diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..cd26117
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,13 @@
+
+.spyproject/*
+
+data/*
+figs/*
+output/*
+stats/*
+__pycache__/*
+
+*/.ipynb_checkpoints/*.ipynb
+.ipynb_checkpoints
+
+*.pyc
diff --git a/AllFunctions.py b/AllFunctions.py
deleted file mode 100644
index 2f50353..0000000
--- a/AllFunctions.py
+++ /dev/null
@@ -1,515 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Mar 8 09:16:54 2018
-
-@author: u1490431
-"""
-'''
- All imports and all functions needed for Distraction Paper
- Run this script before anything else
-
- Contains:
-
- loadmatfile, distractedOrNot, remcheck, distractionCalc2...
-'''
-
-# Import modules --------------------------------------------------------
-
-import numpy as np
-import scipy.io as sio
-
-import matplotlib.pyplot as plt
-import pandas as pd
-import os
-import matplotlib as mpl
-import itertools
-import matplotlib.mlab as mlab
-#import seaborn as sb
-import statistics as stats
-# Set plot parameters and styles
-
-#sb.set_context("paper")
-#sb.set_style("white")
-
-# Plot settings, font / size / styles
-Calibri = {'fontname':'Calibri'}
-Size = {'fontsize': 20}
-label_size = 14
-plt.rcParams['xtick.labelsize'] = label_size
-plt.rcParams['ytick.labelsize'] = label_size
-
-# Functions -------------------------------------------------------------
-def mapTTLs(matdict):
- for x in ['LiA_', 'La2_']:
- try:
- licks = getattr(matdict['output'], x)
- except:
- print('File has no ' + x)
-
- lickson = licks.onset
- licksoff = licks.offset
-
- return lickson, licksoff
-
-
-'''
- Loads a matlab converted TDT file, produces output session
- dictionary of data from Synapse tankfiles. Photometry data
- and TTLs for licks, distractors etc.
-
-'''
-
-def loadmatfile(file):
- a = sio.loadmat(file, squeeze_me=True, struct_as_record=False)
- print(type(a))
- sessiondict = {}
- sessiondict['blue'] = a['output'].blue
- sessiondict['uv'] = a['output'].uv
- sessiondict['fs'] = a['output'].fs
-
- try:
-
- sessiondict['licks'] = a['output'].licks.onset
- sessiondict['licks_off'] = a['output'].licks.offset
- except: ## find which error it is
-
- sessiondict['licks'], sessiondict['licks_off'] = mapTTLs(a)
-
-
-
- sessiondict['distractors'] = distractionCalc2(sessiondict['licks'])
-
-# #write distracted or not to produce 2 lists of times, distracted and notdistracted
- #distracted, notdistracted= distractedOrNot(sessiondict['distractors'], sessiondict['licks'])
-#
- sessiondict['distracted'], sessiondict['notdistracted'] = distractedOrNot(sessiondict['distractors'], sessiondict['licks'])
- # sessiondict['notdistracted'] = notdistracted
-
- # ''' sessiondict['lickRuns'] = lickRunCalc(sessiondict['licks']) '''
-
- return sessiondict
-# -----------------------------------------------------------------
-
-def distractedOrNot(distractors, licks):
- distracted = []
- notdistracted = []
- lickList = []
- for l in licks:
- lickList.append(l)
-
-
- for index, distractor in enumerate(distractors):
- if distractor in licks:
-
- ind = lickList.index(distractor)
- try:
- if (licks[ind+1] - licks[ind]) > 1:
- distracted.append(licks[ind])
- else:
- if (licks[ind+1] - licks[ind]) < 1:
- notdistracted.append(licks[ind])
- except IndexError:
- print('last lick was a distractor!!!')
- distracted.append(licks[ind])
-
- return(distracted, notdistracted)
-
-
-def remcheck(val, range1, range2):
- # function checks whether value is within range of two decimels
- if (range1 < range2):
- if (val > range1) and (val < range2):
- return True
- else:
- return False
- else:
- if (val > range1) or (val < range2):
- return True
- else:
- return False
-
-
-def distractionCalc2(licks, pre=1, post=1):
- licks = np.insert(licks, 0, 0)
- b = 0.001
- d = []
- idx = 3
-
- while idx < len(licks):
- if licks[idx]-licks[idx-2] < 1 and remcheck(b, licks[idx-2] % 1, licks[idx] % 1) == False:
- d.append(licks[idx])
- b = licks[idx] % 1
- idx += 1
- try:
- while licks[idx]-licks[idx-1] < 1:
- b = licks[idx] % 1
- idx += 1
- except IndexError:
- pass
- else:
- idx +=1
-# print(len(d))
-
-# print(d[-1])
- if d[-1] > 3599:
- d = d[:-1]
-
-# print(len(d))
-
- return d
-
-
-# LickCalc ============================================================
-# Looking at function from Murphy et al (2017)
-
-"""
-This function will calculate data for bursts from a train of licks. The threshold
-for bursts and clusters can be set. It returns all data as a dictionary.
-"""
-def lickCalc(licks, offset = [], burstThreshold = 0.25, runThreshold = 10,
- binsize=60, histDensity = False):
-
- # makes dictionary of data relating to licks and bursts
- if type(licks) != np.ndarray or type(offset) != np.ndarray:
- try:
- licks = np.array(licks)
- offset = np.array(offset)
- except:
- print('Licks and offsets need to be arrays and unable to easily convert.')
- return
-
- lickData = {}
-
- if len(offset) > 0:
- lickData['licklength'] = offset - licks
- lickData['longlicks'] = [x for x in lickData['licklength'] if x > 0.3]
- else:
- lickData['licklength'] = []
- lickData['longlicks'] = []
-
- lickData['licks'] = np.concatenate([[0], licks])
- lickData['ilis'] = np.diff(lickData['licks'])
- lickData['freq'] = 1/np.mean([x for x in lickData['ilis'] if x < burstThreshold])
- lickData['total'] = len(licks)
-
- # Calculates start, end, number of licks and time for each BURST
- lickData['bStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
- lickData['bInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
- lickData['bEnd'] = [lickData['licks'][i-1] for i in lickData['bInd'][1:]]
- lickData['bEnd'].append(lickData['licks'][-1])
-
- lickData['bLicks'] = np.diff(lickData['bInd'] + [len(lickData['licks'])])
- lickData['bTime'] = np.subtract(lickData['bEnd'], lickData['bStart'])
- lickData['bNum'] = len(lickData['bStart'])
- if lickData['bNum'] > 0:
- lickData['bMean'] = np.nanmean(lickData['bLicks'])
- else:
- lickData['bMean'] = 0
-
- lickData['bILIs'] = [x for x in lickData['ilis'] if x > burstThreshold]
-
- lickData['bILIs'] = [x for x in lickData['ilis'] if x > burstThreshold]
-
- # Calculates start, end, number of licks and time for each RUN
- lickData['rStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
- lickData['rInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
- lickData['rEnd'] = [lickData['licks'][i-1] for i in lickData['rInd'][1:]]
- lickData['rEnd'].append(lickData['licks'][-1])
-
- lickData['rLicks'] = np.diff(lickData['rInd'] + [len(lickData['licks'])])
- lickData['rTime'] = np.subtract(lickData['rEnd'], lickData['rStart'])
- lickData['rNum'] = len(lickData['rStart'])
-
- lickData['rILIs'] = [x for x in lickData['ilis'] if x > runThreshold]
- try:
- lickData['hist'] = np.histogram(lickData['licks'][1:],
- range=(0, 3600), bins=int((3600/binsize)),
- density=histDensity)[0]
- except TypeError:
- print('Problem making histograms of lick data')
-
- return lickData
-
-def asnumeric(s):
- try:
- x = float(s)
- return x
- except ValueError:
- return float('nan')
-
-def medfilereader(filename, varsToExtract = 'all',
- sessionToExtract = 1,
- verbose = False,
- remove_var_header = False):
- if varsToExtract == 'all':
- numVarsToExtract = np.arange(0,26)
- else:
- numVarsToExtract = [ord(x)-97 for x in varsToExtract]
-
- f = open(filename, 'r')
- f.seek(0)
- filerows = f.readlines()[8:]
- datarows = [asnumeric(x) for x in filerows]
- matches = [i for i,x in enumerate(datarows) if x == 0.3]
- if sessionToExtract > len(matches):
- print('Session ' + str(sessionToExtract) + ' does not exist.')
- if verbose == True:
- print('There are ' + str(len(matches)) + ' sessions in ' + filename)
- print('Analyzing session ' + str(sessionToExtract))
-
- varstart = matches[sessionToExtract - 1]
- medvars = [[] for n in range(26)]
-
- k = int(varstart + 27)
- for i in range(26):
- medvarsN = int(datarows[varstart + i + 1])
-
- medvars[i] = datarows[k:k + int(medvarsN)]
- k = k + medvarsN
-
- if remove_var_header == True:
- varsToReturn = [medvars[i][1:] for i in numVarsToExtract]
- else:
- varsToReturn = [medvars[i] for i in numVarsToExtract]
-
- if np.shape(varsToReturn)[0] == 1:
- varsToReturn = varsToReturn[0]
- return varsToReturn
-
-
-def MetaExtractor (metafile):
- f = open(metafile, 'r')
- f.seek(0)
- Metafilerows = f.readlines()[1:]
- tablerows = []
-
- for row in Metafilerows:
- items = row.split(',')
- tablerows.append(items)
-
- MedFilenames, RatID, Date, Day, Session, Drug, TotLicks, Distractions, \
- NonDistractions, PercentDistracted = [], [], [], [], [], [], [], [], [], []
-
- for i, lst in enumerate(tablerows):
- MedFilenames = MedFilenames + [lst[1]]
- RatID = RatID + [lst[2]]
- Date = Date + [lst[3]]
- #Day = Day + [lst[3]]
- Session = Session + [lst[4]]
- # Drug = Drug + [lst[5]]
- TotLicks = TotLicks + [lst[6]]
- Distractions = Distractions + [lst[8]]
- #NonDistractions = NonDistractions + [lst[8]]
- PercentDistracted = PercentDistracted + [lst[9]]
-
- return ({'MedFilenames':MedFilenames, 'RatID':RatID, 'Date':Date, 'Session':Session, \
- 'TotLicks':TotLicks, 'Distractions':Distractions, \
- 'PercentDistracted':PercentDistracted})
-
-def time2samples(self):
- tick = self.output.Tick.onset
- maxsamples = len(tick)*int(self.fs)
- if (len(self.data) - maxsamples) > 2*int(self.fs):
- print('Something may be wrong with conversion from time to samples')
- print(str(len(self.data) - maxsamples) + ' samples left over. This is more than double fs.')
-
- self.t2sMap = np.linspace(min(tick), max(tick), maxsamples)
-
-def snipper(data, timelock, fs = 1, t2sMap = [], preTrial=10, trialLength=30,
- adjustBaseline = True,
- bins = 0):
-
- if len(timelock) == 0:
- print('No events to analyse! Quitting function.')
