX2mR9ZUWW9S33xk5KSlw9Ogv5QqeWV3OrFmz+P3vf8+sWbMICwtDq9Xy6quvtlr3asJDfvvtt5w8eZK///3vODs7i8XKsmXLWLZsmfj6hRdewNfXF39/f1avXs3333/foYzio0aNIioq6or1NBoNTzzxBOXl5Z0+FxkZGRkrp06dYsmSJZw6daqnmyIjIyMjYwMohM6kze1mqqurcXNzo6qqqlkYyIaGBrKysoiIiMDe3r5D+7JGV2uLc+c6FkZap9Ph4ODQoWPKdB5ZX2npqL5X8xuTgZSUFDGoiEzXcvToUYYPH86RI0fkPDkSIfdfaZH1lRZZX2npLfq2ZRu0Rr+JrhYTYzFkrjVPjkrVbyTrEWR9pUXWV1o8PT17ugkyMleN3H+lRdZXWmR9pcUW9e1X7moxMc0jq1lLRw0cgPr6eukaKCPrKzGyvtJy/Pjxnm6CjMxVI/dfaZH1lRZZX2mxRX37lZEjIyMjIyMjIyMjI9P3kdzI+eGHHxg1ahQODg54e3szd+5cqQ8pKU5OTj3dhD6NrK+0yPpKy4gRI3q6CX2WmJgY1q1bR0xnpt5lOoXcf6VF1ldaZH2lxRb1ldTI+frrr7nzzju5++67OXHiBHv27GHRokVSHlJyGhsbe7oJfRpZX2mR9ZWWvLy8nm5Cn8XFxYXw8HA515OEyP1XWmR9pUXWV1psUV/JjByj0cjy5ct55ZVXWLZsGbGxscTFxXHbbbdJdchuwWAw9HQT+jSyvtIi6ysthYWFPd2EPkt+fj4vvPAC+fn5Pd2UPovcf6VF1ldaZH2lxRb1lczIOXr0KPn5+SiVSq677joCAgKYNWsWZ86ckeqQ3YJCoejpJvRpZH2lRdZXWuToddJRXFzMl19+SXFxcU83pc8i919pkfWVFllfabFFfSUzcs6fPw/AM888w5///GfWr1+Ph4cHkyZNajMxpF6vp7q6ulnpbVwpJrfMtSHrKy2yvtIybdq0nm6CjMxVI/dfaZH1lRZZX2mxRX07bZY988wzPPvss+3WOXToEGazGYCnnnqKefPmAfDBBx8QHBzMmjVruP/++1t87qWXXmp131u2bMHJyYmpU6dy8OBBdDod3t7emEwmqqqqAMSEhQ0NDYDFf7u+vh6TyYSdnR2Ojo7UXEqSc3ldZ2dnGhoaMBqNKJVKnJ2dRQNLq9WiVCrR6XQACIKAWq1uta5Go0GlUolhep2cnGhsbMRgMKBQKHB1dRXbe3ldR0dHjEYjjY2NYt3q6mrxeBqNhrq6uhZ1Adzc3KipqcFsNreo6+DggNlsRq/XA5aL3NraWsxmMyqVCnt7e2pra1ut2xkN26t7uYbt6W0ymXB2dhbrNtVQqVTi4uLSpoat6W3VsD29rRp2VO/OaNhe3a7qs53R22Aw4OXl1Wb/tmpYV1cnHmvjxo0AhISE4O3tzbFjxwBISkqioKCAgoIC7OzsmD59Olu2bMFkMhEYGEhgYCCHDx8G4LrrrqO0tJQLFy4AMHPmTLZv305jYyN+fn6Eh4dz4MABAIYMGUJ1dTXZ2dkAzJgxgz179lBfX4+3tzexsbHs3bsXgIEDB9LQ0EBmZiaAOEbU1tbi4eHBwIED+fnnnwGIj4/HbDZz7tw5ACZNmsTx48fFhGLDhg1jx44dgGWRu0qlIiUlBYDx48dz9uxZysvLcXJyYvTo0WzduhWAyMhIHB0dOX36NDk5OSxYsICMjAxKSkqwt7dn4sSJbNq0CYCwsDDc3d05ceIEACNHjiQ3N5eioiLUajVTp05l06ZNCIJAcHAwvr6+HD16FIDhw4dTVFQkzpDPmDGDrVu3YjQaCQgIIDg4mEOHDgEwdOhQysvLyc3NFfXesWMHer0eX19fIiMj2b9/PwCDBw+mtraWrKwsAKZPn87evXupr6/Hy8uL+Ph49uzZA8CAAQNobGwkIyMDgClTpnD48GFqampwd3dnyJAh7Nq1C4C4uDgA0tLSAJg4cSInT56ksrISFxcXkpKS2L59OwDR0dFoNBrOnj0LwLhx40hNTaWsrAxHR0fGjh0rfuc5OTn4+/tz6tQpAEaPHs358+e5ePEiWq2WyZMni302NDQUT09PMfTpiBEjyMvLo7CwEJVKxbRp09i8eTNms5mgoCD8/f05cuQIAMOGDePixYvk5eWhUChITk5m27ZtGAwG/P39CQ0N5eDBgwAkJiZSWVlJTk4OAMnJyezatYuGhgZ8fHyIjo5m3759AAwaNIj6+nrxRuC0adPYv38/dXV1eHp6MmDAALHPJiQkYDQaSU9PB2Dy5MkcPXpUTIY3dOhQdu7cCUBsbCxKpZLU1FSxz545c4aKigqcnZ0ZOXIk27ZtAyAqKgp7e3vRs2Ls2LGcO3eOI0eOkJCQwLhx49i8eTMA4eHhuLq6cvLkSQBGjRpFdnY2xcXFaDQapkyZIo8RdGyM+OGHHwgLC2PMmDHyGEHXjxHvv/8+YWFhRERE4OzsLI8RXTxGrFu3Dh8fHxwdHXt0jLC2v0MInaSkpERISUlpt+h0OmHbtm0CIOzevbvZ50eOHCk8+eSTre67oaFBqKqqEsuFCxcEQKiqqmpWT6fTCWfPnhV0Ol1nm3/NVFZWtrs9LCxMcHFxEerr68X3qqqqBHt7eyEuLk7q5on8+9//FhITEwU7OzvhpZdearduSUmJMH/+fMHDw0MICQkRPv300zbr/upXv2r1ex0zZowACIWFhWI9pVIpnD17Vqzz+eefC5MmTWq3LVfSV+ba6Ki+Pfkbs2V++umnnm5Cn+XIkSMCIBw5cqSnm9JnkfuvtMj6Sousr7T0Fn2rqqpatQ1ao9MzOd7e3nh7e1+x3vDhw9FqtaSlpTF+/HjAsug5OzubsLCwVj+j1WrRarWdbVK3otForljH39+f7777jjvuuAOAtWvXEhISInXTmhEYGMjzzz/P//73vyvWXb58OQ4ODhQWFpKRkcHUqVO57rrrGDBgQKv1Y2JiWLVqlfi9ZmVlUVZW1qKem5sbzz33HJ999lmH290RfWWuHllfaQkKCurpJvRZvLy8mDt3Ll5eXj3dlD6L3H+lRdZXWmR9pcUW9ZVsTY6rqyvLli1j5cqVbNq0ibS0NB544AEA5s+fL9Vh2yU9HY4ebVkuzfJ1iI4svFq4cCGrVq0SX69atapF6OxTp04xbtw43N3dSUpKEqeFO4sgCK2+f8sttzB79uwOrcH46aef+NOf/oRWq2XgwIHccsstzdp/OXPnzuW7774TI3V99tlnLFy4sEW93/zmN2zYsKHVqcXs7Gzs7e15++238fX1JSQkhB07dvDJJ58QEBBAaGioOMUq03XY4sJBW8Lf37+nm9BnCQsL4913323zJpnMtSP3X2mR9ZUWWV9psUV9Jc2T88orr7BgwQLuvPNORowYQU5ODtu2bcPDw0PKw7ZKejrExsLw4S1LbGzHDR3rmo72mDFjBkePHqW8vJyioiLS09OZOHGiuL2xsZGbbrqJRYsWUVJSwooVK5g9e7a41uRy3n77bYYOHUpoaCj33HMP69evZ9euXTz44IOir+K10tRYEgSh3Sh47u7ujBo1SvSx/Pzzz1vNf+Tp6clvf/tbnnvuuVb309jYSHZ2Nvn5+SxfvpwlS5Zw8uRJcnJy+MMf/sCjjz56bScl04KO9F+Zq8fqqy3T9eh0Or7++mtx/ZhM1yP3X2mR9ZUWWV9psUV9JTVy1Go1//jHPyguLqa6uprNmzczcOBAKQ/ZJpfWZPPpp3DkyC/l00+bb+8KVCoVt9xyC2vWrOGLL75g/vz5KJW/SL1//37s7Ox48MEHUavVLFiwgJiYGHHhYVP0ej3Z2dmsX7+eI0eOMGbMGN577z3+8Y9/MGHChC7JQJucnMzf/vY3dDodp06dYu3atVe8GF60aBGrVq3i+PHjODg4EBsb22q93/3ud/zwww+tzuYIgsBTTz2FWq1m3rx55Ofn89hjj6HRaJg3bx5nzpwRA1jIyMj0b1JSUli2bJm40FtGRkZGRqY9+p3vSkICDBt29Z93dHTsUL3Fixfzpz/9CZ1Ox3vvvUdlZaW4raCggNDQ0Gb1w8LCKCgoaLEfrVbLrbfeyvPPP095eTnTp0/no48+wsnJia+++oozZ85cs+H41ltv8dvf/pawsDDCwsJYuHChGAGsLWbPns0jjzyCh4cHixcvbrOel5cXv/3tb3n++eeZPXt2i3OzutM5ODgAiLo4ODhgMBhobGwUI4vJXDsd7b8yV8ewaxlcZGR6GLn/Sousr7TI+kqLLeor6UxOX8RoNHao3pgxY8jPz6e2tpahQ4c22xYYGCiGybSSm5tLYGBgi/3o9XqefPJJJk+ezMKFCzlw4AAJCQmEhYWxZ8+eFsbS1eDj48OaNWu4ePEihw4doqKigqSkpHY/Y29vz8yZM/nPf/4jBlhoi8cff5z169eLYSLbo6P6ylwdsr7ScvHixZ5ugozMVSP3X2mR9ZUWWV9psUV9+91MzrXS2NgozjpcibVr1zZzU7MyevRoDAYDb7/9Nvfeey/ffPMNaWlpJCcnt6ir0WjYsmWLuJ9bb721Q8c2Go0YjUZMJhNGo5GGhgbUajV2dnYt6mZmZuLp6YmzszNff/01u3fv5r333rviMZ577jnuvvtuAgIC2q3n5eXFAw88wFtvvcXgwYPbrdsZfWU6j6yvtOTl5fWYS66MzLUi919pkfWVFllfabFFffvdTE5KSvPIalK6dw8ZMoRBgwa1eF+j0bBu3To++eQTvLy8ePnll/nuu+9wc3NrUVehULRqKF2J559/HgcHBz799FOefvppHBwc+OSTTwDYvXs3zs7OYt0DBw4QHx+Pu7s7b7/9Nj/88EOH3JqCg4ObBVRoj8cff1xMpikj01dRKBQ93YQ+i0KhQK1WyxpLiKyttMj6Sousr7TYor4Koa0YxL0Aa8ZWa7ZhKw0NDWRlZREREdHh9RrW6Gptce4cxMRca4tlZPoGV/Mbk5GRkZGRkZGRkrZsg9boNzM5MTEWQ6ZpZDVr6YyBU11dLW1D+zmyvtIi6yst27Zt6+km9GlkfaVF1ldaZH2lRdZXWmxR3361JqcrZmp68cRXn0DWV1pkfaXFmiBXputJSUnhvvvu4/vvvychIaGnm9MnkfuvtMj6Sousr7TYor79Zianq1Cr1T3dhD6NrK+0yPpKiy1mhLYVdDodmZmZcjJQCZH7r7TI+kqLrK+02KK+spHTSTQaTU83oU8j6ystsr7S0hUh3WVkegq5/0qLrK+0yPpKiy3qKxs5naSurq6nm9CnkfWVFllfaTl48GBPN0FG5qqR+6+0yPpKi6yvtNiivrKRIyMjIyMjIyMjIyPTp5CNnE7SkfwxMlePrK+0yPpKS2JiYk83oc8SERHBe++9R0RERE83pc8i919pkfWVFllfabFFfWUjp5MYjcaebkKfRtZXWmR9paWysrKnm9Bn8fDwYMKECXh4ePR0U/oscv+VFllfaZH1lRZb1Fc2cjpJY2NjTzehTyPrKy2yvtKSk5PT003osxQXF/Paa69RXFzc003ps8j9V1pkfaVF1ldabFHffmvk6PXS7Dc8PJz9+/c3e2/ZsmU888wz0hxQItLS0pg9ezbe3t74+PiwZMkSKioq2qy/bds2EhMTcXZ2ZtKkSWRnZ7dZV6FQEB0d3ey99PR0FAoF8+bNa1Zv7Nixzepdf/31fPjhh1d1TjIyMrZLfn4+//nPf8jPz+/ppsjIyMjI2AD90sh5911wcbE8dhZXV9eub1AvpKqqittvv53MzEyys7NpbGxkxYoVrdYtLS3ltttu46WXXqKqqorZs2ezcOHCdvevVCo5cOCA+HrVqlXExMSgUjXPT5uamsqmTZuu/YRkgP7Tf3uK5OTknm6CjMxVI/dfaZH1lRZZX2mxRX37nZHz7ruwbBkkJFgeO2vo1NbWXtPxP/zwQ5KTk7n33ntxcXEhKSmJ/Px8HnzwQdzc3Bg1ahQFBQUAmM1m5s6di6+vL56ensyfP5/y8nIAduzYQVBQkPh6zZo1xMXFdTpRniAIrb4/cuRI7rrrLtzc3HBycuLee+9tM3zgvn37iImJ4YYbbsDOzo7HH3+c48ePk56e3uZxFy5cyKpVq8TXn3/+OQsXLmyxZuSxxx7j2Wef7dQ5ybTNtfZfmfbZtWtXTzdBRuaqkfuvtMj6Sousr7TYor79ysixGjgPPwzHjlkeO2vomP+/vfuOiupa2wD+DL2jMAIqRUSsiCj2rgFL1GtJdBlr1HjlqgST3C8a9aqJNVETE29iSbyiscYbo9GoEez1imBvoEIs2LCASp2Z/f3BYiIRZSDsHOb4/NZikTmz55x3HneQ17PPGYPhT9exZ88evP7663jw4AG8vb3RqlUrtGvXDvfv30e1atUwd+5c49g+ffogOTkZycnJePz4MT755BMAQPv27fHGG29g7NixuHfvHiIjIxEdHQ17e/vnjnfnzh2MHDkSfn5+aNSoEaZPn44jR45g48aNGDJkiEk1Hz58GPXq1Xvh80U1S+fOnXvh+H79+uGnn36CXq9HXFwctFptkXdNevvtt3Hz5k3ExMSYVCe9XFnMX3qx7OxspUsgKjXOX7mYr1zMVy5zzPeVaXKebXC+/BKwsMj/XtJG54/LqYoSHh6OChUqGL+WL19e6Pn69eujd+/esLa2Rs+ePeHo6Ih+/frBysoKvXr1wunTpwHkL+kaNGgQHB0d4erqivfeew8HDx407mfOnDmIi4tD+/btMXjwYLRo0aLIeo4ePYquXbvi7NmzWLFiBTIzMzFp0iRs27YN//rXv4p9PydPnsRXX331wrEtWrRAYmIifvnlF+Tl5WHu3LnIyclBZmbmC/fp7u6OBg0aIDY2FqtXr8aAAQMA5F+H8yxra2tMnDiRZ3PKiCnzl0qvUqVKSpegWq6urmjbti1cXV2VLkW1OH/lYr5yMV+5zDHfV6LJ+WODU/B7tEZT8kbHzs6u2DExMTF49OiR8WvYsGGFnvfw8DD+t729faGJY29vb/xUep1Oh3HjxsHPzw8uLi548803cf/+feNYBwcH9O/fHxcuXMC77777wnq6deuGu3fv4p133sHXX3+NsLAwxMTEYObMmdi8efNL30tycjJ69OiBZcuWvfBMjlarxYYNGzB58mR4eXnhxo0bqFevHqpWrfrSfQ8cOBDff/89Nm7ciH79+gHIb+z+aNiwYbhx4wZiY2Nfuj8qninzl0rvjzfUoLITEBCALVu2ICAgQOlSVIvzVy7mKxfzlcsc81V9k5OTk9/EBAcDCxb83uAU0GjytwcH548r7q5rf+U1DatXr8aBAwdw5MgRZGRk4L///W+hZWFJSUlYtGgR+vbtiw8++OCF+1m1ahWSkpLw9ttvo0GDBpg1axbc3d3RoUMHeHt7v/B1t2/fRnh4OP71r3+hV69eL601PDwcJ06cwP379zFjxgzcunULQUFBL31Nz5498fPPPyMoKMjY6On1+ufGWVtb46OPPuLZnDLAa3LkOnLkiNIlqFZeXh62b9+OvLw8pUtRLc5fuZivXMxXLnPMV/VrV2xtgYUL88/UjBtX+EwOAAiRv/30aWDx4vzx5cXjx49ha2uLChUqIC0tDfPmzTM+ZzAYMHToUEyaNAkRERFo0KABfvjhB+MZkWcNHjwYlpaWxsf/+Mc/ij12eno6OnfujCFDhuDvf/97seNPnjyJoKAgZGRkYOzYsRg0aBDc3d1f+hoHBwfExMRAq9UWu/9hw4Zh1qxZePLkCfr371/seCJSlzNnzqB///6Ij49Ho0aNlC6HiIjKOdWfyQGAUaPyG5iFC4GoqPzGBsj/HhWVv33x4vxxxSnqwn5ZCu5u5uHhgTZt2qBLly7G5+bNmwdLS0tERUXB3t4ey5cvR2RkJO7evfvcfp5tcEy1adMmnD59Gp999hmcnJyMXwUiIiIQERFhfDxjxgy4ubkhMDAQWq0Wn376qUnHadasWaHlJ0UtVwMAGxsbfPTRR8a7yVHp/JXz91VU3NlLovKM81cu5isX85XLHPPViBfdQ7gcyMjIgKurK9LT0wt9vkd2djaSk5Ph7+9fomsMnr02Z8GC/DM4JWlwCo7N6xrkYb5ymZpvaf8fe9UlJSUhMDBQ6TJUKSEhAaGhoTyTIxHnr1zMVy7mK1d5yfdFvUFRXokzOQWePaPTsGHJGxwAyCnuoh36U5ivXMxXrqtXrypdAlGpcf7KxXzlYr5ymWO+qr8m548KGprIyJI3OEREREREVP69UsvVnpWTU7qbDAghnvssFyo7zFcuU/PlcrXS0el0/CwiSfR6PdLT0+Hq6lqq6wypeJy/cjFfuZivXOUlXy5XM0Fp76LGW/DKxXzlYr5yHT16VOkSVMvS0hLnz59ngyMR569czFcu5iuXOeb7yjY5pWUwGJQuQdWYr1zMV66CD/KlspeUlISoqCgkJSUpXYpqcf7KxXzlYr5ymWO+bHJKqDycqlMz5isX85XLzc1N6RJU6/Hjx0hISMDjx4+VLkW1OH/lYr5yMV+5zDFfNjklxOsT5GK+cjFfuerWrat0CUSlxvkrF/OVi/nKZY75sskpIV7TIBfzlYv5ynXw4EGlSyAqNc5fuZivXMxXLnPMl00OERERERGpitQmJzExET179oRWq4WLiwtatWqFPXv2yDykyUr7mYjFLfepVq0aXFxckJWVZdyWkZEBe3t71K5du3QHLUeio6MREhICZ2dnVK9eHYsXLzbpdV26dHlpdtHR0dBoNPjiiy8KbZ84cSI0Gg3WrVtXaNySJUuMY27fvs3bTpuIy9XkqlOnjtIlqJaPjw8++eQT+Pj4KF2KanH+ysV85WK+cpljvlKbnG7dukGn02H37t2Ij49HSEgIunfvjtu3b8s8bLGWLAGcnfO/y+Dl5YWff/7Z+Hjjxo2q+Ys5JycHixcvxsOHD7FlyxZMnToV+/fvf+lrNm3aZNIyqRo1amD9+vXGx0IIrF+/HgEBAYXGVaxYEbNmzUJeXl7p3gSRJDqdTukSVKtSpUoYOHAgKlWqpHQpqsX5KxfzlYv5ymWO+UprctLS0nD58mVMmDABwcHBCAwMxJw5c5CZmYlz587JOmyxliwBIiKAOnXyv5e00cnOzi52zFtvvYXVq1cbH69evRoDBgwoNEaj0WDRokXw9fWFVqvF+vXrsXXrVlSvXh0eHh6Fftn/9ttvERgYCGdnZwQHB2Pv3r3GWurWrYu1a9cCAB49egRvb2/s3r27ZG8K+Q2FKUaNGoXmzZvDysoK9erVQ1hYGOLi4l44Pjs7G5MnT8acOXOK3XdAQAAcHR2RkJAAADh8+DB8fHzg7e1daFzTpk3h4+OD5cuXF7mfatWqYf78+ahZsyZcXFywYMECHDt2DHXr1oWbm9tzZ4teJabMXyo93t5YngcPHmDx4sV48OCB0qWoFuevXMxXLuYrlznmK63JcXd3R506dbBy5Uo8ffoUOp0OS5YsgaenJ0JDQ2Ud9qUKGpzISODEifzvpWl0ihMeHo6EhAQ8ePAAt2/fRlJSEtq2bfvcuEOHDiExMRGLFi3C6NGj8eOPP+Ls2bNYtmwZxo4dC71eDwCoUqUKdu3ahfT0dERGRqJ///7IycmBnZ0dVqxYgXHjxuHWrVuIiorC3/72N3Ts2LHIuhYtWoSQkBD4+vpixIgR2Lp1K/bv348xY8bg+PHjJX6fer0ex44dQ7169V44Zs6cOejfv/9zjcqL9O3bF2vWrAEArFmzBgMHDixy3NSpU196Nmfbtm2Ii4tDbGwsxo8fj7lz5+LQoUPYs2cPJk6ciHv37plUDxGVDykpKZg7dy5SUlKULoWIiMyAtA/N0Gg0iImJQc+ePeHs7AwLCwt4enpix44dqFChQpGvycnJQc4zF8tkZGSUWT3PNjhffgloNPnfgfztADBqVPH7cXZ2LnaMlZUVevXqhQ0bNiArKwt9+/aFhcXz/eSHH34IOzs79OnTB/3798fo0aPh4OCAHj164PHjx0hNTYWPjw+6detmfM3IkSMxZcoUJCUlISgoCE2aNMGIESMQFhaGrKwsnD59usiacnJykJKSgq1bt8LW1habN2/G0qVLAQADBgxAkyZNin/zfzB58mRUrVoVnTt3LvL5lJQU/PDDD0hISDB5ieKQIUPQvHlzzJo1C5s3b8aMGTMKnRUrEB4ejqpVqyI6Oho9evR47vmoqCi4urqiadOm8PLyQr9+/VCxYkVUrFgRvr6+uHjx4iu57MWU+Uul1759e6VLICo1zl+5mK9czFcuc8y3xE3OtGnT8PHHH790TFxcHEJDQzF69Gh4eHjgwIEDsLe3x3fffYfu3bsjLi4OlStXfu51s2fPLnLfsbGxcHR0RMeOHXHs2DFkZWVBq9VCr9cjPT0dwO8XVBcsx3F2dkZmZib0ej1WrLBFVJQdxo4V+PJLDQquUS9odIQQiIjQICsrCyNG6ODk5GRssGxtbWFhYWG8kYBGo4GlpSV0Oh0sLCwKjbWxsQGQf5venj17Yvr06cjMzMQXX3xhHFNQL5B/bUnBY2trazg7OyM9PR0ajQZ2dna4c+cOXFxcsGPHDsyZMwdXr1417r+gAQKA4cOHY/bs2Zg0aRIsLCyg0+mMn0xrb28Pg8GAnJwchIeHY/r06bh37x7atWuHb775Bu7u7li9ejWOHj2KRo0aGccCwMmTJ40NVsuWLbFjxw7jB/F9//33+PHHH7Fjxw5kZGQUytvS0hIODg4YO3YsJkyYAADIzc01vn8nJydkZ2cXyjAzMxM6nQ729vaoWbMmPvjgAzRo0ADOzs4wGAzIzMw0Hlun0yE9PR0TJkzAu+++i5YtWxq35+bmwmAwwNHREUB+o2xrawsXFxfk5eUhMzMTNjY2SE9PR1ZWlrEuV1dXZGRkQAgBa2tr2NjYGDN0cHAw7hsAXFxc8OTJExgMBlhZWcHOzs54zdGzeRc39mVztiDDgvf8x7HPZvjHsX+cs8+O1el0cHNze+H8dnR0RG5uLp4+fWo81q+//gog/8JvrVaLEydOAAAaN26M1NRUpKamwtLSEmFhYYiNjYVer0eVKlVQpUoV4xnChg0bIi0tDdevXwcAdO7cGXv27EFubi48PT1RrVo1/O9//wMABAcHIyMjw/gv9uHh4Th06BAyMzOh1WpRs2ZNHD58GABQr149ZGdn48qVKwBg/Bnx5MkTVKxYEfXq1TPe9rJ27dowGAxITEwEALRr1w4nT55Eeno6XFxc0KhRI+NS0MDAQFhZWeHChQsAgNatW+P8+fN48OABHB0d0bx5c+zatQsAUL16dTg4OODs2bO4desW+vTpg8uXL+PevXuws7ND27ZtsXPnTgCAn58fKlSogFOnTgHIX3p57do13L59G9bW1ujYsSN27twJIQS8vb3h4eFhXL4ZGhqK27dv4+bNm7CwsEB4eDh27doFnU6HypUrw9vb27h0NCQkBA8ePMC1a9eMee/duxc5OTnw8PBA9erVcfToUQBA/fr18eTJEyQnJwMAwsLCcPjwYWRmZsLd3R21a9fGoUOHAOR/TkJubi4uX74MAOjQoQOOHz+Ox48fo0KFCggODjZeo1erVi0AwKVLlwAAbdu2xenTp/Ho0SM4OzujcePGxhvR1KhRAzY2Njh//jwAoFWrVrh48SLu378PBwcHtGzZ0vhn/ttvv8HLywtnzpwBADRv3hxXr17F3bt3YWtri/bt2xvnrK+vL9zc3HDy5EkAQJMmTXDjxg3cunULVlZWeO211xATEwODwYCqVavCy8sL8fHxAIBGjRrh7t27uHHjBjQaDTp16oTdu3cjLy8PXl5e8PX1xbFjxwAADRo0wKNHj/Dbb78BADp16oT9+/cjOzsblSpVQo0aNXDkyBEAQFBQEDIzM40/z1977TUcPXoUT58+hZubG+rWrWucs3Xq1IFOpzMuE2nfvj0SEhKQkZEBV1dXhISEYN++fQCAmjVrwsLCAhcvXjTO2XPnzuHhw4dwcnJC06ZNjUuZAwICYGdnZ1w63rJlSyQmJuLMmTMICAhAq1atEBMTA+D3m+kU/ANas2bNkJKSgjt37sDGxgYdOnTgzwiY9jMiJiYGlStXRosWLfgzAmX/M2Lt2rWoXLky/P394eTkxJ8RZfwzYseOHXBxcYGDg4OiPyMK6jeJKKF79+6JCxcuvPQrKytLxMbGCgsLC5Genl7o9TVq1BCzZ88uct/Z2dkiPT3d+HX9+nUB4Ll9ZGVlifPnz4usrKxi683OFsLaWojgYCH0+qLH6PX5z1tb549/mUePHr30eT8/P3HkyBEhhBABAQGiTp06Qggh9uzZI2rVqmUcB0DcunXL+NjW1lYkJycbH7u6uooLFy6I7OxsYWdnJ3799Veh0+mEEEJ4eXmJPXv2CCGEMBgMIiwsTAwcOFBotVpx48aNIuvKzs4WHTp0EGvXrhUbN24Uw4cPF56ensLLy0uMHj1aZGRkvPyNP2PdunXC29u7UL1FqVChgvD09BSenp5Cq9UKAMLT01NcvHjxubHLly8XnTt3Fo8ePRLR0dFCo9GI9evXCyGEaNeunVi7dm2hcQVatGghpk+fLp6dys/+GQghRK1atYx5CSFEgwYNxPbt201+v2pS3PwtUJL/x+h3O3bsULoE1YqPjxcARHx8vNKlqBbnr1zMVy7mK1d5yTc9Pb3I3qAoJT6To9VqodVqix2XmZkJAM8t07KwsIDBYCjyNba2trC1tS1pSS9lawssXJi/JG3cuN+XqhUQIn/76dPA4sX541/G0tLS5GNv3LixyGVqJZGTk4Pc3Fzj0qovv/yy0PUkBXc62759O6ZNm4aRI0di27Ztz+3HxsYGsbGxxnp69+5dqnp27tyJyMhIxMbGolq1ai8de+nSJeOf9fXr19GmTRucPHnypfPH0tISffv2haenp0mnRqdOnfrcTR3oxUoyf6nkXF1dlS5BtRwdHREUFGQ8S0tlj/NXLuYrF/OVyxzzlXbjgRYtWqBixYoYOnQoTp06hcTERPzf//0fkpOTC11j8lcYNSq/gVm4EIiKym9sgPzvUVH52xcvNu2aHAcHB5OPGxwcjKCgoFJWnc/FxQVz585FeHg4vLy8cP/+fdSoUQMAkJycjMmTJyM6OhpWVlaYMmUKbty4gf/85z/P7Uej0fzphgvIX1L48OFDtGzZEk5OTnByckJEwUVNyF8adeDAAQCAh4cHvLy84OXlZWzSvLy8YGX14t7awcEBDg4OxX6uToHOnTujZs2af/JdvTpKMn+p5EJCQpQuQbVq1aqFuLg44xIXKnucv3IxX7mYr1zmmK9GCBPvHVwKx48fx6RJk3D8+HHk5eWhXr16mDJlCrp27WrS6wvWFBashy2QnZ2N5ORk+Pv7l+jDDZ+9+cCCBflncErS4AD515SYYzdrLpivXKbmW9r/x151v/766wtvxEF/HvOVi/nKxXzlYr5ylZd8X9QbFEXa3dWA/AuKCi42Kg8KGpmICGDfvt+XqJna4BARkTISEhLQpUsXxMfHo1GjRkqXQ0RE5ZzUJqc8KmhoIiNL1+DwX7XlYr5yMV+5uHSSzBnnr1zMVy7mK5c55vvKNTlAfmPz9tvF32SAiKgkyuK6NyKlcP7KxXzlYr5ymWO+5ldxGSltg1Pw2SEkB/OVi/nKVaL79xOVM5y/cjFfuZivXOaYr1k3ORLvmUD0SnvRbd6JiIiIzIHUu6v9WS+6g4Jer0dSUhIcHBxQqVIlaJ794BvJCj6NnuRgvnIVl68QArm5ubh37x70ej0CAwPN8hS1Up4+fcrPcZEkOzsbiYmJqFmzJq8tk4TzVy7mKxfzlau85Ftu7q4mi6WlJby9vXHjxg2kpKT8pcfOyckp8w8spd8xX7lMzdfBwQG+vr5scEro3LlzaNq0qdJlqJKdnR2ys7PZ4EjE+SsX85WL+cpljvmaZZMD5H/oZGBgIPLy8v7S4x48eBCtW7f+S4/5KmG+cpmSr6WlJaysrP7SM6Rq8fDhQ6VLUK3k5GRMmDABy5Ytg7+/v9LlqBLnr1zMVy7mK5c55mu2TQ6Q/8vYX720yd7env+SKBHzlYv5yuXk5KR0Car18OFD7NmzBw8fPmSTIwnnr1zMVy7mK5c55muW1+QoKS8vD9bW1kqXoVrMVy7mKxfzlSchIQGhoaH8MFCJOH/lYr5yMV+5yku+JekNuOC+hHbv3q10CarGfOVivnIxXzJnnL9yMV+5mK9c5phvuV6uVnCSKSMjQ+FKfvf06dNyVY/aMF+5mK9czFeeJ0+eGL8zYzk4f+VivnIxX7nKS74FNZiyEK1cL1e7ceMGfHx8lC6DiIiIiIjKievXr8Pb2/ulY8p1k2MwGJCamgpnZ+dycaenjIwM+Pj44Pr16+XmGiE1Yb5yMV+5mK9czFcu5isX85WL+cpVnvIVQuDx48eoUqVKsR9zUa6Xq1lYWBTbpSnBxcVF8T9kNWO+cjFfuZivXMxXLuYrF/OVi/nKVV7ydXV1NWkcbzxARERERESqwiaHiIiIiIhUhU1OCdja2mLq1KmwtbVVuhRVYr5yMV+5mK9czFcu5isX85WL+cplrvmW6xsPEBERERERlRTP5BARERERkaqwySEiIiIiIlVhk0NERERERKrCJoeIiIiIiFSFTU4pJSYmomfPntBqtXBxcUGrVq2wZ88epctSlV9++QXNmjWDvb09tFot+vTpo3RJqpOTk4OQkBBoNBqcPHlS6XJUISUlBSNGjIC/vz/s7e0REBCAqVOnIjc3V+nSzNY333wDf39/2NnZITQ0FAcOHFC6JFWYPXs2mjRpAmdnZ3h4eKBXr164dOmS0mWp1uzZs6HRaDBu3DilS1GNmzdvYtCgQXB3d4eDgwNCQkIQHx+vdFmqoNPpMHnyZOPfZdWrV8cnn3wCg8GgdGkmY5NTSt26dYNOp8Pu3bsRHx+PkJAQdO/eHbdv31a6NFX48ccfMXjwYAwbNgynTp3CoUOHMGDAAKXLUp0PP/wQVapUUboMVbl48SIMBgOWLFmCc+fO4YsvvsDixYsxceJEpUszS+vXr8e4ceMwadIknDhxAm3atEHXrl1x7do1pUsze/v27cOYMWNw9OhRxMTEQKfToVOnTnj69KnSpalOXFwcli5diuDgYKVLUY2HDx+iVatWsLa2xvbt23H+/HnMnz8fFSpUULo0Vfj000+xePFi/Pvf/8aFCxfw2WefYe7cuVi4cKHSpZlOUIndu3dPABD79+83bsvIyBAARGxsrIKVqUNeXp6oWrWq+O6775QuRdW2bdsmateuLc6dOycAiBMnTihdkmp99tlnwt/fX+kyzFLTpk1FREREoW21a9cWEyZMUKgi9bp7964AIPbt26d0Kary+PFjERgYKGJiYkS7du1EVFSU0iWpwvjx40Xr1q2VLkO1unXrJoYPH15oW58+fcSgQYMUqqjkeCanFNzd3VGnTh2sXLkST58+hU6nw5IlS+Dp6YnQ0FClyzN7CQkJuHnzJiwsLNCwYUNUrlwZXbt2xblz55QuTTXu3LmDkSNH4vvvv4eDg4PS5aheeno63NzclC7D7OTm5iI+Ph6dOnUqtL1Tp044fPiwQlWpV3p6OgBwrpaxMWPGoFu3bggLC1O6FFX5+eef0bhxY/Tt2xceHh5o2LAhvv32W6XLUo3WrVtj165dSExMBACcOnUKBw8exOuvv65wZaazUroAc6TRaBATE4OePXvC2dkZFhYW8PT0xI4dO3iatAxcvXoVADBt2jR8/vnnqFatGubPn4927dohMTGRfwH/SUIIvP3224iIiEDjxo2RkpKidEmqduXKFSxcuBDz589XuhSzk5aWBr1eD09Pz0LbPT09uTS4jAkh8P7776N169YICgpSuhzVWLduHRISEhAXF6d0Kapz9epVLFq0CO+//z4mTpyIY8eO4d1334WtrS2GDBmidHlmb/z48UhPT0ft2rVhaWkJvV6PmTNn4q233lK6NJPxTM4zpk2bBo1G89Kv48ePQwiB0aNHw8PDAwcOHMCxY8fQs2dPdO/eHbdu3VL6bZRbpuZbcFHbpEmT8MYbbyA0NBTLly+HRqPBhg0bFH4X5Zep+S5cuBAZGRn46KOPlC7ZrJia77NSU1PRpUsX9O3bF++8845ClZs/jUZT6LEQ4rlt9OeMHTsWp0+fxtq1a5UuRTWuX7+OqKgorFq1CnZ2dkqXozoGgwGNGjXCrFmz0LBhQ4waNQojR47EokWLlC5NFdavX49Vq1ZhzZo1SEhIwIoVKzBv3jysWLFC6dJMphFCCKWLKC/S0tKQlpb20jHVqlXDoUOH0KlTJzx8+BAuLi7G5wIDAzFixAhMmDBBdqlmydR8jxw5go4dO+LAgQNo3bq18blmzZohLCwMM2fOlF2qWTI13/79+2PLli2FfknU6/WwtLTEwIEDzeoH2F/J1HwLfplJTU1Fhw4d0KxZM0RHR8PCgv+mVFK5ublwcHDAhg0b0Lt3b+P2qKgonDx5Evv27VOwOvWIjIzEpk2bsH//fvj7+ytdjmps2rQJvXv3hqWlpXGbXq+HRqOBhYUFcnJyCj1HJePn54fw8HB89913xm2LFi3CjBkzcPPmTQUrUwcfHx9MmDABY8aMMW6bMWMGVq1ahYsXLypYmem4XO0ZWq0WWq222HGZmZkA8NwvLRYWFmZ1a72/mqn5hoaGwtbWFpcuXTI2OXl5eUhJSYGfn5/sMs2Wqfl+9dVXmDFjhvFxamoqOnfujPXr16NZs2YySzRrpuYL5N/WtEOHDsazkGxwSsfGxgahoaGIiYkp1OQULBemP0cIgcjISPz000/Yu3cvG5wy9tprr+HMmTOFtg0bNgy1a9fG+PHj2eD8Sa1atXrulueJiYn8PaGMZGZmPvd3l6WlpVn9nssmpxRatGiBihUrYujQoZgyZQrs7e3x7bffIjk5Gd26dVO6PLPn4uKCiIgITJ06FT4+PvDz88PcuXMBAH379lW4OvPn6+tb6LGTkxMAICAgAN7e3kqUpCqpqalo3749fH19MW/ePNy7d8/4nJeXl4KVmaf3338fgwcPRuPGjdGiRQssXboU165dQ0REhNKlmb0xY8ZgzZo12Lx5M5ydnY3XObm6usLe3l7h6syfs7Pzc9c3OTo6wt3dndc9lYH33nsPLVu2xKxZs9CvXz8cO3YMS5cuxdKlS5UuTRV69OiBmTNnwtfXF/Xq1cOJEyfw+eefY/jw4UqXZjoF7+xm1uLi4kSnTp2Em5ubcHZ2Fs2bNxfbtm1TuizVyM3NFR988IHw8PAQzs7OIiwsTJw9e1bpslQpOTmZt5AuQ8uXLxcAivyi0vn666+Fn5+fsLGxEY0aNeItjsvIi+bp8uXLlS5NtXgL6bK1ZcsWERQUJGxtbUXt2rXF0qVLlS5JNTIyMkRUVJTw9fUVdnZ2onr16mLSpEkiJydH6dJMxmtyiIiIiIhIVbhQnIiIiIiIVIVNDhERERERqQqbHCIiIiIiUhU2OUREREREpCpscoiIiIiISFXY5BARERERkaqwySEiIiIiIlVhk0NERERERGVi//796NGjB6pUqQKNRoNNmzaVeB9CCMybNw81a9aEra0tfHx8MGvWrBLtw6rERyUiIiIiIirC06dP0aBBAwwbNgxvvPFGqfYRFRWFnTt3Yt68eahfvz7S09ORlpZWon1ohBCiVEcnIiIiIiJ6AY1Gg59++gm9evUybsvNzcXkyZOxevVqPHr0CEFBQfj000/Rvn17AMCFCxcQHByMs2fPolatWqU+NperERERERHRX2LYsGE4dOgQ1q1bh9OnT6Nv377o0qULkpKSAABbtmxB9erVsXXrVvj7+6NatWp455138ODBgxIdh00OERERERFJd+XKFaxduxYbNmxAmzZtEBAQgH/+859o3bo1li9fDgC4evUqfvvtN2zYsAErV65EdHQ04uPj8eabb5boWLwmh4iIiIiIpEtISIAQAjVr1iy0PScnB+7u7gAAg8GAnJwcrFy50jhu2bJlCA0NxaVLl0xewsYmh4iIiIiIpDMYDLC0tER8fDwsLS0LPefk5AQAqFy5MqysrAo1QnXq1AEAXLt2jU0OERERERGVHw0bNoRer8fdu3fRpk2bIse0atUKOp0OV65cQUBAAAAgMTERAODn52fysXh3NSIiIiIiKhNPnjzB5cuXAeQ3NZ9//jk6dOgANzc3+Pr6YtCgQTh06BDmz5+Phg0bIi0tDbt370b9+vXx+uuvw2AwoEmTJnBycsKCBQtgMBgwZswYuLi4YOfOnSbXwSaHiIiIiIjKxN69e9GhQ4fntg8dOhTR0dHIy8vDjBkzsHLlSty8eRPu7u5o0aIFPv74Y9SvXx8AkJqaisjISOzcuROOjo7o2rUr5s+fDzc3N5PrYJNDRERERESqwltIExERERGRqrDJISIiIiIiVWGTQ0REREREqsImh4iIiIiIVIVNDhERERERqQqbHCIiIiIiUhU2OUREREREpCpscoiIiIiISFXY5BARERERkaqwySEiIiIiIlVhk0NERERERKrCJoeIiIiIiFTl/wHOfommgFwCOwAAAABJRU5ErkJggg==", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final Optimized Anchor (UC-based):\n", + "Design: {'D': np.float64(1.8913237564654963), 'L': np.float64(11.099208044881985), 'zlug': np.float64(7.3994720299213235)}\n", + "Capacity Results: {'Hmax': np.float64(2680903.350073319), 'Vmax': np.float64(3516302.6906043873), 'Ha': np.float64(2186977.238360048), 'Va': np.float64(2635582.2104549985), 'zlug': np.float64(7.3994720299213235), 'z0': np.float64(1.75), 'UC': np.float64(0.4999999981738827), 'Weight pile': 457496.7673970701}\n", + "\n", + "Final Optimized Anchor:\n", + "Design: {'D': np.float64(1.8913237564654963), 'L': np.float64(11.099208044881985), 'zlug': np.float64(7.3994720299213235)}\n", + "Capacity Results: {'Hmax': np.float64(2680903.350073319), 'Vmax': np.float64(3516302.6906043873), 'Ha': np.float64(2186977.238360048), 'Va': np.float64(2635582.2104549985), 'zlug': np.float64(7.3994720299213235), 'z0': np.float64(1.75), 'UC': np.float64(0.4999999981738827), 'Weight pile': 457496.7673970701}\n" + ] + } + ], + "source": [ + "anchor.getSizeAnchor(\n", + " geom = [anchor.dd['design']['L'], anchor.dd['design']['D']],\n", + " geomKeys = ['L', 'D'],\n", + " geomBounds = [(5.0, 15.0), (1.0, 4.0)],\n", + " loads = None,\n", + " lambdap_con = [3, 6],\n", + " zlug_fix = False,\n", + " safety_factor = {'SF_combined': 2},\n", + " plot = True\n", + ")\n", + "\n", + "print('\\nFinal Optimized Anchor:')\n", + "print('Design:', anchor.dd['design'])\n", + "print('Capacity Results:', anchor.anchorCapacity)" + ] + }, + { + "cell_type": "markdown", + "id": "b7c5fff6", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "490a71e1", + "metadata": {}, + "source": [ + "### Step 11: Optimized anchor material costs\n", + "We assess the cost of the optimized suction pile defined by the manufacturing cost (USD/kg)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "a439735f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mass: 46635.76 kg\n", + "Material unit cost: 10.25 USD/kg\n", + "Material cost: 478016.50 USD [2024]\n" + ] + } + ], + "source": [ + "anchor.getCostAnchor()\n", + "\n", + "print(f\"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg\")\n", + "print(f\"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg\")\n", + "print(f'Material cost: {anchor.cost[\"Material cost\"]:.2f} USD [2024]')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "raft-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/05_Anchors/inputs/GulfOfMaine_bathymetry_100x100.txt b/examples/05_Anchors/inputs/GulfOfMaine_bathymetry_100x100.txt new file mode 100644 index 00000000..22b0bc97 --- /dev/null +++ b/examples/05_Anchors/inputs/GulfOfMaine_bathymetry_100x100.txt @@ -0,0 +1,104 @@ +--- MoorPy Bathymetry Input File --- +nGridX 100 +nGridY 100 + -4420.52 -4345.53 -4270.54 -4195.55 -4120.57 -4045.58 -3970.59 -3895.60 -3820.61 -3745.62 -3670.63 -3595.64 -3520.66 -3445.67 -3370.68 -3295.69 -3220.70 -3145.71 -3070.72 -2995.74 -2920.75 -2845.76 -2770.77 -2695.78 -2620.79 -2545.80 -2470.81 -2395.83 -2320.84 -2245.85 -2170.86 -2095.87 -2020.88 -1945.89 -1870.90 -1795.92 -1720.93 -1645.94 -1570.95 -1495.96 -1420.97 -1345.98 -1270.99 -1196.01 -1121.02 -1046.03 -971.04 -896.05 -821.06 -746.07 -671.08 -596.10 -521.11 -446.12 -371.13 -296.14 -221.15 -146.16 -71.17 3.81 78.80 153.79 228.78 303.77 378.76 453.75 528.74 603.72 678.71 753.70 828.69 903.68 978.67 1053.66 1128.64 1203.63 1278.62 1353.61 1428.60 1503.59 1578.58 1653.57 1728.55 1803.54 1878.53 1953.52 2028.51 2103.50 2178.49 2253.48 2328.46 2403.45 2478.44 2553.43 2628.42 2703.41 2778.40 2853.39 2928.37 3003.36 +-3873.36 176.051 176.899 177.747 178.595 179.442 179.494 178.011 176.528 175.045 173.562 172.871 172.659 172.447 172.235 172.023 172.577 173.212 173.848 174.483 175.080 175.503 175.927 176.350 176.773 176.902 176.690 176.479 176.267 176.056 175.370 174.523 173.676 172.829 172.010 172.221 172.432 172.643 172.854 173.000 173.002 173.004 173.006 173.007 173.256 173.677 174.098 174.519 174.939 174.812 174.602 174.391 174.181 174.003 174.004 174.006 174.007 174.008 174.100 174.310 174.521 174.732 174.942 174.551 173.917 173.282 172.648 172.013 172.426 172.847 173.268 173.689 174.225 175.069 175.914 176.758 177.602 177.745 177.323 176.901 176.479 176.057 175.281 174.435 173.589 172.743 171.946 171.523 171.101 170.678 170.255 169.916 169.705 169.494 169.283 169.072 169.291 169.714 170.137 170.561 170.984 +-3803.23 176.470 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pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_0 pro_0 pro_0 pro_0 pro_0 +2859.23 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_2 pro_2 pro_2 pro_2 pro_2 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_0 pro_0 pro_0 pro_0 pro_0 +2929.37 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_2 pro_2 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_0 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_0 pro_0 pro_0 pro_0 pro_0 +2999.50 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_0 pro_0 pro_0 pro_0 pro_0 pro_0 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_0 pro_0 pro_0 pro_0 pro_0 +3069.63 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_0 pro_0 pro_0 pro_0 pro_0 pro_0 pro_0 pro_0 pro_0 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_0 pro_0 pro_0 pro_0 pro_0 +--- SOIL TYPES --- +Class gamma Su0 k alpha phi UCS Em +(name) (kN/m^3) (kPa) (kPa/m) (-) (deg) (MPa) (MPa) +pro_0 8.00 14.0 2.8 0.7 - - - +pro_1 8.00 12.0 2.4 0.7 - - - +pro_2 8.00 10.0 2.0 0.7 - - - +pro_3 8.00 8.0 1.6 0.7 - - - +pro_4 8.00 6.0 1.2 0.7 - - - \ No newline at end of file diff --git a/examples/05_Anchors/inputs/GulfOfMaine_soil_profiles.yaml b/examples/05_Anchors/inputs/GulfOfMaine_soil_profiles.yaml new file mode 100644 index 00000000..d0308033 --- /dev/null +++ b/examples/05_Anchors/inputs/GulfOfMaine_soil_profiles.yaml @@ -0,0 +1,116 @@ +pro_0: + layers: + - soil_type: clay + top: 0 + bottom: 12 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 10.0 + Su_bot: 20.0 + - soil_type: clay + top: 12 + bottom: 22 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 15.0 + Su_bot: 25.0 + - soil_type: clay + top: 22 + bottom: 30 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 55.0 + Su_bot: 70.0 +pro_1: + layers: + - soil_type: clay + top: 0 + bottom: 5 + gamma_top: 8.2 + gamma_bot: 8.2 + Su_top: 12.0 + Su_bot: 22.0 + - soil_type: clay + top: 5 + bottom: 15 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 22.0 + Su_bot: 22.0 + - soil_type: clay + top: 15 + bottom: 30 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 22.0 + Su_bot: 50.0 +pro_2: + layers: + - soil_type: clay + top: 0 + bottom: 8 + gamma_top: 8.4 + gamma_bot: 8.4 + Su_top: 14.0 + Su_bot: 14.0 + - soil_type: clay + top: 8 + bottom: 18 + gamma_top: 8.4 + gamma_bot: 8.4 + Su_top: 15.0 + Su_bot: 25.0 + - soil_type: clay + top: 18 + bottom: 30 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 25.0 + Su_bot: 25.0 + +pro_3: + layers: + - soil_type: clay + top: 0 + bottom: 15 + gamma_top: 8.6 + gamma_bot: 8.6 + Su_top: 20.0 + Su_bot: 26.0 + - soil_type: clay + top: 15 + bottom: 25 + gamma_top: 8.6 + gamma_bot: 8.6 + Su_top: 20.0 + Su_bot: 40.0 + - soil_type: clay + top: 25 + bottom: 30 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 40.0 + Su_bot: 40.0 +pro_4: + layers: + - soil_type: clay + top: 0 + bottom: 3 + gamma_top: 8.8 + gamma_bot: 8.8 + Su_top: 10.0 + Su_bot: 10.0 + - soil_type: clay + top: 3 + bottom: 10 + gamma_top: 8.8 + gamma_bot: 8.8 + Su_top: 16.0 + Su_bot: 40.0 + - soil_type: clay + top: 10 + bottom: 30 + gamma_top: 8.8 + gamma_bot: 8.8 + Su_top: 40.0 + Su_bot: 60.0 \ No newline at end of file diff --git a/examples/Inputs/GulfOfMaine_bathymetry_100x100.txt b/examples/Inputs/GulfOfMaine_bathymetry_100x100.txt new file mode 100644 index 00000000..22b0bc97 --- /dev/null +++ b/examples/Inputs/GulfOfMaine_bathymetry_100x100.txt @@ -0,0 +1,104 @@ +--- MoorPy Bathymetry Input File --- +nGridX 100 +nGridY 100 + -4420.52 -4345.53 -4270.54 -4195.55 -4120.57 -4045.58 -3970.59 -3895.60 -3820.61 -3745.62 -3670.63 -3595.64 -3520.66 -3445.67 -3370.68 -3295.69 -3220.70 -3145.71 -3070.72 -2995.74 -2920.75 -2845.76 -2770.77 -2695.78 -2620.79 -2545.80 -2470.81 -2395.83 -2320.84 -2245.85 -2170.86 -2095.87 -2020.88 -1945.89 -1870.90 -1795.92 -1720.93 -1645.94 -1570.95 -1495.96 -1420.97 -1345.98 -1270.99 -1196.01 -1121.02 -1046.03 -971.04 -896.05 -821.06 -746.07 -671.08 -596.10 -521.11 -446.12 -371.13 -296.14 -221.15 -146.16 -71.17 3.81 78.80 153.79 228.78 303.77 378.76 453.75 528.74 603.72 678.71 753.70 828.69 903.68 978.67 1053.66 1128.64 1203.63 1278.62 1353.61 1428.60 1503.59 1578.58 1653.57 1728.55 1803.54 1878.53 1953.52 2028.51 2103.50 2178.49 2253.48 2328.46 2403.45 2478.44 2553.43 2628.42 2703.41 2778.40 2853.39 2928.37 3003.36 +-3873.36 176.051 176.899 177.747 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pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_1 pro_0 pro_0 pro_0 pro_0 pro_0 +--- SOIL TYPES --- +Class gamma Su0 k alpha phi UCS Em +(name) (kN/m^3) (kPa) (kPa/m) (-) (deg) (MPa) (MPa) +pro_0 8.00 14.0 2.8 0.7 - - - +pro_1 8.00 12.0 2.4 0.7 - - - +pro_2 8.00 10.0 2.0 0.7 - - - +pro_3 8.00 8.0 1.6 0.7 - - - +pro_4 8.00 6.0 1.2 0.7 - - - \ No newline at end of file diff --git a/examples/Inputs/GulfOfMaine_soil_profiles.yaml b/examples/Inputs/GulfOfMaine_soil_profiles.yaml new file mode 100644 index 00000000..71b43efd --- /dev/null +++ b/examples/Inputs/GulfOfMaine_soil_profiles.yaml @@ -0,0 +1,67 @@ +pro_0: + layers: + - soil_type: clay + top: 0 + bottom: 10 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 10.0 + Su_bot: 20.0 + - soil_type: clay + top: 10 + bottom: 20 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 15.0 + Su_bot: 25.0 +pro_1: + layers: + - soil_type: clay + top: 0 + bottom: 20 + gamma_top: 8.2 + gamma_bot: 8.2 + Su_top: 12.0 + Su_bot: 22.0 +pro_2: + layers: + - soil_type: clay + top: 0 + bottom: 5 + gamma_top: 8.4 + gamma_bot: 8.4 + Su_top: 14.0 + Su_bot: 24.0 + - soil_type: clay + top: 5 + bottom: 20 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 15.0 + Su_bot: 25.0 + +pro_3: + layers: + - soil_type: clay + top: 0 + bottom: 5 + gamma_top: 8.6 + gamma_bot: 8.6 + Su_top: 16.0 + Su_bot: 26.0 + - soil_type: clay + top: 5 + bottom: 20 + gamma_top: 8.0 + gamma_bot: 8.0 + Su_top: 25.0 + Su_bot: 35.0 +pro_4: + layers: + - soil_type: clay + top: 0 + bottom: 20 + gamma_top: 8.8 + gamma_bot: 8.8 + Su_top: 18.0 + Su_bot: 28.0 \ No newline at end of file diff --git a/examples/Inputs/GulfOfMaine_soil_uniform_100x100.txt b/examples/Inputs/GulfOfMaine_soil_uniform_100x100.txt new file mode 100644 index 00000000..14d5d515 --- /dev/null +++ b/examples/Inputs/GulfOfMaine_soil_uniform_100x100.txt @@ -0,0 +1,112 @@ +--- MoorPy Soil Input File --- +nGridX 100 +nGridY 100 + -4420.52 -4345.53 -4270.54 -4195.55 -4120.57 -4045.58 -3970.59 -3895.60 -3820.61 -3745.62 -3670.63 -3595.64 -3520.66 -3445.67 -3370.68 -3295.69 -3220.70 -3145.71 -3070.72 -2995.74 -2920.75 -2845.76 -2770.77 -2695.78 -2620.79 -2545.80 -2470.81 -2395.83 -2320.84 -2245.85 -2170.86 -2095.87 -2020.88 -1945.89 -1870.90 -1795.92 -1720.93 -1645.94 -1570.95 -1495.96 -1420.97 -1345.98 -1270.99 -1196.01 -1121.02 -1046.03 -971.04 -896.05 -821.06 -746.07 -671.08 -596.10 -521.11 -446.12 -371.13 -296.14 -221.15 -146.16 -71.17 3.81 78.80 153.79 228.78 303.77 378.76 453.75 528.74 603.72 678.71 753.70 828.69 903.68 978.67 1053.66 1128.64 1203.63 1278.62 1353.61 1428.60 1503.59 1578.58 1653.57 1728.55 1803.54 1878.53 1953.52 2028.51 2103.50 2178.49 2253.48 2328.46 2403.45 2478.44 2553.43 2628.42 2703.41 2778.40 2853.39 2928.37 3003.36 +-3873.36 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 +-3803.23 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 +-3733.10 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 +-3662.97 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-3592.83 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-3522.70 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-3452.57 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-3382.44 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-3312.31 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-3242.18 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-3172.05 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-3101.92 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 +-3031.78 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 +-2961.65 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-2891.52 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-2821.39 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-2751.26 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-2681.13 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-2611.00 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-2540.87 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 +-2470.74 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 +-2400.60 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 +-2330.47 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 +-2260.34 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 +-2190.21 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-2120.08 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-2049.95 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 +-1979.82 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 +-1909.69 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 +-1839.55 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 +-1769.42 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 +-1699.29 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_1 +-1629.16 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 +-1559.03 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 +-1488.90 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 +-1418.77 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 +-1348.64 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 +-1278.51 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 +-1208.37 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 +-1138.24 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 +-1068.11 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-997.98 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-927.85 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-857.72 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-787.59 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-717.46 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-647.32 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 +-577.19 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 +-507.06 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 +-436.93 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 +-366.80 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 +-296.67 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 +-226.54 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 +-156.41 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +-86.28 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +-16.14 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +53.99 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +124.12 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +194.25 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +264.38 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +334.51 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +404.64 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +474.77 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +544.91 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +615.04 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +685.17 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +755.30 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +825.43 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_4 mud_4 mud_4 mud_4 mud_4 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +895.56 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +965.69 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1035.82 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1105.95 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1176.09 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1246.22 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1316.35 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1386.48 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1456.61 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1526.74 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1596.87 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1667.00 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1737.14 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1807.27 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1877.40 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +1947.53 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 +2017.66 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_3 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 +2087.79 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_2 mud_3 mud_3 mud_3 mud_3 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mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 +3069.63 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_0 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_1 mud_0 mud_0 mud_0 mud_0 mud_0 +--- SOIL TYPES --- +Class gamma Su0 k alpha phi UCS Em +(name) (kN/m^3) (kPa) (kPa/m) (-) (deg) (MPa) (MPa) +mud_0 8.00 14.0 2.8 0.7 - - - +mud_1 8.00 12.0 2.4 0.7 - - - +mud_2 8.00 10.0 2.0 0.7 - - - +mud_3 8.00 8.0 1.6 0.7 - - - +mud_4 8.00 6.0 1.2 0.7 - - - \ No newline at end of file diff --git a/examples/Inputs/OntologySample200m.yaml b/examples/Inputs/OntologySample200m.yaml index a59e0a1c..cc2402c6 100644 --- a/examples/Inputs/OntologySample200m.yaml +++ b/examples/Inputs/OntologySample200m.yaml @@ -43,23 +43,42 @@ site: soil_types: mud_soft: - Su0 : [2.39] # [kPa] - k : [1.41] # [kPa/m] - gamma : [10] # [kN/m^3] - depth: [0] # [m] + layers: + - soil_type: clay + top: 0 + bottom: 50 + gamma_top: 10.0 + gamma_bot: 10.0 + Su_top: 2.39 + Su_bot: 59.39 mud_firm: - Su0 : [23.94] # [kPa] - k : [2.67] # [kPa/m] - gamma: [15] - depth: [0] # [m] - mud_hard: - Su0: [50] - k: [1.0] - gamma: [9.5] - depth: [0] + layers: + - soil_type: clay + top: 0 + bottom: 50 + gamma_top: 10.0 + gamma_bot: 50.0 + Su_top: 23.4 + Su_bot: 157.44 + mud_hard: + layers: + - soil_type: clay + top: 0 + bottom: 50 + gamma_top: 8.5 + gamma_bot: 8.5 + Su_top: 50.0 + Su_bot: 100.00 rock: - UCS: [5] # [MPa] - Em: [7] # [MPa] + layers: + - soil_type: rock + top: 0 + bottom: 50 + UCS_top: 5.0 + UCS_bot: 5.0 + Em_top: 7.0 + Em_bot: 7.0 + metocean: extremes: # extreme values for specified return periods (in years) @@ -297,9 +316,10 @@ mooring_connector_types: anchor_types: drag-embedment1: - type : DEA # type of anchor - A : 10 # net area of anchor's fluke [m^2] - zlug : 20 # embedded depth of padeye [m] + type : DEA # type of anchor + B : 5 # net area of anchor's fluke [m^2] + L : 2 + zlug : 10 # embedded depth of padeye [m] suction_pile1: type : suction_pile L : 16.4 # length of pile [m] @@ -1542,7 +1562,7 @@ platforms: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1565,7 +1585,7 @@ platforms: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1638,7 +1658,7 @@ platforms: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1661,7 +1681,7 @@ platforms: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) diff --git a/examples/Inputs/OntologySample200m_1turb.yaml b/examples/Inputs/OntologySample200m_1turb.yaml index c55aad76..31882ffc 100644 --- a/examples/Inputs/OntologySample200m_1turb.yaml +++ b/examples/Inputs/OntologySample200m_1turb.yaml @@ -1163,7 +1163,7 @@ platform: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1186,7 +1186,7 @@ platform: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1295,7 +1295,7 @@ anchor_types: zlug : 10 # embedded depth of padeye [m] suction1: - type : suction_pile + type : suction L : 16.4 # length of pile [m] D : 5.45 # diameter of pile [m] zlug : 9.32 # embedded depth of padeye [m] diff --git a/examples/Inputs/OntologySample200m_uniformArray.yaml b/examples/Inputs/OntologySample200m_uniformArray.yaml index 21f8c8a1..dbecc412 100644 --- a/examples/Inputs/OntologySample200m_uniformArray.yaml +++ b/examples/Inputs/OntologySample200m_uniformArray.yaml @@ -1175,7 +1175,7 @@ platforms: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1198,7 +1198,7 @@ platforms: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) diff --git a/examples/Inputs/OntologySample200mbis_1turb.yaml b/examples/Inputs/OntologySample200mbis_1turb.yaml new file mode 100644 index 00000000..1cd07b83 --- /dev/null +++ b/examples/Inputs/OntologySample200mbis_1turb.yaml @@ -0,0 +1,1323 @@ +type: draft/example of floating array ontology under construction +name: +comments: +# Site condition information +site: + general: + water_depth : 200 # [m] uniform water depth + rho_water : 1025.0 # [kg/m^3] water density + rho_air : 1.225 # [kg/m^3] air density + mu_air : 1.81e-05 # air dynamic viscosity + #... + + boundaries: # project or lease area boundary, via file or vertex list + file: # filename of x-y vertex coordinates [m] + x_y: # list of polygon vertices in order [m] + - [-3000, -3000] + - [-3000, 3000] + - [3000, 3000] + - [3000, -3000] + + bathymetry: + file: './bathymetry200m_sample.txt' + + seabed: + x : [-10901, 0, 10000] + y : [-10900, 0, 10000 ] + + type_array: + - [mud_soft , mud_firm , mud_soft] + - [mud_soft , mud_firm , mud_soft] + - [mud_soft , mud_firm , mud_soft] + + soil_types: + mud_soft: + layers: + - soil_type: clay + top: 0 + bottom: 50 + gamma_top: 10.0 + gamma_bot: 10.0 + Su_top: 2.39 + Su_bot: 59.39 + mud_firm: + layers: + - soil_type: clay + top: 0 + bottom: 50 + gamma_top: 10.0 + gamma_bot: 50.0 + Su_top: 23.4 + Su_bot: 157.44 + sand_dense: + layers: + - soil_type: sand + top: 0 + bottom: 20 + gamma_top: 9.0 + gamma_bot: 9.0 + phi_top: 28 + phi_bot: 33 + Dr_top: 50 + Dr_bot: 55 + + metocean: + extremes: # extreme values for specified return periods (in years) + keys : [ Hs , Tp , WindSpeed, TI, Shear, Gamma, CurrentSpeed ] + data : + 1: [ 1 ,2 ,3 ] + 10: [ 1 , 2 , 3 ] + 50: [ 1 , 2 , 3 ] + 500: [ 1 , 2 , 3 ] + + probabalistic_bins: + keys : [ prob , Hs , Tp, WindSpeed, TI, Shear, Gamma, CurrentSpeed, WindDir, WaveDir, CurrentDir ] + data : + - [ 0.010 , 1 , 1 ] + - [ 0.006 , 1 , 1 ] + - [ 0.005 , 1 , 1 ] + + time_series : + file: 'metocean_timeseries.csv' + + resource : + file: 'windresource' + + RAFT_cases: + keys : [wind_speed, wind_heading, turbulence, turbine_status, yaw_misalign, wave_spectrum, wave_period, wave_height, wave_heading ] + data : # m/s deg % or e.g. IIB_NTM string deg string (s) (m) (deg) + - [ 0, 0, 0, operating, 0, JONSWAP, 12, 6, 0 ] + # - [ 16, 0, IIB_NTM, operating, 0, JONSWAP, 12, 6, 30 ] + # - [ 10.59, 0, 0.05, operating, 0, JONSWAP, 15.75, 11.86, 0 ] + + RAFT_settings: + min_freq : 0.001 # [Hz] lowest frequency to consider, also the frequency bin width + max_freq : 0.20 # [Hz] highest frequency to consider + XiStart : 0 # sets initial amplitude of each DOF for all frequencies + nIter : 4 # sets how many iterations to perform in Model.solveDynamics() +# ----- Array-level inputs ----- + +# Wind turbine array layout +array: + keys : [ID, topsideID, platformID, mooringID, x_location, y_location, heading_adjust] + data : # ID# ID# ID# [m] [m] [deg] + - [fowt0, 1, 1, ms1, -1600, -1600, 180 ] # 2 array, shared moorings + # - [FOWT3, 1, 1, ms3, 1600, -1600, 0 ] + # - [FOWT4, 1, 2, ms4, -1600, 0, 0 ] + # - [FOWT5, 1, 1, ms5, 0, 0, 45 ] + # - [FOWT6, 1, 1, ms10, 1600, 0, 0 ] + # - [FOWT7, 1, 1, ms6, -1600, 1600, 0 ] + # - [FOWT8, 1, 1, ms6, 0, 1600, 0 ] + # - [FOWT9, 1, 1, ms6, 1600, 1600, 0 ] + +# ----- turbines and platforms ----- + +topsides: + + - type : turbine + mRNA : 991000 # [kg] RNA mass + IxRNA : 0 # [kg-m2] RNA moment of inertia about local x axis (assumed to be identical to rotor axis for now, as approx) [kg-m^2] + IrRNA : 0 # [kg-m2] RNA moment of inertia about local y or z axes [kg-m^2] + xCG_RNA : 0 # [m] x location of RNA center of mass [m] (Actual is ~= -0.27 m) + hHub : 150.0 # [m] hub height above water line [m] + Fthrust : 1500.0E3 # [N] temporary thrust force to use + + I_drivetrain: 318628138.0 # full rotor + drivetrain inertia as felt on the high-speed shaft + + nBlades : 3 # number of blades + Zhub : 150.0 # hub height [m] + Rhub : 3.97 # hub radius [m] + precone : 4.0 # [deg] + shaft_tilt : 6.0 # [deg] + overhang : -12.0313 # [m] + aeroMod : 1 # 0 aerodynamics off; 1 aerodynamics on + + + blade: + precurveTip : -3.9999999999999964 # + presweepTip : 0.0 # + Rtip : 120.96999999936446 # rotor radius + + # r chord theta precurve presweep + geometry: + - [ 8.004, 5.228, 15.474, 0.035, 0.000 ] + - [ 12.039, 5.321, 14.692, 0.084, 0.000 ] + - [ 16.073, 5.458, 13.330, 0.139, 0.000 ] + - [ 20.108, 5.602, 11.644, 0.192, 0.000 ] + - [ 24.142, 5.718, 9.927, 0.232, 0.000 ] + - [ 28.177, 5.767, 8.438, 0.250, 0.000 ] + - [ 32.211, 5.713, 7.301, 0.250, 0.000 ] + - [ 36.246, 5.536, 6.232, 0.246, 0.000 ] + - [ 40.280, 5.291, 5.230, 0.240, 0.000 ] + - [ 44.315, 5.035, 4.348, 0.233, 0.000 ] + - [ 48.349, 4.815, 3.606, 0.218, 0.000 ] + - [ 52.384, 4.623, 2.978, 0.178, 0.000 ] + - [ 56.418, 4.432, 2.423, 0.100, 0.000 ] + - [ 60.453, 4.245, 1.924, 0.000, 0.000 ] + - [ 64.487, 4.065, 1.467, -0.112, 0.000 ] + - [ 68.522, 3.896, 1.056, -0.244, 0.000 ] + - [ 72.556, 3.735, 0.692, -0.415, 0.000 ] + - [ 76.591, 3.579, 0.355, -0.620, 0.000 ] + - [ 80.625, 3.425, 0.019, -0.846, 0.000 ] + - [ 84.660, 3.268, -0.358, -1.080, 0.000 ] + - [ 88.694, 3.112, -0.834, -1.330, 0.000 ] + - [ 92.729, 2.957, -1.374, -1.602, 0.000 ] + - [ 96.763, 2.800, -1.848, -1.895, 0.000 ] + - [ 100.798, 2.637, -2.136, -2.202, 0.000 ] + - [ 104.832, 2.464, -2.172, -2.523, 0.000 ] + - [ 108.867, 2.283, -2.108, -2.864, 0.000 ] + - [ 112.901, 2.096, -1.953, -3.224, 0.000 ] + - [ 116.936, 1.902, -1.662, -3.605, 0.000 ] + # station(rel) airfoil name + airfoils: + - [ 0.00000, circular ] + - [ 0.02000, circular ] + - [ 0.15000, SNL-FFA-W3-500 ] + - [ 0.24517, FFA-W3-360 ] + - [ 0.32884, FFA-W3-330blend ] + - [ 0.43918, FFA-W3-301 ] + - [ 0.53767, FFA-W3-270blend ] + - [ 0.63821, FFA-W3-241 ] + - [ 0.77174, FFA-W3-211 ] + - [ 1.00000, FFA-W3-211 ] + + + airfoils: + - name : circular # + relative_thickness : 1.0 # + data: # alpha c_l c_d c_m + - [ -179.9087, 0.00010, 0.35000, -0.00010 ] + - [ 179.9087, 0.00010, 0.35000, -0.00010 ] + - name : SNL-FFA-W3-500 # + relative_thickness : 0.5 # + data: # alpha c_l c_d c_m + - [ -179.9660, 0.00000, 0.08440, 0.00000 ] + - [ -170.0000, 0.44190, 0.08440, 0.31250 ] + - [ -160.0002, 0.88370, 0.12680, 0.28310 ] + - [ -149.9998, 0.96740, 0.29270, 0.26320 ] + - [ -139.9999, 0.78010, 0.49700, 0.20480 ] + - [ -130.0001, 0.62930, 0.71610, 0.19320 ] + - [ -120.0003, 0.47850, 0.92460, 0.20080 ] + - [ -109.9999, 0.31890, 1.09850, 0.21360 ] + - [ -100.0000, 0.15530, 1.21820, 0.22210 ] + - [ -90.0002, 0.00000, 1.27070, 0.21980 ] + - [ -79.9998, -0.15530, 1.21820, 0.19600 ] + - [ -70.0000, -0.31890, 1.09850, 0.16350 ] + - [ -60.0001, -0.47840, 0.92460, 0.12850 ] + - [ -49.9997, -0.62930, 0.71610, 0.09650 ] + - [ -39.9999, -0.78010, 0.49700, 0.07160 ] + - [ -30.0001, -0.96740, 0.29270, 0.05220 ] + - [ -20.0002, -1.02810, 0.14990, -0.00630 ] + - [ -19.7499, -1.02430, 0.14720, -0.00890 ] + - [ -19.2502, -1.00520, 0.14470, -0.00990 ] + - [ -18.9999, -0.99710, 0.14330, -0.01050 ] + - [ -18.7500, -1.00520, 0.14030, -0.01100 ] + - [ -18.5002, -0.99950, 0.13860, -0.01160 ] + - [ -18.2499, -0.99080, 0.13730, -0.01200 ] + - [ -18.0000, -0.98150, 0.13600, -0.01260 ] + - [ -17.4998, -0.97640, 0.13220, -0.01350 ] + - [ -17.2500, -0.97050, 0.13060, -0.01390 ] + - [ -17.0002, -0.96550, 0.12900, -0.01430 ] + - [ -16.7498, -0.96620, 0.12680, -0.01470 ] + - [ -16.5000, -0.95440, 0.12580, -0.01510 ] + - [ -16.2502, -0.94440, 0.12460, -0.01550 ] + - [ -15.9998, -0.94050, 0.12290, -0.01580 ] + - [ -15.7500, -0.94330, 0.12060, -0.01610 ] + - [ -15.5002, -0.93300, 0.11950, -0.01640 ] + - [ -15.2498, -0.92110, 0.11850, -0.01680 ] + - [ -14.7502, -0.91580, 0.11500, -0.01730 ] + - [ -14.4998, -0.90700, 0.11380, -0.01750 ] + - [ -14.2500, -0.89590, 0.11270, -0.01780 ] + - [ -14.0002, -0.89260, 0.11100, -0.01810 ] + - [ -13.7498, -0.88080, 0.11000, -0.01840 ] + - [ -13.5000, -0.87220, 0.10890, -0.01860 ] + - [ -13.2502, -0.86600, 0.10750, -0.01880 ] + - [ -12.9998, -0.86260, 0.10590, -0.01880 ] + - [ -12.7500, -0.84890, 0.10510, -0.01920 ] + - [ -12.5002, -0.83630, 0.10420, -0.01940 ] + - [ -12.2498, -0.83630, 0.10230, -0.01940 ] + - [ -12.0000, -0.82710, 0.10130, -0.01960 ] + - [ -11.7502, -0.81410, 0.10040, -0.01980 ] + - [ -11.4998, -0.80040, 0.09970, -0.02000 ] + - [ -11.0002, -0.78900, 0.09710, -0.01990 ] + - [ -10.7498, -0.78620, 0.09560, -0.01960 ] + - [ -10.5000, -0.77470, 0.09480, -0.01940 ] + - [ -10.2502, -0.77010, 0.09400, -0.01840 ] + - [ -9.9998, -0.76740, 0.09250, -0.01830 ] + - [ -9.7500, -0.75060, 0.09170, -0.01920 ] + - [ -9.5002, -0.72900, 0.09120, -0.02050 ] + - [ -9.2498, -0.70950, 0.09020, -0.02240 ] + - [ -9.0000, -0.68550, 0.08950, -0.02470 ] + - [ -8.7502, -0.65900, 0.08910, -0.02670 ] + - [ -8.4998, -0.63190, 0.08870, -0.02870 ] + - [ -8.2500, -0.60190, 0.08790, -0.03200 ] + - [ -8.0002, -0.57180, 0.08750, -0.03450 ] + - [ -7.7498, -0.54240, 0.08730, -0.03670 ] + - [ -7.5000, -0.50980, 0.08680, -0.03990 ] + - [ -7.2502, -0.47670, 0.08640, -0.04300 ] + - [ -6.9998, -0.44540, 0.08620, -0.04530 ] + - [ -6.7500, -0.41420, 0.08600, -0.04760 ] + - [ -6.5002, -0.37910, 0.08560, -0.05100 ] + - [ -6.2498, -0.34600, 0.08530, -0.05380 ] + - [ -6.0000, -0.31440, 0.08520, -0.05600 ] + - [ -5.7502, -0.28170, 0.08500, -0.05860 ] + - [ -5.4998, -0.24610, 0.08470, -0.06190 ] + - [ -5.2500, -0.21330, 0.08460, -0.06440 ] + - [ -5.0002, -0.18270, 0.08450, -0.06630 ] + - [ -4.7498, -0.14940, 0.08430, -0.06880 ] + - [ -4.5000, -0.11580, 0.08420, -0.07150 ] + - [ -4.2502, -0.08370, 0.08400, -0.07370 ] + - [ -3.9998, -0.05290, 0.08400, -0.07560 ] + - [ -3.7500, -0.02250, 0.08390, -0.07740 ] + - [ -3.5002, 0.00890, 0.08380, -0.07930 ] + - [ -3.2498, 0.03920, 0.08380, -0.08110 ] + - [ -3.0000, 0.06860, 0.08380, -0.08260 ] + - [ -2.7502, 0.09740, 0.08380, -0.08380 ] + - [ -2.4998, 0.12600, 0.08380, -0.08520 ] + - [ -2.2500, 0.15550, 0.08380, -0.08670 ] + - [ -2.0002, 0.18530, 0.08380, -0.08830 ] + - [ -1.7498, 0.21460, 0.08370, -0.08970 ] + - [ -1.5000, 0.24300, 0.08370, -0.09100 ] + - [ -1.2502, 0.27130, 0.08380, -0.09210 ] + - [ -0.9998, 0.30060, 0.08380, -0.09360 ] + - [ -0.7500, 0.32950, 0.08380, -0.09490 ] + - [ -0.5002, 0.35780, 0.08380, -0.09610 ] + - [ -0.2498, 0.38570, 0.08380, -0.09720 ] + - [ 0.0000, 0.41350, 0.08380, -0.09830 ] + - [ 0.2298, 0.44250, 0.08390, -0.09950 ] + - [ 0.4698, 0.47150, 0.08390, -0.10080 ] + - [ 0.7002, 0.50030, 0.08390, -0.10190 ] + - [ 0.9402, 0.52860, 0.08400, -0.10290 ] + - [ 1.1700, 0.55670, 0.08400, -0.10400 ] + - [ 1.3997, 0.58500, 0.08410, -0.10500 ] + - [ 1.6398, 0.61350, 0.08410, -0.10610 ] + - [ 1.8701, 0.64170, 0.08420, -0.10720 ] + - [ 2.1102, 0.66970, 0.08420, -0.10820 ] + - [ 2.3400, 0.69750, 0.08430, -0.10910 ] + - [ 2.5697, 0.72510, 0.08430, -0.11000 ] + - [ 2.8098, 0.75280, 0.08440, -0.11090 ] + - [ 3.0401, 0.78070, 0.08450, -0.11190 ] + - [ 3.2802, 0.80830, 0.08460, -0.11280 ] + - [ 3.5099, 0.83580, 0.08460, -0.11370 ] + - [ 3.7403, 0.86310, 0.08470, -0.11460 ] + - [ 3.9798, 0.89020, 0.08470, -0.11530 ] + - [ 4.2101, 0.91730, 0.08480, -0.11610 ] + - [ 4.4502, 0.94440, 0.08490, -0.11700 ] + - [ 4.6799, 0.97130, 0.08500, -0.11780 ] + - [ 4.9102, 0.99810, 0.08510, -0.11850 ] + - [ 5.1497, 1.02490, 0.08520, -0.11920 ] + - [ 5.3801, 1.05150, 0.08530, -0.11990 ] + - [ 5.6201, 1.07790, 0.08530, -0.12060 ] + - [ 5.8499, 1.10410, 0.08540, -0.12120 ] + - [ 6.0802, 1.13020, 0.08560, -0.12180 ] + - [ 6.3197, 1.15600, 0.08570, -0.12240 ] + - [ 6.5501, 1.18180, 0.08580, -0.12300 ] + - [ 6.7901, 1.20760, 0.08590, -0.12350 ] + - [ 7.0199, 1.23340, 0.08600, -0.12400 ] + - [ 7.2502, 1.25890, 0.08610, -0.12450 ] + - [ 7.4903, 1.28410, 0.08620, -0.12500 ] + - [ 7.7200, 1.30880, 0.08640, -0.12540 ] + - [ 7.9601, 1.33310, 0.08650, -0.12570 ] + - [ 8.1899, 1.35700, 0.08670, -0.12590 ] + - [ 8.4202, 1.38100, 0.08690, -0.12620 ] + - [ 8.6603, 1.40540, 0.08700, -0.12650 ] + - [ 8.8900, 1.42950, 0.08710, -0.12670 ] + - [ 9.1198, 1.45310, 0.08730, -0.12700 ] + - [ 9.8801, 1.51540, 0.08790, -0.12650 ] + - [ 10.6398, 1.57490, 0.08860, -0.12560 ] + - [ 11.4001, 1.61510, 0.08950, -0.12140 ] + - [ 12.1501, 1.64430, 0.09120, -0.11630 ] + - [ 12.9099, 1.68240, 0.09300, -0.11330 ] + - [ 13.6702, 1.71460, 0.09540, -0.11070 ] + - [ 14.4202, 1.73620, 0.09890, -0.10800 ] + - [ 15.1799, 1.76270, 0.10240, -0.10630 ] + - [ 15.9403, 1.77060, 0.10760, -0.10420 ] + - [ 16.6903, 1.76390, 0.11440, -0.10250 ] + - [ 17.4500, 1.76040, 0.12110, -0.10130 ] + - [ 18.2097, 1.72510, 0.13100, -0.10010 ] + - [ 18.9701, 1.70350, 0.13990, -0.09980 ] + - [ 19.7201, 1.67840, 0.14920, -0.10010 ] + - [ 20.4798, 1.65050, 0.15910, -0.10160 ] + - [ 21.2401, 1.62270, 0.16910, -0.10360 ] + - [ 21.9901, 1.60670, 0.17780, -0.10640 ] + - [ 22.7499, 1.59720, 0.18580, -0.10990 ] + - [ 23.5102, 1.58920, 0.19370, -0.11360 ] + - [ 24.2602, 1.58150, 0.20140, -0.11800 ] + - [ 25.0199, 1.55630, 0.21350, -0.12490 ] + - [ 25.7802, 1.52720, 0.22670, -0.13250 ] + - [ 26.5302, 1.49820, 0.23990, -0.14000 ] + - [ 27.2900, 1.46910, 0.25310, -0.14760 ] + - [ 28.0497, 1.44010, 0.26630, -0.15510 ] + - [ 28.8100, 1.41100, 0.27950, -0.16270 ] + - [ 29.5600, 1.38200, 0.29270, -0.17030 ] + - [ 30.3198, 1.36220, 0.30780, -0.17400 ] + - [ 31.0801, 1.34240, 0.32300, -0.17770 ] + - [ 31.8301, 1.32250, 0.33810, -0.18150 ] + - [ 32.5898, 1.30270, 0.35320, -0.18520 ] + - [ 33.3502, 1.28290, 0.36840, -0.18890 ] + - [ 34.1002, 1.26310, 0.38350, -0.19260 ] + - [ 34.8599, 1.24330, 0.39870, -0.19640 ] + - [ 35.6202, 1.22340, 0.41380, -0.20010 ] + - [ 36.3800, 1.20360, 0.42890, -0.20390 ] + - [ 37.1300, 1.18380, 0.44410, -0.20760 ] + - [ 37.8903, 1.16400, 0.45920, -0.21130 ] + - [ 38.6500, 1.14420, 0.47430, -0.21500 ] + - [ 39.4000, 1.12430, 0.48950, -0.21880 ] + - [ 40.1598, 1.10640, 0.50520, -0.22180 ] + - [ 40.9201, 1.09050, 0.52140, -0.22420 ] + - [ 41.6701, 1.07450, 0.53760, -0.22660 ] + - [ 42.4298, 1.05860, 0.55380, -0.22890 ] + - [ 43.1901, 1.04260, 0.57010, -0.23130 ] + - [ 43.9401, 1.02670, 0.58630, -0.23370 ] + - [ 44.6999, 1.01070, 0.60250, -0.23610 ] + - [ 45.4602, 0.99480, 0.61880, -0.23840 ] + - [ 46.2199, 0.97880, 0.63500, -0.24080 ] + - [ 46.9699, 0.96280, 0.65120, -0.24320 ] + - [ 47.7302, 0.94690, 0.66750, -0.24550 ] + - [ 48.4900, 0.93090, 0.68370, -0.24790 ] + - [ 49.2400, 0.91500, 0.69990, -0.25030 ] + - [ 49.9997, 0.89900, 0.71610, -0.25270 ] + - [ 60.0001, 0.68360, 0.92460, -0.28330 ] + - [ 70.0000, 0.45560, 1.09850, -0.31560 ] + - [ 79.9998, 0.22190, 1.21820, -0.34820 ] + - [ 90.0002, 0.00000, 1.27070, -0.37730 ] + - [ 100.0000, -0.15530, 1.21820, -0.38770 ] + - [ 109.9999, -0.31890, 1.09850, -0.38650 ] + - [ 120.0003, -0.47840, 0.92460, -0.38060 ] + - [ 130.0001, -0.62930, 0.71610, -0.38030 ] + - [ 139.9999, -0.78010, 0.49700, -0.40320 ] + - [ 149.9998, -0.96740, 0.29270, -0.48540 ] + - [ 160.0002, -0.88370, 0.12680, -0.53250 ] + - [ 170.0000, -0.44180, 0.08440, -0.39060 ] + - [ 179.9660, 0.00000, 0.08440, 0.00000 ] + - name : FFA-W3-211 # + relative_thickness : 0.211 # + data: # alpha c_l c_d c_m + - [ -179.9087, 0.00000, 0.02464, 0.00000 ] + - [ -177.7143, 0.05403, 0.02534, 0.09143 ] + - [ -175.4286, 0.10805, 0.02742, 0.18286 ] + - [ -173.1429, 0.16208, 0.03088, 0.27429 ] + - [ -170.8572, 0.21610, 0.03570, 0.36571 ] + - [ -168.5716, 0.27013, 0.05599, 0.39192 ] + - [ -166.2857, 0.32415, 0.08143, 0.37898 ] + - [ -164.0000, 0.37818, 0.11112, 0.36605 ] + - [ -161.7145, 0.43220, 0.14485, 0.35312 ] + - [ -159.4284, 0.48623, 0.18242, 0.34768 ] + - [ -157.1428, 0.54025, 0.22359, 0.36471 ] + - [ -154.8573, 0.59428, 0.26810, 0.38175 ] + - 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[ 177.7143, -0.06960, 0.03228, -0.11429 ] + - [ 179.9087, 0.00000, 0.03169, 0.00000 ] + - name : FFA-W3-360 # + relative_thickness : 0.36 # + data: # alpha c_l c_d c_m + - [ -179.9087, 0.00000, 0.03715, 0.00000 ] + - [ -177.7143, 0.07178, 0.03774, 0.09143 ] + - [ -175.4286, 0.14356, 0.03951, 0.18286 ] + - [ -173.1429, 0.21534, 0.04245, 0.27429 ] + - [ -170.8572, 0.28713, 0.04653, 0.36571 ] + - [ -168.5716, 0.35891, 0.05174, 0.40313 ] + - [ -166.2857, 0.43069, 0.06068, 0.40814 ] + - [ -164.0000, 0.50247, 0.08651, 0.41315 ] + - [ -161.7145, 0.57425, 0.11586, 0.41816 ] + - [ -159.4284, 0.64603, 0.14856, 0.42627 ] + - [ -157.1428, 0.71781, 0.18439, 0.44370 ] + - [ -154.8573, 0.78960, 0.22313, 0.46114 ] + - [ -152.5714, 0.86138, 0.26453, 0.47857 ] + - [ -150.2857, 0.93316, 0.30832, 0.49600 ] + - [ -148.0000, 1.00494, 0.35424, 0.48830 ] + - [ -143.8571, 0.91898, 0.44192, 0.46784 ] + - [ -139.7143, 0.84406, 0.53379, 0.44803 ] + - [ -135.5714, 0.77483, 0.62793, 0.43697 ] + - [ -131.4286, 0.70790, 0.72238, 0.42591 ] + - 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[ 159.4286, -0.64603, 0.14856, -0.42627 ] + - [ 161.7143, -0.57425, 0.11586, -0.43530 ] + - [ 164.0000, -0.50247, 0.08651, -0.45315 ] + - [ 166.2857, -0.43069, 0.06068, -0.47100 ] + - [ 168.5714, -0.35891, 0.05174, -0.48884 ] + - [ 170.8571, -0.28713, 0.04653, -0.45714 ] + - [ 173.1429, -0.21534, 0.04245, -0.34286 ] + - [ 175.4286, -0.14356, 0.03951, -0.22857 ] + - [ 177.7143, -0.07178, 0.03774, -0.11429 ] + - [ 179.9087, 0.00000, 0.03715, 0.00000 ] + + + + pitch_control: + GS_Angles: [0.06019804, 0.08713416, 0.10844806, 0.12685912, 0.14339822, 0.1586021 , 0.17279614, 0.18618935, 0.19892772, 0.21111989, 0.22285021, 0.23417256, 0.2451469 , 0.25580691, 0.26619545, 0.27632495, 0.28623134, 0.29593266, 0.30544521, 0.314779 , 0.32395154, 0.33297489, 0.3418577 , 0.35060844, 0.35923641, 0.36774807, 0.37614942, 0.38444655, 0.39264363, 0.40074407] + GS_Kp: [-0.9394215 , -0.80602855, -0.69555026, -0.60254912, -0.52318192, -0.45465531, -0.39489024, -0.34230736, -0.29568537, -0.25406506, -0.2166825 , -0.18292183, -0.15228099, -0.12434663, -0.09877533, -0.0752794 , -0.05361604, -0.0335789 , -0.01499149, 0.00229803, 0.01842102, 0.03349169, 0.0476098 , 0.0608629 , 0.07332812, 0.0850737 , 0.0961602 , 0.10664158, 0.11656607, 0.12597691] + GS_Ki: [-0.07416547, -0.06719673, -0.0614251 , -0.05656651, -0.0524202 , -0.04884022, -0.04571796, -0.04297091, -0.04053528, -0.03836094, -0.03640799, -0.03464426, -0.03304352, -0.03158417, -0.03024826, -0.02902079, -0.02788904, -0.02684226, -0.02587121, -0.02496797, -0.02412567, -0.02333834, -0.02260078, -0.02190841, -0.0212572 , -0.02064359, -0.0200644 , -0.01951683, -0.01899836, -0.01850671] + Fl_Kp: -9.35 + wt_ops: + v: [3.0, 3.266896551724138, 3.533793103448276, 3.800689655172414, 4.067586206896552, 4.334482758620689, 4.601379310344828, 4.868275862068966, 5.135172413793104, 5.402068965517241, 5.6689655172413795, 5.935862068965518, 6.2027586206896554, 6.469655172413793, 6.736551724137931, 7.00344827586207, 7.270344827586207, 7.537241379310345, 7.804137931034483, 8.071034482758622, 8.337931034482759, 8.604827586206897, 8.871724137931036, 9.138620689655173, 9.405517241379311, 9.672413793103448, 9.939310344827586, 10.206206896551725, 10.473103448275863, 10.74, 11.231724137931035, 11.723448275862069, 12.215172413793104, 12.706896551724139, 13.198620689655172, 13.690344827586207, 14.182068965517242, 14.673793103448276, 15.16551724137931, 15.657241379310346, 16.14896551724138, 16.640689655172416, 17.13241379310345, 17.624137931034483, 18.11586206896552, 18.607586206896553, 19.099310344827586, 19.591034482758623, 20.082758620689653, 20.57448275862069, 21.066206896551726, 21.557931034482756, 22.049655172413793, 22.54137931034483, 23.03310344827586, 23.524827586206897, 24.016551724137933, 24.508275862068963, 25.0] + pitch_op: [-0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, 3.57152, 5.12896, 6.36736, 7.43866, 8.40197, 9.28843, 10.1161, 10.8974, 11.641, 12.3529, 13.038, 13.6997, 14.3409, 14.9642, 15.5713, 16.1639, 16.7435, 17.3109, 17.8673, 18.4136, 18.9506, 19.4788, 19.9989, 20.5112, 21.0164, 21.5147, 22.0067, 22.4925, 22.9724] + omega_op: [2.1486, 2.3397, 2.5309, 2.722, 2.9132, 3.1043, 3.2955, 3.4866, 3.6778, 3.8689, 4.0601, 4.2512, 4.4424, 4.6335, 4.8247, 5.0159, 5.207, 5.3982, 5.5893, 5.7805, 5.9716, 6.1628, 6.3539, 6.5451, 6.7362, 6.9274, 7.1185, 7.3097, 7.5008, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56] + gear_ratio: 1 + torque_control: + VS_KP: -38609162.66552 + VS_KI: -4588245.18720 + + + tower: # (could remove some entries that don't apply for the tower) + dlsMax : 5.0 # maximum node splitting section amount; can't be 0 + + name : tower # [-] an identifier (no longer has to be number) + type : 1 # [-] + rA : [ 0, 0, 15] # [m] end A coordinates + rB : [ 0, 0, 144.582] # [m] and B coordinates + shape : circ # [-] circular or rectangular + gamma : 0.0 # [deg] twist angle about the member's z-axis + + # --- outer shell including hydro--- + stations : [ 15, 28, 28.001, 41, 41.001, 54, 54.001, 67, 67.001, 80, 80.001, 93, 93.001, 106, 106.001, 119, 119.001, 132, 132.001, 144.582 ] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB + d : [ 10, 9.964, 9.964, 9.967, 9.967, 9.927, 9.927, 9.528, 9.528, 9.149, 9.149, 8.945, 8.945, 8.735, 8.735, 8.405, 8.405, 7.321, 7.321, 6.5 ] # [m] diameters if circular or side lengths if rectangular (can be pairs) + t : [ 0.082954, 0.082954, 0.083073, 0.083073, 0.082799, 0.082799, 0.0299, 0.0299, 0.027842, 0.027842, 0.025567, 0.025567, 0.022854, 0.022854, 0.02025, 0.02025, 0.018339, 0.018339, 0.021211, 0.021211 ] # [m] wall thicknesses (scalar or list of same length as stations) + Cd : 0.0 # [-] transverse drag coefficient (optional, scalar or list of same length as stations) + Ca : 0.0 # [-] transverse added mass coefficient (optional, scalar or list of same length as stations) + # (neglecting axial coefficients for now) + CdEnd : 0.0 # [-] end axial drag coefficient (optional, scalar or list of same length as stations) + CaEnd : 0.0 # [-] end axial added mass coefficient (optional, scalar or list of same length as stations) + rho_shell : 7850 # [kg/m3] material density + +platform: + + type : FOWT + potModMaster : 1 # [int] master switch for potMod variables; 0=keeps all member potMod vars the same, 1=turns all potMod vars to False (no HAMS), 2=turns all potMod vars to True (no strip) + dlsMax : 5.0 # maximum node splitting section amount for platform members; can't be 0 + qtfPath : 'IEA-15-240-RWT-UMaineSemi.12d' # path to the qtf file for the platform + rFair : 58 + zFair : -14 + + members: # list all members here + + - name : center_column # [-] an identifier (no longer has to be number) + type : 2 # [-] + rA : [ 0, 0, -20] # [m] end A coordinates + rB : [ 0, 0, 15] # [m] and B coordinates + shape : circ # [-] circular or rectangular + gamma : 0.0 # [deg] twist angle about the member's z-axis + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + # --- outer shell including hydro--- + stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB + d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) + t : 0.05 # [m] wall thicknesses (scalar or list of same length as stations) + Cd : 0.6 # [-] transverse drag coefficient (optional, scalar or list of same length as stations) + Ca : 0.93 # [-] transverse added mass coefficient (optional, scalar or list of same length as stations) + CdEnd : 0.6 # [-] end axial drag coefficient (optional, scalar or list of same length as stations) + CaEnd : 1.0 # [-] end axial added mass coefficient (optional, scalar or list of same length as stations) + rho_shell : 7850 # [kg/m3] + # --- handling of end caps or any internal structures if we need them --- + cap_stations : [ 0 ] # [m] location along member of any inner structures (in same scaling as set by 'stations') + cap_t : [ 0.001 ] # [m] thickness of any internal structures + cap_d_in : [ 0 ] # [m] inner diameter of internal structures (0 for full cap/bulkhead, >0 for a ring shape) + + + - name : outer_column # [-] an identifier (no longer has to be number) + type : 2 # [-] + rA : [51.75, 0, -20] # [m] end A coordinates + rB : [51.75, 0, 15] # [m] and B coordinates + heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) + shape : circ # [-] circular or rectangular + gamma : 0.0 # [deg] twist angle about the member's z-axis + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + # --- outer shell including hydro--- + stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB + d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) + t : 0.05 # [m] wall thicknesses (scalar or list of same length as stations) + Cd : 0.6 # [-] transverse drag coefficient (optional, scalar or list of same length as stations) + Ca : 0.93 # [-] transverse added mass coefficient (optional, scalar or list of same length as stations) + CdEnd : 1.0 # [-] end axial drag coefficient (optional, scalar or list of same length as stations) + CaEnd : 0.7 # value of 3.0 gives more heave response # [-] end axial added mass coefficient (optional, scalar or list of same length as stations) + rho_shell : 7850 # [kg/m3] + # --- ballast --- + l_fill : 1.4 # [m] + rho_fill : 5000 # [kg/m3] + # --- handling of end caps or any internal structures if we need them --- + cap_stations : [ 0 ] # [m] location along member of any inner structures (in same scaling as set by 'stations') + cap_t : [ 0.001 ] # [m] thickness of any internal structures + cap_d_in : [ 0 ] # [m] inner diameter of internal structures (0 for full cap/bulkhead, >0 for a ring shape) + + + - name : pontoon # [-] an identifier (no longer has to be number) + type : 2 # [-] + rA : [ 5 , 0, -16.5] # [m] end A coordinates + rB : [ 45.5, 0, -16.5] # [m] and B coordinates + heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) + shape : rect # [-] circular or rectangular + gamma : 0.0 # [deg] twist angle about the member's z-axis + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + # --- outer shell including hydro--- + stations : [0, 40.5] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB + d : [12.4, 7.0] # [m] diameters if circular or side lengths if rectangular (can be pairs) + t : 0.05 # [m] wall thicknesses (scalar or list of same length as stations) + Cd : [1.5, 2.2 ] # [-] transverse drag coefficient (optional, scalar or list of same length as stations) + Ca : [2.2, 0.2 ] # [-] transverse added mass coefficient (optional, scalar or list of same length as stations) + CdEnd : 0.0 # [-] end axial drag coefficient (optional, scalar or list of same length as stations) + CaEnd : 0.0 # [-] end axial added mass coefficient (optional, scalar or list of same length as stations) + rho_shell : 7850 # [kg/m3] + l_fill : 40.5 # [m] + rho_fill : 1025.0 # [kg/m3] + + + - name : upper_support # [-] an identifier (no longer has to be number) + type : 2 # [-] + rA : [ 5 , 0, 14.545] # [m] end A coordinates + rB : [ 45.5, 0, 14.545] # [m] and B coordinates + heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) + shape : circ # [-] circular or rectangular + gamma : 0.0 # [deg] twist angle about the member's z-axis + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + # --- outer shell including hydro--- + stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB + d : 0.91 # [m] diameters if circular or side lengths if rectangular (can be pairs) + t : 0.01 # [m] wall thicknesses (scalar or list of same length as stations) + Cd : 0.0 # [-] transverse drag coefficient (optional, scalar or list of same length as stations) + Ca : 0.0 # [-] transverse added mass coefficient (optional, scalar or list of same length as stations) + CdEnd : 0.0 # [-] end axial drag coefficient (optional, scalar or list of same length as stations) + CaEnd : 0.0 # [-] end axial added mass coefficient (optional, scalar or list of same length as stations) + rho_shell : 7850 # [kg/m3] + + +# ----- Mooring system ----- + +# Mooring system descriptions (each for an individual FOWT with no sharing) +mooring_systems: + + ms1: + name: 2-line semi-taut polyester mooring system with a third line shared + + keys: [MooringConfigID, heading, anchorType, lengthAdjust] + data: + - [ semitaut-poly_1, 150 , suction1, 0 ] + - [ semitaut-poly_1, 270 , suction1, 0 ] + - [ semitaut-poly_1, 30 , suction1, 0 ] + + +# Mooring line configurations +mooring_line_configs: + + semitaut-poly_1: # mooring line configuration identifier + + name: Semitaut polyester configuration 1 # descriptive name + + span: 642 + + sections: #in order from anchor to fairlead + - mooringFamily: chain # ID of a mooring line section type + d_nom: .1549 + length: 497.7 # [m] usntretched length of line section + adjustable: True # flags that this section could be adjusted to accommodate different spacings... + - connectorType: h_link + - mooringFamily: polyester # ID of a mooring line section type + d_nom: .182 + length: 199.8 # [m] length (unstretched) + +# Mooring connector properties +mooring_connector_types: + + h_link: + m : 140 # [kg] component mass + v : 0.13 # [m^3] component volumetric displacement + +# Anchor type properties +anchor_types: + + drag-embedment1: + type : DEA # type of anchor + A : 10 # net area of anchor's fluke [m^2] + zlug : 8 # embedded depth of padeye [m] + + suction1: + type : suction + L : 8.40 # length of pile [m] + D : 2.45 # diameter of pile [m] + zlug : 5.32 # embedded depth of padeye [m] + diff --git a/examples/Inputs/OntologySample600m_shared.yaml b/examples/Inputs/OntologySample600m_shared.yaml index 675d2f94..ef355e76 100644 --- a/examples/Inputs/OntologySample600m_shared.yaml +++ b/examples/Inputs/OntologySample600m_shared.yaml @@ -37,7 +37,7 @@ site: type_array: - [mud_soft , mud_firm , mud_soft] - - [mud_soft , mud_soft , mud_soft] + - [mud_soft , mud_soft , mud_soft] - [mud_soft , mud_firm , mud_soft] soil_types: # dictionary-based approach @@ -1212,7 +1212,7 @@ platforms: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1235,7 +1235,7 @@ platforms: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1484,17 +1484,17 @@ mooring_connector_types: # Anchor type properties anchor_types: suction_pile1: - type : suction_pile + type : suction L : 16.4 # length of pile [m] D : 5.45 # diameter of pile [m] zlug : 9.32 # embedded depth of padeye [m] d-g_pile1: - type : dandg_pile + type : dandg L : 50 # length of pile [m] D : 3 # diameter of pile [m] zlug : 0 # embedded depth [m] driven_pile1: - type : driven_pile + type : driven L : 20 # pile length [m] D : 1.5 # pile diameter [m] zlug : 3 # embedded depth [m diff --git a/examples/Inputs/checkyaml.yaml b/examples/Inputs/checkyaml.yaml new file mode 100644 index 00000000..59a84805 --- /dev/null +++ b/examples/Inputs/checkyaml.yaml @@ -0,0 +1,1331 @@ +site: + seabed: + x: [-10901.0, 0.0, 10000.0] + y: [-10900.0, 0.0, 10000.0] + type_array: + - [mud_soft, mud_firm, mud_soft] + - [mud_soft, mud_firm, mud_soft] + - [mud_soft, mud_firm, mud_soft] + soil_types: + mud_soft: + layers: + - Su0: [2.39] + k: [1.41] + depth: [0] + top: 0 + bottom: 50 + soil_type: mud_soft + Su0: [2.39] + k: [1.41] + alpha: [0.7] + gamma: [8.7] + phi: [0.0] + UCS: [7.0] + Em: [50.0] + mud_firm: + layers: + - Su0: [23.94] + k: [2.67] + depth: [0] + top: 0 + bottom: 50 + soil_type: mud_firm + Su0: [2.39] + k: [1.41] + alpha: [0.7] + gamma: [8.7] + phi: [0.0] + UCS: [7.0] + Em: [50.0] + bathymetry: + x: [-3000.0, 3500.0, 10000.0] + y: [-3000.0, 3500.0, 10000.0] + depths: + - [200.1, 207.25625, 240.0] + - [200.34375, 206.133984375, 208.3125] + - [210.7, 196.29375000000002, 185.0] + boundaries: + x_y: + - [-2000.0, -2000.0] + - [-2000.0, 8000.0] + - [8000.0, 8000.0] + - [8000.0, -2000.0] + - [-2000.0, -2000.0] + general: + water_depth: 200.0 + rho_air: 1.225 + rho_water: 1025.0 + mu_air: 1.81e-05 +array: + keys: [ID, topsideID, platformID, mooringID, x_location, y_location, + z_location, heading_adjust] + data: + - [fowt0, 1, 1, ms0, -600.0, -800.0, 0.0, 0.0] + - [fowt1, 1, 1, ms0, 1100.0, -800.0, 0.0, 0.0] + - [fowt2, 1, 1, ms0, 2800.0, -800.0, 0.0, 0.0] + - [fowt3, 1, 1, ms0, 4500.0, -800.0, 0.0, 0.0] + - [fowt4, 1, 1, ms0, 6200.0, -800.0, 0.0, 0.0] + - [fowt5, 1, 1, ms0, -600.0, 1100.0, 0.0, 0.0] + - [fowt6, 1, 1, ms0, 1100.0, 1100.0, 0.0, 0.0] + - [fowt7, 1, 1, ms0, 2800.0, 1100.0, 0.0, 0.0] + - [fowt8, 1, 1, ms0, 4500.0, 1100.0, 0.0, 0.0] + - [fowt9, 1, 1, ms0, 6200.0, 1100.0, 0.0, 0.0] + - [fowt10, 1, 1, ms0, -600.0, 3000.0, 0.0, 0.0] + - [fowt11, 1, 1, ms0, 1100.0, 3000.0, 0.0, 0.0] + - [fowt12, 1, 1, ms0, 2800.0, 3000.0, 0.0, 0.0] + - [fowt13, 1, 1, ms0, 4500.0, 3000.0, 0.0, 0.0] + - [fowt14, 1, 1, ms0, 6200.0, 3000.0, 0.0, 0.0] + - [fowt15, 1, 1, ms0, -600.0, 4900.0, 0.0, 0.0] + - [fowt16, 1, 1, ms0, 1100.0, 4900.0, 0.0, 0.0] + - [fowt17, 1, 1, ms0, 2800.0, 4900.0, 0.0, 0.0] + - [fowt18, 1, 1, ms0, 4500.0, 4900.0, 0.0, 0.0] + - [fowt19, 1, 1, ms0, 6200.0, 4900.0, 0.0, 0.0] + - [fowt20, 1, 1, ms0, -600.0, 6800.0, 0.0, 0.0] + - [fowt21, 1, 1, ms0, 1100.0, 6800.0, 0.0, 0.0] + - [fowt22, 1, 1, ms0, 2800.0, 6800.0, 0.0, 0.0] + - [fowt23, 1, 1, ms0, 4500.0, 6800.0, 0.0, 0.0] + - [fowt24, 1, 1, ms0, 6200.0, 6800.0, 0.0, 0.0] +platform: + type: FOWT + potModMaster: 1 + dlsMax: 5.0 + qtfPath: IEA-15-240-RWT-UMaineSemi.12d + rFair: 58 + zFair: -14 + members: + - name: center_column + type: 2 + rA: [0, 0, -20] + rB: [0, 0, 15] + shape: circ + gamma: 0.0 + potMod: false + stations: [0, 1] + d: 10.0 + t: 0.05 + Cd: 0.6 + Ca: 0.93 + CdEnd: 0.6 + CaEnd: 1.0 + rho_shell: 7850 + cap_stations: [0] + cap_t: [0.001] + cap_d_in: [0] + dlsMax: 5.0 + headings: 0.0 + - name: outer_column + type: 2 + rA: [51.75, 0, -20] + rB: [51.75, 0, 15] + heading: [60, 180, 300] + shape: circ + gamma: 0.0 + potMod: false + stations: [0, 35] + d: 12.5 + t: 0.05 + Cd: 0.6 + Ca: 0.93 + CdEnd: 1.0 + CaEnd: 0.7 + rho_shell: 7850 + l_fill: 1.4 + rho_fill: 5000 + cap_stations: [0] + cap_t: [0.001] + cap_d_in: [0] + dlsMax: 5.0 + headings: [60.0, 180.0, 300.0] + - name: pontoon + type: 2 + rA: [5, 0, -16.5] + rB: [45.5, 0, -16.5] + heading: [60, 180, 300] + shape: rect + gamma: 0.0 + potMod: false + stations: [0, 40.5] + d: [12.4, 7.0] + t: 0.05 + Cd: [1.5, 2.2] + Ca: [2.2, 0.2] + CdEnd: 0.0 + CaEnd: 0.0 + rho_shell: 7850 + l_fill: 40.5 + rho_fill: 1025.0 + dlsMax: 5.0 + headings: [60.0, 180.0, 300.0] + - 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[170.8571, -0.27841, 0.04114, -0.45714] + - [173.1429, -0.2088, 0.03702, -0.34286] + - [175.4286, -0.1392, 0.03406, -0.22857] + - [177.7143, -0.0696, 0.03228, -0.11429] + - [179.9087, 0.0, 0.03169, 0.0] + - name: FFA-W3-360 + relative_thickness: 0.36 + data: + - [-179.9087, 0.0, 0.03715, 0.0] + - [-177.7143, 0.07178, 0.03774, 0.09143] + - [-175.4286, 0.14356, 0.03951, 0.18286] + - [-173.1429, 0.21534, 0.04245, 0.27429] + - [-170.8572, 0.28713, 0.04653, 0.36571] + - [-168.5716, 0.35891, 0.05174, 0.40313] + - [-166.2857, 0.43069, 0.06068, 0.40814] + - [-164.0, 0.50247, 0.08651, 0.41315] + - [-161.7145, 0.57425, 0.11586, 0.41816] + - [-159.4284, 0.64603, 0.14856, 0.42627] + - [-157.1428, 0.71781, 0.18439, 0.4437] + - [-154.8573, 0.7896, 0.22313, 0.46114] + - [-152.5714, 0.86138, 0.26453, 0.47857] + - [-150.2857, 0.93316, 0.30832, 0.496] + - [-148.0, 1.00494, 0.35424, 0.4883] + - [-143.8571, 0.91898, 0.44192, 0.46784] + - [-139.7143, 0.84406, 0.53379, 0.44803] + - [-135.5714, 0.77483, 0.62793, 0.43697] + - [-131.4286, 0.7079, 0.72238, 0.42591] + - [-127.2857, 0.64116, 0.8152, 0.4215] + - [-123.1429, 0.57335, 0.90444, 0.42058] + - [-119.0, 0.50388, 0.98826, 0.42024] + - [-114.8571, 0.43261, 1.06493, 0.42168] + - [-110.7143, 0.35981, 1.13285, 0.42312] + - [-106.5714, 0.28603, 1.19061, 0.42258] + - [-102.4286, 0.21209, 1.23704, 0.42163] + - [-98.2857, 0.13899, 1.27116, 0.41864] + - [-94.1429, 0.06787, 1.29229, 0.41277] + - [-90.0, 0.0, 1.3, 0.4069] + - [-85.8571, -0.06787, 1.29229, 0.39426] + - [-81.7143, -0.13899, 1.27116, 0.38162] + - [-77.5714, -0.21209, 1.23704, 0.36676] + - [-73.4286, -0.28603, 1.19061, 0.35033] + - [-69.2857, -0.35981, 1.13285, 0.33362] + - [-65.1429, -0.43261, 1.06493, 0.31561] + - [-61.0, -0.50388, 0.98826, 0.29759] + - [-56.8571, -0.57335, 0.90444, 0.27989] + - [-52.7143, -0.64116, 0.8152, 0.2623] + - [-48.5714, -0.7079, 0.72238, 0.24491] + - [-44.4286, -0.77483, 0.62793, 0.22794] + - [-40.2857, -0.84406, 0.53379, 0.21097] + - [-36.1429, -0.91898, 0.44192, 0.13525] + - [-32.0, -1.00494, 0.35424, 0.05517] + - [-28.0, -1.11306, 0.20494, 0.03211] + - [-24.0, -1.05425, 0.15434, 0.01268] + - [-20.0, -0.98247, 0.10967, -0.00282] + - [-18.0, -0.94173, 0.09249, -0.00741] + - [-16.0, -0.89333, 0.07597, -0.01107] + - [-14.0, -0.85472, 0.06054, -0.0125] + - [-12.0, -0.82348, 0.04641, -0.01177] + - [-10.0, -0.79541, 0.03441, -0.01082] + - [-8.0, -0.6365, 0.02548, -0.02769] + - [-6.0, -0.39095, 0.01994, -0.05107] + - [-4.0, -0.13071, 0.01653, -0.07148] + - [-2.0, 0.16173, 0.01507, -0.09179] + - [-1.0, 0.31121, 0.01477, -0.10119] + - [0.0, 0.45956, 0.01465, -0.10988] + - [1.0, 0.60566, 0.01466, -0.11776] + - [2.0, 0.74868, 0.01481, -0.12477] + - [3.0, 0.88862, 0.01507, -0.13098] + - [4.0, 1.02544, 0.01544, -0.13648] + - [5.0, 1.15878, 0.01593, -0.1413] + - [6.0, 1.28822, 0.01654, -0.1454] + - [7.0, 1.41282, 0.01731, -0.14875] + - [8.0, 1.5309, 0.01831, -0.15118] + - [9.0, 1.64065, 0.01963, -0.15262] + - [10.0, 1.73926, 0.0215, -0.1531] + - [11.0, 1.81971, 0.02445, -0.15254] + - [12.0, 1.87065, 0.02966, -0.15121] + - [13.0, 1.89221, 0.0377, -0.14969] + - [14.0, 1.8791, 0.04824, -0.14562] + - [15.0, 1.88111, 0.05838, -0.14358] + - [16.0, 1.86359, 0.06992, -0.14095] + - [18.0, 1.73324, 0.10166, -0.13711] + - [20.0, 1.59357, 0.13916, -0.14082] + - [24.0, 1.46708, 0.21002, -0.15693] + - [28.0, 1.44834, 0.282, -0.17979] + - [32.0, 1.43563, 0.35424, -0.20147] + - [36.1429, 1.31283, 0.44192, -0.22409] + - [40.2857, 1.2058, 0.53379, -0.24619] + - [44.4286, 1.1069, 0.62793, -0.26133] + - [48.5714, 1.01129, 0.72238, -0.27648] + - [52.7143, 0.91594, 0.8152, -0.29062] + - [56.8571, 0.81907, 0.90444, -0.30424] + - [61.0, 0.71982, 0.98826, -0.31787] + - [65.1429, 0.61801, 1.06493, -0.33154] + - [69.2857, 0.51401, 1.13285, -0.34522] + - [73.4286, 0.40862, 1.19061, -0.35846] + - [77.5714, 0.30299, 1.23704, -0.37161] + - [81.7143, 0.19855, 1.27116, -0.38405] + - [85.8571, 0.09695, 1.29229, -0.39547] + - [90.0, 0.0, 1.3, -0.4069] + - [94.1429, -0.06787, 1.29229, -0.41277] + - [98.2857, -0.13899, 1.27116, -0.41864] + - [102.4286, -0.21209, 1.23704, -0.42163] + - [106.5714, -0.28603, 1.19061, -0.42258] + - [110.7143, -0.35981, 1.13285, -0.42312] + - [114.8571, -0.43261, 1.06493, -0.42168] + - [119.0, -0.50388, 0.98826, -0.42024] + - [123.1429, -0.57335, 0.90444, -0.42058] + - [127.2857, -0.64116, 0.8152, -0.4215] + - [131.4286, -0.7079, 0.72238, -0.42591] + - [135.5714, -0.77483, 0.62793, -0.43697] + - [139.7143, -0.84406, 0.53379, -0.44803] + - [143.8571, -0.91898, 0.44192, -0.46784] + - [148.0, -1.00494, 0.35424, -0.4883] + - [150.2857, -0.93316, 0.30832, -0.496] + - [152.5714, -0.86138, 0.26453, -0.47857] + - [154.8571, -0.7896, 0.22313, -0.46114] + - [157.1429, -0.71781, 0.18439, -0.4437] + - [159.4286, -0.64603, 0.14856, -0.42627] + - [161.7143, -0.57425, 0.11586, -0.4353] + - [164.0, -0.50247, 0.08651, -0.45315] + - [166.2857, -0.43069, 0.06068, -0.471] + - [168.5714, -0.35891, 0.05174, -0.48884] + - [170.8571, -0.28713, 0.04653, -0.45714] + - [173.1429, -0.21534, 0.04245, -0.34286] + - [175.4286, -0.14356, 0.03951, -0.22857] + - [177.7143, -0.07178, 0.03774, -0.11429] + - [179.9087, 0.0, 0.03715, 0.0] + pitch_control: + GS_Angles: [0.06019804, 0.08713416, 0.10844806, 0.12685912, 0.14339822, + 0.1586021, 0.17279614, 0.18618935, 0.19892772, 0.21111989, 0.22285021, + 0.23417256, 0.2451469, 0.25580691, 0.26619545, 0.27632495, 0.28623134, + 0.29593266, 0.30544521, 0.314779, 0.32395154, 0.33297489, 0.3418577, + 0.35060844, 0.35923641, 0.36774807, 0.37614942, 0.38444655, 0.39264363, + 0.40074407] + GS_Kp: [-0.9394215, -0.80602855, -0.69555026, -0.60254912, -0.52318192, + -0.45465531, -0.39489024, -0.34230736, -0.29568537, -0.25406506, + -0.2166825, -0.18292183, -0.15228099, -0.12434663, -0.09877533, + -0.0752794, -0.05361604, -0.0335789, -0.01499149, 0.00229803, 0.01842102, + 0.03349169, 0.0476098, 0.0608629, 0.07332812, 0.0850737, 0.0961602, + 0.10664158, 0.11656607, 0.12597691] + GS_Ki: [-0.07416547, -0.06719673, -0.0614251, -0.05656651, -0.0524202, + -0.04884022, -0.04571796, -0.04297091, -0.04053528, -0.03836094, + -0.03640799, -0.03464426, -0.03304352, -0.03158417, -0.03024826, + -0.02902079, -0.02788904, -0.02684226, -0.02587121, -0.02496797, + -0.02412567, -0.02333834, -0.02260078, -0.02190841, -0.0212572, + -0.02064359, -0.0200644, -0.01951683, -0.01899836, -0.01850671] + Fl_Kp: -9.35 + wt_ops: + v: [3.0, 3.266896551724138, 3.533793103448276, 3.800689655172414, + 4.067586206896552, 4.334482758620689, 4.601379310344828, + 4.868275862068966, 5.135172413793104, 5.402068965517241, + 5.6689655172413795, 5.935862068965518, 6.2027586206896554, + 6.469655172413793, 6.736551724137931, 7.00344827586207, + 7.270344827586207, 7.537241379310345, 7.804137931034483, + 8.071034482758622, 8.337931034482759, 8.604827586206897, + 8.871724137931036, 9.138620689655173, 9.405517241379311, + 9.672413793103448, 9.939310344827586, 10.206206896551725, + 10.473103448275863, 10.74, 11.231724137931035, 11.723448275862069, + 12.215172413793104, 12.706896551724139, 13.198620689655172, + 13.690344827586207, 14.182068965517242, 14.673793103448276, + 15.16551724137931, 15.657241379310346, 16.14896551724138, + 16.640689655172416, 17.13241379310345, 17.624137931034483, + 18.11586206896552, 18.607586206896553, 19.099310344827586, + 19.591034482758623, 20.082758620689653, 20.57448275862069, + 21.066206896551726, 21.557931034482756, 22.049655172413793, + 22.54137931034483, 23.03310344827586, 23.524827586206897, + 24.016551724137933, 24.508275862068963, 25.0] + pitch_op: [-0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, + -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, + -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, + -0.25, 3.57152, 5.12896, 6.36736, 7.43866, 8.40197, 9.28843, 10.1161, + 10.8974, 11.641, 12.3529, 13.038, 13.6997, 14.3409, 14.9642, 15.5713, + 16.1639, 16.7435, 17.3109, 17.8673, 18.4136, 18.9506, 19.4788, 19.9989, + 20.5112, 21.0164, 21.5147, 22.0067, 22.4925, 22.9724] + omega_op: [2.1486, 2.3397, 2.5309, 2.722, 2.9132, 3.1043, 3.2955, 3.4866, + 3.6778, 3.8689, 4.0601, 4.2512, 4.4424, 4.6335, 4.8247, 5.0159, 5.207, + 5.3982, 5.5893, 5.7805, 5.9716, 6.1628, 6.3539, 6.5451, 6.7362, 6.9274, + 7.1185, 7.3097, 7.5008, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, + 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, + 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56] + gear_ratio: 1 + torque_control: + VS_KP: -38609162.66552 + VS_KI: -4588245.1872 + tower: + dlsMax: 5.0 + name: tower + type: 1 + rA: [0, 0, 15] + rB: [0, 0, 144.582] + shape: circ + gamma: 0.0 + stations: [15, 28, 28.001, 41, 41.001, 54, 54.001, 67, 67.001, 80, 80.001, + 93, 93.001, 106, 106.001, 119, 119.001, 132, 132.001, 144.582] + d: [10, 9.964, 9.964, 9.967, 9.967, 9.927, 9.927, 9.528, 9.528, 9.149, 9.149, + 8.945, 8.945, 8.735, 8.735, 8.405, 8.405, 7.321, 7.321, 6.5] + t: [0.082954, 0.082954, 0.083073, 0.083073, 0.082799, 0.082799, 0.0299, + 0.0299, 0.027842, 0.027842, 0.025567, 0.025567, 0.022854, 0.022854, + 0.02025, 0.02025, 0.018339, 0.018339, 0.021211, 0.021211] + Cd: 0.0 + Ca: 0.0 + CdEnd: 0.0 + CaEnd: 0.0 + rho_shell: 7850 +array_mooring: + anchor_keys: [ID, type, x, y, embedment] + anchor_data: [] + line_keys: [MooringConfigID, endA, endB, headingA, headingB, lengthAdjust] + line_data: [] +mooring_systems: + ms0: + keys: [MooringConfigID, heading, anchorType, lengthAdjust] + data: + - ['0', 150.0, drag-embedment1, 0] + - ['0', 270.0, drag-embedment1, 0] + - ['0', 30.0, drag-embedment1, 0] +mooring_line_configs: + '0': + name: '0' + span: 642.0 + sections: + - type: 0 + length: 497.7 + - connectorType: h_link + - type: 1 + length: 199.8 +mooring_line_types: + 0: + name: 0 + d_vol: 0.27882 + m: 479.88020000000006 + EA: 2053741190.7845445 + w: 4093.6781602294 + MBL: 20893381.207590003 + EAd: 0.0 + EAd_Lm: 0.0 + d_nom: 0.1549 + cost: 2005.5584560000004 + notes: made with getLineProps + material: chain + Cd: 1.333 + CdAx: 0.639 + Ca: 1.0 + CaAx: 0.5 + 1: + name: 1 + d_vol: 0.14405282886514317 + m: 22.491196000000002 + EA: 142830688.0 + w: 56.75848886217392 + MBL: 10202192.0 + EAd: 118345427.2 + EAd_Lm: 40.0 + d_nom: 0.182 + cost: 441.9688 + notes: made with getLineProps + material: polyester + Cd: 2.021 + CdAx: 0.0 + Ca: 1.1 + CaAx: 0.15 +mooring_connector_types: + h_link: + m: 140.0 + v: 0.13 + type: h_link + CdA: 0 +anchor_types: + drag-embedment1: + type: DEA + A: 10 + zlug: 10 +cables: [] +dynamic_cable_configs: {} +cable_types: {} +cable_appendages: {} diff --git a/examples/Inputs/output_MD.dat b/examples/Inputs/output_MD.dat index 38578132..3837a191 100644 --- a/examples/Inputs/output_MD.dat +++ b/examples/Inputs/output_MD.dat @@ -11,22 +11,22 @@ TypeName Diam Mass/m Cd Ca CdEnd CaEnd ----------------------- BODIES ------------------------------------------------------ ID Attachment X0 Y0 Z0 r0 p0 y0 Mass CG* I* Volume CdA* Ca* (#) (-) (m) (m) (m) (deg) (deg) (deg) (kg) (m) (kg-m^2) (m^3) (m^2) (-) -1 free -1499.91 1499.96 0.00 0.00 0.00 -0.00 1.9911e+07 0.00|0.00|-2.54 0.000e+00 19480.10 0.00 0.00 +1 free -599.89 -799.82 0.00 -0.00 0.00 0.00 1.9911e+07 0.00|0.00|-2.54 0.000e+00 19480.10 0.00 0.00 ---------------------- RODS --------------------------------------------------------- ID RodType Attachment Xa Ya Za Xb Yb Zb NumSegs RodOutputs (#) (name) (#/key) (m) (m) (m) (m) (m) (m) (-) (-) ---------------------- POINTS ------------------------------------------------------- ID Attachment X Y Z Mass Volume CdA Ca (#) (-) (m) (m) (m) (kg) (m^3) (m^2) (-) -1 Fixed -1150.00 893.78 -204.21 0.00 0.00 0.00 0.00 -2 Free -1391.15 1311.56 -137.43 140.00 0.13 0.00 0.00 -3 Coupled -1470.91 1449.73 -14.00 0.00 0.00 0.00 0.00 -4 Fixed -2200.00 1500.00 -203.86 0.00 0.00 0.00 0.00 -5 Free -1717.38 1499.97 -137.30 140.00 0.13 0.00 0.00 -6 Coupled -1557.91 1499.96 -14.00 0.00 0.00 0.00 0.00 -7 Fixed -1150.00 2106.22 -204.07 0.00 0.00 0.00 0.00 -8 Free -1391.18 1688.34 -137.38 140.00 0.13 0.00 0.00 -9 Coupled -1470.91 1550.19 -14.00 0.00 0.00 0.00 0.00 +1 Fixed -250.00 -1406.22 -200.71 0.00 0.00 0.00 0.00 +2 Free -491.58 -987.52 -138.05 140.00 0.13 0.00 0.00 +3 Body1 29.00 -50.23 -14.00 0.00 0.00 0.00 0.00 +4 Fixed -1300.00 -800.00 -200.70 0.00 0.00 0.00 0.00 +5 Free -816.60 -799.86 -138.04 140.00 0.13 0.00 0.00 +6 Body1 -58.00 -0.00 -14.00 0.00 0.00 0.00 0.00 +7 Fixed -250.00 -193.78 -201.34 0.00 0.00 0.00 0.00 +8 Free -491.48 -612.04 -138.29 140.00 0.13 0.00 0.00 +9 Body1 29.00 50.23 -14.00 0.00 0.00 0.00 0.00 ---------------------- LINES -------------------------------------------------------- ID LineType AttachA AttachB UnstrLen NumSegs LineOutputs (#) (name) (#) (#) (m) (-) (-) diff --git a/examples/duplicate_platform.py b/examples/duplicate_platform.py index e40b77c9..2328375e 100644 --- a/examples/duplicate_platform.py +++ b/examples/duplicate_platform.py @@ -32,6 +32,12 @@ # make new moorpy array project.getMoorPyArray() +for line in rep_pf.mooringSystem(project).lineList: + xB, yB, zB = line.rB + #z_anchor, soil_label = get_depth_and_soil(xB, yB) + #print(f' Anchor at ({xB:.1f}, {yB:.1f}) → Depth = {z_anchor:.2f} m') + + # plot the new system project.plot3d() plt.show() \ No newline at end of file diff --git a/examples/example_anchors.py b/examples/example_anchors.py deleted file mode 100644 index d496b11f..00000000 --- a/examples/example_anchors.py +++ /dev/null @@ -1,148 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Example showing how to call forces and -anchor capacity functions, along with safety factors and material costs. -""" -# import necessary packages -from famodel.project import Project -import os - -os.chdir('./Inputs/') - -# set yaml file location and name -ontology_file = 'OntologySample200m_1turb.yaml' - -# create project class -project = Project(file=ontology_file) -project.getMoorPyArray() - -# let's choose a single anchor from the array to look at -anch = project.anchorList['fowt0a'] - -# now let's get the mudline and lug forces on this anchor -anch.getLugForces() # getLugForces calls getMudlineForces() to get the anchor forces at both locations - -# establish a factor of safety in horizontal (Ha) and vertical (Va) directions -minfs = {'Ha': 1.8, 'Va': 2} - -# let's get the loads with the factor of safety included -loads_with_FS = {'Ha':anch.loads['Ha']*minfs['Ha'],'Va':anch.loads['Va']*minfs['Va']} - -# get anchor capacity for one anchor (this case is for suction pile in clay) -anch.getAnchorCapacity(loads=loads_with_FS) # loads are used in capacity calculation, so let's send in the loads with factor of safety applied - -# get anchor cost -startGeom = [10,2,6.6] -geomKeys = ['L','D','zlug'] -geomBounds = [(5, 50), (1, 7), (3.3,16.7)] -FSDiff_max = {'Ha':5,'Va':5} -anch.getSize(startGeom,geomKeys,geomBounds,minfs=minfs,FSdiff_max=FSDiff_max, plot=True) -anch.getCost() -print('\nClay suction pile capacity is: ',anch.anchorCapacity) -print('Clay suction pile safety factor is: ',anch.getFS()) -print('Clay suction pile cost is: ', anch.cost,'\n') -# try suction pile with sand -newdd = anch.dd -anch.soilProps['sand'] = anch.soilProps.pop('mud_firm') -anch.soilProps['sand']['phi'] = 33 -anch.soilProps['sand']['Dr'] = 70 -anch.soilProps['sand']['delta'] = 25 -# update anchor loads at lug point (mudline load should be constant), then get anchor capacity -anch.getLugForces() -anch.getSize(startGeom,geomKeys,geomBounds,plot=True) -anch.getAnchorCapacity(loads=loads_with_FS) -anch.getCost() -print('\nSand suction pile capacity is: ',anch.anchorCapacity,' N') -print('Sand suction pile safety factor is: ',anch.getFS()) -print('Sand suction pile cost is: ', anch.cost,' USD\n') - -# check plate anchor type -newdd['type'] = 'DEA' -newdd['design'] = {'type':'DEA','A':20,'zlug':20,'beta':10} -anch.soilProps['clay'] = anch.soilProps.pop('sand') - -startGeom = [10,20] -geomKeys = ['A','zlug'] - -anch.getLugForces() -anch.getAnchorCapacity(loads=loads_with_FS) -# let's fix the zlug for the plate anchor - set fix_zlug=True to prevent it being changed -anch.getSize(startGeom,geomKeys,minfs={'Ha':2,'Va':0}, fix_zlug = True) -anch.getCost() -print('\nClay plate capacity is: ',anch.anchorCapacity,' N') -print('Clay plate safety factor is: ',anch.getFS()) -print('Clay plate cost is: ', anch.cost,' USD\n') - -# check drilled and grouted pile anchor type -newdd['type'] = 'dandg_pile' -newdd['design'] = {'type':'dandg_pile','L':50,'D':3,'zlug':0} -anch.soilProps['rock'] = anch.soilProps.pop('clay') # soil_properties has default rock info in there already, just change name - -# startGeom = [5,50] -# geomKeys = ['L','D'] -anch.getLugForces() -# anch.getSize(startGeom,geomKeys,minfs={'Ha':2,'Va':2}) -anch.getAnchorCapacity(loads=loads_with_FS) -print('\nRock drilled and grouted pile capacity is: ',anch.anchorCapacity,' N') -print('Rock drilled and grouted pile safety factor is: ',anch.getFS()) - -# check driven pile anchor in rock -newdd['type'] = 'driven' -anch.soilProps['weak_rock'] = anch.soilProps.pop('rock') -newdd['design'] = {'type':'driven','L':20,'D':1.5,'zlug':-3} # zlug should be negative (above mudline) for rock! - -anch.getLugForces() -anch.getAnchorCapacity(loads=loads_with_FS) -print('\nWeak rock driven pile capacity is: ',anch.anchorCapacity,' N') -print('Weak rock driven pile safety factor is: ',anch.getFS()) - -# check driven pile anchor in clay -anch.soilProps['clay'] = anch.soilProps.pop('weak_rock') -newdd['design'] = {'type':'driven','L':40,'D':4,'zlug':10} - -anch.getLugForces() -anch.getAnchorCapacity(loads=loads_with_FS) -print('\nClay driven pile capacity is: ',anch.anchorCapacity,' N') -print('Clay driven pile safety factor is: ',anch.getFS()) - -# check driven pile anchor in sand -anch.soilProps['sand'] = anch.soilProps.pop('clay') -anch.soilProps['sand']['Dr'] = 50 - -anch.getLugForces() -anch.getAnchorCapacity(loads=loads_with_FS) -print('\nSand driven pile capacity is: ',anch.anchorCapacity,' N') -print('Sand driven pile safety factor is: ',anch.getFS()) - -# check helical pile anchor with sand -newdd['type'] = 'helical_pile' -newdd['design'] = {'type':'helical_pile','L':25.1,'d':1,'D':5.01, 'zlug':5} - -anch.getLugForces() -anch.getAnchorCapacity(loads=loads_with_FS) -print('\nSand helical pile capacity is: ',anch.anchorCapacity,' N') -print('Sand helical pile safety factor is: ',anch.getFS()) - -# check helical pile anchor with clay -anch.soilProps['clay'] = anch.soilProps.pop('sand') -newdd['type'] = 'helical_pile' -newdd['design'] = {'type':'helical_pile','L':25.1,'d':1,'D':5.01,'zlug':5} - -anch.getLugForces() -anch.getAnchorCapacity(loads=loads_with_FS) -print('\nClay helical pile capacity is: ',anch.anchorCapacity,' N') -print('Clay helical pile safety factor is: ',anch.getFS()) - -# check torpedo anchor in clay -newdd['type'] = 'torpedo_pile' -newdd['design'] = {'type':'torpedo_pile','D1':3,'D2':1.1,'L1':10,'L2':4,'zlug':16} - -anch.getLugForces() -anch.getAnchorCapacity(loads=loads_with_FS) -print('\nClay torpedo pile capacity is: ',anch.anchorCapacity,' N') -print('Clay torpedo pile safety factor is: ',anch.getFS()) - - - - - diff --git a/examples/example_driver.py b/examples/example_driver.py index ad1ac38b..a4dd85f2 100644 --- a/examples/example_driver.py +++ b/examples/example_driver.py @@ -77,27 +77,35 @@ # plot motion envelopes with 2d plot project.plot2d(save=True,plot_bathymetry=False) - +#%% Section 5: Anchor capabilities #### get anchor capacities, loads, and safety factors #### print('\nGetting anchor capacities, loads, and safety factors\n') # let's look at one anchor in the farm # define anchor to analyze anchor = project.anchorList['FOWT1a'] -# get anchor capacity -anchor.getAnchorCapacity() + +name, soil_def = project.getSoilAtLocation(anchor.r[0], anchor.r[1]) +profile_map = [{'name': name, 'layers': soil_def['layers']}] +anchor.setSoilProfile(profile_map) + +Hm = anchor.loads['Hm'] +Vm = anchor.loads['Vm'] +zlug = anchor.dd['design']['zlug'] + +# Now use these in lug and capacity checks +anchor.getLugForces(Hm, Vm, zlug) +anchor.getCapacityAnchor(Hm, Vm, zlug) capacities = anchor.anchorCapacity -# get anchor loads at mudline and anchor lug depth (if applicable) -loads = anchor.getLugForces() + # size an anchor -starting_geometry = [15,20] # geometry values -starting_geom_labels = ['A','zlug'] # corresponding labels for the geometry list -min_safety_factors = {'Ha':2,'Va':2} # minimum safety factors -FSdiff_max = {'Ha':.1,'Va':.1} # allowable difference between actual and desired FS for final result -anchor.getSize(starting_geometry, starting_geom_labels, minfs=min_safety_factors, - FSdiff_max=FSdiff_max) +geom_start = [anchor.dd['design']['B'], anchor.dd['design']['L']] # geometry values +geom_labels = ['B','L'] # corresponding labels for the geometry list +geom_bounds = [(0.5, 4.0), (0.5, 4.0)] +safety_factor = {'SF_combined': 1.0} # minimum safety factors +anchor.getSizeAnchor(geom_start, geom_labels, geom_bounds, loads = None, safety_factor={'SF_combined': 1.0}) # get safety factor -sfs = anchor.getFS() +sfs = anchor.getSafetyFactor() print('\nAnchor safety factors: ',sfs) # NOTE that Va will show as 'inf' because there is no vertical force on the anchor. diff --git a/famodel/anchors/README.md b/famodel/anchors/README.md index 84788f66..58ccb495 100644 --- a/famodel/anchors/README.md +++ b/famodel/anchors/README.md @@ -1,229 +1,666 @@ # Anchors Library -This subpackage of FAModel contains the Anchor class as well as modules for anchor capacity -calculations. +This subpackage of FAModel contains the anchor class and all modules for the capacity under extreme loads and the installation assessments -## Anchor Class -The anchor class contains properties and methods related to mooring anchors. -The supported anchor types are below, with the associated FAModel name in italics. -- Plate anchors - - *DEA* (drag-embedment anchors) - - *SEPLA* (suction embedded plate anchors) - - *DEPLA* (dynamically embedded plate anchors) - - *VLA* (vertically loaded anchors) - - *plate* (unspecified plate anchor) -- *suction_pile* (Suction caisson/ suction bucket anchors) -- *torpedo_pile* (Torpedo pile anchors) -- *helical_pile* (Helical pile anchors) -- *driven_pile* (Driven pile anchors) -- *dandg_pile* (Drilled and grouted piles) - - -The anchor class stores properties and methods that enable a wide range of modeling - from capacity to cost to loads, and more. The [anchor capacity modules](#anchor-capacity-modules) are integrated with the anchor class through the getAnchorCapacity() method. -### Anchor Properties -- **r** : anchor [x,y,z] position -- **dd** : anchor design dictionary, containing geometric properties, soil properties at the anchor location, cost -- **ms** : moorpy system associated with this anchor point -- **aNum** : anchor index in array mooring list (generally only used for shared moorings) -- **mpAnchor** : moorpy point object that models this anchor -- **anchorCapacity** : dictionary with horizontal and vertical capacity of the anchor. Generally these are loads in [N], but can also be displacements (generally for driven or drilled and grouted piles) -- **loads** : dictionary of loads on the anchor, and the method used to obtain these loads (static or dynamic modeling). Loads include horizontal (H) and vertical (V) loads, as well as the angle of the load (theta). The keys for these loads will either include an m (for loads at the mudline) or a (for loads at the anchor lug). -- **soilProps** : dictionary of soil property information at the location of the anchor -- **failure_probability** : dictionary of probabilities for failure of the anchor +## Seabed Conditions +Introduction to different soil types -### Anchor Methods -- **makeMoorPyAnchor()** : Creates a MoorPy point object representing the anchor in a moorpy system -- **getAnchorCapacity()** : Calls anchor capacity functions for the correct anchor type -- **getFS()** : Computes safety factor for loads on the anchor -- **getCost()** : Finds costs of anchor from MoorProps and stores in design dictionary -- **getMass()** : Finds mass and/or UHC of anchor from MoorProps and stores in design dictionary -- **getMudlineForces()** : Finds forces on anchor at mudline using MoorPy Point.getForces method. Use max_force=True to obtain the maximum forces on that anchor from the platform.getWatchCircle() method. For more information on the getWatchCircle() calculations, see the [Platform ReadMe](../platform/README.md). An additional anchor.loads dictionary entry is included to describe the mudline load type. 'mudline_load_type'='max' if max_force=True, and 'mudline_load_type'='current_state' if max_force=False. -- **getLugForces()** : Finds forces at the anchor lug location with getTransferFunction function in capacity_loads.py. -The getTransferLoad function requires **maximum** mudline forces as an input. These forces can be sent in as a dictionary, or anchor.loads dictionary will be searched for 'Hm' and 'Vm' values with additional key-value pair 'mudline_force_type':'max' to indicate these mudline forces are maximums. -If there are no max mudline forces in the anchor.loads dictionary, getMudlineForces(max_force=True) will be called. Stores results in loads dictionary. If lug is at mudline or no lug provided, equates mudline forces with lug forces. ->[!NOTE] ->The getTransferFunction function called by getLugForces() is tuned to work with maximum loads on the anchor. Some anchor configuration, load, and soil condition combinations may produce invalid results in getTransferFunction. For example, the output Va may show as negative. In that case, getLugForces() will warn the user of the invalidity of the result and assign the anchor lug forces = mudline forces. +Heterogenous soil (mixed layers). Map of soil properties for horizontal and vertical spatial-variability. +The reference elevation of the pile is the pile head (z = 0 m), from here all elevations are derived. Thus, Z0 (mudline elevation) is the distance between the pile head and the top of the first layer of soil. Main padeye locations depend on their relative elevation to z0, if zlug > z0 mooring line is embedded below the mudline elevation +### Soil properties +##### Input +- profile_map + - location_name: CPT or reference in the system (-) + - x, y: coordinates of the anchor within the lease area (m), (m) + - layers (at least one): + - top, bottom: depth for top and bottom for each layer (m), (m) + - soil_type: clay/mud, sand and (weak) rock (-) + - soil properties: + - clay/mud: + - gamma: submerged soil unit weight (kN/m³) + - Su: undrained shear strength (kPa) + - sand: + - gamma: submerged soil unit weight: (kN/m³) + - phi: internal friction angle (deg) + - Dr: relative density (%) + - (weak) rock, + - UCS: unconfined compressive strength at failure (MPa) + - Em: rock mass modulus (MPa) -### Anchor Type Requirements +>[!NOTE] +Driven piles are only possible on weak rock, defined here as up to UCS = 5 MPa -Different geometric properties and soil conditions are needed for each anchor type. See the [Anchor Capacity Modules](#anchor-capacity-modules) section for details on the requirements of each anchor type. +> [!IMPORTANT] +Units within FAModel follow the SI exclusively. The input soil parameters units follow common industry convention. Soil parameters conversion units to Pa and N/m³ take place in the capacity_soils module exclusively. There is no need to convert units. + profile_map = [ + { + 'location_name': 'CPT_1', + 'x': 498234, 'y': 5725141, + 'layers': [ + { + 'top': 1.0, 'bottom': 6.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.0, + 'Su_top': 10, 'Su_bot': 50}, + { + 'top': 6.0, 'bottom': 15.0, + 'soil_type': 'sand', + 'gamma_top': 8.0, 'gamma_bot': 8.0, + 'phi_top': 32, 'phi_bot': 38, + 'Dr_top': 70, 'Dr_bot': 75}, + { + 'top': 15.0, 'bottom': 30.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 9.0, + 'Su_top': 100, 'Su_bot': 200}] + } + ] + Note: + - z0 = 1 m, meaning the pile head is 1 m above the mudline + - soil_type: clay, sand, clay + - this method allows different soil types and gaps in between soil layers of the same or different soil type +##### Output +- z0: depth of the mudline relative to the pile head (m) +- soil types: + - clay/mud: + - f_gamma: effective unit soil weigtht at depth (N/m³) + - f_Su: undrained shear strength at depth (Pa) + - f_sigma_v_eff: effective vertical stress at depth (Pa) + - f_alpha: adhesion factor from API correlation (-) + - sand: + - f_gamma: effective unit soil weigtht at depth (N/m³) + - f_phi: friction angle at depth (deg) + - f_sigma_v_eff: effective vertical stress at depth (Pa) + - f_Dr: relative density at depth (%) + - f_delta: skin friction angle at depth (-) + - rock: + - f_UCS: unconfined compressive strength at depth (Pa) + - f_Em: rock mass modulus at depth (deg) +------------------------------------------------------------------------------ -## Anchor Capacity Modules -The following list shows the required soil conditions, soil properties, geometry, and load information for anchor capacity calculations of each anchor type. Soil classification for clay and sand can be found in [Soil Classification Parameters](#soil-classification-parameters). +Soil classification for clay, sand and rock can be found in [Soil Classification Parameters](#soil-classification-parameters). >[!NOTE] ->Some anchor capacity functions require input loads at the anchor lug point. These loads can be sent in to the getAnchorCapacity() method, or the getAnchorCapacity() method will calculate the loads by calling getLugLoads(). The input loads must be maximum or large loads on the anchor. - -### DEA/SEPLA/DEPLA/VLA/plate - - soil condition: clay/mud - - Su0 (undrained shear strength of soil at mudline) [kPa] - - k (rate of change in shear strength with depth) [kPa/m] - - gamma (submerged soil unit weight) [kN/m^3] - - geometry - - A (area of plate) [m^2] - - zlug (embedded depth of bridle/padeye below mudline - positive is below mudline, negative is above mudline) [m] - - beta (OPTIONAL - angle of plate after keying) [deg] - - loads: None -### suction_pile (Suction caisson/ suction bucket anchors) - - soil conditions - - sand - - phi (internal friction angle) [deg] - - Dr (relative density) [%] - - delta (interface friction angle at soil-anchor line) [deg] ***only needed for loads calculation* - - clay/mud - - Su0 (undrained shear strength of soil at mudline) [kPa] - - k (rate of change in shear strength with depth) [kPa/m] - - alpha (adhesion factor) [-] - - geometry - - L (length of pile) [m] - - D (diameter of pile) [m] - - zlug (embedded depth of padeye below mudline) [m] - - loads - - Ha, Va (horizontal and vertical loads on padeye of anchor) -### torpedo_pile (Torpedo pile anchors) - - soil condition: clay/mud - - Su0 (undrained shear strength of soil at mudline) [kPa] - - k (rate of change in shear strength with depth) [kPa/m] - - alpha (adhesion factor) [-] - - geometry - - D1 (wing diameter) [m] - - D2 (shaft diameter) [m] - - L1 (wing length) [m] - - L2 (shaft length) [m] - - zlug (embedded depth of padeye below mudline) [m] - - loads: None -### helical_pile (Helical pile anchors) - - soil conditions - - sand - - phi (internal friction angle) [deg] - - gamma (submerged soil unit weight) [kN/m^3] - - alpha_star (empirical adhesion factor **can use alpha instead*) [-] - - clay/mud - - Su0 (undrained shear strength of soil at mudline) [kPa] - - k (rate of change in shear strength with depth) [kPa/m] - - gamma (submerged soil unit weight) [kN/m^3] - - alpha_star (empirical adhesion factor **can use alpha instead*) [-] - - geometry - - D (helix diameter) [m] - - L (shaft length) [m] - - d (shaft diameter) [m] - - loads: None -### driven_pile (Driven pile anchors) - - soil conditions: - - weak rock (up to UCS = 5 MPa) - - UCS (unconfined compressive strength at failure) - - Em (rock mass modulus) - - sand - - phi (internal friction angle) [deg] - - gamma (submerged soil unit weight) [kN/m^3] - - Dr (relative density) [%] - - clay/mud - - Su0 (undrained shear strength of soil at mudline) [kPa] - - k (rate of change in shear strength with depth) [kPa/m] - - gamma (submerged soil unit weight) [kN/m^3] - - geometry - - L (length of pile) [m] - - D (diameter of pile) [m] - - zlug (embedded depth of padeye below mudline) [m] - - loads - - Ha, Va (horizontal and vertical loads on padeye of anchor) - - #### Output notes - The general output is a lateral and rotational displacement or bending moment. In getAnchorCapacity, the driven pile capacity function is called in a while loop with incremented horizontal input forces until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function. +>Some anchor capacity functions require input loads at the anchor lug point. These loads can be sent in to the getLugLoads() method, or the getAnchorCapacity() method will calculate the loads by calling getLugLoads(). +The input loads must be maximum or large loads on the anchor. + +### Soil classification parameters + +The soft, medium and hard clay soil classes are distinguished by the following parameter ranges: +| clay/mud | N-Value | Eff. unit weight, gamma (kN/m³) | Void ratio, e (-) | Water content, (%) | Undrained shear strength, Su (kN/m2) | +|:-----------------:|:--------:|:---------------------------------:|:----------:|:--------------------------------------:|:-------------------------------:| +| Very Soft | 0 - 2 | 5.5 - 8.5 | 0.9 - 1.4 | 30 - 50 | 0 - 12.5 | +| Soft | 2 - 4 | 5.5 - 8.5 | 0.9 - 1.4 | 30 - 50 | 12.5 - 25 | +| Medium | 4 - 8 | 5.5 - 8.5 | 0.9 - 1.4 | 30 - 50 | 25 - 50 | +| Stiff | 8 - 15 | 8.5 - 12 | ~ 0.6 | 20 - 30 | 50 - 100 | +| Very Stiff | 15 - 30 | 8.5 - 12 | ~ 0.6 | 20 - 30 | 100 - 200 | +| Hard | < 30 | 8.5 - 12 | ~ 0.6 | 20 - 30 | > 200 | + + +Sand can also be classified ranging from soft to hard. and are chracterize by the following ranges: + +| sand | N-Value | Eff. unit weight, gamma (kN/m³) | Void ratio, e (-) | Water content, (%)| Eff. friction angle, phi (deg) | Relative density, Dr (%) | +|:----------------:|:--------:|:----------------------------:|:----------:|:--------------------------------------:|:-------------------:|:--------------------:| +| Very Loose | > 4 | 7 - 9.5 | ~ 0.8 | 25 - 30 | < 30 | < 15 | +| Loose | 4 - 10 | 7 - 9.5 | ~ 0.8 | 25 - 30 | 30 - 35 | 15 - 35 | +| Compact | 10 - 30 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | 35 - 40 | 35 - 65 | +| Dense | 30 - 50 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | 40 - 45 | 65 - 85 | +| Very Dense | < 50 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | > 45 | > 85 | + +## Anchor Types +The supported anchor types are listed below with their associated FAModel names in italics. Anchors types have specific [anchor capacity](#anchor-capacity-modules) and [anchor installation](#anchor-installation-modules) application modules, these are shown for clarity below as well. + +| | Capacity | Installation | +|--------------------------------------------------------|----------------|--------------| +|*DEA* (drag-embedment anchors) | Plate | Drag | +|*SEPLA* (suction embedded plate anchors) | Plate | Suction | +|*DEPLA* (dynamically embedded plate anchors) | Plate | Dynamic | +|*VLA* (vertically loaded anchors) | Plate | Drag | +|*suction* (suction caisson/suction bucket anchors) | Suction | Suction | +|*torpedo* (torpedo pile anchors) | Torpedo | Dynamic | +|*helical* (helical pile anchors) | Helical/Driven | Torque-crowd | +|*driven* (driven pile anchors) | Driven | Driven | +|*dandg* (drilled and grouted pile anchors) | Drilled&Grout | Drilled | + +### Anchor geometrical properties +#### DEA/SEPLA/DEPLA/VLA/plate +##### Short definition of the anchor concepts included in plates. Variables involved in the design: +- soil condition: + - z, gamma, Su: clay soil parameters (m), (kN/m3), (kPa) +- geometry: + - B: width of plate - dimension contained in the vertical plane (m) + - L: length of plate - dimension perpendicular to the vertical plane (m) + - zlug: embedded depth of bridle/padeye below mudline (m) + - beta: angle of plate with horizontal plane (deg) +- loads: + - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N) - For non-rock soil, the hinge (bending moment) is also considered as a failure mode along with the lateral and rotational displacement *IF* the zlug is positive (below the mudline) -### dandg_pile (Drilled and grouted piles) - - soil condition: rock - - UCS (unconfined compressive strength at failure) - - Em (rock mass modulus) - - geometry - - L (length of pile) [m] - - D (diameter of pile) [m] - - zlug (lug location (lug above mudline has negative zlug)) [m] - - loads - - Ha, Va (horizontal and vertical loads on padeye of anchor) -#### Output notes - The general output is a lateral and rotational displacement. In getAnchorCapacity, the drilled and grouted pile function is called in a while loop with incremented horizontal input forces until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function. +#### suction_pile (suction caisson/ suction bucket anchors) +##### Short definition of the suction anchor. Variables involved in the design: +- soil condition: + - location_name: + - x, y: CPT or reference name + - layers: + - z, gamma, Su: clay soil parameters (m), (kN/m3), (kPa) + - z, gamma, phi, Dr: sand soil parameters (m), (kN/m3), (deg), (%) + +- geometry: + - D: diameter of pile (m) + - L: length of pile (m) + - zlug: embedded depth of padeye below mudline (m) +- loads: + - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N) + +#### torpedo_pile (torpedo pile anchors) +##### Short definition of the suction anchor. Variables involved in the design: +- soil condition: + - z, gamma, Su: clay soil parameters () +- geometry + - D1: wing diameter (m) + - D2: shaft diameter (m) + - L1: wing length (m) + - L2: shaft length (m) + - zlug: embedded depth of padeye below mudline (m) +- loads + - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N) + +#### helical_pile (helical pile anchors) +##### Short definition of the helical anchor. Variables involved in the design: +- soil condition: + - z, gamma, Su: clay soil parameters (m), (kN/m3), (kPa) + - z, gamma, phi, Dr: sand soil parameters (m), (kN/m3), (deg), (%) +- geometry + - D: helix diameter (m) + - L: shaft length (m) + - d: shaft diameter (m) + - zlug: embedded depth of padeye below mudline (m) +- loads + - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N) + +#### driven_pile (driven pile anchors) +##### Short definition of the helical anchor. Variables involved in the design: +- soil condition: + - z, gamma, Su: clay soil parameters (m), (kN/m3), (kPa) + - z, gamma, phi, Dr: sand soil parameters (m), (kN/m3), (deg), (%) + - z, UCS, Em: (weak) rock parameters (m), (MPa), (MPa) +- geometry + - L: length of pile (m) + - D: diameter of pile (m) + - zlug: embedded depth of padeye below mudline (m) +- loads + - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N) + +> [IMPORTANT!] The general output is a lateral and rotational displacement. In getCapacityAnchor, the driven pile capacity function is called in a while loop with variable (increase or decrease) input geometrical properties until at least one of the accepting criteria past set failure criteria, thus providing a horizontal force capacity output. + + > [NOTE!] For non-rock soil, the hinge (bending moment) is also considered as a failure mode along with the lateral and rotational displacement -------------------------------------------------------------------------------- -> [!IMPORTANT] -> A positive zlug denotes a lug/padeye/bridle below the mudline, while a negative zlug denotes a lug/padeye/bridle above the mudline. Anchors in rock should have a zlug >= 0. +#### dandg_pile (drilled and grouted pile anchors) +##### Short definition of the helical anchor. Variables involved in the design: +- soil condition: + - z, UCS, Em: (weak) rock parameters (m), (MPa), (MPa) +- geometry + - L: length of pile (m) + - D: diameter of pile (m) + - zlug: lug location (m) +- loads + - Ha, Va: horizontal and vertical loads on padeye of anchor + +## Loads +Loads derived from MoorPy and DRAFT are considered at a fixed point at mudline elevation. These loads need to be transfered from mudline to lug penetration when the main padeye is below the mudline. The transfer function uses soil properties (profile), mooring line properties (line_type, d and w), loads and lueg depth (zlug) to calculate loads at lug elevation (main padeye) + +> [!NOTE] It is cautious to condiser as input the tension load magnitude at mudline since the load will be equal or larger to the tension at lug penetration. Conversely, the angle of the load at lug penetration will equal or larger to the angle at mudline. Therefore, yielding to more vertical componenent. Therefore, Tm >= Ta and thetam <= thetaa + +##### Input +- profile_map: soil profile +- Tm: tension of the load on mudline (N) +- thetam: angle of the load on mudline (deg) +- zlug: main padeye embeddment (m) +- line_type: type of mooring line ('chain' or 'wire') (-) +- d: mooring line diameter (m) +- w: mooring line unit weight (N/m) + +##### Output +- Ta: tension of the load on padeye of anchor (N) +- thetaa: angle of the load on padeye of anchor (deg) +- Ha: horizontal component of the load on padeye (N) +- Va: vertical component of the load on padeye (N) +- length: length of the embedded line (m) +- drag: depth of the embedded line (m) + + +The getTransferLoad function expects maximum mudline forces as input. These can be: + + - Passed directly as a dictionary + - Retrieved from the anchor.loads dictionary using the keys 'Hm' and 'Vm', with the flag 'mudline_force_type': 'max'. + +If no such values are found, getMudlineForces(max_force=True) will be called automatically to obtain them. + +When the lug is located at the mudline, or no lug depth is specified, the function assumes lug forces are equal to mudline forces. + +>[!NOTE] See getLugForces() (#anchor-capacity-modules) for more details on the load transfer mechanism from mudline to lug elevation (i.e., below the seabed). + +The getTransferFunction, used internally by getLugForces, is calibrated for **maximum load conditions**. In some cases—depending on anchor geometry, load magnitude, and soil conditions—the function may produce invalid results (e.g., negative vertical load Va). +When this occurs, getLugForces() issues a warning and defaults to assigning **lug forces equal to mudline forces.** + +>[!NOTE] Some anchor capacity functions (e.g., suction, driven, helical) require loads to be applied at the lug elevation. These can be passed directly into getCapacityAnchor(), or if not provided, the method will internally compute them using getLugForces(). + +Always ensure that the loads used in these methods represent maximum or near-maximum force levels to ensure valid and conservative capacity estimates. + + + +## Equipment + +### Installation vessel +#### Pullard force +Drag installation. Input + - Tmax: maximum pullard force (N) + +#### Crane capacity +Dynamic installation. Output + - Wp: dynamically installed plate/pile weight (N) + +### Installation device +#### Suction pump +Suction installation. Output + - delta_u_suction: maximum underpressure given by the suction pump during installation (Pa) + - delta_u_retrieve: maxumum overpressure given by the suction pump during retrieval/removal (Pa) -> [!NOTE] -> Load inputs to the capacity functions (with the exception of driven & drilled and grouted anchors) are in kN, while the anchor loads dictionary is in N. This conversion is automatically completed in the getAnchorCapacity() function so no manual load conversion is required. Load outputs are automatically converted in the getAnchorCapacity function where necessary. +#### Hydraulic drive head +Torque-crowd installation. Output + - Torque: torque (Nm) + - Force: crowd compressive force (N) + +#### Hammer +Driven installation. Input + - hammer_params: + - r_m: ram mass (kg) + - h: strock height (m) + - efficiency: efficiency of the hammer (-) + +#### Drill head +Drilled installation ----------------------------------------------------------------------------- -### Model Fidelity + +## Anchor Class +The anchor class contains properties and methods related to mooring anchors. -There are two levels of fidelity in these models: +The anchor class stores properties and methods that enable a wide range of modeling. +The [anchor capacity modules](#anchor-capacity-modules) and the [anchor installation modules](#anchor-installation-modules) are integrated with FAModel through the anchor class and its methods. + +### Anchor modules +Introduction + +Inspection of the folder: anchors_famodel -- Level 1 basic models are soil-dependent capacity curves for a - range of anchor types based on performing curve fits to - published information in anchor manuals and standards. -- Level 2 intermediate models are quantitative calculations for - suction caissons and plate anchors that account for quantitative - soil properties as well as their variation with embedment depth. +| | | | +|----------------------------------------------|----------------|--------------| +|![Plate anchor](images/Plateanchors/Plate.png)|![Suction pile anchor](images/Suctionpiles/Suction.png)|![Torpedo pile anchor](images/Torpedopiles/Torpedo.png)| +|![Helical pile anchor](images/Helicalpiles/Helical.png)|![Driven pile anchor](images/Drivenpiles/Driven.png)|![Drilled and grouted pile anchor](images/Drilledandgroutedpiles/Drilled.png)| -This plot gives an example of the capacity curves that can be -produced by the intermediate model (holding capacity for a suction -embedded plate anchor) as a function of surface shear strength: -![Capacities](images/SEPLA_curves_small.PNG) +#### Anchor capacity modules +Analytical static capacity models for extreme load conditions. These models include static soil-structure interaction but the cyclic loading conditions are not covered yet. They will need to follow from further research. -### Implemented level-1 model anchor and soil types +- **capacity_plate** : + - getCapacityPlate(profile_map, location_name, D, L, zlug, Ha, Va, plot) + - capacityPlate dict: + - 'Capacity' + - 'Horizontal max.', 'Vertical max.' + - 'Unity check' + - 'Weight plate' -| | DEA | Suction | VLA | SEPLA | -|-------------|-----------|---------|-----|-------| -| Soft clay | X | X | X | X | -| Medium clay | X | X | X | X | -| Hard clay | X | | | | -| Sand | X | | | | +- **capacity_suction** : + - getCapacitySuction(profile_map, location_name, D, L, zlug, Ha, Va, thetalug, psilug, plot) + - capacitySuction dict: + - 'Horizontal max.', 'Vertical max.' + - 'Unity check' + - 'Weight pile' + +- **capacity_torpedo** : + - getCapacityTorpedo(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot) + - capacityTorpedo dict: + - 'Horizontal max.', 'Vertical max.' + - 'Unity check' + - 'Weight pile' -### Parameters needed for level-2 anchor capacity models +- **capacity_helical** : + - getCapacityHelical(profile_map, location_name, D, L, d, zlug, Ha, Va, plot) + - capacityHelical dict: + - 'Horizontal max.', 'Vertical max.' + - 'Lateral displacement', 'Rotational displacement' + - 'Bending moment' + - 'Plastic moment' + - 'Unity check (vertical)', 'Unity check (horizontal)' + - 'Weight pile' -| **Anchor type** | **Suction** | **Suction** | **VLA** | **SEPLA** | -|------------------------|-------------|-------------|----------|-----------| -| **Soil type** | **Clay** | **Sand** | **Clay** | **Clay** | -| **Anchor parameters** | | | | | -| Diameter | x | x | | | -| Length | x | x | | | -| Area | | | X | X | -| Thickness | ratio | ratio | ratio | ratio | -| Embedment depth | | | X | X | -| **Soil parameters** | | | | | -| gamma | X | X | X | X | -| Su0 | X | | X | X | -| k | X | | X | X | -| alpha | X | | | | -| phi | | X | | | +- **capacity_driven** : + - getCapacityDriven(profile_map, location_name, L, D, zlug, Ha, Va, plot) + - capacityDriven dict: + - 'Horizontal max.', 'Vertical max.' + - 'Lateral displacement', 'Rotational displacement' + - 'Bending moment' + - 'Plastic moment' + - 'Unity check (vertical)', 'Unity check (horizontal)' + - 'Weight pile' + +- **capacity_dandg** : + - getCapacityDandG(profile_map, location_name, L, D, zlug, Ha, Va, plot) + - capacityDandG dict: + - 'Horizontal max.', 'Vertical max.' + - 'Lateral displacement', 'Rotational displacement' + - 'Bending moment' + - 'Plastic moment' + - 'Unity check (vertical)', 'Unity check (horizontal)' + - 'Weight pile' +- **capacity_load** : + - getTransferFunction(profile_map, Tm, thetam, zlug, line_type, d, w, plot) + - capacityLoads dict: + - 'Tm', 'thetam' + - 'Hm', 'Vm' + - 'Ta', 'thetaa' + - 'Ha', 'Va' + - 'length' + - 'drag_values' + - 'depth_values' -These models will continue to be expanded as data sources and time permit. +#### Anchor installation modules +Analytical installation models for main anchor types. -## Soil Classification Parameters +- **installation_drag** : + - getInstallationPlate(profile_map, location_name, B, Lf, Ls, Lca, Lj, plot) + - installationDrag dict: + - 'Capacity' + - 'embedment_depth' + - 'drag_distance' + - 'Weight plate' -The soft, medium, and hard clay soil classes are distinguished by the following parameter ranges: -| Soil Type (Clay) | N-Value | Effective Sat. Unit Weight, kN/m3 | Void Ratio | Natural Water Content in Sat. State, % | Undrained Shear Strength, kN/m2 | -|:-----------------:|:--------:|:---------------------------------:|:----------:|:--------------------------------------:|:-------------------------------:| -| Very Soft | 0 - 2 | 5.5 - 8.5 | 0.9 - 1.4 | 30 - 50 | 0 - 12.5 | -| Soft | 2 - 4 | 5.5 - 8.5 | 0.9 - 1.4 | 30 - 50 | 12.5 - 25 | -| Medium | 4 - 8 | 5.5 - 8.5 | 0.9 - 1.4 | 30 - 50 | 25 - 50 | -| Stiff | 8 - 15 | 8.5 - 12 | ~ 0.6 | 20 - 30 | 50 - 100 | -| Very Stiff | 15 - 30 | 8.5 - 12 | ~ 0.6 | 20 - 30 | 100 - 200 | -| Hard | < 30 | 8.5 - 12 | ~ 0.6 | 20 - 30 | > 200 | +- **installation_suction** : + - getInstallationSuction(profile_map, location_name, D, L, plot) + - installationSuction dict: + - 'Fi', 'Fo', 'Qw' + - 'Rsuction', 'Rretrieval' + - 'SWP_depth' +- **buckling_suction** : + - getBucklingSuction(profile_map, location_name, D, L, plot) + - installationBuckling dict: + - 'UC' + - 'PE' -Sand can also be classified ranging from soft to hard, however only a single sand class is supported at this time. In the future, sand classes will follow the parameter ranges in the following table: +- **installation_dynamic** : + - getInstallationTorpedo(profile_map, location, D1, D2, L1, L2, ballast, drop_height, plot) + - installationDynamic dict: + - 'final_depth' + - 'v_max', 'v_impact' + +- **installation_torque** : + - getInstallationHelical(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot) + - installationTorque dict: + - 'Force', 'Torque' + - 'sigma_helix', 'sigma_core', 'sigma_weld' + - 'failire_mode' + +- **installation_driven** : + - getInstallationDriven(profile_map, location_name, D, L, hammer_params, J_shaft, J_toe, plot) + - installationDriven dict + +- **installation_drill** : + - getInstallationDrill(profile_map, location_name, D, L, driller_params, plot) + - installationDrill dict + +#### Anchor support modules + +- **anchor_soils** : + - clay_profile(profile) + - **return:** z0, f_gamma, f_Su, f_sigma_v_eff, f_alpha + - sand_profile(profile) + - **return:** z0, f_gamma, f_phi, f_Dr, f_sigma_v_eff, f_delta + - rock_profile(profile) + - **return:** z0, f_UCS, f_Em + +- **anchor_solvers** : + - fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant) + - **return:** y, Mi, Mp, hinge_formed, hinge_location + +- **anchor_pycurves** : + - py_Matlock(z, D, Su, sigma_v_eff, gamma, z0, return_curve) + - **return:** f, (y, p) + - py_API(z, D, phi, sigma_v_eff, Dr, z0, return_curve) + - **return:** f, (y, p) + - py_Reese(z, D, UCS, Em, z0, return_curve) + - **return:** f, (y, p) + - py_Lovera(z, D, UCS, Em, z0, delta_grout, E_grout, delta_crushed, return_curve) + - **return:** f, (y, p) + +- **anchor_plots** : + - plot_pile(layers, y, z, D, L, z0, zlug, hinge_location) + - plot_suction(layers, L, D, z0, zlug) + - plot_suction(layers, D1, D2, L1, L2, z0, zlug) + - plot_helical(layers, D, L, d, z0, zlug, n_helix, spacing) + - plot_plate(layers, B, L, z0, zlug, beta) + - plot_load(layers, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug) + - plot_pycurve(pycurve_data) + +### Anchor class methods + +- **Anchor.setSoilProfile(profile_map)** + + Assign a soil profile directly to the anchor from a single CPT (Cone Penetration Test) record. + + This method sets the internal soil_profile, extracts soil types, and organizes layer properties by soil type. It assumes that the input contains only one CPT entry. + + **Parameters** + - **profile_map** : list of dict. A list containing exactly one dictionary representing a CPT profile. The dictionary has: + - 'location_name': a string indicating the name of the CPT + - 'x', 'y': coordinates of the CPT location + - 'layers': a list of layer dictionaries, each with a 'soil_type' key and relevant soil parameters. + + **Raises** + - **ValueError** : If profile_map contains more than one CPT. + + **Attributes Updated** + - **self.soil_profile** : list of dict. Stores the soil layers from the CPT. + - **self.profile_name** : str. Name of the CPT, default is "CPT_Assigned" if not provided. + - **self.soil_type_list** : list of str. Unique soil types present in the profile. + - **self.soil_type** : str. If a single soil type is present, its name; otherwise, 'mixed'. + - **self.soilProps** : dict. Dictionary grouping layer properties (excluding soil_type) by soil type. + +- **Anchor.interpolateSoilProfile(profile_map)** + + Interpolate a soil profile for the anchor location from the four nearest CPTs using inverse distance weighting. + + This method assumes all CPTs share the same layer structure. Each soil parameter is interpolated layer-by-layer based on the relative proximity of the CPTs to the anchor. + + **Parameters** + - **profile_map** : list of dict. A list containing at least four CPT profile dictionaries. Each dictionary has: + - 'location_name': a string indicating the name of the CPT + - 'x', 'y': coordinates of the CPT location + - 'layers': a list of layer dictionaries, each with a 'soil_type' key and relevant soil parameters. + + **Raises** + - **ValueError** : If fewer than four CPTs are provided in profile_map. + + **Attributes Updated** + - **self.soil_profile** : list of dict. Stores the soil layers from the CPT. + - **self.profile_name** : str. Set to "Interpolated_2D". + - **self.soil_type_list** : list of str. Unique soil types present in the interpolated profile. + - **self.soil_type** : str. If a single soil type is present, its name; otherwise, 'mixed'. + - **self.soilProps** : dict. Dictionary grouping layer properties (excluding soil_type) by soil type. + +- **Anchor.makeMoorPyAnchor(ms)** + + Create and register a MoorPy anchor point within the given MoorPy system using the anchor's location and design properties. + + The anchor is added as a fixed point in the MoorPy model (Point object) and its mass and diameter are assigned if available. The method also sets the point type based on the anchor type. + + **Parameters** + - **ms** : MoorPy system instance. The MoorPy system in which the anchor will be created. + + **Attributes Updated** + - **self.mpAnchor** : MoorPy Point. Reference to the created MoorPy anchor point in the system. + +- **Anchor.getLineProperties()** + + Retrieve the mooring line type, diameter and unit weight from the anchor's attached mooring object. + + This method inspects the attached Mooring object and extracts relevant line properties from its first section. If no chain is present, the method assumes no load transfer below the mudline and returns None for diameter and weight. + + **Parameters** + - **line_type** : str. Type of mooring line (e.g., 'chain', 'wire'). + - **d** : float or None. Nominal diameter of the mooring line (m) or None if not applicable. + - **w** : float or None. Unit weight of the mooring line (N/m) or None if not applicable. + + **Raises** + - **ValueError** : If no mooring line attachment is found for the anchor. + +- **Anchor.getMudlineForces(max_force=False, lines_only=False, seabed=True, xyz=False, project=None)** + + Compute the forces acting on the anchor at the mudline, either from the current state of the MoorPy system or as the maximum expected force based on platform excursion. + + If max_force=True, the method retrieves the extreme load on the anchor using either the provided project’s arrayWatchCircle() method or the attached platform’s getWatchCircle() method. Otherwise, it calculates the current forces using MoorPy's getForces(). + + **Parameters** + - **max_force** : bool, optional. If True, compute the maximum expected mudline force based on platform excursion. Default is False. + - **lines_only** : bool, optional. If True, considers only mooring line forces (ignores seabed and body effects). Default is False. + - **seabed** : bool, optional. If True, includes seabed reaction force in the calculation. Default is True. + - **xyz** : bool, optional. If True, returns forces in the x, y, z directions. Otherwise returns only the relevant DOFs. Default is False + - **project** : bool, optional. Project object with a arrayWatchCircle() method. Used to compute global platform excursions when max_force=True. + + **Attributes Updated** + - **self.loads** : dict. Stores the computed mudline force components and metadata: + - 'Hm': horizontal force magnitude at mudline (N) + - 'Vm': vertical force at mudline (N) + - 'thetam': angle of applied load at mudline (deg) + - 'method': load computation method (currently 'static') + - 'mudline_load_type': 'current_state' or 'max_force', depending on the mode used + +- **Anchor.getLugForces(Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False)** + + Calculate the horizontal and vertical loads at the anchor lug (Ha, Va) based on the mudline loads and the load transfer profile along the mooring line. + + If the padeye depth zlug is embedded below the mudline, the method computes the lug loads using the load transfer model. Otherwise, it assigns the mudline loads directly to the lug. + + **Parameters** + - **Hm** : float. Horizontal mudline load (N). + - **Vm** : float. Vertical mudline load (N). + - **zlug** : float. Padeye embedment depth (m). + - **line_type** : str, optional. Type of mooring line ('chain' or 'wire'). If not provided, inferred from attachments or defaults to 'chain'. + - **d** : float, optional. Mooring line diameter (m). + - **w** : float, optional. Mooring line unit weight (N/m). + - **plot** : bool, optional. If True, generates plots of load transfer and geometry. Default is False. + + **Raises** + - **ValueError** : If the soil profile is not assigned to the anchor before calling this method. + + **Attributes Updated** + - **self.anchorCapacity** : dict. Stores the anchor's computed capacity results, including: + - 'Hmax': maximum horizontal capacity (N). + - 'Vmax': maximum vertical capacity (N). + - 'Ha','Va': lug-level horizontal and vertical loads (N). + - 'UC' or 'Unity check (horizontal)', 'Unity check (vertical)' : capacity utilization checks. + - 'Lateral displacement', 'Rotational displacement' : optional displacement results (if available) + - 'Weight pile', 'Weight plate' : self-weight of pile or plate depending on type. + - 'zlug' : lug depth (m), and 'z0' : mudline reference elevation (m) + +- **Anchor.getCapacityAnchor(Hm, Vm, zlug, line_type=None, d=None, w=None, mass_update=False, plot=False)** + + Calculate the load capacity of the anchor based on its type, geometry and local soil profile. + + This method computes the anchor resistance against applied horizontal and vertical loads using the appropriate capacity model for the anchor type. It optionally performs load transfer from the mudline to the lug and updates the anchor's capacity results. + + **Parameters** + - **Hm** : float. Horizontal mudline load (N). + - **Vm** : float. Vertical mudline load (N). + - **zlug** : float. Padeye embedment depth (m). + - **line_type** : str, optional. Type of mooring line ('chain' or 'wire'). If not provided, inferred from attachments or defaults to 'chain'. + - **d** : float, optional. Mooring line diameter (m). + - **w** : float, optional. Mooring line unit weight (N/m). + - **mass_update** : bool, optional. If True, updates the anchor's weight based on computed capacity. Default is False. + - **plot** : bool, optional. Whether to generate a plot of the load transfer profile. Default is False. + + **Raises** + - **ValueError** : If the anchor type is unknown or if the soil profile is not properly assigned. + + **Attributes Updated** + - **layers** : list of dict. The soil profile layers used in the load transfer calculation. + - **Ha** : float. Horizontal load at the lug (N). + - **Va** : float. Vertical load at the lug (N). + +- **Anchor.getSizeAnchor(geom, geomKeys, geomBounds=None, loads=None, lambda_con=[3, 6], zlug_fix=True, safety_factor={'SF_combined':1.0}, plot=False)** + + Generalized optimization method to determine the appropriate geometry for all anchor types based on load conditions and safety factor requirements. + + For suction, torpedo, and plate-type anchors, the method minimizes the difference between the calculated and target Unity Check (UC). For driven, helical, and dandg anchors, the method iteratively searches for the smallest geometry that satisfies combined UC, lateral and rotational displacement limits. + + **Parameters** + - **geom** : : list of float. Initial geometry values (e.g., [L, D]). + - **geomKeys** : list of str. Keys that define the geometry variables to optimize (e.g., ['L', 'D']). + - **geomBounds** : list of tuple, optional. Bounds for geometry values, e.g., [(5.0, 20.0), (1.0, 4.0)]. + - **loads** : dict, optional. Dictionary containing mudline forces ('Hm', 'Vm'). Defaults to self.loads. + - **lambdap_con** : list of float, optional. Minimum and maximum slenderness ratio constraints [L/D_min, L/D_max]. Default is [4, 8]. + - **zlug_fix** : float, optional. If False, allows zlug to vary with geometry. Default is True. + - **safety_factor** : bool, optional. Dictionary with safety factor targets (e.g., {'SF_combined': 1.5}). Default is {'SF_combined': 1.0}. + - **plot** : bool, optional. If True, generates plots the final capacity results. Default is False. + + **Raises** + - **ValueError** : If the anchor type is not supported for this optimization method. + + **Attributes Updated** + - **self.dd['design']** : list of dict. The soil profile layers used in the load transfer calculation. + - **self.anchorCapacity** : float. Horizontal load at the lug (N). + - Ha, Va : float. Lug loads (N). + - Ha, Va : float. Lug loads (N). + - UC or 'Unity check (horizontal)', 'Unity check (vertical)' + - Optional displacements and weights if applicable + +- **Anchor.getSafetyFactor(ms=None)** + + Estimate the material cost of the anchor using the MoorPy Point object and its getCost_and_MBL() method. + + If no MoorPy system is provided, the method initializes one and registers the anchor. Cost is based on mass and design parameters, and the Minimum Breaking Load (MBL) is also computed. + + **Parameters** + - **ms** : MoorPy system instance, optional. If provided, uses the existing system. Otherwise, creates a new MoorPy system internally. + + **Raises** + - **KeyError** : If self.mass is not defined and neither 'Weight pile' nor 'Weight plate' is available in self.anchorCapacity. This typically indicates that getCapacityAnchor() has not been called before getCostAnchor(). + + **Attributes Updated** + - **self.cost** : dict. Stores cost-related metrics for the anchor: + - 'Material cost' : Estimated anchor material cost. + - 'MBL' : Minimum Breaking Load (from MoorPy). + - 'unit_cost' : Cost per unit mass (cost/mass). + - **self.mpAnchor.m** : float. Mass of the MoorPy anchor point (assigned if self.mass is not already set). + +### Anchor Object Properties + +- **r** : anchor [x, y, z] position +- **dd** : anchor design dictionary, containing geometric properties, soil properties at the anchor location, cost +- **ms** : MoorPy system associated with this anchor point +- **aNum** : anchor index in array mooring list (generally only used for shared moorings) +- **mpAnchor** : MoorPy point object that models this anchor +- **anchorCapacity** : dictionary with horizontal and vertical capacity of the anchor. Generally these are loads in [N], but can also be displacements (generally for driven or drilled and grouted piles) +- **loads** : dictionary of loads on the anchor, and the method used to obtain these loads (static or dynamic modeling). +Loads include mooring line tension (T) with the angle of the load (theta) as well as horizontal (H) and vertical (V) loads components. +The keys for these loads will either include an m (for loads at the mudline) or a (for loads at the anchor lug / main padeye). +- **soilProps** : dictionary of soil property information at the location of the anchor +- **failure_probability** : dictionary of probabilities for failure of the anchor + +### Anchor Type Requirements + +Different geometric properties and soil conditions are needed for each anchor type. See the [Anchor Capacity Modules](#anchor-capacity-modules) section for details on the requirements of each anchor type. + +## Anchor Capacity Modules +The following list shows the required soil conditions, load information and geometrical properties involved in the anchors' capacity calculations. + + +#### Output notes + The general output is a lateral and rotational displacement. In getAnchorCapacity, the drilled and grouted pile function is called in a while loop with incremented horizontal input forces + until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function. + -| Soil Type (sand) | N-Value | Eff. Sat. Unit Weight, kN/m3 | Void Ratio | Natural Water Content in Sat. State, % | Eff. friction Angle | Relative density (%) | -|:----------------:|:--------:|:----------------------------:|:----------:|:--------------------------------------:|:-------------------:|:--------------------:| -| Very Loose | > 4 | 7 - 9.5 | ~ 0.8 | 25 - 30 | < 30 | < 15 | -| Loose | 4 - 10 | 7 - 9.5 | ~ 0.8 | 25 - 30 | 30 - 35 | 15 - 35 | -| Compact | 10 - 30 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | 35 - 40 | 35 - 65 | -| Dense | 30 - 50 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | 40 - 45 | 65 - 85 | -| Very Dense | < 50 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | > 45 | > 85 | -Conversion functions are under development to switch between categories (level 1 anchor models) -and quantitative soil metrics (level 2 anchor models). \ No newline at end of file diff --git a/famodel/anchors/README_FMO.md b/famodel/anchors/README_FMO.md deleted file mode 100644 index c4498589..00000000 --- a/famodel/anchors/README_FMO.md +++ /dev/null @@ -1,267 +0,0 @@ -# Anchors Library - -This subpackage of FAModel contains the Anchor class as well as modules for analytical static anchor capacity assessment. - -## Anchor Class -The anchor class contains properties and methods related to mooring anchors. - -The anchor class stores properties and methods that enable a wide range of modeling - from capacity to cost to loads, and more. -The [anchor capacity modules](#anchor-capacity-modules) are integrated with the anchor class through the getAnchorCapacity() method. - -### Anchor Files -Introduction -Inspection of the folder: anchors_famodel - -#### Anchor Capacity Files -- **capacity_plate** : getCapacityPlate -- **capacity_suction** : getCapacitySuction -- **capacity_torpedo** : getCapacityTorpedo -- **capacity_driven** : getCapacityDriven -- **capacity_dandg** : getCapacityDandG -- **capacity_helical** : getCapacityHelical -- **capacity_load** : getTransferFunction - -#### Anchor Support Files -- **capacity_soils** : clay_profile, sand_profile and rock_profile -- **capacity_solvers** : fd_solver for non-linear solver -- **capacity_pycurves** : py_Matlock, py_API, py_Reese and py_Lovera -- **capacity_plots** : plot_driven, plot_suction, plot_helical,plot_plate, plot_dandg, plot_load, plot_pycurve - -### Anchor Methods -- **setSoilProfile()** : Assign a bilinearly interpolated soil profile from the 4 nearest CPTs -- **makeMoorPyAnchor()** : Creates a MoorPy point object representing the anchor in a moorpy system -- **getMudlineForces()** : Finds forces on anchor at mudline using MoorPy Point.getForces method. Use max_force=True to obtain the maximum forces on that anchor from the platform.getWatchCircle() method. -For more information on the getWatchCircle() calculations, see the [Platform ReadMe](../platform/README.md). An additional anchor.loads dictionary entry is included to describe the mudline load type. -'mudline_load_type'='max' if max_force=True, and 'mudline_load_type'='current_state' if max_force=False. -- **getLugForces()** : Finds forces at the anchor lug location with getTransferFunction function in capacity_loads.py -- **getCapacityAnchor()** : Calls anchor capacity functions for the correct anchor type using the getLugForces embedded in the method -- **getSizeAnchor()** : Calls sizing anchor functions for the correct anchor type using the getLugForces embedded in the method -- **getFS()** : Computes safety factor for loads on the anchor -- **getCost()** : Finds costs of anchor from MoorProps and stores in design dictionary -- **getCombinedPlot()** : Create a plot showing the anchor and the inverse catenary overlay in the same coordinate system. - -### Anchor Object Properties -- **r** : anchor [x,y,z] position -- **dd** : anchor design dictionary, containing geometric properties, soil properties at the anchor location, cost -- **ms** : moorpy system associated with this anchor point -- **aNum** : anchor index in array mooring list (generally only used for shared moorings) -- **mpAnchor** : moorpy point object that models this anchor -- **anchorCapacity** : dictionary with horizontal and vertical capacity of the anchor. Generally these are loads in [N], but can also be displacements (generally for driven or drilled and grouted piles) -- **loads** : dictionary of loads on the anchor, and the method used to obtain these loads (static or dynamic modeling). -Loads include mooring line tension (T) with the angle of the load (theta) as well as horizontal (H) and vertical (V) loads components. -The keys for these loads will either include an m (for loads at the mudline) or a (for loads at the anchor lug / main padeye). -- **soilProps** : dictionary of soil property information at the location of the anchor -- **failure_probability** : dictionary of probabilities for failure of the anchor - -### Anchor Type Requirements - -Different geometric properties and soil conditions are needed for each anchor type. See the [Anchor Capacity Modules](#anchor-capacity-modules) section for details on the requirements of each anchor type. - -## Anchor Capacity Modules -The following list shows the required soil conditions, load information and geometrical properties involved in the anchors' capacity calculations. -Soil classification for clay, sand and rock can be found in [Soil Classification Parameters](#soil-classification-parameters). ->[!NOTE] ->Some anchor capacity functions require input loads at the anchor lug point. These loads can be sent in to the getAnchorCapacity() method, or the getAnchorCapacity() method will calculate the loads by calling getLugLoads(). -The input loads must be maximum or large loads on the anchor. - -### Soil Properties -Introduction to different soil types -- clay/mud - - gamma (submerged soil unit weight) (kN/m^3) - - Su (undrained shear strength of soil at mudline) (kPa) - -- sand - - gamma (submerged soil unit weight) (kN/m^3) - - phi (internal friction angle) (deg) - - Dr (relative density) (%) - -- rock (weak rock up to UCS = 5 MPa) - - UCS (unconfined compressive strength at failure) (MPa) - - Em (rock mass modulus) (MPa) - -### Soil Classification Parameters - -The soft, medium, and hard clay soil classes are distinguished by the following parameter ranges: -| Soil Type (clay) | N-Value | Eff. Sat. Unit Weight, kN/m3 | Void Ratio | Natural Water Content in Sat. State, % | Undrained Shear Strength, kN/m2 | -|:-----------------:|:--------:|:---------------------------------:|:----------:|:--------------------------------------:|:-------------------------------:| -| Very Soft | 0 - 2 | 5.5 - 8.5 | 0.9 - 1.4 | 30 - 50 | 0 - 12.5 | -| Soft | 2 - 4 | 5.5 - 8.5 | 0.9 - 1.4 | 30 - 50 | 12.5 - 25 | -| Medium | 4 - 8 | 5.5 - 8.5 | 0.9 - 1.4 | 30 - 50 | 25 - 50 | -| Stiff | 8 - 15 | 8.5 - 12 | ~ 0.6 | 20 - 30 | 50 - 100 | -| Very Stiff | 15 - 30 | 8.5 - 12 | ~ 0.6 | 20 - 30 | 100 - 200 | -| Hard | < 30 | 8.5 - 12 | ~ 0.6 | 20 - 30 | > 200 | - - -Sand can also be classified ranging from soft to hard, however only a single sand class is supported at this time. In the future, sand classes will follow the parameter ranges in the following table: - -| Soil Type (sand) | N-Value | Eff. Sat. Unit Weight, kN/m3 | Void Ratio | Natural Water Content in Sat. State, % | Eff. friction Angle | Relative density (%) | -|:----------------:|:--------:|:----------------------------:|:----------:|:--------------------------------------:|:-------------------:|:--------------------:| -| Very Loose | > 4 | 7 - 9.5 | ~ 0.8 | 25 - 30 | < 30 | < 15 | -| Loose | 4 - 10 | 7 - 9.5 | ~ 0.8 | 25 - 30 | 30 - 35 | 15 - 35 | -| Compact | 10 - 30 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | 35 - 40 | 35 - 65 | -| Dense | 30 - 50 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | 40 - 45 | 65 - 85 | -| Very Dense | < 50 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | > 45 | > 85 | - -Conversion Units - -Z0 explanaition on the pile head reference and how for zlug > z0 mooring line is embedded and below the mudline elevation - -### Loads -- Loads at mudline elevation - - Tm, thetam (Tension and angle of the load on mudline) - - Hm, Vm (Horizontal and vertical loads on mudline) -- Loads at mudline elevation - - Ta, thetaa (Tension and angle of the load on padeye of anchor) - - Ha, Va (Horizontal and vertical loads on padeye of anchor) -- Transfer functions: soil properties (profile) mooring line properties (line_type, d and w), loads and zlug - -> [!NOTE] check getLugForces for more details on this transfer function from mudline to lug elevation (below the seabed) -The getTransferLoad function requires **maximum** mudline forces as an input. These forces can be sent in as a dictionary, or anchor.loads dictionary will be searched for 'Hm' and 'Vm' values with additional -key-value pair 'mudline_force_type':'max' to indicate these mudline forces are maximums. - -If there are no max mudline forces in the anchor.loads dictionary, getMudlineForces(max_force=True) will be called. Stores results in loads dictionary. -If lug is at mudline or no lug provided, equates mudline forces with lug forces. ->[!NOTE] ->The getTransferFunction function called by getLugForces() is tuned to work with maximum loads on the anchor. Some anchor configuration, load, and soil condition combinations may produce invalid results in getTransferFunction. -For example, the output Va may show as negative. In that case, getLugForces() will warn the user of the invalidity of the result and assign the anchor lug forces = mudline forces. - -------------------------------------------------------------------------------- -> [!IMPORTANT] -> A positive zlug denotes a lug/padeye/bridle below the mudline, while a negative zlug denotes a lug/padeye/bridle above the mudline. Anchors in rock should have a zlug >= 0. - -> [!NOTE] -> Load inputs to the capacity functions (with the exception of driven & drilled and grouted anchors) are in kN, while the anchor loads dictionary is in N. This conversion is automatically completed in the getAnchorCapacity() -function so no manual load conversion is required. Load outputs are automatically converted in the getAnchorCapacity function where necessary. - ------------------------------------------------------------------------------ - -### Anchor Types -The supported anchor types are below, with the associated FAModel name in italics. -- Plate anchors - - *DEA* (drag-embedment anchors) - - *SEPLA* (suction embedded plate anchors) - - *DEPLA* (dynamically embedded plate anchors) - - *VLA* (vertically loaded anchors) - - *plate* (unspecified plate anchor) -- *suction_pile* (Suction caisson/ suction bucket anchors) -- *torpedo_pile* (Torpedo pile anchors) -- *helical_pile* (Helical pile anchors) -- *driven_pile* (Driven pile anchors) -- *dandg_pile* (Drilled and grouted piles) - -### Anchor geometrical properties -#### DEA/SEPLA/DEPLA/VLA/plate -- soil condition: clay -- geometry - - A (area of plate) (m^2) - - zlug (embedded depth of bridle/padeye below mudline - positive is below mudline, negative is above mudline) (m) - - beta (OPTIONAL - angle of plate with horizontal plane) (deg) -- loads - - Ha, Va (horizontal and vertical loads on padeye of anchor) - -#### suction_pile (Suction caisson/ suction bucket anchors) -- soil condition: clay and sand -- geometry - - L (length of pile) (m) - - D (diameter of pile) (m) - - zlug (embedded depth of padeye below mudline) (m) -- loads - - Ha, Va (horizontal and vertical loads on padeye of anchor) - -#### torpedo_pile (Torpedo pile anchors) -- soil condition: clay -- geometry - - D1 (wing diameter) (m) - - D2 (shaft diameter) (m) - - L1 (wing length) (m) - - L2 (shaft length) (m) - - zlug (embedded depth of padeye below mudline) (m) -- loads - - Ha, Va (horizontal and vertical loads on padeye of anchor) - -#### helical_pile (Helical pile anchors) -- soil condition: clay and sand -- geometry - - D (helix diameter) (m) - - L (shaft length) (m) - - d (shaft diameter) (m) - - zlug (embedded depth of padeye below mudline) (m) -- loads - - Ha, Va (horizontal and vertical loads on padeye of anchor) - -#### driven_pile (Driven pile anchors) -- soil condition: clay, sand and (weak) rock -- geometry - - L (length of pile) (m) - - D (diameter of pile) (m) - - zlug (embedded depth of padeye below mudline) (m) -- loads - - Ha, Va (horizontal and vertical loads on padeye of anchor) - -#### Output notes - The general output is a lateral and rotational displacement or bending moment. In getCapacityAnchor, the driven pile capacity function is called in a while loop with incremented horizontal - input forces until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function. - - For non-rock soil, the hinge (bending moment) is also considered as a failure mode along with the lateral and rotational displacement - -#### dandg_pile (Drilled and grouted piles) -- soil condition: rock -- geometry - - L (length of pile) (m) - - D (diameter of pile) (m) - - zlug (lug location) (m) -- loads - - Ha, Va (horizontal and vertical loads on padeye of anchor) - -#### Output notes - The general output is a lateral and rotational displacement. In getAnchorCapacity, the drilled and grouted pile function is called in a while loop with incremented horizontal input forces - until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function. - - -### Model Fidelity - -There are two levels of fidelity in these models: - -- Level 1 basic models are soil-dependent capacity curves for a - range of anchor types based on performing curve fits to - published information in anchor manuals and standards. -- Level 2 intermediate models are quantitative calculations for - suction caissons and plate anchors that account for quantitative - soil properties as well as their variation with embedment depth. - -This plot gives an example of the capacity curves that can be -produced by the intermediate model (holding capacity for a suction -embedded plate anchor) as a function of surface shear strength: - -![Capacities](images/SEPLA_curves_small.PNG) - -### Implemented level-1 model anchor and soil types - -| | DEA | Suction | VLA | SEPLA | -|-------------|-----------|---------|-----|-------| -| Soft clay | X | X | X | X | -| Medium clay | X | X | X | X | -| Hard clay | X | | | | -| Sand | X | | | | - -### Parameters needed for level-2 anchor capacity models - -| **Anchor type** | **Suction** | **Suction** | **VLA** | **SEPLA** | -|------------------------|-------------|-------------|----------|-----------| -| **Soil type** | **Clay** | **Sand** | **Clay** | **Clay** | -| **Anchor parameters** | | | | | -| Diameter | x | x | | | -| Length | x | x | | | -| Area | | | X | X | -| Thickness | ratio | ratio | ratio | ratio | -| Embedment depth | | | X | X | -| **Soil parameters** | | | | | -| gamma | X | X | X | X | -| Su0 | X | | X | X | -| k | X | | X | X | -| alpha | X | | | | -| phi | | X | | | - - -These models will continue to be expanded as data sources and time permit. - diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index 2ec40b3e..56771b55 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -3,71 +3,66 @@ """ import moorpy as mp import numpy as np +from scipy.optimize import minimize from famodel.famodel_base import Node from famodel.mooring.mooring import Mooring +import matplotlib.pyplot as plt +from collections import defaultdict import famodel.platform.platform import shapely as sh - class Anchor(Node): - def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, - g=9.81, rho=1025): + def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, + g=9.81, rho=1025, profile_map=None): ''' + Initialize an Anchor object. + Parameters ---------- - dd: dictionary - Design dictionary that contains all information on an anchor for a mooring line/shared mooring - { - type: # anchor type (plate,suction_pile,torpedo_pile,helical_pile,driven_pile,dandg_pile) - design: # all geometric info from yaml file, only need to include info relevant to your anchor type - A plate anchor area - D anchor diameter (or helix diameter for helical piles) - D1 torpedo anchor wing diameter - D2 torpedo anchor shaft diameter - d helical pile shaft diameter - L pile anchor length - L1 torpedo anchor wing length - L2 torpedo anchor shaft length - zlug padeye z elevation (+ down into the soil) - beta angle of plate anchor after keying (optional) - cost: - matCost: # material cost - instCost: # installation cost - decomCost: # decomissioning cost - } - ms: system object - MoorPy system object the anchor is in - - r: list - Location of anchor in x,y,z - - aNum: int - entry number in project.anchorList dictionary (may remove...) - id: str/int - unique id of this object - g: float - acceleration due to gravity in m/s^2 - rho: float - density of water in kg/m^3 + dd : dict + Design dictionary containing all information on the anchor. + ms : MoorPy system object + MoorPy system instance. + r : list of float + Anchor position coordinates (x, y, z) (m) + aNum : int, optional + Index in anchor list. + id : str or int, optional + Unique anchor identifier. + g : float, optional + Gravity. + rho : float, optional + Water density. + profile_map : list of dict, optional + Full soil profile map for selecting local soil layers. ''' - # Initialize as a node - Node.__init__(self,id) - - # Design description dictionary for this Anchor + + from famodel.famodel_base import Node + Node.__init__(self, id) + self.dd = dd - - # MoorPy system this anchor is in self.ms = ms - - # x,y,z location of anchor self.r = r - - # anchor index in array mooring list (only used for shared moorings/anchors) self.aNum = aNum - - # MoorPy anchor object + self.g = g + self.rho = rho + + if dd and 'type' in dd: + self.anchType = dd['type'] + else: + self.anchType = 'suction' + print(f"[Anchor] No type provided. Defaulting to 'suction'.") + + self.soil_type = None + self.soil_profile = None + self.profile_name = None + self.soil_type_list = [] + self.mpAnchor = None + self.capacity_format = None + self.mass = dd.get('design', {}).get('mass', None) if dd else None + self.point_num = 0 # initialize point number # get environment info self.g = g # acceleration due to gravity (m/s^2) @@ -101,1025 +96,1289 @@ def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, } ''' self.soilProps = {} + self.loads = {} + self.anchorCapacity = {} + self.cost = {} self.failure_probability = {} - - # environmental impact self.env_impact = {} - - # self.cost = {} - - - def makeMoorPyAnchor(self, ms): - '''Create a MoorPy anchor object in a moorpy system - Parameters - ---------- - ms : class instance - MoorPy system - - Returns - ------- - ms : class instance - MoorPy system + # Assign soil profile if map is provided + if profile_map is not None: + if len(profile_map) == 1: + self.setSoilProfile(profile_map) + elif len(profile_map) >= 4: + self.interpolateSoilProfile(profile_map) + else: + raise ValueError("profile_map must contain either 1 or ≥4 CPTs for soil assignment.") + def setSoilProfile(self, profile_map): ''' - # create anchor as a fixed point in MoorPy system - ms.addPoint(1,self.r) - # assign this point as mpAnchor in the anchor class instance - self.mpAnchor = ms.pointList[-1] + Assign a soil profile directly from a single CPT. + Assumes profile_map is a list with only one entry. + ''' + if len(profile_map) != 1: + raise ValueError("setSoilProfile expects a profile_map with exactly one CPT.") - # add mass if available - if 'm' in self.dd['design'] and self.dd['design']['m']: - self.mpAnchor.m = self.dd['design']['m'] - # set anchor diameter - if 'd' in self.dd['design'] and self.dd['design']['d']: - self.mpAnchor.d = self.dd['design']['d'] - # set the point as an anchor entity - self.mpAnchor.entity= {'type': 'anchor'} - if 'type' in self.dd: - self.mpAnchor.entity['anchor_type']=self.dd['type'] - - return(ms) + cpt = profile_map[0] + self.soil_profile = profile_map + self.profile_name = cpt.get('name', 'CPT_Assigned') + # Extract soil types from layers + layers = cpt['layers'] + soil_types = [layer['soil_type'] for layer in layers] + self.soil_type_list = list(set(soil_types)) + self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed' - def getAnchorCapacity(self,ground_cons=None,installAdj=1,profile=None,loads=None,plot=True): + # Group layers by soil type + soilProps = defaultdict(list) + for layer in layers: + layer_copy = layer.copy() + soil_type = layer_copy.pop('soil_type') + soilProps[soil_type].append(layer_copy) + self.soilProps = dict(soilProps) + + print(f"[Anchor] Assigned soil profile from {self.profile_name} with soil types {self.soil_type_list}") + + + def interpolateSoilProfile(self, profile_map): + ''' + Interpolate a soil profile from the 4 nearest CPTs in profile_map. + ''' + if len(profile_map) < 4: + raise ValueError("interpolateSoilProfile requires at least 4 CPTs.") + + x_anchor, y_anchor = self.r[0], self.r[1] + + # Sort CPTs by distance + distances = [np.hypot(p['x'] - x_anchor, p['y'] - y_anchor) for p in profile_map] + idx_sorted = np.argsort(distances) + CPTs = [profile_map[i] for i in idx_sorted[:4]] + + # Inverse distance weighting + x = np.array([cpt['x'] for cpt in CPTs]) + y = np.array([cpt['y'] for cpt in CPTs]) + d = np.hypot(x - x_anchor, y - y_anchor) + w = 1 / np.maximum(d, 1e-3)**2 + w /= np.sum(w) + + # Interpolate layer-by-layer (assumes same layer structure) + layers_list = [cpt['layers'] for cpt in CPTs] + n_layers = len(layers_list[0]) + interpolated_layers = [] + + for i in range(n_layers): + base_layer = layers_list[0][i] + layer = {'soil_type': base_layer['soil_type']} + + for key in base_layer: + if key == 'soil_type': + continue + if all(key in l[i] for l in layers_list): + vals = [l[i][key] for l in layers_list] + layer[key] = np.dot(w, vals) + + interpolated_layers.append(layer) + + self.soil_profile = interpolated_layers + self.profile_name = "Interpolated_2D" + + # Extract soil types + soil_types = [layer['soil_type'] for layer in self.soil_profile] + self.soil_type_list = list(set(soil_types)) + self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed' + + # Group interpolated layers by soil type + soilProps = defaultdict(list) + for layer in self.soil_profile: + layer_copy = layer.copy() + soil_type = layer_copy.pop('soil_type') + soilProps[soil_type].append(layer_copy) + self.soilProps = dict(soilProps) + + print(f"[Anchor] Interpolated soil profile: {self.profile_name} with soil types {self.soil_type_list}") + + def makeMoorPyAnchor(self, ms): ''' - Calls anchor capacity functions developed by Felipe Moreno for the correct anchor type + Create a MoorPy anchor object in a MoorPy system. Parameters ---------- - ground_conds : dict, optional - Ground conditions dictionary with the key as the soil type name, values as soil info such as UCS,Em,phi,gamma,effective stress,etc. The default is None. - If no dict provided, the ground conds will be pulled from the anchor soilProps property - installAdj : float, optional - Adjustment to the capacity based on installation (dummy variable for now, but future installation functions - will dictate this value) - profile : 2D array, optional - 2d array of depths (m) and corresponding undrained shear strength (Pa). Su must not be zero - (change to small value such as .001), but z must always start at 0. Ex: array([z1,Su1],[z2,Su2],...) - Used only for driven pile and drilled and grouted pile anchors. - loads : dict, optional - Dictionary of loads on the anchor at the lug point in [N]. If not provided, will use the loads dictionary property - of the anchor. If this is empty and it is needed for the capacity function (i.e. driven piles) then - the anchor.getLugForces() function will be called. + ms : MoorPy system instance + The MoorPy system to add the anchor to. Returns ------- - results : dict - Dictionary of capacity of the anchor (generally a max force [N] in H and V, but can be a max displacement (driven, dandg piles)) + ms : MoorPy system instance + The updated MoorPy system with the anchor added. + ''' + anchType = self.anchType or 'suction' - ''' - # - - - - set details - - - - - anchType = self.dd['type'] - geom = self.dd['design']# geometric anchor information + # Create anchor as a fixed point in MoorPy system + ms.addPoint(1, self.r) - if not ground_cons: - soil = next(iter(self.soilProps.keys()), None) # soil type - ground_conds = self.soilProps[soil] - else: - soil = next(iter(ground_cons.keys())) - ground_conds = ground_cons[soil] - - for key,prop in ground_conds.items(): - if isinstance(prop,list) or isinstance(prop,np.ndarray): - if len(prop)>1: - print('Warning: Only homogeneous soils are supported at this time. Only the first item in a property list will be used.') - break - else: - ground_conds[key] = prop[0] - - - if loads: - # find out if mudline loads or anchor loads - if not 'Ha' in loads: - # get loads at lug - loads = self.getLugForces(mudloads=loads,plot=plot) - else: - loads = self.loads - - + # Assign this point as mpAnchor in the anchor class instance + self.mpAnchor = ms.pointList[-1] + + # Set mass if available + if 'mass' in self.dd.get('design', {}): + self.mpAnchor.m = self.dd['design']['mass'] + + # Set diameter if available + if 'd' in self.dd.get('design', {}): + self.mpAnchor.d = self.dd['design']['d'] + + # Set dummy design to get PointType from MoorPy + design = {f"num_a_{anchType}": 1} + pointType = ms.setPointType(design, source=None) + self.mpAnchor.entity = pointType + + return ms - # logic to determine what functions to call based on anchor type and soil type... - - # - - - - plate anchors - - - - - if anchType == 'SEPLA' or anchType == 'DEA' or anchType == 'DEPLA' or anchType == 'VLA' or anchType == 'plate': - from .anchors_famodel.capacity_plate import getCapacityPlate - if 'clay' in soil or 'mud' in soil: - # write or overwrite beta in geom dictionary from loads function - if anchType != 'DEA': - if not 'beta' in geom: - if not 'thetaa' in loads: - # calculate thetaa from Ha and Va - loads['thetaa'] = np.arctan2(loads['Va'],loads['Ha']) - # loads = self.getLugForces(plot=plot) - geom['beta'] = 90 - loads['thetaa'] - else: - geom['beta'] = 0 - if 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds: - results = getCapacityPlate(geom['A'], geom['beta'], geom['zlug'], 'clay', ground_conds['gamma'], - Su0=ground_conds['Su0'], k=ground_conds['k']) - else: - raise Exception('Ground conditions dictionary needs Su0, k, gamma information for clay plate anchors') - else: - print(f'Warning: Soil type {soil} is not compatible with plate anchors (SEPLA/DEPLA/DEA/VLA)') - - # - - - - suction buckets - - - - - elif 'suction' in anchType: - from .anchors_famodel.capacity_suction import getCapacitySuction - # check loads have been calculated (needed for capacity function in this case) - if not 'Ha' in loads: - # call getMPForces function - loads = self.getLugForces(plot=plot) - if 'sand' in soil: - if 'phi' in ground_conds and 'Dr' in ground_conds: - results = getCapacitySuction(geom['D'], geom['L'], geom['zlug'], - loads['Ha']/1000, loads['Va']/1000, - 'sand', ground_conds['gamma'], - phi=ground_conds['phi'], - Dr=ground_conds['Dr'], plot=plot) - else: - raise Exception('Ground conditions dictionary needs phi and relative density information for sand suction pile anchor') - elif 'clay' in soil or 'mud' in soil: - if 'Su0' in ground_conds and 'k' in ground_conds and 'alpha' in ground_conds:# and 'gamma_sub' in ground_conds: - results = getCapacitySuction(geom['D'],geom['L'], geom['zlug'], - loads['Ha']/1000, loads['Va']/1000, - 'clay', ground_conds['gamma'], - Su0=ground_conds['Su0'], - k=ground_conds['k'], plot=plot) - results['Horizontal max.'] = results['Horizontal max.'] - results['Vertical max.'] = results['Vertical max.'] - - else: - raise Exception('Ground conditions dictionary needs Su0, k, and alpha information for clay suction pile anchor') - else: - print(f'Warning: Soil type {soil} is not compatible with suction pile anchor') - - # - - - - helical piles - - - - - elif 'helical' in anchType: - from .anchors_famodel.capacity_helical import getCapacityHelical - if 'sand' in soil: - if 'phi' in ground_conds and 'gamma' in ground_conds: - results = getCapacityHelical(geom['D'], geom['L'], geom['d'], - geom['zlug'], 'sand', - ground_conds['gamma'], - phi=ground_conds['phi'], - Dr=ground_conds['Dr']) - results['Vertical max.'] = results['Capacity'] - else: - raise Exception('Ground conditions dictionary needs phi, gamma and relative density information for clay helical pile anchor') - elif 'clay' in soil or 'mud' in soil: - if not 'alpha_star' in ground_conds: - ground_conds['alpha_star'] = ground_conds['alpha'] - if 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds: - results = getCapacityHelical(geom['D'], geom['L'], geom['d'], - geom['zlug'], 'clay', - ground_conds['gamma'], - Su0=ground_conds['Su0'], - k=ground_conds['k']) - results['Vertical max.'] = results['Capacity'] - else: - raise Exception('Ground conditions dictionary needs Su0, k, gamma, and alpha_star information for clay helical pile anchor') - else: - print(f'Warning: Soil type {soil} is not compatible with helical pile anchor') - - # - - - - torpedo piles - - - - - elif 'torpedo' in anchType: - from .anchors_famodel.capacity_torpedo import getCapacityTorpedo - if 'clay' in soil or 'mud' in soil: - if 'Su0' in ground_conds and 'k' in ground_conds and 'alpha' in ground_conds: - results = getCapacityTorpedo(geom['D1'], geom['D2'], - geom['L1'], geom['L2'], - geom['zlug'], 'clay', - ground_conds['Su0'], - ground_conds['k'], - ground_conds['alpha']) - results['Horizontal max.'] = results['Horizontal max.'] - results['Vertical max.'] = results['Vertical max.'] - else: - raise Exception('Ground conditions dictionary needs Su0, k, and alpha information') - else: - print('Warning: Soil type {soil} is not compatible with torpedo pile anchor') - - # - - - - driven piles - - - - - elif 'driven' in anchType: # driven pile anchor - # check loads have been calculated (needed for capacity function in this case) - if not 'Ha' in loads: - # call getLugForces function - loads = self.getLugForces(plot=plot) - H_inc = loads['Ha']*0.1 # increment H by 10% of Ha load in the while loops to back-calc max H from displacements - H = 0 - # check soil - if 'weak_rock' in soil: - from .anchors_famodel.capacity_drivenrock import getCapacityDrivenRock - - if not profile: - if 'UCS' in ground_conds and 'Em' in ground_conds: - profile = [[0,ground_conds['UCS'],ground_conds['Em']], - [75,ground_conds['UCS'],ground_conds['Em']]] #profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['UCS'],ground_conds['Em']))] - else: - raise Exception('Ground conditions dictionary needs UCS, Em, and depth information for weak rock driven pile anchor') - - y, z, results = getCapacityDrivenRock(profile, geom['L'], geom['D'], - geom['zlug'], loads['Va'], - loads['Ha'], plot=plot) - - # loop through, calling capacity with larger H values until a displacement value goes above limit - while results['Lateral displacement']< 0.05*geom['D'] and results['Rotational displacement'] < 0.25: - # increment H - H += H_inc - # call capacity function - y, z, results = getCapacityDrivenRock(profile, geom['L'], - geom['D'], geom['zlug'], - loads['Va'], H=H, plot=plot) - - - elif 'sand' in soil: - from .anchors_famodel.capacity_drivensoil import getCapacityDrivenSoil - if profile or ('gamma' in ground_conds and 'Dr' in ground_conds and 'phi' in ground_conds): - if not profile: - profile = [[0,ground_conds['phi'],ground_conds['gamma'],ground_conds['Dr']], - [75,ground_conds['phi'],ground_conds['gamma'],ground_conds['Dr']]] #profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['phi'],ground_conds['gamma']))] - - y, z, results = getCapacityDrivenSoil(profile, 'sand', - geom['L'], geom['D'], - geom['zlug'], loads['Va'], - loads['Ha'], plot=plot) - if geom['zlug'] > 0: - # need to check bending moment if lug is below mudline (+ zlug) - # loop through, calling capacity with larger H values until a displacement value goes above limit - while results['Lateral displacement']<= 0.05*geom['D'] and results['Bending moment'] <= results['Plastic moment']: - # increment H by 10% of load - H += H_inc - # call capacity function - y, z, results = getCapacityDrivenSoil(profile,'clay', - geom['L'], geom['D'], - geom['zlug'], loads['Va'], - H=H, plot=plot) - - else: - while results['Lateral displacement']<= 0.05*geom['D'] and results['Rotational displacement'] <= 0.25: - # increment H by 10% of load - H += H_inc - # call capacity function - y, z, results = getCapacityDrivenSoil(profile, 'clay', - geom['L'], geom['D'], - geom['zlug'], loads['Va'], - H=H, plot=plot) - else: - raise Exception('Ground conditions dictionary needs phi, gamma, and depth information for sand driven pile anchor') - elif 'clay' in soil or 'mud' in soil: - from .anchors_famodel.capacity_drivensoil import getCapacityDrivenSoil - #if profile or ('Su' in ground_conds and 'gamma' in ground_conds and 'depth' in ground_conds) or ('Su0' in ground_conds and 'k' in ground_conds): - if not profile: - if 'Su' in ground_conds and 'depth' in ground_conds and 'gamma' in ground_conds: - profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['Su'],ground_conds['gamma']))] - elif 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds: - Su = ground_conds['Su0']+ground_conds['k']*75 - profile = [[0,ground_conds['Su0'],ground_conds['gamma']],[75,Su,ground_conds['gamma']]] - else: - raise Exception('Ground conditions dictionary needs information for clay driven pile anchor') - - y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'],loads['Ha'], plot=plot) - - if geom['zlug'] > 0: - # need to check bending moment if lug is below mudline (+ zlug) - # loop through, calling capacity with larger H values until a displacement value goes above limit - while results['Lateral displacement']<= 0.05*geom['D'] and results['Bending moment'] <= results['Plastic moment']: - # increment H by 10% of load - H += H_inc - # call capacity function - y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'], H=H, plot=plot) - - else: - while results['Lateral displacement']<= 0.05*geom['D'] and results['Rotational displacement'] <= 0.25: - # increment H by 10% of load - H += H_inc - # call capacity function - y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'], H=H, plot=plot) - - - else: - print(f'Warning: Soil type {soil} is not compatible with driven pile anchors') - - # - - - - drilled and grouted piles - - - - - elif 'dandg' in anchType: # drill and grout pile - from .anchors_famodel.capacity_dandg import getCapacityDandG - # check for correct soil - if 'rock' in soil: - # check loads have been calculated (needed for capacity function in this case) - if not 'Ha' in loads: - # call getMPForces function - loads = self.getLugForces(plot=plot) - # check for correct ground properties - if profile or ('UCS' in ground_conds and 'Em' in ground_conds): - if not profile: - profile = [[0,ground_conds['UCS'],ground_conds['Em']],[75,ground_conds['UCS'],ground_conds['Em']]] #[list(x) for x in list(zip(ground_conds['depth'],ground_conds['UCS'],ground_conds['Em']))] - - # call capacity function once to get displacement values - y, z, results = getCapacityDandG(profile,geom['L'],geom['D'], - geom['zlug'], loads['Va'], - loads['Ha'], plot=plot) - H_inc = loads['Ha']*0.1 # increment H by 10% of Ha load - H = H_inc # start H at 10% of Ha load - # loop through, calling capacity with larger H values until a displacement value goes above limit - while results['Lateral displacement']< 0.05*geom['D'] and results['Rotational displacement'] < 0.25: - # call capacity function - y, z, results = getCapacityDandG(profile, geom['L'], geom['D'], - geom['zlug'], loads['Va'], - H=H, plot=plot) - # increment H - H += H_inc + def getLineProperties(self): + ''' + Retrieve line_type, diameter and unit weight from attached mooring. + + Returns + ------- + line_type : str + Type of mooring line ('chain' or 'wire') + d : float + Nominal diameter (m) + w : float + Unit weight (N/m) + ''' + for att in self.attachments.values(): + if isinstance(att['obj'], Mooring): + mtype = att['obj'].dd['sections'][0]['type']['material'].lower() + if 'chain' not in mtype: + print('No chain below seafloor, setting Ta=Tm (no load transfer).') + return mtype, None, None, True else: - raise Exception('Ground conditions dictionary need UCS and Em information for drill and grout pile') - else: - print(f'Warning: soil type {soil} is not compatible with drill and grout pile') - - # - - - - anchor type not recognized or supported - - - - - else: - raise Exception(f'Anchor type {anchType} is not supported at this time') - - # - - - - save relevant results in dictionary using common terms - - - - - # capacity = cap*installAdj ??? OR is installAdj an input to the capacity functions? - # save capacity - if 'dandg' in anchType or 'driven' in anchType: # will take in dandg, dandg_pile, driven, driven_pile - self.anchorCapacity['Lat_max'] = results['Lateral displacement'] # [deg] - if 'Rotational displacement' in results: - self.anchorCapacity['Rot_max'] = results['Rotational displacement'] # [deg] - elif 'Bending moment' in results: - self.anchorCapacity['Mbend_max'] = results['Bending moment'] - self.anchorCapacity['Va_max'] = results['Axial capacity'] # [N] - self.anchorCapacity['Ha_max'] = H + d_nom = att['obj'].dd['sections'][0]['type']['d_nom'] + w_nom = att['obj'].dd['sections'][0]['type']['w'] + return 'chain', d_nom, w_nom, False + raise ValueError('No mooring line attachment found for anchor.') + + def getMudlineForces(self, max_force=False, lines_only=False, seabed=True, xyz=False, project=None): + ''' + Find forces on anchor at mudline using the platform.getWatchCircle method + or the MoorPy Point.getForces method. Optionally computes the maximum force + based on platform excursion using the project's arrayWatchCircle method or + the attached platform's getWatchCircle method. - else: - if 'Horizontal max.' in results: - self.anchorCapacity['Ha_max'] = results['Horizontal max.']*1000 # [N] - self.anchorCapacity['Va_max'] = results['Vertical max.']*1000 # [N] - self.mass = results['Weight']*1000/self.g # mass in [kg] - - # add on extra for drag-embedment anchors (flukes) - if 'DEA' in anchType: - self.mass *= 1.75 - - - return(results) - - def getMudlineForces(self, max_force=False,lines_only=False, seabed=True, xyz=False,project=None): - '''Find forces on anchor at mudline using the platform.getWatchCircle method or MoorPy Point.getForces method. - Optionally, get forces at anchor lug location with getTransferLoad function in capacity_loads.py. - Stores in loads dictionary Parameters ---------- - max_force : boolean, optional - Find and save the maximum force on the anchor (True) or just get force at the current MoorPy system state (False) - lines_only : boolean, optional - Calculate forces from just mooring lines (True) or not (False). Default is false - seabed : boolean, optional - Include effect of seabed pushing up the anchor (True) or not (False). Default is true - xyz : boolean, optional - Return forces in x,y,z DOFs (True) or only the enabled DOFs (False). Default is false - + max_force : bool, optional + If True, computes the maximum expected force on the anchor + using platform excursion. Default is False. + lines_only : bool, optional + Calculate forces from just mooring lines (True) or not (False). Default is False. + seabed : bool, optional + Include effect of seabed pushing up the anchor (True) or not (False). Default is True. + xyz : bool, optional + Return forces in x, y, z DOFs (True) or only the enabled DOFs (False). Default is False. + project : object, optional + Project object that can run arrayWatchCircle(). Used only if max_force is True. + + Returns + ------- + dict + Dictionary containing mudline forces. ''' Platform = famodel.platform.platform.Platform + if max_force: if project: - # get watch circle of platform(s) project.arrayWatchCircle() else: - # find platform associated with this anchor for att in self.attachments.values(): - if isinstance(att['obj'],Mooring): + if isinstance(att['obj'], Mooring): for attM in att['obj'].attached_to: - if isinstance(attM,Platform): - locx,locy,maxVals = attM.getWatchCircle() - # call getForces method from moorpy point object + if isinstance(attM, Platform): + locx, locy, maxVals = attM.getWatchCircle() + Hm = np.sqrt(maxVals[0]**2 + maxVals[1]**2) + Vm = maxVals[2] + thetam = np.degrees(np.arctan2(Vm, Hm)) + self.loads['Hm'] = Hm + self.loads['Vm'] = Vm + self.loads['thetam'] = thetam + self.loads['mudline_load_type'] = 'max_force' + break else: loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz) - self.loads['Hm'] = np.sqrt(loads[0]**2+loads[1]**2) # mudline forces in [N] - self.loads['Vm'] = loads[2] # [N] - self.loads['thetam'] = np.degrees(np.arctan(self.loads['Vm']/self.loads['Hm'])) # [deg] + Hm = np.sqrt(loads[0]**2 + loads[1]**2) + Vm = loads[2] + thetam = np.degrees(np.arctan2(Vm, Hm)) + self.loads['Hm'] = Hm + self.loads['Vm'] = Vm + self.loads['thetam'] = thetam self.loads['mudline_load_type'] = 'current_state' - - # loads determined from moorpy are static + self.loads['method'] = 'static' - - return(self.loads) - - def getLugForces(self, mudloads=None, max_force=True, plot=False): + return self.loads + + def getLugForces(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False): ''' - Find forces on an anchor at the lug point based on the mudline forces and angles. Calls getTransferFunction script + Calculate the lug forces Ha and Va based on mudline loads using local soil profile. Parameters ---------- - mudloads : dict, optional - Dictionary of max mudline forces. The default is None. + Hm : float + Horizontal mudline load (N) + Vm : float + Vertical mudline load (N) + zlug : float + Padeye embedment depth (m) + line_type : str, optional + Type of mooring line ('chain' or 'wire') + d : float, optional + Mooring line diameter (m) + w : float, optional + Mooring line unit weight (N/m) + plot : bool, optional + Whether to plot the load transfer profile Returns ------- - loads: dict - Dictionary of loads at the lug point [N] - + Ha : float + Horizontal load at lug (N). + Va : float + Vertical load at lug (N). ''' from .anchors_famodel.capacity_load import getTransferLoad + from .anchors_famodel.support_plots import plot_load + + # Ensure soil profile is available + if self.soil_profile is None or self.soil_type is None: + raise ValueError("Anchor soil profile or soil type is not assigned. Use setSoilProfile first.") + + soil_profile = self.soil_profile + soil_type = self.soil_type + + # Determine mudline depth + z0 = soil_profile[0]['layers'][0]['top'] - nolugload = False - - if not mudloads: - if not self.loads: - # get max mudline forces first - self.getMudlineForces(max_force=max_force) - elif not 'mudline_load_type' in self.loads: - raise KeyError("Loads dictionary must specify 'mudline_load_type'='current_state' or 'mudline_load_type'='max', where 'max' indicates the loads are maximum loads.") - elif max_force and self.loads['mudline_load_type'] != 'max': - # need max forces, not current state - self.getMudlineForces(max_force=True) - mudloads = self.loads - else: - # check syntax - if not 'Hm' in mudloads or not 'Vm' in mudloads: - raise KeyError('Mudline load dictionary must have Hm and Vm for horizontal load and vertical load (in [N]) at the mudline') - if not 'thetam' in mudloads: - mudloads['thetam'] = np.degrees(np.arctan(mudloads['Vm']/mudloads['Hm'])) - - def makeEqual_TaTm(mudloads): - mudloads['Ha'] = mudloads['Hm'] # [N] - mudloads['Va'] = mudloads['Vm'] # [N] - mudloads['thetaa'] = mudloads['thetam'] # [deg] - - if 'zlug' in self.dd['design']: - if self.dd['design']['zlug'] > 0: - # get line type - for att in self.attachments.values(): - if isinstance(att['obj'],Mooring): - mtype = att['obj'].dd['sections'][0]['type']['material'] - if not 'chain' in mtype: - print('No chain on seafloor, setting Ta=Tm') - nolugload = True - break - else: - md = att['obj'].dd['sections'][0]['type']['d_nom'] - mw = att['obj'].dd['sections'][0]['type']['w'] - soil = next(iter(self.soilProps.keys()), None) - ground_conds = self.soilProps[soil] - # update soil conds as needed to be homogeneous - for key,prop in ground_conds.items(): - if isinstance(prop,list) or isinstance(prop,np.ndarray): - if len(prop)>1: - print('Warning: Only homogeneous soils are supported at this time. Only the first item in a property list will be used.') - break - else: - ground_conds[key] = prop[0] - - Tm = np.sqrt(mudloads['Hm']**2+mudloads['Vm']**2) # [N] - if 'clay' in soil or 'mud' in soil and not nolugload: - # Tm, thetam, zlug, line_type, d, soil_type, Su0=None, k=None, w=None - try: - loadresults = getTransferLoad(Tm/1000,mudloads['thetam'], - self.dd['design']['zlug'],mtype,md, - 'clay',Su0=ground_conds['Su0'], - k=ground_conds['k'],w=mw/1000, - plot=plot) # output Ha and Va (convert weight to kN/m) - except Exception as e: - print(e) - print('Unable to get loads at anchor lug location. Setting Ta = Tm') - nolugload = True - elif 'sand' in soil and not nolugload: - soil = 'sand' - try: - loadresults = getTransferLoad(Tm/1000, self.loads['thetam'], - self.dd['design']['zlug'], - mtype, md, soil, - gamma=ground_conds['gamma'], - phi=ground_conds['phi'], - delta=ground_conds['delta'], - w=mw/1000,plot=plot) # output Ha and Va (convert weight to kN/m) - except Exception as e: - print(e) - print('Unable to get loads at anchor lug location. Setting Ta = Tm') - nolugload = True - elif 'rock' in soil and not nolugload: - raise ValueError('zlug should be <= 0 for rock.') - - # if loadresults['V']<0: - # # results are invalid - # print('Warning: invalid results for the combination of anchor ',self.dd['type'],' soil ',soil,' and loads ',mudloads,'. Setting Ha=Hm, Va=Vm, thetaa=thetam') - # makeEqual_TaTm(mudloads) - if nolugload: - makeEqual_TaTm(mudloads) - else: - mudloads['Ha'] = loadresults['H']*1000 # [N] - mudloads['Va'] = loadresults['V']*1000 # [N] - mudloads['thetaa'] = loadresults['angle'] # [deg] - else: - # Ha = Hm because zlug is at mudline or above - makeEqual_TaTm(mudloads) + # Load transfer if padeye is embedded + if zlug > z0: + # Check if padeye is embedded in rock + if any(layer.get('soil_type') == 'rock' for layer in self.soil_profile[0]['layers']): + raise ValueError('[Warning] Padeye depth is embedded in rock. Embedded line in rock is not possible.') + + + if line_type is None or d is None or w is None: + try: + line_type, d, w = self.getLineProperties() + except ValueError: + print('[Warning] No mooring attachment found. Trying anchor-level line properties...') + line_type = getattr(self, 'line_type', None) + d = getattr(self, 'd', None) + w = getattr(self, 'w', None) + + if any(v is None for v in [line_type, d, w]): + print('[Fallback] Using default chain properties.') + line_type = 'chain' + d = 0.16 + w = 5500.0 + + layers, loads = getTransferLoad( + profile_map=self.soil_profile, + Tm=np.sqrt(Hm**2 + Vm**2), + thetam=np.degrees(np.arctan2(Vm, Hm)), + zlug=zlug, + line_type=line_type, + d=d, + w=w, + plot=plot) + + Ta = loads['Ta'] + thetaa = loads['thetaa'] + Ha = Ta*np.cos(np.deg2rad(thetaa)) + Va = Ta*np.sin(np.deg2rad(thetaa)) + else: - print('No zlug given, assuming loads at mudline = loads at anchor lug') - makeEqual_TaTm(mudloads) + Ha = Hm + Va = Vm + layers = self.soil_profile[0]['layers'] + - if not 'method' in mudloads: - # assume mudloads are static unless told otherwise - # loads determined from moorpy are static - mudloads['method'] = 'static' - else: - mudloads['method'] = mudloads['method'] - - return mudloads - - def getFS(self, loads=None, acceptance_crit=None): + if plot == True: + plot_load(layers, loads['drag_values'], loads['depth_values'], + loads['Tm'], loads['thetam'], loads['Ta'], + loads['thetaa'], zlug=zlug) + + return layers, Ha, Va + + def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_update=False, plot=False): ''' - Compute safety factor for loads on the anchor - + Calculate anchor capacity based on anchor type and local soil profile. + Parameters ---------- - loads : dict, optional - Dictionary of loads on the anchor. - acceptance_crit : dict, optional - Dictionary of acceptable factors of safety for each load type. - Key is the load type, and value is the minimum acceptable safety factor. - Default is None (in which case no comparison between FS and acceptance criteria is calculated) - + Hm : float + Horizontal mudline load (N) + Vm : float + Vertical mudline load (N) + zlug : float + Padeye embedment depth (m) + line_type : str, optional + Type of mooring line ('chain' or 'wire') + d : float, optional + Mooring line diameter (m) + w : float, optional + Mooring line unit weight (N/m) + mass_update : bool, optional + Whether to update the mass when is not assigned + plot : bool, optional + Whether to plot the load transfer and pile geometry + Returns ------- - FS : dict - Dictionary of safety factors (often horizontal and vertical load SFs, but could be displacement SFs (drilled and grouted/driven piles)) - acceptance : dict - Dictionary of bools that state whether the FS>=acceptance_crit for each load - acceptance_margin : dict - Dictionary of difference between FS and acceptance criteria for each load type - - + results : dict + Capacity results dictionary from the selected capacity function. ''' - if not loads: - if not 'Ha' in self.loads: - self.getLugForces() - loads = self.loads - if not self.anchorCapacity: - self.getAnchorCapacity() - - # look for load dictionary key in capacity dictionary - FS = {} - acceptance = {} - acceptance_margin = {} - for Lkey,Lval in loads.items(): - for Ckey,Cval in self.anchorCapacity.items(): - if Lkey in Ckey: - if Lval == 0: - FS[Lkey] = float('inf') - else: - FS[Lkey] = Cval/Lval - if acceptance_crit and Lkey in acceptance_crit: - if Lval == 0 or acceptance_crit[Lkey] == 0: - acceptance[Lkey] = True - else: - acceptance[Lkey] = acceptance_crit[Lkey]<=FS[Lkey] - acceptance_margin[Lkey] = FS[Lkey] - acceptance_crit[Lkey] - - if acceptance_crit: - return(FS,acceptance,acceptance_margin) - else: - return(FS) - - def makeBuffer(self, buff_rad=50): - point = sh.Point(self.r[:2]) - buff = point.buffer(buff_rad) - return buff - - def getCost(self,costDict='default'): - '''find costs of anchor and store in design dictionary + + from .anchors_famodel.capacity_plate import getCapacityPlate + from .anchors_famodel.capacity_suction import getCapacitySuction + from .anchors_famodel.capacity_torpedo import getCapacityTorpedo + from .anchors_famodel.capacity_helical import getCapacityHelical + from .anchors_famodel.capacity_driven import getCapacityDriven + from .anchors_famodel.capacity_dandg import getCapacityDandG + + capacity_dispatch = { + 'suction': getCapacitySuction, + 'sepla': getCapacityPlate, + 'dea': getCapacityPlate, + 'depla': getCapacityPlate, + 'vla': getCapacityPlate, + 'plate': getCapacityPlate, + 'torpedo': getCapacityTorpedo, + 'helical': getCapacityHelical, + 'driven': getCapacityDriven, + 'dandg': getCapacityDandG} - Parameters - ---------- - costDict : dictionary or yaml, optional - Dictionary of various costs for anchors. Sub costs that can be included are: - material : material costs + print('[DEBUG] profile_name:', self.profile_name) + print('[DEBUG] soil_profile passed as profile_map:') + for entry in self.soil_profile: + print(entry.get('name'), list(entry.keys())) + + + print(f'[Debug] mass_update = {mass_update}') + anchType_clean = self.dd['type'].lower().replace(' ', '') + capacity_func = capacity_dispatch.get(anchType_clean) + if capacity_func is None: + raise ValueError(f"Unknown anchor type '{self.anchType}' for anchor capacity calculation.") + + if self.soil_profile is None or self.soil_type is None: + raise ValueError("Soil profile or soil type not set for this anchor.") + + soil_profile = self.soil_profile + soil_type = self.soil_type + z0 = soil_profile[0]['layers'][0]['top'] + + if line_type is None or d is None or w is None: + try: + line_type, d, w = self.getLineProperties() + except ValueError: + print('[Warning] No mooring attachment found. Trying anchor-level line properties...') + line_type = getattr(self, 'line_type', None) + d = getattr(self, 'd', None) + w = getattr(self, 'w', None) + + if any(v is None for v in [line_type, d, w]): + print('[Fallback] Using default chain properties.') + line_type = 'chain' + d = 0.16 + w = 5500.0 + + # Load transfer if padeye is embedded below mudline + if zlug > z0: + layers, Ha, Va = self.getLugForces( + Hm, Vm, + zlug=zlug, + line_type=line_type, + d=d, + w=w, + plot=False) + + Ta = np.sqrt(Ha**2 + Va**2) + thetaa = np.degrees(np.arctan2(Va, Ha)) - ''' - if isinstance(costDict,str) and costDict != 'default': - import yaml - costDict = yaml.load(costDict, Loader=yaml.FullLoader) - anchType = self.dd['type'] - if costDict == 'default': - matCostDict = {'DEA':5.705,'suction_pile':4.435,'gravity':1.905} # mean values from Task 49 Design Basis ranges - instCostDict = {} - decomCostDict = {} + print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") + else: - matCostDict = costDict['material'] - if 'install' in costDict: - instCostDict = costDict['install'] - if 'decom' in costDict: - decomCostDict = costDict['decom'] - keyFail = True - # check if mass info is available - if not self.mass: - if 'soil_properties' in self.dd: - # need mass - call capacity functions - self.getAnchorCapacity(plot=False) - else: - print('Soil properties needed to calculate anchor mass for cost. Setting cost to 0.') - self.mass = 0 + Ha = Hm + Va = Vm + Ta = np.sqrt(Ha**2 + Va**2) + thetaa = np.degrees(np.arctan2(Va, Ha)) + + print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") + + # --- Call the appropriate capacity function --- + if anchType_clean in ['sepla', 'dea', 'depla', 'vla', 'plate']: + self.capacity_format = 'plate' + B = self.dd['design']['B'] + L = self.dd['design']['L'] + print(f"[Final Check] Ha = {Ha}, Va = {Va}, anchor = {self.anchType}") + beta = 90.0 - np.degrees(np.arctan2(Va, Ha)) + self.dd['design']['beta'] = beta + layers, results = capacity_func( + profile_map=self.soil_profile, + location_name=self.profile_name, + B=B, L=L, zlug=zlug, + beta=beta, + Ha=Ha, Va=Va, + plot=plot) - # sort by type of anchor - for Ckey,Cval in matCostDict.items(): - if anchType in Ckey: - self.cost['materials'] = matCostDict[Ckey]*self.mass - # self.cost['install'] = instCostDict[Ckey] - # self.cost['decom'] = decomCostDict[Ckey] - keyFail = False - # raise error if anchType not found in cost dictionary - if keyFail: - raise KeyError(f'anchor type {anchType} not found in material cost dictionary') + elif anchType_clean == 'suction': + self.capacity_format = 'envelope' + D = self.dd['design']['D'] + L = self.dd['design']['L'] + zlug = self.dd['design']['zlug'] + layers, results = capacity_func( + profile_map=self.soil_profile, + location_name=self.profile_name, + D=D, L=L, zlug=zlug, + Ha=Ha, Va=Va, + thetalug=5, psilug=7.5, + plot=plot) - return(sum(self.cost.values())) + elif anchType_clean == 'torpedo': + self.capacity_format = 'envelope' + D1 = self.dd['design']['D1'] + D2 = self.dd['design']['D2'] + L1 = self.dd['design']['L1'] + L2 = self.dd['design']['L2'] + ballast = self.dd['design'].get('ballast', 0.0) + layers, results = capacity_func( + profile_map=self.soil_profile, + location_name=self.profile_name, + D1=D1, D2=D2, L1=L1, L2=L2, + zlug=zlug, + ballast=ballast, + Ha=Ha, Va=Va, + plot=plot) - - - # def getSuctionSize(self,D,L,loads=None,minfs={'Ha':1.6,'Va':2},LD_con=[4,8]): - # ''' + elif anchType_clean == 'helical': + self.capacity_format = 'component' + D = self.dd['design']['D'] + L = self.dd['design']['L'] + d = self.dd['design']['d'] + zlug = self.dd['design']['zlug'] + layers, results = capacity_func( + profile_map=self.soil_profile, + location_name=self.profile_name, + D=D, L=L, d=d, + zlug=zlug, + Ha=Ha, Va=Va, + plot=plot) + + elif anchType_clean == 'driven': + self.capacity_format = 'component' + L = self.dd['design']['L'] + D = self.dd['design']['D'] + zlug = self.dd['design']['zlug'] + layers, y, z, results = capacity_func( + profile_map=self.soil_profile, + location_name=self.profile_name, + L=L, D=D, zlug=zlug, + Ha=Ha, Va=Va, + plot=plot) + elif anchType_clean == 'dandg': + self.capacity_format = 'component' + L = self.dd['design']['L'] + D = self.dd['design']['D'] + zlug = self.dd['design']['zlug'] + layers, y, z, results = capacity_func( + profile_map=self.soil_profile, + location_name=self.profile_name, + L=L, D=D, zlug=zlug, + Ha=Ha, Va=Va, + plot=plot) - # Parameters - # ---------- - # D : float - # Diameter of suction bucket - # L : float - # Length of suction bucket - # loads : dict, optional - # Dictionary of maximum anchor loads in horizontal and vertical directions. The default is None. - # minfs : dict,optoinal - # Minimum factors of safety in horizontal and vertical directions - # LD_con : float - # Constraint for L/D parameter - - # Returns - # ------- - # None. - - # ''' - # from scipy.optimize import minimize - # anchType = self.dd['type'] - # if not loads: - # loads = self.loads - - # if not 'Ha' in loads: - # loads = self.getLugForces(mudloads=loads) - - # loads['Ha'] = minfs['Ha']*loads['Ha'] - # loads['Va'] = minfs['Va']*loads['Va'] - - # if not 'zlug' in self.dd['design']: - # self.dd['design']['zlug'] = (2/3)*L - - # # Define the objective function: Minimize |UC - 1| (aim for UC to be 1) - # def objective(vars): - # D, L = vars - # self.dd['design']['D'] = D - # self.dd['design']['L'] = L - # self.dd['design']['zlug'] = (2/3)*L - # results = self.getAnchorCapacity(plot=False) - # return abs(results['UC'] - 1) + else: + raise ValueError(f"Anchor type '{self.anchType}' not supported.") + + # --- Store results --- + self.anchorCapacity = { + 'Hmax': results.get('Horizontal max.', np.nan), + 'Vmax': results.get('Vertical max.', np.nan), + 'Ha': Ha, + 'Va': Va, + 'zlug': zlug, + 'z0': z0} - # def conFun(vars,LD_con): - # D, L = vars - # if L/D >= LD_con[0] and L/D <= LD_con[1]: - # conval = 1 - # else: - # conval = -1 - - # return(conval) + # Correct UC format + if anchType_clean in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']: + self.anchorCapacity['UC'] = results.get('Unity check', np.nan) - # # Initial guess for D and L - # initial_guess = [D, L] # Input values for D and L + elif anchType_clean in ['helical', 'driven', 'dandg']: + self.anchorCapacity['Unity check (horizontal)'] = results.get('Unity check (horizontal)', np.nan) + self.anchorCapacity['Unity check (vertical)'] = results.get('Unity check (vertical)', np.nan) - # # Bounds for D and L (adjust as needed) - # bounds = [(1, 5), (5, 50)] # Bounds for D and L + # Copy over lateral and rotational displacements + if 'Lateral displacement' in results: + self.anchorCapacity['Lateral displacement'] = results['Lateral displacement'] + if 'Rotational displacement' in results: + self.anchorCapacity['Rotational displacement'] = results['Rotational displacement'] - # # constraints - # constraints = [{'type':'ineq','fun':conFun,'args':(LD_con,)}] + # Weight calculated via dimensions + if mass_update == False: + if 'Weight pile' in results: + self.anchorCapacity['Weight pile'] = results['Weight pile'] + if 'Weight plate' in results: + self.anchorCapacity['Weight plate'] = results['Weight plate'] + else: + if 'Weight pile' in results: + if self.mass is None: + self.mass = results['Weight pile']/self.g + self.anchorCapacity['Weight pile'] = self.mass*self.g + if 'Weight plate' in results: + if self.mass is None: + self.mass = results['Weight plate']/self.g + self.anchorCapacity['Weight plate'] = self.mass*self.g + + # print(f"[DEBUG] Stored Lateral displacement in anchorCapacity: {self.anchorCapacity['Lateral displacement']:.6f}") + + def getSizeAnchor(self, geom, geomKeys, geomBounds=None, loads=None, lambdap_con=[4, 8], + zlug_fix=True, safety_factor={'SF_combined': 1.0}, plot=False): + ''' + Generalized optimization method for all anchor types, using dictionary-based safety factors. + ''' + + anchType_clean = self.dd['type'].lower().replace('', '') + + if loads is None: + loads = self.loads + + Hm = loads['Hm'] + Vm = loads['Vm'] + + line_type = getattr(self, 'line_type', 'chain') + d = getattr(self, 'd', 0.16) + w = getattr(self, 'w', 5000.0) + + def update_zlug(): + if anchType_clean == 'suction' and not zlug_fix and 'zlug' not in geomKeys: + self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] + elif anchType_clean in ['driven', 'helical'] and not zlug_fix: + ratio = self.dd['design'].get('zlug_ratio', self.dd['design']['zlug']/self.dd['design']['L']) + self.dd['design']['zlug_ratio'] = ratio + self.dd['design']['zlug'] = ratio*self.dd['design']['L'] + + def get_lambda(): + if anchType_clean == 'torpedo': + L = self.dd['design']['L1'] + self.dd['design']['L2'] + A_wing = (self.dd['design']['D1'] - self.dd['design']['D2']) * self.dd['design']['L1'] + A_shaft = self.dd['design']['D2'] * L + D = (A_wing + A_shaft) / L + elif anchType_clean in ['driven', 'dandg', 'helical', 'suction']: + L = self.dd['design']['L'] + D = self.dd['design']['D'] + elif anchType_clean in ['plate', 'sepla', 'dea', 'depla', 'vla']: + L = self.dd['design']['L'] + D = self.dd['design']['B'] + else: + raise ValueError(f'lambda not defined for anchor type: {anchType_clean}') + return L/D + + def constraint_lambda_min(vars): + return get_lambda() - lambdap_con[0] + + def constraint_lambda_max(vars): + return lambdap_con[1] - get_lambda() + + if anchType_clean in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']: + target_UC = 1.0/safety_factor.get('SF_combined', 1.0) + + def objective_uc(vars): + for i, key in enumerate(geomKeys): + self.dd['design'][key] = vars[i] + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False) + UC = self.anchorCapacity.get('UC', 2.0) + return (UC - target_UC)**2 + + def constraint_uc_envelope(vars): + return self.anchorCapacity.get('UC', 0.0) - target_UC + + constraints_uc = [ + {'type': 'ineq', 'fun': constraint_lambda_min}, + {'type': 'ineq', 'fun': constraint_lambda_max}, + {'type': 'ineq', 'fun': constraint_uc_envelope}, + ] + + result_uc = minimize( + objective_uc, + geom, + method='COBYLA', + bounds=geomBounds if geomBounds else None, + constraints=constraints_uc, + options={'rhobeg': 0.1, 'catol': 0.01, 'maxiter': 500} + ) + + endGeom = dict(zip(geomKeys, result_uc.x)) + self.dd['design'].update(endGeom) + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=plot) + + print('\nFinal Optimized Anchor (UC-based):') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + + def termination_condition(): + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_lat = 0.05*self.dd['design']['D'] # 5% of the pile diameter + limit_rot = 5.0 # 5 deg + + if UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot: + print('[Termination Condition Met] All four limits satisfied.') + return 'terminate' + + return 'continue' - # # Run the optimization to find D and L that satisfy UC close to 1 - # solution = minimize(objective, initial_guess, bounds=bounds,method="COBYLA", - # constraints=constraints,options={'rhobeg':0.1, 'catol':0.001}) + def termination_condition_dandg(): + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_lat = 0.05*self.dd['design']['D'] # 5% of the pile diameter + limit_rot = 5.0 # 5 deg + + if UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot: + print('[Termination Condition Met] All four limits satisfied.') + return 'terminate' + + return 'continue' + + def is_valid(value): + return np.isfinite(value) and not np.isnan(value) and abs(value) < 1e6 - # # Extract the optimized values of D and L - # self.dd['design']['D'], self.dd['design']['L'] = solution.x - # self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] - # results = self.getAnchorCapacity(plot=False) - - - def getSize(self, geom, geomKeys, geomBounds=None, loads=None, minfs={'Ha':1.6,'Va':2}, - LD_con=[4,8], fix_zlug=False, FSdiff_max=None, plot=False): - ''' + if anchType_clean in ['helical', 'driven']: + L0, D0 = geom if len(geom) == 2 else [5.0, 1.0] + self.dd['design']['L'] = L0 + self.dd['design']['D'] = D0 + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False) + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_disp = 0.05*D0 # 5% of the pile diameter + limit_rot = 5.0 # 5 deg + direction = 'shrink' if (UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_disp and disp_rot <= limit_rot) else 'grow' - Parameters - ---------- - geom: list - starting guess geometry values - geomKeys : list - List of keys that match the geom list values i.e. 'L','D','zlug' - geomBounds : list,optional - List of upper and lower bounds for each geometry value. - Each entry should be a tuple of upper and lower bounds for each geometry i.e. [(5,10),(10,20)] - loads : dict, optional - Dictionary of maximum anchor loads in horizontal and vertical directions (not including factor of safety). The default is None. - minfs : dict,optional - Minimum factors of safety in horizontal and vertical directions - LD_con : float - Constraint for L/D parameter - fix_zlug : bool - Boolean to decide if zlug should be altered as geometric values are altered. - True = fixed zlug, False = zlug may be changed - plot : bool - Boolean controls if capacity plots are generated or not for the final configuration + max_iter = 200 + iter_count = 0 - Returns - ------- - None. + if direction == 'shrink': + for D in np.arange(D0, 0.49, -0.05): + self.dd['design']['D'] = D + for L in np.arange(L0, 1.95, -0.25): + self.dd['design']['L'] = L + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False) + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + iter_count += 1 + if not all(is_valid(v) for v in [UC_h, UC_v, disp_lat, disp_rot]): + continue + if termination_condition(): + print(f'\nTermination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + else: + for D in np.arange(D0, 3.05, 0.05): + self.dd['design']['D'] = D + for L in np.arange(L0, 50.25, 0.25): + self.dd['design']['L'] = L + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False) + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + iter_count += 1 + status = termination_condition() + if status == 'terminate': + print(f'Termination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + elif status == 'continue': + continue + status = termination_condition() + if status == 'terminate': + print(f'\nTermination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + + if anchType_clean in ['dandg']: + L0, D0 = geom if len(geom) == 2 else [5.0, 1.0] + self.dd['design']['L'] = L0 + self.dd['design']['D'] = D0 + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False) + + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_disp = 0.05*D0 # 5% of the pile diameter + limit_rot = 5.0 # 5 deg + direction = 'shrink' if (UC_v <= 1.0 and disp_lat <= limit_disp and disp_rot <= limit_rot) else 'grow' + max_iter = 200 + iter_count = 0 + + if direction == 'shrink': + for D in np.arange(D0, 0.49, -0.05): + self.dd['design']['D'] = D + for L in np.arange(L0, 1.95, -0.25): + self.dd['design']['L'] = L + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False) + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + iter_count += 1 + if not all(is_valid(v) for v in [UC_v, disp_lat, disp_rot]): + continue + if termination_condition_dandg(): + print(f'\nTermination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + else: + for D in np.arange(D0, 3.05, 0.05): + self.dd['design']['D'] = D + for L in np.arange(L0, 50.25, 0.25): + self.dd['design']['L'] = L + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False) + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + iter_count += 1 + status = termination_condition_dandg() + if status == 'terminate': + print(f'Termination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + elif status == 'continue': + continue + status = termination_condition_dandg() + if status == 'terminate': + print(f'\nTermination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + + print('[Warning] While-loop search reached bounds without meeting criteria.') + + else: + raise ValueError(f"Anchor type '{anchType_clean}' not supported for safety factor input.") + + def getSizeAnchor2(self, geom, geomBounds=None, loads=None, lambdap_con=[3, 6], + zlug_fix=True, safety_factor={'SF_combined': 1.0}, plot=False): ''' - # - - - - Objective and Constraint Functions - - # Define the objective function: Minimize weight of anchor (cost is dependent on weight) - def objective(vars, args): + Grid-based optimization method for envelope anchors (suction, torpedo, plate). + Evaluates UC over a grid of L and D, and selects the point closest to target UC. + ''' + import matplotlib.pyplot as plt + from matplotlib import cm + import matplotlib.colors as mcolor + import numpy as np - geomKeys = args['geomKeys'] - input_loads = args['input_loads'] - fix_zlug = args['fix_zlug'] + anchType_clean = self.dd['type'].lower().replace('', '') - newGeom = dict(zip(geomKeys,vars)) - self.dd['design'].update(newGeom) - if 'suction' in self.dd['type'] and not fix_zlug: - self.dd['design']['zlug'] = (2/3)*newGeom['L'] - - if 'Hm' in input_loads or 'Vm' in input_loads: - anchor_loads = self.getLugForces(mudloads=input_loads) - input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary - # get results - results = self.getAnchorCapacity(loads=input_loads, plot=False) - - return(results['Weight']) - - # constraint for suction bucket sizing only. May add more constraints for other anchors in the future... - def conFun_LD(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): - newGeom = dict(zip(geomKeys, vars)) - self.dd['design'].update(newGeom) + if loads is None: + loads = self.loads + + Hm = loads['Hm'] + Vm = loads['Vm'] + + line_type = getattr(self, 'line_type', 'chain') + d = getattr(self, 'd', 0.16) + w = getattr(self, 'w', 5000.0) + + if anchType_clean not in ['suction', 'torpedo', 'plate']: + raise ValueError(f"Grid-based getSizeAnchor only supports envelope anchors, not '{anchType_clean}'") + + UC_target = 1.0/safety_factor.get('SF_combined', 1.0) + + # Unpack bounds and generate grid + L_vals = np.linspace(geomBounds[0][0], geomBounds[0][1], 10) + D_vals = np.linspace(geomBounds[1][0], geomBounds[1][1], 10) + + L_grid, D_grid = np.meshgrid(L_vals, D_vals) + UC_grid = np.full_like(L_grid, np.nan, dtype=float) + mask = np.full_like(L_grid, False, dtype=bool) + + best_UC, best_L, best_D = None, None, None + results = [] + + for i in range(D_grid.shape[0]): # loop over D + for j in range(D_grid.shape[1]): # loop over L + D = D_grid[i, j] + L = L_grid[i, j] + lambdap = L/D + + if not (lambdap_con[0] <= lambdap <= lambdap_con[1]): + continue + + mask[i, j] = True + self.dd['design']['L'] = L + self.dd['design']['D'] = D + + if anchType_clean == 'suction' and not zlug_fix: + self.dd['design']['zlug'] = (2/3)*L - if 'Hm' in input_loads or 'Vm' in input_loads: - anchor_loads = self.getLugForces(mudloads=input_loads) - input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary + try: + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, + mass_update=True, plot=False) + UC = self.anchorCapacity.get('UC', np.nan) + results.append({ + 'L': L, + 'D': D, + 'UC': UC}) - results = self.getAnchorCapacity(loads=input_loads, plot=False) + if UC > 1e-2 and UC < 10.0: + UC_grid[i, j] = UC + # Find UC closest to target + if best_UC is None or abs(UC - UC_target) < abs(best_UC - UC_target): + best_UC = UC + best_L = L + best_D = D + + except: + continue + + # Update best result + # if best_L is not None and best_D is not None: + self.dd['design']['L'] = best_L + self.dd['design']['D'] = best_D + if anchType_clean == 'suction' and not zlug_fix: + self.dd['design']['zlug'] = (2/3)*best_L + + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, + mass_update=True, plot=plot) + + print('\nFinal Optimized Anchor (Grid-based):') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + + # else: + # print('[Warning] No valid combination found in the grid.') + + # Optional plot + + if plot: + fig, ax = plt.subplots(figsize=(6, 8)) + vmin, vmax = 0.01, 10 + levels = np.logspace(np.log10(vmin), np.log10(vmax), 21) + cp = ax.contourf(D_grid, L_grid, UC_grid, levels=levels, cmap='coolwarm', norm=mcolor.LogNorm(vmin=vmin, vmax=vmax)) + fig.colorbar(cp, ax=ax, label='Unity check (UC)') + ax.contour(D_grid, L_grid, UC_grid, levels=levels, colors='k', linewidths=0.3, alpha=0.3) + ax.contour(D_grid, L_grid, UC_grid, levels=[1.0], colors='red', linewidths=2, linestyles='--') + ax.set_xlabel('Diameter (m)') + ax.set_ylabel('Length (m)') + ax.set_title('Unity Check (UC') + ax.plot(best_D, best_L, 'ro', label='Best match') + ax.annotate('Best match', (best_D, best_L), textcoords="offset points", xytext=(10,10), ha='center', color='red') + ax.legend() + plt.grid(True) + plt.tight_layout() + plt.show() - convalA = newGeom['L']/newGeom['D'] - LD_con[0] - convalB = LD_con[1] - newGeom['L']/newGeom['D'] - conval = min([convalA,convalB]) - # if newGeom['L']/newGeom['D'] >= LD_con[0] and newGeom['L']/newGeom['D'] <= LD_con[1]: - # conval = 1 - # else: - # conval = -1 + #UC_target = 1.0 + closest = min(results, key=lambda x: abs(x['UC'] - UC_target)) + print("Closest to UC_target:") + print(closest) - return(conval) - # constraint to ensure unity check > 1 for suction buckets - def conFun_Suction(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): - if 'Hm' in input_loads or 'Vm' in input_loads: - anchor_loads = self.getLugForces(mudloads=input_loads) - input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary - results = self.getAnchorCapacity(loads=input_loads, plot=False) - #conval = results['UC'] - 1 - conval = 1 - results['UC'] - # convalB = 1 - results['UC'] - return(conval) - - def conFun_DandG(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): + return results + + def getSizeAnchor_BO(self, + geom=[10.0, 2.0], + geomKeys=['L', 'D'], + geomBounds=[(5.0, 15.0), (1.0, 4.0)], + loads=None, + lambdap_con=[3, 6], + zlug_fix=False, + safety_factor={'SF_combined': 1.0}, + n_calls=25, + plot=False, + verbose=True): + ''' + Bayesian optimization to find (D, L) for UC closest to UC_target. + Uses scikit-optimize for surrogate model and efficient sampling. + ''' + from skopt import gp_minimize + from skopt.space import Real + from skopt.utils import use_named_args + import numpy as np - newGeom = dict(zip(geomKeys, vars)) - self.dd['design'].update(newGeom) - if 'Hm' in input_loads or 'Vm' in input_loads: - anchor_loads = self.getLugForces(mudloads=input_loads) - input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary - results = self.getAnchorCapacity(loads=input_loads, plot=False) + if loads is None: + loads = self.loads - return np.array([0.05*newGeom['D'] - results['Lateral displacement'] , 0.25 - results['Rotational displacement']]) - - def conFunH(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): - # if 'suction' in self.dd['type']: - # results = self.getAnchorCapacity(plot=False) - # conval = results['UC'] - 1 - # # if results['UC'] < 1: - # # conval = -1*(results['UC']) - # else: - if 'Hm' in input_loads or 'Vm' in input_loads: - anchor_loads = self.getLugForces(mudloads=input_loads) - input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary - minfs = dict(Ha=minfs['Hm'], Va=minfs['Vm']) - FS, _, _ = self.getFS(loads=input_loads, acceptance_crit=minfs) - conval = FS['Ha'] - 1 - # for key,val in FS.items(): - - # if val/minfs[key]<1: - # if -1*(1-val/minfs[key]) < conval: - # conval = -1*(1-val/minfs[key]) - return(conval) - - def conFunV(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): - if 'Hm' in input_loads or 'Vm' in input_loads: - anchor_loads = self.getLugForces(mudloads=input_loads) - input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary - minfs = dict(Ha=minfs['Hm'], Va=minfs['Vm']) - FS, _, _ = self.getFS(loads=input_loads, acceptance_crit=minfs) - # special case for DEAs - if minfs['Va'] == 0: - conval = 1 - else: - conval = FS['Va'] - 1 - - # print('FS_V',FS['Va']) - return(conval) - - def conBounds(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): + Hm = loads['Hm'] + Vm = loads['Vm'] - newGeom = dict(zip(geomKeys, vars)) - self.dd['design'].update(newGeom) + line_type = getattr(self, 'line_type', 'chain') + d = getattr(self, 'd', 0.16) + w = getattr(self, 'w', 5000.0) - if 'Hm' in input_loads or 'Vm' in input_loads: - anchor_loads = self.getLugForces(mudloads=input_loads) - input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary - results = self.getAnchorCapacity(loads=input_loads, plot=False) + UC_target = 1.0 / safety_factor.get('SF_combined', 1.0) - bound_L_lower = newGeom['L'] - geomBounds[0][0] - bound_L_upper = geomBounds[0][1] - newGeom['L'] - bound_D_lower = newGeom['D'] - geomBounds[1][0] - bound_D_upper = geomBounds[1][1] - newGeom['D'] + # Define the search space + space = [ + Real(geomBounds[1][0], geomBounds[1][1], name='D'), + Real(geomBounds[0][0], geomBounds[0][1], name='L') + ] - return np.array([bound_L_lower, bound_L_upper, bound_D_lower, bound_D_upper]) - - # - - - - - Setup & Optimization - from scipy.optimize import minimize - from copy import deepcopy - - anchType = self.dd['type'] - - # loads['Ha'] = minfs['Ha']*loads['Ha'] - # loads['Va'] = minfs['Va']*loads['Va'] - startGeom = dict(zip(geomKeys,geom)) - print('start geometry: ',startGeom) - # apply initial guess geometry - self.dd['design'].update(startGeom) - - if not 'zlug' in self.dd['design']: - if 'suction' in anchType and not fix_zlug: - self.dd['design']['zlug'] = (2/3)*startGeom['L'] + @use_named_args(space) + def objective(**params): + D = params['D'] + L = params['L'] + + # Apply lambda constraint + lambdap = L/D + if not (lambdap_con[0] <= lambdap <= lambdap_con[1]): + return 100.0 + + self.dd['design']['D'] = D + self.dd['design']['L'] = L + if not zlug_fix: + self.dd['design']['zlug'] = (2/3)*L + + try: + self.getCapacityAnchor( + Hm=Hm, + Vm=Vm, + zlug=self.dd['design']['zlug'], + line_type=line_type, + d=d, + w=w, + mass_update=True, + plot=False) + + UC = self.anchorCapacity.get('UC', np.nan) + except: + UC = np.nan + + if verbose: + print(f"Evaluated D={D:.3f}, L={L:.3f} -> UC={UC:.3f}") + + if not np.isfinite(UC): + return 100.0 + + if UC < UC_target: + return (UC_target - UC)**2 * 0.5 # less penalty for overdesign else: - self.dd['design']['zlug'] = 0 - - # if zlug is fixed, remove it from design variables - if fix_zlug and 'zlug' in geomKeys: - zlug_loc = geomKeys.index('zlug') - startGeom.pop('zlug') - geomKeys.remove('zlug') - geom.pop(zlug_loc) - if geomBounds: - geomBounds.pop(zlug_loc) - - if not loads: + return (UC - UC_target)**2 * 10 # higher penalty for failure + + # Run Bayesian optimization + res = gp_minimize( + objective, + space, + x0=[geom[1], geom[0]], + n_calls=n_calls, + random_state=42, + verbose=verbose + ) + + # Best result + best_D, best_L = res.x + self.dd['design']['D'] = best_D + self.dd['design']['L'] = best_L + if not zlug_fix: + self.dd['design']['zlug'] = (2/3)*best_L + + self.getCapacityAnchor( + Hm=Hm, + Vm=Vm, + zlug=self.dd['design']['zlug'], + line_type=line_type, + d=d, + w=w, + mass_update=True, + plot=plot + ) + UC = self.anchorCapacity.get('UC', np.nan) + + print('\nBayesian Optimized Anchor:') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + print(f'Best UC: {UC:.4f} (target: {UC_target})') + + results = {'D': best_D, 'L': best_L, 'UC': UC, 'result': res} + + return results + # PATCH for GRADIENT method: wrap getCapacityAnchor in safe evaluator + def safe_get_uc(self, Hm, Vm, zlug, line_type, d, w, verbose=False): + try: + self.getCapacityAnchor(Hm, Vm, zlug, line_type, d, w, True, False) + return self.anchorCapacity.get('UC', np.nan) + except Exception as e: + if verbose: + print(f"[Safe Error] {str(e)}") + return np.nan + + def getSizeAnchor_gradient(self, + geom=[10.0, 2.0], + geomKeys=['L', 'D'], + geomBounds=[(5.0, 15.0), (1.0, 4.0)], + loads=None, + lambdap_con=[3, 6], + zlug_fix=False, + safety_factor={'SF_combined': 1.0}, + step_size=0.2, + tol=0.05, + max_iter=30, + verbose=True): + ''' + Gradient-based optimization with early stopping to match UC_target. + ''' + import numpy as np + + if loads is None: loads = self.loads - - if not 'Ha' in loads: - loads = self.getLugForces(mudloads=loads) - - # suction bucket needs to be loads*FS because of capacity envelope calculations in capacity function - if ('Hm' in loads and 'Vm' in loads) and ('Hm' in minfs and 'Vm' in minfs): - input_loads = {'Hm':loads['Hm']*minfs['Hm'], 'Vm':loads['Vm']*minfs['Vm']} - else: - input_loads = {'Ha':loads['Ha']*minfs['Ha'],'Va':loads['Va']*minfs['Va']} + + Hm = loads['Hm'] + Vm = loads['Vm'] + + line_type = getattr(self, 'line_type', 'chain') + d = getattr(self, 'd', 0.16) + w = getattr(self, 'w', 5000.0) + + UC_target = 1.0 / safety_factor.get('SF_combined', 1.0) + + L, D = geom + + for iter in range(max_iter): + lambdap = L / D + if not (lambdap_con[0] <= lambdap <= lambdap_con[1]): + if verbose: + print(f"[Iter {iter}] λ = {lambdap:.2f} out of bounds. Terminating.") + break + + self.dd['design']['L'] = L + self.dd['design']['D'] = D + if not zlug_fix: + self.dd['design']['zlug'] = (2/3)*L + + UC0 = self.safe_get_uc(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, verbose=verbose) + + if not np.isfinite(UC0): + break + + if verbose: + print(f"[Iter {iter}] L={L:.2f}, D={D:.2f}, UC={UC0:.3f}") + + if abs(UC0 - UC_target) < tol: + print("Early stopping: UC within tolerance.") + break + + # Gradient estimate + delta = 0.1 + UC_L = self.safe_get_uc(Hm, Vm, (2/3)*(L + delta), line_type, d, w, verbose=verbose) + UC_D = self.safe_get_uc(Hm, Vm, (2/3)*L, line_type, d, w, verbose=verbose) + + grad_L = (UC_L - UC0)/delta if np.isfinite(UC_L) else 0.0 + grad_D = (UC_D - UC0)/delta if np.isfinite(UC_D) else 0.0 + + # Update + L -= step_size * grad_L + D -= step_size * grad_D + L = np.clip(L, geomBounds[0][0], geomBounds[0][1]) + D = np.clip(D, geomBounds[1][0], geomBounds[1][1]) + + if not (lambdap_con[0] <= L/D <= lambdap_con[1]): + if verbose: + print("Terminated: lambda constraint violated after update.") + break + + self.dd['design']['L'] = L + self.dd['design']['D'] = D + self.dd['design']['zlug'] = (2/3)*L + self.getCapacityAnchor(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, True, True) + + print('\nGradient Optimized Anchor:') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + + return {'D': D, 'L': L, 'UC': self.anchorCapacity.get('UC', np.nan)} + + def getSafetyFactor(self): + ''' + Calculate the safety factor based on the unity checks stored in capacity results. - + Returns + ------- + dict + Dictionary containing safety factors. + ''' + + anchType_clean = self.anchType.lower().replace(' ', '') + + if anchType_clean in ['helical', 'driven', 'dandg']: + UC_v = self.anchorCapacity.get('Unity check (vertical)', None) + UC_h = self.anchorCapacity.get('Unity check (horizontal)', None) + + if UC_v is None or UC_h is None: + print("Warning: Vertical or horizontal unity check (UC) not found in capacity results. Returning NaN.") + return {'SF_vertical': np.nan, 'SF_horizontal': np.nan} + + SF_v = 1.0/UC_v if UC_v != 0 else np.inf + SF_h = 1.0/UC_h if UC_h != 0 else np.inf + + return {'SF_vertical': SF_v, 'SF_horizontal': SF_h} - - # Initial guess for geometry - initial_guess = geom # [val for val in startGeom.values()] # Input values for geometry - # geomKeys = [key for key in startGeom.keys()] - - # Bounds and constraints - if 'suction' in anchType: - # bounds = [(1, 7), (5, 50),()] # Bounds for D and L - # constraints - - constraints = [{'type':'ineq','fun':conFun_LD,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conFun_Suction,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}] - - elif 'dandg' in anchType: - constraints = [{'type':'ineq','fun':conFun_LD,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conFun_DandG,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}] - - else: - constraints = [{'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}] - - # Run the optimization to find sizing that satisfy UC close to 1 - print('optimizing anchor size') - - if 'suction' in anchType or 'dandg' in anchType: - solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), - method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) else: - solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), - method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) - - FS, acceptance, FSdiff = self.getFS(loads=input_loads, acceptance_crit=minfs) - - # adjust starting value if you're far off from the acceptance criteria (in either direction) - if FSdiff_max: - count = 0 - while count<10 and (np.any([abs(FSdiff[key])>FSdiff_max[key] for key in FSdiff.keys()]) or np.any([diff<0 for diff in FSdiff.values()])): - if np.any([diff<.02 for key,diff in FSdiff.items() if minfs[key]>0]) and np.all([diff>=0 for diff in FSdiff.values()]): - # exit loop if you're as close as can be on one of the FS even if other is above diff requirements UNLESS an FS is below minimum reqiured FS - break - print('Factor of Safety not close enough to minimum factor of safety, trying again with adjusted initial guess.') - print(FS) - # calculate new percent difference of FS from min fs - diffPCT = [FSdiff[key]/FS[key] for key in FSdiff] - # create adjustment coefficient based on this or .25, whichever is lower - adjust_coeff = np.min([np.min(diffPCT),0.25]) - # adjust initial guess values by adjustment coefficient - for i,val in enumerate(initial_guess): - initial_guess[i] = val - val*adjust_coeff - # update zlug for suction buckets as needed to be 2/3L - if 'suction' in anchType and not fix_zlug: - zlug_loc = geomKeys.index('zlug') - L_loc = geomKeys.index('L') - initial_guess[zlug_loc] = (2/3)*initial_guess[L_loc] - - print('new initial guess',initial_guess) - # re-run optimization - if 'suction' in anchType or 'dandg' in anchType: - solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), - method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) - else: - solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), - method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) - # re-determine FS and diff from minFS - FS, acceptance, FSdiff = self.getFS(loads=input_loads, acceptance_crit=minfs) - count += 1 + UC = self.anchorCapacity.get('UC', None) + + if UC is None: + print("Warning: Unity check (UC) not found in capacity results. Returning NaN.") + return {'SF_combined': np.nan} + + SF = 1.0/UC if UC != 0 else np.inf + + return {'SF_combined': SF} + + def getCostAnchor(self, ms=None): + ''' + Assign material cost using a Point object and getCost_and_MBL(). + ''' + + # Create or use existing MoorPy system + if ms is None: + ms = mp.System() + + # Create MoorPy Point using makeMoorPyAnchor + self.makeMoorPyAnchor(ms) + + # Check if mass is assigned + if self.mass is None: + if 'Weight pile' in self.anchorCapacity: + self.mass = self.anchorCapacity['Weight pile'] / self.g + elif 'Weight plate' in self.anchorCapacity: + self.mass = self.anchorCapacity['Weight plate'] / self.g + else: + raise KeyError("Missing 'Weight pile' or 'Weight plate' in anchorCapacity. \ + Run getCapacityAnchor() before getCostAnchor(), or define self.mass explicitly.") - # Extract the optimized values of geometry - endGeom = dict(zip(geomKeys,solution.x)) - print('End geometry: ',endGeom) - self.dd['design'].update(endGeom) - if 'suction' in anchType and not fix_zlug: - self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] - results = self.getAnchorCapacity(loads=input_loads,plot=plot) - - # # check if anchor loads are available - # if not self.loads: - # # if not, check if theres a moorpy anchor object and calculate loads from that - # if self.mpAnchor: - # print("Need anchor loads to obtain cost, using getMPForces to determine loads in MoorPy") - # self.getLugForces() - # elif self.ms: - # print('Need anchor loads to obtain cost, creating a MoorPy anchor object and using getMPForces to determine loads in MoorPy') - # self.makeMoorPyAnchor(self.ms) - # self.getLugForces() - # else: - # raise Exception("Need anchor loads to obtain cost") - # # check again if there are loads - # if self.loads: - # c = self.dd['cost'] # set location for clarity - # # calculate individual costs and total cost for the anchor - # c['matCost'], c['instCost'], c['decomCost'] = mp.Point.getcost(self.mpAnchor) - # c['totCost'] = c['matCost'] + c['instCost'] + c['decomCost'] + # Assign mass to MoorPy point + self.mpAnchor.m = self.mass + + cost, MBL, info = self.mpAnchor.getCost_and_MBL() + + # Store results + self.cost = { + 'Material cost': cost, + 'MBL': MBL, + 'unit_cost': cost/self.mpAnchor.m } + + return self.cost + + def getCombinedPlot(self): + ''' + Create a plot showing the suction pile and the inverse catenary overlay in the same coordinate system. + ''' + from anchors_famodel.capacity_load import getTransferLoad + from anchors_famodel.capacity_plots import plot_suction + + if self.anchType.lower() != 'suction': + raise NotImplementedError("getCombinedPlot only supports suction piles.") + + # Extract design inputs + design = self.dd['design'] + D = design['D'] + L = design['L'] + zlug = design['zlug'] + + if self.soil_profile is None or self.soil_type is None: + raise ValueError("Soil profile or type not assigned. Use setSoilProfile first.") + + soil_profile = self.soil_profile + soil_type = self.soil_type + z0 = soil_profile[0]['top'] + Hm = self.loads['Hm'] + Vm = self.loads['Vm'] + thetam = self.loads.get('thetam', np.degrees(np.arctan2(Vm, Hm))) + + line_type = getattr(self, 'line_type', 'chain') + d = getattr(self, 'd', 0.16) + w = getattr(self, 'w', 5000.0) + + # Get inverse catenary path + layers, result = getTransferLoad( + profile_map=[{'layers': self.soil_profile}], + Tm=np.sqrt(Hm**2 + Vm**2), + thetam=thetam, + zlug=zlug, + line_type=line_type, + d=d, + w=w, + plot=False + ) + + drag_values = np.array(result['drag_values']) + depth_values = -np.array(result['depth_values'])[::-1] + + x_start = D/2 + drag_values[0] + z_start = zlug + drag_transformed = x_start - drag_values + depth_transformed = z_start + (depth_values- depth_values[0]) + + # Plot suction pile + plot_suction(soil_profile, L, D, z0=z0, zlug=zlug, title='Suction Pile and Mooring Line Load Path') + + + # Overlay inverse catenary path + plt.plot(drag_transformed, depth_transformed, color='b', lw=2.0, label='Inverse catenary') + plt.plot(drag_transformed[-1], depth_transformed[-1], 'ro', label='Mudline end') + plt.plot( drag_transformed[0], depth_transformed[0], 'go', label='Embedded end') + + n = 2e6 + Tm = result['Tm'] + Ta = result['Ta'] + thetaa = result['thetaa'] + + plt.arrow(drag_transformed[-1], depth_transformed[-1], + Tm*np.cos(np.deg2rad(thetam))/n, -Tm*np.sin(np.deg2rad(thetam))/n, + head_width=0.25, head_length=0.5, color='r', label='Mudline load') - # def getMass(self,uhc_mode): - # '''find mass and/or UHC of anchor from MoorProps and store in design dictionary - # Parameters - # ---------- - # uhc_mode : boolean - # True : obtain UHC from mass - # False : obtain Masss and UHC from loads - # ''' - # if uhc_mode: # if looking for UHC given mass - # if self.dd['design']['m']: # check anchor mass is given - # self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=1, mass_int=self.dd['design']['m'], anchor=self.dd['type'], soil_type=self.anchorProps['soil_type']) - # else: - # raise Exception("Need anchor mass to calculate UHC when uhc_mode = 1") - # else: # if looking for mass and UHC given loads - # if self.loads: # check the loads section exists - # self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=0, fx=self.loads['ff'], fz=self.loads['fz'], anchor=self.dd['type'],soil_type=self.dd['soil_type'],method=self.loads['method']) - # elif self.mpAnchor: - # print("Need anchor loads to obtain mass, using getMPForces to determine loads in MoorPy") - # self.getLugForces() - # self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=0, fx=self.loads['ff'], fz=self.loads['fz'], anchor=self.dd['type'],soil_type=self.dd['soil_type'],method=self.loads['method']) - # elif self.ms: - # print('Need anchor loads to obtain mass, creating a MoorPy anchor object and using getMPForces to determine loads in MoorPy') - # self.makeMoorPyAnchor(self.ms) - # self.getLugForces() - # self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=0, fx=self.loads['ff'], fz=self.loads['fz'], anchor=self.dd['type'],soil_type=self.dd['soil_type'],method=self.loads['method']) - # else: - # raise Exception("Need anchor loads to obtain mass") + plt.arrow(drag_transformed[0], depth_transformed[0], + Ta*np.cos(np.deg2rad(thetaa))/n, -Ta*np.sin(np.deg2rad(thetaa))/n, + head_width=0.25, head_length=0.5, color='g', label='Padeye load') + + xmax = max(drag_transformed[-1] + D, 2*D) + plt.xlim(-D, xmax) + plt.legend() + plt.grid(True) + plt.tight_layout() + plt.show() + diff --git a/famodel/anchors/anchor_capacity.py b/famodel/anchors/anchor_capacity.py deleted file mode 100644 index e3acb56b..00000000 --- a/famodel/anchors/anchor_capacity.py +++ /dev/null @@ -1,153 +0,0 @@ -"""The anchor capacity calculation 'switchboard' that holds generic -anchor capacity functions and calls the specific calculation functions -from other modules depending on the soil and anchor information.""" - -import matplotlib.pyplot as plt -import numpy as np - -import moorpy.MoorProps as mprop - -from .capacity_plate import getCapacityPlate -from .capacity_suction import getCapacitySuction -from .capacity_dandg import * - - - - - -def anchorCapacity(anchor, soil, display=0): - '''Calculate anchor holding capacity based on specified anchor and soil - information. - - Parameters - ---------- - anchor : dictionary - anchor description - soil : dictionary - soil description. Can be a keyword ([_/soft/medium/hard] clay, or sand) - for the level 1 model, or a soilProps dict for the level 2 model. - model_level : int - 1 or 2. - - Returns - ------- - UHC: float - required anchor ultimate holding capacity [kN] - info: dict - dictionary with additional information depending on the model. - ''' - - - if model_level == 1: # soil keyword indicates level 1 models - - - # calls level 1 anchor capacity function, with anchor/soil types and default assumptions - uhc, mass, info = mprop.getAnchorMass(uhc_mode=True, mass_int=anchor['mass'], - anchor=anchor['type'], soil_type=soil['class'], - method='static', display=0) - - #fx, fz = anchor_curves.anchorCapacity(0, 0, 0, anchor=anchor['type'], - # soil_type=soil['class'], display=display) - - - elif model_level==2: # dict indicates a soilProps dictionary - - # >>> we probably need anchor details too then ... - - - # For now the anchor properties get checked in this function - # but in the future they coudl be moved to the individual functions. - - if anchor['type'] == 'DEA': - # make curves from - pass - - elif anchor['type'] == 'SCA': - - L = getFromDict(anchor, 'length') - D = getFromDict(anchor, 'diameter', default=L/6) - thick = getFromDict(anchor, 'thickness', default=L/100) - F_ang = np.degrees(np.atan2(Fz, Fx)) # load inclination angle [deg] - - if soil['class'] == 'clay': - - gamma = getFromDict(soil, 'gamma', default=4.7) - Su0 = getFromDict(soil, 'So0' , default=2.39) - k = getFromDict(soil, 'k' , default=1.41) - alpha = getFromDict(soil, 'alpha', default=0.7) - SF = 2 - - results = getCapacitySuction(L, L_D_aspect=L/D, D_t_aspect=D/thick, - A_angle=F_ang, Su0=Su0, k=k, - Alpha=alpha, gamma=gamma, J=1/SF) - - elif soil['class'] == 'sand': - - gamma = getFromDict(soil, 'gamma', default=9.0) - phi = getFromDict(soil, 'phi' , default=30) - results = getCapacitySuction(L, L_D_aspect=L/D, D_t_aspect=D/thick, - A_angle=F_ang, gamma=gamma, Phi=phi) - - else: - #raise Exception(f"soil class '{soil.class}' is not supported.") - pass - - - elif anchor['type'] == 'VLA': - - # same plate capacity calc as SEPLA for now - will in future consider angle - - A = getFromDict(anchor, 'area') - thick = getFromDict(anchor, 'thickness', default=np.sqrt(L)/40) - H = getFromDict(anchor, 'embedment') # embedment depth [m] - - if soil['class'] == 'clay': - - gamma = getFromDict(soil, 'gamma', default=4.7) - Su0 = getFromDict(soil, 'So0' , default=2.39) - k = getFromDict(soil, 'k' , default=1.41) - - results = getCapacityPlate(A, B_t_aspect=np.sqrt(L)/thick, - Hs=H, Bita=30, Los=0.05, - gamma=gamma, So0=So0, k=k) - else: - raise Exception("Only clay soil is supported for this anchor type.") - - - elif anchor['type'] == 'SEPLA': - - A = getFromDict(anchor, 'area') - thick = getFromDict(anchor, 'thickness', default=np.sqrt(L)/40) - H = getFromDict(anchor, 'embedment') # embedment depth [m] - - if soil['class'] == 'clay': - - gamma = getFromDict(soil, 'gamma', default=4.7) - Su0 = getFromDict(soil, 'So0' , default=2.39) - k = getFromDict(soil, 'k' , default=1.41) - - results = getCapacityPlate(A, B_t_aspect=np.sqrt(L)/thick, - Hs=H, Bita=30, Los=0.05, - gamma=gamma, So0=So0, k=k) - else: - raise Exception("Only clay soil is supported for this anchor type.") - - else: - raise Exception(f"Anchor type '{anchor.type}' is not yet supported in hte intermediate anchor model set") - - - - print(f"UHC input: fx:{fx} fz:{fz} -- Mass: {mass}, Cost: {cost}") - info["UHC input"] = fx,fz #[kN] - info["Capacity_sf"] = capacity_sf #[kN] - info["Mass"] = mass #[mT] - info["Cost"] = cost #[$/mT] - #info["Length"] = L - info["Area"] = area - - else: - raise Exception("Model level must be 1 or 2") - - - return capacity, info - diff --git a/famodel/anchors/anchor_conflict_backup.py b/famodel/anchors/anchor_conflict_backup.py new file mode 100644 index 00000000..ff882af2 --- /dev/null +++ b/famodel/anchors/anchor_conflict_backup.py @@ -0,0 +1,1153 @@ +"""Anchor class for FAModel, containing information and key methods for anchors of mooring lines + Work in progress +""" +import moorpy as mp +import numpy as np +from famodel.famodel_base import Node +from famodel.mooring.mooring import Mooring +import famodel.platform.platform +from collections import defaultdict +import shapely as sh + + +class Anchor(Node): + + def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, + g=9.81, rho=1025): + ''' + Parameters + ---------- + dd: dictionary + Design dictionary that contains all information on an anchor for a mooring line/shared mooring + { + type: # anchor type (plate,suction_pile,torpedo_pile,helical_pile,driven_pile,dandg_pile) + design: # all geometric info from yaml file, only need to include info relevant to your anchor type + A plate anchor area + D anchor diameter (or helix diameter for helical piles) + D1 torpedo anchor wing diameter + D2 torpedo anchor shaft diameter + d helical pile shaft diameter + L pile anchor length + L1 torpedo anchor wing length + L2 torpedo anchor shaft length + zlug padeye z elevation (+ down into the soil) + beta angle of plate anchor after keying (optional) + cost: + matCost: # material cost + instCost: # installation cost + decomCost: # decomissioning cost + } + ms: system object + MoorPy system object the anchor is in + + r: list + Location of anchor in x,y,z + + aNum: int + entry number in project.anchorList dictionary (may remove...) + id: str/int + unique id of this object + g: float + acceleration due to gravity in m/s^2 + rho: float + density of water in kg/m^3 + ''' + # Initialize as a node + Node.