- Data:
USGS NWIS and
-
USGS PEAKFQ
+
+
+
diff --git a/hydroshift/templates/footer.html b/hydroshift/templates/footer.html
index 8bac78e..81d64e1 100644
--- a/hydroshift/templates/footer.html
+++ b/hydroshift/templates/footer.html
@@ -1,18 +1,19 @@
diff --git a/hydroshift/utils/data_retrieval.py b/hydroshift/utils/data_retrieval.py
index d029899..71c1607 100644
--- a/hydroshift/utils/data_retrieval.py
+++ b/hydroshift/utils/data_retrieval.py
@@ -1,4 +1,7 @@
+from functools import cached_property
import logging
+import time
+import traceback
from typing import List
import numpy as np
@@ -9,8 +12,10 @@
from dataretrieval import NoSitesError, nwis
from scipy.stats import genpareto
-from hydroshift.consts import REGULATION_MAP
+from hydroshift.consts import REGULATION_MAP, MAX_CACHE_ENTRIES
+from hydroshift.errors import GageNotFoundException
from hydroshift.utils.common import group_consecutive_years
+logger = logging.getLogger(__name__)
class Gage:
@@ -18,12 +23,22 @@ class Gage:
def __init__(self, gage_id: str):
"""Construct class."""
- self.gage_id = gage_id
+ self.gage_id = self.validate_id(gage_id)
self.site_data = load_site_data(gage_id)
+ self.data_catalog = get_site_catalog(gage_id)
+
+ @staticmethod
+ def validate_id(idx: str):
+ if idx is None:
+ raise GageNotFoundException()
+ if not idx.isnumeric():
+ raise GageNotFoundException()
+ return idx
@property
@st.cache_data(
- hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)}
+ hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)},
+ max_entries=MAX_CACHE_ENTRIES
)
def latitude(self) -> float:
"""Latitude of gage."""
@@ -31,7 +46,8 @@ def latitude(self) -> float:
@property
@st.cache_data(
- hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)}
+ hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)},
+ max_entries=MAX_CACHE_ENTRIES
)
def longitude(self) -> float:
"""Longitude of gage."""
@@ -39,7 +55,8 @@ def longitude(self) -> float:
@property
@st.cache_data(
- hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)}
+ hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)},
+ max_entries=MAX_CACHE_ENTRIES
)
def elevation(self) -> float:
"""Elevation of gage."""
@@ -47,18 +64,23 @@ def elevation(self) -> float:
@property
@st.cache_data(
- hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)}
+ hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)},
+ max_entries=MAX_CACHE_ENTRIES
)
def mean_basin_elevation(self) -> float:
"""Average elevation of gage watershed."""
- row = [
- r for r in self.streamstats["characteristics"] if r["variableTypeID"] == 6
- ] # Get ELEV param
- return row[0]["value"]
+ try:
+ row = [
+ r for r in self.streamstats["characteristics"] if r["variableTypeID"] == 6
+ ] # Get ELEV param
+ return row[0]["value"]
+ except (KeyError, IndexError):
+ return None
@property
@st.cache_data(
- hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)}
+ hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)},
+ max_entries=MAX_CACHE_ENTRIES
)
def streamstats(self) -> pd.DataFrame:
"""Load AMS for this site."""
@@ -69,7 +91,8 @@ def streamstats(self) -> pd.DataFrame:
@property
@st.cache_data(
- hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)}
+ hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)},
+ max_entries=MAX_CACHE_ENTRIES
)
def ams(self) -> pd.DataFrame:
"""Load AMS for this site."""
@@ -87,14 +110,16 @@ def missing_dates_ams(self) -> list:
@property
@st.cache_data(
- hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)}
+ hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)},
+ max_entries=MAX_CACHE_ENTRIES
)
def flow_stats(self) -> pd.DataFrame:
"""Load flow statistics for this site."""
return get_flow_stats(self.gage_id)
@st.cache_data(
- hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)}
+ hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)},
+ max_entries=MAX_CACHE_ENTRIES
)
def get_daily_values(self, start_date: str, end_date: str) -> pd.DataFrame:
"""Load daily mean discharge for this site."""