- raise Exception('no events')
- nSnips = len(timelock)
- pps = int(fs) # points per sample
- pre = int(preTrial*pps)
-# preABS = preTrial
- length = int(trialLength*pps)
-# converts events into sample numbers
- event=[]
- if len(t2sMap) > 1:
- for x in timelock:
- event.append(np.searchsorted(t2sMap, x, side="left"))
- else:
- event = [x*fs for x in timelock]
-
- avgBaseline = []
- snips = np.empty([nSnips,length])
-
- for i, x in enumerate(event):
- start = int(x) - pre
- avgBaseline.append(np.mean(data[start : start + pre]))
-# print(x)
- try:
- snips[i] = data[start : start+length]
- except: # Deals with recording arrays that do not have a full final trial
- snips = snips[:-1]
- avgBaseline = avgBaseline[:-1]
- nSnips = nSnips-1
-
- if adjustBaseline == True:
- snips = np.subtract(snips.transpose(), avgBaseline).transpose()
- snips = np.divide(snips.transpose(), avgBaseline).transpose()
-
- if bins > 0:
- if length % bins != 0:
- snips = snips[:,:-(length % bins)]
- totaltime = snips.shape[1] / int(fs)
- snips = np.mean(snips.reshape(nSnips,bins,-1), axis=2)
- pps = bins/totaltime
-
- return snips, pps
-
-def trialsFig(ax, trials1, trials2, pps=1, preTrial=10, scale=5, noiseindex = [],
- plotnoise=True,
- eventText='event',
- ylabel=''):
-
- if len(noiseindex) > 0:
- trialsNoise = np.array([i for (i,v) in zip(trials1, noiseindex) if v])
- trials1 = np.array([i for (i,v) in zip(trials1, noiseindex) if not v])
- if plotnoise == True:
- ax.plot(trialsNoise.transpose(), c='red', alpha=0.4)
-
- ax.plot(trials1.transpose(), c='lightblue', alpha=0.6)
-
-
- ax.plot(trials2.transpose(), c='thistle', alpha=0.6)
- ax.plot(np.mean(trials2, axis=0), c='purple', linewidth=2)
- ax.plot(np.mean(trials1,axis=0), c='blue', linewidth=2)
- ax.set(ylabel = ylabel)
- ax.xaxis.set_visible(False)
-
- scalebar = scale * pps
-
- yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
- scalebary = (yrange / 10) + ax.get_ylim()[0]
- scalebarx = [ax.get_xlim()[1] - scalebar, ax.get_xlim()[1]]
-
- ax.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
- ax.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), str(scale) +' s', ha='center',va='top', **Calibri, **Size)
-
- ax.spines['right'].set_visible(False)
- ax.spines['top'].set_visible(False)
- ax.spines['bottom'].set_visible(False)
-
- xevent = pps * preTrial
- ax.plot([xevent, xevent],[ax.get_ylim()[0], ax.get_ylim()[1] - yrange/20],'--')
- ax.text(xevent, ax.get_ylim()[1], eventText, ha='center',va='bottom', **Calibri, **Size)
-
- return ax
-
-
-
-
-def med_abs_dev(data, b=1.4826):
- median = np.median(data)
- devs = [abs(i-median) for i in data]
- mad = np.median(devs)*b
-
- return mad
-
-def findnoise(data, background, t2sMap = [], fs = 1, bins=0, method='sd'):
-
- bgSnips, _ = snipper(data, background, t2sMap=t2sMap, fs=fs, bins=bins)
-
- if method == 'sum':
- bgSum = [np.sum(abs(i)) for i in bgSnips]
- bgMAD = med_abs_dev(bgSum)
- bgMean = np.mean(bgSum)
- elif method == 'sd':
- bgSD = [np.std(i) for i in bgSnips]
- bgMAD = med_abs_dev(bgSD)
- bgMean = np.mean(bgSD)
-
- return bgMAD, bgMean
-
-
-def makerandomevents(minTime, maxTime, spacing = 77, n=100):
- events = []
- total = maxTime-minTime
- start = 0
- for i in np.arange(0,n):
- if start > total:
- start = start - total
- events.append(start)
- start = start + spacing
- events = [i+minTime for i in events]
- return events
-
-
-def makephotoTrials(self, bins, events, threshold=10):
- bgMAD = findnoise(self.data, self.randomevents,
- t2sMap = self.t2sMap, fs = self.fs, bins=bins,
- method='sum')
- blueTrials, self.pps = snipper(self.data, events,
- t2sMap = self.t2sMap, fs = self.fs, bins=bins)
- UVTrials, self.pps = snipper(self.dataUV, events,
- t2sMap = self.t2sMap, fs = self.fs, bins=bins)
- sigSum = [np.sum(abs(i)) for i in blueTrials]
- sigSD = [np.std(i) for i in blueTrials]
- noiseindex = [i > bgMAD*threshold for i in sigSum]
-
- return blueTrials, UVTrials, noiseindex
-
-
-def removenoise(snipsIn, noiseindex):
- snipsOut = np.array([x for (x,v) in zip(snipsIn, noiseindex) if not v])
- return snipsOut
-
-def trialsMultShadedFig(ax, trials, pps = 1, scale = 5, preTrial = 10,
- eventText = 'event', ylabel = '',
- linecolor=['purple', 'blue'], errorcolor=['thistle', 'lightblue'],
- title=''):
-
- for i in [0, 1]:
- yerror = [np.std(i)/np.sqrt(len(i)) for i in trials[i].T]
- y = np.mean(trials[i],axis=0)
- x = np.arange(0,len(y))
-
- ax.plot(x, y, c=linecolor[i], linewidth=2)
-
- errorpatch = ax.fill_between(x, y-yerror, y+yerror, color=errorcolor[i], alpha=0.8)
-
- ax.set(ylabel = ylabel)
- ax.xaxis.set_visible(False)
-
- scalebar = scale * pps
-
- yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
- scalebary = (yrange / 10) + ax.get_ylim()[0]
- scalebarx = [ax.get_xlim()[1] - scalebar, ax.get_xlim()[1]]
-
- ax.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2) # below in '' = 5
- ax.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '5 s', ha='center',va='top', **Calibri, **Size)
-
- ax.spines['right'].set_visible(False)
- ax.spines['top'].set_visible(False)
- ax.spines['bottom'].set_visible(False)
-
- xevent = pps * preTrial
- ax.plot([xevent, xevent],[ax.get_ylim()[0], ax.get_ylim()[1] - yrange/20],'--')
- ax.text(xevent, ax.get_ylim()[1], eventText, ha='center',va='bottom', **Calibri, **Size)
- ax.set_title(title, fontsize=14)
-
- return ax, errorpatch
-
-def nearestevents(timelock, events, preTrial=10, trialLength=30):
-# try:
-# nTrials = len(timelock)
-# except TypeError:
-# nTrials = 1
- data = []
- start = [x - preTrial for x in timelock]
- end = [x + trialLength - preTrial for x in start]
- for start, end in zip(start, end):
- data.append([x for x in events if (x > start) & (x < end)])
- for i, x in enumerate(data):
- data[i] = x - timelock[i]
-
- return data
-
-
diff --git a/CORRECTIONS_FILES.py b/CORRECTIONS_FILES.py
deleted file mode 100644
index baef16e..0000000
--- a/CORRECTIONS_FILES.py
+++ /dev/null
@@ -1,2426 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Jul 31 15:39:40 2019
-
-@author: kate
-"""
-
-## UPDATED CORRECTIONS FILE
-
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri May 25 09:08:16 2018
-@author: u1490431
-"""
-
-"""
-Chapter 4 - Distraction and photometry in VTA
-"""
-
-# Import modules --------------------------------------------------------
-
-import numpy as np
-import scipy.io as sio
-
-import matplotlib.pyplot as plt
-import pandas as pd
-import os
-import matplotlib as mpl
-import itertools
-import matplotlib.mlab as mlab
-#import seaborn as sb
-import statistics as stats
-
-# Functions -------------------------------------------------------------
-def mapTTLs(matdict):
- for x in ['LiA_', 'La2_']:
- try:
- licks = getattr(matdict['output'], x)
- except:
- print('File has no ' + x)
-
- lickson = licks.onset
- licksoff = licks.offset
-
- return lickson, licksoff
-
-
-def loadmatfile(file):
- a = sio.loadmat(file, squeeze_me=True, struct_as_record=False)
- print(type(a))
- sessiondict = {}
- sessiondict['blue'] = a['output'].blue
- sessiondict['uv'] = a['output'].uv
- sessiondict['fs'] = a['output'].fs
-
- try:
-
- sessiondict['licks'] = a['output'].licks.onset
- sessiondict['licks_off'] = a['output'].licks.offset
- except: ## find which error it is
-
- sessiondict['licks'], sessiondict['licks_off'] = mapTTLs(a)
-
-
-
- sessiondict['distractors'] = distractionCalc2(sessiondict['licks'])
-
-# #write distracted or not to produce 2 lists of times, distracted and notdistracted
- #distracted, notdistracted= distractedOrNot(sessiondict['distractors'], sessiondict['licks'])
-#
- sessiondict['distracted'], sessiondict['notdistracted'] = distractedOrNot(sessiondict['distractors'], sessiondict['licks'])
- # sessiondict['notdistracted'] = notdistracted
-
- # ''' sessiondict['lickRuns'] = lickRunCalc(sessiondict['licks']) '''
-
- return sessiondict
-
-def distractedOrNot(distractors, licks):
- distracted = []
- notdistracted = []
- lickList = []
- for l in licks:
- lickList.append(l)
-
-
- for index, distractor in enumerate(distractors):
- if distractor in licks:
-
- ind = lickList.index(distractor)
- try:
- if (licks[ind+1] - licks[ind]) > 1:
- distracted.append(licks[ind])
- else:
- if (licks[ind+1] - licks[ind]) < 1:
- notdistracted.append(licks[ind])
- except IndexError:
- print('last lick was a distractor!!!')
- distracted.append(licks[ind])
-
- return(distracted, notdistracted)
-
-
-def remcheck(val, range1, range2):
- # function checks whether value is within range of two decimels
- if (range1 < range2):
- if (val > range1) and (val < range2):
- return True
- else:
- return False
- else:
- if (val > range1) or (val < range2):
- return True
- else:
- return False
-
-
-def distractionCalc2(licks, pre=1, post=1):
- licks = np.insert(licks, 0, 0)
- b = 0.001
- d = []
- idx = 3
-
- while idx < len(licks):
- if licks[idx]-licks[idx-2] < 1 and remcheck(b, licks[idx-2] % 1, licks[idx] % 1) == False:
- d.append(licks[idx])
- b = licks[idx] % 1
- idx += 1
- try:
- while licks[idx]-licks[idx-1] < 1:
- b = licks[idx] % 1
- idx += 1
- except IndexError:
- pass
- else:
- idx +=1
-# print(len(d))
-
-# print(d[-1])
- if d[-1] > 3599:
- d = d[:-1]
-
-# print(len(d))
-
- return d
-
-# LickCalc ============================================================
-# Looking at function from Murphy et al (2017)
-
-"""
-This function will calculate data for bursts from a train of licks. The threshold
-for bursts and clusters can be set. It returns all data as a dictionary.
-"""
-def lickCalc(licks, offset = [], burstThreshold = 0.25, runThreshold = 10,
- binsize=60, histDensity = False):
-
- # makes dictionary of data relating to licks and bursts
- if type(licks) != np.ndarray or type(offset) != np.ndarray:
- try:
- licks = np.array(licks)
- offset = np.array(offset)
- except:
- print('Licks and offsets need to be arrays and unable to easily convert.')
- return
-
- lickData = {}
-
- if len(offset) > 0:
- lickData['licklength'] = offset - licks
- lickData['longlicks'] = [x for x in lickData['licklength'] if x > 0.3]
- else:
- lickData['licklength'] = []
- lickData['longlicks'] = []
-
- lickData['licks'] = np.concatenate([[0], licks])
- lickData['ilis'] = np.diff(lickData['licks'])
- lickData['freq'] = 1/np.mean([x for x in lickData['ilis'] if x < burstThreshold])
- lickData['total'] = len(licks)
-
- # Calculates start, end, number of licks and time for each BURST
- lickData['bStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
- lickData['bInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
- lickData['bEnd'] = [lickData['licks'][i-1] for i in lickData['bInd'][1:]]
- lickData['bEnd'].append(lickData['licks'][-1])
-
- lickData['bLicks'] = np.diff(lickData['bInd'] + [len(lickData['licks'])])
- lickData['bTime'] = np.subtract(lickData['bEnd'], lickData['bStart'])
- lickData['bNum'] = len(lickData['bStart'])
- if lickData['bNum'] > 0:
- lickData['bMean'] = np.nanmean(lickData['bLicks'])
- else:
- lickData['bMean'] = 0
-
- lickData['bILIs'] = [x for x in lickData['ilis'] if x > burstThreshold]
-
- lickData['bILIs'] = [x for x in lickData['ilis'] if x > burstThreshold]
-
- # Calculates start, end, number of licks and time for each RUN
- lickData['rStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
- lickData['rInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
- lickData['rEnd'] = [lickData['licks'][i-1] for i in lickData['rInd'][1:]]
- lickData['rEnd'].append(lickData['licks'][-1])
-
- lickData['rLicks'] = np.diff(lickData['rInd'] + [len(lickData['licks'])])
- lickData['rTime'] = np.subtract(lickData['rEnd'], lickData['rStart'])
- lickData['rNum'] = len(lickData['rStart'])
-
- lickData['rILIs'] = [x for x in lickData['ilis'] if x > runThreshold]
- try:
- lickData['hist'] = np.histogram(lickData['licks'][1:],
- range=(0, 3600), bins=int((3600/binsize)),
- density=histDensity)[0]
- except TypeError:
- print('Problem making histograms of lick data')
-
- return lickData
-
-def asnumeric(s):
- try:
- x = float(s)
- return x
- except ValueError:
- return float('nan')
-
-def time2samples(self):
- tick = self.output.Tick.onset
- maxsamples = len(tick)*int(self.fs)
- if (len(self.data) - maxsamples) > 2*int(self.fs):
- print('Something may be wrong with conversion from time to samples')
- print(str(len(self.data) - maxsamples) + ' samples left over. This is more than double fs.')