__init__(self,id) + + # Design description dictionary for this Anchor + self.dd = dd + + # MoorPy system this anchor is in + self.ms = ms + + # x,y,z location of anchor + self.r = r + + # anchor index in array mooring list (only used for shared moorings/anchors) + self.aNum = aNum + + # MoorPy anchor object + self.mpAnchor = None + + # get environment info + self.g = g # acceleration due to gravity (m/s^2) + self.rho = rho # density of fluid (kg/m^3) + + # anchor mass + if 'mass' in self.dd['design']: + self.mass = self.dd['design']['mass'] + else: + self.mass = None + + # Dictionaries for additional information + # anchor capacity + self.anchorCapacity = {} + self.safety_factors = {} # calculated safety factor + self.safety_factors_required = {} # minimum allowable safety factor + + # anchor costs + self.cost = {} + + self.loads = {} + ''' + { + Hm: # horizontal maximum anchor loads at mudline [N] + Vm: # vertical maximum anchor loads at mudline [N] + thetam: # angle of load at the mudline [rad] + Ha: # horizontal maximum loads at lug + Va: # vertical maximum loads at lug + thetaa: # angle of load at lug + method: # dynamic or static method of calculation + } + ''' + self.soilProps = {} + self.failure_probability = {} + + # environmental impact + self.env_impact = {} + + + # self.cost = {} + + def setSoilProfile(self, profile_map): + ''' + Assign a soil profile directly from a single CPT. + Assumes profile_map is a list with only one entry. + ''' + if len(profile_map) != 1: + raise ValueError("setSoilProfile expects a profile_map with exactly one CPT.") + + cpt = profile_map[0] + self.soil_profile = cpt['layers'] + self.profile_name = cpt.get('name', 'CPT_Assigned') + + # Extract soil types from layers + soil_types = [layer['soil_type'] for layer in self.soil_profile] + self.soil_type_list = list(set(soil_types)) + self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed' + + # Group layers by soil type + soilProps = defaultdict(list) + for layer in self.soil_profile: + layer_copy = layer.copy() + soil_type = layer_copy.pop('soil_type') + soilProps[soil_type].append(layer_copy) + self.soilProps = dict(soilProps) + + print(f"[Anchor] Assigned soil profile from {self.profile_name} with soil types {self.soil_type_list}") + + + def makeMoorPyAnchor(self, ms): + '''Create a MoorPy anchor object in a moorpy system + Parameters + ---------- + ms : class instance + MoorPy system + + Returns + ------- + ms : class instance + MoorPy system + + ''' + # create anchor as a fixed point in MoorPy system + ms.addPoint(1,self.r) + # assign this point as mpAnchor in the anchor class instance + self.mpAnchor = ms.pointList[-1] + + # add mass if available + if 'm' in self.dd['design'] and self.dd['design']['m']: + self.mpAnchor.m = self.dd['design']['m'] + # set anchor diameter + if 'd' in self.dd['design'] and self.dd['design']['d']: + self.mpAnchor.d = self.dd['design']['d'] + # set the point as an anchor entity + self.mpAnchor.entity= {'type': 'anchor'} + if 'type' in self.dd: + self.mpAnchor.entity['anchor_type']=self.dd['type'] + + return(ms) + + + def getAnchorCapacity(self,ground_cons=None,installAdj=1,profile=None,loads=None,plot=True): + ''' + Calls anchor capacity functions developed by Felipe Moreno for the correct anchor type + + Parameters + ---------- + ground_conds : dict, optional + Ground conditions dictionary with the key as the soil type name, values as soil info such as UCS,Em,phi,gamma,effective stress,etc. The default is None. + If no dict provided, the ground conds will be pulled from the anchor soilProps property + installAdj : float, optional + Adjustment to the capacity based on installation (dummy variable for now, but future installation functions + will dictate this value) + profile : 2D array, optional + 2d array of depths (m) and corresponding undrained shear strength (Pa). Su must not be zero + (change to small value such as .001), but z must always start at 0. Ex: array([z1,Su1],[z2,Su2],...) + Used only for driven pile and drilled and grouted pile anchors. + loads : dict, optional + Dictionary of loads on the anchor at the lug point in [N]. If not provided, will use the loads dictionary property + of the anchor. If this is empty and it is needed for the capacity function (i.e. driven piles) then + the anchor.getLugForces() function will be called. + + Returns + ------- + results : dict + Dictionary of capacity of the anchor (generally a max force [N] in H and V, but can be a max displacement (driven, dandg piles)) + + ''' + # - - - - set details - - - - + anchType = self.dd['type'] + geom = self.dd['design']# geometric anchor information + + if not ground_cons: + soil = next(iter(self.soilProps.keys()), None) # soil type + ground_conds = self.soilProps[soil] + else: + soil = next(iter(ground_cons.keys())) + ground_conds = ground_cons[soil] + + for key,prop in ground_conds.items(): + if isinstance(prop,list) or isinstance(prop,np.ndarray): + if len(prop)>1: + print('Warning: Only homogeneous soils are supported at this time. Only the first item in a property list will be used.') + break + else: + ground_conds[key] = prop[0] + + + if loads: + # find out if mudline loads or anchor loads + if not 'Ha' in loads: + # get loads at lug + loads = self.getLugForces(mudloads=loads,plot=plot) + else: + loads = self.loads + + + + # logic to determine what functions to call based on anchor type and soil type... + + # - - - - plate anchors - - - - + if anchType == 'SEPLA' or anchType == 'DEA' or anchType == 'DEPLA' or anchType == 'VLA' or anchType == 'plate': + from .anchors_famodel.capacity_plate import getCapacityPlate + if 'clay' in soil or 'mud' in soil: + # write or overwrite beta in geom dictionary from loads function + if anchType != 'DEA': + if not 'beta' in geom: + if not 'thetaa' in loads: + # calculate thetaa from Ha and Va + loads['thetaa'] = np.arctan2(loads['Va'],loads['Ha']) + # loads = self.getLugForces(plot=plot) + geom['beta'] = 90 - loads['thetaa'] + else: + geom['beta'] = 0 + if 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds: + results = getCapacityPlate(geom['A'], geom['beta'], geom['zlug'], 'clay', ground_conds['gamma'], + Su0=ground_conds['Su0'], k=ground_conds['k']) + else: + raise Exception('Ground conditions dictionary needs Su0, k, gamma information for clay plate anchors') + else: + print(f'Warning: Soil type {soil} is not compatible with plate anchors (SEPLA/DEPLA/DEA/VLA)') + + # - - - - suction buckets - - - - + elif 'suction' in anchType: + from .anchors_famodel.capacity_suction import getCapacitySuction + # check loads have been calculated (needed for capacity function in this case) + if not 'Ha' in loads: + # call getMPForces function + loads = self.getLugForces(plot=plot) + if 'sand' in soil: + if 'phi' in ground_conds and 'Dr' in ground_conds: + results = getCapacitySuction(geom['D'], geom['L'], geom['zlug'], + loads['Ha']/1000, loads['Va']/1000, + 'sand', ground_conds['gamma'], + phi=ground_conds['phi'], + Dr=ground_conds['Dr'], plot=plot) + else: + raise Exception('Ground conditions dictionary needs phi and relative density information for sand suction pile anchor') + elif 'clay' in soil or 'mud' in soil: + if 'Su0' in ground_conds and 'k' in ground_conds and 'alpha' in ground_conds:# and 'gamma_sub' in ground_conds: + results = getCapacitySuction(geom['D'],geom['L'], geom['zlug'], + loads['Ha']/1000, loads['Va']/1000, + 'clay', ground_conds['gamma'], + Su0=ground_conds['Su0'], + k=ground_conds['k'], plot=plot) + results['Horizontal max.'] = results['Horizontal max.'] + results['Vertical max.'] = results['Vertical max.'] + + else: + raise Exception('Ground conditions dictionary needs Su0, k, and alpha information for clay suction pile anchor') + else: + print(f'Warning: Soil type {soil} is not compatible with suction pile anchor') + + # - - - - helical piles - - - - + elif 'helical' in anchType: + from .anchors_famodel.capacity_helical import getCapacityHelical + if 'sand' in soil: + if 'phi' in ground_conds and 'gamma' in ground_conds: + results = getCapacityHelical(geom['D'], geom['L'], geom['d'], + geom['zlug'], 'sand', + ground_conds['gamma'], + phi=ground_conds['phi'], + Dr=ground_conds['Dr']) + results['Vertical max.'] = results['Capacity'] + else: + raise Exception('Ground conditions dictionary needs phi, gamma and relative density information for clay helical pile anchor') + elif 'clay' in soil or 'mud' in soil: + if not 'alpha_star' in ground_conds: + ground_conds['alpha_star'] = ground_conds['alpha'] + if 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds: + results = getCapacityHelical(geom['D'], geom['L'], geom['d'], + geom['zlug'], 'clay', + ground_conds['gamma'], + Su0=ground_conds['Su0'], + k=ground_conds['k']) + results['Vertical max.'] = results['Capacity'] + else: + raise Exception('Ground conditions dictionary needs Su0, k, gamma, and alpha_star information for clay helical pile anchor') + else: + print(f'Warning: Soil type {soil} is not compatible with helical pile anchor') + + # - - - - torpedo piles - - - - + elif 'torpedo' in anchType: + from .anchors_famodel.capacity_torpedo import getCapacityTorpedo + if 'clay' in soil or 'mud' in soil: + if 'Su0' in ground_conds and 'k' in ground_conds and 'alpha' in ground_conds: + results = getCapacityTorpedo(geom['D1'], geom['D2'], + geom['L1'], geom['L2'], + geom['zlug'], 'clay', + ground_conds['Su0'], + ground_conds['k'], + ground_conds['alpha']) + results['Horizontal max.'] = results['Horizontal max.'] + results['Vertical max.'] = results['Vertical max.'] + else: + raise Exception('Ground conditions dictionary needs Su0, k, and alpha information') + else: + print('Warning: Soil type {soil} is not compatible with torpedo pile anchor') + + # - - - - driven piles - - - - + elif 'driven' in anchType: # driven pile anchor + # check loads have been calculated (needed for capacity function in this case) + if not 'Ha' in loads: + # call getLugForces function + loads = self.getLugForces(plot=plot) + H_inc = loads['Ha']*0.1 # increment H by 10% of Ha load in the while loops to back-calc max H from displacements + H = 0 + # check soil + if 'weak_rock' in soil: + from .anchors_famodel.capacity_drivenrock import getCapacityDrivenRock + + if not profile: + if 'UCS' in ground_conds and 'Em' in ground_conds: + profile = [[0,ground_conds['UCS'],ground_conds['Em']], + [75,ground_conds['UCS'],ground_conds['Em']]] #profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['UCS'],ground_conds['Em']))] + else: + raise Exception('Ground conditions dictionary needs UCS, Em, and depth information for weak rock driven pile anchor') + + y, z, results = getCapacityDrivenRock(profile, geom['L'], geom['D'], + geom['zlug'], loads['Va'], + loads['Ha'], plot=plot) + + # loop through, calling capacity with larger H values until a displacement value goes above limit + while results['Lateral displacement']< 0.05*geom['D'] and results['Rotational displacement'] < 0.25: + # increment H + H += H_inc + # call capacity function + y, z, results = getCapacityDrivenRock(profile, geom['L'], + geom['D'], geom['zlug'], + loads['Va'], H=H, plot=plot) + + + elif 'sand' in soil: + from .anchors_famodel.capacity_drivensoil import getCapacityDrivenSoil + if profile or ('gamma' in ground_conds and 'Dr' in ground_conds and 'phi' in ground_conds): + if not profile: + profile = [[0,ground_conds['phi'],ground_conds['gamma'],ground_conds['Dr']], + [75,ground_conds['phi'],ground_conds['gamma'],ground_conds['Dr']]] #profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['phi'],ground_conds['gamma']))] + + y, z, results = getCapacityDrivenSoil(profile, 'sand', + geom['L'], geom['D'], + geom['zlug'], loads['Va'], + loads['Ha'], plot=plot) + if geom['zlug'] > 0: + # need to check bending moment if lug is below mudline (+ zlug) + # loop through, calling capacity with larger H values until a displacement value goes above limit + while results['Lateral displacement']<= 0.05*geom['D'] and results['Bending moment'] <= results['Plastic moment']: + # increment H by 10% of load + H += H_inc + # call capacity function + y, z, results = getCapacityDrivenSoil(profile,'clay', + geom['L'], geom['D'], + geom['zlug'], loads['Va'], + H=H, plot=plot) + + else: + while results['Lateral displacement']<= 0.05*geom['D'] and results['Rotational displacement'] <= 0.25: + # increment H by 10% of load + H += H_inc + # call capacity function + y, z, results = getCapacityDrivenSoil(profile, 'clay', + geom['L'], geom['D'], + geom['zlug'], loads['Va'], + H=H, plot=plot) + else: + raise Exception('Ground conditions dictionary needs phi, gamma, and depth information for sand driven pile anchor') + elif 'clay' in soil or 'mud' in soil: + from .anchors_famodel.capacity_drivensoil import getCapacityDrivenSoil + #if profile or ('Su' in ground_conds and 'gamma' in ground_conds and 'depth' in ground_conds) or ('Su0' in ground_conds and 'k' in ground_conds): + if not profile: + if 'Su' in ground_conds and 'depth' in ground_conds and 'gamma' in ground_conds: + profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['Su'],ground_conds['gamma']))] + elif 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds: + Su = ground_conds['Su0']+ground_conds['k']*75 + profile = [[0,ground_conds['Su0'],ground_conds['gamma']],[75,Su,ground_conds['gamma']]] + else: + raise Exception('Ground conditions dictionary needs information for clay driven pile anchor') + + y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'],loads['Ha'], plot=plot) + + if geom['zlug'] > 0: + # need to check bending moment if lug is below mudline (+ zlug) + # loop through, calling capacity with larger H values until a displacement value goes above limit + while results['Lateral displacement']<= 0.05*geom['D'] and results['Bending moment'] <= results['Plastic moment']: + # increment H by 10% of load + H += H_inc + # call capacity function + y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'], H=H, plot=plot) + + else: + while results['Lateral displacement']<= 0.05*geom['D'] and results['Rotational displacement'] <= 0.25: + # increment H by 10% of load + H += H_inc + # call capacity function + y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'], H=H, plot=plot) + + + else: + print(f'Warning: Soil type {soil} is not compatible with driven pile anchors') + + # - - - - drilled and grouted piles - - - - + elif 'dandg' in anchType: # drill and grout pile + from .anchors_famodel.capacity_dandg import getCapacityDandG + # check for correct soil + if 'rock' in soil: + # check loads have been calculated (needed for capacity function in this case) + if not 'Ha' in loads: + # call getMPForces function + loads = self.getLugForces(plot=plot) + # check for correct ground properties + if profile or ('UCS' in ground_conds and 'Em' in ground_conds): + if not profile: + profile = [[0,ground_conds['UCS'],ground_conds['Em']],[75,ground_conds['UCS'],ground_conds['Em']]] #[list(x) for x in list(zip(ground_conds['depth'],ground_conds['UCS'],ground_conds['Em']))] + + # call capacity function once to get displacement values + y, z, results = getCapacityDandG(profile,geom['L'],geom['D'], + geom['zlug'], loads['Va'], + loads['Ha'], plot=plot) + H_inc = loads['Ha']*0.1 # increment H by 10% of Ha load + H = H_inc # start H at 10% of Ha load + # loop through, calling capacity with larger H values until a displacement value goes above limit + while results['Lateral displacement']< 0.05*geom['D'] and results['Rotational displacement'] < 0.25: + # call capacity function + y, z, results = getCapacityDandG(profile, geom['L'], geom['D'], + geom['zlug'], loads['Va'], + H=H, plot=plot) + # increment H + H += H_inc + else: + raise Exception('Ground conditions dictionary need UCS and Em information for drill and grout pile') + else: + print(f'Warning: soil type {soil} is not compatible with drill and grout pile') + + # - - - - anchor type not recognized or supported - - - - + else: + raise Exception(f'Anchor type {anchType} is not supported at this time') + + # - - - - save relevant results in dictionary using common terms - - - - + # capacity = cap*installAdj ??? OR is installAdj an input to the capacity functions? + # save capacity + if 'dandg' in anchType or 'driven' in anchType: # will take in dandg, dandg_pile, driven, driven_pile + self.anchorCapacity['Lat_max'] = results['Lateral displacement'] # [deg] + if 'Rotational displacement' in results: + self.anchorCapacity['Rot_max'] = results['Rotational displacement'] # [deg] + elif 'Bending moment' in results: + self.anchorCapacity['Mbend_max'] = results['Bending moment'] + self.anchorCapacity['Va_max'] = results['Axial capacity'] # [N] + self.anchorCapacity['Ha_max'] = H + + else: + if 'Horizontal max.' in results: + self.anchorCapacity['Ha_max'] = results['Horizontal max.']*1000 # [N] + self.anchorCapacity['Va_max'] = results['Vertical max.']*1000 # [N] + self.mass = results['Weight']*1000/self.g # mass in [kg] + + # add on extra for drag-embedment anchors (flukes) + if 'DEA' in anchType: + self.mass *= 1.75 + + + return(results) + + def getMudlineForces(self, max_force=False,lines_only=False, seabed=True, xyz=False,project=None): + '''Find forces on anchor at mudline using the platform.getWatchCircle method or MoorPy Point.getForces method. + Optionally, get forces at anchor lug location with getTransferLoad function in capacity_loads.py. + Stores in loads dictionary + Parameters + ---------- + max_force : boolean, optional + Find and save the maximum force on the anchor (True) or just get force at the current MoorPy system state (False) + lines_only : boolean, optional + Calculate forces from just mooring lines (True) or not (False). Default is false + seabed : boolean, optional + Include effect of seabed pushing up the anchor (True) or not (False). Default is true + xyz : boolean, optional + Return forces in x,y,z DOFs (True) or only the enabled DOFs (False). Default is false + + ''' + Platform = famodel.platform.platform.Platform + if max_force: + if project: + # get watch circle of platform(s) + project.arrayWatchCircle() + else: + # find platform associated with this anchor + for att in self.attachments.values(): + if isinstance(att['obj'],Mooring): + for attM in att['obj'].attached_to: + if isinstance(attM,Platform): + locx,locy,maxVals = attM.getWatchCircle() + # call getForces method from moorpy point object + else: + loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz) + self.loads['Hm'] = np.sqrt(loads[0]**2+loads[1]**2) # mudline forces in [N] + self.loads['Vm'] = loads[2] # [N] + self.loads['thetam'] = np.degrees(np.arctan(self.loads['Vm']/self.loads['Hm'])) # [deg] + self.loads['mudline_load_type'] = 'current_state' + + # loads determined from moorpy are static + self.loads['method'] = 'static' + + return(self.loads) + + def getLugForces(self, mudloads=None, max_force=True, plot=False): + ''' + Find forces on an anchor at the lug point based on the mudline forces and angles. Calls getTransferFunction script + + Parameters + ---------- + mudloads : dict, optional + Dictionary of max mudline forces. The default is None. + + Returns + ------- + loads: dict + Dictionary of loads at the lug point [N] + + ''' + from .anchors_famodel.capacity_load import getTransferLoad + + nolugload = False + + if not mudloads: + if not self.loads: + # get max mudline forces first + self.getMudlineForces(max_force=max_force) + elif not 'mudline_load_type' in self.loads: + raise KeyError("Loads dictionary must specify 'mudline_load_type'='current_state' or 'mudline_load_type'='max', where 'max' indicates the loads are maximum loads.") + elif max_force and self.loads['mudline_load_type'] != 'max': + # need max forces, not current state + self.getMudlineForces(max_force=True) + mudloads = self.loads + else: + # check syntax + if not 'Hm' in mudloads or not 'Vm' in mudloads: + raise KeyError('Mudline load dictionary must have Hm and Vm for horizontal load and vertical load (in [N]) at the mudline') + if not 'thetam' in mudloads: + mudloads['thetam'] = np.degrees(np.arctan(mudloads['Vm']/mudloads['Hm'])) + + def makeEqual_TaTm(mudloads): + mudloads['Ha'] = mudloads['Hm'] # [N] + mudloads['Va'] = mudloads['Vm'] # [N] + mudloads['thetaa'] = mudloads['thetam'] # [deg] + + if 'zlug' in self.dd['design']: + if self.dd['design']['zlug'] > 0: + # get line type + for att in self.attachments.values(): + if isinstance(att['obj'],Mooring): + mtype = att['obj'].dd['sections'][0]['type']['material'] + if not 'chain' in mtype: + print('No chain on seafloor, setting Ta=Tm') + nolugload = True + break + else: + md = att['obj'].dd['sections'][0]['type']['d_nom'] + mw = att['obj'].dd['sections'][0]['type']['w'] + soil = next(iter(self.soilProps.keys()), None) + ground_conds = self.soilProps[soil] + # update soil conds as needed to be homogeneous + for key,prop in ground_conds.items(): + if isinstance(prop,list) or isinstance(prop,np.ndarray): + if len(prop)>1: + print('Warning: Only homogeneous soils are supported at this time. Only the first item in a property list will be used.') + break + else: + ground_conds[key] = prop[0] + + Tm = np.sqrt(mudloads['Hm']**2+mudloads['Vm']**2) # [N] + if 'clay' in soil or 'mud' in soil and not nolugload: + # Tm, thetam, zlug, line_type, d, soil_type, Su0=None, k=None, w=None + try: + loadresults = getTransferLoad(Tm/1000,mudloads['thetam'], + self.dd['design']['zlug'],mtype,md, + 'clay',Su0=ground_conds['Su0'], + k=ground_conds['k'],w=mw/1000, + plot=plot) # output Ha and Va (convert weight to kN/m) + except Exception as e: + print(e) + print('Unable to get loads at anchor lug location. Setting Ta = Tm') + nolugload = True + elif 'sand' in soil and not nolugload: + soil = 'sand' + try: + loadresults = getTransferLoad(Tm/1000, self.loads['thetam'], + self.dd['design']['zlug'], + mtype, md, soil, + gamma=ground_conds['gamma'], + phi=ground_conds['phi'], + delta=ground_conds['delta'], + w=mw/1000,plot=plot) # output Ha and Va (convert weight to kN/m) + except Exception as e: + print(e) + print('Unable to get loads at anchor lug location. Setting Ta = Tm') + nolugload = True + elif 'rock' in soil and not nolugload: + raise ValueError('zlug should be <= 0 for rock.') + + # if loadresults['V']<0: + # # results are invalid + # print('Warning: invalid results for the combination of anchor ',self.dd['type'],' soil ',soil,' and loads ',mudloads,'. Setting Ha=Hm, Va=Vm, thetaa=thetam') + # makeEqual_TaTm(mudloads) + if nolugload: + makeEqual_TaTm(mudloads) + else: + mudloads['Ha'] = loadresults['H']*1000 # [N] + mudloads['Va'] = loadresults['V']*1000 # [N] + mudloads['thetaa'] = loadresults['angle'] # [deg] + else: + # Ha = Hm because zlug is at mudline or above + makeEqual_TaTm(mudloads) + else: + print('No zlug given, assuming loads at mudline = loads at anchor lug') + makeEqual_TaTm(mudloads) + + if not 'method' in mudloads: + # assume mudloads are static unless told otherwise + # loads determined from moorpy are static + mudloads['method'] = 'static' + else: + mudloads['method'] = mudloads['method'] + + return mudloads + + def getFS(self, loads=None, acceptance_crit=None): + ''' + Compute safety factor for loads on the anchor + + Parameters + ---------- + loads : dict, optional + Dictionary of loads on the anchor. + acceptance_crit : dict, optional + Dictionary of acceptable factors of safety for each load type. + Key is the load type, and value is the minimum acceptable safety factor. + Default is None (in which case no comparison between FS and acceptance criteria is calculated) + + Returns + ------- + FS : dict + Dictionary of safety factors (often horizontal and vertical load SFs, but could be displacement SFs (drilled and grouted/driven piles)) + acceptance : dict + Dictionary of bools that state whether the FS>=acceptance_crit for each load + acceptance_margin : dict + Dictionary of difference between FS and acceptance criteria for each load type + + + ''' + if not loads: + if not 'Ha' in self.loads: + self.getLugForces() + loads = self.loads + if not self.anchorCapacity: + self.getAnchorCapacity() + + # look for load dictionary key in capacity dictionary + FS = {} + acceptance = {} + acceptance_margin = {} + for Lkey,Lval in loads.items(): + for Ckey,Cval in self.anchorCapacity.items(): + if Lkey in Ckey: + if Lval == 0: + FS[Lkey] = float('inf') + else: + FS[Lkey] = Cval/Lval + if acceptance_crit and Lkey in acceptance_crit: + if Lval == 0 or acceptance_crit[Lkey] == 0: + acceptance[Lkey] = True + else: + acceptance[Lkey] = acceptance_crit[Lkey]<=FS[Lkey] + acceptance_margin[Lkey] = FS[Lkey] - acceptance_crit[Lkey] + + if acceptance_crit: + return(FS,acceptance,acceptance_margin) + else: + return(FS) + + def makeBuffer(self, buff_rad=50): + point = sh.Point(self.r[:2]) + buff = point.buffer(buff_rad) + return buff + + def getCost(self,costDict='default'): + '''find costs of anchor and store in design dictionary + + Parameters + ---------- + costDict : dictionary or yaml, optional + Dictionary of various costs for anchors. Sub costs that can be included are: + material : material costs + + ''' + if isinstance(costDict,str) and costDict != 'default': + import yaml + costDict = yaml.load(costDict, Loader=yaml.FullLoader) + anchType = self.dd['type'] + if costDict == 'default': + matCostDict = {'DEA':5.705,'suction_pile':4.435,'gravity':1.905} # mean values from Task 49 Design Basis ranges + instCostDict = {} + decomCostDict = {} + else: + matCostDict = costDict['material'] + if 'install' in costDict: + instCostDict = costDict['install'] + if 'decom' in costDict: + decomCostDict = costDict['decom'] + keyFail = True + # check if mass info is available + if not self.mass: + if 'soil_properties' in self.dd: + # need mass - call capacity functions + self.getAnchorCapacity(plot=False) + else: + print('Soil properties needed to calculate anchor mass for cost. Setting cost to 0.') + self.mass = 0 + + # sort by type of anchor + for Ckey,Cval in matCostDict.items(): + if anchType in Ckey: + self.cost['materials'] = matCostDict[Ckey]*self.mass + # self.cost['install'] = instCostDict[Ckey] + # self.cost['decom'] = decomCostDict[Ckey] + keyFail = False + # raise error if anchType not found in cost dictionary + if keyFail: + raise KeyError(f'anchor type {anchType} not found in material cost dictionary') + + return(sum(self.cost.values())) + + + + # def getSuctionSize(self,D,L,loads=None,minfs={'Ha':1.6,'Va':2},LD_con=[4,8]): + # ''' + + + # Parameters + # ---------- + # D : float + # Diameter of suction bucket + # L : float + # Length of suction bucket + # loads : dict, optional + # Dictionary of maximum anchor loads in horizontal and vertical directions. The default is None. + # minfs : dict,optoinal + # Minimum factors of safety in horizontal and vertical directions + # LD_con : float + # Constraint for L/D parameter + + # Returns + # ------- + # None. + + # ''' + # from scipy.optimize import minimize + # anchType = self.dd['type'] + # if not loads: + # loads = self.loads + + # if not 'Ha' in loads: + # loads = self.getLugForces(mudloads=loads) + + # loads['Ha'] = minfs['Ha']*loads['Ha'] + # loads['Va'] = minfs['Va']*loads['Va'] + + # if not 'zlug' in self.dd['design']: + # self.dd['design']['zlug'] = (2/3)*L + + # # Define the objective function: Minimize |UC - 1| (aim for UC to be 1) + # def objective(vars): + # D, L = vars + # self.dd['design']['D'] = D + # self.dd['design']['L'] = L + # self.dd['design']['zlug'] = (2/3)*L + # results = self.getAnchorCapacity(plot=False) + # return abs(results['UC'] - 1) + + # def conFun(vars,LD_con): + # D, L = vars + # if L/D >= LD_con[0] and L/D <= LD_con[1]: + # conval = 1 + # else: + # conval = -1 + + # return(conval) + + # # Initial guess for D and L + # initial_guess = [D, L] # Input values for D and L + + # # Bounds for D and L (adjust as needed) + # bounds = [(1, 5), (5, 50)] # Bounds for D and L + + # # constraints + # constraints = [{'type':'ineq','fun':conFun,'args':(LD_con,)}] + + # # Run the optimization to find D and L that satisfy UC close to 1 + # solution = minimize(objective, initial_guess, bounds=bounds,method="COBYLA", + # constraints=constraints,options={'rhobeg':0.1, 'catol':0.001}) + + # # Extract the optimized values of D and L + # self.dd['design']['D'], self.dd['design']['L'] = solution.x + # self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] + # results = self.getAnchorCapacity(plot=False) + + + def getSize(self, geom, geomKeys, geomBounds=None, loads=None, minfs={'Ha':1.6,'Va':2}, + LD_con=[4,8], fix_zlug=False, FSdiff_max=None, plot=False): + ''' + + + Parameters + ---------- + geom: list + starting guess geometry values + geomKeys : list + List of keys that match the geom list values i.e. 'L','D','zlug' + geomBounds : list,optional + List of upper and lower bounds for each geometry value. + Each entry should be a tuple of upper and lower bounds for each geometry i.e. [(5,10),(10,20)] + loads : dict, optional + Dictionary of maximum anchor loads in horizontal and vertical directions (not including factor of safety). The default is None. + minfs : dict,optional + Minimum factors of safety in horizontal and vertical directions + LD_con : float + Constraint for L/D parameter + fix_zlug : bool + Boolean to decide if zlug should be altered as geometric values are altered. + True = fixed zlug, False = zlug may be changed + plot : bool + Boolean controls if capacity plots are generated or not for the final configuration + + Returns + ------- + None. + + ''' + # - - - - Objective and Constraint Functions + + # Define the objective function: Minimize weight of anchor (cost is dependent on weight) + def objective(vars, args): + + geomKeys = args['geomKeys'] + input_loads = args['input_loads'] + fix_zlug = args['fix_zlug'] + + newGeom = dict(zip(geomKeys,vars)) + self.dd['design'].update(newGeom) + if 'suction' in self.dd['type'] and not fix_zlug: + self.dd['design']['zlug'] = (2/3)*newGeom['L'] + + if 'Hm' in input_loads or 'Vm' in input_loads: + anchor_loads = self.getLugForces(mudloads=input_loads) + input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary + # get results + results = self.getAnchorCapacity(loads=input_loads, plot=False) + + return(results['Weight']) + + # constraint for suction bucket sizing only. May add more constraints for other anchors in the future... + def conFun_LD(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): + newGeom = dict(zip(geomKeys, vars)) + self.dd['design'].update(newGeom) + + if 'Hm' in input_loads or 'Vm' in input_loads: + anchor_loads = self.getLugForces(mudloads=input_loads) + input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary + + results = self.getAnchorCapacity(loads=input_loads, plot=False) + + convalA = newGeom['L']/newGeom['D'] - LD_con[0] + convalB = LD_con[1] - newGeom['L']/newGeom['D'] + conval = min([convalA,convalB]) + # if newGeom['L']/newGeom['D'] >= LD_con[0] and newGeom['L']/newGeom['D'] <= LD_con[1]: + # conval = 1 + # else: + # conval = -1 + + return(conval) + # constraint to ensure unity check > 1 for suction buckets + def conFun_Suction(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): + if 'Hm' in input_loads or 'Vm' in input_loads: + anchor_loads = self.getLugForces(mudloads=input_loads) + input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary + results = self.getAnchorCapacity(loads=input_loads, plot=False) + #conval = results['UC'] - 1 + conval = 1 - results['UC'] + # convalB = 1 - results['UC'] + return(conval) + + def conFun_DandG(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): + + newGeom = dict(zip(geomKeys, vars)) + self.dd['design'].update(newGeom) + if 'Hm' in input_loads or 'Vm' in input_loads: + anchor_loads = self.getLugForces(mudloads=input_loads) + input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary + results = self.getAnchorCapacity(loads=input_loads, plot=False) + + return np.array([0.05*newGeom['D'] - results['Lateral displacement'] , 0.25 - results['Rotational displacement']]) + + def conFunH(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): + # if 'suction' in self.dd['type']: + # results = self.getAnchorCapacity(plot=False) + # conval = results['UC'] - 1 + # # if results['UC'] < 1: + # # conval = -1*(results['UC']) + # else: + if 'Hm' in input_loads or 'Vm' in input_loads: + anchor_loads = self.getLugForces(mudloads=input_loads) + input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary + minfs = dict(Ha=minfs['Hm'], Va=minfs['Vm']) + FS, _, _ = self.getFS(loads=input_loads, acceptance_crit=minfs) + conval = FS['Ha'] - 1 + # for key,val in FS.items(): + + # if val/minfs[key]<1: + # if -1*(1-val/minfs[key]) < conval: + # conval = -1*(1-val/minfs[key]) + return(conval) + + def conFunV(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): + if 'Hm' in input_loads or 'Vm' in input_loads: + anchor_loads = self.getLugForces(mudloads=input_loads) + input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary + minfs = dict(Ha=minfs['Hm'], Va=minfs['Vm']) + FS, _, _ = self.getFS(loads=input_loads, acceptance_crit=minfs) + # special case for DEAs + if minfs['Va'] == 0: + conval = 1 + else: + conval = FS['Va'] - 1 + + # print('FS_V',FS['Va']) + return(conval) + + def conBounds(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): + + newGeom = dict(zip(geomKeys, vars)) + self.dd['design'].update(newGeom) + + if 'Hm' in input_loads or 'Vm' in input_loads: + anchor_loads = self.getLugForces(mudloads=input_loads) + input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va']) # overwrite the input_loads dictionary + results = self.getAnchorCapacity(loads=input_loads, plot=False) + + bound_L_lower = newGeom['L'] - geomBounds[0][0] + bound_L_upper = geomBounds[0][1] - newGeom['L'] + bound_D_lower = newGeom['D'] - geomBounds[1][0] + bound_D_upper = geomBounds[1][1] - newGeom['D'] + + return np.array([bound_L_lower, bound_L_upper, bound_D_lower, bound_D_upper]) + + # - - - - - Setup & Optimization + from scipy.optimize import minimize + from copy import deepcopy + + anchType = self.dd['type'] + + # loads['Ha'] = minfs['Ha']*loads['Ha'] + # loads['Va'] = minfs['Va']*loads['Va'] + startGeom = dict(zip(geomKeys,geom)) + print('start geometry: ',startGeom) + # apply initial guess geometry + self.dd['design'].update(startGeom) + + if not 'zlug' in self.dd['design']: + if 'suction' in anchType and not fix_zlug: + self.dd['design']['zlug'] = (2/3)*startGeom['L'] + else: + self.dd['design']['zlug'] = 0 + + # if zlug is fixed, remove it from design variables + if fix_zlug and 'zlug' in geomKeys: + zlug_loc = geomKeys.index('zlug') + startGeom.pop('zlug') + geomKeys.remove('zlug') + geom.pop(zlug_loc) + if geomBounds: + geomBounds.pop(zlug_loc) + + if not loads: + loads = self.loads + + if not 'Ha' in loads: + loads = self.getLugForces(mudloads=loads) + + # suction bucket needs to be loads*FS because of capacity envelope calculations in capacity function + if ('Hm' in loads and 'Vm' in loads) and ('Hm' in minfs and 'Vm' in minfs): + input_loads = {'Hm':loads['Hm']*minfs['Hm'], 'Vm':loads['Vm']*minfs['Vm']} + else: + input_loads = {'Ha':loads['Ha']*minfs['Ha'],'Va':loads['Va']*minfs['Va']} + + + + + # Initial guess for geometry + initial_guess = geom # [val for val in startGeom.values()] # Input values for geometry + # geomKeys = [key for key in startGeom.keys()] + + # Bounds and constraints + if 'suction' in anchType: + # bounds = [(1, 7), (5, 50),()] # Bounds for D and L + # constraints + + constraints = [{'type':'ineq','fun':conFun_LD,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conFun_Suction,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}] + + elif 'dandg' in anchType: + constraints = [{'type':'ineq','fun':conFun_LD,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conFun_DandG,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}] + + else: + constraints = [{'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}] + + # Run the optimization to find sizing that satisfy UC close to 1 + print('optimizing anchor size') + + if 'suction' in anchType or 'dandg' in anchType: + solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), + method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) + else: + solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), + method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) + + FS, acceptance, FSdiff = self.getFS(loads=input_loads, acceptance_crit=minfs) + + # adjust starting value if you're far off from the acceptance criteria (in either direction) + if FSdiff_max: + count = 0 + while count<10 and (np.any([abs(FSdiff[key])>FSdiff_max[key] for key in FSdiff.keys()]) or np.any([diff<0 for diff in FSdiff.values()])): + if np.any([diff<.02 for key,diff in FSdiff.items() if minfs[key]>0]) and np.all([diff>=0 for diff in FSdiff.values()]): + # exit loop if you're as close as can be on one of the FS even if other is above diff requirements UNLESS an FS is below minimum reqiured FS + break + print('Factor of Safety not close enough to minimum factor of safety, trying again with adjusted initial guess.') + print(FS) + # calculate new percent difference of FS from min fs + diffPCT = [FSdiff[key]/FS[key] for key in FSdiff] + # create adjustment coefficient based on this or .25, whichever is lower + adjust_coeff = np.min([np.min(diffPCT),0.25]) + # adjust initial guess values by adjustment coefficient + for i,val in enumerate(initial_guess): + initial_guess[i] = val - val*adjust_coeff + # update zlug for suction buckets as needed to be 2/3L + if 'suction' in anchType and not fix_zlug: + zlug_loc = geomKeys.index('zlug') + L_loc = geomKeys.index('L') + initial_guess[zlug_loc] = (2/3)*initial_guess[L_loc] + + print('new initial guess',initial_guess) + # re-run optimization + if 'suction' in anchType or 'dandg' in anchType: + solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), + method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) + else: + solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), + method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) + # re-determine FS and diff from minFS + FS, acceptance, FSdiff = self.getFS(loads=input_loads, acceptance_crit=minfs) + count += 1 + + # Extract the optimized values of geometry + endGeom = dict(zip(geomKeys,solution.x)) + print('End geometry: ',endGeom) + self.dd['design'].update(endGeom) + if 'suction' in anchType and not fix_zlug: + self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] + results = self.getAnchorCapacity(loads=input_loads,plot=plot) + + # # check if anchor loads are available + # if not self.loads: + # # if not, check if theres a moorpy anchor object and calculate loads from that + # if self.mpAnchor: + # print("Need anchor loads to obtain cost, using getMPForces to determine loads in MoorPy") + # self.getLugForces() + # elif self.ms: + # print('Need anchor loads to obtain cost, creating a MoorPy anchor object and using getMPForces to determine loads in MoorPy') + # self.makeMoorPyAnchor(self.ms) + # self.getLugForces() + # else: + # raise Exception("Need anchor loads to obtain cost") + # # check again if there are loads + # if self.loads: + # c = self.dd['cost'] # set location for clarity + # # calculate individual costs and total cost for the anchor + # c['matCost'], c['instCost'], c['decomCost'] = mp.Point.getcost(self.mpAnchor) + # c['totCost'] = c['matCost'] + c['instCost'] + c['decomCost'] + + + # def getMass(self,uhc_mode): + # '''find mass and/or UHC of anchor from MoorProps and store in design dictionary + # Parameters + # ---------- + # uhc_mode : boolean + # True : obtain UHC from mass + # False : obtain Masss and UHC from loads + # ''' + # if uhc_mode: # if looking for UHC given mass + # if self.dd['design']['m']: # check anchor mass is given + # self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=1, mass_int=self.dd['design']['m'], anchor=self.dd['type'], soil_type=self.anchorProps['soil_type']) + # else: + # raise Exception("Need anchor mass to calculate UHC when uhc_mode = 1") + # else: # if looking for mass and UHC given loads + # if self.loads: # check the loads section exists + # self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=0, fx=self.loads['ff'], fz=self.loads['fz'], anchor=self.dd['type'],soil_type=self.dd['soil_type'],method=self.loads['method']) + # elif self.mpAnchor: + # print("Need anchor loads to obtain mass, using getMPForces to determine loads in MoorPy") + # self.getLugForces() + # self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=0, fx=self.loads['ff'], fz=self.loads['fz'], anchor=self.dd['type'],soil_type=self.dd['soil_type'],method=self.loads['method']) + # elif self.ms: + # print('Need anchor loads to obtain mass, creating a MoorPy anchor object and using getMPForces to determine loads in MoorPy') + # self.makeMoorPyAnchor(self.ms) + # self.getLugForces() + # self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=0, fx=self.loads['ff'], fz=self.loads['fz'], anchor=self.dd['type'],soil_type=self.dd['soil_type'],method=self.loads['method']) + # else: + # raise Exception("Need anchor loads to obtain mass") \ No newline at end of file diff --git a/famodel/anchors/anchor_map.py b/famodel/anchors/anchor_map.py deleted file mode 100644 index 525d569f..00000000 --- a/famodel/anchors/anchor_map.py +++ /dev/null @@ -1,889 +0,0 @@ -"""Anchor class for FAModel, containing information and key methods for anchors of mooring lines - Work in progress -""" -import moorpy as mp -import numpy as np -from scipy.optimize import minimize -from famodel.famodel_base import Node -from famodel.mooring.mooring import Mooring -import famodel.platform.platform -import matplotlib.pyplot as plt - -class Anchor(Node): - - def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, - g=9.81, rho=1025, profile_map=None): - ''' - Initialize an Anchor object. - - Parameters - ---------- - dd : dict - Design dictionary containing all information on the anchor. - ms : MoorPy system object - MoorPy system instance. - r : list of float - Anchor position coordinates (x, y, z) (m) - aNum : int, optional - Index in anchor list. - id : str or int, optional - Unique anchor identifier. - g : float, optional - Gravity. - rho : float, optional - Water density. - profile_map : list of dict, optional - Full soil profile map for selecting local soil layers. - ''' - - from famodel.famodel_base import Node - Node.__init__(self, id) - - self.dd = dd - self.ms = ms - self.r = r - self.aNum = aNum - self.g = g - self.rho = rho - - self.anchType = dd.get('type') if dd else None - self.soil_type = None - self.soil_profile = None - self.profile_name = None - self.soil_type_list = [] - - self.mpAnchor = None - self.capacity_format = None - self.mass = dd.get('design', {}).get('mass', None) if dd else None - - self.anchorCapacity = {} - self.cost = {} - self.loads = {} - self.soilProps = {} - self.failure_probability = {} - self.env_impact = {} - - # --- Assign soil profile if map is provided --- - if profile_map is not None: - self.setSoilProfile(profile_map) - - def setSoilProfile(self, profile_map): - ''' - Assign a bilinearly interpolated soil profile from the 4 nearest CPTs. - - Parameters - ---------- - profile_map : list of dict - Each CPT must have keys: 'x', 'y', and 'layers' - - Returns - ------- - None - ''' - import numpy as np - - x_anchor, y_anchor = self.r[0], self.r[1] - - # Sort all CPTs by distance - distances = [np.hypot(p['x'] - x_anchor, p['y'] - y_anchor) for p in profile_map] - idx_sorted = np.argsort(distances) - CPTs = [profile_map[i] for i in idx_sorted[:4]] - - # Extract positions and weights (inverse distance squared) - x = np.array([cpt['x'] for cpt in CPTs]) - y = np.array([cpt['y'] for cpt in CPTs]) - dx = x - x_anchor - dy = y - y_anchor - d = np.hypot(dx, dy) - w = 1/np.maximum(d, 1e-3)**2 - w /= np.sum(w) - - # Interpolate layer-by-layer - layers_list = [cpt['layers'] for cpt in CPTs] - n_layers = len(layers_list[0]) - interpolated_layers = [] - - for i in range(n_layers): - layer = {'soil_type': layers_list[0][i]['soil_type']} - keys = layers_list[0][i].keys() - - for key in keys: - if key == 'soil_type': - continue - if all(key in l[i] for l in layers_list): - vals = [l[i][key] for l in layers_list] - layer[key] = np.dot(w, vals) - - interpolated_layers.append(layer) - - self.soil_profile = interpolated_layers - self.profile_name = f'Interpolated_2D' - - # Assign soil type - soil_types = [layer['soil_type'] for layer in self.soil_profile] - self.soil_type_list = list(set(soil_types)) - self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed' - - print(f"[Anchor] Assigned interpolated soil profile: {self.profile_name} weighting with soil types {self.soil_type_list}") - - def makeMoorPyAnchor(self, ms): - ''' - Create a MoorPy anchor object in a MoorPy system. - - Parameters - ---------- - ms : MoorPy system instance - The MoorPy system to add the anchor to. - - Returns - ------- - ms : MoorPy system instance - The updated MoorPy system with the anchor added. - ''' - import moorpy as mp - - # Create anchor as a fixed point in MoorPy system - ms.addPoint(1, self.r) - - # Assign this point as mpAnchor in the anchor class instance - self.mpAnchor = ms.pointList[-1] - - # Set mass if available - if 'mass' in self.dd.get('design', {}): - self.mpAnchor.m = self.dd['design']['mass'] - - # Set diameter if available - if 'd' in self.dd.get('design', {}): - self.mpAnchor.d = self.dd['design']['d'] - - # Set the point as an anchor entity - self.mpAnchor.entity = {'type': 'anchor'} - if 'type' in self.dd: - self.mpAnchor.entity['anchor_type'] = self.dd['type'] - - return ms - - def getLineProperties(self): - ''' - Retrieve line_type, diameter and unit weight from attached mooring. - - Returns - ------- - line_type : str - Type of mooring line ('chain' or 'wire') - d : float - Nominal diameter (m) - w : float - Unit weight (N/m) - ''' - for att in self.attachments.values(): - if isinstance(att['obj'], Mooring): - mtype = att['obj'].dd['sections'][0]['type']['material'].lower() - if 'chain' not in mtype: - print('No chain below seafloor, setting Ta=Tm (no load transfer).') - return mtype, None, None, True - else: - d_nom = att['obj'].dd['sections'][0]['type']['d_nom'] - w_nom = att['obj'].dd['sections'][0]['type']['w'] - return 'chain', d_nom, w_nom, False - raise ValueError('No mooring line attachment found for anchor.') - - def getMudlineForces(self, lines_only=False, seabed=True, xyz=False): - ''' - Find forces on anchor at mudline using the platform.getWatchCircle method or MoorPy Point.getForces method. - - Parameters - ---------- - lines_only : boolean, optional - Calculate forces from just mooring lines (True) or not (False). Default is False. - seabed : boolean, optional - Include effect of seabed pushing up the anchor (True) or not (False). Default is True. - xyz : boolean, optional - Return forces in x,y,z DOFs (True) or only the enabled DOFs (False). Default is False. - - Returns - ------- - dict - Dictionary containing mudline forces. - ''' - loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz) - - self.loads = { - 'Hm': np.sqrt(loads[0]**2 + loads[1]**2), - 'Vm': loads[2], - 'thetam': np.degrees(np.arctan2(loads[2], np.sqrt(loads[0]**2 + loads[1]**2))), - 'method': 'static', - 'mudline_load_type': 'current_state' - } - - return self.loads - - def getLugForces(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False): - ''' - Calculate the lug forces Ha and Va based on mudline loads using local soil profile. - - Parameters - ---------- - Hm : float - Horizontal mudline load (N) - Vm : float - Vertical mudline load (N) - zlug : float - Padeye embedment depth (m) - line_type : str, optional - Type of mooring line ('chain' or 'wire') - d : float, optional - Mooring line diameter (m) - w : float, optional - Mooring line unit weight (N/m) - plot : bool, optional - Whether to plot the load transfer profile - - Returns - ------- - Ha : float - Horizontal load at lug (N). - Va : float - Vertical load at lug (N). - ''' - from famodel.anchors.anchors_famodel_map.capacity_load_map import getTransferLoad - - # Ensure soil profile is available - if self.soil_profile is None or self.soil_type is None: - raise ValueError("Anchor soil profile or soil type is not assigned. Use setSoilProfile first.") - - soil_profile = self.soil_profile - soil_type = self.soil_type - - # Determine mudline depth - z0 = soil_profile[0]['top'] - - # Load transfer if padeye is embedded - if zlug > z0: - if line_type is None or d is None or w is None: - try: - line_type, d, w = self.getLineProperties() - except ValueError: - print('[Warning] No mooring attachment found. Trying anchor-level line properties...') - line_type = getattr(self, 'line_type', None) - d = getattr(self, 'd', None) - w = getattr(self, 'w', None) - - if any(v is None for v in [line_type, d, w]): - print('[Fallback] Using default chain properties.') - line_type = 'chain' - d = 0.16 - w = 5000.0 - - layers, loads = getTransferLoad( - profile_map=[{'layers': self.soil_profile}], - Tm=np.sqrt(Hm**2 + Vm**2), - thetam=np.degrees(np.arctan2(Vm, Hm)), - zlug=zlug, - line_type=line_type, - d=d, - w=w, - plot=plot - ) - - Ta = loads['Ta'] - thetaa = loads['thetaa'] - Ha = Ta*np.cos(np.deg2rad(thetaa)) - Va = Ta*np.sin(np.deg2rad(thetaa)) - - else: - Ha = Hm - Va = Vm - - return layers, Ha, Va - - def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False): - ''' - Calculate anchor capacity based on anchor type and local soil profile. - - Parameters - ---------- - Hm : float - Horizontal mudline load (N) - Vm : float - Vertical mudline load (N) - zlug : float - Padeye embedment depth (m) - line_type : str, optional - Type of mooring line ('chain' or 'wire') - d : float, optional - Mooring line diameter (m) - w : float, optional - Mooring line unit weight (N/m) - plot : bool, optional - Whether to plot the load transfer and pile geometry - - Returns - ------- - results : dict - Capacity results dictionary from the selected capacity function. - ''' - from famodel.anchors.anchors_famodel_map.capacity_plate_map import getCapacityPlate - from famodel.anchors.anchors_famodel_map.capacity_suction_map import getCapacitySuction - from famodel.anchors.anchors_famodel_map.capacity_torpedo_map import getCapacityTorpedo - from famodel.anchors.anchors_famodel_map.capacity_helical_map import getCapacityHelical - from famodel.anchors.anchors_famodel_map.capacity_driven_map import getCapacityDriven - from famodel.anchors.anchors_famodel_map.capacity_dandg_map import getCapacityDandG - from famodel.anchors.anchors_famodel_map.capacity_load_map import getTransferLoad - from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_load - # import numpy as np - - capacity_dispatch = { - 'suction': getCapacitySuction, - 'sepla': getCapacityPlate, - 'dea': getCapacityPlate, - 'depla': getCapacityPlate, - 'vla': getCapacityPlate, - 'plate': getCapacityPlate, - 'torpedo': getCapacityTorpedo, - 'helical': getCapacityHelical, - 'driven': getCapacityDriven, - 'dandg': getCapacityDandG - } - - anchType_clean = self.anchType.lower().replace(' ', '') - capacity_func = capacity_dispatch.get(anchType_clean) - if capacity_func is None: - raise ValueError(f"Unknown anchor type '{self.anchType}' for anchor capacity calculation.") - - if self.soil_profile is None or self.soil_type is None: - raise ValueError("Soil profile or soil type not set for this anchor.") - - soil_profile = self.soil_profile - soil_type = self.soil_type - z0 = soil_profile[0]['top'] - - # Load transfer if padeye is embedded below mudline - if zlug > z0: - if line_type is None or d is None or w is None: - try: - line_type, d, w = self.getLineProperties() - except ValueError: - print('[Warning] No mooring attachment found. Trying anchor-level line properties...') - line_type = getattr(self, 'line_type', None) - d = getattr(self, 'd', None) - w = getattr(self, 'w', None) - - if any(v is None for v in [line_type, d, w]): - print('[Fallback] Using default chain properties.') - line_type = 'chain' - d = 0.16 - w = 5000.0 - - else: - layers, resultsLoad = getTransferLoad( - profile_map=[{'layers': soil_profile}], - Tm=np.sqrt(Hm**2 + Vm**2), - thetam=np.degrees(np.arctan2(Vm, Hm)), - zlug=zlug, - line_type=line_type, - d=d, - w=w, - plot=False - ) - if plot: - plot_load( - layers, - resultsLoad['drag_values'], - resultsLoad['depth_values'], - resultsLoad['Tm'], - resultsLoad['thetam'], - resultsLoad['Ta'], - resultsLoad['thetaa'], - zlug=zlug - ) - - Ta = resultsLoad['Ta'] - thetaa = resultsLoad['thetaa'] - Ha = Ta*np.cos(np.deg2rad(thetaa)) - Va = Ta*np.sin(np.deg2rad(thetaa)) - - print(f'Input Hm = {Hm}, Vm = {Vm}, zlug = {zlug}') - print(f'Input Ha = {Ha}, Va = {Va}, zlug = {zlug}') - print(f'Input Ta = {Ta}, thetaa = {(thetaa)}') - else: - Ha = Hm - Va = Vm - - # --- Call the appropriate capacity function --- - if anchType_clean in ['sepla', 'dea', 'depla', 'vla', 'plate']: - self.capacity_format = 'plate' - B = self.dd['design']['B'] - L = self.dd['design']['L'] - beta = self.dd['design'].get('beta', 0.0) - layers, results = capacity_func( - profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}], - location_name=self.profile_name, - B=B, L=L, zlug=zlug, - beta=beta, - Ha=Ha, Va=Va, - plot=plot - ) - - elif anchType_clean == 'suction': - self.capacity_format = 'envelope' - D = self.dd['design']['D'] - L = self.dd['design']['L'] - zlug = self.dd['design']['zlug'] - layers, results = capacity_func( - profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}], - location_name=self.profile_name, - D=D, L=L, zlug=zlug, - Ha=Ha, Va=Va, - thetalug=5, psilug=7.5, - plot=plot - ) - - elif anchType_clean == 'torpedo': - self.capacity_format = 'envelope' - D1 = self.dd['design']['D1'] - D2 = self.dd['design']['D2'] - L1 = self.dd['design']['L1'] - L2 = self.dd['design']['L2'] - ballast = self.dd['design'].get('ballast', 0.0) - layers, results = capacity_func( - profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}], - location_name=self.profile_name, - D1=D1, D2=D2, L1=L1, L2=L2, - zlug=zlug, - ballast=ballast, - Ha=Ha, Va=Va, - plot=plot - ) - - elif anchType_clean == 'helical': - self.capacity_format = 'component' - D = self.dd['design']['D'] - L = self.dd['design']['L'] - d = self.dd['design']['d'] - zlug = self.dd['design']['zlug'] - layers, results = capacity_func( - profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}], - location_name=self.profile_name, - D=D, L=L, d=d, - zlug=zlug, - Ha=Ha, Va=Va, - plot=plot - ) - - elif anchType_clean in ['driven', 'dandg']: - self.capacity_format = 'component' - L = self.dd['design']['L'] - D = self.dd['design']['D'] - zlug = self.dd['design']['zlug'] - layers, y, z, results = capacity_func( - profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}], - location_name=self.profile_name, - L=L, D=D, zlug=zlug, - Ha=Ha, Va=Va, - plot=plot - ) - - else: - raise ValueError(f"Anchor type '{self.anchType}' not supported.") - - # --- Store results --- - self.capacity_results = { - 'Hmax': results.get('Horizontal max.', np.nan), - 'Vmax': results.get('Vertical max.', np.nan), - 'UC': results.get('Unity check', np.nan), - 'Ha': Ha, - 'Va': Va, - 'zlug': zlug, - 'z0': z0 - } - - if 'Weight pile' in results: - self.capacity_results['Weight pile'] = results['Weight pile'] - if 'Weight plate' in results: - self.capacity_results['Weight plate'] = results['Weight plate'] - - if anchType_clean in ['driven', 'dandg']: - self.capacity_results['Lateral displacement'] = results.get('Lateral displacement', np.nan) - self.capacity_results['Rotational displacement'] = results.get('Rotational displacement', np.nan) - - return results - - def getSafetyFactor(self): - ''' - Calculate the safety factor based on the unity checks stored in capacity results. - - Returns - ------- - dict - Dictionary containing safety factors. - ''' - anchType_clean = self.anchType.lower().replace(' ', '') - - if anchType_clean in ['helical', 'driven', 'dandg']: - UC_v = self.capacity_results.get('Unity check (vertical)', None) - UC_h = self.capacity_results.get('Unity check (horizontal)', None) - - if UC_v is None or UC_h is None: - print("Warning: Vertical or horizontal unity check (UC) not found in capacity results. Returning NaN.") - return {'SF_vertical': np.nan, 'SF_horizontal': np.nan} - - SF_v = 1.0/UC_v if UC_v != 0 else np.inf - SF_h = 1.0/UC_h if UC_h != 0 else np.inf - - return {'SF_vertical': SF_v, 'SF_horizontal': SF_h} - - else: - UC = self.capacity_results.get('UC', None) - - if UC is None: - print("Warning: Unity check (UC) not found in capacity results. Returning NaN.") - return {'SF_combined': np.nan} - - SF = 1.0/UC if UC != 0 else np.inf - - return {'SF_combined': SF} - - def getCostAnchor(self, costDict='default'): - ''' - Calculate the cost of the anchor based on material, installation, and decommissioning costs. - - Parameters - ---------- - costDict : str or dict, optional - If 'default', uses mean values from Task 49 Design Basis ranges. - If dict or yaml path, loads user-defined cost dictionaries. - - Returns - ------- - float - Total cost of the anchor. - ''' - if isinstance(costDict, str) and costDict != 'default': - import yaml - costDict = yaml.load(costDict, Loader=yaml.FullLoader) - - anchType = self.dd['type'] - - if costDict == 'default': - matCostDict = { - 'suction_pile': 4.435, - 'DEA': 5.705, - 'SEPLA': 5.705, - 'DEPLA': 5.705, - 'VLA': 5.705, - 'torpedo_pile': 5.0, - 'helical_pile': 6.0, - 'driven_pile': 4.0, - 'dandg_pile': 5.5 - } - instCostDict = { - 'suction_pile': 2.0, - 'DEA': 1.5, - 'SEPLA': 1.5, - 'DEPLA': 1.5, - 'VLA': 1.5, - 'torpedo_pile': 2.5, - 'helical_pile': 3.0, - 'driven_pile': 2.0, - 'dandg_pile': 2.2 - } - decomCostDict = { - 'suction_pile': 1.0, - 'DEA': 0.8, - 'SEPLA': 0.8, - 'DEPLA': 0.8, - 'VLA': 0.8, - 'torpedo_pile': 1.2, - 'helical_pile': 1.5, - 'driven_pile': 1.0, - 'dandg_pile': 1.1 - } - else: - matCostDict = costDict.get('material', {}) - instCostDict = costDict.get('install', {}) - decomCostDict = costDict.get('decom', {}) - - keyFail = True - - # Ensure mass is available - if self.mass is None or self.mass == 0: - # Try to extract from capacity_results if already available - if 'Weight pile' in self.capacity_results: - self.mass = self.capacity_results['Weight pile']/self.g - elif 'Weight plate' in self.capacity_results: - self.mass = self.capacity_results['Weight plate']/self.g - else: - # If capacity_results missing, attempt to calculate capacity to retrieve weight - if 'soil_properties' in self.dd: - self.getAnchorCapacity(plot=False) - if 'Weight pile' in self.capacity_results: - self.mass = self.capacity_results['Weight pile']/self.g - elif 'Weight plate' in self.capacity_results: - self.mass = self.capacity_results['Weight plate']/self.g - else: - print('Warning: Weight not found after capacity calculation, setting mass to 0.') - self.mass = 0 - else: - print('Soil properties needed to calculate anchor mass for cost. Setting mass to 0.') - self.mass = 0 - - # Calculate material cost based on mass - if anchType in matCostDict: - self.cost['Material Cost'] = matCostDict[anchType]*self.mass - keyFail = False - else: - raise KeyError(f'Anchor type {anchType} not found in material cost dictionary.') - - # Install and decom costs if available - self.cost['Installation Cost'] = instCostDict.get(anchType, 0.0) - self.cost['Decommissioning Cost'] = decomCostDict.get(anchType, 0.0) - - # Total cost - self.cost['Total Cost'] = (self.cost['Material Cost'] + - self.cost['Installation Cost'] + - self.cost['Decommissioning Cost']) - - return sum(self.cost.values()) - - def getSizeAnchor(self, geom, geomKeys, geomBounds=None, loads=None, - minfs={'Ha': 1.6, 'Va': 2.0}, lambdap_con=[4, 8], zlug_fix=False, plot=False): - ''' - Generalized optimization method for all anchor types. - ''' - - anchType_clean = self.dd['type'].lower().replace(' ', '') - - if loads is None: - loads = self.loads - - Hm = loads['Hm'] - Vm = loads['Vm'] - - line_type = getattr(self, 'line_type', 'chain') - d = getattr(self, 'd', 0.16) - w = getattr(self, 'w', 5000.0) - - def update_zlug_if_suction(): - if anchType_clean == 'suction' and not zlug_fix and 'zlug' not in geomKeys: - self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] - - # --- Stage 1: Safety Optimization to reach UC <= 1 --- - def safety_objective(vars): - for i, key in enumerate(geomKeys): - self.dd['design'][key] = vars[i] - update_zlug_if_suction() - - _, Ha, Va = self.getLugForces(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=False) - - self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=False) - - if self.capacity_format == 'envelope': - UC = self.capacity_results.get('UC', 2.0) - elif self.capacity_format == 'component': - UC = max( - self.capacity_results.get('Unity check (horizontal)', 2.0), - self.capacity_results.get('Unity check (vertical)', 2.0) - ) - elif self.capacity_format == 'plate': - UC = self.capacity_results.get('UC', 2.0) - else: - UC = 2.0 - return (UC - 1.0)**2 - - minimize( - safety_objective, - geom, - method='COBYLA', - bounds=geomBounds if geomBounds else None, - options={'rhobeg': 0.02, 'catol': 0.001, 'maxiter': 1500} - ) - - # --- Stage 2: Weight Minimization with constraints --- - if anchType_clean != 'torpedo': - def weight_objective(vars): - for i, key in enumerate(geomKeys): - self.dd['design'][key] = vars[i] - update_zlug_if_suction() - - _, Ha, Va = self.getLugForces(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=False) - - self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=False) - - return self.capacity_results.get('Weight pile', - self.capacity_results.get('Weight plate', - self.capacity_results.get('Weight', 1e9))) - - def constraint_uc_envelope(vars): - return 1.0 - self.capacity_results.get('UC', 2.0) - - def constraint_uc_component(vars): - return 1.0 - max( - self.capacity_results.get('Unity check (horizontal)', 2.0), - self.capacity_results.get('Unity check (vertical)', 2.0) - ) - - def constraint_fs_horizontal(vars): - return (self.capacity_results.get('Horizontal max.', 0)/self.capacity_results.get('Ha', 1)) - minfs['Ha'] - - def constraint_fs_vertical(vars): - return (self.capacity_results.get('Vertical max.', 0)/self.capacity_results.get('Va', 1)) - minfs['Va'] - - def constraint_lambda_min(vars): - anchType_clean = self.dd['type'].lower().replace(' ', '') - - if anchType_clean == 'torpedo': - L = self.dd['design']['L1'] + self.dd['design']['L2'] - A_wing = (self.dd['design']['D1'] - self.dd['design']['D2'])*self.dd['design']['L1'] - A_shaft = self.dd['design']['D2']*L - D = (A_wing + A_shaft)/L - elif anchType_clean in ['driven', 'dandg', 'helical', 'suction']: - L = self.dd['design']['L'] - D = self.dd['design']['D'] - elif anchType_clean in ['plate', 'sepla', 'dea', 'depla', 'vla']: - L = self.dd['design']['L'] - D = self.dd['design']['B'] - else: - raise ValueError(f'lambda constraints not defined for anchor type: {anchType_clean}') - return (L/D) - lambdap_con[0] - - def constraint_lambda_max(vars): - anchType_clean = self.dd['type'].lower().replace(' ', '') - - if anchType_clean == 'torpedo': - L = self.dd['design']['L1'] + self.dd['design']['L2'] - A_wing = (self.dd['design']['D1'] - self.dd['design']['D2'])*self.dd['design']['L1'] - A_shaft = self.dd['design']['D2']*L - D = (A_wing + A_shaft)/L - elif anchType_clean in ['driven', 'dandg', 'helical', 'suction']: - L = self.dd['design']['L'] - D = self.dd['design']['D'] - elif anchType_clean in ['plate', 'sepla', 'dea', 'depla', 'vla']: - L = self.dd['design']['L'] - D = self.dd['design']['B'] # use plate width - else: - raise ValueError(f'lambda constraints not defined for anchor type: {anchType_clean}') - return lambdap_con[1] - (L/D) - - if self.capacity_format == 'envelope': - constraints = [ - {'type': 'ineq', 'fun': constraint_uc_envelope}, - {'type': 'ineq', 'fun': constraint_fs_horizontal}, - {'type': 'ineq', 'fun': constraint_fs_vertical}, - {'type': 'ineq', 'fun': constraint_lambda_min}, - {'type': 'ineq', 'fun': constraint_lambda_max}, - ] - elif self.capacity_format == 'component': - constraints = [ - {'type': 'ineq', 'fun': constraint_uc_component}, - {'type': 'ineq', 'fun': constraint_lambda_min}, - {'type': 'ineq', 'fun': constraint_lambda_max}, - ] - elif self.capacity_format == 'plate': - constraints = [ - {'type': 'ineq', 'fun': constraint_uc_envelope} - ] - else: - raise ValueError(f"Unknown capacity_format: {self.capacity_format}") - - result = minimize( - weight_objective, - [self.dd['design'][key] for key in geomKeys], - method='COBYLA', - constraints=constraints, - bounds=geomBounds if geomBounds else None, - options={'rhobeg': 0.5, 'catol': 0.01, 'maxiter': 100} - ) - - endGeom = dict(zip(geomKeys, result.x)) - print('Optimized geometry:', endGeom) - self.dd['design'].update(endGeom) - - update_zlug_if_suction() - - self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=plot) - - print('\nFinal Optimized Anchor:') - print('Design:', self.dd['design']) - print('Capacity Results:', self.capacity_results) - - - def getCombinedPlot(self): - ''' - Create a plot showing the suction pile and the inverse catenary overlay in the same coordinate system. - ''' - from famodel.anchors.anchors_famodel_map.capacity_load_map import getTransferLoad - from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_suction - - if self.anchType.lower() != 'suction': - raise NotImplementedError("getCombinedPlot only supports suction piles.") - - # Extract design inputs - design = self.dd['design'] - D = design['D'] - L = design['L'] - zlug = design['zlug'] - - if self.soil_profile is None or self.soil_type is None: - raise ValueError("Soil profile or type not assigned. Use setSoilProfile first.") - - soil_profile = self.soil_profile - soil_type = self.soil_type - z0 = soil_profile[0]['top'] - - Hm = self.loads['Hm'] - Vm = self.loads['Vm'] - thetam = self.loads.get('thetam', np.degrees(np.arctan2(Vm, Hm))) - - line_type = getattr(self, 'line_type', 'chain') - d = getattr(self, 'd', 0.16) - w = getattr(self, 'w', 5000.0) - - # Get inverse catenary path - layers, result = getTransferLoad( - profile_map=[{'layers': self.soil_profile}], - Tm=np.sqrt(Hm**2 + Vm**2), - thetam=thetam, - zlug=zlug, - line_type=line_type, - d=d, - w=w, - plot=False - ) - - drag_values = np.array(result['drag_values']) - depth_values = -np.array(result['depth_values'])[::-1] - - x_start = D/2 + drag_values[0] - z_start = zlug - drag_transformed = x_start - drag_values - depth_transformed = z_start + (depth_values- depth_values[0]) - - # Plot suction pile - plot_suction(soil_profile, L, D, z0=z0, zlug=zlug, title='Suction Pile and Mooring Line Load Path') - - - # Overlay inverse catenary path - plt.plot(drag_transformed, depth_transformed, color='b', lw=2.0, label='Inverse catenary') - plt.plot(drag_transformed[-1], depth_transformed[-1], 'ro', label='Mudline end') - plt.plot( drag_transformed[0], depth_transformed[0], 'go', label='Embedded end') - - n = 2e6 - Tm = result['Tm'] - Ta = result['Ta'] - thetaa = result['thetaa'] - - plt.arrow(drag_transformed[-1], depth_transformed[-1], - Tm*np.cos(np.deg2rad(thetam))/n, -Tm*np.sin(np.deg2rad(thetam))/n, - head_width=0.25, head_length=0.5, color='r', label='Mudline load') - - plt.arrow(drag_transformed[0], depth_transformed[0], - Ta*np.cos(np.deg2rad(thetaa))/n, -Ta*np.sin(np.deg2rad(thetaa))/n, - head_width=0.25, head_length=0.5, color='g', label='Padeye load') - - xmax = max(drag_transformed[-1] + D, 2*D) - plt.xlim(-D, xmax) - plt.legend() - plt.grid(True) - plt.tight_layout() - plt.show() diff --git a/famodel/anchors/anchor_profile.py b/famodel/anchors/anchor_profile.py deleted file mode 100644 index 611c5b0a..00000000 --- a/famodel/anchors/anchor_profile.py +++ /dev/null @@ -1,915 +0,0 @@ -"""Anchor class for FAModel, containing information and key methods for anchors of mooring lines - Work in progress -""" -import moorpy as mp -import numpy as np -from scipy.optimize import minimize -from famodel.famodel_base import Node -from famodel.mooring.mooring import Mooring -import famodel.platform.platform - -class Anchor(Node): - - def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, g=9.81, rho=1025): - ''' - Initialize an Anchor object. - - Parameters - ---------- - dd : dict - Design dictionary containing all information on the anchor: - { - type : str - Anchor type ('plate', 'suction_pile', 'torpedo_pile', 'helical_pile', 'driven_pile', 'dandg_pile') - design : dict - Geometric properties (e.g., A, D, D1, D2, d, L, L1, L2, zlug, beta) - cost : dict - Cost breakdown (matCost, instCost, decomCost) - } - ms : MoorPy system object - The MoorPy system instance the anchor is added to. - r : list of float - Location of the anchor in (x, y, z) coordinates (m). - aNum : int, optional - Entry number for anchor within the project anchorList dictionary. - id : str or int, optional - Unique identifier for the anchor object. - g : float, optional - Gravitational acceleration (m/s²). Default is 9.81. - rho : float, optional - Water density (kg/m³). Default is 1025. - ''' - - from famodel.famodel_base import Node - Node.__init__(self, id) - - self.dd = dd - self.ms = ms - self.r = r - self.aNum = aNum - self.g = g - self.rho = rho - - # Initialize anchor type and soil type - self.anchType = dd.get('type') if dd else None - self.soil_type = None - - # Initialize MoorPy anchor object - self.mpAnchor = None - - # Extract mass if available - self.mass = dd.get('design', {}).get('mass', None) if dd else None - - # Initialize other dictionaries - self.anchorCapacity = {} - self.cost = {} - self.loads = {} - self.soilProps = {} - self.failure_probability = {} - self.env_impact = {} - - def makeMoorPyAnchor(self, ms): - ''' - Create a MoorPy anchor object in a MoorPy system. - - Parameters - ---------- - ms : MoorPy system instance - The MoorPy system to add the anchor to. - - Returns - ------- - ms : MoorPy system instance - The updated MoorPy system with the anchor added. - ''' - import moorpy as mp - - # Create anchor as a fixed point in MoorPy system - ms.addPoint(1, self.r) - - # Assign this point as mpAnchor in the anchor class instance - self.mpAnchor = ms.pointList[-1] - - # Set mass if available - if 'mass' in self.dd.get('design', {}): - self.mpAnchor.m = self.dd['design']['mass'] - - # Set diameter if available - if 'd' in self.dd.get('design', {}): - self.mpAnchor.d = self.dd['design']['d'] - - # Set the point as an anchor entity - self.mpAnchor.entity = {'type': 'anchor'} - if 'type' in self.dd: - self.mpAnchor.entity['anchor_type'] = self.dd['type'] - - return ms - - def getLineProperties(self): - ''' - Retrieve line_type, diameter and unit weight from attached mooring. - - Returns - ------- - line_type : str - Type of mooring line ('chain' or 'wire'). - d : float - Nominal diameter (m). - w : float - Unit weight (N/m). - nolugload : bool - True if no lug load transfer should be applied. - ''' - for att in self.attachments.values(): - if isinstance(att['obj'], Mooring): - mtype = att['obj'].dd['sections'][0]['type']['material'].lower() - if 'chain' not in mtype: - print('No chain on seafloor, setting Ta=Tm (no load transfer).') - return mtype, None, None, True - else: - d_nom = att['obj'].dd['sections'][0]['type']['d_nom'] - w_nom = att['obj'].dd['sections'][0]['type']['w'] - return 'chain', d_nom, w_nom, False - raise ValueError('No mooring line attachment found for anchor.') - - def getMudlineForces(self, lines_only=False, seabed=True, xyz=False): - ''' - Find forces on anchor at mudline using the platform.getWatchCircle method or MoorPy Point.getForces method. - - Parameters - ---------- - lines_only : boolean, optional - Calculate forces from just mooring lines (True) or not (False). Default is False. - seabed : boolean, optional - Include effect of seabed pushing up the anchor (True) or not (False). Default is True. - xyz : boolean, optional - Return forces in x,y,z DOFs (True) or only the enabled DOFs (False). Default is False. - - Returns - ------- - dict - Dictionary containing mudline forces. - ''' - loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz) - - self.loads = { - 'Hm': np.sqrt(loads[0]**2 + loads[1]**2), - 'Vm': loads[2], - 'thetam': np.degrees(np.arctan2(loads[2], np.sqrt(loads[0]**2 + loads[1]**2))), - 'method': 'static', - 'mudline_load_type': 'current_state' - } - - return self.loads - - def getLugForces(self, ground_conds, Hm, Vm, thetam, zlug, line_type=None, d=None, w=None, plot=False): - ''' - Calculate the lug forces Ha and Va based on mudline loads. - - Parameters - ---------- - ground_conds : dict - Dictionary of ground conditions where keys are soil types. - Hm : float - Horizontal mudline load (N). - Vm : float - Vertical mudline load (N). - thetam : float - Mudline load angle (deg). - zlug : float - Padeye embedment depth (m). - line_type : str, optional - Type of mooring line ('chain' or 'wire'). - d : float, optional - Mooring line diameter (m). - w : float, optional - Mooring line unit weight (N/m). - plot : bool, optional - Whether to plot the load transfer profile. - - Returns - ------- - Ha : float - Horizontal load at lug (N). - Va : float - Vertical load at lug (N). - ''' - from famodel.anchors.anchors_famodel_profile.capacity_load import getTransferLoad - - if self.soil_type is None: - self.soil_type = self.dd.get('design', {}).get('soil_type') - - soil_profile = self.dd.get('soil_properties', {}).get(self.soil_type) - - # Determine mudline depth - z0 = soil_profile[0][0] - - # Load transfer if padeye is embedded - if zlug > z0: - # Fallback mechanism for line properties - if line_type is None or d is None or w is None: - try: - line_type, d, w, _ = self.getLineProperties() - except ValueError: - print('[Warning] No mooring attachment found. Trying anchor-level line properties...') - line_type = getattr(self, 'line_type', None) - d = getattr(self, 'd', None) - w = getattr(self, 'w', None) - - if any(v is None for v in [line_type, d, w]): - print('[Fallback] Using default chain properties.') - line_type = 'chain' - d = 0.16 - w = 5000.0 - - loads = getTransferLoad( - soil_profile, self.soil_type, np.sqrt(Hm**2 + Vm**2), - thetam, zlug, line_type, d, w, plot=plot - ) - Ta = loads['Ta'] - thetaa = loads['thetaa'] - Ha = Ta*np.cos(thetaa) - Va = Ta*np.sin(thetaa) - - else: - Ha = Hm - Va = Vm - - return Ha, Va - - - def getCapacityAnchor(self, ground_conds, Hm, Vm, thetam, zlug, line_type=None, d=None, w=None, plot=False): - ''' - Calculate anchor capacity based on anchor type and ground conditions. - - Parameters - ---------- - ground_conds : dict - Dictionary of ground conditions where keys are soil types. - Hm : float - Horizontal mudline load (N). - Vm : float - Vertical mudline load (N). - thetam : float - Mudline load angle (deg). - zlug : float - Padeye embedment depth (m). - line_type : str, optional - Type of mooring line ('chain' or 'wire'). - d : float, optional - Mooring line diameter (m). - w : float, optional - Mooring line unit weight (N/m). - plot : bool, optional - Whether to plot the results. - - Returns - ------- - None - Updates anchor object with capacity results. - ''' - from famodel.anchors.anchors_famodel_profile.capacity_plate import getCapacityPlate - from famodel.anchors.anchors_famodel_profile.capacity_suction import getCapacitySuction - from famodel.anchors.anchors_famodel_profile.capacity_torpedo import getCapacityTorpedo - from famodel.anchors.anchors_famodel_profile.capacity_helical import getCapacityHelical - from famodel.anchors.anchors_famodel_profile.capacity_driven import getCapacityDriven - from famodel.anchors.anchors_famodel_profile.capacity_dandg import getCapacityDandG - from famodel.anchors.anchors_famodel_profile.capacity_load import getTransferLoad - import numpy as np - - # --- Dispatch dictionary --- - capacity_dispatch = { - 'suction': getCapacitySuction, - 'sepla': getCapacityPlate, - 'dea': getCapacityPlate, - 'depla': getCapacityPlate, - 'vla': getCapacityPlate, - 'plate': getCapacityPlate, - 'torpedo': getCapacityTorpedo, - 'helical': getCapacityHelical, - 'driven': getCapacityDriven, - 'dandg': getCapacityDandG - } - - # Normalize anchor type - anchType_clean = self.anchType.lower().replace(' ', '') - - # Find function - capacity_func = capacity_dispatch.get(anchType_clean) - if capacity_func is None: - raise ValueError(f"Unknown anchor type '{self.anchType}' for anchor capacity calculation.") - - # Get ground conditions - soil_profile = ground_conds.get(self.soil_type) - if soil_profile is None: - raise ValueError(f"Ground condition '{self.soil_type}' not found in provided ground_conds.") - - # Determine if load transfer is needed - z0 = soil_profile[0][0] - - # Load transfer if padeye is embedded - if zlug > z0: - if line_type is None or d is None or w is None: - try: - line_type, d, w, nolugload = self.getLineProperties() - except ValueError: - print('[Warning] No mooring attachment found. Trying anchor-level line properties...') - line_type = getattr(self, 'line_type', None) - d = getattr(self, 'd', None) - w = getattr(self, 'w', None) - nolugload = False - - if line_type is None or d is None or w is None: - print('[Fallback] Using default chain properties.') - line_type = 'chain' - d = 0.16 - w = 5000.0 - - if nolugload: - Ha, Va = Hm, Vm - else: - loads = getTransferLoad(soil_profile, self.soil_type, Tm=np.sqrt(Hm**2 + Vm**2), thetam=thetam, zlug=zlug, line_type=line_type, d=d, w=w, plot=plot) - Ta = loads['Ta'] - thetaa = loads['thetaa'] - Ha = Ta*np.cos(thetaa) - Va = Ta*np.sin(thetaa) - else: - loads = getTransferLoad(soil_profile, self.soil_type, Tm=np.sqrt(Hm**2 + Vm**2), thetam=thetam, zlug=zlug, line_type=line_type, d=d, w=w, plot=plot) - Ta = loads['Ta'] - thetaa = loads['thetaa'] - Ha = Ta*np.cos(thetaa) - Va = Ta*np.sin(thetaa) - else: - Ha = Hm - Va = Vm - - # Call the capacity function based on anchor type - if anchType_clean == 'suction': - D = self.dd['design']['D'] - L = self.dd['design']['L'] - zlug = self.dd['design']['zlug'] - - results = capacity_func(soil_profile, self.soil_type, D, L, zlug, Ha, Va, plot=plot) - - elif anchType_clean in ['sepla', 'dea', 'depla', 'vla', 'plate']: - results = capacity_func(soil_profile, self.soil_type, self.B, self.L, zlug, self.beta, Ha, Va, plot=plot) - - elif anchType_clean == 'torpedo': - results = capacity_func(soil_profile, self.soil_type, self.D1, self.D2, self.L1, self.L2, zlug, self.ballast, Ha, Va, plot=plot) - - elif anchType_clean == 'helical': - results = capacity_func(soil_profile, self.soil_type, self.D, self.L, self.d, zlug, Ha, Va, plot=plot) - - elif anchType_clean == 'driven': - y, z, results = capacity_func(soil_profile, self.soil_type, self.L, self.D, zlug, Ha, Va, plot=plot) - - elif anchType_clean == 'dandg': - y, z, results = capacity_func(soil_profile, self.soil_type, self.L, self.D, zlug, Ha, Va, plot=plot) - - else: - raise ValueError(f"Anchor type '{self.anchType}' not supported.") - - # --- Standardize and store capacity results --- - self.capacity_results = { - 'Hmax': results.get('Horizontal max.', np.nan), - 'Vmax': results.get('Vertical max.', np.nan), - 'UC': results.get('Unity check', np.nan), - 'Ha': Ha, - 'Va': Va, - 'zlug': zlug, - 'z0': z0 - } - - # Add mass if weight is available - if 'Weight pile' in results: - self.capacity_results['Weight pile'] = results['Weight pile'] - if 'Weight plate' in results: - self.capacity_results['Weight plate'] = results['Weight plate'] - - # Special cases for displacement-based anchors - if anchType_clean in ['driven_pile', 'dandg_pile']: - self.capacity_results['Lateral displacement'] = results.get('Lateral displacement', np.nan) - self.capacity_results['Rotational displacement'] = results.get('Rotational displacement', np.nan) - - return results - - def getSafetyFactor(self): - ''' - Calculate the safety factor based on the unity checks stored in capacity results. - - Returns - ------- - dict - Dictionary containing safety factors. - ''' - anchType_clean = self.anchType.lower().replace(' ', '') - - if anchType_clean in ['helical', 'driven', 'dandg']: - UCv = self.capacity_results.get('Unity check (vertical)', None) - UCh = self.capacity_results.get('Unity check (horizontal)', None) - - if UCv is None or UCh is None: - print("Warning: Vertical or Horizontal Unity Check not found in capacity results. Returning NaN.") - return {'SF_vertical': np.nan, 'SF_horizontal': np.nan} - - SFv = 1.0/UCv if UCv != 0 else np.inf - SFh = 1.0/UCh if UCh != 0 else np.inf - - return {'SF_vertical': SFv, 'SF_horizontal': SFh} - - else: - UC = self.capacity_results.get('UC', None) - - if UC is None: - print("Warning: Unity Check (UC) not found in capacity results. Returning NaN.") - return {'SF_combined': np.nan} - - SF = 1.0/UC if UC != 0 else np.inf - - return {'SF_combined': SF} - - def getCost(self, costDict='default'): - ''' - Calculate the cost of the anchor based on material, installation, and decommissioning costs. - - Parameters - ---------- - costDict : str or dict, optional - If 'default', uses mean values from Task 49 Design Basis ranges. - If dict or yaml path, loads user-defined cost dictionaries. - - Returns - ------- - float - Total cost of the anchor. - ''' - if isinstance(costDict, str) and costDict != 'default': - import yaml - costDict = yaml.load(costDict, Loader=yaml.FullLoader) - - anchType = self.dd['type'] - - if costDict == 'default': - matCostDict = { - 'suction_pile': 4.435, - 'DEA': 5.705, - 'SEPLA': 5.705, - 'DEPLA': 5.705, - 'VLA': 5.705, - 'torpedo_pile': 5.0, - 'helical_pile': 6.0, - 'driven_pile': 4.0, - 'dandg_pile': 5.5 - } - instCostDict = { - 'suction_pile': 2.0, - 'DEA': 1.5, - 'SEPLA': 1.5, - 'DEPLA': 1.5, - 'VLA': 1.5, - 'torpedo_pile': 2.5, - 'helical_pile': 3.0, - 'driven_pile': 2.0, - 'dandg_pile': 2.2 - } - decomCostDict = { - 'suction_pile': 1.0, - 'DEA': 0.8, - 'SEPLA': 0.8, - 'DEPLA': 0.8, - 'VLA': 0.8, - 'torpedo_pile': 1.2, - 'helical_pile': 1.5, - 'driven_pile': 1.0, - 'dandg_pile': 1.1 - } - else: - matCostDict = costDict.get('material', {}) - instCostDict = costDict.get('install', {}) - decomCostDict = costDict.get('decom', {}) - - keyFail = True - - # Ensure mass is available - if self.mass is None or self.mass == 0: - # Try to extract from capacity_results if already available - if 'Weight pile' in self.capacity_results: - self.mass = self.capacity_results['Weight pile']/self.g - elif 'Weight plate' in self.capacity_results: - self.mass = self.capacity_results['Weight plate']/self.g - else: - # If capacity_results missing, attempt to calculate capacity to retrieve weight - if 'soil_properties' in self.dd: - self.getAnchorCapacity(plot=False) - if 'Weight pile' in self.capacity_results: - self.mass = self.capacity_results['Weight pile']/self.g - elif 'Weight plate' in self.capacity_results: - self.mass = self.capacity_results['Weight plate']/self.g - else: - print('Warning: Weight not found after capacity calculation, setting mass to 0.') - self.mass = 0 - else: - print('Soil properties needed to calculate anchor mass for cost. Setting mass to 0.') - self.mass = 0 - - # Calculate material cost based on mass - if anchType in matCostDict: - self.cost['Material Cost'] = matCostDict[anchType]*self.mass - keyFail = False - else: - raise KeyError(f'Anchor type {anchType} not found in material cost dictionary.') - - # Install and decom costs if available - self.cost['Installation Cost'] = instCostDict.get(anchType, 0.0) - self.cost['Decommissioning Cost'] = decomCostDict.get(anchType, 0.0) - - # Total cost - self.cost['Total Cost'] = (self.cost['Material Cost'] + - self.cost['Installation Cost'] + - self.cost['Decommissioning Cost']) - - return sum(self.cost.values()) - - - def getSize(self, geom, geomKeys, geomBounds=None, loads=None, minfs={'Ha':1.0,'Va':1.0}, - lambdap_con=[4,6], zlug_fix=False, FSdiff_max=None, plot=False): - ''' - Resize the anchor dimensions to meet the target safety factor and geometric constraints. - - Parameters - ---------- - geom : list - Starting guess geometry values. - geomKeys : list - List of keys matching the geom list values (e.g., 'L', 'D', 'zlug'). - geomBounds : list, optional - List of tuples of upper and lower bounds for each geometry value. - loads : dict, optional - Dictionary of maximum anchor loads. - minfs : dict, optional - Minimum factors of safety in horizontal and vertical directions. - lambdap_con : list, optional - Constraint for L/D parameter as [min, max]. - zlug_fix : bool, optional - Whether zlug should be fixed (True) or updated (False). - FSdiff_max : dict, optional - Maximum allowable difference between achieved FS and target FS. - plot : bool, optional - Whether to plot results. - - Returns - ------- - None - ''' - from scipy.optimize import minimize - import numpy as np - - anchType = self.dd['type'] - - if loads is None: - loads = self.loads - - # Compute thetam internally from Hm and Vm - Hm = loads['Hm'] - Vm = loads['Vm'] - thetam = np.degrees(np.arctan2(Vm, Hm)) - zlug = loads['zlug'] - - # Read mooring properties from anchor attributes - line_type = self.line_type - d = self.d - w = self.w - - Ha, Va = self.getLugForces( - ground_conds=self.dd.get('soil_properties'), - Hm=Hm, - Vm=Vm, - thetam=thetam, - zlug=zlug, - line_type=line_type, - d=d, - w=w, - plot=plot - ) - - ground_conds = self.dd.get('soil_properties') - - input_loads = {'Ha': Ha*minfs['Ha'], 'Va': Va*minfs['Va']} - - # Objective: minimize weight - def objective_(vars, geomKeys, Ha, Va, ground_conds, thetam, zlug, plot): - newGeom = dict(zip(geomKeys, vars)) - self.dd['design'].update(newGeom) - if 'suction' in self.dd['type'] and not zlug_fix: - self.dd['design']['zlug'] = (2/3)*newGeom['L'] - results = self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot) - return results.get('Weight pile', results.get('Weight plate', 1e6)) - - # Constraints - def conFun_lambdap_(vars, lambdap_con, geomKeys): - newGeom = dict(zip(geomKeys, vars)) - lambdap = newGeom['L']/newGeom['D'] - return min(lambdap - lambdap_con[0], lambdap_con[1] - lambdap) - - def conFun_UC_(vars, Ha, Va, ground_conds, thetam, zlug, plot): - results = self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot) - return results.get('Unity check', 0) - 1 - - def conFun_H_(vars, Ha, Va, ground_conds, thetam, zlug, plot): - results = self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot) - FS = self.getFS() - return FS.get('SF_horizontal', FS.get('SF_combined', 0)) - 1 - - def conFun_V_(vars, minfs, Ha, Va, ground_conds, thetam, zlug, plot): - results = self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot) - FS = self.getFS() - if minfs['Va'] == 0: - return 1 - return FS.get('SF_vertical', FS.get('SF_combined', 0)) - 1 - - # Initial geometry setup - startGeom = dict(zip(geomKeys, geom)) - self.dd['design'].update(startGeom) - - if not 'zlug' in self.dd['design']: - if 'suction' in anchType and not zlug_fix: - self.dd['design']['zlug'] = (2/3)*startGeom['L'] - else: - self.dd['design']['zlug'] = 0 - - if zlug_fix and 'zlug' in geomKeys: - zlug_loc = geomKeys.index('zlug') - geomKeys.pop(zlug_loc) - geom.pop(zlug_loc) - if geomBounds: - geomBounds.pop(zlug_loc) - - initial_guess = geom - - # Setup constraints - constraints = [] - - if 'suction' in anchType: - constraints.append({'type': 'ineq', 'fun': conFun_lambdap_, 'args': (lambdap_con, geomKeys)}) - - if 'torpedo' in anchType or 'suction' in anchType: - constraints.append({'type': 'ineq', 'fun': conFun_UC_, 'args': (Ha, Va, ground_conds, thetam, zlug, plot)}) - else: - constraints.append({'type': 'ineq', 'fun': conFun_H_, 'args': (Ha, Va, ground_conds, thetam, zlug, plot)}) - constraints.append({'type': 'ineq', 'fun': conFun_V_, 'args': (minfs, Ha, Va, ground_conds, thetam, zlug, plot)}) - - print('Starting optimization of anchor size') - - if geomBounds is None: - solution = minimize(objective_, initial_guess, args=(geomKeys, Ha, Va, ground_conds, thetam, zlug, plot), - method='COBYLA', constraints=constraints, - options={'rhobeg': 0.1, 'catol': 0.001}) - else: - solution = minimize(objective_, initial_guess, args=(geomKeys, Ha, Va, ground_conds, thetam, zlug, plot), - method='COBYLA', constraints=constraints, bounds=geomBounds, - options={'rhobeg': 0.1, 'catol': 0.001}) - - # Update final geometry - endGeom = dict(zip(geomKeys, solution.x)) - print('Optimized geometry: ', endGeom) - self.dd['design'].update(endGeom) - - if 'suction' in anchType and not zlug_fix: - self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] - zlug = self.dd['design']['zlug'] # update local zlug - - self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot) - - def getSizeSuction(self, geom, geomKeys, geomBounds=None, loads=None, minfs={'Ha':1.6,'Va':2}, - lambdap_con=[4,8], zlug_fix=False, plot=False): - ''' - Two-stage optimization: - Stage 1 - Grow anchor to satisfy UC <= 1. - Stage 2 - Minimize weight while keeping UC <= 1 and satisfying L/D constraints. - ''' - - anchType = self.dd['type'] - - if loads is None: - loads = self.loads - - Hm = loads['Hm'] - Vm = loads['Vm'] - zlug = self.dd['design']['zlug'] - thetam = np.degrees(np.arctan2(Vm, Hm)) - - line_type = self.line_type - d = self.d - w = self.w - - initial_guess = [self.dd['design']['L'], self.dd['design']['D']] - bounds = geomBounds if geomBounds else [(5.0, 30.0), (2.0, 5.0)] - - ground_conds = self.dd.get('soil_properties') - - # --- Stage 1: Safety First --- - def safety_objective(vars): - L, D = vars - self.dd['design']['L'] = L - self.dd['design']['D'] = D - self.dd['design']['zlug'] = (2/3) * L - - Ha, Va = self.getLugForces( - ground_conds=ground_conds, - Hm=Hm, - Vm=Vm, - thetam=thetam, - zlug=self.dd['design']['zlug'], - line_type=line_type, - d=d, - w=w, - plot=False - ) - - self.getCapacityAnchor( - ground_conds=ground_conds, - Hm=Hm, - Vm=Vm, - thetam=thetam, - zlug=self.dd['design']['zlug'], - line_type=line_type, - d=d, - w=w, - plot=False - ) - - UC = self.capacity_results.get('UC', 2.0) - return max(0.0, UC - 1.0)**2 - - minimize( - safety_objective, - initial_guess, - method='COBYLA', - bounds=bounds, - options={'rhobeg': 0.1, 'catol': 0.001, 'maxiter': 300} - ) - - # --- Stage 2: Weight Minimization --- - def weight_objective(vars): - L, D = vars - self.dd['design']['L'] = L - self.dd['design']['D'] = D - self.dd['design']['zlug'] = (2/3) * L - - Ha, Va = self.getLugForces( - ground_conds=ground_conds, - Hm=Hm, - Vm=Vm, - thetam=thetam, - zlug=self.dd['design']['zlug'], - line_type=line_type, - d=d, - w=w, - plot=False - ) - - self.getCapacityAnchor( - ground_conds=ground_conds, - Hm=Hm, - Vm=Vm, - thetam=thetam, - zlug=self.dd['design']['zlug'], - line_type=line_type, - d=d, - w=w, - plot=False - ) - - return self.capacity_results.get('Weight pile', 1e9) - - def constraint_uc(vars): - L, D = vars - return 1.0 - self.capacity_results.get('UC', 2.0) - - def constraint_fs_horizontal(vars): - L, D = vars - return (self.capacity_results.get('Hmax', 0) / self.capacity_results.get('Ha', 1)) - minfs['Ha'] - - def constraint_fs_vertical(vars): - L, D = vars - return (self.capacity_results.get('Vmax', 0) / self.capacity_results.get('Va', 1)) - minfs['Va'] - - def constraint_lambda_min(vars): - L, D = vars - return (L/D) - lambdap_con[0] - - def constraint_lambda_max(vars): - L, D = vars - return lambdap_con[1] - (L/D) - - result = minimize( - weight_objective, - [self.dd['design']['L'], self.dd['design']['D']], - method='COBYLA', - constraints=[ - {'type': 'ineq', 'fun': constraint_fs_horizontal}, - {'type': 'ineq', 'fun': constraint_fs_vertical}, - {'type': 'ineq', 'fun': constraint_lambda_min}, - {'type': 'ineq', 'fun': constraint_lambda_max} - ], - bounds=bounds, - options={'rhobeg': 0.5, 'catol': 0.01, 'maxiter': 100} - ) - - # Update final geometry - endGeom = dict(zip(geomKeys, result.x)) - print('Optimized geometry:', endGeom) - self.dd['design'].update(endGeom) - - if 'suction' in anchType and not zlug_fix: - self.dd['design']['zlug'] = (2/3) * self.dd['design']['L'] - - self.getCapacityAnchor( - ground_conds=ground_conds, - Hm=Hm, - Vm=Vm, - thetam=thetam, - zlug=self.dd['design']['zlug'], - line_type=line_type, - d=d, - w=w, - plot=plot - ) - - print('\nFinal Optimized Anchor:') - print('Design:', self.dd['design']) - print('Capacity Results:', self.capacity_results) - - def getCombinedPlot(self): - ''' - Create a single plot showing the suction pile and the inverse catenary overlay in the same coordinate system. - ''' - from famodel.anchors.anchors_famodel_profile.capacity_load import getTransferLoad - from famodel.anchors.anchors_famodel_profile.capacity_plots import plot_suction - import numpy as np - import matplotlib.pyplot as plt - - if self.anchType.lower() != 'suction': - raise NotImplementedError("getCombinedPlot only supports suction piles for now.") - - # Extract key parameters - design = self.dd['design'] - D = design['D'] - L = design['L'] - zlug = design['zlug'] - soil_type = self.soil_type - soil_profile = self.dd['soil_properties'][soil_type] - z0 = soil_profile[0][0] - - Hm = self.loads['Hm'] - Vm = self.loads['Vm'] - thetam = self.loads.get('thetam', np.degrees(np.arctan2(Vm, Hm))) - - line_type = getattr(self, 'line_type', 'chain') - d = getattr(self, 'd', 0.16) - w = getattr(self, 'w', 5000.0) - - # Get inverse catenary path - result = getTransferLoad( - soil_profile, soil_type, np.sqrt(Hm**2 + Vm**2), thetam, zlug, line_type, d, w, plot=False - ) - drag_values = np.array(result['drag_values']) - depth_values = np.array(result['depth_values']) - depth_values = -depth_values[::-1] - Tm = result['Tm']; thetam = result['thetam'] - Ta = result['Ta']; thetaa = result['thetaa'] - - # Transform to suction pile coordinate system - x_start = D/2 + drag_values[0] - z_start = zlug - drag_transformed = x_start - drag_values - depth_transformed = z_start + (depth_values - depth_values[0]) - - # Set up plot - fig, ax = plt.subplots(figsize=(5, 5)) - - # Plot suction pile - plot_suction(soil_profile, soil_type, L, D, zlug=zlug, title='', ax=ax) - - # Overlay inverse catenary - ax.plot(drag_transformed, depth_transformed, color='b', lw=2.0, label='Inverse Catenary') - - # Annotate ends - ax.plot(drag_transformed[-1], depth_transformed[-1], 'ro', label='Mudline End') - ax.plot(drag_transformed[0], depth_transformed[0], 'go', label='Embedded End') - - n = 2e6 - # Add load vectors - ax.arrow(drag_transformed[-1], depth_transformed[-1], Tm*np.cos(np.deg2rad(thetam))/n, -Tm*np.sin(np.deg2rad(thetam))/n, - head_width=0.25, head_length=0.5, color='r', label='Mudline Load') - ax.arrow(drag_transformed[0], depth_transformed[0], Ta*np.cos(thetaa)/n, -Ta*np.sin(thetaa)/n, - head_width=0.25, head_length=0.5, color='g', label='Padeye Load') - - # Finalize plot - xmax = max(drag_transformed[-1] + D, 2*D) - ax.set_xlim(-D, xmax) - ax.set_title('Suction Pile with Inverse Catenary') - ax.legend() - ax.grid(True) - plt.tight_layout() - plt.show() diff --git a/famodel/anchors/anchors_famodel/__init__.py b/famodel/anchors/anchors_famodel/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/famodel/anchors/anchors_famodel/capacity_dandg.py b/famodel/anchors/anchors_famodel/capacity_dandg.py index 284c4339..0f8e2d47 100644 --- a/famodel/anchors/anchors_famodel/capacity_dandg.py +++ b/famodel/anchors/anchors_famodel/capacity_dandg.py @@ -1,17 +1,13 @@ import numpy as np import matplotlib.pyplot as plt -from scipy.interpolate import interp1d -from scipy import linalg -import inspect +from .support_soils import rock_profile +from .support_solvers import fd_solver +from .support_pycurves import py_Lovera +from .support_plots import plot_pile, plot_pycurve -################################### -#### Pile Geometry and Loading #### -################################### - -def getCapacityDandG(profile, L, D, zlug, V, H, plot=True): - ''' - Models a laterally loaded pile using the p-y method. The solution for +def getCapacityDandG(profile_map, location_name, L, D, zlug, Ha, Va, plot=False): + '''Models a laterally loaded pile using the p-y method. The solution for lateral displacements is obtained by solving the 4th order ODE, EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method. @@ -19,43 +15,46 @@ def getCapacityDandG(profile, L, D, zlug, V, H, plot=True): Assumes that EI remains constant with respect to curvature i.e. pile material remains in the elastic region. - Input: - ----- - profile - A 2D array of depths (m) and corresponding undrained shear strength(Pa) - Eg: array([[z1,UCS1],[z2,UCS2],[z3,UCS3]...]) - Use small values for Su (eg: 0.001) instead of zeros to avoid divisions - by zero but always start z at 0.0 - Example of a valid data point at the mudline is [0.0, 0.001] - L - Length of pile (m) - D - Outer diameter of pile (m) - V - Axial force at pile head (N) - H - Force at pile head (N) - M - Moment at pile head (N*m) - n - Number of elements (50 by default) - iterations - Number of iterations to repeat calculation in order obtain convergence of 'y' - (A better approach is to iterate until a predefined tolerance is achieved) - - Output: - ------ - y - Lateral displacement at each node, length = n + 5, (n+1) real nodes and 4 imaginary nodes - z - Vector of node locations along pile - resultsDandG- Dictionary with results + Parameters + ---------- + profile : array + Rock profile as a 2D array: (z (m), UCS (MPa), Em (MPa)) + soil_type : string + Select soil condition, 'rock' + L : float + Pile length (m) + D : float + Pile diameter (m) + zlug : float + Load eccentricity above the mudline or depth to mudline relative to the pile head (m) + Ha : float + Horizontal load at pile lug elevation (N) + Va : float + Vertical load at pile lug elevation (N) + plot : bool + Plot the p-y curve and the deflection pile condition if True + + Returns + ------- + y : array + Lateral displacement at each node (n+1 real + 4 imaginary) + z : array + Node location along pile (m) + resultsDandG : dict + Dictionary with lateral, rotational, vertical and pile weight results ''' - - # Extract optional keyword arguments - # ls = 'x' - - n = 50; iterations = 10; loc = 2 - # Resistance factor - nhuc = 1; nhu = 0.3 - delta_r = 0.08 # Mean roughness height [m] + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + + n = 50; loc = 2 # Number of nodes (-) + tol = 1e-16; max_iter = 50 # Iteration parameters (-) + nhuc = 1; nhu = 0.3 # Resistance factor (-) + delta_r = 0.08 # Mean roughness height (m) - # Convert L and D to floating point numbers to avoid rounding errors - L = float(L) - D = float(D) t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD E = 200e9 # Elastic modulus of pile material (Pa) + fy = 350e6 # Steel's yield strength (Pa) rhows = 66.90e3 # Submerged steel specific weight (N/m3) rhow = 10e3 # Water specific weight (N/m3) @@ -75,299 +74,159 @@ def PileWeight(Len, Dia, tw, rho): # Initialize and assemble array/list of p-y curves at each real node z = np.zeros(N) - py_funs = [] k_secant = np.zeros(N) - DQ = [] + py_funs = [] + DQ = []; pycurve_data = [] - for i in [0,1]: # Top two imaginary nodes + z0 = min(layer['top'] for layer in layers) + + for i in [0, 1]: # Top two imaginary nodes z[i] = (i - 2)*h py_funs.append(0) k_secant[i] = 0.0 - for i in range(2,n+3): # Real nodes + for i in range(2, n+3): # Real nodes z[i] = (i - 2)*h - # Extract rock profile data - zlug, f_UCS, f_Em = rock_profile(profile) - UCS, Em = f_UCS(z[i]), f_Em(z[i]) - py_funs.append(py_Reese(z[i], D, zlug, UCS, Em, plot=plot)) - k_secant[i] = py_funs[i](y[i])/y[i] + z_depth = z[i] + + matched_layer = next((layer for layer in layers if layer['top'] <= z_depth <= layer['bottom']), None) + if matched_layer is None or z_depth < matched_layer['top']: + py_funs.append(lambda y_val: np.zeros_like(y_val)) + k_secant[i] = 0.0 + DQ.append(0.0) + continue + + profile = [[matched_layer['top'], matched_layer['UCS_top'], matched_layer['Em_top']], + [matched_layer['bottom'], matched_layer['UCS_bot'], matched_layer['Em_bot']]] + z0_local, f_UCS, f_Em = rock_profile(profile) + + if z_depth < z0_local: + py_funs.append(lambda y_val: np.zeros_like(y_val)) + k_secant[i] = 0.0 + DQ.append(0.0) + continue + + UCS = f_UCS(z_depth) + Em = f_Em(z_depth) + py_f, (y_vals, p_vals) = py_Lovera(z_depth, D, UCS, Em, zlug, z0, return_curve=True) + py_funs.append(py_f) + pycurve_data.append((y_vals, p_vals, z_depth, 'rock')) + # print(f"z_depth = {z_depth:.2f} m, UCS = {f_UCS(z_depth):.2e} Pa, Em = {f_Em(z_depth):.2e} Pa") + SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D alpha = 0.36*SCR - 0.0005 fs = alpha*UCS - Dq = np.pi*D*fs*z[i] + Dq = np.pi*D*fs*z_depth DQ.append(Dq) + k_val = py_funs[i](y[i]) + k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 for i in [n+3, n+4]: # Bottom two imaginary nodes z[i] = (i - 2)*h py_funs.append(0) k_secant[i] = 0.0 - - # Track k_secant and current displacements - - y1 = np.linspace(-2.*D, 2.*D, 500) - if plot: - plt.plot(y1, py_funs[loc](y1)) - plt.xlabel('y (m)'), plt.ylabel('p (N/m)') - plt.grid(True) - - for j in range(iterations): - # if j == 0: print 'FD Solver started!' - y = fd_solver(n, N, h, EI, V, H, zlug, k_secant) - if plot: - plt.plot(y[loc], k_secant[loc]*y[loc]) - + + Wp = PileWeight(L, D, t, rhows + rhow) + Wtip = DQ[-1] if DQ else 0.0 + Vmax = Wp + Wtip + + for j in range(max_iter): + y_old = y.copy() + y, *_ = fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant) + + # Update stiffness for i in range(2, n+3): - k_secant[i] = py_funs[i](y[i])/y[i] - - # print(f'y_max = {max(y):.3f} m') - # print(f'rot_max = {np.rad2deg((y[2] - y[3])/h):.3f} deg') - - resultsDandG = {} - resultsDandG['Lateral displacement'] = y[2] - resultsDandG['Rotational displacement'] = np.rad2deg((y[2] - y[3])/h) - resultsDandG['Axial capacity'] = DQ[-1] - resultsDandG['Weight'] = PileWeight(L, D, t, (rhows + rhow)) - - return y[2:-2], z[2:-2], resultsDandG - -################# -#### Solvers #### -################# - -def fd_solver(n, N, h, EI, V, H, zlug, k_secant): - ''' - Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for - non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves - are linear. - - Input: - ----- - n - Number of elements - N - Total number of nodes - h - Element size - EI - Flexural rigidity of pile - V - Axial force at pile head - H - Shear at pile head/tip - M - Moment at pile head/tip - k_secant - Secant stiffness from p-y curves - - Output: - ------ - y - Lateral displacement at each node - ''' - M = H*zlug + if callable(py_funs[i]): + k_secant[i] = py_funs[i](y[i])/y[i] if y[i] != 0 else 0.0 - # Initialize and assemble matrix - X = np.zeros((N,N)) - - # (n+1) finite difference equations for (n+1) real nodes - for i in range(0,n+1): - X[i,i] = 1.0 - X[i,i+1] = -4.0 + V*h**2/EI - X[i,i+2] = 6.0 - 2*V*h**2/EI + k_secant[i+2]*h**4/EI - X[i,i+3] = -4.0 + V*h**2/EI - X[i,i+4] = 1.0 - - # Curvature at pile head - X[n+1,1] = 1.0 - X[n+1,2] = -2.0 - X[n+1,3] = 1.0 - - # Shear at pile head - X[n+2,0] = -1.0 - X[n+2,1] = 2.0 - V*h**2/EI - X[n+2,2] = 0.0 - X[n+2,3] = -2.0 + V*h**2/EI - X[n+2,4] = 1.0 - - # Curvature at pile tip - X[n+3,-2] = 1.0 - X[n+3,-3] = -2.0 - X[n+3,-4] = 1.0 - - # Shear at pile tip - X[n+4,-1] = 1.0 - X[n+4,-2] = -2.0 + V*h**2/EI - X[n+4,-3] = 0.0 - X[n+4,-4] = 2.0 - V*h**2/EI - X[n+4,-5] = -1.0 - - # Initialize vector q - q = np.zeros(N) - - # Populate q with boundary conditions - q[-3] = 2*H*h**3 # Shear at pile head - # q[-4] = M*h**2 # Moment at pile head - - y = linalg.solve(EI*X, q) - - return y - -############################### -#### P-Y Curve Definitions #### -############################### - - -def py_Reese(z, D, zlug, UCS, Em, plot=True): - - ''' - Returns an interp1d interpolation function which represents the Reese (1997) p-y curve at the depth of interest. - - Important: Make sure to import the interp1 function by running 'from scipy.interpolate import interp1d' in the main program. + # Check convergence + if np.linalg.norm(y - y_old, ord=2) < tol: + print(f'[Converged in {j+1} iterations]') + break + else: + print('[Warning: Solver did not converge]') - Input: - ----- - z - Depth relative to pile head (m) - D - Pile diameter (m) - zlug - Load eccentricity above the mudline or depth to mudline relative to the pile head (m) - UCS - Undrained shear strength (Pa) - Em - Effectve vertical stress (Pa) - RQD - Rock quality designation, measures the quality of the rock core taken from a borehole. - Typically ranges from 25% (very weathered rock) to 100% (fresh rock). - Output: - ------ - Returns an interp1d interpolation function which represents the p-y curve at the depth of interest. - 'p' (N/m) and 'y' (m). - ''' - z0 = 0 - - #from scipy.interpolate import interp1d - #global var_Reese - - RQD = 69 # Assumed good rock quality (hard rock) - Dref = 0.305; nhu = 0.3; E = 200e9 - t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD - I = np.pi*(D**4 - (D - 2*t)**4)/64.0 - EI = E*I - alpha = -0.00667*RQD + 1 - krm = 0.0005 - - if (z - z0) < 0: - p_ur = 0 - else: - if z < 3*D: - p_ur = alpha*UCS*D*(1 + 1.4*z/D) - #kir = (100 +400*z/(3*D)) - else: - p_ur = 5.2*alpha*UCS*D - #kir = 500 - - kir = (D/Dref)*2**(-2*nhu)*(EI/(Em*D**4))**0.284 - Kir = kir*Em - y_rm = krm*D - y_a = (p_ur/(2*y_rm**0.25*Kir))**1.333 - - # Normalized lateral displacement - N = 20 - y = np.concatenate((-np.logspace(5,-3,N),[0],np.logspace(-3,5,N))) + if plot: + plot_pycurve(pycurve_data) + + fig, ax = plt.subplots(figsize=(3, 5)) + y0 = np.zeros_like(z[2:-2]) + ax.plot(y0, z[2:-2], 'k', label='Original pile axis') + ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape') + ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') + ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)') + ax.set_xlabel('Lateral displacement (m)') + ax.set_ylabel('Depth (m)') + ax.set_xlim([-0.1*D, 0.1*D]) + ax.set_ylim([L + 5, -2]) + ax.grid(ls='--') + ax.legend() + + # Relevant index of nodes + zlug_index = int(zlug/h); print(zlug_index) + ymax_index = np.argmax(y); print(ymax_index) - p=[]; P=[]; - for i in range (len(y)): - if abs(y[i]) < y_a: - P = np.sign(y[i])*Kir*y[i] - elif abs(y[i]) > y_a: - P = min((p_ur/2)*(abs(y[i])/y_rm)**0.25,p_ur) - p.append(P) - - p = np.array(p).squeeze() - for j in range(len(y)): - if y[j] < 0: - p[j] = -1*p[j] - elif y[j] > 0: - p[j] = p[j] - - #var_Reese = inspect.currentframe().f_locals + resultsDandG = { + 'Vertical max.': Vmax, + 'Lateral displacement': y[ymax_index], + 'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h), + 'Bending moment': None, + 'Plastic moment': None, + 'Plastic hinge': None, + 'Hinge location': None, + 'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf, + 'Unity check (horizontal)': 0.0, # Placeholder; no Mp or Mi in current model + 'Weight pile': PileWeight(L, D, t, rhows + rhow), + 'p-y model': 'Lovera (2023)'} - f = interp1d(y, p) # Interpolation function for p-y curve - if plot: - plt.plot(y, p) - plt.xlabel('y (m)') - plt.ylabel('p (N/m)'), - plt.title('PY Curves - Reese (1997)') - plt.grid(True) - plt.xlim([-0.03*D, 0.03*D]) - plt.ylim([min(p), max(p)]) - - return f # This is f (linear interpolation of y-p) + return layers, y[2:-2], z[2:-2], resultsDandG -####################### -#### Rock Profile ##### -####################### - -def rock_profile(profile): - ''' - Define the (weak) rock profile used by the p-y analyzer. Outputs 'interp1d' functions containing - UCS and Em profiles to be used by the p-y curve functions. - - Input: - ----- - profile - A 2D tuple in the following format: ([depth (m), UCS (MPa), Em (MPa), py-model]) - The soil profile should be defined relative to the pile/tower head (i.e. point of lateral load application) - so that any load eccentricities can be taken into account. An example soil profile is shown below. - Eg: array([[z0,UCS0,Em0, 'Reese'], - [z1,UCS1,Em1, 'Reese'], - [z2,UCS2,Em2, 'Reese'], - ...]) - *The current program cannot define layers with different p-y models. But it will added in the future. - - Output: - ------ - z0 - Depth of mudline relative to the pile head (m) - f_UCS - 'interp1d' function containing undrained shear strength profile (Pa) - f_Em - 'interp1d' function containing effective vertical stress profile (Pa) - ''' - - # Depth of mudline relative to pile head - z0 = float(profile[0][0]) +if __name__ == '__main__': - # Extract data from soil_profile array and zero strength virtual soil layer - # from the pile head down to the mudline - depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m - UCS = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # MPa - Em = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # MPa + profile_map = [ + { + 'name': 'CPT_rock_1', + 'x': 502000, + 'y': 5725000, + 'layers': [ + { + 'top': 2.0, 'bottom': 5.0, + 'soil_type': 'rock', + 'UCS_top': 8.0, 'UCS_bot': 8.0, # MPa + 'Em_top': 100, 'Em_bot': 200 # MPa + }, + { + 'top': 5.0, 'bottom': 9.0, + 'soil_type': 'rock', + 'UCS_top': 10.0, 'UCS_bot': 10.0, # MPa + 'Em_top': 200, 'Em_bot': 300 # MPa + }, + { + 'top': 9.0, 'bottom': 30.0, + 'soil_type': 'rock', + 'UCS_top': 20.0, 'UCS_bot': 20.0, # MPa + 'Em_top': 300, 'Em_bot': 400 # MPa + } + ] + } + ] + + D = 3.0 # Diameter (m) + L = 10.0 # Length (m) + zlug = 1 # Padeye elevation (m) + Ha = 5.0e6 # Horizontal load (N) + Va = 3.0e5 # Vertical load (N) + + layers, y, z, results = getCapacityDandG(profile_map, 'CPT_rock_1', L, D, zlug, Ha, Va, plot=True) + + print('\n--- Results for DandG Pile in Layered Rock ---') + for key, val in results.items(): + print(f'{key}: {val:.3f}' if isinstance(val, float) else f'{key}: {val}') + + plot_pile(layers, y, z, D, L, layers[0]['top'], zlug) - # Define interpolation functions - f_UCS = interp1d(depth, UCS*1e6, kind='linear') # Pa - f_Em = interp1d(depth, Em*1e6, kind='linear') # Pa - #var_rock_profile = inspect.currentframe().f_locals - - return z0, f_UCS, f_Em - -if __name__ == '__main__': - profile = np.array([[0.0, 5, 7, 'Name of p-y model'], - [25.0, 5, 7, 'Name of p-y model']]) - L = 15 - D = 1 - zlug = 0 - H0 = 318763.5 - V0 = 297554.3 - H = 1e4; V = 1e4 - - values_H =[]; values_V =[] - - y, z, results = getCapacityDandG(profile, L=L, D=D, zlug=zlug, V=V0, H=H0) - - while results['Lateral displacement']< 0.05*D and results['Rotational displacement'] < 0.25: - - y, z, results = getCapacityDandG(profile, L=L, D=D, zlug=zlug, V=V, H=H) - - H += 10000 - - values_H.append(H); H_ratio = np.array(values_H)/H0 - - y0 = np.zeros(len(z)) - #Plot deflection profile of pile - fig, ax = plt.subplots(figsize=(3,5)) - ax.plot(y0,z,'black') - ax.plot(y,z,'r') - ax.set_xlabel('Displacement [m]') - ax.set_ylabel('Depth below pile head [m]') - ax.set_ylim([L + 2, -2]) - ax.set_xlim([-0.1*D, 0.1*D]) - ax.grid(ls='--') - fig.show() \ No newline at end of file diff --git a/famodel/anchors/anchors_famodel_map/capacity_driven_map.py b/famodel/anchors/anchors_famodel/capacity_driven.py similarity index 81% rename from famodel/anchors/anchors_famodel_map/capacity_driven_map.py rename to famodel/anchors/anchors_famodel/capacity_driven.py index cd7c58ee..561bc2b7 100644 --- a/famodel/anchors/anchors_famodel_map/capacity_driven_map.py +++ b/famodel/anchors/anchors_famodel/capacity_driven.py @@ -1,12 +1,12 @@ import numpy as np import matplotlib.pyplot as plt -from .capacity_soils_map import clay_profile, sand_profile, rock_profile -from .capacity_solvers import fd_solver -from .capacity_pycurves_map import py_Matlock, py_API, py_Reese -from .capacity_plots_map import plot_pile, plot_pycurve +from .support_soils import clay_profile, sand_profile, rock_profile +from .support_solvers import fd_solver +from .support_pycurves import py_Matlock, py_API, py_Reese +from .support_plots import plot_pile, plot_pycurve -def getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=True): +def getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=False): '''Models a laterally loaded pile using the p-y method. The solution for lateral displacements is obtained by solving the 4th order ODE, EI*d4y/dz4 EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. @@ -51,7 +51,7 @@ def getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=True) layers = profile_entry['layers'] n = 50; loc = 2 # Number of nodes (-) - tol = 1e-16; max_iter = 50 # Iteration parameters (-) + tol = 1e-16; max_iter = 100 # Iteration parameters (-) nhuc = 1; nhu = 0.3 # Resistance factor (-) delta_r = 0.08 # Mean roughness height (m) @@ -101,9 +101,9 @@ def SoilWeight(Len, Dia, tw, gamma_soil): continue soil_type = matched_layer['soil_type'] - + if soil_type == 'clay': - profile = [[matched_layer['top'], matched_layer['Su_top'], matched_layer['gamma_top']], + profile = [[matched_layer['top'], matched_layer['Su_top'], matched_layer['gamma_top']], [matched_layer['bottom'], matched_layer['Su_bot'], matched_layer['gamma_bot']]] z0_local, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile) if z_depth < z0_local: @@ -115,7 +115,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil): sigma_v_eff = f_sigma_v_eff(z_depth) gamma = f_gamma(z_depth) alpha = f_alpha(z_depth) - py_f, (y_vals, p_vals) = py_Matlock(z_depth, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=z0_local, return_curve=True) + py_f, (y_vals, p_vals) = py_Matlock(z_depth, D, gamma, Su, sigma_v_eff, z0=z0_local, return_curve=True) py_funs.append(py_f) pycurve_data.append((y_vals, p_vals, z_depth, 'clay')) Vo = np.pi*D*alpha*Su*z_depth**2 @@ -124,7 +124,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil): k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 elif soil_type == 'sand': - profile = [[matched_layer['top'], matched_layer['phi_top'], matched_layer['gamma_top'], matched_layer['Dr_top']], + profile = [[matched_layer['top'], matched_layer['phi_top'], matched_layer['gamma_top'], matched_layer['Dr_top']], [matched_layer['bottom'], matched_layer['phi_bot'], matched_layer['gamma_bot'], matched_layer['Dr_bot']]] z0_local, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile) if z_depth < z0_local: @@ -132,9 +132,11 @@ def SoilWeight(Len, Dia, tw, gamma_soil): k_secant[i] = 0.0 PileShaft.append(0.0) continue + phi = f_phi(z_depth) sigma_v_eff = f_sigma_v_eff(z_depth) + Dr = f_Dr(z_depth) delta = f_delta(z_depth) - py_f, (y_vals, p_vals) = py_API(z_depth, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=z0_local, return_curve=True) + py_f, (y_vals, p_vals) = py_API(z_depth, D, phi, sigma_v_eff, Dr, z0=z0_local, return_curve=True) py_funs.append(py_f) pycurve_data.append((y_vals, p_vals, z_depth, 'sand')) fs = delta * sigma_v_eff @@ -144,7 +146,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil): k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 elif soil_type in ['rock', 'weak_rock']: - profile = [[matched_layer['top'], matched_layer['UCS_top'], matched_layer['Em_top']], + profile = [[matched_layer['top'], matched_layer['UCS_top'], matched_layer['Em_top']], [matched_layer['bottom'], matched_layer['UCS_bot'], matched_layer['Em_bot']]] z0_local, f_UCS, f_Em = rock_profile(profile) if z_depth < z0_local: @@ -154,7 +156,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil): continue UCS = f_UCS(z_depth) Em = f_Em(z_depth) - py_f, (y_vals, p_vals) = py_Reese(z_depth, D, zlug, f_UCS, f_Em, z0=z0_local, return_curve=True) + py_f, (y_vals, p_vals) = py_Reese(z_depth, D, UCS, Em, z0=z0_local, return_curve=True) py_funs.append(py_f) pycurve_data.append((y_vals, p_vals, z_depth, 'rock')) SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D @@ -166,7 +168,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil): k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 else: - raise ValueError(f"Unsupported soil type: {matched_layer['soil_type']}") + raise ValueError(f"Unsupported soil type: {matched_layer.get('soil_type')} at depth z = {z_depth:.2f} m") for i in [n+3, n+4]: z[i] = (i - 2)*h @@ -216,22 +218,25 @@ def SoilWeight(Len, Dia, tw, gamma_soil): ax.legend() # Relevant index of nodes - zlug_index = int(zlug/h) - ymax_index = np.argmax(y) + y_pile = y[2:-2] + z_pile = z[2:-2] + ymax_index = np.argmax(np.abs(y_pile)) resultsDriven = { - 'Weight': PileWeight(L, D, t, rhows + rhow), + 'Horizontal max.': abs(Mi)/abs(zlug) if zlug != 0 else 1e-6, 'Vertical max.': Vmax, - 'Lateral displacement': y[ymax_index], - 'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h), + 'Lateral displacement': y_pile[ymax_index], + 'Rotational displacement': np.rad2deg(abs(y_pile[ymax_index - 1] - y_pile[ymax_index])/h), 'Bending moment': abs(Mi), 'Plastic moment': Mp, 'Plastic hinge': hinge_formed, - 'Hinge location': hinge_location, - 'Horizontal max.': abs(Mi)/abs(zlug) if zlug != 0 else 1e-6, + 'Hinge location': hinge_location, 'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf, - 'Unity check (horizontal)': Ha/(abs(Mi)/abs(zlug)) if zlug != 0 else np.inf - } + 'Unity check (horizontal)': Ha/(abs(Mi)/abs(zlug)) if zlug != 0 else np.inf, + 'Weight pile': PileWeight(L, D, t, rhows + rhow)} + + print(f"Max lateral displacement: {y_pile[ymax_index]:.6f} m at z = {z_pile[ymax_index]:.2f} m") + print(f"Deflected tip: {y_pile[-1]:.6f} m at z = {z_pile[-1]:.2f} m") return layers, y[2:-2], z[2:-2], resultsDriven @@ -246,28 +251,33 @@ def SoilWeight(Len, Dia, tw, gamma_soil): 'top': 1.0, 'bottom': 6.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.0, - 'Su_top': 60, 'Su_bot': 200}, - # { - # 'top': 6.0, 'bottom': 15.0, - # 'soil_type': 'clay', - # 'gamma_top': 8.0, 'gamma_bot': 8.0, - # 'Su_top': 200, 'Su_bot': 400}, + 'Su_top': 60, 'Su_bot': 80}, { 'top': 6.0, 'bottom': 15.0, - 'soil_type': 'sand', + 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.0, - 'phi_top': 32, 'phi_bot': 38, - 'Dr_top': 70, 'Dr_bot': 75}, + 'Su_top': 80, 'Su_bot': 400}, + # { + # 'top': 6.0, 'bottom': 15.0, + # 'soil_type': 'sand', + # 'gamma_top': 8.0, 'gamma_bot': 8.0, + # 'phi_top': 32, 'phi_bot': 38, + # 'Dr_top': 70, 'Dr_bot': 75}, { 'top': 15.0, 'bottom': 30.0, 'soil_type': 'clay', - 'gamma_top': 8.0, 'gamma_bot': 9.0, + 'gamma_top': 8.0, 'gamma_bot': 8.0, 'Su_top': 200, 'Su_bot': 400}] + # { + # 'top': 0.0, 'bottom': 30.0, + # 'soil_type': 'rock', + # 'UCS_top': 5.0, 'UCS_bot': 5.0, + # 'Em_top': 7.0, 'Em_bot': 7.0}] } ] D = 2.5 # Diameter (m) - L = 25.0 # Length (m) + L = 15.0 # Length (m) zlug = 3 # Padeye depth (m) Ha = 5.0e5 # Horizontal load (N) Va = 1.5e5 # Vertical load (N) diff --git a/famodel/anchors/anchors_famodel/capacity_drivenrock.py b/famodel/anchors/anchors_famodel/capacity_drivenrock.py deleted file mode 100644 index d44f6e2f..00000000 --- a/famodel/anchors/anchors_famodel/capacity_drivenrock.py +++ /dev/null @@ -1,373 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from scipy.interpolate import interp1d -from scipy import linalg -import inspect - -################################### -#### Pile Geometry and Loading #### -################################### - -def getCapacityDrivenRock(profile, L, D, zlug, V, H, plot=True): - ''' - Models a laterally loaded pile using the p-y method. The solution for - lateral displacements is obtained by solving the 4th order ODE, - EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. - EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method. - - Assumes that EI remains constant with respect to curvature i.e. pile - material remains in the elastic region. - - Input: - ----- - profile - A 2D array of depths (m) and corresponding undrained shear strength(Pa) - Eg: array([[z1,UCS1],[z2,UCS2],[z3,UCS3]...]) - Use small values for Su (eg: 0.001) instead of zeros to avoid divisions - by zero but always start z at 0.0 - Example of a valid data point at the mudline is [0.0, 0.001] - L - Length of pile (m) - D - Outer diameter of pile (m) - V - Axial force at pile head (N) - H - Force at pile head (N) - M - Moment at pile head (N*m) - n - Number of elements (50 by default) - iterations - Number of iterations to repeat calculation in order obtain convergence of 'y' - (A better approach is to iterate until a predefined tolerance is achieved) - - Output: - ------ - y - Lateral displacement at each node, length = n + 5, (n+1) real nodes and 4 imaginary nodes - z - Vector of node locations along pile - resultsDrivenRock - Dictionary with results - ''' - - # Extract optional keyword arguments - # ls = 'x' - n = 50; iterations = 10; loc = 2 - - # Resistance factor - nhuc = 1; nhu = 0.3 - delta_r = 0.08 # Mean roughness height [m] - - # Convert L and D to floating point numbers to avoid rounding errors - L = float(L) - D = float(D) - t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD - E = 200e9 # Elastic modulus of pile material (Pa) - rhows = 66.90e3 # Submerged steel specific weight (N/m3) - rhow = 10e3 # Water specific weight (N/m3) - - # Pile geometry - I = (np.pi/64.0)*(D**4 - (D - 2*t)**4) - EI = E*I - h = L/n # Element size - N = (n + 1) + 4 # (n+1) Real + 4 Imaginary nodes - - # Dry and wet mass of the pile - def PileWeight(Len, Dia, tw, rho): - Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho - return Wp - - # Array for displacements at nodes, including imaginary nodes. - y = np.ones(N)*(0.01*D) # An initial value of 0.01D was arbitrarily chosen - - # Initialize and assemble array/list of p-y curves at each real node - z = np.zeros(N) - py_funs = [] - k_secant = np.zeros(N) - DQ = [] - - for i in [0,1]: # Top two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - for i in range(2,n+3): # Real nodes - z[i] = (i - 2)*h - # Extract rock profile data - zlug, f_UCS, f_Em = rock_profile(profile) - UCS, Em = f_UCS(z[i]), f_Em(z[i]) - py_funs.append(py_Reese(z[i], D, zlug, UCS, Em)) - k_secant[i] = py_funs[i](y[i])/y[i] - SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D - alpha = 0.36*SCR - 0.0005 - fs = alpha*UCS - Dq = np.pi*D*fs*z[i] - DQ.append(Dq) - - for i in [n+3, n+4]: # Bottom two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - # Track k_secant and current displacements - - y1 = np.linspace(-2.*D, 2.*D, 500) - if plot: - plt.plot(y1, py_funs[loc](y1)) - plt.xlabel('y (m)'), plt.ylabel('p (N/m)') - plt.grid(True) - - for j in range(iterations): - # if j == 0: print 'FD Solver started!' - y = fd_solver(n, N, h, EI, V, H, zlug, k_secant) - - if plot: - plt.plot(y[loc], k_secant[loc]*y[loc]) - - for i in range(2, n+3): - k_secant[i] = py_funs[i](y[i])/y[i] - - # print(f'y_max = {y[2]:.3f} m') - # print(f'rot_max = {np.rad2deg((y[2] - y[3])/h):.3f} deg') - - resultsDrivenRock = {} - resultsDrivenRock['Lateral displacement'] = y[2] - resultsDrivenRock['Rotational displacement'] = np.rad2deg((y[2] - y[3])/h) - resultsDrivenRock['Axial capacity'] = DQ[-1] - resultsDrivenRock['Weight'] = PileWeight(L, D, t, (rhows + rhow)) - - return y[2:-2], z[2:-2], resultsDrivenRock - -################# -#### Solvers #### -################# - -def fd_solver(n, N, h, EI, V, H, zlug, k_secant): - ''' - Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for - non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves - are linear. - - Input: - ----- - n - Number of elements - N - Total number of nodes - h - Element size - EI - Flexural rigidity of pile - V - Axial force at pile head - H - Shear at pile head/tip - M - Moment at pile head/tip - k_secant - Secant stiffness from p-y curves - - Output: - ------ - y - Lateral displacement at each node - ''' - M = H*zlug - - # Initialize and assemble matrix - X = np.zeros((N,N)) - - # (n+1) finite difference equations for (n+1) real nodes - for i in range(0,n+1): - X[i,i] = 1.0 - X[i,i+1] = -4.0 + V*h**2/EI - X[i,i+2] = 6.0 - 2*V*h**2/EI + k_secant[i+2]*h**4/EI - X[i,i+3] = -4.0 + V*h**2/EI - X[i,i+4] = 1.0 - - # Curvature at pile head - X[n+1,1] = 1.0 - X[n+1,2] = -2.0 - X[n+1,3] = 1.0 - - # Shear at pile head - X[n+2,0] = -1.0 - X[n+2,1] = 2.0 - V*h**2/EI - X[n+2,2] = 0.0 - X[n+2,3] = -2.0 + V*h**2/EI - X[n+2,4] = 1.0 - - # Curvature at pile tip - X[n+3,-2] = 1.0 - X[n+3,-3] = -2.0 - X[n+3,-4] = 1.0 - - # Shear at pile tip - X[n+4,-1] = 1.0 - X[n+4,-2] = -2.0 + V*h**2/EI - X[n+4,-3] = 0.0 - X[n+4,-4] = 2.0 - V*h**2/EI - X[n+4,-5] = -1.0 - - # Initialize vector q - q = np.zeros(N) - - # Populate q with boundary conditions - q[-3] = 2*H*h**3 # Shear at pile head - # q[-4] = M*h**2 # Moment at pile head - - y = linalg.solve(EI*X, q) - - return y - -############################### -#### P-Y Curve Definitions #### -############################### - -def py_Reese(z, D, zlug, UCS, Em): - ''' - Returns an interp1d interpolation function which represents the Reese (1997) p-y curve at the depth of interest. - - Important: Make sure to import the interp1 function by running 'from scipy.interpolate import interp1d' in the main program. - - Input: - ----- - z - Depth relative to pile head (m) - D - Pile diameter (m) - zlug - Load eccentricity above the mudline or depth to mudline relative to the pile head (m) - UCS - Undrained shear strength (Pa) - Em - Effectve vertical stress (Pa) - RQD - Rock quality designation, measures the quality of the rock core taken from a borehole. - Typically ranges from 25% (very weathered rock) to 100% (fresh rock). - - Output: - ------ - Returns an interp1d interpolation function which represents the p-y curve at the depth of interest. - 'p' (N/m) and 'y' (m). - ''' - z0 = 0 - - #from scipy.interpolate import interp1d - #global var_Reese - - RQD = 52 # Assumed fair rock quality (moderately weathered rocks) - Dref = 0.305; nhu = 0.3; E = 200e9 - t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD - I = np.pi*(D**4 - (D - 2*t)**4)/64.0 - EI = E*I - alpha = -0.00667*RQD + 1 - krm = 0.0005 - - if (z - z0) < 0: - p_ur = 0 - else: - if z < 3*D: - p_ur = alpha*UCS*D*(1 + 1.4*z/D) - #kir = (100 +400*z/(3*D)) - else: - p_ur = 5.2*alpha*UCS*D - #kir = 500 - - kir = (D/Dref)*2**(-2*nhu)*(EI/(Em*D**4))**0.284 - Kir = kir*Em - y_rm = krm*D - y_a = (p_ur/(2*y_rm**0.25*Kir))**1.333 - - # Normalized lateral displacement - N = 20 - y = np.concatenate((-np.logspace(5,-3,N),[0],np.logspace(-3,5,N))) - - p=[]; P=[]; - for i in range (len(y)): - if abs(y[i]) < y_a: - P = np.sign(y[i])*Kir*y[i] - elif abs(y[i]) > y_a: - P = min((p_ur/2)*(abs(y[i])/y_rm)**0.25,p_ur) - p.append(P) - - p = np.array(p).squeeze() - for j in range(len(y)): - if y[j] < 0: - p[j] = -1*p[j] - elif y[j] > 0: - p[j] = p[j] - - #var_Reese = inspect.currentframe().f_locals - - f = interp1d(y, p) # Interpolation function for p-y curve - - plt.plot(y, p) - plt.xlabel('y (m)') - plt.ylabel('p (N/m)'), - plt.title('PY Curves - Reese (1997)') - plt.grid(True) - plt.xlim([-0.03*D, 0.03*D]) - plt.ylim([min(p), max(p)]) - - return f # This is f (linear interpolation of y-p) - -####################### -#### Rock Profile ##### -####################### - -def rock_profile(profile): - ''' - Define the (weak) rock profile used by the p-y analyzer. Outputs 'interp1d' functions containing - UCS and Em profiles to be used by the p-y curve functions. - - Input: - ----- - profile - A 2D tuple in the following format: ([depth (m), UCS (MPa), Em (MPa), py-model]) - The soil profile should be defined relative to the pile/tower head (i.e. point of lateral load application) - so that any load eccentricities can be taken into account. An example soil profile is shown below. - Eg: array([[z0,UCS0,Em0, 'Reese'], - [z1,UCS1,Em1, 'Reese'], - [z2,UCS2,Em2, 'Reese'], - ...]) - *The current program cannot define layers with different p-y models. But it will added in the future. - - plot_profile - Plot Su vs depth profile. Choose 'Yes' to plot. - - Output: - ------ - z0 - Depth of mudline relative to the pile head (m) - f_UCS - 'interp1d' function containing undrained shear strength profile (Pa) - f_Em - 'interp1d' function containing effective vertical stress profile (Pa) - ''' - - # Depth of mudline relative to pile head - z0 = float(profile[0][0]) - - # Extract data from soil_profile array and zero strength virtual soil layer - # from the pile head down to the mudline - depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m - UCS = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # MPa - Em = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # MPa - - # Define interpolation functions - f_UCS = interp1d(depth, UCS*1e6, kind='linear') # Pa - f_Em = interp1d(depth, Em*1e6, kind='linear') # Pa - - #var_rock_profile = inspect.currentframe().f_locals - - return z0, f_UCS, f_Em - -if __name__ == '__main__': - - profile = np.array([[0.0, 5, 7, 'Name of p-y model'], - [25.0, 5, 7, 'Name of p-y model']]) - - L = 20 - D = 1.5 - zlug = 2*D - H0 = 3187635 - V0 = 2975543 - - H = 1e4; V = 1e4 - - values_H =[]; values_V =[] - - y, z, results = getCapacityDrivenRock(profile, L=L, D=D, zlug=zlug, V=V0, H=H0) - - while results['Lateral displacement']< 0.05*D and results['Rotational displacement'] < 0.25: - - y, z, results = getCapacityDrivenRock(profile, L=L, D=D, zlug=zlug, V=V, H=H) - - H += 10000 - - values_H.append(H); H_ratio = np.array(values_H)/H0 - - y0 = np.zeros(len(z)) - #Plot deflection profile of pile - fig, ax = plt.subplots(figsize=(3,5)) - ax.plot(y0,z,'black') - ax.plot(y,z,'r') - ax.set_xlabel('Displacement [m]') - ax.set_ylabel('Depth below pile head [m]') - ax.set_ylim([L + 2,-2]) - ax.set_xlim([-0.1*D, 0.1*D]) - ax.grid(ls='--') - fig.show() \ No newline at end of file diff --git a/famodel/anchors/anchors_famodel/capacity_drivensoil.py b/famodel/anchors/anchors_famodel/capacity_drivensoil.py deleted file mode 100644 index b4cee31c..00000000 --- a/famodel/anchors/anchors_famodel/capacity_drivensoil.py +++ /dev/null @@ -1,628 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt - -################################### -#### Pile Geometry and Loading #### -################################### - -def getCapacityDrivenSoil(profile, soil_type, L, D, zlug, V, H, plot=True): - - '''Models a laterally loaded pile using the p-y method. The solution for - lateral displacements is obtained by solving the 4th order ODE, EI*d4y/dz4 - - F*d2y/dz2 + ky = 0 using the finite difference method. - - Assumes that EI remains constant with respect to curvature i.e. pile - material remains in the elastic region. - - Input: - ----- - profile - A 2D array of depths (m) and corresponding undrained shear strength(Pa) - Eg: array([[z1,Su1],[z2,Su2],[z3,Su3]...]) - Use small values for Su (eg: 0.001) instead of zeros to avoid divisions by zero but always start z at 0.0 - Example of a valid data point at the mudline is [0.0, 0.001] - soil_type - Select soil condition, 'clay' or 'sand' - Assigns which p-y model to use, 'Matlock' or 'API'. - L - Length of pile (m) - D - Outer diameter of pile (m) - zlug - Load eccentricity above the mudline or depth to mudline relative to the pile head (m) - V - Axial force at pile head (N), vertically downwards is postive. - H - Force at pile head (N), shear causing clockwise rotation of pile is positive. - M - Moment at pile head (N*m), moments causing tension on left side of pile is positive. - n - Number of elements (50 by default) - iterations - Number of iterations to repeat calculation in order obtain convergence of 'y' - (A better approach is to iterate until a predefined tolerance is achieved but this requires additional - coding so, I will implement this later.) - - Output: - ------ - y - Lateral displacement at each node, length = n + 5, (n+1) real nodes and 4 imaginary nodes - z - Vector of node locations along pile - ''' - - # Extract optional keyword arguments - - ls = 'x' - n = 25; iterations = 10; loc=2 - - # Convert L and D to floating point numbers to avoid rounding errors - L = float(L) - D = float(D) - t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD - E = 200e9 # Elastic modulus of pile material (Pa) - fy = 350e6 # Yield strength of pile material (Pa) - rhows = 66.90e3 # Submerged steel specific weight (N/m3) - rhow = 10e3 # Water specific weight (N/m3) - - # Pile geometry - I = (np.pi/64.0)*(D**4 - (D - 2*t)**4) - EI = E*I - h = L/n # Element size - N = (n + 1) + 4 # (n+1) Real + 4 Imaginary nodes - - # Outer and inner surface of the pile skirt - def PileSurface(Len, Dia): - Sp = np.pi*Dia*Len - return Sp - # Dry and wet mass of the pile - def PileWeight(Len, Dia, tw, rho): - Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho - return Wp - # Mass of the soil plug - def SoilWeight(Len, Dia, tw, gamma_soil): - Wsoil =(np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil - return Wsoil - - # Array for displacements at nodes, including imaginary nodes. - y = np.ones(N)*(0.01*D) # An initial value of 0.01D was arbitrarily chosen - - # Initialize and assemble array/list of p-y curves at each real node - z = np.zeros(N) - py_funs = []; PileShaft =[] - k_secant = np.zeros(N) - - for i in [0, 1]: # Top two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - # Extract soil profile data - if soil_type == 'clay': - z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile) - - elif soil_type == 'sand': - z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile) - - for i in range(2, n+3): # Real nodes - z[i] = (i - 2)*h - if soil_type == 'clay': - Su, sigma_v_eff, gamma, alpha = f_Su(z[i]), f_sigma_v_eff(z[i]), f_gamma(z[i]), f_alpha(z[i]) - py_funs.append(py_Matlock(z[i], D, zlug, Su, sigma_v_eff, gamma, plot=plot)) - Vo = np.pi*D*alpha*Su*z[i]**2 - PileShaft.append(Vo) - Vmax = PileWeight(L, D, t, rhows) + SoilWeight(L, D, t, gamma) + PileShaft[-1] - - elif soil_type == 'sand': - phi, sigma_v_eff, gamma, Dr, delta = f_phi(z[i]), f_sigma_v_eff(z[i]), f_gamma(z[i]), f_Dr(z[i]), f_delta(z[i]) - py_funs.append(py_API(z[i], D, zlug, phi, sigma_v_eff, Dr, plot=plot)) - fs = delta*sigma_v_eff - Vo = np.pi*D*fs*z[i] - PileShaft.append(Vo) - Vmax = PileWeight(L, D, t, rhows) + SoilWeight(L, D, t, gamma) + PileShaft[-1] - - k_secant[i] = py_funs[i](y[i])/y[i] - - for i in [n+3, n+4]: # Bottom two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - y1 = np.linspace(-2.*D, 2.*D, 500) - if plot: - plt.plot(y1, py_funs[loc](y1)) - plt.xlabel('y (m)'), plt.ylabel('p (N/m)') - plt.grid(True) - - for j in range(iterations): - # if j == 0: print 'FD Solver started!' - y, Mi, Mp, hinge_formed, hinge_location = fd_solver(n, N, h, D, t, fy, EI, V, H, zlug, k_secant) - - for i in range(2, n+3): - k_secant[i] = py_funs[i](y[i])/y[i] - - - resultsDrivenSoil = {} - # Populate q with boundary conditions - if zlug <= 0: - # print(f'y_max = {max(y):.3f} m') - # print(f'rot_max = {np.rad2deg((y[2] - y[3])/h):.3f} deg') - - resultsDrivenSoil['Lateral displacement'] = max(y) - resultsDrivenSoil['Rotational displacement'] = np.rad2deg((y[2] - y[3])/h) - resultsDrivenSoil['Axial capacity'] = Vmax - resultsDrivenSoil['Pile weight'] = PileWeight(L, D, t, (rhows + rhow)) - - else: - # print(f'y_max = {max(y):.3f} m') - # print(f'Mi = {Mi:.3f} Nm') - - resultsDrivenSoil['Lateral displacement'] = max(y) - resultsDrivenSoil['Bending moment'] = Mi - resultsDrivenSoil['Plastic moment'] = Mp - resultsDrivenSoil['Plastic hinge'] = hinge_formed - resultsDrivenSoil['Hinge location'] = hinge_location - resultsDrivenSoil['Axial capacity'] = Vmax - resultsDrivenSoil['Weight'] = PileWeight(L, D, t, (rhows + rhow)) - - return y[2:-2], z[2:-2], resultsDrivenSoil - -################# -#### Solvers #### -################# - -def fd_solver(n, N, h, D, t, fy, EI, V, H, zlug, k_secant): - '''Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for - non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves - are linear. - - Input: - ----- - n - Number of elements - N - Total number of nodes - h - Element size - EI - Flexural rigidity of pile - V - Axial force at pile head/zlug depth - H - Shear at pile head/zlug depth - M - Moment at pile head/zlug depth - zlug - Load eccentricity above the mudline or depth to mudline relative to the pile head (m) - k_secant - Secant stiffness from p-y curves - - Output: - ------ - y_updated - Lateral displacement at each node - ''' - - from scipy import linalg - - # Identify the node corresponding to zlug - zlug_index = int(zlug/h) # Index for the node corresponding to zlug - - # Initialize and assemble matrix - X = np.zeros((N, N)) - - # (n+1) finite difference equations for (n+1) real nodes - for i in range(0, n+1): - X[i,i] = 1.0 - X[i,i+1] = -4.0 + V*h**2/EI - X[i,i+2] = 6.0 - 2*V*h**2/EI + k_secant[i+2]*h**4/EI - X[i,i+3] = -4.0 + V*h**2/EI - X[i,i+4] = 1.0 - - # Curvature at pile head - X[n+1,1] = 1.0 - X[n+1,2] = -2.0 - X[n+1,3] = 1.0 - - # Shear at pile head - X[n+2,0] = -1.0 - X[n+2,1] = 2.0 - V*h**2/EI - X[n+2,2] = 0.0 - X[n+2,3] = -2.0 + V*h**2/EI - X[n+2,4] = 1.0 - - # Curvature at pile tip - X[n+3,-2] = 1.0 - X[n+3,-3] = -2.0 - X[n+3,-4] = 1.0 - - # Shear at pile tip - X[n+4,-1] = 1.0 - X[n+4,-2] = -2.0 + V*h**2/EI - X[n+4,-3] = 0.0 - X[n+4,-4] = 2.0 - V*h**2/EI - X[n+4,-5] = -1.0 - - # Initialize vector q - q = np.zeros(N) - #M = H*abs(zlug) - - # Populate q with boundary conditions - if zlug <= 0: - q[-3] = 2*H*h**3 # Shear at pile head - #q[-4] = M*h**2 # Moment at pile head - else: - q[zlug_index] = 2*H*h**3 # Shear at pile head - #q[zlug_index + 1] = M*h**2 # Moment at pile head - - y = linalg.solve(EI*X, q) - - # Compute the plastic moment capacity Mp - Zp = (1/6)*(D**3 - (D - 2*t)**3) # Plastic section modulus for hollow pile (m3) - Mp = Zp*fy # Plastic moment capacity (N/m) - - # Check for plastic hinge formation - Mi, Mp, hinge_formed, hinge_location = plastic_hinge(y, h, EI, Mp) - - return y, Mi, Mp, hinge_formed, hinge_location - -############################### -#### P-Y Curve Definitions #### -############################### - -def py_Matlock(z, D, zlug, Su, sigma_v_eff, gamma, plot=True): - - '''Returns an interp1d interpolation function which represents the Matlock (1970) p-y curve at the depth of interest. - Important: Make sure to import the interp1 function by running 'from scipy.interpolate import interp1d' in the main program. - - Input: - ----- - z - Depth relative to pile head (m) - D - Pile diameter (m) - zlug - Load eccentricity above the mudline or depth to mudline relative to the pile head (m) - Su - Undrained shear strength (Pa) - sigma_v_eff - Effective vertical stress (Pa) - gamma - Effective unit weight of the soil (kN/m3) - - Output: - ------ - Returns an interp1d interpolation function which represents the p-y curve at the depth of interest. - 'p' (N/m) and 'y' (m). - ''' - - from scipy.interpolate import interp1d - - z0 = 0 - - # Strain at half the strength as defined by Matlock (1970). - # Typically ranges from 0.005 (stiff clay) to 0.02 (soft clay). - epsilon_50 = 0.02 - # p-y curve properties - J = 0.5 - - if zlug < 0: - # Scenario 1: zlug is negative (above mudline) - if (z - z0) < 0: - # No p-y curve between z = 0 and zlug - Nc = 0.0 - z_cr = 1.0 # Dummy value to avoid crashing - else: - # Calculate p-y curve below zlug - Nc = 3.0 + sigma_v_eff/Su + J*(z - abs(zlug))/D - if Nc > 9.0: - Nc = 9.0 - z_cr = 6.0*D/(gamma*D/Su + J) - - else: - # Scenario 2: zlug is positive (below mudline) - # Calculate p-y curve for the entire pile (all depths) - Nc = 3.0 + sigma_v_eff/Su + J*(z - zlug)/D - if Nc > 9.0: - Nc = 9.0 - z_cr = 6.0 * D/(gamma*D/Su + J) - - p_ult = Su*Nc*D - y_50 = 2.5*epsilon_50*D - - # Normalized lateral displacement - Y = np.concatenate((-np.logspace(3,-4,100),[0],np.logspace(-4,3,100))) - - # Normalized p-y curves - P = 0.5*np.sign(Y)*abs(Y)**(1.0/3.0) # sign(Y) and abs(Y) used since negative numbers cannot be raised to fractional powers - # Expression equivalent to P = 0.5*Y**(1.0/3.0) for Y>=0 - for i in range(0,len(Y)): - if P[i] > 1.0: P[i] = 1.0 - elif P[i] < -1.0: P[i] = -1.0 - - # Un-normallized p-y curves - p = P*p_ult - y = Y*y_50 - - f = interp1d(y, p, kind='linear') # Interpolation function for p-y curve - - # Plot of p-y curve and check if 'k' is calculated correctly - if plot: - plt.plot(y, p,'-') - plt.xlabel('y (m)') - plt.ylabel('p (N/m)') - plt.title('PY Curves - Matlock (1970)') - plt.grid(True) - plt.xlim([-2*D, 2*D]) - - return f # This is f (linear interpolation of y-p) - -def py_API(z, D, zlug, phi, sigma_v_eff, Dr, plot=True): - - '''Returns an interp1d interpolation function which represents the Matlock (1970) p-y curve at the depth of interest. - - Important: Make sure to import the interp1 function by running 'from scipy.interpolate import interp1d' in the main program. - - Input: - ----- - z - Depth relative to pile head (m) - D - Pile diameter (m) - zlug - Load eccentricity above the mudline or depth to mudline relative to the pile head (m) - phi - Internal friction angle (deg) - sigma_v_eff - Effectve vertical stress (Pa) - - Output: - ------ - Returns an interp1d interpolation function which represents the p-y curve at the depth of interest. - 'p' (N/m) and 'y' (m). - ''' - - from scipy.interpolate import interp1d - - # Interpolate coefficients depending on the effective friction angle - phi_ref = [ 20, 25, 30, 35, 40] - C1_ref = [0.80, 1.25, 1.90, 3.00, 4.50] - C2_ref = [1.60, 2.10, 2.60, 3.40, 4.30] - C3_ref = [ 10, 15, 30, 55, 105] - - C1 = np.interp(phi, phi_ref, C1_ref) - C2 = np.interp(phi, phi_ref, C2_ref) - C3 = np.interp(phi, phi_ref, C3_ref) - - if (z - zlug) < 0: - # p-y curves for the virtual soil layer between the pile head and the mudline should have p=0 - p_ult = 0.0 - else: - try: - p_ult = min(C1*z + C2*D, C3*D)*sigma_v_eff - except ZeroDivisionError: - print("Division by zero! phi = 0.0 so z_cr cannot be calculated.") - - # Dr = 0.75 # Relative density of the soil (assumed) - k = (54.6*Dr**2 + 0.8*Dr + 1.8)*1e3 - - # Normalized lateral displacement - N = 20 - y = np.concatenate((-np.logspace(3,-4,N),[0],np.logspace(-4,3,N))) - A = max(3 - 0.8*z/D, 0.9) - ε = 1e-6 - p = A*p_ult*np.tanh(k*z*y/(A*p_ult + ε)) - - f = interp1d(y, p, kind='linear') # Interpolation function for p-y curve - - if plot: - # Plot of p-y curve and check if 'k' is calculated correctly - plt.plot(y, p,'-') - plt.xlabel('y (m)') - plt.ylabel('p (N/m)') - plt.title('PY Curves - API (1993)') - plt.grid(True) - plt.xlim([-0.10*D, 0.10*D]) - # plt.ylim([min(y), max(y)]) # Adjust x-axis limits to match y values - - return f # This is f (linear interpolation of y-p) - -######################## -#### Plastic Hinge ##### -######################## - -def plastic_hinge(y, h, EI, Mp): - ''' - Check for plastic hinge formation along the pile. - - Parameters: - ---------- - y : ndarray - Lateral displacements at each node. - h : float - Element size (distance between nodes). - EI : float - Flexural rigidity of the pile (N*m²). - Mp : float - Plastic moment capacity of the pile section. - - Returns: - ------- - Mp : float - Plastic moment of the pile section (Nm) - hinge_formed : bool - True if a plastic hinge forms, False otherwise. - hinge_location : int - Index of the node where the plastic hinge forms (if any). - ''' - - hinge_formed = False - hinge_location = -1 - Mi = [] - - # Loop through each internal node and compute the bending moment - for i in range(1, len(y)-1): - # Approximate the bending moment at node i - Mint = EI*(y[i+1] - 2*y[i] + y[i-1])/h**2 - Mi.append(Mint) - - # Check if the moment exceeds the plastic moment capacity - if Mint >= Mp: - hinge_formed = True - hinge_location = i - break # Stop at the first plastic hinge formation - - return max(Mi), Mp, hinge_formed, hinge_location - - -######################## -#### Soil Profiles ##### -######################## - -def clay_profile(profile): - '''Define the clay profile used by the p-y analyzer. Outputs 'interp1d' functions containing Su and sigma'_v - profiles to be used by the p-y curve functions. - - Input: - ----- - profile - A 2D tuple in the following format: ([Depth (m), Su (kPa), gamma (kN/m^3), py-model, model parameter]) - The soil profile should be defined relative to the pile/tower head (i.e. point of lateral load application) - so that any load eccentricities can be taken into account. An example soil profile is shown below. - Eg: array([[z0, Su0, gamma0, 'Matlock', 0.02], - ...]) - - *The current program cannot define layers with different p-y models. But it will added in the future. - - Output: - ------ - z0 - Depth of mudline relative to the pile head (m) - f_Su - 'interp1d' function containing undrained shear strength profile (Pa) - f_sigma_v_eff - 'interp1d' function containing effective vertical stress profile (Pa) - f_gamma - 'interp1d' function containing effective unit weight (kN/m3) - f_alpha - Adhesion factor for clays - ''' - - from scipy.interpolate import interp1d - - # Depth of mudline relative to pile head - z0 = float(profile[0][0]) - - # Extract data from profile array and zero strength virtual soil layer - # from the pile head down to the mudline - depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m - Su = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # kPa - gamma = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # kN/m3 - - # Calculate sigma_v_eff at each depth - sigma_v_eff = np.zeros(len(depth)) - - for i in range(1, len(depth)): - sigma_v_eff[i] = sigma_v_eff[i-1] + gamma[i-1]*(depth[i] - depth[i-1]) - - # Define interpolation functions - f_Su = interp1d(depth, Su*1000, kind='linear') # Pa - f_sigma_v_eff = interp1d(depth, sigma_v_eff*1000, kind='linear') # Pa - f_gamma = interp1d(depth, gamma*1000, kind='linear') # N/m3 - - # Calculate f_psi and f_alpha at each depth (not as a scalar) - f_psi = lambda z: f_Su(z) / f_sigma_v_eff(z) - - def calc_alpha(z): - psi_val = f_psi(z) - if psi_val <= 1.0: - return min(0.5*psi_val**-0.50, 1) - else: - return min(0.5*psi_val**-0.25, 1) - - # Create an interpolated adhesion factor function - f_alpha = lambda z: calc_alpha(z) - - return z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha - -def sand_profile(profile): - '''Define the sand profile used by the p-y analyzer. Outputs 'interp1d' functions containing Su and sigma'_v - profiles to be used by the p-y curve functions. - - Input: - ----- - profile - A 2D tuple in the following format: ([Depth (m), Su (kPa), gamma (kN/m^3), py-model, model parameter]) - The soil profile should be defined relative to the pile/tower head (i.e. point of lateral load application) - so that any load eccentricities can be taken into account. An example soil profile is shown below. - Eg: array([[z0, phi, gamma0, Dr, 'API', 0.02], - ...]) - - *The current program cannot define layers with different p-y models. But it will added in the future. - - Output: - ------ - z0 - Depth of mudline relative to the pile head (m) - f_phi - 'interp1d' function containing effective friction angle (deg) - f_sigma_v_eff - 'interp1d' function containing effective vertical stress profile (Pa) - f_gamma - 'interp1d' function containing effective unit weight (N/m3) - f_Dr - Relative density of the soil (%) - f_delta - Skin friction factor (sand/steel) - ''' - - from scipy.interpolate import interp1d - - # Depth of mudline relative to pile head - z0 = float(profile[0][0]) - - # Extract data from profile array and zero strength virtual soil layer - # from the pile head down to the mudline - depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m - phi = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # deg - gamma = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # kN/m3 - Dr = np.concatenate([np.array([0]), np.array([row[3] for row in profile],dtype=float)]) # % - - # Calculate sigma_v_eff and static loading factor at each depth - sigma_v_eff = np.zeros(len(depth)) - - for i in range(1, len(depth)): - sigma_v_eff[i] = sigma_v_eff[i-1] + gamma[i-1]*(depth[i] - depth[i-1]) - - # Define interpolation functions - f_phi = interp1d(depth, phi, kind='linear') # deg - f_sigma_v_eff = interp1d(depth, sigma_v_eff*1000, kind='linear') # Pa - f_gamma = interp1d(depth, gamma*1000, kind='linear') # N/m3 - f_Dr = interp1d(depth, Dr, kind='linear') # % - - # Define delta as a function of Dr - def calc_delta(Dr_val): - if 35 <= Dr_val < 50: - return 0.29 - elif 50 <= Dr_val < 65: - return 0.37 - elif 65 <= Dr_val < 85: - return 0.46 - elif Dr_val >= 85: - return 0.56 - else: - return 0 # Default or error value for very low Dr values - - # Apply delta calculation to Dr profile - delta_values = np.array([calc_delta(Dr_val) for Dr_val in Dr]) - f_delta = interp1d(depth, delta_values, kind='linear') # Interpolated delta values - - return z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta - -if __name__ == '__main__': - - # CLAY - profile = np.array([[ 0.0, 10, 8], - [75.0, 245, 5]]) - # SAND - # profile = np.array([[ 0.0, 32, 8, 75], - # [75.0, 38, 9, 85]]) - - L = 20 - D = 1.5 - zlug = 5*D - - H0 = 4260000 - V0 = 1590000 - - H = 1e6; V = 1e6 - - values_H =[]; values_V =[] - - y, z, results = getCapacityDrivenSoil(profile, soil_type='clay', L=L, D=D, zlug=zlug, V=V, H=H) - - # while results['Lateral displacement']<= 0.05*D and results['Rotational displacement'] <= 0.25: - - # y, z, results = getCapacityDrivenSoil(profile, soil_type='clay', L=L, D=D, zlug=zlug, V=V, H=H) - - # H += 10000 - - # values_H.append(H); H_ratio = np.array(values_H)/H0 - - while results['Lateral displacement']<= 0.05*D and results['Bending moment'] <= results['Plastic moment']: - - y, z, results = getCapacityDrivenSoil(profile, soil_type='sand', L=L, D=D, zlug=zlug, V=V, H=H) - - H += 100000 - - values_H.append(H); H_ratio = np.array(values_H)/H0 - - # y, z, results = getCapacityDrivenSoil(profile, soil_type='sand', L=L, D=D, zlug=zlug, V=V, H=H) - - - y0 = np.zeros(len(z)) - #Plot deflection profile of pile - fig, ax = plt.subplots(figsize=(3,5)) - ax.plot(y0,z,'black') - ax.plot(y,z,'r') - ax.set_xlabel('Displacement [m]') - ax.set_ylabel('Depth below pile head [m]') - ax.set_ylim([L + 2,-2]) - ax.set_xlim([-0.1*D, 0.1*D]) - ax.grid(ls='--') - fig.show() \ No newline at end of file diff --git a/famodel/anchors/anchors_famodel/capacity_helical.py b/famodel/anchors/anchors_famodel/capacity_helical.py index 0ccae2f4..3e52fe59 100644 --- a/famodel/anchors/anchors_famodel/capacity_helical.py +++ b/famodel/anchors/anchors_famodel/capacity_helical.py @@ -1,93 +1,173 @@ import numpy as np +from .capacity_driven import getCapacityDriven, plot_pile +from .support_soils import clay_profile, sand_profile +from .support_plots import plot_helical + +def getCapacityHelical(profile_map, location_name, D, L, d, zlug, Ha, Va, plot=False): + '''Calculate the vertical and horizontal capacity of a helical pile using a soil profile. + The calculation is based on the soil profile, anchor geometry and inclined load. -def getCapacityHelical(D, L, d, zlug, soil_type, gamma, Su0=None, k=None, phi=None, Dr=None): - - '''Calculate the inclined vertical load capacity of a helical pile in clay. - The calculation is based on the soil properties and anchor geometry. - Parameters ---------- + profile : array + Soil profiles (z, parameters) + Clay soil profile (z, Su, gamma) + Sand soil profile (z, phi, gamma, Dr) + soil_type : string + Select soil condition, 'clay' or 'sand' D : float - Helix diameter [m] + Helix diameter (m) L : float - Length shaft [m] + Shaft length (m) d : float - Pile shaft diameter [m] + Shaft diameter (m) zlug : float - Embedded depth of the lug [m] - soil_type : string - Select soil condition, 'clay' or 'sand' - gamma: float - Effective unit weight of the soil [kN/m3] - Su0 : float - Undrained shear strength at the mudline (clay only) [kPa] - k : float - Undrained shear strength gradient (clay only) [kPa/m] - phi : float - Angle of internal friction (sand only) [deg] - Dr : float - Relative density of the soil (%) (sand only) [-] - - + Depth to padeye (m) + Ha : float + Horizontal load applied at padeye (N) + Va : float + Vertical load applied at padeye (N) + plot : bool + Plot the p-y curve and the deflection pile condition if True + Returns ------- - Qu: float - Maximum vertical capacity [kN] + y : array + Lateral displacement at each node (real nodes only) + z : array + Node depth positions corresponding to y (m) + resultsHelical : dict + Dictionary containing displacements, moment capacity, hinge state and vertical capacity ''' - - rhos= 78.50 # Dry steel unit weight (kN/m3) - t = (6.35 + D*20)/1e3 # Suction pile wall thickness (m), API RP2A-WSD - - # Dry and wet mass of the pile + + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + + t = (6.35 + D*20)/1e3 # Helical pile wall thickness (m), API RP2A-WSD + rhows = 66.90e3 # Submerged steel specific weight (kN/m3) + rhow = 10e3 # Water specific weight (kN/m3) + def PileWeight(Len, Dia1, Dia2, tw, rho): - Wp = ((np.pi/4)*((Dia1**2 - (Dia1 - 2*tw)**2)*Len + (np.pi/4)*Dia2**2*tw))*rho - return Wp - # Define alpha coefficient (clay) - if soil_type == 'clay': - Su_av_L = Su0 + k*(L - D) # Undrained shear strength values (average) - sigma_v_eff = gamma*zlug # Effective soil stress (kN/m2) - psi_val = Su_av_L/sigma_v_eff # Su/p0' for point in question (API DP 2A-WSD) - if psi_val <= 1.0: - alpha = min(0.5*psi_val**-0.50, 1) - else: - alpha = min(0.5*psi_val**-0.25, 1) + return ((np.pi/4)*((Dia1**2 - (Dia1 - 2*tw)**2)*Len + (np.pi/4)*Dia2**2*tw))*rho + + z_helix = zlug + (L - D) + matched_layer = next((layer for layer in layers if layer['top'] <= z_helix <= layer['bottom']), None) + if matched_layer is None: + raise ValueError(f"No soil layer found at z = {z_helix:.2f} m") + + if matched_layer['soil_type'] == 'clay': + profile = [[matched_layer['top'], matched_layer['Su_top'], matched_layer['gamma_top']], + [matched_layer['bottom'], matched_layer['Su_bot'], matched_layer['gamma_bot']]] + z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile) + + z_helix = np.clip(z_helix, matched_layer['top'], matched_layer['bottom']) + Su = f_Su(z_helix) + sigma_v_eff = max(f_sigma_v_eff(z_helix), 1.0) + psi_val = Su/sigma_v_eff + alpha = min(0.5*psi_val**-0.50, 1) if psi_val <= 1.0 else min(0.5 * psi_val**-0.25, 1) + + Nc = min(6.0*(1 + 0.2*d/D), 9) + Qh = ((np.pi/4)*(D**2 - d**2)*Nc*Su + f_gamma(z_helix)*D)*0.75 + Qs = np.pi*d*L*alpha*Su + Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs + + elif matched_layer['soil_type'] == 'sand': + profile = [[matched_layer['top'], matched_layer['phi_top'], matched_layer['gamma_top'], matched_layer['Dr_top']], + [matched_layer['bottom'], matched_layer['phi_bot'], matched_layer['gamma_bot'], matched_layer['Dr_bot']]] + z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile) + + z_helix = np.clip(z_helix, matched_layer['top'], matched_layer['bottom']) + gamma = f_gamma(z_helix) + Dr = f_Dr(z_helix) + delta = f_delta(z_helix) + phi = f_phi(z_helix) + + Nq = 0.5*(12*phi)**(phi/54) + Qh = (np.pi/4)*(D**2 - d**2)*Nq*gamma*z_helix + Qs = np.pi*d*L*delta*gamma*z_helix + Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs - # Define delta as a function of Dr (sand) - def calc_delta(Dr_val): - if 35 <= Dr_val < 50: - return 0.29 - elif 50 <= Dr_val < 65: - return 0.37 - elif 65 <= Dr_val < 85: - return 0.46 - elif Dr_val >= 85: - return 0.56 + + Wp = PileWeight(L, D, d, t, (rhows + rhow)) + + # Unity Check based only on vertical capacity + UC_vertical = Va/Qu + + # Compute horizontal capacity using p-y method + layers, y, z, results_lateral = getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=False) + # Plotting + if plot: + plot_pile(layers, y, z, D, L, z0=layers[0]['top'], zlug=zlug, hinge_location=None) + + Hcap = results_lateral['Horizontal max.'] + UC_horizontal = Ha/Hcap if Hcap != 0 else np.inf + + resultsHelical = { + 'Horizontal max.': Hcap, + 'Vertical max.': Qu, + 'Lateral displacement': results_lateral['Lateral displacement'], + 'Rotational displacement': results_lateral['Rotational displacement'], + 'Unity check (horizontal)': UC_horizontal, + 'Unity Check (vertical)': UC_vertical, + 'Weight pile': Wp,} + + if matched_layer['soil_type'] == 'clay': + resultsHelical['Su @ helix'] = Su + resultsHelical['alpha'] = alpha + elif matched_layer['soil_type'] == 'sand': + resultsHelical['Dr @ helix'] = Dr + resultsHelical['delta'] = delta + resultsHelical['phi'] = phi + + return layers, resultsHelical + +if __name__ == '__main__': + + profile_map = [ + { + 'name': 'CPT_1', + 'x': 498234, 'y': 5725141, + 'layers': [ + { + 'top': 1.0, 'bottom': 3.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 9.0, + 'Su_top': 60, 'Su_bot': 50}, + # { + # 'top': 3.0, 'bottom': 7.0, + # 'soil_type': 'clay', + # 'gamma_top': 15.0, 'gamma_bot': 25.0, + # 'Su_top': 100, 'Su_bot': 150}, + { + 'top': 3.0, 'bottom': 7.0, + 'soil_type': 'sand', + 'gamma_top': 8.0, 'gamma_bot': 8.0, + 'phi_top': 32, 'phi_bot': 38, + 'Dr_top': 70, 'Dr_bot': 75}, + { + 'top': 7.0, 'bottom': 15.0, + 'soil_type': 'clay', + 'gamma_top': 25.0, 'gamma_bot': 50.0, + 'Su_top': 200, 'Su_bot': 400}] + } + ] + + D = 1.5 # Helix diameter (m) + L = 12.0 # Pile length (m) + d = 0.5 # Shaft diameter (m) + zlug = 3 # Padeye depth (m) + Ha = 30e3 # Horizontal load (N) + Va = 50e3 # Vertical load (N) + + layers, resultsHelical = getCapacityHelical(profile_map, 'CPT_1', D, L, d, zlug, Ha, Va, plot=True) + for key, val in resultsHelical.items(): + if isinstance(val, float): + print(f"{key}: {val:.3f}") else: - return 0 # Default or error value for very low Dr values - - Wp = PileWeight(L, D, d, t, rhos) + print(f"{key}: {val}") - # ----- Clay case ----- - if soil_type == 'clay': - Nc = 6.0*(1 + 0.2*d/D); - Nc = np.where(Nc < 9, Nc, 9) - # Su is calculated, at the depth of the helix minus one helical plate diameter - # A reduction of 25% is applied for a moderately sensitive clay - Qh = ((np.pi/4)*(D**2 - d**2)*Nc*(Su0 + k*(L - D)) + gamma*D)*0.75 - Qs = np.pi*d*L*alpha*(Su0 + k*(L - D)) - Qu = Qh + Qs - - # ----- Sand case ----- - else: - delta = calc_delta(Dr) - Nq = 0.5*(12*phi)**(phi/54) - Qh = (np.pi/4)*(D**2 - d**2)*Nq*gamma*L - Qs = np.pi*d*L*delta*gamma*L - Qu = Qh + Qs - - resultsHelical = {} - resultsHelical['Capacity'] = Qu # Vertical capacity - resultsHelical['Weight'] = Wp # Dry weight of the helical pile (kN) - - return resultsHelical \ No newline at end of file + plot_helical(layers, D=D, L=L, d=d, z0=layers[0]['top'], zlug=zlug, n_helix=1, spacing=1.0) + + + diff --git a/famodel/anchors/anchors_famodel/capacity_load.py b/famodel/anchors/anchors_famodel/capacity_load.py index 33553bdd..55a8b5d6 100644 --- a/famodel/anchors/anchors_famodel/capacity_load.py +++ b/famodel/anchors/anchors_famodel/capacity_load.py @@ -1,225 +1,210 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed May 29 15:53:52 2024 -@author: fmoreno -""" - -import yaml # Allow access to config file for user inputs import numpy as np import matplotlib.pyplot as plt -from scipy.optimize import fsolve +from .support_soils import clay_profile, sand_profile +from .support_plots import plot_load +def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=False): + '''Calculate the transfer load from mudline to main padeye using a layered soil profile. -def getAnchorLoad(Tm, thetam, zlug, d, soil_type, gamma, Su0, k): - - '''Calculate the inclined load capacity of a Suction Caisson Anchor in sand or clay. - The calculation is based on the soil properties, anchor geometry, and the angle of inclined load. - Offshore Geotechnical Engineering (Randolph , page 323) - Parameters ---------- - Tm : float - Mooring line load at mudlevel [kN] - thetam : float - Mooring line angle at mudlevel [deg] - zlug : float - Embedded depth of the lug [m] - soil_type: string - Select soil condition, 'clay' or 'sand' + profile_map : list of dicts + Soil profile in profile_map format + Tm : float + Mooring line load at mudlevel (N) + thetam : float + Mooring line angle at mudlevel (deg) + zlug : float + Embedment depth of the lug (m) + line_type : str + 'chain' or 'wire' d : float - Chain diameter [m] - Su0 : float - Undrained shear strength at the mudline (clay) [kPa] - k : float - Undrained shear strength gradient (clay) [kPa/m] - - Returns - ------- - Ta : float - Inclined load magnitude at the anchor lug [kN] - thetaa : float - Inclined load angle at the anchor lug [deg] - ''' - - # Setting bearing capacity values per soil type - if soil_type == 'clay': - Nc = 8.5; Ab=2.5; nhu=0.40 # Nc - Bearing capacity factor (9 and 14) DNV-RP-E301 - elif soil_type == 'sand': # Ab - Effective unit bearing area (2.5 - 2.6 times chain dia) - Nc = 9; Ab=2.5; nhu=0.35 # nhu - Friction coefficient between the mooring line and soil - - thetam = np.radians(thetam) - - if soil_type == 'clay': - Su_av_lug = Su0*zlug + k*zlug**2/2 - zaQav = Ab*d*Nc*Su_av_lug - - elif soil_type == 'sand': - zaQav = Ab*d*Nc*gamma*zlug**2/2 - - def LoadTransfer(beta): - return(2*zaQav*np.e**(nhu*(beta - thetam)) - Tm*(beta**2 - thetam**2)) - - thetaa = fsolve(LoadTransfer, thetam) - thetaa = thetaa[0] - Ta = Tm/(np.e**(nhu*(thetaa - thetam))) - - H = Ta*np.cos(thetaa) - V = Ta*np.sin(thetaa) - - resultsLoad = {} - resultsLoad['load'] = Ta # Load magnitude @ lug - resultsLoad['angle'] = np.rad2deg(thetaa) # Load angle @ lug - resultsLoad['H'] = H # Horizontal component @ lug - resultsLoad['V'] = V # Vertical component @ lug - - return resultsLoad - -def getTransferLoad(Tm, thetam, zlug, line_type, d, soil_type, Su0=None, - k=None, gamma=None, phi= None, delta=None, w=None, plot=False): - '''Calculate the transfer load from the mudline to the main padeye - elevation using the DNV standards. The calculation is based on the - mooring line properties, anchor geometry and the load from MoorPy and - RAFT. - - Parameters - ---------- - Tm : float - Mooring line load at mudlevel [kN] - thetam : float - Mooring line angle at mudlevel [deg] - zlug : float - Embedded depth of the lug [m] - line_type = string - Select line type, 'chain' or 'wire' - d : float - Chain diameter [m] - soil_type = string - Select soil condition, 'clay' or 'sand' - Su0 : float - Undrained shear strength at the mudline (clay only) [Pa] - k : float - Undrained shear strength gradient (clay only) [Pa/m] - gamma: float - Effective unit weight of the soil (sand only) [N/m3] - phi : float - Friction angle (sand only) [deg] - delta: float - Interface friction angle at soil-anchor line (sand only) [deg] + Chain diameter (m) w : float - Mooring line unit weight [N/m] - + Mooring line unit weight (N/m) + plot : bool + Show plot + Returns ------- - Ta : float - Inclined load magnitude at the anchor lug [kN] - thetaa : float - Inclined load angle at the anchor lug [deg] + dict + Dictionary with transferred load components and depth. ''' - - deltas = 0.2 - - # Include element weight in terms of d and match it with deltas - if line_type == 'chain': - Et = 10; En = 2.5; W = w*deltas; + + deltas = 0.2 # discretization step + + # Line mechanical properties + if line_type == 'chain': + Et, En = 10, 2.5 elif line_type == 'wire': - Et = np.pi; En = 1; W = w*deltas; - - T = Tm; theta = np.deg2rad(thetam); - Su = Su0; - drag = 0; depth = 0.1 - - T_values = []; Su_values = []; - drag_values = []; depth_values = []; + Et, En = np.pi, 1 + W = w*deltas + + # Soil layer access + layers = profile_map[0]['layers'] + z0 = min(layer['top'] for layer in layers) + Nc = 8.5 + + if all(layer['soil_type'] in ['rock', 'weak_rock'] for layer in layers): + print('[Bypass] Skipping load transfer — soil is all rock.') + Ha = Tm*np.cos(np.deg2rad(thetam)) + Va = Tm*np.cos(np.deg2rad(thetam)) + return Ha, Va + + # Initial values + z0 = min(layer['top'] for layer in layers) + T = Tm + theta = np.deg2rad(thetam) + drag = 0 + depth = z0 + 0.01 + + # Tracing lists + drag_values, depth_values = [], [] - # Setting bearing capacity values per soil type - if soil_type == 'clay': - Nc = 8.5; alpha = 0.7; - elif soil_type == 'sand': - nhu = 0.5 - Nq = np.exp(np.pi*np.tan(np.deg2rad(phi)))*(np.tan(np.deg2rad(45 + phi/2)))**2 - # print(Nq) - while (zlug - depth) >= 0: - if soil_type =='clay': - dtheta = (En*d*Nc*Su - W*np.cos(theta))/T*deltas - dT = (Et*d*alpha*Su + W*np.sin(theta))*deltas - - elif soil_type =='sand': - dtheta = (En*d*Nq*gamma*depth - W*np.cos(theta))/T*deltas - dT = (Et*d*gamma*depth*np.tan(np.rad2deg(delta)) + W*np.sin(theta))*deltas + matched_layer = next((layer for layer in layers if layer['top'] <= depth <= layer['bottom']), None) + if matched_layer is None: + break - ddrag = deltas*np.cos(theta) - ddepth = deltas*np.sin(theta) - theta += dtheta; T -= dT; - - drag += ddrag; depth += ddepth - if Su: - Su = Su0 + k*depth - - # Ensure consistency in load transfer - if abs(Tm - T) > 0.75*Tm: # More than 75% loss - raise Exception(f"Warning: Load transfer is unrealistic. Initial load Tm = {Tm/1e6:.2f} MN and current load T = {T/1e6:.2f} MN differ by more than 75 %") - break # Exit the loop if load transfer is unrealistic + if matched_layer['soil_type'] == 'clay': + matched_layer = next((layer for layer in layers if layer['soil_type'] == 'clay' and layer['top'] <= depth <= layer['bottom']), None) + if matched_layer is None: + break + profile = [[matched_layer['top'], matched_layer['gamma_top'], matched_layer['Su_top']], + [matched_layer['bottom'], matched_layer['gamma_bot'], matched_layer['Su_bot']]] + z0_local, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile) + + Su = f_Su(depth) + alpha = f_alpha(depth) + d_theta = (En*d*Nc*Su - W*np.cos(theta))/T*deltas + dT = (Et*d*alpha*Su + W*np.sin(theta))*deltas + + elif matched_layer['soil_type'] == 'sand': + matched_layer = next((layer for layer in layers if layer['soil_type'] == 'sand' and layer['top'] <= depth <= layer['bottom']), None) + if matched_layer is None: + break - # Check for excessive load angles - if not (0 < np.rad2deg(theta) < 90): - raise Exception(f"Warning: Load angle is unrealistic: {np.rad2deg(theta):.2f} deg") - break # Exit the loop if the angle becomes unreasonable + profile = [[matched_layer['top'], matched_layer['gamma_top'], matched_layer['phi_top'], matched_layer['Dr_top']], + [matched_layer['bottom'], matched_layer['gamma_bot'], matched_layer['phi_bot'], matched_layer['Dr_bot']]] + z0_local, f_gamma, f_phi, f_Dr, f_sigma_v_eff, f_delta = sand_profile(profile) + + gamma_z = f_gamma(depth) + delta_z = f_delta(depth) + phi = f_phi(depth) + Nq = np.exp(np.pi*np.tan(np.deg2rad(phi)))*(np.tan(np.deg2rad(45 + phi/2)))**2 + # print(f'Nq = {Nq:.2f}, depth = {depth:.2f} m') + d_theta = (En*d*Nq*gamma_z*depth - W*np.cos(theta))/T*deltas + dT = (Et*d*gamma_z*depth*np.tan(np.deg2rad(delta_z)) + W*np.sin(theta))*deltas - T_values.append(T); Su_values.append(Su) - drag_values.append(drag); depth_values.append(depth); - - Ta = T; thetaa = theta - # print(thetaa); print(Ta) - H = Ta*np.cos(thetaa); V = Ta*np.sin(thetaa) - length_values = deltas*len(drag_values) - - resultsLoad = {} - resultsLoad['diff'] = (Tm - Ta)/1e6 # Difference - resultsLoad['load'] = Ta/1e6 # Load magnitude @ lug - resultsLoad['angle'] = np.rad2deg(thetaa) # Load angle @ lug - resultsLoad['H'] = H # Horizontal component @ lug - resultsLoad['V'] = V # Vertical component @ lug - resultsLoad['length'] = length_values # Length of the embedded line - - # Plot of the line and extreme line tension - drag_values = [-1*i for i in drag_values] - depth_values = [-1*j for j in depth_values] - - if plot: - fig, ax = plt.subplots(figsize=(20, 5)); n = 2000000 - ax.scatter(drag_values[-1], depth_values[-1], color='g', zorder=5) - ax.scatter(0, 0, color='r', zorder=4) - ax.arrow(0, 0, Tm*np.cos(np.deg2rad(thetam))/n, Tm*np.sin(np.deg2rad(thetam))/n, - head_width=0.25, head_length=0.5, color='r', zorder=3) - ax.arrow(drag_values[-1], depth_values[-1], Ta*np.cos(thetaa)/n, Ta*np.sin(thetaa)/n, - head_width=0.25, head_length=0.5, color='g',zorder=2) - ax.plot(drag_values, depth_values,color='b', zorder=1) - - #Set labels and title - plt.xlabel('Drag distance [m]') - plt.ylabel('Embedded depth [m]') - plt.suptitle('Inverse catenary profile in soil DNV') - plt.grid(True) - - return resultsLoad + elif matched_layer['soil_type'] in ['rock', 'weak_rock']: + raise ValueError(f"Unsupported soil type: {matched_layer['soil_type']}. Mooring line cannot be embedded in rock.") + + d_drag = deltas*np.cos(theta) + d_depth = deltas*np.sin(theta) + + theta += d_theta + T -= dT + drag += d_drag + depth += d_depth + + if not (0 < np.rad2deg(theta) < 90): + print(f"[Warning] Line angle reached {np.rad2deg(theta):.2f}°, stopping at drag = {-drag:.2f} m") + break + + drag_values.append(-drag); + depth_values.append(-depth); + + if np.rad2deg(theta) >= 90: + print(f"[Correction] Clipping angle to 90° to avoid negative horizontal load (Ha).") + theta = np.deg2rad(90) + Ta = T; thetaa = theta + Hm = Tm*np.cos(np.deg2rad(thetam)); Vm = Tm*np.cos(np.deg2rad(thetam)) + Ha = Ta*np.cos(thetaa); Va = Ta*np.sin(thetaa) + + print(f'Input Tm = {Tm} N, thetam = {thetam}°, zlug = {zlug} m') + print(f'Output Hm = {Hm} N, Vm = {Vm} N') + print(f'Output Ta = {Ta} N, thetaa = {np.rad2deg(thetaa)}°') + print(f'Output Ha = {Ha} N, Va = {Va} N') + + resultsLoad = { + 'Tm': Tm, 'thetam': thetam, + 'Hm': Hm, 'Vm': Vm, + 'Ta': Ta, 'thetaa': np.rad2deg(thetaa), + 'Ha': Hm, 'Va': Vm, + 'length': deltas*len(drag_values), + 'drag_values': drag_values, + 'depth_values': depth_values} + + return layers, resultsLoad if __name__ == '__main__': - - Tm = 1.16e6 - thetam = 0 - zlug = 10 - line_type ='chain' - d = 0.160 - soil_type ='sand' - Su0 = 2.4*1e3 - k = 1.41*1e3 - gamma = 9e3 - phi = 35 - delta = 27 - w = 4093 - - resultsDNV = getTransferLoad(Tm, thetam, zlug, line_type, d, soil_type, Su0, k, gamma, phi, delta, w) - #results = getAnchorLoad(Tm, thetam, zlug, d, soil_type, gamma, Su0, k) \ No newline at end of file + + # profile_map = [ + # { + # 'name': 'CPT_1', + # 'x': 498234, 'y': 5725141, + # 'layers': [ + # { + # 'top': 1.0, 'bottom': 2.0, + # 'soil_type': 'clay', + # 'gamma_top': 8.0, 'gamma_bot': 8.0, + # 'Su_top': 10, 'Su_bot': 25}, + # { + # 'top': 2.0, 'bottom': 8.0, + # 'soil_type': 'clay', + # 'gamma_top': 8.0, 'gamma_bot': 8.0, + # 'Su_top': 25, 'Su_bot': 50}, + # { + # 'top': 8.0, 'bottom': 16.0, + # 'soil_type': 'clay', + # 'gamma_top': 8.0, 'gamma_bot': 8.0, + # 'Su_top': 50, 'Su_bot': 100} + # ] + # } + # ] + profile_map = [ + { + 'name': 'CPT_1', + 'x': 498234, 'y': 5725141, + 'layers': [ + # { + # 'top': 0.0, 'bottom': 5.0, + # 'soil_type': 'sand', + # 'gamma_top': 9.5, 'gamma_bot': 9.5, + # 'phi_top': 28, 'phi_bot': 30, + # 'Dr_top': 70, 'Dr_bot': 70}, + { + 'top': 0.0, 'bottom': 5.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.0, + 'Su_top': 25, 'Su_bot': 25}, + # { + # 'top': 0.0, 'bottom': 3.0, + # 'soil_type': 'sand', + # 'gamma_top': 9.5, 'gamma_bot': 9.5, + # 'phi_top': 30, 'phi_bot': 38, + # 'Dr_top': 65, 'Dr_bot': 75}, + { + 'top': 5.0, 'bottom': 15.0, + 'soil_type': 'sand', + 'gamma_top': 9.5, 'gamma_bot': 9.5, + 'phi_top': 35, 'phi_bot': 40, + 'Dr_top': 77, 'Dr_bot': 85} + ] + } + ] + + Tm = 4978442 # Load at mudline (N) + thetam = 30 # Angle at mudline (deg) + zlug = 8.5 # Padeye depth (m) + line_type = 'chain' + d = 0.25 # Chain diameter (m) + w = 5000 # Line weight (N/m) + + layers, resultsLoad = getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w, plot=True) + + plot_load(layers, resultsLoad['drag_values'], resultsLoad['depth_values'], + resultsLoad['Tm'], resultsLoad['thetam'], resultsLoad['Ta'], + resultsLoad['thetaa'], zlug=zlug) \ No newline at end of file diff --git a/famodel/anchors/anchors_famodel/capacity_plate.py b/famodel/anchors/anchors_famodel/capacity_plate.py index bc371ff7..825252d4 100644 --- a/famodel/anchors/anchors_famodel/capacity_plate.py +++ b/famodel/anchors/anchors_famodel/capacity_plate.py @@ -1,102 +1,177 @@ import numpy as np +import matplotlib.pyplot as plt +from .support_soils import clay_profile +from .support_plots import plot_plate + +def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot=False): + '''Calculate the plate anchor capacity using clay soil layers from profile_map. + The calculation is based on the soil profile, anchor geometry and inclined load. -def getCapacityPlate(A, beta, zlug, soil_type, gamma, Su0=None, k=None): - - '''Calculate the inclined load capacity of a plate in clay at a given depth. - The calculation is based on the soil properties, anchor geometry and the angle of inclined load. - The plate is assumed to be inclined perpendicular to the tension at the main padeye depth. - Parameters ---------- - A : float - Plate area, assumed to be square so that width B = sqrt(A). [m^2] + profile_map : list of dict + Soil profile map with coordinates and layers per location. + location_name : str + Name of the location to select the soil profile. + B : float + Plate width (m) + L : float + Plate length (m) + zlug : float + Embedment depth of the main padeye (m) beta : float - Angle of the plate after keying process [deg] - zlug: float - Embedded depth of the main padeye [m] - soil_type : string - Specify 'sand' or 'clay'. This affects what other soil parameters are used. - gamma: float - Effective unit weight of the soil [kN/m3] - Su0 : float - Undrained shear strength at the mudline [kPa] - k : float - Undrained shear strength gradient [kPa/m] - + Inclination angle of the plate (deg) + Ha : float + Applied horizontal load (N) + Va : float + Applied vertical load (N) + plot : bool + Whether to generate plots. + Returns ------- - Tmax: float - Maximum capacity [kN] + Dictionary with Capacity, Weight, UC, etc. ''' - - - Los=0.05 # Key lost fraction due to the keying process, default 0.05 (-) - rhos= 78.50 # Dry steel unit weight (kN/m3) - - B = round(np.sqrt(A),2) # Anchor width (and length, approximated as square) (m) - zlug_B = zlug/B # Anchor depth range/ width of the plate - B_t = 40 # Aspect ratio plate width to thickness, default is 40 - t = round(B/B_t, 2) # Thickness of the plate, which it depends on the width (m) + + # Extract and filter clay layers from profile_map + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = [layer for layer in profile_entry['layers'] if layer['soil_type'] == 'clay'] + + if not layers: + raise ValueError('Plate anchor capacity model only supports clay soils.') + + # Build the profile array: [[z, Su, gamma], ...] + profile = [] + for layer in layers: + profile.append([layer['top'], layer['gamma_top'], layer['Su_top']]) + profile.append([layer['bottom'], layer['gamma_bot'], layer['Su_bot']]) + + print("layer gamma_top (raw):", layer['gamma_top']) + print("layer gamma_bot (raw):", layer['gamma_bot']) + + profile = np.array(sorted(profile, key=lambda x: x[0])) + + # Parameters and constants + Los = 0.05 + B_t = 40 + rhows = 66.90e3 # Submerged steel (N/m3) + rhow = 10e3 # Seawater (N/m3) + + # Evaluate interpolated Su and gamma + z0, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile) + t = round(B/B_t, 2) + V_steel = round(B*L*t, 2) + zlug_B = zlug/B + + # Profile check points + npts = 10 + z_offsets = np.linspace(-0.5, 0.5, npts)*B*np.sin(np.deg2rad(beta)) + z_points = zlug + z_offsets; print(z_points) + + Su_vals = [f_Su(z) for z in z_points] + gamma_10 = f_gamma(z_points[2]); print(gamma_10) + gamma_vals = [f_gamma(z) for z in z_points]; print("gamma_vals:", [f"{val:.2f}" for val in gamma_vals], "N/m3") + Su = np.mean(Su_vals); print(f"Su: {Su:.2f} Pa") + gamma = np.mean(gamma_vals); print(f"gamma: {gamma:.2f} N/m3") - #t=np.sqrt(0.006*A)/4 - V = round(A*t,2) # Steel volume (m3) - W = V*rhos # Plate weight (kg) - Su = Su0 + k*zlug # Undrained shear strength at plate depth + print("Profile being sent to clay_profile():") + for row in profile: + print(f"z = {row[0]:.2f} m, gamma = {row[1]:.2f} kN/m³, Su = {row[2]:.2f} kPa") - # ----- anchor pullout capacity ----- + # Shear strength gradient + k = np.polyfit(z_points, Su_vals, 1)[0] + print(f"k: {k:.2f}") - # Anchor Pullout capacity factor in weightless clay with breakaway base, soil homogeneous - Nco_0_0 = 2.483*np.log(zlug_B) + 1.974 # angle = 0 deg - Nco_90_0 = 2.174*np.log(zlug_B) + 3.391 # angle = 90 deg + # Pile weight including auxiliary parts + Wp = 1.35*V_steel*(rhows + rhow) - kBSh = k*B/Su # Degree of soil non-homogeneity + # Capacity factors + Nco_0_0 = 2.483*np.log(zlug_B) + 1.974 + Nco_90_0 = 2.174*np.log(zlug_B) + 3.391 + kBSh = k*B/Su + print(f"kBSh: {kBSh:.2f}") f0 = np.where(zlug_B < 4, 1.77*(zlug_B**0.3) - 1.289, 0.192*zlug_B + 0.644) - f90 = np.where(zlug_B < 4, 0.68*(zlug_B**0.5) - 0.41 , 0.153*zlug_B + 0.341) + f90 = np.where(zlug_B < 4, 0.68*(zlug_B**0.5) - 0.410, 0.153*zlug_B + 0.341) - # Non-homogeneity adjustment factor for anchor ultimate pullout capacity - S_kB_0 = 1 - f0 *kBSh + S_kB_0 = 1 - f0*kBSh S_kB_90 = 1 - f90*kBSh + Nco_0 = S_kB_0*Nco_0_0 + Nco_90 = S_kB_90*Nco_90_0 + Nco = Nco_0 + (Nco_90 - Nco_0)*(beta/90)**2 - # Anchor Pullout capacity factor in weightless clay with breakaway base, soil nonhomogeneous - Nco_0 = S_kB_0*Nco_0_0 - Nco_90 = S_kB_90*Nco_90_0 - - # Anchor pullout capacity factor in weightless clay with no breakaway base - Nco = Nco_0 + (Nco_90 - Nco_0)*(beta/90)**2 - - # Uplift bearing capacity factor, soil homogeneous Nco_s_0_0 = np.where(2.90*zlug_B + 6.02 <= 11.59, 2.90*zlug_B + 6.02, 11.596) Nco_s_90_0 = np.where(2.72*zlug_B + 4.02 <= 11.59, 2.72*zlug_B + 4.02, 11.596) - # ----- ultimate anchor capacity factor ----- - - # Non-homogeneity factor for anchor ultimate pullout capacity - S_s_kB_0 = np.where(zlug_B <= 2, 1 + (0.8 - 0.3*zlug_B)*kBSh - (0.383*kBSh**1.36), 1) # Angle = 0 - + S_s_kB_0 = np.where(zlug_B <= 2, 1 + (0.8 - 0.3*zlug_B)*kBSh - (0.383*kBSh**1.36), 1) f90s = np.where(zlug_B <= 3, 0.267*zlug_B, 0.6) - S_s_kB_90 = 1 - f90s*kBSh # Angle = 90 - - # Anchor ultimate holding capacity in with breakaway base, soil nonhomogeneous - Nco_s_0 = S_s_kB_0 *Nco_s_0_0 + S_s_kB_90 = 1 - f90s*kBSh + Nco_s_0 = S_s_kB_0*Nco_s_0_0 Nco_s_90 = S_s_kB_90*Nco_s_90_0 - - # Anchor ultimate holding capacity in with no breakaway base, soil nonhomogeneous Nco_s = Nco_s_90 + (Nco_s_0 - Nco_s_90)*((90 - beta)/90)**2 - - # ----- final results ----- - Nc_final = np.minimum(Nco + (gamma*zlug)/Su, Nco_s) # anchor pullout capacity factor [kN] - qu = Nc_final*Su # The bearing pressure capacity of the anchor plate - Tmax = round(qu*(1 - Los)*A,2) # The bearing tension force capacity of the anchor plate - Hmax = Tmax*np.cos(np.deg2rad(beta)) - Vmax = Tmax*np.sin(np.deg2rad(beta)) - - resultsPlate = {} - resultsPlate['Capacity'] = Tmax # Capacity at specified loading angle - resultsPlate['Horizontal max.'] = Hmax # Maximum horizontal capacity in clay - resultsPlate['Vertical max.'] = Vmax # Maximum vertical capacity in clay - resultsPlate['Weight'] = W # Dry weight of the plate (kN) + Nc_final = max(Nco + (gamma*zlug)/Su, Nco_s) + print(f"Nc_star: {Nco + (gamma*zlug)/Su:.2f}") + print(f"Nc_star: {Nco_s:.2f}") + qu = Nc_final*Su + Tmax = round(qu*(1 - Los)*B*L, 2) + Hmax = Tmax*np.cos(np.deg2rad(90 - beta)) + Vmax = Tmax*np.sin(np.deg2rad(90 - beta)) + + Ta = np.sqrt(Ha**2 + Va**2) + UC = Ta/Tmax + + resultsPlate = { + 'Capacity': Tmax, + 'Horizontal max.': Hmax, + 'Vertical max.': Vmax, + 'Unity check': UC, + 'Weight plate': Wp + } - return resultsPlate \ No newline at end of file + return layers, resultsPlate + +if __name__ == '__main__': + profile_map = [ + { + 'name': 'CPT_1', + 'x': 498234, 'y': 5725141, + 'layers': [ + { + 'top': 0.0, 'bottom': 9.5, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.5, + 'Su_top': 10, 'Su_bot': 25 + }, + { + 'top': 9.5, 'bottom': 11.5, + 'soil_type': 'clay', + 'gamma_top': 8.5, 'gamma_bot': 8.5, + 'Su_top': 25, 'Su_bot': 45 + }, + { + 'top': 11.5, 'bottom': 25.0, + 'soil_type': 'clay', + 'gamma_top': 8.5, 'gamma_bot': 9.0, + 'Su_top': 45, 'Su_bot': 50 + } + ] + } + ] + + B = 2.0 + L = 2.0 + zlug = 10.0 + Ha = 350e3 + Va = 400e3 + alpha = np.rad2deg(np.arctan2(Va, Ha)) + beta = 90 - alpha + + layers, results = getCapacityPlate(profile_map, 'CPT_1', B, L, zlug, beta, Ha, Va) + + print("\n--- Plate Anchor Capacity Results ---") + for key, val in results.items(): + print(f"{key}: {val:.2f}") + + plot_plate(layers, B, L, z0 = layers[0]['top'], zlug=zlug, beta=beta, title='Plate Anchor in Layered Soil') diff --git a/famodel/anchors/anchors_famodel/capacity_suction.py b/famodel/anchors/anchors_famodel/capacity_suction.py index d16b75a1..d94059c8 100644 --- a/famodel/anchors/anchors_famodel/capacity_suction.py +++ b/famodel/anchors/anchors_famodel/capacity_suction.py @@ -1,53 +1,54 @@ -import yaml import numpy as np import matplotlib.pyplot as plt -from scipy.optimize import fsolve -#from famodel.anchors.capacity_load import getAnchorLoad -#from famodel.anchors.anchors_famodel.capacity_load import getTransferLoad +import matplotlib.colors as mcolors +from .support_soils import clay_profile, sand_profile -def getCapacitySuction(D, L, zlug, H, V, soil_type, gamma, Su0=None, k=None, phi=None, Dr=None, plot=True): - +def getCapacitySuction(profile_map, location_name, D, L, zlug, Ha, Va, thetalug=5, psilug=7.5, plot=False): '''Calculate the inclined load capacity of a suction pile in sand or clay following S. Kay methodology. - The calculation is based on the soil properties, anchor geometry and inclined load. + The calculation is based on the soil profile, anchor geometry and inclined load. Parameters ---------- + profile : array + Soil profile as a 2D array: (z, parameters) + Clay soil profile (z (m), Su (kPa), gamma (kN/m³)) + Sand soil profile (z (m), phi (deg), gamma (kN/m³), Dr (%)) + soil_type : string + Select soil condition, 'clay' or 'sand' D : float - Suction pile diameter [m] + Suction pile diameter (m) L : float - Suction anchor length [m] + Suction pile length from pile head (m) zlug: float - Embedded depth of the main padeye [m] - soil_type : string - Select soil condition, 'clay' or 'sand' - gamma: float - The effective unit weight of the soil. [kN/m3] - Su0 : float - Undrained shear strength at the mudline (clay only) [kPa] - k : float - Undrained shear strength gradient (clay only) [kPa/m] - phi : float - Angle of internal friction (sand only) [deg] - Dr : float - Relative density of the soil (%) (sand only) [-] - + Embedded depth of the main padeye (m) + thetalug: float + Angle of tilt misaligment (deg) (default value: 5.0) + psilug: float + Angle of twist misaligment (deg) (default value: 7.5) + Ha : float + Horizontal load at pile lug elevation (N) + Va : float + Vertical load at pile lug elevation (N) + plot : bool + Plot the capacity envelope if True + Returns ------- - Hmax : float - Maximum horizontal capacity [kN] - Vmax : float - Maximum vertical capacity [kN] - ''' - - lambdap = L/D; m = 2/3; # Suction pile slenderness ratio + Dictionary with capcity, weigths and UC. + ''' + + # Retrieve soil layers from map + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + + z0 = layers[0]['top'] # Mudline elevation + lambdap = (L - z0)/D # Suction pile slenderness ratio t = (6.35 + D*20)/1e3 # Suction pile wall thickness (m), API RP2A-WSD rlug = D/2 # Radial position of the lug - thetalug = 5 # Angle of tilt misaligment, default is 5. (deg) - psilug = 7.5 # Angle of twist misaligment, default is 7.5. (deg) - rhows = 66.90 # Submerged steel specific weight (kN/m3) - rhow = 10 # Water specific weight (kN/m3) - + rhows = 66.90e3 # Submerged steel specific weight (N/m3) + rhow = 10e3 # Water specific weight (N/m3) + # Outer and inner surface of the pile skirt def PileSurface(Len, Dia): Sp = np.pi*Dia*Len @@ -67,267 +68,454 @@ def rlugTilt(r, z, theta): def zlugTilt(r, z, theta): Z = r*np.sin(np.deg2rad(theta)) + z*np.cos(np.deg2rad(theta)) return Z - # Define delta as a function of Dr - def calc_delta(Dr_val): - if 35 <= Dr_val < 50: - return 0.29 - elif 50 <= Dr_val < 65: - return 0.37 - elif 65 <= Dr_val < 85: - return 0.46 - elif Dr_val >= 85: - return 0.56 - else: - return 0 # Default or error value for very low Dr values - - if soil_type == 'clay': - # Definitions for cohesive soils - Nc = min (6.2*(1 + 0.34*np.arctan(lambdap)),9) # End-bearing capacity factor - ez = (Su0*L**2/2 + k*L**3/3)/(Su0*L + k*L**2/2)#; print(ez) - Np_fixed = 10.25; Np_free = 4 # From Np vs L/D chart from CAISSON_VHM - Su_av_L = Su0 + k*zlug # Undrained shear strength values (average) - Su_tip = Su0 + k*L # Undrained shear strength values (tip) - sigma_v_eff = gamma*zlug # Effective soil stress (kN/m2) - psi_val = Su_av_L/sigma_v_eff # Su/p0' for point in question (API DP 2A-WSD) - #zlug = ez # Optimized depth of the lug - - if psi_val <= 1.0: - alpha = min(0.5*psi_val**-0.50, 1) - else: - alpha = min(0.5*psi_val**-0.25, 1) - - Hmax = Np_fixed*L*D*Su_av_L; - H0 = Np_free*L*D*Su_av_L; - Mmax = Np_fixed*L*L*D*Su_av_L; + # Ellipse crossing with constant values + def horizontal_cross(H, M, M_target): + crossings = [] + for i in range(len(M_rot) - 1): + if (M[i] - M_target) * (M[i+1] - M_target) < 0: + # Interpolation to get more precise value at crossing + H_cross = np.interp(M_target, [M[i], M[i+1]], [H[i], H[i+1]]) + crossings.append(H_cross) + return crossings + def vertical_cross(H, M, H_target): + crossings = [] + for i in range(len(H) - 1): + if (H[i] - H_target) * (H[i+1] - H_target) < 0: + # Interpolation to get more precise value at crossing + M_cross = np.interp(H_target, [H[i], H[i+1]], [M[i], M[i+1]]) + crossings.append(M_cross) + return crossings + + Np_fixed = 11.65 + Np_free = 3.5 + Nc = min(6.2*(1 + 0.34*np.arctan(lambdap)), 9) + + # Initialize + sum_ez_weighted = 0.0 + Hmax_final = 0.0 + Vmax_final = 0.0 + layer_data = [] + + # Profile check points + npts = 10 + + for layer in layers: + soil_type = layer['soil_type'] + z_top = layer['top'] + z_bot = layer['bottom'] + + if soil_type == 'clay': + # Prepare soil profile for clay + profile = [ + [z_top, layer['gamma_top'], layer['Su_top']], + [z_bot, layer['gamma_bot'], layer['Su_bot']] + ] + + z_ref, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile) + + # Clip the layer first + z_top_clip = max(z_top, z0) + z_bot_clip = min(z_bot, z0 + (L - z0)) + dz_clip = z_bot_clip - z_top_clip; print(f'dz_clip = {dz_clip:.2f} m') + + if dz_clip <= 0: + continue # Skip layers fully above or below + + # Calculate properties over clipped dz + z_vals = np.linspace(z_top_clip, z_bot_clip, npts) + Su_vals = f_Su(z_vals) + Su_total = np.trapz(Su_vals, z_vals) + Su_moment = np.trapz(Su_vals*z_vals, z_vals) + + ez_layer = Su_moment/Su_total + Su_av_z = f_Su(ez_layer) + + print(f'ez_layer = {ez_layer:.2f} m') + print(f'Su_av_z (at ez_layer) = {Su_av_z:.2f} Pa') + + Su_bot = f_Su(z_bot_clip) + gamma_vals = f_gamma(z_vals) + gamma_av = np.mean(gamma_vals) + + # Calculate Hmax for clay + Hmax_layer = Np_fixed*D*dz_clip*Su_av_z; + + layer_data.append({ + 'z_top': z_top_clip, + 'z_bot': z_bot_clip, + 'dz': dz_clip, + 'Hmax_layer': Hmax_layer, + 'ez_layer': ez_layer + }) + + sigma_v_eff = f_sigma_v_eff(np.mean(z_vals)) + alpha_av = float(f_alpha(np.mean(z_vals))) + + # Side shear To and Ti + To = PileSurface(dz_clip, D)*alpha_av*Su_av_z + Ti = PileSurface(dz_clip, D - 2*t)*alpha_av*Su_av_z + + # Tip resistance + if abs(z_bot_clip - (z0 + (L - z0))) < 1e-3: # tip check + Tbase = (np.pi/12)*D**3*Su_bot + else: + Tbase = 0.0 + + Tmax = min(To + Ti, To + Tbase) + + # Torque induced by horizontal load + T = Ha*rlug*np.sin(np.deg2rad(psilug)) + + nhuT = T/Tmax + nhuV = Ha/To + nhuVstar = np.sqrt(nhuV**2 - nhuT**2) + alphastar = alpha_av*(nhuVstar/nhuV); print(f"alphastar = {alphastar:.3f}") + + # Constant weight + Pile_Head = PileWeight(z0, D, t, rhows) + + # Vertical failure modes + Vmax1 = None + if np.isclose(z_bot_clip, z0 + (L - z0), atol=0.1): + Vmax1 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + Nc*Su_bot*(np.pi/4)*D**2 + # else: + # Vmax1 = np.inf # No tip resistance unless at tip + + Vmax2 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + PileSurface(dz_clip, D - 2*t)*alphastar*Su_av_z + Vmax3 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + SoilWeight(dz_clip, D, t, gamma_av) + + Vmax_candidates = [v for v in [Vmax1, Vmax2, Vmax3] if v is not None] + Vmax_layer = max(Vmax_candidates) + + # Sum vertical capacities + Vmax_final += Vmax_layer + + # Print layer debug info + print(f"Vmax_layer = {Vmax_layer:.2f} N") + print(f"Vmax1 = {Vmax1:.2f} N" if Vmax1 is not None else "Vmax1 = not applicable") + print(f"Vmax2 = {Vmax2:.2f} N") + print(f"Vmax3 = {Vmax3:.2f} N") + + elif soil_type == 'sand': + # Prepare soil profile for sand + profile = [ + [z_top, layer['gamma_top'], layer['phi_top'], layer['Dr_top']], + [z_bot, layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']] + ] - # M modifies the Hmax capacity - M = - V*rlugTilt(rlug,zlug,thetalug) - H*(zlugTilt(rlug,zlug,thetalug) - ez) - def f(Hmax): - return m*(Hmax/(L*D*(Su0 + k*zlug)) - Np_fixed) + M*(Hmax/(L*D*(Su0 + k*zlug))/(Hmax*L)) - Hmax = fsolve(f,5) + z_ref, f_gamma, f_phi, _, f_sigma_v_eff, f_delta = sand_profile(profile) - # Torsion capacity - Fo = PileSurface(L, D)*alpha*Su_av_L - To = Fo - Ti = PileSurface(L,(D - 2*t))*alpha*Su_av_L - Tbase = np.pi*D**3*Su_tip/12 - Tmax = min(To + Ti, To + Tbase) + # Clip the layer within pile embedded length + z_top_clip = max(z_top, z0) + z_bot_clip = min(z_bot, z0 + (L - z0)) + dz_clip = z_bot_clip - z_top_clip - # Introduce twist effects due to installation misaligment - T = H*rlug*np.sin(np.deg2rad(psilug)) - nhuT = T/Tmax; nhuV = H/Fo; - nhuVstar = np.sqrt(nhuV**2 - nhuT**2) - alphastar = alpha*(nhuVstar/nhuV) + if dz_clip <= 0: + continue # Skip non-overlapping layers - # "Plugged" (Reverse end bearing capacity - passive suction) - Vmax1 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alphastar*Su_av_L + Nc*Su_tip*(np.pi/4)*D**2) - # "Coring" - Vmax2 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alphastar*Su_av_L + PileSurface(L,(D - 2*t))*alphastar*Su_av_L) - # "Leaking" - Vmax3 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alphastar*Su_av_L + SoilWeight(L, D, t, gamma)) - Vmax = min(Vmax1, Vmax2, Vmax3) - ''' - print("\n--- Parameter-Based Version ---") - print(f"Su_av_L = {Su_av_L:.3f} kPa") - print(f"sigma'_v(zlug) = {sigma_v_eff:.3f} kPa") - print(f"psi_val = {psi_val:.3f}") - print(f"alpha (API) = {alpha:.3f}") - print(f"Hmax = {Hmax[0]:.2f} kN") - print(f"Vmax = {Vmax:.2f} kN") - ''' - - elif soil_type == 'sand': - # Definition for non-cohesive soils - Nq = np.e**(np.pi*np.tan(np.radians(phi)))*np.tan(np.radians(45) + np.radians(phi)/2)**2 # Lateral-bearing capacity factor - sigma_av_L = gamma*L/2 # Effective stress (average) - sigma_tip = gamma*L # Effective stress (tip) - Hmax = 0.5*D*Nq*gamma*L**2 - - M = - V*rlugTilt(rlug,zlug,thetalug) - H*(zlugTilt(rlug,zlug,thetalug) - zlug) + # Calculate properties over clipped dz + z_vals = np.linspace(z_top_clip, z_bot_clip, npts) + phi_vals = f_phi(z_vals) + sigma_vals = f_sigma_v_eff(z_vals) + delta_vals = f_delta(z_vals) - # Torsion capacity - delta = calc_delta(Dr) - To = PileSurface(L, D)*delta*sigma_av_L - Ti = PileSurface(L, (D -2*t))*delta*sigma_av_L - Tbase = np.pi*D**3*sigma_tip/12 - Tmax = min(To + Ti, To + Tbase) + phi_av = np.mean(phi_vals) + sigma_av = np.mean(sigma_vals) + delta_av = np.mean(delta_vals) - # Introduce twist effects due to installation misaligment - T = H*rlug*np.sin(np.deg2rad(psilug)) - Fo = delta*sigma_av_L*L*np.pi*D - nhuT = T/Tmax; nhuV = H/Fo; - nhuVstar = np.sqrt(nhuV**2 - nhuT**2) - deltastar = delta*(nhuVstar/nhuV) - - # "Coring" - Vmax2 = PileWeight(L, D, t, rhows) + PileSurface(L, D)*deltastar*sigma_av_L + PileSurface(L,(D - 2*t))*deltastar*sigma_av_L - # "Leaking" - Vmax3 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*deltastar*sigma_av_L + SoilWeight(L, D, t, gamma)) - Vmax = min(Vmax2, Vmax3) - # def y(depth): - # return np.e**(-depth) - 1 + depth - # Ze = D/(4*7); Zi = D/(4*5) - # Vmax = 7*gamma*Ze**2*y(L/Ze)*PileSurface(L, D)/L + 5*gamma*Zi**2*y(L/Zi)*PileSurface(L,(D - 2*t))/L - ''' - print("\n--- Parameter-Based (Sand) ---") - print(f"phi = {phi:.2f} deg") - print(f"gamma = {gamma:.2f} kN/m3") - print(f"deltastar = {deltastar:.2f} -") - print(f"sigma_av_L = {sigma_av_L:.2f} kN") - print(f"sigma_tip = {sigma_tip:.2f} kN") - ''' - # Pile weight (inc. stiffening plus vent) assessed as a factor - Wp = 1.10*PileWeight(L, D, t, (rhows + rhow)) - # Submerged weight of the soil plug - Ws = SoilWeight(L, D, t, gamma) - - # Capacity envelope - aVH = 0.5 + lambdap; bVH = 4.5 + lambdap/3 - #print('Env. exp = ' +str(aVH)+' '+str(bVH)) - UC = (H/Hmax)**aVH + (V/Vmax)**bVH - x = np.cos(np.linspace (0, np.pi/2, 100)) - y = (1 - x**bVH)**(1/aVH) - X = Hmax*x; Y = Vmax*y - if plot: - plt.plot(X, Y, color = 'b') - plt.plot(H, V, '*', color = 'r') + sigma_tip = f_sigma_v_eff(z_bot_clip) + gamma_vals = f_gamma(z_vals) + gamma_av = np.mean(gamma_vals) - # Set labels and title - plt.xlabel('Horizontal capacity [kN]') - plt.ylabel('Vertical capacity [kN]') - plt.suptitle('VH suction pile capacity envelope') - plt.axis([0, 1.3*max(X[0], H), 0, 1.3*max(Y[-1], V)]) - plt.grid(True) - plt.show() - - resultsSuction = {} - if soil_type == 'clay': - resultsSuction['Horizontal max.'] = Hmax[0] # Maximum horizontal capacity in clay - elif soil_type == 'sand': - resultsSuction['Horizontal max.'] = Hmax # Maximum horizontal capacity in sand - resultsSuction['Vertical max.'] = Vmax # Maximum vertical capacity - if soil_type == 'clay': - resultsSuction['UC'] = UC[0] # Unity check in clay - elif soil_type == 'sand': - resultsSuction['UC'] = UC # Unity check in sand - resultsSuction['Weight'] = Wp # Dry weight of the suction pile (kN) - resultsSuction['Weight Soil'] = Ws # Submerged weight of the soil plug (kN) - resultsSuction['t'] = t # Pile thikness in [m] - - return resultsSuction + Nq = np.e**(np.pi*np.tan(np.radians(phi_av)))*(np.tan(np.radians(45) + np.radians(phi_av)/2))**2 + + # Calculate Hmax for sand + Hmax_layer = 0.5*Nq*D*gamma_av*dz_clip**2 + + layer_data.append({ + 'z_top': z_top_clip, + 'z_bot': z_bot_clip, + 'dz': dz_clip, + 'Hmax_layer': Hmax_layer, + 'ez_layer': np.mean(z_vals) + }) + + # Side friction + To = PileSurface(dz_clip, D)*delta_av*sigma_av + Ti = PileSurface(dz_clip, D - 2*t)*delta_av*sigma_av + + if abs(z_bot_clip - (z0 + (L - z0))) < 1e-3: + Tbase = np.pi/4*D**2*sigma_tip + else: + Tbase = 0.0 + + Tmax = min(To + Ti, To + Tbase) -def getCapacitySuctionSimp(D, L, zlug, H, V, gamma, Su0, k, alpha): + # Torque induced by horizontal load + T = Ha*rlug*np.sin(np.deg2rad(psilug)) + nhuT = T/Tmax + nhuV = Ha/To + nhuVstar = np.sqrt(nhuV**2 - nhuT**2) + deltastar = delta_av*(nhuVstar/nhuV) + + # Constant weight + Pile_Head = PileWeight(z0, D, t, rhows) + + # Vertical failure modes + Vmax2 = Pile_Head + PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*deltastar*sigma_av + PileSurface(dz_clip, D - 2*t)*deltastar*sigma_av + Vmax3 = Pile_Head + PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*deltastar*sigma_av + SoilWeight(dz_clip, D, t, gamma_av) + + Vmax_layer = min(Vmax2, Vmax3) + + # Sum vertical capacities + Vmax_final += Vmax_layer + + print(f"Vmax_layer (sand) = {Vmax_layer:.2f} N") + print(f"Vmax2 (sand) = {Vmax2:.2f} N") + print(f"Vmax3 (sand) = {Vmax3:.2f} N") - ''' - Parameters - ---------- - D : float - Suction pile diameter [m] - L : float - Suction anchor length [m] - Tm : float - Mooring line load at mudlevel [kN] - thetam : float - Mooring line angle at mudlevel [deg] - zlug : float - Embedded depth of the lug [m] - gamma: float + # Hmax_final and weighted ez + for data in layer_data: + z_top = data['z_top'] + z_bot = data['z_bot'] + Hmax_layer = data['Hmax_layer'] + ez_layer = data['ez_layer'] + dz_layer = data['dz'] + + z_embedded_start = z0 + z_embedded_end = L - z0 + + if z_top >= z_embedded_start and z_bot <= z_embedded_end: + sum_ez_weighted += Hmax_layer*ez_layer + Hmax_final += Hmax_layer + print(f'Hmax_layer = {Hmax_layer:.2f} m') + + elif z_top < z_embedded_end and z_bot > z_embedded_start: + dz_inside = min(z_bot, z_embedded_end) - max(z_top, z_embedded_start) + if dz_inside > 0: + ratio = dz_inside/dz_layer + sum_ez_weighted += Hmax_layer*ratio*ez_layer + Hmax_final += Hmax_layer*ratio + # print(f'ez_layer (partial) = {ez_layer:.2f} m') + + ez_global = sum_ez_weighted/Hmax_final + print(f'ez_global = {ez_global:.2f} m') + print(f'Hmax_final = {Hmax_final:.2f} m') + + # Calculate coupled moment + M = -Va*rlugTilt(rlug, zlug, thetalug) - Ha*(zlugTilt(rlug, zlug, thetalug) - ez_global) + Mv = -Va*rlugTilt(rlug, zlug, thetalug) + print(f"rlug_eff = {rlugTilt(rlug, zlug, thetalug):.2f} m") + print(f"zlug_eff = {zlugTilt(rlug, zlug, thetalug):.2f} m") + print(f"M = {M:.2f} Nm") - Su0 : float - Undrained shear strength at the mudline (clay only)[kPa] - k : float - Undrained shear strength gradient (clay only) [kPa/m] - alpha : float - Skin friction coefficient (outer and inner - clay only) [-] - rhows : float - Submerged steel density [t/m3] + # MH Ellipse Parameters for Clay (Kay 2014) + # ΔφMH (piecewise based on L/D) + if 0.5 <= lambdap < 1.125: + delta_phi = 0.32 + 4.32*lambdap; #print(delta_phi) + elif 1.125 <= lambdap < 2.0: + delta_phi = 7.13 - 1.71*lambdap; #print(delta_phi) + elif 2.0 <= lambdap <= 8.0: + delta_phi = 2.25 - 0.25*lambdap; #print(delta_phi) + else: + raise ValueError('L/D out of bounds for MH ellipse.') - Returns - ------- - Hmax : float - Maximum horizontal capacity [kN] - Vmax : float - Maximum vertical capacity [kN] - UC: float - Capacity unity check for given load [-] - ''' + phi_MH = -np.arctan(ez_global/(L - z0)) - np.deg2rad(delta_phi) + a_MH = Np_fixed/np.cos(phi_MH) + delta_bMH = 0.45*(lambdap)**(-0.9) if lambdap <= 1.5 else 0 + b_MH = -Np_free*np.sin(phi_MH) + delta_bMH + print(f"delta_phi = {delta_phi:.2f} deg") + print(f"phi_MH = {np.rad2deg(phi_MH):.2f} deg") + print(f"a_MH = {a_MH:.2f}") + print(f"b_MH = {b_MH:.2f}") + + # MH Ellipse Parameters for Clay (Kay 2015) + # VH (piecewise based on L/D) + if 0.5 <= lambdap < 1.5: + a_VH = 9/4 + (5/3)*lambdap; + elif 0.5 <= lambdap < 1.25: + b_VH = 23/4 - (13/5)*lambdap; + elif 1.5 <= lambdap <= 6.0: + a_VH = 47/12 - (5/9)*lambdap; + b_VH = 50/19 - (2/19)*lambdap; + else: + raise ValueError('L/D ratio out of bounds for MH ellipse formulation.') + a_VH = 0.5 + lambdap; b_VH = 4.5 + lambdap/3 + # a_VH = 4.5 + lambdap/2; b_VH = 4.5 + lambdap/4 + print(f"a_VH = {a_VH:.2f}") + print(f"b_VH = {b_VH:.2f}") + + # Scale VH ellipse based on vertical load ratio (Kay 2015) + shrink_factor = 1 - ((Va/Vmax_final)**b_VH)**(2/a_VH) + + plt.figure(figsize=(10, 5)) + theta = np.linspace(0, 2*np.pi, 400) + shrink_factors = np.linspace(0.0, 1.0, 5) + # Define colormap + cmap = plt.colormaps['Greys'] + norm = mcolors.Normalize(vmin=min(shrink_factors), vmax=max(shrink_factors)) - lambdap = L/D; # Suction pile slenderness ratio - t = 10*D/1e3 # Thickness of the pile - Np_fixed = 10.25; # From Np vs L/D chart from CAISSON_VHM - rhows=66.90 + for s_f in shrink_factors: + color = cmap(norm(s_f)) + x_ellipse = Hmax_final*s_f*np.cos(theta) + y_ellipse = Vmax_final*s_f*np.sin(theta) + H_rot = np.cos(phi_MH)*x_ellipse - np.sin(phi_MH)*y_ellipse + M_rot = np.sin(phi_MH)*x_ellipse + np.cos(phi_MH)*y_ellipse + plt.plot(H_rot, M_rot, color=color, alpha=0.5) - Su_av_L = Su0 + k*zlug; # Undrained shear strength values (average) - Su_tip = Su0 + k*L; # Undrained shear strength values (tip) - Nc = min (6.2*(1 + 0.34*np.arctan(lambdap)),9) # End-bearing capacity factor + x_ellipse_prime = Hmax_final*shrink_factor*np.cos(theta) + y_ellipse_prime = Vmax_final*shrink_factor*np.sin(theta) + H_rot_prime = np.cos(phi_MH)*x_ellipse_prime - np.sin(phi_MH)*y_ellipse_prime + M_rot_prime = np.sin(phi_MH)*x_ellipse_prime + np.cos(phi_MH)*y_ellipse_prime + Hlim = 1.2*Hmax_final + plt.xlim(-Hlim, Hlim) + plt.ylim(-Hlim, Hlim) + plt.grid(True, color='gray', linestyle='--', lw=0.5, alpha=0.8) - # Outer and inner surface of the pile skirt - def PileSurface(Len, Dia): - Sp = np.pi*Dia*Len - return Sp - # Dry and wet mass of the pile - def PileWeight(Len, Dia, tw, rho): - Wp = ((np.pi/4)*((Dia**2 - (Dia - 2*tw)**2)*Len + (np.pi/4)*Dia**2*tw))*rho - return Wp - # Weight of the soil plug - def SoilWeight(Len, Dia, tw, gamma_soil): - Wsoil =(np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil - return Wsoil + # Highlight the actual one + plt.plot(H_rot_prime, M_rot_prime, 'b', label= f'MH ellipse w/ V/Vmax = {shrink_factor:.3f}') + plt.axhline(0, color='k', linestyle='--', lw=1.0) + plt.axvline(0, color='k', linestyle='--', lw=1.0) - Hmax = Np_fixed*L*D*Su_av_L - # "Plugged" (Reverse end bearing capacity - passive suction) - Vmax1 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alpha*Su_av_L + Nc*Su_tip*(np.pi/4)*D**2) - # "Coring" - Vmax2 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alpha*Su_av_L + PileSurface(L,(D - 2*t))*alpha*Su_av_L) - # "Leaking" - Vmax3 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alpha*Su_av_L + SoilWeight(L, D, t, gamma)) - # print(Vmax1); print(Vmax2); print(Vmax3) - Vmax = min(Vmax1,Vmax2,Vmax3) + # Plot horizontal line at constant M and Mv + H_plot = np.linspace(min(1.3*H_rot), max(1.3*H_rot), 100) + M_plot = np.full_like(H_plot, M) # Constant moment + Mv_plot = np.full_like(H_plot, Mv) # Constant moment + plt.plot(H_plot, M_plot, 'r', lw=1.0, label='Moment line') + plt.plot(H_plot, Mv_plot, 'r', lw=0.5, label='Vertical moment line') + plt.legend(loc='lower left', fontsize='small') + + H_roots = horizontal_cross(H_rot_prime, M_rot_prime, M) + Hmax_v = 0.1 + if H_roots: + Hmax_pos = max([r for r in H_roots if r >= 0], default=None) + Hmax_neg = min([r for r in H_roots if r < 0], default=None) + if M > 0 and Hmax_neg is not None: + Hmax_v = abs(Hmax_neg) + plt.plot(Hmax_neg, M, 'ro', label=f'Hmax,v = {Hmax_neg/1e6:.1f} MN', zorder=20) + plt.legend(loc='lower left') + elif M <= 0 and Hmax_pos is not None: + Hmax_v = abs(Hmax_pos) + plt.plot(Hmax_pos, M, 'ro', label=f'Hmax,v = {Hmax_pos/1e6:.1f} MN', zorder=20) + plt.legend(loc='lower left') + else: + print('[WARNING] No valid Hmax crossing found for moment cut.') + else: + print('[WARNING] No intersection between moment line and ellipse.') - # Submerged pile weight (inc. stiffening plus vent) assessed as a factor - Wp = 1.00*PileWeight(L, D, t, (rhows)) - # Submerged weight of the soil plug - Ws = SoilWeight(L, D, t, gamma) + # Find relevant intercept + H_v_roots = horizontal_cross(H_rot_prime, M_rot_prime, 0.0) + M_v_roots = vertical_cross(H_rot_prime, M_rot_prime, 0.0) + idx_maxH = np.argmax(H_rot_prime) + H_at_maxH = H_rot_prime[idx_maxH] + M_at_maxH = M_rot_prime[idx_maxH] + idx_minM = np.argmin(M_rot_prime) + H_at_minM = H_rot_prime[idx_minM] + M_at_minM = M_rot_prime[idx_minM] - # H = Tm*np.cos(np.deg2rad(thetam)); V = Tm*np.sin(np.deg2rad(thetam)) + # Plotting + if plot: + plt.scatter(H_v_roots[0], 0.0, s=25, facecolors='white', edgecolors='blue', + marker='s',label=f'Ho ≈ {H_v_roots[0]/1e6:.1f} MN', zorder=10) + plt.legend(loc='lower left', fontsize='small') + plt.scatter(0.0, M_v_roots[0], s=25, facecolors='white', edgecolors='blue', + marker='s', label=f'Mo ≈ {M_v_roots[0]/1e6:.1f} MNm', zorder=10) + plt.legend(loc='lower left', fontsize='small') + plt.scatter(H_at_maxH, M_at_maxH, s=25, facecolors='white', edgecolors='blue', + marker='D', label=f'Hmax ≈ {H_at_maxH/1e6:.1f} MN', zorder=10) + plt.legend(loc='lower left', fontsize='small') + plt.scatter(H_at_minM, M_at_minM, s=25, facecolors='white', edgecolors='blue', + marker='D', label=f'Mmax ≈ {M_at_minM/1e6:.1f} MNm', zorder=10) + plt.legend(loc='lower left', fontsize='small') + + # Constant weight + pile_head = PileWeight(z0, D, t, rhows); print(f"pile_head = {pile_head:.2f} N") + Vmax_final += pile_head; print(f"Vmax_final = {Vmax_final:.2f} N") + + Wp = 1.10*PileWeight(L, D, t, rhows + rhow) # Capacity envelope - aVH = 0.5 + lambdap; bVH = 4.5 + lambdap/3 - # print('Env. exp =' +str(aVH)+str(bVH)) - UC = (H/Hmax)**aVH + (V/Vmax)**bVH + a_VH = 0.5 + lambdap; b_VH = 4.5 + lambdap/3 + # Unity check + UC = (Ha/Hmax_v)**a_VH + (Va/Vmax_final)**b_VH + plt.figure(figsize=(6, 5)) + x = np.linspace(0, 1, 100) + y = (1 - x**b_VH)**(1/a_VH) - x = np.cos(np.linspace (0,np.pi/2,1000)) - y = (1 - x**bVH)**(1/aVH) - X = Hmax*x; Y = Vmax*y + # Plotting + if plot: + plt.figure(figsize=(6, 5)) + plt.plot(Hmax_v*x, Vmax_final*y, 'b', label='VH Envelope') + plt.plot(Ha, Va, 'go', label='Applied load') + plt.xlabel('Horizontal capacity (N)') + plt.ylabel('Vertical capacity (N)') + plt.title('VH suction pile capacity envelope') + plt.axis([0, 1.3*max(Hmax_v, Ha), 0, 1.3*max(Vmax_final, Va)]) + plt.grid(True) + plt.legend() + plt.show() - #H_good = Hmax*np.exp(np.log(0.5)/aVH) - #V_good = Vmax*np.exp(np.log(0.5)/bVH) - - resultsSuctionSimp = {} - resultsSuctionSimp['Horizontal max.'] = Hmax # Capacity at specified loading angle - resultsSuctionSimp['Vertical max.'] = Vmax # Capacity at specified loading angle - resultsSuctionSimp['UC'] = UC # Unity check - resultsSuctionSimp['Weight'] = Wp # Pile weight in kN - resultsSuctionSimp['Weight Soil'] = Ws # in kN - resultsSuctionSimp['t'] = t - - return resultsSuctionSimp + resultsSuction = { + 'Horizontal max.': Hmax_v, + 'Vertical max.': Vmax_final, + 'Unity check': UC, + 'Weight pile': Wp} + + return layers, resultsSuction if __name__ == '__main__': - - D = 2.0 # Diameter in meters - L = 10.0 # Length in meters - zlug = (2/3)*L # Padeye depth - H = 1500.0 # Horizontal load in kN - V = 1000.0 # Vertical load in kN - - gamma = 8 - Su0 = 25 - k = 0 - - phi = 50 - Dr = 75 - - results_clay = getCapacitySuction(D, L, zlug, H, V, 'clay', gamma, Su0=Su0, k=k, phi=phi, Dr=Dr, plot=True) - - # results_sand = getCapacitySuction(D, L, zlug, H, V, 'sand', gamma, Su0=Su0, k=k, phi=phi, Dr=Dr, plot=True) \ No newline at end of file + + profile_map = [ + { + 'name': 'CPT_1', + 'x': 498234, 'y': 5725141, + 'layers': [ + { + 'top': 0.0, 'bottom': 20.0, + 'soil_type': 'clay', + 'gamma_top': 8.5, 'gamma_bot': 8.5, + 'Su_top': 2.4, 'Su_bot': 30.3}] + # { + # 'top': 10.0, 'bottom': 20.0, + # 'soil_type': 'clay', + # 'gamma_top': 8.5, 'gamma_bot': 8.5, + # 'Su_top': 13.95, 'Su_bot': 30.3}] + # { + # 'top': 30.0, 'bottom': 36.0, + # 'soil_type': 'clay', + # 'gamma_top': 9.0, 'gamma_bot': 9.5, + # 'Su_top': 75, 'Su_bot': 100}, + # { + # 'top': 36.0, 'bottom': 55.0, + # 'soil_type': 'clay', + # 'gamma_top': 9.5, 'gamma_bot': 9.5, + # 'Su_top': 100, 'Su_bot': 100}] + } + ] + + + # Pile and load properties + D = 3.34 # Pile diameter (m) + L = 20.0 # Pile length (m) + zlug = (2/3)*L # Lug depth (m) + theta = 5 # Tilt misalignment angle (deg) + psi = 7.5 # Twist misalignment angle (deg) + Ha = 1e6 # Applied horizontal load (N) + Va = 5.7e6 # Applied vertical load (N) + + # Calculate + layers, resultsSuction = getCapacitySuction( + profile_map, 'CPT_1', # Soil properties and location of the pile + D, L, zlug, # Pile geometrical properties + Ha, Va, # Pile loading conditions + thetalug=theta, psilug=psi, # Pile misaligment tolerances + plot=False + ) + + # print('\n--- Suction Pile Capacity Results ---') + # print(f"Hmax_final = {resultsSuction['Hmax_final']:.2f} N") + # print(f"Vmax_final = {resultsSuction['Vmax_final']:.2f} N") + # print(f"Unity check (UC) = {resultsSuction['UnityCheck']:.4f}") + # print(f"Total Moment (M_total) = {resultsSuction['M_total']:.2f} Nm") + + # plot_suction(layers, L, D, z0 = layers[0]['top'], zlug=zlug) diff --git a/famodel/anchors/anchors_famodel/capacity_torpedo.py b/famodel/anchors/anchors_famodel/capacity_torpedo.py index 1d493f1e..93393b74 100644 --- a/famodel/anchors/anchors_famodel/capacity_torpedo.py +++ b/famodel/anchors/anchors_famodel/capacity_torpedo.py @@ -1,95 +1,271 @@ -import yaml import numpy as np import matplotlib.pyplot as plt -from scipy.optimize import fsolve -from matplotlib import cm -from mpl_toolkits import mplot3d - -def getCapacityTorpedo(D1, D2, L1, L2, zlug, soil_type, Su0, k, alpha): - +from .support_soils import clay_profile +from .support_plots import plot_torpedo + +def getCapacityTorpedo(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot=False): '''Calculate the inclined load capacity of a torpedo pile in clay following S. Kay methodology. - The calculation is based on the holding capacity of the suction pile as if it fully was embedded in soil. - + The calculation is based on the soil profile, anchor geometry and inclined load. + Parameters ---------- + profile : array + Clay soil profile (z, Su, gamma) + Clay soil profile (z (m), Su (kPa), gamma (kN/m³)) + soil_type : string + Select soil condition, 'clay' D1 : float - Torpedo pile wing diameter. [m] + Wing diameter (m) D2 : float - Torpedo pile shaft diameter. [m] + Shaft diameter (m) L1 : float - Torpedo pile wing length. [m] + Winged section length (m) L2 : float - Torpedo pile shaft length, excluding wing. [m] + Shaft section length (m) zlug : float - Torpedo pile embedded depth at main padeye elevation. [m] - soil_type : string - Select soil condition, 'clay' or 'sand' - gamma: float - The effective unit weight of the soil. [kN/m3] - Su0 : float - The Undrained shear strength at the mudline. [kPa] - k : float - The Undrained shear strength gradient. [kPa/m] - alpha : float - The skin friction coefficient. [-] - + Padeye embedment depth (m) + Ha : float + Horizontal load at pile lug elevation (N) + Va : float + Vertical load at pile lug elevation (N) + plot : bool + Plot the capacity envelope if True + Returns ------- - Hmax : float - Maximum horizontal capacity [kN] - Vmax : float - Maximum vertical capacity [kN] + Dictionary with capcity, weigth and UC. ''' - rhos= 78.50 # Dry steel unit weight (kN/m3) - t = (6.35 + D2*20)/1e3 # Torpedo pile wall thickness (m), API RP2A-WSD - - L = L1 + L2; - Dstar = (D1*L1 + (D1 + 2*D2)*L2)/L # Plane 1 (four fins) - #Dstar = (D1*L1 + np.sqrt(2)*(D1/2 + D2)*L2)/L # Plane 2 (four fins) - #rlug = D2/2; zlug = zlug; - lambdap = L/Dstar; print('lambdap = ' +str(lambdap)) - a = zlug; b = zlug + L1; c = zlug + L1 + L2; - Wp = 850 # Weight of the pile with ballast [kN] - # Dry and wet mass of the pile + # Retrieve soil layers from map + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + + L = L1 + L2 + t = (6.35 + D2*20)/1e3 + rhows = 66.90e3 + rhow = 10e3 + def PileWeight(Len1, Len2, Dia1, Dia2, tw, rho): - Wp = ((np.pi/4)*(Dia1**2 - (Dia1 - 2*tw)**2)*(Len1 + Len2) + 4*Len2*Dia2*tw)*rho - return(Wp) - W = PileWeight(L1, L2, D1, D2, t, rhos) # Weight of the steel pile [kN] + return ((np.pi/4)*(Dia1**2 - (Dia1 - 2*tw)**2)*(Len1 + Len2) + 4*Len2*Dia2*tw)*rho + + def PileWingedSurface(length, diameter): + return np.pi*diameter*length + + def PileShaftSurface(length, diameter1, diameter2): + return 8*length*(diameter1 - diameter2) + + z_start = zlug + z_wing = zlug + L1 + z_end = zlug + L + + layer_data = [] + Vmax_total = 0.0 - # Dry and wet mass of the pile - def PileSurface(Len1, Len2, Dia1, Dia2): - Sp = np.pi*Dia1*(Len1 + Len2) + 8*Len2*Dia2*0.9 - return(Sp) + # Profile check points + npts = 10 + + for layer in layers: + if layer['soil_type'] != 'clay': + raise ValueError('Torpedo pile capacity model only supports clay soils.') + + z_layer_top = layer['top'] + z_layer_bot = layer['bottom'] + + z_clip_top = max(z_layer_top, z_start) + z_clip_bot = min(z_layer_bot, z_end) + + if z_clip_bot <= z_clip_top: + continue + + segments = [] + if z_clip_bot <= z_wing: + segments.append((z_clip_top, z_clip_bot, D1)) + elif z_clip_top >= z_wing: + segments.append((z_clip_top, z_clip_bot, D2)) + else: + segments.append((z_clip_top, z_wing, D1)) + segments.append((z_wing, z_clip_bot, D2)) + + for z_seg_top, z_seg_bot, D in segments: + dz_seg = z_seg_bot - z_seg_top + if dz_seg <= 0: + continue + + profile = [ + [z_seg_top, layer['Su_top'], layer['gamma_top']], + [z_seg_bot, layer['Su_bot'], layer['gamma_bot']] + ] + z_ref, f_Su, _, f_gamma, f_alpha = clay_profile(profile) + + z_vals = np.linspace(z_seg_top, z_seg_bot, npts) + Su_vals = f_Su(z_vals) + alpha_vals = np.array([f_alpha(z) for z in z_vals]) + Su_total = np.trapz(Su_vals, z_vals) + Su_moment = np.trapz(z_vals*Su_vals, z_vals) + print("xxxxxxxxxxxxxxxxxxxxxxxxx") + Su_av_z = Su_total/dz_seg + print(f"Su_av_z = {Su_av_z:.2f} Pa") + ez_layer = Su_moment /Su_total + print(f"dz_seg = {dz_seg:.2f} m") + print(f"ez_layer = {ez_layer:.2f} m") + alpha_av = np.mean(alpha_vals) + print(f"alpha_av = {alpha_av:.2f}") + + Np_free = 3.45 + Hmax_layer = Np_free*dz_seg*D*Su_av_z + print(f"Hmax_layer = {Hmax_layer:.2f} N") + print(f"D = {D:.2f} m") + + surface_area = PileWingedSurface(dz_seg, D) if D == D1 else PileShaftSurface(dz_seg, D1, D2) + Vmax_layer = surface_area*alpha_av*Su_av_z + Vmax_total += Vmax_layer + print(f"Vmax_layer = {Vmax_layer:.2f} N") + + layer_data.append({ + 'z_top': z_seg_top, + 'z_bot': z_seg_bot, + 'dz': dz_seg, + 'Hmax_layer': Hmax_layer, + 'ez_layer': ez_layer, + 'Su_av_z': Su_av_z, + 'D_used': D + }) + + if not layer_data: + raise ValueError('No overlapping clay layers within pile depth.') + + sum_Hmax = 0.0 + sum_ez_weighted = 0.0 + + for data in layer_data: + z_top = data['z_top'] + z_bot = data['z_bot'] + Hmax_layer = data['Hmax_layer'] + ez_layer = data['ez_layer'] + dz_layer = data['dz'] + + z_embedded_start = zlug + z_embedded_end = zlug + L + + if z_top >= z_embedded_start and z_bot <= z_embedded_end: + sum_ez_weighted += Hmax_layer*ez_layer + sum_Hmax += Hmax_layer + elif z_top < z_embedded_end and z_bot > z_embedded_start: + dz_inside = min(z_bot, z_embedded_end) - max(z_top, z_embedded_start) + if dz_inside > 0: + ratio = dz_inside/dz_layer + sum_ez_weighted += Hmax_layer*ratio*ez_layer + sum_Hmax += Hmax_layer * ratio + + ez_global = sum_ez_weighted/sum_Hmax + print(f'ez_global = {ez_global:.2f} m') + print(f'sum_Hmax = {sum_Hmax:.2f} N') + + Vmax_total += PileWeight(L1, L2, D1, D2, t, rhows) + ballast + Wp = 1.10 * PileWeight(L1, L2, D1, D2, t, rhows + rhow) + ballast + + ez_ratio = (ez_global - zlug)/L; print(f"ez_ratio = {ez_ratio:.2f} m") + + # Average effective width + L = L1 + L2 + A_wing_plane_1 = (D1 - D2)*L1 + A_wing_plane_2 = (D1 - D2)*np.cos(np.deg2rad(45))/2*L1 + A_shaft = D2*L - ez_Su_den = D1*Su0*(b - a) + 0.5*D1*k*(b**2 - a**2) + D2*Su0*(c - b) + 0.5*D2*k*(c**2 - b**2) - ez_Su_num = D1*Su0*(a**2 - a*b) + 0.33*D1*k*(b**3 - a**3) + b**2*(0.5*D1*Su0 - 0.5*D1*a*k) - a**2*(0.5*D1*Su0 - 0.5*D1*a*k)\ - + D2*Su0*(b**2 - b*c) + 0.33*D2*k*(c**3 - b**3) + c**2*(0.5*D2*Su0 - 0.5*D2*b*k) - b**2*(0.5*D2*Su0 - 0.5*D2*b*k) - ez_Su = ez_Su_num/ez_Su_den - ez_Su_L = ez_Su/L - # print('ez_Su = ' +str(ez_Su)) - Np_free = 3.45 # From Np vs L/D chart from CAISSON_VHM - - Hmax = L*Dstar*Np_free*(Su0 + k*(zlug + ez_Su)) - # print('Hmax = ' +str(Hmax)) - Vmax = PileSurface(L1, L2, D1, D2)*alpha*(Su0 + k*(zlug + ez_Su)) + Wp - # print('Vmax = ' +str(Vmax)) - - #aVH = 0.5 + L/Dstar; bVH = 4.5 - L/(3*Dstar) - aVH = 4.5 + L/(2*Dstar); bVH = 3.5 - L/(4*Dstar) - #H = Ta*np.cos(np.deg2rad(resultsLoad['angle'])); V = Ta*np.sin(np.deg2rad(resultsLoad['angle'])) - #UC = (H/Hmax)**aVH + (V/Vmax)**bVH + # Choose based on direction: + plane = '1' # or '2' - deg = [0, 15, 30, 45, 60, 75, 90] - x = np.cos(np.deg2rad(deg)) - y = (1 - x**bVH)**(1/aVH) - X = Hmax*x/1e3; Y = Vmax*y/1e3 # in MN - - resultsTorpedo = {} - resultsTorpedo['Horizontal max.'] = Hmax #Hmax[0] # Capacity at specified loading angle - resultsTorpedo['Vertical max.'] = Vmax # Capacity at specified loading angle - resultsTorpedo['Weight'] = W # Dry weight of the helical pile (kN) - - return resultsTorpedo \ No newline at end of file + if plane == '1': + Dstar = (A_wing_plane_1 + A_shaft)/L + elif plane == '2': + Dstar = (A_wing_plane_2 + A_shaft)/L + + # Assign aVH and bVH based on ez_su/L + if np.isclose(ez_ratio, 2/3, atol=0.05): + aVH = 0.5 + L/Dstar + bVH = 4.5 - L/(3*Dstar) + mode = 'deep mobilization (2/3)' + elif 0.40 <= ez_ratio <= 0.75: + aVH = 4.5 + L/(2*Dstar) + bVH = 3.5 - L/(4*Dstar) + mode = 'moderate mobilization (1/2 – 3/4)' + # else: + # aVH = 4.0 + # bVH = 4.0 + # mode = 'default exponents (fallback)' + print(f'Interaction exponents set to aVH = {aVH:.2f}, bVH = {bVH:.2f} [{mode}]') + + UC = (Ha/sum_Hmax)**aVH + (Va/Vmax_total)**bVH + + if plot: + deg = np.linspace(0, 90, 20) + x = np.cos(np.deg2rad(deg)) + y = (1 - x**bVH)**(1/aVH) + X = sum_Hmax*x + Y = Vmax_total*y + + plt.figure(figsize=(6, 5)) + plt.plot(X, Y, color='blue', label='VH Envelope') + plt.plot(Ha, Va, 'o', color='red', label='Load Point') + plt.xlabel('Horizontal Capacity (N)') + plt.ylabel('Vertical Capacity (N)') + plt.title('VH torpedo pile capacity envelope') + plt.grid(True) + plt.legend() + plt.tight_layout() + plt.show() + + resultsTorpedo = { + 'Horizontal max.': sum_Hmax, + 'Vertical max.': Vmax_total, + 'Unity check': UC, + 'Weight pile': Wp, + 'ez_global': ez_global, + 'layer_data': layer_data} + + return layers, resultsTorpedo + +if __name__ == '__main__': + + profile_map = [ + { + 'name': 'CPT_1', + 'x': 498234, 'y': 5725141, + 'layers': [ + { + 'top': 0.0, 'bottom': 20.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.5, + 'Su_top': 50, 'Su_bot': 70}, + { + 'top': 20.0, 'bottom': 25.0, + 'soil_type': 'clay', + 'gamma_top': 8.5, 'gamma_bot': 8.5, + 'Su_top': 80, 'Su_bot': 100}, + { + 'top': 25.0, 'bottom': 50.0, + 'soil_type': 'clay', + 'gamma_top': 8.5, 'gamma_bot': 9.0, + 'Su_top': 125, 'Su_bot': 150}] + } + ] + + D1 = 3.0 + D2 = 1.5 + L1 = 11.0 + L2 = 5.0 + zlug = 15.0 + ballast = 10000 + Ha = 6.0e6 + Va = 8.0e6 + + layers, results = getCapacityTorpedo(profile_map, 'CPT_1', D1, D2, L1, L2, zlug, ballast, Ha, Va) + + # print("\n--- Torpedo Pile Capacity Results ---") + # for key, val in results.items(): + # if key != 'layer_data': + # print(f"{key}: {val:.2f}") + + plot_torpedo(layers, D1, D2, L1, L2, z0 = layers[0]['top'], zlug=zlug) diff --git a/famodel/anchors/anchors_famodel/installatioin_torque.py b/famodel/anchors/anchors_famodel/installatioin_torque.py new file mode 100644 index 00000000..1829ab0e --- /dev/null +++ b/famodel/anchors/anchors_famodel/installatioin_torque.py @@ -0,0 +1,176 @@ + +import numpy as np +import matplotlib.pyplot as plt +from support_soils import sand_profile + +def getInstallationHelical(profile_map, location_name, D, L, d, plot=True): + # Constants and geometry + dH = 0.05 # m + psi_p = 16.5 # degrees + delta_crit = 24 # degrees + Fr = 0.01 + + Dh = D # Helix diameter + Dc = d # Core diameter + ph = Dh/3 # Pitch + E = 210e9 # Pa + th = 0.10 # m + tc = 0.03 # m + f_y = 350e6 # Pa + f_y_weld = 250e6 # Pa (typical weld yield strength) + k = 1.04 # Bending coefficient + + profile_entry = next((p for p in profile_map if p['name'] == location_name), None) + if not profile_entry: + raise ValueError(f"Location '{location_name}' not found in profile_map") + + layers = profile_entry['layers'] + + # Assemble sand profile + profile = [] + for layer in layers: + if layer['soil_type'] == 'sand': + profile.append([layer['top'], layer['gamma_top'], layer['phi_top'], layer['Dr_top']]) + profile.append([layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]) + + z0, f_gamma, f_phi, f_Dr, f_sigma_v_eff, f_delta = sand_profile(profile) + + def qc_depth(H): + qc0, depth0 = 0, 0 + qc1, depth1 = 8e6, 6 + qc2, depth2 = 40e6, 16 + m1 = (qc1 - qc0)/(depth1 - depth0) + m2 = (qc2 - qc1)/(depth2 - depth1) + if H <= depth1: + return m1 * H + elif H <= depth2: + return m2 * (H - depth1) + qc1 + else: + return qc2 + + def qc_average(H, Dh): + depths = np.linspace(max(0, H - 1.5*Dh), min(H + 1.5*Dh, 20), int(1.5*Dh/dH)) + return np.mean([qc_depth(z) for z in depths]) + + def calculate_torque(Dc, Dh, Fr, ph, th, dH, H): + gamma = f_gamma(H) + delta_crit_rad = np.radians(delta_crit) + a = Fr/np.tan(delta_crit_rad) + theta = np.arctan(ph/(np.pi*Dh)) + K0 = 1 - np.sin(np.radians(32)) + Tc = np.sum([a*qc_average(z, Dh)*np.tan(delta_crit_rad)*dH*(Dc**2/2) for z in np.arange(0, H, dH)]) + Tb = qc_average(H, Dh)*np.pi*(Dc**3)*np.tan(delta_crit_rad)/12 + Th = (a*qc_average(H, Dh)*np.tan(delta_crit_rad + theta)*np.pi*(Dh**3 - Dc**3)/(12*K0) + + a*qc_average(H, Dh)*th*np.tan(delta_crit_rad)*np.pi*(Dh**2)/2 + + a*qc_average(H, Dh)*th*(Dh**2 - Dc**2)/8) + return Tc + Tb + Th + + def calculate_force(Dc, Dh, Fr, ph, th, dH, H): + gamma = f_gamma(H) + delta_crit_rad = np.radians(delta_crit) + a = Fr/np.tan(delta_crit_rad) + K0 = 1 - np.sin(np.radians(32)) + Fc = np.sum([0.6*a*qc_average(z, Dh)*np.tan(delta_crit_rad)*dH*np.pi*Dc for z in np.arange(0, H, dH)]) + Fb = 0.6*qc_average(H, Dh)*np.pi*(Dc**2)/4 + Fh = (a*qc_average(H, Dh)*np.pi*(Dh**2 - Dc**2)/(4*K0) + + a*qc_average(H, Dh)*th*np.pi*Dh/K0 + + qc_average(H, Dh)*th*(Dh - Dc)/2) + return Fc + Fb + Fh + + def calculate_core(T, Fy_c, Dc, tc, E, H, K=2): + tau = 16*(T/np.pi)*(Dc/(Dc**4 - (Dc - 2*tc)**4)) + sigma_y = 4*(Fy_c/np.pi)/((Dc**2 - (Dc - 2*tc)**2)) + sigma_eq_c = np.sqrt(sigma_y**2 + 3 * tau**2) + I = (np.pi/64)*(Dc**4 - (Dc - 2*tc)**4) + Fy_cr = np.pi**2*E*I/(K*H)**2 + return sigma_eq_c, Fy_cr + + def calculate_helix(Fy_max, Dh, Dc, th, k): + q = 4*Fy_max/(np.pi*(Dh**2 - Dc**2)) + return k*q*Dh**2/(4*th**2) + + def calculate_weld_stress(Fy_max, th, Dh, weld_length): + M = Fy_max * th + Q = Fy_max + Aw = weld_length * th + sigma_w = M / (weld_length * th**2 / 6) + tau_w = Q / Aw + sigma_eq_w = np.sqrt(sigma_w**2 + 3 * tau_w**2) + return sigma_eq_w + + def calculate_axial_capacity(Dh, H): + gamma = f_gamma(H) + # phi_p = 6.6 + 11*np.log10(qc_average(H, Dh)/np.sqrt(gamma*H)) + phi_p = f_phi(H) + Fps = np.tan(np.radians(psi_p)) + np.cos(np.radians(phi_p - psi_p))*\ + (np.tan(np.radians(phi_p) - np.tan(np.radians(psi_p)))) + Fs1 = 2*Fps + Fs2 = (4/3)*Fps*np.tan(np.radians(psi_p)) + Fu = (1 + Fs1*(H/Dh) + Fs2*(H/Dh)**2)*gamma*(np.pi/4)*(Dh**2)*H + return Fu + + Tmax = 8e6 + H_max = L + H_values = np.linspace(0.1, H_max, 100) + results = { + 'H': [], 'Fu': [], 'Torque': [], 'Force': [], + 'SigmaHelix': [], 'SigmaCore': [], 'BucklingLimit': [], 'SigmaWeld': [], 'FailureMode': [] + } + + for H in H_values: + T = calculate_torque(Dc, Dh, Fr, ph, th, dH, H) + F_inst = calculate_force(Dc, Dh, Fr, ph, th, dH, H) + sigma_helix = calculate_helix(F_inst, Dh, Dc, th, k) + sigma_core, Fy_cr = calculate_core(T, F_inst, Dc, tc, E, H) + sigma_weld = calculate_weld_stress(F_inst, th, Dh, weld_length=np.pi*Dc) + Fu = calculate_axial_capacity(Dh, H) + + if T > Tmax: + failure_mode = 'Torque limit' + elif F_inst > Fy_cr: + failure_mode = 'Core buckling' + elif sigma_helix > f_y: + failure_mode = 'Helix stress' + elif sigma_weld > f_y_weld: + failure_mode = 'Weld stress' + else: + failure_mode = 'OK' + + if failure_mode == 'OK': + results['H'].append(H) + results['Fu'].append(Fu) + results['Torque'].append(T) + results['Force'].append(F_inst) + results['SigmaHelix'].append(sigma_helix) + results['SigmaCore'].append(sigma_core) + results['BucklingLimit'].append(Fy_cr) + results['SigmaWeld'].append(sigma_weld) + results['FailureMode'].append(failure_mode) + + if plot: + plt.figure(figsize=(7, 5)) + plt.plot(results['H'], results['Fu'], label=f'Dh/Dc = {Dh/Dc:.2f}') + plt.title("Fu vs H") + plt.xlabel("Depth H (m)") + plt.ylabel("Axial Capacity Fu (N)") + plt.grid(True) + plt.legend() + plt.tight_layout() + plt.show() + + return results + +if __name__ == '__main__': + profile_map = [ + { + 'name': 'CPT_1', + 'x': 0, 'y': 0, + 'layers': [ + {'top': 0.0, 'bottom': 5.0, 'soil_type': 'sand', 'gamma_top': 10, 'gamma_bot': 11, 'phi_top': 32, 'phi_bot': 36, 'Dr_top': 55, 'Dr_bot': 65}, + {'top': 5.0, 'bottom': 20.0, 'soil_type': 'sand', 'gamma_top': 11, 'gamma_bot': 12, 'phi_top': 36, 'phi_bot': 38, 'Dr_top': 65, 'Dr_bot': 80} + ] + } + ] + + results = getInstallationHelical(profile_map, 'CPT_1', D=1.5, L=10.0, d=0.5, plot=True) + \ No newline at end of file diff --git a/famodel/anchors/anchors_famodel/installation_buckling.py b/famodel/anchors/anchors_famodel/installation_buckling.py new file mode 100644 index 00000000..ee00f615 --- /dev/null +++ b/famodel/anchors/anchors_famodel/installation_buckling.py @@ -0,0 +1,164 @@ +import numpy as np +import matplotlib.pyplot as plt +from support_soils import clay_profile, sand_profile +from installation_suction import getInstallationSuction + +def compute_Zs(s, r, t, nu): + return (s**2/(r*t))*np.sqrt(1 - nu**2) + +def compute_C(psi, rho, xi): + return psi*np.sqrt(1 + (rho*xi/psi)**2) + +def gamma_M(lam_bar): + if lam_bar < 0.5: + return 1.15 + elif lam_bar <= 1.0: + return 0.85 + 0.6 * lam_bar + else: + return 1.45 + +def getBucklingSuction(profile_map, location_name, D, L, t=None, fy=345e6): + ''' + Shell buckling capacity during suction pile installation using DNV-RP-C202 and Colliard & Wallerand effective length. + ''' + E = 2.1e11 + nu = 0.3 + + if t is None: + t = D / 200 + + r = D/2 - t/2 + + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + main_type = layers[0]['soil_type'].lower() + if main_type == 'clay': + clay_input = [[layers[0]['top'], layers[0]['gamma_top'], layers[0]['Su_top']], + [layers[0]['bottom'], layers[0]['gamma_bot'], layers[0]['Su_bot']]] + _, f_gamma, _, _, _ = clay_profile(clay_input) + elif main_type == 'sand': + sand_input = [[layers[0]['top'], layers[0]['gamma_top'], layers[0]['phi_top']], + [layers[0]['bottom'], layers[0]['gamma_bot'], layers[0]['phi_bot']]] + _, f_gamma, _, _, _ = sand_profile(sand_input) + else: + raise ValueError("Unsupported soil type") + + depths = np.arange(0.1, L + 0.1, 0.1) # start from 0.1 to avoid division by zero + suction_results = getInstallationSuction(profile_map, location_name, D, L, gamma_m_install=1.5, gamma_m_retrieval=1.25) + pe_values = suction_results['delta_u_suction'] + pe_values = np.full_like(pe_values, 300e3) + + UC_list = [] + LB_list = [] + PE_list = [] + for z, pe in zip(depths, pe_values): + # Colliard & Wallerand effective length + x = L - z # exposed length + L_B = L*(1 + 2*(x/L) - 0.0435*(x/L)**2) + s = L_B + LB_list.append(s) + PE_list.append(pe/1e3) + + Zs = compute_Zs(s, r, t, nu) + + # fEa (Axial) + psi_a = 4.0 + xi_a = 0.702*Zs + rho_a = 0.5*(1 + r/(150 * t))**-0.5 + C_a = compute_C(psi_a, rho_a, xi_a) + fEa = C_a*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2 + + # fEm (Bending) + C_m = C_a + fEm = fEa + + # fEtau (Shear) + psi_t = 5.34 + (s/L)**2 + xi_t = 0.856*(s/L)*Zs**(3/4) + rho_t = 0.6 + C_t = compute_C(psi_t, rho_t, xi_t) + fEtau = C_t*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2 + + # fEh (Hoop) + psi_h = (1 + (s/L)**2)**2 + xi_h = 1.04*(s/L)*np.sqrt(Zs) + rho_h = 0.6 + C_h = compute_C(psi_h, rho_h, xi_h) + fEh = C_h*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2 + + sigma_a = 0.5*pe*r/t + sigma_m = 0 + sigma_h = pe*r/t + tau = 0 + + sigma_j = np.sqrt((sigma_a)**2 - (sigma_a)*sigma_h + sigma_h**2 + 3*tau**2) + + sigma_a0 = max(0, -sigma_a) + sigma_m0 = max(0, -sigma_m) + sigma_h0 = max(0, -sigma_h) + + lam_bar_sq = (fy/sigma_j)*( + (sigma_a0/fEa) + + (sigma_m0/fEm) + + (sigma_h0/fEh) + + (tau/fEtau) + ) + lam_bar = np.sqrt(lam_bar_sq) + + gammaM = gamma_M(lam_bar) + fksd = fy/np.sqrt(1 + lam_bar**4)/gammaM + + UC = sigma_j/fksd + UC_list.append(UC) + + fig, axs = plt.subplots(1, 3, figsize=(18, 6)) + + axs[0].plot(UC_list, depths, label='UC', color='blue') + axs[0].invert_yaxis() + axs[0].set_xlabel('Unity check') + axs[0].set_ylabel('Depth (m)') + axs[0].set_title('Shell buckling UC vs. Depth') + axs[0].grid(True) + axs[0].legend() + + axs[1].plot(LB_list, depths, label='Buckling Length (L_B)', color='blue') + axs[1].invert_yaxis() + axs[1].set_xlabel('Effective buckling length (m)') + axs[1].set_ylabel('Depth (m)') + axs[1].set_title('Buckling Length vs. Depth') + axs[1].grid(True) + axs[1].legend() + + axs[2].plot(PE_list, depths, label='Underpressure', color='green') + axs[2].invert_yaxis() + axs[2].set_xlabel('Underpressure (kPa)') + axs[2].set_ylabel('Depth (m)') + axs[2].set_title('Suction Pressure vs. Depth') + axs[2].grid(True) + axs[2].legend() + + plt.tight_layout() + plt.show() + + return { + 'depths': depths.tolist(), + 'UC_list': UC_list, + 'LB_list': LB_list + } + +if __name__ == '__main__': + profile_map = [ + { + 'name': 'CPT_1', + 'x': 0, 'y': 0, + 'layers': [ + { + 'top': 0.0, 'bottom': 20.0, + 'soil_type': 'clay', + 'gamma_top': 9.0, 'gamma_bot': 9.0, + 'Su_top': 5.0, 'Su_bot': 45.0 + } + ] + } + ] + getBucklingSuction(profile_map, 'CPT_1', D=2.0, L=10.4) diff --git a/famodel/anchors/anchors_famodel/installation_buckling2.py b/famodel/anchors/anchors_famodel/installation_buckling2.py new file mode 100644 index 00000000..39e13aa1 --- /dev/null +++ b/famodel/anchors/anchors_famodel/installation_buckling2.py @@ -0,0 +1,157 @@ +import numpy as np +import matplotlib.pyplot as plt +from support_soils import clay_profile, sand_profile +from support_pycurves import py_Matlock, py_API +from installation_suction import getInstallationSuction + +def compute_Zs(s, r, t, nu): + return (s**2/(r*t))*np.sqrt(1 - nu**2) + +def compute_C(psi, rho, xi): + return psi*np.sqrt(1 + (rho*xi/psi)**2) + +def gamma_M(lam_bar): + if lam_bar < 0.5: + return 1.15 + elif lam_bar <= 1.0: + return 0.85 + 0.6*lam_bar + else: + return 1.45 + +def getBucklingSuction(profile_map, location_name, D, L): + E = 2.1e11 + fy = 325e6 + nu = 0.3 + t = (2.35 + D*20)/1e3; print(t) # Suction pile wall thickness (m), API RP2A-WSD + + R = D/2 - t/2 + + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + main_type = layers[0]['soil_type'].lower() + if main_type == 'clay': + clay_input = [[layers[0]['top'], layers[0]['gamma_top'], layers[0]['Su_top']], + [layers[0]['bottom'], layers[0]['gamma_bot'], layers[0]['Su_bot']]] + _, f_gamma, f_Su, f_sigma_v_eff, _ = clay_profile(clay_input) + elif main_type == 'sand': + sand_input = [[layers[0]['top'], layers[0]['gamma_top'], layers[0]['phi_top']], + [layers[0]['bottom'], layers[0]['gamma_bot'], layers[0]['phi_bot']]] + _, f_gamma, f_phi, f_Dr, f_sigma_v_eff, _ = sand_profile(sand_input) + else: + raise ValueError("Unsupported soil type") + + depths = np.arange(0.1, L + 0.1, 0.1) + suction_results = getInstallationSuction(profile_map, location_name, D, L, gamma_m_install=1.5, gamma_m_retrieval=1.25) + pe_values = np.interp(depths, suction_results['depths'], suction_results['delta_u_suction']) + + def soil_type_map(z): + for layer in layers: + if layer['top'] <= z <= layer['bottom']: + return layer['soil_type'].lower() + raise ValueError(f"No soil type defined at depth {z}") + + alpha_list = [] + y_disp = 0.001 + for z in depths: + stype = soil_type_map(z) + if stype == 'clay': + py_func, _ = py_Matlock(z, D, f_gamma(z), f_Su(z), f_sigma_v_eff(z), return_curve=True) + elif stype == 'sand': + py_func, _ = py_API(z, D, f_phi(z), f_sigma_v_eff(z), f_Dr(z), return_curve=True) + else: + raise ValueError(f"Unsupported soil type at depth {z}") + stiffness = py_func(y_disp)/y_disp if y_disp != 0 else 0 + alpha_list.append(stiffness) + alpha_array = np.array(alpha_list) + integral_total = np.trapz(alpha_array, depths) + alpha_z = np.cumsum(alpha_array)*(depths[1] - depths[0])/integral_total + + UC_list = [] + PE_list = [] + + for z, pe, alpha in zip(depths[:-1], pe_values[:-1], alpha_z[:-1]): + s = L # constant buckling length + PE_list.append(pe) + + Zs = compute_Zs(s, R, t, nu) + + psi_a = 4.0 + xi_a = 0.702*Zs + rho_a = 0.5*(1 + R/(150*t))**-0.5 + C_a = compute_C(psi_a, rho_a, xi_a) + fEa = C_a*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2 + + C_m = C_a + fEm = fEa + + psi_h = (1 + (s/L)**2)**2 + xi_h = 1.04*(s/L)*np.sqrt(Zs) + rho_h = 0.6 + C_h = compute_C(psi_h, rho_h, xi_h) + fEh = C_h*(np.pi**2 *E/(12*(1 - nu**2)))*(t/s)**2 + + psi_t = 5.34 + (s/L)**2 + xi_t = 0.856*(s/L)*Zs**(3/4) + rho_t = 0.6 + C_t = compute_C(psi_t, rho_t, xi_t) + fEtau = C_t*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2 + + sigma_a = 0.5*pe*R*(1 - alpha)/t + sigma_m = 0 + sigma_h = pe*R*(1 - alpha)/t + tau = 0 + + sigma_j = np.sqrt((sigma_a)**2 - (sigma_a)*sigma_h + sigma_h**2 + 3*tau**2) + + sigma_a0 = max(0, -sigma_a) + sigma_m0 = max(0, -sigma_m) + sigma_h0 = max(0, -sigma_h) + + lam_bar_sq = (fy/sigma_j)*( + (sigma_a0/fEa) + + (sigma_m0/fEm) + + (sigma_h0/fEh) + + (tau/fEtau) + ) + lam_bar = np.sqrt(lam_bar_sq) + + gammaM = gamma_M(lam_bar) + fksd = fy/np.sqrt(1 + lam_bar**4)/gammaM + + UC = sigma_j/fksd + UC_list.append(UC) + + plt.figure(figsize=(6, 6)) + plt.plot(UC_list, depths[:-1], label='UC (with confinement)', color='darkred') + plt.gca().invert_yaxis() + plt.xlabel('Unity Check') + plt.ylabel('Depth (m)') + plt.title('Shell Buckling') + plt.grid(True) + plt.legend() + plt.tight_layout() + plt.show() + + return { + 'depths': depths.tolist(), + 'UC_list': UC_list, + 'PE_list': PE_list, + 'alpha_z': alpha_z.tolist() + } + +if __name__ == '__main__': + profile_map = [ + { + 'name': 'CLAY_INSTALL', + 'x': 0, 'y': 0, + 'layers': [ + { + 'top': 0.0, 'bottom': 20.0, + 'soil_type': 'clay', + 'gamma_top': 9.0, 'gamma_bot': 9.0, + 'Su_top': 5.0, 'Su_bot': 85.0 + } + ] + } + ] + getBucklingSuction(profile_map, 'CLAY_INSTALL', D=4.0, L=17.0) diff --git a/famodel/anchors/anchors_famodel/capacity_drag.py b/famodel/anchors/anchors_famodel/installation_drag.py similarity index 95% rename from famodel/anchors/anchors_famodel/capacity_drag.py rename to famodel/anchors/anchors_famodel/installation_drag.py index d17969e2..c476c3ce 100644 --- a/famodel/anchors/anchors_famodel/capacity_drag.py +++ b/famodel/anchors/anchors_famodel/installation_drag.py @@ -1,14 +1,9 @@ -""" -Drag embedment anchor capacity calculation functions in intermidiate model, -currently set up for clay soils. -Lead author: Felipe Moreno. -""" import yaml # Allow access to config file for user inputs import numpy as np import matplotlib.pyplot as plt -def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En, +def getInstallationDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En, nhu, Su0, k, Ne, thetae0, z0, Nn_max, Nt_max, Nm_max, m, n, p, q, plot=True): @@ -78,7 +73,7 @@ def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En, Vf = Af*tf Vs = Ls*ts*Ws*2 Va = round(Vf + Vs,1) - W = Va*rhos + Wp = Va*rhos # The Anchor Initial Condition Su = Su0 + k*z0 # Undrained shear strength at the initial embedded depth, kPa @@ -104,7 +99,7 @@ def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En, thetaf_values = [] z = z0; x = x0; Ta = Ta0 - xmax = 60; Tmax = 30000; + xmax = 60; Tmax = 3000; for _ in range(3000): thetaaf = thetaf + thetaa @@ -164,10 +159,10 @@ def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En, plt.show() resultsDrag = {} - resultsDrag['capacity'] = max(Ta_values) - resultsDrag['W'] = W + resultsDrag['Capacity'] = max(Ta_values) resultsDrag['embedment_depth'] = z resultsDrag['drag_distance'] = x + resultsDrag['Weight plate'] = Wp return resultsDrag @@ -189,7 +184,7 @@ def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En, m = configDrag['m']; n = configDrag['n']; p = configDrag['p']; q = configDrag['q']; z0 = configDrag['z0']; - resultsDrag = getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm,En, + resultsDrag = getInstallationDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm,En, nhu, Su0, k, Ne, thetae0, z0, Nn_max, Nt_max, Nm_max, m, n, p, q) diff --git a/famodel/anchors/anchors_famodel/installation_driven.py b/famodel/anchors/anchors_famodel/installation_driven.py new file mode 100644 index 00000000..9bb85e9d --- /dev/null +++ b/famodel/anchors/anchors_famodel/installation_driven.py @@ -0,0 +1,186 @@ +import numpy as np +import matplotlib.pyplot as plt +from capacity_soils_map import clay_profile, sand_profile, rock_profile + +def getInstallationDriven(profile_map, location_name, D, L, hammer, J_shaft, J_toe, plot=True, refusal_threshold=0.002, refusal_count=10): + dz = 0.24 + z0 = 1.0 + max_depth = L + N = int((max_depth - z0) / dz) + + t_wall = (6.35 + D * 20) / 1e3 + D_inner = D - 2 * t_wall + A = np.pi / 4 * (D**2 - D_inner**2) + E = 2.1e11 + rhos = 7850 + g = 9.81 + + m = rhos*A*dz + k = E*A/dz + dt = N/np.sqrt(E/rhos) + + m_r = hammer['m_r'] + h = hammer['h'] + eta = hammer['efficiency'] + E_hammer = eta*m_r*g*h + v0 = np.sqrt(2 * E_hammer / m_r) + + soil = profile_map[location_name] + layers = soil['layers'] + + def compute_Rdynamic(v_local, z, J): + for layer in layers: + if layer['top'] <= z <= layer['bottom']: + if layer['soil_type'] == 'clay': + profile = [[layer['top'], layer['gamma_top'], layer['Su_top']], + [layer['bottom'], layer['gamma_bot'], layer['Su_bot']]] + _, _, _, _, f_alpha = clay_profile(profile) + return J*f_alpha(z)*v_local*np.pi*D + elif layer['soil_type'] == 'sand': + profile = [[layer['top'], layer['gamma_top'], layer['phi_top'], layer['Dr_top']], + [layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]] + _, _, _, _, _, f_delta = sand_profile(profile) + return J*f_delta(z)*v_local*np.pi*D + elif layer['soil_type'] == 'rock': + profile = [[layer['top'], layer['UCS_top'], layer['Em_top']], + [layer['bottom'], layer['UCS_bot'], layer['Em_bot']]] + _, f_UCS, _ = rock_profile(profile) + return J*f_UCS(z)*v_local*np.pi*D + return 0.0 + + def compute_Rstatic(z): + for layer in layers: + if layer['top'] <= z <= layer['bottom']: + if layer['soil_type'] == 'clay': + profile = [[layer['top'], layer['gamma_top'], layer['Su_top']], + [layer['bottom'], layer['gamma_bot'], layer['Su_bot']]] + _, _, _, _, f_alpha = clay_profile(profile) + return f_alpha(z)*np.pi*D*dz + elif layer['soil_type'] == 'sand': + profile = [[layer['top'], layer['gamma_top'], layer['phi_top'], layer['Dr_top']], + [layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]] + _, _, _, _, _, f_delta = sand_profile(profile) + return f_delta(z)*np.pi*D*dz + elif layer['soil_type'] == 'rock': + profile = [[layer['top'], layer['UCS_top'], layer['Em_top']], + [layer['bottom'], layer['UCS_bot'], layer['Em_bot']]] + _, f_UCS, _ = rock_profile(profile) + return f_UCS(z)*np.pi*D*dz + return 0.0 + + penetration = 0.0 + total_energy = 0.0 + toe_displacement = [] + blow_counts = [] + consecutive_small_blows = 0 + + blow = 0 + while penetration < (max_depth - z0): + u = np.zeros(N + 1) + v = np.zeros(N + 1) + a = np.zeros(N + 1) + + # Apply initial velocity to first node to help energy propagate + v[0] = v0 + T = 0.5 + nsteps = int(T/dt) + + for step in range(nsteps): + F_internal = np.zeros(N + 1) + for i in range(1, N): + F_internal[i] = k*(u[i - 1] - 2*u[i] + u[i + 1]) + + F_internal[N] = k*(u[N - 1] - u[N]) + + for i in range(N + 1): + z = dz*i + z0 + + if i == N: + F_internal[i] -= compute_Rdynamic(v[i], z, J_toe) + F_internal[i] += compute_Rstatic(z) + else: + F_internal[i] -= compute_Rdynamic(v[i], z, J_shaft) + + if step < 10: + print(f" Step {step}: z = {z:.2f}, Rd = {compute_Rdynamic(v[i], z, J_shaft):.2f}, v = {v[i]:.4f}, u = {u[i]:.4f}") + + a = np.nan_to_num(F_internal / m, nan=0.0, posinf=0.0, neginf=0.0) + v += a * dt + u += v * dt + + if np.any(np.abs(u) > 5): + print("Displacement blew up. Stopping.") + break + + delta_z = u[-1] + penetration += delta_z + + toe_displacement.append(penetration) + blow_counts.append(blow + 1) + total_energy += E_hammer + + print(f"Blow {blow + 1}: Δz = {delta_z:.5f} m, Total Penetration = {penetration:.3f} m") + + if abs(delta_z) < refusal_threshold: + consecutive_small_blows += 1 + else: + consecutive_small_blows = 0 + + if consecutive_small_blows >= refusal_count: + print("Refusal criteria met: 10 consecutive blows with <2 mm displacement") + break + + blow += 1 + + if plot: + plt.figure(figsize=(8, 4)) + plt.plot(blow_counts, toe_displacement, marker='o') + plt.xlabel('Blow Count') + plt.ylabel('Cumulative Toe Displacement (m)') + plt.title('Toe Displacement vs Blow Count') + plt.grid(True) + plt.tight_layout() + plt.show() + + return { + 'blow_counts': blow_counts, + 'toe_displacement': toe_displacement, + 'total_energy': total_energy, + 'final_depth': penetration, + 'total_counts': len(blow_counts) + } + +if __name__ == '__main__': + profile_map = { + 'CPT_1': { + 'type': 'clay', + 'x': 498234, 'y': 5725141, + 'layers': [ + { + 'top': 1.0, 'bottom': 6.