@@ -104,17 +129,19 @@ def missing_dates_daily_values(self, start_date: str, end_date: str) -> list:
"""Get missing dates for mean daily value series."""
return check_missing_dates(self.get_daily_values(start_date, end_date), "daily")
+ @property
@st.cache_data(
- hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)}
+ hash_funcs={"hydroshift.utils.data_retrieval.Gage": lambda x: hash(x.gage_id)},
+ max_entries=MAX_CACHE_ENTRIES
)
- def get_monthly_values(self) -> pd.DataFrame:
+ def monthly_values(self) -> pd.DataFrame:
"""Load monthly mean discharge for this site."""
return get_monthly_values(self.gage_id)
@property
def missing_dates_monthly_values(self) -> list:
"""Get missing dates for mean monthly value series."""
- return check_missing_dates(self.get_monthly_values(), "monthly")
+ return check_missing_dates(self.monthly_values, "monthly")
def get_regulation_summary(self, major_codes=["3", "9"]) -> List[str]:
"""Run regulation summary for gage."""
@@ -155,13 +182,23 @@ def get_regulation_summary(self, major_codes=["3", "9"]) -> List[str]:
def raise_warnings(self):
"""Create any high level data warnings."""
- regulation_results = self.get_regulation_summary()
- if regulation_results["minor"]:
- for result in regulation_results["minor"]:
- st.warning(result)
- if regulation_results["major"]:
- for result in regulation_results["major"]:
- st.error(result)
+ if not self.ams_valid:
+ st.error("Gage has no annual maxima series data available")
+ if not self.dv_valid:
+ st.error("Gage has no daily value data available")
+ if not self.flow_stats_valid:
+ st.error("Gage has no flow statistics data available")
+ if not self.monthly_values_valid:
+ st.error("Gage has no monthly flow statistics data available")
+
+ if self.ams_valid:
+ regulation_results = self.get_regulation_summary()
+ if regulation_results["minor"]:
+ for result in regulation_results["minor"]:
+ st.warning(result)
+ if regulation_results["major"]:
+ for result in regulation_results["major"]:
+ st.error(result)
@property
def has_regional_skew(self) -> bool:
@@ -179,13 +216,59 @@ def regional_skew(self) -> float:
except (KeyError, IndexError):
return None
if val == 9999: # California Eq. from USGS SIR 2010-5260 NL-ELEV eq
+ if self.mean_basin_elevation is None:
+ return None
val = (0 - 0.62) + 1.3 * (
1 - np.exp(0 - ((self.mean_basin_elevation) / 6500) ** 2)
)
return val
+ @property
+ def ams_valid(self) -> bool:
+ """Whether this gage has AMS data."""
+ sub = self.data_catalog[self.data_catalog["data_type_cd"] == "pk"]
+ if len(sub) > 0:
+ if sub["count_nu"].values[0] > 0:
+ return True
+ return False
+
+ @property
+ def dv_valid(self) -> bool:
+ """Whether this gage has daily value data."""
+ sub = self.data_catalog[(self.data_catalog["data_type_cd"] == "dv") & (self.data_catalog["parm_cd"] == "00060")]
+ if len(sub) > 0:
+ if sub["count_nu"].values[0] > 0:
+ return True
+ return False
+
+ @property
+ def flow_stats_valid(self) -> bool:
+ """Whether this gage has flow statistics data."""
+ if self.flow_stats is None:
+ return False
+ return True
+
+ @property
+ def monthly_values_valid(self) -> bool:
+ """Whether this gage has flow statistics data."""
+ return self.monthly_values is not None
+
+ @property
+ def available_plots(self) -> list[str]:
+ plots = []
+ if self.flow_stats_valid:
+ plots.append("Daily Flow Statistics")
+ if self.ams_valid:
+ plots.extend(["Annual Peak Flow (AMS)", "Log-Pearson III (LP3) Analysis", "AMS Seasonal Ranking"])
+ if self.dv_valid:
+ plots.append("Daily Mean Streamflow")
+ if self.monthly_values_valid:
+ plots.append("Monthly Mean Streamflow")
+ return plots
-@st.cache_data
+
+
+@st.cache_data(max_entries=MAX_CACHE_ENTRIES)
def get_ams(gage_id):
"""Fetches Annual Maximum Series (AMS) peak flow data for a given gage."""