-
- self.t2sMap = np.linspace(min(tick), max(tick), maxsamples)
-
-def snipper(data, timelock, fs = 1, t2sMap = [], preTrial=10, trialLength=30,
- adjustBaseline = True,
- bins = 0):
-
- if len(timelock) == 0:
- print('No events to analyse! Quitting function.')
- raise Exception('no events')
- nSnips = len(timelock)
- pps = int(fs) # points per sample
- pre = int(preTrial*pps)
-# preABS = preTrial
- length = int(trialLength*pps)
-# converts events into sample numbers
- event=[]
- if len(t2sMap) > 1:
- for x in timelock:
- event.append(np.searchsorted(t2sMap, x, side="left"))
- else:
- event = [x*fs for x in timelock]
-
- avgBaseline = []
- snips = np.empty([nSnips,length])
-
- for i, x in enumerate(event):
- start = int(x) - pre
- avgBaseline.append(np.mean(data[start : start + pre]))
-# print(x)
- try:
- snips[i] = data[start : start+length]
- except: # Deals with recording arrays that do not have a full final trial
- snips = snips[:-1]
- avgBaseline = avgBaseline[:-1]
- nSnips = nSnips-1
-
- if adjustBaseline == True:
- snips = np.subtract(snips.transpose(), avgBaseline).transpose()
- snips = np.divide(snips.transpose(), avgBaseline).transpose()
-
- if bins > 0:
- if length % bins != 0:
- snips = snips[:,:-(length % bins)]
- totaltime = snips.shape[1] / int(fs)
- snips = np.mean(snips.reshape(nSnips,bins,-1), axis=2)
- pps = bins/totaltime
-
- return snips, pps
-
-def makerandomevents(minTime, maxTime, spacing = 77, n=100):
- events = []
- total = maxTime-minTime
- start = 0
- for i in np.arange(0,n):
- if start > total:
- start = start - total
- events.append(start)
- start = start + spacing
- events = [i+minTime for i in events]
- return events
-
-def med_abs_dev(data, b=1.4826):
- median = np.median(data)
- devs = [abs(i-median) for i in data]
- mad = np.median(devs)*b
-
- return mad
-
-def findnoise(data, background, t2sMap = [], fs = 1, bins=0, method='sd'):
-
- bgSnips, _ = snipper(data, background, t2sMap=t2sMap, fs=fs, bins=bins)
-
- if method == 'sum':
- bgSum = [np.sum(abs(i)) for i in bgSnips]
- bgMAD = med_abs_dev(bgSum)
- bgMean = np.mean(bgSum)
- elif method == 'sd':
- bgSD = [np.std(i) for i in bgSnips]
- bgMAD = med_abs_dev(bgSD)
- bgMean = np.mean(bgSD)
-
- return bgMAD, bgMean
-# Distracted or not peaks
-# Manuall add the lists here
-# Start with THPH1 and 2 lick days
-# Then THPH1 and 2 distraction
-# Then THPH1 and 2 habituation
-
-# WHICH RATS DID NOT HAVE SIGNAL?
-# THPH1 AND 2
-# Lick day
-TDTfiles_thph_lick = ['thph1-1_lick6', 'thph1-2_lick6', 'thph1-3_lick6', 'thph1-4_lick6', 'thph1-5_lick6',\
- 'thph1-6_lick6', 'thph2-1_lick3', 'thph2-2_lick3','thph2-3_lick3','thph2-4_lick3', \
- 'thph2-5_lick3','thph2-6_lick3', 'thph2-7_lick6', 'thph2-8_lick6']
-
-# Modelled distractors change variable names for this script or script section
-# Distraction day
-TDTfiles_thph_dis = ['thph1-3_distraction1', \
- 'thph1-4_distraction1','thph1-5_distraction1','thph1-6_distraction1', \
- 'thph2-1_distraction', 'thph2-2_distraction', 'thph2-3_distraction', \
- 'thph2-4_distraction', 'thph2-5_distraction', 'thph2-6_distraction', \
- 'thph2-7_distraction', 'thph2-8_distraction'] #'thph1-1_distraction1', 'thph1-2_distraction1'
-
-# Habituation day
-TDTfiles_thph_hab = ['thph1-3_distraction2',\
- 'thph1-4_distraction2', 'thph1-5_distraction2','thph1-6_distraction2',\
- 'thph2-1_habituation', 'thph2-2_habituation', 'thph2-3_habituation', \
- 'thph2-4_habituation', 'thph2-5_habituation', 'thph2-6_habituation', \
- 'thph2-7_habituation'] #['thph1-1_distraction2','thph1-2_distraction2',
-
-
-TDTfilepath = '/Volumes/KP_HARD_DRI/All_Matlab_Converts/BIG CONVERSION 14 AUG 2018/THPH matfiles/'
-
-savepath = '/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures'
-
-# LICKING ANALYSIS **************************************************************************
-# Loop through files and calculate burst and run lengths
-# Assign empty lists for storing arrays of burst/run lengths
-
-# Lengths, all burst or run lengths here NOT
-allBursts = []
-allBurstsTimes = []
-allRuns = []
-allRunTimes = []
-
-allRunIndices = []
-allrILIs = []
-allbILIs = []
-allRatBlue = []
-allRatUV = []
-allRatFS = []
-allRatLicks = []
-
-for filename in TDTfiles_thph_lick:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlue.append(ratdata['blue'])
- allRatUV.append(ratdata['uv'])
- allRatFS.append(ratdata['fs'])
- allRatLicks.append(ratdata['licks'])
- burstanalysis = lickCalc(ratdata['licks'], offset=ratdata['licks_off'])
- burstList = burstanalysis['bLicks'] # n licks per burst
- runList = burstanalysis['rLicks'] # n licks per run
- burstListTimes = burstanalysis['bStart'] # Actual times of start of runs
- runListTimes = burstanalysis['rStart'] # Actual times of start of bursts
- allBursts.append(burstList)
- allRuns.append(runList)
- allRunTimes.append(runListTimes)
- allBurstsTimes.append(burstListTimes)
-# allRunIndices.append(indexRunList)
-# allrILIs.append(runILIs)
-# allbILIs.append(burstILIs)
-
-# Make the list of lists into one long list for histogram
-MergedBurstList = list(itertools.chain.from_iterable(allBursts))
-MergedRunList = list(itertools.chain.from_iterable(allRuns))
-
-# Descriptives - aggregated data
-meanburstlength = round(np.mean(MergedBurstList))
-medburstlen = round(np.median(MergedBurstList))
-meanrunlength = round(np.mean(MergedRunList))
-medrunlen = round(np.median(MergedRunList))
-
-## Make the snips
-
-
-blueMeansBurst = []
-uvMeansBurst = []
-blueMeansRuns = []
-uvMeansRuns = []
-allbluesnips = []
-alluvsnips = []
-
-for i, val in enumerate(allRunTimes):
-
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRunTimes[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRunTimes[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
-
-# Might not need the noise index, this is just for trials fig
-
-# fig = plt.figure()
-# ax = plt.subplot(1,1,1)
-# ax.set_ylim([-0.03, 0.03])
-# #ax.set_ylim([-0.05, 0.05])
-# trialsMultShadedFig(ax, [uvSnips,blueSnips], ppsBlue, eventText='First Lick in Run')
-# plt.text(250,0.03, '{}'.format(len(allRunTimes[i])) + ' Runs' )
-#
-# fig2 = plt.figure()
-# ax2 = plt.subplot(1,1,1)
-# ax2.set_ylim([-0.2, 0.2])
-# trialsFig(ax2, blueSnips, uvSnips, ppsBlue, eventText='First Lick in Run') #noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRunTimes[i])) + ' Runs' )
-#
-# filepath ='/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/'
-# ratname = str(i+1) +'.pdf'
-#
-# fig2.savefig(filepath+ratname)
-
- # these four lines used later to define means plot (made after runs)
- blueMean = np.mean(blueSnips, axis=0)
- blueMeansRuns.append(blueMean)
- uvMean = np.mean(uvSnips, axis=0)
- uvMeansRuns.append(uvMean)
- allbluesnips.append(blueSnips)
- alluvsnips.append(uvSnips)
-
-# All runs and all bursts (representative rat, etc.)
-# Then segregate by long and short (for all rats quartiles not for each rat)
-
-
-#########################################################################################
-# Average of all runs, all rats, all trials
-## Mean of ALL runs and ALL rats on multishaded figure
-
-#linecolor=['purple', 'blue'], errorcolor=['thistle', 'lightblue']
-
-
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.03, 0.03])
-ax.set_ylim([-0.05, 0.05])
-trialsMultShadedFig(ax, [np.asarray(uvMeansRuns[2:]),np.asarray(blueMeansRuns)], ppsBlue, eventText='First Lick in Run', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'])
-plt.text(250,0.03, '{}'.format(len(MergedRunList)) + ' Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/All_Runs_All_Rats.pdf')
-
-
-## Shows every single trial for each rat for runs - to choose representative sample
-#for index, sniplist in enumerate(allbluesnips):
-# for ind, lis in enumerate(sniplist):
-# fig = plt.figure(figsize=(6,3))
-# ax = plt.subplot(1,1,1)
-# ax = plt.plot(allbluesnips[index][ind])
-# plt.text(250,0, '{}'.format([index,ind]))
-
-#########################################################################################
-
-
-# Individual trial 1 - 13,1
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[13][1] , color='blue')
-ax.plot(alluvsnips[13][1] , color='purple')
-triallicks = nearestevents(allRunTimes[13],allRatLicks[13])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[1]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90, c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH2.8_Run2.pdf')
-
-
-# Individual trial 1 - 13,6
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[13][6] , color='blue')
-ax.plot(alluvsnips[13][6] , color='purple')
-triallicks = nearestevents(allRunTimes[13],allRatLicks[13])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[6]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90, c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH2.8_Run7.pdf', bbox_inches="tight")
-
-
-# Individual trial 1 - 12, 11
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[12][11] , color='blue')
-ax.plot(alluvsnips[12][11] , color='purple')
-triallicks = nearestevents(allRunTimes[12],allRatLicks[12])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[11]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH2.7_Run12.pdf', bbox_inches="tight")
-
-# Individual trial 1 - 11, 10
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[11][10] , color='blue')
-ax.plot(alluvsnips[11][10] , color='purple')
-triallicks = nearestevents(allRunTimes[11],allRatLicks[11])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[10]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH2.6_Run11.pdf', bbox_inches="tight")
-
-# Individual trial 1 - 4, 33
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[4][33] , color='blue')
-ax.plot(alluvsnips[4][33] , color='purple')
-triallicks = nearestevents(allRunTimes[4],allRatLicks[4])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[33]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH1.5_Run34.pdf', bbox_inches="tight")
-
-# Individual trial 1 - 3, 13
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[3][13] , color='blue')
-ax.plot(alluvsnips[3][13] , color='purple')
-triallicks = nearestevents(allRunTimes[3],allRatLicks[3])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[13]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH1.4_Run14.pdf', bbox_inches="tight")
-
-# Individual trial 1 - 3, 13 ----- TESTER FOR SCALE
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[3][13] , color='black')
-ax.plot(alluvsnips[3][13] , color='black')
-triallicks = nearestevents(allRunTimes[3],allRatLicks[3])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[13]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-# Making x scale
-scale = 5
-scalebar = scale * ppsBlue
-yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
-scalebary = (yrange / 10) + ax.get_ylim()[0]
-scalebarx = [ax.get_xlim()[1] - scalebar, ax.get_xlim()[1]]
-ax.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-ax.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), str(scale) +' s', ha='center',va='top')
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_SCALE.pdf', bbox_inches="tight")
-
-
-###########################################################################################
-
-# Long and short runs, 25th and 75th percentiles
-
-## Find short and long run lengths
-aggregateLowerQuart = np.percentile(MergedRunList,25)
-aggregateUpperQuart = np.percentile(MergedRunList,75)
-
-allLogIndRuns = []
-for runLicksList in allRuns:
- logIndRuns = [] # so 14 empty arrays exist and then don't (add to larger)
- for item in runLicksList:
- if item < aggregateLowerQuart:
- logIndRuns.append('LOWER')
- else:
- if item > aggregateUpperQuart:
- logIndRuns.append('UPPER')
-
- else:
- logIndRuns.append('MIDDLE')
- allLogIndRuns.append(logIndRuns)
-
-# 14 lists of logical indices for whether the n licks in that run was L,M,U
-
-
-
-# Final lists of run times sorted by u.m.l and stored by rat (14 lists)
-lowerqRunTimes = []
-uppqRunTimes = []
-mid50RunTimes = []
-lowerLengths = []
-upperLengths = []
-
-# Might need the index and the value for both lists ??