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.0, + 'Su_top': 10, 'Su_bot': 20}, + { + 'top': 6.0, 'bottom': 15.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.0, + 'Su_top': 80, 'Su_bot': 100}, + { + 'top': 15.0, 'bottom': 30.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 9.0, + 'Su_top': 100, 'Su_bot': 200} + ] + } + } + + D = 1.0 + L = 12.0 + hammer = {'m_r': 85000, 'h': 5.5, 'efficiency': 0.85} + J_shaft = 0.05 + J_toe = 0.05 + + results = getInstallationDriven(profile_map, 'CPT_1', D, L, hammer, J_shaft, J_toe, plot=True) + for key, val in results.items(): + print(f"{key}: {val}") diff --git a/famodel/anchors/anchors_famodel/installation_driven2.py b/famodel/anchors/anchors_famodel/installation_driven2.py new file mode 100644 index 00000000..db007b64 --- /dev/null +++ b/famodel/anchors/anchors_famodel/installation_driven2.py @@ -0,0 +1,167 @@ +import numpy as np +import matplotlib.pyplot as plt +from capacity_soils_map import clay_profile, sand_profile, rock_profile + +def compute_Rstatic(z): + for layer in layers: + if layer['top'] <= z <= layer['bottom']: + if layer['soil_type'] == 'clay': + profile = [[layer['top'], layer['gamma_top'], layer['Su_top']], + [layer['bottom'], layer['gamma_bot'], layer['Su_bot']]] + _, _, _, _, f_alpha = clay_profile(profile) + return f_alpha(z)*np.pi*D*dz + elif layer['soil_type'] == 'sand': + profile = [[layer['top'], layer['gamma_top'], layer['phi_top'], layer['Dr_top']], + [layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]] + _, _, _, _, _, f_delta = sand_profile(profile) + return f_delta(z)*np.pi*D*dz + elif layer['soil_type'] == 'rock': + profile = [[layer['top'], layer['UCS_top'], layer['Em_top']], + [layer['bottom'], layer['UCS_bot'], layer['Em_bot']]] + _, f_UCS, _ = rock_profile(profile) + return f_UCS(z)*np.pi*D*dz + return 0.0 + +def compute_Rdynamic(v_local, z, J): + for layer in layers: + if layer['top'] <= z <= layer['bottom']: + if layer['soil_type'] == 'clay': + profile = [[layer['top'], layer['gamma_top'], layer['Su_top']], + [layer['bottom'], layer['gamma_bot'], layer['Su_bot']]] + _, _, _, _, f_alpha = clay_profile(profile) + return J*f_alpha(z)*v_local*np.pi*D + elif layer['soil_type'] == 'sand': + profile = [[layer['top'], layer['gamma_top'], layer['phi_top'], layer['Dr_top']], + [layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]] + _, _, _, _, _, f_delta = sand_profile(profile) + return J*f_delta(z)*v_local*np.pi*D + elif layer['soil_type'] == 'rock': + profile = [[layer['top'], layer['UCS_top'], layer['Em_top']], + [layer['bottom'], layer['UCS_bot'], layer['Em_bot']]] + _, f_UCS, _ = rock_profile(profile) + return J*f_UCS(z)*v_local*np.pi*D + return 0.0 + +def getInstallationDriven(profile_map, location_name, D_input, L, hammer, J_shaft, J_toe, plot): + global D, dz, layers + D = D_input + soil = profile_map[location_name] + layers = soil['layers'] + z0 = layers[0]['top'] + + N = 100 + z = np.linspace(z0, L, N) + dz = z[1] - z[0] + + dt = 0.001 + t_max = 2.0 + time = np.arange(0, t_max, dt) + + u = np.zeros((len(time), N)) + v = np.zeros((len(time), N)) + F = np.zeros((len(time), N)) + + rho_steel = 7850 + t = 0.05 + A = np.pi * ((D / 2)**2 - ((D / 2) - t)**2) + m_total = A * L * rho_steel + M_prime = m_total / N + + m_r = hammer['m_r'] + h = hammer['h'] + eff = hammer['efficiency'] + E = m_r * 9.81 * h * eff + + blow_count = 0 + penetration = 0.0 + z_vals = [] + blow_vals = [] + refusal_counter = 0 + refusal_limit = 10 + min_set = 0.002 + + while penetration < L and refusal_counter < refusal_limit: + blow_count += 1 + v0 = np.sqrt(2 * E / m_r) + v[0, 0] = v0 + + for n in range(1, len(time)): + for i in range(1, N - 1): + u_xx = (u[n - 1, i + 1] - 2 * u[n - 1, i] + u[n - 1, i - 1]) / dz**2 + F[n, i] = M_prime * u_xx + + v[n] = v[n - 1] + (F[n] / M_prime) * dt + u[n] = u[n - 1] + v[n] * dt + + delta_z = max(u[-1, :]) + tip_depth = penetration + delta_z + + Rs = compute_Rstatic(tip_depth) + Rt = 9.0 * compute_Rstatic(tip_depth) + R_total = Rs + Rt + compute_Rdynamic(v[-1, -1], tip_depth, J_shaft) + + penetration += delta_z + z_vals.append(penetration) + blow_vals.append(blow_count) + + if delta_z < min_set: + refusal_counter += 1 + else: + refusal_counter = 0 + + if plot: + plt.figure() + plt.plot(z_vals, blow_vals, label='Blows vs Penetration') + plt.xlabel('Penetration Depth [m]') + plt.ylabel('Blow Count') + plt.title('Driveability Simulation') + plt.grid(True) + plt.legend() + plt.show() + + return { + 'z': z, + 'z_vals': z_vals, + 'blow_vals': blow_vals, + 'u': u, + 'v': v, + 'F': F, + 'dt': dt, + 'dz': dz + } + + +if __name__ == '__main__': + profile_map = { + 'CPT_1': { + 'type': 'clay', + 'x': 498234, 'y': 5725141, + 'layers': [ + { + 'top': 1.0, 'bottom': 6.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.0, + 'Su_top': 10, 'Su_bot': 20}, + { + 'top': 6.0, 'bottom': 15.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.0, + 'Su_top': 80, 'Su_bot': 100}, + { + 'top': 15.0, 'bottom': 30.0, + 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 9.0, + 'Su_top': 100, 'Su_bot': 200} + ] + } + } + + D = 1.0 + L = 12.0 + hammer = {'m_r': 155000, 'h': 5.5, 'efficiency': 0.85} + J_shaft = 0.05 + J_toe = 0.05 + + results = getInstallationDriven(profile_map, 'CPT_1', D, L, hammer, J_shaft, J_toe, plot=True) + for key, val in results.items(): + print(f"{key}: {val}") \ No newline at end of file diff --git a/famodel/anchors/anchors_famodel/installation_dynamic.py b/famodel/anchors/anchors_famodel/installation_dynamic.py new file mode 100644 index 00000000..95c97834 --- /dev/null +++ b/famodel/anchors/anchors_famodel/installation_dynamic.py @@ -0,0 +1,164 @@ + +import numpy as np +import matplotlib.pyplot as plt +from support_soils import clay_profile + +def PileWeight(L1, L2, D1, D2, tw, rho): + return ((np.pi/4)*(D1**2 - (D1 - 2*tw)**2)*(L1 + L2) + 4*L2*D2*tw)*rho + +def PileVolume(L1, L2, D1, D2, tw): + return (np.pi/4)*D1**2*(L1 + L2) + 4*L2*D2*tw + +def PileWingedSurface(length, diameter1, diameter2): + return 8*length*(diameter1 - diameter2) + +def PileShaftSurface(length, diameter): + return np.pi*diameter*length + +def getInstallationDynamic(profile_map, location_name, D1, D2, L1, L2, ballast, drop_height, plot=True): + """ + Deterministic installation model of a torpedo pile in clay based on time-domain integration. + Implements the model by True (1976) as adapted in Kazue et al. (2020), accounting for layered soil. + """ + # Constants + rhows = 66.90e3 # Submerged steel specific weight (N/m3) + rhow = 10e3 # Water specific weight (N/m3) + Sti = 3.0 + Se = 5.0 + Ce = 0.02 + delta = 0.9 + Nc = 9.0 + CD = 2.7 + dt = 0.002 + tmax = 15.0 + beta = 28/27 + g = 9.81 + + # Geometry + D = D1 # use the wing diameter for frontal area + L = L1 + L2 + t = (6.35 + D2*20)/1e3 # assumed same as in getCapacityTorpedo + + # Retrieve soil profile and construct full profile list + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + profile = [] + for layer in layers: + if layer['soil_type'] != 'clay': + continue + profile.append([layer['top'], layer['Su_top'], layer['gamma_top']]) + profile.append([layer['bottom'], layer['Su_bot'], layer['gamma_bot']]) + + # Sort and remove duplicates if needed + # profile = sorted(list({(z, su, g) for z, su, g in profile}), key=lambda x: x[0]) + z_ref, f_gamma, f_Su, _, f_delta = clay_profile(profile) + + # Precompute parameters + Af = np.pi*(D2**2)/4 + 4*(D2 - D1)*t + As = PileWingedSurface(L1, D1, D2) + PileShaftSurface(L1 + L2, D2) + Vol = PileVolume(L1, L2, D1, D2, t) + Wp = PileWeight(L1, L2, D1, D2, t, rhows) + ballast; #print(f'Wp = {Wp:.2f} N') + M = Wp/g + Mprime = M + 2*rhow*Vol + + # Closed-form solution for v_impact from free fall in water + CD_water = 1.2 + A_water = Af + vt = np.sqrt((2*Mprime*g)/(rhow*CD_water*A_water)) + t_impact = (vt/g)*np.arccosh(np.exp(g*drop_height/vt**2)) + v_impact = vt*np.tanh(g*t_impact/vt) + + # Initial conditions + t = [0.0] + z = [0.0] + v = [v_impact] + a = [Wp/Mprime] + + # Integration constants + beta1 = dt**2*(0.5 - beta) + beta2 = dt**2*beta + gamma1 = -0.5*dt + gamma2 = 1.5*dt + nsteps = int(tmax/dt) + + for i in range(nsteps): + zn = z[-1] + vn = v[-1] + an = a[-1] + + Su = f_Su(zn) + gamma = f_gamma(zn) + delta = f_delta(zn) + rho = gamma/g + Se_dot = Se/(1 + (1/np.sqrt(Ce*vn/(Su*D2)) + 0.06)) if vn > 0 else 1.0 + + Mprime_local = M + 2*rho*Vol + FD = 0.5*rhow*CD*Af*vn*abs(vn) + FT = Su*Nc*Af*Se_dot + FS = Su*As*delta*Se_dot/Sti + + f_total = (Wp - Vol*gamma) - FD - FT - FS + an1 = f_total/Mprime_local + + zn1 = zn + dt*vn + beta1*an + beta2*an1 + vn1 = vn + gamma1*an + gamma2*an1 + + if vn1 < 0: + break # penetration stops + + t.append(t[-1] + dt) + z.append(zn1) + v.append(vn1) + a.append(an1) + + if plot: + fig, ax1 = plt.subplots() + ax1.plot(t, z, 'b', label='Penetration depth (m)') + ax1.set_xlabel('Time (s)') + ax1.set_ylabel('Depth (m)') + ax1.grid(True) + + ax2 = ax1.twinx() + ax2.plot(t, v, 'r', label='Velocity (m/s)') + ax2.set_ylabel('Velocity (m/s)') + + fig.suptitle('Torpedo Pile Installation Response') + fig.legend(loc='upper right') + plt.tight_layout() + plt.show() + + return { + 'final_depth': z[-1], + 'max_velocity': max(v), + 'penetration_time': t[-1] + } + +if __name__ == '__main__': + profile_map = [ + { + 'name': 'CPT_1', + 'x': 0, 'y': 0, + 'layers': [ + {'top': 0.0, 'bottom': 30.0, 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.5, + 'Su_top': 5, 'Su_bot': 20}, + {'top': 30.0, 'bottom': 100.0, 'soil_type': 'clay', + 'gamma_top': 8.5, 'gamma_bot': 9.0, + 'Su_top': 20, 'Su_bot': 60} + ] + } + ] + + location = 'CPT_1' + D1 = 3.0 + D2 = 1.5 + L1 = 5.0 + L2 = 5.0 + ballast = 350000 + drop_height = 200 + + results = getInstallationDynamic(profile_map, location, D1, D2, L1, L2, ballast, drop_height) + + print("\n--- Torpedo Installation Results ---") + for k, v in results.items(): + print(f"{k}: {v:.2f}") diff --git a/famodel/anchors/anchors_famodel/installation_dynamic2.py b/famodel/anchors/anchors_famodel/installation_dynamic2.py new file mode 100644 index 00000000..f55c7f21 --- /dev/null +++ b/famodel/anchors/anchors_famodel/installation_dynamic2.py @@ -0,0 +1,152 @@ +import numpy as np +import matplotlib.pyplot as plt +from support_soils import clay_profile + +def PileWeight(L1, L2, D1, D2, tw, gamma): + return ((np.pi/4)*(D1**2 - (D1 - 2*tw)**2)*(L1 + L2) + 4*L2*D2*tw)*gamma + +def PileVolume(L1, L2, D1, D2, tw): + return (np.pi/4)*D1**2*(L1 + L2) + 4*L2*D2*tw + +def PileWingedSurface(length, diameter1, diameter2): + return 8*length*(diameter1 - diameter2) + +def PileShaftSurface(length, diameter): + return np.pi*diameter*length + +def getInstallationDynamic(profile_map, location_name, D1, D2, L1, L2, ballast, drop_height, plot=True): + """ + Penetration of torpedo pile using depth-based formulation (Eq. 2.10). + """ + # Constants + rhows = 66.90e3 # Submerged unit weight of steel (N/m³) + rhow = 10e3 # Unit weight of water (N/m³) + Sti = 2.0 # Installation strain-rate index (-), affects side friction strain-rate correction + Se = 5.0 # Strain-rate multiplier (-), empirical factor for rate effects + Ce = 0.02 # Strain-rate coefficient (-), controls shape of strain-rate correction + Nc = 9.0 # Bearing capacity factor for undrained clay [-], used in tip resistance (q = Nc * Su) + CD = 2.7 # Drag coefficient (-), for a cylindrical body falling in water + dz = 1 # Depth increment (m), used in depth-stepping integration + g = 9.81 # Gravitational acceleration (m/s²) + + + # Geometry + D = D1 + L = L1 + L2 + t = (6.35 + D2*20)/1e3 + + # Soil profile + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + profile = [] + for layer in layers: + if layer['soil_type'] != 'clay': continue + profile.append([layer['top'], layer['gamma_top'], layer['Su_top']]) + profile.append([layer['bottom'], layer['gamma_bot'], layer['Su_bot']]) + # profile = sorted(list({(z, su, g) for z, su, g in profile}), key=lambda x: x[0]) + z0, f_gamma, f_Su, _, f_delta = clay_profile(profile) + # print('\n--- f_gamma and f_Su vs Depth ---') + # z_start = z0 + # z_end = profile[-1][0] + # z_vals = np.linspace(z_start, z_end, 10) + # for z_val in z_vals: + # gamma_val = f_gamma(z_val) + # Su_val = f_Su(z_val) + # print(f'z = {z_val:.1f} m → gamma = {gamma_val:.2f} N/m³, Su = {Su_val:.2f} Pa') + + # Parameters + Af = np.pi*(D2**2)/4 + 4*(D2 - D1)*t + As = PileWingedSurface(L1, D1, D2) + PileShaftSurface(L1 + L2, D2) + Vol = PileVolume(L1, L2, D1, D2, t) + Wp = PileWeight(L1, L2, D1, D2, t, rhows + rhow) + ballast; print(f'Wp = {Wp:.2f} N') + M = Wp/g + Mprime = M + 2*f_gamma(2*L/3)*Vol + + CD_water = 1.2 + vt = np.sqrt((2*Mprime*g)/(rhow*CD_water*Af)) + t_impact = (vt/g)*np.arccosh(np.exp(g*drop_height/vt**2)) + v_impact = vt*np.tanh(g*t_impact/vt) + + # Loop + v = [v_impact]; #print(v) + z = [0.0] + term1_values, term2_values = [0.0], [0.0] + i = 0 + while v[-1] > 0: + zi = z[-1] + vi = v[-1] + Sui = f_Su(zi); #print(f'Sui = {Sui:.2f} Pa') + gammai = f_gamma(zi); #print(f'gammai = {gammai:.2f} N/m3') + rhoi = gammai/g; #print(f'rhoi = {rhoi:.2f} kg/m3') + deltai = f_delta(zi) + Mprime_local = M + 2*rhoi*Vol + term1 = (Wp - Vol*gammai) - (0.5*CD*rhoi*Af*vi**2); #print(f'term1 = {term1:.2f} N') + term2 = Sui*(Af*Nc + As*deltai/Sti) + Se_dot = Se/(1 + (1/np.sqrt(Ce*vi/(Sui*D)) + 0.06)); #print(f'Se_dot = {Se_dot:.2f}') + top = 2*dz*(term1 - term2)*Se_dot; #print(f'top = {top:.2f}') + bottom = vi*Mprime_local; #print(f'bottom = {bottom:.2f}') + vi1 = vi + top/bottom + if vi1 < 0.01: + print(f'Stopping due to low velocity at z = {zi:.2f} m') + break + + v.append(vi1) + z.append(zi + dz) + term1_values.append(term1) + term2_values.append(term2) + i += 1 + #print(f'z = {zi:.2f} m | term1 (drive) = {term1:.2f} N | term2 (resist) = {term2:.2f} N | net = {term1 - term2:.2f} N') + + if plot: + fig, ax1 = plt.subplots() + ax1.plot(z, v, 'b', label='Velocity vs Depth') + ax1.set_xlabel('Depth (m)') + ax1.set_ylabel('Velocity (m/s)') + + ax2 = ax1.twinx() + ax2.plot(z, term1_values, 'r', label='Drive (N)') + ax2.plot(z, term2_values, 'g', label='Resist (N)') + ax2.set_ylabel('Forces (N)') + + ax1.grid(True) + plt.title('Depth-based Penetration of Torpedo Pile') + plt.legend(loc='upper right') + plt.tight_layout() + plt.show() + + return { + 'final_depth': z[-1], + 'v_max': max(v), + 'v_impact': v_impact, + 'steps': i + } + +if __name__ == '__main__': + profile_map = [ + { + 'name': 'CPT_1', + 'x': 0, 'y': 0, + 'layers': [ + {'top': 0.0, 'bottom': 60.0, 'soil_type': 'clay', + 'gamma_top': 8.5, 'gamma_bot': 8.5, + 'Su_top': 5, 'Su_bot': 85}, + {'top': 40.0, 'bottom': 400.0, 'soil_type': 'clay', + 'gamma_top': 8.5, 'gamma_bot': 8.5, + 'Su_top': 85, 'Su_bot': 805} + ] + } + ] + + location = 'CPT_1' + D1 = 3.0 + D2 = 1.5 + L1 = 10.0 + L2 = 5.0 + ballast = 10000 + drop_height = 20 + + results = getInstallationDynamic(profile_map, location, D1, D2, L1, L2, ballast, drop_height) + + print("\n--- Torpedo Installation Results ---") + for k, v in results.items(): + print(f"{k}: {v:.2f}") diff --git a/famodel/anchors/anchors_famodel/installation_dynamic3.py b/famodel/anchors/anchors_famodel/installation_dynamic3.py new file mode 100644 index 00000000..625b4161 --- /dev/null +++ b/famodel/anchors/anchors_famodel/installation_dynamic3.py @@ -0,0 +1,126 @@ + +import numpy as np +import matplotlib.pyplot as plt +from installation_dynamic import getInstallationDynamic +from scipy.stats import lognorm + +def getInstallationDynamicMC(profile_map, location, D1, D2, L1, L2, ballast, drop_height, N_sim=2000): + np.random.seed(16) + + # Lognormal distribution parameters + Suk_mean, Suk_std = 1.9, 0.9 + Sti_mean, Sti_std = 3.2, 1.0 + + Suk_samples = np.random.lognormal(mean=np.log(Suk_mean), sigma=Suk_std/Suk_mean, size=N_sim) + Sti_samples = np.random.lognormal(mean=np.log(Sti_mean), sigma=Sti_std/Sti_mean, size=N_sim) + + final_depths = [] + + for Suk, Sti in zip(Suk_samples, Sti_samples): + # Update profile with consistent linear Su(z) + profile = [dict(profile_map[0])] + Su0 = profile[0]['layers'][0]['Su_top'] + for layer in profile[0]['layers']: + z_top = layer['top'] + z_bot = layer['bottom'] + layer['Su_top'] = Su0 + Suk*z_top + layer['Su_bot'] = Su0 + Suk*z_bot + + # Override getInstallationDynamic with fixed Sti + result = getInstallationDynamic(profile, location, D1, D2, L1, L2, ballast, drop_height, plot=False) + + final_depths.append(result['final_depth']) + + # Fit lognormal distribution to the data + shape, loc, scale = lognorm.fit(final_depths, floc=0) + x = np.linspace(min(final_depths), max(final_depths), 500) + pdf = lognorm.pdf(x, shape, loc=loc, scale=scale) + + # Create subplot + fig, axs = plt.subplots(1, 2, figsize=(12, 5)) + + # Fit lognormals + s_Suk, loc_Suk, scale_Suk = lognorm.fit(Suk_samples, floc=0) + s_Sti, loc_Sti, scale_Sti = lognorm.fit(Sti_samples, floc=0) + + # Generate x values for plotting + x_Suk = np.linspace(min(Suk_samples), max(Suk_samples), 500) + x_Sti = np.linspace(min(Sti_samples), max(Sti_samples), 500) + + # Compute PDFs + pdf_Suk = lognorm.pdf(x_Suk, s_Suk, loc=loc_Suk, scale=scale_Suk) + pdf_Sti = lognorm.pdf(x_Sti, s_Sti, loc=loc_Sti, scale=scale_Sti) + + # Compute max density from histograms and PDFs for setting common ylim + hist_Suk_vals, _ = np.histogram(Suk_samples, bins=40, density=True) + hist_Sti_vals, _ = np.histogram(Sti_samples, bins=40, density=True) + y_max = max(max(hist_Suk_vals), max(hist_Sti_vals), max(pdf_Suk), max(pdf_Sti)) * 1.1 + + # Plot Suk PDF + axs[0].hist(Suk_samples, bins=40, density=True, alpha=0.6, color='lightgreen', edgecolor='g', label='Samples') + axs[0].plot(x_Suk, pdf_Suk, 'r', label='Fitted Lognormal') + axs[0].set_title('Lognormal Fit for $S_{uk}$') + axs[0].set_xlabel('Suk (kPa/m)') + axs[0].set_ylabel('Density') + axs[0].set_ylim(0, y_max) + axs[0].legend() + axs[0].grid(True) + + # Plot Sti PDF + axs[1].hist(Sti_samples, bins=40, density=True, alpha=0.6, color='lightblue', edgecolor='b', label='Samples') + axs[1].plot(x_Sti, pdf_Sti, 'r', label='Fitted Lognormal') + axs[1].set_title('Lognormal Fit for $S_{ti}$') + axs[1].set_xlabel('Sti (dimensionless)') + axs[1].set_ylabel('Density') + axs[1].set_ylim(0, y_max) + axs[1].legend() + axs[1].grid(True) + + # Plot histogram with fitted lognormal + plt.figure(figsize=(8, 5)) + plt.hist(final_depths, bins=40, alpha=0.6, density=True, color='lightsalmon', edgecolor='r', label='Simulated PDF') + plt.plot(x, pdf, 'r', lw=2, label='Fitted Lognormal') + plt.axvline(np.median(final_depths), color='k', linestyle='--', label=f'Median = {np.median(final_depths):.2f} m') + plt.title('Monte Carlo Simulation with Fitted Lognormal Distribution') + plt.xlabel('Penetration (m)') + plt.ylabel('Density') + plt.legend() + plt.grid(True) + plt.tight_layout() + plt.show() + + # Print parameters + print(f'Lognormal fit parameters:') + print(f' mean = {np.mean(final_depths):.2f}') + print(f' mean - 1 std deviation = {np.mean(final_depths) - np.std(final_depths):.2f}') + print(f' mean + 1 std deviation = {np.mean(final_depths) + np.std(final_depths):.2f}') + + return final_depths + +# Example usage in main +if __name__ == '__main__': + profile_map = [ + { + 'name': 'CPT_1', + 'x': 0, 'y': 0, + 'layers': [ + {'top': 0.0, 'bottom': 30.0, 'soil_type': 'clay', + 'gamma_top': 8.0, 'gamma_bot': 8.5, + 'Su_top': 5, 'Su_bot': 20}, + {'top': 30.0, 'bottom': 180.0, 'soil_type': 'clay', + 'gamma_top': 8.5, 'gamma_bot': 9.0, + 'Su_top': 20, 'Su_bot': 150} + ] + } + ] + + location = 'CPT_1' + D1 = 3.0 + D2 = 1.2 + L1 = 5.0 + L2 = 5.0 + ballast = 300000 + drop_height = 200 + + # Run Monte Carlo Simulation + depths = getInstallationDynamicMC(profile_map, location, D1, D2, L1, L2, ballast, drop_height) diff --git a/famodel/anchors/anchors_famodel/installation_suction.py b/famodel/anchors/anchors_famodel/installation_suction.py new file mode 100644 index 00000000..7c474082 --- /dev/null +++ b/famodel/anchors/anchors_famodel/installation_suction.py @@ -0,0 +1,215 @@ +import numpy as np +import matplotlib.pyplot as plt +from support_soils import clay_profile + +def PileWeight(Len, Dia, tw, rho): + return ((np.pi/4)*((Dia**2 - (Dia - 2*tw)**2)*Len + (np.pi/4)*Dia**2*tw))*rho + +def getInstallationSuction(profile_map, location_name, D, L, gamma_m_install=1.5, gamma_m_retrieval=1.25): + ''' + Installation and retrieval pressure assessment for suction piles in clay using a layered profile. + Returns a dictionary with pressure values and resistances. + ''' + # Constants and geometry + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + + rhows = 66.90e3 # Submerged steel specific weight (N/m3) + rhow = 10e3 # Water specific weight (N/m3) + + WT = D/200; print(WT) + t = (6.35 + D*20)/1e3; print(t) # Suction pile wall thickness (m), API RP2A-WSD + Di = D - 2*WT + Asi = np.pi * Di + Aso = np.pi * D + Awall = 0.25 # m² + Aplug = np.pi * Di**2 / 4 + Nc_strip_deep = 7.5 + Nc_circle = 9.0 + alphaD_su = 0.25 + Wp = PileWeight(L, D, WT, rhows); print(Wp) + Wsub_steel = 750e3 # in N + + # Convert layer data into clay profile format + z0 = layers[0]['top'] + z_bot = layers[0]['bottom'] + gamma_top = layers[0]['gamma_top'] + gamma_bot = layers[0]['gamma_bot'] + Su_top = layers[0]['Su_top'] + Su_bot = layers[0]['Su_bot'] + + clay_input = [ + [z0, gamma_top, Su_top], + [z_bot, gamma_bot, Su_bot] + ] + + _, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(clay_input) + + # Diagnostic: evaluate and plot alpha(z) + z_plot = np.linspace(z0, z_bot, 100) + alpha_plot = f_alpha(z_plot) + + # print('\n--- Adhesion Factor α(z) ---') + # for z_check in [0, 2, 4, 6, 8, 10, 12, 14, 17]: + # print(f"z = {z_check:>5.2f} m → α = {f_alpha(z_check):.3f}") + + # plt.figure(figsize=(5, 4)) + # plt.plot(alpha_plot, z_plot, label='α(z)', color='purple') + # plt.gca().invert_yaxis() + # plt.grid(True) + # plt.xlabel('Adhesion Factor α') + # plt.ylabel('Depth (m)') + # plt.title('API Clay Adhesion Factor vs Depth') + # plt.tight_layout() + # plt.show() + + # Prepare output arrays + depths = np.arange(0, L + 0.1, 0.1) + Rsuction_list = [] + delta_u_suction_list = [] + delta_u_retrieval_list = [] + delta_u_all_install_list = [] + delta_u_all_retrieval_list = [] + SWP_depth = None + + for L in depths: + z_tip = L + z_mid = L/2 + z_tip_ext = L + alphaD_su*Di + + su_av_L = f_Su(z_mid) + int_su = (su_av_L)*L + su_tip = f_Su(z_tip) + su_av_tip = f_Su(z_tip_ext) + # alpha_i = alpha_o = float(f_alpha(z_mid)) + alpha_i = alpha_o = 0.3 + + Fi = Asi*alpha_i*int_su + Fo = Aso*alpha_o*int_su + Qw = Awall*Nc_strip_deep*su_tip + + Rsuction = Fi + Fo + Qw + Rretrieval = Rsuction + delta_u_suction = max((Rsuction - Wp)/Aplug, 0.0) + delta_u_retrieval = (Rretrieval + Wp)/Aplug + delta_u_all_install = Fi/Aplug + Nc_circle*su_av_tip/gamma_m_install + delta_u_all_retrieval = Fi/Aplug + Nc_circle*su_av_tip/gamma_m_retrieval + + Rsuction_list.append(Rsuction) + delta_u_suction_list.append(delta_u_suction) + delta_u_retrieval_list.append(delta_u_retrieval) + delta_u_all_install_list.append(delta_u_all_install) + delta_u_all_retrieval_list.append(delta_u_all_retrieval) + + if SWP_depth is None and Rsuction >= Wp: + SWP_depth = L + + # Plotting + fig, axs = plt.subplots(1, 3, figsize=(10, 7)) + + axs[0].plot(Rsuction_list, depths, label='Installation Resistance', color='blue') + if SWP_depth is not None: + axs[0].axvline(Wp, color='red', linestyle='--', label=f'SWP = {SWP_depth:.2f} m') + axs[0].set_xlabel('Installation Resistance (N)') + axs[0].set_ylabel('Penetration (m)') + axs[0].set_title('Installation Resistance vs Penetration') + axs[0].grid(True) + axs[0].invert_yaxis() + axs[0].legend() + + axs[1].plot(delta_u_suction_list, depths, label='Underpressure', color='green') + axs[1].plot(delta_u_all_install_list, depths, label='Δu allowable install', color='orange') + if SWP_depth is not None: + axs[1].axhline(SWP_depth, color='red', linestyle='--', label=f'SWP = {SWP_depth:.2f} m') + axs[1].set_xlabel('Underpressure (Pa)') + axs[1].set_ylabel('Penetration (m)') + axs[1].set_title('Underpressure vs Penetration') + axs[1].grid(True) + axs[1].invert_yaxis() + axs[1].legend() + + axs[2].plot(delta_u_retrieval_list, depths, label='Overpressure', color='green') + axs[2].plot(delta_u_all_retrieval_list, depths, label='Δu allowable retrieve', color='orange') + axs[2].set_xlabel('Overpressure (Pa)') + axs[2].set_ylabel('Penetration (m)') + axs[2].set_title('Overpressure vs Penetration') + axs[2].grid(True) + axs[2].invert_yaxis() + axs[2].legend() + + plt.tight_layout() + plt.show() + + # Final state outputs + L = L + z_tip = L + z_mid = L/2 + z_tip_ext = L + alphaD_su*Di + + su_av_L = f_Su(z_mid) + int_su = su_av_L*L + su_tip = f_Su(z_tip) + su_av_tip = f_Su(z_tip_ext) + alpha_i = alpha_o = float(f_alpha(z_mid)) + alpha_i = alpha_o = 0.3 + + Fi = Asi*alpha_i*int_su + Fo = Aso*alpha_o*int_su + Qw = Awall*Nc_strip_deep*su_tip + + Rsuction = Fi + Fo + Qw + Rretrieval = Rsuction + + delta_u_suction = max((Rsuction - Wp)/Aplug, 0.0) + delta_u_retrieval = (Rretrieval + Wp)/Aplug + + delta_u_all_install = Fi/Aplug + Nc_circle*su_av_tip/gamma_m_install + delta_u_all_retrieval = Fi/Aplug + Nc_circle*su_av_tip/gamma_m_retrieval + + return { + 'layers': layers, + 'depths': depths, + 'z0': z0, + 'D': D, + 'L': L, + 'Di': Di, + 'Asi': Asi, # m + 'Aso': Aso, # m + 'Aplug': Aplug, # m² + 'su_av_L': su_av_L/1e3, # kPa + 'int_su': int_su/1e3, # kN/m + 'su_tip': su_tip/1e3, # kPa + 'su_av_tip': su_av_tip/1e3, # kPa + 'Fi': Fi/1e3, # kN + 'Fo': Fo/1e3, # kN + 'Qw': Qw/1e3, # kN + 'Rsuction': Rsuction/1e6, # MN + 'Rretrieval': Rretrieval/1e6, # MN + 'delta_u_suction': delta_u_suction_list, # kPa + 'delta_u_retrieval': delta_u_retrieval/1e3, # kPa + 'delta_u_all_install': delta_u_all_install/1e3, # kPa + 'delta_u_all_retrieval': delta_u_all_retrieval/1e3, # kPa + 'SWP_depth': SWP_depth} + +if __name__ == '__main__': + profile_map = [ + { + 'name': 'CLAY_INSTALL', + 'x': 0, 'y': 0, + 'layers': [ + { + 'top': 0.0, 'bottom': 20.0, + 'soil_type': 'clay', + 'gamma_top': 9.0, 'gamma_bot': 9.0, + 'Su_top': 5.0, 'Su_bot': 45.0 + } + ] + } + ] + + results = getInstallationSuction(profile_map, 'CLAY_INSTALL', D=4.0, L=17.0) + for k, v in results.items(): + if isinstance(v, float): + print(f"{k:<25} = {v:.2f}") + else: + print(f"{k:<25} = {v}") \ No newline at end of file diff --git a/famodel/anchors/anchors_famodel_map/capacity_plots_map.py b/famodel/anchors/anchors_famodel/support_plots.py similarity index 89% rename from famodel/anchors/anchors_famodel_map/capacity_plots_map.py rename to famodel/anchors/anchors_famodel/support_plots.py index d733fade..c01a68fc 100644 --- a/famodel/anchors/anchors_famodel_map/capacity_plots_map.py +++ b/famodel/anchors/anchors_famodel/support_plots.py @@ -3,6 +3,28 @@ import matplotlib.pyplot as plt def plot_pile(layers, y, z, D, L, z0=None, zlug=None, hinge_location=None): + '''Plot the soil profile and a driven pile with deflected shape in layered soil. + + Parameters + ---------- + layers : list of dicts + Each layer has 'top', 'bottom', 'soil_type', and strength parameters + such as 'Su_top' (clay), 'phi_top' (sand) or 'UCS_top' (rock) + y : array-like + Lateral displacement profile from FD solution (typically y[2:-2]) + z : array-like + Depth values associated with displacement points (typically z[2:-2]) + D : float + Pile diameter (m) + L : float + Embedded pile length (m) + z0 : float, optional + Mudline elevation m) from pile head reference (z = 0) + zlug : float, optional + Depth of the padeye below pile head (m) + hinge_location : int, optional + Index of plastic hinge location in y/z arrays. + ''' fig, ax = plt.subplots(figsize=(5, 5)) lambdap = L / D @@ -74,7 +96,7 @@ def plot_pile(layers, y, z, D, L, z0=None, zlug=None, hinge_location=None): plt.tight_layout() plt.show() -def plot_suction(layers, L, D, z0=None, zlug=None, title='Suction Pile and Soil Layers'): +def plot_suction(layers, L, D, z0=None, zlug=None): '''Plot the soil profile and a suction pile geometry using updated profile_map structure. Parameters: @@ -87,8 +109,6 @@ def plot_suction(layers, L, D, z0=None, zlug=None, title='Suction Pile and Soil Pile diameter (m) zlug : float Padeye depth (m, referenced to pile head = 0) - title : string - Plot title ''' fig, ax = plt.subplots(figsize=(8, 5)) xmax = 2*D @@ -142,13 +162,13 @@ def plot_suction(layers, L, D, z0=None, zlug=None, title='Suction Pile and Soil ax.set_ylabel('Depth (m)') ax.set_xlim(-xmax, xmax) ax.set_ylim(L + 2*D, -D) - ax.set_title(title) + ax.set_title('Suction Pile and Soil Layers') ax.grid() ax.legend() plt.tight_layout() plt.show() -def plot_torpedo(layers, D1, D2, L1, L2, z0, zlug, title='Torpedo Pile and Soil Layers'): +def plot_torpedo(layers, D1, D2, L1, L2, z0, zlug): '''Plot the soil layers and geometry of a torpedo pile using absolute depth for soil and pile head at z=0. Parameters: @@ -165,8 +185,6 @@ def plot_torpedo(layers, D1, D2, L1, L2, z0, zlug, title='Torpedo Pile and Soil Winged length (m) L2 : float Shaft length (m) - title : str - Plot title ''' fig, ax = plt.subplots(figsize=(7, 7)) @@ -226,13 +244,13 @@ def plot_torpedo(layers, D1, D2, L1, L2, z0, zlug, title='Torpedo Pile and Soil ax.set_ylim(zmax, zmin) ax.set_xlabel('Horizontal extent (m)') ax.set_ylabel('Depth (m)') - ax.set_title(title) + ax.set_title('Torpedo Pile and Soil Layers') ax.grid() ax.legend() plt.tight_layout() plt.show() -def plot_helical(layers, D, L, d, z0, zlug, n_helix=1, spacing=1.0, title='Helical Pile and Soil Layers'): +def plot_helical(layers, D, L, d, z0, zlug, n_helix=1, spacing=1.0): '''Plot a helical pile in layered soil with shaft and angled helices, starting at zlug. Parameters: @@ -251,8 +269,6 @@ def plot_helical(layers, D, L, d, z0, zlug, n_helix=1, spacing=1.0, title='Helic Number of helices (typically 1) spacing : float Vertical spacing between helices (m) - title : str - Plot title ''' fig, ax = plt.subplots(figsize=(5, 6)) @@ -322,13 +338,13 @@ def plot_helical(layers, D, L, d, z0, zlug, n_helix=1, spacing=1.0, title='Helic ax.set_ylim(L + D, min(zlug - D, min(layer['top'] for layer in layers) - 2)) ax.set_xlabel('Horizontal extent (m)') ax.set_ylabel('Depth (m)') - ax.set_title(title) + ax.set_title('Helical Pile and Soil Layers') ax.grid() ax.legend() plt.tight_layout() plt.show() -def plot_plate(layers, B, L, z0, zlug, beta, title='Plate Anchor in Layered Soil'): +def plot_plate(layers, B, L, z0, zlug, beta): '''Plot soil layers and an inclined plate anchor centered at zlug. Parameters: @@ -345,8 +361,6 @@ def plot_plate(layers, B, L, z0, zlug, beta, title='Plate Anchor in Layered Soil Center embedment of the plate (m) beta : float Inclination angle of plate (deg) - title : str - Plot title ''' fig, ax = plt.subplots(figsize=(5, 5)) xmax = 3*B @@ -397,7 +411,7 @@ def plot_plate(layers, B, L, z0, zlug, beta, title='Plate Anchor in Layered Soil ax.set_ylim(zmax, zmin) ax.set_xlabel("Horizontal extent (m)") ax.set_ylabel("Depth (m)") - ax.set_title(title) + ax.set_title('Plate Anchor in Layered Soil') ax.legend(loc='lower right') ax.grid(True) plt.tight_layout() @@ -454,7 +468,7 @@ def plot_load(layers, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug): elif soil == 'sand': phi = layer.get('phi_top', 30) gamma = layer.get('gamma_top', 10) - color_fill = plt.cm.YlOrBr(phi / max_phi) + color_fill = plt.cm.YlOrBr(phi/max_phi) label_soil = f'ϕ = {phi:.0f}°, γ = {gamma:.1f} kN/m³' else: color = 'gray' @@ -469,7 +483,7 @@ def plot_load(layers, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug): scale = 2e6 # Arrow scaling factor for better visual readability # Plot the inverse catenary profile - ax.plot(drag_values, depth_values, color='b', label='Mooring line') + ax.plot(drag_values, depth_values, color='k', label='Mooring line') # Load arrows ax.arrow(0, -layers[0]['top'], @@ -478,19 +492,29 @@ def plot_load(layers, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug): ax.arrow(drag_values[-1], depth_values[-1], Ta*np.cos(np.deg2rad(thetaa))/scale, Ta*np.sin(np.deg2rad(thetaa))/scale, - head_width=0.25, head_length=0.5, color='g', label='Padeye Load') + head_width=0.25, head_length=0.5, color='g', label='Lug load') + + ax.plot(0, -layers[0]['top'], 'ro', zorder=5) if zlug is not None: ax.plot(drag_values[-1], -zlug, 'go', label=f'Padeye (zlug = {zlug:.2f} m)') + # Coordinates of lug arrow tail + xlug = drag_values[-1] + ylug = depth_values[-1] + + # Mark the base of the lug vector with a green dot and depth label + ax.plot(xlug, ylug, 'go', zorder=5) + ax.annotate(f'z = {-ylug:.2f} m', (xlug - 0.5, ylug - 0.75), color='g') + # Add mudline and padeye markers - ax.axhline(-layers[0]['top'], color='k', linestyle='--', lw=1.5, label=f'Mudline') + ax.axhline(-layers[0]['top'], color='b', linestyle='--', lw=1.5, label=f'Mudline') # Annotate loads ax.annotate(f"{Tm/1e6:.2f} MN", (Tm*np.cos(np.deg2rad(thetam))/scale, - -layers[0]['top'] + Tm*np.sin(np.deg2rad(thetam))/scale), color='r') + -layers[0]['top']), color='r') ax.annotate(f"{Ta/1e6:.2f} MN", (drag_values[-1] + Ta*np.cos(np.deg2rad(thetaa))/scale, - depth_values[-1] + Ta*np.sin(np.deg2rad(thetaa))/scale), color='g') + depth_values[-1]), color='g') # Deduplicate legend entries handles, labels = ax.get_legend_handles_labels() @@ -501,8 +525,8 @@ def plot_load(layers, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug): ax.set_ylabel('Embedded depth (m)') ax.set_title('Inverse Catenary in Layered Soil') ax.grid(True) - #ax.set_ylim(min(zlug - 10, min(depth_values) - 5), max(15, max(depth_values) + 5)) - ax.legend(loc='lower left') + ax.set_ylim(min(zlug - 10, min(depth_values) - 5), max(5, max(depth_values) + 5)) + ax.legend(loc='lower right') plt.tight_layout() plt.show() @@ -515,7 +539,7 @@ def plot_pycurve(pycurve_data): pycurve_data : list of tuples Each tuple must be (y_vals, p_vals, z_depth, soil_type) ''' - fig, ax = plt.subplots(figsize=(6, 5)) + fig, ax = plt.subplots(figsize=(6, 5), constrained_layout=True) for y, p, z, soil in pycurve_data: label = f'{soil.capitalize()} @ z = {z:.1f} m' @@ -525,6 +549,5 @@ def plot_pycurve(pycurve_data): ax.set_ylabel('Soil resistance p (N/m)') ax.set_title('p–y Curves at Various Depths') ax.grid(True) - ax.legend() - plt.tight_layout() + # ax.legend(fontsize='small') plt.show() diff --git a/famodel/anchors/anchors_famodel_map/capacity_pycurves_map.py b/famodel/anchors/anchors_famodel/support_pycurves.py similarity index 86% rename from famodel/anchors/anchors_famodel_map/capacity_pycurves_map.py rename to famodel/anchors/anchors_famodel/support_pycurves.py index 377a9a8f..c8cf40e0 100644 --- a/famodel/anchors/anchors_famodel_map/capacity_pycurves_map.py +++ b/famodel/anchors/anchors_famodel/support_pycurves.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt from scipy.interpolate import interp1d -def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, return_curve=False): +def py_Matlock(z, D, gamma, Su, sigma_v_eff, z0=None, return_curve=False): ''' Generate Matlock (1970) p–y curve at a given depth in clay. Parameters @@ -12,8 +12,6 @@ def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, return_curve=F Depth relative to pile head (m) D : float Pile diameter (m) - zlug : float - Load eccentricity above or below mudline (m) f_Su : function Undrained shear strength (Pa) f_sigma_v_eff : function @@ -31,9 +29,9 @@ def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, return_curve=F Interpolation function for p–y relationship (N/m vs m) ''' - Su = f_Su(z) - sigma_v_eff = f_sigma_v_eff(z) - gamma = f_gamma(z) + # Su = f_Su(z) + # sigma_v_eff = f_sigma_v_eff(z) + # gamma = f_gamma(z) # Strain at half the strength as defined by Matlock (1970). # Typically ranges from 0.005 (stiff clay) to 0.02 (soft clay). @@ -63,11 +61,11 @@ def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, return_curve=F y = Y*y_50 p = P*p_ult - f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0) # Interpolation function for p-y curve + f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0) return (f, (y, p)) if return_curve else f -def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, return_curve=False): +def py_API(z, D, phi, sigma_v_eff, Dr, z0=None, return_curve=False): ''' Generate API RP2A (1993) p–y curve at a given depth in sand. Parameters @@ -76,8 +74,6 @@ def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, return_curve=False): Depth relative to pile head (m) D : float Pile diameter (m) - zlug : float - Load eccentricity above or below mudline (m) f_phi : function Friction angle (deg) f_sigma_v_eff : function @@ -95,9 +91,9 @@ def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, return_curve=False): Interpolation function for p–y relationship (N/m vs m) ''' - phi = f_phi(z) - sigma_v_eff = f_sigma_v_eff(z) - Dr = f_Dr(z) + # phi = f_phi(z) + # sigma_v_eff = f_sigma_v_eff(z) + # Dr = f_Dr(z) # Interpolate coefficients depending on the effective friction angle phi_ref = [ 20, 25, 30, 35, 40] @@ -128,14 +124,14 @@ def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, return_curve=False): A = max(3 - 0.8*z/D, 0.9) # Apply API p–y formulation - ε = 1e-6 # prevent division by zero - p = A*p_ult*np.tanh(k*z*y/(A*p_ult + ε)) + epsilon = 1e-6 # prevent division by zero + p = A*p_ult*np.tanh(k*z*y/(A*p_ult + epsilon)) f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0) return (f, (y, p)) if return_curve else f -def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, return_curve=False): +def py_Reese(z, D, UCS, Em, z0=None, return_curve=False): ''' Generate Reese (1997) p–y curve at a given depth in weak rock. Parameters @@ -144,8 +140,6 @@ def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, return_curve=False): Depth relative to pile head (m) D : float Pile diameter (m) - zlug : float - Load eccentricity above or below mudline (m) f_UCS : function Unconfined compressive strength UCS(z) (Pa) f_Em : function @@ -161,8 +155,8 @@ def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, return_curve=False): Interpolation function for p–y relationship (N/m vs m) ''' - UCS = f_UCS(z) - Em = f_Em(z) + # UCS = f_UCS(z) + # Em = f_Em(z) RQD = 52 # Assumed fair rock quality (moderately weathered rocks) Dref = 0.305; # Reference diamter (m) @@ -191,13 +185,13 @@ def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, return_curve=False): # Normalized lateral displacement N = 20 - y = np.concatenate((-np.logspace(4,-3,N),[0],np.logspace(-3,4,N))) - y = np.linspace(-0.02*D, 0.02*D, 200) # ±2 cm + y = np.concatenate((-np.logspace(.1,-2,N),[0],np.logspace(-2,.1,N))) + # y = np.linspace(-0.2*D, 0.2*D, 200) # ±2 cm p = [] for val in y: if abs(val) < y_a: - p_val = np.sign(val)*Kir*val + p_val = Kir*val else: p_val = np.sign(val)*min((p_ur/2)*(abs(val)/y_rm)**0.25, p_ur) p.append(p_val) @@ -206,7 +200,7 @@ def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, return_curve=False): return (f, (y, p)) if return_curve else f -def py_Lovera(z, D, f_UCS, f_Em, zlug, z0, delta_grout=0.075, E_grout=20e9, delta_crushed=0.025, return_curve=False): +def py_Lovera(z, D, UCS, Em, zlug, z0, delta_grout=0.075, E_grout=20e9, delta_crushed=0.025, return_curve=False): ''' Generate Lovera (2019) p–y curve at a given depth for layered rock interfaces. Parameters @@ -242,7 +236,7 @@ def py_Lovera(z, D, f_UCS, f_Em, zlug, z0, delta_grout=0.075, E_grout=20e9, delt return lambda y: np.zeros_like(y) # Retrieve elastic modulus at depth - Em = f_Em(z) + # Em = f_Em(z) nu = 0.3 # Typical Poisson's ratio for rock G_rock = Em/(2*(1 + nu)) k_rock = 4*G_rock diff --git a/famodel/anchors/anchors_famodel_map/capacity_soils_map.py b/famodel/anchors/anchors_famodel/support_soils.py similarity index 100% rename from famodel/anchors/anchors_famodel_map/capacity_soils_map.py rename to famodel/anchors/anchors_famodel/support_soils.py diff --git a/famodel/anchors/anchors_famodel_map/capacity_solvers.py b/famodel/anchors/anchors_famodel/support_solvers.py similarity index 97% rename from famodel/anchors/anchors_famodel_map/capacity_solvers.py rename to famodel/anchors/anchors_famodel/support_solvers.py index b63b0826..e73b9251 100644 --- a/famodel/anchors/anchors_famodel_map/capacity_solvers.py +++ b/famodel/anchors/anchors_famodel/support_solvers.py @@ -50,8 +50,11 @@ def fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant): Index of the node with hinge formation ''' - # Initialize and assemble matrix + # Initialize + N = n + 5 X = np.zeros((N, N)) + q = np.zeros(N) + # k_secant = np.zeros(N) # (n+1) finite difference equations for (n+1) real nodes for i in range(0, n+1): @@ -84,13 +87,11 @@ def fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant): X[n+4, -3] = 0.0 X[n+4, -4] = 2.0 - Va*h**2/EI X[n+4, -5] = -1.0 - - # Initialize vector q - q = np.zeros(N) # Always apply shear # Index of the node where the horizontal load is applied (padeye) zlug_index = int(zlug/h) + #zlug_index = max(1, int(zlug/h)) # avoid node 0 q[zlug_index] = 2*Ha*h**3 y = linalg.solve(EI*X, q) diff --git a/famodel/anchors/anchors_famodel_map/capacity_dandg_map.py b/famodel/anchors/anchors_famodel_map/capacity_dandg_map.py deleted file mode 100644 index aafc2af4..00000000 --- a/famodel/anchors/anchors_famodel_map/capacity_dandg_map.py +++ /dev/null @@ -1,233 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from .capacity_soils_map import rock_profile -from .capacity_solvers import fd_solver -from .capacity_pycurves_map import py_Lovera -from .capacity_plots_map import plot_pile, plot_pycurve - -def getCapacityDandG(profile_map, location_name, L, D, zlug, Ha, Va, plot=True): - '''Models a laterally loaded pile using the p-y method. The solution for - lateral displacements is obtained by solving the 4th order ODE, - EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. - EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method. - - Assumes that EI remains constant with respect to curvature i.e. pile - material remains in the elastic region. - - Parameters - ---------- - profile : array - Rock profile as a 2D array: (z (m), UCS (MPa), Em (MPa)) - soil_type : string - Select soil condition, 'rock' - L : float - Pile length (m) - D : float - Pile diameter (m) - zlug : float - Load eccentricity above the mudline or depth to mudline relative to the pile head (m) - Ha : float - Horizontal load at pile lug elevation (N) - Va : float - Vertical load at pile lug elevation (N) - plot : bool - Plot the p-y curve and the deflection pile condition if True - - Returns - ------- - y : array - Lateral displacement at each node (n+1 real + 4 imaginary) - z : array - Node location along pile (m) - resultsDandG : dict - Dictionary with lateral, rotational, vertical and pile weight results - ''' - - profile_entry = next(p for p in profile_map if p['name'] == location_name) - layers = profile_entry['layers'] - - n = 50; loc = 2 # Number of nodes (-) - tol = 1e-16; max_iter = 50 # Iteration parameters (-) - nhuc = 1; nhu = 0.3 # Resistance factor (-) - delta_r = 0.08 # Mean roughness height (m) - - t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD - E = 200e9 # Elastic modulus of pile material (Pa) - fy = 350e6 # Steel's yield strength (Pa) - rhows = 66.90e3 # Submerged steel specific weight (N/m3) - rhow = 10e3 # Water specific weight (N/m3) - - # Pile geometry - I = (np.pi/64.0)*(D**4 - (D - 2*t)**4) - EI = E*I - h = L/n # Element size - N = (n + 1) + 4 # (n+1) Real + 4 Imaginary nodes - - # Dry and wet mass of the pile - def PileWeight(Len, Dia, tw, rho): - Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho - return Wp - - # Array for displacements at nodes, including imaginary nodes. - y = np.ones(N)*(0.01*D) # An initial value of 0.01D was arbitrarily chosen - - # Initialize and assemble array/list of p-y curves at each real node - z = np.zeros(N) - k_secant = np.zeros(N) - py_funs = [] - DQ = []; pycurve_data = [] - - z0 = min(layer['top'] for layer in layers) - - for i in [0, 1]: # Top two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - for i in range(2, n+3): # Real nodes - z[i] = (i - 2)*h - z_depth = z[i] - - matched_layer = next((layer for layer in layers if layer['top'] <= z_depth <= layer['bottom']), None) - if matched_layer is None or z_depth < matched_layer['top']: - py_funs.append(lambda y_val: np.zeros_like(y_val)) - k_secant[i] = 0.0 - DQ.append(0.0) - continue - - profile = [[matched_layer['top'], matched_layer['UCS_top'], matched_layer['Em_top']], - [matched_layer['bottom'], matched_layer['UCS_bot'], matched_layer['Em_bot']]] - z0_local, f_UCS, f_Em = rock_profile(profile) - - if z_depth < z0_local: - py_funs.append(lambda y_val: np.zeros_like(y_val)) - k_secant[i] = 0.0 - DQ.append(0.0) - continue - - UCS = f_UCS(z_depth) - Em = f_Em(z_depth) - py_f, (y_vals, p_vals) = py_Lovera(z_depth, D, f_UCS, f_Em, zlug, z0, return_curve=True) - py_funs.append(py_f) - pycurve_data.append((y_vals, p_vals, z_depth, 'rock')) - # print(f"z_depth = {z_depth:.2f} m, UCS = {f_UCS(z_depth):.2e} Pa, Em = {f_Em(z_depth):.2e} Pa") - - SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D - alpha = 0.36*SCR - 0.0005 - fs = alpha*UCS - Dq = np.pi*D*fs*z_depth - DQ.append(Dq) - k_val = py_funs[i](y[i]) - k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 - - for i in [n+3, n+4]: # Bottom two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - Wp = PileWeight(L, D, t, rhows + rhow) - Wtip = DQ[-1] if DQ else 0.0 - Vmax = Wp + Wtip - - for j in range(max_iter): - y_old = y.copy() - y, *_ = fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant) - - # Update stiffness - for i in range(2, n+3): - if callable(py_funs[i]): - k_secant[i] = py_funs[i](y[i])/y[i] if y[i] != 0 else 0.0 - - # Check convergence - if np.linalg.norm(y - y_old, ord=2) < tol: - print(f'[Converged in {j+1} iterations]') - break - else: - print('[Warning: Solver did not converge]') - - - if plot: - plot_pycurve(pycurve_data) - - fig, ax = plt.subplots(figsize=(3, 5)) - y0 = np.zeros_like(z[2:-2]) - ax.plot(y0, z[2:-2], 'k', label='Original pile axis') - ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape') - ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') - ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)') - ax.set_xlabel('Lateral displacement (m)') - ax.set_ylabel('Depth (m)') - ax.set_xlim([-0.1*D, 0.1*D]) - ax.set_ylim([L + 5, -2]) - ax.grid(ls='--') - ax.legend() - - # Relevant index of nodes - zlug_index = int(zlug/h); print(zlug_index) - ymax_index = np.argmax(y); print(ymax_index) - - resultsDandG = { - 'Weight pile': PileWeight(L, D, t, rhows + rhow), - 'Vertical max.': Vmax, - 'Lateral displacement': y[ymax_index], - 'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h), - 'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf, - 'Unity check (horizontal)': 0.0, # Placeholder; no Mp or Mi in current model - 'Bending moment': None, - 'Plastic moment': None, - 'Plastic hinge': None, - 'Hinge location': None, - 'p-y model': 'Lovera (2023)', - } - - return layers, y[2:-2], z[2:-2], resultsDandG - -if __name__ == '__main__': - - profile_map = [ - { - 'name': 'CPT_rock_1', - 'x': 502000, - 'y': 5725000, - 'layers': [ - { - 'top': 2.0, 'bottom': 5.0, - 'soil_type': 'rock', - 'UCS_top': 1.0, 'UCS_bot': 2.0, # MPa - 'Em_top': 100, 'Em_bot': 200 # MPa - }, - { - 'top': 5.0, 'bottom': 9.0, - 'soil_type': 'rock', - 'UCS_top': 2.0, 'UCS_bot': 3.0, # MPa - 'Em_top': 200, 'Em_bot': 300 # MPa - }, - { - 'top': 9.0, 'bottom': 30.0, - 'soil_type': 'rock', - 'UCS_top': 3.0, 'UCS_bot': 6.0, # MPa - 'Em_top': 300, 'Em_bot': 400 # MPa - } - ] - } - ] - - D = 3.0 # Diameter (m) - L = 10.0 # Length (m) - zlug = 1 # Padeye elevation (m) - Ha = 8.0e6 # Horizontal load (N) - Va = 3.0e6 # Vertical load (N) - - layers, y, z, results = getCapacityDandG(profile_map, 'CPT_rock_1', L, D, zlug, Ha, Va, plot=True) - - print('\n--- Results for DandG Pile in Layered Rock ---') - for key, val in results.items(): - print(f'{key}: {val:.3f}' if isinstance(val, float) else f'{key}: {val}') - - plot_pile(layers, y, z, D, L, layers[0]['top'], zlug) - - - - - diff --git a/famodel/anchors/anchors_famodel_map/capacity_helical_map.py b/famodel/anchors/anchors_famodel_map/capacity_helical_map.py deleted file mode 100644 index 4a495a4e..00000000 --- a/famodel/anchors/anchors_famodel_map/capacity_helical_map.py +++ /dev/null @@ -1,172 +0,0 @@ - -import numpy as np -from .capacity_driven_map import getCapacityDriven, plot_pile -from .capacity_soils_map import clay_profile, sand_profile -from .capacity_plots_map import plot_helical - -def getCapacityHelical(profile_map, location_name, D, L, d, zlug, Ha, Va, plot=True): - '''Calculate the vertical and horizontal capacity of a helical pile using a soil profile. - The calculation is based on the soil profile, anchor geometry and inclined load. - - Parameters - ---------- - profile : array - Soil profiles (z, parameters) - Clay soil profile (z, Su, gamma) - Sand soil profile (z, phi, gamma, Dr) - soil_type : string - Select soil condition, 'clay' or 'sand' - D : float - Helix diameter (m) - L : float - Shaft length (m) - d : float - Shaft diameter (m) - zlug : float - Depth to padeye (m) - Ha : float - Horizontal load applied at padeye (N) - Va : float - Vertical load applied at padeye (N) - plot : bool - Plot the p-y curve and the deflection pile condition if True - - Returns - ------- - y : array - Lateral displacement at each node (real nodes only) - z : array - Node depth positions corresponding to y (m) - resultsHelical : dict - Dictionary containing displacements, moment capacity, hinge state and vertical capacity - ''' - - profile_entry = next(p for p in profile_map if p['name'] == location_name) - layers = profile_entry['layers'] - - t = (6.35 + D*20)/1e3 # Helical pile wall thickness (m), API RP2A-WSD - rhows = 66.90e3 # Submerged steel specific weight (kN/m3) - rhow = 10e3 # Water specific weight (kN/m3) - - def PileWeight(Len, Dia1, Dia2, tw, rho): - return ((np.pi/4)*((Dia1**2 - (Dia1 - 2*tw)**2)*Len + (np.pi/4)*Dia2**2*tw))*rho - - z_helix = zlug + (L - D) - matched_layer = next((layer for layer in layers if layer['top'] <= z_helix <= layer['bottom']), None) - if matched_layer is None: - raise ValueError(f"No soil layer found at z = {z_helix:.2f} m") - - if matched_layer['soil_type'] == 'clay': - profile = [[matched_layer['top'], matched_layer['Su_top'], matched_layer['gamma_top']], - [matched_layer['bottom'], matched_layer['Su_bot'], matched_layer['gamma_bot']]] - z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile) - - z_helix = np.clip(z_helix, matched_layer['top'], matched_layer['bottom']) - Su = f_Su(z_helix) - sigma_v_eff = max(f_sigma_v_eff(z_helix), 1.0) - psi_val = Su/sigma_v_eff - alpha = min(0.5*psi_val**-0.50, 1) if psi_val <= 1.0 else min(0.5 * psi_val**-0.25, 1) - - Nc = min(6.0*(1 + 0.2*d/D), 9) - Qh = ((np.pi/4)*(D**2 - d**2)*Nc*Su + f_gamma(z_helix)*D)*0.75 - Qs = np.pi*d*L*alpha*Su - Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs - - elif matched_layer['soil_type'] == 'sand': - profile = [[matched_layer['top'], matched_layer['phi_top'], matched_layer['gamma_top'], matched_layer['Dr_top']], - [matched_layer['bottom'], matched_layer['phi_bot'], matched_layer['gamma_bot'], matched_layer['Dr_bot']]] - z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile) - - z_helix = np.clip(z_helix, matched_layer['top'], matched_layer['bottom']) - gamma = f_gamma(z_helix) - Dr = f_Dr(z_helix) - delta = f_delta(z_helix) - phi = f_phi(z_helix) - - Nq = 0.5*(12*phi)**(phi/54) - Qh = (np.pi/4)*(D**2 - d**2)*Nq*gamma*z_helix - Qs = np.pi*d*L*delta*gamma*z_helix - Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs - - - Wp = PileWeight(L, D, d, t, (rhows + rhow)) - - # Unity Check based only on vertical capacity - UC_vertical = Va/Qu - - # Compute horizontal capacity using p-y method - layers, y, z, results_lateral = getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=True) - - plot_pile(layers, y, z, D, L, z0=layers[0]['top'], zlug=zlug, hinge_location=None) - - Hcap = results_lateral['Horizontal max.'] - UC_horizontal = Ha/Hcap if Hcap != 0 else np.inf - - resultsHelical = { - 'Vertical max.': Qu, - 'Weight': Wp, - 'Unity Check (Vertical)': UC_vertical, - 'Horizontal max.': Hcap, - 'Unity Check (Horizontal)': UC_horizontal - } - - if matched_layer['soil_type'] == 'clay': - resultsHelical['Su @ helix'] = Su - resultsHelical['Alpha'] = alpha - elif matched_layer['soil_type'] == 'sand': - resultsHelical['Dr @ helix'] = Dr - resultsHelical['Delta'] = delta - resultsHelical['Phi'] = phi - - return layers, resultsHelical - -if __name__ == '__main__': - - profile_map = [ - { - 'name': 'CPT_1', - 'x': 498234, 'y': 5725141, - 'layers': [ - { - 'top': 1.0, 'bottom': 3.0, - 'soil_type': 'clay', - 'gamma_top': 8.0, 'gamma_bot': 9.0, - 'Su_top': 60, 'Su_bot': 50}, - { - 'top': 3.0, 'bottom': 7.0, - 'soil_type': 'clay', - 'gamma_top': 15.0, 'gamma_bot': 25.0, - 'Su_top': 100, 'Su_bot': 150}, - # { - # 'top': 6.0, 'bottom': 15.0, - # 'soil_type': 'sand', - # 'gamma_top': 8.0, 'gamma_bot': 8.0, - # 'phi_top': 32, 'phi_bot': 38, - # 'Dr_top': 70, 'Dr_bot': 75}, - { - 'top': 7.0, 'bottom': 15.0, - 'soil_type': 'clay', - 'gamma_top': 25.0, 'gamma_bot': 50.0, - 'Su_top': 200, 'Su_bot': 400}] - } - ] - - D = 1.5 # Helix diameter (m) - L = 12.0 # Pile length (m) - d = 0.5 # Shaft diameter (m) - zlug = 3 # Padeye depth (m) - Ha = 30e3 # Horizontal load (N) - Va = 50e3 # Vertical load (N) - - print("--- Clay Profile ---") - layers, resultsHelical = getCapacityHelical(profile_map, 'CPT_1', D, L, d, zlug, Ha, Va, plot=True) - for key, val in resultsHelical.items(): - if isinstance(val, float): - print(f"{key}: {val:.3f}") - else: - print(f"{key}: {val}") - - plot_helical(layers, D=D, L=L, d=d, z0=layers[0]['top'], zlug=zlug, n_helix=1, spacing=1.0, title='Helical Pile in Sand Profile') - - - diff --git a/famodel/anchors/anchors_famodel_map/capacity_load_map.py b/famodel/anchors/anchors_famodel_map/capacity_load_map.py deleted file mode 100644 index 7ebca3b7..00000000 --- a/famodel/anchors/anchors_famodel_map/capacity_load_map.py +++ /dev/null @@ -1,212 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from .capacity_soils_map import clay_profile, sand_profile -from .capacity_plots_map import plot_load - -def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=False): - '''Calculate the transfer load from mudline to main padeye using a layered soil profile. - - Parameters - ---------- - profile_map : list of dicts - Soil profile in profile_map format - Tm : float - Mooring line load at mudlevel (N) - thetam : float - Mooring line angle at mudlevel (deg) - zlug : float - Embedment depth of the lug (m) - line_type : str - 'chain' or 'wire' - d : float - Chain diameter (m) - w : float - Mooring line unit weight (N/m) - plot : bool - Show plot - - Returns - ------- - dict - Dictionary with transferred load components and depth. - ''' - - deltas = 0.2 # discretization step - - # Line mechanical properties - if line_type == 'chain': - Et, En = 10, 2.5 - elif line_type == 'wire': - Et, En = np.pi, 1 - W = w*deltas - - # Soil layer access - layers = profile_map[0]['layers'] - z0 = min(layer['top'] for layer in layers) - Nc = 8.5 - - # Initial values - z0 = min(layer['top'] for layer in layers) - T = Tm - theta = np.deg2rad(thetam) - drag = 0 - depth = z0 + 0.01 - - # Tracing lists - drag_values, depth_values = [], [] - - while (zlug - depth) >= 0: - matched_layer = next((layer for layer in layers if layer['top'] <= depth <= layer['bottom']), None) - if matched_layer is None: - break - - if matched_layer['soil_type'] == 'clay': - matched_layer = next((layer for layer in layers if layer['soil_type'] == 'clay' and layer['top'] <= depth <= layer['bottom']), None) - if matched_layer is None: - break - profile = [[matched_layer['top'], matched_layer['gamma_top'], matched_layer['Su_top']], - [matched_layer['bottom'], matched_layer['gamma_bot'], matched_layer['Su_bot']]] - z0_local, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile) - - Su = f_Su(depth) - alpha = f_alpha(depth) - d_theta = (En*d*Nc*Su - W*np.cos(theta))/T*deltas - dT = (Et*d*alpha*Su + W*np.sin(theta))*deltas - - elif matched_layer['soil_type'] == 'sand': - matched_layer = next((layer for layer in layers if layer['soil_type'] == 'sand' and layer['top'] <= depth <= layer['bottom']), None) - if matched_layer is None: - break - - profile = [[matched_layer['top'], matched_layer['gamma_top'], matched_layer['phi_top'], matched_layer['Dr_top']], - [matched_layer['bottom'], matched_layer['gamma_bot'], matched_layer['phi_bot'], matched_layer['Dr_bot']]] - z0_local, f_gamma, f_phi, f_Dr, f_sigma_v_eff, f_delta = sand_profile(profile) - - gamma_z = f_gamma(depth) - delta_z = f_delta(depth) - phi = f_phi(depth) - Nq = np.exp(np.pi*np.tan(np.deg2rad(phi)))*(np.tan(np.deg2rad(45 + phi/2)))**2 - print(f'Nq = {Nq:.2f}, depth = {depth:.2f} m') - d_theta = (En*d*Nq*gamma_z*depth - W*np.cos(theta))/T*deltas - dT = (Et*d*gamma_z*depth*np.tan(np.deg2rad(delta_z)) + W*np.sin(theta))*deltas - - else: - raise ValueError(f"Unsupported soil type: {matched_layer['soil_type']}") - - d_drag = deltas*np.cos(theta) - d_depth = deltas*np.sin(theta) - - theta += d_theta - T -= dT - drag += d_drag - depth += d_depth - - if abs(Tm - T) > 0.75*Tm: - raise Exception(f"Load transfer unrealistic: Tm = {Tm/1e6:.2f} MN vs T = {T/1e6:.2f} MN") - if not (0 < np.rad2deg(theta) < 90): - raise Exception(f"Load angle unrealistic: {np.rad2deg(theta):.2f} deg") - - drag_values.append(-drag); - depth_values.append(-depth); - - Ta = T; thetaa = theta - - print(f'Input Tm = {Tm}, thetam = {thetam}, zlug = {zlug}') - print(f'Output Hm = {Tm*np.cos(np.deg2rad(thetam))}, Vm = {Tm*np.sin(np.deg2rad(thetam))}') - print(f'Output Ta = {Ta}, thetaa = {np.rad2deg(thetaa)}') - print(f'Output Ha = {Ta*np.cos(thetaa)}, Va = {Ta*np.sin(thetaa)}') - - resultsLoad = { - 'Tm': Tm, - 'thetam': thetam, - 'Ta': Ta, - 'thetaa': np.rad2deg(thetaa), - 'length': deltas*len(drag_values), - 'drag_values': drag_values, - 'depth_values': depth_values - } - - return layers, resultsLoad - - -if __name__ == '__main__': - - profile_map = [ - { - 'name': 'CPT_1', - 'x': 498234, 'y': 5725141, - 'layers': [ - { - 'top': 1.0, 'bottom': 2.0, - 'soil_type': 'clay', - 'gamma_top': 8.0, 'gamma_bot': 8.0, - 'Su_top': 10, 'Su_bot': 25}, - { - 'top': 2.0, 'bottom': 8.0, - 'soil_type': 'clay', - 'gamma_top': 8.0, 'gamma_bot': 8.0, - 'Su_top': 25, 'Su_bot': 50}, - { - 'top': 8.0, 'bottom': 16.0, - 'soil_type': 'clay', - 'gamma_top': 8.0, 'gamma_bot': 8.0, - 'Su_top': 50, 'Su_bot': 100} - ] - } - ] - # profile_map = [ - # { - # 'name': 'CPT_1', - # 'x': 498234, 'y': 5725141, - # 'layers': [ - # # { - # # 'top': 0.0, 'bottom': 5.0, - # # 'soil_type': 'sand', - # # 'gamma_top': 9.5, 'gamma_bot': 9.5, - # # 'phi_top': 28, 'phi_bot': 30, - # # 'Dr_top': 70, 'Dr_bot': 70}, - # { - # 'top': 0.0, 'bottom': 5.0, - # 'soil_type': 'clay', - # 'gamma_top': 8.0, 'gamma_bot': 8.0, - # 'Su_top': 25, 'Su_bot': 25}, - # { - # 'top': 5.0, 'bottom': 10.0, - # 'soil_type': 'sand', - # 'gamma_top': 9.5, 'gamma_bot': 9.5, - # 'phi_top': 32, 'phi_bot': 36, - # 'Dr_top': 70, 'Dr_bot': 70}, - # { - # 'top': 10.0, 'bottom': 15.0, - # 'soil_type': 'sand', - # 'gamma_top': 9.5, 'gamma_bot': 9.5, - # 'phi_top': 42, 'phi_bot': 45, - # 'Dr_top': 70, 'Dr_bot': 70} - # ] - # } - # ] - - Tm = 6e6 # Load at mudline (N) - thetam = 10 # Angle at mudline (deg) - zlug = 8 # Padeye depth (m) - line_type = 'chain' - d = 0.16 # Chain diameter (m) - w = 5000 # Line weight (N/m) - - layers, resultsLoad = getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w, plot=True) - - # print("\n--- Transfer Load Results ---") - # for key, val in resultsLoad.items(): - # if isinstance(val, float): - # print(f"{key}: {val:.3f}") - # elif isinstance(val, list): - # print(f"{key}:") - # for v in val: - # print(f" {v:.3f}") - # else: - # print(f"{key}: {val}") - - plot_load(layers, resultsLoad['drag_values'], resultsLoad['depth_values'], - resultsLoad['Tm'], resultsLoad['thetam'], resultsLoad['Ta'], - resultsLoad['thetaa'], zlug=zlug) \ No newline at end of file diff --git a/famodel/anchors/anchors_famodel_map/capacity_plate_map.py b/famodel/anchors/anchors_famodel_map/capacity_plate_map.py deleted file mode 100644 index 56e50b44..00000000 --- a/famodel/anchors/anchors_famodel_map/capacity_plate_map.py +++ /dev/null @@ -1,177 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from .capacity_soils_map import clay_profile -from .capacity_plots_map import plot_plate - -def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot=True): - '''Calculate the plate anchor capacity using clay soil layers from profile_map. - The calculation is based on the soil profile, anchor geometry and inclined load. - - Parameters - ---------- - profile_map : list of dict - Soil profile map with coordinates and layers per location. - location_name : str - Name of the location to select the soil profile. - B : float - Plate width (m) - L : float - Plate length (m) - zlug : float - Embedment depth of the main padeye (m) - beta : float - Inclination angle of the plate (deg) - Ha : float - Applied horizontal load (N) - Va : float - Applied vertical load (N) - plot : bool - Whether to generate plots. - - Returns - ------- - Dictionary with Capacity, Weight, UC, etc. - ''' - - # Extract and filter clay layers from profile_map - profile_entry = next(p for p in profile_map if p['name'] == location_name) - layers = [layer for layer in profile_entry['layers'] if layer['soil_type'] == 'clay'] - - if not layers: - raise ValueError('Plate anchor capacity model only supports clay soils.') - - # Build the profile array: [[z, Su, gamma], ...] - profile = [] - for layer in layers: - profile.append([layer['top'], layer['gamma_top'], layer['Su_top']]) - profile.append([layer['bottom'], layer['gamma_bot'], layer['Su_bot']]) - - print("layer gamma_top (raw):", layer['gamma_top']) - print("layer gamma_bot (raw):", layer['gamma_bot']) - - profile = np.array(sorted(profile, key=lambda x: x[0])) - - # Parameters and constants - Los = 0.05 - B_t = 40 - rhows = 66.90e3 # Submerged steel (N/m3) - rhow = 10e3 # Seawater (N/m3) - - # Evaluate interpolated Su and gamma - z0, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile) - t = round(B/B_t, 2) - V_steel = round(B*L*t, 2) - zlug_B = zlug/B - - # Profile check points - npts = 10 - z_offsets = np.linspace(-0.5, 0.5, npts)*B*np.sin(np.deg2rad(beta)) - z_points = zlug + z_offsets; print(z_points) - - Su_vals = [f_Su(z) for z in z_points] - gamma_10 = f_gamma(z_points[2]); print(gamma_10) - gamma_vals = [f_gamma(z) for z in z_points]; print("gamma_vals:", [f"{val:.2f}" for val in gamma_vals], "N/m3") - Su = np.mean(Su_vals); print(f"Su: {Su:.2f} Pa") - gamma = np.mean(gamma_vals); print(f"gamma: {gamma:.2f} N/m3") - - print("Profile being sent to clay_profile():") - for row in profile: - print(f"z = {row[0]:.2f} m, gamma = {row[1]:.2f} kN/m³, Su = {row[2]:.2f} kPa") - - # Shear strength gradient - k = np.polyfit(z_points, Su_vals, 1)[0] - print(f"k: {k:.2f}") - - # Pile weight including auxiliary parts - Wp = 1.35*V_steel*(rhows + rhow) - - # Capacity factors - Nco_0_0 = 2.483*np.log(zlug_B) + 1.974 - Nco_90_0 = 2.174*np.log(zlug_B) + 3.391 - kBSh = k*B/Su - print(f"kBSh: {kBSh:.2f}") - - f0 = np.where(zlug_B < 4, 1.77*(zlug_B**0.3) - 1.289, 0.192*zlug_B + 0.644) - f90 = np.where(zlug_B < 4, 0.68*(zlug_B**0.5) - 0.410, 0.153*zlug_B + 0.341) - - S_kB_0 = 1 - f0*kBSh - S_kB_90 = 1 - f90*kBSh - Nco_0 = S_kB_0*Nco_0_0 - Nco_90 = S_kB_90*Nco_90_0 - Nco = Nco_0 + (Nco_90 - Nco_0)*(beta/90)**2 - - Nco_s_0_0 = np.where(2.90*zlug_B + 6.02 <= 11.59, 2.90*zlug_B + 6.02, 11.596) - Nco_s_90_0 = np.where(2.72*zlug_B + 4.02 <= 11.59, 2.72*zlug_B + 4.02, 11.596) - - S_s_kB_0 = np.where(zlug_B <= 2, 1 + (0.8 - 0.3*zlug_B)*kBSh - (0.383*kBSh**1.36), 1) - f90s = np.where(zlug_B <= 3, 0.267*zlug_B, 0.6) - S_s_kB_90 = 1 - f90s*kBSh - Nco_s_0 = S_s_kB_0*Nco_s_0_0 - Nco_s_90 = S_s_kB_90*Nco_s_90_0 - Nco_s = Nco_s_90 + (Nco_s_0 - Nco_s_90)*((90 - beta)/90)**2 - - Nc_final = max(Nco + (gamma*zlug)/Su, Nco_s) - print(f"Nc_star: {Nco + (gamma*zlug)/Su:.2f}") - print(f"Nc_star: {Nco_s:.2f}") - qu = Nc_final*Su - Tmax = round(qu*(1 - Los)*B*L, 2) - Hmax = Tmax*np.cos(np.deg2rad(90 - beta)) - Vmax = Tmax*np.sin(np.deg2rad(90 - beta)) - - Ta = np.sqrt(Ha**2 + Va**2) - UC = Ta/Tmax - - resultsPlate = { - 'Capacity': Tmax, - 'Horizontal max.': Hmax, - 'Vertical max.': Vmax, - 'Unity check': UC, - 'Weight plate': Wp - } - - return layers, resultsPlate - -if __name__ == '__main__': - profile_map = [ - { - 'name': 'CPT_1', - 'x': 498234, 'y': 5725141, - 'layers': [ - { - 'top': 0.0, 'bottom': 9.5, - 'soil_type': 'clay', - 'gamma_top': 8.0, 'gamma_bot': 8.5, - 'Su_top': 10, 'Su_bot': 25 - }, - { - 'top': 9.5, 'bottom': 11.5, - 'soil_type': 'clay', - 'gamma_top': 8.5, 'gamma_bot': 8.5, - 'Su_top': 25, 'Su_bot': 45 - }, - { - 'top': 11.5, 'bottom': 25.0, - 'soil_type': 'clay', - 'gamma_top': 8.5, 'gamma_bot': 9.0, - 'Su_top': 45, 'Su_bot': 50 - } - ] - } - ] - - B = 2.0 - L = 2.0 - zlug = 10.0 - Ha = 350e3 - Va = 400e3 - alpha = np.rad2deg(np.arctan2(Va, Ha)) - beta = 90 - alpha - - layers, results = getCapacityPlate(profile_map, 'CPT_1', B, L, zlug, beta, Ha, Va) - - print("\n--- Plate Anchor Capacity Results ---") - for key, val in results.items(): - print(f"{key}: {val:.2f}") - - plot_plate(layers, B, L, z0 = layers[0]['top'], zlug=zlug, beta=beta, title='Plate Anchor in Layered Soil') diff --git a/famodel/anchors/anchors_famodel_map/capacity_suction_map.py b/famodel/anchors/anchors_famodel_map/capacity_suction_map.py deleted file mode 100644 index db296616..00000000 --- a/famodel/anchors/anchors_famodel_map/capacity_suction_map.py +++ /dev/null @@ -1,401 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from scipy.optimize import fsolve -from .capacity_soils_map import clay_profile, sand_profile -from .capacity_plots_map import plot_suction - - -def PileSurface(Len, Dia): - return np.pi*Dia*Len - -def PileWeight(Len, Dia, tw, rho): - return ((np.pi/4)*((Dia**2 - (Dia - 2*tw)**2)*Len + (np.pi/4)*Dia**2*tw))*rho - -def SoilWeight(Len, Dia, tw, gamma_soil): - return (np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil - -def rlugTilt(r, z, theta): - return r*np.cos(np.deg2rad(theta)) - z*np.sin(np.deg2rad(theta)) - -def zlugTilt(r, z, theta): - return r*np.sin(np.deg2rad(theta)) + z*np.cos(np.deg2rad(theta)) - -def getCapacitySuction(profile_map, location_name, D, L, zlug, Ha, Va, thetalug=5, psilug=7.5, plot=True): - '''Calculate the inclined load capacity of a suction pile in sand or clay following S. Kay methodology. - The calculation is based on the soil profile, anchor geometry and inclined load. - - Parameters - ---------- - profile : array - Soil profile as a 2D array: (z, parameters) - Clay soil profile (z (m), Su (kPa), gamma (kN/m³)) - Sand soil profile (z (m), phi (deg), gamma (kN/m³), Dr (%)) - soil_type : string - Select soil condition, 'clay' or 'sand' - D : float - Suction pile diameter (m) - L : float - Suction pile length from pile head (m) - zlug: float - Embedded depth of the main padeye (m) - thetalug: float - Angle of tilt misaligment (deg) (default value: 5.0) - psilug: float - Angle of twist misaligment (deg) (default value: 7.5) - Ha : float - Horizontal load at pile lug elevation (N) - Va : float - Vertical load at pile lug elevation (N) - plot : bool - Plot the capacity envelope if True - - Returns - ------- - Dictionary with capcity, weigths and UC. - ''' - - # Retrieve soil layers from map - profile_entry = next(p for p in profile_map if p['name'] == location_name) - layers = profile_entry['layers'] - - z0 = layers[0]['top'] # Mudline elevation - lambdap = (L - z0)/D # Suction pile slenderness ratio - t = (6.35 + D*20)/1e3 # Suction pile wall thickness (m), API RP2A-WSD - rlug = D/2 # Radial position of the lug - thetalug = 5 # Angle of tilt misaligment, default is 5. (deg) - psilug = 7.5 # Angle of twist misaligment, default is 7.5. (deg) - rhows = 66.90e3 # Submerged steel specific weight (N/m3) - rhow = 10e3 # Water specific weight (N/m3) - - Np_fixed = 10.25 - Np_free = 4.0 - Nc = min(6.2*(1 + 0.34*np.arctan(lambdap)), 9) - - # Initialize - sum_ez_weighted = 0.0 - sum_Hmax = 0.0 - Vmax_final = 0.0 - layer_data = [] - - # Profile check points - npts = 10 - - for layer in layers: - soil_type = layer['soil_type'] - z_top = layer['top'] - z_bot = layer['bottom'] - - if soil_type == 'clay': - # Prepare soil profile for clay - profile = [ - [z_top, layer['gamma_top'], layer['Su_top']], - [z_bot, layer['gamma_bot'], layer['Su_bot']] - ] - - z_ref, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile) - - # Clip the layer first - z_top_clip = max(z_top, z0) - z_bot_clip = min(z_bot, z0 + (L - z0)) - dz_clip = z_bot_clip - z_top_clip; # print(f'dz_clip = {dz_clip:.2f} m') - - if dz_clip <= 0: - continue # Skip layers fully above or below - - # Calculate properties over clipped dz - z_vals = np.linspace(z_top_clip, z_bot_clip, npts) - Su_vals = f_Su(z_vals) - Su_total = np.trapz(Su_vals, z_vals) - Su_moment = np.trapz(Su_vals*z_vals, z_vals) - - Su_av_z = Su_total/dz_clip; # print(f'Su_av_z = {Su_av_z:.2f} Pa') - ez_layer = Su_moment/Su_total; - Su_bot = f_Su(z_bot_clip) - gamma_vals = f_gamma(z_vals) - gamma_av = np.mean(gamma_vals) - - # Calculate Hmax for clay - Hmax_layer = Np_fixed*D*dz_clip*Su_av_z; print(f'Su_av_z = {Su_av_z:.2f} Pa') - - layer_data.append({ - 'z_top': z_top_clip, - 'z_bot': z_bot_clip, - 'dz': dz_clip, - 'Hmax_layer': Hmax_layer, - 'ez_layer': ez_layer - }) - - sigma_v_eff = f_sigma_v_eff(np.mean(z_vals)) - alpha_av = float(f_alpha(np.mean(z_vals))) - - # Side shear To and Ti - To = PileSurface(dz_clip, D)*alpha_av*Su_av_z - Ti = PileSurface(dz_clip, D - 2*t)*alpha_av*Su_av_z - - # Tip resistance - if abs(z_bot_clip - (z0 + (L - z0))) < 1e-3: # tip check - Tbase = (np.pi/12)*D**3*Su_bot - else: - Tbase = 0.0 - - Tmax = min(To + Ti, To + Tbase) - - # Torque induced by horizontal load - T = Ha*rlug*np.sin(np.deg2rad(psilug)) - - nhuT = T/Tmax - nhuV = Ha/To - nhuVstar = np.sqrt(nhuV**2 - nhuT**2) - alphastar = alpha_av*(nhuVstar/nhuV); print(f"alphastar = {alphastar:.3f}") - - # Constant weight - Pile_Head = PileWeight(z0, D, t, rhows) - - # Vertical failure modes - if np.isclose(z_bot_clip, z0 + (L - z0), atol=0.1): - Vmax1 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + Nc*Su_bot*(np.pi/4)*D**2 - else: - Vmax1 = np.inf # No tip resistance unless at tip - - Vmax2 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + PileSurface(dz_clip, D - 2*t)*alphastar*Su_av_z - Vmax3 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + SoilWeight(dz_clip, D, t, gamma_av) - - Vmax_layer = min(Vmax1, Vmax2, Vmax3) - - # Sum vertical capacities - Vmax_final += Vmax_layer - - # Print layer debug info - print(f"Vmax_layer = {Vmax_layer:.2f} N") - print(f"Vmax1 = {Vmax1:.2f} N") - print(f"Vmax2 = {Vmax2:.2f} N") - print(f"Vmax3 = {Vmax3:.2f} N") - - elif soil_type == 'sand': - # Prepare soil profile for sand - profile = [ - [z_top, layer['gamma_top'], layer['phi_top'], layer['Dr_top']], - [z_bot, layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']] - ] - - z_ref, f_gamma, f_phi, _, f_sigma_v_eff, f_delta = sand_profile(profile) - - # Clip the layer within pile embedded length - z_top_clip = max(z_top, z0) - z_bot_clip = min(z_bot, z0 + (L - z0)) - dz_clip = z_bot_clip - z_top_clip - - if dz_clip <= 0: - continue # Skip non-overlapping layers - - # Calculate properties over clipped dz - z_vals = np.linspace(z_top_clip, z_bot_clip, npts) - phi_vals = f_phi(z_vals) - sigma_vals = f_sigma_v_eff(z_vals) - delta_vals = f_delta(z_vals) - - phi_av = np.mean(phi_vals) - sigma_av = np.mean(sigma_vals) - delta_av = np.mean(delta_vals) - - sigma_tip = f_sigma_v_eff(z_bot_clip) - - Nq = np.e**(np.pi*np.tan(np.radians(phi_av)))*(np.tan(np.radians(45) + np.radians(phi_av)/2))**2 - - # Calculate Hmax for sand - Hmax_layer = 0.5*Nq*D*gamma_av*dz_clip**2 - - layer_data.append({ - 'z_top': z_top_clip, - 'z_bot': z_bot_clip, - 'dz': dz_clip, - 'Hmax_layer': Hmax_layer, - 'ez_layer': np.mean(z_vals) - }) - - # Side friction - To = PileSurface(dz_clip, D)*delta_av*sigma_av - Ti = PileSurface(dz_clip, D - 2*t)*delta_av*sigma_av - - if abs(z_bot_clip - (z0 + (L - z0))) < 1e-3: - Tbase = np.pi/4*D**2*sigma_tip - else: - Tbase = 0.0 - - Tmax = min(To + Ti, To + Tbase) - - # Torque induced by horizontal load - T = Ha*rlug*np.sin(np.deg2rad(psilug)) - nhuT = T/Tmax - nhuV = Ha/To - nhuVstar = np.sqrt(nhuV**2 - nhuT**2) - deltastar = delta_av*(nhuVstar/nhuV) - - # Vertical failure modes - Vmax2 = Pile_Head + PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*deltastar*sigma_av + PileSurface(dz_clip, D - 2*t)*deltastar*sigma_av - Vmax3 = Pile_Head + PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*deltastar*sigma_av + SoilWeight(dz_clip, D, t, gamma_av) - - Vmax_layer = min(Vmax2, Vmax3) - - # Sum vertical capacities - Vmax_final += Vmax_layer - - print(f"Vmax_layer (sand) = {Vmax_layer:.2f} N") - print(f"Vmax2 (sand) = {Vmax2:.2f} N") - print(f"Vmax3 (sand) = {Vmax3:.2f} N") - - # sum Hmax and weighted ez - for data in layer_data: - z_top = data['z_top'] - z_bot = data['z_bot'] - Hmax_layer = data['Hmax_layer'] - ez_layer = data['ez_layer'] - dz_layer = data['dz'] - - z_embedded_start = z0 - z_embedded_end = z0 + (L - z0) - - if z_top >= z_embedded_start and z_bot <= z_embedded_end: - sum_ez_weighted += Hmax_layer*ez_layer - sum_Hmax += Hmax_layer - # print(f'ez_layer (full) = {ez_layer:.2f} m') - - elif z_top < z_embedded_end and z_bot > z_embedded_start: - dz_inside = min(z_bot, z_embedded_end) - max(z_top, z_embedded_start) - if dz_inside > 0: - ratio = dz_inside/dz_layer - sum_ez_weighted += Hmax_layer*ratio*ez_layer - sum_Hmax += Hmax_layer * ratio - # print(f'ez_layer (partial) = {ez_layer:.2f} m') - - ez_global = sum_ez_weighted/sum_Hmax - # print(f'sum_ez_weighted = {sum_ez_weighted:.2f}') - print(f'ez_global = {ez_global:.2f} m') - print(f'Hmax = {sum_Hmax:.2f} m') - - # Calculate moment and Hmax_final - M_total = -Va*rlugTilt(rlug, zlug, thetalug) - Ha*(zlugTilt(rlug, zlug, thetalug) - ez_global) - # print(f"rlug_eff = {rlugTilt(rlug, zlug, thetalug):.2f} m") - # print(f"zlug_eff = {zlugTilt(rlug, zlug, thetalug):.2f} m") - print(f"M_total = {M_total:.2f} Nm") - - # ΔφMH from Kay 2014 - if 0.5 <= lambdap < 1.125: - delta_phi = 0.32 + 4.32*lambdap; #print(delta_phi) - elif 1.125 <= lambdap < 2.0: - delta_phi = 7.13 - 1.71*lambdap; #print(delta_phi) - elif 2.0 <= lambdap <= 6.0: - delta_phi = 4.55 - 0.425*lambdap; #print(delta_phi) - else: - raise ValueError('L/D out of bounds for MH ellipse.') - - phi_MH = -np.arctan(ez_global/(L - z0)) - np.deg2rad(delta_phi) - a_MH = Np_fixed/np.cos(phi_MH) - delta_bMH = 0.45*(lambdap)**(-0.9) if lambdap <= 1.5 else 0 - b_MH = -Np_free*np.sin(phi_MH) + delta_bMH - print('M cos(phi)/a_MH =', (M_total*np.cos(phi_MH))/a_MH) - print('M sin(phi)/b_MH =', (M_total*np.sin(phi_MH))/b_MH) - - def f(H_var): - term1 = ((M_total*np.cos(phi_MH) + H_var*np.sin(phi_MH))/a_MH)**2 - term2 = ((M_total*np.sin(phi_MH) - H_var*np.cos(phi_MH))/b_MH)**2 - return term1 + term2 - 1 - - try: - Hmax_final = max(fsolve(f, sum_Hmax*0.8)[0], 0.0) - except: - Hmax_final = 0.0 - - print(f"Hmax_final (MH ellipse) = {Hmax_final:.2f} N") - - # Constant weight - pile_head = PileWeight(z0, D, t, rhows); print(f"pile_head = {pile_head:.2f} N") - Vmax_final += pile_head; print(f"Vmax_final = {Vmax_final:.2f} N") - - # Unity check - UC = (Ha/Hmax_final)**(0.5 + lambdap) + (Va/Vmax_final)**(4.5 + lambdap/3) if Hmax_final and sum_Hmax else np.inf - - # Plotting - if plot: - x = np.linspace(0, 1, 100) - y = (1 - x**(4.5 + lambdap/3))**(1/(0.5 + lambdap)) - - plt.figure(figsize=(6, 5)) - plt.plot(Hmax_final*x, Vmax_final*y, 'b', label='VH Envelope') - plt.plot(Ha, Va, 'ro', label='Applied Load') - plt.xlabel('Horizontal Capacity (N)') - plt.ylabel('Vertical Capacity (N)') - plt.title('VH suction pile capacity envelope') - plt.grid(True) - plt.legend() - plt.tight_layout() - plt.show() - - resultsSuction = { - 'Horizontal max.': Hmax_final, - 'Vertical max.': Vmax_final, - 'Unity check': UC, - # 'Weight pile': Wp, - # 'Weight soil': Wsoil, - 't': t - } - - return layers, resultsSuction - -if __name__ == '__main__': - - profile_map = [ - { - 'name': 'CPT_1', - 'x': 498234, 'y': 5725141, - 'layers': [ - { - 'top': 2.0, 'bottom': 4.0, - 'soil_type': 'clay', - 'gamma_top': 8.0, 'gamma_bot': 8.5, - 'Su_top': 25, 'Su_bot': 50}, - { - 'top': 4.0, 'bottom': 8.0, - 'soil_type': 'clay', - 'gamma_top': 8.5, 'gamma_bot': 9.0, - 'Su_top': 50, 'Su_bot': 75}, - { - 'top': 8.0, 'bottom': 16.0, - 'soil_type': 'clay', - 'gamma_top': 9.0, 'gamma_bot': 9.5, - 'Su_top': 75, 'Su_bot': 100}, - { - 'top': 16.0, 'bottom': 25.0, - 'soil_type': 'clay', - 'gamma_top': 9.5, 'gamma_bot': 9.5, - 'Su_top': 100, 'Su_bot': 100}] - } - ] - - - # Pile and load properties - D = 2.5 # Pile diameter (m) - L = 10.0 # Pile length (m) - zlug = 8.0 # Lug depth (m) - theta = 5 # Tilt misalignment angle (deg) - psi = 7.5 # Twist misalignment angle (deg) - Ha = 6e6 # Applied horizontal load (N) - Va = 2e6 # Applied vertical load (N) - - # Calculate - layers, resultsSuction = getCapacitySuction( - profile_map, 'CPT_1', # Soil properties and location of the pile - D, L, zlug, # Pile geometrical properties - Ha, Va, # Pile loading conditions - thetalug=theta, psilug=psi, # Pile misaligment tolerances - plot=True - ) - - # print('\n--- Suction Pile Capacity Results ---') - # print(f"Hmax_final = {resultsSuction['Hmax_final']:.2f} N") - # print(f"Vmax_final = {resultsSuction['Vmax_final']:.2f} N") - # print(f"Unity check (UC) = {resultsSuction['UnityCheck']:.4f}") - # print(f"Total Moment (M_total) = {resultsSuction['M_total']:.2f} Nm") - - plot_suction(layers, L, D, z0 = layers[0]['top'], zlug=zlug, title='Suction Pile and Soil Layers') diff --git a/famodel/anchors/anchors_famodel_map/capacity_torpedo_map.py b/famodel/anchors/anchors_famodel_map/capacity_torpedo_map.py deleted file mode 100644 index adf5580f..00000000 --- a/famodel/anchors/anchors_famodel_map/capacity_torpedo_map.py +++ /dev/null @@ -1,272 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from .capacity_soils_map import clay_profile -from .capacity_plots_map import plot_torpedo - -def getCapacityTorpedo(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot=True): - '''Calculate the inclined load capacity of a torpedo pile in clay following S. Kay methodology. - The calculation is based on the soil profile, anchor geometry and inclined load. - - Parameters - ---------- - profile : array - Clay soil profile (z, Su, gamma) - Clay soil profile (z (m), Su (kPa), gamma (kN/m³)) - soil_type : string - Select soil condition, 'clay' - D1 : float - Wing diameter (m) - D2 : float - Shaft diameter (m) - L1 : float - Winged section length (m) - L2 : float - Shaft section length (m) - zlug : float - Padeye embedment depth (m) - Ha : float - Horizontal load at pile lug elevation (N) - Va : float - Vertical load at pile lug elevation (N) - plot : bool - Plot the capacity envelope if True - - Returns - ------- - Dictionary with capcity, weigth and UC. - ''' - - # Retrieve soil layers from map - profile_entry = next(p for p in profile_map if p['name'] == location_name) - layers = profile_entry['layers'] - - L = L1 + L2 - t = (6.35 + D2*20)/1e3 - rhows = 66.90e3 - rhow = 10e3 - - def PileWeight(Len1, Len2, Dia1, Dia2, tw, rho): - return ((np.pi/4)*(Dia1**2 - (Dia1 - 2*tw)**2)*(Len1 + Len2) + 4*Len2*Dia2*tw)*rho - - def PileWingedSurface(length, diameter): - return np.pi*diameter*length - - def PileShaftSurface(length, diameter1, diameter2): - return 8*length*(diameter1 - diameter2) - - z_start = zlug - z_wing = zlug + L1 - z_end = zlug + L - - layer_data = [] - Vmax_total = 0.0 - - # Profile check points - npts = 10 - - for layer in layers: - if layer['soil_type'] != 'clay': - raise ValueError('Torpedo pile capacity model only supports clay soils.') - - z_layer_top = layer['top'] - z_layer_bot = layer['bottom'] - - z_clip_top = max(z_layer_top, z_start) - z_clip_bot = min(z_layer_bot, z_end) - - if z_clip_bot <= z_clip_top: - continue - - segments = [] - if z_clip_bot <= z_wing: - segments.append((z_clip_top, z_clip_bot, D1)) - elif z_clip_top >= z_wing: - segments.append((z_clip_top, z_clip_bot, D2)) - else: - segments.append((z_clip_top, z_wing, D1)) - segments.append((z_wing, z_clip_bot, D2)) - - for z_seg_top, z_seg_bot, D in segments: - dz_seg = z_seg_bot - z_seg_top - if dz_seg <= 0: - continue - - profile = [ - [z_seg_top, layer['Su_top'], layer['gamma_top']], - [z_seg_bot, layer['Su_bot'], layer['gamma_bot']] - ] - z_ref, f_Su, _, f_gamma, f_alpha = clay_profile(profile) - - z_vals = np.linspace(z_seg_top, z_seg_bot, npts) - Su_vals = f_Su(z_vals) - alpha_vals = np.array([f_alpha(z) for z in z_vals]) - - Su_total = np.trapz(Su_vals, z_vals) - Su_moment = np.trapz(z_vals*Su_vals, z_vals) - print("xxxxxxxxxxxxxxxxxxxxxxxxx") - Su_av_z = Su_total/dz_seg - print(f"Su_av_z = {Su_av_z:.2f} Pa") - ez_layer = Su_moment /Su_total - print(f"dz_seg = {dz_seg:.2f} m") - print(f"ez_layer = {ez_layer:.2f} m") - alpha_av = np.mean(alpha_vals) - print(f"alpha_av = {alpha_av:.2f}") - - Np_free = 3.45 - Hmax_layer = Np_free*dz_seg*D*Su_av_z - print(f"Hmax_layer = {Hmax_layer:.2f} N") - print(f"D = {D:.2f} m") - - surface_area = PileWingedSurface(dz_seg, D) if D == D1 else PileShaftSurface(dz_seg, D1, D2) - Vmax_layer = surface_area*alpha_av*Su_av_z - Vmax_total += Vmax_layer - print(f"Vmax_layer = {Vmax_layer:.2f} N") - - layer_data.append({ - 'z_top': z_seg_top, - 'z_bot': z_seg_bot, - 'dz': dz_seg, - 'Hmax_layer': Hmax_layer, - 'ez_layer': ez_layer, - 'Su_av_z': Su_av_z, - 'D_used': D - }) - - if not layer_data: - raise ValueError('No overlapping clay layers within pile depth.') - - sum_Hmax = 0.0 - sum_ez_weighted = 0.0 - - for data in layer_data: - z_top = data['z_top'] - z_bot = data['z_bot'] - Hmax_layer = data['Hmax_layer'] - ez_layer = data['ez_layer'] - dz_layer = data['dz'] - - z_embedded_start = zlug - z_embedded_end = zlug + L - - if z_top >= z_embedded_start and z_bot <= z_embedded_end: - sum_ez_weighted += Hmax_layer*ez_layer - sum_Hmax += Hmax_layer - elif z_top < z_embedded_end and z_bot > z_embedded_start: - dz_inside = min(z_bot, z_embedded_end) - max(z_top, z_embedded_start) - if dz_inside > 0: - ratio = dz_inside/dz_layer - sum_ez_weighted += Hmax_layer*ratio*ez_layer - sum_Hmax += Hmax_layer * ratio - - ez_global = sum_ez_weighted/sum_Hmax - print(f'ez_global = {ez_global:.2f} m') - print(f'sum_Hmax = {sum_Hmax:.2f} N') - - Vmax_total += PileWeight(L1, L2, D1, D2, t, rhows) + ballast - Wp = 1.10 * PileWeight(L1, L2, D1, D2, t, rhows + rhow) + ballast - - ez_ratio = (ez_global - zlug)/L; print(f"ez_ratio = {ez_ratio:.2f} m") - - # Average effective width - L = L1 + L2 - A_wing_plane_1 = (D1 - D2)*L1 - A_wing_plane_2 = (D1 - D2)*np.cos(np.deg2rad(45))/2*L1 - A_shaft = D2*L - - # Choose based on direction: - plane = '1' # or '2' - - if plane == '1': - Dstar = (A_wing_plane_1 + A_shaft)/L - elif plane == '2': - Dstar = (A_wing_plane_2 + A_shaft)/L - - # Assign aVH and bVH based on ez_su/L - if np.isclose(ez_ratio, 2/3, atol=0.05): - aVH = 0.5 + L/Dstar - bVH = 4.5 - L/(3*Dstar) - mode = 'deep mobilization (2/3)' - elif 0.40 <= ez_ratio <= 0.75: - aVH = 4.5 + L/(2*Dstar) - bVH = 3.5 - L/(4*Dstar) - mode = 'moderate mobilization (1/2 – 3/4)' - # else: - # aVH = 4.0 - # bVH = 4.0 - # mode = 'default exponents (fallback)' - print(f'Interaction exponents set to aVH = {aVH:.2f}, bVH = {bVH:.2f} [{mode}]') - - UC = (Ha/sum_Hmax)**aVH + (Va/Vmax_total)**bVH - - if plot: - deg = np.linspace(0, 90, 20) - x = np.cos(np.deg2rad(deg)) - y = (1 - x**bVH)**(1/aVH) - X = sum_Hmax*x - Y = Vmax_total*y - - plt.figure(figsize=(6, 5)) - plt.plot(X, Y, color='blue', label='VH Envelope') - plt.plot(Ha, Va, 'o', color='red', label='Load Point') - plt.xlabel('Horizontal Capacity (N)') - plt.ylabel('Vertical Capacity (N)') - plt.title('VH torpedo pile capacity envelope') - plt.grid(True) - plt.legend() - plt.tight_layout() - plt.show() - - resultsTorpedo = { - 'Horizontal max.': sum_Hmax, - 'Vertical max.': Vmax_total, - 'Unity check': UC, - 'Weight pile': Wp, - 'ez_global': ez_global, - 'layer_data': layer_data - } - - return layers, resultsTorpedo - -if __name__ == '__main__': - - profile_map = [ - { - 'name': 'CPT_1', - 'x': 498234, 'y': 5725141, - 'layers': [ - { - 'top': 0.0, 'bottom': 20.0, - 'soil_type': 'clay', - 'gamma_top': 8.0, 'gamma_bot': 8.5, - 'Su_top': 50, 'Su_bot': 70}, - { - 'top': 20.0, 'bottom': 25.0, - 'soil_type': 'clay', - 'gamma_top': 8.5, 'gamma_bot': 8.5, - 'Su_top': 80, 'Su_bot': 100}, - { - 'top': 25.0, 'bottom': 50.0, - 'soil_type': 'clay', - 'gamma_top': 8.5, 'gamma_bot': 9.0, - 'Su_top': 125, 'Su_bot': 150}] - } - ] - - D1 = 3.0 - D2 = 1.5 - L1 = 11.0 - L2 = 5.0 - zlug = 15.0 - ballast = 10000 - Ha = 6.0e6 - Va = 8.0e6 - - layers, results = getCapacityTorpedo(profile_map, 'CPT_1', D1, D2, L1, L2, zlug, ballast, Ha, Va) - - # print("\n--- Torpedo Pile Capacity Results ---") - # for key, val in results.items(): - # if key != 'layer_data': - # print(f"{key}: {val:.2f}") - - plot_torpedo(layers, D1, D2, L1, L2, z0 = layers[0]['top'], zlug=zlug, title='Torpedo Pile in Clay Profile') diff --git a/famodel/anchors/anchors_famodel_profile/capacity_dandg.py b/famodel/anchors/anchors_famodel_profile/capacity_dandg.py deleted file mode 100644 index 1970a91c..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_dandg.py +++ /dev/null @@ -1,272 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from scipy.interpolate import interp1d -from scipy import linalg -from .capacity_soils import rock_profile -from .capacity_pycurves import py_Lovera -from .capacity_plots import plot_pile - -def getCapacityDandG(profile, soil_type, L, D, zlug, Ha, Va, plot=True): - '''Models a laterally loaded pile using the p-y method. The solution for - lateral displacements is obtained by solving the 4th order ODE, - EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. - EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method. - - Assumes that EI remains constant with respect to curvature i.e. pile - material remains in the elastic region. - - Parameters - ---------- - profile : array - Rock profile as a 2D array: (z (m), UCS (MPa), Em (MPa)) - soil_type : string - Select soil condition, 'rock' - L : float - Pile length (m) - D : float - Pile diameter (m) - zlug : float - Load eccentricity above the mudline or depth to mudline relative to the pile head (m) - Ha : float - Horizontal load at pile lug elevation (N) - Va : float - Vertical load at pile lug elevation (N) - plot : bool - Plot the p-y curve and the deflection pile condition if True - - Returns - ------- - y : array - Lateral displacement at each node (n+1 real + 4 imaginary) - z : array - Node location along pile (m) - resultsDandG : dict - Dictionary with lateral, rotational, vertical and pile weight results - ''' - - n = 50; loc = 2 # Number of nodes (-) - tol = 1e-16; max_iter = 50 # Iteration parameters (-) - nhuc = 1; nhu = 0.3 # Resistance factor (-) - delta_r = 0.08 # Mean roughness height (m) - - t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD - E = 200e9 # Elastic modulus of pile material (Pa) - rhows = 66.90e3 # Submerged steel specific weight (N/m3) - rhow = 10e3 # Water specific weight (N/m3) - - # Pile geometry - I = (np.pi/64.0)*(D**4 - (D - 2*t)**4) - EI = E*I - h = L/n # Element size - N = (n + 1) + 4 # (n+1) Real + 4 Imaginary nodes - - # Dry and wet mass of the pile - def PileWeight(Len, Dia, tw, rho): - Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho - return Wp - - # Array for displacements at nodes, including imaginary nodes. - y = np.ones(N)*(0.01*D) # An initial value of 0.01D was arbitrarily chosen - - # Initialize and assemble array/list of p-y curves at each real node - z = np.zeros(N) - k_secant = np.zeros(N) - py_funs = [] - DQ = [] - z0, f_UCS, f_Em = rock_profile(profile) - - - for i in [0, 1]: # Top two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - for i in range(2, n+3): # Real nodes - z[i] = (i - 2)*h - if z[i] < z0: - # No p-y curve above mudline - py_funs.append(lambda y_val: np.zeros_like(y_val)) - k_secant[i] = 0.0 - DQ.append(0.0) - else: - py_funs.append(py_Lovera(z[i], D, f_UCS, f_Em, zlug, z0, plot=True)) - # print(f"z = {z[i]:.2f} m, UCS = {f_UCS(z[i]):.2e} Pa, Em = {f_Em(z[i]):.2e} Pa") - UCS = f_UCS(z[i]) - Em = f_Em(z[i]) - SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D - alpha = 0.36*SCR - 0.0005 - fs = alpha*UCS - Dq = np.pi*D*fs*z[i] - DQ.append(Dq) - Vmax = PileWeight(L, D, t, rhows) + DQ[-1] - - k_secant[i] = py_funs[i](y[i])/y[i] - - for i in [n+3, n+4]: # Bottom two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - for j in range(max_iter): - y_old = y.copy() - y = fd_solver(n, N, h, EI, Ha, Va, zlug, z0, k_secant) - - # Update stiffness - for i in range(2, n+3): - if callable(py_funs[i]): - k_secant[i] = py_funs[i](y[i])/y[i] if y[i] != 0 else 0.0 - - # Check convergence - if np.linalg.norm(y - y_old, ord=2) < tol: - print(f'[Converged in {j+1} iterations]') - break - else: - print('[Warning: Solver did not converge]') - - if plot: - y1 = np.linspace(-2.*D, 2.*D, 500) - plt.plot(y1, py_funs[loc](y1)) - plt.xlabel('y (m)'), plt.ylabel('p (N/m)') - plt.grid(True) - - fig, ax = plt.subplots(figsize=(3, 5)) - y0 = np.zeros_like(z[2:-2]) - # Plot original vertical pile - ax.plot(y0, z[2:-2], 'k', label='Original pile axis') - # Plot deflected shape - ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape') - # Padeye marker - ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') - # Mudline marker - ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)') - - ax.set_xlabel('Lateral displacement (m)') - ax.set_ylabel('Depth (m)') - ax.set_xlim([-0.1*D, 0.1*D]) - ax.set_ylim([L + 5, -2]) # Downward is positive z - ax.grid(ls='--') - ax.legend() - - # Relevant index of nodes - zlug_index = int(zlug/h); print(zlug_index) - ymax_index = int(np.max(y)); print(ymax_index) - - resultsDandG = { - 'Weight pile': PileWeight(L, D, t, rhows + rhow), - 'Vertical max.': Vmax, - 'Lateral displacement': y[ymax_index], - 'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h), - 'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf, - 'Unity check (horizontal)': 0.0, # Placeholder; no Mp or Mi in current model - 'Bending moment': None, - 'Plastic moment': None, - 'Plastic hinge': None, - 'Hinge location': None, - 'p-y model': 'Lovera (2023)', - } - - return y[2:-2], z[2:-2], resultsDandG - -def fd_solver(n, N, h, EI, Ha, Va, zlug, z0, k_secant): - '''Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for - non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves - are linear. - - Parameters - ---------- - n : int - Number of elements (-) - N : int - Total number of nodes (-) - h : float - Element size (m) - EI : float - Flexural rigidity of the pile (Nm²) - Ha : float - Horizontal load at padeye (N) - Va : float - Vertical load at padeye (N) - zlug : float - Padeye depth from pile head (m) - z0 : float - Mudline elevation from pile head (m) - k_secant : array - Secant stiffness at each node - - Returns - ------- - y : array - Lateral displacement at each node (n+1 real + 4 imaginary) - ''' - - # Initialize and assemble matrix - X = np.zeros((N, N)) - - # (n+1) finite difference equations for (n+1) real nodes - for i in range(0,n+1): - X[i, i] = 1.0 - X[i, i+1] = -4.0 + Va*h**2/EI - X[i, i+2] = 6.0 - 2*Va*h**2/EI + k_secant[i+2]*h**4/EI - X[i, i+3] = -4.0 + Va*h**2/EI - X[i, i+4] = 1.0 - - # Curvature at pile head - X[n+1, 1] = 1.0 - X[n+1, 2] = -2.0 - X[n+1, 3] = 1.0 - - # Shear at pile head - X[n+2, 0] = -1.0 - X[n+2, 1] = 2.0 - Va*h**2/EI - X[n+2, 2] = 0.0 - X[n+2, 3] = -2.0 + Va*h**2/EI - X[n+2, 4] = 1.0 - - # Curvature at pile tip - X[n+3, -2] = 1.0 - X[n+3, -3] = -2.0 - X[n+3, -4] = 1.0 - - # Shear at pile tip - X[n+4, -1] = 1.0 - X[n+4, -2] = -2.0 + Va*h**2/EI - X[n+4, -3] = 0.0 - X[n+4, -4] = 2.0 - Va*h**2/EI - X[n+4, -5] = -1.0 - - # Initialize vector q - q = np.zeros(N) - - # Index of the node where the horizontal load is applied (padeye) - zlug_index = int(zlug/h) - q[zlug_index] = 2*Ha*h**3 - - # Solve for displacement - y = linalg.solve(EI*X, q) - - return y - -if __name__ == '__main__': - - profile_rock = np.array([ - [ 2.0, 2, 200], - [ 5.0, 2, 200], - [ 9.0, 2, 200], - [30.0, 2, 200] - ]) - - D = 3.0 # Diameter (m) - L = 10.0 # Length (m) - zlug = 1 # Padeye elevation (m) - Ha = 8.0e6 # Horizontal load (N) - Va = 3.0e6 # Vertical load (N) - - y, z, resultsDandG = getCapacityDandG(profile_rock, 'rock', L, D, zlug, Ha, Va, plot=True) - for key, val in resultsDandG.items(): - if isinstance(val, float): - print(f"{key}: {val:.3f}") - else: - print(f"{key}: {val}") - - plot_pile(profile_rock, 'rock', y, z, D, L, profile_rock[0, 0], zlug) diff --git a/famodel/anchors/anchors_famodel_profile/capacity_driven.py b/famodel/anchors/anchors_famodel_profile/capacity_driven.py deleted file mode 100644 index 485f7e8c..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_driven.py +++ /dev/null @@ -1,418 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from scipy.interpolate import interp1d -from scipy import linalg -from .capacity_soils import clay_profile, sand_profile, rock_profile -from .capacity_pycurves import py_Matlock, py_API, py_Reese -from .capacity_plots import plot_pile - -def getCapacityDriven(profile, soil_type, L, D, zlug, Ha, Va, plot=True): - '''Models a laterally loaded pile using the p-y method. The solution for - lateral displacements is obtained by solving the 4th order ODE, EI*d4y/dz4 - EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. - EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method. - - Assumes that EI remains constant with respect to curvature i.e. pile - material remains in the elastic region. - - Parameters - ---------- - profile : array - Soil profile as a 2D array: (z, parameters) - Clay: (z (m), Su (kPa), gamma (kN/m³)) - Sand: (z (m), phi (deg), gamma (kN/m³), Dr (%)) - Rock: (z (m), UCS (MPa), Em (MPa)) - soil_type : string - Select soil condition: 'clay', 'sand', or '(weak) rock' - L : float - Pile length (m) - D : float - Pile diameter (m) - zlug : float - Depth of padeye from pile head (m) - Ha : float - Horizontal load applied at padeye (N) - Va : float - Vertical load applied at padeye (N) - plot : bool - Plot the p-y curve and the deflection pile condition if True - - Returns - ------- - y : array - Lateral displacement at each node (real nodes only) - z : array - Node depth positions corresponding to y (m) - resultsDriven : dict - Dictionary containing displacements, moment capacity, hinge state and vertical capacity - ''' - - n = 50; iterations = 10; loc = 2 - nhuc = 1; nhu = 0.3 # Resistance factor (-) - delta_r = 0.08 # Mean roughness height (m) - - t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD - E = 200e9 # Elastic modulus of pile material (Pa) - fy = 350e6 # Yield strength of pile material (Pa) - rhows = 66.90e3 # Submerged steel specific weight (N/m3) - rhow = 10e3 # Water specific weight (N/m3) - - # Pile geometry - I = (np.pi/64.0)*(D**4 - (D - 2*t)**4) - EI = E*I - h = L/n # Element size - N = (n + 1) + 4 # (n+1) Real + 4 Imaginary nodes - - # Outer and inner surface of the pile skirt - def PileSurface(Len, Dia): - Sp = np.pi*Dia*Len - return Sp - # Dry and wet mass of the pile - def PileWeight(Len, Dia, tw, rho): - Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho - return Wp - # Mass of the soil plug - def SoilWeight(Len, Dia, tw, gamma_soil): - Wsoil =(np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil - return Wsoil - - # Array for displacements at nodes, including imaginary nodes. - y = np.ones(N)*(0.01*D) # An initial value of 0.01D was arbitrarily chosen - - # Initialize and assemble array/list of p-y curves at each real node - z = np.zeros(N) - py_funs = []; PileShaft =[] - k_secant = np.zeros(N) - DQ = [] - - for i in [0, 1]: # Top two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - for i in range(2, n+3): # Real nodes - z[i] = (i - 2)*h - if soil_type == 'clay': - z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile) - if z[i] < z0: - # No p-y curve above mudline - py_funs.append(lambda y_val: np.zeros_like(y_val)) - k_secant[i] = 0.0 - PileShaft.append(0.0) - else: - Su = f_Su(z[i]) - sigma_v_eff = f_sigma_v_eff(z[i]) - gamma = f_gamma(z[i]) - alpha = f_alpha(z[i]) - py_funs.append(py_Matlock(z[i], D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=z0, plot=plot)) - Vo = np.pi*D*alpha*Su*z[i]**2 - PileShaft.append(Vo) - k_val = py_funs[i](y[i]) - k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 - - elif soil_type == 'sand': - z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile) - if z[i] < z0: - # No p-y curve above mudline - py_funs.append(lambda y_val: np.zeros_like(y_val)) - k_secant[i] = 0.0 - PileShaft.append(0.0) - else: - phi = f_phi(z[i]) - sigma_v_eff = f_sigma_v_eff(z[i]) - gamma = f_gamma(z[i]) - delta = f_delta(z[i]) - py_funs.append(py_API(z[i], D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=z0, plot=plot)) - fs = delta * sigma_v_eff - Vo = np.pi*D*fs*z[i] - PileShaft.append(Vo) - k_val = py_funs[i](y[i]) - k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 - - elif soil_type in ['rock', 'weak_rock']: - z0, f_UCS, f_Em = rock_profile(profile) - if z[i] < z0: - # No p-y curve above mudline - py_funs.append(lambda y_val: np.zeros_like(y_val)) - k_secant[i] = 0.0 - DQ.append(0.0) - else: - UCS = f_UCS(z[i]) - Em = f_Em(z[i]) - py_funs.append(py_Reese(z[i], D, zlug, f_UCS, f_Em, z0=z0, plot=plot)) - SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D - alpha = 0.36*SCR - 0.0005 - fs = alpha*UCS - Dq = np.pi*D*fs*z[i] - DQ.append(Dq) - k_val = py_funs[i](y[i]) - k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 - - for i in [n+3, n+4]: # Bottom two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - # Compute individual contributions to total vertical load - Wp = PileWeight(L, D, t, rhows) # Pile self-weight (wet) - Wsoil = SoilWeight(L, D, t, gamma) if soil_type in ['clay', 'sand'] else 0.0 # Soil plug only in soil profiles - Wshaft = PileShaft[-1] if soil_type in ['clay', 'sand'] else 0.0 # Shaft resistance for soils - Wtip = DQ[-1] if soil_type in ['rock', 'weak_rock'] else 0.0 # Tip resistance for rock - - # Compute total vertical capacity - Vmax = Wp + Wsoil + Wshaft + Wtip - - for j in range(iterations): - y, Mi, Mp, hinge_formed, hinge_location = fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant) - for i in range(2, n+3): - if callable(py_funs[i]): - k_secant[i] = py_funs[i](y[i])/y[i] - - if plot: - y1 = np.linspace(-2.*D, 2.*D, 500) - plt.plot(y1, py_funs[loc](y1)) - plt.xlabel('y (m)'), plt.ylabel('p (N/m)') - plt.grid(True) - - fig, ax = plt.subplots(figsize=(3, 5)) - y0 = np.zeros_like(z[2:-2]) - # Plot original vertical pile - ax.plot(y0, z[2:-2], 'k', label='Original pile axis') - # Plot deflected shape - ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape') - # Padeye marker - ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') - # Mudline marker - ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)') - - ax.set_xlabel('Lateral displacement (m)') - ax.set_ylabel('Depth (m)') - ax.set_xlim([-0.1*D, 0.1*D]) - ax.set_ylim([L + 5, -2]) # Downward is positive z - ax.grid(ls='--') - ax.legend() - - # Relevant index of nodes - zlug_index = int(zlug/h); print(zlug_index) - ymax_index = int(np.max(y)); print(ymax_index) - - resultsDriven = { - 'Weight pile': PileWeight(L, D, t, rhows + rhow), - 'Vertical max.': Vmax, - 'Horizontal max.': abs(Mi)/abs(zlug) if zlug != 0 else 1e-6, - 'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf, - 'Unity check (horizontal)': Ha/(abs(Mi)/abs(zlug)) if zlug != 0 else np.inf, - 'Lateral displacement': y[ymax_index], - 'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h), - 'Bending moment': abs(Mi), - 'Plastic moment': Mp, - 'Plastic hinge': hinge_formed, - 'Hinge location': hinge_location, - 'p-y model': 'Matlock (1970)' if soil_type == 'clay' else 'API RP2A (1993)' if soil_type == 'sand' else 'Reese (1997)', - } - - - return y[2:-2], z[2:-2], resultsDriven - -def fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant): - '''Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for - non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves - are linear. - - Parameters - ---------- - n : int - Number of elements - N : int - Total number of nodes (real + imaginary) - h : float - Element size (m) - D : float - Pile diameter (m) - t : float - Pile wall thickness (m) - fy : float - Yield strength of pile material (Pa) - EI : float - Flexural rigidity of the pile (Nm²) - Ha : float - Horizontal load at pile lug elevation (N) - Va : float - Vertical load at pile lug elevation (N) - zlug : float - Depth of padeye from pile head (m) - z0 : float - Mudline depth from pile head (m) - k_secant : array - Secant stiffness from p-y curves at each node - - Returns - ------- - y : array - Lateral displacement at each node - Mi : float - Maximum internal bending moment (Nm) - Mp : float - Plastic moment capacity of the pile (Nm) - hinge_formed : bool - Whether plastic hinge is formed - hinge_location : int - Index of the node with hinge formation - ''' - - # Initialize and assemble matrix - X = np.zeros((N, N)) - - # (n+1) finite difference equations for (n+1) real nodes - for i in range(0, n+1): - X[i, i] = 1.0 - X[i, i+1] = -4.0 + Va*h**2/EI - X[i, i+2] = 6.0 - 2*Va*h**2/EI + k_secant[i+2]*h**4/EI - X[i, i+3] = -4.0 + Va*h**2/EI - X[i, i+4] = 1.0 - - # Curvature at pile head - X[n+1, 1] = 1.0 - X[n+1, 2] = -2.0 - X[n+1, 3] = 1.0 - - # Shear at pile head - X[n+2, 0] = -1.0 - X[n+2, 1] = 2.0 - Va*h**2/EI - X[n+2, 2] = 0.0 - X[n+2, 3] = -2.0 + Va*h**2/EI - X[n+2, 4] = 1.0 - - # Curvature at pile tip - X[n+3, -2] = 1.0 - X[n+3, -3] = -2.0 - X[n+3, -4] = 1.0 - - # Shear at pile tip - X[n+4, -1] = 1.0 - X[n+4, -2] = -2.0 + Va*h**2/EI - X[n+4, -3] = 0.0 - X[n+4, -4] = 2.0 - Va*h**2/EI - X[n+4, -5] = -1.0 - - # Initialize vector q - q = np.zeros(N) - - # Always apply shear - # Index of the node where the horizontal load is applied (padeye) - zlug_index = int(zlug/h) - q[zlug_index] = 2*Ha*h**3 - - y = linalg.solve(EI*X, q) - - # Compute the plastic moment capacity Mp - Zp = (1/6)*(D**3 - (D - 2*t)**3) # Plastic section modulus for hollow pile (m3) - Mp = Zp*fy # Plastic moment capacity (N/m) - - # Check for plastic hinge formation - Mi, Mp, hinge_formed, hinge_location = plastic_hinge(y, h, EI, Mp) - - return y, Mi, Mp, hinge_formed, hinge_location - -def plastic_hinge(y, h, EI, Mp): - '''Check for plastic hinge formation along the pile. - - Parameters - ---------- - y : array - Lateral displacements at each node - h : float - Element size (m) - EI : float - Flexural rigidity of the pile (Nm²) - Mp : float - Plastic moment capacity (Nm) - - Returns - ------- - Mi_max : float - Maximum internal moment along the pile (Nm) - Mp : float - Plastic moment capacity (Nm) - hinge_formed : bool - True if plastic hinge is formed - hinge_location : int - Node index where hinge forms (if any) - ''' - - hinge_formed = False - hinge_location = -1 - Mi_all = [] - - # Loop through each internal node and compute the bending moment - for i in range(1, len(y) - 1): - Mi = EI * (y[i+1] - 2*y[i] + y[i-1])/h**2 - Mi_all.append(Mi) - - if abs(Mi) >= Mp and not hinge_formed: - hinge_formed = True - hinge_location = i - - Mi_max = max(Mi_all, key=abs) if Mi_all else 0.0 - - return Mi_max, Mp, hinge_formed, hinge_location - -if __name__ == '__main__': - - profile_clay = np.array([ - [ 1.0, 600, 8], - [ 6.0, 200, 8], - [15.0, 400, 8], - [30.0, 600, 9] - ]) - - profile_sand = np.array([ - [ 2.0, 28, 8, 75], - [10.0, 34, 9, 75], - [15.0, 36, 10, 75], - [40.0, 45, 9, 85] - ]) - - profile_rock = np.array([ - [ 2.0, 0.5, 1e3], - [ 5.0, 2.0, 2e4], - [30.0, 1.0, 5e4] - ]) - - D = 2.5 # Diameter (m) - L = 25.0 # Length (m) - zlug = 1 # Padeye depth (m) - - # === CLAY === - y_clay, z_clay, resultsDriven_clay = getCapacityDriven(profile_clay, 'clay', L, D, zlug, Ha=5.0e6, Va=1.5e5, plot=True) - for key, val in resultsDriven_clay.items(): - if isinstance(val, float): - print(f"{key}: {val:.3f}") - else: - print(f"{key}: {val}") - - plot_pile(profile_clay, 'clay', y_clay, z_clay, D, L, profile_clay[0, 0], zlug, resultsDriven_clay.get('Hinge location')) - - # # === SAND === - # y_sand, z_sand, resultsDriven_sand = getCapacityDriven(profile_sand, 'sand', L, D, zlug, Ha=2.5e6, Va=2.0e6, plot=True) - # for key, val in resultsDriven_sand.items(): - # if isinstance(val, float): - # print(f"{key}: {val:.3f}") - # else: - # print(f"{key}: {val}") - - # plot_pile(profile_sand, 'sand', y_sand, z_sand, D, L, profile_sand[0, 0], zlug, resultsDriven_sand.get('Hinge location')) - - # # === ROCK === - # y_rock, z_rock, resultsDriven_rock = getCapacityDriven(profile_rock, 'rock', L, D, zlug, Ha=3.5e6, Va=3.0e6, plot=True) - # for key, val in resultsDriven_rock.items(): - # if isinstance(val, float): - # print(f"{key}: {val:.3f}") - # else: - # print(f"{key}: {val}") - - # plot_pile(profile_rock, 'rock', y_rock, z_rock, D, L, profile_rock[0, 0], zlug, resultsDriven_rock.get('Hinge location')) - - - diff --git a/famodel/anchors/anchors_famodel_profile/capacity_helical.py b/famodel/anchors/anchors_famodel_profile/capacity_helical.py deleted file mode 100644 index d425aea8..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_helical.py +++ /dev/null @@ -1,152 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from .capacity_driven import getCapacityDriven, plot_pile -from .capacity_soils import clay_profile, sand_profile -from .capacity_plots import plot_helical - -def getCapacityHelical(profile, soil_type, D, L, d, zlug, Ha, Va, plot=True): - '''Calculate the vertical and horizontal capacity of a helical pile using a soil profile. - The calculation is based on the soil profile, anchor geometry and inclined load. - - Parameters - ---------- - profile : array - Soil profiles (z, parameters) - Clay: (z (m), Su (kPa), gamma (kN/m³)) - Sand: (z (m), phi (deg), gamma (kN/m³), Dr (%)) - soil_type : string - Select soil condition, 'clay' or 'sand' - D : float - Helix diameter (m) - L : float - Shaft length (m) - d : float - Shaft diameter (m) - zlug : float - Depth to padeye (m) - Ha : float - Horizontal load at pile lug elevation (N) - Va : float - Vertical load at pile lug elevation (N) - plot : bool - Plot the p-y curve and the deflection pile condition if True - - Returns - ------- - Dictionary with capacity, displacements, weight and UC. - ''' - - t = (6.35 + D*20)/1e3 # Helical pile wall thickness (m), API RP2A-WSD - rhows = 66.90e3 # Submerged steel specific weight (kN/m3) - rhow = 10e3 # Water specific weight (kN/m3) - - def PileWeight(Len, Dia1, Dia2, tw, rho): - return ((np.pi/4)*((Dia1**2 - (Dia1 - 2*tw)**2)*Len + (np.pi/4)*Dia2**2*tw))*rho - - if soil_type == 'clay': - z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile) - - z_helix = np.clip(zlug + (L - D), profile[0, 0], profile[-1, 0]) - Su = f_Su(z_helix) - sigma_v_eff = max(f_sigma_v_eff(z_helix), 1.0) - psi_val = Su/sigma_v_eff - alpha = min(0.5*psi_val**-0.50, 1) if psi_val <= 1.0 else min(0.5 * psi_val**-0.25, 1) - - Nc = min(6.0*(1 + 0.2*d/D), 9) - Qh = ((np.pi/4)*(D**2 - d**2)*Nc*Su + f_gamma(z_helix)*D)*0.75 - Qs = np.pi*d*L*alpha*Su - Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs - - elif soil_type == 'sand': - z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile) - - z_helix = np.clip(zlug + (L - D), profile[0, 0], profile[-1, 0]) - gamma = f_gamma(z_helix) - Dr = f_Dr(z_helix) - delta = f_delta(z_helix) - phi = f_phi(z_helix) - - Nq = 0.5*(12*phi)**(phi/54) - Qh = (np.pi/4)*(D**2 - d**2)*Nq*gamma*z_helix - Qs = np.pi*d*L*delta*gamma*z_helix - Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs - - # Pile weight (inc. auxilary items) assessed as a factor - Wp = 1.10*PileWeight(L, D, d, t, (rhows + rhow)) - - # Unity Check based only on vertical capacity - UC_vertical = Va/Qu - - # Compute horizontal capacity using p-y method - y, z, results_lateral = getCapacityDriven(profile, soil_type, L, d, zlug, Ha, Va, plot=True) - if soil_type == 'clay': - plot_pile(profile_clay, 'clay', y, z, D, L, z0, zlug, hinge_location=None) - elif soil_type == 'sand': - plot_pile(profile_sand, 'sand', y, z, D, L, z0, zlug, hinge_location=None) - - Hcap = Ha if 'Plastic moment' not in results_lateral else results_lateral['Bending moment']/abs(zlug) if zlug != 0 else 1e-6 - UC_horizontal = Ha/Hcap if Hcap != 0 else np.inf - - resultsHelical = { - 'Weight pile': Wp, - 'Vertical max.': Qu, - 'Horizontal max.': Hcap, - 'Unity check (vertical)': UC_vertical, - 'Unity check (horizontal)': UC_horizontal, - 'Lateral displacement': results_lateral.get('Lateral displacement', None), - 'Rotational displacement': results_lateral.get('Rotational displacement', None), - 'Bending moment': results_lateral.get('Bending moment', None), - 'Plastic moment': results_lateral.get('Plastic moment', None), - 'Plastic hinge': results_lateral.get('Plastic hinge', None), - 'Hinge location': results_lateral.get('Hinge location', None), - 'p-y model': results_lateral.get('p-y model', None), - } - - return resultsHelical - -if __name__ == '__main__': - - profile_clay = np.array([ - [ 1.0, 10, 8.0], - [ 5.0, 15, 8.5], - [10.0, 25, 8.5], - [25.0, 50, 9.0] - ]) - - profile_sand = np.array([ - [ 1.0, 28, 9.5, 40, 60], - [ 5.0, 34, 10.0, 42, 70], - [ 8.0, 38, 10.0, 44, 75], - [15.0, 38, 11.5, 45, 85] - ]) - - D = 1.8 # Helix diameter (m) - L = 12.0 # Pile length (m) - d = 0.8 # Shaft diameter (m) - zlug = 2 # Padeye depth (m) - Va = 50e3 # Vertical load (N) - Ha = 30e3 # Horizontal load (N) - - # === CLAY === - # resultsHelical_clay = getCapacityHelical(profile_clay, 'clay', D, L, d, zlug, Ha, Va, plot=True) - # for key, val in resultsHelical_clay.items(): - # if isinstance(val, float): - # print(f"{key}: {val:.3f}") - # else: - # print(f"{key}: {val}") - - # plot_helical(profile_clay, 'clay', D, L, d, zlug, n_helix=1, spacing=1.0, title='Helical Pile in Clay Profile') - - # === SAND === - resultsHelical_sand = getCapacityHelical(profile_sand, 'sand', D, L, d, zlug, Ha, Va, plot=True) - for key, val in resultsHelical_sand.items(): - if isinstance(val, float): - print(f"{key}: {val:.3f}") - else: - print(f"{key}: {val}") - - plot_helical(profile_sand, 'sand', D, L, d, zlug, n_helix=1, spacing=1.0, title='Helical Pile in Sand Profile') - - - diff --git a/famodel/anchors/anchors_famodel_profile/capacity_load.py b/famodel/anchors/anchors_famodel_profile/capacity_load.py deleted file mode 100644 index 7c75ed1e..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_load.py +++ /dev/null @@ -1,189 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from .capacity_soils import clay_profile, sand_profile -from .capacity_plots import plot_load - -def getTransferLoad(profile, soil_type, Tm, thetam, zlug, line_type, d, w=None, plot=True): - '''Calculate the transfer load from mudline to main padeye using a layered soil profile. - - Parameters - ---------- - profile : array - Soil profile as a 2D array: (z, parameters) - Clay soil profile (z (m), Su (kPa), gamma (kN/m³)) - Sand soil profile (z (m), phi (deg), gamma (kN/m³), Dr (%)) - soil_type : string - Select soil condition, 'clay' or 'sand' - Tm : float - Mooring line load at mudlevel (N) - thetam : float - Mooring line angle at mudlevel (deg) - zlug : float - Embedment depth of the lug (m) - line_type : string - Select mooring line type, 'chain' or 'wire' - d : float - Mooring line diameter (m) - w : float - Mooring line unit weight (N/m) - plot : bool - Plot the inverse catenary mooring line profile if True - - Returns - ------- - dict - Dictionary with transferred load components and depth. - ''' - - deltas = 0.2 # discretization step - - profile = np.array(profile) - - # Line mechanical properties - if line_type == 'chain': - Et, En = 10, 2.5 - elif line_type == 'wire': - Et, En = np.pi, 1 - W = w*deltas - - # Soil profile and interpolators - if soil_type == 'clay': - Nc = 8.5 - z0, f_Su, _, f_gamma, f_alpha = clay_profile(profile) - elif soil_type == 'sand': - nhu = 0.5 - z0, f_phi, _, f_gamma, _, f_delta = sand_profile(profile) - - # Initial values - T = Tm - theta = np.deg2rad(thetam) - drag = 0 - depth = z0 + 0.1 - - # Tracing lists - drag_values, depth_values = [], [] - - while (zlug - depth) >= 0: - if soil_type == 'clay': - Su = f_Su(depth) - alpha = f_alpha(depth) - dtheta = (En*d*Nc*Su - W*np.cos(theta))/T*deltas - dT = (Et*d*alpha*Su + W*np.sin(theta))*deltas - elif soil_type == 'sand': - gamma_z = f_gamma(depth) - delta_z = f_delta(depth) - phi = f_phi(depth) - Nq = np.exp(np.pi*np.tan(np.deg2rad(phi)))*(np.tan(np.deg2rad(45 + phi/2)))**2 - print(f'Nq = {Nq:.2f}, depth = {depth:.2f} m') - dtheta = (En*d*Nq*gamma_z*depth - W*np.cos(theta))/T*deltas - dT = (Et*d*gamma_z*depth*np.tan(np.deg2rad(delta_z)) + W*np.sin(theta))*deltas - - ddrag = deltas*np.cos(theta) - ddepth = deltas*np.sin(theta) - - theta += dtheta - T -= dT - drag += ddrag - depth += ddepth - - if abs(Tm - T) > 0.75*Tm: - raise Exception(f"Load transfer unrealistic: Tm = {Tm/1e6:.2f} MN vs T = {T/1e6:.2f} MN") - - if not (0 < np.rad2deg(theta) < 90): - raise Exception(f"Load angle unrealistic: {np.rad2deg(theta):.2f} deg") - - drag_values.append(-drag) - depth_values.append(-depth) - - Ta = T; thetaa = theta - # H = Ta*np.cos(thetaa) - # V = Ta*np.sin(thetaa) - - if plot: - plot_load( - profile, soil_type, - drag_values, depth_values, - Tm, thetam, - Ta, thetaa, - zlug - ) - - resultsLoads = { - 'Tm': Tm, - 'thetam': thetam, - 'Ta': Ta, - 'thetaa': thetaa, - 'length': deltas*len(drag_values), - 'drag_values': drag_values, - 'depth_values': depth_values - } - - return resultsLoads - -if __name__ == '__main__': - - # Define a clay profile: [depth (m), Su (kPa), gamma (kN/m3)] - # profile_clay = np.array([ - # [ 0.0, 0, 0.0], - # [ 2.0, 25, 8.0], - # [ 8.0, 50, 8.0], - # [16.0, 100, 8.0] - # ]) - - # Define a sand profile: [depth (m), phi (deg), gamma (kN/m3), Dr (%)] - profile_sand = np.array([ - [ 0.0, 30, 9.5, 70], - [ 5.0, 30, 9.5, 70], - [10.0, 30, 9.5, 70], - [15.0, 30, 9.5, 70] - ]) - - # # Input parameters - # Tm = 1.2e7 # Load at mudline (N) - # thetam = 10 # Angle at mudline (deg) - # zlug = 8.3 # Padeye depth (m) - # line_type = 'chain' - # d = 0.16 # Chain diameter (m) - # w = 4093 # Line weight (N/m) - - # # Run transfer load calculation - # results = getTransferLoad(profile_clay, soil_type, Tm, thetam, zlug, line_type, d, w, plot=True) - # print("\n--- Transfer Load Results (Clay) ---") - # for key, val in results.items(): - # if isinstance(val, float): - # print(f"{key}: {val:.3f}") - # elif isinstance(val, list): - # print(f"{key}:") - # for v in val: - # print(f" {v:.3f}") - # else: - # print(f"{key}: {val}") - - # plot_load(profile_clay, soil_type, results['drag_values'], results['depth_values'], results['Tm'], results['thetam'], results['Ta'], results['thetaa'], zlug) - - - # Input parameters - Tm = 8.2e6 - thetam = 10 - zlug = 8 - line_type = 'chain' - d = 0.25 - soil_type = 'sand' - w = 4093 - - results = getTransferLoad(profile_sand, soil_type, Tm, thetam, zlug, line_type, d, w, plot=True) - - # print("\n--- Transfer Load Results (Sand) ---") - # for key, val in results.items(): - # if isinstance(val, float): - # print(f"{key}: {val:.3f}") - # elif isinstance(val, list): - # print(f"{key}:") - # for v in val: - # print(f" {v:.3f}") - # else: - # print(f"{key}: {val}") - - plot_load(profile_sand, soil_type, results['drag_values'], results['depth_values'], results['Tm'], results['thetam'], results['Ta'], results['thetaa'], zlug) - diff --git a/famodel/anchors/anchors_famodel_profile/capacity_plate.py b/famodel/anchors/anchors_famodel_profile/capacity_plate.py deleted file mode 100644 index cb718fa2..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_plate.py +++ /dev/null @@ -1,143 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from .capacity_soils import clay_profile -from .capacity_plots import plot_plate - -def getCapacityPlate(profile, soil_type, B, L, zlug, beta, Ha, Va, plot=True): - '''Calculate the plate anchor capacity using a full clay soil profile and return capacity + UC. - The calculation is based on the soil profile, anchor geometry and inclined load. - - Parameters - ---------- - profile : ndarray - Clay soil profile as a 2D array: [[depth, Su, gamma], ...] - soil_type : str - Currently only 'clay' is supported. - B : float - Plate width (m) - L : float - Plate length (m) - zlug : float - Embedment depth of the main padeye (m) - beta : float - Inclination angle of the plate (deg) - Ha : float - Applied horizontal load (kN) - Va : float - Applied vertical load (kN) - plot : bool - Placeholder for future use. - - Returns - ------- - Dictionary with capacity, weight and UC. - ''' - - Los = 0.05 - B_t = 40 - rhows = 66.90e3 # Submerged steel specific weight (kN/m3) - rhow = 10e3 # Water specific weight (kN/m3) - - # Extract soil parameters from profile - z0, f_Su, _, f_gamma, _ = clay_profile(profile) - - # Geometry - t = round(B/B_t, 2) - V_steel = round(B*L*t, 2) - zlug_B = zlug/B - - # Define 5 evaluation points along inclined width - N = 5 - z_offsets = np.linspace(-0.5, 0.5, N)*B*np.sin(np.deg2rad(beta)) - z_points = zlug + z_offsets; print(z_points) - - # Evaluate Su and gamma at these points - Su_vals = [f_Su(z) for z in z_points] - gamma_10 = f_gamma(z_points[2]); print(gamma_10) - gamma_vals = [f_gamma(z) for z in z_points]; print("gamma_vals:", [f"{val:.2f}" for val in gamma_vals], "N/m3") - Su = np.mean(Su_vals); print(f"Su: {Su:.2f} Pa") - gamma = np.mean(gamma_vals); print(f"gamma: {gamma:.2f} N/m3") - - print("Profile being sent to clay_profile():") - for row in profile: - print(f"z = {row[0]:.2f} m, Su = {row[1]:.2f} kPa, gamma = {row[2]:.2f} kN/m³") - - # Compute shear strength gradient k from linear fit - k = np.polyfit(z_points, Su_vals, 1)[0] - print(f"k: {k:.2f}") - - # Pile weight (inc. auxiliary elements) assessed as a factor - Wp = 1.35*V_steel*(rhows + rhow) - - # Capacity factors - Nco_0_0 = 2.483*np.log(zlug_B) + 1.974 - Nco_90_0 = 2.174*np.log(zlug_B) + 3.391 - kBSh = k*B/Su - print(f"kBSh: {kBSh:.2f}") - - f0 = np.where(zlug_B < 4, 1.77*(zlug_B**0.3) - 1.289, 0.192*zlug_B + 0.644) - f90 = np.where(zlug_B < 4, 0.68*(zlug_B**0.5) - 0.410, 0.153*zlug_B + 0.341) - - S_kB_0 = 1 - f0*kBSh - S_kB_90 = 1 - f90*kBSh - Nco_0 = S_kB_0*Nco_0_0 - Nco_90 = S_kB_90*Nco_90_0 - Nco = Nco_0 + (Nco_90 - Nco_0)*(beta/90)**2 - - Nco_s_0_0 = np.where(2.90*zlug_B + 6.02 <= 11.59, 2.90*zlug_B + 6.02, 11.596) - Nco_s_90_0 = np.where(2.72*zlug_B + 4.02 <= 11.59, 2.72*zlug_B + 4.02, 11.596) - - S_s_kB_0 = np.where(zlug_B <= 2, 1 + (0.8 - 0.3*zlug_B)*kBSh - (0.383*kBSh**1.36), 1) - f90s = np.where(zlug_B <= 3, 0.267*zlug_B, 0.6) - S_s_kB_90 = 1 - f90s*kBSh - Nco_s_0 = S_s_kB_0*Nco_s_0_0 - Nco_s_90 = S_s_kB_90*Nco_s_90_0 - Nco_s = Nco_s_90 + (Nco_s_0 - Nco_s_90)*((90 - beta)/90)**2 - - # Existing capacity factor and base pressure - Nc_final = max(Nco + (gamma*zlug)/Su, Nco_s) # anchor pullout capacity factor [kN] - print(f"Nc_star: {Nco + (gamma*zlug)/Su:.2f}") - print(f"Nc_star: {Nco_s:.2f}") - qu = Nc_final*Su # Bearing pressure capacity of the anchor plate - Tmax = round(qu*(1 - Los)*B*L, 2) # Bearing tension force capacity of the anchor plate - Hmax = Tmax*np.cos(np.deg2rad(90 - beta)) - Vmax = Tmax*np.sin(np.deg2rad(90 - beta)) - - Ta = np.sqrt(Ha**2 + Va**2) - UC = Ta/Tmax - - resultsPlate = { - 'Capacity': Tmax, - 'Horizontal max.': Hmax, - 'Vertical max.': Vmax, - 'Unity check': UC, - 'Weight plate': Wp - } - - return resultsPlate - -if __name__ == '__main__': - - profile_clay = np.array([ - [ 0.0, 10, 8.0], - [ 9.5, 25, 8.5], - [11.5, 45, 8.5], - [25.0, 50, 9.0] - ]) - - B = 2.0 # Plate width (m) - L = 2.0 # Plate length (m) - zlug = 10.0 # Padeye depth (m) - Ha = 350e3 # Horizontal load (N) - Va = 400e3 # Vertical load (N) - alpha = np.rad2deg(np.arctan2(Va, Ha)) # Load angle from horizontal (deg) - beta = 90 - alpha # Plate angle after keying (m) - - results = getCapacityPlate(profile_clay, 'clay', B, L, zlug, beta, Ha, Va) - print("\n--- Plate Anchor Capacity Results ---") - for key, val in results.items(): - print(f"{key}: {val:.2f}") - - # plot_plate(profile_clay, 'clay', B, L, zlug, beta, title='Inclined Plate Anchor in Clay') - diff --git a/famodel/anchors/anchors_famodel_profile/capacity_plots.py b/famodel/anchors/anchors_famodel_profile/capacity_plots.py deleted file mode 100644 index fb096f3e..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_plots.py +++ /dev/null @@ -1,435 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt - -def plot_pile(profile, soil_type, y, z, D, L, z0=None, zlug=None, hinge_location=None): - '''Plots the deflected shape of the pile alongside the original vertical line. - - Parameters: - ---------- - profile : - - soil_type : str - - y : np.array - Lateral displacements (m) - z : np.array - Depths from pile head (m) - D : float - Pile diameter (m) - L : float - Pile length (m) - z0 : float, optional - Depth of the mudline from the pile head (m) - zlug : float, optional - Depth of the padeye from the pile head (m) - hinge_location : int, optional - Node index where a plastic hinge formed (if any) - label : str - Label for deflected shape line - color : str - Line color for deflected shape - ''' - fig, ax = plt.subplots(figsize=(3, 5)) - - lambdap = L / D - - # Adjust horizontal scale based on slenderness - if lambdap >= 4: - xmax = 5*D # Slender (e.g., driven pile) - elif lambdap <= 2: - xmax = 2*D # Stubby (e.g., drilled & grouted) - else: - xmax = 3*D # Intermediate case - - # Mudline marker - if z0 is not None: - ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)') - #ax.fill_betweenx(z, -0.05 * D, 0.05 * D, where=(z >= z0), color='lightgray', alpha=0.3, label='Soil zone') - - # Padeye marker (on right wall of pile) - if zlug is not None: - ax.plot(D/2, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') - - # Draw pile as rectangle (from head to tip) - pile = plt.Rectangle((-D/2, 0), D, L, edgecolor='k', facecolor='none', lw=2, label='Driven Pile') - ax.add_patch(pile) - - seen_labels = set() - # Plot soil layers as background fills - for i in range(len(profile) - 1): - z_top = profile[i][0] - z_bot = profile[i+1][0] - if soil_type == 'clay': - Su = profile[i][1] - color = plt.cm.Oranges(Su/np.max(profile[:, 1])) - label = f'Su = {Su:.0f} kPa' - elif soil_type == 'sand': - phi = profile[i][1] - gamma = profile[i][2] - color = plt.cm.YlOrBr(phi/np.max(profile[:, 1])) - label = f'ϕ = {phi:.0f}°, γ = {gamma:.1f} kN/m³' - elif soil_type == 'rock': - UCS = profile[i][1] - Em = profile[i][2] - color = plt.cm.Greys(UCS/np.max(profile[:, 1])) - label = f'UCS = {UCS:.2f} MPa, Em = {Em:.1f} MPa' - - # Only assign label if not already used - if label not in seen_labels: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label) - seen_labels.add(label) - else: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4) - - ax.set_xlabel('Lateral displacement (m)') - ax.set_ylabel('Depth (m)') - ax.set_xlim(-xmax, xmax) - ax.set_ylim([L + 5, -2]) # Downward is positive z - ax.grid(ls='--') - ax.legend() - ax.set_title('Pile Deflection Profile') - plt.tight_layout() - plt.show() - - -def plot_suction(profile, soil_type, L, D, zlug=None, title='Suction Pile and Soil Layers', ax=None): - '''Plot the soil profile and a suction pile geometry using array structure for profile. - - Parameters: - ---------- - profile : list or ndarray of lists (z, Su or phi or UCS, gamma) - soil_type : str - 'clay', 'sand', or 'rock' - L : float - Embedded length (m) - D : float - Pile diameter (m) - zlug : float - Padeye depth (m, referenced to pile head = 0) - title : string - Plot title - ''' - import numpy as np - import matplotlib.pyplot as plt - - profile = np.array(profile) - if ax is None: - fig, ax = plt.subplots(figsize=(5, 5)) - xmax = 2*D - - # Split numerical values from profile - z_vals = [float(row[0]) for row in profile] - values = [float(row[1]) for row in profile] - - seen_labels = set() - # Plot soil layers as background fills - for i in range(len(z_vals) - 1): - z_top = z_vals[i] - z_bot = z_vals[i+1] - val = values[i] - - if soil_type == 'clay': - color = plt.cm.Oranges(val / max(values)) - label = f'Su = {val:.0f} kPa' - elif soil_type == 'sand': - color = plt.cm.YlOrBr(val / max(values)) - label = f'ϕ = {val:.0f}°' - - if label not in seen_labels: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label) - seen_labels.add(label) - else: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4) - - # Draw pile geometry - x_left = -D/2; x_right = D/2 - z_top = 0; z_bot = L - ax.plot([x_left, x_left], [z_top, z_bot], color='k', lw=2.0, label='Suction Pile') - ax.plot([x_right, x_right], [z_top, z_bot], color='k', lw=2.0) - ax.plot([x_left, x_right], [z_top, z_top], color='k', lw=2.0) - - # Reference lines - ax.axhline(z_vals[0], color='k', linestyle='--', lw=1.5, label='Mudline') - ax.axhline(L, color='b', linestyle='--', lw=1.5, label='Pile Tip') - - # Padeye marker - if zlug is not None and 0 <= zlug <= L: - ax.plot(x_right, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') - - ax.set_xlabel('Horizontal extent (m)') - ax.set_ylabel('Depth (m)') - ax.set_xlim(-xmax, xmax) - ax.set_ylim(L + 2*D, -D) - ax.set_title(title) - ax.grid() - ax.legend() - plt.tight_layout() - plt.show() - - -def plot_torpedo(profile, soil_type, D1, D2, L1, L2, zlug, title='Torpedo Pile and Soil Layers'): - '''Plot the soil layers and geometry of a torpedo pile using absolute depth for soil and pile head at z=0. - - Parameters: - ---------- - profile : - list or array of (z, Su, 'clay') - soil_type : str - 'clay' - D1 : float - Wing diameter (at tip) (m) - D2 : float - Shaft diameter (at head) - L1 : float - Winged length (m) - L2 : float - Shaft length (m) - title : str - Plot title - ''' - fig, ax = plt.subplots(figsize=(7, 7)) - - xmax = 5*max(D1, D2) - z1 = zlug + L1 # interface between L1 and L2 - z_tip = z1 + L2 # pile tip - - # Split numerical values from profile - z_vals = [float(row[0]) for row in profile] - values = [float(row[1]) for row in profile] - - seen_labels = set() - # Plot soil layers as background fills - for i in range(len(z_vals) - 1): - z_top = z_vals[i] - z_bot = z_vals[i+1] - val = values[i] - - if soil_type == 'clay': - color = plt.cm.Oranges(val/max(values)) - label = f'Su = {val:.0f} kPa' - - # Only assign label if not already used - if label not in seen_labels: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label) - seen_labels.add(label) - else: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4) - - # Draw winged section (upper) - ax.add_patch(plt.Rectangle((-D1/2, zlug), D1, L1, edgecolor='k', facecolor='none', lw=2, label='Winged Section')) - - # Draw shaft section (lower) - ax.add_patch(plt.Rectangle((-D2/2, z1), D2, L2, edgecolor='k', facecolor='none', lw=2, label='Shaft Section')) - - # Reference lines - ax.axhline(z_vals[0], color='k', linestyle='--', lw=1.0, label='Mudline') - ax.axhline( z_tip, color='b', linestyle='--', lw=1.0, label='Pile Tip') - - # Padeye marker - if zlug is not None: - ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') - - - ax.set_xlim(-xmax, xmax) - ax.set_ylim(max(z_vals) + 0.5*D1, min(zlug - 2*D1, z_vals[0] - 2)) - ax.set_xlabel('Horizontal extent (m)') - ax.set_ylabel('Depth (m)') - ax.set_title(title) - ax.grid() - ax.legend() - plt.tight_layout() - plt.show() - -def plot_helical(profile, soil_type, D, L, d, zlug, n_helix=1, spacing=1.0, title='Helical Pile and Soil Layers'): - '''Plot a helical pile in layered soil, with the pile starting at zlug and the helix near the pile tip. - - Parameters: - ---------- - profile : list or array of (z, Su or phi, ...) - soil_type : - 'clay' or 'sand' - D : float - Helix diameter (m) - L : float - Pile length (m) - d : float - Shaft diameter (m) - zlug : float - Embedment depth of pile head (m) - n_helix : int - Number of helices (-) - spacing : float - Vertical spacing between helices (m) - title : str - Plot title - ''' - fig, ax = plt.subplots(figsize=(5, 5)) - - xmax = 3*max(D, d) - - # Extract soil data - z_vals = [float(row[0]) for row in profile] - values = [float(row[1]) for row in profile] - - seen_labels = set() - # Plot soil layers as background fills - for i in range(len(z_vals) - 1): - z_top = z_vals[i] - z_bot = z_vals[i+1] - val = values[i] - - if soil_type == 'clay': - color = plt.cm.Oranges(val/max(values)) - label = f'Su = {val:.0f} kPa' - elif soil_type == 'sand': - color = plt.cm.YlOrBr(val / max(values)) - label = f'ϕ = {val:.0f}°' - - # Only assign label if not already used - if label not in seen_labels: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label) - seen_labels.add(label) - else: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4) - - # Draw shaft - ax.add_patch(plt.Rectangle((-d/2, 0), d, L, edgecolor='k', facecolor='none', lw=2, label='Shaft')) - - # Draw helices - z_helix_base = L - D # Base helix depth - for i in range(n_helix): - z_helix = z_helix_base - i*spacing - if z_helix < zlug: - break - ax.plot([-D/2, D/2], [z_helix - d/2, z_helix + d/2], color='k', lw=2, label='Helix' if i == 0 else None) - - # Reference lines - ax.axhline(z_vals[0], color='k', linestyle='--', lw=1.0, label='Mudline') - ax.axhline( L, color='b', linestyle='--', lw=1.0, label='Pile Tip') - - # Padeye marker - if zlug is not None: - ax.plot(d/2, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') - - ax.set_xlim(-xmax, xmax) - ax.set_ylim(max(z_vals), min(zlug - 0.5*d, z_vals[0] - 2)) - ax.set_xlabel('Horizontal extent (m)') - ax.set_ylabel('Depth (m)') - ax.set_title(title) - ax.grid() - ax.legend() - plt.tight_layout() - plt.show() - -def plot_plate(profile, B, L, zlug, beta, title='Plate Anchor in Clay Profile'): - '''Plot soil layers and an inclined plate anchor centered at zlug. - - Parameters: - ---------- - profile : ndarray of shape (n, 3), with (depth, Su, gamma) - B : float - Plate width (m) - L : float - Plate length (m) - zlug : float - Center embedment of the plate (m) - beta : float - Inclination angle of plate (deg) - title : str - Plot title - ''' - fig, ax = plt.subplots(figsize=(5, 5)) - - xmax = 3*B - - # Extract soil data - layer_depths = profile[:, 0] - layer_depths = np.append(layer_depths, [profile[0][-1]]) - - # Inclined plate geometry - dx = (B/2)*np.cos(np.deg2rad(beta)) - dz = (B/2)*np.sin(np.deg2rad(beta)) - plate_x = [-dx, dx] - plate_z = [zlug - dz, zlug + dz] - - seen_labels = set() - # Plot soil layers as background fills - for i in range(len(layer_depths) - 1): - z_top = layer_depths[i] - z_bot = layer_depths[i+1] - Su = profile[i][1] - color = plt.cm.Oranges(Su/np.max(profile[:, 1])) - label = f'Su = {Su:.0f} kPa' - - # Only assign label if not already used - if label not in seen_labels: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label) - seen_labels.add(label) - else: - ax.axhspan(z_bot, z_top, color=color, alpha=0.4) - - # Plot inclined plate - ax.plot(plate_x, plate_z, color='k', lw=1.5, label='Plate') - - # Reference lines - ax.axhline(0, color='k', linestyle='--', lw=1.0, label='Mudline') - - # Padeye marker - if zlug is not None: - ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') - - ax.set_xlim(-xmax, xmax) - ax.set_ylim(25, -1) - ax.set_xlabel("Horizontal extent (m)") - ax.set_ylabel("Depth (m)") - ax.set_title(title) - ax.legend(loc='lower right') - ax.grid(True) - plt.tight_layout() - plt.show() - -def plot_load(profile, soil_type, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug): - - fig, ax = plt.subplots(figsize=(12, 6)) - n = 2e6 - - # Plot the inverse catenary profile - ax.plot(drag_values, depth_values, color='b', label='Mooring line') - - # Add load vectors - ax.arrow(0, -profile[0][0], Tm*np.cos(np.deg2rad(thetam))/n, Tm*np.sin(np.deg2rad(thetam))/n, - head_width=0.25, head_length=0.5, color='r', label='Mudline Load') - ax.arrow(drag_values[-1], depth_values[-1], Ta*np.cos(thetaa)/n, Ta*np.sin(thetaa)/n, - head_width=0.25, head_length=0.5, color='g', label='Padeye Load') - - #ax.set_aspect('equal', adjustable='datalim') - - # Plot soil layers as background fills - for i in range(len(profile) - 1): - z_top = -profile[i][0] - z_bot = -profile[i+1][0] - if soil_type == 'clay': - Su = profile[i][1] - gamma = profile[i][2] - color = plt.cm.Oranges(Su/np.max(profile[:, 1])) - label = f'Su = {Su:.0f} kPa' - elif soil_type == 'sand': - phi = profile[i][1] - gamma = profile[i][2] - color = plt.cm.YlOrRd(phi/np.max(profile[:, 1])) - label = f'ϕ = {phi:.0f}°, γ = {gamma:.1f} kN/m³' - ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label) - - # Deduplicate legend entries - handles, labels = ax.get_legend_handles_labels() - unique = dict(zip(labels, handles)) - ax.legend(unique.values(), unique.keys(), loc='lower left') - - ax.set_xlabel('Drag distance [m]') - ax.set_ylabel('Embedded depth [m]') - ax.set_title('Inverse Catenary in Layered Soil') - ax.grid(True) - - ax.annotate(f"{Tm/1e6:.2f} MN", (Tm*np.cos(np.deg2rad(thetam))/n, -profile[0][0] + Tm*np.sin(np.deg2rad(thetam))/n), color='r') - ax.annotate(f"{Ta/1e6:.2f} MN", (drag_values[-1] + Ta*np.cos(thetaa)/n, depth_values[-1] + Ta*np.sin(thetaa)/n), color='g') diff --git a/famodel/anchors/anchors_famodel_profile/capacity_pycurves.py b/famodel/anchors/anchors_famodel_profile/capacity_pycurves.py deleted file mode 100644 index 67eb67f8..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_pycurves.py +++ /dev/null @@ -1,298 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from scipy.interpolate import interp1d - -def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, plot=True): - ''' Generate Matlock (1970) p–y curve at a given depth in clay. - - Parameters - ---------- - z : float - Depth relative to pile head (m) - D : float - Pile diameter (m) - zlug : float - Load eccentricity above or below mudline (m) - f_Su : function - Undrained shear strength (Pa) - f_sigma_v_eff : function - Effective vertical stress (Pa) - f_gamma : function - Effective unit weight (kN/m³) - z0 : float, optional - Mudline depth (m). If provided, disables resistance above this level - plot : bool - Plot the resulting p–y curve if True - - Returns - ------- - f : interp1d - Interpolation function for p–y relationship (N/m vs m) - ''' - - Su = f_Su(z) - sigma_v_eff = f_sigma_v_eff(z) - gamma = f_gamma(z) - - # Strain at half the strength as defined by Matlock (1970). - # Typically ranges from 0.005 (stiff clay) to 0.02 (soft clay). - epsilon_50 = 0.02 - J = 0.5 - - # No soil resistance above mudline - if z0 is not None and z < z0: - return lambda y_val: np.zeros_like(y_val) - - # Calculate ultimate resistance and shape parameters - Nc = 3.0 + sigma_v_eff/Su + J*z/D - Nc = min(Nc, 9.0) - z_cr = 6.0 *D/(gamma*D/Su + J) - - p_ult = Su * Nc * D - y_50 = 2.5 * epsilon_50 * D - - # Normalized lateral displacements - Y = np.concatenate((-np.logspace(3, -4, 100), [0], np.logspace(-4, 3, 100))) - P = 0.5 * np.sign(Y)*np.abs(Y)**(1.0/3.0) - P = np.clip(P, -1.0, 1.0) - - # Un-normallized p-y curves - y = Y*y_50 - p = P*p_ult - - f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0) # Interpolation function for p-y curve - - # Plot of p-y curve and check if 'k' is calculated correctly - if plot: - plt.plot(y, p,'-') - plt.xlabel('y (m)') - plt.ylabel('p (N/m)') - plt.title('PY Curves - Matlock (1970)') - plt.grid(True) - plt.xlim([-2*D, 2*D]) - - return f - -def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, plot=True): - ''' Generate API RP2A (1993) p–y curve at a given depth in sand. - - Parameters - ---------- - z : float - Depth relative to pile head (m) - D : float - Pile diameter (m) - zlug : float - Load eccentricity above or below mudline (m) - f_phi : function - Friction angle (deg) - f_sigma_v_eff : function - Effective vertical stress (Pa) - f_Dr : function - Relative density (-) - z0 : float, optional - Mudline depth (m). If provided, disables resistance above this level - plot : bool - Plot the resulting p–y curve if True - - Returns - ------- - f : interp1d - Interpolation function for p–y relationship (N/m vs m) - ''' - - phi = f_phi(z) - sigma_v_eff = f_sigma_v_eff(z) - Dr = f_Dr(z) - - # Interpolate coefficients depending on the effective friction angle - phi_ref = [ 20, 25, 30, 35, 40] - C1_ref = [0.80, 1.25, 1.90, 3.00, 4.50] - C2_ref = [1.60, 2.10, 2.60, 3.40, 4.30] - C3_ref = [ 10, 15, 30, 55, 105] - - C1 = np.interp(phi, phi_ref, C1_ref) - C2 = np.interp(phi, phi_ref, C2_ref) - C3 = np.interp(phi, phi_ref, C3_ref) - - # Disable p–y curve above mudline - if z0 is not None and z < z0: - return lambda y_val: np.zeros_like(y_val) - - # Compute ultimate lateral resistance - p_ult = min(C1*z + C2*D, C3*D) * sigma_v_eff - - # Compute initial stiffness k (kN/m3 → N/m3) - k = (54.6*Dr**2 + 0.8*Dr + 1.8)*1e3 - - # Normalized displacement range - N = 20 - y = np.concatenate((-np.logspace(3, -4, N), [0], np.logspace(-4, 3, N))) - - # Shape coefficient A - A = max(3 - 0.8*z/D, 0.9) - - # Apply API p–y formulation - ε = 1e-6 # prevent division by zero - p = A * p_ult * np.tanh(k*z*y/(A*p_ult + ε)) - - f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0) - - if plot: - # Plot of p-y curve and check if 'k' is calculated correctly - plt.plot(y, p,'-') - plt.xlabel('y (m)') - plt.ylabel('p (N/m)') - plt.title('PY Curves - API (1993)') - plt.grid(True) - plt.xlim([-0.10*D, 0.10*D]) - - return f - -def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, plot=True): - ''' Generate Reese (1997) p–y curve at a given depth in weak rock. - - Parameters - ---------- - z : float - Depth relative to pile head (m) - D : float - Pile diameter (m) - zlug : float - Load eccentricity above or below mudline (m) - f_UCS : function - Unconfined compressive strength UCS(z) (Pa) - f_Em : function - Young's modulus Em(z) (Pa) - z0 : float, optional - Mudline depth (m). If provided, disables resistance above this level - plot : bool - Plot the resulting p–y curve if True - - Returns - ------- - f : interp1d - Interpolation function for p–y relationship (N/m vs m) - ''' - - UCS = f_UCS(z) - Em = f_Em(z) - - RQD = 52 # Assumed fair rock quality (moderately weathered rocks) - Dref = 0.305; nhu = 0.3; E = 200e9 - t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD - I = np.pi*(D**4 - (D - 2*t)**4)/64.0 - EI = E*I - alpha = -0.00667*RQD + 1 - krm = 0.0005 - - if z < z0: - # Above mudline, no resistance - p_ur = 0 - else: - if z < 3*D: - p_ur = alpha*UCS*D*(1 + 1.4*z/D) - #kir = (100 +400*z/(3*D)) - else: - p_ur = 5.2*alpha*UCS*D - #kir = 500 - - kir = (D/Dref)*2**(-2*nhu)*(EI/(Em*D**4))**0.284 - Kir = kir*Em - y_rm = krm*D - y_a = (p_ur/(2*y_rm**0.25*Kir))**1.333 - - # Normalized lateral displacement - N = 20 - y = np.concatenate((-np.logspace(4,-3,N),[0],np.logspace(-3,4,N))) - - p = [] - for val in y: - if abs(val) < y_a: - p_val = np.sign(val) * Kir * val - else: - p_val = np.sign(val)*min((p_ur/2)*(abs(val)/y_rm)**0.25, p_ur) - p.append(p_val) - - f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0) - - if plot: - plt.plot(y, p) - plt.xlabel('y (m)') - plt.ylabel('p (N/m)'), - plt.title('PY Curves - Reese (1997)') - plt.grid(True) - plt.xlim([-0.03*D, 0.03*D]) - #plt.ylim([min(p), max(p)]) - - return f - -def py_Lovera(z, D, f_UCS, f_Em, zlug, z0, plot=True): - ''' Generate Lovera (2019) p–y curve at a given depth for layered rock interfaces. - - Parameters - ---------- - z : float - Depth relative to pile head (m) - D : float - Pile diameter (m) - f_UCS : function - Unconfined compressive strength UCS(z) (Pa) - f_Em : function - Young's modulus Em(z) (Pa) - zlug : float - Load eccentricity (m) - z0 : float - Mudline depth (m). If provided, disables resistance above this level - delta_grout : float, optional - Grout annulus thickness (m) (default value: delta_grout=0.075) - E_grout : float, optional - Grout elastic modulus (Pa) (default value: E_grout=20e9) - delta_crushed : float, optional - Crushed rock annulus thickness (m) (default value: delta_crushed=0.025) - plot : bool - Plot the resulting p–y curve if True - - Returns - ------- - f : interp1d - Interpolation function for p–y relationship (N/m vs m) - ''' - - # Default values - delta_grout = 0.075 - E_grout = 20e9 - delta_crushed = 0.025 - - if z < z0: - return lambda y: np.zeros_like(y) - - # Retrieve elastic modulus at depth - Em = f_Em(z) - nu = 0.3 # Typical Poisson's ratio for rock - G_rock = Em/(2*(1 + nu)) - k_rock = 4*G_rock - - # Set E_crushed as 25% of intact rock modulus if not given - E_crushed = 0.25*Em - - # Compute total stiffness from linear components - k_eq = 1.0/(0.4*delta_grout/E_grout + delta_crushed/E_crushed + 1.0/k_rock) - - y = np.linspace(-0.03*D, 0.03*D, 200) - p = k_eq*y - f = interp1d(y, p, fill_value="extrapolate") - - if plot: - plt.plot(y, p, '-') - plt.xlabel('y (m)') - plt.ylabel('p (N/m)') - plt.title('PY Curves - Lovera (2019)') - plt.grid(True) - plt.xlim([-0.1*D, 0.1*D]) - plt.ylim([min(p), max(p)]) - plt.show() - - return f - diff --git a/famodel/anchors/anchors_famodel_profile/capacity_soils.py b/famodel/anchors/anchors_famodel_profile/capacity_soils.py deleted file mode 100644 index 93d4ae19..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_soils.py +++ /dev/null @@ -1,176 +0,0 @@ - -import numpy as np -from scipy.interpolate import interp1d - -def clay_profile(profile): - ''' Create interpolated functions for a clay soil profile. - Calculates Su, effective vertical stress, unit weight and adhesion factor. - - Parameters - ---------- - profile : array - Clay profile as 2D array: (z, Su, gamma) - Depth (m), undrained shear strength Su (kPa) and effective unit weight gamma (kN/m³) - - Returns - ------- - z0 : float - Depth of mudline relative to pile head (m) - f_Su : interp1d - Undrained shear strength, Su(z) (Pa) - f_sigma_v_eff : interp1d - Effective vertical stress, σ'v(z) (Pa) - f_gamma : interp1d - Effective unit weight of the soil, γ'(z) (N/m³) - f_alpha : function - Adhesion factor from API correlation, α (-) - ''' - - # Depth of mudline relative to pile head - z0 = float(profile[0][0]) - - # Extract data from profile array and zero strength virtual soil layer - # from the pile head down to the mudline - depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m - Su = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # kPa - gamma = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # kN/m3 - - # Calculate sigma_v_eff at each depth - sigma_v_eff = np.zeros(len(depth)) - - for i in range(1, len(depth)): - sigma_v_eff[i] = sigma_v_eff[i-1] + gamma[i-1]*(depth[i] - depth[i-1]) - - # Define interpolation functions - f_Su = interp1d(depth, Su*1e3, kind='linear') # Pa - f_sigma_v_eff = interp1d(depth, sigma_v_eff*1e3, kind='linear') # Pa - f_gamma = interp1d(depth, gamma*1e3, kind='linear') # N/m3 - - # Calculate f_psi and f_alpha at each depth (not as a scalar) - f_psi = lambda z: f_Su(z)/np.maximum(f_sigma_v_eff(z), 1.0) - - def calc_alpha(psi): - # Avoid divide-by-zero or log(0) by setting a floor - psi = np.maximum(psi, 1e-6) - if np.ndim(psi) == 0: - psi = float(psi) - # API-style adhesion factor: two regimes - return min(0.5*psi**-0.50, 1) if psi <= 1.0 else min(0.5*psi**-0.25, 1) - else: - return np.where( - psi <= 1.0, - np.minimum(0.5*psi**-0.50, 1), - np.minimum(0.5*psi**-0.25, 1) - ) - - # Create an interpolated adhesion factor function - # Create an interpolated adhesion factor function - def f_alpha(z): - psi_val = f_psi(z) - alpha_val = calc_alpha(psi_val) - return np.atleast_1d(alpha_val)[0] if np.ndim(alpha_val) == 0 else alpha_val - - return z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha - -def sand_profile(profile): - ''' Create interpolated functions for a sand soil profile. - Calculates phi, effective stress, unit weight, relative density, and skin friction factor. - - Parameters - ---------- - profile : array - Sand profile as 2D array: (z, phi, gamma, Dr) - Depth (m), friction angle, phi (deg), effective unit weight, gamma (kN/m³) and relative density, Dr (%) - - Returns - ------- - z0 : float - Depth of mudline relative to pile head (m) - f_phi : interp1d - Friction angle, φ(z) (deg) - f_sigma_v_eff : interp1d - Effective vertical stress, σ'v(z) (Pa) - f_gamma : interp1d - Effective unit weight of the soil, γ'(z) (N/m³) - f_Dr : interp1d - Relative density, Dr(z) (-) - f_delta : interp1d - Skin friction factor, δ(z) (-) - ''' - - # Depth of mudline relative to pile head - z0 = float(profile[0][0]) - - # Extract data from profile array and zero strength virtual soil layer - # from the pile head down to the mudline - depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m - phi = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # deg - gamma = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # kN/m3 - Dr = np.concatenate([np.array([0]), np.array([row[3] for row in profile],dtype=float)]) # - - - # Calculate sigma_v_eff and static loading factor at each depth - sigma_v_eff = np.zeros(len(depth)) - - for i in range(1, len(depth)): - sigma_v_eff[i] = sigma_v_eff[i-1] + gamma[i-1]*(depth[i] - depth[i-1]) - - # Define interpolation functions - f_phi = interp1d(depth, phi, kind='linear') # deg - f_sigma_v_eff = interp1d(depth, sigma_v_eff*1e3, kind='linear') # Pa - f_gamma = interp1d(depth, gamma*1e3, kind='linear') # N/m3 - f_Dr = interp1d(depth, Dr, kind='linear') # - - - # Define delta as a function of Dr - def calc_delta(Dr_val): - if 35 <= Dr_val < 50: - return 0.29 - elif 50 <= Dr_val < 65: - return 0.37 - elif 65 <= Dr_val < 85: - return 0.46 - elif Dr_val >= 85: - return 0.56 - else: - return 0 # Default or error value for very low Dr values - - # Apply delta calculation to Dr profile - delta_values = np.array([calc_delta(Dr_val) for Dr_val in Dr]) - f_delta = interp1d(depth, delta_values, kind='linear') # Interpolated delta values - - return z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta - -def rock_profile(profile): - ''' Create interpolated functions for a weak rock profile. - Calculates unconfined compressive strength (UCS) and Young’s modulus (Em). - - Parameters - ---------- - profile : array - Rock profile as 2D array: (z, UCS, Em) - Depth (m), unconfined compressive strength, UCS (MPa), Young's modulus, Em (MPa) - - Returns - ------- - z0 : float - Depth of rockline relative to pile head (m) - f_UCS : interp1d - Unconfined compressive strength, UCS(z) (Pa) - f_Em : interp1d - Young's modulus, Em(z) (Pa) - ''' - - # Depth of rockline relative to pile head - z0 = float(profile[0][0]) - - # Extract data from soil_profile array and zero strength virtual soil layer - # from the pile head down to the mudline - depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m - UCS = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # MPa - Em = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # MPa - - # Define interpolation functions - f_UCS = interp1d(depth, UCS*1e6, kind='linear') # Pa - f_Em = interp1d(depth, Em*1e6, kind='linear') # Pa - - return z0, f_UCS, f_Em - diff --git a/famodel/anchors/anchors_famodel_profile/capacity_suction.py b/famodel/anchors/anchors_famodel_profile/capacity_suction.py deleted file mode 100644 index e322048a..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_suction.py +++ /dev/null @@ -1,293 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from scipy.optimize import fsolve -from scipy.optimize import root_scalar -from .capacity_soils import clay_profile, sand_profile -from .capacity_plots import plot_suction - -def getCapacitySuction(profile, soil_type, D, L, zlug, Ha, Va, plot=True): - '''Calculate the inclined load capacity of a suction pile in sand or clay following S. Kay methodology. - The calculation is based on the soil profile, anchor geometry and inclined load. - - Parameters - ---------- - profile : array - Soil profile as a 2D array: (z, parameters) - Clay soil profile (z (m), Su (kPa), gamma (kN/m³)) - Sand soil profile (z (m), phi (deg), gamma (kN/m³), Dr (%)) - soil_type : string - Select soil condition, 'clay' or 'sand' - D : float - Suction pile diameter (m) - L : float - Suction pile length from pile head (m) - zlug: float - Embedded depth of the main padeye (m) - thetalug: float - Angle of tilt misaligment (deg) (default value: 5.0) - psilug: float - Angle of twist misaligment (deg) (default value: 7.5) - Ha : float - Horizontal load at pile lug elevation (N) - Va : float - Vertical load at pile lug elevation (N) - plot : bool - Plot the capacity envelope if True - - Returns - ------- - Dictionary with capcity, weigths and UC. - ''' - - z0 = profile[0][0] - lambdap = (L - z0)/D; m = 2/3; # Suction pile slenderness ratio - t = (6.35 + D*20)/1e3 # Suction pile wall thickness (m), API RP2A-WSD - rlug = D/2 # Radial position of the lug - thetalug = 5 # Angle of tilt misaligment, default is 5. (deg) - psilug = 7.5 # Angle of twist misaligment, default is 7.5. (deg) - rhows = 66.90e3 # Submerged steel specific weight (N/m3) - rhow = 10e3 # Water specific weight (N/m3) - - # Outer and inner surface of the pile skirt - def PileSurface(Len, Dia): - Sp = np.pi*Dia*Len - return Sp - # Dry and wet mass of the pile - def PileWeight(Len, Dia, tw, rho): - Wp = ((np.pi/4)*((Dia**2 - (Dia - 2*tw)**2)*Len + (np.pi/4)*Dia**2*tw))*rho - return Wp - # Mass of the soil plug - def SoilWeight(Len, Dia, tw, gamma_soil): - Wsoil =(np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil - return Wsoil - # Tilt and twist effects due to installation misaligments - def rlugTilt(r, z, theta): - R = r*np.cos(np.deg2rad(theta)) - z*np.sin(np.deg2rad(theta)) - return R - def zlugTilt(r, z, theta): - Z = r*np.sin(np.deg2rad(theta)) + z*np.cos(np.deg2rad(theta)) - return Z - - if soil_type == 'clay': - z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile) - z_vals = np.linspace(z0, L, 10) - - Su_vals = f_Su(z_vals) - ez = np.trapz(z_vals*Su_vals, z_vals)/np.trapz(Su_vals, z_vals); - ez_soil = ez; print(f"ez_soil = {ez_soil:.2f} m") - gamma_vals = f_gamma(z_vals) - - Su_av_L = f_Su(ez_soil) - Su_tip = f_Su(L); # print(f"Su_tip = {Su_tip:.2f} Pa") - sigma_v_eff = f_sigma_v_eff(ez_soil) - gamma_av = np.trapz(gamma_vals, z_vals)/(L - z0); # print(f"gamma_av = {gamma_av:.2f} kN/m³") - alpha_av = float(f_alpha(ez_soil)) - - Nc = min(6.2*(1 + 0.34*np.arctan(lambdap)), 9) - Np_fixed = 10.25; Np_free = 4 - Hmax = Np_fixed*(L - z0)*D*Su_av_L; print(f'Su_av_L = {Su_av_L:.2f} Pa') - print(f'Hmax = {Hmax:.2f} N') - - M = -Va*rlugTilt(rlug, zlug, thetalug) - Ha*(zlugTilt(rlug, zlug, thetalug) - ez_soil) - # print(f"rlug_eff = {rlugTilt(rlug, zlug, thetalug):.2f} m") - # print(f"zlug_eff = {zlugTilt(rlug, zlug, thetalug):.2f} m") - print(f"M = {M:.2f} Nm") - - # --- MH Ellipse Parameters for Clay (Kay 2014) --- - # ΔφMH (piecewise based on L/D) - if 0.5 <= lambdap < 1.125: - delta_phi = 0.32 + 4.32*lambdap; # print(delta_phi) - elif 1.125 <= lambdap < 2.0: - delta_phi = 7.13 - 1.71*lambdap; # print(delta_phi) - elif 2.0 <= lambdap <= 8.0: - delta_phi = 4.55 - 0.425*lambdap; # print(delta_phi) - else: - raise ValueError('L/D ratio out of bounds for MH ellipse formulation.') - - phi_MH = -np.arctan(ez_soil/(L -z0)) - np.deg2rad(delta_phi) - a_MH = Np_fixed/np.cos(phi_MH) - delta_bMH = 0.45*(lambdap)**(-0.9) if lambdap <= 1.5 else 0 - b_MH = -Np_free*np.sin(phi_MH) + delta_bMH - print('M cos(phi)/a_MH =', (M*np.cos(phi_MH))/a_MH) - print('M sin(phi)/b_MH =', (M*np.sin(phi_MH))/b_MH) - - # Solve MH ellipse for Hmax - def f(Hmax): - term1 = ((M*np.cos(phi_MH) + Hmax*np.sin(phi_MH))/a_MH)**2 - term2 = ((M*np.sin(phi_MH) - Hmax*np.cos(phi_MH))/b_MH)**2 - return term1 + term2 - 1 - - try: - Hmax = max(fsolve(f, Hmax*0.8)[0], 0.0) - except: - Hmax = 0.0 - - print(f'Hmax (MH ellipse) = {Hmax:.2f} N') - - To = PileSurface((L - z0), D)*alpha_av*Su_av_L - Ti = PileSurface((L - z0), D - 2*t)*alpha_av*Su_av_L - Tbase = np.pi*D**3*Su_tip/12 - Tmax = min(To + Ti, To + Tbase) - - T = Ha*rlug*np.sin(np.deg2rad(psilug)) - nhuT = T/Tmax - nhuV = Ha/To - nhuVstar = np.sqrt(nhuV**2 - nhuT**2) - alphastar = alpha_av*(nhuVstar/nhuV); print(f"alphastar = {alphastar:.3f}") - - Vmax1 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*alphastar*Su_av_L + Nc*Su_tip*(np.pi/4)*D**2 - Vmax2 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*alphastar*Su_av_L + PileSurface((L - z0), D - 2*t)*alphastar*Su_av_L - Vmax3 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*alphastar*Su_av_L + SoilWeight((L - z0), D, t, gamma_av) - Vmax = min(Vmax1, Vmax2, Vmax3) - - print(f"Vmax1 = {Vmax1:.2f} N"); print(f"Vmax2 = {Vmax2:.2f} N"); print(f"Vmax3 = {Vmax3:.2f} N") - - - elif soil_type == 'sand': - z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile) - z_vals = np.linspace(z0, L, 10) - - sigma_v_vals = f_sigma_v_eff(z_vals) - ez = np.trapz(z_vals*sigma_v_vals, z_vals) / np.trapz(sigma_v_vals, z_vals) - ez_soil = ez - z0 - - phi_vals = f_phi(z_vals) - gamma_vals = f_gamma(z_vals) - sigma_v_vals = f_sigma_v_eff(z_vals) - Dr_vals = f_Dr(z_vals) - delta_vals = f_delta(z_vals) - - phi_av = np.trapz(phi_vals, z_vals)/(L - z0) - gamma_av = np.trapz(gamma_vals, z_vals)/(L - z0) - delta_av = np.trapz(delta_vals, z_vals)/(L - z0) - sigma_av_L = np.trapz(sigma_v_vals, z_vals)/(L - z0) - sigma_tip = f_sigma_v_eff(L) - - Nq = np.e**(np.pi*np.tan(np.radians(phi_av)))*(np.tan(np.radians(45) + np.radians(phi_av)/2))**2 - Hmax = 0.5*D*Nq*gamma_av*(L - z0)**2 - Np_free = 3.0 - - M = -Va*rlugTilt(rlug, zlug, thetalug) - Ha*(zlugTilt(rlug, zlug, thetalug) - ez_soil) - - # --- MH Ellipse Parameters for Clay (Kay 2014) --- - # ΔφMH (piecewise based on L/D) - if 0.5 <= lambdap < 1.125: - delta_phi = 0.32 + 4.32*lambdap - elif 1.125 <= lambdap < 2.0: - delta_phi = 7.13 - 1.71*lambdap - elif 2.0 <= lambdap <= 6.0: - delta_phi = 4.55 - 0.425*lambdap - else: - raise ValueError('L/D ratio out of bounds for MH ellipse formulation.') - - phi_MH = -np.arctan(ez_soil/(L - z0)) - np.deg2rad(delta_phi) - a_MH = Nq/np.cos(phi_MH) - delta_bMH = 0.45*(lambdap)**(-0.9) if lambdap <= 1.5 else 0 - b_MH = -Nq*np.sin(phi_MH) + delta_bMH - - # Solve MH ellipse for Hmax - def f(Hmax): - term1 = ((M*np.cos(phi_MH) + Hmax*np.sin(phi_MH))/a_MH)**2 - term2 = ((M*np.sin(phi_MH) - Hmax*np.cos(phi_MH))/b_MH)**2 - return term1 + term2 - 1 - - Hmax = fsolve(f, 0.8*Hmax)[0] - print(f'Hmax (MH ellipse) = {Hmax:.2f} N') - - To = PileSurface((L - z0), D)*delta_av*sigma_av_L - Ti = PileSurface((L - z0), D - 2*t)*delta_av*sigma_av_L - Tbase = np.pi*D**3*sigma_tip/12 - Tmax = min(To + Ti, To + Tbase) - - T = Ha*rlug*np.sin(np.deg2rad(psilug)) - Fo = delta_av*sigma_av_L*L*np.pi*D - nhuT = T/Tmax - nhuV = Ha/Fo - nhuVstar = np.sqrt(nhuV**2 - nhuT**2) - deltastar = delta_av*(nhuVstar/nhuV) - - Vmax2 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*deltastar*sigma_av_L + PileSurface((L - z0), D - 2*t)*deltastar*sigma_av_L - Vmax3 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*deltastar*sigma_av_L + SoilWeight((L - z0), D, t, gamma_av) - Vmax = min(Vmax2, Vmax3) - - # Pile weight (inc. stiffening plus vent) assessed as a factor - Wp = 1.10*PileWeight(L, D, t, (rhows + rhow)) - # Submerged weight of the soil plug - Wsoil = SoilWeight((L - z0), D, t, gamma_av) - - # Capacity envelope - aVH = 0.5 + lambdap; bVH = 4.5 + lambdap/3 - # print('Env. exp = ' +str(aVH)+' '+str(bVH)) - UC = (Ha/Hmax)**aVH + (Va/Vmax)**bVH - x = np.cos(np.linspace (0, np.pi/2, 100)) - y = (1 - x**bVH)**(1/aVH) - X = Hmax*x; Y = Vmax*y - - if plot: - plt.figure(figsize=(6, 5)) - plt.plot(X, Y, color = 'b', label='VH Envelope') - plt.plot(Ha, Va, 'o', color = 'r', label='Load Point') - - # Set labels and title - plt.xlabel('Horizontal capacity (N)') - plt.ylabel('Vertical capacity (N)') - plt.suptitle('VH suction pile capacity envelope') - plt.axis([0, 1.3*max(X[0], Ha), 0, 1.3*max(Y[-1], Va)]) - plt.legend() - plt.grid(True) - plt.show() - - resultsSuction = { - 'Horizontal max.': Hmax, - 'Vertical max.': Vmax, - 'Unity check': UC, - 'Weight pile': Wp, - 'Weight soil': Wsoil, - 't': t - } - - return resultsSuction - -if __name__ == '__main__': - - # Clay profile: [depth (m), Su (kPa), gamma (kN/m³)] - profile_clay = np.array([ - [ 2.0, 25, 8.5], - [ 4.0, 25, 8.5], - [ 6.0, 25, 8.5], - [20.0, 25, 8.5] - ]) - - # Sand profile: [depth (m), phi (deg), gamma (kN/m³), Dr(%)] - profile_sand = np.array([ - [ 1.0, 28, 8.0, 75], - [ 5.0, 35, 8.5, 75], - [ 8.0, 38, 9.0, 75], - [20.0, 42, 9.5, 75] - ]) - - D = 3.0 # Diameter (m) - L = 10.0 # Length (m) - zlug = 8.0 # Padeye depth (m) - Ha = 2.0e6 # Horizontal load (N) - Va = 3.0e6 # Vertical load (N) - - # === CLAY === - resultsSuction_clay = getCapacitySuction(profile_clay, 'clay', D, L, zlug, Ha, Va, plot=True) - for key, val in resultsSuction_clay.items(): - print(f"{key}: {val:.2f}") - - # Plot suction pile with the clay profile - profile_clay_plot = [(float(z), float(Su), 'clay') for z, Su, _ in profile_clay] - plot_suction(profile_clay_plot, 'clay', L, D, zlug) - - # === SAND === - # resultsSuction_sand = getCapacitySuction(profile_sand, 'sand', D, L, zlug, Ha, Va, plot=True) - # for key, val in resultsSuction_sand.items(): - # print(f"{key}: {val:.2f}") - - # # Sand profile formatted for plotting - # profile_sand_plot = [(float(z), float(phi), 'sand') for z, phi, _, _ in profile_sand] - # plot_suction(profile_sand_plot, 'sand', L, D, zlug, title='Suction Pile in Sand Profile') - diff --git a/famodel/anchors/anchors_famodel_profile/capacity_torpedo.py b/famodel/anchors/anchors_famodel_profile/capacity_torpedo.py deleted file mode 100644 index 5495a956..00000000 --- a/famodel/anchors/anchors_famodel_profile/capacity_torpedo.py +++ /dev/null @@ -1,159 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from .capacity_soils import clay_profile -from .capacity_plots import plot_torpedo - -def getCapacityTorpedo(profile, soil_type, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot=True): - '''Calculate the inclined load capacity of a torpedo pile in clay following S. Kay methodology. - The calculation is based on the soil profile, anchor geometry and inclined load. - - Parameters - ---------- - profile : array - Clay soil profile (z, Su, gamma) - Clay soil profile (z (m), Su (kPa), gamma (kN/m³)) - soil_type : string - Select soil condition, 'clay' - D1 : float - Wing diameter (m) - D2 : float - Shaft diameter (m) - L1 : float - Winged section length (m) - L2 : float - Shaft section length (m) - zlug : float - Padeye embedment depth (m) - Ha : float - Horizontal load at pile lug elevation (N) - Va : float - Vertical load at pile lug elevation (N) - plot : bool - Plot the capacity envelope if True - - Returns - ------- - Dictionary with capcity, weigth and UC. - ''' - - t = (6.35 + D2*20)/1e3 # Torpedo pile wall thickness (m), API RP2A-WSD - rhows = 66.90e3 # Submerged steel specific weight (N/m3) - rhow = 10e3 # Water specific weight (N/m3) - - # Average effective width - L = L1 + L2 - A_wing_plane_1 = (D1 - D2)*L1 - A_wing_plane_2 = (D1 - D2)*np.cos(np.deg2rad(45))/2*L1 - A_shaft = D2*L - - # Choose based on direction: - plane = '1' # or '2' - - if plane == '1': - Dstar = (A_wing_plane_1 + A_shaft)/L - elif plane == '2': - Dstar = (A_wing_plane_2 + A_shaft)/L - - z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile) - - a = zlug - c = zlug + L1 + L2 - profile_depth = profile[-1, 0] - - if c > profile_depth: - raise ValueError( - f'Soil profile does not cover the full pile length.\n' - f' → Pile tip depth: {c:.2f} m\n' - f' → Soil profile depth: {profile_depth:.2f} m\n' - f'Extend the soil profile to at least the pile tip depth to run the capacity model.' - ) - - z_vals = np.linspace(a, c, 100) - Su_vals = f_Su(z_vals) - alpha_vals = np.array([f_alpha(z) for z in z_vals]) - - ez_soil = np.trapz(z_vals*Su_vals, z_vals)/np.trapz(Su_vals, z_vals) - Su_e = f_Su(ez_soil) - alpha_e = f_alpha(ez_soil) - print(f"Su_e = {Su_e:.2f} kPa, ez_soil = {ez_soil:.2f} m, alpha_e = {alpha_e:.2f}") - - def PileWeight(Len1, Len2, Dia1, Dia2, tw, rho): - return ((np.pi/4)*(Dia1**2 - (Dia1 - 2*tw)**2)*(Len1 + Len2) + 4*Len2*Dia2*tw)*rho - - def PileSurface(Len1, Len2, Dia1, Dia2): - return np.pi*Dia1*(Len1 + Len2) + 8*Len2*Dia2*0.9 - - Np_free = 3.45 - Hmax = Np_free*L*Dstar*Su_e - Vmax = PileSurface(L1, L2, D1, D2)*alpha_e*Su_e + PileWeight(L1, L2, D1, D2, t, rhows) + ballast - - # Pile weight (inc. auxiliary elements) assessed as a factor - Wp = 1.10*PileWeight(L1, L2, D1, D2, t, (rhows + rhow)) + ballast - - # Calculate actual ez_su to L ratio - ez_ratio = (ez_soil - zlug)/L; print(f"ez_ratio = {ez_ratio:.2f} m") - - # Assign aVH and bVH based on ez_su/L - if np.isclose(ez_ratio, 2/3, atol=0.05): - aVH = 0.5 + L/Dstar - bVH = 4.5 - L/(3*Dstar) - mode = 'deep mobilization (2/3)' - elif 0.45 <= ez_ratio <= 0.75: - aVH = 4.5 + L/(2*Dstar) - bVH = 3.5 - L/(4*Dstar) - mode = 'moderate mobilization (1/2 – 3/4)' - print(f'Interaction exponents set to aVH = {aVH:.2f}, bVH = {bVH:.2f} [{mode}]') - - UC = (Ha/Hmax)**aVH + (Va/Vmax)**bVH - - deg = np.linspace(0, 90, 20) - x = np.cos(np.deg2rad(deg)) - y = (1 - x**bVH)**(1/aVH) - X = Hmax*x; Y = Vmax*y - - if plot: - plt.plot(X, Y, color='blue', label='VH Envelope') - plt.plot(H, V, 'o', color='red', label='Load Point') - plt.xlabel('Horizontal Load (N)') - plt.ylabel('Vertical Load (N)') - plt.title('VH torpedo pile capacity envelope') - plt.grid(True) - plt.legend() - plt.axis([0, 1.3*max(X[0], H), 0, 1.3*max(Y[-1], V)]) - plt.show() - - resultsTorpedo = { - 'Horizontal max.': Hmax, - 'Vertical max.': Vmax, - 'Unity check': UC, - 'Weight pile': Wp - } - - return resultsTorpedo - -if __name__ == '__main__': - - profile_clay = np.array([ - [ 0.0, 50, 8.0], - [20.0, 50, 8.5], - [25.0, 50, 8.5], - [50.0, 50, 9.0] - ]) - - D1 = 3.0 # Wing diameter (m) - D2 = 1.5 # Shaft diamter (m) - L1 = 11.0 # Winged section length (m) - L2 = 10.0 # Shaft section length (m) - zlug = 15.0 # Padeye depth (m) - ballast = 10000 # Ballast load (N) - H = 6.0e6 # Horizontal load (N) - V = 8.0e6 # Vertical load (N) - - results = getCapacityTorpedo(profile_clay, 'clay', D1, D2, L1, L2, zlug, ballast, H, V, plot=True) - print("\n--- Torpedo Pile Capacity Results ---") - for key, val in results.items(): - print(f"{key}: {val:.2f}") - - plot_torpedo(profile_clay, 'clay', D1, D2, L1, L2, zlug, title='Torpedo Pile in Clay Profile') - diff --git a/famodel/anchors/getCapacityAnchor_profile.py b/famodel/anchors/getCapacityAnchor_profile.py deleted file mode 100644 index 4e7cf491..00000000 --- a/famodel/anchors/getCapacityAnchor_profile.py +++ /dev/null @@ -1,94 +0,0 @@ - -from famodel.anchors.anchor_profile import Anchor -from famodel.anchors.anchors_famodel_profile.capacity_plots import plot_load -import numpy as np - -# Define the soil profile -soil_profile = np.array([ - [ 1.0, 10, 8.0], - [ 2.0, 25, 8.5], - [ 8.0, 50, 9.0], - [16.0, 100, 9.5], - [25.0, 100, 9.5] -]) - -# Create Anchor object -anchor = Anchor( - dd={ - 'type': 'suction', - 'design': {'D': 2.5, 'L': 10.0, 'zlug': 6.0, 'soil_type': 'clay'}, - 'soil_properties': {'clay': soil_profile} - }, - ms=None, - r=[0.0, 0.0, 0.0], - aNum=0, - id='A1', - g=9.81, - rho=1025 -) - -# Assign loads manually -anchor.loads = { - 'Hm': 3e6, # Horizontal mudline load (N) - 'Vm': 1e6 # Vertical mudline load (N) -} - -# Also assign mooring line properties manually -anchor.line_type = 'chain' -anchor.d = 0.16 # Chain diameter (m) -anchor.w = 5000.0 # Nominal submerged weight (N/m) - -# --- Step 1: Compute Lug Forces --- -Ha, Va = anchor.getLugForces( - ground_conds=anchor.dd['soil_properties'], - Hm=anchor.loads['Hm'], - Vm=anchor.loads['Vm'], - thetam=np.degrees(np.arctan2(anchor.loads['Vm'], anchor.loads['Hm'])), - zlug=anchor.dd['design']['zlug'], - line_type=anchor.line_type, - d=anchor.d, - w=anchor.w, - plot=True -) - -# Print Lug Forces -print('\nLug Forces Computed:') -print(f'Ha = {Ha:.2f} N') -print(f'Va = {Va:.2f} N') - -# --- Step 2: Compute Anchor Capacity --- -anchor.getCapacityAnchor( - ground_conds=anchor.dd['soil_properties'], - Hm=anchor.loads['Hm'], - Vm=anchor.loads['Vm'], - thetam=np.degrees(np.arctan2(anchor.loads['Vm'], anchor.loads['Hm'])), - zlug=anchor.dd['design']['zlug'], - line_type=anchor.line_type, - d=anchor.d, - w=anchor.w, - plot=True -) - -# Print Capacity Results -print('\nCapacity Results:') -for key, value in anchor.capacity_results.items(): - print(f'{key}: {value:.2f}') - -# --- Step 3: Optimize Anchor Geometry --- -anchor.getSizeSuction( - geom=[anchor.dd['design']['L'], anchor.dd['design']['D']], - geomKeys=['L', 'D'], - geomBounds=[(5.0, 15.0), (2.0, 6.0)], - loads=None, - minfs={'Ha': 1.0, 'Va': 1.0}, - lambdap_con=[3, 6], - zlug_fix=False, - plot=True -) - -print('\nFinal Optimized Anchor:') -print('Design:', anchor.dd['design']) -print('Capacity Results:', anchor.capacity_results) - -# --- Step 4: Visualize Anchor Geometry --- -anchor.getCombinedPlot() \ No newline at end of file diff --git a/famodel/anchors/getCapacityHelical_map.py b/famodel/anchors/getCapacityHelical_map.py deleted file mode 100644 index 1710e413..00000000 --- a/famodel/anchors/getCapacityHelical_map.py +++ /dev/null @@ -1,70 +0,0 @@ - -from anchor_map import Anchor - -# --- Define soil profile --- -profile_map = [ - { - 'name': 'CPT_H1', - 'x': 0.0, 'y': 0.0, - 'layers': [ - {'top': 1.0, 'bottom': 10.0, 'soil_type': 'sand', 'gamma_top': 10.0, 'gamma_bot': 11.0, 'phi_top': 30, 'phi_bot': 32, 'Dr_top': 60, 'Dr_bot': 60}, - {'top': 10.0, 'bottom': 20.0, 'soil_type': 'sand', 'gamma_top': 11.0, 'gamma_bot': 11.5, 'phi_top': 36, 'phi_bot': 38, 'Dr_top': 60, 'Dr_bot': 80} - ] - } -] - -# --- Create helical anchor object --- -anchor = Anchor( - dd = { - 'type': 'helical', - 'design': { - 'D': 1.7, # Helix diameter (m) - 'L': 12.0, # Depth (m) - 'd': 0.3, # Shaft diameter (m) - 'zlug': 4.0 # Padeye depth (m) - } - }, - r = [0.0, 0.0, 0.0] -) - -# Assign loads and mooring info -anchor.loads = { - 'Hm': 8.8e6, - 'Vm': 1.2e6 -} -anchor.line_type = 'chain' -anchor.d = 0.16 -anchor.w = 5000.0 - -# Assign local soil -anchor.setSoilProfile(profile_map) - -# --- Step 1: Lug Forces --- -layers, Ha, Va = anchor.getLugForces( - Hm = anchor.loads['Hm'], - Vm = anchor.loads['Vm'], - zlug = anchor.dd['design']['zlug'], - line_type = anchor.line_type, - d = anchor.d, - w = anchor.w, - plot = True -) - -print('\nLug Forces Computed:') -print(f'Ha = {Ha:.2f} N') -print(f'Va = {Va:.2f} N') - -# --- Step 2: Capacity --- -anchor.getCapacityAnchor( - Hm = anchor.loads['Hm'], - Vm = anchor.loads['Vm'], - zlug = anchor.dd['design']['zlug'], - line_type = anchor.line_type, - d = anchor.d, - w = anchor.w, - plot = True -) - -print('\nCapacity Results:') -for key, val in anchor.capacity_results.items(): - print(f'{key}: {val:.2f}') diff --git a/famodel/anchors/getCapacityHelical_sand.py b/famodel/anchors/getCapacityHelical_sand.py deleted file mode 100644 index 1710e413..00000000 --- a/famodel/anchors/getCapacityHelical_sand.py +++ /dev/null @@ -1,70 +0,0 @@ - -from anchor_map import Anchor - -# --- Define soil profile --- -profile_map = [ - { - 'name': 'CPT_H1', - 'x': 0.0, 'y': 0.0, - 'layers': [ - {'top': 1.0, 'bottom': 10.0, 'soil_type': 'sand', 'gamma_top': 10.0, 'gamma_bot': 11.0, 'phi_top': 30, 'phi_bot': 32, 'Dr_top': 60, 'Dr_bot': 60}, - {'top': 10.0, 'bottom': 20.0, 'soil_type': 'sand', 'gamma_top': 11.0, 'gamma_bot': 11.5, 'phi_top': 36, 'phi_bot': 38, 'Dr_top': 60, 'Dr_bot': 80} - ] - } -] - -# --- Create helical anchor object --- -anchor = Anchor( - dd = { - 'type': 'helical', - 'design': { - 'D': 1.7, # Helix diameter (m) - 'L': 12.0, # Depth (m) - 'd': 0.3, # Shaft diameter (m) - 'zlug': 4.0 # Padeye depth (m) - } - }, - r = [0.0, 0.0, 0.0] -) - -# Assign loads and mooring info -anchor.loads = { - 'Hm': 8.8e6, - 'Vm': 1.2e6 -} -anchor.line_type = 'chain' -anchor.d = 0.16 -anchor.w = 5000.0 - -# Assign local soil -anchor.setSoilProfile(profile_map) - -# --- Step 1: Lug Forces --- -layers, Ha, Va = anchor.getLugForces( - Hm = anchor.loads['Hm'], - Vm = anchor.loads['Vm'], - zlug = anchor.dd['design']['zlug'], - line_type = anchor.line_type, - d = anchor.d, - w = anchor.w, - plot = True -) - -print('\nLug Forces Computed:') -print(f'Ha = {Ha:.2f} N') -print(f'Va = {Va:.2f} N') - -# --- Step 2: Capacity --- -anchor.getCapacityAnchor( - 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zBy5}4aTj1itOFYO!9$16sh>HM8v9VB3Lc|Kg%f(0=99Ofo11~tgEic6`8yc|!U@^pF3@Bxd KYxzpIAN~h(KxicZ literal 0 HcmV?d00001 diff --git a/famodel/geography.py b/famodel/geography.py index ec4219c1..f930a901 100644 --- a/famodel/geography.py +++ b/famodel/geography.py @@ -3,29 +3,17 @@ import os import numpy as np import matplotlib.pyplot as plt - import moorpy as mp from moorpy.helpers import set_axes_equal import yaml - import pandas as pd import geopandas as gpd from shapely.geometry import Point, Polygon, LineString - - - - import famodel.seabed.seabed_tools as sbt - - - - from pyproj import CRS from pyproj.aoi import AreaOfInterest from pyproj.database import query_utm_crs_info - - def getLatLongCRS(epsg_code=4326): '''Returns a coordinate reference system (CRS) object from the pyproj package of a 'wordly' CRS with units of latitude and longitude @@ -92,7 +80,6 @@ def getTargetCRS(longitudes, latitudes): return target_crs - def getCustomCRS(long, lat): '''Seemingly way too simple of a method to create a pyproj CRS centered around a custom geographical point @@ -113,8 +100,6 @@ def getCustomCRS(long, lat): return custom_crs - - def getLeaseCoords(lease_name): # read in the BOEM shapefile that contains all Wind Energy Lease Areas (can use other shapefiles for aliquots) @@ -140,17 +125,17 @@ def getLeaseCoords(lease_name): raise ValueError(f"The lease area name '{lease_area}' is not supported yet") # extract the longitude and latitude coordinates of the lease area - area_longs, area_lats = lease_area.geometry.unary_union.exterior.coords.xy + #area_longs, area_lats = lease_area.geometry.unary_union.exterior.coords.xy + area_longs, area_lats = lease_area.geometry.union_all().exterior.coords.xy + # calculate the centroid of the lease area centroid = ( lease_area.geometry.centroid.values.x[0], lease_area.geometry.centroid.values.y[0] ) - return area_longs, area_lats, centroid - - - + return area_longs, area_lats, centroid + def convertLatLong2Meters(longs, lats, centroid, latlong_crs, target_crs, return_centroid=False): '''input longs/lats need to be in EPSG:4326 CRS Longs and Lats need to be in pairs, i.e., the first entry to longs and @@ -206,7 +191,6 @@ def convertLatLong2Meters(longs, lats, centroid, latlong_crs, target_crs, return else: return xs, ys - def convertMeters2LatLong(xs, ys, centroid, latlong_crs, target_crs, mesh=False): '''Input xs and ys need to be in the target CRS Xs and Ys need to be in pairs, i.e. the first entry to xs and the @@ -250,9 +234,6 @@ def convertMeters2LatLong(xs, ys, centroid, latlong_crs, target_crs, mesh=False) return longs, lats - - - def getMapBathymetry(gebcofilename): # load the GEBCO bathymetry file @@ -277,7 +258,6 @@ def getMapBathymetry(gebcofilename): return longs, lats, depths, ncols, nrows - def convertBathymetry2Meters(longs, lats, depths, centroid, centroid_utm, latlong_crs, target_crs, ncols, nrows, xs=[], ys=[]): @@ -331,8 +311,6 @@ def convertBathymetry2Meters(longs, lats, depths, centroid, centroid_utm, return bathXs, bathYs, bath_depths - - def writeBathymetryFile(moorpy_bathymetry_filename, bathXs, bathYs, bath_depths, soil=False): '''Write a MoorDyn/MoorPy-style bathymetry text file based on provided x and y grid line values and a 2D array of depth values.''' @@ -355,8 +333,6 @@ def writeBathymetryFile(moorpy_bathymetry_filename, bathXs, bathYs, bath_depths, f.write('\n') f.close() - - def getLeaseAndBathymetryInfo(lease_name, gebco_file, bath_ncols=100, bath_nrows=100): # initialize the conventional lat/long CRS @@ -369,12 +345,15 @@ def getLeaseAndBathymetryInfo(lease_name, gebco_file, bath_ncols=100, bath_nrows custom_crs = getCustomCRS(centroid[0], centroid[1]) # convert the lease boundary to meters - lease_xs, lease_ys, centroid_utm = convertLatLong2Meters(lease_longs, lease_lats, centroid, latlong_crs, custom_crs, return_centroid=True) + lease_xs, lease_ys, centroid_utm = convertLatLong2Meters(lease_longs, lease_lats, centroid, + latlong_crs, custom_crs, return_centroid=True) # get bathymetry information from a GEBCO file (or other) bath_longs, bath_lats, bath_depths, ncols, nrows = getMapBathymetry(gebco_file) + # convert bathymetry to meters - bath_xs, bath_ys, bath_depths = convertBathymetry2Meters(bath_longs, bath_lats, bath_depths, centroid, centroid_utm, latlong_crs, custom_crs, bath_ncols, bath_nrows) + bath_xs, bath_ys, bath_depths = convertBathymetry2Meters(bath_longs, bath_lats, bath_depths, centroid, centroid_utm, + latlong_crs, custom_crs, bath_ncols, bath_nrows) # export to MoorPy-readable file bathymetryfile = f'bathymetry_{bath_ncols}x{bath_nrows}.txt' writeBathymetryFile(bathymetryfile, bath_xs, bath_ys, bath_depths) @@ -393,10 +372,6 @@ def getLeaseAndBathymetryInfo(lease_name, gebco_file, bath_ncols=100, bath_nrows return info - - - - def getSoilType(x, y, centroid, latlong_crs, custom_crs, soil_file): """Function to return the name of the soil below a specific x/y coordinate by creating shapely polygons based on the shapefile data. It loops through all polygons in the shapefile and if the x/y position is contained in that polygon, it returns the soil of that polygon.""" @@ -439,8 +414,6 @@ def getSoilType(x, y, centroid, latlong_crs, custom_crs, soil_file): return soil_type - - def getSoilGrid(centroid, latlong_crs, custom_crs, soil_file, nrows=100, ncols=100, xbound=None, ybound=None): """Note: can make the outer shapely shape have 'holes' of the inner shapely shapes""" @@ -486,6 +459,7 @@ def getSoilGrid(centroid, latlong_crs, custom_crs, soil_file, nrows=100, ncols=1 ys = np.linspace(ybound[0], ybound[-1], nrows) else: ys = np.linspace( np.min([np.min(soil_ys[i]) for i in range(len(soil_shapes))]), np.max([np.max(soil_ys[i]) for i in range(len(soil_shapes))]), nrows) + soil_grid = np.zeros([len(ys), len(xs)]) # for each manmade grid point, loop through all the polygons and determine whether that grid point is within the shape or not @@ -507,8 +481,6 @@ def getSoilGrid(centroid, latlong_crs, custom_crs, soil_file, nrows=100, ncols=1 soil_grid = np.array(soil_grid_list) # saving to list and then changing to np.array because I couldn't figure out how else to do it with strings return xs, ys, soil_grid - - def plot3d(lease_xs, lease_ys, bathymetryfilename, area_on_bath=False, args_bath={}, xbounds=None, ybounds=None, zbounds=None): '''Plot aspects of the Project object in matplotlib in 3D''' @@ -520,12 +492,12 @@ def plot3d(lease_xs, lease_ys, bathymetryfilename, area_on_bath=False, args_bath fig = plt.figure(figsize=(6,4)) ax = plt.axes(projection='3d') - if xbounds != None: - ax.set_xlim(xbounds[0], xbounds[1]) - if ybounds != None: - ax.set_ylim(ybounds[0], ybounds[1]) - if zbounds != None: - ax.set_zlim(zbounds[0], zbounds[1]) + # if xbounds != None: + # ax.set_xlim(xbounds[0], xbounds[1]) + # if ybounds != None: + # ax.set_ylim(ybounds[0], ybounds[1]) + # if zbounds != None: + # ax.set_zlim(zbounds[0], zbounds[1]) # plot the lease area in a red color, if desired ax.plot(lease_xs, lease_ys, np.zeros(len(lease_xs)), color='r', zorder=100) @@ -535,7 +507,7 @@ def plot3d(lease_xs, lease_ys, bathymetryfilename, area_on_bath=False, args_bath # !!!! include option to plot entire bathymetry file or not if isinstance(bathymetryfilename, str): - bathGrid_Xs, bathGrid_Ys, bathGrid = sbt.readBathymetryFile(bathymetryfilename) # parse through the MoorDyn/MoorPy-formatted bathymetry file + bathGrid_Xs, bathGrid_Ys, bathGrid = sbt.readBathymetryFile(bathymetryfilename) # parse through the MoorDyn/MoorPy-formatted bathymetry file X, Y = np.meshgrid(bathGrid_Xs, bathGrid_Ys) # create a 2D mesh of the x and y values bath = ax.plot_surface(X, Y, -bathGrid, rstride=1, cstride=1, vmin=args_bath['zlim'][0], vmax=args_bath['zlim'][1], @@ -558,8 +530,6 @@ def plot3d(lease_xs, lease_ys, bathymetryfilename, area_on_bath=False, args_bath return fig, ax - - def projectAlongSeabed(x, y, bathXs, bathYs, bath_depths): '''Project a set of x-y coordinates along a seabed surface (grid), returning the corresponding z coordinates.''' @@ -925,7 +895,7 @@ def addState(self, ax, states=[], kwargs={}): # get bathymetry information from a GEBCO file (or other) bath_longs, bath_lats, bath_depths, ncols, nrows = getMapBathymetry('bathymetry/gebco_2023_n41.3196_s40.3857_w-125.2881_e-123.9642.asc') # convert bathymetry to meters - ncols = 500 + ncols = 500C:/Users/fmoreno/Downloads/GEBCO_25_Jun_2025_3c682d73375c/GEBCO_25_Jun_2025_3c682d73375c/gebco_2024_n44.2_s44.0_w12.5_e12.8.asc nrows = 500 bath_xs, bath_ys, bath_depths = convertBathymetry2Meters(bath_longs, bath_lats, bath_depths, centroid, centroid_utm, latlong_crs, custom_crs, ncols, nrows) # export to MoorPy-readable file @@ -942,11 +912,73 @@ def addState(self, ax, states=[], kwargs={}): lease_name = 'GulfofMaine_ResearchArray' gebco_file = __location__+'\\..\\geography\\gebco_2024_n44.1458_s41.4761_w-70.9497_e-66.2146.asc' info = getLeaseAndBathymetryInfo(lease_name, gebco_file) + + x_center = np.mean(info['lease_xs']) + y_center = np.mean(info['lease_ys']) + + zoom = 8000 + + xbounds = [x_center - zoom, x_center + zoom] + ybounds = [y_center - zoom, y_center + zoom] + + fig, ax = plot3d(info['lease_xs'], info['lease_ys'], 'bathymetry_100x100.txt', + area_on_bath=True, args_bath={'cmap': 'gist_earth', 'zlim': [-500, 50]}, + xbounds=xbounds, ybounds=ybounds) + + plt.show() + + # Load bathymetry data manually + with open('GulfOfMaine_bathymetry_100x100.txt', 'r') as f: + lines = f.readlines() + + nGridX = int(lines[1].split()[1]) + nGridY = int(lines[2].split()[1]) + x_vals = np.array([float(val) for val in lines[3].split()]) + y_vals = [] + z_matrix = [] + + for line in lines[4:4+nGridY]: + parts = line.split() + y_vals.append(float(parts[0])) + z_matrix.append([float(z) for z in parts[1:]]) + + + # Extract y and z + z_vals = np.array(z_matrix) + y_vals = np.array(y_vals) + + + # Now crop using xbounds and ybounds + xmask = (x_vals >= xbounds[0]) & (x_vals <= xbounds[1]) + ymask = (y_vals >= ybounds[0]) & (y_vals <= ybounds[1]) + + x_crop = x_vals[xmask] + y_crop = y_vals[ymask] + z_crop = z_vals[ymask][:, xmask] + + # Plot manually using Axes3D + X, Y = np.meshgrid(x_crop, y_crop) + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + ax.plot_surface(X, Y, z_crop, cmap='gist_earth') + ax.set_title('Zoomed Bathymetry') + + lease_xs = info['lease_xs'] + lease_ys = info['lease_ys'] + ax.plot(lease_xs, lease_ys, zs=200, zdir='z', color='r', linewidth=2, label='Lease Area') + ax.legend() plt.show() - a = 2 + + + # plot3d(info['lease_xs'], info['lease_ys'], 'bathymetry_100x100.txt', area_on_bath=False, args_bath={'zlim':[-3000, 500], 'cmap': 'gist_earth'}) + # xbounds=(info['bath_xs'].min(), info['bath_xs'].min()), + # ybounds=(info['bath_ys'].min(), info['bath_ys'].min()), + # zbounds=(info['bath_depths'].min(), info['bath_depths'].min()) + + diff --git a/famodel/mooring/mooringOntology.yaml b/famodel/mooring/mooringOntology.yaml index 90d548a5..3d582873 100644 --- a/famodel/mooring/mooringOntology.yaml +++ b/famodel/mooring/mooringOntology.yaml @@ -1194,7 +1194,7 @@ platforms: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1217,7 +1217,7 @@ platforms: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) diff --git a/famodel/project.py b/famodel/project.py index 31340c2d..1bc0ae93 100644 --- a/famodel/project.py +++ b/famodel/project.py @@ -1,6 +1,9 @@ """Project class for FAModel, containing information and key methods for the site information and design information that make up a project.""" +import sys +sys.path.append(r'C:\Code\FAModel_anchors') + import os import numpy as np import matplotlib.pyplot as plt @@ -1002,7 +1005,6 @@ def seabedIntersect(self, r, u): return r_i - def projectAlongSeabed(self, x, y): '''Project a set of x-y coordinates along a seabed surface (grid), returning the corresponding z coordinates.''' @@ -1022,206 +1024,148 @@ def projectAlongSeabed(self, x, y): return z - - # METHODS TO USE WITH ANCHOR TOOLS - - def loadSoil(self, filename=None, yaml=None): + def loadSoil(self, filename=None, yaml=None, soil_mode='uniform', profile_source=None): ''' - Load geoetechnical information from an input file (format TBD), convert to - a rectangular grid, and save the grid to the floating array object (TBD). - - The input file should provide rows with the following entries: - - x coordinate - - y coordinate - - class - soil classification name ('clay', 'sand', or 'rock' with optional modifiers) - - gamma* - soil effective unit weight [kPa] (all soils) - - Su0* - undrained shear strength at mudline [kPa] (clay - - K* - undrained shear strength gradient [kPa/m] (clay - - alpha* - soil skin friction coefficient [-] (clay soils) - - phi* - angle of internal friction [deg] (sand soils) - - Some (*) parameters are optional depending on the soil class and mode. + Load geotechnical information from input file or YAML. + Supports two soil modes: 'uniform' and 'layered'. - Irregular sampling points will be supported and interpolated to a - rectangular grid. - - Paramaters + Parameters ---------- - filename : path - path/name of file containing soil data + filename : str, optional + Path to .txt/.dat file with soil labels/profile IDs and coordinates + yaml : dict, optional + Dictionary containing soil data and properties (used when filename is None) + soil_mode : str + Either 'uniform' or 'layered' + profile_source : str, optional + Path to YAML file with layered profile definitions (only used if soil_mode='layered') ''' xs = None ys = None soil_names = None - if filename is not None: # if the filename option was selected, then that means there is at least a grid in the file, and maybe soil type information - if filename[-3:]=='shp': - raise ValueError("Geography-related operations not directly supported in Project class") - - elif filename[-3:]=='txt' or filename[-3:]=='dat': + soilProps = None - # load in the grid portion of the soil input file - xs, ys, soil_names = sbt.readBathymetryFile(filename, dtype=str) # read MoorDyn-style file + # Case 1: File input (grid + properties) + if filename is not None: + if filename.endswith('.shp'): + raise ValueError("Shapefiles not supported in Project class") - soilProps = sbt.getSoilTypes(filename) # load in the soil property information (if there is any) - - # regardless of whether there is soil type information in the file, if there is soil information in the yaml, read that in - if yaml: - soilProps = yaml['soil_types'] # if there is a yaml file as input, load in the soil props that way (overwrites the other one) + elif filename.endswith('.txt') or filename.endswith('.dat'): + # Load label/profile_id grid + xs, ys, soil_names = sbt.readBathymetryFile(filename, dtype=str) + + # Load soil properties + soilProps = sbt.getSoilTypes(filename, soil_mode=soil_mode, profile_source=profile_source) + if yaml: + soilProps = yaml.get('soil_types', soilProps) # allow overwriting via YAML - elif filename is None: # if the filename option was not selected - if yaml: # and if there was a yaml option selected, simply read in that yaml information + # Case 2: YAML only (no filename) + elif filename is None: + if yaml: xs = yaml['x'] ys = yaml['y'] soil_names = yaml['type_array'] - soilProps = yaml['soil_types'] - else: # but if there was no yaml option selected (and no file option selected) -> set default values - print('Warning: No soil grid nor soil properties were selected, but this function ran -> use preprogrammed default values') + raw_soil_types = yaml['soil_types'] + + # Ensure all soil types have a 'layers' field + soilProps = {} + for key, entry in raw_soil_types.items(): + if 'layers' in entry: + soilProps[key] = entry + else: + # Wrap old flat format into single-layer profile (optional fallback) + layer = dict(entry) + layer.setdefault('top', 0) + layer.setdefault('bottom', 50) + layer.setdefault('soil_type', key) + soilProps[key] = {'layers': [layer]} + else: + print('[Warning] No soil input provided — using default values') xs = [0] ys = [0] - soil_names = ['mud'] - soilProps = dict(mud={'Su0':[2.39], 'k':[1.41], 'gamma':[10], 'depth':[0]}, - rock={'UCS':[5], 'Em':[7], 'depth':[0]}) - + soil_names = [['mud']] # note: should be 2D to match grid structure + soilProps = { + 'mud': {'layers': [{ + 'soil_type': 'clay', + 'top': 0, 'bottom': 50, + 'gamma_top': 10, 'gamma_bot': 10, + 'Su_top': 2.39, 'Su_bot': 59.39 + }]}, + 'rock': {'layers': [{ + 'soil_type': 'rock', + 'top': 0, 'bottom': 50, + 'UCS_top': 5, 'UCS_bot': 5, + 'Em_top': 7, 'Em_bot': 7 + }]} + } + + else: - raise ValueError("Something is wrong") - - ''' - # check that correct soil properties are being provided for the different soil types - for soil in soilProps: - if 'rock' in soil or 'hard' in soil: - if not 'UCS' in soilProps[soil] or not 'Em' in soilProps[soil]: - raise ValueError('Rock soil type requires UCS and Em values') - elif 'sand' in soil: - if not 'phi' in soilProps[soil] or not 'gamma' in soilProps[soil]: - raise ValueError('Sand soil type requires phi and gamma values') - elif 'clay' in soil: - if not 'Su0' in soilProps[soil] or not 'k' in soilProps[soil]: - raise ValueError('Clay soil type requires Su0 and k values') - elif 'mud' in soil or 'mud_soft': - if not 'Su0' in soilProps[soil] or not 'k' in soilProps[soil]: - raise ValueError('Mud soil type requires Su0 and k values') - else: - raise ValueError(f'Soil type {soil} not recognized. Soil type key must contain one of the following keywords: rock, sand, clay, mud') - ''' - - # make sure the soilProps dictionary has all the required information (should be updated later with exact properties based on anchor capacity functions) - # setting each soil type dictionary with all the values, just in case they need them for whatever reason - here are the default values - # the default types (and values) are set if there is no other information provided - for key,props in soilProps.items(): - props['Su0'] = getFromDict(props, 'Su0' , shape=-1, dtype=list, default=[2.39], index=None) - props['k'] = getFromDict(props, 'k' , shape=-1, dtype=list, default=[1.41], index=None) - props['alpha'] = getFromDict(props, 'alpha', shape=-1, dtype=list, default=[0.7] , index=None) - props['gamma'] = getFromDict(props, 'gamma', shape=-1, dtype=list, default=[4.7] , index=None) - props['phi'] = getFromDict(props, 'phi' , shape=-1, dtype=list, default=[0.0] , index=None) - props['UCS'] = getFromDict(props, 'UCS' , shape=-1, dtype=list, default=[7.0] , index=None) - props['Em'] = getFromDict(props, 'Em' , shape=-1, dtype=list, default=[50.0], index=None) - - for k,prop in props.items(): - if 'array' in type(prop).__name__: - # clean up property type - props[k] = np.array(prop) - - + raise ValueError("Invalid combination of filename/yaml inputs") + + # --- Set defaults only for uniform mode --- + if soil_mode == 'uniform': + for key, props in soilProps.items(): + props['Su0'] = getFromDict(props, 'Su0', shape=-1, dtype=list, default=[2.39]) + props['k'] = getFromDict(props, 'k', shape=-1, dtype=list, default=[1.41]) + props['alpha'] = getFromDict(props, 'alpha', shape=-1, dtype=list, default=[0.7]) + props['gamma'] = getFromDict(props, 'gamma', shape=-1, dtype=list, default=[8.7]) + props['phi'] = getFromDict(props, 'phi', shape=-1, dtype=list, default=[0.0]) + props['UCS'] = getFromDict(props, 'UCS', shape=-1, dtype=list, default=[7.0]) + props['Em'] = getFromDict(props, 'Em', shape=-1, dtype=list, default=[50.0]) + + # ensure no array-like leftovers + for k, prop in props.items(): + if hasattr(prop, '__array__'): + props[k] = np.array(prop) + + # --- Store to project --- self.soilProps = soilProps - - - if xs is not None: self.soil_x = np.array(xs) self.soil_y = np.array(ys) self.soil_names = np.array(soil_names) - - - # update soil info for anchor if needed + + self.soil_mode = soil_mode + print(f"Loaded soilProps keys: {list(soilProps.keys())}") + + # --- Update anchor objects if available --- if self.anchorList: - for anch in self.anchorList.values(): - name, props = self.getSoilAtLocation(anch.r[0],anch.r[1]) - anch.soilProps = {name:props} - - # load data from file - - # interpolate onto grid defined by grid_x, grid_y - - # save - ''' - self.soil_class - self.soil_gamma - self.soil_Su0 - self.soil_K - self.soil_alpha - self.soil_phi - ''' - pass - + for anchor in self.anchorList.values(): + name, props = self.getSoilAtLocation(anchor.r[0], anchor.r[1]) + anchor.soilProps = {name: props} def getSoilAtLocation(self, x, y): ''' - Interpolate soil properties at specified location from the soil - properties grid and return a dictionary of soil properties that - can be used in anchor capacity calculations. - - Parameters - ---------- - x : float - x coordinate in array reference frame [m]. - y : float - y coordinate in array reference frame [m]. + Retrieve the soil information at a specific location, supporting both uniform and layered modes. Returns - ------- - soilProps : dictionary - Dictionary of standard MoorPy soil properties. + ------- + (str, dict or list): soil name or profile ID, and associated soil properties or layered profile ''' - - # NEW: finds the specific soil grid point that the xy point is closest to and assigns it that soil type if self.soil_x is not None: - ix = np.argmin([abs(x-soil_x) for soil_x in self.soil_x]) - iy = np.argmin([abs(y-soil_y) for soil_y in self.soil_y]) - - soil_name = self.soil_names[iy, ix] - - soil_info = self.soilProps[soil_name] - - return soil_name, soil_info - else: - pass - - ''' - # SIMPLE HACK FOR NOW - rocky, _,_,_,_ = sbt.interpFromGrid(x, y, self.soil_x, self.soil_y, self.soil_rocky) - - return rocky - ''' - ''' - soilProps = {} - + ix = np.argmin([abs(x - sx) for sx in self.soil_x]) + iy = np.argmin([abs(y - sy) for sy in self.soil_y]) + soil_id = self.soil_names[iy, ix] # could be label or profile_id - if self.seabed_type == 'clay': - - soilProps['class'] = 'clay' - soilProps['gamma'] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_gamma) - soilProps['Su0' ] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_Su0 ) - soilProps['k' ] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_k ) - soilProps['alpha'] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_alpha) - soilProps['phi' ] = None - - elif self.seabed_type == 'sand': - soilProps['class'] = 'sand' - soilProps['gamma'] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_gamma) - soilProps['Su0' ] = None - soilProps['k' ] = None - soilProps['alpha'] = None - soilProps['phi' ] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_phi ) - - # note: for sand, can assume homogeneous angle of internal fricton + if self.soil_mode == 'uniform': + soil_info = self.soilProps[soil_id] + return soil_id, soil_info + + elif self.soil_mode == 'layered': + profile_layers = self.soilProps[soil_id] # list of layer dicts + return soil_id, profile_layers + + else: + raise ValueError(f"Unknown soil_mode: {self.soil_mode}") + + print(f"[DEBUG] soil_id at location ({x}, {y}) is: {soil_id}") + print(f"[DEBUG] Available soilProps keys: {list(self.soilProps.keys())}") else: - raise ValueError(f"Unsupported seabed type '{self.seabed_type}'.") - - return soilProps - ''' + raise ValueError("No soil grid defined") # # ----- Anchor def updateAnchor(self,anch='all',update_loc=True): @@ -1251,39 +1195,20 @@ def updateAnchor(self,anch='all',update_loc=True): name, props = self.getSoilAtLocation(x,y) # update soil anchor.soilProps = {name:props} - - - # def calcAnchorCapacity(self, anchor): - # '''Compute holding capacity of a given anchor based on the soil - # info at its position. The anchor object's anchor properties and - # location will be used to determine the holding capacity, which - # will be saved to the anchor object. - - # Parameters - # ---------- - # anchor : MoorPy Anchor object (derived from Point) - # The anchor object in question. - # ''' - - # # interpolate soil properties/class based on anchor position - # anchor.soilProps = self.getSoilAtLocation(anchor.r[0], anchor.r[1]) - - # # fill in generic anchor properties if anchor info not provided - # if not type(anchor.anchorProps) == dict: - # anchor.anchorProps = dict(type='suction', diameter=6, length=12) - - # # apply anchor capacity model - # capacity, info = anchorCapacity(anchorProps, soilProps) - - # # save all information to the anchor (attributes of the Point) - # anchor.soilProps = soilProps - # anchor.anchorCapacity = capacity - # anchor.anchorInfo = info - - # # also return it - # return capacity - + def setSoilAtLocation(self, anchor): + name, props = self.getSoilAtLocation(anchor.r[0], anchor.r[1]) + + # Add required metadata + layer = dict(props) # shallow copy of props + layer['soil_type'] = name # or force to 'clay'/'rock' if needed + layer['top'] = props.get('top', 0) + layer['bottom'] = props.get('bottom', 50) + # Wrap in expected profile_map format + profile_map = [{'name': name, 'layers': [layer]}] + anchor.setSoilProfile(profile_map) + + def setCableLayout(self): # 2-D diff --git a/famodel/seabed/seabed_tools.py b/famodel/seabed/seabed_tools.py index ae70c24c..7011a68e 100644 --- a/famodel/seabed/seabed_tools.py +++ b/famodel/seabed/seabed_tools.py @@ -5,11 +5,6 @@ import matplotlib.pyplot as plt import numpy as np - - - - - def readBathymetryFile(filename, dtype=float): with open(filename, 'r') as f: @@ -68,37 +63,82 @@ def writeBathymetryFile(filename, grid_x, grid_y, grid_depth): row = [y] + list(grid_depth[i, :]) f.write(" ".join(map(str, row)) + "\n") -def getSoilTypes(filename): - '''function to read in a preliminary input text file format of soil type information''' +import yaml +import os - soilProps = {} +def getSoilTypes(filename, soil_mode='layered', profile_source=None): + ''' + Load soil properties or layered profiles depending on soil_mode. - f = open(filename, 'r') - - for line in f: - if line.count('---') > 0 and (line.upper().count('SOIL TYPES') > 0): - line = next(f) # skip this header line, plus channel names and units lines - var_names = line.split() - line = next(f) - line = next(f) - while line.count('---') == 0: - entries = line.split() - soilProps[entries[0]] = {} - for iv,var in enumerate(var_names[1:]): - # convert entries to strings unless there is - if entries[iv+1] == '-': - soilProps[entries[0]][var] = [0] - else: - soilProps[entries[0]][var] = [float(entries[iv+1])] - line = next(f) - - f.close() + Parameters + ---------- + filename : str + Path to .txt file containing grid and profile/soil label definitions + soil_mode : str + 'uniform' or 'layered' + profile_source : str or None + Path to YAML file with layered soil profiles (used only for 'layered') - return soilProps + Returns + ------- + soilProps : dict + Dictionary of soil type properties (uniform) or layered profiles (layered) + ''' + soilProps = {} + used_labels = [] + with open(filename, 'r') as f: + lines = f.readlines() + for i, line in enumerate(lines): + if line.strip().startswith('---') and 'SOIL TYPES' in line.upper(): + break + + # Extract used labels from the SOIL TYPES section + for line in lines[i+3:]: + if '---' in line: + break + entries = line.strip().split() + label = entries[0] + used_labels.append(label); print(label) + + if soil_mode == 'uniform': + var_names = lines[i+1].split() + for line in lines[i+3:]: + if '---' in line: + break + entries = line.strip().split() + label = entries[0] + soilProps[label] = {} + for iv, var in enumerate(var_names[1:]): + val = entries[iv+1] + soilProps[label][var] = [float(val)] if val != '-' else [0.0] + + elif soil_mode == 'layered': + if profile_source is None: + raise ValueError("profile_source (path to YAML) is required for layered mode.") + + # Load the full YAML file of profiles + with open(profile_source, 'r') as f: + all_profiles = yaml.safe_load(f) + + # Reassign each label to the actual layer list directly + for label in used_labels: + if label not in all_profiles: + raise KeyError(f'Profile ID {label} not found in YAML: {profile_source}') + soilProps[label] = all_profiles[label]['layers'] # now a list of layer dicts + + print(f"[DEBUG] Loaded profiles from YAML: {list(soilProps.keys())}") + if used_labels: + print(f"[DEBUG] Example layers for {used_labels[0]}: {soilProps[used_labels[0]]}") + else: + print("[WARNING] No profile labels were found in the soil grid.") + else: + raise ValueError(f"Unrecognized soil_mode '{soil_mode}'") + + return soilProps def convertLatLong2Meters(zerozero, lats, longs): '''Convert a list of latitude and longitude coordinates into @@ -141,11 +181,6 @@ def convertLatLong2Meters(zerozero, lats, longs): return Xs, Ys - - - - - def processASC(gebcofilename, lat, lon, outfilename=""): '''Process an ASC file of bathymetry information and convert into a rectangular bathymetry grid in units of m relative to the @@ -266,9 +301,16 @@ def processGeotiff(filename, lat, lon, outfilename="processGeotiff.txt", **kwarg #lats, _ = rasterio.transform.xy(tiff.transform, 0, range(tiff.width-1,-1,-1)) height, width = tiff.shape cols, rows = np.meshgrid(np.arange(width), np.arange(height)) - longs_mesh, lats_mesh = rasterio.transform.xy(tiff.transform, rows, cols) - longs = np.array(longs_mesh)[0,:] - lats = np.flip(np.array(lats_mesh)[:,0]) + + # rasterio.transform.xy returns flat lists, so reshape after + longs_list, lats_list = rasterio.transform.xy(tiff.transform, rows, cols) + + longs_array = np.array(longs_list).reshape((height, width)) + lats_array = np.array(lats_list).reshape((height, width)) + + longs = longs_array[0, :] # all x-coords from first row + lats = np.flip(lats_array[:, 0]) # all y-coords from first column (flip to make it south to north) + # lats data provided from top left corner, i.e., latitudes are descending. It seems that the following interpolation functions (getDepthFromBathymetry) # can only work if latitudes start small and increase, meaning that the first latitude entry has to be the bottom left corner @@ -707,11 +749,14 @@ def getPlotBounds(latsorlongs_boundary, zerozero, long=True): if __name__ == '__main__': - centroid = (40.928, -124.708) #humboldt - xs = np.arange(-30000,30001,400) - ys = np.arange(-40000,40001,400) + centroid = (44.1, 12.65) + # Choose grid that fits within 25x22 km — e.g., ±10 km to be safe + xs = np.arange(-10000, 10001, 400) # 50 x points → 20 km total + ys = np.arange(-10000, 10001, 400) # 50 y points - xs, ys, depths = processGeotiff('humboldt.tif', centroid[0], centroid[1], xs=xs, ys=ys, outfilename='test output.txt') + xs, ys, depths = processGeotiff('gebco_2024_n44.2_s44.0_w12.5_e12.8.tif', + centroid[0], centroid[1], xs=xs, ys=ys, + outfilename='test output.txt') import moorpy as mp ms = mp.System(depth=np.max(depths), bathymetry='test output.txt') diff --git a/famodel/seabed/test output.txt b/famodel/seabed/test output.txt new file mode 100644 index 00000000..d7761f3c --- /dev/null +++ b/famodel/seabed/test output.txt @@ -0,0 +1,55 @@ +--- MoorPy Bathymetry Input File --- +nGridX 51 +nGridY 51 + -10000.00 -9600.00 -9200.00 -8800.00 -8400.00 -8000.00 -7600.00 -7200.00 -6800.00 -6400.00 -6000.00 -5600.00 -5200.00 -4800.00 -4400.00 -4000.00 -3600.00 -3200.00 -2800.00 -2400.00 -2000.00 -1600.00 -1200.00 -800.00 -400.00 0.00 400.00 800.00 1200.00 1600.00 2000.00 2400.00 2800.00 3200.00 3600.00 4000.00 4400.00 4800.00 5200.00 5600.00 6000.00 6400.00 6800.00 7200.00 7600.00 8000.00 8400.00 8800.00 9200.00 9600.00 10000.00 +-10000.00 -59.765 -58.906 -38.079 -27.261 -27.565 -27.146 -24.467 -23.345 -23.486 -27.393 -41.435 -53.706 -30.342 -18.322 -19.148 -17.880 -16.607 -14.551 -14.170 -12.598 -10.169 -8.602 -11.852 -9.802 -5.174 -4.550 -3.576 1.920 5.710 7.117 7.900 8.582 8.988 9.000 9.000 9.000 9.600 9.897 10.000 10.000 10.000 10.000 10.000 10.897 11.000 11.000 11.000 11.000 11.000 11.204 11.934 +-9600.00 -61.993 -56.979 -35.704 -26.878 -25.706 -24.045 -22.552 -21.525 -21.101 -25.824 -35.156 -44.625 -26.111 -13.920 -13.592 -13.706 -13.778 -12.570 -11.591 -10.076 -7.681 -6.540 -5.940 -4.525 -3.400 -1.060 2.510 5.746 6.856 7.832 8.371 8.925 9.000 9.000 9.209 9.762 9.921 10.000 10.000 10.000 10.000 10.000 10.645 11.000 11.000 11.000 11.000 11.000 11.751 11.808 12.000 +-9200.00 -69.080 -54.023 -41.091 -37.848 -30.482 -23.653 -21.163 -20.399 -19.637 -23.969 -35.319 -46.270 -31.407 -16.493 -10.337 -10.024 -10.495 -9.571 -8.372 -7.056 -5.412 -4.298 -2.870 -2.107 0.332 2.514 5.651 6.922 7.720 8.628 8.809 9.000 9.000 9.049 9.729 10.000 10.000 10.000 10.000 10.000 10.000 10.408 10.944 11.000 11.000 11.000 11.000 11.517 12.000 12.000 12.000 +-8800.00 -66.667 -55.758 -57.224 -59.801 -42.220 -25.509 -20.499 -19.082 -17.691 -20.628 -32.444 -43.204 -28.311 -13.821 -8.035 -7.031 -7.039 -6.116 -5.583 -5.684 -3.729 -2.163 -1.555 0.727 3.436 5.231 7.036 7.492 8.539 9.000 9.000 9.000 9.000 9.531 10.000 10.000 10.000 10.000 10.032 10.487 10.487 10.824 11.000 11.000 11.000 11.215 11.481 11.917 12.000 12.000 12.000 +-8400.00 -54.952 -64.936 -83.616 -91.950 -62.900 -31.396 -20.589 -17.759 -15.745 -17.086 -23.848 -29.242 -17.347 -8.385 -6.202 -4.767 -4.291 -4.023 -4.152 -5.086 -3.140 -1.459 0.260 3.818 5.210 6.856 8.054 8.356 9.000 9.000 9.000 9.000 9.313 10.000 10.000 10.000 10.000 10.000 10.066 11.000 11.000 11.000 11.000 11.000 11.087 11.639 12.000 12.000 12.000 12.000 12.000 +-8000.00 -43.799 -73.192 -101.596 -118.193 -87.675 -41.901 -20.768 -16.685 -14.787 -15.054 -15.456 -13.884 -10.024 -7.523 -5.127 -3.371 -3.000 -3.518 -3.807 -4.000 -3.487 -1.509 3.039 5.522 6.523 7.720 8.764 9.000 9.000 9.000 9.107 9.219 9.908 10.000 10.000 10.000 10.146 10.217 10.269 11.000 11.000 11.000 11.000 11.212 11.410 12.000 12.000 12.000 12.000 12.000 12.000 +-7600.00 -32.692 -58.037 -71.587 -99.977 -96.847 -53.373 -21.105 -15.044 -13.923 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18.069 18.915 19.000 19.030 19.555 20.000 20.087 21.000 21.029 21.557 22.000 22.084 23.000 23.028 23.604 24.741 25.000 25.193 26.027 26.559 diff --git a/tests/simple_farm.yaml b/tests/simple_farm.yaml index 22e56373..8b8b65d9 100644 --- a/tests/simple_farm.yaml +++ b/tests/simple_farm.yaml @@ -1166,7 +1166,7 @@ platform: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1189,7 +1189,7 @@ platform: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) diff --git a/tests/testOntology.yaml b/tests/testOntology.yaml index b554d5f0..4ee8b8be 100644 --- a/tests/testOntology.yaml +++ b/tests/testOntology.yaml @@ -32,27 +32,53 @@ site: file: #'../bathymetry200m_sample.txt' seabed: - x : [-1901, 0, 1900] - y : [-1900, 2, 1900 ] + x : [-1901, 0, 1900 ] + y : [-1900, 2, 1900 ] type_array: - [mud_soft , mud_firm , mud_soft] - - [mud_soft , mud_soft , mud_soft] + - [mud_soft , mud_soft , mud_soft] - [mud_soft , mud_firm , mud_soft] - - soil_types: # dictionary-based approach - mud_soft: - Su0 : [2.39] # [kPa] - k : [1.41] # [kPa/m] - depth: [0] # [m] - mud_firm: - Su0 : [23.94] # [kPa] - k : [2.67] # [kPa/m] - depth: [0] # [m] - rock: - UCS : [7] # [MPa] - Em : [50] # [MPa] - depth: [0] # [m] + + soil_types: + mud_soft: + layers: + - soil_type: clay + top: 0 + bottom: 50 + gamma_top: 10.0 + gamma_bot: 10.0 + Su_top: 2.39 + Su_bot: 59.39 + mud_firm: + layers: + - soil_type: clay + top: 0 + bottom: 50 + gamma_top: 10.0 + gamma_bot: 50.0 + Su_top: 23.4 + Su_bot: 157.44 + sand: + layers: + - soil_type: sand + top: 1 + bottom: 50 + gamma_top: 8.5 + gamma_bot: 8.5 + phi_top: 28.0 + phi_bot: 32.00 + Dr_top: 50.0 + Dr_bot: 85.00 + weak_rock: + layers: + - soil_type: rock + top: 0 + bottom: 50 + UCS_top: 5.0 + UCS_bot: 5.0 + Em_top: 7.0 + Em_bot: 7.0 metocean: extremes: # extreme values for specified return periods (in years) @@ -82,7 +108,7 @@ site: - [ 10.5, 0, 0.01, operating, 0, JONSWAP, 12, 6, 0 ] RAFT_settings: - min_freq : 0.001 # [Hz] lowest frequency to consider, also the frequency bin width + min_freq : 0.001 # [Hz] lowest frequency to consider, also the frequency bin width max_freq : 0.10 # [Hz] highest frequency to consider XiStart : 0 # sets initial amplitude of each DOF for all frequencies nIter : 4 # sets how many iterations to perform in Model.solveDynamics() @@ -1212,7 +1238,7 @@ platforms: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1235,7 +1261,7 @@ platforms: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1484,17 +1510,17 @@ mooring_connector_types: # Anchor type properties anchor_types: suction_pile1: - type : suction_pile + type : suction L : 16.4 # length of pile [m] D : 5.45 # diameter of pile [m] zlug : 9.32 # embedded depth of padeye [m] d-g_pile1: - type : dandg_pile + type : dandg L : 50 # length of pile [m] D : 3 # diameter of pile [m] zlug : 0 # embedded depth [m] driven_pile1: - type : driven_pile + type : driven L : 20 # pile length [m] D : 1.5 # pile diameter [m] zlug : 3 # embedded depth [m @@ -1504,14 +1530,14 @@ anchor_types: zlug : 10 #beta: 30 torpedo_pile1: - type: torpedo_pile + type: torpedo D1 : 3 D2: 1.1 L1: 10 L2: 4 zlug: 16 helical_pile1: - type: helical_pile + type: helical L : 25.1 d : 1 D : 5.01 diff --git a/tests/test_anchors.py b/tests/test_anchors.py index 0292c66a..f82f55a7 100644 --- a/tests/test_anchors.py +++ b/tests/test_anchors.py @@ -3,6 +3,20 @@ import numpy as np import os +# --- Helper goes at module level --- +def assign_soil(anchor, soil_label, project): + soil_def = project.soilProps[soil_label] + layers = soil_def['layers'] + print('[DEBUG] assign_soil: soil_label =', soil_label) + print('[DEBUG] assign_soil: soil_def =', soil_def) + profile_map = [{ + 'name': 'CPT_Assigned', + 'x': 0, 'y': 0, + 'layers': layers}] + + anchor.setSoilProfile(profile_map) + anchor.profile_name = 'CPT_Assigned' + def test_anchor_loads(): # load in famodel project @@ -10,93 +24,137 @@ def test_anchor_loads(): project = Project(file=os.path.join(dir,'testOntology.yaml'), raft=False) project.getMoorPyArray(cables=1) anch = project.anchorList['FOWT1a'] + + assign_soil(anch, 'mud_soft', project) - # get lug loads on anchor - anch.getLugForces(plot=False) + # Force calculation + anch.getMudlineForces(max_force=False) - assert('Ha' in anch.loads) - assert('Hm' in anch.loads) - assert(anch.loads['Ha'] != anch.loads['Hm']) + # Extract mudline loads + Hm = anch.loads.get('Hm') + Vm = anch.loads.get('Vm') + zlug = anch.dd['design']['zlug'] + + # Compute lug loads + _, Ha, Va = anch.getLugForces(Hm, Vm, zlug, plot=False) + anch.loads['Ha'] = Ha + anch.loads['Va'] = Va + # Assertions + assert 'Ha' in anch.loads + assert 'Hm' in anch.loads + assert anch.loads['Ha'] != anch.loads['Hm'] + def test_anchor_capacities(): # load in famodel project (suction pile anchor) dir = os.path.dirname(os.path.realpath(__file__)) project = Project(file=os.path.join(dir,'testOntology.yaml'), raft=False) project.getMoorPyArray(cables=1) anch = project.anchorList['FOWT1a'] + + assign_soil(anch, 'mud_firm', project) # fill in load dictionary to skip watch circle run - loads = {'Ha':4522222,'Va':3948278} + loads = {'Ha':4.5e6, 'Va':1.9e6} # get capacity and safety factor - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - - # try suction pile with sand - soil = anch.soilProps - soil['sand'] = soil.pop(next(iter(soil.keys()))) - soil['sand']['phi'] = 33 - soil['sand']['Dr'] = 50 + Ha = loads['Ha'] + Va = loads['Va'] + zlug = anch.dd['design']['zlug'] + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + + # try SUCTION PILE with sand + assign_soil(anch, 'sand', project) # get capacity and safety factor - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - - # check plate anchor type - newdd = anch.dd - newdd['type'] = 'plate' - newdd['design'] = {'type':'plate','A':20,'zlug':10,'beta':10} - anch.soilProps['clay'] = anch.soilProps.pop('sand') - # new loads - loads['Ha'] = 1000000 + Ha = loads['Ha'] + Va = loads['Va'] + zlug = anch.dd['design']['zlug'] + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + + # check PLATE ANCHOR type + anch.dd['type'] = 'plate' + anch.dd['design'] = {'B':5, 'L':2, 'zlug':10, 'beta':10} + assign_soil(anch, 'mud_soft', project) + # new horizontal load + loads['Ha'] = 2e6 loads['Va'] = 0 + Ha = loads['Ha'] + Va = loads['Va'] + zlug = anch.dd['design']['zlug'] # get capacity and safety factor - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - - # check drilled and grouted anchor type (need to change material to rock) - loads = {'Ha':4522222,'Va':3948278} # go back to original loads - newdd['type'] = 'dandg_pile' - newdd['design'] = {'type':'dandg_pile','L':50,'D':3,'zlug':0} - soil['rock'] = soil.pop('clay') # soil_properties has default rock info in there already, just change name - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - - # check driven pile anchor in rock and clay - newdd['type'] = 'driven' - soil['weak_rock'] = soil.pop('rock') - newdd['design'] = {'type':'driven','L':20,'D':1.5,'zlug':-3} - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - - soil['clay'] = soil.pop('weak_rock') - newdd['design'] = {'type':'driven','L':30,'D':2,'zlug':3} - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - - soil['sand'] = soil.pop('clay') - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - soil['sand']['Dr'] = 50 - - # check helical pile anchor with sand - newdd['type'] = 'helical_pile' - newdd['design'] = {'type':'helical_pile','L':25.1,'d':1,'D':5.01,'zlug':5} - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - - # check helical pile anchor with clay - soil['clay'] = soil.pop('sand') - newdd['type'] = 'helical_pile' - newdd['design'] = {'type':'helical_pile','L':25.1,'d':1,'D':5.01,'zlug':5} - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - - # check torpedo anchor - newdd['type'] = 'torpedo_pile' - newdd['design'] = {'type':'torpedo_pile','D1':3,'D2':1.1,'L1':10,'L2':4,'zlug':16} - anch.getAnchorCapacity(loads=loads, plot=False) - anch.getFS(loads=loads) - + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + + # check DRILLED & GROUTED PILE (need to change material to rock) + loads = {'Ha':4.5e5, 'Va':1.9e5} # again assign new loads + anch.dd['type'] = 'dandg' + anch.dd['design'] = {'L':10, 'D':3, 'zlug':0} + assign_soil(anch, 'weak_rock', project) + Ha = loads['Ha'] + Va = loads['Va'] + zlug = anch.dd['design']['zlug'] + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + + # check DRIVEN PILES in soils and rock + anch.dd['type'] = 'driven' + anch.dd['design'] = {'L':20, 'D':1.5, 'zlug': 3} + assign_soil(anch, 'mud_firm', project) + Ha = loads['Ha'] + Va = loads['Va'] + zlug = anch.dd['design']['zlug'] + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + + anch.dd['design'] = {'L':30, 'D':2, 'zlug':3} + assign_soil(anch, 'sand', project) + Ha = loads['Ha'] + Va = loads['Va'] + zlug = anch.dd['design']['zlug'] + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + + assign_soil(anch, 'weak_rock', project) + Ha = loads['Ha'] + Va = loads['Va'] + # change the padeye back to mudline elevation in rock + anch.dd['design']['zlug'] = 0 + zlug = anch.dd['design']['zlug'] + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + + # check HELICAL PILE with sand + anch.dd['type'] = 'helical' + anch.dd['design'] = {'L':15, 'd':1.25, 'D':2.00, 'zlug':3} + assign_soil(anch, 'sand', project) + Ha = loads['Ha'] + Va = loads['Va'] + zlug = anch.dd['design']['zlug'] + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + + # check HELICAL PILE with clay + anch.dd['type'] = 'helical' + anch.dd['design'] = {'L':12, 'd':0.5, 'D':1.5, 'zlug':3} + assign_soil(anch, 'mud_firm', project) + Ha = loads['Ha'] + Va = loads['Va'] + zlug = anch.dd['design']['zlug'] + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + + # check TORPEDO PILE + anch.dd['type'] = 'torpedo' + anch.dd['design'] = {'D1':3, 'D2':1.1, 'L1':10, 'L2':4, 'zlug':16} + assign_soil(anch, 'mud_soft', project) + Ha = loads['Ha'] + Va = loads['Va'] + zlug = anch.dd['design']['zlug'] + anch.getCapacityAnchor(Ha, Va, zlug=zlug, plot=False) + anch.getSafetyFactor() + From 8efe33fd6c0458855d754eabef19fad51dc6a517 Mon Sep 17 00:00:00 2001 From: Stein Date: Wed, 30 Jul 2025 14:40:10 -0600 Subject: [PATCH 02/15] Anchor.getSizeAnchor updates to support previous anchor design work To start, I created a new "display" variable that can toggle on/off print statements--in anchor.py and capacity function files--to make the output file clear to read - NOTE: This was only done for suction piles and dandg piles. The display parameter will have to be called from the other anchor type capacity function scripts, which I did not prioritize right now Second, I ran into problems with anchor.dd['type'] values not being exact strings of 'suction', 'dandg', etc. - I corrected the type values in my Project on my front-end, but also added a check in Anchor.__init__ to make sure the type values are exactly what they should be - In makeMoorPyAnchor, it references MoorPy.getPointProps['AnchorProps'], which contain cost information for certain types of anchors. If the anchor type does not exist in getPointProps (e.g., 'dandg'), then it defaults to a suction pile getSizeAnchor - Implemented functionality to support safety factors on different components (includes Hm, Vm, and UC right now) - Updated a lot of if statements to check for the anchType just in case the string is something different but includes the main anchor type name - Added a constraint function for the design variable bounds, since COBYLA does not recognize bounds - Moved the code that updated the anchor design dictionary and recalculated the capacity to the first constraint function, since with COBYLA, the constraints are evaluated first, and this led to UC's that didn't correspond - Added a check to the dandg (and driven/helical) sections to ensure the L/D constraints were being met - - However, the UC constraints weren't being met; this may require a reevaluation of the methods that we're using for design, since we're not using COBYLA for these anymore Updated getSizeAnchor2 to work with what I had too capacity_suction bounds upgrade for design iterations that have a L/D greater than 6 --- famodel/anchors/anchor.py | 145 ++++++++++++------ .../anchors/anchors_famodel/capacity_dandg.py | 12 +- .../anchors/anchors_famodel/capacity_load.py | 10 +- .../anchors_famodel/capacity_suction.py | 110 ++++++------- 4 files changed, 164 insertions(+), 113 deletions(-) diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index 56771b55..9350d631 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -14,7 +14,7 @@ class Anchor(Node): def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, - g=9.81, rho=1025, profile_map=None): + g=9.81, rho=1025, profile_map=None, display=0): ''' Initialize an Anchor object. @@ -53,6 +53,13 @@ def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, else: self.anchType = 'suction' print(f"[Anchor] No type provided. Defaulting to 'suction'.") + + # raise errors/warnings if the anchor type is not what it needs to be + anchor_type_options = ['suction', 'sepla', 'dea', 'depla', 'vla', 'plate', 'torpedo', 'helical', 'driven', 'dandg'] + if self.anchType not in anchor_type_options: + raise ValueError(f"The anchor 'type' needs to explicitly be one of {anchor_type_options} (Case not sensitive)") + if self.anchType not in ['drag-embedment', 'gravity', 'suction', 'SEPLA', 'VLA', 'driven']: + print('Warning: The anchor type provided does not have any cost coefficients. This will default to a suction pile') self.soil_type = None self.soil_profile = None @@ -110,6 +117,8 @@ def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, self.interpolateSoilProfile(profile_map) else: raise ValueError("profile_map must contain either 1 or ≥4 CPTs for soil assignment.") + + self.display = display def setSoilProfile(self, profile_map): ''' @@ -137,7 +146,7 @@ def setSoilProfile(self, profile_map): soilProps[soil_type].append(layer_copy) self.soilProps = dict(soilProps) - print(f"[Anchor] Assigned soil profile from {self.profile_name} with soil types {self.soil_type_list}") + if self.display > 0: print(f"[Anchor] Assigned soil profile from {self.profile_name} with soil types {self.soil_type_list}") def interpolateSoilProfile(self, profile_map): @@ -228,6 +237,8 @@ def makeMoorPyAnchor(self, ms): self.mpAnchor.d = self.dd['design']['d'] # Set dummy design to get PointType from MoorPy + if anchType not in list(ms.pointProps['AnchorProps'].keys()): + anchType = 'suction' # default to a suction pile just to get the MoorPy pointProps working design = {f"num_a_{anchType}": 1} pointType = ms.setPointType(design, source=None) self.mpAnchor.entity = pointType @@ -317,7 +328,7 @@ def getMudlineForces(self, max_force=False, lines_only=False, seabed=True, xyz=F self.loads['method'] = 'static' return self.loads - def getLugForces(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False): + def getLugForces(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False, display=0): ''' Calculate the lug forces Ha and Va based on mudline loads using local soil profile. @@ -388,7 +399,7 @@ def getLugForces(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False) line_type=line_type, d=d, w=w, - plot=plot) + plot=plot, display=display) Ta = loads['Ta'] thetaa = loads['thetaa'] @@ -408,7 +419,7 @@ def getLugForces(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False) return layers, Ha, Va - def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_update=False, plot=False): + def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_update=False, plot=False, display=0): ''' Calculate anchor capacity based on anchor type and local soil profile. @@ -456,13 +467,13 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u 'driven': getCapacityDriven, 'dandg': getCapacityDandG} - print('[DEBUG] profile_name:', self.profile_name) - print('[DEBUG] soil_profile passed as profile_map:') + if self.display > 1: print('[DEBUG] profile_name:', self.profile_name) + if self.display > 1: print('[DEBUG] soil_profile passed as profile_map:') for entry in self.soil_profile: - print(entry.get('name'), list(entry.keys())) + if self.display > 1: print(entry.get('name'), list(entry.keys())) - print(f'[Debug] mass_update = {mass_update}') + if self.display > 1: print(f'[Debug] mass_update = {mass_update}') anchType_clean = self.dd['type'].lower().replace(' ', '') capacity_func = capacity_dispatch.get(anchType_clean) if capacity_func is None: @@ -479,13 +490,13 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u try: line_type, d, w = self.getLineProperties() except ValueError: - print('[Warning] No mooring attachment found. Trying anchor-level line properties...') + if self.display > 0: print('[Warning] No mooring attachment found. Trying anchor-level line properties...') line_type = getattr(self, 'line_type', None) d = getattr(self, 'd', None) w = getattr(self, 'w', None) if any(v is None for v in [line_type, d, w]): - print('[Fallback] Using default chain properties.') + if self.display > 0: print('[Fallback] Using default chain properties.') line_type = 'chain' d = 0.16 w = 5500.0 @@ -498,12 +509,12 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u line_type=line_type, d=d, w=w, - plot=False) + plot=False, display=display) Ta = np.sqrt(Ha**2 + Va**2) thetaa = np.degrees(np.arctan2(Va, Ha)) - print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") + if self.display > 1: print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") else: Ha = Hm @@ -511,14 +522,14 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u Ta = np.sqrt(Ha**2 + Va**2) thetaa = np.degrees(np.arctan2(Va, Ha)) - print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") + if self.display > 1: print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") # --- Call the appropriate capacity function --- if anchType_clean in ['sepla', 'dea', 'depla', 'vla', 'plate']: self.capacity_format = 'plate' B = self.dd['design']['B'] L = self.dd['design']['L'] - print(f"[Final Check] Ha = {Ha}, Va = {Va}, anchor = {self.anchType}") + if self.display > 1: print(f"[Final Check] Ha = {Ha}, Va = {Va}, anchor = {self.anchType}") beta = 90.0 - np.degrees(np.arctan2(Va, Ha)) self.dd['design']['beta'] = beta layers, results = capacity_func( @@ -540,7 +551,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u D=D, L=L, zlug=zlug, Ha=Ha, Va=Va, thetalug=5, psilug=7.5, - plot=plot) + plot=plot, display=display) elif anchType_clean == 'torpedo': self.capacity_format = 'envelope' @@ -594,7 +605,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u location_name=self.profile_name, L=L, D=D, zlug=zlug, Ha=Ha, Va=Va, - plot=plot) + plot=plot, display=display) else: raise ValueError(f"Anchor type '{self.anchType}' not supported.") @@ -641,41 +652,48 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u # print(f"[DEBUG] Stored Lateral displacement in anchorCapacity: {self.anchorCapacity['Lateral displacement']:.6f}") def getSizeAnchor(self, geom, geomKeys, geomBounds=None, loads=None, lambdap_con=[4, 8], - zlug_fix=True, safety_factor={'SF_combined': 1.0}, plot=False): + zlug_fix=True, safety_factor={}, plot=False, display=0): ''' Generalized optimization method for all anchor types, using dictionary-based safety factors. ''' + self.display = display anchType_clean = self.dd['type'].lower().replace('', '') if loads is None: loads = self.loads + + sf_uc = safety_factor.get('SF_combined', 1.0) + sf_Hm = safety_factor.get('Hm', 1.0) + sf_Vm = safety_factor.get('Vm', 1.0) - Hm = loads['Hm'] - Vm = loads['Vm'] + Hm = loads['Hm']*sf_Hm + Vm = loads['Vm']*sf_Vm line_type = getattr(self, 'line_type', 'chain') d = getattr(self, 'd', 0.16) w = getattr(self, 'w', 5000.0) def update_zlug(): - if anchType_clean == 'suction' and not zlug_fix and 'zlug' not in geomKeys: + if 'suction' in anchType_clean and not zlug_fix and 'zlug' not in geomKeys: self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] - elif anchType_clean in ['driven', 'helical'] and not zlug_fix: + elif np.any([name in anchType_clean for name in ['driven', 'helical']]) and not zlug_fix: ratio = self.dd['design'].get('zlug_ratio', self.dd['design']['zlug']/self.dd['design']['L']) self.dd['design']['zlug_ratio'] = ratio self.dd['design']['zlug'] = ratio*self.dd['design']['L'] + elif 'dandg' in anchType_clean: + self.dd['design']['zlug'] = 0 def get_lambda(): - if anchType_clean == 'torpedo': + if 'torpedo' in anchType_clean: L = self.dd['design']['L1'] + self.dd['design']['L2'] A_wing = (self.dd['design']['D1'] - self.dd['design']['D2']) * self.dd['design']['L1'] A_shaft = self.dd['design']['D2'] * L D = (A_wing + A_shaft) / L - elif anchType_clean in ['driven', 'dandg', 'helical', 'suction']: + elif np.any([name in anchType_clean for name in ['driven', 'dandg', 'helical', 'suction']]): L = self.dd['design']['L'] D = self.dd['design']['D'] - elif anchType_clean in ['plate', 'sepla', 'dea', 'depla', 'vla']: + elif np.any([name in anchType_clean for name in ['plate', 'sepla', 'dea', 'depla', 'vla']]): L = self.dd['design']['L'] D = self.dd['design']['B'] else: @@ -683,22 +701,37 @@ def get_lambda(): return L/D def constraint_lambda_min(vars): + for i, key in enumerate(geomKeys): + self.dd['design'][key] = vars[i] + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) return get_lambda() - lambdap_con[0] def constraint_lambda_max(vars): return lambdap_con[1] - get_lambda() + + def constraint_bounds(vars): + con_bound_return = np.zeros(len(geomKeys)*2) + for i,var in enumerate(geomKeys): + con_bound_return[2*i] = self.dd['design'][var] - geomBounds[i][0] + con_bound_return[2*i+1] = geomBounds[i][1] - self.dd['design'][var] + return con_bound_return - if anchType_clean in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']: - target_UC = 1.0/safety_factor.get('SF_combined', 1.0) + if np.any([name in anchType_clean for name in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']]): + target_UC = 1.0/sf_uc def objective_uc(vars): + ''' for i, key in enumerate(geomKeys): self.dd['design'][key] = vars[i] update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, mass_update=True, plot=False) - UC = self.anchorCapacity.get('UC', 2.0) - return (UC - target_UC)**2 + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) + ''' + #UC = self.anchorCapacity.get('UC', 2.0) + #return (UC - target_UC)**2 + return self.anchorCapacity.get('Weight pile') def constraint_uc_envelope(vars): return self.anchorCapacity.get('UC', 0.0) - target_UC @@ -707,13 +740,13 @@ def constraint_uc_envelope(vars): {'type': 'ineq', 'fun': constraint_lambda_min}, {'type': 'ineq', 'fun': constraint_lambda_max}, {'type': 'ineq', 'fun': constraint_uc_envelope}, + {'type': 'ineq', 'fun': constraint_bounds}, ] result_uc = minimize( objective_uc, geom, method='COBYLA', - bounds=geomBounds if geomBounds else None, constraints=constraints_uc, options={'rhobeg': 0.1, 'catol': 0.01, 'maxiter': 500} ) @@ -722,7 +755,7 @@ def constraint_uc_envelope(vars): self.dd['design'].update(endGeom) update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, mass_update=True, plot=plot) + line_type=line_type, d=d, w=w, mass_update=True, plot=plot, display=display) print('\nFinal Optimized Anchor (UC-based):') print('Design:', self.dd['design']) @@ -738,7 +771,7 @@ def termination_condition(): limit_rot = 5.0 # 5 deg if UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot: - print('[Termination Condition Met] All four limits satisfied.') + if self.display > 0: print('[Termination Condition Met] All four limits satisfied.') return 'terminate' return 'continue' @@ -751,7 +784,7 @@ def termination_condition_dandg(): limit_rot = 5.0 # 5 deg if UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot: - print('[Termination Condition Met] All four limits satisfied.') + if self.display > 0: print('[Termination Condition Met] All four limits satisfied.') return 'terminate' return 'continue' @@ -765,7 +798,7 @@ def is_valid(value): self.dd['design']['D'] = D0 update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, mass_update=True, plot=False) + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] @@ -782,15 +815,17 @@ def is_valid(value): for D in np.arange(D0, 0.49, -0.05): self.dd['design']['D'] = D for L in np.arange(L0, 1.95, -0.25): + if L/D > lambdap_con[1] or L/D < lambdap_con[0]: + continue self.dd['design']['L'] = L update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, mass_update=True, plot=False) + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) - print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + if self.display > 0: print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') iter_count += 1 if not all(is_valid(v) for v in [UC_h, UC_v, disp_lat, disp_rot]): continue @@ -803,15 +838,17 @@ def is_valid(value): for D in np.arange(D0, 3.05, 0.05): self.dd['design']['D'] = D for L in np.arange(L0, 50.25, 0.25): + if L/D > lambdap_con[1] or L/D < lambdap_con[0]: + continue self.dd['design']['L'] = L update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, mass_update=True, plot=False) + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) - print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + if self.display > 0: print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') iter_count += 1 status = termination_condition() if status == 'terminate': @@ -828,13 +865,13 @@ def is_valid(value): print('Capacity Results:', self.anchorCapacity) return - if anchType_clean in ['dandg']: + if 'dandg' in anchType_clean: L0, D0 = geom if len(geom) == 2 else [5.0, 1.0] self.dd['design']['L'] = L0 self.dd['design']['D'] = D0 update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, mass_update=True, plot=False) + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) @@ -850,14 +887,16 @@ def is_valid(value): for D in np.arange(D0, 0.49, -0.05): self.dd['design']['D'] = D for L in np.arange(L0, 1.95, -0.25): + if L/D > lambdap_con[1] or L/D < lambdap_con[0]: + continue self.dd['design']['L'] = L update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, mass_update=True, plot=False) + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) - print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + if self.display > 0: print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') iter_count += 1 if not all(is_valid(v) for v in [UC_v, disp_lat, disp_rot]): continue @@ -870,14 +909,16 @@ def is_valid(value): for D in np.arange(D0, 3.05, 0.05): self.dd['design']['D'] = D for L in np.arange(L0, 50.25, 0.25): + if L/D > lambdap_con[1] or L/D < lambdap_con[0]: + continue self.dd['design']['L'] = L update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, mass_update=True, plot=False) + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) - print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + if self.display > 0: print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') iter_count += 1 status = termination_condition_dandg() if status == 'terminate': @@ -894,13 +935,13 @@ def is_valid(value): print('Capacity Results:', self.anchorCapacity) return - print('[Warning] While-loop search reached bounds without meeting criteria.') + if self.display > 0: print('[Warning] While-loop search reached bounds without meeting criteria.') else: raise ValueError(f"Anchor type '{anchType_clean}' not supported for safety factor input.") def getSizeAnchor2(self, geom, geomBounds=None, loads=None, lambdap_con=[3, 6], - zlug_fix=True, safety_factor={'SF_combined': 1.0}, plot=False): + zlug_fix=True, safety_factor={}, plot=False): ''' Grid-based optimization method for envelope anchors (suction, torpedo, plate). Evaluates UC over a grid of L and D, and selects the point closest to target UC. @@ -915,8 +956,12 @@ def getSizeAnchor2(self, geom, geomBounds=None, loads=None, lambdap_con=[3, 6], if loads is None: loads = self.loads - Hm = loads['Hm'] - Vm = loads['Vm'] + sf_uc = safety_factor.get('SF_combined', 1.0) + sf_Hm = safety_factor.get('Hm', 1.0) + sf_Vm = safety_factor.get('Vm', 1.0) + + Hm = loads['Hm']*sf_Hm + Vm = loads['Vm']*sf_Vm line_type = getattr(self, 'line_type', 'chain') d = getattr(self, 'd', 0.16) @@ -925,7 +970,7 @@ def getSizeAnchor2(self, geom, geomBounds=None, loads=None, lambdap_con=[3, 6], if anchType_clean not in ['suction', 'torpedo', 'plate']: raise ValueError(f"Grid-based getSizeAnchor only supports envelope anchors, not '{anchType_clean}'") - UC_target = 1.0/safety_factor.get('SF_combined', 1.0) + UC_target = 1.0/sf_uc # Unpack bounds and generate grid L_vals = np.linspace(geomBounds[0][0], geomBounds[0][1], 10) diff --git a/famodel/anchors/anchors_famodel/capacity_dandg.py b/famodel/anchors/anchors_famodel/capacity_dandg.py index 0f8e2d47..0d123cf0 100644 --- a/famodel/anchors/anchors_famodel/capacity_dandg.py +++ b/famodel/anchors/anchors_famodel/capacity_dandg.py @@ -6,7 +6,7 @@ from .support_pycurves import py_Lovera from .support_plots import plot_pile, plot_pycurve -def getCapacityDandG(profile_map, location_name, L, D, zlug, Ha, Va, plot=False): +def getCapacityDandG(profile_map, location_name, L, D, zlug, Ha, Va, plot=False, display=0): '''Models a laterally loaded pile using the p-y method. The solution for lateral displacements is obtained by solving the 4th order ODE, EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. @@ -141,10 +141,10 @@ def PileWeight(Len, Dia, tw, rho): # Check convergence if np.linalg.norm(y - y_old, ord=2) < tol: - print(f'[Converged in {j+1} iterations]') + if display > 0: print(f'[Converged in {j+1} iterations]') break else: - print('[Warning: Solver did not converge]') + if display > 0: print('[Warning: Solver did not converge]') if plot: @@ -164,8 +164,10 @@ def PileWeight(Len, Dia, tw, rho): ax.legend() # Relevant index of nodes - zlug_index = int(zlug/h); print(zlug_index) - ymax_index = np.argmax(y); print(ymax_index) + zlug_index = int(zlug/h) + if display > 0: print(zlug_index) + ymax_index = np.argmax(y) + if display > 0: print(ymax_index) resultsDandG = { 'Vertical max.': Vmax, diff --git a/famodel/anchors/anchors_famodel/capacity_load.py b/famodel/anchors/anchors_famodel/capacity_load.py index 55a8b5d6..81854743 100644 --- a/famodel/anchors/anchors_famodel/capacity_load.py +++ b/famodel/anchors/anchors_famodel/capacity_load.py @@ -4,7 +4,7 @@ from .support_soils import clay_profile, sand_profile from .support_plots import plot_load -def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=False): +def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=False, display=0): '''Calculate the transfer load from mudline to main padeye using a layered soil profile. Parameters @@ -122,10 +122,10 @@ def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=Fa Hm = Tm*np.cos(np.deg2rad(thetam)); Vm = Tm*np.cos(np.deg2rad(thetam)) Ha = Ta*np.cos(thetaa); Va = Ta*np.sin(thetaa) - print(f'Input Tm = {Tm} N, thetam = {thetam}°, zlug = {zlug} m') - print(f'Output Hm = {Hm} N, Vm = {Vm} N') - print(f'Output Ta = {Ta} N, thetaa = {np.rad2deg(thetaa)}°') - print(f'Output Ha = {Ha} N, Va = {Va} N') + if display > 0: print(f'Input Tm = {Tm} N, thetam = {thetam}°, zlug = {zlug} m') + if display > 0: print(f'Output Hm = {Hm} N, Vm = {Vm} N') + if display > 0: print(f'Output Ta = {Ta} N, thetaa = {np.rad2deg(thetaa)}°') + if display > 0: print(f'Output Ha = {Ha} N, Va = {Va} N') resultsLoad = { 'Tm': Tm, 'thetam': thetam, diff --git a/famodel/anchors/anchors_famodel/capacity_suction.py b/famodel/anchors/anchors_famodel/capacity_suction.py index d94059c8..f28391c8 100644 --- a/famodel/anchors/anchors_famodel/capacity_suction.py +++ b/famodel/anchors/anchors_famodel/capacity_suction.py @@ -4,7 +4,7 @@ import matplotlib.colors as mcolors from .support_soils import clay_profile, sand_profile -def getCapacitySuction(profile_map, location_name, D, L, zlug, Ha, Va, thetalug=5, psilug=7.5, plot=False): +def getCapacitySuction(profile_map, location_name, D, L, zlug, Ha, Va, thetalug=5, psilug=7.5, plot=False, display=0): '''Calculate the inclined load capacity of a suction pile in sand or clay following S. Kay methodology. The calculation is based on the soil profile, anchor geometry and inclined load. @@ -116,7 +116,8 @@ def vertical_cross(H, M, H_target): # Clip the layer first z_top_clip = max(z_top, z0) z_bot_clip = min(z_bot, z0 + (L - z0)) - dz_clip = z_bot_clip - z_top_clip; print(f'dz_clip = {dz_clip:.2f} m') + dz_clip = z_bot_clip - z_top_clip + if display > 1: print(f'dz_clip = {dz_clip:.2f} m') if dz_clip <= 0: continue # Skip layers fully above or below @@ -130,15 +131,15 @@ def vertical_cross(H, M, H_target): ez_layer = Su_moment/Su_total Su_av_z = f_Su(ez_layer) - print(f'ez_layer = {ez_layer:.2f} m') - print(f'Su_av_z (at ez_layer) = {Su_av_z:.2f} Pa') + if display > 1: print(f'ez_layer = {ez_layer:.2f} m') + if display > 1: print(f'Su_av_z (at ez_layer) = {Su_av_z:.2f} Pa') Su_bot = f_Su(z_bot_clip) gamma_vals = f_gamma(z_vals) gamma_av = np.mean(gamma_vals) # Calculate Hmax for clay - Hmax_layer = Np_fixed*D*dz_clip*Su_av_z; + Hmax_layer = Np_fixed*D*dz_clip*Su_av_z layer_data.append({ 'z_top': z_top_clip, @@ -169,7 +170,8 @@ def vertical_cross(H, M, H_target): nhuT = T/Tmax nhuV = Ha/To nhuVstar = np.sqrt(nhuV**2 - nhuT**2) - alphastar = alpha_av*(nhuVstar/nhuV); print(f"alphastar = {alphastar:.3f}") + alphastar = alpha_av*(nhuVstar/nhuV) + if display > 1: print(f"alphastar = {alphastar:.3f}") # Constant weight Pile_Head = PileWeight(z0, D, t, rhows) @@ -191,10 +193,10 @@ def vertical_cross(H, M, H_target): Vmax_final += Vmax_layer # Print layer debug info - print(f"Vmax_layer = {Vmax_layer:.2f} N") - print(f"Vmax1 = {Vmax1:.2f} N" if Vmax1 is not None else "Vmax1 = not applicable") - print(f"Vmax2 = {Vmax2:.2f} N") - print(f"Vmax3 = {Vmax3:.2f} N") + if display > 0: print(f"Vmax_layer = {Vmax_layer:.2f} N") + if display > 0: print(f"Vmax1 = {Vmax1:.2f} N" if Vmax1 is not None else "Vmax1 = not applicable") + if display > 0: print(f"Vmax2 = {Vmax2:.2f} N") + if display > 0: print(f"Vmax3 = {Vmax3:.2f} N") elif soil_type == 'sand': # Prepare soil profile for sand @@ -270,9 +272,9 @@ def vertical_cross(H, M, H_target): # Sum vertical capacities Vmax_final += Vmax_layer - print(f"Vmax_layer (sand) = {Vmax_layer:.2f} N") - print(f"Vmax2 (sand) = {Vmax2:.2f} N") - print(f"Vmax3 (sand) = {Vmax3:.2f} N") + if display > 0: print(f"Vmax_layer (sand) = {Vmax_layer:.2f} N") + if display > 0: print(f"Vmax2 (sand) = {Vmax2:.2f} N") + if display > 0: print(f"Vmax3 (sand) = {Vmax3:.2f} N") # Hmax_final and weighted ez for data in layer_data: @@ -288,7 +290,7 @@ def vertical_cross(H, M, H_target): if z_top >= z_embedded_start and z_bot <= z_embedded_end: sum_ez_weighted += Hmax_layer*ez_layer Hmax_final += Hmax_layer - print(f'Hmax_layer = {Hmax_layer:.2f} m') + if display > 0: print(f'Hmax_layer = {Hmax_layer:.2f} m') elif z_top < z_embedded_end and z_bot > z_embedded_start: dz_inside = min(z_bot, z_embedded_end) - max(z_top, z_embedded_start) @@ -299,15 +301,15 @@ def vertical_cross(H, M, H_target): # print(f'ez_layer (partial) = {ez_layer:.2f} m') ez_global = sum_ez_weighted/Hmax_final - print(f'ez_global = {ez_global:.2f} m') - print(f'Hmax_final = {Hmax_final:.2f} m') + if display > 1: print(f'ez_global = {ez_global:.2f} m') + if display > 0: print(f'Hmax_final = {Hmax_final:.2f} m') # Calculate coupled moment M = -Va*rlugTilt(rlug, zlug, thetalug) - Ha*(zlugTilt(rlug, zlug, thetalug) - ez_global) Mv = -Va*rlugTilt(rlug, zlug, thetalug) - print(f"rlug_eff = {rlugTilt(rlug, zlug, thetalug):.2f} m") - print(f"zlug_eff = {zlugTilt(rlug, zlug, thetalug):.2f} m") - print(f"M = {M:.2f} Nm") + if display > 1: print(f"rlug_eff = {rlugTilt(rlug, zlug, thetalug):.2f} m") + if display > 1: print(f"zlug_eff = {zlugTilt(rlug, zlug, thetalug):.2f} m") + if display > 1: print(f"M = {M:.2f} Nm") # MH Ellipse Parameters for Clay (Kay 2014) # ΔφMH (piecewise based on L/D) @@ -324,10 +326,10 @@ def vertical_cross(H, M, H_target): a_MH = Np_fixed/np.cos(phi_MH) delta_bMH = 0.45*(lambdap)**(-0.9) if lambdap <= 1.5 else 0 b_MH = -Np_free*np.sin(phi_MH) + delta_bMH - print(f"delta_phi = {delta_phi:.2f} deg") - print(f"phi_MH = {np.rad2deg(phi_MH):.2f} deg") - print(f"a_MH = {a_MH:.2f}") - print(f"b_MH = {b_MH:.2f}") + if display > 1: print(f"delta_phi = {delta_phi:.2f} deg") + if display > 1: print(f"phi_MH = {np.rad2deg(phi_MH):.2f} deg") + if display > 1: print(f"a_MH = {a_MH:.2f}") + if display > 1: print(f"b_MH = {b_MH:.2f}") # MH Ellipse Parameters for Clay (Kay 2015) # VH (piecewise based on L/D) @@ -335,55 +337,55 @@ def vertical_cross(H, M, H_target): a_VH = 9/4 + (5/3)*lambdap; elif 0.5 <= lambdap < 1.25: b_VH = 23/4 - (13/5)*lambdap; - elif 1.5 <= lambdap <= 6.0: + elif 1.5 <= lambdap < 20.0: # need to set a maximum of 6 based on installation pump requirements a_VH = 47/12 - (5/9)*lambdap; b_VH = 50/19 - (2/19)*lambdap; else: raise ValueError('L/D ratio out of bounds for MH ellipse formulation.') a_VH = 0.5 + lambdap; b_VH = 4.5 + lambdap/3 # a_VH = 4.5 + lambdap/2; b_VH = 4.5 + lambdap/4 - print(f"a_VH = {a_VH:.2f}") - print(f"b_VH = {b_VH:.2f}") + if display > 0: print(f"a_VH = {a_VH:.2f}") + if display > 0: print(f"b_VH = {b_VH:.2f}") # Scale VH ellipse based on vertical load ratio (Kay 2015) shrink_factor = 1 - ((Va/Vmax_final)**b_VH)**(2/a_VH) - plt.figure(figsize=(10, 5)) + if plot: plt.figure(figsize=(10, 5)) theta = np.linspace(0, 2*np.pi, 400) shrink_factors = np.linspace(0.0, 1.0, 5) # Define colormap - cmap = plt.colormaps['Greys'] + if plot: cmap = plt.colormaps['Greys'] norm = mcolors.Normalize(vmin=min(shrink_factors), vmax=max(shrink_factors)) for s_f in shrink_factors: - color = cmap(norm(s_f)) + if plot: color = cmap(norm(s_f)) x_ellipse = Hmax_final*s_f*np.cos(theta) y_ellipse = Vmax_final*s_f*np.sin(theta) H_rot = np.cos(phi_MH)*x_ellipse - np.sin(phi_MH)*y_ellipse M_rot = np.sin(phi_MH)*x_ellipse + np.cos(phi_MH)*y_ellipse - plt.plot(H_rot, M_rot, color=color, alpha=0.5) - + if plot: plt.plot(H_rot, M_rot, color=color, alpha=0.5) + x_ellipse_prime = Hmax_final*shrink_factor*np.cos(theta) y_ellipse_prime = Vmax_final*shrink_factor*np.sin(theta) H_rot_prime = np.cos(phi_MH)*x_ellipse_prime - np.sin(phi_MH)*y_ellipse_prime M_rot_prime = np.sin(phi_MH)*x_ellipse_prime + np.cos(phi_MH)*y_ellipse_prime Hlim = 1.2*Hmax_final - plt.xlim(-Hlim, Hlim) - plt.ylim(-Hlim, Hlim) - plt.grid(True, color='gray', linestyle='--', lw=0.5, alpha=0.8) - + if plot: plt.xlim(-Hlim, Hlim) + if plot: plt.ylim(-Hlim, Hlim) + if plot: plt.grid(True, color='gray', linestyle='--', lw=0.5, alpha=0.8) + # Highlight the actual one - plt.plot(H_rot_prime, M_rot_prime, 'b', label= f'MH ellipse w/ V/Vmax = {shrink_factor:.3f}') - plt.axhline(0, color='k', linestyle='--', lw=1.0) - plt.axvline(0, color='k', linestyle='--', lw=1.0) - + if plot: plt.plot(H_rot_prime, M_rot_prime, 'b', label= f'MH ellipse w/ V/Vmax = {shrink_factor:.3f}') + if plot: plt.axhline(0, color='k', linestyle='--', lw=1.0) + if plot: plt.axvline(0, color='k', linestyle='--', lw=1.0) + # Plot horizontal line at constant M and Mv H_plot = np.linspace(min(1.3*H_rot), max(1.3*H_rot), 100) M_plot = np.full_like(H_plot, M) # Constant moment Mv_plot = np.full_like(H_plot, Mv) # Constant moment - plt.plot(H_plot, M_plot, 'r', lw=1.0, label='Moment line') - plt.plot(H_plot, Mv_plot, 'r', lw=0.5, label='Vertical moment line') - plt.legend(loc='lower left', fontsize='small') + if plot: plt.plot(H_plot, M_plot, 'r', lw=1.0, label='Moment line') + if plot: plt.plot(H_plot, Mv_plot, 'r', lw=0.5, label='Vertical moment line') + if plot: plt.legend(loc='lower left', fontsize='small') H_roots = horizontal_cross(H_rot_prime, M_rot_prime, M) Hmax_v = 0.1 @@ -392,17 +394,17 @@ def vertical_cross(H, M, H_target): Hmax_neg = min([r for r in H_roots if r < 0], default=None) if M > 0 and Hmax_neg is not None: Hmax_v = abs(Hmax_neg) - plt.plot(Hmax_neg, M, 'ro', label=f'Hmax,v = {Hmax_neg/1e6:.1f} MN', zorder=20) - plt.legend(loc='lower left') + if plot: plt.plot(Hmax_neg, M, 'ro', label=f'Hmax,v = {Hmax_neg/1e6:.1f} MN', zorder=20) + if plot: plt.legend(loc='lower left') elif M <= 0 and Hmax_pos is not None: Hmax_v = abs(Hmax_pos) - plt.plot(Hmax_pos, M, 'ro', label=f'Hmax,v = {Hmax_pos/1e6:.1f} MN', zorder=20) - plt.legend(loc='lower left') + if plot: plt.plot(Hmax_pos, M, 'ro', label=f'Hmax,v = {Hmax_pos/1e6:.1f} MN', zorder=20) + if plot: plt.legend(loc='lower left') else: - print('[WARNING] No valid Hmax crossing found for moment cut.') + if display > 0: print('[WARNING] No valid Hmax crossing found for moment cut.') else: - print('[WARNING] No intersection between moment line and ellipse.') - + if display > 0: print('[WARNING] No intersection between moment line and ellipse.') + # Find relevant intercept H_v_roots = horizontal_cross(H_rot_prime, M_rot_prime, 0.0) M_v_roots = vertical_cross(H_rot_prime, M_rot_prime, 0.0) @@ -429,8 +431,10 @@ def vertical_cross(H, M, H_target): plt.legend(loc='lower left', fontsize='small') # Constant weight - pile_head = PileWeight(z0, D, t, rhows); print(f"pile_head = {pile_head:.2f} N") - Vmax_final += pile_head; print(f"Vmax_final = {Vmax_final:.2f} N") + pile_head = PileWeight(z0, D, t, rhows) + if display > 0: print(f"pile_head = {pile_head:.2f} N") + Vmax_final += pile_head + if display > 0: print(f"Vmax_final = {Vmax_final:.2f} N") Wp = 1.10*PileWeight(L, D, t, rhows + rhow) @@ -438,7 +442,7 @@ def vertical_cross(H, M, H_target): a_VH = 0.5 + lambdap; b_VH = 4.5 + lambdap/3 # Unity check UC = (Ha/Hmax_v)**a_VH + (Va/Vmax_final)**b_VH - plt.figure(figsize=(6, 5)) + if plot: plt.figure(figsize=(6, 5)) x = np.linspace(0, 1, 100) y = (1 - x**b_VH)**(1/a_VH) @@ -454,7 +458,7 @@ def vertical_cross(H, M, H_target): plt.grid(True) plt.legend() plt.show() - + resultsSuction = { 'Horizontal max.': Hmax_v, 'Vertical max.': Vmax_final, From b808038bd9909646e2b9d2632a6bf4e717d46185 Mon Sep 17 00:00:00 2001 From: Moreno Date: Thu, 31 Jul 2025 14:11:40 -0600 Subject: [PATCH 03/15] Modifications in anchor class to include mass_update flag and minor capacity model examples updates --- famodel/anchors/anchor.py | 30 +++++++++++-------- .../anchors/anchors_famodel/capacity_dandg.py | 2 +- .../anchors/anchors_famodel/capacity_load.py | 2 +- .../anchors_famodel/capacity_suction.py | 5 ++-- 4 files changed, 23 insertions(+), 16 deletions(-) diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index 9350d631..76d6c08c 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -634,20 +634,18 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u self.anchorCapacity['Rotational displacement'] = results['Rotational displacement'] # Weight calculated via dimensions - if mass_update == False: + if not mass_update: if 'Weight pile' in results: self.anchorCapacity['Weight pile'] = results['Weight pile'] if 'Weight plate' in results: self.anchorCapacity['Weight plate'] = results['Weight plate'] else: if 'Weight pile' in results: - if self.mass is None: - self.mass = results['Weight pile']/self.g - self.anchorCapacity['Weight pile'] = self.mass*self.g + self.mass = results['Weight pile']/self.g + self.anchorCapacity['Weight pile'] = results['Weight pile'] if 'Weight plate' in results: - if self.mass is None: - self.mass = results['Weight plate']/self.g - self.anchorCapacity['Weight plate'] = self.mass*self.g + self.mass = results['Weight plate']/self.g + self.anchorCapacity['Weight plate'] = results['Weight plate'] # print(f"[DEBUG] Stored Lateral displacement in anchorCapacity: {self.anchorCapacity['Lateral displacement']:.6f}") @@ -1312,9 +1310,17 @@ def getSafetyFactor(self): return {'SF_combined': SF} - def getCostAnchor(self, ms=None): + def getCostAnchor(self, ms=None, mass_update=True): ''' Assign material cost using a Point object and getCost_and_MBL(). + + Parameters + ---------- + ms : MoorPy System, optional + The mooring system to which the anchor point belongs. If None, a new one is created. + mass_update : bool, optional + If True, update mpAnchor mass from self.mass. + If False, preserve existing mpAnchor.m if already set. ''' # Create or use existing MoorPy system @@ -1324,12 +1330,12 @@ def getCostAnchor(self, ms=None): # Create MoorPy Point using makeMoorPyAnchor self.makeMoorPyAnchor(ms) - # Check if mass is assigned - if self.mass is None: + # Assign self.mass if missing + if self.mass is None or mass_update: if 'Weight pile' in self.anchorCapacity: - self.mass = self.anchorCapacity['Weight pile'] / self.g + self.mass = self.anchorCapacity['Weight pile']/self.g elif 'Weight plate' in self.anchorCapacity: - self.mass = self.anchorCapacity['Weight plate'] / self.g + self.mass = self.anchorCapacity['Weight plate']/self.g else: raise KeyError("Missing 'Weight pile' or 'Weight plate' in anchorCapacity. \ Run getCapacityAnchor() before getCostAnchor(), or define self.mass explicitly.") diff --git a/famodel/anchors/anchors_famodel/capacity_dandg.py b/famodel/anchors/anchors_famodel/capacity_dandg.py index 0d123cf0..fdb5acb8 100644 --- a/famodel/anchors/anchors_famodel/capacity_dandg.py +++ b/famodel/anchors/anchors_famodel/capacity_dandg.py @@ -220,7 +220,7 @@ def PileWeight(Len, Dia, tw, rho): Ha = 5.0e6 # Horizontal load (N) Va = 3.0e5 # Vertical load (N) - layers, y, z, results = getCapacityDandG(profile_map, 'CPT_rock_1', L, D, zlug, Ha, Va, plot=True) + layers, y, z, results = getCapacityDandG(profile_map, 'CPT_rock_1', L, D, zlug, Ha, Va, plot=True, display=0) print('\n--- Results for DandG Pile in Layered Rock ---') for key, val in results.items(): diff --git a/famodel/anchors/anchors_famodel/capacity_load.py b/famodel/anchors/anchors_famodel/capacity_load.py index 81854743..479af3c5 100644 --- a/famodel/anchors/anchors_famodel/capacity_load.py +++ b/famodel/anchors/anchors_famodel/capacity_load.py @@ -203,7 +203,7 @@ def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=Fa d = 0.25 # Chain diameter (m) w = 5000 # Line weight (N/m) - layers, resultsLoad = getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w, plot=True) + layers, resultsLoad = getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w, plot=True, display=1) plot_load(layers, resultsLoad['drag_values'], resultsLoad['depth_values'], resultsLoad['Tm'], resultsLoad['thetam'], resultsLoad['Ta'], diff --git a/famodel/anchors/anchors_famodel/capacity_suction.py b/famodel/anchors/anchors_famodel/capacity_suction.py index f28391c8..328c97ee 100644 --- a/famodel/anchors/anchors_famodel/capacity_suction.py +++ b/famodel/anchors/anchors_famodel/capacity_suction.py @@ -504,7 +504,7 @@ def vertical_cross(H, M, H_target): zlug = (2/3)*L # Lug depth (m) theta = 5 # Tilt misalignment angle (deg) psi = 7.5 # Twist misalignment angle (deg) - Ha = 1e6 # Applied horizontal load (N) + Ha = 1e6 # Applied horizontal load (N) Va = 5.7e6 # Applied vertical load (N) # Calculate @@ -513,7 +513,8 @@ def vertical_cross(H, M, H_target): D, L, zlug, # Pile geometrical properties Ha, Va, # Pile loading conditions thetalug=theta, psilug=psi, # Pile misaligment tolerances - plot=False + plot=True, + display=1 ) # print('\n--- Suction Pile Capacity Results ---') From 30ca6d118d88af553b26bc7de81a3dcb8cd0c910 Mon Sep 17 00:00:00 2001 From: Moreno Date: Thu, 31 Jul 2025 18:08:53 -0600 Subject: [PATCH 04/15] Updated project; replaced anchor_soil example with layered/uniform variants --- ...