try:
@@ -194,8 +277,8 @@ def get_ams(gage_id):
else:
df = nwis.get_record(service="peaks", sites=[gage_id], ssl_check=True)
except NoSitesError:
- logging.warning(f"Peaks could not be found for gage id: {gage_id}")
- return {"peaks": None, "lp3": None, "missing_years": None}
+ logger.debug(f"Peaks could not be found for gage id: {gage_id}")
+ return pd.DataFrame()
df["season"] = ((df.index.month % 12 + 3) // 3).map(
{1: "Winter", 2: "Spring", 3: "Summer", 4: "Fall"}
@@ -205,26 +288,25 @@ def get_ams(gage_id):
return df
-@st.cache_data
+@st.cache_data(max_entries=MAX_CACHE_ENTRIES)
def get_flow_stats(gage_id):
"""Fetches flow statistics for a given gage."""
try:
df = nwis.get_stats(sites=gage_id, parameterCd="00060", ssl_check=True)[0]
except IndexError:
- logging.warning(f"Flow stats could not be found for gage_id: {gage_id}")
+ logger.debug(f"Flow stats could not be found for gage_id: {gage_id}")
return None
return df
-@st.cache_data
+@st.cache_data(max_entries=MAX_CACHE_ENTRIES)
def load_site_data(gage_number: str) -> dict:
"""Query NWIS for site information"""
try:
resp = nwis.get_record(sites=gage_number, service="site", ssl_check=True)
-
except ValueError:
- raise ValueError(f"Gage {gage_number} not found")
+ raise GageNotFoundException()
return {
"site_no": resp["site_no"].iloc[0],
@@ -237,35 +319,48 @@ def load_site_data(gage_number: str) -> dict:
"alt_datum_cd": resp["alt_datum_cd"].iloc[0],
}
+@st.cache_data(max_entries=MAX_CACHE_ENTRIES)
+def get_site_catalog(gage_number: str) -> dict:
+ """Query NWIS for site information"""
+ try:
+ df = nwis.what_sites(sites=gage_number, seriesCatalogOutput='true', ssl_check=True)[0]
+ except Exception as e:
+ logger.error("Error querying site: %s", e, exc_info=True)
+ return df
-@st.cache_data
+
+@st.cache_data(max_entries=MAX_CACHE_ENTRIES)
def get_daily_values(gage_id, start_date, end_date):
"""Fetches mean daily flow values for a given gage."""
try:
- dv = nwis.get_dv(gage_id, start_date, end_date, ssl_check=True)[0]
+ dv = nwis.get_dv(gage_id, start_date, end_date, ssl_check=True, parameterCd ="00060")[0]
except Exception:
- logging.warning(f"Daily Values could not be found for gage_id: {gage_id}")
+ logger.debug(f"Daily Values could not be found for gage_id: {gage_id}")
return None
return dv
-@st.cache_data
+@st.cache_data(max_entries=MAX_CACHE_ENTRIES)
def get_monthly_values(gage_id):
"""Fetches mean monthly flow values for a given gage and assigns a datetime column based on the year and month."""
try:
- mv = nwis.get_stats(gage_id, statReportType="monthly", ssl_check=True)[0]
+ mv = nwis.get_stats(gage_id, statReportType="monthly", ssl_check=True, parameterCd = "00060")[0]
except Exception:
- logging.warning(f"Monthly Values could not be found for gage_id: {gage_id}")
+ logger.debug(f"Monthly Values could not be found for gage_id: {gage_id}")
return None
mv = mv.rename(columns={"year_nu": "year", "month_nu": "month"})
mv["date"] = pd.to_datetime(mv[["year", "month"]].assign(day=1))
- mv = mv.sort_values("date")
+ mv = mv.sort_values("date").set_index("date")
+
+ full_range = pd.date_range(mv.index.min(), mv.index.max(), freq="MS")
+
+ mv = mv.reindex(full_range)
- return mv
+ return mv.reset_index(names="date")
def check_missing_dates(df, freq):
diff --git a/hydroshift/utils/jinja.py b/hydroshift/utils/jinja.py
index f087be9..ce3620d 100644
--- a/hydroshift/utils/jinja.py
+++ b/hydroshift/utils/jinja.py
@@ -19,7 +19,7 @@ def check_for_consts(template_name: str, context: dict) -> dict:
return context
-@st.cache_data
+@st.cache_data(max_entries=consts.MAX_CACHE_ENTRIES)
def render_template(template_name: str, context: dict = {}) -> str:
"""Load a template from the environment and format it."""