-for i, listofindices in enumerate(allLogIndRuns):
- templower = []
- tempupper = []
- tempmid = []
- for j, runIndex in enumerate(listofindices):
-
- #print(i,j) i is the list index and j is the item index
-
- if runIndex == 'LOWER':
- lowrun = allRunTimes[i][j]
- templower.append(lowrun)
- lowerLengths.append(allRuns[i][j])
-
- if runIndex == 'UPPER':
- upprun = allRunTimes[i][j]
- tempupper.append(upprun)
- upperLengths.append(allRuns[i][j])
-#
- if runIndex == 'MIDDLE':
- midrun = allRunTimes[i][j]
- tempmid.append(midrun)
-
- lowerqRunTimes.append(templower)
- uppqRunTimes.append(tempupper)
- mid50RunTimes.append(tempmid)
-
- ## Figure how to also get the actual lengths? Must have done this. Line 603
- ## runLicksList inside allRuns
-# _________________________________________________________________________
-
-
-#allign to lowerqRunTimes
-#allign to uppqRunTimes
-
-uvMeans_short_run = []
-blueMeans_short_run = []
-uvMeans_long_run = []
-blueMeans_long_run = []
-# Makes tonnes of individual plots
-# Individual rats might look odd as low Ns, but mean of mean will be better
-# Repeat this with bursts if looks like might be useful
-
-for i, val in enumerate(lowerqRunTimes):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], lowerqRunTimes[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], lowerqRunTimes[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-#
-# fig12 = plt.figure()
-# ax10 = plt.subplot(1,1,1)
-# ax10.set_ylim([-0.03, 0.03])
-# #ax.set_ylim([-0.05, 0.05])
-# trialsMultShadedFig(ax10, [uvSnips,blueSnips], ppsBlue, eventText='First Lick Short Run')
-# plt.text(250,0.03, '{}'.format(len(lowerqRunTimes[i])) + ' short runs' )
-##
-# fig13 = plt.figure()
-# ax11 = plt.subplot(1,1,1)
-# ax11.set_ylim([-0.2, 0.2])
-# trialsFig(ax11, blueSnips, uvSnips, ppsBlue, eventText='First Lick in Short Run', noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(lowerqRunTimes[i])) + ' short runs' )
-
-# # these four lines used later to define means plot (made after runs)
- blueMeanSHORT = np.mean(blueSnips, axis=0)
- blueMeans_short_run.append(blueMeanSHORT)
- uvMeanSHORT = np.mean(uvSnips, axis=0)
- uvMeans_short_run.append(uvMeanSHORT)
-
-MergedRunList_Short = list(itertools.chain.from_iterable(lowerqRunTimes))
-# Average of all SHORT runs, all rats, all trials
-## Mean of ALL SHORT runs and ALL rats on multishaded figure
-
-#linecolor=['purple', 'blue'], errorcolor=['thistle', 'lightblue']
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.03, 0.03])
-ax.set_ylim([-0.05, 0.05])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_short_run),np.asarray(blueMeans_short_run)], ppsBlue, eventText='First Lick in Short Run', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'])
-plt.text(250,0.03, '{}'.format(len(MergedRunList_Short)) + ' Short Runs' ) ## Edit this to be all
-fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Short_Runs_All_Rats.pdf', bbox_inches="tight")
-
-
-## ===============================================================
-
-# Photometry figures (individual) for LONG runs
-
-for i, val in enumerate(uppqRunTimes):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], uppqRunTimes[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], uppqRunTimes[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-
-# fig14 = plt.figure()
-# ax12 = plt.subplot(1,1,1)
-# ax12.set_ylim([-0.03, 0.03])
-# #ax.set_ylim([-0.05, 0.05])
-# trialsMultShadedFig(ax12, [uvSnips,blueSnips], ppsBlue, eventText='First Lick Long Run')
-# plt.text(250,0.03, '{}'.format(len(uppqRunTimes[i])) + ' long runs' )
-#
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.2, 0.2])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='First Lick in Long Run', noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(uppqRunTimes[i])) + ' long runs' )
-
-# # these four lines used later to define means plot (made after runs)
- blueMeanLONG = np.mean(blueSnips, axis=0)
- blueMeans_long_run.append(blueMeanLONG)
- uvMeanLONG = np.mean(uvSnips, axis=0)
- uvMeans_long_run.append(uvMeanLONG)
-
-MergedRunList_Long = list(itertools.chain.from_iterable(uppqRunTimes))
-# Average of all SHORT runs, all rats, all trials
-## Mean of ALL SHORT runs and ALL rats on multishaded figure
-
-# Not sure how to turn the scale off here (removed ppsBlue)
-#linecolor=['purple', 'blue'], errorcolor=['thistle', 'lightblue']
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.03, 0.03])
-ax.set_ylim([-0.05, 0.05])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_long_run),np.asarray(blueMeans_long_run)], ppsBlue, eventText='First Lick in Long Run', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Long_Runs_All_Rats.pdf', bbox_inches="tight")
-
-
-fig = plt.figure(figsize=(6,3))
-ax= plt.subplot(1,1,1)
-ax.set_ylim([-0.05, 0.05])
-LONG_SHORTrunMultFig = trialsMultShadedFig(ax, [np.asarray(blueMeans_short_run),np.asarray(blueMeans_long_run)], ppsBlue, eventText=''
- , linecolor=['k', 'firebrick'], errorcolor=['darkgrey', 'darkorange'], scale=0)
-ax.set(ylabel = chr(916) + 'F')
-ax.yaxis.label.set_size(14)
-
-
-# Simple figure legend
-import matplotlib.patches as mpatches
-
-orange_patch = mpatches.Patch(color='darkorange', label='Long Runs')
-grey_patch = mpatches.Patch(color='darkgrey', label='Short Runs')
-plt.legend(handles=[orange_patch, grey_patch], fontsize=14)
-plt.show()
-fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/ShortANDLong_Runs_All_Rats.pdf', bbox_inches="tight")
-
-
-######
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# DISTRACTION FILES (minus the first 2) - this was run with all included
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-
-allRatBlue = []
-allRatUV = []
-allRatFS = []
-allRatLicks = []
-allRatDistractors = []
-allRatDistracted = []
-allRatNotDistracted = []
-blueMeans_distractor = []
-uvMeans_distractor = []
-blueMeans_distracted = []
-uvMeans_distracted = []
-blueMeans_notdistracted = []
-uvMeans_notdistracted = []
-allbluesnips = []
-alluvsnips = []
-
-for filename in TDTfiles_thph_dis:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlue.append(ratdata['blue'])
- allRatUV.append(ratdata['uv'])
- allRatFS.append(ratdata['fs'])
- allRatLicks.append(ratdata['licks'])
- allRatDistractors.append(ratdata['distractors'])
- allRatDistracted.append(ratdata['distracted'])
- allRatNotDistracted.append(ratdata['notdistracted'])
- figure12 = plt.figure(figsize=(6,3))
- ax6 = plt.subplot(111)
- ax6.spines['right'].set_visible(False)
- ax6.xaxis.set_visible(False)
- ax6.spines['top'].set_visible(False)
- ax6.spines['bottom'].set_visible(False)
- ax6.set(ylabel = 'Trials')
- ax6.yaxis.label.set_size(14)
- distractionrasterFig(ax6, ratdata['distractors'], ratdata['licks'], pre=1, post=10, sortevents='yes', sortdirection='dec')
-
-
-for i, val in enumerate(allRatDistractors):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatDistractors[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatDistractors[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-# Individual plots to choose a representative rat
-
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distractor') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistractors[i])) + ' distractors' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_' + str(i) + '.pdf', bbox_inches="tight")
-
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractor.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractor.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractor),np.asarray(blueMeans_distractor)], ppsBlue, eventText='Distractor', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_All_Rats.pdf', bbox_inches="tight")
-
-
-
-for i, val in enumerate(allRatDistracted):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatDistracted[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatDistracted[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-# Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistracted[i])) + ' distracted' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_' + str(i) + '.pdf', bbox_inches="tight")
-#
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distracted.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distracted.append(uvMeanDISTRACTED)
- allbluesnips.append(blueSnips)
- alluvsnips.append(uvSnips)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distracted),np.asarray(blueMeans_distracted)], ppsBlue, eventText='Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_All_Rats.pdf', bbox_inches="tight")
-
-
-
-
-for i, val in enumerate(allRatNotDistracted):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatNotDistracted[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatNotDistracted[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-
-# Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Not Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatNotDistracted[i])) + ' not distracted' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_' + str(i) + '.pdf', bbox_inches="tight")
-
-# Means for not distracted trials here MULT SHADED FIG
-
- blueMeanNOTDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_notdistracted.append(blueMeanNOTDISTRACTED)
- uvMeanNOTDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_notdistracted.append(uvMeanNOTDISTRACTED)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_notdistracted),np.asarray(blueMeans_notdistracted)], ppsBlue, eventText='Not Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_All_Rats.pdf', bbox_inches="tight")
-
-
-########################################################################################################
-
-
-# (1) Make plots of each trial or pick ones that look good
-
-# Individual trial Run this with [0] --> [13] for each rat all trials saved
-# Manually chose trials to run single trials for
-
-# Indices all moved by +2
-# 2.7 - 3, 6, 9
-# 2.6 - 3, 9, 14
-# 2.5 - 5
-# 2.1 - 0
-# 1.5 - 0
-
-#for ind, snip in enumerate(allbluesnips[13]):
-# print(ind)
-# f = plt.figure(figsize=(6,3))
-# ax = plt.subplot(111)
-# ax.plot(allbluesnips[13][ind] , color='blue')
-# ax.plot(alluvsnips[13][ind] , color='purple')
-#
-#
-# triallicks = nearestevents(allRatDistractors[13],allRatLicks[13])
-# trialdistractors = nearestevents(allRatDistractors[13],allRatDistractors[13])
-# trialdistracted = nearestevents(allRatDistractors[13],allRatDistracted[13])
-# trialnotdistracted = nearestevents(allRatDistractors[13],allRatNotDistracted[13])
-#
-# xvals1 = [(x+10)*10 for x in triallicks[ind]]
-# xvals1 = [(x+10)*10 for x in triallicks[ind]]
-# #xvals2 = [(x+10)*10 for x in trialdistractors[trial]]
-# xvals3 = [(x+10)*10 for x in trialdistracted[ind]]
-# xvals4 = [(x+10)*10 for x in trialnotdistracted[ind]]
-# yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-# #yvals2 = [ax.get_ylim()[1] + 0.005] * len(xvals2)
-# yvals3 = [ax.get_ylim()[1] + 0.02] * len(xvals3)
-# yvals4 = [ax.get_ylim()[1] + 0.02] * len(xvals4)
-# #ax.scatter(xvals, yvals)
-# ax.scatter(xvals1, yvals1, marker='|', s=90, c='k')
-# #ax.scatter(xvals2, yvals2, marker='*')
-# ax.scatter(xvals3, yvals3, marker='o', facecolors= 'k', edgecolors='k', linewidth=2, s=60)
-# ax.scatter(xvals4, yvals4, marker='o', facecolors= 'none', edgecolors='k', linewidth=2, s=60)
-#
-# # ax.set_ylim([-0.0, 0.05])
-# ax.spines['right'].set_visible(False)
-# ax.spines['top'].set_visible(False)
-# ax.spines['left'].set_visible(False)
-# ax.spines['bottom'].set_visible(False)
-# ax.xaxis.set_visible(False)
-# ax.yaxis.set_visible(False)
-# f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/THPH2.8_trial_' + str(ind) +'.pdf', bbox_inches="tight")
-
-### REMEMBER the axes have been removed and the scales are different
-
-### Are these just alligned to distracted or both??? Check which list you use and how many trials
-
-## Smaller time period before ? Less than a second so you know no distractors are present????