{anchor_soil.py => anchor_soil_layered.py} | 2 +- examples/05_Anchors/anchor_soil_uniform.py | 73 +++++++++++++++++++ famodel/anchors/anchor.py | 2 +- famodel/project.py | 25 +++++++ 4 files changed, 100 insertions(+), 2 deletions(-) rename examples/05_Anchors/{anchor_soil.py => anchor_soil_layered.py} (94%) create mode 100644 examples/05_Anchors/anchor_soil_uniform.py diff --git a/examples/05_Anchors/anchor_soil.py b/examples/05_Anchors/anchor_soil_layered.py similarity index 94% rename from examples/05_Anchors/anchor_soil.py rename to examples/05_Anchors/anchor_soil_layered.py index 5d76b294..def10f7d 100644 --- a/examples/05_Anchors/anchor_soil.py +++ b/examples/05_Anchors/anchor_soil_layered.py @@ -23,7 +23,7 @@ # Step 3: Assign local soil profile from project (nearest neighbor lookup) soil_id, soil_profile = proj.getSoilAtLocation(anchor.r[0], anchor.r[1]) anchor.soilProps = {soil_id: soil_profile} -anchor.setSoilProfile([{ 'name': soil_id, 'layers': soil_profile }]) # ensures `anchor.soil_profile` is set +anchor.setSoilProfile([{'name': soil_id, 'layers': soil_profile}]) # ensures `anchor.soil_profile` is set # Step 4: Assign loads and line anchor.loads = {'Hm': 2e6, 'Vm': 1.5e6} diff --git a/examples/05_Anchors/anchor_soil_uniform.py b/examples/05_Anchors/anchor_soil_uniform.py new file mode 100644 index 00000000..e416de4b --- /dev/null +++ b/examples/05_Anchors/anchor_soil_uniform.py @@ -0,0 +1,73 @@ + +import sys +sys.path.append(r'C:\Code\FAModel_anchors\famodel') + +from project import Project +from anchors.anchor import Anchor + +# Step 1: Initialize and load soil +proj = Project() +proj.loadSoil( + filename='inputs/GulfOfMaine_soil_layered_100x100.txt', + soil_mode='uniform') # 'uniform' soil does not need from the profile_source yaml file + +for label, props in proj.soilProps.items(): + print(f"{label}: {props}") + +# Convert to profile_map format so anchor capacity models can use it +proj.convertUniformToLayered(default_layer=50.0) + +for label, props in proj.profile_map.items(): + print(f"{label}: {props}") + +# Step 2: Create and register an anchor at a known position in the grid +anchor = Anchor( + dd = {'type': 'suction', 'design': {'D': 3.5, 'L': 12.0, 'zlug': 9.67}}, + r = [54.0, -4450.0, 0.0]) + +# Step 3: Assign local soil profile from project (nearest neighbor lookup) +soil_id, _ = proj.getSoilAtLocation(anchor.r[0], anchor.r[1]) +soil_profile = proj.profile_map[soil_id] # get compatible layered format +anchor.soilProps = {soil_id: soil_profile} +anchor.setSoilProfile([{'name': soil_id, 'layers': soil_profile}]) # ensures `anchor.soil_profile` is set + +# Step 4: Assign loads and line +anchor.loads = {'Hm': 1e6, 'Vm': 5e4} +anchor.line_type = 'chain' +anchor.d = 0.16 +anchor.w = 5000.0 + +# Step 5: Run capacity check and optimization +anchor.getLugForces( + Hm=anchor.loads['Hm'], Vm=anchor.loads['Vm'], + zlug = anchor.dd['design']['zlug'], + d=anchor.d, w=anchor.w, + plot=True) + +anchor.getCapacityAnchor( + Hm=anchor.loads['Hm'], Vm=anchor.loads['Vm'], + zlug = anchor.dd['design']['zlug'], + line_type=anchor.line_type, d=anchor.d, w=anchor.w, + mass_update=True, + plot=True) +anchor.getCostAnchor() +print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') + +results = anchor.getSizeAnchor( + geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], + geomKeys = ['L', 'D'], + geomBounds = [(8.0, 15.0), (2.0, 4.0)], + loads = None, + lambdap_con = [3, 6], + zlug_fix = False, + safety_factor = {'SF_combined': 1}, + plot = True) + +# Step 6: Report +print('\nFinal Optimized Anchor:') +print('Design:', anchor.dd['design']) +print('Capacity Results:', anchor.anchorCapacity) +anchor.getCostAnchor() +print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') + + diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index 76d6c08c..f8fc902f 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -205,7 +205,7 @@ def interpolateSoilProfile(self, profile_map): self.soilProps = dict(soilProps) print(f"[Anchor] Interpolated soil profile: {self.profile_name} with soil types {self.soil_type_list}") - + def makeMoorPyAnchor(self, ms): ''' Create a MoorPy anchor object in a MoorPy system. diff --git a/famodel/project.py b/famodel/project.py index 1bc0ae93..da60c4ba 100644 --- a/famodel/project.py +++ b/famodel/project.py @@ -1166,6 +1166,31 @@ def getSoilAtLocation(self, x, y): print(f"[DEBUG] Available soilProps keys: {list(self.soilProps.keys())}") else: raise ValueError("No soil grid defined") + + def convertUniformToLayered(self, default_layer=50.0): + ''' + Converts self.soilProps (uniform format) into profile_map (layered format) + using a default thickness and assuming uniform clay profile. + Matches the structure of layered CPT-based soil profiles. + ''' + self.profile_map = {} + + for name, props in self.soilProps.items(): + name = str(name) + gamma = float(props['gamma'][0]) + Su0 = float(props['Su0'][0]) + k = float(props['k'][0]) + + layer = { + 'soil_type': 'clay', + 'top': 0.0, + 'bottom': default_layer, + 'gamma_top': gamma, + 'gamma_bot': gamma, + 'Su_top': Su0, + 'Su_bot': Su0 + k * default_layer} + + self.profile_map[name] = [layer] # just layers! # # ----- Anchor def updateAnchor(self,anch='all',update_loc=True): From 3509fc8021eb32cb6b1620544873fc8c419af272 Mon Sep 17 00:00:00 2001 From: Moreno Date: Wed, 13 Aug 2025 10:05:57 -0600 Subject: [PATCH 05/15] Save WIP changes before switching to main --- examples/05_Anchors/anchor_dandg.py | 6 +- examples/05_Anchors/anchor_driven_soil.py | 8 +- examples/05_Anchors/anchor_helical.py | 13 +- examples/05_Anchors/anchor_plate.py | 19 +-- examples/05_Anchors/anchor_soil_uniform.py | 4 - examples/05_Anchors/anchor_suction.py | 6 +- examples/05_Anchors/anchor_torpedo.py | 3 +- examples/05_Anchors/example_suction.ipynb | 49 ++++-- famodel/anchors/README.md | 2 +- famodel/anchors/anchor.py | 156 +++++++++++------- .../anchors_famodel/capacity_driven.py | 10 +- .../anchors_famodel/capacity_helical.py | 2 +- .../anchors/anchors_famodel/capacity_load.py | 12 +- .../anchors/anchors_famodel/capacity_plate.py | 18 +- .../anchors_famodel/capacity_torpedo.py | 26 +-- famodel/project.py | 2 +- 16 files changed, 193 insertions(+), 143 deletions(-) diff --git a/examples/05_Anchors/anchor_dandg.py b/examples/05_Anchors/anchor_dandg.py index a5fef24d..95764ce9 100644 --- a/examples/05_Anchors/anchor_dandg.py +++ b/examples/05_Anchors/anchor_dandg.py @@ -30,8 +30,8 @@ # Assign mooring loads anchor.loads = { 'Hm': 5.0e6, - 'Vm': 2.5e5 -} + 'Vm': 2.5e5} + anchor.line_type = 'chain' anchor.d = 0.16 anchor.w = 5000.0 @@ -57,7 +57,7 @@ anchor.getSizeAnchor( geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], geomKeys = ['L', 'D'], - geomBounds = [(2.0, 70.0), (0.25, 3.0)], + geomBounds = [(2.0, 30.0), (2.25, 5.0)], loads = None, lambdap_con = [4, 50], zlug_fix = True, diff --git a/examples/05_Anchors/anchor_driven_soil.py b/examples/05_Anchors/anchor_driven_soil.py index f12bc5ab..4ea2efd1 100644 --- a/examples/05_Anchors/anchor_driven_soil.py +++ b/examples/05_Anchors/anchor_driven_soil.py @@ -20,7 +20,7 @@ 'type': 'driven', 'design': { 'L': 25.0, # Embedded length - 'D': 4.25, # Diameter + 'D': 2.00, # Diameter 'zlug': 3.0 # Padeye depth } }, @@ -29,7 +29,7 @@ # Assign mooring loads anchor.loads = { - 'Hm': 2.0e6, + 'Hm': 7.0e5, 'Vm': 2.5e5} anchor.line_type = 'chain' @@ -71,9 +71,9 @@ anchor.getSizeAnchor( geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], geomKeys = ['L', 'D'], - geomBounds = [(2.0, 70.0), (0.25, 3.0)], + geomBounds = [(2.0, 50.0), (0.25, 3.5)], loads = None, - lambdap_con = [4, 50], + lambdap_con = [3, 50], zlug_fix = True, safety_factor = {'SF_horizontal': 1, 'SF_vertical': 1}, plot = True) \ No newline at end of file diff --git a/examples/05_Anchors/anchor_helical.py b/examples/05_Anchors/anchor_helical.py index 7adbb02d..3c6c81e5 100644 --- a/examples/05_Anchors/anchor_helical.py +++ b/examples/05_Anchors/anchor_helical.py @@ -30,8 +30,8 @@ # --- Assign mooring loads and properties --- anchor.loads = { - 'Hm': 80e4, - 'Vm': 50e5} + 'Hm': 8e5, + 'Vm': 1e5} anchor.line_type = 'chain' anchor.d = 0.16 @@ -48,8 +48,7 @@ line_type = anchor.line_type, d = anchor.d, w = anchor.w, - plot = True -) + plot = True) print('\nLug Forces Computed:') print(f'Ha = {Ha:.2f} N') @@ -63,8 +62,7 @@ line_type = anchor.line_type, d = anchor.d, w = anchor.w, - plot = False -) + plot = False) print('\nCapacity Results:') for key, val in anchor.anchorCapacity.items(): @@ -79,5 +77,4 @@ lambdap_con = [6, 15], zlug_fix = True, safety_factor = {'SF_horizontal': 1, 'SF_vertical': 1}, - plot = False -) + plot = False) diff --git a/examples/05_Anchors/anchor_plate.py b/examples/05_Anchors/anchor_plate.py index aa841f73..f04c3675 100644 --- a/examples/05_Anchors/anchor_plate.py +++ b/examples/05_Anchors/anchor_plate.py @@ -19,14 +19,13 @@ # --- Create plate anchor --- anchor = Anchor( dd = {'type': 'plate', 'design': {'B': 3.0, 'L': 6.0, 'zlug': 14.0, 'beta': 30.0}}, - r = [100.0, 100.0, 0.0] -) + r = [0.0, 0.0, 0.0]) # --- Assign load and mooring properties --- anchor.loads = { 'Hm': 3.5e6, - 'Vm': 2.5e6 -} + 'Vm': 2.5e6} + anchor.line_type = 'chain' anchor.d = 0.16 anchor.w = 5000.0 @@ -42,8 +41,7 @@ line_type = anchor.line_type, d = anchor.d, w = anchor.w, - plot = True -) + plot = True) print('\nLug Forces Computed:') print(f'Ha = {Ha:.2f} N') @@ -57,8 +55,7 @@ line_type = anchor.line_type, d = anchor.d, w = anchor.w, - plot = True -) + plot = True) print('\nCapacity Results:') for key, value in anchor.anchorCapacity.items(): @@ -72,6 +69,6 @@ loads = None, lambdap_con = [2, 4], # less critical for plates zlug_fix = True, - safety_factor = {'SF_combined': 3}, - plot = True -) + safety_factor = {'SF_horizontal': 2, 'SF_vertical': 3}, + #safety_factor = {'SF_combined': 3}, + plot = True) diff --git a/examples/05_Anchors/anchor_soil_uniform.py b/examples/05_Anchors/anchor_soil_uniform.py index e416de4b..203f88b7 100644 --- a/examples/05_Anchors/anchor_soil_uniform.py +++ b/examples/05_Anchors/anchor_soil_uniform.py @@ -1,7 +1,4 @@ -import sys -sys.path.append(r'C:\Code\FAModel_anchors\famodel') - from project import Project from anchors.anchor import Anchor @@ -57,7 +54,6 @@ geom = [anchor.dd['design']['L'], anchor.dd['design']['D']], geomKeys = ['L', 'D'], geomBounds = [(8.0, 15.0), (2.0, 4.0)], - loads = None, lambdap_con = [3, 6], zlug_fix = False, safety_factor = {'SF_combined': 1}, diff --git a/examples/05_Anchors/anchor_suction.py b/examples/05_Anchors/anchor_suction.py index 12a10b05..aa1b0e44 100644 --- a/examples/05_Anchors/anchor_suction.py +++ b/examples/05_Anchors/anchor_suction.py @@ -60,8 +60,8 @@ D = anchor.dd['design']['D'] zlug = anchor.dd['design']['zlug'] # Access matched profile -layers = anchor.soil_profile -z0 = layers[0]['top'] +layers = anchor.soil_profile[0]['layers'] +z0 = layers[0]['top'] plot_suction(layers, L=L, D=D, z0=z0, zlug=zlug) @@ -121,7 +121,7 @@ loads = None, lambdap_con = [3, 6], zlug_fix = False, - safety_factor = {'SF_combined': 1}, + safety_factor = {'SF_horizontal': 2, 'SF_vertical': 3}, plot = True) print('\nFinal Optimized Anchor:') diff --git a/examples/05_Anchors/anchor_torpedo.py b/examples/05_Anchors/anchor_torpedo.py index 28cdc25c..9201f289 100644 --- a/examples/05_Anchors/anchor_torpedo.py +++ b/examples/05_Anchors/anchor_torpedo.py @@ -87,8 +87,7 @@ lambdap_con = [2, 8], zlug_fix = True, safety_factor = {'SF_combined': 1}, - plot = True -) + plot = True) print('\nFinal Optimized Anchor:') print('Design:', anchor.dd['design']) diff --git a/examples/05_Anchors/example_suction.ipynb b/examples/05_Anchors/example_suction.ipynb index 8df5784c..39e9bd8b 100644 --- a/examples/05_Anchors/example_suction.ipynb +++ b/examples/05_Anchors/example_suction.ipynb @@ -25,10 +25,22 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 7, "id": "9f2d8d4b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'famodel'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mfamodel\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchors\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchor\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Anchor\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mfamodel\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchors\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchors_famodel\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msupport_plots\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m plot_suction\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'famodel'" + ] + } + ], "source": [ "from famodel.anchors.anchor import Anchor\n", "from famodel.anchors.anchors_famodel.support_plots import plot_suction" @@ -45,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 5, "id": "935551c4", "metadata": {}, "outputs": [], @@ -105,16 +117,19 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 6, "id": "3aab0b15", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "zlug: 8.67\n", - "L: 12.0\n" + "ename": "NameError", + "evalue": "name 'Anchor' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m anchor \u001b[38;5;241m=\u001b[39m \u001b[43mAnchor\u001b[49m(\n\u001b[0;32m 2\u001b[0m dd \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msuction\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2.5\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m12.0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8.67\u001b[39m}},\n\u001b[0;32m 3\u001b[0m r \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m250.0\u001b[39m, \u001b[38;5;241m250.0\u001b[39m, \u001b[38;5;241m0.0\u001b[39m]\n\u001b[0;32m 4\u001b[0m )\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug:\u001b[39m\u001b[38;5;124m'\u001b[39m, anchor\u001b[38;5;241m.\u001b[39mdd[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL:\u001b[39m\u001b[38;5;124m'\u001b[39m, anchor\u001b[38;5;241m.\u001b[39mdd[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", + "\u001b[1;31mNameError\u001b[0m: name 'Anchor' is not defined" ] } ], @@ -138,17 +153,19 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 3, "id": "368fac90", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Anchor] Interpolated soil profile: Interpolated_2D with soil types ['clay']\n", - "zlug: 8.67\n", - "L: 12.0\n" + "ename": "NameError", + "evalue": "name 'anchor' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43manchor\u001b[49m\u001b[38;5;241m.\u001b[39minterpolateSoilProfile(profile_map)\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug:\u001b[39m\u001b[38;5;124m'\u001b[39m, anchor\u001b[38;5;241m.\u001b[39mdd[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL:\u001b[39m\u001b[38;5;124m'\u001b[39m, anchor\u001b[38;5;241m.\u001b[39mdd[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", + "\u001b[1;31mNameError\u001b[0m: name 'anchor' is not defined" ] } ], diff --git a/famodel/anchors/README.md b/famodel/anchors/README.md index 58ccb495..b4c8f170 100644 --- a/famodel/anchors/README.md +++ b/famodel/anchors/README.md @@ -37,7 +37,7 @@ Units within FAModel follow the SI exclusively. The input soil parameters units profile_map = [ { - 'location_name': 'CPT_1', + 'name': 'CPT_1', 'x': 498234, 'y': 5725141, 'layers': [ { diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index f8fc902f..8e477754 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -58,8 +58,8 @@ def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, anchor_type_options = ['suction', 'sepla', 'dea', 'depla', 'vla', 'plate', 'torpedo', 'helical', 'driven', 'dandg'] if self.anchType not in anchor_type_options: raise ValueError(f"The anchor 'type' needs to explicitly be one of {anchor_type_options} (Case not sensitive)") - if self.anchType not in ['drag-embedment', 'gravity', 'suction', 'SEPLA', 'VLA', 'driven']: - print('Warning: The anchor type provided does not have any cost coefficients. This will default to a suction pile') + # if self.anchType not in ['drag-embedment', 'gravity', 'suction', 'SEPLA', 'VLA', 'driven']: + # print('Warning: The anchor type provided does not have any cost coefficients. This will default to a suction pile') self.soil_type = None self.soil_profile = None @@ -148,7 +148,6 @@ def setSoilProfile(self, profile_map): if self.display > 0: print(f"[Anchor] Assigned soil profile from {self.profile_name} with soil types {self.soil_type_list}") - def interpolateSoilProfile(self, profile_map): ''' Interpolate a soil profile from the 4 nearest CPTs in profile_map. @@ -188,23 +187,28 @@ def interpolateSoilProfile(self, profile_map): interpolated_layers.append(layer) - self.soil_profile = interpolated_layers + self.soil_profile = [{ + 'name': 'Interpolated_2D', + 'x': x_anchor, + 'y': y_anchor, + 'layers': interpolated_layers}] self.profile_name = "Interpolated_2D" # Extract soil types - soil_types = [layer['soil_type'] for layer in self.soil_profile] + layers = self.soil_profile[0]['layers'] + soil_types = [layer['soil_type'] for layer in layers] self.soil_type_list = list(set(soil_types)) self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed' # Group interpolated layers by soil type soilProps = defaultdict(list) - for layer in self.soil_profile: + for layer in self.soil_profile[0]['layers']: layer_copy = layer.copy() soil_type = layer_copy.pop('soil_type') soilProps[soil_type].append(layer_copy) self.soilProps = dict(soilProps) - print(f"[Anchor] Interpolated soil profile: {self.profile_name} with soil types {self.soil_type_list}") + if self.display > 0: print(f"[Anchor] Interpolated soil profile: {self.profile_name} with soil types {self.soil_type_list}") def makeMoorPyAnchor(self, ms): ''' @@ -467,10 +471,10 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u 'driven': getCapacityDriven, 'dandg': getCapacityDandG} - if self.display > 1: print('[DEBUG] profile_name:', self.profile_name) - if self.display > 1: print('[DEBUG] soil_profile passed as profile_map:') + if self.display > 0: print('[DEBUG] profile_name:', self.profile_name) + if self.display > 0: print('[DEBUG] soil_profile passed as profile_map:') for entry in self.soil_profile: - if self.display > 1: print(entry.get('name'), list(entry.keys())) + if self.display > 0: print(entry.get('name'), list(entry.keys())) if self.display > 1: print(f'[Debug] mass_update = {mass_update}') @@ -514,7 +518,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u Ta = np.sqrt(Ha**2 + Va**2) thetaa = np.degrees(np.arctan2(Va, Ha)) - if self.display > 1: print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") + if self.display > 0: print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") else: Ha = Hm @@ -522,7 +526,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u Ta = np.sqrt(Ha**2 + Va**2) thetaa = np.degrees(np.arctan2(Va, Ha)) - if self.display > 1: print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") + if self.display > 0: print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}") # --- Call the appropriate capacity function --- if anchType_clean in ['sepla', 'dea', 'depla', 'vla', 'plate']: @@ -538,7 +542,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u B=B, L=L, zlug=zlug, beta=beta, Ha=Ha, Va=Va, - plot=plot) + plot=plot, display=display) elif anchType_clean == 'suction': self.capacity_format = 'envelope' @@ -567,7 +571,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u zlug=zlug, ballast=ballast, Ha=Ha, Va=Va, - plot=plot) + plot=plot, display=display) elif anchType_clean == 'helical': self.capacity_format = 'component' @@ -581,7 +585,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u D=D, L=L, d=d, zlug=zlug, Ha=Ha, Va=Va, - plot=plot) + plot=plot, display=display) elif anchType_clean == 'driven': self.capacity_format = 'component' @@ -593,7 +597,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u location_name=self.profile_name, L=L, D=D, zlug=zlug, Ha=Ha, Va=Va, - plot=plot) + plot=plot, display=display) elif anchType_clean == 'dandg': self.capacity_format = 'component' @@ -656,15 +660,16 @@ def getSizeAnchor(self, geom, geomKeys, geomBounds=None, loads=None, lambdap_con ''' self.display = display - anchType_clean = self.dd['type'].lower().replace('', '') + anchType_clean = self.dd['type'].strip().lower() + print(f"[Debug] Anchor type parsed: '{anchType_clean}'") if loads is None: loads = self.loads - sf_uc = safety_factor.get('SF_combined', 1.0) - sf_Hm = safety_factor.get('Hm', 1.0) - sf_Vm = safety_factor.get('Vm', 1.0) - + sf_Hm = safety_factor.get('Hm', safety_factor.get('SF_horizontal', 1.0)) + sf_Vm = safety_factor.get('Vm', safety_factor.get('SF_vertical', 1.0)) + sf_uc = safety_factor.get('SF_combined', max(sf_Hm, sf_Vm)) # conservative by default + Hm = loads['Hm']*sf_Hm Vm = loads['Vm']*sf_Vm @@ -729,7 +734,11 @@ def objective_uc(vars): ''' #UC = self.anchorCapacity.get('UC', 2.0) #return (UC - target_UC)**2 - return self.anchorCapacity.get('Weight pile') + #return self.anchorCapacity.get('Weight pile') + if any(name in anchType_clean for name in ['plate', 'sepla', 'dea', 'depla', 'vla']): + return self.anchorCapacity.get('Weight plate') + else: + return self.anchorCapacity.get('Weight pile') def constraint_uc_envelope(vars): return self.anchorCapacity.get('UC', 0.0) - target_UC @@ -759,34 +768,59 @@ def constraint_uc_envelope(vars): print('Design:', self.dd['design']) print('Capacity Results:', self.anchorCapacity) return - + + def near_border(): + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_lat = 0.05*self.dd['design']['D'] # 10% of the pile diameter + limit_rot = 10.0 # 10 deg + + near_UC_h = 0.95 <= UC_h <= 1.0 + near_UC_v = 0.95 <= UC_v <= 1.0 + near_disp_lat = 0.95*limit_lat <= disp_lat <= limit_lat + near_disp_rot = 4.75 <= disp_rot <= limit_rot + + return near_UC_h or near_UC_v or near_disp_lat or near_disp_rot + def termination_condition(): UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) - limit_lat = 0.05*self.dd['design']['D'] # 5% of the pile diameter - limit_rot = 5.0 # 5 deg - - if UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot: - if self.display > 0: print('[Termination Condition Met] All four limits satisfied.') - return 'terminate' - + limit_lat = 0.05*self.dd['design']['D'] # 10% of the pile diameter + limit_rot = 10.0 # 10 deg + + all_satisfied = (UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot) + + if all_satisfied: + if near_border(): + if self.display > 0: print('[Termination] All criteria satisfied and near border.') + return 'terminate' + else: + if self.display > 0: print('[Safe but not near border] Continue shrinking...') + return 'continue' return 'continue' def termination_condition_dandg(): UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) - limit_lat = 0.05*self.dd['design']['D'] # 5% of the pile diameter - limit_rot = 5.0 # 5 deg - - if UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot: - if self.display > 0: print('[Termination Condition Met] All four limits satisfied.') - return 'terminate' - + limit_lat = 0.05*self.dd['design']['D'] # 10% of the pile diameter + limit_rot = 10.0 # 10 deg + + all_satisfied = (UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot) + + if all_satisfied: + if near_border(): + if self.display > 0: print('[Termination] All criteria satisfied and near border.') + return 'terminate' + else: + if self.display > 0: print('[Safe but not near border] Continue shrinking...') + return 'continue' return 'continue' - + def is_valid(value): return np.isfinite(value) and not np.isnan(value) and abs(value) < 1e6 @@ -794,6 +828,8 @@ def is_valid(value): L0, D0 = geom if len(geom) == 2 else [5.0, 1.0] self.dd['design']['L'] = L0 self.dd['design']['D'] = D0 + Lmin, Lmax = geomBounds[0] + Dmin, Dmax = geomBounds[1] update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) @@ -802,17 +838,17 @@ def is_valid(value): UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) - limit_disp = 0.05*D0 # 5% of the pile diameter - limit_rot = 5.0 # 5 deg + limit_disp = 0.10*D0 # 10% of the pile diameter + limit_rot = 10.0 # 10 deg direction = 'shrink' if (UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_disp and disp_rot <= limit_rot) else 'grow' max_iter = 200 iter_count = 0 if direction == 'shrink': - for D in np.arange(D0, 0.49, -0.05): - self.dd['design']['D'] = D - for L in np.arange(L0, 1.95, -0.25): + for L in np.arange(L0, Lmin - 1e-6, -0.25): + self.dd['design']['L'] = L + for D in np.arange(Dmax, Dmin - 1e-6, -0.05): if L/D > lambdap_con[1] or L/D < lambdap_con[0]: continue self.dd['design']['L'] = L @@ -832,10 +868,10 @@ def is_valid(value): print('Design:', self.dd['design']) print('Capacity Results:', self.anchorCapacity) return - else: - for D in np.arange(D0, 3.05, 0.05): - self.dd['design']['D'] = D - for L in np.arange(L0, 50.25, 0.25): + elif direction == 'grow': + for L in np.arange(L0, Lmax + 1e-6, 0.25): + self.dd['design']['L'] = L + for D in np.arange(Dmin, Dmax + 1e-6, 0.05): if L/D > lambdap_con[1] or L/D < lambdap_con[0]: continue self.dd['design']['L'] = L @@ -862,11 +898,17 @@ def is_valid(value): print('Design:', self.dd['design']) print('Capacity Results:', self.anchorCapacity) return + else: + raise ValueError(f"Unknown optimization direction: {direction}") + + if self.display > 0: print('[Warning] While-loop search reached bounds without meeting criteria.') if 'dandg' in anchType_clean: L0, D0 = geom if len(geom) == 2 else [5.0, 1.0] self.dd['design']['L'] = L0 self.dd['design']['D'] = D0 + Lmin, Lmax = geomBounds[0] + Dmin, Dmax = geomBounds[1] update_zlug() self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) @@ -874,17 +916,17 @@ def is_valid(value): UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) - limit_disp = 0.05*D0 # 5% of the pile diameter - limit_rot = 5.0 # 5 deg + limit_disp = 0.10*D0 # 10% of the pile diameter + limit_rot = 10.0 # 10 deg direction = 'shrink' if (UC_v <= 1.0 and disp_lat <= limit_disp and disp_rot <= limit_rot) else 'grow' max_iter = 200 iter_count = 0 if direction == 'shrink': - for D in np.arange(D0, 0.49, -0.05): - self.dd['design']['D'] = D - for L in np.arange(L0, 1.95, -0.25): + for L in np.arange(L0, Lmin - 1e-6, -0.25): + self.dd['design']['L'] = L + for D in np.arange(Dmax, Dmin - 1e-6, -0.05): if L/D > lambdap_con[1] or L/D < lambdap_con[0]: continue self.dd['design']['L'] = L @@ -903,10 +945,10 @@ def is_valid(value): print('Design:', self.dd['design']) print('Capacity Results:', self.anchorCapacity) return - else: - for D in np.arange(D0, 3.05, 0.05): - self.dd['design']['D'] = D - for L in np.arange(L0, 50.25, 0.25): + elif direction == 'grow': + for L in np.arange(L0, Lmax + 1e-6, 0.25): + self.dd['design']['L'] = L + for D in np.arange(Dmin, Dmax + 1e-6, 0.05): if L/D > lambdap_con[1] or L/D < lambdap_con[0]: continue self.dd['design']['L'] = L @@ -932,6 +974,8 @@ def is_valid(value): print('Design:', self.dd['design']) print('Capacity Results:', self.anchorCapacity) return + else: + raise ValueError(f"Unknown optimization direction: {direction}") if self.display > 0: print('[Warning] While-loop search reached bounds without meeting criteria.') diff --git a/famodel/anchors/anchors_famodel/capacity_driven.py b/famodel/anchors/anchors_famodel/capacity_driven.py index 561bc2b7..83961540 100644 --- a/famodel/anchors/anchors_famodel/capacity_driven.py +++ b/famodel/anchors/anchors_famodel/capacity_driven.py @@ -6,7 +6,7 @@ from .support_pycurves import py_Matlock, py_API, py_Reese from .support_plots import plot_pile, plot_pycurve -def getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=False): +def getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=False, display=0): '''Models a laterally loaded pile using the p-y method. The solution for lateral displacements is obtained by solving the 4th order ODE, EI*d4y/dz4 EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. @@ -196,10 +196,10 @@ def SoilWeight(Len, Dia, tw, gamma_soil): # Check convergence if np.linalg.norm(y - y_old, ord=2) < tol: - print(f'[Converged in {j+1} iterations]') + if display > 0: print(f'[Converged in {j+1} iterations]') break else: - print('[Warning: Solver did not converge]') + if display > 0: print('[Warning: Solver did not converge]') if plot: plot_pycurve(pycurve_data) @@ -235,8 +235,8 @@ def SoilWeight(Len, Dia, tw, gamma_soil): 'Unity check (horizontal)': Ha/(abs(Mi)/abs(zlug)) if zlug != 0 else np.inf, 'Weight pile': PileWeight(L, D, t, rhows + rhow)} - print(f"Max lateral displacement: {y_pile[ymax_index]:.6f} m at z = {z_pile[ymax_index]:.2f} m") - print(f"Deflected tip: {y_pile[-1]:.6f} m at z = {z_pile[-1]:.2f} m") + if display > 0: print(f"Max lateral displacement: {y_pile[ymax_index]:.6f} m at z = {z_pile[ymax_index]:.2f} m") + if display > 0: print(f"Deflected tip: {y_pile[-1]:.6f} m at z = {z_pile[-1]:.2f} m") return layers, y[2:-2], z[2:-2], resultsDriven diff --git a/famodel/anchors/anchors_famodel/capacity_helical.py b/famodel/anchors/anchors_famodel/capacity_helical.py index 3e52fe59..f88fe892 100644 --- a/famodel/anchors/anchors_famodel/capacity_helical.py +++ b/famodel/anchors/anchors_famodel/capacity_helical.py @@ -4,7 +4,7 @@ from .support_soils import clay_profile, sand_profile from .support_plots import plot_helical -def getCapacityHelical(profile_map, location_name, D, L, d, zlug, Ha, Va, plot=False): +def getCapacityHelical(profile_map, location_name, D, L, d, zlug, Ha, Va, plot=False, display=0): '''Calculate the vertical and horizontal capacity of a helical pile using a soil profile. The calculation is based on the soil profile, anchor geometry and inclined load. diff --git a/famodel/anchors/anchors_famodel/capacity_load.py b/famodel/anchors/anchors_famodel/capacity_load.py index 479af3c5..cece7f19 100644 --- a/famodel/anchors/anchors_famodel/capacity_load.py +++ b/famodel/anchors/anchors_famodel/capacity_load.py @@ -47,7 +47,7 @@ def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=Fa Nc = 8.5 if all(layer['soil_type'] in ['rock', 'weak_rock'] for layer in layers): - print('[Bypass] Skipping load transfer — soil is all rock.') + if display > 0: print('[Bypass] Skipping load transfer — soil is all rock.') Ha = Tm*np.cos(np.deg2rad(thetam)) Va = Tm*np.cos(np.deg2rad(thetam)) return Ha, Va @@ -93,7 +93,7 @@ def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=Fa delta_z = f_delta(depth) phi = f_phi(depth) Nq = np.exp(np.pi*np.tan(np.deg2rad(phi)))*(np.tan(np.deg2rad(45 + phi/2)))**2 - # print(f'Nq = {Nq:.2f}, depth = {depth:.2f} m') + if display > 0: print(f'Nq = {Nq:.2f}, depth = {depth:.2f} m') d_theta = (En*d*Nq*gamma_z*depth - W*np.cos(theta))/T*deltas dT = (Et*d*gamma_z*depth*np.tan(np.deg2rad(delta_z)) + W*np.sin(theta))*deltas @@ -109,17 +109,17 @@ def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=Fa depth += d_depth if not (0 < np.rad2deg(theta) < 90): - print(f"[Warning] Line angle reached {np.rad2deg(theta):.2f}°, stopping at drag = {-drag:.2f} m") + if display > 0: print(f"[Warning] Line angle reached {np.rad2deg(theta):.2f}°, stopping at drag = {-drag:.2f} m") break drag_values.append(-drag); depth_values.append(-depth); if np.rad2deg(theta) >= 90: - print(f"[Correction] Clipping angle to 90° to avoid negative horizontal load (Ha).") + if display > 0: print(f"[Correction] Clipping angle to 90° to avoid negative horizontal load (Ha).") theta = np.deg2rad(90) Ta = T; thetaa = theta - Hm = Tm*np.cos(np.deg2rad(thetam)); Vm = Tm*np.cos(np.deg2rad(thetam)) + Hm = Tm*np.cos(np.deg2rad(thetam)); Vm = Tm*np.sin(np.deg2rad(thetam)) Ha = Ta*np.cos(thetaa); Va = Ta*np.sin(thetaa) if display > 0: print(f'Input Tm = {Tm} N, thetam = {thetam}°, zlug = {zlug} m') @@ -131,7 +131,7 @@ def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=Fa 'Tm': Tm, 'thetam': thetam, 'Hm': Hm, 'Vm': Vm, 'Ta': Ta, 'thetaa': np.rad2deg(thetaa), - 'Ha': Hm, 'Va': Vm, + 'Ha': Hm, 'Va': Va, 'length': deltas*len(drag_values), 'drag_values': drag_values, 'depth_values': depth_values} diff --git a/famodel/anchors/anchors_famodel/capacity_plate.py b/famodel/anchors/anchors_famodel/capacity_plate.py index 825252d4..59a1f91d 100644 --- a/famodel/anchors/anchors_famodel/capacity_plate.py +++ b/famodel/anchors/anchors_famodel/capacity_plate.py @@ -4,7 +4,7 @@ from .support_soils import clay_profile from .support_plots import plot_plate -def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot=False): +def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot=False, display=0): '''Calculate the plate anchor capacity using clay soil layers from profile_map. The calculation is based on the soil profile, anchor geometry and inclined load. @@ -47,8 +47,8 @@ def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot= profile.append([layer['top'], layer['gamma_top'], layer['Su_top']]) profile.append([layer['bottom'], layer['gamma_bot'], layer['Su_bot']]) - print("layer gamma_top (raw):", layer['gamma_top']) - print("layer gamma_bot (raw):", layer['gamma_bot']) + if display > 0: print("layer gamma_top (raw):", layer['gamma_top']) + if display > 0: print("layer gamma_bot (raw):", layer['gamma_bot']) profile = np.array(sorted(profile, key=lambda x: x[0])) @@ -75,13 +75,13 @@ def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot= Su = np.mean(Su_vals); print(f"Su: {Su:.2f} Pa") gamma = np.mean(gamma_vals); print(f"gamma: {gamma:.2f} N/m3") - print("Profile being sent to clay_profile():") + if display > 0: print("Profile being sent to clay_profile():") for row in profile: - print(f"z = {row[0]:.2f} m, gamma = {row[1]:.2f} kN/m³, Su = {row[2]:.2f} kPa") + if display > 0: print(f"z = {row[0]:.2f} m, gamma = {row[1]:.2f} kN/m³, Su = {row[2]:.2f} kPa") # Shear strength gradient k = np.polyfit(z_points, Su_vals, 1)[0] - print(f"k: {k:.2f}") + if display > 0: print(f"k: {k:.2f}") # Pile weight including auxiliary parts Wp = 1.35*V_steel*(rhows + rhow) @@ -90,7 +90,7 @@ def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot= Nco_0_0 = 2.483*np.log(zlug_B) + 1.974 Nco_90_0 = 2.174*np.log(zlug_B) + 3.391 kBSh = k*B/Su - print(f"kBSh: {kBSh:.2f}") + if display > 0: print(f"kBSh: {kBSh:.2f}") f0 = np.where(zlug_B < 4, 1.77*(zlug_B**0.3) - 1.289, 0.192*zlug_B + 0.644) f90 = np.where(zlug_B < 4, 0.68*(zlug_B**0.5) - 0.410, 0.153*zlug_B + 0.341) @@ -112,8 +112,8 @@ def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot= Nco_s = Nco_s_90 + (Nco_s_0 - Nco_s_90)*((90 - beta)/90)**2 Nc_final = max(Nco + (gamma*zlug)/Su, Nco_s) - print(f"Nc_star: {Nco + (gamma*zlug)/Su:.2f}") - print(f"Nc_star: {Nco_s:.2f}") + if display > 0: print(f"Nc_star: {Nco + (gamma*zlug)/Su:.2f}") + if display > 0: print(f"Nc_star: {Nco_s:.2f}") qu = Nc_final*Su Tmax = round(qu*(1 - Los)*B*L, 2) Hmax = Tmax*np.cos(np.deg2rad(90 - beta)) diff --git a/famodel/anchors/anchors_famodel/capacity_torpedo.py b/famodel/anchors/anchors_famodel/capacity_torpedo.py index 93393b74..7c7e13aa 100644 --- a/famodel/anchors/anchors_famodel/capacity_torpedo.py +++ b/famodel/anchors/anchors_famodel/capacity_torpedo.py @@ -4,7 +4,7 @@ from .support_soils import clay_profile from .support_plots import plot_torpedo -def getCapacityTorpedo(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot=False): +def getCapacityTorpedo(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot=False, display=0): '''Calculate the inclined load capacity of a torpedo pile in clay following S. Kay methodology. The calculation is based on the soil profile, anchor geometry and inclined load. @@ -104,24 +104,23 @@ def PileShaftSurface(length, diameter1, diameter2): Su_total = np.trapz(Su_vals, z_vals) Su_moment = np.trapz(z_vals*Su_vals, z_vals) - print("xxxxxxxxxxxxxxxxxxxxxxxxx") Su_av_z = Su_total/dz_seg - print(f"Su_av_z = {Su_av_z:.2f} Pa") + if display > 0: print(f"Su_av_z = {Su_av_z:.2f} Pa") ez_layer = Su_moment /Su_total - print(f"dz_seg = {dz_seg:.2f} m") - print(f"ez_layer = {ez_layer:.2f} m") + if display > 0: print(f"dz_seg = {dz_seg:.2f} m") + if display > 0: print(f"ez_layer = {ez_layer:.2f} m") alpha_av = np.mean(alpha_vals) - print(f"alpha_av = {alpha_av:.2f}") + if display > 0: print(f"alpha_av = {alpha_av:.2f}") Np_free = 3.45 Hmax_layer = Np_free*dz_seg*D*Su_av_z - print(f"Hmax_layer = {Hmax_layer:.2f} N") - print(f"D = {D:.2f} m") + if display > 0: print(f"Hmax_layer = {Hmax_layer:.2f} N") + if display > 0: print(f"D = {D:.2f} m") surface_area = PileWingedSurface(dz_seg, D) if D == D1 else PileShaftSurface(dz_seg, D1, D2) Vmax_layer = surface_area*alpha_av*Su_av_z Vmax_total += Vmax_layer - print(f"Vmax_layer = {Vmax_layer:.2f} N") + if display > 0: print(f"Vmax_layer = {Vmax_layer:.2f} N") layer_data.append({ 'z_top': z_seg_top, @@ -160,13 +159,14 @@ def PileShaftSurface(length, diameter1, diameter2): sum_Hmax += Hmax_layer * ratio ez_global = sum_ez_weighted/sum_Hmax - print(f'ez_global = {ez_global:.2f} m') - print(f'sum_Hmax = {sum_Hmax:.2f} N') + if display > 0: print(f'ez_global = {ez_global:.2f} m') + if display > 0: print(f'sum_Hmax = {sum_Hmax:.2f} N') Vmax_total += PileWeight(L1, L2, D1, D2, t, rhows) + ballast Wp = 1.10 * PileWeight(L1, L2, D1, D2, t, rhows + rhow) + ballast - ez_ratio = (ez_global - zlug)/L; print(f"ez_ratio = {ez_ratio:.2f} m") + ez_ratio = (ez_global - zlug)/L; + if display > 0: print(f"ez_ratio = {ez_ratio:.2f} m") # Average effective width L = L1 + L2 @@ -195,7 +195,7 @@ def PileShaftSurface(length, diameter1, diameter2): # aVH = 4.0 # bVH = 4.0 # mode = 'default exponents (fallback)' - print(f'Interaction exponents set to aVH = {aVH:.2f}, bVH = {bVH:.2f} [{mode}]') + if display > 0: print(f'Interaction exponents set to aVH = {aVH:.2f}, bVH = {bVH:.2f} [{mode}]') UC = (Ha/sum_Hmax)**aVH + (Va/Vmax_total)**bVH diff --git a/famodel/project.py b/famodel/project.py index da60c4ba..21ea8a9a 100644 --- a/famodel/project.py +++ b/famodel/project.py @@ -2,7 +2,7 @@ the site information and design information that make up a project.""" import sys -sys.path.append(r'C:\Code\FAModel_anchors') +sys.path.append(r'C:\Code\FAModel') import os import numpy as np From e8856766c625f379d1797048caec6e2537248e15 Mon Sep 17 00:00:00 2001 From: Moreno Date: Tue, 30 Sep 2025 16:51:00 -0600 Subject: [PATCH 06/15] Fix tests/test_anchors.py after merge edits --- tests/test_anchors.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/tests/test_anchors.py b/tests/test_anchors.py index 3391d65b..19fb4231 100644 --- a/tests/test_anchors.py +++ b/tests/test_anchors.py @@ -5,7 +5,6 @@ import matplotlib.pyplot as plt import pytest -<<<<<<< HEAD # --- Helper goes at module level --- def assign_soil(anchor, soil_label, project): soil_def = project.soilProps[soil_label] @@ -20,13 +19,8 @@ def assign_soil(anchor, soil_label, project): anchor.setSoilProfile(profile_map) anchor.profile_name = 'CPT_Assigned' - -def test_anchor_loads(): - # load in famodel project -======= @pytest.fixture def project(): ->>>>>>> dev dir = os.path.dirname(os.path.realpath(__file__)) return(Project(file=os.path.join(dir,'testOntology.yaml'), raft=False)) @@ -45,7 +39,6 @@ def test_anchor_loads(project): Vm = anch.loads.get('Vm') zlug = anch.dd['design']['zlug'] -<<<<<<< HEAD # Compute lug loads _, Ha, Va = anch.getLugForces(Hm, Vm, zlug, plot=False) anch.loads['Ha'] = Ha @@ -56,10 +49,7 @@ def test_anchor_loads(project): assert 'Hm' in anch.loads assert anch.loads['Ha'] != anch.loads['Hm'] -def test_anchor_capacities(): -======= def test_anchor_capacities(project): ->>>>>>> dev # load in famodel project (suction pile anchor) project.getMoorPyArray(cables=1) anch = project.anchorList['FOWT1a'] From b0f66a69f3f9804aa0b6a90a03560c3fb5eb570b Mon Sep 17 00:00:00 2001 From: Moreno Date: Thu, 2 Oct 2025 09:41:50 -0600 Subject: [PATCH 07/15] Tests: resolve conflicts in test_anchors.py --- tests/test_anchors.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_anchors.py b/tests/test_anchors.py index 19fb4231..5be1c189 100644 --- a/tests/test_anchors.py +++ b/tests/test_anchors.py @@ -5,6 +5,7 @@ import matplotlib.pyplot as plt import pytest +print('hola') # --- Helper goes at module level --- def assign_soil(anchor, soil_label, project): soil_def = project.soilProps[soil_label] From 2dcb3b3b490339f576f7242da5a35bbbb4002f71 Mon Sep 17 00:00:00 2001 From: Moreno Date: Thu, 2 Oct 2025 09:43:08 -0600 Subject: [PATCH 08/15] Anchors: update setLineProperties and getForces methods for consistency --- famodel/anchors/anchor.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index 27ef5f74..39a5d754 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -270,13 +270,13 @@ def getLineProperties(self): ''' for att in self.attachments.values(): if isinstance(att['obj'], Mooring): - mtype = att['obj'].dd['sections'][0]['type']['material'].lower() + mtype = att['obj'].sections()[0]['type']['material'].lower() if 'chain' not in mtype: print('No chain below seafloor, setting Ta=Tm (no load transfer).') return mtype, None, None, True else: - d_nom = att['obj'].dd['sections'][0]['type']['d_nom'] - w_nom = att['obj'].dd['sections'][0]['type']['w'] + d_nom = att['obj'].sections()[0]['type']['d_nom'] + w_nom = att['obj'].sections()[0]['type']['w'] return 'chain', d_nom, w_nom, False raise ValueError('No mooring line attachment found for anchor.') @@ -326,7 +326,10 @@ def getMudlineForces(self, max_force=False, lines_only=False, seabed=True, xyz=F self.loads['mudline_load_type'] = 'max_force' break else: - loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz) + # loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz) + # MoorPy Body.getForces does not accept seabed/lines_only in current API. + # Get forces (total), optionally post-process seabed if needed. + loads = self.mpAnchor.getForces() Hm = np.sqrt(loads[0]**2 + loads[1]**2) Vm = loads[2] thetam = np.degrees(np.arctan2(Vm, Hm)) From 4485556577c9cf1bae9b1a9169661e79a407db4a Mon Sep 17 00:00:00 2001 From: Moreno Date: Thu, 2 Oct 2025 09:44:08 -0600 Subject: [PATCH 09/15] Project: add convertLayeredToUniform method to complement convertUniformToLayered() --- famodel/project.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/famodel/project.py b/famodel/project.py index 44c74bdc..6c75d77e 100644 --- a/famodel/project.py +++ b/famodel/project.py @@ -1346,6 +1346,40 @@ def convertUniformToLayered(self, default_layer=50.0): 'Su_bot': Su0 + k * default_layer} self.profile_map[name] = [layer] # just layers! + + def convertLayeredToUniform(self): + ''' + Converts self.profile_map (layered format) into soilProps (uniform format) + assuming a single clay layer with linear Su(z) = Su0 + k*z. + Matches the structure expected by uniform soil models. + ''' + self.soilProps = {} + + for name, layers in self.profile_map.items(): + if not layers or len(layers) != 1: + raise ValueError('convertLayeredToUniform only supports a single-layer profile') + + layer = layers[0] + if str(layer.get('soil_type', '')).lower() != 'clay': + raise ValueError('convertLayeredToUniform only supports clay') + + top = float(layer['top']) + bot = float(layer['bottom']) + Su_top = float(layer['Su_top']) + Su_bot = float(layer['Su_bot']) + gamma = float(layer['gamma_top']) # gamma_top == gamma_bot in your format + + if bot <= top: + raise ValueError('Invalid layer thickness (bottom <= top)') + + thickness = bot - top + k = (Su_bot - Su_top)/thickness + Su0 = Su_top - k*top + + self.soilProps[name] = { + 'gamma': [gamma], + 'Su0': [Su0], + 'k': [k]} # # ----- Anchor def updateAnchor(self,anch='all',update_loc=True): From 38ba6b7a557d0571056f8b7dbf4bf9ac574980d5 Mon Sep 17 00:00:00 2001 From: Moreno Date: Fri, 3 Oct 2025 14:36:29 -0600 Subject: [PATCH 10/15] Updated anchor type labels in the yaml files --- famodel/anchors/README.md | 2 +- tests/mooring_ontology.yaml | 2 +- tests/mooring_ontology_parallels.yaml | 2 +- tests/platform_ontology.yaml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/famodel/anchors/README.md b/famodel/anchors/README.md index b4c8f170..bbce4321 100644 --- a/famodel/anchors/README.md +++ b/famodel/anchors/README.md @@ -110,7 +110,7 @@ Sand can also be classified ranging from soft to hard. and are chracterize by th | Very Dense | < 50 | 9.5 - 11.5 | ~ 0.45 | 12 - 16 | > 45 | > 85 | ## Anchor Types -The supported anchor types are listed below with their associated FAModel names in italics. Anchors types have specific [anchor capacity](#anchor-capacity-modules) and [anchor installation](#anchor-installation-modules) application modules, these are shown for clarity below as well. +The supported anchor types are listed below with their associated FAModel labels in italics (*Labels are not case sensitive*). Anchors types have specific [anchor capacity](#anchor-capacity-modules) and [anchor installation](#anchor-installation-modules) application modules, these are shown for clarity below as well. | | Capacity | Installation | |--------------------------------------------------------|----------------|--------------| diff --git a/tests/mooring_ontology.yaml b/tests/mooring_ontology.yaml index aa06dd1d..f5da3059 100644 --- a/tests/mooring_ontology.yaml +++ b/tests/mooring_ontology.yaml @@ -152,7 +152,7 @@ mooring_connector_types: # Anchor type properties anchor_types: suction_pile1: - type : suction_pile + type : suction L : 16.4 # length of pile [m] D : 5.45 # diameter of pile [m] zlug : 9.32 # embedded depth of padeye [m] \ No newline at end of file diff --git a/tests/mooring_ontology_parallels.yaml b/tests/mooring_ontology_parallels.yaml index e98647ae..92794cd7 100644 --- a/tests/mooring_ontology_parallels.yaml +++ b/tests/mooring_ontology_parallels.yaml @@ -170,7 +170,7 @@ mooring_connector_types: # Anchor type properties anchor_types: suction_pile1: - type : suction_pile + type : suction L : 16.4 # length of pile [m] D : 5.45 # diameter of pile [m] zlug : 9.32 # embedded depth of padeye [m] \ No newline at end of file diff --git a/tests/platform_ontology.yaml b/tests/platform_ontology.yaml index 0dae6a53..a5124e23 100644 --- a/tests/platform_ontology.yaml +++ b/tests/platform_ontology.yaml @@ -1239,7 +1239,7 @@ mooring_line_types: # Anchor type properties anchor_types: suction_pile1: - type : suction_pile + type : suction L : 16.4 # length of pile [m] D : 5.45 # diameter of pile [m] zlug : 9.32 # embedded depth of padeye [m] \ No newline at end of file From dd22138f97ba206645cb71bb14148549e8eab78d Mon Sep 17 00:00:00 2001 From: Moreno Date: Wed, 8 Oct 2025 11:00:25 -0600 Subject: [PATCH 11/15] Change anchor label from 'dandg' to 'drilled' --- .../{anchor_dandg.py => anchor_drilled.py} | 5 +- famodel/anchors/anchor.py | 36 +- ...{capacity_dandg.py => capacity_drilled.py} | 468 +++++++++--------- tests/test_anchors.py | 3 +- 4 files changed, 255 insertions(+), 257 deletions(-) rename examples/05_Anchors/{anchor_dandg.py => anchor_drilled.py} (97%) rename famodel/anchors/anchors_famodel/{capacity_dandg.py => capacity_drilled.py} (94%) diff --git a/examples/05_Anchors/anchor_dandg.py b/examples/05_Anchors/anchor_drilled.py similarity index 97% rename from examples/05_Anchors/anchor_dandg.py rename to examples/05_Anchors/anchor_drilled.py index 95764ce9..e252be61 100644 --- a/examples/05_Anchors/anchor_dandg.py +++ b/examples/05_Anchors/anchor_drilled.py @@ -17,15 +17,14 @@ # --- Create driven pile anchor --- anchor = Anchor( dd = { - 'type': 'dandg', + 'type': 'drilled', 'design': { 'L': 10.0, # Embedded length 'D': 2.85, # Diameter 'zlug': 1.0 # Padeye depth } }, - r = [0.0, 0.0, 0.0] -) + r = [0.0, 0.0, 0.0]) # Assign mooring loads anchor.loads = { diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index 39a5d754..72868ede 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -55,7 +55,7 @@ def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, print(f"[Anchor] No type provided. Defaulting to 'suction'.") # raise errors/warnings if the anchor type is not what it needs to be - anchor_type_options = ['suction', 'sepla', 'dea', 'depla', 'vla', 'plate', 'torpedo', 'helical', 'driven', 'dandg'] + anchor_type_options = ['suction', 'sepla', 'dea', 'depla', 'vla', 'plate', 'torpedo', 'helical', 'driven', 'drilled'] if self.anchType not in anchor_type_options: raise ValueError(f"The anchor 'type' needs to explicitly be one of {anchor_type_options} (Case not sensitive)") # if self.anchType not in ['drag-embedment', 'gravity', 'suction', 'SEPLA', 'VLA', 'driven']: @@ -466,7 +466,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u from .anchors_famodel.capacity_torpedo import getCapacityTorpedo from .anchors_famodel.capacity_helical import getCapacityHelical from .anchors_famodel.capacity_driven import getCapacityDriven - from .anchors_famodel.capacity_dandg import getCapacityDandG + from .anchors_famodel.capacity_drilled import getCapacityDrilled capacity_dispatch = { 'suction': getCapacitySuction, @@ -478,7 +478,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u 'torpedo': getCapacityTorpedo, 'helical': getCapacityHelical, 'driven': getCapacityDriven, - 'dandg': getCapacityDandG} + 'drilled': getCapacityDrilled} if self.display > 0: print('[DEBUG] profile_name:', self.profile_name) if self.display > 0: print('[DEBUG] soil_profile passed as profile_map:') @@ -608,7 +608,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u Ha=Ha, Va=Va, plot=plot, display=display) - elif anchType_clean == 'dandg': + elif anchType_clean == 'drilled': self.capacity_format = 'component' L = self.dd['design']['L'] D = self.dd['design']['D'] @@ -636,7 +636,7 @@ def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_u if anchType_clean in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']: self.anchorCapacity['UC'] = results.get('Unity check', np.nan) - elif anchType_clean in ['helical', 'driven', 'dandg']: + elif anchType_clean in ['helical', 'driven', 'drilled']: self.anchorCapacity['Unity check (horizontal)'] = results.get('Unity check (horizontal)', np.nan) self.anchorCapacity['Unity check (vertical)'] = results.get('Unity check (vertical)', np.nan) @@ -693,7 +693,7 @@ def update_zlug(): ratio = self.dd['design'].get('zlug_ratio', self.dd['design']['zlug']/self.dd['design']['L']) self.dd['design']['zlug_ratio'] = ratio self.dd['design']['zlug'] = ratio*self.dd['design']['L'] - elif 'dandg' in anchType_clean: + elif 'drilled' in anchType_clean: self.dd['design']['zlug'] = 0 def get_lambda(): @@ -702,7 +702,7 @@ def get_lambda(): A_wing = (self.dd['design']['D1'] - self.dd['design']['D2']) * self.dd['design']['L1'] A_shaft = self.dd['design']['D2'] * L D = (A_wing + A_shaft) / L - elif np.any([name in anchType_clean for name in ['driven', 'dandg', 'helical', 'suction']]): + elif np.any([name in anchType_clean for name in ['driven', 'drilled', 'helical', 'suction']]): L = self.dd['design']['L'] D = self.dd['design']['D'] elif np.any([name in anchType_clean for name in ['plate', 'sepla', 'dea', 'depla', 'vla']]): @@ -812,7 +812,7 @@ def termination_condition(): return 'continue' return 'continue' - def termination_condition_dandg(): + def termination_condition_drilled(): UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) @@ -912,7 +912,7 @@ def is_valid(value): if self.display > 0: print('[Warning] While-loop search reached bounds without meeting criteria.') - if 'dandg' in anchType_clean: + if 'drilled' in anchType_clean: L0, D0 = geom if len(geom) == 2 else [5.0, 1.0] self.dd['design']['L'] = L0 self.dd['design']['D'] = D0 @@ -949,7 +949,7 @@ def is_valid(value): iter_count += 1 if not all(is_valid(v) for v in [UC_v, disp_lat, disp_rot]): continue - if termination_condition_dandg(): + if termination_condition_drilled(): print(f'\nTermination criteria met.') print('Design:', self.dd['design']) print('Capacity Results:', self.anchorCapacity) @@ -969,7 +969,7 @@ def is_valid(value): disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) if self.display > 0: print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') iter_count += 1 - status = termination_condition_dandg() + status = termination_condition_drilled() if status == 'terminate': print(f'Termination criteria met.') print('Design:', self.dd['design']) @@ -977,7 +977,7 @@ def is_valid(value): return elif status == 'continue': continue - status = termination_condition_dandg() + status = termination_condition_drilled() if status == 'terminate': print(f'\nTermination criteria met.') print('Design:', self.dd['design']) @@ -1339,7 +1339,7 @@ def getSafetyFactor(self): anchType_clean = self.anchType.lower().replace(' ', '') - if anchType_clean in ['helical', 'driven', 'dandg']: + if anchType_clean in ['helical', 'driven', 'drilled']: UC_v = self.anchorCapacity.get('Unity check (vertical)', None) UC_h = self.anchorCapacity.get('Unity check (horizontal)', None) @@ -1760,7 +1760,7 @@ def conFun_Suction(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, mi # convalB = 1 - results['UC'] return(conval) - def conFun_DandG(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): + def conFun_Drilled(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): newGeom = dict(zip(geomKeys, vars)) self.dd['design'].update(newGeom) @@ -1879,9 +1879,9 @@ def conBounds(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}] - elif 'dandg' in anchType: + elif 'drilled' in anchType: constraints = [{'type':'ineq','fun':conFun_LD,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, - {'type':'ineq','fun':conFun_DandG,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, + {'type':'ineq','fun':conFun_Drilled,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, {'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}, {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}] @@ -1893,7 +1893,7 @@ def conBounds(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): # Run the optimization to find sizing that satisfy UC close to 1 print('optimizing anchor size') - if 'suction' in anchType or 'dandg' in anchType: + if 'suction' in anchType or 'drilled' in anchType: solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) else: @@ -1926,7 +1926,7 @@ def conBounds(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs): print('new initial guess',initial_guess) # re-run optimization - if 'suction' in anchType or 'dandg' in anchType: + if 'suction' in anchType or 'drilled' in anchType: solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs), method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001}) else: diff --git a/famodel/anchors/anchors_famodel/capacity_dandg.py b/famodel/anchors/anchors_famodel/capacity_drilled.py similarity index 94% rename from famodel/anchors/anchors_famodel/capacity_dandg.py rename to famodel/anchors/anchors_famodel/capacity_drilled.py index fdb5acb8..929e2af5 100644 --- a/famodel/anchors/anchors_famodel/capacity_dandg.py +++ b/famodel/anchors/anchors_famodel/capacity_drilled.py @@ -1,234 +1,234 @@ - -import numpy as np -import matplotlib.pyplot as plt -from .support_soils import rock_profile -from .support_solvers import fd_solver -from .support_pycurves import py_Lovera -from .support_plots import plot_pile, plot_pycurve - -def getCapacityDandG(profile_map, location_name, L, D, zlug, Ha, Va, plot=False, display=0): - '''Models a laterally loaded pile using the p-y method. The solution for - lateral displacements is obtained by solving the 4th order ODE, - EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. - EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method. - - Assumes that EI remains constant with respect to curvature i.e. pile - material remains in the elastic region. - - Parameters - ---------- - profile : array - Rock profile as a 2D array: (z (m), UCS (MPa), Em (MPa)) - soil_type : string - Select soil condition, 'rock' - L : float - Pile length (m) - D : float - Pile diameter (m) - zlug : float - Load eccentricity above the mudline or depth to mudline relative to the pile head (m) - Ha : float - Horizontal load at pile lug elevation (N) - Va : float - Vertical load at pile lug elevation (N) - plot : bool - Plot the p-y curve and the deflection pile condition if True - - Returns - ------- - y : array - Lateral displacement at each node (n+1 real + 4 imaginary) - z : array - Node location along pile (m) - resultsDandG : dict - Dictionary with lateral, rotational, vertical and pile weight results - ''' - - profile_entry = next(p for p in profile_map if p['name'] == location_name) - layers = profile_entry['layers'] - - n = 50; loc = 2 # Number of nodes (-) - tol = 1e-16; max_iter = 50 # Iteration parameters (-) - nhuc = 1; nhu = 0.3 # Resistance factor (-) - delta_r = 0.08 # Mean roughness height (m) - - t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD - E = 200e9 # Elastic modulus of pile material (Pa) - fy = 350e6 # Steel's yield strength (Pa) - rhows = 66.90e3 # Submerged steel specific weight (N/m3) - rhow = 10e3 # Water specific weight (N/m3) - - # Pile geometry - I = (np.pi/64.0)*(D**4 - (D - 2*t)**4) - EI = E*I - h = L/n # Element size - N = (n + 1) + 4 # (n+1) Real + 4 Imaginary nodes - - # Dry and wet mass of the pile - def PileWeight(Len, Dia, tw, rho): - Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho - return Wp - - # Array for displacements at nodes, including imaginary nodes. - y = np.ones(N)*(0.01*D) # An initial value of 0.01D was arbitrarily chosen - - # Initialize and assemble array/list of p-y curves at each real node - z = np.zeros(N) - k_secant = np.zeros(N) - py_funs = [] - DQ = []; pycurve_data = [] - - z0 = min(layer['top'] for layer in layers) - - for i in [0, 1]: # Top two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - for i in range(2, n+3): # Real nodes - z[i] = (i - 2)*h - z_depth = z[i] - - matched_layer = next((layer for layer in layers if layer['top'] <= z_depth <= layer['bottom']), None) - if matched_layer is None or z_depth < matched_layer['top']: - py_funs.append(lambda y_val: np.zeros_like(y_val)) - k_secant[i] = 0.0 - DQ.append(0.0) - continue - - profile = [[matched_layer['top'], matched_layer['UCS_top'], matched_layer['Em_top']], - [matched_layer['bottom'], matched_layer['UCS_bot'], matched_layer['Em_bot']]] - z0_local, f_UCS, f_Em = rock_profile(profile) - - if z_depth < z0_local: - py_funs.append(lambda y_val: np.zeros_like(y_val)) - k_secant[i] = 0.0 - DQ.append(0.0) - continue - - UCS = f_UCS(z_depth) - Em = f_Em(z_depth) - py_f, (y_vals, p_vals) = py_Lovera(z_depth, D, UCS, Em, zlug, z0, return_curve=True) - py_funs.append(py_f) - pycurve_data.append((y_vals, p_vals, z_depth, 'rock')) - # print(f"z_depth = {z_depth:.2f} m, UCS = {f_UCS(z_depth):.2e} Pa, Em = {f_Em(z_depth):.2e} Pa") - - SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D - alpha = 0.36*SCR - 0.0005 - fs = alpha*UCS - Dq = np.pi*D*fs*z_depth - DQ.append(Dq) - k_val = py_funs[i](y[i]) - k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 - - for i in [n+3, n+4]: # Bottom two imaginary nodes - z[i] = (i - 2)*h - py_funs.append(0) - k_secant[i] = 0.0 - - Wp = PileWeight(L, D, t, rhows + rhow) - Wtip = DQ[-1] if DQ else 0.0 - Vmax = Wp + Wtip - - for j in range(max_iter): - y_old = y.copy() - y, *_ = fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant) - - # Update stiffness - for i in range(2, n+3): - if callable(py_funs[i]): - k_secant[i] = py_funs[i](y[i])/y[i] if y[i] != 0 else 0.0 - - # Check convergence - if np.linalg.norm(y - y_old, ord=2) < tol: - if display > 0: print(f'[Converged in {j+1} iterations]') - break - else: - if display > 0: print('[Warning: Solver did not converge]') - - - if plot: - plot_pycurve(pycurve_data) - - fig, ax = plt.subplots(figsize=(3, 5)) - y0 = np.zeros_like(z[2:-2]) - ax.plot(y0, z[2:-2], 'k', label='Original pile axis') - ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape') - ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') - ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)') - ax.set_xlabel('Lateral displacement (m)') - ax.set_ylabel('Depth (m)') - ax.set_xlim([-0.1*D, 0.1*D]) - ax.set_ylim([L + 5, -2]) - ax.grid(ls='--') - ax.legend() - - # Relevant index of nodes - zlug_index = int(zlug/h) - if display > 0: print(zlug_index) - ymax_index = np.argmax(y) - if display > 0: print(ymax_index) - - resultsDandG = { - 'Vertical max.': Vmax, - 'Lateral displacement': y[ymax_index], - 'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h), - 'Bending moment': None, - 'Plastic moment': None, - 'Plastic hinge': None, - 'Hinge location': None, - 'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf, - 'Unity check (horizontal)': 0.0, # Placeholder; no Mp or Mi in current model - 'Weight pile': PileWeight(L, D, t, rhows + rhow), - 'p-y model': 'Lovera (2023)'} - - return layers, y[2:-2], z[2:-2], resultsDandG - -if __name__ == '__main__': - - profile_map = [ - { - 'name': 'CPT_rock_1', - 'x': 502000, - 'y': 5725000, - 'layers': [ - { - 'top': 2.0, 'bottom': 5.0, - 'soil_type': 'rock', - 'UCS_top': 8.0, 'UCS_bot': 8.0, # MPa - 'Em_top': 100, 'Em_bot': 200 # MPa - }, - { - 'top': 5.0, 'bottom': 9.0, - 'soil_type': 'rock', - 'UCS_top': 10.0, 'UCS_bot': 10.0, # MPa - 'Em_top': 200, 'Em_bot': 300 # MPa - }, - { - 'top': 9.0, 'bottom': 30.0, - 'soil_type': 'rock', - 'UCS_top': 20.0, 'UCS_bot': 20.0, # MPa - 'Em_top': 300, 'Em_bot': 400 # MPa - } - ] - } - ] - - D = 3.0 # Diameter (m) - L = 10.0 # Length (m) - zlug = 1 # Padeye elevation (m) - Ha = 5.0e6 # Horizontal load (N) - Va = 3.0e5 # Vertical load (N) - - layers, y, z, results = getCapacityDandG(profile_map, 'CPT_rock_1', L, D, zlug, Ha, Va, plot=True, display=0) - - print('\n--- Results for DandG Pile in Layered Rock ---') - for key, val in results.items(): - print(f'{key}: {val:.3f}' if isinstance(val, float) else f'{key}: {val}') - - plot_pile(layers, y, z, D, L, layers[0]['top'], zlug) - - - - - + +import numpy as np +import matplotlib.pyplot as plt +from .support_soils import rock_profile +from .support_solvers import fd_solver +from .support_pycurves import py_Lovera +from .support_plots import plot_pile, plot_pycurve + +def getCapacityDrilled(profile_map, location_name, L, D, zlug, Ha, Va, plot=False, display=0): + '''Models a laterally loaded pile using the p-y method. The solution for + lateral displacements is obtained by solving the 4th order ODE, + EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method. + EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method. + + Assumes that EI remains constant with respect to curvature i.e. pile + material remains in the elastic region. + + Parameters + ---------- + profile : array + Rock profile as a 2D array: (z (m), UCS (MPa), Em (MPa)) + soil_type : string + Select soil condition, 'rock' + L : float + Pile length (m) + D : float + Pile diameter (m) + zlug : float + Load eccentricity above the mudline or depth to mudline relative to the pile head (m) + Ha : float + Horizontal load at pile lug elevation (N) + Va : float + Vertical load at pile lug elevation (N) + plot : bool + Plot the p-y curve and the deflection pile condition if True + + Returns + ------- + y : array + Lateral displacement at each node (n+1 real + 4 imaginary) + z : array + Node location along pile (m) + resultsDandG : dict + Dictionary with lateral, rotational, vertical and pile weight results + ''' + + profile_entry = next(p for p in profile_map if p['name'] == location_name) + layers = profile_entry['layers'] + + n = 50; loc = 2 # Number of nodes (-) + tol = 1e-16; max_iter = 50 # Iteration parameters (-) + nhuc = 1; nhu = 0.3 # Resistance factor (-) + delta_r = 0.08 # Mean roughness height (m) + + t = (6.35 + D*20)/1e3 # Pile wall thickness (m), API RP2A-WSD + E = 200e9 # Elastic modulus of pile material (Pa) + fy = 350e6 # Steel's yield strength (Pa) + rhows = 66.90e3 # Submerged steel specific weight (N/m3) + rhow = 10e3 # Water specific weight (N/m3) + + # Pile geometry + I = (np.pi/64.0)*(D**4 - (D - 2*t)**4) + EI = E*I + h = L/n # Element size + N = (n + 1) + 4 # (n+1) Real + 4 Imaginary nodes + + # Dry and wet mass of the pile + def PileWeight(Len, Dia, tw, rho): + Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho + return Wp + + # Array for displacements at nodes, including imaginary nodes. + y = np.ones(N)*(0.01*D) # An initial value of 0.01D was arbitrarily chosen + + # Initialize and assemble array/list of p-y curves at each real node + z = np.zeros(N) + k_secant = np.zeros(N) + py_funs = [] + DQ = []; pycurve_data = [] + + z0 = min(layer['top'] for layer in layers) + + for i in [0, 1]: # Top two imaginary nodes + z[i] = (i - 2)*h + py_funs.append(0) + k_secant[i] = 0.0 + + for i in range(2, n+3): # Real nodes + z[i] = (i - 2)*h + z_depth = z[i] + + matched_layer = next((layer for layer in layers if layer['top'] <= z_depth <= layer['bottom']), None) + if matched_layer is None or z_depth < matched_layer['top']: + py_funs.append(lambda y_val: np.zeros_like(y_val)) + k_secant[i] = 0.0 + DQ.append(0.0) + continue + + profile = [[matched_layer['top'], matched_layer['UCS_top'], matched_layer['Em_top']], + [matched_layer['bottom'], matched_layer['UCS_bot'], matched_layer['Em_bot']]] + z0_local, f_UCS, f_Em = rock_profile(profile) + + if z_depth < z0_local: + py_funs.append(lambda y_val: np.zeros_like(y_val)) + k_secant[i] = 0.0 + DQ.append(0.0) + continue + + UCS = f_UCS(z_depth) + Em = f_Em(z_depth) + py_f, (y_vals, p_vals) = py_Lovera(z_depth, D, UCS, Em, zlug, z0, return_curve=True) + py_funs.append(py_f) + pycurve_data.append((y_vals, p_vals, z_depth, 'rock')) + # print(f"z_depth = {z_depth:.2f} m, UCS = {f_UCS(z_depth):.2e} Pa, Em = {f_Em(z_depth):.2e} Pa") + + SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D + alpha = 0.36*SCR - 0.0005 + fs = alpha*UCS + Dq = np.pi*D*fs*z_depth + DQ.append(Dq) + k_val = py_funs[i](y[i]) + k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0 + + for i in [n+3, n+4]: # Bottom two imaginary nodes + z[i] = (i - 2)*h + py_funs.append(0) + k_secant[i] = 0.0 + + Wp = PileWeight(L, D, t, rhows + rhow) + Wtip = DQ[-1] if DQ else 0.0 + Vmax = Wp + Wtip + + for j in range(max_iter): + y_old = y.copy() + y, *_ = fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant) + + # Update stiffness + for i in range(2, n+3): + if callable(py_funs[i]): + k_secant[i] = py_funs[i](y[i])/y[i] if y[i] != 0 else 0.0 + + # Check convergence + if np.linalg.norm(y - y_old, ord=2) < tol: + if display > 0: print(f'[Converged in {j+1} iterations]') + break + else: + if display > 0: print('[Warning: Solver did not converge]') + + + if plot: + plot_pycurve(pycurve_data) + + fig, ax = plt.subplots(figsize=(3, 5)) + y0 = np.zeros_like(z[2:-2]) + ax.plot(y0, z[2:-2], 'k', label='Original pile axis') + ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape') + ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)') + ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)') + ax.set_xlabel('Lateral displacement (m)') + ax.set_ylabel('Depth (m)') + ax.set_xlim([-0.1*D, 0.1*D]) + ax.set_ylim([L + 5, -2]) + ax.grid(ls='--') + ax.legend() + + # Relevant index of nodes + zlug_index = int(zlug/h) + if display > 0: print(zlug_index) + ymax_index = np.argmax(y) + if display > 0: print(ymax_index) + + resultsDandG = { + 'Vertical max.': Vmax, + 'Lateral displacement': y[ymax_index], + 'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h), + 'Bending moment': None, + 'Plastic moment': None, + 'Plastic hinge': None, + 'Hinge location': None, + 'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf, + 'Unity check (horizontal)': 0.0, # Placeholder; no Mp or Mi in current model + 'Weight pile': PileWeight(L, D, t, rhows + rhow), + 'p-y model': 'Lovera (2023)'} + + return layers, y[2:-2], z[2:-2], resultsDandG + +if __name__ == '__main__': + + profile_map = [ + { + 'name': 'CPT_rock_1', + 'x': 502000, + 'y': 5725000, + 'layers': [ + { + 'top': 2.0, 'bottom': 5.0, + 'soil_type': 'rock', + 'UCS_top': 8.0, 'UCS_bot': 8.0, # MPa + 'Em_top': 100, 'Em_bot': 200 # MPa + }, + { + 'top': 5.0, 'bottom': 9.0, + 'soil_type': 'rock', + 'UCS_top': 10.0, 'UCS_bot': 10.0, # MPa + 'Em_top': 200, 'Em_bot': 300 # MPa + }, + { + 'top': 9.0, 'bottom': 30.0, + 'soil_type': 'rock', + 'UCS_top': 20.0, 'UCS_bot': 20.0, # MPa + 'Em_top': 300, 'Em_bot': 400 # MPa + } + ] + } + ] + + D = 3.0 # Diameter (m) + L = 10.0 # Length (m) + zlug = 1 # Padeye elevation (m) + Ha = 5.0e6 # Horizontal load (N) + Va = 3.0e5 # Vertical load (N) + + layers, y, z, results = getCapacityDrilled(profile_map, 'CPT_rock_1', L, D, zlug, Ha, Va, plot=True, display=0) + + print('\n--- Results for Drilled Pile in Layered Rock ---') + for key, val in results.items(): + print(f'{key}: {val:.3f}' if isinstance(val, float) else f'{key}: {val}') + + plot_pile(layers, y, z, D, L, layers[0]['top'], zlug) + + + + + diff --git a/tests/test_anchors.py b/tests/test_anchors.py index 5be1c189..aa7b1c5d 100644 --- a/tests/test_anchors.py +++ b/tests/test_anchors.py @@ -5,7 +5,6 @@ import matplotlib.pyplot as plt import pytest -print('hola') # --- Helper goes at module level --- def assign_soil(anchor, soil_label, project): soil_def = project.soilProps[soil_label] @@ -91,7 +90,7 @@ def test_anchor_capacities(project): # check DRILLED & GROUTED PILE (need to change material to rock) loads = {'Ha':4.5e5, 'Va':1.9e5} # again assign new loads - anch.dd['type'] = 'dandg' + anch.dd['type'] = 'drilled' anch.dd['design'] = {'L':10, 'D':3, 'zlug':0} assign_soil(anch, 'weak_rock', project) Ha = loads['Ha'] From 440764608d14051a8f63cfd2fe930b99c8c74c02 Mon Sep 17 00:00:00 2001 From: lsirkis <149831604+lsirkis@users.noreply.github.com> Date: Tue, 4 Nov 2025 16:43:00 -0700 Subject: [PATCH 12/15] Uncomment example run step in CI workflow GitHub CI should run example. --- .github/workflows/CI_FAModel.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/CI_FAModel.yml b/.github/workflows/CI_FAModel.yml index 4a8aa2f7..d446148b 100644 --- a/.github/workflows/CI_FAModel.yml +++ b/.github/workflows/CI_FAModel.yml @@ -55,10 +55,10 @@ jobs: cd tests pytest . - # - name: Example run - # run: | - # cd examples - # python example_driver.py false + - name: Example run + run: | + cd examples + python example_driver.py false From 21bf46a4ff8ed8273ecf8fb70bcca3101bfb73d1 Mon Sep 17 00:00:00 2001 From: lsirkis Date: Wed, 5 Nov 2025 13:13:36 -0700 Subject: [PATCH 13/15] Editing some files to make merge easier -- no major changes to functionality -- tests and examples will not work at this time, this is a WIP for the merge --- .github/workflows/CI_FAModel.yml | 12 +- README.md | 10 +- .../05_visual_lease_boundaries.py | 4 +- .../01_Visualization/07_3D-visual_platform.py | 6 +- .../07_3D-visual_platform.yaml | 12 +- .../08_3D-visual_turbine.yaml | 18 +- .../01_platform.yaml | 12 +- .../02_FOWT.yaml | 63 +- examples/08_Design_Adjustment/01_Fairleads.py | 33 + examples/08_Design_Adjustment/03_rotations.py | 41 + examples/OntologySample200m.yaml | 40 +- examples/OntologySample200m_1turb.yaml | 18 +- examples/OntologySample200m_uniformArray.yaml | 18 +- examples/OntologySample600m_shared.yaml | 18 +- examples/duplicate_platform.py | 14 +- examples/example_driver.py | 16 +- famodel/anchors/anchor.py | 7 +- famodel/design/CableDesign.py | 1312 +++++++++ famodel/design/CableLayout_functions.py | 1034 +++++++ famodel/design/LineDesign.py | 2293 +++++++++++++++ famodel/design/LinearSystem.py | 2083 ++++++++++++++ famodel/design/fadsolvers.py | 1857 ++++++++++++ famodel/design/layout.py | 2485 +++++++++++++++++ famodel/design/layout_helpers.py | 1178 ++++++++ famodel/mooring/mooringOntology.yaml | 4 +- famodel/project.py | 3 - famodel/seabed_tools.py | 310 ++ tests/simple_farm.yaml | 18 +- tests/testOntology.yaml | 22 +- tests/test_LineDesign.py | 200 ++ tests/test_anchors.py | 4 +- 31 files changed, 13010 insertions(+), 135 deletions(-) create mode 100644 examples/08_Design_Adjustment/01_Fairleads.py create mode 100644 examples/08_Design_Adjustment/03_rotations.py create mode 100644 famodel/design/CableDesign.py create mode 100644 famodel/design/CableLayout_functions.py create mode 100644 famodel/design/LineDesign.py create mode 100644 famodel/design/LinearSystem.py create mode 100644 famodel/design/fadsolvers.py create mode 100644 famodel/design/layout.py create mode 100644 famodel/design/layout_helpers.py create mode 100644 famodel/seabed_tools.py create mode 100644 tests/test_LineDesign.py diff --git a/.github/workflows/CI_FAModel.yml b/.github/workflows/CI_FAModel.yml index d446148b..e099140b 100644 --- a/.github/workflows/CI_FAModel.yml +++ b/.github/workflows/CI_FAModel.yml @@ -40,6 +40,7 @@ jobs: - name: Extras run: | conda install -y pytest meson ninja nlopt + pip install scipy==1.11.2 # specific SciPy version needed for current LineDesign test results conda info - name: Conda Install famodel @@ -48,17 +49,16 @@ jobs: - name: Overwrite MoorPy run: | + pip uninstall -y moorpy # need to uninstall the old version to install the current dev branch pip install git+https://github.com/NREL/MoorPy@dev - - - name: Test run - run: | - cd tests - pytest . - name: Example run run: | cd examples python example_driver.py false - + - name: Test run + run: | + cd tests + pytest . diff --git a/README.md b/README.md index 02a13d22..0dae1648 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,12 @@ -# FAModel +# Floating Array Design Toolset -The FAModel (or Floating Array Model) package serves as a high-level library for +The Floating Array Design (FAD) Toolset is a collection of tools for +modeling and designing arrays of floating offshore structures. It was +originally designed for floating wind systems but has applicability +for many offshore applications. + +A core part of the FAD Toolset is the Floating Array Model (FAModel), +which serves as a high-level library for efficiently modeling a floating wind array. It combines site condition information and a description of the floating array design, and contains functions for evaluating the array's behavior considering the site conditions. For example, it combines diff --git a/examples/01_Visualization/05_visual_lease_boundaries.py b/examples/01_Visualization/05_visual_lease_boundaries.py index 95e15551..676a0bf4 100644 --- a/examples/01_Visualization/05_visual_lease_boundaries.py +++ b/examples/01_Visualization/05_visual_lease_boundaries.py @@ -9,9 +9,11 @@ from famodel import Project import matplotlib.pyplot as plt +import os # define name of ontology input file -input_file = '05_visual_lease_boundaries.yaml' +dir = os.path.dirname(os.path.realpath(__file__)) +input_file = os.path.join(dir,'05_visual_lease_boundaries.yaml') # initialize Project class with input file, we don't need RAFT for this so mark False project = Project(file=input_file,raft=False) diff --git a/examples/01_Visualization/07_3D-visual_platform.py b/examples/01_Visualization/07_3D-visual_platform.py index 655e867d..7f9d5993 100644 --- a/examples/01_Visualization/07_3D-visual_platform.py +++ b/examples/01_Visualization/07_3D-visual_platform.py @@ -7,14 +7,16 @@ from famodel import Project import matplotlib.pyplot as plt +import os # define name of ontology input file -input_file = 'examples/01_Visualization/07_3D-visual_platform.yaml' +dir = os.path.dirname(os.path.realpath(__file__)) +input_file = os.path.join(dir,'07_3D-visual_platform.yaml') # initialize Project class with input file, we don't need RAFT for this so mark False project = Project(file=input_file,raft=True) # plot -project.plot3d(fowt=True) +project.plot3d(plot_fowt=True) plt.show() \ No newline at end of file diff --git a/examples/01_Visualization/07_3D-visual_platform.yaml b/examples/01_Visualization/07_3D-visual_platform.yaml index 602f73bd..6c4df180 100644 --- a/examples/01_Visualization/07_3D-visual_platform.yaml +++ b/examples/01_Visualization/07_3D-visual_platform.yaml @@ -34,12 +34,12 @@ platform: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -56,13 +56,13 @@ platform: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -82,7 +82,7 @@ platform: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -103,7 +103,7 @@ platform: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) diff --git a/examples/01_Visualization/08_3D-visual_turbine.yaml b/examples/01_Visualization/08_3D-visual_turbine.yaml index a27a5e4f..8d059f2c 100644 --- a/examples/01_Visualization/08_3D-visual_turbine.yaml +++ b/examples/01_Visualization/08_3D-visual_turbine.yaml @@ -36,8 +36,8 @@ topsides: nBlades : 3 # number of blades Zhub : 150.0 # hub height [m] Rhub : 3.97 # hub radius [m] - precone : 4.0 # [deg] - shaft_tilt : 6.0 # [deg] + precone : -4.0 # [deg] + shaft_tilt : -6.0 # [deg] overhang : -12.0313 # [m] aeroMod : 1 # 0 aerodynamics off; 1 aerodynamics on @@ -1059,7 +1059,7 @@ topsides: dlsMax : 5.0 # maximum node splitting section amount; can't be 0 name : tower # [-] an identifier (no longer has to be number) - type : 1 # [-] + type : rigid # [-] rA : [ 0, 0, 15] # [m] end A coordinates rB : [ 0, 0, 144.582] # [m] and B coordinates shape : circ # [-] circular or rectangular @@ -1085,12 +1085,12 @@ platform: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1107,13 +1107,13 @@ platform: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1133,7 +1133,7 @@ platform: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1154,7 +1154,7 @@ platform: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) diff --git a/examples/03_Frequency_Domain_Analysis_RAFT/01_platform.yaml b/examples/03_Frequency_Domain_Analysis_RAFT/01_platform.yaml index 56f523a3..a860d818 100644 --- a/examples/03_Frequency_Domain_Analysis_RAFT/01_platform.yaml +++ b/examples/03_Frequency_Domain_Analysis_RAFT/01_platform.yaml @@ -22,12 +22,12 @@ platform: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -44,13 +44,13 @@ platform: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -70,7 +70,7 @@ platform: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -91,7 +91,7 @@ platform: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) diff --git a/examples/03_Frequency_Domain_Analysis_RAFT/02_FOWT.yaml b/examples/03_Frequency_Domain_Analysis_RAFT/02_FOWT.yaml index a05ac7ef..533b143e 100644 --- a/examples/03_Frequency_Domain_Analysis_RAFT/02_FOWT.yaml +++ b/examples/03_Frequency_Domain_Analysis_RAFT/02_FOWT.yaml @@ -11,9 +11,48 @@ site: array: keys : [ID, topsideID, platformID, mooringID, x_location, y_location, heading_adjust] data : # ID# ID# ID# [m] [m] [deg] - - [fowt0, 1, 1, 0, -1600, -1600, 0 ] - - [fowt1, 1, 1, 0, 0, -1600, 0 ] # 2 array, shared moorings + - [fowt0, 1, 1, ms1, -1600, -1600, 0 ] + - [fowt1, 1, 1, ms1, 0, -1600, 0 ] # 2 array, shared moorings +# ----- Mooring system ----- + +# Mooring system descriptions (each for an individual FOWT with no sharing) +mooring_systems: + + ms1: + name: 3-line semi-taut polyester mooring system + + keys: [MooringConfigID, heading, anchorType, lengthAdjust] + data: + - [ semitaut-poly_1, 150 , drag-embedment1, 0 ] + - [ semitaut-poly_1, 270 , drag-embedment1, 0 ] + - [ semitaut-poly_1, 30 , drag-embedment1, 0 ] + + +# Mooring line configurations +mooring_line_configs: + + semitaut-poly_1: # mooring line configuration identifier, matches MooringConfigID + + name: Semitaut polyester configuration 1 # descriptive name + + span: 642 # 2D x-y distance from fairlead to anchor + + sections: #in order from anchor to fairlead + - mooringFamily: chain # ID of a mooring line section type + d_nom: .1549 # nominal diameter of material [m] + length: 497.7 # [m] usntretched length of line section + - mooringFamily: polyester # ID of a mooring line section type + d_nom: .182 # nominal diameter of material [m] + length: 199.8 # [m] length (unstretched) + + + +# Anchor type properties +anchor_types: + + drag-embedment1: + type : DEA # type of anchor (drag-embedment anchor) topsides: @@ -30,8 +69,8 @@ topsides: nBlades : 3 # number of blades Zhub : 150.0 # hub height [m] Rhub : 3.97 # hub radius [m] - precone : 4.0 # [deg] - shaft_tilt : 6.0 # [deg] + precone : -4.0 # [deg] + shaft_tilt : -6.0 # [deg] overhang : -12.0313 # [m] aeroMod : 1 # 0 aerodynamics off; 1 aerodynamics on @@ -1053,7 +1092,7 @@ topsides: dlsMax : 5.0 # maximum node splitting section amount; can't be 0 name : tower # [-] an identifier (no longer has to be number) - type : 1 # [-] + type : rigid # [-] rA : [ 0, 0, 15] # [m] end A coordinates rB : [ 0, 0, 144.582] # [m] and B coordinates shape : circ # [-] circular or rectangular @@ -1073,16 +1112,18 @@ topsides: platform: type : FOWT z_location : 0 # optional to put the depth of this platform type + rFair : 58 + zFair : -15 members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1099,13 +1140,13 @@ platform: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1125,7 +1166,7 @@ platform: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1146,7 +1187,7 @@ platform: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) diff --git a/examples/08_Design_Adjustment/01_Fairleads.py b/examples/08_Design_Adjustment/01_Fairleads.py new file mode 100644 index 00000000..3524f6f8 --- /dev/null +++ b/examples/08_Design_Adjustment/01_Fairleads.py @@ -0,0 +1,33 @@ +# -*- coding: utf-8 -*- +""" +Simple driver file to create an array showing moorings attached to fairelead objects. + +This allows you to connect moorings to platforms at a specific point and then run +mooring headings independent of the heading of this connection point. +The input file only contains the bare minimum information to build a 2d plot +of the turbine locations and moorings with fairleads (no cables, platform design, turbines, + site condition information, etc.) +""" + +from famodel import Project +import matplotlib.pyplot as plt +import os + +# define name of ontology input file +dir = os.path.dirname(os.path.realpath(__file__)) +input_file = os.path.join(dir,'01_Fairleads.yaml') + +# initialize Project class with input file, we don't need RAFT for this so mark False +project = Project(file=input_file,raft=False) + +# plot +project.plot2d() + + +# to moorings plot in 3d, we'll need to add depth and create a moorpy model of the system +project.depth = 200 # depth added because we did not include the site conditions section of the yaml +project.getMoorPyArray() +project.plot3d() + +plt.show() + diff --git a/examples/08_Design_Adjustment/03_rotations.py b/examples/08_Design_Adjustment/03_rotations.py new file mode 100644 index 00000000..ab58299a --- /dev/null +++ b/examples/08_Design_Adjustment/03_rotations.py @@ -0,0 +1,41 @@ +# -*- coding: utf-8 -*- +""" +Simple driver file to create an array with moorings and rotate the platforms and array. + +This allows you to rotate platforms including all of their moorings, anchors, and fairleads +The input file only contains the bare minimum information to build a 2d plot +of the turbine locations and moorings with fairleads (no cables, platform design, turbines, + site condition information, etc.) +""" + +from famodel import Project +import matplotlib.pyplot as plt +import os + +# define name of ontology input file +dir = os.path.dirname(os.path.realpath(__file__)) +input_file = os.path.join(dir,'01_Fairleads.yaml') + +# initialize Project class with input file, we don't need RAFT for this so mark False +project = Project(file=input_file,raft=False) + +# plot +project.plot2d() + +# let's rotate a platform but keep in the same x,y position +pf_loc = project.platformList['fowt0'].r +new_heading = 143 # [deg] +project.platformList['fowt0'].setPosition(r=pf_loc, heading=new_heading, + degrees=True, project=project) + +# plot again to see the difference +project.plot2d() + +# let's now change the platform's position +new_r = [-2000, -2200] +project.platformList['fowt0'].setPosition(r=new_r, project=project) + +# plot again +project.plot2d() + + diff --git a/examples/OntologySample200m.yaml b/examples/OntologySample200m.yaml index c68b11a0..22e2d05e 100644 --- a/examples/OntologySample200m.yaml +++ b/examples/OntologySample200m.yaml @@ -420,18 +420,18 @@ dynamic_cable_configs: span: 1590 cable_type: dynamic_cable_66 # ID of a cable section type1 A: 300 - length: 1610 # [m] length (unstretched) + length: 1660 # [m] length (unstretched) sections: - type: Buoyancy_750m #_w_buoy # (section properties including averaged effect of buoyancy modules) L_mid: 510 # [m] from end A - N_modules: 6 + N_modules: 6.5 spacing: 18 # [m] V: 2 - type: Buoyancy_750m #_w_buoy # (section properties including averaged effect of buoyancy modules) L_mid: 1040 # [m] from end A - N_modules: 6 + N_modules: 6.5 spacing: 18 # [m] V: 2 @@ -534,8 +534,8 @@ topsides: nBlades : 3 # number of blades Zhub : 150.0 # hub height [m] Rhub : 3.97 # hub radius [m] - precone : 4.0 # [deg] - shaft_tilt : 6.0 # [deg] + precone : -4.0 # [deg] + shaft_tilt : -6.0 # [deg] overhang : -12.0313 # [m] aeroMod : 1 # 0 aerodynamics off; 1 aerodynamics on @@ -1557,7 +1557,7 @@ topsides: dlsMax : 5.0 # maximum node splitting section amount; can't be 0 name : tower # [-] an identifier (no longer has to be number) - type : 1 # [-] + type : rigid # [-] rA : [ 0, 0, 15] # [m] end A coordinates rB : [ 0, 0, 144.582] # [m] and B coordinates shape : circ # [-] circular or rectangular @@ -1587,7 +1587,7 @@ platforms: - name: fairlead1 r_rel: [58, 0, -14] headings: [30, 150, 270, 35] # headings in degrees for the fairlead (if multiple headings, the fairlead will be repeated for each heading) - Jtubes : # list of Jtube coordinates for the platform relative to platform coordinate and 0-degree heading + JTubes : # list of Jtube coordinates for the platform relative to platform coordinate and 0-degree heading - name: Jtube1 r_rel: [5, 0, -20] headings: [90, 210, 330] # headings in degrees for the Jtube (if multiple headings, the Jtube will be repeated for each heading) @@ -1597,12 +1597,12 @@ platforms: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1619,13 +1619,13 @@ platforms: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1645,7 +1645,7 @@ platforms: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1666,7 +1666,7 @@ platforms: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1692,7 +1692,7 @@ platforms: - name: fairlead1 r_rel: [58, 0, -14] headings: [30, 150, 270, 35] # headings in degrees for the fairlead (if multiple headings, the fairlead will be repeated for each heading) - Jtubes: + JTubes: - name: Jtube1 r_rel: [5, 0, -20] headings: [90, 210, 330] # headings in degrees for the Jtube (if multiple headings, the Jtube will be repeated for each heading) @@ -1701,12 +1701,12 @@ platforms: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1723,13 +1723,13 @@ platforms: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1749,7 +1749,7 @@ platforms: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1770,7 +1770,7 @@ platforms: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) diff --git a/examples/OntologySample200m_1turb.yaml b/examples/OntologySample200m_1turb.yaml index 31882ffc..738755bd 100644 --- a/examples/OntologySample200m_1turb.yaml +++ b/examples/OntologySample200m_1turb.yaml @@ -106,8 +106,8 @@ topsides: nBlades : 3 # number of blades Zhub : 150.0 # hub height [m] Rhub : 3.97 # hub radius [m] - precone : 4.0 # [deg] - shaft_tilt : 6.0 # [deg] + precone : -4.0 # [deg] + shaft_tilt : -6.0 # [deg] overhang : -12.0313 # [m] aeroMod : 1 # 0 aerodynamics off; 1 aerodynamics on @@ -1129,7 +1129,7 @@ topsides: dlsMax : 5.0 # maximum node splitting section amount; can't be 0 name : tower # [-] an identifier (no longer has to be number) - type : 1 # [-] + type : rigid # [-] rA : [ 0, 0, 15] # [m] end A coordinates rB : [ 0, 0, 144.582] # [m] and B coordinates shape : circ # [-] circular or rectangular @@ -1158,12 +1158,12 @@ platform: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1180,13 +1180,13 @@ platform: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1206,7 +1206,7 @@ platform: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1227,7 +1227,7 @@ platform: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) diff --git a/examples/OntologySample200m_uniformArray.yaml b/examples/OntologySample200m_uniformArray.yaml index dbecc412..91a33d5a 100644 --- a/examples/OntologySample200m_uniformArray.yaml +++ b/examples/OntologySample200m_uniformArray.yaml @@ -118,8 +118,8 @@ topsides: nBlades : 3 # number of blades Zhub : 150.0 # hub height [m] Rhub : 3.97 # hub radius [m] - precone : 4.0 # [deg] - shaft_tilt : 6.0 # [deg] + precone : -4.0 # [deg] + shaft_tilt : -6.0 # [deg] overhang : -12.0313 # [m] aeroMod : 1 # 0 aerodynamics off; 1 aerodynamics on @@ -1141,7 +1141,7 @@ topsides: dlsMax : 5.0 # maximum node splitting section amount; can't be 0 name : tower # [-] an identifier (no longer has to be number) - type : 1 # [-] + type : rigid # [-] rA : [ 0, 0, 15] # [m] end A coordinates rB : [ 0, 0, 144.582] # [m] and B coordinates shape : circ # [-] circular or rectangular @@ -1170,12 +1170,12 @@ platforms: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1192,13 +1192,13 @@ platforms: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1218,7 +1218,7 @@ platforms: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1239,7 +1239,7 @@ platforms: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) diff --git a/examples/OntologySample600m_shared.yaml b/examples/OntologySample600m_shared.yaml index 390e592f..d87ea1e4 100644 --- a/examples/OntologySample600m_shared.yaml +++ b/examples/OntologySample600m_shared.yaml @@ -158,8 +158,8 @@ topsides: nBlades : 3 # number of blades Zhub : 150.0 # hub height [m] Rhub : 3.97 # hub radius [m] - precone : 4.0 # [deg] - shaft_tilt : 6.0 # [deg] + precone : -4.0 # [deg] + shaft_tilt : -6.0 # [deg] overhang : -12.0313 # [m] aeroServoMod : 2 # 0 aerodynamics off; 1 aerodynamics on (no control); 2 aerodynamics and control on @@ -1179,7 +1179,7 @@ topsides: dlsMax : 5.0 # maximum node splitting section amount; can't be 0 name : tower # [-] an identifier (no longer has to be number) - type : 1 # [-] + type : rigid # [-] rA : [ 0, 0, 15] # [m] end A coordinates rB : [ 0, 0, 144.582] # [m] and B coordinates shape : circ # [-] circular or rectangular @@ -1219,12 +1219,12 @@ platforms: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1241,13 +1241,13 @@ platforms: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1267,7 +1267,7 @@ platforms: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1288,7 +1288,7 @@ platforms: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) diff --git a/examples/duplicate_platform.py b/examples/duplicate_platform.py index 0ed3fb40..21d19e44 100644 --- a/examples/duplicate_platform.py +++ b/examples/duplicate_platform.py @@ -10,9 +10,9 @@ #### INPUTS #### dir = os.path.dirname(os.path.realpath(__file__)) -filename = dir+'\OntologySample200m.yaml' # yaml file to make initial platform(s) -rep_pf_name = 'FOWT1' # platform to replicate (look at yaml file array data table to get platform names) -new_pf_loc = [0,0] +filename = os.path.join(dir,'OntologySample200m_1turb.yaml') # yaml file to make initial platform(s) +rep_pf_name = 'fowt0' # platform to replicate (look at yaml file array data table to get platform names) +new_pf_loc = [0,-1600,0] # first load in single platform from yaml @@ -30,12 +30,6 @@ # make new moorpy array project.getMoorPyArray() -for line in rep_pf.mooringSystem(project).lineList: - xB, yB, zB = line.rB - #z_anchor, soil_label = get_depth_and_soil(xB, yB) - #print(f' Anchor at ({xB:.1f}, {yB:.1f}) → Depth = {z_anchor:.2f} m') - - # plot the new system -project.plot3d() +project.plot3d(plot_fowt=True) plt.show() \ No newline at end of file diff --git a/examples/example_driver.py b/examples/example_driver.py index 69c276da..690062f9 100644 --- a/examples/example_driver.py +++ b/examples/example_driver.py @@ -21,9 +21,11 @@ from famodel.project import Project import os import matplotlib.pyplot as plt +from copy import deepcopy # set yaml file location and name -ontology_file = "OntologySample200m.yaml" +dir = os.path.dirname(os.path.realpath(__file__)) +ontology_file = os.path.join(dir,"OntologySample200m.yaml") #%% Section 1: Project without RAFT print('Creating project without RAFT\n') @@ -31,7 +33,7 @@ # create project object project = Project(file=ontology_file, raft=False) # create moorpy system of the array, include cables in the system -project.getMoorPyArray(cables=True) +project.getMoorPyArray() # plot in 3d, using moorpy system for the mooring and cable plots project.plot2d() project.plot3d() @@ -41,12 +43,11 @@ #create project object, automatically create RAFT object (and automatically create moorpy system in the process!) project = Project(file=ontology_file,raft=True) # plot in 3d, use moorpy system for mooring and cables, use RAFT for platform, tower, and turbine visuals -project.plot3d(fowt=True,draw_boundary=False,boundary_on_bath=False,save=True) +project.plot3d(plot_fowt=True,plot_boundary=False,plot_boundary_on_bath=False,save=True) # get location of RAFT model (stored as array property in project class) model = project.array -model.mooring_currentMod = 0 # temp requirement to work with changes in RAFT -model.ms.moorMod = 0 # temp requirement to work with changes in RAFT + print('Running RAFT case') # run cases model.analyzeCases() @@ -110,11 +111,12 @@ #### add marine growth to the mooring lines and cables #### print('\nAdding marine growth\n') # marine growth dictionary is read in from YAML, see Ontology ReadMe for description +reg_line_d = deepcopy(project.mooringList['FOWT1a'].ss.lineList[1].type['d_nom']) project.getMarineGrowth(display=False) # moorpy system lines with marine growth are stored in the respective objects under ss_mod (pristine lines are stored under ss) # check the difference in nominal diameter for a given line: -reg_line_d = project.mooringList['FOWT1a'].ss.lineList[1].type['d_nom'] -mg_line_d = project.mooringList['FOWT1a'].ss_mod.lineList[-1].type['d_nom'] +mg_line_d = project.mooringList['FOWT1a'].ss.lineList[-1].type['d_nom'] + print('\nPristine line polyester nominal diameter just below surface: ',reg_line_d) print('Marine growth line polyester nominal diameter just below surface: ',mg_line_d) diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index 72868ede..c579c0a7 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -38,13 +38,18 @@ def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, Full soil profile map for selecting local soil layers. ''' - from famodel.famodel_base import Node + # Initialize as a node Node.__init__(self, id) + # Design description dictionary for this Anchor self.dd = dd + # MoorPy system this anchor is in self.ms = ms + # x,y,z location of anchor self.r = r + # anchor index in array mooring list self.aNum = aNum + self.g = g self.rho = rho diff --git a/famodel/design/CableDesign.py b/famodel/design/CableDesign.py new file mode 100644 index 00000000..cd182529 --- /dev/null +++ b/famodel/design/CableDesign.py @@ -0,0 +1,1312 @@ +# Draft Cable Design class adapted from LineDesign - R + +import moorpy as mp +from moorpy.subsystem import Subsystem + +from famodel.cables.dynamic_cable import DynamicCable +import famodel.cables.cable_properties as cprops + +from fadesign.fadsolvers import dopt2, doptPlot +from moorpy.helpers import getLineProps, getFromDict +from copy import deepcopy + +import numpy as np +import matplotlib.pyplot as plt +import yaml +import time + + +class CableDesign(DynamicCable): + ''' + The Dynamic Cable class inherits the properties of MoorPy's Subsystem class + (i.e. solving for equilibrium) for the purpose of quasi-static design and + analysis. Eventually the DynamicCable class will live in FAModel, and this + code will be streamlined to inherit it and just add design methods. + + Example allVars vector: X = [span, L, ...] + where < > section repeats and is composed of + B - net buoyancy provided by all modules on this section [kN] + Lmid - the buoyancy section midpoint along the cable arc length [m] + Ls - the length of this buoyancy section (centered about the midpoint) [m] + Xindices + specify the design variable number, or optional key characters: + c - constant, will not be changed + r - the AllVars value will be interpreted as a ratio to the total length + In other words, the actual value will be the specified value times L. + ''' + + def __init__(self, depth, cableType, buoyType, n=3, i_buoy=[1], mgdict = None, **kwargs): + '''Creates a DynamicCable object to be used for evaluating or + optimizing a dynamic cable design. + + Parameters + ---------- + depth : float + Water depth + span : float + Horizontal distance of dynamic cable [m]. + n : int + Number of sections (typically alternating: cable, cable+buoyancy, ...) + i_buoy : list + List of section indices that can have buoyancy modules. + cableType : dict + Dictionary of bare cable properties. + buoyType : dict + Dictionary of buoyancy module properties. + name : string + Name of dictionary entry in cableProps yaml file to get data from. + X0 : array + Initial design variable values (length n). + offset : float + Maximum mean/steady offset in surge [m]. + ''' + + self.display = getFromDict(kwargs, 'display', default=0) + + # add the parameters set by the input settings dictionary + self.name = getFromDict(kwargs, 'name', dtype=str, default='no name provided') + + # set up the mooring system object with the basics from the System class + rho = getFromDict(kwargs, 'rho', default=1025.0) + g = getFromDict(kwargs, 'g' , default=9.81) + + # ----- set model-specific parameters ----- + + self.shared = getFromDict(kwargs, 'shared', default=0) # flag to indicate shared line + self.rBFair = getFromDict(kwargs, 'rBFair', shape=-1, default=[0,0,0]) # [m] end coordinates relative to attached body's ref point + self.nLines = n # number of sections + self.i_buoy = i_buoy # index of any sections that have buoyancy modules + self.bs = [0 for i in range(0,len(i_buoy))] + + #-------set marine growth parameters--------------------- + self.MG = getFromDict(kwargs, 'MG', default = False) + if self.MG: + if mgdict == None: + raise Exception('mgdict must be provied if MG == True') + else: + self.mgdict = mgdict + + # ============== set the design variable list ============== + self.ignore_static = getFromDict(kwargs, 'ignore_static', default=False) + + self.allVars = getFromDict(kwargs, 'allVars' , shape=2 + 3*len(self.i_buoy)) + + # set the design variable type list + if 'Xindices' in kwargs: + self.Xindices = list(kwargs['Xindices']) + if not len(self.Xindices)==len(self.allVars): + raise Exception("Xindices must be the same length as allVars") + else: + raise Exception("Xindices must be provided.") + + + # number of design variables (the design vector is the length of each + # find the largest integer to determine the number of desired design variables + self.nX = 1 + max([ix for ix in self.Xindices if isinstance(ix, int)]) + + + # check for errors in Xindices + for i in range(self.nX): + if not i in self.Xindices: + raise Exception(f"Design variable number {i} is missing from Xindices.") + # entries must be either design variable index or constant/solve/ratio flags + valid = list(range(self.nX))+['c','r'] + for xi in self.Xindices: + if not xi in valid: + raise Exception(f"The entry '{xi}' in Xindices is not valid. Must be a d.v. index, 'c', or 'r'.") + + # check for 'r' variable option + self.rInds = [i for i,xi in enumerate(self.Xindices) if xi=='r'] + for i in range(len(self.rInds)): + if self.allVars[self.rInds[i]] >= 1.0 or self.allVars[self.rInds[i]] <= 0.0: + raise Exception("The ratio variable needs to be between 1 and 0") + + + # ----- Initialize some objects ----- + + self.span = self.allVars[0] + self.L = self.allVars[1] + + # Store the bare cable type by itself for easy access (TODO: reduce redundancy) + self.cableType = cableType + self.buoyType = buoyType + + # make a dummy design dictionary for Mooring to make a Subsystem with??? + dd = {} + + # The bare cable properties dict + dd['cable_type'] = cableType + + #length properties + dd['length'] = self.L + + #span + dd['span'] = self.span + + # Buoyancy section properties + + for i in range(len(i_buoy)): + + # Net buoyancy per buoyancy module [N] + F_buoy = (rho - buoyType['density'])*g*buoyType['volume'] + + # Buoyancy + if self.shared == 2 and i ==0: + N_modules = 1000*self.allVars[3*i+2] / (F_buoy) / 2 # split buoyancy force across the full length + else: + N_modules = 1000*self.allVars[3*i+2] / F_buoy # my not be an integer, that's okay + + + # L_mid (position along cable) + if self.Xindices[3*i + 3] == 'r': + + #set equal to ratio * cable length + L_mid = self.allVars[3*i+3] * self.L + else: + L_mid = self.allVars[3*i+3] + + # Spacing + spacing = self.allVars[3*i+4] / (N_modules - 1) + + if N_modules > 0: + if not 'buoyancy_sections' in dd: + dd['buoyancy_sections'] = [] + dd['buoyancy_sections'].append(dict(L_mid=L_mid, + module_props=buoyType, + N_modules = N_modules, + spacing = spacing)) + + # Call Mooring init function (parent class) + if self.shared == 1: + + DynamicCable.__init__(self, 'designed cable', dd=dd, + rA=[self.span,0,self.rBFair[2]], rB=self.rBFair, + rad_anch=self.span, rad_fair=self.rBFair[0], z_anch=-depth, + z_fair=self.rBFair[2], rho=rho, g=g, span=self.span, length=self.L, shared = self.shared) # arbitrary initial length + + elif self.shared == 2: + DynamicCable.__init__(self, 'designed cable', dd=dd, + rA=[-0.5*self.span-self.rBFair[0], 0, -1], rB=self.rBFair, + rad_anch=self.span, rad_fair=self.rBFair[0], z_anch=-depth, + z_fair=self.rBFair[2], rho=rho, g=g, span=self.span, length=self.L, shared = self.shared) # arbitrary initial length + + else: + DynamicCable.__init__(self, 'designed cable', dd=dd, + rA=[self.span,0,-depth], rB=self.rBFair, + rad_anch=self.span, rad_fair=self.rBFair[0], z_anch=-depth, + z_fair=self.rBFair[2], rho=rho, g=g, span=self.span, length=self.L, shared = self.shared) # arbitrary initial length + + + + # now make Subsystem, self.ss + self.createSubsystem(case=int(self.shared)) + self.ss.eqtol= 0.05 # position tolerance to use in equilibrium solves [m] + + # amplification factors etc. + self.DAFs = getFromDict(kwargs, 'DAFs', shape=self.nLines+2, default=1.0) # dynamic amplication factor for each line section, and anchor forces (DAFS[-2] is for vertical load, DAFS[-1] is for horizontal load) + self.Te0 = np.zeros([self.nLines,2]) # undisplaced tension [N] of each line section end [section #, end A/B] + self.LayLen_adj = getFromDict(kwargs, 'LayLen_adj', shape=0, default=0.0) # adjustment on laylength... positive means that the dynamic lay length is greater than linedesign laylength + self.damage = getFromDict(kwargs, 'damage', shape = -1, default = 0.0) #Lifetime fatigue damage from previous iteration for wind/wave headings in self.headings and the 180 degree reverse of self.headings + #self.unload(f'{configuration}.dat') + + + # ----- Set solver and optimization settings ----- + self.x_mean = getFromDict(kwargs, 'offset', default=0) + self.x_ampl = getFromDict(kwargs, 'x_ampl', default=10) # [m] expected wave-frequency motion amplitude about mean + + self.eqtol = 0.002 # position tolerance to use in equilibrium solves [m] + self.noFail = False # can be set to True for some optimizers to avoid failing on errors + + self.iter = -1 # iteration number of a given optimization run (incremented by updateDesign) + self.log = dict(x=[], f=[], g=[]) # initialize a log dict with empty values + + + # ----- optimization stuff ----- + # get design variable bounds and last step size + self.Xmin = getFromDict(kwargs, 'Xmin' , shape=self.nX, default=np.zeros(self.nX)) # minimum bounds on each design variable + self.Xmax = getFromDict(kwargs, 'Xmax' , shape=self.nX, default=np.zeros(self.nX)+1000) # maximum bounds on each design variable + self.dX_last = getFromDict(kwargs, 'dX_last', shape=self.nX, default=[]) # 'last' step size for each design variable + + if len(self.Xmin) != self.nX or len(self.Xmax) != self.nX or len(self.dX_last) != self.nX: + raise Exception("The size of Xmin/Xmax/dX_last does not match the number of design variables") + + #set up initial design variable values from allVars input + self.X0 = np.array([self.allVars[self.Xindices.index(i)] for i in range(self.nX)]) + + # initialize the vector of the last design variables, which each iteration will compare against + self.Xlast = np.zeros(self.nX) + + self.X_denorm = np.ones(self.nX) # normalization factor for design variables + self.obj_denorm = 1.0 # normalization factor for objective function + + + # ----- set up the constraint functions and lists ----- + + if 'constraints' in kwargs: + self.constraints = kwargs['constraints'] + else: + self.constraints = {} + #raise ValueError('A constraints dictionary must be passed when initializing a new Mooring') + + # a hard-coded dictionary that points to all of the possible constraint functions by name + self.confundict = {"max_total_length" : self.con_total_length, # maximum total length of combined line sections + "min_lay_length" : self.con_lay_length, # minimum length of a line section on the seabed + "max_lay_length" : self.con_max_lay_length, # minimum length of a line section on the seabed + "tension_safety_factor" : self.con_strength, # minimum ratio of MBL/tension for a section + "overall_tension_safety_factor" : self.con_overall_strength, # minimum ratio of MBL/tension for all sections + "curvature_safety_factor":self.con_curvature, # minimum ratio of curvature_limit/curvature for a section + "max_curvature" :self.con_max_curvature, # minimum ratio of curvature_limit/curvature for a section + "min_sag" : self.con_min_sag, # minimum for the lowest point of a section + "max_sag" : self.con_max_sag, # maximum for the lowest point of a section + "max_hog" : self.con_max_hog, # maximum for the highest point of a section + "max_touchdown_range" : self.con_max_td_range, # maximum for the lowest point of a section + } + + # set up list of active constraint functions + self.conList = [] + self.convals = np.zeros(len(self.constraints)) # array to hold constraint values + self.con_denorm = np.ones(len(self.constraints)) # array to hold constraint normalization constants + self.con_denorm_default = np.ones(len(self.constraints)) # default constraint normalization constants + + for i, con in enumerate(self.constraints): # for each list (each constraint) in the constraint dictionary + + # ensure each desired constraint name matches an included constraint function + if con['name'] in self.confundict: + + # the constraint function for internal use (this would be called in UpdateDesign) + def internalConFun(cc, ii): # this is a closure so that Python doesn't update index and threshold + def conf_maker(X): + def func(): + # compute the constraint value using the specified function + val = self.confundict[cc['name']](X, cc['index'], cc['threshold']) + + # record the constraint value in the list + self.convals[ii] = val / self.con_denorm[ii] # (normalized) + self.constraints[ii]['value'] = val # save to dict (not normalized) + + return val + return func() + return conf_maker + + # make the internal function and save it in the constraints dictionary + con['fun'] = internalConFun(con, i) + + # the externally usable constraint function maker + def externalConFun(name, ii): # this is a closure so that Python doesn't update index and threshold + def conf_maker(X): + def func(): + # Call the updatedesign function (internally avoids redundancy) + self.updateDesign(X) + + # get the constraint value from the internal list + val = self.convals[ii] + + return val + return func() + return conf_maker + + # add the conf function to the conList + self.conList.append(externalConFun(con['name'], i)) + + # Save the default/recommended normalization constant + + if con['name'] in ['max_total_length']: + self.con_denorm_default[i] = con['threshold'] # sum([line.L for line in self.ss.lineList]) + + elif con['name'] in ['tension_safety_factor', 'curvature_safety_factor']: + self.con_denorm_default[i] = 4*con['threshold'] + + elif con['name'] in ['max_curvature']: + self.con_denorm_default[i] = 4*con['threshold'] + + elif con['name'] in ['min_lay_length', 'min_sag', 'max_sag', 'max_hog', 'max_touchdown_range']: + self.con_denorm_default[i] = depth + + else: + raise ValueError("Constraint parameter "+con['name']+" is not a supported constraint type.") + + + + # ----- Set up the cable properties ----- + ''' + # For now, this will load the cable properties YAML and manually add + # the selected cable type to the MoorPy system. + with open(cableProps) as file: + source = yaml.load(file, Loader=yaml.FullLoader) + + # Get dictionary of the specified cable type from the yaml + di = source['cable_types'][name] + + cableType = self.makeCableType(di, name) # Process/check it into a new dict + # ^^^ I forget why this is done + + # Save some constants for use when computing buoyancy module stuff + + self.d0 = cableType['d_vol'] # diameter of bare dynamic cable + self.m0 = cableType['m'] # mass/m of bare dynamic cable + self.w0 = cableType['w'] # weight/m of bare dynamic cable + + #self.rho_buoy = cableType['rho_buoy'] # aggregate density of buoyancy modules [kg/m^3] + ''' + + ''' + # ----- set up the dynamic cable in MoorPy ----- + + lengths = self.allVars[:self.nLines] # Length of each section [m] (first n entries of allVars) + types = [] + + # Set up line types list + for i in range(self.nLines): + # Give the buoyancy sections their own type so they can be adjusted independently + if i in self.i_buoy: + types.append(deepcopy(cableType)) + + else: # All bare cable sections can reference the same type + types.append(cableType) + + # call to the Subsystem method to put it all together + if self.shared == True: + + # set second platform connection at the same coordinates as the first platform connection + self.rAFair = self.rBFair + self.makeGeneric(lengths, types, suspended = 1) + else: + self.makeGeneric(lengths, types) + ''' + # initialize and equilibrate this initial cable + self.ss.initialize() + self.ss.maxIter = 5000 + self.ss.setOffset(0) + self.updateDesign(self.X0, normalized=False) # assuming X0/AllVars is not normalized + + + + def updateDesign(self, X, display=0, display2=0, normalized=True): + '''updates the design with the current design variables (X). + + Example allVars vector: X = [span, L, ...] + where < > section repeats and is composed of + B - net buoyancy provided by all modules on this section [N] + Lmid - the buoyancy section midpoint along the cable arc length + Ls - the length of this buoyancy section (centered about the midpoint) + Xindices + specify the design variable number, or optional key characters: + c - constant, will not be changed + r - the AllVars value will be interpreted as a ratio to the total length + In other words, the actual value will be the specified value times L. + + If self.shared==2, then the buoyancy sections are measured from the + center point of the cable, and are assumed to be mirrored on both sides. + ''' + + # Design vector error checks + if len(X)==0: # if any empty design vector is passed (useful for checking constraints quickly) + return + elif not len(X)==self.nX: + raise ValueError(f"DynamicCable.updateDesign passed design vector of length {len(X)} when expecting length {self.nX}") + elif any(np.isnan(X)): + raise ValueError("NaN value found in design vector") + + # If X is normalized, denormalize (scale) it up to the full values + if normalized: + X = X*self.X_denorm + + + # If any design variable has changed, update the design and the metrics + if not all(X == self.Xlast): + + self.Xlast = np.array(X) # record the current design variables + + if self.display > 1: + print("Updated design") + print(X) + + self.iter += 1 + + # ----- Apply the design variables to update the design ----- + + # Update span + dvi = self.Xindices[0] # design variable index - will either be an integer or a string + if dvi in range(self.nX): # only update if it's tied to a design variable (if it's an integer) + self.span = X[dvi] + self.dd['span'] = self.span + + # Update total cable length + dvi = self.Xindices[1] + if dvi in range(self.nX): + self.L = X[dvi] + self.dd['length'] = X[dvi] # some redundancy - need to streamline DynamicCable + + + # Update each buoyancy section + for i in range(len(self.i_buoy)): + + bs = self.dd['buoyancy_sections'][i] # shorthand for the buoyancy section dict + + # Net buoyancy per buoyancy module [N] + F_buoy = (self.rho - bs['module_props']['density'])*self.g*bs['module_props']['volume'] + + + # Buoyancy + dvi = self.Xindices[3*i+2] # buoyancy design variable index + if dvi in range(self.nX): # only update if it's tied to a design variable + if self.shared == 2 and i == 0: + bs['N_modules'] = 1000*X[dvi] / F_buoy / 2 # my not be an integer, that's okay + else: + bs['N_modules'] = 1000*X[dvi] / F_buoy # my not be an integer, that's okay + + + # L_mid (position along cable) + dvi = self.Xindices[3*i+3] # buoyancy design variable index + if dvi in range(self.nX): # only update if it's tied to a design variable + bs['L_mid'] = X[dvi] + elif dvi == 'r': + bs['L_mid'] = self.allVars[3*i+3] * self.L + + # Spacing + dvi = self.Xindices[3*i+4] # buoyancy design variable index + if dvi in range(self.nX): # only update if it's tied to a design variable + length = X[dvi] + else: + length = self.allVars[3*i+4] + + bs['spacing'] = length / (bs['N_modules'] - 1) + + #store the buoyancy module spacing + self.bs[i] = bs['spacing'] + + + + # get these design dictionary changes applied in DynamicCable + if len(self.i_buoy) > 0: + self.updateSubsystem() + else: + self.ss.lineList[0].setL(self.L) + + ''' + for i in range(self.nLines): # go through each section + + # update the section length from the design variables + dvi = self.Xindices[i] #design variable index + + # only update if design variable is in list (not constant) + if dvi in range(self.nX): + L=X[dvi] + self.ss.lineList[i].setL(L) + + # if the line has buoyancy, apply the buoyancy design variable + if i in self.i_buoy: + + # check if design variable is in list (not constant) + dvi = self.Xindices[self.nLines + self.i_buoy.index(i)] + if dvi in range(self.nX): + B = X[dvi] # take the buoyancy per unit length design variable [N/m] + + #handle cases where buoyancy is fixed + else: + B = self.allVars[self.nLines + i - 1] + + # compute what diameter of buoyancy module is needed to achieve this buoyancy per unit length + d_inner = self.d0 # inner diameter for buoyancy module [m] + rho_buoy = self.rho_buoy # constant density of buoyancy modules + + d_outer = np.sqrt(((4*B)/((self.rho-self.rho_buoy)*np.pi*self.g))+d_inner**2) # required outer diameter of buoyancy modules (assuming spread over section length) [m] + m_buoy = rho_buoy*(np.pi/4*(d_outer**2 - d_inner**2)) # mass per meter of spread buoyancy module [kg/m] + m = self.m0 + m_buoy # mass per unit length of combined cable + spread buoyancy modules [kg/m] + w = m*self.g - self.rho*(np.pi/4*(d_outer**2))*self.g # weight per unit length [N/m] + + # update line properties + self.ss.lineTypes[i]['m'] = m + self.ss.lineTypes[i]['w'] = w + self.ss.lineTypes[i]['d_vol'] = d_outer + ''' + + + # ----- evaluate constraints ----- + # Evaluate any constraints in the list, at the appropriate displacements. + # The following function calls will fill in the self.convals array. + + #increase solveEquilibrium tolerance + self.ss.eqtol = 0.05 + + + if self.MG: + self.addMarineGrowth(self.mgdict) + self.ss_mod.eqtol = 0.05 + self.ss_mod.maxIter = 5000 + + + # ZERO OFFSET: + self.ss.setOffset(0) + self.ss.calcCurvature() + + if self.MG: + self.ss_mod.setOffset(0) + self.ss_mod.calcCurvature() + + # Save tensions # these aren't used anywhere... not saving the MG tensions + for i, line in enumerate(self.ss.lineList): + self.Te0[i,0] = np.linalg.norm(line.fA) + self.Te0[i,1] = np.linalg.norm(line.fB) + + # Call any constraints that evaluate at the undisplaced position + for con in self.constraints: + if con['offset'] == 'zero': + con['fun'](X) + + # MAX OFFSET: + self.ss.setOffset(self.x_mean+self.x_ampl) # apply static + dynamic offsets + self.ss.calcCurvature() + + if self.MG: + self.ss_mod.setOffset(self.x_mean+self.x_ampl) + self.ss_mod.calcCurvature() + + # Call any constraints needing a positive displacement + for con in self.constraints: + if con['offset'] == 'max': + con['fun'](X) + + self.min_lay_length = self.ss.getLayLength() # record minimum lay length + + if self.MG: + self.min_lay_length = min([self.ss.getLayLength(), self.ss_mod.getLayLength()]) + + # MIN OFSET: + self.ss.setOffset(-self.x_mean-self.x_ampl) # apply static + dynamic offsets + self.ss.calcCurvature() + + if self.MG: + self.ss_mod.setOffset(-self.x_mean-self.x_ampl) + self.ss_mod.calcCurvature() + + # Call any constraints needing a negative displacement + for con in self.constraints: + if con['offset'] == 'min': + con['fun'](X) + + self.max_lay_length = self.ss.getLayLength() # record maximum lay length + if self.MG: + self.max_lay_length = max([self.ss.getLayLength(), self.ss_mod.getLayLength()]) + + # OTHER: + self.ss.setOffset(0) # restore to zero offset and static EA + if self.MG: + self.ss_mod.setOffset(0) + + # or at least set back to static states + + # Call any constraints that depend on results across offsets + for con in self.constraints: + if con['offset'] == 'other': + con['fun'](X) + + + # --- evaluate objective function --- + + # calculate the cost of each section + self.cost = {} + + if self.ignore_static: # option to ignore static portion of cable in cost calcs + L = self.L - self.min_lay_length + else: + L = self.L + + self.cost['cable'] = L*self.cableType['cost'] + + self.cost['buoyancy'] = 0 + + if 'buoyancy_sections' in self.dd: + for bs in self.dd['buoyancy_sections']: + self.cost['buoyancy'] += bs['N_modules']*self.buoyType['cost'] + + self.cost['total'] = self.cost['cable'] + self.cost['buoyancy'] + + self.obj_val = self.cost['total'] / self.obj_denorm # normalize objective function value + + # could also add a cost for touchdown protection sleeve based on + # the touchdown point range of motion + # e.g. c_touchdwown_protection = (self.max_lay_length - self.min_lay_length) * cost_factor + + + # ----- write to log ----- + + # log the iteration number, design variables, objective, and constraints + self.log['x'].append(list(X)) + self.log['f'].append(list([self.obj_val])) + self.log['g'].append(list(self.convals)) + + + # provide some output? + if display > 5: + f = self.objectiveFun(X, display=1) + print("Line lengths are ") + for line in self.ss.lineList: + print(line.L) + + print(f"Cost is {f}") + self.evaluateConstraints(X, display=1) + self.ss.plotProfile() + plt.show() + + + def objectiveFun(self, X, display=0, normalized=True): + '''Update the design (if necessary) and return the objective function + (cost) value.''' + + self.updateDesign(X, display=display, normalized=normalized) + + if display > 1: + print(f"Cost is {self.cost['total']:.1f} and objective value is {self.obj_val:.3f}.") + + return self.obj_val + + + def evaluateConstraints(self, X, display=0, normalized=True): + '''Update the design (if necessary) and display the constraint + values.''' + + self.updateDesign(X, display=display, normalized=normalized) + + if display > 1: + for i, con in enumerate(self.constraints): + print(f" Constraint {i:2d} value of {con['value']:8.2f} " + +f"for {con['name']}: {con['threshold']} of {con['index']} at {con['offset']} displacement.") + + return self.convals + + + def setNormalization(self): + '''Set normalization factors for optimization + (based on initial design state).''' + + # design variables + self.X_denorm = np.array(self.Xlast) + # objective + self.obj_denorm = self.cost['total'] + # constraints + self.con_denorm = self.con_denorm_default + + + def clearNormalization(self): + '''Clear any normalization constants to unity so no scaling is done.''' + self.X_denorm = np.ones(self.nX) + self.obj_denorm = 1.0 + self.con_denorm = np.ones(len(self.constraints)) + + + def optimize(self, gtol=0.03, maxIter=40, nRetry=0, plot=False, display=0, stepfac=4, method='dopt'): + '''Optimize the design variables according to objectve, constraints, bounds, etc. + ''' + + # reset iteration counter + self.iter = -1 + + # clear optimization progress tracking lists + self.log['x'] = [] + self.log['f'] = [] + self.log['g'] = [] + + # set combined objective+constraints function for dopt + def eval_func(X): + '''DynamicCable object evaluation function''' + + self.updateDesign(X) + f = self.obj + g = np.array(self.convals) # needs to be a copy to not pass by ref + oths = dict(status=1) + + return f, g, [], [], oths, False + + # set the display value to use over the entire process + self.display = display + self.method = method + + # Set starting point to normalized value + X0 = self.X0 / self.X_denorm + dX_last = self.dX_last / self.X_denorm + Xmax = self.Xmax / self.X_denorm + Xmin = self.Xmin / self.X_denorm + + # call optimizer to perform optimization + if method=='dopt': + + if display > 0: print("\n --- Beginning CableDesign2 optimize iterations using DOPT2 ---") + + X, min_cost, infodict = dopt2(eval_func, X0, tol=0.001, a_max=1.4, maxIter=maxIter, stepfac=stepfac, + Xmin=Xmin, Xmax=Xmax, dX_last=dX_last, display=self.display) + + elif method in ['COBYLA', 'SLSQP']: + + from scipy.optimize import minimize + + if self.display > 0: print("\n --- Beginning CableDesign2 optimize iterations using COBYLA ---") + + condict = [dict(type="ineq", fun=con) for con in self.conList] + cons_tuple = tuple(condict) + + if method=='COBYLA': + result = minimize(self.objectiveFun, X0, constraints=cons_tuple, method="COBYLA", + options={'maxiter':maxIter, 'disp':True, 'rhobeg':0.1}) + #options={'maxiter':maxIter, 'disp':True, 'rhobeg':10.0}) + + elif method=='SLSQP': + result = minimize(self.objectiveFun, X0, constraints=cons_tuple, method='SLSQP', + bounds = list(zip(Xmin, Xmax)), + options={'maxiter':maxIter, 'eps':0.02,'ftol':1e-6, 'disp': True, 'iprint': 99}) + + X = result.x + + #elif method=='CMNGA': + # from cmnga import cmnga + + # bounds = np.array([[self.Xmin[i], self.Xmax[i]] for i in range(len(self.Xmin))]) + + # X, min_cost, infoDict = cmnga(self.objectiveFun, bounds, self.conList, dc=0.2, nIndivs=12, nRetry=100, maxGens=20, maxNindivs=500 ) + + #, maxIter=maxIter, stepfac=stepfac, Xmin=self.Xmin, Xmax=self.Xmax, dX_last=self.dX_last, display=self.display) + + else: + raise Exception('Optimization method unsupported.') + + # make sure it's left at the optimized state + self.updateDesign(X) + + # plot + if plot: + self.plotOptimization() + + return X, self.cost['total'] # , infodict + + + def plotOptimization(self): + + if len(self.log['x']) == 0: + print("No optimization trajectory saved (log is empty). Nothing to plot.") + return + + fig, ax = plt.subplots(len(self.X0)+1+len(self.constraints),1, sharex=True, figsize=[6,8]) + fig.subplots_adjust(left=0.4) + Xs = np.array(self.log['x']) + Fs = np.array(self.log['f']) + Gs = np.array(self.log['g']) + + for i in range(len(self.X0)): + ax[i].plot(Xs[:,i]) + #ax[i].axhline(self.Xmin[i], color=[0.5,0.5,0.5], dashes=[1,1]) + #ax[i].axhline(self.Xmax[i], color=[0.5,0.5,0.5], dashes=[1,1]) + + ax[len(self.X0)].plot(Fs) + ax[len(self.X0)].set_ylabel("cost", rotation='horizontal') + + for i, con in enumerate(self.constraints): + j = i+1+len(self.X0) + ax[j].axhline(0, color=[0.5,0.5,0.5]) + ax[j].plot(Gs[:,i]) + ax[j].set_ylabel(f"{con['name']}({con['threshold']})", + rotation='horizontal', labelpad=80) + + ax[j].set_xlabel("function evaluations") + + # ::::::::::::::::::::::::::::::: constraint functions ::::::::::::::::::::::::::::::: + + # Each should return a scalar C where C >= 0 means valid and C < 0 means violated. + + + def con_total_length(self, X, index, threshold): + '''This ensures that the total length of the Mooring does not result in a fully slack Mooring + (ProfileType=4) in its negative extreme mean position''' + # ['max_line_length', index, threshold] # index and threshold are completely arbitrary right now + + Lmax = (self.span-self.rBFair[0]-self.x_mean + self.depth+self.ss.rBFair[2]) # (3-14-23) this method might now be deprecated with more recent updates to ensure the combined line lengths aren't too large + total_linelength = sum([self.ss.lineList[i].L for i in range(self.nLines)]) + c = Lmax-total_linelength + + return c + + + def con_lay_length(self, X, index, threshold, display=0): + '''This ensures there is a minimum amount of line on the seabed at the +extreme displaced position.''' + + if self.MG: + minlaylength = min([self.ss.getLayLength(iLine=index),self.ss_mod.getLayLength(iLine=index)]) + else: + minlaylength = self.ss.getLayLength(iLine=index) + + return minlaylength - threshold + self.LayLen_adj + + def con_max_lay_length(self, X, index, threshold, display=0): + '''This ensures there is a minimum amount of line on the seabed at the +extreme displaced position.''' + + if self.MG: + minlaylength = min([self.ss.getLayLength(iLine=index),self.ss_mod.getLayLength(iLine=index)]) + else: + minlaylength = self.ss.getLayLength(iLine=index) + + return threshold - minlaylength + + def con_max_td_range(self, X, index, threshold, display=0): + '''Ensures the range of motion of the touchdown point betweeen the + range of offsets is less then a certain distance. + This constraint is for the system as a whole (index is ignored) and + must have offset='other' so that it's evaluated at the end.''' + return threshold - (self.max_lay_length - self.min_lay_length) + + """ + def con_buoy_contact(self, X, index, threshold, display=0): + '''This ensures the first line node doesn't touch the seabed by some minimum clearance in the +extreme displaced position.''' + return self.getPointHeight(index) - threshold + <<<< seems funny <<< + """ + + def con_strength(self, X, index, threshold, display=0): + '''This ensures the MBL of the line is always greater than the maximum + tension the line feels times a safety factor.''' + if self.MG: + minsf = min([self.ss.getTenSF(index),self.ss_mod.getTenSF(index)]) + else: + minsf = self.ss.getTenSF(index) + return minsf - threshold + + def con_overall_strength(self, X, index, threshold, display=0): + '''This ensures the MBL of the line is always greater than the maximum + tension the line feels times a safety factor. *** checks all line sections ***''' + + sfs = [] + + #check both ss_mod and ss if there's marine growth + if self.MG: + + #iterate through linelist and append safety factors + for index in range(0, len(self.ss_mod.lineList)): + minsf = self.ss_mod.getTenSF(index) + sfs.append(minsf - threshold) + + for index in range(0, len(self.ss.lineList)): + minsf = self.ss.getTenSF(index) + sfs.append(minsf - threshold) + + return min(sfs) + + + + def con_curvature(self, X, index, threshold, display=0): + '''Ensure that the MBR of the cable is always greater than the maximum + actual curvature times a safety factor.''' + if self.MG: + mincsf = min([ self.ss.getCurvSF(index), self.ss_mod.getCurvSF(index)]) + else: + mincsf = self.ss.getCurvSF(index) + return mincsf - threshold + + def con_max_curvature(self, x, index, threshold, display=0): + '''Ensures that the MBR divided by the maximum curvature over the + entire cable is greater than a threshold safety factor. + + >>> make a single set of cable props for the line overall + >>> then there will be more for the buoyancy sections ''' + if self.MG: + maxks = max([max(self.ss.Ks), max(self.ss_mod.Ks)]) + else: + maxks = max(self.ss.Ks) + return 1 /( self.cableType['MBR'] * maxks ) - threshold + + + def con_min_sag(self, X, index, threshold, display=0): + '''Ensure the lowest point of a line section is below + a minimum depth.''' + if self.MG: + minsag = min([self.ss.getSag(index), self.ss_mod.getSag(index)]) + else: + minsag = self.ss.getSag(index) + return threshold - minsag + + def con_max_sag(self, X, index, threshold, display=0): + '''Ensures the lowest point of a line section is above + a certain maximum depth.''' + if self.MG: + maxsag = max([self.ss.getSag(index), self.ss_mod.getSag(index)]) + else: + maxsag = self.ss.getSag(index) + return maxsag - threshold + + def con_max_hog(self, X, index, threshold, display=0): + '''Ensures the highest point of a line section is below + a certain maximum depth ''' + if self.MG: + maxhog = max([self.ss.getHog(index), self.ss_mod.getHog(index)]) + else: + maxhog = self.ss.getHog(index) + return threshold - maxhog + + # ----- utility functions ----- + + def plotProfile(self, iPoint=1, Xuvec=[1,0,0], Yuvec=[0,0,1], ax=None, color=None, title="", slack=False, displaced=True, figsize=(6,4)): + '''Plot the mooring profile in undisplaced and extreme displaced positions + + Parameters + ---------- + Xuvec : list, optional + plane at which the x-axis is desired. The default is [1,0,0]. + Yuvec : lsit, optional + plane at which the y-axis is desired. The default is [0,0,1]. + ax : axes, optional + Plot on an existing set of axes + color : string, optional + Some way to control the color of the plot ... TBD <<< + title : string, optional + A title of the plot. The default is "". + slack : bool, optional + If false, equal axis aspect ratios are not enforced to allow compatibility in subplots with axis constraints. + displaced : bool, optional + If true (default), displaced line profiles are also plotted. + + Returns + ------- + fig : figure object + To hold the axes of the plot + ax: axis object + To hold the points and drawing of the plot + + ''' + + # if axes not passed in, make a new figure + if ax == None: + fig, ax = plt.subplots(1,1, figsize=figsize) + ax.set_xlabel('Horizontal distance (m)') + ax.set_ylabel('Depth (m)') + + if self.MG: + fig1, ax1 = plt.subplots(1,1, figsize=figsize) + ax1.set_xlabel('Horizontal distance (m)') + ax1.set_ylabel('Depth (m)') + else: + fig = plt.gcf() # will this work like this? <<< + + + if displaced: + offsets = [0, self.x_mean+self.x_ampl, -self.x_mean-self.x_ampl] + else: + offsets = [0] + + for x in offsets: + alph = 1 if x==0 else 0.5 # make semi-transparent for offset profiles + + self.ss.setOffset(x) + + ax.plot(x, 0,'ko',markersize = 2) # draw platform reference point + + if self.shared == 2: # plot other half too if it's a shared line where only half is modeled <<< + for i, line in enumerate(self.ss.lineList): + if i in self.i_buoy: + self.ss.lineList[i].color = [.6,.6,.0] + else: + self.ss.lineList[i].color = [.3,.5,.5] + self.ss.drawLine2d(0, ax, color = 'self', Xoff = -self.ss.span/2) + + #store ss cos_th before plotting the flipped half cable + self.ss.cos_th = -self.ss.cos_th + self.ss.drawLine2d(0, ax, color = 'self', Xoff = self.ss.span/2) + self.ss.cos_th = -self.ss.cos_th + + else: + for i, line in enumerate(self.ss.lineList): + if color==None: # alternate colors so the segments are visible + if i in self.i_buoy: + line.drawLine2d(0, ax, color=[.6,.6,.0], alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec) + else: + line.drawLine2d(0, ax, color=[.3,.5,.5], alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec) + else: + line.drawLine2d(0, ax, color=color, alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec) + + if self.MG: + alph = 1 if x==0 else 0.5 # make semi-transparent for offset profiles + + self.ss_mod.setOffset(x) + + ax1.plot(x, 0,'ko',markersize = 2) # draw platform reference point + + if self.shared == 2: # plot other half too if it's a shared line where only half is modeled <<< + for i, line in enumerate(self.ss_mod.lineList): + + #check if linetype name has buoy in it (**** this is highly dependent on naming convention) + if line.type['name'].split("_")[-1][:4] == 'buoy': + self.ss_mod.lineList[i].color = [.6,.6,.0] + else: + self.ss_mod.lineList[i].color = [.3,.5,.5] + self.ss_mod.drawLine2d(0, ax1, color = 'self', Xoff = -self.ss.span/2) + + #store ss cos_th before plotting the flipped half cable + self.ss_mod.cos_th = -self.ss_mod.cos_th + self.ss_mod.drawLine2d(0, ax1, color = 'self', Xoff = self.ss_mod.span/2) + self.ss_mod.cos_th = -self.ss_mod.cos_th + + else: + for i, line in enumerate(self.ss_mod.lineList): + if color==None: # alternate colors so the segments are visible + + #check if linetype name has buoy in it (**** this is highly dependent on naming convention) + if line.type['name'].split("_")[-1][:4] == 'buoy': + line.drawLine2d(0, ax1, color=[.6,.6,.0], alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec) + else: + line.drawLine2d(0, ax1, color=[.3,.5,.5], alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec) + else: + line.drawLine2d(0, ax1, color=color, alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec) + + # make legend entries available + if displaced: + if not color==None: + ax.plot(np.nan, np.nan, color=color, alpha=1, label="undisplaced") + ax.plot(np.nan, np.nan, color=color, alpha=0.5, label="displaced") + + #ax.plot([self.ss.lineList[0].rA[0], 0], [-self.depth, -self.depth], color='k') + # only force equal aspect ratio if "slack" keyword isn't specified (so that sharex=True, sharey-True plots are possible) + if not slack: + ax.axis("equal") + + ax.set_title(title) + #ax.set_ylim(-1,1) + + + self.ss.setOffset(0) # set back to its neutral position + + if self.MG: + + if not slack: + ax1.axis("equal") + + ax1.set_title(title + " Marine Growth") + + self.ss_mod.setOffset(0) + + return fig, ax, fig1, ax1 # return the figure and axis object in case it will be used later to update the plot + + else: + return fig, ax + + def plotCurves(self, ax=[], color="k", title=""): + '''Plot key performance curves for the cable as a function of offset + + Parameters + ---------- + ax : axes, optional + Plot on an existing set of axes + title : string, optional + A title of the plot. The default is "". + + Returns + ------- + fig : figure object + To hold the axes of the plot + ax: axis object + To hold the points and drawing of the plot + + ''' + + # if axes not passed in, make a new figure + if len(ax) == 0: + fig, ax = plt.subplots(2,1, sharex=True) + newFig=True + else: + if not len(ax) == 2: + raise Exception("ax provided to plotCurves must be a list of 2 axes.") + fig = plt.gcf() + newFig = False + + x = np.linspace(-self.x_mean_high-self.x_ampl, self.x_mean_low+self.x_ampl, 50) + + Fx = np.zeros(len(x)) + Ts = np.zeros([len(x), len(self.ss.lineList)]) + + # calculate values at each offset point + for i in range(len(x)): # go through each offset point + + self.ss.setOffset(x[i]) # offset the desired amount + + Fx[i] = self.ss.fB_L[0] # get horizontal mooring force + + for j in range(len(self.ss.lineList)): # get upper end tension of each line segment + Ts[i,j] = self.ss.lineList[j].TB + + # plots + ax[0].plot(x, -Fx/1e3, c=color) + + for j in range(len(self.ss.lineList)): + ax[1].plot(x, Ts[:,j]/1e3, c=color, dashes=[5-0.5*j, 0.5*j], label=f"segment {j+1}") + + ax[0].set_ylabel("Fx (kN)") + ax[1].set_ylabel("Tension (kN)") + if newFig: ax[1].legend() + ax[1].set_xlabel("Offset (m)") + #fig.set_title(title) + + self.ss.setOffset(0) # restore to undisplaced position + + return fig, ax # return the figure and axis object in case it will be used later to update the plot + + """ + def makeCableType(self, di, name): + '''sets up a cableType dictinoary by reading in from a dictionary a + a specified name entry.''' + + # a few calculations + d = float(di['d']) # [m] + m = float(di['m']) # [kg/m] + w = (m - np.pi/4*d**2 *self.rho)*self.g + + # make and fill in a cableType dictionary, which will go in MoorPy's lineTypes dictionary + cableType = dict(name=name) + cableType['d_vol'] = float(di['d']) # [m] + cableType['m'] = m # [kg/m + cableType['w'] = w # [N/m] wet weight per unit length] + cableType['EA'] = getFromDict(di, 'EA') # [N] axial stiffness + cableType['EI'] = getFromDict(di, 'EI' , default=0) # [N-m^2] bending stiffness + cableType['MBL'] = getFromDict(di, 'MBL', default=0) # [N] minimum breaking load + cableType['MBR'] = getFromDict(di, 'MBR', default=0) # [m] minimum bend radius + cableType['A_con'] = getFromDict(di, 'A' , default=0) # [mm^2] conductor area + cableType['dynamic'] = getFromDict(di, 'dynamic', dtype=bool, default=True) + cableType['DC'] = getFromDict(di, 'DC' , dtype=bool, default=False) + cableType['cable_cost'] = getFromDict(di, 'cable_cost', default=0) # $/m dynamic cable cost + cableType['buoy_cost'] = getFromDict(di, 'buoy_cost', default=0) # cost of each module + cableType['buoy_length'] = getFromDict(di, 'buoy_length', default=0) # meters for each buoyancy module + cableType['L_BM'] = getFromDict(di, 'L_BM', default=0) # [m] center to center spacing between two buoyancy modules + cableType['D_BM'] = getFromDict(di, 'D_BM', default=0) # [m] Diameter of buoyancy module + cableType['V_BM'] = getFromDict(di, 'V_BM', default=0) # [m] volume of buoyancy module + cableType['rho_buoy'] = getFromDict(di, 'rho_buoy', default=500) # [kg/m^3] aggregate density of buoyancy module + if cableType['V_BM'] <= 0: + raise Exception("Volume of buoyancy module must be greater than zero") + + return cableType + """ + def updateHyroCoeffs(self, C_dnc = 1.2, C_dnb = 1.2, C_dab1 = 1, C_dab2 = 0, C_dac = 0, C_anb = 1, C_anc = 1, C_aab = 0.5 , C_aac = 0): + ''' + + + Parameters + ---------- + C_dnc : Normal drag coeff for the cable. The default is 1.2. + C_dnb : Normal drag coeff for the buoyancy module. The default is 1.2. + C_dab1 : Drag coefficient for exposed ends of buoyancy module. The default is 1. + C_dab2 : Axial drag coefficient for buoyancy module (skin friction). The default is 0. + C_dac : Axial drag coefficient for cable (skin friction). The default is 0. + C_anb : Normal added mass coefficient for buoyancy module. The default is 1. + C_anc : Normal added mass coefficient for cable. The default is 1. + C_aab : Axial added mass coefficient for buoyancy module. The default is 0.5 (assumed sphere added mass coeff). + C_aac : Axial added mass coefficient for cable. The default is 0. + + Returns + ------- + None. + + ''' + #iterate through list of line properties + buoycount = -1 + for i in (range(0, len(self.ss.lineTypes))): + linetype = self.ss.lineTypes[i] + if linetype['name'].split("_")[-1][:4] == 'buoy': + buoycount += 1 + deq = linetype['d_vol'] # volume equiv diameter for buoy section + dc = self.cableType['d_vol'] # diameter of cable + db = self.buoyType['d'] # diameter of buoy + Lbs = self.bs[buoycount] + if Lbs == 0: + ValueError('Buoyancy module spacing is zero') + Lb = self.buoyType['l'] + + self.ss.lineTypes[i]['Cd'] = 1/(Lbs * deq)*(C_dnc * dc * (Lbs - Lb) + C_dnb * db * Lb) + self.ss.lineTypes[i]['CdAx'] = 1 / (Lbs * deq) *(C_dab1 * (db**2 - dc**2)/4 + C_dab2 * db * Lb + C_dac * dc * (Lbs - Lb)) + self.ss.lineTypes[i]['Ca'] = 1 / (Lbs * deq**2) * (C_anb * db**2 * Lb + C_anc * dc**2 *(Lbs - Lb)) + self.ss.lineTypes[i]['CaAx'] = 1 / (Lbs * deq**2) * (C_aab * db**2 * Lb + C_aac * dc**2 *(Lbs - Lb)) + else: + self.ss.lineTypes[i]['CdAx'] = 0.0 + self.ss.lineTypes[i]['Ca'] = 1.0 + if self.MG: + for i in (range(0, len(self.ss_mod.lineTypes))): + linetype = self.ss_mod.lineTypes[i] + if linetype['name'].split("_")[-1][:4] == 'buoy': + buoycount += 1 + deq = linetype['d_vol'] # volume equiv diameter for buoy section + dc = self.cableType['d_vol'] # diameter of cable + db = self.buoyType['d'] # diameter of buoy + Lbs = self.bs[buoycount] + if Lbs == 0: + ValueError('Buoyancy module spacing is zero') + Lb = self.buoyType['l'] + + self.ss_mod.lineTypes[i]['Cd'] = 1/(Lbs * deq)*(C_dnc * dc * (Lbs - Lb) + C_dnb * db * Lb) + self.ss_mod.lineTypes[i]['CdAx'] = 1 / (Lbs * deq) *(C_dab1 * (db**2 - dc**2)/4 + C_dab2 * db * Lb + C_dac * dc * (Lbs - Lb)) + self.ss_mod.lineTypes[i]['Ca'] = 1 / (Lbs * deq**2) * (C_anb * db**2 * Lb + C_anc * dc**2 *(Lbs - Lb)) + self.ss_mod.lineTypes[i]['CaAx'] = 1 / (Lbs * deq**2) * (C_aab * db**2 * Lb + C_aac * dc**2 *(Lbs - Lb)) + else: + self.ss_mod.lineTypes[i]['CdAx'] = 0.0 + self.ss_mod.lineTypes[i]['Ca'] = 1.0 + + +# ----- Main Script ----- +if __name__ == '__main__': + + # EXAMPLE + + depth = 800 + configuration = 'Humboldt' + + settings = {} + settings['rBFair'] = [0,0,-14] # relative attachment coordinate on FOWT [m] + settings['span'] = 950 # relative attachment coordinate on FOWT [m] + + settings['offset'] = 80 # mean surge offsets in either direction [m] + settings['x_ampl'] = 5 # additional dynamic surge amplitude about the mean [m] + + + # design variables: initial values, min and max bounds + settings['Xindices'] = ['c', 0, 1, 2, 'c'] # order of design variables. multiple line segments can have the same design variable. 'c' flag means that it stays constant + # span L B1[kN] Lmid1 Spread + settings['allVars'] = [950, 1100, 100, 613, 300] # must be the same length as Xindices + settings['Xmin'] = [100, 100, 100] # must be same length as # of design variables + settings['Xmax'] = [1200, 800, 1000] # must be same length as # of design variables + settings['dX_last'] = [10, 10, 10] # must be same length as # of design variables + + # set up constraints + settings['constraints'] = [dict(name='min_lay_length', index=0, threshold= 80, offset='max'), # ensure there is at least 20 m of cable along the seabed + dict(name='max_sag', index=1, threshold=5-depth, offset='min')] # ensure the start of the buoyancy section stays 5 m off the seabed + + # also add a tension safety factor constraint for each section + + for i in range(3): + settings['constraints'].append(dict(name='tension_safety_factor', index=i, threshold=2.0, offset='max')) + + # add a curvature safety factor constraint for each offset of the cable or section of the cable + for i in range(3): + settings['constraints'].append(dict(name='curvature_safety_factor', index=i, threshold=2.0, offset='min')) + + # add a maximum touchdown point range of motion constraint + settings['constraints'].append(dict(name='max_touchdown_range', index=0, threshold=50.0, offset='other')) + + # load property coefficients + cable_props = cprops.loadCableProps(None) # load default property scaling coefficients + cableType = cprops.getCableProps(400, 'dynamic_cable_66', cableProps=cable_props) + + buoy_props = cprops.loadBuoyProps(None) # load default property scaling coefficients + buoyType = cprops.getBuoyProps(1, 'Buoyancy_750m', buoyProps=buoy_props) + + + #set up the object + dc = CableDesign(depth, cableType, buoyType, n=3, i_buoy=[1], **settings) + + #plot initial design + dc.plotProfile(title='initial (X0)') + dc.setNormalization() + + X, min_cost = dc.optimize(maxIter=3, plot=False, display=2, stepfac=4, method='COBYLA') + #X, min_cost = dc.optimize(maxIter=8, plot=False, display=1, stepfac=4, method='SLSQP') + #X, min_cost = dc.optimize(maxIter=2, plot=False, display=1, stepfac=4, method='dopt') + + dc.objectiveFun(X, display=2) + dc.evaluateConstraints(X, display=2) + dc.updateDesign(X, display=0) + dc.plotProfile(title= 'dopt') + dc.plotOptimization() + #dc.unload('Humboldt.dat') + + + plt.show() diff --git a/famodel/design/CableLayout_functions.py b/famodel/design/CableLayout_functions.py new file mode 100644 index 00000000..cdc40be1 --- /dev/null +++ b/famodel/design/CableLayout_functions.py @@ -0,0 +1,1034 @@ +# -*- coding: utf-8 -*- +import os +import numpy as np +from sklearn.cluster import SpectralClustering +# from sklearn.cluster import SpectralClustering +from scipy.spatial.distance import cdist, pdist, squareform +import networkx as nx +import math +import pandas as pd +import matplotlib.pyplot as plt +from famodel.cables.cable_properties import * +from shapely.geometry import Point, LineString, MultiPoint +import shapely as sh +from copy import deepcopy + + +# TODO: rename and reorder inputs + +def getCableLayout(turb_coords, subs_coords, conductor_sizes, + cableProps_type, turb_rating_MW, turb_cluster_id=[], turb_subs_id=[], + n_cluster_sub=0, n_tcmax=8, plot=False, oss_rerouting=False, + substation_id=None): + ''' Function creating the cable layout of whole wind farm, including + estimation of cable conductor sizes. It currently supports a single + substation. + + Parameters + ---------- + turb_coords : 2D array + Coordinates of each turbine, provided as an N-by-2 array of [x,y] values [m]. + subs_coords : list or array + Substation [x,y] coordinates [m]. + conductor_sizes ; list + Conductor sizes to be allowed when sizing cables [mm^2]. + cableProps_type : string + Name of cable type in cableProps property scaling coefficients yaml. + turb_rating_MW : float + Turbine rated power [MW] + turb_cluster_id : list (optional) + The index of the cluster (integers starting from zero) that each + turbine belongs to. This is specified to determines the clusters. + turb_subs_id : list (optional) + The index of the substation (integers starting from zero) that each + turbine should feed to. + n_cluster_sub : int (optional) + Then number of clusters per substation to create if clustering automatically + (turb_cluster_id should not be specified in this case). + n_tcmax : int (optional) + Then number of clusters to create if clustering automatically + (turb_cluster_id should not be specified in this case). + plot : bool (optional, default False) + Displays a plot of the array cable network if True. + + Returns + ------- + iac_dic : list of dicts + List of an array cable information dictionary for each cable. + ''' + + # Handle if coordinates are inputted as lists rather than arrays + if type(turb_coords) == list: + turb_coords = np.array(turb_coords) + n_turb = turb_coords.shape[0] # number of turbines + + if type(subs_coords) == list: + subs_coords = np.array(subs_coords) + + if subs_coords.shape == (2,): # if just an x,y pair, put in a 2D array + subs_coords = np.array([subs_coords]) + n_subs = subs_coords.shape[0] # number of substations + + + # Get cable properties + iac_props = [] + for A in conductor_sizes: + cprops = getCableProps(A, cableProps_type, cableProps=None, source='default', name="", rho=1025.0, g=9.81) + iac_props.append(cprops) + + + # ----- Divide turbines among substations ----- + + if len(turb_subs_id) > 0: # if substation assignment indices are provided + + subs_labels_unique, subs_labels_counts = np.unique(turb_subs_id,return_counts=True) + if not n_subs == len(subs_labels_unique): + raise Exception("There are more unique entries in turb_subs_id than number of subs_coords provided.") + turb_subs_id = np.array(turb_subs_id) + + # Check that substation labels are integers counting up from 0 + for i in range(n_subs): + if not i in subs_labels_unique: + raise Exception(f"provided substation assignment labels must be integers counting up from 0. Integer {i} was not found.") + + else: # If no substation assignments are provided, divide the turbines by distance + # determine max # of turbines allowed per substation + max_turbs_per_substation = n_cluster_sub*n_tcmax + 5 # num clusters x num turbs per cluster + a few extra + turb_subs_id = assignSubstationTurbines(turb_coords, subs_coords, max_turbs_per_substation) + + + # ----- Handle turbine clustering ----- + + if len(turb_cluster_id) > 0: # if cluster indices are provided + + cluster_labels_unique, cluster_labels_counts = np.unique(turb_cluster_id,return_counts=True) + n_cluster = len(cluster_labels_unique) + turb_cluster_id = np.array(turb_cluster_id) + + + # Check that cluster labels are integers counting up from 0 + cluster_subs_id = [] + for i in range(n_cluster): + cluster_subs_id.append(int(np.unique(turb_subs_id[turb_cluster_id==i]))) + if not i in cluster_labels_unique: + raise Exception(f"provided cluster labels must be integers counting up from 0. Integer {i} was not found.") + + # TODO: figure out how to deal with inconsistencies between turbine cluster vs substation assignments + + else: # If no clusters are provided, create clusters + + n_cluster = 0 + if n_cluster_sub == 0: # if number of clusters (per substation) not specified, use default + n_cluster_sub = int(np.ceil(n_turb/n_tcmax/n_subs)) + + # cluster turbines (for each substation if multiple) + turb_cluster_id = [None]*n_turb # cluster ID of each turbine + cluster_labels_counts = [] # the number of turbines in each cluster + cluster_subs_id = [] # substation ID of each cluster + for i in range(n_subs): + turbs = np.where(turb_subs_id==i)[0] + cluster_id, labels_counts = clusteringSpectral(turb_coords[turbs], + subs_coords[i,:], n_cluster_sub, n_tcmax) + + # Store each turbine's cluster ID (adjusting IDs for multiple substations) + for ii,cid in enumerate(cluster_id): + turb_cluster_id[turbs[ii]] = int(cid + n_cluster) + cluster_subs_id += list([int(x) for x in np.zeros(len(labels_counts)) + i]) + + cluster_labels_counts += list(labels_counts) + + n_cluster += len(labels_counts) # tally up actual number of clusters + + + + # ----- Figure out cable connections for each cluster ----- + if not substation_id: + substation_id = [] + for i in range(n_subs): + substation_id.append(n_turb + i) + + index_map = [] # maps local turbine id within each cluster to the global turbine list index + + # The main outputs of this part of the code (one entry per cable) + global_edge_list = [] # global end connection ids of each cable (a, b) + upstreamturb_count = [] # number of turbines upstream of each cable + cable_cluster_id = [] # id number of the cluster each cable belongs to + + cable_types = [] # list of the cable type dict for each cable + + for ic in range(n_cluster): # for each cluster + # Select indices of points per cluster + cluster_ind = np.where(np.array(turb_cluster_id) == ic)[0] + + # Index of the substation for this cluster + #isubs = turb_subs_id[cluster_ind[0]] + isubs = cluster_subs_id[ic] + + # ----- Make coordinate lists for each cluster, and index map ----- + + # Make array of just the coordinates in the cluster + cluster_coords = turb_coords[cluster_ind,:] + + #cluster_arrays.append(cluster_coords) + # Make list of global turbine indicies that are within this cluster + index_map.append(np.arange(n_turb)[cluster_ind]) + + + # Distances from substation to turbine locations for cluster + distances = np.linalg.norm(cluster_coords - subs_coords[isubs,:], axis=1) + # Find the index of the closest turbine to substation + gate_index0 = np.argmin(distances) + + # Calculate minimum spanning tree for the cluster + cluster_edge_list = minimum_spanning_tree(cluster_coords, gate_index0) + # This is a list of [a, b] pairs of turbine indices where, within each + # pair, the power flow is from b to a, and a is closer to the substation. + + # Get number of upstream turbines per turbine, counting th + iac_upstreamturb_count_ic = getUpstreamTurbines(cluster_edge_list) + # iac_upstreamturb_count_ic is now a list giving the number of + # upstream turbines for each cable, with the same indexing as + # cluster_edge_list. + + # Convert cluster edge list into global turbine IDs + for ia, ib in cluster_edge_list: + global_edge_list.append([index_map[ic][ia], + index_map[ic][ib]]) + cable_cluster_id.append(ic) + + upstreamturb_count.append(iac_upstreamturb_count_ic[ib] + 1) + + # determine which substation this cable goes to based on cluster to substation index mapping + subid = substation_id[isubs] + + # Add the cable that goes from the substation to the cluster gate + global_edge_list.append([subid, index_map[ic][gate_index0]]) + cable_cluster_id.append(ic) + upstreamturb_count.append(cluster_labels_counts[ic]) # (cable to substation) + + # Get cable id and assign cable to turbine + #iac_cab2turb_ic2 = getCableID(cluster_coords, gate_coords[ic], + # cluster_edge_list, iac_upstreamturb_count_ic) + # iac_cab2turb_ic = [[el[1], i, el[0]] for i, el in enumerate(cluster_edge_list)] + # Above is no longer used <<< + + + # ----- Size cables and generate dictionary of cable information ----- + + # results of the previous stage are stored in + # - global_edge_list + # - upstreamturb_count + # - cable_cluster_id + + # combine coordinates for easy plotting of everything + coords = np.vstack([turb_coords, subs_coords]) + + iac_dic = [] # list of dictionaries for each cable's information + + # loop through ALL cables + for i in range(len(global_edge_list)): + + # Size cable to support cumulative power up to this point + required_rated_power = turb_rating_MW * upstreamturb_count[i] + selected_cable = selectCable(required_rated_power, iac_props) + + cable_types.append(selected_cable) + + # note: turb_id_A/B is currently opposite of cluster_edge_list [a,b] <<< + turb_id_A = global_edge_list[i][1] + turb_id_B = global_edge_list[i][0] + + coordinates = [[coords[turb_id_A][0], coords[turb_id_A][1]], + [coords[turb_id_B][0], coords[turb_id_B][1]]] + + iac_dic.append({'cluster_id': cable_cluster_id[i], + 'turbineA_glob_id': turb_id_A, # row_id_A, + 'turbineB_glob_id': turb_id_B, # row_id_B, + 'cable_id': i, # this is the global id + 'upstream_turb_count': upstreamturb_count[i], + '2Dlength': np.linalg.norm(coords[turb_id_A] - coords[turb_id_B]), + 'coordinates': coordinates, # end/turbine coordinates: [[xA,yA],[xB,yB]] + 'conductor_area': selected_cable['A'], + 'cable_costpm': selected_cable['cost']}) + + + """ + + # >>> This section has draft rerouting capability for cable to substation. <<< + # oss_rerouting : cable rerouting to avoid intersections of cables between clusters and substation. True = on, False = off + intersection_join = False # ? + + # GATE ROUTING + # Create a list to store the connections + gate_connections = [] + gate_line = LineString(gate_coords) + # Connect OSS coordinates to each point along the gate line + for point in gate_line.coords: + connection_line = LineString([subs_coords, point]) + gate_connections.append(connection_line) + + # Loop over each cluster + for ic in range(n_cluster): + + # Check for intersection + # Overwrite new path when there is an intersection + # Define the first connection + connection = gate_connections[ic] + # Find the intersection between the first connection and the gate line + intersection = connection.intersection(gate_line) + + # Check if there is an intersection + # Multipoint means, there is another intersection, except the target gate + if intersection_join: + if intersection.geom_type == "MultiPoint": + # Create new path + if ic == (len(gate_coords)) and oss_rerouting == 1: + # If last gate leads to an intersection + connection_new = [subs_coords, gate_coords[ic-1], gate_coords[ic]] + + elif ic >= len(gate_coords) - 1: + connection_new = [subs_coords, gate_coords[ic-1], gate_coords[ic]] + + else: + connection_new = [subs_coords, gate_coords[ic+1], gate_coords[ic]] + # Create a new LineString with the updated coordinates + new_line = LineString(connection_new) + + ''' + # Second interation - check if new line is also intersecting + intersection = connection.intersection(new_line) + if intersection.geom_type == "MultiPoint": + # Create new path + if ic == range(len(gate_coords)): + # If last gate leads to an intersection + connection_new = [subs_coords, gate_coords[ic-2], gate_coords[ic-1], gate_coords[ic]] + else: + connection_new = [subs_coords, gate_coords[ic+2], gate_coords[ic+1], gate_coords[ic]] + # Create a new LineString with the updated coordinates + new_line = LineString(connection_new) + ''' + + + # Replace gate connection with new line + gate_connections[ic] = new_line + """ + + + # Make cable layout plot + if plot == 1: + plotCableLayout(iac_dic, turb_coords, subs_coords, save=False) + + # cable_id = np.array([a['cable_id'] for a in iac_dic]) + # ia = np.array([a['turbineA_glob_id'] for a in iac_dic]) + # ib = np.array([a['turbineB_glob_id'] for a in iac_dic]) + # cid =np.array([a['cluster_id'] for a in iac_dic]) + + return iac_dic, global_edge_list, cable_types + + +# ----- Cluster turbines ----- +def clusteringSpectral(turb_coords, subs_coords, n_cluster, n_tcmax): + ''' Clustering wind turbines based on their angles from a single + substation using Spectral Clustering. + + Input: + self.turb_coords : turbines coordinates + self.subs_coords : offshore substation coordinates + self.n_cluster : amount of clusters + n_tcmax : max amount of turbines per cluster + + Output: + self.cluster_arrays : list with turbine coordinates per cluster + self.turb_cluster_id : array with cluster ID per turbine location + + https://scikit-learn.org/stable/modules/clustering.html#spectral-clustering + ''' + # ----- Clustering with Spectral clustering + # Output: labels + # Calculate vectors from root to each point + vectors = turb_coords - subs_coords + # Calculate angles (in radians) between vectors and x-axis + angles = np.arctan2(vectors[:, 1], vectors[:, 0]) + # Rescale angles to [0, 2*pi] + angles[angles < 0] += 2 * np.pi + # Reshape angles to column vector for clustering + angles = angles.reshape(-1, 1) + + # Calculate Euclidean distance from each point to the root + # Clustering using spectral with angles as features + spectral_clustering = SpectralClustering(n_clusters=n_cluster, random_state = 0, affinity='nearest_neighbors', n_neighbors=n_tcmax) + spectral_clustering.fit(angles) + + # ----- Cluster labels + turb_cluster_id = spectral_clustering.labels_ + # ----- Number of turbines per cluster + cluster_labels_unique, cluster_labels_counts = np.unique(turb_cluster_id, return_counts=True) + ''' + # ----- Cluster locations array + cluster_arrays = [] + + for name in cluster_labels_unique: + #name=0 + # Select indices of points per cluster + cluster_ind = np.where(turb_cluster_id == name)[0] + cluster_points = turb_coords[cluster_ind] + cluster_arrays.append(cluster_points) + ''' + return turb_cluster_id, cluster_labels_counts + + +def getclusterGates(turb_coords, subs_coords, turb_cluster_id): + ''' Get gates of turbines cluster, meaning the closest turbines to oss from each cluster. + Input: + turb_coords : turbine coordinates - list of x,y pairs + turb_cluster_id : cluster ID of each turbine + subs_coords : substation coordinates - list of x,y pairs + + Output: + gate_coords : list of gate coordinates per cluster + gate_index : index of the turbine that is the gate per cluster + ''' + cluster_names = np.unique(turb_cluster_id) + gate_coords = np.zeros((len(cluster_names),2)) + gate_index = np.zeros(len(cluster_names)) + + for i in cluster_names : + # Get locations in current cluster + cluster_ind = np.where(turb_cluster_id == i)[0] + cluster_points = turb_coords[cluster_ind] + # Calculate distances from OSS to Turb locations for cluster + distances = np.linalg.norm(cluster_points - subs_coords, axis=1) + # Find the index of the turbine with the minimum distance to OSS + gate_index0 = np.argmin(distances) + # Get the closest location to OSS for current cluster + gate_coords[i,:] = cluster_points[gate_index0] + gate_index[i] = gate_index0 + return gate_coords, gate_index + + +def minimum_spanning_tree(points, start_index): + '''Find edges that form a minimum spanning tree of the provided node + points, starting from a specified node. + X are edge weights of fully connected graph. + This function is adapted from the 'Simplistic Minimum Spanning Tree in Numpy' + from Andreas Mueller, 2012. + https://peekaboo-vision.blogspot.com/2012/02/simplistic-minimum-spanning-tree-in.html + If only one point is provided, an empty list will be returned. + + Input: + points : List of turbine coordinate x,y pairs + start_index : index of which point to start at, which corresponds to + the turbine that will be attached to the substation. + + Output: + spanning_edges : list of lists + Collection of node pairs for each edge, where in each [a,b] pair, a + is the ID of the node closer to the substation. + ''' + + X = squareform(pdist(points)) + + n_vertices = X.shape[0] + spanning_edges = [] + + # initialize with start_index: + visited_vertices = [start_index] + num_visited = 1 + # exclude self connections: + diag_indices = np.arange(n_vertices) + X[diag_indices, diag_indices] = np.inf # set self-distances to infinite to exclude them + + while num_visited != n_vertices: + # define new edge as shortest distance between visited vertices and others + new_edge = np.argmin(X[visited_vertices], axis=None) + # 2d encoding of new_edge from flat, get correct indices + new_edge = divmod(new_edge, n_vertices) + new_edge = [visited_vertices[new_edge[0]], new_edge[1]] + # add edge to tree + spanning_edges.append(new_edge) + visited_vertices.append(new_edge[1]) + # remove all edges inside current tree so they aren't considered for the next new_edge + X[tuple(visited_vertices), new_edge[1]] = np.inf + X[new_edge[1], tuple(visited_vertices)] = np.inf + num_visited += 1 + + return spanning_edges + + +def selectCable(required_rated_power, cableTypes): + '''Selected the cable type from a list that is the smallest option to + exceed the required rated power.''' + + closest_rated_power = float('inf') # Initialize with positive infinity to find the closest power + selected_cable = None + + # Iterate through the list and find the closest power that is >= required_rated_power + for cable_props_dict in cableTypes: + if cable_props_dict['power'] >= required_rated_power and cable_props_dict['power'] < closest_rated_power: + + closest_rated_power = cable_props_dict['power'] + selected_cable = cable_props_dict + + if not selected_cable: + raise Exception(f"No cable provided meets the required rated power of {required_rated_power}.") + breakpoint() + + return selected_cable + +def assignSubstationTurbines(turb_coords, sub_coords, max_turbines): + ''' + Function to split turbines between substations based on which substation a turbine is closest to. + + Parameters + ---------- + turb_coords : array + Array of turbine x,y coordinates + sub_coords : array + Array of substation x,y coordinates + max_turbines : int + Maximum number of turbines allowed per substation + + Returns + ------- + turb_subs_id : list + The index of substation that each turbine should feed to + ''' + turb_subs_id = np.zeros((len(turb_coords[:,0]))) # array of substations associated with each turbine + turbs_for_oss = [] # list of turbine ids for each substation + distlist = [] # list of distances for each turbine from each substation + noss = len(sub_coords[:,0]) # number of substations + + # create list where each entry is an array of distances from turbine coords to a specific oss coord + for oo in range(noss): + turbs_for_oss.append([]) + distlist.append(np.linalg.norm(turb_coords - sub_coords[oo], axis=1)) + + # find which oss is closest to each point + for idx in range(len(distlist[0])): + turb_subs_id[idx] = int(np.argmin([dist[idx] for dist in distlist])) + # list of turbine ids broken out by substation + turbs_for_oss = [list(np.where(turb_subs_id==subid)[0]) for subid in range(noss)] + + rturbs_for_oss = deepcopy(turbs_for_oss) + # if an oss has too many turbines, need to switch some turbines to another oss + overfilled_oss = [oo for oo in range(noss) if len(turbs_for_oss[oo])>max_turbines] + if len(overfilled_oss)>0: + # find oss with least number of turbines + uoss = np.argmin([len(turbs_for_oss[oo]) for oo in range(noss)]) # underfilled oss + + # for each overfilled oss, switch some turbines to the underfilled oss + for ooss in overfilled_oss: + turbine_ids = np.array(turbs_for_oss[ooss]) # ids of turbines currently associated with overfilled oss + # find difference in distance between each turbine and the over- and under-filled oss + dist_disparity_margin = [distlist[uoss][tidx]-distlist[ooss][tidx] for tidx in turbine_ids] + # sort list of indices by decreasing distance difference + sorted_dist_disp = np.flip(np.argsort(dist_disparity_margin)) + rturbs_for_oss[ooss] = list(turbine_ids[sorted_dist_disp[:max_turbines]]) # update overfilled oss turb list with turbines of largest distance disparity + rturbs_for_oss[uoss].extend(list(turbine_ids[sorted_dist_disp[max_turbines:]])) # add remaining turbines to underfilled oss + + # update turb_subs_id + for oo,ossid in enumerate(rturbs_for_oss): + for tid in ossid: # tid is the turbine index/id number + turb_subs_id[tid] = int(oo) + + # return vals + return(turb_subs_id) + + +""" + +# IN WORK => BACKLOG! +# ----- Advanced routing ----- +def advancedCableRouting(iac_edges, cluster_arrays, exclusion_coords): + '''Wrapping method to perform advanced cable routing, considering obstacles + iac_edges : list of array with edge IDs + cluster_arrays : List of arrays with turbine coordinates per cluster + exclusion_coords : List of arrays with exclusion zone coordinates + ''' + + # Check cable intersection + intersecting_lines, lines = checkCableIntersections(iac_edges, cluster_arrays, exclusion_coords) + + nearby_lines = getObstacles(intersecting_lines, lines, buffer_distance=2000) + + obstacles_list = [nearby_lines, lines, exclusion_polygons_sh] + + + +def checkCableIntersections(iac_edges, cluster_arrays, exclusion_coords): + '''Wrapping method to perform advanced cable routing, considering obstacles + Input: + iac_edges : list of array with edge IDs + cluster_arrays : List of arrays with turbine coordinates per cluster + exclusion_coords : List of arrays with exclusion zone coordinates + + Output: + intersecting_indices : list of array with edge IDs + + ''' + # Exclusion zones + exclusion = exclusion_coords + exclusion_polygons_sh = [] # List to store polygons + + # Create exclusion zone polygons + for ie in range(len(exclusion)): + exclusion_polygon = sh.Polygon(exclusion[ie]) + exclusion_polygons_sh.append(exclusion_polygon) + + # Convert iac_edges and respective coordinates into Shapely LineString objects and identify intersecting lines + intersecting_indices = [] + # Loop over clusters + for ic in range(len(iac_edges)): + edges = iac_edges[ic] + coords = cluster_arrays[ic] + + # Loop over cables in cluster + for ie in range(len(edges)): + start, end = edges[ie] + line = LineString([coords[start], coords[end]]) + + # Check if the line intersects the exclusion polygon and get iac_edge index + if line.intersects(exclusion_polygon): + intersecting_indices.append((ic, ie)) + + + + + + # Get insecting edges + iac_edges[intersecting_indices[0][0]][intersecting_indices[0][1]] + + + cluster_arrays[intersecting_indices[0][0],iac_edges[intersecting_indices[0][0]][intersecting_indices[0][1]]] + + # Convert iac_edges and respective coordinates into Shapely LineString objects + lines = [] + for edges, coords in zip(iac_edges, cluster_arrays): + for edge in edges: + start, end = edge + line = LineString([coords[start], coords[end]]) + lines.append(line) + + # Identify lines that intersect with the exclusion polygon + intersecting_lines = [line for line in lines if line.intersects(exclusion_polygons_sh[0])] + + return intersecting_lines, lines + + +def getObstacles(intersecting_lines, lines, buffer_distance): + '''Wrapping method to perform advanced cable routing, considering obstacles + intersecting_lines : list of array shapely lines + buffer_distance : distance, integer + ''' + combined_buffer = intersecting_lines[0].buffer(buffer_distance) + for intersecting_line in intersecting_lines[1:]: + combined_buffer = combined_buffer.union(intersecting_line.buffer(buffer_distance)) + + # Identify lines that intersect with the buffer + # Currently cables only, later include mooring lines as well + nearby_lines = [line for line in lines if line.intersects(combined_buffer) and line not in intersecting_lines] + + return nearby_lines + + + #x,y = nearby_lines[0].coords.xy + + + # Plotting - with nearby lines + plt.figure(figsize=(10, 10)) + + # Plot all lines in blue + for line in lines: + x, y = line.xy + plt.plot(x, y, marker='o', color='blue') + + # Plot intersecting lines in red + for intersecting_line in intersecting_lines: + x, y = intersecting_line.xy + plt.plot(x, y, marker='o', color='red') + + # Plot nearby lines in orange + for line in nearby_lines: + x, y = line.xy + plt.plot(x, y, marker='o', color='orange') + + # Plot the combined buffer + #x, y = combined_buffer.exterior.xy + #plt.plot(x, y, color='green', linestyle='--') + + plt.xlabel('X') + plt.ylabel('Y') + plt.title('Lines and Buffer Around Intersecting Line') + plt.grid(True) + plt.show() + + + + + # Plotting - different lines only + plt.figure(figsize=(10, 10)) + for line in lines: + x, y = line.xy + plt.plot(x, y, marker='o') + + plt.xlabel('X') + plt.ylabel('Y') + plt.title('Shapely Lines from Edges and Coordinates') + plt.grid(True) + plt.show() + + # Display the results + for line in lines: + print(line) + + # Plotting - insecting lines + plt.figure(figsize=(10, 10)) + + # Plot all lines + for line in lines: + x, y = line.xy + plt.plot(x, y, marker='o', color='blue') + + # Plot intersecting lines in red + for line in intersecting_lines: + x, y = line.xy + plt.plot(x, y, marker='o', color='red') + + # Plot the exclusion polygon + for polygon in exclusion_polygons_sh: + x, y = polygon.exterior.xy + plt.plot(x, y, color='green') + + plt.xlabel('X') + plt.ylabel('Y') + plt.title('Shapely Lines and Exclusion Polygon') + plt.grid(True) + plt.show() + +""" + + +def getUpstreamTurbines(edge_list): + '''Calculate the number of turbines upstream of each turbine. + Input: + edge_list : list of list pairs + List of the object ids at the ends of each cable [a ,b], where power + flows from b to a. + + Output: + self.iac_upstreamturb : upstream turbines per cable + self.iac_upstreamturb_count : amount of upstream turbines per cable + ''' + + if len(edge_list) == 0: + return [] # if there is only one turbine in the cluster + + # Create a directed graph from the iac edge list + G = nx.DiGraph() + G.add_edges_from(edge_list) + + # Initialize a list to store neighbors (turbines) of each point + neighbors_list = [] + # Iterate over each point and find its neighbors until a point has no neighbors + for point in range(np.max(edge_list) + 1): + neighbors = bfs_neighbors(G, point) + # Directly append neighbors which might be a set or None + neighbors_list.append(neighbors if neighbors is not None else None) + # Neighbor count + neighbor_count = [len(neighbors) if neighbors is not None else 0 for neighbors in neighbors_list] + iac_upstreamturb_count = [nc for nc in neighbor_count] + + return iac_upstreamturb_count + + +# Function to perform BFS traversal +def bfs_neighbors(graph, start_point): + '''Breadth-First Search. It's a algorithm for searching or traversing tree or graph data structures. + The algorithm starts at a chosen node of a graph and explores all of the neighbor nodes at the present + depth prior to moving on to the nodes at the next depth level. + + Input: + graph : network graph + start_point : start point + + Output: + neighbors : list of neighbors of each point + ''' + + neighbors = set() # Set to store neighbors + visited = set() # Set to store visited points + queue = [start_point] # Initialize queue with start point + + while queue: + # Dequeue a point from the queue + current_point = queue.pop(0) + # Check if current point has neighbors + if current_point not in visited: + visited.add(current_point) + current_neighbors = set(graph.neighbors(current_point)) + neighbors |= current_neighbors # Union operation to add neighbors + queue.extend(current_neighbors - visited) # Add unvisited neighbors to the queue + + # Replace empty set with None + if not neighbors: + neighbors = None + + return neighbors + + +""" +#Seems like the below function is equivalent to +#iac_cab2turb_ic = [[el[1], i, el[0]] for i, el in enumerate(cluster_edge_list)] +#but should double check if it has additional functionality: +def getCableID(coords, gate_coord, edge_list, iac_upstreamturb_count): + '''Identify cable (edge) number related to turbines. + Input: + self.iac_edges : list of inter array cable edges per cluster + self.iac_upstreamturb_count : amount of upstream turbines per cable + gate_coord : list of gate coordinates per cluster + + Output: + self.iac_ID : Downflow cable ID at each wind turbine + self.iac_cab2turb : List with cables and turbines, without 999 (gates) + + ''' + + # Identify cable (edge) number related to turbines + # These cables are in flow direction of the respective wind turbine + #breakpoint() + iac_ID = [] + TurbB_ID = [] + + + cab_id = np.zeros(len(iac_upstreamturb_count), dtype=int) + turb_id_B = np.zeros(len(iac_upstreamturb_count), dtype=int) + + gate_index = np.where((coords == gate_coord).all(axis=1))[0][0] + + # Iterate through the range of points + for turb_id_A in range(np.min(edge_list), np.max(edge_list) + 1): + # If gate point, then skip, because a gate point does not have a inner cluster cable + if turb_id_A == gate_index: + cab_id[turb_id_A] = 999 + turb_id_B[turb_id_A] = 200 # Index for substation + else: + connected_edges = [] + edge_neighbor_counts = [] + edge_points = [] + + # Find edges connected to the point and their respective neighbor counts + for edge_index, (start, end) in enumerate(edge_list): + if start == turb_id_A or end == turb_id_A: + connected_edges.append(edge_index) + + # Add neighbor count for the opposite end of the edge + target_point = end if start == turb_id_A else start + edge_neighbor_counts.append(iac_upstreamturb_count[target_point]) + + # Select the edge (cable) that leads to the turbine with the most neighbors + if edge_neighbor_counts: + max_neighbors_index = np.argmax(edge_neighbor_counts) + selected_edge_index = connected_edges[max_neighbors_index] + cab_id[turb_id_A] = selected_edge_index + + # Get Index of turbine B + cable = edge_list[selected_edge_index] + turb_id_B[turb_id_A] = cable[cable != turb_id_A] + + #iac_cab2turb = relateCab2Turb(iac_ID, TurbB_ID) + + #iac_edges[iac_ID[ic]] + array = np.column_stack((np.arange(len(cab_id)), cab_id, turb_id_B)) + iac_cab2turb = array[array[:, 1] != 999] + + return iac_cab2turb +""" + + +# ----- Plot wind farm layout ----- +def plotCableLayout(iac_dic, turb_coords, subs_coords, gate_connections=[], exclusion_coords=[], save=False): + '''Plot wind farm Cable layout. + + ''' + + # combine coordinates for easy plotting of everything + coords = np.vstack([turb_coords, subs_coords]) + + # Exclusion zones + if len(exclusion_coords) > 0: + exclusion = exclusion_coords + exclusion_polygons_sh = [] # List to store polygons + + # Create exclusion polygons + for ie in range(len(exclusion)): + exclusion_polygon = sh.Polygon(exclusion[ie]) + exclusion_polygons_sh.append(exclusion_polygon) + + + # Set font sizes + #fsize_legend = 12 # Legend + #fsize_ax_label = 12 # Ax Label + #fsize_ax_ticks = 12 # Ax ticks + #fsize_title = 16 # Title + + # Create a colormap and a legend entry for each unique cable section + # Find unique values + # Convert dictionary into data frame + iac_df=pd.DataFrame(iac_dic) + + unique_cables = np.unique([a['conductor_area'] for a in iac_dic]) + colors = plt.cm.viridis(np.linspace(0, 1, len(unique_cables))) # Create a colormap based on the number of unique sections + section_to_color = {sec: col for sec, col in zip(unique_cables, colors)} + + + plt.figure(figsize=(10, 6)) + + # ----- Lease area boundary + #shape_polygon = sh.Polygon(self.boundary) + #x, y = self.boundary_sh.exterior.xy + #plt.plot(x, y, label='Boundary', linestyle='dashed', color='black') + + # Plot Turbines + plt.scatter(coords[:-1, 0], coords[:-1, 1], color='red', label='Turbines') + + # Annotate each point with its index + for i in range(coords.shape[0]-1): #, point in enumerate(cluster_arrays[ic]): + plt.annotate(str(i), coords[i,:], textcoords="offset points", xytext=(0, 10), ha='center') + + # Loop over edges / cable ids + for i in range(len(iac_dic)): + + # Cable selection + color = section_to_color[iac_dic[i]['conductor_area']] + label = f"Section {int(iac_dic[i]['conductor_area'])} mm²" if int(iac_dic[i]['conductor_area']) not in plt.gca().get_legend_handles_labels()[1] else "" + + ia = iac_dic[i]['turbineA_glob_id'] + ib = iac_dic[i]['turbineB_glob_id'] + + plt.plot( coords[[ia,ib], 0], coords[[ia,ib], 1], color=color, label=label) + + plt.text( np.mean(coords[[ia,ib], 0]), np.mean(coords[[ia,ib], 1]), str(i), fontsize=9, color='black') + + + # Turbines + # plt.scatter(cluster_arrays[ic][:, 0], cluster_arrays[ic][:, 1], color='red', label='Turbines') + # Plot gate as a diamond marker + #plt.scatter(self.gate_coords[ic][0], self.gate_coords[ic][1], marker='D', color='green', label='Gate') + + """ + ## ----- Cables Gates to OSS + # TODO: updated cable_id below >>> + iac_oss = iac_df[iac_df['cable_id'] >= 100] + iac_array_oss = iac_oss.values + + for i in range(n_cluster): + cable_section_size = int(iac_array_oss[i, 9]) # Assuming cable section size is in the 7th column + color = section_to_color.get(cable_section_size, 'black') # Default to black if section size not found + connection = gate_connections[i] + x_connection, y_connection = connection.xy + label = f'Section {cable_section_size} mm²' if cable_section_size not in plt.gca().get_legend_handles_labels()[1] else "" + plt.plot(x_connection, y_connection, color=color, label=label) + + #plt.plot([gate_coords[i][0], x_oss], [gate_coords[i][1], y_oss], color=color, label=f'Section {cable_section_size} mm²' if cable_section_size not in plt.gca().get_legend_handles_labels()[1] else "") + """ + + if len(exclusion_coords) > 0: + for ie in range(len(exclusion)): + shape_polygon = exclusion_polygons_sh[ie]#sh.Polygon(self.exclusion[i]) + x, y = shape_polygon.exterior.xy + plt.plot(x, y, linestyle='dashed', color='orange', label='Exclusion Zone') + #ax.plot([], [], linestyle='dashed', color='orange', label='Exclusion Zone') + + # turbine locations + #ax.scatter(x0, y0, c='black', s=12, label='Turbines') + + + # ----- OSS + plt.scatter(subs_coords[:,0], subs_coords[:,1], label='substation', marker='*', color='black', s=100) + + + # Set plot title and labels + plt.title('Wind Turbine Cluster - Cable Conductor Sizes') + plt.xlabel('X (m)') + plt.ylabel('Y (m)') + + # Create a custom legend for the unique cable sections + handles, labels = plt.gca().get_legend_handles_labels() + by_label = dict(zip(labels, handles)) # Removing duplicate labels + plt.legend(by_label.values(), by_label.keys(),loc='upper left', fancybox=True, ncol=2) + plt.gca().set_aspect('equal', adjustable='box') # Set aspect ratio to be equal + + # Create a custom legend for the unique cable sections + #handles, labels = plt.gca().get_legend_handles_labels() + #by_label = dict(zip(labels, handles)) # Removing duplicate labels + #sorted_labels = sorted(by_label.keys()) # Sort the labels alphabetically + #sorted_handles = [by_label[label] for label in sorted_labels] # Get handles corresponding to sorted labels + #plt.legend(sorted_handles, sorted_labels, loc='upper center', bbox_to_anchor=(0.5, -0.1), fancybox=True, ncol=2) + #plt.gca().set_aspect('equal', adjustable='box') # Set aspect ratio to be equal + + + plt.grid(True) + + # ----- Save plot with an incremented number if it already exists + if save: + counter = 1 + output_filename = f'wind farm layout_{counter}.png' + while os.path.exists(output_filename): + counter += 1 + output_filename = f'wind farm layout_{counter}.png' + + # Increase the resolution when saving the plot + plt.savefig(output_filename, dpi=300, bbox_inches='tight') # Adjust the dpi as needed + + +# Test Script +if __name__ == '__main__': + + + turb_coords = [[ 0, 1000], + [ 0, 2000], + [ 0, 3000], + [ 0, 4000], + [ 0, 5000], + [ 1000, 0], + [ 1000, 1000], + [ 1000, 2000], + [ 1000, 3000], + [ 2000, 2000], + [ 2000, 3000], + [ 2000, 4000], + [ 2000, 5000]] + + cluster_id = [ 0, 0, 0, 0, 0, + 1, 1, 1, 1, 1, + 1, 1, 2] + + subs_coords = [ 1400, 200] + + conductor_sizes = np.array([300, 630, 1000]) + + cableProps_type = 'dynamic_cable_66' + turb_rating_MW = 15 + + + #iac_dic = getCableLayout(conductor_sizes, cableProps_type, turb_rating_MW, turb_coords, subs_coords, plot=1) + iac_dic, connections, types = getCableLayout(turb_coords, subs_coords, conductor_sizes, cableProps_type, turb_rating_MW, turb_cluster_id=[], plot=1) + + cable_id = np.array([a['cable_id'] for a in iac_dic]) + ia = np.array([a['turbineA_glob_id'] for a in iac_dic]) + ib = np.array([a['turbineB_glob_id'] for a in iac_dic]) + cid =np.array([a['cluster_id'] for a in iac_dic]) + + + # set up a CableSystem!! + from famodel.cables.cable_system import CableSystem + cs = CableSystem(turb_coords) + + cs.update(connections, types, coords=turb_coords, + powers=[15]*len(turb_coords), + subcoords=subs_coords) + + cs.checkConnectivity() + + plt.show() + \ No newline at end of file diff --git a/famodel/design/LineDesign.py b/famodel/design/LineDesign.py new file mode 100644 index 00000000..b38210be --- /dev/null +++ b/famodel/design/LineDesign.py @@ -0,0 +1,2293 @@ +# New version of LineDesign that uses Subsystem + +import moorpy as mp # type: ignore +#import moordesign.MoorSolve as msolve +from famodel.design.fadsolvers import dsolve2, dopt2, doptPlot +from moorpy.MoorProps import getAnchorProps # type: ignore +from moorpy.helpers import (loadLineProps, getLineProps, # type: ignore + rotationMatrix, getFromDict) + +from famodel.mooring.mooring import Mooring + +import numpy as np +import matplotlib.pyplot as plt +import yaml +import time + + + +class LineDesign(Mooring): + ''' + The LineDesign class inherits from Mooring, which includes a Subsystem. For some cases where offsets + need to be computed, this class will also add a System to look at N line case. + + - The dynamic component of the design process will utilize various dynamic amplification factors (DAFs) + that will be used an input into this class to design the Moorings + - Design variables are imported through the 'allVars' variable,designated by the 'Xindices' variable, + and stored in the "X" variable + - The objective is calculated using the method self.objectiveFun to evaluate the cost of the Mooring + - Constraints are initialized by a global dictionary of all possible constraints: self.confundict + - Each constraint in confundict has a corresponding function (method) to evaluate that constraint + - The key is a string (e.g. "min_lay_length") that the user can pass in through a list, with a corresponding + number to designate the Line in the Mooring for the constraint to apply to, and its quantitative limit + - There is a member function (e.g. con_lay_length) that pertains to each constraint. It accepts the design + vector X, updates the design to ensure the mooring system properties are updated, and evaluates the constraint. + It returns a negative scalar if the constraint is not met by the quantity specified by the user + + Other notable capabilities + - Shared vs anchored moorings designated by "shared" parameter + - Anchor spacing can be a design variable. Turbine spacing for a shared line needs to be defined. + - rBFair is the fairlead coordinates relative to the attached body's reference point for an anchored line in Quadrant 1. + For example, rBFair can be something like [7.875,0,-21] or [5.57,5.57,-21] or [3.93,6.82,-21] + For a shared line, the fairlead coordinates are assumed to be the same for both bodies but flipped + - Design variables can be given one of four designations in Xindices: an integer, 'c' (constant), + 's' (to be solved for), or 'r' (like a constant, but can be set as a ratio wrt another variable) + + Example allVars vector: X = [A or W0, L1, D1, ...] where < > section repeats + For anchor lines, the first entry is anchor spacing. For shared lines, the first entry is midpoint weight. + Example Mooring: (anchor at spacing A)---- L1,D1-----(W1)------L2,D2------(W2)------L3,D3------(end B) + Clump weights and buoyancy floats are not specified directly. They are both 'weights' and can have either a postitive or negative value + + ''' + + def __init__(self, depth, lineProps=None, **kwargs): + '''Creates a LineDesign Mooring object to be used for evaluating or optimizing a mooring line design. + + Parameters + ---------- + depth : float + Water depth + + Keyword Arguments + ----------------- + solve_for : string + Keyword indicating which built-in design algorithm to use, if any. Options are: + 'tension' - adjusts a line section length to achieve a target horizontal tension on the line. + 'offset' - adjusts a line section length to achieve a target mean offset considering all lines. + 'stiffness' - adjusts a line section length to achieve a target undisplaced surge stiffness considering all lines. + 'ghost' - adjusts anchor spacing to achieve a target minimum laid length - IN PROGRESS. + 'fancy' - adjusts a line section length to ensure mean offset is less than a target value - IN PROGRESS. + 'none' - makes no adjustment. + All options except none require that one of the line section lengths be set as solved ('s') rather than fixed/variable. + DAFs : float or float array, optional + Dynamic amplification factors to use to scale up quasi-static predicted deviations from mean + values to approximate dynamic ones. Provide a scalar or an n+1 array where n is the number + of line sections and the last entry is the DAF to be used for anchor loads. Default is 1. + + ''' + self.settings = kwargs + + self.display = getFromDict(kwargs, 'display', default=0) + + # add the parameters set by the input settings dictionary + self.name = getFromDict(kwargs, 'name', dtype=str, default='no name provided') + lineTypeNames = getFromDict(kwargs, 'lineTypeNames' , dtype=str, shape=-1, default=[]) + + + # set up the mooring system object with the basics from the System class + rho = getFromDict(kwargs, 'rho', default=1025.0) + g = getFromDict(kwargs, 'g' , default=9.81) + self.depth = depth # used? + + # ----- Set properties for Mooring object and its Subsystem ----- + # set model-specific parameters + self.shared = getFromDict(kwargs, 'shared', dtype=bool, default=False) + self.span = getFromDict(kwargs, 'span', default=0) # [m] horizontal extent of mooring (formerly "spacing") + + # set remaining Mooring-specific parameters + self.rBFair = getFromDict(kwargs, 'rBFair', shape=-1, default=[0,0,0]) # [m] end coordinates relative to attached body's ref point + self.nLines = len(lineTypeNames) # number of sections in the mooring line + + + + # ============== set the design variable list ============== + self.solve_for = getFromDict(kwargs, 'solve_for', dtype=str, default='offset') # whether to solve for offsets assuming 3 lines, or solve for mean horizontal tension of this line (for use with shared array design tools) + + self.allVars = getFromDict(kwargs, 'allVars' , shape=3*len(lineTypeNames)) + + # set the design variable type list + if 'Xindices' in kwargs: + self.Xindices = list(kwargs['Xindices']) + if not len(self.Xindices)==len(self.allVars): + raise Exception("Xindices must be the same length as allVars") + else: + raise Exception("Xindices must be provided.") + + # find the largest integer to determine the number of desired design variables + self.nX = 1 + max([ix for ix in self.Xindices if isinstance(ix, int)]) + + # check for errors in Xindices + for i in range(self.nX): + if not i in self.Xindices: + raise Exception(f"Design variable number {i} is missing from Xindices.") + valid = list(range(self.nX))+['c','s','r','g'] # entries must be either design variable index or constant/solve/ratio flags + for xi in self.Xindices: + if not xi in valid: + raise Exception(f"The entry '{xi}' in Xindices is not valid. Must be a d.v. index, 'c', 's', or 'r'.") + + # find the length solve index 's' and make sure it's valid + sInds = [i for i,xi in enumerate(self.Xindices) if xi=='s'] + if len(sInds) == 1: + if (sInds[0]-1)%3 == 0: + self.iL = int((sInds[0]-1)/3) # this is the line index whose length will be adjusted in the dsolve inner loop + else: + raise Exception("The 's' flag in Xindices must be at a line length (i.e. the 2nd, 5th, 8th...) position.") + elif len(sInds) == 0: + if self.solve_for in ['none', 'ghost']: + self.iL = 0 # arbitrary line index. The index won't matter when solve_for = 'none' + else: + raise Exception("A single 's' flag for line length solving must be provided in Xindices") + else: + raise Exception("A single 's' flag for line length solving must be provided in Xindices") + + # check for 'r' variable option + self.rInds = [i for i,xi in enumerate(self.Xindices) if xi=='r'] + for i in range(len(self.rInds)): + if self.allVars[self.rInds[i]] >= 1.0 or self.allVars[self.rInds[i]] <= 0.0: + raise Exception("The ratio variable needs to be between 1 and 0") + + + # set up the mooring system for the specific configuration type + ''' + Just makes the connections, sizing happens later. + + Example allVars vector: X = [A or W0, L1, D1, ...] where < > section repeats + For anchor lines, the first entry is anchor spacing. For shared lines, the first entry is midpoint weight. + Example Mooring: (anchor at spacing A)---- L1,D1-----(W1)------L2,D2------(W2)------L3,D3------(end B) + Clump weights and buoyancy floats are not specified directly. They are both 'weights' and can have either a postitive or negative value + ''' + + # first set the weight, length, and diameter lists based on the allVars inputs. Don't worry about design variables yet. Create the units list too. + + if self.shared==1: + if self.span == 0: raise Exception("For shared arrangements, a span must be provided to the Mooring object.") + Ws = self.allVars[0::3].tolist() + else: + self.span = self.allVars[0]*10 - self.rBFair[0] # in tens of meters + Ws = self.allVars[3::3].tolist() + + Ls = self.allVars[1::3].tolist() + Ds = self.allVars[2::3].tolist() + + unitPattern = ['t', 'm', 'mm'] + self.allVarsUnits = [unitPattern[i % 3] for i in range(len(self.allVars))] + if self.shared==0: + self.allVarsUnits[0] = 'm' + # if any of the input lengths are in ratio form, convert them to real value form + # (this can currently only handle 1 ration variable per Mooring) + if len(self.rInds) > 0: + self.nsll_ratio = self.allVars[self.rInds[0]] + self.allVars[self.rInds[0]] = self.nsll_ratio*self.span + Ls = self.allVars[1::3].tolist() # reset the Ls variable + + # ==================================================================== + + + + # ----- Initialize some objects ----- + if self.shared==1: + shared=1 + shareCase=2 # assumed symmetric and we model half the shared line. + elif self.shared==0: + shared=0 + shareCase=0 + # make a dummy design dictionary for Mooring to make a Subsystem with??? + dd = dict(subcomponents={}) + + # Create subcomponents list: alternating Connectors and Sections + # Pattern: [Connector, Section, Connector, Section, ..., Connector] + # Total length: 2*nLines + 1 (nLines sections + nLines+1 connectors) + dd['subcomponents'] = [{} for i in range(2*self.nLines + 1)] + + # the sizing function coefficients to use in the design + self.lineProps = loadLineProps(lineProps) + + # Build alternating subcomponents list + for i in range(self.nLines): + # Connector at position 2*i (even indices: 0, 2, 4, ...) + connector_idx = 2*i + section_idx = 2*i + 1 + + # Initialize connector properties (will be populated below) + dd['subcomponents'][connector_idx] = {'m': 0, 'v': 0, 'CdA': 0} + # Assign section properties + dd['subcomponents'][section_idx]['type'] = getLineProps(Ds[i], + material=lineTypeNames[i], name=i, lineProps=self.lineProps) + dd['subcomponents'][section_idx]['L'] = Ls[i] + + # Add final connector at end + dd['subcomponents'][2*self.nLines] = {'m': 0, 'v': 0, 'CdA': 0} + + # Assign props of first connector if shared (midpoint weight) + if self.shared==1: + pointDict = self.getClumpMV(Ws[0]) + dd['subcomponents'][0]['m'] = pointDict['m'] + dd['subcomponents'][0]['v'] = pointDict['v'] + + # Assign props for intermediate connectors + for i in range(self.nLines-1): + # Intermediate connectors are at positions 2, 4, 6, ... (2*(i+1)) + connector_idx = 2*(i+1) + pointDict = self.getClumpMV(Ws[ i + 1*(self.shared==1)]) + + dd['subcomponents'][connector_idx]['m'] = pointDict['m'] + dd['subcomponents'][connector_idx]['v'] = pointDict['v'] + # CdA could be added here if needed + + # General mooring dimension info + dd['span' ] = self.span + dd['zAnchor' ] = -self.depth + dd['rad_fair'] = np.abs(self.rBFair[0]) + dd['z_fair' ] = self.rBFair[2] + + # super().__init__(depth=depth, rho=rho, g=g, lineProps=lineProps) # if we're a subsystem + + # Call Mooring init function (parent class) + + + Mooring.__init__(self, dd=dd, rho=rho, g=g, shared=shared, lineProps=self.lineProps) + # The above will also create Mooring self parameters like self.rad_anch + + # Save a copy of the original anchoring radius to use with the + # solve_for=ghost option to adjust the chain length. + self.rad_anch0 = float(self.rad_anch) + + self.createSubsystem(case=int(shareCase)) + if self.shared==1: + self.ss.rA[2] = self.rBFair[2] + + # HARDCODING THIS FOR NOW (MIDPOINT WEIGHT MUST BE UPDATED) + pointDict = self.getClumpMV(.5*Ws[0]) + + self.dd['subcomponents'][0]['m'] = pointDict['m'] + self.dd['subcomponents'][0]['v'] = pointDict['v'] + + self.ss.pointList[0].m = pointDict['m'] + self.ss.pointList[0].v = pointDict['v'] + + self.ss.eqtol = getFromDict(kwargs, 'eqtol', default=0.002) # position tolerance to use in equilibrium solves [m] + + # load a custom line props scaling dict if provided ?? + #self.ss.lineProps = lineProps + + + # identify number of line sections and initialize dynamic amplification factors + self.DAFs = getFromDict(kwargs, 'DAFs', shape=self.nLines+2, default=1.0) # dynamic amplication factor for each line section, and anchor forces (DAFS[-2] is for vertical load, DAFS[-1] is for horizontal load) + self.Te0 = np.zeros([self.nLines,2]) # undisplaced tension [N] of each line section end [section #, end A/B] + self.LayLen_adj = getFromDict(kwargs, 'LayLen_adj', shape=0, default=0.0) # adjustment on laylength... positive means that the dynamic lay length is greater than linedesign laylength + self.damage = getFromDict(kwargs, 'damage', shape = -1, default = 0.0) #Lifetime fatigue damage *(MBL/dT/dx)^m in list with same order as fatigue_headings + self.fatigue_headings = getFromDict(kwargs, 'fatigue_headings', shape = -1, default = [0]) #loading directions for fatigue damage, same order as self.damage + self.ms_fatigue_index = int(getFromDict(kwargs, 'ms_fatigue_index', shape = 0, default = 1)) #index of line in full moorpy system for fatigue damage evaluation. linelist follows the order in headings + self.corrosion_mm = getFromDict(kwargs, 'corrosion_mm', default=0) # [mm] the corrosion of line material over a 25 year lifetime + + # ----- Set solver and optimization settings ----- + + self.x_target = getFromDict(kwargs, 'x_target', default=0) # [m] target mean offset at rated load (e.g. from LinearSystem) - only used in solve_for offset or ghost + self.x_mean_in = getFromDict(kwargs, 'x_mean_in', default=0) + self.x_mean_out = getFromDict(kwargs, 'x_mean_out', default=0) + #self.x_mean_max = getFromDict(kwargs, 'x_mean_max', default=self.x_mean) # set the maximum tolerable mean offset to match the initial target mean offset << appears no longer really used + self.x_ampl = getFromDict(kwargs, 'x_ampl' , default=10) # [m] expected wave-frequency motion amplitude about mean + #self.x_extreme = getFromDict(kwargs, 'xextreme' , default=self.xmax) # >>> same as below, but leaving for now for backward compatibility <<< + #self.x_extr_pos = getFromDict(kwargs, 'x_extr_pos', default=self.xmax) # [m] expected maximum extreme offset (mean + dynamics) + #self.x_extr_neg = getFromDict(kwargs, 'x_extr_neg', default=-self.x_extr_pos) # [m] expected maximum extreme negative offset (negative of xextreme unless provided separately) + self.fx_target = getFromDict(kwargs, 'fx_target') # [N] the expected thrust force or target horizontal line tension + self.kx_target = getFromDict(kwargs, 'kx_target', default=0) # [N/m] the target horizontal line stiffness + if self.solve_for == 'ghost': + self.lay_length_target = getFromDict(kwargs, 'lay_target') # [m] Target laid length - required when solve_for is ghost + + self.headings = getFromDict(kwargs, 'headings' , shape=-1, default=[60, 180, 300]) # [deg] headings of the mooring lines (used only when solve_for is 'offset', 'stiffness', or 'fancy') + + # >>> TODO: add something that adjusts headings to give min/max offsets in -/+ x direction <<< + + self.noFail = False # can be set to True for some optimizers to avoid failing on errors + + self.iter = -1 # iteration number of a given optimization run (incremented by updateDesign) + self.log = dict(x=[], f=[], g=[], time=[], xe=[], a=[]) # initialize a log dict with empty values + + # set the anchor type and initialize the horizontal and vertical capacities of the anchor + self.anchorType = getFromDict(kwargs, 'anchorType', dtype=str, default='drag-embedment') + self.anchorFx = 0.0 + self.anchorFz = 0.0 + + + # ----- optimization stuff ----- + # get design variable bounds and last step size + self.Xmin = getFromDict(kwargs, 'Xmin' , shape=self.nX) # minimum bounds on each design variable + self.Xmax = getFromDict(kwargs, 'Xmax' , shape=self.nX) # maximum bounds on each design variable + self.dX_last = getFromDict(kwargs, 'dX_last', shape=self.nX, default=[]) # 'last' step size for each design variable + if len(self.Xmin) != self.nX or len(self.Xmax) != self.nX or len(self.dX_last) != self.nX: + raise Exception("The size of Xmin/Xmax/dX_last does not match the number of design variables") + + + # initialize the vector of the last design variables, which each iteration will compare against + self.Xlast = np.zeros(self.nX) + + # fill in the X0 value (initial design variable values) based on provided allVars and Xindices (uses first value if a DV has multiple in allVars) + self.X0 = np.array([self.allVars[self.Xindices.index(i)] for i in range(self.nX)]) + self.X0Units = np.array(self.allVarsUnits)[[self.Xindices.index(i) for i in range(self.nX)]] # corresponding units + self.X_denorm = np.ones(self.nX) # normalization factor for design variables + self.obj_denorm = 1.0 # normalization factor for objective function + + + # ----- Set up the constraint functions and lists ----- + + if 'constraints' in kwargs: + self.constraints = kwargs['constraints'] + else: + self.constraints = [] + + # a hard-coded dictionary that points to all of the possible constraint functions by name + self.confundict = {"min_Kx" : self.con_Kx, # a minimum for the effective horizontal stiffness of the mooring + "max_offset" : self.con_offset, # a maximum for the horizontal offset in the extreme loaded position + "min_lay_length" : self.con_lay_length, # a minimum for the length of Line 1 on the seabed at x=x_extr_pos (replaces anchor_uplift) + "rope_contact" : self.con_rope_contact, # a margin off the seabed for Point 2 (bottom of Line 2) at x=x_extr_neg + "tension_safety_factor" : self.con_strength, # a minimum ratio of MBL/tension for all lines in the Mooring at x=x_extr_pos + "min_sag" : self.con_min_sag, # a minimum for the lowest point's depth at x=x_extr_pos + "max_sag" : self.con_max_sag, # a maximum for the lowest point's depth at x=x_extr_neg + "max_total_length" : self.con_total_length, # a maximum line length + "min_yaw_stiff" : self.con_yaw_stiffness, # a minimum yaw stiffness for the whole system about the extreme negative position + "max_damage" : self.con_damage, # a maximum fatigue damage for a specified mooring line (scales from provided damage from previous iteration) + "min_tension" : self.con_min_tension, # a minimum line tension + "min_angle" : self.con_min_angle, # a minimum inclination angle for the line + "max_angle" : self.con_max_angle # a maximum inclination angle for the line + } + + # a hard-coded dictionary of the units associated with the constraints + conUnitsDict = {"min_Kx" : "N/m", + "max_offset" : "m", + "min_lay_length" : "m", + "rope_contact" : "m", + "tension_safety_factor" : "-", + "min_sag" : "m", + "max_sag" : "m", + "max_total_length" : "m", + "min_yaw_stiff" : "Nm/deg", + "max_damage" : "-", + "min_tension" : "N", + "min_angle" : "deg", + "max_angle" : "deg" + } + + # add units to the constraints dictionary + for con in self.constraints: + if con['name'] in conUnitsDict: + con['unit'] = conUnitsDict[con['name']] + else: + raise Exception(f"The constraint name '{con['name']}' is not recognized.") + # set up list of active constraint functions + self.conList = [] + self.convals = np.zeros(len(self.constraints)) # array to hold constraint values + self.con_denorm = np.ones(len(self.constraints)) # array to hold constraint normalization constants + self.con_denorm_default = np.ones(len(self.constraints)) # default constraint normalization constants + + for i, con in enumerate(self.constraints): # for each list (each constraint) in the constraint dictionary + + # ensure each desired constraint name matches an included constraint function + if con['name'] in self.confundict: + + # the constraint function for internal use (this would be called in UpdateDesign) + def internalConFun(cc, ii): # this is a closure so that Python doesn't update index and threshold + def conf_maker(X): + def func(): + # compute the constraint value using the specified function + val = self.confundict[cc['name']](X, cc['index'], cc['threshold']) + + # record the constraint value in the list + self.convals[ii] = val / self.con_denorm[ii] # (normalized) + self.constraints[ii]['value'] = val # save to dict (not normalized) + + return val + return func() + return conf_maker + + # make the internal function and save it in the constraints dictionary + con['fun'] = internalConFun(con, i) + + # the externally usable constraint function maker + def externalConFun(name, ii): # this is a closure so that Python doesn't update index and threshold + def conf_maker(X): + def func(): + # Call the updatedesign function (internally avoids redundancy) + try: + self.updateDesign(X) + + # get the constraint value from the internal list + val = self.convals[ii] + except: + val = -1000 + + return val + return func() + return conf_maker + + # add the conf function to the conList + self.conList.append(externalConFun(con['name'], i)) + + # Save the default/recommended normalization constant + + if con['name'] in ['max_total_length']: + self.con_denorm_default[i] = con['threshold'] # sum([line.L for line in self.ss.lineList]) + + elif con['name'] in ['min_Kx', 'tension_safety_factor', 'min_yaw_stiff', 'max_damage']: + self.con_denorm_default[i] = con['threshold'] + + elif con['name'] in ['max_offset', 'min_lay_length', 'rope_contact', 'min_sag', 'max_sag', 'max_touchdown_range']: + self.con_denorm_default[i] = depth + + else: + raise ValueError("Constraint parameter "+con['name']+" is not a supported constraint type.") + + + + # ensure each constraint is applicable for the type of mooring + if self.shared==1: + if any([con['name'] in ["min_lay_length", "rope_contact"] for con in self.constraints]): + raise ValueError("You are using a constraint that will not work for a shared mooring line") + else: + if any([con['name'] in ["min_sag", "max_sag"] for con in self.constraints]): + raise ValueError("You are using a sag constraint that will not work for an anchored mooring line") + + if not self.solve_for in ['none', 'ghost']: + if any([con['name'] == "max_offset" for con in self.constraints]): + raise ValueError("The offset constraints should only be used when solve_for is none or ghost") + + if self.solve_for == 'ghost': + if any([con['name'] == "min_lay_length" for con in self.constraints]): + print('Warning: having a min_lay_length cosntraint may conflict with lay_length_target in solve_for ghost.') + #ind = [con['name'] for con in self.constraints].index('min_lay_length') + #self.lay_length_target = self.constraints[ind]['threshold'] + if shared: + raise Exception("solve_for ghost can't be used for shared lines") + if not self.Xindices[0] == 'c': + raise Exception("solve_for ghost requires the Xindices[0] to be 'c'.") + + + # ============================================================= + + # make the mooring system + # self.makeGenericMooring( Ls, Ds, lineTypeNames, Ws, suspended=int(self.shared)) + + if self.solve_for in ['none', 'offset'] and len(self.headings) == 0: + raise Exception('When solve_for is none or offset, line headings must be provided.') + + # If needed, make a MoorPy System to use for determining offsets + self.ms = None + + + # These options require forces/stiffnesses of the whole mooring system + if self.solve_for in ['none', 'offset', 'ghost']: + + self.ms = mp.System(depth=self.depth, rho=self.rho, g=self.g) + #lineProps=lineProps) + + # Add a coupled body to represent the platform + self.ms.addBody(-1, np.zeros(6), DOFs=[0,1]) + + # Set up Subsystems at the headings + for i, heading in enumerate(self.headings): + + rotMat = rotationMatrix(0, 0, np.radians(heading)) + + # create end Points for the line + self.ms.addPoint(1, np.matmul(rotMat, [self.rad_anch, 0, -self.depth])) + self.ms.addPoint(1, np.matmul(rotMat, [self.rad_fair, 0, self.z_fair]), body=1) + + # Make subsystem and attach it + ss = mp.Subsystem(mooringSys=self.ms, depth=self.depth, + span=self.span, rBfair=[-self.rad_fair, 0, self.z_fair]) + + # set up the Subsystem design, with references to the types in dd + types = [self.dd['subcomponents'][i]['type'] for i in range(1, len(self.dd['subcomponents']), 2)] + ss.makeGeneric(lengths=Ls, types=types) + self.ms.lineList.append(ss) # add the SubSystem to the System's lineList + ss.number = i+1 + + # attach it to the respective points + self.ms.pointList[2*i+0].attachLine(i+1, 0) + self.ms.pointList[2*i+1].attachLine(i+1, 1) + + self.ms.initialize() + + # initialize the created mooring system + self.ss.initialize(daf_dict = {'DAFs': self.DAFs}) + self.ss.setOffset(0) + self.updateDesign(self.X0, normalized=False) # assuming X0/AllVars is not normalized + + + def updateDesign(self, X, display=0, display2=0, normalized=True): + '''updates the design with the current design variables using improved Fx/Kx solver methods + ''' + start_time = time.time() + + # reset modifiers to mooring design (corrosion/creep/marine_growth) + self.reset() + + # Design vector error checks + if len(X)==0: # if any empty design vector is passed (useful for checking constraints quickly) + return + elif not len(X)==self.nX: + raise ValueError(f"LineDesign.updateDesign passed design vector of length {len(X)} when expecting length {self.nX}") + elif any(np.isnan(X)): + raise ValueError("NaN value found in design vector") + + # If X is normalized, denormalize (scale) it up to the full values + if normalized: + X = X*self.X_denorm + + # If any design variable has changed, update the design and the metrics + if not all(X == self.Xlast): + + self.Xlast = np.array(X) # record the current design variables + + self.iter += 1 + + if self.display > 2: + print(f"Iteration {self.iter}") + + if self.display > 1: + print("Updated design") + print(X) + + + # ----- Apply the design variables to update the design ----- + + # update anchor spacing + dvi = self.Xindices[0] # design variable index - will either be an integer or a string + if dvi in range(self.nX): # only update if it's tied to a design variable (if it's an integer) + if self.shared==1: # if it's a shared line, this would be the midpoint weight (we divide by two because we're simulating half the line) + + pointDict = self.getClumpMV(.5*X[dvi]) + + self.dd['subcomponents'][0]['m'] = pointDict['m'] + self.dd['subcomponents'][0]['v'] = pointDict['v'] + + self.ss.pointList[0].m = pointDict['m'] + self.ss.pointList[0].v = pointDict['v'] + + # arbitrary depth of self.depth/2. Will be equilibrated soon + #self.ss.pointList[0].setPosition([ -0.5*self.span, 0, -0.5*self.depth]) + + else: + # if it's an anchor line, this would be the anchor spacing + + dv = X[dvi]*10 + self.ss.span = dv - np.abs(self.rBFair[0]) # update the span of the ld.ss subsystem + self.dd['span'] = dv - np.abs(self.rBFair[0]) # can update the ld.subsystem's design dictionary too + + self.ss.pointList[0].setPosition([-self.ss.span, 0, -self.depth]) + + self.setAnchoringRadius(dv) + + + # Update section lengths or diameters + for i in range(self.nLines): + + # length + dvi = self.Xindices[3*i+1] # design variable index + if dvi in range(self.nX): # only update if it's tied to a design variable + + # Modify section 1 length (if using ghost option) + if i==0 and self.solve_for=='ghost': + L_new = X[dvi] + (self.rad_anch - self.rad_anch0) + else: + L_new = X[dvi] + + self.setSectionLength(L_new, i) + if self.ms: + for ss in self.ms.lineList: + ss.lineList[i].setL(L_new) + + #elif dvi=='r': # if the line length is a ratio variable, update it to stay the same proportion of the updated anchor spacing + # self.ss.lineList[i].setL(self.nsll_ratio*self.span) + + # diameter + dvi = self.Xindices[3*i+2] # design variable index + if dvi in range(self.nX): # only update if it's tied to a design variable + lineType = getLineProps(X[dvi], + material=self.dd['subcomponents'][2*i+1]['type']['material'], + name=i, lineProps=self.lineProps) + # use the update method to preserve refs to the original dict - this 'points'/connects to the subsystem object too! + self.dd['subcomponents'][2*i+1]['type'].update(lineType) + # update the ss as well + self.ss.lineList[i].type.update(lineType) + + # apply corrosion to the mooring's MBL dictionary (which gets references in the getTenSF constraint in subsystem) + self.setCorrosion(corrosion_mm=self.corrosion_mm) + + # update the intermediate points if they have any weight or buoyancy + for i in range(self.nLines-1): + dvi = self.Xindices[3*i+3] # design variable index + if dvi in range(self.nX): # only update if it's tied to a design variable + + pointDict = self.getClumpMV(X[dvi]) + + self.dd['subcomponents'][2*(i+1)]['m'] = pointDict['m'] + self.dd['subcomponents'][2*(i+1)]['v'] = pointDict['v'] + + self.ss.pointList[i+1].m = pointDict['m'] # update clump buoyancy + self.ss.pointList[i+1].v = pointDict['v'] # update clump mass + + if self.ms: # also update things in the ms if there is one + for ss in self.ms.lineList: + ss.pointList[i+1].m = pointDict['m'] + ss.pointList[i+1].v = pointDict['v'] + + + # ----- Screen design to make sure it's physically feasible ----- + + # >>> TODO: check for negative line lengths that somehow get set <<< + + + Lmax = 0.95*(self.ss.span + self.depth+self.rBFair[2]) + + if sum([self.ss.lineList[i].L for i in range(self.nLines)]) > Lmax: # check to make sure the total length of line is less than the maximum L shape (helpful for GA optimizations) + + if self.solve_for=='none': + self.x_mean_in = -1e3 + self.x_mean_out = 1e3 + self.x_mean_eval = 1e3 # arbitrary high number to set the offset (and constraints) + + for i,con in enumerate(self.constraints): + val = -1e3 + self.convals[i] = val / self.con_denorm[i] # (normalized) + self.constraints[i]['value'] = val # save to dict (not normalized) + + + else: + + # ----- Length adjustment (seems sketchy) ----- + # print(self.ms.bodyList[0].r6[2]) + # set x0 as a 1D list of the line length to be solved for + x0 = [self.ss.lineList[self.iL].L] + + # maximum length of the segment being sized to avoid fully slack + Lmax = 0.99*(self.ss.span + self.depth+self.rBFair[2]) - sum([self.ss.lineList[i].L for i in range(self.nLines) if i != self.iL]) + + # >>> may need a different Lmax for shared lines <<< + + if x0[0] >= Lmax: + x0[0] = 0.8*Lmax + + + # ----- Solver process ----- + + # call dsolve2 to tune line length - eval function depends on solve_for + # note: use a high stepfac so that dsolve2 detects a nonzero slope even when the slope is quite shallow + if self.solve_for == "tension": + x, y, info = dsolve2(self.func_TH_L, x0, tol=[0.4*self.ss.eqtol], args=dict(direction='horizontal'), + Xmin=[10], Xmax=[Lmax], dX_last=[10], maxIter=40, + stepfac=100, display=self.display-1) + elif self.solve_for == "offset": + + args = dict(xOffset=self.x_target, display=self.display-1) + + x, y, info = dsolve2(self.func_fx_L, x0, args=args, + tol=[0.4*self.ss.eqtol], Xmin=[10], Xmax=[Lmax], + dX_last=[10], stepfac=100, maxIter=40, + display=self.display-1) + + elif self.solve_for == "none": + pass + # >>> can remove this from if else block once solve_for error check is done in init func <<< + + elif self.solve_for == 'stiffness': + + x, y, info = dsolve2(self.func_kx_L, x0, args=dict(display=display2), + tol=[0.4*self.ss.eqtol], Xmin=[10], Xmax=[Lmax], + dX_last=[10], stepfac=100, display=self.display-1) + + # this solves for the line length to meet a stiffness equality constraint + # which means that we can still have an offset constraint since the line + # length isn't being solved for to meet a certain offset + + + elif self.solve_for == 'fancy': # a new option to allow lower mean offsets (need to rename!) + # Outer loop determines offset that gives target tension SF, inner loop adjusts line length to achieve said offset + def tuneLineLengthsForOffset(xCurrent, args): # this function does the standard "offset"-mode solve, but now it can be done in the loop of another solve + + args = dict(xOffset=xCurrent[0], fx_target=self.fx_target) + + # tune line length until thrust force is balanced at this mean offset + x, y, info = dsolve2(self.func_fx_L, x0, args=args, + tol=[0.4*self.ss.eqtol], Xmin=[10], Xmax=[Lmax], + dX_last=[10], stepfac=100, display=0) + + stopFlag = False if info['success'] else True # if the line length solve was unsuccessful, set the flat to stop the mean offset solve + + # check strength constraint at this offset + some dynamic additional offset + # (doing this manually here for now, and avoiding the strength constaint at higher levels >>> do not use tension_safety_factor! <<<) + '''This ensures the MBL of the line is always greater than the maximum tension the line feels times a safety factor''' + self.ss.lineList[self.iL].setL(x[0]) # make sure the design is up to date (in terms of tuned line length) + self.ss.setOffset(xCurrent[0] + 10) # offset the body the desired amount (current mean offset + wave offset) + cMin = self.ss.getMinSF(display=display) - 2.0 # compute the constraint value + + print(f" xmax={xCurrent[0]:8.2f} L={x[0]:8.3f} dFx={y[0]:8.0f} minSF={self.getMinSF():7.3f}") + #breakpoint() + + return np.array([cMin]), dict(status=1), stopFlag # return the constraint value - we'll actually solve for this to be zero - finding the offset that just barely satisifes the SFs + + # solve for mean offset that will satisfy tension safety factor constraint (after dynamic wave offset is added) + x, y, info = dsolve2(tuneLineLengthsForOffset, [5], tol=[4*self.ss.eqtol], Xmin=[1], Xmax=[4*self.x_target], dX_last=[5], stepfac=10, display=1) + + + elif self.solve_for=='ghost': + '''Use very large anchor spacing and compute an imaginary + anchor spacing and line length based on the desired lay + length.''' + + # Compute the offset with the adjusted design variables + self.x_mean_out = self.getOffset(self.fx_target) + self.ms.bodyList[0].setPosition([0,0,0,0,0,0]) # ensure body is re-centered + + # self.span and self.ss.span seems redundant. Does LD/Mooring need it?? <<< + + # figure out tension in least laid length scenario... + self.ss.setOffset(self.x_mean_out) # apply max static offsets + self.ss.setDynamicOffset(self.x_mean_out + self.x_ampl) # move to dynamic offset + max_anchor_tension = self.ss.TeD[0,0] # save tension at anchor + + # Set anchoring radius a bit larger than needed, and evaluate once (ss only) + length_to_add = 0.2 * self.rad_anch + new_length = self.dd['subcomponents'][1]['L'] + length_to_add/(1 + max_anchor_tension/self.ss.lineList[0].EA) + self.rad_anch = float(self.rad_anch + length_to_add) + self.ss.span = self.rad_anch - self.rBFair[0] + self.ss.setEndPosition([-self.rad_anch, 0, -self.depth], endB=False) + Mooring.setSectionLength(self, new_length, 0) # ss only, skip ms + + # Figure out lay length + self.ss.setOffset(self.x_mean_out) # apply max static offsets + self.ss.setDynamicOffset(self.x_mean_out + self.x_ampl) # move to dynamic offset + max_anchor_tension = self.ss.TeD[0,0] # save tension at anchor + min_lay_length = self.ss.getLayLength() # record minimum lay length + + # Adjust anchor positions to hit target + unused_length = min_lay_length - self.lay_length_target + new_length = self.dd['subcomponents'][1]['L'] - unused_length + new_spacing = self.rad_anch - unused_length*(1 + max_anchor_tension/self.ss.lineList[0].EA) + self.setAnchoringRadius(new_spacing) + self.setSectionLength(new_length, 0) + + # Update the Subsystem solutions after the adjustments + self.ss.staticSolve() + for ss in self.ms.lineList: + ss.staticSolve() + + #print(f"{self.iter} {self.ss.offset:6.2f}m offset, {self.rad_anch:6.2f} rad_anch, {self.ss.lineList[0].L:6.2f} L") + + else: + raise Exception("solve_for must one of 'offset', 'tension', 'none', 'stiffness, 'fancy', or 'ghost'") + + + if not self.solve_for in ['none', 'ghost']: + if info["success"] == False: + print("Warning: dsolve2 line length tuning solve did not converge.") + #breakpoint() # <<<< handle non convergence <<< + else: + #>>>>> deal with nonzero y - penalize it somehow - for optimizer <<<<< + + # ensure system uses latest tuned line length + #self.ss.lineList[self.iL].setL(x[0]) + self.setSectionLength(x[0], self.iL) + + + # ----- Compute (or set) high and low mean offsets ----- + # (solve for the offsets at which the horizontal mooring reactions balance with fx_target) + if self.solve_for in ['none', 'ghost']: + self.x_mean_out = self.getOffset(self.fx_target) + self.x_mean_in = -self.getOffset(-self.fx_target) + if self.display > 1: print(f" Found offsets x_mean_out: {self.x_mean_out:.2f}, x_mean_in: {self.x_mean_in:.2f}") + self.ms.bodyList[0].setPosition([0,0,0,0,0,0]) # ensure body is re-centered + + # x_mean_in is the offset when the input headings are flipped, representing the opposite loading direction. + # This will only be worst-case/best-case offsets when one of the input headings is either completely upwind or completely downwind. + + + # ----- Evaluate system state and constraint values at offsets ----- + + # Evaluate any constraints in the list, at the appropriate displacements. + # The following function calls will fill in the self.convals array. + + + # ZERO OFFSET: + self.ss.setOffset(0) + + # get undisplaced tensions of each line section and anchors + for i, line in enumerate(self.ss.lineList): + self.Te0[i,0] = np.linalg.norm(line.fA) + self.Te0[i,1] = np.linalg.norm(line.fB) + + + + # Call any constraints that evaluate at the undisplaced position + #self.calcCurvature() + for con in self.constraints: + if con['offset'] == 'zero': + con['fun'](X) + + + # MAX OFFSET: + self.ss.setOffset(self.x_mean_out) # apply static offsets + self.ss.setDynamicOffset(self.x_mean_out + self.x_ampl) # move to dynamic offset + # save maximum anchor tensions for use in cost calculations (includes DAF) + self.anchorFx = self.ss.anchorFxD + self.anchorFz = self.ss.anchorFzD + + self.min_lay_length = self.ss.getLayLength() # record minimum lay length + #print(f"{self.iter} {self.ss.offset:6.2f}m offset, {self.rad_anch:6.2f} rad_anch, {self.ss.lineList[0].L:6.2f} L") + #print(f"Min lay length is {self.min_lay_length}") + self.x_mean_eval = float(self.x_mean_out) # the x_mean value to evaluate if there's an offset constraint + + # Call any constraints needing a positive displacement + for con in self.constraints: + if con['offset'] == 'max': + con['fun'](X) + + + # MIN OFFSET: + self.ss.setOffset(-self.x_mean_in) # apply static offset + self.ss.setDynamicOffset(-self.x_mean_in + -self.x_ampl) # peak offset + + self.max_lay_length = self.ss.getLayLength() # record maximum lay length + + self.x_mean_eval = float(self.x_mean_in) # the x_mean value to evaluate if there's an offset constraint + + # Call any constraints needing a negative displacement + for con in self.constraints: + if con['offset'] == 'min': + con['fun'](X) + + + # OTHER: + self.ss.setOffset(0) # restore to zero offset and static EA + # or at least set back to static states + + # Call any constraints that depend on results across offsets + for con in self.constraints: + if con['offset'] == 'other' or con['offset'] == 'zero': + con['fun'](X) + + ############################################################ + + # ----- Evaluate objective function ----- + + # Calculate the cost from all components in the Mooring + self.lineCost = 0.0 + for line in self.ss.lineList: + self.lineCost += line.L*line.type['cost'] + + # Adjust cost for active length in case of ghost option + if self.solve_for == 'ghost': + + # the length beyond the minimum lay length is not used + unused_length = self.min_lay_length - self.lay_length_target + + # just adjust costs from first section + self.lineCost -= unused_length * self.ss.lineList[0].type['cost'] + + # also adjust and record anchor position ??? (would be nice to show on plots) + + + # calculate anchor cost (using anchor forces calculated when the mooring's constraints were analyzed) + if self.shared==1: + self.anchorCost = 0.0 + self.anchorMatCost = 0.0 + self.anchorInstCost = 0.0 + self.anchorDecomCost = 0.0 + else: + self.anchorMatCost, self.anchorInstCost, self.anchorDecomCost = getAnchorProps(self.anchorFx, self.anchorFz, type=self.anchorType) + self.anchorCost = self.anchorMatCost + self.anchorInstCost + self.anchorDecomCost + + # calculate weight/float cost + self.wCost = 0.0 + self.WF = 1.0 # weight factor: a multiplier for the weight cost per unit mass (kg) + for point in self.ss.pointList: + if point.number > 1 and point.number < self.nLines+1: + self.wCost += abs(point.m + point.v*self.rho)*self.WF + + # if it's shared, we need to double the line costs since it's mirrored + if self.shared==1: + self.lineCost = self.lineCost*2 + self.wCost = self.wCost*2 + + # total cost for all 3 moorings + self.cost = self.lineCost + self.anchorCost + self.wCost + + if self.display > 1: + print(' Cost is ',self.cost) + + + # >>> dynamic_L = self.ss.lineList[0].L - self.min_lay_length #(for line [0] only...) + + self.obj_val = self.cost / self.obj_denorm # normalize objective function value + + + # ----- write to log ----- + + # log the iteration number, design variables, objective, and constraints + self.log['x'].append(list(X)) + self.log['f'].append(list([self.obj_val*self.obj_denorm])) + self.log['g'].append(list(self.convals*self.con_denorm)) + self.log['time'].append(time.time() - start_time) + self.log['xe'].append(self.ss.lineList[self.iL].L) + self.log['a'].append((self.ss.span + self.rBFair[0])/10) + + # TODO: log relevant tuned values (line length, lay length, etc.) for each solve_for option <<< + + # provide some output + if self.display > 5: + f = self.objectiveFun(X, display=1) + + Fx = self.fB_L[0] # get horizontal force from mooring on body + + print(f"Fx: {Fx/1e3:7.1f} vs target of {self.fx_target/1e3:7.1f}") + + print("Line lengths are ") + for line in self.ss.lineList: + print(line.L) + + print("Line input diameters are ") + for lineType in self.lineTypes.values(): + print(lineType['input_d']) + + print(f"Cost is {f}") + + self.evaluateConstraints(X, normalized=False, display=1) + + self.plotProfile() + plt.show() + + + def objectiveFun(self, X, display=0, normalized=True): + '''objective of the optimization. Set to minimize cost''' + + self.updateDesign(X, display=display, normalized=normalized) + + if display > 1: + print(f"Cost is {self.cost:.1f} and objective value is {self.obj_val:.3f}.") + + return float(self.obj_val) # return a copy + + + def evaluateConstraints(self, X, display=0, normalized=True): + '''Update the design (if necessary) and display the constraint + values.''' + + self.updateDesign(X, display=display, normalized=normalized) + + if display > 1: + for i, con in enumerate(self.constraints): + print(f" Constraint {i:2d} value of {con['value']:8.2f} " + +f"for {con['name']}: {con['threshold']} of {con['index']} at {con['offset']} displacement.") + + return np.array(self.convals) # return a copy + + + def setNormalization(self): + '''Set normalization factors for optimization + (based on initial design state).''' + + # design variables + self.X_denorm = np.array(self.Xlast) + # objective + self.obj_denorm = self.cost + # constraints + self.con_denorm = self.con_denorm_default + + + def clearNormalization(self): + '''Clear any normalization constants to unity so no scaling is done.''' + self.X_denorm = np.ones(self.nX) + self.obj_denorm = 1.0 + self.con_denorm = np.ones(len(self.constraints)) + + + def optimize(self, gtol=0.03, maxIter=40, nRetry=0, plot=False, display=0, stepfac=4, method='dopt', continued=False): + '''Optimize the design variables according to objectve, constraints, bounds, etc. + ''' + + # set the display value to use over the entire process + self.display = display + + if not continued: + # reset iteration counter + self.iter = -1 + + # clear optimization progress tracking lists + self.log['x'] = [] + self.log['f'] = [] + self.log['g'] = [] + self.log['time'] = [] + self.log['xe'] = [] + self.log['a'] = [] + + # Set starting point to normalized value + X0 = self.X0 / self.X_denorm + + else: + # Update starting point to the Xlast value + self.updateDesign(self.Xlast, normalized=False) + X0 = self.Xlast / self.X_denorm + + dX_last = self.dX_last / self.X_denorm + Xmax = self.Xmax / self.X_denorm + Xmin = self.Xmin / self.X_denorm + + def eval_func(X, args): + '''Mooring object evaluation function condusive with MoorSolve.dopt2''' + + f = self.objectiveFun(X, display=display) + g = self.evaluateConstraints(X, display=display) + oths = dict(status=1) + Fx = self.ss.fB_L[0] + + return f, g, [self.ss.lineList[self.iL].L], Fx, oths, False + + + # set noFail for GAs in case they come up with crazy designs (avoids exceptions) + if 'GA' in method: + self.noFail = True + else: + self.noFail = False + + + + + # call optimizer to perform optimization + # --------- dopt method: Newton Iteration ----------- + if method=='dopt': + + if display > 0: print("\n --- Beginning LineDesign2 optimize iterations using DOPT2 ---") + + X, min_cost, infodict = dopt2(eval_func, X0, tol=4*self.ss.eqtol, a_max=1.4, maxIter=maxIter, stepfac=stepfac, + Xmin=Xmin, Xmax=Xmax, dX_last=dX_last, display=4) #self.display) + + + # Retry procedure if things don't work + for i in range(nRetry): + print(f" Mooring optimization attempt {i} was UNSUCCESFUL") + print(f" Message from dopt on attempt {i}: {infodict['message']}") + + self.updateDesign(X) # update the mooring using the optimized design variables + G = self.evaluateConstraints(X) # evaluate the constraints of the mooring + + # check how far the constraints are off + c_rel = G / np.array([con[2] for con in self.constraints], dtype=float) # get relative value of constraints (denominator converts dict values to a np.array) + i_kx = [i for i,con in enumerate(self.constraints) if con=='min_Kx'][0] # index of Kx constraint + + if display > 1: print(f' stiffness is {c_rel[i_kx]*100+100.:5.1f}% of target') + c_rel[i_kx] = 0.0 + # zero the kx constraint since it's okay to break it (that's why we iterate with LinearSystem) + + + + if np.min(c_rel) < -gtol: + + # try to catch some known problem cases + if stepfac==10: + print(' retrying optimization with step size (stepfac) boosted from 10 to 100') + stepfac = 100 + + else: + self.updateDesign(X) # make sure it's left at the optimized state + break # out of ideas, so that's the best we can do with this design problem + + # rerun the optimizer with modified settings + X, min_cost, infodict = dopt2(eval_func, X0, tol=0.001, a_max=1.4, maxIter=maxIter, + Xmin=Xmin, Xmax=Xmax, dX_last=dX_last) + + + else: # if successful + self.updateDesign(X) # make sure it's left at the optimized state + break # exit the retry loop + + if i==nRetry-1: # re-check the design if we did all retries, since otherwise it won't be done by the loop above + self.updateDesign(X) # update the mooring using the optimized design variables + G = self.evaluateConstraints(X, display=0) # evaluate the constraints of the mooring + + # check how far the constraints are off + #c_rel = G / np.fromiter(self.constraints.values(), dtype=float) # get relative value of constraints (denominator converts dict values to a np.array) + c_rel = G / np.array([con[2] for con in self.constraints], dtype=float) # get relative value of constraints (denominator converts dict values to a np.array) + i_kx = [i for i,con in enumerate(self.constraints) if con=='min_Kx'][0] # index of Kx constraint + if display > 1: print(f' stiffness is {c_rel[i_kx]*100+100.:5.1f}% of target') + c_rel[i_kx] = 0.0 + + + # --------- COBYLA method ----------- + elif method in ['COBYLA', 'SLSQP']: + + from scipy.optimize import minimize + + if self.display > 0: print("\n --- Beginning LineDesign2 optimize iterations using COBYLA ---") + + condict = [dict(type="ineq", fun=con) for con in self.conList] + cons_tuple = tuple(condict) + + if method=='COBYLA': + result = minimize(self.objectiveFun, X0, constraints=cons_tuple, + method="COBYLA", options={'maxiter':maxIter, + 'disp':True, 'rhobeg':0.1, 'catol':0.001}) # 'rhobeg':10.0 + + elif method=='SLSQP': + result = minimize(self.objectiveFun, X0, constraints=cons_tuple, + method='SLSQP', bounds = list(zip(Xmin, Xmax)), + options={'maxiter':maxIter, 'eps':0.02, + 'ftol':1e-6, 'disp': True, 'iprint': 99}) + + X = result.x + + + # --------- Bayesian method ----------- + elif method == 'bayesian': + + from bayes_opt import BayesianOptimization + from scipy.optimize import NonlinearConstraint + + if self.display > 0: print("\n --- Beginning LineDesign2 optimize iterations using Bayesian Optimization ---") + + # --- make list of decision variable names --- + # design parameter names [A or W0, L1, D1, ...] + ''' + param_names = [] + for i in range( (2+self.nX)//3): + param_names = param_names + [f'W{i}', f'L{i+1}', f'D{i+1}'] + if not self.shared: + param_names[0] = 'A' + ''' + dvnames = [str(i) for i in range(self.nX)] + + # --- set up constraints --- + def constraint_function(**kwargs): + + # Reconstruct decision variable vector + X = np.zeros(self.nX) + for i in range(self.nX): + X[i] = kwargs[dvnames[i]] + + # Call and evaluate each constraint? + G = self.evaluateConstraints(X, display=0) + + return G + + # Make constraint objects (valid values are from zero to infinity) + zeros = np.zeros(len(self.conList)) + constraint = NonlinearConstraint(constraint_function, zeros, zeros+np.inf) + + # Make objective function to maximize + def negativeObjectiveFun(**kwargs): + + # Reconstruct decision variable vector + X = np.zeros(self.nX) + for i in range(self.nX): + X[i] = kwargs[dvnames[i]] + + # Negative version of objective function + return -1*self.objectiveFun(X, display=0) + + # Bounded region of parameter space + pbounds = {} + for i in range(self.nX): + pbounds[dvnames[i]] = (Xmin[i], Xmax[i]) + + # Set up optimizer + optimizer = BayesianOptimization( + f=negativeObjectiveFun, constraint=constraint, + pbounds=pbounds, verbose=2, random_state=1) + + # Find some valid starting points using a random search + pts = 0 # how many valid starting points found so far + for i in range(1000): + x = optimizer.space.random_sample() + print(x) + if optimizer.constraint.allowed(optimizer.space.probe(x)[1]): + print('Registering start point') + print(x) + optimizer.space.probe(x) + pts += 1 + + if pts > 3: # <<< How many valid starting points to ensure + break + + # Do the optimization + optimizer.maximize( + init_points=4, # <<< Total number of start points before iterating + n_iter=maxIter) # <<< Number of points to iterate through + + print(optimizer.max) + + X = np.array(list(optimizer.max['params'].values())) + + + # ----- CMNGA ----- + elif method=='CMNGA': + from cmnga import cmnga # type: ignore + + bounds = np.array([[Xmin[i], Xmax[i]] for i in range(len(Xmin))]) + + X, min_cost, infoDict = cmnga(self.objectiveFun, bounds, self.conList, + dc=0.03, nIndivs=14, nRetry=500, maxGens=20, maxNindivs=600 ) + + + # --------- Genetic Algorithm ---------- + elif method=='GA': + + # import the GA from scipy to save from importing if other optimizers are selected + from scipy.optimize import differential_evolution, NonlinearConstraint + + # initialize storage variables for the GA, including an iterator variable to track through LineDesign + n = 100000 + self.XsGA = np.zeros([self.nX, n]) + self.CsGA = np.zeros([len(self.constraints), n]) + self.FsGA = np.zeros([3,n]) + + # initialize some GA parameters + self.popsize = 2 + self.maxiter = 40 + + self.popsize = 15 + self.maxiter = 1000 + + # bounds + bounds = [(Xmin[i], Xmax[i]) for i in range(len(Xmin))] + # constraints + constraints = tuple([ NonlinearConstraint(self.conList[i], 0, np.inf) for i in range(len(self.conList)) ]) + + # run the GA + result = differential_evolution(self.objectiveFun, bounds, maxiter=self.maxiter, + constraints=constraints, popsize=self.popsize, tol=0.1, disp=True, polish=False) + + # this doesn't require the initial design variable vector, it searches the whole design space initially + + # set the number of individuals in the population (for some reason, it means NP = popsize*N, where N is # of DV's) + if self.popsize==1: + self.NP = self.popsize*self.nX + 1 + else: + self.NP = self.popsize*self.nX + + # organize the stored variables better (trim the excess zeros) + XsGA = np.zeros([len(self.XsGA), len(np.trim_zeros(self.XsGA[0,:]))]) + for i in range(len(self.XsGA)): + XsGA[i,:] = np.trim_zeros(self.XsGA[i,:]) + self.XsGA = np.array(XsGA) + maxCsGA = 0 + for i in range(len(self.CsGA)): + if len(np.trim_zeros(self.CsGA[i,:])) > maxCsGA: + maxCsGA = len(np.trim_zeros(self.CsGA[i,:])) + CsGA = np.zeros([len(self.CsGA), maxCsGA]) + for i in range(len(self.CsGA)): + CsGA[i,:len(np.trim_zeros(self.CsGA[i,:]))] = np.trim_zeros(self.CsGA[i,:]) + self.CsGA = np.array(CsGA) + FsGA = np.zeros([len(self.FsGA),len(self.CsGA[0])]) + for i in range(len(FsGA)): + FsGA[i,:] = np.array(self.FsGA[i,0:len(self.CsGA[0])]) + self.FsGA = FsGA + + X = result.x + + + # --------- Particle Swarm Algorithm ---------- + elif method=='PSO': + + from pyswarm import pso + + xopt, fopt = pso(self.objectiveFun, Xmin, Xmax, f_ieqcons=self.getCons4PSO, + maxiter=50, swarmsize=1000, debug=True) + + # TODO: Either implement a change in the pyswarm.pso function to rerun if a feasible design isn't found in the first generation OR + # implement a try/except statement in updateDesign for when solveEquilibrium errors occur (which will happen if a feasible design isn't found) + + X = xopt + + + else: + raise Exception('Specified optimization method not recognized.') + + # make sure it's left at the optimized state + self.updateDesign(X) + + # save a couple extra metrics + #self.infodict['weight'] = -self.fB_L[2] + + # check whether optimization passed or failed based on constraint values + self.evaluateConstraints(X, display = 5) + if min(self.convals) > -0.01: + self.success = True + else: + self.success = False + + if plot: + self.plotOptimization() + + return X, self.cost #, infodict + + + def getCons4PSO(self, X): + conList = [] + for con in self.constraints: + conList.append(con['value']) + return conList + + + def plotOptimization(self, layout="tall", return_fig=False): + '''Plot the optimization trajectory, including design variables, constraints and cost. + + Parameters + ---------- + layout : str + "tall" (default) or "grid" layout for subplots. The grid will place all d.v.s in the first column, all constraints in the second column, and cost in the third column. + ''' + if len(self.log['x']) == 0: + print("No optimization trajectory saved (log is empty). Nothing to plot.") + return + + Xs = np.array(self.log['x']) + Fs = np.array(self.log['f']) + Gs = np.array(self.log['g']) + + n_dv = len(self.X0) + n_con = len(self.constraints) + + if layout=="tall": + n_rows = n_dv + n_con + 1 + fig, axes = plt.subplots(n_rows, 1, sharex=True, figsize=[6, 1.5*n_rows]) + axes = axes.reshape(-1, 1) + elif layout=="grid": + n_rows = max(n_dv, n_con, 1) + fig, axes = plt.subplots(n_rows, 3, sharex=True, figsize=[12, 1.5*n_rows]) + if n_rows == 1: + axes = axes[np.newaxis, :] + + # --- Column 1: Design variables --- + for i in range(n_dv): + ax = axes[i, 0] + ax.plot(Xs[:, i], color='blue') + ax.set_ylabel(f"d.v.