context = check_for_consts(template_name, context)
diff --git a/hydroshift/utils/plots.py b/hydroshift/utils/plots.py
index 2d84cfd..401ca31 100644
--- a/hydroshift/utils/plots.py
+++ b/hydroshift/utils/plots.py
@@ -6,6 +6,7 @@
from plotly.subplots import make_subplots
from scipy.stats import norm
+from hydroshift.consts import MAX_CACHE_ENTRIES
from hydroshift.utils.common import classify_regulation
from hydroshift.utils.ffa import LP3Analysis
@@ -88,6 +89,9 @@ def plot_flow_stats(stats_df, gage_id):
"""Plot Flow Statistics using Plotly with month-abbreviation x-axis labels."""
# Ensure data is sorted
# Create a datetime column
+ p_cols = ['p05_va', 'p10_va', 'p20_va', 'p25_va', 'p50_va', 'p75_va', 'p80_va', 'p90_va', 'p95_va']
+ stats_df[p_cols] = stats_df[p_cols].ffill()
+ stats_df[p_cols] = stats_df[p_cols].fillna(0)
stats_df["date"] = pd.to_datetime(
{
"year": 2000, # dummy leap year to support Feb 29
@@ -101,6 +105,7 @@ def plot_flow_stats(stats_df, gage_id):
stats_df["day_of_year"] = stats_df["month_nu"] * 30 + stats_df["day_nu"]
fig = go.Figure()
# Percentile bands
+
fig.add_trace(
go.Scatter(
x=stats_df["date"],
@@ -370,7 +375,7 @@ def plot_cpm_heatmap(pval_df: pd.DataFrame):
return fig
-@st.cache_data
+@st.cache_data(max_entries=MAX_CACHE_ENTRIES)
def combo_cpm(ams_df: pd.DataFrame, pval_df: pd.DataFrame, cps: dict = {}) -> Figure:
"""Plot a change point model with peak flows and statistical analysis."""
# Create subplots
@@ -442,7 +447,7 @@ def combo_cpm(ams_df: pd.DataFrame, pval_df: pd.DataFrame, cps: dict = {}) -> Fi
# Update layout
fig.update_layout(
- coloraxis=dict(colorscale=custom_color_scale, cmin=0.05, cmax=0.001),
+ coloraxis=dict(colorscale=custom_color_scale, cmax=0.05, cmin=0.001),
coloraxis_colorbar=dict(
orientation="h",
yanchor="bottom",
diff --git a/pyproject.toml b/pyproject.toml
index 38df551..5208099 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,6 +7,7 @@ requires-python = ">=3.12"
dependencies = [
"dataretrieval==1.0.11",
"folium==0.19.4",
+ "friendlywords>=1.2.0",
"jinja2>=3.1.6",
"kaleido==0.2.0",
"leafmap>=0.43.1",
@@ -18,7 +19,8 @@ dependencies = [
"radian>=0.6.13",
"rasterio>=1.4.3",
"scipy==1.15.2",
- "streamlit==1.42.2",
+ "streamlit==1.49.0",
+ "streamlit-cookies-manager>=0.2.0",
"streamlit-folium==0.24.0",
]
diff --git a/scripts/logo.py b/scripts/logo.py
new file mode 100644
index 0000000..e652d0e
--- /dev/null
+++ b/scripts/logo.py
@@ -0,0 +1,50 @@
+import os
+import matplotlib.colors as mcolors
+import matplotlib.patheffects as pe
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import norm
+
+from PIL import Image
+
+
+n = 100
+c1 = "#4287f5"
+c1_accent = mcolors.to_hex([c * 0.8 for c in mcolors.to_rgb(c1)])
+c2 = "#f5a742"
+c2_accent = mcolors.to_hex([c * 0.8 for c in mcolors.to_rgb(c2)])
+d1 = norm(1.5)
+d2 = norm(-1.5)
+s1 = d1.rvs(n)
+s2 = d2.rvs(n)
+fcs = [c1] * n + [c2] * n
+ecs = [c1_accent] * n + [c2_accent] * n
+combo = np.append(s1, s2)
+pdf_range = np.linspace(d1.mean() + (3 * d1.std()), d2.mean() - (3 * d2.std()), 1000)