-
-'''
-#### Representative rat
-## repeat on LICKING DAY - with modelled distractors and modelled distraction / or not
-### Then look at white noise vs non white noise
-## Then white noise on habituation day AND all distractors on habituation day (not distracted trials as very few, maybe)
-## THEN compare the peaks with stats and export to SPSS for t-tests?
-'''
-################################################################################################
-################################################################################################
-
-## Subsetting data, to get peaks
-
-## Variables : stored blue and uv snips for different conditions (means for each rat, lists of 12 - not 14 1.1 and 1.2 removed)
-## Do this all on a uv subtracted baseline?
-
-# UV is essentially zero but check this
-
-# (1) UV signal subtracted from the blue
-# (2) Derive the peaks by following rules:
-# (3) Compare using ANOVA or t-tests
-
-## Expects list of 12 rats with mean snips in each field
-def uvSubtractor(rat_snip_means_list_blue, uv):
- subtractedSignal = []
- for ind, rat in enumerate(rat_snip_means_list_blue):
- subtractedSignal.append(rat_snip_means_list_blue[ind] - uv[ind])
-
- return subtractedSignal
-
-bkgnd_sub_Runs = uvSubtractor(blueMeansRuns, uvMeansRuns)
-bkgnd_sub_Short_Runs = uvSubtractor(blueMeans_short_run, uvMeans_short_run)
-bkgnd_sub_Long_Runs = uvSubtractor(blueMeans_long_run, uvMeans_long_run)
-bkgnd_sub_Distractor = uvSubtractor(blueMeans_distractor, uvMeans_distractor)
-bkgnd_sub_Distracted = uvSubtractor(blueMeans_distracted, uvMeans_distracted)
-bkgnd_sub_Notdistracted = uvSubtractor(blueMeans_notdistracted, uvMeans_notdistracted)
-
-## Expects list of 12 rats with mean snips in each field
-## Give it the background subtracted snips or just the blue (as list of lists with eachlist a rat)
-
-
-
-def PhotoPeaksCalc(snips_all_rats):
-
- allRat_peak = []
- allRat_t = []
- allRat_pre = []
- allRat_post = []
- allRat_base = []
-
- for rat in snips_all_rats:
- pre_event = np.mean(rat[0:50]) # Average for 5 seconds, 10 seconds before event
- peak = np.max(rat[100:130]) ## Minus the average of the first 5 seconds and after 100 points (slice)
- peak_range = rat[100:130]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- pre_event = np.mean(rat[50:100])
- post_event = np.mean(rat[100:300])
- baseline = np.mean(rat[0:50])
-
- allRat_peak.append(peak)
- allRat_t.append(t)
- allRat_pre.append(pre_event)
- allRat_post.append(post_event)
- allRat_base.append(baseline)
-
-
- return allRat_peak, allRat_t, allRat_pre, allRat_post, allRat_base
-
-### Photometry peak variables - remember these lists will be unequal distractors 12 rats not 14?
-### Should maybe take out the first 2 of the licks too? As they had no signal ???
-## Manually removed the first 2 rats in SPSS -- should also remove them here (check later about the photo plots)
-
-# All runs
-peak_runs, t_runs, pre_runs, post_runs, baseline_runs = PhotoPeaksCalc(bkgnd_sub_Runs[2:])
-# Short runs
-peak_short_runs, t_short_runs, pre_short_runs, post_short_runs, baseline_short_runs = PhotoPeaksCalc(bkgnd_sub_Short_Runs[2:])
-# Long runs
-peak_long_runs, t_long_runs, pre_long_runs, post_long_runs, baseline_long_runs = PhotoPeaksCalc(bkgnd_sub_Long_Runs[2:])
-# Distractors
-peak_distractor, t_distractor, pre_distractor, post_distractor, baseline_distractor = PhotoPeaksCalc(bkgnd_sub_Distractor)
-# Distracted
-peak_distracted, t_distracted, pre_distracted, post_distracted, baseline_distracted = PhotoPeaksCalc(bkgnd_sub_Distracted)
-# Not distracted
-peak_notdistracted, t_notdistracted, pre_notdistracted, post_notdistracted, baseline_notdistracted = PhotoPeaksCalc(bkgnd_sub_Notdistracted)
-
-
-## Run the distraction code on the lick day data (minus rats 1 and 2) to get modelled distractor
-## peaks
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# DISTRACTION FILES (minus the first 2) - this was run with all included
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-
-allRatBlueMOD = []
-allRatUVMOD = []
-allRatFSMOD = []
-allRatLicksMOD = []
-allRatDistractorsMOD = []
-allRatDistractedMOD = []
-allRatNotDistractedMOD = []
-blueMeans_distractorMOD = []
-uvMeans_distractorMOD = []
-blueMeans_distractedMOD = []
-uvMeans_distractedMOD = []
-blueMeans_notdistractedMOD = []
-uvMeans_notdistractedMOD = []
-allbluesnipsMOD = []
-alluvsnipsMOD = []
-
-for filename in TDTfiles_thph_lick[2:]:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlueMOD.append(ratdata['blue'])
- allRatUVMOD.append(ratdata['uv'])
- allRatFSMOD.append(ratdata['fs'])
- allRatLicksMOD.append(ratdata['licks'])
- allRatDistractorsMOD.append(ratdata['distractors'])
- allRatDistractedMOD.append(ratdata['distracted'])
- allRatNotDistractedMOD.append(ratdata['notdistracted'])
-
-
-for i, val in enumerate(allRatDistractorsMOD):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueMOD[i], allRatDistractorsMOD[i], fs=allRatFSMOD[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVMOD[i], allRatDistractorsMOD[i], fs=allRatFSMOD[i], bins=300)
-
- randevents = makerandomevents(allRatBlueMOD[i][300], allRatBlueMOD[i][-300])
- bgMad, bgMean = findnoise(allRatBlueMOD[i], randevents, fs=allRatFSMOD[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-# Individual plots to choose a representative rat
-
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distractor') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistractors[i])) + ' distractors' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_' + str(i) + '.pdf', bbox_inches="tight")
-
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractorMOD.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractorMOD.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractorMOD),np.asarray(blueMeans_distractorMOD)], ppsBlue, eventText='Modelled Distractor', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Modelled_Distractors_All_Rats.pdf', bbox_inches="tight")
-
-
-
-for i, val in enumerate(allRatDistractedMOD):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueMOD[i], allRatDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVMOD[i], allRatDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
-
- randevents = makerandomevents(allRatBlueMOD[i][300], allRatBlueMOD[i][-300])
- bgMad, bgMean = findnoise(allRatBlueMOD[i], randevents, fs=allRatFSMOD[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-# Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistracted[i])) + ' distracted' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_' + str(i) + '.pdf', bbox_inches="tight")
-#
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distractedMOD.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distractedMOD.append(uvMeanDISTRACTED)
- allbluesnipsMOD.append(blueSnips)
- alluvsnipsMOD.append(uvSnips)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractedMOD),np.asarray(blueMeans_distractedMOD)], ppsBlue, eventText='Distracted trial MOD', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_All_Rats.pdf', bbox_inches="tight")
-
-for i, val in enumerate(allRatNotDistractedMOD):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueMOD[i], allRatNotDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVMOD[i], allRatNotDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
-
- randevents = makerandomevents(allRatBlueMOD[i][300], allRatBlueMOD[i][-300])
- bgMad, bgMean = findnoise(allRatBlueMOD[i], randevents, fs=allRatFSMOD[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-
-## Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Not Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatNotDistractedMOD[i])) + ' not distracted' )
-## fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_' + str(i) + '.pdf', bbox_inches="tight")
-
-# Means for not distracted trials here MULT SHADED FIG
-
- blueMeanNOTDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_notdistractedMOD.append(blueMeanNOTDISTRACTED)
- uvMeanNOTDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_notdistractedMOD.append(uvMeanNOTDISTRACTED)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_notdistractedMOD),np.asarray(blueMeans_notdistractedMOD)], ppsBlue, eventText='Not Distracted trial MOD', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_All_Rats.pdf', bbox_inches="tight")
-
-bkgnd_sub_Distractor_MOD = uvSubtractor(blueMeans_distractorMOD, uvMeans_distractorMOD)
-bkgnd_sub_Distracted_MOD = uvSubtractor(blueMeans_distractedMOD, uvMeans_distractedMOD)
-bkgnd_sub_Notdistracted_MOD = uvSubtractor(blueMeans_notdistractedMOD, uvMeans_notdistractedMOD)
-# Distractors (in this case only these peaks in SPSS, may decide to use DIS and NOTDIS later though)
-peak_distractorMOD, t_distractorMOD, pre_distractorMOD, post_distractorMOD, baseline_distractorMOD = PhotoPeaksCalc(bkgnd_sub_Distractor_MOD)
-# Distracted
-peak_distractedMOD, t_distractedMOD, pre_distractedMOD, post_distractedMOD, baseline_distractedMOD = PhotoPeaksCalc(bkgnd_sub_Distracted_MOD)
-# Not distracted
-peak_notdistractedMOD, t_notdistractedMOD, pre_notdistractedMOD, post_notdistractedMOD, baseline_notdistractedMOD = PhotoPeaksCalc(bkgnd_sub_Notdistracted_MOD)
-
-
-
-
-
-
-## NOW - make all photo peaks again from HABITUATION day not lick day or distraction day
-
-# Repeat everything (not the licking just distraction) and make all of the snips and peaks
-# Then transfer these to SPSS
-
-## Run the distraction code on the HABITUATION DAY data (minus rats 1 and 2)
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# HABITUATION FILES (minus the first 2)
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-allRatBlueHAB = []
-allRatUVHAB = []
-allRatFSHAB = []
-allRatLicksHAB = []
-allRatDistractorsHAB = []
-allRatDistractedHAB = []
-allRatNotDistractedHAB = []
-blueMeans_distractorHAB = []
-uvMeans_distractorHAB = []
-blueMeans_distractedHAB = []
-uvMeans_distractedHAB = []
-blueMeans_notdistractedHAB = []
-uvMeans_notdistractedHAB = []
-allbluesnipsHAB = []