{i+1} ({self.X0Units[i]})", rotation=90, fontsize=10, va='center') + + # --- Column 2 / stacked: Constraints --- + for i, con in enumerate(self.constraints): + idx = i if layout == "grid" else n_dv + i + ax = axes[idx, 1 if layout == "grid" else 0] + ax.axhline(0, color=[0.5,0.5,0.5]) + tol = 0.005 * (max(Gs[:, i])-min(Gs[:, i])) + color = 'green' if Gs[-1, i] >= -tol else 'red' + ax.plot(Gs[:, i], color=color) + if con['name']=='tension_safety_factor': + con_name = 'SF' + else: + con_name = con['name'] + ax.set_ylabel(f"{con_name} ({con['unit']})", + rotation=90, + va='center', fontsize=10) + # Show threshold value inside plot + ax.text(0.98, 0.90, f"{con['threshold']}", + transform=ax.transAxes, + va='top', ha='right', fontsize=8, color='black') + + # --- Column 3 / stacked: Cost --- + if layout == "grid": + ax_cost = axes[0, 2] + else: + ax_cost = axes[-1, 0] + ax_cost.plot(Fs/1e6, color='black') + ax_cost.set_ylabel('cost (M$)', rotation=90, va='center', fontsize=10) + + # remove unused axes if layout='grid' + if layout=="grid": + for i in range(n_dv, n_rows): + axes[i, 0].axis('off') + + for i in range(n_con, n_rows): + axes[i, 1].axis('off') + + for i in range(1, n_rows): + axes[i, 2].axis('off') + + # --- X labels only on bottom subplots --- + for i in range(n_rows): + for j in range(axes.shape[1]): + if axes[i, j].has_data(): + if layout == "tall": + if i == n_rows-1: # only bottom row + axes[i, j].set_xlabel("function evaluations") + else: + axes[i, j].set_xlabel("") + elif layout == "grid": + if (i == n_dv-1 and j == 0) or (i == n_con-1 and j == 1) or (i == 0 and j == 2): + axes[i, j].set_xlabel("function evaluations") + else: + axes[i, j].set_xlabel("") + + + plt.tight_layout() + + if return_fig: + return fig, axes + + def plotGA(self): + '''A function dedicated to plotting relevant GA outputs''' + + # determine how many "generations" the GA went through + gens = [0]; m=3 + gens.append(self.NP*m) + feasible=False + while len(gens) < self.maxiter+1: + while not feasible and len(gens) < self.maxiter+1: + m=2 + nextgen = gens[-1] + (self.NP*m) + gens.append(nextgen) + for f in self.FsGA[0,gens[-2]:nextgen]: + if f>0: + feasible=True + m=1 + if len(gens) < self.maxiter+1: + nextgen = gens[-1] + (self.NP*m) + gens.append(nextgen) + if len(gens) != self.maxiter+1: raise ValueError('Something is not right') + + + #Ls = self.allVars[1::3].tolist() + #Ds = self.allVars[2::3].tolist() + #Ws = self.allVars[3::3].tolist() + + # set the x-axis vector of each individual that was evaluated + iters = np.arange(1, self.iter+1 + 1, 1) + + # plot the change in design variables across each individual + chainL = [self.XsGA[0,i] for i in range(len(self.XsGA[0]))] + chainD = [self.XsGA[1,i] for i in range(len(self.XsGA[1]))] + #polyL = [self.XsGA[2,i] for i in range(len(self.XsGA[2]))] + #polyD = [self.XsGA[3,i] for i in range(len(self.XsGA[3]))] + + fig, ax = plt.subplots(2,1, sharex=True) + ax[0].plot(iters, chainL, label='chain') + #ax[0].plot(iters, polyL, label='polyester') + ax[1].plot(iters, chainD, label='chain') + #ax[1].plot(iters, polyD, label='polyester') + ax[1].set_xlabel('individual evaluated') + ax[1].set_ylabel('line diameter (mm)') + ax[0].set_ylabel('line length (m)') + ax[0].legend() + ax[1].legend() + #for i in range(len(gens)): + #ax[0].axvline(x=gens[i], color='k') + + # plot the change in each constraint of each individual across the optimization + Cnames = ['lay_length','rope_contact','offset','strength0','strength1'] + Cline = np.zeros_like(self.CsGA) + fig, ax = plt.subplots(len(self.CsGA), 1, sharex=True) + for i in range(len(self.CsGA)): + for j in range(len(self.CsGA[i])): + if self.CsGA[i,j] < -9000: + ax[i].plot(iters[j], 0, 'rx') + Cline[i,j] = 0 + else: + ax[i].plot(iters[j], self.CsGA[i,j], 'bo') + Cline[i,j] = self.CsGA[i,j] + ax[i].set_ylabel(f'{Cnames[i]}') + ax[i].plot(iters, Cline[i,:], 'g') + ax[i].plot(iters, np.zeros(len(iters)), 'r') + ax[-1].set_xlabel('individual evaluated') + + # plot the change in objective (cost) of each individual across the optimization + Fnames = ['Line Cost', 'Anchor Cost', 'Total Cost'] + fig, ax = plt.subplots(1, 1, sharex=True) + ax.plot(iters, self.FsGA[0,:], label='Line Cost') + ax.plot(iters, self.FsGA[1,:], label='Anchor Cost') + ax.plot(iters, self.FsGA[2,:], label='Total Cost') + ax.set_ylabel('Cost ($)') + ax.set_xlabel('individual evaluated') + ax.legend() + ''' + # to calculate all the iterations (individuals) that had all nonzero constraints + a=[] + for j in range(len(ld.CsGA[0])): + if np.all(ld.CsGA[:,j]>0): + a.append(j) + + # attempting to plot only the nonzero points on the plot + for i in range(len(ld.FsGA)): + for j in range(len(ld.FsGA[i])): + if ld.FsGA[i,j]==np.nan: + ld.FsGA[i,j] = None + ''' + + + + def storeGA(self, val, i, type='X', name='', index=0): + '''function to store the design variable vector, constraint values, and objective results for each iteration, based on self.iter, + where self.iter is updated every time updateDesign is called''' + + #if method=='GA': + if type=='X': + self.XsGA[:,i] = val + + elif type=='C': + confunnames = [self.confundict[con[0]].__name__ for con in self.constraints] + for c in range(len(np.unique(confunnames))): + if name==confunnames[c]: + self.CsGA[c+index,i] = val + + elif type=='F': + self.FsGA[:,i] = val + + + """ + def checkGA(self, type='normal'): + '''function to check the feasibility of a design, mostly used in a GA, to ensure that LineDesign can even run it. + More specifically, if the GA comes up with a design with sum of line lengths longer than span+depth, it will return False''' + + total_linelength = sum([self.ss.lineList[i].L for i in range(self.nLines)]) + + if type=='normal': + Lmax0 = self.span-self.rBFair[0] + self.depth+self.rBFair[2] # maximum possible line length allowable in equilibrium position + if total_linelength > Lmax0: + return False + else: + return True + + elif type=='offset': + Lmax1 = self.span-self.rBFair[0]-self.x_mean_out-self.x_ampl + self.depth+self.rBFair[2] # maximum possible line length allowable in offset position + if total_linelength > Lmax1: + return False + else: + return True + """ + + + # :::::::::: solver functions :::::::::: + + # the original function from LineDesign, for tuning the line's horizontal tension + def func_TH_L(self, Xl, args): + '''Apply specified section L, return the horizontal pretension error.''' + self.setSectionLength(Xl[0], self.iL) + # option to setOffset? + self.ss.staticSolve() + if args['direction']=='horizontal': + Fx = abs(self.ss.fB_L[0]) # horizontal fairlead tension + elif args['direction']=='norm': + Fx = np.linalg.norm(self.ss.fB_L) + + return np.array([Fx - self.fx_target]), dict(status=1) , False + + + def func_kH_L(self, Xl, args): + '''Apply specified section L, return the horizontal stiffness error.''' + self.ss.lineList[self.iL].setL(Xl[0]) + # option to setOffset? + self.staticSolve() + Kx = self.KB_L[0,0] # horizontal inline stiffness + + return np.array([Kx - self.kx_target]), dict(status=1) , False + + + def func_fx_L(self, Xl, args): + '''Apply specified section L, return the Fx error when system is offset.''' + '''Function for solving line length that achieves equilibrium at a specified offset. + Expects xOffset, fx_target, and angles as keys in args dictionary. + Receives line length and returns net force at xOffset.''' + + if self.ms: + for ss in self.ms.lineList: + ss.lineList[self.iL].setL(Xl[0]) + self.ms.bodyList[0].setPosition([args['xOffset'], 0,0,0,0,0]) + self.ms.solveEquilibrium() + Fx = -self.ms.bodyList[0].getForces()[0] + else: + self.ss.lineList[self.iL].setL(Xl[0]) + self.ss.setOffset(args['xOffset']) + Fx = np.abs(self.ss.fB_L[0]) # horizontal fairlead tension. + + if 'display' in args: + if args['display'] > 2: + print(f" Xl is {Xl[0]:6.3f} and Fx is {Fx/1e3:10.0f} kN so error is {(Fx+self.fx_target)/1e3:8.0f} kN") + + return np.array([Fx - self.fx_target]), dict(status=1), False + + + def func_kx_L(self, Xl, args): # evaluate how close the system horizontal stiffness is compared to the kx_target + + for ss in self.ms.lineList: # go through each Subsystem + ss.lineList[self.iL].setL(Xl[0]) # update the section length + + # option to setOffset? + self.ms.bodyList[0].setPosition([0, 0,0,0,0,0]) # apply offset + self.ms.solveEquilibrium() + Kx = self.ms.getCoupledStiffness()[0,0] # mooring system stiffness in x + + if 'display' in args: + if args['display'] > 1: + print(f" Xl is {Xl[0]:6.3f} and Kx is {Kx/1e3:10.0f} kN/m so error is {(Kx+self.kx_target)/1e3:8.0f} kN/m") + + return np.array([Kx - self.kx_target]), dict(status=1), False + + + + def func_fx_x(self, X, args): + + self.ms.bodyList[0].setPosition([X[0], 0,0,0,0,0]) # apply offset + self.ms.solveEquilibrium() + FxMoorings = self.ms.bodyList[0].getForces()[0] # net mooring force in x + FxApplied = args['FxApplied'] + + return np.array([FxApplied + FxMoorings]), dict(status=1), False + + + + def step_fx_x(self, X, args, Y, oths, Ytarget, err, tols, iter, maxIter): + ''' this now assumes tols passed in is a vector''' + + FxMoorings = self.ms.bodyList[0].getForces()[0] # net mooring force in x + FxApplied = args['FxApplied'] + + dY = FxApplied + FxMoorings + + Kx = self.ms.bodyList[0].getStiffnessA(lines_only=True)[0,0] + + if Kx > 0: + dX = dY/Kx + + else: # backup case, just move 10 m + + dX = np.sign(dY)*10 + + return np.array([dX]) + + + + + def setAnchoringRadius(self, a): + '''Sets the anchoring radius, including of any LineDesign MoorPy + System. Input is the anchoring radius from platform centerline [m]. + ''' + + if a < 0: + raise Exception("The value passed to setAnchoringRadius must be positive.") + + self.rad_anch = float(a) + + self.dd['span'] = self.rad_anch - self.rBFair[0] + self.ss.span = float(self.dd['span']) + + self.ss.setEndPosition([-self.rad_anch, 0, -self.depth], endB=False) + + # Now handle the MoorPy system, if there is one, moving the anchor points + if self.ms: + for i, heading in enumerate(self.headings): + rotMat = rotationMatrix(0, 0, np.radians(heading)) + self.ms.pointList[2*i].setPosition(np.matmul(rotMat, [self.rad_anch, 0, -self.depth])) + + # set subsystem span if needed... <<< + self.ms.lineList[i].span = float(self.dd['span']) + + def setSectionLength(self, L, i): + '''Sets the length of a section, including in the MoorPy System if there + is one. Overrides Mooring.setSectionLength''' + + # First call the Mooring version of this method, which handles the subsystem + Mooring.setSectionLength(self, L, i) + + # Now handle the MoorPy system, if there is one + if self.ms: + for ss in self.ms.lineList: + ss.lineList[i].setL(L) + + + # ::::::::::::::::::::::::::::::: constraint functions ::::::::::::::::::::::::::::::: + + # Each should return a scalar C where C >= 0 is valid and C < 0 is violated. + + def con_Kx(self, X, index, value, display=0): + '''This ensures Kx, the effective horizontal stiffness, is greater than a given value. + Note: this constraint doesn't use the index input.''' + + Kx = self.ss.KB_L[0,0] # get effective horizontal stiffness at current/undisplaced position + c = Kx - value + + return c + + + def con_total_length(self, X, index, value): + '''This ensures that the total length of the Mooring does not result in a fully slack Mooring + (ProfileType=4) in its negative extreme mean position''' + # ['max_line_length', index, value] # index and value are completely arbitrary right now + + Lmax = 0.95*(self.ss.span + self.depth+self.rBFair[2]) + + total_linelength = sum([self.ss.lineList[i].L for i in range(self.nLines)]) + c = Lmax-total_linelength + + return c + + # ----- offset constraints ----- + + def getOffset(self, FxApplied, headings=[]): + '''Computes the horizontal offset of the body in response to an + applied horizontal force, considering all mooring lines, by solving + for offset at which mooring reaction force equals FxApplied.''' + + # Ensure everything is switched back to status stiffnesses + self.ms.revertToStaticStiffness() + + # Solve for the surge offset that matches the applied force + ''' + x, y, info = dsolve2(self.func_fx_x, [0], step_func=self.step_fx_x, + args=dict(FxApplied=FxApplied, + heading=headings), tol=[0.01], Xmin=[-1e5], + Xmax=[1e5], dX_last=[10], stepfac=4, display=0) + + return x[0] + ''' + self.ms.bodyList[0].f6Ext = np.array([FxApplied, 0,0, 0,0,0]) + self.ms.solveEquilibrium(DOFtype='both') + #offset = self.ms.bodyList[0].r6[0] + #self.ms.bodyList[0].f6Ext = [0,0,0,0,0,0] + #self.ms.bodyList[0].setPosition([0,0,0,0,0,0]) + #self.ms.solveEquilibrium() + #return offset + return self.ms.bodyList[0].r6[0] + + + def con_offset0(self, X, index, value): + '''This ensures that the system does not offset by a certain amount in its unloaded position''' + + # placeholder, this method may not make sense as-is + return value - self.getOffset(0) + + + def con_offset(self, X, index, value): + '''This ensures that the system does not offset by a certain amount in its fully loaded position''' + + return value - abs(self.x_mean_eval) + + # ----- lay length constraints ----- + + def con_lay_length(self, X, index, threshold, display=0): + '''This ensures there is a minimum amount of line on the seabed at the +extreme displaced position.''' + return self.ss.getLayLength(iLine=index) - threshold + self.ss.LayLen_adj + + def con_max_td_range(self, X, index, threshold, display=0): + '''Ensures the range of motion of the touchdown point betweeen the + range of offsets is less then a certain distance. + This constraint is for the system as a whole (index is ignored) and + must have offset='other' so that it's evaluated at the end.''' + return threshold - (self.max_lay_length - self.min_lay_length) + + + # ----- rope contact constraints ----- + + def con_rope_contact(self, X, index, threshold, display=0): + '''Ensures the first line node doesn't touch the seabed by some + minimum clearance.''' + + return self.ss.getPointHeight(index) - threshold # compute the constraint value + + + # ----- strength constraints ----- + + def con_strength(self, X, index, threshold, display=0): + '''This ensures the MBL of the line is always greater than the maximum + tension the line feels times a safety factor.''' + return self.ss.getTenSF(index) - threshold + + def con_min_tension(self, X, index, threshold, display = 0): + '''Ensures that the minimum line tension is above a threshold''' + return self.ss.getMinTen(index) - threshold + + def con_curvature(self, X, index, threshold, display=0): + '''Ensure that the MBR of the cable is always greater than the maximum + actual curvature times a safety factor.''' + return self.ss.getCurvSF(index) - threshold + + + def getDamage(self, index, display=0): + ''' method to predict fatigue damage based on previous iteration''' + + damage = self.damage + + if sum(damage) == 0: + raise ValueError("Fatigue damage from previous iteration was not provided") + + + sumdamage = 0 + + + #fatigue_headings are loading direction for fatigue dynamic factor calculation. must match order of damage in self.damage + for i, ang in enumerate(self.fatigue_headings): + + #apply fx_target at direction in fatigue_headings + self.ms.bodyList[0].f6Ext = np.array([self.fx_target*np.cos(np.radians(ang)), self.fx_target*np.sin(np.radians(ang)),0, 0,0,0]) + self.ms.solveEquilibrium(DOFtype='both') + + #store offset + offsetx = self.ms.bodyList[0].r6[0] + offsety = self.ms.bodyList[0].r6[1] + + #tension 1 + Ten1 = max(np.linalg.norm(self.ms.lineList[self.ms_fatigue_index].lineList[index].fA),np.linalg.norm(self.ms.lineList[self.ms_fatigue_index].lineList[index].fB)) + + #set force back to zero + self.ms.bodyList[0].f6Ext = [0,0,0,0,0,0] + + #add dx to previous offset to get dtdx (slope of tension-displacement curve) + dx = 0.5 + self.ms.bodyList[0].setPosition(np.array([offsetx + dx*np.cos(np.radians(ang)),offsety+dx*np.sin(np.radians(ang)),0,0,0,0])) # move the body by the change in distance + self.ms.solveEquilibrium() + + #tension 1 + Ten2 = max(np.linalg.norm(self.ms.lineList[self.ms_fatigue_index].lineList[index].fA),np.linalg.norm(self.ms.lineList[self.ms_fatigue_index].lineList[index].fB)) + + #slope of tension-displacement curve at fx_target applied at ang + dTdx = (Ten2 - Ten1)/dx + + #ratio is based on fatigue damage equation (Tension/MBL)^m, where m = 3 for chain + MBL_corroded = self.ms.lineList[self.ms_fatigue_index].lineList[index].type['MBL'] * ( (self.ms.lineList[self.ms_fatigue_index].lineList[index].type['d_nom'] - (self.corrosion_mm/1000)) / self.ms.lineList[self.ms_fatigue_index].lineList[index].type['d_nom'] )**2 + ratio = (dTdx/ MBL_corroded)**3 + + #ratio is multipled by the inputted previous iteration damage*MBL1/dTdx1 + sumdamage = sumdamage + ratio * damage[i] + + + return sumdamage + + + def con_damage(self, X, index, threshold, display=0): + '''constraint method to ensure the scaled fatigue damage meets required fatigue damage''' + + return threshold - self.getDamage(index, display=display) + + + def getYawStiffness(self, x_offset, display=0): + '''method to calculate the yaw stiffness of the whole mooring system using an analytical equation''' + + yawstiff = 0 + # calculate stiffness in different situations + for i, ang in enumerate(self.headings): + spacing_x = self.span*np.cos(np.radians(ang)) - x_offset # x distance from offset fairlead to anchor point + spacing_y = self.span*np.sin(np.radians(ang)) # y distance from offset fairlead to anchor point + spacing_xy= np.linalg.norm([spacing_x, spacing_y]) # radial distance from offset fairlead to anchor point + self.setPosition(spacing_xy-self.span) + tau0 = self.ss.fB_L[0] # calculate the horizontal tension on the body from the 1 line + + # analytic equation for yaw stiffness for each mooring line heading + yawstiff += (-tau0/spacing_xy)*self.ss.rBFair[0]**2 + -tau0*self.ss.rBFair[0] + + self.ss.setOffset(0) # restore to zero offset and static EA + + return yawstiff + + + def con_yaw_stiffness0(self, X, value, display=0): + '''constraint method to ensure the yaw stiffness of the mooring system represented by this line design meets a certain yaw stiffness requirement, + quasi-statically, and in the undisplaced position''' + + c = self.getYawStiffness(x_offset=0, display=display) - value # compute the constraint value + + return c + + def con_yaw_stiffness(self, X, index, value, display=0): + '''constraint method to ensure the yaw stiffness of the mooring system represented by this line design meets a certain yaw stiffness requirement, + quasi-statically, and in the extreme displaced position''' + + try: + bodyPosition = np.array([-self.x_mean_in-self.x_ampl, 0,0,0,0,0]) + c = self.getYawStiffness(x_offset=bodyPosition[0], display=display) - value # compute the constraint value + + except Exception as e: + if self.noFail: + c = -60000 + else: + raise(e) + + return c + + + # ----- shared line sag constraints ----- + + def con_min_sag(self, X, index, threshold, display=0): + '''Ensure the lowest point of a line section is below + a minimum depth.''' + return threshold - self.ss.getSag(index) + + def con_max_sag(self, X, index, threshold, display=0): + '''Ensures the lowest point of a line section is above + a certain maximum depth.''' + return self.ss.getSag(index) - threshold + + # ----- angle constraints ----- + def con_min_angle(self, X, index, threshold, display=0): + '''Ensure the angle of a line section is above a minimum value.''' + return self.ss.getAng(index) - threshold + + def con_max_angle(self, X, index, threshold, display=0): + '''Ensure the angle of a line section is below a maximum value.''' + return threshold - self.ss.getAng(index) + + # ----- utility functions ----- + + def plotProfile(self, Xuvec=[1,0,0], Yuvec=[0,0,1], ax=None, color=None, title="", slack=False, displaced=True, figsize=(6,4), label=None): + '''Plot the mooring profile in undisplaced and extreme displaced positions + + Parameters + ---------- + Xuvec : list, optional + plane at which the x-axis is desired. The default is [1,0,0]. + Yuvec : lsit, optional + plane at which the y-axis is desired. The default is [0,0,1]. + ax : axes, optional + Plot on an existing set of axes + color : string, optional + Some way to control the color of the plot ... TBD <<< + title : string, optional + A title of the plot. The default is "". + slack : bool, optional + If false, equal axis aspect ratios are not enforced to allow compatibility in subplots with axis constraints. + dispalced : bool, optional + If true (default), displaced line profiles are also plotted. + + Returns + ------- + fig : figure object + To hold the axes of the plot + ax: axis object + To hold the points and drawing of the plot + + ''' + + # if axes not passed in, make a new figure + if ax == None: + fig, ax = plt.subplots(1,1, figsize=figsize) + ax.set_xlabel('Horizontal distance (m)') + ax.set_ylabel('Depth (m)') + else: + fig = plt.gcf() # will this work like this? <<< + + + if displaced: + offsets = [0, self.x_mean_out+self.x_ampl, -self.x_mean_in-self.x_ampl] + else: + offsets = [0] + + for x in offsets: + + alph = 1 if x==0 else 0.5 # make semi-transparent for offset profiles + + self.ss.setOffset(x) + + #ax.plot(self.rB[0], self.rB[2],'ko',markersize = 2) # fairlead location + ax.plot(x, 0,'ko',markersize = 2) # platform ref point location + # self.ss.drawLine2d(0,ax) + for i, line in enumerate(self.ss.lineList): + if i != 0: + label = None + if color==None: # alternate colors so the segments are visible + if line.type['material'][0]=='c': + line.drawLine2d(0, ax, color=[.1, 0, 0], alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec, Xoff=-self.rad_fair, label=label) + if self.shared==1: # plot other half too if it's a shared line where only half is modeled <<< + line.drawLine2d(0, ax, color=[.1, 0, 0], alpha=alph, Xuvec=-np.array(Xuvec), Yuvec=Yuvec, Xoff=-self.span-self.rad_fair, label=label) + elif 'nylon' in line.type['material']: + line.drawLine2d(0, ax, color=[.8,.8,.2], alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec,Xoff=-self.rad_fair, label=label) + else: + line.drawLine2d(0, ax, color=[.3,.5,.5], alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec,Xoff=-self.rad_fair, label=label) + if self.shared==1: # plot other half too if it's a shared line where only half is modeled <<< + line.drawLine2d(0, ax, color=[.3,.5,.5], alpha=alph, Xuvec=-np.array(Xuvec), Yuvec=Yuvec, Xoff=-self.span-self.rad_fair, label=label) + else: + line.drawLine2d(0, ax, color=color, alpha=alph, Xuvec=Xuvec, Yuvec=Yuvec,Xoff=-self.rad_fair, label=label) + if self.shared==1: # plot other half too if it's a shared line where only half is modeled <<< + line.drawLine2d(0, ax, color=color, alpha=alph, Xuvec=-np.array(Xuvec), Yuvec=Yuvec, Xoff=-self.span-self.rad_fair, label=label) + + ''' + # plot points/weights/floats along the line >>> needs to be updated to account for Xuvec and Yuvec <<< + for point in self.pointList: + if point.number > 1 and point.number < self.nLines+1: + if point.v > 0: + ax.plot(point.r[0],point.r[2],'yo',markersize=5) + elif point.m > 0: + ax.plot(point.r[0],point.r[2],'ko',markersize=5) + else: + ax.plot(point.r[0],point.r[2],'bo',markersize=1) + ''' + + # make legend entries available + if displaced: + if not color==None: + ax.plot(np.nan, np.nan, color=color, alpha=1, label="undisplaced") + ax.plot(np.nan, np.nan, color=color, alpha=0.5, label="displaced") + + #ax.plot([self.ss.lineList[0].rA[0], 0], [-self.depth, -self.depth], color='k') + # only force equal aspect ratio if "slack" keyword isn't specified (so that sharex=True, sharey-True plots are possible) + if not slack: + ax.axis("equal") + + ax.set_title(title) + #ax.set_ylim(-1,1) + + self.ss.setOffset(0) # return to undisplaced position + self.ss.solveEquilibrium(tol=self.ss.eqtol) + + return fig, ax # return the figure and axis object in case it will be used later to update the plot + + + def plotCurves(self, ax=[], color="k", title=""): + '''Plot key performance curves for the mooring as a function of offset + + Parameters + ---------- + ax : axes, optional + Plot on an existing set of axes + title : string, optional + A title of the plot. The default is "". + + Returns + ------- + fig : figure object + To hold the axes of the plot + ax: axis object + To hold the points and drawing of the plot + + ''' + + # if axes not passed in, make a new figure + if len(ax) == 0: + fig, ax = plt.subplots(2,1, sharex=True) + newFig=True + else: + if not len(ax) == 2: + raise Exception("ax provided to plotCurves must be a list of 2 axes.") + fig = plt.gcf() + newFig = False + + x = np.linspace(-self.x_mean_in-self.x_ampl, self.x_mean_out+self.x_ampl, 50) + + Fx = np.zeros(len(x)) + Ts = np.zeros([len(x), len(self.ss.lineList)]) + + # calculate values at each offset point + for i in range(len(x)): # go through each offset point + + self.ss.setOffset(x[i]) # offset the desired amount + + Fx[i] = self.ss.fB_L[0] # get horizontal mooring force + + for j in range(len(self.ss.lineList)): # get upper end tension of each line segment + Ts[i,j] = self.ss.lineList[j].TB + + # plots + ax[0].plot(x, -Fx/1e3, c=color) + + for j in range(len(self.ss.lineList)): + ax[1].plot(x, Ts[:,j]/1e3, c=color, dashes=[5-0.5*j, 0.5*j], label=f"segment {j+1}") + + ax[0].set_ylabel("Fx (kN)") + ax[1].set_ylabel("Tension (kN)") + if newFig: ax[1].legend() + ax[1].set_xlabel("Offset (m)") + #fig.set_title(title) + + self.ss.setOffset(0) # restore to undisplaced position + + return fig, ax # return the figure and axis object in case it will be used later to update the plot + + + def dump(self): + '''Puts info about the mooring into a dictionary and returns it.''' + + self.objectiveFun([]) # ensure things are calculated + + info = dict(arrangement={}, design={}, performance={}, cost={}) # the dictionary and its top-level entries + + info['arrangement']['name'] = self.name + ''' + info['design']['X' ] = self.Xlast # design variables + info['design']['Gdict' ] = self.evaluateConstraints([])[1] # dict of constraint names and values of evaluated constraint functions + info['design']['Ls' ] = [line.L for line in self.ss.lineList] # length of each segment + info['design']['Ds' ] = [line.type['input_d'] for line in self.ss.lineList] # *input* diameter of each segment + info['design']['lineTypes' ] = [line.type['name'] for line in self.ss.lineList] # line type of each segment (may be redundant with what's in arrangement) + info['design']['anchorType'] = self.anchorType # (may be redundant with what's in arrangement) + info['design']['span' ] = self.span # platform-platform of platfom-anchor horizontal span just in case it's changed + info['design']['Ltot' ] = sum([line.L for line in self.ss.lineList]) # total mooring length + + info['performance']['Fx'] = self.fB_L[0] + info['performance']['Kx'] = self.bodyList[0].getStiffness(tol=self.eqtol)[0,0] + + info['cost']['total' ] = self.cost + info['cost']['line' ] = self.lineCost + if not self.shared: + info['cost']['anchor' ] = self.anchorMatCost + info['cost']['install'] = self.anchorInstCost # eventually should sort out if this represents the total installation cost + info['cost']['decom' ] = self.anchorDecomCost + ''' + + # this version converts out of numpy format for yaml export (should make a better system for this) + info['design']['X' ] = self.Xlast.tolist() # design variables + #info['design']['Gdict' ] = self.evaluateConstraints([])[1] # dict of constraint names and values of evaluated constraint functions + info['design']['Ls' ] = [float(line.L ) for line in self.ss.lineList] # length of each segment + info['design']['Ds' ] = [float(line.type['d_nom']) for line in self.ss.lineList] # *input* diameter of each segment + info['design']['lineTypes' ] = [str(line.type['material']) for line in self.ss.lineList] # line type of each segment (may be redundant with what's in arrangement) + info['design']['anchorType'] = self.anchorType # (may be redundant with what's in arrangement) + info['design']['span' ] = float(self.span) # platform-platform of platfom-anchor horizontal span just in case it's changed + info['design']['Ltot' ] = float(sum([line.L for line in self.ss.lineList])) # total mooring length + + info['performance']['Fx'] = float(self.fB_L[0] ) + info['performance']['Kx'] = float(self.KB_L[0,0]) + + info['cost']['total' ] = float(self.cost ) + info['cost']['line' ] = float(self.lineCost) + if not self.shared==1: + info['cost']['anchor' ] = float(self.anchorMatCost ) + info['cost']['install'] = float(self.anchorInstCost ) # eventually should sort out if this represents the total installation cost + info['cost']['decom' ] = float(self.anchorDecomCost) + + + return info + + + def adjustConstraint(self, key, value): + '''Modifies the value of an existing constraint''' + for con in self.constraints: + if con[0] == key: + con[2] = value + + @staticmethod + def getClumpMV(weight, rho=1025.0, g=9.81, **kwargs): + + '''A function to provide a consistent scheme for converting a clump weight/float magnitude to the + mass and volume to use in a MoorPy Point.''' + + if weight >= 0: # if the top point of the intermediate line has a clump weight + pointvol = 0.0 + pointmass = weight*1000.0 # input variables are in units of tons (1000 kg), convert to kg + else: + pointvol = -weight*1200.0/rho # input variables are still in tons. Assume additional 20% of BM mass + pointmass = -weight*200.0 + + return dict(m=pointmass, v=pointvol) + + + +if __name__ == '__main__': + + # Example case from Stein + ''' + settings = {} + settings['rBFair'] = [58,0,-14] + settings['x_ampl'] = 10 # xmax value is designed to be the "target" offset, used for solve_for = 'tension' + settings['fx_target'] = 1.95e6 + settings['solve_for'] = 'none' + settings['headings'] = [60, 180, 300] + + settings['name'] = 'chain-poly-chain' + settings['lineTypeNames'] = ['chain','polyester','chain'] + settings['anchorType'] = 'suction' + settings['allVars'] = [1000/10, 100, 120, 0, 800, 200, 0, 100, 120] + settings['Xindices'] = ['c', 0, 'c', 'c', 1, 2, 'c', 'c', 'c'] + settings['Xmin'] = [10, 10, 10] + settings['Xmax'] = [500, 10000, 500] + settings['dX_last'] = [10, 10, 10] + + settings['constraints'] = [dict(name='rope_contact' , index=1, threshold=5 , offset='min'), + dict(name='max_offset' , index=0, threshold=60, offset='max')] + + for j in range(len(settings['lineTypeNames'])): + settings['constraints'].append(dict(name='tension_safety_factor', index=j, threshold=2.0, offset='max')) + + depth = 766.765 + ld = LineDesign(depth, **settings) + + ld.setNormalization() # turn on normalization (important for COBYLA etc) + + start_time = time.time() + #X, min_cost = ld.optimize(maxIter=12, plot=False, display=3, stepfac=4, method='dopt') + X, min_cost = ld.optimize(maxIter=10, plot=True, display=3, stepfac=4, method='COBYLA') + print("optimize time: {:8.2f} seconds".format((time.time() - start_time))) + ld.objectiveFun(X, display=2) + ld.evaluateConstraints(X, display=0) + ld.updateDesign(X, display=0) + ld.plotProfile() + plt.show() + ''' + + + depth = 200 + + settings = {} + settings['rBFair'] = [58,0,-14] + settings['x_ampl'] = 10 + settings['fx_target'] = 1.95e6 + settings['headings'] = [60, 180, 300] + + settings['solve_for'] = 'none' + #settings['solve_for'] = 'ghost' + + settings['name'] = 'DEA-chain-poly' # <<< semitaut option + settings['lineTypeNames'] = ['chain','polyester'] + settings['anchorType'] = 'drag-embedment' + ''' + settings['allVars'] = [800/10, 400, 120, 0, 400, 200,] + settings['Xindices'] = ['c', 0, 1, 'c', 2, 3] + settings['Xmin'] = [10, 10, 10, 10] + settings['Xmax'] = [10000, 500, 800, 500] + settings['dX_last'] = [10, 10, 10, 10] + ''' + settings['allVars'] = [1000/10, 800, 120, 0, 80, 200,] + settings['Xindices'] = ['c', 0, 1, 'c', 'c', 2] + settings['Xmin'] = [400, 10, 10] + settings['Xmax'] = [2000, 500, 500] + settings['dX_last'] = [10, 10, 10] + + ''' + settings['name'] = 'DEA-chain' # <<< catenary option + settings['lineTypeNames'] = ['chain'] + settings['anchorType'] = 'drag-embedment' + settings['lay_target'] = 200 + settings['allVars'] = [1000/10, 1000, 120] + settings['Xindices'] = ['c', 0, 1] + settings['Xmin'] = [500, 50] + settings['Xmax'] = [1500, 300] + settings['dX_last'] = [10, 10] + + + #settings['solve_for'] = 'offset' + settings['solve_for'] = 'tension' + settings['Xindices'] = ['c', 's', 0] + settings['Xmin'] = [10] + settings['Xmax'] = [500] + settings['dX_last'] = [10] + settings['x_target'] = 34.560922734165835 + settings['x_mean_out'] = 34.560922734165835 + settings['x_mean_in'] = 60 + ''' + settings['constraints'] = [dict(name='min_lay_length', index=0, threshold=20, offset='max'), + dict(name='max_offset' , index=0, threshold=25, offset='max'), + dict(name='rope_contact' , index=1, threshold=5 , offset='min')] + + for j in range(len(settings['lineTypeNames'])): + settings['constraints'].append(dict(name='tension_safety_factor', index=j, threshold=2.0, offset='max')) + + + + + ld = LineDesign(depth, **settings) + + ld.setNormalization() # turn on normalization (important for COBYLA etc) + + start_time = time.time() + #X, min_cost = ld.optimize(maxIter=20, plot=False, display=3, stepfac=4, method='dopt') + #X, min_cost = ld.optimize(maxIter=40, plot=True, display=3, stepfac=4, method='COBYLA') + #X, min_cost = ld.optimize(maxIter=40, plot=True, display=3, stepfac=4, method='CMNGA') + #X, min_cost = ld.optimize(maxIter=40, plot=True, display=3, stepfac=4, method='PSO') + X, min_cost = ld.optimize(maxIter=40, plot=True, display=0, stepfac=4, method='bayesian') + + print('') + print('Analyzing Results:') + print( " optimize time: {:8.2f} seconds".format((time.time() - start_time))) + print( ' design variables (normalized): ', [f"{x:8.3f}" for x in X]) + print( ' design variables (denormalized): ', [f"{x:8.2f}" for x in X*ld.X_denorm]) + print(f' solved line length: {ld.ss.lineList[ld.iL].L:8.2f} m') + print('') + + ld.objectiveFun(X, display=2) + ld.evaluateConstraints(X, display=2) + ld.updateDesign(X, display=0) + ld.plotProfile() + plt.show() + + a = 2 diff --git a/famodel/design/LinearSystem.py b/famodel/design/LinearSystem.py new file mode 100644 index 00000000..e150eb90 --- /dev/null +++ b/famodel/design/LinearSystem.py @@ -0,0 +1,2083 @@ +import moorpy as mp # type: ignore +import fadesign.MoorSolve as msolve +import numpy as np +import scipy +import matplotlib as mpl +import matplotlib.pyplot as plt +import fadesign.conceptual.graph_helpers as gh +# Old shared moorings linear modeling code from 2021 / updated in 2024 by Rudy Alkarem + +def getUnitAndLength( rA, rB ): + + dr = rB-rA + l = np.linalg.norm(dr) + u = dr/l + + return u, l + + +class LinearSystem(): + '''2D array representation with linear mooring properties for optimization. + + + some conventions/flags: + - shared (formerly profile) + - net: False for normal/shared line; True for >2 interconnected lines + + + ''' + + + def __init__(self, coords, intraMat, nPtfm, interMats=[], + interCoords=None, inits=None, profileMap=None, intersectZ=None, + rFair=0, zFair=0, depth=600., fmax=1e6, xmax=40.0, plots=0, nonlin=1.0, + center=True, old_mode=True): + '''Formerly makeSimpleSystem in Array.py, this sets up a LinearSystem + from a coordinates list and adjacency matrix. + + Parameters + ---------- + coords : 2D list + intra-cell coordinates + intraMat : 2D array + Adjacency matrix for intra-cell lines (within)... + nPtfm : int + Number of intra-cell platforms or turbines + interMats : list of 2D arrays + Adjacency matrix for inter-cell lines (between)... + interCoords : list of 2D lists + inter-cell spacing (coordinates of center of neighboring cell w.r.t. the center of the unit cell) + inits: dictionary + initial tension and stiffness values and hybrid line-related characteristics. + In the "old_mode", this requires mooring groups to be provided with the following: + - tenA # [N] Initial tension(Anchored) + - tenS # [N] Initial tension(Shared) + - klA # [N/m] Initial stiffness (Anchored) + - klS # [N/m] Initial stiffness (Shared) + - tenTen # [N] Initial tendon tension (Hybrid) + - w + In the newer more general mode, this requires mooring groups to be provided with: + - kl + - kt + - + profileMap: 2D list + allows the user to manually define the profiles of each mooring group (0: anchored, 1: shared, 2: hybrid) + intersectZ: 2D list + depth of midpoint for all anchors (if the z value is not zero, it is a hybrid system) + rFair : float + Radius of fairleads from platform centerline (just a uniform value for now..) + zFair : float + Z elevation of fairleads (just a uniform value for now..) + depth : float, optional + Depth of the water in the system. The default is 600 + nonlin : float + A multiplier to compensate for nonlinearitize in a real mooring system. 1: linear, >1: add some margin from the watch circles + center : bool + If true, the system will be shifted so that its center is at 0,0 + old_mode : bool + If true (default) functions the old way with assumed weight-tension + -stiffness relations. If False, uses a more generic approach. + ''' + + self.old_mode = bool(old_mode) + + # key array layout info + self.coords = np.array(coords) + self.intraMat = np.array(intraMat) + self.interMats = np.array(interMats) + self.nPtfm = nPtfm + if np.any(intersectZ==None): + self.intersectZ = np.zeros(len(coords)) + else: + self.intersectZ = intersectZ + self.interCoords = interCoords + if inits: + self.inits = inits + + self.profileMap = profileMap + # lists of objects to be created + #self.bodyList = [] # one for each FOWT + #self.pointList = [] # for each anchor and also each attachment point (on the FOWT bodies) + self.mooringGroups = [] # a list of dictionaries for the linear properties of each mooring group in the array + + # arrays for each actual mooring line (not groups), i.e. each anchor or shared line + self.u = [] # list of line unit vectors + self.l = [] # list of line horiztonal spacings + self.group = [] # line group index + self.endA = [] # id of what the line is connected to (this corresponds to the row or column index in the adjacency matrix) + self.endB = [] + self.rA = [] # coordinates of the connected lines at end A + self.rB = [] # coordinates of the connected lines at end B + self.angA = [] # offset angles (deg about yaw axis) of fairlead attachment on platform [deg] + self.angB = [] + self.boundary = [] # a boolean to check if the line is a boundary (inter-cell) line + # parameters + self.depth = depth + self.fmax = fmax + + self.xmax = xmax # max offset (watch circle radius) constraint + self.nonlin = nonlin + self.angles = np.arange(0,360, 15) # wind angles to consider + + self.anchor_cost_factor = 1.0 # this factor is used to scale the cost of anchor lines (whereas shared line costs aren't scaled). Can be adjusted externally. + + + # shift the coordinates of all the bodies and anchors so that the center of the plot is ideally at (0,0) + if center: + cx = np.mean([self.coords[i,0] for i in range(len(self.coords))]) + cy = np.mean([self.coords[i,1] for i in range(len(self.coords))]) + #mss.transform(trans=[-cx, -cy]) + self.coords = self.coords - [cx,cy] + + self.center = [np.mean([self.coords[i,0] for i in range(len(self.coords))]), + np.mean([self.coords[i,1] for i in range(len(self.coords))])] + + # also add some generic line types (which map to LineDesigns), + # one for each line grouping as defined in the entries of intraMat + maxGroupNumber = np.max(intraMat) + netGroup = 0 + if interMats: + for interMat in interMats: + maxGroupNumber = np.max([maxGroupNumber, np.max(interMat)]) + + if self.old_mode: # make mooring groups using the old approach (compatible with conceptDesign) + + for i in range(maxGroupNumber): # loop up to maximum number in adjacency matrix (one for each line grouping) + #if profileMap: # MH: not needed <<< + # shared = self.profileMap[i] + #else: + if interMats: + shared = i+1 in intraMat[:nPtfm, :nPtfm] or np.any([i+1 in interMat for interMat in interMats]) + else: + shared = i+1 in intraMat[:nPtfm, :nPtfm] + + if inits: + + if not np.all(self.intersectZ==0) and i+1 in intraMat[:nPtfm, nPtfm:] and shared==1: + percent_droop = 100 - self.intersectZ[np.where(i+1==intraMat[:nPtfm, :])[1][0]]/self.depth * 100 # Check the depth of that anchor point to determine the percent_droop, if any + intersectDeg = np.sum(intraMat[self.intersectZ > 0, :] > 0, axis=1)[netGroup] + net = True + tendON = True + netGroup += 1 + if percent_droop <= 20: # if the line has low drop, a tendon is too expensive + tendON = False + else: + net = False + percent_droop = 50 + + if shared==0: # anchored + self.mooringGroups.append(dict(type=i+1, ten=self.inits['tenA'], kl=self.inits['klA'], w=self.inits['w'], shared=shared, net=False, cost=1)) + elif shared==1: # shared or hybrid (net) + if net: + self.mooringGroups.append(dict(type=i+1, ten=self.inits['tenS'], kl=self.inits['klS'], w=self.inits['w'], shared=shared, percent_droop=percent_droop, net=net, tendON=tendON, tenTen=self.inits['tenTen'], intersectDeg=intersectDeg, cost=1)) + else: + self.mooringGroups.append(dict(type=i+1, ten=self.inits['tenS'], kl=self.inits['klS'], w=self.inits['w'], shared=shared, percent_droop=percent_droop, net=net, cost=1)) + else: + self.mooringGroups.append(dict(type=i+1, kl=100, ten=1000, w=1500, tenTen=1000, shared=shared, net=False, cost=1)) + + else: # new more general/simple approach for mooring groups + # note: inits must be provided as an input in this case (defining each mooring group) + + # figure out shared from place in intraMat and interMats... + groupNumbers = [] + profileMap = [] # says whether each group is anchored or shared line + n = self.intraMat.shape[0] + for i in range(n): + for j in range(n): + a = self.intraMat[i,j] + # if the entry is nonzero and not already stored, add it + if a > 0: + if not a in groupNumbers: + groupNumbers.append(a) + if i < self.nPtfm and j < self.nPtfm: + profileMap.append(1) # flag as shared line + shared = 1 + elif i > self.nPtfm and j > self.nPtfm: + raise Exception("This appears to be an anchor-anchor mooring!") + else: + profileMap.append(0) # flag as anchored line + shared = 0 + + # set up the mooring Group + self.mooringGroups.append(dict(type=a, shared=shared, net=False, cost=1)) + self.mooringGroups[-1].update(self.inits[a-1]) # add in input info on the mooring group + + + # now go through interMat (intercell matrices) and do similarly + for interMat in self.interMats: + n = interMat.shape[0] + for i in range(n): + for j in range(n): + a = interMat[i,j] + # if the entry is nonzero and not already stored, add it + if a > 0: + if not a in groupNumbers: + groupNumbers.append(a) + if i < self.nPtfm and j < self.nPtfm: + profileMap.append(1) # flag as shared line + shared = 1 + elif i > self.nPtfm and j > self.nPtfm: + raise Exception("This appears to be an anchor-anchor mooring!") + else: + profileMap.append(0) # flag as anchored line + shared = 0 + + # set up the mooring Group + self.mooringGroups.append(dict(type=a, shared=shared, net=False, cost=1)) + self.mooringGroups[-1].update(self.inits[a-1]) # add in input info on the mooring group + + # not sure what to do about nets in the above!! + + + # make lines using adjacency matrix (and add corresponding points if they're on a platform) + #linenum = 1 + for iA in range(len(coords)): + for iB in range(iA): + k = int(intraMat[iA,iB]) # the number (if positive) indicates the lineDesign type or grouping + if k > 0: + dr = self.coords[iB,:] - self.coords[iA,:] + l = np.linalg.norm(dr) + self.l.append(l) # store length <<<<<<<< need to subtract fairlead radii... ? + self.u.append(np.round(dr/l, 2)) # store unit vector + self.group.append(k) # store lineDesign type (starts at 1, need to subtract 1 for index) + + self.endA.append(iA) # end A attachment object index + self.endB.append(iB) + self.rA.append(self.coords[iA, :]) + self.rB.append(self.coords[iB, :]) + self.angA.append(0.0) # to be handled later <<<<<<< + self.angB.append(0.0) + self.boundary.append(False) + # fill in ratios in the corresponding line design for convenience - eventually need to check/enforce consistency <<<< + mg = self.mooringGroups[k-1] + shared = mg['shared'] + + if self.old_mode: + mg['ten__w'] = mg['ten']/mg['w'] + mg['kl__w' ] = mg['kl' ]/mg['w'] + mg['kt__kl'] = mg['ten']/l/mg['kl'] # kt/ k = ten/l/k + else: + pass # <<< nothing needed here?? + + mg['l'] = l # store a copy so it's acccessible in the mooringGroups list + mg['dl_max'] = xmax # maximum extension of this mooring group (initial value equal to xmax) + mg['dl_min'] = -xmax # maximum compression of this mooring group (must be negative, initial value equal to -xmax) + + # Add inter-cell lines: + if interMats: + for interMat, interCoord in zip(interMats, interCoords): + interMat = np.array(interMat) + interCoord = np.array(interCoord) + for iA in range(self.nPtfm): + for iB in range(iA): + if iA < self.nPtfm and iB < self.nPtfm: + b = int(interMat[iA, iB]) + if b > 0: + intercenter = self.center + interCoord + rotMat = np.array([[np.cos(np.pi), -np.sin(np.pi)], + [np.sin(np.pi), np.cos(np.pi)]]) + intercenterp = np.matmul(rotMat, intercenter) + intercoordsiB = intercenter + (self.coords[iB, :] - self.center) + intercoordsiA = intercenter + (self.coords[iA, :] - self.center) + intercoordsiBp = intercenterp + (self.coords[iB, :] - self.center) + intercoordsiAp = intercenterp + (self.coords[iA, :] - self.center) + # compute both distances and choose the smallest one + drBA = intercoordsiB - self.coords[iA,:] + lBA = np.linalg.norm(drBA) + drBAp = intercoordsiBp - self.coords[iA,:] + lBAp = np.linalg.norm(drBAp) + if lBAp > lBA: + dr = drBA + # not that it matters but for consistency, choose the right A and B: + self.endA.append(iA) + self.endB.append(iB) + self.rA.append(self.coords[iA, :]) + self.rB.append(intercoordsiB) + + self.endA.append(iB) + self.endB.append(iA) + self.rA.append(self.coords[iB, :]) + self.rB.append(intercoordsiAp) + else: + dr = drBAp + self.endA.append(iA) + self.endB.append(iB) + self.rA.append(self.coords[iA, :]) + self.rB.append(intercoordsiBp) + + self.endA.append(iB) + self.endB.append(iA) + self.rA.append(self.coords[iB, :]) + self.rB.append(intercoordsiA) + l = np.linalg.norm(dr) + self.l.append(l) + self.l.append(l) + self.u.append(dr/l) + self.u.append(-dr/l) + self.group.append(b) + self.group.append(b) + self.angA.append(0.0) + self.angB.append(0.0) + self.angA.append(0.0) + self.angB.append(0.0) + self.boundary.append(True) + self.boundary.append(True) + # not sure about this part: + mg = self.mooringGroups[b-1] + shared = mg['shared'] + if self.old_mode: + mg['ten__w'] = mg['ten']/mg['w'] + mg['kl__w' ] = mg['kl' ]/mg['w'] + mg['kt__kl'] = mg['ten']/l/mg['kl'] # kt/ k = ten/l/k + else: + pass # <<< nothing needed here? + mg['l'] = l + mg['dl_max'] = xmax + mg['dl`_min'] = -xmax + + + print("end of intermat setup?") + + self.nLines = len(self.l) + + # should make some error checks for consistent properties (spacing, shared) in mooring groups <<< + + # now also make the structure matrix + self.StructureMatrix = np.zeros([2*self.nPtfm, self.nLines]) # rows: DOFs; columns: lines + + for j in range(self.nLines): + if self.endA[j] < self.nPtfm: # only if not an anchor + self.StructureMatrix[self.endA[j]*2 , j] = self.u[j][0] + self.StructureMatrix[self.endA[j]*2 + 1, j] = self.u[j][1] + + if self.endB[j] < self.nPtfm: # only if not an anchor + self.StructureMatrix[self.endB[j]*2 , j] = -self.u[j][0] + self.StructureMatrix[self.endB[j]*2 + 1, j] = -self.u[j][1] + + + # remember, you may want to call calibrate (or similar) to set up better + # values for ten__w, kl__w, and kt__kl for each mooring object assuming a continuous catenary line + + + def preprocess(self, plots=0, display=0): + '''Initializes things... + + Does all the things that can be done once the lineDesign characteristics are set (f/l/k and f/w) + + the mooring system objects to their initial positions if applicable? + + contents of former getTensionMatrix and getKnobs are now here''' + + # ensure the points (and line ends) are in the right positions + #for point in self.pointList: + # point.setPosition(Point.r) + + # update line properties so that initial values are in place + #self.calibrate_kt_over_k(plots=plots) + + # fill in the initial line stiffnesses and generate the system stiffness matrix so it's in place + #self.updateStiffnesses(np.ones(self.nLines)) + + # draw initial mooring system if desired + #if plots==1: + # self.plot(title="Mooring system at initialization") + + + #def getTensionMatrix(self): + '''Tension matrix defines the contribution of each line group's weight to each line's tension + + Essentially it is just a mapping from each line group's weight to each individual line's tension. + There is only one nonzero entry per row - i.e. each line's tension is just based on a single group's stiffness. + This seems simple and maybe doesn't need to be a matrix?? + ''' + + self.TensionMatrix = np.zeros([self.nLines, len(self.mooringGroups)]) + + for j in range(self.nLines): + + i = self.group[j]-1 + + if self.old_mode: + self.TensionMatrix[j, i] = self.mooringGroups[i]['ten__w'] + else: # NEW - USING TENSIONS DIRECTLY RATHER THAN WEIGHT RATIOS + self.TensionMatrix[j, i] = self.mooringGroups[i]['ten'] + + if self.boundary[j]: + self.TensionMatrix[j, i] *= 0.5 #MH: this seems suspect <<< + + + + #def getKnobs(self): + '''based on structure and tension matrices, calculatesd self.Knobs_k, which is used by c_to_k when optimizing stiffness.''' + + # Null space of Structure Matrix + N1 = scipy.linalg.null_space(self.StructureMatrix)#, rcond = 0.0001) + + # null space of N1 augmented with tension matrix + N2 = scipy.linalg.null_space(np.hstack([N1, -self.TensionMatrix])) #, rcond = 0.0001) + #N2 = scipy.linalg.null_space(np.append(N1, -self.TensionMatrix,1))#, rcond = 0.0001) + + # nullspace matrix containing basis vectors of valid line weight solutions for equilibrium given line groupings + # (we skip the top part of the vectors--the coefficients for feasible tension combinations--because tensions can be calculated directly from weights) + self.wBasis = N2[-len(self.mooringGroups):] + + # self.getSystemStiffness() + # print(self.SystemStiffness) + + # check to make sure there is at least on design variable + if np.prod(self.wBasis.shape)==0: + raise Exception("No knobs available") + + # normalize each weight basis vector and flip signs on any that are mostly negative + self.nBasis = self.wBasis.shape[1] # store the number of vectors + #print(self.nKnobs) + + for i in range(self.nBasis): + self.wBasis[:,i] = self.wBasis[:,i] / np.linalg.norm(self.wBasis[:,i]) * np.sign(np.sum(self.wBasis[:,i])) + #self.Knobs_k[:,i] = self.Knobs_k[:,i] / np.linalg.norm(self.Knobs_k[:,i]) + + + #Create Initial guess for q (the knob values multiplied by the weight basis vectors) + self.q0 = np.zeros(self.nBasis)+100 + + # lower any initial knob values for basis vectors that contain negatives to avoid any negative weights to start with + #for j in range(len(self.mooringGroups)): + # for i in range(self.nBasis): + # wtemp = np.sum(self.wBasis[:,i]*self.q0 + # if any(self.wBasis[:,i] < 0): + # self.q0[i] = 0.0 + # raise the knob of the all-positive basis vector if needed to make all weights positive + for i in range(self.nBasis): + w = np.matmul(self.wBasis, self.q0) # initial weights per unit length of each line group + if any(w < 0) and all(self.wBasis[:,i] > 0): # if there's a negative line weight and this basis vector is all positive + + q_i_add = np.max(-w/self.wBasis[:,i]) + if display > 0: + print(f' to resolve negative initial line tension ({w}), increasing q0[{i}] by {2*q_i_add}') + + self.q0[i] += 2*q_i_add + + + + + def getSystemStiffness(self): + '''Calculates the stiffness matrix of the SimpleSystem, based on current positions and LineType info''' + + #If were to generalize so each point has 3 or more degrees of freedom, replace each 2 with a 3 + #If we were to further generalize to consider points and bodies, we have to put more carefull thought into the indexing + + + # self.SystemStiffness = np.zeros([2*len(self.coords), 2*len(self.coords)]) + # MH: Changed back to nPtfm x nPtfm. Not sure why anchors were included. Maybe for hybrid? <<< + self.SystemStiffness = np.zeros([2*self.nPtfm, 2*self.nPtfm]) + + for j in range(self.nLines): + + # first get the line's stiffness matrix (xA, yA, xB, yB) + cc = self.u[j][0]*self.u[j][0] # cos(theta)*cos(theta) + ss = self.u[j][1]*self.u[j][1] + cs = self.u[j][0]*self.u[j][1] + + # Find Transformation Matrices: + transMat_inline = np.array([ [ cc, cs,-cc,-cs], + [ cs, ss,-cs,-ss], + [-cc,-cs, cc, cs], + [-cs,-ss, cs, ss]]) + + transMat_perpendicular = np.array([ [ ss,-cs,-ss, cs ], + [-cs, cc, cs,-cc ], + [-ss, cs, ss,-cs ], + [ cs,-cc,-cs, cc ]]) + # Lookup inline and perpendicular stiffness values for this line type (assumes a certain line spacing, etc.) + mg = self.mooringGroups[self.group[j]-1] + if self.old_mode: + kl = mg['kl__w']*mg['w'] + kt = mg['kt__kl']*kl + mg['kl'] = kl + else: # the new mode uses stiffness values that are already provided + kl = mg['kl'] + kt = mg['kt'] + + # Multiply stiffness values by transformation matrix + K_inline = kl * transMat_inline + + #Force in y direction from displacement in y direction caused by tension in x direction + K_perpendicular = kt * transMat_perpendicular + + # Note: Force in x direction from displacement in y direction caused by tension in x direction is neglected as second-order + K_sum = K_inline + K_perpendicular + + # now apply to the appropriate spots in the system stiffness matrix + iA = self.endA[j] + iB = self.endB[j] + + # MH: re-adding the old logic here >>> + if self.endA[j]>> MH: this part may be for hybrid shared moorings >>> + boundaryLineCounti = np.sum(self.boundary[:j]) + if boundaryLineCounti % 2 == 0: # only count the stiffness of the boundary line once + self.SystemStiffness[iA*2:iA*2+2, iA*2:iA*2+2] += K_sum[:2,:2] + self.SystemStiffness[iB*2:iB*2+2, iB*2:iB*2+2] += K_sum[2:,2:] + self.SystemStiffness[iA*2:iA*2+2, iB*2:iB*2+2] += K_sum[:2,2:] + self.SystemStiffness[iB*2:iB*2+2, iA*2:iA*2+2] += K_sum[2:,:2] + if iA >= self.nPtfm: + tau = mg.get('tenTen', 1000) + tau__L = tau/self.intersectZ[iA] # intentionally will be set to inf if it's an anchor + # Only apply if there is a tendon + if mg.get('net', False) and not mg.get('tendON', False): + tau__L = 0 + + self.SystemStiffness[iA*2:iA*2+2, iA*2:iA*2+2] += (np.array([[1, 0],[0, 1]]) * tau__L) + ''' + """ + >>> MH: maybe this was a clever approach to remove anchor row/columns? >>> + # remove any rows and columns in the stiffness matrix with infinity values + finite_mask = np.isfinite(self.SystemStiffness).all(axis=1) + self.SystemStiffness = self.SystemStiffness[np.ix_(finite_mask, finite_mask)] + self.nAnch = int(np.sum(finite_mask==False)/2) + + >>> RA: This works for non-hybrid designs but need to think of a new way to include + net/hybrid designs. + """ + self.nAnch = len(self.coords) - self.nPtfm #MH: temporarily filling this in here <<< + + # self.SystemStiffness[np.abs(self.SystemStiffness) < 1e-5] = 0 + # calculate inverse of stiffness matrix to avoid solving multiple times later + self.K_inverse = np.linalg.inv(self.SystemStiffness) + + + def get_x(self, f, theta=0, heading=0, removeHybrid=True): + '''Get displacement in all dof using linear model. Nonlinear factor included. + This assumes system in equilibrium when no external force is applied. + + theta is wind directions in radians, heading is the wind direction in degrees + ''' + + #if watch_circles > 0: + if not hasattr(self, 'K_inverse'): + raise Exception("In a Linear System, getSystemStiffness must be called before calling get_x.") + + + if theta==0: + theta = np.radians(heading) + + if np.isscalar(f): # thrust force and direction + F = [f*np.cos(theta), f*np.sin(theta)]*(len(self.coords)-self.nAnch) + F[2*self.nPtfm:] = [0, 0]*(len(self.coords) - self.nPtfm - self.nAnch) + + elif len(f)==2: # x and y force components to be applied to each turbine + F = [f[0], f[1]]*(len(self.coords)-self.nAnch) + F[2*self.nPtfm:] = [0, 0]*(len(self.coords) - self.nPtfm - self.nAnch) + + elif len(f)==2*(len(self.coords)-self.nAnch): # full vector of forces + F = f + + else: + raise ValueError("Invalid format of f provided to get_x") + + + #Use linear algebra to solve for x vector (solve for offsets based on stiffness matrix and force vector) Nonlinear factor included here. + xi = np.matmul(self.K_inverse, F)*self.nonlin + + # also figure out peak tensions etc here? <<<< + + + if removeHybrid: + # remove hybrid and anchor points + xi = xi[:2*self.nPtfm] + return xi + + + + def windsweep(self, f=0): + ''' gets offsets and changes in line spacing across wind directions. + ''' + + if f == 0: + f=self.fmax + + self.xi_sweep = np.zeros([len(self.angles), 2*self.nPtfm]) # holds displacement vector (x and y of each platform) for each wind direction + self.dl_sweep = np.zeros([len(self.angles), self.nLines]) # change in each line's spacing for each wind direction + + for i,angle in enumerate(self.angles): + + xi = self.get_x(f, heading=angle) # Get the offsets in each DOF + self.xi_sweep[i,:] = xi + + for il in range(self.nLines): # loop through line indices + dl = 0.0 + iA = self.endA[il] + iB = self.endB[il] + if iA < self.nPtfm: # if this end is attached to a platform + dl += np.sum( xi[2*iA : 2*iA+2] * self.u[il]) # calculate extension as dot product of displacement and line unit vector + if iB < self.nPtfm: # if this end is attached to a platform + dl -= np.sum( xi[2*iB : 2*iB+2] * self.u[il]) # calculate extension as -dot product of displacement and line unit vector + + self.dl_sweep[i, il] = dl + + # also compute watch circle area (by summation of areas of triangles) + self.areas = np.zeros(self.nPtfm) + for i in range(self.nPtfm): + + for j in range(-1, len(self.angles)-1): + self.areas[i] += self.xi_sweep[j,2*i] * (self.xi_sweep[j+1,2*i+1] - self.xi_sweep[j-1,2*i+1]) * 0.5 + + return + + def getCost(self): + '''Calculate the cost function of the line for the spring model''' + #Assume cost is proportional to line length and mass + + #self.line_cost = [self.l[i]*self.mooringGroups[self.group[i]-1]['w'] for i in range(self.nLines)] <<< this was double counting + self.line_cost = [] + + for i in range(self.nLines): + + self.line_cost.append(self.l[i]*self.mooringGroups[self.group[i]-1]['w']) # Cost Function for each line [kg m] + + if self.mooringGroups[self.group[i]-1]['shared'] != 1: # If it's an anchor mooring + self.line_cost[-1] = self.line_cost[-1]*self.anchor_cost_factor # include an anchorcost factor + elif self.mooringGroups[self.group[i]-1]['net'] and self.mooringGroups[self.group[i]-1]['tendON']: # if there's an anchor in a hybrid system + self.line_cost[-1] = self.line_cost[-1]*self.anchor_cost_factor/self.mooringGroups[self.group[i]-1]['intersectDeg'] # include a shared anchorcost factor + # sloppily store the cost in the mooring group as well for use in vizualization + self.mooringGroups[self.group[i]-1]['cost'] = self.line_cost[-1] + + self.cost = np.sum(np.array(self.line_cost)) # save total cost + + return(self.cost) + + + def getWeight(self): + '''Calculate the total system mooring line (wet) weight''' + + return sum([self.l[i]*self.mooringGroups[self.group[i]-1]['w'] for i in range(self.nLines)]) + + + def optimize(self, display=0): + '''solve the cheapeast system for a given input force, wind angle, and maximum displacement''' + + if not self.old_mode: + raise Exception("LinearSystem.optimize only works when old_mode = True") + + if display > 1: + print(f'Beginning LinearSystem optimization.') + + print('weight basis vectors are:') + print(self.wBasis) + + def dopt_fun(q): + '''evaluation function for the dopt solver. This function includes both + design variables and constraints. This function inputs q which is an array of knob values. + ''' + + # ----- calculate line weight from q, and upate line types and system stiffness matrix ------------ + w = np.matmul(self.wBasis, q) # weights per unit length of each line group + + for i, mg in enumerate(self.mooringGroups): + mg['w'] = w[i] # update wet weight per unit length [N/m] + + self.getSystemStiffness() # need this for evaluating constraints + + # ---- objective function - mooring system mass or cost ----- + f = self.getCost() + + + # ----- constraint values - margin from max offsets, and line weights (must be positive) ----- + ''' + Finds how much a certain stiffness will undershoot the maximum design displacement + This function returns a list of undershoots for each degree of freedom. + ''' + + self.windsweep() # update offset and line extension numbers for each wind direction + + self.offsets = np.zeros([len(self.angles), self.nPtfm]) # store offset distance (radius) for each direction + + for i,angle in enumerate(self.angles): # Calculate the hypotenuse of each platform's offset + self.offsets[i,:] = [np.linalg.norm(self.xi_sweep[i, 2*k:2*k+2]) for k in range(self.nPtfm)] + + peak_offsets = np.max(self.offsets, axis=0) # get largest displacement of each platform (over the range of wind directions) + + offset_margin = [self.xmax - peak_offset for peak_offset in peak_offsets] # substract maximum from resultant values to get undershoot + + g = np.hstack([offset_margin, w.tolist()]) + + + # return the objective and constraint values + return f, g, w, 0, 0, False + + + dX_last = np.zeros(self.nBasis)+1e5 + + + # call the optimizer (set display=2 for a desecription of what's going on) + #q, f, res = msolve.dopt(dopt_fun, q0, tol=0.001, maxIter=70, a_max=1.5, dX_last=dX_last, display=max(display-1,0)) + #q, f, res = msolve.dopt(dopt_fun, self.q0, tol=0.002, stepfac=100, maxIter=100, a_max=1.5, dX_last=dX_last, display=display-1) + q, f, res = msolve.dopt2(dopt_fun, self.q0, tol=0.002, stepfac=100, maxIter=100, a_max=1.5, dX_last=dX_last, display=display-1) + + + # check the results - and retry up to 4 times + for i in range(4): + if display > 0: print(f" Message from dopt: {res['message']}") + + f, g, w, _, _, _ = dopt_fun(q) # get latest objective and constraint values + + #if display>0: + # if res['success'] == False: + # print('LinearSystem Mooring optimization was UNSUCCESSFUL: '+res['message']) + # else: + # print(f"LinearSystem Mooring optimization was successful after {res['iter']} iterations.") + + # check for overly stiff or soft solution (and the rerun with better starting points) + if self.nPtfm==1: + offset_margin = g[0] # <<< can this be simplified? + else: + offset_margin = np.min(g[:-len(q)-1]) # this is the closest any watch circle comes to the limit + + if offset_margin > 0.05*self.xmax: # if the closest it gets to the target watch circles is more than 5% short + if display > 0: print(f' LinearSystem optimization attempt {i} detected overly small watch circles (largest is {offset_margin:5.1f} m from the limit).') + if display > 1: print(' Retrying the optimization with lighter starting points (q0)') + self.q0 = 0.3*self.q0 + + + elif offset_margin < -0.1*self.xmax: # if it overshoots the target watch circles by more than 10% + if display > 0: print(f' LinearSystem optimization attempt {i} detected extreme watch circles (largest is {-offset_margin:5.1f} m over the limit).') + if display > 1: print(' Retrying the optimization with heavier starting points (q0)') + self.q0 = 10.0*self.q0 + + else: # otherwise, call it succsessful + if display>0: print(f" LinearSystem optimization attempt {i} was successful after {res['iter']} iterations.") + break + + # this is where we rerun dopt with the modified settings + q, f, res = msolve.dopt(dopt_fun, self.q0, tol=0.002, stepfac=100, maxIter=100, a_max=1.5, dX_last=dX_last, display=display-1) + + + if display>1: + + if res['success'] == False: + print('Final LinearSystem Mooring optimization was UNSUCCESSFUL: '+res['message']) + else: + print(f"Final LinearSystem Mooring optimization was successful after {res['iter']} iterations.") + + + + # plotting + + if ((res['success'] == False and display >0) or display > 1) and self.nPtfm>1: # plot the optimization if it's likely desired + + n = len(q) + fig, ax = plt.subplots(n+3, 1, sharex=True) + Xs = np.array(res["Xs"]) + Fs = np.array(res["Fs"]) + Gs = np.array(res["Gs"]) + iter = res["iter"] + + for i in range(n): + ax[i].plot(Xs[:iter+1,i]) + ax[i].set_ylabel(f"q{i}") + + ax[n].plot(Xs[:iter+1,n:]) + ax[n].set_ylabel("weights") + + ax[n+1].plot(Fs[:iter+1]) + ax[n+1].set_ylabel("cost") + + #m = len(self.Knobs_k) + ax[n+2].plot(Gs[:iter+1,:-n-1]) + #ax[n+3].plot(Gs[:iter+1,-n-1:]) + ax[n+2].set_ylabel("g offsets") + #ax[n+3].set_ylabel("g weights") + ax[n+2].set_xlabel("iteration") + + #breakpoint() + if display > 1: + plt.show() + breakpoint() + + + #For debugging purposes: + self.res = res + self.q = q + + + # get max extension of each line group's spacing and store it in the mooring group + dl_max = np.max(self.dl_sweep, axis=0) + dl_min = np.min(self.dl_sweep, axis=0) + #print((" group: "+"".join([" {:6d}"]*self.nLines)).format(*self.group )) + #print((" dl_max: "+"".join([" {:6.1f}"]*self.nLines)).format(*dl_max.tolist() )) + #print((" dl_min: "+"".join([" {:6.1f}"]*self.nLines)).format(*dl_min.tolist() )) + for i, mg in enumerate(self.mooringGroups): + mg['dl_max'] = np.mean(dl_max[[j for j, k in enumerate(self.group) if k==i+1]]) # take the mean from any lines in this mooring group (group i+1) + mg['dl_min'] = np.mean(dl_min[[j for j, k in enumerate(self.group) if k==i+1]]) + + # update each mooring group's weight and tension values + for i, mg in enumerate(self.mooringGroups): + mg['w'] = w[i] # update wet weight per unit length [N/m] + mg['ten'] = mg['ten__w']*mg['w'] # update line tension [N] + + if np.round(w[i], 3) == 0: + w[i] = 0.0 + + if w[i] < 0: + raise ValueError("breakpoint due to negative weight") + + self.getSystemStiffness() # need this for evaluating constraints + + return q + + + + def optimize2(self, display=0): + '''NEW: Figure out what mooringGroup stiffnesses will achieve the + desired watch circles, for a given input force, wind angle, and + maximum displacement''' + + if self.old_mode: + raise Exception("LinearSystem.optimize2 only works when old_mode = False") + + if display > 1: + print(f'Beginning LinearSystem optimization2.') + + print('tension basis vectors are:') + print(self.wBasis) + + + self.iter=0 # reset iteration counter + + def dopt_fun(kls): + '''evaluation function for the dopt solver. This function includes + both design variables and constraints. This function inputs kls, + inline stiffness values. + ''' + + # ----- upate line types and system stiffness matrix ------------ + + for i, mg in enumerate(self.mooringGroups): + mg['kl'] = kls[i] # update inline stiffness [N/m] + + self.getSystemStiffness() # need this for evaluating constraints + + # ---- objective function - mooring system mass or cost ----- + # approximate cost as product of line length and stiffness + line_stiffness_costs = [self.l[i]*self.mooringGroups[self.group[i]-1]['kl'] for i in range(self.nLines)] + f = sum(line_stiffness_costs) + + + # ----- constraint values - margin from max offsets, and line weights (must be positive) ----- + ''' + Finds how much a certain stiffness will undershoot the maximum design displacement + This function returns a list of undershoots for each degree of freedom. + ''' + + self.windsweep() # update offset and line extension numbers for each wind direction + + # store offset distance (radius) for each direction + self.offsets = np.zeros([len(self.angles), self.nPtfm]) + for i,angle in enumerate(self.angles): + self.offsets[i,:] = [np.linalg.norm(self.xi_sweep[i, 2*k:2*k+2]) for k in range(self.nPtfm)] + + peak_offsets = np.max(self.offsets, axis=0) # get largest displacement of each platform (over the range of wind directions) + + offset_margin = [self.xmax - peak_offset for peak_offset in peak_offsets] # substract maximum from resultant values to get undershoot + + g = np.hstack([offset_margin, kls.tolist()]) # constraints are offset and positive stiffness + + self.iter = self.iter + 1 # update counter (this isn't actually iterations, it's function calls) + if display > 3 and self.iter%20 == 0: + sys.plot2d(watch_circles=4, line_val="stiffness") + plt.show() + + + # return the objective and constraint values + return f, g, [], 0, 0, False + + n = len(self.mooringGroups) + kls0 = np.array([mg['kl'] for mg in self.mooringGroups]) # starting point + dX_last = np.zeros(n) + 0.01*np.mean(kls0) # step size + + + # call the optimizer (set display=2 for a desecription of what's going on) + #q, f, res = msolve.dopt(dopt_fun, q0, tol=0.001, maxIter=70, a_max=1.5, dX_last=dX_last, display=max(display-1,0)) + #q, f, res = msolve.dopt(dopt_fun, self.q0, tol=0.002, stepfac=100, maxIter=100, a_max=1.5, dX_last=dX_last, display=display-1) + kls, f, res = msolve.dopt2(dopt_fun, kls0, tol=0.002, stepfac=20, maxIter=40, a_max=1.4, dX_last=dX_last, display=display-1) + + + # check the results - and retry up to 4 times + for i in range(4): + if display > 0: print(f" Message from dopt: {res['message']}") + + f, g, _, _, _, _ = dopt_fun(kls) # get latest objective and constraint values + + #if display>0: + # if res['success'] == False: + # print('LinearSystem Mooring optimization was UNSUCCESSFUL: '+res['message']) + # else: + # print(f"LinearSystem Mooring optimization was successful after {res['iter']} iterations.") + + # check for overly stiff or soft solution (and the rerun with better starting points) + if self.nPtfm==1: + offset_margin = g[0] # <<< can this be simplified? + else: + offset_margin = np.min(g[:-len(kls)-1]) # this is the closest any watch circle comes to the limit + + if offset_margin > 0.05*self.xmax: # if the closest it gets to the target watch circles is more than 5% short + if display > 0: print(f' LinearSystem optimization attempt {i} detected overly small watch circles (largest is {offset_margin:5.1f} m from the limit).') + if display > 1: print(' Retrying the optimization with lighter starting points (q0)') + self.q0 = 0.3*self.q0 + + + elif offset_margin < -0.1*self.xmax: # if it overshoots the target watch circles by more than 10% + if display > 0: print(f' LinearSystem optimization attempt {i} detected extreme watch circles (largest is {-offset_margin:5.1f} m over the limit).') + if display > 1: print(' Retrying the optimization with heavier starting points (q0)') + self.q0 = 10.0*self.q0 + + else: # otherwise, call it succsessful + if display>0: print(f" LinearSystem optimization attempt {i} was successful after {res['iter']} iterations.") + break + + # this is where we rerun dopt with the modified settings + q, f, res = msolve.dopt(dopt_fun, kls0, tol=0.002, stepfac=100, maxIter=100, a_max=1.5, dX_last=dX_last, display=display-1) + + + if display>1: + + if res['success'] == False: + print('Final LinearSystem Mooring optimization was UNSUCCESSFUL: '+res['message']) + else: + print(f"Final LinearSystem Mooring optimization was successful after {res['iter']} iterations.") + + + + # plotting + + if True: #((res['success'] == False and display >0) or display > 1) and self.nPtfm>1: # plot the optimization if it's likely desired + + n = len(kls) + fig, ax = plt.subplots(n+3, 1, sharex=True) + Xs = np.array(res["Xs"]) + Fs = np.array(res["Fs"]) + Gs = np.array(res["Gs"]) + iter = res["iter"] + + for i in range(n): + ax[i].plot(Xs[:iter+1,i]) + ax[i].set_ylabel(f"kls{i}") + + ax[n].plot(Xs[:iter+1,n:]) + ax[n].set_ylabel("weights") + + ax[n+1].plot(Fs[:iter+1]) + ax[n+1].set_ylabel("cost") + + #m = len(self.Knobs_k) + ax[n+2].plot(Gs[:iter+1,:-n-1]) + #ax[n+3].plot(Gs[:iter+1,-n-1:]) + ax[n+2].set_ylabel("g offsets") + #ax[n+3].set_ylabel("g weights") + ax[n+2].set_xlabel("iteration") + + + + #For debugging purposes: + self.res = res + self.kls = kls + + + # get max extension of each line group's spacing and store it in the mooring group + dl_max = np.max(self.dl_sweep, axis=0) + dl_min = np.min(self.dl_sweep, axis=0) + #print((" group: "+"".join([" {:6d}"]*self.nLines)).format(*self.group )) + #print((" dl_max: "+"".join([" {:6.1f}"]*self.nLines)).format(*dl_max.tolist() )) + #print((" dl_min: "+"".join([" {:6.1f}"]*self.nLines)).format(*dl_min.tolist() )) + for i, mg in enumerate(self.mooringGroups): + mg['dl_max'] = np.mean(dl_max[[j for j, k in enumerate(self.group) if k==i+1]]) # take the mean from any lines in this mooring group (group i+1) + mg['dl_min'] = np.mean(dl_min[[j for j, k in enumerate(self.group) if k==i+1]]) + + # update each mooring group's kl + for i, mg in enumerate(self.mooringGroups): + mg['kl'] = kls[i] + + if np.round(kls[i], 3) == 0: + kls[i] = 0.0 + + if kls[i] < 0: + raise ValueError("breakpoint due to negative kl") + + self.getSystemStiffness() # need this for evaluating constraints + + return kls + + + def plot2d(self, ax=None, **kwargs): + '''Plots 2d view of simple system mooring configuration, optionally including additional properties + + Parameters + ---------- + ax : matplotlib axes + The axes to draw the plot on. A new figure is created and returned if this is not provided. + show_lines : string + Specifies whether to show lines: none, anch, shared, all (default) + watch_circles: float + Specifies whether to draw watch circles (and at what scale, >0) or not to draw them (0) + line_val : string + Specifies what to show for the lines: uniform, two, groups (default), stiffness, cost, weight, tension + colormap : int or string + Specifies what colormap to use. + colorbar : int + 0 - don't draw one, 1 - draw as normal, 2 - draw on seperate axes specified in kwarg cbax. + colorscale : string + Specify linear or log (for logarithmic) + cbax : plt.Axes + Only used if colorbar=2 + show_axes : bool + Whether to show the axes of the figure or not (hide them). + labels : string + Whether to label lines (l), points (p or t for turbine, a for anchor), etc. '' means no labels. + title : string + Text to add above the figure (otherwise default text will be shown). + line_color + line_style + line_width + ''' + + + #plt.ion() #Turn on interactive mode + + # initialize some plotting settings + n = self.nLines + + # some optional argument processing and setting default values if not supplied + + line_val = kwargs.get("line_val" , "groups" ) # get the input value, or use "groups" as default + show_lines = kwargs.get("show_lines" , "all" ) # get the input value, or use "groups" as default + watch_circles = kwargs.get("watch_circles", 0 ) # + colormap = kwargs.get("colormap" , 0 ) # + colorbar = kwargs.get("colorbar" , 1 ) # + colorscale = kwargs.get("colorscale" , "linear" ) # + show_axes = kwargs.get("show_axes" , True ) # + labels = kwargs.get("labels" , '' ) # + title = kwargs.get("title" , [] ) # + figsize = kwargs.get("figsize" , (5,5) ) # + wea = kwargs.get("wea" , None ) # + #center = kwargs.get("center" , 1 ) # turns on and off whether the plot is centered or not + + # receive or use default uniform line color/style/width (may be overriden by non-uniform color coding options in line_val) + colors = [kwargs.get("line_color", 'black')]*n + styles = [kwargs.get("line_style", 'solid')]*n + thicks = [kwargs.get("line_width", 2)]*n + + + + # set up colormap + if colormap == 0 or colormap == "rainbow" or colormap == "jet": + #Create Rainbow colormap (I still incorrectly use 'jet' sometimes when I want a rainbow colormap, so I will keep support for that keyworkd) + cmap = mpl.cm.rainbow + + elif colormap == 1 or colormap == 'aut': #Create autumn colormap + cmap = mpl.cm.autumn + + else: + raise ValueError("invalide colormap input provided to plot2d.") + + + # set whether colormap will be linear of logarithmic + if colorscale == "linear": + normalizer = mpl.colors.Normalize + elif colorscale == "log" or colorscale == "logarithmic": + normalizer = mpl.colors.LogNorm + else: + raise ValueError("colorscale must be 'linear' or 'log'.") + + # set up color map bounds if provided + if "val_lim" in kwargs: + + def getLineColors(values): + norm = normalizer(vmin=kwargs["val_lim"][0], vmax=kwargs["val_lim"][1]) #Scale to min and max values + s_m = mpl.cm.ScalarMappable(norm=norm, cmap = cmap) # create Scalar Mappable for colormapping + return s_m.to_rgba(values), s_m + + else: + + def getLineColors(values): + if min(values) == max(values): + norm = normalizer(vmin=0, vmax=max(values)) # if only one value, start scale at zero + else: + norm = normalizer(vmin=min(values), vmax=max(values)) # set scaling to min and max values + s_m = mpl.cm.ScalarMappable(norm = norm, cmap = cmap) # create Scalar Mappable for colormapping + return s_m.to_rgba(values), s_m + + ''' + # get arrays of all the values of interest up-front + line_k = [0.001*self.lineTypes[key].k for key in self.lineTypes] + line_c = [0.001*self.lineTypes[key].cost for key in self.lineTypes] # note: this is when the cost parameter has been repurposed from $/m to $/line + line_m = [ self.lineTypes[key].mlin for key in self.lineTypes] + line_w = [ self.lineTypes[key].w for key in self.lineTypes] + line_t = [0.001*self.lineTypes[key].t for key in self.lineTypes] + line_kt_k = [ self.lineTypes[key].kt_over_k for key in self.lineTypes] + line_MBL = [0.001*self.lineTypes[key].MBL for key in self.lineTypes] + line_MSF = [ MBL/t for MBL,t in zip(line_MBL,line_t)] # safety factor + ''' + + line_k = [self.mooringGroups[self.group[i]-1]['kl' ]/1e3 for i in range(n)] # stiffness in kN/m + line_t = [self.mooringGroups[self.group[i]-1]['ten']/1e6 for i in range(n)] # tension in MN + line_w = [self.mooringGroups[self.group[i]-1]['w'] for i in range(n)] # wet weight per length in N/m + line_m = [self.mooringGroups[self.group[i]-1]['w']/9.81 for i in range(n)] # wet weight per length in kg/m + #line_t_w = [self.mooringGroups[self.group[i]-1]['ten__w'] for i in range(n)] # can add this in later + #line_k_w = [self.mooringGroups[self.group[i]-1]['kl__w'] for i in range(n)] # can add this in later + if self.old_mode: + line_kt_k = [self.mooringGroups[self.group[i]-1]['kt__kl'] for i in range(n)] + line_cost = [self.mooringGroups[self.group[i]-1]['cost'] for i in range(n)] + + + clist = ['tab:blue','tab:cyan','tab:green','tab:olive','tab:brown','tab:purple', + 'tab:red','tab:orange','tab:blue','tab:pink','tab:gray'] + + + # set up line data display - Detetermine which line variable we are using + if line_val == 'uniform': # all lines drawn black and solid + pass + + # elif line_val == 'two': # distinguishes shared vs. anchor lines + # for i in range(n): + # if self.mooringGroups[i]['shared']: + # colors[i] = "blue" + # styles[i] = "solid" + # else: + # colors[i] = "black" + # styles[i] = "dashed" + + elif line_val == 'groups': + for i in range(n): + ii = self.group[i]-1 + colors[i] = clist[ii] + + elif line_val == 'shared': + for i in range(n): + ii = self.group[i]-1 + if self.mooringGroups[ii]['shared']: + colors[i] = 'tab:cyan' + else: + colors[i] = 'tab:pink' + colorbar = 0 + + elif line_val == 'stiffness': + colors, s_m = getLineColors(line_k) # get colors corresponding to each line type + colorbar_label = 'Effective stiffness (kN/m)' + line_var = 'k' + + + elif line_val == 'weight': + colors, s_m = getLineColors(line_w) + colorbar_label = 'Wet weight (N/m)' + line_var = 'weight' + + elif line_val == 'mass': + colors, s_m = getLineColors(line_m) + colorbar_label = 'Wet mass (kg/m)' + line_var = 'weight' + + elif line_val == 'tension': + colors, s_m = getLineColors(line_t) + colorbar_label = 'Horizontal tension (MN)' + line_var = 'T' + + elif line_val == 'kt_over_k': + colors, s_m = getLineColors(line_kt_k) + colorbar_label = 'Line kt/k (-)' + line_var = 'kt/k' + + elif line_val == 'cost': + colors, s_m = getLineColors(line_cost) + colorbar_label = 'Line cost [?]' + line_var = 'cost' + + + else: + raise ValueError('Incorrect line_val given') + + + # set up axes + if ax == None: # if no axes passed in, make a new figure + fig, ax = plt.subplots(1,1, figsize=figsize, constrained_layout=True) + else: + fig = ax.get_figure() # otherwise plot on the axes passed in + + + + # # plot each mooring line, colored differently for each line type + for i in range(n): + + # shousner: I don't understand how the j var found an integer + #j = int(Line.type[4:])-1 # index of LineType + ii = self.group[i]-1 + + shared = self.mooringGroups[ii]['shared']==1 + + rA = self.rA[i] + rB = self.rB[i] + + + + if not (show_lines=="none" or (show_lines=="anch" and shared) or (show_lines=="shared" and not shared)): + if self.boundary[i]: + l, = ax.plot([rA[0], rB[0]],[rA[1], rB[1]], color=colors[i], linestyle='--', lw=thicks[i]) + else: + l, = ax.plot([rA[0], rB[0]],[rA[1], rB[1]], color=colors[i], linestyle=styles[i], lw=thicks[i]) + if 'l' in labels: + coord = 0.5*(rA + rB) # position label at midpoint between line ends + ax.text(coord[0], coord[1], f"{i+1}", bbox=dict(facecolor='none', edgecolor='k')) + + + # display colorbar + if not line_val in ["uniform", "two", "groups"]: + if colorbar==2: + if 'cbax' in kwargs: + #if isinstance(colorbar, plt.Axes): # if an axes has been passed in via colorbar + plt.gcf().colorbar(s_m, label=colorbar_label, ax=kwargs["cbax"], shrink=0.4, aspect=12) # put the colorbar on that axes + else: + raise ValueError("An axes to put the colorbar beside must be provided (as 'cbax') when colorbar=2") + + elif colorbar == 1: # make a regular colorbar on the current axes + cax = plt.gca().inset_axes([1.1, 0, 0.05, 1]) + plt.gcf().colorbar(s_m, label=colorbar_label, cax=cax) + elif colorbar == 0: # don't make a colorbar + pass + else: + raise ValueError("Unrecognized entry for colorbar when calling plot2d.") + + + #plot each platform and anchor + #for i in range(self.coords.shape()[0]): # loop through each platform or anchor + for i in range(len(self.intraMat)): + + # platform + if i < self.nPtfm: + ax.plot(self.coords[i,0], self.coords[i,1], 'ko', markersize = 6) + + # plot watch circles if requested + if watch_circles > 0: + if not hasattr(self, 'xi_sweep'): + raise Exception("In a Linear System, windsweep must be called before trying to plot watch circles.") + + scale = watch_circles + + center_x = self.coords[i,0] + center_y = self.coords[i,1] + + # plot calculated displacement envelopes + #disps_x = self.xi_sweep[:,2*i] * scale + #disps_y = self.xi_sweep[:,2*i+1] * scale + #ax.plot(center_x + disps_x, center_y + disps_y,'k',lw=1.5, alpha = 0.6) + + watch_circle_coords = np.column_stack([self.xi_sweep[:,2*i ]*scale + center_x, + self.xi_sweep[:,2*i+1]*scale + center_y]) + + # ax.add_patch(mpl.patches.Polygon(watch_circle_coords, lw=1, ec=[(self.depth - np.max(self.intersectZ))/self.depth,0,0,1.0], fc=[0,0,0,0])) + ax.add_patch(mpl.patches.Polygon(watch_circle_coords, lw=1, ec=[0,0,0,0.6], fc=[0,0,0,0])) + + # Plot the boundaries + r = self.xmax * scale + + thetas = np.linspace(0, 2 * np.pi, 201) + xs, ys = (np.array(()),np.array(())) + for theta in thetas: + xs = np.append(xs,r * np.cos(theta) + center_x) + ys = np.append(ys,r * np.sin(theta) + center_y) + ax.plot(xs,ys,'r--', lw=1, alpha = 0.5) + + + if 't' in labels: + coord = np.array([self.coords[i,0], self.coords[i,1],0]) + np.array([250, 150,0]) + ax.text(coord[0], coord[1], f"T{i+1}", fontweight='bold')#, bbox=dict(facecolor='none', edgecolor='k', boxstyle='circle,pad=0.3')) + elif 'p' in labels: + coord = np.array([self.coords[i,0], self.coords[i,1],0]) + np.array([200, 200,0]) + ax.text(coord[0], coord[1], str(i+1))#, bbox=dict(facecolor='none', edgecolor='k', boxstyle='circle,pad=0.3')) + + # anchor + elif i >= self.nPtfm and self.intersectZ[i] > 0: + ax.plot(self.coords[i,0], self.coords[i,1], 'ko', markersize=6, mfc='cyan') + if 'h' in labels: + coord = np.array([self.coords[i,0], self.coords[i,1],0]) + np.array([200, 200,0]) + ax.text(coord[0], coord[1], "Hbrd"+str(i+1-self.nPtfm), bbox=dict(facecolor='none', edgecolor='c', boxstyle='circle,pad=0.3')) + else: + ax.plot(self.coords[i,0], self.coords[i,1], 'ko', markersize=6, mfc='none') + if 'a' in labels: + coord = np.array([self.coords[i,0], self.coords[i,1],0]) + np.array([200, 200,0]) + ax.text(coord[0], coord[1], "Anch"+str(i+1-self.nPtfm), bbox=dict(facecolor='none', edgecolor='k', boxstyle='circle,pad=0.3')) + + + + + #Uncomment to hard code labels + #plt.legend(shadow=True, loc="upper left") <<< still need to sort out legend + if line_val == 'groups': + + from matplotlib.lines import Line2D + handles = [] + for i in range(len(self.mooringGroups)): + handles.append(Line2D([0], [0], label=f'Group {i}', color=clist[i])) + + plt.legend(handles=handles) + + ax.set_aspect('equal') + + if not show_axes: + ax.axis('off') + + if len(title) > 0: + plt.title(title) + + # if this made a new figure, return its handles + #if axes == None: + + # Plot the WEA boundaries if given: + if wea: + x, y = wea.exterior.xy + plt.plot(x, y, color='green', linestyle='--') + + return fig, ax + + + + + + #note: method analyzeWind(self) made some nice plots with binning offsets by direction according to severity + # See code prior to March 20 for this capability. + + + + def eigenAnalysis(self, plot=0, M=1e6, deg=0): + ''' + deg + first desired direction of turbine 1 for organizing eigenmodes. Default 0 (deg) + ''' + + v1 = [[ np.cos(np.radians(deg))], [np.sin(np.radians(deg))]] + v2 = [[-np.sin(np.radians(deg))], [np.cos(np.radians(deg))]] + + + #Take code and ideas from patrick and run eigen analysis + + #Define and Populate Mass Matrix + if np.isscalar(M): + self.MassMatrix = np.zeros((2*self.nPtfm, 2*self.nPtfm)) + np.fill_diagonal(self.MassMatrix, M) + else: # if it's a full matrix + if M.shape != self.SystemStiffness.shape: + S = self.SystemStiffness + raise ValueError(f'The mass matrix is of size {M.shape[0]}x{M.shape[1]} and needs to be of size {S.shape[0]}x{S.shape[1]}') + else: + self.MassMatrix = M + + + #Calculate eigenvalues and eigenvectors (note: eigenvectors or mode shapes are *columns*) + self.eigenvalues, self.eigenvectors = np.linalg.eig(np.matmul(np.linalg.inv(self.MassMatrix), self.SystemStiffness)) + + #Calculate Natrual Frequency + self.nat_freqs = np.real(np.sqrt(self.eigenvalues)) + + #Find Indicies to sort from smallest to largest + sort_indices = np.argsort(self.nat_freqs) + + + #Use sort_indices to sort natrual frequency, eigenvalues and eigenvectors + self.nat_freqs = np.array([self.nat_freqs[i] for i in sort_indices]) + self.eigenvalues = np.array([self.eigenvalues[i] for i in sort_indices]) + self.eigenvectors = np.transpose([self.eigenvectors[:,i] for i in sort_indices]) + self.periods = np.pi * 2 / self.nat_freqs + + #Round periods to 5 decimals + self.periods = np.round(self.periods,5) + + #Pretty plots + #Loop through each eigen vector + #Re-orient eigenvector pairs to look nice + for period in set(self.periods): + count = np.count_nonzero(self.periods == period) + + #If there are duplicates, re-order them so that they are orthogonal + if count == 2: + #print('re-normalizing modes for period {}'.format(period)) + + #Get indicies + ind = [i for i in range(len(self.periods)) if self.periods[i] == period] + eigs = np.empty([len(self.periods),0]) + for i in ind: + #eigs is a nxc matrix where n is the number of DOF and c is the period count + eigs = np.column_stack((eigs,self.eigenvectors[:,i])) + #print(eigs) + + #Make orthogonal + eigs = scipy.linalg.orth(eigs) + + #A elegant bit of linear algebra used to get desired directions + #desired directions + dir1 = np.array(v1) + dir2 = np.array(v2) + dirs = np.append(dir1,dir2,axis = 1) + + #current directions + current = eigs[:2,:2] + + #get the weights needed + #weights1 = np.matmul(np.linalg.inv(current),dir1) + #weights2 = np.matmul(np.linalg.inv(current),dir2) + weights = np.matmul(np.linalg.inv(current),dirs) + + #trasform eigs using weights + eigs = np.matmul(eigs,weights) + + #Update variables + for i in ind: + self.eigenvectors[:,i] = eigs[:,0] + eigs = np.delete(eigs, 0, axis = 1) + + + #Plot Things + if plot == 1: + + def closestDivisors(n): + a = round(np.sqrt(n)) + while n%a > 0: a -= 1 + return int(a),int(n//a) + + rows, cols = closestDivisors(len(self.eigenvalues)) + + fig,ax = plt.subplots(rows,cols) + #Loop through each eigen values + for ind in range(len(self.eigenvalues)): + # np.unravel_index() allows linear indexing of a 2D array, like in matlab + if len(np.shape(ax)) == 2: + plt_ind = np.unravel_index(ind,[rows,cols],'F') + else: + plt_ind = ind + + self.eigenPlot(ind, ax=ax[plt_ind]) + + ''' + #size eigenvector #TODO: Change this based on the size of the plot + eigenvector = self.eigenvectors[:,ind] * 1500 + + + #Loop through each point + for i in range(self.nPtfm): + r = np.array([self.coords[i,0], self.coords[i,1]]) + + ax[plt_ind].plot(r[0], r[1], 'ko', markersize = 2) + + ax[plt_ind].quiver(np.array(r[0]), + np.array(r[1]), + np.array(eigenvector[2*i]), + np.array(eigenvector[2*i+1]) + ,units='xy', scale=1) + + ax[plt_ind].set_aspect('equal') + ax[plt_ind].set_xticks([]) + ax[plt_ind].set_yticks([]) + ax[plt_ind].set_axis_off() + ax[plt_ind].set_xlim(ax[plt_ind].get_xlim()[0] - 1000, ax[plt_ind].get_xlim()[1] + 1000) + ax[plt_ind].set_ylim(ax[plt_ind].get_ylim()[0] - 1000, ax[plt_ind].get_ylim()[1] + 1000) + ax[plt_ind].set_title('T = {:.3f}s'.format(self.periods[ind])) + ''' + #TODO + #Nice way to make them perpindicular + #Animation of them rotating + + #Collective Mode + #is there a slick way to add the two together + #anti-collective mode + + #kx collective + #kt collective + + #kx anti-collective + #kt anti-collective + + + #1. Singlue turbine - 2 modes, same period + #2. 2 turbines - 4 modes, all permuatataions of kt/kx, col/anti-col + # no two modes have the same peiord. + #3. 4 turbine square - 8 modes. 2 copies of all perumatations of kt/kx + #4 3 turbines triangle - 6 modes. Things get weird because all motion + # includes combinations of kt and kx + #5 6 turbine hexagon - 12 modes. Theres 120 deg symmetry so I expect + # atleast 3 copies of all permutations. I expect these permutations + # to look similar to the triangle + #6 7 turbine hexagon - 14 modes. There are 120 deg symmetry so I again + # expect 3 copies of all permutations. Things get weird because 14 + # is not divisable by 3, so I don't quite know where that leads + + + # new method to plot any given eigenmode + def eigenPlot(self, ind, ax=None, period=True, figsize=(5,5), length=800, color='k', line_width=4, head_size=3): + '''Plot an eigenmode of a Linear System. i is the mode index. eigenAnalysis must be called first.''' + + if ax == None: + fig, ax = plt.subplots(1,1, figsize=figsize) + else: + fig = ax.get_figure() + + # get largest length of an eigenvector horizontal motion vector + maxLength = max(np.hypot(self.eigenvectors[0::2,ind], self.eigenvectors[1::2,ind])) + + # scale eigenvector to desired length + eigenvector = self.eigenvectors[:,ind] * length/maxLength + + #Loop through each point + for i in range(self.nPtfm): + r = np.array([self.coords[i,0], self.coords[i,1]]) + + ax.plot(r[0], r[1], 'ko', markersize=2) + + ax.quiver(np.array(r[0]), + np.array(r[1]), + np.array(eigenvector[2*i]), + np.array(eigenvector[2*i+1]), + units='dots', width=line_width, color=color, zorder=10, + headwidth=head_size, headlength=head_size, headaxislength=head_size, + angles='xy', scale_units='xy', scale=1) + #units='xy', scale=1) + + ax.set_aspect('equal') + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_axis_off() + #ax.set_xlim(ax.get_xlim()[0] - length, ax.get_xlim()[1] + length) + #ax.set_ylim(ax.get_ylim()[0] - length, ax.get_ylim()[1] + length) + if period: + ax.set_title('T = {:.3f}s'.format(self.periods[ind])) + + return fig, ax + + # :::::::::::: methods below here to eventually be moved to separate code ::::::::::::::: + + def calibrate(self, percent_droop=50, percent_drag=60, plots=0): + + + def laylength_eval(X, args): + '''Function to be solved for lay length target''' + + # Step 1. break out design variables and arguments into nice names + L = X[0] + [Xf,Zf,EA,W] = args + + # Step 2. do the evaluation (this may change mutable things in args) + (Fx1, Fy1, Fx2, Fy2, info) = mp.catenary(Xf, Zf, L, EA, W) + + # Step 3. group the outputs into objective function value and others + Y = info["LBot"] # objective function + oths = dict(message="none") # other outputs - returned as dict for easy use + + return np.array([Y]), oths, False + + + def laylength_step(X, args, Y, oths, Ytarget, err, tols, iter, maxIter): + '''Stepping functions for achieving lay length target''' + + L = X[0] + [Xf,Zf,EA,W] = args + LBot = Y[0] + + if LBot <= 0: # if no seabed contact, increase line length by 10% of spacing + dL = 0.1*Xf + + else: # get numerical derivative + deltaL = 2*tols[0] # step size + (Fx1, Fy1, Fx2, Fy2, info) = mp.catenary(Xf, Zf, L+deltaL, EA, W) # evaluate LBot in perturbed case + LBot2 = info["LBot"] + dLBot_dL = (LBot2-LBot)/deltaL # derivative + + # adjust as per Netwon's method + dL = -err[0]/dLBot_dL + + return np.array([dL]) # returns dX (step to make) + + + def droop_eval(X, args): + '''Function to be solved for shared droop target''' + + # Step 1. break out design variables and arguments into nice names + L = X[0] + [Xf,Zf,EA,W,cb] = args + + # Step 2. do the evaluation (this may change mutable things in args) + (Fx1, Fy1, Fx2, Fy2, info) = mp.catenary(Xf, Zf, L, EA, W, cb) + + # Step 3. group the outputs into objective function value and others + Y = info["Zextreme"] # objective function + oths = dict(message="none") # other outputs - returned as dict for easy use + + return np.array([Y]), oths, False + + + def droop_step(X, args, Y, oths, Ytarget, err, tols, iter, maxIter): + '''Stepping functions for achieving shared droop target''' + + L = X[0] + [Xf,Zf,EA,W,cb] = args + Zmin = Y[0] + + if Zmin >= -tols[0]: # if nearly no droop at all (in which case derivative will be near zero), add length + dL = 0.1*Xf + + else: # get numerical derivative + deltaL = 2*tols[0] # step size + (Fx1, Fy1, Fx2, Fy2, info) = mp.catenary(Xf, Zf, L+deltaL, EA, W, cb) # evaluate droop in perturbed case + Zmin2 = info["Zextreme"] + dZmin_dL = (Zmin2-Zmin)/deltaL # derivative + + # adjust as per Netwon's method + dL = -err[0]/dZmin_dL + + return np.array([dL]) # returns dX (step to make) + + + # initialize 3D coordinates (can probably go in init) + coords = np.zeros([len(self.coords),3]) + for j in range(len(self.coords)): + if j < self.nPtfm: + coords[j,:] = np.array([self.coords[j][0], self.coords[j][1], 0]) + else: + coords[j,:] = np.array([self.coords[j][0], self.coords[j][1], -self.depth+self.intersectZ[j]]) + + # Just need to get an initial Fx to send to LineDesign -> assume it's a simple catenary for now + + # Loop through each mooring line and update its properties + for ii in range(np.max(self.intraMat)): + mg = self.mooringGroups[ii] # the mooring group shortcut + i = self.group.index(ii+1) # the index of endA/endB where this mooring line object occurs first + + rA = coords[self.endA[i]] # starting coordinates of the line + rB = coords[self.endB[i]] # ending coordinates of the line + + # initialize line parameters + Xf = np.linalg.norm((rA - rB)[0:2]) # horizontal distance (a.k.a. L_xy) + Zf = np.linalg.norm((rA - rB)[2 ]) # vertical distance (aka depth) + L = 1.2*np.hypot(Xf, Zf) # unstretched line length (design variable) + EA = 1232572089.6 # EA value of 120mm chain + W = 2456.820077481978 # W value of 120mm chain + cb = -self.depth # the distance down from end A to the seabed + + + # if anchored, adjust line length to have line on seabed for percent_drag of spacing + if mg['shared']==0: # anchored line + X0 = [L] + LBotTarget = [percent_drag/100*Xf] # target lay length is percent_drag of horizontal anchor spacing + args = [Xf,Zf,EA,W] # the other (constant) arguments needed by catenary + X, Y, info = msolve.dsolve2(laylength_eval, X0, Ytarget=LBotTarget, step_func=laylength_step, args=args, maxIter=20) + + # set line length to the solved value + L = X[0] + # Call catenary function with resized line length + (Fx1, Fy1, Fx2, Fy2, info) = mp.catenary(Xf, Zf, L, EA, W, plots=plots) + + # if shared, adjust the line length to have the line droop for percent_droop of spacing + elif mg['shared']==1 and not mg['net']: # shared line + X0 = [L] + DroopTarget = [-percent_droop/100*self.depth - rB[2]] # target droop elevation relative to fairlead (from percent_droop of depth) + args = [Xf,Zf,EA,W,cb] # the other (constant) arguments needed by catenary + X, Y, info = msolve.dsolve2(droop_eval, X0, Ytarget=DroopTarget, step_func=droop_step, args=args, maxIter=20) + + # set line length to the solved value + L = X[0] + + # Call catenary function with resized line length + (Fx1, Fy1, Fx2, Fy2, info) = mp.catenary(Xf, Zf, L, EA, W, cb, plots = plots) + + elif mg['net'] and not mg.get('tendON', False): # hybrid line (without tendon) + Xf *= 2 + Zf *= 0 + X0 = [2*L] + DroopTarget = [rA[2]] + args = [Xf,Zf,EA,W,cb] + X, Y, info = msolve.dsolve2(droop_eval, X0, Ytarget=DroopTarget, step_func=droop_step, args=args, maxIter=20) + + # set line length to the solved value + L = X[0] + + # Call catenary function with resized line length + (Fx1, Fy1, Fx2, Fy2, info) = mp.catenary(Xf, Zf, L, EA, W, cb, plots = plots) + + elif mg['net'] and mg.get('tendON', False): # hybrid line (with tendon) - assume shared with zero droop + X0 = [L] + DroopTarget = [0] # target droop elevation relative to fairlead (from percent_droop of depth)== + args = [Xf,Zf,EA,W,cb] # the other (constant) arguments needed by catenary + X, Y, info = msolve.dsolve2(droop_eval, X0, Ytarget=DroopTarget, step_func=droop_step, args=args, maxIter=20) + + # set line length to the solved value + L = X[0] + + # Call catenary function with resized line length + (Fx1, Fy1, Fx2, Fy2, info) = mp.catenary(Xf, Zf, L, EA, W, cb, plots = plots) + + Fx = np.abs(info['HF']) # horizontal tension component at fairlead [N] + Kx = info['stiffnessB'][0,0] # effective horizontal stiffness at fairlead [N/m] + kt_over_k = Fx / Kx / Xf # kt/Kx = Fx/L_xy / Kx + + + if plots == 2: + plt.title('Catenary Line Profile for Mooring Line: {}'.format(ii+1)) + + print('Force for Mooring Line {}: {}'.format(ii, Fx)) + print('Stiffness for Mooring Line {}: {}'.format(ii, Kx)) + print('\n{} '.format('kt/k for Mooring Line {}: {}'.format(ii, kt_over_k))) + + + # Update mooringGroups dictionary values with + mg['ten__w'] = Fx/W + mg['kl__w'] = Kx/W + mg['kt__kl'] = kt_over_k + mg['L'] = L # <<<< this is a shortcut that should be done outside of LinearSystem in future + + + + def makeMoorPySystem(self): + ''' sets up a *very simple* MoorPy system using points for the FOWTS, and no bodies ''' + + ms = mp.System(depth=self.depth) + + + # make free points for platforms (assumed at z=0) and fixed points for anchors + for i in range(len(self.coords)): + if i < self.nPtfm: + #ms.addPoint(0, np.hstack([self.coords[i][:2], 0]), m=1e9, v=2e9*ms.rho) # add buoyancy as double the mass - this should make it equilibrate at z=0 + ms.addPoint(0, np.hstack([self.coords[i][:2], 0]), DOFs=[0,1]) # specify as free to move in x and y only + else: + ms.addPoint(1, np.hstack([self.coords[i][:2], -self.depth])) + + + # also add some generic line types, one for each line grouping as defined in the entries of intraMat + for i in range(np.max(self.intraMat)): + shared = i+1 in self.intraMat[:self.nPtfm, :self.nPtfm] # detect if it's a shared line (True) or not (False, assumed anchored) + massden = self.mooringGroups[i]['w']/9.81 + + ms.lineTypes[f"type{i+1}"] = mp.LineType(f"type{i+1}", 0.0, massden, 1.0e15) #, shared=shared) + + + # make lines using adjacency matrix + linenum = 1 + for i in range(len(self.coords)): + for j in range(i): + k = np.int(self.intraMat[i,j]) # the entry in the intraMat corresponds to the line type number (starting from 1) + if k > 0: + ml = self.mooringGroups[k-1] + ms.addLine(ml['L'], f'type{k}') + ms.pointList[i].attachLine(linenum, 1) + ms.pointList[j].attachLine(linenum, 0) + linenum = linenum + 1 + ''' this should be done to coords if it happens anywhere + if self.center: + cx = np.mean([point.r[0] for point in ms.pointList]) + cy = np.mean([point.r[1] for point in ms.pointList]) + ms.transform(trans=[-cx, -cy]) + ''' + + ms.initialize() + + return ms + + + def getYawStiffness(self, rf): + yawStiffness = np.zeros(self.nPtfm) + for i in range(self.nPtfm): + connectedLines = np.where(np.abs(self.StructureMatrix[i*2:i*2+1, :]) > 0)[1] + for j in connectedLines: + if self.boundary[j]: + lineYawStiffness = self.mooringGroups[self.group[j] - 1]['ten']/(2*self.l[j]) * rf**2 + else: + lineYawStiffness = self.mooringGroups[self.group[j] - 1]['ten']/self.l[j] * rf**2 + yawStiffness[i] += lineYawStiffness + return yawStiffness + + + def removeRedundantGroups(self): + ''' + this method: + 1) removes any zero weight groups (lower than 1% of mean weight) + 2) merge mooring groups that are similar to one another (mooring groups that their w are 5% different from the w range), and + 3) reformulates the system and its mooring groups + ''' + # calculate the mean weight for the LinearSystem: + wSum = sum(mg['w'] for mg in self.mooringGroups) + wMax = max(mg['w'] for mg in self.mooringGroups) + wMin = min(mg['w'] for mg in self.mooringGroups) + wMean = wSum / len(self.mooringGroups) + + # find the indices of mooring groups to keep and the ones to remove + remove_indices = [i for i, mg in enumerate(self.mooringGroups) if mg['w'] < 0.01 * wMean] + keep_indices = list(set(range(len(self.mooringGroups))) - set(remove_indices)) + + # Update mooringGroups and profileMap + self.mooringGroups = [self.mooringGroups[i] for i in keep_indices] + self.profileMap = [self.profileMap[i] for i in keep_indices] + + # create a mapping from old indices to new indices + index_mapping = {old: new for new, old in enumerate(keep_indices)} + + # remove lines that belong to redundant mooring groups + new_l, new_u, new_endA, new_endB, new_rA, new_rB, new_angA, new_angB, new_boundary, new_group = [], [], [], [], [], [], [], [], [], [] + + for i in range(len(self.l)): + if self.group[i] - 1 in keep_indices: # subtract 1 because group starts at 1, not 0 + new_group.append(index_mapping[self.group[i] - 1] + 1) + new_l.append(self.l[i]) + new_u.append(self.u[i]) + new_endA.append(self.endA[i]) + new_endB.append(self.endB[i]) + new_rA.append(self.rA[i]) + new_rB.append(self.rB[i]) + new_angA.append(self.angA[i]) + new_angB.append(self.angB[i]) + new_boundary.append(self.boundary[i]) + + # remove from intra-cell adjacency matrix + for remIdx in remove_indices: + for i, mg in enumerate(self.group): + if mg == remIdx + 1: + remA = self.endA[i] + remB = self.endB[i] + self.intraMat[remA, remB], self.intraMat[remB, remA] = 0, 0 + + unique_intra_groups = sorted(np.unique(self.intraMat[self.intraMat > 0])) + intra_group_mapping = {old: new for new, old in enumerate(unique_intra_groups, start=1)} + for old, new in intra_group_mapping.items(): + self.intraMat[self.intraMat == old] = new + # update the properties with the new filtered lists + self.l = new_l + self.u = new_u + self.endA = new_endA + self.endB = new_endB + self.rA = new_rA + self.rB = new_rB + self.angA = new_angA + self.angB = new_angB + self.boundary = new_boundary + self.group = new_group + self.nLines = len(self.l) + self.StructureMatrix = np.zeros([2*self.nPtfm, self.nLines]) # rows: DOFs; columns: lines + + # merge mooring Groups + removeIndex = [] + for i, mg1 in enumerate(self.mooringGroups[:-1]): + for j in range(i+1, len(self.mooringGroups)): + mg2 = self.mooringGroups[j] + # Check following conditions: + # if the difference in weight is minimial, + # if they both have the same shared map, + # and if they have the same length: + con1 = np.abs(mg2['w'] - mg1['w'])/(wMax - wMin) < 0.05 + con2 = self.profileMap[i]==self.profileMap[i+1] + con3 = np.round(mg1['l'], 2)==np.round(mg2['l'], 2) + if con1 and con2 and con3: + self.mooringGroups[i]['w'] = (mg1['w'] + mg2['w']) / 2 + removeIndex.append(j) + self.group = [i+1 if g==j+1 else g for g in self.group] + idx1, idx2 = np.where(self.intraMat==j+1) + self.intraMat[idx1, idx2] = i+1 + + if removeIndex: + for idx in sorted(np.unique(removeIndex), reverse=True): + del self.mooringGroups[idx] + del self.profileMap[idx] + + unique_groups = sorted(np.unique(self.group)) + unique_intra_groups = sorted(np.unique(self.intraMat[self.intraMat > 0])) + group_mapping = {old: new for new, old in enumerate(unique_groups, start=1)} + intra_group_mapping = {old: new for new, old in enumerate(unique_intra_groups, start=1)} + self.group = [group_mapping[g] for g in self.group] + + for old, new in intra_group_mapping.items(): + self.intraMat[self.intraMat == old] = new + + for j in range(self.nLines): + if self.endA[j] < self.nPtfm: # only if not an anchor + self.StructureMatrix[self.endA[j]*2 , j] = self.u[j][0] + self.StructureMatrix[self.endA[j]*2 + 1, j] = self.u[j][1] + + if self.endB[j] < self.nPtfm: # only if not an anchor + self.StructureMatrix[self.endB[j]*2 , j] = -self.u[j][0] + self.StructureMatrix[self.endB[j]*2 + 1, j] = -self.u[j][1] + + # Check if any row/column in the intraMat is empty, delete it, and delete the corresponding self.coords: + empty_rows = np.where(~self.intraMat.any(axis=1))[0] + empty_cols = np.where(~self.intraMat.any(axis=0))[0] + empty_indices = np.unique(np.concatenate((empty_rows, empty_cols))) + if len(empty_indices) > 0: + # Remove empty rows and columns from intraMat + self.intraMat = np.delete(self.intraMat, empty_indices, axis=0) + self.intraMat = np.delete(self.intraMat, empty_indices, axis=1) + self.coords = np.delete(self.coords, empty_indices, axis=0) + self.intersectZ = np.delete(self.intersectZ, empty_indices, axis=0) + + # Reassign the 'type' of each mooring group after deletion + for i, group in enumerate(self.mooringGroups): + group['type'] = i + 1 # Reassign types from 1 to len(mooringGroups) + + self.preprocess() + self.optimize() + +# ------- test script + +if __name__ == '__main__': + + import Array as array + + from moorpy.helpers import printVec, printMat + + # specify the array layouts and their parameters + T = 2000. + A = 1200. + depth = 600. + + ''' + # ----- old examples ----- + + #coords, intraMat, nPtfm, name = array.layout_pair_4_anchs(T, A, deg=120) + #coords, intraMat, nPtfm, name = array.layout_triangle_3_anchs(T, A) + coords, intraMat, nPtfm, name = array.layout_1_square_8_anchs(T, A) + + sys = LinearSystem(coords, intraMat, nPtfm, depth=600., fmax=1e6, + xmax=0.1*min(T,A)) + + + + sys.preprocess() + + q= sys.optimize() + print(q) + sys.plot2d(watch_circles=1, line_val="stiffness") + + #sys.eigenAnalysis(plot=1) + + #sys.updateDesign() + + + # ----- newer more advanced example ----- + ''' + print("New LinearSystem example") + # def __init__(self, coords, intraMat, nPtfm, interMats=None, + # interCoords=None, inits=None, profileMap=None, intersectZ=None, + # rFair=0, zFair=0, depth=600., fmax=1e6, xmax=40.0, plots=0, nonlin=1.0, + # center=True, old_mode=True): + + #coords, intraMat, nPtfm, name = array.Grid3x3(T, A) + #coords, intraMat, nPtfm, name = array.Fat_Hexagon(T, A, fathexagontype='min_linetypes') + coords, intraMat, nPtfm, name = array.Square(T, A, type='water-strider') + + + mooringGroupDict = [ + dict(w=1500, ten=100000, kl=10000, kt=50, shared=False), + #dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True)] #, + ''' + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True)] + ''' + + sys = LinearSystem(coords, intraMat, nPtfm, depth=600., fmax=1e6, + xmax=0.1*min(T,A), inits=mooringGroupDict, old_mode=False) + + + sys.preprocess() + + + sys.getSystemStiffness() + sys.windsweep() # figure out watch circles + + sys.plot2d(watch_circles=4, line_val="stiffness") + + + # now try a stiffness optimization + + sys.optimize2(display=2) + sys.plot2d(watch_circles=4, line_val="stiffness") + + ''' + + print("New LinearSystem example - with inter-array shared lines") + # def __init__(self, coords, intraMat, nPtfm, interMats=None, + # interCoords=None, inits=None, profileMap=None, intersectZ=None, + # rFair=0, zFair=0, depth=600., fmax=1e6, xmax=40.0, plots=0, nonlin=1.0, + # center=True, old_mode=True): + + coords, intraMat, nPtfm, name = array.Grid3x3(T, A) + + # remove anchored lines on side E-W turbines (turbine 3 and 5) + intraMat[20,3] = 0 + intraMat[14,5] = 0 + + # make interMats + interMats = [] + a = np.zeros([9,9]) + a[5,3] = 3 # connect turbines 3 and 5 with a shared line + print(a) + interMats.append(np.array(a)) + + # specify a lateral pattern spaced 6 km apart so shared lines all have same length + interCoords = [[600,0]] + + mooringGroupDict = [ + dict(w=1500, ten=100000, kl=10000, kt=500, shared=False), + dict(w=1500, ten=100000, kl=10000, kt=500, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=500, shared=True)] + + + sys = LinearSystem(coords, intraMat, nPtfm, depth=600., fmax=1e6, + xmax=0.1*min(T,A), + interMats = interMats, interCoords = interCoords, + inits=mooringGroupDict, old_mode=False) + + sys.getSystemStiffness() + sys.windsweep() # figure out watch circles + + sys.plot2d(watch_circles=1, line_val="stiffness") + ''' + + # ----- example with inter-array shared lines! ----- + + + ''' + + import fadesign.conceptual.Cell as cell + #coords, intraMat, nPtfm, interMats, interCoords = cell.Grid3x3(T, A) # no inter shared lines! + #coords, intraMat, nPtfm, interMats, interCoords = cell.honeycombPattern(T) # + coords, intraMat, nPtfm, interMats, interCoords = cell.grid() # + + breakpoint() + + mooringGroupDict = [ + dict(w=1500, ten=100000, kl=10000, kt=50, shared=False), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True), + dict(w=1500, ten=100000, kl=10000, kt=50, shared=True)] + + + sys = LinearSystem(coords, intraMat, nPtfm, interCoords=interCoords, + depth=600., fmax=1e6, + xmax=0.1*min(T,A), inits=mooringGroupDict, old_mode=False) + + sys.getSystemStiffness() + sys.windsweep() # figure out watch circles + + sys.plot2d(watch_circles=1, line_val="stiffness") + + ''' + + + plt.show() \ No newline at end of file diff --git a/famodel/design/fadsolvers.py b/famodel/design/fadsolvers.py new file mode 100644 index 00000000..b1471eae --- /dev/null +++ b/famodel/design/fadsolvers.py @@ -0,0 +1,1857 @@ +# a file to hold the custom solvers used in FAD + +import numpy as np +import matplotlib.pyplot as plt +import time +#from scipy.optimize import fsolve +#import scipy.optimize + + +# ================================ original above / modified below =========================================== + + +""" +def eval_func1(X, args): + '''returns target outputs and also secondary outputs for constraint checks etc.''' + + # Step 1. break out design variables and arguments into nice names + + # Step 2. do the evaluation (this may change mutable things in args) + + # Step 3. group the outputs into objective function value and others + + return Y, oths + + + +def step_func1(X, args, Y, oths, Ytarget, err, tol, iter, maxIter): + '''General stepping functions, which can also contain special condition checks or other adjustments to the process + + ''' + + # step 1. break out variables as needed + + # do stepping, as well as any conditional checks + + return dL # returns dX (step to make) +""" + + + +def dsolve1D(eval_func, step_func, X0, Ytarget, args, tol=0.0001, maxIter=20, Xmin=-np.inf, Xmax=np.inf): + ''' + Assumes the function is positive sloped (so use -X if negative-sloped) + + tol - relative convergence tolerance (relative to step size, dX) + Xmin, Xmax - bounds. by default start bounds at infinity + ''' + + X = 1*X0 # start off design variable + + + print(f"Starting dsolve1D iterations>>> aiming for Y={Ytarget}") + + for iter in range(maxIter): + + + # call evaluation function + Y, oths = eval_func(X, args) + + # compute error + err = Y - Ytarget + + print(f" new iteration with X={X:6.2f} and Y={Y:6.2f}") + + # update/narrow the bounds (currently this part assumes that the function is positively sloped) << any N-D equivalent? + if err > 0:# and L < LUpper: # + Xmax = 1.0*X + elif err < 0:# and L > LLower: # + Xmin = 1.0*X + + if iter==maxIter-1: + print("Failed to find solution after "+str(iter)+" iterations, with error of "+str(err)) + breakpoint() + break + + #>>>> COULD ALSO HAVE AN ITERATION RESTART FUNCTION? >>> + # that returns a restart boolean, as well as what values to use to restart things if true. How? + + else: + dX = step_func(X, args, Y, oths, Ytarget, err, tol, iter, maxIter) + + + # check for convergence + if np.abs(dX) < tol*(np.abs(X)+tol): + print("Equilibrium solution completed after "+str(iter)+" iterations with error of "+str(err)+" and dX of "+str(dX)) + print("solution X is "+str(X)) + break + + + # Make sure we're not diverging by keeping things within narrowing bounds that span the solution. + # I.e. detect potential for oscillation and avoid bouncing out and then back in to semi-taut config + # Use previous values to bound where the correct soln is, and if an iteration moves beyond that, + # stop it and put it between the last value and where the bound is (using golden ratio, why not). + if dX > 0 and X+dX >= Xmax: # if moving up and about to go beyond previous too-high value + X = X + 0.62*(Xmax-X) # move to mid point between current value and previous too-high value, rather than overshooting + print("<--|") + elif dX < 0 and X+dX <= Xmin: # if moving down and about to go beyond previous too-low value + X = X + 0.62*(Xmin-X) #0.5*(L+LLower) # move to mid point between current value and previous too-low value, rather than overshooting + print("|-->") + else: + X = X+dX + + + return X, Y, dict(iter=iter, err=err) + + + +# X, Y, info = dsolve1D(eval_func1, step_func1, X0, Ytarget, args, tol=tol, maxIter=maxIter) + + + + +# TODO: add default step_func (finite differencer), Ytarget, and args + +def dsolve(eval_func, X0, Ytarget=[], step_func=None, args=[], tol=0.0001, maxIter=20, + Xmin=[], Xmax=[], a_max=2.0, dX_last=[], display=0): + ''' + PARAMETERS + ---------- + eval_func : function + function to solve (will be passed array X, and must return array Y of same size) + X0 : array + initial guess of X + Ytarget : array (optional) + target function results (Y), assumed zero if not provided + stp_func : function (optional) + function use for adjusting the variables (computing dX) each step. + If not provided, Netwon's method with finite differencing is used. + args : list + A list of variables (e.g. the system object) to be passed to both the eval_func and step_func + tol : float + *relative* convergence tolerance (applied to step size components, dX) + Xmin, Xmax + Bounds. by default start bounds at infinity + a_max + maximum step size acceleration allowed + dX_last + Used if you want to dictate the initial step size/direction based on a previous attempt + ''' + success = False + + # process inputs and format as arrays in case they aren't already + + X = np.array(X0, dtype=np.float64) # start off design variable + N = len(X) + + Xs = np.zeros([maxIter,N]) # make arrays to store X and error results of the solve + Es = np.zeros([maxIter,N]) + dXlist = np.zeros([maxIter,N]) + dXlist2 = np.zeros([maxIter,N]) + + + # check the target Y value input + if len(Ytarget)==N: + Ytarget = np.array(Ytarget, dtype=np.float64) + elif len(Ytarget)==0: + Ytarget = np.zeros(N, dtype=np.float64) + else: + raise TypeError("Ytarget must be of same length as X0") + + + # if a step function wasn't provided, provide a default one + if step_func==None: + if display>1: + print("Using default finite difference step func") + + def step_func(X, args, Y, oths, Ytarget, err, tol, iter, maxIter): + + J = np.zeros([N,N]) # Initialize the Jacobian matrix that has to be a square matrix with nRows = len(X) + + for i in range(N): # Newton's method: perturb each element of the X variable by a little, calculate the outputs from the + X2 = np.array(X) # minimizing function, find the difference and divide by the perturbation (finding dForce/d change in design variable) + deltaX = tol*(np.abs(X[i])+tol) + X2[i] += deltaX + Y2, _, _ = eval_func(X2, args) # here we use the provided eval_func + + J[:,i] = (Y2-Y)/deltaX # and append that column to each respective column of the Jacobian matrix + + if N > 1: + dX = -np.matmul(np.linalg.inv(J), Y-Ytarget) # Take this nth output from the minimizing function and divide it by the jacobian (derivative) + else: + + dX = np.array([-(Y[0]-Ytarget[0])/J[0,0]]) + + if display > 1: + print(f" step_func iter {iter} X={X[0]:9.2e}, error={Y[0]-Ytarget[0]:9.2e}, slope={J[0,0]:9.2e}, dX={dX[0]:9.2e}") + + return dX # returns dX (step to make) + + + + # handle bounds + if len(Xmin)==0: + Xmin = np.zeros(N)-np.inf + elif len(Xmin)==N: + Xmin = np.array(Xmin, dtype=np.float64) + else: + raise TypeError("Xmin must be of same length as X0") + + if len(Xmax)==0: + Xmax = np.zeros(N)+np.inf + elif len(Xmax)==N: + Xmax = np.array(Xmax, dtype=np.float64) + else: + raise TypeError("Xmax must be of same length as X0") + + + + if len(dX_last)==0: + dX_last = np.zeros(N) + else: + dX_last = np.array(dX_last, dtype=np.float64) + + if display>1: + print(f"Starting dsolve iterations>>> aiming for Y={Ytarget}") + + + for iter in range(maxIter): + + + # call evaluation function + Y, oths, stop = eval_func(X, args) + + # compute error + err = Y - Ytarget + + if display>1: + print(f" new iteration #{iter} with X={X} and Y={Y}") + + Xs[iter,:] = X + Es[iter,:] = err + + # stop if commanded by objective function + if stop: + break + + + if iter==maxIter-1: + if display>0: + print("Failed to find solution after "+str(iter)+" iterations, with error of "+str(err)) + breakpoint() + break + + #>>>> COULD ALSO HAVE AN ITERATION RESTART FUNCTION? >>> + # that returns a restart boolean, as well as what values to use to restart things if true. How? + + else: + dX = step_func(X, args, Y, oths, Ytarget, err, tol, iter, maxIter) + + + #if display>2: + # breakpoint() + + # Make sure we're not diverging by keeping things from reversing too much. + # Track the previous step (dX_last) and if the current step reverses too much, stop it part way. + # Stop it at a plane part way between the current X value and the previous X value (using golden ratio, why not). + + # get the point along the previous step vector where we'll draw the bounding hyperplane (could be a line, plane, or more in higher dimensions) + Xlim = X - 0.62*dX_last + + # the equation for the plane we don't want to recross is then sum(X*dX_last) = sum(Xlim*dX_last) + if np.sum((X+dX)*dX_last) < np.sum(Xlim*dX_last): # if we cross are going to cross it + + alpha = np.sum((Xlim-X)*dX_last)/np.sum(dX*dX_last) # this is how much we need to scale down dX to land on it rather than cross it + + if display > 2: + print(" limiting oscillation with alpha="+str(alpha)) + print(f" dX_last was {dX_last}, dX was going to be {dX}, now it'll be {alpha*dX}") + print(f" dX_last was {dX_last/1000}, dX was going to be {dX/1000}, now it'll be {alpha*dX/1000}") + + dX = alpha*dX # scale down dX + + # also avoid extreme accelerations in the same direction + if np.linalg.norm(dX_last) > tol: # only worry about accelerations if the last step was non-negligible + for i in range(N): + + if abs(dX_last[i]) < tol: # set the maximum permissible dx in each direction based an an acceleration limit + dX_max = a_max*10*tol*np.sign(dX[i]) + else: + dX_max = a_max*dX_last[i] + + if dX_max == 0.0: # avoid a divide-by-zero case (if dX[i] was zero to start with) + dX[i] = 0.0 + else: + a_i = dX[i]/dX_max # calculate ratio of desired dx to max dx + + if a_i > 1.0: + + if display > 2: + print(f" limiting acceleration ({1.0/a_i:6.4f}) for axis {i}") + print(f" dX_last was {dX_last}, dX was going to be {dX}") + + #dX = dX*a_max/a_i # scale it down to the maximum value + dX[i] = dX[i]/a_i # scale it down to the maximum value (treat each DOF individually) + + if display > 2: + print(f" now dX will be {dX}") + + dXlist[iter,:] = dX + if iter==196: + breakpoint() + # enforce bounds + for i in range(N): + + if X[i] + dX[i] < Xmin[i]: + dX[i] = Xmin[i] - X[i] + + elif X[i] + dX[i] > Xmax[i]: + dX[i] = Xmax[i] - X[i] + + dXlist2[iter,:] = dX + # check for convergence + if all(np.abs(dX) < tol*(np.abs(X)+tol)): + + if display>0: + print(f"dsolve converged. iter={iter}, X={X}, error={err} and dX={dX}") + + #if abs(err) > 10: + # breakpoint() + + if any(X == Xmin) or any(X == Xmax): + success = False + print("Warning: dsolve ended on a bound.") + else: + success = True + + break + + dX_last = 1.0*dX # remember this current value + + + X = X + dX + + + return X, Y, dict(iter=iter, err=err, dX=dX_last, oths=oths, Xs=Xs, Es=Es, success=success, dXlist=dXlist, dXlist2=dXlist2) + + +def dsolve2(eval_func, X0, Ytarget=[], step_func=None, args=[], tol=0.0001, maxIter=20, + Xmin=[], Xmax=[], a_max=2.0, dX_last=[], stepfac=4, display=0): + ''' + PARAMETERS + ---------- + eval_func : function + function to solve (will be passed array X, and must return array Y of same size) + X0 : array + initial guess of X + Ytarget : array (optional) + target function results (Y), assumed zero if not provided + stp_func : function (optional) + function use for adjusting the variables (computing dX) each step. + If not provided, Netwon's method with finite differencing is used. + args : list + A list of variables (e.g. the system object) to be passed to both the eval_func and step_func + tol : float or array + If scalar, the*relative* convergence tolerance (applied to step size components, dX). + If an array, must be same size as X, and specifies an absolute convergence threshold for each variable. + Xmin, Xmax + Bounds. by default start bounds at infinity + a_max + maximum step size acceleration allowed + dX_last + Used if you want to dictate the initial step size/direction based on a previous attempt + ''' + success = False + start_time = time.time() + # process inputs and format as arrays in case they aren't already + + X = np.array(X0, dtype=np.float64) # start off design variable + N = len(X) + + Xs = np.zeros([maxIter,N]) # make arrays to store X and error results of the solve + Es = np.zeros([maxIter,N]) + dXlist = np.zeros([maxIter,N]) + dXlist2 = np.zeros([maxIter,N]) + + + # check the target Y value input + if len(Ytarget)==N: + Ytarget = np.array(Ytarget, dtype=np.float64) + elif len(Ytarget)==0: + Ytarget = np.zeros(N, dtype=np.float64) + else: + raise TypeError("Ytarget must be of same length as X0") + + # ensure all tolerances are positive + if np.isscalar(tol) and tol <= 0.0: + raise ValueError('tol value passed to dsovle2 must be positive') + elif not np.isscalar(tol) and any([toli <= 0 for toli in tol]): + raise ValueError('every tol entry passed to dsovle2 must be positive') + + + # handle bounds + if len(Xmin)==0: + Xmin = np.zeros(N)-np.inf + elif len(Xmin)==N: + Xmin = np.array(Xmin, dtype=np.float64) + else: + raise TypeError("Xmin must be of same length as X0") + + if len(Xmax)==0: + Xmax = np.zeros(N)+np.inf + elif len(Xmax)==N: + Xmax = np.array(Xmax, dtype=np.float64) + else: + raise TypeError("Xmax must be of same length as X0") + + + # if a step function wasn't provided, provide a default one + if step_func==None: + if display>1: + print("Using default finite difference step func") + + def step_func(X, args, Y, oths, Ytarget, err, tols, iter, maxIter): + ''' this now assumes tols passed in is a vector''' + J = np.zeros([N,N]) # Initialize the Jacobian matrix that has to be a square matrix with nRows = len(X) + + for i in range(N): # Newton's method: perturb each element of the X variable by a little, calculate the outputs from the + X2 = np.array(X) # minimizing function, find the difference and divide by the perturbation (finding dForce/d change in design variable) + deltaX = stepfac*tols[i] # note: this function uses the tols variable that is computed in dsolve based on the tol input + X2[i] += deltaX + Y2, _, _ = eval_func(X2, args) # here we use the provided eval_func + + J[:,i] = (Y2-Y)/deltaX # and append that column to each respective column of the Jacobian matrix + + if N > 1: + dX = -np.matmul(np.linalg.inv(J), Y-Ytarget) # Take this nth output from the minimizing function and divide it by the jacobian (derivative) + else: + # if the result of the eval_func did not change, increase the stepfac parameter by a factor of 10 and calculate the Jacobian again + if J[0,0] == 0.0: + + stepfacb = stepfac*10 + + J = np.zeros([N,N]) # Initialize the Jacobian matrix that has to be a square matrix with nRows = len(X) + for i in range(N): # Newton's method: perturb each element of the X variable by a little, calculate the outputs from the + X2b = np.array(X) # minimizing function, find the difference and divide by the perturbation (finding dForce/d change in design variable) + deltaXb = stepfacb*tols[i] # note: this function uses the tols variable that is computed in dsolve based on the tol input + X2b[i] += deltaXb + Y2b, _, _ = eval_func(X2b, args) # here we use the provided eval_func + J[:,i] = (Y2b-Y)/deltaXb # and append that column to each respective column of the Jacobian matrix + + if J[0,0] == 0.0: # if the Jacobian is still 0, maybe increase the stepfac again, but there might be a separate issue + #breakpoint() + raise ValueError('dsolve2 found a zero gradient - maybe a larger stepfac is needed.') + + # if the Jacobian is all good, then calculate the dX + dX = np.array([-(Y[0]-Ytarget[0])/J[0,0]]) + + if display > 1: + print(f" step_func iter {iter} X={X[0]:9.2e}, error={Y[0]-Ytarget[0]:9.2e}, slope={J[0,0]:9.2e}, dX={dX[0]:9.2e}") + + return dX # returns dX (step to make) + + + if len(dX_last)==0: + dX_last = np.zeros(N) + else: + dX_last = np.array(dX_last, dtype=np.float64) + + if display>0: + print(f"Starting dsolve iterations>>> aiming for Y={Ytarget}") + + + for iter in range(maxIter): + + + # call evaluation function + Y, oths, stop = eval_func(X, args) + + # compute error + err = Y - Ytarget + + if display>2: + print(f" new iteration #{iter} with X={X} and Y={Y}") + + Xs[iter,:] = X + Es[iter,:] = err + + # stop if commanded by objective function + if stop: + break + + # handle tolerances input + if np.isscalar(tol): + tols = tol*(np.abs(X)+tol) + else: + tols = np.array(tol) + + # check maximum iteration + if iter==maxIter-1: + if display>0: + print("Failed to find solution after "+str(iter)+" iterations, with error of "+str(err)) + + # looks like things didn't converge, so if N=1 do a linear fit on the last 30% of points to estimate the soln + if N==1: + + m,b = np.polyfit(Es[int(0.7*iter):iter,0], Xs[int(0.7*iter):iter,0], 1) + X = np.array([b]) + Y = np.array([0.0]) + if display>1: + print(f"Using linear fit to estimate solution at X={b}") + + break + + #>>>> COULD ALSO HAVE AN ITERATION RESTART FUNCTION? >>> + # that returns a restart boolean, as well as what values to use to restart things if true. How? + + else: + dX = step_func(X, args, Y, oths, Ytarget, err, tols, iter, maxIter) + + + #if display>2: + # breakpoint() + + # Make sure we're not diverging by keeping things from reversing too much. + # Track the previous step (dX_last) and if the current step reverses too much, stop it part way. + # Stop it at a plane part way between the current X value and the previous X value (using golden ratio, why not). + + # get the point along the previous step vector where we'll draw the bounding hyperplane (could be a line, plane, or more in higher dimensions) + Xlim = X - 0.62*dX_last + + # the equation for the plane we don't want to recross is then sum(X*dX_last) = sum(Xlim*dX_last) + if np.sum((X+dX)*dX_last) < np.sum(Xlim*dX_last): # if we cross are going to cross it + + alpha = np.sum((Xlim-X)*dX_last)/np.sum(dX*dX_last) # this is how much we need to scale down dX to land on it rather than cross it + + if display > 2: + print(" limiting oscillation with alpha="+str(alpha)) + print(f" dX_last was {dX_last}, dX was going to be {dX}, now it'll be {alpha*dX}") + print(f" dX_last was {dX_last/1000}, dX was going to be {dX/1000}, now it'll be {alpha*dX/1000}") + + dX = alpha*dX # scale down dX + + # also avoid extreme accelerations in the same direction + for i in range(N): + + if abs(dX_last[i]) > tols[i]: # only worry about accelerations if the last step was non-negligible + + dX_max = a_max*dX_last[i] # set the maximum permissible dx in each direction based an an acceleration limit + + if dX_max == 0.0: # avoid a divide-by-zero case (if dX[i] was zero to start with) + breakpoint() + dX[i] = 0.0 + else: + a_i = dX[i]/dX_max # calculate ratio of desired dx to max dx + + if a_i > 1.0: + + if display > 2: + print(f" limiting acceleration ({1.0/a_i:6.4f}) for axis {i}") + print(f" dX_last was {dX_last}, dX was going to be {dX}") + + #dX = dX*a_max/a_i # scale it down to the maximum value + dX[i] = dX[i]/a_i # scale it down to the maximum value (treat each DOF individually) + + if display > 2: + print(f" now dX will be {dX}") + + dXlist[iter,:] = dX + #if iter==196: + #breakpoint() + + # enforce bounds + for i in range(N): + + if X[i] + dX[i] < Xmin[i]: + dX[i] = Xmin[i] - X[i] + + elif X[i] + dX[i] > Xmax[i]: + dX[i] = Xmax[i] - X[i] + + dXlist2[iter,:] = dX + # check for convergence + if all(np.abs(dX) < tols): + + if display>0: + print("Iteration converged after "+str(iter)+" iterations with error of "+str(err)+" and dX of "+str(dX)) + print("Solution X is "+str(X)) + + #if abs(err) > 10: + # breakpoint() + + if display > 0: + print("Total run time: {:8.2f} seconds = {:8.2f} minutes".format((time.time() - start_time),((time.time() - start_time)/60))) + + + if any(X == Xmin) or any(X == Xmax): + success = False + print("Warning: dsolve ended on a bound.") + else: + success = True + + break + + dX_last = 1.0*dX # remember this current value + + + X = X + dX + + + return X, Y, dict(iter=iter, err=err, dX=dX_last, oths=oths, Xs=Xs, Es=Es, success=success, dXlist=dXlist, dXlist2=dXlist2) + + +def dsolvePlot(info): + '''Plots dsolve or dsolve solution process based on based dict of dsolve output data''' + + n = info['Xs'].shape[1] # number of variables + + if n < 8: + fig, ax = plt.subplots(2*n, 1, sharex=True) + for i in range(n): + ax[ i].plot(info['Xs'][:info['iter']+1,i]) + ax[n+i].plot(info['Es'][:info['iter']+1,i]) + ax[-1].set_xlabel("iteration") + else: + fig, ax = plt.subplots(n, 2, sharex=True) + for i in range(n): + ax[i,0].plot(info['Xs'][:info['iter']+1,i]) + ax[i,1].plot(info['Es'][:info['iter']+1,i]) + ax[-1,0].set_xlabel("iteration, X") + ax[-1,1].set_xlabel("iteration, Error") + plt.show() + + +def dopt(eval_func, X0, tol=0.0001, maxIter=20, Xmin=[], Xmax=[], a_max=1.2, dX_last=[], display=0, stepfac=10): + ''' + Multi-direction Newton's method solver. + + tol - *relative* convergence tolerance (applied to step size components, dX) + Xmin, Xmax - bounds. by default start bounds at infinity + a_max - maximum step size acceleration allowed + stepfac - factor to increase step size to relative to tol*X0 + ''' + start_time = time.time() + + success = False + lastConverged = False # flag for whether the previous iteration satisfied the convergence criterion + + # process inputs and format as arrays in case they aren't already + if len(X0) == 0: + raise ValueError("X0 cannot be empty") + + X = np.array(X0, dtype=np.float64) # start off design variable (optimized) + + # do a test call to see what size the results are + f, g, Xextra, Yextra, oths, stop = eval_func(X) #, XtLast, Ytarget, args) + + N = len(X) # number of design variables + Nextra = len(Xextra) # additional relevant variables calculated internally and passed out, for tracking + m = len(g) # number of constraints + + Xs = np.zeros([maxIter, N + Nextra]) # make arrays to store X and error results of the solve + Fs = np.zeros([maxIter]) # make arrays to store objective function values + Gs = np.zeros([maxIter, m]) # make arrays to store constraint function values + + + + if len(Xmin)==0: + Xmin = np.zeros(N)-np.inf + elif len(Xmin)==N: + Xmin = np.array(Xmin, dtype=np.float64) + else: + raise TypeError("Xmin must be of same length as X0") + + if len(Xmax)==0: + Xmax = np.zeros(N)+np.inf + elif len(Xmax)==N: + Xmax = np.array(Xmax, dtype=np.float64) + else: + raise TypeError("Xmax must be of same length as X0") + + + if len(dX_last)==N: + dX_last = np.array(dX_last, dtype=np.float64) + elif len(dX_last)==0: + dX_last = np.zeros(N) + else: + raise ValueError("dX_last input must be of same size as design vector, if provided") + #XtLast = 1.0*Xt0 + + # set finite difference step size + #dX_fd = 4.0 #0.5# 1.0*dX[i] # this is gradient finite difference step size, not opto step size + dX_fd = stepfac*X*tol # set dX_fd as function of tolerance and initial values + + + + if display > 0: + print("Starting dopt iterations>>>") + + for iter in range(maxIter): + iter_start_time = time.time() + + # call evaluation function (returns objective val, constrain vals, tuned variables, tuning results) + f, g, Xextra, Yextra, oths, stop = eval_func(X) #, XtLast, Ytarget, args) + + if display > 1: print("") + if display > 0: + + if isinstance(Xextra, list): + XextraDisp = Xextra + else: + XextraDisp = Xextra.tolist() + + print((" >> Iteration {:3d}: f={:8.2e} X="+"".join([" {:9.2f}"]*len(X))+" Xe="+"".join([" {:9.2f}"]*len(Xextra))).format(*( + [ iter , f ] + X.tolist() + XextraDisp) )) + + + if display > 1: print(f"\n Constraint values: {g}") + + Xs[iter,:] = np.hstack([X, Xextra]) + Fs[iter] = f + Gs[iter,:] = g + + + # stop if commanded by objective function + if stop: + message = 'Received stop command from objective function' + break + + # temporarily display output + #print(np.hstack([X,Y])) + + + if iter==maxIter-1: + + print("Failed to converge after "+str(iter)+" iterations") + + if any(X == Xmin) or any(X == Xmax) or any(g < 0.0): + for i in range(N): + if X[i] == Xmin[i] : print(f" Warning: Design variable {i} ended on minimum bound {Xmin[i]}.") + if X[i] == Xmax[i] : print(f" Warning: Design variable {i} ended on maximum bound {Xmax[i]}.") + + for j in range(m): # go through each constraint + if g[j] < 0: # if a constraint will be violated + print(f" Warning: Constraint {j} was violated by {-g[j]}.") + else: + print(" No constraint or bound issues.") + + success = False + break + + #>>>> COULD ALSO HAVE AN ITERATION RESTART FUNCTION? >>> + # that returns a restart boolean, as well as what values to use to restart things if true. How? + + else: # this is where we get derivatives and then take a step + + #dX = step_func(X, args, Y, oths, Ytarget, err, tol, iter, maxIter) + # hard coding a generic approach for now + + dX = np.zeros(N) # optimization step size to take + + X2 = np.array(X, dtype=np.float64) + + Jf = np.zeros([N]) + Jg = np.zeros([N,m]) + Hf = np.zeros([N]) # this is just the diagonal of the Hessians + Hg = np.zeros([N,m]) + + for i in range(N): # loop through each variable + + # could do repetition to hone in when second derivative is large, but not going to for now + # or if first derivative is zero (in which case take a larger step size) + + X2[i] += dX_fd[i] # perturb + + fp, gp, Xtp, Yp, othsp, stopp = eval_func(X2) + X2[i] -= 2.0*dX_fd[i] # perturb - + fm, gm, Xtm, Ym, othsm, stopm = eval_func(X2) + X2[i] += dX_fd[i] # restore to original + + # for objective function and constraints (note that g may be multidimensional), + # fill in diagonal values of Jacobian and Hession (not using off-diagonals for now) + Jf[i] = (fp-fm) /(2*dX_fd[i]) + Jg[i,:] = (gp-gm) /(2*dX_fd[i]) + Hf[i] = (fm-2.0*f+fp) /dX_fd[i]**2 + Hg[i,:] = (gm-2.0*g+gp) /dX_fd[i]**2 + + #breakpoint() + + # If we're currently violating a constraint, fix it rather than worrying about the objective function + # This step is when new gradients need to be calculated at the violating point + # e.g. in cases where the constraint functions are flat when not violated + if any(g < 0.0): + + if display > 3: + print(" CONSTRAINT HANDLING SECTION") + for i in range(len(Jg)): + print(f" Jg[{i}] = {np.round(Jg[i],5)}") + #print((" Jg[{:3d}] = "+"".join([" {:6.2f}"]*m).format(*([i]+Jg[i].tolist())))) + + g0 = [] + gradg = [] + #sqg = [] + + # first get the gradient of each active constraint + stepdir = np.zeros(N) # this is the direction we will step in + + for j in range(m): # go through each constraint + if g[j] < 0: # if a constraint will be violated + if np.sum(np.abs(Jg[:,j])) == 0.0: + print(f"dopt error, zero Jacobian for constraint {j}. g(X) may be flat or dX_fd may be too small") + stop=True # set flag to exit iteration + message = f"Error, zero Jacobian for constraint {j}. g(X) may be flat or dX_fd may be too small" + break + + g0.append( g[j]) # constraint value at the current location + gradg.append(Jg[:,j]) # gradient for each active constraint <<< doesn't work so well + #sqg.append( np.sum(Jg[:,j]*Jg[:,j])) # gradient dotted with itself (i.e. sum of squares) + + + # OG output for comparison + stepdir_i = 1.0*Jg[:,j] # default is to assume we're moving in the same direction as the gradient since that's most efficient + for i in range(N): + if (X[i]==Xmin[i] and Jg[i,j]<0) or (X[i]==Xmax[i] and Jg[i,j]>0): # but if any dimension is on its bound, and the gradient is to move in that direction + stepdir_i[i] = 0.0 # set its component to zero instead (other dimensions will now have to move farther) + alph = (0.0-g[j])/np.sum(Jg[:,j]*stepdir_i) # for our selected step direction, find how far to move to get to zero + if np.sum(Jg[:,j]*stepdir_i) == 0.0: + print('NaN isue') + + dXcon = stepdir_i*alph *1.1 # step is step direction vector (possibly gradient) times alpha (plus a little extra for margin) - add the step command from each violated constraint + + if display > 3: + print(f' - Looking at g[{j}]') + print(" stepdir_i = "+"".join([" {:.5f}"]*len(stepdir_i)).format(*(stepdir_i.tolist()))) + print(" alph = ",alph) + print(" g0 = ",g0) + print(" gradg = ",gradg) + + if display > 1: + print((" Con {:3d} OG correction"+"".join([" {:9.2f}"]*N)).format(*( [j]+ dXcon.tolist()) )) + + + # now zero any dimensions that are about to cross a bound (if we're already at the bound) + for i in range(N): + + for j in range(len(g0)): # look through each active constraint (but apply zeroing to all active constraints for now) + if (X[i]==Xmin[i] and gradg[j][i]<0) or (X[i]==Xmax[i] and gradg[j][i]>0): # but if any dimension is on its bound, and the gradient is to move in that direction + for k in range(len(g0)): + gradg[k][i] = 0.0 # set its component to zero instead (other dimensions will now have to move farther) + if display > 3: print('gradg',gradg) + if display > 3: print(' - No bounds issues') + sqg = [ np.sum(jac*jac) for jac in gradg] # update the gradient dotted with itself (i.e. sum of squares) + + + if display > 3: print(' - Find stepdir') + # now sort out a combined step direction depending on the active constraints + if len(g0) == 2 and np.sum(gradg[0]*gradg[1]) < 0 and N>1: # if two active constraints in opposing directions + c1 = g0[0]/sqg[0] * ( np.sum(gradg[0]*gradg[1]) * gradg[1]/sqg[1] - gradg[0] ) + + c2 = g0[1]/sqg[1] * ( np.sum(gradg[0]*gradg[1]) * gradg[0]/sqg[0] - gradg[1] ) + stepdir = c1 + c2 + if display > 3: print(f' A: c1={c1}, c2={c2}') + + else: # all other cases - assume we're moving in the same direction as the gradient since that's most efficient + #c2 = [(-g0[j])/np.sum(gradg[j]*gradg[j])*gradg[j] for j in range(len(g0))] # compute step directions that will zero each constraint + + c = np.zeros([len(g0), N]) + for j in range(len(g0)): # compute step directions that will zero each constraint + if np.sum(gradg[j]*gradg[j]) > 0: # just leave it as zero if any direction has a zero derivative + c[j,:] = -g0[j] / np.sum(gradg[j]*gradg[j]) * gradg[j] + if display > 3: print(f' B: c={c}') + else: + if display > 0: + print(f' dopt warning: zero gradient squared for active constraint {j} at iter={iter} and X={X}') + + #stepdir=sum(c2) + stepdir = np.sum(c, axis=0) # step is step direction vector (possibly gradient) times alpha (plus a little extra for margin) - add the step command from each violated constraint + if display > 3: print(' stepdir = ',stepdir) + + + if np.linalg.norm(stepdir)==0: + stop = True + break + + + if display > 3: print(' - Find alpha') + # now find how large the step needs to be to satisfy each active constraint + alpha = 0.0 # this is the scalar that determines how far we will step in the direction + for j in range(m): # go through each constraint + if g[j] < 0: # if a constraint will be violated + alpha_i = (0.0-g[j])/np.sum(Jg[:,j]*stepdir)# for this constraint, find how far to move along the step direction to get to zero + + alpha = np.max([alpha, alpha_i]) + if display > 3: print(' alpha_i =',alpha_i) + # if an acceleration limit will be applied in some dimension, it'd be nice to revise the direction and recompute <<< + + #dXcon = stepdir*alpha *1.1 # step is step direction vector (possibly gradient) times alpha (plus a little extra for margin) - add the step command from each violated constraint + + #if display > 1: + #print(f" Constraint {j:3d} active).") + #print((" Con {:3d} correction: "+"".join([" {:9.2f}"]*N)).format(*( [j]+ dXcon.tolist()) )) + #if display > 2: + # print((" J = "+"".join([" {:9.2e}"]*m)).format(*Jg[:,j].tolist() )) + # print((" H = "+"".join([" {:9.2e}"]*m)).format(*Hg[:,j].tolist() )) + + dX = stepdir*alpha *1.1 # step is step direction vector (possibly gradient) times alpha (plus a little extra for margin) + + + if display > 1: + print((" Total constraint step (dX) :"+"".join([" {:9.2f}"]*N)).format(*dX.tolist()) ) + + #if iter==4 or iter==5: + # breakpoint() + + # if the above fails, we could try backtracking along dX_last until the constriant is no longer violated... + + # at the end of this, the step will be a summation of the steps estimated to resolve each constraint - good idea? + + # otherwise make an optimization step + else: + if display > 3: print(" OPTIMIZATION STEP SECTION") + + # figure out step size in each dimension + dxType = ['none']*N + for i in range(N): + if Hf[i] <= 0.1*abs(Jf[i])/np.linalg.norm(dX_last): # if the hessian is very small or negative, just move a fixed step size + #dX[i] = -Jf[i]/np.linalg.norm(Jf) * np.abs(dX_last[i]) * a_max*0.9 + dX[i] = -Jf[i]/np.linalg.norm(Jf) * np.linalg.norm(dX_last) * a_max + if display > 3: print(Jf[i], np.linalg.norm(Jf), np.linalg.norm(dX_last), dX_fd[i]) + # but make sure the step size is larger than the convergence tolerance + if abs(dX[i]) <= tol*(np.abs(X[i])+tol): + dX[i] = np.sign(dX[i])*tol*(np.abs(X[i])+tol)*1.1 + + dxType[i] = 'fixed' + else: + dX[i] = -Jf[i]/Hf[i] + + dxType[i] = 'hessian' + + #dX[i] = -Jf[i]/np.linalg.norm(Jf) * np.linalg.norm(dX_last) * a_max # << trying a fixed step size approach (no hessian) + + if display > 1: + print((" Minimization step, dX = "+"".join([" {:9.2f}"]*N)).format(*dX.tolist() )) + if display > 2: + print((" step type "+"".join([" {:9}"]*N)).format(*dxType )) + if display > 2: + print((" J = "+"".join([" {:9.2f}"]*N)).format(*Jf.tolist() )) + print((" H = "+"".join([" {:9.2f}"]*N)).format(*Hf.tolist() )) + #breakpoint() + + if any(np.isnan(dX)): + breakpoint() + + dX_min0 = np.array(dX) + + # respect bounds (handle each dimension individually) + for i in range(N): + if X[i] + dX[i] < Xmin[i]: + dX[i] = Xmin[i] - X[i] + elif X[i] + dX[i] > Xmax[i]: + dX[i] = Xmax[i] - X[i] + + dX_minB = np.array(dX) + + # deal with potential constraint violations in making the step (based on existing gradients) + # respect constraints approximately (ignore cross-couplings...for now) + X2 = X + dX # save jump before constraint correction + for j in range(m): # go through each constraint + g2j = g[j] + np.sum(Jg[:,j]*dX) # estimate constraint value after planned step + if g2j < 0: # if the constraint will be violated + + # option 1: assume we complete the step, then follow the constraint gradient up to resolve the constraint violation + alpha = -g2j / np.sum(Jg[:,j]*Jg[:,j]) # assuming we follow the gradient, finding how far to move to get to zero + + if display > 2 and alpha > 2000: + breakpoint() + + dX = dX + alpha*Jg[:,j]*1.05 # step size is gradient times alpha (adding a little extra for margin) + + + # option 2: just stop short of where the constraint would be violated along the original dX path (inferior because it gets bogged up when against a constraint) + #alpha = -g[j] / np.sum(Jg[:,j]*dX) + #dX = alpha * dX * 0.95 + + if display > 1: + print((" trimin step: (j={:2d},{:6.3f}):"+"".join([" {:9.2f}"]*N)).format( + *( [j, alpha] + dX.tolist()))) + + + # this is how to stop the dX vector at the approximate constraint boundary (not good for navigation) + #for j in len(g): # go through each constraint + # if g[j] + np.sum(Jg[:,j]*dX) < 0: # if a constraint will be violated + # alpha = -g[j]/np.sum(Jg[:,j]*dX) # find where the constraint boundary is (linear approximation) + # dX = dX*alpha # shrink the step size accordingly (to stop at edge of constraint) + + if stop: + break + + + # Make sure we're not diverging by keeping things from reversing too much. + # Track the previous step (dX_last) and if the current step reverses too much, stop it part way. + + ''' + # Original approach: Stop it at a plane part way between the current X value and the previous X value (using golden ratio, why not). + # This means scaling down the full vector (while preserving its direction). The downside is this limits all dimensions. + # get the point along the previous step vector where we could draw a bounding hyperplane (could be a line, plane, or more in higher dimensions) + Xlim = X - 0.62*dX_last + # the equation for the plane we don't want to recross is then sum(X*dX_last) = sum(Xlim*dX_last) + if np.sum((X+dX)*dX_last) < np.sum(Xlim*dX_last): # if we cross are going to cross it + alpha = np.sum((Xlim-X)*dX_last)/np.sum(dX*dX_last) # this is how much we need to scale down dX to land on it rather than cross it + dX = alpha*dX # scale down dX + if display > 1: + print((" (alpha={:9.2e}) to "+"".join([" {:8.2e}"]*N)).format( + ''' + # Revised approach: only scale down the directions that have reversed sign from the last step. + for i in range(N): + if np.sign(dX[i])==-np.sign(dX_last[i]) and abs(dX_last[i]) > tol: # if this dimension is reversing direction + + ratio = np.abs(dX[i]/dX_last[i]) # if it's reversing by more than 62% of the last step in this dimension, limit it + if ratio > 0.62: + dX[i] = dX[i]*0.62/ratio # scale down dX so that it is just 62% of the dX_last + + if display > 1: + print((" oscil limit: (i={:2d},{:6.3f},{:7.3f}):"+"".join([" {:9.2f}"]*N)).format( + *( [i, 0.62/ratio, ratio] + dX.tolist() ))) + + + # also avoid extreme accelerations in the same direction + if np.linalg.norm(dX_last) > tol: # only worry about accelerations if the last step was non-negligible + for i in range(N): + + # set the maximum permissible dx in each direction based an an acceleration limit + if abs(dX_last[i]) < tol: + dX_max = a_max*10*tol*np.sign(dX[i]) + #if abs(dX_last[i]) < tol*(np.abs(X[i])+tol): + # dX_max = a_max*tol*(np.abs(X[i])+tol)*np.sign(dX[i]) + else: + dX_max = a_max*dX_last[i] + #print('dX_max',dX_max, dX_last, tol, dX) + if dX_max == 0.0: # avoid a divide-by-zero case (if dX[i] was zero to start with) + dX[i] = 0.0 + else: + a_i = dX[i]/dX_max # calculate ratio of desired dx to max dx + #print('a_i',a_i, i, dX[i]) + if a_i > 1.0: + + #dX = dX/a_i # scale it down to the maximum value <<< this needs to be in the conditional if X[i] > Xmin[i] and X[i] < Xmax[i]: # limit this direction if it exceeds the limit and if it's not on a bound (otherwise things will get stuck) + # NOTE: if this has problems with making the dX too small, triggering convergence, try the individual approach below <<< + dX[i] = dX[i]/a_i # scale it down to the maximum value (treat each DOF individually) + #print(dX[i]) + if display > 1: + print((" accel limit: (i={:2d},{:6.3f},{:7.3f}):"+"".join([" {:9.2f}"]*N)).format( + *( [i, 1.0/a_i, a_i ] + dX.tolist()))) + + + + # enforce bounds + for i in range(N): + if X[i] + dX[i] < Xmin[i]: + dX[i] = Xmin[i] - X[i] + if display > 3: print(f" Minimum bounds adjustment for dX[{i}]") + elif X[i] + dX[i] > Xmax[i]: + dX[i] = Xmax[i] - X[i] + if display > 3: print(f" Maximum bounds adjustment for dX[{i}]") + + + # check for convergence + if all(np.abs(dX) < tol*(np.abs(X)+tol)): + + if lastConverged: # only consider things converged if the last iteration also satisfied the convergence criterion + + if display>0: + print(f"Optimization converged after {iter} iterations with dX of {dX}") + print(f"Solution X is "+str(X)) + print(f"Constraints are "+str(g)) + + if any(X == Xmin) or any(X == Xmax): + if display>0: + for i in range(N): + if X[i] == Xmin[i] : print(f" Warning: Design variable {i} ended on minimum bound {Xmin[i]}.") + if X[i] == Xmax[i] : print(f" Warning: Design variable {i} ended on maximum bound {Xmax[i]}.") + + success = True + message = "converged on one or more bounds" + + elif any(X == Xmin) or any(X == Xmax) or any(g < 0.0): + if display>0: + for j in range(m): # go through each constraint + if g[j] < 0: # if a constraint will be violated + print(f" Warning: Constraint {j} was violated by {-g[j]}.") + + success = False + message = f"converged with one or more constraints violated (by max {-min(g):7.1e})" + + else: + success = True + message = "converged with no constraint violations or active bounds" + break + + else: + lastConverged = True # if this is the first time the convergence criterion has been met, note it and keep going + message = "convergence criteria only met once (need twice in a row)" + else: + lastConverged = False + message = "not converged" + + if display > 2: + print(f" Convergence message: {message}") + + dX_last = 1.0*dX # remember this current value + #XtLast = 1.0*Xt + #if iter==3: + #breakpoint() + X = X + dX + + if display > 1: + + print((" dopt iteration finished. dX= "+"".join([" {:9.2f}"]*N)).format(*(dX.tolist()))) + print(" iteration run time: {:9.2f} seconds".format(time.time() - iter_start_time)) + + + if display > 2: + print(f" Convergence message: {message}") + + if display > 0: + print(" total run time: {:8.2f} seconds = {:8.2f} minutes".format((time.time() - start_time),((time.time() - start_time)/60))) + + return X, f, dict(iter=iter, dX=dX_last, oths=oths, Xs=Xs, Fs=Fs, Gs=Gs, Xextra=Xextra, g=g, Yextra=Yextra, + success=success, message=message) + + + +def dopt2(eval_func, X0, tol=0.0001, maxIter=20, Xmin=[], Xmax=[], a_max=1.2, dX_last=[], display=0, stepfac=10, args=[]): + ''' + Gradient descent solver with some line search capability + + tol - *relative* convergence tolerance (applied to step size components, dX) + Xmin, Xmax - bounds. by default start bounds at infinity + a_max - maximum step size acceleration allowed + stepfac - factor to increase step size to relative to tol*X0 + ''' + start_time = time.time() + + success = False + lastConverged = False # flag for whether the previous iteration satisfied the convergence criterion + + # process inputs and format as arrays in case they aren't already + if len(X0) == 0: + raise ValueError("X0 cannot be empty") + + X = np.array(X0, dtype=np.float64) # start off design variable (optimized) + + # do a test call to see what size the results are + f, g, Xextra, Yextra, oths, stop = eval_func(X, args) #, XtLast, Ytarget, args) + + N = len(X) # number of design variables + Nextra = len(Xextra) # additional relevant variables calculated internally and passed out, for tracking + m = len(g) # number of constraints + + Xs = np.zeros([maxIter, N + Nextra]) # make arrays to store X and error results of the solve + Fs = np.zeros([maxIter]) # make arrays to store objective function values + Gs = np.zeros([maxIter, m]) # make arrays to store constraint function values + + + + if len(Xmin)==0: + Xmin = np.zeros(N)-np.inf + elif len(Xmin)==N: + Xmin = np.array(Xmin, dtype=np.float64) + else: + raise TypeError("Xmin must be of same length as X0") + + if len(Xmax)==0: + Xmax = np.zeros(N)+np.inf + elif len(Xmax)==N: + Xmax = np.array(Xmax, dtype=np.float64) + else: + raise TypeError("Xmax must be of same length as X0") + + + if len(dX_last)==N: + dX_last = np.array(dX_last, dtype=np.float64) + elif len(dX_last)==0: + dX_last = np.zeros(N) + else: + raise ValueError("dX_last input must be of same size as design vector, if provided") + #XtLast = 1.0*Xt0 + + # set finite difference step size + #dX_fd = 4.0 #0.5# 1.0*dX[i] # this is gradient finite difference step size, not opto step size + dX_fd = stepfac*X*tol # set dX_fd as function of tolerance and initial values + dX_fd0 = np.array(dX_fd) + + if display > 3: print(f" dX_fd is {dX_fd}") + + if display > 0: + print("Starting dopt iterations>>>") + + for iter in range(maxIter): + iter_start_time = time.time() + + Xsave = np.array(X) + + if any(X == Xmin): + Xbadj = np.array(X) + for ixmin in np.where(X==Xmin)[0]: + Xbadj[ixmin] = X[ixmin]*(1+tol) # badj = bound adjustment + elif any(X == Xmax): + Xbadj = np.array(X) + for ixmax in np.where(X==Xmax)[0]: + Xbadj[ixmax] = X[ixmax]*(1-tol) + else: + Xbadj = np.array(X) + + X = np.array(Xbadj) + + # call evaluation function (returns objective val, constrain vals, tuned variables, tuning results) + f, g, Xextra, Yextra, oths, stop = eval_func(X, args) #, XtLast, Ytarget, args) + + if display > 1: print("") + if display > 0: + + if isinstance(Xextra, list): + XextraDisp = Xextra + else: + XextraDisp = Xextra.tolist() + + print((" >> Iteration {:3d}: f={:8.2e} X="+"".join([" {:9.2f}"]*len(X))+" Xe="+"".join([" {:9.2f}"]*len(Xextra))).format(*( + [ iter , f ] + X.tolist() + XextraDisp) )) + + + if display > 1: + print(f"\n Constraint values: {g}") + elif display > 0 and any(g < 0.0): + print(f" Constraint values: {g}") + + Xs[iter,:] = np.hstack([X, Xextra]) + Fs[iter] = f + Gs[iter,:] = g + + + # stop if commanded by objective function + if stop: + message = 'Received stop command from objective function' + break + + # temporarily display output + #print(np.hstack([X,Y])) + + + if iter==maxIter-1: + + print("Failed to converge after "+str(iter)+" iterations") + + if any(X == Xmin) or any(X == Xmax) or any(g < 0.0): + for i in range(N): + if X[i] == Xmin[i] : print(f" Warning: Design variable {i} ended on minimum bound {Xmin[i]}.") + if X[i] == Xmax[i] : print(f" Warning: Design variable {i} ended on maximum bound {Xmax[i]}.") + + for j in range(m): # go through each constraint + if g[j] < 0: # if a constraint will be violated + print(f" Warning: Constraint {j} was violated by {-g[j]}.") + else: + print(" No constraint or bound issues.") + + success = False + break + + #>>>> COULD ALSO HAVE AN ITERATION RESTART FUNCTION? >>> + # that returns a restart boolean, as well as what values to use to restart things if true. How? + + else: # this is where we get derivatives and then take a step + + #dX = step_func(X, args, Y, oths, Ytarget, err, tol, iter, maxIter) + # hard coding a generic approach for now + + dX = np.zeros(N) # optimization step size to take + + X2 = np.array(X, dtype=np.float64) + + Jf = np.zeros([N]) + Jg = np.zeros([N,m]) + Hf = np.zeros([N]) # this is just the diagonal of the Hessians + Hg = np.zeros([N,m]) + + for i in range(N): # loop through each variable + + # could do repetition to hone in when second derivative is large, but not going to for now + # or if first derivative is zero (in which case take a larger step size) + + dX_fd = np.array(dX_fd0) # make a copy of the original dX_fd to store temporary values + + X2[i] += dX_fd0[i] # perturb + by original dX_fd0 + if X2[i] > Xmax[i]: # if the perturbed+ X2 value goes above the bounds + X2[i] = Xmax[i] # set the perturbed+ X2 value to the max bound + dX_fd[i] = Xmax[i] - X[i] # and set the temp dX_fdi value to how much that new perturbation is + + fp, gp, Xtp, Yp, othsp, stopp = eval_func(X2, args) # evaluate at the proper X2 position + + X2[i] -= 2.0*dX_fd[i] # perturb - by updated dX_fd + if X2[i] < Xmin[i]: # if the perturbed- X2 value goes under the bounds + X2[i] = Xmin[i] # set the perturbed- X2 value to the min bound + dX_fd[i] = X[i] - Xmin[i] # and set the temp dX_fd value to how much that new perturbation is + fm, gm, Xtm, Ym, othsm, stopm = eval_func(X2, args) # evaluate at the proper X2 position + + X2[i] += dX_fd[i] # restore to original + + # for objective function and constraints (note that g may be multidimensional), + # fill in diagonal values of Jacobian and Hessian (not using off-diagonals for now) + Jf[i] = (fp-fm) /(2*dX_fd[i]) + Jg[i,:] = (gp-gm) /(2*dX_fd[i]) + Hf[i] = (fm-2.0*f+fp) /dX_fd[i]**2 + Hg[i,:] = (gm-2.0*g+gp) /dX_fd[i]**2 + #if i==0: print(fp, fm, dX_fd[i], Jf[i]) + + #breakpoint() + + # If we're currently violating a constraint, fix it rather than worrying about the objective function + # This step is when new gradients need to be calculated at the violating point + # e.g. in cases where the constraint functions are flat when not violated + if any(g < 0.0): + + if display > 3: + print(" Constraint step") + for i in range(len(Jg)): + print(f" Jg[{i}] = {np.round(Jg[i],5)}") + #print((" Jg[{:3d}] = "+"".join([" {:6.2f}"]*m).format(*([i]+Jg[i].tolist())))) + + g0 = [] + gradg = [] + #sqg = [] + + # first get the gradient of each active constraint + stepdir = np.zeros(N) # this is the direction we will step in + + for j in range(m): # go through each constraint + if g[j] < 0: # if a constraint will be violated + if np.sum(np.abs(Jg[:,j])) == 0.0: + print(f"dopt error, zero Jacobian for constraint {j}. g(X) may be flat or dX_fd may be too small") + stop=True # set flag to exit iteration + message = f"Error, zero Jacobian for constraint {j}. g(X) may be flat or dX_fd may be too small" + break + + g0.append( g[j]) # constraint value at the current location + gradg.append(Jg[:,j]) # gradient for each active constraint <<< doesn't work so well + #sqg.append( np.sum(Jg[:,j]*Jg[:,j])) # gradient dotted with itself (i.e. sum of squares) + + + # OG output for comparison + stepdir_i = 1.0*Jg[:,j] # default is to assume we're moving in the same direction as the gradient since that's most efficient + for i in range(N): + if (X[i]==Xmin[i] and Jg[i,j]<0) or (X[i]==Xmax[i] and Jg[i,j]>0): # but if any dimension is on its bound, and the gradient is to move in that direction + stepdir_i[i] = 0.0 # set its component to zero instead (other dimensions will now have to move farther) + alph = (0.0-g[j])/np.sum(Jg[:,j]*stepdir_i) # for our selected step direction, find how far to move to get to zero + if np.sum(Jg[:,j]*stepdir_i) == 0.0: + print('NaN isue') + + dXcon = stepdir_i*alph *1.1 # step is step direction vector (possibly gradient) times alpha (plus a little extra for margin) - add the step command from each violated constraint + + if display > 3: + print(f' - Looking at g[{j}]') + print(" stepdir_i = "+"".join([" {:.5f}"]*len(stepdir_i)).format(*(stepdir_i.tolist()))) + print(" alph = ",alph) + print(" g0 = ",g0) + print(" gradg = ",gradg) + + if display > 1: + print((" Con {:3d} OG correction"+"".join([" {:9.2f}"]*N)).format(*( [j]+ dXcon.tolist()) )) + + + # now zero any dimensions that are about to cross a bound (if we're already at the bound) + for i in range(N): + + for j in range(len(g0)): # look through each active constraint (but apply zeroing to all active constraints for now) + if (X[i]==Xmin[i] and gradg[j][i]<0) or (X[i]==Xmax[i] and gradg[j][i]>0): # but if any dimension is on its bound, and the gradient is to move in that direction + for k in range(len(g0)): + gradg[k][i] = 0.0 # set its component to zero instead (other dimensions will now have to move farther) + if display > 3: print('gradg',gradg) + if display > 3: print(' - No bounds issues') + sqg = [ np.sum(jac*jac) for jac in gradg] # update the gradient dotted with itself (i.e. sum of squares) + + + if display > 3: print(' - Find stepdir') + # now sort out a combined step direction depending on the active constraints + if len(g0) == 2 and np.sum(gradg[0]*gradg[1]) < 0 and N>1: # if two active constraints in opposing directions + c1 = g0[0]/sqg[0] * ( np.sum(gradg[0]*gradg[1]) * gradg[1]/sqg[1] - gradg[0] ) + + c2 = g0[1]/sqg[1] * ( np.sum(gradg[0]*gradg[1]) * gradg[0]/sqg[0] - gradg[1] ) + stepdir = c1 + c2 + if display > 3: print(f' A: c1={c1}, c2={c2}') + + else: # all other cases - assume we're moving in the same direction as the gradient since that's most efficient + #c2 = [(-g0[j])/np.sum(gradg[j]*gradg[j])*gradg[j] for j in range(len(g0))] # compute step directions that will zero each constraint + + c = np.zeros([len(g0), N]) + for j in range(len(g0)): # compute step directions that will zero each constraint + if np.sum(gradg[j]*gradg[j]) > 0: # just leave it as zero if any direction has a zero derivative + c[j,:] = -g0[j] / np.sum(gradg[j]*gradg[j]) * gradg[j] + if display > 3: print(f' B: c={c}') + else: + if display > 0: + print(f' dopt warning: zero gradient squared for active constraint {j} at iter={iter} and X={X}') + + #stepdir=sum(c2) + stepdir = np.sum(c, axis=0) # step is step direction vector (possibly gradient) times alpha (plus a little extra for margin) - add the step command from each violated constraint + if display > 3: print(' stepdir = ',stepdir) + + + if np.linalg.norm(stepdir)==0: + stop = True + break + + + if display > 3: print(' - Find alpha') + # now find how large the step needs to be to satisfy each active constraint + alpha = 0.0 # this is the scalar that determines how far we will step in the direction + for j in range(m): # go through each constraint + if g[j] < 0: # if a constraint will be violated + alpha_i = (0.0-g[j])/np.sum(Jg[:,j]*stepdir)# for this constraint, find how far to move along the step direction to get to zero + + alpha = np.max([alpha, alpha_i]) + if display > 3: print(' alpha_i =',alpha_i) + # if an acceleration limit will be applied in some dimension, it'd be nice to revise the direction and recompute <<< + + #dXcon = stepdir*alpha *1.1 # step is step direction vector (possibly gradient) times alpha (plus a little extra for margin) - add the step command from each violated constraint + + #if display > 1: + #print(f" Constraint {j:3d} active).") + #print((" Con {:3d} correction: "+"".join([" {:9.2f}"]*N)).format(*( [j]+ dXcon.tolist()) )) + #if display > 2: + # print((" J = "+"".join([" {:9.2e}"]*m)).format(*Jg[:,j].tolist() )) + # print((" H = "+"".join([" {:9.2e}"]*m)).format(*Hg[:,j].tolist() )) + + dX = stepdir*alpha *1.1 # step is step direction vector (possibly gradient) times alpha (plus a little extra for margin) + + + if display > 1: + print((" Total constraint step (dX) :"+"".join([" {:9.2f}"]*N)).format(*dX.tolist()) ) + + #if iter==4 or iter==5: + # breakpoint() + + # if the above fails, we could try backtracking along dX_last until the constriant is no longer violated... + + # at the end of this, the step will be a summation of the steps estimated to resolve each constraint - good idea? + + # otherwise (no constraints violated) make an optimization step + else: + + # start by doing a line search down the slope + + dir = -Jf/np.linalg.norm(Jf) # direction to move along + if display > 1: print(f" beginning line search in steepest descent direction, u={dir}") + step = dir * tol*np.linalg.norm(X) * 2 + #print('dir',dir,'step',step) + j_active = -1 # index of constraint that is limiting the step + + dX = np.zeros_like(X) + X2 = X + dX + flast = 1.0*f + glast = 1.0*g + step2 = 1.0*step + for k in range(100): # now do a line search + + step2 = step*(2**k) # double step size each time + + dX = dX + step2 # <<< looks like I'm actually trippling the step - need to fix at some point <<< + + + # check for bound violation + if any(X2 + dX < Xmin) or any(X2 + dX > Xmax): + dX = dX - step2 + if display > 3: + print(f" ----- next step will cross bounds, so will use X={X+dX} and dX={dX}") + break + + # evaluate the function + fl, gl, Xtl, Yl, othsl, stopl = eval_func(X + dX, args) + if display > 3: + print((" line searching: k={:2d} f={:6.3f}):"+"".join([" {:9.2f}"]*N)).format( + *( [k, fl] + (X+dX).tolist()))) + + # check for increasing f + if fl > flast: + dX = dX - step2 + if display > 3: + print(f" ----- f increasing ----- so will use X={X+dX} and dX={dX}") + break + + # check for constraint violation + if any(gl < 0): + + frac = -glast/(gl-glast) # how much of the step (fraction) could be taken until each constraint is violated + backfrac = (1.0-frac)*(frac > 0) # how much of the step to backtrace (with filtering to exclude negatives from constraints that are decreasing) + j_active = np.argmax(backfrac) + + dXog = 1.0*dX + #breakpoint() + + dX = dX - backfrac[j_active]*step2 - 2*dir*tol*np.linalg.norm(X) # back up, and also keep a 2*tol margin from the boundary + + # normal case: # find -Jf component tangent with constraint surface to move along + tandir = -Jf + np.sum(Jg[:,j_active]*Jf)*Jg[:,j_active]/np.sum(Jg[:,j_active]*Jg[:,j_active]) + + ''' ...not sure this next part really works/helps at all... + # >>>>>>> make it so that if we've backed up further from the constraint than the last X, + #then move along direction of previous dX! (to avoid potential sawtooth bouncing along constraint boundary) + # IF the constraint direction points more toward the constraint than the previous dX direction. + if iter > 0 and np.sum(dX*step) < 0: # line search has us moving further away from constraint boundary + + tandir = tandir/np.linalg.norm(tandir) + lastdir = dX_last/np.linalg.norm(dX_last) + + if np.sum(Jg[:,j_active]*lastdir) > np.sum(Jg[:,j_active]*tandir): + print(f"special case. Using {lastdir} rather than {tandir}") + tandir = lastdir + ''' + + + if display > 3: + print(f" ----- constraint violated ----- {gl} ") + print(f" will back up to X={X+dX} and do line search along constraint") + + print(dXog) + print(dX) + print(gl) + fl2, gl2, Xtl2, Yl2, othsl2, stopl2 = eval_func(X + dX, args) + if display > 3: print(gl2) + + break + + + flast = 1.0*fl + glast = 1.0*gl + + #for i in range(N): + #dX[i] = -Jf[i]/np.linalg.norm(Jf) * np.linalg.norm(dX_last) * a_max # << trying a fixed step size approach (no hessian) + + if display > 1: + print((" Minimization step, dX = "+"".join([" {:9.2f}"]*N)).format(*dX.tolist() )) + if display > 2: + print((" J = "+"".join([" {:9.2f}"]*N)).format(*Jf.tolist() )) + + if any(np.isnan(dX)): + breakpoint() + + dX_min0 = np.array(dX) + + # respect bounds (handle each dimension individually) <<< + for i in range(N): + if X[i] + dX[i] < Xmin[i]: + dX[i] = Xmin[i] - X[i] + elif X[i] + dX[i] > Xmax[i]: + dX[i] = Xmax[i] - X[i] + + dX_minB = np.array(dX) + + + # but do a line search tangent to whatever constraint boundary is limiting if applicable (and if tangent is clear) + if j_active >= 0 and N > 1 and not any(np.isnan(tandir)): + #tandir = -Jf + np.sum(Jg[:,j_active]*Jf)*Jg[:,j_active]/np.sum(Jg[:,j_active]*Jg[:,j_active]) # find -Jf component tangent with constraint surface to move along + step = tandir/np.linalg.norm(tandir) * tol*np.linalg.norm(X) + if display > 3: + print(f"Constraint normal vector is {Jg[:,j_active]/np.linalg.norm(Jg[:,j_active])}") + print(f" beginning line search along constraint {j_active} boundary, u={tandir/np.linalg.norm(tandir)}") + + X3 = X + dX + step3=0 + for k in range(100): # now do a line search + + # evaluate the function + fl, gl, Xtl, Yl, othsl, stopl = eval_func(X3, args) + if display > 3: + print((" line searching: k={:2d} f={:6.3f}):"+"".join([" {:9.2f}"]*N)).format( + *( [k, fl] + X3.tolist()))) + + # check for increasing f + if k>0: + if fl > flast: + X3 = X3 - step3 + if display > 3: + print(f" ----- f increasing ----- so will use previous X={X3} and dX={X3 - X}") + break + + # check for constraint violation + if any(gl < 0): + X3 = X3 - step3 + # could instead back up to an intermediate point, and offset by the 2*tol margin too + if display > 3: + print(f" ----- constraint violated ----- {gl} --- so will use previous") + break + + flast = 1.0*fl + step3 = step*(1.6**k) # increase step size each time + + # check for bound violation + if any(X3 + step3 < Xmin) or any(X3 + step3 > Xmax): + if display > 3: + print(f" ----- next step will cross bounds, so stopping here") + break + + X3 = X3 + step3 + + dX = X3 - X # undo the last step (which was bad) and calculated overall effective dX + + + # this is how to stop the dX vector at the approximate constraint boundary (not good for navigation) + #for j in len(g): # go through each constraint + # if g[j] + np.sum(Jg[:,j]*dX) < 0: # if a constraint will be violated + # alpha = -g[j]/np.sum(Jg[:,j]*dX) # find where the constraint boundary is (linear approximation) + # dX = dX*alpha # shrink the step size accordingly (to stop at edge of constraint) + + if stop: + break + + + # Make sure we're not diverging by keeping things from reversing too much. + # Track the previous step (dX_last) and if the current step reverses too much, stop it part way. + + + # Original approach: Stop it at a plane part way between the current X value and the previous X value (using golden ratio, why not). + # This means scaling down the full vector (while preserving its direction). The downside is this limits all dimensions. + # get the point along the previous step vector where we could draw a bounding hyperplane (could be a line, plane, or more in higher dimensions) + Xlim = X - 0.62*dX_last + # the equation for the plane we don't want to recross is then sum(X*dX_last) = sum(Xlim*dX_last) + if np.sum((X+dX)*dX_last) < np.sum(Xlim*dX_last): # if we cross are going to cross it + ratio = np.sum((Xlim-X)*dX_last)/np.sum(dX*dX_last) # this is how much we need to scale down dX to land on it rather than cross it + dX = ratio*dX # scale down dX + if display > 1: + print((" oscil limit: ( reducing by factor {:6.3f} :"+"".join([" {:9.2f}"]*N)).format( + *( [ratio] + dX.tolist() ))) + + # Revised approach: only scale down the directions that have reversed sign from the last step. + ''' + for i in range(N): + if np.sign(dX[i])==-np.sign(dX_last[i]) and abs(dX_last[i]) > tol: # if this dimension is reversing direction + + ratio = np.abs(dX[i]/dX_last[i]) # if it's reversing by more than 62% of the last step in this dimension, limit it + if ratio > 0.62: + dX[i] = dX[i]*0.62/ratio # scale down dX so that it is just 62% of the dX_last + + if display > 1: + print((" oscil limit: (i={:2d},{:6.3f},{:7.3f}):"+"".join([" {:9.2f}"]*N)).format( + *( [i, 0.62/ratio, ratio] + dX.tolist() ))) + ''' + + # also avoid extreme accelerations in the same direction + if np.linalg.norm(dX_last) > tol: # only worry about accelerations if the last step was non-negligible + for i in range(N): + + # set the maximum permissible dx in each direction based an an acceleration limit + #if abs(dX_last[i]) < tol: + # dX_max = a_max*10*tol*np.sign(dX[i]) + if abs(dX_last[i]) < tol*(np.abs(X[i])+tol): + dX_max = a_max*tol*(np.abs(X[i])+tol)*np.sign(dX[i]) + else: + dX_max = a_max*dX_last[i] + #print('dX_max',dX_max, dX_last, tol, dX) + if dX_max == 0.0: # avoid a divide-by-zero case (if dX[i] was zero to start with) + dX[i] = 0.0 + else: + a_i = dX[i]/dX_max # calculate ratio of desired dx to max dx + #print('a_i',a_i, i, dX[i]) + if a_i > 1.0: + + # Option 1. the directoin-preserving approach: (could have problems with making the dX too small, triggering convergence) + dX = dX/a_i # scale it down to the maximum value <<< this needs to be in the conditional if X[i] > Xmin[i] and X[i] < Xmax[i]: # limit this direction if it exceeds the limit and if it's not on a bound (otherwise things will get stuck) + # Option 2. the individual approach below <<< + #dX[i] = dX[i]/a_i # scale it down to the maximum value (treat each DOF individually) + #print(dX[i]) + if display > 1: + print((" accel limit: (i={:2d},by {:6.3f} :"+"".join([" {:9.2f}"]*N)).format( + *( [i, 1.0/a_i] + dX.tolist()))) + + + # enforce bounds + for i in range(N): + if X[i] + dX[i] < Xmin[i]: + dX[i] = Xmin[i] - X[i] + #dX[i] = Xmin[i]*(1+tol) - X[i] + if display > 2: print(f" Minimum bounds adjustment for dX[{i}]") + elif X[i] + dX[i] > Xmax[i]: + dX[i] = Xmax[i] - X[i] + #dX[i] = Xmax[i]*(1-tol) - X[i] + if display > 2: print(f" Maximum bounds adjustment for dX[{i}]") + + + # check for convergence + if all(np.abs(dX) < tol*(np.abs(X)+tol)): + + if lastConverged: # only consider things converged if the last iteration also satisfied the convergence criterion + + if display>0: + print(f"Optimization converged after {iter} iterations with dX of {dX}") + print(f"Solution X is "+str(X)) + print(f"Constraints are "+str(g)) + + if any(X == Xmin) or any(X == Xmax): + if display>0: + for i in range(N): + if X[i] == Xmin[i] : print(f" Warning: Design variable {i} ended on minimum bound {Xmin[i]}.") + if X[i] == Xmax[i] : print(f" Warning: Design variable {i} ended on maximum bound {Xmax[i]}.") + + success = True + message = "converged on one or more bounds" + + elif any(X == Xmin) or any(X == Xmax) or any(g < 0.0): + if display>0: + for j in range(m): # go through each constraint + if g[j] < 0: # if a constraint will be violated + print(f" Warning: Constraint {j} was violated by {-g[j]}.") + + success = False + message = f"converged with one or more constraints violated (by max {-min(g):7.1e})" + + else: + success = True + message = "converged with no constraint violations or active bounds" + break + + else: + lastConverged = True # if this is the first time the convergence criterion has been met, note it and keep going + message = "convergence criteria only met once (need twice in a row)" + else: + lastConverged = False + message = "not converged" + + if display > 2: + print(f" Convergence message: {message}") + + dX_last = 1.0*dX # remember this current value + #XtLast = 1.0*Xt + #if iter==3: + #breakpoint() + X = X + dX + + if display > 0: + print((" dopt iteration finished. dX= "+"".join([" {:9.2f}"]*N)).format(*(dX.tolist()))) + if display > 2: + print(" iteration run time: {:9.2f} seconds".format(time.time() - iter_start_time)) + + + if display > 2: + print(f" Convergence message: {message}") + + if display > 0: + print(" total run time: {:8.2f} seconds = {:8.2f} minutes".format((time.time() - start_time),((time.time() - start_time)/60))) + + runtime = time.time() - start_time #seconds + + return X, f, dict(iter=iter, dX=dX_last, oths=oths, Xs=Xs, Fs=Fs, Gs=Gs, Xextra=Xextra, g=g, Yextra=Yextra, + success=success, message=message, time=runtime) + + +def doptPlot(info): + + n = info['Xs'].shape[1] # number of DVs + m = info['Gs'].shape[1] # number of constraints + + fig, ax = plt.subplots(n+1+m,1, sharex=True) + Xs = np.array(info["Xs"]) + Fs = np.array(info["Fs"]) + Gs = np.array(info["Gs"]) + iter = info["iter"] + + for i in range(n): + ax[i].plot(Xs[:iter+1,i]) + + ax[n].plot(Fs[:iter+1]) + ax[n].set_ylabel("objective") + + for i in range(Gs.shape[1]): + j = i+1+n + ax[j].axhline(0, color=[0.5,0.5,0.5]) + ax[j].plot(Gs[:iter+1,i]) + ax[j].set_ylabel(f'con {i}') + + ax[j].set_xlabel("iteration") + + plt.show() + + + +# ------------------------------ sample functions ---------------------------- + + +def eval_func1(X, args): + '''returns target outputs and also secondary outputs for constraint checks etc.''' + + # Step 1. break out design variables and arguments into nice names + + # Step 2. do the evaluation (this may change mutable things in args) + y1 = (X[0]-2)**2 + X[1] + y2 = X[0] + X[1] + + # Step 3. group the outputs into objective function value and others + Y = np.array([y1, y2]) # objective function + oths = dict(status=1) # other outputs - returned as dict for easy use + + return Y, oths, False + + + +def step_func1(X, args, Y, oths, Ytarget, err, tol, iter, maxIter): + '''General stepping functions, which can also contain special condition checks or other adjustments to the process + + ''' + + # get numerical derivative + J = np.zeros([len(X),len(X)]) # Initialize the Jacobian matrix that has to be a square matrix with nRows = len(X) + + for i in range(len(X)): # Newton's method: perturb each element of the X variable by a little, calculate the outputs from the + X2 = np.array(X) # minimizing function, find the difference and divide by the perturbation (finding dForce/d change in design variable) + deltaX = tol*(np.abs(X[i])+tol) + X2[i] += deltaX + Y2, extra = eval_func1(X2, args) + + J[:,i] = (Y2-Y)/deltaX # and append that column to each respective column of the Jacobian matrix + + dX = -np.matmul(np.linalg.inv(J), Y) # Take this nth output from the minimizing function and divide it by the jacobian (derivative) + + return dX # returns dX (step to make) + + + + + +## ============================== below is a new attempt at the catenary solve ====================================== +# <<< moved to Catenary.py >>> + + + + + + + + + + + + +''' + +# test run + + +#Catenary2(100, 50, 130, 1e8, 100, plots=1) + +print("\nTEST 1") +catenary(576.2346666666667, 514.6666666666666, 800, 4809884.623076923, -2.6132152062554828, CB=-64.33333333333337, HF0=0, VF0=0, Tol=1e-05, MaxIter=50, plots=2) +print("\nTEST 2") +catenary(88.91360441490338, 44.99537159734132, 100.0, 854000000.0000001, 1707.0544275185273, CB=0.0, HF0=912082.6820817506, VF0=603513.100376363, Tol=1e-06, MaxIter=50, plots=1) +print("\nTEST 3") +catenary(99.81149090002897, 0.8459770263789324, 100.0, 854000000.0000001, 1707.0544275185273, CB=0.0, HF0=323638.97834178555, VF0=30602.023233123222, Tol=1e-06, MaxIter=50, plots=1) +print("\nTEST 4") +catenary(99.81520776134033, 0.872357398602503, 100.0, 854000000.0000001, 1707.0544275185273, CB=0.0, HF0=355255.0943810993, VF0=32555.18285808794, Tol=1e-06, MaxIter=50, plots=1) +print("\nTEST 5") +catenary(99.81149195956499, 0.8459747131565791, 100.0, 854000000.0000001, 1707.0544275185273, CB=0.0, HF0=323645.55876751675, VF0=30602.27072107738, Tol=1e-06, MaxIter=50, plots=1) +print("\nTEST 6") +catenary(88.91360650151807, 44.99537139684605, 100.0, 854000000.0000001, 1707.0544275185273, CB=0.0, HF0=912082.6820817146, VF0=603513.100376342, Tol=1e-06, MaxIter=50, plots=1) +''' +''' +maxIter = 10 +# call the master solver function +X0 = [2,2] +Ytarget = [0,0] +args = [] +X, Y, info = dsolve(eval_func1, step_func1, X0, Ytarget, args, maxIter=maxIter) +''' diff --git a/famodel/design/layout.py b/famodel/design/layout.py new file mode 100644 index 00000000..7ef553cb --- /dev/null +++ b/famodel/design/layout.py @@ -0,0 +1,2485 @@ +import os +import moorpy as mp +from moorpy.helpers import getFromDict +import numpy as np +import math +import pandas as pd +import matplotlib.pyplot as plt +from matplotlib.animation import FuncAnimation +from matplotlib.colors import LogNorm +from matplotlib.collections import PolyCollection +from matplotlib.ticker import AutoLocator, AutoMinorLocator +from matplotlib.ticker import FuncFormatter +from scipy.interpolate import NearestNDInterpolator +from scipy import interpolate, optimize +from scipy.optimize import minimize, differential_evolution, NonlinearConstraint +from scipy.spatial.distance import cdist, pdist, squareform +from scipy.spatial import distance +from scipy import optimize +from sklearn.cluster import SpectralClustering # KMeans + +import random +import csv +# from moorpy.helpers import getFromDict +# from shapely import Point, Polygon +import shapely as sh +from shapely.geometry import Point, LineString, MultiLineString, Polygon, MultiPolygon +from shapely.ops import unary_union, nearest_points + +import shapely.geometry +import shapely.affinity +from shapely.affinity import translate +#import shapely.affinity as sa +import networkx as nx + +import yaml +#import raft +from copy import deepcopy + +from moorpy.helpers import set_axes_equal + +import fadesign + +from famodel.project import Project +from famodel.mooring.mooring import Mooring +from famodel.anchors.anchor import Anchor +from famodel.platform.platform import Platform +from famodel.cables.cable import Cable +from famodel.cables.cable_properties import loadCableProps, getCableProps +from famodel.substation.substation import Substation + +from fadesign.layout_helpers import getLower, makeMooringListN +from fadesign.CableLayout_functions import getCableLayout + +import floris +from floris import FlorisModel + +# from floris.turbine_library import TurbineInterface, TurbineLibrary + +from pyswarm import pso + +# Import PySwarms +#import pyswarms as pso +#from pyswarms.utils.functions import single_obj as fx +#from pyswarms.utils.plotters import (plot_cost_history, plot_contour, plot_surface) +#from pyswarms.utils.plotters.formatters import Mesher + + +# SPYDER +# Interactive plots on +#%matplotlib qt +# Interactive plots off +#%matplotlib inline + + +class Layout(Project): + '''A class to store and work with the layout information of a wind farm.''' + + def __init__(self, X, Xu, Xdb =[], wind_rose = [], ss = None, mooringAdjuster = None, **kwargs): + + '''Create a new Layout object that can be used for optimization. + The Layout class allows storage of various data for the layout design + problem, such as the boundaries, the seabed bathymetry, and wind rose. + This initialization function sets those data. Many of the data inputs + are optional and default to not being used. + + For FREE LAYOUT OPTIMIZATION: X = [x,y,phi], Xu = [] + For UNIFORM GRID LAYOUT OPTIMIZATION: X = [], + Xu = [ spacing_x, spacing_y, trans_x, trans_y, rotang, skew] + + + Parameters + ---------- + X : 1D array + Design vector considered within the optimization [x,y,phi] + x, y : turbine positions in (m) + phi : turbine heading in (deg) + Xu : 1D array + Design vector considered within the uniform grid optimization + [grid_spacing_x,grid_spacing_y,grid_trans_x, grid_trans_y, grid_rotang, grid_skew] + grid_spacing_x, y : x,y turbine spacing in (m) + grid_trans_x, y : x,y translation of entire grid in (m) + grid_rotang : rotation of grid around centroid of lease area (deg) + grid_skew : skew angle of grid (deg) + Xdb : + ??? + nTurbines : int + Number of turbines to work with. + boundary_coords : 2D array + List of x coordinates of lease area boundary vertices (m). + List of y coordinates of lease area boundary vertices (m). + + grid_x : 1D array + List of x coordinates of bathymetry grid (km). + grid_y : 1D array + List of y coordinates of bathymetry grid (km). + grid_depth : 2D array + Matrix of depth values corresponding to x,y coordinates (m). + + wind_rose : FLORIS wind rose + A wind rose of wind speeds, direction, frequency and TI + ss : MoorPy Subsystem, optional + A MoorPy Subsystem to adapt for a 3D representation of each mooring line. + mode : string + 'LCOE', 'AEP' or 'CAPEX'. + rotation_mode : Bool + True : considering rotation as part of the design vector as design variable + False: not considering rotation as design variable + + turb_minrad=360 + Radius of turbine buffer zone. + moor_minrad=50 + Radius of mooring buffer zone. + moorOpt_mode : string + 'basic' : Basic mooring layout, without MoorPy + 'advanced' : Mooring layout considers MoorPy input + + + + ''' + # Initialize Project aspects to start with + super().__init__() + + self.display = getFromDict(kwargs, 'display', default=0) + + # add seabed bathymetry (based on file for now) + self.bathymetry_file = getFromDict(kwargs, 'bathymetry_file', dtype=str, default = '') + self.loadBathymetry(self.bathymetry_file) + self.soil_file = getFromDict(kwargs, 'soil_file', dtype=str, default = '') + self.loadSoil(self.soil_file) + self.cable_mode= getFromDict(kwargs, 'cable_mode', default = True) + + + # ----- Optimization modes ----- + self.optimizer = getFromDict(kwargs, 'optimizer', dtype=str, default = '') # Optimizer + self.obj_penalty = getFromDict(kwargs, 'obj_penalty', default = True) # Use penalty factor in objective function yes (1) or no (0) + self.mode = getFromDict(kwargs, 'mode', dtype=str, default = 'LCOE') # Optimization mode + self.rotation_mode = getFromDict(kwargs, 'rotation_mode', default = True) # Rotation included as Design Variable or not + self.alternate_rows = getFromDict(kwargs, 'alternate_rows', default = False ) + self.log = dict(x=[], f=[], g=[]) # initialize a log dict with empty values + self.iter = -1 # iteration number of a given optimization run (incremented by updateDesign) + self.parallel = getFromDict(kwargs, 'parallel', default = False) + self.infeasible_obj_update = getFromDict(kwargs, 'infeasible_obj_update', default = False) # set True to update objective function even when layout violates constraints + + + # ----- Turbine quantity and positions ----- + self.nt = int(getFromDict(kwargs, 'n_turbines', default = 67)) # Number of turbines + self.turb_coords= np.zeros((self.nt,2)) # Turbine positions (x,y) [m] + self.turb_depth = np.zeros(self.nt) + + self.turb_minrad = getFromDict(kwargs, 'turb_minrad', default = 200) + self.moor_minrad = getFromDict(kwargs, 'moor_minrad', default = 20) + self.anchor_minrad = getFromDict(kwargs, 'anchor_minrad', default = 50) + + self.turb_mindist_m = np.zeros(self.nt) # currently inactive + self.con_turb_turb = np.zeros(self.nt) # distance to turbine + # distance to boundary - considering anchor radius + self.con_turb_boundary = np.zeros(self.nt) + # distance to boundary - center of WTG + self.turb_dist_tb2_m = np.zeros(self.nt) + + if np.size(Xu) != 0: + self.Xlast = np.zeros((self.nt)*2) # X vector containting X and Y coordinates + else: + self.Xlast = np.zeros_like(X) + + self.obj_value = 0 + self.turb_rating_MW = getFromDict(kwargs, 'turb_rating_MW', default = 15) # Rating of each turbine in MW + # IAC System parameters + self.iac_voltage_kV = getFromDict(kwargs, 'iac_voltage_kV', default = 66) # Voltage level in kV + self.iac_type = 'dynamic_cable_66' # Cable type, as defined in cable properties YAML + + # Turbine electrical current in ampere A + self.turb_I = (self.turb_rating_MW * 1e6) / (self.iac_voltage_kV * 1e3) + + # Cable conductor sizes for 66 kV transmission system + # List to be further specified + + #dir = os.path.dirname(os.path.realpath(__file__)) + #with open(os.path.join(dir,"CableProps_default.yaml")) as file: + # source = yaml.load(file, Loader=yaml.FullLoader) + #As = source['conductor_size']['size_A_df'] + self.iac_typical_conductor = getFromDict(kwargs, 'iac_typical_conductor',shape=-1, default = [0]) + if len(self.iac_typical_conductor)==1 and self.iac_typical_conductor[0] == 0: + self.iac_typical_conductor = np.array([ 70, 95, 120, 150, 185, 240, 300, 400, 500, 630, + 800, 1000, 1200,1400,1600,200,2500,3000,4000]) + + + # ----- Offshore Substation ----- + self.noss = int(getFromDict(kwargs,'noss', default = 1)) + self.oss_coords_initial = getFromDict(kwargs, 'oss_coords', shape=-1, default = np.zeros((self.noss,2))) # initial OSS coordinates + # adjust to a nested list [[x,y]] if given oss coords as [x,y] for compatibility with situations with multiple oss + if self.oss_coords_initial.shape == (2,): + self.oss_coords_initial = np.array([self.oss_coords_initial]) + # for now we'll set oss_coords = oss_coords_initial but this could change in generateGridPoints if using uniform grid layout + self.oss_coords = deepcopy(self.oss_coords_initial) + self.oss_minrad = getFromDict(kwargs, 'oss_minrad', default = self.turb_minrad*2) + self.static_substations = getFromDict(kwargs, 'static_substations', dtype=bool, default = False) + + # create substation platform object + for oo in range(self.noss): + r = [self.oss_coords[oo][0], self.oss_coords[oo][1], 0] + self.platformList[self.nt+oo] = Platform(id=self.nt+oo,r=r,rFair=ss.rad_fair,zFair=ss.z_fair) + self.platformList[self.nt+oo].entity = 'Substation' + + + + + # ----- Turbine Cluster ----- + self.n_cluster = int(getFromDict(kwargs, 'n_cluster', default = 9))# Amount of turbine cluster for cable routing + #self.n_tcmax = (np.ceil(self.nt/self.n_cluster)).astype(int) + self.n_tcmax = round(self.nt/(self.n_cluster*self.noss)) + + # ----- set default obj. values ----- + self.aep = 0 + self.obj_value = None + + + print("setting up areas/geometry") + # ----- Lease area boundary polygon ----- + #self.boundary = boundary_coords#list(zip(boundary_x, boundary_y)) + self.boundary_coords = getFromDict(kwargs, 'boundary_coords', shape = -1, default = np.array([(0, 0),(10000, 0),(10000, 10000), (0,10000) ])) + self.setBoundary(self.boundary_coords[:,0], self.boundary_coords[:,1]) + self.boundary_sh = sh.Polygon(self.boundary) + + # set up any interior sub boundaries (useful for mulitple separate uniform grids) + self.sub_boundary_coords = getFromDict(kwargs, 'sub_boundary_coords', shape=-1, + default = []) + self.sub_boundary = [] + self.sub_boundary_sh = [] + self.sub_boundary_centroid = [] + self.sub_boundary_centroid_x = [] + self.sub_boundary_centroid_y = [] + for subb in self.sub_boundary_coords: + subb = np.array(subb) + # save as project sub boundaries + self.sub_boundary.append(np.vstack([[subb[i,0],subb[i,1]] for i in range(len(subb))])) + + # if the boundary doesn't repeat the first vertex at the end, add it + if not all(subb[0,:] == subb[-1,:]): + self.sub_boundary[-1] = np.vstack([self.sub_boundary[-1], subb[0,:]]) + # create sub boundary shapely polygon + self.sub_boundary_sh.append(sh.Polygon(self.sub_boundary[-1])) + # create sub boundary centroid and store centroid coords + self.sub_boundary_centroid.append(self.sub_boundary_sh[-1].centroid) + self.sub_boundary_centroid_x.append(self.sub_boundary_centroid[-1].x) + self.sub_boundary_centroid_y.append(self.sub_boundary_centroid[-1].y) + + # trim the bathymetry grid to avoid excess + self.trim_grids = getFromDict(kwargs,'trimGrids',default=True) + if self.trim_grids: + self.trimGrids() + + # Get centroid of lease area + self.boundary_centroid = self.boundary_sh.centroid + self.boundary_centroid_x, self.boundary_centroid_y = self.boundary_centroid.x, self.boundary_centroid.y + + # Safety margin + self.boundary_margin = getFromDict(kwargs, 'boundary_margin', default = 0) #margin applied to exterior boundary + # Calculate the buffered polygon with safety margin + # internal: safety margin, to ensure that there is enough space for mooring system without crossing lease area boundaries + # idea: Safety margin dependent on water depth? + self.boundary_sh_int = self.boundary_sh.buffer(-self.boundary_margin) + #self.grid_x_min, self.grid_y_min, self.grid_x_max, self.grid_y_max = self.boundary_sh_ext.bounds + + ''' + # Calculate the total area of the buffered polygon + self.total_area_ext = self.boundary_sh_ext.area + self.total_area_int = self.boundary_sh_int.area + self.a_f=round(self.total_area_ext/self.total_area_int) + + # Maximum x and y distance in boundary shape + def max_distance(x_values): + if len(x_values) < 2: + return 0 + x_max = max(x_values) + x_min = min(x_values) + return abs(x_max - x_min) + + self.bound_dist_x = max_distance(self.boundary[:,0]) + self.bound_dist_y = max_distance(self.boundary[:,1]) + ''' + + # INTIAL: Parse the design vector and store updated positions internally + #x_pos, y_pos = X[:len(X)//2], X[len(X)//2:] + # ONLY FOR FREE OPTIMIZATION + if np.size(Xu) == 0 and np.size(Xdb) == 0: + if self.rotation_mode: + x_pos, y_pos, rot_rad = X[:self.nt], X[self.nt:2*self.nt], X[2*self.nt:] + #self.turb_rot_deg = rot_deg + self.turb_rot= rot_rad#np.radians(rot_deg) + else: + x_pos, y_pos = X[:len(X)//2], X[len(X)//2:] + + self.turb_rot = getFromDict(kwargs, 'turb_rot', shape = self.nt, default = np.zeros(self.nt))#rot_rad#np.radians(turb_rot) + + # Turbine positons: INPUT in [m] + self.turb_coords[:,0]= x_pos + self.turb_coords[:,1]= y_pos + # UNIFORM GRID LAYOUT OPTIMIZATION + else: + self.turb_rot = getFromDict(kwargs, 'turb_rot', shape = self.nt, default = np.zeros(self.nt)) + self.turb_coords = np.zeros((self.nt,2)) + + # X = self.generateGridPoints(Xu) + #x_pos, y_pos = X[:len(X)//2], X[len(X)//2:] + #self.turb_coords[:,0]= x_pos + #self.turb_coords[:,1]= y_pos + + # ----- Exclusion zone polygons ----- + # Turbine distances to exclusion zones (if any) + self.exclusion = getFromDict(kwargs, 'exclusion_coords', shape = -1, default = []) + self.turb_dist_tez1_m = np.zeros((self.nt*len(self.exclusion))) + self.turb_dist_tez2_m = np.zeros((self.nt*len(self.exclusion))) + self.exclusion_polygons_sh = [] # List to store polygons + + # Create exclusion polygons + for ie in range(len(self.exclusion)): + exclusion_polygon = sh.Polygon(self.exclusion[ie]) + self.exclusion_polygons_sh.append(exclusion_polygon) + + + # ----- Wind data ----- + self.wind_rose = wind_rose + + + # ----- Mooring system variables ----- + print("setting up mooringList") + self.mooringList = makeMooringListN(ss, 3*self.nt) # make Moorings + + for mooring in self.mooringList.values(): # hackily set them up + mooring.dd['sections'] = [] + mooring.dd['connectors'] = [] + for i,sec in enumerate(mooring.ss.lineList): + mooring.dd['connectors'].append({'CdA':0,'m':0,'v':0}) + mooring.dd['sections'].append({'type':mooring.ss.lineList[i].type, + 'L':mooring.ss.lineList[i].L}) + mooring.dd['connectors'].append({'CdA':0,'m':0,'v':0}) + mooring.adjuster = mooringAdjuster # set the designer/adjuster function + + + # ----- Platforms ----- + for i in range(self.nt): + r = [self.turb_coords[i][0],self.turb_coords[i][1],0] + self.platformList[i] = Platform(id=i, r=r, heading=0, mooring_headings=[0,120,240],rFair=ss.rad_fair,zFair=ss.z_fair) + self.platformList[i].entity = 'FOWT' + + for j in range(3): + self.platformList[i].attach(self.mooringList[i*3+j], end='b') + + + # ---- Anchors ---- + self.anchorList = {} + if 'anchor_settings' in kwargs: + anchor_settings = True + else: + anchor_settings = False + # set up anchor design dictionary + ad = {'design':{}, 'cost':{}} + if anchor_settings and 'anchor_design' in kwargs['anchor_settings']: + anchor_design_initial = kwargs['anchor_settings']['anchor_design'] + ad['type'] = kwargs['anchor_settings']['anchor_type'] + else: + print('No anchor type given, defaulting to suction bucket anchor.') + anchor_design_initial = {'D':3.0,'L':16.5,'zlug':10} + ad['type'] = 'suction_pile' + ad['design'] = anchor_design_initial # INPUT or not??? + for i, moor in enumerate(self.mooringList.values()): + if self.soil_x is not None: # get soil conditions at anchor location if soil info available + name, props = self.getSoilAtLocation(moor.rA[0], moor.rA[1]) + + # create anchor object + anch = Anchor(dd=ad,aNum=i,id=moor.id) + anch.soilProps = {name:props} + self.anchorList[anch.id] = anch + # attach to mooring line + moor.attachTo(anch,end='a') + if 'mass' in ad: + anch.mass = ad['mass'] + elif anchor_settings and 'mass' in kwargs['anchor_settings']: + anch.mass = kwargs['anchor_settings']['mass'] + + + # --- develop anchor types --- + self.anchorTypes = {} + self.anchorMasses = {} + self.anchorCosts = {} + # pull out mean depth + meandepth = np.mean(-self.grid_depth) + pf = self.platformList[0] + # artificially set platform at 0,0 + pf.setPosition([0,0],project=self) # put in a random place and reposition moorings + # create ms for this platform + msPF = pf.mooringSystem() + # set depth artificially to mean depth + msPF.depth = -meandepth + # set mooring object depth artificially for now + for moor in pf.getMoorings().values(): + moor.dd['zAnchor'] = meandepth + moor.z_anch = meandepth + moor.ss.depth = -meandepth + moor.rad_fair = 58 + moor.z_fair = -14 + # call set position function again to use adjuster function on all moorings + pf.setPosition([0,0]) + + # get anchors connected to this platform + anchors = pf.getAnchors() + # choose one (all should be same) + anch = anchors[0] + + # keep zlug constant? + if anchor_settings and 'fix_zlug' in kwargs['anchor_settings']: + fix_zlug=kwargs['anchor_settings']['fix_zlug'] + else: + fix_zlug=False + # set minimum allowable FS + if anchor_settings and 'FS_min' in kwargs['anchor_settings']: + minfs = kwargs['anchor_settings']['FS_min'] + else: + minfs = {'Ha':2,'Va':2} + # set FSdiff_max if provided + if anchor_settings and 'FSdiff_max' in kwargs['anchor_settings']: + FSdiff_max = kwargs['anchor_settings']['FSdiff_max'] + else: + FSdiff_max = None + # create anchor for each soil type + for name, soil in self.soilProps.items(): + if anchor_settings and 'anchor_resize' in kwargs['anchor_settings'] and kwargs['anchor_settings']['anchor_resize']: + # get anchor forces from array watch circle + pf.getWatchCircle(ms = msPF) + # get loads dictionary but get rid of any Ha Va loads that might already be there + anch.loads = {'Hm':anch.loads['Hm'],'Vm':anch.loads['Vm'], + 'thetam':anch.loads['thetam'], 'mudline_load_type':'max'} + # update soil type for anchor + anch.soilProps = {name:soil} + geom = [val for val in anch.dd['design'].values()] + geomKeys = [key for key in anch.dd['design'].keys()] + anch.getSize(geom,geomKeys, FSdiff_max=FSdiff_max, + fix_zlug=fix_zlug, minfs=minfs) + + self.anchorTypes[name] = deepcopy(anch.dd) if anch.dd else {} + self.anchorMasses[name] = deepcopy(anch.mass) if anch.mass else 0 + try: + self.anchorCosts[name] = deepcopy(anch.getCost()) + except: + self.anchorCosts[name] = 0 + + + + self.ms_na = 3 # Number of anchors per turbine. For now ONLY 3 point mooring system. + #self.ms_anchor_depth = np.zeros((self.nt*self.ms_na)) # depths of anchors + self.anchor_coords= np.zeros((self.nt*self.ms_na,2)) # anchor x-y coordinate list [m] + self.ms_bufferzones_pos = np.zeros((self.nt,), dtype=object) # Buffer zones for moorign system + self.ms_bufferzones_rout = np.zeros((self.nt,), dtype=object) + self.ms_bufferzones_rout_points = np.zeros((self.nt,), dtype=object) + + + # ----- Initialize the FLORIS interface fi ----- + self.use_FLORIS = getFromDict(kwargs,'use_FLORIS', default = False) + if self.use_FLORIS: # If using FLORIS, initialize it + print("initializing FLORIS") + # How to do this more elegant? + dirname = '' #'./_input/' + #flName = 'gch_floating.yaml' + if self.parallel: + from floris import ParFlorisModel + self.floris_file = getFromDict(kwargs, 'floris_file', dtype = str, default = '') + self.flow = ParFlorisModel(self.floris_file) + + else: + self.floris_file = getFromDict(kwargs, 'floris_file', dtype = str, default = '') + self.flow = FlorisModel(self.floris_file) #FlorisInterface + + # FLORIS inputs x y positions in m + self.flow.set(layout_x=self.turb_coords[:,0], + layout_y=self.turb_coords[:,1], + wind_data = self.wind_rose + ) + #run floris simulation + # self.flow.run() + + # # SAVE INITIAL AEP + # self.aep0 = self.flow.get_farm_AEP() + + # ----- Wind Turbine Data ----- + # https://nrel.github.io/floris/turbine_interaction.html + # self.ti = TurbineInterface.from_internal_library("iea_15MW.yaml") + + if self.display > 0: + self.plotWakes(wind_spd = 10, wind_dir = 270, ti = 0.06) + + else: # if not using FLORIS, indicate it with a None + self.flow = None + + print("updating layout") + if np.size(Xu) != 0: + self.updateLayoutUG(Xu) + elif np.size(Xdb) != 0: + self.db_ext_spacing = getFromDict(kwargs, 'db_ext_spacing', default = [0, 1, 0, 1]) + self.updateLayoutDB(Xdb) + else: + self.updateLayoutOPT(X) + + + + + + def generateGridPoints(self, Xu, trans_mode, boundary_index=-1): + ''' Generate uniform grid points and save resulting coordinates into vector X. + This transforms the uniform grid (UG) design variables into the design variables of + the free layout optimization. + + trans_mode = 'x': Shear transformation in x direction only + trans_mode = 'xy': Shear transformation in x and y direction + ''' + grid_spacing_x = Xu[0] + grid_spacing_y = Xu[1] + grid_trans_x = Xu[2] + grid_trans_y = Xu[3] + grid_rotang = Xu[4] + grid_skew = Xu[5] + + if boundary_index >= 0: + boundary = self.sub_boundary_sh[boundary_index] + bound_centroid_y = self.sub_boundary_centroid_y[boundary_index] + bound_centroid_x = self.sub_boundary_centroid_x[boundary_index] + + else: + boundary = self.boundary_sh + bound_centroid_y = self.boundary_centroid_y + bound_centroid_x = self.boundary_centroid_x + + if self.rotation_mode: + if len(Xu) != 7: + raise ValueError('If rotation mode is True, Xu[6] is turbine rotation') + self.turb_rot = np.radians(Xu[6]) + + # Check if self.grid_spacing_x/y is equal to 0, if so, set it to 1000 m + if grid_spacing_x == 0: + grid_spacing_x = self.turb_minrad*0.5 + if grid_spacing_y == 0: + grid_spacing_y = self.turb_minrad*0.5 + + # Shear transformation + # Calculate trigonometric values + cos_theta = np.cos(np.radians(grid_rotang)) + sin_theta = np.sin(np.radians(grid_rotang)) + tan_phi = np.tan(np.radians(grid_skew)) + + # Transmoration matrix, considering shear transformatio and rotation + # Default: shear direction in x direction only + # xy: shear direction in x and direction + if trans_mode == 'xy': + # Compute combined x and y shear + transformation_matrix = np.array([[cos_theta-sin_theta*tan_phi, -sin_theta + tan_phi * cos_theta], + [sin_theta+cos_theta*tan_phi, sin_theta*tan_phi+cos_theta]]) + else: + # default transformation: x shear only + transformation_matrix = np.array([[cos_theta, -sin_theta + tan_phi * cos_theta], + [sin_theta, sin_theta*tan_phi+cos_theta]]) + + # Generate points in the local coordinate system + points = [] + + # Lease area shape: Get min and max xy coordinates and calculate width + min_x, min_y, max_x, max_y = boundary.bounds # self.boundary_sh.bounds + xwidth = abs(max_x-min_x) + ywidth = abs(max_y-min_y) + + + # LOCAL COORDINATE SYSTEM WITH (0,0) LEASE AREA CENTROID + # Therefore, +/- self.boundary_centroid_y/x cover the entire area + # Loop through y values within the boundary_centroid_y range with grid_spacing_y increments + column_count = 0 + rotations = [] + grid_position =[] + for y in np.arange(-bound_centroid_y-ywidth, bound_centroid_y+ywidth, grid_spacing_y): + column_count += 1 + row_count = 0 + # Loop through x values within the boundary_centroid_x range with grid_spacing_x increments + for x in np.arange(-bound_centroid_x-xwidth, bound_centroid_x+xwidth, grid_spacing_x): + + row_count += 1 + # Apply transformation matrix to x, y coordinates + local_x, local_y = np.dot(transformation_matrix, [x, y]) + # Add grid translation offsets to local coordinates + local_x += grid_trans_x + local_y += grid_trans_y + # Create a Point object representing the transformed coordinates + # Transform back into global coordinate system with by adding centroid to local coordinates + point = Point(local_x + bound_centroid_x, local_y + bound_centroid_y) + points.append(point) + + if self.alternate_rows: + rotations.append(self.turb_rot + np.radians(180 * (column_count % 2))) + #store column, row for each turbine + grid_position.append([column_count, row_count]) + + + # remove points that are not in boundaries + bound_lines = boundary.boundary # get boundary lines for shapely analysis + out_lines = [bound_lines] + # keep only points inside bounds + points_ib = [pt for pt in points if (boundary.contains(pt))] + if self.alternate_rows: + self.turb_rot = [rotations[ind] for ind in range(0, len(points)) if boundary.contains(points[ind])] + self.grid_positions = [grid_position[ind] for ind in range(0, len(points)) if boundary.contains(points[ind])] + + points_ibe = points_ib + # remove points in exclusion zones + if self.exclusion_polygons_sh: + for ie in range(len(self.exclusion)): + points_ibe = [pt for pt in points_ibe if not self.exclusion_polygons_sh[ie].contains(pt)] + out_lines.append(self.exclusion_polygons_sh[ie].boundary) # get boundary lines for exclusion zones + + return(points_ibe) + + + def pareGridPoints(self,points_ibe): + ''' + Function to pare number of grid points down to desired amount, place oss + at closest grid points (if substations allowed to move) and return + array of x, y(, rotation) values. Sorts points by distance from all borders + (lease boundary, inner boundaries, exclusion zones) and keeps the nt points + furthest from all boundaries + + Parameters + ---------- + points_ibe : list + List of shapely point objects that are inside all boundaries and outside all exclusion zones + + Returns + ------- + X : np.ndarray + 1D array of concatenated x, y(, rotation) for each turbine + + ''' + # determine number of points to keep (usually # turbines + # substations) + if self.static_substations: + # in this case, keep substations where they are + nt = self.nt + else: + nt = self.nt + self.noss + + # create list of boundary lines from outside boundary, exclusion zones, and inner boundaries + out_lines = [self.boundary_sh.boundary] + if len(self.sub_boundary_sh) > 0: + for sub in self.sub_boundary_sh: + out_lines.append(sub.boundary) + for ie in range(len(self.exclusion)): + out_lines.append(self.exclusion_polygons_sh[ie].boundary) + + lines = MultiLineString(out_lines) + point_dists = [pt.distance(lines) for pt in points_ibe] # get min dist between bounds and each point + points_ibe = np.array(points_ibe) + # get indices of sorting by descending minimum distance + points_sorted_idx = [int(ind) for ind in np.flip(np.argsort(point_dists,kind='stable'))] + furthest_points = list([points_ibe[i] for i in range (0, len(points_ibe)) if i in points_sorted_idx[:nt]]) # pull out the points that are furthest from bounds + self.grid_positions = list(self.grid_positions[i] for i in range (0, len(points_ibe)) if i in points_sorted_idx[:nt]) + if self.alternate_rows: + furthest_rotations = list(self.turb_rot[i] for i in range (0, len(points_ibe)) if i in points_sorted_idx[:nt]) + + + # add points outside lease area if more points are needed + min_x, min_y, max_x, max_y = self.boundary_sh.bounds + if len(points_sorted_idx)< nt: + # determine remaining number of turbines to add + leftover = nt-len(points_sorted_idx) + # choose point outside bounds for leftovers + leftover_loc = Point(min_x-1,min_y-1) + furthest_points.extend([leftover_loc]*leftover) + if self.alternate_rows: + furthest_rotations.extend([0]*leftover) + + # put substation(s) in place closest to oss_coords if substations can move + if not self.static_substations: + for oo in range(self.noss): + # make a multipoint fro + turb_multipoint = sh.MultiPoint(furthest_points) + oss_point_start = Point(self.oss_coords_initial[oo]) + # find point closest to initial oss coord & set as new oss position + oss_point = nearest_points(turb_multipoint,oss_point_start)[0] + # remove turbine from new oss position (extra turbines have been placed already) + if oss_point in furthest_points: + if self.alternate_rows: + del furthest_rotations[furthest_points.index(oss_point)] + + index = furthest_points.index(oss_point) + furthest_points.remove(oss_point) + self.grid_positions.remove(self.grid_positions[index]) + self.oss_coords[oo] = [oss_point.x, oss_point.y] + else: + print('Could not find nearby point for oss, setting oss to initial coords') + self.oss_coords[oo] = self.oss_coords_initial[oo] + + # save points furthest from bounds into turb_coords + x_coords = np.array([point.x for point in furthest_points])#/1000 + y_coords = np.array([point.y for point in furthest_points])#/1000 + for i,coord in enumerate(self.turb_coords): + coord[0] = x_coords[i] + coord[1] = y_coords[i] + + #update grid_positions row and column coordinates based on minimum + self.grid_positions = np.array(self.grid_positions) + self.grid_positions[:,0] = self.grid_positions[:,0] - min(self.grid_positions[:,0]) + self.grid_positions[:,1] = self.grid_positions[:,1] - min(self.grid_positions[:,1]) + + # Return Design Vector X with x,y coordinates, same as used for the free layout optimization. + # Coordinates in (km) + # This completes the interface + if self.rotation_mode: + + if self.alternate_rows: + self.turb_rot = furthest_rotations + X = np.concatenate((self.turb_coords[:,0], self.turb_coords[:,1], self.turb_rot)) + else: + X = np.concatenate((self.turb_coords[:,0], self.turb_coords[:,1], self.turb_rot*np.ones((nt)))) + else: + X = np.concatenate((self.turb_coords[:,0], self.turb_coords[:,1])) + + return X + + + def updateLayout(self, X, level=0, refresh=False): + '''Update the layout based on the specified design vector, X. This + will adjust the turbine positions stored in the Layout object as + well as those in the FLORIS and any other sub-objects. + + Parameters + ---------- + X + Design vector. + level + Analysis level to use. Simplest is 0. + refresh : bool + If true, forces a re-analysis, even if this design vector is old. + ''' + if len(X)==0: # if any empty design vector is passed (useful for checking constraints quickly) + if refresh: + X = np.array(self.Xlast) + else: + return + if np.array_equal(X, self.Xlast) and not refresh: + #if all(X == self.Xlast) and not refresh: # if X is same as last time + #breakpoint() + pass # just continue, skip the update steps + + + elif any(np.isnan(X)): + raise ValueError("NaN value found in design vector") + + else: # Update things iff the design vector is valid and has changed + if self.display > 1: + print("Updated design") + + self.iter += 1 # update internal iteration counter + + # Parse the design vector and store updated positions internally + if self.rotation_mode: + x_pos, y_pos, rot_rad = X[:self.nt], X[self.nt:2*self.nt], X[2*self.nt:] + #self.turb_rot = np.radians(rot_deg) + self.turb_rot = rot_rad + else: + x_pos, y_pos = X[:len(X)//2], X[len(X)//2:] + #self.turb_rot = self.turb_rot_const + + self.turb_coords[:,0]= x_pos + self.turb_coords[:,1]= y_pos + + # Update things for each turbine + #breakpoint() + #print(self.nt, len(self.turb_depth), X) + #print(self.turb_coords) + + # Update Paltform class + for i in range(self.nt): + self.platformList[i].setPosition(self.turb_coords[i], heading=self.turb_rot[i], degrees=False, project = self) + # switch anchor type + anchs = self.platformList[i].getAnchors() + for anch in anchs.values(): + name, props = self.getSoilAtLocation(anch.r[0],anch.r[1]) + atype = self.anchorTypes[name] + anch.dd.update(atype) + anch.mass = self.anchorMasses[name] + anch.cost['materials'] = self.anchorCosts[name] + anch.soilProps = {name:props} + + # Get depth at turbine postion + self.turb_depth[i] = -self.getDepthAtLocation( + self.turb_coords[i,0], self.turb_coords[i,1]) + # update substation platform location(s) + for oo in range(self.noss): + self.platformList[self.nt+oo].setPosition(self.oss_coords[oo], + heading=self.turb_rot[i], + degrees=False,project=self) + + + # Anchor locations - to be repalced when integration is further advanced + #for j in range(3): + # im = i*3 + j # index in mooringList + # self.ms_anchor_depth[im] = self.mooringList[im].z_anch#self.mooringList[im].rA[2] OLD, not needed anymore + # self.anchor_coords[im,:] = self.mooringList[im].rA[:2] + + ''' + # Calculate anchor position based on headings + + theta = self.turb_rot[i] # turbine heading + #R = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]]) + + headings = np.radians([60,180,300]) + + for j in range(len(headings)): + + im = i*3 + j # index in mooringList + + # heading of the mooring line + heading_i = headings[j] + theta + + # adjust the whole Mooring + #self.mooringList[im].reposition(self.turb_coords[i,:], + # heading=heading_i, project=self, level=level) + #self.mooringList[im].reposition(r_center=self.turb_coords[i,:], + # heading=heading_i, project=self, level=level) + ''' + # get the anchor location from the mooring + #self.anchor_coords[im,:] = self.mooringList[im].rA[:2] + #self.ms_anchor_depth[im] = self.mooringList[im].rA[2] + #self.mooringList[0].z_anch + #self.mooringList[0].rA + #self.mooringList[0].rA + + + + # ----- evaluate constraints ----- + + # ----- Calculate buffer zone shape around mooring lines and anchors. ----- + # ISO 19901-7: 100 m safety zone to other offshore assets, therefore 50 m per mooring line is recommended + + # SAFE BUFFERZONES IN PLATFORM OBJECT? + + + + + # Create LineString geometries and buffer them + for i in range(self.nt): + # Buffer group for turbine positioning + buffer_group_pos = [] + # Buffer group for cable routing + buffer_group_rout = [] + + for j in range(self.ms_na): + im = 3*i + j # global index of mooring/anchor + + moor_bf_start = get_point_along_line(self.turb_coords[i,:], self.mooringList[im].rA[:2], self.turb_minrad) + # Buffer zone mooring line + #line = LineString([self.turb_coords[i,:], self.mooringList[im].rA[:2]]) + line = LineString([moor_bf_start, self.mooringList[im].rA[:2]]) + mooringline_buffer = line.buffer(self.moor_minrad) + + # Buffer zone anchor + # Create a point at coordinates (x, y) + point = Point(self.mooringList[im].rA[:2]) + # Create a buffer around the anchor with a radius of X + anchor_buffer = point.buffer(self.anchor_minrad) + + # Buffer zone turbine + # Create a point at coordinates (x, y) + point = Point(self.turb_coords[i,:],) + # Create a buffer around the anchor with a radius of X + turb_buffer = point.buffer(self.turb_minrad) + + # Buffer group for turbine positioning + buffer_group_pos.append(mooringline_buffer) + buffer_group_pos.append(anchor_buffer) + buffer_group_pos.append(turb_buffer) + + # Buffer group for cable routing + buffer_group_rout.append(mooringline_buffer) + buffer_group_rout.append(anchor_buffer) + + # Combine the buffered lines connected to the same turbine into one polygon + polygon = unary_union(buffer_group_pos) # Combine buffers for each turbine + if isinstance(polygon, MultiLineString): + # Convert MultiLineString to Polygon + polygon = Polygon(polygon) + self.ms_bufferzones_pos[i] = polygon + + polygon = unary_union(buffer_group_rout) # Combine buffers for each turbine + if isinstance(polygon, MultiLineString): + # Convert MultiLineString to Polygon + polygon = Polygon(polygon) + self.ms_bufferzones_rout[i] = polygon + + #envelopes['buffer_zones']['shape'] + + + + # ----- Overlap between mooring zones ----- + # Create an empty 2D array to store the areas of intersection + intersection_areas = np.zeros((self.nt, self.nt)) + # Calculate and fill the array with the areas of intersection + for i in range(self.nt): + for j in range(i + 1, self.nt): + polygon1 = self.ms_bufferzones_pos[i] + polygon2 = self.ms_bufferzones_pos[j] + # Calculate intersection + intersection = polygon1.intersection(polygon2) + # Fill the array with the area of intersection + intersection_areas[i, j] = intersection.area*(-1) + intersection_areas[j, i] = intersection.area*(-1) + + self.con_moor_moor = getLower(intersection_areas) # get lower diagonal + + + # ----- Overlap between mooring zones and boundary ----- + # Calculate areas of the parts of polygons outside the boundary + self.con_moor_boundary = np.zeros(self.nt) + # Iterate over polygons and fill the array with areas + for i, polygon in enumerate(self.ms_bufferzones_pos): + if isinstance(polygon, (Polygon, MultiPolygon)) and polygon.intersects( self.boundary_sh): + # Calculate the intersection with the boundary polygon + intersection = polygon.intersection( self.boundary_sh) + # Calculate the area of the parts outside the boundary + outside_area = polygon.difference(intersection).area + # Fill the array with the area + self.con_moor_boundary[i] = -outside_area + + + # ----- Between exclusion zones and turbines ----- + self.con_moor_ez_m2 = np.zeros((self.nt*len(self.exclusion))) + r = 0 + for ie in range(len(self.exclusion)): + #exclusion_polygon = sh.Polygon(self.exclusion[ie]) + # Iterate over polygons and fill the array with areas + for i, polygon in enumerate(self.ms_bufferzones_pos): + if isinstance(polygon, (Polygon, MultiPolygon)) and polygon.intersects(self.exclusion_polygons_sh[ie]): + # Calculate the intersection with the boundary polygon + intersection = polygon.intersection(self.exclusion_polygons_sh[ie]) + # Calculate the area of the parts inside exclusion areas + inside_area = polygon.difference(intersection).area + # Fill the array with the area + self.con_moor_ez_m2[r] = -inside_area + r += 1 + + + # ----- Margin between turbines ----- + + # Distance matrix between turbines + distances = cdist(self.turb_coords, self.turb_coords) + + dists = distances [np.tril_indices_from( distances , k=-1)] # get lower diagonal + + # Reduce by buffer radius (for each turbine) then store + self.con_turb_turb = dists - 2*self.turb_minrad + + # ----- Margin between turbines and OSS ----- + # Distance matrix between turbines + #distances = cdist(self.turb_coords,self.oss_coords) + #print(self.oss_coords) + #print(self.turb_coords) + #dists = distance.cdist(self.oss_coords, self.turb_coords, 'euclidean') + #dists = distances [np.tril_indices_from( distances , k=-1)] # get lower diagonal + dists = [] + for oo in range(self.noss): + dists.extend(np.linalg.norm(self.turb_coords - self.oss_coords[oo], axis=1)) + # Reduce by buffer radius (for each turbine) then store + self.con_turb_oss = np.array(dists) - self.oss_minrad + + # ----- margin between turbines and lease area boundary ----- + r = 0 + self.con_turb_ez_m = np.zeros((self.nt*len(self.exclusion))) + self.con_oss_boundary = np.zeros(self.noss) + self.con_oss_ez_m = np.zeros((self.noss*len(self.exclusion))) + coords = np.zeros((self.nt+self.noss,2)) + coords[:self.nt] = self.turb_coords + coords[self.nt:] = self.oss_coords + isturb=True + for i in range(self.nt+self.noss): + if i>=self.nt: + isturb = False + # Create a Shapely Point for the given xy of turbine or oss + p_turb = Point(coords[i,0], coords[i,1]) + + #breakpoint() + # Find the nearest point on the shape to the given point + p_bound = nearest_points(self.boundary_sh.exterior, p_turb)[0] + + # Calculate the Euclidean distance between point and nearest point on boundary + distance_within = p_turb.distance(p_bound) + + # If point is outside boundary, give the distance a negative sign + if not p_turb.within(self.boundary_sh): + distance_within = -abs(distance_within) + + # Reduce by buffer radius, then add to constraint list + if isturb: + self.con_turb_boundary[i] = distance_within - self.turb_minrad + else: + self.con_oss_boundary[i-self.nt] = distance_within - self.oss_minrad + + for ie in range(len(self.exclusion)): + p_exclusion = nearest_points(self.exclusion_polygons_sh[ie].exterior, p_turb)[0] + dist_outside = p_turb.distance(p_exclusion) + # if turbine is inside exclusion zone, give distance - sign + if p_turb.within(self.exclusion_polygons_sh[ie]): + dist_outside = -abs(dist_outside) + + if isturb: + self.con_turb_ez_m[r] = dist_outside + else: + self.con_oss_ez_m[r-self.nt*len(self.exclusion)] = dist_outside + r += 1 + + # could handle exclusion zones in this same loop + # # ----- margin between turbines and exclusion zones ----- + # # Optimize: creat point once, together with above + # if len(self.exclusion) > 0: + # r = 0 + # for ie in range(len(self.exclusion)): + # #exclusion_polygon = sh.Polygon(self.exclusion[ie]) + # #breakpoint() + + # for i in range(self.nt): + # # Create a Shapely Point for the given xy + # #point = Point(x_pos[i], y_pos[i]) + # point = Point(self.turb_coords[i,0], self.turb_coords[i,1]) + # # Find the nearest point on the shape to the given point + # nearest_point = nearest_points(self.exclusion_polygons_sh[ie].exterior, point)[0] + # # Calculate the Euclidean distance between WTG anchor radius and nearest point on shape + # # Reduce distance by radius (distance has to be equal or greater than anchor radius) + # self.turb_dist_tez1_m[r] = point.distance( + # nearest_point) - self.turb_minrad + # # Calculate the Euclidean distance between WTG center and shape + # self.turb_dist_tez2_m[r] = point.distance(nearest_point) + # # Check if turbine is outside the boundary + # # Ensure if point is outside shape, distance is always negative + # if point.within(self.exclusion_polygons_sh[ie]): + # self.turb_dist_tez1_m[r] = -abs(self.turb_dist_tez1_m[r]) + # # Weight the contraints so that the turbines stay within the specifified area + # self.turb_dist_tez2_m[r] = -abs(self.turb_dist_tez2_m[r]) + # r =+1 + + + + + + + # ----- Concatenate constraints vector ----- + + # Note: exclusions are temporarily skipped, but can be added back in to the below + + #!! QUESTION MB: Should this be considered at all as a constraint? I think it is more important that + # anchor buffer zones do not exceed the lease boundaries, but not a wind turbine spacing parameter. + + # distances + constraint_vals_m = np.concatenate([self.con_turb_turb, self.con_turb_boundary, + self.con_turb_oss, self.con_turb_ez_m, + self.con_oss_boundary, self.con_turb_ez_m]) + constraint_vals_km = constraint_vals_m/1000 + + # areas + constraint_vals_m2 = np.concatenate([self.con_moor_moor, self.con_moor_boundary, self.con_moor_ez_m2]) + constraint_vals_km2 = constraint_vals_m2/(1000**2) + # Combine constraint values (scaling to be around 1) + self.con_vals = 10*np.concatenate([constraint_vals_km, constraint_vals_km2]) + + + # Sum of Constraint values + negative_values = [val for val in self.con_vals if val < 0] + + + if not negative_values: + self.con_sum = 0 + # ----- Cable Layout - ONLY FOR FEASIBLE LAYOUT + if self.cable_mode: + + self.iac_dic,_,_ = getCableLayout(self.turb_coords, self.oss_coords, self.iac_typical_conductor, + self.iac_type, self.turb_rating_MW, turb_cluster_id=[], + n_cluster_sub=self.n_cluster, n_tcmax=self.n_tcmax, plot=False, oss_rerouting=1) + + # Save cables in cable objects + self.addCablesConnections(self.iac_dic,cableType_def=self.iac_type) + + else: + self.con_sum = sum(negative_values) # sum of all values below zero + + + if self.optimizer == 'PSO': + # PSO constraints only + # Constraints above zero 0: satisfied (often it is g < 0 for satisfied constraints for a PSO) + # Solution: Sum of negative constraint values, because it has to be one value only + self.con_vals = self.con_sum + + + # Penalty factor: (1+abs(self.con_vals)) or 1 + if self.obj_penalty == 1: # penalty ON + f_pentalty = (1+abs(self.con_sum)) + else: # penalty OFF + f_pentalty = 1 + + + # ----- evaluate objective function ----- + # compute the objective function value + # objective function includes a constraint term, leading to a penalty when constraints are not satisfied + # (1+abs(self.con_vals)) + # objective funciton + if not negative_values or self.infeasible_obj_update or not self.obj_value: + if self.mode == 'LCOE': # minimize LCOE (this LCOE version focuses on mooring and cable costs/AEP) + self.getLCOE() + #self.constraintFuns_penalty(X) + self.obj_value = self.lcoe*1e5*f_pentalty #(1+abs(self.con_vals)) #+ self.cost_penalty / self.aep#self.getLCOE() #+ self.constraintFuns_penalty(X)/self.aep + elif self.mode == 'LCOE2': # minimize LCOE (this LCOE version includes opex estimates and platform/turbine cost estimates) + self.getLCOE2() + self.obj_value = self.lcoe*f_pentalty + elif self.mode == 'AEP': # maximize AEP + self.getAEP(display = self.display) + self.obj_value = -self.aep/1e12/f_pentalty #-self.getAEP() #+ self.constraintFuns_penalty(X) # minus, because algorithm minimizes the objective function + elif self.mode == 'CAPEX': # maximize AEP + self.getCost() + #self.constraintFuns_penalty(X) + self.obj_value = (self.cost_total/1e7)*f_pentalty #+ self.cost_penalty#+ abs(self.con_vals)#self.constraintFuns_penalty#(X)/1e7 #self.getCAPEX() #+ self.constraintFuns_penalty(X) + + else: + raise Exception( + "The layout 'mode' must be either LCOE, AEP or CAPEX.") + + ''' + # ----- write to log ----- + # only log if the design has significantly changed + if np.linalg.norm(X - self.Xlast) > 100: # <<< threshold should be customized + # log the iteration number, design variables, objective, and constraints + self.log['x'].append(list(X)) + self.log['f'].append(list([self.obj_value])) + # Check if self.con_vals is an integer - Different optimizer require different constraints + if isinstance(self.con_vals, int): + # Convert self.con_vals to a list before appending to self.log['g'] + self.log['g'].append([self.con_vals]) + else: + # If self.con_vals is already iterable, directly append it to self.log['g'] + self.log['g'].append(list(self.con_vals)) + ''' + + self.Xlast = np.array(X) # record the current design variables + + + def updateLayoutUG(self, Xu, level=0, refresh=False): + '''Interface from uniform grid design variables to turbine coordinates.''' + + X_points = [] + # create grid points + if len(self.sub_boundary_sh) > 0: + # determine # of grid variables per sub boundary + nXu = 7 if self.rotation_mode else 6 + + # create grid points for each sub grid + for ind in range(len(self.sub_boundary_sh)): + # pull out relevant design variables + Xus = Xu[nXu*ind:nXu*(ind+1)] + # convert km to m for first 4 variables + Xum = np.hstack([[x*1000 for x in Xus[0:4]], Xus[4:]]) + # generate grid points + X_points.extend(self.generateGridPoints(Xum,trans_mode='x',boundary_index=ind)) + else: + # create grid points for entire grid + Xum = np.hstack([[x*1000 for x in Xu[0:4]], Xu[4:]]) # convert first 4 entries from km to m + # generate grid points + X_points.extend(self.generateGridPoints(Xum,trans_mode='x')) + + # pare down grid points to those furthest from boundaries & optionally add substation(s) in grid + X = self.pareGridPoints(X_points) + + self.updateLayout(X, level, refresh) # update each turbine's position + + #def updateLayoutOPTUG(self, Xu): + # '''Interface from uniform grid design variables to turbine coordinates.''' + # X = self.generateGridPoints(Xu) + # X2 = np.array(X) # make a copy of the design vector + # X2[:2*self.nt] = X[:2*self.nt]#*1000 # convert coordinates from km to m + # self.updateLayout(X2) + + + def updateLayoutDB(self, Xdb, level=0, refresh=False): + '''Interface for Dogger Bank style layouts.''' + + ### Xdb[0] and Xdb[1] are exterior spacings. db_ext_spacing allows the user to set what spacing each side uses (in order of coordinates) + interior =self.boundary_sh.buffer(-self.mooringList[0].rad_anch - self.anchor_minrad) ### this buffer should ensure anchor stays within boundary --- need to check + coords = list(interior.exterior.coords) + + from shapely.geometry import LineString + + #iterate through boundaries + points = [] + for i in range(0, len(coords) - 1): + + # connect exterior coordinates in order + line = LineString([coords[i], coords[i+1]]) + + # db_ext_spacing input allows the user to set which boundaries use which outer spacing + # determine number of turbines that will fit + num = math.floor(line.length/Xdb[self.db_ext_spacing[i]]) + + #interpolate along the side for the num turbines + if i == 0: + points.extend([line.interpolate(i/num , normalized = True) for i in range(num)]) + else: + + #after the first side, start turbines at +spacing so there isn't overlap at the corner + points.extend([line.interpolate(i/num , normalized = True) for i in range(1, num)]) + + + xs = [point.coords[0][0] for point in points] + ys = [point.coords[0][1] for point in points] + + + + #fill the interio using generateGridPoints + interiorinterior = interior.buffer(-self.mooringList[0].rad_anch - self.anchor_minrad) ### again this buffer needs to be checked + + #store original exterior boundary and numturbines + boundary_sh_int = self.boundary_sh_int + nt = self.nt + + interior_nt = nt - len(xs) + if interior_nt < 0: + interior_nt = 0 + + self.boundary_sh_int = interiorinterior + self.nt = interior_nt + + if self.nt > 0: + + X = self.generateGridPoints(Xdb[2:],trans_mode='x') + #combined exterior and interior turbines into X vector + Xall = list(X[:self.nt]) + xs + list(X[self.nt:]) + ys + x_coords = list(X[:self.nt]) + xs + y_coords = list(X[self.nt:]) + ys + else: + print('Exterior coords filled the required number of turbines') + + xs = xs[:nt] + ys = ys[:nt] + + Xall = xs + ys + x_coords = xs + y_coords = ys + + #revert boundary and nt + self.boundary_sh_int = boundary_sh_int + self.nt = nt + self.turb_coords = np.zeros((self.nt,2)) + + + #create buffers for exterior points (generateGridPoints did this for interior already) + for i in range(0, len(xs)): + point = Point(xs[i], ys[i]) + self.platformList[interior_nt + i].setPosition([point.x,point.y], heading=None, degrees=False, project = self) + atts = [x['obj'] for x in self.platformList[interior_nt + i].attachments.values()] + mList = [x for x in atts if type(x)==Mooring] + + # switch anchor type + anchs = self.platformList[i].getAnchors() + for anch in anchs.values(): + name, props = self.getSoilAtLocation(anch.r[0],anch.r[1]) + atype = self.anchorTypes[name] + anch.dd.update(atype) + anch.mass = self.anchorMasses[name] + anch.cost['materials'] = self.anchorCosts[name] + anch.soilProps = {name:props} + + # Get depth at turbine postion + self.turb_depth[interior_nt + i] = -self.getDepthAtLocation( + point.x, point.y) + buffer_group_pos = [] + + + for j in range(self.ms_na): + # im = 3*len(points) + j # global index of mooring/anchor + moor_bf_start = get_point_along_line([point.x, point.y], mList[j].rA[:2],self.turb_minrad) + # Buffer zone mooring line + #line = LineString([self.turb_coords[i,:], self.mooringList[im].rA[:2]]) + line = LineString([moor_bf_start, mList[j].rA[:2]]) + mooringline_buffer = line.buffer(self.moor_minrad) + + # Buffer zone anchor + # Create a point at coordinates (x, y) + point1 = Point(mList[j].rA[:2]) + # Create a buffer around the anchor with a radius of X + anchor_buffer = point1.buffer(self.anchor_minrad) + + # Buffer zone turbine + # Create a buffer around the anchor with a radius of X + turb_buffer = point.buffer(self.turb_minrad) + + # Buffer group for turbine positioning + buffer_group_pos.append(mooringline_buffer) + buffer_group_pos.append(anchor_buffer) + buffer_group_pos.append(turb_buffer) + polygon = unary_union(buffer_group_pos) # Combine buffers for each turbine + + if self.boundary_sh_int.contains(polygon): + # If the point is within the shape, append it to the list of bufferzones + + self.ms_bufferzones_pos[interior_nt + i] = polygon + + + + if len(x_coords) < self.nt: + for i in range(len(points)): + self.turb_coords[i,0] = x_coords[i] + self.turb_coords[i,1] = y_coords[i] + else: + self.turb_coords[:,0] = x_coords + self.turb_coords[:,1] = y_coords + + self.updateLayout(Xall, level, refresh) + + + + def updateLayoutOPT(self, X): + '''Wrapper for updateLayout that uses km instead of m.''' + X2 = np.array(X) # make a copy of the design vector + X2[:2*self.nt] = X[:2*self.nt]*1000 # convert coordinates from km to m + self.updateLayout(X2) + + + + # ----- OBJECTIVE FUNCTION ----- + def objectiveFunUG(self, Xu): + '''The general objective function. Will behave differently depending + on settings. Only input is the design variable vector, Xu.''' + # print('Xu in objective function: ',Xu) + # X = self.generateGridPoints(Xu,trans_mode='x') + + # update the layout with the specified design vector + # self.updateLayoutUG(X) + #Xum = np.hstack([[x*1000 for x in Xu[0:4]], Xu[4:]]) # convert first 4 entries from km to m + self.updateLayoutUG(Xu) + #self.updateLayoutOPTUG(X) + return self.obj_value + + + def objectiveFunDB(self, Xdb): + '''The general objective function. Will behave differently depending + on settings. Only input is the design variable vector, Xu.''' + + # update the layout with the specified design vector + # self.updateLayoutUG(X) + + self.updateLayoutDB(Xdb) + + #self.updateLayoutOPTUG(X) + + return self.obj_value + + + def objectiveFun(self, X): + '''The general objective function. Will behave differently depending + on settings. Only input is the design variable vector, X.''' + + # update the layout with the specified design vector + self.updateLayoutOPT(X) + + return self.obj_value + + + # ----- ANCHORS ----- + + + + # ----- AEP / FLORIS ----- + def getAEP(self, display = 0): + '''Compute AEP using FLORIS, based on whatever data and turbine + positions are already stored in the Layout object. + (updateLayout should have been called before this method.''' + + # FLORIS inputs positions in m + self.flow.set(layout_x=self.turb_coords[:,0], + layout_y=self.turb_coords[:,1] ) + + #run floris simulation + self.flow.set(wind_data = self.wind_rose) + self.flow.run() + + #frequencies must be in list + self.aep = self.flow.get_farm_AEP() + + if display > 0: + self.plotWakes(wind_spd = 10, wind_dir = 270, ti = 0.06) + #return self.aep + """ + # ----- CAPEX ----- + def getCAPEXMooring(self): + '''Compute CAPEX of mooring systems. Currently test function only''' + self.capex_mooring = sum(abs(self.ms_anchor_depth))*1000 + #capex_mooring = sum(self.turb_depth**2) + #return self.capex_mooring + + # ----- TOTAL CAPEX function + def getCAPEX(self): + '''Compute TOTAL CAPEX, adding sub-methods together. Currently test function only''' + self.getCAPEXMooring() + self.capex_total = self.capex_mooring + """ + # ----- TOTAL CAPEX function + def getCost(self): + '''Compute TOTAL CAPEX, adding sub-methods together. Currently test function only''' + + CapEx_mooring = 0 + for mooring in self.mooringList.values(): + CapEx_mooring += mooring.getCost() + + CapEx_anchors = 0 + for anchor in self.anchorList.values(): + CapEx_anchors += anchor.getCost() + + CapEx_cables = 0 + for cable in self.cableList.values(): + CapEx_cables += cable.getCost() + + + + # Include cable costs for feasible layouts + #if self.con_sum == 0: + #self.cost_cable = sum(self.iac_cost) + # self.cost_total = CapEx+self.cost_cable + #else: + self.cost_total = CapEx_mooring + CapEx_cables + CapEx_anchors + return self.cost_total + + + # ----- LCOE ----- + def getLCOE(self): + '''Compute LCOE = CAPEX / AEP. Currently test function and based on CAPEX only.''' + self.getAEP(display = self.display) + self.getCost() + self.lcoe = self.cost_total/self.aep#self.getCOST()/(self.getAEP()/ 1.0e6) # [$ / MWh]self.getAEP() + #return self.cost + + # ----- LCOE ----- + def getLCOE2(self): + '''updated LCOE function using capex, opex, and fcr assumptions from previous projects''' + + farm_capacity = self.turb_rating_MW * self.nt * 1000 # kW + + capex = 3748.8 * farm_capacity # $ does NOT include moorings/cables. + #from DeepFarm LCOE report GW scale individual wind farm, substracted mooring system and array system costs + opex = 62.51 *farm_capacity # $ annually. from DeepFarm LCOE report + fcr = 5.82/100 # fixed charge rate %. from DeepFarm LCOE report + + self.getAEP() + self.getCost() + self.lcoe = ((self.cost_total+capex)*fcr+ opex)/self.aep*1e6 # [$ / MWh] + + #return self.cost + + # ----- PENALTY FUNCTION ----- + def constraintFuns_penalty(self, X): + '''Penalty function to better guide the optimization. Only input is the design variable vector, X.''' + self.getCost() + self.constraintFuns(X) + + #con_vals = self.con_vals#self.constraintFuns(X) + # Get the indices of negative values + #negative_indices = np.where(con_vals < 0)[0] + #return self.getCAPEX()*0.1*abs(np.sum(con_vals[negative_indices]))#*1e3*self.nt**2 + #self.cost_penalty = self.cost_total*0.5*abs(np.sum(con_vals[negative_indices]))#*1e3*self.n + + self.cost_penalty = self.con_vals + + #return self.cost_penalty + + + + # ----- CONSTRAINTS FUNCTION ----- + # -------------------------------- + def constraintFunsUG(self, Xu): + '''The general constraints function. Will behave differently depending + on settings. Only input is the design variable vector, X.''' + + #X = self.generateGridPoints(Xu) + #Xum = np.hstack([[x*1000 for x in Xu[0:4]], Xu[4:]]) # convert first 4 entries from km to m + # print(Xu) + # if any([x>2500 for x in Xu]): + # breakpoint() + # update the layout with the specified design vector + self.updateLayoutUG(Xu) + #self.updateLayoutOPTUG(Xu) + return self.con_vals + + def constraintFunsDB(self, Xdb): + '''The general constraints function. Will behave differently depending + on settings. Only input is the design variable vector, X.''' + + # update the layout with the specified design vector + self.updateLayoutDB(Xdb) + + return self.con_vals + + + def constraintFuns(self, X): + '''The general constraints function. Will behave differently depending + on settings. Only input is the design variable vector, X.''' + + # update the layout with the specified design vector + self.updateLayoutOPT(X) + + return self.con_vals + + + def calcDerivatives(self): + '''Compute the derivatives about the current state of the layout, + for use with optimizers that accept a Jacobian function. + This is explicitly designed for when variables are x, y, and h. + >>> PLACEHOLDER <<< + ''' + + nDOF = 3*self.nt + ''' + # Perturb each DOF in turn and compute AEP results + J_AEP = np.zeros([nt,nt]) + + # Perturp each turbine and figure out the effects on cost and constraint + for i in range(nt): + J_CONS_i = np.zeros([ng, 3]) # fill in each row of this (or each column?) + + + # then combine them into overall matrices + + J_cost + + J_constraints.... + + # >>>> need to straighten out constraint vectors... + ''' + + def saveLOG(self, filename): + + with open(filename, 'w', newline='') as csvfile: + writer = csv.writer(csvfile) + # Write header + writer.writerow(['it','x', 'f', 'g']) + # Write data + for i in range(len(self.log['x'])): + it = [i] + x_values = self.log['x'][i] # Design variables + f_value = self.log['f'][i] # Result of objective function + g_values = self.log['g'][i] # Scalar, either -1 or 1 + + writer.writerow([it, x_values, f_value, g_values]) + + + # ----- Plot wind farm layout ----- + def plotLayout(self, ax=None, bare=False, save=False): + '''Plot wind farm layout.''' + + # if axes not passed in, make a new figure + if ax == None: + fig, ax = plt.subplots(1,1, figsize=[6,6]) + else: + fig = ax.get_figure() + + # Set font sizes + fsize_legend = 12 # Legend + fsize_ax_label = 12 # Ax Label + fsize_ax_ticks = 12 # Ax ticks + fsize_title = 16 # Title + + x0 = self.turb_coords[:,0] + y0 = self.turb_coords[:,1] + + + # Plot the layout, using the internally stored information. + + #breakpoint() + # ----- Bathymetry / contourf + + #num_levels = 10 # Adjust this value as needed + X, Y = np.meshgrid(self.grid_x, self.grid_y) + #breakpoint() + depth_min =np.min(self.grid_depth) + depth_min=math.floor(depth_min / 10) * 10 + depth_max =np.max(self.grid_depth) + depth_max=math.ceil(depth_min / 10) * 10 + + depth_range = depth_max- depth_min + + if depth_range < 100: + steps_m = 10 + else: + steps_m = 100 + + num_levels = round((depth_max- depth_min)/steps_m) + + + if depth_min != depth_max: + contourf = ax.contourf(X, Y, self.grid_depth, num_levels, cmap='Blues', vmin=depth_min, vmax=depth_max) + #contourf = ax.contourf(X, Y, self.grid_depth[x_indices, y_indices], num_levels, cmap='Blues', vmin=0, vmax=1000) + #contourf.norm.autoscale([0,1]) + + #contourf.set_clim(0, 1000) + + # Add colorbar with label + if not bare: + cbar = plt.colorbar(contourf, ax=ax, fraction=0.04, label='Water Depth (m)') + # Set the font size for the colorbar label and ticks + #cbar.ax.yaxis.label.set_fontsize(fsize_ax_label) + #cbar.ax.tick_params(axis='y', labelsize=fsize_ax_ticks) + + + # seabed + X, Y = np.meshgrid(self.soil_x, self.soil_y) + ax.scatter(X, Y, s=4, cmap='cividis_r', vmin=-0.5, vmax=1.5) + + # ----- OSS + for oo in self.oss_coords: + ax.scatter(oo[0],oo[1], color='red', marker='*', label='OSS', s=100) + circle = plt.Circle((oo[0], oo[1]), self.oss_minrad, edgecolor=[.5,0,0,.8], + facecolor='none', linestyle='dashed', lw=0.8) + + # (AEP: {aep / 1.0e9:.2f} GWh,\n CAPEX: M$ {cost/1.0e6:.2f},\n LCOE: {lcoe:.2f} $/MWh)' + + # plt.scatter(x, y, color='blue', marker='D') + # plt.scatter(optimized_x_pos, optimized_y_pos, label=f'Optimized Positions (AEP: {optimized_aep / 1.0e9:.2f} GWh)', color='red', marker='D') + + # Anchors + #plt.scatter(self.anchor_coords[:,0], self.anchor_coords[:,1], + # label='Anchor Positions', color='red', marker='.') + + # Plot mooring buffer zones + for i, polygon in enumerate(self.ms_bufferzones_pos): + if isinstance(polygon, MultiPolygon): + for poly in polygon: + x, y = poly.exterior.xy + ax.plot(x, y,color='red') + else: + x, y = polygon.exterior.xy + #ax.plot(x, y,color='red') + ax.fill(x, y,color=[.6,.3,.3,.6]) + # Add a single legend entry outside the loop + if not bare: + legend_entry = ax.fill([], [], color=[.6,.3,.3,.6], label='Mooring Buffer Zone') + + # Add a legend with fontsize + if not bare: + ax.legend(handles=legend_entry) #, fontsize=fsize_legend) + + # ----- mooring lines + for i in range(self.nt): + for j in range(3): + plt.plot([self.turb_coords[i,0], self.mooringList[3*i+j].rA[0]], + [self.turb_coords[i,1], self.mooringList[3*i+j].rA[1]], 'k', lw=0.5) + + # plt.plot([self.turb_coords[i,0], self.anchor_coords[3*i+j,0]], + # [self.turb_coords[i,1], self.anchor_coords[3*i+j,1]], 'k', lw=0.5) + + + + # ----- Minimum distance + i = 0 + for x, y in zip(x0, y0): + if i == 0: + circle = plt.Circle((x, y), self.turb_minrad, edgecolor=[.5,0,0,.8], + facecolor='none', linestyle='dashed', label='Turbine Buffer Zone', lw=0.8) + else: + circle = plt.Circle((x, y), self.turb_minrad, edgecolor=[.5,0,0,.8], + facecolor='none', linestyle='dashed', lw=0.8) + i =+ 1 + ax.add_patch(circle) + # Add a legend to the axes with fontsize + if not bare: + ax.legend() #fontsize=fsize_legend) + # plt.gca().add_patch(circle) + + # ----- Lease area boundary + #shape_polygon = sh.Polygon(self.boundary) + x, y = self.boundary_sh.exterior.xy + ax.plot(x, y, label='Boundary', linestyle='dashed', color='black') + + # ----- Sub boundaries + for subb in self.sub_boundary_sh: + x,y = subb.exterior.xy + ax.plot(x,y, label='Sub-boundary', linestyle=':', color='blue') + + + # ----- Exclusion zones + if len(self.exclusion) !