+pdf1 = d1.pdf(pdf_range)
+pdf2 = d2.pdf(pdf_range)
+
+fig, axs = plt.subplots(ncols=2, gridspec_kw={"width_ratios": [3, 1], "wspace": 0.01}, figsize=(4, 2))
+axs[0].scatter(range(len(combo)), combo, ec=ecs, fc=fcs, lw=0.35, s=2.2)
+axs[0].axvline(n + 0.5, ls="dashed", c="k", alpha=0.3, lw=0.7)
+axs[1].fill_betweenx(pdf_range, 0, pdf1, fc=c1, ec=c1_accent)
+axs[1].fill_betweenx(pdf_range, 0, pdf2, fc=c2, ec=c2_accent)
+for ax in axs:
+ ax.set_xticks([])
+ ax.set_yticks([])
+ ax.set_frame_on(False)
+fig.tight_layout()
+fig.savefig("hydroshift/images/logo_base.png", transparent=True, dpi=600, bbox_inches="tight", pad_inches=0)
+
+
+fig, ax = plt.subplots(figsize=(1, 1))
+ax.fill_between(pdf_range, 0, pdf1, fc=c1, ec=c1_accent)
+ax.fill_between(pdf_range, 0, pdf2, fc=c2, ec=c2_accent)
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_frame_on(False)
+fig.tight_layout()
+fig.savefig("hydroshift/images/tmp.png", transparent=True, dpi=300, bbox_inches="tight", pad_inches=0)
+png_image = Image.open("hydroshift/images/tmp.png")
+png_image.save("hydroshift/images/favicon.ico", format='ICO', sizes=[(32, 32), (64, 64)])
+os.remove("hydroshift/images/tmp.png")
diff --git a/uv.lock b/uv.lock
index db553f3..ef7d559 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,14 +1,14 @@
version = 1
-revision = 1
+revision = 2
requires-python = ">=3.12"
[[package]]
name = "affine"
version = "2.4.0"
source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132 }
+sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" }
wheels = [
- { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662 },
+ { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662, upload-time = "2023-01-19T23:44:28.833Z" },
]
[[package]]
@@ -22,9 +22,18 @@ dependencies = [
{ name = "packaging" },
{ name = "typing-extensions", marker = "python_full_version < '3.14'" },
]
-sdist = { url = "https://files.pythonhosted.org/packages/16/b1/f2969c7bdb8ad8bbdda031687defdce2c19afba2aa2c8e1d2a17f78376d8/altair-5.5.0.tar.gz", hash = "sha256:d960ebe6178c56de3855a68c47b516be38640b73fb3b5111c2a9ca90546dd73d", size = 705305 }
+sdist = { url = "https://files.pythonhosted.org/packages/16/b1/f2969c7bdb8ad8bbdda031687defdce2c19afba2aa2c8e1d2a17f78376d8/altair-5.5.0.tar.gz", hash = "sha256:d960ebe6178c56de3855a68c47b516be38640b73fb3b5111c2a9ca90546dd73d", size = 705305, upload-time = "2024-11-23T23:39:58.542Z" }
wheels = [
- { url = "https://files.pythonhosted.org/packages/aa/f3/0b6ced594e51cc95d8c1fc1640d3623770d01e4969d29c0bd09945fafefa/altair-5.5.0-py3-none-any.whl", hash = "sha256:91a310b926508d560fe0148d02a194f38b824122641ef528113d029fcd129f8c", size = 731200 },
+ { url = "https://files.pythonhosted.org/packages/aa/f3/0b6ced594e51cc95d8c1fc1640d3623770d01e4969d29c0bd09945fafefa/altair-5.5.0-py3-none-any.whl", hash = "sha256:91a310b926508d560fe0148d02a194f38b824122641ef528113d029fcd129f8c", size = 731200, upload-time = "2024-11-23T23:39:56.4Z" },
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
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