-alluvsnipsHAB = []
-
-for filename in TDTfiles_thph_hab:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlueHAB.append(ratdata['blue'])
- allRatUVHAB.append(ratdata['uv'])
- allRatFSHAB.append(ratdata['fs'])
- allRatLicksHAB.append(ratdata['licks'])
- allRatDistractorsHAB.append(ratdata['distractors'])
- allRatDistractedHAB.append(ratdata['distracted'])
- allRatNotDistractedHAB.append(ratdata['notdistracted'])
-
-
-for i, val in enumerate(allRatDistractorsHAB):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueHAB[i], allRatDistractorsHAB[i], fs=allRatFSHAB[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVHAB[i], allRatDistractorsHAB[i], fs=allRatFSHAB[i], bins=300)
-
- randevents = makerandomevents(allRatBlueHAB[i][300], allRatBlueHAB[i][-300])
- bgMad, bgMean = findnoise(allRatBlueHAB[i], randevents, fs=allRatFSHAB[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-# Individual plots to choose a representative rat
-
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distractor') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistractors[i])) + ' distractors' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_' + str(i) + '.pdf', bbox_inches="tight")
-
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractorHAB.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractorHAB.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractorHAB),np.asarray(blueMeans_distractorHAB)], ppsBlue, eventText='Distractor', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Habituation_Distractors_All_Rats.pdf', bbox_inches="tight")
-
-
-
-for i, val in enumerate(allRatDistractedHAB):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueHAB[i], allRatDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVHAB[i], allRatDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
-
- randevents = makerandomevents(allRatBlueHAB[i][300], allRatBlueHAB[i][-300])
- bgMad, bgMean = findnoise(allRatBlueHAB[i], randevents, fs=allRatFSHAB[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-# Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistracted[i])) + ' distracted' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_' + str(i) + '.pdf', bbox_inches="tight")
-#
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distractedHAB.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distractedHAB.append(uvMeanDISTRACTED)
- allbluesnipsHAB.append(blueSnips)
- alluvsnipsHAB.append(uvSnips)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractedHAB),np.asarray(blueMeans_distractedHAB)], ppsBlue, eventText='Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_All_Rats.pdf', bbox_inches="tight")
-
-for i, val in enumerate(allRatNotDistractedHAB):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueHAB[i], allRatNotDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVHAB[i], allRatNotDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
-
- randevents = makerandomevents(allRatBlueHAB[i][300], allRatBlueHAB[i][-300])
- bgMad, bgMean = findnoise(allRatBlueHAB[i], randevents, fs=allRatFSHAB[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-
-## Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Not Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatNotDistractedMOD[i])) + ' not distracted' )
-## fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_' + str(i) + '.pdf', bbox_inches="tight")
-
-# Means for not distracted trials here MULT SHADED FIG
-
- blueMeanNOTDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_notdistractedHAB.append(blueMeanNOTDISTRACTED)
- uvMeanNOTDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_notdistractedHAB.append(uvMeanNOTDISTRACTED)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_notdistractedHAB),np.asarray(blueMeans_notdistractedHAB)], ppsBlue, eventText='Not Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_All_Rats.pdf', bbox_inches="tight")
-
-bkgnd_sub_Distractor_HAB = uvSubtractor(blueMeans_distractorHAB, uvMeans_distractorHAB)
-bkgnd_sub_Distracted_HAB = uvSubtractor(blueMeans_distractedHAB, uvMeans_distractedHAB)
-bkgnd_sub_Notdistracted_HAB = uvSubtractor(blueMeans_notdistractedHAB, uvMeans_notdistractedHAB)
-# Distractors (in this case only these peaks in SPSS, may decide to use DIS and NOTDIS later though)
-peak_distractorHAB, t_distractorHAB, pre_distractorHAB, post_distractorHAB, baseline_distractorHAB = PhotoPeaksCalc(bkgnd_sub_Distractor_HAB)
-# Distracted
-peak_distractedHAB, t_distractedHAB, pre_distractedHAB, post_distractedHAB, baseline_distractedHAB = PhotoPeaksCalc(bkgnd_sub_Distracted_HAB)
-# Not distracted
-peak_notdistractedHAB, t_notdistractedHAB, pre_notdistractedHAB, post_notdistractedHAB, baseline_notdistractedHAB = PhotoPeaksCalc(bkgnd_sub_Notdistracted_HAB)
-
-
-
-
-
-
-
-## DISTRACTION
-
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Aug 20 20:17:44 2018
-@author: u1490431
-"""
-
-''' Makes all of the bar scatters, not just for distraction, for licking
- Need to add labels and titles so we know which plts are which
- Check which scripts need to be run first and make sure to comment out
- the seaborn import that causes the plotting problems (no black outline)
- and strange grid
-
-'''
-
-
-
-## run this script AFTER the ch4 analysis script (photometry licking days and dis, not barscatter)
-def MetaExtractorTHPH (metafile):
- f = open(metafile, 'r')
- f.seek(0)
- Metafilerows = f.readlines()[1:]
- tablerows = []
-
- for row in Metafilerows:
- items = row.split(',')
- tablerows.append(items)
-
- TDTFile, MedFilenames, RatID, Date, Session, Include, Licks, Totdistractors, Distracted, \
- Percentdistracted, Note, Endcol = [],[],[],[],[],[],[],[],[],[],[],[]
-
- for i, lst in enumerate(tablerows):
-
-
-
- TDTFile = TDTFile + [lst[0]]
- MedFilenames = MedFilenames + [lst[1]]
- RatID = RatID + [lst[2]]
- Date = Date + [lst[3]]
- Session = Session + [lst[4]]
- Include = Include + [lst[5]]
- Licks = Licks + [lst[6]]
- Totdistractors = Totdistractors + [lst[7]]
- Distracted = Distracted + [lst[8]]
- Percentdistracted = Percentdistracted + [lst[9]]
- Note = Note + [lst[10]]
- Endcol = Endcol + [lst[11]]
-
-
- return ({'MedFilenames':MedFilenames, 'RatID':RatID, 'Date':Date, 'Session':Session, \
- 'Include':Include, 'Licks':Licks, 'PercentDistracted':Percentdistracted})
-
-
-
-def subsetter2(dictionary, dates, dis=False, verbose=False):
- '''
- SUBSETTER KP
- # Subsets data according to date, reads in dictionnary produced from metafile
- # and subsets into variable based on date(s) and drug condition
- # if distraction day argument is given as True adds the distractor type
- # to the output lists for later processing
-
- Adapted to include & include == 1 (because not all had the same date for last lick day)
- In metafile do not include 2.7 and 2.8 on the lick 3 day but do include them on lick 6
-
-
- '''
- subset = []
-
- for ind, filename in enumerate(dictionary['MedFilenames']):
- path = medfolder + filename
- onsets, offsets, med_dis_times, dis_type = medfilereader(path, ['b', 'c', 'i', 'j'], remove_var_header = True) # e onset, f offset
-
- if dis == True:
- if dictionary['Date'][ind] in dates and dictionary['Include'][ind] == '1':
- subset.append([onsets, offsets, dis_type, dictionary['RatID'][ind]])
-
- elif dis==False:
- if dictionary['Date'][ind] in dates and dictionary['Include'][ind] == '1':
- subset.append([onsets, offsets, dictionary['RatID'][ind]])
-
- if verbose: #assumes true
- print('filename, or comment ...')
- return subset
-
-
-
-def percentdisgroup(distractiondict):
- ''' Discalc_sal_M == distractiondict '''
-
- percent_dis_group = []
- for rat in distractiondict:
- percentage = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percent_dis_group.append(percentage)
- return percent_dis_group
-
-
-
-def disbygroup(dictionary):
- ''' Prodcues times of distracted and not distracted as 2 lists
- takes a dictionary of grouped rat data
- '''
-
- dis = []
- for rat in dictionary:
-
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- dis.append([distracted, notdistracted])
-
- return dis
-
-# Functions
-
-"""
-This function will create bar+scatter plots when passed a 1 or 2 dimensional
-array. Data needs to be passed in as a numpy object array, e.g.
-data = np.empty((2), dtype=np.object)
-data[0] = np.array(allData['nCasLicks'][index])
-data[1] = np.array(allData['nMaltLicks'][index])
-Various options allow specification of colors and paired/unpaired plotting.
-It can return the figures, axes, bars, and scatters for further modification.
-e.g.
-fig1, ax1, barlist1, sc1 = jmf.barscatter(data)
-for i in barlist1[1].get_children():
- i.set_color('g')
-"""
-def barscatter(data, transpose = False, unequal=False,
- groupwidth = .75,
- barwidth = .9,
- paired = False,
- spaced = False,
- barfacecoloroption = 'same', # other options 'between' or 'individual'
- barfacecolor = ['white'],
- baredgecoloroption = 'same',
- baredgecolor = [''],
- baralpha = 1,
- scatterfacecoloroption = 'same',
- scatterfacecolor = ['white'],
- scatteredgecoloroption = 'same',
- scatteredgecolor = ['grey'],
- scatterlinecolor = 'grey', # Don't put this value in a list
- scattersize = 80,
- scatteralpha = 1,
- linewidth=1,
- ylabel = 'none',
- xlabel = 'none',
- grouplabel = 'auto',
- itemlabel = 'none',
- barlabels = [],
- barlabeloffset=0.1,
- grouplabeloffset=0.2,
- yaxisparams = 'auto',
- show_legend = 'none',
- xrotation=0,
- legendloc='upper right',
- ax=[]):
-
- if unequal == True:
- dims = np.ndim(data)
- data_obj = np.ndarray((np.shape(data)), dtype=np.object)
- for i1, dim1 in enumerate(data):
- for i2, dim2 in enumerate(dim1):
- data_obj[i1][i2] = np.array(dim2, dtype=np.object)
- data = data_obj
-
- if type(data) != np.ndarray or data.dtype != np.object:
- dims = np.shape(data)
- if len(dims) == 2 or len(dims) == 1:
- data = data2obj1D(data)
-
- elif len(dims) == 3:
- data = data2obj2D(data)
-
- else:
- print('Cannot interpret data shape. Should be 2 or 3 dimensional array. Exiting function.')