=0: + for ie in range(len(self.exclusion)): + shape_polygon = self.exclusion_polygons_sh[ie]#sh.Polygon(self.exclusion[i]) + x, y = shape_polygon.exterior.xy + ax.plot(x, y, linestyle='dashed', color='orange', label='Exclusion Zone') + #ax.plot([], [], linestyle='dashed', color='orange', label='Exclusion Zone') + + # turbine locations + ax.scatter(x0, y0, c='black', s=12, label='Turbines') + + + + if self.cable_mode: + # ----- Cables + # Create a colormap and a legend entry for each unique cable section + # Find unique values + unique_cables = np.unique([x['conductor_area'] for x in self.iac_dic]) #(self.iac_dic['minimum_con'].values) + colors = plt.cm.viridis(np.linspace(0, 1, len(unique_cables))) # Create a colormap based on the number of unique sections + section_to_color = {sec: col for sec, col in zip(unique_cables, colors)} + + + # ----- Cables in Cluster + # Cable array + iac_array = self.iac_dic + count = 0 + # Loop over each cluster + for ic in range(self.n_cluster*self.noss): + # Plot vertices + #plt.scatter(self.cluster_arrays[ic][:, 0], self.cluster_arrays[ic][:, 1], color='red', label='Turbines') + + # Annotate each point with its index + #for i, point in enumerate(self.cluster_arrays[ic]): + #plt.annotate(str(i), (point[0], point[1]), textcoords="offset points", xytext=(0, 10), ha='center') + + # Get index of cluster + #ind_cluster = np.where(iac_array[:, 0] == 0)[0] + # Loop over edges / cable ids + len_cluster = len(np.where(np.array([x['cluster_id']==ic for x in iac_array]))[0]) + for i in range(len_cluster): + ix = np.where((np.array([x['cluster_id']== ic for x in iac_array])) & (np.array([y['cable_id']== count for y in iac_array]) ))[0] + if len(ix)<1: + breakpoint() + ind = ix[0] + #ind = np.where((iac_array[:, 0] == ic) & (iac_array[:, 2] == i))[0][0] + # Plot edge + #edge = self.iac_edges[ic][i] + start = iac_array[ind]['coordinates'][0]#self.cluster_arrays[ic][edge[0]] + end = iac_array[ind]['coordinates'][1] + # Cable selection + color = section_to_color[iac_array[ind]['conductor_area']] + ax.plot([start[0], end[0]], [start[1], end[1]], color=color, label=f'Section {int(iac_array[ind]["conductor_area"])} mm²' if int(iac_array[ind]["conductor_area"]) not in plt.gca().get_legend_handles_labels()[1] else "") + #plt.text((start[0] + end[0]) / 2, (start[1] + end[1]) / 2, str(i), fontsize=9, color='black') + # for sid in oss_ids: + # if iac_array[ix]['turbineA_glob_id'] == sid or iac_array[ix]['turbineB_glob_id'] == sid: + # iac_array_oss.append(iac_array[ix]) + # iac_oss_id.append(sid) + + count += 1 + + # Plot gate as a diamond marker + #plt.scatter(self.gate_coords[ic][0], self.gate_coords[ic][1], marker='D', color='green', label='Gate') + + + ## ----- Cables Gates to OSS + + # for i in range(self.n_cluster): + # cable_section_size = int(iac_array_oss[i]['conductor_area']) # Assuming cable section size is in the 7th column + # color = section_to_color.get(cable_section_size, 'black') # Default to black if section size not found + # oss_coord = self.substationList[iac_oss_id[i]].r + # ax.plot([iac_array_oss[i]['coordinates'][1][0], oss_coord[0]], [iac_array_oss[i]['coordinates'][1][1],oss_coord[1]], color=color, label=f'Section {cable_section_size} mm²' if cable_section_size not in plt.gca().get_legend_handles_labels()[1] else "") + + + + ''' + # NEW: TURBINE CLUSTER AND CABLES + # Plot turbines by cluster + for label in set(self.cluster_labels): + cluster_turbines = [self.turb_coords[i] for i, lbl in enumerate(self.cluster_labels) if lbl == label] + if cluster_turbines: # Check if list is not empty + x, y = zip(*cluster_turbines) + ax.scatter(x, y, label=f'Cluster {label}') + + # Plot edges + for i in range(len(self.cluster_edges)): + P = self.cluster_arrays[i] + for edge in self.cluster_edges[i]: + i, j = edge + plt.plot([P[i, 0], P[j, 0]], [P[i, 1], P[j, 1]], color ='black') + + # Plot OSS and gates + ax.scatter(*self.oss_coords, color='red', marker='*', label='OSS') + ax.scatter(self.gate_coords[:, 0], self.gate_coords[:, 1], color='black', marker='d', label='Gates') + + # Legend adjustment might be needed depending on the number of elements + #ax.legend(loc='upper center', fancybox=True, ncol=2) + ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, ncol=2) + ''' + + + + # ----- Additional plot customization + # Set x- and y-axis ticks fontsize + if not bare: + ax.set_xticks(ax.get_xticks()) + ax.set_yticks(ax.get_yticks()) + ax.set_xticklabels(ax.get_xticklabels()) #, fontsize=fsize_ax_ticks) + ax.set_yticklabels(ax.get_yticklabels()) #, fontsize=fsize_ax_ticks) + + # # Define a custom formatter to divide ticks by 1000 + # def divide_by_1000(value, tick_number): + # return f'{value/1000:.0f}' + + # # Apply the custom formatter to the x and y axis ticks + # ax.xaxis.set_major_formatter(FuncFormatter(divide_by_1000)) + # ax.yaxis.set_major_formatter(FuncFormatter(divide_by_1000)) + + #ax.axis("equal") + ax.set_aspect('equal') + + # # Use AutoLocator for major ticks + # ax.xaxis.set_major_locator(AutoLocator()) + # ax.yaxis.set_major_locator(AutoLocator()) + # # Use AutoMinorLocator for minor ticks + # ax.xaxis.set_minor_locator(AutoMinorLocator()) + # ax.yaxis.set_minor_locator(AutoMinorLocator()) + + # ax.set_xlim([self.grid_x[0], self.grid_x[-1]]) + # ax.set_ylim([self.grid_y[0], self.grid_y[-1]]) + #ax.set_xlim([x_min_bounds-1000, x_max_bounds+1000]) + #ax.set_ylim([y_min_bounds-1000, y_max_bounds+1000]) + + + #plt.title('Optimized Wind Farm Layout',fontsize=fsize_title) + plt.xlabel('x (km)') #,fontsize=fsize_ax_label) + plt.ylabel('y (km)') #,fontsize=fsize_ax_label) + #plt.legend(loc='upper center', bbox_to_anchor=( + # 0.5, -0.2), fancybox=True, ncol=3) + #plt.legend(loc='upper center', fancybox=True, ncol=2) + handles, labels = plt.gca().get_legend_handles_labels() + unique_labels = list(set(labels)) # Get unique labels + unique_labels.sort() # Sort the unique labels alphabetically + unique_handles = [handles[labels.index(label)] for label in unique_labels] # Get handles corresponding to unique labels + plt.legend(unique_handles, unique_labels, loc='upper center', bbox_to_anchor=(0.5, -0.1), fancybox=True, ncol=2) + plt.gca().set_aspect('equal', adjustable='box') # Set aspect ratio to be equal + #ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), fancybox=True, ncol=2) + + #breakpoint() + # Set plot area to around the lease area + + + # Calc plot bounds + #offset = 1000 + #offset_polygon = translate(self.boundary_sh, xoff=offset, yoff=offset) + # Get bounds + #x_min_bounds, y_min_bounds, x_max_bounds, y_max_bounds = offset_polygon.bounds + # Round to next 100 + #x_min_bounds, y_min_bounds = [math.floor(v / 1000) * 1000 for v in (x_min, y_min)] + #x_max_bounds, y_max_bounds = [math.ceil(v / 1000) * 1000 for v in (x_max, y_max)] + + + + # ----- Save plot with an incremented number if it already exists + if save: + counter = 1 + output_filename = f'wind farm layout_{counter}.png' + while os.path.exists(output_filename): + counter += 1 + output_filename = f'wind farm layout_{counter}.png' + + # Increase the resolution when saving the plot + plt.savefig(output_filename, dpi=300, bbox_inches='tight') # Adjust the dpi as needed + + # also print some output + + if self.flow: # if FLORIS + print('AEP:', self.aep) + + self.getCost() + print('Cost:', self.cost_total) + + # for mooring in self.mooringList.values(): + # print(mooring.cost) + + """ + def plot3d(self, ax=None, figsize=(10,8), fowt=None, save=False, + draw_boundary=True, boundary_on_bath=True, args_bath={}, draw_axes=True): + '''Plot aspects of the Project object in matplotlib in 3D. + + TODO - harmonize a lot of the seabed stuff with MoorPy System.plot... + + Parameters + ---------- + ... + ''' + + # color map for soil plotting + import matplotlib.cm as cm + from matplotlib.colors import Normalize + cmap = cm.cividis_r + norm = Normalize(vmin=-0.5, vmax=1.5) + #print(cmap(norm(np.array([0,1])))) + + + # if axes not passed in, make a new figure + if ax == None: + fig = plt.figure(figsize=figsize) + ax = plt.axes(projection='3d') + else: + fig = ax.get_figure() + + # try icnraesing dpeht grid density for nicer plot + xs = np.arange(-1000,8000,500) + ys = np.arange(-1000,9500,500) + #self.setGrid(xs, ys) + zs = np.zeros([len(ys), len(xs)]) + for i in range(len(ys)): + for j in range(len(xs)): + zs[i,j] = self.getDepthAtLocation(xs[j], ys[i]) + X, Y = np.meshgrid(xs, ys) # 2D mesh of seabed grid + + + # plot the bathymetry in matplotlib using a plot_surface + #X, Y = np.meshgrid(self.grid_x, self.grid_y) # 2D mesh of seabed grid + ax.plot_surface(X, Y, -zs, **args_bath) + ''' + # interpolate soil rockyness factor onto this grid + xs = self.grid_x + ys = self.grid_y + rocky = np.zeros([len(ys), len(xs)]) + for i in range(len(ys)): + for j in range(len(xs)): + rocky[i,j], _,_,_,_ = sbt.interpFromGrid(xs[j], ys[i], + self.soil_x, self.soil_y, self.soil_rocky) + # apply colormap + rc = cmap(norm(rocky)) + bath = ax.plot_surface(X, Y, -self.grid_depth, facecolors=rc, **args_bath) + ''' + #bath = ax.plot_surface(X, Y, -self.grid_depth, **args_bath) + # + + + # also if there are rocky bits... (TEMPORARY) + ''' + X, Y = np.meshgrid(self.soil_x, self.soil_y) + Z = np.zeros_like(X) + xs = self.soil_x + ys = self.soil_y + for i in range(len(ys)): + for j in range(len(xs)): + Z[i,j] = -self.getDepthAtLocation(xs[j], ys[i]) + ax.scatter(X, Y, Z+5, c=self.soil_rocky, s=6, cmap='cividis_r', vmin=-0.5, vmax=1.5, zorder=0) + ''' + + # plot the project boundary + if draw_boundary: + boundary = np.vstack([self.boundary, self.boundary[0,:]]) + ax.plot(boundary[:,0], boundary[:,1], np.zeros(boundary.shape[0]), + 'b--', zorder=100, lw=1, alpha=0.5) + + # plot the projection of the boundary on the seabed, if desired + if boundary_on_bath: + boundary_z = self.projectAlongSeabed(boundary[:,0], boundary[:,1]) + ax.plot(boundary[:,0], boundary[:,1], -boundary_z, 'k--', zorder=10, lw=1, alpha=0.7) + + # plot the Moorings + for mooring in self.mooringList: + #mooring.subsystem.plot(ax = ax, draw_seabed=False) + if mooring.subsystem: + mooring.subsystem.drawLine(0, ax, shadow=False) + + # plot the FOWTs using a RAFT FOWT if one is passed in (TEMPORARY) + if fowt: + for i in range(self.nt): + xy = self.turb_coords[i,:] + fowt.setPosition([xy[0], xy[1], 0,0,0,0]) + fowt.plot(ax, zorder=20) + + # Show full depth range + ax.set_zlim([-np.max(self.grid_depth), 0]) + + set_axes_equal(ax) + if not draw_axes: + ax.axis('off') + + ax.view_init(20, -130) + ax.dist -= 3 + fig.tight_layout() + + # ----- Save plot with an incremented number if it already exists + if save: + counter = 1 + output_filename = f'wind farm 3d_{counter}.png' + while os.path.exists(output_filename): + counter += 1 + output_filename = f'wind farm 3d_{counter}.png' + + # Increase the resolution when saving the plot + plt.savefig(output_filename, dpi=300, bbox_inches='tight') # Adjust the dpi as needed + """ + + def playOptimization(self): + '''A very slow clunky way to animate the optimization''' + fig, ax = plt.subplots(1,1) + + self.updateLayout(self.log['x'][0]) + self.plotLayout(ax=ax) + + def animate(i): + ax.clear() + self.updateLayout(self.log['x'][i]) + self.plotLayout(ax=ax, bare=True) + + ani = FuncAnimation(fig, animate, frames=len(self.log['x']), + interval=500, repeat=True) + + return ani + + + + + + + + + + + + + + + + + + + + + + def plotOptimization(self): + + if len(self.log['x']) == 0: + print("No optimization trajectory saved (log is empty). Nothing to plot.") + return + + + + fig, ax = plt.subplots(5,1, sharex=True, figsize=[6,8]) + fig.subplots_adjust(left=0.4) + + X = np.array(self.log['x']) + Fs = np.array(self.log['f']) + Gs = np.array(self.log['g']) + + + if self.rotation_mode: + x_pos, y_pos, rot_rad = X[:,:self.nt], X[:,self.nt:2*self.nt], X[:,2*self.nt:] + else: + x_pos, y_pos = X[:,:len(X)//2], X[:,len(X)//2:] + rot_rad = np.zeros_like(x_pos) + + for i in range(self.nt): + ax[0].plot(x_pos[:,i]) + ax[1].plot(y_pos[:,i]) + ax[2].plot(rot_rad[:,i]) + + ax[3].plot(Fs) + ax[3].set_ylabel("cost", rotation='horizontal') + + Gs_neg = Gs*(Gs < 0) + ax[4].plot(np.sum(Gs_neg, axis=1)) + ax[4].set_ylabel("constaint violation sum", rotation='horizontal') + ''' + for i, con in enumerate(self.constraints): + j = i+1+len(X) + ax[j].axhline(0, color=[0.5,0.5,0.5]) + ax[j].plot(Gs[:,i]) + ax[j].set_ylabel(f"{con['name']}({con['threshold']})", + rotation='horizontal', labelpad=80) + ''' + ax[-1].set_xlabel("iteration roughly") + + + + """ + nX = len(self.log['x'][0]) + fig, ax = plt.subplots(nX+1+1,1, sharex=True, figsize=[6,8]) + fig.subplots_adjust(left=0.4) + Xs = np.array(self.log['x']) + Fs = np.array(self.log['f']) + Gs = np.array(self.log['g']) + + for i in range(nX): + ax[i].plot(Xs[:,i]) + #ax[i].axhline(self.Xmin[i], color=[0.5,0.5,0.5], dashes=[1,1]) + #ax[i].axhline(self.Xmax[i], color=[0.5,0.5,0.5], dashes=[1,1]) + + ax[nX].plot(Fs) + ax[nX].set_ylabel("cost", rotation='horizontal') + + Glist = Gs.ravel() + + ax[nX+1].plot(np.sum(Glist[Glist<0])) + ax[nX+1].set_ylabel("constaint violation sum", rotation='horizontal') + ''' + for i, con in enumerate(self.constraints): + j = i+1+len(X) + ax[j].axhline(0, color=[0.5,0.5,0.5]) + ax[j].plot(Gs[:,i]) + ax[j].set_ylabel(f"{con['name']}({con['threshold']})", + rotation='horizontal', labelpad=80) + ''' + ax[-1].set_xlabel("iteration roughly") + """ + + def plotCost(self): + '''Makes a bar chart of the cost breakdown.''' + + + def plotWakes(self, wind_spd, wind_dir, ti): + '''uses floris tools to plot wakes''' + import floris.layout_visualization as layoutviz + from floris.flow_visualization import visualize_cut_plane + + fmodel = self.flow + + # Create the plotting objects using matplotlib + fig, ax = plt.subplots() + + + layoutviz.plot_turbine_points(fmodel, ax=ax) + layoutviz.plot_turbine_labels(fmodel, ax=ax) + ax.set_title("Turbine Points and Labels") + ax.set_xlabel('X (m)') + ax.set_ylabel('Y (m)') + + + + fmodel.set(wind_speeds=[wind_spd], wind_directions=[wind_dir], turbulence_intensities=[ti]) + horizontal_plane = fmodel.calculate_horizontal_plane( + x_resolution=200, + y_resolution=100, + height=90.0, + ) + + # Plot the flow field with rotors + fig, ax = plt.subplots() + visualize_cut_plane( + horizontal_plane, + ax=ax, + label_contours=False, + title="Horizontal Flow with Turbine Rotors and labels", + ) + ax.set_xlabel('X (m)') + ax.set_ylabel('Y (m)') + + # Plot the turbine rotors + layoutviz.plot_turbine_rotors(fmodel, ax=ax) + + plt.show() + +# Calculate offset from the turbine to create buffer zones for cable routing +def get_point_along_line(start, end, diste): + # Convert inputs to numpy arrays + start = np.array(start) + end = np.array(end) + # Calculate the direction vector from start to end + direction = end - start + # Normalize the direction vector + length = np.linalg.norm(direction) + unit_direction = direction / length + # Calculate the new point at the specified distance along the direction vector + new_point = start + unit_direction * diste + return new_point + +# def mooringAdjuster1(mooring, project, r, u, level=0): +# '''Custom function to adjust a mooring, called by +# Mooring.adjust. Fairlead point should have already +# been adjusted.''' + +# ss = mooring.ss # shorthand for the mooring's subsystem + +# T_target = 1e6 # target mooring line pretension [N] (hardcoded example) +# i_line = 0 # line section to adjust (if multiple) (hardcoded example) + +# #>>> pit in better prpfile <<< + +# # Find anchor location based on desired relation +# r_i = np.hstack([r + 58*u, -14]) # fairlead point +# slope = 0.58 # slope from horizontal +# u_a = np.hstack([u, -slope]) # direct vector from r_i to anchor +# r_anch = project.seabedIntersect(r_i, u_a) # seabed intersection + +# # save some stuff for the heck of it +# mooring.z_anch = r_anch[2] +# mooring.anch_rad = np.linalg.norm(r_anch[:2]-r) + +# mooring.setEndPosition(r_anch, 'a') # set the anchor position + +# # Estimate the correct line length to start with +# ss.lineList[0].setL(np.linalg.norm(mooring.rB - mooring.rA)) + +# # Next we could adjust the line length/tension (if there's a subsystem) +# if level==1: # level 1 analysis (static solve to update node positions) +# ss.staticSolve() + +# elif level==2: # adjust pretension (hardcoded example method for now) + +# def eval_func(X, args): +# '''Tension evaluation function for different line lengths''' +# ss.lineList[i_line].L = X[0] # set the first line section's length +# ss.staticSolve(tol=0.0001) # solve the equilibrium of the subsystem +# return np.array([ss.TB]), dict(status=1), False # return the end tension + +# # run dsolve2 solver to solve for the line length that matches the initial tension +# X0 = [ss.lineList[i_line].L] # start with the current section length +# L_final, T_final, _ = dsolve2(eval_func, X0, Ytarget=[T_target], +# Xmin=[1], Xmax=[1.1*np.linalg.norm(ss.rB-ss.rA)], +# dX_last=[1], tol=[0.1], maxIter=50, stepfac=4) +# ss.lineList[i_line].L = L_final[0] + + +# # Compute anchor size and cost +# soilr = project.getSoilAtLocation(*r_anch[:2]) +# if 'rock' in soilr: +# rocky = 1 +# else: +# rocky = 0 + +# anchor_cost = 300e3 + rocky*200e3 +# mooring.cost['anchor'] = anchor_cost + + + # getWatchCircle() method + # getMudlineForces(, max_forces=True) + + + + + +if __name__ == '__main__': + + # Wind rose + from floris import WindRose + wind_rose = WindRose.read_csv_long( + 'humboldt_rose.csv', wd_col="wd", ws_col="ws", freq_col="freq_val", ti_col_or_value=0.06) + + + # ----- LEASE AREA BOUNDARIES ----- + WestStart = 10000 + NorthStart = 10000 + boundary_coords = np.array([ + (0, 0), + (WestStart, 0), + (WestStart, NorthStart), + (0,NorthStart) + ]) + + + + # Make a sample Subsystem to hold the mooring design (used for initialization) + print("Making subsystem") + newFile = '..\scripts\input_files\GoMxOntology.yaml' + project = Project(file=newFile,raft=0) + project.getMoorPyArray() + ss = deepcopy(project.ms.lineList[0]) + + # ----- Set optimization mode + opt_mode = 'CAPEX' + #opt_mode = 'AEP' + #opt_mode = 'LCOE' + # remember to set use_FLORIS accordingly when initializing Layout + + # set substation location + oss_coords = np.array([0, 0]) + + + + #layouttype = 'freelayout' + layouttype = 'uniformgridlayout' + + # ----- UNIFORM GRID ----- + if layouttype == 'uniformgridlayout': + + + + #[grid_spacing_x, grid_spacing_y, grid_trans_x, grid_trans_y, grid_rotang, grid_skew, optional turb_rotation] + Xu = [1000/1000, 1000/1000, 500/1000, 150/1000, 45, 0, 0] + + + + # Amount of wind turbines + nt = 20 + + #rotation mode and turbine rotation + rotation_mode = True + rot_rad=np.zeros((nt)) + + + # Boundaries of design variables for PSO + boundaries_UG=np.array([[0.5, 3],[0.5, 3], [-.5, .5], [-.5, .5], [0, 180], [0,0.2],[0, 180] ]) + + #cable routing + cable_mode = True + + # ----- FREE LAYOUT ----- + elif layouttype == 'freelayout': + + #first iteration turbine coordinates + gulfofmaine_int = np.array([ + [3000, 2000], + [2000, 2000], + [0, 2000], + [1000, 2000], + [2000, 2000], + [1000, 100], + [2000, 100], + [0, 4000], + [1000, 4000], + [2000, 4000] + ]) + + + x_coords = gulfofmaine_int[:, 0] # x coordinates + y_coords = gulfofmaine_int[:, 1] # y coordinates + + nt = len(x_coords) # Number of wind turbines + + #first iteration rotations + rot_deg = np.zeros((nt)) + + + # ----- Bounds vectors for design variables ----- + # FOR OPTIMIZER ONLY + # Lease area boundaries + boundaries_x = np.tile([(min(boundary_coords[:,0]), max(boundary_coords[:,0]))], (nt, 1)) + boundaries_y = np.tile([(min(boundary_coords[:,1]), max(boundary_coords[:,1]))], (nt, 1)) + # Rotation in rad 0 - 360*pi/180 + boundaries_rot = np.tile([(0.001, 6.283)], (nt, 1)) + # Combine into one array + + + + # ----- Set rotation mode + # If True, rotations are considered as design variable, therefore included + # into same vector as x and y. Otherwise not. + rotation_mode = True + rot_rad = np.deg2rad(rot_deg) # Rotations need to be in rad for the optimization + x = np.array(x_coords/1000) #km + y = np.array(y_coords/1000) #km + + # Create flattened array xy for initial positions for Layout [km, rad] + if rotation_mode: + xy = np.concatenate((x, y, rot_rad)) + boundary_xy = np.concatenate((boundaries_x/1000, boundaries_y/1000, boundaries_rot)) + else: + xy = np.concatenate((x, y)) + boundary_xy = np.concatenate((boundaries_x/1000, boundaries_y/1000)) + + # cable routing + cable_mode = True + + + + # ----- Initialize LAYOUT class ----- + print("Initializing Layout") + + settings = {} + settings['n_turbines'] = nt + settings['turb_rot'] = rot_rad + settings['rotation_mode'] = rotation_mode + settings['cable_mode'] = cable_mode + settings['oss_coords'] = oss_coords + settings['boundary_coords'] = boundary_coords + settings['bathymetry_file'] = '..\scripts\input_files\GulfOfMaine_bathymetry_100x100.txt' + settings['soil_file'] = '..\scripts\input_files\soil_sample.txt' + settings['floris_file']='gch_floating.yaml' + #settings['exclusion_coords'] = exclusion_coords + settings['use_FLORIS'] = False + settings['mode'] = opt_mode + settings['optimizer'] ='PSO' + settings['obj_penalty'] = 1 + settings['parallel'] = False + settings['n_cluster'] = 3 + + # set up anchor dictionary + anchor_settings = {} + anchor_settings['anchor_design'] = {'L':20,'D':4.5,'zlug':13.3} # geometry of anchor + anchor_settings['anchor_type'] = 'suction' # anchor type + anchor_settings['anchor_resize'] = True # bool to resize the anchor or not + anchor_settings['fix_zlug'] = False # bool to keep zlug the same when resizing anchor + anchor_settings['FSdiff_max'] = {'Ha':.2,'Va':.2} # max allowed difference between FS and minimum FS + anchor_settings['FS_min'] = {'Ha':2,'Va':2} # horizontal and vertical minimum safety factors + + settings['anchor_settings'] = anchor_settings + + + if layouttype == 'freelayout': + layout1 = Layout(X=xy, Xu=[], wind_rose = wind_rose, ss=ss, **settings) + elif layouttype == 'uniformgridlayout': + layout1 = Layout(X=[], Xu=Xu, wind_rose = wind_rose, ss=ss, **settings) + + + + ''' + # ----- Sequential Least Squares Programming (SLSQP) + if layouttype == 'freelayout': + res = minimize(fun=layout1.objectiveFun, x0=xy, method='SLSQP', + bounds = boundary_xy, + constraints={'type': 'ineq', 'fun': layout1.constraintFuns}, + options={'maxiter':100, 'eps':0.02,'ftol':1e-6, 'disp': True, 'iprint': 99}) + #options={'maxiter': 5000,'eps': 0.2, 'finite_diff_rel_step': '2-point', 'ftol': 1e-6, 'disp': True, 'iprint': 99}) + elif layouttype == 'uniformgridlayout': + res = minimize(fun=layout1.objectiveFunUG, x0=Xu, method='SLSQP', + bounds = boundaries_UG, + constraints={'type': 'ineq', 'fun': layout1.constraintFunsUG}, + options={'maxiter':1, 'eps':0.02,'ftol':1e-6, 'disp': True, 'iprint': 99}) + ''' + + ''' + # ----- Constrained Optimization BY Linear Approximation (COBYLA) + res = minimize(fun=layout1.objectiveFun, x0=xy, method='COBYLA', + constraints={'type': 'ineq', 'fun': layout1.constraintFuns}, + options={'maxiter':2000,'catol': 1e-6, 'tol': 1e-6, 'disp': True}) + ''' + + + ''' + # ----- Differential Evolution (DE) + # NonlinearConstraint + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.NonlinearConstraint.html#scipy.optimize.NonlinearConstraint + #cons_fun = NonlinearConstraint(fun, lb, ub, jac='2-point', hess=, keep_feasible=False, finite_diff_rel_step=None, finite_diff_jac_sparsity=None) + cons_fun = NonlinearConstraint(layout1.constraintFuns, lb = 0, ub = np.inf) + + # takes FOREVER and is not the best solution + res = differential_evolution(func=layout1.objectiveFun, bounds=boundary_xy, args=(), strategy='best1bin', + maxiter=1000, popsize=25, tol=0.01, mutation=(0.5, 1.0), + recombination=0.7, seed=None, callback=None, disp=True, polish=True, + init='latinhypercube', atol=0, updating='immediate', workers=1, + constraints=cons_fun, x0=xy, integrality=None, vectorized=False) + ''' + + + # ----- Particle Swarm Optimization + + # Other PSO (PSO with Scipy interface, but not that elaborated?) + # https://github.com/jerrytheo/psopy?tab=readme-ov-file + + + # Pyswarm (NOT pyswarms) + # https://pythonhosted.org/pyswarm/ + + if layouttype == 'freelayout': + res, fopt = pso(layout1.objectiveFun, lb=boundary_xy[:,0], ub=boundary_xy[:,1], f_ieqcons=layout1.constraintFuns, + swarmsize=20, omega=0.72984, phip=0.6, phig=0.8, maxiter=20, minstep=1e-8, minfunc=1e-8, debug=True) + elif layouttype == 'uniformgridlayout': + res, fopt = pso(layout1.objectiveFunUG, lb=boundaries_UG[:,0], ub=boundaries_UG[:,1], f_ieqcons=layout1.constraintFunsUG, + swarmsize=20, omega=0.72984, phip=0.6, phig=0.8, maxiter=20, minstep=1e-8, minfunc=1e-8, debug=True) + + + if layouttype == 'freelayout': + layout1.updateLayoutOPT(res) # make sure it is using the optimized layout => ONLY NEEDED WHEN INPUT WAS in km + layout1.updateLayout(X=[], level=2, refresh=True) # do a higher-fidelity update + layout1.plotLayout(save=True) + + elif layouttype == 'uniformgridlayout': + + #optimized_xy_m = [1400, 1400, 500, 1000, 45, 0] + + layout1.updateLayoutUG(Xu=res, level=2, refresh=True) # do a higher-fidelity update + layout1.plotLayout(save=True) + + + + plt.show() + + + + + +########################## END +# ARCHIVE diff --git a/famodel/design/layout_helpers.py b/famodel/design/layout_helpers.py new file mode 100644 index 00000000..c69ef09c --- /dev/null +++ b/famodel/design/layout_helpers.py @@ -0,0 +1,1178 @@ +import moorpy as mp +from moorpy.helpers import dsolve2 +import numpy as np +import matplotlib.pyplot as plt +from shapely import Point, Polygon +from numpy import random +from copy import deepcopy +import time + + +from famodel.mooring.mooring import Mooring +from famodel.seabed_tools import getDepthFromBathymetry +from famodel.project import Project +from famodel.design.fadsolvers import dsolve2 + + +def create_initial_layout(lease_xs, lease_ys, ms, grid_x, grid_y, grid_depth, update_ms=True, display=0): + ''' + The first iteration of a layout generator function based off of Katherine's previous work in summer 2023. + The idea is to come up with turbine locations within a lease area boundary that can be oriented various directions, + not overlap other mooring systems, and not extend outside of a lease area boundary. + In reality, I'd imagine this function would become obsolete, as we could populate a lease area with random points and + then optimize their positions, but the capabilities within this function can be used as a starting point to + incorporate into the optimization process. + + Right now, it loops through a "grid" of x and y positions, spaced relatively close together and calculates the anchor positions + of that turbine (using an adjustable mooring system orientation) by extending or shortening the mooring line until it + contacts the seabed bathymetry along that path. Using these new anchor positions, the function then checks each turbine + position on whether 1) it, and the anchor x/y positions are within the lease area bounds and 2) the given triangle that + connects the anchor points overlaps any other existing triangles (i.e., footprints). If it satisfies those two criteria, + then it appends that turbine x/y position to the list of valid points. + + Parameters + ---------- + lease_xs : float, array + The x coordinates of coordinate pairs defining a layout boundary, relative to a certain point (usually a centroid) + lease_ys : float, array + The y coordinates of coordinate pairs defining a layout boundary, relative to a certain point (usually a centroid) + ms : MoorPy System object + A MoorPy System object defining a mooring system + grid_x : float, array + The x coordinates of coordinate pairs defining a bathymetry grid + grid_y : float, array + The y coordinates of coordinate pairs defining a bathymetry grid + grid_depth: float, 2D matrix + The depth (z coordinates) of the grid defined by grid_x and grid_y + + Returns + ------- + xs : float, array + A list of the x coordinates of turbine locations within the array + ys : float, array + A list of the y coordinates of turbine locations within the array + footprintList : list, Polygon objects + A list of shapely Polygon objects of each mooring system footprint based on anchor locations + ''' + + coords = [] + + area = Polygon([(lease_xs[i], lease_ys[i]) for i in range(len(lease_xs))]) + + # Brainstorm different initialization approaches + # - Placing one at a starting point and filling in from there + # - Choosing a predetermined number of turbine and making them fit + # - Placing anchors and working backwards + # not sure which one is the best right now; will stick with the first one of choosing a starting point and filling in around + + # Placing approaches: + # make a very fine xlocs and ylocs grid + # loop through and find the first point in the lease area that has all 3 anchors in the lease area + # - the next point that's tested will obviously be way too close to the first point, but this will allow for better placement + # make an "orientation" variable in case we want to switch the orientations (ms.transform) (typically either 180 or 0) (later todo item) + + xlocs = np.arange(np.min(lease_xs), np.max(lease_xs)+1, 1000) # set a really small spacing between + ylocs = np.arange(np.min(lease_ys), np.max(lease_ys)+1, 1000) + + # if you want to change the "starting" point, you will need to rearrange the xlocs and ylocs variables + # something like "xlocs = np.hstack([(xlocs[int(len(xlocs))/2):], xlocs[:int(len(xlocs))/2)]])" + + # count how many anchor points there are + anchors = [point.number for point in ms.pointList if point.r[2]==-ms.depth] # <<<<< might need to change this assumption later on checking if it's on the seabed + fairleads = ms.bodyList[0].attachedP + + # initialize a couple storage/bool variables + invalid = False + footprintList = [] + msList = [] + counter = 0 + + # placeholder to deal with the mooring system orientation + orientation = -180 + + # loop through the xlocs and ylocs variables to test x/y positions to place turbines + for ix in range(len(xlocs)): + for iy in range(len(ylocs)): + anchorGlobalTempList = [] + anchorLocalTempList = [] + + # set the x/y position of a point to test + point = [xlocs[ix], ylocs[iy]] + + # orient the mooring system around that point by a certain amount + orientation = 0 + #orientation += 180 + #orientation = random.choice([0,90,180,270]) + ms.transform(rot=orientation) + + # reinitialize the mooring system after reorientation + ms.initialize() + ms.solveEquilibrium() + + # loop through the anchors in the mooring system and evaluate whether they meet the criteria + for i,anchornum in enumerate(anchors): + + old_anchor_point = ms.pointList[anchornum-1].r + np.array([point[0], point[1], 0]) + fairlead_point = ms.pointList[fairleads[i]-1].r + np.array([point[0], point[1], 0]) + # update the anchor point based on how close it is to the bathymetry + new_anchor_point = getUpdatedAnchorPosition(old_anchor_point, fairlead_point, grid_x, grid_y, grid_depth) + + # check to make sure the updated anchor point is within the lease area boundary + if not area.contains(Point(new_anchor_point[0], new_anchor_point[1])): + invalid = True + + # save the anchor point for later, regardless of whether it fits or not + anchorGlobalTempList.append(new_anchor_point) + anchorLocalTempList.append(new_anchor_point - np.array([point[0], point[1], 0])) + + + # create new lists/polygons of using the anchor positions of this one turbine point + #anchorList.append([anchor_point for anchor_point in anchorGlobalTempList]) + + # create a shapely polygon made up of the anchor points + new_boundary = Polygon( [(anchor_point[0], anchor_point[1]) for anchor_point in anchorGlobalTempList] ) + + # check to make sure that the newly created polygon does not intersect any other polygons + for moor_sys in footprintList: + if moor_sys.intersects(new_boundary): + invalid = True + + + + + + # if all checks pass, then include this point to the list of coordinates of the farm and include the boundary polygon to reference later + if invalid==False: + + # save the point to a list of "coords" + coords.append(point) + if display > 0: print(f"Appending Point ({point[0]:6.1f}, {point[1]:6.1f}) to the coords list") + + # add to the counter for the number of turbines that meet criteria + counter += 1 + if display > 0: print(f'nTurbines = {counter}') + + # save the polygon footprint of the anchor points + footprintList.append(Polygon( [(anchor_point[0], anchor_point[1]) for anchor_point in anchorGlobalTempList] ) ) + + + # if you want to update the mooring system line lengths to match pretension + if update_ms: + if display > 0: print(f"Updating the mooring system at Point ({point[0]:6.1f}, {point[1]:6.1f}) ") + + # create a copy of the MoorPy System and of the anchor point list + mscopy = deepcopy(ms) + + # adjust the MoorPy System line lengths to keep the same pretension (calls another internal function) + #ms_new = adjustMS4Pretension(mscopy, anchorLocalTempList) + ms_new = adjustMS4Bath(mscopy, point, grid_x, grid_y, grid_depth, display=display) + + else: + ms_new = deepcopy(ms) + + # save the adjusted (or not adjusted mooring system) + msList.append(ms_new) + + + + # reset the invalid flag variable in case it was changed to true + invalid = False + + + # extract the x and y variables from the list of points + xs = [xy[0] for xy in coords] + ys = [xy[1] for xy in coords] + + return xs, ys, footprintList, msList + + + +def create_layout(bound_xs, bound_ys, subsystem, grid_x, grid_y, grid_depth, + spacing_x, spacing_y, headings=[60, 180, 300]): + ''' + Create a rectangular grid layout. + + Parameters + ---------- + lease_xs : float, array + The x coordinates of coordinate pairs defining a layout boundary, relative to a certain point (usually a centroid) + lease_ys : float, array + The y coordinates of coordinate pairs defining a layout boundary, relative to a certain point (usually a centroid) + subsystem : MoorPy Subsystem object + A MoorPy Subsystem object defining the mooring configuration to be used. + grid_x : float, array + The x coordinates of coordinate pairs defining a bathymetry grid + grid_y : float, array + The y coordinates of coordinate pairs defining a bathymetry grid + grid_depth: float, 2D matrix + The depth (z coordinates) of the grid defined by grid_x and grid_y + spacing_x : float + The x spacing between turbines [m]. + spacing_y : float + The y spacing between turbines [m]. + + Returns + ------- + xs : float, array + A list of the x coordinates of turbine locations within the array + ys : float, array + A list of the y coordinates of turbine locations within the array + footprintList : list, Polygon objects + A list of shapely Polygon objects of each mooring system footprint based on anchor locations + ''' + + + # make sure the subsystem is initialized + subsystem.initialize() + + # save dimensions from the subsystem + rad_anch = np.linalg.norm(subsystem.rA[:2]) + rad_fair = np.linalg.norm(subsystem.rB[:2]) + z_anch = subsystem.rA[2] + z_fair = subsystem.rB[2] + + # initialize some lists + coords = [] + mooringList = [] + footprintList = [] + + # make the bounds into a shapely Polygon + area = Polygon([(bound_xs[i], bound_ys[i]) for i in range(len(bound_xs))]) + + # Grid of turbine locations (only those in the boundaries will be kept) + xlocs = np.arange(np.min(lease_xs), np.max(lease_xs)+1, spacing_x) + ylocs = np.arange(np.min(lease_ys), np.max(lease_ys)+1, spacing_y) + + mooring_count = 0 + + # loop through the xlocs and ylocs variables to test x/y positions to place turbines + for ix in range(len(xlocs)): + for iy in range(len(ylocs)): + + valid = True # flag for whether the turbine position satisfies requirements + + # set the x/y position of a point to test + point = [xlocs[ix], ylocs[iy]] + + # make sure the turbine location is in the boundary + if not area.contains(Point(point)): + valid = False + + # assume "orientation" is always 0 + # initialize a list + anchorlist = [] + ssList = [] + + # at the current grid point, set the anchor and fairlead points of the subsystem using a list of line heading angles and adjust for bathymetry + for ang in headings: + + if not valid: + break + + th = np.radians(ang) + + # set the local anchor and fairlead points + r_anch = np.hstack([rad_anch*np.array([np.cos(th), np.sin(th)])+point, z_anch]) + r_fair = np.hstack([rad_fair*np.array([np.cos(th), np.sin(th)])+point, z_fair]) + + mooring_count += 1 + print(f"Mooring count is {mooring_count}.") + + ss = deepcopy(subsystem) # make a copy from the original since we'll be iterating on this object + + # set the anchor and fairlead points of the subsystem + #subsystem_copy.pointList[0].setPosition(r_anch) + ss.setEndPosition(r_anch, endB=0) + #subsystem_copy.pointList[-1].setPosition(r_fair) + ss.setEndPosition(r_fair, endB=1) + ss.staticSolve() + + + # adjust subsystem for bathymetry (adjusting anchor points and line lengths) + adjustSS4Bath(ss, grid_x, grid_y, grid_depth, display=0) + + new_anchor_point = ss.rA + anchorlist.append(new_anchor_point) + + ssList.append(ss) # add it to a temporary list for just this turbine + + # if any new anchor point is outside the bounds of the Polygon area, then this point is invalid + if not area.contains(Point(new_anchor_point)): + valid = False + + # if not valid, skip the rest of this point in the for loop + if not valid: + continue + + # after checking all new anchor points for each line heading, check to make sure the new footprint doesn't overlap with any others + new_footprint = Polygon( [(anchor_point[0], anchor_point[1]) for anchor_point in anchorlist] ) + + # check to make sure that the newly created polygon does not intersect any other polygons + for footprint in footprintList: + if footprint.intersects(new_footprint): + valid = False + + # make the moorings and add to the master lists if valid + if valid: + + for i, ss in enumerate(ssList): + mooringList.append(Mooring(subsystem=ss, rA=ss.rA, rB=ss.rB)) + + coords.append(point) + + footprintList.append( Polygon( [(anchor_point[0], anchor_point[1]) for anchor_point in anchorlist] ) ) + + + return np.array(coords), mooringList, footprintList + + +def create_rotated_layout(bound_xs, bound_ys, spacing_x, spacing_y, grid_rotang, grid_skew_x, grid_skew_y, grid_trans_x, grid_trans_y, fullMPsystem = True, ms = None, rad_anch = None, rotations=None, center=None): + ''' + Create a rectangular grid layout. + + Parameters + ---------- + bound_xs : list + The x coordinates of coordinate pairs defining a layout boundary, relative to a certain point (usually a centroid) + bound_ys : list + The y coordinates of coordinate pairs defining a layout boundary, relative to a certain point (usually a centroid) + spacing_x : float + The x spacing between turbines [m]. + spacing_y : float + The y spacing between turbines [m]. + grid_rotang : float + Rotation of y axis in deg (0 deg is due North, 90 deg is due West) + grid_skew_x : float + Angle of parallelogram between adjacent rows in deg + grid_skew_y : float + Angle of parallelogram between adjacent columns in deg + grid_trans_x : float + x offset to add to all turbine positions + grid_trans_y : float + y offset to add to all turbine positions + fullMPsystem : bool + if True, create/rotation full moorpy systems (slower). if False, use rad_anch as circular buffer zone + ms : MoorPy system + mooring system to rotate, need to input if fullMPsystem = True + rad_anch : float + mooring anchoring radius. need to input if fullMPsystem = False + rotations: list + list of two mooring orientations in deg relative to the rotated y axis (used for every other row). not used if fullMPsystem = False + center: list + the coordinate of the center of the layout. Default: the midpoint of the x and y bounds + Returns + ------- + x_coords : array + array of turbine x coordinates + y_coords : array + array of turbine y coordinates + moorings : list + list of mooring systems + area : shapely polygon + polygon of boundary + ''' + + #boundary of area + area = Polygon([(bound_xs[i], bound_ys[i]) for i in range(len(bound_xs))]) + + # Shear transformation in X + # Calculate trigonometric values + cos_theta = np.cos(np.radians(-grid_rotang)) + sin_theta = np.sin(np.radians(-grid_rotang)) + tan_phi_x = np.tan(np.radians(grid_skew_x)) + tan_phi_y = np.tan(np.radians(grid_skew_y)) + + # Compute combined rotation and skew transformation matrix + transformation_matrix = np.array([[cos_theta - sin_theta*tan_phi_y, cos_theta*tan_phi_x - sin_theta], + [sin_theta + cos_theta*tan_phi_y, sin_theta*tan_phi_x + cos_theta]]) + + # Generate points in the local coordinate system + points = [] + moorings = [] + labels_list = [] # list with grid labels, so that each point now to which horizontal or vertical line it belongs + break_flag = False + # LOCAL COORDINATE SYSTEM WITH (0,0) LEASE AREA CENTROID + # Therefore, +/- self.boundary_centroid_y/x cover the entire area + # Loop through y values within the boundary_centroid_y range with grid_spacing_y increments + iy = 0 + + ywidth = np.max(bound_ys) - np.min(bound_ys) + xwidth = np.max(bound_xs) - np.min(bound_xs) + if center==None: + ycenter = (np.max(bound_ys) + np.min(bound_ys))/2 + xcenter = (np.max(bound_xs) + np.min(bound_xs))/2 + else: + xcenter = center[0] + ycenter = center[1] + + for y in np.arange(np.min(bound_ys) - ywidth*1.0, np.max(bound_ys) + ywidth*1.0, spacing_y): # extending by 1.0*width in x and y to make sure rotations include everything + # Loop through x values within the boundary_centroid_x range with grid_spacing_x increments + ix = 0 + for x in np.arange(np.min(bound_xs) - xwidth*1.0, np.max(bound_xs) + xwidth*1.0, spacing_x): + # Apply transformation matrix to x, y coordinates + local_x, local_y = np.dot(transformation_matrix, [x - xcenter, y - ycenter]) + # Add grid translation offsets to local coordinates + local_x += grid_trans_x + local_y += grid_trans_y + + + # Create a Point object representing the transformed coordinates + # Transform back into global coordinate system with by adding centroid to local coordinates + #point = Point(local_x + np.min(bound_xs), local_y + np.min(bound_ys)) + point = Point(local_x + xcenter, local_y + ycenter) + + if fullMPsystem: + + if ms == None: + raise ValueError('NEED TO INPUT MOORPY SYSTEM') + + # Check if the point lies within the specified shape (boundary_sh_int) + + # deep copy of mooring system to apply translations and rotation + mss = deepcopy(ms) + + # select every other column for rotation and add to farm rotation (mooring rotation is relative to y') + rot = rotations[iy % 2] + grid_rotang + + mss.transform(trans = [point.x, point.y], rot = -rot) #moorpy rotation convention is opposite + mss.initialize() + mss.solveEquilibrium() + + contained = True + for l in mss.lineList: + anchor = Point(l.rA[0], l.rA[1]) + + if not area.contains_properly(anchor): + contained = False + + else: + if rad_anch == None: + raise ValueError('NEED TO INPUT RAD_ANCH') + buff = point.buffer(rad_anch) + contained = True + + if not area.contains_properly(buff): + contained = False + + if contained: + # If the point is within the shape, append it to the list of points + points.append(point) + + if fullMPsystem: + moorings.append(mss) + # Save grid label + labels_list.append([ix,iy-1]) # y -1 so that labels are again starting at 0 + # If the number of points collected reaches the desired threshold (nt), set break_flag to True and exit the loop + # if len(points) >= self.nt: + # break_flag = True + # break + ix += 1 + iy += 1 + # If break_flag is True, exit the outer loop as well + if break_flag: + break + + x_coords = np.array([point.x for point in points])#/1000 + y_coords = np.array([point.y for point in points])#/1000 + + return(x_coords, y_coords, moorings, area) + + + +def getUpdatedAnchorPosition(old_anchor_point, fairlead_point, grid_x, grid_y, grid_depth, ratio=1000): + ''' + Compute a new anchor position for a taut mooring line by looking along the + a line from old anchor to fairlead and seeing where it intersects the seabed. + + Paramaters + ---------- + old_anchor_point : float, array + list of a xyz coordinate of an anchor point + fairlead_point : float, array + list of a xyz coordinate of a fairlead point + grid_x : float, array + The x coordinates of coordinate pairs defining a bathymetry grid + grid_y : float, array + The y coordinates of coordinate pairs defining a bathymetry grid + grid_depth: float, 2D matrix + The depth (z coordinates) of the grid defined by grid_x and grid_y + ratio: int or float (optional) + the value of how far to extend a mooring line until it intersects the bathymetry grid plane + + Returns + ------- + new_anchor_point : float, array + list of a xyz coordinate of the updated anchor point so that it intersects the local bathymetry grid plane + ''' + + # calculate the actual depth based on bathymetry of the x/y coordinates of the anchor + x = old_anchor_point[0] + y = old_anchor_point[1] + #depth, nvec, ix0, iy0 = getDepthFromBathymetry(x, y, grid_x, grid_y, grid_depth) # needed to adjust gDFB function <<<<<< can change later + depth, nvec = getDepthFromBathymetry(x, y, grid_x, grid_y, grid_depth) # needed to adjust gDFB function <<<<<< can change later + + # create points of a line that connect the fairlead to the anchor + p0 = fairlead_point + p1 = old_anchor_point + + ''' + # but adjust the "anchor" point to way below the bathymetry, if it is found that the initial anchor position is above the bathymetry + if p1[2] > -depth: + p1 = np.array([p0[0]+ratio*(p1[0]-p0[0]), p0[1]+ratio*(p1[1]-p0[1]), p0[2]+ratio*(p1[2]-p0[2])]) + ''' + + # Find the intersection point between the mooring Line (assumed straight) + # and the bathymetry grid panel + u = p1 - p0 # vector from fairlead to original anchor + w = np.array([x, y, -depth]) - p0 # vector from fairlead to a point on the grid panel of the original anchor + + fac = np.dot(nvec, w) / np.dot(nvec, u) # fraction along u where it crosses the seabed (can be greater than 1) + + new_anchor_point = p0 + u*fac + + return new_anchor_point + + + +""" +def getInterpNums(xlist, xin, istart=0): # should turn into function in helpers + ''' + Paramaters + ---------- + xlist : array + list of x values + xin : float + x value to be interpolated + istart : int (optional) + first lower index to try + + Returns + ------- + i : int + lower index to interpolate from + fout : float + fraction to return such that y* = y[i] + fout*(y[i+1]-y[i]) + ''' + + nx = len(xlist) + + if xin <= xlist[0]: # below lowest data point + i = 0 + fout = 0.0 + + elif xlist[-1] <= xin: # above highest data point + i = nx-1 + fout = 0.0 + + else: # within the data range + + # if istart is below the actual value, start with it instead of + # starting at 0 to save time, but make sure it doesn't overstep the array + if xlist[min(istart,nx)] < xin: + i1 = istart + else: + i1 = 0 + + for i in range(i1, nx-1): + if xlist[i+1] > xin: + fout = (xin - xlist[i] )/( xlist[i+1] - xlist[i] ) + break + + return i, fout + +def getDepthFromBathymetry(x, y, bathGrid_Xs, bathGrid_Ys, bathGrid, point_on_plane=False): #BathymetryGrid, BathGrid_Xs, BathGrid_Ys, LineX, LineY, depth, nvec) + ''' interpolates local seabed depth and normal vector + + Parameters + ---------- + x, y : float + x and y coordinates to find depth and slope at [m] + bathGrid_Xs, bathGrid_Ys: float, array + The x and y coordinates defining a bathymetry grid + bathGrid: float, 2D matrix + The depth (z coordinates) of the grid defined by bathGrid_Xs and bathGrid_Ys + point_on_plane: bool (optional): + determines whether to return the indices that go with the bathGrid arrays to return a point on the bathymetry grid plane + + Returns + ------- + depth : float + local seabed depth (positive down) [m] + nvec : array of size 3 + local seabed surface normal vector (positive out) + ix0 : int + index of the point on the bathymetry grid plane that goes with bathGrid_Xs + iy0 : int + index of the point on the bathymetry grid plane that goes with bathGrid_Xs + ''' + + # get interpolation indices and fractions for the relevant grid panel + ix0, fx = getInterpNums(bathGrid_Xs, x) + iy0, fy = getInterpNums(bathGrid_Ys, y) + + + # handle end case conditions + if fx == 0: + ix1 = ix0 + else: + ix1 = min(ix0+1, bathGrid.shape[1]) # don't overstep bounds + + if fy == 0: + iy1 = iy0 + else: + iy1 = min(iy0+1, bathGrid.shape[0]) # don't overstep bounds + + + # get corner points of the panel + c00 = bathGrid[iy0, ix0] + c01 = bathGrid[iy1, ix0] + c10 = bathGrid[iy0, ix1] + c11 = bathGrid[iy1, ix1] + + # get interpolated points and local value + cx0 = c00 *(1.0-fx) + c10 *fx + cx1 = c01 *(1.0-fx) + c11 *fx + c0y = c00 *(1.0-fy) + c01 *fy + c1y = c10 *(1.0-fy) + c11 *fy + depth = cx0 *(1.0-fy) + cx1 *fy + + # get local slope + dx = bathGrid_Xs[ix1] - bathGrid_Xs[ix0] + dy = bathGrid_Ys[iy1] - bathGrid_Ys[iy0] + + if dx > 0.0: + dc_dx = (c1y-c0y)/dx + else: + dc_dx = 0.0 # maybe this should raise an error + + if dx > 0.0: + dc_dy = (cx1-cx0)/dy + else: + dc_dy = 0.0 # maybe this should raise an error + + nvec = np.array([dc_dx, dc_dy, 1.0])/np.linalg.norm([dc_dx, dc_dy, 1.0]) # compute unit vector + + if not point_on_plane: + return depth, nvec + else: + return depth, nvec, ix0, iy0 +""" + + + +def adjustMS4Bath(ms, ms_xy, grid_x, grid_y, grid_depth, iLine=-1, nLines_in_ms=3, nLines_in_line=3, display=0, extend=True): + '''Function that updates a MoorPy System object's anchor positions in response to bathymetry and then updates + the line lengths to keep the same pretension that was there before the bathymetry adjustments + + Parameters + ---------- + ms : MoorPy System object + A MoorPy System object defining a mooring system + ms_xy: float, array + The 2D x/y position of the system's coordinate system relative to a reference point (e.g., a centroid) + to reference the proper bathymetry location + grid_x : float, array + The x coordinates of coordinate pairs defining a bathymetry grid + grid_y : float, array + The y coordinates of coordinate pairs defining a bathymetry grid + grid_depth: float, 2D matrix + The depth (z coordinates) of the grid defined by grid_x and grid_y + iLine: int, optional + the index of the line object that is to be adjusted (among the indices of line objects from one anchor to one fairlead, like a subsystem) + nLines: int, optional + the number of mooring lines that surround the MoorPy Body + display: int, optional + an option for print statement outputting + extend: boolean, optional + True for updating anchor positions to bathymetry along the vector of the mooring line, or False for dropping/lifting the anchor at the same x/y position + + Returns + ------- + ms: MoorPy System object + The updated MoorPy System object with new anchor positions and line lengths that match the initial pretensions + ''' + + # NOTE: This function can probably be put in system.py as a method since it adjusts a System object + if np.any([isinstance(line, mp.Subsystem) for line in ms.lineList]): + subsystem_flag = True + else: + subsystem_flag = False + + ### COLLECT INFORMATION ABOUT THE INPUT MOORING SYSTEM (OR SUBSYSTEMS(S)) ### + + T_init_list = [np.linalg.norm(ss.fB_L[0:2]) for ss in ms.lineList] + + # collect point numbers for all anchor points + anchors = [point.number for point in ms.pointList if point.type==1 and point.number not in ms.bodyList[0].attachedP] + # collect point numbers for all anchor points if there are any subsystems in the lineList + #anchors_subsystem = [anchornum for anchornum in anchors for line in ms.lineList for linenum in ms.pointList[anchornum-1].attached if isinstance(line, mp.Subsystem) and linenum==line.number] + # split anchor point numbers up based on whether they are attached to a subsystem or just a line + #anchors_lines = list(set(anchors).difference(anchors_subsystem)) + + # collect point numbers for all "fairleads" + # (fairleads are defined as the points attached to the body where "upper_points" are the points that the lines that are to be adjusted are attached to at the top) + if not subsystem_flag: + iLines = np.arange(iLine, 1e3, nLines_in_line, dtype=int)[:nLines_in_ms] # create a list of the indices of all lines in a mooring system to vary (doesn't always need to be line connected to the fairlead) + upper_points = np.sort([point.number for point in ms.pointList for iL in iLines if all(point.r==ms.lineList[iL].rB)]) # collect the numbers of the points where the lines of interest are attached to at the top + + fairleads = [point.number for point in ms.pointList if point.type==1 and point.number in ms.bodyList[0].attachedP] # collect the numbers of the points that are fairleads + # collect point numbers for all "upper_points" if there are any subsystems in the list + #upper_points_subsystem = [fairleadnum for fairleadnum in upper_points for line in ms.lineList for linenum in ms.pointList[fairleadnum-1].attached if isinstance(line, mp.Subsystem) and linenum==line.number] + # split the upper_points list up based on whether they are attached to a subsystem or not + #upper_points_lines = np.sort(list(set(upper_points).difference(upper_points_subsystem))) + + if not subsystem_flag: + # collect line numbers that are attached to the points of interest + #lower_lines = [line.number for line in ms.lineList if not isinstance(line, mp.Subsystem) for point in ms.pointList if point.number in anchors if all(line.rA==point.r)] + upper_lines = [line.number for line in ms.lineList if not isinstance(line, mp.Subsystem) for point in ms.pointList if point.number in upper_points if all(line.rB==point.r)] + fairleads_lines = [line.number for line in ms.lineList if not isinstance(line, mp.Subsystem) for point in ms.pointList if point.number in fairleads if all(line.rB==point.r)] + # collect the upper tensions of each line attached to the points of interest + upper_lines_TB = [ms.lineList[linenum-1].TB for linenum in upper_lines] + fairleads_lines_TB = [ms.lineList[linenum-1].TB for linenum in fairleads_lines] + + # separate the subsystem objects from the rest to use later, separately from the Line objects + subsystems = [line for line in ms.lineList if isinstance(line, mp.Subsystem)] + + ### CALCULATE AND SET NEW ANCHOR POSITIONS FOR ONLY LINE OBJECTS ### + for i,anchornum in enumerate(anchors): + anchor_point_local = ms.pointList[anchornum-1].r + anchor_point_global = anchor_point_local + np.array([ms_xy[0], ms_xy[1], 0]) + fairlead_point_local = ms.pointList[fairleads[i]-1].r + fairlead_point_global = fairlead_point_local + np.array([ms_xy[0], ms_xy[1], 0]) + + if extend: # if you wish to "extend" or "retract" the anchor point along the vector of the mooring line + new_anchor_point_global = getUpdatedAnchorPosition(anchor_point_global, fairlead_point_global, grid_x, grid_y, grid_depth) + if new_anchor_point_global[2] < anchor_point_global[2]: + if display > 0: print("'Extending' the anchor point to the bathymetry, along the vector of the mooring line") + elif new_anchor_point_global[2] > anchor_point_global[2]: + if display > 0: print("'Retracting' the anchor point to the bathymetry, along the vector of the mooring line") + else: + if display > 0: print("No change in the anchor depth") + else: # if you wish to "drop" or "lift" the anchor point at the same x/y position + new_depth, _ = ms.getDepthFromBathymetry(anchor_point_global[0], anchor_point_global[1]) + new_anchor_point_global = np.array([anchor_point_global[0], anchor_point_global[1], -new_depth]) + if new_anchor_point_global[2] < anchor_point_global[2]: + if display > 0: print("'Dropping' the anchor point to the bathymetry, at the same x/y position") + elif new_anchor_point_global[2] > anchor_point_global[2]: + if display > 0: print("'Lifting' the anchor point to the bathymetry, at the same x/y position") + else: + if display > 0: print("No change in the anchor depth") + + new_anchor_point_local = new_anchor_point_global - np.array([ms_xy[0], ms_xy[1], 0]) + ms.pointList[anchornum-1].setPosition(new_anchor_point_local) + # setPosition sets the point.r value to the input, and also updates the end position of the line object + # setPosition also doesn't allow the input position to be less than ms.depth (which shouldn't matter if the input ms to this function is already at seabedMod=2) + if subsystem_flag: + ms.lineList[i].setEndPosition(new_anchor_point_local, endB=0) + ms.lineList[i].depth = -new_anchor_point_local[2] + ms.lineList[i].pointList[0].setPosition(np.array([ms.lineList[i].pointList[0].r[0], 0, new_anchor_point_local[2]])) + + # resolve for equilibrium + ms.solveEquilibrium() + + if subsystem_flag: + for i,ss in enumerate(subsystems): + L = adjustSS4Pretension(ss, i_line=1, T_init=T_init_list[i], horizontal=True, display=3, tol=0.001) + ss.lineList[1].setL(L) + ms.solveEquilibrium() + + else: + ## update line lengths to match pretension ## + def eval_func(X, args): + '''Tension evaluation function for different line lengths''' + L = X[0] # extract the solver variable + # set args variables + ms_copy = args['ms'] #ms_copy = deepcopy(args['ms']) + iLineX = args['iLineX'] + iLineFair = args['iLineFair'] + # set System variables and solve for new tension + ms_copy.lineList[iLineX].L = L + ms_copy.solveEquilibrium() + T = np.linalg.norm(ms_copy.lineList[iLineFair].fB) + return np.array([T]), dict(status=1), False + + #upper_lines_byL = [ upper_lines[i] for i in np.flip(np.argsort([ms.lineList[ul-1].L for ul in upper_lines])) ] + #fairleads_lines_byL = [ fairleads_lines[i] for i in np.flip(np.argsort([ms.lineList[ul-1].L for ul in upper_lines])) ] + + # loop through the upper lines and run dsolve2 solver to solve for the line length that matches that initial tension + for i,upper_linenum in enumerate(upper_lines): + # set initial variables + T_init = fairleads_lines_TB[i] #T_init = upper_lines_TB[i] + EA = ms.lineList[upper_linenum-1].type['EA'] + L_init = ms.lineList[upper_linenum-1].L + X0 = [ 10 ] #X0 = [ L_init/(T_init/EA+1) ] # setting to start at 10 to start from really taut and extend to longer, always + if display > 0: print(f" Updating Line {upper_linenum} length to match pretension") + + # run dsolve2 to solve for the line length that produces the same initial tension + L_final, T_final, _ = dsolve2(eval_func, X0, Ytarget=[T_init], args=dict(ms=ms, iLineX=upper_linenum-1, iLineFair=fairleads_lines[i]-1), maxIter=200, stepfac=4, display=display, tol=1e-4) + # has the option to solve for an intermediate line length that results in the same tension on the fairlead line (different line) + + # set the new line length into the ms System + ms.lineList[upper_linenum-1].L = L_final[0] + if display > 0: print(f' L0 = {X0[0]:6.1f}, LF = {L_final[0]:6.1f}') + if display > 0: print(f' T0 = {T_init:8.2e}, TF = {T_final[0]:8.2e}') + + + ms.solveEquilibrium() + + return ms + + +def adjustSS4Pretension(ssin, i_line=0, T_init=None, horizontal=False, display=0, stepfac=10, tol=0.01): + + ss = deepcopy(ssin) + + if T_init==None: + if horizontal: + T_init = np.linalg.norm(ss.fB_L[0:2]) + else: + T_init = ss.TB # save the initial pretension + + # can update the subsystem initially if need be (Subsystem.staticSolve is the equivalent to System.solveEquilibrium) + #ss.staticSolve() + #T0 = ss.TB + + # update line lengths to match pretension + def eval_func(X, args): + '''Tension evaluation function for different line lengths''' + L = X[0] + ss.lineList[i_line].L = L + ss.staticSolve() + if horizontal: + T = np.linalg.norm(ss.fB_L[0:2]) + else: + T = ss.TB + return np.array([T]), dict(status=1), False + + # run dsolve2 solver to solve for the upper line length that matches the initial tension + L_init = ss.lineList[i_line].L + if display > 0: print(f" Updating Subsystem {ss.number}'s Line {ss.lineList[i_line].number} length to match pretension") + + #X0 = [L_init] + X0 = [10] + + # run dsolve2 to solve for the line length that sets the same pretension + L_final, T_final, _ = dsolve2(eval_func, X0, Ytarget=[T_init], + Xmin=[1], Xmax=[1.1*np.linalg.norm(ss.rB-ss.rA)], + dX_last=[1], tol=[tol], + maxIter=200, stepfac=stepfac, display=display) + + #ss.lineList[i_line].setL(L_final[0]) # assign the solved_for length + if display > 0: print(f' L_init = {L_init:6.1f}, LF = {L_final[0]:6.1f}') + if display > 0: print(f' T_init = {T_init:8.2e}, TF = {T_final[0]:8.2e}') + + #ss.staticSolve() # reset the subsystem + + return L_final[0] + #return ss + + + + + +def adjustMooring(mooring, layout, r_fair, r_anch, adjust={}): + '''Adjust a Mooring object for change in layout considering the seabed, + which is contained in Project object that is also passed in. + The Mooring adjustment should work regardless of whether the mooring + is only 2D or also includes a 3D representation via MoorPy Subsystem. + + When a subsystem is involved, a dictionary can be past via 'adjust' + to ask for the pretension to be adjusted to a desired value. + + Parameters + ---------- + mooring : Mooring object + The Mooring to be adjusted. + layout : Layout object + An object of the Layout class that contains seabed information. + r_fair : float, array + Absolute xyz coordinate of a fairlead point [m]. + r_anch : float, array + Absolute xyz coordinate of the anchor point (guess to be adjusted) [m]. + adjust : dict + Dictionary specifying a method of adjusting the mooring to maintain a + desired characteristic. Currently only pretension is supported: + {'pretension' : {'target' : XXX N, 'i': line index to adjust}}. + ''' + + # Update the anchor position if it isn't already on the seabed + if not np.isclose(r_anch[2], layout.getDepthAtLocation(*r_anch[:2])): + r_anch = layout.getUpdatedAnchorPosition(r_fair, r_anch) + + # Set the mooring end positions (this will update any Subsystem too) + mooring.setEndPosition(r_anch, 'a') + mooring.setEndPosition(r_fair, 'b') + + # If requested, update the line lengths to maintain pretension + if mooring.subsystem and 'pretension' in adjust: + target = adjust['pretension']['target'] + i_line = int(adjust['pretension']['i']) + + ss = mooring.subsystem # shorthand + + # update line lengths to match pretension + def eval_func(X, args): + '''Tension evaluation function for different line lengths''' + ss.lineList[i_line].L = X[0] # set specified line section's length + ss.staticSolve(tol=0.0001) # solve the equilibrium of the subsystem + return np.array([ss.TB]), dict(status=1), False # return the end tension + + # run dsolve2 solver to solve for the upper line length that matches the initial tension + X0 = [ss.lineList[i_line].L] # initial value is current section length + + L_final, T_final, _ = dsolve2(eval_func, X0, Ytarget=[target], + Xmin=[1], Xmax=[1.1*np.linalg.norm(ss.rB-ss.rA)], + dX_last=[1], tol=[0.1], + maxIter=200, stepfac=4, display=2) + + ss.lineList[i_line].L = L_final[0] + + print(f"Adjusted mooring to pretension of {T_final[0]:.0f} N and {L_final[0]:.2f}-m section length.") + + +def makeMooringListN(subsystem0, N): + '''Simple function for making a mooringList of N mooring objects, by + by duplication one provided subsystem. They can be positioned later. + ''' + + #mooringList = [] + # Initialize empty list + #mooringList = [None] * N + mooringList = {} + + for i in range(N): + + # Make a copy from the original + ss = deepcopy(subsystem0) + + #mooringList.append(Mooring(subsystem=ss,id=i)) + mooringList[i] = Mooring(subsystem=ss,id=i) + #mooringList[i].rA = ss.rA + #mooringList[i].rB = ss.rB + # Make a new mooring object to hold the copied subsystem + #mooringList.append(Mooring(subsystem=ss, rA=ss.rA, rB=ss.rB)) + + return mooringList + + +def getLower(A): + '''Return a vector of the serialized lower-triangular elements of matrix A''' + return A[np.tril_indices_from(A , k=-1)] + +if __name__ == '__main__': + + + # initialize the area bounds + lease_xs = np.array([ 2220.61790941, 2220.61787966, 3420.61793096, 3420.61791961, + 3420.61801382, 3420.61803977, 3420.61807596, 3420.61811471, + 3420.61822821, 4620.61806679, 4620.61816982, 4620.6182304 , + 4620.61832506, 4620.61827356, 4620.61840937, 4620.61846802, + 5820.61842928, 5820.61837593, 5820.61846352, 5820.61851395, + 5820.61860812, 7020.61852522, 7020.61862679, 7020.61856713, + 5820.61872489, 5820.61881681, 5820.61883604, 4620.61889509, + 4620.61904038, 4620.61903256, 3420.619089 , 3420.61917709, + 3420.61920431, 2220.61926344, 2220.61944036, 2220.61939526, + 1020.61946852, -179.38041543, -179.38053362, -179.38049454, + -179.38063026, -179.38059316, -179.38071736, -179.38082411, + -179.38081907, -179.38086534, -179.38087289, -179.38099606, + -179.38098055, -1379.38097106, -1379.38100559, -1379.38111504, + -2579.38099329, -2579.38099701, -2579.38112028, -2579.38119101, + -3779.38113702, -3779.38108251, -3779.38118818, -4979.38109205, + -4979.38117059, -4979.38120063, -6179.38111446, -6179.38120826, + -7379.38107666, -7379.38119559, -8579.38111169, -8579.38121027, + -7379.38119975, -7379.38133324, -7379.38133537, -7379.38136608, + -7379.3815011 , -7379.38148217, -6179.38158388, -6179.38169844, + -4979.38166381, -4979.38179166, -3779.38188846, -2579.38201223, + -1379.38197896, -1379.38206961, -179.38223745, -179.38215968, + 1020.61770826, 2220.61761805, 2220.61770748, 2220.61772366, + 2220.6178225 , 2220.61783243, 2220.61790941]) + + lease_ys = np.array([ 10997.93747912, 9797.9374786 , 9797.93762135, 8597.93777362, + 7397.93779263, 6197.93779256, 4997.9378827 , 3797.93806299, + 2597.93810968, 2597.93821722, 1397.93821124, 197.93829277, + -1002.06153985, -2202.06150557, -3402.0613888 , -4602.0614069 , + -4602.06132321, -5802.06115355, -7002.06101498, -8202.06101547, + -9402.06093466, -9402.06087921, -10602.06081225, -11802.06066334, + -11802.06074832, -13002.06064456, -14202.06056929, -14202.06071115, + -15402.06052442, -16602.06047346, -16602.0605191 , -17802.06042198, + -19002.060349 , -19002.06047655, -20202.06042422, -21402.06027959, + -21402.06033391, -21402.06033968, -20202.06050384, -19002.06057741, + -17802.06066473, -16602.06066129, -15402.06078244, -14202.06092037, + -13002.06096561, -11802.06102581, -10602.06121353, -9402.06119237, + -8202.0613002 , -8202.06134893, -7002.06145163, -5802.06156475, + -5802.06157941, -4602.06171868, -3402.06175304, -2202.06190779, + -2202.0618502 , -1002.06194537, 197.93795779, 197.93795451, + 1397.93778023, 2597.93771457, 2597.93761946, 3797.93767253, + 3797.93747917, 4997.93739719, 4997.93747426, 6197.9373365 , + 6197.93731811, 7397.93724719, 8597.93717933, 9797.93711807, + 10997.93706522, 12197.93690464, 12197.93699804, 13397.93696353, + 13397.93699185, 14597.93692719, 14597.93693641, 14597.93707605, + 14597.93701544, 15797.93706205, 15797.93707378, 16997.93692194, + 16997.93704183, 16997.93701933, 15797.93709866, 14597.93727451, + 13397.93732149, 12197.93746482, 10997.93747912]) + + # initialize dummy mooring system to use to organize turbines within a layout + ms_type = 1 + + if ms_type==1: + ms = mp.System(file='sample_deep.txt') + + elif ms_type==2: + depth = 200 # water depth [m] + angles = np.radians([60, 300]) # line headings list [rad] + rAnchor = 600 # anchor radius/spacing [m] + zFair = -21 # fairlead z elevation [m] + rFair = 20 # fairlead radius [m] + lineLength= 650 # line unstretched length [m] + typeName = "chain1" # identifier string for the line type + + ms = mp.System(depth=depth) + + # add a line type + ms.setLineType(dnommm=120, material='chain', name=typeName) # this would be 120 mm chain + + # Add a free, body at [0,0,0] to the system (including some properties to make it hydrostatically stiff) + ms.addBody(1, np.zeros(6), m=1e6, v=1e3, rM=100, AWP=1e3) + + # For each line heading, set the anchor point, the fairlead point, and the line itself + for i, angle in enumerate(angles): + + # create end Points for the line + ms.addPoint(1, [rAnchor*np.cos(angle), rAnchor*np.sin(angle), -depth]) # create anchor point (type 0, fixed) + ms.addPoint(1, [ rFair*np.cos(angle), rFair*np.sin(angle), zFair]) # create fairlead point (type 0, fixed) + + # attach the fairlead Point to the Body (so it's fixed to the Body rather than the ground) + ms.bodyList[0].attachPoint(2*i+2, [rFair*np.cos(angle), rFair*np.sin(angle), zFair]) + + # add a Line going between the anchor and fairlead Points + ms.addLine(lineLength, typeName, pointA=2*i+1, pointB=2*i+2) + + # ----- Now add a SubSystem line! ----- + ss = mp.Subsystem(mooringSys=ms, depth=depth, spacing=rAnchor, rBfair=[10,0,-20]) + + # set up the line types + ms.setLineType(180, 'chain', name='one') + ms.setLineType( 50, 'chain', name='two') + + # set up the lines and points and stuff + ls = [350, 300] + ts = ['one', 'two'] + ss.makeGeneric(lengths=ls, types=ts) + + # add points that the subSystem will attach to... + ms.addPoint(1, [-rAnchor, 100, -depth]) # Point 5 - create anchor point (type 0, fixed) + ms.addPoint(1, [ -rFair , 0, zFair]) # Point 6 - create fairlead point (type 0, fixed) + ms.bodyList[0].attachPoint(6, [-rFair, 0, zFair]) # attach the fairlead Point to the Body + + #ms.addLine(length, type, pointA, pointB) + + # string the Subsystem between the two points! + ms.lineList.append(ss) # add the SubSystem to the System's lineList + ss.number = 3 + ms.pointList[4].attachLine(3, 0) # attach it to the respective points + ms.pointList[5].attachLine(3, 1) # attach it to the respective points + + #ms.bodyList[0].type = 1 + + + + ms.initialize() # make sure everything's connected + ms.solveEquilibrium() # equilibrate + + + + + + + # create a subsystem + ss = mp.Subsystem(depth=2000, spacing=2000, rBfair=[10,0,-20]) + + # set up the line types + ss.setLineType(180, 'polyester', name='one') + + # set up the lines and points and stuff + lengths = [2000] + types = ['one'] + ss.makeGeneric(lengths, types) + + # plotting examples + ss.setEndPosition([-2000 , 0,-2000], endB=0) + ss.setEndPosition([-10, 0, -20], endB=1) + ss.staticSolve() + + #ss.pointList[0].setPosition(np.array([-2000, 0, -2000])) + #ss.pointList[-1].setPosition(np.array([-10, 0, -20])) + #ss.solveEquilibrium() + + + + + + + # initialize dummy bathymetry variables to check anchor depths + grid_x = np.array([-65000, 65000]) + grid_y = np.array([-65000, 65000]) + grid_depth = np.array([[2367, -211], + [3111, -338]]) + + start_time = time.time() + + coords, mooringList, footprintList = create_layout(lease_xs, lease_ys, ss, grid_x, grid_y, grid_depth, + spacing_x=8400, spacing_y=8600) + + end_time = time.time() - start_time + print(end_time, end_time/60) + # create a layout of turbine positions + #xs, ys, footprintList, msList = create_initial_layout(lease_xs, lease_ys, ms, grid_x, grid_y, grid_depth, display=1) + + # plot the result + fig, ax = plt.subplots(1,1) + ax.plot(coords[:,0], coords[:,1], color='k', marker='o', linestyle='') + ax.plot(lease_xs, lease_ys, color='r') + for polygon in footprintList: + x, y = polygon.exterior.coords.xy + ax.plot(x, y, color='b', alpha=0.5) + ax.set_aspect('equal') + + + # try making a Project with the above + + project = Project() + + project.loadBathymetry('bathymetry_sample.txt') + + project.mooringList = mooringList + + project.plot3d() + + plt.show() + + + a = 2 + + + + + # Next Steps: + # - be able to adjust the starting point to see if there are any other arrangements that can fit more turbines + # - be able to adjust the "bounds" on each turbine (i.e., change from a circle around each turbine to maybe a triangle) + # - be able to account for bathymetry for each anchor point (will likely need FAModel integration, as this is already set up to do this) + + + +def convertm2km(coords): + above_1000 = any(coord > 1000 for coord in coords) + if above_1000: + coords = [coord / 1000 for coord in coords] + return coords + + + + + + + + + + + diff --git a/famodel/mooring/mooringOntology.yaml b/famodel/mooring/mooringOntology.yaml index 3d582873..90d548a5 100644 --- a/famodel/mooring/mooringOntology.yaml +++ b/famodel/mooring/mooringOntology.yaml @@ -1194,7 +1194,7 @@ platforms: rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1217,7 +1217,7 @@ platforms: heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) diff --git a/famodel/project.py b/famodel/project.py index 6c75d77e..c958cfe4 100644 --- a/famodel/project.py +++ b/famodel/project.py @@ -1,9 +1,6 @@ """Project class for FAModel, containing information and key methods for the site information and design information that make up a project.""" -import sys -sys.path.append(r'C:\Code\FAModel') - import os import numpy as np import matplotlib.pyplot as plt diff --git a/famodel/seabed_tools.py b/famodel/seabed_tools.py new file mode 100644 index 00000000..6baeffb6 --- /dev/null +++ b/famodel/seabed_tools.py @@ -0,0 +1,310 @@ +"""A set of functions for processing seabed information for a Project.""" + + +import os +import matplotlib.pyplot as plt +import numpy as np + + + + + + +def readBathymetryFile(filename, dtype=float): + + with open(filename, 'r') as f: + # skip the header + line = next(f) + # collect the number of grid values in the x and y directions from the second and third lines + line = next(f) + nGridX = int(line.split()[1]) + line = next(f) + nGridY = int(line.split()[1]) + # allocate the Xs, Ys, and main bathymetry grid arrays + bathGrid_Xs = np.zeros(nGridX) + bathGrid_Ys = np.zeros(nGridY) + bathGrid = np.zeros([nGridY, nGridX], dtype=dtype) # MH swapped order June 30 + # read in the fourth line to the Xs array + line = next(f) + bathGrid_Xs = [float(line.split()[i]) for i in range(nGridX)] + strlist = [] + # read in the remaining lines in the file into the Ys array (first entry) and the main bathymetry grid + for i in range(nGridY): + line = next(f) + entries = line.split() + bathGrid_Ys[i] = entries[0] + if dtype==float: + bathGrid[i,:] = entries[1:] + if dtype==str: + strlist.append(entries[1:]) + if dtype==str: + bathGrid = np.array(strlist) + + return bathGrid_Xs, bathGrid_Ys, bathGrid + + +def getSoilTypes(filename): + '''function to read in a preliminary input text file format of soil type information''' + + soilProps = {} + + f = open(filename, 'r') + + for line in f: + if line.count('---') > 0 and (line.upper().count('SOIL TYPES') > 0): + line = next(f) # skip this header line, plus channel names and units lines + var_names = line.split() + line = next(f) + line = next(f) + while line.count('---') == 0: + entries = line.split() + soilProps[entries[0]] = {} + for iv,var in enumerate(var_names[1:]): + # convert entries to strings unless there is + if entries[iv+1] == '-': + soilProps[entries[0]][var] = [0] + else: + soilProps[entries[0]][var] = [float(entries[iv+1])] + line = next(f) + + f.close() + + return soilProps + + +def processBoundary(filename, lat, lon,meters=True): + '''Reads boundary information from a CSV file and stores the boundary + coordinate list in a set of arrays. This function can be extended to + deal with multiple boundary sets. + + Parameters + ---------- + filename : string + Filename containing columns of x and y coordinates of boundary. + lat : float + lattitude of reference point to use for array y grid + long : float + lattitude of reference point to use for array x grid + + Returns + ------- + Xs : array + x values of grid points [m] + Ys : array + y values of grid points [m] + ''' + + import pandas as pd + + zerozero = (lat, lon) # lattitude and longitude of reference point (grid origin) + + delin = pd.read_csv(filename) + longs = np.array(delin['X_UTM10']) + lats = np.array(delin['Y_UTM10']) + + if meters: + Xs = longs + Ys = lats + #else: + #Xs, Ys = convertLatLong2Meters(zerozero, lats, longs) + + return Xs, Ys + + + + + + + + + +def resampleGrid(x_new, y_new, x_old, y_old, grid_values): + '''Interpolate an existing array of values on a rectangular grid to a new + rectangular grid. + + Parameters + ---------- + x_new : list + x values of the new grid to interpolate to + y_new : list + y values of the new grid to interpolate to + x_old : list + x values of the original grid + y_old : list + x values of the original grid + grid_values : 2D array + The values on the old grid to be interpolated from (dimensions must + match the length of y_old and x_old, in that order). + + Returns + ------- + grid_values_new : 2D array + Interpolated grid values on y_new and x_new grid lines. + ''' + + grid_values_new = np.zeros([len(y_new), len(x_new)]) + + for i in range(len(y_new)): + for j in range(len(x_new)): + grid_values_new[i,j], _ = getDepthFromBathymetry(x_new[j], y_new[i], + x_old, y_old, grid_values) + + return grid_values_new + + +def getInterpNums(xlist, xin, istart=0): # should turn into function in helpers + ''' + Paramaters + ---------- + xlist : array + list of x values + xin : float + x value to be interpolated + istart : int + first lower index to try + + Returns + ------- + i : int + lower index to interpolate from + fout : float + fraction to return such that y* = y[i] + fout*(y[i+1]-y[i]) + ''' + + if np.isnan(xin): + raise Exception('xin value is NaN.') + + nx = len(xlist) + + if xin <= xlist[0]: # below lowest data point + i = 0 + fout = 0.0 + + elif xlist[-1] <= xin: # above highest data point + i = nx-1 + fout = 0.0 + + else: # within the data range + + # if istart is below the actual value, start with it instead of + # starting at 0 to save time, but make sure it doesn't overstep the array + if xlist[min(istart,nx)] < xin: + i1 = istart + else: + i1 = 0 + + for i in range(i1, nx-1): + if xlist[i+1] > xin: + fout = (xin - xlist[i] )/( xlist[i+1] - xlist[i] ) + break + + return i, fout + + +def interpFromGrid(x, y, grid_x, grid_y, values): + '''Interpolate from a rectangular grid of values.''' + + # get interpolation indices and fractions for the relevant grid panel + ix0, fx = getInterpNums(grid_x, x) + iy0, fy = getInterpNums(grid_y, y) + + # handle end case conditions + if fx == 0: + ix1 = ix0 + else: + ix1 = min(ix0+1, values.shape[1]) # don't overstep bounds + + if fy == 0: + iy1 = iy0 + else: + iy1 = min(iy0+1, values.shape[0]) # don't overstep bounds + + # get corner points of the panel + c00 = values[iy0, ix0] + c01 = values[iy1, ix0] + c10 = values[iy0, ix1] + c11 = values[iy1, ix1] + + # get interpolated points and local value + cx0 = c00 *(1.0-fx) + c10 *fx + cx1 = c01 *(1.0-fx) + c11 *fx + c0y = c00 *(1.0-fy) + c01 *fy + c1y = c10 *(1.0-fy) + c11 *fy + value = cx0 *(1.0-fy) + cx1 *fy + + # get local slope + dx = grid_x[ix1] - grid_x[ix0] + dy = grid_y[iy1] - grid_y[iy0] + + # deal with being on an edge or a zero-width grid increment + if dx > 0.0: + dc_dx = (c1y-c0y)/dx + else: + dc_dx = c0y*0 # maybe this should raise an error + + if dy > 0.0: + dc_dy = (cx1-cx0)/dy + else: + dc_dy = cx0*0 # maybe this should raise an error + + # return the interpolated value, the derivatives, and the grid indices + return value, dc_dx, dc_dy, ix0, iy0 + + + +def getDepthFromBathymetry(x, y, grid_x, grid_y, grid_depth, index=False): + ''' interpolates local seabed depth and normal vector + + Parameters + ---------- + x, y : float + x and y coordinates to find depth and slope at [m] + + Returns + ------- + depth : float + local seabed depth (positive down) [m] + nvec : array of size 3 + local seabed surface normal vector (positive out) + index : bool, optional + If True, will also retun ix and iy - the indices of the intersected + grid panel. + ''' + + # Call general function for 2d interpolation + depth, dc_dx, dc_dy, ix0, iy0 = interpFromGrid(x, y, grid_x, grid_y, grid_depth) + + # Compute unit vector of the seabed panel + nvec = np.array([dc_dx, dc_dy, 1.0])/np.linalg.norm([dc_dx, dc_dy, 1.0]) + + if index: + return depth, nvec, ix0, iy0 + else: + return depth, nvec + + + + + + + + +if __name__ == '__main__': + + centroid = (40.928, -124.708) #humboldt + xs = np.arange(-30000,30001,400) + ys = np.arange(-40000,40001,400) + + xs, ys, depths = processGeotiff('humboldt.tif', centroid[0], centroid[1], xs=xs, ys=ys, outfilename='test output.txt') + + import moorpy as mp + ms = mp.System(depth=np.max(depths), bathymetry='test output.txt') + ms.initialize() + ms.plot(hidebox=True, args_bath={'cmap':'viridis'}) + ''' + # try converting to a different grid + x_new = np.arange(-20000, 20001, 800) + y_new = np.arange(-20000, 20001, 800) + depths_new = resampleGrid(x_new, y_new, xs, ys, depths) + ''' + plt.show() diff --git a/tests/simple_farm.yaml b/tests/simple_farm.yaml index 8b8b65d9..c05945de 100644 --- a/tests/simple_farm.yaml +++ b/tests/simple_farm.yaml @@ -114,8 +114,8 @@ turbine: nBlades : 3 # number of blades Zhub : 150.0 # hub height [m] Rhub : 3.97 # hub radius [m] - precone : 4.0 # [deg] - shaft_tilt : 6.0 # [deg] + precone : -4.0 # [deg] + shaft_tilt : -6.0 # [deg] overhang : 12.0313 # [m] aeroServoMod : 2 # 0 aerodynamics off; 1 aerodynamics on (no control); 2 aerodynamics and control on @@ -1133,7 +1133,7 @@ turbine: dlsMax : 5.0 # maximum node splitting section amount; can't be 0 name : tower # [-] an identifier (no longer has to be number) - type : 1 # [-] + type : rigid # [-] rA : [ 0, 0, 15] # [m] end A coordinates rB : [ 0, 0, 144.582] # [m] and B coordinates shape : circ # [-] circular or rectangular @@ -1161,12 +1161,12 @@ platform: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1183,13 +1183,13 @@ platform: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1209,7 +1209,7 @@ platform: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1230,7 +1230,7 @@ platform: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) diff --git a/tests/testOntology.yaml b/tests/testOntology.yaml index 3ddd8406..0ea7abe4 100644 --- a/tests/testOntology.yaml +++ b/tests/testOntology.yaml @@ -161,7 +161,7 @@ array_mooring: # Array cables (compact table format, without routing info) array_cables: - keys: [ AttachA, AttachB, DynCableA, DynCableB, JtubeA, JtubeB, headingA, headingB, cableType, lengthAdjust] + keys: [ AttachA, AttachB, DynCableA, DynCableB, JTubeA, JTubeB, headingA, headingB, cableType, lengthAdjust] data: - [ FOWT1, FOWT3, suspended_1, NONE, 2, 3, 180, 0, NONE, 0] - [ FOWT3, FOWT4, lazy_wave1, lazy_wave1, 3, 1, 285, 80, static_cable_36, 0] @@ -1205,7 +1205,7 @@ topsides: dlsMax : 5.0 # maximum node splitting section amount; can't be 0 name : tower # [-] an identifier (no longer has to be number) - type : 1 # [-] + type : rigid # [-] rA : [ 0, 0, 15] # [m] end A coordinates rB : [ 0, 0, 144.582] # [m] and B coordinates shape : circ # [-] circular or rectangular @@ -1233,7 +1233,7 @@ platforms: - name: fairlead1 r_rel: [40.5,0,-20] headings: [270, 30, 150] # headings in degrees for the fairlead (if multiple headings, the fairlead will be repeated for each heading) - Jtubes : # list of Jtube coordinates for the platform relative to platform coordinate and 0-degree heading + JTubes : # list of Jtube coordinates for the platform relative to platform coordinate and 0-degree heading - name: Jtube1 r_rel: [5, 0, -20] headings: [90, 210, 330] # headings in degrees for the Jtube (if multiple headings, the Jtube will be repeated for each heading) @@ -1241,12 +1241,12 @@ platforms: members: # list all members here - name : center_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 0, 0, -20] # [m] end A coordinates rB : [ 0, 0, 15] # [m] and B coordinates shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 1] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 10.0 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1263,13 +1263,13 @@ platforms: - name : outer_column # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [51.75, 0, -20] # [m] end A coordinates rB : [51.75, 0, 15] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) shape : circ # [-] circular or rectangular gamma : 0.0 # [deg] twist angle about the member's z-axis - potMod : False # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory + potMod : True # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory # --- outer shell including hydro--- stations : [0, 35] # [-] location of stations along axis. Will be normalized such that start value maps to rA and end value to rB d : 12.5 # [m] diameters if circular or side lengths if rectangular (can be pairs) @@ -1289,7 +1289,7 @@ platforms: - name : pontoon # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, -16.5] # [m] end A coordinates rB : [ 45.5, 0, -16.5] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1310,7 +1310,7 @@ platforms: - name : upper_support # [-] an identifier (no longer has to be number) - type : 2 # [-] + type : rigid # [-] rA : [ 5 , 0, 14.545] # [m] end A coordinates rB : [ 45.5, 0, 14.545] # [m] and B coordinates heading : [ 60, 180, 300] # [deg] heading rotation of column about z axis (for repeated members) @@ -1558,12 +1558,12 @@ cables: attachID: FOWT1 # FOWT/substation/junction ID heading: 270 # [deg] heading of attachment at end A dynamicID: lazy_wave1 # ID of dynamic cable configuration at this end - Jtube: 2 + JTube: 2 endB: attachID: FOWT2 heading: 270 dynamicID: lazy_wave1 - Jtube: 2 + JTube: 2 routing_x_y_r: - [700,150,20] # [x (m), y (m), radius (m)] - [900,150,20] diff --git a/tests/test_LineDesign.py b/tests/test_LineDesign.py new file mode 100644 index 00000000..b5e11eb5 --- /dev/null +++ b/tests/test_LineDesign.py @@ -0,0 +1,200 @@ +import numpy as np +import moorpy as mp +from famodel.design.LineDesign import LineDesign +from pathlib import Path + + + +moorpy_dir = Path(mp.__file__).parent +moorprops_file = moorpy_dir / "library" / "MoorProps_default.yaml" +moorprops_file = str(moorprops_file) + + +def test_DEA_Chain_COBYLA_none(): + + depth = 200 + + settings = {} + settings['rBFair'] = [58,0,-14] + settings['x_ampl'] = 10 + settings['fx_target'] = 1.95e6 + 465284.3 # from 0.71^2 m/s * 9.23e5 current + settings['kx_target'] = 2e4 + settings['headings'] = [60, 180, 300] + + settings['name'] = 'DEA-chain' + settings['lineTypeNames'] = ['chain'] + settings['anchorType'] = 'drag-embedment' + settings['allVars'] = [1000/10, 1000, 120] + + # solve_for = none setup + settings['solve_for'] = 'none' + settings['x_target'] = 35 + settings['x_mean_out'] = 35 + settings['x_mean_in'] = 60 + + settings['Xindices'] = [0, 1, 2] + settings['Xmin'] = [10, 10, 10] + settings['Xmax'] = [1000, 1000, 120] + settings['dX_last'] = [10, 10, 10] + + settings['constraints'] = [dict(name='min_lay_length', index=0, threshold=20, offset='max'), + dict(name='max_offset' , index=0, threshold=60, offset='min'), + dict(name='max_offset' , index=0, threshold=35, offset='max')] + for j in range(len(settings['lineTypeNames'])): + settings['constraints'].append(dict(name='tension_safety_factor', index=j, threshold=2.0, offset='max')) + + + # run LineDesign + ld1 = LineDesign(depth, lineProps=moorprops_file, **settings) + ld1.setNormalization() + X, min_cost = ld1.optimize(maxIter=400, plot=False, display=2, stepfac=4, method='COBYLA') + ld1.updateDesign(X, display=0) + X1 = np.array(X)*ld1.X_denorm + X1[0] *= 10 + + assert abs(X1[0] - 1033.070) < 0.01 + assert abs(X1[1] - 1036.494) < 0.01 + assert abs(X1[2] - 102.920) < 0.01 + + + +def test_DEA_Chain_COBYLA_offset(): + + depth = 200 + + settings = {} + settings['rBFair'] = [58,0,-14] + settings['x_ampl'] = 10 + settings['fx_target'] = 1.95e6 + 465284.3 # from 0.71^2 m/s * 9.23e5 current + settings['kx_target'] = 2e4 + settings['headings'] = [60, 180, 300] + + settings['name'] = 'DEA-chain' + settings['lineTypeNames'] = ['chain'] + settings['anchorType'] = 'drag-embedment' + settings['allVars'] = [1000/10, 1000, 120] + + settings['solve_for'] = 'offset' + settings['x_target'] = 34.4 + settings['x_mean_out'] = 34.4 + settings['x_mean_in'] = 60 + + settings['Xindices'] = [0, 's', 1] + settings['Xmin'] = [10, 10] + settings['Xmax'] = [500, 500] + settings['dX_last'] = [10, 10] + + settings['constraints'] = [dict(name='min_lay_length', index=0, threshold=20, offset='max')] + for j in range(len(settings['lineTypeNames'])): + settings['constraints'].append(dict(name='tension_safety_factor', index=j, threshold=2.0, offset='max')) + + + # run LineDesign + ld2 = LineDesign(depth, lineProps=moorprops_file, **settings) + ld2.setNormalization() + X, min_cost = ld2.optimize(maxIter=400, plot=False, display=2, stepfac=4, method='COBYLA') + ld2.updateDesign(X, display=0) + X2 = np.array(X)*ld2.X_denorm + X2[0] *= 10 + + assert abs(X2[0] - 1033.144) < 0.01 + assert abs(ld2.ss.lineList[0].L - 1036.553) < 0.01 + assert abs(X2[1] - 102.869) < 0.01 + + +def test_DEA_Chain_COBYLA_tension(): + + depth = 200 + + settings = {} + settings['rBFair'] = [58,0,-14] + settings['x_ampl'] = 10 + settings['fx_target'] = 1.95e6 + 465284.3 # from 0.71^2 m/s * 9.23e5 current + settings['kx_target'] = 2e4 + settings['headings'] = [60, 180, 300] + + settings['name'] = 'DEA-chain' + settings['lineTypeNames'] = ['chain'] + settings['anchorType'] = 'drag-embedment' + settings['allVars'] = [1000/10, 1000, 120] + + settings['solve_for'] = 'tension' + settings['x_target'] = 34.4 + settings['x_mean_out'] = 34.4 + settings['x_mean_in'] = 60 + settings['fx_target'] = 570217.4 + + settings['Xindices'] = [0, 's', 1] + settings['Xmin'] = [10, 10] + settings['Xmax'] = [500, 500] + settings['dX_last'] = [10, 10] + + settings['constraints'] = [dict(name='min_lay_length', index=0, threshold=20, offset='max')] + for j in range(len(settings['lineTypeNames'])): + settings['constraints'].append(dict(name='tension_safety_factor', index=j, threshold=2.0, offset='max')) + + + # run LineDesign + ld3 = LineDesign(depth, lineProps=moorprops_file, **settings) + ld3.setNormalization() + X, min_cost = ld3.optimize(maxIter=400, plot=False, display=2, stepfac=4, method='COBYLA') + ld3.updateDesign(X, display=0) + X3 = np.array(X)*ld3.X_denorm + X3[0] *= 10 + + assert abs(X3[0] - 1033.145) < 0.01 + assert abs(ld3.ss.lineList[0].L - 1036.553) < 0.01 + assert abs(X3[1] - 102.869) < 0.01 + + + +''' +def test_DEA_Chain_Polyester_COBYLA_none(): + + depth = 200 + + settings = {} + settings['rBFair'] = [58,0,-14] + settings['x_ampl'] = 10 + settings['fx_target'] = 1.95e6 + 465284.3 # from 0.71^2 m/s * 9.23e5 current + settings['headings'] = [60, 180, 300] + + settings['solve_for'] = 'none' + settings['x_target'] = 35 + settings['x_mean_out'] = 35 + settings['x_mean_in'] = 60 + + settings['name'] = 'DEA-chain-rope' + settings['lineTypeNames'] = ['chain','polyester'] + settings['anchorType'] = 'drag-embedment' + + settings['solve_for'] = 'none' + settings['allVars'] = [750/10, 600, 100, 0, 100, 180] + settings['Xindices'] = [0, 1, 2, 'c', 3, 4] + settings['Xmin'] = [10, 10, 10, 10, 10] + settings['Xmax'] = [500, 1500, 300, 1000, 300] + settings['dX_last'] = [10, 10, 10, 10, 10] + + settings['constraints'] = [dict(name='min_lay_length', index=0, threshold=20, offset='max'), + dict(name='max_offset' , index=0, threshold=60, offset='min'), + dict(name='max_offset' , index=0, threshold=35, offset='max'), + dict(name='rope_contact' , index=1, threshold=5 , offset='min')] + for j in range(len(settings['lineTypeNames'])): + settings['constraints'].append(dict(name='tension_safety_factor', index=j, threshold=2.0, offset='max')) + + ld = LineDesign(depth, lineProps=moorprops_file, **settings) + ld.setNormalization() + X, min_cost = ld.optimize(maxIter=4000, plot=False, display=3, stepfac=4, method='COBYLA') + ld.updateDesign(X, display=0) + X = np.array(X)*ld1.X_denorm + X[0] *= 10 + + assert abs(X[0] - 986.242) < 0.001 + assert abs(X[1] - 856.697) < 0.001 + assert abs(X[2] - 106.746) < 0.001 + assert abs(X[3] - 131.426) < 0.001 + assert abs(X[4] - 179.636) < 0.001 +''' + + + diff --git a/tests/test_anchors.py b/tests/test_anchors.py index aa7b1c5d..d97e57c2 100644 --- a/tests/test_anchors.py +++ b/tests/test_anchors.py @@ -9,8 +9,8 @@ def assign_soil(anchor, soil_label, project): soil_def = project.soilProps[soil_label] layers = soil_def['layers'] - print('[DEBUG] assign_soil: soil_label =', soil_label) - print('[DEBUG] assign_soil: soil_def =', soil_def) + # print('[DEBUG] assign_soil: soil_label =', soil_label) + # print('[DEBUG] assign_soil: soil_def =', soil_def) profile_map = [{ 'name': 'CPT_Assigned', 'x': 0, 'y': 0, From a3a69dcd318fc3060f1c28042e949627b2b21c64 Mon Sep 17 00:00:00 2001 From: lsirkis Date: Thu, 6 Nov 2025 15:26:48 -0700 Subject: [PATCH 14/15] Remove extra getSizeAnchor functions to AnchorDesign_temp.py -- remove extra getSizeAnchor functions into temp storage in AnchorDesign_temp.py (new file, could eventually turn into a full anchor design script) -- small bug fixes in examples -- resolve remaining conflicts in geography.py --- examples/05_Anchors/anchor_soil_layered.py | 10 +- examples/05_Anchors/anchor_soil_uniform.py | 8 +- examples/05_Anchors/anchor_suction.py | 4 +- examples/05_Anchors/example_suction.ipynb | 3380 +------------------- famodel/anchors/AnchorDesign_temp.py | 669 ++++ famodel/anchors/anchor.py | 388 +-- famodel/geography.py | 370 --- 7 files changed, 771 insertions(+), 4058 deletions(-) create mode 100644 famodel/anchors/AnchorDesign_temp.py diff --git a/examples/05_Anchors/anchor_soil_layered.py b/examples/05_Anchors/anchor_soil_layered.py index def10f7d..714eaefd 100644 --- a/examples/05_Anchors/anchor_soil_layered.py +++ b/examples/05_Anchors/anchor_soil_layered.py @@ -1,9 +1,7 @@ -import sys -sys.path.append(r'C:\Code\FAModel_anchors\famodel') -from project import Project -from anchors.anchor import Anchor +from famodel import Project +from famodel.anchors.anchor import Anchor # Step 1: Initialize and load soil proj = Project() @@ -44,7 +42,7 @@ line_type=anchor.line_type, d=anchor.d, w=anchor.w, mass_update=True, plot=True) -anchor.getCostAnchor() +anchor.getCost() print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') results = anchor.getSizeAnchor( @@ -61,7 +59,7 @@ print('\nFinal Optimized Anchor:') print('Design:', anchor.dd['design']) print('Capacity Results:', anchor.anchorCapacity) -anchor.getCostAnchor() +anchor.getCost() print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') diff --git a/examples/05_Anchors/anchor_soil_uniform.py b/examples/05_Anchors/anchor_soil_uniform.py index 203f88b7..01886c7f 100644 --- a/examples/05_Anchors/anchor_soil_uniform.py +++ b/examples/05_Anchors/anchor_soil_uniform.py @@ -1,6 +1,6 @@ -from project import Project -from anchors.anchor import Anchor +from famodel import Project +from famodel.anchors.anchor import Anchor # Step 1: Initialize and load soil proj = Project() @@ -47,7 +47,7 @@ line_type=anchor.line_type, d=anchor.d, w=anchor.w, mass_update=True, plot=True) -anchor.getCostAnchor() +anchor.getCost() print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') results = anchor.getSizeAnchor( @@ -63,7 +63,7 @@ print('\nFinal Optimized Anchor:') print('Design:', anchor.dd['design']) print('Capacity Results:', anchor.anchorCapacity) -anchor.getCostAnchor() +anchor.getCost() print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]') diff --git a/examples/05_Anchors/anchor_suction.py b/examples/05_Anchors/anchor_suction.py index aa1b0e44..ccc4ea57 100644 --- a/examples/05_Anchors/anchor_suction.py +++ b/examples/05_Anchors/anchor_suction.py @@ -106,7 +106,7 @@ print(f'{key}: {value:.2f}') # --- Step 4: Compute Costs --- -anchor.getCostAnchor() +anchor.getCost() print(f"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg") print(f"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg") @@ -129,7 +129,7 @@ print('Capacity Results:', anchor.anchorCapacity) # # --- Step 6: Compute Costs --- -anchor.getCostAnchor() +anchor.getCost() print(f"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg") print(f"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg") diff --git a/examples/05_Anchors/example_suction.ipynb b/examples/05_Anchors/example_suction.ipynb index 39e9bd8b..34149187 100644 --- a/examples/05_Anchors/example_suction.ipynb +++ b/examples/05_Anchors/example_suction.ipynb @@ -25,22 +25,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "9f2d8d4b", "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'famodel'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mfamodel\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchors\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchor\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Anchor\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mfamodel\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchors\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchors_famodel\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msupport_plots\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m plot_suction\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'famodel'" - ] - } - ], + "outputs": [], "source": [ "from famodel.anchors.anchor import Anchor\n", "from famodel.anchors.anchors_famodel.support_plots import plot_suction" @@ -57,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "935551c4", "metadata": {}, "outputs": [], @@ -117,19 +105,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "3aab0b15", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'Anchor' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m anchor \u001b[38;5;241m=\u001b[39m \u001b[43mAnchor\u001b[49m(\n\u001b[0;32m 2\u001b[0m dd \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msuction\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2.5\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m12.0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8.67\u001b[39m}},\n\u001b[0;32m 3\u001b[0m r \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m250.0\u001b[39m, \u001b[38;5;241m250.0\u001b[39m, \u001b[38;5;241m0.0\u001b[39m]\n\u001b[0;32m 4\u001b[0m )\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug:\u001b[39m\u001b[38;5;124m'\u001b[39m, anchor\u001b[38;5;241m.\u001b[39mdd[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL:\u001b[39m\u001b[38;5;124m'\u001b[39m, anchor\u001b[38;5;241m.\u001b[39mdd[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", - "\u001b[1;31mNameError\u001b[0m: name 'Anchor' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "zlug: 8.67\n", + "L: 12.0\n" ] } ], @@ -153,19 +138,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "368fac90", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'anchor' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43manchor\u001b[49m\u001b[38;5;241m.\u001b[39minterpolateSoilProfile(profile_map)\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug:\u001b[39m\u001b[38;5;124m'\u001b[39m, anchor\u001b[38;5;241m.\u001b[39mdd[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzlug\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL:\u001b[39m\u001b[38;5;124m'\u001b[39m, anchor\u001b[38;5;241m.\u001b[39mdd[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdesign\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", - "\u001b[1;31mNameError\u001b[0m: name 'anchor' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "zlug: 8.67\n", + "L: 12.0\n" ] } ], @@ -186,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 15, "id": "71419ebe", "metadata": {}, "outputs": [ @@ -194,13 +176,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "zlug: 7.3994720299213235\n", - "L: 11.099208044881985\n" + "zlug: 8.67\n", + "L: 12.0\n", + "{'soil_type': 'clay', 'top': 1.75, 'bottom': 3.5, 'gamma_top': 8.25, 'gamma_bot': 8.75, 'Su_top': 12.5, 'Su_bot': 27.5}\n" ] }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -217,7 +200,8 @@ "print('zlug:', anchor.dd['design']['zlug'])\n", "print('L:', anchor.dd['design']['L'])\n", "# Access matched profile\n", - "layers = anchor.soil_profile\n", + "layers = anchor.soil_profile[0]['layers']\n", + "print(layers[0])\n", "z0 = layers[0]['top'] \n", "\n", "plot_suction(layers, L=L, D=D, z0=z0, zlug=zlug)\n", @@ -235,19 +219,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "38df38f6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zlug: 8.67\n", - "L: 12.0\n" - ] - } - ], + "outputs": [], "source": [ "anchor.loads = {\n", " 'Hm': 3e6,\n", @@ -269,23 +244,13 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 17, "id": "4ae865bd", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.67\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3375980.0732258284, thetaa = 55.648978744279006\n", - "Output Ha = 1904935.434154513, Va = 2787196.1621888806\n" - ] - }, { "data": { - "image/png": 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", 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Hj2P16tUoXLiw2vtJ9r///U/5c0lvBqucEBsbi127dqUoL1q0KFxcXDR+3YMHD8acOXNw6dKlVFdrXrRoERo2bIhGjRph0KBBcHBwwIcPH3D//n0cPHgQx44dSzdeIyMjLFmyBD179sTbt2/Rvn17WFpa4vXr17h27Rpev36NZcuWAQD++OMPeHl5wd3dHaNGjYJMJsPs2bNRqFChNK8cJYuJiYGDgwM6dOiAZs2awdbWFh8/fkRYWBgWLVqEChUqoG3btgAAPz8/LF26FD179sSjR4/g5OSEU6dOYcaMGfDx8Unxe0tEeUTu3sNNRJRSWrMwFSpUKEXb33//PdVZYWQymWBraysAECZMmJDqcT5+/Cj89ttvQrly5QQ9PT3B1NRUcHJyEkaMGCFERkYq2wEQhgwZkmL79evXC66uroKVlZWgp6cnFCtWTOjYsaPw77//qrR7/fq1MGzYMMHR0VHQ1dUVzMzMhBo1aggTJkwQPn78mOH7IZVKhfXr1wtNmzYVzMzMBB0dHaFo0aKCt7e3sGXLFkEmkynbzpo1S3BwcBDEYrFQoUIFYeXKlam+R1evXhUaNGggGBoaCgAEFxcXjeJNngFn7ty5KeIFIPz+++/K58+ePRPatWsnFClSRDA2Nha8vLyE69evp5hVJ7Wfe2ocHByEChUqZPi+JUtrFiZN+tO3XFxclDMMfftIfi/Vfd1fa9KkiWBmZibEx8enWh8RESH06dNHKF68uKCrqysULVpUqF+/vjBt2rQUr3fnzp2p7uP48eOCr6+vYGZmJujq6grFixcXfH19U7Q/cOCA4OzsLOjp6Ql2dnbCrFmz1Hp/EhMThXnz5gne3t6CnZ2dIBaLBX19faFChQrC2LFjhTdv3qi0f/PmjTBw4EDBxsZG0NHREezt7YXx48cLCQkJKu04CxNR3iEShM9TIRAREeVx//77r3LGosGDB+d2OFkqKioK9vb2+PnnnzFnzpzcDoeIKE1MIIiIKM978OABHj9+jF9//RVPnjzB/fv3YWhomNthZYlnz57h4cOHmDt3Lo4dO4a7d++iePHiuR0WEVGauA4EERHleX/88Qfc3d3x8eNH7Ny5s8AkD4DixuomTZrgxo0b2Lx5M5MHIsrzeAWCiIiIiIjUVqCuQEyePBkikUjlkdGiN8ePH0eNGjWgr6+PkiVLYvny5TkULRERERFR/lPgpnGtVKkSjhw5onyura2dZtuIiAj4+PigX79+2LRpE06fPo3BgwejaNGiaNeuXU6ES0RERESUrxS4BEJHRyfDqw7Jli9fDjs7OyxcuBAAUKFCBVy8eBHz5s1jAkFERERElIoCl0Dcu3cPxYoVg1gsRp06dTBjxgyULFky1bbh4eHw8PBQKfP09MTq1ashkUigq6ub6naJiYlITExUPpfL5Xj79i3Mzc1VVoAlIiIiIsoPBEHAhw8fUKxYMWhppX+XQ4FKIOrUqYMNGzagbNmyePXqFaZNm4b69evjxo0bMDc3T9E+MjISVlZWKmVWVlaQSqWIjo6GjY1NqseZOXMmpkyZki2vgYiIiIgotzx9+hQlSpRIt02BSiC8vb2V/3ZyckK9evVQqlQprF+/HiNHjkx1m2+vGCRPSpXelYTx48er7C8mJgZ2dnaIiIiAsbHx97wEJYlEgtDQULi6uqZ5JYQKNvYBYh8g9gFiH6Cc6gMfPnyAo6OjWueyBSqB+FahQoXg5OSEe/fupVpvbW2NyMhIlbKoqCjo6OikesUimVgshlgsTlFuZmYGExOT7wv6M4lEAkNDQ5ibm/MD4wfFPkDsA8Q+QOwDlFN9IHnf6gzHL1DTuH4rMTERt27dSnMoUr169RASEqJSFhwcjJo1a/KXlIiIiIgoFQUqgRg9ejSOHz+OiIgInDt3Du3bt0dsbCx69uwJQDH0yM/PT9l+4MCBePz4MUaOHIlbt25hzZo1WL16NUaPHp1bL4GIiIiIKE8rUEOYnj17hi5duiA6OhpFixZF3bp1cfbsWdjb2wMAXr58iSdPnijbOzo6IiAgACNGjMDSpUtRrFgxLF68mFO4EhERERGloUAlENu2bUu3ft26dSnKXFxccPny5WyKiIiIiIioYClQQ5iIiIiIiCh7MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1MYEgIiIiIiK1FagEYubMmahVqxaMjY1haWmJ1q1b486dO+luExYWBpFIlOJx+/btHIqaiIiIiCj/KFAJxPHjxzFkyBCcPXsWISEhkEql8PDwQFxcXIbb3rlzBy9fvlQ+ypQpkwMRExERERHlLzq5HUBWCgwMVHm+du1aWFpa4tKlS2jcuHG621paWqJw4cLZGB0RERERUf5XoK5AfCsmJgYAYGZmlmHbatWqwcbGBm5ubggNDc3u0IiIiIiI8qUCdQXia4IgYOTIkWjYsCEqV66cZjsbGxv8888/qFGjBhITE7Fx40a4ubkhLCwszasWiYmJSExMVD6PjY0FAEgkEkgkkiyJP3k/WbU/yn/YB4h9gNgHiH2AcqoPaLJ/kSAIQjbGkmuGDBkCf39/nDp1CiVKlNBo2xYtWkAkEuHAgQOp1k+ePBlTpkxJUb5lyxYYGhpmKl4iIiIiotwSHx+Prl27IiYmBiYmJum2LZAJxM8//4x9+/bhxIkTcHR01Hj76dOnY9OmTbh161aq9aldgbC1tUV0dHSGb7i6JBIJQkJC4O7uDl1d3SzZJ+Uv7APEPkDsA8Q+QDnVB2JjY2FhYaFWAlGghjAJgoCff/4Ze/fuRVhYWKaSBwC4cuUKbGxs0qwXi8UQi8UpynV1dbP8B5sd+6T8hX2A2AeIfYDYByi7+4Am+y5QCcSQIUOwZcsW7N+/H8bGxoiMjAQAmJqawsDAAAAwfvx4PH/+HBs2bAAALFy4EA4ODqhUqRKSkpKwadMm7N69G7t3786110FERERElFcVqARi2bJlAIAmTZqolK9duxa9evUCALx8+RJPnjxR1iUlJWH06NF4/vw5DAwMUKlSJfj7+8PHxyenwiYiIiIiyjcKVAKhzu0c69atU3k+duxYjB07NpsiIiIiIiIqWAr0OhBERERERJS1mEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHamEAQEREREZHaCmQC8ffff8PR0RH6+vqoUaMGTp48mW7748ePo0aNGtDX10fJkiWxfPnyHIqUiIiIiCh/KXAJxPbt2zF8+HBMmDABV65cQaNGjeDt7Y0nT56k2j4iIgI+Pj5o1KgRrly5gl9//RXDhg3D7t27czhyIiIiIqK8r8AlEH/++Sd++ukn9O3bFxUqVMDChQtha2uLZcuWpdp++fLlsLOzw8KFC1GhQgX07dsXffr0wbx583I4ciIiIiKivE8ntwPISklJSbh06RJ++eUXlXIPDw+cOXMm1W3Cw8Ph4eGhUubp6YnVq1dDIpFAV1c3xTaJiYlITExUPo+NjQUASCQSSCSS730Zyn19/X/68bAPEPsAsQ8Q+wDlVB/QZP8FKoGIjo6GTCaDlZWVSrmVlRUiIyNT3SYyMjLV9lKpFNHR0bCxsUmxzcyZMzFlypQU5cHBwTA0NPyOV5BSSEhIlu6P8h/2AWIfIPYBYh+g7O4D8fHxarctUAlEMpFIpPJcEIQUZRm1T6082fjx4zFy5Ejl89jYWNja2sLDwwMmJiaZDVuFRCJBSEgI3N3dU70KQgUf+wCxDxD7ALEPUE71geQRNeooUAmEhYUFtLW1U1xtiIqKSnGVIZm1tXWq7XV0dGBubp7qNmKxGGKxOEW5rq5ulv9gs2OflL+wDxD7ALEPEPsAZXcf0GTfBeomaj09PdSoUSPFJZ6QkBDUr18/1W3q1auXon1wcDBq1qzJX1QiIiIiom8UqAQCAEaOHIlVq1ZhzZo1uHXrFkaMGIEnT55g4MCBABTDj/z8/JTtBw4ciMePH2PkyJG4desW1qxZg9WrV2P06NG59RKIiIiIiPKsAjWECQA6deqEN2/eYOrUqXj58iUqV66MgIAA2NvbAwBevnypsiaEo6MjAgICMGLECCxduhTFihXD4sWL0a5du9x6CUREREREeVaBSyAAYPDgwRg8eHCqdevWrUtR5uLigsuXL2dzVERERERE+V+BG8JERERERETZhwkEERERERGpjQkEERERERGpjQkEERERERGpjQkEERERERGprUDOwkRERERElGcJAhAdDdy5A9y9q3jcuQM8fAicOwfo6+d2hOliAkFERERElB3i4oB7974kCHfvAjdvKv7/8WPK9tWrA2/eAMWL53ysGmACQURERESUWRIJ8OjRlysJyUnC7dtAVNSXdjo6iisPMlnKfXh7A7/8AjRqBIhEORZ6ZjGBICIiIiJKjyAAkZEphxzduAE8efIlKdDSArS1FUnFt6TSL//W1lYkCt26AWPGAJUq5czryCJMIIiIiIiIkj19Cpw8+SVZuHEDuH8f+PRJUS8SKa4mSKWKxOJrcrnikRYtLcX9DYMHA8OH5/mhSmlhAkFERERElGzqVGDVKsXJPpAyIRCE1K8wpEUkUmxTtCgwejQwYABgapp18eYCTuNKRERERJRs4EDAySnjqwkZ0dZW/L90aWDNGsWVjbFj833yADCBICIiIiL6okYN4PJl4M8/FUOVNJW8TZ06wMGDipupe/cGxOKsjTMXcQgTERERERGgGGp08CAwciTw4IFqXfJQpLTo6Chupm7eHBg3DqhbN3tjzUW8AkFEREREP66nTwE/P0WCoKUFtGr1JXmwsgK2bAF27VLcw6CVyqmztjagq6u4ynD7NrB3b4FOHgAmEERERET0I5FKgeXLAQMDRdJgZwds3PilfuhQxfoNyVO3dukCtGunmJFp0KAviYZIBBgbK9ZvePoU+OcfoGzZ3HtdOYhDmIiIiIioYLt6VTED0tGjKeuqVVPc79CkSfr7MDUF/vpLcbViwgTFUKWffgKMjLIj4jyNCQQRERERFSwfPwJz5gB//JF6/fTpinUYDA0133ft2kBIyHeFl98xgSAiIiKi/E0QgKAgxc3Pt26lrPf1BWbPzncrPudVTCCIiIiIKP95+RKYOBFYvTplXZEiimFJPXp8WY+BsgwTCCIiIiLK+2QyYP16xVWGmJiU9f36AVOmADY2OR/bD4YJBBERERHlTTduAGPGAIcPp6yrWFFxlcHDQzEjEuUYJhBERERElDfExwMLFgC//ZZ6/aRJioTiB5z5KC9hAkFEREREuUMQgLAwxbCkq1dT1jdrBsydC1StmsOBUXqYQBARERFRznn9Gpg8Gfj775R1BgaKKxA//QTo8DQ1r+JPhoiIiIiyj1wObNsGjBihWOH5Wz16KNZlsLXN+dgoU5hAEBEREVHWunsXGDcO2LcvZV2pUoqbn1u04M3P+RQTCCIiIiL6PgkJwF9/KW5wTs24ccAvvwCFC+doWJQ9mEAQERERkeZOnwZGjQLOnUtZ16gRMG8eULt2zsdF2Y4JBBERERFl7O1bYNo0xU3O39LSUpQPHAjo6eV8bJSjtHI7ACIiIiLKgwQB2LULsLNT3Ktgbq6aPHTsCDx4oGgnkwHDhhXs5GHZMsDZGTAxUTzq1Ut9gbtvJSYCEyYA9vaAWKy4B2TNmi/1N24A7doBDg6K93nhwoz3GRamaFukiGL42NfOn1fUfX1/SXL7ypUVP6uvFS4MrFuX8TG/wgSCiIiIiBQiIoDOnRUnm1paQIcOwNOniroSJYAdOxSzKgkCsH07ULJk7sabk0qUAGbNAi5eVDyaNgVatVIkAOnp2BE4ehRYvRq4cwfYuhUoX/5LfXy84n2cNQuwttYsJmNjYO9e1bI1axRJX2oePAA2bNDsGKlgAkFERET0o0pKAhYvVqy5IBIpTmS3b/9SP3w4EB2tSBiePlUkFD/qzEktWgA+PkDZsorH9OmKFbHPnk17m8BA4PhxICBAsSieg4PivpD69b+0qVVLsVhe586KKxSa6NlT9WrGp0+KKXN79ky9/c8/A7//nvKqhYaYQBARERH9SC5cABo3ViQCYjHwv/99GdZSuzZw8qQiYRAExZAlc/PcjTcvkskUJ+pxcYqhTGk5cACoWROYMwcoXlyReIwerTjRzwo9eih+Xk+eKJ7v3q1IUqpXT7398OGAVKqYMes7MIEgIiIiKshiYhRTqCaPi09OEpLNmaM4oRUExYxKDRvmXqx53X//Ka46iMWKG8b37gUqVky7/cOHwKlTwPXrirYLFyruKxkyJGvisbQEvL2/3MOwZg3Qp0/a7Q0NFVcgZs5U9ItMYgJBRERElBfJZBAdP47iJ05AdPx4yptf0yIIwMGDQOnSioShcGFg9uwv9a1aAbdvf7nKMGYMoK+fLS+hwClXDrh6VTFsadAgxVChmzfTbi+XK34GmzcrEjcfH8UieuvWZd1ViD59FPt7+BAIDwe6dUu//U8/ARYWqn1CQ0wgiIiIiPKaPXsABwfouLuj5p9/QsfdXTE0Zc+e1Ns/fQr4+X25+bllS8UNs4DiZHHjRkUCIgiK1aHLlcupV1Kw6OkpErOaNRXf4lepAixalHZ7GxvF0CVT0y9lFSoofg7PnmVNTD4+insafvpJcZ9GRkPOdHQU0/EuWgS8eJGpQzKBICIiIspL9uwB2rdPeYL5/LmifM8exTj2FSsUQ1JEIsWsOxs3fmk7aBAQGak4UX39GujeXZFYUNYSBMU0rWlp0EBxkv7x45eyu3cVP4sSJbImBm1txb0QYWHpD1/6WocOQKVKwJQpmTokF5LLQnFxip/ht7S1Va8MxsWlvQ8tLcDAQLWtrq56bePjFf04NSKR4jMmM20/fVJcgUtLoUKZa5uQkP7VWE3aJn9+AorfY6k0a9oaGHz5vE1KAiSSrGmrr/+lr6TVViIBEhK0IZN96QMSiaJ9WsRixRcLmraVStP//NPT+xKDJm1lsvQnetDV/TJluCZt5fL0r/xq0lZH58ukF4Kg+N3Iiraa/N6n1Ta5D3z9OZDaZ0Ra+BnxRX7+jJBIROn+LVBty88IIJ9/RshkMBj2P4gEASnmOvr8Sylv1x6foA9A9PlhCFR2AmbOhFbTJjAwFKXcbyr4GfGFOr/3upN/hczdG/plbCH6+AHYtg1CWBgS9gVC/vl91v19PEQvniNppWKqVIPOXaH1xx9A795I+m0KZJHREI8eA5lfHyTJDYA4AElJ0Lp9E2IxoJWUBDx/DsmFq5CIjSCUKp3q3wJ9GZB8ypmUBEh++QMYPEZx9SEO0E4A9KH4+evrf2krkQBJn2PV+n0W9Ft7KvaRmH5fSUHIhKSkJOHJkyfC7du3hTdv3mRmFwVKTEyMAEAAYoQvAwq/PHy8ZYJcJlE+DA3lqbYDBMHFRS7IZRIhMSFe2Ldvn2BhkXbbmjXlKvu1t0+7bcWKqm0rVky7rb29atuaNdNua2Gh2tbFJe22hoaqbX28ZWm2BQSVtu3bpd/2Q+yXtj390m/7KvJL20GD0m/78MGXtqNGpd/2v3+/tJ00Kf22585+aTt7tjTdtsFBCcq2S5ak3/bgAamy7ZrV6bfdvu1L2+3b0m+7ZvWXtgcPpN92yZIvbY8dTb/t7Nlf2p47K0m37aRJX36P/vs3/bajRn1p+/BB+m0HDfrS9lVk+m17+n1p+yE2/bbt26n+3qfXNjOfEckPfkYU7M+IxIR4oWfP6+m2PXb0y+8RPyPy/2eEC0LT3uirhwtCU63iZ8SXR1Z/RqxCHyEC9oJcT0+QFy0qyN2aCouaB6q0WYueQihclM8fPpAI8hv/CXI3NyFRx0B4ghLCPIwU9BH/5f1CRKpBfb2fbx83lx4RBECQv3md6nlEK+wVBEAAPn9GHFW0XzE7WqVdIDwEARB6Yq2gOI+FEBMTk+G5r9pXID5+/IjNmzdj69atOH/+PBK/+nqhRIkS8PDwQP/+/VGrVi0N0pcfhCwJiHv7VYE1kPJ7ha/avgGkn9NkQY4veeO3bSVAXPSX54Il0ryoJJcCca+/el4UQBpfZwkyIC7qq+NYAEhjZUlB/k1bcwBpzWEsfNPWDIr8OA0qbYsAMEizqeK1CYp/SwsDMEy7bXw0EPf56w2JKYBC6bR9A8R9/llITAAYpd3201sg7vNXFknGAIzTafsOiPv89WNSIQCmabdNjPmy30RDAIXTbpvwHoj7/LuZZACgSAb7/fx1XqI+ALO02ybFAnGfv6JLEANIZ3xl4gcg7vPXbgl6ACzS2e/HL195fNIFUDSdtnFA3IfPbXUAWKbdVhIPxMUq/h2vDcAqnbafgLjPM1HEa0Hx+5kGaQIQ917x7zgRAJu028oSgbh3XxUUS6dtJj4jkglW4GcECu5nhFSNm2YT3gFxny8l8DPic9v8+xlhg5dpb/OVNNvxM+KLLP6M6IvVAIBXtyNR1ELxGXF7pOrf795Yp7pR/BvA3gzYuwG//maC+YtTfkY8hgNEEPDfuShUqqD4jJg8wxhTZ6X9GfGhojOE2OcAkhS/J9+cR+xHa4iSX0/CO6BRBQixz5H0j2pf8ELQV89i0zzet0SCIAgZNVqwYAGmT58OBwcHtGzZErVr10bx4sVhYGCAt2/f4vr16zh58iT27t2LunXrYsmSJShTpozaQeR3sbGxMDU1xfO792BikvKHnZkhTBKpFIePX0TjmrWgq5P6Lz4vPX6Rn4cnpDmESSpD8KnLaNmsGvTFig90Dk9Q/DtfD0/QoG1yH/BoWF35OcAhTJlrm18/IyRSKQ4cuQS3ejXT/FvAIUwp2+bnzwjt06dh0K5d2ht+9mn3bsgaNEhRzs+IzLXNy58Rqf0tUOc8Ipm6nxGxsR9QvGwZxMTEwMTEJO0dQs17IM6cOYPQ0FA4OTmlWl+7dm306dMHy5cvx+rVq3H8+PEfKoFIVqgQUMg4jSz/63bpfOmkpCV8bquV5h+Nbxmm88XX97Q1SOfLt+9pq5/OF4Df01ZskPZ3F9/TVk9f8cipthKpAH19GbR1tQEtRR/QFSse6tCkrY6e4pHVbbW1gEJpfEH1PW21tNT8PdKwrQjZ0xaZbJvcB9L7HNBkv/yMUMhXnxFaAnR1BbX/FvAzQiEvf0Z8+vQJJ06FI+hIKJwqVUBvv67A123dG0AoXgx48RKiVM7QBZEIKF4M+u4NUr/x8hv8jNC8bV77jMjob4Em+03v917dWYIBNROInTt3qrUzsViMwYMHq390IiIiogJMEATcuXsfgSFHERRyDMdPhSPh86WUJo0bKBMIJW1tYO50oFsfCCKRShIhJH/tPWeaWskDUXbhLExEREREWSg29gOOhp1AYMgxBB8JxeMnT1Xqixezgad7U/h6uae+g1bNgc1rgDETgOcvvt5QkTy0ap6N0RNlTOMEIiEhAUuWLEFoaCiioqIg/2ag2uXLl7MsOCIiIqK8Ti6X4+q1/xB45BiCQo4h/NxFSL8aGK+npweXhvXh0cwVXu5NUbFCOYhEaUyUkKxVc6C5NyQnTuPasdOo0rQBdBqrN2yJKLtpnED06dMHISEhaN++PWrXrp3xLwARERFRARMd/QbBR8MQFHIMQUdCEfX6tUp92TKl4NmsKTybucKlUX0UKqTB4P5k2toQGtXHc6k2nBvVYfJAeYbGCYS/vz8CAgLQIJU7/4mIiIgKIqlUivMXLyMwRHGV4eLlq/h6Iksjo0Jwa9JYeZXB0cE+F6Mlyl4aJxDFixeHsbEGt/QTERER5UMvXkZ+vsJwDCHHjuPdu/cq9VWcKsHTvSm83N1Qv24t6OmpOf0UUT6ncQIxf/58jBs3DsuXL4e9PbNrIiIiKhgkEglOh59HYMhRBAYfw7/Xb6jUFylSGB5uTeDZrCk8mrmimE06i8oRFWAaJxA1a9ZEQkICSpYsCUNDQ+jqqk7K/Pbt2zS2JCIiIspbnjx9hsCQYwgMPoqjYSfw4cNHZZ1IJEKtGtXg6d4U3h5uqFWjGrR5HwKR5glEly5d8Pz5c8yYMQNWVla8iZqIiIjyjcTERJw8fRaHg48i6Mgx3Lx1R6W+qIUFPJu5wsvDDe5NXVC0qEUuRUqUd2mcQJw5cwbh4eGoUqVKdsRDRERElKUiHj1WXmU4dvwk4uLilXVaWlqoW7sGvDzc4OXuhupVnaGlpZWL0RLlfRonEOXLl8enT5+yIxYiIiKi75aQkIATp8JxOPgoAkOO4s7d+yr11laW8HJv+vkqQxMUKVI4dwIlyqc0TiBmzZqFUaNGYfr06XByckpxD4SJiUmWBUdERESkjocRjxQJQ/BRhJ44jfj4L1cZtLW1Ub9uLXi5u8Hbww1VnCtzCDbRd9A4gfDy8gIAuLm5qZQLggCRSASZTJY1kRERERGlITExESdOhSMg6EiqVxmK2Vgrb35u5uqCwoVNcylSooJH4wQiNDQ0O+L4bo8ePcIff/yBY8eOITIyEsWKFUP37t0xYcKEdOdl7tWrF9avX69SVqdOHZw9eza7QyYiIiINPHr8BIeDj+Jw0BEcO34qxVWGBvVqK68yODtV4lUGomyicQLh4uKSHXF8t9u3b0Mul2PFihUoXbo0rl+/jn79+iEuLg7z5s1Ld1svLy+sXbtW+ZwLwRAREeW+pKQknDx9VnmV4dbtuyr1NtZW8PJw41UGohymVgLx5MkT2NnZqb3T58+fo3jx4pkOKjO8vLyUw6sAoGTJkrhz5w6WLVuWYQIhFothbc3FYIiIiHLbs+cvlFcZjoQex8ePcco6bW1t1KtTE94ezXgvA1EuUiuBqFWrFlq2bIl+/fqhdu3aqbaJiYnBjh07sGjRIgwYMAA///xzlgaaGTExMTAzM8uwXVhYGCwtLVG4cGG4uLhg+vTpsLS0TLN9YmIiEhMTlc9jY2MBABKpFBKp9PsD/7yvr/9PPx72AWIfoB+hD0ilUpw9fxGBwUcRGHIM/12/qVJvZVkUHs1c4eXuBjfXxiozJkl/gPsuf4Q+QOnLqT6gyf5FgiAIGTV6+/YtZsyYgTVr1kBXVxc1a9ZEsWLFoK+vj3fv3uHmzZu4ceMGatasid9++w3e3t7f9QKywoMHD1C9enXMnz8fffv2TbPd9u3bYWRkBHt7e0RERGDixImQSqW4dOkSxGJxqttMnjwZU6ZMSVG+ZcsWGBoaZtlrICIiKojev3+Py5cv49KlS7hy5YrKvQwikQhly5ZF9erVUaNGDZQsWZLrMhDlgPj4eHTt2hUxMTEZzqqqVgKRLCEhAQEBATh58iQePXqET58+wcLCAtWqVYOnpycqV6783cF/K62T9a9duHABNWvWVD5/8eIFXFxc4OLiglWrVml0vJcvX8Le3h7btm1D27ZtU22T2hUIW1tbvH58CyaFC2t0vLRIpFIcOXkJzRrVgK6OxreqUAHAPkDsA1RQ+oBcLselK9cQ+Hlo0qUr11Tqzc2KKK4yeLihWVMXWJib51KkeU9B6QOUeTnVB2Lfv0dR+wpqJRAaRaGvr4+2bdumeWKdHYYOHYrOnTun28bBwUH57xcvXsDV1RX16tXDP//8o/HxbGxsYG9vj3v37qXZRiwWp3p1QldHJ8t/sNmxT8pf2AeIfYDyYx94/z4GQUeO4XDQERwOPobX0dEq9dWrOsPHyx0+ns1Qq0Y1aGtr51Kk+UN+7AOUtbK7D2iy7zzfEy0sLGBhYaFW2+fPn8PV1RU1atTA2rVrM3XJ882bN3j69ClsbGw03paIiOhHJQgCrt+4hYCgIwgICsGZsxdU1oYyMTGGh1sT5Q3Q1tZWuRgtEX2PPJ9AqOvFixdo0qQJ7OzsMG/ePLx+/VpZ9/UMS+XLl8fMmTPRpk0bfPz4EZMnT0a7du1gY2ODR48e4ddff4WFhQXatGmTGy+DiIgo34iPj8ex46fgHxiCw0FH8OTpM5X6ihXKwdujGXy93NGgXm3o6urmUqRElJUKTAIRHByM+/fv4/79+yhRooRK3de3edy5cwcxMTEAFNPB/ffff9iwYQPev38PGxsbuLq6Yvv27TA2Ns7R+ImIiPKDR4+fwD8wBAGBIQg9cRoJCQnKOn19fTR1aagcmuRgr/4U8ESUfxSYBKJXr17o1atXhu2+TiYMDAwQFBSUjVERERHlbxKJBGfOnod/oGJo0s1bd1Tq7WxLwMfLHb5ezeDauCFnIyT6ARSYBIKIiIiyRnT0GwSGHIN/YDCCjoTi/fsYZZ22tjbq160FH093+Hq5o1LF8lzMjegHk6kE4u7duwgLC0NUVBTkcrlK3aRJk7IkMCIiIsoZgiDgv+s34R8YAv/AYJw9f0nl77u5uRm83d3g4+UOz2auKou5EdGPR+MEYuXKlRg0aBAsLCxgbW2t8q2DSCRiAkFERJQPfPr0CaHHT+FQYAj8Dwfj6bPnKvVVnCrBx8sdzb09ULtmdU6zSkRKGicQ06ZNw/Tp0zFu3LjsiIeIiIiyyfMXL+EfGIJDAUE4GnYSnz59Utbp6+ujmWvjz/czuMO2RPFcjJSI8jKNE4h3796hQ4cO2RELERERZaHkFaAPHQ7GoYAgXLn2n0q9bYni8PX2QHMvd7i6NISBgUEuRUpE+YnGCUSHDh0QHByMgQMHZkc8RERE9B0+fvyIkGPHcehwMAICQ/Aq6su6SCKRCHVr14Svtzuae3nAqXJF3gBNRBpTK4FYvHix8t+lS5fGxIkTcfbsWTg5OaVYFGbYsGFZGyERERGl68nTZzgYEIRDAUEIPXEaSUlJyjpjYyN4NnOFr5cHvD3cYGlZNBcjJaKCQK0EYsGCBSrPjYyMcPz4cRw/flylXCQSMYEgIiLKZnK5HBcuXfmcNATj3+s3VOpLOjqgubcHmnt7oHHDetDT08ulSImoIFIrgYiIiMjuOIiIiCgdcXFxCDl2HAcDglIMTdLS0kL9urXQ3NsTLXw8Ub5cGQ5NIqJso/E9EFOnTsXo0aNTrDT56dMnzJ07l9O4EhERZZFnz18gMDAQy5YuRtiJ00hMTFTWGRsbwcu9KZp7e8Lbww0WFua5GCkR/Ug0TiCmTJmCgQMHpkgg4uPjMWXKFCYQREREmSQIAq5c/RcHDwfjoH8gLl/9V6Xe0cEezb090MLHk0OTiCjXaJxACIKQ6mXRa9euwczMLEuCIiIi+lEkJiYi9PgpHPh8E/Sz5y+UdSKRCGXLlkWPzu3Qqrk3KlYox6FJRJTr1E4gihQpApFIpPww+/oDTCaT4ePHj5zalYiISA3R0W/gHxiCgwGBCDoSiri4eGWdoaEhPNyaoIWPJzzcm+LS9Qfwdq0DXR2Nv/MjIsoWan8aLVy4EIIgoE+fPpgyZQpMTU2VdXp6enBwcEC9evWyJUgiIqL87t79B9h/KBAHAwJxOvw85HK5sq6YjTWa+3iipY8nmjZpBH19fQCARCoF8CCXIiYiSp3aCUTPnj0BAI6Ojqhfv36K9R+IiIjoC5lMhnMXLuGAfyAO+Afi9p17KvVVnSujha8XWvp6oXpVZw5NIqJ8Q+ProS4uLpDJZNi1axdu3boFkUiEChUqoFWrVtDh5VUiIvqBxcfHI+TYcRzwD4T/4RBEvf4y1aqOjg5cGzdEC1/FVKv2dra5GCkRUeZpfMZ//fp1tGrVCpGRkShXrhwA4O7duyhatCgOHDgAJyenLA+SiIgor3r9OhqHDgfjgH8ggo+G4dOnT8o6U1MT+Hg0Q8vmXvByd4OpqUkuRkpElDU0TiD69u2LSpUq4eLFiyhSpAgA4N27d+jVqxf69++P8PDwLA+SiIgoL0m+n+GA/2GcOXtB5X4GO9sSaNncC618vdG4YT0O+SWiAkfjBOLatWsqyQOgmKFp+vTpqFWrVpYGR0RElBfI5XJcuHQF+w8dxgH/QNy8dUelvloVJ7Rs7o1Wvl6o4lyZ9zMQUYGmcQJRrlw5vHr1CpUqVVIpj4qKQunSpbMssPxIkCRCSIrPuKE6+5LKFP9P+gRBrp0l+6T8hX2A2AdyV1JSEkJPhmO/fzAOHA7Gy8goZZ2Ojg5cGtRBSx93tPByh71diS8bSj5ByKIY2AeIfYByqg8IksSMG32mcQIxY8YMDBs2DJMnT0bdunUBAGfPnsXUqVMxe/ZsxMbGKtuamPxgYz3fRwCyQlmzL9nnPz/vIgBtfpP1Q2IfIPaBHBf7IQ6BJy5gf8gpBISeQ+zHOGWdUSEDeLnURiv3hvB2qY0ipsafaxKBt9k01Sr7ALEPUE71gQ9xGbf5TOMEonnz5gCAjh07Ki/RCoLihbVo0UL5XCQSQSaTabr7/E1sBBgUzpp9yeQAPij2p62VNfuk/IV9gNgHckRkVDQOBB/H/sBQHDt1HklJEmWdVVFztPRsglZeTdC0QW2IxXo5Gxz7ALEPUE71gST1kxONE4jQ0FBNN/lxaIsh0tHPkl2JoEi+RNpiiHR4yfJHxD5A7APZ5/7Dx9gbcAT7Ao7g7KVryi/CAKBMSXu09mmG1t5uqFOjCrS0cu+kjX2A2Acox/qAtljtpplaB4KIiCg/EQQBV6/fwl7/I9h3+Aiu31Jd1K12dWe08nZDa283lC9TkjdBExGlI1Mrv508eRIrVqzAw4cPsXPnThQvXhwbN26Eo6MjGjZsmNUxEhERaUwmk+HUuUvYd/go9gUcweOnL5R1Ojo6aFK/Flp5u6GVtxtKFLPOxUiJiPIXjROI3bt3o0ePHujWrRsuX76MxETFHdsfPnzAjBkzEBAQkOVBEhERqSMxMQlHT4Zjj38IDgQeQ/Sbd8o6AwN9eLk2RGufZmju0QRFCpvmYqRERPmXxgnEtGnTsHz5cvj5+WHbtm3K8vr162Pq1KlZGhwREVFGPn6Mw+FjJ7HXPwT+Icfx4auZk4oUNkULjyZo7dMMHk0awNDQIBcjJSIqGDROIO7cuYPGjRunKDcxMcH79++zIiYiIqJ0vXn7DgeDw7Av4AiCQk8hMTFJWVfM2hKtfZqhjU8zNK5XkytBExFlMY0TCBsbG9y/fx8ODg4q5adOnULJkiWzKi4iIiIVLyKjsC/gCPb6hyDszAWVqcJLOdihbXN3tPFphtrVnXN15iQiooJO4wRiwIAB+N///oc1a9ZAJBLhxYsXCA8Px+jRozFp0qTsiJGIiH5Qj548x+5DwdjrH4IzF66o1DlXLIc2vu5o6+uOyhXKcOYkIqIconECMXbsWMTExMDV1RUJCQlo3LgxxGIxRo8ejaFDh2ZHjERE9AO5fe+hMmm4/O9Nlbq6Naook4ZSjna5FCER0Y8tU9O4Tp8+HRMmTMDNmzchl8tRsWJFGBkZZXVsRET0AxAEAdeu38Ye/xDs8Q/GzTsPlHVaWlpoXK8m2jb3QGtOt0pElCdkKoEAAENDQ9SsWTMrYyEioh+EIAi4dO0Gdh0Mwu6DwXjw6ImyTldXF26N6qKNbzO08nKDZVHzXIyUiIi+pVYC0bZtW7V3uGfPnkwHQ0REBZdcLse5S9ew62Aw9vgHqyzspq8vhmeThmjb3B0tPF1R2NQkFyMlIqL0qJVAmJp+WWxHEATs3bsXpqamyisQly5dwvv37zVKNIiIqOCTyWQ4ff4ydh1U3NPw/OUrZZ2hoQF8m7mgXQsP+Lg1hpFRoVyMlIiI1KVWArF27Vrlv8eNG4eOHTti+fLl0NbWBqD4AzF48GCYmPAbIyKiH51MJsOJ8IvYeSAQe/2P4NXraGWdsVEhtPBwRbsWHvB0bciF3YiI8iGN74FYs2YNTp06pUweAEBbWxsjR45E/fr1MXfu3CwNkIiI8j6pVIrjZy5g54Eg7As4gqjoN8q6wqYmaOXVFO1aeKBZ4/rQ1xfnYqRERPS9NE4gpFIpbt26hXLlyqmU37p1C3K5PMsCIyKivE0qlSL01DnsPKhIGqLfvFPWmRUxRStvN7Rv4Qm3RnWhp6eXi5ESEVFW0jiB6N27N/r06YP79++jbt26AICzZ89i1qxZ6N27d5YHSEREeYdEIkHoqfPYeTAQ+wKO4M3b98o6c7PCaO3TDB1aeMG1YW3o6urmXqBERJRtNE4g5s2bB2trayxYsAAvX74EANjY2GDs2LEYNWpUlgdIRES56+srDXv9Q1SSBgvzImjj444OLT3hUr8WkwYioh+AxgmElpYWxo4di7FjxyI2NhYAePM0EVEBI5VKEXb6PHYeCMLegBCV4UlFLcxUkgYdnUwvKURERPnQd33qM3EgIio4pFIpToRfxI79gdjjH6ySNFiYF0FbXw8mDURE9H0JBBER5W8ymQynzl3Cjv2B2H0wWGX2JHOzwmjj646OLb3QpEFtJg1ERASACQQR0Q9HLpfj7MVr2L7/MHYdCMTLV6+VdWZFTFWSBt7TQERE32ICQUT0AxAEAReu/Ift+w5j18EgPH3+UllnamKMNj7N0LG1N9wa1WXSQERE6WICQURUQAmCgH9v3MG2fQHYse8wIp48U9YZFTJEKy83dGztDY8mDSAWc50GIiJSj1oJxOLFi9Xe4bBhwzIdDBERfb879yOwbW8Atu8LwO17D5XlhoYGaOHRBB1becOraSMYGOjnYpRERJRfqZVALFiwQOX569evER8fj8KFCwMA3r9/D0NDQ1haWjKBICLKBY+ePMf2fQHYvu8wrl6/pSwXi/Xg49YYHVt7o7l7ExQqZJiLURIRUUGgVgIRERGh/PeWLVvw999/Y/Xq1ShXrhwA4M6dO+jXrx8GDBiQPVESEVEKL19FYcf+QGzfG4Czl64py3V0dODRpD46tfZBK283mBgb5WKURERU0Gh8D8TEiROxa9cuZfIAAOXKlcOCBQvQvn17dOvWLUsDJCKiL969j8HuQ8HYtjcAoafOQRAEAIBIJIJrwzro1NobbX3dYW5WJJcjJSKigkrjBOLly5eQSCQpymUyGV69epUlQRER0Rdx8fEIPHoCW/f4I/DYKZXP4Ho1q6JzW1+0b+EBGyvLXIySiIh+FBonEG5ubujXrx9Wr16NGjVqQCQS4eLFixgwYACaNWuWHTESEf1wkpKS4H/kJBau3o6uFy8i/tMnZZ1zxXLo3NYXnVv7wMGueC5GSUREPyKNE4g1a9agZ8+eqF37ywJDUqkUnp6eWLVqVZYHSET0o5DL5Th59iK27PbHroNBePc+RllX0t4Wndv6oEsbX1QqXyYXoyQioh+dxglE0aJFERAQgLt37+L27dsQBAEVKlRA2bJlsyM+IqICTRAEXLt+G1v2HMK2vQF49iJSWWdjVRQ1a9fFuIFdUa9mFYhEolyMlIiISCHTC8k5ODhAEASUKlUKOjpcj46ISBMPHz3F1r3+2LL7EG7dfaAsNzUxRrsWHujatjnq166O4EsRqFWtNJMHIiLKMzQ+84+Pj8fPP/+M9evXAwDu3r2LkiVLYtiwYShWrBh++eWXLA+SiKggeB39Ftv3H8bW3YcQfvGqslws1kNz9ybo2q45vN0aQ19fDACQSGW5FCkREVHatDTdYPz48bh27RrCwsKgr/9lFdNmzZph+/btWRqcphwcHCASiVQeGSU0giBg8uTJKFasGAwMDNCkSRPcuHEjhyImooIuLi4eW/f4o3nXASjm1BjDxk9D+MWr0NLSQrPG9bB60XRE3jiFnWsWoY2vuzJ5ICIiyqs0vgKxb98+bN++HXXr1lW5pF6xYkU8ePAgnS1zxtSpU9GvXz/lcyOj9BdQmjNnDv7880+sW7cOZcuWxbRp0+Du7o47d+7A2Ng4u8MlogJIKpXi2Mlz2LzrAPb4H0FcfLyyrmbVyujS1hed2/hw2lUiIsqXNE4gXr9+DUvLlH/04uLi8sQYXWNjY1hbW6vVVhAELFy4EBMmTEDbtm0BAOvXr4eVlRW2bNnClbWJSG2CIODStRvYvPsgtu0JwKvX0co6R7sS6Na+Bbq1b4FypR1zMUoiIqLvp/EQplq1asHf31/5PDlpWLlyJerVq5d1kWXS7NmzYW5ujqpVq2L69OlISkpKs21ERAQiIyPh4eGhLBOLxXBxccGZM2dyIlwiyuceP32O6QuWo1LD5qjt0QGLVmzAq9fRMDcrjEG9u+DUoS24fyEYU38ZxuSBiIgKBI2vQMycORNeXl64efMmpFIpFi1ahBs3biA8PBzHjx/PjhjV9r///Q/Vq1dHkSJFcP78eYwfPx4RERFprk8RGamYLtHKykql3MrKCo8fP07zOImJiUhMTFQ+j42NBQBIpPIsu+lRIpOr/J9+POwDeVdM7AfsPhiELbsP4eTZi8pyfX0xWni4onPb5vBo0gB6ep/Xysnkz5B9gNgHiH2AcqoPSKTq718kCIKg6QH+++8/zJs3D5cuXYJcLkf16tUxbtw4ODk5abqrDE2ePBlTpkxJt82FCxdQs2bNFOW7d+9G+/btER0dDXNz8xT1Z86cQYMGDfDixQvY2Ngoy/v164enT58iMDBQo5i2bNkCQ0PDjF4SEeVDUqkUly9fRlhYGC5cuACJRAJAcRW2cuXKaNKkCerVq8fPACIiypfi4+PRtWtXxMTEwMTEJN22mUogclJ0dDSio6PTbePg4KAyI1Sy58+fo0SJEjh79izq1KmTov7hw4coVaoULl++jGrVqinLW7VqhcKFCyunqv1WalcgbG1tEXXzFEwLF1bzlaVPIpPjyKWHaFajJHS1NR5pRgUA+0DuU9zXcB2bdh7Ezv2HEf32nbKuQtlS6NauBTq39YVtcZt09pJ57APEPkDsA5RTfSDm/XtYVmyoVgKh1hCm5CE66sjogJqysLCAhYVFpra9cuUKAKhcXfiao6MjrK2tERISokwgkpKScPz4ccyePTvN/YrFYojFKada1NXRgq6OdqZiTYuudtbvk/IX9oGc9/T5S2zefQgbd+xXWeTNqqgFOrf1QY8OLVHNqWKOTRzBPkDsA8Q+QNndB3R11E9O1EogChcurPYfSpksdxY+Cg8Px9mzZ+Hq6gpTU1NcuHABI0aMQMuWLWFnZ6dsV758ecycORNt2rSBSCTC8OHDMWPGDJQpUwZlypTBjBkzYGhoiK5du+bK6yCi3PHxYxz2BhzBhu37cOzUOSRfnDUw0Edrbzd079AS7i71oaOj8a1jREREBYpafwlDQ0OV/3706BF++eUX9OrVSznrUnh4ONavX4+ZM2dmT5RqEIvF2L59O6ZMmYLExETY29ujX79+GDt2rEq7O3fuICYmRvl87Nix+PTpEwYPHox3796hTp06CA4O5hoQRD8AuVyO0FPnsHHHfuw+FKKyXoNL/Vro0bEV2rfwhIlx+uvJEBER/UjUSiBcXFyU/546dSr+/PNPdOnSRVnWsmVLODk54Z9//kHPnj2zPko1VK9eHWfPns2w3be3fIhEIkyePBmTJ0/OpsiIKK+5+yAC67fvx6adB/D0+UtleWlHO/To2Ard27eEo32JXIyQiIgo79L4Wnx4eDiWL1+eorxmzZro27dvlgRFRJTVYmI/YPu+w9iwfR/OXLiiLC9saoKOrbzh17EV6tWqmicWxCQiIsrLNE4gbG1tsXz5csyfP1+lfMWKFbC1tc2ywIiIvpdMJsPRE2exfvte7A04goQExexpWlpa8GraEH6dWqOlZ1Po66ecFIGIiIhSp3ECsWDBArRr1w5BQUGoW7cuAODs2bN48OABdu/eneUBEhFp6s79CKzfvg8bd+zH85evlOUVy5VCr85t0a19c9hYWeZihERERPmXxgmEj48P7t69i2XLluH27dsQBAGtWrXCwIEDeQWCiHJN7IeP2LH/MNZu2YPwi1eV5UUKm6JLW1/06twGNapU4hAlIiKi75Sp+QhtbW0xY8aMrI6FiEgjcrkcJ8IvYu2W3dh1KBifPiUAUAxR8nZrhJ6d26CFhyvEYr1cjpSIiKjgUCuB+Pfff9XeobOzc6aDISJSx+Onz7F++z6s37YPEU+eKcvLlymJXp3boEfHlhyiRERElE3USiCqVlXMTCIIgsrl/+QpUb8uy62F5IioYEtISMTegCNYu2U3jp48q/z8MTYqhE5tfNCnS1vUqVGFQ5SIiIiymVoJREREhPLfV65cwejRozFmzBiVheTmz5+POXPmZE+URPTDuvLfTazevBtbdh/C+5hYZblrwzro3aUt2vq6w9DQIBcjJCIi+rGolUDY29sr/92hQwcsXrwYPj4+yjJnZ2fY2tpi4sSJaN26dZYHSUQ/lnfvY7Bl9yGs2bIbV/67pSy3LW6DXp3boFfnNlzojYiIKJdofBP1f//9B0dHxxTljo6OuHnzZpYERUQ/HrlcjrDT57Fm827sCQhRrtmgp6eL1t5u6NO1Pdwa14W2tnYuR0pERPRj0ziBqFChAqZNm4bVq1dDX18fAJCYmIhp06ahQoUKWR4gERVsLyKjsHbrHqzZvFvlhminCmXRp1s7dG/fAuZmRXIxQiIiIvqaxgnE8uXL0aJFC9ja2qJKlSoAgGvXrkEkEuHQoUNZHiARFTxSqRQBR05g9eZd8A85DrlcDgAwMTZCl7a++Klbe67ZQERElEdpnEDUrl0bERER2LRpk3IhuU6dOqFr164oVKhQdsRIRAXEw0dPsXrLbqzbugcvX71WljesUwN9u7dH+xaevCGaiIgoj8vUQnKGhobo379/VsdCRAVQYmIS9h0+glUbd+LoybPKcgvzIvDr2Bp9u7dH+TIlczFCIiIi0kSmEoiNGzdixYoVePjwIcLDw2Fvb48FCxagZMmSaNWqVVbHSET50N0HEfhnw06s374Xb96+B6BYM8bdpT769uiAlp6u0NPjCtFERET5jcYJxLJlyzBp0iQMHz4c06ZNUy4cV6RIESxcuJAJBNEPLDExCXv8Q7By4w6EnT6vLC9uY4XeXdqiT9d2cLArnosREhER0ffSOIFYsmQJVq5cidatW2PWrFnK8po1a2L06NFZGhwR5Q937kdg5UbVqw1aWlrwadYY/Xp0hLdbI+joZOqCJxEREeUxGv9Fj4iIQLVq1VKUi8VixMXFZUlQRJT3JV9t+GfDdhw/c0FZXqKYNX7q1h59uraFbXGbXIyQiIiIsoPGCYSjoyOuXr2qsjo1ABw+fBgVK1bMssCIKG+6//Ax/tm4A+u27UX0m3cAFFcbmns0Qb8eHeDVtBEXeyMiIirANE4gxowZgyFDhiAhIQGCIOD8+fPYunUrZs6ciVWrVmVHjESUyyQSCQ4GhWLF+u0IOX5GWZ58teGnbu1Qoph1LkZIREREOUXjBKJ3796QSqUYO3Ys4uPj0bVrVxQvXhyLFi1C586dsyNGIsolT569wKpNu7B68y7lug0ikQjebo0woGdn3ttARET0A8rUX/5+/fqhX79+iI6Ohlwuh6WlZVbHRUS5RC6XIyj0FJav26aySrRVUQv06dYO/bp34ExKREREP7BMf3UYFRWFO3fuQCQSQSQSoWjRolkZFxHlsOg377Bmy26sWL8dEU+eKcubNqqLAT07oZVXU67bQERERJonELGxsRgyZAi2bt2q/GZSW1sbnTp1wtKlS2FqaprlQRJR9hAEAWcvXsWytVux82AQEhOTAACFTU3Qq3MbDOjZCeVKO+ZylERERJSXaJxA9O3bF1evXoW/vz/q1asHkUiEM2fO4H//+x/69euHHTt2ZEecRJSF4uLisWXPISxbuw1Xr99SltesWhmDendBp1beMDQ0yMUIiYiIKK/SOIHw9/dHUFAQGjZsqCzz9PTEypUr4eXllaXBEVHWunM/An+v3Yr12/Yi9sNHAIC+vhid2/hiUK/OqFXNKZcjJCIiorxO4wTC3Nw81WFKpqamKFKkSJYERURZRyaTwT/kOJau3qwyBWtpRzsM7NUFvTq3hlmRwrkXIBEREeUrGicQv/32G0aOHIkNGzbAxkaxymxkZCTGjBmDiRMnZnmARJQ50W/eYfXmXVi+fhseP30BQDEFa3OPJhjSpxuaudSDlpZWLkdJRERE+Y1aCUS1atUgEomUz+/duwd7e3vY2dkBAJ48eQKxWIzXr19jwIAB2RMpEanl4tXrWLp6M7btC1DeFG1WxBQ/dWuPgT07w9G+RC5HSERERPmZWglE69atszkMIvoeSUlJ2HUwGH+t2oSzl64py6s7V8TQvt3RqZU3DAz0czFCIiIiKijUSiB+//337I6DiDIh8tVrrNiwAyvWb0NkVDQAQFdXF51aeWFwn66oU6OKytVDIiJ1yAUBSVIht8MgABKpHDo6OkiQyCET+Hn+I8rKPqCrLYK21vf3o0wvJAcAHz9+VK4FkczExOS7AiKijJ2//C/+WrUJ2/cHQiKRAABsrIpiQM9O6N+jI6ytuLAjEWVOklSOR2+SIAfvkcoLBADW1tZ4FitABGluh0O5IGv7gABTfRGsTXS/6wtGjROIiIgIDB06FGFhYUhISPgSjiBAJBJBJpNlOhgiSlvyMKUlqzbi3KV/leX1albF0L7d0a65O1eKJqLvIggCXsZIoK2njxLWVtDiFcxcJwjAx4QkGOnrgT+OH1NW9QFBEBCfkIDXr6OBWAlsTDN/zqBxAtGtWzcAwJo1a2BlZcXhEUTZ7PWbt1izeReWrd2Kl69eAwD09HTRqZU3hvbtzrUbiCjLSOXAJylQrJg5DHnfVJ4gCECSXLFmD0+5fkxZ2QeS74d8HRUFS2Mh08OZNE4g/v33X1y6dAnlypXL1AGJSD3/3byDJUv+xqlTJ5WzKVlbWmBQ7y7o36MjrCwtcjlCIipoZHIBgAi6ut81wpmI8jBDfX0AIkhkOZhA1KpVC0+fPmUCQZQNkhd9W/TPBoSeOqcsr1m1Mv7X3w8dWnpymBIRZTsR+FU3UUGVFaOHNE4gVq1ahYEDB+L58+eoXLkydHV1VeqdnZ2/OyiiH82Hj3FYu3UPlqzchAePngAAtLW1UadOHcwYNwiN6tbgcEEiIiLKEzROIF6/fo0HDx6gd+/eyjKRSMSbqIky4cmzF1iyajNWbtyB2A8fAQCFTU3Qr0cH9PfrjBsv4lCvVmkmD0SUuwS54pFTRFqKB6Wq90/98f79e+zdvSO3QymwtPQMsWfnNrRu1TK3Q8mTNP7t7NOnD6pVq4bw8HA8fPgQERERKv8nooydv/wvuvQfiVK1PDD/7zWI/fAR5Uo7YunsSXh6NRSzJ42GXQmb3A6TiEiROHx6D3x6l4OP9xolLFFRURgweCjsS5WFvlFh2Ng6wMu3JcLPnst442x04uQptGzdDsXtS0JLzxD79h9QqZdIJBg3/jc4V6sFo8IWKG5fEj1798WLFy++67jrNmyElp6h8lHMzhGdunRHRMSj79pvXrB5yzZUrVEHhUzNUczOEX369sebN2/SbH/t2r/o2r0n7EqWgaGJGSo6VcOiJUu/Ow7XZp7Q0jPEtu07VcoXLv4LjmXKp2i/bsNG1GvokunjLVu2DD4tWsHDuzn6DRwMQcjddVo0vgLx+PFjHDhwAKVLl86OeIgKLJlMhv2Hj2LB8vU4ff6ysrxpo7oYMbAnvN0aQ0uL37gRUR4jyAFBBohEOXNVIPl4glzt47Xv1BUSiQTrVq9ESUdHvIp6haPHwvD27dtsDjZ9cXFxcHZ2Qq+ePdC+U9cU9fHx8bhy9Sp++/UXVHF2wrv37zFi1Bi0atsBF86e/q5jm5iY4Pb1qxAEAbfv3MXAwT+jVdsOuHLxLLS1tb9r37nl1Okz6NmnL/6cNwctfH3w/MULDBoyDP0GDMaeXdtT3ebSlSuwKGqBjevWwLZECZwJP4sBg4dCW1sLQwcP+q549PX1MXHyFLRr2zrFkP5vHTzoj5Ytmmf6WIMGDcL9e3cQGnYcZ8LPYv6cWbm69prGnwRNmzbFtWvXsiMWogLp48c4LF65EWXreKF9n//h9PnL0NXVRc9OrXHl2F4c2b0Wvu5NmDwQUd6WPKwoJx4aeP/+PU6dPoNZM6bBtYkL7O3tULtWLYwfNwa+Pt4AgEePHkNLzxBXr15T2U5LzxBhx09k6dv0NW8vT0ybOhlt27ROtd7U1BTBhw+hY4d2KFeuLOrWqY3FC+fj0uUrePLkqdrHuXT5MqyK22P6zNnKMpFIBGtra9jY2MC1iQsm/fYrrt+4gfv3H+DCxYvw8G6Ooja2KGxhjSZuHrh85Yraxws7fgJGhS0we+58lfIHDx5CW1wIV65cVXtfmjh77jwcHOwxbOhgODo6oGGD+ujf7ydcvHw5zW369OqJxQvmw6VxI5Qs6Yju3bqgV88e2LvvQJrbpGbqtBmwLuGg0oc6d+qAmJhYrFy9Jt1tExISEHzkKFo29wUAOJYpj2kzZqFn774wLlIUDqXLYf+Bg3j9+jVat+0A4yJF4VytFi5euqSyn5XLl+Hy+XCMHjkcxsbGGsWf1TQ+Y2nRogVGjBiByZMnY/fu3Thw4IDKg4gUXkRGYfy0P2FXrSmGT5iBiCfPYFbEFL+OGIBHl49g7ZKZqFI55WVOIiJSn5GREYyMjLDvwEEkJiZm6b4HDvkZxkWKpvvQ5ERfHTExsRCJRChc2FSt9mHHT6CZpy8mT5qACePHpdkuef5/iUSCDx8+wq97N5w4FoLwk2EoXboUfFu2wYcPH9Q6Zv16dbHi778wafJUvH79Wlm+as06VHF2QrVqVVPdbvOWbRm+n5u3bEv3uM+ePUfA4UAIgoBXr15h95698PH2UivuZLExsTArUkSttoIg4H8jR2PNuvU4GXoEVatWUdaZmJhg/Lgx+GP6TMTFxaW5j6PHQmFtbYVKlSoqyxYu/gv169fF5fPh8PH2gl/vvujZpy+6de2CS+fOoHSpkujZu59yqFLy4s36+vrYsGkzrl37N9Vj5RSNhzANHDgQADB16tQUdbyJmgi4cfse5v+9Fpt3H4JEIgEAlClpj+EDeqJnp9YwNDTI5QiJiAoOHR0drF31D/oPGoIV/6xC9WpV0bhRQ3Tu2AHOzt+30ObU3ydi9Ijh6bYpVizr7ldLSEjA+AkT0bVzJ7WGp+w/cBB+vfti+dIl6NK5Y5rtnj17hnl/LkSJEsVRtmwZVK5cSaV+xd9/wcyyGI6fOInmvj4ZHldPTw/dunbGvD8XYPfefRjYvx+kUik2bNqEX38Zm+Z2LVv4ok7tWunu28rKMs26+vXqYtP6tejczQ8JCQmQSqVo2dwXSxb+mWHMycLPnsOOXbtxaP+eDNtKpTL07N0XFy9dxqmwoyhRokSKNoMH9sfiv5biz4WLMXHC+FT3s//goRTDl3y8PDGgX18AwKQJ47F8xUrUrFEDHdq3BQCMHTMK9Rs1watXr2BlZY3evXsjPu4j3rx5C7emril+hjlN4wRCLs/BWRiI8glBEBB66hzm/70Gh4+eVJY3rFMDowb3RgtPVw5RIiLKJu3atoavjxdOnjqN8LPnEBQcgrnzF2Dlir/Ry69HpvdraWkJS8u0T2izkkQiQZdufpDL5Vi6ZGGG7c9duIBDAYexY+smtGndKkV9TEwMjIsUhSAIiI+PR/VqVbF7x1bo6ekhKioKk6b8gdDQ43gVFQWZTIb4+Hg8earZ1ZT27dpix87dGNi/H/wDDuPdu/fo2rlTmu2NjY2/a+jNzZu38L+RozBxwnh4ujfDy8hIjP1lAgYO+Rmr/1me4fY3btxE63YdMXHCeLg3c8uw/cgxYyEWixF+MgwWFqkv3ioWizFl0kT8PHwkBg3ol6JeEAQc8g/A1k0bVMqdnCor/21lZaUoq/xV2ed+FxX1GlZW1ti6dStMDPPOauQ8oyH6DlKpFFv3+KNms3Zo1q43Dh89CS0tLbRv4YkzAVtx4uAmtPJ2Y/JARJTN9PX14d7MDZN++xWnT4Sip193TJ46DQCUn8Ffz1yTfIU4PTk1hEkikShmSXr0GMGHD6l19aFUyZIoX64c1q7bgKSkpBT1xsbGuHLhLP69fAEf3r3GxXNnUKtmTQBA7779cfnyFSyYPwenjx/DlQtnYW5ujqSkjN+Tr3Vo1xYnTp7Cy5cvsXrNOrRt3QpF0hka9L1DmGbNmYcG9ephzKgRcHZ2gqeHO5YuWYi16zbg5cuX6cZ68+YtuHn6oG+fXvjt11/Uen3N3Jri+fMXCAo+km677t26wMHeHtO+ugcl2fkLF5CUJEHDBvVVyr++6Tp5qvavV4BPLsurX9yrfQXCx8cHW7duhampYkze9OnTMWTIEBQuXBgA8ObNGzRq1Ag3b97MlkCJ8pK4uHis2boHfy5bi8dPFdPtGRjoo3eXthgxoCdKOdrlcoRERD+2ihUqYP+BQwCAokUV3x6/jIxEtc/1V9UYQ54TQ5iSk4d79x/gWMhhmJubq7Wdhbk5du/YCtdmXujctQe2b92kclKqpaWF0qVLpbrtyVNnsHTxQuW9A0+fPkN0dLTGsZctWwaVK1XCoiV/43BQMIICDqbb/nuHMMXHx0NHR/XUNXlGqfSmNb1x4ybcPH3g170bpv8xJd3jq8Tb3BctfH3Qza83tLW10blTh1TbaWlpYca0KWjXsQsGfnMVYv+BQ/D19sq3M1+lRe0EIigoSOXmpNmzZ6NLly7KBEIqleLOnTtZHiBRdhEEAVeib6KaRUW1F2p7Hf0Wf63ejKVrNuPtuxgAQFELMwz9qRsG9+4CczP1bsoiIqKs8ebNG3Ts0h29e/rB2akyjI2NcfHSZcyd/ydatlDMemNgYIC6dWpj9tz5cLC3R/SbN5j4e8Ynkt87hOnjx4+4f/+B8nnEo8e4evUazMzMYGdnC6lUig6duuLy1as4uHc3ZDIZIiMjAQBmZmbQ09PLML6jwQFo6u6Nrt17YuvmDSlOsFNTulRJbNqyFTVrVEfsh1iM/WUCDAwyd39e+3ZtMHnqNNjb28G1SfrrHHzvEKbmzX3Qf+AQLFvxDzzd3fEyMhIjRo1B7Vo1UaxYMQDA3n378etvv+PW9asAFMlDUw9veDRzw8jhPyvfX21tbRQtWjTDY7Zp3Qob1q6GX++foKOjg/bt2qTaztfHG3Vq18I/K1erJEEHDwVg8qTfMv2a8yq1E4hvM7vcXsAiLxLiYyDXzZr3RS5TXLKSx7+DXJvDX7LSo48vsfHBYay9548ncZHY2ngqOjimPxby4ZPnWLhqG9bu9MenBEUiXdKuGEb264qe7X1goC8GIEAel3VzjrMPEPsA5XQfkMtEEAQDxToM8s+Tosg/r0ItAyDKgeEUggBArjguMp6YxcjQALVr1sDCxUvw4GEEJBIJbEsUR9/evfDruNHK17F6xVL8NGAwatVriHJly2D29D/g2byV4jjy7JkA5uKFi2jq+eWm5FFjFLMk9ezeFWtXrcCzJ09w4JA/AKBarboq2x4LCkATl0af3w8AcimUA+AFOQABkMtgbVkURwMPwdXDG939emHz+jWf3zuk+bpWr/gbA4YMQ/Xa9WBnWwLTp07GmF8mKPb7eZvefQfg0eMnCA05nO5r7NC2FSZNnorefj0gEuRANp4e9ureFR9iY7H07+UYPXY8Chc2RVMXF8yaPlUZd8z797hz967y+c5du/H69Wts3roNm7d+GR5lb2eHiLs30j/g5/ejfZuWkMtWwK/3T9ASCWjbupXi5yIIKu/xrGlT0KBJM8V7IJfhwYOHuP/gATybuar+LASovNffHg/Al/8LcsXPHlDtA99DkEEQ5JB/ioE86csPTIiPVXsXIkHNTEBLSwuRkZHKTNzY2BjXrl1DyZIlAQCvXr1CsWLFfshZmGJjY2Fqaoq3O8bCxFCcJfuUCFoIklWDp/YV6ObEB3YB90GWiN3vbmFt9FWc/vhlvGopcRGcqdAH5jqGqW539WEk5u46g52nbkIuV/yq1Chtg9Ht6qFt/QrQzsY/6OwDxD5AOd0HErSN8NiyERxLFIO+3ufvGAUBkH4CkJN9UAvQMciak6V8ToAIsTCECeIhys6z82+4Nm8Pl4b1MPmXURm21SpSAns2rUJrX82mUy3o/lz6D46GnYT/zo3ftZ+s7gMJSVJEPHsB+6iT0Jd9VJbHxifCrOMcxMTEZHgfjtpXIEQiUYphHuoO+/hhGBcDTAtnzb7kAKIBFHHkre6ZJBPkCH17CxtenMLuVxeRKJdAC4o+qwURzHQLIaj2rzA3UL2EKQgCTl69i9kbDyHo7H/Kco86lTG6mw9ca1TImb7PPkDsA5TjfUAP0NIBtPUAna9W1tXR+/JNeE7IqVWv8wspFD+DHPLhw0c8ePQEB3dtBXTU/GJUS1f9tj+IEra2+GX0iKx5X7KyD8i0FL/nprYAvroBX/u92rvQaAhTr169IBYr3oSEhAQMHDgQhQoVAoAsX7wlX9IrBJGBegu/ZCT5iyaRgSk/QzV058NzbHgainVPjiIy8T10RFqQCp+HAUCACCKItXQRWH8KHAuXVG4nCAL8T17E7DV7cObabQCKK28d3OtjbO+2qFrOMUdfB/sAsQ9QTvcBkaCjOHHX0gK0vr7ps2DdAJqvJOdtIm0gh763NTY1xZO71zXbKEWfoY7t22XNjrK6D2jJAJEWRPrGEImkX8o/qT8Ll9oJRM+ePVWed+/ePUUbPz8/tQ9MlJXeJX3EtuensPbxEVyKeQBtkRZkn5OG5OQhmZZIhN11fkG1z8mDVCrDjpDTmLN2D/679xgAoKerg54tm2K0X2uUtsu6RYKIiIgKmqy8/4/yB7UTiLVr12ZnHEQak8ilCIq6gvVPjuFA5AXIBBmSU3OZkPZY3X+qDoGnZTUkJCZh3YFjmL9hHx4+ewUAMDLUx8AOXhjerQVsiprlxMsgIiIiylc0XomaKLddi4nAhqeh2PAkFG8lH1WuNmQ0/cPU8l3RoWh9LNi4H/M37MfL6HcAAIvCJhjWtTkGd/JGEROjbH4FRERERPkXEwjKF14lvMfW5yew+vER3PzwVOW+hvSuNiQTAehRtAlwAii5ZQCi3yumKithZY7Rfq3xUxt3GBrw5i8iIiKijDCBoDxLLsix5+VZrHt8FEFRVxTTJn++wvDtfQ3pEcUBJf+zxv5T5xHzMR4AUMrWGmN7tYVfiybQ+2rlTiIiIiJKHxMIyrOS5FJ0ujA38zv4AIjOAKJLIjxIUqw8WbGkLcb3bY+O7g2go8PZIoiIiIg0VWAmBgwLC1OuVfHt48KFC2lu16tXrxTt69atm2Z7yjliLV1sqjES3Uq4oIiuYrpgbXXmMYwBEABgESCEA/IkAdUrlMSu+eNwbedCdPVuzOSBiIiIKJMKzBWI+vXr4+XLlyplEydOxJEjR1CzZs10t/Xy8lKZZUpPL+cWa6G0iUQidCnRCF1KNIJckOPy+4dY9TgEKx8HK9voiLQhFT6vfv4ewCkAVwB8LqpW2RF/DOgGrwbVufAhEVEmCXIpIJfl3AG1tCHSKjCnKFmud/8heB8Tg73bN+V2KAWWViEz7Nm2Ea1b+OZ2KHlSgbkCoaenB2tra+XD3NwcBw4cQJ8+fTI8cRSLxSrbmplx+s68RgQRZt7bpUweiuqZYH31/6FT8YYw+WAAHACwBMBFADJAZC/Cknn9cGHDfHg3rMHkgYgokwS5FHj7EHhzL+cebx8qjqumqKjXGPDzCNiXc4J+EWvYOJaHV8t2CD93PhvfmYzNnLsAtRu5wcTKDlb2ZdGmU3fcuXtPpc3Hjx8xdORY2JapBEPzYqhYvQ6WrVzzXcddt3ELtAqZKR/FSlZApx69EfHo8XftNy/YvG0nqtZphEIWxVGsZAX0GTAEb96kvw7FhUuX0cynNYoUc4BZcUd4tmiLq9f++644XL1aQKuQGbbt3K1SvvCvZXCsUCVF+3Ubt6BeE/dMH2/ZsmXwadMBHs3boN+Q/0HIyZXhU1Fg0/sDBw4gOjoavXr1yrBtWFgYLC0tUbhwYbi4uGD69OmwtLRMs31iYqLKytuxsYoZfSQCIFH/3t50SeWq//+RBb26jM4X5wEADLQMEFjvd9QxK4cHT1/i6IH/EOefqLziYFqmEJIaybG1/Sh4WdWAVHHndb7EPkDsA5TTfUAqANACBEHxAADIZIAsSbEKbk6sNCz/fDyZDIJIvdOU9l17QiKVYO2Kv1HS0QGvoqJwLOwE3rx9j9w8zzp+6jQG9f8JtapXg1Qmw29TpsGzZTtcvxiOQoUUQ3NHjJuAsBOnsGHVCjjY2yH46DEMHTEGNtbWaNXcR/knTFD+B0j+05bWaxMAmJgY49aV8xAEAbfv3MOg/41Eqw5dcTn8BLS18+cw3lNnzqJnv0H4c9Z0NPfxwvMXLzH4fyPRd/D/sGfbxlS3+fDhA7xatUdLX2/8tWAupFIpJk+fBa9W7fHk7nXopjORisrvQYpKQF9fHxOnzkDbVi1T7Ofb7Q74B6Klr0+m+qMAYNCgQbh/5yZCj5/EmXMXMG/GHzAxMdF8Z1/FJpUDkq++X5VoEFuBTSBWr14NT09P2NraptvO29sbHTp0gL29PSIiIjBx4kQ0bdoUly5dglic+rSeM2fOxJQpU1KUH40qBMOPWftN95Hn/OYcqIGtzluVz27ef4kpO5cgLCwMcrnir2rVqlXRsWNHVKxYUdEoCQh8mhuxZj32AWIfoJzqAzo6IlhbAx+lIiRpKY4pkomgKxNB0NYBhBw4bRBEEMnkkEhFEISMX3dMTAxOhZ/FoUOHUKN+AwBAERs7lK+iGL4cKwGePHmCKlWq4MSJE3ByclJu5+DggIMHD6Jhw4bZ8lK2f/Pt9KIlS1GmTBmcuHANDRooYj199gI6de6C6vUaAQA69+iN5avX48zFq3D1/DJ85sNXZ3oSueLkL/Zz2dWrV9GhQwcMGDAAo0ePRoJMBEAEQzNrAECN+jYYPWYsBgwYgKt3IvDhwwf88ccf+PfffyGRSODk5IQZM2agSpWU35yn5tSpU+jUqRPGjBmD4cOHK8sjIiJQo0YNhIWFwdnZWeP3KyPHz16EnZ0devYbCAAwL+4Av169sWjRIuV78a0rtx7g3bv3GD3uV9iUKAEAGDlmHPY0bIibEc/h6OiY5vE+yUTK/c6ZMwerVq3C7t274eTkBKkAtGvXDoGBgViyagP69u0LAEiQiSAXoBJPQkICQo6FYuz4XxErEcHZ2Rl+fn64f/8+Dh06hCJFimD27NmoXbs2hg0bhhMnTsDOzg5Lly5FtWrVlPuZv3AJYmJisGzZMgj6Jmm+5owkSUX4JAVORooglX7ZR3x8IbX3kecTiMmTJ6d6sv61CxcuqNzn8OzZMwQFBWHHjh0Z7r9Tp07Kf1euXBk1a9aEvb09/P390bZt21S3GT9+PEaOHKl8HhsbC1tbW7hZxsG0iEGGx1SHVK74g9GsuACdAjPQTH3jrq/HP4+DlM8fuC/H+6h4zF6zC5sOhUImUyQOng2q49d+HVHHqdznlvn0ckMqfvQ+QOwDlPN9IFEQ8EwHMNIRoK/7+fNUJADanx9aOfAZK1J8v66vIwA6GR/PsLAhjIyMEHL4EJrWq5Hql39Gn/dTSEeAyefXJf9cZqj9pexbg4aNxObtO9M9/vWL4bCzLZFhnAAQFR8DALAtWlh5zMb16yI48DAG9uqKYjY2CDtxCg8ePMDiuTNhoquYvPyDRARjXQHJp3q6WoCOFmCiKyDsxCm07dId0ydPwqB+fQAI0NcWIBJB5XWZGekDAMRCEmI+fUDv7p1Ro+pMAMCfS5aiU8eOuHPtAoyNjTN8Hc0a1MLyJQvw08ChGNSrG4oWtQAAbN+8AVWcKqNhDSek9vd48/adGDRsZIryry1b/Ce6deqQap1r/VqYNm0aTh0LhrdHM0RFvYb/gf1o7uWR5s+weoVSsDA3x84tGzF+zEjIZDLs2LIRlSqUR6WSJaCTTh8z0BZgrCPHiLHjsf9QAE6GBKBM6VIABOiIAHNTI/w6ZgTmzJ2LAX6dUahQIehrC9D65r0/efQ4rK0sUce5PABF/bJlf2Pa7xMxZfxoLPxrGQYOHID6deugd49u+HPGFPwycQqGDBqI/y6GAyIRXn9IRFFjMcTGYmzfthUdW/mgqrNTuu9lWhLkAgx0gEbWAsSiL3HGvItTex95PoEYOnQoOnfunG4bBwcHledr166Fubk5WrZsqfHxbGxsYG9vj3v37qXZRiwWp/oBpStS/FJnJR2trN9nXnb27R00OPmL8vne2uPhLDjgt9kbseFQKKRSxVglz/rV8PvAzqjjVDa3Qs0xP1ofoJTYByin+oAsOWcQKR4AoLwIIPr8yG5fHU+d29d0dXWwdsVf6D90OFasXofqVZ3RuGEDdG7fFs5OlRS7En3ZtfLfX/0/reNMnTgeo4cPTff4xYtZqxWnIAgYPf43NKxfF06VKyrLF8+fhf5DhsOubGXo6OhAS0sLK5cuQqMGn2eETP6ZfPs6ABzwD4Bf34FYvngBunRsp9yn8i38/I9nz59j/qIlKFG8GMqVLa1yfABYsWQBzIo74sTpM2ju7ZnhaxGL9dC9cwfMX7gEew4cxMC+vSGVSrFh8zb8OnZkmu9HK18v1K1VI919W1kWTXP7BvXqYNOaFejS8yckJCRAKpWipa83lvw5O81tTEyMERp4AK07dce02Yrh0GXLlELg/t3Q1U3/NFgmk6JXv0G4eOUKTh09jBLFi3+p/Hy8wf1/wuK/V2DBX39j4i9jvlR/Fc8B/wC09PVWKfPxcMfAvr0AAJPGj8HyVWtQq0Y1dGzXGgAwbtQw1Hf1RFRUFKysrNC7d2/Ef4jFm7dv4ebqAqdKFdTqd6lJ3k5HS3HumkxXg/3l+QTCwsICFhYWarcXBAFr166Fn59fuuPa0vLmzRs8ffoUNjY2Gm9LmfdJlogyIYPwMvEdAKCFdS0sLPETZq7ajQ4H5igTB/d6VfH7gE6oV6V8boZLRER5SLvWLeHr5YGTp8MRfv4CgkKOYu6CxVi5dBF69eia6f1aWhaFpWXRLIlx6Mix+Pf6DZw8EqBSvvjvFTh74SL279wCe1tbnDh9BkNGjIGNtRWaNW2S5v7OXbiEQ4eDsGPTWrRp2TxFfUxMLIwtbSEIAuLj41G9ahXs3roBenp6iIp6jUnTZiL0+Em8ioqCTCZHfHw8njx9ptFrat+2FXbs3ouBfXvDPzAY796/R9eO7dNsb2xsrNYVjrTcvHUb/xs9HhN/GQ3PZm54GRmJsRN+x8BhI7F62ZJUt/n06RN+GvQzGtStgy3rVkImk2H+or/g26Yjzp88CgODtEeOjBw3AWKxGOGhwbCwME+1jVgsxpTfxuPnUWMxqG+fFPWCIOBQQBC2rl+lUv51Emdlpbjv1qnSV2Wf78WNeh0NKysrbN26FSa6QqaThqxW4L7TOnbsGCIiIvDTTz+lWl++fHns3bsXgGLmg9GjRyM8PByPHj1CWFgYWrRoAQsLC7Rp0yYnw/6hzbm3B0aHOiuTh7NV56DEMXOUbz0Uq/aEQCqVoVndKji5diYC//6dyQMREaWgr68PdzdXTBo/FqePBaFn9y6YPH0WAEBLS3G68/XMNRKJJMN9Dhw2EsaWtuk+1Dnp/nnUOBz0P4xjhw+ofIv96dMnTJg8DfNnTUMLHy84O1XC0IH90LFda8xf9Fe6+yxV0gHly5bB2g2bkZSUlKLe2NgIV8KP49/zp/Ah6ikung5FrRrVAQC9BwzB5SvXsGD2DJw+Gogr4cdhbm6W6n7S06FNK5w4dQYvX0Zi9bqNaNuyOYoUKZxm+83bdmb4fm7elvaQsVnzFqJBvdoYM2IYnJ0qwdPdDUsXzsPaDZvx8mVkqtts2bELjx4/xZoVf6FWjeqoW7sWNq9diYjHT7D/UECq2yRr1rQJnr94iaAjx9Jt171LRzjY2SmvcHzt/MVLSJIkoWF91TXGvv6SO3mmyNTKku/1zGvy/BUITa1evRr169dHhQoVUq2/c+cOYmIUYxC1tbXx33//YcOGDXj//j1sbGzg6uqK7du3f1eGTOq59eEpKh8bpnw+z6E3nh95gyYzf0NCouJDrGltJ/w+sDMaVquY1m6IiIhSqFi+nPIEsejnb49fRr5C8i2pV//NeBrPqb+Nx+j/pT+EqZiNdZp1giDg51HjsO+AP0IDD8DRwV6lXiKRQCKRQOubr5W1tbUzPHG0MDfH7i0b4OrdAp39fsL2jWtUTkC1tLRQulTJVLc9eeYsli6YCx8vxbSiT589Q3T0m3SPl5qyZUqjcsUKWPT3ChwOPoKgA7vTbd/S1wt11BjClJb4T/HQ0VE9ddXW/pwcpnEPZHz8J2hpaalM5578XC5P/x6blr7eaOHjhW69+0NbWwudO7RLtZ2WlhZmTJ2Edl38MLBvb5W6/YcOw9fTI9/OfJWWApdAbNmyJd36r799MDAwQFBQUDqtKTtI5TI0PPkLLry/DwBw0rGH98Pq+H32VsR9SgAANKxWAVMGd0WTmpVzM1QiIsrj3rx5i47de6O3Xzc4V64EY2MjXLx8BXMXLEFLX28Air/3dWvXxOw/F8LB3hbRb95i4tTpGe77e4cwDRkxBlt37MK+7ZthbGSEyMhXAABTUxMYGBjAxMQELo0aYOyE32FgYAB7O1scP3kaG7dsx/xZ09SK76j/fjT1aYmuvfpi6/rVKU6wU1O6pCM2bd2OmtWrIvbDB+XxM6N921aYPG0W7O1s4erSKN223zuEqbm3F/oPHY5lK9fAs1lTvIyMxIixE1C7ZnUU+zz0fO+BQ/j19z9w68o5AIB70yYYO+F3DBkxBj8P7Ae5XI7Z8xdBR0cbro0znn2rTcvm2LBqGfz6DoKOjg7at2mVajtfLw/UqVUD/6xZr5IEHfQ/jMm//ZLqNvlZgRvCRHnb+ifHID7YXpE8JACDH3vj8azXmLNmL+I+JaBmxdIIWDoJYaunM3kgIspL5DJALs2Bh2YrXhsZFULtWjWw8K9lcPH0hVOtBpj0x0z07d0Df/05R9lu9bIlkEgkqNXIDcPHjMcfkyZk9TuUwvKVaxATEwtXrxYoVqqC8rF9115lm63rVqFWjWro3mcAKtWoh9l/LsK03yek+CY7LdbWVjjqvx//3biF7n36QybL+P1bvWwJ3r2PQfX6TeDXdxB+HtQflkVV7zft3X8IXL1aZLivDm1aQS6Xo7dft2xftLVXj66YP2salq5YCadaDdCxex+UK1Mau7duULaJiYlVWayvfLmyOLBzC/67fgP1m3qisYcvXryMxOF9O2GTztWjr7Vv0wrr/lkKv76DsGf/wTTbzfrjdyQkJCifP3gYgfsPI+DZrGkmXm3eJhJyeym7AiA2NhampqZ4e3QlTM3Uv+E7PRI5EPhUBC9boUDMvvLsUzTsg/spnkgAj0fVcMn/Pt68/wAAcCpjjymDuqBlk9pcNfqzgtYHSHPsA5TTfSBB0MEjLRs42pWAvlgPwFcrUcs0Gx//XbT1ALOSEGkVuIESGhM+rymQ0zfQunq1gEujBpg8IeNvz7UKmWHPto1o3cI3w7Y/kj8XL8XR0OPw35vxsgLpyeo+kJCYhIgnz+Agfwl90ZcV32PeRsPMrR9iYmIyXKSOv5mUrQRBQIcLc7D35VlABhT6Vx8mZwwQ/PoKAKCcQ3H8PrAzOrjXV97kRkREeYdISweCWUmNrwx8Fy1tJg+56MOHD3jwMAIHd23NuDGlqUTxYvhl9IjcDiNb8LeTsk3Aq0tocXYaIAdwAyh+1hzPn79BHBJgZ10UkwZ2Qg/fJtDRKVg3FhERFTQiLR2AJ/Q/DGNjYzy5ez23w8j3OrYruDN68tOAsty7pI+wONxDsfjNPcD8pDHePP2A53iDokVMMKFvB/Rv7wmxnubrdBAREVHeIo97m9shUA5jAkFZasR/q7H44SHgCYAjAJ4Ab/ABJkaGGO3XGv/r1hxGhpmb6YGIiIiIch8TCMoSZ9/eQYOTvwBRAI4CuKMo1xfr4ecuvhjTszXMC6d/Qw4RERER5X1MIOi7fJIlouyRwXjx6i0QBuAqAEGxsEuf1s0wsX9HFLdMffl3IiIiIsp/mEBQps29txe/XNoAnAJwDsDnmcDaNK2LaUO7obxjidwMj4iIiIiyARMI0tjtD89QKehn4DyAkwA+r5nSqFpFzBruh7rO5XIzPCIiIiLKRkwgSG1SuQyNTozH+ZP3FPc5xCjKK5e2w/Sfu8O3UU0uAkdERERUwDGBILWsf3IMffYsAYIBvFSUFbc0x5TBXeDXvAm0tbmWAxFRQSUkfcrxlahFepyxLy29+w/B+5gY7N2+KbdDKbC4unb6mEBQup5/egO7TX0VU7LeVZQZFzLAuN5t8b+uLWBoIM7V+IiIKHsJSZ8g3DgMJMTm3EH1TYBK3monEVFRrzHxjxkIDD6CV1GvUaRwYVRxqoTfJ4xDvTq1sznYtE2ePgtTZ8xRKbOytMTLiNsAAIlEgt+mTMfhoBA8fPQYpiYmaObqgpl/TEIxG5tMH3fdxi3oM3Co8rm1lRUaNaiLWX9MhqODfab3mxcsXbEKS1esxKPHT2FnWwK/jhkJv26d02x/7d/rmD1/IU6Fn0X0m7dwsLfFgJ96439DBn5XHK5eLXD85GlsWbcSnTu0U5Yv/GsZFi1djohb11Tar9u4BStWr0V4WEimjrds2TIcDz0KmVQKe3s7/PPXwlwd9cEEglIlCAJaHZ0O/22XgMtQzqzUv50HJg3oBEuzwrkdIhER5QRZkiJ50NYDdHLgSyNpouJ4siQA6iUQ7bv1hEQiwbp//kZJRwe8iorC0dATePvufbaGqo5KFcoj5NBe5fOvr9jHx3/ClavX8Nsvo1HFqTLevX+PEWN/RasO3XDh1LHvOq6JiTFuXzkPQRBw++49DBw2Eq06dMWVsyfy7aiBZSvX4Nffp+KfvxaiVo3qOH/xEvoPHYEiRQqjhY9XqttcunIVFkUtsHH1CtiWKI4zZ89jwM8joK2tjaED+31XPPr6+pg4dQbatW4JXd30F8c9GBCIlr4+mT7WoEGDcP/OTYQeP4kz5y5g/sw/YGKSe9Pja+XakSnP2vfkLHRGt4X/hEvAJQAC0LJJbfy3azH+Gj+AyQMR0Y9IRwyRnkG2PzRNUt6/j8GpM2cx64/JcHVpBHs7W9SuWQPjx4yAr5cHAODR4yfQKmSGq9f+U9lOq5AZwk6cytK36Vs6OjqwtrZSPooWtVDWmZqaIPjQXnRs1wblypZB3dq1sHj+bFy6chVPnj5T+xiXrlyFlX1ZTJ8zX1kmEolgbW0FGxtruLo0wqTxY3H95i3cf/AQFy5dhkfzNihqVxqFbezRxLM5Ll+5ls4RVIWdOAWjoiUwe/4ilfIHDyOgbWSOK1f/VXtfmti0dTv69+mFTu3boqSjAzp3aIc+ft0w589FaW7Tp2d3LJ43Cy6NGqCkowO6d+mIXj26Yu/+Qxode+rMObB2KKfShzp3aIuY2FisXLsh3W0TEhIQfDQULX0VSY5jhSqYNnseevYdBGNLWziUd8b+QwF4/ToarTt2g7GlLZxrNcDFy1dU9rNy6WJcPnMco4cPhbGxsUbxZzUmEKT0LukjtKe1QTu/2cAxAElAzUqlEbpqGvYuGI9yDsVzO0QiIiIVRkaFYGRkhH0H/ZGYmJil+x44bCSMLW3TfWR0on/vwUMUL1URJStWRZeeP+FhxKN028fExEIkEqGwqXrfLoedOIVmvq0x+bdfMGHsqDTbGRjoA1AMm/rw4SP8unXBiWB/hIcGo3SpkvBt2wkfPnxQ65j169bGiiULMOmPGXj9OlpZvmrdRlRxqoxqVZ1T3W7ztp0Zvp+bt+1M87iJiUnQ11dNMA0MDHD+4mVIJBK1YgeA2JhYmKn5ZaggCPjf6F+wZv0mnDwSgKpVnJR1JsbGGD96BP6YORdxcXFp7uNo2AlYW1miUsUKyrKFfy1D/Xp1cPlMGHy8PODXdyB69huEbp074NLpUJQuVRI9+w2CIAgAFEkIAOjri7Fh8zZc+/e62q83O3AIEwEAuu6bj+2rTwGfPweLWZph1v/80MWrEbS0mGcSEVHepKOjg7Ur/kL/ocOxYvU6VK/qjMYNG6Bz+7Zwdqr0Xfue+tt4jP7f0HTbFLOxTrOuTs0aWL/yb5QtXRqvoqIwfc58NGjqhesXz8Dc3CxF+4SEBIyfNBVdO7ZXa3jK/kMB8Os7EMsXL0CXju3SbPfs+XPMW7gEJYoXQ9kypVG5UkWV+hVLFsCsuCOOnzqD5t6eGR5XT08P3Tp3wLyFS7B7/0EM7NsbUqkUGzZvw69jR6a5XUtfL9SpVSPdfVtZFk2zzqNZU6xetwmtW/iietUquHTlKtZu2AyJRILo6DewSednkSz83Hns2LMPh3Zvy7CtVCpFz76DcPHKFZw6ehgliqf8InVw/5+w+O8V+HPJ35j4y5hU97P/UABa+nqrlPl4uGPAT70AAJN+GYPlK9egZo1q6NC2NQBg7MhhqO/qiVevomBlZYXevXsj/kMs3rx9CzdXF1SuVAG5iQnED27/rXNoO3MW8PmKnFisi1/7tMfIHq14gzQREeUL7Vq3hK+XB06eDkf4+QsICjmKuQsWY+XSRejVo2um92tpWRSW6ZzQZsTb0135bydURL06tVC6cg2s37wVI4cNUWkrkUjQpWdfyOVyLF04N8N9n7twCYcOB2HHprVo07J5ivqYmFgYW9pCEATEx8ejetUq2L11A/T09BAV9RqTps1E6PGTeBUVBZlMjvj4eI2GTQFA+7atsGP3Xgzs2xv+gcF49/49unZsn2Z7Y2Pj7xp6M/GX0Yh89Qr1mnhAEARYWVqiZ/cumLtgsVr3ddy4eQutO3bHxPFj4O7mmmH7keMmQCwWIzw0GBYW5qm2EYvFmPLbePw8aiwG9e2Tol4QBBwKCMLW9atUyp0qf0nirKwsFWVfJXZWloqyqNfRsLKywtatW2GiKyCvzJbPr5Z/UNEfY1B4VFe09fucPIgAvxauuH9gOX7r3zHfJA/LIgJRNXQ4Cvt3RWH/rmhwYhwOv7qU7jan3txEo5PjUTSgBwod7ISKR4di4YMDKdotenAQFY4MQaGDnWAf1Bcj/1uDhHSmMQyLvg7t/W1gHtA9Rbvz7+5Ce38baO9vk6K987FhkAkylfZm/t2w4en33UBHRPQj0dfXh7ubKyaNH4vTx4LQs3sXTJ4+CwCUV9KTh4MAUGvIS1YMYfpaoUKF4FSpAu49eKhSLpFI0KlHH0Q8eozgg3vUuvpQqqQDypctg7UbNiMpKeXfJmNjI1wJP45/z5/Ch6inuHg6FLVqVAcA9B4wBJevXMOC2TNw+mggroQfh7m5War7SU+HNq1w4tQZvHwZidXrNqJty+YoUqRwmu2/dwiTgYEB1iz/C3HRzxFx6yoe3/kXDvZ2MDY2SvMEP9nNW7fh5tMafXv74bdxo9V6fc2aNsHzFy8RdCT9v8fdu3SEg50dps2el6Lu/MVLSJIkoWH9uirlX990nTybUmplcrlcrVhzGq9A/L+9u46r6vwDOP65dKM0CAh2KxbGbEWs4XR25+yaOhs7ZrfTqdg1df7smt2F0+l0BiKKgQEikvf+/kCvXqmLEqLf9+vFa57nPPE9l2d6vvc855xvjEqlos2KmaxbfgzePpHP0yMf8wZ1pWTB3Jkb3CdwNrZmYqE25DGNv2y5MvAQP5yZzIWq0yls4ZpoG1NdI3q416WYRU5M9Yw4/uwa3S8vwkTXiK5u8Tfcrbl/hKHXVvG7Ry8qWBXgZvhDOl6cA8CMogm/YfiQuZ4xW4PP0MK5krpseeBBXI1tCXzzNEH9268fszLwMB1y1vikz0AIIURChQrkZ9uOXQDYvj25DH70GI+3+/3/vpJEy/c+dwnTx6Kiorh+4ybfVSyvLnuXPPx36zZ/7f5fokubEmNjbc3mtSupVqcBzdt2YsOqZRonoDo6OuTJnSvRtsdOnmb+zKnU9Y6/QnI/KIiQkGdaH8c7+fLmoUihgsxe8Bu79x1g7/82J1v/c5cwvaOvr69eTrThjy3U966d7HLrf65dp0bdhrRt1ZwJo0ek2P/7eOvQoK43rTp0RVdXR+NxrR/S0dFh4thRNG7Rlm6dO2js27ZjN/Vqe2XZJ18lRRKILCxOFcexZ9cJjnyBo1F2KlkXRFeR9ATdev40P46dAvfjt63tzFk4qDuNapTLsm+QbuBQRmN7fKHWLArYy+kXN5NMIDyy5cIj2/u/VN1M7NgafJrjz66pE4jTL25Q0aoALZ0rq+s0d67EuRf/pRhTW5dqLA88qE4g3sRFsSHoOL1z1WP8zYTfrPTKVZcxN9bTwrkSRroG2h24EEIIAJ49e07T1h3o0LYVxYoUxtzcjPMXLzF15lz1unNjY2PKlS3NlBmzcMvpQsiz54wcOyHFvj93CdPAoSNpUNcbVxdnnjx9yoQp0wl79Yp2rVoA8Wvsm7Rqz0X/y2z/Yz1xcXE8evQYACur7BgYJP9vgp2dLQd3bqN63e9p2b4z61YsRU8v5VO7PLncWb1uA6VLliDs1SsGD/fF2PjTXtz3YyMfRo+fTE5XF6pVqZRs3c9dwnTzv1ucPX8RzzKlePHyJTPnLuDqtev4LV6grrP1fzsY5juO65fOAPHJQ/U6PnjVqMaA3j3Un6+urq7GE7GS8sP39Vn5+0Ladu6Onp4eP/7gk2i9et5eeJYpxeJlKzSSoO07dzN6xJBPPuYvlSxhyqK2PDxFrn0/UePESFpfmEGNEyPJte8ntjw8laBu8LPnOPZox49d4pMHhYGC0T2ac2/bEhrXLJ9lk4ePxaniWB90jNdxkZTPnl/rdpde3uHU8xtUtnl/s11Fq4JceHmbsy/i35535/Ujdj++QF375L85AWjtUoXjz64RGBF/tWHzw1O4mdjhkS3xKzx9czUgVhnH/Lu7tI5ZCCEyXGxU/Evl0vmH2NQ9ScnMzJSyZUoxa95CqtSuR9EyFRk1bhKdO7Rh3oz3L3FbunAuMTExlKlUg36DhjJu1PC0/oQSePDwIS3bd6FAibI0btEOAwN9Th3aR05XFwCCHjzkfzt3E/TgIR7lK+OUu6D65+Tps1qN4eBgz8Gd27jyz3Vad+xKXFxcim2WLpzLi5ehlKxQlbadu9O7e1fsPjqZ7tC1J9W8G6TYV5MffFAqlXRo2yrdzyfi4uKYMWc+JcpVxqtBIyIjozhxcA9uOd9/YRgaGsaNm++/7Nu0dRtPQ0JYs2GTxudbtrL2V/1//MEHv8Xzadu5O1u2bU+y3uRxvuqnJUH8Y21v3blL7ZrVU3mkXz6F6sMFgeKThIWFYWlpyfODS7C0Sjmb1UaMEvbcV+DtokL/ozRvy8NTND33Kx//4t79b7uxzGAaOZUnJiaW9ovmsH7NMXj793GdmqVYNLAbzvZpE+eX4ErYPSoeHUKkMhozXSNWlx6g1Ym+697OPI0OJVapxLdAM0bkb6qxf96dnQy86ocKFbGqOLq5eTO/+E9J9nc45Co1TozkWd3VdLw4F49s7ozM34yaJ0bi4+CJi4ktjc9OJs5na4L6Gx4cZ8S11dyqtQhLfVOsdrZiapFO2FEj0Tkgvg3J/T0gvg0ZPQciVXoE6Dji7uqMkWH8t9+Z9SZqRSreRP01U6kgLEaR4TfQVvNuQJVKFRk9POVvz3VMrdiyfhUNG9TLgMiyjhlz5nPw0BF2bt34Wf2k9RyIjIrmbmAQbspgjBSx6vLQ5yFY1ehCaGhoivfhyBKmLCZOFUf/K0sTJA8AKuKTiAFXlxF9K4ZW42bC28czO7pnZ8OoQVQskbmP/UoP+c2cuFh1Bi9jXrMl+BQdLs7hUMXxFLJwSbbdke8mEB4XyennNxh2bRW5TR3Vy44Oh1xl4s0/mFe8K57Z83ErPJj+V5fieCN7gkQjMR1y1qD/laW0cq7Cqec32VBmEMeeXU+yfifXmsy69T9+/W8rEwq1Tt0HIIQQ6UhhYAyF67x9M3QG0TWQ5CETvXr1itt37rL9j3WZHUqW5pzDiSED+2d2GOlCEogs5tiz6wRFJn2jkwq4/yaEVgvjkweFCczu34VuP9T+6m7gecdAR588Zo4AlM6eh/MvbjHnzg4WleiebDt3U3sAilrk5ElUKGP/Xa9OIHyvr6W1SxU656ylrvM6LpJulxcyLN+P6CiS/yqwrn1JuvsvpIv/fOo7lMbaIIVMXkeXsQVb0vHSXHq6f/qr7oUQIj3En8zLCf23wtzcnMCbmfuisq9B08Y/pFwpi5IEIosJjnyhXUVz+MGnHEsG9CS7hVn6BvWFUaEiSqn9GykTaxMRF4XOR7cI6Sp0Uani66ZEV6FLa5eqTL21lZ3lRmoVQ5McFZl+60/G3tiQqtiFEEKIzKR8/TyzQxAZTBKILMbO0FKregv7/kTXUt7pHE3mG35tNd72JXExtuFV7Bs2BB3jcMg/7Cr//qR92LVVPHjznBWl+gKw4M4uXExsKWAW/wi448+uM/3WNnrlev/Nf32HMsy8/T9KWLrHL2F6HYzvv2tp4FAm2SddfWhswRYMzNMQawPtnzgxsVAb6pwaq3V9IYQQQoiMJglEFqPQ4ttvgDyujukcyZfhcdRL2l2YRXDUCyz1TChm4cau8iOpZVdCXSc48gX3P3j/ghIVw6+t4m7EE/QUuuQ2dWBioTb89PYRrgDD8zVBgYJR/67lwZvn2BpaUN++NONTcX+CgY4+Nob6KVf8QHXbYlSzKcr+p/6paieEEEIIkVEkgchiHkdp9xQMbetldb97JP+CH4DlJftobPfKVY9euZJ/UoSeji6jCjRjVIFmWsdS1aaI+glLiWno6KmxP6n6eyr4Au+evqL18EIIIYQQGUIeDJjFOBplT9N6QgghhBBCpIYkEFlMJeuCOBtZk9RjgBWAi7ENlay/vse1CiGEEEKIzCcJRBajq9BlZtFOAAmSiHfbM4p01PpGXyGEEEIIIVJD7oHIgho5lWdjmcH0v7JU450QzsY2zCjSkUZO5TMxOiGEEF+b6IhwYqOiMmw8PUNDDEy+rUeQp7Vq3g0oXrQIs6ZOyuxQvkoB9wLJVagEF08eoUTxopkdToaTBCKLauRUHh/Hshx7dp3gyBc4GmWnknVBufIghBAiTUVHhHNhyyoiXmr5HqI0YJItO6UatdE6iXjy5Ckjx01kz74DPH7ylOzZslG8aGF8h/9Cec+y6Rxt0o4eP8m0WXO5cOkywY8esWX9Kho20HyIh0qlYszEKSxZtpIXL1/iWaYU82b8SuFC8UuRnz9/wbBxkzly+BD3gx5gY22FT4N6jBs5DEvL5F9SmpzREyYzduKvAOjo6ODk6IBXzepMGjMKW1ubTz/oTBYTE8OkaTNZuWY9Dx4Gkz9vHiaP88Xbq2ay7fbuP8joCZP55/oNjIwMqVyxPFMnjsPdLecnx+JesDj3Au9z8tBeypUtoy7vN2gol69c5dCe7Rr1R0+YzI2b/7FuxdJPGm+Y7zguXvJHpVJRpnQpxvsO/+TYUyJLmLIwXYUuVW2K0MK5ElVtikjyIIQQIs3FRkUR8fIF+oZGmFhmT/cffUMjIl6+SNUVjx9btePvK1fxW7yAG5fPsW3TGqpU+o7nL16m3wejhdevX1OsaBHmzpiSZJ1fZ8xh5twFzJ0xhbNHD+Bgb4dXg8a8evUKgIfBwTx69IhfJ4zl77PHWf7bfPbuP0jnHr0/O77CBQvw8PZ17t34mwWzp7Nj117aden+2f1mphFjJrB46QrmTJvCPxdO8VPnDjRq0ZZL/n8n2ebO3QAaNmtNtSqVuXTqCHv+/IOQZ89p3KLtZ8djZGTEkJFjtKq7fecevq9XN+WKSZg4ZiS6urr8c/0GK1av/eR+tCEJhBBCCCFSpG9kjIGJabr/6BsZpyquly9DOX7yNJPHjaZalUrkdHWhbOlSDB3Un3re8e/3CbgXiI6pFf6Xr2i00zG14vDR42n6OX2oTu1ajPcdTiOfBonuV6lUzJ6/iGGDfqaRTwOKFC6E3+IFRLyJYO3GzQAUKVyIlStX0qCuN7lzuVO9amXG+w5n+669xMbGah3Lnn0HyOaYk5Vr1qvL9PT0cHCwJ4eTE/Xr1KZ3j67sO3iIN2/esGffASrVrEN2JzdsXHLToHFzbt+5q/V4fqvWYpXDndXrNmqUHzx0BANLOx4/fqJ1X6mxet1Ghg7qT13vWuRyd6N7l47UrlmdGXPmJ9nmov9l4uLiGO87nNy53CnpUZyf+/bi8pWrxMTEaDWuUqmkS8++5C9ehnuB75/B3rVjO06fPc+uPfuTbX8/KIir165T5+2VEh1TK35b6keDxs0xs82Bp6cnp86c5dbtO1TzboCZrTMVqnkl+J3s3LqRM0f307v7T1rF/akkgRBCCCFElmVmZoqZmRl/bt9JVBrfp9GtzwDM7VyS/Qm8H/TJ/d8NuMejx4/xqlFNXWZoaEiV7ypy6vTZJNuFhoVhYWGOnp52K9HXb9pMs7YdWbFkIW1bNU+ynrGREUqlktjYWF5HRNC/dw/OHj3IgR1/oqOjQ6PmbVAqlVqN2ezHH/hlQD8GDBmukegs9VtFg7re2NvbJdpu4tQZKX7mx06cSnLcqOgojIwMExzX8VOnk2xTuqQHurq6LF+1hri4OEJDw1i9bgNeNaqhr5/yC2Gjo6Np1qYDFy76c2z/LnK6uqj3ueV05adO7RnmOzbZz+5/O/dQuWIFsmWzVJeNnzyVNi2bcfHkEfLmzUvrjl3p1nsAQ37ux7ljBwHoPWAwEJ+Mvpv/xkbGjJs8lRfpeAVO7oEQQgghRJalp6fH8t/m0bVXP35b6kfJEsWo/F1Fmv/YiGJFC39W32NHDGVg3+RfWOrk6PDJ/T96/BgAe3tbjXI7O1sCAxN/k+izZ88ZP3kaXTu212qMBYuXMnz0OP7csIZqVSolWe/fGzdZ9PtyypYuibm5OY0bfq+x//cFc7B3y8e16/9SpHChFMc1Njbm5749mTZ7Lgf+Ooy3V02eP3/B1u07+WPtiiTbdevUgaaNGibbdw4nxyT31a5RnZlzF1C5YgVy53Ln4KEjbNu5m7i4uCTbuOV0Ze//NtO0dQe69R5AXFwc5T3LsHPLxiTbvBP++jX1GzXjTWQkh/ZsT/S+lBG/DMRv9VrWrN9Em5aJv6D2fzt28X39Ohpl7du0pGnjH1CpoG/fvnh5eTHil4HUrlUDgD49fqJjt/ilbLGxsdSo64Oenh5PQ0Lo0+MnsmfPlmL8n0oSCCGEEEJkaY0bfk89by+OnTjFqbPn2Lv/IFNnzmHJ/Nm0b9Pyk/u1s7PFzs425YqfSfHRg9lVKhUKRcI3PoWFhVG/cTMKFciP77DBKfa7Zdt2Hj95yrEDuyhbulSC/Vf+uYa5nQtxcXFERUVRtdJ3/DZvJgC379xl1NiJnD53npBnz1AqVQAE3n+gVQIB8cldwwb12Lh5K95eNVm1bgPWVlZ4vz0BToyVVXasrD79Zbizpk6ia69+FPTwRKFQkDuXO+3btMRvVdL3BDx69JguPfvStlVzWjRpzKvwcHzHT6JJq/bs27El0d/FOy3bd8Y5hxMHdv6JiYlJonVsbW34uW8vfMdPotmPPyTYHxYWxpHjJ1k8f7ZGebEi7xNgO7v4KzZFi7z/7O3t7IiMjCQsLAwLCwuOH9yTZJxpTZYwCSGEECLLMzIyolaNaowaOpgTf+2lXesWjJ4wGYh/yhDEn5i/o83a9vRewuRgbw/Ao4/uB3j6NAR7O80lPq9evaJOwyaYmZqxZf0qrZbWlChWFFsbG5avWqtx7O/kz5eHS6eO8M+FU0Q8e8jB3dvIkzsXAN83acGz589ZPG8Wpw/v5/ThfQBEx0Sn6hib/ODD1rfLy5atWE271i3Q1U36oS+fu4TJ1taGrRtWE/40iIB/L3P90hnMTE1xd3NNss38xb9jYW7OrxPG4FGiGJW/q8CqpYs4ePgIZ86dT/b46tSuxd9Xr3H6bPL1BvTuwZs3kSxYnPAJS7v3HaBg/nwaS58Ajd/xuyRGXy9h2bvkLiPJFQghhBBCfHUKFcjPth27ALC1sQYg+NFjPN7u9//7ShIt30vvJUzubjlxsLdn/1+H8ShRDIhfT3/k+AkmjxutrhcWFkazJo0xNDRk26Y1GBkZadV/Lnc3pk0aRzXv79HV1WXejF819hvoG6gThg89e/ac6//eZNGcmVSqGP9uqeMnk76HIDnVq1ZGT0+PsZOmcvXadbasX5Vs/c9dwvSOkZEROZyciImJYcu27TRJps+IiDfo6mp+p66rE5/kpHTPR/fOHSlSqCA+TVuxY/N6qlSqmGg9MzMzRgwZyJiJU6hfp7bGvv/t2E2Det4pHtOXRBIIIYQQQmRZz549p2nrDnRo24piRQpjbm7G+YuXmDpzLt/Xi19TbmxsTLmypZkyYxZuOV0IefackWMnpNj35y5hCg8P59bt90/JuRtwD//LV7Cyyo6rizMKhYK+PbsxadoM8ubJRd7cuZg0dSYmxia0bNoYiL/y0LhxY6LeRLBq6W+Ehb0iLCz+Ea+2tjbJfpsPkC9vHv7avY1q3t+jp6ur1YvlsmfPhrW1FUuWrcDRwZ7A+0EMHTX2kz4DPT09fOrXZcr0WVSpVJHcudyTrf+5S5jOnDvPg4fBlChWlAcPgxkzYQpKpZLB/fuo68xbtIQ//7eTA7v+BKCetxez5i1k7KRf45cwvQpn+Ohx5HR1waN4sRTH7N29K3FxcTT4sQW7tm7kuwrlEq3XtWM7Zs1byPpNW/AsE7+kLDY2lt37D7C/79ZPPubMIAmEEEIIIVIUE/nmixzHzMyUsmVKMWveQm7fvUtMTCwuzjno3KENwwYNUNdbunAunbr3pkylGuTPm4cp40dT+/vGaR2+hvMX/ale5/3NyD8PGQFAu1YtWL44/rGigwf04U3kG3r2G6R+kdze//2Bubk5ABcuXeb8+fjlMXmLat7HcOeaP245k16a807+fHk5uOtP9ZWI6ZPHJ1tfR0eHdX6/03fQEIqWqUj+vHmYPW0y1bw1H0dbzbsBbq6u6mNJSpMffFi2YjUd27ZOMdbPFRkZxcixE7hz9x5mZqbU9arFyqULNZ5uFPLsGbfvvk/sqletzJrli5k6cy5TZ87FxNiY8p5l2P3nJoyNtXuscL9e3VEqldRr1JTdf26iQjnPBHX09fUZO3IYrTp0UZcdOXYCM1NTSnmU+PSDzgQKVWKL4kSqhIWFYWlpyfODS7C0Spu3N8YoYc99Bd4uKvTlTpVvkswBIXNAZPQciFTpEaDjiLurM0aGBkDWeBP110ylgrAYBRb6KpK5lzdTuBcsju+wX1K8UT3gXiC5CpXg4skjlCheNIOiyxr6DBxCbGwsC2ZNS7JOWs+ByKho7gYG4aYMxkjx/hG7oc9DsKrRhdDQUCwskn/LuVyBEEIIIUSSDEzMKNWoTareDP259AwNJXn4wv174ybmZmbJvldCpKxIoQKUL1s2s8NINUkghBBCCJEsAxMzOaEXGgrkz8ff505kdhhZnrbv8/jSSAIhhBBCCCHSjVtOV5Svn2d2GCINyapaIYQQQgghhNYkgRBCCCGEEEJoTRIIIYQQQgghhNYkgRBCCCGEEEJoTRIIIYQQQgghhNYkgRBCCCGESCM6plb8uX1nuo4RcC8QHVMr/C9fAeDw0ePomFrx8mVouo4rxDuSQAghhBAiS+vQtSc6plZ06zMgwb4e/QaiY2pFh649MySWh7evU8erZoaM9U6FcmV5ePs6lpbJvz1YiLQiCYQQQgghsjwX5xxs+GMLb968UZdFRkayftNmXF2c03386OhoABwc7DE0NEz38T5kYGCAg4M9CoUiQ8cV3y5JIIQQQgiR5ZUsURxXZ2e2bNuhLtuybQcuOXLgUbyYRt2oqCj6DByCfc58GFs5UqlmHc5duKhR58ixE3hWrolRdgecchVkyMgxxMbGqvdX825ArwGDGfDLcGxd8+DVoBGguYTp3VKjLdu2U73O95ja5KCEZyVOnTmrMdaS5StwzVcEU5scNGrehhlz5pPdyU3rY/94CZPfqrVkd3Jj7/6DFCrpibmdC3V8fiQ4+JFGu+Ur11CopCfGVo4U9PBkweKlWo8pvm1ZJoGYMGECFSpUwMTEhGzZsiVaJzAwkAYNGmBqaoqNjQ19+vRRfyOQlKioKHr37o2NjQ2mpqZ8//33BAUFpcMRCCGEECI9tW/TEr9Va9Xby1euoUPbVgnqDR7uy5Y/t+O3eD4XThwiT65cePv8yPPnLwB48PAh9Ro1o3QpD/xPH2XB7GksW7ma8VOmafSzcs169PT0OH5gN4vmzEgyrhFjxvNz315cOnWEfHlz07J9F3UycuLUabr3+Zk+PX7i0qkj1KxelYlTk+5LWxERb5g+ex4rf1/Ekb07CAwKYtCwUer9S5avYMSYCYz3HcG1i6eZMHoEo8ZNZMXqdZ89tvj66WV2ANqKjo6mSZMmlC9fnqVLE2bIcXFx1KtXD1tbW44fP86zZ89o164dKpWKuXPnJtlvv3792L59O+vXr8fa2pqff/6Z+vXrc+HCBXR1ddPzkIQQQogvmkqlIiIiIlPGNjExSfWSnDYtmjHMdxwB9wJRKBScOH2GdSt+58ixE+o6r1+/ZtHvy1n+23zq1K4FwOL5s9hf8DBLV6xiUP8+LFi8DBfnHMyb8SsKhYIC+fPxMPgRQ0aOYdTQwejoxH//mieXO79OGJNiXD/37UU9by8ARg8fQpHSFbh1+w4F8udj3qIl1PGqycB+vQHIlzcPp06fZceevak69o/FxMSwcM4McudyB6DnT10YN2mqev/4ydOYNmkcjXwaAODulpNr/95g8TI/2rVu8Vlji69flkkgxoyJ/x/Uz88v0f379u3j2rVr3L9/HycnJwCmT59O+/btmTBhAhYWCW8sCg0NZenSpaxatYqaNeNveFq9ejUuLi4cOHCA2rVrp8/BCCGEEFlAREQE5nYumTL2qyf3MTU1TVUbGxtr6nnXYsWadahUKup518LGxlqjzu07AcTExFCxvKe6TF9fn7KlS3L9xk0A/r1xk/JlS2skMBXLeRIeHk7Qg4fqeypKlSyhVVzFihRW/9nRwQGAJ09DKJA/Hzdu3qLh9/U06pcpXfKzEwgTExN18hA/rj1Pnj4F4OnTEO4HPaBzjz507dVPXSc2NhbLRM6XhPhYlkkgUnLq1CmKFCmiTh4AateuTVRUFBcuXKBatWoJ2ly4cIGYmBi8vLzUZU5OThQpUoSTJ09KAiGEEEJkMR3atqb3gMEAzJsxNcF+lUoFkODqhkqlUpepVCpIZH98u/dlpiYmWsWkr6ev/vO7MZRKZYJxPx7rc+jra57iKRQKdb/vxl48bxaeZUpp1JPVF0IbX00C8ejRI+zt7TXKsmfPjoGBAY8ePUqyjYGBAdmzZ9cot7e3T7INxN83ERUVpd4OCwsDIEYFMcpPPQJNsUrN/4pvj8wBIXNAZPQciFUBOqBSxf8YG5sQ9vh+xgz+EWNjE7Q9j1a9+1FB7Zo1iI6OAcCrZvX4Y/lgf+5c7hgYGHDs5GlaNv0RiF/uc/7iJfr27IZKBQXz52fLtu0ole9P7k+ePou5uRlOjk7xcb2NLbEY331+7/apPqj34X9VKsifLy9nz1/U6Of8Rf/3dT44xo/HVPHROKqP2nzQ54d17OzsyOHkyJ27AbRs1iTR+MWXI7E58Fn9ve0jVgkxH+SuManoO1MTiNGjR6uXJiXl3LlzlC5dWqv+ElsrmVhmn5KU2kyaNCnRuA8+McUkPG0foXbggTyS7Vsnc0DIHBAZNQf09BQ4OEB4rIJoHQWgAAOzDBn7Y69iU67zTowy/mQoLEYB6HHq9GkAXiv14O2+mHf7Dczo2LEjg4f7YmSeHWdnZ+bMmcPriDc0admWsBgFrTt0YvaCRXTr/wtdunTh1q1b+E6YTI8ePQmP04W4+GQrWql4O6amN3Hx5eGx8ftex76v9+ptWcTbOh06d6VevXpMmrUAb29vjh49yu59BwDNvl998OeP+42Ie9+3ToyCyDgFKhUa7d+8rfOubPAvQxgyZAgGphbUrFmTqKgo/P39efnyJT17Zsw7M0TqvEpkrn2K6FgFb2Lh2CMFsbHv+4yI0H7JYKYmEL169aJ58+bJ1nFzc9OqLwcHB86cOaNR9uLFC2JiYhJcmfiwTXR0NC9evNC4CvHkyRMqVKiQ5FhDhw5lwID3L6sJCwvDxcWFGnavscxurFW8KYlVxv+DUTOHCr0s86wskZZkDgiZAyKj50CUSkWQHpjpqTDSzzpfQ+vrgJ4OWLyN2cLa/O2e+G09nfg67/ZPHz8KPZR079aNV+HhlC5Zgj3b/sDV1hJQYZHTkR2bN/DLCF8qVaqEVfbsdGrXmrFDf0ZP722fCjDQUan7/JCxbny52du6pnrv6ynflpm8reNVyZMFs6czbtKvTJgwAa8a1enfqzvzf/sdC30VKuJPHM31Vbw71fu4XxPd+G3zt9tGuioUCjRiM35b511Zr05tsDI3Zvqsufj6+mJqakLRQoXo07NbosckMk9ic+BzRCpVGOtBJQcVhor3v+vQF6+17kOhSouFdhnIz8+Pfv368fLlS43y3bt3U79+fYKCgnB0dARgw4YNtGvXjidPniR5E7WtrS2rV6+madOmAAQHB+Ps7MyuXbu0vgciLCwMS0tLnh9cgqWVzecd4FsxSthzX4G3iwp9OXH4JskcEDIHREbPgUiVHgE6jri7OmNkaJD+A4pEdenZlxs3/+Po/l3qKwkW+qqPb8sQ34i0ngORUdHcDQzCTRmMkeL9pb7Q5yFY1ehCaGhooufNH8oy/yQFBgbi7+9PYGAgcXFx+Pv74+/vT3h4OABeXl4UKlSINm3acOnSJQ4ePMjAgQPp0qWL+kN48OABBQoU4OzZ+Be4WFpa0qlTJ37++WcOHjzIpUuXaN26NUWLFlU/lUkIIYQQIj1NmzWXy39f5dbtO8xduJiVa9bTtlXyKzSEyExZ5ibqUaNGsWLFCvW2h4cHAIcOHaJq1aro6uqyc+dOevToQcWKFTE2NqZly5ZMm/b+pS8xMTHcuHFD45nWM2fORE9Pj6ZNm/LmzRtq1KiBn5+fPIVACCGEEBni3IWLTJ05l1fh4eRyz8nsaZPp3L5tZoclRJKyTALh5+eX5Dsg3nF1dWXHjh1J7ndzc0vwaDQjIyPmzp2b7MvmhBBCCCHSy4ZVyzM7BCFSJcssYRJCCCGEEEJkPkkghBBCCCGEEFqTBEIIIYQQQgihNUkghBBCCCGEEFqTBEIIIYQQQgihNUkghBBCCCGEEFqTBEIIIYQQQgs6plb8uX0nAAH3AtExtcL/8hUADh89jo6pFS9fhqZrDNW8G9Bv0NB0HSMjxxFZU5Z5D4QQQgghMsn9IAh5lnHj2ViDi7PW1Tt07cmKNevo2qk9i+bM0NjXo99AFi1ZRrtWLVi+eH5aR6pWoVxZHt6+jqWlRbqNIcSXQhIIIYQQQiTtfhAUL4MiKirDhlQZGsLlc6lKIlycc7Dhjy3MnDIBY2NjACIjI1m/aTOuqejnUxkYGODgYJ/u4wjxJZAlTEIIIYRIWsizDE0egPjxUnnFo2SJ4rg6O7Nl2w512ZZtO3DJkQOP4sU06roXLM6seQs1yjzKVWb0hMnq7f9u3aaKVz2MrRwpXKoc+w8eSnb8j5cw+a1aS3YnN/buP0ihkp6Y27lQx+dHgoMfabRbvnINhUp6YmzlSEEPTxYsXpqq437x4iXtOnfHKoc7pjY5qNuwCf/duq3e/+zZc1q264xL3sKY2uSgWJmKrNu4WaOP169f065zd8ztXHDKVZDps+elKgbx7ZEEQgghhBBfhfZtWuK3aq16e/nKNXRo2yrV/SiVShq3aIuuri6nDu1j4ewZDBk5JtX9RES8Yfrseaz8fRFH9u4gMCiIQcNGqfcvWb6CEWMmMN53BNcunmbC6BGMGjeRFavXaT1Gh596cv7SJbZtXMvJv/aiUqmo16gZMTExAERGRVLSozjb/1jPlXMn6NKxHW07d+PMufPqPgYN9+XQ0WNsWbeSvf/bzJFjJ7hw6XKqj1d8O2QJkxBCCCG+Cm1aNGOY7zgC7gWiUCg4cfoM61b8zpFjJ1LVz4G/DnP9xk3u/umPc44cAEwYPYK6PzRNVT8xMTEsnDOD3LncAej5UxfGTZqq3j9+8jSmTRpHI58GALi75eTavzdYvMyPdq1bpNj/f7du87+duzl+cDcVynkCsHrZYlzzF+XP7Ttp0qghOZycGNivt7pN7+5d2bv/IJu2bMOzTGnCw8NZtmI1K5YsoFaNagD4LV6AS74iqTpW8W2RBEIIIYQQXwUbG2vqeddixZp18d/Ee9fCxsY61f1cv3ETVxdndfIAUN6zbKr7MTExUScPAI4O9jx5+hSAp09DuB/0gM49+tC1Vz91ndjYWCwttLsR+/qNm+jp6eFZprS6zNraivx583D9xk0A4uLimDx9Fhv/2MqD4GCioqKJiorC1NQEgNt3AoiOjtY4Piur7OTPmyfVxyu+HZJACCGEEOKr0aFta3oPGAzAvBlTE62jo6ODSqXSKIuJjVH/+eN9AApF6mPR19c8zVIoFOq+lUolAIvnzcKzTCmNerq6ulr1n1ic78oVbwOePns+s+YtZOaUiRQtXAhTUxP6Dx5GdHRMsn0IkRy5B0IIIYQQXw3vWjWIjo4hOjqG2rWqJ1rH1saa4EeP1dthYWHcDQhUbxcqkJ/A+0E8DA5Wl506cy5N47S3tyOHkyN3AgLIkzuXxo+7W06t+ihUID+xsbEa9zM8e/acm7duUzB/PgCOnzzF9/Xq0LpFU4oXK0Iudzf+u31HXT9Pbnf09fU5ffb98b148ZKbH9yILcTH5AqEEEIIIb4aurq6XLt4Sv3nxFSrUpkVq9fRoK432bNZMmrcJI26NatXJX++PLTr3INpk8YR9uoVI8ZMSPNYfYf/Qt+BQ7EwN6eOV02ioqI5f/ESL16+ZECfnim2z5snNz7169K1Vz8WzZmJuZkZQ0eNIYeTIz716wKQO5c7W7Zt5+TpM2TPlo0Zcxfw6PFjdYJhZmZGx3atGTzcF2srK+ztbBkxZgI6OvIds0iaJBBCCCGE+KpYpHAPwdCB/bgbEECDH5tjaWHB2JHDuBtwT71fR0eHLetW0blHHzyr1MQtpyuzp06iTsMmaRpn5/ZtMTE2YdqsufwyYjSmpiYULVyIvj27ad3HskXz6DdoKA1+bE50dAyVK5Zn55YN6OvrAzByyCAC7gXi7dMEE2NjunRsS8P69QgNC1P3MXXCGF6Hv8anaSvMzcwY0KeHxn4hPqZQyeK3zxYWFoalpSXPDy7B0somTfqMUcKe+wq8XVToy5cA3ySZA0LmgMjoORCp0iNAxxF3V2eMDA3iC7PIi+S+VioVhMUosNBXfdJ9GCLrS+s5EBkVzd3AINyUwRgpYtXloc9DsKrRhdDQ0BSTcLkCIYQQQoikuTjD5XOoUvlit89iYy3JgxBfMEkghBBCCJE8F2c5oRdCqMlFcSGEEEIIIYTWJIEQQgghhBBCaE0SCCGEEEIIIYTWJIEQQgghhBBCaE0SCCGEEEIIIYTWJIEQQgghhBBCaE0SCCGEEEIIIYTWJIEQQgghhEilw0ePo2NqxcuXoek+Tvbs2dN9HCFSQ14kJ4QQQohkBYYFEfIm495EbWNsjauF9i+u69C1Jy9DQ9m6YXU6RiWEeEcSCCGEEEIkKTAsiAK/lyEyLirDxjTSNeTfzudSlUQIITKOLGESQgghRJJC3jzL0OQBIDIuKs2uePitWkt2JzeNsj+370TH1EqjbPyUadjnzIeFvSude/RhyMgxeJSrnKqxNv/5P4qULo9RdgfcCxZn+ux5GvtXr9tIme+qY2HviqN7AVq178KTJ0816uzas5/8xctgYu1E9TrfExAYmKoYhMgIkkAIIYQQ4pu2Zv0mJv46g8njfDl//C9cXZxZ9PuyVPVx4ZI/zdp0pNmPjfj77HF8h/3CqHGT8Fu1Vl0nOjqasSOH4n/6KFs3rOLuvXt0+Kmnev/9oCAat2xLHa+aXDp1hE7t2jBs1Ng0O04h0oosYRJCCCHEN23eosV0bNeaDm1bATBq6GD2HzxEePhrrfuYOWcBNapWZuSQQQDky5uHa//eYNrsubRv0xKAju1aq+vncndj9rTJeFauSXh4OGZmZixcspxc7jmZ+etEFAoF+fPl5co/1/h1xuw0PFohPp9cgRBCCCHEN+3Gf7coW6qkRlmZj7ZTcv3GTSqU99Qoq1jek/9u3SEuLg6AS/5/07BpK9wKFMPC3pVq3t8DEHg/CIB/b9zEs0xpFAqFuo9yZcuk+niESG+SQAghhBDiq6Wjo4NKpdIoi4mJSVDvw5N2IEGblKhUqmT7eP36NbV9GmNmasqqpb9x9ugBtqxbCUB0dMwnjSlEZpEEQgghhBBfLVsba169Cuf16/fLkfz/vqpRJ3/ePJy9cFGj7MIl/1SNU6hAfk6cPK1RdvL0WfLlyY2uri7/3vyPkJBnTBo3ikoVy1Mgfz6ePA3RqF+wQH7OnDuvUfbxthBfAkkghBBCCJHlhYaF4X/5isZP4P0gPMuUxsTEhGG+47h1+w5rN/zBitXrNNr26taVZStWs2L1Ov67dZvxU6bx99V/ElxRSM6APj05ePgo4yZP5eZ/t1ixeh3zf/udn/v2AsDV2RkDAwPmLlzCnbsB/G/nbsZPmabRR7fOHbh9J4ABvwznxs3/Eo1ViC+BJBBCCCGEyPIOHz1OyQpVNH58x03Cyio7q5YuYve+AxQr+x3rN23Gd/hgjbatmjdhyMB+DBo+ilIVq3E34B7tWrXAyMhQ6/FLehRnw6plbPhjC0XLVMR3/CTGjBiivoHa1taG5b/N54+t2yhcqjxTps9i6kTNJyy5ujjzx9oV7Ni9lxLlKvPb0uWMHz3i8z8cIdKYQiUL7j5bWFgYlpaWPD+4BEsrmzTpM0YJe+4r8HZRoS9p3jdJ5oCQOSAyeg5EqvQI0HHE3dUZI0MD4Nt9kZxX/R9wsLdn5dJFmRYDgEoFYTEKLPRVpOKCiPiKpPUciIyK5m5gEG7KYIwUsery0OchWNXoQmhoKBYWFsn2IY9xFUIIIUSSXC2c+bfzuTR7sZs2bIytMzR5iIiIYNHvy6ldszq6urqs27SZA4eOsG/7lgyLQYisRBIIIYQQQiTL1cI5U68GpDeFQsHuvQeY8Ot0oqKiyZ83D3+sXUHN6lUzOzQhvkiSQAghhBDim2ZsbMz+nVszOwwhsgxZVSuEEEIIIYTQmiQQQgghhBBCCK1JAiGEEEIIIYTQmiQQQgghhBBCCK1JAiGEEEIIIYTQmiQQQgghhBBCCK1JAiGEEEII8ZHDR4+jY2rFy5ehmR0Kz58/x8EtHwH3ArWq716wOLPmLUznqERUVBQ58xflwiX/zA4lw0kCIYQQQog0FaeM43DgcdZd38zhwOPEKePSdbwOXXuiY2qFjqkVBpZ25C7swcChI3n9+nW6jptRZs6cSf26tXHL6ZrZoaSp8PBweg0YjEvewphYO1GopCcLlyxLsd3Ll6H07D8Ip1wFMbZypFBJT3bt2a/e716wuHo+fPjTs/+gNI3f0NCQn/v2YsiIMWnab1YgL5ITQgghRJrZcnM7/Q4OJSj8obrM2cyJWTUm0Shfg3Qb17tWDZYtmkdMbAzHTpymS8++vI6IYOHs6ek2ZkZ48+YNq1atYueWDZkdSprr/8twDh89zqqlv+GW05V9B/+iZ79BODk64FO/bqJtoqOj8WrQCDtbGzatWY5zjhzcD3qAuZmZus7ZoweJi3uftF69dh2vBo1o8oNPmh9Dq2ZNGDzcl+v/3qBggfxp3v+XSq5ACCGEECJNbLm5nSbb2mskDwAPwoNpsq09W25uT7exDQ0NcXCwx8XZmZbNfqRlsx/Ztn0XAKvXbaTMd9WxsHfF0b0Ardp34cmTpxrtd+3ZT/7iZTCxdqJ6ne8TXS508vQZqnjVw8TaCdd8RegzcIj6KsfYSb9SrEzFBG1KV6zGqHET1dvLV66hUElPjK0cKejhyYLFS5M9rt37DqCnp0d5z7Lqsg+vuHz4c/jo8QTtA+4FomNqhf/lK+qyly9DE9T/387d5CtWWn38K1avS/clXKfPnKNtq+ZUrfwdbjld6dqxPcWLFuH8xUtJtlm2cg3PX7xg64bVVCxfjpyuLnxXoRzFixVR17G1tcHBwV79s2P3XnLncqdKpYS/n3dGT5iMR7nKLFuxmpz5i2Ju50L3vj8TFxfHrzPm4OheAPuc+Zjwq2ZCam1tRYVyZVm3afPnfyBZiCQQQgghhPhscco4+h0cigpVgn3vyvr/NSzdlzO9Y2xsTExsDBD/rfXYkUPxP32UrRtWcffePTr81FNd935QEI1btqWOV00unTpCp3ZtGDpqrEZ/V65ew9unCT98X5/LZ46xfuVSTpw8Te8BgwHo2LYV1/69wbkLF9Vt/r7yD5cu/0371i0BWLJ8BSPGTGC87wiuXTzNhNEjGDVuIitWr0vyOI6dOEmJEiU0ymZNncTD29fVP316/ISdrS0F8uX9pM8q4F4gTVq1x6d+XS6dOkLXju0YMWZ8iu3qNmyCuZ1Lsj/JqVihHNt37uHBw4eoVCoOHTnGzVu3qV2zRpJttu/cTfmyZejZfxAObvkpWroCE6fO0Lji8KHo6GjWbNhEh7atUCgUycZz+24Ae/YfYPefm1jrt4TlK9dQv1Ezgh4+5PDe7Uwe58vIMRM4ffacRrsypUpy/OTpZPv+2sgSJiGEEEJ8tmNBpxJcefiQChX3Xz3gWNApqrp+l66xnD1/gXUb/6BG1coAdGzXWr0vl7sbs6dNxrNyTcLDwzEzM2PhkuXkcs/JzF8nolAoyJ8vL1f+ucavM2ar202bNZcWTRvTr1d3APLmyc3saZOpWrs+C2ZPxzlHDmrXrM7yVWspU6okAMtXraFKpYrkcncDYPzkaUybNI5GPvFLudzdcnLt3xssXuZHu9YtEj2WgMD7ODo6apRZWlpgaWkBwJZt2/ltqR/7d2zFwcH+kz6vRb8vJ3/ePEydGJ805c+Xl6vX/mXir8kv/1qyYDZv3kR+0pgAc6ZNpmvPfrjkLYKenh46OjosmT+b7yqUS7LNnYB7/HXkGC2b/cjOrRv479Zteg0YTGxsLKOGDk5Q/8/tO3n5MpT2SXy+H1IqlSxdOBdzc3MKFSxAtcrfceO/W+zcuhEdHR3y58vLrzPncPjoCcqVLaNul8PJkU1btLvB/WshCYQQQgghPlvw68dpWi+1duzei7mdC7GxscTExOBTvy5zpk0B4JL/34yZOAX/v6/w/MVLlEolAIH3gyhUsAD/3riJZ5nSGt9Ql/cso9H/BX9/bt2+y9oNf6jLVCoVSqWSuwH3KFggP507tKVT997MmDweXV1d1m74g2mTxgHw9GkI94Me0LlHH7r26qfuIzY2FksLiySP682bN9jaGya675L/37Tr0oN5M39N9qQ7JTf/u0XpUh4aZWVLl0yxXQ4np08eE2DOgt84fe482zatJaeLC0dPnKRn/0E4OthTs3rVRNsolUrsbG1YPG8Wurq6lPIowcPgR0ybNS/RBGLZitXU8aqJ00dJWGLccrpgbm6u3ra3s0NXVxcdHZ0Pymx58lRz+ZuxsREREW+0POqvgyQQQgghhPhsjqbaffutbb3Uqlb5OxbMno6+vj5Ojg7o6+sD8Pr1a2r7NMarejVWLf0NWxtrAu8H4e3zI9HR8UucVKqEy64+plQq6dqpPX26d02wz9XFGYAGdb0xNDBk6/92YmhoQFR0FI0bNlC3B1g8bxaeZUpptNfV1U1yXBtra16+fJmg/NGjx/g0bUnHtq3o1K5Nku3fnfx+eIwxMTEadVQqVYLlPdp8JnUbNuFYCkt3Xj25n2j5mzdvGD56PFvWr6KetxcAxYoWxv/vK0yfPS/JBMLRwR59PX2Nz6xg/nw8evyY6OhoDAwM1OX3Au9z4NARNq9bmeKxAOjr6WtsKxQK9Tz6sOzd7/Kd589fYmtjrdUYX4ssk0BMmDCBnTt34u/vj4GBQYL/mS5fvszkyZM5fvw4ISEhuLm50a1bN/r27Ztsv1WrVuXIkSMaZc2aNWP9+vVpfQhCCCHEV6uSc3mczZx4EB6c6H0QChQ4mztRybl8uoxvampKnty5EpT/e/M/QkKeMWncKFyc40/0z1/016hTsEB+tu3YqVF2+ux5je2SxYtz7dq/iY7xjp6eHm1bNcdv1RoMDQ1p9mMjTExMALC3tyOHkyN3AgJo1byJ1sflUbwoK9dt0iiLjIykYbNWFMiXlxlTJiTb/t2JbfCjx7y7xuD/9xWNOvnz5WX33v0aZcndyPzO5yxhiomJISYmBp2PEhddXd0EJ+gfqlDOk3Ub/0CpVKqTo5u3buPo4KCRPED8EjI7W1t1gpJe/rl2HY/ixdJ1jC9NlkkgoqOjadKkCeXLl2fp0oRPLLhw4QK2trasXr0aFxcXTp48SdeuXdHV1aVXr17J9t2lSxfGjn1/s5SxsXGaxy+EEEJ8zXR1dJlVYxJNtrVHgUIjiVAQf5I4s/pEdHWS/rY9Pbg6O2NgYMDchUvo1rkDV69dZ/yUaRp1unXuwIw58xnwy3B+6tSeC5cuJ7ixefCAvpSv5kXP/oPo0r4tpqYmXL9xk/1/HWbu9Cnqep3bt6FQyfjlRMcP7tbow3f4L/QdOBQLc3PqeNUkKiqa8xcv8eLlSwb06UlivGrUYJjvOF68eImVVbb4eHsP4H7QQw7s3MrTpyHqulZW2ROcRBsbG1OubGmmzJiFW04XQp49Z+RYzaTjp07tmTl3Ab+MGE2ndq3x//uK+viTu/H4c5YwWVhYUKVSRQYP98XY2Jicri4cOXaCVWs3MH3y+xu423XujpOTI5PGjgKge5cOzFu0hL6DhtK7Wxf+u32HSVNn0ruH5pUhpVKJ36q1tG3VHD299D3dPXbyFGNHDkvXMb40WeYpTGPGjKF///4ULVo00f0dO3Zkzpw5VKlShVy5ctG6dWs6dOjAli1bUuzbxMQEBwcH9Y+lpWVahy+EEEJ89Rrla8AmHz9ymGmuN3c2d2KTj1+6vgciKba2Niz/bT5/bN1G4VLlmTJ9lvpm4XdcXZz5Y+0KduzeS4lylflt6XImjB6hUadY0cIc3rudW7duU9mrHiUrVGXU2Ik4fnTjct48ualQriz58+XBs0xpjX2d27dlyfzZrFi9jmJlv6Oqd31WrFmHu1vOJOMvWqQQHh4ebNzyp7rsyPETBD96ROFS5XHKXVD9c/L02UT7WLpwLjExMZSpVIN+g4YybtRwjf3ubjnZtMaPrf/bQXHPSiz6fTnDBv8MgKGhQWJdpol1fr9TppQHrTv+FP+7mTGb8b7D6da5g7pOYFAQjx69v2/GxdmZvf/7g/MXLlLcsxJ9Bw6hT8+fGPJzP42+D/x1mMD7QXRs2yrd4gc4deYsoWFh/PjD9+k6zpdGodJmkdsXxM/Pj379+iW6HvBjrVu3JjIykj/++CPJOlWrVuWff/5BpVJhb29PnTp18PX11biJJiVhYWFYWlry/OASLK1stG6XnBgl7LmvwNtFhX6WSfNEWpI5IGQOiIyeA5EqPQJ0HHF3dcboM04c45RxHAs6RfDrxzia2lPJuXyGX3nILCqVioIennTt2C7Jqwqp6w/+2Lmf0aNGcuX8SY0betPThF+n89vvywm8eTVDxsuqmrZuT4nixRg2aEC6jaFSQViMAgt9FSk8iVYrkVHR3A0Mwk0ZjJEiVl0e+jwEqxpdCA0NxSKZG/shCy1hSq1Tp06xceNGdu7cmWy9Vq1a4e7ujoODA1evXmXo0KFcvnyZ/fv3J9kmKiqKqKgo9XZYWBgAMar4v+zTQqxS87/i2yNzQMgcEBk9B2JVgE78CcvnfL2oo9Cliovmo1qz1teVn+bJk6esXr+BBw+Dad+6VZocswrw8vLiQcBtgh48VN/HkdYWLl5K6VIeWFtZceL0GabNmkvPrl2+id/bp4qKiqJYkSL069k9XT8n1Yf/TYs59baPWCXEfJCQxKSi70xNIEaPHs2YMWOSrXPu3DlKly6dbJ2P/fPPP/j4+DBq1Chq1aqVbN0uXbqo/1ykSBHy5s1L6dKluXjxIiVLJv4Is0mTJiUa98EnppiEp0Fq+IEDD9K2P5H1yBwQMgdERs0BPT0FDg4QHqsgWkfmXWo55sqPtbU1M2fORNcsO2ExKbfRVoeu8e+fSMs+P/TPf3cY/+t0Xrx4gbOzMz179qJ///6Excg8SJKOEb0HDCIGiEmn38uHXqXR7yI6VsGbWDj2SEFs7Ps+IyJMte4jU5cwhYSEEBISkmwdNzc3jIyM1NspLWG6du0a1apVo3PnzkyYkPyTCRKjUqkwNDRk1apVNGvWLNE6iV2BcHFx4fGBJVhmT5slTLHK+H8wauZQoSdLF75JMgeEzAGR0XMgSqVHkJ4jbi6ft4RJpB0V8SeO5voq5FT+25TWcyAyKpqA+0E4xwZj+OESphch2NfMAkuYbGxssLFJmxNuiL/yUL16ddq1a/dJycO7PmJiYhK89fFDhoaGGBomfKmLvoI0X6Oqp5P2fYqsReaAkDkgMmoOxL39SlGhIE3WWos08O53gvxOvllpPAfe9aGnE3/u+o5+KvrOMv8kBQYG4u/vT2BgIHFxcfj7++Pv7094eDgQf+JfrVo1atWqxYABA3j06BGPHj3i6QdvC3zw4AEFChTg7Nn4pxTcvn2bsWPHcv78eQICAti1axdNmjTBw8ODihUrZspxCiGEEEII8SXLMjdRjxo1ihUrVqi3PTziX4dy6NAhqlatyqZNm3j69Clr1qxhzZo16no5c+YkICAAiH9pyY0bN4iIiADAwMCAgwcPMnv2bMLDw3FxcaFevXr4+vom+1ZIIYQQQgghvlVZJoHw8/PDz88vyf2jR49m9OjRyfbh5uam8Wp2FxeXBG+hFkIIIYQQQiQtyyxhEkIIIYQQQmQ+SSCEEEIIIYQQWpMEQgghhBAiBTqmVvy5Pf7ltAH3AtExtcL/8hUADh89jo6pFS9fhmZmiEJkGEkghBBCCJGldejaEx1TK7r1GZBgX49+A9ExtaJD157pNn6FcmV5ePs6lpbJPztfiK+FJBBCCCGEyPJcnHOw4Y8tvHnzRl0WGRnJ+k2bcXVxTtexDQwMcHCwRyEvahDfCEkghBBCCJHllSxRHFdnZ7Zs26Eu27JtBy45cuBRvJi6zL1gcWbNW6jR1qNcZUZPmKze/u/Wbap41cPYypHCpcqx/+ChZMf+eAmT36q1ZHdyY+/+gxQq6Ym5nQt1fH4kOPiRRrvlK9dQqKQnxlaOFPTwZMHipZ98/EJkpCzzGFchhBBCZI7Xr5Pep6sLRkba1dXRAWPjlOuamqYuvnfat2mJ36q1tGreBIg/Qe/QthVHjp3Qug+lUknjFm2xsbHm1KF9hL16Rf/Bw1IdS0TEG6bPnsfK3xeho9ChTeefGDRsFKuXLwZgyfIVjB4/hbkzpuBRvBiXLv9N1179MDUxoV3rFqkeT4iMJAmEEEIIIZJlbpc9yX11a8ewY0u4etveLRsREYkv5alSKYZDe97XdS9kSUhIwsUQytcvPinONi2aMcx3HAH3AlEoFJw4fYZ1K35PVQJx4K/DXL9xk7t/+uOcIwcAE0aPoO4PTVMVS0xMDAvnzCB3LncAev7UhXGTpqr3j588jWmTxtHIpwEA7m45ufbvDRYv85MEQnzxJIEQQgghxFfBxsaaet61WLFmHSqVinretbCxsU5VH9dv3MTVxVmdPACU9yyb6lhMTEzUyQOAo4M9T54+BeDp0xDuBz2gc48+dO3VT10nNjYWSwu5EVt8+SSBEEIIIUSyXj1J+oqArq7m9uOAl0nW1fnoYsPda2n/2NMObVvTe8BgAObNmJpgv46ODiqVSqMsJjZG/eeP9wF8yr3R+vqap1gKhULdt1KpBGDxvFl4limlUU/34w9UiC+QJBBCCCGESFZq7klIr7ra8q5Vg+jo+ISgdq3qCfbb2lgT/OixejssLIy7AYHq7UIF8hN4P4iHwcE4OToCcOrMuTSN0d7ejhxOjtwJCFDfryFEViIJhBBCCCG+Grq6uly7eEr9549Vq1KZFavX0aCuN9mzWTJq3CSNejWrVyV/vjy069yDaZPGEfbqFSPGTEjzOH2H/0LfgUOxMDenjldNoqKiOX/xEi9evmRAn/R7Z4UQaUESCCGEEEJ8VSySuY9g6MB+3A0IoMGPzbG0sGDsyGHcDbin3q+jo8OWdavo3KMPnlVq4pbTldlTJ1GnYdpeKejcvi0mxiZMmzWXX0aMxtTUhKKFC9G3Z7c0HUeI9KBQJbbYT6RKWFgYlpaWPD+4BEsrmzTpM0YJe+4r8HZRoS9v6/gmyRwQMgdERs+BSJUeATqOuLs6Y2RokP4DihSpVBAWo8BCX/VJ92KIrC+t50BkVDR3A4NwUwZjpIhVl4c+D8GqRhdCQ0OTTcJBXiQnhBBCCCGESAVJIIQQQgghhBBakwRCCCGEEEIIoTVJIIQQQgghhBBakwRCCCGEEEIIoTVJIIQQQgihQR7PKMTXKy2evyoJhBBCCCEA0EUJKhUxMbEpVxZCZEkRkVGgUqJP3Cf3IS+SE0IIIQQAeigxUUbw9NkL9PR00ZEXD2Q6lQqiYxVEKuU9EN+qtJoDKlV88vA05BnZlGHo6nz6pQhJIIQQQggBgEIBDoQSEGnAvftRmR2OeOtNLBjLGds3Lc3mgEpJNmUY9opXn9WNTEchhBBCqBko4sijekSMUk/uhfgCxCrh2CMFlRxU6MnC829SWs4BfeI+68rDO5JACCGEEEKDjgIMkfsgvgQxCoiNVWCoUKEvS5i+SV/iHJBcVgghhBBCCKE1SSCEEEIIIYQQWpMEQgghhBBCCKE1uQciDajevpEj7PUbFAYRadJnjBIiIhSEhavQlzTvmyRzQMgcEDIHhMwBkVFzIOz1G+D9eW1yJIFIA69exT8Ky+37PpkciRBCCCGEEJ/u1atXWFpaJltHodImzRDJUiqVPHz4EHNzcxRp9JaXsLAwXFxcuH//PhYWFmnSp8haZA4ImQNC5oCQOSAyag6oVCpevXqFk5MTOjrJX+qQKxBpQEdHB2dn53Tp28LCQv7C+MbJHBAyB4TMASFzQGTEHEjpysM7sppOCCGEEEIIoTVJIIQQQgghhBBakwTiC2VoaIivry+GhoaZHYrIJDIHhMwBIXNAyBwQX+IckJuohRBCCCGEEFqTKxBCCCGEEEIIrUkCIYQQQgghhNCaJBBCCCGEEEIIrUkCkUXcvHkTHx8fbGxssLCwoGLFihw6dCizwxIZbOfOnXh6emJsbIyNjQ2NGjXK7JBEJoiKiqJEiRIoFAr8/f0zOxyRQQICAujUqRPu7u4YGxuTO3dufH19iY6OzuzQRDpasGAB7u7uGBkZUapUKY4dO5bZIYkMMmnSJMqUKYO5uTl2dnY0bNiQGzduZHZYgCQQWUa9evWIjY3lr7/+4sKFC5QoUYL69evz6NGjzA5NZJDNmzfTpk0bOnTowOXLlzlx4gQtW7bM7LBEJhg8eDBOTk6ZHYbIYP/++y9KpZLffvuNf/75h5kzZ7Jo0SKGDRuW2aGJdLJhwwb69evH8OHDuXTpEpUqVaJOnToEBgZmdmgiAxw5coSePXty+vRp9u/fT2xsLF5eXrx+/TqzQ5OnMGUFISEh2NracvToUSpVqgTAq1evsLCw4MCBA9SoUSOTIxTpLTY2Fjc3N8aMGUOnTp0yOxyRiXbv3s2AAQPYvHkzhQsX5tKlS5QoUSKzwxKZZOrUqSxcuJA7d+5kdigiHXh6elKyZEkWLlyoLitYsCANGzZk0qRJmRiZyAxPnz7Fzs6OI0eOULly5UyNRa5AZAHW1tYULFiQlStX8vr1a2JjY/ntt9+wt7enVKlSmR2eyAAXL17kwYMH6Ojo4OHhgaOjI3Xq1OGff/7J7NBEBnr8+DFdunRh1apVmJiYZHY44gsQGhqKlZVVZoch0kF0dDQXLlzAy8tLo9zLy4uTJ09mUlQiM4WGhgJ8Ef/PSwKRBSgUCvbv38+lS5cwNzfHyMiImTNnsmfPHrJly5bZ4YkM8O7bxdGjRzNixAh27NhB9uzZqVKlCs+fP8/k6ERGUKlUtG/fnm7dulG6dOnMDkd8AW7fvs3cuXPp1q1bZoci0kFISAhxcXHY29trlNvb28vy5W+QSqViwIABfPfddxQpUiSzw5EEIjONHj0ahUKR7M/58+dRqVT06NEDOzs7jh07xtmzZ/Hx8aF+/foEBwdn9mGIz6DtHFAqlQAMHz6cxo0bU6pUKZYvX45CoWDTpk2ZfBTic2g7B+bOnUtYWBhDhw7N7JBFGtN2Dnzo4cOHeHt706RJEzp37pxJkYuMoFAoNLZVKlWCMvH169WrF3///Tfr1q3L7FAAuQciU4WEhBASEpJsHTc3N06cOIGXlxcvXrzAwsJCvS9v3rx06tSJIUOGpHeoIp1oOwdOnTpF9erVOXbsGN999516n6enJzVr1mTChAnpHapIJ9rOgebNm7N9+3aNE4e4uDh0dXVp1aoVK1asSO9QRTrRdg4YGRkB8clDtWrV8PT0xM/PDx0d+S7waxQdHY2JiQmbNm3ihx9+UJf37dsXf39/jhw5konRiYzUu3dv/vzzT44ePYq7u3tmhwOAXmYH8C2zsbHBxsYmxXoREREACf6R0NHRUX8zLbImbedAqVKlMDQ05MaNG+oEIiYmhoCAAHLmzJneYYp0pO0cmDNnDuPHj1dvP3z4kNq1a7NhwwY8PT3TM0SRzrSdAwAPHjygWrVq6quQkjx8vQwMDChVqhT79+/XSCD279+Pj49PJkYmMopKpaJ3795s3bqVw4cPfzHJA0gCkSWUL1+e7Nmz065dO0aNGoWxsTFLlizh7t271KtXL7PDExnAwsKCbt264evri4uLCzlz5mTq1KkANGnSJJOjExnB1dVVY9vMzAyA3Llz4+zsnBkhiQz28OFDqlatiqurK9OmTePp06fqfQ4ODpkYmUgvAwYMoE2bNpQuXZry5cuzePFiAgMD5b6Xb0TPnj1Zu3Yt27Ztw9zcXH3vi6WlJcbGxpkamyQQWYCNjQ179uxh+PDhVK9enZiYGAoXLsy2bdsoXrx4ZocnMsjUqVPR09OjTZs2vHnzBk9PT/766y+yZ8+e2aEJITLAvn37uHXrFrdu3UqQNMpq5K9Ts2bNePbsGWPHjiU4OJgiRYqwa9cuufL8jXj3+N6qVatqlC9fvpz27dtnfEAfkHsghBBCCCGEEFqTxZNCCCGEEEIIrUkCIYQQQgghhNCaJBBCCCGEEEIIrUkCIYQQQgghhNCaJBBCCCGEEEIIrUkCIYQQQgghhNCaJBBCCCGEEEIIrUkCIYQQQgghhNCaJBBCCCHSjZ+fH9myZVNvjx49mhIlSmRaPOlt6dKleHl5fVYfT548wdbWlgcPHqRRVEIIkbYkgRBCiCymffv2KBQKFAoF+vr62NvbU6tWLZYtW4ZSqczs8JI1cOBADh48qFXdrJZsREVFMWrUKEaOHPlZ/djZ2dGmTRt8fX3TKDIhhEhbkkAIIUQW5O3tTXBwMAEBAezevZtq1arRt29f6tevT2xsbJLtYmJiMjDKhMzMzLC2ts7UGNLL5s2bMTMzo1KlSp/dV4cOHVizZg0vXrxIg8iEECJtSQIhhBBZkKGhIQ4ODuTIkYOSJUsybNgwtm3bxu7du/Hz81PXUygULFq0CB8fH0xNTRk/fjxxcXF06tQJd3d3jI2NyZ8/P7Nnz9boPzY2lj59+pAtWzasra355ZdfaNeuHQ0bNkw2Lj8/P1xdXTExMeGHH37g2bNnGvs/vqpw+PBhypYti6mpKdmyZaNixYrcu3cPPz8/xowZw+XLl9VXW94d14wZMyhatCimpqa4uLjQo0cPwsPDNWLIli0be/fupWDBgpiZmakTrg8tW7aMwoULY2hoiKOjI7169VLvCw0NpWvXrtjZ2WFhYUH16tW5fPlysse+fv16vv/+e42y9u3b07BhQyZOnIi9vT3ZsmVjzJgxxMbGMmjQIKysrHB2dmbZsmUa7YoWLYqDgwNbt25NdkwhhMgMkkAIIcRXonr16hQvXpwtW7ZolPv6+uLj48OVK1fo2LEjSqUSZ2dnNm7cyLVr1xg1ahTDhg1j48aN6jZTpkxhzZo1LF++nBMnThAWFsaff/6Z7PhnzpyhY8eO9OjRA39/f6pVq8b48eOTrB8bG0vDhg2pUqUKf//9N6dOnaJr164oFAqaNWvGzz//TOHChQkODiY4OJhmzZoBoKOjw5w5c7h69SorVqzgr7/+YvDgwRp9R0REMG3aNFatWsXRo0cJDAxk4MCB6v0LFy6kZ8+edO3alStXrvC///2PPHnyAKBSqahXrx6PHj1i165dXLhwgZIlS1KjRg2eP3+e5PEcO3aM0qVLJyj/66+/ePjwIUePHmXGjBmMHj2a+vXrkz17ds6cOUO3bt3o1q0b9+/f12hXtmxZjh07luxnLoQQmUIlhBAiS2nXrp3Kx8cn0X3NmjVTFSxYUL0NqPr165dinz169FA1btxYvW1vb6+aOnWqejs2Nlbl6uqa5LgqlUrVokULlbe3d4J4LC0t1du+vr6q4sWLq1QqlerZs2cqQHX48OFE+/uwbnI2btyosra2Vm8vX75cBahu3bqlLps/f77K3t5eve3k5KQaPnx4ov0dPHhQZWFhoYqMjNQoz507t+q3335LtM2LFy9UgOro0aMa5e3atVPlzJlTFRcXpy7Lnz+/qlKlSurt2NhYlampqWrdunUabfv376+qWrVqUocthBCZRq5ACCHEV0SlUqFQKDTKEvtWfNGiRZQuXRpbW1vMzMxYsmQJgYGBQPzyncePH1O2bFl1fV1dXUqVKpXs2NevX6d8+fIaZR9vf8jKyor27dtTu3ZtGjRowOzZsxMsM0rMoUOHqFWrFjly5MDc3Jy2bdvy7NkzXr9+ra5jYmJC7ty51duOjo48efIEiH/K0cOHD6lRo0ai/V+4cIHw8HCsra0xMzNT/9y9e5fbt28n2ubNmzcAGBkZJdhXuHBhdHTe/3Nrb29P0aJF1du6urpYW1ur43vH2NiYiIiIlD4OIYTIcJJACCHEV+T69eu4u7trlJmammpsb9y4kf79+9OxY0f27duHv78/HTp0IDo6WqPex4mISqVKduyU9idm+fLlnDp1igoVKrBhwwby5cvH6dOnk6x/79496tatS5EiRdi8eTMXLlxg/vz5gOYN4vr6+hrtFAqFOj5jY+NkY1IqlTg6OuLv76/xc+PGDQYNGpRoG2traxQKRaI3PScWS2JlHz9B6/nz59ja2iYbqxBCZAZJIIQQ4ivx119/ceXKFRo3bpxsvWPHjlGhQgV69OiBh4cHefLk0fhm3dLSEnt7e86ePasui4uL49KlS8n2W6hQoQQn/8klA+94eHgwdOhQTp48SZEiRVi7di0ABgYGxMXFadQ9f/48sbGxTJ8+nXLlypEvXz4ePnyY4hgfMjc3x83NLcnHyZYsWZJHjx6hp6dHnjx5NH5sbGwSbWNgYEChQoW4du1aqmJJztWrV/Hw8Eiz/oQQIq1IAiGEEFlQVFQUjx494sGDB1y8eJGJEyfi4+ND/fr1adu2bbJt8+TJw/nz59m7dy83b95k5MiRnDt3TqNO7969mTRpEtu2bePGjRv07duXFy9eJLgq8aE+ffqwZ88efv31V27evMm8efPYs2dPkvXv3r3L0KFDOXXqFPfu3WPfvn3cvHmTggULAuDm5sbdu3fx9/cnJCSEqKgocufOTWxsLHPnzuXOnTusWrWKRYsWpeKTizd69GimT5/OnDlz+O+//7h48SJz584FoGbNmpQvX56GDRuyd+9eAgICOHnyJCNGjOD8+fNJ9lm7dm2OHz+e6lgSExERwYULFz77pXRCCJEeJIEQQogsaM+ePTg6OuLm5oa3tzeHDh1izpw5bNu2DV1d3WTbduvWjUaNGtGsWTM8PT159uwZPXr00Kjzyy+/0KJFC9q2bUv58uUxMzOjdu3aia7xf6dcuXL8/vvvzJ07lxIlSrBv3z5GjBiRZH0TExP+/fdfGjduTL58+ejatSu9evXip59+AqBx48Z4e3tTrVo1bG1tWbduHSVKlGDGjBlMmTKFIkWKsGbNGiZNmpSKTy5eu3btmDVrFgsWLKBw4cLUr1+f//77D4hfTrRr1y4qV65Mx44dyZcvH82bNycgIAB7e/sk++zSpQu7du0iNDQ01fF8bNu2bbi6uqbJOyWEECKtKVSfsmhVCCHEN0WpVFKwYEGaNm3KuHHjMjucL1bTpk3VS7I+R9myZenXrx8tW7ZMo8iEECLtyBUIIYQQCdy7d48lS5Zw8+ZNrly5Qvfu3bl7966c0KZg6tSpmJmZfVYfT5484ccff6RFixZpFJUQQqQtuQIhhBAigfv379O8eXOuXr2KSqWiSJEiTJ48mcqVK2d2aEIIITKZJBBCCCGEEEIIrckSJiGEEEIIIYTWJIEQQgghhBBCaE0SCCGEEEIIIYTWJIEQQgghhBBCaE0SCCGEEEIIIYTWJIEQQgghhBBCaE0SCCGEEEIIIYTWJIEQQgghhBBCaE0SCCGEEEIIIYTW/g9bCGSQcGi+9QAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -331,65 +296,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 18, "id": "aea072d6", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Debug] mass_update = False\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.67\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3375980.0732258284, thetaa = 55.648978744279006\n", - "Output Ha = 1904935.434154513, Va = 2787196.1621888806\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.67\n", - "Output Ha = 1904935.4341545128, Va = 2787196.162188881, zlug = 8.67\n", - "Output Ta = 3375980.073225829, thetaa = 55.648978744279006\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.382\n", - "Vmax_layer = 280523.02 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 280523.02 N\n", - "Vmax3 = 241917.02 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.388\n", - "Vmax_layer = 977721.41 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 977721.41 N\n", - "Vmax3 = 697709.86 N\n", - "dz_clip = 5.00 m\n", - "ez_layer = 9.68 m\n", - "Su_av_z (at ez_layer) = 67381.35 Pa\n", - "alphastar = 0.381\n", - "Vmax_layer = 4714446.61 N\n", - "Vmax1 = 4714446.61 N\n", - "Vmax2 = 2131059.03 N\n", - "Vmax3 = 1378013.04 N\n", - "dz_clip = -4.00 m\n", - "Hmax_layer = 1068338.04 m\n", - "Hmax_layer = 4213508.43 m\n", - "ez_global = 7.51 m\n", - "Hmax_final = 11659911.93 m\n", - "rlug_eff = 0.49 m\n", - "zlug_eff = 8.75 m\n", - "M = -3719492.55 Nm\n", - "delta_phi = 1.23 deg\n", - "phi_MH = -37.45 deg\n", - "a_MH = 14.68\n", - "b_MH = 2.13\n", - "a_VH = 4.60\n", - "b_VH = 5.87\n", - "pile_head = 65180.03 N\n", - "Vmax_final = 6037871.08 N\n" - ] - }, { "data": { "image/png": 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", @@ -465,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 20, "id": "2858630b", "metadata": {}, "outputs": [ @@ -480,7 +390,7 @@ } ], "source": [ - "anchor.getCostAnchor()\n", + "anchor.getCost()\n", "\n", "print(f\"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg\")\n", "print(f\"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg\")\n", @@ -498,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 21, "id": "304da340", "metadata": {}, "outputs": [ @@ -506,2417 +416,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.0\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.0\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.0\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.382\n", - "Vmax_layer = 280523.02 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 280523.02 N\n", - "Vmax3 = 241917.02 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.388\n", - "Vmax_layer = 977721.41 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 977721.41 N\n", - "Vmax3 = 697709.86 N\n", - "dz_clip = 5.00 m\n", - "ez_layer = 9.68 m\n", - "Su_av_z (at ez_layer) = 67381.35 Pa\n", - "alphastar = 0.381\n", - "Vmax_layer = 4714446.61 N\n", - "Vmax1 = 4714446.61 N\n", - "Vmax2 = 2131059.03 N\n", - "Vmax3 = 1378013.04 N\n", - "dz_clip = -4.00 m\n", - "Hmax_layer = 1068338.04 m\n", - "Hmax_layer = 4213508.43 m\n", - "ez_global = 7.51 m\n", - "Hmax_final = 11659911.93 m\n", - "rlug_eff = 0.55 m\n", - "zlug_eff = 8.08 m\n", - "M = -2654716.69 Nm\n", - "delta_phi = 1.23 deg\n", - "phi_MH = -37.45 deg\n", - "a_MH = 14.68\n", - "b_MH = 2.13\n", - "a_VH = 4.60\n", - "b_VH = 5.87\n", - "pile_head = 65180.03 N\n", - "Vmax_final = 6037871.08 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.066666666666666\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.066666666666666\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.066666666666666\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.382\n", - "Vmax_layer = 280523.02 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 280523.02 N\n", - "Vmax3 = 241917.02 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.388\n", - "Vmax_layer = 977721.41 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 977721.41 N\n", - "Vmax3 = 697709.86 N\n", - "dz_clip = 5.10 m\n", - "ez_layer = 9.74 m\n", - "Su_av_z (at ez_layer) = 67694.92 Pa\n", - "alphastar = 0.383\n", - "Vmax_layer = 4770807.37 N\n", - "Vmax1 = 4770807.37 N\n", - "Vmax2 = 2190495.24 N\n", - "Vmax3 = 1414096.12 N\n", - "dz_clip = -3.90 m\n", - "Hmax_layer = 1068338.04 m\n", - "Hmax_layer = 4213508.43 m\n", - "ez_global = 7.58 m\n", - "Hmax_final = 11886754.80 m\n", - "rlug_eff = 0.54 m\n", - "zlug_eff = 8.14 m\n", - "M = -2625933.52 Nm\n", - "delta_phi = 1.22 deg\n", - "phi_MH = -37.44 deg\n", - "a_MH = 14.67\n", - "b_MH = 2.13\n", - "a_VH = 4.64\n", - "b_VH = 5.88\n", - "pile_head = 65180.03 N\n", - "Vmax_final = 6094231.84 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.0\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.0\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.0\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.382\n", - "Vmax_layer = 294553.72 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 294553.72 N\n", - "Vmax3 = 257302.57 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.388\n", - "Vmax_layer = 1020831.37 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 1020831.37 N\n", - "Vmax3 = 735946.66 N\n", - "dz_clip = 5.00 m\n", - "ez_layer = 9.68 m\n", - "Su_av_z (at ez_layer) = 67381.35 Pa\n", - "alphastar = 0.381\n", - "Vmax_layer = 5051461.65 N\n", - "Vmax1 = 5051461.65 N\n", - "Vmax2 = 2222396.23 N\n", - "Vmax3 = 1448165.42 N\n", - "dz_clip = -4.00 m\n", - "Hmax_layer = 1111071.56 m\n", - "Hmax_layer = 4382048.77 m\n", - "ez_global = 7.51 m\n", - "Hmax_final = 12126308.41 m\n", - "rlug_eff = 0.60 m\n", - "zlug_eff = 8.08 m\n", - "M = -2798831.67 Nm\n", - "delta_phi = 1.26 deg\n", - "phi_MH = -37.49 deg\n", - "a_MH = 14.68\n", - "b_MH = 2.13\n", - "a_VH = 4.44\n", - "b_VH = 5.81\n", - "pile_head = 70824.69 N\n", - "Vmax_final = 6437671.43 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.982130175536096\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.982130175536096\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.982130175536096\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.383\n", - "Vmax_layer = 267237.19 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 267237.19 N\n", - "Vmax3 = 227542.82 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.388\n", - "Vmax_layer = 936475.62 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 936475.62 N\n", - "Vmax3 = 661643.15 N\n", - "dz_clip = 4.97 m\n", - "ez_layer = 9.66 m\n", - "Su_av_z (at ez_layer) = 67297.38 Pa\n", - "alphastar = 0.381\n", - "Vmax_layer = 4381689.40 N\n", - "Vmax1 = 4381689.40 N\n", - "Vmax2 = 2028361.30 N\n", - "Vmax3 = 1302390.78 N\n", - "dz_clip = -4.03 m\n", - "Hmax_layer = 1027168.32 m\n", - "Hmax_layer = 4051135.70 m\n", - "ez_global = 7.49 m\n", - "Hmax_final = 11152427.49 m\n", - "rlug_eff = 0.50 m\n", - "zlug_eff = 8.06 m\n", - "M = -2533815.29 Nm\n", - "delta_phi = 1.19 deg\n", - "phi_MH = -37.42 deg\n", - "a_MH = 14.67\n", - "b_MH = 2.13\n", - "a_VH = 4.75\n", - "b_VH = 5.92\n", - "pile_head = 60000.40 N\n", - "Vmax_final = 5645402.62 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.968928702525485\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.968928702525485\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.968928702525485\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.383\n", - "Vmax_layer = 253949.36 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 253949.36 N\n", - "Vmax3 = 213365.55 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.389\n", - "Vmax_layer = 894799.42 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 894799.42 N\n", - "Vmax3 = 625721.83 N\n", - "dz_clip = 4.95 m\n", - "ez_layer = 9.65 m\n", - "Su_av_z (at ez_layer) = 67235.37 Pa\n", - "alphastar = 0.381\n", - "Vmax_layer = 4059873.87 N\n", - "Vmax1 = 4059873.87 N\n", - "Vmax2 = 1929735.84 N\n", - "Vmax3 = 1229931.07 N\n", - "dz_clip = -4.05 m\n", - "Hmax_layer = 985281.02 m\n", - "Hmax_layer = 3885932.88 m\n", - "ez_global = 7.48 m\n", - "Hmax_final = 10656507.21 m\n", - "rlug_eff = 0.45 m\n", - "zlug_eff = 8.04 m\n", - "M = -2399145.75 Nm\n", - "delta_phi = 1.14 deg\n", - "phi_MH = -37.37 deg\n", - "a_MH = 14.66\n", - "b_MH = 2.12\n", - "a_VH = 4.93\n", - "b_VH = 5.98\n", - "pile_head = 54986.19 N\n", - "Vmax_final = 5263608.84 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.98319669640548\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.98319669640548\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.98319669640548\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.384\n", - "Vmax_layer = 240934.05 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 240934.05 N\n", - "Vmax3 = 199681.97 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.389\n", - "Vmax_layer = 853555.73 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 853555.73 N\n", - "Vmax3 = 590698.27 N\n", - "dz_clip = 4.97 m\n", - "ez_layer = 9.66 m\n", - "Su_av_z (at ez_layer) = 67302.39 Pa\n", - "alphastar = 0.381\n", - "Vmax_layer = 3770930.94 N\n", - "Vmax1 = 3770930.94 N\n", - "Vmax2 = 1854188.89 N\n", - "Vmax3 = 1172231.33 N\n", - "dz_clip = -4.03 m\n", - "Hmax_layer = 943537.67 m\n", - "Hmax_layer = 3721297.74 m\n", - "ez_global = 7.49 m\n", - "Hmax_final = 10247596.94 m\n", - "rlug_eff = 0.40 m\n", - "zlug_eff = 8.05 m\n", - "M = -2252875.71 Nm\n", - "delta_phi = 1.09 deg\n", - "phi_MH = -37.32 deg\n", - "a_MH = 14.65\n", - "b_MH = 2.12\n", - "a_VH = 5.13\n", - "b_VH = 6.04\n", - "pile_head = 50241.16 N\n", - "Vmax_final = 4915661.89 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.0260784418972\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.0260784418972\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.0260784418972\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.384\n", - "Vmax_layer = 230888.20 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 230888.20 N\n", - "Vmax3 = 189264.17 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.389\n", - "Vmax_layer = 821429.12 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 821429.12 N\n", - "Vmax3 = 563784.35 N\n", - "dz_clip = 5.04 m\n", - "ez_layer = 9.70 m\n", - "Su_av_z (at ez_layer) = 67503.95 Pa\n", - "alphastar = 0.382\n", - "Vmax_layer = 3572663.63 N\n", - "Vmax1 = 3572663.63 N\n", - "Vmax2 = 1818508.67 N\n", - "Vmax3 = 1141524.26 N\n", - "dz_clip = -3.96 m\n", - "Hmax_layer = 910817.63 m\n", - "Hmax_layer = 3592250.41 m\n", - "ez_global = 7.54 m\n", - "Hmax_final = 10016185.59 m\n", - "rlug_eff = 0.36 m\n", - "zlug_eff = 8.09 m\n", - "M = -2112176.19 Nm\n", - "delta_phi = 1.04 deg\n", - "phi_MH = -37.27 deg\n", - "a_MH = 14.64\n", - "b_MH = 2.12\n", - "a_VH = 5.33\n", - "b_VH = 6.11\n", - "pile_head = 46694.67 N\n", - "Vmax_final = 4671675.62 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.071808646017427\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.071808646017427\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.071808646017427\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.384\n", - "Vmax_layer = 221466.71 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 221466.71 N\n", - "Vmax3 = 179612.64 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 791062.42 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 791062.42 N\n", - "Vmax3 = 538644.96 N\n", - "dz_clip = 5.11 m\n", - "ez_layer = 9.74 m\n", - "Su_av_z (at ez_layer) = 67719.12 Pa\n", - "alphastar = 0.383\n", - "Vmax_layer = 3391245.16 N\n", - "Vmax1 = 3391245.16 N\n", - "Vmax2 = 1786501.75 N\n", - "Vmax3 = 1113768.92 N\n", - "dz_clip = -3.89 m\n", - "Hmax_layer = 879722.76 m\n", - "Hmax_layer = 3469612.74 m\n", - "ez_global = 7.59 m\n", - "Hmax_final = 9802617.52 m\n", - "rlug_eff = 0.32 m\n", - "zlug_eff = 8.13 m\n", - "M = -1987645.24 Nm\n", - "delta_phi = 0.99 deg\n", - "phi_MH = -37.22 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.53\n", - "b_VH = 6.18\n", - "pile_head = 43462.90 N\n", - "Vmax_final = 4447237.19 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.134836617745083\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.134836617745083\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.134836617745083\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.384\n", - "Vmax_layer = 217286.79 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 217286.79 N\n", - "Vmax3 = 175368.64 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 777515.41 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 777515.41 N\n", - "Vmax3 = 527525.03 N\n", - "dz_clip = 5.20 m\n", - "ez_layer = 9.79 m\n", - "Su_av_z (at ez_layer) = 68016.06 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 3337860.11 N\n", - "Vmax1 = 3337860.11 N\n", - "Vmax2 = 1802474.45 N\n", - "Vmax3 = 1119287.48 N\n", - "dz_clip = -3.80 m\n", - "Hmax_layer = 865797.80 m\n", - "Hmax_layer = 3414692.94 m\n", - "ez_global = 7.66 m\n", - "Hmax_final = 9822765.87 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 8.19 m\n", - "M = -1913941.07 Nm\n", - "delta_phi = 0.96 deg\n", - "phi_MH = -37.18 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.12\n", - "a_VH = 5.66\n", - "b_VH = 6.22\n", - "pile_head = 42058.90 N\n", - "Vmax_final = 4374721.22 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.128098362499056\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.128098362499056\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.128098362499056\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.384\n", - "Vmax_layer = 204673.39 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 204673.39 N\n", - "Vmax3 = 162708.37 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 736353.01 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 736353.01 N\n", - "Vmax3 = 494101.60 N\n", - "dz_clip = 5.19 m\n", - "ez_layer = 9.79 m\n", - "Su_av_z (at ez_layer) = 67984.30 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 3055268.25 N\n", - "Vmax1 = 3055268.25 N\n", - "Vmax2 = 1704894.70 N\n", - "Vmax3 = 1050360.20 N\n", - "dz_clip = -3.81 m\n", - "Hmax_layer = 823283.12 m\n", - "Hmax_layer = 3247015.71 m\n", - "ez_global = 7.65 m\n", - "Hmax_final = 9322538.75 m\n", - "rlug_eff = 0.25 m\n", - "zlug_eff = 8.18 m\n", - "M = -1773403.69 Nm\n", - "delta_phi = 0.89 deg\n", - "phi_MH = -37.12 deg\n", - "a_MH = 14.61\n", - "b_MH = 2.11\n", - "a_VH = 5.92\n", - "b_VH = 6.31\n", - "pile_head = 37935.66 N\n", - "Vmax_final = 4034230.31 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.064733812581633\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.064733812581633\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.064733812581633\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200778.12 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200778.12 N\n", - "Vmax3 = 158844.34 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 723554.22 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 723554.22 N\n", - "Vmax3 = 483821.94 N\n", - "dz_clip = 5.10 m\n", - "ez_layer = 9.73 m\n", - "Su_av_z (at ez_layer) = 67685.82 Pa\n", - "alphastar = 0.383\n", - "Vmax_layer = 2934560.60 N\n", - "Vmax1 = 2934560.60 N\n", - "Vmax2 = 1633123.89 N\n", - "Vmax3 = 1004880.85 N\n", - "dz_clip = -3.90 m\n", - "Hmax_layer = 810000.62 m\n", - "Hmax_layer = 3194629.72 m\n", - "ez_global = 7.58 m\n", - "Hmax_final = 9007383.29 m\n", - "rlug_eff = 0.24 m\n", - "zlug_eff = 8.12 m\n", - "M = -1755541.43 Nm\n", - "delta_phi = 0.89 deg\n", - "phi_MH = -37.11 deg\n", - "a_MH = 14.61\n", - "b_MH = 2.11\n", - "a_VH = 5.96\n", - "b_VH = 6.32\n", - "pile_head = 36697.38 N\n", - "Vmax_final = 3895590.32 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.998215467623575\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.998215467623575\n", - "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 7.998215467623575\n", - "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 199945.41 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 199945.41 N\n", - "Vmax3 = 158021.18 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 720812.78 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 720812.78 N\n", - "Vmax3 = 481627.11 N\n", - "dz_clip = 5.00 m\n", - "ez_layer = 9.68 m\n", - "Su_av_z (at ez_layer) = 67372.96 Pa\n", - "alphastar = 0.382\n", - "Vmax_layer = 2879193.91 N\n", - "Vmax1 = 2879193.91 N\n", - "Vmax2 = 1582650.96 N\n", - "Vmax3 = 974616.00 N\n", - "dz_clip = -4.00 m\n", - "Hmax_layer = 807151.64 m\n", - "Hmax_layer = 3183393.38 m\n", - "ez_global = 7.51 m\n", - "Hmax_final = 8804738.14 m\n", - "rlug_eff = 0.24 m\n", - "zlug_eff = 8.05 m\n", - "M = -1774666.08 Nm\n", - "delta_phi = 0.89 deg\n", - "phi_MH = -37.12 deg\n", - "a_MH = 14.61\n", - "b_MH = 2.11\n", - "a_VH = 5.93\n", - "b_VH = 6.31\n", - "pile_head = 36434.85 N\n", - "Vmax_final = 3836386.95 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.932052105212464\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.932052105212464\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.932052105212464\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 198416.10 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 198416.10 N\n", - "Vmax3 = 156512.04 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 715773.02 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 715773.02 N\n", - "Vmax3 = 477598.68 N\n", - "dz_clip = 4.90 m\n", - "ez_layer = 9.62 m\n", - "Su_av_z (at ez_layer) = 67062.26 Pa\n", - "alphastar = 0.381\n", - "Vmax_layer = 2810131.86 N\n", - "Vmax1 = 2810131.86 N\n", - "Vmax2 = 1528398.93 N\n", - "Vmax3 = 941545.42 N\n", - "dz_clip = -4.10 m\n", - "Hmax_layer = 801910.52 m\n", - "Hmax_layer = 3162722.52 m\n", - "ez_global = 7.44 m\n", - "Hmax_final = 8580006.44 m\n", - "rlug_eff = 0.24 m\n", - "zlug_eff = 7.98 m\n", - "M = -1800507.88 Nm\n", - "delta_phi = 0.90 deg\n", - "phi_MH = -37.13 deg\n", - "a_MH = 14.61\n", - "b_MH = 2.11\n", - "a_VH = 5.91\n", - "b_VH = 6.30\n", - "pile_head = 35954.70 N\n", - "Vmax_final = 3760275.68 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.865586696499585\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.865586696499585\n", - "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.865586696499585\n", - "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 197449.65 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 197449.65 N\n", - "Vmax3 = 155560.13 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 712584.83 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 712584.83 N\n", - "Vmax3 = 475054.60 N\n", - "dz_clip = 4.80 m\n", - "ez_layer = 9.57 m\n", - "Su_av_z (at ez_layer) = 66750.64 Pa\n", - "alphastar = 0.379\n", - "Vmax_layer = 2753492.76 N\n", - "Vmax1 = 2753492.76 N\n", - "Vmax2 = 1478731.12 N\n", - "Vmax3 = 911638.29 N\n", - "dz_clip = -4.20 m\n", - "Hmax_layer = 798592.51 m\n", - "Hmax_layer = 3149636.33 m\n", - "ez_global = 7.36 m\n", - "Hmax_final = 8378262.49 m\n", - "rlug_eff = 0.25 m\n", - "zlug_eff = 7.92 m\n", - "M = -1819224.36 Nm\n", - "delta_phi = 0.91 deg\n", - "phi_MH = -37.14 deg\n", - "a_MH = 14.61\n", - "b_MH = 2.11\n", - "a_VH = 5.88\n", - "b_VH = 6.29\n", - "pile_head = 35652.62 N\n", - "Vmax_final = 3699179.85 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.798991463084559\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3414465.46817813, thetaa = 51.581346423704765\n", - "Output Ha = 2121758.7150329417, Va = 2675203.616280948\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.798991463084559\n", - "Output Ha = 2121758.715032941, Va = 2675203.616280948, zlug = 7.798991463084559\n", - "Output Ta = 3414465.46817813, thetaa = 51.58134642370477\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 196874.23 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 196874.23 N\n", - "Vmax3 = 154994.01 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 710685.36 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 710685.36 N\n", - "Vmax3 = 473540.48 N\n", - "dz_clip = 4.70 m\n", - "ez_layer = 9.51 m\n", - "Su_av_z (at ez_layer) = 66438.92 Pa\n", - "alphastar = 0.377\n", - "Vmax_layer = 2705430.48 N\n", - "Vmax1 = 2705430.48 N\n", - "Vmax2 = 1432335.01 N\n", - "Vmax3 = 883958.63 N\n", - "dz_clip = -4.30 m\n", - "Hmax_layer = 796614.80 m\n", - "Hmax_layer = 3141836.27 m\n", - "ez_global = 7.29 m\n", - "Hmax_final = 8192744.37 m\n", - "rlug_eff = 0.25 m\n", - "zlug_eff = 7.85 m\n", - "M = -1857294.69 Nm\n", - "delta_phi = 0.92 deg\n", - "phi_MH = -37.14 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.11\n", - "a_VH = 5.84\n", - "b_VH = 6.28\n", - "pile_head = 35473.25 N\n", - "Vmax_final = 3648463.32 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.7325628950142224\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3414465.46817813, thetaa = 51.581346423704765\n", - "Output Ha = 2121758.7150329417, Va = 2675203.616280948\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.7325628950142224\n", - "Output Ha = 2121758.715032941, Va = 2675203.616280948, zlug = 7.7325628950142224\n", - "Output Ta = 3414465.46817813, thetaa = 51.58134642370477\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 195825.56 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 195825.56 N\n", - "Vmax3 = 153963.57 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 707221.33 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 707221.33 N\n", - "Vmax3 = 470782.30 N\n", - "dz_clip = 4.60 m\n", - "ez_layer = 9.45 m\n", - "Su_av_z (at ez_layer) = 66128.49 Pa\n", - "alphastar = 0.376\n", - "Vmax_layer = 2648458.05 N\n", - "Vmax1 = 2648458.05 N\n", - "Vmax2 = 1383719.44 N\n", - "Vmax3 = 854590.09 N\n", - "dz_clip = -4.40 m\n", - "Hmax_layer = 793006.36 m\n", - "Hmax_layer = 3127604.63 m\n", - "ez_global = 7.22 m\n", - "Hmax_final = 7993393.93 m\n", - "rlug_eff = 0.25 m\n", - "zlug_eff = 7.78 m\n", - "M = -1876357.33 Nm\n", - "delta_phi = 0.92 deg\n", - "phi_MH = -37.15 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.11\n", - "a_VH = 5.81\n", - "b_VH = 6.27\n", - "pile_head = 35147.33 N\n", - "Vmax_final = 3586652.27 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.677288410606607\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3414465.46817813, thetaa = 51.581346423704765\n", - "Output Ha = 2121758.7150329417, Va = 2675203.616280948\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.677288410606607\n", - "Output Ha = 2121758.715032941, Va = 2675203.616280948, zlug = 7.677288410606607\n", - "Output Ta = 3414465.46817813, thetaa = 51.58134642370477\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 202798.06 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 202798.06 N\n", - "Vmax3 = 160845.34 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 730196.38 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 730196.38 N\n", - "Vmax3 = 489150.03 N\n", - "dz_clip = 4.52 m\n", - "ez_layer = 9.41 m\n", - "Su_av_z (at ez_layer) = 65870.59 Pa\n", - "alphastar = 0.374\n", - "Vmax_layer = 2758674.00 N\n", - "Vmax1 = 2758674.00 N\n", - "Vmax2 = 1392151.06 N\n", - "Vmax3 = 864978.49 N\n", - "dz_clip = -4.48 m\n", - "Hmax_layer = 816897.59 m\n", - "Hmax_layer = 3221831.27 m\n", - "ez_global = 7.15 m\n", - "Hmax_final = 8096223.75 m\n", - "rlug_eff = 0.28 m\n", - "zlug_eff = 7.73 m\n", - "M = -1982366.87 Nm\n", - "delta_phi = 0.97 deg\n", - "phi_MH = -37.20 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.61\n", - "b_VH = 6.20\n", - "pile_head = 37337.42 N\n", - "Vmax_final = 3729005.87 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.665922524227427\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n", - "Output Ha = 2154823.310507873, Va = 2655538.689355862\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.665922524227427\n", - "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.665922524227427\n", - "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 196174.16 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 196174.16 N\n", - "Vmax3 = 154305.93 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 708373.20 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 708373.20 N\n", - "Vmax3 = 471699.01 N\n", - "dz_clip = 4.50 m\n", - "ez_layer = 9.40 m\n", - "Su_av_z (at ez_layer) = 65817.60 Pa\n", - "alphastar = 0.374\n", - "Vmax_layer = 2619818.77 N\n", - "Vmax1 = 2619818.77 N\n", - "Vmax2 = 1344383.57 N\n", - "Vmax3 = 831736.86 N\n", - "dz_clip = -4.50 m\n", - "Hmax_layer = 794206.50 m\n", - "Hmax_layer = 3132337.95 m\n", - "ez_global = 7.14 m\n", - "Hmax_final = 7843864.78 m\n", - "rlug_eff = 0.26 m\n", - "zlug_eff = 7.72 m\n", - "M = -1926167.79 Nm\n", - "delta_phi = 0.94 deg\n", - "phi_MH = -37.16 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.11\n", - "a_VH = 5.75\n", - "b_VH = 6.25\n", - "pile_head = 35255.54 N\n", - "Vmax_final = 3559621.67 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.599299557791959\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n", - "Output Ha = 2154823.310507873, Va = 2655538.689355862\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.599299557791959\n", - "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.599299557791959\n", - "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 195724.83 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 195724.83 N\n", - "Vmax3 = 153864.68 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 706888.43 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 706888.43 N\n", - "Vmax3 = 470517.44 N\n", - "dz_clip = 4.40 m\n", - "ez_layer = 9.34 m\n", - "Su_av_z (at ez_layer) = 65507.31 Pa\n", - "alphastar = 0.372\n", - "Vmax_layer = 2575602.34 N\n", - "Vmax1 = 2575602.34 N\n", - "Vmax2 = 1300634.14 N\n", - "Vmax3 = 805649.13 N\n", - "dz_clip = -4.60 m\n", - "Hmax_layer = 792659.46 m\n", - "Hmax_layer = 3126236.47 m\n", - "ez_global = 7.07 m\n", - "Hmax_final = 7668689.19 m\n", - "rlug_eff = 0.26 m\n", - "zlug_eff = 7.65 m\n", - "M = -1953647.14 Nm\n", - "delta_phi = 0.95 deg\n", - "phi_MH = -37.17 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.11\n", - "a_VH = 5.70\n", - "b_VH = 6.23\n", - "pile_head = 35116.08 N\n", - "Vmax_final = 3513331.68 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.53340544846019\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n", - "Output Ha = 2154823.310507873, Va = 2655538.689355862\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.53340544846019\n", - "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.53340544846019\n", - "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 197610.86 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 197610.86 N\n", - "Vmax3 = 155718.82 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 713116.82 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 713116.82 N\n", - "Vmax3 = 475478.87 N\n", - "dz_clip = 4.30 m\n", - "ez_layer = 9.29 m\n", - "Su_av_z (at ez_layer) = 65200.95 Pa\n", - "alphastar = 0.371\n", - "Vmax_layer = 2577607.38 N\n", - "Vmax1 = 2577607.38 N\n", - "Vmax2 = 1271420.39 N\n", - "Vmax3 = 789811.18 N\n", - "dz_clip = -4.70 m\n", - "Hmax_layer = 799146.30 m\n", - "Hmax_layer = 3151820.44 m\n", - "ez_global = 6.99 m\n", - "Hmax_final = 7573363.72 m\n", - "rlug_eff = 0.27 m\n", - "zlug_eff = 7.59 m\n", - "M = -2008087.24 Nm\n", - "delta_phi = 0.97 deg\n", - "phi_MH = -37.19 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.12\n", - "a_VH = 5.61\n", - "b_VH = 6.20\n", - "pile_head = 35702.93 N\n", - "Vmax_final = 3524037.99 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.466940379053499\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.466940379053499\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.466940379053499\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 196644.71 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 196644.71 N\n", - "Vmax3 = 154768.34 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 709927.45 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 709927.45 N\n", - "Vmax3 = 472936.67 N\n", - "dz_clip = 4.20 m\n", - "ez_layer = 9.23 m\n", - "Su_av_z (at ez_layer) = 64892.47 Pa\n", - "alphastar = 0.369\n", - "Vmax_layer = 2523844.77 N\n", - "Vmax1 = 2523844.77 N\n", - "Vmax2 = 1225690.08 N\n", - "Vmax3 = 762107.62 N\n", - "dz_clip = -4.80 m\n", - "Hmax_layer = 795825.49 m\n", - "Hmax_layer = 3138723.25 m\n", - "ez_global = 6.92 m\n", - "Hmax_final = 7384462.35 m\n", - "rlug_eff = 0.28 m\n", - "zlug_eff = 7.52 m\n", - "M = -2044839.88 Nm\n", - "delta_phi = 0.98 deg\n", - "phi_MH = -37.19 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.12\n", - "a_VH = 5.57\n", - "b_VH = 6.19\n", - "pile_head = 35401.81 N\n", - "Vmax_final = 3465818.74 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.528345537398288\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n", - "Output Ha = 2154823.310507873, Va = 2655538.689355862\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.528345537398288\n", - "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.528345537398288\n", - "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 191489.13 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 191489.13 N\n", - "Vmax3 = 149720.01 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.391\n", - "Vmax_layer = 692864.61 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 692864.61 N\n", - "Vmax3 = 459393.52 N\n", - "dz_clip = 4.29 m\n", - "ez_layer = 9.28 m\n", - "Su_av_z (at ez_layer) = 65177.44 Pa\n", - "alphastar = 0.371\n", - "Vmax_layer = 2456531.86 N\n", - "Vmax1 = 2456531.86 N\n", - "Vmax2 = 1233020.90 N\n", - "Vmax3 = 762760.60 N\n", - "dz_clip = -4.71 m\n", - "Hmax_layer = 778027.14 m\n", - "Hmax_layer = 3068526.82 m\n", - "ez_global = 6.99 m\n", - "Hmax_final = 7361457.45 m\n", - "rlug_eff = 0.25 m\n", - "zlug_eff = 7.58 m\n", - "M = -1940635.38 Nm\n", - "delta_phi = 0.94 deg\n", - "phi_MH = -37.15 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.11\n", - "a_VH = 5.74\n", - "b_VH = 6.25\n", - "pile_head = 33812.71 N\n", - "Vmax_final = 3374698.32 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.467198257566559\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.467198257566559\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.467198257566559\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 199070.56 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 199070.56 N\n", - "Vmax3 = 157157.45 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 717930.56 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 717930.56 N\n", - "Vmax3 = 479322.23 N\n", - "dz_clip = 4.20 m\n", - "ez_layer = 9.23 m\n", - "Su_av_z (at ez_layer) = 64893.66 Pa\n", - "alphastar = 0.369\n", - "Vmax_layer = 2570776.56 N\n", - "Vmax1 = 2570776.56 N\n", - "Vmax2 = 1239396.50 N\n", - "Vmax3 = 771938.93 N\n", - "dz_clip = -4.80 m\n", - "Hmax_layer = 804154.83 m\n", - "Hmax_layer = 3171574.04 m\n", - "ez_global = 6.92 m\n", - "Hmax_final = 7462364.83 m\n", - "rlug_eff = 0.29 m\n", - "zlug_eff = 7.52 m\n", - "M = -2072151.45 Nm\n", - "delta_phi = 0.99 deg\n", - "phi_MH = -37.20 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.52\n", - "b_VH = 6.17\n", - "pile_head = 36159.86 N\n", - "Vmax_final = 3523937.54 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.400809287203985\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.400809287203985\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.400809287203985\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200208.23 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200208.23 N\n", - "Vmax3 = 158280.88 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721678.26 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721678.26 N\n", - "Vmax3 = 482319.75 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.18 m\n", - "Su_av_z (at ez_layer) = 64586.08 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557372.63 N\n", - "Vmax1 = 2557372.63 N\n", - "Vmax2 = 1205455.44 N\n", - "Vmax3 = 752641.00 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808051.22 m\n", - "Hmax_layer = 3186941.31 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7340231.71 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.46 m\n", - "M = -2119835.45 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.45\n", - "b_VH = 6.15\n", - "pile_head = 36517.63 N\n", - "Vmax_final = 3515776.75 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.334659934620789\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n", - "Output Ha = 2218182.0096819005, Va = 2615301.443540927\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.334659934620789\n", - "Output Ha = 2218182.0096819005, Va = 2615301.443540927, zlug = 7.334659934620789\n", - "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 201762.59 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 201762.59 N\n", - "Vmax3 = 159818.83 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 726792.83 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 726792.83 N\n", - "Vmax3 = 486418.02 N\n", - "dz_clip = 4.00 m\n", - "ez_layer = 9.12 m\n", - "Su_av_z (at ez_layer) = 64280.15 Pa\n", - "alphastar = 0.365\n", - "Vmax_layer = 2551829.96 N\n", - "Vmax1 = 2551829.96 N\n", - "Vmax2 = 1173891.23 N\n", - "Vmax3 = 734980.53 N\n", - "dz_clip = -5.00 m\n", - "Hmax_layer = 813364.49 m\n", - "Hmax_layer = 3207896.79 m\n", - "ez_global = 6.77 m\n", - "Hmax_final = 7231118.91 m\n", - "rlug_eff = 0.31 m\n", - "zlug_eff = 7.39 m\n", - "M = -2186006.01 Nm\n", - "delta_phi = 1.03 deg\n", - "phi_MH = -37.22 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.36\n", - "b_VH = 6.12\n", - "pile_head = 37008.76 N\n", - "Vmax_final = 3517394.13 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.367735235084056\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n", - "Output Ha = 2218182.0096819005, Va = 2615301.443540927\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.367735235084056\n", - "Output Ha = 2218182.0096819005, Va = 2615301.443540927, zlug = 7.367735235084056\n", - "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200985.91 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200985.91 N\n", - "Vmax3 = 159049.91 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 724238.04 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 724238.04 N\n", - "Vmax3 = 484369.80 N\n", - "dz_clip = 4.05 m\n", - "ez_layer = 9.15 m\n", - "Su_av_z (at ez_layer) = 64433.05 Pa\n", - "alphastar = 0.366\n", - "Vmax_layer = 2554651.42 N\n", - "Vmax1 = 2554651.42 N\n", - "Vmax2 = 1189687.39 N\n", - "Vmax3 = 743827.80 N\n", - "dz_clip = -4.95 m\n", - "Hmax_layer = 810711.05 m\n", - "Hmax_layer = 3197431.63 m\n", - "ez_global = 6.81 m\n", - "Hmax_final = 7285791.25 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.42 m\n", - "M = -2159357.06 Nm\n", - "delta_phi = 1.02 deg\n", - "phi_MH = -37.22 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.40\n", - "b_VH = 6.13\n", - "pile_head = 36763.02 N\n", - "Vmax_final = 3516638.39 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.402328930637527\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.402328930637527\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.402328930637527\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 203323.99 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 203323.99 N\n", - "Vmax3 = 161367.30 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 731923.95 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 731923.95 N\n", - "Vmax3 = 490538.20 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.18 m\n", - "Su_av_z (at ez_layer) = 64593.11 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2618168.22 N\n", - "Vmax1 = 2618168.22 N\n", - "Vmax2 = 1223180.09 N\n", - "Vmax3 = 765347.70 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 818690.10 m\n", - "Hmax_layer = 3228900.88 m\n", - "ez_global = 6.85 m\n", - "Hmax_final = 7440529.26 m\n", - "rlug_eff = 0.31 m\n", - "zlug_eff = 7.46 m\n", - "M = -2154084.22 Nm\n", - "delta_phi = 1.03 deg\n", - "phi_MH = -37.23 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.38\n", - "b_VH = 6.13\n", - "pile_head = 37504.80 N\n", - "Vmax_final = 3590920.97 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.387262874558214\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.387262874558214\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.387262874558214\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 194526.39 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 194526.39 N\n", - "Vmax3 = 152689.26 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 702925.62 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 702925.62 N\n", - "Vmax3 = 467367.43 N\n", - "dz_clip = 4.08 m\n", - "ez_layer = 9.16 m\n", - "Su_av_z (at ez_layer) = 64523.38 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2442151.12 N\n", - "Vmax1 = 2442151.12 N\n", - "Vmax2 = 1166741.18 N\n", - "Vmax3 = 725788.37 N\n", - "dz_clip = -4.92 m\n", - "Hmax_layer = 788528.44 m\n", - "Hmax_layer = 3109943.77 m\n", - "ez_global = 6.83 m\n", - "Hmax_final = 7131536.23 m\n", - "rlug_eff = 0.28 m\n", - "zlug_eff = 7.44 m\n", - "M = -2062716.68 Nm\n", - "delta_phi = 0.99 deg\n", - "phi_MH = -37.18 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.12\n", - "a_VH = 5.56\n", - "b_VH = 6.19\n", - "pile_head = 34745.26 N\n", - "Vmax_final = 3374348.39 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399868809039603\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399868809039603\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399868809039603\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 197098.22 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 197098.22 N\n", - "Vmax3 = 155214.33 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 711424.87 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 711424.87 N\n", - "Vmax3 = 474129.82 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64581.72 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2497443.65 N\n", - "Vmax1 = 2497443.65 N\n", - "Vmax2 = 1188082.69 N\n", - "Vmax3 = 740184.52 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 797384.86 m\n", - "Hmax_layer = 3144873.36 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7241136.90 m\n", - "rlug_eff = 0.28 m\n", - "zlug_eff = 7.45 m\n", - "M = -2085188.94 Nm\n", - "delta_phi = 1.00 deg\n", - "phi_MH = -37.19 deg\n", - "a_MH = 14.62\n", - "b_MH = 2.12\n", - "a_VH = 5.51\n", - "b_VH = 6.17\n", - "pile_head = 35543.03 N\n", - "Vmax_final = 3441509.77 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.401280820159469\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.401280820159469\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.401280820159469\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 198651.52 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 198651.52 N\n", - "Vmax3 = 156744.14 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 716549.26 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 716549.26 N\n", - "Vmax3 = 478218.60 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.18 m\n", - "Su_av_z (at ez_layer) = 64588.26 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2527816.21 N\n", - "Vmax1 = 2527816.21 N\n", - "Vmax2 = 1197328.35 N\n", - "Vmax3 = 746738.23 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 802718.09 m\n", - "Hmax_layer = 3165907.52 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7292898.26 m\n", - "rlug_eff = 0.29 m\n", - "zlug_eff = 7.45 m\n", - "M = -2102012.31 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.20 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.48\n", - "b_VH = 6.16\n", - "pile_head = 36028.45 N\n", - "Vmax_final = 3479045.44 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.404969278223096\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.404969278223096\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.404969278223096\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200252.40 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200252.40 N\n", - "Vmax3 = 158324.53 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721823.67 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721823.67 N\n", - "Vmax3 = 482436.15 N\n", - "dz_clip = 4.11 m\n", - "ez_layer = 9.18 m\n", - "Su_av_z (at ez_layer) = 64605.33 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2560434.10 N\n", - "Vmax1 = 2560434.10 N\n", - "Vmax2 = 1208205.08 N\n", - "Vmax3 = 754302.10 N\n", - "dz_clip = -4.89 m\n", - "Hmax_layer = 808202.34 m\n", - "Hmax_layer = 3187537.35 m\n", - "ez_global = 6.85 m\n", - "Hmax_final = 7351484.53 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.46 m\n", - "M = -2118126.36 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.45\n", - "b_VH = 6.15\n", - "pile_head = 36531.55 N\n", - "Vmax_final = 3519041.72 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.392551909093574\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.392551909093574\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.392551909093574\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200418.54 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200418.54 N\n", - "Vmax3 = 158488.76 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 722370.64 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 722370.64 N\n", - "Vmax3 = 482874.05 N\n", - "dz_clip = 4.09 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64547.86 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557010.14 N\n", - "Vmax1 = 2557010.14 N\n", - "Vmax2 = 1201607.66 N\n", - "Vmax3 = 750506.23 N\n", - "dz_clip = -4.91 m\n", - "Hmax_layer = 808770.78 m\n", - "Hmax_layer = 3189779.27 m\n", - "ez_global = 6.83 m\n", - "Hmax_final = 7327159.07 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2126596.87 Nm\n", - "delta_phi = 1.02 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.43\n", - "b_VH = 6.14\n", - "pile_head = 36583.92 N\n", - "Vmax_final = 3516383.25 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.39671089582197\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.39671089582197\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.39671089582197\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200348.96 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200348.96 N\n", - "Vmax3 = 158419.97 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 722141.58 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 722141.58 N\n", - "Vmax3 = 482690.65 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64567.11 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557890.21 N\n", - "Vmax1 = 2557890.21 N\n", - "Vmax2 = 1203741.72 N\n", - "Vmax3 = 751723.22 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808532.74 m\n", - "Hmax_layer = 3188840.42 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7334873.38 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2123599.58 Nm\n", - "delta_phi = 1.02 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36561.98 N\n", - "Vmax_final = 3516942.73 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.398765463164171\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.398765463164171\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.398765463164171\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200283.88 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200283.88 N\n", - "Vmax3 = 158355.64 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721927.32 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721927.32 N\n", - "Vmax3 = 482519.12 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64576.62 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557735.79 N\n", - "Vmax1 = 2557735.79 N\n", - "Vmax2 = 1204630.47 N\n", - "Vmax3 = 752204.75 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808310.06 m\n", - "Hmax_layer = 3187962.18 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7337730.23 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2121773.85 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36541.47 N\n", - "Vmax_final = 3516488.45 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.398967367270157\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.398967367270157\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.398967367270157\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200475.34 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200475.34 N\n", - "Vmax3 = 158544.92 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 722557.64 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 722557.64 N\n", - "Vmax3 = 483023.78 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64577.55 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2561515.84 N\n", - "Vmax1 = 2561515.84 N\n", - "Vmax2 = 1205784.76 N\n", - "Vmax3 = 753023.08 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808965.11 m\n", - "Hmax_layer = 3190545.68 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7344156.40 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2123824.98 Nm\n", - "delta_phi = 1.02 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36601.84 N\n", - "Vmax_final = 3521150.66 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.398361136666139\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.398361136666139\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.398361136666139\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 199901.12 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 199901.12 N\n", - "Vmax3 = 157977.43 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 720666.94 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 720666.94 N\n", - "Vmax3 = 481510.41 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64574.74 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2550184.33 N\n", - "Vmax1 = 2550184.33 N\n", - "Vmax2 = 1202322.59 N\n", - "Vmax3 = 750569.08 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 807000.03 m\n", - "Hmax_layer = 3182795.44 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7324879.64 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2117672.10 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.45\n", - "b_VH = 6.15\n", - "pile_head = 36420.91 N\n", - "Vmax_final = 3507173.30 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3997901611151775\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3997901611151775\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3997901611151775\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200248.80 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200248.80 N\n", - "Vmax3 = 158320.97 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721811.82 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721811.82 N\n", - "Vmax3 = 482426.67 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64581.36 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557608.37 N\n", - "Vmax1 = 2557608.37 N\n", - "Vmax2 = 1205059.48 N\n", - "Vmax3 = 752434.61 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808190.03 m\n", - "Hmax_layer = 3187488.79 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7339073.09 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2120834.02 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36530.41 N\n", - "Vmax_final = 3516199.41 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399278551221133\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399278551221133\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399278551221133\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200267.08 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200267.08 N\n", - "Vmax3 = 158339.04 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721872.01 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721872.01 N\n", - "Vmax3 = 482474.85 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64578.99 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557686.71 N\n", - "Vmax1 = 2557686.71 N\n", - "Vmax2 = 1204849.43 N\n", - "Vmax3 = 752322.84 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808252.58 m\n", - "Hmax_layer = 3187735.49 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7338426.50 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2121311.88 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36536.17 N\n", - "Vmax_final = 3516361.97 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399327343324559\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399327343324559\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399327343324559\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200315.00 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200315.00 N\n", - "Vmax3 = 158386.41 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 722029.79 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 722029.79 N\n", - "Vmax3 = 482601.15 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64579.22 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2558631.75 N\n", - "Vmax1 = 2558631.75 N\n", - "Vmax2 = 1205137.35 N\n", - "Vmax3 = 752527.06 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808416.55 m\n", - "Hmax_layer = 3188382.19 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7340031.12 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2121826.25 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36551.28 N\n", - "Vmax_final = 3517527.81 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399180964245797\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399180964245797\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399180964245797\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200171.25 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200171.25 N\n", - "Vmax3 = 158244.33 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721556.47 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721556.47 N\n", - "Vmax3 = 482222.27 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64578.54 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2555797.15 N\n", - "Vmax1 = 2555797.15 N\n", - "Vmax2 = 1204273.64 N\n", - "Vmax3 = 751914.46 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 807924.64 m\n", - "Hmax_layer = 3186442.09 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7335217.41 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2120283.14 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36505.98 N\n", - "Vmax_final = 3514030.84 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399534460150541\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399534460150541\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399534460150541\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200258.04 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200258.04 N\n", - "Vmax3 = 158330.11 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721842.25 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721842.25 N\n", - "Vmax3 = 482451.03 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64580.17 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557649.57 N\n", - "Vmax1 = 2557649.57 N\n", - "Vmax2 = 1204955.08 N\n", - "Vmax3 = 752379.16 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808221.66 m\n", - "Hmax_layer = 3187613.52 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7338753.25 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2121074.04 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36533.33 N\n", - "Vmax_final = 3516283.19 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.39940652842244\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.39940652842244\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.39940652842244\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200262.58 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200262.58 N\n", - "Vmax3 = 158334.60 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721857.21 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721857.21 N\n", - "Vmax3 = 482462.99 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64579.58 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557668.58 N\n", - "Vmax1 = 2557668.58 N\n", - "Vmax2 = 1204902.39 N\n", - "Vmax3 = 752351.10 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808237.20 m\n", - "Hmax_layer = 3187674.81 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7338590.63 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2121193.20 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36534.76 N\n", - "Vmax_final = 3516323.13 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399418649509588\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399418649509588\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399418649509588\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200274.56 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200274.56 N\n", - "Vmax3 = 158346.44 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721896.66 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721896.66 N\n", - "Vmax3 = 482494.58 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64579.64 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557904.84 N\n", - "Vmax1 = 2557904.84 N\n", - "Vmax2 = 1204974.34 N\n", - "Vmax3 = 752402.14 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808278.20 m\n", - "Hmax_layer = 3187836.52 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7338991.70 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2121321.87 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36538.53 N\n", - "Vmax_final = 3516614.60 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3993822862479135\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3993822862479135\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3993822862479135\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200238.62 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200238.62 N\n", - "Vmax3 = 158310.91 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721778.30 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721778.30 N\n", - "Vmax3 = 482399.83 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64579.47 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557196.10 N\n", - "Vmax1 = 2557196.10 N\n", - "Vmax2 = 1204758.49 N\n", - "Vmax3 = 752249.02 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808155.19 m\n", - "Hmax_layer = 3187351.39 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7337788.51 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2120935.87 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36527.21 N\n", - "Vmax_final = 3515740.23 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3994720299213235\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3994720299213235\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3994720299213235\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200260.26 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200260.26 N\n", - "Vmax3 = 158332.30 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721849.55 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721849.55 N\n", - "Vmax3 = 482456.87 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64579.89 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557658.86 N\n", - "Vmax1 = 2557658.86 N\n", - "Vmax2 = 1204929.37 N\n", - "Vmax3 = 752365.47 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808229.24 m\n", - "Hmax_layer = 3187643.44 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7338673.91 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2121132.20 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36534.03 N\n", - "Vmax_final = 3516302.69 N\n", - "[Debug] mass_update = True\n", - "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3994720299213235\n", - "Output Hm = 3000000.0000000005, Vm = 3000000.0000000005\n", - "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n", - "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3994720299213235\n", - "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3994720299213235\n", - "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n", - "[Branch Check] Entered zlug>z0 for anchor suction\n", - "dz_clip = 1.75 m\n", - "ez_layer = 2.74 m\n", - "Su_av_z (at ez_layer) = 20960.65 Pa\n", - "alphastar = 0.385\n", - "Vmax_layer = 200260.26 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 200260.26 N\n", - "Vmax3 = 158332.30 N\n", - "dz_clip = 3.50 m\n", - "ez_layer = 5.44 m\n", - "Su_av_z (at ez_layer) = 41334.23 Pa\n", - "alphastar = 0.390\n", - "Vmax_layer = 721849.55 N\n", - "Vmax1 = not applicable\n", - "Vmax2 = 721849.55 N\n", - "Vmax3 = 482456.87 N\n", - "dz_clip = 4.10 m\n", - "ez_layer = 9.17 m\n", - "Su_av_z (at ez_layer) = 64579.89 Pa\n", - "alphastar = 0.367\n", - "Vmax_layer = 2557658.86 N\n", - "Vmax1 = 2557658.86 N\n", - "Vmax2 = 1204929.37 N\n", - "Vmax3 = 752365.47 N\n", - "dz_clip = -4.90 m\n", - "Hmax_layer = 808229.24 m\n", - "Hmax_layer = 3187643.44 m\n", - "ez_global = 6.84 m\n", - "Hmax_final = 7338673.91 m\n", - "rlug_eff = 0.30 m\n", - "zlug_eff = 7.45 m\n", - "M = -2121132.20 Nm\n", - "delta_phi = 1.01 deg\n", - "phi_MH = -37.21 deg\n", - "a_MH = 14.63\n", - "b_MH = 2.12\n", - "a_VH = 5.44\n", - "b_VH = 6.15\n", - "pile_head = 36534.03 N\n", - "Vmax_final = 3516302.69 N\n" + "[Debug] Anchor type parsed: 'suction'\n" ] }, { "data": { - "image/png": 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", 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", 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OnDlDQ0MDZrOZpUuXav/eGRkZxMfHc+jQIQBmzZrFuXPnOHfuHAaDgRUrVrBp0yZcLhepqamkpqayf/9+AGbMmEFzczNnz57VYnbLli04HA6SkpLIzs5mz549AEydOpXOzk4tJm666SZ27NhBb28v8fHxFBQUsHPnTgAmTZqEzWajvLwcQPqIIO4jPH/zqqoqkpOTpY+QPoIzZ85w5swZ3G639BFIH7Fp0yYA2traOHfunPQRQdpHeNp/JfxqKl1BQQFf/vKX+dGPfqTdt2PHDhYtWkRdXR3JycnDfsZut2O327XvOzs7ycjIkKl0Y8CxY8eYPHmyr5sh/IjEhBioqqqK7373u/zP//wPWVlZvm6O8BPST4ihJCaC29VMpfOrEaPk5GTq6+sH3dfY2IjRaCQuLu6CP2OxWLBYLKPRPOFnLpQoi7FNYkIMlJWVxZNPPkl8fLyvmyL8iPQTYiiJCeHhV2uM5s+fP2ye54YNG5g1axYmk8lHrRL+yjM0LYSHxIQYqK+vj9dee42+vj5fN0X4EeknxFASE8JjRBOj7u5uDh8+rM3DrKys5PDhw9r81B/96Efcc8892vMfeughberDiRMnePbZZ3nmmWd45JFHRrKZQgghgtCJEyd46KGHBlU6FUIIIS5mRKfS7d+/n6VLl2rff/e73wXgS1/6Es8//zx1dXVakgQwbtw43nvvPf7pn/6Jxx9/nNTUVB577DFuv/32kWymCFBSflcMJTEhhLgc6SfEUBITwmNEE6Mbb7yRS9V2eP7554fdd8MNN2jVUoS4lMbGRhISEnzdDOFHJCaEEJcj/YQYSmJCePjVGiMhrkZNTY2vmyD8jMSEEOJypJ8QQ0lMCA9JjETA0ul0vm6C8DMSE2IgnU6HyWSSuBCDSDyIoSQmhMeo7WM0Wq6mVrkQQgghhBAieF1NbiAjRiJgeXZbFsJDYkIMJTEhhpKYEENJTAgPSYxEwHI6nb5ugvAzEhNioBMnTvDAAw9IuW4xiPQTYiiJCeEhiZEIWLJTtRhKYkIM1NfXR3l5uWzwKgaRfkIMJTEhPEa0XLcQIykzM9PXTQhqbrcbu92uHQ6HY9j3/f39uFwu7evFbrvd7oueZ+gyR71ej8FgwGAwDLp9ocNoNGI2mzGZTJjNZlwuF2fPntW+9xyyAF8I4SHvHWIoiQnhIYmRCFh79+5l1apVvm5GQFAUhd7eXrq7u+nt7aWnp+eiX/v6+rDb7QE5taCsrIy8vLwLPmY2m7FYLISEhGC1Wq/oa1hYGCEhIZJUCRFE5L1DDCUxITwkMRIiCNjtdjo6OrSjs7Nz0O3Ozk76+/uv6bWNRiMWiwWLxaIlF57bRqMRo9E4aATnQrf1ev0VJxeKomgjTkMPt9s96Hun04nT6cThcOBwOGhpaSE2Nlb73uFwaK/r+b6rq+uqfn+9Xk9YWNhlj/DwcMLDwzEYDFf1+kIIIYTwD5IYiYA1bdo0XzdhVDmdTtra2mhpaRl29PT0XPbndTodISEhhIaGah/mPbcHfg0NDcVqtWpJUCB90K+vrx80V1xRFC15stvt2Gw2bDYbfX19l/3que12u+nq6rqihEqn0xEWFkZERASRkZHa14G3IyIisFgsMgo1CsaNG8dTTz3FuHHjfN0U4UfG2nuHuDyJCeEhiZEIWO3t7UG5YNLtdtPc3Ex9fT319fU0NDTQ0tJCR0fHsPU4A4WEhBAVFUVkZCRRUVHDbkdERARUknMthsaETqfT1hmFhYVd9eu5XC56enoueXR3d2u3XS4X3d3ddHd3U1dXd9HXNZvN2t8mOjp62BEeHi6JkxfExMSwePFiYmJifN0U4UeC9b1DXDuJCeEhiZEIWFVVVUyYMMHXzbguTqdTS4AGJkIXm/ZmtVqJi4sbdsTGxmKxWEa59f7H2zFhMBi0EZ/L8azj6uzspKur64JfOzs7sdlsOBwOmpubaW5uvuBrGY3GCyZNMTExxMbGyrqnK9TQ0MD//M//8B//8R8kJSX5ujnCTwTDe4fwLokJ4SGJkRCjRFEU2tvbOXv2LDU1NZw9e5aGhoYLVmwzm80kJyeTnJxMUlISCQkJxMXFERoaKh+I/ZRnGl1YWBgpKSkXfZ7T6dSSpI6ODtrb2wcdHR0d9Pf3a9MkL8STIMfGxhIbGzvodmho6Ej9igGntraWP/3pTzz00EOSGAkhhLgsnXKpuTkBqLOzk6ioKDo6Oq7oKq8IXIqi+HWSoCgKbW1tVFZWcubMGc6cOXPBdSoRERGkpKRoiVBycjIxMTF+/bv5K3+PiSvhcrno6uoaljC1tbXR1tZGZ2fnJX8+JCRES5JiY2OJj4/XEmuTyTRKv4V/OHjwIEVFRRw4cICZM2f6ujnCTwRDPyG8S2IiuF1NbiAjRiJgbd++nRtuuMHXzRjEs6Hk6dOnqaysHPYh1mAwkJycTEZGBhkZGaSnpxMVFeWj1gYff4yJq2UwGLSpcxfidDppbW0ddLS0tNDa2kpnZyd9fX3U1tZSW1s76Od0Oh3R0dEkJCRoyZLnttVqHYXfTAj/EAz9hPAuiQnhIYmRCFg2m83XTUBRFBoaGjh9+jSnT5/m7NmzgwokGAwG0tPTyc7OZty4caSlpY25q/ajyR9iYqSZTCaSkpIuODVsaNLU0tJCc3MzTU1N9PX1aaNOpaWlg34uIiJiULKUmJhIUlKSJEwiKI2FfkJcHYkJ4SGJkQhYCQkJPjmvoiicO3eOEydOUFJSQmtr66DHExMTyc/PJzc3l4yMDEmERpGvYsJfXCxpUhSFnp4eLUnyfG1qatJKkXd1dVFZWTno56KiorTX8xxxcXHo9frR/LWuWVRUFEuWLJFRWTHIWO8nxHASE8JD1hiJgNXZ2Tmqf+PGxkaOHDnC8ePHaW9v1+43Go3k5uaSn59Pfn6+fAjzodGOiWBgs9m0CnmeZKmhoYGOjo4LPt9gMJCQkDAsYQoPDx/lll8ZiQkxlMSEGEpiIrjJGiMxJuzatYtVq1aN6Dl6enooLi7myJEjg/alMZvN5OfnM3HiRPLz8zGbzSPajrHO5YKhFcwvdEln27Y9rFlzEwYDyDraK2O1WklPTyc9PX3Q/TabjYaGBhoaGmhsbNRuOxwOrbT8QJ4iIgOPyMhIny5odjqdvP/++3z605+WkVuhGY33DhFYJCaEhyRGQgyhKArV1dXs27ePkpISrZy2wWAgPz+fqVOnkp+fLx+0LqKnB5qazh8dHdDVBZ2d578OvN3VBTYb2O3q4XCcv+353uW60rPfpN0ymwcfJtPg70NDISwMwsPPfx142/M1MhJiYiA6Wv0aE6PeH+yJl9VqJSsri6ysLO0+T8n5oclSS0uLNh1v4Pql0NDQYcnSaFZcLC4u5vOf/7xUpRNCCHFFJDESAWvy5MlefT2Hw8GRI0fYt28fjY2N2v1paWlMmzaNyZMnj9k9YhQFWlrg7NnzR3U11NefT4AaG9WvfX2+bq3K4VCPkWA0Dk6UYmIgNhYSEiAx8fyRlHT+dlhY4CdTOp2OmJgYYmJiBm2G6HA4aGhooK6uTjsaGxvp7e2lvLyc8vJy7bkWi0VLktLS0khLSyM6OlpK5YpR4+33DhH4JCaEhyRGImD19vZ67XX27t3Lnj176Pv4U73JZGLq1KnMmjXrkpt1BhObDcrKoLRUPU6fhqoqNQGqqbm6hMdsVpOEhAQ1gYiMVI+IiOG3IyIgJAQsFvXnLJYL3zYaBycWF7pdVlZOdnaulhQ5necTpKFHb686utXdff7rwNuerx0d0NZ2/nA61Wl9zc3qcaVCQgYnTGlp6pGefv52WhpERQVeAmU2m7US9B79/f00NjYOSpYaGhqw2+3avl4eYWFhWpLkOUJCQnzwm4ixwFvvHSJ4SEwID0mMRMCqqKggPz//mn++q6uLjz76iIMHD+J0OgGIjY1lzpw5TJ8+PWhLFXd2wtGjcOQInDhxPhGqrr7wup2BEhMhMxMyMtQjNfV8AjTwiIjwzYf7xsYyZszIHbHXVxQ1ofIkSe3t52+3tqqjZkOPhgY1qezrUxPNqqpLnyMsbHCilJEB2dnnj8xMCITQNBqNpKamkpqaqt3ncrlobm7m3LlznDt3jtraWhoaGujp6aG0tHTQNLy4uLhBiVJycjJGo7xliet3ve8dIvhITAgPeZcRY05vby87duxg7969WkKUkpLCokWLKCwsDJhSxJejKHDuHBw+rB6HDqlfB8xqGiYqCsaPh4ICyM+HcePOJ0Hp6erozVim06mJS1iY+u9xpXp61ATJkyzV10NtrToSV1t7/mhrU5/rSVYvJjl5cLLkOcaNU7/6ay0Qg8GgVbGbMWMGoI4s1dfXU1tbS01NDbW1tdoeTC0tLRw9elT72YGbI2dmZhIREeHLX0cIIUSQkXLdImD19/df1RXk/v5+du3axUcffYTdbgcgIyODpUuXMm7cuIBf49DfryY+O3acP86du/Bz09Nh+nSYOPF8IlRQoI72BPI/w9XGhL/p7VX/ZgOTpupqdZTpzBn16O6+9Gvo9ZCVpSa2+fmQl3f+67hx/ps0DdTb26uNKHkSpgtNdYmOjiYzM1NLlBISEgZd2HC5XHR0dBAVFYXBYBjNX0H4sUDvJ4T3SUwEt6vJDSQxEgHro48+YtGiRZd9nqIonDp1ivXr19PW1gZAcnIyy5YtIz8/P2ATIocDdu6EzZvho49gzx71g/VABgMUFqpJkOeYNg3i433Q4FFwpTERqBRFnbLnSZKGHhUVw2NgIL1eHVHyJEsTJqjxMXGiOgrlr/8VPNXwampqqK6u5uzZszQ0NDD07ctisZCenq4lS+np6ezduzeoY0JcvWDvJ8TVk5gIbrKPkRgTenp6LvuctrY23n33XcrKygCIjIzkpptuYvLkyQGZEFVUwPr1sG6dmhANHT2Ijob582HhQvWYM0ctSz1WXElMBDKdDuLi1KOoaPjjigJ1dWoRjdOn1cNzu6xMTZoqKtRjw4bBPxsVpSZJnkTJczs7W02ofGlgNbwpU6YAYLfbqamp4ezZs1RXV1NTU4Pdbh9UBa+1tZW33nqLf/u3f2PhwoVkZWVJUQcR9P2EuHoSE8JDEiMRsGJjYy/6mKIo7Nu3j02bNuFwODAYDCxYsIDFixcH1GasLhd8+CG88Qa8/776AXegxES46SZYvFhNhCZO9P2HWF+6VEyMBTqdWhAjNRWWLBn8mCdp8iRJpaVq8Y0TJ9REqaMDdu9Wj4GsVnW65eTJMGUKTJ2qHqmpvh1hslgs5ObmkpurFttwu900NDRw9uxZLVmqq6ujurqavXv3cvbsWQCSkpLIysoiOzubrKwswsLCfPdLCJ8Y6/2EGE5iQnjIVDoRsHp6ei74oaa9vZ0333xTKwecnZ3NrbfeSlxc3Ci38Nr098OWLfD3v6sJUVPT+ceMRliwAFavhlWr1KlxYzkRGupiMSEuzWZTE6aSkvPJ0okTcOrUxfeCio0dnChNmaImT/70z799+3ZuuOEGfvvb32I0Gmm+QH31hISEQYmSFHQIftJPiKEkJoKbrDGSxGhMWL9+PatWrRp036lTp3jjjTew2WyYTCZuuukmZs+eHRDT5o4cgRdfhJdfViuYecTEwKc+BZ/8JCxbpk55Ehd2oZgQ187lgspKNWE6dkwt815crCZMLtfw5+t0kJurJkozZsDMmeqUv6Sk0W87wMGDBykqKuLAgQPMnDmT7u5uqqqqqKqq4syZM4M2cvaIi4sjOzubcePGMW7cOPmwFISknxBDSUwEN1ljJMYcl8vF5s2b2bFjBwBpaWncfvvtfj883tkJL70ETz6pfuj0iIuDz3wGbr8dbrwRTCafNVGMYQaDWqghL09NzD1sNnVEqbhYjVtPwlRfr07TKyuD118///zUVDVJGnikp4/+VLzw8HAmTZrEpEmTALX63cBEqaGhQSsTfuDAAUAt1DJu3DhycnLIysoKqKm4Qgghro6MGImAVV1dTWZmJjabjVdffVVbcD1v3jxuuukmvy7PW1wMf/iDmhR5CiiYzXDrrXDPPepUOfn8dfU8MSF8o7FRje0jR+DgQfU4efLCGwfHx59PkoqKYO5c7ydLTU1N/PGPf+Shhx4iISHhss/v6+ujurqaM2fOUFFRQcPAoVtAr9eTnp5OTk4OOTk5pKWl+XU/Iy5M+gkxlMREcJOpdJIYjQkVFRXExcXxf//3fzQ0NGA2m1m7di0TJ070ddMuSFFg2zb4z/9Uq8p5TJgADz8Md92lrtsQ166iooKcnBxfN0MM0N2tjih5EqWDB+H4cXUt3VDJyWqCNGeOesyeff1TR68nJnp6eqisrKSiooKKigra29sHPW42m8nKytISpcTExICYtjvWST8hhpKYCG6SGEliNCb89a9/paamhq6uLiIiIvjiF79ISkqKr5s1jKLAe+/Bf/yHutcQqAUTbrsNvv51daqcfJbyDpknHhhsNnVk6eBBOHAA9u9Xk6cLrVuaMGFwsjR16uVHU0+fhq4u6Ojo4Pnnn+fee+8lKiqKiAh1/6Zr1dbWpiVJlZWVwzadjYiIIDc3l7y8PHJycggdS7XyA4j0E2IoiYngJmuMRNBrampi06ZNpKWlkZiYyJ133kmUH1Yl2LYNfvxjdSNWUEsff/nL8L3vqYvUhRiLrFZ1NGj27PP39fbCoUPqxYO9e9WjslKdinfyJLzwgvo8iwVmzTq/V9f8+TBwltzp01BQ4PkuCvg2L754/vHS0mtPjmJiYigqKqKoqAhFUWhoaNCSpDNnztDV1cXhw4c5fPgwOp2OtLQ0LVFKS0tDLyUkhRDCr8mIkQg4zc3NPP/887S3t5Oens4999zjd1dmT56Ef/qn81PmQkLgG99QEyJfVegaC+x2OxaL5aKPK4qC2+3G5XLhdru121dCr9df8JCpUyOnsRH27VOTJE/C1NY2/HkFBecTpehotXDJSy+pG9R6nDihTlc9cEBd1+Rt/f39VFdXU1ZWRllZ2bCKdyEhIeTk5JCXl0dubq68P/nQ5foJMfZITAQ3mUoniVHQ6u7u5k9/+hMdHR309vby7//+736VFHV1wc9+Bo8+qq6hMJnggQfgX/4F/HCWX0BRFAWn04ndbsdut2Oz2bTbDocDp9PJ8ePHGTduHP39/fT39+N0OnE6nYMSIW/zJEhGo/Gyh9lsxmw2YzKZsFgsmEymQffJiMKlKYo6IrRzJ+zYoR4nTlz4uUMToIMH1SIPI5UYDdXZ2Ul5eTllZWWUl5djs9kGPZ6YmEheXh4FBQVkZmbK334U7dq1i/nz5/u6GcKPSEwEN5lKJ4KS0+nkL3/5Cx0dHcTFxTFjxgy/Soree09Ngmpr1e9vuQV++1u11LG4PLvdTk9PD729vcOOvr4++vr6LpvY1NbWYrVar/icA0d+LmXgSNNQnoSr/0LVBK6S2WzGarVqh8ViGfY1JCSEkJCQMVkNTadTR4cKCuDee9X7Wlth167zidLu3RfflBbUUafCQnUUdyRFRkYyY8YMZsyYgdvtpra2VhtNOnfuHI2NjTQ2NrJz506sViv5+fkUFBSQl5dHyEg3bozr7Oz0dROEn5GYEB6SGImAoCgKb775JrW1tYSGhnLnnXdSWlrq62YB0N6uTpt7/nn1+9xc+N3v4BOf8GWr/JPL5aKrq+uCh91uv6LXMBqNWpIw8PCMvkyfPh2TyYTRaNS+Go1GLQEyGAzXPA1OURQtSRp4uFwuXC6XNlJ1ocPpdGojW3a7Xfve4XBoSZXn+yt5kx6YJIWGhmq3Pd+HhYVhGgMbYMXGqv/XPP/f9uyBefMu/vyHH4ZvfUst5HDDDeqxYAGEh49cG/V6PRkZGWRkZLB06VJ6e3u10aTTp0/T29tLcXExxcXF6HQ6MjMzKSgooKCggPj4eJmu6WX+uB5V+JbEhPCQqXQiIBw4cIB33nkHvV7Pvffeq+1fdDWjAyNhzx743Oegulq9mv1P/6RWn/OjgSyfcTgctLe309bWRnt7O+3t7XR0dFxy1MdqtRIWFqZ90A8LCxv0wd9qtV5ypMQfYuJauFwunE6nNj1w4DTBgffZbDb6+vqueF2UxWIhLCxs2BHMiZNnytzF1hjFx0Nz8+CfMRrVn7nhBli6FBYvhrCw0WmvZzTp1KlTlJaWDlubFBMToyVJWVlZGI1yPfN6BWo/IUaOxERwkzVGkhgFlaamJp566imcTicrV65kwYIFgG/LayoK/O//wiOPgNOpjhK98IK6+HssUhSFzs5OmpubtaOrq+uCzzWbzURERAw7wsPDr/uD+lgouaooCg6HQ5teOHCqYW9vLzabjZ6eHhyXmk/2MavVOuxv4DkCNWkaXJVuuFOn1HL527adP6qrBz/HZFKr3S1fDitWqNXzRuufo729ndLSUkpLS6msrByUBJvNZvLy8pgwYQL5+fky5e4ajYV+QlwdiYngJmuMRNDwTKFzOp3k5ub6xeJIhwO++lW0EsCf+Qw88wyMpTxcURQ6OjpoaGigoaGB5ubmC34QDwsLIyYmhujoaKKjo4mJiSE0NFSmBl0HnU6nTR+Mjo6+6PMcDge9vb309PRc8HA4HNooVFNT07CfDwkJITw8nMjISCIjI4mKiiIyMpKQkBC//vvl56slubu64MSJE9x115289NLLFBYWDtrHKC8P7rtPvX3mjJogbd0KmzeridL27erxk5+o0+xuvFFNlJYvh8mTR27vsejoaObMmcOcOXNwOBxUVFRoiVJ3dzclJSWUlJSg1+vJzs5mwoQJTJgwQS4ECiGEF8iIkfBrnil0FouFb3zjG0RERGiPVVZWMm7cuFFtT1sb3H47bNkCBgP85jfqegU//pzoNQ6Hg4aGBurq6qivrx+2uaXRaCQ2Npb4+Hji4+OJi4sb9fKnvoiJQGW32+nu7tYOz1qv7u7uS673MplMgxIlz21/THgPHjxIUVERBw4cYOYVlqJTFCgvh02b4IMP1ESptXXwc5KSYNkydTRp5UpITx+Bxg9rl0JdXR0nT57k5MmTw6bcpaWlMWHCBAoLC4mPjx/5BgUw6SfEUBITwU1GjERQ6O3tZdOmTQAsXbp0UFIEjHp526Ym9WpxcbF6BfnVV2H16lFtwqiz2+3U1tZy9uxZGhoaBq0PMhgMJCYmkpSUREJCAtHR0T6vlCYlj6+cZ9QpLi5u2GOepMmTLHV0dNDZ2Ul3dzdOp5OWlhZaWloG/YzRaNQSpYEjhIG2N4hOp44m5eXBQw+B2w2HD6tJ0qZN8OGH0NAAf/mLegBMmqT2BatXw6JF6ga23m+XjtTUVFJTU1m2bBktLS1aklRTU0NtbS21tbV88MEHxMfHa0lSamqq3yWsvib9hBhKYkJ4SGIk/NauXbvo6+sjKSmJOXPmDHv85MmTZGVljUpbmprUK8THjqn7Eb3/PkybNiqnHnVOp5Oamhqqq6uHJUORkZEkJyeTkpJCQkKC3y0EH82YCGYXS5o8VQU7Ozu1o6Ojg66uLvr7+2ltbaV1yPBKaGioliR5Eqbw8PCA+bCu16v7Hs2cCd//PtjtaknwTZtg40a1/Pfx4+rxm9+ohVduvPF8opSXNzIjynFxcSxcuJCFCxfS1dXFqVOnOHnyJJWVlTQ3N/PRRx/x0UcfERkZyYQJE5g4caLsl/Qx6SfEUBITwsO/PtUI8bG+vj727t0LqKNFvnwz7+hQR4o8SdHWrZde3B2oWltbKS8vp6qqatCePNHR0WRkZJCZmTls1E6MLQaDQUtuBnK5XPT09GiJkqcKYVdXl7YX1blz57TnG43GYWvPoqKifD7ieCUslvNlvv/jP6ClRU2S1q+Hdeugrk7d0+y999Tnjxt3PklauhRG4r9QREQEs2bNYtasWdhsNk6fPs3Jkyc5ffo0nZ2d7N27l7179xIeHs7EiRMlSRJCiIuQNUbCL23fvp3NmzeTmJjIww8/fMGryz09PYSNcE1dpxNuvln94BOMSZHL5eLMmTOUl5cPutIfERFBdnY2GRkZAfX/aDRiQlw5p9OpJUmesu0dHR0XLDeu1+uJiYkhNjaW2NhY4uLiiIiIuK6RJZvNRmlpKQUFBaNSildR1Km2niTpww/VPsTDbFZHk265RT1GeklDf38/FRUVlJSUcPLkSWw2m/ZYeHg4hYWFTJo0acwlSdJPiKEkJoKblOuWxCigKYrCo48+SkdHB5/+9KeZOnXqBZ+3d+/eC06x86YHHoA//Und0+TDD2HGjBE93ahxOp2Ul5dz6tQp+vr6APWDaXp6Onl5eSQkJATMVKeBRiMmxPVxu910dnYOSpja2touWNXQZDINSpRiY2MJvcpNwnwZE93d6sWUdevU6bcVFYMfnzhRTZBuvVXdlHYkZ6a6XC4qKio4fvz4mE+SpJ8QQ0lMBDcpviACWmVlJR0dHVitVgoH7tA4RFtb24i246WX1KRIr4dXXgmOpMjhcHD69GlOnTqlfRANDQ0lPz+fcePGBfwGdyMdE+L66fX6YdPxFEWhp6eHlpYWbZ1SW1sbTqdTKwnvERISoiVJ8fHxxMbGXnStW2VlJT/84Q955plnfFJxKjz8/OgQqHso/eMf6vHhh1BSoh7/9V8QGwtr1qjPXbUKYmK82xaDwUB+fj75+fnDkqTu7m727dvHvn37tCRp8uTJZGZmBuQFksuRfkIMJTEhPCQxEn7nyJEjAEyePPmSm0yGh4ePWBvKy+Hhh9Xb//Zv5z/YBCqXy0V5eTnHjx/XSjFHRERQWFhIVlZWQKztuBIjGRNi5Oh0Om1jWc8CaM/I0sBkqaOjg76+PmpqaqipqQHOT8GLj48nISGBuLg4bePTtrY2tmzZQltbm1+U4h0/Xj2+9z1ob1en3L3zjjqa1NoKL7+sHgaDWt3uU5+CtWu9P+XuQkmSZ7rdwCQpKiqKyZMnM2XKFJKSkoImSZJ+QgwlMSE8ZCqd8CuKovDf//3f9PT0cO+995KdnX3R5zqdzksmTtfeBnUdwPbtsGSJuo9JIOcN9fX1HDhwgK6uLgCioqKYOHEiGRkZQTdlZqRiQviH/v5+2traaG1tpaWlhebm5mH7aYGa9MfHx1NfX8/atWvZv38/RUVFPmjxlenvVyvdeUaTjh8f/Pi0aXDbbWqSNHXqyO2b5nK5qKys5NixY5w4cWLQflYJCQlMmTKFyZMnExsbOzINGCXST4ihJCaCm6wxksQoYNXV1fHkk09iNpv5wQ9+cMmRjPXr17Nq1Sqvt+Hll+GuuyAkRJ3mconczK/ZbDYOHz7MmTNnALBarUyePJmcnJygS4g8RiomhH9SFIXe3l6amppobm6mubmZjo4OPG9rlZWV/PjHP+bXv/41c+fOJT4+nsTERGJiYvz6/0BFhTqS9Oab6gWaARXzGTdOTZBuuw0WLBi5izb9/f2cPn2a4uJiSktLB1WqTE9PZ8qUKUyaNCkgr7RLPyGGkpgIbrLGSASsyspKALKzs30yvaunBx55RL39//5f4CZF586dY+/evdhsNnQ6Hfn5+UyZMkWuiImgotPpCAsLIywsTBtddjgcNDc309LSQnt7O6BeDfZsgApqUYeEhAQSExNJTEwkOjrarxKlnBz49rfVo7lZHUV64w3YsAEqK+G3v1WPhAT45CfVJGn5cu9uLGs0GiksLKSwsBCbzcbJkycpLi6moqJCm8q4bt06cnJymDx5MoWFhQG/RlEIIWTESPiV119/naNHj7J8+XIWL158yeeWlZWRl5fn1fP/5jdqYpSTo44WWSxeffkR53K5OHLkCKWlpYA6bW7OnDnDNuoMViMREyJw1dXV8ctf/pIHH3wQk8lEU1MTjY2Nwyrgmc1mLUlKTEwkKirKL9fT9PSo65LefFMdUfo47wPU/ZFuvRU+9zm1eMNI5Sjd3d0cP36c4uJibZ0XqInUhAkTmDZtGrm5uX6VaA4l/YQYSmIiuMlUOkmMAtYTTzxBQ0MDX/jCFxg/fvwln1tTU0N6errXzt3XpyZE9fXwzDPwla947aVHRV9fHzt37qSpqQmA8ePHM3Xq1KAprHAlvB0TIvANjQm32017ezuNjY00NjbS1NSEc+BmQ4DFYiEpKYmEhASSkpKuez+lkeB0qtPs3nhDTZQ+HgwDzidJn/2surHsSCVJbW1tHDt2jKNHj2r9DqgL2adOncq0adNISkoamZNfB+knxFASE8HtanKDEb+k84c//EErA1xUVMSHH3540edu3boVnU437Dh58uRIN1P4AbfbTXNzMwCJiYmXff7xoSuUr9Nf/6omRZmZ6hqjQNLR0cGmTZtoamrCbDazZMkSZsyYMaaSIvB+TIjA1tnZyQsvvEBnZ6d2n16vJzY2lgkTJrBkyRJuu+02VqxYwbRp00hOTsZoNGK326murubAgQO89957/OMf/2Dv3r2cPXt2UEECXzKZ1Olzv/89VFfDzp3wne9Aejp0dcH//Z86xS4hAb74RTV5+njLMq+JiYlh8eLFfO1rX+OBBx5g7ty5hIaG0t3dzc6dO3niiSd48skn2b17Nz09Pd49+XWQfkIMJTEhPEZ0jdFf//pXvvOd7/CHP/yBhQsX8uSTT7JmzRpKSkrIzMy86M+dOnVqUEaXkJAwks0UfqKvrw+XywXgk9G+559Xvz7wgLpDfaBoaWlh27ZtOBwOIiMjWbx4MREREb5ulhA+V1ZWxr/+67+yZs0aZs6cecHn6PV64uPjiY+Pp7CwEJfLRWtrqzai1NzcTE9PDxUVFVRUVKDT6YiLiyM5OZnk5GRiY2N9Pm1Mr4f589XjN7+BPXvg1Vfh73+Hs2fhL39Rj/Dw89PtvDmSpNPpSE1NJTU1lZUrV3L69GltSm9dXR11dXVs2LCB/Px8pk2bRkFBwUX3nhJCCF8a0al0c+fOZebMmTzxxBPafYWFhaxdu5Zf/epXw56/detWli5dSltb26DN/66GTKULXE1NTTz++OOEhITwgx/84LLP7+rq8loCcOaMWu1Jp4OqKsjI8MrLjriWlha2bNlCf38/cXFxLFmyBEugLYzyIm/GhAh8Bw8epKioiAMHDlw0Mbqc/v5+mpqaqKuro6GhgY6OjkGPm81mkpOTSUpKIiUlhdDQUG803Svcbti7F/72t/NJkkdUFNx+uzqadOONI1Pdrre3l2PHjnHkyBGt8AWom/ROnjyZmTNnkpKS4v0TX4b0E2IoiYng5hdV6RwOBwcOHOCHP/zhoPtXrlzJzp07L/mzM2bMwGazMXHiRP71X/+VpUuXXvS5drt90NSGgVMmRGDx7EdypR8sSktLvbY3yXvvqV+XLAmcpKijo4Pt27fT399PUlISixYtGvNV57wZE0KAWlQgJSVF+wDf09NDfX099fX1NDQ04HA4qK6uprq6GlALniQnJ5OSkkJCQoJPp7Pq9TBvnnr893+rSdKrr6rThmtr4dln1SM5GT7/efjCF2D2bO/tkxQaGsqcOXOYM2cOTU1NHDlyhKNHj9LZ2altIpucnMzMmTOZMmWKtjHvSJN+QgwlMSE8Riwxam5uxuVyDVt4mZSURH19/QV/JiUlhaeeeoqioiLsdjt//vOfWb58OVu3bmXJkiUX/Jlf/epX/PSnPx12/6ZNmwgLC2PZsmXs3buX7u5uYmJimDRpEh999BEAEyZMwO12axW8brjhBg4fPqxllDNnzmTr1q0A5OfnYzQaOXHiBACLFi2ipKSE1tZWwsLCmDdvHh988AEAOTk5hIaGcuzYMQDmz59PWVkZTU1NWK1WlixZwoYNGwDIysoiOjqaI0eOADBnzhyqq6upr6/HZDKxbNkyNmzYgKIopKenk5iYyMGDBwEoKiqivr6e2tpa9Ho9N910Ex988AH9/f2kpKSQnp7Ovn37AJg+fTqtra3am/eqVavYunUrdrudxMREcnJy2L17NwBTpkyhu7tbK529YsUKdu7cSW9vL3FxcUyYMIEdO3YAMHHiRBwOB2VlZQAsXbqU/fv309XVRXR0NFOnTmX79u0AWjGFU6dOAbBkyRKOHj1Ke3s7ERERxMXFUVZWRnNzM+Xl5ZjNZkpKSgBYuHAhJ0+epKWlhdDQUBYsWMD+/ftpbm5m3LhxhIeHU1xcDMC8efOoqKigsbERi8XCjTfeyPr16wHIzMwkNjaWw4cPAzB79mxqamr485+TgURWr4aNGzfidrtJS0sjOTmZAwcOADBz5kwaGxupqalBp9OxcuVKNm/ejNPpJDk5mczMTPbu3QvAtGnTaG9vp6qqClAvCGzfvh2bzUZCQgJ5eXns2rULgMmTJ9Pb20tFRQUAy5cv1+bkx8bGMnHiRC1mCwsL6e/vp6SkhBMnTpCamkpXVxeRkZEcOHCA6dOns23bNgAKCgrQ6/XaGr1FixZx/Phx2traCA8PZ86cOWzevBmA3NxcrFarNs96wYIFlJaW0tzcTGhoKAsXLmTjxo2AWko9MjKSo0ePAurI8JkzZ2hoaMBsNrN06VLt3zsjI4P4+HgOHToEwKxZszh37hznzp3DYDCwYsUKNm3ahMvl0qbi7N+/H1AvkDQ3N3P248vcq1atYsuWLTgcDpKSksjOzmbPnj0ATJ06lc7OTi0mbrrpJnbs2EFvby/x8fEUFBRoF2QmTZqEzWajvLwcQPqIAOojZs2axZYtWwDIy8u7bB/h+ZtXVVWRnJx8XX1EXV0dRqOR5cuXU1FRgdvtJi8vD6vVyo4dO7SYaGpq0v79J0yYQEtLC+Hh4RQUFJCbmztqfcTp06cBuPHGGzl48KB2xfQXv5jOihXbOHYshkOHJvD++2HU1xt49FF49FFIT7exZEktn/hEB5/97BSv9RG1tbW4XC6mTZtGdnY2zz33HNXV1TQ1NVFeXs7jjz9OZmYmt912G0ajkbq6uhHpI86cOcOZM2eYMWOG9BHSR7Bp0yZALSSSkpLitT4iED5HXKyPCMbPEVdTq2DEptKdO3eOtLQ0du7cyfz587X7f/GLX/DnP//5iht56623otPpePvtty/4+IVGjDIyMmQqXQA6c+YMzz//PAkJCXz961+/7PM//PDDy5b0vhJuN8TEQGcn7N8P/n7RyO12s23bNhoaGoiIiGDFihVjevrcQN6KCREcjh8/zqpVq1i/fj2TJk0a8fPZ7XYaGxupq6vj3Llz2Gw27TGdTkd8fLz2xh0ZGekXle4cDnV/pP/7P3jrLfh44B6AGTPUqXZf/CKkpnr/3L29vRQXF3Pw4EEaGhq0+2NjY5kxYwbTp08fkelN0k+IoSQmgptflOt2OByEhoby6quvctttt2n3f/vb3+bw4cNaNno5v/jFL3jppZe0KyyXI2uMAldVVRXPPfcccXFxfPOb37zs891ut1cWPXvWF5lM6j4h/j4braSkhKNHj2I0GlmxYsU1r8cLRt6KCRE8fBUTiqLQ0tJCXV0dtbW12mazHhEREaSkpJCamurzKXcePT3w9ttqkrRuHfT3q/fr9XDTTfClL8HateDtGW+KonDu3DkOHjxIcXGxts+UXq8nPz+fmTNnkp+f77W/o/QTYiiJieDmF+W6zWYzRUVF2rCZx8aNG1mwYMEVv86hQ4d8sjhTjD7PrukDr7JeytDYulYfj5xTWOj/SVF7e7s2taKoqEiSoiG8FRMiePgqJjwjRFOmTGH16tXceuutFBUVkZKSgl6vp6uri9LSUrZu3cqbb77Jzp07qaqqGran0mgKC1PXGb3zjrp1wRNPwMKF6qj6+vXqyFFyMtx/P3z4IXjrsqpOpyMtLY1bb72VRx55hE996lNkZmbidrs5deoUf/nLX/jtb3/Lli1bhhW/uBbST4ihJCaEx4jWy/zud7/L3XffzaxZs5g/fz5PPfUU1dXVPPTQQwD86Ec/ora2lhdffBGARx99lOzsbCZNmoTD4eCll17itdde47XXXhvJZgo/ER4eDqjTK1wu16hdQf14GjkTJozK6a6ZoigcPHgQt9tNamoq2dnZvm6SEH7t6NGj3HHHHWzfvp2pU6f6tC1hYWHk5+eTn5+P0+mkoaFBmydvs9m0Ag4Gg4Hk5GTS09NJTU312TTZuDh46CH1KCuDP/8ZXnxRHWF/5hn1GDcO7rlHPXJyvHNes9nMjBkzmDFjBk1NTRw6dIgjR47Q1dXFtm3b2L59O+PHj2fWrFnk5ub6xXREIUTwGNHE6I477qClpYWf/exn1NXVMXnyZN577z2ysrIAqKur0xbxgTr97pFHHqG2tpaQkBAmTZrEu+++y8033zySzRR+IiQkBL1ej9vtpqen57LDnd5KDDwbtl/BnrI+VVdXR2NjIwaDgaKiIvlAcAGSLIqB+vv76ejooN8zJ8xPmEwm0tPTSU9P16bc1dbWUlNTQ1dXF7W1tdpi+KSkJNLT00lLS9NG1UdbXh789Kfwk5+oI0UvvqhWt6usVO//6U9h0SK49151jyRvLQtKSEhg5cqVLF++nBMnTrB//37OnDnDyZMnOXnyJDExMcyaNYsZM2ZcVZl06SfEUBITwmNE9zHyBVljFNgeffRR2tvbuffeey/bUdXV1XllmuWDD8JTT8G//7v6xu+PFEVh48aNtLa2MmHCBKZPn+7rJvklb8XESHK73bhcLpxOJ/39/bjdbu1wuVyDvh/aPQ/9Xq/Xo9PpLvhVr9djMBiGHZ7njAXe2MdoNCmKQkdHBzU1NZw9e3bQtDGdTkdCQoKWUPl6v6TeXnjjDXjhBdi06fy0urAwuOMOdbrdvHneK/3t0dTUxP79+zly5Ig27dpgMDBp0iRmzZpFRkbGZeM7EPoJMbokJoKbX+xjJMS1SEpKor29nYaGhssmRkePHvVKR+YpajhKW2hck+bmZlpbWzEYDBQWFvq6OX7LWzFxLRRFob+/H4fDgd1ux+FwaLedTidOpxOXy+Xz0QudTofRaBx0mEwmTCbToO/NZjMmk8kvigKMFTqdjujoaKKjo5k8eTJdXV2cPXuWmpoaWltbaWxspLGxkYMHDxIXF0dmZiYZGRk+SZJCQ+HOO9WjthZeekndE6m09Pz+SBMnwn33wd13Q0KCd86bkJDAmjVrWL58OceOHWP//v2cO3eOo0ePcvToUZKSkpg1axZTp0696DREX/YTwj9JTAgPSYyEX0lKSuLUqVM0NjaO2jk9hWj8eezUs59GVlaWlOb2A06nk76+vmHHlSY9nuTEYDBgNBq1EZ6hx8Ar3xe6Cu4ZVVIURbs9dARq4OF5ridRuxKepMlsNg86LBYLFosFk8k0ZkagRltERAQTJ05k4sSJ9PT0UFNTQ01NDc3NzbS0tNDS0sLhw4eJj48nKyuLjIwMn/QPaWnwgx/AP/8z7NgBTz8Nf/sblJTA974HP/yhWs3uvvtgxQrwRq5tNpuZOXMmM2fOpLa2lv3793Ps2DEaGhp499132bRpE9OnT2fOnDnExcVd/wmFEGOCTKUTfqWkpIS//e1vJCUl8fDDD1/yue3t7V6pynb//epC4p//HP7lX6775bzO5XLx5ptv4nQ6WbFiBfHx8b5ukt/yVkwM5Fnz1t3drX31lBO+EJPJhMViGZRAmM3mYaM0o51MeJImz6iV5/BM6fN89Yx6ORwO3G73ZV9Xr9drSdLAw2q1YrFYfFoCt7u7m507d7JgwQKtuEsw6Ovro6amRtsg1cOzJikrK4u0tDRMPiyz2dEBr7yiJkkf77UIQGYmfOUr8OUvq7e9qa+vjyNHjmgbPYN6QSE/P5+5c+eSk5ODTqcbkX5CBDaJieAmU+lEwPIU5mhoaKCnp4ewsLCLPvfMmTNeWWsTG6t+bWm57pcaEQ0NDTidTkJDQ+XK52V4IyYURaG3t5fOzk46Ojro7u4eliDodDosFgshISGDDovF4rdTz3Q6nbbOyGw2X/b5iqLgcrlwOBw4nU4tWRo4VdBut+N2u7URswud02q1YrVaCQkJGfR1NP6dwsPDSUxMDKqkCNRCNZ4Kdz09PVpFu7a2Nurq6qirq8NgMJCWlkZmZiYpKSmjHpdRUer6zQcfhCNH1ItPf/4zVFer6zl/9jP4xCfg4Ydh1arzI/fXIyQkhHnz5jF37lwqKirYs2cPpaWl2hEfH8/cuXMBmD179vWfUAQNb32eEIFPEiPhV8LCwkhKSqKhoYGqqiomTpx40ecO3Cn9eiQnq1/r673ycl7nuSKcnJwsU5Yu41pjwu1209XVRXt7O21tbcNGhEwmE+Hh4YSHhxMWFkZYWJjfJkDeMnAt0sUoioLdbr/g0dfXNyhpamtrG/SznsQyNDRU+2q1Wr0a4zU1NfzsZz/jscceIz093Wuv60/CwsIoLCyksLCQzs5OqqurqaqqoqurS0uYPFXwsrKySExMHPVRvGnT4LHH4D//Uy3Y8PTTsGWLul/SO++oZb8ffFAdSfLGWiSdTkdubi65ubm0tLSwd+9eDh8+THNzM++++y7V1dW0trYyZ84cYmJirv+EIuB56/OECHySGAm/M27cOBoaGigrK7tkYnQlV72vhGe95dmzXnk5r/NMCZEpdJd3tTHR29urrdcYuObGYDAQGRmpHd7+wB4sBo4IDaUoCg6HA5vNRl9fHzabTbvtdDq1BKq9vV37Gb1ePyhZCgsLIzQ09JqT0MbGRt544w3+9V//NWgTo4EiIyOZPHkykyZNoq2tTUuMent7qayspLKykpCQELKyshg3bhxRUVGj2r6QEHWT2C9+EU6dgj/+EZ5/Xi37/cMfwr/9G3zmM+oo0sKF3qloFxcXx5o1a1i2bBmHDx9mz549VFZWsmvXLnbv3k1BQQHz5s0jOztb/o+PYd76PCECn6wxEn6nsrKSF154gZCQEB555JERvzJ/+DDMmAExMep0On97b3z99ddxOBysWrVKrm56gdvtpq2tjYaGBrq7u7X7TSYT0dHRxMTEEBkZ6dO1McGuv7+fvr4+ent7B311uVzDnutJvjwjdWFhYYSEhFxRvxBo5bpHgqIoNDU1UV1dzdmzZ7F7ynCiJg3Z2dlkZmb6rKhLby/89a9qkrR37/n7J09WN5e9+27w5lu5oiiUlZWxe/duragNqCPyCxYsYNKkSUE/GizEWHM1uYEkRsLvuN1ufvOb39DT08Ndd91FXl7eBZ+3fv16Vq1add3ns9vVvTdcLnXUyJ8uLDudTl577TUAbr/9dp8upg4El4oJt9tNY2MjDQ0N2odDvV5PVFQU8fHxREVFSTLkQ55peQOTpZ6engsWutDpdNqIkmeK44VG9SQxGszlclFXV0dlZSV1dXXa2jm9Xk9aWhrjxo0jOTnZZ/8PDhxQE6T/+z81YQK1b/7Sl+Cb34QJE7xzHk8/0dTUpE2z84wYR0ZGMm/ePIqKiqQC6Bjirc8Twj9J8QUR0PR6PRMnTmTfvn0cO3bsoomRt1gsUFgIx47Bvn3+lRh5FrR7SiaLq6coCi0tLdTW1moJkclkIjExkcTERPl39RMXm5bndDrp6ekZdDidTnp7e+nt7dXW4BmNRi1J8qwFE4MZDAZtg1ibzUZVVRVnzpyhra2Ns2fPcvbsWaxWqzbVbrSrdBUVwZ/+BL/+tVqo4Ykn4MQJ+MMf1GPVKvjWt2D1au8Ua0hISOATn/gEy5YtY//+/ezZs4fOzk42bNjAtm3bKCoqYu7cuaM+5VAI4TuSGAm/NGXKFPbt28fx48dZvXr1BdcwZGRkeO18S5aoidGWLXDbbV572evm2RfnUgvgxXlDY6Knp4fKykp6P778bLFYSElJIT4+XkaHAoRniqPnQ7pn7ZInSfKUUe/v76e9vV1bs6TT6ejo6GDt2rUYjUacTqckwQNYrVbGjx/P+PHjaWtr48yZM1RVVWGz2Th16hSnTp0iJiaGnJwcsrKyRnUNRnS0OkL0jW/A1q1q4Ya33oL169UjP199/EtfurZpdkP7iZCQEBYvXsz8+fMpLi5m586dNDU1sXPnTnbv3s3kyZNZsGAByZ5KPSLoePPzhAhsMpVO+CVFUXjiiSdobGxkzZo1WonVgRobG0lMTPTK+f7+d/jsZ9V57cXFXnlJr2hubmbTpk2Eh4dzyy23+Lo5fs8TE263m9raWurr61EUBaPRSEpKCklJSZIQBSG3201vby/d3d3a4ZmCN7Dsf2hoKBEREdohidJgLpeL+vp6KisrOXfunDbVzmg0kp6eTm5uLvHx8T4pUlBZCY8/rla06+hQ74uIUPdD+sY31GTpSl3uvcOzDmnnzp1UVlZq9+fk5LBgwQJyc3OlUEOQ8ebnCeF/ZI2RJEZBYd++fbz77rvEx8fz9a9/fdgbkTfnBDc3Q2IiKIr6Bpyd7ZWXvW5tbW2sX78eq9XK2rVrfd0cv7d+/XqWLl1KWVmZVlghLi6OzMxM+RA8xtjtdhobG/nrX//KDTfcwIXe6gYmSpGRkTIyO4Ddbqeqqory8nI6PJkIEBUVRU5ODtnZ2T5Zg9PdrU6ze+wxOHlSvU+ng5tvhu98B5Yvv3wBnat57zh37hy7du3i+PHjWqKYnJzM4sWLKSwslAstQULWGAW3q8kN5H+08FtTp07FbDbT3NzM6dOnR/Rc8fFwww3q7b//fURPdVU8H9Q8U+rEpfX393P8+HG6u7sxGo3k5+eTm5srSdEYZLFYaGpq4vvf/z4Gg4EZM2aQl5dHUlISoaGhgFqu3bM1wKFDhygpKaG2tpaurq5hm/qONRaLhYKCAlavXs2KFSvIycnBaDTS0dHBoUOHeOutt9i5cycNDQ0XTDpHSni4Ws77+HF1Wt0nPqFe0Hr3XbjpJrXC6EsvwYDq+9clNTWV22+/nW9961vMnz8fs9lMfX09r776Ko8//jiHDh26YDVFIURgkhEj4dc2btzIjh07SE9P57777hs0atTS0kJcXJzXzvXEE/C1r8GsWWoRBn8wsCrdpz/9adlr4RI6Ozs5cuQIFouF0NBQ8vLyLrg2TYwdl6pK53Q66erqoquri87OTq3QicfAvayioqIklgCHw0F1dTXl5eWDNuyNjIwkLy+P7Oxsn/RRp0+rI0jPPnu+ml16Onz72/DVr8LQ2gnX897R19fHnj172LNnjxYzUVFRLFiwgJkzZ8pFmADl7c8Twr/IVDpJjIJGd3c3jz76KP39/dxzzz3k5ORojxUXFzNlyhSvnauhAdLS1LLdx47BpElee+nr8uabb2Kz2Vi5ciWxsbG+bo5f6urq4tSpU9TX15OXl0d+fr7sRSKuqly33W6ns7OTjo4OOjs7h43SWq1WoqOjiYqKIiIiYsxPoWptbaW8vJyqqqpBRWKysrLIz88f9Yp2apvUct+PPab256CuQ3rwQbWanWd9vTfeO+x2OwcOHGDnzp3atN2wsDDmzZvH7NmzJZEOMN7+PCH8i0ylE0EjPDxc+0CzdevWQVM2zp0759VzJSXBJz+p3v7jH7360tclPDwcUP9ji+HsdjtlZWW43W5sNhsFBQWSFImrZrFYSEhIIC8vjxkzZjBp0iTS09O1zX5tNhv19fWcOnWKQ4cOcfr0aZqamrT9b8aa2NhYZs+ezac+9SmKioqIioqiv7+f8vJy1q1bxwcffEBVVdWoTjOLjYUf/xjOnFGLNBQWQlcX/Pd/Q06Oulns4cPeee+wWCwsWLCA73znO9xyyy3ExMTQ09PDBx98wG9/+1s++OADenp6rvs8YnR4+/OECFyy0lT4vUWLFnHo0CGqq6s5deoUEz7e5W8kPvw+9BC88Qa8+CL86lfqfHZfi42Npbm5mdbWVrL9pSqEn3C73ZSVleF0OgkNDZVNWsUger2e0NDQq44JnU5HWFgYYWFhpKam4nK56OjooKOjg/b2dpxOJ21tbdp0srCwMKKjo4mJiSEkJGRMVSwzmUzk5+eTl5dHU1MTZWVl1NTU0NTURFNTE1arldzcXPLy8ggJCRmVNlmtcN99asW6999XE6OtW9W1Ry+9BEVFs7Ba1W0arvdPZTQamTVrFjNnzuTYsWN8+OGHNDU18eGHH7J7925mzZrFwoULtQtcwj/JxTThIVPpREDYvHkz27dvJy4ujq997Wsj1om53TB+PJSVwaOPqnPUfa2qqopdu3YRGxvLypUrfd0cv1JXV8fZs2cxGo1MmjRJdqoXI05RFHp7e2lvb6ejo0ObRuVhsViIiYkhJiaG8PDwMZUkefT19VFeXk55ebm2Dkev15OZmUlBQYFPpgTv3w+/+Q28+qo6XRpg4UJ1hGnNmutPkDwUReHUqVN8+OGH1NbWAmryOHv2bBYuXCgbDwvhA7LGSBKjoGO323nsscfo6enR9jXatGkTK1as8Pq5nnxSHTlKTYWKCvD1Z+2+vj7eeustdDodn/zkJ0ftqqu/czgcHD16FLfbTU5ODvHx8SMWEyJwjXRMOJ1ObWPZjo6OQdXsTCaTliSNxXVJLpeL2tpabdqhR0JCAgUFBaSlpY36v0lFBXz72zVs2JDOx1tdMW2amiDdfjt465qboiiUl5ezZcuWQQnSnDlzWLBggSRIfkbeO4KbrDESQcdisbB06VIAtmzZQldX14jNXb/3XjUpOncOXnhhRE5xVUJCQoiNjUVRFOrq6nzdHL9RX1+P2+0mIiJCqyYkZXPFQCUlJdx3332UlJSM2DlMJhMJCQnk5+drJcHj4uIwGAw4nU4aGxs5deoUhw8fpqKiYljyFMwMBgOZmZksX76clStXkp2djV6vp6mpiR07dvDuu+9y8uRJbTPe0ZCTA9/4xnHOnIFHHoGwMDhyBO64Q12T9Oyz4I3m6HQ68vLyuP/++7nzzjtJS0vD6XSyY8cOHn30UTZu3ChrkPyIvHcIDxkxEgHD7Xbz9NNPc+7cOSZPnsz48eNHrIrM736nbhaYng6lpeDrQZpjx45x7NgxUlJSuMGz4dIY5nK5OHz4MC6Xi4KCAq0CllQWEgNdTVU6b3O73XR1dWlrkQYWafCMJMXGxhIRETGmptv19fVRVlZGWVkZdrsdUNfp5OTkMH78+FEZSRnYT7S2wv/+r9rneyqQp6fD97+vlvr2Vt+vKAqnT59m69at2kJ/s9msjSB59tYSviHvHcFNptJJYhS06urqeOqpp1AUhU984hPMnj17RM7T1wcTJkB1Nfzyl/CjH43Iaa5YV1cX7777rkyn+1hraytlZWVYrVamTJmifbCUvSjEQL5MjAZSFIXu7m5aWloumCTFxsYSGxs7ptYk9ff3U1VVRWlpKR0dHYC6DikjI4MJEyYQExMzYue+UD/R1QVPPaWuQ/IMzKekwA9/CA88oBZ08IaLJUhz585l4cKFUubbR+S9I7jJVDoRtFJSUpg3bx4AzzzzjHbF0dtCQtSECNTqdI2NI3KaKxYREUFCQgKKolBZWenbxviB9vZ2AKKjowd9kNy/f7+PWiTExel0OiIiIsjOzmb69OmMHz+ehIQEjEYjTqeThoYGTpw4wdGjR6mpqcFms/m6ySPOaDSSm5vL6tWrufHGG0lOTsbtdlNVVcX69evZunUr9fX1jMS12wv1ExER8L3vqWuQnngCMjPVBOnb34bcXHVUyRt/Fp1OR0FBAV/96lf5whe+QEpKCg6Hgw8//JDf/e537NixY8yWgPclee8QHpIYiYCzdOlSYmJi6O7uZt26dSN2ni98AYqK1CuJP/zhiJ3mink2ty0rKxvz86E9c/Ojhm5pL4Sf0+l0REVFMW7cOKZPn05BQQHx8fEYDAbsdjvnzp3j6NGjlJSU0NDQMGyj2WCj0+lITk7mxhtvZOXKlWRlZaHT6aivr2fr1q1s2LCBqqqqUVuXZbWqxXdOn1b3s8vIUNebfutbaoL0+997L0EaP348DzzwAJ///OdJSEigr6+PjRs38thjj7F///4x388L4QsylU4EpKqqKh5//HFCQ0P5/Oc/r+1t5G07d6olXQG2bVP3vfAVl8vFO++8g81mY968eWN2TyO3282BAwdQFIVp06YNKtHd2NhIYmKiD1sn/El7eztvv/02n/zkJ7V1aP7K5XLR3t5OS0sLHR0d2kiJXq8nKiqK+Pj4MbNPV3d3N6WlpVRUVGiJYXh4OIWFhWRnZ1/3dg1X00/Y7fDcc+oMgrNn1fvS0tQqdvfd572qpW63m+LiYrZs2aKNiMfGxrJs2TImTZo0ZqZY+oq8dwQ3WWMkidGY8PTTT1NTU0NYWBgPP/zwiG2g9+CD6tzzCRPUXdN9Wb77+PHjFBcXExUVxapVq8bEh6Sh7HY7R44cQa/XU1RUNOgDQ0lJCRMnTvRh64S/CcSYcDqdtLS00NzcTG9vr3a/yWQiLi6OhISEMbHO0G63U1ZWRmlpqTZtOjQ0lAkTJpCTk4PReG171F9LTNjtasW6X/4SamrU+9LT4f/9P3UjWZPpmpoyTH9/PwcOHGD79u3ayHhycjLLly8nLy9PEqQREoj9hLhyssZIjAmpqakkJSXR09PD66+/PmJTLf6//w8SE+HkSfj5z0fkFFcsPz8fs9lMR0cH1dXVvm2Mj3iu5eh0umEfEs56LukKgVrS/de//jX19fW+bspVMZlMJCcnM3nyZCZPnkxKSgpmsxmn00l9fT3FxcWcOHGC5ubmoJ5uZbFYmDRpErfeeiszZswgJCSE3t5eDh48yD/+8Q9Onjx5TetxrqWfsFjg4YfVzb8ff1wdNaqpUS+cTZwIr7yibhB+vYxGI3PnzuXb3/42y5Ytw2KxUF9fz8svv8zzzz8vfdwIkX9X4SGJkQhYBoOBz3zmM5hMJioqKti+ffuInCcmRp1XDurVwt27R+Q0V8RsNmvTBo8dOxbUH4qEuF7nzp3j+eef16p/BaLQ0FAyMjKYNm0aBQUFxMTEoNPp6OrqoqKigsOHD3PmzJmg3hPHaDQyfvx4brnlFmbNmkVYWBg2m43Dhw/zzjvvcPz48VHbC8liga99TU2Qfvc7SEhQb3vWpL7/PnhjHo7ZbGbJkiV8+9vfZsGCBRiNRqqqqnjmmWd49dVXafPUFhdCeJVMpRMB7+jRo7z++uvodDruuusucnNzR+Q8d94J//d/kJenTqnz1cblTqeTd999F5vNxrRp0ygsLPRNQ3ykv7+fgwcPAlBUVHTd6w3E9Rk4gudv/KVct7c5HA6am5tpamoaVJkzLCyMxMREYmNjg/r/hcvlorq6mpKSErq6ugA1kRg/fjwFBQWYvDWv7Qp0dcGjj8Kvf63eBnUt6q9+BQsWeO88nZ2dbN26lUOHDqEoCgaDgblz57J48eIxMa1SiOsha4wkMRoTtmzZwtKlSwF45513OHDgAKGhoTzwwAMjstC6rQ2mTIHaWrj/fvjTn7x+iitWWVnJnj17MBqN3HzzzWNqc0BFUThw4ABut5upU6cO2vdjYEyIa+N2u3G5XLjdbu22oii43W4URdEOYNDXgYmR57ZnuqNer9due74feoxEYhWsiZGHoih0dXXR1NREW1ubNp3YaDQSHx9PYmJiUO+L43a7OXv2LCUlJdpeSGazmcLCQvLy8i6aII1EP9HcrE67/v3v1fVIALfeCr/4hfq+4S0NDQ1s2LCB8vJyAEJCQrjhhhuYPXt2UCfDI03eO4KbrDESY8LAqROrV68mJSWF3t5eXnnllRGZVhETA88/DzodPP00vPSS109xxbKzs4mPjx80ejJW6HQ67cPewIXpwKhNpwkWiqLgcDjo7e2ls7OTtrY2Wltb6ejooKuri56eHmw2G3a7HafTSX9//6CkaWiS5DkGJlX9/f04HA7sdjs2m42+vj56enro6uqio6ODtrY2WlpaaG1tpb29na6uLnp7e7HZbDidTu08YjidTkdkZCS5ublMmzaNjIwMLBYL/f392lokzwaqwfhvqNfrycrKYvXq1cyfP5/IyEgcDgdHjhzh3Xff5eTJkxcsdz4S/UR8PPz3f6tlvu+/H/R6eOcdmDYNvvSl8wUbrldSUhJ33303d911F4mJifT19bFu3Toef/xxTpw4EZR/59Eg7x3CQ0aMRMA6fPgw06dP177v6Ojgqaeeoqenh4kTJ/LZz352RK5C/+Qn8LOfQWgo7N8PvprJ1tbWxsaNG3G73SxcuJCMjAzfNMQHzpw5Q2NjI8nJyWRmZmr3D40JMZzL5cLhcOBwOOjv77/gBymDwaCN5BgMhguO+gwcFfIY+FoDE6WhI06exGlggnUpnnZ4DqPRqLXxUioqKnjwwQd58skntX3Agp2iKLS3t9PY2KiNogBYrVYSExNJSEgI2pEFt9tNdXU1x48f16bYWa1WJk6cSG5urvZ7j0Y/ceqUWrHu1VfV70NC1A1kf/AD8FYBVbfbzaFDh9i8ebO2xiwrK4tVq1aRmprqnZOMEfLeEdxkKp0kRmNCe3v7sClz1dXVvPDCC7hcLm644YYRGRp3uWDlSti8Wa1GtHu3umu6LxQXF3P8+HGsViurV68O6mkzA7W0tFBeXk5ISAhTBsxTuVBMiPMjQzabbVgyZDAYMJlMV5VweLttQ0eZPKNSnq8Xe5samCh5jqFtH8sxYbPZaGxspLm5WRs5MRqNJCQkkJSUhNls9nELR4bb7ebMmTMcP35cSxjCwsKYPHkyWVlZdHZ2jlpM7N0L3/0u7Nihfp+UpFY3/fKXwVv5qd1uZ8eOHezcuVP7O0+bNo2bbrppxLaxCDZjuZ8YCyQxksRoTFi/fj2rVq0adv+hQ4d46623AFi7du2IXAVqaIAZM6CuDj75SXjjDXXqxGhzuVxs2LCBjo4OUlJSWLJkiV8ugve2/v5+Dh8+jNvtZvLkydoaq4vFxFg1cKrcwAqGJpMJs9mM2WwesfU93qIoipYseabmeW5fyMBESVEUXn/9dT73uc8FbRJwJVwuFy0tLdTX12Oz2QB1pC82NpaUlJSgXaPocrmorKykpKREm3YbFRVFZ2cnn/vc50Yt7hVFfY/453+Gj5cGMWWKOvVu5Urvnaejo4PNmzdz5MgRQC13fsMNNzB37tygHSX0FnnvCG6yxkiMaTNmzGDhwoUAvP3229oiVW9KSlLf6CwWePttdcqELxgMBubPn4/BYKCuro5Tp075piGjzGg0EhUVBaijR2I4l8tFZ2cnXV1duFwu9Ho9oaGhxMTEEBUVRUhIiDZNzp/pdDqMRiMWi4XQ0FAiIyOJiYkhNjaWqKgowsLCsFgs2gc/l8uF3W6np6eHXbt2cffdd7N79276+vouOnUw2BkMBhITE5kyZQr5+flERESgKAotLS0cO3aMkydP0t7eHnT/NgaDgby8PG6++WamT5+u7QFXVlbG5s2baW5uHpV26HTw6U9DSQn89rfqetXiYli1CtasgePHvXOeqKgobrvtNu6//37S0tKw2+1s2LCBJ554YkTeB4UIRjJiJAJWXV0dKSkpF3zMc6W4uLgYs9nMl7/85Ys+93q89BLcfbd6++WX4Ytf9PoprkhZWRn79+9Hr9ezbNky4uPjfdOQUdTW1sbp06cxGo1MmzZNSw5H4u8caBwOB93d3bjdbnQ6HSEhIYSEhPh9EnS93G43/f392rF//36WL1/Opk2bmDZtGqCuVzIajZjNZm0K4VjU09NDfX09ra2tWkIUGhpKamqqtldSsLHb7Zw8eVKrYAqQnp7O1KlTR/XzQmurOp3u978Hp1OdbfC1r6lrV2NivHMORVE4dOgQH3zwgTadsLCwkFWrVsmUsQuQ947gJiNGYkzo7Oy86GM6nY61a9cybtw4HA4HL7/88ohsiHfXXer0CFDnjG/d6vVTXJHc3FwyMzNxu93s2rVr0N4mwSo6Ohqr1Up/fz9NTU3ApWNirHA4HHR1deF2uzEajURHRxMaGhqUH3SH0uv1mM1mbWTJM6oYEhKiTRt0u91a4tjW1kZbWxvd3d04HI6gGzG5lLCwMHJzc5k6dSrJyckYDAZ6e3spKyvj2LFjNDc3a+W/g4XFYmHatGkUFRWRk5ODTqejpqaGdevWsX///lHrN2Nj4X/+Rx1Buv12cLvVJKmgQN0Gwhv7dut0OmbOnMk3v/lN5s6di16v58SJE/z+979n69atOJ3O6z9JEJH3DuEhiZEIWGfOnLnk4waDgTvuuIOkpCS6u7t54YUXRqTz++Uv1Tc3hwPWrlWnSIw2nU7HrFmzCA8Pp6enh507d150DUaw0Ol0JCcnA1BfX4/L5bpsTAS7/v5+urq6UBQFi8VCVFTUmB0RgfMV86xWqzYFz5MomkwmdDodLpcLm81GZ2cnra2tdHZ2YrPZgi4puBiLxUJmZiZTp04lLS0No9FIX18fFRUVFBcX09jYGHT/FvX19cyZM4fVq1eTnp6O2+2mrKyMd999l1OnTo1a35mXB3//O3zwAUyapO6F9MADMHeuWtTHG6xWK2vWrOHBBx8kOzub/v5+tm7dyuOPP87Jkye9c5IgMNbfO8R5khiJoGa1Wrn77ruJjY2lvb2dF154ge7ubq+ew2BQp9QtWgQdHeqc8bNnvXqKK2I2m1m0aBFGo5GGhgZth/RgFh8fj8ViweFwUF9f7+vm+JSiKHR3d6MoCmazmfDw8DExSnQ1POuVQkNDiYqKIiYmhsjISKxWKwaDQStW4RlN6ujooK+vL+gvMoBakCMtLU3bD8lkMmG32zlz5gxHjx6loaEh6BKkqKgoFi1axLJly4iJicHhcHDo0CHWrVtHTU3NqPWfy5bBoUPw6KMQGQkHDsD8+XDvveCtbi0pKYkvfelLfOYznyEyMpL29nZeeeUVXnnllUFl3YUY62SNkQhYbrf7issKd3R08Oyzz9LR0UFSUhL33nsvISEhXm1Pa6uaHJ04ARMmwLZtkJjo1VNckdraWj766CMURWHGjBmMHz9+9BsxilpbWykrK0Ov1zNp0iSv/10Dhd1up6urC71eT3R09KiW3PZXbrcbu92OxWK57L+Hp/rdwD2eBvIUgLiS1woGLpeL5uZm6urqtM0vLRYLqampxMXFBfS/wYXeOzwlvo8ePapV7ktKSmLGjBmjuianoQF+9CN47jn1+8hIde+8b34TTCbvnMPhcLB9+3Z27tyJ2+3GbDazbNky5syZE9B/1+txNZ8nROCRct2SGI0JH374IYsXL77i57e0tPDcc8/R3d1Namoqd999t9c/RFdXq8nR2bPqjudbtnhvMe3VOHnyJIcPH0an07F48eKg3uxPURROnTpFZ2cn9fX13HrrrWNypKSzsxOHw0FoaGjQll++FlfbT3hcbCPcgVXyPOuWgpnb7aa5uZlz585pCZLVatUSpED8v3apmHA6nZw4cUKbUqfT6cjNzWXKlClYLJZRa+OePfCNb6ibiINa3vvJJ9WRJG9pbGzknXfe4ezHUxxSU1O55ZZbgvr94mKutZ8QgUGKL4gxwbMvxZWKi4vjnnvuITQ0lHPnzvHiiy/S19fn1TZlZsKmTWo57yNH4Oab4eMN2EfV+PHjycnJQVEUdu7cqRUnCEY6nY6srCz0ej09PT1B/btejKIo2gjHWN6vZ6jS0lK+/vWvU1paetU/azAYCAkJ0abchYWFYTKZUBQFp9OpTbfr6uoK6sINer1eK/WdmZmJyWTCZrNRUVHBsWPHBlW1CxSXeu8wmUxMnTqVm2++mczMTBRFoaysjPfee4+KiopR+13nzlWTo6efhrg4de3qwoVq9br2du+cIzExka985SvceuutWK1Wzp07x5/+9CfWrVunJcFjxdV+nhDBSxIjEbCupSR1YmIi9957L2FhYdTV1fHCCy94vUMsKICNG9WRot274ROfGP3kSKfTUVRUREpKCv39/Xz44YcjUpXPX4SEhJCenk5oaChnz571esLr7xRF0dZ/jOViC0N1d3dTXFx83esK9Xr9sCTJs4Gs3W6ns7OTtra2YRvpBhODwUBycjJTp04lIyNDK9JQVlbGiRMn6PLFFaBrdCXvHWFhYSxYsIBly5YRFRWF3W5n7969fPDBB7S2to5CK9Uy3vfdBydPquuNFAWeeAIKC+Fvf1O/v16e94pvfOMbTJ48GUVR2L1795grzjAWtrgQV0am0omA1dXVRURExDX9bGNjIy+88AI9PT0kJSVxzz33EBYW5tX27dsHN92kFmSYPx/efx8+rh48avr7+9m2bRtNTU1YrVaWL19+zf9m/k5RFI4cOaJNJyssLBwzSYLb7aa1tRWdTkdsbGxATm8aCQcPHqSoqIgDBw4wc+ZMr79+f38/drsdu90+qDCByWTCarViNpuD9m/R399PQ0ODVhESICYmhoyMDKxWq49bd2lX+97hcrkoKyujuLiY/v5+dDodeXl5TJkyZVRHaLduhQcfBM8A6M03w+OPQ3a2987hqc7nuZBWWFjIJz7xCcLDw713Ej90PZ8nhP+TqXRiTNi5c+c1/6xn5Cg8PJyGhgaee+45r1fmmT1bLcMaEwO7dsGKFWqBhtFkNBpZvHgxMTEx2Gw2tm7dqm32F2x0Oh11dXWYTCZ6e3s5c+ZMwE3xuVaeD9+KooyZ39kfGI1GwsLCiImJISIiQkuEnE4nXV1dtLe309vbG3TV3ED93dPS0pg6dSqJiYnodDra2tooLi6mqqrKr/fJudr3DoPBwPjx4/nEJz5BVlYWiqJw+vRp3nvvvVHtZ268EY4ehX//dzCb4b33YOJE+PWvYUitkGuWl5fH1772NRYtWqTtffSHP/yBY8eOBXXfcj2fJ0RwkcRIjFkJCQl8+ctfJioqiubmZp599lmam5u9eo6iIrUAQ3y8uoh22TIY7SUwZrOZG264gYiICHp6etiyZUvQJkd6vZ68vDx0Oh0tLS1jpoS3TqfTRseGVlMTI0+n02GxWIiMjNT2SdLr9bhcLnp7e7W1SMH4tzGZTGRnZzNp0iSio6NRFIWGhgaOHj1KXV1dUCWFISEhzJ8/n6VLlxIVFYXNZmP37t1s37591PpUi0WtUnf0qJoo9fWpm4zPm+e9PfRMJhMrVqzgq1/9KsnJyfT29vL3v/+dv/3tb17f7kIIfyNT6UTAqqmpIT09/bpfp6Ojgz//+c80NzcTFhbGXXfdRUpKihdaeN7x47B8uVqKddIktUDDx3uTjpqenh62bt1KV1cXYWFh3HjjjUE3dcATEw0NDVRVVQGQm5tLXFycj1s28rq7u7HZbFit1qCf9nKlmpubee655/jyl7886msIPHsi9fX1DUqITCYTISEh2gazwaajo4OzZ89qazdDQkLIzMwkarTnEV+CN947XC4XJ0+epKSkBJfLhdFoZMqUKeTn549apUJFgRdegO9+F9ra1HLe//Zv8IMfeK+0t8vl4sMPP2T79u243W5CQkK4+eabmTx5clDFr7c+Twj/JOW6JTEaE8rKysjLy/PKa/X09PDSSy9RV1eHxWLh85//POPGjfPKa3uUlqojRrW1kJsL69apO5+Ppr6+PrZs2UJnZychISEsXbo0qP6fDIyJ6upq6uvr0ev1jB8/PuiSwKGcTicdHR3odDpiYmKCvoz0lfJmP3GtnE4nNpttUPU6o9FISEhIUK5DUhSF5uZmampqtCl1sbGxZGRkjGrJ64vxZkx0dnayb98+rRpmbGwss2fPJmYU92moq4OHHoK331a/nzEDnn8epk713jnq6+t58803tVH4CRMm8IlPfCJo+lV/6CfEyJE1RmJMKC8v99prhYWFce+995KdnY3dbuell16i2FvzEj5WUKBu+jpuHJSXw4IFaoGG0eRJhqKioujr62Pz5s20e6v2qx8YGBMZGRnExMTgdrspLS0N+ikgRqNRq5Q21qryXUxzczO///3vvT5F9mqZTCYiIiKIjo4mJCQEnU5Hf38/XV1ddHR0YLfbg2r9hk6nIyEhgSlTppCUlIROp6O1tZXi4mLOnTvn8+l13nzviIyMZNmyZcyePRuz2UxraysbN27k6NGjo1ahMCUF3nwTXn4ZYmPh0CGYNQt+9jPw1lKv5ORkvvrVr7J06VIMBgMnT57kD3/4A0ePHg2K2PVmTIjAJomREB+zWCzcddddTJo0CZfLxWuvvcZHH33k1U4/Nxd27oSZM9W1RjfeqFarG00hISEsW7ZMK8iwefPmoNz7R6fTkZOTQ2RkJC6Xi9LS0qBdWwXq7+vZ2NVmswVt2eirUV1dze9+9zuqq6t93RRAXcTvKdbgWYc0MEEKtv2QjEYjWVlZTJo0iYiICNxuNzU1NRw7diygyntfjmcT2DVr1pCRkYHb7aakpIQNGzaMWmlvnQ6++EV12vbatWpC9JOfwJw56p563mAwGLjhhht44IEHSElJoa+vj9dff52///3vcjFGBA2ZSicCltPpxOStidQDKIrChg0b2LVrFwCzZ89mzZo1Xp2a1NUFn/kMbNgABgP86U/w5S977eWviN1u58MPP6S5uRmDwcD8+fMDfo71hWLCkxR1dXVhMpkoKCjweml2f6EoirbhqNFoJCoqKuimaV2NkS7Xfb3cbjc2mw2bzaaNophMJkJDQ0ekb/MlRVFoaWnh7Nmz2vS6xMRE0tPTMRqNo9qWkXrv8KipqWH//v3YbDb0ej2TJk1iwoQJo7Z9gKLAK6/AN76hVkI1meAXv4DvfU/dG8kbXC4XH330Edu2bcPtdhMZGcltt93m9Snoo2WkY0L4lkylE2PC3r17R+R1dTodq1atYs2aNeh0Ovbt28df/vIXbDab184REQHvvAN33w0uF3zlK/Dzn3tnw74rZbFYuPHGG0lLS8PlcrFjxw5Onz49eg0YAReKCYPBQH5+PuHh4TidTk6dOhVUV6sH0ul0hIWFaSMRspu7f9Pr9YSGhmqV7Dylvjs6Oujs7AyqKnY6nY74+HimTJlCYmIioO4nd+zYsVHffHqk3js80tPTWb16tTZ6VFxczAcffDBq05Z1OvjCF6CkBD71KXX06J//Wd0y4uxZ75zDM3p03333ERcXR2dnJy+++CIbN24MyNHqkY4JETgkMRIBa6TXjMydO5fPfe5zmEwmTp8+zTPPPOPVaRFms1pR6Ec/Ur//f/9PHTXyYv51WUajkYULF5KXl4eiKBw4cCCg54xfLCaMRiMFBQVERETQ39/PqVOnvL5vlb/wTNcCtdiG3W73cYvE5XgSpJiYGKxWKzqdDofDQUdHBz09PT5fk+NNRqOR7OxsJkyYgNVqxeFwcPr0acrKykZt76PRWG9otVpZsGAB8+fP19YebdiwgRMnToxa/5qUBG+8oc5ICA1Vt46YOhVefdV750hLS+PBBx9k5syZKIrCjh07ePrppwNuenawr0EVV04SIxGwRqPqT2FhIV/+8peJjIykqamJP/3pT1RUVHjt9XU6+OUv1d3LDQY1UVq6FEZz+x29Xk9RURFTpkwBoKSkhF27dgXk1epLxYQnOYqKisLtdnP69GlaWlpGsXWjx2KxaOuNuru7cTgcPm6Rb4SHhzNz5syAKV+u1+sJDw8nOjoas9msFdJob2/HZrMF7AWLC4mMjGTSpEmkpKRoxRlGa/RotCrG6XQ6srKyWLNmDSkpKbjdbo4cOcLWrVtHbU2OTgf333++IEN7O3zuc3DvvdDZ6Z1zmM1mPvnJT3LHHXcQGhpKXV0dTz31FPv27QuYmB3NKoLCv8kaIxGwenp6Rm2tSHd3N6+88go1NTXo9XpWr17N7Nmzvbp+Y9Mm+Oxn1Teu9HR46y21SMNoqqioYP/+/bjdbmJjY1m0aJH2ATsQXElMuN1uKioqtNG/jIwMkpOTg24tjqIodHd3Y7fb0el0REREYDabfd2sUTea/YS3ORwOent7tYsUJpOJ8PDwUVurMlp6enqorKzUpn4mJiaSkZExYr+nL2JCURQqKio4dOgQ/f39WCwW5s6dS2pq6qi1wemEn/4UfvUrcLvVCqkvvwzz53vvHF1dXbz55ptalbeCggLWrl3r9+8jgdxPiMvzqzVGf/jDHxg3bhxWq5WioiI+/PDDSz5/27ZtFBUVYbVaycnJ4Y9//ONIN1EEqI8++mjUzhUeHs69997LtGnTcLvdvPfee/zjH//w6lzqFStg714YPx5qamDRIvjb37z28lckJyeHG2+8EYvFok398HWp46txJTGh1+vJzc0l+eMdds+ePUtVVVVQTVcC9Wp1eHg4FotFK8ow1qbVud1uNm/eHLB/W7PZTFRUFGFhYdr6o/b2dnp7ewPmSvyVCAsLY+LEidoFisbGRo4fPz5i05tG873Dw1O5buXKlcTExGC329m+fTsHDx4ctTU5JpO6lnXrVsjKgspKWLwY/vu/vbe+NSIigrvuuovVq1djNBopLS3lySefpKamxjsnGCG+iAnhn0Y0MfrrX//Kd77zHf7lX/6FQ4cOsXjxYtasWXPR0qmVlZXcfPPNLF68mEOHDvHjH/+Yb33rW7z22msj2UwhrojRaGTt2rWsXLkSnU7HgQMH+POf/+zVEtD5+bBnD6xeDX19cMcd6k7mo/m5LjExkZUrVxIdHY3NZmPLli1UVlaOXgNGgU6nIzMzk6ysLO2DWGlp6aitcRgtnuTIMy2ru7vbq0VE/N3hw4f55Cc/yeHDh33dlGum0+kICQkZNL2ut7eXzs7OgFzkfjF6vZ7MzEzGjx+P2WzGZrNx4sQJamtrgyoJjIyMZMWKFYwfPx6A0tJSNm7cOKprHhcvVkt4f/7zavGf738fli9X1yAdPDj4uJZ6PDqdjnnz5nH//fcTFxdHR0cHzz77LLt37w6qv6UITiM6lW7u3LnMnDmTJ554QruvsLCQtWvX8qtf/WrY83/wgx/w9ttvc+LECe2+hx56iCNHjmilky9HptKNHVVVVWRlZfnk3KdPn+bvf/87drudyMhIPvOZz5CZmem113e54Ac/gN/8Rv3+5pvhz39WN+8bLU6nkz179mhX+goKCpg2bZpfT+O5lphoa2ujoqICl8uF2WwmLy8vYNakXClFUejp6dGSopCQEK0KWjDz93LdV0tRFBwOh1aQwVOF0GKxBNXfsr+/n6qqKm0NYGRkJLm5uV4rp+zL946Bzp07x969e7HZbBiNRmbPnj2q7VIUeOop+OY3L70RbGmpetHuWtjtdt5++22OHz8OqJ8BP/WpT2G1Wq/tBUeIv8SEGBl+MZXO4XBw4MABVq5cOej+lStXsnPnzgv+zK5du4Y9f9WqVezfvz/oruSK6+fL6TH5+fncf//9JCQk0NnZyfPPP8+uXbu8djXMYFCnNzz/PFit8N576nqjffu88vJXxGQysXDhQiZNmgSoVza3bNni1yWgryUmYmJimDhxIiEhITgcDk6ePBlwFZUux/MB2jPPv6+vj87OzoCdYjZW6XQ6LBYLUVFRmEwmbRSwu7s7qK7EG41GcnNzycnJQa/X09nZyfHjx71WZt9f4j41NZVVq1aRlJREf38/u3bt4sCBA6M2EqjTwYMPwnPPqd+/9BIcOHD+eOkl9f7r+We3WCx85jOf4eabb8ZgMHDixAmefPJJ6urqrv8X8CJ/iQnheyO2q1pzczMul4ukpKRB9yclJVF/kZJb9fX1F3x+f38/zc3NpKSkDPsZu90+aN5858dlVg4fPjzoqm9MTAzjxo3DZrNRUlIy7HU8VxNPnTo1bGpUdnY2sbGxNDU1cXbIJgARERHk5+fjcrk4coHtpadMmYLJZKK8vHzYUHlaWhpJSUm0tbUNm6oUEhJCYWEhAIcOHRr2pldYWEhISMigq2oeSUlJpKWl0dXVNWxfGpPJpFUfKy4uHpZw5ufnExERQW1tLQ0NDYMei4uLIysri76+vkGjeqC+Yc+YMQOAEydODKu4M27cOGJiYmhoaKC2tnbQY1FRUeTm5uJ0OikuLmYozyjF6dOnB70x7ty5kzvuuIOEhARaW1s5c+bMoJ8LCwvTpiscPHhw2OtOnDgRq9VKZWXlsEpIKSkppKSk0NnZSVlZ2aDHLBYLkyZNIiEhgXnz5vHBBx9QXl7O888/z7Zt27j//vuJj4+npqaGxsbGQT8bHx9PZmYmvb29nDx5ctBjer2e6dOnA2plOJvNxpQp8Oyz6h4UVVU5LFoUzb//ez0rV55j4AXi6OhocnJycDgcHDt2bNjvOn36dPR6PaWlpcPm7WdmZhIfH09zc/Owaa7h4eFMmTKFqKgo/va3v1FZWcnhw4eZOnUq8fHxTJ48GbPZTEVFxbA9OlJTU0lOTqa9vX1YJT+r1crEiRMB9f/q0DelCRMmEBoaSnV19bA1Tp5NIbu7uyktLR302J49e3j44YcBOH78+LA1NXl5eURGRlJXVzfsjTkyMhKr1Up9fT3vv/8+sbGxJCcnaxv7Bksf4XA4CA8PJzk5mZqaGurr6wdtsBlMfcTANviijwA4evTosAqPBQUFhIeHX3MfYTAYqKmpoaOjQ6tWZzQamTx5MnFxcdTX13Pu3LlBPzuSfURBQQFut/uCUxavt48IDQ3l3Xffpa+vj+LiYpKSkoiLi6OwsPCa+gij0UhdXR3jxo276j5ipD5HpKamEh8fT3FxMRs2bGDPnj1MmzaNkJAQYOT7iOjoOiCFwsILF/xR/x+d//96LX2EZ0Rs586dtLW18cQTT5Cfn8/EiRO10U5f9BGgFuApLS0lKioqaPoIOP85YqCcnByio6ODqo+Ay3+OGPp3vSRlhNTW1iqAsnPnzkH3//znP1fGjx9/wZ/Jz89XfvnLXw6676OPPlIApa6u7oI/85Of/EQBLnssXbpU2bNnj3LkyJELPr5u3Tqlr69PmTx58rDHvv/97yvl5eXKz372s2GPzZw5U/nwww+VlpaWC77uK6+8onR0dChLliwZ9thXv/pV5cSJE8pTTz017LHc3Fzlgw8+UBRFUUwm07DH//jHPypNTU3Kpz/96WGPfe5zn1OOHDmivPXWW8Mei4+PV9atW6coiqLEx8cPe/w///M/ldraWuWBBx4Y9tiqVauUffv2KXv37h32mMlkUtatW6fY7XaloKBg2OM//vGPlcrKSuVf/uVfhj02d+5cZceOHUpNTc0F/w1fe+01paurS5k3b96wx772ta8pp06dUh577LFhj02YMEHZsmWLoijKBV/32WefVVpaWpSbb7552GN33nmnUlxcrPz1r38d9lhKSoqyfv16RVEUJSoqatjja9euVY4cOaJ86UtfGvbYLbfcohw4cEDZtm3bsMdCQ0OVdevWKU6nU8nOzr7A7/OCok5++OWwxxYtWqTs2rVLOX369AV/17ffflvp7u5WZs6cOeyxb3/728rp06eV//qv/xr22JQpU5Tt27crvb29F3zdF198UWlra1NWrFgx7LF7771XOX78uPLCCy8MeywzM1PZuHGjoiiKEhoaOuzx//3f/1UaGhqUz3/+88Meu+2225RDhw4p69evH/ZYeHi4sm7dOsXlcilpaWnDHv/5z3+unD17VvnmN7857LGlS5cqu3fvVjZt2nTB3zWY+og77rhDOXbs2AXjO9j6CEB5/fXX/aqP+J//+R+lrq7O633Ef/3XfymVlZXKI488MuwxX/URf/7zn/2qj4iKilL++Mc/XnMfMZKfI44dO3bB1x3pPmLp0u8poCgHDgz+fHXggKKAosCMQT93PX3EihUrlJ///OfK/fffP+wxX36O+Mtf/jIm+oif/OQnSlVV1ZjtIzo6Oi6YSww0YmuMHA4HoaGhvPrqq9x2223a/d/+9rc5fPgw27ZtG/YzS5YsYcaMGfzud7/T7nvjjTf43Oc+R29v7wXnF19oxCgjI4Nt27bJiFGQjxjZ7Xby8vJ8NmIEg6/0NDY2snHjRm0PmVmzZmk7vHtcz5WeceNyeO65aL7//Xrc7nNkZ8Ovfw05OaN3pcflcnHy5Elt3dGsWbNYtGgRdXV1fjFi5HK5mD17NnD1I0YD+4g9e/ZQW1tLf38/er2e5ORkli9fjk6nC5o+IiUlhfr6eu3/stFoJDQ0FKvVGjR9hNPpRKfTMX36dLq7u33eR3h4+2qwy+Wit7eX9PR0oqOj6e7uHvY3D/SrwS6Xi9bWVhoaGnC73UycOJGpU6dSX19/1SNGBQUFWK1WvxkxGthH7Nq1i8OHD9PV1YVOp6OgoIBbbrkFs9k8Yn3Eu+/WccstKRw4MHjE6OBBKCqCf/7nE9xxx/WNGHnExcWRmZnJ1q1beeWVV4DzBX/Cw8N9NmIUERFBb29v0PYRHmN5xOiGG264ojVGI158oaioiD/84Q/afRMnTuRTn/rURYsvvPPOO4M6nIcffpjDhw9L8QUxzO7du5k3b56vmzFIb28vb7zxhpaQTps2jZtvvhmLxeK1c3z0kVqt7tw5CAmB3/4WHngARnPtdWVlJQcOHKC/vx+r1cqcOXNGdT+Oi/FmTDidTioqKrQPIp4PRQOnnQUDu90+aDG/JzkKlsX8/thPjARlyL5VoaGh2lSsYNLV1UVZWRlOpxOz2Ux+fv5V7z/j7zHR39/PgQMHtEQnJyeHoqKiESt840mAXnoJPs6jADhxAu66S31v+a//gu99z7vvM+Xl5fz973+nr6+PiIgI7rjjDtLT0713gqvg7zEhro9fFF8A+O53v8vTTz/Ns88+y4kTJ/inf/onqqureeihhwD40Y9+xD333KM9/6GHHqKqqorvfve7nDhxgmeffZZnnnmGRx55ZCSbKQLUaJY3vVKhoaF88Ytf1EYXjhw5wpNPPjns6tb1WLRI3cV85Uq1pPdDD8GnPw1DLhCPqHHjxnHTTTdpJb23b9+uJUq+5M2YMJlMFBQUkJmZiV6vp62tjWPHjvll3F0Pz2J+Tynonp4eOjo6gqLgTUVFBd///veHXWkMRp7S7FarVfs7Dr3iHgwiIiK09UUOh4MTJ04Mu1J/Of7+f9hoNDJnzhxmzJiBTqejoqKCbdu2jdg+ZBER6te77lITJM9x113q/YqilvS+5x71PcdbcnNz+epXv0pCQgJdXV08//zzFxxVHw3+HhNi9IxoYnTHHXfw6KOP8rOf/Yzp06ezfft23nvvPa0kYl1d3aAht3HjxvHee++xdetWpk+fzn/8x3/w2GOPcfvtt49kM0WA8tcRQZ1Ox+LFi7n33nuJioqitbWVZ555hu3bt3ut8k1iIrz/vlrO22SCN9+EqVPhgw+88vJXJCoqiptuuomCggJALWG+cePGYUPho8nbMaHT6UhOTqawsBCr1YrD4eDUqVNUVlb6PAn0JoPBQEREBBEREej1evr7++ns7KS7uzugqzW1t7fz0Ucf+TQmR9PQ6oMDS7QHE6vVSmFhIdHR0bjdbsrKyq6qkqS/vncMpNPpGD9+PIsXL8ZkMmlTtUfiA3x+vlqSe2BFOs9x6hT87/+qlVJfegmWLFE3IPeW2NhY7r//fiZMmEB/fz9vvPEG69evH/V+JxBiQoyOEZ1K5wsylW7ssNvtXp2iNhJsNhv/+Mc/tPm6WVlZfPrTnyYqKspr5zh0CL74RTh5Up3m8Mgj6u7mZrPXTnFZdXV17NmzB5vNhsFgYNq0aeTn54/6dKyRjAmXy6XNmVcUBbPZTHZ2NtHR0SNyPl9xu9309PRoV6f1ej0hISEBOb0u2PYxulLKx5vA9vX1odPpiIiIwDyaHcIoURSFM2fOaElReno6KSkpl43TQHjvGMiT4Hd3d2MymVi0aNGwCr4jbcsW+Oxn1ZkJaWnqFhJTp3rv9RVFYevWrdr689zcXD772c+O2n5HgRYT4ur4zVQ6IUbS1q1bfd2Ey7Jardx+++2sXbsWs9lMVVUVTzzxhLbZnTfMmKFe2XvwQXXKw69/DfPnq4nSaElJSWH16tWkpqbicrk4ePAgW7ZsGbZAc6SNZEwYDAYyMzOZMGGCNnpUWloadKNHer2eiIgIoqKiMBqNWqLU3t6O3W4Pqv1ygtXAtWKetUejtTfOaNLpdGRnZ2vrG2tqaqipqblsjAbCe8dA0dHRrFixgoSEBJxOJ9u2bRu2wH2kLV2q7qM3cSLU1qpTujdt8t7r63Q6li5dyuc+9zmtGM0zzzwzaqO9gRYTYuRIYiTECPNUxXrooYdIS0vDZrPx6quv8tZbb3ltznhoKPzxj/DGGxAbqy6mnTFDLcwwWp+HrFYrixcvpqioCKPRSGNjI+vWreP06dNB9WE6IiKCSZMmkZycjE6no6mpieLiYlpaWoLq9zSZTERFRREeHo5er8flctHV1UVHRwcOhyOoftdg5JlWZzKZcLvddHV1BeXfTKfTkZ6eTmZmJqCOXp89ezbofler1cqNN95IRkYGbrebXbt2Das6O9LGjVOL/9xwg7rp65o18OKL3j3HxIkTue+++4iMjKSpqYmnn356WPU0IUaSJEYiYOXn5/u6CVclNjaWr3zlKyxZsgSdTsehQ4d44oknhpVYvR5r18LRo3DTTWCzwXe/CzfeCEMqhY4YnU5Hfn4+q1evJjExUauutHXr1mHla0fCaMXE0NEjp9NJeXk5p06dCqo1HTqdDqvVSnR0NKGhoeh0Om39UWdnp98XaEhNTeWRRx7xi4qJvuCZRudZNxaMxRg8kpOTtfXL9fX1l0yOAu29w8NgMDB//nzy8vJQFIUDBw5QXFw8qklgTAysXw9f+AL098OXvqRO3fZmE5KTk7n//vtJSkqiu7ub5557jlOnTnnvBBcQqDEhvE/WGImAVV1drV0lDDRnzpzhzTff1KYJzJkzhxUrVnhtHYCiwJ/+pJZX7e5Wy3r/53/C178O+lG6HKIoCmVlZRw5coT+/n6MRiPTp08nNzd3xNaq+CIm3G63tueJ2+1Gr9dr+1foR+sfe5S43W76+vqw2WzahzGTyURISAgmk8kv1yAFcj/hLXa7XdsXJzo6esTKPvuDxsZGbS+atLQ00tLShj0n0GNCURRKSkq0/Xpyc3OZNWvWqP7/c7vhxz9W31cA7r8fnngCvLmbgd1u59VXX6WsrAydTsfNN9+s7VPnbYEeE+LSZI2RGBOGbiAZSLKzs3n44YeZNWsWAHv37uWJJ56gqqrKK6+v06l7GxUXw7JlaonVb31LvT1alYs9o0erVq0iISGB/v5+9u/fz+bNm0esNKovYkKv15OWlsbkyZOJiorC7XZTW1vLsWPHgq4aml6vJywsjOjoaK0Yg9PppLOzk46ODr9bg9Te3s6zzz4bdH+Hq2WxWLRy7L29vb5uzohKTEzURo4utMEoBPZ7B6h966RJk5g9ezY6nY7y8nL27t07qpXc9Hr4//4/ePxx9fbTT8PnPw8Oh/fOYbFY+MIXvkBRURGKovDuu++ybdu2EeljAj0mhPdIYiSEj1gsFm655RbuvvtuoqKiaGtr4/nnn2fdunVem6KUnQ0bN6pvXqGhsG2bWknoD39Qr/iNhoiICJYuXcqMGTMwGo00NTWxfv16jh07FlQLwq1WKwUFBeTl5WE2m7HZbJSWlnLq1Kmgm8JkMBgIDw8nOjqakJAQbYpdV1cX7e3tg0aUfKmiooKf/vSnY2Ifo8vxlPB2OBwBXYL9SiQlJWkjRVVVVbSM5iZvoyg3N5f58+ej1+uprKwc9eQI4Gtfg9deU6ugvvYa3Habd/c6MhgM3HLLLdxwww0AbNmyhffff98v+hcRnGQqnQhYPT09V73jub+y2Wxs2LCBgwcPAhAXF8enPvUprw7tV1TAV76iJkcACxfCU0+pVYZGS09PDwcOHNAW00ZGRjJr1iwSExO99vr+EBOe0t6NjY243W50Oh2JiYmkpqZiMpl83Tyvc7vd2Gw2bDab9sFMr9djsViwWq0+m7o1Vst1X4xn496wsDBCQkJ83ZwRpSgK1dXVNDQ0oNfrKSws1PoGf+knvOXs2bPs2rULt9tNVlYWc+fOHfVpvBs2qGtc+/rUmQlvvQXh4d49x549e3j//fcBmDJlCmvXrvVa3xJsMSEGk6l0YkwoKSnxdRO8xmq18slPfpK77rqLyMhIWlpaeO6551i3bh0OL81NyMmBzZvhsccgLAx27IDp0+Hf/k0t1DAawsLCWLx4MQsWLMBqtdLZ2cnmzZvZu3evVyr0+UtMeIozTJ48mZiYGBRFoaGhgeLiYurr64Puir1eryc0NJSYmBjCwsIwGAzaeqT29nY6Ozulkp0f8Kxh9PeiGd6g0+nIzMzUNoE9ffq01pf6Sz/hLRkZGSxYsAC9Xk9VVRW7d+8e9T5m5UpYt05NhjZvhlWrwNszpufOncvtt9+OXq+nuLiYv/71r17bKiHYYkJcO0mMRMBqbW31dRO8Li8vj6997WtMnz4dRVHYvXs3jz/+uNfKsur18M1vQkkJ3HILOJ3wH/8B06adH0kaaZ4PLGvWrCE3NxdQpzy99957lJeXX9eHZ3+LCavVSn5+PhMmTCA0NJT+/n6qq6s5duwYra2tQZco6HQ6QkJCiI6OJjIyUlvX4nA46OzspL29nb6+vqBLDAOFZ7QymPbduhSdTkdubi4hISE4HA7KyspQFMXv+glvSE9PZ+HChej1eqqrq9m/f/+o9y9LlsAHH0B0NOzcCcuXqxvCetOUKVP44he/iMlkorS0lL/85S9eSfSDMSbEtZHESASsYB32tlqtrF27lrvuuovo6Gg6Ojp4+eWXefXVV722YWpmJrz9Nrz6KiQnQ2mpWtb7/vthtN4fLBYLs2fPZvny5URFRWG329m3bx+bNm265jcpf42JyMhIJk2axLhx4zCZTNhsNsrKyigpKaGjoyMoEySz2UxkZCQxMTGEhIRoeyH19PTQ1tZGZ2fniBdrsFqtZGdnY7VaR+wcgcQzvcrtdgddzF2MwWAgPz8fo9FId3c3tbW1fttPXK+0tDQWLFiATqejoqKCo0ePjnob5syBLVsgPl7deHzlSvB27ZO8vDzuvPNOzGYz5eXlvPzyy9c9syJYY0JcPVljJAKWpwR0MHM4HGzdulWbGmG1WrnpppuYOXOm10qztrfDj36kbhALkJgIjz6qVhgareqvLpeLsrIyjh07htPpRKfTkZOTw9SpU7FYLFf8OoEQEy6Xi/r6eurr67XiE5GRkaSnpxPu7Un5fkRRFOx2O3a7fdAVXs9aJLPZjNFo9HrJ4UCIidHidru1iw5xcXF+WV59pLS0tFBeXo5OpyMvL4+YmBhfN2nElJeXs2/fPgCmT5/OhAkTRr0NJSXqxbamJpg3T12DFBHh3XNUV1fz8ssvY7fbyczM5M4777yq94uBpJ8IbrLGSIwJH3zwga+bMOLMZjMrV67kq1/9KqmpqdhsNt555x2ef/55mpqavHKO6Gh1/4kPP1QLMTQ2whe/qC6gPXbMK6e4LIPBwPjx47n55pvJzs5GURTKy8t59913KSsru+KpV4EQEwaDgbS0NKZOnUpycjJ6vZ7O/5+98w6Pqtr+9zuTTHrvvUAqkFBC70hXUAERKSp27P1rwd6u996f3atiueLVIDZQsdBBei8JkISEJKT3XmcyM78/jnNIL5AySfb7PPuZmTN7ztlzZs8+57PX2muVlXHu3DkSExP7bDhlQ7JYe3v7BlYkw1qk0tJSSkpKqKyspK6urtMsGr2hT3QX9QNj9CdRBJIQdHV1Ra/Xs3Xr1j4VEbMxAwcOZOjQoQCcOnWqU5OIt5dBg6SIqI6OcOgQXHMNdHaObz8/P2655RYsLCxIS0tj3bp1l205EuOEwIAQRgJBL8DT05M777yTOXPmYGZmxsWLF/nkk0/YvXt3p60XmDgRTp6UsphbWsLu3VJwhsce6/xFtC1haWnJ2LFjmT59Og4ODqjVao4dO8bWrVvJycnpnkZ0EyqVCj8/PyIiInB1dUWhUFBcXMyZM2dISkrqswIJJHFobW2No6MjdnZ2mJubo1Ao0Gq1csCG0tJSqqqqrkgknTp1ioULF3Lq1KnO/QK9FMNNY3+dGffz88Pc3BydTidHxuyrhIWFyZaio0ePkpeX1+1tGDpUshTZ2UkTb4sWdW6eI5DcB2+99VYsLCy4ePEi69ev7xfBRQRdhxBGgl7LgAEDeroJ3YpSqWTs2LHcd999hISEoNVq2b17Nx999FGnBWcwM4PVqyU3iAULQKuFd96B0FD4+mvoLsdbV1dXZs2aRVRUFGZmZpSUlLB792727NlDWVlZi5/rjX3C3NycwMBAhgwZgpOTEyAtBD5z5gyJiYlUdvY0qxFhWItka2uLk5MTtra2skiqq6ujqqqKkpIS2ZKk0Wg6JJJ0Oh1VVVUi2AOSK2PN3+EnL9fdqLdjYmKCv78/jo6O5OTk9OnJB4VCwdChQ/Hz80On07F///4eGUtGjoQ//5Ty6G3ZAnfc0fk59Dw9PVmxYgVmZmYkJyfz/fffd9gi2BuvHYKuQQgjQa/FkLCwv+Hg4MDSpUtZvHgxtra2FBUVER0dzbfffktxcXGnHCMgADZskMKvhoRAbi7ccgtMmgTdNfmuVCoJDg7mmmuuISQkBKVSSVZWFps3b+bEiRPNhvfuzX3C0tKSoKAghgwZIq//KC4u5uzZsyQmJnZa4A1jRaFQYG5ujq2tLY6OjtjY2GBmZtbAklRaWkpxcTHl5eXU1tYKwdMBKisr0el0mJiYyGG7+yMODg44OTmh1+vJyMjo6eZ0KQqFgtGjR+Pk5ERtbS179+7tEWvK+PHw449gYgLffANPPdX5x/Dx8WH58uWoVCoSExP58ccfOzQ+9OZrh6BzEcEXOot774XMzO47noC8vLxOSwzaW9HqdBQWFkqCSK9HoVDg5OyMk6NjpyX40+qk5LDnz0sWJAWScAoNlSxM3YWmro6y0lJ51lupVGJra4u1tbW8XqIv9QmtVktNTQ3qejcyKlNTzC0spCAFPdi27kQP6HU6dDodOr2+icVIqVCgUCrlx/rnpaS0lD179jB58mQc7O27td3Ggh6pL2m1WhRIbnTdnfzT2MjNzcXsb6uZrY1Nn3ctrNNqyc/PR6fVYmFpiZOTU4+MH+npcPKU9HzwYBjYBUaayqoqMjMy0Ov12Ds44O7u3q7v2peuHb0Gb29pgXM30BFtIISRoNeyZcsWZs+e3dPNMAry8/P5448/5EW2Dg4OzJkzh9DQ0E5bZJ2eDk88Ad9/z9/HkJLD3n9/9wqknJwcTp8+LVvHbGxsiIiIwM/Pj61bt/a5PlFdXU12djaFhYWyKLCxscHDwwNHR8d+tYher9dTV1eHWq1Go9E0WV+nUChQqVSoVCpMTU2JiYlh5MiRHD9+nBEjRvRQq3sOnU5HWVmZfJ6sra2xtLTs4Vb1PFu2bCEkJIT8/Hzs7e0JDQ3t6SZ1OQUFBezcuROdTtdjkeoA/vlPePppKafer79KQRk6m7i4OL7//nv0ej1Tp05l6tSpbX5G3E/0bYQwEsKoX1BWViZ+43ro9XrOnTvHli1b5HU4wcHBzJkzB2dn5047zo4d8OijEBsrvQ4Kgn//G667rvvCe+t0OlJTU4mJiZEtSI6OjgQGBhIcHNwnxUJNTQ05OTkUFBTILiIWFhZ4eHjg7OyMiYlJD7ew+9HpdGg0GlkoNXadqa6uJi4ujhEjRmBnZ9dvrCWG9UT115QIUXSJsrIyzMzMiI2NRa/XExkZ2S9yXRnCeCuVSmbMmCGvaexO9Hq4+274/HMpfPeBAzBkSOcf59ixY/z2228AzJs3j5EjR7ZaX9xP9G2EMBLCqF9w4sSJfjkL3BZqtZo9e/Zw8OBBtFotJiYmjBs3jkmTJnXaomutFr78Ep57Tlp/BFLOirfegu78STQaDYmJicTFxaHRaMjOzmbo0KFERkbi4uLSfQ3pRjQaDXl5eeTm5sqWAJVKhbu7O66urqhUqh5uYc+g1+vRarWyJckglHJycvDw8AAki5JSqcTU1LRB6StCWq/Xo9Fo5Gh+BoQoaojh2nH+/HlKSkrw8PDAz8+vp5vV5ej1eg4cOEB6ejq2trbMmjWrR8YLtRpmz5Yin/r7w9Gj4Ora+cfZtWsXf/31FwqFgqVLlxISEtJiXXE/0bcReYwE/YLOyuPT1zAzM2PGjBnce++9DBw4EK1Wy759+3j//fc5fvx4pyxYNzGBO++ExER49lkwN5cuciNHwm23QXdFwlWpVAwaNIh58+YRFhZGTU0NeXl5bN++nX379lHaXXHGuxGVSoW3tzdDhw7F398fc3NzNBoNGRkZnD59mpSUlD4dbaslFAoFpqamWFpaygEcysrKeO6558jLy8PExEQWT7W1tVRWVlJaWkpRURElJSWUl5dTXV2NWq1Gq9V2Wh6lrsYghiorKykuLpZd5wwR/ww5owSXMFw7XP++G6/vptqXUSgUjBw5Emtra8rLyzl+/HiPtMPMTArGEBQEFy9KefO6Iq3U1KlTGTFiBHq9nh9//JFcwyxeM4j7CYEBIYwEvZb+4PpwJbi4uLBixQqWLl2Ks7MzlZWVbNq0iU8++YQLFy50yjFsbeH11yEhQbq46fWwdi0EB8NLL0F5eaccpk3Mzc0ZNmwYUVFRDBgwAIVCQUZGBps3b+bQoUOUd1dDuhETExPc3d2JjIxk4MCBWFtbo9PpyM/P58yZM8THx1NUVNQvbviaQ6FQUFJSwi+//EJNTQ2Ojo44OTlhZ2eHlZUVZmZmKJVKed2SQSyVlZVRXFxMcXExpaWllJeXU1VVRW1trWyF6slzahBCNTU1lJeXy+2srq5Gp9OhVCqxtLSUc0T1RxfLtjBcO+zt7TExMZGtbP0Bc3Nzxo4di0KhIDU1tcci8zk7w88/S2G8t2+XrhedjUKh4JprriEwMBC1Ws23337bYshycT8hMCBc6QS9Fv3fUdgEbaPVajl69Ch//fUX1dXVgLT+aNasWfKsaWdw6JCUEPbgQem1q6vkbnfPPZJVqasx9InS0lJiY2Pli75SqcTf35/BgwdjY2PT9Q3pAfR6PRUVFeTl5TUQRObm5ri5ueHq6trno2815sSJE0RFRbUYfEGv16PT6dBqtdTV1cnR29qyGCmVSrkY3PMab2tc2oP+76h7hnYZLFyGNhoem0TmUypRqVSYm5ujUqnEuNgG9a8diYmJFBcX4+3tjbe3dw+3rPs4ffo0cXFxWFpaMnfu3B4L4b5uHSxfLj3ftAnmzev8Y1RXV/PZZ59RVFSEr68vt956a5OxUNxP9G3EGiMhjPoFIopMx6muruavv/7iyJEj8uzyyJEjmTp1aqflcdDrJTeJ1aslVzuQwnu/+iosXSq54XUVjfuEIVGqIcu9UqkkICCAwYMHY21t3XUN6WHUajV5eXnk5+fLeUuUSiVOTk64urpiY2PTL24C2hJGLWEQJIbSWJh0lMbiqP5zwyW48WNr1F8nZYjA1x9+z86i/jiRm5vLxYsXcXR0JDg4uIdb1n3U1dWxZcsWysvLCQoKajM4QVfy4IPw4Yfg5AQxMVIU586moKCAzz//nJqaGkaOHMm8RgpM3E/0bcQaI4FA0CyWlpbMmTOH+++/n7CwMHQ6HUeOHOH999/nwIEDTcIfXw4KBSxeDGfPwiefgKcnpKbCzTfD8OHw+++SeOoOnJycmDx5MjNmzMDT0xOdTkdycjK///47x44d65FM8N2BmZkZPj4+DB06lMDAQKysrNDpdBQUFBAXF8fZs2cbBG8QNMSwXsnc3BwrKytsbGywt7fH0dERZ2dnHBwcsLOzk/NoWVpaytYaQ+S7xuJHZ8jF9Le4qi+6DNYhgyhSKBSYmJhgamqKmZkZlpaWWFtbY2dnh5OTk+wmZ2VlJSxEV4hh/VV/caUzYGpqyqhRowApWl1nJQe/HAxBe4qKYOVK6Iq8zS4uLtxwww0oFAqOHTvG6dOnO/8ggj6BsBgJei3x8fE9louhr5CSksKWLVvIyckBJJ/7adOmERkZ2Wlhjauq4P334c03wRALYdIk6fX48Z1yCJm2+kRBQQGxsbHyIlyDBSk8PBxbW9vObYwRodfrqaysJD8/n8LCQjkAh1KpxNnZGVdX1waJcvsKGRkZPPfcc7z22mv4+Ph0+/Hru8YZXtd/Dxpakzrqeie4POqPExqNhpMnTwIwatSofnfuDxw4QFpaGm5ubkybNq3Hvn98vCSOqqvhnXfgkUe65ji7d+9m9+7dqFQq7rzzTtzd3f8+vrif6MsIVzohjPoF9cPwCi4fnU7H6dOn2bVrl5z/yM3NjenTpxMSEtJpF8qiIim53/vvw9+ph5g3D155RbIkdQbt7RN5eXmy1QSkG1I/Pz/Cw8NxcHDonMYYKXV1dRQWFpKfn99gltzKygoXFxecnZ37VMhvMU4IGlO/T+h0Oo4dOwbAiBEj+t06vMrKSv744w+0Wi0TJ07skQkEA598AvfeK0WtO30aukKn6PV6oqOjSUpKwsnJiXvuuQdzc3MxTvRxhDBq5csbIhBdjp+4wLjYt28fEydO7Olm9Bk0Gg2xsbEcP36c2tpaADw8PBg3bly7FiWrVKp2RcDKyICXX4b//veSy8T110tRiYYOvYIvQMf9xPPz8zl37hzZ2dnyNh8fHwYNGtQjyQ+7E0Owhvz8fIqKimQrkkKhwMHBARcXF+zt7Xt1QtSKigo+++wz7rrrrj4bdEPQceqPE3q9nmPHjqHX6xk2bFiPBSHoSWJiYjh37hyOjo7MmjWrx6xGej1ccw38+SdMmahl58t7UeZmS/7YkyZ12gLVqqoq1qxZQ2lpKcOHD+e6664Ta4z6OEIYtfDl1Wo12dnZ/c6XuK9SXV0t8nN0AXq9ntraWmpra2V3H5VKhYWFRavCR6FQ4OPj0+4b0PPnJWvRunWX1hzdcAO8+OLlZ0K/3ItbUVERcXFxZGRkyN/Z09OT8PBwXF1d+7x7jcGKVFhYSEVFhbxdpVLh7OyMi4tLpwXn6E4uN/iCoG9Tf5zQarVyPp+oqKh+Gd68traWTZs2UVdX1+NWo4sX4ZnQDfyz9mF8qRdK3McH3nsPFi7spONcZO3atej1epYsWUJaWpoQRn0YIYya+fI6nY7ExERMTExwdXXFzMysz9/s9HXq6ur6ndtDd6LVaqmsrKTG4PeGFPrZ2tq62VCnBtes4ODgDt1cxMVJAum77ySBpFDAjTdKAik8vGNtLi4uxtHRsWMfqkdpaSlxcXGkpaXJFhRnZ2fCw8Px8vLq1daT9lJdXU1BQQEFBQVyRDuQXO2cnZ1xcnLCvDtir3cCQhgJmqP+OFFbW8vp06dRKpVERUX12/sCg9XI2dmZmTNn9lxDNmxAv+gG9OgbRgcz/C4//thp4mjHjh3s3bsXS0tLbrrpJvz9/TtlvwLjQwijZr58TU0NKSkp+Pv798qZT0FTqqqqxG/ZDdTV1VFWVtZAIFlZWWFra9tAAFVXV5OamkpgYOBlJcs7e1ZysfvhB+m1QiGF937hBQgNbd8+Tp8+zdAr9cdDcsGKi4sjNTVVdru1tbUlNDSUgICAfiHI9Xo9paWlFBQUUFJSIgtFkM6Fs7Mzjo6ORr0eSQgjQXPUHyeKi4tJTEzE0tKSiIiIHm5Zz1FdXc2mTZvQ6XTMnDkTZ2fn7m+EVgsBAegzMmhWnioUkuUoJaVT3Oq0Wi2ff/452dnZmJmZ8cwzz/RbYdzX6Ygw6vtX90Z0yYyvVntpNbmg29BVVnZf3Od+jCngZG6OxsSEyspKNBoNmtJSikpLsbSwwNLKChOlEkVtLajVksK5jIvLYOD7pyFxEaxZAzt3Qdw6WP4tzJgBd9wBbaUZqTxwQPo/XiE2wCgTEyK8vUlPTyc9PR2NRsOFmBjSzczw8/PD19e3T69HUAAOf5c6nY6ysjJKS0upqqpCq9eTB+QrFNjY2GBnZ4ednZ3RuSFZxsUx/O9HgcBA/XGiNicHq4ICyYJUz0La37AEwqqryc7KIvO333DuCZF47Bi0JIpAut6np8MXX0An5F0yAW4MCuLHU6e4ePEiF374gaCgoCver6ADhIWBkU1w9zuL0eXOZrdKZaXkD9SLeOnTT8kpLOSTZ55h9/HjrPrHP4j/8UcAbCZPJvnnn3Hr44vPBZ1LDZBSUEDgqlVYXLzY080RCAQCgUBgzBw/LsVp72KExai7sbDo+GKIegQMGkRRcTG5yclyMIGysjLcBwzA39eX+L9zLAQMGsT6tWsZO3q0/NlVDz2Eh7s7L61e3bGDurhIM2bh4ZCfL8XH/Ps7VOTnX/Z36UoeeOwxRkVFcevy5fK2ux54AHMzMz58++0Gdd//6CN++uUX/tqyBYBjJ07w5OrVZGZn88aLL3LDggUN6i9cupSIwYN5+bnnuv6LdBIXkpO55e67OXn6NGEhIXz5yScMbWGW78SpUzzw+OOcOXcOF2dnnn/qKW67+WZAckl89Kmn2LhpEwqFgicfeYQnHn4YkFzK5ixYQFxCAjqdjhFDh/Kft98mNDQUjUZDVWUlmr+ThGrq6qjV6ahZt65TJx/On5ci2G3bBoZZnDGjJQtSVNRlGacuG51OR25uLikpKZSXlwNS0AkXFxf8/PxwdnbuV64YNTU1siXJEMkQJMu8ISGpra1tv3A9FPQ+KisrSUlJQWliQmhIiNFZPLsbvV7Pvn37qKqqIjIyEk9Pz+5twLFjcM89bddbs6ZTLEYGtFotP/30E8XFxYSEhDBt2rRO27egDYwwd5S4WnUGJiZgbX35n1co8PDw4NcdO1iyZAkAG374AV9fX1AqL+1boQBLy4bHUqkkUdPR45uZSZ+1tpb2Wf84RsqWnTtZ/dJLcjtLS0tZsXIlixcv5t3//KfBzde6n37i9ttvl+tu/usvZl99NbW1tUT/9BM3rFgh1y0tLeXPbdv4x7//bfTnoD5L77iDa665hh27dvH555+zYNkyzp8/3+xN6C333MPSpUvZd+AAp06dYsqUKUy46ipCQkJ4/Y03SMnIIOnCBUpLS7nqqquIiIpi9uzZmJuZ8dkXXxD69yKfjz/+mFvvvZfDhw9jBqjs7amtraW8vJy66mqqtVrWHjnCkCFDGD9+fKesAQsZAW/eBLclSElhv/kGTh6BT45ICWJXr4a5c6W/x9atW5k1a9YVH7MllIAn4KHXk5eXR3x8PNnZ2RQB54uKsNdqCQkJwd/fv1+IAYu/i6teT1VVFcXFxRQVFVFVU0MFkIskHO2srXFycsLBwaHb1yR1dZ8Q9D4MfSI9Pp4qc3NcXV0xCQzs6Wb1OArAwcyMzLNnuWBvj2d3r8sbOhRefRUyM5t3kzesMbrjjk4L3Q2SS506IYHspCSyy8sJcXdvV4oKQd+k74dY6iUsXbqU6Oho+XV0dDTLli27on1WV1fzwAMP4OXlhY+PD//85z/b9TmFQkFOTg4AAQEB/POf/yQoKAhXV1deeuklud5vv/1GaGgotra2+Pr68u233wLS7MuLL76Iv78/Hh4ePP7449T9bVWoz9atW5kwYYL8OjAwkPvvvx+AkpIS7Ozs5M9duHABKyurJjNYkydPxtLSkm3btsnbkpOTOXnyJDfccIO8zRCedcWKFfz555+UlJTI7/30008MGTKE0NBQdu/eTVhYGM8//zwODg6EhoZy7tw5XnvtNZycnAgPD+fs2bPyZ++77z68vLxwcHBg1qxZpKWlAZCQkICLiwtJSUkAHDp0CA8PD/Ly8tr1G7RFQkICCQkJPPPMM1hYWPDAAw+g1Wo5cOBAs/UvXrzIsmXLUCqVjBgxgvDwcBISEgDYvHkzjz76KHZ2dvj6+nL77bfzv//9D5DCNYeHh6NUKtHr9SiVSlJSUuT9KhQKLCws5Jw3JiYm1NXVsW/fPt5991127NhBZWVlp3zn0FD48ktITIT77gNzczhwQMp7ERUF338PzXSzLkGhUODu7s6UKVO4+uqrCQ4OxtTUlNLSUo4ePcqvv/7K6dOn+01qAIVCgbW1NT4+PkRERDBkyBC8vb2xsrKSgzikpKRw6tQpOW9UVVUVXe3JffbsWVauXNngPysQGPpkWVkZSqWy+y0jRoxBEOTm5nZ/vkcTEykkNzRxBdChkLTSu+92qigy4OrqKgfk2Lx5c5ePTQLjpd8KI71eWhrU1aW9/62ZM2dy4sQJioqKyMnJITExkcmTJ1/Rd3ziiScoLS3l/PnzHDlyhP/9739s2rSpw/v56aefOHjwIIcPH+aLL77gt99+A+DOO+/kv//9L+Xl5Rw9elQeVN5++20OHDjA8ePHiY+P58SJE3z88cdN9jtu3DhOnjxJdXU1mZmZgJS0FWD//v2MGjVKnnXfvHlzkxwDhpDrS5cuZd26dfL2devWMXfuXDlBp+GmbNiwYQwcOJBhw4bx008/Nai/vJ57XlJSEq6urhQUFDBr1iyuvvpqLC0tycvLY968eTxXz91u4sSJxMXFkZOTg4+PDw899BAAoaGhPPvss6xcuZLKykpWrlzJ+++/j5ubW5PzsG/fPhwcHFoszXHu3DlCQ0MbLP6PjIxs8QbwgQce4Ouvv6auro4jR46Qnp7OmDFj5PfrXwT0en2T/URGRsoC7Kmnnmqyf4VCgbm5Oba2tlxzzTV4enqiVqvZu3cv7777Lps3b6asrKzZtnWUgAD4z3+kwERPPCEZ+U6ehCVL4J57pvKf/0B36hE7OzuioqK49tprGT58ONbW1qjVauLi4vjtt984cOAAeXl5/eZCq1AosLKywtvbmyFDhhAREYGPjw/W1tZyUtn09HTOnDlDTEwMaWlplJWVNYh611nU1taSnZ3dwM1PIPD09JQneNzc3Dp/3XEvxtHREXNzczQaDYWFhd3fgIULpZDcjSw2Gfjw5sjOC9XdGB8fH2bMmIFKpSI9PV1MpvRj+q0wqqoCG5uuL+29QTM1NeX666/nhx9+YP369SxevLjZCHozZ85scNP85ZdfNrs/vV7Pl19+yVtvvYWNjQ1eXl7ce++9/Ph3gIWO8Mgjj+Dq6sqAAQO45557ZFGhUqk4c+YMFRUVeHh4MGjQIAC++OILXn/9dVxcXHBwcODxxx9v9ri2traEh4dz5MgR9u7dy/XXX49araa4uJi9e/cyceJEuW5zwsggmlasWMHPP/8sz843Fjrbt29n2rRp8tqPFStWyNa57Oxs9uzZw0033STXd3Bw4MEHH8TU1JSFCxdSWFjIo48+Kr+OiYmR6y5btgx7e3ssLCx46qmnZGFnOG8KhYLRo0cTERHBjTfe2Oz5nThxIiUlJS2W5qioqGiygNDOzq5Bcs76zJkzhy+++AILCwvGjx/P66+/Lou0WbNm8e6771JSUkJqaipr165tYumIiYmhrKyMTz75hPA21tMFBgZy9913c9NNN+Ht7Y1Go+HQoUO89957bNq0iaKiolY/3148PeHf/5YSAr7wAjg7Q0aGGQ88AH5+Uh6k7lwuZ2ZmRmhoKNdccw0TJ07Ezc0NnU5HWloaO3fuZPPmzSQmJjbIDdQfsLS0xMvLi8GDBzNs2DACAgJwcHBAqVRSW1tLTk4O8fHxnDp1igsXLjTJnyQQdCZ6vR6NRoNarcbCwkK4TDVCoVDI14YeEUYgiZ/UVNi1C9atI+2rXQSSwurjC0lO7ppDurm5YWtry6RJkwDpvqHbLWYCo6DfCiNjZPny5axbt67JjX19tm3b1uCm+bbbbmu2Xn5+PtXV1YSEhMgi6tlnn70sV676WbB9fX3Jzs4G4Mcff+TXX3/F29ubWbNmER8fD0BaWloDAbd8+XLyW7hDnTRpEnv37mXv3r1MmjSJ8ePHs3///gbCSK1Wc/ToUXnAMmC4eR8yZAgDBgzg119/5eTJk2RmZjJ//ny53pYtW5gzZ478esmSJRw4cICsrCzWr1/P1KlT8fDwkN93cXGRRZSlpSXOzs6ySLW0tGzgGvb6668TFBSEnZ0do0ePbnAhUSqVrFy5knPnzvHw38EMOgsbG5smFpiysjJsbGya1C0sLGT+/Pm8++671NbWcurUKV544QUOHz4MwHPPPUdAQADh4eHMnTuXxYsXN3uzYGlpyZ133sntt99OcXFxq+1TKBSEhYVx5513cvPNN+Pv7y9nmP/ggw/YsGFDi32iozg7S/mP0tLgvvvOMWAAFBZKSWP9/ODee+Fvj8ZuQalU4uPjw1VXXcXs2bMZOHCg7GZ3/PhxfvnlF44dO9ai6O3LmJmZ4ebmRkhICMOHDycoKAgXFxdUKhV1dXUUFhbKrrBnzpwhPT29y6xJgv6HXq/n4sWLnD9/HqVSycCBA/t9wIXmMHhbtDXOdykmJjB1Kixdit8tU5k52wS9Hj76qGsOd+LECUDyZLGxsaGkpITTp093zcEERk2/FUZWVlBR0fWlI2vPx40bR2ZmJhUVFQwbNuyKvp+LiwsWFhZcvHhRFlFlZWX8+eefHd5XRkaG/Dw9PV0WEWPGjOH3338nLy+P4cOHy+uDvL292bt3r3zc0tJSzp071+y+J06cyL59+9i/fz8TJ05k0qRJbN++nVOnTjF27FhAcjUbOXJkqzljVqxYIYvKhQsXNnCN2LZtW4NM3q6urkyfPp3169e3KkLb4q+//mLNmjX8+eeflJaWcuTIkQbvFxYW8vzzz3PzzTfz5JNPtjj7tHfvXmxsbFoszTFo0CASEhIazKzHxMQwePDgJnWTk5Oxt7dnwYIFmJiYMGTIEKZOncqePXsAsLa25tNPPyU7O5u4uDgUCgUjW4j4Y3CFMojjtlAoFAwcOJDbbruN22+/naCgIPR6PTExMfznP//hu+++Iysrq137agsrK7j22nTOn5fWG40aJaUX++QTCAmBRYvg0KFOOVS7cXR0ZNSoUVx77bWMGDFCXjeXlJTE5s2b2bFjBxcvXuyXM5MmJiY4OTkxYMAAhg0bRnh4OF5eXlj/HQClqqqK7Oxs4uPjOXnyJImJieTm5lJTU9Nv3BIFnYderyctLU2eHBwwYIDc1wQNMbhwl5aW9mxD6vH37QVff921a0lVKpU8Kbtnz55+OTb3d/qtMFIopLUJXV06Grl3w4YNfP/991f8/ZRKJbfeeitPPPGEnLU+Li6uyc17e3j//fcpKCggJSWFTz/9lEWLFqFWq1m3bh1lZWWoVCpsbGzkmbc77riD1atXk5OTg16vJzU1lb/++qvZfU+aNIl9+/ah0Whwc3Nj0qRJ/Pe//yUsLAxbW1vgUuCExtSPeLZs2TK2bdvGN99800DoxMXF4eTk1GRtz/Lly3nvvfc4e/YsCy/TZ7m8vBxTU1OcnZ2prKzktddea/D+fffdx+LFi/nqq68wMzPjrbfeavEcVFRUtFiaIzQ0lNDQUN58801qa2v56KOPMDExYfz48U3qhoSEUF5ezqZNm9Dr9cTHx7Nz5045y3tGRgY5OTlotVq2bdvGl19+yaOPPgpIGeL37NmDWq2msrKSZ599FgcHB4LbyrTaDH5+fqxYsYK7775bdseLi4vj008/5ZtvviE1NfWKb3ijoqIwMYHFi+HwYdi9WwrOoNfDhg0wbhxMmNC9gRpAspSEhIQwd+5crrrqKvz8/FAqleTn53Pw4EHZ2mlMNyLdiUKhwNbWFh8fHwYPHszw4cMZMGAAzs7OqFQqtFotxcXFXLx4kZiYGGJiYkhOTqagoAC1Wt3ifoOCgvjuu+9E0sZ+jlarJSkpidzcXABGjhwpW0UETTFMyFVWVhrNJMScOZJ3QF4e7NzZ+fuPiopq8FxYjfov/VYYGSuRkZEMGTKkU/b19ttvY21tTUREBE5OTtxyyy2XZRpfsGABY8eOZdSoUaxcuVJ2U/vqq6/w9/fH0dGRbdu28d7f0WSeeOIJRo8ezfjx47G3t2f+/Pmkp6c3u293d3e8vLzk6HQDBw7ExsamzfVFQINId97e3owbNw6FQsFVV13V5mevv/56ioqKmD9/vizAOsqcOXMYN24c/v7+RERENBAlP/zwAydOnOAf//gHCoWC//73v/zzn/8krhMTAa9bt47Nmzfj4ODAZ599xoYNG+R1V2+88QZz584FwN7enu+++47nn38eOzs7Zs2axQMPPCC7FyYmJjJq1ChsbW156qmnWL9+PV5eXgBoNBoefvhhnJ2d8fPz49SpU/zxxx9XFHLZy8uLJUuWcN999xEZGYlCoSApKYm1a9fy+eefc/bs2ct2nTJEUwRpUmLKFPjtNzhzBm67TYpQf+CAFKghMFAK/92dbvQG//3x48czf/58IiIisLKyora2loSEBP7880927NhBSkpKv15no1KpcHFxkYOlDB48GB8fH2xtbeW1SQUFBSQnJ3Pq1CliYmJITU2lqKiowXmzs7Nj0KBBbSb0E/RdqqqqOHfuHMXFxbL7nAjG0TqGfIp1dXWtTjx0JyoVGJbp1gvg22nUv3aoVCr5nuTQoUNGIw4F3YNC38d+8Zay29bU1JCSkkJgYKCIQNMBAgICWL9+vezW1t1kZ2czYcIEkptZcVlaWoq9vX2rn589ezarV6++4gh/gra53P9YUVERB/7Or2QQu46OjowbN47hw4d3SIS1ZF00kJ0tudZ98ok08whSGq+bb4aHHoJmPBG7HJ1OR05ODsnJyWRlZcmiUKVS4e/vz4ABA8Tsdj20Wi0VFRWUlZVRXl7e7Ky2lZUVNjY2VFRU8I9//IN///vfstgX9A90Oh2xsbGyCFKpVAQFBWFra9vmOCGQJve0Wi3z5883GpfDPXukyS5nZ8jN7dyo3Y37RE1NDW+//TZqtZpbb72VQJHnqlfTkjZoDmExEhg1ZWVl/Otf/2r2PUU7/BSnT5/OuHHjOrtZgk7EycmJefPm8eijjzJlyhQsLS0pLi7mjz/+4J133mHXrl3tzoXUXCTH+nh6SoEaLl6EtWth2DCoroZPP4UhQ2DWLPj9d+jOtf5KpRIvLy8mTpzI/PnziYyMxMbGBo1GQ1JSElu3bmXLli0kJiaKmW6ktUn29vb4+voyaNAghg8fTnBwMB4eHrJ7bVVVFXl5eZw4cYJ169bx119/ceHCBXJzc7slf5KgZyktLeXEiRPy/0WpVDJkyBDZO6CtcUKA7BpvTIFPxo0DOzvJyv93rIROo3GfsLCwkFOQGAIVCfoHwmIkaJWethgJeg+d9R9Tq9WcOnWKgwcPyq6fpqamDBs2jPHjx3eq9USvh717pZyCP/98SRAFB8MDD8Att0ALqaS6FL1eT25uLsnJyWRkZMg3JwYRFRAQgKenp4io1QwajYaKigrKy8s5cuSIvM4vLCxMrmNiYoK1tTU2NjZYW1tjbW3danAXQe+gvLycrKysBmv1rK2tGTRoULsm0gSX2LBhA2q1mquvvtqoXFEXLoSNG+HVV6FeSsEuIT8/n//85z8oFAoee+yxy3a7F/Q8HbEYmXZTmwS9lNTU1J5uQouUlZUZ1YAt6BzMzMwYPXo0I0eOJC4ujv3795OVlcWxY8c4fvw4YWFhjB8/Hh8fnyY3Ozt27GD69OntPpZCAZMnSyU1VUoc+9lnkJgIDz8MzzwDy5bBfffB8OGd/EVbbZcCDw8PPDw8qK2tJTU1ldTUVIqLi8nIyCAjIwNzc3P8/f0JCAjA0dFR3Pj9jUqlwtHREUdHRwoKCgBpgsfHx4fy8nIqKirQarWUlZU1CHlvZmbWRCwJ4Wn86PV6SktLycnJkX9Pw1o+b29ved1lfTo6TvQ3DLmegCtaT9oVXHWVJIwOHuzc/TbXJ1xdXfH19ZUTvooJ4v6BEEaCXksfM3YKGqFUKhk8eDCDBg3i4sWL7N+/n8TEROLi4oiLi8Pb25sxY8YwePBg+Qa27gpCzQUESAljX3xRCgn70UdS0IbPP5fK2LFSTqQbb4TuNDqbm5vLUQgNSXhTU1Opqanh/PnznD9/Hnt7ewICAggICJAXTgsuYUhyDdK4UV1dTWVlJRUVFVRWVlJdXY1arZYTTBuwsLDAysqqQRGWJeNArVZTWFhIXl6e7DKnUChwcXHB09OzVav1lYwT/QG1Wi1fX42tvxsySRw/Lln8O2s+qKU+ERERQXp6OjExMUIY9ROEMBL0WoxtJkvQNSgUCvmmPy8vj4MHDxIbG0tmZiYbNmxg27ZtjBo1iqioKDw9Pa/4eDY2kgBatQr27YOPP4Yff5RyIB06BI89BrffDvfcAwMHdsIX7AAODg4MGzaMyMhIcnJySE1NJTMzk9LSUk6fPk1MTAzu7u4EBga2OFven3B0dOTqq6/G0dFR3qZQKGSR4+rqCkgBHaqqqmShVFlZSW1tLTU1NdTU1FBUVCR/XqVSyZ+3tLTEysoKCwsLsW6lG9BoNBQXF1NUVER5ebl8825qaoqLiwvu7u6Ym5u3uZ/OGCf6MuXl5YAUxMTYrKaRkaBUSsEXsrKgmVzkl0VLfWLw4MFs3ryZrKwsioqKRCCcfoBYYyTotdTV1fX7Gz9jojv/Y5WVlRw/fpyjR4/KF3FTU1MCAgKYMWOGnIS4s8jNhS++kKLZGSLPKxQwe7Ykoq6+GnqqK6rVatLT00lNTSU/P1/ebmpqio+PD35+fri7uxvdDU53cbk3MxqNhqqqqgalpeSyCoUCc3NzLC0tsbS0lC1N5ubm/fa8dwZ6vZ7KykpKS0spLS1tEoHQ1tYWV1dXHB0dO3SexQ1u6yQnJ3PkyBHc3d2ZNm1aTzenCcHBkJQEW7dCvdztV0RrfeKrr74iJSWFuXPnMmbMmM45oKBb6cgaIyGMBL2W9oTrFnQfPfEf02q1nD17lsOHD5OZmUlSUhJBQUEEBAQwduxYQkJCOnUmX6uVotZ9/DFs3nxpu7e3lCfp9tul/Eg9RXl5ORcvXiQ1NbVBcmAzMzN8fHzw9/fH1dW131g3ampq+Pbbb1m6dGmn9EmtVkt1dbUslKqrq6murm7RDUehUGBmZoa5uTkWFhZNHvvL79BeDJa78vLyBuvB6mNtbY2TkxNOTk7tsg41hwjX3TpHjhwhOTmZ8PBwOTKbMTF9upTk1dQUPvxQst5fKa31if3797Nt2zZCQkJYtmzZlR9M0O2I4AsCgaBfYGJiQmRkJBEREWRkZPDJJ5+gVCrldTiOjo6MHj2aYcOGdcraGxMTuPZaqSQlwZo18OWXkJkJr70Gr78OM2bAnXfCddfBZd63XTa2trYMGTKEwYMHU1hYSFpaGmlpadTU1JCcnExycjIWFhb4+fnh5+eHs7Nznw7acO7cOW6//XaGDh3KiBEjrnh/JiYm2NjYYGNjI2/T6/XU1dXJFiWDWDIIptraWmpraxsEeoBLoslQzM3Nmzzvq9YmvV4vuyrWF5rNWeQM4dkNxdjWvPQ1DBExAdzc3Hq4Nc1TVSU9DhokuTxD54ijlhg4cCDbtm0jJSVFeKr0A8SvK+hU6of3XrVqFSEhITz22GNdcixDzhKBQKFQ4Ovry0MPPYSFhQVHjx7l+PHjFBcXs2XLFnbu3MmQIUMYNWpUpyX6DAqSgjW89hr88osUoGHbtkvFxUUK933nnRAe3imHbDeGReguLi4MGzaM/Px80tLSSE9PbxC0wdraGl9fX/z8/ERku8tEoVCgUqnkG3cDBsFUU1MjiyPDmqWamhq0Wq28vSVMTU0xMzPD1NQUlUrVbDE1NcXExMSoRJROp0Oj0aBWq+VHtVotn4Pa2toW8+OYmZlhY2ODra0tNjY2WFlZdXq/HDZsWKfury9RVFREZWWlvG7L2FizRlrr+cADUpqFRx7pHHHUWp9wd3fHysqKqqoqcnNz8e6shU0Co0QIIyMgICCAoqIicnNz5VntsrIy3N3d8ff3Jz4+vodbKJGamkpYWBg1NTXtqv/JJ590aXvq6upEAAZBA4qKiggPD2fGjBlMmTKFmJgYjhw5Qm5uLidPnuTkyZN4e3szatQoBg8e3Cn9x9xcilR3442QkgL//a9UsrLg7belMmEC3HUXLF4M3a3nlUol7u7uuLu7M2LECHJzc0lLSyMjI4PKykri4+OJj4/Hzs4OX19fvL29hUjqBAyCSaVSNcl/Ul80GURDffGgVqupq6uTS3tQKpWYmJjIQqm+YFIqlS2W+u2t/2hop16vR6fTNXg0tF+r1aLVaps8N4R6bqu9FhYWDSL/WVtbd8uYXlRUhLu7e5cfpzdy8eJFALy9vY3u+rpmjSSCHngA3n9fWuf53nvSe1cqjlrrEwqFAi8vL5KSksjOzhbCqI8jhJGR4OHhwa+//sqSJUsAKbmar69vD7fKuFGr1SI0saABaWlphP9tnlGpVERFRTFixAgyMjI4evQoZ8+eJTMzk8zMTLZs2cKwYcMYOXIkzs7OnXL8wEAp8eCLL0prkD77TFqTtH+/VB58UBJQK1dKYqm7tYeJiQleXl54eXlRV1dHdnY2aWlpZGVlUVZWxtmzZzl79izW1tb4+Pjg4+ODi4uLEEmdTH3R1BIGi5JGo2m2GARIXV2dLFoMlhpjQKlUolKpMDMzkx8Na6ssLCwwMzPrsX5Vf5wQXEKtVpOSkgJIE7bGhEEUPfigJIYMXaezxFFbfcLT05OkpCSysrI6vnNBr0IIIyNh6dKlREdHy8IoOjqaZcuWsX79erlObGwsq1at4uzZswQFBfHhhx/KcfUDAgJ48MEHWbNmDTk5ObzyyiuMHz+elStXkpOTw/PPP8+jjz4KQHV1NU8++SQbNmxAqVTy4IMP8tRTTwGwcuVKnJycOH36NEeOHGH8+PGsX78eR0dHZs2aRW1trexff/78+VbdklauXElYWBhPP/00L730EsnJyVRXV7N582YGDx7Md999h7+/v/zd7r//fmJjYxkwYABr1qxhpCFhgUBwBRjc7Hx9fZk9ezYnT57k2LFjlJSUcPDgQQ4ePMjAgQMZNWpUpwVrMDWFefOkkpUFa9dKUe2Sk6XHL76QQn3feqvkbvf336BbMTU1lc+LRqORBWN2djaVlZUkJCSQkJCAhYWFLJJcXV2NymWrL2NiYtIud2G9Xt/EclP/0SCYWir199P4uUKhQKFQoFQqmzyvb5Fq/Nzg4icEde8iKSkJjUaDvb19p0f2vBJqayVBFBkJ777bdEJJoZC2//WXVG/lys5f32k4H3l5eZ27Y4HRIYSRkTBz5ky++OILioqKUKvVJCYm8swzz8jCSK1WM3/+fJ588knuvvtufvrpJ+bNm8eFCxdkv/Y//viDo0ePkpCQwKRJk7j22mvZv38/aWlpjB07lhUrVuDq6soTTzxBWVkZ58+fp6ysjJkzZzJo0CDmz58PwHfffceWLVsICgrimmuu4cMPP+T5559n69athIWFNYh21RE2bNjAn3/+ybfffsvtt9/OK6+8whdffEF5eTlz587lgw8+4Nprr2XTpk0sWLCAxMTEViNJiYh0gsa0FWnK2tqaiRMnMn78eC5cuMDRo0dJTEzkwoULXLhwATs7O6Kiohg+fHibkWvai5cXPPssPP20lBdp7Vr44Qe4cAFeeEEq06ZJImnRIimPUnejUqnkXFF1dXXk5OSQkZFBVlYWNTU1JCUlkZSUhJmZGV5eXvj4+ODh4WH0i5BHjBjR5xNBG0SKqanpZUdp62+IiHRNqampIS4uDoCwsDCjErXm5vDBB5JF6JFHGlqMQEr0+sgjEBMjpVS4nL9BW33CkAuttLS04zsX9Cq67KpWXFzMQw89xK+//grAtddeywcffICDg0OLn1m5ciVfffVVg21jxozh0KFDXdPIqiro6vU7YWHtWlRgamrK9ddfzw8//EB1dTWLFy9uMHN96NAhTExMuP/++wG46aabeO+999i6dSuLFy8G4OGHH8be3p7Ro0fj4eHBjTfeiKOjI46Ojvj5+REfH4+LiwtffvklqampcnSle++9lx9//FEWRkuWLGHIkCEALFq0iJ07d3bKqZg1axaTJk2S2//CCy8A8PvvvxMZGcmCBQsAuP7663nttdc4ePBgqzkUysvLm/juC/o3u3fvZurUqW3WUyqVBAcHExwcTHFxMcePH+fEiROUlZWxa9cudu/eTVBQEFFRUQQHB3eKlUSphMmTpfLBB7BhA3z1lRR2dtcuqdx/P9xwgySSpkyRPtPdGPIf+fj4oNVqycvLIyMjg8zMTGpqauSIf6ampnh6euLl5YWnp6fRpkFob58Q9B9En2hKTEwMGo0GR0dHo3Ojg0vucatWSULIsMZIr4eHH5bG1E8+ufw1Rm31CcNEmSGEvLCc9126TBgtW7aMjIwMNv+d7OPuu+/m5ptvZtOmTa1+bs6cOXz55Zfy6y4NzRkfD1FRXbd/gOPHoZ1hYpcvX87TTz9NdXU1n376KSUlJfJ7WVlZ+Pn5Najv7+/fwN+1fmhNS0tLOau74XVlZSX5+flUV1cTEhIiv6fT6ZgwYUKz+7GysrpsC1FjWtpvWloaO3bsaCCaNRoN2dnZre6vpahGgv5LaxG+WsLR0ZEZM2YwdepUzp07x4kTJ0hNTSUxMZHExERsbGwYNmwYI0aM6LSkkNbWcPPNUklLg6+/lixJSUmSWPrqK/Dzg6VLYflyiIjolMN2GBMTEzw9PfH09CQqKoqCggJZJFVWVpKenk56ejoKhQJnZ2d5/ZK9vb1RzDgnJCRw77338vPPPxMaGtrTzREYCZczTvRlsrKySE5OBiQrqzH8d5ujvjiCS1HprlQUQdt9wtraGhMTE7RaLRUVFcJjpQ/TJcIoLi6OzZs3c+jQITlL8Geffca4ceNISEho9QJlbm7efb6tYWGScOnqY7STcePGkZmZiZmZGcOGDWP37t3ye15eXqSnpzeon5aWxqJFizrUHBcXFywsLLh48WKH/9hdNVh6e3tzzTXXsGHDhg59ztgi5gh6nivJu2FqakpkZCSRkZEUFhZy8uRJTp06RUVFBfv27WPfvn0EBgYyYsQIwsPDO82NzM8PVq+W3O0OHpRE0fr1kmD65z+lMmSIJJCWLu2Z9UggWdnc3Nxwc3Nj+PDhFBcXk5mZSVZWFsXFxRQUFFBQUEBMTAzW1tayNcnNza3HXO4MUfcqKyt75PgC48RY8/P0BNXV1Rw5cgSAkJCQBhOqxsjtt8N990mJXffsueQ+d6V5jNrqE4a8Y9XV1UYT4ETQNXTJ1ergwYPY29vLoghg7Nix2Nvbc+DAgVaF0e7du3Fzc8PBwYEpU6bw+uuvd90gZmXVbmtOd2EIiNCYsWPHotFo+Pjjj7nrrrvYuHEjCQkJzJo1q0P7VyqV3HrrrTzxxBP8+9//xs7OjoSEBMrLyxk9enSrn3VxcZEtOZ6enh06bmvMmzePZ555hl9//ZVrrrkGtVrNX3/9xbhx41oVbyLRn6AxAwYM6JT9ODs7M2PGDKZNm8b58+c5ceIESUlJpKSkkJKSgqWlpZw0tLPGJ4UCxo+Xyrvvwh9/QHS0FNXuzBl45hmpTJwoiaTFi6GTguldRlsVODk54eTkREREBFVVVWRlZZGVlUVubi6VlZXyuiRTU1Pc3d1la5KIJCnoaTprnOjt1NXVsW/fPmpqarC3tycyMrKnm9QmJ06ATifdvsXFdY4ogvb1CcMET3tD6At6J13iwZ6Tk9PszYKbmxs5OTktfm7u3LlER0ezc+dO3nrrLY4ePcpVV13VqonTkFG8funNREZGyut76mNmZsYvv/zC119/jbOzM2+++Sa//vrrZZlz3377baytrYmIiMDJyYlbbrmF4uLiNj9nbW3NU089RUREBA4ODp0WttLe3p7ffvuN9957D1dXVwICAvj000/b/JyYBRY0prPXI5qYmBAeHs7y5ct55JFHmDp1Kvb29lRXV3Po0CE++ugjPvvsM44ePUp1dXWnHdfSUgrEsGED5ORIyWOnTZPE0759cO+94OEB8+dL1qWe/itYWVkRFBTE5MmTWbBgAZMnTyYoKAgrKyvq6urIzMzk6NGj/PLLL2zZsoXTp0+Tm5uLVqvt2YYL+iVdtm65F6HT6Thy5AiFhYWYmZkxceJEow+mAmBwpJk1C8rLO0cUQfv6hGFdkRBGfRuFvgMhe1566SVefvnlVuscPXqUrVu38tVXX5GQkNDgveDgYO644w6efvrpdh0vOzsbf39/1q9fz8KFCzvUpp9++glra2uuuuoqjhw5QnV1NS4uLgQHB8ud2rBY2JCw1NbWlqqqKnlhnZWVFeXl5c3WtbGxoaamhrq6OpRKJTY2NrIoMzc3R6lUyjdKrdU1ZDWvqqoCJPFhyBauUCiws7OTo6A0rmu46VCr1XLdsrIy9Hq9nDfCIB7q1wVJjJSXl6PT6ZrUtbS0RKfTyYLUzs6OiooKdDodpqamWFhYyOuDGtftyDlsrW57zmFBQYGcG6N+3frnUKlUYmtr2+I5bO58G85ha+fbcA7be747cg5bq9tZffZyzndL/dtwDisrK8nIyCA8PJy//voLAF9fX1xcXDh58iQAI0eOlC0LJiYmzJgxg+3bt6PVamWLwrFjxwAYPnw4BQUFsgvp7Nmz2bVrF2q1Gnd3dwICAjh8+DAgTSiUlZWxe/duAgICmDlzJvv376eqqgoXFxdCQkI4cOAAAIMHD6ampoYLFy4AyGNERUUFjo6ODB48mH379gFSdCadTsf58+cBmDJlCidOnCAhIYGMjAw0Go3sm+/q6kpwcDDm5uZ4eXkxefJkzp07R1FREdbW1owdO5YdO3YA0uyklZUVZ86cASQ32qSkJPLz87GwsGDy5Mls3boVkNYSOjg4cPr0afLzzUlJGc26dRAffymoi7m5ljFj8liwQMOCBZbEx58AICoqipycHDIzM1EqlcycOZMdO3ZQV1eHp6cnPj4+HD16FJAyvxcVFZGWliaf7927d1NbW4ubmxsDBgyQbx4iIiKoqKiQc57MmDGDAwcOUFVVhbOzM2FhYezfvx+9Xo+vry9ZWVmcPXuWyspKAgICyMrKQq1WY21tzZAhQ8jKysLOzo6hQ4eiUCjka8fkyZOJiYmhpKQEW1tbRo4cya5duwAICgrCzMyMc+fOATBhwgTi4+MpLCzEysqK8ePH8/HHH/Pggw+yYcMGxowZQ2xsLCBZ4JOTk8nLy8Pc3JypU6eyZcsWAPz8/HBycuLUqVMAjBo1ioyMDLKzszE1NWX69Ols27YNnU6Ht7c3Hh4eHP/bJXvEiBFysAqFQsGsWbPYuXMnGo0GDw8P/Pz8ZLeloUOHUlJSIifUnDVrFnv27KGmpgZXV1eCgoI4ePAgAEOGDKGqqkrua9OnT+fQoUNUVlbi5OTEoEGD5D4bHh5OXV0diYmJAEydOlUOLGJvb8+wYcPk/6chPL0hifjEiRM5e/YsxcXF2NjYMHr0aDn4zsCBA7GwsODs2bMAjB8/nvPnz1NQUICVlRUTJkxg27ZtgJQ+ws7OjpiYGEAKopSamkpubi5mZmZMmzZNPt/dPUYYgofcddddXTZGnDp1itLSUuzs7BgxYoTsFh8cHIypqakc/W3ixImdPkYAjB49mrS0NHJyclCpVFx11VVs3boVvV4vh93fsGEDhYWFeHt7M3DgQKqqqnpkjAAYNGgQarWapKQkAKZNm8axY8coLy/HwcGByMhI9uzZA8Arr0zmwAFL7rknngULLl7xGLF9+3ZAChg2efLkVseIO++8k5qaGu655x5CQkLEGNGLxoj4+HgWLVok/y9bo0PCyOBD3hoBAQGsW7eOxx57rEHwAAAHBwfeeecdbrvttvYekuDgYO688045z05jamtrG1iUysrK8PX1bfLla2pqSElJITAw0GijJwk6hlqtFu50RoQx/MeysrJaza3V2VRWVhIbG8upU6caWMNtbGyIjIxk6NChLWZTv1Li4mDdOqn8fS0EpMAO8+dLiWTnzJGsT8ZCdXU1ubm55OTkkJOTI4t2A9bW1ri7u+Ph4YG7u/sVh58uKioiOjqa5cuXd1rgDEHvp7vHCWNCq9Vy5MgRLl68iFKpZPz48fj4+PR0s9pFdjZ4e0uR6JKTpYTanUV7+sRrr71GXV0djzzySKsRlgXGh0H0tUcYdchu6uLigouLS5v1xo0bR2lpKUeOHJHXrRw+fJjS0lLGjx/f7uMVFhaSnp7e6noWc3NzkbuhnyKi0gka01kRFNuLYZZ37Nix5OTkcOrUKWJjY6moqODAgQMcOHAAT09Phg0bRkRERLsSdraX8HB49VV45RUphsz330vl4kXJvW79eiknUn2R1NNzQpaWlnK+JL1eT0lJiSyS8vPzqaysJDk5meTkZHkdk0EkOTs7dzhErpOTE7NnzxaiSNCA7h4njAW1Ws3+/fvJzc1FqVQyZsyYXiOKQBrT9HppHWZniiJou09oNJom3kaCvkmHLEYdYe7cuWRlZbFmzRpACtft7+/fIFx3WFgY//jHP1iwYAEVFRW89NJLLFq0CE9PT1JTU3n22WdJS0sjLi6u3flqWlKFxjCbLehcSktLRchMI8IY/mNbtmzp8eSNWq2WpKQkTp06xfnz5+V1NEqlkpCQEIYOHSq71HQ2ej0cPSolkP3+eymynQFbW7j2Wilow6xZxmVJAslvPz8/XxZKjRMpmpqa4uzsjJubG+7u7jg6OrYplPLz83nppZd46aWXjD7alqD7MIZxorspKSlh//79lJeXY2pqysSJE7svAnAnoNVCaKiUGPujj6R1lp1JW30iLy+Pjz76CHNzc55++mmjDWkuaJ4usxh1hOjoaB566CE5atq1117Lhx9+2KBOQkKCfPEzMTEhNjaW//3vf5SUlODp6cm0adP47rvvRBJPgUDQazAxMSE0NJTQ0FCqqqo4c+YMp06dIisri/j4eOLj47GwsGDQoEFEREQQEBDQaRdZhQJGj5bKv/4FR45csiRlZEhR7qKjJXe7OXNg4UK45howhvkFQ8JYg4dAVVWVLJLy8vKoqakhNzeX3NxcYmNjMTU1xcXFRQ4h3pxQSk9P56OPPuKOO+4QwkjQL9Hr9aSmpnL8+HHq6uqwtrZmwoQJvc6KunGjJIqcnOCWW7r/+IWFhYDkOSVEUd+myyxGPYWwGPUf9Hq9GKCMCGP4jxlzRvK8vDxOnz5NbGxsg+iZdnZ2DBkyhIiICDw8PLqkT+t0cPiwJJB++gnqp0RTqWD6dFiwAK67DrpoSdQVodfrKS0tJT8/n7y8PPLy8ppEKzU1NcXV1RU3NzdcXV3lAApRUVEcP36cEUaWmkHQcxjzONGZlJaW8ueff8qv3d3dGTduXK+7B9JqpcwqMTHw/POS+3DnH6P1PrFnzx527txJZGRki8HABMZLRyxGQhgJei3l5eXCmmhEGMN/bO/evUyaNKlHjt1edDodaWlpxMTEcO7cuQYBCFxdXYmIiCAiIgJHR8cuOb5eL+UC2bBBmoX9O0AWIFmcJkyQRNKCBZ3vx99ZGISSQSTl5eXJESANmJqaUlhYyKpVq9i8eTPTpk0TwVoEQO8YJ64EvV7PuXPn5AhrIEXlCwsLazZPorGzZg2sWgWOjpCY2DX529rqE9988w1JSUnMmTOHsWPHdn4DBF2KUbjSCQRdjQi+IGiMIbS6MaNUKuUABFdffTVJSUnExMRw/vx58vPz2blzJzt37sTX15eIiAgGDx6MtbV1px1foYCoKKm8/jrEx0sCaeNGaX3Svn1SefxxGDZMWpc0f740Y2ss91QKhQIHBwccHBwICQmRAznk5eXJViW1Wk1RUREAJ06coKSkBHt7ezmIkIuLC9bW1sLq3A/pDePE5VJQUMDJkydl1y+QQiEHGussRxvk58Nzz0nPX36565Jat9YntFqtHKI8ICCgaxogMBqEMBL0WnpDMjpB9+LcVVfNLsLU1JSwsDDCwsKora0lLi6O2NhYkpOTSU9PJz09nc2bNxMYGMjgwYMJCwvr1Mh2AGFh8MwzUklPh59/lkTSX3/BqVNSeeUV8PSEefMkkTR9upR53lhQKBQ4Ojri6OhIaGgoOp2OsrIyjh07RkREBM7OzrJ4KikpkfOlWFpaNhBKDg4O/cLFqr/T28aJ9lBSUsK5c+fkG3hTU1NCQkIIDw9HpVL1cOsuD70e7r4bCgpgyBDJatRVtNYnMjMzUavVWFpadln6BYHxIFzp+jjR0dH8+OOPbNy48bL3sXLlSsLCwtqdmLe76Ao/8frftTPOXX/CGP5jFRUV2NjY9MixO5Py8nLOnj1LbGwsmZmZ8nalUsmAAQNkkWTZhaHlCgrgt99g0ybYuhXqR7O1sJDE0fz5kljy9u6yZlwxhj5RXV0t5+IrKCiguLi4idXZxMQEBwcHnJ2dcXR0xNnZGVtbW2FV6mP0lXECpMSkZ8+eJSMjA5AmCQIDA4mIiOjS8aE7+O9/4Y47pHWQhw/D8OFdd6zW+sQff/zBkSNHGDp0KAsWLOi6Rgi6DOFK14uYOXMms2fP5oknnmiw/bHHHqOwsJCvvvqqQ/tTKBRkZ2fLYTiXL1/O8uXLO629xkRFRUWHw3UHBASwfv36dvkI9+Vz11fZv39/nwjDa2trK+dHKioq4ty5c5w9e5bs7GySkpJISkpi06ZNDBw4kMGDBxMaGtrpN0EuLrBypVRqayUL0qZNUrl4EX7/XSogudnNny9FuIuKMh6XO61Wy9atW7nuuuuwtLTE19cXX19fQAoPXlRURGFhIfn5+RQUFKBWqyksLGzghqRSqXB0dMTJyQknJyecnZ2xsrISYqkX09vHCb1eT05ODomJiWRlZQHStd/X15dBgwb1ieSjR47AffdJz19+uWtFEbTcJ7RaLWfPngUgIiKiaxshMAqEMOphVqxYwbvvvttAGOl0Or777ju+/PLLdu9Ho9H0WnO5QCBoGScnJyZOnMjEiRMpLCyURZLhxigxMRETE5MGIqmzLXbm5lLuo1mz4P334cyZSyLp8GEpmMOJE9INjKsrzJ4thQOfNUt63VOcPn2aRYsWNRuVztTUVA71HR4ejl6vp6KiQhZLxcXFFBcXo9Fo5AAPBiwsLBqIJQcHByGWBF1ObW0tKSkpXLhwgfLyckASRH5+foSHh/cJQQSQmQnXXy9NyMybB//3fz3XloSEBCorK7G2tmbAgAE91xBBt2Ek83r9l4ULF5KQkEBcvdBQu3fvRqvVMn36dNLS0rjmmmtwdnYmPDyczZs3y/UCAgL417/+RWhoKIMGDZJzRg0cOBAbGxsOHjzI2rVrmTNnjvyZnTt3MnLkSOzs7AgODmbv3r0AfPbZZwQHB2Nra0tkZCS7d+9uV/sDAgJ46623CAkJwc7OjnfffZcjR44waNAgnJyceOedd+S6RUVF3HTTTbi4uBAUFMTnn38uv7dy5UoeeeQRpkyZgo2NDcuWLSMnJ4cZM2Zgb2/P8uXL5USZAP/5z3+IiorCxcWFW2+9lcrKSgDWrl3LrFmzuPfee7Gzs2Pw4MGcOnUKgDvvvJO0tDSuuuoqbGxs+O6771r9bvXP3e7duwkLC+Pll1/GycmJwMBAtm3b1uC7LVu2DDc3NwYMGNBhS5+gcxg0aFBPN6FLcXZ2ZtKkSaxatYoHHniAq666Cnd3d7RaLefPn2fjxo38+9//Zt26dZw4cUL+X3QmCgVERMCzz8LBg5CTA19+KeVEsrWVFkt/8w2sWCGF/h49Gl54Qapb7y9sdCgUCmxtbfH392fEiBFMnz6dhQsXMmfOHEaNGkVQUBCOjo4olUpqamrIzs7m7Nmz7N27l02bNrFx40Z27tzJiRMnSE5OpqioqMGYJTAeetM4odVqyczM5MCBA/z666+cOnWK8vJyzMzMCAkJ4eqrr2bcuHF9RhTl50sTKtnZMHiwlHetO5b9Ndcn9Ho9+/fvByAqKqpXRvQTdBxhMephbG1tufbaa1m3bh2vvvoqAOvWreOmm25CoVAwf/587r77bn755ReOHj3K/PnzOXPmjOwq9/PPP7N3717s7OywsLBAoVBw4cIF+f2EhAT5WMnJySxYsIDo6Gjmzp0rLygE8PLyYseOHfj4+PDFF19w0003cfHiRczNzdv8Dn/88QdHjx4lISGBSZMmce2117J//37S0tIYO3YsK1aswNXVlfvvvx9TU1PS0tJISkpixowZhIWFMXHiRAB++OEHduzYgaurKyNGjGDevHn873//w8vLi5EjR/Lbb79x3XXX8cMPP/Dpp5/y+++/4+fnxx133MELL7zAW2+9BcCuXbu4++67+fDDD3nxxRd5/PHH2bFjB59//jnbt29vtytdY5KSkrC1tSUvL4///ve/rFq1igsXLgBw8803M2TIENLT00lJSeGqq65i2LBhDB06tMPHEVw+jUM292VcXFyYPHkykydPJj8/X7Yk5eXlcf78ec6fP49CocDf35+wsDDCw8M77HraHtzcLrncaTRw4ABs3gx//gmnT0uR7o4ehVdflcLtzpolWZNmz5YCOhgzSqVSjn5nQKvVytYkg2WpvLwctVrdxLKkVCqxtbXF0dERe3t7HB0dcXBw6FfrXI0RYx8ndDodeXl5pKWlkZGR0aC9jo6OBAUF4efn1+e8RIqLpfHh3Dnw8pIs0m0sB+k0musTFy9eJDMzE1NTU8aMGdM9DRH0OEIYGQErVqzg4Ycf5tVXX6W2tpaffvqJrVu3cuTIETQaDffffz8A48aNY+rUqfz555/cdtttADz66KO4ubm16zjffvst1113HfPmzQPAz89Pfu+aa66Rn99111288MILJCYmMmTIkDb3+/DDD2Nvb8/o0aPx8PDgxhtvlCNE+fn5ER8fj5OTEz/99BMXLlzAysqKyMhI7rjjDr799ltZGC1ZsoSwsDAApk6dio2NjTyLM336dGJiYrjuuuv44osvWL16NR4eHlhaWvLss89yzTXXyMIoIiKCG264AYBly5bxySeftOv8tIW9vT2PPvooCoWCFStWcM8991BRUUFFRQV79+7l119/xcTEhLCwMJYtW8aGDRuEMOpmkpKSGDhwYE83o9txdXVlypQpTJkyhby8POLj44mLiyM7O5vU1FRSU1PZvHkzXl5ehIeHExYWhmsX+LipVDBlilT+8Q/IypICN/z5p/RYXAzffScVkCxPM2bAzJkwaRL0hvXwJiYmchS74OBgQBJLpaWllJaWUlxcLEe/U6vV8vb6WFhYYGdn16RYWloKd7xuwBjHCYMVMjs7m5ycnAY36paWlvj5+eHv74+jo2Of7CPZ2TB3rjSZ4uYGO3Z0bx61xn1Cr9ezfft2AIYNG9apKRMExo0QRvfeKzm0dgXe3vDxx21Wmz17NmVlZRw6dIjs7GxcXV0ZNWoU33//PYmJiQ1mK+vq6oiKipJf+/j4tLs5GRkZLfrI/vzzz7zyyiskJycDUlSs+guQW6O+MLO0tGxww2VpaUllZSX5+flotdoG7fX392fLli0d2g9AWload9xxB0qlUr5AaDSaZvdjZWVFRf1wWleAq6urfDxDyOSKigrS0tKorKxsEO5Tq9WKwA2CHsGwbmby5MmUlJQQFxdHfHw8aWlpZGVlkZWVxY4dO3BxcZEtSV5eXl1ys+XldcmaVFcnLag2WJOOHYPYWKm8844kqsaNk4TSjBkwahT0loj8JiYm8nojQ74YvV5PVVWVLJYMjxUVFdTU1FBTU9PAugRSoIfmBJO1tbVw4+lj1NXVyYE/srOzKSoqon6QYHNzc3x9ffHz82tw7emLJCRIVuTUVMn9dts2KY1ATxIbG0tGRgZmZmZMmTKlZxsj6FZ6yWWnC2mHcOlqVCoVN954I+vWrSM7O1u+ofb29iYiIoITJ060+NmODJa+vr4NXOsM1NbWsnTpUn755RemT5+OiYkJnp6edGYkd1dXV5RKJRkZGXJUqLS0NLy8vDq8L29vb958803mzZvX4ZuFrri4eHt74+Dg0G4hKeg6pk2b1tNNMCocHBwYN24c48aNo6KigoSEBOLj40lOTqagoIB9+/axb98+7OzsCA0NJTQ0lICAgC7JEWZqCuPHS+WVV6Rw4Dt3wvbt0o1Qairs2SOVF16QXGimTr1kUQoNldY3dYSIiAgyMjLabVXvTBQKBdbW1lhbWzcY5zQaDeXl5ZSWllJeXk5ZWRmlpaVUVlai0WiaRMUDSXjZ2Nhga2uLjY1Ng2JlZSVEUwfpiXGitraWgoIC8vPzyc/PbzZcvKOjI15eXnh6euLk5NQvftctW2DpUsmaHBwsTZz0RIyD+n2ipqZGXkM8adIkbG1tu79Bgh5DCCMjYfny5Vx//fVUVFTwxhtvAFK2ao1Gw6effsrKlSsBOHz4MP7+/g3c4Orj5uZGamqqvMaoPkuXLmXYsGH88ccfzJkzR15j5OrqKj8CvPfee+Tn53fq9zMxMWHhwoWsXr2aNWvWcOHCBb744gt+/PHHDu/rjjvu4PXXX5dzNWRnZ3P69OkGQSZawnB+LmeNUUt4e3szatQoXnjhBZ5++mnMzMyIiYnBwsKiVy3y7QscO3aM8ePH93QzjBIbGxuioqKIioqipqaGxMRE4uPjSUxMpKysjKNHj3L06FHMzMwYOHAgISEhhISEdJkLiYsL3HijVACSkyWRtH275EZTVAS//ioVkKxPU6deKkFBbQsllUrFxYsX8TaiREsqlUq2LtVHq9XKQqmxcKrvqtcYpVKJtbW1LJysrKxkQWZlZYW5uXmftjZcDl09TlRXV8vr0AyluUAoVlZWuLq64u7ujqenZ6/PO9QRdDp44w1pEkSvhzFjpDVFPRXFsn6f+OOPPygvL8fJyYlx48b1TIMEPYYQRkbC+PHjsbW1JTAwUPZbNzU15bfffuPhhx9m9erV6PV6Ro4c2eqamRdeeIHrrruO2traBhHsAAIDA/npp5948sknWbJkCZ6envz3v/9l4MCB/Pvf/2bmzJkoFAruvfdegoKCOv07/uc//+G+++7Dx8cHe3t7XnnlFSZNmtTh/dx0000UFxezaNEisrOz8fT0ZNWqVe0SRk899RQPPfQQq1at4tNPP+VGw13ZFRIdHc1jjz3GgAEDUKvVDBkypEFEPkH3YAhhK2gdCwsLIiIiiIiIQKPRkJKSQkJCAufPn6e8vJy4uDji4uJQKBR4e3sTEhJCaGgobm5uXXaTPWCAlOX+7rul6HWnTl0SSnv3SuuV1q2TCkiBG+oLpeDgpkLpwoULPPbYY0RHRxvdmpLGGJLLNo4uptPpqKyslNczlpeXy88rKirQ6XSUl5dTXl5OdnZ2k/2amprKIsnwaGlp2aCoVKp+JZ46Y5wwuEoaxGt9QVtTU9PsZ+zs7HB1dcXFxQU3N7d+G+I9PR1uv136bwPccw+8956UFqCnMPSJs2fPEhMTg0KhYOHChV1iPRcYNwp9Z/pLGQEtZbetqakhJSWFwMBAERGoj9CXspf3BYzhP3b48GERPegK0Ov1ZGdnc/78eRISEprcaDs4OMiWpK5yuWuO6mopX9Lu3VI5eBAaB5Hy8LgkkqZMkVzvTp48QVRUVLN5jPoCOp2O6urqBqKpqqqKyspKqqqqqK6ubtd+TE1Nm4glc3NzLCwsMDc3b/C8L9wotmec0Ol01NTUNDif9R8rKyupq6tr9rMKhQI7Ozs5CJEhGqGZmVlXfJ1eg14P110nWYYALC3hww8lkdTTHD58mAEDBvD5559TW1vLlClThGt2H6IlbdAcQhgJei06na5f+GD3FozhP1ZdXd2v3FG6mrKyMhITE0lISCA5ObnBjaCZmRkDBgwgKCiIoKCgbs2j0lgoHTokJYOsj6srDB58gt27o1i79jhLl46gv92XarXaBjfyhufV1dXU1NRQXV3d4dDVpqamslAyMzNDpVI1eaz/3NTUFFNTU0xMTOTH7hy39Xo9dXV1aDQa1Go1dXV1lJeXo1Qqqauro7a2Vg6GYXheW1tLbW1tm+tsDeHYbW1tsbOzk5/b29v3uVDaV0pqasMoc6NGSfnOQkJ6rEkNKCwsJDo6mqKiIvz9/bnlllsw6Y4ESoJuQQgjIYz6BaWlpV2Sl0VweRjDf2zLli3Mnj27R47d19FoNCQnJ8s5khq7IxnCVwcFBeHv79+tloWamqYWJUkonQCigONYWIxg9GiYOBEmTJCCQPSRnJhXhEajkUVS/dJYJNTU1DQJFnC5mJiYYGJigkqlQqlUykWhUKBQKJpsq3+bYnhe/1Gn06HVauVHQzG8bnybc+HChXa5ViqVSiwtLZus27KyssLGxkZEC2wHFRXw5pvw//7fpckLpRIqK8FYbsU0Gg1PP/00tra2ODg4cPfdd8uRZwV9g44Io95vExcIBAJBl6NSqeTIdQaXu6SkJJKSksjIyKCgoICCggIOHjyISqUiICCAoKAggoODmwQa6GwsLC7lT3rxRekG7PhxKV/S+++DvT2Ull6KegfSeqTBgy8JpbFjYeDAjke+6+0YLDxtRd4yWF7qiyW1Wo1arZatMRqNpsFztVqNVqulrq6ugbXRIFy6M9GqUqnE1NQUMzMzrKyscHNzQ6VSNXEVtLCwaOBCKITP5VFXB//7Hzz3nJSjCGDaNEkkjR7ds22rT11dHd999x25ubm4uLiwbNkyIYr6OcJiJOi11NbWYt6TqzUFDTCG/1hqaioBAQE9cuz+TE1NDcnJybJQKisra/C+k5OT7HIXEBDQbWstcnNz+eCDD3jggQcpKXFn/37Ytw/274fExKb1nZ2lm7YxY6QyejR0sabrN+j1elkQGYRSfcuOwfLT3HNomGrBYFkyYLBAGdz0lEplg20qlQoTExP5M2Kc6Dq0Wvj2W3j5ZUhKkrYNGCBZjK6/3rgmHrRaLT/88APx8fFUVFTw4IMP4u/v39PNEnQBwpVOCKN+gRBGxoUx/MfEDU/Po9frycvLk0VSWloaWq1Wfl+pVOLr68uAAQMYMGAA3t7eXTor31KfyM2FAwckkbR/P5w82XSdEkhrIAxCacwYiIyk361V6muIcaLzqamR1gz9v/8nJWwFKST/U0/Bgw/2bMS55qitreX777/nwoULmJqaMnnyZCZPntzTzRJ0EcKVTtAvqKmpEcJI0ICEhARxw9PDKBQK3N3dcXd3Z8KECdTW1pKamkpiYiJJSUmUlJRw8eJFLl68yK5duzA3NycgIEAWSi4uLp0Wwri4uJjPPvuMJ554AkdHxwbvubvDggVSASnK3enT0lolQ0lMhPPnpfL111I9c3MYPhyioi6V8HAQa+17D2Kc6DwKCuCTT+CDDyAvT9rm5ARPPgkPPADGGDi2oqKC6OhosrOzMTMz48Ybb+TChQs93SyBkSCEkUAgEAi6DHNzc3ltEkhiJTk5WS7V1dUkJCSQ8Pc0s62trSySBgwYcEVZ51NSUnjjjTdYtGhRE2HUGDMzKVLWqFHSDR1AYSEcOdJQLBUXS1HwDh269FkLC8mSVF8sDRokLEuCvoleL+UWW7MGfvzxUuh8X194+GG46y5oY1K+x8jKyuL777+npKQEa2trli1bhre3txBGAhnhSifotYhw3caFMfzHRLju3oVerycnJ4cLFy6QnJxMWlpak9wwrq6uBAYGEhAQgL+/P9bW1u3e/4kTnZvHSK+X1k0cOyYFdzh+HE6cgEZLqgBJFBnE0rBh0vOICLgCnSfoJMQ4cXmkp0sJlteuhfj4S9tHjIDHH4fFi43bcnry5El+//136urqcHJyYvny5Tg7OwOiT/R1hCudoF9QVVUlErwKGhATEyMSvPYiFAoFnp6eeHp6MnHiRDQaDenp6bI1KTs7m/z8fPLz8zly5AggCaWAgAC5dEQoXXl7IThYKkuXStt0Orhw4ZJIMjyWlEgC6tixhvsYMACGDpWEUmSk9DwwUAphLOgexDjRfgoLYcMGiI6Gv/66tN3aWvoP3HMPjBzZc+1rD7W1tfz555+cOnUKgNDQUBYsWNBgAk/0CYEBIYzag1Yr2Y2zs8HTEyZNgk5M/BUQEMD69esZO3asvG3VqlV4eHjw0ksvddpxjImqqioeffRRNm7ciEKh4Mknn+SJJ55osf7atWt57rnnKCsrY9GiRaxZs6bBgu769W677TZeffVVnnvuOXn7s88+yz/+8Q++/fZbbrrpJrneJ598wj333ANATk4Onp6ebSb1ExgvJSUlPd0EwRWgUqlkFzqQZnFTUlK4ePEiqamp5ObmykLp6NGjQEOh5O/v3+2TJUrlJbF0003SNr0ekpMvCaWYGGn9UlaWtD05GTZuvLQPa2vJmjR0qPQ4eLDkiufqalxRvPoKYpxonbQ0+PlnqY/u3SvdAhmYPBmWL5f6urG6y9UnMTGR33//nZKSEhQKBdOmTWPSpElN1jGKPiEwIIRRW2zYIDnNZmRc2ubjA++9BwsX9ly7ejmvv/46KSkpJCUlUVpaylVXXUVERESzyTljY2N57LHH2Lp1K8HBwVx//fW89tpr/N///V+z+w4KCmLdunWyMNLr9Xz33XdNEvo5OjryxhtvcPvtt4ss5X2EK1mPIjA+LC0tGTRoEIMGDQKkCRWDSGpJKLm4uBAQEICfnx8ajYaQkJBud5FRKKScSAMHSu5FBgoKJJFkKKdPw9mzUrLLxuuWQFrEPmhQ0+LlJQTTlSDGiYZUV0th7Ldtg61bpX5Zn6FDJevQ0qXg59czbewoVVVV/Otf/5JfOzo6cv3117cYjlv0CYEBIYxaY8MGuOEGafqvPpmZ0vYff+wWcbR27VrWrVuHv78/69evJzQ0lF9++YU33niDb775hrCwMDZu3IiXlxc6nY4bbriBffv2UVdXx/Tp01mzZg1OTk7s3r2b5cuXExsbi5OTEz/88APPPfccp06danDjUF1djbu7O7GxsfIgsn37dh555BHOnDnTKd9p8+bNvPbaa9jZ2WFnZ8ftt9/O//73v2aF0bp161iyZAkj/7bXP//889x5550tWtMGDhxIcXExJ06cYMSIERw4cABfX98m9UaPHk1FRQVffvkld999d6d8L0HPMtLYfToEV4SVlRXh4eGEh4cDl4SSQSzl5OTIiWaP/e3Ddscdd3D27FnKysrw8/PD3d29x9YmurjAVVdJxUBdnRT1rr5QOndOsioVFUk3rPv2NdyPnd0lkRQSIpXgYEmIiWUSbdPfxwmdDmJjLwmhvXulcNsGFAop6fGCBVLuob8NuL0CvV5PXFwcmzZtkreZmJiwatWqVqPY9vc+IbiEEEYtodVKlqLm3Kr0emnkeOQRuO66TnWra4ldu3bx/fff89FHH7F48WImTJjAv/71L9577z2WL1/Ov//9b9555x0AFi5cyNdff01dXR1LlizhlVde4d1332Xq1KksWrSIBx54gPfee48HH3yQjRs3NplNtbS0ZN68efzwww+ye9v333/PkiVLmm3bvHnz2Nf4yv03Tz/9NE8//XSz79V3WdPr9Zw9e7bZeufOnWsgmIYOHUpKSgp5eXl4eHg0+5nly5ezbt06RowYwbp161i+fDnR0dFN6r344ovcdddd3Hbbbc3uR9C72LVrV7PiWtA3aU4opaWlkZqaSnp6OtnZ2cTExFBVVSVP6piZmeHr64uvry9+fn74+Ph0W8LZ5jA1vSRyDK54IM3iJyRIIql+SUqSgj00Z2FSKKTIYAbXPoNgCgmR1jEJw7hEfxsnSkqkvnLwoFQOH24aMMTbG2bNgpkzYcYMyY2zt5Gdnc2WLVtITU2Vt40ePZqrr766zc/2tz4haBkhjFpi796G7nON0eulEC1798LUqVd8uJkzZ2JST2BVV1fzzDPPyK8jIiJY8HfCjeuuu47ExERuvPFGAK6//no+//xzQEqeuGLFCvlzjz76KKtXr5Zfv/nmmwwdOpSpU6dy8803M27cuGbbs2TJEl5//XWeeOIJ6urq2LhxI/v372+27m+//dbh7ztr1izeffddxo8fT0lJCWvXrm2xbkVFRYMoIobnlZWVLX5myZIljB49mjfeeINffvmF1157rVlhNHPmTLy9vVm7di3z58/v8PcQCATGg5WVFWFhYYSFhQFw5MgRXn31VT7++GMsLCxIT0+ntraWCxcuyOF5lUolHh4e+Pr64uPjg4+PDw4ODp2WS+lysbSUotkNG9Zwe22tlF/JIJQSEy/lWyotldaHpKXBjh0NP2diIrlBBQRIIslQDK89PPpvAIjaWuNLQHq5lJZK1qDTp6U1bgcPQlxc03rW1tKty8yZkiAKC+u97pklJSX89ddfnDp1Cr1ej6mpKRMmTGDChAk9Oukh6J0IYdQS2dmdW68Ntm3b1iT4Qn3c3Nzk55aWlrjWm86xtLSURUJdXR1PPPEEGzdupLi4GL1ej4uLi1zXysqKm266iddff53Nmze32J45c+Zw6623kpqaSkJCAj4+PoSEhFzx9zTw3HPP8eijjxIeHo6DgwOLFy/mwIEDzda1sbGhrN70luG5k5NTi/t3d3cnLCyMZ599lpEjR7aaw+TFF1/knnvuYc6cOZf5bQTGQlBQUE83QWBEmJqaUldXx4gRIxgxYgQ6nY68vDzS09NJS0sjLS2N0tJSsrKyyMrK4vDhwwBYW1vj7e2Nj48P3t7eeHt7G02aB3NzGDJEKvXR66U1TOfPNxRLhudVVZCSIpVdu5rfb0DAJaHk7y8tpzUUb+++46ZXf5xYswYefFBKUPp3HJ5egVotuVvGxjZcs3bxYvP1g4Jg3LhLZcgQyVrZmyktLWXv3r2cPHlSDsYUERHBjBkzsLe379C+xLVDYKCX/y26EE/Pzq3XTURHR7N3714OHjyIl5cXW7ZskaOugRSh5eOPP2bx4sU8/vjjfP/9983ux9zcnOuuu44ffviB+Pj4Ft3oAObOncvevXubfe/ZZ5/l2WefbbLd2tqaTz/9VH793HPPtejjO2jQIGJjY+XXp0+fJjAwsM0wvcuWLeO2225j/fr1rdabNWsWnp6efPXVV63WExg/YnZQ0BoG65CHhwejRo0CpJsrg1DKzMwkJyeHyspKzp8/z/nz5+XPuri4yBYlb2/vHl2r1BwKheT+5OoqrQ+pj15/KSJeauolgZSSIr1OT5esJgkJUmkJZ+eGYslQPD3B3V0qrq7Gf8NtGCfWrIFVq6Sw6Ya5SGMSR7W10m+UlHRJ4Bqep6VJa4Waw9f3Uij4MWNg7FioN7fa6ykoKODQoUMNBNGAAQOYNm1as+uJ24O4dggMGPnw1YNMmiSN+JmZza8zUiik9ydN6v62tUJ5eTnm5uY4ODhQUFDA//t//09+T6fTceutt7J69WpWrVrF0KFD+f7772WXvICAAF566SVWrlwJSO5oq1evJi0tTY741Bx//vlnh9uZkZGBqakprq6u7Ny5ky+//LLFYyxbtoypU6dy1113MXDgQF5//XVWrFhBdXV1q4PZ4sWLcXd3Z2o7XB1ffPFFli1b1uHvITAuzp07d9kXRkH/xN7eHnt7e4b8bYKpq6sjOzubzMxMMjIyyMzMpLi4WA7qYMiFolKp8PT0xMvLCy8vLzw9PXF2djYqsWRAoZAsPt7ezV+yNBpJHNUXTenpkjd5Rob0vLpaymlTWNg0alnjYzk7XxJKjYuLixRtz9FRenRykpLhdifnzp3jjz98WbVKsha9+660ZLi7xJFOJ637yc+XbjEM59lQDNvy8lrfj7W1tDbNIIIMSYRbcabotej1etLS0jhw4ADnz5+X1ygHBAQwbdq0FqPNtRdx7RAYEMKoJUxMpJDcN9wgjfT1xZHBEffdd7sl8EJHuOWWW/j9999xc3PD19eXO++8k8TERAD+3//7f5iYmPDwww+jVCr58ssvWbhwIVOnTsXR0ZHCwsIG7nwzZ87k5ptvbpBXpLNITEzklltuobCwkLCwMNavX4+Xl5f8vo2NDX/++SeTJk0iIiKCt956i/nz58t5jFavXk1N/TA6zWBlZdVu97jZs2cTEhLCocarmQUCQb/C1NRUDs5goLKysoFQyszMpKamRnbHM2BmZoaHh4csmDw9PXFxcTFKsVQflUqKPNbSMK/XSzfyjW/gDaIpJwdycyVXPp1OeiwokCLstQdr60siyVAcHMDWFmxspGJ43vjRxgYsLCRxZW4ulbYsVr//7sMHH0ii6L33pEv6e+9J77UljnQ6SUhWVkJ5OVRUSKXx8/JyKC6+dC4KCiRRaXhsydrT3LkJDpZc4eo/BgdLQrO3rgtqL7W1tcTGxnL8+HGy6y1dCA0NZfz48VcsiASCxij0fSybZVlZGfb29pSWljZYsF9TU0NKSgqBgYEd8xVvLo+Rr68kivpQHqODBw/y/vvv8+233/Z0U9qNVqttELBC0LNc9n+sE6moqOj2BJ8C46W6upozZ84wZMiQTs1lpNfrKSwsJDMzk6ysLLKzs8nOzkaj0TSpq1KpmhVLfXHs0mqlG//c3JZLUdGlUlLSvEPGlaJUSgKpvlgyM5O2l5ZKlpgHHoD3328oLPR6eOgh+PBDyeplbi6t5dFopEe1umGy0yvFzk7KSdXYNdHb+9Kji0vfFz+N0ev1ZGVlceLECWJjY1Gr1YA0aTF06FDGjRvXYO10ZyCuHX2blrRBcwhh1B60Win6XHa25Ew9aZLRWYr6I5WVlW2uMxJ0H8YgjI4dOybyUQga0F19QqfTUVhY2EAoZWdnyzd19TExMcHV1RV3d3c8PDxwd3fH3d29341nOp0kVOqLpfqiqb4lpjnrjOGxtrZjAisyEk6ebD4Kn04Hw4dLwQzawty8eQuW4bmjoyRsDMXZueFzsaylIUVFRcTGxhITE0NhYaG83cXFhaioKIYOHYqVlVWXHFtcO/o2HRFGwpWuPZiYdEpIbkHnUldX19NNEBgZ9S+mAsHFixd57rnnWLNmTZe73CiVSlxdXXF1dWXo0KHAJctSY7FUW1tLTk4OOTk5nK63YMfGxkYWSQbB1FetSyAJE0dHqQwcePn70eulRLlqtSSSDKX+a7VaEj0//wxvvSU5gjRnMXr4YUkUPfUULFkiiRczM8ndsP6jlZXIC9UZFBUVER8fz7lz58io55mjUqkICwsjKioKf3//Lg+fL64dAgNCGAl6Lcbuty/ofrpqNlHQOyksLGTLli0UFhb2yFoEhUKBi4sLLi4uREZGApJYKikpITc3Vy45OTkUFxdTUVFBRUWFnGMJJOuSi4sLbm5usvBydXXF0dGxzwqmjqJQSCJFpZLW5LTGxIkAibz1VrC8tsiwjPjhhyU3uk8+Ma7odH0JvV5PTk4O8fHxxMfHk5ubK7+nUCgYMGAAkZGRhIWFYd6NyaXEtUNgQAgjQa9F+AMLGjN+/PieboJA0CoKhQJHR0ccHR3lRLQAarWavLy8BmIpNzeX2tpaeVt9TExMcHZ2biCWXF1dcXJywtTY42X3MP/85wCCgy8FWjBEpfvgAyGKuoKqqiouXLhAUlISFy5coKKiQn5PqVTi7+9PeHg4gwYN6rHrurh2CAyI0VPQazH4jAoEBrZv387s2bN7uhkCQYcxMzOT8yQZ0Ov1lJaWkpubS0FBAfn5+XIxCKm8RjGdlUolTk5OuLi44OzsjLOzM05OTjg7O2NjY9PlLkm9ge3bt3PPPdI4sWoV/PWX5D4nRFHnUF1dTVpaGhcvXiQ1NZXs7GzqL2c3MzNjwIABhIWFERISYhTWGnHtEBgQwkggEAgEAiNEoVDg4OCAg4MDoaGh8na9Xk9ZWVkDoZSfn09eXh61tbVyzqXGmJmZySKpvmBydnbG0tKy34kmgwh68EEhii4Xg2toRkYGGRkZXLx4kdzcXBrH9XJ3dycoKIigoCB8fX2FVVNgtIieKei1dKf/saB3EBgY2NNNEBgR7u7u3H333bi7u/d0UzoVhUIhJ6YNCgqSt+v1eioqKmShVFRURGFhIUVFRRQXF6NWq+WgD42xtLTE0dERBweHJo8ODg596ka2/jhxzz2wcqUUYU7QOnq9nvLycnJzc8nOzpbzelVWVjap6+zsTEBAAP7+/gQGBmJra9sDLW4/4tohMNB3RjpBv0MEXxA0Rqw7E9TH29ubF198sUHy6L6MQqHA1tYWW1vbJkm5tVotxcXFslAqLCyUn5eWllJdXU11dTVZWVnN7tvW1lYWSfUFk729Pba2tpj1otjTjccJIYqaotFoGqx5M5Tq6uomdU1MTPDw8MDHxwdfX1/8/f2NXgg1Rlw7BAaEMDICAgICWL9+PWPHjpW3rVq1Cg8PD1566aUuP/7evXuZO3eu/Fqn01FTU0Nubi6urq4N6qalpTFo0CD5tV6vp6qqimPHjhEVFdVk3wqFgoEDB5KUlCRvS0xMJCQkhNmzZ7N582a53rhx4zhw4IBcb86cOdx0002sXLmy2XZXV1f3qouxoOuJjY3tNzfBgrYpLy/nm2++4d577+11N2qdjSG6XXOJMTUajWxVKikpoaSkRH5usDSVl5dTXl5Oenp6s/u3tLTEzs6u1WIsVn4xTkjodDpKSkoaWBYNz4uLi5u4w4E0Ieni4oK7uzve3t74+Pjg4eHR6y2Kok8IDPTunizoFCZNmtQgSsz777/PDz/80EQUAfj5+TWo++uvv/Lggw8yYsSIFvevVCo5fPgwY8aMASA6Oprg4OAm9eLj49m6dSuzZs26kq8jEAgEgDQJ89RTTzFjxoxWx6j+jkqlkvMnNUav11NdXd1ALBmel5aWUlpailqtli1OjaPn1cfMzAwbGxtsbGywtrZu9VFMel05Go2GsrKyBqW8vJzi4mJZCOt0uhY/b2Vl1SABsbu7O66urr1eBAkErSF6dxskJkrZtRtjawvN3Nt3GR988AHvvPMO5eXlzJ07lw8//LDN7L2N0ev17VpcGx0dze23396ufUZHR7Ns2bJW97t06VKio6NlYfTtt9+ydOlSDh8+3KDeo48+yssvv9xuYdTfssQL2qa+1VUgEFw5CoUCKysrrKysWpxRr62tbXID3rhUV1ejVqtlq0RbqFQqrK2tsbKywtLSsk06DU4AADBjSURBVF3FwsKiXTftvXWc0Ov11NbWUlVV1WIpLy9vcM7bwtTUFCcnJzkQh+G5i4tLv4pi2Fv7hKDzEcKoFRITISSk5ffPn+8ecbRlyxbefPNNtm/fjp+fH7fccguPPfYYn3/+eZO6ubm5PPfcc2zduhVnZ2cWLFjAjBkzyM7OZuPGjXz99detHispKYlTp06xePHiNttVXl7Opk2bOHLkSKv1brzxRubMmcM777zDiRMncHFxITAwsIkwWrlyJV988QXbtm1j5syZbR5frVaLmStBA5KTkxk+fHhPN0Mg6FeYm5vLeZRawmC9qKiooLKystVHjUaDRqORrVMdwcTEBDMzM8zMzDA3N5ef199mcAlXqVSYmJhgamqKiYlJg+eNtymVSlkk1BcLjbcpFAr0ej1arVYuOp2uwev629RqtVxqa2sbvK6/zWCRa83C0xxmZmZNXBrt7e1lIWRra9tvxE9riGuHwIC4q2wFg6Xom28gPPzS9rg4WLGieUvS5TJz5swGWcyrq6t55plnAPjuu+9YtWoV4X834o033iAqKqpZYXTo0CHmzp3L22+/TWpqKuvWrWP16tUMGDCA559/vs12REdHM2fOHJycnNqsu2HDBoKDgxkyZEir9ZydnRk6dCjbt2/nzz//ZNmyZc3WU6lUPPvss7z88svtEkYajabNOoL+ReOcLgKBwDhQqVRyaPC2UKvVslAyCIK2Sk1NjSxIDNtaIikpqVWXP2PHzMxMtuLVL4Z1Xra2tg3WdQnh0zbi2iEwIIRROwgPh652T9+2bVuT4AsGsrKymDJlivza39+fyspKSktLmyQ4veaaa/j888+58847cXR0ZPHixbz22msUFBTwv//9jyeffLLVdqxbt45XX321XW2Ojo5m+fLl7aq7fPlyvv76a/bs2cPx48f5/fffm61322238cYbb7B9+/Y29ymi0gkaYyyLuwXGgUqlwsXFBZVK1dNNEXQAQ76l9kzQGTC4mTVneWn82srKivDwcDQajWzBqaura/K8/jatVisfp/4xm3tUKBSYmJigVCplq5OhNN6mUqmatW41fm1paSmLH+Ep0fmIa4fAgPh39QK8vLxIS0uTX6elpWFlZdVEFAF88803JCYmsnLlSlJTU3njjTdYtGgRXl5ebVqMjh49SnZ2NvPnz2+zTTk5OezevZsvvviiXd/huuuu45577mHixImtuluoVCqeeeYZXn755TbXEPX3KFOCpkydOrWnmyAwIiIiIsjPz+/pZgi6AYVCgYWFBRYWFm3WFeOEoDGiTwgMiCn3XsDixYtZs2YN8fHxVFZWsnr1am666aZm695888289dZbzJ07l3vvvZcdO3ZQUlLCuXPnWLp0aavHiY6OZuHChVhaWrbZpvXr1zN+/Hh8fX3b9R2srKzYtm0bH3zwQZt1b7vtNtLS0jh69Gir9UpLS9t1bEH/YcuWLT3dBIGRIfqEoDGiTwgaI/qEwIAQRu0gLg5OnLhU4uK69/hz587lySefZO7cufj7+2Nubs5bb73VbN3665Q6glar5bvvvmvWNW7VqlUNXPugY250BsaMGcPAgQPbrGdmZsYzzzzTrshFAoFA0BKxsbGsWLGC2NjYnm6KQCAQCHoBCn1zGbx6MWVlZdjb21NaWtognHVNTQ0pKSkEBga2y9QOxhOVTtA81dXV7bJuCbqHy/mPdTZxcXFykBKB4MSJE0RFRXH8+HGRx0ggI8YJQWNEn+jbtKQNmkOsMWqF4GBJ/BhDHiNBU8QCVEFjOrJYWyAQ9E/EOCFojOgTAgPCla4NgoOliHSNixBFPU9VVVVPN0FgZJw6daqnmyAQCIwcMU4IGiP6hMCAEEYCgUAgEAgEAoGg3yOEkaDX0lY4b0H/Y9SoUT3dBIERERwczC+//EKwMPEL6iHGCUFjRJ8QGOgyYfT6668zfvx4rKyscHBwaNdn9Ho9L730El5eXlhaWjJ16lTOnj3bVU0U9HLUanVPN0FgZGRkZPR0EwRGhK2tLQEBASLnmaABYpwQNEb0CYGBLhNGarWaxYsXc++997b7M//61794++23+fDDDzl69CgeHh7MnDmT8uaiHwj6PRqNpqebIDAysrOze7oJAiMiMzOT119/nczMzJ5uisCIEOOEoDGiTwgMdJkwevnll3n00UeJiIhoV329Xs+7777L6tWrWbhwIUOGDOGrr76iqqqKdevWdVUzBb0YhULR000QGBkiUqGgPrm5uXz//ffk5ub2dFMERoQYJwSNEX1CYMBo1hilpKSQk5PDrFmz5G3m5uZMmTKFAwcO9GDLBMZKW7HoBf2P6dOn93QTBAKBkSPGCUFjRJ8QGDAaYZSTkwOAu7t7g+3u7u7ye81RW1tLWVlZgyLoH4jfWtCYbdu29XQTBAKBkSPGCUFjRJ8QGOiQ7fCll17i5ZdfbrXO0aNHGTly5GU3qLF7lF6vb9Vl6h//+Eezbdq+fTvW1tZcddVVHDlyhOrqalxcXNBqtZSWlgJgYWEBQE1NDSAt1K2qqkKr1WJiYoKVlZW8vqlxXRsbG2pqaqirq0OpVGJjYyPfqJubm6NUKqmurm6zrpmZGeHh4RQVFXH+/HlcXFxQq9UUFhYSEhKCv78/hw8fluuamprK+XusrKyoq6tDrVajUCiws7OjrKwMvV6PSqXCzMyMysrKJnUB7O3tKS8vR6fTNaj7zTff8M4775CdnY2Pjw/r1q1jxIgRVFRUoNPpMDU1xcLCgjlz5nDw4EH5fNfU1HD77bfzySefNDmHK1as4Ntvv2XHjh1MmDBBPodz587l4MGDJCQk4OXlxf33388333zDwYMHiYyMRKlU8s033/Df//6Xv/76q8k5rK2tpbS0tMn5tra2Rq1Wo9FoUCqV2Nrayr9543NYv27jc9ja+Tacw/aebzs7uybnsKKiAgBLS0t0Oh21tbVt1u2sPtu4bkf6bEvnu7KyUj7Wli1bAPD19cXFxYWTJ08CMHLkSLKyssjKysLExIQZM2awfft2tFotXl5eeHl5cezYMQCGDx9OQUEB6enpAMyePZtdu3ahVqtxd3cnICBA/m9ERkZSVlZGcnIyW7ZsYebMmezfv5+qqipcXFwICQmRLc+DBw+mpqaGCxcuAMhjREVFBY6OjgwePJh9+/YBEBYWhk6n4/z58wBMmTKFU6dOydmzR4wYwe7duwEpApqpqSlxcXEATJw4kXPnzlFUVIS1tTVjx45lx44dAAwYMAArKyvOnDkDwLhx40hKSiI/Px8LCwsmT57M1q1bAfD398fBwYHTp08DMHr0aNLS0sjJyUGlUnHVVVexdetW9Ho9Pj4+uLm5ceLECQCioqLIyckhMzMTpVLJzJkz2bFjB3V1dXh6euLj48PRo0cBGDZsGEVFRaSlpcnne/fu3dTW1uLm5saAAQM4dOgQABEREVRUVJCSkgLAjBkzOHDgAFVVVTg7OxMWFsb+/fsBGDRoEGq1mqSkJACmTZvGsWPHKC8vx8HBgcjISPbs2QNAaGgoAAkJCQBMnjyZmJgYSkpKsLW1ZeTIkezatQuAoKAgzMzMOHfuHAATJkwgPj6ewsJCrKysGD9+vPybX7x4EQ8PD2JjYwEYO3YsycnJ5OXlYW5uztSpU+U+6+fnh5OTk5zXZNSoUWRkZJCdnY2pqSnTp09n27Zt6HQ6vL298fDw4Pjx4wCMGDGCvLw8MjIyUCgUzJo1i507d6LRaPDw8MDPz48jR44AMHToUEpKSrh48SIAs2bNYs+ePdTU1ODq6kpQUJA8xg4ZMoSqqiqSk5MBaXb70KFDVFZW4uTkxKBBg+Q+Gx4eTl1dHYmJiQBMnTqVEydOyJnfhw0bxl9//QVASEgISqWS+Ph4uc+ePXuW4uJibGxsGD16NDt37gRg4MCBWFhYyAGRxo8fz/nz5ykoKMDKyooJEybIN5cBAQHY2dkRExMDwJgxY0hNTSU3NxczMzOmTZvWY2NEamoqqamp6HQ6MUYgxojt27cDUFxcTFZWlhgj+ugYYWh/u9B3gPz8fH1cXFyrpbq6usFnvvzyS729vX2b+75w4YIe0J84caLB9muvvVZ/yy23tPi5mpoafWlpqVzS09P1gL60tLRBverqav25c+eatM8Y8Pf31wcHB+vXr18vb/vyyy/1wcHB+tDQ0G5rx6+//qofPHiw/syZM3qdTqdPSkrSFxYWtvk5jUajd3Nz02/fvr3Z92+99VZ9cHCwftWqVfK25ORkfUhIiB7QZ2dny/UcHR31S5culet9++23+ilTpjS736qqqg58O0FXYwz/sdjY2B47tsD4SE1N1S9cuFCfmpra000RGBFinBA0RvSJvk1paWmz2qA5OuRK5+LiQlhYWKvFMEvdUQIDA/Hw8GhgzlSr1fz111+MHz++xc+Zm5tjZ2fXoHQmiYlw4kTT8rfQ7jSWLl1KdHS0/Do6Opply5Y1qBMbG8uECRNwcHBg5MiR8sxMR9Hr9c1uf/XVV3nnnXcYPHgwCoWCgQMH4uTk1Ob+tm3bhqmpKdOmTWuxzsKFC/n111/lSHLr1q1j6dKlTerdeeed/Pnnn82q+9TUVCwsLPj4449xc3MjJCSE3bt388UXX+Dp6Ymfn588yyHon3h4ePR0EwRGhL+/P2vWrMHf37+nmyIwIsQ4IWiM6BMCA122xigtLY1Tp06RlpaGVqvl1KlTnDp1SnYJAskEvXHjRkByoXvkkUd444032LhxI2fOnGHlypVYWVk1EQjdRWIihIRAVFTTEhLSueJo5syZnDhxgqKiInJyckhMTGTy5Mny+2q1mvnz57Ns2TLy8/N54oknmDdvnuwi1piPP/6YYcOG4efnxx133MFvv/3Gnj17uP/++2UTY320Wi0nT54kNjYWHx8fAgMDeeWVV1oUUfWJjo5m6dKlKJUtdycHBwfGjBkjm0a//fbbZn9XJycn7rvvPl599dVm96NWq0lNTSUzM5N77rmHFStWcO7cOS5evMj//d//8cgjj7TZXkHfxeCuIBAAVFdX89NPP8lunwIBiHFC0BTRJwQGuiw+4QsvvMBXX30lvx4+fDgAu3btYurUqYDkI1r/xv7//u//qK6u5r777qO4uJgxY8awdevWHkvOZ0if9M03EB5+aXtcHKxYcen9zsDU1JTrr7+eH374gerqahYvXtxAaBw6dAgTExPuv/9+AG666Sbee+89tm7dyuLFixvsq7a2ltTUVH777TfMzc355Zdf+PTTTwFYtmxZsxmec3NzqaurY8eOHZw5c4aysjLmzp2Lr68vt912W4vtrqqq4ueff2bv3r1tfsdly5YRHR2Nj48PlpaWhISENFvvscceY+DAgc1ajfR6PatXr0alUjF//nyef/55nn76aczMzFi0aBGPPfYYOp2uVZEmEAj6B3FxcaxatYpRo0YxYsSInm6OQCAQCIycLhNGa9euZe3ata3WaWyNUCgUvPTSS7z00ktd1azLIjwcuuOaunz5cp5++mmqq6v59NNPKSkpkd/LysrCz8+vQX1/f3+ysrKa7Mfc3JwFCxbw2muvUVRUxIwZM/jqq6+wtrbmxx9/5OzZswwePLjBZywtLQF46qmncHBwwMHBgfvvv58//vijVWH0888/4+fnJwvf1pg3bx4PPfQQjo6OLF++vMV6zs7O3Hfffbz22mvMmzevyXczuEsa3PxcXV3l76DRaFCr1Zft0ino3YibX4FA0BZinBA0RvQJgQExrW5EjBs3jszMTCoqKhg2bFiD97y8vOTIOwbS0tLw8vJqsp/a2lqeffZZpk6dytKlSzl8+DDh4eH4+/uzf//+JgILwNHRscm+2utG15rIqY+FhQWzZ8/ms88+Y8mSJa3Wffzxx/ntt9/kyDPNUVdX167jCvoPeXl5Pd0EgUBg5IhxQtAY0ScEBkSqXyNjw4YNzbqBjR07Fo1Gw8cff8xdd93Fxo0bSUhIaJAQ14CZmRnbt2+X97NgwYJ2HXvlypX861//Yvjw4ZSXl/PJJ5/w+OOPt1i/oKCAbdu28eGHH7bz20kBHm677TY8PT1brefs7My9997L+++/T0RERLN1DIEcBAIDGRkZTayhAoFAUB8xTggaI/qEwICwGLWDuLiGEen+TkHQJURGRjJkyJAm283MzPjll1/4+uuvcXZ25s033+TXX3/F3t6+SV2FQnFZa2xefPFFOW/BqFGjWLhwIbfeeisAe/fuxcbGpkH97777jlGjRhEYGNjuY/j4+DQIKtEajz/+uJwHSCBoD63lPBP0PxQKBSqVSvQLQQNEfxA0RvQJgQGFvj3+Ur0IQ4IqQ3I1AzU1NaSkpBAYGNju9SeGqHQtcf48BAdfaYsFgr7B5fzHBAKBQCAQCLqSlrRBcwiLUSsEB0vi5/jxpkWIop6nrKysp5sgMDIMGbgFAgOiTwgaI/qEoDGiTwgMiDVGbSDEj/HSx4ydgk5ArDsT1CcuLo67776bTZs2EV4/54KgXyPGCUFjRJ8QGBAWI0GvRaVS9XQTBEaGyF4uqE91dTUXLlwQCV4FDRDjhKAxok8IDAhhJOi1mJmZ9XQTBEZGc6HoBQKBoD5inBA0RvQJgQEhjAS9lsrKyp5ugsDIOHLkSE83QSAQGDlinBA0RvQJgQEhjAQCgUAgEAgEAkG/RwgjQa/Fysqqp5sgMDKGDh3a000QGBGBgYF8+umnHcq1Juj7iHFC0BjRJwQGhDAS9Frq6up6ugkCI6OkpKSnmyAwIhwdHZk0aRKOjo493RSBESHGCUFjRJ8QGBDCSNBrUavVPd0EgZFx8eLFnm6CwIjIzc3l7bffJjc3t6ebIjAixDghaIzoEwIDQhh1gNrartlvQEAAhw4darBt1apVvPTSS11zwC7ku+++Izg4GBsbG6699lqKioparHvNNdfg5uaGvb09Y8aM4eDBgy3WVSgUBAUFNdh24cIFFAoFc+bMaVBv/PjxDerNmTOHtWvXXt4XEggEvZbMzEw+++wzMjMze7opAoFAIOgFCGHUTtasAVtb6VHQPHFxcdxzzz18++23FBcX4+/vz/33399i/X/9619kZWVRWlrKc889x4IFC1pN2qpUKjl8+LD8+pdffiG4mQy88fHxbN269cq+jKBXMmvWrJ5ugkAgMHLEOCFojOgTAgNCGLWDNWtg1SoID5ceu1scrV27llmzZnHXXXdha2vLyJEjyczM5P7775etLVlZWQDodDoWLlyIm5sbTk5OLF68WLba7N69G29vb/n1Dz/8QGhoaIeTH7YkXrZv387s2bMZOXIkKpWKZ599lp9++qnFsNqDBw/G1NQUvV6PUqkkNzeXqqqqFo+7dOlSoqOj5dfr1q1j6dKlTeo9+uijvPzyyx36ToK+wZ49e3q6CQKBwMgR44SgMaJPCAwIYdQGBlH04INw8qT02BPiaNeuXVx99dUUFRXh4+PDhAkTmDJlCoWFhQQEBPDvf/9brrtw4UJSUlJISUmhvLycV155BYCpU6eyaNEiHnjgAfLz83nwwQdZu3YtlpaWTY6Xm5vLXXfdhb+/PyNGjODVV1/l4MGDbNiwgVtuuaXFdtYXTXq9Ho1GQ2JiYov1582bh4WFBfPmzeOhhx7C2tq6xbo33ngjGzduRKvVcvToUZydnZuNNrVy5UoyMzPZtm1bi/sS9E1qamp6ugkCgcDIEeOEoDGiTwgMCGHUCvVF0XvvgVIpPXaFOJo5cyYODg5y+fLLLxu8HxERwYIFC1CpVFx33XVYW1tz4403YmpqyvXXX09MTAwguZutWLECa2tr7O3tefTRR9m3b5+8nzfffJOjR48ydepUbr75ZsaNG9dsew4dOsTcuXM5c+YMX331FVVVVaxevZo//viD559/vtnPTJ8+nS1btnDkyBFqa2v5xz/+gUKhaNUK9Ntvv1FeXs6PP/7IiBEjWj1Hzs7ODB06lO3btxMdHc2NN97YbD2DtUpYjfofrq6uPd0EgRFhb2/P5MmTsbe37+mmCIwIMU4IGiP6hMCAEEYt0FgUKRTSdoWia8TRtm3bKCkpkcttt93W4H03Nzf5uaWlZYM/saWlpeyuVldXxyOPPIK/vz92dnbccMMNFBYWynWtrKy46aabiIuL46GHHmqxPddccw15eXnceeed/Oc//2HGjBls27aN119/nV9++aXZzwwaNIiPP/6YW2+9FV9fXzw8PLC1tcXb27vV725mZsaiRYt46623iIuLa7Xu8uXL+frrr9mwYQPLli1rsd5tt91GRkYG27dvb3V/gr5F4wAdgv7NwIED2bRpEwMHDuzppgiMCDFOCBoj+oTAgBBGzVBbKwmfyEh4991LosiAQiFtj4yU6nVVtLrLITo6mr1793Lw4EHKysr48ccfG7i3JSYm8vHHH7N48WIef/zxFvfzzTffkJiYyMqVKxk6dChvvPEGzs7OTJs2DR8fnxY/t2zZMuLi4sjLy2PJkiVYWlq2Wr8+dXV1pKSktFrnuuuu49dff2XIkCHNugAaUKlUPPPMM8Jq1M9oLbKhoP+h0Wj4888/0Wg0Pd0UgREhxglBY0SfEBgw7ekGGCPm5vDBB5JF6JFHGlqMAPT6/9/evQdFdZ5/AP8u9/uiclmQi4QIyoiIEJSMGJsEhTQ20an1QpGodUQTi2kzlmJbtaMZEy/R0VZyMWAmGokBbWJMWlNBjJKEAEYiY2KbxaCCKHJZbFkU3t8f+9utu6zcZDkH9vuZ2dk9Z99zznPYZ1999j0X3fzz54HsbF17udBoNHB0dISnpydu3ryJbdu2Gd7r7OxEWloa1q1bh/T0dERFReH99983e0haamoqbG1tDdMrV67s1fbLy8sxadIk1NbWYsWKFcjMzDRaj97ly5dRWVmJJ598EgqFAm+++SauXLmCmJiYbtfv4uKCEydOwMvLq8dYlixZgpdffhmtra1YsGBBr+InouGjsrISCxYsQFlZWY+H6hIREXHE6D5WrNAVPbt3AxkZumII0D1nZOjmZ2fr2snJ4sWLoVQq4ePjg4SEBKN7/Gzbtg22trbIyMiAs7MzcnJysHr1atTX13dZj7lipjdWrlwJDw8PxMbGYvr06cjIyDC8l56ejvT0dMP05s2b4ePjA5VKhby8PHz00Ufw9fXtcRtTpkxBaGhotyNGgO4Qvd///vfd3kuJhpcJEyZIHQIRyRz7CTLFnCA9hejuxjFDUEtLC5RKJZqbm+Hh4WGY39bWBrVajZCQEDg5OfV6ffeea7Rzp26kSK5FkbVpa2vr02dJltXf79hAunTpktl7W5F1Ki8vR0xMDEeMyAj7CTLFnBje7lcbmMMRox7cO3IUHc2iSE60cjq5i2Thhx9+kDoEIpI59hNkijlBejzHqBf0RdDq1SyKiIiIiIiGIx5K1wdarbwutGDthBBQmF4ykCQjh0Pp7t69Czs7/t5DOh0dHWhuboZSqez3eZM0/LCfIFPMieGNh9JZCIsieWltbZU6BJKZL774QuoQSEZsbW1RVVXFooiMsJ8gU8wJ0mNhRENWZ2en1CGQzOhvdEwE6E6ozsjIwKVLl6QOhWSE/QSZYk6QHgsjGrI47E2mRo4cKXUIJCMajQbl5eXQaDRSh0Iywn6CTDEnSI+FEQ1ZvFQ3mYqIiJA6BCKSOfYTZIo5QXosjGjI4jlGZOrzzz+XOgQikjn2E2SKOUF6LIyIiIiIiMjqsTDqA0vdT3TMmDHw8PDAf//7X8O8lpYWODs7Y9y4cZbZ6CDSarVYtmwZvL294eXlhdTU1G5PdMzNzUVAQAA8PDywZMkStLe3m233/vvvQ6FQYNOmTUbzs7KyoFAocOjQIcP6FAoFXn/9dUOburo6Xup7GBo/frzUIZCMBAYG4s9//jMCAwOlDoVkhP0EmWJOkB4Lo156/XXA3V33bAkqlQoffvihYbqgoGDY/GO+e/duVFZW4rvvvoNarcb169exZcsWs20rKyvxm9/8BkePHkVNTQ2qq6u7FD73evjhh3Hw4EHDtBACeXl5CA0NNWo3YsQIvPzyy7hz587A7BTJ0t27d6UOgWTE29sbKSkp8Pb2ljoUkhH2E2SKOUF6LIx64fXXgfR0YPx43bMliqOFCxfiwIEDhukDBw5g0aJFRm0UCgX27t2LoKAgeHl5IS8vD8eOHcNDDz0EHx8f5OXlGdq++eabGDt2LNzd3TFx4kQUFRUB0N2EMyIiAu+99x4AoKmpCQEBATh58mSfY+7tvYEvX76M5ORkjBw5Eu7u7nj22WdRVVVltu3Bgwcxf/58xMbGQqlU4o9//CPeffdds23v3LmD0NBQuLu7o7y8HABw9uxZBAYGIiAgwKhtXFwcAgMDkZOTY3ZdY8aMwfbt2xEWFgYPDw/s3LkTX331FSIiIjBy5Ei89tprvdpXkhYvy0z3unXrFrKzs3Hr1i2pQyEZYT9BppgTpMfCqAf6omj1aqCiQvdsieIoMTER5eXluHXrFurq6nDp0iVMnz69S7szZ87g+++/x969e7Fq1Srk5+fj22+/xb59+/DCCy+go6MDAODv749//vOfaG5uxurVq7FgwQJotVo4OTlh//79WLNmDWpra5GRkYGf/exnePzxx83GtXfvXkyaNAlBQUFYtmwZjh07huLiYjz//PP4+uuve7VvaWlpKC4uxo0bN9DU1IT8/HwkJiaabVtVVYXIyEjDdFRUFNRqtdFhhqZSUlIMo0YHDx5ESkqK2Xbr16/vdtTo+PHjKC0txWeffYbf/e532Lp1K86cOYPCwkJkZWXhxo0bvdpfIpKH6upqbN26FdXV1VKHQkREQwALo27cWxTt2gXY2OieLVEc2dnZ4dlnn8Xhw4dx6NAhzJs3DzY2XT+etWvXwsnJCXPnzkVTUxNWrVoFFxcXzJ49GxqNBteuXQMA/PSnP0VQUBBsbGywfPlyKBQKwy8ijzzyCJYtW4Ynn3wSp0+fxquvvmo2Jq1Wi+rqahw7dgxlZWWIj4/HG2+8gW3btiEhIQGPPPJIr/YtNDQUnp6e8PX1xahRo2Bra4tf/epXZtu2trbCw8PDMK1/be4KdPrLdc+fPx+HDx9Ge3s7/va3v+HnP/+52XUnJiZi9OjRyM3NNft+RkYGlEol4uLioFKp8Itf/AIjRoxAVFQUgoKCcPHixV7tL0lnxowZUodARDLHfoJMMSdIj4XRfZgWRfrz9BUKyxVH+pGP7kY9fHx8AAC2trawt7c3OnbeycnJcFGDo0ePYvLkyfD09ISnpyfq6+vR0NBgaLt06VJUVVVh6dKlcHNzM7stR0dHzJkzB5s2bcLzzz+Pzs5O7N+/Hx988AE6Oztx4cKFLsucPn0abm5ucHNzQ3JyMgBg1apVcHV1RXNzMxobG+Hl5YUXX3zR7Dbd3NzQ0tJimNa/Nhej/qIMvr6+GDduHLKyshAbG4sRI0aYXTfQ/aiR/m8LAM7OzkZ/W2dnZ94ZewjQH1JJRHQ/7CfIFHOC9FgYmaHV6gqfiROBnTv/VxTpKRS6+RMn6toN1NXq4uPjcfXqVbS2tmLSpEn9Xo9Wq8XChQuxZcsWNDQ0oKmpCT4+PoZzgoQQWLlyJVJSUrBr1y5cvXr1vuvJysrCjBkzsHDhQnz55ZcYP348goODcebMGQQFBXVZJiEhAa2trWhtbcUnn3wCADh//jyWLFkCd3d3eHh4YOnSpfc9pykiIgKVlZWG6W+++QYhISFwdnbu0razs9PwetGiRdixY0eX87JMzZw5E35+fti/f3+37WhoureoJiIyh/0EmWJOkJ6d1AHIkaMjsHu3bkRozRrjESMAEEI3//x5IDtb136gFBQUmD2Eri+0Wi3a29sNIx67du0yOj8mOzsbjY2N+OSTT7BhwwYsX74cx48f77IeBwcHfPbZZ4Z45syZ0694YmNj8c477yA+Ph5CCOTm5hqdR3SvRYsWYcaMGVi+fDlCQ0OxefNm/PKXvzTb9t6/07x58+Dr69ur4fD169f3WEDR0KRUKqUOgWTE1dUVEyZMgKurq9ShkIywnyBTzAnS44jRfaxYoSt6du8GMjJ0xRCge87I0M3Pzta1G0gTJ07EhAkTHmgdHh4e2Lp1KxITE6FSqdDQ0ICHH34YAKBWq/GHP/wBubm5sLOzw5/+9CdcuXIFb7/9dpf1KBSKBy7SAGDr1q24ffs2goKCEBQUBI1Ggx07dhjed3Nzw+nTpwEAkZGR2L59O2bPno2AgAAEBgZi3bp1Ztfr4OBgeO3i4oKkpCTDeUfdmTVrFsLCwh5wr0iOHmSklYaf8PBwlJaWIjw8XOpQSEbYT5Ap5gTpKURvr7k8RLS0tECpVKK5udnoJP62tjao1WqEhIT06j/Peveea7Rzp26kyFJFEfVNc3Mzf+WRkf5+xwbS3//+d8yaNUuSbZM8MSfIFHOCTDEnhrf71Qbm8FC6HuiLn/R04NSp/x0+x6KIiEjeysvLkZSUhLKyMkyePFnqcIiISOZYGPWCvghavZpFkZxINSpB8sVDJImoJ+wnyBRzgvRYGPXSihXAc88N7IUWiGhgDcQ5cUQ0vLGfIFPMCdJjJvQBiyJ5aWtrkzoEkhnehJeIesJ+gkwxJ0jP6gqje+99Q0QDZ5hdx4WIiIisjNUcSufg4AAbGxtcu3YN3t7ecHBwgML0zq00pNjZ2XHUSCaEELhx4wYUCgXs7e0li2PatGmSbZvkJyIiAt988w3PHyAj7CfIFHOC9KymMLKxsUFISAhqa2tx7do1qcOhAaDVauHI4xtlQ6FQICAgALa2tpLFcOHCBcTFxUm2fZIXJycntLW18UItZIT9BJliTpCe1RRGgG7UKCgoCHfv3kVHR4fU4dAD+vzzz/krj4zY29tLWhQBQGNjo6TbJ3lRq9XIzMzEvn37EBISInU4JBPsJ8gUc4L0rKowAmA41EfKw31oYDg7O/OXYDLi5uYmdQgkI42NjSgsLERjYyMLIzJgP0GmmBOkZ3UXX6Dhg8PeZIo5QUQ9YT9BppgTpMfCiIaskydPSh0CyQxzgoh6wn6CTDEnSG/YHUqnv2RwS0uLxJGQpd2+fZufMxlhTtC9WltbDc/MC9JjP0GmmBPDm/6z7c1tRRRimN185MqVKwgMDJQ6DCIiIiIikomamhoEBAR022bYFUadnZ24du0a3N3deZ+iYaylpQWBgYGoqamBh4eH1OGQDDAnyBRzgkwxJ8gUc2L4E0JAo9HA398fNjbdn0U07A6ls7Gx6bEapOHDw8ODHRkZYU6QKeYEmWJOkCnmxPCmVCp71Y4XXyAiIiIiIqvHwoiIiIiIiKweCyMakhwdHbF+/Xo4OjpKHQrJBHOCTDEnyBRzgkwxJ+hew+7iC0RERERERH3FESMiIiIiIrJ6LIyIiIiIiMjqsTAiIiIiIiKrx8KIiIiIiIisHgsjGhI2b96MRx99FC4uLvD09OzVMkIIbNiwAf7+/nB2dsaMGTNw4cIFywZKg6axsRGpqalQKpVQKpVITU1FU1NTt8s899xzUCgURo+pU6cOTsBkEX/9618REhICJycnxMTE4PTp0922P3XqFGJiYuDk5ISHHnoI2dnZgxQpDZa+5ERRUVGXPkGhUODixYuDGDFZUnFxMWbPng1/f38oFAocPXq0x2XYT1gvFkY0JLS3t2PevHlYuXJlr5d59dVXsWPHDuzZswelpaVQqVRITEyERqOxYKQ0WBYtWoRz587h008/xaeffopz584hNTW1x+WSkpJQW1treBw/fnwQoiVLyMvLw5o1a7Bu3TpUVFQgISEBycnJ+PHHH822V6vVeOqpp5CQkICKigpkZWXh17/+NfLz8wc5crKUvuaE3nfffWfUL4wdO3aQIiZLu337NqKiorBnz55etWc/YeUE0RCSk5MjlEplj+06OzuFSqUSW7ZsMcxra2sTSqVSZGdnWzBCGgxVVVUCgPjiiy8M80pKSgQAcfHixfsul5aWJp555plBiJAGQ1xcnEhPTzeaN27cOJGZmWm2/dq1a8W4ceOM5q1YsUJMnTrVYjHS4OprThQWFgoAorGxcRCiI6kBEEeOHOm2DfsJ68YRIxqW1Go16urqMHPmTMM8R0dHPPbYYzh79qyEkdFAKCkpgVKpxJQpUwzzpk6dCqVS2ePnW1RUBB8fH4SFhWH58uWor6+3dLhkAe3t7SgrKzP6jgPAzJkz75sDJSUlXdrPmjULX3/9Ne7cuWOxWGlw9Ccn9KKjo+Hn54cnnngChYWFlgyTZI79hHVjYUTDUl1dHQDA19fXaL6vr6/hPRq66urq4OPj02W+j49Pt59vcnIyDhw4gJMnT2L79u0oLS3F448/Dq1Wa8lwyQJu3ryJjo6OPn3H6+rqzLa/e/cubt68abFYaXD0Jyf8/PzwxhtvID8/HwUFBQgPD8cTTzyB4uLiwQiZZIj9hHWzkzoAsl4bNmzAxo0bu21TWlqK2NjYfm9DoVAYTQshuswj+ehtTgBdP1ug5893/vz5htcTJkxAbGwsgoOD8fHHH2Pu3Ln9jJqk1NfvuLn25ubT0NWXnAgPD0d4eLhhOj4+HjU1Ndi2bRumT59u0ThJvthPWC8WRiSZF154AQsWLOi2zZgxY/q1bpVKBUD3y4+fn59hfn19fZdfgkg+epsT58+fx/Xr17u8d+PGjT59vn5+fggODsalS5f6HCtJy8vLC7a2tl1GArr7jqtUKrPt7ezsMGrUKIvFSoOjPzlhztSpU/Huu+8OdHg0RLCfsG4sjEgyXl5e8PLyssi6Q0JCoFKpcOLECURHRwPQHX9+6tQpvPLKKxbZJj243uZEfHw8mpub8dVXXyEuLg4A8OWXX6K5uRmPPvpor7fX0NCAmpoao+KZhgYHBwfExMTgxIkTmDNnjmH+iRMn8Mwzz5hdJj4+Hh999JHRvH/84x+IjY2Fvb29ReMly+tPTphTUVHBPsGKsZ+wclJe+YGoty5fviwqKirExo0bhZubm6ioqBAVFRVCo9EY2oSHh4uCggLD9JYtW4RSqRQFBQWisrJSLFy4UPj5+YmWlhYpdoEGWFJSkpg4caIoKSkRJSUlIjIyUjz99NNGbe7NCY1GI37729+Ks2fPCrVaLQoLC0V8fLwYPXo0c2KIOnTokLC3txf79u0TVVVVYs2aNcLV1VVUV1cLIYTIzMwUqamphvY//PCDcHFxES+++KKoqqoS+/btE/b29uKDDz6QahdogPU1J1577TVx5MgR8f3334tvv/1WZGZmCgAiPz9fql2gAabRaAz/ZwAgduzYISoqKsTly5eFEOwnyBgLIxoS0tLSBIAuj8LCQkMbACInJ8cw3dnZKdavXy9UKpVwdHQU06dPF5WVlYMfPFlEQ0ODSElJEe7u7sLd3V2kpKR0ueTuvTnxn//8R8ycOVN4e3sLe3t7ERQUJNLS0sSPP/44+MHTgPnLX/4igoODhYODg5g8ebI4deqU4b20tDTx2GOPGbUvKioS0dHRwsHBQYwZM0bs3bt3kCMmS+tLTrzyyisiNDRUODk5iREjRohp06aJjz/+WIKoyVL0l2Q3faSlpQkh2E+QMYUQ/39GGRERERERkZXi5bqJiIiIiMjqsTAiIiIiIiKrx8KIiIiIiIisHgsjIiIiIiKyeiyMiIiIiIjI6rEwIiIiIiIiq8fCiIiIiIiIrB4LIyIiIiIikkxxcTFmz54Nf39/KBQKHD16tE/Lb9iwAQqFosvD1dW1T+thYURERERERJK5ffs2oqKisGfPnn4t/9JLL6G2ttboERERgXnz5vVpPSyMiIiIiIhIMsnJydi0aRPmzp1r9v329nasXbsWo0ePhqurK6ZMmYKioiLD+25ublCpVIbH9evXUVVVhWXLlvUpDrsH2QkiIiIiIiJLWrJkCaqrq3Ho0CH4+/vjyJEjSEpKQmVlJcaOHdul/VtvvYWwsDAkJCT0aTscMSIiIiIiIln697//jffeew+HDx9GQkICQkND8dJLL2HatGnIycnp0l6r1eLAgQN9Hi0COGJEREREREQyVV5eDiEEwsLCjOZrtVqMGjWqS/uCggJoNBosXry4z9tiYURERERERLLU2dkJW1tblJWVwdbW1ug9Nze3Lu3feustPP3001CpVH3eFgsjIiIiIiKSpejoaHR0dKC+vr7Hc4bUajUKCwvx4Ycf9mtbLIyIiIiIiEgyra2t+Ne//mWYVqvVOHfuHEaOHImwsDCkpKRg8eLF2L59O6Kjo3Hz5k2cPHkSkZGReOqppwzLvf322/Dz80NycnK/4lAIIcQD7w0REREREVE/FBUV4Sc/+UmX+WlpacjNzcWdO3ewadMmvPPOO7h69SpGjRqF+Ph4bNy4EZGRkQB0h9wFBwdj8eLF2Lx5c7/iYGFERERERERWj5frJiIiIiIiq8fCiIiIiIiIrB4LIyIiIiIisnosjIiIiIiIyOqxMCIiIiIiIqvHwoiIiIiIiKweCyMiIiIiIrJ6LIyIiIiIiMjqsTAiIiIiIiKrx8KIiIiIiIisHgsjIiIiIiKyeiyMiIiIiIjI6v0f+TtFFL6CA6QAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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2yuBNugrnyY1xLtommHmEtsohtFUGoavA0xBtVjmEtsohtFUGb9JVOE8CgUAgEAgEAoFAcAmIantuXG1vZGSEgIAAV5vhlQhtlUNoqwxCV2UYHx/nxIkTzJ07V6z1NMOINqscQlvlENoqg7vrKqrteQk1NTWuNsFrEdoqh9BWGYSuyuDn54dKpRKOkwKINqscQlvlENoqgzfpKpwnN6avr8/VJngtQlvlENoqg9BVGc6cOcMPfvADzpw542pTvA7RZpVDaKscQltl8CZdhfPkxhgMBleb4LUIbZVDaKsMQldl6OvrY9u2bV71xe4uiDarHEJb5RDaKoM36SrmPLnxnCebzYZGo3G1GV6J0FY5hLbKIHRVhpKSEoqKiiguLmbBggWuNserEG1WOYS2yiG0VQZ311XMefISdu7c6WoTvBahrXIIbZVB6CrwNESbVQ6hrXIIbZXBm3QVzpNAIBAIBAKBQCAQXALCeXJjUlNTXW2C1yK0VQ6hrTIIXZUhOjqar371q0RHR7vaFK9DtFnlENoqh9BWGbxJV62rDRCcH3euh+/pCG2VQ2irDEJXZYiPj+eHP/whcXFxrjbF6xBtVjmEtsohtFUGb9JVRJ7cmMrKSleb4LUIbZVDaKsMQldlMBqNvPbaaxiNRleb4nWINqscQlvlENoqgzfpKpwngUAgEFy11NXV8Z3vfIe6ujpXmyIQCAQCD0A4T27MkiVLXG2C1yK0VQ6hrTIIXQWehmizyiG0VQ6hrTJ4k67CeXJjGhoaXG2C1yK0VQ6hrTIIXQWehmizyiG0VQ6hrTJ4k67CeXJjuru7XW2C1yK0VQ6hrTIIXQWehmizyiG0VQ6hrTJ4k67CeXJjfHx8XG2C1yK0VQ6hrTIIXZVBp9MRERGBTqdztSleh2izyiG0VQ6hrTJ4k64qSZIkVxsx2wwPDxMcHMzQ0BBBQUGuNkcgEAgEAoFAIBC4iMvxDUTkyY3Ztm2bq03wWoS2yiG0VQahq3IIbZVB6KocQlvlENoqgzfpKpwngUAgEFy1VFZWcs8993jVGiQCgUAgUA7hPLkxSUlJrjbBaxHaKofQVhmErspgsVjo7e3FYrG42hSvQ7RZ5RDaKofQVhm8SVfhPLkxYWFhrjbBaxHaKofQVhmErgJPQ7RZ5RDaKofQVhm8SVfhPLkxZWVlrjbBaxHaKofQVhmErgJPQ7RZ5RDaKofQVhm8SVfhPAkEAoFAIBAIBALBJSBKlbtxqfL+/n6vCnO6E0Jb5RDaKoPQVRmMRiN79uxhzZo1BAYGutocr0K0WeUQ2iqH0FYZ3F1XUarcS2htbXW1CV6L0FY5hLbKIHRVhsDAQFJSUoTjpACizSqH0FY5hLbK4E26CufJjeno6HC1CV6L0FY5hLbKIHRVhra2Nn7605/S1tbmalO8DtFmlUNoqxxCW2XwJl2F8+TGaLVaV5vgtQhtlUNoqwxCV2Xo6urif//3f+nq6nK1KV6HaLPKIbRVDqGtMniTrmLOkxvPeRIIBAKBspSUlFBUVERxcTELFixwtTkCgUAgcAFuNefpueeeIzU1FV9fX4qKivjoo4/Oe+4DDzyASqWasuXl5cnnbNmyZdpzJiYmlL6VWWfHjh2uNsFrEdoqh9BWGYSuAk9DtFnlENoqh9BWGbxJV0WdpzfffJNvfOMbfP/736e0tJSVK1eyadMmmpubpz3/6aefpqOjQ95aWloICwvjzjvvnHReUFDQpPM6Ojrw9fVV8lZcgt1ud7UJXovQVjmEtsogdBV4GqLNKofQVjmEtsrgTboq6jz9+te/5ktf+hJf/vKXycnJYfPmzSQmJvL8889Pe35wcDAxMTHydvz4cQYGBnjwwQcnnadSqSadFxMTo+RtuIz4+HhXm+C1CG2VQ2irDEJXZQgPD+f2228nPDzc1aZ4HaLNKofQVjmEtsrgTboq5jyZzWaKi4vZsGHDpP0bNmzg0KFDl/QeL7/8MuvWrSM5OXnS/pGREZKTk0lISOCmm26itLR0xux2J7zVKXQHhLbKIbRVBqGrMiQnJ/O73/1uyveM4MoRbVY5hLbKIbRVBm/SVTHnqbe3F5vNRnR09KT90dHRdHZ2XvT6jo4OPvzwQ7785S9P2p+dnc2WLVt47733eP311/H19WX58uXU1dWd971MJhPDw8OTNk+guLjY1SZ4LUJb5RDaKoPQVRnGx8d56623GB8fd7UpXodos8ohtFUOoa0yeJOuitcNVKlUk36XJGnKvunYsmULISEhfO5zn5u0f8mSJSxZskT+ffny5SxYsID/9//+H88888y07/Wzn/2MJ598csr+nTt34u/vz9q1azl27BgjIyOEhoaSl5fHgQMHAIezZrfbqa2tBWD16tWUlZXJ1TgWLFjA3r17AcjMzESr1VJdXQ3AihUrOHnyJP39/fj7+7NkyRJ27doFQFpaGgaDgRMnTgCwdOlS6uvr6enpwdfXl1WrVtHU1MS2bdtITk4mJCSE8vJyABYtWkRzczOdnZ3odDrWrl3L9u3bkSSJhIQEoqKiKCkpAaCoqIjOzk7a2tpQq9WsX7+eXbt2YbVaiY2NJSEhgY8//hiAgoIC+vv75TlpGzduZO/evZhMJqKiokhLS+PIkSMAzJs3j5GRERobGwFYt24dhw4dYmxsjPDwcLKzszl48CAAubm5mM1m6uvrAVizZg3Hjx/HaDQSEhLC/Pnz2b9/PwBz5swB4NSpUwCsWrWKiooKBgcHCQwMZOHChezZsweAjIwM9Ho9J0+elNtCTU0NfX19GAwGli1bxs6dOwFITU0lICCAyspKwOFQl5aW0t3djY+PD9deey3btm0DICkpibCwMMrKygC45ppraG1tpaOjA61Wy3XXXceOHTuw2+3Ex8cTExMjdwoLFiygu7ub1tZWVCoVGzZsYPfu3VgsFmJiYkhKSuLYsWMA5OfnMzg4yJkzZwBHVHb//v1MTEwQGRlJRkYGhw8fBmDu3LmMjY3R0NAAwHXXXceRI0cYHR0lLCyM3Nxcuc3m5ORgtVrlBwrXXnstJSUlciWZgoIC9u3bB0BWVhZqtZqamhq5zVZVVTEwMEBAQACLFi1i9+7dAKSnp+Pr60tVVRUAy5Yto7a2lt7eXgwGA8uXL2fHjh00NTVx6tQpgoKCqKioAGDx4sU0NTXR1dWFXq9nzZo1st6JiYlERETIEeSFCxfS3t5Oe3s7Go2GdevWsXPnTmw2G3FxccTFxXH8+HEACgsL6e3tpaWlRW6ze/bswWw2Ex0dTUpKCkePHgVg/vz5DA8P09TUBMD69es5ePAgY2NjREREkJWVJUfF8/LymJiY4PTp0wBu0UeYTCZKSkom9RHbt28HEH3EFfQRL7/8Mo888ghRUVEsXrxY7iOWLFlCQ0OD6COuoI9wZqCc20cApKSkiD7iCvqIpqYm6urqLjqOEH3E5fcRXV1dbNu27aLjCNFHXF4f0dTURH19/SWNI2D2+win/ZeCYqXKzWYzBoOBv/71r9x2223y/kcffZSysjJZ9OmQJImsrCxuuukmfvOb31z0s77yla/Q2trKhx9+OO1xk8mEyWSSfx8eHiYxMdHtS5X39PQQGRnpajO8EqGtcghtlUHoqgyiVLlyiDarHEJb5RDaKoO76+oWpcr1ej1FRUVTShPu2LGDZcuWXfDaffv2UV9fz5e+9KWLfo4kSZSVlREbG3vec3x8fAgKCpq0eQLd3d2uNsFrEdoqh9BWGYSuAk9DtFnlENoqh9BWGbxJV0Wr7T3++OO89NJL/OEPf6C6uprHHnuM5uZmHnroIQCeeOIJ7rvvvinXvfzyyyxevJi5c+dOOfbkk0+ybds2GhoaKCsr40tf+hJlZWXye3oTra2trjbBaxHaKofQVhmErgJPQ7RZ5RDaKofQVhm8SVdF5zzdfffd9PX18dRTT9HR0cHcuXP54IMP5KpGHR0dU9Z8Ghoa4q233uLpp5+e9j0HBwf56le/SmdnJ8HBwRQWFrJ//34WLVqk5K24hEuZGyb4bAhtlUNoqwxCV2VQqVTodDqhrwIITZVDaKscQltl8CZdFZvz5M5cTl6jQCAQCAQCgUAg8F7cYs6T4MpxVjASzDxCW+UQ2iqD0FU5hLbKIHRVDqGtcghtlcGbdBXOkxtjsVhcbYLXIrRVDqGtMghdlaG6upqvfvWrcmlowcwh2qxyCG2VQ2irDN6kq3Ce3BhvWo3Z3RDaKofQVhmErsowPj7O6dOnxSK5CiDarHIIbZVDaKsM3qSrcJ7cmKSkJFeb4LUIbZVDaKsMQleBpyHarHIIbZVDaKsM3qSrcJ7cGOcK0oKZR2irHEJbZRC6CjwN0WaVQ2irHEJbZfAmXYXzJBAIBAKBQCAQCASXgHCe3Jj8/HxXm+C1CG2VQ2irDEJXZUhNTeXFF18kNTXV1aZ4HaLNKofQVjmEtsrgTboK58mNGRwcdLUJXovQVjmEtsogdFWG0NBQVq5cSWhoqKtN8TpEm1UOoa1yCG2VwZt0Fc6TG3PmzBlXm+C1CG2VQ2irDEJXZejq6uLXv/41XV1drjbF6xBtVjmEtsohtFUGb9JVOE8CgUAguGppa2vj97//PW1tba42RSAQCAQegEqSJMnVRsw2w8PDBAcHMzQ0RFBQkKvNOS+SJKFSqVxthlfi7dra7XYmJiYYHx9nYmKCiYkJTCYTFosFq9Uqv579s8ViwW63I0nSpA2Y8rtarUatVqPRaKa8qlQqtFotGo0GvV6PTqe74KvzZ2/+e8wE3t5mXUVJSQlFRUUUFxezYMECV5vjVYg2qxxCW+UQ2iqDu+t6Ob6BdpZsEnwG9u/fz+rVq11thlfiidqazWaMRiMjIyMYjcZJ28jICOPj47KzZDKZXGZnU1MTKSkpl3WNWq3Gz88PX19ffH19z/uzv7//pE2v17t1ZzyTeGKbFVzdiDarHEJb5RDaKoM36SqcJzdmYmLC1SZ4Le6orSRJjIyM0N/fP2kbGBigv7//M9ms1+tl58PHxwedTodWqz3vqzNy5NyASb8799ntdmw227SvOp2OwsJCOZplNpsxm83yz+e+Ot9vdHSU0dHRy7o/rVY7xaFybgEBAQQFBREYGEhQUBA6ne6y9XMn3LHNCgQXQrRZ5RDaKofQVhm8SVfhPLkxkZGRrjbBa3G1tuPj43R1ddHZ2Sm/9vb2YrFYLnidTqcjMDBwyhYQEIDBYJgUpfH19UWj0czSHX1KeHj4Jac/SZKExWKZkmI43c/j4+OMjY3JTpbZbMZqtTI0NMTQ0NBFP8vX15egoKBJDpXzNTg4mJCQEHx8fK709hXD1W3WWwkODmbVqlUEBwe72hSvQ7RZ5RDaKofQVhm8SVcx58mN5zwNDw+7tX2ezGxqazKZaG1tpbW1lba2Nrq6us472Fer1QQHBxMWFiZvoaGhhIWFERwc7BFparOlrcVikR2pc7ezUxuHh4cv6pQ68fPzIyQkRHamzt18fX0VvqvzI/oD5RDaKoPQVTmEtsohtFUGd9dVzHnyEg4fPszGjRtdbYZXoqS2Y2NjNDU10djYSHNzM93d3Uz3jCIkJISYmBiio6OJiYkhKiqKkJAQl0SLZpLZarc6nU52ai6EJEmYTCbZkTr3dXh4mKGhIcbGxuR5Yx0dHdO+l4+Pj+zMOrfw8HDCwsIICAhQ1LEV/YEyWCwWPvzwQ26//XaPT+10N0SbVQ6hrXIIbZXBm3QVzpNAcIXY7XZaWlqora2loaGBzs7OKc5SaGgoiYmJxMfHyw6TK6MYVxMqlUpOY7xQ2oDZbGZwcJChoSEGBwenbKOjo5hMJjo7O+ns7JxyvU6nm+RUObeIiAjFHSvBZ6eyspLPf/7zotqeQCAQCC4J4Ty5MXPnznW1CV7LlWprsVior6+npqaGuro6xsbGJh2PiooiNTWVlJQUEhMTCQgIuKLP8yQ8td3q9XqioqKIioqa9rjFYmFwcFAu4HH2Njg4iMVioaura9rFVn18fIiIiCAiIoLIyEj559DQ0EuONHqqroKrF9FmlUNoqxxCW2XwJl2F8+TGnDsgF8wcn0Vbu91OY2MjlZWVVFdXTyoH7ufnR2ZmJhkZGaSmphIYGDiT5noU3tpudTodkZGR00avbDYbQ0ND9Pf309fXN6Vaoslkoq2tbcpCrGq1mrCwsEkOVVRUFBEREVNSyLxVV4H3ItqscghtlUNoqwzepKtwntyYhoYGMjMzXW2GV3I52g4PD1NSUkJxcTFGo1HeHxwcTG5uLnPmzCEpKQm1Wq2UuR7F1dhuNRqNnKaXkZEx6ZjVamVgYICenh56e3snbWazWf75bFQqFeHh4XIkLDo6mhMnTpCeni7amcBjuBr7gtlCaKscQltl8CZdhfMkEJyH5uZmDh06RG1tLXa7HXBEmPLy8pg3bx5JSUliHovgomi12mkjVpIkMTw8PMmZ6unpobu7m7GxMXnfyZMnAaivr6e2tpbIyMhJTlV0dPRVlRYqEAgEAoErEaXK3bhsotVqRasV/q0SnE9bSZKoq6vjwIEDNDc3y/tTUlJYuHAh2dnZ4m9yEUS7vTKciyV3d3fT3d1NV1cX3d3ddHZ2yk78uQQGBhITE0NMTAyxsbHExMQQGhoqnPtLwJlyGRwc7PGVLt0N0Rcoh9BWOYS2yuDuuopS5V7CkSNHWLFihavN8Eqm07apqYkdO3bI81I0Gg0FBQUsWbLEqxZ3UxrRbq8MlUolL36cnp4u79+/fz95eXmTHKquri76+/vlNa3q6urk8318fGSHyulURUZGCgfhHDQaDSdPnhRtVgFEX6AcQlvlENoqgzfpKpwnN2Z0dNTVJngtZ2vb29vL9u3bqa2tBRxV16655hqWLFlyVRd++KyIdqsM4+PjhIeHEx4eTk5OjrzfbDbT1dVFZ2cnHR0ddHZ20t3djclk4syZM5w5c0Y+V6PREBUVRVxcHPHx8cTFxV31DlVdXR2PPvoob7zxhtfk47sLoi9QDqGtcghtlcGbdBXOkxsTFhbmahO8lrCwMKxWKwcOHOCjjz7CZrOhVqtZuHAhq1evxt/f39Umeiyi3SrD+XTV6/UkJiaSmJgo77PZbPT29sprUjmdqomJCTo6Oujo6KC4uBhwzMmKjY0lLi5OdqrCw8OvmpQ/o9FISUnJpGIwgplB9AXKIbRVDqGtMniTrsJ5cmNyc3NdbYLXEhYWxu9+9zt6enoAyMzM5Prrryc8PNzFlnk+ot0qw+XoqtFo5GIS+fn5gGMu1dDQEO3t7bS3t9PW1kZ7ezsmk4mWlhZaWlrk6318fIiNjZWjUwkJCQQFBV01DpVgZhB9gXIIbZVDaKsM3qSrcJ7cmAMHDrBx40ZXm+FVSJLEsWPH+O1vf0taWhoBAQFs2rSJ3NxcMTCcIUS7VYYr1VWlUhESEkJISIj8JSZJEv39/bIj1d7eTkdHByaTiaamJpqamuTrAwMDSUhIIDExkYSEBGJjY6esRSUQnI3oC5RDaKscQltl8CZdhfMkuGqwWCy8++67VFVVYbfbyc7O5tZbb8XPz8/VpgkELsG5nlR4eDjz588HHItB9/T0yM5Ua2srXV1dGI1Gqqurqa6uBhwL/MbExMjOVEJCAiEhIeIhhEAgEAi8GuE8uTFnTwoXXBmjo6O8/vrrtLa2otFouPPOO7nlllvEQE8BRLtVhtnSVa1Wyyl/hYWFgOPBg9ORam1tpaWlhZGREdnBOnr0KAD+/v5ydCopKYm4uDi3Lk0LkJiYyFNPPTVpzphgZhB9gXIIbZVDaKsM3qSre3+rXeVYrVZXm+AVGI1GtmzZQl9fH35+ftx9993Y7XbhOCmEaLfK4EpddTodycnJJCcnA5/Onzrbmers7GR0dJRTp05x6tQpwFGMIj4+nqSkJJKSkkhMTMTX19dl9zEdkZGRfPGLXxTLESiA6AuUQ2irHEJbZfAmXYXz5MbU1dWRlpbmajM8mtHRUV599VX6+voICQnhnnvuISIigm3btgltFUK0W2VwJ13Pnj81d+5cwPHF2NHRQWtrK83NzTQ3NzM6OjqpXLpKpSIqKkp2ppKTk12+UHl/fz8vvPAC3/3ud72qGpQ74E5t1tsQ2iqH0FYZvElX4TwJvBaLxcKf//xnenp6CAoK4v777yc0NNTVZgkEXolWq5VLpi9dulQuRuF0pJqbm+nr66Orq4uuri4+/vhjAEJCQmRHKiUlhbCwsFmNCjc1NfHLX/6Sz3/+88J5EggEAsFFUUmSJCn5Ac899xy//OUv6ejoIC8vj82bN7Ny5cppz927dy9r1qyZsr+6uprs7Gz597feeov/+q//4vTp06Snp/PTn/6U22677ZJtGh4eJjg4mKGhIZc/9bwQJpMJHx8fV5vhkUiSxLvvvkt5eTkGg4EvfelLk8qQC22VQ2irDN6g68jIyCRnqrOzE7vdPumcwMBAUlJS5E1pZ6qkpISioiKKi4tZsGCBYp9zNeINbdZdEdoqh9BWGdxd18vxDRSNPL355pt84xvf4LnnnmP58uX87ne/Y9OmTZw8eZKkpKTzXnfq1KlJhp+di3748GHuvvtufvzjH3PbbbfxzjvvcNddd3HgwAEWL16s5O3MOiUlJSxdutTVZngkxcXFlJeXo1arufPOO6es3yS0VQ6hrTJ4g64BAQHk5ubKpdLNZjOtra1yal9LSwtGo5HKykoqKyuB2XemBDOHN7RZd0VoqxxCW2XwJl0VdZ5+/etf86UvfYkvf/nLAGzevJlt27bx/PPP87Of/ey810VFRRESEjLtsc2bN7N+/XqeeOIJAJ544gn27dvH5s2bef3112f8HlzJ8PCwq03wSIaGhti+fTsA69atIzU1dco5QlvlENoqgzfqqtfrSUtLk/PgLRYLbW1t8hpTra2tF3SmUlNTCQ0NFc6Um+KNbdZdENoqh9BWGbxJV8WcJ7PZTHFxMd/97ncn7d+wYQOHDh264LWFhYVMTEyQm5vLD37wg0mpfIcPH+axxx6bdP7GjRvZvHnzjNnuLgQHB7vaBI/kgw8+wGw2y3MvpkNoqxxCW2W4GnTV6XSyYwSOIhStra0XdKZCQkJIS0sjNTWV1NRUAgICLusz/f39mTt3Lv7+/jN9O1c9V0ObdRVCW+UQ2iqDN+mqmPPU29uLzWYjOjp60v7o6Gg6OzunvSY2NpYXX3yRoqIiTCYTf/rTn7juuuvYu3cvq1atAqCzs/Oy3hMceZYmk0n+3VO834KCAleb4HG0tLRw6tQp1Gr1BddxEtoqh9BWGa5GXbVa7XmdqcbGRlpbWxkcHKSkpISSkhLA8X3gdKaSk5MvmmM/Z84cPv74Y7croe4NXI1tdrYQ2iqH0FYZvElXxavtnTt4lSTpvAPaOXPmMGfOHPn3pUuX0tLSwq9+9SvZebrc9wT42c9+xpNPPjll/86dO/H392ft2rUcO3aMkZERQkNDycvL48CBAwBkZ2djt9upra0FYPXq1ZSVlckTyhYsWMDevXsByMzMRKvVUl1dDcCKFSs4efIk/f39+Pv7s2TJEnbt2gVAWloaBoOBEydOyPdaX19PT08Pvr6+rFq1ij/+8Y+kpKSQnJxMSEgI5eXlACxatEiebK3T6Vi7di3bt29HkiQSEhKIioqSBxJFRUV0dnbS1taGWq1m/fr17Nq1C6vVSmxsLAkJCXLVq4KCArk6Fjgienv37sVkMhEVFUVaWhpHjhwBYN68eYyMjNDY2Ag40uMOHTrE2NgY4eHhZGdnc/DgQQByc3Mxm83U19cDsGbNGo4fP47RaCQkJIT58+ezf/9+uQ0A8joxq1atoqKigsHBQQIDA1m4cCF79uwBICMjA71ez8mTJwFYvnw5r7zyCvX19eTl5REWFsa2bdsA5CfSzqfVJpOJxMREuru78fHx4dprr5XPTUpKIiwsjLKyMgCuueYaWltb6ejoQKvVct1117Fjxw7sdjvx8fHExMRQXFwMwIIFC+ju7qa1tRWVSsWGDRvYvXs3FouFmJgYkpKSOHbsGAD5+fkMDg7KZZw3bNjA/v37mZiYIDIykoyMDA4fPgzA3LlzGRsbo6GhAYDrrruOI0eOMDo6SlhYGLm5uXKbzcnJwWq1UldXB8C1115LSUmJPBmyoKCAffv2AZCVlYVaraampkZus1VVVQwMDBAQEMCiRYvYvXs3AOnp6fj6+lJVVQXAsmXLqK2tpbe3F4PBwPLly9mxYwdNTU1ce+21BAUFUVFRAcDixYtpamqiq6sLvV7PmjVrZL0TExOJiIigtLQUgIULF8qLr2o0GtatW8fOnTux2WzExcURFxfH8ePHAUeUure3l5aWFrnN7tmzB7PZTHR0NCkpKfICrvPnz2d4eJimpiYA1q9fz8GDBxkbGyMiIoKsrCw5Kp6Xl8fExASnT58GcIs+wmQykZCQMKmPcKanXm19REpKCiaTicjISFJSUti9ezf19fWMjo5is9nk942IiCAhIQG73U5sbCy33HILdXV19PX1YTAYWLZsGTt37qSpqYk1a9ZM6iOWLFlCQ0OD6COuoI8wm83ExcVN6SMAUlJSRB9xBX1EU1MTa9euveg44mrtI+CzjyPefvttoqOjJ/URMHUcIfqIy+sjmpqauO666y5pHAGz30c47b8UFKu2ZzabMRgM/PWvf51UCe/RRx+lrKxMFv1i/PSnP+W1116TO5KkpCQee+yxSal7v/nNb9i8ebPceM5lushTYmKi21fb27ZtGxs3bnS1GR5Db28vzz77LGq1mkceeeSCZcmFtspxJdra7XbMZjMmk0l+df5stVqxWCzYbDasVqu82Ww2JEnCbrcjSdKUn1Uq1bSbWq1GrVaj0WjQarWo1epJr1qtFp1ON2XT6/VotVr0ej0ajWaG1Ts/os1enNHRUZqammhoaKChoYGBgYFJx/V6PampqWRkZJCenk5YWJiotqcgos0qh9BWOYS2yuDuurpFtT29Xk9RURE7duyY5Dzt2LGDW2+99ZLfp7S0lNjYWPn3pUuXsmPHjknO0/bt21m2bNl538PHx8etyyOej6ysLFeb4FE4n/BkZGRcdD0noa1yXEhbq9WK0WhkZGSEsbExRkdHGR8fZ2xsjLGxMSYmJlB49YQZRa/X4+Pjg6+vr9zPOLez9xkMBvR6/RUVNhBt9uL4+/uTl5dHXl4eAAMDAzQ0NNDY2EhDQwNjY2OcOnVKfiJ99rpOFovFJTZ7M6LNKofQVjmEtsrgTboqmrb3+OOPc++997Jw4UKWLl3Kiy++SHNzMw899BDgqJTX1tbGq6++Cjgq6aWkpJCXl4fZbOa1117jrbfe4q233pLf89FHH2XVqlX8/Oc/59Zbb+Xvf/87O3fulEON3oRarXa1CR6DJEly6sKl5NUKbZVDrVZjs9kYGhpiYGCAoaEhhoeHMRqNjI6OXtJ7OJ2Ss191Op0cETp702g0qNXqSREl589OnFGoczdnFMtut096de63WCxy1Ovs6JfFYkGSJMxmM2azGaPReNF70mg0GAwG/Pz85M1gMMj7DAYDPj4+522bos1ePqGhoRQVFVFUVIQkSXR2dlJfX8/p06dpbm6mv7+fjo4OALZs2UJNTQ3p6elkZGQQHR0tqvhdIaLNKofQVjmEtsrgTboq6jzdfffd9PX18dRTT9HR0cHcuXP54IMPSE5OBqCjo0POiwVHqt83v/lN2tra8PPzIy8vj3/+85/ccMMN8jnLli3jjTfe4Ac/+AH/9V//RXp6Om+++abXrfEEUFNTI2sluDCDg4MMDg6iVqvJyMi46PlC25lDkiRGR0fp7u6mr6+PAwcOEB8fP2XxUyd6vZ7AwED8/f1l5+FsJ+JCDoS7YLfbsVgsTExMyKmFExMTmM3mKfucrzabDaPReEFHS61WYzAY8Pf3JyAggICAAPnnEydOiDZ7BahUKmJjY4mNjWXlypWYTCYaGxvlnHm73U5jYyONjY3s3LmTgIAA2ZHKyMjAz8/PxXfgeYh+VjmEtsohtFUGb9JV8YIRDz/8MA8//PC0x7Zs2TLp929/+9t8+9vfvuh7/su//Av/8i//MhPmCbwE53y3+Ph49Hq9i63xfsbHx+ns7KSrq4uenp5JEaWxsTHsdjt6vZ7Q0FBCQ0MJDAwkKCiIwMBAfHx8PP6Jvlqtvqx0YJvNJqcnjo+PT/r57H12u52RkRFGRkbo6uqa9B6nT5/GZDLJzpS/vz+BgYGytt6g62zi4+NDdnY2Y2NjgONhX0BAAPX19TQ2NjIyMkJ5eTnl5eWoVCoSExPJysoiMzOTqKgoobVAIBBcpShWMMKduZxJYa5kdHRUrD1yiWzdupUjR46wdOnSS5qQKLS9fIxGI62trbS2ttLX1zfpmFqtJjw8nIiICPz8/IiLi8Pf318MMC8Du93O+Pg4o6OjjI6OMjIyIr+OjIxgNBrR6XTnvV6v1xMUFCQ7qc5Xf3//WS1s4WlMTExQW1tLVlaWXK7carXS0tJCfX09dXV1dHd3T7omODiYzMxMsrKySE1NveDf5WpG9LPKIbRVDqGtMri7rm5RMEJw5VRVVbFo0SJXm+EROKtqnT0B/EIIbS8Nk8kkr6kzODg46Vh4eDjR0dFERUURHh4uDyCPHTvmVRNDZwu1Wo2/v/95v1wOHz5MTk7OJMfKaDQyPDzM2NgYZrOZ3t5eent7p7xvQECA7FgFBwcTHBxMYGCgcKoAX19fJiYmJq3zpNVq5UV3169fz+DgIHV1ddTW1tLY2MjQ0BDHjx/n+PHj8rmZmZlkZmZetFjN1YToZ5VDaKscQltl8CZdhfPkxpxbZldwfoaGhgAICQm5pPOFtudHkiR6eno4ffo0LS0t8twltVpNVFQU8fHxJCQknHcOiNBWGYaHhwkJCZm2jVutVkZGRuTCHMPDw/LPVqtV/v1s1Gq17EgFBwcTEhJCcHAwfn5+V1XEsLGxke9+97u8/PLLpKamTntOSEgI11xzDddccw0Wi4XGxkbZmRoaGqKurk5eDyUyMpKsrCyys7OJj493+/l7SiL6AuUQ2iqH0FYZvElX4Ty5MQEBAa42wWNwlhm+1PlOQtupSJJER0cHJ0+enBS9CA0NJS0tjaSkpEua4yO0VYYL6arVaqd1rCRJYmxsTHaohoaGGBoaYnBwEKvVysDAwLRrITkdqZCQEEJDQwkODvbaKNXAwAB79uxhYGDgvM7T2eh0OrKyssjKyuKGG26gp6dHdqRaWlro6emhp6eHgwcP4u/vLztSaWlpV116n+gLlENoqxxCW2XwJl3FnCc3nvNksViuui/bz8rmzZsZHBzky1/+MgkJCRc9X2g7mc7OTioqKujv7wccZbVTUlLkhUQvB6GtMsykrs4KiU5HyvlqNBqnXWdLrVbLjlRoaChhYWFe41DN5CK54+PjnD59mlOnTlFXV8fExIR8TKfTkZaWRnZ2NllZWW6d+z9TiL5AOYS2yiG0VQZ311XMefISdu/e7darMbsTzjSj85XHPhehrYPR0VFKS0tpbW0FHBGM9PR0srOzP3NpZqGtMsykriqVSi6FHh8fL+93rs/ldKYGBwcZGBjAbDbT398vO9fwadrfuQ6VVnv1fq34+fkxd+5c5s6di81m48yZM5w6dYqamhqGhobkBXpVKhUJCQlkZ2czZ84cIiIiXG26Ioi+QDmEtsohtFUGb9L16v2WE3gVBoOBgYEBueyw4MJIkkRDQwOlpaVYrVZ5fazc3NxJE+cFVxcajYawsLBJ0UZnlKq/v19O8+vv78dsNk9J+3NGqMLDw+UtICDgqppD5USj0ZCWlkZaWhrXX389XV1dsiPV0dFBS0sLLS0t7Nixg/DwcHJycsjJySEuLu6q1EsgEAg8BeE8uTHp6emuNsFjcKbAnL3e0IW4mrW1WCwcPXpUjjZFRkaycOFCgoODZ+T9r2ZtlcRVup4dpUpKSgI+nUt1tjM1MDDAxMSEHKFyFlDw8fEhLCyMiIgIwsPDCQsLc6u12GJjY3nkkUeIjY1V7DNUKhUxMTHExMSwevVqhoeHZUeqqalJXlz6wIEDBAcHk52dTW5uLomJiR5dcEL0BcohtFUOoa0yeJOuwnlyY0QE4NJxTpQ/d/2h83G1ams0Gvnoo48YHh5GrVaTn59PVlbWjD7pvlq1VRp30lWlUsll1Z1zDJ0OVV9fn7wNDAxgMpno6Oigo6NDvjYoKGhSdCo4ONhl0ZbY2Fi+/e1vK+o8nUtQUJBcvc9kMlFXV0d1dTV1dXUMDQ1x9OhRjh49ir+/P9nZ2eTk5JCamupxc8zcqc16G0Jb5RDaKoM36SqcJzemqqrqkoofCCA6OhqArq6uSzr/atR2cHCQvXv3MjExgcFgYPny5YSHh8/451yN2s4G7q7r2Q6VM0Jls9kYHByc5FCNjIzI86oaGhoAR4W/iIgIIiMjiYyMJDQ0dNYcheHhYf74xz/yyCOPuKSAkI+PjzxPymKx0NDQQHV1NadOnWJ0dJTi4mKKi4vx9fVlzpw55OTkkJ6e7tYTr524e5v1ZIS2yiG0VQZv0lU4TwKvwOk8dXR0IEmSmDNwDkajkT179mAymQgJCWH16tWfuSCEQHCpaDQaObrkxJna19fXR29vL319fZjNZtrb22lvb590ndOZOnsR5pmmvr6eH/zgB2zatOmKq+1dKTqdjjlz5jBnzhxsNhtNTU1UV1dTU1PDyMgI5eXllJeXo9PpyMzMJC8vj8zMTLdKgxQIBAJvR5Qqd+NS5UajkcDAQFeb4RHYbDZ+/vOfYzabeeihh4iJibng+VeTtiaTiZ07d2I0GgkLC2P16tWXtF7TZ+Vq0nY28VZdndGp3t5eeY0kk8k06RyVSkVISIjsTEVGRs5YCshMlipXCrvdTmtrK9XV1Zw8eVJeFBw+dbjy8vLIyMhwq4iUt7ZZd0BoqxxCW2Vwd11FqXIvoba2lqKiIleb4RFoNBqSk5Opq6ujoaHhos7T1aKtJEkcO3YMo9FIQEAAK1euVNRxgqtH29nGW3U9Ozo1Z84cJEnCaDTKjlRPTw+jo6NycYra2lrAMc8xKiqK6OhoIiMjvTr6olarSUpKIikpiQ0bNsiLWVdVVTEwMMCJEyc4ceIEer1+kiPl6rLx3tpm3QGhrXIIbZXBm3QVzpMb09vb62oTPIqMjAzq6uqoqalh2bJlFzz3atG2sbGRtrY21Go1y5cvn5VUvatF29nmatHVWVAiKChIrs40NjY2yZk6ex2q2tpaVCoVYWFhREVFERUVRUREhFtFYGYSlUpFXFwccXFxXHfddXR0dFBVVUVVVRWDg4NUVlZSWVmJj4+P7Eilp6e7xJG6WtqsKxDaKofQVhm8SVfhPLkxBoPB1SZ4FDk5OWzdupXm5mYGBwflCnzTcTVoazabqaioAGD+/PmEhobOyudeDdq6gqtZV4PBQHJyMsnJyYBj3lR3dzfd3d10dXVhNBrlghTV1dWo1WrCw8PlyFR4ePh5C1D4+PgQHx+veERWCc52pNatW0dbW5vsSA0PD1NRUUFFRQU+Pj5kZ2czd+5c0tLSZq0Yx9XcZpVGaKscQltl8CZdxZwnN57zZLfbPXqND1fwxz/+kcbGRtauXcuqVavOe97VoG1VVRWVlZUEBQWxcePGWRswXQ3augKh6/kZGxujq6tLdqjOXe9No9EQFRVFTEwM0dHRU0qje5u2kiTR2tpKVVUVJ0+eZHh4WD5mMBjIy8tj3rx5JCYmKlpcx9t0dSeEtsohtFUGd9f1cnwD4Ty5sfO0bds2Nm7c6GozPIqysjLeffddgoODefTRR8/7j+rt2tpsNv7xj38wMTHB0qVL5Sf2s4G3aStJ0NcHbW3Q2godHTA46NiGhia/jo+DxfLpZjY7Xq1WUKlAq526aTTg6wsBAZO3wMBPfw4NhZaWEtatW0BEBISHQ1iY41rBZCRJYmRkZFJkamJiYtI5BoNBdqRiYmLYu3evV7XZs5EkiZaWFk6cOEFVVdUkxzIkJIS5c+cyb948uWLpTOJtfYE7IbRVDqGtMri7rqJghOCqJS8vj+3btzM0NER1dTV5eXmuNsklOAeMvr6+XrOugpLYbHD6NNTUwKlTjq221uEstbfDOcXfXMQCnnxy8p7QUIiIgJgYiI+HuLjpN39/11jsClQqFYGBgQQGBpKeno4kSQwNDdHV1UVHRwc9PT2MjY3R0NBAQ0MDzc3NPPXUU/zxj39k1apVhIWFedxitBdCpVLJxSauv/56GhsbqayspLq6msHBQQ4cOMCBAweIiopi3rx5zJs374IpzwKBQHC1I5wnNyYlJcXVJngcOp2Oa665hn379nH48GFyc3OnTUvxdm2d6+UkJCTM+kDQE7RtaYH9++H4cSguhpISOCfTawqRkZCQ4HBGwsIgOBhCQia/Ggyg003e9HpHhAkcEahzN4vF4ZyNjEy/DQ9Dfz+0tY0zOupHb68jygUwMODY6uoubHtYGKSkQGrq9K9elIo+BWeZ85CQEObMmYPVaqW3t5eOjg46Ozux2WyMjo5SW1uLxWJBp9PJEanY2Fj8vcjzVKvVpKenk56ezo033khdXR2VlZXU1tbS3d3Nrl272LVrF4mJicybN4+8vLwrun9P6As8FaGtcghtlcGbdBXOkxvjzimF7sw111zDwYMHaW1t5fTp02RkZEw5x9u17enpASA2NnbWP9sdtTUa4cMPYccO2LPHEWU6Fz8/mDPn0y0rC5KTHQ5TbCy4up5AR8cgsbGOaolWq8Oh6uuDnh7o7HSkFba3T97a2hxOYX+/Yyspmf69o6MhM9Nxz857z8qC9HTX3/dMo9VqiYmJkZczcLbXmJgYfHx8MJlMtLa20traCjhS25xFGcLCwtw6Z/9y0Ol05Obmkpuby8TEBNXV1VRWVtLY2EhLSwstLS1s3bqVjIwM8vPzmTNnzmVX7HPHvsBbENoqh9BWGbxJV+E8uTEVFRUuGfx6OgEBAVxzzTUcPnyYXbt2kZ6ePiX65M3a2mw2jEYjwKxV2Dsbd9F2ZAT+9jfHtnPn5NQ7tRoWLoQlS6CoyLFlZ7v3HKKzddVqISrKseXkXPi64WE4cwaamqCxcerr0BB0dTm2AwcmX6tWOyJTWVkOffLyYN48yM11zMnyBpzl+/Pz8ykoKGBgYIDOzk46Ojro6+uTS6KfPHkSHx8fYmNj5c1b1pby9fWlsLCQwsJCjEYjVVVVVFRU0N7eTm1tLbW1tfj6+pKXl0d+fv4lF5pwl77AGxHaKofQVhm8SVfhPAm8khUrVlBcXCwvJnk1zX2amJiQq9rMxrpO7kZxMfzud/D66w4HyklmJtx8M6xdCytXghc9BLsgQUEOh2fevOmPDw46InG1tZ9uzjlfRiM0NDi2rVsnX5eSAnPnTt7mzHEUv/BUnCXOw8PDycvLw2Qy0dHRQXt7O52dnZhMJpqammhqakKtVhMREUFcXByxsbEEBQUpWrlutggMDGTJkiUsWbKE3t5eysvLqaioYGhoiOLiYoqLiwkNDSU/P5/58+cTFhbmapMFAoFgVhHV9tx4BHWxtYoEF2bv3r3s3buX4OBgvva1r01aNNObte3v72f79u34+flx6623zvrnu0rb/fvhxz92RJmcZGbCvffC7bc7oiXuPLaVJEnenKhUKnmbbV0lyZEO6HSoTp6Eqio4ccJRcXA6NBqHzoWFsGCB47WgwL0d1ZGREQ4dOsSyZcsICAg473k2m42+vj7a29vp6OhgaGho0vGAgADi4uJISEggIiLCa9L7wNE2m5qaKC8v5+TJk5jNZvlYUlIS+fn55ObmTnlY4839rKsR2iqH0FYZ3F1XUar8IniK81RWVkZBQYGrzfBYLBYLzz77LENDQ6xatYq1a9fKx7xZW6fzZDAYuOWWW2b982db27o6eOQR2LbN8btWC3fdBV/9KqxaNbsOkyRJWK1WzGazvFksFiwWCzabDavVis1mkze73T6t03QuKpWKzs5OYmNj0Wg0qNVq+dX5s0ajQavVnnfT6XQzFhnp6/vUkTpxwvFzZaWjeMV0ZGQ4HCmnU7VwoaPcurvwWdrsyMgI7e3ttLe3093djd1ul4/5+PjIjlR0dPRlzxVyZ8xmMzU1NZSXl9PQ0CC3W61Wy5w5cygoKCA9PR21Wu3V/ayrEdoqh9BWGdxdV1Gq3Evo6upytQkejU6n4/rrr+fNN9/k4MGD5OfnE/7JiM2btXU+8bZarS75/NnS1mKBn/wE/r//z7Gekk4H//Zv8N3vOlLKlESSJEwmE2NjY0xMTDA+Ps74+LicMqnE542MjGC32z/z+6tUKnQ6HTqdDr1eP+lnvV6Pj48Per3+kiIm4eEOx/TsdaglyVGgorTUUZiitNSxNTdDfb1j++tfPz0/IwMWLYLFix2vBQWuSflrbW3lqaee4plnnrmssv4BAQFkZWWRlZWFxWKhq6uLtrY22traMJlMNDY20tjYKBeoSEhIIDY2Fh8Pr8Ch1+uZP38+8+fPZ3h4mMrKSsrLy+nu7qaqqoqqqiqCgoLIz89nYGDArQdLnow3f4e5GqGtMniTrsJ5cmO8ZTKyK8nOziYzM5O6ujreffddHnzwQdRqtVdr60ydMZvNWK3WWX/qPRvatrfD3Xd/WuBg40Z49lnHgFwJJElidHSU4eFhRkZGGB0dxWKxTHvudE6KXq+fFB1y/qxWq1GpVLLD4nxVqVRTIlLDw8PMnz9fdqCckauzX61W63k3SZLkaNjoeeqyq1Qq2ZFyOlPOn319fdFqteeNXqlUjsqECQmOuWVOenuhrOxTh+r48U+dqfp6+MtfHOfpdA4HyulQLVni+HsqHTns7u7mnXfe4Qc/+MFnXhNNp9ORkJBAQkICdrudnp4e2ZEaHR2Vq/ep1WoiIyNJSEggPj4eg4fXiA8KCmL58uUsW7aMrq4uSktLqaysZHh4mI8++ojGxkaMRiOFhYXk5uZ6db872wgtlUNoqwzepKtI23PjtD3BzDA0NMRzzz2HyWRiw4YNLFu2zNUmKYokSbz99ttYLBY2bdpEcHCwq02aUU6cgHXrHNXhAgPhxRcdjtRMD7JtNhtDQ0MMDAwwNDQ0JZLnLMjh3Hx9ffHz87vk6M1scm46oTOV0PmzyWTCZDJdNKql1Wrx9fWV79X56uPjc1kpgf398PHHcPQoHDvmeO3tnXpeVBSsWAHLlzteCwsdTtZMUlJSQlFREcXFxSxYsGBG31uSJAYGBmhra6O1tXXKPKnw8HASEhJITEy84HwrT8JqtVJbW0tpaSn19fWy86/X65k7dy6FhYUkJCR4RXENgUDgPYg5TxfBU5ynbdu2sXHjRleb4RWUlJTw3nvvodVq+epXv0ppaalXa7tz5056e3tZvHgxqamps/rZSrbbigq47jrHQHvuXHj7bUdRiJnCGWHq7u5mYGAAm80mH9NqtQQGBhIUFIS/vz8Gg2FWnSSl+wNJkiY5UmazWf7Z+fv5vi7UarUcnfLz88NgMGAwGC7ZqZIkR+l0pzN15IijauJZdQkAx1pcixd/6lAtXepYnPhKUNJ5Ohej0Sg7Un19fZP0DAsLIzEx0ascqbfffpvIyEhKS0vp7++X90dERFBYWMj8+fMJ9Jaa97OMGB8oh9BWGdxdVzHnSSA4h8LCQqqrq6mrq+Nvf/sbycnJrjZJUSIiIujt7aW3t3fWnSel6O6GTZscjtPChbB9O8zUMlbOCEFHR8eklDYfHx/CwsIICQkhICDAq5+WO1P29Hr9tANau93OxMSEPMfr7J/tdrs872vgrKoRGo1mkjNlMBjw8/NDc86CWioVpKY6ts9/3rFvYsLhQB04AAcPOrb+fti717GBYx2qBQtgzRrHtmKFe68/FRgYSHZ2NtnZ2YyPj9PW1kZLSwvd3d309/fT399PeXm51zhS/v7+rFy5khUrVtDc3ExpaSlVVVX09vayY8cOdu3aRVZWFkVFRXKRCYFAIHB3hPPkxiQmJrraBK9BpVJx66238sILL0ypjOWNREVFUVNTQ3t7O5IkzeqgX4l2a7fDPfc45jplZ8OOHTBTFU+Hh4dpaWmRnSa1Wk1YWBiRkZFu5TC5uj9Qq9WyA3Q2zrlUZxfOGBsbY3x8HJvNxsjICCNnLbilUqnw9fWV3ysgIACDwTDFofL1dUSXli93/G63Q02Nw4lyOlSnTzvmUB0/Dr/8paNU+jXXOByptWth2TI437SiujrHOlYdHbFs2PBdOjpiKSlxOF8zGc08H35+fmRkZJCRkcHExAStra0XdKSSkpLw9/dX3rAZxNlmVSoVycnJJCcns2nTJqqqqigtLaWlpYWamhpqamoIDg5mwYIFFBYWunVGiLvg6v7AmxHaKoM36SrS9ty4k+7u7iYqKsrVZngVjY2NvPrqq4yMjHDvvfcyf/58V5ukCDabjXfeeQer1cq6deuIiIiYtc9Wot1u2QIPPugYCH/8sWMtoSvFarXS0tJCT08P4IiSxMTEEBUVNWlNMHfB0/oDSZKYmJhgbGxs0jZdoQ2VSoWfnx/+/v74+/sTEBCAr6/vRSMRbW2wZ8+nW2Pj5OM6naPwhDMytWSJwymrq4OsrPO/b23t7DhQ03GuI3X2V3R4eLjsSHlCsYmLtdmenh6Ki4spLy9nfHwccDjpIhp1cTytP/AkhLbK4O66ijlPF8FTnCd3zw/1VPbs2cPLL79MTk4O//Zv/0ZsbKyrTVKEI0eO0NTURHp6Otdcc82sfe5Mt1uLBdLSoLUVfvEL+Na3rvw9x8bGqKurw2QyAY5IXXx8vFs6TU68pT+wWCyyI+WsXGg+d3ITjkH02c5UQEDARas1nTkz2ZlqaZl83NcXVq+GvDz49a/htdcgJ+fT49XVjghncbEjHdDVnM+RUqlUREZGkpycTEJCgtuWP7/UNmuxWKiurqa4uJgzZ87I+0U06vx4S3/gjghtlcHddXWrOU/PPfccv/zlL+no6CAvL4/NmzezcuXKac99++23ef755ykrK8NkMpGXl8ePfvSjSWJv2bKFBx98cMq14+Pj+LpikRCBx7F69Wq2bt2KxWLhjTfe4Ktf/arHpcNcCmlpaTQ1NXHmzBkKCgrc2jG4EO++63CcoqPh61+/8vcbGhqivr4em82Gj48PaWlpYtL6LKLT6QgODp5UBdJZPn10dJSRkRHGxsawWq0YjUaMRqN8no+PD4GBgQQEBBAYGIivr++ktMrkZHjgAccmSY60vrOdqc5Ox2LKzgWVc3Lcw0k6H76+vnJq3/j4OK2trTQ3N9PT00N3dzfd3d0UFxcTFxdHUlIScXFxHrkgr06nk9eO6unpoaSkhLKyMoaGhtizZw979+6Vo1EZGRkiGiUQCFyKopGnN998k3vvvZfnnnuO5cuX87vf/Y6XXnqJkydPkpSUNOX8b3zjG8TFxbFmzRpCQkJ45ZVX+NWvfsXRo0cpLCwEHM7To48+yqlTpyZdGxMTc8l2eUrkqa+vT17UVTCztLW18fbbb9PX10dSUhL333//lDkXno4kSWzdupWhoSHy8/PJOfsRu4LMdLu9+2743/91LH77s59d2XuNjIxw6tQpbDYbQUFBpKene4xTeTX1B85FiJ2RKadDde7XlU6nk6NSgYGBF6yAKElw8qTDcfrf/3VU9js3wlRSAkVF8Mc/whe/6JhD5Y6Mjo7S3NzMmTNnGBwclPc715tKTk4mKirK5U7GlbRZq9VKdXU1x48fnxSNCgkJYeHChRQWFnrlQ69L5WrqD2Yboa0yuLuubpO2t3jxYhYsWMDzzz8v78vJyeFzn/scP7vEUVBeXh533303//f//l/A4Tx94xvfmPSFcbl4ivNUWVnJvHnzXG2GV1JZWUlsbCy///3vMZlMzJ8/n9tuu81tigPMFI2NjRw9ehRfX19uuummWXkqPZPtVpIgJsZRae/gQUcBgM+KxWLhxIkTWCwWgoODyczMdPng8nK42vuDs4tPGI1GRkZGphR+UavVcjn5wMBA/P39p/2fdjpJ53OeAMLCYMMGuOkmR5XHsDAl7+6zMzg4KDtSZ1eK9PX1JSkpieTkZMLCwlzSt81Um+3t7aW4uJiysjJ5bpRGoyEvL49rrrnmqlw36mrvD5REaKsM7q7r5fgGio0czGYzxcXFbNiwYdL+DRs2cOjQoUt6D7vdjtFoJOycb62RkRE51/umm26itLR0xux2J9rb211tgtfS3t5OREQEd911F2q1moqKCnbv3u1qs2YcZ4WuiYkJamtrZ+UzZ7Ld9vQ4HCeVyrFA6pXQ1NSExWLBYDB4ZOrP1d4faDQagoODiY+PJzs7mwULFpCbm0tiYiKhoaHodDrsdjtDQ0O0tLRw8uRJSktLqauro6ura9rIVXW1w2FybtXVjv3+/o6y6G+84ZgDFRUF117rmCdVVzf7934hQkJCmD9/PjfddBPXXXcdGRkZ+Pj4yP/zO3bs4MMPP6S6upqxsbFZtW2m2mxERAQbN27k8ccf53Of+xxxcXHYbDYqKip4+eWXefHFFykpKZm2GIm3crX3B0oitFUGb9JVscfQvb292Gw2oqOjJ+2Pjo6ms7Pzkt7jf/7nfxgdHeWuu+6S92VnZ7NlyxbmzZvH8PAwTz/9NMuXL6e8vJzM85RHci7y6GR4ePgz3NHs421pZO6EU9v09HRuueUW3n33XT766COCg4NZuHChi62bOTQaDfPmzePIkSOcPHmS1NRU/Pz8FP/MmcLZVURGOhZI/awYjUYGBgZQqVSkpaV55P+WJ9qsJGq1Wk7ZA0eq3/j4OEajkeHhYYxGI1arlYGBAXntKZ1OR1BQEGZzCBDOPfdM/97Hjjmcpw8+gPfeg6oq2LfPsf3nf8KcOXDLLXDzzY6Fet1hmpGziERkZCSFhYV0dXVx5swZ2traGB4epry8nIqKCmJiYkhJSSE+Pl7xSPRMt1mdTkdBQQEFBQW0tbXx8ccfc+LECTo6OnjvvffYvn07BQUFXHPNNW6dHjQTiP5AOYS2yuBNuiqWttfe3k58fDyHDh1i6dKl8v6f/vSn/OlPf6KmpuaC17/++ut8+ctf5u9//zvr1q0773l2u50FCxawatUqnnnmmWnP+dGPfsSTTz45Zf9bb72Fv78/a9eu5dixY4yMjBAaGkpeXh4HDhwAHM6a3W6Xn9qvXr1ansgaFBTEggUL2PvJio2ZmZlotVqqP3l8uWLFCk6ePEl/fz/+/v4sWbKEXbt2AY7J/AaDgRMnTgCwdOlS6uvr6enpwdfXl1WrVrF9+3YAkpOTCQkJoby8HIBFixbR3NxMZ2cnOp2OtWvXsn37diRJIiEhgaioKEpKSgAoKiqis7OTtrY21Go169evZ9euXVitVmJjY0lISODjjz8GoKCggP7+fpqbmwHYuHEje/fuxWQyERUVRVpaGkeOHAFg3rx5jIyM0PhJbeB169Zx6NAhxsbGCA8PJzs7m4MHDwKQm5uL2Wymvr4egDVr1nD8+HGMRqP81HT//v0AzJkzB0Ce07Zq1SoqKioYHBwkMDCQhQsXsmfPHgAyMjLQ6/WcPHkSgOXLl1NTU0NfXx8Gg4Fly5axc+dOAFJTUwkICKCyshKAJUuW0NDQQHd3Nz4+PqhUKn7/+9+jUqm48847Wbx4MWVlZQBcc801tLa20tHRgVar5brrrmPHjh3Y7Xbi4+OJiYmhuLgYgAULFtDd3U1raysqlYoNGzawe/duLBYLMTExJCUlcezYMQDy8/MZHByU8/k3bNjA/v37mZiYIDIykoyMDA4fPgzA3LlzGRsbo6GhAYDrrruOI0eOMDo6SlhYGLm5uXKbzcnJwWq1UvfJI/LVq1ezZcsW+vr6SExM5K677mLfvn0AZGVloVar5f/HFStWUFVVxcDAAAEBASxatEiOyKWnp+Pr60tVVRUAy5Yto7a2lt7eXgwGA8uXL2fHjh0ApKSkEBQUREVFBeBI4W1qaqKrqwu9Xs+aNWvY9sms/cTERCIiIuQI8sKFC2lvb6e9vZ26ulAeeWQRUVHjvPrqfuLi4oiLi+P48eOAY/Hj3t5eWj4pq7Zx40b27NmD2WwmOjqalJQUjh49Kkew9Xq9nNq0fv16Dh48yNjYGBEREWRlZclR8by8PCYmJjh9+jSA6CM8sI+QJInExET5f8FqtZKQkEBvby/j4+P09oYSGZlGa2srWq1WXpC2ra2G+PixSX1EX18wvb1LeOWVPiorQ7HZPo1aBgWZuf56iWuu6SQtrY7gYJVb9RHDw8PYbDa0Wq38N4+IiECj0WC32wkPD+fGG2+ktLRUTlspKCjwmD7CYrEQFhbGa6+9xvDwMIGBgQQGBiJJEnPmzOGmm26iv7//on0EwPz58xkeHqapqUn0EVdBHwEzO4649tpr5TablJREWFiY14wjrr32WkpKSjyyj9BoNKxbt46dO3dis9nOO46oqanhjjvucO2cJ7PZjMFg4K9//Su33XabvP/RRx+lrKxMFn063nzzTR588EH++te/cuONN170s77yla/Q2trKhx9+OO3x6SJPiYmJbj/naefOnRd0HAWfnXO1lSSJ999/n+LiYtRqNV/4whfOG8n0RPr6+ti5cyeSJLFq1Sri4uIU+6yZbLcnTsC8eY75Jn19n+09bDYbpaWl2O125s6d6xHr40yH6A+uDLvdzujoKMPDwwwPDzMyMiKn8TU0NJCRkUFgYCAhISEEBwfLD1XOZWgItm6Ff/zDEZn6JKgFgF7vmCd1xx2OyJS7zZMyGo00NTXR1NQ0aX5UYGAgqamppKSkzOj/x2y2WUmSqK+v5+OPP6aurk7+24aGhrJo0SIKCwu9qiKv6A+UQ2irDO6uq1vMedLr9RQVFckepJMdO3aw7AKzvl9//XUeeOAB/vKXv1yS4yRJEmVlZRdcq8fHx4egoKBJmydgs9lcbYLXcq62KpWKG2+8kXnz5mG323nzzTflp4/eQHh4uPw07vjx49OuqzNTzGS7TU52vPb3Tx6kXg7OwgI+Pj6KpywqiegPrgxnMYn4+HhycnIoLCwkIyODwcFBvvWtb1FfX8/Q0BBnzpyhoqKCiooKmpqaGBwcnKR9cLCjAuRrrznm4+3d60jly8wEsxnef9+xoHN0NFx/Pfz+9465e+5AYGAg8+bN46abbmLNmjWkpKSg1WoxGo1UVFTwj3/8g71793LmzJkZaW+z2WZVKhWZmZn867/+K1//+tdZvnw5fn5+DAwMsG3bNn7961/zwQcf0PdZn8K4GaI/UA6hrTJ4k66KJjw//vjj3HvvvSxcuJClS5fy4osv0tzczEMPPQTAE088QVtbG6+++irgcJzuu+8+nn76aZYsWSLPjfLz85PXBHnyySdZsmQJmZmZDA8P88wzz1BWVsZvf/tbJW/FJSgZHbjamU5btVrN5z73OcxmM6dOneIvf/kL99xzz7Rl9T2RuXPn0tbWhtFopLS0lMWLFyvyOTPZbgMDIS4O2tsdZaaXL7/895iYmADAYDB4dEUu0R/MLFqtlrCwMCIiImhtbSUxMVHOSBgZGcFkMslrKanVaoKDgwkJCSEkJEQub6/VOhbdXb0afvlLRxt96y3429+gsvLTNaUeeshRcOKOO+C228DV63KrVCqio6OJjo7GYrHQ0tJCY2MjPT09dHZ20tnZiY+PDykpKaSnp3/mB46uarOhoaGsX7+ea6+9loqKCo4cOUJPTw/Hjh3j2LFjZGZmsmTJEtLS0jy2TxD9gXIIbZXBm3RVtFQ5OBbJ/cUvfkFHRwdz587lN7/5DatWrQLggQceoKmpSc71vfbaa6dN57v//vvZsmULAI899hhvv/02nZ2dBAcHU1hYyI9+9KNJ86ouhqeUKnf3mviezIW0tVqt/OUvf6GhoQG9Xs8Xv/hFkp0hEA+nu7tbng+yePFiUlNTZ/wzZrrd3nEHvP02PPkkfLJiwWXR2tpKe3s7UVFRpKSkzJhds43oD5ShpKSEoqIiiouLWfBJ3XKbzcbw8DBDQ0MMDQ1NSvtWqVT4+/sTGhpKSEjIlIV6ndTWfupIfTJ15JPrHQ8B/uVfHG07IUHxW7xknGl9jY2NkyrzRUZGkp6eTkJCwmUVmXCXNitJEo2NjRw5cmRSSl9kZCSLFy9m/vz56PV6F1t5ebiLtt6I0FYZ3F1Xt1nnyV3xFOdp27ZtbNy40dVmeCUX09ZisfD666/LDtS//uu/evTA+2yqqqqorKxEq9Wybt06QkJCZvT9Z7rdvvwyfPnLcM01jipol0tbWxttbW0e7zyJ/kAZpnOezsZZxW9gYIDBwcFJc4XAsZ5SSEgIoaGhBAQETOtINTZ+6kh9UpsAcDhSK1fCF77gcKYiImb89j4Tdrudjo4OGhoaaG9vl50NvV5PSkoKaWlpl9RvuGOb7e/v5+jRo5SWlsrpy35+fixYsIBFixbJWS7ujjtq6y0IbZXB3XV1izlPAoEno9Pp+MIXvkB6ejpms5k///nPcoUaTyc3N5fY2FisViuHDh1y+7VRbrgB1Gr4+GNHWtTl4iyP6k351oLZQ6VSYTAYiI+PJy8vj4KCApKTkwkODkatVjMxMUFnZyfV1dWUlZXR1NTE0NDQpDWlUlPhm9+EI0eguRk2b4YVKxyLQO/fD//xH45UvhtvdMylMhpdd7/gSGGOj49n5cqV3HzzzcybNw9/f3/MZjO1tbVs3bqVHTt20NDQ4Pb9x7mEhYWxadMm/vM//5Prr7+esLAwxsfHOXjwIE8//bSc2SIQCATnQ0Se3Djy1N3dTVRUlKvN8EouVVur1cobb7xBfX09Go2GO+64g9zc3FmwUFkmJibYvn07Y2NjxMXFsWLFihlbNFaJdnvbbfDuu/Dww3C50xsHBgaoq6vDYDAwd+7cGbVrNhH9gTIMDg7y3nvvccstt1x2FNZmszE0NMTAwABDQ0NYrVb5mE6nIyQkhLCwMAIDA6f9/2ppgTffhL/8Bc5e693Pz7GG1Be+AJs2gY/PZ727mcNut9PV1cXp06dpb2/HbrcDjvtMTk4mMzNzStTGE9qs3W6nrq6OI0eOyCWzwVEGfNmyZaSnp7vlvChP0NZTEdoqg7vrKtL2LoKnOE8nT570ioG6O3I52lqtVt5++21OnjwpV+XzhoV0+/r62L17NzabjczMTBYsWDAjgwQl2u2uXbBuHfj7w+nTjkpml4rZbKasrAyVSkVhYaHiC4MqhegPlGMmtLXb7fJizAMDA5MiMlqtltDQ0As6UqdOweuvO7ZPlgMCPq3ud//9jgV53WEcPz4+TlNTEw0NDRjPCpNFR0eTkZFBfHw8arXa49pse3s7hw8fpqqqSnYOo6KiWLZsGXPnznWrvsPTtPUkhLbK4O66irQ9L8G5qJ9g5rkcbbVaLf/yL//CwoUL5fWg9u/fj6c/dwgPD2fp0qWoVCrq6urkBRyvFCXa7dq1sGgRjI7Cj398edfq9Xr8/PyQJInBwcEZt222EP2BMnR2dvLLX/7yilO1nBX5UlJSKCgoYM6cOURFRaHT6bBarfT09HDq1CnKyspobGyckto3Zw786EdQUwPHjzvKn8fHO9aVevFFR5GJrCxH+3f1Kgp+fn7k5ORwww03cO2115KQkIBKpaKrq4uDBw/y/vvvU1VV5XGpznFxcdxxxx18/etfZ+nSpej1erq7u3n33XfZvHkzH330EePj4642ExD9gZIIbZXBm3QVzpNAcAmo1WpuvPFGuVLk7t272bp1q8c7UAkJCeTn5wNQVlYmrwrvbqhU8POfO37+3e8cT+kvh7BPVivt7++fYcsEnk57eztbtmyhvb19xt5TpVJNcqSys7PP60g1NzdPWrBXpYKiIvjVrxzzo3btckSd/P2hvt5RcTI1FdasgVdece38KJVKRUxMDCtWrOCmm24iNzcXX19fxsbGqKyspKKigsOHD9PT0+NRfWVISAgbN27k8ccfZ/369QQFBTEyMsKuXbv4zW9+w4cffujRD2IEAsGVIdL23DhtT+CeHD16lA8//BCAefPmceutt7pVOsflIkkSxcXF1NfXo1arWbFihduux3DTTfDPfzoqlO3d6ygkcSlMTExQWVmJJEnMmzfPoxfLFcwsF6u2N5NIkoTRaKS/v39Kap+vry/h4eGEh4fj6+s75dqREUfJ/ldfhd27HcUmAAwGuP12+Ld/c6w3NUNTFz8zNpuNlpYW6urqJi1IGxoaSkZGBsnJyR7XX9psNk6cOMGhQ4fo6uoCHA/U5s2bx/Lly916HodAILg0RNqel7Bnzx5Xm+C1XIm2ixcv5vbbb0etVlNZWcmrr746aU0UT0OlUrFgwQKSk5Ox2+0cOHDgilKYlGy3zz7reAL/0Ufw/POXfp2znDRAR0eHMsYpjOgPPB+VSkVQUBApKSnk5+eTlZVFeHi4XLWvra2NiooKTp48SVdX1yTnKiAA7rsPdu50pO399387Uv3GxhwV+taudaT1/exn4MomrtFoSElJYf369QQEBJCWloZGo2FgYICPP/6Y9957j7KyMo/qMzUaDfn5+Tz00EPce++9pKWlYbfbKS8v57nnnuP111+ntbV1Vm0S/YFyCG2VwZt0Fc6TG+Ncg0Iw81yptvPnz+eLX/wivr6+NDc38/vf/56enp4Zsm72UavVLFq0iISEBNmB6u7u/kzvpWS7TUmB/+//c/z87W9DdfWlXxsbGws4CmWcu1aPJ+BO/YEkSdjtdmw2G1arFYvFMmWzWq3YbDbsdjuSJHlU2tZsoFarCQkJIT09ncLCQtLS0ggODkalUjEyMsKZM2coKyujtraW/v5+uYABQFISPPGEo/0fPQr//u8QGOgopvK970FiItx6K/zjH3BWAcBZR6fTsWjRIm655RYKCgoICAjAbDZTU1PD+++/z+HDhydFp9wdlUpFeno69913H1/5ylfIzc1FpVJx6tQpXnrpJbZs2UJ9ff2stHV36g+8DaGtMniTriJtz43T9srKyigoKHC1GV7JTGnb09PDX/7yFwYGBvD19eWuu+4iLS3tyg10ETabjQMHDtDR0YFWq2XlypVEX05pO5Rvt3Y7rF/vSF3KzXUsnOvvf2nXnj59mr6+PgIDA8nOznbLEsTnY7b7A0mSsFqtk5ygs52hy0WlUqFWq+XXs3/WaDTy62z/TRoaGvj3f/93fve737nF/67FYqGvr2+Kk6/T6QgPDyciIgKDwTDlutFRxyK8L70EBw58uj82Fh580JHWl54+G3fwKee2Wefiu6dOnZr0cCYyMpI5c+YQFxc3Y0smzBa9vb0cPHiQ8vJy2cGNjY1lxYoV5OTkKHY/YnygHEJbZXB3XUWp8ovgKc7T4ODgZa87Irg0ZlLb0dFR3nzzTZqbm1Gr1dxwww0eXcrcarXy0Ucf0dXVhUajYfny5Zc1B2o22m1XFxQUQGcnfPGL8Kc/XVoJZ5PJRGVlJXa7neTk5Mt2DF3JbOhqtVoxm81y5OhiXw9OB2g6nNGmy/2KcTpWGo0GjUaDVquVnSulHCt37WvHx8fp7e2lr69v0lNbf39/IiIiCA8Pn3b+UE0NvPwy/PGPcHZAfMMGx4K8N90EszHt6EK69vf3U1tbS3Nzs+x0BAQEkJmZSVpaGjqdTnkDZ5ChoSEOHz5McXGxnG4ZHh7O8uXLyc/Plxfrninctc16A0JbZXB3XYXzdBE8xXnatm0bGzdudLUZXslMa2u1WnnvvfeoqKgAYMmSJWzYsMHjnqI6sdlsHDp0iLa2NtRqNYsXLyY5OfmSrp2tdrtvn2Oeh90OP/2pI13pUujq6uLMmTOo1Wry8vI8pniEUrpKkoTZbGZ8fHzSIq/AJAfm7AiRSqWSt0t5/7M3u90uR6+cPzsjWmenpp2LSqWa4lA57boSzGYz//u//8tdd92FXq+/ovdSCkmSGBoaore3l8HBQVkntVpNaGgoERERBAUFTfl7mM2O1L2XXoJt2z4tMpGQ4Ej1+/KXISZGObsvpc2Oj49TV1dHfX297CDqdDrS0tLIysrC/1LDym7C2NgYR48e5dixY3JZ8+DgYFauXElBQcGMFcsQ4wPlENoqg7vrKgpGCASzjFar5bbbbmPNmjUAHDlyhFdffdUj59aAY9C8bNkyuYjEkSNHOH36tKvNmsTq1fDMM46fv/99x+Kil0JUVBRBQUHY7XZOnz6NzWZTzkg3x2q1MjQ0hNFoxGq1olKp0Ov1BAQEEBoaSmhoKEFBQRgMBnx9fdHpdJcdBTo7NU+r1aLX6/H19cXPzw9/f38CAwMJCQkhLCyMsLAwQkJCCAwMxGAw4OPjg1arRaVSyWmEJpOJ0dFRhoeH5ap1RqOR8fFxzGbzBR2w6Thx4gT33nsvJ06c+CwSzgoqlYqQkBAyMjLIz88nKSkJg8GA3W6nr6+PU6dOUVFRQUdHx6QiE3o93HEHfPihYz7Ud74DERHQ2gr/9V+OuVF33+14EOGqx6h+fn7Mnz+fW265hYULFxIUFITFYuHUqVPyvKiBgQHXGPcZMBgMrFmzhm984xts2LCBgIAAhoaGeP/993nmmWc4duzYlIcUAoHAsxCRJzeOPHV0dMiT3AUzi5Lanjx5knfffRez2UxQUBB33XUXCQkJinyW0tjtdoqLi2XHae7cueTl5V1w4Dzb7fY//xN+/WvHQPHDDx3RqIthNpupqqrCYrEQHh5OWlqa289/mmldTSaTvL6QWq3G19cXX19ft4yWnl2gwlmkwjkX61ycESqdTodWq0Wn013wnmazVPlMIkkSY2Nj9PT00N/fLw/IndGoqKgoAgICprRrk8kxN+q55+DQoU/35+bCww871pQKCJgZGz9Lm5UkSZ4X5SwLDo55RDk5OURGRrr9/+rZWCwWSkpKOHDgAMZPFuUKDAxk+fLlFBUVfeb0RDE+UA6hrTK4u64ibe8ieIrzdOrUKebMmeNqM7wSpbXt6enhjTfeoK+vD41Gww033EBRUZFin6ckkiRRUVFB9Sel7VJTU1m4cOF5c/hnu93a7XDnnY41cAwG2L4dli+/+HXDw8OcOnUKSZJITEx0604dZlZXs9mM0WhEkiQ50uSOTtPFsNvtshN1IYfKGfXS6XRy9MyJpzpPZ2Oz2RgYGKC7u5uRkRF5v8FgIDIy8rxzo8rLHSX/X3vNUXACIDjYkc73ta85qlteCVfaZgcGBqipqaG5uVmeOxceHk5OTg7x8fEe5URZrVZKS0s5cOAAQ0NDgGOO17Jly1i4cOFlp4yK8YFyCG2Vwd11FWl7XkJTU5OrTfBalNY2MjKSr371q2RnZ2Oz2fjHP/7Be++955HpGiqVivz8fBYuXIharaaxsZH9+/eft+zobLdbtRr+/GfHZPixMdi0CT7++OLXBQUFkZSUBEBLSwu9vb0KW3plzJSudrtdjjj5+voSGBjokY4TOKIser0ePz8/AgMD5VTDwMBA/Pz85JQ/m80mR9oGBgYYGBhgZGQEk8l02Wl+7ohGoyEiIoLc3Fzy8vKIiopCrVYzNjbGmTNnKC8vp6mpaUoacX4+vPACtLU5UmCzsmBoCP7nfxyV+e64w7Gm2md9xHqlbTY0NJSlS5dyww03kJGRgUajoa+vjwMHDrB161YaGxs9Ju1Wq9VyzTXX8PWvf52bb76ZkJAQRkZG2L59O08//TQHDx68rFLOYnygHEJbZfAmXT3zG1Mg8AB8fHy4++67ue6661CpVJSUlPCHP/yBwcFBV5v2mcjIyGDFihVotVq6urrYtWuX28zp8vWFd95xzIMyGh2O1JEjF78uKiqKmE9mzDc2NspPhL0Zp8Og0Wjw9/f3qKf3l4JGo8HHxwd/f39CQkImzdvS6XSyMzUxMYHRaGR4eBhwFC6wWCwevx6Vv78/KSkpFBQUkJycjJ+fHzabje7ubqqqqjh58iT9/f2T7jM4GB55xLFu1D//6fj/sdsd0dxVq2DhQnj1VUfKnysIDAxk4cKF3HTTTeTm5qLT6RgaGuLo0aN88MEHnDp1atJcL3dGo9FQVFTEI488wq233kpoaCijo6Ps2LGDp59+msOHD3vMvQgEVysibc+N0/bsdrvHPhF2d2Zb29OnT/PWW28xNjaGr68vt956Kzk5ObP2+TPJwMAA+/fvZ3x8HB8fH5YtWzap5Lcr263R6Ig8HTzoWPvpvfcuPgdKkiQaGhro6+tDrVaTlZXllv3CTOk6PDyM2WzG39/fYyoNziTOVD+LxSKXZTebzej1erlMul6vR6fTodfrPd65lCSJkZERuru7GRgYkCNtPj4+REVFERkZOW1KX1WVIxr1pz/BJ0XjiI52pPM9/DCEhV38s5XqC8xmM6dPn+bUqVNMTEwAoNfrmTNnDllZWR5V5txut1NZWcm+ffvo7+8HHM7iypUrWbBgwXmr84nxgXIIbZXB3XUVc54ugqc4Tx999BErV650tRleiSu0HRwc5G9/+xutra0ALFq0iA0bNsxY6drZZGxsjAMHDtDf349arSY/P5+srCxUKpXL2+3oKHzuc7BzJ/j4wF//CjfffOFr7HY79fX1DA4Ouq0DNVO6DgwMYLPZCA4O9qhBplLYbDaOHTvG3LlzsVgsk9L4VCqV7ESdO1fKE7FYLHR3d9Pd3S1HN5wpf9HR0fj6+k65pq8Pfv97ePZZR3ofOB5MfOUr8Nhj8Enm67Qo3RfYbDaampqoqamRizE4najMzEy3LT0/HXa7nfLycvbu3StHwIODg1m9evW060S5up/1ZoS2yuDuuoo5T17C2NiYq03wWlyhbUhICA8++CDLP6lmcOzYMV566SW3n2szHQaDgbVr15KSkoLdbqe0tJSjR49itVpd3m79/R1r29x6qyPN6Lbb4MUXL3yNWq0mIyODkJAQ7HY7tbW1bpdeOVO6OiMpV+Fzs2k5ffo0//7v/05HRwehoaEEBwfj5+eHRqOR18ByzpUaGhpifHzcY+dJ6XQ64uPjyc/PJzU1FYPBgM1mo6uri8rKSmpraxkeHp7UNsLD4bvfhcZGx9zC/HzHA4rNmx3zou67Dyorp/88pfsCjUZDeno6mzZtYunSpQQFBWE2m6msrOT999/nxIkTlzWPyJWo1WoKCwt55JFHuPHGGwkMDGRoaIj33nuP3/72t1RUVExqd67uZ70Zoa0yeJOuwnlyYyIiIlxtgtfiKm01Gg3r16/nnnvuwd/fn87OTl588UXKyspcYs+VoNVqWbx4MYWFhajVapqamti9ezcBM1Xn+Arw9XVEnO6/H2w2x4KgTzzhmMdxPs51oOrq6tzKsZ2pNut8gu2JxUuUYGRkhMrKSkZGRuRIk3O+VEhICP7+/nKEzmKxMDo66vGOlFqtJjIykry8PObMmUNISAiSJDE4OEhNTQ1VVVX09vZOcqJ0OvjXf4XSUseCu9ddB1arI61v/ny44QbYu3dycYnZ6mfVajXJyclcf/31LF26lODgYMxmMydOnJCdKJOrJmxdJmcXlti4cSP+/v709/fz9ttv8/zzz3Py5EkkSRLjAwUR2iqDN+kq0vbcLDXnbIxGI4GBga42wytxB22NRiNvv/02jY2NAOTn53PDDTfg4+PjUrs+C52dnRw6dAiz2YwkSaxcudIt1raSJHjqKfjRjxy/3303vPIKXGiqj91up7Gxkb6+PgASExOJiYlx+dyXmWqzExMTjIyMoNFoCAkJcfl9uZpLLVVus9kwm83yPCknKpUKrVaLj4+PPG/KExkfH6erq4ve3t5J86JiYmKIjIyc9r6Ki+EXv3CsG+X0IRcvdizAe8MNMDLimn5WkiRaWlqoqqqSU+B0Oh1ZWVlkZWV5VB9rNps5evQohw4dYvyTyWexsbEsXbqU+fPnu9g678QdxgfeiLvrKtL2vIRDZ69gKJhR3EHbwMBA7r33XtauXYtKpaK8vJwXXniB5uZmV5t22cTExLBx40bCw8NpaGjgwIEDlJaWuryMsEoFP/whbNkCWi28+SasWAFnzpz/GrVaTVpamlyFr6WlhZaWFpenuc1Um3UWQbDZbKKq12Wg0Wjw8/MjODiY0NBQOSIlSRIWi0VO7TMajfJDBE/Cz89PrtKXkJCATqfDZDLJpc47OjqmRCuLihz/U7W1jiISvr5w9CjcdJOjQt8vf1l/wWivUqhUKpKSkrj++utZtmwZwcHBWCwWqqqqeP/996msrPSYdD69Xs/KlSt59NFHufbaa/Hx8aGjo4Nf/OIX/OlPf6Kjo8PVJnod7jA+8Ea8SVfhPAkELkStVrNq1SoeeOABgoODGRgY4JVXXmHnzp0udzwuF39/f9auXStX3jt16hS7d+92i3Lm998PO3ZARASUlDgGdrt3n/985+ArMTERcETWamtrvSLVTa1Wy4UBRkdHPW6Q7w6c60gZDAZ5jpTJZGJ4eJjBwUHGxsY87v9Yq9USFxfH/PnzSU5OxsfHB4vFQktLCxUVFbS2tk5xutPT4be/haYm+Pa3HfMOS0rgxz8uJD/f4WC5Qoaznajly5cTEhIyyYmqqanxmP9pX19frr32Wr7+9a+zZMkS1Go1p0+f5ne/+x1/+9vf5Ep9AoFAeUTanhun7bW2trpF6pM34o7aTkxMsHXrVnn+U0xMDLfffjtRUVGuNewycVYTPHbsmFwCetGiRW6hd3Ozo4BESYljcd1f/tJRMexCmWv9/f00NDRgt9vx9fUlMzPTJSW+Z7LNOue32Gw2fH193WKemqvo7e3llVde4cEHH7yinHxJkrBarZhMJsxm86S5UDqdDl9fX48sfW632+nv76ejo0NOG3POmYqNjZ22ol1vr6OgxDPP2DEaHc9os7Phe9+DL3zBEQV2BZIk0draSmVlpby+l5+fH3l5eaSmpnpUNcUTJ05QW1tLZWUlkiShVqtZuHAhq1evxt/f39XmeTTuOD7wBtxdV1Gq/CJ4ivNUX19PRkaGq83wStxZ2+rqav7xj38wNjaGRqPhuuuuY+nSpR4z6HJqOzIywuHDh+W5Q2lpaRQWFrq8PPb4ODz0kGPRT4DPf95Rje9CqdhjY2PU1dVhMpnkCl8hISGzYq+TmW6zZrMZo9GIJEkEBARMW6b6amGmtXVW6TOZTJMW3nVG/Xx8fDxqoA6fOtwdHR2MjIwAF3eiiosbeP/9NDZvBmfxyqwsePJJuOsuxwMMV2C32zlz5gwnTpyQI+OBgYHMnTuXpKQkj+hrnW22s7OTnTt3Ul9fDzjS/JYtW8bSpUs9am6XO+HO4wNPxt11FXOevITTp0+72gSvxZ21zcnJ4eGHHyYrKwubzcb27dv54x//6Hals8+HU9uAgADWrl1LTk4OKpWKhoYGtm/f7vIKdn5+jjlQzzzjeAL+xhtQWAjHj5//GoPBQG5uLkFBQdhsNmpra2ltbZ3VlLeZbrN6vV6OoI2OjnrMHJCZpre3l2effXZG26VKpcLHx4egoCBCQkIwGAyo1WrsdjtjY2MMDg7KixV7yvNLlUpFaGgoOTk5ZGdnExQUhN1up6uri4qKCs6cOTOlDfX21vHDHzrmGP73fzvKntfWOqJPBQXw979Prs43W6jValJTU7nhhhtYsGABvr6+GI1GDh8+zLZt22hvb3f7v4uzP4iJieGee+7h/vvvJy4uDrPZzN69e3nmmWc4fvy4R1aDdDXuPD7wZLxJV+E8CQRuSEBAAF/4whe4+eab0ev1NDU18fzzz3P8+HG3/1I/G41GQ35+PmvWrMHf3x+j0cju3bupqqpy6Ze6SgWPPOIorZyUBKdPw7Jl8D//c/5y5s5qXc45Xe3t7dTU1HhMCeTp8PPzw9fXF0mS5EIHVxvNzc08/fTTihVq0Wg0GAwGQkNDCQwMRK/Xy5Ep59yoiYkJj/m/VqlUBAUFkZ2dTXZ2NoGBgRd1ooKCHEsFNDQ4ql8GBTnWhvrc52DJEsd8RFfcvkajISsrixtvvJF58+ah0+kYHBxk//797N69m56entk36jOSmprKV77yFe68807CwsIYHR3l/fff54UXXvCqQatA4A6ItD03TtuzWCwuT3HyVjxJ2/7+ft599115cJeSksItt9xCWFiYiy2bnvNpazabKS4u5swnpe7Cw8NZvHixy/8HBwbgK1+Bt95y/L5xI/zxj/CJjzQt/f39NDY2YrPZ0Gq1pKWlKZ7Gp1SbPdtxUqlU8gD/auFSS5XPJDabjYmJCUwmk/wQwZnS5+vr61Hlzp3tp62tDaPRCDjuJTo6moiIiGnnB/b3O+YbPvMMONfNXL0afvITRzVMV2Eymaiurqaurk4u9JGYmEh+fr7bzQu8UH9gs9k4fvw4e/fuleepZWZmsmHDBiIjI2fTTI/Ek8YHnoS76yrS9ryEY8eOudoEr8WTtA0LC+OBBx7g+uuvR6fTyVGow4cPu2VKxvm01ev1LF26lCVLlqDT6ejr62Pbtm3U1NS49D5CQx0L6r7wgqPU8rZtkJ8P779//mvCwsLIy8vD398fq9VKbW0tzc3NilZWU6rNOh0mHx8feSA8MTGhyGcJHGg0GnkhXn9/fzQajZzSNzAwwMjIiMdU6Ts7EjVnzhwCAgKw2+10dHSwdetWOjo6ptxLWBj87GeOSNSjj4JeD/v2wcqVjmjUqVOuuRcfHx8KCgq46aabSEtLQ6VS0dLSwgcffEBZWZlbRWYv1B9oNBoWL148qTJfXV0dzz//PB988AFjTo9VMC2eND7wJLxJV+E8uTHOSbmCmcfTtFWr1SxZsoSHH36Y1NRULBYL27Zt4w9/+IPbpZZcTNuUlBSuv/56YmNjsdlslJWVsWvXLrn6lStQqeDf/90x72nuXOjqgptvhgcf/HSi+7n4+vqSk5MjrwfV2dnJyZMnFSvNrmSbValUctEISZIYGRkRZcxnAbVajZ+fHyEhIQQGBsrrRk1MTDA4OOhxTlRwcDA5OTlkZmZiMBiYmJigpaWFyspKenp6prSn6GhHVb76ekf0V612zIPKy4P/83+gu9s19+Ln58eiRYvYuHEjMTEx2O12ampq+Oc//0ltba1b/E0upT/w8/Pj+uuv5//8n/9DdnY2drudY8eO8cwzz3Do0CGPKdM+23ja+MBT8CZdhfPkxoSGhrraBK/FU7UNDQ3lvvvu4+abb8bHx4fW1lZeeOEF9u/f7xZf6HBp2vr7+7Nq1SoWLVo0KQpVXV3t0ihUXh4cOwb/+Z8Oh2rLFocztXXr9Oer1WqSkpLIyspCr9czPj7OyZMnaWtrm/H7ULrNqlQq/P39MRgMAIyPjzM8POyW0c2ZJCAggAULFrg0LevsAhPBwcHyvCinE2U0Gj1moOssLJGXl0dsbCw+Pj6YzWYaGxs5ceIEAwMDU5yoxERHxcsTJxwPLWw2eO45yMiAn/7009S+2SYkJITVq1ezatUqgoODMZlMlJSUsHXrVtra2lz6cOFy+oPw8HA+//nPc//99xMTE8PExATbt2/nueeeo6amRjwkOQdPHR+4O96kq5jz5MZznkZHR8V6DQrhDdoODw/z/vvvU1tbCziqLt18883Ex8e71K7L1XZ0dJTjx4/T0dEBOL7oFy5c6PKO9uBBeOABx1NxgC9/2VFQ4nxdhtVq5cyZM3Jpdn9/f1JTU2Vn5EqZzTZrMpkYGRmR148JCAjw6nlQ7tgfWCwWxsfH5VQxlUqFXq+XF+T1BEZHR/Hz86O7u5uOjg55cd2goCCSkpLO+7+xdy9885tQXOz4PT7eMR/qvvtcW968oaGBEydOyGmtUVFRFBQUuGT+6Wdts3a7nfLycnbt2iVHAjIyMti0aRPh4eEzbaZH4o79gTfg7rq61Zyn5557jtTUVHx9fSkqKuKjjz664Pn79u2jqKgIX19f0tLSeOGFF6ac89Zbb5Gbm4uPjw+5ubm88847SpnvUg4cOOBqE7wWb9A2KCiIL3zhC9x+++34+fnR2dnJSy+9xD//+U+Xzlm5XG3PjkLp9Xr6+vrYsWMHZWVlLn3avnw5lJfD17/u+P2llxxRqPPNhdJqtaSnp5Oeno5Wq2V0dHRGo1Cz2WZ9fHwIDg5Gq9Vit9sZHh722jQ+u93O7t273S7CptPp5FLnzvloJpOJwcFBRkdH3c7e6Thw4ABqtZqYmBjmzZtHbGwsarWa4eFhqqqqaGpqkh2qs7n2WkcE+M9/huRkaGtzpNAuXgxHj87+fYAjypyRkcGNN95ITk4OGo2G7u5uduzYwfHjx2e96uZn7Q/UajWFhYU88sgjrFixAo1GQ319Pc899xw7d+50q3ldrsIbxgfuiDfpqqjz9Oabb/KNb3yD73//+5SWlrJy5Uo2bdp03pKwjY2N3HDDDaxcuZLS0lK+973v8fWvf523nGWwgMOHD3P33Xdz7733Ul5ezr333stdd93FUVf1qAKBC1GpVMyfP5+vfe1r5OfnI0kSH3/8Mc8++ywnTpzwmMGuSqUiLS2NTZs2kZiYKM8xcE44dxUGAzz9tONJeGoqtLQ40oruvBPa26e/Jjw8nLlz5xIaGordbqetrY2qqiq5EpmnoNVqCQ4OlhfPHR8fZ3BwcNrBridTVlbGLbfcQllZmatNmRatVktgYCDBwcHynCjn32JsbMxj/se1Wi2JiYnMmzePsLAwJEmiu7ubiooKOjo6pjiDajX8679CTY2jMl9QkGNO4pIlDkfq8GEoKZm61dUpex86nY78/HxuuOEGkpKSkCSJ+vp6/vnPf1JfX+8RTi04HpCsW7eOhx9+mMzMTGw2GwcOHPC47w6BwBUomra3ePFiFixYwPPPPy/vy8nJ4XOf+xw/+9nPppz/ne98h/fee4/q6mp530MPPUR5eTmHDx8G4O6772Z4eJgPP/xQPuf6668nNDSU119//ZLs8pS0vTNnzpCcnOxqM7wSb9W2sbGR999/X04dS09P58Ybb5zVtJKZ0LatrY2SkhK5+EJycjKFhYXyQN4VjI7Cj34Ev/mNY05GYKCjYthDD8F0WVSSJDEwMMCZM2dkhyMqKoqEhAS0Wu1lf74r26zZbGZkZAS73Y5KpcLX1xeDwYBKpXKJPTOJK0qVf1YkScJisTA2NiZHZZ2V+3Q6ndv9PS7UZo1GI83NzfL/uK+vL8nJyQQHB097fmenY62oLVsu/rm1tZCZ+Vmtvjy6u7spLi5maGgIcMzrKCoqIiIiQtHPncn+QJIkamtr2bp1KwMDA4CjsM8NN9xAVFTUjHyGJ+Gt4wNX4+66ukXannNNlw0bNkzav2HDBg4dOjTtNYcPH55y/saNGzl+/Lg8+DjfOed7T0/GU55geSLeqm1qair/8R//wZo1a9BqtZw+fZrnnnuO/fv3z1oK3ExoGx8fz/XXX09WVhYqlYozZ87wwQcfUFdX57K/nb+/4wn48eOwaBEYjfC1rzkW150uaKFSqQgLC2PevHny2ird3d2cOHGCvr6+y36y68o2q9frJ6WPOSMfIsVndnHOewoODiYwMBCNRoPNZmN4eBij0eg2RWOcXKjNBgYGkpubS1paGnq9nomJCU6dOkV9ff207SomBl55BY4cgdxcx77XXnPMi3Jur73m2D+bQd6oqCg2btzIggUL0Ov1DAwMsHPnTo4ePapo+vRM9gcqlYo5c+bw8MMPy98dTU1NvPDCC2zduvWqW7rAW8cHrsabdL38x5+XSG9vLzabjehzVpqMjo6ms7Nz2ms6OzunPd9qtdLb20tsbOx5zznfe4Jj8vPZ+cjOkshlZWWTKiyFhoaSmprKxMQEJ0+enPI+zqeSp06dmlKOOCUlhbCwMHp6emhpaZl0LDAwUA6Ll5eXT3lf58rmp0+flp9eARw6dIg777yT6OhoBgYGaGxsnHSdn58fOTk5AJSWlk4ZjOXk5ODn5zdpEruT6Oho4uPjMRqN1J2T56DT6Zg3bx4AlZWVU9J0MjMzCQwMpK2tja6urknHwsPDSU5OZnx8fFIEERwddGFhIQDV1dXy4n1OUlNTCQ0Npauri7a2tknHgoODSU9Px2KxUFlZybnk5+ej0Wioq6ubkh6VmJhIZGQk/f39NDU1AQ5tly1bhr+/P3PmzAEcT6DPJTc3F19fXxobG+Unck5iY2OJjY1leHiYemdVgU/w8fEhLy8PgIqKiimOS1ZWFgEBAbS2ttJ9Tj3eiIgIkpKSGBsbo6amZtIxtVpNQUEBACdPnpzypeZcrHXOnDlyGkZLSwt//vOf2b17N/fffz/x8fGcOHFiyr0WFBSgVqupra2dUlI0KSmJiIgIent7p6TdBgQEkJWVhd1up6ysTNbWydy5c9Hr9TQ0NDB4Tt3vuLg4YmJiGBwcpKGhYdIxX19fFixYQEpKCm+88QZDQ0OcOnWKwMBAcnJyWLp0KQaDgebmZnp7eydd64zwjIyMyAU1nGi1WubPnw9AVVXVlLkKGRkZBAUF0dHRMSVlMDQ0lIKCVHbvnuDHPz7Js8865mYUFcHdd8Nzzy0gJGT6PiIqKorh4WE6OjqoqKjA39+fmJgY/Pz8LqmPqK2txW63T+ojwOFozmYfYbFYsFqtzJkzh+HhYerq6tBqtZOKGHhSH3G2DWf3EU7cuY+wWq2UlZVhMpmQJAmVSoWfnx9LlixBpVJdsI/o7Oyk/Zzc05CQENLS0jCbzTPSRzj7gnP7iHPJycmhp6eHkpISampqOHr0KJGRkYSFhZGQkDCpj9Dp4Ikn/Lj33hxycmC6YKHjbzpOdnb2rPYRsbGxdHV1yY7gRx99RGZmJgkJCXJUcKbGEef2s+cbR8Cl9xEnTpwgMDCQlStXcvjwYRobG/noo4+oqqoiPz+f0NDQSdFNbx1H7NmzZ8r/8nTjCCfu3EdcyThipvuI+vp6UlNTL2kccS5XMo7I/eRpS1lZ2RQH7uw+4ty/6wWRFKKtrU0CpEOHDk3a/5Of/ESaM2fOtNdkZmZK//3f/z1p34EDByRA6ujokCRJknQ6nfSXv/xl0jmvvfaa5OPjc15bfvjDH0rARbc1a9ZIR48elcrLy6c9vnXrVml8fFyaO3fulGPf+ta3pNOnT0tPPfXUlGMLFiyQPvroI6mvr2/a933jjTekoaEhadWqVVOOfeUrX5Gqq6ulF198ccqx9PR0adeuXbIu5x5/4YUXpJ6eHun222+fcuyuu+6SysvLpb///e9TjkVEREhbt26VJEmSIiIiphz/+c9/LrW1tUlf/epXpxzbuHGj9PHHH0vHjh2bckyn00lbt26VTCaTlJWVNeX49773PamxsVH6/ve/P+XY4sWLpYMHD0qtra3TavjWW29JRqNRWrJkyZRjDz/8sHTq1CnpmWeemXIsOztb2rNnjyRJ0rTv+4c//EHq6+uTbrjhhinHvvjFL0qVlZXSm2++OeVYbGystG3bNkmSJCk4OHjK8V//+tdSR0eHdP/99085dtNNN0nFxcXSvn37phwzGAzS1q1bJYvFIqWkpEw5/sMf/lA6c+aM9M1vfnPKsaSkJOlLX/qS9PTTT097r++99540MjIiLViwYMqxRx99VKqrq5N+8YtfTDk2b948af/+/dLY2Ni07/unP/1JGhgYkNatWzfl2AMPPCBVVVVJf/zjH6e1d8eOHZIkSZLBYJhy/IknnpCam5ulz3/+81OO3XbbbVJpaam0bdu2KceCg4OlrVu3SjabTYqPj59y/Cc/+YnU0tIiPfLII1OOXayPCA42Sb/9rVnKy5u+j6irq5O+853vTDl2KX3E3//+d7fqI/bu3Sv19vZKUVFRU457Wh8BSG+//bbX9BEHDx6UxsfHL7uPWLFihXT48GGprq5u2ntVuo9Yu3btlGP33XffNH1EoQSSVFw8+bu+uFiSQPrkONL/+3//T+rq6pr1PmLHjh3SCy+8MO29eto44u6775a+9a1vSdnZ2VOOees4Yrq/jRhHXHkfcc8997jdOGK6PmJoaOi8/oQTxeY8mc1mDAYDf/3rX7ntttvk/Y8++ihlZWXs27dvyjWrVq2isLCQp59+Wt73zjvvcNdddzE2NoZOpyMpKYnHHnuMxx57TD7nN7/5DZs3b+bMmTPT2jJd5CkxMZF9+/a5deTJZDKRlpYmIk/MfOTJZDLh4+Nz1TwxMplMnDx5ktbWVqxWK/39/RQUFJCfny/Pv5mpyJNTWycz+cTIZDJRV1dHW1sbcXFxBAYGEhYWRkBAAOqzahgrGXmaro84fBh+9StoanL0ETk5p/jmN0f55E8FTO4jTp8+TVdXl/z/HhwczLJlywgNDaWiooJzmTdvHjabjba2NpdHnuDTPsJms3Hs2DHGPlmIR61W4+Pjw9y5cwkKCvKIPsJisaBSqSgoKGBkZMSjnyqbzWZMJhN5eXmoVCoaGxtRqVSTogWzGXly9gWX81R5YGCAwcFBurq6sFqtREZGkpeXh7+/v/wdX13txz335FBcPDnyVFLiiALn5FTz05+Os3Hj7Eae4NM+YmxsjK1bt1JfX4/NZkOlUpGamsodd9yBVqu94nHEuf3sTESepusjMjMzOX78OO+//z4jIyNotVoWLlzI3LlziYuL88pxxIkTJ6akjorIk4Mr6SOc/3PuHHlavXr1Jc15UrxgRFFREc8995y8Lzc3l1tvvfW8BSP+8Y9/TBqU/Md//AdlZWWTCkYYjUY++OAD+ZxNmzYREhLidQUjjhw5wpIlS1xthldytWrb1dXFhx9+KHf+ISEhbNy4kezs7BmbbD4b2vb29lJcXCx/EYWEhFBQUEBMTIyin3shLBb47W8dRSWcY5d//Vf4xS8c69RMx9DQEM3NzfIAwGAwkJiYOO2keXdts9InRQxGR0flOTcajQaDwYBer3e7IgbT4a7afhbsdjujo6PygF+r1RIQEPCZipRcKVeiq8VioaWlRXZ6fHx8SE1NJSgoSHaSXnsNPhn3A1BdDffc4/hZr4cf/AC++13Q6a70Tj47o6OjlJSUyAP5wMBAioqKrrivmu0229PTw/vvvy87sDExMdx0000kJCTMmg2zhTf1B+6Eu+vqFgUjAB5//HFeeukl/vCHP1BdXc1jjz1Gc3MzDz30EABPPPEE9913n3z+Qw89xJkzZ3j88ceprq7mD3/4Ay+//DLf/OY35XMeffRRtm/fzs9//nNqamr4+c9/zs6dO/nGN76h5K24hHOfHglmjqtV2+joaO6//37uvPNOgoKCGBwc5M033+S1116jp6dnRj5jNrSNiIhg/fr1FBUVodfrGRwcZO/evXz00UcuKwmu08E3vuGo9PWVr4BKBX/5C2RlwQ9/OP0k9uDgYPLy8khKSkKr1TI2NsapU6eora2VozlO3LXNOosYhISEyBFAm82G0WhkaGgIs9ns1mWPGxoa+Na3vjXliaWnolarCQwMJCgoCLVajdVqZWhoiImJiVn/O1xJm9XpdKSlpZGVlYWPjw8mk4mamhqampowGBxO+j33OJwo5+Z0nFasALMZ/u//dawNNU2QYdbw9/dnxYoVrFixAoPBgNFoZO/evRw7duyKCq7Mdn8QGRnJAw88wK233iqvK/jyyy+7fF1BJXDXvtbT8SZdFXWe7r77bjZv3sxTTz1FQUEB+/fv54MPPpBLFXZ0dEwK3aWmpvLBBx+wd+9eCgoK+PGPf8wzzzzDHXfcIZ+zbNky3njjDV555RXmz5/Pli1bePPNN1m8eLGSt+IS3Dkq5ulczdqqVCry8vL42te+xqpVq9BoNJw+fZrnn3+ebdu2XfEX4Wxpq1aryczM5MYbbyQrKwu1Wk1bWxsffvghpaWls75opZOoKHjxRfj4Y0clvrExeOopR+nkF15wRKjOvQ/nIqLR0dGoVCoGBwenLCLq7m3WWcI8NDQUg8EgD9z/f/bOO7yt6mzgP0mesrz3indsx7ETssjeEwKBAAEyIJQVWmZJS4FS6IC28FGg0LLKasmgQCghkB1CprOcxFkecex47yHbWpZ0vz+ELlZiO3Zi2bJyf89zHklX516d++ro6L73XWq1mqamJjGpgaPR2NjInj17LnIFGehYFVo3NzcEQaClpYWWlpY+/Q56Y876+fkxdOhQMWV2dXU1ev1JsrJabDLtWVteHuzaZbFKBQTA0aMwahT89a+WEgP9gUwmIyoqinnz5pH0Yw71c+fOsWnTpovcorpLf6wHVpe5C+sK/uMf/7jI9XEg4+hr7UDFmeRqV7c9R2WguO1d6NMs0XtIsv2J+vp6Nm/eTG5uLmBxHZs2bRojR460iSPqLv0lW7VazbFjx8SLEWvsTUJCwmWdR28gCLBuncV1yOrOnpwMf/kLLFhgsU5diE6no6SkRHRJVCgUhIWF4efnh5eXVx+O/sowm83odDp0Op3oZ+7i4oKnp6dDufMNpDpPl4MgCOh0OrGgrrXorqKj4mS9TG+vBWq1msLCQvR6PTKZjLCwMCIjIzv9fVdUwAMPwIYNltdjx8Inn1iswf1JdXU1hw4dEq3ksbGxXHPNNT2SlSP8hxUWFvLNN99QX18PQEZGBvPmzcPT07Nfx3WlOIJsnRFHl6vDuO1JXBk7d+7s7yE4LZJsfyIgIIA777yTpUuXEhQUhEaj4dtvv+Xtt98mLy+vx3eq+0u2Pj4+TJ48mSlTpuDr64ter+fIkSNs3LiR0tLSfrF6yGRwyy1w+jS8+SYEBUFuLtx8M0yaZEk0cSEeHh4kJSWRmpqKSqUSE0V88803VFRUOFwtn86Qy+UolUr8/PxsLFHNzc00Njai1Wod0hLlbFjTl1/oxtcXdd96ey3w8fFh6NChBAcHIwgCFRUVHSYNsBIeDuvXw4cfWopaZ2bC8OGW32J/Tj1rbajk5GRkMhlFRUVs3LjxogQHXeEI/2HWuoLjx49HJpORnZ3NP/7xj4uSPAw0HEG2zogzyVVSniQkJABLBqmHHnqI6667DqVSSU1NDatXr+Y///lPl3XUHI3w8HDmzJnDyJEjcXd3p7m5mT179rBt27aLMhL1Fa6uloK6BQXw7LPg6Ql791rc+hYsgA6ScIr1rBITE/H09EQQBEpKSjhx4gRVVVUDpuBgR0qUyWSitbWVhoYGNBrNgDmXgYyrqyu+vr64uLhgNptRq9UXZUAbCCgUCuLi4khMTMTFxYXW1lZOnTpFdXV1h8q4TAb33AMnT8LMmaDVwqOPWn53FySY7FNcXFy45pprmDFjBj4+Puh0Onbv3s3BgwcH1Pfi6urK7NmzuffeewkKCqKlpYXPPvuML7744qJsghISzoLktufAbnvnzp0jPj6+v4fhlEiy7RrrH3lmZqaYZnf48OFMnz4db2/vLvd1JNm2tbWRk5NDbm6ueKc9PDycYcOG4efn12/jKiuzBLN//DFY9YZFiyyZ+tpnD7MiCALHjx9HJpOJsVzu7u5ERkYSGBjoMC5w3UEQBPR6PVqtVrSiWZNOeHh44OLi0qfnU1lZyauvvsqTTz7Zr9ka+wqz2Uxzc7OYot3HxwdXO6Wjs/daYDAYOHfunFj4PigoiJiYmE5dEgUB3noLVq60JJSIjIQ1ayxW4P7EZDJx4sQJcnNzEQQBlUrF2LFjCQoK6nQfR1pnrRiNRn744Qf27t2L2WzGy8uL6667Tky3PVBwRNk6A44u157oBpLy5MDKU3FxMYMGDervYTglkmy7R0NDA9u3bxfrObi5uTFhwgTGjRuHm5tbh/s4omy1Wi2nTp3i3LlzmM1mZDIZMTExpKen92scUU4O/P73sHat5bVcDkuWWLLzJSTY9i0uLiYqKoqamhrKy8vFu9MeHh6Eh4cTGBjYb7Fdl4M1xblWq7W50+7i4oKHhwfu7u59pkQ54py1J4Ig0NzcjMFgQC6X4+PjY5dU5n0hV0EQqKysFF1zlUolCQkJXcbdHDsGt99uSS4hl1tuWjzzDPRBGFiXVFdXc+DAAVpbW5HJZAwZMoQhQ4Z0qAw68pwtLy/n66+/Fus3DRkyhPnz56NUKvt5ZN3DkWU7kHF0uUoxT07CQPcbdmQk2XYPf39/br31Vu69916ioqIwGAx8//33vPnmmxw5cqRDdytHlK2npyejRo1i3rx5DBo0CEEQKCoq4ttvv+3XzHwpKZY739nZljgosxn+8x9LUokHHoD2dQTPnDmDXC4nNDSUjIwMoqOjcXV1RafTUVhYyIkTJ6iurh4wLnBWa5Ovry++vr6ismQ0GmlpaaGhocGmdpS9aGxs5MMPP3S6bHtdIZPJ8Pb2xtXVVXThs4ec+2ItkMlkhIeHk5ycjKurKxqNhtOnT4tJDDpi+HBLZr677rL85n73O5g1Cy6o1drnWGOhYmNjEQSBU6dOsX379g7LLzjiOmslIiKCBx54gClTpiCXyzl9+jRvv/32RUVgHRVHlu1AxpnkKlmeHMXy9NBDFl+edlRXV4vpWSV6F0m2PUcAmpubqampwfijpcDVzY3goCBU3t5YbQQDQbYGgwG1Wi0qTTK5HJVKhcrLq1+tN42NkJML1tAsuQyioiEpEVpbL5ar1QVOr9dj/nEpl8vleLi7O1RGu+4iCAJmsxmT2WwTvyKXyZArFMjlcnr7jBqbmti1axeTJ0/Gr4PixM6MIAgYjUbMgoBcJsPF1bVX5dvXa4HZbKZVoxFddD3c3fHw9OzynEpKIPuEJY25pweMHg396NErotFqaWpsFC3lfv7+KNtZ0wbCOgsWF/CKigqxppWfnx/BwcEObSUfKLIdaHQq18hIePvtvh/QBUhue5fAIZWnDmhtbR1QqYkHEpJsLx+j0cjhw4fZtWuXWMg1MjKSmTNnEhcXN2Bka3X3yc7OFtOCu7m5kZyczODBg+0WB9Id9u2D556DHTssrxUKWLSojeeec+0wJspkMlFTU0NFRYXoAufm5kZoaCghISF9kpa6N7G69Ol0Otra2kRFSiaT4e7ujru7e6/FRjl7qvJLYTKZaGpqwmw2o1Qqe9W1qj/WAmtiFWuSm4CAAOLi4rr8DeTkwE03WTJhenhY6rQtW9ZHA+4CjUZDZmammOgmPj6eESNGiIkyBsI6C5bY023btnHgwAHAEpu2cOFCIiIi+nlkHTOQZDuQcHS5SsrTJRgoytOhQ4cYPXp0fw/DKZFke+Xo9Xr27dvH/v37xbuKCQkJBAUFMW/evH4eXfexXmydOnVKrIDu5uYmZrrrbyXqT3+CjRstr2UyuPVW+O1vISPj4v4mk4na2lqbO70KhYKQkBBCQ0M7jVNzZEwmk2hda+9aplAoREXqSpTDq115Astvubm52WLh8PPrNWW7P9fZmpoaioqKxAQMSUlJXf6Wm5pg6dKfakI98QS8/DLYIRSsR5jNZk6dOsXp06cRBAFfX1/Gjx9PXl7egPsPKygo4H//+x/Nzc3I5XKmTp3KxIkTHc4KJV0f2AdHl6sU8+QkdOWzLXFlSLK9ctzd3Zk2bRqPPvoo1157LQqFgoKCAlatWsUXX3wxYGQsk8kYNGgQc+bMYdy4cfj4+GAwGDh+/DgbNmwgJyenT2ridMT48fDdd3D4MIwfX4UgwOefw7BhllTLBw/a9lcoFGJMVFxcHJ6enphMJioqKsjOzqawsLDTmjiOikKhEFOd+/r64uHhIaY712g0NDQ0iHWjBkoNLEfDzc0NV1dXBEHo1fnRn2tAcHAwycnJuLi40NLSwunTp7s8N19f+Ppry40JgNdeg7lzLa60/YlcLic9PZ0pU6bg4eFBU1MTW7duJT8/v38HdhkkJCTw0EMPMWTIEMxmMzt27ODjjz92uHjDgfLfNdBwJrlKypMD48jmzYGOJNveQ6VSMW/ePB5++GEyMjJwd3fn5MmTvPXWW2zYsEG05jg6crmcmJgY5s6dy9ixY/H29kav13Ps2DE2bNhgk+68rxk5Ev7613yys+GOOywWqPXr4dprYcoUy93y9nki5HI5wcHBDB06lKSkJFQqFWazmZqaGk6ePEl+fj4tLS39ci6Xi0wmw9XVFZVKhb+/P97e3mJcl9FoFOtG9VSR8vDwIDY2Fg8PDzufgeMik8lEdz29Xt9rxYv7e5318fFhyJAheHh4oNfrycnJEV2NO0Iuhz/+Eb74Ary8YPt2SxrzkpI+HHQnhIWFMWfOHEJDQzEajZSWlpKVlTXgbhgolUpuu+02br75Ztzd3SkuLuadd94hJyenv4cm0t/z1llxJrlKbnsO7LZnNBrtkj5WQpKtPSktLeWHH34Q74wqFApGjRrFxIkTL1kjypEwm82cP3+eU6dOiYqGp6cnKSkpxMfH97k7X/s5m5sLf/kLrFoF1izfQ4ZY6tcsXgzu7hfv39zcTGVlpRjfBZZCvKGhofj7+w+45BJWzGYzBoMBvV6P0Wi0ufB3dXUVrSoKhaLTc5TWA4v7amNjIyaTCR8fn15x8XQUuba1tZGXl0draysuLi4kJSVdci06dgyuuw4qKizx7N9917GrbF9jdeM7ceIEcrmckJAQxo8fPyCV/8bGRr744gtKS0sBGDt2LDNnzuz3OeMo89bZcHS5Sm57TsL27dv7ewhOiyRb+3Hq1CmWLFnCPffcQ2xsLCaTiQMHDvDGG2+wZcuWAVN1Xi6XExcXx7x58xg9ejReXl5otVqOHj3Khg0bOHXqVJ+mOG8/Z5OT4aOPoLAQfvUr8PGB06fhZz+D+HhLrMaFBj9vb2+SkpJIT08Xs101Nzdz9uxZsrOzbZJNDCTkcjkeHh74+vri7++PSqXC1dUVmUxGW1sbra2tNDY20tjYSGtrq00CCivSevCTZQ/oNQuro8jV1dWV5ORkvL29MRqN5ObmXtIiPnw4ZGZabkqUlcHEibBrV9+MtyusbnxyuRxXV1eqq6vZsmULdXV1/T20HuPn58c999zD+PHjAcjMzOTDDz/sd/cuR5m3zoYzyVVSniQkJOxCTEwMd999N3fffTfR0dEYjUb27dvHG2+8wbZt27p0n3EkFAoFCQkJXHfddYwePVp05ztx4gQbNmzg+PHj/RZHFBlpUZSKiy2PERFQXg5PPQXR0RZL1Pnztvt4enoSFxdHRkYGERERuLq6otfrKSkp4fjx4xQWFg6Y7+ZCOlKkrK59JpMJrVZLU1MTDQ0NNDc3o9frycrKYuHChRw7dqy/h9/vWC1zzuiQ4uLiwuDBg/H19cVsNpOfn99h/aT2DBoEe/ZYXGObmy0xUFu39tGAL4G/vz8zZ87Ex8cHjUbDjh07KCoq6u9h9RiFQsHs2bNZvHgxSqWS8vJy3n33XU6dOtXfQ5OQ6BTJbc+B3fby8/NJSkrq72E4JZJs7UdHshUEgYKCAnbs2EF5eTlgSTgxduxYxo0bN6BcTsxmMyUlJZw5c0YMdFYoFMTHx5OSkmI3v+7uzFmDAVavhv/7P7Bee8jlljTMjz1mid+40HPNbDZTX19PZWWljdLk4+NDaGgofn5+A9alz4rZbKatrY22tjYMBoNNIeHjx48zc+ZM9uzZw+jRo0Wr1dWIWq3GYDDg5eWFZ7uaQpeLI66zZrOZs2fP0tjYiEKh6NZvVqu1ZLn87jtwc7PERN1wQx8NuBOssm1ra+PAgQOi69vQoUNJS0sbkHNYrVbzxRdfUPxjdfBRo0Yxd+7cPnf1csR56ww4ulylVOWXYKAoT2VlZURGRvb3MJwSSbb2oyvZCoJAXl4e33//vViHxcPDg3HjxnHttdcOKCVKEATKy8s5ffq06DIjl8uJjY0lNTW11+O7ejJnzWZLevPXX4dt237aPny4RYm64w5LPZv2CIJAS0sLVVVVNDQ0iNYHd3d3QkJCCAoK6te07b2FtTCswWCgra2NI0eOMHPmTLZt28awYcNE9zVXV1dcXFx6rZ6Uo2M2m8Xv3c/Pr1cuWB11nTWZTOTn56NWq3F1dSU1NfWSa4/BAHfeCevWWdKXf/UVzJ/fRwPugPayFQSB7Oxszpw5A0BsbCyjR48ecPXdwDIPv//+e/bs2YMgCERGRnL77bf36bWao87bgY6jy1WKeXISTp482d9DcFok2dqPrmQrk8lITk7mwQcfZNGiRYSEhKDT6fj+++957bXX2LFjx4BxGZPJZGJx4GnTphEaGorZbObcuXN899137Nu3r1fjEHoyZ+VyuP56i4vRyZPwwAPg6WkJgr/nHos70u9+ZwmGb38+3t7eJCYmkpGRQXh4OC4uLjYufWfPnkWtVg9oty6rcuTl5SWmPwfEelGCIGAwGGhtbaWpqYn6+nqamprQaDQXWa2cBaviLAgCLi4uvXbR7ajrrEKhELNQWpNJXCrOy80NPvvMcuPBaLRYoqxFrPuD9rKVyWQMGzaM0aNHI5fLKSoqYufOnX0ak9lbyOVyZsyYwZIlS/D09KSsrIx3332X8xf6H9sRR523Ax1nkqukPElISPQ5MpmMIUOGsGLFCm699VZCQkLQ6/Xs2rWL119/nW3btg2YxBIymYzQ0FCmTZvGzJkziYyMRBAEiouL2bp1K9u3b6esrKzfFI60NHj3XSgttWToi46GmhpLSuaYGMvd9F27oP3w3N3diY6OZtiwYcTFxYmpzuvr68nJyeHEiRMDNsHEhVgLdFprSfn5+eHl5YW7uztyuRxBEGhra0Oj0aBWq8V06C0tLeh0uosy/A00TCYTdXV1GAwGZDIZKpXqqrC0WRUod3d3dDodZ8+evaRi7OIC//63pcaaXg833mhJKuEoJCQkMHnyZFxdXampqRlQ6+iFJCYm8sADDxAWFkZrayuffPIJBw4cGNC/NQnnQXLbc2C3PbVa7dDjG8hIsrUflyNbQRA4c+YMu3btEt35XF1dGTVqFOPHjx9QKc7BkoI3NzeX8+fPixdkPj4+JCcnExMTc1kuUb01Z41Gi8vRG2/A3r0/bU9LgxUrYNkyS8HQC9FoNFRXV1NXVyfWlpHL5fj7+xMcHIy3t/eAvOjWaDQcPnyYUaNGibWOrAiCYBMvZTQaO6yrI5PJRIuN1dWvq9TojoC1IO6FcW69kaLcykBYZzUaDWfOnMFkMhEeHk50dPQl99HrLTFPW7dCYKBFgUpM7IPBtqMr2TY1NbFr1y5aW1tRKpVMmTJFtLAONNra2li/fj0nTpwAYNiwYcyfP9+uLsQDYd4ORBxdrlLM0yUYKMpTVlYWI0aM6O9hOCWSbO3HlcjWGhO1a9cuysrKAEuWrBEjRjBhwoQBdwGg0WjIz8+noKAAg8EAWGK8kpKSSExMxL2jgkydYI85e/QovP22pV6U9Rray8tSK+qhh+Caay7ex2QyUV9fT3V1tc1dbU9PT4KCgggMDOzVC/C+oCeyNZlMGI1GUZEyGo0dWixkMhlyuRyFQiEqVQqFArlcLlq7+hqrFc1gMFzkgqhSqXo95nCgrLP19fWcPXsWgOTk5G6tM62tMG0aHDpkKR2wfz/4+9t7pD9xKdlqNBp++OEHmpqacHd3Z8qUKQQEBPTdAHsRQRDIzMxk69atmM1mwsPDuf322/Hz87PL5w2UeTvQcHS5SjFPTkJNTU1/D8FpkWRrP65EttaYqPvuu4+lS5eKKc4PHjzI3//+d7755pt+rwHSE5RKJcOGDeOGG27gmmuuwcvLC51Ox4kTJ1i/fj2HDx++ZLpkK/aYs9dcA++9Z0lv/uablpo2ra3w/vswYgSMHQsff/yTYgUWd6fg4GDS0tJIS0sjJCQEhUKBVqsVY6Py8vKor68fEPFBxcXFPP/882KGr0uhUChwd3fHy8sLHx8f/P398ff3x9vbG09PT1xdXUV3P5PJhMFgQKvV0tzcTGNjI/X19dTX19PY2EhzczOtra3odDr0ej1tbW2YTKYrdk2yWsyMRiN6vZ7W1lbUajX19fWo1Wp0Oh1msxmFQoGXlxcBAQF2SdYyUNbZgIAAQkJCADh37ly36lx5ecHXX1vcYHNz4bbbwKg3wc6dsGaN5bEDK2VvcSnZKpVKpk+fTmBgIHq9nh07dlBdXW238dgTmUzGuHHjWLZsGUqlkoqKCt5//33xBltvM1Dm7UDDmeQqWZ4c2PL0ww8/MGXKlP4ehlMiydZ+9KZsBUGgqKiIH374QaxhIpPJSEtLY8KECYSHh/fK5/QV1jTnubm5ohJoTTyRlJRESEhIp65efTFnBQF277ZYo778EqwhTT4+FmvUvffCyJEXpzu3WqNqa2ttlEEXFxcCAgIICgrCy8vLId3YsrKyGDlyJEeOHOnVu6JmsxmTySRap6zPu6tQWi1XMpnMpnWEIAg2zWw2d6qAyeVy3NzccHNzs3ta9oG0zprNZk6dOoVWqyU4OJi4uLhu7Xf8OEyYALNb1/GR92P4Npf+9GZUlMU/duHCXh9vd2Xb1tbGnj17qKqqQqFQMHnyZEJDQ3t9PH1FY2Mja9eupbKyEhcXFxYuXMiQIUN69TMG0rwdSDi6XCW3vUswUJQnQRAc8mLDGZBkaz/sJdvi4mJ2795Nfn6+uC0xMZGJEycSExMzoL5PQRCoqakhNzfX5u6pr68vSUlJxMTEXOTT39dztqoKPvzQYoUqLPxp+7BhFiVqyRLoyAtIp9NRW1tLXV2dTbYvR3Xrs5fy1BlWi5RVubI+WpWerhSfnnKh22Bfx2INtHW2ublZTPfdk3IDe365jvGv3QoItu481nP/4oteV6B6IluTycTevXspLy/HxcWFyZMni5a2gYher+fLL78kLy8PgFmzZjF+/Phem2sDbd4OFBxdrpLydAkGivK0efNm5syZ09/DcEok2doPe8u2srKSvXv3cvLkSfEiMyoqiokTJ5KcnOzQi3NHqNVq8vLyKCoqEt2F3NzciI+PJzExEZVKBfTfnDWbLR5IH3xgsUZZ9SF3d8v14L33WmI/LgzjEQQBtVpNbW0tDQ0NosVFJpPh4+NDQEAA/v7+fV4A80L6Wnm6FBdakC7c1tFf9oXWKWtcVX//FgbiOltUVER1dTXe3t6kpKRcWoYmE8TGIpSW0mFPmcxigSoshF6su9RT2ZpMJvbs2UNFRQUuLi5MmTKF4ODgXhtPX2M2m9m0aRMHDx4EYOTIkVx33XW9kmZ/IM7bgYCjy7UnukH//mtJWNBoICfnos3e+fkwgBc3R0aSrf2wt2zDgFvi4pgVGMjx48fJzc3FVFHBD4cOcdzPj+HDh5OYmDhgCkT6AKPkcoZFR1NWVkZJSQkajYaq3FyqZTKCgoIYNGhQv81ZOTDdD6Y/Cf+8DzZtsmTry8uHnDXwqzUQGWEpGHrddZYYEAAZ4PtjM5rNYsxPa2srbUAVUCOXo1Kp8PX1RaVS9ct35nnmDNf8+OgIyH5sAANjBnfOQFxnI9va0OTnYzKbaflRieqSw4ehM8UJLL6wJSWWuw+jRvXaOHsqWwUwwdOToz/WLssuLGTkyJF2S7pgb+TAdWFhRMfFsW/fPso3bGDTsWPMnDmzR4l4OmIgztuBQJdyTUmBC7KdOjKS5ckRLE9ZWZZAAgkJCQkJCQkJCYmriSNHLFmK+hHJ8jTQSEmxTJwLqK2tJSgoqB8G5PxIsrUf/SVbg8HAmTNnyM7OFmvXuLq6kpqaytChQwdcrSiAlpYWiktKKC8ro7m5GaVSiaurK+Hh4URFRTnEOWm1Fre+b7+11Lsx/3g7ztUFpkyB66+H8RMsr9sjCAI6nY6mpibUarWYyh0siSZ8fHzw9fVFqVTa3f1MWg/sw0CVq16vJz8/H5lMdumSAocPw4MPXvqg777bq5anK5Ftm9HI4UOHUKvVKJVKxowZc8XWmv6mpqaGjRs3otVq8fHxYf78+Ze9Pg7UeevodCnXlJS+HcwVIlmeOtAuTSYTbdY0U/3Inj17mDhxYn8PwymRZGs/upKtq6ur3V2zjEYj2dnZ7Nu3j9raWsASOJ+Wlsb48eMHXIY+sCiGa9euJTAw0CabXXBwMAkJCURHRzuEm2J5OaxeDZ98AidP/rQ9KAjuvNOSse/aay/O1icIAi0tLWIa7/brr6urq0068N6ukdTS0sL777/P/fffL8aXSfQOjh7j0BU5OTmo1Wqio6O7XjN+jHmirMzionchDhLzdCFarZbt27fT0tJCYGAg06ZN6/f4wyulvr6ef//73zQ2NuLj48OyZcsuK65rIM9bR8bR5SoljLgEXQmopaWF0tLSXst2dCVotVo8PT37exhOiSRb+9GVbGUyGVFRUX1ykSoIAvn5+ezfv5/CduniYmNjGTduHIMHD+73gPqesHnzZmbPnk1lZSUFBQWUl5eLCQXc3d2Ji4sjISHBIaxRgmBJ4fyf/1gK8FZV/fRebCzccYelZWR0rEip1Wrq6upobGy0qbnj4uIiKlI+Pj69okg5WsIIZ8LRL5a6oqqqivPnz+Pj40PKpe6Kr1sHt95qed7u2sGMDJkMZHbIttcbslWr1Wzbtg2DwUBUVBQTJkwYUGtiR6jVav7zn/9QU1ODUqlkyZIlREZG9ugYA3neOjKOLldJeboEnQnIZDKRn5+PUqkkODi43xcRo9E44O8EOSqSbO1HZ7K1pufWaDQkJSX1qaWkoqKC/fv3c/LkSVHhCAoKYuzYsQwbNuyitOCOSENDA/7+/uJrjUZDYWEhBQUFopsiQGhoKAkJCURGRjqENcpohK1bLYrU+vWWIrxWUlJ+UqSSky/e1/xjoomGhgYaGhpsLFIKhQI/Pz/8/f3x9fW97HOVlCf7ceGcHUhotVpOnDiBXC5n5MiRl74eWLcOHnsMSn+q81RMNHW/fZ1r/tj7dZ56S7Y1NTV8//33mM1mhg0bRmpqai+Mrn/RaDSsWrWKsrIy3NzcuPPOO7tdtwsG9rx1ZBxdrpLydAk6E5BOp6OwsJDY2FiHsEpoNBqUAyj7yEBCkq396Eq2Wq2WoqIi4uLi8PDw6OORWX77Bw4c4MiRI+h0OgCUSiWjR49m9OjRDu22dfz4cYYNG3bRdrPZTEVFBWfPnqWyslK0mnt4eBAfH098fLzDnJdGY4mNWrvW8tiuDBTDh1uUqNtvt1inLkQQBBtFqn2MlFwux9fXFz8/P/z8/HqkDEvKk/3obM4OBMxmM0eOHEEQBIYPH9692mQmk6XKdEUFb34RzuPrJrHgZgXr1vX++HpTtgUFBRw6dAiZTMaUKVMICwvrleP2J3q9nrVr11JYWIiLiwt33nknCQkJ3dp3IM9bR8bR5doT5al3ncedhP62OFlxhLgrZ0WSrf3oSrb9/dvy8fFh1qxZPPHEE8ydOxc/Pz80Gg0//PADr732GuvWrbMpWutIVFZWdrhdLpcTGRnJlClTmD9/PmlpaXh4eKDT6Th9+jTffvst33//PefPn7dxgesPlEq47TZLvajqavj3vy3pzV1c4Ngx+M1vIC4Oxo+HN96wZHi2Yq0PFRMTI94hDwsLw93dHbPZTENDA4WFhRw9epTTp09TVlaGRqNxCBfsq5XO5uxAQC6Xi0p4e0W9SxQKmDoV7ryTab+fihkF33wDP4Ze9iq9KduEhATi4+MRBIH9+/fT2t48PEBxd3dnyZIlJCcnYzQaWbNmDefOnevWvgN53joyziRXSXlyYPr7QvOFF15gxYoVAOzcudPG71ulUlFdXd1fQ7ti+lu2zsxAkK27uztjx47l0Ucf5bbbbiM6OhqTyUR2djbvv/8+//rXvzhx4gQmk6m/hyrSHWuKl5cX6enp3HDDDUyYMIGwsDAEQaCqqor9+/ezfv16Dh8+TH19fb8rFT4+sGyZxQJVWQnvvQfTp1tioPbvh8cfh0GDYMwY+POfITf3p31lMhne3t4MGjSIjIwMhg4dSmRkpGhha2lpoaysjJMnT5KdnU1RURGNjY2iy2Z7XFxc8PX1ldx47cBAcIftDpezpg0dCvHxFrfV8HBLsr3epLdlO2LECPz9/dHr9Rw4cKDf14fewMXFhUWLFokK1OrVq7ulQDnLvHU0nEmudlWeGhoaWLZsGb6+vvj6+rJs2TIaGxs77d/W1sZTTz1Feno6Xl5eREREcNddd1FeXm7Tb+rUqRdVVL/jjjvseSr9QnuzYWxsLD4+Pmi1WnGbWq3G09PTRqmJjY0lMzPT5jgrVqzghRde6NWxtbS0EBIS0qvH7A0efvhhPvnkE5tt999/Pw8//LDNNh8fH/7+978zZcoUcdvhw4eZNm0agwcP5osvvrjo2AsXLuT555+3z8DtREFBARMmTECpVDJixAiOHz/ead+pU6fi4eGBSqVCpVIxb948m/czMzMZO3YsKpWKqKgo/vvf/wKQm5vL/PnzCQoKIjg4mJ///Oc0NDTY9bx6C2sWvnvvvZcHHniAYcOGoVAoKC0t5csvv+T1119n165dDnEndvr06d3uq1AoiI6OZurUqaI1ysvLC4PBwNmzZ9myZQubN28mNzcXfXvfuX4iMBDuvx+2b7ckLXvjDZg0yaJIHToEzzxjiY8aMgR++1tLaTzrtZ1MJkOpVBIZGcmQIUMYPnw4cXFx+Pv7I5fL0ev1VFdXk5eXR1ZWFvn5+VRVVYlumxkZGTQ2NpKRkdGPEnBOejJnHQ1BEMSbJ5ebmMSap2DIEFixoncVqN6WrYuLCxMmTMDFxYXq6mry8/N79fj9hUKhuEiBKioq6nKfgTxvHRlnkqtdlafFixdz7NgxNm3axKZNmzh27BjLli3rtL9GoyErK4vnnnuOrKws1q1bR15eHjfeeONFfe+//34qKirE9m5v39ZxAJqammxeh4WFsX79evH1unXriI6O7uthOTTWjGTtWbp0Kf/9739tXJaamppYvXo1S5YsEbdt2rSJOXPmsGTJElatWmVzjKamJjZu3MjixYvtewK9zJ133sns2bOpr6/nZz/7GTfffHOXrlsff/wxLS0ttLS0sHHjRnF7RUUFt9xyC8899xyNjY0cP36ckT8Wdm5qamLRokUUFBRQVFREa2srK1eutPu59TYRERHcfPPNPPHEE0ybNg2VSkVzczM7duzgb3/7G//73/+oqKjot/Ft2bLlsvZTqVSkp6dz/fXXM3XqVAYNGoRCoaCxsZGjR4/y9ddfs3fvXpvsff1JeDg8+ijs2gUVFRaL1Ny54OoKZ87Aiy9aaorHxcETT1hCTNobCN3c3AgODiYpKYlrrrmGwYMHExISgpubm+jed/78ebKzs0Wr1IYNGxzKyugsXO6cdQT0ej0mkwm5XH5ZNZDefdcyNx9+GI4ehUce6V0Fyh6yValUYkxKdna2TVmEgYxCoeC2225j8ODBogvfhTfl2zOQ560j40xytZvydObMGTZt2sS//vUvxo0bx7hx43j//ffZsGEDue39L9rh6+vL1q1bxbsEY8eO5c033+TIkSMUFxfb9FUqlYSFhYnN19fXXqfiMNx55502F/WrVq264ot5rVbLww8/TEREBFFRUfz1r3/t1n4ymUz0X42NjeWvf/0riYmJBAcH21i5NmzYQHJyMt7e3kRHR7NmzRrAktnw+eefJyYmhrCwMJ588skOL+q3bNnChAkTxNdxcXH84he/ABBrOVj3KygoQKlUXlSTY/LkyXh6erJ161ZxW1FREUePHuVWa3pZfkqjuXTpUjZu3GhjJf3yyy8ZOnQoycnJogvjc889h5+fH8nJyZw+fZo//elPBAQEkJqayqlTp8R9f/7znxMREYGfnx+zZ88W53Jubi5BQUGcPXsWsFh2wsLCes0dMjc3l9zcXJ5++mk8PDx4+OGHMZlM7Nu3r8fHeu2111i+fDnXX389Li4uBAYGisG3Y8aM4a677sLX1xcvLy/uvvtuDh482Cvn0B+oVCqmTJnCE088wS233EJUVBQmk4ljx47x7rvv8uGHH3Ly5Mk+v9i+UjcauVxOWFgY48eP58Ybb2TkyJH4+/tjNpspKSlh165dbNiwgezsbNRqdS+N+soIDbVYpDZutMRIffop3HKLJXbq/Hl4/XWYPBkiIiz9vv7aNpufNSNfbGwsw4YNIy0tTSwuLJPJ0Ol0ZGZmct999/H5559z5swZysvLaWlpcQq3pf5mIMvQevNSqVT22PL07rsWRenhh+Hvfwe53GJN7U0Fyl6yTUxMJDQ0FKPRyMGDBwf0d9geqwtfXFwcer2eTz/9VKwDeCHOcs6OhjPJ1W7K0/79+/H19eXaa68Vt40dOxZfX98eXbw1NTUhk8nw8/Oz2b5q1SqCgoJIS0tj5cqVdrlDIgiWP2J7t87m04XZfWbNmkVWVhb19fVUVlaSn5/P5MmTr+gcV65cSVNTE3l5eRw8eJB///vffPPNNz0+zpdffsn+/fs5cOAAH3zwARs2bADgvvvu48MPP6S5uZlDhw6Jd7X+9re/sW/fPo4cOUJOTg5ZWVm8/fbbFx133LhxHD16FK1WKwby79mzB4C9e/cyevRoMVbBajm6EJlMxp133snq1avFbevWrWPevHkEBAQAlnlWWFjI8OHDSUhIYPjw4Xz55Zdi/wutVGfPniU4OJja2lpmz57Nddddh6enJ9XV1cyfP5/f/va3Yt+JEydy5swZKisriYqK4tFHHwUgOTmZZ555huXLl9Pa2sry5cv5+9//3qE75J49e8RMYh21jjh9+jTJyck28ygjI8NGsbuQRx55hODgYGbNmkV2dra43ZqJKS0tjfDwcJYtW9apa97hw4dJS0vr9DMGCgqFgvT0dO677z7uu+8+MjIyUCgUFBcX88UXX/Daa6+xY8eOiyzE9iIqKqrXjuXu7k5SUhJz5sxhzpw5DB48GDc3NzQaDadPn+a7775j69at5OXlie5t/Y2fHyxZAl98ATU18NVXlpgpPz+LYvWvf8FNN1lcAOfNg7festQltSKTyUR38NTUVEaMGEFSUhIqlYqqqioMBgPNzc2UlpZy+vRpjh07xtmzZ6mqqkKr1TrVH39f0Ztzti8RBEG8sLb+R3QXq+L0yCMWxckaLiWT9a4CZS/ZymQyxowZg4uLCzU1NRfduB7IuLi4cMcddxAREYFGo+Hf//53h+v3QJ23jo4zydVuylNlZWWHF4EhISHdzrih0+n4zW9+w+LFi23if5YsWcKaNWvYuXMnzz33HF9++SULuyhAp9frUavVNq07aDSgUtm/tSvRYsOFAcwuLi7cdNNNfP7556xdu5bbbrutwztis2bNsrmw/uijjzo8viAIfPTRR7z66quoVCoiIiJ46KGHOoz3uRSPP/44wcHBxMfH8+CDD4qKh6urKydPnqSlpYWwsDCGDBkCwAcffMCLL75IUFAQfn5+PPnkkx1+rre3N6mpqRw8eJDdu3dz0003YTAYaGhoYPfu3UycOFHs25nyBBbXvf/9739iPZz//ve/NsrQtm3bmDZtmhgYvHTpUtHKV1FRwa5du2zi6vz8/HjkkUdwcXFh4cKF1NXV8cQTT4iv2yseixcvxtfXFw8PD5566ilR+bPKzfpnlZ6ezqJFizoc/8SJE2lsbOy0dURLS8tF6TZ9fHxoaWnpsP/LL79MYWEhxcXFzJo1i+uuu07sW1ZWxqpVq/jqq684e/YsRqORxx9//KJjHDt2jLfffpvnnnuuw88YqERFRbFw4UIef/xxpk6dire3Ny0tLezatYvXX3+dtWvXUlBQYNcLbHvFGPr7+zNixAgWLFjAhAkTCA8PRy6XU1dXR1ZWFuvXr2fXrl0Oka3PilJpUZT+/W+L4rRli+WiNC7Okv580ybL6/h4SEuDp56yuAG2H75CocDf35+IiAgAkpKSiImJwd/fH4VCQVtbG/X19Zw/f54TJ05w7NgxCgoKqK6uRqfTScpUN3DEuNjuUF9fT2trK3K5nMDAwG7vp9db5l1GhsUqemGeCZnMsj0jw9LvSsIN7SlbLy8v8b/6+PHjTpWd1t3dnaVLlxIUFIRarWb16tUXxX0O1Hnr6DiTXHucXuiFF17g97//fZd9Dh06BHScoUYQhG5lrmlra+OOO+7AbDbzz3/+0+a9+++/X3w+dOhQkpKSGDVqFFlZWR3W6fjzn//c4Zi3bduGl5cX06dP5+DBg2i1WoKCgjCZTDQ1Nf3o/mF/d8CmpiY8PFTodDqMRiNyuRyVSkVjYyNubm6i5aClpYUFCxbwxz/+EY1Gw2uvvSYqgta7J4IgsHHjRtLT0wGLy8FDDz2ETqdDrVbj4+ODWq1GEAQaGhrQarUkJSUBlu/LbDZz7bXXisdra2ujqalJvPvc/i6NTqejqakJs9lMZGQkzc3NmM1mQkJC2L17N01NTXz88cf83//9H7/+9a8ZOXIkf/3rXxk5cqR4gW6dC4IgEB4eLi5i1s/z9vZm7NixbN26lerqambPnk1tbS1btmzhhx9+4LnnnqOpqQmDwcChQ4cYMWIETU1Nogyt8klMTCQuLo61a9eSmJhIWVkZM2bMEPtu3ryZyZMn09TUhJubGwsXLmTlypXk5OSwYcMGJk2ahKenp3i8gIAA1Go1bm5uuLq64u/vT3NzM0qlEplMRktLC01NTfj6+vLcc8+xatUqamtrkclkqNVqmpqaUCqVGI1GFi1axKOPPso777wjytDFxQUPDw9RefH09MRsNovysSpBHfW11k+SyWQ0NDRgNpvRaDSYTCbq6+tRKpXi92jtq9PpSE5OxtPTE51Ox4MPPsi//vUvMjMzGT16NG5ubtxxxx3Exsai1Wp5/PHHmT9/Pq2treKcrampYf78+fztb38jISEBg8EgJjixJitobW0Vv9vNmzcDEB0dTVBQEEePHgVg1KhRlJeXU15ejkKhYObMmWzbtg2TyURERAQREREcPnwYgGuuuYba2lpKfsxpPWfOHL7//nsMBgOhoaHExsZy4MABwGJ1U6vVYrDwrFmz2Lt3LxqNhqCgIAYPHixaxdPS0tDpdBQUFACIa0RLSwv+/v7cf//9rF27lry8PIxGI/v372fDhg14e3uzaNEiZDIZer0eHx8fRowYwc6dOwHLBbqLiwtnzpwBLErx6dOnqa+vx8vLi7Fjx7J9+3YA4uPjUSqVnDx5ErDcBIqKiqKmpgYPDw8mT54s+pDHxMTg5+cnJgQZM2YMxcXFVFZW4urqyvTp09myZQuCIBAVFUVISAhZWVkAjBw5ksrKSsrKypDL5cyaNYtNmzZRXV0tuibu3r2b3bt3ExUVhUqlwmw24+3tzdy5c9m5cyd6vZ6QkBDi4+PFhDXp6em0tLRQ+KP5Z+bMmezbtw+NRkNgYCApKSns3bsXgCFDhojJLACmTZvG4cOHaW5uxs/Pj4yMDHbt2gVYLLaA6P49efJkfHyyuf76Rm6/3RuVahRvvlnIwYPBnD7tz+nTMk6fhpdfBn9/gTFj6hk2rIxJk1qZN2+M+J1XVlYSExPDuXPnEASBjIwM8vLyqKmpEWVsdUf19fXF29ub6upqXF1dGT16NNXV1VRWVuLi4sKMGTPYunWruDaGhYVx5MgRwJLVrLq6mtLSUmQyGbNnz2bHjh20tbURFhbGoEGDxM8ZNmwYjY2NnD9/HoDZs2eza9cudDodwcHBJCYmsn//fsDyX6jRaMRsYjNmzCAzM5PW1lYCAgIYMmSIeOMmNTUVo9EoJgWYOnUqWVlZYp2T4cOH88MPPwAwePBg5HI5OTk54pw9deoUDQ0NqFQqxowZw44dOwBLymsPDw/Rum0wGIiIiKC2thalUsmECRNE92lrEiTrjaZrr72WoqIiqqqqcHNzY9q0af2yRtTX14vFtCdNmkRmZma314jDhw+yYoUfb76ZxmOPCfz97zIbBUoQ4LHHBLKzZfz61+fYuTP/steIoqIipk+fbrNGjBs3jrNnz/bKGhEYGEhJSQkGg4GQkBBUKpXNGrF9+3aMRiPh4eFERUWJ133Dhw+nvr5etFjNmTPHIdaI7OxsGhsb8fb2ZtSoUURERHD8+HHq6+t5++23GTx4MAqFggkTJrBx40ZCQ0NRKpWMHz+ebdu2AZawAZVKxYkTJwCLN9W5c+eorq7G3d2dqVOninN20KBBBAQEcOzYMQBGjx5NaWkpFRUVV+0aUVRUxIwZM2zWiPHjx5OXl+cQa4R1/N1C6CE1NTXCmTNnumxarVb44IMPBF9f34v29/X1FT788MMuP8NgMAg33XSTkJGRIdTW1l5yTGazWXB1dRXWrl3b4fs6nU5oamoSW0lJiQAITU1NNv20Wq1w+vRpQavV/nhcQWhpsX8zmzs+r8bGRvF5TEyMsH//fkEQBCEhIUFITU0VBEEQvv/+eyE5ObnDflYefPBB4fnnn7/o+CaTSfDw8LD5nPY8//zzwoMPPtjh5wBCRUWF+JmrVq0S3/vjH/8o3H333TbH0ul0wq9//Wth+vTpgiAIQmJionD8+PGOT/wCPv/8c2HOnDnCsGHDhKqqKuGjjz4SHnvsMUGpVApqtVoQBEHYvn27cP3113d5nJdfflm44YYbhJUrVwqLFy+2eS82Nlaoqqqy2XbdddcJr776qjBq1Cjh448/FrdfKIv9+/cLMTEx4uujR48KoaGhgiAIws6dO4Xo6GghLy9PMJvNQk5OjtD+Z1dbWyuEh4cLy5YtE8aOHSsYjcYOx75r1y7By8ur09YROTk5go+Pj2AwGMRtgwYNEn744Ycu5WQlNTVV2L59uyAIgrB48WLh97//vfjeyZMnhaCgIPF1RUWFkJCQILz77rudzidBuPg35gxUV1cL3333nfDnP/9ZeP7554Xnn39e+OMf/yh89dVXQmlpqWDu7AfeQzZt2tQrx+kpTU1NQnZ2trB+/XphzZo1Yvv666+Fo0ePCg0NDf0yru5QXy8Ia9YIwtKlghAQIAiWy1dLk8kEYdQoQbjnniMCIGRmHun0OCaTSWhqahJKS0uFM2fOCIcOHRIOHDhg07KysoS8vDyhvLxcUKvVgslk6sMzdUz6a85eLkajUTh16pRw4MAB4cSJE52ux5finXcsc+zhh3/6fzebBeGRRyzb33nnysfaF7I9d+6csGbNGmHdunU2/yPOQllZmfCnP/1JeP7554Wvv/5aXKsH2rwdKDi6XJuamjrUDTqix5anoKAggoKCLtlv3LhxNDU1cfDgQcaMGQPAgQMHaGpqYvz48Z3u19bWxqJFi8jPz+f777/vlsn81KlTtLW1XZQowIq7u/tlZcuRycDLq8e79RpKpbLD7evWrbvs1Kntkcvl3H333axcuZJXXnkFHx8fcnNzaW5uFr+z7vL3v/+d2bNn09zczHvvvcc//vEPDAYDX3zxBfPnzxfTXysUCgDuvfdenn32Wd5//31CQ0M5f/4858+ft0kdbmXSpEksX76cmJgYQkJCmDRpEo8++igpKSl4e3sDPyV76IrFixfzu9/9jkOHDtm4Mp45c4aAgICLTMpLlizh6aefpqampku30K5obm4WEyy0trbypz/9yeb9n//859x22228/vrrTJ06lVdffZVf//rXHcqgM3e7zkhOTiY5OZm//OUv/PrXv+aDDz5AoVB0+PtrbGzk0KFDTJ48GZlMxj/+8Q/q6+sZNWoUAMuXL+eBBx5g6dKlhIeH8+c//5nrr78esFgj58yZw1133cUDDzzgVC4e3SE4OJh58+YxY8YMTp48ycGDB6msrOTYsWMcO3aMiIgIRo4cydChQy9rHbJizW7Y1/j4+JCens7QoUOpra2lqKiIkpISNBoNOTk55OTk4Ofnx6BBg4iOjhZ/k46Avz/ccYelmUxw4ABs2GCpLZWdDYcPw+HDicAmpk1LZNo0mDULZs+G1NSf3K7kcjk+Pj6iG6zJZKK1tZXm5mbUajWtra20tbXR0NAgxgLK5XK8vLzEtU+lUjlVnZPu0F9z9nJoa2sjLy+P1tZWXFxcSExMFP+vesqDD1oefyyTyBtvWOqWvfkmvPPOT+9fCX0h25iYGE6fPk1zczOFhYUMHjzY7p/Zl0RERHDrrbeydu1asrKyCA0N5dprrx1Q83Yg4UxytVvMU2pqKnPnzuX+++8nMzOTzMxM7r//fubPny+aVAFSUlL46quvADAajdx6660cPnyYVatWYTKZqKyspLKyUqzwXVBQwB/+8AcOHz5MUVER3333HbfddhvXXHONTVY2Z6Cz+AJrUcje4G9/+5tYWDMgIIC77rrrsmr03HzzzYwdO5bRo0ezfPlybrjhBgA++eQTMY5g69atvPHGG4AlUcWYMWMYP348vr6+3HDDDaJbxYWEhoYSEREhfr8JCQmoVKpuxztZiYyMZNy4cchkMiZNmnTJfW+66Sbq6+u54YYbLvuCcO7cuYwbN46YmBjS09NtFJfPP/+crKws/vznPyOTyfjwww/561//Krpr9AarV69m06ZN+Pn58f7777Nu3Toxlu6ll14Sazm1tbXx9NNPExgYSFhYGN988w3fffedeLE4a9YsnnjiCSZMmEBUVBRms5nXXnsNgP/9739kZ2fz8ssvo1Kp8Pf3F4uVXk24ubkxYsQIHnzwQe677z6GDRuGi4sL5eXlfPPNN7z66qusX7+esrKyy4qX6e/q7DKZjODgYEaPHs2CBQuYOHEiUVFRyOVyGhsbyc7O5ttvv2XLli3k5OQ4RG2s9igUMH48vPQSHD9uqSf1ySewdKkPgYHT0Wp9+O47S/rztDSIjoZ77oHVqy1xVbbHUuDj40NkZCSpqalcc801pKamEh0djb+/P66urpjNZpqbm6moqCA/P5+jR4+SnZ3NuXPnqKqqorm52enTo/f3nO0u1dXVnDx5ktbWVlxdXUlOThZdmi+XqVMtj2+9Bddc07uKE/SNbOVyuagwnT171inj/JKTk8XyJps3b6aoqGjAzNuBhjPJVSbY8ddQX1/Po48+KtYmuvHGG3nrrbdssoPJZDI++ugjli9fTlFREXFxcR0e6/vvv2fq1KmUlJSwdOlSMQlBdHQ0119/Pc8//3y3s+JYfTabmppsAup1Oh2FhYXExcVd8cLZG1hjZhyd2NhY1q5dy9ixY/vl8ysqKpgwYUK3KodbaS/bOXPm8Oyzz15x5kIJC13NW0f7jdkbjUbD8ePHOXLkiE1a3LCwMEaMGEFGRka35dAd62p/oNfrKSsro7i4mOrqaptaUUFBQaJFytPTsx9H2TkVFRX86le/4p57XuHo0XC2brUkl7gwyeCwYTBliqVNngxdOWAIgoBerxdrprW0tIjJatojk8nw9PTEy8sLpVIpPvaGZ4Ej4Khz1orRaOTYsWPinPXw8CApKalX5uqyZZbU+unpkJNjUZ56S3GCvpNtW1sb69evp62tjenTpztV0L8VQRBYt24dJ06cwMvLi4SEhMv2NpHoHEdfDzrTDTqix257PSEgIIBPP/20yz7tdbfY2NhL3tmIjo4Wg9Scne4k1pCwTPiXX365R/u0l+2MGTMYN25cbw/rqkWatz+hVCoZN24cY8eOpbi4mCNHjnD69GkqKyvFdOBpaWmMGDGC6OjoLmXnqBfU7u7uxMfHEx8fj06no6SkhJKSEmpqaqitraW2tpajR48SHBzMoEGDiIqKcijFuaKiglWrVvHLX/6SlSvDWbkStFrYu9eSxW/rVjh2zGKtOn7ckn4aYOhQiyI1dapFmWp/TSmTyfDw8MDDw0N0czcajbS0tNDa2iq2trY2NBqNjWIlk8lQKpVi8/T0RKlUXpR9dSDgqHPWZDJRU1NDRUWFjbKflpZ22a567dm+3aI4yWTwwQeW7HpX4LHbIX0lW1dXV6Kjozl37hzFxcVOqTzJZDJuvPFGampqqKysZM+ePdx0000OO38HKs4kT7tanhyVgWJ5Gij0t+VJYuAg/cYshamzs7M5cuSITUHk4OBgRo4cSUZGRqfxjgMJrVZLcXExxcXF1NXVidvlcjkhISFER0cTGRnZ7/MgKyuLkSNHcuTIkQ6ztQJUVcEPP/zUOiqVlpr6kzI1ZQqEhXX9uYIg0NbWZqNMaTSaTmMG3dzcREXK+ujh4eFUFyT2Rq/XU11dTU1NjegW7+npiZ+fH1FRUb1y4+f8eRg1CmprLTFPHZQvHHBUVlayc+dOPDw8WLBggdPeIGtoaOCdd95Br9czefJkpk+f3t9DkuhDemJ5kpQnB1aerKnFJXofSbb2oyvZOtpvrD8RBIGysjKOHDnCyZMnxYtmhUJBSkoK11xzDfHx8eLF8fbt25kxY0Z/DvmyaW1tFRWp9jGVMpmMoKAgoqKiiIqKwqsfMvR0R3m6kJoai2vfzp0WZerHzMU2JCXBuHGWOKvx42HIEEvcVVcIgiCm9NdoNGi1WjQazUV1aKxY3f6sVi4PDw/c3d3x8PBwiOQUjjBnrXUB6+vraW5uFre7u7sTHh5OcHBwrykD5eUwcyacOQMjRsCePWAvb9W+lK3JZOKrr77CaDQyd+7cTguzOwMnT57k5ZdfJiEhgbvvvpvY2Nj+HpLT4AjrQVc4jNuexJVxFeq1fYYkW/shybZ7yGQyUWmYM2cOJ06cICsri4qKCk6dOsWpU6fw8fFh2LBhDB8+3GEK1F4OXl5epKamkpqaSnNzMyUlJZSWllJfX09NTQ01NTUcPXqUwMBAUSaOlLXvQoKD4ZZbLA0sVobduy2K1M6dlkx++fmW9u9/W/r4+MC11/6kTF17LVwYGiiTycTssO1jeE0mk40ypdVq0Wq1GI3Gi9z+rFjrv12oWLm5ueHi4tIn1oP+mLNW98jm5maampoucon09vYmNDQUPz+/XpVBfj7MnQvnzkFkJKxbZz/FCfpWtgqFgqCgICorK6mpqXFq5Wno0KHEx8eLcVA///nPr/obfb3FQP4PuxBJeXJgHOHOobMiydZ+SLLtOR4eHowePZrRo0dTUVHBsWPHyM7ORq1WiwVqZTIZISEhpKWliYWzByLe3t4MGTKEIUOG0NraSmlpKaWlpdTW1lJXV0ddXR3Hjx/H19eX6OhooqKi8PX1tdvFvr+/P9dddx3+/v6XfYygILj5ZksDaGiAzEzYt8/SDhwAtdoSP/Vj/UdkMktWv9GjLW3UqM5jYxQKBd7e3jYKpdVKpdVq0el0YtPr9ej1elGJ6KjEgVwuF5U0Nzc38dHV1RVXV1fc3Nx6Jfans/IhvYE1KYf1/LVaLa2trWJhbisymQwvLy8CAgIICAiwy2/n4Yfh44+htRXi42HbNoiJ6fWPscGesu2IwMBAKisraWxs7NPP7Q9uuOEG9u7dS319PVu3bhWzB0tcGX09Z+2J5LbnwG57RqNxQAYJDwQk2dqPrmTraL8xR8ZoNJKbm8uxY8c4e/YsGo0GT09P3NzcGDJkCNdccw2DBg1ymvgDrVZLWVkZpaWlF2Xt8/b2JjIykoiICIKCgno9zqe+vr7b2VovB6MRTp60KFL791seO0oO6upqyeo3atRPSlVqKvR0qTKZTOj1ehulSqfTYTAYxLIfl0KhUIjKlKurKy4uLjZNoVCIz+VyOXK5HIVCgUwmE+fk5cjVbDZjMpkwGo0YjUZMJhNtbW3i2K1Nr9fbzJH2eHh4oFKp8PX1xcfHx243dEpKYNCgn15PmgSffQZ9cY1o7zl7IUVFRWRmZhISEuL0sUD19fWo1Wo+/vhjAO6+++5OM0FLdJ++nrM9RYp5ugQDRXkaKKnKByKSbO2HlKq891Gr1bz//vu4ubnZJF8ICAhg+PDhZGRkOJUrjV6vp7y8nLKyMioqKmzqIbm5uREREUFERATh4eFXfGGs0+lYs2YNd955Z5/Oyaoqi3Xq8GE4dMjS6usv7qdUWtJdDx9uUayGD7e8vtxSamazWVQ+9Hq9+NyqoLS1tV1R/SmZTCYqU3l5eaSkpIgKVXtFXxAEsZnNZvGxM4WoI+RyOR4eHnh6eoqtL4oRNzTAX/8Kr78O7cPR2tp6ruheLn2d9rm6upodO3bg6+sr1gd0Vqyy/fbbbzl06BD+/v784he/kG64XiFSqnIJCQkJiT7Dx8eHoUOHMnv2bEpKSjh27BgnT56kvr6eHTt2sGPHDmJjY8nIyGDIkCEDXjF1d3cnLi6OuLg42traqKiooLy8nPLycgwGA0VFRRQVFYmZ+yIiIoiMjLyshBOnT5/mZz/7GcOGDet2wojeIDQUFiywNABBgKKinxSpw4fhyBFobra4/R048NO+MhkkJNgqVEOHWqwglzLKWRWOruaI1dpjVaisVqD2rb11qL3SIwgCJpMJk8mE2WzuNNlFV8hkMhQKhY11y83NzaZZ3Q770vKan29JVf/RRxYXPbBYm+66C+67r8+G0S9YFYfOskE6IzNnziQ3N5eGhgb279/PpEmT+ntIEg6CZHlyYMtTW1ubU8aPtE9tvmLFCgYPHswvf/nLPh2Ds8rWEehKto72GxtIVFVVERoaKr42GAycPn2a48ePU1RUJCbqcHFxISUlhWHDhpGQkOBUqazNZjO1tbWiVap99jQAPz8/IiMjCQ8PJzAwsFsX1peTba+vMJshL8+2ztSxY1BR0XF/pdLi5jdkiG2Li7t0pr8rxWo5sipNZrOZ6upqAgMDxffbP1otUXK53Oa5VWmSyWTo9b1fH6mnNDTAl1/C6tWWZCDWK6aMDHjxRbj+eosy29dcuB7Ym/r6erZs2YKXl5fTxwC1l212djbr1q3Dzc2NRx55xKET2Tg6fT1ne4pkeXISjEajeBEaGxtLfX09VVVVYvVztVpNaGgoMTEx5OTk9OdQRYqKikhJSUGn03Wr/zvvvGPnEXVMe9lK9C6SbO1DfX29zR+Pm5sbw4cPZ/jw4TQ1NZGdnU12djY1NTWcPHmSkydP4uXlRXp6OhkZGYSHhw/4+CirpSkkJIThw4ejVqtFRaq2tpbGxkYaGxs5deoUHh4ehIeHEx4eTmhoKO79fRV+GcjlkJJiaXfc8dP2mhpbZer4ccjNBY3GYq06csT2OO7ulmMkJ0Niom0LC+udi//21iIrWq32si82330XHnkE3nwTHnzwysfXXQTBorBu3AibNsH330P7MLH58+GJJ2DatP5RmqxcuB7Ym/alFJyd9rJNT0/n0KFDlJSUsH37dm666ab+HdwApq/nrD2RlCcHxmAwiIoSQFhYGOvXr+f2228HYN26dURHR/fX8AY0F8pWoveQZGsfiouLSU1N7fA9X19fJk2axMSJE6moqCA7O5sTJ07Q2tpKZmYmmZmZBAcHM2zYMNLT050m3s/HxwcfHx9SUlLQ6/Wie19FRYVo5SwsLEQmkxEYGCgqU/7+/gNakQwOttQTmjnzp21GIxQUWGoMnT79UztzBnS6n5StC/Hy+kmRSkiA2FiL+9+gQZaMcVdSDq+rOdsV775rKTCbkWF5BPspUAaDpU6XNTPivn1QXGzbJyMDFi+2KLD2zqLXXS5XtpeLNWujMxTwvhTtZSuTyZgzZw7/+te/yM7OZvLkyQ6d9MCR6es5a08k5WkAceedd7Jq1SpReVq1ahWLFy9m7dq1Yp8TJ06wYsUKTp06RWJiIm+99RZjx44FLNarRx55hHfffZfKykr+8Ic/MH78eJYvX05lZSXPPfccTzzxBGC5Y/irX/2KdevWIZfLeeSRR3jqqacAWL58OQEBARw/fpyDBw8yfvx41q5di7+/P7Nnz0av16P6MZo5Ly+PiIiITs9p+fLlpKSk8Jvf/IYXXniBc+fOodVq2bRpE2lpaXz22WfE/PhvdeLECX7xi19w4sQJ4uPjeffddxk1alTvC1pCYoAik8nEZAqzZs2ioKCA7OxscnJyqKmpYdu2bWzfvp2YmBjS09NJTU11moshd3d3YmNjiY2NxWQyUVNTQ2VlJRUVFTQ1NVFbW0ttbS0nTpzAw8ODsLAwwsPDu519ztFxcbFYlpKTof3NcZMJzp+3KFL5+XD27E+P589bYnc6U6zAUovKqkwNGgRRURZrVWio5TEsDEJCLJkCewOr4vTII5aEDI8/fuUKlCBY3O8KCy1xZXl5FoXpxAnIybEonu1xc4PJky11m667zuIKebXT1NQEcFUWl4+KiiIpKYn8/Hx27dolWZ8kJOXJkbnw7vCsWbP44IMPqK+vx2AwkJ+fz9NPPy0qTwaDgRtuuIFf/epXPPDAA3z55ZfMnz+fgoIC8Vjfffcdhw4dIjc3l0mTJnHjjTeyd+9eiouLGTt2LEuXLiU4OJiVK1eiVqvJy8tDrVYza9YshgwZIvo6f/bZZ2zevJnExESuv/563nrrLZ577jm2bNlCSkpKh7VFusO6devYuHEja9as4Wc/+xl/+MMf+OCDD2hubmbevHm8+eab3HjjjXzzzTfcfPPN5OfnX1bsjLPceXdEJNnah55mKVIoFAwePJjBgwej0+k4ffo02dnZYrKFoqIivv32WxITE0lPTyc5OXlA149qj0KhICwsjLCwMIYPH05ra6uoSFVVVaHT6UQZyGQytmzZgqurK7W1tQQEBDhVnJhCYak9FB9/8XsGg0WZsCpUBQUWq0txsUWxqq+HpqafFI2uCAy0KFLBweDvb2l+fnM4dMj63PLo7W0pHuvpaYnRsj739IQPP4SHHrIoTm+8YXGLe+MNy/FXrLDEgN19N2i1tq2lxVKouK7O8mh9XlVlOY+iIkvijc7w84Nx4yzFi8eNsxQwvtxshn1FX2ctq6qqAiAoKKhPP7c/6Ei2U6dOJT8/n+zsbKZMmXJFdeGuVhw5015PkZSnS6HRWG5N2ZOUFMu/yAU0Nzfb+Iu7uLhw00038fnnn6PVarntttts/uQzMzNRKBT84he/AOCOO+7gjTfeYMuWLdx2220APPbYY/j6+jJmzBjCwsJYtGgR/v7++Pv7M2jQIHJycggKCuKjjz6iqKgIlUqFSqXioYce4osvvhCVp9tvv52hQ4cCcMstt7Bjx45eEcXs2bPFjDZ33HEHv/vd7wD49ttvycjI4OYfq1DedNNN/OlPf2L//v1Mmzatx59zoWwleg9JtvZh586dTJ069bL29fDwYMSIEYwYMYKmpiZOnjzJiRMnqKysJC8vj7y8PFxdXUlOTmbo0KEkJiY6VVpeLy8vEhISSEhIwGQyUVdXR0VFBZWVlTQ0NHDkyBFiY2M5deoUbm5uhISEEBoaSmhoKN7e3gPaxa8r3Nxg8GBL64iWFksto/YKVUUFVFZaWlWVpRmNFmWlXRb9y+bhh39SnOAnBUoQ4Oc/t7TLJSzM4pYYH2/JTpiRYUn7Hh3dv/FLl8OVrAc9Ra1W09TUhEwmc5qYla7oSLaRkZEkJCRQUFDA4cOHmTVrVv8MbgDTl3PW3jjPv6O9yMmBkSPt+xlHjkAHWZ46qnexZMkSfvOb36DVannvvfdsqn2Xl5czqH3FPiAmJoby8nLxdUhIiPjc09OT4OBgm9etra3U1NSg1WoZ3O4f1Ww2M2HChA6Po1QqL9vSdCGdHbe4uJjt27fb1LKxpjC+HHpSS0SiZ0iytQ+Xk/K5I3x9fZkwYQITJkwQk0ucOHGC+vp6MdGEh4cHQ4YMYejQocTGxjqZJUYhJp0YNmwYx48f57nnnuMPf/gD7u7uGAwGSktLKS0tBSzrolWRCgsLu6ri+VQqi8taV25rZrPFQlVVZVGoamosLnINDXD0aCF+fnHi64YGi0J2oeWofQa79oqTFasCtWsXZGdbtrm6goeHxWKlUlksX0FBPz1aW0yMRWGKibH0dRZ6az3oDgUFBQCEh4cPyMQrPaUz2Y4ZM4aCggKysrKYOnWqlBiph/TlnLU3kvJ0KVJSLk5dZI/P6ICOfpjjxo2jrKxMzLS1c+dO8b2IiAhKSkps+hcXF3PLLbf0aDhBQUF4eHhw/vz5Hrtg2esObWRkJNdffz3r1q3rleNJi579kGRrH9rfWOgtgoODmTZtGlOnTqW8vFxUnpqbm8nKyiIrKwuVSkVaWhppaWlER0c7nRXGZDJx9uxZkpOTGT58OA0NDVRXV1NZWUltbS1arVZ08QOL8hkaGioqYM7i6ni5yOU/KSppabbvHT3ayDXXdL2/IFjcB//5T/jlL+Gxxyy1lNpPM0GwbM/OtihRP/953xWjdVTssR50hFarFZWnxMTEPvnM/qYz2SYlJeHn50djYyOnT59m2LBhfTyygU1fzdm+4CpffrqBUtmhVagv6OxP2ZrE4ULGjh1LW1sbb7/9Nvfffz9fffUVubm5zJ49u0efK5fLufvuu1m5ciWvvPIKPj4+5Obm0tzczJgxY7rcNygoSLQIhYeH9+hzu2L+/Pk8/fTTrF+/nuuvvx6DwcAPP/zAuHHjLivG5mq/4LEnkmztQ3xHQSu9hEwmIzIyksjISGbNmkVxcTEnTpzg9OnTtLS0cODAAQ4cOICPjw9DhgwhLS2NqKgop1Ok5HI5gYGBBAYGkpqaitFopLa2lqqqKqqrq6mvr6epqYmmpiby8vKQyWQEBAQQHBxMSEgIQUFB0vxvR3fmrExmSaX+xBOWv9sVK36yNMlkPylOb70F77zTt2nLHRl7rgftyc7Oxmg0EhQU1Kv/6Y5MZ7KVy+XiTeuTJ09KylMP6as52xc4jy+GE9JqLWF+ARkZGWK8UXvc3Nz4+uuv+c9//kNgYCB/+ctfWL9+/WUpF3/729/EGjEBAQHcddddNDQ0XHI/Ly8vnnrqKdLT0/Hz87NxGbwSfH192bBhA2+88QbBwcHExsby3nvvXfbxOpOtxJUjydY+ZGZm9snnyOVyYmNjueGGG1i5ciWLFy8mIyMDd3d31Go1mZmZfPDBB7z22mts2rSJkpISnLXWuouLC2FhYQwbNoxZs2Zx0003MXHiRBITE/H29kYQBOrq6sjJyWHXrl189dVXbN68maNHj1JaWupUbiqXQ0/n7IMPWhSkN9+0KExms+XxzTclxelC+mI9KCsrE1P9Dx8+3OlulnRGV7JN+9G8as0MLNF9+uo/rC+QCc76r9cFnVURttYFiYuLu6wMbr1NU1OTlLnMTkiytR9dydbRfmMDic2bN/drtiKj0UhBQQGnTp0iNzfXRjEYyBaprKwsRo4cyZEjRxjRQy+D1tZWqqurqampoaamhuYOUrr5+fmJlqng4OCrat5f7pxtX+cpO1tSnDrC3uuBWq1m27ZtGAwGUlJSGD58uN0+y9G4lGz/+c9/Ul1dzcKFC8nIyOjDkQ1s+vs/7FJ0pht0hOS258BcTYHJfY0kW/shydY+pKen9+vnu7i4kJycTHJy8kWKlNUilZmZiY+PD2lpaQwZMmRAKFKxsbH8/e9/JzY2tsf7enl5ERcXR1xcHAAajUZUpGpqamhqaqKxsZHGxkby8/MBi6IZHBxMUFAQQUFBqFQqh5fR5XK5c9aqKD3yiKQ4dYY91wONRsPu3bsxGAwEBQX1+9rT11zqfBMTE6murqaoqEhSnnqAM80jSXlyYKSsZfZDkq39kGRrH3oro2VvcKEidfbsWU6fPk1OTg5qtZr9+/ezf/9+VCoVKSkppKamEhsbi0Kh6O+hX0RAQABz5swhICDgio+lVCqJiYkRC3vrdDpqampE61RjYyNqtRq1Wi0G4bu7uxMUFERgYCBBQUEEBAQ4TZr4K5mzDz4Iy5db4qEkLsZe60Frays7d+6kubkZLy8vJkyY4JC/W3tyKdnGxMSwb98+zp8/30cjcg4c6T/sSnGOFdpJ0ev1V5WLR18iydZ+SLK1D4WFhTblAxwFFxcXUlJSSElJoa2tTbRI5eXl0dLSwuHDhzl8+DAeHh4kJyeTmppKQkKCw2RlrKmp4Y033uCFF16wKd3QG3h4eBAdHU10dDRg+W3U1NRQW1tLXV0d9fX16PV6ysrKKCsrAywxZ35+fjYKlVKpHJDWqSuds5Li1Dn2WA9qa2vZs2cPOp0OlUrFtGnTrkpPgkvJ1loSpq6uDq1We1XK6HJw1P+wy0FSniQkJCQkegVXV1dRkTKZTBQWFnLmzBlycnJobW3l+PHjHD9+HFdXV5KSkkhNTSUpKalfle2SkhL++c9/cu+99/a68nQh7u7uREVFERUVBVjSpDc0NFBXV0ddXR21tbVoNBrq6+upr68X9/P09CQwMJCAgAACAgLw9/e/KurtSPQNgiBw6tQpTp8+jdlsxt/fn4kTJ+Ll5dXfQ3NIPD098fb2prm5mbq6OvH3LHH1ICWMcOCEEYIgDMi7jQMBSbb2oyvZOtpvbCBhMpkGrPuM2WympKSEM2fOcObMGZqamsT3FAoFcXFxpKamkpycjEql6tOxXUnCCHvQ2toqWqbq6upoaGjo0BVWpVKJypRVoXIUa56VgTxnHZ3ekm1NTQ3bt28XX0dFRXHttdc63FzqS7oj208++YTCwkJuuummqyqZxpXg6OuBlDDCSWhpacHb27u/h+GUSLK1H5Js7cO+ffuYNGlSfw/jspDL5WIs0Jw5c6isrBQVqZqaGs6ePcvZs2f55ptviIqKEuOpgoODr7qbHF5eXnh5eYlxU0ajkfr6ehoaGkSLVHNzMy0tLbS0tFBcXAxYanX5+Pjg7+8vKlS+vr79ehE8kOeso3Olsm1ra+PMmTOcPn1a3JaUlMSIESOuut/chXRHttaMss4Ux2NvnGk9kJQnB0YKvLcfkmzthyRb+6DRaPp7CL2CTCYjPDyc8PBwpk+fTm1trejaV1ZWRmlpKaWlpWzfvh1/f39RkRo0aJBD37W0Fy4uLoSEhBASEiJu0+v1NspUQ0MDra2tYgHfoqIiwCJrlUqFn58ffn5++Pv74+vr22cxVM4yZx2Ry5WtwWCgoKCA3NxcdDqduH3y5MlERET01vAGNN2RrdVz4mqv5dYTnGk9kJQnB8ZZMi45IpJs7YckW/sQGBjY30OwC0FBQUyaNIlJkybR3NxMXl4eubm5nDt3joaGBjEFuoeHB0lJSQwePLhX46S8vb0ZO3bsgLKWuru7ExYWRlhYmLhNq9WKClVdXR2NjY1otVqam5tpbm6mpKRE7Ovm5iYqUv7+/vj5+eHj49PryqmzzllHoKey1Wq15OXlcfbsWdra2gDL3B8+fDgRERFXvbWpPd2RrZubGyApTz3BmdYD6SrHgemrmJBVq1bxxRdf8NVXX132MZYvX05KSgq/+c1venFk9uNKZNv+XHtDds6GFMtkH1JSUvp7CHbH29ubkSNHMnLkSAwGA+fOnSM3N5e8vDxaW1s5ceIEJ06cEN0Ak5OTSUpKuqI/5aSkJLZu3drnsVa9jaenJ56enjbWA51OJ9aZamxspKGhgebmZgwGA1VVVVRVVYl95XI5KpUKHx8ffH198fHxEdvlKlVXw5ztL7ojW5PJRHl5OYWFhVRUVGANcff19SUlJeWqteZeiu7KFqSbhT3BmdYD6Vt3YFpaWvD19WXWrFnMmTOHlStX2rz/y1/+krq6Oj755JMeHVcmk1FRUSHetVyyZAlLlizptXEPBKyy7YjY2FjWrl3L2LFjL3mcq1F2l6Ir2UpcPnv37nXo6uy9jZubm5i5z2w2U1ZWRm5uLrm5udTU1FBYWEhhYSGbNm0iICBAtEjFxMT06ILGZDKxZcsWFixY4HQXkh4eHhdZqEwmk03xXmszGAxiDarS0lKxv9X1r70y5evri7e39yXjqa62OduXdCZbk8lEdXU1ZWVllJSU2FhGgoODSU5OJjIyUrI0dUF35q1VrlYLlMSlcab1QFKeBgBLly7l9ddft1GezGYzn332GR999FG3j9PW1nZVZ9CRkJAYmMjlcrFe0syZM6mvrxctUsXFxdTX14vufa6ursTHx5OUlERSUtIlFfnjx49zyy23OEy2PXujUCjEhBJWBEFAq9WiVqtpamoSlaimpiYMBoPo+metRWXF09MTlUqFt7c3KpVKbN1RrCR6j9bWVqqrqykvL6eiogKj0Si+5+npSWxsLHFxcZfMICbRfVpbWwGkGk9XKfL+HoBE51h/lAsXLiQ3N5czZ86I7+3cuROTycSMGTMoLi7m+uuvJzAwkNTUVDZt2iT2i42N5eWXXyY5OZkhQ4Ywe/ZsABISElCpVOzfv5+PP/6YuXPnivvs2LGDUaNG4ePjQ1JSErt37wbg/fffJykpCW9vbzIyMti5c2e3ziM2NpZXX32VwYMH4+Pjw+uvv87BgwcZMmQIAQEBvPbaa2Lf+vp67rjjDoKCgkhMTORf//qX+N7y5ct5/PHHmTJlCiqVisWLF1NZWcnMmTPx9fVlyZIloikd4B//+AdJSUkEBQVx9913i4vdxx9/zC233MJDDz2Ej48PaWlpHDt2DID77ruP4uJipk+fjkql4rPPPuvy3NrLbufOnaSkpPD73/+egIAA4uLi2Lp1q825LV68mJCQEOLj43tsMRwoSH8m9mHIkCH9PQSHISAggHHjxnH33Xfz61//mttvv50RI0bg7e1NW1sbubm5bNiwgddee423336bbdu2cf78eSmZSSfIZDKUSiVhYWEkJyczevRoZsyYwc0338yCBQuYNm0aI0eOJDExkZCQENE1V6vVUlNTw7lz58jOzmbfvn1s2bKFL7/8kv/97380NTVx4MABTp06RVFREdXV1bS2tkrfwxVgNptRq9V4e3uTmZnJN998wzfffMOBAwcoKSnBaDTi6elJYmIiU6ZM4YYbbmDYsGGS4tQDurPW1tbWApaYTYnu4Uz/YZLlyYGx/sF4e3tz4403snr1av74xz8CsHr1au644w5kMhk33HADDzzwAF9//TWHDh3ihhtu4OTJk6Krxv/+9z92796Nj48PHh4eyGQyCgoKxPdzc3PFzzx37hw333wzq1atYt68eZSVlWEwGACIiIhg+/btREVF8cEHH3DHHXdw/vz5bhVr/O677zh06BC5ublMmjSJG2+8kb1791JcXMzYsWNZunQpwcHB/OIXv8DFxYXi4mLOnj3LzJkzSUlJYeLEiQB8/vnnbN++neDgYEaMGMH8+fP597//TUREBKNGjWLDhg0sWLCAzz//nPfee49t27YREhLCvffey+9+9zteffVVAH744QdWrFjBW2+9xfPPP8+TTz7J9u3b+de//sW2bdu67bZ3IWfPnsXb25vq6mo+/PBDVqxYQUFBAQDLli1j6NChlJSUUFhYyPTp0xk+fDjDhg3r8ec4MtKFkX2w/g4lbHF3dyc1NZXU1FQEQaCqqoq8vDzy8/MpLS0VY3v27NmDh4cHCQkJYpPcS7tGJpOJsVShoaE27+n1ejFdektLi036dJ1Oh06n67RGlVVZu7B5eXmhVCrx9PTEzc3tqncta2tro7m5mYaGBhoaGkQXS6PRSENDA/7+/oDFMuvv709YWBgREREEBARc9bK7Ei611ra1tVFXVwdIylNPcKb/MEl5cmD0er14h2/p0qU89thj/PGPf0Sv1/Pll1+yZcsWDh48SFtbG7/4xS8AGDduHFOnTmXjxo3cc889ADzxxBM2aW67Ys2aNSxYsID58+cDMGjQIPG966+/Xnx+//3387vf/Y78/HyGDh16yeM+9thj+Pr6MmbMGMLCwli0aBH+/v74+/szaNAgcnJyCAgI4Msvv6SgoAClUklGRgb33nsva9asEZWn22+/XQw6nDp1KiqVSrybMWPGDLKzs1mwYAEffPABzz77rFgr5ZlnnuH6668XlachQ4Zw6623ArB48WLeeeedbsnnUvj6+vLEE08gk8lYunQpDz74oHhBsXv3btavX49CoSAlJYXFixezbt06p1Oe2s9bid7j7NmzJCQk9PcwHBqZTCbG+EyePBmNRkNBQQH5+fmcPXsWjUbDqVOnOHXqFGCJAbEG0bd3dZK4NO7u7ri7u3eYrMNgMNDS0sLmzZvJyMigubkZjUZDa2srGo0Gs9lMa2ur6A3QEXK5HE9PTzw8PLps7u7uuLq6DkhlwWQyodVq0Wg04qPVRbK5udkmlXh7XFxcMBgMpKWlERwcTGBgoOQm2Ytcaq0tLi7GbDaLMYAS3cOZ/sPsqjw1NDTw6KOPsn79egBuvPFG3nzzTfz8/DrdZ/ny5Re5M1177bVkZmaKr/V6PStXrmTNmjVotVpmzJjBP//5T6KiouxyHjz0EFzg691rREbC229fstucOXNQq9VkZmZSUVFBcHAwo0eP5r///S/5+fk2MjUajYwcOVJ83RO5lJaWEh8f3+F7//vf//jDH/7AuXPnAGhubhbvvlyK9sqbp6cnwcHBNq9bW1upqanBZDLZjDcmJobNmzf36DhgWdzuvfdeHnjgAfF9a3pWwGY/pVLZa4Xu2hf1VCqVAGIhy9bWVpsLDZPJJCWbkJCwI0qlkvT0dNLT08WkEwUFBZw9e5aysjJqamqoqKgALC64OTk5JCQkiO5pA/GC3BFwc3MT46oudNURBAGdTicqUu2bdZter++WgmVFJpPh6uoqKlLtn7u5ueHm5oarqysuLi64uLigUChQKBTicxcXF+RyOXK5HJlMZtOs2wRB6LQZjUZMJpP4aH3e1taGwWDAYDCg1+ttnlvP81J4eHiIKeWtaeW9vb3ZunUr6enpl/0dSVw+Z8+eBSzhD9IacXViV+Vp8eLFlJaWijE4DzzwAMuWLeObb77pcr+5c+faJEK4MJvJ448/zjfffMPatWsJDAzkySefZP78+Rw5csQ+2ZK6odzYg/Z1R1xdXVm0aBGrV6+moqJCvOiOjIwkPT2drKysTo/Tkx93dHS0jRufFb1ez5133snXX3/NjBkzUCgUhIeHi3dte4Pg4GDkcjmlpaVER0cDFiXocgr3RUZG8pe//IUbb7yxw/e7mif2WAwjIyPx8/PrtrI5kBlI9XIGEtOmTevvIQxo2iedmDp1KlqtlsLCQnJycggLC8NsNnPu3DnOnTvH1q1b8fb2Ft374uPj8fLy6u9TGHB0NGfbuwJ2hslkQqfTodVq0el06PV6m9ftm9FoRBAEUTGxB1blyR4oFArRVVGpVIrJN7y9vbtMvCGtB/ajK9maTCZOnDgBwODBg/tqSE6BM81ZuylPZ86cYdOmTWRmZnLttdcCloQD48aNIzc3l+Tk5E73tRYA7IimpiY++OAD/vOf/zBz5kwAPv30U6Kjo9m2bZvTpEEESzXm9rVHlixZwk033URLSwsvvfQSYLHKtbW18d5777F8+XIADhw4QExMjI3LXXtCQkIoKirqUMZ33nknw4cP57vvvmPu3LlizFNwcLD4CPDGG29QU1PTq+erUChYuHAhzz77LO+++y4FBQV88MEHfPHFFz0+1r333suLL77I0KFDiY+Pp6KiguPHj4vJHbqKy7HK53JinjojMjKS0aNH87vf/Y7f/OY3uLm5kZ2djYeHh1MFUcLF81aidzh8+DDjx4/v72E4DZ6engwZMoQhQ4YQGhpKcnIyZ8+epaCggKKiIpqbmzl27JiYTCY0NJS4uDji4+OJiYnpVqzn1c7lzlmFQoGXl1e3FNYLLTxdPe/MQmR93pmCdCnFSS6X21ixrJYsq9Xrwubu7n7FsV3SemA/upJtbm4uLS0tqFQqSXnqIc40Z+2mPO3fvx9fX19RcQIYO3Ysvr6+7Nu3r0vlaefOnYSEhODn58eUKVN48cUXRXetI0eO0NbWJmaNA0sig6FDh7Jv3z6nUp7aZ44DGD9+PN7e3sTFxZGUlARYfJ83bNjAY489xrPPPosgCIwaNarLGJ7f/e53LFiwAL1eb5OZDyAuLo4vv/ySX/3qV9x+++2Eh4fz4YcfkpCQwCuvvMKsWbOQyWQ89NBDJCYm9vo5/+Mf/+DnP/85UVFR+Pr68oc//IFJkyb1+Dh33HEHDQ0NXHfddZSVlREeHs6KFStE5amrP8OnnnqKRx99lBUrVvDee++xaNGiyz6f9qxatYpf/vKXxMfHYzAYGDp0qE2mQWfhwnkr0Ts0Nzf39xCckoKCAp588klWrVrF2LFjGTt2LEajkeLiYgoKCigoKKCyslJMPJGZmYlcLicyMlJUpqKioqRimR3QF3PWqqz0RpZPQRAwm82iO1775xe6813o2tfXSOuB/ehMtoIgsGfPHgCuueYap6sLZ2+cac7KBDvZol966SU+/vhj8vLybLYPHjyYe+65h6effrrD/T777DNUKhUxMTEUFhby3HPPYTQaOXLkCO7u7qxevZp77rnnIl/h2bNnExcXx7vvvnvRMfV6vU1/tVpNdHQ0TU1NNsF+Op2OwsJC4uLiHCLg3Xp3Q6L3kWRrP7qSraP9xgYSBw4csLkZJdE7ZGVlMXLkyC7rPLW2tlJUVMS5c+coLCykvr7e5n1XV1cGDRokKlNhYWH9ckHtaEhz1n5IsrUfncn21KlTfP7557i5ufHYY49Jrrw9xNHnrFqtxtfX9yLdoCN6fKvshRde4Pe//32XfQ4dOgR0HDtivYPTGbfffrv4fOjQoYwaNYqYmBi+/fZbFi5c2Ol+XR33z3/+c4dj3rZtG15eXkyfPp2DBw+i1WoJCgoSK7AD4gWeNeuNt7c3Go0Gk8kk+ipbtekL+6pUKtEnWy6Xo1KpUKvVgMU1US6Xo9VqO+1rNBppamrCzc0NFxcXNBoNAF5eXqIbgkwmw8fHRxzvhX2VSiVGoxGDwSD2VavVCIIgBtNaA3Lb9wVL5rjm5mbMZvNFfT09PTGbzaJS6uPjQ0tLC2azGRcXFzw8PMQkDBf27YkMu+rbHRl2Jm+lUolGo6GtrQ25XI63t3enMuxI3lYZdiVvqwy7K++eyLCrvr01Zy9X3taA8PZ9rTJsbW0VP8uaCCQ6OpqgoCCOHj0KwKhRoygvL6e8vByFQsHMmTPZtm0bJpOJiIgIIiIiOHz4MGC5+1dbW0tJSQlgSazy/fffYzAYCA0NJTY2lgMHDgCQkZGBWq2mqKgIgFmzZrF37140Gg1BQUEMHjyYffv2AZCWloZOpxPTzFvXiJaWFvz9/UlLSxPvQKakpGA2m8UbRVOmTOHYsWPiAjxixAixJlpSUhIuLi5izbaJEydy+vRp6uvr8fLyYuzYsWzfvh2A+Ph4lEolJ0+eFM81KyuLmpoaPDw8mDx5Mlu2bAEsiVX8/Pw4fvw4AGPGjKG4uJjKykpcXV2ZPn06W7ZsQRAEoqKiCAkJEeMkR44cSWVlJWVlZcjlcmbNmsX27dsxGo2Eh4cTFRUlrunDhw+nvr6e4uJiUd47d+5Er9eL9cusyX3S09NpaWmhsLAQgJkzZ7Jv3z40Gg2BgYGkpKSwd+9ewJL90mAwiIHY06ZN4/DhwzQ3N+Pn50dGRga7du0CEL0WrLGZkydPJjs7m8bGRry9vRk1ahTff/89AImJibi5uXH69GkAJkyYQE5ODnV1dSiVSsaPHy9+5+fPnycsLEyMZxg7diznzp2juroad3d3pk6dSmlpKcnJyfj5+dHc3MwPP/xAZWUlAQEB7Nu3j82bNyOXy0lLS6OpqYng4GCGDx9OWlqaOL9HjBhBdXU1paWlyGQyZs+ezY4dO2hrayMsLIxBgwZx8OBBAIYNG0ZjYyPnz58HLDcJd+3ahU6nIzg4mMTERPbv3w9Y/i81Go2Y0GfGjBlkZmbS2toqJm6wztnU1FSMRiP5+fmAJXNpVlaWePEwfPhwfvjhB8Byw1Mul5OTkyPO2VOnTtHQ0IBKpWLMmDHs2LEDsATQe3h4iNkMr7nmGo4cOUJtbS1KpZIJEyaIte9iY2Px8fEhOzsbsLigFxUVUVVVhZubG9OmTZPWCDpfI6zfX/s1Yty4cZw9e1ZaI65wjTCbzWzevFlcI7Zt24bBYGD//v3o9XpCQ0PZs2dPh2uEdc4OGjSIgIAA0e139OjRlJaWUlFRgYuLCzNmzGDr1q2YzWYiIyMJCwvjyJEjgPOuEUajkbNnz9qsEePHjycvL88h1gjr+LtDjy1PtbW1YnGwzoiNjWX16tX88pe/pLGx0eY9Pz8/XnvtNTGNdndISkrivvvu46mnnmLHjh3MmDGD+vp6scYBWCbQTTfd1KGSNFAtT01NTVIdEjshydZ+dCVbR/uNDSQ2b97sVG7JjkJ3LE9dIQgCNTU1FBYWcu7cOYqKii7yjPDw8GDQoEHExsYSExNDeHj4VWGZkuas/ZBkaz86ku3XX3/N0aNH8ff356GHHrookZnEpXH0OWtXy1NQUFC3ioKNGzeOpqYmDh48yJgxYwCLya6pqalHAWN1dXWUlJQQHh4OWO6AuLq6snXrVjEWpaKigpMnT/Lyyy93eAxrPQoJCQkJCYneRCaTERISQkhICNdeey1ms5mKigqKioo4f/4858+fR6fTkZeXJ1oe3N3dGTRoEDExMcTGxhIeHi7FT0hIOCjZ2dkcPXoUmUzGTTfdJClOEvZLGJGamsrcuXO5//77xTikBx54gPnz59ski0hJSeHPf/4zN998My0tLbzwwgvccssthIeHU1RUxDPPPENQUBA333wzYHGDuvfee3nyyScJDAwkICCAlStXkp6eLmbfcxakO/P2Q5Kt/ZBkax+6SrIjcflERkby7LPPEhkZ2SvHsyaTiIyMFF2AKisrOX/+vKhQ6XQ68vPzRZcYNzc3oqOjGTRoEIMGDSIyMtIpLtCkOWs/JNnaj/ayLSkp4euvvwYs7mgxMTH9NawBjzPNWbumB1q1ahWPPvqomBnvxhtv5K233rLpk5ubK8aaKBQKTpw4wb///W8aGxsJDw9n2rRpfPbZZza1Y1577TVcXFxYtGiRWCT3448/lu7cSUhISEj0iNDQUO677z5CQ0Ptcny5XC762I8bNw6z2Ux1dTVFRUWiMqXVasXMftZ9rLENgwYNIjo6WqqfJiHRx1RWVrJmzRpMJhMpKSlMnz69v4ck4SDYVXkKCAjg008/7bJP+5ArT09PMfirKzw8PHjzzTd58803r3iMjoxOp5PcDe2EJFv7IcnWPuTm5hIbG9vfw3A6Gtdyf+oAAFE3SURBVBoaeP/991m5cqVNHK29sCpGYWFhjB07FkEQqK6upri4WGxNTU1isLM1uD4gIMDGOhUUFGSXgt69iTRn7YckW/uRm5uLh4cHn3zyCVqtlsjISBYuXOjwvzdHx5nmrFSYQkJCQkLiqqWwsJCXXnqJW265pU+UpwuRyWSEhoYSGhrK6NGjAUvSleLiYkpKSiguLqaqqor6+nrq6+vFbGmenp5ER0cTGRlJVFQUkZGRksushEQvUF5eTlZWFnq9nqioKJYuXeoUbrQSvYekPDkwkpuG/ZBkaz8k2dqHyZMn9/cQJPoIX19f0tPTSU9PByzW3NLSUlGhKi0tRavV2iShkMlkBAUFERUVJbbg4OB+zeonzVn7Icm29xEEgQMHDlBQUICLiwsxMTEsXrxY8qToJZxpzkrKkwOj0WikQq52QpKt/ZBkax+ys7MdusCghP3w8PAgMTGRxMREAEwmE5WVlZSWloqtoaGBmpoaampqxFonbm5uNpapqKioPv1tSnPWfkiy7V1aW1v55ptvyMnJobKykhtuuIH58+dLsfS9iDPNWecvNNFXmEywcyesWWN5NJl64ZA/HSM2Nlb0fbeyYsUKXnjhhSv+HEfHaDSSnp5OSkpKp30yMzPJyMjAz8+PkJAQ7r77brFo7IV8/PHHeHt786c//clm+zPPPINMJmPt2rViP5lMJmaLBEsAqeT33DWmXpj7EhdzYc08iasXhUJBZGQk1157LbfccguPPfYYv/rVr7jzzjuZNGkS8fHxuLu7YzAYKCwsZPfu3axdu5b/+7//429/+xtr165l165dnD17VizwbQ+kOWs/JNn2Hhs3buSVV14hJycHhULB0KFDufHGGyXFqZdxpjkrWZ56g3Xr4LHHoLT0p21RUfDGG7Bw4WUfVvrhWnjrrbfw9fXtsjhzYmIiGzduJDIyEo1Gw4MPPsgf//hH/vrXv3bYPyEhgdWrV/Pb3/4WsJjrP/vsMxISEmz6+fv789JLL/Gzn/0MV1fX3jspJ0aat/ZBcoe0D56engwePBhPT8/+HsoV4eXlRXJyspgO2Gw2U1tba2OdqqmpQa1Wo1arycnJEff18/MTMwJaW2/ET0lz1n5Isr1yGhsb2bx5M2fOnBG33X///Zw7d066SWoHnGnOSsrTlbJuHdx6K7TLGghAWZll+xdfXLYCpVQqu933448/ZvXq1cTExLB27VqSk5P5+uuveemll/j0009JSUnhq6++IiIiArPZzK233sqePXswGo3MmDGDd999l4CAAHbu3MmSJUs4ceIEAQEBfP755/z2t7/l2LFjNhcXWq2W0NBQTpw4IdY92LZtG48//jgnT568rPPtiKqqKt577z1eeeUVnnzyyU77tS/cLAgCMpmMwsLCTvsnJCTQ2NhIVlYWI0aMYN++fURHR1/Ub8yYMbS0tPDRRx/xwAMPXNnJXCX0ZN5KdJ9Ro0b19xCcktTUVE6cOOF0AeFyuVws3jtixAgA9Ho9lZWVYia/8vJy6urqaGxspLGxkdOnT4v7BwQEEBERQXh4uJgd0MvLq0djkOas/ZBke/no9Xp2795NZmYmRqNR3P7rX/8apVJJQEBAP47OeXGmOSu57V0JJpPF4nSh4gQ/bXv88ct24Wtubu5R/++//57rrruO+vp6oqKimDBhAlOmTKGuro7Y2FheeeUVse/ChQspLCyksLCQ5uZm/vCHPwAwdepUbrnlFh5++GFqamp45JFH+Pjjjy+6K+vp6cn8+fP5/PPPxW3//e9/uf322zsc2/z58/Hz8+uw/eUvf+n0nJ566imeeeaZbv1pFxcX4+fnh0ql4quvvuLhhx/utK/JZGLJkiWsXr0agNWrV7NkyZIO+z7//PO89NJLtLW1XXIMEj2ftxLd4/vvv+/vITgtV4ts3d3diYmJYdy4cdxyyy088sgj/OY3v+Huu+9m1qxZpKWliRkH6+vrOXnyJFu3buU///kPr7zyCq+++iqrVq1i+/btnDp1irq6OptyIxdytci1P5Bk23N0Oh27du3i9ddfF28ex8XFiSEQ1ht/kmztgzPJVbI8XQm7d9u66l2IIEBJiaXf1KlX/HGzZs2ycYnSarU8/fTT4uv09HRuvvlmABYsWEB+fj6LFi0C4KabbuJf//oXYLkjuXTpUnG/J554gmeffVZ8/Ze//IVhw4YxdepUli1bxrhx4zocz+23386LL77IypUrMRqNfPXVV+zdu7fDvhs2bOjx+e7fv5+8vDw++ugjfvjhh0v2HzRoEI2NjVRXV/Puu+8SHh7eZf/bb7+dMWPG8NJLL/H111/zpz/9iVWrVl3Ub9asWURGRvLxxx9zww039Pg8JCQkHJejR49yww03cODAAa655pr+Hk6f4+HhQVxcHHFxceI2jUZDRUUF5eXlVFZWUllZSV1dHc3NzTQ3N5Ofny/2dXNzEy1TYWFhhIaGEhwc7HSWPImBS3NzM4cOHeLgwYPodDoAAgMDmTVrFsnJyZKLnkSPkZSnK6Gionf7XcCF6TG3bt3K2LFjxdcrVqyweT8kJER87unpSXBwsM3r1tZWwJKAYeXKlXz11Vc0NDQgCIKN25tSqeSOO+7gxRdfZNOmTZ2Ob+7cudx9990UFRWRm5tLVFQUgwcPvqxzvRCz2cyjjz7KP//5zx4vbCEhIcybN4+77rqL/fv3d9hHLpcTGhpKSkoKzzzzDKNGjeqyxsvzzz/Pgw8+yNy5c3s0lqsRKa2rfbBmWpPoXQRBoK2trUsLytWGUqkkISHBJgZUr9dTXV0tKlOVlZVUVVVhMBjE4r7t8ff3RxAEXFxcRPfBoKAgKSayl5DWg64RBIHi4mIOHjzImTNnMJvNAAQHBzN58mTS0tI6TeMvydY+OJNcJeXpSriEZaPH/S7AXvU5Vq1axe7du9m/fz8RERFs3ryZBx98UHw/Pz+ft99+m9tuu40nn3yS//73vx0ex93dnQULFvD555+Tk5PTqcsewLx589i9e3eH7z3zzDM888wzNtvUajVZWVmipcdgMKBWqwkLC+PcuXOXjKsxm80UFBR02Qdg8eLF3HPPPWKGvc6YPXs24eHhfPLJJ5c85tVOf9aVcWakO/kS/Ym7uzvR0dE2saFms5m6ujpRmaqoqKC6upqWlhYaGhpoamqyybAll8sJDAwUlamQkBCCg4Px9/eXlKoeIq0HHdPY2Eh2djbHjx+nrq5O3B4TE8O1115LamrqJW/ISrK1D84kV0l5uhImTbJk1Ssr6zjuSSazvD9p0mUdXqvV2mWyNTc34+7ujp+fH7W1tfzf//2f+J7ZbObuu+/m2WefZcWKFQwbNoz//ve/ovtfbGwsL7zwAsuXLwcsrm/PPvssxcXFHDp0qNPP3LhxY4/G6OvrS1lZmfh63759PPXUU+zevbvDrFjfffcdCQkJDB48mMrKSp577jmmTZvW6fGtd6Fuu+02QkNDmdoNt8rnn3+exYsX9+g8rkbsNW+vdk6fPt1hUhMJif5CLpcTHBxMcHCwWNAXLG5/1dXVrF+/nvj4eKqrq6murkan04m1qE6dOmVznICAAIKCgi5qvZH1zxmR1oOfaGxs5MyZM5w5c8bGAurq6kp6ejpjxowhLCys28eTZGsfnEmukvJ0JSgUlnTkt95qUZTaK1DWOxuvv27p50DcddddfPvtt4SEhBAdHc19990n+rD/3//9HwqFgsceewy5XM5HH33EwoULmTp1Kv7+/tTV1dm4Ds6aNYtly5YRHx9PfHx8r41RJpPZLHYBAQEoFAqbbSqVio0bNzJp0iSqqqp4+OGHqaqqwtfXl3nz5tkkyOgMpVLZbVe8OXPmMHjw4IvqbUlISEhI/IRSqSQ2Npbk5GTmzJkDWNyompubRUWqurqaqqoqamtraWtro7a2tsNyFCqVisDAQFGZCgwMJCAgAD8/P1xcpEuYqxGz2UxZWRlnz54lNzeXyspK8T2ZTEZcXBzDhg0jNTVVupEnYRdkwlXo6K1Wq/H19aWpqQkfHx9xu06no7CwkLi4uJ7d7eqozlN0tEVxuoI6TyaTyaFcGfbv38/f//531qxZ099DuWIcTbbORFeyvezfmAQtLS2oVKr+HobTodVqOXnyJEOHDh3wtZ4cje7MWUEQUKvVovJUW1tLXV0dtbW1qNXqTveTyWT4+PgQEBAgNn9/f/G5s180X03rgdlspqamhvPnz1NYWMi5c+fQ6/Xi+zKZjJiYGFJTU0lNTbW5rrscribZ9iWOLtfOdIOOkG7b9AYLF8KCBZasehUVlhinSZOu2OKk0+l6XFfDnowbN67TzHsDDUeTrTMhydY+5OTkOFWdDEfB09MTmUwmKU52oDtzViaT4evri6+v70VFyvV6vahIWVt9fT319fUYDAaamppoamrqsKafSqXC399fLInh6+tr8zjQi54783rQ0tJCRUUFFRUVlJSUUFJSImbJs+Lp6Ul8fDyJiYkMHjy4V/9znFm2/YkzyVVSnnoLhaJX0pG3p33xNoneRZKt/ZBkax/aBz9L9B7nz5/nt7/9Le+++65Y8Fuid7jSOevu7k5ERAQRERE22wVBoLW1lfr6ehoaGkSFyvpao9HQ0tJCS0sLJSUlHR5bqVRepFT5+vri7e2Nt7c3KpXKob0TnGE9MJvNNDY2islGrI8tLS0X9XVzcyM6OpqYmBgSEhIIDw+3W3IiZ5CtI+JMcpWUJwdGylpmPyTZ2g9JtvbhUhkmJS6Puro6Nm/eTF1dnaQ89TL2mrMymQyVSoVKpWLQoEEXva/T6URFqrGxUcz6Z33U6/VoNBo0Gg3l5eWdfo6Xl5eoTF3YVCoVXl5eKJXKfnERHCjrgdlsprm5mfr6eurq6sRm/X5MJtNF+8hkMgIDAwkPDycqKopBgwYRGhraZ/8tA0W2Aw1nkqukPDkwjuwbOtCRZGs/JNnah/Hjx/f3ECQkekR/zVkPD48OLVZWdDpdh0qVtQhwc3MzJpOJ1tZWWltbbRISdISrqytKpVJUpto/enl54eHhcVFzd3e/ImWgv9cDo9GIVqtFq9Wi0WhQq9U0NzfbPKrValpaWsTsth1hrQMWFhZGeHi4WGi5P2PW+lu2zoozyVVSnhwYa/CaRO8jydZ+SLK1D9u2bRMzl0lIDAQcdc56eHgQFhbWafpqQRDQaDQ2ytSFraWlBY1Gg9FopK2tTYy/6gnu7u42ypS7uztubm64urp22VxcXNi7dy9TpkxBLpd32gRBEJvZbLZ5tD43mUy0tbV12XQ6nagoWZvBYOj2ecrlcvz9/QkMDBSzJVqf+/j4XLLuUl/jqPN2oONMcpWUJwkJCQkJCQmJH5HJZKLVqKv6QIIgYDAY0Gg0tLa2XvRofa7T6WxaW1sbYEmIodfre6x0AZw9e5bS9hl++wFrohVPT0+8vb3x8fHBx8fnoucqlUpy55ZwKiTlyYFxd3fv7yE4LZJs7YckW/sQFxfX30NwSkJDQ3nggQcIDQ3t76E4Hc4+Z2UymWgx8vf37/Z+JpPJRpnS6/Xic6sl61LN1dWV4OBgzGZzpw0sVh+ZTIZMJhOft9+mUChsrFodWb7c3d1FJal98/DwcDirUW/g7PO2v3AmuUrKkwMj3amxH5Js7YckW/sgxZLZh8jISJ5//vlO42MkLh9pznaMQqEQLVuXS3l5uTRn7YQ0b+2DM8lVuspxYLRarfg8NjaWzMxMm/dXrFjBCy+80CdjaWlpYeLEiQQGBuLv78+MGTPIycnpsG91dTWLFi0iNDSUgIAAbrjhBoqLizs9tkwmIzEx0WZbfn4+MpmMuXPn2vS7MOBw7ty5fPzxxz0+n/aylehdJNnahxMnTvT3EJyS5uZmPv30U5qbm/t7KE6HNGfthyRb+yHJ1j44k1wl5UmiW7i7u/P+++9TU1NDXV0dCxcu5O677+6wb2trKxMnTuTUqVNUVlaSmJjIPffc0+Xx5XI5Bw4cEF+vWrWKpKSki/rl5OSwZcuWKzsZCQkJiR/Jz8/nqaeeIj8/v7+HIiEhISExAJCUp14gPx+ysi5uV/pf3FOT/ptvvkl8fDzBwcHcddddqNXqHn+mIAgdbnd1dSU1NVXM4COXyzus6g4Wv9ZHH32UoKAg3Nzc+PnPf87Bgwe7/Nw777yTVatWia/XrFnDnXfeeVG/J554gt///vc9OKOO6c1q5BK2SLK1D2PHju3vIUhI9AhpztoPSbb2Q5KtfXAmuUrK0xWSnw+DB8PIkRe3wYOvTIHqSSrQzZs385e//IVvv/2WoqIiWltb+eUvf9lh36qqKu6//35iYmIYMWIEf/zjH9m/fz/r1q3jrrvu6vJzMjIy8PDw4OGHH+app57q1tj27dtHWlpal30WLVrEV199hclk4tChQwQFBXUYXLh8+XLKysrYunVrtz67M3oiW4meIcnWPpw7d66/hyAh0SOkOWs/JNnaD0m29sGZ5CopT1eI1U3+00/hyJGf2qef2r5/OVjTmVqZNWsWfn5+Yvvoo4/E9z777DNWrFhBamoqXl5evPTSS6xdu7bD42ZmZjJv3jxOnjzJJ598gkaj4dlnn+W7777jueee63JM2dnZqNVq3nnnHVJTUy95DiUlJfzmN7/hT3/6U5f9AgMDGTZsGNu2bWPVqlUsXry4w36urq4888wzV2x9ulC2Er2HJFv7UF1d3d9DkJDoEdKctR+SbO2HJFv74ExylZSnXiI1FUaM+Kl1Q6+4JBdmLdu6dSuNjY1iax9HVF5ezqBBg8TXMTExtLa2dlg/4vrrr6e6upr77ruPf/zjH8ycOZOtW7fy4osv8vXXX19yXJ6entx333387Gc/o6GhodN+9fX1zJ07l2eeeYaZM2de8rhLlizhP//5D+vWrWPRokWd9rvnnnsoLS1l27ZtlzxmZ0gZ4eyHJFv7IKWAtw+urq4EBQXh6ura30NxOqQ5az8k2doPSbb2wZnkKl3lODDe3t7d7hsREWGT0a64uBilUomvr+9FfT/99FPy8/NZvnw5w4YN46WXXiIwMJBp06YRFRXVrc8TBIGWlhYqKio6fL+lpYXrrruOBQsW8Mgjj3TrmAsWLGD9+vUMHTqU4ODgTvu5urry9NNPX5H1qSeylegZkmztw9SpU/t7CE5Jeno6NTU1pKen9/dQnA5pztoPSbb2Q5KtfXAmuUrKkwPTk6rjt912G++++y45OTm0trby7LPPcscdd3TYd9myZbz66qvMmzePhx56iO3bt9PY2Mjp06c7TNIAcPz4cXbt2oXBYKC1tZVnnnkGPz+/DjPiGQwGFi5cSFpaGi+99FK3z0GpVLJ161befPPNS/a95557KC4u5tChQ90+fnsup6K7RPeQZGsfNm/e3N9DcFok2doHSa72Q5Kt/ZBkax+cSa6S8tRLnDljm2nvzJm+/fx58+bxq1/9innz5hETE4O7uzuvvvpqh30VCkWPj9/W1sZjjz1GYGAggwYN4tixY3z33Xeiq8uKFStYsWIFAPv372fr1q2sXbsWlUoltq5qPVm59tprSUhIuGQ/Nzc3nn76aerr63t8LhISEhJWTpw4wdKlS52qBomEhISEhP2QCZ3lpnZi1Go1vr6+NDU14ePjI27X6XQUFhYSFxeHh4dHt45lzbbXGXl50IFxpltotVo8PT0vb2eJLpFkaz+6ku3l/MYkLJw5c6ZbSVokekZWVhYjR47kyJEjjBgxor+H41RIc9Z+SLK1H5Js7YOjy7Uz3aAjXPpoTE5LUpJFQeooq5639+UrTgAuLtLXYy8k2doPSbb2ISAgoL+HICHRI6Q5az8k2doPSbb2wZnkale3vYaGBpYtW4avry++vr4sW7aMxsbGLveRyWQdtldeeUXsM3Xq1Ive7yy+py9ISrLNtGdtV6I4AWg0mt4ZoMRFSLK1H5Js7cOxY8f6ewgSEj1CmrP2Q5Kt/ZBkax+cSa52vUW8ePFiSktL2bRpEwAPPPAAy5Yt45tvvul0nwuzt23cuJF7772XW265xWb7/fffzx/+8AfxteSCJSEhISEhISEhISFhT+ymPJ05c4ZNmzaRmZnJtddeC8D777/PuHHjyM3NJTk5ucP9wsLCbF5//fXXTJs2jfj4eJvtSqXyor7OhpeXV38PwWmRZGs/JNnah9GjR/f3EJySpKQkvv766w4zh0pcGdKctR+SbO2HJFv74ExytZvb3v79+/H19RUVJ4CxY8fi6+vLvn37unWMqqoqvv32W+69996L3lu1ahVBQUGkpaWxcuVKmjsKOhrgGAyG/h6C0yLJ1n5IsrUPpaWl/T0Ep8Tb25vY2FipPpkdkOas/ZBkaz8k2doHZ5Kr3ZSnyspKQkJCLtoeEhJCZWVlt47xySef4O3tzcKFC222L1myhDVr1rBz506ee+45vvzyy4v6tEev16NWq23aQKCtra2/h+C0SLK1H5Js7UNnBaklroyysjJefPFFysrK+nsoToc0Z+2HJFv7IcnWPjiTXHvstvfCCy/w+9//vss+1sKlMpnsovcEQehwe0d8+OGHLFmy5KKUxvfff7/4fOjQoSQlJTFq1CiysrI6TDX75z//ucMxb9u2DS8vL6ZPn87BgwfRarUEBQVhMpnEQp/Wz9bpdIDlLqVGo8FkMqFQKFAqlaLV68K+KpUKnU6H0WhELpejUqlExc3d3R25XI5Wq+20r8FgoKmpCTc3N1xcXMRAfC8vLwwGA21tbchkMnx8fMTxXthXqVRiNBoxGAxiX7VajSAIuLq64ubmRmtr60V9AXx9fWlubsZsNl/U19PTE7PZjF6vB8DHx4eWlhbMZjMuLi54eHjQ0tLSYd+eyLCrvt2RYWfyFgQBjUZDW1sbcrkcb2/vTmXYkbytMuxK3lYZdlfePZFhV317a85errwNBgM6nc6mr1WGra2t4mdZC+ZFR0cTFBTE0aNHARg1ahTl5eWUl5ejUCiYOXMm27Ztw2QyERERQUREBIcPHwbgmmuuoba2lpKSEgDmzJnD999/j8FgIDQ0lNjYWA4cOABARkYGarWaoqIiAGbNmsXevXvRaDQEBQUxePBg0SqelpaGTqejoKAAQFwjWlpa8Pf3Jy0tjT179gCQkpKC2WwmLy8PgClTpnDs2DEx3emIESPYuXMnYHERc3Fx4cyPheAmTpzI6dOnqa+vx8vLi7Fjx7J9+3YA4uPjUSqVnDx5EgCTyURWVhY1NTV4eHgwefJktmzZAkBMTAx+fn4cP34cgDFjxlBcXExlZSWurq5Mnz6dLVu2IAgCUVFRhISEkJWVBcDIkSOprKykrKwMuVzOrFmz2L59O0ajkfDwcKKiosQ1ffjw4dTX14s12+bMmcPOnTvR6/WEhIQQHx9PZmYmAOnp6bS0tFBYWAjAzJkz2bdvHxqNhsDAQFJSUti7dy8AQ4YMwWAwcPbsWQCmTZvG4cOHaW5uxs/Pj4yMDHbt+v/27jwuqnr/H/hrWGZgWAbZBGSRVEQFkcUFcMEN9WaldDEV18wrWYZlda1+NzHNr926pdmidk38KkZZkpZZggKS4hJILrigggqKCMou++f3x3w5Oc4AMzAfZxzez8djHjRnPnPmc159OPKec87nHAYA4ZTvixcvAgBGjhyJ06dPo6ysDFZWVggKCkJKSgoAoHfv3hCLxcjJyQEAhIaG4sKFCygtLYVUKkVISAgSExPx3XffYfr06Rg6dKhwv6dhw4bh6tWrKC4uhkQiQVhYmDBm3d3dYWtrK1wAPXjwYBQUFODWrVswMTHB2LFjkZSUhObmZvTo0QNOTk7IzMwEAAQEBKC4uBgFBQUQiUQIDw/HoUOH0NDQACcnJ7i7u+PEiRMAAD8/P5SVleHatWsAgPDwcBw+fBi1tbVwcHBA7969kZGRAUD+b2FNTQ2uXr0KABg7diyOHTuG6upq2Nraon///sKY7devHxobG5GbmwtAPglTVlaWMFXvoEGDkJaWBgDw8vKCkZERLly4IIzZc+fO4d69e7C0tMSQIUNw6NAhAECvXr1gZmaGc+fOCWM2MzMTJSUlkEqlCA0NRVJSEgCgZ8+esLa2xunTpwHI7/GXn5+P27dvQywWY/To0bSPQOv7iOvXryM3N1dhHxEcHIzLly/TPqKT+4g7d+7gt99+E/YRycnJAABPT09YWlrSPqKD+4jr16/j8uXLCvuIkJAQXLp0SS/2ES39V4fG93kqKSlBSUlJm2169uyJnTt34rXXXlOaXc/GxgaffPIJ5s+f3+Y60tPTMXLkSGRnZ8PPz6/NtowxSCQSbN++Hc8995zS63V1dcIfnYB8Lnc3Nzet3OeJEKI++h0j+obu80QIIUST+zxpfNqevb09vL2923yYmZkhODgY5eXlQnUMAMePH0d5eTlCQkLa/ZwtW7YgMDCw3cIJAM6dO4eGhgY4OzurfF0ikcDa2lrh8Th4XE4vfBxRtvxQtny0fBtHyOOCxiw/lC0/lC0fhpQrt2ue+vXrh4kTJ2LhwoU4duwYjh07hoULF2Ly5MkKM+15e3sjMTFR4b0VFRXYtWsXXnjhBaX1XrlyBe+99x7++OMP5Ofn45dffkFkZCT8/f0RGhrKa3N04sGDgi2HL1tOgwLkOZmbm8Pb2/uR9em1117DE088IRz6bjlMrsqkSZNgaWkpPExMTLBkyRKVbefNmweRSCQcMm4REhICkUgkXCc3b948GBsbC6c0AEBCQgLCwsI02g4ND7gSDVC2fDQ3N+u6C4RohMYsP5QtP5QtH4aUK9eb5MbHx8PX1xfh4eEIDw/HwIEDsX37doU2Fy9eFK41aZGQkADGGGbMmKG0TrFYjIMHD2LChAno27cvXnnlFYSHhyM5ORnGxsY8N+eRE4vFCs+dnJywd+9e4fnu3bvh5ub2SPskk8lw4MABlJeX45///CemTJnS6kyH+/fvR1VVFaqqqlBWVgY7OztMmTKl1XX36dMH8fHxwvO8vDyUlpaq7MOqVas6tR0PZ0u0h7Llo0ePHrrugkGys7NDREQE7OzsdN0Vg0Njlh/Klh/Klg9DypVr8WRra4sdO3YIM9zt2LEDNjY2Cm0YY5g3b57Csn/84x+oqamBTCZTWqebmxvS0tJQWlqKuro6XL58GevXr4etrS3HLWlbbi6QlaX8+L/r7DrMxERxPo8ZM2YoFBfx8fGYOXOmQpszZ84gNDQUNjY2CAoKEi7M1FRrRw9WrFiB3r17w8jICJGRkTA3Nxcugm1LUlISTExMMHr06FbbREREYO/evcJsbTt37lRZQL/wwgvYv3+/yov78vPzYWZmhi+//BKOjo5wc3NDamoqtmzZAmdnZ7i7uyMtLU0pW6I9lC0fhn5fO13x8PDApk2b4OHhoeuuGBwas/xQtvxQtnwYUq5ci6euIDcX8PICAgOVH15enSugWmZwazF+/HhkZWXh7t27KCoqQm5uLkaOHCm8Xl9fj6eeegozZ87EnTt38Prrr2Py5MlKR/ZafPnllxg0aBDc3d2xYMEC/Pzzzzh8+DBeeuklYRaStuTn5+Pu3bvo3bt3u23j4+MxY8YMGBm1PuRsbGwwdOhQYfaUb775Rqk4BORF+eLFi1s9+lRfX4/8/HwUFhYiJiYGs2bNQk5ODq5du4Y333wTS5cuVcqWaA9ly0fLTExEu+7fv48ffvhB4ZRooh00ZvmhbPmhbPkwpFypeOqkljPWduwAMjP/euzYofi6NpiYmGDKlCnYtWsXEhISEBkZqVCMHDt2DMbGxnjppZdgamqK6dOno0+fPsJUpQ+qq6tDfn4+fv75Z2RmZiI4OBibN2/GRx99hBEjRrR7J+iGhgbMnTsXb7zxhsojhA+qqanBjz/+iKioqHa3cebMmYiPj0d2djbMzc3h5eWlst1rr72Gffv2qTz6xBjDO++8A1NTUzz77LMoLCzE8uXLIRaL8eyzz+LcuXMGde4tIaTjzp8/j+joaIXrKAkhhJDW0Pk1WtKvH6DtWW6lUqnSsqioKCxfvhz379/H5s2bFaaCv3nzJtzd3RXae3h44ObNm0rrkUgkmDp1KlavXo27d+9i3Lhx2LZtGywsLPD999/j3LlzGDBggMp+tZxq6ejoiNjY2Ha348cff4S7uzv8/f3bbTt58mS88sor6NatW5vFlp2dHRYvXozVq1dj8uTJStvWMqOiubk5AMDBwUF43tDQQKeWcaRq3JLOo2m0yeOGxiw/lC0/lC0fhpQrHXnSY42NjUrLgoODUVhYiKqqKgwaNEjhNRcXF+FGgC2uX78OFxcXpfXU1dXh7bffRlhYGGbMmIHjx4+jX79+8PDwwJEjR5SKsActWbIEN2/exI4dO9o8Da9FfHy8WkedAPlNWydMmICvvvpK5T27HrRs2TL8/PPPwo3wNKEqW6IdlC0fxcXFuu4CIRqhMcsPZcsPZcuHIeVKX7/rsfr6euHIyYN2796tsmgZNmwYGhoa8OWXX2LhwoVITEzExYsXER4ertRWLBYjOTlZWM/UqVPV6tOKFStw5MgRpKWlQSKRtNu+pKQESUlJ+Oyzz9RaPwCsWrUK8+fPb/W+XS3s7Ozw4osv4tNPP4Wvr6/a6wfk2RI+Whu3pHMKCgpaPRpMiD6iMcsPZcsPZcuHIeVKR5605Px5xZn2eJ4+P3DgQPj4+CgtF4vF2LNnD7Zv3w47OzusXbsWe/fuVXlNkkgkUuuo0cPee+89nD9/Hi4uLsL9m1pmAExPT4elpaVC+2+//RaDBw+Gp6en2p/h6uqqMBFGW5YtW0aFEOkSRCKRrrtgkEQiEUxNTSlfDihTfihbfihbPgwpVxHrgne0rKiogEwmQ3l5uXBtDADU1tYiLy8Pnp6eMDMzU2tdLbPttebSJaBPn872mBDD0JHfMUIIIYQQnlqrDVShI0+d1KePvEB6cKa9lkdnC6eKigrtdZQooGz5oWz5OHTokK67YLAoWz4oV34oW34oWz4MKVe65kkLeB1Z6oIHBR8ZypYfypaPlptHE+06f/48/vGPf+Cnn35Cv379dN0dg0Jjlh/Klh/Klg9DypWOPOkxU1NTXXfBYFG2/FC2fBjS3dn1yf3793HlyhW6SS4HNGb5oWz5oWz5MKRcqXjSY2KxWNddMFiULT+ULR9t3T6AEH1EY5YfypYfypYPQ8qViic9Vl1dresuGCzKlh/Klo8TJ07ouguEaITGLD+ULT+ULR+GlCsVT4QQQgghhBCiBiqe9JhUKtV1FwwWZcsPZcuHn5+frrtgkDw9PbF582aN7kVH1ENjlh/Klh/Klg9DypWKJz3W2Nio6y4YLMqWH8qWj7KyMl13wSB169YNI0aMQLdu3XTdFYNDY5YfypYfypYPQ8qViic9Vl9fr+suGCzKlh/Klo9r167pugsG6fbt2/j4449x+/ZtXXfF4NCY5Yey5Yey5cOQcqXiScvq6vist2fPnjh27JjCsujoaMTGxvL5wEdg7dq1EIlEStulSkJCAkQiERISElptIxKJ0Lt3b4Vlubm5EIlEmDhxokK78PBwhXYTJ05EXFycZhtACHnsFRYW4quvvkJhYaGuu0IIIeQxQMWTFm3aBFhZyX9qg7W1tXZWpIcKCwuxc+dOteb9r66uxurVqzFgwIB22xoZGeH48ePC8/j4ePRRcRfj3NxcHDhwQLNOE7UY8rjVpYcLfkL0HY1ZfihbfihbPgwpVyqetGTTJiA6GujXT/5TGwVUVVWV2m3j4uIQHh6OhQsXwsrKCkFBQSgsLMRLL70EmUyGoUOH4ubNmwCA5uZmREREwNHREba2toiMjMTdu3cBAKmpqejRo4fwfNeuXejbt6/GN5BkjLX5+rJly7By5UpIJJJ217Vq1SosWLAA9vb27badMWMG4uPjhefffPMNZsyYodRu8eLFWLlyZbvrI5rTZNwS9R0+fFjXXSBEIzRm+aFs+aFs+TCkXKl40oKWwmnJEuDUKflPbRRQzc3NGrVPSUnB3/72N9y9exeurq4IDQ3FqFGjUFpaip49e+LDDz8U2kZERCAvLw95eXmorKzEe++9BwAICwvDs88+i5dffhl37tzBkiVLEBcXB3Nzc6XPu337NhYuXAgPDw8EBARg1apVyMjIwO7duzFnzpxW+5mamoqSkhJMnTq13W26dOkS9u/fj5dfflmtDKZNm4bExEQ0NTXh5MmTsLe3VzmL1owZM1BYWIikpCS11kvUp+m4Jeqpra3VdRcI0QiNWX4oW34oWz4MKVcqnjrpwcJp/XrAyEj+UxsFlImJicLz8ePHw8bGRnhs3bpV4XVfX19MnToVpqameOaZZ2BhYYFp06bBxMQEU6ZMwenTpwHIT22bNWsWLCwsIJPJ8Oqrr+L3338X1rN27VqcPHkSYWFhmD17NoKDg1X279ixY5g0aRLOnj2Lbdu2oaamBu+88w5++eUX/Otf/1L5nsbGRrz66qtYt26dWhnExMTggw8+gKmpqVrt7ezs4Ofnh+TkZMTHx2PmzJkq25mbm+Ptt9+mo08cPDxuiXY4ODjougsGSSaTYeTIkZDJZLruisGhMcsPZcsPZcuHIeVKxVMnPFw4iUTy5SKRdgooMzMzhedJSUkoKysTHvPnz1d43dHRUfhvc3NzhYFqbm6O6upqAPICZunSpfDw8IC1tTX+/ve/o7S0VGgrlUoxffp0nD9/Hq+88kqr/XvyySdRXFyMF154AZ9//jnGjRuHpKQkvP/++9izZ4/K93z++ecYPnw4fHx82t3+PXv2wMTERGGyB3VERUVh+/bt2L17N6ZNm6ayjUQiwfz581FQUIDk5GSN1k/a9vC4Jdrx8GQoRDt69eqFn376Cb169dJ1VwwOjVl+KFt+KFs+DClXKp46qK5OXhwNHAisW/dX4dRCJJIvHzhQ3q4js/DxunYkPj4e6enpyMjIQEVFBb7//nuFa5Ryc3Px5ZdfIjIyEsuWLWt1PTt27EBubi7mzZsHPz8/rFmzBnZ2dhg9ejRcXV1VviclJQXx8fFwcnKCk5MTbty4gSeffFLpKFpL28OHDwttjx49iujoaOEUw9Y888wz2Lt3L3x8fFr9pqO6uhqmpqZ466236OiTltE1T3xkZGTougsGqaGhAfv370dDQ4Ouu2JwaMzyQ9nyQ9nyYUi50vk1HSSRABs2yI8sLV2qeOQJABiTLz99Gti4Ud5eX1RWVkIikcDGxgYlJSX46KOPhNeam5sxd+5cvPPOO4iOjoafnx++++47lUdwZs+eDWNjY+H5iy++2O5nx8XFKZz3OnjwYGzatAlhYWFKbVetWoXly5cLzyMiIjB37txWT8VrIZVKkZSUpNYEE/Pnz8eaNWtQVVWF6dOnt9ueEGJYzpw5g+nTpyMzMxMBAQG67g4hhBA9R8VTJyxaJP8ZHS3/2VJAMQbExMiLq40b/2qnKVWTNGjDnDlzsG/fPjg6OsLNzQ0vvPACcnNzAQAfffQRjI2NERMTAyMjI2zduhUREREICwtTOC0QgELhpC4bGxulddja2kIqlQKQ37sKADZu3AgrKytYWVkJbcViMWQymcKy1gwdOrTN11tOLROLxXjrrbfUKvyIeniN265OnVNdCdEnNGb5oWz5oWz5MKRcRay9OaUNUEVFBWQyGcrLyxXuSVNbW4u8vDx4enpqdN3Gg9c+rVsnP+LU2cKppT90/QgflC0/bWXb0d8xIj+dVtU9y0jnZGVlITAwkI48cUBjlh/Klh/Klg99z7W12kAVuuZJCxYtkhdKGzYA/v7aKZwAoK4jF0oRtVC2/FC2fFy9elXXXSBEIzRm+aFs+aFs+TCkXOm0PS1pKZSWLNFO4UQIIYQQQgjRL3TanhZO23tQXZ32JodgjEH08DR+RCsoW37aypZO2+u4xsZGuocWB01NTSgvL4dMJuvQdZykdTRm+aFs+aFs+dD3XOm0PR3S5qx6NOUzP5QtP5QtH8eOHdN1FwySsbExcnJyqHDigMYsP5QtP5QtH4aUKxVPeqy5uVnXXTBYlC0/lC0fLTe5JtqVm5uLmJgYYcZRoj00ZvmhbPmhbPkwpFypeNJj+nx483FH2fJD2fJha2ur6y4YpMrKSmRlZaGyslLXXTE4NGb5oWz5oWz5MKRcuRZP77//PkJCQiCVSpXu79MaxhhiY2Ph4uICc3NzhIWF4dy5cwpt6urqsGTJEtjb28PCwgJPP/00CgoKOGyBbtE1IfxQtvxQtnz0799f110gRCM0ZvmhbPmhbPkwpFy5Fk/19fWIjIzU6Aak//73v/Hxxx/js88+w8mTJ+Hk5ITx48crfCu4dOlSJCYmIiEhAb///juqqqowefJkNDU18dgMnaFrR/ihbPmhbPn4/fffdd0FQjRCY5YfypYfypYPQ8qV6/k1K1euBADExcWp1Z4xhnXr1uGdd95BREQEAGDbtm3o3r07du7ciUWLFqG8vBxbtmzB9u3bMW7cOADAjh074ObmhuTkZEyYMIHLthBCCCGEEEK6Nr265ikvLw9FRUUIDw8XlkkkEowaNQpHjx4FAGRmZqKhoUGhjYuLC3x8fIQ2uqTN+4M+ePpTz549YW1tjfv37wvLKioqYG5uDm9vb+19qI7s2bMH3t7ekMlkcHZ2xmuvvdbmkcQLFy5g/PjxsLKyQvfu3bF+/XqV7eLi4iASibB69WqF5e+//z5EIhESEhIU2m3atEloU1RURNOZdwCdtsdHv379dN0Fg+Tm5ob33nsPbm5uuu6KwaExyw9lyw9ly4ch5apXxVNRUREAoHv37grLu3fvLrxWVFQEsViMbt26tdrmYXV1daioqFB48LBpE2BlJf/Jg5OTE/bu3Ss83717t8H8gx8UFIT09HSUl5cjJycHp0+fxubNm1W2vX//PiZNmoQ5c+agpKQEly9fViimH9a7d2/s3LlTeM4Yw/fff49evXoptOvWrRvWrFmDhoYG7WwUIVrU2Nio6y4YJAcHB0RFRcHBwUHXXTE4NGb5oWz5oWz5MKRcNT5tLzY2VjgdrzUnT55EUFBQhzv18Lf96tzQtK02//M//6Oyz8nJybCwsMCYMWNw4sQJ3L9/H/b29sJNE4G/vkWvra0FAFhZWaGmpgZNTU0wNjaGVCpFZWUltm4V49VXzTFwIEN0tAj379/HkiVi1NbWorGxEUZGRrC0tBQKN4lEAiMjI+FIkqWlpcq2YrEYYrEYADB16lTExcXh2WefRX19PbZt24Znn30WiYmJQn9tbGywYcMGfPDBB6ipqcGnn34KMzMzLFu2DNXV1fjss88wadIkMMawY8cOrFu3DkVFRXB3d8f69esRGhqKiooKjBo1CitWrMDkyZNx9+5dhISEIC4uDkOGDAEAmJubo7m5GXX/d6jN2toaVVVVaG5uhomJCczMzFBVVQXGGKRSqUJbVRlaWloCkBe6dXV1aGxsxIULF9Dc3KzU9osvvsDgwYMRGRkJkUiE5uZmuLi4oKmpSSnDmpoaeHh4oLy8HMePH4e3tzeOHTsGFxcXGBkZoaamRrieLiAgANXV1di0aRMWLVok/L9qbGxEfX09vL29sWjRImzbtg1FRUV46623EBoaikWLFqGoqAhvvPEG3njjDaE9AMhkMlRUVIAxBlNTU4jFYmG6TqlUqtC2tQw1yVuTMauq7YPj8OG26o7Z+vp6WFlZKbS1sLBAfX09qqurhc/67bffAMi/+be3t8epU6cAyAvpmzdv4ubNmzA2Nsa4ceOQnJyMpqYmuLi4wMXFBX/88QcAwN/fHyUlJbhx4wYAYMKECUhJSUF9fT26d++Onj174vjx4wCAgQMHoqKiAvn5+QCA8ePH48iRI6ipqYG9vT28vLyEo9cDBgxAbW0trly5AgDCPqKqqgrdunXDgAEDhPO3vb290dzcjEuXLgEARo0ahezsbOFGewEBAUhNTQUA9OnTByYmJjh//jwAYPjw4cjJycHdu3dhYWGBYcOG4eDBgwCAJ554AlKpFGfPnhV+N8rKynDnzh2YmZlh5MiROHDgAADAw8MDNjY2+PPPPwEAQ4YMwfXr11FUVARTU1OMGTMGBw4cAGMMrq6ucHR0RFZWFgAgMDAQRUVFKCwshJGREcaPH4+DBw+isbERzs7OcHV1xcmTJwEAgwYNwt27d3H9+nUh79TUVNTV1cHR0RFPPPGEcC8PX19fVFVVIS8vDwAwbtw4HD16FDU1NbCzs4O3tzeOHDkCQH4hcX19PS5fvgwAGD16NP744w9UVlbCxsYGAwcOxOHDhwEAffv2BQBcvHgRADBy5EicPn0aZWVlsLKyQlBQEFJSUgDIvzgRi8XIyckBAISGhuLChQsoLS2FVCpFSEgIEhMT8csvvyA6Ohqurq44c+YMAGDYsGG4evUqiouLIZFIEBYWJoxZd3d32NraIjs7GwAwePBgFBQU4NatWzAxMcHYsWORlJSE5uZm9OjRA05OTsjMzAQg388UFxejoKAAIpEI4eHhOHToEBoaGuDk5AR3d3ecOHECAODn54eysjJcu3YNABAeHo7Dhw+jtrYWDg4O6N27NzIyMgAAPj4+qKmpwdWrVwEAY8eOxbFjx1BdXQ1bW1v0799fGLP9+vVDY2OjMD17WFgYsrKyhJtEDho0CGlpaQAALy8vGBkZ4cKFC8KYPXfuHO7duwdLS0sMGTIEhw4dAgD06tULZmZmwiRP9fX1uHfvHkpKSiCVShEaGoqkpCQAf51Ncfr0aQDA0KFDkZ+fj9u3b0MsFmP06NG0j0Dr+4j8/HyMGTNGYR8RHByMy5cv0z6ik/uI33//Hbm5ucI+Ijk5GQDg6ekJS0tL2kd0cB+Rn5+PsWPHKuwjQkJCcOnSJb3YR7T0Xy1MQ3fu3GHnz59v83H//n2F92zdupXJZLJ2133lyhUGgGVlZSksf/rpp9mcOXMYY4wdPHiQAWB3795VaDNw4ED27rvvqlxvbW0tKy8vFx43btxgAFh5eblCu/v377OcnByl/rdn40bGAMaWLGGsqUn+E5Av74yysjLhvz08PFh6ejrr0aMHKy0tZbdu3WIeHh7s4MGDrG/fvkI7ACwqKordv3+ffffdd8zW1pbNmzePVVdXs7179zJ7e3vW2NjIGGPs559/ZteuXWNNTU1s8+bNrHv37qy2tpYxxtiJEyeYo6Mju3nzJpszZw578cUXW+3nF198wfz8/Jibmxt7/vnn2U8//cTS0tLY4sWL2YkTJ9Te3vT0dGZtbc0AMEdHR3b+/HmV7ebNm8defvllNmTIEGZvb8+eeuopdv36dZVtt27dyiZMmMDWr1/Pli1bxhhjbPHixWz9+vVs1KhR7JtvvlFod+DAAebh4cHq6+vZrVu32IO/Ih4eHmzMmDGsrKyMHT9+nInFYvb3v/+d3b17l2VnZzMzMzNWXFys9vYaqgfH7cM6+jtGGPv111913QWDlJmZyQCwzMxMXXfF4NCY5Yey5Yey5UPfcy0vL1dZG6ii8Wl79vb28Pb2bvPR0WsePD094eTkJFSdgPybq7S0NISEhACQfwNiamqq0ObWrVs4e/as0OZhEokE1tbWCg9t2bQJiI4GliwB1q8HjIzkP5cskS/vzCl8VlZWCs9NTEwwZcoU7Nq1CwkJCYiMjISRkfL/wjfffBNmZmaIiIhAWVkZFi9eDKlUiqeeegqVlZW4efMmAODJJ5+Eu7s7jIyMsHDhQohEIuFbhsGDB2PBggUYN24c0tPT8e9//1tlH+vq6pCfn4+ff/4ZmZmZCA4OxubNm/HRRx9hxIgRGDx4sNrbO3z4cJSXl+Pq1auIjo5WOjWzRWFhIbZv347PPvsMBQUF6Nu3L2bNmtXmup977jns2rUL9fX12LNnD6KiolS2Gz9+PHr06NHqJCcxMTGQyWQYMmQInJycMG3aNHTr1g1+fn5wd3fX7JsLA/XwuCXaERYWpusuEKIRGrP8ULb8ULZ8GFKuXK95un79OrKzs3H9+nU0NTUhOzsb2dnZClMZe3t7IzExEYD8dL2lS5dizZo1SExMxNmzZzFv3jxIpVLMnDkTgPw0qAULFmDZsmU4ePAgTp06hVmzZsHX11eYfe9RebhwajlrUCTSTgFVU1OjtCwqKgo7d+7Ezp07Wy0AHB0dAQDGxsYwNTVVOJffzMxMOG3sxx9/REBAAGxsbGBjY4Pi4mKUlpYKbZ9//nnk5OTg+eefF06re5hEIsHUqVOxevVqvPTSS2hubsa2bdvw/fffo7m5WekeXQCQnp4OS0tLWFpaYtKkSUqve3p6wtfXF0uXLlX5mebm5oiIiMDgwYMhkUgQGxuL9PT0NqfI7t69O7y9vfH2228jKCgIEomk1bYrVqxo9dqnlmxb+vFgtubm5gZ1B+2OUjVuSee1nEJDyOOCxiw/lC0/lC0fhpQr1+Lp3Xffhb+/P1asWIGqqir4+/vD399fOM8QkJ+T2nK9DiA/arJ06VIsXrwYQUFBKCwsxIEDBxS+zf7kk08wZcoUTJs2DaGhoZBKpfjpp59gbGzMc3MU1NXJi6OBA4F16/4qnFqIRPLlAwfK23VkFj5Vs80FBwejsLAQVVVVGDRoUEe6DkB+xGjGjBlYu3YtSktLUVZWBkdHRzDGAMivIXvxxRcRFRWF9evXo7CwsNX1vP322wgLC8OMGTNw/Phx9OvXDx4eHjhy5Ajc3d2V3jNixAhUVVWhqqoK+/fvV7ne5uZm4Vzyh/n4+Khc3tL31sycORMff/wxZs6c2eZMfuHh4XB2dsa2bdvaXB9RzdDut6YveE10QwgvNGb5oWz5oWz5MKRcud7nKS4urt17PD38B69IJEJsbCxiY2NbfY+ZmRk2bNiADRs2aKGXHSORABs2yI8sLV2qeOQJABiTLz99Gti4Ud5eU60Vg7t371Z5up4m6urqUF9fLxw5Wb9+Pe7cuSO8vnHjRty7dw/79+9HbGwsFi5ciF9++UVpPWKxGMnJyUJ/pk6d2qH+7Nq1C0OHDoW7uzsuX76MtWvXYvz48Srbzpo1CyNGjMCpU6fg4+ODVatWYeTIke2eLhYZGYnu3bsjLCys3VlfVqxYIRztJJp5lF9idCUymUzXXTBIFhYW8PHxgYWFha67YnBozPJD2fJD2fJhSLnq1VTlj5tFi+SF0YYNQEyMvGAC5D9jYuTLN26Ut+sIqVSqcvnAgQNbPfqiLmtra3z44YcYP348nJycUFpait69ewOQ32/r//2//4e4uDiYmJjg3XffRUFBAb7++mul9YhEok4XcgCQm5uLkJAQYfbD0aNHK8yQaGlpifT0dADyWWA+//xzTJ06FQ4ODjhz5gy2b9/e7mdIpVJMnDgRZmZmrWbbYsKECfDy8urcRnVR7WVLOqYzR5pJ6/r27YuTJ08KM3QR7aExyw9lyw9ly4ch5Spi7Z3rZIBaplpsmSa0RW1tLfLy8uDp6anRpBcPXvu0bp38iFNnCycAKC8vN6hKXZ9Qtvy0lW1Hf8eIfGr3CRMm6LobBomy5YNy5Yey5Yey5UPfc22tNlCF62l7XUVLgRQdDaSl/XWqXmcKJ0IIIfxlZWVh4sSJyMzMREBAgK67QwghRM9R8aQlLYXSkiXaK5zom3l+KFt+KFs+6DRS8rihMcsPZcsPZcuHIeVKxZMWLVoEzJvXsckhCCGkLdq4tpCQR4nGLD+ULT+ULR+GlKvhbIme0GbhVFtbq72VEQWULT+ULR90A2byuKExyw9lyw9ly4ch5UrFkwpdcA4NQh4J+t0ihBBCyOOMZtt7YEaNpqYm5ObmQiqVwsHBAaKH73z7iDU1NdE9czihbPlpLVvGGO7cuYOamhr06dOH8tdQdXU13YuIg9raWly6dAleXl50vZ6W0Zjlh7Llh7LlQ99zpdn2OsjY2Biurq4oKChAfn6+rruDuro6SOgCKi4oW37aylYkEsHV1ZUKpw44d+4chgwZoutuGBwzMzPU1tZS4cQBjVl+KFt+KFs+DClXKp4eYmlpiT59+qChoUHXXcHvv/+O4cOH67obBomy5aetbE1NTalw6qB79+7pugsGKS8vD8uXL8eWLVvg6emp6+4YFBqz/FC2/FC2fBhSrlQ8qWBsbKwXf+CZm5vTt6GcULb8ULZ8WFpa6roLBunevXtISUnBvXv3qHjSMhqz/FC2/FC2fBhSrnTNUzvnNepSQ0MDTE1Ndd0Ng0TZ8kPZ8kG58pGVlYXAwEC6SS4HNGb5oWz5oWz50PdcNakNaLY9PXbo0CFdd8FgUbb8ULZ8UK7kcUNjlh/Klh/Klg9DyrVLnrbXcrCtoqJCxz1pW3V1td738XFF2fJD2fJBufJRVVUl/KR8tYvGLD+ULT+ULR/6nmtL39Q5Ia9LnrZXUFAANzc3XXeDEEIIIYQQoidu3LgBV1fXNtt0yeKpubkZN2/ehJWVlc7v5dSaiooKuLm54caNG3p9XdbjiLLlh7Llg3Llh7Llg3Llh7Llh7Ll43HIlTGGyspKuLi4wMio7auauuRpe0ZGRu1WlfrC2tpabwfa446y5Yey5YNy5Yey5YNy5Yey5Yey5UPfc5XJZGq1owkjCCGEEEIIIUQNVDwRQgghhBBCiBqoeNJTEokEK1asgEQi0XVXDA5lyw9lywflyg9lywflyg9lyw9ly4eh5dolJ4wghBBCCCGEEE3RkSdCCCGEEEIIUQMVT4QQQgghhBCiBiqeCCGEEEIIIUQNVDwRQgghhBBCiBqoeNKh999/HyEhIZBKpbCxsVHrPYwxxMbGwsXFBebm5ggLC8O5c+cU2tTV1WHJkiWwt7eHhYUFnn76aRQUFHDYAv107949zJ49GzKZDDKZDLNnz0ZZWVmb7xGJRCofH374odAmLCxM6fXp06dz3hr90pFs582bp5TbsGHDFNrQmNUs14aGBvzzn/+Er68vLCws4OLigjlz5uDmzZsK7brimP3iiy/g6ekJMzMzBAYGIj09vc32aWlpCAwMhJmZGZ544gls3LhRqc0PP/yA/v37QyKRoH///khMTOTVfb2mSba7d+/G+PHj4eDgAGtrawQHB+O3335TaBMXF6dyv1tbW8t7U/SKJrmmpqaqzOzChQsK7WjMymmSrap/q0QiEQYMGCC0oTELHD58GE899RRcXFwgEonw448/tvseg9vPMqIz7777Lvv444/Za6+9xmQymVrvWbt2LbOysmI//PADO3PmDHvuueeYs7Mzq6ioENpER0ezHj16sKSkJJaVlcVGjx7N/Pz8WGNjI6ct0S8TJ05kPj4+7OjRo+zo0aPMx8eHTZ48uc333Lp1S+Hx9ddfM5FIxK5cuSK0GTVqFFu4cKFCu7KyMt6bo1c6ku3cuXPZxIkTFXIrLS1VaENjVrNcy8rK2Lhx49i3337LLly4wDIyMtjQoUNZYGCgQruuNmYTEhKYqakp++qrr1hOTg6LiYlhFhYW7Nq1ayrbX716lUmlUhYTE8NycnLYV199xUxNTdn3338vtDl69CgzNjZma9asYefPn2dr1qxhJiYm7NixY49qs/SCptnGxMSwDz74gJ04cYJdunSJvfXWW8zU1JRlZWUJbbZu3cqsra2V9r9diaa5pqSkMADs4sWLCpk9uK+kMSunabZlZWUKmd64cYPZ2tqyFStWCG1ozDL2yy+/sHfeeYf98MMPDABLTExss70h7mepeNIDW7duVat4am5uZk5OTmzt2rXCstraWiaTydjGjRsZY/JfflNTU5aQkCC0KSwsZEZGRuzXX3/Vet/1TU5ODgOg8AuXkZHBALALFy6ovZ5nnnmGjRkzRmHZqFGjWExMjLa6+tjpaLZz585lzzzzTKuv05jVzpg9ceIEA6Dwh0FXG7NDhgxh0dHRCsu8vb3Z8uXLVbZ/8803mbe3t8KyRYsWsWHDhgnPp02bxiZOnKjQZsKECWz69Ola6vXjQdNsVenfvz9buXKl8Fzdf/sMmaa5thRP9+7da3WdNGblOjtmExMTmUgkYvn5+cIyGrOK1CmeDHE/S6ftPUby8vJQVFSE8PBwYZlEIsGoUaNw9OhRAEBmZiYaGhoU2ri4uMDHx0doY8gyMjIgk8kwdOhQYdmwYcMgk8nU3v7bt29j3759WLBggdJr8fHxsLe3x4ABA/D666+jsrJSa33Xd53JNjU1FY6OjvDy8sLChQtRXFwsvEZjtvNjFgDKy8shEomUTgHuKmO2vr4emZmZCuMIAMLDw1vNMSMjQ6n9hAkT8Mcff6ChoaHNNl1hbLboSLYPa25uRmVlJWxtbRWWV1VVwcPDA66urpg8eTJOnTqltX7ru87k6u/vD2dnZ4wdOxYpKSkKr9GY1c6Y3bJlC8aNGwcPDw+F5V15zHaEIe5nTXTdAaK+oqIiAED37t0Vlnfv3h3Xrl0T2ojFYnTr1k2pTcv7DVlRUREcHR2Vljs6Oqq9/du2bYOVlRUiIiIUlkdFRcHT0xNOTk44e/Ys3nrrLfz5559ISkrSSt/1XUeznTRpEiIjI+Hh4YG8vDz861//wpgxY5CZmQmJREJjVgtjtra2FsuXL8fMmTNhbW0tLO9KY7akpARNTU0q94+t5VhUVKSyfWNjI0pKSuDs7Nxqm64wNlt0JNuH/ec//0F1dTWmTZsmLPP29kZcXBx8fX1RUVGB9evXIzQ0FH/++Sf69Omj1W3QRx3J1dnZGZs3b0ZgYCDq6uqwfft2jB07FqmpqRg5ciSA1sc1jVn1c7h16xb279+PnTt3Kizv6mO2IwxxP0vFk5bFxsZi5cqVbbY5efIkgoKCOvwZIpFI4TljTGnZw9Rpo8/UzRVQzgfQbPu//vprREVFwczMTGH5woULhf/28fFBnz59EBQUhKysLAQEBKi1bn3EO9vnnntO+G8fHx8EBQXBw8MD+/btUypQNVmvvntUY7ahoQHTp09Hc3MzvvjiC4XXDHXMtkXT/aOq9g8v78g+1xB1NIdvvvkGsbGx2LNnj8IXBcOGDVOYPCY0NBQBAQHYsGEDPv30U+11XM9pkmvfvn3Rt29f4XlwcDBu3LiBjz76SCieNF2nIetoDnFxcbCxscGUKVMUltOY7RhD289S8aRlL7/8cruzWfXs2bND63ZycgIgr+KdnZ2F5cXFxULF7uTkhPr6ety7d0/hm/zi4mKEhIR06HP1gbq5nj59Grdv31Z67c6dO0rfaqiSnp6Oixcv4ttvv223bUBAAExNTZGbm/tY/yH6qLJt4ezsDA8PD+Tm5gKgMduZXBsaGjBt2jTk5eXh0KFDCkedVDGUMauKvb09jI2Nlb6pfHD/+DAnJyeV7U1MTGBnZ9dmG03G/OOuI9m2+Pbbb7FgwQLs2rUL48aNa7OtkZERBg8eLOwbDF1ncn3QsGHDsGPHDuE5jdnOZcsYw9dff43Zs2dDLBa32barjdmOMMT9LF3zpGX29vbw9vZu8/HwEQ11tZx+8+ApN/X19UhLSxP+yAwMDISpqalCm1u3buHs2bOP9R+i6uYaHByM8vJynDhxQnjv8ePHUV5ertb2b9myBYGBgfDz82u37blz59DQ0KBQyD6OHlW2LUpLS3Hjxg0hNxqzHcu1pXDKzc1FcnKy8I9QWwxlzKoiFosRGBiodEpiUlJSqzkGBwcrtT9w4ACCgoJgamraZpvHeWxqqiPZAvIjTvPmzcPOnTvx5JNPtvs5jDFkZ2cb5PhUpaO5PuzUqVMKmdGY7Vy2aWlpuHz5ssrrnh/W1cZsRxjkfvZRz1BB/nLt2jV26tQptnLlSmZpaclOnTrFTp06xSorK4U2ffv2Zbt37xaer127lslkMrZ792525swZNmPGDJVTlbu6urLk5GSWlZXFxowZ0+WmfR44cCDLyMhgGRkZzNfXV2na54dzZYyx8vJyJpVK2Zdffqm0zsuXL7OVK1eykydPsry8PLZv3z7m7e3N/P39u0yujGmebWVlJVu2bBk7evQoy8vLYykpKSw4OJj16NGDxuwDNM21oaGBPf3008zV1ZVlZ2crTJlbV1fHGOuaY7ZlauItW7awnJwctnTpUmZhYSHMlrV8+XI2e/ZsoX3LFLqvvvoqy8nJYVu2bFGaQvfIkSPM2NiYrV27lp0/f56tXbtWr6fQ5UXTbHfu3MlMTEzY559/3upU+bGxsezXX39lV65cYadOnWLz589nJiYm7Pjx4498+3RF01w/+eQTlpiYyC5dusTOnj3Lli9fzgCwH374QWhDY1ZO02xbzJo1iw0dOlTlOmnMyv9db/l7FQD7+OOP2alTp4SZXrvCfpaKJx2aO3cuA6D0SElJEdoAYFu3bhWeNzc3sxUrVjAnJycmkUjYyJEj2ZkzZxTWe//+ffbyyy8zW1tbZm5uziZPnsyuX7/+iLZK90pLS1lUVBSzsrJiVlZWLCoqSmla14dzZYyxTZs2MXNzc5X3wbl+/TobOXIks7W1ZWKxmPXq1Yu98sorSvcrMnSaZltTU8PCw8OZg4MDMzU1Ze7u7mzu3LlK45HGrGa55uXlqdx3PLj/6Kpj9vPPP2ceHh5MLBazgIAAlpaWJrw2d+5cNmrUKIX2qampzN/fn4nFYtazZ0+VX57s2rWL9e3bl5mamjJvb2+FP1S7Ek2yHTVqlMrxOXfuXKHN0qVLmbu7OxOLxczBwYGFh4ezo0ePPsIt0g+a5PrBBx+wXr16MTMzM9atWzc2fPhwtm/fPqV10piV03R/UFZWxszNzdnmzZtVro/G7F/T5bf2u90V9rMixv7vqi1CCCGEEEIIIa2ia54IIYQQQgghRA1UPBFCCCGEEEKIGqh4IoQQQgghhBA1UPFECCGEEEIIIWqg4okQQgghhBBC1EDFEyGEEEIIIYSogYonQgghhBBCCFEDFU+EEEIIIYQQvXb48GE89dRTcHFxgUgkwo8//qjR+2NjYyESiZQeFhYWGq2HiidCCCGEEEKIXquuroafnx8+++yzDr3/9ddfx61btxQe/fv3R2RkpEbroeKJEEIIIYQQotcmTZqE1atXIyIiQuXr9fX1ePPNN9GjRw9YWFhg6NChSE1NFV63tLSEk5OT8Lh9+zZycnKwYMECjfph0pmNIIQQQgghhBBdmz9/PvLz85GQkAAXFxckJiZi4sSJOHPmDPr06aPU/r///S+8vLwwYsQIjT6HjjwRQgghhBBCHltXrlzBN998g127dmHEiBHo1asXXn/9dQwfPhxbt25Val9XV4f4+HiNjzoBdOSJEEIIIYQQ8hjLysoCYwxeXl4Ky+vq6mBnZ6fUfvfu3aisrMScOXM0/iwqngghhBBCCCGPrebmZhgbGyMzMxPGxsYKr1laWiq1/+9//4vJkyfDyclJ48+i4okQQgghhBDy2PL390dTUxOKi4vbvYYpLy8PKSkp2Lt3b4c+i4onQgghhBBCiF6rqqrC5cuXhed5eXnIzs6Gra0tvLy8EBUVhTlz5uA///kP/P39UVJSgkOHDsHX1xd/+9vfhPd9/fXXcHZ2xqRJkzrUDxFjjHV6awghhBBCCCGEk9TUVIwePVpp+dy5cxEXF4eGhgasXr0a//u//4vCwkLY2dkhODgYK1euhK+vLwD56X0eHh6YM2cO3n///Q71g4onQgghhBBCCFEDTVVOCCGEEEIIIWqg4okQQgghhBBC1EDFEyGEEEIIIYSogYonQgghhBBCCFEDFU+EEEIIIYQQogYqngghhBBCCCFEDVQ8EUIIIYQQQogaqHgihBBCCCGEEDVQ8UQIIYQQQgghaqDiiRBCCCGEEELUQMUTIYQQQgghhKiBiidCCCGEEEIIUcP/B9AAVNPRqTnZAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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qp6SkhBUrVlBcXMzy5ct93ZyAJPqvskS+yhL5Kstf8hVrchS0b98+XzchoIl8lSXyVZbIV1Az0X+VJfJVlshXWWrMVxQ5giAIgiAIgiAEFFHkzFJmZqavmxDQRL7KEvkqS+SrnPj4eB599FHi4+N93ZSAJfqvskS+yhL5KkuN+ep93QC18Yc9wgOZyFdZIl9liXyVk5yczNe//nWSkpJ83ZSAJfqvskS+yhL5KkuN+YqRnFmqqKjwdRMCmshXWSJfZYl8lWO1Wnn55ZexWq2+bkrAEv1XWSJfZYl8laXGfEWRIwiCIPi9uro6/vVf/5W6ujpfN0UQBEFQAVHkzNLatWt93YSAJvJVlshXWSJfQc1E/1WWyFdZIl9lqTFfUeTMUmNjo6+bENBEvsoS+SpL5Cuomei/yhL5Kkvkqyw15iuKnFnq7e31dRMCmshXWSJfZYl8BTUT/VdZIl9liXyVpcZ8RZEzS0FBQb5uQkAT+SpL5Ksska9yDAYDMTExGAwGXzclYIn+qyyRr7JEvspSY74aSZIkXzfiakZHRwkPD2dkZISwsDBfN0cQBEEQBEEQBB+ZTW0gRnJmaffu3b5uQkAT+SpL5Ksska+yRL7KEvkqS+SrLJGvstSYryhyBEEQBL9XUVHBgw8+qMqzGgRBEIT5J4qcWUpLS/N1EwKayFdZIl9liXyV43A46O/vx+Fw+LopAUv0X2WJfJUl8lWWGvMVRc4sRUVF+boJAU3kqyyRr7JEvoKaif6rLJGvskS+ylJjvqLImaWysjJfNyGgiXyVJfJVlshXUDPRf5Ul8lWWyFdZasxXFDmCIAiCIAiCIAQUsYX0LA0ODqpyyE4tRL7KEvkqS+SrHKvVysGDB9myZQsWi8XXzQlIov8qS+SrLJGvsvwlX7GFtILa29t93YSAJvJVlshXWSJf5VgsFjIyMkSBoyDRf5Ul8lWWyFdZasxXFDmz1NXV5esmBDSRr7JEvsoS+Sqno6ODb3/723R0dPi6KQFL9F9liXyVJfJVlhrzVbzI6ejo4MEHHyQ6OhqTyURRURHFxcVKf1vF6PV6XzchoIl8lSXyVZbIVzk9PT384Q9/oKenx9dNCVii/ypL5Ksska+y1JivomtyhoaGWLZsGVu2bOGf/umfiIuLo6GhgYyMDLKzs6/7+f64JkcQBEGYfyUlJaxYsYLi4mKWL1/u6+YIgiAIPuA3a3KefvppUlNT+dWvfsXq1avJyMhg27ZtN1Tg+Ku9e/f6ugkBTeSrLJGvskS+gpqJ/qsska+yRL7KUmO+ihY5b775JitXruSjH/0ocXFxLFu2jJ///OdXfbzNZmN0dHTazd+43W5fNyGgiXyVJfJVlshXUDPRf5Ul8lWWyFdZasxX0Ql2jY2N/PSnP+VLX/oS//Zv/8aZM2f453/+Z4KCgvjUpz414/Hf/e53eeqpp2Z8fN++fZjNZrZu3cqZM2cYGxsjMjKSRYsWcezYMQDy8/Nxu93U1tYCsGnTJsrKyuThrOXLl3Po0CEAcnNz0ev1VFVVAXDbbbdx8eJFBgcHMZvNrF27lv379wOQlZWFyWTiwoULAERGRlJSUkJfXx/BwcFs3LiRPXv2AJCenk5ERATnz58HYPXq1bS2ttLd3Y3BYGDr1q3s2bMHSZJISUkhLi6OkpISAFasWEF3dzcdHR1otVruuOMO9u/fj9PpJDExkZSUFM6ePQtAUVERg4ODtLa2ArBz504OHTqEzWYjLi6OrKwsTp06BUBhYSFjY2M0NTUBsH37dk6cOMHExATR0dHk5+dz/PhxAAoKCrDb7dTX1wOwZcsWzp07h9VqJSIigiVLlnDkyBEAFixYAEBNTQ0AGzdupLy8nOHhYSwWCytXruTgwYMA5OTkYDQauXjxIgAbNmygurqagYEBTCYT69evZ9++fQBotVo6OzupqKgAYO3atTQ2NtLb20tQUBCbN29m9+7dAKSlpREVFSUfULVq1Sra29vp6upCr9ezbds29u7di9vtJjk5mYSEBHk92PLly+nt7aW9vR2NRsOOHTs4cOAADoeDhIQE0tLSOHPmDABLly5leHiYlpYWAHbs2MGRI0eYmpoiNjaWnJwcTp48CcDixYuZmJigsbERgG3btnHq1CnGx8eJioqioKBA7rMLFy7E6XRSV1cHwObNmykpKZGHYouKijh8+DAAeXl5aLVaqqur5T5bWVnJ0NAQoaGhrF69mgMHDgCQnZ1NcHAwlZWVAKxfv57a2lr6+/uxWq243W75FZmMjAzCwsIoLy8HYM2aNTQ3N9PT04PRaGTLli1y3qmpqcTExFBaWgrAypUr6ezspLOzE51Ox/bt29m3bx8ul4ukpCSSkpI4d+4cAMuWLaO/v5+2tja5zx48eBC73U58fDwZGRmcPn0agCVLljA6OkpzczMAd9xxB8ePH2diYoKYmBjy8vI4ceIEAIsWLWJqaoqGhgYAn18j+vv7GR0dpb6+Xlwj5vgaceHCBTZt2sTIyIi4Rih4jTh69CgbNmwQ1wjm/hrR39/P7t27WbdunbhGMPfXCG++mZmZhIaGimvEHF8j7HY7u3fvxmQy+fQa4W3/jVB0TY7RaGTlypXyxQbgn//5nzl79qz8y7yUzWbDZrPJ/x4dHSU1NdWv1uT09/cTExPj62YELJGvskS+yhL5KkvkqyyRr7JEvsoS+SrLX/L1mzU5iYmJFBQUTPvYwoUL5VcOLhcUFERYWNi0m79R885waiDyVZbIV1kiX+VMTk7y5z//mcnJSV83JWCJ/qsska+yRL7KUmO+ihY5GzZskIchvWpra0lPT1fy2wqCIAgBpqqqiscee0yeHiQIgiAI16JokfPFL36RU6dO8Z3vfIf6+npeeeUVXnjhBZ544gklv62ixNalyhL5KkvkqyyRr6Bmov8qS+SrLJGvstSYr6JFzqpVq3jttdf43e9+x+LFi/nmN7/JM888wyc+8Qklv62ient7fd2EgCbyVZbIV1kiX0HNRP9VlshXWSJfZakxX0WLHIAPfOADVFRUMDU1RVVVFZ/97GeV/paKam9v93UTAprIV1kiX2WJfAU1E/1XWSJfZYl8laXGfBUvcgKNRqPxdRMCmshXWSJfZYl8laPRaDAYDCJjBYlslSXyVZbIV1lqzFfRLaRv1my2iRMEQRAEQRAEIXD5zRbSgch7UJKgDJGvskS+yhL5KkvkqyyRr7JEvsoS+SpLjfmKImeWHA6Hr5sQ0ES+yhL5Kkvkq5yqqioeffRRsYW0gkT/VZbIV1kiX2WpMV9R5MxSQkKCr5sQ0ES+yhL5Kkvkq5zJyUkaGhrEYaAKEv1XWSJfZYl8laXGfEWRM0tpaWm+bkJAE/kqS+SrLJGvoGai/ypL5Ksska+y1JivKHJm6cyZM75uQkAT+SpL5Ksska+gZqL/KkvkqyyRr7LUmK8ocgRBEARBEARBCCiiyJmlpUuX+roJAU3kqyyRr7JEvsrJzMzkhRdeIDMz09dNCVii/ypL5Ksska+y1Jiv3tcNUJvh4WFVLr5SC5HvlUmShN1uZ2pqCpvNNu3t1NQUdrsdp9N51ZvL5cLpdNLe3k5iYiLe47Gu9hZAp9Oh1WrR6XTXfN9gMGAwGDAajRiNRvn9y98ajUaCg4PR6/WqPFTsRoj+q5zIyEhuv/12IiMjfd2UgCX6r7JEvsoS+SpLjfmKImeWWlpayM/P93UzAtatlK/T6cRqtTI2NsbY2Bjj4+NXfDsxMYHNZmMuzu2tr6/HbrfPQevfP51OR0hICMHBwQQHB1/x/ZCQEEwmE2azWb6p4bT7W6n/zreenh5++MMf8s1vfpP4+HhfNycgif6rLJGvskS+ylJjvqLIEQSFuFwuRkZGGB4evuLNarXOunDR6XQEBQURHBw87W1QUBAGgwG9Xi/fdDrdtH/r9XpOnDjB7bffDiAXDJe/vbT9brcbl8s17f1LP+ZyuXA4HDgcDux2u/z20vcv/ZgkSbhcLrmwmw2DwTCt6Ln8FhYWhsViISwsDIPBMKuvLfi/jo4Ofv7zn/PYY4+JIkcQBEG4Lo00Fy8PK2R0dJTw8HBGRkYICwvzdXMAz3Qef381Wc3UmK/D4WBgYIC+vr5pt8HBQdxu9zU/V6/XExoaKt/MZvOMt2azWS5obnaqly/z9U65m5yclKfZXfr+pf+enJxkYmJCHs1yOp2z+l7BwcFy0eMtfC59Gx4ejslkmvMs1Nh/1aKkpIQVK1ZQXFzM8uXLfd2cgCT6r7JEvsoS+SrLX/KdTW0gRnJm6ciRI2zatMnXzQhY/p7v+Pg4XV1ddHZ20tnZSW9vL0NDQ1cdkTEYDERERFz1psQT7WvxZb4ajUYedZoNSZJwOByMj49f9TY2Nsbo6ChWq1VeuzQ1NUVvb+9Vv+7lv5vw8PBp/zabzbP+3fh7/xWEaxH9V1kiX2WJfJWlxnxFkTNLU1NTvm5CQPOnfF0uF93d3bS2ttLW1kZHRwcjIyNXfGxISAixsbEzbhaLxS9e+fDyp3xvlEajkTcuuN6ic0mSsNlsWK1WrFarXPh433rfHxsbw+FwyKNuV6LX6+XCJyoqatotMjISvX7m5VON+QqCl+i/yhL5Kkvkqyw15iuKnFmKjY31dRMCmi/zdblcdHR00NjYSEtLC+3t7TgcjhmPi46OJikpiaSkJBISEoiNjX1fr/r7QqD3X41GI29icK2f1el0Mjo6Om2N1KXrp0ZHR3E6nQwMDDAwMEBDQ8OM7xMWFjaj+NFqtTgcDrEmSAHh4eFs3LiR8PBwXzclYAX69cHXRL7KEvkqS435ijU5szQ6Ouo3bQlE853v8PAwtbW1NDQ00NzcjM1mm3Z/SEgIqamppKWlkZKSQmJi4qynW/kT0X9vjMvlYnR0lJGREYaGhhgaGmJwcJCBgQEGBwdn9BMvm81GUFAQ4eHhxMTEEBMTQ2xsrPy+WophfyX6r7JEvsoS+SpL5Kssf8lXrMlR0MmTJ9m5c6evmxGwlM5XkiS6urqorq6mpqaGnp6eafeHhISQlZVFZmYm6enpxMTEBNSTUtF/b4xOpyMyMpLIyEgyMjKm3SdJEhMTEwwODs64nTx5krS0NEZGRhgZGZkxAhQSEiIXPJcWQBEREWi14mzma3E4HLzzzjvcd999YqRMIeL6oCyRr7JEvspSY76iyBFuCT09PVRUVHDhwgWGh4flj2s0GtLS0sjNzSUrK4vExMSAKmqEuafRaORd71JTU6fdl5qaysaNG+nv75dvfX199Pf3Mzw8zOTkJG1tbbS1tU37PL1eT2xsLHFxccTHx8tvQ0NDRX98T0VFBQ888IDYXU0QBEG4IaLImaXFixf7ugkBbS7zHR8fp6ysjPPnz0/bZctoNJKTk8OCBQvIzc3FZDLN2ff0d6L/KquwsBCTyURaWhppaWnT7nM4HAwODspFz6U3p9NJV1cXXV1d0z4nJCRkWtETFxdHXFycqqdMCv5LXB+UJfJVlshXWWrMVxQ5szQxMeHrJgS0m81XkiSampooLi6muroal8sFeKYf5eXlsXjxYvLy8m7Z6S6i/yrrWvkaDAbi4+NnHGTpdrsZHh6mp6eH3t5e+e3AwACTk5M0NzfT3Nw87XMiIiJISEggISGBxMREEhISCAsLE6M+wk0R1wdliXyVJfJVlhrzFUXOLDU2NpKbm+vrZgSs95uv0+mkvLycEydO0N/fL388JSWF5cuXU1BQQHBw8Fw2VZVE/1XW+8lXq9XKu7MtXLhQ/rjT6aSvr29a4dPT04PVapV3gauurpYfbzKZphU9iYmJREdHi8JHuGHi+qAska+yRL7KUmO+osgRVM1ms3H69GnOnDnD2NgYAEFBQSxZsoQVK1aQkJDg4xYKwvuj1+tJTEwkMTFx2scnJyfp6emhu7ubrq4uuru76evrY2JigsbGRhobG+XHGo1G4uPj5aInOTmZ2NhYscmBIAiCEPDEFtKz5HQ6r3gIoDA3bjRfu93O2bNnOXbsGJOTk4DnHI21a9eyfPlysWbhKkT/VZav8nU6nfT29spFT1dXFz09PVc858lgMJCYmEhSUhLJyckkJSURFRXl9yM+LpeLkZERwsPD0el0vm5OQBLXB2WJfJUl8lWWv+QrtpBW0KlTp7jtttt83YyAdb183W43paWlHDx4UB65iYmJYePGjSxatEg8+bkO0X+V5at89Xq9fECtl9vtZmBgQC56urq66OzsxGaz0draSmtrq/zYkJAQ+fO9hY+/vLDkpdPpuHjxoui/ChLXB2WJfJUl8lWWGvMVRc4sjY+P+7oJAe1a+ba1tfH222/LO1BFRkayefNmCgsLxfSbGyT6r7L8KV+tVktsbCyxsbEUFhYCno05BgYG6OjooKOjg87OTrq7u5mcnKShoWHauT4Wi4Xk5GRSU1NJSUkhKSnJpxt21NXV8YUvfIFXX31VdfPC1cKf+m8gEvkqS+SrLDXmK4qcWYqKivJ1EwLalfK12Wzs2bOH4uJiwLPmZsuWLaxatUqM3MyS6L/K8vd8NRqNfBDp0qVLAc80sN7e3mmFT29vL1arlerqanlzA61WS0JCAqmpqXLhEx4ePm/T3KxWKyUlJVit1nn5frcif++/aifyVZbIV1lqzFesyZml8fFxzGazr5sRsC7Pt6mpiddff52RkREAli1bxvbt28Xv4H0S/VdZgZKv3W6nq6uLjo4O+fBS7/TQS1ksFrngSU1NJTExUbE52yUlJaxYsUIcBqqgQOm//krkqyyRr7L8JV+xJkdBx44dY+fOnb5uRsDy5ut2u9m3bx8nTpwAPFPT7rnnHjIyMnzbQJUT/VdZgZKv0WgkPT2d9PR0wDPNbWRkhLa2Ntrb22lra6O7uxur1crFixe5ePEi4Fk3k5SURFpaGunp6aSmphISEuLLH0WYhUDpv/5K5Ksska+y1JivKHIEvzM2Nsaf/vQn+QDElStXsmPHDoxGo28bJgi3KI1GQ0REBBEREfL6HofDQWdn57TCZ3x8XB75OX78OBqNhri4ONLT0+XCx2Kx+PinEQRBEG4FosiZpUsP6xPmXkxMDC+88AKjo6MYjUY+/OEPU1BQ4OtmBQzRf5V1K+VrMBhmjPYMDQ3JO7e1tLQwMDBAT08PPT09nDlzBvDM6/YWPGlpaTe8fXVqairf+MY3SE1NVfTnupXdSv3XF0S+yhL5KkuN+YoiZ5acTqevmxCwWlpa+P3vf4/JZCImJoaPfexjxMbG+rpZAUX0X2XdyvlqNBqioqKIioqiqKgI8IzKegue1tZWuru7GRwcZHBwkLKyMgBCQ0PJyMggIyODzMzMqxY9sbGxfOITnxDXBAXdyv13Poh8lSXyVZYa8xVFzizV1dWRlZXl62YEnLq6On7/+9/T2dnJ1q1b+fjHPy7m8itA9F9liXynCw0NpaCgQB6NnZqaor29nZaWFlpaWujo6GBsbIwLFy5w4cIFwLOZQWZmplz0REZGAjA4OMjzzz/PV7/6VVXu8qMGov8qS+SrLJGvstSY77wVOd/97nf5t3/7N77whS/wzDPPzNe3FVSgubmZ3//+9zidTlJSUvjkJz/p0/M4BEFQRnBwMDk5OeTk5ACeVwY7Ojpobm6mqamJtrY2rFYr5eXllJeXAxAREUFGRgZTU1N873vf44EHHhBFjiAIgnBd87KF9NmzZ7n//vsJCwtjy5YtN1zk+OMW0jabjaCgIF83I2B0dXXx61//GpvNxoIFC7jnnnswmUy+blbAEv1XWSLfm+NwOGhvb6epqYnm5mba29txu92A51rxwgsv8OUvf5nbb7+drKwssrKyxPViDon+qyyRr7JEvsryl3xnUxsofkz82NgYn/jEJ/j5z38uTztQs5KSEl83IWCMj4/zu9/9DpvNRmZmJh/96Ec5f/68r5sV0ET/VZbI9+YYDAYyMzPZunUrjzzyCF/96ld58MEHue222+S1OKOjo5SUlPCnP/2J733ve/zsZz9j3759NDY2qnLOuD8R/VdZIl9liXyVpcZ8FZ+u9sQTT7Br1y62b9/Ot771rWs+1mazYbPZ5H+Pjo4q3bxZ88c2qZHb7eZPf/oTo6OjxMTE8MADD6DX60W+ChP5KkvkO7eMRqM8vS0qKopvf/vb3HnnnZhMJhobG+np6aGrq4uuri6OHTsm7/iWlZVFdnY2cXFxN7Rzm+Ah+q+yRL7KEvkqS435KlrkvPrqq5SUlHD27Nkbevx3v/tdnnrqqRkf37dvH2azma1bt3LmzBnGxsaIjIxk0aJFHDt2DID8/Hzcbje1tbUAbNq0ibKyMnk4a/ny5Rw6dAiA3Nxc9Ho9VVVVANx2221cvHiRwcFBzGYza9euZf/+/QDydAjvotigoCBKSkro6+sjODiYjRs3smfPHgDS09OJiIiQRyNWr14t7yhkMBjYunUre/bsQZIkUlJSiIuLkyvjFStW0N3dTUdHB1qtljvuuIP9+/fjdDpJTEwkJSVFzrGoqIjBwUFaW1sB2LlzJ4cOHcJmsxEXF0dWVhanTp0CoLCwkLGxMZqamgDYvn07J06cYGJigujoaPLz8zl+/DgABQUF2O126uvrAdiyZQvnzp3DarUSERHBkiVLOHLkCAALFiwAoKamBoCNGzdSXl7O8PAwFouFlStXcvDgQQBycnIwGo3ygYEbNmzglVde4ciRI5hMJh577DH5d2O32+ns7KSiogKAtWvX0tjYSG9vL0FBQWzevJndu3cDyNvPendpWrVqFe3t7XR1daHX69m2bRt79+7F7XaTnJxMQkICxcXFACxfvpze3l7a29vRaDTs2LGDAwcO4HA4SEhIIC0tTd7ydunSpQwPD9PS0gLAjh07OHLkCFNTU8TGxpKTk8PJkycBWLx4MRMTEzQ2NgKwbds2Tp06xfj4OFFRURQUFMh9duHChTidTurq6gDYvHkzJSUl8lBsUVERhw8fBiAvLw+tVkt1dbXcZysrKxkaGiI0NJTVq1dz4MABALKzswkODqayshKA9evXU1tbS39/PwMDA7jdbvbu3QtARkYGYWFh8vqHNWvW0NzcTE9PD0ajkS1btsh5p6amEhMTQ2lpKeA5v6izs5POzk50Oh3bt29n3759uFwukpKSSEpK4ty5cwAsW7aM/v5+2tra5D578OBB7HY78fHxpKenc/LkSSRJoqCggJGREZqbm5EkiU2bNnHmzBkmJyeJjIwkOztb/r+Qn5+P3W6npaUFjUYjZ+jNu7CwUO7f83GN6OrqYnR0lPr6enGNuMlrRHV1NQMDA5hMJtavX09JSQnZ2dmEhITIZ/UkJCQQGxvL6dOnqa6uxuFw4HA4ePfddwFITExk4cKF2Gw2EhIS2LRpk7hGXOMa0dXVxdGjR9mwYYPfXSMyMjI4ffo0AEuWLGF0dFQ+S+2OO+7g+PHjTExMEBMTQ15ennyQ9KJFi5iamqKhoQHAp88jurq62L17N+vWrRPXCAWuEd58MzMzCQ0NFc8j5vgaMTo6yu7duzGZTD69RnjbfyMUW5PT1tbGypUr2bNnD0uXLgU84RcVFV11Tc6VRnJSU1P9ak3O1NQUwcHBvm6GqvX19fH888/jcrm47777WLJkiXyfyFdZSuYrSRJ2u52pqSlsNhuTk5Py+3a7HYfDgd1ux+l0ym8dDgdOp1NedzHXNBoNOp0OvV4/7abT6TAYDPLNaDRiNBpnvB8UFERQUBBa7Y3N7BX9V1nXyleSJPr6+mhoaKChoYGWlhYcDse0x8THx5Obm0tOTg6pqanodLr5aLZqiP6rLJGvskS+yvKXfGezJkexIuf111/n3nvvnfZHxOVyodFo0Gq12Gy26/6B8ceNB3bv3s3OnTt93QzVkiSJX/3qV7S2tpKXl8fHP/7xadNJRL7Kupl8JUlicnKSsbExxsfHmZiYYHx8XL5NTEzMSbHiLUy0Wu204sLbTy7tL263G0mScLvd096fSxqNBqPRSHBwMEFBQTPemkwmgoODMZlMHDp0iDvvvHNOv7/wd7Ppv06nk7a2NhobG2loaKCrq4tL/9wFBQWRlZUlFz3+8jfGl8T1V1kiX2WJfJXlL/nOpjZQbLratm3b5KFCr4cffpj8/Hz+9V//VbyCdouqqamhtbUVg8HArl27xHx5P+QtZoaHhxkdHWV0dJSRkRFGR0dnvDJ+JZcWBCEhIQQFBcmjI3q9Xh4l8d68Iyveokar1d5Uv7i06HE6nTidTlwul/z+pTfv6JL37eXv2+12JEmaMcp8NU1NTTidTkJCQjCZTISEhEx73/tW9PvZKykp4c4776S4uJjly5df9/F6vZ7MzEwyMzPZtm0bExMTNDQ0UFdXR319PRMTE1RVVcnTjbyjPLm5uaSkpIi/UYIgCCqnWJFjsVhYvHjxtI+ZzWaio6NnfFxN8vLyfN0E1ZIkSZ5fu3btWsLDw2c8RuSrrCvla7PZ5FPoBwYGGBwcZGpq6oqfr9VqMZlMmM3mGTfviIavnxx6R4K8U9JuhtvtnjYFz/vW+/7U1BQTExPy1LzIyEjGxsYYGxu76tfU6XRyZqGhoYSGhsrvm81mcUaUQkwmE4WFhRQWFuJ2u+nq6pILno6ODnp6eujp6eHYsWMEBQWRnZ1NTk4OeXl5hIaG+rr580Jcf5Ul8lWWyFdZasx33g4DDRQ3OjdfmKmuro6enh6CgoJYv379FR8j8lWWd6poX18fPT099Pb2MjIycsXHWSwWwsPDCQsLk28Wi8XnRcx80mq1BAcH39A8ZJfLRV1dHdHR0XLh471d+m+XyyWPkF1JcHDwtALIYrHI2YsCaG5otVqSk5NJTk5m8+bNjI+P09DQQH19vTzKc/HiRXmRc3JyMnl5eSxYsID4+PiAHYkT119liXyVJfJVlhrzndcix7sriZpVV1eTnp7u62ao0qU7koSEhFzxMSLfuSdJEkNDQ7S3t7N//36SkpK4fCmexWIhOjqayMhIoqOjiYiIQK8Xr4HMhk6no6Wlhfz8/Ks+xu12MzExIa9ruvTt2NiYPGo0NTXFwMDAjM83mUxy0eMtfMLCwsQUuJtkNptZsmQJS5YsmTbKU1dXR0dHh3w7ePAg4eHhcsGTkZERUP9PxPVXWSJfZYl8laXGfAPn6iz4NavVKm/LeSPz6YWb43a76e/vp729nY6ODsbHxwGYmJhAkiTCw8OJi4sjPj6e2NhYvzjF+Fag1WrlEZorsdvtMwqf0dFRrFarPDVuYmKCnp6eaZ+n1+vlwic8PFy+mUwmUfzM0uWjPFarlbq6OmpqamhsbGRkZISzZ89y9uxZjEYj2dnZ5OXlkZeXh9ls9nXzBUEQhPcotrvaXPDH3dXGx8fFH7L3obi4mL/+9a+kpKTwmc985qqPE/neHO9ZBk1NTUxMTMgf1+v1JCYmEhUVRUZGxlVH0oSbo2T/tdlsWK1WrFarXPiMjo4yNjZ21R3ljEajXPBEREQQERFBeHi4Kqe9TU1NUVtbS15ens+2MXU4HDQ1NVFTU0NtbS1Wq1W+T6PRkJKSQl5eHgsXLiQmJsYnbbwZ4vqrLJGvskS+yvKXfP1id7VAVVlZyerVq33dDNXxHgx2vYVrIt/Zc7vdtLW10dDQQG9vr/xxo9FISkoKycnJxMfHo9frOXPmjChwFKRk//We2XP5k2eXy8X4+Pi0nfCGh4exWq3Y7Xb6+vro6+ub9jlms1kueiIiIoiMjMRsNvv1qE9wcLDPz2kwGAzyqI0kSXR1dckFT1dXF21tbbS1tbF//35iYmJYuHAh+fn5JCUl+XW2XuL6qyyRr7JEvspSY76iyJmloaEhXzdBdSRJkk/uzc7OvuZjRb43zul00tTURHV1tTwdTaPRkJCQQGZmJsnJyTM2CRD5KssX+ep0Onmq2qVcLhdWq5Xh4WGGh4fl4mdyclI+26ijo0N+vNFoJDIykqioKPmtPxU+TU1NfPWrX+XFF18kMzPT181Bo9HIJ3Jv2bKF0dFRamtrqa6upqmpif7+fo4ePcrRo0cJDw8nPz+f/Px80tPT/XYBr7g+KEvkqyyRr7LUmK8ocmbpVtlKdC4NDw9js9nkKVPXIvK9PqfTKa8R8G71HBwcTE5ODpmZmdccThb5Ksuf8tXpdPJIzaVsNptc8Fx6s9vt8jbKXv5U+AwNDXHw4EGGhob8osi5XFhYGCtXrmTlypVMTU1RV1dHVVUV9fX1jIyMcPr0aU6fPo3JZGLBggXk5+eTnZ3tVxsX+FP/DUQiX2WJfJWlxnzFmpxZcjgcqpzP7kvV1dW8+uqrJCQk8Nhjj13zsSLfq5MkiebmZioqKuT1Nmazmfz8fDIzM2/oyZLIV1lqzdflcjEyMsLQ0BBDQ0MMDg4yPDx8xbU+RqORqKgooqOjiYmJISoqal42rigpKWHFihU3fBiov3A4HDQ2NlJVVUVNTQ2Tk5PyfUajkdzcXBYuXEheXh5Go9GHLVVv/1ULka+yRL7K8pd8xZocBR04cICdO3f6uhmq4t0KNzY29rqPFfle2fDwMOfOnaO/vx/wFDeLFy+e9dQXka+y1JqvTqcjKiqKqKgo+WNXK3zsdjvd3d10d3fLjw0LC5OLnujoaMLCwvx2StZ8MxgMLFiwgAULFuB2u2lpaaGqqorq6mpGR0eprKyksrISvV5Pbm4uixYt8lnBo9b+qxYiX2WJfJWlxnxFkSMozvvKpT/syqE2brdbPpTQ7XZjMBgoKCggLy/vljqUU5h/1yp8BgYG5Jt3l7fR0VGampoAz25+lxY90dHRYptyPNtTZ2ZmkpmZyV133UVnZydVVVVcvHiRwcFBqqqqqKqq8ouCRxAEQe1EkTNL11s4L8zkLXJuZFcvke/fjY+Pc+rUKXlnrJSUFJYvX47JZHrfX1Pkq6xAz/fSwic3NxfwrPEZGBigv79fLnycTueM9T1hYWHExsbKt9m+6JGYmMiTTz553XV9aqHRaOTzeLZt20Z3dzcXL16ksrLSZwVPoPdfXxP5Kkvkqyw15iuKnFny5falamW32wFu6I+zyNejv7+fY8eOMTU1hcFgYMWKFWRkZNz01xX5KutWzDcoKEjeZQw8o4+jo6PTih7vSM/o6CgNDQ2AZ2TXW/DExcURGhp6zQ0NEhMT+Z//838GTJFzKY1GQ2JiIomJiWzduvWqBY/BYCA3N1cezZ3rgudW7L/zSeSrLJGvstSYryhyZqmyspKUlBRfN0NVvNOqXC7XdR8r8oW2tjZOnTqFy+UiMjKS9evXY7FY5uRri3yVJfL1TMny7uqWk5MDeEZ7+vv75TN7hoaG5G2sm5ubAc8f0Li4OLnwCQ8Pn1b0jI6O8tJLL/Hkk0/6zUY0SrhSwVNZWSlPafNOXzUYDOTn51NYWEh2dvacTF8V/VdZIl9liXyVpcZ8RZEjKM6765fT6fRxS/xfS0sLp06dQpIkUlJSWLNmjV/sZiIINyMoKEiemgWeXXoGBgbo6+ujt7eXgYEBpqamaG1tpbW1FfCM/MbFxREfH098fDx1dXV87Wtf46677lLV7mo349KCxzulzbtRwdDQEBUVFVRUVBASEkJBQQGFhYWkp6f7zdlGgiAIviS2kJ4lq9U6Z6+q3yr27dvHsWPHWLNmDXfdddc1H3sr59ve3s7x48eRJImsrCxWrlw55ztU3cr5zgeR7/vjcrnkoqevr4/+/v4ZL4p0dXXxpS99iTfffJNt27bd1No0tZMkic7OTioqKrhw4QJjY2PyfWFhYSxevJjCwkISEhJmVfCI/qsska+yRL7K8pd8xRbSCqqtrWXFihW+boaqhIeHAzAyMnLdx96q+Q4ODsojOFlZWaxatUqRV2Nv1Xzni8j3/dHpdMTFxREXFwd4ip6hoSF6e3vp6emhv79fPvj2woULjI+PExYWRlxcHAkJCcTGxt5Su7ddumnBjh075POzqqqqGB0d5cSJE5w4cYLo6GgKCwspLCwkOjr6ul9X9F9liXyVJfJVlhrzFUXOLHnPKRFunLfIGR4evu5jb8V87XY7x48fx+l0kpiYyMqVKxWbbnIr5jufRL5zQ6fTERMTQ0xMDAUFBTidTg4cOAAgr9XxbmRQX1+PRqMhMjKS+Ph4EhISiImJuWW2WNdqtWRlZZGVlcWuXbuor6+noqKCmpoaBgYGOHToEIcOHSIpKYmlS5eyePHiq+5sJ/qvskS+yhL5KkuN+YoiZ5Zu5SkS75f3FcT+/n5cLtc1n3zcivmeO3eO8fFxQkNDWb9+vaKHKN6K+c4nka8y9Hq9PHKxadMmcnJy6Ovrk7epHh0dZXBwUN6JTK/Xk5CQQEJCAomJibfMGV16vZ78/Hzy8/Ox2WzU1NRQUVFBQ0MDnZ2ddHZ2snv3bnJzc1m6dCl5eXnymkkQ/VdpIl9liXyVpcZ8xZqcWXK73eIk71mSJImnn36aqakpHnvsMRISEq762Fst3+7ubg4dOoRWq2Xr1q3ExMQo+v1utXznm8hXWVfLd2JiQi54uru75altXuHh4XLBExsbe8uM8niNj49z4cIFzp8/T2dnp/zx4OBgFi9ezNKlS0lJSUGSJNF/FSSuD8oS+SrLX/KdTW3g+9aqzN69e33dBNXRaDTyGRodHR3XfOytlK/b7aa0tBSAnJwcxQscuLXy9QWRr7Kulq/JZCIzM5O1a9dyzz33sGPHDgoLC4mNjUWj0TAyMkJNTQ2HDh3itdde48iRI9TV1WG1Wuf5J/ANs9nMmjVrePTRR3niiSe47bbbCAsLY2pqinPnzvHiiy/y7LPP8oMf/IChoSFfNzdgieuDskS+ylJjvmK6mjAvUlNTaWxspKGhQXUL15TS0dHByMgIRqORRYsW+bo5guDXysvL+djHPsaRI0dYsmTJVR+n0WiIiooiKiqKRYsWYbPZ5BGerq4uJicn5albABaLhaSkJJKTk4mJifGLVyqVFBsby/bt29m6dSvNzc2cP3+eqqoqBgcHqa+v50c/+hHp6eksXbqURYsW3VIbOgiCEFhEkTNLc3Hq/K0oNzeXw4cP09DQcM11ObdSvjU1NYAnm/l6InEr5esLIl/lOJ1ORkZGZn3eVlBQEGlpaaSlpSFJEiMjI3R1ddHV1UV/fz9Wq5WamhpqamowGo0kJSWRlJREYmJiQJ9RdfmGBVVVVbz77rtMTU3R0tJCS0sL77zzDosWLWLZsmWkpaWJ83dukrg+KEvkqyw15iuKnFnyl7VBapOcnIzZbGZ8fJzW1lYyMzOv+LhbJV+r1Up/fz8ajUY+FX4+3Cr5+orI179pNBoiIiKIiIhg4cKFOBwOuru76ejooKurC5vNRnNzM83NzWi1WuLi4khOTiYpKSmgNy8wGo0sXbqUuLg4zGYz5eXllJWV0d/fT1lZGWVlZURHR7Ns2TKWLl3qF2dlqJG4PihL5KssNeYripxZKi8vJzEx0dfNUB2NRkNeXh6lpaVUVFRctci5VfJtb28HIC4ujpCQkHn7vrdKvr4i8lUXg8FAamoqqampuN1u+vv75also6OjdHd3093dTXFxMZGRkfK0tsjIyIAc1SgvL2fnzp3cdtttbNiwgfb2dkpKSqisrGRgYIB9+/axf/9+cnNzWbZsGXl5ebfcJg43Q1wflCXyVZYa8xVFjjBvli5dSmlpKZWVldx1110BPRXkenp7ewHPCJfgvyRJwul0Tru5XC7cbveMmyRJjI+P09zcfMWvpdVq0Wg0aLXaGe9rtVr0ej06nQ6dTie/H4hPpP2Vd+QmLi6OoqIiRkdH6ezspKOjg/7+foaGhhgaGqKyshKTyURycjKpqakBu45Ho9HIBeBdd91FZWUlpaWltLa2UltbS21tLWazmaVLl7Js2TJiY2N93WRBEIRpxBbSszQ8PExERISvm6FKkiTxox/9iOHhYe67774rLh6+FfKVJInXXnsNu93Ojh07iIqKmrfvfSvkOxsul4vJyUnsdjs2mw273T7tfZfLxWwukVNTUwQHB89Z+7wFj8FgkN9e/r7BYMBoNAb8K+pjY2OcOHGC9evXExoaOq/f22azySM8XV1d09YFBQcHywWP2renvpHrQ39/P6WlpZw/f56xsTH54ykpKaxYsYJFixZhNBoVbqk6ieuvskS+yvKXfGdTG4iRnFlqbm6mqKjI181QJY1Gw7Jlyzh48CCnT5+msLBwxivVt0K+U1NT2O12NBoN4eHh8/q9b4V8r0SSJGw2G+Pj40xOTjI5OcnExAQ2m+2GPt9bbHhvl47AeEdldDod9fX1Vx2dkyRpxsiP932XyyXfnE4nbrcbQP7YjbRTr9djNBoJCgrCYDAQFBSE0WjEaDQSHByMXq9X9chQaGgocXFx817ggGfzgszMTDIzM3G5XPT09NDe3k57eztTU1M0NDTQ0NCA0WgkJSWFlJQU4uPjVVfw3Mj1ISYmhjvuuIOtW7dSX19PaWkptbW1ch67d+9myZIlrFy5kri4uPlpuErcqtff+SLyVZYa8xVFziz19PT4ugmqtnLlSo4ePUpHRwetra2kp6dPu/9WyHd8fByAkJCQeX8SdCvkC56CYnJykpGREcbGxhgbG8PhcFzxsQaDgeDgYLlA8BYGRqMRg8GATqe74elIFy5cmJMpiJcWPk6nE4fDgcPhuOL73hEn73S6iYmJK35NnU5HcHAwQUFBBAUFye8HBwdjMBj8vgBqb2/nG9/4Bj/+8Y9JSUnxWTt0Op28A9uKFSvo6+ujra2Njo4OpqamaGxspLGxEYPBQHJyMikpKSQkJKDX+/+f29lcH3Q6HQsWLGDBggWMjY1RVlZGcXExQ0NDnDlzhjNnzpCamsrKlSspKCi4pacne90q119fEfkqS435+v9V18+IYfib453DXVxczIkTJ2YUObdCvt4n2744fyKQ83W73YyOjjI0NMTIyAh2u33a/VqtFpPJhMlkIiQkRL7N9smXywV9fdDdDV1dMDgIo6OeW0XFAv72N7BaYWoKnE7PzeH4+1uNBvT6v98Mhr+/DQ313rTv3QyEhkJYGERHQ0yM521EBHjrY0mScLlc8lS7y2+XTr0bHx+Xi+xL6fV6goODCQkJmfY2KCjIb4qf3t5eXnvtNb72ta/5tMi5lE6nIyEhgYSEBFasWEF/fz9tbW20t7czOTkp79Sm1+tJSkoiLS2NxMREvx3heb/Xh9DQUHmzgsbGRs6dO0dNTQ1tbW20tbXx7rvvsnTpUlasWHFLr90J5OuvPxD5KkuN+Yo1OcK86+/v57/+67+QJInPfe5zqtut42Z1dHRw9OhRoqOjueOOO3zdHFWTJImxsTH6+voYHh6etlZCq9VisVgICwsjNDQUs9l8wyMyvb1QVzf91tAAnZ2e+96bTeYzGg1ERXkKnthYSEqafktO/vv7FounALTZbNhsNqampma8f7U/A1qtVi4GvQWiyWTyyahESUkJK1asoLi4mOXLl8/7958NSZIYGBiQC55LC0vvlLa0tDTi4uICctMC8GyTX1paSklJCcPDw/LH09PTWblyJQsXLlTF6JYgCP5FrMlR0O7du9m5c6evm6FqMTExFBYWUl5ezv79+3nwwQfl+26FfL2vjLt98Ew5UPJ1uVz09/fT19c3bXqW0WgkMjKSiIgILBbLDT2B7O2FM2eguNhzO3fOM0JzLRoNxMVBQoJndCU83DPaMjTUwuLF6VgsEBIyfaTGe4O/j/BcOspjt8P4OIyNeW5W69/fHxmBgQHo7/d8XJI8/x4YgNraa7c1KgoyM7VkZoaQmRlCVhZkZnpuCxaAweBmamqKqakpJicnp711u91XHP0xGo3Tih6TyeRXoz6+ptFoiImJISYmhqKiIoaGhmhpaaGtrY2JiQl5SltwcDCpqamkp6cTHR3t8/zm8vpgsVjYuHEjt912Gw0NDRQXF1NTUyMfNGoymVi+fDkrV670i8XM8yFQrr/+SuSrLDXmK4ocwSc2b97MhQsXqK+vp6WlZca0tUDmHfK9fDqVcH0ul4u+vj66urrkaX9arZbo6Giio6OxWCzXfaI4NgZHjsC+fZ5bRcXMx2g0kJoKubl/v+XkQEoKJCZ6Rk+u9CL07t3V7NypbF+22z1T5Pr7PUVOT4+nKOvsnHkbHfU8dnDQU8Bd6edMStKSl2ciP9/EwoWwcCHk50NSkoTdbmNiYkLeqMG7WYN3Ktylr9AbDAZMJhOhoaHyW7EOw1PwREVFERUVRVFREX19fbS2ttLW1sbU1BR1dXXU1dVhNptJS0sjLS2NiIgInxc8c0Wr1ZKbm0tubi6jo6OUlpZSXFzM6Ogox44d4/jx4yxYsIDVq1eTmZkZMD+3IAi+J4qcWUpNTfV1EwJCVFQUy5cv59y5c+zevZvPfOYzaLXaWyJf7xbD3lfK53O6ilrzlSSJoaEhWltb5eIwKCiIhIQEoqOjrzvtZXQU3nwTfv972LPHUyh4aTSeJ/YrV8KKFZ5bURG8nwPu5yNfo9EzgpSQcP3Hjo5CczM0Nc28NTbCxAR0dHhuBw9O/9zQUA35+cEsXBgsFz9Ll0JKipOpqb8XPd4iyOFwMDIywsjIiPw1goKCZhQ+73c9SkxMDA888AAxMTHv6/P9gUajkc/iWbZsGT09PbS2tspT2qqqqqiqqiI8PJy0tDQyMjIwv5+O+D4p3X/DwsLYtGkTt99+O7W1tZw5c4bGxkaqq6uprq4mJiaG1atXs3TpUp+sWVSaWq+/aiHyVZYa8xVrcmapt7dXbIs5R8bGxnjuueeYmpriAx/4ACtXrrwl8nW73fz5z3/G5XKxa9cuLBbLvH1vNeZrt9tpbm6WRw2CgoJISkoiOjr6ugXi2bPwk5/Aq696NgLwysiAO+6A7dth61bPlLO5oKZ8JcmzgUJjo2fKW1UVVFd73tbXezZYuJKwME+xU1T097cLF7pxuyfkqW3j4+NMTU3NWOuj0WgICQnBYrEQGhqKxWKZ1WJWNeU7G06nk66uLlpaWujq6sJ1SfhxcXFkZGSQmpqq+MiYL/Lt6+vj7NmzlJWVyS9gGI1GioqKWLVqVUBtVBCo/ddfiHyV5S/5zqY2ULTI+e53v8tf/vIXqqurCQkJYf369Tz99NMsWLDghj7fH4scNc5J9GenT5/mnXfeISQkhCeffJKjR4/eEvnu3r2boaEhNmzYMK+vjqit/46MjNDY2IjD4UCr1ZKYmEhCQsI1RwMkCd55B77xDTh9+u8fX7AAPvYxuP9+KCjwjODMNbXlezV2u2ejhUsLn8pKz+1Ksyx1Os8Ut6IiWLYMVq+GoiIXkjQ+rfC50nk/QUFBcsETGhpKSEjIFacsTUxM8Mtf/pJHHnkEk8mkwE/tH+x2Ox0dHTQ3N9Pb2ysXinq9npSUFDIyMhTbsMCX/ddms3H+/HnOnDlDf3+//PGsrCxWr15NXl6e6jdpCJTrg78S+SrLX/L1m40HDh8+zBNPPMGqVatwOp38+7//Ozt27ODixYvzOgQv+K9Vq1ZRWlpKd3c3e/bsISQkxNdNmhfR0dEMDQ3R19enyiHg+dDX10dzczOSJGEymcjOzr5u/zhxAr74Rc9GAgBBQZ6i5p/+CdauVaawCURGI/IUtUs5HJ6ip6wMzp/3vC0r86wN8hZBv/2t57E6nY7Fi8NYsyaM1athzRooLLQzOek5t8hqtcprfGw2GwMDA4Dnyby36AkLC8NkMqHRaKiurubJJ59k/fr1fr+72s0wGo3ywaPj4+O0tLTQ3NzM6OiovCW1yWQiPT2djIyMeT9QWClBQUGsXr2aVatW0dTUxJkzZ6ipqZE3aYiIiGDNmjUsW7ZMnvIrCIJwLfM6Xa2vr4+4uDgOHz7Mxo0br/t4fxzJGRgYIDo62tfNCChtbW388pe/RJIk7rrrLtasWePrJimutbWVEydOEB4ezl133TVv31ct/benp4eWlhbAsxYjPT39mqM3Q0Pwla/Aiy96/m0ywRNPwJe/7NkFbb6oJd+5JEmeTQ68BU9xsWcErbNz5mNDQz1rn1av9hSd69a5CAn5e9EzPj4+baoWeIoei8VCc3MzO3bs4Ny5c6xYsWJefjZ/4d2Surm5edq6NPCsb8zMzCQtLe2m17H4W/8dHh7m3LlzFBcXMzk5CXiKoWXLlrFmzRoiIyN93MLZ8bd8A43IV1n+kq/fjORczrsgNSoqaj6/7Zzq7Oz0i19yIElNTWXdunWcOHGCP/7