- return
-
- # Check if transpose = True
- if transpose == True:
- data = np.transpose(data)
-
- # Initialize arrays and calculate number of groups, bars, items, and means
-
- barMeans = np.zeros((np.shape(data)))
- items = np.zeros((np.shape(data)))
-
- nGroups = np.shape(data)[0]
- groupx = np.arange(1,nGroups+1)
-
- if len(np.shape(data)) > 1:
- grouped = True
- barspergroup = np.shape(data)[1]
- barwidth = (barwidth * groupwidth) / barspergroup
-
- for i in range(np.shape(data)[0]):
- for j in range(np.shape(data)[1]):
- barMeans[i][j] = np.nanmean(data[i][j])
- items[i][j] = len(data[i][j])
-
- else:
- grouped = False
- barspergroup = 1
-
- for i in range(np.shape(data)[0]):
- barMeans[i] = np.nanmean(data[i])
- items[i] = len(data[i])
-
- # Calculate x values for bars and scatters
-
- xvals = np.zeros((np.shape(data)))
- barallocation = groupwidth / barspergroup
- k = (groupwidth/2) - (barallocation/2)
-
- if grouped == True:
-
- for i in range(np.shape(data)[0]):
- xrange = np.linspace(i+1-k, i+1+k, barspergroup)
- for j in range(barspergroup):
- xvals[i][j] = xrange[j]
- else:
- xvals = groupx
-
- # Set colors for bars and scatters
-
- barfacecolorArray = setcolors(barfacecoloroption, barfacecolor, barspergroup, nGroups, data)
- baredgecolorArray = setcolors(baredgecoloroption, baredgecolor, barspergroup, nGroups, data)
-
- scfacecolorArray = setcolors(scatterfacecoloroption, scatterfacecolor, barspergroup, nGroups, data, paired_scatter = paired)
- scedgecolorArray = setcolors(scatteredgecoloroption, scatteredgecolor, barspergroup, nGroups, data, paired_scatter = paired)
-
- # Initialize figure
- if ax == []:
- fig = plt.figure()
- ax = fig.add_subplot(111)
-
- # Make bars
- barlist = []
- barx = []
- for x, y, bfc, bec in zip(xvals.flatten(), barMeans.flatten(),
- barfacecolorArray, baredgecolorArray):
- barx.append(x)
- barlist.append(ax.bar(x, y, barwidth,
- facecolor = bfc, edgecolor = bec,
- zorder=-1))
-
- # Uncomment these lines to show method for changing bar colors outside of
- # function using barlist properties
- #for i in barlist[2].get_children():
- # i.set_color('r')
-
- # Make scatters
- sclist = []
- if paired == False:
- for x, Yarray, scf, sce in zip(xvals.flatten(), data.flatten(),
- scfacecolorArray, scedgecolorArray):
- for y in Yarray:
- if spaced == True:
- sclist.append(ax.scatter(x+np.random.random(size=1)*barallocation, y, s = scattersize,
- c = scf,
- edgecolors = sce,
- zorder=20))
- else:
- sclist.append(ax.scatter(x, y, s = scattersize,
- c = scf,
- edgecolors = sce,
- zorder=20))
-
- elif grouped == True:
- for x, Yarray, scf, sce in zip(xvals, data, scfacecolorArray, scedgecolorArray):
- for y in np.transpose(Yarray.tolist()):
- sclist.append(ax.plot(x, y, '-o', markersize = scattersize/10,
- color = scatterlinecolor,
- linewidth=linewidth,
- markerfacecolor = scf,
- markeredgecolor = sce,
- zorder=20))
- elif grouped == False:
- for n,_ in enumerate(data[0]):
- y = [y[n-1] for y in data]
- sclist.append(ax.plot(xvals, y, '-o', markersize = scattersize/10,
- color = scatterlinecolor,
- linewidth=linewidth,
- markerfacecolor = scfacecolorArray[0],
- markeredgecolor = scedgecolorArray[0],
- zorder=20))
-
- # Label axes
- if ylabel != 'none':
- plt.ylabel(ylabel)
-
- if xlabel != 'none':
- plt.xlabel(xlabel)
-
- # Set range and tick values for Y axis
- if yaxisparams != 'auto':
- ax.set_ylim(yaxisparams[0])
- plt.yticks(yaxisparams[1])
-
- # X ticks
- ax.tick_params(
- axis='x', # changes apply to the x-axis
- which='both', # both major and minor ticks are affected
- bottom='off', # ticks along the bottom edge are off
- top='off') # labels along the bottom edge are off
-
- if grouplabel == 'auto':
- plt.tick_params(labelbottom='off')
- else:
- if len(barlabels) > 0:
- plt.tick_params(labelbottom='off')
- yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
- offset = ax.get_ylim()[0] - yrange*grouplabeloffset
- for idx, label in enumerate(grouplabel):
- ax.text(idx+1, offset, label, va='top', ha='center')
- else:
- plt.xticks(range(1,nGroups+1), grouplabel)
-
- if len(barlabels) > 0:
- if len(barlabels) != len(barx):
- print('Wrong number of bar labels for number of bars!')
- else:
- yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
- offset = ax.get_ylim()[0] - yrange*barlabeloffset
- for x, label in zip(barx, barlabels):
- ax.text(x, offset, label, va='top', ha='center',rotation=xrotation)
-
- # Hide the right and top spines and set bottom to zero
- ax.spines['right'].set_visible(False)
- ax.spines['top'].set_visible(False)
- ax.spines['bottom'].set_position('zero')
-
- if show_legend == 'within':
- if len(itemlabel) != barspergroup:
- print('Not enough item labels for legend!')
- else:
- legendbar = []
- legendtext = []
- for i in range(barspergroup):
- legendbar.append(barlist[i])
- legendtext.append(itemlabel[i])
- plt.legend(legendbar, legendtext, loc=legendloc)
-
- return ax, barx, barlist, sclist
-
-#plt.savefig('foo.png')
-
-# To do
-# check if n's are the same for paired and if not default to unpaired
-# add color options for scatters
-# add alpha options etc
-# add axis options
-# remove white background
-# work out how to export or save as pdf, tiff, eps etc
-# work out how to return handles to scatters so can be altered outside of function
-# make help doc
-# make html file to show usage using ijupyt
-
-def setcolors(coloroption, colors, barspergroup, nGroups, data, paired_scatter = False):
-
- nColors = len(colors)
-
- if (paired_scatter == True) & (coloroption == 'within'):
- print('Not possible to make a Paired scatter plot with Within setting.')
- coloroption = 'same'
-
- if coloroption == 'within':
- if nColors < barspergroup:
- print('Not enough colors for this option! Reverting to one color.')
- coloroption = 'same'
- elif nColors > barspergroup:
- colors = colors[:barspergroup]
- coloroutput = [colors for i in data]
- coloroutput = list(chain(*coloroutput))
-
- if coloroption == 'between':
- if nColors < nGroups:
- print('Not enough colors for this option! Reverting to one color.')
- coloroption = 'same'
- elif nColors > nGroups:
- colors = colors[:nGroups]
- if paired_scatter == False:
- coloroutput = [[c]*barspergroup for c in colors]
- coloroutput = list(chain(*coloroutput))
- else:
- coloroutput = colors
-
- if coloroption == 'individual':
- if nColors < nGroups*barspergroup:
- print('Not enough colors for this color option')
- coloroption = 'same'
- elif nColors > nGroups*barspergroup:
- coloroutput = colors[:nGroups*barspergroup]
- else:
- coloroutput = colors
-
- if coloroption == 'same':
- coloroutput = [colors[0] for x in range(len(data.flatten()))]
-
- return coloroutput
-
-def data2obj1D(data):
- obj = np.empty(len(data), dtype=np.object)
- for i,x in enumerate(data):
- obj[i] = np.array(x)
- return obj
-
-def data2obj2D(data):
- obj = np.empty((np.shape(data)[0], np.shape(data)[1]), dtype=np.object)
- for i,x in enumerate(data):
- for j,y in enumerate(x):
- obj[i][j] = np.array(y)
- return obj
-
-
-
-
-################################################################################################
-################################################################################################
-
-## BEHAVIOUR SUBSETTING, PERCENT DISTRACTED AND PLOTS [THPH1, THPH2]
-
-metafile = '/Volumes/KP_HARD_DRI/kp259/THPH1AND2/THPH1&2Metafile.csv'
-extract_data = MetaExtractorTHPH(metafile)
-# Folder with all medfiles (THPH1 and THPH2)
-medfolder = '/Volumes/KP_HARD_DRI/kp259/THPH1AND2/med/'
-
-
-
-'''
- Info DPCP1(16) and DPCP2(16) males, DPCP3(24) females :
-
- THPH1 |THPH2.1 - 2.6|THPH2.7 - 2.8|CONDITION
- -----------|-------------|-------------|-----------
- 170620 |170809 |170814 |last lick
- 170621 |170810 |170815 |dis
- 170622 |170811 |170816 |hab 1
-
- All included, can later only subset the first 12 rats and ignore the
- unequal numbers from no habituation on rat 2.8
-'''
-
-
-last_lick = subsetter2(extract_data, ['170620', '170809', '170814'])
-distraction= subsetter2(extract_data, ['170621', '170810', '170815'], dis=True)
-habituation = subsetter2(extract_data, ['170622', '170811', '170806'], dis=True)
-
-
-discalcLick = []
-discalcDis = []
-discalcHab = []
-
-for rat in last_lick:
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- discalcLick.append([distracted, notdistracted])
-
-for rat in distraction:
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- discalcDis.append([distracted, notdistracted])
-
-for rat in habituation:
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- discalcHab.append([distracted, notdistracted])
-
-percentdisLick = []
-percentdisDis = []
-percentdisHab = []
-
-for rat in discalcLick:
- percent = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percentdisLick.append(percent)
-
-for rat in discalcDis:
- percent = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percentdisDis.append(percent)
-
-for rat in discalcHab:
- percent = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percentdisHab.append(percent)
-
-
-##########################################################################################
-
-# PERCENT DISTRACTED - THPH1 AND THPH2
-
-# For barscatter plots excluded rats 2.7 and 2.8 (as not all point on all days)
-data = [[percentdisLick[0:12], percentdisDis[0:12], percentdisHab]]
-
-col3 = ['#FFE5A5','#FFE5A5','#FFE5A5']
-col3 = ['#a2e283','#4cbb17','#257200']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-ax, barx, barlist, sclist = barscatter(data, transpose=False, ax=ax,paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Percent distracted (%)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/PercentDisBarScatter.pdf', bbox_inches="tight")
-
-
- # Barscatters to make:
-
-## PERHAPS only care about distractORS as not many distracted trials on this habituation day?
-
-## UNEQUAL NUMBERS HERE, ISSUE FOR BARSCATTER - Maybe just index missing the LAST rat for ALL comparisons
-
-################################################################################################
-################################################################################################
-
-## Short versus long runs of licks ORANGES X 2 (ORANGE AND BRICK OR GREY AND BRICK)
-def MultBy100(list):
- output = [x*100 for x in list]
-
- return output
-shortVlongPeak = [MultBy100(peak_short_runs), MultBy100(peak_long_runs)]
-shortVlongt = [t_short_runs, t_long_runs]
-shortVlongPre = [MultBy100(pre_short_runs), MultBy100(pre_long_runs)]
-shortVlongPost = [MultBy100(post_short_runs), MultBy100(post_long_runs)]
-
-mpl.rcParams['figure.subplot.wspace'] = 0.6
-mpl.rcParams['figure.subplot.right'] = 1
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(shortVlongPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#ffb349','#ef7700'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(shortVlongt, ax=ax[1], transpose=False, paired=True, barfacecolor=['#ffb349','#ef7700'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(shortVlongPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#ffb349','#ef7700'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(shortVlongPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#ffb349','#ef7700'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/ShortVsLongPeaksBarScatter.pdf', bbox_inches="tight")
-
-################################################################################################
-################################################################################################
-
-## All runs versus all distractors (BRICK AND GREEN or ORANGE AND GREEN)
-runVdisPeak = [MultBy100(peak_runs), MultBy100(peak_distractor)]
-runVdist = [t_runs, t_distractor]
-runVdisPre = [MultBy100(pre_runs), MultBy100(pre_distractor)]
-runVdisPost = [MultBy100(post_runs), MultBy100(post_distractor)]
-
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(runVdisPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#ef7700','#4cbb17'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(runVdist, ax=ax[1], transpose=False, paired=True, barfacecolor=['#ef7700','#4cbb17'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(runVdisPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#ef7700','#4cbb17'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(runVdisPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#ef7700','#4cbb17'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/RunVsDisPeaksBarScatter.pdf', bbox_inches="tight")
-
-
-################################################################################################
-################################################################################################
-
-## Distracted vs not distracted (GREEN X 2)
-disVnotPeak = [MultBy100(peak_notdistracted),MultBy100(peak_distracted)]
-disVnott = [t_notdistracted,t_distracted]
-disVnotPre = [MultBy100(pre_notdistracted),MultBy100(pre_distracted)]
-disVnotPost = [MultBy100(post_notdistracted),MultBy100(post_distracted)]
-
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(disVnotPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(disVnott, ax=ax[1], transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(disVnotPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(disVnotPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/DisVsNotDisPeaksBarScatter.pdf', bbox_inches="tight")
-
-
-################################################################################################
-################################################################################################
-
-## Modelled versus distracion day presented distractors (GREY and GREEN)
-modVdisPeak = [MultBy100(peak_distractorMOD), MultBy100(peak_distractor)]
-modVdist = [t_distractorMOD, t_distractor]
-modVdisPre = [MultBy100(pre_distractorMOD), MultBy100(pre_distractor)]
-modVdisPost = [MultBy100(post_distractorMOD), MultBy100(post_distractor)]
-
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(modVdisPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(modVdist, ax=ax[1], transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(modVdisPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(modVdisPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/ModVsDisPeaksBarScatter.pdf', bbox_inches="tight")
-
-################################################################################################
-################################################################################################
-## Distraction day vs habituation day (GREEN, light and dark)
-disVhabPeak = [MultBy100(peak_distractor), MultBy100(peak_distractorHAB)]
-disVhabt = [t_distractor, t_distractorHAB]
-disVhabPre = [MultBy100(pre_distractor), MultBy100(pre_distractorHAB)]
-disVhabPost = [MultBy100(post_distractor), MultBy100(post_distractorHAB)]
-
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(disVhabPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(disVhabt, ax=ax[1], transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(disVhabPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(disVhabPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/DisVsHabPeaksBarScatter.pdf', bbox_inches="tight")
-
-#???????