xjyxZsiTgR3Ti4+PRarWMjIwwOjo6b0W8Gvrv4OCgXOAkJiaSkpJyzR2XzpzxjNa89yk88gh85zsQHz8frZ1ODfnONY3Gcy5PcjLs2vX3j3d0eIqdM2c8b8+d8+xsd+iQ5+ahIz8/nI0bw7n9drjtNonY2AmsVitWq5XR0VEaG3VMTNhobrYBy9i9u4/29jYSEswUFZkDcoH65S7dknrZsmV0dnbS3NxMV1cXg4ODDA4OUlpaSnJyMtnZ2cTHx7+vXcr8rf9GRESwfft2Nm3aRHl5OadOnaKvr49Tp05x+vRp8vPzWbduHampqarYlc3f8g00Il9lqTHfeRvJkSSJe+65h6GhIY4ePXrFx3inLXiNjo6SmprqVyM5/jInMdA4HA5+9rOfcerUKe69917uu+8+VfzRuhmHDx+mq6uLwsJCFi1aNC/f09/77+TkJJWVlbjdbuLj40lLS7tmP3jlFXjoIc80qqwseOkluO22+Wvv5fw9X19yuTxre7xFz/Hjnultl0tLg9tv99xSUyV27br67/+PfzxPbi6Eh4cTHh5OWFjY+969TY2mpqbk6WxDQ0Pyx81mM1lZWWRmZs5q/ZK/919JkmhoaODkyZM0NDTIH09KSmLdunUUFBT49e/f3/NVO5GvsvwlX78cyfn85z9PeXk5x44du+pjvvvd7/LUU0/N+Pi+ffswm81s3bqVM2fOMDY2RmRkJIsWLZK/Xn5+Pm63m9r3TsbbtGkTZWVlcgjLly/n0HsvH+bm5qLX66mqqgLgtttu4+LFiwwODmI2m1m7di379+8HPIseTSYTFy5cADy74JSUlNDX10dwcDAbN25kz549gOck54iICM6fPw/A6tWraW1tpbu7G4PBwNatW9mzZw+SJJGSkkJcXBwlJSUArFixgu7ubjo6OtBqtdxxxx3s378fp9Mpv5p99uxZAIqKihgcHKS1tRWAnTt3cujQIWw2G3FxcWRlZXHq1CkACgsLGRsbo6mpCYDt27dz4sQJJiYmiI6OJj8/n+PHjwNQUFCA3W6nvr4egC1btnDu3DmsVisREREsWbKEI0eOAMibR9TU1ACwceNGysvLGR4exmKxsHLlSg6+tydtTk4ORqORixcvArBhwwaqq6sZGBjAZDKxfv16Dhw4QEREBHa7nRMnTtDd3U1OTg5r166lsbGR3t5egoKC2Lx5M7t37wYgLS2NqKgoysrKAM/6nvb2drq6utDr9Wzbto29e/fidrtJTk4mISGB4vcOC1m+fDm9vb20t7ej0WjYsWMHBw4cwOFwkJCQQFpaGmfeW9ixdOlShoeH5dGFHTt2cOTIEaampoiNjSUnJ4eTJ08CsHjxYvmwP4Bt27Zx6tQpxsfHiYqKoqCgQO6zYWFhDA0N8fbbb9PW1saWLVsoKSmR/wMXFRVx+PBhAHnRbXV1tdxnKysrGRoaIjQ0lNWrV3PgwAEAsrOzCQ4OpvK9Z5Dr16+ntraW/v5+Ojs7cbvd7N27F4CMjAzCwsIoLy8HYM2aNTQ3N9PT04PRaGTLli1y3qmpqcTExFBaWgrAypUr6ezspLOzE51Ox/bt29m3bx8ul4ukpCSSkpI4d+4cAMuWLaO/v5+2tja5zx48eBC73U58fDwZGRmcOnUKq9Uq75pWVVVFdXU1d9xxB8ePH2diYoKYmBjy8vI4ceIE77yTwo9/XIAkaVi/vof/8T8usGbNRo4f9901orW1ldHRUerr68U14grXiPHxcpKTh/n0py08++xK3njjGJWVEbS3Z1FcHEJ5uYHWVi2//a13bY+nwHn55enrg6qq4MEHoba2i/h4My0tLYyPj6PX61myZAnV1dUYDAYyMjKIjo5W7TVi4cKFOJ1O6urqAM/5YpdfI5qbm+XHdnZ2cv78eVwuF8PDw+zevRuj0Uh6ejo7duyQ+/fVrhGtra0cPXqUDRs2+OU14vR7O4ls376d7Oxs9uzZQ1NTE5Ik8cMf/hCDwcCaNWv44Ac/KLdh0aJFTE1NyUWRL59HtLa2snv3btatWyeuEVe5RtzM8whvvpmZmYSGhlLx3iFogfw84kauEXP1PKKvr4/du3djMpl8eo3wtv9GzMtIzpNPPsnrr7/OkSNHyMzMvOrj1DCSIyjr6NGj7N+/H4PBwKOPPhpQ24dezul08uabb2K329m4cSNJSUm+bpJPDQwM0NDQgFarpbCw8JrTkP72N/jQhzzrQR5/HJ59FlS+8ZIAjI/DqVOew1qPHvVsJGGzedb5XLrXQEmJ5zyjvXtdrFgxKp/Pc/nubUFBQbfcKI/T6aStrY3Gxkb6+vrkj4eEhMijO6GhoT5s4dwaHx/n3LlznD17lrGxMcCzecPy5ctZu3YtERERvm2gIAhzym+2kJYkiSeffJLXXnuNQ4cOkZubO6vP98eNB/bt28f27dt93YyAtXfvXrq7u2loaCAuLo7PfvazAX1qemlpKTU1NcTHx7NlyxbFv5+/9l9Jkrh48SLj4+MkJyeTnJx81cc2NHi2KbZa4dFH4fnn/WfXNH/NV61On/ZsUHC1Igc8b7dtg+3bJVatmmJqylPwWK1W3G63/DlarRaLxUJERASRkZGzOp9HrTxrmhppamqaVgDGx8eTnZ1NcnLytMJPzf3X6XRy4cIFTp06RXd3N+D5nS9evJgNGzYQ74tFepdRc75qIPJVlr/k6zfT1Z544gleeeUV3njjDSwWi3zhCQ8PV+3C8st3/hHmltvt5t577+X555+nt7eXN998M6DX5+Tl5VFXV0dPTw99fX2Kj1z5a/+dmPAcJKnVaq952Jh35MZq9azZeO45/ylwwH/zVasbeX2juNhz+7//V0NoaAjbt4dw990J7NzpwmKZPsrjfb+lpYXQ0FC54AkODg7Ia0xYWBhFRUUUFhbS0dFBY2MjPT098i04OJjs7Gyys7MxmUyq7r96vZ6ioiKWLl1KY2Mjx48fp7GxkfLycsrLy8nJyeG2224jPT3dZ79rNeerBiJfZakxX0WLnJ/+9KeAZ47gpX71q1/x0EMPKfmtFXOrTylSWlJSEqGhoXz0ox/lpZdeoqKigsTERNavX+/rpinCbDaTkZFBY2MjFy5cYPPmzYr+AfbX/uvdeTE8PPyaI3fvvAN79njOv3nxxRt7Ejyf/DVftXtv2cOMf7/7LvT3w759sHs3dHXB6697bqBj6dJIdu2K5K67JJYunWJsbJihoSHGx8cZG/NsXd3e3k5wcDCRkZFEREQQGhoacAWPTqcjLS2NtLQ0eW1FY2OjvNFHVVUVycnJhISEIEmSqn9+jUYjF26dnZ2cOHGCyspK6uvrqa+vJzk5mQ0bNpCfnz/vh4uK64OyRL7KUmO+83pOzmz543Q1f9knPFBdmu+ZM2d4++230Wg0fOITnyAnJ8fHrVPG+Pg4b731Fm63W/G1Of7af2tqahgZGSE9Pf2a00p27vQUOV/6EvzgB/PYwBvkr/mqVV0d5OVd/f7aWvDOgna7PQeUvvUWvP22Z23PpX/dIiPhzjs921zv2OFAkoYYHh5mdHR02rQ2g8FAZGQkkZGRWCyWeX8iPF9cLhcdHR3U1dXJa3cmJydJSEggJyeHjIyMgJkqPDg4yMmTJyktLcXpdAKeoyzWr19PUVERev387MEkrg/KEvkqy1/ynU1tEJhXbwV5d3kQlHFpvqtWrWLZsmVIksSf/vSnaYtoA4nZbCbvvWdyZWVlig4J+2v/9a4XuNZ2t52dngJHo4HPf36+WjY7/pqvWuXmegqZ4mJ4+eUqYDkvv1xFcfH0Agc8G08sWwZf+5pnw4LeXs+ubB//uKfAGRqC3/3OsytbUpKBT34yjgMH8khIWEZOTg7R0dHo9XocDge9vb3U1NRQVlZGU1MTIyMj+PHrge+Ld3Rn27Zt3HnnneTk5NDd3c3IyAjFxcW8+eabFBcXy6OsahYVFcWuXbv44he/yKZNmwgJCWFwcJC//e1vPPPMMxw/fnzaAatKEdcHZYl8laXGfOf1MFBBmA2NRsOuXbvkbUV/+9vf8pnPfCagdgbyKigooLm5mdHRUWpqaigoKPB1k+aVw+EAuOYrx+/tJMry5XCNTRqFAPP3QmYSKGXhwslpmxBcTUwMfOITnpvT6dnE4K234M03Pefz7NnjuT3+uI5166K4994o7rnHTVyclaGhIYaGhnA4HPT19dHX1yeP8ERFRWGxWFQ9petyERERrFy5kp6eHnJzc6mvr2d0dJS6ujrq6uqIj48nNzeXpKQkVY9smc1mtmzZwoYNGygtLeXEiROMjIywd+9ejh07xrp161i9ejXBwcG+bqogCHNATFebpd7e3msujBZuzpXynZiY4Be/+AWDg4MkJSXx0EMPBeTOSM3NzZw6dQqdTsedd96JxWKZ8+/hr/333LlzuN1ulixZctUnGF/8IjzzDPzLv8D/+3/z2rwb5q/5BoLh4WHefPNNPvShD930tsC1tfDaa/CXv3gOJ71UYSHcdx/cd59EerqVoaFBueDxMhgMREVFERUVFVBreLz9V5Ikenp6qK+vp6OjQx7FCg0NJS8vj8zMzICYyuZyuaioqODo0aMMDAwAnm3HV69ezdq1azGbzXP6/cT1QVkiX2X5S75iupqC+vv7fd2EgHalfE0mEw8++CAmk4nOzk7+9Kc/qXKXj+tJT08nISEBl8vF6dOnp60TmCv+2n+929he62fu7PS8zciYhwa9T/6abyDwjjbMxbkneXnwr//qGd1pa/Ps0rd1K+h0UFEBTz0FS5dqWL8+jP/+7wxMpqUsWLCA2NhYeUpbT08PVVVVnD9/nra2NiYnJ2/+h/Qxb//VaDQkJCRw22238YEPfICCggKMRiNjY2OUlJTw17/+lbKyMsbHx33c4puj0+koKiriiSee4CMf+QhxcXHYbDaOHj3KM888w+7du7FarXP2/cT1QVkiX2WpMV9R5MyS9zRmQRlXyzcqKoqPf/zj6PV6amtreeONNwJujrxGo2HVqlUYDAb6+/vlk4bnkr/2X++rwpcf5ngp79IAfz7bz1/zDQTd3d1873vfk48imCspKfDEE7B/P/T0wK9/7Tlo1miEixfhP/8T8vO1bNsWzp//nEl0dBF5eXnExMSg0+mw2+10dXVRUVFBZWUlPT0900Z91ORK/ddsNrNkyRI+9KEPsXLlSiwWC3a7nerqat566y1OnjzJ4OCgD1o7d7zn6fzTP/0TDzzwAElJSTgcDk6ePMmPfvQj3nrrLYaHh2/6+4jrg7JEvspSY76iyBFUIzU1lfvvvx+tVkt5eTlvv/12wBU6ZrOZlStXAnDx4kV6e3t93KL54d1wYGJi4hqP8bxV+YvHwvvU2dnJr3/9azq9Q3oKiI6GT38a3njDU/D86leeHf10Os/mB1/5CmRkaPngByN4550skpI8mxZERkai0WgYHx+npaWFsrIy6urqGBwcVGRE1hf0ej05OTncfffdbNy4kfj4eNxuNy0tLezZs4f9+/fT3t6u6p9Xo9GQn5/PZz/7WR588EFSU1NxOp2cPXuWH//4x7z55ptzUuwIgjA/xJocQXUqKir4y1/+giRJ3H777Wzbts3XTZpzp0+fpqmpCbPZzI4dOwgKCvJ1kxTV3d1Na2srYWFh5OfnX/Exn/0s/OIXnlfWn3pqnhso+FxJSQkrVqyguLiY5Tey88Ac6uuDP//Zszvb0aN/35paq4U77oCHHoK773YwOTnIwMAAY2Nj8ufq9XqioqKIjo4OqPU7AENDQ9TU1NDa2ioXNxaLRV63M19bMytFkiRaWlo4cuQIjY2NgGeK2/Lly7n99tvF8xJB8AGxJkdBB71bPAmKuJF8CwsL2bVrFwBHjx7l0KFDCrdq/i1fvhyLxcL4+DgnTpyYs1dH/bX/etdZWK1W+RyLyy1a5HlbXj5PjXof/DXfq5EkKeBGQ5UQGwuPPQaHD0NrK/zwh7B6tedsnt27PdtUp6UZ+M//jGdkpIDFiwtJSkrCaDTidDrp7e2lqqqKCxcu0NXV5bfT2WbbfyMjI1m7di0f/OAH5XU7VquV4uJi/vrXv1JZWXnNKaj+TqPRkJGRwac+9SkeeeQRsrKycLlcnD17lh/96Ee88847s1qzo7brg9qIfJWlxnzV/TKLD8zHXvq3shvNd+XKldjtdvbs2SMXOZs3b1auYfPMYDCwfv169u/fT09PD2VlZXPy6rW/9t/g4GBMJhMTExMMDg5ecQcX749/4oTnyaU/7mTr63wlScLtduNyuXC5XLjdbvnmLWguLWy8by8dXfC+r9Vq0Wg0025arXbGzXvfrSIlxbPT3xe/CPX18JvfwEsveYqfF17w3HJzQ/j0p1N48MFkIiOt9Pf3MzQ0xOTkJG1tbXR0dBAREUFsbCxhYWF+k9/77b8hISEsWbKEhQsX0tzcTE1NDWNjY1RUVFBdXU12djYLFiwgJCRkjls8f9LS0vjUpz5Fc3MzBw8epKWlhdOnT1NcXMyqVavYsGHDdY838PX1IdCJfJWlxnxFkTNL1zqNXbh5s8l3/fr1SJLE3r17A7LQ8b5KeuzYMWprawkPDyc7O/umvqY/99/o6GgmJibo7e0lNjZ2xhO/tWshPNxzyOPp07BunY8aeg3zna8kSTgcDhwOB06nE6fT+b5GZi79HO/7Nzp6qNFo0Ol0aLXaaW+978/VE/iIiAi2b98+J7urzZWcHPjGN+B//2/PKM9LL8Gf/gR1dZ5DSf/jPzRs3RrGQw+F8eEPu5iYGKC/v5+xsTEGBwcZHBwkKCiI2NhYYmJifL41/s32X4PBQG5uLtnZ2bS1tVFVVcXw8DDV1dXU1dWRmZlJfn6+qs86y8jI4KGHHqK5uZkDBw7Q1tbGyZMnOXfuHKtXr2bDhg1XPdTYn6+/gUDkqyw15ivW5MzS8PCwX/2RDTTvJ9/jx4+zd+9eADZt2sTmzZv95pXRuVBZWUlFRQVarZbNmzff1D71/tx/nU4nZWVluN1uFixYQHh4+IzHfOIT8Mornt2wnnvOB428jvnIV5Ik7Ha7fLv8Eu4tOrxFxuUjLpeOvFz6/+TyQsd7u3QU6NKRIe/tWrxt0ev1cptupvjx5/7rNTbmWb/z61/DpTNpIyM9mxp87nOQljZBX18fAwMD8vRMjUZDREQEcXFxPhvdmet8JUmiq6uLixcvTtueOi0tjYULF/r97/J6JEmioaGBgwcP0tHRAYDRaGTNmjVs2LBhxplfaui/aibyVZa/5Dub2kAUObO0e/dudu7c6etmBKz3m++JEyfYs2cPAGvXrmXnzp0BU+hIksTJkydpbW3FYDCwdetWIiMj39fX8vf+29LSQk9PD2azmYKCghm/w717YccOCA2F9nbPyI4/UTJfSZKYmppiampq2jlROp0Og8GAXq+Xi4n56vuXTo+7fJqcy+W66qiSVquV2+u9aa8z/9But/OHP/yB+++/3+cjHjequdkzne2Xv4SWlr9/fNMmT7Fzzz0uJiaG6Ovrm7a2IyQkhLi4OKKjo+d18b5S/VeSJPr6+qiqqqKrq0v+eFJSEgUFBcTExMz595xPkiRRV1fHwYMH5Z8vJCSE22+/XT4WAPz/+qt2Il9l+Uu+YuMB4Zazfv167r77bgBOnTrFm2++qeqtTC+l0WhYvXo1sbGxOBwODh8+PKcH1PmTpKQkdDod4+PjVzx7Y/t2WLjQ82r588/7oIE+4nA4GB4eZnx8HJfLhVarJSQkhIiICCIiIggNDSU4OBi9Xj+vxb13pMZoNBIcHIzZbCYsLIyIiAiioqKIjIzEYrFgMpkICgqS2+d2u7Hb7UxMTDA6Osrg4CBDQ0NYrVYmJyevOO3uwoULfPKTn+TChQvz9vPdrIwMz26ADQ3w1lue83e0Ws/Utv/v/4P0dB1PPx2DXr+QwsJC4uPj0el0TE5O0tLSwvnz52lubr7m1upqoNFoiIuLY9OmTezYsYO0tDQ0Gg2dnZ3s27ePQ4cOqfKgQS+NRkNeXh6PPvooDzzwALGxsUxOTrJnzx6effZZSkpKAubvkSCoiRjJmaWuri4SExN93YyAdbP5nj9/ntdffx1JkigoKOC+++5T/TamXna7nYMHDzI0NITZbGb79u2zXsirhv7b2dlJe3s7RqORxYsXz/j9/eY3nmk/ERGeJ49RUb5p55XMdb6SJDE5Ocnk5CSSJKHVauWCQa0jlZIkyeuHvDfvlLhLXTraYzAYKC8vZ+XKlT7ZQnoutbfDiy96tkNvb//7x7dtg89/Hu6+28XQUD+9vb1MTk7K94eFhREfH09ERIRiv/v5vD5YrVaqqqpobm6WC4CEhAQWL16s+pEdt9tNeXk5Bw8eZOS9U4xjYmJYsmQJt99+u2r/7/o7Nfx9UzN/yVeM5ChodHTU100IaDeb79KlS7n//vvR6XRcvHiR3/72t0xNTc1R63zLaDSyceNGeWvpQ4cOzfpnU0P/jY+PJzg4GLvdfsUTlj/xCViyBIaH/e+8nLnOd3JykomJCSRJIjg4mMjISIKDg1X9JEmj0WAwGAgJCcFisRAZGUlkZCTh4eGYTCaMRiNarXbaaM/IyIj8ZHFqaup9b7DgD1JS4Otfh6Ymz6Gjd98NGg3s3w/33gv5+TpeeSWe1NTF5OfnExUVhUajYXR0lLq6OioqKujp6Zk2ZXGuzOf1wWKxsHr1au6++26ysrLQarV0d3cHxMiOVqulqKiIJ598kp07d2Iymejv7+cPf/gDv/jFL2hqavJ1EwOSGv6+qZka8xVFziw1Nzf7ugkBbS7yXbhwIZ/4xCcwGo00NTXxy1/+Un6CpHYhISFs2rSJkJAQRkZGOHjw4KwKHTX0X51OR2ZmJhqNhr6+PoaGhi67H77/fc/7zz4Lp075oJFXMZf5ep/gA5jN5oA7SPJSWq0Wg8GAyWQiLCyMyMhIIiIiMJvNBAUFodVq5aJmcnKS4eFheXqbzWZT5VQgvd4zfe2ttzwFz1e/6tmcoKkJvvQlSE3V8LWvhSFJOSxdupTExET0ej1TU1PyVLa2trY53dbVF9eH0NDQgC129Ho969at45//+Z/ZuHEjY2NjdHR08NJLL/Hyyy/T3d3t6yYGFDX8fVMzNeYrihwhIGVlZfHII49gsVjo7e3lF7/4RcD8QQkNDWXLli3TCp1Lp7UEAovFIm9X2dTUNONAwTvugE9+0nPy/D/+IwTIYJ1MkiTGx8cBzxlCaj5f5P3QaDTo9fppoz0WiwXwbFPsXddjs9mwWq0MDQ0xMjJy1fU8/i49Hb77Xc/0tZ/9DAoKPOvOnn0WFiyA++4zUl2dypIlS0lPTyc4OBin00lXVxfnz5+noaFB7i9qda1i5+jRowwPD/u6ie9bcHAwW7du5cMf/jCrV69Gq9VSX1/Pz372M15//XVVvkIuCGog1uTMktvtvu4uQML7N9f5joyM8Nvf/pbe3l6MRiP3338/OTk5c/b1fclqtXLw4EEmJiYICwuTC59rUVP/dbvdVFdXMzY2RmhoKPn5+dPaPjjoeTLY0+PZqcofNiKYq3wdDgcjIyNotVoiIiJU8ztTkreo8a5HcjqdOBwO7Ha7vA2zl3czBKPROO+bMcwFSYJ9++BHP/KM9HgtWgRf+Qo88IDExMQwPT09054gh4eHk5SU9L5H/fzp+jA2NsbFixflNTveracXL14sF7xq4813cHCQAwcOyJtoGAwGNmzYwPr161Wzc6A/8qf+G4j8JV+xJkdBx48f93UTAtpc5xseHs4jjzxCZmYmdrudV155hdLS0jn9Hr5isVjYsmULZrOZ0dFRDhw4wNjY2DU/R039V6vVkp2djV6vZ2xsjKampmmv0EdFeTYh0Gg8r36/8ooPG/ueucrX4XAAnic//vBHxR9otVrOnTsnn7Hjnd4WERFBZGQkoaGhGI1GNBoNLpeLyclJRkZGGBoaYnx8HIfDoZoRHo3GM1r5t79BbS08+aRn2/TKSnjoIcjJ0fDrX0eSkpLPokWLiI6ORqPRMDIyQlVVlXwI52x/Xn+6PnhHdu68807S0tKQJImWlhbeeecdzp49q8od57z5RkVF8ZGPfITPfOYzpKam4nA4OHToEM8++yylpaWqnH7pD/yp/wYiNeYr/nrOkhovrGqiRL7BwcE8+OCDLFmyBLfbzRtvvMG+ffsC4g+JxWJh69atmM1mrFYr+/btm7GG5VJq679BQUFkZ2ej0WgYGBigs7Nz2v07dnhOlgf47GehuNgHjbzEXOXr7Zs6nW5Ovl4gqK2t5YknnqC2tnbGfTqdjuDgYMLCwoiKiiIsLExey+N2u6cVPGNjY6oqeHJz4cc/9kxle/ppSEjwvP+lL0FaGjz9tJmwsGwKCwuJi4tDq9UyNjZGbW0tlZWVDAwM3PDP6o/Xh7CwMNavX8+OHTtITEzE7XbT0NDAW2+9RVlZ2YyprP7s8nxTUlJ45JFHuP/++4mMjMRqtfLGG2/wwgsviM0J3gd/7L+BRI35iiJnltS+taW/UypfnU7Hvffey8aNGwE4duwYr776akDsvObdTjoiIoKpqSkOHDhAT0/PFR+rxv4bHh5ORkYGAB0dHfT19U27/+tfh507YWICPvABaG31QSPfo8Z81WJsbIyKiorrjlZqNBqMRqO8licsLIzg4GC54JmammJkZITh4WEmJiYU2aVMCeHh8D//p2djghde8BQ/Q0PwzW961vR85SvB6PUZLFmyhMTERHQ6HRMTEzQ0NFBRUUF/f/91ix1/7r9RUVFs2rSJrVu3Ehsbi8vlorq6mrfeeouqqipV/B6vlK9Go6GgoIAnnniCHTt2EBwcTHd3Ny+99BK/+93vVL3xwnzz5/4bCNSYr1iTM0tWq1W184HVYD7yLS8v580338TpdBITE8MDDzygyv+8l7Pb7Rw7doze3l60Wi1r164lLS1t2mPU3H/b2tro6upCo9GQlZVFdHS0fN/oKGzYABcuwOLFnsMWfXF+zlzlOzExwcTEBEFBQar9fc21kpISVqxY8b7PyZEkCYfDgc1mw263T3vCbzAYCAoKkrevVgOXC15/Hf7P/4Fz5zwfMxo9G3H8r/8FiYlOent76enpkac/BgcHk5SUJE9vu5xarg+SJNHV1UV5ebm8IYHZbGbJkiXyQaP+6EbynZiY4NChQ5w7d05eA7FmzRo2bdpEcHDwPLVUndTSf9XKX/IVa3IUdOLECV83IaDNR75LlizhkUceISwsjP7+fn7+859TV1en+PdVmtFoZNOmTaSmpuJ2uzl58iQ1NTXTnsypuf+mpKQQFxeHJEk0NjZOm5YXFuZZoJ2Q4Cl07rrLU/jMt7nK13sAqpqmVfm7S0d4oqKisFgs8hoeh8PB2NiYqqaz6XTwD/8AZ854NinYuBHsdvjpTyE7G558Uo/TmcSSJUtITU3FYDAwNTVFY2MjFy5cuOI0NrVcHzQaDUlJSezYsYM1a9YQEhLC+Pg4J0+eZO/evfT29vq6iVd0I/maTCbuvvtuHn/8cRYsWCBfy5977jnKysr8vl/6klr6r1qpMV9R5Ai3pKSkJB599FHS0tKw2Wy88sorHDt2TPV/QHQ6HevWrSMnJwdJkigtLeXs2bOqmMpxPRqNhvT0dGJiYpAkifr6egYHB+X709Jg716IjvY88fvAB0Ctu+p6NxzwHogpzC2NRkNQUJB8Jo/ZbEav1yNJkjydzbsltb+v3dNoYNs2z+jlwYOweTM4HJ7dBnNy4PHHdTgciSxZsoSUlBT0ej2Tk5M0NDRw4cIFBgcHVXvd02q1ZGZmsmvXLgoLC9Hr9fLOZUeOHFH11swxMTF8/OMf58EHHyQ6OpqxsTFef/11XnzxxRlrEwVBuDIxXW2W2tvbSUlJ8XUzAtZ85+tyuXj77bcpfm/F+uLFi/nQhz6k+m08JUmitrZWfuUvNjaW2267jb6+PtX3X+9IzsDAABqNhszMzGnTDYuLYetWz0jO+vWeEZ6IiPlp21z2X++UNb1eT3h4uN9OwZkv/f39/OpXv+Lhhx9WZHqpJEk4nU5sNhs2m01+4u8tiIKDg+URNn935Ag89RQcOOD5d1AQPP64ZxpbVJSLnp4euru75a23zWYzKSkpWK1WVV8fpqamuHDhAo2NjfJUr9zcXBYtWuQX1/T3e31wuVycOnWKw4cPY7fb0Wg0LF++nG3btmEymRRoqTqJ52fK8pd8Z1MbiCJnlurr6wPmnBV/5It8JUni3LlzvPPOO7jdbmJiYrj//vuJi4ub13YoobOzk5MnT+JwOAgNDSUlJYWioiJfN+umSZJEc3OzvAlBWloaCQkJ8v2nTnmmrA0PQ1ER7N4N8/HrnMv+63a7GR4exu12Yzabb7kDQa9kvq4P3jN5bDbbtDN4jEYjwcHB8oGk/u7YMfiP/4BDhzz/Dg2FL3/ZszNbSIhTLna8I71TU1OsWLECs9nsu0bPgdHRUcrKyuQRj+DgYAoLC8nMzPTpmqub7b9Wq5W9e/dSXl4OQEhICFu2bGHlypWqWUumJPH8TFn+kq9Yk6OghoYGXzchoPkiX41Gw6pVq/j0pz+NxWKR1+mUlZXNe1vmWlJSEtu3byc0NJSxsTHeffdd2tvbfd2sm6bRaMjIyJALm9bWVlpaWuRX39eu9Tyxi4uDsjLPeoW2NuXbNZf9V6vVyq/STkxMzDjw8lbT39/Pc889Ny+7TWm1WkJCQggPDyc8PFw+gNRutzM6Osrw8DBTU1N+P83rtts8ozm7d8Py5TA2Bv/7f0NWFjz3nJ7o6GSWLFlCfHw8Wq2Wjo4OKisraWhoUPXOk2FhYWzcuJFNmzYRFhbG1NQUZ8+eZe/evTN2Z5xPN3t9sFgs3HfffTz88MMkJCQwOTnJ22+/zQsvvEDbfFzg/Jx4fqYsNeYrihxBeE96ejqPPfYYWVlZOBwOXn/9dd544w15ZyK1Cg8P54477iAuLg6Xy8WxY8coKyvz+7UG16PRaEhNTSU1NRWAnp4e6uvr5Velly71vJKdlgY1NbBuHajtHFjvjl+SJGG1WlX/O7sZra2t/OhHP6J1HvcI9x46arFYiIiIICQkBK1Wi8vlkjcq8Pd1OxqN5zyps2fhD3+AvDzo74cvfhEWLoTXXzeQlpbO4sWL5SldAwMDXLhwgdbWVlUX14mJiezcuZNly5ZhNBoZGhpi//79nDx5UpVnfnilp6fz6KOPsmvXLkJCQuju7ubFF1/kb3/7G5OTk75uniD4DTFdbZYcDgcGg8HXzQhY/pCv2+3m6NGjHDp0CEmSiI+P56Mf/ajqt5l2uVyUlJTIr8bExcWxbt26gJgGNTg4KM/DN5vN5Obmyk/YWlvhzjuhqgpMJvjtb+HDH1amHUr0X7fbzcjICC6XC4PBQFhYmCqmSs21m91Ceq54p7JNTU3JBbVWqyU4OFg+j8efOZ3w6197zpfyrl/fsAH+3/+DoiIHdrud9vZ2RkZGAM8mGMnJycTGxqq6301NTVFRUUFjYyOSJKHX6yksLCQ3N3fefmdKXB8mJibYu3cvpe+9ghMaGsrOnTtZvHixqn9f74c/PH8IZP6Sr5iupqAzZ874ugkBzR/y1Wq1bNq0iU9+8pOYzWZ6enp44YUXuHDhgq+bdlN0Oh12u50NGzag1+vp7e1lz549Pp2+MVeioqJYsGABBoOB8fFxLl68iNVqBTwjOSdPel7NnpiA++7znByvxMs7SvRfrVaLxWJBq9XicDiwWq1+P00qkHmnskVERBAaGopOp8PtdjMxMcHQ0BATExN+PbKj18NnPgO1tZ5CJyQEjh+H1avhgx8cYmjIzIIFC1iwYAEhISE4HA6am5uprKxU9W5lwcHBrFq1ijvuuIOYmBicTielpaXs2bNn3g7cVOL6YDKZuOeee3jooYeIiYlhbGyMP//5z7z88svTdp+8FfjD84dApsZ8RZEzS9c7bVu4Of6Ub1ZWFo899hgZGRnY7Xb+9Kc/8cYbb2Cz2XzdtPdtbGyM1NRUduzYQXh4OJOTkxw8eJCqqirVP3G2WCwsXLiQkJAQ7HY7NTU19PT0IEkS4eGeXdaeeMJT3Hz1q/CpT839FtNK9V+9Xo/FYpHXhYhCx/c0Gg3BwcFERERgsVjkLagnJiYYHh5mcnLSr39HZrNnfU5dnef/AsDu3XHk5cE3vwnBweEsWrSI9PR09Ho9ExMTVFdXU19fr+prYFRUFNu2bWPVqlUYjUaGh4fZt28fZ86cUfznUvLvW0ZGBo899hhbt25Fr9fT0NDAT37yE44ePRoQRwjcCH96/hCI1JivKHJmKTIy0tdNCGj+lq/FYuFTn/oUt99+OxqNhtLSUn72s5+pdvG+N9+wsDC2b99Oeno6breb8+fPc/jwYdXP5w4ODqagoICoqCjcbjctLS00NTXhcrnQ6+G55+DZZz0HKb78MqxZ41mvM1eU7L/etSGXLoD35xGDuRYaGsry5csJDQ31dVOm8W4xHR4eLhc7breb8fFxVWxQkJwML73kWbNTVDTK5CT8539CYSHs26clPj6ewsJC4uLi0Gg0DA4OUlFRQVdXl2r7n0ajITs7m127dpGVlQVAY2Mjb7/9tjydTQlK/33T6/Vs3LiRf/qnfyIrKwun08n+/ft5/vnn53Utm6/42/OHQKPGfMWanFkaHx9X/faa/syf821ubua1115jZGREntJ2++23+/0c/Etdnq/3zJnS0lKcTidBQUGsWbOGpKQkH7by5kmSRHd3N+3t7UiShMlkIjs7W15/dPgwPPAAdHd7ttV98UW4//6b/77z0X+9U9bcbrc8wqPT6RT9nv7Cn68PXpIkYbPZmJyclF9B1+v1mM1mv5jPfi1jY+P87W9mvvQl6OryfOyjH4Uf/hBSUjzrP1paWuSpoCaTifT0dCwWiw9bffP6+vo4d+6cvA4pPj6eVatWzXlBPZ/9V5IkKioq2L17N+PvDVmvXr2a7du3+8WZQUpQw/VBzfwlX79bk/OTn/yEzMxMgoODWbFiBUePHp2Pb6uIY8eO+boJAc2f8/VOB1i8eDFut5uDBw/yq1/9iqGhIV837YZdnq/3Fc077riDyMhIbDYbR44coaSkRNVTHDQaDYmJifI6nYmJCSorK+nr60OSJDZt8uy0tnmzZ1vdj30MnnwSbnbX3Pnov97NB7RaLU6nk5GREex2u+Lf19fcbjcHDhzw+9GDS6exmc3mab8nq9Xq1/+vjh8/xgMPQHU1/Mu/eEY8//hHyM+HH/wAjEYT+fn5ZGVlyf+vqqqqaGpqUvUulLGxsezYsYOlS5ei0+no6enh3XffpaamZk7723z+fdNoNCxZsoTPf/7z8kYdZ86c4ac//SlNTU3z1o755M/PHwKBGvNVvMj5/e9/z7/8y7/w7//+75SWlnL77bdz11133RJDp0LgCQkJ4R/+4R+49957CQoKoq2tjeeff57z58/79ZSU6wkPD2f79u3k5eUBUFtby759+xgeHvZtw25SWFgYixYtIiwsDLfbTVNTEw0NDTidThISYO9ezynw4JnKtmoVnD/v2zbfCL1eT0REBAaDAbfbjdVqZWJiQtV98HrKysr40Ic+pJrzqzQajbxBQUhICBqNBpvNxvDwsN//rsLCPLutFRfD+vWetWtf/rJnG/a33tLQ2hqDw7GYnp5kqqtNHD06zttv1zEwMODXP9e16HQ6Fi5cyJ133klcXJy8McH+/ftVfR0MCQnhQx/6EJ/85CcJDw9naGiIl156ib/97W+qXlslCDdC8elqa9asYfny5fz0pz+VP7Zw4UI+/OEP893vfvean+uP09VaWlpIT0/3dTMClpryHRoa4i9/+Yt8CNuiRYvYtWuXfICjP7qRfDs7Ozl9+jQ2mw2tVkthYSELFixQ1bS8y10+fc1oNJKVlSVfV955Bx56CHp7wWiEb3/bcyr8bH/k+e6/kiQxPj4uH9xoMBjkHb8Cjb9sIf1+OZ1OxsfH5REPf5zCdqX+63bDr37lKXKu91z/j388z9KlJjIyMvzq55ot7zTesrIyHA4HWq2WgoICFi5ceFP/t3z9981ms7Fv3z7Onj0LeF7c+tCHPkR2drbP2jSXfJ1voPOXfP1muprdbqe4uJgdO3ZM+/iOHTs4ceKEkt9aMf4+VULt1JRvZGQkDz/8MFu2bEGr1VJZWcl//dd/UVVV5eumXdWN5JuUlMSdd95JcnKyvCnB/v375Xn4auSdvlZQUEBwcLC8+1praysul4u77oILF+Cee8Buh698BbZt85yxMxvz3X81Gg2hoaHyhgQOh4ORkRFsNptqX1EPVHq9nrCwMHk7cKfTyejoKOPj437zu7pS/9Vq4R//ESorYdMmz8deftkzyuO9vfyy5+OTk3qGhoa4cOGCqkd1vNN477rrLvk6eOHChZse3fb137egoCB27drFpz/9aSIjIxkZGeG///u/efPNN+UXStTM1/kGOjXmq1fyi/f39+NyuYiPj5/28fj4eLq7u2c83mazTRs+9e7JX1ZWNm0BYGRkJJmZmUxNTXHx4sUZX8f7Kl9NTY284M4rIyODqKgo+vr65FfgvSwWC7m5ubhcLs5fYc5KYWEhtbW18uF8l0pOTiY+Pp6hoaEZ811DQkJYuHAhAKWlpTMu/N5tb1taWhgYGJh2X3x8PMnJyVitVurq6qbdZzAYKCwsBKCiomLGnOjc3FwsFgsdHR309PRMuy86Opr09HQmJydnPCnXaDQsW7YMgKqqqhk7bmVmZhIZGUlPTw8dHR3T7gsPDyc7OxuHw0FFRQWX8855rqurm/GkOTU1ldraWsLDw2lubp52n9nsObsBPK/oXs775LWpqWnGGpnExEQSExMZHR2lvr5+2n1BQUEsWrQIgPLy8hmne+fl5REaGkp7ezu9vb3T7ouJiSEtLY1Vq1YxNTXFwYMH6erq4plnniEnJ4cnnngCk8nExYsXZ/wBycrKIiIigu7ubjq9J/K9JyIigqysLOx2+xXP5ikqKkKr1VJbWztjS8e0tDRiYmLo7++fMSU0NDSUpqYm0tPTrzjlx3vieWNjI8PDw5hMJsxmM9XV1QwPDzMyMkJGRgYajWbaIXPeHc3A83/18gthfn4+JpOJ1tbWGedRxMXFkZKSwtjYGLW1tdPu0+v1LFmyBIDKysoZUytycnIICwujq6uLLu8q6fdc6xrhcrmIjY2lr6+P06dP43a7SUxMJDQ0lP/4D9iyJYN/+7coDh3qY+HCNr7wBc/ZOlrt9a8RXV1dZGZm0tDQMO/XiMnJSc6fPy/3YYPBQHh4OEuXLgXUf424tA2Dg4OqukZ4t1/2crvd2O128vLymJycpLy8HJ1Oh17/9z/JvrhGlJaW8o//+I+43e4rXiP+z/8pZN06AwsXwpUG0zw7epVSXV1NRUUFYWFhJCUlERoaqqprBHieR5hMJmJjYxkdHZXXHpWWlrJ582ZWr17NwMDArJ5HnDhxgs997nMYDAafXCMufR6xdu1azpw5w8WLFykpKaG+vp78/HwSEhKmfa6arhEnTpzgYx/7GLGxsaq/RoDnTK6ioiIAv3geceLECXbs2EFeXt5VrxGXP4+4VFJSEgkJCQwPD9PY2Djtvtk8j7j893pNkoI6OjokQDpx4sS0j3/rW9+SFixYMOPxX//61yXgurctW7ZIp0+fls6fP3/F+999911pcnJSWrx48Yz7vvKVr0gNDQ3SN77xjRn3LV++XDp69Kg0MDBwxa/76quvSm+88Ya0cePGGfd99rOflaqqqqQXXnhhxn3Z2dnS/v37JUmSJIPBMOP+559/Xurr65Puu+++Gffdf//90vnz56U33nhjxn0xMTHSu+++K0mSJMXExMy4/+mnn5Y6OjqkRx99dMZ9O3fulM6ePSudOXNmxn0Gg0F69913JZvNJuXl5c24/9/+7d+kpqYm6d///d9n3LdmzRrp+PHjUnt7+xUz/POf/yxZrVZp7dq1M+57/PHHpd/97nfSj3/84xn35efnSwcPHpQkz5V9xu2Xv/ylNDAwIN19990z7vvEJz4hVVRUSL///e9n3JeYmCjt3r1bkiRJCg8Pn3H/D3/4Q6mrq0v69Kc/PeO+D3zgA1JxcbF0+PDhGffp9XrpH//xH6Xy8nIpIyNjxv1f//rXpZaWFunLX/7yjPtuu+026eTJk1JdXd0Vf9Y333xTGhsbk5YvXz7jvi984QtSXV2d9H//7/+dcV9hYaH04osvShMTE1f8uv/93/8tDQ0NSdu3b7/iz/rjH/9Y+vznPz/jvrS0NGnv3r2SJEmSyWSacf+zzz4r9fT0SA888MCM++69916ptLRU2r1794z7wsPDpXfffVdyuVxScnLyjPu/9a1vSW1tbdKTTz45474buUZ0d3dLCxYsmHHfV77yFWnfvhYpJeWZGfdd7xrxve99TxoZGfGba0RsbKx04MABye12B8Q1ApD+8pe/BMQ1wmQySfv27ZP6+vqkrKysGff74hqRm5srHTly5KrXiG9+8y0JJKm4ePrf7uJiSQJJ+uMfG6SXXnppxuelpqaq8hpxtecRH/nIR6Tf/e530te+9rUZ993I8wh/ukZERkZKjz32mPT1r3/9ir8btV0jHn/8cammpiZgrhHvvvuu5HA4/Op5xLWuEdd6HvHQQw9JlZWVV7xGvJ/nESMjI9etQxRdk2O32zGZTPzxj3/k3nvvlT/+hS98gbKyMg4fPjzt8VcayUlNTeXw4cN+M5Ljcrno6OgQIzkKjeRYLBYmJiZU/QpMb28vhw8fJigoSM5k9erV8vbF4LuRnLS0NIxG46xfgUlMTGRsbIwTJ07Q2dmJTqcjOzub9PR0TCaTKl+l9X7dpqYm+WRwo9HI2rVrycjIoLu7jx/8oI3nnvPsumY0whe+YOE738lFo7nyNcL7f87Xr9I6nU4mJyfRaDQUFBSg1+tnvHJ2aXvVcI1wOBxoNBqKiooYGxtT9TUC/v4qrdvtpri4WJ65YDAYMJlM5OTkzPs1wvs35Wqv0jochaxda6C4ePpITkkJrFgBTz0FTz45TFNTI5OTk3R0dDA1NYXRaGT9+vWkpKRQXl6uqmvEpc8jJEmivb2dwcFBQkJCGB8fJyYmhpSUFHl0+1rPI2w2GytXrvSLkRwvg8FAfn4+e/bs4W9/+xtut5vIyEi2bt1KTEyMqq4RNpuNnJwcMZKDMtcIm81GdHS0X4zkbNq06YbW5MzLxgMrVqzgJz/5ifyxgoIC7rnnHlVuPHDq1CnWrl3r62YErEDJ1+l0cvjwYY4dO4YkSZjNZnbt2iX/J/aVm83XarVy7tw5+Y9dZGQkq1evVuUhYZcaGRmhublZfpIUHR1NamoqRqORpib43Oc8O7EBLFsGP/2p5yDRy/lT/5UkiampKSYnJ+U/GMHBwZhMJtVuIuFP+c4l6b2zdbzrc3Q6nXyw6Hy6Xr7eYubll+G959sAVFXBgw963n/gAXjhBbBYPNPy2tra5OuF2WwmKytr2gs+ajQ2Nsbp06fp6+sDICUlhVWrVskvbF2Nv/ff2tpa3njjDcbHx9HpdGzbto1169ZNm57sz/w9X7Xzl3z9ZuMBgC996Uv84he/4Je//CVVVVV88YtfpLW1lccee0zpb62Iy195EeZWoOSr1+vZtm0bn/nMZ4iNjWV8fJw//OEPvPrqq/Irtr5ws/laLBZ5PrrRaGRoaIi9e/dOWwuiRuHh4SxevJj4+Hg0Gg0DAwNUVFTQ09NDRobE7t2eHaYiIjzn66xdC5/9LFz2orNf9d9LtzD2PvmamppieHiYyclJ1S0Kb2xs5Ctf+coVR6TUznu2Tnh4ODqdDpfL5ZPzj67Xf71nfj74oKfY8d68BY5WC6++CqtXw8WLnlei09PTyc3NxWAwMD4+TmVl5YzRGrUJDQ1l69at8qvh7e3t7NmzRy56rsafrg9XkpeXx+OPP05+fj4ul4s9e/bw0ksv+X27vdTSTrVSY76KFzkf+9jHeOaZZ/jGN75BUVERR44c4e233/aLbejeD38ZUQpUgZZvcnIyn/vc59i4cSNarZbq6mqee+45ecH7fJuLfDUaDVlZWdx1112kpaXhdrupqqpi9+7dV9xQRC10Oh3p6ekUFBQQGhqKy+WipaWFixcvMj4+xkMPeQ5J/PSnPY//xS9gwQLPq9be8x39sf9qtVosFgvh4eHo9Xrcbjfj4+MMDw8zNTWlmmJneHiYY8eOqfrMkuvR6/WEh4djNBqRJAmr1Tqvu15dr//m5kJt7fSd1by32lo4ehSSkz3/T1avht//3vN5kZGR086ramxspLm5WZW7NXlpNBry8/PZvn07FouF8fFxDh48SGVl5VV/Ln+8PlzObDbzsY99jA996EMYjUaam5v56U9/esVpY/5GDfmqmRrzVXy62s3wx+lqNpvtukPSwvsXyPn29vby17/+VV4LlpyczAc/+MEZu9koSYl829vbKS4uludcp6WlUVRU5NfnBV2PJEn09fXR3t6O0+lEo9HIc+8NBgPHjsETT0B5uefxq1bBj38My5b5d//1TouanJzE9V5lptPpMJlMGI1Gv56WovZzcmZDuuz8I7PZPC9TvObi+tDb65mydvCg59///M/w/e+DweD5uTo7O+ns7ESSJCwWC9nZ2RiNxjlove84HA6Ki4vlNSDx8fGsXbt2xu9MbX/fBgcH+ctf/kJ7ezvgWad01113+e0ZSGrLV238JV+/mq4WaA4dOuTrJgS0QM43Li6ORx55hA984AMEBQXR0dHBCy+8wN69e+dtWooS+aakpHDXXXeRl5eHRqOhtbWVt99+m5qaGvmJtNpoNBri4uIoLCwkJiZGLnrKy8vp6upi/Xo3xcXwox95Toc/e9ZzGvzOnYPMZnfL+eadFhUREYHZbEar1eJyubBareJ8HT+i0Wgwm83yCwXj4+MzFm4rYS6uD3FxsGcP/K//5fn3j38Mu3bByIjn50pOTiY3Nxe9Xo/VauXixYuqPoMLPIv316xZw5o1a9Dr9fT09LB79+4Zi8zV9vctKiqKRx55hE2bNqHRaCgpKeEXv/iF3043VFu+aqPGfEWRIwjzSKPRsHLlSj7/+c9TUFCA2+3m+PHj/OQnP5mxY4uaGI1Gli9fzo4dO4iJicHpdFJaWsqePXtm/KFXE4PBQFZWFgsXLpSnsLW1tVFRUcHo6CBPPilRUwMPPwwaDRw+nEh+Pnz1q54ndf7q0vU6JpMJjUaD0+nEarUyPDwsih0/oNFoMJlM0wodtRzYqNfDd74Dr70GJpNn047165FfAIiIiKCgoACTySQfzOuvT5xvlEajITMzkx07dhAREcHU1BSHDh2ipqZG1f+XtFotW7Zs4ZOf/CShoaH09PTwwgsvXHF3SUHwN2K62iw1NjaSlZXl62YErFst39raWt566y15Qd+iRYvYsWMH4eHhiny/+chXkiQaGxspLy+XdyvLyMhg6dKlqt5VSZIkBgYGaG9vl0feLBYLaWlpmM1mysrg8ccnOXnS8zPGxMA3vuHZoGCeN8maNbfbzdTUFFNTU/J6Ap1OR0hICEFBQX4xja27u5sf/OAH/I//8T/mdYqnr0mSxMTEhLwleFhYmGLThZS4PpSUwAc/CJ2dnlGeN97wbNwBnoN5L93CPTk5maSkJL/obzfD6XRy7tw5efqa99DotrY2Vf99Gxsb489//rO8vfWyZcu4++67/Wb62q32/GG++Uu+s6kNRJEzS62traSlpfm6GQHrVszXbrdz8OBBTp06hSRJGAwGNm7cyLp16+Z8C9n5zNdms1FeXk5DQwPgWVRdUFDAggUL0Ol089IGJbhcLrq7u+nq6pILgujoaJKTk+np6aW8PI2vfAVqajyPz831FDv33+/Zfcqfud1uec2O92fTarUEBQURHBzs89/brXh9AE+hMzY2hs1mQ6vVEhERocg24Erl297uKXTKyiA4GP78Z7j7bs993rNnvOfYxMbGkp6ertptzr0kSaK+vp7S0lLcbjfh4eHyxiZq5na7OXLkCIcPH0aSJOLi4vjoRz9KbGysr5t2y14f5ou/5CvW5Cjo8gOvhLl1K+ZrNBrZuXMnn/vc50hLS8PhcLB//35++tOfzvkUtvnMNygoiFWrVnHHHXcQHR2N0+mkvLycd955h/b2dtVO4dDpdCQnJ1NYWEh0dPS0LaeLi8+xc6edigp47jnPaE5dHXz8457DE996C/z5x9ZqtYSEhBAZGYnZbEan0+F2u5mcnGR4eBir1eqzrcKHh4f55S9/GdC7q12NRqMhNDRU3h1vbGxMkf8/Sl0fUlI8O6/t2uU5WPfDH/YUOuD52VJTU8nIyECj0dDX10ddXZ1q1/N5aTQacnNz2bp1KyEhIYyMjPDmm2/OOJRUbbRaLZs3b+ZTn/oUoaGh9Pb28sILL1BZWenrpt2Szx/mkxrzFUWOIPiJhIQEHn74Ye677z5CQ0MZGBjg5Zdf5tVXX1X1E7vo6Gi2b98u7zY0NjbGsWPHOHjw4IxTpdUkKCiI7OxsFi1aREREhLxzWXl5OT097Xzuc04aGz2jOGFhcP48fOADcPvtcOSIr1t/bZeu2fFOj/L+fMPDwz7ZfrqxsZGnnnoqIM/JuRHeQkej0WC32+f9DJ2bFRrqWaPzsY+Bw+EZ2fzv//77/XFxceTm5qLVahkZGaGmpkbVZ295xcTEsGPHDmJjY3G5XBw9epS6ujpfN+umZWZm8thjj5GZmYnD4eCPf/wj+/btU/W24ELgEdPVZml8fByz2ezrZgQska+HzWbj0KFD8nk6er2e22+/nQ0bNtzUFDZf5+twOKiurqa6uhqXyyWfubN48WJVr9cBz/WqoaEBh8MBeKbnJSQkEB8fz/Cwjqef5v9n77zDo6rSP/6ZycykTHrvBVKBUEPvLVhQFERpKq6roqKoyxZ1XXWtu659/ansulZARLGhCIQmvXfSSQippNdJpt3fH8NcE0hCArlJJrmf5znPtDP3nvudM+fe9573vC/vvmu5kw0wfTr87W8wblwXNrodGI1GdDoder1eNG6USiUajQYHB4cOd628lN4UQro16urqqKurE3PqdOT6lc4YH0wmyzq1jz+2BOv473/hd7/77fOamhrS0tIwGo1otVpiYmIk71udgclkYteuXeJMTlRUFEOGDLF5tzyz2UxSUhJ79uwBIDIykjlz5nTJeN7V57eeTnfRV3ZXk5AzZ850dRN6NLK+Fuzt7ZkxYwZLliwhPDwco9HItm3beO+990hJSbnqO+hdra9arSY+Pp4bbriB0NBQBEEgMzOTn376iVOnTokGgi3i6uqKwWAgKioKR0dHjEYjubm5HD9+nIaGfF55xURmJjz4oCUQwebNllmdKVNg+/bu7cYGFqPNxcXlMle2+vp6KioqqKysbBK4QEYaHBwcUCqVGI3GDv+/dMb4YGdnMWyWLrX0+d//Hr766rfPnZ2diY2NRa1WU1tbS0pKSo+Y0bEG8hg0aBAA6enp7Ny50+Zm5C5FqVSSmJjInDlzUKvVZGRk8J///KdLomp29fmtp2OL+spGTjuxRoGRkQZZ36b4+vpy9913c9ttt+Hi4kJ5eTlffvkln3766VX5dncXfbVaLWPGjGHKlCniep1Tp07x008/kZGRYbMXyuXl5Xh4eDBgwAD69OmDg4NDE2MH8nn3XRNpaXD//ZYEidu2weTJMHEiJCV1f2PHum7H6spmjb5mMBioqamhvLyc6urqJjM+Mh2HNRAEIEYv7Cg6a3xQKi35c+6/39LfFy2CDRt++9zJyYmYmBjUajV1dXWkpaXZ/BodsIwPcXFxjBs3DpVKRUFBAVu3bu2UHEhSEx8fz7333ou7uztlZWX897//7fSL4u5yfuup2KK+spHTTrrDVF1PRtb3chQKBQMGDGDp0qWMHz8elUpFdnY2K1as4Pvvv29XIr3upq+vry/Tpk1j7NixuLi4UF9fz6FDh/jll19sMjiBVV+FQoG3tzfx8fHNGjv29vn83/+ZyMiAhx4CjcayMHv6dBg7Fn74Abq7nadQKNBoNE1md1Qqlbh2p6qqivLycmprazEYDNf8Wzo4OBAeHo6Dg0MHHYHtotFoADp8hqMzxweFAv7v/2DePMsanblzLdHXrFgNHZVKRU1NDenp6TZ788OKVd/g4GCmTJmCg4MDFRUVJCUl2XxCVLCsK73//vuJiIhAr9fz1VdfsW3btk4bx7vb+a2nYYv6ymty2onRaOwR/sHdFVnfK2M9KZ46dQqwXPCMGzeO0aNHXzFfQXfW12QycfbsWU6dOiXeofbx8SE+Ph5fX98ubl3baElfa46d/Px8MaGjSqXC19cXPz8/LlxQ889/wooVv63ZiYuDP/4RFiyAizfuuz2CIGAymWhoaKChoaHJRamdnR0ajQaNRoNKpbqqtSTduf92JmazWbyrao3w1xF0hb4GgyWcdFIShITAgQPQOA1STU0NqampmEwmvLy86NOnj83m0blU35qaGnbs2EF1dTUODg5MmDABT0/PLmxhx2A2m9m8eTN79+4FLLM8s2bNkrxvyeODtHQXfeU8OR3Jgw9CXp748sKFCzZzwWWLyPq2HZ1Ox4XiYuovujqo1Gp8vL1xcXWlpUsAW9DXGiK3cZhcewcHXF1d0XSTpHMtcSV9BSx5kRrq6zFdNAAUWAxVewcHDAYlZ89aMsNbb9I72EOfPhAWDuquP7+0GQEQzGbMF0vjE41CoUCpVKJUKFAolS3210uxhf7bGVj7EYBGre6wi/6u0ldvgF07oaYWPDxg7JimOaUMBgM1tbWAZUbP0UZn85rT12QyUVpaisFgQKFU4uXpKboj2jqVlZUUFhWBIODo5ERgYCAqCXNtyeODtDTRNygI3n+/S9rRHtvAhk6ZXcQlP+LRjRuZMWNGFzWm5yPr23YcgVBB4NSpUyQlJVFZWQlYXCFmzJhBSEjIZd+xBX2VgCugqqvjzJkznD17VpwRCA4OJj4+Hjc3ty5tY0tcSV8FYA9oBIGKigoKCgqoqamxfKZQ4OnpSXhAAMFGJ1asgDfftGSKJxlc82DJEnj4YegG+diuiOJiUWKZ4TEYDDQ0NFy2VkepVKJWq9FoNKjV6hajTR07doyxY8eye/duBg8e3BmH0G0xm0xUl5eLfYYOMnK6anzQAKEZMHw4VJTDn/rDP/7x2+dqwHDhAtnZ2YAlgpctzng0p68d4GEwsGvXLoqKirCzs2PcuHEEBAR0TSM7EDeg9OxZ1qxZQ0NDA15eXixcuFCy384Wzm+2jC3qK6/JaSd9+vTp6ib0aGR924dCoSA+Pp6lS5cyZcoUNBoNubm5fPTRR6xZs4aSkpIm9W1JXycnJxISErj++uvFRIG5ubn88ssv7Nu3TzQOuhNt1VehUODh4UFcXByxsbG4ubmJLm2nTp2ioCCV++6r5OxZgf/9z+K6VlUF//ynZVZn7lzLGp7uOw/flMbrdzw9PcWABUqlErPZTENDA9XV1ZSXl1NVVYVOp8NkMjUxhsxmM3V1dTa/LqMjsM7iXK3bX0t05fgQGQkffWR5/s9/wi+/NP3c19cX/4t+bFlZWaLbpy3Rkr5qtZoJEyYQHBx8WahpW6dPnz7ce++9uLm5UVpayn//+1/Onz8v2b5kpMMW9ZWNnHbi5OTU1U3o0cj6Xh3Wk+QjjzzC0KFDUSgUJCcn89577/HDDz9QVVUF2Ka+Li4ujBo1ihkzZhAcHIwgCGRnZ/Pzzz9z8OBBai+6sXQH2quvQqHA1dWVmJgY+vfvj6enJwqFQkyGmJZ2ihtvvMDx4yZ++MESbtpkgq+/hgkTYNgw+OST39bx2AKXBixwc3PD0dEROzs7BEFAr9dTW1tLeXk5FRUV1NTUXLa+pzdjNpvFaFwd7dbU1ePD7NmWmUqwRF67dC1+SEgILi4umEwmMjMzba5PtKavnZ0do0eP7pGGjq+vL/fddx+BgYHU1dXx6aefcvr06Q7fT1f3356OLeorGzntxLrYW0YaZH2vDRcXF26++WYeeughYmNjEQSBI0eO8M4775CUlMThw4e7uolXjbu7O+PGjSMxMZGAgADMZrOYY+fAgQPdYmbnWvqvVqslMjKS+Ph4/P39sbOzQ6fTkZ2dzalTJxgyJJcNG/ScOGFJpujgAEePwj33WNzXnnnmomubDaFQKFCr1Wi1Wjw8PMQobRqNBoVCgclkor6+nurqatEdU6fT9VqjRxAEqqurxQTBHW3kdIfx1zpbef68pU83RqFQ0LdvXzGHTl6j9bK2wJX07cmGjrOzM4sXLyYmJgaj0cjXX3/NwYMHO3Qf3aH/9mRsUV/ZyJGR6YH4+Pgwb9487r33XkJDQzEajezatYvvv/+e3bt323TSTU9PTyZOnMjUqVPx8/PDbDZz9uxZfv75525j7FwLDg4OhIaGMmjQIEJDQ7G3t8dgMJCfn8+JEyfQas/yxhs1nD8v8OqrlohUxcXw4osQFmYJybttm+24sjXGmjDR1dVVdGtzdHRsEtHHavSUlZVRXl4uzvRc6t7W0zAYDOICdaVSibOzs81GGWsNJyf44APL83ffhUtv+Gs0GsLDwwEoLCzsVjO5HUFzhk5XJNaUAo1Gwx133MHw4cMRBIGffvqJHTt29Oj/rUzXIkdXaydVVVXdpi09EVnfjkcQBNLS0tiyZQvnz5/H3t4eV1dXJk+ezKBBg1pc6G0rlJSUcOrUKQoLCwHLQvawsDD69euHi4tLp7ZFiv4rCALl5eUUFRU1yaWh1Wrx9fXF1dWTH3+04+23Ydeu374XFWVx+Vm8GLy9O7RJXUJNTQ0HDhxgwIABaDSaZnPEKJVKVCpVk2Lr/VsQBNGQs+Lm5nbFcPFXQ3caf+fMgXXr4NZbLY+XkpGRQVlZGVqtln79+tmEwdcefU0mE3v27CEvLw+NRsOUKVNwd3eXtoGdhCAIbN++nR07dgCI7sjX+ht2p/7bE+ku+sohpCXkyJEjDB06tKub0WOR9ZUOs9nMmjVrKCwsFF1/vL29mTx5ss1cJLRGSUkJp0+fFt07lEoloaGhxMbGdtrFgdT9t6amhgsXLlBWVia6a6lUKry9vfH19SU52YEPP4SVK8E6oaXRWNY63H8/TJrUYYG4uoTG+prNZoxGIwaDAYPB0OJMjp2dnVhUKpX4vLv3d6ur3qWuea6urmIy0I6mO42/yckwYIAlKe6RIzBkSNPPDQYDJ0+exGg00qdPH7xtwJJvr75Go5EdO3ZQXFyMo6Mj06ZNs8mEjC2xb98+frkYYWLw4MHcfPPN13RTojv1355Id9G3PbaBbd/i6gKKi4u7ugk9Gllf6VAqlXh6evLII4+QmJiIo6MjJSUlrF27lg8++ICUlBSbdhvw9vZm4sSJTJs2TVyzk52dzS+//MLOnTsvizQnBVL3X2dnZ/r06cOgQYMICQnB3t4eo9FIYWEhJ06cwMkplVdeKSc318yKFZCQAHo9fPmlJWhBTAz8618W9zZbIycnh2effZacnBzA0p81Gg1arRZ3d3c8PT1xc3NDq9Xi4OAgRh4zmUzo9Xp0Oh3V1dVUVFRQVlZGRUUF1dXV1NbWUl9f36qh1BmYzeYmQRfKy8vR6XSYzWbs7OzQarV4eXlJZuBA9xp/4+Lg9tstz9977/LP1Wq1GG0tLy/PJtZotVdflUrFuHHjcHNzQ6fTsWPHjiYzerbOqFGjuPXWW1EqlRw7doxvv/32mn7H7tR/eyK2qK9s5LQTBxtNQmYryPpKi/Xib8yYMSxbtoxJkyZhb29PUVERX375Jf/5z39IT0/vEcZOYmIiISEhKBQK8vLySEpKYsuWLeTn50t2fJ3Vf9VqNQEBAQwcOJDo6Gjc3d3FqGzp6emcPXuc6647z86d9Rw+DA88AM7OkJ4Of/yjJY/brbfCd99ZjCBboKSkhPXr17dorFqDGDg6OuLs7Iy7u7sYvc3Z2RkHBwcxD48gCBiNRhoaGtDpdNTU1FBZWUl5ebm41qeyslI0gqzBDgwGA0ajEZPJZElw2o5+JAiCOPuk1+upr6+ntraWqqoqcb+Nw2dbo9C5urri7u6Oo6Oj5LNP3W38tUZaW7UKLk4+N8HPzw+1Wk1DQwOlpaWd27ir4Gr0tbe3Z+LEiWi1Wqqqqti9ezcmk0mC1nUNgwYN4rbbbkOpVHLy5EnWrVt31cfX3fpvT8MW9ZXd1dqJIAjd3s3BlpH1lZbm9NXpdOzZs4f9+/eL+TdCQkKYPHkyERERNv97VFdXk5ycTHZ2tniX0N3dnbi4OEJCQjp0zUZX9t/6+nqKi4spKSlpEljC1dUVb29v1GoPvvrKjhUr4NCh377n5QXz58Pdd1tCUnfXn/vIkSMMGzaMw4cPX5PLhNXYMJlMorHS+Hl7TokKhaLJ733pb2/dliAIbdqu1aXuSolRpaK7jb+CYJnRSU2FL76AhQsvr1NQUMD58+fRarX079+/8xvZDq5F38rKSpKSkjAYDERFRTFs2LAObl3XkpKSwtq1azGZTPTr1485c+ZgZ2fXrm10t/7b0+gu+sruahKyadOmrm5Cj0bWV1qa09fR0ZGpU6eybNkyxowZg0ql4vz583z22Wd8+umnnDt3rgta2nG4uLgwYsQIZs6cSWxsLCqVioqKCvbu3cvPP/9MRkZGh90Z7cr+6+DgQEhICIMGDSIyMlKc3amqquLs2bNkZh4nMfEc27fXcvy4wPLl4O8PpaXw739bss0PGGAJ4Wtroajbg0KhwM7ODo1Gg6OjI1qtFldXVzw8PPD09BRnf1xcXNBqtTg6OmJvb49arRYDGVhP9FaDyVqsxlJjo6mx4aRQKMTgCJfu37pvFxcXMVFqZ9Pdxl+FAmbOtDy/6y748MPL63h7e6NUKqmtre32kdauRV83NzdGjhwJcHG29mxHNatbEBsbyx133IGdnR1nzpxh3bp17XZd6279t6dhi/qqrlxFRkamN6DVaklMTGT06NHs2rWLQ4cOkZ2dzccff0zfvn2ZMGECYWFhXd3Mq8bJyYnBgwcTFxdHRkYGaWlp1NTUcOjQIU6dOkVUVBSRkZEdnnuks7GuvfL09KShoYGSkhJKSkpoaGigqKiIoqIinJyceOIJL55/3otff9Xw6acW17UzZ+DPf4Ynn4Rp02DRIpg1C7rJRLrkWA2gttxBts7ONJ6luXS2pvFMj/V5d7gTaktY3SkHDIAlSyzPH3jgt8/VajXu7u6im2FPWph/KcHBwcTHx3Py5EkOHTokztL2FKKjo7njjjtYs2YNp0+fRq1WM2vWLPk/I3PVyDM57cSWL/JsAVlfaWmLvi4uLlx//fU8+uijJCQkoFQqyczM5OOPP+aTTz4hKyvLptfs2Nvb079/f2666SaGDRuGVqulvr6ekydP8sMPP3Dw4EEx+lx76W79197enqCgIAYOHEhMTAyenp4olUrq6uo4f/48p08fJzw8hXffLSEvz8R//gPjxlkiWm3aZLl77utric721VfQlTfKfX19ufvuu/H19e26RjTCOitjdTFTqVSo1eompXE0t8YzQN2V7tZ/P/zQkitn6VJL4tulSy2GzqUzOtboiVf7v+0sOkLffv36ERwcjNlsZt++fTad86w5oqOjmTNnDgqFgmPHjrFhw4Y2n2+6W//tadiivvKanHZSWFgoRnSR6XhkfaXlavStqKhg165dHD16VHTrCgkJYeLEifTt27fbX7hdCZPJRG5uLqmpqZSVlYnvBwQEEB0djb+/f5uP0Rb6r9FopKysjNLS0iZ5d5RKJR4eHnh7e1Nc7MrKlQq+/BJSUn77rpMT3Hwz3HEHXHcddPY6VFvQ15bpTvp++KHFoFm6FN55x+K6JgiwbJnF8Pngg99mdAwGA0ePHgVg2LBh7V7L0Vl0lL4Gg4FffvmF2tpaIiIiRDe2nsSJEyf49ttvEQSB8ePHM3Xq1Ct+pzv1355Id9G3PbaBTburmUymTr+LcerUqR6TkKs7IusrLW3R13pn2nph7+7uzsyZMxk/fjy7d+/myJEjnD9/ni+++ILAwEAmTpxIdHS0zRo7dnZ2hIWFERoaSklJCampqeTl5VFQUEBBQQFubm5ER0cTFhaGStX6kHn8+PFucRJoDZVKha+vL76+vtTX11NWVkZJSQn19fWUlpZSWlqKWq3mnnu8eOIJT86e1bJmjYI1a+DsWUs46i+/tLiw3XKLxeCZNs2Sj0dKampqWL16Nffddx/Ozs7S7qyX0l36r9XAeeQRePvt34JhKBSW19DUdc06c2YwGNDpdN22f3SUvmq1mlGjRrF161aysrIIDAwkJCSkA1rYfRg4cCB6vZ7169ezc+dOXF1dGT58eKvf6S79t6dii/ra7ExOTU0Nubm5ne42o9PpcHR07NR99iZkfaWlrfo6OTkREBDQbE6O6upq9uzZw6FDh8SbDP7+/kyYMIG4uDibNXYaU1NTQ1paGllZWeIx2tvbExkZSWRkZIsabty4kRkzZnRmUzsEQRCora2ltLSUsrKyJjeP7O3tLy6K9+TMGSe++spi8OTm/vZ9V1e48UaL0XP99eDi0vFt7KjoajIt0x36b0ODpf/ExVlc1JqLv2A2W5KDJidDdTXY20NycjLV1dX07dsXLy+vzm94G+hofU+ePMnp06fRaDRcf/31PfLcuWPHDrZt24ZCoeCOO+4gNja2xbrdof/2ZLqLvu2ZybFJI8dkMpGeno6TkxM+Pj6delFlNBqveDdX5uqR9ZWWK+krCAJ6vZ7i4mJMJhNRUVEtRnmqra1l7969HDhwQAw97ePjw7hx4xgwYEC3dRlpD3q9nqysLNLS0sTITUqlktDQUCIjI/Hy8moy/pSXl+Ph4dFVze0QzGYzlZWVYsLMxpHnrAaPu7snx487sWaNgm++gYKC376v0Vhmdm69FW66Cfz8OqZdspEjPd2l/7Y0kwMtu6ylpaVRUVFBREQEPj4+XdPwK9DR+ppMJrZs2UJZWRmhoaGMGTOmw7bdXRAEgfXr13P48GFUKhV33313i7NW3aX/9lS6i7493sipr68nKyuL8PDwTr9zUVdXh5OTU6fuszch6ystbdW3rq6Oc+fOERERccUEYDqdjn379rF//37q6+sBS7jTMWPGMGTIEEkztHcWZrOZvLw80tLSmmR99vDwIDIyUnRlO378OIMGDerClnYsJpOpicHTOKSrg4ODaPCcPOnId98p+PZbS8JRKwoFjB1rmeG55Rbo2/fq2yIbOdLTnfpve9bkgCWscnl5OWFhYfh1lGXdwUihb1lZGZs3b0YQBCZOnEhAQECHbr87YDabWbNmDampqTg5OXH//fc363bdnfpvT6S76Ntr8uR0hVtMT4tk0t2Q9ZWWturbnhwdjo6OTJ48mccee4ypU6ei1WqprKxkw4YNvPXWW2zfvp26urqrbXK3QKlUEhISwtSpU5k+fToRERHY2dlRXl7OwYMH+eGHHzhy5AjZ2dld3dQOxc7ODk9PTyIjIxkyZAiRkZFihLb6+nry8/M5c+YUTk4nePjhHA4erOLUKYEXX4SEBMtF6a5dsHw5REZCbCw88QQkJVnckmS6F4WFhV3dBJEHHrAYMv/+Nzz6qMVFrSUDB34b29RqdRe0tm1Ioa+npydRUVGA5UZAR+X86k4olUrmzJlDQEAAdXV1rF69WvQeaEx36r89EVvU16aNnK6gO643eO6551hycRXm9u3bm/isOjs7c+HCha5qWrvpjvr2JKTU18HBgfHjx/PYY48xc+ZMPD09qaurY/v27bz55pts2LCBiooKyfbfWXh5eTFy5EhuvvlmBg8ejLOzM3q9nrS0NM6cOcP27dvJzc1tdyK77s6lBk/fvn3x8PBAqVTS0NBAYWEhqakp6PXHmD//LBs3lpGVZeLdd2HKFLCzs2Suf/NNmD4dvLwsszsrVsD581fev0qlws3NTXZnlZDuZiDcfrvF/fHf/7aswWnJwBEEgYaLVnN3znMllb7x8fHY29tTXV1NVlaWJPvoajQaDfPmzcPZ2ZmioiLWrVt32Zrs7tZ/exq2qK9s5LSTK02NhYeH4+rqik6nE9+rqqrC0dGxifERHh7Ovn37mnx3yZIlPPfccx3a3pqamm6TV6IxS5cu5dNPP23y3n333cdTTz11Wd133nmHiRMniq8PHTrE5MmTiY6O5uuvv76s/uzZs3n22Wc7vtESkpmZydixY3FycmLo0KEcP378it/Zu3cvSqWSV199tcn7+/btY9SoUTg7OxMcHMxXX33V5PN77733opuROwsWLOjQ4wDLQJiQkMDSpUuZO3cuAQEBGAwG9u/fzzvvvMO3335rU4Z3S9jb2xMbG8uNN97IhAkTCAwMpE+fPhQWFrJr1y5++uknTp8+3WQs6CnY2dnh5eVFVFSUOMPj7e2NSqXCYDBQUlJCRkYGJSVHSUxM48sviykoMLB2LdxzD/j7W3LufP+95YI1NBTi4y2JSLdvb36WZ+DAgVRUVDBw4MBOP97ewpQpU7q6CU345z8tyUADAy1BBpozcMDiMmswGFAqld168b1U+qrVagYMGABYImgajUZJ9tPVuLm5MW/ePFQqFSkpKezYsaPJ592t//Y0bFFfyYyc7Oxs7r33XiIiInB0dKRv3748++yzzU4x2hJtSTbm7+/PDz/8IL5et25djwvveK1s3LiRxMTEJu8tWrSINWvWXDZAr1q1ioULF4qvf/nlF2bMmMHChQtZuXJlk7pWNykpLt6lZP78+SQmJlJWVsbvfvc7br311lZPVGazmccff/yykJoFBQXMmTOHZ555hoqKCo4fP86wYcPEzxctWoSzszNZWVkUFxfzxz/+UbJjUiqV9O/fn/vvv5+77rqLPn36YDabOX78OP/3f//HqlWryM7OtunEomCZHQsMDGTChAk4OTkRFxeHvb09tbW1nDx5kh9//JE9e/ZQWFho88faHNYZnj59+jBkyBBiY2Px9/fH3t4es9lMRUUFWVlZZGcfo1+/M7z4Yj5pabUcPmxxaxszxhJB69Qpy0Xt5Mng6WnJw/Paa3DkiMVVCWDTpk1de7A9nO6k75Ejlt8f4P/+zxJFrTkDBywLosFyE7I9rradjZT69unTB2dnZ3HNck8lODiYm266CbBEXjt79qz4WXfqvz0RW9RXstEgJSUFs9nMhx9+yOnTp3nzzTf54IMPmr1T39OYP39+k4vvlStXXvNFt06nY+nSpQQGBhIcHMw//vGPNn1PoVCIfpTh4eH84x//IDIyEh8fnyazRuvXrycmJgYXFxdCQkJYvXo1YFl4/OyzzxIWFoa/vz9/+MMfmr343rRpE2PHjhVfR0RE8PDDDwOWZJKurq7i9zIzM8UQxY2ZMGECDg4ObN68WXzv7NmzHD16lNtuu018zxrGcNGiRZe5QH3zzTcMGDCAmJgY0XXvmWeewd3dnZiYGM6cOcOLL76Ip6cncXFxnD59WvzuQw89RGBgIO7u7iQmJpKTkwNAamoq3t7eZGRkAJaZEn9//w6bjUhNTSU1NZUnn3wSBwcHli5dislkYs+ePS1+Z8WKFYwcOZK4uLgm77/55pssXryYG2+8EZVKhZeXF30vrvY+ffo0x44d44033sDNzQ21Ws2QIUM65BhaQ6FQ0KdPH+666y7uv/9++vfvj0KhIC0tjU8++YQVK1Zw4sSJHuFLrtFoGDRoEDfffDOjRo3Cy8sLs9lMTk4O27dvZ/369Zw+fVqM1NbTUCgUuLq6EhoaysCBAxkwYABBQUFotVoEQRBD/ycnn0ahsLi1/fBDKfn5BlavhjvvBF9fqKuDjRvhT3+CYcPAxwcSE09z++2/5+efT9MDbcVuQXcxwgsKLK6MJhPcdhvMmmUJE90cJpNJHIu7a+hoK1Lqa2dnR0xMDGC5/uoJ42lLDBo0iGHDhiEIAt98842Y1Li79N+eii3qK5mRc9111/Hxxx+TmJhInz59uPnmm1m+fDnr1q3r8H0JgsX1QeoiCLQpUtT06dM5cuQIZWVlFBYWkp6ezoQJE67pGJcvX05lZSVpaWkcOHCAzz77jB9//LHd2/nmm2/Yu3cv+/fv56OPPmL9+vUA/P73v+d///sf1dXVHDx4UIyg8cYbb7Bnzx4OHz5MSkoKR44c4f33379su6NHj+bo0aPodDry8vIA2LVrFwC7d+9m+PDhoi+9dSbmUqxx8FetWiW+t2rVKq6//no8PT0By0xNVlYWgwcPpm/fvgwePJhvvvmmSf3Gsz4ZGRn4+PhQUlJCYmIiN9xwA46Ojly4cIGZM2fy17/+Vaw7btw4kpOTKSwsJDg4mEcffRSAmJgYnnrqKRYvXkxtbS2LFy/mnXfeadYNcNeuXbi7u7dYmuPMmTPExMQ06VsDBw5sYoA1pqysjLfeeqtZ18aDBw+iUCjo378/AQEB3HnnneJdzkOHDhEdHc2iRYvw8vJixIgR7Ny5s9l9SEVgYCBz587lkUceISEhAbVaTUFBAevWrePtt99m165dNu3eFRwcDFguOMLDw5k+fTozZswgOjoajUYjzu6sX7+eHTt2kJub22MvRhQKBU5OTgQFBdG/f38GDx5MeHg4Hh4e2NnZiW5tmZmZ5OQcY+DAM7zySh4ZGTUcPy7w5pswc6YlZ0pZGWze3EBl5XluvLGBsDCL29v//meJ5maD595uibX/dhomk8U/cfVqy6PJRGGhZRbv/HmIjrZEWWuNoqIiDAaDGN68OyO1vtZomLW1tRQ0juveA7nuuuvw9/entraWr7/+GrPZ3Pn9t5dhi/p26rxuZWVlq4NQQ0MDVVVVTUpbqKsDZ2fpS10dbVr0qlKpuOWWW1i7di1ffvklc+fObXYKffr06U0ugD/++ONmtycIAh9//DGvv/46zs7OBAYG8uCDDza7HuVKPPbYY/j4+NCnTx8eeOAB0UBQq9WcOnWKmpoa/P396devHwAfffQRL730Et7e3ri7u/OHP/yh2f26uLgQFxfHgQMH2LlzJ7fccgt6vZ7y8nJ27tzJuHHjxLotGTkACxcu5LvvvhOjcV1qtCQlJTF58mRxAf2iRYvEWbOCggJ+/fVX5s2bJ9Z3d3fnkUceQaVSMXv2bEpLS3n88cfF1ydOnBDrLliwADc3NxwcHPjzn/8sGmlW3RQKBSNGjCA+Pp7bb7+92faPGzeOioqKFktz1NTUXLbWy9XVlZqammbrP/XUUzz22GPNxqvPy8tj5cqVfPvtt2RkZGA0GnnsscfEz7Zs2cK0adMoLCzkL3/5C7fccgtlZWXN7kdKPD09mTlzJo8//jhTp07F2dmZqqoqkpKSeOONN/j5558pLS3t9HZdK80Zvh4eHgwdOlSc3fH19UUQBAoKCti1axc//vgjx44da/N4Z6toNBp8fX3FdTyxsbEEBATg5OQkzvLk5eWRnHwGo/EoM2dm8NFHF8jLq2fXLkHMcK9SWS6AP/kE7r3XciEcEGC54//223D4MPTQJQmS06nrN9etg/Bwi3/iggUweTKG4HCeH7yOEycsM3o//2xxXWyJuro68vPzAcsFWHcPXCO1viqVioiICMDiMdGTUavVzJ07F3t7e86dO8eePXu65frjnoQt6ttpYWoyMzN59913ef3111us88orr/D8889f9n5SUhJarZYpU6Zw4MABdDod3t7eYg4Hi+eHm3SNv0hlZSVOTgJqtRqj0YhSqRQvzuC3WZ6amhpmzZrFCy+8QF1dHW+++aZYx7qmRxAENmzYQHx8PGDJMP/ggw9SX19PVVUVrq6uVFVVIQgC5eXl6HQ6MUykQqHAbDYzcuRIcXsGg4HKykoxT0njtUP19fVUVlZiNpsJCgqiuroas9mMr68vO3fupLKykk8++YR//etf/OlPf2LYsGH84x//YNiwYeTk5DB9+nTx5CEIAgEBAWIkG+v+XFxcGDVqFJs3b+bChQskJiZSUlLCpk2b2LFjB8888wyVlZXo9XoOHjzI0KFDqaysvEzDsLAwIiIi+PLLL4mMjCQvL4+pU6eKdTdu3MiECROorKxEo9Ewe/Zsli9fTkpKCuvXr2f8+PE4OjqK2/P09KSqqgqNRoNarcbDw4Pq6mqcnJxQKBTU1NRQWVmJm5sbzzzzDCtXrqSkpASFQkFVVdXF39wJo9HI7bffzqOPPsoHH3wgaqhSqXBwcBANEkdHR8xms6iP1Vhprq41/4xCoaC8vByz2UxdXR0mk4mysjKcnJzE39Fad//+/ezdu5d33nmH2tpa9Ho9DQ0NmM1mqqurxQg04eHh6HQ6MdKZ1T0qLCyM3/3ud1RVVTF16lQiIiL49ddfmTx5MgBarRa9Xk9tba34227cuBGAkJAQvL29OXr0KAAJCQnk5+eTn5+PnZ0d06ZNIykpCZPJRGBgIIGBgRw6dAiAIUOGUFJSwvmLYbRmzJjBtm3b0Ov1+Pn5sXjxYtauXUtycjJqtZqff/6Z1atXExwczP3338/58+fF/310dLToyte/f3/q6+vFE7p1jKipqcHDw4P+/fuLxmpsbCxms5m0tDQAJk6cyLFjx8RY+0OHDmX79u0AREVFoVKpSE5OBizG65kzZygrK0Or1TJq1Ci2bNkCWHzhnZycOHXqFNnZ2cyfP5+MjAyKi4txcHBgwoQJoi9zWFgY/fr1o7q6mpKSEpycnDh37hynT59GqVQyYsQIioqKcHd3JywsDF9fX44cOQLAsGHDKCwsJC8vD6VSyfTp09myZQtGo5GAgACCg4M5ePAgAIMHD6asrEx0uZwxYwbbt2+noaEBX19f+vTpIwY+iY+Pp6amRvTjnzZtGnv27KGurg4vLy9iY2PZvXs3AP369UOv14uum5MnT+bQoUNUV1fj7u7OwIED+fXXXwFE15nU1FTA4o564sQJKioqcHFxISEhgb179wIWV1qDwUBqaioGg4Hg4GBycnLQ6XSo1Wr69u1Lnz4WvT/+OAulMozvv6/k9GkP0tPdKSqyJCW1Tuo6OhqJi6tg7FiBiRPtUCqP4ORkYvjw4eTm5lJQUIBKpWLq1Kls3rxZHBv9/f05fPgwAEOHDuXChQvk5uaiUChITExk69atGAwG/P39CQ0N5cCBA4DFfaaiooJz584BkJiYyK+//kp9fT0+Pj5ERkaKxzpgwADq6urEtQRTp05l37591NbW4unpSb9+/cQ+GxcXh9FoJP1i8qFJkyZx5MgRMU/E4MGDxcXX0dHRKJVKUlJSxD57+vRpysvLcXZ2ZsSIEWzduhWAvn374uDgIM4WjxkzhrS0NA4dOkS/fv0YO3as6DZsDaZjvSE0cuRIsrOzKSoqQqPRMHny5HaPEV47djD4pZdAEGhsltgV5vEet6F3X8mTu+eTk7ONjAzLGBEeHs7+/fsBy0x3eXk5J0+exGw2M3ToUDHIR3ceI9avX094eDijR49udYxwd3cXg8+MGDGCnJwcCgsLUavVTJkyhU2bNiEIAsHBwZeNEQ0NDWRmZnL27FmGDx/O3r17bXqM2LZtGwCRkZFoNBrOnDkDwNixY8nKysLNzY2DBw+yZcsW9uzZw6BBg4iIiMDZ2ZmTJ08CMGrUKM6ePcuFCxewt7dn0qRJYp8NDQ3F09OTY8eOAchjRCtjxIYNG/Dz88PJyUnyMaK16whr+9uE0E6effZZAWi1HDx4sMl38vLyhMjISOHee+9tddv19fVCZWWlWM6fPy8AQmVlZZN6Op1OOHPmjKDT6QRBEASzWRBqaqQvZrMgVFRUtHoMYWFhwt69ewVBEIS+ffsKcXFxgiAIwrZt24SYmJhm61l54IEHhGefffaybZpMJsHBwaHFfT/77LPCAw880Ox+AKGgoEDc58qVK8XPXnjhBeHuu+9usq36+nrhT3/6kzBlyhRBEAQhMjJSOH78eKvHbGXt2rXCjBkzhEGDBglFRUXCxx9/LCxbtkxwcnISqqqqBEEQhC1btgg33nhji9uoqKgQ/vnPfwo33XSTsHz5cmHx4sVNPg8PDxeKioqavHfDDTcIr7/+upCQkCB88skn4vuXarF3714hLCxMfH306FHBz89PEARB2L59uxASEiKkpaUJZrNZSElJERr/PUpKSoSAgADhzjvvFEaNGiUYjcZm2//rr78KWq22xdIcKSkpgqurq6DX68X3QkNDhR07dlxW98033xS0Wq3g5+cn+Pn5CQ4ODoKzs7Pw+9//XhAEQViwYIHw/PPPi/VPnToleHt7C4IgCJs2bWpy/IIgCAkJCcL69esv28+l/7HOwmw2C2fPnhVWrlwpPPvss2L58MMPhePHjwsGg6FT29Nefvnll3bVNxqNQm5urvDrr78KX375pbB69Wph9erVwtq1a4V9+/YJhYWFgtlslqi13ROz2SxUVVUJubm5wpkzZ4SDBw8K+/fvFz799FMBED799FPh+PHjQlZWllBSUiJUVemFX38VhJdfFoTrrxcEV1dBsDiw/VYUCkHo318Q7rlHEN5/XxAOHxaERn83mYu0t/9eFUajIAQHX/4jXSwmFIIxMMRSrwX0er1w+vRpYf/+/cKxY8eajJ3dmU7RV7CM9atXrxbS0tI6ZX9didlsFlavXi08++yzwtKlS7v9OcKW6az+eyUqKyubtQ2ao90zOUuXLm3iDtQc4eHh4vP8/HwmT57M6NGjWbFiRavfs7e3v6oY9woFaLXt/tqVMZng4t1sAOpAq1BAa4uGBQF0OqitZd3KlSit9XU6S4gg63cb1RMxGCzxMi/ZvhK4e8EClj/2GK+9+CKurq6kpqVRXVPDiIQEy3cMhub3AxY/u4uLit556y0Sx46luqaGFR9+yHtvvIG+vJyvv/uOmdddh7OzM84aDXYAtbXce+edPP2Xv/Cff/8bP19fzuXkcC4nh4njx1926OOHDmXxrl2EhYTgq9UyftgwHn30UWKjo3FRKqG2lo3r1zNj0qQWNdQqFCy45Rb+9re/cfDAAT7/73/FuskpKXi6u+Or1Tb5/sLbbuPJZ5+luKSE2TNm/PbZpVrodL8t4LrkdXVxMSo7O7wcHKi9cIEXretdLtZ96P77mXvLLbz1z38y6brreP2VV/jT4483q0FNUVGzx9Z4e42JCQ4mJiqKV//+d/70+ON89Omn2CmVjBk06LL69y9cyLybbxZfL/vjH4nq25fly5ZBbS2L583j/kceYdHs2QT4+/PKCy9w40VNJg0fjkIQ+HTFChbNm8dPv/xC1tmzjB448PJ2NTRY+tXp05Y/WCehACKAiNhYyv38OHnyJGlpaZgKCth7+DBHHRyIi4ujX79+ODs7d1q72soIlcoSFqqN2AFBQJBWiy44mIKCAvLy8qirq6MiPZ1jWGYHAwIC8Pf3x8XFRaKWdx8UgMvFEgSYBIE6nQ4frZZVy5cTqdOhPHaMGsDq0Olvb89dA5xYMsIJh6edOH9ew9FjCo4dhePHobAIOA3HTsOxj2EFYK+BmBjo3/+3EhJiifTWW2lv/70qDh2C3NwWP1YiQP55+OgjS0bZS9Dr9Zw7dw5zQwMudnZERESgvnjHvrvTKfoCkVVVmLOyqKyqsoSl68EogFkhITTs3YtHVRVHP/rosqijMh3DZf03NhacnLquQW2g3UaOt7c33t7ebaqbl5fH5MmTGTZsGB9//HG3Du3YLPX1luD8jbiiYAYDZGeDiwsD7ews7yUnQ06O5aLRur1G9UQqKiwZ8y7ZJ8Ab99zDU++9R/zQoVTX1REVEsKLDz5ose5KSizfbW4/AGlplpW7BgO3jhzJqHHjqKiu5qHbbuOmiAj0KSl8umIFDy9bhtlsZlB0NB8++SQkJ7M8MRFDfj5jxo+npLKSMH9//nzXXdBMH/ADAr28GBsbC8nJ9AWc7e0ZFxMjtueX9etZ+8orzR6jVd8gYPSAAaRkZzPF1/e3765axYzBgy/77i1RUTxQWspNY8fi0vjkeakW2dkW3a2vz561OO8nJ3NdcDCjY2IIi43F292dP915J19c/O3WJiVx5OBBjq9ahSIlhf8tX86IxYu5KSaGuIv+z9fKqqef5u7nnuPl114jNiyMdS++iOri9PPLH3/MzqNH2fDOOzgBjYcUx4YGnOvqcC8ogIICpgcE8PhttzF20iT0RiMzRo3izT/9CZKTUQPfv/oq977wAg8/9hhRISGse+UVPIuKoDnDrKQEliyBi9PrnY0HMOFisRUuXyXVdhyBPheLzG/Y8ZvR09Z/W/TFckdrlfTAyYtFBri2/tvhtBAvWgNEdW5LOozO0jeCtv9XegKOwN3WF5991oUt6dlc1n8PH4ahQ7uiKW1GIQjSxKXJz89n4sSJhIaG8tlnn2FnveDHkkemLVh9Cq3+sFasceCtkUQk49KZHKCmthZnSaaNpCe8Xz++/OQTRo0Y0SX7LygsZOy0aZw9darFOq3pO2PWLJ7+4x+Z0CiIgUz7aGv/rW9oIOv8eSKMRhy6yWJek8lEdnY2p0+fbhI5yMvLi/79+xMZFYW6DYFBpGTPnj2MGTOmw7ZnMpkoLi4mPz+fkpISMYSnQqHAx8eHwMBAvL29m4yvPZXi4mLefvttli1bho+Pj/i+wWhEV1dHXV0dOp0OnU6H2ZpY5yLWJJHW4uDgSGGhmjNnFJw+bcnRk5oKDS2kcQsOsgQ4iIqylPBwy6yPLSUAb9ArsNe0frrv6P5r5fx52LIF1q8Hj6xD/IcWEt405sMPxZkc63otK46OjoSEhLQp2ml3Qip9L0UQBLZt24bBYGDUqFG4uUm/ZrmrEQSBd955BwcHBwIDA5k5c2a3D0Rha1zWf7toJqcl26A5JLsi2LRpExkZGWRkZFwWdk4iu6rjsbO7zA/ObDJJ5BvXCSgU4OjYZe2vMhr552uvtbr/1vSdmpjI6ClTbOvKopvR5v5rZweai/48Ut5IaAd2QN/hw+k7dy6FhYUcOHCAkydPUmAwcCo9HcfcXIYOHcrw4cNbDNctNbWlpR16Z8sO8L9Y6uvrycnJITs7m7KyMsqA1MpKNDodISEhhIaG4uPjY3sz5m3k/JEjvLRhA7NffBGfRhqrLxbrqc5sNlNbW0tNTY1YDAaD6Nomfi9cSd/+WgYu1qLVanFw0JKdreb4cTh2zOLmdvw45OfD0TwgD9j22/ft7KBPH8t5vnGJiYHulq7lww/hkUfg3XdbTqgJHdd/Gxpg715ISoKffrLoacXZcRD/ULyAR10eCpq5FlAoIDgY7r0XE3DhwgUKCgowxsZe/FhBv2HDbLKfd/T40BIKQFldTXlhISWhobhdzJXWk1EAIbNmcebMGQqMRsI1GjGwk0zH0Fn9tyORbCanI+jymZweRnh4OF9++SWjRo3q6qbIdHNs5T+m0+k4evQoBw4cEEN0KxQKYmJiSEhIoG/fvj3ybp41Ss+5c+fEcOvw2x3u0NBQvLy8etSxHzlyhGHDhnH48GGGtuNEKwgC9fX1osFTW1uLTqdr9mabvb09Tk5OaLUWw8cS4VAtGjzHj8OZM5ZZn9aWOnh7WwygiAjLrE/jx9DQzr1v8OGHFq/TgQPhxAn44IPWDZ32IgiWmZqDB+HAAcvj/v2WpaBW7OxgyhRLmO877gC3LessL6wbsHKxvxq//JLi8eMpLCzEYDAAluilPj4+BAUFdVzjezCHDx8mPT2duLg4Me9db+DXX39l69atuLu7s3Tp0jal/ZCxLbrFTE5PxRre2RbJzs7u6iZcEVvW1xboafo6OjoyZswYRo0aRXp6Ovv37+fs2bOkpKSQkpKCh4cHw4YNY8iQIWg7YQZzy5YtTJ06VfL9WHNrxcfHc+HCBXJycsjNzUWn05GWlkZaWhparZbQ0FBCQ0Nxd3fvUQZPe1AoFKKbmtXNzWQyUXfRxa22tlYMmd7Q0EBDQ4OYQBcs+TiCghyJinLirruccHJywt7egaIiJSkpNCmpqZalgCUllnIxeuxlBAb+5vIWEPBbCQz87bm7+7XH/LAaOI88Am+9BY89hphvqDlDp7X+W1dnMWbS0y3HmZpqWe6ZnAwXLlxe388Ppk2D6dMtSV2bzG7Nng1ffw3LljUJQmAODKT46ac536cP5ovh5u3t7QkKCuoRRntnjQ+AGJiltrVAST2MLVu2MGHCBA4ePEhFRQWHDh2Sb+p2IJ3ZfzsK2chpJ9144qtHIOsrLT1VX6VSSUxMDDExMRQXF3Po0CGOHz9OeXk5SUlJbNu2jbi4OBISEggLC5PsYsnYyVkolUol/v7++Pv7M2zYMIqKijh37hx5eXnU1taSnJxMcnIyrq6uosHTk4zcq8XOzg4XF5cm0epMJpNo8NTW1lJXV0dDQwMGgwGDwdAkWavVcAoNdSAmxpE77nDA0dERe3t76uvtSEuzxDnJyrr8sbbW4gJ3MYdli2g0lkSYnp4WA6Hxo7u7xetUq7UkqrY+12ot31OpLDmD/v53WLrUkiRVobA8CoLF0MnKgptusiw7ray0lP37g9i1y/K8pOS3dubnW95rCZUK4uNh+HBLGTXKEq2u1b/Z7NkIN99M/ebN1GVmUu7gQFn//pZpH7MZJycn/Pz88PLysknXtObozPFBfdGt2zoT1hswGo2o1WomT57MDz/8wI4dOxg8eHC39kawJTr7/NYRyEZOO1HL60EkRdZXWnqDvj4+Plx//fVMmzaNU6dOcfjwYXJzczl16hSnTp3C29ubhIQEBg0ahKOjY4fuOyAgoEO31x7s7OzE5GlGo5H8/HxycnIoKCigqqpKPH4PDw9CQ0MJDg62qZDUHh4e3HDDDXh4SBOjys7ODldX1yZGoMlkEoMZ1DUKbmA0GsXXjVEoFGg0GrRaR4YNc2DMGHscHBywt7dHo9GgUCgpLbUYGFlZkJcHFwMjNinl5ZbAkIWFlnK1LF0K77zzm7GhUFheA/zjH5bSlNhWt6fVWgIvREdb1h5ZHwcMsCz3vBKCINDQ0CAmYa6qqsLg5SVO89jZ2eHh4YGPjw/Ozs42P3NzKZ05PljdtGzxwvRqseo7ePBg9uzZQ0lJCYcOHWKcHKyoQ+jK89vVIq/JaSdGo1H28ZQQWV9paau+trImp60UFBRw+PBhTpw4gV5vCaGlUqkYMGAACQkJBAUFdcgFVVlZGZ6ente8nY7EYDCQm5tLTk4ORUVFTSKPubu7ExISQlBQEG5ubt3+orI76CsIAnq9XjR+6uvrqa+vF42flrAaQPb2TQ0fa1Gr1eKMRX29xQWsrMxSSkstxfq8stIyI9S41NRYHvV6i6EUHw9Hjzaf98dshiFD4ORJS7AEDw9wcwNHxwZ8fOxxc7PYHYGBTYuLS9td6KwGjVUnazCIS2cWVCoVrq6ueHp64ubm1qMjBXZm/83KymL//v34+/szadKkTtlnV9NY3+PHj/Ptt9/i7OzMsmXLesUNPqnpDuMvyGtyJKW2trZXhGPsKmR9paW36hsQEMDMmTOZPn06J0+e5NChQxQWFnLs2DGOHTuGv78/Q4YMYeDAgdc0u3Pw4EFmzJjRgS2/dtRqNREREURERNDQ0MD58+c5f/48xcXFVFRUUFFRwcmTJ3FxcSEkJITg4GA8PDy6ncFTX1/P999/z/z587vU8FYoFGLi6sZR/ARBwGg0igaPdY2P9dFsNotrfhq7vjXerlqtbmL4+PmpCQpSoVarUalUqFSW51cyBKxrcR577DdXtd/aaXm/uSAEGzdub1f/tRp8DQ0N4qP1mHU6HSaT6bLvKJVKtFotLi4uuLm5odVqe4w72pXozPHBanD3ppuGjfUdMGAAW7dupbKykmPHjskJQjuA7nh+uxK9p/fLyMj0euzt7UlISGDYsGHk5eVx6NAhTp06RWFhIRs2bGDz5s3ExcUxZMgQIiIiut2F/rVib29PZGQkkZGRNDQ0kJ+fz/nz5yksLKS6upozZ85w5swZtFotwcHBBAcH4+3t3S10OHPmDL/73e8YNGhQu6KrdRZWI0WtVl/mBigIAgaDQTQCrIaAXq9Hr9djMBgwm83i6yuhVCpFY6e5ctNNdlRWavnzn90u5g9RoFBYDJxHHxX4978VvPmmjnnz9OJaG6vBUlZWhtlsxmQyYTabxWI0GsViMBgwGo2YTKZW1/kplUocHBxwcnLC0dERZ2fnXmXUdCXWgANO3TwjvVTY2dkxZswYNmzYwIEDB0hISOgW45hM5yIbOe2kNw0YjUNOL1myhOjoaJ544glJ99mb9O0KZH0tKBQK8SJ+xowZnDx5kiNHjlBYWMjJkyc5efIkHh4eDB48mMGDB7d59mvw4MHSNrwDsbe3F2d4DAYD+fn55ObmUlBQQG1tLampqaSmpuLg4CBq5ePj06PdiaTC6qqm0WiaXQdlnQWyGjnW0tigsAZAsBodDQ0Nre5z0iT48599+Mc/IgCBt99WsGyZxcD585+zGDOmmEb5NQFLRK6MjIx2HZtSqRTd8KwuePb29heTrjrIBk0jOnN8sM4W2tK6u2vlUn0HDRpEUlISxcXF5ObmEhIS0jUN6yHY0vnNimzktBNr9I6WCA8Pp6ysjKKiItHtpaqqCj8/P8LCwkhJSemsprZKdnY2sbGx1NfXt6n+Bx98IHGLLFxJX5lrQ9b3chwdHRkxYgQjRoygoKCAI0eOcPLkScrLy9m2bRvbt2+nb9++DB06lJiYmFYv8svKyvDz8+vE1ncMarWasLAwwsLCMBqNFBYWkpubS35+PvX19WJiZ7VaTUBAAAEBAQQGBmJvb9/VTe8RNJ4Fai3UuSAImM3mJjMpLRWz2cy99xpxciri2Wf9+PVXgRMnFDzzTB63314LODXZP0B1dTWurq4olcrLitVdrnGxutHJd8jbRmeND4IgUFJSAiBZoI7uyKX6Ojg40L9/f44dO8aRI0dkI+cascXzm2zktBO9Xn9Fn31/f39++OEH7rjjDgDWrVsn/7naSFv0lbl6ZH1bJyAggBtvvJHExESSk5M5cuQI2dnZ4kW+Vqtl4MCBDB06VMy50picnBzi4uK6oOUdh0qlEmduTCYTFy5c4Pz586LBk5OTQ05ODgqFAm9vbwIDAwkKCsLFxUW+2JUYhUIhuqS1lb/9zZKz5pFHFBfX4AQBzSfUzM3NJTa29QhrMldPZ40PZWVl6PV6VCpVt1go3lk0p+/QoUM5duwYp0+f5sYbb+xVa5Q6Gls8v8nzyBIwf/58Vq5cKb5euXIlCxYsaFLn5MmTjB07Fnd3dxISEti3b5/4WXh4OK+//jrR0dG4urry1ltvceDAAfr164enpydvvvmmWFen07F06VICAwMJDg7mH41igi5evJgnnniCqVOn4uLiwowZM8Qkd4mJiTQ0NODs7IyzszP5V0jasHjxYl599VUAnnvuOe666y7mzp2Li4sLo0aN4ty5c02ObcKECWIixkOHDl2FijIyXYdarWbgwIEsXryYRx99lPHjx+Pi4kJtbS179+7lvffeY8WKFezfv/+yMMI9CTs7OwICAhgxYgSzZs1i+vTp9O/fH3d3dwRBoLi4mOPHj/Pzzz/z888/c/ToUYqKippdcC7TdTzwAFRXN58AVKbnYT0fBwYG9np3wZCQEFxcXNDr9WRlZXV1c2Q6md7d+6+CtvjmT58+nSNHjlBWVkZhYSHp6elMmDBB/Fyv13PTTTexYMECiouLWb58OTNnzqSyUba1n3/+mYMHD5KUlMSf//xnXnvtNXbv3s22bdt46qmnKC4uBmD58uVUVlaSlpbGgQMH+Oyzz/jxxx/F7axZs4a3336b4uJijEYj//73vwHYtGkT9vb21NTUUFNTQ2BgYLt0WLduHY8++ijl5eVER0fz97//HbC4O1x//fU8/vjjlJSU8Mwzz3Drrbe22S2uN0b+6kxkfduPp6cnU6dO5fHHH2f+/PnExsaiVCrJz89nw4YNvP7663z55ZekpKQwbdq0rm6uZCgUCry8vIiPj+e6667jpptuYtiwYQQEBKBUKqmuriY1NZVt27bx/fffs2fPHrKzs9v8378SQ4cORRCEbhl0wBZoi2ehrUVOsjU6Q1+DwSAaOeHh4ZLvrzvRnL4KhYKYmBiAbrNcwFaxxfGh58zb1dWB1B04NpZqk+mKC/lUKhW33HILa9euRafTMXfu3CZ3U/bt24ednR0PP/wwAPPmzePtt99m06ZNzJ07F4Bly5bh5ubGiBEj8Pf35/bbb8fDw0NM5JeSkoK3tzcff/wx2dnZ4ozMgw8+yNdff81NN90EwB133MGAAQMAmDNnDlu3bu0QKRITExk/frzY/r/97W8A/PTTTwwcOJBbb70VgFtuuYUXX3yRvXv3Mnny5Ctut7q6ulctlOxsZH2vHqVSSUxMDDExMdTW1nLq1CmOHTtGQUEBKSkppKSkUFBQwKxZsxg8eDABAQE92n1Lq9USFRVFVFQUBoOBwsJC8vPzyc/Pp6GhoYlbm4eHBwEBAfj7+19TBvvt27f3mpwfXYGsr7R0hr5nz54VvTT8/f0l3Vd3oyV9Y2NjOXToEOnp6Z3fqB6ELY4PPcfISUmBYcOk3cfhw5j79m1T1YULF/KXv/wFnU7HihUrqKioED/Lz88nNDS0Sf2wsLAmLmO+vr7ic0dHxyb+/46OjtTW1lJcXIxOpyM6Olr8zGw2M3bs2Ga34+TkRE1NTZvafyVa2m5OTg5btmxpkj/CYDBQUFDQpu02TlQo0/HI+nYMWq2WkSNHMnLkSC5cuMCxY8c4ceIEtbW1HDhwgAMHDuDj48OgQYMYOHDgFROW2TpqtZqQkBBCQkIwm82UlZWRl5dHYWEh5eXllJWVUVZWxunTpy/mf/HD398ff3//VhfaNyY1NZUHH3yQ7777TrwzK9OxXClim8y1IbW+DQ0NnDlzBoC4uLhe56rWkr6hoaEolUqqqqqorKyUPRquElscH3qOkRMbC4cPS76PtsalGj16NHl5eWg0GgYPHsz27dvFzwIDAzl//nyT+jk5OcyZM6ddzfH29sbBwYFz5861+08r1R3moKAgbrzxRtatW3dV35cjf0mLrG/H4+vrS2JiItOmTePHH3/EYDCQkpJCcXExSUlJbNmyhT59+jBw4EBiY2N7fEQypVKJt7c33t7eDBo0CJ1OR2FhoVgaJyQFiwulv78/AQEBrYaorq2tJSUlRcz/IdPxNL55JdPxSK3viRMnaGhowM3Nrde5qkHL+mo0Gvz9/cW8YLKRc3XY4vjQc4wcJyfoBF9tzcUswm1h3bp1zd5JGTVqFAaDgffff5/77ruPb7/9ltTUVBITE9vVFqVSyd13383y5ct57bXXcHV1JTU1lerqakaMGNHqd729vcUZloCAgHbttzVmzpzJk08+yQ8//MCNN96IXq9nx44djB49uk0Di0aj6bC2yFyOrK90KJVKJk2ahJubG/X19Zw5c4Zjx46Rk5NDZmYmmZmZqFQqYmJiiI+PJzIysldE+nF0dBTz8ZjNZsrLyyksLKSgoIDS0lIqKyuprKwkNTUVlUqFt7c3fn5++Pn54e7u3uvuRnclffr06eom9Gik1Dc3N5fMzEwAhg0b1ivzWbWmb0hICPn5+eTl5Yku/DLtwxbHB/ns0U7acxdx4MCBzf6ZNBoN33//PZ9//jleXl68+uqr/PDDD1d1d+GNN95Aq9USHx+Pp6cnd911lxhBrTW0Wi1//vOfiY+Px93d/YrR1dqKm5sb69ev5+2338bHx4fw8HBWrFjR5u/Ld2mlRdZXWqxREh0cHBg6dCi/+93vePTRR5k8eTJeXl4YjUZOnz7Nl19+yeuvv86PP/5IdnZ2q1njexJKpRIvLy/69+/PtGnTuOWWWxgzZgx9+vTB0dFRzNFz/PhxNm3axHfffceuXbtIT0+nurq6q5vf42kc5VOm45FK36qqKg4cOABY1p/Y4h33jqA1fb29vQFLeG2Zq8MWxweF0I3PrlVVVbi5uVFZWdnEp72+vp6srCwiIiJwcHDo1DbJ/pzSIusrLW3Vtyv/Y7bMxo0bW4xAIwgCBQUFnDx5klOnTjW5aHd1dWXAgAHEx8fj7+/fowMWtIQgCFRWVlJUVMSFCxe4cOECBoNB/DwrK4unnnqKTz75hPHjx+Pn59fm9TwybaO1/itz7Uihr06nIykpidraWry9vZk8eXKvnMWB1vXNzMzk888/x8fHRwz6JNM+usv40JJt0Bw931eig5ETKUqLrK+0yPpKS3x8fIufKRQKAgMDCQwMZPr06Zw7d46TJ09y5swZqqqq2LNnD3v27MHb21ucBe5NifwUCgXu7u64u7sTExMjurYVFRVRVFSETqfjd7/7HQaDQbxr7ezsjJ+fHz4+Pvj4+MhGzzXSWv+VuXY6Wt+6ujq2b99ObW0tLi4ujBs3rtcaONC6vh4eHgBNgkDJtA9bHB9kI6edyNGppEXWV1pkfaWlrdELlUqluE7lhhtuID09nZMnT5KWlkZJSQlbt25l69atBAQE0L9/f/r37y+epHsLVtc2Ly8v+vXrh8lkIi4uDq1WS1FREWVlZWKeL+taBK1Wi4+PD97e3vj4+ODq6torZ8Wulo6KvinTPB2pb1VVFTt27KC2thatVsvEiRN7/ax7a/patTEYDJjNZnmt31Vgi+ODbOS0k4aGhl4/kEiJrK+0yPpKS1ZWVpOQ7m1BpVIRFxdHXFwc9fX1pKSkcPLkSbKysigoKKCgoICkpCQCAwNFg6dxiPbeQllZGZ9//jnPPfcc8fHxGAwGiouLuXDhAsXFxZSXl1NbW0ttbS3Z2dkA2Nvbi7M83t7eeHh4yBc3rXA1/Vem7XSUvvn5+ezduxeDwYCLiwuTJk2SZzFpXd/GQXf0er18HrwKbHF8kI0cGRkZmW6Cg4MDgwcPZvDgwWLI5NOnT5OVlSUm2ty8eTNBQUGiwdNb1rCdP3+e//u//+Pee+/Fx8cHtVotuv+B5Q5taWkpxcXFlJSUUFpaSkNDA7m5ueTm5gIWg9LLywtfX1+8vLzw9PSUIw7K2Awmk4mTJ0+SmpqKIAj4+PgwduxY+YK9DTR24zO2I0qujG0jGzntpKcn9etqZH2lRdZXWqZNm9Zh29JqtQwbNoxhw4ZRW1tLcnIyp0+fJjs7m7y8PPLy8ti0aRPBwcH079+ffv369RqDpznUarWYYBQsF4Tl5eUUFxeLho9erxfX+IBlHZCrq6voFufl5YWrq2uvne3pyP4rcznXou+5c+fYu3ev+DoyMpIhQ4b06jU4l9Kavo2DmMg3Nq4OWxwfZCOnndTU1ODi4tLVzeixyPpKi6yvtOzZs4fx48d3+Ha1Wi0JCQkkJCRQU1MjGjznzp0TZyo2btxIcHCw6PrWm4IWNIednZ2YlDQuLk6M3tZ4pqempkbM03P27Fngt9mexqW33CmXqv/KWLgafRsaGjh9+jRpaWnie+PGjSM4OLijm2fztKavXq8HLDc25KTYV4ctjg+ykdNO5IXb0iLrKy2yvtJSV1cn+T6cnZ0ZPnw4w4cPp7q6muTkZE6dOkVOTo5o8GzevBk/Pz/R4PH19e31C/AbR2+LiooCLKHSS0tLmxSj0dhktgcsmnt6euLp6YmHhwceHh498m5wZ/Tf3kx79DUajWRmZnL69GnxAh0gMTGx19/AaInW9LUumnd0dOz1Y+HVYovjg2zktJPekKG8K5H1lRZZX2nx8vLq1P25uLgwYsQIRowYQXV1NSkpKaSkpJCVlSVeqG/fvh1PT0/R4AkKCrLJk7yLiwujRo3q0JlIBwcHgoKCCAoKAiw3AaqqqpoYPVVVVWIUt5ycnCbtsRo81mJvb99hbesKOrv/9jbaoq/BYCAjI4PU1FTq6+sBS5LtIUOGiK6YMs3Tmr6lpaUAsoF4Ddji+CAnA20nJpOpy3xgV65cyddff82333571dtYvHgxsbGx/OUvf+nAlnUcHalv42PtCO16Am3VV04GenXU1NTg7Ozc1c1Ap9ORlpZGcnIyGRkZTRbauri4iAZPWFiYTa0/6Qp99Xo9ZWVllJWVUV5eTllZGbW1tc3W1Wq1lxk+Dg4ONmNUdpf+21NpTd+amhrOnj1LRkaGOHOj1Wrp168fERERNvU/7Spa03fHjh1s27aNQYMGceutt3Zyy3oG3WV8kJOBSkhNTU2Li3unT5/OjBkzWL58eZP3n3jiCUpLS/n000/btS+FQkFBQYF492bhwoUsXLjw6hpuI7Sm76WEh4fz5ZdfMmrUqCvW7Q3atYX26CvTfnbv3t0tMkI7OjoyaNAgBg0ahF6vJyMjg+TkZNLS0qiurubAgQMcOHAAR0dHoqKiiImJITIyslvPRJhMJjZt2sSsWbM69UaTRqNpEtAALOskysvLm5Tq6moxhLU1mhtYwli7ubnh7u6Om5ubWLrjuoDu0n97KpfqazKZKCgoIDMzk8LCQqz3nF1dXYmLiyM0NFQOLNAOWuu/1llYazRGmfZji+ODbOR0IIsWLeKtt95qYuSYzWbWrFnDxx9/3ObtGAyGbnkClJGRsT00Gg39+vWjX79+GI1GsrKySE5OJiUlhbq6Ok6cOMGJEyews7MjPDycmJgYoqOju10unuPHjzNnzhwOHz7M0KFDu7Qt9vb2lxk+er2eiooKccbHavg0NDRw4cIFLly40GQbWq32MsPHxcVFvqjt4ZhMJoqLi8X1c1aXNAB/f3/69u1LUFCQPHPTgZhMJtHICQ8P79rGyHQq8r+onTg6Orb42ezZs0lNTSU5OVl8b/v27ZhMJqZOnUpOTg433ngjXl5exMXF8csvv4j1wsPD+ec//0lMTAz9+vUjMTERgL59++Ls7MzevXv55JNPuO6668TvbN26lYSEBFxdXYmKimLnzp0A/Oc//yEqKgoXFxcGDhzI9u3b23Rs4eHhvP7660RHR+Pq6spbb73FgQMH6NevH56enrz55pti3bKyMubNm4e3tzeRkZH897//FT9bvHgxjz32GBMnTsTZ2ZkFCxZQWFjItGnTcHNzY+HChZhMJrH+e++9R1RUFN7e3ixdulR0Bfnkk09ITEzkwQcfxNXVlf79+3Ps2DEAfv/735OTk8OUKVNwdnZmzZo1rR5bY+22b99ObGwszz//PJ6enkRERLB58+Ymx7ZgwQJ8fX3p06dPu2fgujOt9V+Za6dfv35d3YRWUalUREVFcfPNN7N8+XJ+97vfMXbsWLy9vTGZTGRmZvLzzz/z1ltv8cEHH7Bt2zby8/Ppxl7N3QaNRoOvry+xsbGMHj2aG264gTlz5pCYmMjIkSOJiYnB399f/A/W1taSl5fHmTNn2Lt3L7/88gvffPMNGzZsYNeuXRw/fpyzZ89SUlJCQ0NDpxxDd++/torBYCAvLw+TycQPP/zA9u3bycjIoL6+HgcHB/r168fMmTOZNGkSISEhsoFzlbTUf7OzszEYDDg5OeHr69vJreo52OL4IM/ktJPWolO5uLhw8803s2rVKl544QUAVq1axbx581AoFNx0003cf//9fP/99xw8eJCbbrqJU6dOiXcDv/vuO3bu3Imrq6vox52ZmSl+npqaKu7r7Nmz3HrrraxcuZLrr7+evLw80Y83MDCQLVu2EBwczEcffcS8efM4d+5cm1xRfv75Zw4ePEhqairjx4/n5ptvZvfu3eTk5DBq1CgWLVqEj48PDz/8MCqVipycHDIyMpg2bRqxsbGMGzcOgLVr17JlyxZ8fHwYOnQoM2fO5LPPPiMwMJCEhATWr1/PrFmzWLt2LStWrCApKQlfX18WL17M3/72N15//XUAtm3bxv3338+///1vnn32Wf7whz+wZcsW/vvf/5KUlNRmd7VLycjIwMXFhQsXLvC///2PJUuWkJmZCcCdd97JgAEDOH/+PFlZWUyZMoXBgwczaNCgdu+nuyFHV5OWxlGQujtKpZLQ0FBCQ0OZPn06JSUlpKWlkZqaSk5ODoWFhRQWFrJjxw5cXFyIjo4mJiaGiIgIeaa5jahUKjEqW2MaGhqorKykoqJCDGFdWVmJwWAQn1+Kvb09rq6uuLi4iMXV1RWtVtthsz+21H+7MyaTibKyMoqKiigsLKSsrAyz2Ux5ebkYoCI4OJiQkBB8fHzk2bsOoqX+e+LECcBykW4r6+O6I7Y4PshGTjtpaGhodSH2okWLWLZsGS+88AINDQ188803bNq0iQMHDmAwGHj44YcBGD16NJMmTWLDhg3cc889ADz++ONtvsuwevVqZs2axcyZMwEIDQ0VP7vxxhvF5/fddx9/+9vfSE9PZ8CAAVfc7rJly3Bzc2PEiBH4+/tz++23iwtoQ0NDSUlJwdPTk2+++YbMzEycnJwYOHAg9957L6tXrxaNnDvuuIPY2FgAJk2ahLOzs3gXYOrUqZw4cYJZs2bx0Ucf8fTTTxMWFgbAY489xrx580QjJz4+nttuuw2ABQsW8MEHH7RJnyvh5ubG448/jkKhYNGiRTzwwANiBKWdO3fyww8/YGdnR2xsLAsWLGDdunU9wsi5Uv+VuTYyMjLo27dvVzfjqrDmlBkzZgx1dXWkp6eTmppKRkYG1dXVHD58mMOHD6NWq4mIiCAyMpKoqCg8PDy6uuk2h729Pb6+vk3Ge0EQqKuro7q6mqqqqiaPdXV1NDQ0iIlNG6NQKHB0dESr1aLVanF2dm7yvD0hc225/3YV1t+ttLSUsrIy8bGxtwJYboJWV1czefJkfHx85NkaCWiu/zY0NIjeNT3hHN6V2OL40LOMnAcfhLw8abYdFATvv3/FajNmzKCqqop9+/ZRUFCAj48Pw4cP56uvviI9Pb2Jn7vRaGTYsGHi6/Yk98rNzaVPnz7Nfvbdd9/x97//XUxuV11dLYZPvBKNT7qOjo74+Pg0eV1bW0txcTEmk6lJe8PCwti4cWO7tgOWxYD33nsv999/P2A5YTSOBNV4O05OTmKs+2vFx8dHPPE7OTkBiCFia2trm4RKNJlMctACmV6Fk5OTGLjAaDSSnZ1NamoqaWlpVFZWkpaWJiYn9Pb2JioqiqioKEJDQ+Uw5VeJQqEQjZNLQwUbDAaqq6ubFKsBZDQaqauro66u7jIDCCwzdpcaPU5OTjg6OopFrVbLd7jbgPV3aDz7VlFRgU6nu6yuvb09fn5++Pn54e/vj1arZePGjfj5+XVBy3svBw8eRK/X4+PjIydQ7YX0rLNRG4yQa+VKORrUajW33347q1atoqCgQLw4DgoKIj4+niNHjrT43facZEJCQpq4r1lpaGhg/vz5fP/990ydOhU7OzsCAgI61KfeehcqNzeXkJAQwGKsXE3UkqCgIF599VVuvvlmwOJO1dY7XFKclIOCgnB3d2+zUWhrdGSOEZnLmTx5clc3ocNRqVRERkYSGRnJDTfcQHFxMenp6aSnp5OTk0NJSQklJSXs3bsXjUZDnz59iIqKIjIyskMj+cXHx5Obm9srferVanWzbm+CINDQ0EBNTY0Y2a3x87q6Osxms2gYtYRKpRKNnX379uHg4CAaQvb29tjb2+Pg4IBGo+nRxpAgCOj1+st0tM7y19bWNnsuVSqVuLm54eXlJRYXF5fLtOqJ40N34lJ99Xo9e/bsAWD8+PE9uu92BrbYfzvFyGloaGDkyJEcP36co0ePMnjw4M7YrSTU1dVdMU74woULueWWW6ipqeHll18GYOTIkRgMBlasWMHixYsB2L9/P2FhYU1czRrj6+tLdnZ2swnA5s+fz+DBg/n555+57rrrxDU5Pj4+4iPA22+/3ezdvWvBzs6O2bNn8/TTT/Phhx+SmZnJRx99xNdff93ubd1777289NJLDBgwgD59+oh5AhoHWGgJqz5XsyanJYKCghg+fDh/+9vf+Mtf/oJGo+HEiRPi4lBbpy39V+bqOXToEGPGjOnqZkiGQqEQ3azGjh1LfX09Z8+eFY2empoaMSEpWP6jVoMnJCTkmmZ51Go1586dExN3ylh+DwcHBxwcHPD29r7sc7PZjE6na3LBrtPpqKurQ6fTodPp0Ov1GI1GqqurOX/+vHjjqqX9WY2exoaPWq0WH5t7bmdnJ5bOxGw2YzAY0Ov14qP1eX19PTqdjvr6+ibPL3UzuxQHB4cmEfGs4cHb0rd7+vjQ1Vyq7+7du6mrq8PDw6NN7voyrWOL/bdTjJw//elPBAYGcvz48c7YnaRcaQAEGDNmDC4uLkRERBAVFQVY7pStX7+eZcuW8fTTTyMIAgkJCa2uMfnb3/7GrFmzaGhoaBKJDSAiIoJvvvmGP/7xj9xxxx0EBATwv//9j759+/Laa68xffp0FAoFDz74IJGRkdd20M3w3nvv8dBDDxEcHIybmxt///vfGT9+fLu3M2/ePMrLy7nhhhvIy8vDz8+Phx56qE1Gzp///GceffRRlixZwooVK7j99tuv5lAuY+XKlTzxxBP06dMHvV7PgAEDmkSWs2Xa0n9lrp7W7pb3RKzGf79+/RAEgcLCQtHgyc3NFUMn7969G7VaTXh4OH379qVv3754e3u3685qZmYmTzzxBCtXrrQ5v/CuorGrWks0vuBPSkpi0KBBogGk0+loaGigoaEBvV6PIAiiUXC17bEaOyqVCpVKhZ2dHQqFAqVSedlj41l9a9AUQRDEApYxraXS2PW5PVjXOFnXN1kfrUGBrpbeNj50No31LS0tZdeuXYAlh6G8BurascX+qxAkjg26YcMGnnjiCb755hv69+/frpmclrKadmU29u6S8bWnIusrLW3Vtyv/Y7bM/v37GTlyZFc3o1tQV1dHZmYm6enpnD179rL1dK6urqLB06dPH3FtXEscOXKEYcOGdYs8OT2V1vqvyWRCr9dTX18vGj5W48c6O9J41qTx864OQa5SqZrMOGk0Guzt7XF0dMTBwUF8tBap1pXJ44O0WPU1m8189tlnZGdnExkZycKFC2VXtQ6gu/TflmyD5pB0JqeoqIj77ruP77777oonMEAcNK1UVVVJ2byroi3HIXP1yPpKi6yvtAwcOLCrm9BtcHJyIj4+nvj4eARB4MKFC2RmZpKZmcm5c+eoqqri6NGjHD16FIVCQUBAAH369KFv377X7Nomc3W01n/t7OzEQAXtQRAEzGYzRqOxyQxL40drneYeBUEQL1Ctd+MVCkWT96wzQtZifU+tVoufdQfk8UFarPru2LGD7OxsNBoNN9xwg2zgdBC22H8lO4sIgsDixYtZsmQJCQkJZGdnX/E7r7zyCs8///xl7yclJaHVapkyZQoHDhxAp9OJyeus+QSsd5ut0+guLi7U1dVhMpmws7PDyclJnGq7tK6zszP19fUYjUaUSiXOzs6igWVvb49SqRSjpwiCgFqtbrauRqNBpVJRV1cHWDJaW+9kKRQKXF1dxfZeWtfJyQmj0YherxfrVlVVifvTaDRiRLLGdcESDrm6uhqz2XxZXUdHR8xms2g8urq6UlNTg9lsRqVS4eDgIN5hvbRuezRsre6lGramt8lkwtnZWazbWEOlUomLi0uLGjant1XD1vS2athWvdujYWt1O6rPtkdvg8GAl5dXi/3bqmFtba24L2vUvJCQELy9vTl69CgACQkJ5Ofnk5+fj52dHdOmTSMpKQmTyURgYCCBgYEcOnQIgCFDhlBSUsL58+cBSxTCbdu2odfr8fPzIzw8nP379wOWgbSqqkocM6ZPny76Vnt7exMdHS0uJu3fvz/19fVijiPrGFFTU4OHhwf9+/cXXRZiY2Mxm81iVLCJEydy7Ngx8W7Q0KFDxcS5UVFRqFQqMfTouHHjOHPmDGVlZWi1WkaNGsWWLVsAxFmIU6dOkZ2dzfz588nIyKC4uBgHBwcmTJjApk2bAEsUQnd3d9F1d8SIEWJOGrVazZQpU9i0aROCIBAcHIyvr68YrGTYsGEUFhaSl5eHUqlk+vTpbNmyBaPRSEBAAMHBwRw8eBCAwYMHU1ZWJmb5njFjBtu3b6ehoUFMcrtv3z7AsqC/pqaGrKwsAKZNm8aePXuoq6vDy8uL2NhYdu/eDVjyTOj1ejIyMgDLQtRDhw5RXV2Nu7s7AwcO5NdffwUgJiYG+C2/14QJE7Czs8PX15ewsDB8fHz49ttvKSgoQKFQUFFRIX63b9++2NnZ4ezsTEREBDNnzhR/83PnzuHv78/JkycBGDVqFGfPnuXChQvY29szadIksc+Ghobi6ekpJhEePnw4ubm5FBQUoFKpmDp1Kps3b8ZsNhMUFIS/vz+HDx8GYOjQoVy4cIHc3FwUCgWJiYls3boVg8GAv78/oaGhHDhwALCEpq2oqODcuXMAJCYm8uuvv1JfX4+Pjw+RkZHs3bsXgAEDBlBXVydGv5w6dSr79u2jtrYWT09P+vXrJ/bZuLg4jEYj6enpgCUc/5EjR8Q7mYMHD2bHjh0AREdHo1QqxfVQ48aN4/Tp05SXl+Ps7MyIESPYunWrqK+DgwOnT58GLC7WaWlpHDp0iH79+jF27FgxQXJ4eDiurq5irpGRI0eSnZ1NUVERGo2GyZMnX9MYYf0vdPQYYe2z3WmMWL9+PeHh4YwePVoeI2h+jDhx4gQVFRW4uLiQkJDAtm3bAIiMjESj0XDmzBkAxo4dS0pKCqWlpTg5OTFmzBg+++wzNBoNR48eRa1WExkZycGDB+UxooPGiG+//RY/Pz+cnJw6dYy49DrC2v620G53teeee65ZQ6QxBw8eZM+ePaxZs4Zff/0VOzs7srOziYiIaNVdrbmZnJCQkG7lrlZZWdmhEYNkmiLrKy1t1Vd2V7s6Nm7cyIwZM7q6GTZHdXU1Z8+eJTMzs1nXNgcHBwRB4KmnnmLz5s1MnTpVvjsrAXL/lRZZX2lZtWoV2dnZ6PV6hg0bxk033dTVTepRdJf+K6m72tKlS5k3b16rdcLDw3nxxRfZt28f9vb2TT5LSEhg4cKFfPrpp5d9zxqxpTsjX/BJi6yvtMj6Sov1zqRM+3BxcRHz8lhd27KyssjKyiI7O5v6+npqamoYP348v/zyC0ePHiUiIoLw8HAiIiLw9PSUjZ4OQO6/0iLrKx1lZWWcPHkSe3t7IiIiuP7667u6ST0OW+y/7TZyrFmxr8Q777zDiy++KL7Oz89nxowZrFmzplssXJKRkZGR6X4oFAoxieKoUaMwm80UFBSQlZVFYGAgOp2O2tpaTp06xalTpwCLW6jV6AkLC8PDw0M2emRkegkXLlzg888/R6fTERYWxrx58+Q1fTKAhGtyLs39Yo3o1LdvX5vOOltfX9/tZ5tsGVlfaZH1lZbU1FTCw8O7uhk9CqVSSVBQEE5OTmzYsIHHH3+curo6cabn/PnzVFVVcfz4cXEdg4uLC2FhYWIeMl9fX9noaQNy/5UWWd+OJz8/ny+++IK6ujqMRiOLFi2Sz3ESYYv9VzZ1ZWRkZGS6PVlZWbz88svMmTOHoUOHEhoaysSJEzEYDJw/f56srCxycnLIzc2lurq6yUyPo6MjoaGhouETEBAg582QkbFxTp06xffff4/BYCAoKIihQ4fKKShkmtBpRk54eHiXx8rvCFxcXLq6CT0aWV9pkfWVlgkTJnR1E3odarWaPn360KdPHwCMRiN5eXmcO3eOc+fOcf78eXQ6HampqWIUJ41GQ0hICGFhYYSEhBAUFIRGo+nKw+gWyP1XWmR9Owaz2czWrVvFCGORkZHMnTtXTBgrIw222H/lmZx2UldXJ98pkBBZX2mR9ZWWEydOyGsOuxiVSiXO2IAlLH1hYaFo9OTk5KDT6cScPWBxifPz8yMkJEQsbm5uvc7FTe6/0iLre+2Ul5fz7bffiqGvx44dy9SpU1Eqld0mWWVPxRb7b++brzeZYPt2WL3a8mgytfPrrdcPDw8XY8tbWbJkCc8991z72mlDfPLJJwwePBgXFxf69OnDBx980GLdl19+GWdnZ7HY29sTHx8vft5Y308++QSFQtEkgAXAU089hUKh4Msvv2xS78MPPxTrFBYW9roLlLZwpf4rc21UVFR0dRNkLsHOzo6goCDGjBnD/Pnz+dOf/sSDDz7IDTfcwIABA3BzcxODGxw4cIBvvvmGt956izfeeIOvvvqKvXv3kpubi9Fo7OpDkRy5/0qLrO/VIwgCn332GW+//TY5OTnY29tz2223MX36dNH1VNZXWmxR3941k7NuHSxbBrm5v70XHAxvvw2zZ7dpE90lc3J3oqGhgQ8++ICEhARSU1OZMmUK/fr1a3Zq86mnnuKpp54SX8+ePZv+/fuLry/VNzIyklWrVvHXv/4VsAx0a9asoW/fvk3qeXh48PLLL/O73/0OtVrdkYfXo5D7r7TI7oDS4ejoSHR0NI6Ojte0ncbR20aMGAFY8i6cP39eLAUFBVRXV3PmzBkx+aBKpSIwMJDg4GCCgoIICgrqcbM9cv+VFlnfq6O4uJj33ntPfO3p6cldd92Fu7t7k3qyvtJii/r2npmcdevgttuaGjgAeXmW99eta9NmnJycrqkZn3zyCYmJidx3331iRt+8vDwefvhh3NzcGDlyJPn5+YDF73T27Nn4+vri6enJ3LlzKSsrA2D79u0EBQWJr9euXUtMTIyYud6KTqfD1dVVzLILkJSUxIABA67pOBrzwAMPMGrUKFQqFf3792fatGliVuXWqKio4Oeff2bhwoXie5fq27dvX1xcXMSMznv27CEkJOSyCH0jRowgJCSEjz/+uAOOqOdyrf1XpnUSEhK6ugk9lri4OE6ePElcXFyHb9vV1ZX+/ftz3XXXcd999/Hkk09yzz33MG3aNGJiYnBycsJoNJKTk8OePXtYu3Ytb731Fv/6179YtWoVO3bsICMj47Lx19aQ+6+0yPq2j/r6epKSkpp4h9jb2/Pggw9eZuCArK/U2KK+vcPIMZksMzjNBT6wvvfYY21yXauurr7m5mzbto0bbriBsrIygoODGTt2LBMnTqS0tJTw8HBee+01se7s2bPFUKnV1dX8/e9/B2DSpEnMmTOHpUuXUlxczCOPPMInn3xy2V1OR0dHZs6cydq1a8X3vvrqK+64445m2zZz5kzc3d2bLa+++uoVj81kMnHgwIEmszMt8fXXXzNgwABiY2PF95rTd+HChaxatQqwZDRubBQ15tlnn+Xll1/GYDBccd+9lY7ovzIts23btq5uQo+ms/RVq9WEhYUxbtw45s+fzx//+EceeeQRbrnlFhISEggMDESpVFJbW0taWhrbtm3jiy++4B//+AfvvPMO33zzDfv27eP8+fM2NR7J/VdaZH3bhtFoZO/evbzzzjvs2rULk8lEdHQ0y5Yt48knn2zRW0PWV1psUd/e4a62c+flMziNEQQ4f95Sb9Kka97d9OnTm7gF6XQ6nnzySfF1fHw8t956KwCzZs0iPT2d22+/HYBbbrmF//73v4BlMeyiRYvE7z3++OM8/fTT4utXX32VQYMGMWnSJO68805Gjx7dbHvuuOMOXnrpJZYvX47RaOTbb79l9+7dzdZdv379VR61hb/+9a8EBQUxY8aMK9ZduXJliwZLY+644w5GjBjByy+/zPfff8+LL77IypUrL6s3ffp0goKC+OSTT7jpppuuqv0yMjLdk6NHj3LTTTexf/9+hgwZ0qn7VigUeHl54eXlxeDBgwHLhVhhYSF5eXliKS0tpaysTMy+DpZx3NfXl4CAALH4+fnJ0dxkZC7BYDBw7Ngxdu3aRWVlJWBJQG+dUe1JrqEynUPvMHIKCjqsXluSTG3evJlRo0aJr5csWdLkc19fX/G5o6MjPj4+TV7X1tYClpPo8uXL+fbbbykvL0cQBLy9vcW6Tk5OzJs3j5deeolffvmlxfZcd9113H333WRnZ5OamkpwcDDR0dFXPI728sEHH7Bu3Tp27959xcEoNzeXXbt2iTM0VprT18/Pj9jYWJ566ikSEhLw8PBocbvPPvssDzzwANddd93VHUQPR06SJi2RkZFd3YQeiyAIGAyGbpOKQKVSERwc3MR1VqfTkZ+f38TwqampobCwkMLCQo4ePQr8ZjQ1Nnz8/f2veb3RtSL3X2mR9W2e+vp6Dh48yL59+8TrH1dXVyZNmsTgwYPbnNNK1ldabFHf3mHkBAR0WL3OTCC3cuVKdu7cyd69ewkMDGTjxo088MAD4ufp6em8//77zJ07lz/84Q989dVXzW7H3t6eWbNmsXbtWlJSUlp0VQO4/vrr2blzZ7OfXRo0oDFr1qzhpZdeYufOnU0MsZZYvXo1kyZNIuASzVvSd8GCBdxzzz1iRLWWSExMJCAggE8//fSKbeiNyAkQpUW+O9+7cXR0pG/fvmJgFEEQqKqqoqCgoEmprq6mpKSEkpISccYHLAFUrAaPv78/fn5+uLq6dtodbLn/Sousb1MKCws5ePAgJ06cEN063d3dGTNmDEOGDGl3ECFZX2mxRX17h5EzfrwlilpeXvPrchQKy+fjx19xUzqdrtN+6Orqauzt7XF3d6ekpIR//etf4mdms5m7776bp59+miVLljBo0CC++uor0e0tPDyc5557jsWLFwMWl6+nn36anJycVoMCbNiwod3t3LRpE4888ghJSUmEh4e36TsrV67kscceu+z9lvSdO3cufn5+TGqDO+Gzzz7LggUL2tSO3kZn9t/eyJkzZwgJCenqZsh0ExQKBW5ubri5uTVZe1hTU0NBQQGFhYWi4VNeXi4Wa0Q3AAcHB3x9fcWIcH5+fvj6+koyKyv3X2mR9bXM2iQnJ3P48GFyGy0j8PPzY+zYsfTv3/+qo4DK+kqLLerbO4wcOztLmOjbbrMYNI0NHesdsrfestTrRtx111389NNP+Pr6EhISwu9//3vS09MB+Ne//oWdnR3Lli1DqVTy8ccfM3v2bCZNmoSHhwelpaVNXOamT5/OnXfe2SQzeEfxyiuvUF5ezpgxY8T3Fi1aJEZEcXZ2ZsOGDYy/aESeOXOG1NRUZrcxbDdYXPPa6oI2Y8YMoqOjL8tXJCMjI9MdcHZ2JioqiqioKPE9nU7XxOi5cOECxcXF1NfXk5OTIyY/tOLu7t7E6PHz88PT01MOEy/T7TCZTGRmZnL8+HFSU1PFnFN2dnbExcUxfPhwQkND5TU3Mh2OQuguDs7NUFVVhZubG5WVlbi6uorv19fXk5WVRUREBA4ODm3fYHN5ckJCLAZOGy+4TSZTtz+JWKOSrF69uqub0m5sQV9bpq36XvV/rJdTU1ODs7NzVzejR6LT6Th16hQDBgzo8rUrnYXJZKKkpISioiKKioq4cOECRUVFVFVVNVtfqVTi6emJj48PPj4+eHt7i49tcf2R+6+09CZ99Xo9mZmZJCcnk5aWRn19vfiZj48PgwYNYvDgwR2qR2/StyvoLvq2ZBs0R++YybEyezbMmmWJolZQYFmDM358u2Zw6uvr0Wq1Ejby2hk9enSLkda6O7agry0j6ystKSkpNplLwBZwdHREoVD0GgMHLHe6rbM1jdHpdE2MHutzvV4vrvVJTk4W61vd5i41fry8vERdQe6/UtPT9S0rKyMzM5OMjAzOnj3bJHy6s7MzAwYMYNCgQfj7+0sya9PT9e1qbFHf3mXkgMWguYYw0dZpVhlpkPWVFllfaSktLe3qJvRYzp07x1//+lc+/PBDwsLCuro5XYqjoyPh4eFN1kBagxyUlJRQXFxMcXGx+Lyuro6KigoqKipEl2crDg4OeHl54enpSWZmJhqNRnzdmwzKzqCnjQ81NTXk5OSQlZVFZmammJzciru7O3FxccTFxREcHCx54Juepm93wxb17X1GzjUiR6eSFllfaZH1lRYnJ6eubkKPpbS0lI0bN1JaWtrrjZzmaBzkwBrdzUptba1o8DQ2gqqqqqivrxfDXZ87d466ujrxe05OTnh6eopGj4eHBx4eHri7u+Ps7CyvoWgntjw+mM1mSktLycvLIycnh3Pnzl120atUKgkNDaVv375ERUXh5+fXqX3ElvW1BWxRX9nIaSfdwR+xJyPrKy2yvtLSOPiGjEx3QavVotVqLzMODQaDmLy0tLSUkpISKioqKC0tpbq6mrq6Ourq6ppEwbKiUqlwc3PD3d0dd3d30fixFq1WKxtBl2Ar44PVoMnPz6egoID8/HwKCwvR6/VN6ikUCnx9fQkLC6Nv376Eh4d3aS42W9HXVrFFfWUjp51YFzzJSIOsr7TI+kpLUlISM2bM6OpmyMi0CbVa3WTNz8aNG7nlllsAy8LxxgZQWVkZ5eXlVFRUUFVVhdFopLS0tEUXFrVajaura5Pi4uLS5HVvM4S62/hgNpspKysT13FZi3V916VoNBr8/f0JCQkhLCyMkJCQbuXS2N307WnYor6ykSMjIyMjIyPTBOsFrb+//2Wfmc1mqqqqqKioEA0faykvL6e6uhqDwdCqEQQW96bGho+zs7M462R9bn1sb2JIGcs6rfr6eiorK8Vi/Y1KSkooKyvDZDI1+121Wk1AQACBgYHio5eXl+zyLGNTyEZOO+nKqdjegKyvtMj6SktERERXN6HH4ufnx/33339ZpDGZjgZJhqYAAD0+SURBVKOt/VepVIpuac0lgDaZTFRWVlJVVdWkVFdXi89ramowm83ixfeV0Gg0TQwfJycnHB0dcXBwwNHRsUmxvqfRaLrVTFFHjQ9msxmdTkddXR21tbWXlcZGTUNDQ6vbUqvVeHt7Nwk3bo28Z2sGjTz+Sost6isbOe3E1v70toasr7TI+kqLvOZJOoKCgnj22WcJDAzs6qb0WDqq/9rZ2eHp6Ymnp2eLdUwmEzU1NU0Mn9raWmpqasSLdetzo9HYxH2urSiVStHo0Wg0bSpqtRo7OzuxqFSqJq+t7ymVyjYbUGazGaPRSENDA4WFhRiNRkwmk1iMRqN4jA0NDa0W6zqp9qQ41Gq1YlAKNzc3PDw8RMPG1dW1WxmC14I8/kqLLeorGzntRKfTodFoWvw8PDycL7/8klGjRonvLVmyBH9/f5577jnJ25eamsof/vAH9u3bh0KhYMaMGbz77rt4eHg0W//GG2/k4MGDNDQ0EBsby1tvvdVijh2FQkHfvn3JyMgQ30tPTyc6OpoZM2bwyy+/iPVGjx7Nnj17xHrXXXcd8+bNY/Hixa22/0r6ylwbsr7ScvLkSfkiXCKqq6v54osvePDBB3Fxcenq5vRIOrP/2tnZiRfdrSEIAnq9XjR4rI91dXXU19ej0+maFOt7RqMRs9ksGkzdgYyMDCIjIztse46OjqJ7n7U4OTnh6uqKu7u7qG9vcfWTx19psUV9ZSOnh1FZWcntt9/OypUrUalU3HPPPSxfvpyPPvqo2fr//Oc/iYmJQaVS8eOPP3LrrbdSUFDQ4p0dpVLJ/v37GTlyJAArV64kKirqsnopKSls2rSJxMTEjjs4GRmZXkt6ejp//vOfmTZtGkOHDu3q5sh0EgqFAnt7e+zt7fHy8mrz9wwGg2j4NDQ0oNfrmy2XfmadZbl0tqW599qCIAjiDJCDgwMuLi7NzhKpVCrxODUajfj80uLk5CQWu3YkMpeR6Y30KiMnPR2qqy9/38UFmrlOb5aOyBb/7rvv8uabb1JdXc3111/Pv//9b1xdXdu1DUEQmjVERowYwYgRI8TX9913H0888USL2+nfv7+4PaVSSVFREXV1dS0e5/z581m5cqVo5KxevZr58+ezf//+JvUef/xxnn/++XYbOR2hr0zLyPpKS+MZXBkZW6Mn9V+1Wi1GeOsuVFZWytEtJaQn9d/uiC3q22sc9NPTIToahg27vERHWz5vC82FVWwPGzdu5NVXX+Wnn34iOzub2traFo2QoqIi7rvvPsLCwhg6dCgvvPACe/fuZd26ddx1111t2t+ePXtEQ6YlZs6ciYODAzNnzuTRRx9t9UL49ttv59tvv8VkMnHw4EG8vb2bXYy2ePFi8vLy2Lx5c5vaaeVa9ZVpHVlfaTl79mxXN0FG5qqR+6+0yPpKi6yvtNiivr1mJsc6g/PFFxAX99v7ycmwaFHzMzzNYTAYrlhn+vTpTaaRdTodTz75JABr1qxhyZIlxF1sxMsvv8ywYcP473//e9l29u3bx/XXX88bb7xBdnY2q1at4umnn6ZPnz4888wzV2zHsWPHeOedd/j1119brbd+/Xr0ej0//vgjNTU1rdb18vJi0KBBJCUlsWHDBhYsWNBsPbVazVNPPcXzzz/P9OnTr9hWK23RV+bqkfWVlgsXLnR1E2Rkrhq5/0qLrK+0yPpKiy3q22tmcqzExcHQob+VxgZPW2hLdKrNmzc3yRtwzz33iJ/l5+cTGhoqvg4LCxNDPl7KjTfeyIULF/j973/Pe++9x7Rp09i8eTMvvfQS33//fattyMrK4qabbuKjjz664kwOWMJzzpkzh9dff53k5ORW6y5cuJDPP/+cdevWcfvtt7dY75577iE3N5ekpKQr7t+KHP1LWmR9pUUO0S0d1lC3vWURdVcg919pkfWVFllfabFFfeUrnnZyrVF9AgMDycnJEV/n5OTg5OTUrJ/uF198QXp6OosXL2bQoEG8/PLLeHl5MXnyZIKDg1vcR2FhIdOnT+eZZ54Rs1e3FaPRSFZWVqt1Zs2axQ8//MCAAQPw8fFpsZ5arebJJ5/k+eefb/P+5ahJ0iLrKy2TJk3q6ib0WOLj4ykuLiY+Pr6rm9JjkfuvtMj6Sousr7TYor6ykdNO2pK0rDXmzp3Lhx9+SEpKCrW1tTz99NPMmzev2bp33nknr7/+Otdffz0PPvggW7ZsoaKigjNnzjB//vwW2zdjxgzuuusu7r///lbbcu7cOdavX099fT0NDQ38+9//Jjc3l2HDhrX6PScnJzZv3sy77757xeO95557yMnJ4eDBg1esa22/jHTI+krLxo0bu7oJPRpZX2mR9ZUWWV9pkfWVFlvUt9cZOcnJcOTIb+UKnlkdzvXXX88f//hHrr/+esLCwrC3t+f1119vtu7VhIf87rvvOHHiBP/85z9xdnYWi5UlS5awZMkS8fVLL72Er68v/v7+rFmzhh9//LFNGcVHjhxJ3759r1hPo9Hw5JNPtiuBm4yMjMylnDx5kkWLFnHy5MmuboqMjIyMjA2gENqTNreTqaqqws3NjcrKyiZhIOvr68nKyiIiIgIHB4c2bcsaXa0l0tLaFkZap9Ph6OjYpn3KtB9ZX2lpq75X8x+TgeTkZDGoiEzHcuTIEYYNG8bhw4flPDkSIfdfaZH1lRZZX2npLvq2ZBs0R6+JrhYVZTFkrjVPjkrVayTrEmR9pUXWV1o8PT27ugkyMleN3H+lRdZXWmR9pcUW9e1V7mpRUU0jq1lLWw0cgLq6OukaKCPrKzGyvtJy7Nixrm6CjMxVI/dfaZH1lRZZX2mxRX17lZEjIyMjIyMjIyMjI9PzkdzI+emnnxg5ciSOjo54e3sze/ZsqXcpKVqttqub0KOR9ZUWWV9pGT58eFc3occSFRXF999/T1R7pt5l2oXcf6VF1ldaZH2lxRb1ldTI+eabb7jzzju55557OH78OLt372bBggVS7lJy9Hp9VzehRyPrKy2yvtKSm5vb1U3osbi4uBAeHi7nepIQuf9Ki6yvtMj6Sost6iuZkWM0Glm2bBmvvfYaS5YsITo6mpiYGG677TapdtkpGAyGrm5Cj0bWV1pkfaWloKCgq5vQY8nLy+Oll14iLy+vq5vSY5H7r7TI+kqLrK+02KK+khk5R44cIS8vD6VSyZAhQwgICOD666/n9OnTUu2yU1AoFF3dhB6NrK+0yPpKixy9TjqKior46quvKCoq6uqm9Fjk/istsr7SIusrLbaor2RGztmzZwF47rnn+Otf/8r69evx8PBg4sSJLSaGbGhooKqqqknpblwpJrfMtSHrKy2yvtIyderUrm6CjMxVI/dfaZH1lRZZX2mxRX3bbZY999xzPP/8863WOXjwIGazGYCnn36aOXPmAPDxxx8THBzM2rVreeCBBy773iuvvNLstpOSktBqtUyZMoUDBw6g0+nw9vbGZDJRWVkJICYsrK+vByz+23V1dZhMJuzs7HBycqL6YpKcS+s6OztTX1+P0WhEqVTi7OwsGlj29vYolUp0Oh0AgiCgVqubravRaFCpVGKYXq1Wi16vx2AwoFAocHV1Fdt7aV0nJyeMRiN6vV6sW1VVJe5Po9FQW1t7WV0ANzc3qqurMZvNl9V1dHTEbDbT0NAAWC5ya2pqMJvNqFQqHBwcqKmpabZuezRsre6lGramt8lkwtnZWazbWEOlUomLi0uLGjant1XD1vS2athWvdujYWt1O6rPtkdvg8GAl5dXi/3bqmFtba24r40bNwIQEhKCt7c3R48eBSAhIYH8/Hzy8/Oxs7Nj2rRpJCUlYTKZCAwMJDAwkEOHDgEwZMgQSkpKOH/+PAAzZsxg27Zt6PV6/Pz8CA8PZ//+/QAMHDiQqqoqsrOzAZg+fTq7d++mrq4Ob29voqOj2bNnDwD9+/envr6ezMxMAHGMqKmpwcPDg/79+7Nr1y4AYmNjMZvNpKWlATBx4kSOHTsmJhQbOnQo27dvByyL3FUqFcnJyQCMGzeOM2fOUFZWhlarZdSoUWzZsgWAPn364OTkxKlTpzh37hzz5s0jIyOD4uJiHBwcmDBhAps2bQIgLCwMd3d3jh8/DsCIESPIycmhsLAQtVrNlClT2LRpE4IgEBwcjK+vL0eOHAFg2LBhFBYWijPk06dPZ8uWLRiNRgICAggODubgwYMADB48mLKyMnJyckS9t2/fTkNDA76+vvTp04d9+/YBEB8fT01NDVlZWQBMmzaNPXv2UFdXh5eXF7GxsezevRuAfv36odfrycjIAGDy5MkcOnSI6upq3N3dGThwIL/++isAMTExAKSmpgIwYcIETpw4QUVFBS4uLiQkJLBt2zYAIiMj0Wg0nDlzBoCxY8eSkpJCaWkpTk5OjBkzRvzNz507h7+/PydPngRg1KhRnD17lgsXLmBvb8+kSZPEPhsaGoqnp6cY+nT48OHk5uZSUFCASqVi6tSpbN68GbPZTFBQEP7+/hw+fBiAoUOHcuHCBXJzc1EoFCQmJrJ161YMBgP+/v6EhoZy4MABAAYNGkRFRQXnzp0DIDExkV9//ZX6+np8fHyIjIxk7969AAwYMIC6ujrxRuDUqVPZt28ftbW1eHp60q9fP7HPxsXFYTQaSU9PB2DSpEkcOXJETIY3ePBgduzYAUB0dDRKpZKUlBSxz54+fZry8nKcnZ0ZMWIEW7duBaBv3744ODiInhVjxowhLS2Nw4cPExcXx9ixY9m8eTMA4eHhuLq6cuLECQBGjhxJdnY2RUVFaDQaJk+eLI8RtG2M+OmnnwgLC2P06NHyGEHHjxEfffQRYWFhRERE4OzsLI8RHTxGfP/99/j4+ODk5NSlY4S1/W1CaCfFxcVCcnJyq0Wn0wlbt24VAGHnzp1Nvj9ixAjhqaeeanbb9fX1QmVlpVjOnz8vAEJlZWWTejqdTjhz5oyg0+na2/xrpqKiotXPw8LCBBcXF6Gurk58r7KyUnBwcBBiYmKkbp7Ie++9JwwaNEiws7MTXnnllVbrFhcXC3PnzhU8PDyEkJAQ4Ysvvmix7t13393s7zp69GgBEAoKCsR6SqVSOHPmjFhn9erVwsSJE1tty5X0lbk22qpvV/7HbJlffvmlq5vQYzl8+LAACIcPH+7qpvRY5P4rLbK+0iLrKy3dRd/KyspmbYPmaPdMjre3N97e3lesN2zYMOzt7UlNTWXcuHGAZdFzdnY2YWFhzX7H3t4ee3v79japU9FoNFes4+/vzw8//MAdd9wBwLp16wgJCZG6aU0IDAzkxRdf5H//+98V6y5btgxHR0cKCgrIyMhgypQpDBkyhH79+jVbPyoqipUrV4q/a1ZWFqWlpZfVc3Nz44UXXmDVqlVtbndb9JW5emR9pSUoKKirm9Bj8fLyYvbs2Xh5eXV1U3oscv+VFllfaZH1lRZb1FeyNTmurq4sWbKEZ599lk2bNpGamsqDDz4IwNy5c6Xabaukp8ORI5eXi7N8baItC6/mz5/PypUrxdcrV668LHT2yZMnGTt2LO7u7iQkJIjTwu1FEIRm37/llluYOXNmm9Zg/PLLL/zlL3/B3t6e/v37c8sttzRp/6XMnj2bH374QYzUtWrVKubPn39Zvd///vds2LCh2anF7OxsHBwceP/99/H19SUkJITt27fz+eefExAQQGhoqDjFKtNx2OLCQVvC39+/q5vQYwkLC+PDDz9s8SaZzLUj919pkfWVFllfabFFfSXNk/Paa68xb9487rzzToYPH865c+fYunUrHh4eUu62WdLTIToahg27vERHt93Qsa7paI3p06dz5MgRysrKKCwsJD09nQkTJoif6/V6brrpJhYsWEBxcTHLly9n5syZ4lqTS3n//fcZPHgwoaGh3Hvvvaxfv55ff/2Vhx9+WPRVvFYaG0uCILQaBc/d3Z2RI0eKPparV69uNv+Rp6cnDz30EC+88EKz29Hr9WRnZ5OXl8eyZctYtGgRJ06c4Ny5c/zpT3/iscceu7aDkrmMtvRfmavH6qst0/HodDq++eYbcf2YTMcj919pkfWVFllfabFFfSU1ctRqNf/6178oKiqiqqqKzZs3079/fyl32SIX12TzxRdw+PBv5Ysvmn7eEahUKm655RbWrl3Ll19+ydy5c1Eqf5N637592NnZ8fDDD6NWq5k3bx5RUVHiwsPGNDQ0kJ2dzfr16zl8+DCjR49mxYoV/Otf/2L8+PEdkoE2MTGRf/zjH+h0Ok6ePMm6deuueDG8YMECVq5cybFjx3B0dCQ6OrrZek888QQ//fRTs7M5giDw9NNPo1armTNnDnl5eTz++ONoNBrmzJnD6dOnxQAWMjIyvZvk5GSWLFkiLvSWkZGRkZFpjV7nuxIXB0OHXv33nZyc2lRv4cKF/OUvf0Gn07FixQoqKirEz/Lz8wkNDW1SPywsjPz8/Mu2Y29vz6233sqLL75IWVkZ06ZN49NPP0Wr1fL1119z+vTpazYc33nnHR566CHCwsIICwtj/vz5YgSwlpg5cyaPPvooHh4eLFy4sMV6Xl5ePPTQQ7z44ovMnDnzsmOzutM5OjoCiLo4OjpiMBjQ6/ViZDGZa6et/Vfm6hh6LYOLjEwXI/dfaZH1lRZZX2mxRX0lncnpiRiNxjbVGz16NHl5edTU1DB48OAmnwUGBophMq3k5OQQGBh42XYaGhp46qmnmDRpEvPnz2f//v3ExcURFhbG7t27LzOWrgYfHx/Wrl3LhQsXOHjwIOXl5SQkJLT6HQcHB2bMmMF//vMfMcBCS/zhD39g/fr1YpjI1mirvjJXh6yvtFy4cKGrmyAjc9XI/VdaZH2lRdZXWmxR3143k3Ot6PV6cdbhSqxbt66Jm5qVUaNGYTAYeP/997nvvvv49ttvSU1NJTEx8bK6Go2GpKQkcTu33nprm/ZtNBoxGo2YTCaMRiP19fWo1Wrs7Owuq5uZmYmnpyfOzs5888037Ny5kxUrVlxxHy+88AL33HMPAQEBrdbz8vLiwQcf5J133iE+Pr7Vuu3RV6b9yPpKS25ubpe55MrIXCty/5UWWV9pkfWVFlvUt9fN5CQnN42sJqV798CBAxkwYMBl72s0Gr7//ns+//xzvLy8ePXVV/nhhx9wc3O7rK5CoWjWULoSL774Io6OjnzxxRc888wzODo68vnnnwOwc+dOnJ2dxbr79+8nNjYWd3d33n//fX766ac2uTUFBwc3CajQGn/4wx/EZJoyMj0VhULR1U3osSgUCtRqtayxhMjaSousr7TI+kqLLeqrEFqKQdwNsGZstWYbtlJfX09WVhYRERFtXq9hja7WEmlpEBV1rS2WkekZXM1/TEZGRkZGRkZGSlqyDZqj18zkREVZDJnGkdWspT0GTlVVlbQN7eXI+kqLrK+0bN26taub0KOR9ZUWWV9pkfWVFllfabFFfXvVmpyOmKnpxhNfPQJZX2mR9ZUWa4JcmY4nOTmZ+++/nx9//JG4uLiubk6PRO6/0iLrKy2yvtJii/r2mpmcjkKtVnd1E3o0sr7SIusrLbaYEdpW0Ol0ZGZmyslAJUTuv9Ii6ystsr7SYov6ykZOO9FoNF3dhB6NrK+0yPpKS0eEdJeR6Srk/istsr7SIusrLbaor2zktJPa2tqubkKPRtZXWmR9peXAgQNd3QQZmatG7r/SIusrLbK+0mKL+spGjoyMjIyMjIyMjIxMj0I2ctpJW/LHyFw9sr7SIusrLYMGDerqJvRYIiIiWLFiBREREV3dlB6L3H+lRdZXWmR9pcUW9ZWNnHZiNBq7ugk9GllfaZH1lZaKioqubkKPxcPDg/Hjx+Ph4dHVTemxyP1XWmR9pUXWV1psUV/ZyGkner2+q5vQo5H1lRZZX2k5d+5cVzehx1JUVMQbb7xBUVFRVzelxyL3X2mR9ZUWWV9psUV9e62R09AgzXbDw8PZt29fk/eWLFnCc889J80OJSI1NZWZM2fi7e2Nj48PixYtory8vMX6W7duZdCgQTg7OzNx4kSys7NbrKtQKIiMjGzyXnp6OgqFgjlz5jSpN2bMmCb1rrvuOj755JOrOiYZGRnbJS8vj//85z/k5eV1dVNkZGRkZGyAXmnkfPghuLhYHtuLq6trxzeoG1JZWcntt99OZmYm2dnZ6PV6li9f3mzdkpISbrvtNl555RUqKyuZOXMm8+fPb3X7SqWS/fv3i69XrlxJVFQUKlXT/LQpKSls2rTp2g9IBug9/berSExM7OomyMhcNXL/lRZZX2mR9ZUWW9S31xk5H34IS5ZAXJzlsb2GTk1NzTXt/5NPPiExMZH77rsPFxcXEhISyMvL4+GHH8bNzY2RI0eSn58PgNlsZvbs2fj6+uLp6cncuXMpKysDYPv27QQFBYmv165dS0xMTLsT5QmC0Oz7I0aM4K677sLNzQ2tVst9993XYvjAvXv3EhUVxQ033ICdnR1/+MMfOHbsGOnp6S3ud/78+az8//buPC6qev8f+GsA2RcFBFRWEURFRHHNfQG3zKX0umZkJqWE1r1fTb1qpWappXkLtLwuuV5LLU1TVNz1iuC+gYq5oLmAgCADM3N+f8yPuaICA/HxMMfX8/HggXPmM3Pe8/IjzptzzmdWrzbcXrt2LYYMGfLMNSMTJkzAJ598Uq7XRCX7q/OXSrd//365SyCqMM5fsZivWMxXLFPM96VqcooanOho4MQJ/ffyNjo6ne4v15GQkIBevXohIyMDnp6eaNu2LTp27IgHDx7A19cXc+fONYwdMGAA0tLSkJaWhpycHHz66acAgE6dOuH111/HuHHjcO/ePURHR2P58uWwsbF5Zn9//vknRo8eDR8fHzRr1gyfffYZjhw5go0bN+LNN980qubDhw+jUaNGJd7/vGbp3LlzJY4fNGgQNm3aBK1Wi8TERLi6uj531aS33noLt27dQnx8vFF1UukqY/5SyfLz8+UugajCOH/FYr5iMV+xTDHfl6bJebLBWbgQMDPTfy9vo/P06VTPEx4ejurVqxu+li1bVuz+xo0bo3///qhWrRr69u0LOzs7DBo0CBYWFujXrx9Onz4NQH9K1/Dhw2FnZwcnJydMmDABBw8eNDzPnDlzkJiYiE6dOmHEiBFo06bNc+s5evQoevbsibNnz2LFihXIy8vDlClTsG3bNvzzn/8s8/WcPHkS33zzTYlj27Rpg5SUFPz2228oLCzE3LlzoVarkZeXV+Jzuri4oEmTJti1axdWr16NoUOHAtBfh/OkatWqYfLkyTyaU0mMmb9UcTVr1pS7BMVycnJChw4d4OTkJHcpisX5KxbzFYv5imWK+b4UTc7TDU7R+2iVqvyNjrW1dZlj4uPj8fDhQ8NXZGRksfvd3NwMf7axsSk2cWxsbAyfSq/RaDB+/Hj4+PjA0dERb7zxBh48eGAYa2tri8GDB+PChQv44IMPSqynd+/euHv3Lt555x18++236NatG+Lj4zFr1iz88ssvpb6WtLQ09OnTB0uXLi3xSI6rqys2bNiAqVOnwsPDAzdv3kSjRo1Qp06dUp972LBh+PHHH7Fx40YMGjQIgL6xe1pkZCRu3ryJXbt2lfp8VDZj5i9V3NMLalDl8ff3x5YtW+Dv7y93KYrF+SsW8xWL+YplivkqvslRq/VNTEgIsGDB/xqcIiqVfntIiH5cWauuvchrGlavXo0DBw7gyJEjyM7Oxk8//VTstLDU1FTExsZi4MCB+Oijj0p8nlWrViE1NRVvvfUWmjRpgtmzZ8PFxQWdO3eGp6dniY+7c+cOwsPD8c9//hP9+vUrtdbw8HCcOHECDx48wMyZM3H79m0EBweX+pi+ffvi119/RXBwsKHR02q1z4yrVq0aPv74Yx7NqQS8JkesI0eOyF2CYhUWFmL79u0oLCyUuxTF4vwVi/mKxXzFMsV8FX/uipUVsGiR/kjN+PHFj+QAgCTpt58+DcTF6cdXFTk5ObCyskL16tVx//59zJs3z3CfTqfDyJEjMWXKFERFRaFJkyb4z3/+Yzgi8qQRI0bA3NzccPu9994rc99ZWVno3r073nzzTbz77rtljj958iSCg4ORnZ2NcePGYfjw4XBxcSn1Mba2toiPj4erq2uZzx8ZGYnZs2fj0aNHGDx4cJnjiUhZzpw5g8GDByMpKQnNmjWTuxwiIqriFH8kBwDGjNE3MIsWATEx+sYG0H+PidFvj4vTjyvL8y7sF6VodTM3Nze0b98ePXr0MNw3b948mJubIyYmBjY2Nli2bBmio6Nx9+7dZ57nyQbHWJs3b8bp06fx5Zdfwt7e3vBVJCoqClFRUYbbM2fOhLOzMwICAuDq6oovvvjCqP20atWq2OknzztdDQAsLS3x8ccfG1aTo4p5kfP3ZVTW0UuiqozzVyzmKxbzFcsU81VJJa0hXAVkZ2fDyckJWVlZxT7fIz8/H2lpafDz8yvXNQZPXpuzYIH+CE55GpyiffO6BnGYr1jG5lvRf2Mvu9TUVAQEBMhdhiIlJycjLCyMR3IE4vwVi/mKxXzFqir5ltQbPM9LcSSnyJNHdJo2LX+DAwDqsi7aob+E+YrFfMW6evWq3CUQVRjnr1jMVyzmK5Yp5qv4a3KeVtTQREeXv8EhIiIiIqKq76U6Xe1JanXFFhmQJOmZz3KhysN8xTI2X56uVjEajYafRSSIVqtFVlYWnJycKnSdIZWN81cs5isW8xWrquTL09WMUNFV1LgEr1jMVyzmK9bRo0flLkGxzM3Ncf78eTY4AnH+isV8xWK+Yplivi9tk1NROp1O7hIUjfmKxXzFKvogX6p8qampiImJQWpqqtylKBbnr1jMVyzmK5Yp5ssmp5yqwqE6JWO+YjFfsZydneUuQbFycnKQnJyMnJwcuUtRLM5fsZivWMxXLFPMl01OOfH6BLGYr1jMV6yGDRvKXQJRhXH+isV8xWK+YplivmxyyonXNIjFfMVivmIdPHhQ7hKIKozzVyzmKxbzFcsU82WTQ0REREREiiK0yUlJSUHfvn3h6uoKR0dHtG3bFgkJCSJ3abSKfiZiWaf7+Pr6wtHREY8fPzZsy87Oho2NDYKCgiq20ypk+fLlCA0NhYODA+rWrYu4uDijHtejR49Ss1u+fDlUKhW+/vrrYtsnT54MlUqFdevWFRu3ePFiw5g7d+5w2Wkj8XQ1sRo0aCB3CYrl5eWFTz/9FF5eXnKXolicv2IxX7GYr1immK/QJqd3797QaDTYs2cPkpKSEBoaildffRV37twRudsyLV4MODjov4vg4eGBX3/91XB748aNivmPWa1WIy4uDpmZmdiyZQumT5+O/fv3l/qYzZs3G3WaVL169bB+/XrDbUmSsH79evj7+xcbV6NGDcyePRuFhYUVexFEgmg0GrlLUKyaNWti2LBhqFmzptylKBbnr1jMVyzmK5Yp5iusybl//z4uX76MSZMmISQkBAEBAZgzZw7y8vJw7tw5Ubst0+LFQFQU0KCB/nt5G538/PwyxwwZMgSrV6823F69ejWGDh1abIxKpUJsbCy8vb3h6uqK9evXY+vWrahbty7c3NyKvdn//vvvERAQAAcHB4SEhGDv3r2GWho2bIi1a9cCAB4+fAhPT0/s2bOnfC8K+obCGGPGjEHr1q1hYWGBRo0aoVu3bkhMTCxxfH5+PqZOnYo5c+aU+dz+/v6ws7NDcnIyAODw4cPw8vKCp6dnsXEtW7aEl5cXli1b9tzn8fX1xfz58xEYGAhHR0csWLAAx44dQ8OGDeHs7PzM0aKXiTHzlyqOyxuLk5GRgbi4OGRkZMhdimJx/orFfMVivmKZYr7CmhwXFxc0aNAAK1euRG5uLjQaDRYvXgx3d3eEhYU99zFqtRrZ2dnFvipTUYMTHQ2cOKH/XpFGpyzh4eFITk5GRkYG7ty5g9TUVHTo0OGZcYcOHUJKSgpiY2Px/vvv4+eff8bZs2exdOlSjBs3DlqtFgBQu3Zt7N69G1lZWYiOjsbgwYOhVqthbW2NFStWYPz48bh9+zZiYmLw2muvoUuXLs+tKzY2FqGhofD29saoUaOwdetW7N+/H2PHjsXx48fL/Tq1Wi2OHTuGRo0alThmzpw5GDx48DONSkkGDhyINWvWAADWrFmDYcOGPXfc9OnTSz2as23bNiQmJmLXrl2YOHEi5s6di0OHDiEhIQGTJ0/GvXv3jKqHiKqGa9euYe7cubh27ZrcpRARkQkQ9qEZKpUK8fHx6Nu3LxwcHGBmZgZ3d3f8/vvvqF69+nMf8/nnn+OTTz55ZvuuXbtgZ2eHLl264NixY3j8+DFcXV2h1WqRlZUF4H/XGhT9ptrBwQF5eXnQarUwNzfHjz/a4v33zTBunISFC1VQqYCFC/VHMKKiVFCr1Rg5Mh9mZmawt7c3NFhWVlYwMzMzXGNja2traNqeHmtpaWmooVevXli3bh0ePXqE1157zfD4onoBYMKECVCr1ejatSsePnyIt99+G4WFhejYsSNycnJw6dIl1KlTB507d4alpSVycnIwaNAgTJs2DWfOnEFAQAACAwMxatQodOnSBY8fP8axY8eg0WgMH9pkY2MDnU6H7OxsXLp0CVu2bIFGo8HWrVsRGxsLMzMz9O/fH4GBgSgoKIBOp4P6/1+w9HSGtra2hs+osLa2xtSpU+Hu7o7WrVtDp9M9M/bs2bNYt24djh49irt37xpev729PfLz84tlmJeXB41Gg7/97W/o2LEjJk6ciM2bN2PGjBn48ccfkZeXZ9i3RqNBy5YtUatWLSxdutTQ1Gk0GsNrGD16NJycnBAUFAQ3Nzf069cP9vb28PX1haenJ86ePYvWrVujoKAAAODk5ITs7GxIkoRq1arB0tLSkKGtra3huQHA0dERjx49gk6ng4WFBaytrQ2n4xXlXZRhaWPLmrNP5/3k2CczfHrs03P2ybEqlQqSJJU4v+3s7FBQUIDc3FzDvnbs2AFAf02Eq6srTpw4AQBo3rw50tPTkZ6eDnNzc3Tr1g27du2CVqtF7dq1Ubt2bUPz3LRpU9y/fx83btwAAHTv3h0JCQkoKCiAu7s7fH198d///hcAEBISguzsbMOb2fDwcBw6dAh5eXlwdXVFYGAgDh8+DABo1KgR8vPzceXKFQAw/Ix49OgRatSogUaNGhlWhAkKCoJOp0NKSgoAoGPHjjh58iSysrLg6OiIZs2aGY6SBgQEwMLCAhcuXAAAtGvXDufPn0dGRgbs7OzQunVr7N69GwBQt25dw3zXarXIzs7G5cuXce/ePVhbW6NDhw7YuXMnAMDHxwfVq1fHqVOnAOiPSl6/fh137txBtWrV0KVLF+zcuROSJMHT0xNubm6GI5thYWG4c+cObt26BTMzM4SHh2P37t3QaDSoVasWPD09DUdVQ0NDkZGRgevXrxvy3rt3L9RqNdzc3FC3bl3Dp1c3btwYjx49QlpaGgCgW7duOHz4MPLy8uDi4oKgoCAcOnQIgH4J0YKCAly+fBkA0LlzZxw/fhw5OTmoXr06QkJCDKev1q9fHwBw6dIlAECHDh1w+vRpPHz4EA4ODmjevLnhGs169erB0tIS58+fBwC0bdsWFy9exIMHD2Bra4tXXnnF8Hf+xx9/wMPDA2fOnAEAtG7dGlevXsXdu3dhZWWFTp06Geast7c3nJ2dcfLkSQBAixYtcPPmTdy+fRsWFhbo2rUr4uPjodPpUKdOHXh4eCApKQkA0KxZM9y9exc3b96ESqVCREQE9uzZg8LCQnh4eMDb2xvHjh0DADRp0gQPHz7EH3/8AQCIiIjA/v37kZ+fj5o1a6JevXo4cuQIACA4OBh5eXm4evUqAKBr1644evQocnNz4ezsjIYNGxrmbIMGDaDRaAy/Qe3UqROSk5ORnZ0NJycnhIaGYt++fQCAwMBAmJmZ4eLFi4Y5e+7cOWRmZsLe3h4tW7Y0HOX39/eHtbW14ayKV155BSkpKdBqtThw4ADatm2L+Ph4AP+7zvT06dMAgFatWuHatWv4888/YWlpic6dO/NnBIz7GaHVarFjxw60adOGPyNQ+T8jivL18/ODvb09f0ZU8s+I6tWrY8eOHbC1tZX1Z0RR/UaRymn69OkSgFK/EhMTJZ1OJ7322mtSz549pYMHD0pJSUnSe++9J9WpU0dKT09/7nPn5+dLWVlZhq8bN25IAKSsrKxi4x4/fiydP39eevz4sVE1x8VJEiBJ0dGSpNMVv0+n028H9OPKkpOTU+r9Pj4+0pEjR6TDhw9LHTp0kFq0aCGdOHFCSkhIkOrXr28YB0C6ffu24baVlZWUlpZmuO3k5CRduHBBkiRJ2rRpk9S0aVPJyclJcnJykszMzKS9e/caxqampkoApM8++6zU2o4cOSKNGTNGGjhwoLR48WIpIyNDUqvV0urVq6WzZ88+M37//v2SnZ2dZGdnJ/Xo0aPYfbGxsVJgYKB07969EvfXr18/6aeffpIkSZLS0tIkKyurEscuW7ZM6t69u5STkyNFRERIH330kdS3b19JkiSpY8eO0tq1a4uNkyRJ2rFjh+Tr6ytdv35denIqF/0dFKlfv76UkJBguN2kSRNp+/btJdaiZGXN3yLl/TdGeocPH5a7BMVKSkqSAEhJSUlyl6JYnL9iMV+xmK9YVSXfrKys5/YGz1PuIznjxo3D4MGDSx3j6+uLPXv2YOvWrcjMzISjoyMA4LvvvkN8fDxWrFiBSZMmPfM4KysrWFlZlbekUqnV+tPSQkKABQuApxfhUqn02/ft04976y2gtBKKTiErS5s2bXDr1i1YWloiNDTU8Nuf8tevxpAhQ/DLL7+ga9euMDc3R61atQzX0EiShPfeew/Dhg3DwoULERkZiTp16jz3eSZPnox3330XVlZW2Lp1K6ZNmwaVSoUBAwagT58+zzymffv2z10wYP369Zg1axYOHDgAV1fXEmvfu3cvjhw5grFjx0Kr1UKtVsPDwwP79u0z/AbnaVqtFkOHDkVkZKRhRbWSREREoFatWlixYkWp4+h/jJ2/VDGVfYot0YvE+SsW8xWL+YplivmWu8lxdXUt9Y1tkby8PACAmVnxy37MzMyg0+nKu9sKs7ICFi3SX3szfrz+FLUnGx1J0m8/fRqIiyu9wQEAc3Nzo/e9cePGZ15/eanVahQUFBhWFFq4cGGx60mKVjrbvn07ZsyYgdGjR2Pbtm3PPI+lpSV27dplqKd///4Vqmfnzp2Ijo7Grl274OvrW+rYS5cuGf6ub9y4gfbt2+PkyZOlzh9zc3MMHDgQ7u7u6NSpU5n1TJ8+/ZlFHahk5Zm/VH5OTk5yl6BYdnZ2CA4Ohp2dndylKBbnr1jMVyzmK5Yp5its4YE2bdqgRo0aGDlyJE6dOoWUlBT84x//QFpaGnr37i1qt881Zoy+gVm0CIiJ0Tc2gP57TIx+e1ycflxZbG1tjd5vSEgIgoODK1i1nqOjI+bOnYvw8HB4eHjgwYMHqFevHgAgLS0NU6dOxfLly2FhYYFp06bh5s2b+Pe///3M86hUqr/ccAH666YyMzPxyiuvwN7eHvb29oiKijLcb29vjwMHDgAA3Nzc4OHhAQ8PD0OT5uHhAQuLkntrW1tb2Nralvm5OkW6d++OwMDAv/iqXh7lmb9UfqGhoXKXoFj169dHYmJiiUeB6a/j/BWL+YrFfMUyxXxVkmTk2sEVcPz4cUyZMgXHjx9HYWEhGjVqhGnTpqFnz55GPb7owqmii/6K5OfnIy0tDX5+fuX6cMMnV1dbsEB/BKc8DQ6gv3DeFLtZU8F8xTI234r+G3vZ7dixA927d5e7DMVivmIxX7GYr1jMV6yqkm9JvcHzCFtdDdCvmlC0okJVUNTIREXpr8EpOkXN2AaHiIjkkZycjB49eiApKQnNmjWTuxwiIqrihDY5VVFRQxMdXbEGh7/VFov5isV8xeKpk2TKOH/FYr5iMV+xTDHfl67JAfSNTVmrqBERlVdlXPdGJBfOX7GYr1jMVyxTzNf0Kq4kFW1wij4gkcRgvmIxX7HK9SFlRFUM569YzFcs5iuWKeZr0k2OwDUTiF5qL3KZdyIiIqLKJnR1tb+qpBUUtFotUlNTYWtri5o1a0L19Cd8CqTVavlZIwIxX7HKyleSJBQUFODevXvQarUICAgwyUPUcsnNzeXnuAiSn5+PlJQUBAYG8toyQTh/xWK+YjFfsapKvlVmdTVRzM3N4enpiZs3b+LatWsvdN9qtRpWvJhHGOYrlrH52trawtvbmw1OOZ07dw4tW7aUuwxFsra2Rn5+PhscgTh/xWK+YjFfsUwxX5NscgD9h04GBASgsLDwhe734MGDaNeu3Qvd58uE+YplTL7m5uawsLB4oUdIlSIzM1PuEhQrLS0NkyZNwtKlS+Hn5yd3OYrE+SsW8xWL+YplivmabJMD6N+MvehTm2xsbPibRIGYr1jMVyx7e3u5S1CszMxMJCQkIDMzk02OIJy/YjFfsZivWKaYr0lekyOnwsJCVKtWTe4yFIv5isV8xWK+4iQnJyMsLIwfBioQ569YzFcs5itWVcm3PL0BT7gvpz179shdgqIxX7GYr1jMl0wZ569YzFcs5iuWKeZbpU9XKzrIlJ2dLXMl/5Obm1ul6lEa5isW8xWL+Yrz6NEjw3dmLAbnr1jMVyzmK1ZVybeoBmNORKvSp6vdvHkTXl5ecpdBRERERERVxI0bN+Dp6VnqmCrd5Oh0OqSnp8PBwaFKrPSUnZ0NLy8v3Lhxo8pcI6QkzFcs5isW8xWL+YrFfMVivmIxX7GqUr6SJCEnJwe1a9cu82MuqvTpamZmZmV2aXJwdHSU/S9ZyZivWMxXLOYrFvMVi/mKxXzFYr5iVZV8nZycjBrHhQeIiIiIiEhR2OQQEREREZGisMkpBysrK0yfPh1WVlZyl6JIzFcs5isW8xWL+YrFfMVivmIxX7FMNd8qvfAAERERERFRefFIDhERERERKQqbHCIiIiIiUhQ2OUREREREpChscoiIiIiISFHY5FRQSkoK+vbtC1dXVzg6OqJt27ZISEiQuyxF+e2339CqVSvY2NjA1dUVAwYMkLskxVGr1QgNDYVKpcLJkyflLkcRrl27hlGjRsHPzw82Njbw9/fH9OnTUVBQIHdpJuu7776Dn58frK2tERYWhgMHDshdkiJ8/vnnaNGiBRwcHODm5oZ+/frh0qVLcpelWJ9//jlUKhXGjx8vdymKcevWLQwfPhwuLi6wtbVFaGgokpKS5C5LETQaDaZOnWr4v6xu3br49NNPodPp5C7NaGxyKqh3797QaDTYs2cPkpKSEBoaildffRV37tyRuzRF+PnnnzFixAhERkbi1KlTOHToEIYOHSp3WYrzf//3f6hdu7bcZSjKxYsXodPpsHjxYpw7dw5ff/014uLiMHnyZLlLM0nr16/H+PHjMWXKFJw4cQLt27dHz549cf36dblLM3n79u3D2LFjcfToUcTHx0Oj0SAiIgK5ublyl6Y4iYmJWLJkCUJCQuQuRTEyMzPRtm1bVKtWDdu3b8f58+cxf/58VK9eXe7SFOGLL75AXFwc/vWvf+HChQv48ssvMXfuXCxatEju0ownUbndu3dPAiDt37/fsC07O1sCIO3atUvGypShsLBQqlOnjvTDDz/IXYqibdu2TQoKCpLOnTsnAZBOnDghd0mK9eWXX0p+fn5yl2GSWrZsKUVFRRXbFhQUJE2aNEmmipTr7t27EgBp3759cpeiKDk5OVJAQIAUHx8vdezYUYqJiZG7JEWYOHGi1K5dO7nLUKzevXtLb7/9drFtAwYMkIYPHy5TReXHIzkV4OLiggYNGmDlypXIzc2FRqPB4sWL4e7ujrCwMLnLM3nJycm4desWzMzM0LRpU9SqVQs9e/bEuXPn5C5NMf7880+MHj0aP/74I2xtbeUuR/GysrLg7Owsdxkmp6CgAElJSYiIiCi2PSIiAocPH5apKuXKysoCAM7VSjZ27Fj07t0b3bp1k7sURfn111/RvHlzDBw4EG5ubmjatCm+//57uctSjHbt2mH37t1ISUkBAJw6dQoHDx5Er169ZK7MeBZyF2CKVCoV4uPj0bdvXzg4OMDMzAzu7u74/fffeZi0Ely9ehUAMGPGDHz11Vfw9fXF/Pnz0bFjR6SkpPA/4L9IkiS89dZbiIqKQvPmzXHt2jW5S1K0K1euYNGiRZg/f77cpZic+/fvQ6vVwt3dvdh2d3d3nhpcySRJwocffoh27dohODhY7nIUY926dUhOTkZiYqLcpSjO1atXERsbiw8//BCTJ0/GsWPH8MEHH8DKygpvvvmm3OWZvIkTJyIrKwtBQUEwNzeHVqvFrFmzMGTIELlLMxqP5DxhxowZUKlUpX4dP34ckiTh/fffh5ubGw4cOIBjx46hb9++ePXVV3H79m25X0aVZWy+RRe1TZkyBa+//jrCwsKwbNkyqFQqbNiwQeZXUXUZm++iRYuQnZ2Njz/+WO6STYqx+T4pPT0dPXr0wMCBA/HOO+/IVLnpU6lUxW5LkvTMNvprxo0bh9OnT2Pt2rVyl6IYN27cQExMDFatWgVra2u5y1EcnU6HZs2aYfbs2WjatCnGjBmD0aNHIzY2Vu7SFGH9+vVYtWoV1qxZg+TkZKxYsQLz5s3DihUr5C7NaCpJkiS5i6gq7t+/j/v375c6xtfXF4cOHUJERAQyMzPh6OhouC8gIACjRo3CpEmTRJdqkozN98iRI+jSpQsOHDiAdu3aGe5r1aoVunXrhlmzZoku1SQZm+/gwYOxZcuWYm8StVotzM3NMWzYMJP6AfYiGZtv0ZuZ9PR0dO7cGa1atcLy5cthZsbfKZVXQUEBbG1tsWHDBvTv39+wPSYmBidPnsS+fftkrE45oqOjsXnzZuzfvx9+fn5yl6MYmzdvRv/+/WFubm7YptVqoVKpYGZmBrVaXew+Kh8fHx+Eh4fjhx9+MGyLjY3FzJkzcevWLRkrUwYvLy9MmjQJY8eONWybOXMmVq1ahYsXL8pYmfF4utoTXF1d4erqWua4vLw8AHjmTYuZmZlJLa33ohmbb1hYGKysrHDp0iVDk1NYWIhr167Bx8dHdJkmy9h8v/nmG8ycOdNwOz09Hd27d8f69evRqlUrkSWaNGPzBfTLmnbu3NlwFJINTsVYWloiLCwM8fHxxZqcotOF6a+RJAnR0dHYtGkT9u7dywanknXt2hVnzpwpti0yMhJBQUGYOHEiG5y/qG3bts8seZ6SksL3CZUkLy/vmf+7zM3NTep9LpucCmjTpg1q1KiBkSNHYtq0abCxscH333+PtLQ09O7dW+7yTJ6joyOioqIwffp0eHl5wcfHB3PnzgUADBw4UObqTJ+3t3ex2/b29gAAf39/eHp6ylGSoqSnp6NTp07w9vbGvHnzcO/ePcN9Hh4eMlZmmj788EOMGDECzZs3R5s2bbBkyRJcv34dUVFRcpdm8saOHYs1a9bgl19+gYODg+E6JycnJ9jY2MhcnelzcHB45vomOzs7uLi48LqnSjBhwgS88sormD17NgYNGoRjx45hyZIlWLJkidylKUKfPn0wa9YseHt7o1GjRjhx4gS++uorvP3223KXZjwZV3YzaYmJiVJERITk7OwsOTg4SK1bt5a2bdsmd1mKUVBQIH300UeSm5ub5ODgIHXr1k06e/as3GUpUlpaGpeQrkTLli2TADz3iyrm22+/lXx8fCRLS0upWbNmXOK4kpQ0T5ctWyZ3aYrFJaQr15YtW6Tg4GDJyspKCgoKkpYsWSJ3SYqRnZ0txcTESN7e3pK1tbVUt25dacqUKZJarZa7NKPxmhwiIiIiIlIUnihORERERESKwiaHiIiIiIgUhU0OEREREREpCpscIiIiIiJSFDY5RERERESkKGxyiIiIiIhIUdjkEBERERGRorDJISIiIiKiSrF//3706dMHtWvXhkqlwubNm8v9HJIkYd68eQgMDISVlRW8vLwwe/bscj2HRbn3SkRERERE9By5ublo0qQJIiMj8frrr1foOWJiYrBz507MmzcPjRs3RlZWFu7fv1+u51BJkiRVaO9EREREREQlUKlU2LRpE/r162fYVlBQgKlTp2L16tV4+PAhgoOD8cUXX6BTp04AgAsXLiAkJARnz55F/fr1K7xvnq5GREREREQvRGRkJA4dOoR169bh9OnTGDhwIHr06IHU1FQAwJYtW1C3bl1s3boVfn5+8PX1xTvvvIOMjIxy7YdNDhERERERCXflyhWsXbsWGzZsQPv27eHv74+///3vaNeuHZYtWwYAuHr1Kv744w9s2LABK1euxPLly5GUlIQ33nijXPviNTlERERERCRccnIyJElCYGBgse1qtRouLi4AAJ1OB7VajZUrVxrGLV26FGFhYbh06ZLRp7CxySEiIiIiIuF0Oh3Mzc2RlJQEc3PzYvfZ29sDAGrVqgULC4tijVCDBg0AANevX2eTQ0REREREVUfTpk2h1Wpx9+5dtG/f/rlj2rZtC41GgytXrsDf3x8AkJKSAgDw8fExel9cXY2IiIiIiCrFo0ePcPnyZQD6puarr75C586d4ezsDG9vbwwfPhyHDh3C/Pnz0bRpU9y/fx979uxB48aN0atXL+h0OrRo0QL29vZYsGABdDodxo4dC0dHR+zcudPoOtjkEBERERFRpdi7dy86d+78zPaRI0di+fLlKCwsxMyZM7Fy5UrcunULLi4uaNOmDT755BM0btwYAJCeno7o6Gjs3LkTdnZ26NmzJ+bPnw9nZ2ej62CTQ0REREREisIlpImIiIiISFHY5BARERERkaKwySEiIiIiIkVhk0NERERERIrCJoeIiIiIiBSFTQ4RERERESkKmxwiIiIiIlIUNjlERERERKQobHKIiIiIiEhR2OQQEREREZGisMkhIiIiIiJFYZNDRERERESK8v8APp6JWp7hPiMAAAAASUVORK5CYII=", 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", 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", 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", 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JfJVTVlbGypUrKS0tZcWKFf5uTlAS/VdZIl9liXyVFSj5imtyFLR//35/NyGoiXyVJfJVlshXUDPRf5Ul8lWWyFdZasxXFDmCIAiCIAiCIAQVUeTMUk5Ojr+bENREvsoS+SpL5KucpKQkPv7xj5OUlOTvpgQt0X+VJfJVlshXWWrMV+/vBqhNIKwRHsxEvsoS+SpL5KuctLQ0vv71r5OamurvpgQt0X+VJfJVlshXWWrMV4zkzFJVVZW/mxDURL7KEvkqS+SrnImJCV544QUmJib83ZSgJfqvskS+yhL5KkuN+YoiRxAEQQh4DQ0NfOlLX6KhocHfTREEQRBUQBQ5s7Ru3Tp/NyGoiXyVJfJVlshXUDPRf5Ul8lWWyFdZasxXFDmz1Nzc7O8mBDWRr7JEvsoS+QpqJvqvskS+yhL5KkuN+YoiZ5b6+/v93YSgJvJVlshXWSJfQc1E/1WWyFdZIl9lqTFfUeTMUkhIiL+bENREvsoS+SpL5Kscg8FAfHw8BoPB300JWqL/KkvkqyyRr7LUmK9GkiTJ3424mvHxcaKiohgbGyMyMtLfzREEQRAEQRAEwU9mUxuIkZxZ2rNnj7+bENREvsoS+SpL5Ksska+yRL7KEvkqS+SrLDXmK4ocQRAEIeBVVVXx0EMPqXKvBkEQBGH+iSJnljIzM/3dhKAm8lWWyFdZIl/lOJ1OBgcHcTqd/m5K0BL9V1kiX2WJfJWlxnxFkTNLsbGx/m5CUBP5KkvkqyyRr6Bmov8qS+SrLJGvstSYryhyZqmiosLfTQhqIl9liXyVJfIV1Ez0X2WJfJUl8lWWGvMVRY4gCIIgCIIgCEFFLCE9S8PDw6ocslMLka+yRL7KEvkqZ2JigkOHDrF161YiIiL83ZygJPqvskS+yhL5KitQ8hVLSCuos7PT300IaiJfZYl8lSXyVU5ERATZ2dmiwFGQ6L/KEvkqS+SrLDXmK4qcWerp6fF3E4KayFdZIl9liXyV09XVxXe+8x26urr83ZSgJfqvskS+yhL5KkuN+Spe5HR1dfHQQw8RFxeHyWRi2bJllJaWKv20itHr9f5uQlAT+SpL5Ksska9y+vr6+J//+R/6+vr83ZSgJfqvskS+yhL5KkuN+Sp6Tc7IyAjLly9n69atfPKTnyQxMZGmpiays7PJy8u77vcH4jU5giAIwvwrKytj5cqVlJaWsmLFCn83RxAEQfCDgLkm5/vf/z4ZGRn8+te/Zs2aNWRnZ7N9+/YbKnAC1b59+/zdhKAm8lWWyFdZIl9BzUT/VZbIV1kiX2WpMV9Fi5zXXnuNVatW8b73vY/ExESWL1/OL37xi6s+3m63Mz4+Pu0WaDwej7+bENREvsoS+SpL5Cuomei/yhL5Kkvkqyw15qvoBLvm5maefvppPve5z/GVr3yFs2fP8i//8i+EhITw4Q9/eMbjv/e97/GNb3xjxtf379+P2Wxm27ZtnD17FovFQkxMDIsWLeL48eMAFBUV4fF4qK+vB2Dz5s1UVFTIw1krVqzg8OHDABQUFKDX66mpqQHg9ttv59KlSwwPD2M2m1m3bh0HDhwAIDc3F5PJxMWLFwGIiYmhrKyMgYEBQkND2bRpE3v37gUgKyuL6OhoLly4AMCaNWtob2+nt7cXg8HAtm3b2Lt3L5IkkZ6eTmJiImVlZQCsXLmS3t5eurq60Gq13HHHHRw4cACXy0VKSgrp6emcO3cOgGXLljE8PEx7ezsAu3bt4vDhw9jtdhITE8nNzeX06dMAlJSUYLFYaGlpAWDHjh2cPHmSyclJ4uLiKCoq4sSJEwAUFxfjcDhobGwEYOvWrZw/f56JiQmio6NZsmQJR48eBWDBggUA1NXVAbBp0yYqKysZHR0lIiKCVatWcejQIQDy8/MxGo1cunQJgA0bNlBbW8vQ0BAmk4n169ezf/9+ALRaLd3d3VRVVQGwbt06mpub6e/vJyQkhC1btrBnzx4AMjMziY2NlTeoWr16NZ2dnfT09KDX69m+fTv79u3D4/GQlpZGcnKyfD3YihUr6O/vp7OzE41Gw86dOzl48CBOp5Pk5GQyMzM5e/YsAEuXLmV0dJS2tjYAdu7cydGjR7HZbCQkJJCfn8+pU6cAWLx4MZOTkzQ3NwOwfft2Tp8+jdVqJTY2luLiYrnPLly4EJfLRUNDAwBbtmyhrKxMHopdtmwZR44cAaCwsBCtVkttba3cZ6urqxkZGSE8PJw1a9Zw8OBBAPLy8ggNDaW6uhqA9evXU19fz+DgIBMTE3g8HvkdmezsbCIjI6msrARg7dq1tLa20tfXh9FoZOvWrXLeGRkZxMfHU15eDsCqVavo7u6mu7sbnU7Hjh072L9/P263m9TUVFJTUzl//jwAy5cvZ3BwkI6ODrnPHjp0CIfDQVJSEtnZ2Zw5cwaAJUuWMD4+TmtrKwB33HEHJ06cYHJykvj4eAoLCzl58iQAixYtwmaz0dTUBOD3Y8Tg4CDj4+M0NjaKY8QcHyMuXrzI5s2bGRsbE8cIBY8Rx44dY8OGDeIYwdwfIwYHB9mzZw+33XabOEYw98cIX745OTmEh4eLY8QcHyMcDgd79uzBZDL59Rjha/+NUPSaHKPRyKpVq+SDDcC//Mu/cO7cOfmPeTm73Y7dbpf/PT4+TkZGRkBdkzM4OEh8fLy/mxG0RL7KEvkqS+SrLJGvskS+yhL5Kkvkq6xAyTdgrslJSUmhuLh42tcWLlwov3PwdiEhIURGRk67BRo1rwynBiJfZYl8lSXyVc7U1BR//vOfmZqa8ndTgpbov8oS+SpL5KssNearaJGzYcMGeRjSp76+nqysLCWfVhAEQQgyNTU1PPbYY/L0IEEQBEG4FkWLnM9+9rOcPn2a7373uzQ2NvL73/+eZ599lscff1zJp1WUWLpUWSJfZYl8lSXyFdRM9F9liXyVJfJVlhrzVbTIWb16NS+//DJ/+MMfWLx4Md/61rd48skn+eAHP6jk0yqqv7/f300IaiJfZYl8lSXyFdRM9F9liXyVJfJVlhrzVbTIAXjXu95FVVUVNpuNmpoaPvaxjyn9lIrq7Oz0dxOCmshXWSJfZYl8BTUT/VdZIl9liXyVpcZ8FS9ygo1Go/F3E4KayFdZIl9liXyVo9FoMBgMImMFiWyVJfJVlshXWWrMV9ElpG/WbJaJEwRBEARBEAQheAXMEtLByLdRkqAMka+yRL7KEvkqS+SrLJGvskS+yhL5KkuN+YoiZ5acTqe/mxDURL7KEvkqS+SrnJqaGj7+8Y+LJaQVJPqvskS+yhL5KkuN+YoiZ5aSk5P93YSgJvJVlshXWSJf5UxNTdHU1CQ2A1WQ6L/KEvkqS+SrLDXmK4qcWcrMzPR3E4KayFdZIl9liXwFNRP9V1kiX2WJfJWlxnxFkTNLZ8+e9XcTgprIV1kiX2WJfAU1E/1XWSJfZYl8laXGfEWRIwiCIAiCIAhCUBFFziwtXbrU300IaiJfZYl8lSXyVU5OTg7PPvssOTk5/m5K0BL9V1kiX2WJfJWlxnz1/m6A2oyOjqry4iu1EPlemSRJOBwObDYbdrt92kebzYbD4cDlcl315na7cblcdHZ2kpKSgm97rKt9BNDpdGi1WnQ63TU/NxgMGAwGjEaj/PHyzy//WmhoKHq9XpWbit0I0X+VExMTw8aNG4mJifF3U4KW6L/KEvkqS+SrLDXmK4qcWWpra6OoqMjfzQhat1K+LpeL8fFxrFYrFotF/nj551arlcnJSex2O3Oxb29jYyMOh2MOWv/O6XQ6wsLCCA0NJTQ09Iqfh4WFYTKZMJvN8k0Nu93fSv13vvX19fHjH/+Yb33rWyQlJfm7OUFJ9F9liXyVJfJVlhrzFUWOICjE7XYzOjp61dvExMSsf6ZOpyMkJITQ0NBpH0NCQjAYDOj1evmm0+mm/Vuv13Py5Ek2btwIIBcMV/ooSRIejwe32y1/9N3e/m+n04nT6cThcFz1o+8mSRJut1su5mbDYDBMK3refouMjCQiIoLIyEgMBsOssxUCW1dXF7/4xS947LHHRJEjCIIgXJdGmou3hxUyPj5OVFQUY2NjREZG+rs5gHc6T6C/m6xmaszX5XIxODjIwMDAtNvw8DAej+ea36vX6wkPD5dvZrN5xkez2SwXNDc71cuf+V4+5W5qakqeanelz6emppicnJRHtFwu16yeKzQ0VC54Lv/o+zwqKgqTyTTnWaix/6pFWVkZK1eupLS0lBUrVvi7OUFJ9F9liXyVJfJVVqDkO5vaQIzkzNLRo0fZvHmzv5sRtAI9X6vVSk9PD93d3fT09NDX18fIyMhVp5IZDAaio6OvelPiRPta/JmvRqORR52ioqJu+PskScLpdGK1Wq96s1gsjI+PMzExIRdSNpuNgYGBq/5c398mKipqxt8lKiqK8PDwWf9tAr3/CsK1iP6rLJGvskS+ylJjvqLImSWbzebvJgS1QMrX7XbT29tLe3s7HR0ddHV1MTY2dsXHhoWFkZCQMOMWEREREO98+ARSvjdKo9HICxdc76JzSZKw2+1MTEwwMTEhFz6+j77PLRYLTqdTHnW7Er1eLxdAMTExxMbGEhcXR2xsLDExMej1Mw+fasxXEHxE/1WWyFdZIl9lqTFfUeTMUkJCgr+bENT8ma/b7aarq4vm5mba2tro7OzE6XTOeFxcXBypqamkpqaSnJxMQkICZrM5oIqZqwn2/qvRaORFDK71u/oWfbj8GqmxsTH58/HxcVwuF0NDQwwNDV3xeSIjI4mNjZ1202q1OJ1OcU2QAqKioti0adOsRgGF2Qn244O/iXyVJfJVlhrzFdfkzNL4+HjAtCUYzXe+o6Oj1NfX09TURGtrK3a7fdr9YWFhZGRkkJmZSXp6OikpKYSEhMxb++aa6L83xu12Mz4+ztjYGCMjI4yMjDA8PMzw8DBDQ0Mz+omP3W6Xp+PFx8cTHx9PQkKC/LlaiuFAJfqvskS+yhL5Kkvkq6xAyVdck6OgU6dOsWvXLn83I2gpna8kSfT09FBbW0tdXR19fX3T7g8LCyM3N5ecnByysrKIj48PqpNS0X9vjE6nIyYmhpiYGLKzs6fdJ0kSk5OTctFz+e306dNkZGQwNjbG2NgYTU1N0743LCxMLnguL4Cio6PRasXezNfidDr5+9//zv333y9GyhQijg/KEvkqS+SrLDXmK4oc4ZbQ19dHVVUVFy9eZHR0VP66RqMhMzOTgoICcnNzSUlJCaqiRph7Go1GXvUuIyNj2n2ZmZls3LiRoaEhBgYGGBwclFfeGx0dZWpqio6ODjo6OqZ9n16vJyEhgcTERJKSkuSP72Txg2BVVVXFgw8+KFZXEwRBEG6IKHJmafHixf5uQlCby3ytVisVFRVcuHCB/v5++etGo5H8/HwWLFhAQUEBJpNpzp4z0In+q6zFixdjMpkwmUwzCiDfNT6+osdXAA0ODuJyuejp6aGnp2fa94SFhc0ofBITE1U9ZVIIXOL4oCyRr7JEvspSY76iyJmlyclJfzchqN1svpIk0dLSQmlpKbW1tbjdbsA7/aiwsJDFixdTWFh4y053Ef1XWdfKV6/Xk5SUNGMjS4/Hw+joKH19ffT399Pf309fXx9DQ0NMTU3R1tZGW1vbtO+Jjo4mKSmJlJQUUlJSSE5OJjIyUoz6CDdFHB+UJfJVlshXWWrMVxQ5s9Tc3ExBQYG/mxG03mm+LpeLyspKTp48yeDgoPz19PR0VqxYQXFxMaGhoXPZVFUS/VdZ7yRfrVYrr862cOFC+esul4uBgQG56PF9nJiYkFeBq6urkx9vMplITk6Wi56UlBR5xTdBuBHi+KAska+yRL7KUmO+osgRVM1ut3PmzBnOnj2LxWIBICQkhCVLlrBy5UqSk5P93EJBeGf0er08UnO5qakp+vr66O3tpaenh97eXgYGBpicnKS5uZnm5mb5sUajkaSkJLnoSUtLIyEhQRQ+giAIQtATS0jPksvluuImgMLcuNF8HQ4H586d4/jx40xNTQHefTTWrVvHihUrxDULVyH6r7L8la/L5aK/v18uenp6eujr67viPk8Gg4GUlBRSU1NJS0sjNTWV2NjYgJ/q5na7GRsbIyoqCp1O5+/mBCVxfFCWyFdZIl9lBUq+YglpBZ0+fZrbb7/d380IWtfL1+PxUF5ezqFDh+SRm/j4eDZt2sSiRYvEyc91iP6rLH/lq9fr5Q1qfTweD0NDQ3LR09PTQ3d3N3a7nfb2dtrb2+XHhoWFyd/vK3wC5Y0lH51Ox6VLl0T/VZA4PihL5Ksska+y1JivKHJmyWq1+rsJQe1a+XZ0dPDGG2/IK1DFxMSwZcsWSkpKxPSbGyT6r7ICKV+tVktCQgIJCQmUlJQA3oU5hoaG6Orqoquri+7ubnp7e5mamqKpqWnavj4RERGkpaWRnp5ORkYGqampfl2wo6Ghgc985jP88Y9/VN28cLUIpP4bjES+yhL5KkuN+YoiZ5ZiY2P93YSgdqV87XY7e/fupbS0FPBec7N161ZWr14tRm5mSfRfZQV6vhqNRt6IdOnSpYB3Glh/f79c9HR1ddHf38/ExAS1tbXU1tYC3qIpOTmZjIwMufCJioqat2luExMTlJWVMTExMS/PdysK9P6rdiJfZYl8laXGfMU1ObNktVoxm83+bkbQenu+LS0tvPLKK4yNjQGwfPlyduzYIf4G75Dov8oKlnydTic9PT10dnbS2dlJR0fHFYuLiIgIueBJT08nNTVVsTnbZWVlrFy5UmwGqqBg6b+BSuSrLJGvsgIlX3FNjoKOHz/Orl27/N2MoOXL1+PxsH//fk6ePAl4p6bde++9ZGdn+7eBKif6r7KCJV+DwUBmZiaZmZmAd5rb2NiYXPB0dHTQ29vLxMQENTU11NTUAN7rZlJSUsjKyiIrK4uMjAzCwsL8+asIsxAs/TdQiXyVJfJVlhrzFUWOEHAsFgt/+tOfaG1tBWDVqlXs3LkTo9Ho34YJwi1Ko9EQHR1NdHS0vOu10+mku7t7WuFjtVrl0Z8TJ06g0WhITEwkMzNTLnwiIiL8/NsIgiAItwJR5MzS5Zv1CXMvPj6eZ599lvHxcYxGI+95z3soLi72d7OChui/yrqV8jUYDHLhAt7RntHRUdrb22lra6OtrY2hoSH6+vro6+vj3LlzgHdUNisrSy58bnT56oyMDL75zW+SkZGh6O91K7uV+q8/iHyVJfJVlhrzFUXOLLlcLn83IWi1tbXx4osvYjKZiI+P5/3vfz8JCQn+blZQEf1XWbdyvhqNhpiYGGJiYuRFDSwWi1z0tLe309vby8jICCMjI1RUVAAQHh5OVlYWOTk5ZGdnExcXd8WiJyEhgQ9+8IPimKCgW7n/zgeRr7JEvspSY76iyJmlhoYGcnNz/d2MoNPQ0MCLL75Id3c327Zt4wMf+ICYy68A0X+VJfKdLjw8nOLiYnk01maz0dnZKY/0dHV1YbFYqK6uprq6GvAuZuAreHJycoiOjkaj0TA8PMwzzzzDl7/8ZVWu8qMGov8qS+SrLJGvstSY77wVOd/73vf4yle+wmc+8xmefPLJ+XpaQQVaW1t58cUXcblcpKen86EPfciv+3EIgqCM0NBQ8vPzyc/PB7zvDHZ1ddHa2kpLS4u8iltlZSWVlZUAREVFkZOTg81m4wc/+AEPPvigKHIEQRCE65qXJaTPnTvHAw88QGRkJFu3br3hIicQl5C22+2EhIT4uxlBo6enh9/85jfY7XYWLFjAvffei8lk8nezgpbov8oS+d4cp9NJZ2cnLS0ttLa20tnZicfjAbzHimeffZYvfOELbNy4kdzcXHJzc8XxYg6J/qsska+yRL7KCpR8Z1MbKL5NvMVi4YMf/CC/+MUviImJUfrpFFdWVubvJgQNq9XKH/7wB+x2Ozk5Obzvfe/jwoUL/m5WUBP9V1ki35tjMBjIyclh27ZtPProo3z5y1/mQx/6ELfffrt8Lc74+DhlZWX86U9/4gc/+AE///nP2b9/P83NzaqcMx5IRP9VlshXWSJfZakxX8Wnqz3++OPs3r2bHTt28O1vf/uaj7Xb7djtdvnf4+PjSjdv1gKxTWrk8Xj405/+xPj4OPHx8Tz44IPo9XqRr8JEvsoS+c4to9FIXl4eeXl5xMbG8p3vfIc777wTk8lEc3MzfX199PT00NPTw/Hjx+UV33Jzc8nLyyMxMfGGVm4TvET/VZbIV1kiX2WpMV9Fi5w//vGPlJWVyUuHXs/3vvc9vvGNb8z4+v79+zGbzWzbto2zZ89isViIiYlh0aJFHD9+HICioiI8Hg/19fUAbN68mYqKCnk4a8WKFRw+fBiAgoIC9Hq9vIHd7bffzqVLlxgeHsZsNrNu3ToOHDgAIE+HuHjxIgAhISGUlZUxMDBAaGgomzZtYu/evQBkZWURHR0tj0asWbNGXlHIYDCwbds29u7diyRJpKenk5iYKFfGK1eupLe3l66uLrRaLXfccQcHDhzA5XKRkpJCenq6nOOyZcsYHh6mvb0dgF27dnH48GHsdjuJiYnk5uZy+vRpAEpKSrBYLLS0tACwY8cOTp48yeTkJHFxcRQVFXHixAkAiouLcTgcNDY2ArB161bOnz/PxMQE0dHRLFmyhKNHjwKwYMECAOrq6gDYtGkTlZWVjI6OEhERwapVqzh06BAA+fn5GI1GLl26BMCGDRv4/e9/z9GjRzGZTDz22GPy38bhcNDd3U1VVRUA69ato7m5mf7+fkJCQtiyZQt79uwBIDMzk9jYWHmVptWrV9PZ2UlPTw96vZ7t27ezb98+PB4PaWlpJCcnU1paCsCKFSvo7++ns7MTjUbDzp07OXjwIE6nk+TkZDIzMzl79iwAS5cuZXR0lLa2NgB27tzJ0aNHsdlsJCQkkJ+fz6lTpwBYvHgxk5OTNDc3A7B9+3ZOnz6N1WolNjaW4uJiuc8uXLgQl8tFQ0MDAFu2bKGsrEweil22bBlHjhwBoLCwEK1WS21trdxnq6urGRkZITw8nDVr1nDw4EEA8vLyCA0NlS/kXr9+PfX19QwODjI0NITH42Hfvn0AZGdnExkZKV//sHbtWlpbW+nr68NoNLJ161Y574yMDOLj4ykvLwe8+xd1d3fT3d2NTqdjx44d7N+/H7fbTWpqKqmpqZw/fx6A5cuXMzg4SEdHh9xnDx06hMPhICkpiaysLE6dOoUkSRQXFzM2NkZrayuSJLF582bOnj3L1NQUMTEx5OXlyf8XioqKcDgctLW1odFo5Ax9eZeUlMj9ez6OET09PYyPj9PY2CiOETd5jKitrWVoaAiTycT69espKysjLy+PsLAwSkpKAEhJSSEhIYHTp09TW1uL0+nE6XTy5ptvyvcvXLgQu91OcnIymzdvFseIaxwjenp6OHbsGBs2bAi4Y0R2djZnzpwBYMmSJYyPj8t7qd1xxx2cOHGCyclJ4uPjKSwslDeSXrRoETabjaamJgC/nkf09PSwZ88ebrvtNnGMUOAY4cs3JyeH8PBwcR4xx8eI8fFx9uzZg8lk8usxwtf+G6HYNTkdHR2sWrWKvXv3ysuJbtmyhWXLll31mpwrjeRkZGQE1DU5NpuN0NBQfzdD1QYGBnjmmWdwu93cf//9LFmyRL5P5KssJfOVJAmHw4HNZsNutzM1NSV/7nA4cDqdOBwOXC6X/NHpdOJyueTrLuaaRqNBp9Oh1+un3XQ6HQaDQb4ZjUaMRuOMz0NCQggJCUGrvbGZvaL/Kuta+UqSxMDAAE1NTTQ3N9Pa2orT6Zz2mKSkJAoKCsjPzycjIwOdTjcfzVYN0X+VJfJVlshXWYGS72yuyVGsyHnllVe47777pr2IuN1uNBoNWq0Wu91+3ReYQFx4YM+ePezatcvfzVAtSZL49a9/TXt7O4WFhXzgAx+YNp1E5Kusm8lXkiSmpqawWCxYrVYmJyexWq3ybXJyck6KFV9hotVqpxUXvn5yeX/xeDxIkoTH45n2+VzSaDQYjUZCQ0MJCQmZ8dFkMhEaGorJZOLw4cPceeedc/r8wj/Mpv+6XC46Ojpobm6mqamJnp4eLn+5CwkJITc3Vy56AuU1xp/E8VdZIl9liXyVFSj5zqY2UGy62vbt2+WhQp9HHnmEoqIivvSlL4l30G5RdXV1tLe3YzAY2L17t5gvH4B8xczo6Cjj4+OMj48zNjbG+Pj4jHfGr+TygiAsLIyQkBB5dESv18ujJL6bb2TFV9Rotdqb6heXFz0ulwuXy4Xb7ZY/v/zmG13yfXz75w6HA0mSZowyX01LSwsul4uwsDBMJhNhYWHTPvd9FP1+9srKyrjzzjspLS1lxYoV1328Xq8nJyeHnJwctm/fzuTkJE1NTTQ0NNDY2Mjk5CQ1NTXydCPfKE9BQQHp6eniNUoQBEHlFCtyIiIiWLx48bSvmc1m4uLiZnxdTQoLC/3dBNWSJEmeX7tu3TqioqJmPEbkq6wr5Wu32xkeHmZ4eJihoSGGh4ex2WxX/H6tVovJZMJsNs+4+UY0/H1y6BsJ8k1Juxkej2faFDzfR9/nNpuNqakpJicnsdvtxMTEYLFYsFgsV/2ZOp1Oziw8PJzw8HD5c7PZLPaIUojJZKKkpISSkhIkSaK7u1sueLq6uujr66Ovr4/jx48TEhJCXl4e+fn5FBYWEh4e7u/mzwtx/FWWyFdZIl9lqTHfedsMNFjc6Nx8YaaGhgb6+voICQlh/fr1V3yMyFdZvqmiAwMD9PX10d/fz9jY2BUfFxERQVRUFJGRkfItIiLC70XMfNJqtYSGht7QPGS3201DQwNxcXFMTU3Jxc/lH6empnC73fII2ZWEhoZOK4AiIiLk7EUBNDc0Gg1paWmkpaWxZcuWK47yXLp0Sb7IOS0tjcLCQhYsWEBSUlLQjsSJ46+yRL7KEvkqS435zmuR41uVRM1qa2vJysrydzNU6fIVScLCwq74GJHv3JMkiZGRETo7Ozlw4ACpqam8/VK8iIgI4uLiiImJIS4ujujoaPR68R7IbOh0Otra2igqKrrqYzweD5OTk/J1TW//ePkI0dDQ0IzvN5lMctHjK3wiIyPFFLibdLVRnoaGBrq6uuTboUOHiIqKkgue7OzsoPp/Io6/yhL5Kkvkqyw15hs8R2choE1MTMjLct7IfHrh5ng8HgYHB+ns7KSrqwur1QrA5OQkkiQRFRVFYmIiSUlJJCQkBMQuxrcCrVYrj9BcicPhkBdysFgsTExMMDExwfj4ODabjcnJSSYnJ+nr65v2fXq9Xi58oqKi5JvJZBLFzyy9fZTHYrFQX19PXV0dzc3NjI2Nce7cOc6dOyfv41NYWEhhYSFms9nfzRcEQRDeotjqanMhEFdXs1qt4oXsHTh//jx/+9vfSE9P56Mf/ehVHyfyvTm+vQxaWlqYnJyUv67X60lJSSE2Npbs7OyrjqQJN0fJ/mu326cVPb6PFovlqivKGY1GueCJjo4mOjqaqKgoVU57s9ls1NfXU1hY6LdlTJ1OJy0tLXLRMzExId+n0WhIT0+nsLCQhQsXEh8f75c23gxx/FWWyFdZIl9lBUq+AbG6WrCqrq5mzZo1/m6G6vg2YrvehWsi39nzeDx0dHTQ1NREf3+//HWj0Uh6ejppaWkkJSWh1+s5e/asKHAUpGT/9e3Z8/aTZ7fbjdVqnbYS3ujoKBMTEzgcDgYGBhgYGJj2PWazWS56oqOjiYmJwWw2B/SoT2hoqN/3aTAYDPKoze7du+np6ZELnp6eHjo6Oujo6ODAgQPEx8ezcOFCioqKSE1NDehsfcTxV1kiX2WJfJWlxnxFkTNLIyMj/m6C6kiSJO/cm5eXd83HinxvnMvloqWlhdraWnk6mkajITk5mZycHNLS0mYsEiDyVZY/8tXpdPJUtcu53W4mJiYYHR1ldHRULn6mpqbkKXFdXV3y441GIzExMcTGxsofA6nwaWlp4ctf/jK/+tWvyMnJ8Xdz0Gg08o7cW7ZsYXx8nPr6empra2lpaWFwcJBjx45x7NgxoqKiKCoqoqioiKysrIC9gFccH5Ql8lWWyFdZasxXFDmzdKssJTqXRkdHsdvt8pSpaxH5Xp/L5aKhoYG6ujp5qefQ0FDy8/PJycm55nCyyFdZgZSvTqeTR2ouZ7fb5YLn8pvD4ZCXUfYJpMJnZGSEQ4cOMTIyEhBFzttFRkayatUqVq1ahc1mo6GhgZqaGhobGxkbG+PMmTOcOXMGk8nEggULKCoqIi8vL6AWLgik/huMRL7KEvkqS435imtyZsnpdKpyPrs/1dbW8sc//pHk5GQee+yxaz5W5Ht1kiTR2tpKVVWVfL2N2WymqKiInJycGzpZEvkqS635ut1uxsbGGBkZYWRkhOHhYUZHR694rY/RaCQ2Npa4uDji4+OJjY2dl4UrysrKWLly5Q1vBhoonE4nzc3N1NTUUFdXx9TUlHyf0WgkPz+fhQsXUlhY6PcFQNTaf9VC5Ksska+yAiVfcU2Ogg4ePMiuXbv83QxV8S2Fm5CQcN3HinyvbHR0lPPnzzM4OAh4i5vFixfPeuqLyFdZas1Xp9MRGxtLbGys/LWrFT4Oh4Pe3l56e3vlx0ZGRspFT1xcHJGRkQE7JWu+GQwGFixYwIIFC/B4PLS1tVFbW0tNTQ3j4+Pyfjx6vZ6CggIWLVpEYWEhRqNx3tuq1v6rFiJfZYl8laXGfEWRIyjO985lIKzKoTYej0c+CfJ4PBgMBoqLiyksLLylNuUU5t+1Cp+hoSH55lvlbXx8nJaWFsC7mt/lRU9cXJzfRykCgVarJScnh5ycHO688056enqoqanh0qVLDA0NUVNTQ01NTUAUPIIgCGonipxZut6F88JMviLnRlb1Evn+g9Vq5fTp0/LKWOnp6axYsQKTyfSOf6bIV1nBnu/lhU9BQQHgvcbHV/AMDg4yNDSEy+WacX1PZGQkCQkJ8m22b3qkpKTwxBNPXPe6PrW4fOGCbdu20dfXR3V1NdXV1QwPD/ul4An2/utvIl9liXyVpcZ8RZEzS/5cvlStHA4HwA29OIt8vQYHBzl+/Dg2mw2DwcDKlSvJzs6+6Z8r8lXWrZhvSEiIfLIO3tHH8fHxaUWPb6RnfHxcXk7ebDbLBU9iYiLh4eHXXNAgJSWFf/u3fwuaIudyvlURk5OT/Vrw3Ir9dz6JfJUl8lWWGvMVRc4sVVdXk56e7u9mqIpvWpXb7b7uY0W+0NHRwenTp3G73cTExLB+/XoiIiLm5GeLfJUl8vVOyfKt6uZ7589utzM4OCjv2TMyMiIvY93a2gp4X0ATExPlwicqKmpa0TM+Ps7zzz/PE088ETAL0SjhRgseg8FAUVERJSUl5OXlzcn0VdF/lSXyVZbIV1lqzFcUOYLifKt+uVwuP7ck8LW1tXH69GkkSSI9PZ21a9cGxGomgnAzQkJCSEtLIy0tDfCu0jM0NMTAwAD9/f0MDQ1hs9lob2+nvb0d8I78JiYmkpSURGJiIo2NjXzta1/jrrvuUtXqajfjegVPVVUVVVVVhIWFUVxcTElJCVlZWQGzt5EgCII/iSWkZ2liYmLO3lW/Vezfv5/jx4+zdu1a7rrrrms+9lbOt7OzkxMnTiBJErm5uaxatWrOV6i6lfOdDyLfd8btdstFz8DAAIODgzPeFOnu7ubzn/88r732Gtu3b7+pa9PUTpIkuru7qaqq4uLFi1gsFvm+yMhIFi9eTElJCcnJybMqeET/VZbIV1kiX2UFSr5iCWkF1dfXs3LlSn83Q1WioqIAGBsbu+5jb9V8h4eH5RGc3NxcVq9erci7sbdqvvNF5PvO6HQ6EhMTSUxMBLxFz8jICP39/fT19TE4OIjdbgfg4sWLWK1WIiMjSUxMJDk5mYSEhFtq9TaNRiOPjO3cuZPW1lYuXrzIpUuXGB8f5+TJk5w8eZK4uDhKSkooKSkhLi7uuj9X9F9liXyVJfJVlhrzFUXOLPn2KRFunK/IGR0dve5jb8V8HQ4HJ06cwOVykZKSwqpVqxSbbnIr5jufRL5zQ6fTER8fT3x8PMXFxbhcLg4ePAggX6vjW8igsbERjUZDTEwMSUlJJCcnEx8ff8sssa7VasnNzSU3N5e7776bxsZGqqqqqKurY2hoiMOHD3P48GFSU1NZunQpixcvvurKdqL/KkvkqyyRr7LUmK8ocmbpVp4i8U753kEcHBzE7XZf8+TjVsz3/PnzWK1WwsPDWb9+vaKbKN6K+c4nka8y9Hq9PHKxefNm8vPzGRgYkJepHh8fZ3h4WL4wX6/Xy9eypKSk3DJ7dOn1eoqKiigqKsJut1NXV0dVVRVNTU10d3fT3d3Nnj17KCgoYOnSpRQWFsrXTILov0oT+SpL5KssNeYrrsmZJY/HI3byniVJkvj+97+PzWbjscceIzk5+aqPvdXy7e3t5fDhw2i1WrZt20Z8fLyiz3er5TvfRL7Kulq+k5OTcsHT29uLzWabdn9UVJRc8CQkJNwyozw+VquVixcvcuHCBbq7u+Wvh4aGsnjxYpYuXUp6ejqSJIn+qyBxfFCWyFdZgZLvbGoD/7dWZfbt2+fvJqiOb9M7gK6urms+9lbK1+PxUF5eDkB+fr7iBQ7cWvn6g8hXWVfL12QykZOTw7p167j33nvZuXMnJSUlJCQkoNFoGBsbo66ujsOHD/Pyyy9z9OhRGhoamJiYmOffwD/MZjNr167l4x//OI8//ji33347kZGR2Gw2zp8/z69+9SueeuopfvSjHzEyMuLv5gYtcXxQlshXWWrMV0xXE+ZFRkYGzc3NNDU1qe7CNaV0dXUxNjaG0Whk0aJF/m6OIAS0yspK3v/+93P06FGWLFly1cdpNBpiY2OJjY1l0aJF2O12eYSnp6eHqakpeeoWQEREBKmpqaSlpREfHx8Q71QqKSEhgR07drBt2zba2tq4cOECly5dYnh4mMbGRn7yk5+QlZXF0qVLWbRo0S21oIMgCMFFFDmzNBe7zt+KCgoKOHLkCE1NTde8LudWyreurg7wZjNfJxK3Ur7+IPJVjsvlYmxsbNb7bYWEhJCZmUlmZiaSJDE2NkZPTw89PT0MDg4yMTFBXV0ddXV1GI1GUlNTSU1NJSUlJaj3qNJqteTk5JCTk8Pdd99NbW0tb775JlNTU7S1tdHW1sbf//53Fi1axPLly8nMzBT779wkcXxQlshXWWrMVxQ5sxQo1wapTVpaGmazGavVSnt7Ozk5OVd83K2S78TEBIODg2i1WvLz8+fteW+VfP1F5BvYNBoN0dHRREdHs3DhQpxOJ729vXR1ddHT04Pdbqe1tZXW1la0Wi2JiYmkpaWRmpoa1IsXGI1GlixZQkJCAmazmcrKSioqKhgcHKSiooKKigri4uJYvnw5S5cuDYi9MtRIHB+UJfJVlhrzFUXOLFVWVpKSkuLvZqiORqOhsLCQ8vJyqqqqrlrk3Cr5dnZ2ApCYmEhYWNi8Pe+tkq+/iHzVxWAwkJGRQUZGBh6Ph6GhIbq6uuju7mZ8fJze3l56e3spLS0lOjpaLnhiY2ODclSjsrKSXbt2cfvtt7NhwwY6OzspLy/n4sWLDA0NsX//fg4cOEBBQQHLly+nsLDwllvE4WaI44OyRL7KUmO+osgR5s3SpUspLy+nurqau+66K6inglxPf38/4B3hEgKXJEm4XK5pN7fbjcfjmXGTJAmr1Upra+sVf5ZWq0Wj0aDVamd8rtVq0ev16HQ6dDqd/HkwnkgHKq1WS0JCAgkJCSxbtozx8XG6u7vp6upicHCQ0dFRRkdHqa6uJiwsjPT0dDIyMoL2Oh6NRiMXgHfeeSfV1dWUl5fT3t5OfX099fX1mM1mli5dyvLly0lISPB3kwVBEKYRS0jP0ujoKNHR0f5uhipJksRPfvITRkdHuf/++6948fCtkK8kSbz88ss4HA527txJbGzsvD33rZDvbLjdbqampnA4HNjtdhwOx7TP3W43szlE2mw2QkND56x9voLHYDDIH9/+ucFgwGg0Bv076haLhZMnT7J+/XrCw8Pn9bntdru8WEFPT8+064JCQ0NJS0sjIyND9ctT38jx4fIpbBaLRf56eno6K1euZNGiRRiNRoVbqk7i+Ksska+yAiXf2dQGYiRnllpbW1m2bJm/m6FKGo2G5cuXc+jQIc6cOUNJScmMd6pvhXxtNhsOhwONRkNUVNS8PvetkO+VSJKE3W7HarUyNTXF1NQUk5OT2O32G/p+X7Hhu10+AuMbldHpdDQ2Nl51dE6SpGmjPpePArndbvnmcrnweDwA8tdupJ16vR6j0UhISAgGg4GQkBCMRiNGo5HQ0FD0er2qR4bCw8NJTEyc9wIHvIsX+C7Sd7vd9PX10dnZSWdnJzabjaamJpqamjAajXLBk5SUpLqC50aOD/Hx8fLqbA0NDZSXl1NfXy/nsWfPHpYsWcKqVatITEycn4arxK16/J0vIl9lqTFfUeTMUl9fn7+boGqrVq3i2LFjdHV10dHRQWZm5rT7b4V8rVYrAGFhYfN+EnQr5AvegmJqaoqxsTEsFgsWiwWn03nFxxoMBkJDQ+UCwVcYGI1GDAYDOp3uhqcjXbx4cU6mIF5e+LhcLpxOJ06n84qf+0acfNPpJicnr/gzdTodoaGhhISEEBISIn8eGhqKwWAI+AKos7OTb37zm/z0pz8lPT3db+3Q6XTyCmwrV65kYGBgWsHT0tJCS0sLBoOB1NRUMjIySE5ORq8P/Jfb2RwftFotCxYsYMGCBVgsFioqKigtLWVkZISzZ89y9uxZMjIyWLVqFcXFxbf09GSfW+X46y8iX2WpMd/AP+oGGDEMf3N8c7hLS0s5efLkjCLnVsjXd7Ltj/0ngjlfj8fD+Pg4IyMjjI2N4XA4pt2v1WoxmUyYTCbCwsLk22xPvtxu6O+H3l7o6YHhYRgf996qqor4619hYgJsNnC5pt+cTtBoQK/33gyG6Z+Hh/tu2rduBsLDITIS4uIgPt77MToafPWxJEm43W55qt3bb5dPvbNarXKRfTm9Xk9oaChhYWHTPoaEhARM8dPf38/LL7/M1772Nb8WOZfT6XQkJyeTnJzMihUrGBwcpLOzk46OjmlLMev1elJTU8nMzCQlJSVgR3je6fEhPDxcXqygubmZ0tJSamtr6ejooKOjgzfffJOlS5eycuXKW/ranWA+/gYCka+y1JivuCZHmHeDg4P853/+J5Ik8YlPfEJ1q3XcrK6uLo4dO0ZcXBx33HGHv5ujapIkYbFYGBgYYHR0dNq1ElqtloiICCIjIwkPD8dsNt/wiEx/P9TXQ0PDP25NTdDdDQMD8NZsMr/RaCA21lvwJCRAaurVb5GR3gLQbrdjt9ux2WwzPr/ay4BWq5WLQV+BaDKZ/DIqUVZWxsqVKyktLWXFihXz/vyzIUkSQ0NDdHR00NnZOa2wNBqNpKenk5mZSWJiYlAuWgDeZfJ9ozujo6Py17Oysli1ahULFy5UxeiWIAiBRVyTo6A9e/awa9cufzdD1eLj4ykpKaGyspIDBw7w0EMPyffdCvn63hn3+OFMOVjydbvdDA4OMjAwMG16ltFoJCYmhujoaCIiIm7oBLK/H86ehdJSOH/e+7Gn59rfo9VCYiIkJ3tHV6KivMXEyEgbixdnEREBYWEzR2t853S+UZ3LR3gcDrBawWKZeRsbg6EhGBz0jhhJkvffQ0PeYuxaYmIgN1dLTk7YWzfIyYHcXFiwAAwGDzabDZvNxtTU1LSPHo/niqM/RqNxWtFjMpkCatTH3zQaDfHx8cTHx7Ns2TJGRkZoa2ujo6ODyclJmpubaW5uJjQ0lIyMDDIzM4mPj/d7fnN5fIiIiGDjxo1s2LCBpqYmSktLqa+vl0e3TCYTK1asYNWqVQFxMfN8CJbjb6AS+SpLjfmKIkfwiy1btnDx4kUaGxtpa2sjKyvL302aN74h37dPpxKuz+12MzAwQE9PjzztT6vVEhcXR1xcHBEREdc9UbRY4OhR2L/fe6uqmvkYjQaysiA/HwoKvLf8fEhPh5QU7+jJlWYc7dlTy65dyvZlp9M7RW5w0Fvk9PV5i7Lu7pm3sTEYGfEWbqWlV/49U1O1FBaaKCoysXAhFBXBwoWQmirhcNiZnJyUF2rwLdbgmwp3+Tv0BoMBk8lEeHi4/FFch+EteGJjY4mNjWXZsmUMDAzQ3t5OR0cHNpuNhoYGGhoaMJvNZGRkkJWVRXR0tN8Lnrmi1WopKCigoKCA8fFxysvLKSsrY2xsjOPHj3PixAkWLFjAmjVryMnJCZrfWxAE/xNFzixlZGT4uwlBITY2lhUrVnD+/Hn27NnDRz/6UbRa7S2Rr2+JYd875fM5XUWt+UqSxMjICO3t7XJxGBISQnJyMnFxcded9jI+Dq+9Bi++CHv3ekdNfDQa70n9qlXe28qVsHQpvJMN7ucjX4MBkpK8t+uZmIDWVmhp8d6am//xeUuLd+Soq8t7O3Ro+veGh2soKgqlqCiUhQu9GS1dCosWuZmampSLH9+KdU6nk7GxMcbGxuSfYTQaMZvN027vdIpSfHw8Dz74IPHx8e/o+wOBRqMhMTGRxMREli9fTl9fH+3t7fKUttraWmpra4mMjCQrK4vs7GzM76QjvkNK99/IyEg2b97Mxo0bqa+v5+zZszQ3N8u/d3x8PGvWrGHp0qV+uWZRaWo9/qqFyFdZasxXXJMzS/39/WJZzDlisVj42c9+hs1m413veherVq26JfL1eDz8+c9/xu12s3v3biIiIubtudWYr8PhoLW1VR41CAkJITU1lbi4uGsWiJLknYb29NPe4sZm+8d92dlwxx2wYwds2+adcjYX1JSvJHlHg5qavFPeamqgttb7sbHRu8DClURGeoudpUth2TLvbeFCDx7PpDy1zVcAvf3lRaPREBYWRnh4OBEREYSHh8/qZFZN+c6Gy+Wip6eHtrY2enp6cF8WfmJiItnZ2WRkZCg+MuaPfAcGBjh37hwVFRXyGxhGo5Fly5axevXqoFqoIFj7b6AQ+SorUPKdTW2gaJHzve99j7/85S/U1tYSFhbG+vXr+f73v8+CBQtu6PsDschR45zEQHbmzBn+/ve/ExYWxhNPPMGxY8duiXz37NnDyMgIGzZsmNd3R9TWf8fGxmhubsbpdKLVaklJSSE5Ofmaq1NJErzxBnzzm94ix2fBAnj/++GBB6C42DuCM9fUlu/VOBze4ufywqe62nu70ixLnc47zW3ZMli+HNasgWXL3EiSVS58rFbrFff7CQkJITw8XC58wsLCrjhlaXJykueee45HH30Uk8mkwG8dGBwOB11dXbS2ttLf3y8Xinq9nvT0dLKzsxVbsMCf/ddut3PhwgXOnj3L4OCg/PXc3FzWrFlDYWGh6hdpCJbjQ6AS+SorUPINmIUHjhw5wuOPP87q1atxuVx89atfZefOnVy6dGleh+CFwLV69WrKysro6+tj7969hIWF+btJ8yIuLo6RkREGBgZUOQQ8HwYGBmhtbUWSJEwmE3l5edftHydOwGc/C+fOef8dEuItaj75SVi3TpnCJhgZjchT1C7ndHqLnooKuHDB+7GiwnttkK8I+t3vvI/V6XQsXhzJ2rWRrFkDa9dCSYmDqSnvvkUTExPyNT52u52hoSHAezLvK3giIyMxmUxoNBpqa2t54oknWL9+fcCvrnYzjEajvPGo1Wqlra2N1tZWxsfHaW1tpbW1FZPJJE9nm+8NhZUSEhLCmjVrWL16NS0tLZw9e5a6ujp5kYbo6GjWrl3L8uXL5Sm/giAI1zKv09UGBgZITEzkyJEjbNq06bqPD8SRnKGhIeLi4vzdjKDS0dHBc889hyRJ3HXXXaxdu9bfTVJce3s7J0+eJCoqirvuumvenlct/bevr4+2tjbAey1GVlbWNUdvRkbgi1+EX/3K+2+TCR5/HL7wBe8qaPNFLfnOJUnyLnLgK3hKS+HMGe/X3i483Hvd05o13qLzttvchIX9o+ixWq3TpmqBt+iJiIigtbWVnTt3cv78eVauXDkvv1ugkCSJ4eFhWlpapl2XBt7rG3NycsjMzLzp61gCrf+OjY1x/vx5SktL5VUUQ0JCWL58OWvXriUmJsbPLZydQMs32Ih8lRUo+QbMSM7b+S5IjY2Nnc+nnVPd3d0B8UcOJhkZGdx2222cPHmSl156iSVLlgT9iE5SUhJarZaxsTHGx8fnrYhXQ/8dHh6WC5yUlBTS09OvueLSmTPeaWhvfQv//M/w3e/Ob3Hjo4Z855pGA2lp3tvu3f/4eleX929z9qz34/nz3pXtDh/23rx0FBVFsXFjFJs2we23SyQkTDIxMcHExATj4+M0N+uYnLTT2moHlrNnzwCdnR0kJ5tZtswclBeov51Go5FXEFy+fDk9PT20tLTQ09PD8PAww8PDlJeXk5aWRl5eHklJSe9olbJA679RUVFs376dTZs2UVlZyenTpxkYGOD06dOcOXOGoqIi1q1bR2ZmpipWZQu0fIONyFdZasx33kZyJEni3nvvZWRkhGPHjl3xMb5pCz7j4+NkZGQE1EhOoMxJDDZOp5Of//znnD59mvvuu4/7779fFS9aN+PIkSP09PRQUlLCokWL5uU5A73/Tk1NUV1djcfjISkp6bonL7//PXzkI95pVHl58PzzsGHD/LX37QI9X39yu73X9viKnhMnvNPb3i4jAzZu9N4yMyV277763/+lly5QUOA9GY6KiiIyMvKaI37Bxmaz0d7eTktLCyMjI/LXzWYzubm55OTkzOr6pUDvv5Ik0dTUxOnTp2lsbJS/npqayrp161i0aFFA//0DPV+1E/kqK1DyDciRnE9/+tNUVlZy/Pjxqz7me9/7Ht/4xjdmfH3//v2YzWa2bdvG2bNnsVgsxMTEsGjRIvnnFRUV4fF4qH9rZ7zNmzdTUVEhh7BixQoOv/X2YUFBAXq9npqaGgBuv/12Ll26xPDwMGazmXXr1nHgwAHAe9GjyWTi4sWLgHcVnLKyMgYGBggNDWXTpk3s3bsXQN7f4MKFCwCsWbOG9vZ2ent7MRgMbNu2jb179yJJEunp6SQmJlJWVgbAypUr6e3tpaurC61Wyx133MGBAwdwuVzyu9nn3rrQYNmyZQwPD9Pe3g7Arl27OHz4MHa7ncTERHJzczl9+jQAJSUlWCwWWlpaANixYwcnT55kcnKSuLg4ioqKOHHiBADFxcU4HA75xWPr1q2cP3+eiYkJoqOjWbJkCUePHgWQF4+oq6sDkN9pGx0dJSIiglWrVnHorTVp8/PzMRqNXLp0CYANGzZQW1vL0NAQJpOJ9evXc/DgQaKjo3E4HJw8eZLe3l7y8/NZt24dzc3N9Pf3ExISwpYtW9izZw8AmZmZxMbGUlFRAXiv7+ns7KSnpwe9Xs/27dvZt28fHo+HtLQ0kpOTKX1rs5AVK1bQ399PZ2cnGo2GnTt3cvDgQZxOJ8nJyWRmZnL2ravWly5dyujoqDy6sHPnTo4ePYrNZiMhIYH8/HxOnToFwOLFi+XN/gC2b9/O6dOnsVqtxMbGUlxcLPfZiIgIRkZGeOONN+jo6GDr1q2UlZXJ/4GXLVvGkSNHAOSLbmtra+U+W11dzcjICOHh4axZs4aDBw8CkJeXR2hoKNVvnUGuX7+e+vp6BgcH6e7uxuPxsG/fPgCys7OJjIyksrISgLVr19La2kpfXx9Go5GtW7fKeWdkZBAfH095eTkAq1atoru7m+7ubnQ6HTt27GD//v243W5SU1NJTU3l/PnzACxfvpzBwUE6OjrkPnvo0CEcDgdJSUlkZ2dz+vRpJiYm5FXTampqqK2t5Y477uDEiRNMTk4SHx9PYWEhJ0+e5I030nnqqWIkScOGDX187nMXWbNmEydO+O8Y0d7ezvj4OI2NjeIYcYVjhNVaSVraKA8/HMFTT63i1VePU10dTVdXLqWlYVRWGujo0PL733sLWPAWOC+8MP36oJoaeOghqK/vISnJTFtbG1arFb1ez5IlS6itrcVgMJCdnU1cXJxqjxELFy7E5XLR0NAAePcXe/sxwvd3Ky4upquriwsXLuB2uxkdHWXPnj0YjUaysrLYuXOn3L+vdoxob2/n2LFjbNiwISCPEWfOnJEzy8vLY+/evTQ3NyNJEv/xH/+BwWBgzZo13HPPPfLffNGiRdhsNpqamgD8eh7R3t7Onj17uO2228Qx4irHiJs5j/Dlm5OTQ3h4OFVvbYIWzOcRN3KMmKvziIGBAfbs2YPJZPLrMcLX/hsxLyM5TzzxBK+88gpHjx4lJyfnqo9Tw0iOoKxjx45x4MABDAYDH//4x4Nq+dC3c7lcvPbaazgcDjZt2kRqaqq/m+RXQ0NDNDU1odVqKSkpueY0pL/+Fe6913s9yOOPw09/CipfeEnAu2/P6dNw7Jj3duIE2O3e63wuX2ugrMy7n9HevW5WrRqX9+d5++ptISEht9woj8vlorOzk6amJgYGBuSvh4WFyaM74eHhfmzh3LJarZSWlsqFC3g3pl2xYgW33XYb0dHR/m2gIAhzKmCWkJYkiSeeeIKXX36Zw4cPU1BQMKvvD8SFB/bv38+OHTv83YygtW/fPnp7e2lqaiIxMZGPfexjQb1renl5OXV1dSQlJbF161bFny9Q+68kSVy6dAmr1UpaWhppaWlXfWxjo3eZYosFPv5xeOaZwFk1LVDzVaszZ7wLFFytyAHv17dvhx07JNassWGzeQueiYkJPB6P/D1arZaIiAiio6OJiYnBaDTO828z/7zXNDXT2tqK7bKNopKSksjLyyMtLW1a4afm/utyuaiurubUqVP09vYC3r/54sWLWb9+PcnJyX5uobrzVQORr7ICJd+Ama72+OOP8/vf/55XX32ViIgI+cATFRWl2gvL377yjzC3PB4P9913H8888wz9/f289tprQX19TmFhIQ0NDfT19TEwMKD4yFWg9t/JSe9Gklqt9pqbjUkSfOpT3gJn0yb42c8Cp8CBwM1XrW7k/Y2yMu/tBz/QYDaHsWNHGHffncyuXW4iI6eP8vg+b2trIzw8XC54QkNDg/IYExkZybJlyygpKaG7u5umpib6+vrkW2hoKHl5eeTl5WEymVTdf/V6PUuXLmXJkiU0Nzdz4sQJmpubqayspLKykvz8fDZs2EB2drbf/tZqzlcNRL7KUmO+ihY5Tz/9NOCdI3i5X//613zkIx9R8qkVc6tPKVJaamoq4eHhvO997+P555+nqqqKlJQU1q9f7++mKcJsNpOdnU1zczMXL15ky5Ytir4AB2r/9a28GBUVdc2RuzfegH37vPvf/OpXN3YSPJ8CNV+1e+uyhxn/3rMHBge9fWLPHujpgVdf9d5Ax9KlMdx9dwx33y2xdKkNi2WUkZERrFYrFot36erOzk5CQ0Plgic8PDzoCh6dTkdGRgYZGRlYrVZ57xnfQh81NTWkpaURFhaGJEmq/v01Go1cuPX09HDixAmqq6tpbGyksbGRtLQ0NmzYQFFR0bxvLiqOD8oS+SpLjfnO6z45sxWI09UCZZ3wYHV5vmfPnuWNN95Ao9Hw0EMPkZeX5+fWKcNqtfL666/j8XgUvzYnUPtvXV0dY2NjZGVlkZSUdNXH7dzpPaH9/Ofhhz+cxwbeoEDNV60aGqCw8Or319eDbxa0JHn36Xn9dW8xfPq092s+MTFw553eZa537nQC3oJnfHx82rQ2g8EgFzyRkZHzfiI8X9xuN11dXTQ0NMjX7kxNTZGcnEx+fj7Z2dlBM1V4ZGSEkydPUl5ejsvlArxbWaxfv56lS5fO2+8pjg/KEvkqK1DynU1tEJxHbwX5VnkQlHF5vqtXr2b58uVIksRLL7007SLaYGI2myl860yuoqJC0SHhQO2/vgvGr7XcbXe3t8DRaLyLDQSiQM1XrQoKvIVMaSm88EINsIIXXqihtHR6gQPefrF8OXzta3DyJPT3e1dl+8AHvAXOyAj84Q/eVdlSUw089FACBw8Wkpy8nPz8fOLi4tDr9TidTgYGBqivr6eiooKWlhbGxsYI4PcD3xGdTkdmZibbt2/nzjvvJD8/n97eXsbGxigtLeW1116jtLRUHmVVs5iYGHbv3s1nP/tZNm/eTFhYGMPDw/ztb3/jJz/5CSdOnJixaIUSxPFBWSJfZakx33ndDFQQZkOj0bB79255WdHf/e53fPSjHw2qlYF8iouLaW1tZXx8nLq6OoqLi/3dpHnldDoBrvmO6lsribJiBVxjkUYhyPyjkJkCylm4cGraIgRXEx8PH/yg9+ZyeRcxeP11eO017/48e/d6b5/6lI7bbovlvvtiufdeD4mJE4yMjDAyMiIXPAMDAxgMBmJiYoiNjSUiIkLVU7reLjo6mlWrVtHX10dBQQGNjY2Mj4/T0NBAQ0MDSUlJFBQUkJqaquqRLbPZzNatW9mwYQPl5eWcPHmSsbEx9u3bx/Hjx1m3bh1r164lNDTU300VBGEOiOlqs9Tf33/NC6OFm3OlfCcnJ/nlL3/J8PAwqampfOQjHwnKlZFaW1s5ffo0Op2OO++8k4iIiDl/jkDtv+fPn8fj8bBkyZKrnmD867/CT37i/fgf/zGvzbthgZpvMBgdHeW1117j3e9+900vC1xfDy+/DH/5i3dz0suVlMD998N990lkZ08wMjIsFzw+BoOB2NhYYmNjg+oaHl//lSSJvr4+Ghsb6erqkkexwsPDKSwsJCcnJyimsrndbqqqqjh27BhDQ0OAd9nxNWvWsG7dOsxm85w+nzg+KEvkq6xAyVdMV1PQ4OCgv5sQ1K6Ur8lk4qGHHsJkMtHd3c2f/vQnVa7ycT1ZWVkkJyfjdrs5c+bMtOsE5kqg9l/fMrbX+p27u70fs7PnoUHvUKDmGwx8ow1zse9JYSF86Uve0Z2ODu8qfdu2gU4HVVXwjW/AsmUabrstkt/+NpuwsKUsWLCAhIQEeUpbX18fNTU1XLhwgY6ODqampm7+l/QzX//VaDQkJydz++238653vYvi4mKMRiMWi4WysjL++te/UlFRgdVq9XOLb45Op2PZsmU8/vjjvPe97yUxMRG73c6xY8d48skn2bNnDxMTE3P2fOL4oCyRr7LUmK8ocmbJtxuzoIyr5RsbG8sHPvAB9Ho99fX1vPrqq0E3R16j0bB69WoMBgODg4PyTsNzKVD7r+9d4WvNi/ddGhATMx8temcCNd9g0Nvbyw9+8AN5K4K5kp7uvcbrwAHo64Pf/Abe/W4wGr2ruP37v8PChVq2bYviT3/KITZ2GYWFhcTHx6PT6XA4HPT09FBVVUV1dTV9fX3TRn3U5Er912w2s2TJEt797nezatUqIiIicDgc1NbW8vrrr3Py5El5FEStfPvpfPKTn+TBBx8kNTUVp9PJqVOn+MlPfsLrr7/O6OjoTT+POD4oS+SrLDXmK4ocQTUyMjJ44IEH0Gq1VFZW8sYbbwRdoWM2m1m1ahUAly5dor+/388tmh++BQcmJyev+hjfzBGVv3ksvEPd3d385je/ods3pKeAuDh4+GHvEtR9ffDrX8OuXd4RnrIy+Ld/g5wcLe96VzRvvJFLaqp30YKYmBg0Gg1Wq5W2tjYqKipoaGhgeHhYkRFZf9Dr9eTn53P33XezadMmkpKS8Hg8tLe3s2/fPg4cOEBnZ6eqf1+NRkNRUREf+9jHeOihh8jIyMDlcnHu3Dl++tOf8tprr81JsSMIwvwQ1+QIqlNVVcVf/vIXJEli48aNbN++3d9NmnNnzpyhpaUFs9nMzp07CQkJ8XeTFNXb20t7ezuRkZEUFRVd8TEf/ah3b5x//3fvdCLh1lJWVsbKlSspLS1lxY2sPDCHBgbgz3/2rs527Ng/lqbWamHHDvjIR2D3bidTU8MMDQ1hsVjk79Xr9cTGxhIXFxdU1++Ad2nmuro62tvb5eImmK7bkSSJtrY2jh49SnNzM+Cd4rZixQo2btwozksEwQ/ENTkKOuRb4klQxI3kW1JSwu7duwE4duwYhw8fVrhV82/FihVERERgtVo5derUnL07Gqj913edxcTEhLyPxdstWuT9WFk5T416BwI136uRJCnoRkOVkJAAjz0GR45Aezv8+MewZg14PN4V2v7X/4KMDAP/+38nMTJSzOLFJaSmpmI0GnG5XPT391NTU8PFixfp6ekJ2Olss+2/MTExrFu3jnvuuWfGdTt/+9vfqK6unpelmZWi0WjIzs7mwx/+MI8++ii5ubm43W7OnTvHT37yE/7+97/P6podtR0f1Ebkqyw15iuWkJ4lh8Ph7yYEtRvNd9WqVTgcDvbu3SsXOVu2bFGuYfPMYDCwfv16Dhw4QG9vLxUVFXPy7nWg9t/Q0FBMJhOTk5OMjIyQkJAw4zG+X//UKe/JZSCuZOvvfCVJwuPx4Ha7cbvdeDwe+eYraC4vbHwfLx9d8H2u1WrRaDTTblqtdsbNd9+tIj0dPvtZ762xEf77v723tjb4xS+8t/z8MB5+OJ0PfSiNmJgJBgcHGRkZYWpqio6ODrq6uoiOjiYhIYHIyMiAye+d9t+wsDCWLFnCwoULaW1tpa6uDovFQlVVFTU1NeTn57NgwQLCwsLmuMXzJzMzkw9/+MO0trZy6NAh2traOHPmDKWlpaxevZoNGzZcd3sDfx8fgp3IV1lqzFcUObN0rd3YhZs3m3zXr1+PJEns27cvKAsd37ukx48fp76+nqioKPLy8m7qZwZy/42Li2NycpK+vj7i4+NnnPjddhtERnqvlThzxvvvQDPf+UqShNPpxOl04nK5cLlc72hk5vLv8X1+o6OHGo0GnU6HVqud9tH3+VydwEdHR7Njx445WV1truTnwze/Cf/n/3hHeZ5/Hv70J2/x87//N/z7v2vYujWSj3wkkvvuczM5OcTg4CAWi4Xh4WGGh4cJCQkhISGB+Ph4vy+Nf7P912AwUFBQQF5eHh0dHdTU1DA6OkptbS0NDQ3k5ORQVFSk6r3OsrOz+chHPkJraysHDx6ko6ODU6dOcf78edasWcP69euvuvR0IB9/g4HIV1lqzFdckzNLo6OjAfUiG2zeSb4nTpxg3759AGzevJktW7YEzDujc6G6upqqqiq0Wi1btmy5qXXqA7n/ulwuKioq8Hg8LFiwgKioqBmP+V//y3tdxKc/DU895YdGXsd85CtJEg6HQ769/RDuKzp8RcbbR1wuH3m5/P/J2wsd3+3yUaDLR4Z8t2vxtUWv18ttupniJ5D7r4/F4r1+5ze/gctn0sbEwIc/DJ/4BGRlTTIwMMDQ0JA8PVOj0RAdHU1iYqLfRnfmOl9Jkujp6eHSpUvTlqfOzMxk4cKFAf+3vB5JkmhububQoUN0dnYCYDQaWbt2LRs2bJix55ca+q+aiXyVFSj5zqY2EEXOLO3Zs4ddu3b5uxlB653me/LkSfbu3QvAunXr2LVrV9AUOpIkcerUKdrb2zEYDGzbto2Yd7iOcqD337a2Nvr6+jCbzRQXF8/4G+7bBzt3Qng4dHV5R3YCiZL5SpKEzWbDZrNN2ydKp9NhMBjQ6/VyMTFfff/y6XFvnybndruvOqqk1Wrl9vpu2uvMP3Q4HPzP//wPDzzwgN9HPG5Ua6t3Kttzz3mns/ls3Oi9xuc973EzOTnCwMDAtGs7wsLCSExMJC4uDr1+/iZcKNV/JUliYGCAmpoaenp65K+npqZSXFxMfHz8nD/nfJIkicbGRg4dOiSv/hcWFsbGjRvlbQEg8I+/aifyVVag5CsWHhBuOevXr+fuu+8G4PTp07z22muqXsr0chqNhjVr1pCQkIDT6eTIkSNzukFdIElNTUWn02G1WhkeHp5x/44dsHCh993yZ57xQwP9xOl0Mjo6itVqxe12o9VqCQsLIzo6mujoaMLDwwkNDUWv189rce8bqTEajYSGhmI2m4mMjCQ6OprY2FhiYmKIiIjAZDIREhIit8/j8eBwOJicnGR8fJzh4WFGRkaYmJhgamrqitPuLl68yIc+9CEuXrw4b7/fzcrO9q4G2NQEr7/u3X9Hq/Wu0PbBD0Jmpo7/+3/j0ekWUlJSQlJSEjqdjqmpKdra2rhw4QKtra3XXFpdDTQaDYmJiWzevJmdO3eSmZmJRqOhu7ub/fv3c/jwYVVuNOij0WgoKCjgYx/7GA8++CAJCQlMTU2xd+9ennrqKcrKyoLm9UgQ1ESM5MxST08PKSkp/m5G0LrZfC9cuMArr7yCJEkUFxdz//33z+s7oUpyOBwcOnSIkZERzGYzO3bsmPWFvGrov11dXXR1dWE0Glm8ePGMv9/zz3uX7I2O9p48xsb6pZlXNNf5SpLE1NQUU1NTSJKEVquVCwa1jlRKkiRfP+S7+abEXe7y0R6DwUBlZSWrVq3yyxLSc6mz07sU+i9/6f3cZ+tWeOIJ2L3bzcjIIP39/UxNTcn3R0ZGkpSURHR0tGJ/+/k8PkxMTFBTU0Nra6tcACQnJ7N48WLVj+x4PB4qKys5dOgQY2/tYhwfH8+SJUvYuHGjav/vBjo1vL6pWaDkK0ZyFDQ+Pu7vJgS1m8136dKlPPDAA+h0Oi5dusTvfvc7bDbbHLXOv4xGI5s2bZKXlj58+PCsfzc19N/k5GRCQ0NxOBxX3GH5oYegpARGR70XfQeSuc53amqKyclJJEkiNDSUmJgYQkNDVX2SpNFoMBgMhIWFERERQUxMDDExMURFRWEymTAajWi12mmjPWNjY/LJos1me8cLLASC9HT4+tehpQVeew127/aO7hw6BPffDwsW6Pjd75JIT19MUVERsbGxaDQaxsfHaWhooKqqir6+vmlTFufKfB4fIiIiWLNmDXfffTe5ublotVp6e3uDYmRHq9WybNkynnjiCXbt2oXJZGJwcJD/+Z//4Ze//CUtLS3+bmJQUsPrm5qpMV9R5MxSa2urv5sQ1OYi34ULF/LBD34Qo9FIS0sLzz33nHyCpHZhYWFs3ryZsLAwxsbGOHTo0KwKHTX0X51OR05ODhqNhoGBAUZGRt52P/zwh97Pn3rKu9JaoJjLfH0n+ABmsznoNpK8nFarxWAwYDKZiIyMJCYmhujoaMxmMyEhIWi1WrmomZqaYnR0VJ7eZrfbVTkVSK+He+6Bv/3NW/D8f/+fd1SytRU+/3nIyNDwla9E4vHks3TpUlJTU9Hr9dhsNnkqW0dHx5wu6+qP40N4eHjQFjt6vZ7bbruNf/mXf2Hz5s1YrVa6urp4/vnneeGFF+jt7fV3E4OKGl7f1EyN+YoiRwhKubm5PProo0RERNDf388vf/nLoHlBCQ8PZ+vWrdMKncuntQSDiIgIebnKlpaWGRsK7tzpHdHxeODRRyFIButkkiRhtVoB7x5Cat5f5J3QaDTo9fppoz0RERGAd5li33U9drudiYkJRkZGGBsbu+r1PIEuMxO++13o6IBnn/VufGu1wn/+JyxYAPfdZ+TSpXSWLFlKVlYWoaGhuFwuenp6uHDhAk1NTXJ/UatrFTvHjh1jdHTU3018x0JDQ9m6dSv33nsva9asQavV0tjYyM9//nNeeeUVVb5DLghqIK7JmSWPx3PdVYCEd26u8x0bG+N3v/sd/f39GI1GHnjgAfLz8+fs5/vTxMQEhw4dYnJyksjISLnwuRY19V+Px0NtbS0Wi4Xw8HCKioqmtX1oCIqLob/fu1LV00/7sbFvmat8nU4nY2NjaLVaoqOjVfM3U5KvqPFdj+RyuXA6nTgcDnkZZh/fYghGo3HeF2OYC5IEBw7AT37iXbDA9ypdXAxf/CJ84AMSk5Oj9PX1TTtBjoqKIjU19R2P+gXS8cFisXDp0iX5mh3f0tOLFy+WC1618eU7PDzMwYMH5UU0DAYDGzZsYP369apZOTAQBVL/DUaBkq+4JkdBJ06c8HcTgtpc5xsVFcWjjz5KTk4ODoeD3//+95SXl8/pc/hLREQEW7duxWw2Mz4+zsGDB7FYLNf8HjX1X61WS15eHnq9HovFQktLy7R36OPi4Le/BY3Gu9LaH/7gx8a+Za7ydTqdgPfkJxBeVAKBVqvl/Pnz8h47vult0dHRxMTEEB4ejtFoRKPR4Ha7mZqaYmxsjJGREaxWK06nUzUjPBqNdyXBv/4V6uq8CxKEh8OlS/DII5Cfr+HXv44hLa2IRYsWERcXh0ajYWxsjJqaGnkTztn+voF0fPCN7Nx5551kZmYiSRJtbW38/e9/59y5c6pccc6Xb2xsLO9973v56Ec/SkZGBk6nk8OHD/PUU09RXl6uyumXgSCQ+m8wUmO+4tVzltR4YFUTJfINDQ3loYceYsmSJXg8Hl599VX2798fFC8kERERbNu2DbPZzMTEBPv3759xDcvl1NZ/Q0JCyMvLQ6PRMDQ0JO9B4bNzJ3z1q97PP/pRKC31QyMvM1f5+vqmTqebk58XDOrr63n88cepr6+fcZ9OpyM0NJTIyEhiY2OJjIyUr+XxeDzTCh6LxaKqgqegAH76U+9KbN//PiQnez///OchKwv+7/81ExGRR0lJCYmJiWi1WiwWC/X19VRXVzM0NHTDv2sgHh8iIyNZv349O3fuJCUlBY/HQ1NTE6+//joVFRUzprIGsrfnm56ezqOPPsoDDzxATEwMExMTvPrqqzz77LNicYJ3IBD7bzBRY76iyJkltS9tGeiUylen03HfffexadMmAI4fP84f//jHoFh5zbecdHR0NDabjYMHD9LX13fFx6qx/0ZFRZGdnQ14l5ceGBiYdv//+T+waxdMTnov5L7CgmzzRo35qoXFYqGqquq6o5UajQaj0ShfyxMZGUloaKhc8NhsNsbGxhgdHWVyclKRVcqUEBUF//Zv3kUKnn3WW/yMjMC3v+0tdr74xVD0+myWLFlCSkoKOp2OyclJmpqaqKqqYnBw8LrFTiD339jYWDZv3sy2bdtISEjA7XZTW1vL66+/Tk1NjSr+jlfKV6PRUFxczOOPP87OnTsJDQ2lt7eX559/nj/84Q+qXnhhvgVy/w0GasxXXJMzSxMTE6qdD6wG85FvZWUlr732Gi6Xi/j4eD7wgQ8QFxen6HPOB4fDwfHjx+nv70er1bJu3ToyMzOnPUbN/bejo4Oenh40Gg25ubnT/mZjY7BhA1RXe5eXPnzYP/vnzFW+k5OTTE5OEhISotq/11wrKytj5cqV73ifHEmScDqd2O12HA7HtBN+g8FASEiIvHy1Grjd8Mor3tGdc+e8XzMa4Z//2btSW0qKi/7+fvr6+uTpj6GhoaSmpsrT295OLccHSZLo6emhsrJSXpDAbDZTUlJCVlZWwF6DdSP5Tk5OcvjwYc6fPy9fA7F27Vo2b95MaGjoPLVUndTSf9UqUPIV1+Qo6OTJk/5uQlCbj3yXLFnCo48+SmRkJIODg/ziF7+goaFB8edVmtFoZPPmzWRkZODxeDh16hR1dXXTTubU3H/T09NJTExEkiSam5unTcuLivJeoJ2cDFVVcNddMDEx/22cq3x9G6CqaVpVoLt8hCc2NpaIiAj5Gh6n04nFYlHVdDadDv7pn7xLqO/fD5s3g8PhXYAjLw8+/Wk9TmcqS5YsISMjA4PBgM1mo7m5mYsXL15xGptajg8ajYbU1FR27tzJ2rVrMZlMWK1WTp8+zb59+646ku1vN5KvyWTi7rvv5lOf+hQLFiyQj+U/+9nPqKioCPh+6U9q6b9qpcZ8RZEj3JJSU1P5+Mc/TmZmJjabjd///vecOHFC9S8gOp2O2267jfz8fCRJory8nHPnzqliKsf1aDQasrKyiI+PR5IkGhsbGR4elu/PyoJ9+7wjOGfPwrve5V2GV418Cw74NsQU5pZGoyEkJETek8dsNqPX65EkSZ7O5luSOtCv3dNoYPt27+jloUOwZQs4nfDzn3untH3ykzqczhSWLFlCeno6er2eqakpmpqauHjxIsPDw6o97mm1WnJycrj77rtZsmQJBoOB4eFhDh06xNGjR1W9NLNvlsFDDz1EXFwcFouFV155hV/96lczrk0UBOHKxHS1Wers7CQ9Pd3fzQha852v2+3mjTfeoPStK9ZLSkq45557VL+MpyRJ1NfXy+/8JSQkcPvttzMwMKD6/usbyRkaGkKj0ZCTkzNtrnBpKWzbBuPj3ilsf/sbREfPT9vmsv/6pqzp9XqioqICdgrOfBkcHOTXv/41jzzyiCJzwyVJwuVyYbfbsdvt8om/ryAKDQ2VR9gC3dGj8M1vepehBu80tk99Cr7yFYiNddPX10dvb6+89LbZbCY9PZ2JiQlVHx9sNhsXL16kublZnupVUFDAokWLAuKY/k6PD263m9OnT3PkyBEcDgcajYYVK1awfft2TCaTAi1VJ3F+pqxAyXc2tYEocmapsbExaPZZCUT+yFeSJM6fP8/f//53PB4PCQkJPPDAAyQkJMxrO5TQ3d3NqVOncDqdhIeHk56ezrJly/zdrJsmSRKtra3yIgSZmZkkJyfL958+7Z2yNjoKy5bBnj2QmKh8u+ay/3o8HkZHR/F4PJjN5ltuQ9Arma/jg29PHrvdPm0PHqPRSGhoqLwhaaA7cQK+9jXvKA94l6H+/Ofhc58Dk8klFzu+kV6bzcbKlSsxm83+a/QcGB8f58KFC3R1dQHea5FKSkrIycnx6zVXN9t/JyYm2LdvH5WVlYD399q2bRurVq1SzbVkShLnZ8oKlHzFNTkKampq8ncTgpo/8tVoNKxevZqHH36YiIgIBgYGePbZZ7lw4cK8t2WupaamsmPHDsLDw7FYLLz55pt0dnb6u1k3TaPRkJ2dLRc27e3ttLW1ye++r1vnPbFLTISKCti0ybvsrtLmsv9qtVr5XdrJyckZG17eagYHB/nZz342L6tNabVawsLCiIqKIioqSt6A1OFwMD4+zujoKDabLeCneW3YAAcPeov8lSvBYoFvfMN7zc5TT+mJi0tjyZIlJCUlodVq6erqorq6mqamJlWvPBkZGcnGjRvZvHkzkZGR2Gw2zp07x759+2aszjifbvb4EBERwf3338+jjz5KcnIyNpuNN954g5///Oe0t7fPUSvVS5yfKUuN+YoiRxDekpWVxWOPPUZubi5Op5OXX36Z1157TV6ZSK2ioqK44447SExMxO12c/z4cSoqKgL+WoPr0Wg0ZGRkkJGRAUBfXx+NjY3yu9JLl8KxY5CR4d1Q8bbbQG37wPpW/JIkiYmJCdX/zW5Ge3s7P/nJT+b1ZM636WhERATR0dGEhYWh1Wpxu93yQgWBft2ORuPdT+rcOXjpJSgshMFB72jOwoXw8ssGMjOzWLx4sTyla2hoiIsXL9Le3q7q4jolJYVdu3axfPlyjEYjIyMjHDhwgJMnT6pyzw+fzMxMPv7xj7N7927CwsLo6+vjueee469//StTU1P+bp4gBAwxXW2WnE4nBoPB380IWoGQr8fj4dixYxw+fBhJkkhKSuKBBx5Q/TLTbrebsrIy+d2YxMREbrvttqCYBjU8PCzPwzebzRQUFMgnbO3tcOedUFMDJhP87nfwnvco0w4l+q/H42FsbAy3243BYCAyMlIVU6Xm2s0uIT1XfFPZbDabXFBrtVpCQ0Pl/XgCmcsFv/kNfP3r4Lt+fcMG+PGPYflyJw6Hg87OTsbGxgDvIhhpaWkkJCSout/ZbDaqqqpobm5GkiT0ej0lJSUUFBTM299MiePD5OQk+/bto/ytd3DMZjN33nknixcvVvXf650IhPOHYBYo+Yrpago6e/asv5sQ1AIhX61Wy+bNm/nQhz6E2Wymr6+Pn//851y8eNHfTbspOp0Oh8PBhg0b0Ov19Pf3s3fvXr9O35grsbGxLFiwAIPBgNVq5dKlS0y8tYZ0ZiacOuV9N3tyEu6/H/7f/wMl3t5Rov9qtVoiIiLQarU4nU4mJiYCfppUMPNNZYuOjiY8PBydTofH42FycpKRkREmJycDemRHr4ePfhTq670b6ZpM3mt31q6Fd71rhOFhMwsWLGDBggWEhYXhdDppbW2lurpa1auVhYaGsnr1au644w7i4+NxuVyUl5ezd+/eedtwU4njg8lk4t5775UX5LBarfz5z3/mhRdemLb65K0gEM4fgpka8xVFzixdb7dt4eYEUr65ubk89thjZGdn43A4+NOf/sSrr76q6iV9LRYLGRkZ7Ny5k6ioKKampjh06BA1NTWqP3GOiIhg4cKFhIWF4XA4qKuro6+vD0mS5H10Hn/cW9x86Uvw8MPeomcuKdV/9Xo9ERER8nUhotDxP41GQ2hoKNHR0URERMhLUE9OTjI6OsrU1FRA/43MZu9oTn299/8CwN69iSxYAN/6FoSERLFo0SKysrLQ6/VMTk5SW1tLQ0MDdrvdv42/CbGxsWzfvp3Vq1djNBoZHR1l//79nD17VvHfS8nXN990623btqHX62lqauK//uu/OHbsWFBsIXAjAun8IRipMV9R5MxSTEyMv5sQ1AIt34iICD784Q+zceNGNBoN5eXlPPPMM/KqPWrjyzcyMpIdO3aQlZWFx+PhwoULHDlyRPXzuUNDQykuLiY2NhaPx0NbWxstLS243W70evjZz+Cpp7wbKf72t953r+vq5u75ley/vmtDLr8APpBHDOZaeHg4K1asIDw83N9Nmca3xHRUVJRc7Hg8HqxWqyoWKEhL805fO38eli0bZ2oK/v3foaQE9u3TkpSURElJCYmJiWg0GkZGRqiqqqKnp0e1/U+j0ZCXl8fu3bvJzc0FoLm5mTfeeEOezqYEpV/f9Ho9mzZt4pOf/CS5ubm4XC4OHDjAM888c0ssTBBo5w/BRo35imtyZslqtap+ec1AFsj5tra28vLLLzM2NoZWq2XLli3cfvvtAT8H/3Jvz9e350x5eTkul4vQ0FDWrFlDamqqH1t58yRJore3l87OTiRJwmQykZeXJ19/dOQIvP/90NfnXVb3V7+CBx64+eedj/7rm7Lm8XjkER6dTqfocwaKQD4++EiShN1uZ2pqSn4HXa/XYzabA2I++7VYLFb+9jczn/sc9PR4v/be98J//Aekp3uv/2hra5OngppMJrKysoiIiPBjq2/ewMAA58+fl69DSkpKYtWqVXP+e81n/5UkiaqqKvbs2YP1rV2R16xZw44dOwJizyAlqOH4oGaBkm/AXZPzX//1X+Tk5BAaGsrKlSs5duzYfDytIo4fP+7vJgS1QM43Ozubxx57jMWLF+PxeDh48CC/+c1vGB0d9XfTbtjb8/W9o3nHHXcQExODzWbj6NGjlJWVqXqKg0ajISUlRb5OZ3JykurqagYGBpAkic2bvSutbd7sXVb3/e+HJ56Am101dz76r2/xAa1Wi8vlYmxsTNVTKG+U7/9coI8eXD6NzWw2T/s7TUxMBPT/qxMnjvPgg1BbC//6r94Rzz/9CYqK4Ic/BKPRRFFREbm5ufL/q5qaGlpaWlS9CmVCQgI7d+5k2bJl6PV6+vr62LNnD3V1dXPa3+bz9U2j0bBkyRI+/elPywt1nD17lqeffpqWlpZ5a8d8CuTzh2CgxnwVL3JefPFF/vVf/5WvfvWrlJeXs3HjRu66665bYuhUCD5hYWH80z/9E/fddx8hISG0t7fz9NNPy5uzqVVUVBQ7duygsLAQgPr6evbv36+qAu5KIiMjWbRoEZGRkXg8HlpaWmhqasLlcpGSAvv3w5e/7H3sz34Gq1eDGv6Uer2e6OhoDAYDHo+HiYkJJicnA3pa1M2qqKjg3e9+NxUVFf5uyg3RaDTyAgVhYWFoNBrsdjujo6MB/7eKjPSO3pSWwvr1YLXCF7/oXYb99dc1tLfH43Qupq8vjdpaE8eOWXnjjQaGhoYC+ve6Fp1OR1FREbt27SIpKUlemODAgQOqPg6GhYXx7ne/mw996ENERUUxMjLC888/z9/+9jdVX1slCDdC8elqa9euZcWKFTz99NPy1xYuXMh73vMevve9713zewNxulpbWxtZWVn+bkbQUlO+IyMj/OUvf6GjowOAxYsXc/fdd8sbOAaiG8m3u7ubM2fOYLfb0Wq1lJSUsGDBAlVNy3u7t09fMxqN5ObmyseV11+HRx+F/n4wGuG734XPfhZm+yvPd/+VJAmr1Spv3GgwGOQVv4JNoCwh/U65XC6sVqs84hGIU9iu1H89Hvj1r+ELX4Drneu/9NIFli41kZ2dHVC/12z5pvFWVFTgdDrRarUUFxezcOHCm/q/5e/XN7vdzv79+zl37hzgfXPr3e9+N3l5eX5r01zyd77BLlDyDZjpag6Hg9LSUnbu3Dnt6zt37uTkyZNKPrViAn2qhNqpKd+YmBgeeeQRtm7dilar5eLFi/znf/4nNTU1/m7aVd1Ivqmpqdx5552kpaXJixIcPHhQnoevRr7pa8XFxYSGhsqrr7W3t+N2u9m9G6qq4J57wOHwntDt2AFv1a83bL77r0ajITw8XF6QwOl0MjY2ht1uV+076sFKr9cTGRkpLwfucrkYHx/HarUGzN/qSv1Xq4V//meorvZO7wR44QXvKI/v9sIL3q9PTekZGRnh4sWLqh7V8U3jveuuu+Tj4MWLF296dNvfr28hISHs3r2bhx9+mJiYGMbGxvjtb3/La6+9Jr9Romb+zjfYqTFfvZI/fHBwELfbTVJS0rSvJyUl0dvbO+Pxdrt92vCpb03+ioqKaSvqxMTEkJOTg81m49KlSzN+ju9dvrq6OvmCO5/s7GxiY2MZGBiQ34H3iYiIoKCgALfbzYULF2b83JKSEurr6+XN+S6XlpZGUlISIyMjM+a7hoWFsXDhQgDKy8tnHPh9y962tbUxNDQ07b6kpCTS0tKYmJigoaFh2n0Gg4GSkhIAqqqqZsyJLigoICIigq6uLvr6+qbdFxcXR1ZWFlNTUzNOyjUaDcuXLwegpqZmxopbOTk5xMTE0NfXN2OVsaioKPLy8nA6nVRVVfF2S5cuRafT0dDQMOOkOSMjg/r6eqKiomhtbZ12n9ns3bsBvO/ovp3v5LWlpYWRkZFp96WkpJCSksL4+DiNjY3T7gsJCWHRokUAVFZWztjdu7CwkPDwcDo7O+nv7592X3x8PJmZmaxevRqbzcahQ4fo6enhySefJD8/n8cffxyTycSlS5dmvIDk5uYSHR1Nb28v3b4d+d4SHR1Nbm4uDofjinvzLFu2DK1WS319/YwlHTMzM4mPj2dwcHDGlNDw8HBaWlrIysq64pQf347nzc3NjI6OYjKZMJvN1NbWMjo6yujoKDk5OQDTNpnzrWgG3v+rbz8QFhUVYTKZaG9vn7EfRWJiIunp6VgsFurr66fdp9frWbJkCQDV1dUzplbk5+cTGRlJT08PPb6rpN9yrWOE2+0mISGBgYEBzpw5g8fjISUlhfDwcL7+ddi2LZuvfjWWQ4cGWLiwg3/9V+/moVrt9Y8RPT095OTk0NTUNO/HiKmpKS5cuCD3YYPBQFRUFEuXLgXUf4y4vA3Dw8OqOkb4ll/28Xg8OBwOCgsLmZqaorKyEp1Oh17/j5dkfxwjysvL+ed//mc8Hs8VjxH/9/+WcNttBhYuhCsNpnlX9CqntraWqqoqIiMjSU1NJTw8XFXHCPCeR5hMJhISEhgfH5evPSovL2fz5s2sXbuWoaGhWZ1HnDx5kk984hMYDAa/HCMuP49Yt24dZ8+e5dKlS5SVldHY2EhRURHJycnTvldNx4iTJ0/y/ve/n4SEBNUfI8C7J9eyZcsAAuI84uTJk+zcuZPCwsKrHiPefh5xudTUVJKTkxkdHaW5uXnafbM5j3j73/WaJAV1dXVJgHTy5MlpX//2t78tLViwYMbjv/71r0vAdW9bt26Vzpw5I124cOGK97/55pvS1NSUtHjx4hn3ffGLX5Sampqkb37zmzPuW7FihXTs2DFpaGjoij/3j3/8o/Tqq69KmzZtmnHfxz72MammpkZ69tlnZ9yXl5cnHThwQJIkSTIYDDPuf+aZZ6SBgQHp/vvvn3HfAw88IF24cEF69dVXZ9wXHx8vvfnmm5IkSVJ8fPyM+7///e9LXV1d0sc//vEZ9+3atUs6d+6cdPbs2Rn3GQwG6c0335TsdrtUWFg44/6vfOUrUktLi/TVr351xn1r166VTpw4IXV2dl4xwz//+c/SxMSEtG7duhn3fepTn5L+8Ic/SD/96U9n3FdUVCQdOnRIkrxH9hm35557ThoaGpLuvvvuGfd98IMflKqqqqQXX3xxxn0pKSnSnj17JEmSpKioqBn3//jHP5Z6enqkhx9+eMZ973rXu6TS0lLpyJEjM+7T6/XSP//zP0uVlZVSdnb2jPu//vWvS21tbdIXvvCFGffdfvvt0qlTp6SGhoYr/q6vvfaaZLFYpBUrVsy47zOf+YzU0NAg/b//9/9m3FdSUiL96le/kiYnJ6/4c3/7299KIyMj0o4dO674u/70pz+VPv3pT8+4LzMzU9q3b58kSZJkMplm3P/UU09JfX190oMPPjjjvvvuu08qLy+X9uzZM+O+qKgo6c0335TcbreUlpY24/5vf/vbUkdHh/TEE0/MuO9GjhG9vb3SggULZtz3xS9+Udq/v01KT39yxn3XO0b84Ac/kMbGxgLmGJGQkCAdPHhQ8ng8QXGMAKS//OUvQXGMMJlM0v79+6WBgQEpNzd3xv3+OEYUFBRIR48eveox4lvfel0CSSotnf7aXVoqSSBJL73UJD3//PMzvi8jI0OVx4irnUe8973vlf7whz9IX/va12bcdyPnEYF0jIiJiZEee+wx6etf//oV/zZqO0Z86lOfkurq6oLmGPHmm29KTqczoM4jrnWMuNZ5xEc+8hGpurr6iseId3IeMTY2dt06RNFrchwOByaTiZdeeon77rtP/vpnPvMZKioqOHLkyLTHX2kkJyMjgyNHjgTMSI7b7aarq0uM5Cg0khMREcHk5KSq34Hp7+/nyJEjhISEAN53VFavXi0vXwz+G8nJzMzEaDTO+h2YlJQULBYLJ0+epLu7G51OR15eHllZWZhMJlW+S+v7uS0tLfLO4EajkXXr1pGdnU1v7wA//GEH//mf3lXXjEb4zGci+O53C9BornyM8P2f8/e7tC6Xi6mpKTQaDcXFxej1+hnvnF3eXjUcI5xOJxqNhmXLlmGxWFR9jIB/vEvr8XgoLS2VZy4YDAZMJhP5+fnzfozwvaZc7V1ap7OEdesMlJZOH8kpK4OVK+Eb34AnnhilpaWZqakpurq6sNlsGI1G1q9fT3p6OpWVlao6Rlx+HiFJEl1dXQwNDREWFobVaiU+Pp709HR5dPta5xF2u51Vq1YFxEiOj8FgoKioiL179/K3v/0Nj8dDTEwM27ZtIz4+XlXHCLvdTn5+vhjJQZljhN1uJy4uLiBGcjZv3nxD1+TMy8IDK1eu5L/+67/krxUXF3PvvfeqcuGB06dPs27dOn83I2gFS74ul4sjR45w/PhxJEnCbDbzrne9S36R8pebzXdiYoLz58/LL3YxMTGsWbNGlZuEXW5sbIzW1lb5JCkuLo6MjAyMRiMtLfDxj3tXYgPvyd3TT8OaNTN/TiD1X0mSsNlsTE1NyS8YoaGhmEwm1S4iEUj5ziXprb11fNfn6HQ6eWPR+XS9fH3FzAsvwOWHspoaeOgh7+cPPgjPPgsREd5peR0dHfLxwmw2k5ubO+0NHzWyWCycOXOGgYEBANLT01m9erX8xtbVBHr/bWho4NVXX8VisaDT6di+fTu33XbbtOnJgSzQ81W7QMk3YBYeAPjc5z7HL3/5S5577jlqamr47Gc/S3t7O4899pjST62It7/zIsytYMlXr9ezfft2PvrRj5KQkIDVauXFF1/kj3/8o/yOrT/cbL4RERFs2bKFNWvWYDQaGRkZYd++fdOuBVGjqKgoFi9eTFJSEhqNhqGhIaqqqujr6yM7W2LvXnjuOYiO9p7orVvnLXze9qZzQPXfy5cw9p182Ww2RkdHmZqaUt1F4c3NzXzxi1+84oiU2vn21omKikKn0+F2u/2y/9H1+q9vb8yHHvIWO76br8DR6eCPf/S+AXDpkved6KysLAoKCjAYDFitVqqrq2eM1qhNeHg427Ztk98N7+zsZO/evXLRczWBdHy4koKCAj75yU9SVFSE2+1m7969PP/88wHfbh+1tFOt1Jiv4kXO+9//fp588km++c1vsmzZMo4ePcobb7wREMvQvROBMqIUrIIt37S0ND7xiU+wceNGtFottbW1/OxnP5MveJ9vc5GvRqMhNzeXu+66i8zMTDweDzU1NezZs+eKC4qohU6nIysri+LiYsLDw3G73bS1tXHp0iWsVguPPOLdJPHhh0GS4Be/gAULvO9a+/6Ugdh/tVotERERREVFodfr8Xg8WK1WRkdHsdlsqil2RkdHOX78uKr3LLkevV5PVFQURqMRSZKYmJiY11Wvrtd/Cwqgvn76ymq+W309HDsGaWne/ydr1ngLHvCO+F6+X1VzczOtra2qXK3JR6PRUFRUxI4dO4iIiMBqtXLo0CGqq6uv+nsF4vHh7cxmM+9///t597vfjdFopLW1laeffvqK08YCjRryVTM15qv4dLWbEYjT1ex2+3WHpIV3Lpjz7e/v569//at8LVhaWhr33HPPjNVslKREvp2dnZSWlspzrjMzM1m2bFlA7xd0PZIkMTAwQGdnJy6XC41GI8+9NxgMHD8Ojz/+j41DV6+Gp56CZcsCu//6pkVNTU3hdrsBb3FnMpkwGo0BPS1F7fvkzIb0tv2PzGbzvEzxmovjQ38/fOADcPCg99//8i/wwx+CweD9vbq7u+nu7kaSJMLDw8nPz8doNM5B6/3H6XRSWloqXwOSlJTEunXrZvzN1Pb6Njw8zF/+8hc6OzsB73VKd911V8DugaS2fNUmUPINqOlqwebw4cP+bkJQC+Z8ExMTefTRR9m9ezchISF0dXXx7LPPsm/fvhmLRihFiXzT09O56667KCwsRKPR0N7ezhtvvEFdXZ18Iq02Go2GxMRESkpKiI+Pl4ueyspKenp6WL/eQ2kp/OQn3t3hz53zTmHbtWuY2axuOd9806Kio6Mxm81otVrcbjcTExNif50AotFoMJvN8hsFVqt1xoXbSpiL40NiIuzdC1/5ivffP/0p3H23dyNRjUZDWloaBQUF6PV6LBYL1dXVqt6DC7wX769bt461a9ei1+vp6+tjz549My4yV9vrW2xsLI8++iibN29Go9FQVlbGL3/5y4Cdbqi2fNVGjfmKIkcQ5pFGo2H16tV8+tOfpri4GI/Hw4kTJ/iv//qvGSu2qInRaGTFihXccccdxMXF4XK5KC8vZ9++fdedpx7IDAYDubm5LFy4ELPZjNvtpqOjg6qqKsbHh3niCYm6OvjIR0CjgSNHUigqgi9/GQJ5+vLl1+uYTCY0Gg0ul4uJiQlGR0dFsRMANBoNJpNpWqGjlg0bdTr4znfglVfAbPYu2rFhA/gWDIuOjqa4uBiTyYTT6aS2tjZgT5xnIycnh127dhEdHY3NZuPw4cPU1taq+v+SVqtl69atfOhDHyI8PJy+vj6effbZK64uKQiBRkxXm6Xm5mZyc3P93YygdavlW1dXxxtvvCFf0Ldo0SJ27txJVFSUIs83H/lKkkRzczOVlZXyamU5OTksWbJE1asqSZLE0NAQnZ2d8gXhERERZGZmYjabqaiAT31qilOnvL9jQoJ3Sd2PfQzmeZGsWfN4PNhsNmw2m3w9gU6nIywsjJCQkICYxtbb28uPfvQjPv/5z8/rFE9/kySJyclJeUnwyMhIxaYLKXF8KC+Hd70Luru9ozyvvuod9QTvxryXL+GelpZGampqQPS3m+FyuTh//rw8fc23aXRHR4eqX98sFgt//vOf5eWtly9fzt133x0w09dutfOH+RYo+c6mNhBFziy1t7eTmZnp72YErVsxX4fDwcGDBzlz5gySJGEwGNi8eTPr1q2b8yVk5zNfu91OZWUlTU1NgPei6uLiYhYsWIBOp5uXNijB7XbT29tLT0+PXBDExcWRlpZGX18/lZWZfPGLUFfnfXxBAXzrW/C+90Ggr9zs8Xjka3Z8v5tWqyUkJITQ0FC//91uxeMDeAsdi8WC3W5Hq9USHR2tyDLgSuXb1QX33OMteEJD4c9/9k5hA+/v1tnZKe9jk5CQQFZWlmqXOfeRJInGxkbKy8vxeDxERUXJC5uomcfj4ejRoxw5cgRJkkhMTOR973sfCQkJ/m7aLXt8mC+Bkq+4JkdBb9/wSphbt2K+RqORO++8k0984hNkZmbidDrZv38/Tz/99JxPYZvPfENCQli9evW0KWyVlZX8/e9/p7OzU7VTOHQ6HWlpaZSUlBAXFzdtyenS0vPs2uWgqsq7EEF8PDQ0ePcOWbECXn/duzJboNJqtYSFhRETE4PZbEan0+HxeJiammJ0dJSJiQm/LRU+OjrKc889F9Srq12NRqMhPDxcXh3PYrEo8v9HqeNDWhocPQq7d3s31n3Pe7yFDnh/t4yMDLKzs9FoNAwMDNDQ0KDa6/l8NBoNBQUFbNu2jbCwMMbGxnjttddmbEqqNlqtli1btvDhD3+Y8PBw+vv7efbZZ6murvZ3027J84f5pMZ8RZEjCAEiOTmZRx55hPvuu4/w8HCGhoZ44YUXePHFF1V9YhcXF8eOHTvk1YYsFgvHjx/n8OHDqv69QkJCyMvLY9GiRURHR8srl1VWVtLX18ljj7loboZvftO7OMGFC95pOxs3ek/4Atnl1+z4pkf5fr/R0VG/LD/d3NzMN77xjaDcJ+dG+AodjUaDw+GY9z10blZ4OLz8Mrz//eB0wgMPwG9/+4/7ExMTKSgoQKvVMjY2Rl1dnar33vKJj49n586dJCQk4Ha7OXbsGA0NDf5u1k3LycnhscceIycnB6fTyUsvvcT+/ftVvSy4EHzEdLVZslqtmM1mfzcjaIl8vex2O4cPH5b30zEYDGzcuJH169ff1BQ2f+fru8i4trYWt9uNRqORCwU1X68D3uNVU1OTvFKeXq8nJSWFxMRERv9/9s47PKoy++OfmcxMeu89gVRaKKH3FiwgCKI0FXVVrFjYddV10bWv3V1XYdefFURRbCjSQXqvCSmEhJCekJ5MMu3+/hjmmpBCArlJJrmf57lPZua+c++Zb9555577nvecMhtef908u2NZOz51Kvz97zBmTCca3QYMBgNarRadTic6N0qlEo1Gg52dXbuHVl5OT0oh3RI1NTXU1NSINXXac/1KR4wPRqN5ndonn5iTdfzvf3D33X/sr6qqIjU1FYPBgKOjI9HR0ZL3rY7AaDSye/ducSYnMjKSQYMGWX1YnslkYsuWLezduxeAiIgI5syZ0ynjeWf/vnV3uoq+criahCQlJXW2Cd0aWV8ztra2TJs2jSVLlhAWFoZer2fbtm385z//uaZsPZ2tr1qtpn///txwww2EhISIceu//PILp0+f7rBU2lLg4uKCXq8nMjISe3t7DAYDFy5c4OTJk+h0ebz6qpH0dFiyxJyIYPNm86zOpElgDZk5VSoVzs7OjULZamtrKSsro7y8vEHiAhlpsLOzQ6lUYjAY2v370hHjg42N2bF5+GFz6Oaf/gTffPPHficnJ2JiYlCr1VRXV5OcnNwtZnQsiTzi4uIASEtLY9euXVY3I3c5SqWShIQE5syZg1qt5uzZs/z3v/9tlD67I+js37fujjXqKzs5bcSSBUZGGmR9G+Lj48Odd97JLbfcgrOzMyUlJaxZs4bPP/+c/Pz8Nh+vq+jr6OjIqFGjmDRpkrhe5/Tp0/zyyy+cPXvWai+US0tLcXd3p1+/fvTq1Qs7Ozv0er3o7CgUefz730ZSU+G++8wFErdvh4kTYfx42Lq1a6/ZgT/W7VhC2SzZ1/R6PVVVVZSWllJZWdlgxkem/bAkggDE7IXtRUeND0qluX7OffeZ+/uiRbBhwx/7HRwciI6ORq1WU1NTQ2pqqtWv0QHz+BAbG8uYMWNQqVTk5eWxbdu2DqmBJDX9+/fnnnvuwc3NjZKSEv73v/91+EVxV/l9665Yo76yk9NGusJUXXdG1rcxCoWCfv368fDDDzN27FhUKhUZGRmsWLGCn376qU2F9Lqavj4+PkyZMoVRo0bh7OxMbW0thw8f5rfffrPK5AQWfRUKBV5eXvTv37+Rs3PixAlsbXP5z3+MnD0LDz4IGo15nc6UKeZ6Ij/9BF3dz1MoFGg0mgazOyqVSly7U1FRQWlpKdXV1ej1+mv+X9rZ2REWFoadnV07fQLrRaPRALT7DEdHjg8KBfznP+akHHq9Ofvg8eN/7Lc4OpaioWlpaVZ788OCRd+goCAmTZqEnZ0dZWVlbNmyxeoLooJ5Xel9991HeHg4Op2Ob775hu3bt3fYON7Vft+6G9aor7wmp40YDIZuER/cVZH1vTKWH8XTp08D5gueMWPGMHLkyCvWK+jK+hqNRs6dO8fp06fFO9Te3t70798fHx+fTraudTSnr6XGTm5urljQUaVS4ePjg6+vL4WFav75T1i58o81O7Gx8Oc/w8KFZifIGhAEAaPRSF1dHXV1dQ0uSm1sbNBoNGg0GlQq1VWtJenK/bcjMZlM4l1VS4a/9qAz9NXrzemkt2yB4GA4eBDql0GqqqoiJSUFo9GIp6cnvXr1sto6OpfrW1VVxc6dO6msrMTOzo5x48bh4eHRiRa2DyaTic2bN7Nv3z7APMszc+ZMyfuWPD5IS1fRV66T05488IA5yf8lCgsLreaCyxqR9W09Wq2WwqIiai+FOqjUary9vHB2caG5SwBr0NeSIrd+mlxbOztcXFzQdJGic81xJX0FzHWR6mprMV5yABSYHVVbOzv0OiXnMiAzEyw36e3soFcvCA0Fdef/vrQaARBMJkyXtvo/NAqFAqVSiVKhQKFUNttfL8ca+m9HYOlHABq1ut0u+jtLX50edu+Cqmpwd4fRoxrWlNLr9VRVVwPmGT17K53Na0pfo9HIxYsX0ev1KJRKPD08xHBEa6e8vJz8ggIQBOwdHAgICEAlYa0teXyQlgb6BgbChx92ih1t8Q2s6Cezk7jsn3hs40amTZvWScZ0f2R9W489ECIInD59mi1btlBeXg6YQyGmTZtGcHBwo/dYg75KwAVQ1dSQlJTEuXPnxBmB4OBg+vXrh6ura6fa2BxX0lcB2AIaQaCsrIy8vDyqqqrM+xQKPDw8CPP3J8jgwIoV8M47kJcHJIFrjjlpwUMPme94d3UUlzYl5hkevV5PXV1do7U6SqUStVqNRqNBrVY3m23q+PHjjB49mj179jBw4MCO+AhdFpPRSGVpqdhnaCcnp7PGBw0QchaGDoWyUvhzH/jnP//YrwYMRUVkZGQA5gxe1jjj0ZS+NoC7Xs/u3bspKCjAxsaGMWPG4O/v3zlGtiOuwMVz5/j666+pq6vD09OThQsXSva/s4bfN2vGGvWVZ3LaSFpaGpGRkZ1tRrdF1vfq0Ov17Nu3j927d4t3eGNjY5k8eTJeXl5iO2vUt7KyksTERM6fP48gCCgUCkJDQ+nXrx9OTk6dbV4D2qqvIAhUVlaSl5cnOqkArq6u+Pn5YWvrwurVCt54A5KTzftsbGD2bHj0UfP6HWuL3Knv8Oj1+gYhbQqFArVaLTo9SqVSnKWQU0j/gVarpbq6GrVa3a4Of2ePD+vWwZw55scbNsB11zXcn5WVRX5+PjY2NvTt29fq1me1pK/RaGTfvn1kZ2d3K0cHzDMAq1atory8HAcHB+bPn9/kTbhrpbP7b3enq+grp5CWEAcHh842oVsj63t1qNVqxo0bxyOPPMLgwYNRKBScOXOGDz74gJ9//pmKigrAOvV1dnZmxIgRTJs2jaCgIARBIDMzk19//ZVDhw5RfSmMpSvQVn0VCgUuLi5ER0fTt29fPDw8UCgUYjHEtLTTTJ9eyMmTRn74ASZMMNcZWbvWnH46Ph4++wzaOcmWpFyesMDV1RV7e3tsbGwQBAGdTkd1dTWlpaWUlZVRVVXVaH1PT8ZkMonZuNo7rKmzx4fZs82ppcFcS+fytfjBwcE4OztjNBpJT0+3uj7Rkr42NjaMHDmSoKCgRjV1rB0fHx/uvfdeAgICqKmp4bPPPiMxMbHdz9PZ/be7Y436yk5OG7Es9paRBlnfa8PZ2ZmbbrqJBx98kJiYGARB4MiRI7z//vts2bKFo0ePdraJV42bmxtjxowhISEBf39/TCYT6enp/PLLL13G2bmW/uvo6EhERAT9+/fHz88PGxsbtFotmZmZnD59kiFDstm4UceJE+baInZ2cPQoLF4MISHmwqLWdk1kmblxdHTE3d1dzNKm0WhQKBQYjUZqa2uprKwUZ7q0Wm2PdXosM38mkwmVStXuTk5XGH9ff928Bi07G/72t4b7LMWDLTV0cuqtl7UGrqTv5Y7Orl27yM3N7SDrpMXJyYnFixcTHR2NwWDg22+/5dChQ+16jq7Qf7sz1qiv7OTIyHRDvL29mTdvHnfffTchISEYDAZ2797NDz/8wN69e626uJ6Hhwfjx49n0qRJ+Pr6NnB2Dh48KK5xsVbs7OwICQkhLi6OkJAQbG1t0ev15ObmcvLkSZyczvHOO1VkZQm8+ioEBUFhIbz4otnZmT/fXFy06wYiN4+lYKKLiwseHh64uLhgb2/fIKOPxekpKSmhtLRUnOkxGo1Wl3K8Lej1enGBulKpxMnJyWqzjLWEgwN89JH58b/+BZff8NdoNISFhQGQn5/fJW5utCf1HR2TycSePXs6pbCmFGg0Gm677TaGDh2KIAj88ssv7Ny5s1t/b2U6F3lNThupqKjoMrZ0R2R92x9BEEhNTWXLli1kZ2dja2uLq6srEydOZMCAAc0u9LYWioqKSExMFIujKpVKQkND6dOnD87Ozh1qixT9VxAESktLyc/Pb+DAOTo64uPjg4uLBz/9ZMP778Pu3X+8LyrKXGzxzjuh3rIsq6WqqoqDBw/Sr18/NBpNk466UqlEpVI12Ky9fwuCIDpyFlxdXa+YLv5q6Erj75w55jU6s2bB99833n/27FlKSkpwdHSkT58+VuHwtUVfo9HI3r17ycnJQaPRMGnSJNzc3KQ1sIMQBIEdO3awc+dOADEc+Vr/h12p/3ZHuoq+cgppCTl69GiPX/QqJbK+0mEymfj666/Jy8sT1+h4e3szYcIEq7lIaIni4mISExPFOHalUklISAgxMTEddnEgdf+tqqqisLCQkpISMVxLpVLh5eWFj48PZ87YsWIFrFoFFn9IozFfMN53H4wfb32JCupTX1+TyYTBYECv16PX65udybGxsRE3lUolPu7q/d0Sqnd5aJ6Li4tYDLS96Urj75kz0K+fuSjukSNwuVl6vZ5Tp05hMBjo1atXgwQrXZW26mswGNi5cydFRUXY29szZcoUqyzI2BwHDhxgw4YNAAwcOJCbbrrpmm5KdKX+2x3pKvrKiQckpKioqLNN6NbI+kqHUqnEw8ODRx55hISEBOzt7SkqKmLt2rWsWLGC5ORkqw4b8PLyYvz48UyZMkVcs5OZmclvv/3Grl27KC4ultwGqfuvk5MTvXr1Ii4ujuDgYGxtbTEYDOTn53Py5EkcHFJ49dVSsrNNrFxpTkyg08FXX8HEiRATA2++Cdb4NcvKymL58uVkZWUB5v6s0WhwdHTEzc0NDw8PXF1dcXR0xM7OTiw4ajQa0el0aLVaKisrKSsro6SkhLKyMiorK6murqa2trZFR6kjMJlMDZIulJaWotVqMZlM2NjY4OjoiKenp2QODnSt8Tc2Fm67zfz4P/9pvF+tVuN3qWpoTk6OVazRaqu+KpWKMWPG4OrqilarZefOnQ1m9Kyd4cOHM3v2bJRKJcePH+f777+/pv9jV+q/3RFr1Fd2ctqItaWstDZkfaXFzs4OtVrNqFGjWLp0KRMmTMDW1pb8/HzWrFnDf//7X9LS0rqFs5OQkEBwcDAKhYKcnBy2bNnC1q1byc3NlezzdVT/VavV+Pv7M2DAAKKionBzcxOzsqWlpXHu3Amuu+4Cu3bVcuQI3H8/ODlBair8+c/mOm6zZ8MPP5idIGuguLiY9evXN+usWpIY2Nvb4+TkhJubm5i9zcnJSez7SqUSQRAwGAzU1dWh1WqpqqqivLyc0tJSca1PeXm56ARZkh3o9XoMBgNGo9Fc4LQN/UgQBHH2SafTUVtbS3V1NRUVFeJ5Kyoq0Gq1GI1GMQudi4sLbm5u2NvbSz771NXG34ceMv9dvRrqZVgX8fX1Ra1WU1dXx8WLFzvWuKvgavS1tbVl/PjxODo6UlFRwZ49ezAajRJY1zkMGDCAW265BaVSyalTp1i3bt1Vf76u1n+7G9aorxyu1kYsdTpkpEHWV1qa0ler1bJ3714OHDgg1tgJDg5m4sSJhIeHW/3/o7KykjNnzpCZmSneJXRzcyM2Npbg4OB2XbPRmf23traWoqIiiouL0ev14usuLi54eXmhVrvzzTc2rFwJhw//8T4vL3OygjvvNIcEddV/d3vVybE4G0ajUXRW6j9uy0+iQqFo8P++/H9vOZYgCK06riWk7kqFUaWiq42/ggB9+phrRH3xBSxa1LhNXl4eFy5cwNHRkb59+3a8kW3gWvQtLy9ny5Yt6PV6IiMjGTJkSDtb17kkJyezdu1ajEYjffr0Yc6cOdjY2LTpGF2t/3Y3uoq+criahGzatKmzTejWyPpKS1P62tvbM3nyZJYuXcqoUaNQqVRcuHCBzz//nM8++4zz5893gqXth7OzM8OGDWP69OnExMSgUqkoKytj3759/Prrr5w9e7bd7ox2Zv+1s7MjODiYuLg4IiIixNmdiooKzp07R3r6CRISzrNjRzUnTggsWwZ+flBcbM5iFR8P/fubK813k6y1TaJQKLCxsUGj0WBvb4+joyMuLi64u7vj4eEhzv44Ozvj6OiIvb09tra2qNVqMZGB5Yfe4jBZNouzVN9pqu84KRQKMTnC5ee3nNvZ2RlbW9tOSZjQ1cZfhQKmTzc/vvNOWLGicRsvLy+USiXV1dVdPtPatejr6urK8OHDAS7N1p5rL7O6BDExMdx2223Y2NiQlJTEunXr2hy61tX6b3fDGvVVXbmJjIxMT8DR0ZGEhARGjhzJ7t27OXz4MJmZmXzyySf07t2bcePGERoa2tlmXjUODg4MHDiQ2NhYzp49S2pqKlVVVRw+fJjTp08TGRlJREREu9ce6Wgsa688PDyoq6ujuLiY4uJi6urqKCgooKCgAAcHB554wpMXXvBk504Nn39uDl1LTISnnoKnn4apU2HhQpg5E7rIRLrkWByg1txBtszO1J+luXy2pv5Mj+VxV7gTak1YlqD06wdLlpgf33//H/vVajVubm5imGF3Wph/OUFBQfTv359Tp05x+PBhcZa2uxAVFcW8efNYs2YNiYmJqNVqZs6cKX9nZK4aeSanjVjzRZ41IOsrLa3R19nZmeuvv55HH32UIUOGoFQqSU9P55NPPuHTTz8lIyPDqtfs2Nra0rdvX6ZPn87gwYNxdHSktraWU6dO8dNPP3Ho0CGx8GRb6Wr919bWlsDAQAYMGEB0dDQeHh4olUpqamq4cOECiYknCA9P5l//KiY728jKlTB6tDmj1caNcMcd4ONjXr/zzTfQmTfKfXx8uPPOO/Hx8ek8I+phmZWxhJipVCrUanWDrX42t/ozQF2VrtZ/V6wwzzI+/DAcO2b+u2RJ4xkdS/bEq/3edhTtoW+fPn3EGjr79+9vEJraHYiMjBTX6Bw/fpwNGza0+vemq/Xf7oY16iuvyWkj+fn5YkYXmfZH1ldarkbf0tJSdu/ezfHjx8WwruDgYMaPH0/v3r27/IXblTAajVy4cIHU1FRKSkrE1/39/YmKisLPz6/Vn9Ea+q/BYKCkpISLFy9SWVkpvq5UKnF3d8fLy4vCQhdWrVKwZg2kpPzxXgcHuOkmc9ar666Djl6Hag36WjNdSd8VK8wOzcMPw/vvm0PXBAGWLjU7Ph999MeMjl6v59ixYwAMGTKkzWs5Oor20lev1/Pbb79RXV1NeHi4GMbWnTh58iTff/89giAwduxYJk+efMX3dKX+2x3pKvq2xTew6nA1o9HY4XcxTp8+3W0KcnVFZH2lpTX6Wu5MWy7s3d3dmTFjBuPGjWPPnj0cPXqUCxcu8OWXXxIQEMD48eOJioqyWmfHxsaGsLAwQkNDKSoqIjU1lZycHPLy8sjLy8PV1ZWoqChCQ0NRqVoeMk+cONElfgRaQqVS4ePjg4+PD7W1tZSUlFBcXExtbS0XL17k4sWLaDQa7r7bgyef9CA93ZGvv1bw9deQkQFr1pg3FxdzocbbboMpU8z1eKSkqqqKr776invvvRcnJydpT9ZD6Sr91+LgPPIIvPfeH8kwFArzc2gYumaZOdPr9Wi12i7bP9pLX7VazYgRI9i2bRsZGRn4+/sTEhLSDhZ2HQYMGIBOp2P9+vXs2rULFxcXhg4d2uJ7ukr/7a5Yo75WO5NTVVVFdnZ2h4fNaLVa7O3tO/ScPQlZX2lprb4ODg74+/s3WZOjsrKSvXv3cvjwYfEmg5+fH+PGjSM2NtZqnZ36VFVVkZqayrlz5zAYDIA59CsiIoKIiIhmNdy4cSPTpk3rSFPbBUEQqK6u5uLFi5SUlDS4eWRra3tpUbwHiYkOfPON2eHJyfnj/S4ucOONZqfn+uvB2bn9bWyv7GoyzdMV+m9dnbn/xMaaQ9Sayr9gMsGgQeaCoZWVYGtrzs5VUVFB79698fT07HjDW0F763vq1CkSExPRaDRcf/313fK3c+fOnWzfvh2FQsGtt95KbGxss227Qv/tznQVfdsyk2OVTo7RaCQtLQ0HBwe8vb079KLKYDBc8W6uzNUj6ystV9JXEAR0Oh1FRUUYjUYiIyObzfJUXV3Nvn37OHjwoJh62tvbmzFjxtCvX78uGzLSFnQ6HefOnSMtLU3M3KRUKgkJCSEiIgJPT88G409paSnu7u6dZW67YDKZKC8vFwtm1s88Z3F43Nw8OH7cga+/VvDdd5CfT7025pmdWbPMoW3ttYRGdnKkp6v03+ZmcqD5kLXU1FTKysoIDw/H29u7cwy/Au2tr9FoZOvWrZSUlBASEsKoUaPa7dhdBUEQWL9+PUeOHEGlUnHnnXcSHBzcZNuu0n+7K11F327v5NTW1pKRkUFYWFiH37moqanBwcGhQ8/Zk5D1lZbW6ltTU8P58+cJDw+/YgEwrVbL/v37OXDgALW1tYA53emoUaMYNGiQpBXaOwqTyUR2djapqakNilG6u7sTEREhhrKdOHGCuLi4TrS0fTEajQ0cnvopXe3s7ESH5+RJe374QcH338PZs3+8X6k0JzKYNcu89ep19bbITo70dKX+25Y1OWBOq1xaWkpoaCi+vr6dZ3gLSKFvSUkJmzdvRhAExo8fj7+/f7sevytgMpn4+uuvSUlJwcHBgfvuu6/JsOuu1H+7I11F3x5TJ6czwmK6WyaTroasr7S0Vt+21Oiwt7dn4sSJPPbYY0yePBlHR0fKy8vZsGED7777Ljt27KCmpuZqTe4SWGZvpkyZwtSpUwkPD8fGxobS0lIOHTrETz/9xNGjR8nMzOxsU9sVGxsbPDw8iIiIYNCgQURERIgZ2mpra8nNzSUp6TROTqd4+OEsjhyp5NQpgZdegiFDzGFFu3bBk09C797mEKQnn4StW+HS5J9MFyK//pRcJ3P//WZH5t//hkcfNfel5hwc+GNsU6vVnWBt65BCXw8PDyIjIwHzjYD2qvnVlVAqlcyZMwd/f39qamr46quvxOiB+nSl/tsdsUZ9rdrJ6Qy64nqD559/niWXVmHu2LGDmJgYcZ+TkxOFhYWdZVqb6Yr6diek1NfOzo6xY8fy2GOPMX36dDw8PKipqWHHjh288847bNiwgbKyMsnO31F4enoyfPhwbrrpJuLi4nByckKn05GamkpSUhI7duwgOzu7zYXsujqXOzy9e/fG3d1ddHjy8/NJTj6DXn+c+fPPsWlTCRkZRv71L5g0CWxszJXr337bHM7m6Qk33wz//S9kZ1/5/CqVCldXVzmcVUK6moNw223m8Md//9u8Bqc5B0cQBOouFdTpynWupNK3f//+2NraUllZSUZGhiTn6Gw0Gg3z5s3DycmJgoIC1q1b12hNdlfrv90Na9RXdnLayJWmxsLCwnBxcUGr1YqvVVRUYG9v38D5CAsLY//+/Q3eu2TJEp5//vl2tbeqqqrL1JWoz8MPP8xnn33W4LV7772XZ555plHb999/n/Hjx4vPDx8+zMSJE4mKiuLbb79t1H727NksX768/Y2WkPT0dEaPHo2DgwODBw/mxIkTV3zPvn37UCqVvPbaaw1e379/PyNGjMDJyYmgoCC++eabBvvvueeeS2FGbixYsKBdPweYB8L4+Hgefvhh5s6di7+/P3q9ngMHDvD+++/z/fffW5Xj3Ry2trbExsZy4403Mm7cOAICAujVqxf5+fns3r2bX375hcTExAZjQXfBxsYGT09PIiMjxRkeLy8vVCoVer2e4uJizp49S3HxMaZNS2PNmiLy8vSsXQt33QV+flBVZS5Aet99EBwMAwbAX/8KO3c2PcszYMAAysrKGDBgQId/3p7CpEmTOtuEBvzzn+ZEBAEB5iQDTTk4YA6Z1ev1KJXKLr34Xip91Wo1/fr1A8wZNC3JUrobrq6uzJs3D5VKRXJyMjt37mywv6v13+6GNeormZOTmZnJPffcQ3h4OPb29vTu3Zvly5c3OcVoTbSm2Jifnx8//fST+HzdunXNLpTrqWzcuJGEhIQGry1atIivv/660QC9evVqFi5cKD7/7bffmDZtGgsXLmTVqlUN2lrCpKS4eJeS+fPnk5CQQElJCXfffTc333xziz9UJpOJxx9/vFFKzby8PObMmcNzzz1HWVkZJ06cYMiQIeL+RYsW4eTkREZGBkVFRfz5z3+W7DMplUr69u3Lfffdx+23306vXr0wmUycOHGC//znP6xevZrMzEyrLiwK5tmxgIAAxo0bh4ODA7Gxsdja2lJdXc2pU6f4+eef2bt3L/n5+Vb/WZvCMsPTq1cvBg0aRExMDH5+ftja2mIymSgtLSUjI4PMzOP06ZPESy/lkppazeHDAi++CCNHmtdbnDoFr78OEyaAu7s5S9ubb5ozbFkmxTZt2tSpn7W705X0PXYM3njD/PiDD8xZ1JpycMC8IBrMNyHbEmrb0Uipb69evXBychLXLHdXgoKCmDFjBmDOvHbu3DlxX1fqv90Ra9RXstEgOTkZk8nEihUrSExM5J133uGjjz5q8k59d2P+/PkNLr5XrVp1zRfdWq2Whx9+mICAAIKCgnj99ddb9T6FQiHGUYaFhfH6668TERGBt7d3g1mj9evXEx0djbOzM8HBwXz11VeAeeHx8uXLCQ0Nxc/PjyeffLLJi+9NmzYxevRo8Xl4eDgPPfQQAGVlZbi4uIjvS09PF1MU12fcuHHY2dmxefNm8bVz585x7NgxbrnlFvE1SxrDRYsWNQqB+u677+jXrx/R0dFi6N5zzz2Hm5sb0dHRJCUl8dJLL+Hh4UFsbCyJiYniex988EECAgJwc3MjISGBrKwsAFJSUvDy8uLspRXV+/fvx8/Pr91mI1JSUkhJSeHpp5/Gzs6Ohx9+GKPRyN69e5t9z8qVKxk+fHijdJrvvPMOixcv5sYbb0SlUuHp6Unv3r0BSExM5Pjx47z99tu4urqiVqsZNGhQu3yGllAoFPTu3Zs77riD++67j759+6JQKEhNTeXTTz9l5cqVnDx5slvEkms0GuLi4rjpppsYPnw4np6emEwmsrKy2LFjB+vXrycxMVHM1NbdUCgUuLi4EBISwoABA+jXrx+BgYE4OjoiCIKY+v/MmUSUyuMsWHCOn3++SE6OnlWrYNEi8PaGmhr47Tf4859h8GBzhraEhERuvfVPbNiQSDf0FbsEXcUJz883J6owGGDOHPPj5qLQjEajOBZ31dTRFqTU18bGhujoaMB8/dUdxtPmiIuLY8iQIQiCwHfffScWNe4q/be7Yo36SubkXHfddXzyySckJCTQq1cvbrrpJpYtW8a6deva/VyCANXV0m+CQKsyRU2dOpWjR49SUlJCfn4+aWlpjBs37po+47JlyygvLyc1NZWDBw/y+eef8/PPP7f5ON999x379u3jwIEDfPzxx6xfvx6AP/3pT/zf//0flZWVHDp0SMyg8fbbb7N3716OHDlCcnIyR48e5cMPP2x03JEjR3Ls2DG0Wi05lwpo7N69G4A9e/YwdOhQMZbeMhNzOQqFgttuu43Vq1eLr61evZrrr78eDw8PwDxTk5GRwcCBA+nduzcDBw7ku+++a9C+/qzP2bNn8fb2pri4mISEBG644Qbs7e0pLCxk+vTp/O1vfxPbjhkzhjNnzpCfn09QUBCPPvooANHR0TzzzDMsXryY6upqFi9ezPvvv99kGODu3btxc3NrdmuKpKQkoqOjG/StAQMGNHDA6lNSUsK7777bZGjjoUOHUCgU9O3bF39/f26//XbxLufhw4eJiopi0aJFeHp6MmzYMHbt2tXkOaQiICCAuXPn8vDDDxMfH49arSYvL49169bx3nvvsXv3bqsO7woKCgLMFxzh4eFMnTqVadOmERUVhUajEWd31q9fz86dO8nOzu62FyMKhQIHBwcCAwPp27cvAwcOJCwsDHd3d2xsbMSwtvT0dLKzjzNwYBKvvZbD2bNVHD8u8Pbb5to7Tk5w8SJs3lxHefkFbrihjrAwuPtu+OQTczY3K/zt7ZJY+m+HYTTCjh3w1Vfmv0YjBQVw3XWQlQVRUbByZcuHKCgoQK/Xi+nNuzJS62vJhlldXU1eXp6k5+psrrvuOvz8/Kiurubbb7/FZDJ1fP/tYVijvh06r1teXt7iIFRXV0dFRUWDrTXU1Jh/CKXeampo1aJXlUrFrFmzWLt2LWvWrGHu3LlNTqFPnTq1wQXwJ5980uTxBEHgk08+4a233sLJyYmAgAAeeOCBJtejXInHHnsMb29vevXqxf333y86CGq1mtOnT1NVVYWfnx99+vQB4OOPP+bll1/Gy8sLNzc3nnzyySbP6+zsTGxsLAcPHmTXrl3MmjULnU5HaWkpu3btYsyYMWLb5pwcgIULF/LDDz+I2bgud1q2bNnCxIkTxQX0ixYtEmfN8vLy+P3335k3b57Y3s3NjUceeQSVSsXs2bO5ePEijz/+uPj85MmTYtsFCxbg6uqKnZ0dTz31lOikWXRTKBQMGzaM/v37c+uttzZp/5gxYygrK2t2a4qqqqpGa71cXFyoqqpqsv0zzzzDY4891mS++pycHFatWsX333/P2bNnMRgMPPbYY+K+rVu3MmXKFPLz8/nrX//KrFmzKCkpafI8UuLp6cn06dN5/PHHmTx5Mk5OTlRUVLBlyxbefvttfv31Vy5evNjhdl0rTTm+7u7uDB48mJtuuokRI0bg4+ODIAjk5eWxe/dufv75Z44fP97q8c5a0Wg0+Pj4iOt4YmJi8Pf3x8HBQZzlycnJITk5CaPxGDNmnOX//q+Q3Nxadu8WxAr3KpX5AviTT8yOTmSkec3G3LnmmipHj5pnAGTaToeu31y3DsLCYOJEWLAAJk5EHxTG83HrOHHCPHv3yy/Qkt9SU1NDbm4uYL4A6+qJa6TWV6VSER4eDpgjJrozarWauXPnYmtry/nz59mzZ0+XXH/cnbBGfTssTU16ejr/+te/eOutt5pt8+qrr/LCCy80en3Lli04OjoyadIkDh48iFarxcvLS6zhYI78cJXO+EuUl5fj4CCgVqsxGAwolUrx4gz+mOWpqqpi5syZvPjii9TU1PDOO++IbSxregRBYMOGDfTv3x8wV5h/4IEHqK2tpaKiAhcXFyoqKhAEgdLSUrRarZgmUqFQYDKZGD58uHg8vV5PeXm5WKek/tqh2tpaysvLMZlMBAYGUllZiclkwsfHh127dlFeXs6nn37Km2++yV/+8heGDBnC66+/zpAhQ8jKymLq1Knij4cgCPj7+4uZbCznc3Z2ZsSIEWzevJnCwkISEhIoLi5m06ZN7Ny5k+eee47y8nJ0Oh2HDh1i8ODBlJeXN9IwNDSU8PBw1qxZQ0REBDk5OUyePFlsu3HjRsaNG0d5eTkajYbZs2ezbNkykpOTWb9+PWPHjsXe3l48noeHBxUVFWg0GtRqNe7u7lRWVuLg4IBCoaCqqory8nJcXV157rnnWLVqFcXFxSgUCioqKi79zx0wGAzceuutPProo3z00UeihiqVCjs7O9Ehsbe3x2QyifpYnJWm2lrqzygUCkpLSzGZTNTU1GA0GikpKcHBwUH8P1raHjhwgH379vH+++9TXV2NTqejrq4Ok8lEZWWlmIEmLCwMrVYrZjqzhEeFhoZy9913U1FRweTJkwkPD+f3339n4sSJADg6OqLT6aiurhb/txs3bgQgODgYLy8vjh07BkB8fDy5ubnk5uZiY2PDlClT2LJlC0ajkYCAAAICAjh8+DAAgwYNori4mAsXLgAwbdo0tm/fjk6nw9fXl8WLF7N27VqSkpLQaDT8+uuvfPXVVwQFBXHfffdx4cIF8XsfFRUlhvL17duX2tpa8QfdMkZUVVXh7u5O3759RWc1JiYGk8lEamoqAOPHj+f48eNirv3BgwezY8cOACIjI1GpVJw5cwYwO69JSUmUlJTg6OjIiBEj2Lp1K2COhXdwcOD06dNkZmYyf/58zp49S1FREXZ2dowbN06MZQ4NDaVPnz5UVlZSXFyMg4MD58+fJzExEaVSybBhwygoKMDNzY3Q0FB8fHw4evQoAEOGDCE/P5+cnByUSiVTp05l69atGAwG/P39CQoK4tChQwAMHDiQkpISMeRy2rRp7Nixg7q6Onx8fOjVq5eY+KR///5UVVWJcfxTpkxh79691NTU4OnpSUxMDHv27AGgT58+6HQ6MXRz4sSJHD58mMrKStzc3BgwYAC///47gBg6k5KSApjDUU+ePElZWRnOzs7Ex8ezb98+wBxKq9frSUlJQa/XExQURFZWFlqtFrVaTe/evenVy6z3J59koFSG8uOP5Zw+7c7Zs27k5yv49luw3H9xcDAQE1PG6NEC48fboFQexcHByNChQ8nOziYvLw+VSsXkyZPZvHmzODb6+flx5MgRAAYPHkxhYSHZ2dkoFAoSEhLYtm0ber0ePz8/QkJCOHjwIGAOnykrK+P8+fMAJCQk8Pvvv1NbW4u3tzcRERHiZ+3Xrx81NTXiWoLJkyezf/9+qqur8fDwoE+fPmKfjY2NxWAwkJaWBsCECRM4evSoWCdi4MCB4uLrqKgolEolycnJYp9NTEyktLQUJycnhg0bxrZt2wDo3bs3dnZ24mzxqFGjSE1N5fDhw/Tp04fRo0eLYcOWZDqWG0LDhw8nMzOTgoICNBoNEydObPMY4blzJwNffhkEgfpuiU1+Dh9wCzq3VTy9Zz4XLmwnPd08RoSFhXHgwAHAPNNdWlrKqVOnMJlMDB48WEzy0ZXHiPXr1xMWFsbIkSNbHCPc3NzE5DPDhg0jKyuL/Px81Go1kyZNYtOmTQiCQFBQUKMxoq6ujvT0dM6dO8fQoUPZt2+fVY8R27dvByAiIgKNRkNSUhIAo0ePJiMjA1dXVw4dOsS2bdvYt28fcXFxhIeH4+TkxKlTpwAYMWIE586do7CwEFtbWyZMmCD22ZCQEDw8PDh+/DiAPEa0MEZs2LABX19fHBwcJB8jWrqOsNjfKoQ2snz5cgFocTt06FCD9+Tk5AgRERHCPffc0+Kxa2trhfLycnG7cOGCAAjl5eUN2mm1WiEpKUnQarWCIAiCySQIVVXSbyaTIJSVlbX4GUJDQ4V9+/YJgiAIvXv3FmJjYwVBEITt27cL0dHRTbazcP/99wvLly9vdEyj0SjY2dk1e+7ly5cL999/f5PnAYS8vDzxnKtWrRL3vfjii8Kdd97Z4Fi1tbXCX/7yF2HSpEmCIAhCRESEcOLEiRY/s4W1a9cK06ZNE+Li4oSCggLhk08+EZYuXSo4ODgIFRUVgiAIwtatW4Ubb7yx2WOUlZUJ//znP4UZM2YIy5YtExYvXtxgf1hYmFBQUNDgtRtuuEF46623hPj4eOHTTz8VX79ci3379gmhoaHi82PHjgm+vr6CIAjCjh07hODgYCE1NVUwmUxCcnKyUP/rUVxcLPj7+wu33367MGLECMFgMDRp/++//y44Ojo2uzVFcnKy4OLiIuh0OvG1kJAQYefOnY3avvPOO4Kjo6Pg6+sr+Pr6CnZ2doKTk5Pwpz/9SRAEQViwYIHwwgsviO1Pnz4teHl5CYIgCJs2bWrw+QVBEOLj44X169c3Os/l37GOwmQyCefOnRNWrVolLF++XNxWrFghnDhxQtDr9R1qT1v57bff2tTeYDAI2dnZwu+//y6sWbNG+Oqrr4SvvvpKWLt2rbB//34hPz9fMJlMElnbNTGZTEJFRYWQnZ0tJCUlCYcOHRIOHDggfPbZZwIgfPbZZ8KJEyeEjIwMobi4WKio0Am//y4IL78sCNdfLwguLoJgDmD7Y1MoBKFvX0G46y5B+PBDQThyRBDqfd1kLtHW/ntVGAyCEBTU+J90aTOiEAwBweZ2zaDT6YTExEThwIEDwvHjxxuMnV2ZDtFXMI/1X331lZCamtoh5+tMTCaTsGbNGmH58uXCQw891OV/I6yZjuq/V6K8vLxJ36Ap2jyT8/DDDzcIB2qKsLAw8XFubi4TJ05k5MiRrLxCcK2tre1V5bhXKMDRsc1vuzJGI1y6mw1ADTgqFNDSomFBAK0WqqtZt2oVSkt7rdacIsjy3nrtRPR6c+7Uy46vBO5csIBljz3GGy+9hIuLCympqVRWVTEsPt78Hr2+6fOAOc7u0qKi9999l4TRo6msqmLlihV88Pbb6EpL+faHH5h+3XU4OTnhpNFgA1BdzT23386zf/0r//33v/H18eF8Vhbns7IYP3Zso48+dvBgFu/eTWhwMD6OjowdMoRHH32UmKgonJVKqK5m4/r1TJswoVkNHRUKFsyaxd///ncOHTzIF//7n9j2THIyHm5u+Dg6Nnj/wltu4enlyykqLmb2tGl/7LtcC632jwVclz2vLCpCZWODp50d1YWFvGRZ73Kp7YP33cfcWbN495//ZMJ11/HWq6/yl8cfb1KDqoKCJj9b/ePVJzooiOjISF77xz/4y+OP8/Fnn2GjVDIqLq5R+/sWLmTeTTeJz5f++c9E9u7NsqVLobqaxfPmcd8jj7Bo9mz8/fx49cUXufGSJhOGDkUhCHy2ciWL5s3jl99+I+PcOUYOGNDYrro6c79KTDR/wToIBRAOhMfEUOrry6lTp0hNTcWYl8e+I0c4ZmdHbGwsffr0wcnJqcPsai3DVCpzvFQrsQECgUBHR7RBQeTl5ZGTk0NNTQ1laWkcxzw76O/vj7+/f5f8zO2NAnC+tAUCRkGgRqvF29GR1cuWEaHVojx+nCrAEtDpZ2vLnf0deGC4A3bPOpCVpeH4cQXHjsGJE1BQCCTC8UQ4/gmsBGw1EB0Nffv+sQUHQxdOziU5be2/V8Xhwy0WRlIiQO4F+PhjiI9vtF+n03H+/HlMdXU4X1r7pr50x76r0yH6AhEVFZgyMiivqDCnpevGKICbgoKo3bsX98pKjv7vfwwbNqyzzeqWNOq/MTHg4NB5BrWCNjs5Xl5eeHl5taptTk4OEydOZMiQIXzyySddOrVjk9TWmpPz1+OKgun1kJkJzs4MsLExv3bmjDmIXKf743j12omUlZkr5l12ToC377qLZz74gP6DB1NZU0NkcDAvPfCA2bsrLja/t6nzAKSmQkkJ6PXcPHw4I8aMoayykgdvuYUZ4eHokpP5bOVKHlq6FJPJRFxUFCuefhrOnGFZQgL63FxGjR1LcXk5oX5+PHXHHdBEH/AFAjw9GR0TA2fO0BtwsrVlTHS0aM9v69ez9tVXm/yMFn0DgZH9+pGcmckkH58/3rt6NdMGDmz03lmRkdx/8SIzRo/Guf6P5+VaZGaadbc8P3fOHLx/5gzXBQUxMjqa0JgYvNzc+Mvtt/Plpf/d2i1bOHroECdWr0aRnMz/LVvGsMWLmREdTeyl+OdrZfWzz3Ln88/zyhtvEBMayrqXXkJ1afr5lU8+YdexY2x4/30cgPpDin1dHU41Nbjl5UFeHlP9/Xn8llsYPWECOoOBaSNG8M5f/gJnzqAGfnztNe558UUeeuwxIoODWffqq3gUFEBTjllxMSxZApem1zsad2Dcpc1aaLxKqvXYA70ubTJ/YMMfTk9rv23Rl7bbWmqkA05d2mSAa+u/7U4z+aI1QGTHWtJudJS+4bT+u9IdsAfutDz5/PNOtKR706j/HjliTn/ZhVEIgjR5aXJzcxk/fjwhISF8/vnn2Fgu+DHXkWkNlphCSzysBUseeEsmEcm4fCYHqKquxkmSaSPpCevThzWffsqITrrLkZefz+gpUzh3+nSzbVrSd9rMmTz75z8zrl4SA5m20dr+W1tXR8aFC4QbDNh1kcW8RqORzMxMEhMTG2QO8vT0pF+/fvSOiEDdisQgUrJ3715GjRrVbsczGo0UFRWRm5tLcXGxmMJToVDg7e1NQEAAXl5eDcbX7kpRURHvvfceS5cuxdvbW3xdbzCgramhpqYGrVaLVqvFZCmscwlLkUjLZmdnT36+msREBUlJcPo0pKRAXTNl3IICzZm+IiPNW3g4BAWBNRUAr9MpsNW0/HPf3v3XwoULsHUrrF8P7hmH+S/NFLypz4oV4kyOZb2WBXt7e4KDg1uV7bQrIZW+lyMIAtu3b0ev1zNixAhcXaVfs9zZCILA+++/j52dHQEBAUyfPr3LJ6KwNhr1306ayWnON2gKya4INm3axNmzZzl79myjtHMS+VXtj41Nozg4k9EoUWxcB6BQgL19p9lfYTDwzzfeaPH8Lek7OSGBkZMmWdeVRRej1f3XxgY0l+J5pLyR0AZsgN5Dh9J77lzy8/M5ePAgp06dIk+v53RqKvYXLjB48GCGDh3abLpuqam+eLFd72zZAH6XttraWrKyssjMzKSkpIQSIKW8HI1WS3BwMKGhoXh5eVnfjHkruXD0KC9v2MDsl17Cu57G6kub5afOZDJRXV1NVVWVuOn1ei7PVagOUxLR15E4R0ccHR2xs3MkM1PNiRNw/Lg5zO3ECcjNhWM5QA6w/Y/329hA797m3/noaPNfy9bVMhmvWAGPPAL/+lfzBTWh/fpvXR3s2wdbtpgzpF1a0w2Ak30crytexL0mBwVNXAsoFGYP8p57MAKFhYXk5eVhiIm5tFtBnyFDrLKft/f40BwKQFlZSWl+PsUhIbheqpXWnVEAwTNnkpSURJ7BQJhGIyZ2kmkfOqr/tieSzeS0B50+k9PNCAsLY82aNYwYMaKzTZHp4ljLd0yr1XLs2DEOHjwopuhWKBRER0czdOhQevXq1S3v5lmy9Jw/f15Mtw5/3OEOCQnB09OzW332o0ePMmTIEI4cOcLgNvzQCoJAbW2t6PBUV1ej1WqbvNlma2uLg4MDjpccH3OGQ7Xo8Jw4AUlJkJwMzWR4B8wFTXv1Ms/4hIWZN8vjkJCOvW+wYoU56nTAADh5Ej76qGVHp60IgnmJzcGDcOiQ+e+BA+aloBZsbMyZom+5BebNA9et68xPLAewcKm/GtasoWjsWPLz89Hr9YA5e6m3tzeBgYHtZ3w35siRI6SlpREbGyvWvesJ/P7772zbtg03NzcefvjhVpX9kLEuusRMTnfFkt7ZGsnMzOxsE66INetrDXQ3fe3t7Rk1ahQjRowgLS2NAwcOcO7cOZKTk0lOTsbd3Z34+HgGDhyIYwfMYG7dupXJkydLfh5Lba3+/ftTWFhIVlYW2dnZaLVaUlNTSU1NxdHRkZCQEEJCQnBzc+tWDk9bUCgUYpiaJczNaDRScynErbq6WkyZXldXR11dnVhAF8z1OAID7YmMdOCOOxxwcHDA1taO/HwlKSlmh6f+duECFBWZt0sZjxsREGB2eIKDwd//jy0g4I/Hbm7XnvPD4uA88gi8+y489hhivaGmHJ2W+m9NjdmZSU01bykp5r9nzjS9pM/XF6ZMgalTYfp08PSst3P2bHO+76VLGyQhMAUEUPTss1zo1QvTpXTztra2BAYGdgunvaPGB0BMUlLdUqKkbsbWrVsZN24chw4doqysjMOHD8s3dduRjuy/7YXs5LSRLjzx1S2Q9ZWW7qqvUqkkOjqa6OhoioqKOHz4MCdOnKC0tJTNmzezbds2YmNjiY+PJzQ0VLKLJUMHV6FUKpX4+fnh5+cn1tHJysoiJyeH6upqzpw5w5kzZ3B1dRUdHuf6yU56KDY2Njg7OzfQwmg0ig5PdXU1NTU11NXVodfr0ev1DYq1WhynkBA7oqPtue02O+zt7bG1tUWrtSE11ZznJDMTMjIa/q2uNofAXaph2SwajTnszdOz8V83N3PUaVObRmMumPrdd/CPf8DDD5uLpCoU5r+CYHZ0MjJgxgzzstPycvN24EAgu3ebHxcX/2Fnbq75teb1hP79YehQ8zZiBPTrdwUnbfZshJtuonbzZmrS0ym1s6Okb1/zwUwmHBwc8PX1xdPT0ypD05qiI8cH9aWwbstMWE/AYDCgVquZOHEiP/30Ezt37mTgwIFdOhrBmujo37f2QHZy2ohaXg8iKbK+0tIT9PX29ub6669nypQpnD59msOHD5OTk8Pp06c5ffo0Xl5exMfHExcXh729fbue29/fv12P1xZsbGwIDAwkMDAQg8FAbm4uWVlZ5OXlUV5ezqlTpzh16hTu7u6EhIQQFBRkVQ6Pu7s7N9xwA+7u0uSosrGxwcXFpcFMp9FoFJMZ1NRLbmAwGMTn9VEoFGg0Gpyc7ImPt2P0aFvs7OywtbVFo9GgUCi5eNHsYGRkQE4OXEqMSF6e2ZnIyzMny9TpID/fvF0tDz8M77//h7OhUJifA7z+unlrSEyLx3NwgIgI8xqk6GhzMoboaLND05r1x4IgUFdXJxZhrqioQO/pKU7z2NjY4O7ujre3N05OTlY/c3M5HTk+WMK0rPHC9Gqx6Dtw4ED27t1LcXExhw8fZoycrKhd6Mzft6tFXpPTRgwGgxzjKSGyvtLSWn2tZU1Oa8nLy+Pw4cOcOnUKnc6cQkulUtGvXz/i4+MJDAxslwuqkpISPLrYqnOdTkdOTg5ZWVkUFBQ0yDzm7u5OUFAQgYGBuLq6dvmLyq6gryAI6HQ60fmpra2ltrZWdH6aw+IA2do2dHwsm1qtFmcstFooLDRn/rdsFy/+8be83DwjVF1tXhtU/7HFOerfH44da7ruj8kEgwbBqVPmRAnu7uDiAvb2dXh72+Lqap41Cgw0h9BZNheX1ofQWRwai06WZBCXzyyoVCpcXFzw8PDA1dW1W2cK7Mj+m5GRwYEDB/Dz82PChAkdcs7Opr6+J06c4Pvvv8fJyYmlS5f2iBt8UtMVxl+Q1+RISnV1dY9Ix9hZyPpKS0/V19/fnxkzZpCQkMDJkyc5fPgwBQUFHD9+nOPHj+Pn58egQYMYMGDANc3uHDp0iGnTprWj5deORqMhPDyc8PBw6urquHDhAhcuXKCoqIjS0lJKS0s5deoULi4uBAUFERQUhLu7e5dzeGpra/nxxx+ZP39+pzreCoVCLFxdP4ufIAgYDAbR4bGs8bH8NZlM4pqf+qFv9Y+rVqsbOD5+fmqCglSo1WpUKhUqlfnxlRwBy1qcxx77I1TtDzvNrzeVhGDjxh1t6r8Wh6+urk78a/nMWq0Wo9HY6D1KpRIHBwecnZ1xc3PD0dGx24SjXYmOHB8sDndPumlYX99+/fqxbds2ysvLOX78OEOHDu1k66yfrvj7diV6Tu+XkZHp8dja2jJ06FDi4+PJzs7m8OHDJCYmkp+fz4YNG9i8eTOxsbEMGjSI8PDwLnehf63Y2toSERFBREQEdXV15OTkkJ2dTX5+PhUVFSQlJZGUlISjo6Po8Hh5eXUJHZKSkrj77ruJi4trU3a1jsLipKjV6kZhgIIgoNfrRSfA4gjodDp0Oh16vR6TySQ+vxJKpVJ0dpraZsywobzckaeecr1UP0SBQmF2cB59VODf/1bwzjta5s3TiWttLA5LSUkJJpMJo9GIyWQSN4PBIG56vR6DwYDRaGxxnZ9SqcTOzg4HBwfs7e1xcnLqUU5NZ2JJOODQxSvSS4WNjQ2jRo1iw4YNHDx4kPj4+C4xjsl0LLKT00Z60oBRP+X0kiVLiIqK4oknnpD0nD1J385A1teMQqEgODiY4OBgrrvuOk6ePMmxY8fIz89vsHZl4MCBDBw4sNWzXwMHDpTW8HbE1taWXr160atXL/R6Pbm5uWRnZ5OXl0d1dTUpKSmkpKRgZ2cnOjze3t7dOpxIKiyhahqNpsl1UJZZIMtsiF6vF52f+k6FxRmyzAq1xIQJ8NRT3rz+ejgg8N57CpYuNTs4Tz2VwahRRdSrrwmYM3KdPXu2TZ9NqVSKYXiWEDxbW9tLRVftZIemHh05PlhmC61p3d21crm+cXFxbNmyhaKiIrKzswkODu4cw7oJ1vT7ZkF2ctqIJXtHc4SFhVFSUkJBQYEY9lJRUYGvry+hoaEkJyd3lKktkpmZSUxMDLW1ta1q/9FHH0lskZkr6Stzbcj6Nsbe3p7hw4czbNgw8vLyOHr0KKdOnaK0tJTt27ezY8cOevfuzeDBg4mOjm7xIr+kpARfX98OtL59UKvVhIaGEhoaisFgID8/n+zsbHJzc6mtrRULO6vVavz9/fH39ycgIABbW9vONr1bUH8WqKVU54IgYDKZGsykNLeZTCbuuceAg0MBy5f78vvvAidPKnjuuRxuvbUacGhwfoDKykpcXFxQKpWNNku4XP2wOctj+Q556+io8UEQBIqLiwEkS9TRFblcXzs7O/r27cvx48c5evSo7ORcI9b4+yY7OW1Ep9NdMWbfz8+Pn376idtuuw2AdevWyV+uVtIafWWuHlnf5lEoFAQEBBAQEMC0adNISkri2LFjZGZmihf5jo6ODBgwgMGDB4s1V+qTlZVFbGxsJ1jffqhUKnHmxmg0UlhYyIULF0SHJysri6ysLBQKBV5eXgQEBBAYGIizs7N8sSsxCoVCDElrLX//u7lmzSOPKC6twQkEmi6omZ2dTUxMyxnWZK6ejhofSkpK0Ol0qFSqHuXkNKXv4MGDOX78OImJidx44409ao1Se2ONv2/yPLIEzJ8/n1WrVonPV61axYIFCxq0OXXqFKNHj8bNzY34+Hj2798v7gsLC+Ott94iKioKFxcX3n33XQ4ePEifPn3w8PDgnXfeEdtqtVoefvhhAgICCAoK4vV6OUEXL17ME088weTJk3F2dmbatGlikbuEhATq6upwcnLCycmJ3CsUbVi8eDGvvfYaAM8//zx33HEHc+fOxdnZmREjRnD+/PkGn23cuHG4u7szZMgQDh8+fBUqysh0Hmq1mri4OBYvXswjjzzCmDFjcHZ2prq6mn379vHBBx+wcuVKDhw40CiNcHfCxsYGf39/hg0bxsyZM5k6dSp9+/bFzc0NQRAoKirixIkT/Prrr/z6668cO3aMgoKCJhecy3Qe998PlZVNFwCV6X5Yfo8DAgJ6fHhpcHAwzs7O6HQ6MjIyOtscmQ5GdnLaSGti86dOncrRo0cpKSkhPz+ftLQ0xo0bJ+7X6XTMmDGDBQsWUFRUxLJly5g+fTrl9aqt/frrrxw6dIgtW7bw1FNP8cYbb7Bnzx62b9/OM888Q1FREQDLli2jvLyc1NRUDh48yOeff87PP/8sHufrr7/mvffeo6ioCIPBwL///W8ANm3ahK2tLVVVVVRVVREQENAmHdatW8ejjz5KaWkpUVFR/OMf/wDM4Q7XX389jz/+OMXFxTz33HPcfPPNrQ6L64mZvzoSWd+24+npyZQpU3j88ceZP38+MTExKJVKcnNz2bBhA2+99RZr1qwhOTmZKVOmdLa5kqFQKPD09KR///5cd911zJgxgyFDhuDv749SqaSyspKUlBS2b9/Ojz/+yN69e8nMzGz1d/9KDB48GEEQumTSAWugNZGF1pY5ydroCH31er3o5ISFhUl+vq5EU/oqFApxdrKrLBewVqxxfOg+83Y1NSB1B46JodJovOJCPpVKxaxZs1i7di1arZa5c+c2WHy5f/9+bGxseOihhwCYN28e7733Hps2bWLu3LkALF26FFdXV4YNG4afnx+33nor7u7uYiG/5ORkvLy8+OSTT8jMzBRnZB544AG+/fZbZsyYAcBtt91Gv379AJgzZw7btm1rFykSEhIYO3asaP/f//53AH755RcGDBjAzTffDMCsWbN46aWX2LdvHxMnTrzicSsrK3vUQsmORtb36lEqlURHRxMdHU11dTWnT5/m+PHj5OXlkZycTHJyMnl5ecycOZOBAwfi7+/frcO3HB0diYyMJDIyEr1eT35+Prm5ueTm5lJXV9cgrM3d3R1/f3/8/PyuqYL9jh07ekzNj85A1ldaOkLfc+fOiVEafn5+kp6rq9GcvtHR0Rw6dIi0tLSON6obYY3jQ/dxcpKTYcgQac9x5Aim3r1b1XThwoX89a9/RavVsnLlSsrKysR9ubm5hISENGgfGhraIGTMx8dHfGxvb98g/t/e3p7q6mqKiorQarVERUWJ+0wmE6NHj27yOA4ODlRVVbXK/ivR3HGzsrLYunVrg/oRer2evLy8Vh23fqFCmfZH1rd9cHR0ZPjw4QwfPpzCwkKOHz/OyZMnqa6u5uDBgxw8eBBvb2/i4uIYMGDAFQuWWTtqtVrMVmcymSgpKSEnJ4f8/HxKS0spKSmhpKSExMRENBoNvr6++Pn54efn1+JC+/qkpKTwwAMP8MMPPxAdHS3xJ+qZXCljm8y1IbW+dXV1JCUlARAbG9vjMts1p29ISAhKpZKKigrKy8vliIarxBrHh+7j5MTEwJEjkp+jtXmpRo4cSU5ODhqNhoEDB7Jjxw5xX0BAABcuXGjQPisrizlz5rTJHC8vL+zs7Dh//nybv7RS3WEODAzkxhtvZN26dVf1fjnzl7TI+rY/Pj4+JCQkMGXKFH7++Wf0ej3JyckUFRWxZcsWtm7dSq9evRgwYAAxMTHdPiOZUqnEy8sLLy8v4uLi0Gq15Ofni1v9gqRgDqH08/PD39+/xRTV1dXVJCcni/U/ZNqf+jevZNofqfU9efIkdXV1uLq69rhQNWheX3NhXT9yc3O5cOGC7ORcJdY4PnQfJ8fBATogVltzqYpwa1i3bl2Td1JGjBiBXq/nww8/5N577+X7778nJSWFhISENtmiVCq58847WbZsGW+88QYuLi6kpKRQWVnJsGHDWnyvl5eXOMPi7+/fpvO2xPTp03n66af56aefuPHGG9HpdOzcuZORI0e2amDRaDTtZotMY2R9pUOpVDJhwgRcXV2pra0lKSmJ48ePk5WVRXp6Ounp6ahUKqKjo+nfvz+RkZE9YlGwvb094eHhhIeHYzKZKC0tJT8/n7y8PC5evEh5eTnl5eWkpKSgUqnw8vLC19cXX19f3Nzcetzd6M6kV69enW1Ct0ZKfbOzs0lPTwdgyJAhPWJsuZyW9A0ODiY3N5ecnBwxhF+mbVjj+CD/erSRttxFHDBgQJNfJo1Gw48//sgXX3yBp6cnr732Gj/99NNV3V14++23cXR0pH///nh4eHDHHXeIGdRawtHRkaeeeor+/fvj5uZ2xexqrcXV1ZX169fz3nvv4e3tTVhYGCtXrmz1++W7tNIi6ystliyJdnZ2DB48mLvvvptHH32UiRMn4unpicFgIDExkTVr1vDmm2/y888/k5mZ2WLV+O6EUqnE09OTvn37MmXKFGbNmsWoUaPo1asX9vb2Yo2eEydOsGnTJn744Qd2795NWloalZWVnW1+t6d+lk+Z9kcqfSsqKjh48CAAMTExVnnHvT1oSV8vLy/AnF5b5uqwxvFBIXThX9eKigpcXV0pLy9vENNeW1tLRkYG4eHh2NnZdahNcjyntMj6Sktr9e3M75g1s3HjxmYz0AiCQF5eHqdOneL06dMNLtpdXFzo168fAwYMwNfXt1snLGgOQRAoLy+noKCAwsJCCgsL0ev14v6MjAyeeeYZPv30U8aOHYuvr2+r1/PItI6W+q/MtSOFvlqtli1btlBdXY2XlxcTJ07skbM40LK+6enpfPHFF3h7e4tJn2TaRlcZH5rzDZqi+4SrdRByIUVpkfWVFllfaenfv3+z++oXG506dSrnz5/n1KlTJCUlUVFRwd69e9m7dy/e3t7079+ffv364eHh0YHWdy4KhQI3Nzfc3NyIjo4WQ9sKCgooKChAq9Vy9913o9frxbvWTk5O+Pr64u3tjbe3t+z0XCMt9V+Za6e99a2pqWHHjh1UV1fj7OzMmDFjeqyDAy3raymKWj8JlEzbsMbxQXZy2oicnUpaZH2lRdZXWlqbvVCpVIrrVG644QbS0tI4deoUqampFBUVsW3bNrZt24a/vz99+/alb9++PapyOfwR2ubp6UmfPn0wGo3Exsbi6OhIQUEBJSUlYp0vy1oER0dHvL298fLywtvbGxcXlx45K3a1tFf2TZmmaU99Kyoq2LlzJ9XV1Tg6OjJ+/PgeP+vekr4WbfR6PSaTSV7rdxVY4/ggOzltpK6urscPJFIi6ystsr7SkpGR0SCle2tQqVTExsYSGxtLbW0tycnJnDp1ioyMDPLy8sjLy2PLli0EBgbSt29f+vTp0yBFe0+hpKSEL774gueff57+/fuj1+spKiqisLCQoqIiSktLqa6uprq6mszMTABsbW3FWR5vb285kcEVuJr+K9N62kvf3Nxc9u3bh16vx9nZmQkTJsizmLSsb/2kOzqdTv4dvAqscXyQnRwZGRmZLoKdnR0DBw5k4MCBYsrkxMREMjIyyMnJIScnh02bNhEUFCQ6PD1lDduFCxf4z3/+wz333IO3tzdqtVoM/wPzHdqLFy9SVFREcXExxcXF1NXVkZ2dTXZ2NoCYvc3b21ucJZLTqstYC0ajkVOnTpGSkoIgCHh7ezN69Gj5gr0V1A/jM7QhS66MdSM7OW2kuxf162xkfaVF1ldapkyZ0m7HcnR0ZMiQIQwZMoTq6mrOnDlDYmIimZmZ4oX7xo0bCQ4OFh2envz/VavVYoFRMF8QlpaWUlRUJDo+Op1OrNcD5nVALi4uosPj6emJi4tLj53tac/+K9OYa9H3/Pnz7Nu3T3weERHBoEGDevQanMtpSd/6SUzkUgpXhzWOD7KT00aqqqpwdnbubDO6LbK+0iLrKy179+5l7Nix7X5cR0dH4uPjiY+Pp6qqSnR4zp8/LxbW/O233wgKChJD33pS0oKmsLGxEYuSxsbGitnbLE7PxYsXqa6uFuv0nDt3DjDP9tR3ejw9PXvMnXKp+q+MmavRt66ujsTERFJTU8XXxowZQ1BQUHubZ/W0pK9OpwPMNzbk2durwxrHB9nJaSPywm1pkfWVFllfaampqZH8HE5OTgwdOpShQ4dSWVnJmTNnOH36NFlZWeIMz+bNm/H19RUdHh8fnx6/AL9+9rbIyEjAnH63pKSEixcvipvBYBAzullwcnLCw8MDDw8P3N3dcXd375Z3gzui//Zk2qKvwWAgPT2dxMRE8QIdICEhocffwGiOlvS1LJq3t7fv8WPh1WKN44Ps5LQRlUqWTEpkfaVF1ldaPD09O/R8zs7ODBs2jGHDhlFZWUlycjJnzpwhMzNTvFDfsWMHHh4eosMTGBholT/yzs7OjBgxol1nIu3t7QkMDCQwMBAw3wSoqKho4PSUl5eLWdyysrIa2GNxeCybra1tu9nWGXR0/+1ptEZfvV7P2bNnSUlJoba2FjAX2R40aJAYiinTNC3pe/HiRQDZQbwGrHF8kIuBthGj0dhpMbCrVq3i22+/5fvvv7/qYyxevJiYmBj++te/tqNl7Ud76lv/s7aHdt2B1uorFwO9OqqqqnBycupsM9BqtaSkpHDmzBnS09MbLLR1dnYWHZ7Q0FCrWn/SGfrqdDpxtqe0tFTM4tYUjo6OjRwfOzs7q3Equ0r/7a60pG9VVRXnzp3j7Nmz4syNo6Mjffr0ITw83Kq+p51FS/ru3LmT7du3ExcXx80339zBlnUPusr4IBcDlZCqqqpmsxlNnTqVadOmsWzZsgavP/HEE1y8eJHPPvusTedSKBTk5eWJd28WLlzIwoULr85wK6ElfS8nLCyMNWvWMGLEiCu27QnatYa26CvTdvbs2dMlKkLb29uLWdp0Oh1paWmcOXOG1NRUKisrOXjwIAcPHsTe3p7IyEiio6OJiIjo0jMRRqORTZs2MXPmzA690aTRaBokNADzOgmLw2PZKisrxRTWlmxuYE5j7erqipubG66uruLWFdcFdJX+2125XF+j0UheXh7p6enk5+djuefs7OxMnz59CAkJkRMLtIGW+q9lFtaSjVGm7Vjj+CA7Oe3IokWLePfddxs4OSaTia+//ppPPvmk1cfR6/Vd8gdQRkbG+tBoNGJBUYPBwLlz5zhz5gwpKSnU1NRw8uRJTp48iY2NDWFhYURHRxMVFdXlavGcOHGCOXPmcOTIEQYPHtypttja2jZyfHQ6HWVlZZSUlDRwfOrq6igsLKSwsLDBMRwdHRs5Ps7OzvJFbTfHaDRSVFQkrp+zhKQB+Pn50bt3bwIDA+WZm3bEaDSKTk5YWFjnGiPTocjfojZib2/f7L7Zs2eLISIWduzYgdFoZPLkyWRlZXHjjTfi6elJbGwsv/32m9guLCyMf/7zn0RHR9OnTx8SEhIA6N27N05OTuzbt49PP/2U6667TnzPtm3biI+Px8XFhcjISHbt2gXAf//7XyIjI3F2dmbAgAHs2LGjVZ8tLCyMt956i6ioKFxcXHj33Xc5ePAgffr0wcPDg3feeUdsW1JSwrx58/Dy8iIiIoL//e9/4r7Fixfz2GOPMX78eJycnFiwYAH5+flMmTIFV1dXFi5ciNFoFNt/8MEHREZG4uXlxcMPPyyGgnz66ackJCTwwAMP4OLiQt++fTl+/DgAf/rTn8jKymLSpEk4OTnx9ddft/jZ6mu3Y8cOYmJieOGFF/Dw8CA8PJzNmzc3+GwLFizAx8eHXr16tXkGrivTUv+VuXb69OnT2Sa0iEqlIioqipkzZ7Js2TLuuusuRo0ahaenJ0ajkfT0dH799VfeffddPvroI7Zv305ubi5dOKq5y6DRaPDx8SEmJoaRI0dyww03MGfOHBISEhg+fDjR0dH4+fmJ38Hq6mpycnJISkpi3759/Pbbb3z33Xds2LCB3bt3c+LECc6dOyfW++kIunr/tVb0ej05OTkYjUZ++uknduzYwdmzZ6mtrcXOzo4+ffpw4403MmHCBIKDg2UH5ypprv9mZmai1+txcHDAx8eng63qPljj+CDP5LSRlrJTOTs7c9NNN7F69WpefPFFAFavXs28efNQKBTMmDGD++67jx9//JFDhw4xY8YMTp8+Ld4N/OGHH9i1axcuLi5iHHd6erq4PyUlRTzXuXPnuPnmm1m1ahXXX389OTk5YhxvQEAAW7duJSgoiI8//ph58+Zx/vz5VoWi/Prrrxw6dIiUlBTGjh3LTTfdxJ49e8jKymLEiBEsWrQIb29vHnroIVQqFVlZWZw9e5YpU6YQExPDmDFjAFi7di1bt27F29ubwYMHM336dD7//HMCAgKIj49n/fr1zJw5k7Vr17Jy5Uq2bNmCj48Pixcv5u9//ztvvfUWANu3b+e+++7j3//+N8uXL+fJJ59k69at/O9//2PLli2tDle7nLNnz+Ls7ExhYSH/93//x5IlS0hPTwfg9ttvp1+/fly4cIGMjAwmTZrEwIEDiYuLa/N5uhpydjVpqZ8FqaujVCoJDQ0lNDSUhIQEiouLSUlJISUlhQsXLoj1ZHbu3ImzszNRUVFER0cTHh4uzzS3EpVKJWZlq09dXR3l5eWUlZWJKazLy8vR6/Xi48uxtbXFxcUFZ2dncXNxccHR0bHdZn+sqf92ZYxGIyUlJRQUFJCfn09JSQkmk4nS0lJxnVZgYCDBwcF4e3vLs3ftRHP99+TJk4D5It1a1sd1RaxxfJCdnDZSV1fX4kLsRYsWsXTpUl588UXq6ur47rvv2LRpEwcPHkSv1/PQQw8BMHLkSCZMmMCGDRu46667AHj88cdbfZfhq6++YubMmUyfPh2AkJAQcd+NN94oPr733nv5+9//TlpaGv369bvicZcuXYqrqyvDhg3Dz8+PW2+9VVxAGxISQnJyMh4eHnz33Xekp6fj4ODAgAEDuOeee/jqq69EJ+e2224jJiYGgAkTJuDk5CTeBZg8eTInT55k5syZfPzxxzz77LOEhoYC8NhjjzFv3jzRyenfvz+33HILAAsWLOCjjz5qlT5XwtXVlccffxyFQsGiRYu4//77xQxKu3bt4qeffsLGxoaYmBgWLFjAunXruoWTc6X+K3NtnD17lt69e3e2GVeFpabM6NGjqampIS0tjZSUFM6ePUtlZSVHjhzhyJEjqNVqwsPDiYyMJCIiAnd398423eqwtbXFx8enwXgvCAI1NTVUVlZSUVHR4G9NTQ11dXVijZ/6KBQK7O3tcXR0xNHREScnpwaP25Iy15r7b2dh+b9dvHhRTFBRUlLSIFoBzDdBKysrmThxIt7e3vJsjQQ01X/r6urE6Jru8BvemVjj+NC9nJwHHoCcHGmOHRgIH354xWbTpk2joqKC/fv3k5eXh7e3N0OHDuWbb74hLS2tQZy7wWBgyJAh4vO2FPfKzs6mV69eTe774Ycf+Mc//iEWt6usrBTTJ16J+j+69vb2eHt7N3heXV1NUVERRqOxgb2hoaFs3LixTccB82LAe+65h/vuuw8w/2DUzwRV/zgODg5irvtrxdvbW/zhd3BwABBTxFZXVzdIlWg0GuWkBTI9CgcHB+Li4oiLi8NgMJCZmSnO8lRUVJCamioWJ/Ty8iIyMpLIyEhCQ0Plu9JXiUKhEJ2Ty1MF6/V6KisrG2wWB8hgMFBTU0NNTU0jBwjMM3b1nR8HBwfs7e0bbGq1Wr7D3Qos/4f6s29lZWVotdpGbW1tbfH19cXX1xc/Pz8cHR3ZuHEjvr6+nWB5z+XQoUPodDq8vb3lAqo9kO7l5LTCCblWrlSjQa1Wc+utt7J69Wry8vLEi+PAwED69+/P0aNHm31vW35kgoODG4SvWairq2P+/Pn8+OOPTJ48GRsbG/z9/ds1pt5yFyo7O5vg4GDA7KxcTdaSwMBAXnvtNW666SbAHE7V2jtcUvwoBwYG4ubm1mqn0NpozxojMo2ZOHFiZ5vQ7qhUKiIiIoiIiOCGG26gsLCQtLQ00tLSuHDhAsXFxRQXF7Nv3z40Gg29evUSnZ4rpfdsC/379yc7O7tHxtSr1eomw94EQaCuro6qqioxs1v9xzU1NZhMJtExag6VSoW9vT0ajYb9+/c3cIBsbW2xtbXFzs4OjUbTrZ0hQRDQ6XSNdLTM8ldXVzf5W6pUKnF1dcXT01PcnJ2dG2nVHceHrsTl+up0Ovbu3QvA2LFju3Xf7Qissf92iJNTV1fH8OHDOXHiBMeOHWPgwIEdcVpJqKmpuWKe8IULFzJr1iyqqqp45ZVXABg+fDh6vZ6VK1eyePFiAA4cOEBoaGiDULP6+Pj4kJmZ2WQBsPnz5zNw4EB+/fVXrrvuOnFNjre3t/gX4L333mvy7t61YGNjw+zZs3n22WdZsWIF6enpfPzxx3z77bdtPtY999zDyy+/TL9+/ejVq5dYJ6B+goXmsOhzNWtymiMwMJChQ4fy97//nb/+9a9oNBpOnjwpLg61dlrTf2WunsOHDzNq1KjONkMyFAqFeHd6zJgx1NbWkp6eTlpaGmfPnqWqqork5GSSk5MB8PX1FcPagoODr2mWR61Wc/78ebFwp4z5/2FnZ4ednR1eXl6N9ptMJrRabSPHR6vViptOp8NgMFBZWcmFCxfEG1fNnc/i9NR3fNRqtfi3qcc2Njbi1pGYTCb0ej06nU78a3lcW1uLVqultra2wePLw8wux87OrkFGPEt68NYUWu7u40Nnc7m+e/bsoaamBnd391aF68u0jDX23w5xcv7yl78QEBDAiRMnOuJ0knKlARBg1KhRODs7i3HrYL5Ttn79epYuXcqzzz6LIAjEx8e3uMbk73//OzNnzqSurq5BJjaA8PBwvvvuO/785z9z22234e/vz//93//Ru3dv3njjDaZOnYpCoeCBBx4gIiLi2j50E3zwwQc8+OCDBAUF4erqyj/+8Q/Gjh3b5uPMmzeP0tJSbrjhBnJycvD19eXBBx9slZPz1FNP8eijj7JkyRJWrlzJrbfeejUfpRGrVq3iiSeeoFevXuh0Ovr169cgs5w105r+K3P1tHS3vDtiZ2cnpqcWBIH8/Hxxlic7O5uCggIKCgrYvXs3Go2GsLAwevfuTa9evfDy8mrTndX09HSeeOIJVq1aZXVx4Z2FJVTN0dGx2Tb1L/i3bNlCXFxcAyeorq6Ouro6dDodgiCITsHV2mNxdlQqFSqVChsbGxQKBUqlstHf+rP6lqQpgiCIG5jHtOa2+qHPbcGyxskS4mf5a0kKdLX0tPGho6mv78WLF9m9ezdgrmEor4G6dqyx/yoEiXODbtiwgSeeeILvvvuOvn37tmkmp7mqpp1Zjb2rVHztrsj6Sktr9e3M75g1c+DAAYYPH97ZZnQJampqxFmec+fONVpP5+LiQu/evUWnx7I2rjmOHj3KkCFDukSdnO5KS/3XaDSi0+mora0VHR+L82OZHak/a1L/cWenIFepVA1mnDQaDba2ttjb22NnZyf+tWytmZW5GuTxQVos+ppMJj7//HMyMzOJiIhg4cKFcqhaO9BV+m9zvkFTSDqTU1BQwL333ssPP/xwxR8wQBw0LVRUVEhp3lXRms8hc/XI+kqLrK+0DBgwoLNN6DI4ODjQv39/+vfvjyAIFBYWkp6eTnp6OufPn6eiooJjx45x7NgxFAoF/v7+otNzraFtMldHS/3XxsZGXKfTFgRBwGQyYTAYGsyw1P9radPUX0EQxAtUhULR6LFldkilUqFUKhv8VavV4mxRV0AeH6TFou/OnTvJzMxEo9Fwww03yA5OO2GN/VcyJ0cQBBYvXsySJUuIj48nMzPziu959dVXeeGFFxq9vmXLFhwdHZk0aRIHDx5Eq9Xi5eWF0WgU6wlY7jZbptGdnZ2pqanBaDRiY2ODg4ODONV2eVsnJydqa2sxGAwolUqcnJxEB8vW1halUilmTxEEAbVa3WRbjUaDSqWipqYGMFe0ttzJUigUuLi4iPZe3tbBwQGDwYBOpxPbVlRUiOfTaDRiRrL6bcGcDrmyshKTydSorb29PSaTSXQeXVxcqKqqwmQyoVKpsLOzE++wXt62LRq21PZyDVvS22g04uTkJLatr6FSqcTZ2blZDZvS26JhS3pbNGyt3m3RsKW27dVn26K3Xq/H09Oz2f5t0bC6ulo8lyVrXnBwMF5eXhw7dgyA+Ph4cnNzyc3NxcbGhilTprBlyxaMRiMBAQEEBARw+PBhAAYNGkRxcTEXLlwAzFkIt2/fjk6nw9fXl7CwMA4cOACYB9KKigpxzJg6daoYW+3l5UVUVJS4mLRv377iuhBAHCOqqqpwd3enb9++YshCTEwMJpNJzAo2fvx4jh8/Lt4NGjx4sFg4NzIyEpVKJaYeHTNmDElJSZSUlODo6MiIESPYunUrgDgLcfr0aTIzM5k/fz5nz56lqKgIOzs7xo0bx6ZNmwBzFkI3NzcxdHfYsGFkZWWRn5+PWq1m0qRJbNq0CUEQCAoKwsfHR0xWMmTIEPLz88nJyUGpVDJ16lS2bt2KwWDA39+foKAgDh06BMDAgQMpKSkRq3xPmzaNHTt2UFdXJxa53b9/P2Be0F9VVUVGRgYAU6ZMYe/evdTU1ODp6UlMTAx79uwBzHUmdDodZ8+eBcwLUQ8fPkxlZSVubm4MGDCA33//HYDo6Gjgj/pe48aNw8bGBh8fH0JDQ/H29ub7778nLy8PhUJBWVmZ+N7evXtjY2ODk5MT4eHhTJ8+Xfyfnz9/Hj8/P06dOgXAiBEjOHfuHIWFhdja2jJhwgSxz4aEhODh4SEWER46dCjZ2dnk5eWhUqmYPHkymzdvxmQyERgYiJ+fH0eOHAFg8ODBFBYWkp2djUKhICEhgW3btqHX6/Hz8yMkJISDBw8C5tS0ZWVlnD9/HoCEhAR+//13amtr8fb2JiIign379gHQr18/ampqxOyXkydPZv/+/VRXV+Ph4UGfPn3EPhsbG4vBYCAtLQ0wp+M/evSoeCdz4MCB7Ny5E4CoqCiUSqW4HmrMmDEkJiZSWlqKk5MTw4YNY9u2baK+dnZ2JCYmAuYQ69TUVA4fPkyfPn0YPXq0WCA5LCwMFxcXsdbI8OHDyczMpKCgAI1Gw8SJE69pjLB8F9p7jLD02a40Rqxfv56wsDBGjhwpjxE0PUacPHmSsrIynJ2diY+PZ/v27QBERESg0WhISkoCYPTo0SQnJ3Px4kUcHBwYNWoUn3/+ORqNhmPHjqFWq4mIiODQoUPyGNFOY8T333+Pr68vDg4OHTpGXH4dYbG/NbQ5XO35559v0hGpz6FDh9i7dy9ff/01v//+OzY2NmRmZhIeHt5iuFpTMznBwcFdKlytvLwcV1fXDj1nT0LWV1paq68crnZ1bNy4kWnTpnW2GVZHZWUl586dIz09vcnQNjs7OwRB4JlnnmHz5s1MnjxZvjsrAXL/lRZZX2lZvXo1mZmZ6HQ6hgwZwowZMzrbpG5FV+m/koarPfzww8ybN6/FNmFhYbz00kvs378fW1vbBvvi4+NZuHAhn332WaP3WTK2dGXkCz5pkfWVFllfabHcmZRpG87OzmJdHktoW0ZGBhkZGWRmZlJbW0tVVRVjx47lt99+49ixY4SHh4ubu7u77PS0A3L/lRZZX+koKSnh1KlT2NraEh4ezvXXX9/ZJnU7rLH/ttnJsVTFvhLvv/8+L730kvg8NzeXadOm8fXXX3eJhUsyMjIyMl2P+mmqR4wYgclkIi8vj4yMDAICAtBqtVRXV3P69GlOnz4NmMNCw8PDCQsLIzQ0VHZ6ZGR6EIWFhXzxxRdotVpCQ0OZN2+eZMkjZKwLyXrB5bVfLBmdevfubdVVZ2tra7v8bJM1I+srLbK+0pKSkkJYWFhnm9GtUCqVBAYG4uDgwIYNG3jssceoqakRZ3qys7OpqKjgxIkT4joGZ2dnQkNDxTpkPj4+stPTCuT+Ky2yvu1Pbm4uX375JTU1NRgMBhYtWiT/xkmENfZf2dWVkZGRkenyZGRk8MorrzBnzhwGDx5MaGgoEyZMQK/Xk5WVRWZmJufPnycnJ4fKysoGMz329vaEhISIjo+/v79cN0NGxso5ffo0P/74I3q9nsDAQAYPHiyXoJBpQIc5OWFhYZ2eK789cHZ27mwTujWyvtIi6yst48aN62wTehxqtVpMPQ3m4pY5OTmcP3+e8+fPc+HCBbRaLSkpKWIWJ41GQ3BwMKGhoQQHBxMYGIhGo+nMj9ElkPuvtMj6tg8mk4lt27aJGcYiIiKYO3euWDBWRhqssf/KMzltpKamRr5TICGyvtIi6ystJ0+elNccdjJqtZqwsDAxrMJoNJKXlyc6PVlZWWJKYUtaYaVSia+vL8HBweLm6ura40Lc5P4rLbK+105paSnff/+9mPp69OjRTJ48GaVS2WWKVXZXrLH/9rz5eqMRduyAr74y/zUa2/j2ltuHhYWJueUtLFmyhOeff75tdloRn376KQMHDsTZ2ZlevXrx0UcfNdv2lVdewcnJSdxsbW3p37+/uL++vp9++ikKhaJBAguAZ555BoVCwZo1axq0W7FihdgmPz+/x12gtIYr9V+Za6OsrKyzTZC5DBsbG4KCghg9ejQLFizgqaee4oEHHuCGG26gb9++uLq6iskNDh48yHfffce7777L22+/zTfffMO+ffvIzs7GYDB09keRHLn/Sous79UjCAKff/457733HllZWdja2nLLLbcwdepUMfRU1ldarFHfnjWTs24dLF0K2dl/vBYUBO+9B7Nnt+oQXaVycleirq6Ojz76iPj4eFJSUpg0aRJ9+vRpcmrzmWee4ZlnnhGfz549m759+4rPL9c3IiKC1atX87e//Q0wD3Rff/21GJpiwd3dnVdeeYW7774btVrdnh+vWyH3X2mRwwGlw97enqioKOzt7a/pOPWztw0bNgww14/Kzs7mwoULXLhwgby8PCorK0lKShKLD6pUKgICAggKCiIwMJDAwMBuN9sj919pkfW9OoqKivjggw/E5x4eHtxxxx24ubk1aCfrKy3WqG/PmclZtw5uuaWhgwOQk2N+fd26Vh3GwcHhmsz49NNPSUhI4N577xUr+ubk5PDQQw/h6urK8OHDyc3NBcxxp7Nnz8bHxwcPDw/mzp1LSUkJADt27CAwMFB8vnbtWqKjo8XK9Ra0Wi0uLi5ilV2ALVu20K9fv2v6HPW5//77GTFiBCqVir59+zJlyhSxqnJLlJWV8euvv7Jw4ULxtcv17d27N87OzmJF57179xIcHNwoQ9+wYcMIDg7mk08+aYdP1H251v4r0zLx8fGdbUK3JTY2llOnThEbG9vux3Z1daVv375cd9113HvvvTz99NPcddddTJkyhejoaBwcHDAYDGRlZbF3717Wrl3Lu+++y5tvvsnq1avZuXMnZ8+ebTT+Whty/5UWWd+2UVtby5YtWxpEh9ja2vLAAw80cnBA1ldqrFHfnuHkGI3mGZymEh9YXnvssVaFrlVWVl6zOdu3b+eGG26gpKREDKMYP348Fy9eJCwsjDfeeENsO3v2bDFVamVlJf/4xz8AmDBhAnPmzOHhhx+mqKiIRx55hE8//bTRXU57e3umT5/O2rVrxde++eYbbrvttiZtmz59Om5ubk1ur7322hU/m9Fo5ODBgw1mZ5rj22+/pV+/fsTExIivNaXvwoULWb16NWCuaFzfKarP8uXLeeWVV9Dr9Vc8d0+lPfqvTPNs3769s03o1nSUvmq1mtDQUMaMGcP8+fP585//zCOPPMKsWbOIj48nICAApVJJdXU1qampbN++nS+//JLXX3+d999/n++++479+/dz4cIFqxqP5P4rLbK+rcNgMLBv3z7ef/99du/ejdFoJCoqiqVLl/L00083G60h6yst1qhvzwhX27Wr8QxOfQQBLlwwt5sw4ZpPN3Xq1AZhQVqtlqefflp83r9/f26++WYAZs6cSVpaGrfeeisAs2bN4n//+x9gXgy7aNEi8X2PP/44zz77rPj8tddeIy4ujgkTJnD77bczcuTIJu257bbbePnll1m2bBkGg4Hvv/+ePXv2NNl2/fr1V/mpzfztb38jMDCQadOmXbHtqlWrmnVY6nPbbbcxbNgwXnnlFX788UdeeuklVq1a1ajd1KlTCQwM5NNPP2XGjBlXZb+MjEzX5NixY8yYMYMDBw4waNCgDj23QqHA09MTT09PBg4cCJgvxPLz88nJyRG3ixcvUlJSIlZfB/M47uPjg7+/v7j5+vrK2dxkZC5Dr9dz/Phxdu/eTXl5OWAuQG+ZUe1OoaEyHUPPcHLy8tqtXWuKTG3evJkRI0aIz5csWdJgv4+Pj/jY3t4eb2/vBs+rq6sB84/osmXL+P777yktLUUQBLy8vMS2Dg4OzJs3j5dffpnffvutWXuuu+467rzzTjIzM0lJSSEoKIioqKgrfo628tFHH7Fu3Tr27NlzxcEoOzub3bt3izM0FprS19fXl5iYGJ555hni4+Nxd3dv9rjLly/n/vvv57rrrru6D9HNkYukSUtERERnm9BtEQQBvV7fZUoRqFQqgoKCGoTOarVacnNzGzg+VVVV5Ofnk5+fz7Fjx4A/nKb6jo+fn981rze6VuT+Ky2yvk1TW1vLoUOH2L9/v3j94+LiwoQJExg4cGCra1rJ+kqLNerbM5wcf/92a9eRBeRWrVrFrl272LdvHwEBAWzcuJH7779f3J+WlsaHH37I3LlzefLJJ/nmm2+aPI6trS0zZ85k7dq1JCcnNxuqBnD99deza9euJvddnjSgPl9//TUvv/wyu3btauCINcdXX33FhAkT8L9M8+b0XbBgAXfddZeYUa05EhIS8Pf357PPPruiDT0RuQCitMh353s29vb2DWr2CIJARUUFeXl5DbbKykqKi4spLi4WZ3zAnECl/myPr68vLi4uHXYHW+6/0iLr25D8/HwOHTrEyZMnxbBONzc3Ro0axaBBg9qcREjWV1qsUd+e4eSMHWvOopaT0/S6HIXCvH/s2CseSqvVdtg/urKyEltbW9zc3CguLubNN98U95lMJu68806effZZlixZQlxcHN98840Y9hYWFsbzzz/P4sWLAXPI17PPPktWVlaLSQE2bNjQZjs3bdrEI488wpYtW8TaFFdi1apVPPbYY41eb07fuXPn4uvry4RWhBMuX76cBQsWtMqOnkZH9t+eSFJSEsHBwZ1thkwXQaFQ4Orqiqura4O1h1VVVeTl5ZGfny86PqWlpeJmyegGYGdnh6+vLz4+PqLj4+PjI8msrNx/pUXW1zxrc+bMGY4cOUJ2vWUEvr6+jB49mr59+151FlBZX2mxRn17hpNjY2NOE33LLWaHpr6jY7lD9u675nZdiDvuuINffvkFHx8fgoOD+dOf/kRaWhoAb775JjY2NixduhSlUsknn3zC7NmzmTBhAu7u7ly8eLFByNzUqVO5/fbb6dWrF7169WpXO1999VVKS0sZNWqU+NqiRYvEjChOTk5s2LCBsZecyKSkJFJSUpjdyrTdYA7Na20I2rRp04iKimpUr0hGRkamK+Dk5ERkZCSRkZHia1qttoHTU1hYSFFREbW1tWIh0/q4ubmJTo/F8fHw8JDTxMt0OYxGI+np6Zw4cYKUlBSx5pSNjQ2xsbEMHTqUkJAQec2NTLujELpKgHMTVFRU4OrqSnl5OS4uLuLrtbW1ZGRkEB4ejp2dXesP2FSdnOBgs4PTygtuo9HY5X9ELFlJvvrqq842pc1Yg77WTGv1vervWA+nqqoKJyenzjajW6LVajl9+jT9+vXr9LUrHYXRaKS4uJiCggIKCgooLCykoKCAioqKJtsrlUo8PT3x8vLC29sbb29vvLy88PLyalXoj9x/paUn6avT6UhPT+fMmTOkpqZSW1sr7vP29iYuLo6BAwe2qx49Sd/OoKvo25xv0BQ9YybHwuzZMHOmOYtaXp55Dc7YsW2awamtrcXR0VFCI6+dkSNHNptpratjDfpaM7K+0pKcnGyVtQSsAXt7exQKRY9xcMB8p9syU1MfrVbbwOmxPNbpdBQVFVFUVMSZM2fE9pawufqOj7e3N56enqKuIPdfqenu+paUlJCens7Zs2c5d+5cg/TpTk5O9OvXj7i4OPz8/CSZtenu+nY21qhvz3JywOzQXEOaaMs0q4w0yPpKi6yvtFy8eLGzTei2nD9/nr/97W+sWLGC0NDQzjanU7G3tycsLKzBGkhLkoPi4mLR0bE8rqmpoaysjLKyMjHk2YKdnR2enp54eHiQnp6ORqMRn/ckh7Ij6G7jQ1VVFVlZWWRkZJCeni4WJ7fg5uZGbGwssbGxBAUFSZ74prvp29WwRn17npNzjcjZqaRF1ldaZH2lxcHBobNN6LZcvHiRjRs3cvHixR7v5DRF/SQHluxuFqqrqxs4PxYHqKKigtraWjHd9fnz56mpqRHf5+DggIeHh+j0uLu74+7ujpubG05OTvIaijZizeODyWTi4sWL5Obmcv78ebKysiguLm7QRqlUEhISQu/evYmMjMTX17dD+4g162sNWKO+spPTRrpCPGJ3RtZXWmR9paV+8g0Zma6Co6Mjjo6OjZxDvV4vFi+9ePEixcXFlJaWUlJSQmVlJTU1NdTU1DTIgmVBpVLh6uqKm5sbbm5uovNj2RwdHWUn6DKsZXyo79Dk5eWRm5tLfn4+Op2uQTuFQoGPjw+hoaH07t2bsLCwTq3FZi36WivWqK/s5LQRy4InGWmQ9ZUWWV9p2bJlC9OmTetsM2RkWoVarW6w5mfjxo3MmjULMC8cr+8AlZSUUFpaSllZGRUVFRgMBi5evNhsCItarcbFxaXB5uzs3OB5T3OEutr4YDKZKCkpEWs2WTbL+q7L0Wg0+Pn5ERwcTGhoKMHBwV0qpLGr6dvdsEZ9ZSdHRkZGRkZGpgGWC1o/P79G+0wmExUVFZSVlYmOj2UrLS2lsrISvV7fohME5vCm+o6Pk5OTOOtkeWz529bCkDLmdVp1dXWUlZVRXl5OeXm5+D8qLi6mpKQEo9HY5HvVajX+/v4EBASIfz09PeWQZxmrQnZy2khnTsX2BGR9pUXWV1rCw8M724Rui6+vL/fdd1+jTGMy7Udr+69SqRTD0poqAG00GikvL6eioqLBVllZKT6uqqrCZDKJF99XQqPRNHB8HBwcsLe3x87ODnt7+wab5TWNRtOlZoraa3wQBAGtVkt1dXWTm8UBLS8vp66urgtTOu0AADzaSURBVMVjqdVqMc345Zn3rM2hkcdfabFGfWUnp41Y25fe2pD1lRZZX2mR1zxJR2BgIMuXLycgIKCzTem2tFf/tbGxwcPDAw8Pj2bbGI1GqqqqGjg/1dXVVFVVNfprNBobhM+1FqVSKTo9Go2mVZtarcbGxkbcVCpVg+eW15RKZasdKJPJhMFgoK6ujvz8fAwGA0ajUdwMBgMGgwGdTkddXV2Lm2WdVFtKHDo6OopJKVxdXXF3dxcdGxcXly7lCF4L8vgrLdaor+zktBGtVotGo2l2f1hYGGvWrGHEiBHia0uWLMHPz4/nn39ecvtSUlJ48skn2b9/PwqFgmnTpvGvf/0Ld3f3JtvfeOONHDp0iLq6OmJiYnj33XebrbGjUCjo3bs3Z8+eFV9LS0sjKiqKadOm8dtvv4ntRo4cyd69e8V21113HfPmzWPx4sUt2n8lfWWuDVlfaTl16pR8ES4RlZWVfPnllzzwwAM4Ozt3tjndko7svzY2NuJFd0tYQq7qOz7V1dXU1NSg1WrRarXU1taKjy2b0WjEZDKJ7bsCZ8+eJSIiot2OZ29vL4b3WTYHBwdcXFxwc3MT9e0poX7y+Cst1qiv7OR0M8rLy7n11ltZtWoVKpWKu+66i2XLlvHxxx832f6f//wn0dHRqFQqfv75Z26++Wby8vKavbOjVCo5cOAAw4cPB2DVqlVERkY2apecnMymTZtISEhovw8nIyPTY0lLS+Opp55iypQpDB48uLPNkekgFAoFdnZ2Yj2f1iAIAgaDoYETpNPpWrVZZlkun21p6rXW2mKZAbKzs8PZ2bnJWSKVSoWtrS22trZoNBrx8eWbg4ODuNm0oZC5jExPpEc5OWlpUFnZ+HVnZ2jiOr1J2qNa/L/+9S/eeecdKisruf766/n3v/+Ni4tLm44hCEKTjsiwYcMYNmyY+Pzee+/liSeeaPY4ffv2FY+nVCopKCigpqam2c85f/58Vq1aJTo5X331FfPnz+fAgQMN2j3++OO88MILbXZy2kNfmeaR9ZWW+jO4MjLWRnfpvwqFArVaLWZ46yqUl5fL2S0lpLv0366KNerbYwL009IgKgqGDGm8RUWZ97eGptIqtoWNGzfy2muv8csvv5CZmUl1dXWzTkhBQQH33nsvoaGhDB48mBdffJF9+/axbt067rjjjladb+/evaIj0xzTp0/Hzs6O6dOn8+ijj7Z4IXzrrbfy/fffYzQaOXToEF5eXk0uRlu8eDE5OTls3ry5VXZauFZ9ZVpG1ldazp0719kmyMhcNXL/lRZZX2mR9ZUWa9S3x8zkWGZwvvwSYmP/eP3MGVi0qOkZnqbQ6/VXbDN16tQG08harZann34agK+//polS5YQe8mIV155hSFDhvC///2v0XH279/P9ddfz9tvv01mZiarV6/m2WefpVevXjz33HNXtOP48eO8//77/P777y22W79+PTqdjp9//pmqqqoW23p6ehIXF8eWLVvYsGEDCxYsaLKdWq3mmWee4YUXXmDq1KlXtNVCa/SVuXpkfaWlsLCws02Qkblq5P4rLbK+0iLrKy3WqG+PmcmxEBsLgwf/sdV3eFpDa7JTbd68uUHdgLvuukvcl5ubS0hIiPg8NDSU6urqJlNo3njjjRQWFvKnP/2JDz74gClTprB582ZefvllfvzxxxZtyMjIYMaMGXz88cdXnMkBc3rOOXPm8NZbb3HmzJkW2y5cuJAvvviCdevWceuttzbb7q677iI7O5stW7Zc8fwW5Oxf0iLrKy1yim7psKS67SmLqDsDuf9Ki6yvtMj6Sos16itf8bSRa83qExAQQFZWlvg8KysLBweHJuN0v/zyS9LS0li8eDFxcXG88soreHp6MnHiRIKCgpo9R35+PlOnTuW5554Tq1e3FoPBQEZGRottZs6cyU8//US/fv3w9vZutp1arebpp5/mhRdeaPX55axJ0iLrKy0TJkzobBO6Lf3796eoqIj+/ft3tindFrn/Sousr7TI+kqLNeorOzltpDVFy1pi7ty5rFixguTkZKqrq3n22WeZN29ek21vv/123nrrLa6//noeeOABtm7dSllZGUlJScyfP79Z+6ZNm8Ydd9zBfffd16It58+fZ/369dTW1lJXV8e///1vsrOzGTJkSIvvc3BwYPPmzfzrX/+64ue96667yMrK4tChQ1dsa7FfRjpkfaVl48aNnW1Ct0bWV1pkfaVF1ldaZH2lxRr17XFOzpkzcPToH9sVIrPaneuvv54///nPXH/99YSGhmJra8tbb73VZNurSQ/5ww8/cPLkSf75z3/i5OQkbhaWLFnCkiVLxOcvv/wyPj4++Pn58fXXX/Pzzz+3qqL48OHD6d279xXbaTQann766TYVcJORkZG5nFOnTrFo0SJOnTrV2abIyMjIyFgBCqEtZXM7mIqKClxdXSkvL2+QBrK2tpaMjAzCw8Oxs7Nr1bEs2dWaIzW1dWmktVot9vb2rTqnTNuR9ZWW1up7Nd8xGThz5oyYVESmfTl69ChDhgzhyJEjcp0ciZD7r7TI+kqLrK+0dBV9m/MNmqLHZFeLjDQ7MtdaJ0el6jGSdQqyvtIi6ystHh4enW2CjMxVI/dfaZH1lRZZX2mxRn17VLhaZGTDzGqWrbUODkBNTY10BsrI+kqMrK+0HD9+vLNNkJG5auT+Ky2yvtIi6yst1qhvj3JyZGRkZGRkZGRkZGS6P5I7Ob/88gvDhw/H3t4eLy8vZs+eLfUpJcXR0bGzTejWyPpKi6yvtAwdOrSzTei2REZG8uOPPxLZlql3mTYh919pkfWVFllfabFGfSV1cr777jtuv/127rrrLk6cOMGePXtYsGCBlKeUHJ1O19kmdGtkfaVF1ldasrOzO9uEbouzszNhYWFyrScJkfuvtMj6Sousr7RYo76SOTkGg4GlS5fyxhtvsGTJEqKiooiOjuaWW26R6pQdgl6v72wTujWyvtIi6ysteXl5nW1CtyUnJ4eXX36ZnJyczjal2yL3X2mR9ZUWWV9psUZ9JXNyjh49Sk5ODkqlkkGDBuHv78/1119PYmKiVKfsEBQKRWeb0K2R9ZUWWV9pkbPXSUdBQQHffPMNBQUFnW1Kt0Xuv9Ii6ystsr7SYo36SubknDt3DoDnn3+ev/3tb6xfvx53d3fGjx/fbGHIuro6KioqGmxdjSvl5Ja5NmR9pUXWV1omT57c2SbIyFw1cv+VFllfaZH1lRZr1LfNbtnzzz/PCy+80GKbQ4cOYTKZAHj22WeZM2cOAJ988glBQUGsXbuW+++/v9H7Xn311SaPvWXLFhwdHZk0aRIHDx5Eq9Xi5eWF0WikvLwcQCxYWFtbC5jjt2tqajAajdjY2ODg4EDlpSI5l7d1cnKitrYWg8GAUqnEyclJdLBsbW1RKpVotVoABEFArVY32Vaj0aBSqcQ0vY6Ojuh0OvR6PQqFAhcXF9Hey9s6ODhgMBjQ6XRi24qKCvF8Go2G6urqRm0BXF1dqaysxGQyNWprb2+PyWSirq4OMF/kVlVVYTKZUKlU2NnZUVVV1WTbtmjYUtvLNWxJb6PRiJOTk9i2voZKpRJnZ+dmNWxKb4uGLelt0bC1erdFw5batlefbYveer0eT0/PZvu3RcPq6mrxXBs3bgQgODgYLy8vjh07BkB8fDy5ubnk5uZiY2PDlClT2LJlC0ajkYCAAAICAjh8+DAAgwYNori4mAsXLgAwbdo0tm/fjk6nw9fXl7CwMA4cOADAgAEDqKioIDMzE4CpU6eyZ88eampq8PLyIioqir179wLQt29famtrSU9PBxDHiKqqKtzd3enbty+7d+8GICYmBpPJRGpqKgDjx4/n+PHjYkGxwYMHs2PHDsC8yF2lUnHmzBkAxowZQ1JSEiUlJTg6OjJixAi2bt0KQK9evXBwcOD06dOcP3+eefPmcfbsWYqKirCzs2PcuHFs2rQJgNDQUNzc3Dhx4gQAw4YNIysri/z8fNRqNZMmTWLTpk0IgkBQUBA+Pj4cPXoUgCFDhpCfny/OkE+dOpWtW7diMBjw9/cnKCiIQ4cOATBw4EBKSkrIysoS9d6xYwd1dXX4+PjQq1cv9u/fD0D//v2pqqoiIyMDgClTprB3715qamrw9PQkJiaGPXv2ANCnTx90Oh1nz54FYOLEiRw+fJjKykrc3NwYMGAAv//+OwDR0dEApKSkADBu3DhOnjxJWVkZzs7OxMfHs337dgAiIiLQaDQkJSUBMHr0aJKTk7l48SIODg6MGjVK/J+fP38ePz8/Tp06BcCIESM4d+4chYWF2NraMmHCBLHPhoSE4OHhIaY+HTp0KNnZ2eTl5aFSqZg8eTKbN2/GZDIRGBiIn58fR44cAWDw4MEUFhaSnZ2NQqEgISGBbdu2odfr8fPzIyQkhIMHDwIQFxdHWVkZ58+fByAhIYHff/+d2tpavL29iYiIYN++fQD069ePmpoa8Ubg5MmT2b9/P9XV1Xh4eNCnTx+xz8bGxmIwGEhLSwNgwoQJHD16VCyGN3DgQHbu3AlAVFQUSqWS5ORksc8mJiZSWlqKk5MTw4YNY9u2bQD07t0bOzs7MbJi1KhRpKamcuTIEWJjYxk9ejSbN28GICwsDBcXF06ePAnA8OHDyczMpKCgAI1Gw8SJE+UxgtaNEb/88guhoaGMHDlSHiNo/zHi448/JjQ0lPDwcJycnOQxop3HiB9//BFvb28cHBw6dYyw2N8qhDZSVFQknDlzpsVNq9UK27ZtEwBh165dDd4/bNgw4Zlnnmny2LW1tUJ5ebm4XbhwQQCE8vLyBu20Wq2QlJQkaLXatpp/zZSVlbW4PzQ0VHB2dhZqamrE18rLywU7OzshOjpaavNEPvjgAyEuLk6wsbERXn311RbbFhUVCXPnzhXc3d2F4OBg4csvv2y27Z133tnk/3XkyJECIOTl5YntlEqlkJSUJLb56quvhPHjx7doy5X0lbk2WqtvZ37HrJnffvuts03othw5ckQAhCNHjnS2Kd0Wuf9Ki6yvtMj6SktX0be8vLxJ36Ap2jyT4+XlhZeX1xXbDRkyBFtbW1JSUhgzZgxgXvScmZlJaGhok++xtbXF1ta2rSZ1KBqN5opt/Pz8+Omnn7jtttsAWLduHcHBwVKb1oCAgABeeukl/u///u+KbZcuXYq9vT15eXmcPXuWSZMmMWjQIPr06dNk+8jISFatWiX+XzMyMrh48WKjdq6urrz44ousXr261Xa3Rl+Zq0fWV1oCAwM724Rui6enJ7Nnz8bT07OzTem2yP1XWmR9pUXWV1qsUV/J1uS4uLiwZMkSli9fzqZNm0hJSeGBBx4AYO7cuVKdtkXS0uDo0cbbpVm+VtGahVfz589n1apV4vNVq1Y1Sp196tQpRo8ejZubG/Hx8eK0cFsRBKHJ12fNmsX06dNbtQbjt99+469//Su2trb07duXWbNmNbD/cmbPns1PP/0kZupavXo18+fPb9TuT3/6Exs2bGhyajEzMxM7Ozs+/PBDfHx8CA4OZseOHXzxxRf4+/sTEhIiTrHKtB/WuHDQmvDz8+tsE7otoaGhrFixotmbZDLXjtx/pUXWV1pkfaXFGvWVtE7OG2+8wbx587j99tsZOnQo58+fZ9u2bbi7u0t52iZJS4OoKBgypPEWFdV6R8eypqMlpk6dytGjRykpKSE/P5+0tDTGjRsn7tfpdMyYMYMFCxZQVFTEsmXLmD59urjW5HI+/PBDBg4cSEhICPfccw/r16/n999/56GHHhJjFa+V+s6SIAgtZsFzc3Nj+PDhYozlV1991WT9Iw8PDx588EFefPHFJo+j0+nIzMwkJyeHpUuXsmjRIk6ePMn58+f5y1/+wmOPPXZtH0qmEa3pvzJXjyVWW6b90Wq1fPfdd+L6MZn2R+6/0iLrKy2yvtJijfpK6uSo1WrefPNNCgoKqKioYPPmzfTt21fKUzbLpTXZfPklHDnyx/bllw33twcqlYpZs2axdu1a1qxZw9y5c1Eq/5B6//792NjY8NBDD6FWq5k3bx6RkZHiwsP61NXVkZmZyfr16zly5AgjR45k5cqVvPnmm4wdO7ZdKtAmJCTw+uuvo9VqOXXqFOvWrbvixfCCBQtYtWoVx48fx97enqioqCbbPfHEE/zyyy9NzuYIgsCzzz6LWq1mzpw55OTk8Pjjj6PRaJgzZw6JiYliAgsZGZmezZkzZ1iyZIm40FtGRkZGRqYlelzsSmwsDB589e93cHBoVbuFCxfy17/+Fa1Wy8qVKykrKxP35ebmEhIS0qB9aGgoubm5jY5ja2vLzTffzEsvvURJSQlTpkzhs88+w9HRkW+//ZbExMRrdhzff/99HnzwQUJDQwkNDWX+/PliBrDmmD59Oo8++iju7u4sXLiw2Xaenp48+OCDvPTSS0yfPr3RZ7OE09nb2wOIutjb26PX69HpdGJmMZlrp7X9V+bqGHwtg4uMTCcj919pkfWVFllfabFGfSWdyemOGAyGVrUbOXIkOTk5VFVVMXDgwAb7AgICxDSZFrKysggICGh0nLq6Op555hkmTJjA/PnzOXDgALGxsYSGhrJnz55GztLV4O3tzdq1ayksLOTQoUOUlpYSHx/f4nvs7OyYNm0a//3vf8UEC83x5JNPsn79ejFNZEu0Vl+Zq0PWV1oKCws72wQZmatG7r/SIusrLbK+0mKN+va4mZxrRafTibMOV2LdunUNwtQsjBgxAr1ez4cffsi9997L999/T0pKCgkJCY3aajQatmzZIh7n5ptvbtW5DQYDBoMBo9GIwWCgtrYWtVqNjY1No7bp6el4eHjg5OTEd999x65du1i5cuUVz/Hiiy9y11134e/v32I7T09PHnjgAd5//3369+/fYtu26CvTdmR9pSU7O7vTQnJlZK4Vuf9Ki6yvtMj6Sos16tvjZnLOnGmYWU3K8O4BAwbQr1+/Rq9rNBp+/PFHvvjiCzw9PXnttdf46aefcHV1bdRWoVA06ShdiZdeegl7e3u+/PJLnnvuOezt7fniiy8A2LVrF05OTmLbAwcOEBMTg5ubGx9++CG//PJLq8KagoKCGiRUaIknn3xSLKYpI9NdUSgUnW1Ct0WhUKBWq2WNJUTWVlpkfaVF1ldarFFfhdBcDuIugKViq6XasIXa2loyMjIIDw9v9XoNS3a15khNhcjIa7VYRqZ7cDXfMRkZGRkZGRkZKWnON2iKHjOTExlpdmTqZ1azbG1xcCoqKqQ1tIcj6ystsr7Ssm3bts42oVsj6ystsr7SIusrLbK+0mKN+vaoNTntMVPThSe+ugWyvtIi6ystlgK5Mu3PmTNnuO+++/j555+JjY3tbHO6JXL/lRZZX2mR9ZUWa9S3x8zktBdqtbqzTejWyPpKi6yvtFhjRWhrQavVkp6eLhcDlRC5/0qLrK+0yPpKizXqKzs5bUSj0XS2Cd0aWV9pkfWVlvZI6S4j01nI/VdaZH2lRdZXWqxRX9nJaSPV1dWdbUK3RtZXWmR9peXgwYOdbYKMzFUj919pkfWVFllfabFGfWUnR0ZGRkZGRkZGRkamWyE7OW2kNfVjZK4eWV9pkfWVlri4uM42odsSHh7OypUrCQ8P72xTui1y/5UWWV9pkfWVFmvUV3Zy2ojBYOhsE7o1sr7SIusrLWVlZZ1tQrfF3d2dsWPH4u7u3tmmdFvk/istsr7SIusrLdaor+zktBGdTtfZJnRrZH2lRdZXWs6fP9/ZJnRbCgoKePvttykoKOhsU7otcv+VFllfaZH1lRZr1LfHOjl1ddIcNywsjP379zd4bcmSJTz//PPSnFAiUlJSmD59Ol5eXnh7e7No0SJKS0ubbb9t2zbi4uJwcnJi/PjxZGZmNttWoVAQERHR4LW0tDQUCgVz5sxp0G7UqFEN2l133XV8+umnV/WZZGRkrJecnBz++9//kpOT09mmyMjIyMhYAT3SyVmxApydzX/biouLS/sb1AUpLy/n1ltvJT09nczMTHQ6HcuWLWuybXFxMbfccguvvvoq5eXlTJ8+nfnz57d4fKVSyYEDB8Tnq1atIjIyEpWqYX3a5ORkNm3adO0fSAboOf23s0hISOhsE2Rkrhq5/0qLrK+0yPpKizXq2+OcnBUrYMkSiI01/22ro1NVVXVN5//0009JSEjg3nvvxdnZmfj4eHJycnjooYdwdXVl+PDh5ObmAmAymZg9ezY+Pj54eHgwd+5cSkpKANixYweBgYHi87Vr1xIdHd3mQnmCIDT5+rBhw7jjjjtwdXXF0dGRe++9t9n0gfv27SMyMpIbbvj/9u48PqZ7/x/4azKRfSGJJMgqEkFEiH2PJbaqpeVaq6mqtKSh7v1SXLRFtWip2ya0rqXWq0VLKbHvVyT2LUHUEmqLJEQmmZnz+2N+mStIMknzcTLH6/l45BFz5jNz3vPyEfPOOecz3aFWqzFu3DicOHECqampRe534MCBWLlypfH26tWrMXDgwOeuGRk7diw++eSTUr0mKtpfnb9UvH379sldAlGZcf6KxXzFYr5imWO+r1STU9DgxMQAx48bvpe20dHr9X+5jt27d6N79+548OABvLy80KpVK7Rr1w7379+Hn58fZs+ebRzbt29fpKWlIS0tDdnZ2fj0008BAO3bt8cbb7yB0aNH4+7du4iJicHSpUtha2v73P7+/PNPjBgxAr6+vmjUqBE+++wzHD58GOvXr8dbb71lUs2HDh1CvXr1irz/Rc3S2bNnixzfv39/bNiwATqdDomJiXBzc3vhqklvv/02bt68iYSEBJPqpOKVx/ylouXm5spdAlGZcf6KxXzFYr5imWO+r0yT83SDM38+YGFh+F7aRufZ06lepHPnzqhcubLxa8mSJYXur1+/Pvr06YNKlSqhV69esLe3R//+/WFpaYnevXvj1KlTAAyndA0ZMgT29vZwdnbG2LFjceDAAePzzJo1C4mJiWjfvj2GDh2KFi1avLCeI0eOoFu3bjhz5gyWLVuGnJwcTJo0CVu2bME///nPEl/PiRMn8M033xQ5tkWLFkhJScFvv/2G/Px8zJ49GxqNBjk5OUU+p6urKxo0aIAdO3Zg5cqVGDRoEADDdThPq1SpEiZOnMijOeXElPlLZVe1alW5S1AsZ2dntG3bFs7OznKXolicv2IxX7GYr1jmmO8r0eQ82+AUvI9WqUrf6NjY2JQ4JiEhAQ8fPjR+RUVFFbrf3d3d+GdbW9tCE8fW1tb4qfRarRZjxoyBr68vnJyc8Oabb+L+/fvGsXZ2dhgwYADOnz+PDz/8sMh6evTogTt37uDdd9/Ft99+i06dOiEhIQEzZszAL7/8UuxrSUtLQ8+ePbF48eIij+S4ublh3bp1mDx5Mjw9PXHjxg3Uq1cPNWrUKPa5Bw8ejB9//BHr169H//79ARgau2dFRUXhxo0b2LFjR7HPRyUzZf5S2T27oAaVn4CAAGzatAkBAQFyl6JYnL9iMV+xmK9Y5piv4pscjcbQxISGAvPm/a/BKaBSGbaHhhrGlbTq2su8pmHlypXYv38/Dh8+jKysLPz000+FTgtLTU1FXFwc+vXrh3HjxhX5PCtWrEBqairefvttNGjQADNnzoSrqysiIiLg5eVV5ONu376Nzp0745///Cd69+5dbK2dO3fG8ePHcf/+fUyfPh23bt1CSEhIsY/p1asXfv31V4SEhBgbPZ1O99y4SpUq4eOPP+bRnHLAa3LEOnz4sNwlKFZ+fj62bt2K/Px8uUtRLM5fsZivWMxXLHPMV/HnrlhbAwsWGI7UjBlT+EgOAEiSYfupU0B8vGF8RZGdnQ1ra2tUrlwZ9+7dw5w5c4z36fV6DBs2DJMmTUJ0dDQaNGiA//znP8YjIk8bOnQo1Gq18fb7779f4r4zMzPRpUsXvPXWW3jvvfdKHH/ixAmEhIQgKysLo0ePxpAhQ+Dq6lrsY+zs7JCQkAA3N7cSnz8qKgozZ87Eo0ePMGDAgBLHE5GynD59GgMGDEBSUhIaNWokdzlERFTBKf5IDgCMHGloYBYsAGJjDY0NYPgeG2vYHh9vGFeSF13YL0rB6mbu7u5o06YNunbtarxvzpw5UKvViI2Nha2tLZYsWYKYmBjcuXPnued5usEx1caNG3Hq1Cl8+eWXcHBwMH4ViI6ORnR0tPH29OnT4eLigsDAQLi5ueGLL74waT/NmjUrdPrJi05XAwArKyt8/PHHxtXkqGxe5vx9FZV09JKoIuP8FYv5isV8xTLHfFVSUWsIVwBZWVlwdnZGZmZmoc/3yM3NRVpaGvz9/Ut1jcHT1+bMm2c4glOaBqdg37yuQRzmK5ap+Zb139irLjU1FYGBgXKXoUjJyckIDw/nkRyBOH/FYr5iMV+xKkq+RfUGL/JKHMkp8PQRnYYNS9/gAICmpIt26C9hvmIxX7GuXLkidwlEZcb5KxbzFYv5imWO+Sr+mpxnFTQ0MTGlb3CIiIiIiKjie6VOV3uaRlO2RQYkSXrus1yo/DBfsUzNl6erlY1Wq+VnEQmi0+mQmZkJZ2fnMl1nSCXj/BWL+YrFfMWqKPnydDUTlHUVNS7BKxbzFYv5inXkyBG5S1AstVqNc+fOscERiPNXLOYrFvMVyxzzfWWbnLLS6/Vyl6BozFcs5itWwQf5UvlLTU1FbGwsUlNT5S5FsTh/xWK+YjFfscwxXzY5pVQRDtUpGfMVi/mK5eLiIncJipWdnY3k5GRkZ2fLXYpicf6KxXzFYr5imWO+bHJKidcniMV8xWK+YtWtW1fuEojKjPNXLOYrFvMVyxzzZZNTSrymQSzmKxbzFevAgQNyl0BUZpy/YjFfsZivWOaYL5scIiIiIiJSFKFNTkpKCnr16gU3Nzc4OTmhVatW2L17t8hdmqysn4lY0uk+fn5+cHJywpMnT4zbsrKyYGtri+Dg4LLttAJZunQpwsLC4OjoiJo1ayI+Pt6kx3Xt2rXY7JYuXQqVSoWvv/660PaJEydCpVJhzZo1hcYtXLjQOOb27dtcdtpEPF1NrDp16shdgmJ5e3vj008/hbe3t9ylKBbnr1jMVyzmK5Y55iu0yenRowe0Wi127dqFpKQkhIWF4bXXXsPt27dF7rZECxcCjo6G7yJ4enri119/Nd5ev369Yv5j1mg0iI+PR0ZGBjZt2oSpU6di3759xT5m48aNJp0mVatWLaxdu9Z4W5IkrF27FgEBAYXGValSBTNnzkR+fn7ZXgSRIFqtVu4SFKtq1aoYPHgwqlatKncpisX5KxbzFYv5imWO+Qprcu7du4dLly5hwoQJCA0NRWBgIGbNmoWcnBycPXtW1G5LtHAhEB0N1Klj+F7aRic3N7fEMQMHDsTKlSuNt1euXIlBgwYVGqNSqRAXFwcfHx+4ublh7dq12Lx5M2rWrAl3d/dCb/a///57BAYGwtHREaGhodizZ4+xlrp162L16tUAgIcPH8LLywu7du0q3YuCoaEwxciRI9G8eXNYWlqiXr166NSpExITE4scn5ubi8mTJ2PWrFklPndAQADs7e2RnJwMADh06BC8vb3h5eVVaFzTpk3h7e2NJUuWvPB5/Pz8MHfuXAQFBcHJyQnz5s3D0aNHUbduXbi4uDx3tOhVYsr8pbLj8sbiPHjwAPHx8Xjw4IHcpSgW569YzFcs5iuWOeYrrMlxdXVFnTp1sHz5cjx+/BharRYLFy6Eh4cHwsPDX/gYjUaDrKysQl/lqaDBiYkBjh83fC9Lo1OSzp07Izk5GQ8ePMDt27eRmpqKtm3bPjfu4MGDSElJQVxcHD744AP8/PPPOHPmDBYvXozRo0dDp9MBAKpXr46dO3ciMzMTMTExGDBgADQaDWxsbLBs2TKMGTMGt27dQmxsLF5//XV06NDhhXXFxcUhLCwMPj4+GD58ODZv3ox9+/Zh1KhROHbsWKlfp06nw9GjR1GvXr0ix8yaNQsDBgx4rlEpSr9+/bBq1SoAwKpVqzB48OAXjps6dWqxR3O2bNmCxMRE7NixA+PHj8fs2bNx8OBB7N69GxMnTsTdu3dNqoeIKoarV69i9uzZuHr1qtylEBGRGRD2oRkqlQoJCQno1asXHB0dYWFhAQ8PD/z++++oXLnyCx/z+eef45NPPnlu+44dO2Bvb48OHTrg6NGjePLkCdzc3KDT6ZCZmQngf9caFPym2tHRETk5OdDpdFCr1fjxRzt88IEFRo+WMH++CioVMH++4QhGdLQKGo0Gw4blwsLCAg4ODsYGy9raGhYWFsZrbOzs7IxN27NjraysjDV0794da9aswaNHj/D6668bH19QLwCMHTsWGo0GHTt2xMOHD/HOO+8gPz8f7dq1Q3Z2Ni5evIgaNWogIiICVlZWyM7ORv/+/TFlyhScPn0agYGBCAoKwvDhw9GhQwc8efIER48ehVarNX5ok62tLfR6PbKysnDx4kVs2rQJWq0WmzdvRlxcHCwsLNCnTx8EBQUhLy8Per0emv9/wdKzGdrZ2Rk/o8LGxgaTJ0+Gh4cHmjdvDr1e/9zYM2fOYM2aNThy5Aju3LljfP0ODg7Izc0tlGFOTg60Wi3+9re/oV27dhg/fjw2btyIadOm4ccff0ROTo5x31qtFk2bNkW1atWwePFiY1On1WqNr2HEiBFwdnZGcHAw3N3d0bt3bzg4OMDPzw9eXl44c+YMmjdvjry8PACAs7MzsrKyIEkSKlWqBCsrK2OGdnZ2xucGACcnJzx69Ah6vR6WlpawsbExno5XkHdBhsWNLWnOPpv302OfzvDZsc/O2afHqlQqSJJU5Py2t7dHXl4eHj9+bNzXtm3bABiuiXBzc8Px48cBAI0bN0Z6ejrS09OhVqvRqVMn7NixAzqdDtWrV0f16tWNzXPDhg1x7949XL9+HQDQpUsX7N69G3l5efDw8ICfnx/++9//AgBCQ0ORlZVlfDPbuXNnHDx4EDk5OXBzc0NQUBAOHToEAKhXrx5yc3Nx+fJlADD+jHj06BGqVKmCevXqGVeECQ4Ohl6vR0pKCgCgXbt2OHHiBDIzM+Hk5IRGjRoZj5IGBgbC0tIS58+fBwC0bt0a586dw4MHD2Bvb4/mzZtj586dAICaNWsa57tOp0NWVhYuXbqEu3fvwsbGBm3btsX27dsBAL6+vqhcuTJOnjwJwHBU8tq1a7h9+zYqVaqEDh06YPv27ZAkCV5eXnB3dzce2QwPD8ft27dx8+ZNWFhYoHPnzti5cye0Wi2qVasGLy8v41HVsLAwPHjwANeuXTPmvWfPHmg0Gri7u6NmzZrGT6+uX78+Hj16hLS0NABAp06dcOjQIeTk5MDV1RXBwcE4ePAgAMMSonl5ebh06RIAICIiAseOHUN2djYqV66M0NBQ4+mrtWvXBgBcvHgRANC2bVucOnUKDx8+hKOjIxo3bmy8RrNWrVqwsrLCuXPnAACtWrXChQsXcP/+fdjZ2aFly5bGv/M//vgDnp6eOH36NACgefPmuHLlCu7cuQNra2u0b9/eOGd9fHzg4uKCEydOAACaNGmCGzdu4NatW7C0tETHjh2RkJAAvV6PGjVqwNPTE0lJSQCARo0a4c6dO7hx4wZUKhUiIyOxa9cu5Ofnw9PTEz4+Pjh69CgAoEGDBnj48CH++OMPAEBkZCT27duH3NxcVK1aFbVq1cLhw4cBACEhIcjJycGVK1cAAB07dsSRI0fw+PFjuLi4oG7dusY5W6dOHWi1WuNvUNu3b4/k5GRkZWXB2dkZYWFh2Lt3LwAgKCgIFhYWuHDhgnHOnj17FhkZGXBwcEDTpk2NR/kDAgJgY2NjPKuiZcuWSElJgU6nw/79+9GqVSskJCQA+N91pqdOnQIANGvWDFevXsWff/4JKysrRERE8GcETPsZodPpsG3bNrRo0YI/I1D+PyMK8vX394eDgwN/RpTzz4jKlStj27ZtsLOzk/VnREH9JpFKaerUqRKAYr8SExMlvV4vvf7661K3bt2kAwcOSElJSdL7778v1ahRQ0pPT3/hc+fm5kqZmZnGr+vXr0sApMzMzELjnjx5Ip07d0568uSJSTXHx0sSIEkxMZKk1xe+T683bAcM40qSnZ1d7P2+vr7S4cOHpUOHDklt27aVmjRpIh0/flzavXu3VLt2beM4ANKtW7eMt62traW0tDTjbWdnZ+n8+fOSJEnShg0bpIYNG0rOzs6Ss7OzZGFhIe3Zs8c4NjU1VQIgffbZZ8XWdvjwYWnkyJFSv379pIULF0oPHjyQNBqNtHLlSunMmTPPjd+3b59kb28v2dvbS127di10X1xcnBQUFCTdvXu3yP317t1b+umnnyRJkqS0tDTJ2tq6yLFLliyRunTpImVnZ0uRkZHSuHHjpF69ekmSJEnt2rWTVq9eXWicJEnStm3bJD8/P+natWvS01O54O+gQO3ataXdu3cbbzdo0EDaunVrkbUoWUnzt0Bp/42RwaFDh+QuQbGSkpIkAFJSUpLcpSgW569YzFcs5itWRck3MzPzhb3Bi5T6SM7o0aMxYMCAYsf4+flh165d2Lx5MzIyMuDk5AQA+O6775CQkIBly5ZhwoQJzz3O2toa1tbWpS2pWBqN4bS00FBg3jzg2UW4VCrD9r17DePefhsoroSCU8hK0qJFC9y8eRNWVlYICwsz/van9PVrMHDgQPzyyy/o2LEj1Go1qlWrZryGRpIkvP/++xg8eDDmz5+PqKgo1KhR44XPM3HiRLz33nuwtrbG5s2bMWXKFKhUKvTt2xc9e/Z87jFt2rR54YIBa9euxYwZM7B//364ubkVWfuePXtw+PBhjBo1CjqdDhqNBp6enti7d6/xNzjP0ul0GDRoEKKioowrqhUlMjIS1apVw7Jly4odR/9j6vylsinvU2yJXibOX7GYr1jMVyxzzLfUTY6bm1uxb2wL5OTkAAAsLApf9mNhYQG9Xl/a3ZaZtTWwYIHh2psxYwynqD3d6EiSYfupU0B8fPENDgCo1WqT971+/frnXn9paTQa5OXlGVcUmj9/fqHrSQpWOtu6dSumTZuGESNGYMuWLc89j5WVFXbs2GGsp0+fPmWqZ/v27YiJicGOHTvg5+dX7NiLFy8a/66vX7+ONm3a4MSJE8XOH7VajX79+sHDwwPt27cvsZ6pU6c+t6gDFa0085dKz9nZWe4SFMve3h4hISGwt7eXuxTF4vwVi/mKxXzFMsd8hS080KJFC1SpUgXDhg3DyZMnkZKSgn/84x9IS0tDjx49RO32hUaONDQwCxYAsbGGxgYwfI+NNWyPjzeMK4mdnZ3J+w0NDUVISEgZqzZwcnLC7Nmz0blzZ3h6euL+/fuoVasWACAtLQ2TJ0/G0qVLYWlpiSlTpuDGjRv497///dzzqFSqv9xwAYbrpjIyMtCyZUs4ODjAwcEB0dHRxvsdHBywf/9+AIC7uzs8PT3h6elpbNI8PT1haVl0b21nZwc7O7sSP1enQJcuXRAUFPQXX9WrozTzl0ovLCxM7hIUq3bt2khMTCzyKDD9dZy/YjFfsZivWOaYr0qSTFw7uAyOHTuGSZMm4dixY8jPz0e9evUwZcoUdOvWzaTHF1w4VXDRX4Hc3FykpaXB39+/VB9u+PTqavPmGY7glKbBAQwXzptjN2sumK9YpuZb1n9jr7pt27ahS5cucpehWMxXLOYrFvMVi/mKVVHyLao3eBFhq6sBhlUTClZUqAgKGpnoaMM1OAWnqJna4BARkTySk5PRtWtXJCUloVGjRnKXQ0REFZzQJqciKmhoYmLK1uDwt9piMV+xmK9YPHWSzBnnr1jMVyzmK5Y55vvKNTmAobEpaRU1IqLSKo/r3ojkwvkrFvMVi/mKZY75ml/F5aSsDU7BBySSGMxXLOYrVqk+pIyoguH8FYv5isV8xTLHfM26yRG4ZgLRK+1lLvNOREREVN6Erq72VxW1goJOp0Nqairs7OxQtWpVqJ79hE+BdDodP2tEIOYrVkn5SpKEvLw83L17FzqdDoGBgWZ5iFoujx8/5ue4CJKbm4uUlBQEBQXx2jJBOH/FYr5iMV+xKkq+FWZ1NVHUajW8vLxw48YNXL169aXuW6PRwJoX8wjDfMUyNV87Ozv4+PiwwSmls2fPomnTpnKXoUg2NjbIzc1lgyMQ569YzFcs5iuWOeZrlk0OYPjQycDAQOTn57/U/R44cACtW7d+qft8lTBfsUzJV61Ww9LS8qUeIVWKjIwMuUtQrLS0NEyYMAGLFy+Gv7+/3OUoEuevWMxXLOYrljnma7ZNDmB4M/ayT22ytbXlbxIFYr5iMV+xHBwc5C5BsTIyMrB7925kZGSwyRGE81cs5isW8xXLHPM1y2ty5JSfn49KlSrJXYZiMV+xmK9YzFec5ORkhIeH88NABeL8FYv5isV8xaoo+ZamN+AJ96W0a9cuuUtQNOYrFvMVi/mSOeP8FYv5isV8xTLHfCv06WoFB5mysrJkruR/Hj9+XKHqURrmKxbzFYv5ivPo0SPjd2YsBuevWMxXLOYrVkXJt6AGU05Eq9Cnq924cQPe3t5yl0FERERERBXE9evX4eXlVeyYCt3k6PV6pKenw9HRsUKs9JSVlQVvb29cv369wlwjpCTMVyzmKxbzFYv5isV8xWK+YjFfsSpSvpIkITs7G9WrVy/xYy4q9OlqFhYWJXZpcnBycpL9L1nJmK9YzFcs5isW8xWL+YrFfMVivmJVlHydnZ1NGseFB4iIiIiISFHY5BARERERkaKwySkFa2trTJ06FdbW1nKXokjMVyzmKxbzFYv5isV8xWK+YjFfscw13wq98AAREREREVFp8UgOEREREREpCpscIiIiIiJSFDY5RERERESkKGxyiIiIiIhIUdjklFFKSgp69eoFNzc3ODk5oVWrVti9e7fcZSnKb7/9hmbNmsHW1hZubm7o27ev3CUpjkajQVhYGFQqFU6cOCF3OYpw9epVDB8+HP7+/rC1tUVAQACmTp2KvLw8uUszW9999x38/f1hY2OD8PBw7N+/X+6SFOHzzz9HkyZN4OjoCHd3d/Tu3RsXL16UuyzF+vzzz6FSqTBmzBi5S1GMmzdvYsiQIXB1dYWdnR3CwsKQlJQkd1mKoNVqMXnyZOP/ZTVr1sSnn34KvV4vd2kmY5NTRj169IBWq8WuXbuQlJSEsLAwvPbaa7h9+7bcpSnCzz//jKFDhyIqKgonT57EwYMHMWjQILnLUpz/+7//Q/Xq1eUuQ1EuXLgAvV6PhQsX4uzZs/j6668RHx+PiRMnyl2aWVq7di3GjBmDSZMm4fjx42jTpg26deuGa9euyV2a2du7dy9GjRqFI0eOICEhAVqtFpGRkXj8+LHcpSlOYmIiFi1ahNDQULlLUYyMjAy0atUKlSpVwtatW3Hu3DnMnTsXlStXlrs0Rfjiiy8QHx+Pf/3rXzh//jy+/PJLzJ49GwsWLJC7NNNJVGp3796VAEj79u0zbsvKypIASDt27JCxMmXIz8+XatSoIf3www9yl6JoW7ZskYKDg6WzZ89KAKTjx4/LXZJiffnll5K/v7/cZZilpk2bStHR0YW2BQcHSxMmTJCpIuW6c+eOBEDau3ev3KUoSnZ2thQYGCglJCRI7dq1k2JjY+UuSRHGjx8vtW7dWu4yFKtHjx7SO++8U2hb3759pSFDhshUUenxSE4ZuLq6ok6dOli+fDkeP34MrVaLhQsXwsPDA+Hh4XKXZ/aSk5Nx8+ZNWFhYoGHDhqhWrRq6deuGs2fPyl2aYvz5558YMWIEfvzxR9jZ2cldjuJlZmbCxcVF7jLMTl5eHpKSkhAZGVloe2RkJA4dOiRTVcqVmZkJAJyr5WzUqFHo0aMHOnXqJHcpivLrr7+icePG6NevH9zd3dGwYUN8//33cpelGK1bt8bOnTuRkpICADh58iQOHDiA7t27y1yZ6SzlLsAcqVQqJCQkoFevXnB0dISFhQU8PDzw+++/8zBpObhy5QoAYNq0afjqq6/g5+eHuXPnol27dkhJSeF/wH+RJEl4++23ER0djcaNG+Pq1atyl6Roly9fxoIFCzB37ly5SzE79+7dg06ng4eHR6HtHh4ePDW4nEmShI8++gitW7dGSEiI3OUoxpo1a5CcnIzExES5S1GcK1euIC4uDh999BEmTpyIo0eP4sMPP4S1tTXeeustucsze+PHj0dmZiaCg4OhVquh0+kwY8YMDBw4UO7STMYjOU+ZNm0aVCpVsV/Hjh2DJEn44IMP4O7ujv379+Po0aPo1asXXnvtNdy6dUvul1FhmZpvwUVtkyZNwhtvvIHw8HAsWbIEKpUK69atk/lVVFym5rtgwQJkZWXh448/lrtks2Jqvk9LT09H165d0a9fP7z77rsyVW7+VCpVoduSJD23jf6a0aNH49SpU1i9erXcpSjG9evXERsbixUrVsDGxkbuchRHr9ejUaNGmDlzJho2bIiRI0dixIgRiIuLk7s0RVi7di1WrFiBVatWITk5GcuWLcOcOXOwbNkyuUszmUqSJEnuIiqKe/fu4d69e8WO8fPzw8GDBxEZGYmMjAw4OTkZ7wsMDMTw4cMxYcIE0aWaJVPzPXz4MDp06ID9+/ejdevWxvuaNWuGTp06YcaMGaJLNUum5jtgwABs2rSp0JtEnU4HtVqNwYMHm9UPsJfJ1HwL3sykp6cjIiICzZo1w9KlS2Fhwd8plVZeXh7s7Oywbt069OnTx7g9NjYWJ06cwN69e2WsTjliYmKwceNG7Nu3D/7+/nKXoxgbN25Enz59oFarjdt0Oh1UKhUsLCyg0WgK3Uel4+vri86dO+OHH34wbouLi8P06dNx8+ZNGStTBm9vb0yYMAGjRo0ybps+fTpWrFiBCxcuyFiZ6Xi62lPc3Nzg5uZW4ricnBwAeO5Ni4WFhVktrfeymZpveHg4rK2tcfHiRWOTk5+fj6tXr8LX11d0mWbL1Hy/+eYbTJ8+3Xg7PT0dXbp0wdq1a9GsWTORJZo1U/MFDMuaRkREGI9CssEpGysrK4SHhyMhIaFQk1NwujD9NZIkISYmBhs2bMCePXvY4JSzjh074vTp04W2RUVFITg4GOPHj2eD8xe1atXquSXPU1JS+D6hnOTk5Dz3f5darTar97lscsqgRYsWqFKlCoYNG4YpU6bA1tYW33//PdLS0tCjRw+5yzN7Tk5OiI6OxtSpU+Ht7Q1fX1/Mnj0bANCvXz+ZqzN/Pj4+hW47ODgAAAICAuDl5SVHSYqSnp6O9u3bw8fHB3PmzMHdu3eN93l6espYmXn66KOPMHToUDRu3BgtWrTAokWLcO3aNURHR8tdmtkbNWoUVq1ahV9++QWOjo7G65ycnZ1ha2src3Xmz9HR8bnrm+zt7eHq6srrnsrB2LFj0bJlS8ycORP9+/fH0aNHsWjRIixatEju0hShZ8+emDFjBnx8fFCvXj0cP34cX331Fd555x25SzOdjCu7mbXExEQpMjJScnFxkRwdHaXmzZtLW7ZskbssxcjLy5PGjRsnubu7S46OjlKnTp2kM2fOyF2WIqWlpXEJ6XK0ZMkSCcALv6hsvv32W8nX11eysrKSGjVqxCWOy0lR83TJkiVyl6ZYXEK6fG3atEkKCQmRrK2tpeDgYGnRokVyl6QYWVlZUmxsrOTj4yPZ2NhINWvWlCZNmiRpNBq5SzMZr8khIiIiIiJF4YniRERERESkKGxyiIiIiIhIUdjkEBERERGRorDJISIiIiIiRWGTQ0REREREisImh4iIiIiIFIVNDhERERERKQqbHCIiIiIiKhf79u1Dz549Ub16dahUKmzcuLHUzyFJEubMmYOgoCBYW1vD29sbM2fOLNVzWJZ6r0RERERERC/w+PFjNGjQAFFRUXjjjTfK9ByxsbHYvn075syZg/r16yMzMxP37t0r1XOoJEmSyrR3IiIiIiKiIqhUKmzYsAG9e/c2bsvLy8PkyZOxcuVKPHz4ECEhIfjiiy/Qvn17AMD58+cRGhqKM2fOoHbt2mXeN09XIyIiIiKilyIqKgoHDx7EmjVrcOrUKfTr1w9du3ZFamoqAGDTpk2oWbMmNm/eDH9/f/j5+eHdd9/FgwcPSrUfNjlERERERCTc5cuXsXr1aqxbtw5t2rRBQEAA/v73v6N169ZYsmQJAODKlSv4448/sG7dOixfvhxLly5FUlIS3nzzzVLti9fkEBERERGRcMnJyZAkCUFBQYW2azQauLq6AgD0ej00Gg2WL19uHLd48WKEh4fj4sWLJp/CxiaHiIiIiIiE0+v1UKvVSEpKglqtLnSfg4MDAKBatWqwtLQs1AjVqVMHAHDt2jU2OUREREREVHE0bNgQOp0Od+7cQZs2bV44plWrVtBqtbh8+TICAgIAACkpKQAAX19fk/fF1dWIiIiIiKhcPHr0CJcuXQJgaGq++uorREREwMXFBT4+PhgyZAgOHjyIuXPnomHDhrh37x527dqF+vXro3v37tDr9WjSpAkcHBwwb9486PV6jBo1Ck5OTti+fbvJdbDJISIiIiKicrFnzx5EREQ8t33YsGFYunQp8vPzMX36dCxfvhw3b96Eq6srWrRogU8++QT169cHAKSnpyMmJgbbt2+Hvb09unXrhrlz58LFxcXkOtjkEBERERGRonAJaSIiIiIiUhQ2OUREREREpChscoiIiIiISFHY5BARERERkaKwySEiIiIiIkVhk0NERERERIrCJoeIiIiIiBSFTQ4RERERESkKmxwiIiIiIlIUNjlERERERKQobHKIiIiIiEhR2OQQEREREZGi/D+unpm3QOHMUQAAAABJRU5ErkJggg==", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Final Optimized Anchor (UC-based):\n", - "Design: {'D': np.float64(1.8913237564654963), 'L': np.float64(11.099208044881985), 'zlug': np.float64(7.3994720299213235)}\n", - "Capacity Results: {'Hmax': np.float64(2680903.350073319), 'Vmax': np.float64(3516302.6906043873), 'Ha': np.float64(2186977.238360048), 'Va': np.float64(2635582.2104549985), 'zlug': np.float64(7.3994720299213235), 'z0': np.float64(1.75), 'UC': np.float64(0.4999999981738827), 'Weight pile': 457496.7673970701}\n", - "\n", - "Final Optimized Anchor:\n", - "Design: {'D': np.float64(1.8913237564654963), 'L': np.float64(11.099208044881985), 'zlug': np.float64(7.3994720299213235)}\n", - "Capacity Results: {'Hmax': np.float64(2680903.350073319), 'Vmax': np.float64(3516302.6906043873), 'Ha': np.float64(2186977.238360048), 'Va': np.float64(2635582.2104549985), 'zlug': np.float64(7.3994720299213235), 'z0': np.float64(1.75), 'UC': np.float64(0.4999999981738827), 'Weight pile': 457496.7673970701}\n" - ] - } - ], - "source": [ - "anchor.getSizeAnchor(\n", - " geom = [anchor.dd['design']['L'], anchor.dd['design']['D']],\n", - " geomKeys = ['L', 'D'],\n", - " geomBounds = [(5.0, 15.0), (1.0, 4.0)],\n", - " loads = None,\n", - " lambdap_con = [3, 6],\n", - " zlug_fix = False,\n", - " safety_factor = {'SF_combined': 2},\n", - " plot = True\n", - ")\n", - "\n", - "print('\\nFinal Optimized Anchor:')\n", - "print('Design:', anchor.dd['design'])\n", - "print('Capacity Results:', anchor.anchorCapacity)" - ] - }, - { - "cell_type": "markdown", - "id": "b7c5fff6", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "490a71e1", - "metadata": {}, - "source": [ - "### Step 11: Optimized anchor material costs\n", - "We assess the cost of the optimized suction pile defined by the manufacturing cost (USD/kg)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "a439735f", - "metadata": {}, - "outputs": [ + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final Optimized Anchor (UC-based):\n", + "Design: {'D': 1.427702436403329, 'L': 8.565624503775082, 'zlug': 5.710416335850054}\n", + "Capacity Results: {'Hmax': 0.1, 'Vmax': 1719845.4215692256, 'Ha': 2528247.4234612333, 'Va': 2414423.5137604806, 'zlug': 5.710416335850054, 'z0': 1.75, 'UC': 1.0997495198266297e+39, 'Weight pile': 114372.69697445678}\n", + "\n", + "Final Optimized Anchor:\n", + "Design: {'D': 1.427702436403329, 'L': 8.565624503775082, 'zlug': 5.710416335850054}\n", + "Capacity Results: {'Hmax': 0.1, 'Vmax': 1719845.4215692256, 'Ha': 2528247.4234612333, 'Va': 2414423.5137604806, 'zlug': 5.710416335850054, 'z0': 1.75, 'UC': 1.0997495198266297e+39, 'Weight pile': 114372.69697445678}\n" + ] + } + ], + "source": [ + "anchor.getSizeAnchor(\n", + " geom = [anchor.dd['design']['L'], anchor.dd['design']['D']],\n", + " geomKeys = ['L', 'D'],\n", + " geomBounds = [(5.0, 15.0), (1.0, 4.0)],\n", + " loads = None,\n", + " lambdap_con = [3, 6],\n", + " zlug_fix = False,\n", + " safety_factor = {'SF_combined': 2},\n", + " plot = True\n", + ")\n", + "\n", + "print('\\nFinal Optimized Anchor:')\n", + "print('Design:', anchor.dd['design'])\n", + "print('Capacity Results:', anchor.anchorCapacity)" + ] + }, + { + "cell_type": "markdown", + "id": "b7c5fff6", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "490a71e1", + "metadata": {}, + "source": [ + "### Step 11: Optimized anchor material costs\n", + "We assess the cost of the optimized suction pile defined by the manufacturing cost (USD/kg)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a439735f", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mass: 46635.76 kg\n", + "Mass: 11658.79 kg\n", "Material unit cost: 10.25 USD/kg\n", - "Material cost: 478016.50 USD [2024]\n" + "Material cost: 119502.56 USD [2024]\n" ] } ], "source": [ - "anchor.getCostAnchor()\n", + "anchor.getCost()\n", "\n", "print(f\"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg\")\n", "print(f\"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg\")\n", @@ -3720,7 +522,7 @@ ], "metadata": { "kernelspec": { - "display_name": "raft-env", + "display_name": "fam", "language": "python", "name": "python3" }, @@ -3734,7 +536,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/famodel/anchors/AnchorDesign_temp.py b/famodel/anchors/AnchorDesign_temp.py new file mode 100644 index 00000000..0747824a --- /dev/null +++ b/famodel/anchors/AnchorDesign_temp.py @@ -0,0 +1,669 @@ +# -*- coding: utf-8 -*- +""" +temp storage of different anchor sizing functions that use different optimization methods. Eventually can be converted into a full AnchorDesign class... +""" + +def getSizeAnchor(self, geom, geomKeys, geomBounds=None, loads=None, lambdap_con=[4, 8], + zlug_fix=True, safety_factor={}, plot=False, display=0): + ''' + Generalized optimization method for all anchor types, using dictionary-based safety factors. + ''' + self.display = display + + anchType_clean = self.dd['type'].strip().lower() + print(f"[Debug] Anchor type parsed: '{anchType_clean}'") + + if loads is None: + loads = self.loads + + sf_Hm = safety_factor.get('Hm', safety_factor.get('SF_horizontal', 1.0)) + sf_Vm = safety_factor.get('Vm', safety_factor.get('SF_vertical', 1.0)) + sf_uc = safety_factor.get('SF_combined', max(sf_Hm, sf_Vm)) # conservative by default + + Hm = loads['Hm']*sf_Hm + Vm = loads['Vm']*sf_Vm + + line_type = getattr(self, 'line_type', 'chain') + d = getattr(self, 'd', 0.16) + w = getattr(self, 'w', 5000.0) + + def update_zlug(): + if 'suction' in anchType_clean and not zlug_fix and 'zlug' not in geomKeys: + self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] + elif np.any([name in anchType_clean for name in ['driven', 'helical']]) and not zlug_fix: + ratio = self.dd['design'].get('zlug_ratio', self.dd['design']['zlug']/self.dd['design']['L']) + self.dd['design']['zlug_ratio'] = ratio + self.dd['design']['zlug'] = ratio*self.dd['design']['L'] + elif 'drilled' in anchType_clean: + self.dd['design']['zlug'] = 0 + + def get_lambda(): + if 'torpedo' in anchType_clean: + L = self.dd['design']['L1'] + self.dd['design']['L2'] + A_wing = (self.dd['design']['D1'] - self.dd['design']['D2']) * self.dd['design']['L1'] + A_shaft = self.dd['design']['D2'] * L + D = (A_wing + A_shaft) / L + elif np.any([name in anchType_clean for name in ['driven', 'drilled', 'helical', 'suction']]): + L = self.dd['design']['L'] + D = self.dd['design']['D'] + elif np.any([name in anchType_clean for name in ['plate', 'sepla', 'dea', 'depla', 'vla']]): + L = self.dd['design']['L'] + D = self.dd['design']['B'] + else: + raise ValueError(f'lambda not defined for anchor type: {anchType_clean}') + return L/D + + def constraint_lambda_min(vars): + for i, key in enumerate(geomKeys): + self.dd['design'][key] = vars[i] + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) + return get_lambda() - lambdap_con[0] + + def constraint_lambda_max(vars): + return lambdap_con[1] - get_lambda() + + def constraint_bounds(vars): + con_bound_return = np.zeros(len(geomKeys)*2) + for i,var in enumerate(geomKeys): + con_bound_return[2*i] = self.dd['design'][var] - geomBounds[i][0] + con_bound_return[2*i+1] = geomBounds[i][1] - self.dd['design'][var] + return con_bound_return + + if np.any([name in anchType_clean for name in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']]): + target_UC = 1.0/sf_uc + + def objective_uc(vars): + ''' + for i, key in enumerate(geomKeys): + self.dd['design'][key] = vars[i] + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) + ''' + #UC = self.anchorCapacity.get('UC', 2.0) + #return (UC - target_UC)**2 + #return self.anchorCapacity.get('Weight pile') + if any(name in anchType_clean for name in ['plate', 'sepla', 'dea', 'depla', 'vla']): + return self.anchorCapacity.get('Weight plate') + else: + return self.anchorCapacity.get('Weight pile') + + def constraint_uc_envelope(vars): + return self.anchorCapacity.get('UC', 0.0) - target_UC + + constraints_uc = [ + {'type': 'ineq', 'fun': constraint_lambda_min}, + {'type': 'ineq', 'fun': constraint_lambda_max}, + {'type': 'ineq', 'fun': constraint_uc_envelope}, + {'type': 'ineq', 'fun': constraint_bounds}, + ] + + result_uc = minimize( + objective_uc, + geom, + method='COBYLA', + constraints=constraints_uc, + options={'rhobeg': 0.1, 'catol': 0.01, 'maxiter': 500} + ) + + endGeom = dict(zip(geomKeys, result_uc.x)) + self.dd['design'].update(endGeom) + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=plot, display=display) + + print('\nFinal Optimized Anchor (UC-based):') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + + def near_border(): + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_lat = 0.05*self.dd['design']['D'] # 10% of the pile diameter + limit_rot = 10.0 # 10 deg + + near_UC_h = 0.95 <= UC_h <= 1.0 + near_UC_v = 0.95 <= UC_v <= 1.0 + near_disp_lat = 0.95*limit_lat <= disp_lat <= limit_lat + near_disp_rot = 4.75 <= disp_rot <= limit_rot + + return near_UC_h or near_UC_v or near_disp_lat or near_disp_rot + + def termination_condition(): + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_lat = 0.05*self.dd['design']['D'] # 10% of the pile diameter + limit_rot = 10.0 # 10 deg + + all_satisfied = (UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot) + + if all_satisfied: + if near_border(): + if self.display > 0: print('[Termination] All criteria satisfied and near border.') + return 'terminate' + else: + if self.display > 0: print('[Safe but not near border] Continue shrinking...') + return 'continue' + return 'continue' + + def termination_condition_drilled(): + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_lat = 0.05*self.dd['design']['D'] # 10% of the pile diameter + limit_rot = 10.0 # 10 deg + + all_satisfied = (UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot) + + if all_satisfied: + if near_border(): + if self.display > 0: print('[Termination] All criteria satisfied and near border.') + return 'terminate' + else: + if self.display > 0: print('[Safe but not near border] Continue shrinking...') + return 'continue' + return 'continue' + + def is_valid(value): + return np.isfinite(value) and not np.isnan(value) and abs(value) < 1e6 + + if anchType_clean in ['helical', 'driven']: + L0, D0 = geom if len(geom) == 2 else [5.0, 1.0] + self.dd['design']['L'] = L0 + self.dd['design']['D'] = D0 + Lmin, Lmax = geomBounds[0] + Dmin, Dmax = geomBounds[1] + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) + + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_disp = 0.10*D0 # 10% of the pile diameter + limit_rot = 10.0 # 10 deg + direction = 'shrink' if (UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_disp and disp_rot <= limit_rot) else 'grow' + + max_iter = 200 + iter_count = 0 + + if direction == 'shrink': + for L in np.arange(L0, Lmin - 1e-6, -0.25): + self.dd['design']['L'] = L + for D in np.arange(Dmax, Dmin - 1e-6, -0.05): + if L/D > lambdap_con[1] or L/D < lambdap_con[0]: + continue + self.dd['design']['L'] = L + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + if self.display > 0: print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + iter_count += 1 + if not all(is_valid(v) for v in [UC_h, UC_v, disp_lat, disp_rot]): + continue + if termination_condition(): + print(f'\nTermination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + elif direction == 'grow': + for L in np.arange(L0, Lmax + 1e-6, 0.25): + self.dd['design']['L'] = L + for D in np.arange(Dmin, Dmax + 1e-6, 0.05): + if L/D > lambdap_con[1] or L/D < lambdap_con[0]: + continue + self.dd['design']['L'] = L + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) + UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax'] + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + if self.display > 0: print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + iter_count += 1 + status = termination_condition() + if status == 'terminate': + print(f'Termination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + elif status == 'continue': + continue + status = termination_condition() + if status == 'terminate': + print(f'\nTermination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + else: + raise ValueError(f"Unknown optimization direction: {direction}") + + if self.display > 0: print('[Warning] While-loop search reached bounds without meeting criteria.') + + if 'drilled' in anchType_clean: + L0, D0 = geom if len(geom) == 2 else [5.0, 1.0] + self.dd['design']['L'] = L0 + self.dd['design']['D'] = D0 + Lmin, Lmax = geomBounds[0] + Dmin, Dmax = geomBounds[1] + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) + + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + limit_disp = 0.10*D0 # 10% of the pile diameter + limit_rot = 10.0 # 10 deg + direction = 'shrink' if (UC_v <= 1.0 and disp_lat <= limit_disp and disp_rot <= limit_rot) else 'grow' + + max_iter = 200 + iter_count = 0 + + if direction == 'shrink': + for L in np.arange(L0, Lmin - 1e-6, -0.25): + self.dd['design']['L'] = L + for D in np.arange(Dmax, Dmin - 1e-6, -0.05): + if L/D > lambdap_con[1] or L/D < lambdap_con[0]: + continue + self.dd['design']['L'] = L + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + if self.display > 0: print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + iter_count += 1 + if not all(is_valid(v) for v in [UC_v, disp_lat, disp_rot]): + continue + if termination_condition_drilled(): + print(f'\nTermination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + elif direction == 'grow': + for L in np.arange(L0, Lmax + 1e-6, 0.25): + self.dd['design']['L'] = L + for D in np.arange(Dmin, Dmax + 1e-6, 0.05): + if L/D > lambdap_con[1] or L/D < lambdap_con[0]: + continue + self.dd['design']['L'] = L + update_zlug() + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, mass_update=True, plot=False, display=display) + UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax'] + disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0)) + disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0)) + if self.display > 0: print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg') + iter_count += 1 + status = termination_condition_drilled() + if status == 'terminate': + print(f'Termination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + elif status == 'continue': + continue + status = termination_condition_drilled() + if status == 'terminate': + print(f'\nTermination criteria met.') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + return + else: + raise ValueError(f"Unknown optimization direction: {direction}") + + if self.display > 0: print('[Warning] While-loop search reached bounds without meeting criteria.') + + else: + raise ValueError(f"Anchor type '{anchType_clean}' not supported for safety factor input.") + +def getSizeAnchor2(self, geom, geomBounds=None, loads=None, lambdap_con=[3, 6], + zlug_fix=True, safety_factor={}, plot=False): + ''' + Grid-based optimization method for envelope anchors (suction, torpedo, plate). + Evaluates UC over a grid of L and D, and selects the point closest to target UC. + ''' + import matplotlib.pyplot as plt + from matplotlib import cm + import matplotlib.colors as mcolor + import numpy as np + + anchType_clean = self.dd['type'].lower().replace('', '') + + if loads is None: + loads = self.loads + + sf_uc = safety_factor.get('SF_combined', 1.0) + sf_Hm = safety_factor.get('Hm', 1.0) + sf_Vm = safety_factor.get('Vm', 1.0) + + Hm = loads['Hm']*sf_Hm + Vm = loads['Vm']*sf_Vm + + line_type = getattr(self, 'line_type', 'chain') + d = getattr(self, 'd', 0.16) + w = getattr(self, 'w', 5000.0) + + if anchType_clean not in ['suction', 'torpedo', 'plate']: + raise ValueError(f"Grid-based getSizeAnchor only supports envelope anchors, not '{anchType_clean}'") + + UC_target = 1.0/sf_uc + + # Unpack bounds and generate grid + L_vals = np.linspace(geomBounds[0][0], geomBounds[0][1], 10) + D_vals = np.linspace(geomBounds[1][0], geomBounds[1][1], 10) + + L_grid, D_grid = np.meshgrid(L_vals, D_vals) + UC_grid = np.full_like(L_grid, np.nan, dtype=float) + mask = np.full_like(L_grid, False, dtype=bool) + + best_UC, best_L, best_D = None, None, None + results = [] + + for i in range(D_grid.shape[0]): # loop over D + for j in range(D_grid.shape[1]): # loop over L + D = D_grid[i, j] + L = L_grid[i, j] + lambdap = L/D + + if not (lambdap_con[0] <= lambdap <= lambdap_con[1]): + continue + + mask[i, j] = True + self.dd['design']['L'] = L + self.dd['design']['D'] = D + + if anchType_clean == 'suction' and not zlug_fix: + self.dd['design']['zlug'] = (2/3)*L + + try: + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, + mass_update=True, plot=False) + UC = self.anchorCapacity.get('UC', np.nan) + results.append({ + 'L': L, + 'D': D, + 'UC': UC}) + + if UC > 1e-2 and UC < 10.0: + UC_grid[i, j] = UC + # Find UC closest to target + if best_UC is None or abs(UC - UC_target) < abs(best_UC - UC_target): + best_UC = UC + best_L = L + best_D = D + + except: + continue + + # Update best result + # if best_L is not None and best_D is not None: + self.dd['design']['L'] = best_L + self.dd['design']['D'] = best_D + if anchType_clean == 'suction' and not zlug_fix: + self.dd['design']['zlug'] = (2/3)*best_L + + self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], + line_type=line_type, d=d, w=w, + mass_update=True, plot=plot) + + print('\nFinal Optimized Anchor (Grid-based):') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + + # else: + # print('[Warning] No valid combination found in the grid.') + + # Optional plot + + if plot: + fig, ax = plt.subplots(figsize=(6, 8)) + vmin, vmax = 0.01, 10 + levels = np.logspace(np.log10(vmin), np.log10(vmax), 21) + cp = ax.contourf(D_grid, L_grid, UC_grid, levels=levels, cmap='coolwarm', norm=mcolor.LogNorm(vmin=vmin, vmax=vmax)) + fig.colorbar(cp, ax=ax, label='Unity check (UC)') + ax.contour(D_grid, L_grid, UC_grid, levels=levels, colors='k', linewidths=0.3, alpha=0.3) + ax.contour(D_grid, L_grid, UC_grid, levels=[1.0], colors='red', linewidths=2, linestyles='--') + ax.set_xlabel('Diameter (m)') + ax.set_ylabel('Length (m)') + ax.set_title('Unity Check (UC') + ax.plot(best_D, best_L, 'ro', label='Best match') + ax.annotate('Best match', (best_D, best_L), textcoords="offset points", xytext=(10,10), ha='center', color='red') + ax.legend() + plt.grid(True) + plt.tight_layout() + plt.show() + + #UC_target = 1.0 + closest = min(results, key=lambda x: abs(x['UC'] - UC_target)) + print("Closest to UC_target:") + print(closest) + + return results + +def getSizeAnchor_BO(self, + geom=[10.0, 2.0], + geomKeys=['L', 'D'], + geomBounds=[(5.0, 15.0), (1.0, 4.0)], + loads=None, + lambdap_con=[3, 6], + zlug_fix=False, + safety_factor={'SF_combined': 1.0}, + n_calls=25, + plot=False, + verbose=True): + ''' + Bayesian optimization to find (D, L) for UC closest to UC_target. + Uses scikit-optimize for surrogate model and efficient sampling. + ''' + from skopt import gp_minimize + from skopt.space import Real + from skopt.utils import use_named_args + import numpy as np + + if loads is None: + loads = self.loads + + Hm = loads['Hm'] + Vm = loads['Vm'] + + line_type = getattr(self, 'line_type', 'chain') + d = getattr(self, 'd', 0.16) + w = getattr(self, 'w', 5000.0) + + UC_target = 1.0 / safety_factor.get('SF_combined', 1.0) + + # Define the search space + space = [ + Real(geomBounds[1][0], geomBounds[1][1], name='D'), + Real(geomBounds[0][0], geomBounds[0][1], name='L') + ] + + @use_named_args(space) + def objective(**params): + D = params['D'] + L = params['L'] + + # Apply lambda constraint + lambdap = L/D + if not (lambdap_con[0] <= lambdap <= lambdap_con[1]): + return 100.0 + + self.dd['design']['D'] = D + self.dd['design']['L'] = L + if not zlug_fix: + self.dd['design']['zlug'] = (2/3)*L + + try: + self.getCapacityAnchor( + Hm=Hm, + Vm=Vm, + zlug=self.dd['design']['zlug'], + line_type=line_type, + d=d, + w=w, + mass_update=True, + plot=False) + + UC = self.anchorCapacity.get('UC', np.nan) + except: + UC = np.nan + + if verbose: + print(f"Evaluated D={D:.3f}, L={L:.3f} -> UC={UC:.3f}") + + if not np.isfinite(UC): + return 100.0 + + if UC < UC_target: + return (UC_target - UC)**2 * 0.5 # less penalty for overdesign + else: + return (UC - UC_target)**2 * 10 # higher penalty for failure + + # Run Bayesian optimization + res = gp_minimize( + objective, + space, + x0=[geom[1], geom[0]], + n_calls=n_calls, + random_state=42, + verbose=verbose + ) + + # Best result + best_D, best_L = res.x + self.dd['design']['D'] = best_D + self.dd['design']['L'] = best_L + if not zlug_fix: + self.dd['design']['zlug'] = (2/3)*best_L + + self.getCapacityAnchor( + Hm=Hm, + Vm=Vm, + zlug=self.dd['design']['zlug'], + line_type=line_type, + d=d, + w=w, + mass_update=True, + plot=plot + ) + UC = self.anchorCapacity.get('UC', np.nan) + + print('\nBayesian Optimized Anchor:') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + print(f'Best UC: {UC:.4f} (target: {UC_target})') + + results = {'D': best_D, 'L': best_L, 'UC': UC, 'result': res} + + return results +# PATCH for GRADIENT method: wrap getCapacityAnchor in safe evaluator +def safe_get_uc(self, Hm, Vm, zlug, line_type, d, w, verbose=False): + try: + self.getCapacityAnchor(Hm, Vm, zlug, line_type, d, w, True, False) + return self.anchorCapacity.get('UC', np.nan) + except Exception as e: + if verbose: + print(f"[Safe Error] {str(e)}") + return np.nan + +def getSizeAnchor_gradient(self, + geom=[10.0, 2.0], + geomKeys=['L', 'D'], + geomBounds=[(5.0, 15.0), (1.0, 4.0)], + loads=None, + lambdap_con=[3, 6], + zlug_fix=False, + safety_factor={'SF_combined': 1.0}, + step_size=0.2, + tol=0.05, + max_iter=30, + verbose=True): + ''' + Gradient-based optimization with early stopping to match UC_target. + ''' + import numpy as np + + if loads is None: + loads = self.loads + + Hm = loads['Hm'] + Vm = loads['Vm'] + + line_type = getattr(self, 'line_type', 'chain') + d = getattr(self, 'd', 0.16) + w = getattr(self, 'w', 5000.0) + + UC_target = 1.0 / safety_factor.get('SF_combined', 1.0) + + L, D = geom + + for iter in range(max_iter): + lambdap = L / D + if not (lambdap_con[0] <= lambdap <= lambdap_con[1]): + if verbose: + print(f"[Iter {iter}] λ = {lambdap:.2f} out of bounds. Terminating.") + break + + self.dd['design']['L'] = L + self.dd['design']['D'] = D + if not zlug_fix: + self.dd['design']['zlug'] = (2/3)*L + + UC0 = self.safe_get_uc(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, verbose=verbose) + + if not np.isfinite(UC0): + break + + if verbose: + print(f"[Iter {iter}] L={L:.2f}, D={D:.2f}, UC={UC0:.3f}") + + if abs(UC0 - UC_target) < tol: + print("Early stopping: UC within tolerance.") + break + + # Gradient estimate + delta = 0.1 + UC_L = self.safe_get_uc(Hm, Vm, (2/3)*(L + delta), line_type, d, w, verbose=verbose) + UC_D = self.safe_get_uc(Hm, Vm, (2/3)*L, line_type, d, w, verbose=verbose) + + grad_L = (UC_L - UC0)/delta if np.isfinite(UC_L) else 0.0 + grad_D = (UC_D - UC0)/delta if np.isfinite(UC_D) else 0.0 + + # Update + L -= step_size * grad_L + D -= step_size * grad_D + L = np.clip(L, geomBounds[0][0], geomBounds[0][1]) + D = np.clip(D, geomBounds[1][0], geomBounds[1][1]) + + if not (lambdap_con[0] <= L/D <= lambdap_con[1]): + if verbose: + print("Terminated: lambda constraint violated after update.") + break + + self.dd['design']['L'] = L + self.dd['design']['D'] = D + self.dd['design']['zlug'] = (2/3)*L + self.getCapacityAnchor(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, True, True) + + print('\nGradient Optimized Anchor:') + print('Design:', self.dd['design']) + print('Capacity Results:', self.anchorCapacity) + + return {'D': D, 'L': L, 'UC': self.anchorCapacity.get('UC', np.nan)} \ No newline at end of file diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index e8c625f5..52ce884a 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -997,341 +997,6 @@ def is_valid(value): else: raise ValueError(f"Anchor type '{anchType_clean}' not supported for safety factor input.") - def getSizeAnchor2(self, geom, geomBounds=None, loads=None, lambdap_con=[3, 6], - zlug_fix=True, safety_factor={}, plot=False): - ''' - Grid-based optimization method for envelope anchors (suction, torpedo, plate). - Evaluates UC over a grid of L and D, and selects the point closest to target UC. - ''' - import matplotlib.pyplot as plt - from matplotlib import cm - import matplotlib.colors as mcolor - import numpy as np - - anchType_clean = self.dd['type'].lower().replace('', '') - - if loads is None: - loads = self.loads - - sf_uc = safety_factor.get('SF_combined', 1.0) - sf_Hm = safety_factor.get('Hm', 1.0) - sf_Vm = safety_factor.get('Vm', 1.0) - - Hm = loads['Hm']*sf_Hm - Vm = loads['Vm']*sf_Vm - - line_type = getattr(self, 'line_type', 'chain') - d = getattr(self, 'd', 0.16) - w = getattr(self, 'w', 5000.0) - - if anchType_clean not in ['suction', 'torpedo', 'plate']: - raise ValueError(f"Grid-based getSizeAnchor only supports envelope anchors, not '{anchType_clean}'") - - UC_target = 1.0/sf_uc - - # Unpack bounds and generate grid - L_vals = np.linspace(geomBounds[0][0], geomBounds[0][1], 10) - D_vals = np.linspace(geomBounds[1][0], geomBounds[1][1], 10) - - L_grid, D_grid = np.meshgrid(L_vals, D_vals) - UC_grid = np.full_like(L_grid, np.nan, dtype=float) - mask = np.full_like(L_grid, False, dtype=bool) - - best_UC, best_L, best_D = None, None, None - results = [] - - for i in range(D_grid.shape[0]): # loop over D - for j in range(D_grid.shape[1]): # loop over L - D = D_grid[i, j] - L = L_grid[i, j] - lambdap = L/D - - if not (lambdap_con[0] <= lambdap <= lambdap_con[1]): - continue - - mask[i, j] = True - self.dd['design']['L'] = L - self.dd['design']['D'] = D - - if anchType_clean == 'suction' and not zlug_fix: - self.dd['design']['zlug'] = (2/3)*L - - try: - self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, - mass_update=True, plot=False) - UC = self.anchorCapacity.get('UC', np.nan) - results.append({ - 'L': L, - 'D': D, - 'UC': UC}) - - if UC > 1e-2 and UC < 10.0: - UC_grid[i, j] = UC - # Find UC closest to target - if best_UC is None or abs(UC - UC_target) < abs(best_UC - UC_target): - best_UC = UC - best_L = L - best_D = D - - except: - continue - - # Update best result - # if best_L is not None and best_D is not None: - self.dd['design']['L'] = best_L - self.dd['design']['D'] = best_D - if anchType_clean == 'suction' and not zlug_fix: - self.dd['design']['zlug'] = (2/3)*best_L - - self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], - line_type=line_type, d=d, w=w, - mass_update=True, plot=plot) - - print('\nFinal Optimized Anchor (Grid-based):') - print('Design:', self.dd['design']) - print('Capacity Results:', self.anchorCapacity) - - # else: - # print('[Warning] No valid combination found in the grid.') - - # Optional plot - - if plot: - fig, ax = plt.subplots(figsize=(6, 8)) - vmin, vmax = 0.01, 10 - levels = np.logspace(np.log10(vmin), np.log10(vmax), 21) - cp = ax.contourf(D_grid, L_grid, UC_grid, levels=levels, cmap='coolwarm', norm=mcolor.LogNorm(vmin=vmin, vmax=vmax)) - fig.colorbar(cp, ax=ax, label='Unity check (UC)') - ax.contour(D_grid, L_grid, UC_grid, levels=levels, colors='k', linewidths=0.3, alpha=0.3) - ax.contour(D_grid, L_grid, UC_grid, levels=[1.0], colors='red', linewidths=2, linestyles='--') - ax.set_xlabel('Diameter (m)') - ax.set_ylabel('Length (m)') - ax.set_title('Unity Check (UC') - ax.plot(best_D, best_L, 'ro', label='Best match') - ax.annotate('Best match', (best_D, best_L), textcoords="offset points", xytext=(10,10), ha='center', color='red') - ax.legend() - plt.grid(True) - plt.tight_layout() - plt.show() - - #UC_target = 1.0 - closest = min(results, key=lambda x: abs(x['UC'] - UC_target)) - print("Closest to UC_target:") - print(closest) - - return results - - def getSizeAnchor_BO(self, - geom=[10.0, 2.0], - geomKeys=['L', 'D'], - geomBounds=[(5.0, 15.0), (1.0, 4.0)], - loads=None, - lambdap_con=[3, 6], - zlug_fix=False, - safety_factor={'SF_combined': 1.0}, - n_calls=25, - plot=False, - verbose=True): - ''' - Bayesian optimization to find (D, L) for UC closest to UC_target. - Uses scikit-optimize for surrogate model and efficient sampling. - ''' - from skopt import gp_minimize - from skopt.space import Real - from skopt.utils import use_named_args - import numpy as np - - if loads is None: - loads = self.loads - - Hm = loads['Hm'] - Vm = loads['Vm'] - - line_type = getattr(self, 'line_type', 'chain') - d = getattr(self, 'd', 0.16) - w = getattr(self, 'w', 5000.0) - - UC_target = 1.0 / safety_factor.get('SF_combined', 1.0) - - # Define the search space - space = [ - Real(geomBounds[1][0], geomBounds[1][1], name='D'), - Real(geomBounds[0][0], geomBounds[0][1], name='L') - ] - - @use_named_args(space) - def objective(**params): - D = params['D'] - L = params['L'] - - # Apply lambda constraint - lambdap = L/D - if not (lambdap_con[0] <= lambdap <= lambdap_con[1]): - return 100.0 - - self.dd['design']['D'] = D - self.dd['design']['L'] = L - if not zlug_fix: - self.dd['design']['zlug'] = (2/3)*L - - try: - self.getCapacityAnchor( - Hm=Hm, - Vm=Vm, - zlug=self.dd['design']['zlug'], - line_type=line_type, - d=d, - w=w, - mass_update=True, - plot=False) - - UC = self.anchorCapacity.get('UC', np.nan) - except: - UC = np.nan - - if verbose: - print(f"Evaluated D={D:.3f}, L={L:.3f} -> UC={UC:.3f}") - - if not np.isfinite(UC): - return 100.0 - - if UC < UC_target: - return (UC_target - UC)**2 * 0.5 # less penalty for overdesign - else: - return (UC - UC_target)**2 * 10 # higher penalty for failure - - # Run Bayesian optimization - res = gp_minimize( - objective, - space, - x0=[geom[1], geom[0]], - n_calls=n_calls, - random_state=42, - verbose=verbose - ) - - # Best result - best_D, best_L = res.x - self.dd['design']['D'] = best_D - self.dd['design']['L'] = best_L - if not zlug_fix: - self.dd['design']['zlug'] = (2/3)*best_L - - self.getCapacityAnchor( - Hm=Hm, - Vm=Vm, - zlug=self.dd['design']['zlug'], - line_type=line_type, - d=d, - w=w, - mass_update=True, - plot=plot - ) - UC = self.anchorCapacity.get('UC', np.nan) - - print('\nBayesian Optimized Anchor:') - print('Design:', self.dd['design']) - print('Capacity Results:', self.anchorCapacity) - print(f'Best UC: {UC:.4f} (target: {UC_target})') - - results = {'D': best_D, 'L': best_L, 'UC': UC, 'result': res} - - return results - # PATCH for GRADIENT method: wrap getCapacityAnchor in safe evaluator - def safe_get_uc(self, Hm, Vm, zlug, line_type, d, w, verbose=False): - try: - self.getCapacityAnchor(Hm, Vm, zlug, line_type, d, w, True, False) - return self.anchorCapacity.get('UC', np.nan) - except Exception as e: - if verbose: - print(f"[Safe Error] {str(e)}") - return np.nan - - def getSizeAnchor_gradient(self, - geom=[10.0, 2.0], - geomKeys=['L', 'D'], - geomBounds=[(5.0, 15.0), (1.0, 4.0)], - loads=None, - lambdap_con=[3, 6], - zlug_fix=False, - safety_factor={'SF_combined': 1.0}, - step_size=0.2, - tol=0.05, - max_iter=30, - verbose=True): - ''' - Gradient-based optimization with early stopping to match UC_target. - ''' - import numpy as np - - if loads is None: - loads = self.loads - - Hm = loads['Hm'] - Vm = loads['Vm'] - - line_type = getattr(self, 'line_type', 'chain') - d = getattr(self, 'd', 0.16) - w = getattr(self, 'w', 5000.0) - - UC_target = 1.0 / safety_factor.get('SF_combined', 1.0) - - L, D = geom - - for iter in range(max_iter): - lambdap = L / D - if not (lambdap_con[0] <= lambdap <= lambdap_con[1]): - if verbose: - print(f"[Iter {iter}] λ = {lambdap:.2f} out of bounds. Terminating.") - break - - self.dd['design']['L'] = L - self.dd['design']['D'] = D - if not zlug_fix: - self.dd['design']['zlug'] = (2/3)*L - - UC0 = self.safe_get_uc(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, verbose=verbose) - - if not np.isfinite(UC0): - break - - if verbose: - print(f"[Iter {iter}] L={L:.2f}, D={D:.2f}, UC={UC0:.3f}") - - if abs(UC0 - UC_target) < tol: - print("Early stopping: UC within tolerance.") - break - - # Gradient estimate - delta = 0.1 - UC_L = self.safe_get_uc(Hm, Vm, (2/3)*(L + delta), line_type, d, w, verbose=verbose) - UC_D = self.safe_get_uc(Hm, Vm, (2/3)*L, line_type, d, w, verbose=verbose) - - grad_L = (UC_L - UC0)/delta if np.isfinite(UC_L) else 0.0 - grad_D = (UC_D - UC0)/delta if np.isfinite(UC_D) else 0.0 - - # Update - L -= step_size * grad_L - D -= step_size * grad_D - L = np.clip(L, geomBounds[0][0], geomBounds[0][1]) - D = np.clip(D, geomBounds[1][0], geomBounds[1][1]) - - if not (lambdap_con[0] <= L/D <= lambdap_con[1]): - if verbose: - print("Terminated: lambda constraint violated after update.") - break - - self.dd['design']['L'] = L - self.dd['design']['D'] = D - self.dd['design']['zlug'] = (2/3)*L - self.getCapacityAnchor(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, True, True) - - print('\nGradient Optimized Anchor:') - print('Design:', self.dd['design']) - print('Capacity Results:', self.anchorCapacity) - - return {'D': D, 'L': L, 'UC': self.anchorCapacity.get('UC', np.nan)} def getSafetyFactor(self): ''' @@ -1369,7 +1034,7 @@ def getSafetyFactor(self): return {'SF_combined': SF} - def getCostAnchor(self, ms=None, mass_update=True): + def getCost(self, ms=None, mass_update=True): ''' Assign material cost using a Point object and getCost_and_MBL(). @@ -1556,57 +1221,6 @@ def makeBuffer(self, buff_rad=50): - - - - - def getCost(self,costDict='default'): - '''find costs of anchor and store in design dictionary - - Parameters - ---------- - costDict : dictionary or yaml, optional - Dictionary of various costs for anchors. Sub costs that can be included are: - material : material costs - - ''' - if isinstance(costDict,str) and costDict != 'default': - import yaml - costDict = yaml.load(costDict, Loader=yaml.FullLoader) - anchType = self.dd['type'] - if costDict == 'default': - matCostDict = {'DEA':5.705,'suction_pile':4.435,'gravity':1.905} # mean values from Task 49 Design Basis ranges - instCostDict = {} - decomCostDict = {} - else: - matCostDict = costDict['material'] - if 'install' in costDict: - instCostDict = costDict['install'] - if 'decom' in costDict: - decomCostDict = costDict['decom'] - keyFail = True - # check if mass info is available - if not self.mass: - if self.soilProps: - # need mass - call capacity functions - self.getAnchorCapacity(plot=False) - else: - print('Soil properties needed to calculate anchor mass for cost. Setting cost to 0.') - self.mass = 0 - - # sort by type of anchor - for Ckey,Cval in matCostDict.items(): - if anchType in Ckey: - self.cost['materials'] = matCostDict[Ckey]*self.mass - # self.cost['install'] = instCostDict[Ckey] - # self.cost['decom'] = decomCostDict[Ckey] - keyFail = False - # raise error if anchType not found in cost dictionary - if keyFail: - raise KeyError(f'anchor type {anchType} not found in material cost dictionary') - - return(sum(self.cost.values())) - # def getSuctionSize(self,D,L,loads=None,minfs={'Ha':1.6,'Va':2},LD_con=[4,8]): diff --git a/famodel/geography.py b/famodel/geography.py index 0f4cf7a0..0929f849 100644 --- a/famodel/geography.py +++ b/famodel/geography.py @@ -602,376 +602,6 @@ def getSoilGrid(centroid, latlong_crs, custom_crs, soil_file, nrows=100, ncols=1 return xs, ys, soil_grid -<<<<<<< HEAD - # organize the bathymetry arguments - if len(args_bath)==0: - args_bath = {'zlim':[-3200,500], 'cmap':'gist_earth'} - - fig = plt.figure(figsize=(6,4)) - ax = plt.axes(projection='3d') - - # if xbounds != None: - # ax.set_xlim(xbounds[0], xbounds[1]) - # if ybounds != None: - # ax.set_ylim(ybounds[0], ybounds[1]) - # if zbounds != None: - # ax.set_zlim(zbounds[0], zbounds[1]) - - # plot the lease area in a red color, if desired - ax.plot(lease_xs, lease_ys, np.zeros(len(lease_xs)), color='r', zorder=100) - - # plot the bathymetry in matplotlib using a plot_surface - - # !!!! include option to plot entire bathymetry file or not - - if isinstance(bathymetryfilename, str): - bathGrid_Xs, bathGrid_Ys, bathGrid = sbt.readBathymetryFile(bathymetryfilename) # parse through the MoorDyn/MoorPy-formatted bathymetry file - X, Y = np.meshgrid(bathGrid_Xs, bathGrid_Ys) # create a 2D mesh of the x and y values - bath = ax.plot_surface(X, Y, -bathGrid, rstride=1, cstride=1, - vmin=args_bath['zlim'][0], vmax=args_bath['zlim'][1], - cmap=args_bath['cmap']) - - ''' - # plot the project boundary - if boundary: - ax.plot(self.boundaryXs, self.boundaryYs, np.zeros(len(self.boundaryXs)), color='b', zorder=100, alpha=0.5) - ''' - - # plot the projection of the lease area bounds on the seabed, if desired - if area_on_bath: - lease_zs = projectAlongSeabed(lease_xs, lease_ys, bathGrid_Xs, bathGrid_Ys, bathGrid) - ax.plot(lease_xs, lease_ys, -lease_zs, color='tab:blue', zorder=10, alpha=0.5) - - - set_axes_equal(ax) - ax.axis('off') - - return fig, ax - -def projectAlongSeabed(x, y, bathXs, bathYs, bath_depths): - '''Project a set of x-y coordinates along a seabed surface (grid), - returning the corresponding z coordinates.''' - - if len(x) == len(y): - n = len(x) - z = np.zeros(n) # z coordinate of each point [m] - a = np.zeros(n) # could also do slope (dz/dh) - for i in range(n): - z[i], nvec = sbt.getDepthFromBathymetry(x[i], y[i], bathXs, bathYs, bath_depths) - - else: - z = np.zeros([len(y), len(x)]) - for i in range(len(y)): - for j in range(len(x)): - z[i,j], nvec = sbt.getDepthFromBathymetry(x[j], y[i], bathXs_mesh, bathYs_mesh, bath_depths) - - return z - - - - - - - - - - - - """ - if self.lat0 != 0 and self.lon0 != 0: - # set the centroid of the project and save in a GeoDataFrame - self.centroid = (self.lon0, self.lat0) - self.gdf = gpd.GeoDataFrame({'type':'centroid', 'geometry': [Point(self.centroid)]}, crs=self.latlong_crs) - # set the target coordinate reference system (CRS) that will switch between regular lat/long system and "meters from centroid" system, based on centroid location - information - # extract the numeric code - self. # save the target CRS (UTM 10N = 32610) - - - gdf_leases = gpd.GeoDataFrame({'type': 'lease_area', 'geometry': lease_area.geometry}, crs=getLatLongCRS() ) - - gdf = add2geodataframe(gdf0, gdf_leases) - - # convert lat/long boundary/lease area points to meters away from the centroid - self.lease_xs, self.lease_ys = self.convertLatLong2Meters(self.area_longs, self.area_lats, self.centroid) - - - if which_centroid=='lease_area': - # make a blank copy of the gdf to switch to the target CRS to get the accurate centroid - gdf_utm = self.gdf.copy().to_crs(self.target_crs) - centroid_utm = (gdf_utm.geometry.centroid.values.x[0], gdf_utm.geometry.centroid.values.y[0]) - gdf_centroid = gpd.GeoDataFrame({'type':'centroid', 'geometry': [Point(centroid_utm)]}, crs=self.target_crs) - else: - # assuming the input centroid is in a long/lat pair - gdf_centroid = gpd.GeoDataFrame({'type':'centroid', 'geometry': [Point(which_centroid)]}, crs=self.latlong_crs) - gdf_centroid.to_crs(self.target_crs) - - self.centroid_utm = (gdf_centroid.geometry.values.x[0], gdf_centroid.geometry.values.y[0]) - self.centroid = (gdf_centroid.to_crs(self.latlong_crs).geometry.values.x[0], gdf_centroid.to_crs(self.latlong_crs).geometry.values.y[0]) # assume centroid is focal point of Project - - def initialize_geodataframe(crs, columns=['type','geometry']): - - gdf = gpd.GeoDataFrame(columns=columns, geometry='initialization', crs=crs) - - return gdf - - def add2geodataframe(gdf_to_add_to, gdf_to_add): - - # check to make sure they have the same columns and CRS - - gdf_new = pd.concat([gdf_to_add_to, gdf_to_add]) - - return gdf_new - - - - - - # TODO - - # reference entire CA bathymetry file (maybe) - # plot2d method and a plotGDF method (with bathymetry in the geodataframe using contours) - - # add the coastline - #xcoast, ycoast = sbt.getCoast(self.Xs, self.Ys, self.depths) - #ax.plot(xcoast, ycoast, np.zeros(len(self.Ys)), color='k', zorder=100) - - # need to fix up bounds - #xbmin, xbmax = sbt.getPlotBounds(self.longs_bath, self.centroid, long=True) - #ybmin, ybmax = sbt.getPlotBounds(self.lats_bath, self.centroid, long=False) - - plt.show() - - - - - - - - - # METHODS USED SPECIFICALLY FOR DEEPFARM LCOE ANALYSIS - - def addMap2GDF(self, filename='', states=None): - '''function to include a shapefile of a provided map''' - - # read in the provided filename to add to the geodataframe - usa = gpd.read_file(filename) - # internal list of states, in order, to go with the default U.S. states shapefile - statenamelist = ['Maryland','Iowa','Delaware','Ohio','Pennsylvania','Nebraska','Washington','Puerto Rico','Alabama','Arkansas','New Mexico', # 0-10 - 'Texas','California','Kentucky','Georgia','Wisconsin','Oregon','Missouri','Virginia','Tennessee','Louisiana','New York', # 11-21 - 'Michigan','Idaho','Florida','Alaska','Illinois','Montana','Minnesota','Indiana','Massachusetts','Kansas','Nevada','Vermont', # 22-33 - 'Connecticut','New Jersey','Washington D.C.','North Carolina','Utah','North Dakota','South Carolina','Mississippi','Colorado', # 34-42 - 'South Dakota','Oklahoma','Wyoming','West Virginia','Maine','Hawaii','New Hampshire','Arizona','Rhode Island'] # 43-51 - # insert names of the states into the new gdf - usa.insert(0, 'type', statenamelist) - # set the CRS of the USA pdf to the right CRS - usa.set_crs(crs="EPSG:4326", inplace=True) - self.usa = usa - - for state in states: - state_gs = usa.loc[usa['type']==state] - self.gdf = pd.concat([self.gdf, state_gs]) - - - - def setFarmLayout(self, style='grid', nrows=10, ncols=10, turbine_spacing=2000, nOSS=2): - - if style=='grid': - # for now, this is very custom code specific to the DeepFarm project - farmxspacing = (nrows-1)*turbine_spacing - farmyspacing = (ncols-1)*turbine_spacing - - turbine_distances_from_centroid = [] - oss_distances_from_centroid = [] - for j in reversed(range(ncols)): - for i in range(nrows): - xpos = -(farmxspacing/2)+(i*turbine_spacing) - ypos = -(farmyspacing/2)+(j*turbine_spacing) - turbine_distances_from_centroid.append((xpos, ypos)) - - # add positions of two offshore substations (OSSs) - oss_distances_from_centroid.append((11000.0, 5000.0)) - oss_distances_from_centroid.append((11000.0, -5000.0)) - - if style=='shared': - turbine_xspacing = np.sqrt(2000**2-1000**2) - turbine_yspacing = 2000 - farmxspacing = turbine_xspacing*9 - farmyspacing = turbine_yspacing*9 - - turbine_distances_from_centroid = [] - oss_distances_from_centroid = [] - for j in reversed(range(ncols)): - for i in range(nrows): - xpos = -(farmxspacing/2)+(i*turbine_xspacing) - ypos = -(farmyspacing/2)+(j*turbine_yspacing) - 1000*np.sin(np.radians(30)) + 1000*(i%2) - turbine_distances_from_centroid.append((xpos, ypos)) - - # add positions of two offshore substations (OSSs) - oss_distances_from_centroid.append((5.5*turbine_xspacing, 2.0*turbine_yspacing+1000*np.sin(np.radians(30)))) - oss_distances_from_centroid.append((5.5*turbine_xspacing, -2.5*turbine_yspacing-1000*np.sin(np.radians(30)))) - - if style=='small-shared': - turbine_xspacing = np.sqrt(2000**2-1000**2) - turbine_yspacing = 2000 - farmxspacing = turbine_xspacing*1 - farmyspacing = turbine_yspacing*2 - - turbine_distances_from_centroid = [] - oss_distances_from_centroid = [] - for j in reversed(range(3)): - for i in range(2): - xpos = -(farmxspacing/2)+(i*turbine_xspacing) - ypos = -(farmyspacing/2)+(j*turbine_yspacing) - 1000*np.sin(np.radians(30)) + 1000*(i%2) - turbine_distances_from_centroid.append((xpos, ypos)) - - # add positions of two offshore substations (OSSs) - oss_distances_from_centroid.append((-0.5*turbine_xspacing, 2.0*turbine_yspacing-1000*np.sin(np.radians(30)))) - - - # create a copy of the global gdf and transform it into the easting/northing coordinate reference system - gdf_utm = self.gdf.copy().to_crs(self.target_crs) - xcentroid = gdf_utm.loc[gdf_utm['type']=='centroid'].centroid.x[0] - ycentroid = gdf_utm.loc[gdf_utm['type']=='centroid'].centroid.y[0] - - # create shapely Point objects of the turbine positions relative to the centroid, in the UTM CRS - turbine_geoms = [] - for i,(x,y) in enumerate(turbine_distances_from_centroid): - turbine_geoms.append( Point(xcentroid + x, ycentroid + y) ) - - oss_geoms = [] - for i,(x,y) in enumerate(oss_distances_from_centroid): - oss_geoms.append( Point(xcentroid + x, ycentroid + y) ) - - # make a new gdf to put the turbine data together - turbine_gdf = gpd.GeoDataFrame({'type': ['turbine']*len(turbine_geoms), 'geometry': turbine_geoms}, crs=self.target_crs) - # make a new gdf to put the substation data together - oss_gdf = gpd.GeoDataFrame({'type': 'substation', 'geometry': oss_geoms}, crs=self.target_crs) - # merge these two geodataframes together into one (best way I can find to "add" rows to a dataframe; ignoring index makes all indices a different number) - turbine_gdf = pd.concat([turbine_gdf, oss_gdf], ignore_index=True) - - # convert the turbine/oss coordinates back to regular latitude/longitude (EPSG: 4326) - turbine_gdf = turbine_gdf.to_crs('EPSG:4326') - - # add the turbine gdf to the global gdf - self.gdf = pd.concat([self.gdf, turbine_gdf], ignore_index=True) - - # add local variables in this method to the turbine_gdf to be used later (but don't need for the global gdf) - turbine_gdf.insert(2, 'easting_northing_geometry', turbine_geoms + oss_geoms) - turbine_gdf.insert(3, 'meters_from_centroid', turbine_distances_from_centroid + oss_distances_from_centroid) - - # save this new turbine_gdf for future use - self.turbine_gdf = turbine_gdf - - # make a layout CSV (used for WHaLE/WAVES) - self.makeLayoutCSV() - - - - def makeLayoutCSV(self, filename='layout_test.csv'): - - turbine_longs = [point.coords[0][0] for point in self.turbine_gdf.geometry] - turbine_lats = [point.coords[0][1] for point in self.turbine_gdf.geometry] - - self.turbine_gdf.insert(2, 'longitude', turbine_longs) - self.turbine_gdf.insert(3, 'latitude', turbine_lats) - - turbine_eastings = [point.coords[0][0] for point in self.turbine_gdf['easting_northing_geometry']] - turbine_northings = [point.coords[0][1] for point in self.turbine_gdf['easting_northing_geometry']] - - #self.turbine_gdf.insert(5, 'easting', turbine_eastings) - #self.turbine_gdf.insert(6, 'northing', turbine_northings) - - turbine_x_from_centroid = [point[0] for point in self.turbine_gdf['meters_from_centroid']] - turbine_y_from_centroid = [point[1] for point in self.turbine_gdf['meters_from_centroid']] - - self.turbine_gdf.insert(5, 'easting', turbine_x_from_centroid) - self.turbine_gdf.insert(6, 'northing', turbine_y_from_centroid) - - self.turbine_gdf.insert(8, 'floris_x', turbine_x_from_centroid) - self.turbine_gdf.insert(9, 'floris_y', turbine_y_from_centroid) - - columns = ['type', 'longitude', 'latitude', 'easting', 'northing', 'floris_x', 'floris_y'] - df = pd.DataFrame(self.turbine_gdf) - df.to_csv(filename, columns=columns) - - - def plotGDF(self, kwargs): - '''2D map-like plot''' - - if 'centroid' in kwargs: - centroid_settings = kwargs['centroid'] - if 'label' in centroid_settings: - centroid_label = 'centroid' - if 'map' in kwargs: - map_settings = kwargs['map'] - if 'farm' in kwargs: - farm_settings = kwargs['farm'] - - fig, ax = plt.subplots(1,1) - - if 'centroid' in kwargs: - self.gdf.loc[self.gdf['type']=='centroid'].plot(ax=ax, color=centroid_settings['color'], label=centroid_label) - - if 'boundary' in kwargs: - map_boundary = self.gdf.loc[self.gdf['type']=='California'].boundary - map_boundary.plot(ax=ax, color=map_settings['color']) - - if 'farm' in kwargs: - self.gdf.loc[self.gdf['type']=='turbine'].plot(ax=ax, color=farm_settings['turbine']['color'], label='turbine') - self.gdf.loc[self.gdf['type']=='substation'].plot(ax=ax, color=farm_settings['oss']['color'], label='substation') - - ax.set_xlabel('Longitude') - ax.set_ylabel('Latitude') - ax.legend() - - ax.set_xlim([-124.875, -124.55]) - ax.set_ylim([40.025, 40.25]) - - fig.tight_layout() - - # Some GeoPandas Help - # to plot just one entry of a geoseries: gdf.loc[[0],'geometry'].plot() - # to get the columns of a gdf: gdf.columns - # merging gdf's - # adding columns to gdf's - - return fig, ax - - - - def addPoints(self, ax, pointlist=[], kwargs={}): - - point_settings = kwargs['pointlist'] - - points = gpd.GeoDataFrame({'type':['nrel_channel','nrel_humboldt','nrel_crescent_city','hawaii'], - 'geometry': pointlist}, crs='EPSG:4326') - - points.plot(ax=ax, color=point_settings['color'], marker=point_settings['marker'], label=point_settings['label']) - - - def addState(self, ax, states=[], kwargs={}): - - for state in states: - state_settings=kwargs[state] - - state_geom = self.usa.loc[self.usa['type']==state] - if 'boundary' in state_settings: - state_geom = state_geom.boundary - - newstate = gpd.GeoDataFrame({'type':state, 'geometry':state_geom}, crs='EPSG:4326') - - newstate.plot(ax=ax, color=state_settings['color']) - - - - - - -""" -======= ->>>>>>> dev - if __name__ == '__main__': From 8b1d47d5560ed38c8af4dbcbe7ef404605124b1e Mon Sep 17 00:00:00 2001 From: lsirkis Date: Thu, 6 Nov 2025 15:45:44 -0700 Subject: [PATCH 15/15] Small change anchor.py cost dict for uniformity with other components -- removed MBL and unit_cost from anchor.cost dictionary to match methodology of other component cost dicts. --- famodel/anchors/AnchorDesign_temp.py | 5 ++++- famodel/anchors/anchor.py | 5 +++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/famodel/anchors/AnchorDesign_temp.py b/famodel/anchors/AnchorDesign_temp.py index 0747824a..2223096e 100644 --- a/famodel/anchors/AnchorDesign_temp.py +++ b/famodel/anchors/AnchorDesign_temp.py @@ -1,6 +1,9 @@ # -*- coding: utf-8 -*- """ -temp storage of different anchor sizing functions that use different optimization methods. Eventually can be converted into a full AnchorDesign class... +temp storage of different anchor sizing functions that use different +optimization methods. These were built-in methods to Anchor class. + +Eventually can be converted into a full AnchorDesign class... """ def getSizeAnchor(self, geom, geomKeys, geomBounds=None, loads=None, lambdap_con=[4, 8], diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py index 52ce884a..f0399135 100644 --- a/famodel/anchors/anchor.py +++ b/famodel/anchors/anchor.py @@ -1072,8 +1072,9 @@ def getCost(self, ms=None, mass_update=True): # Store results self.cost = { 'Material cost': cost, - 'MBL': MBL, - 'unit_cost': cost/self.mpAnchor.m } + #'MBL': MBL, + #'unit_cost': cost/self.mpAnchor.m + } return self.cost