-
-# ANALYSIS FOR WHITE NOISE VS NON-WHITE NOISE HERE
-# (7) 4 bars --> wn vs nwn dis day vs hab day - peak, t, pre, post
-
-
-
-## Correlation between PERCENT DISTRACTED and DISTRCTOR PEAK
-import matplotlib as mpl
-from scipy import stats
-import seaborn as sn
-slope, intercept, r_value, p_value, std_err = stats.linregress(percentdisDis[2:], MultBy100(peak_distractor))
-## Plot the scatter
-plt.plot(percentdisDis[2:], MultBy100(peak_distractor),'o', color='limegreen')
-## Add line of best fit
-plt.plot(np.asarray(percentdisDis[2:]), intercept+slope*np.asarray(percentdisDis[2:]), 'limegreen')
-
-plt.legend()
-sn.despine(offset=10, trim=True);
-
-plt.xlabel('Percent distracted', fontsize=14)
-plt.ylabel('Peak response to distractors (% ΔF)', fontsize=14)
-plt.xticks(fontsize=14)
-plt.yticks(fontsize=14)
-plt.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Corr_Percent_DistractorPeak.pdf', bbox_inches="tight")
-plt.show()
-print('Linear regression, Percent distracted VS distractor peak')
-print('R squared = ',r_value**2, ', p value = ', p_value)
-
-
-## Correlation between PERCENT DISTRACTED and DISTRACTED PEAK
-slope, intercept, r_value, p_value, std_err = stats.linregress(percentdisDis[2:], MultBy100(peak_distracted))
-## Plot the scatter
-plt.plot(percentdisDis[2:], MultBy100(peak_distracted),'o', color='#257200')
-## Add line of best fit
-plt.plot(np.asarray(percentdisDis[2:]), intercept+slope*np.asarray(percentdisDis[2:]) ,'#257200')
-
-plt.legend()
-sn.despine(offset=10, trim=True);
-
-plt.xlabel('Percent distracted', fontsize=14)
-plt.ylabel('Peak response distracted (% ΔF)', fontsize=14)
-plt.xticks(fontsize=14)
-plt.yticks(fontsize=14)
-plt.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Corr_Percent_DistractedPeak.pdf', bbox_inches="tight")
-plt.show()
-print('Linear regression, Percent distracted VS distracted peak')
-print('R squared = ',r_value**2, ', p value = ', p_value)
-
-
-
-
-## Correlation between PERCENT DISTRACTED and RUNS PEAK
-slope, intercept, r_value, p_value, std_err = stats.linregress(percentdisDis[2:], MultBy100(peak_runs))
-## Plot the scatter
-plt.plot(percentdisDis[2:], MultBy100(peak_runs),'o', color='darkorange')
-## Add line of best fit
-plt.plot(np.asarray(percentdisDis[2:]), intercept+slope*np.asarray(percentdisDis[2:]), 'darkorange')
-
-plt.legend()
-sn.despine(offset=10, trim=True);
-
-plt.xlabel('Percent distracted', fontsize=14)
-plt.ylabel('Peak response runs (% ΔF)', fontsize=14)
-plt.xticks(fontsize=14)
-plt.yticks(fontsize=14)
-plt.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Corr_Percent_RunsPeak.pdf', bbox_inches="tight")
-
-plt.show()
-print('Linear regression, Percent distracted VS all runs peak')
-print('R squared = ',r_value**2, ', p value = ', p_value)
-
-
-################################################################################################
-################################################################################################
-
-
-# White noise separation - thph1 and thph2
-
-def discalc_modalities(dictionary, modalitykey):
- ''' Calculates distractors, distracted and modalities for dictionary of
- rats by group for just distraction day only
-
- Calculates a grouped percentage of each modality and how distracted
- by that modality rats are on average (by group)
-
- '''
- percent_dis_whitenoise_group = []
- percent_dis_tone_group = []
- percent_dis_combined_group = []
- percent_dis_all_non_WN_group = []
- discalcgroup = []
- ## SAL MALES - DISTRACTION DAY ONLY - DISTRACTOR TYPE ANALYSIS INCLUDED
-# Finds distracted or not (corrects for med slipping issue)
- for rat in dictionary:
- print('a')
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- print(len(distracted), len(notdistracted))
- # work out percentage and add this too
- discalcgroup.append([distracted, notdistracted])
-
- dis_numeric = []
- ndis_numeric = []
- # Modality analysis - calculates which distractors contain different features (whitenoise, tone or combination)
- # Then works out on how many of these trials rats are distracted (individual) before creating a mean
- # Tr and except because there are some cases where there are ZERO distracted or non distracted trials (or both)
- for d in distracted:
- try:
- dis_numeric.append([rat[2][idx] for idx, val in enumerate(discalc) if val == d][0])
- except IndexError:
- pass
-
-
- for nd in notdistracted:
- try:
- ndis_numeric.append([rat[2][idx] for idx, val in enumerate(discalc) if val == nd][0])
- except IndexError:
- pass
-
- # Makes the distracted trial types into integers
- dis_numeric = [int(d) for d in dis_numeric]
- # Counts to work out percentages after finding how many are each modality
- d_whitenoise_count = 0
- d_tone_count = 0
- d_combined_count = 0
-
- dis_type_text = [] #labels the distypes with text labels and adds to the counts
- for d in dis_numeric:
- if d in modalitykey['whitenoise']:
- dis_type_text.append('whitenoise')
- d_whitenoise_count += 1
- elif d in modalitykey['tone']:
- dis_type_text.append('tone')
- d_tone_count += 1
- elif d in modalitykey['combined3']:
- dis_type_text.append('combined3')
- d_combined_count += 1
- # Non-distracted trials by modality
- ndis_numeric = [int(d) for d in ndis_numeric]
- nd_whitenoise_count = 0
- nd_tone_count = 0
- nd_combined_count = 0
-
- ndis_type_text = []
- for d in ndis_numeric:
- if d in modalitykey['whitenoise']:
- ndis_type_text.append('whitenoise')
- nd_whitenoise_count += 1
- elif d in modalitykey['tone']:
- ndis_type_text.append('tone')
- nd_tone_count += 1
- elif d in modalitykey['combined3']:
- ndis_type_text.append('combined3')
- nd_combined_count += 1
-
-## DEBUGGING SECTION
-
-# Not an issue if there are zero distracted trials, because calculates 0%
-# without problem, will be an issue if both 0 dis and 0 non-dis (because no
-# trials of that modality, need to exclude)
-
-# if d_whitenoise_count < 1:
-# print('y', d_whitenoise_count, nd_whitenoise_count)
-# if d_tone_count < 1:
-# print('x', d_tone_count, nd_tone_count)
-# if d_combined_count < 1:
-# print('z', d_combined_count, nd_combined_count)
-
-# WHITENOISE
- if nd_whitenoise_count > 0:
- percent_distracted_whitenoise = d_whitenoise_count / (d_whitenoise_count + nd_whitenoise_count) *100
- elif d_whitenoise_count < 1:
- try:
- percent_distracted_whitenoise = d_whitenoise_count / (d_whitenoise_count + nd_whitenoise_count) *100
- except ZeroDivisionError:
- print('oops')
- percent_distracted_whitenoise = -1
- elif d_whitenoise_count > 1:
- percent_distracted_whitenoise = 100
-# TONE
- ## what to do if both zero
- if nd_tone_count > 0:
- percent_distracted_tone = d_tone_count / (d_tone_count + nd_tone_count) *100
- elif d_tone_count < 1:
- try:
- percent_distracted_tone = d_tone_count / (d_tone_count + nd_tone_count) *100
- except ZeroDivisionError:
- print('oopsT')
- percent_distracted_tone = -1
- elif d_tone_count > 1:
- percent_distracted_tone = 100
-
-# COMBINED
- if nd_combined_count > 0:
- percent_distracted_combined = d_combined_count / (d_combined_count + nd_combined_count) *100
-
- elif d_combined_count < 1:
- try:
- percent_distracted_combined = d_combined_count / (d_combined_count + nd_combined_count) *100
- except ZeroDivisionError:
- print('oopsC')
- percent_distracted_combined = -1
- elif d_combined_count > 1:
- percent_distracted_combined = 100
-
-# NON WHITE NOISE
-
- if nd_combined_count > 0 or nd_tone_count > 0:
- percent_distracted_all_non_WN = (d_tone_count + d_combined_count) / ((d_tone_count + d_combined_count )+ (nd_tone_count + nd_combined_count)) *100
-
- elif d_combined_count < 1 and d_tone_count < 1:
- print('all less')
- try:
- percent_distracted_all_non_WN = (d_tone_count + d_combined_count) / ((d_tone_count + d_combined_count )+ (nd_tone_count + nd_combined_count)) *100
- except ZeroDivisionError:
- print('oopsNWN')
- percent_distracted_all_non_WN = -1
- elif d_combined_count or d_tone_count > 1:
- percent_distracted_all_non_WN = 100
-
-
- percent_dis_whitenoise_group.append(percent_distracted_whitenoise)
- percent_dis_tone_group.append(percent_distracted_tone)
- percent_dis_combined_group.append(percent_distracted_combined)
- percent_dis_all_non_WN_group.append(percent_distracted_all_non_WN)
-
-## Removing the occurrences of -1 which were added to account for zero distractors
-
- percent_dis_whitenoise_group = [x for x in percent_dis_whitenoise_group if x != -1]
- percent_dis_tone_group = [x for x in percent_dis_tone_group if x != -1]
- percent_dis_combined_group = [x for x in percent_dis_combined_group if x != -1]
- percent_dis_all_non_WN_group = [x for x in percent_dis_all_non_WN_group if x != -1]
-
-
- mean_percent_WHITENOISE = np.mean(percent_dis_whitenoise_group) # the average percentage of JUST whitenoise trials that rats are distracted on
- mean_percent_TONE = np.mean(percent_dis_tone_group)
- mean_percent_COMBINED = np.mean(percent_dis_combined_group)
- mean_percent_ALL_NON_WN = np.mean(percent_dis_all_non_WN_group)
-
- return discalcgroup, percent_dis_whitenoise_group, percent_dis_tone_group, \
- percent_dis_combined_group, percent_dis_all_non_WN_group, mean_percent_WHITENOISE, mean_percent_TONE, \
- mean_percent_COMBINED, mean_percent_ALL_NON_WN
-
-
-
-################################################################################################
-################################################################################################
-
-# Distraction day analysis (including modalities)
-modalitykey = {'whitenoise':[1,4], 'tone':[2,5], 'combined3':[3,6]}
-
-
-discalcgroup, percent_dis_whitenoise_group, percent_dis_tone_group, \
-percent_dis_combined_group, percent_dis_all_non_WN_group, mean_percent_WHITENOISE, \
-mean_percent_TONE, mean_percent_COMBINED, mean_percent_ALL_NON_WN = discalc_modalities(distraction, modalitykey)
diff --git a/Ch4_analysis_distraction.py b/Ch4_analysis_distraction.py
deleted file mode 100644
index c836123..0000000
--- a/Ch4_analysis_distraction.py
+++ /dev/null
@@ -1,979 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Aug 20 20:17:44 2018
-
-@author: u1490431
-"""
-
-''' Makes all of the bar scatters, not just for distraction, for licking
- Need to add labels and titles so we know which plts are which
- Check which scripts need to be run first and make sure to comment out
- the seaborn import that causes the plotting problems (no black outline)
- and strange grid
-
-'''
-
-
-
-## run this script AFTER the ch4 analysis script (photometry licking days and dis, not barscatter)
-def MetaExtractorTHPH (metafile):
- f = open(metafile, 'r')
- f.seek(0)
- Metafilerows = f.readlines()[1:]
- tablerows = []
-
- for row in Metafilerows:
- items = row.split(',')
- tablerows.append(items)
-
- TDTFile, MedFilenames, RatID, Date, Session, Include, Licks, Totdistractors, Distracted, \
- Percentdistracted, Note, Endcol = [],[],[],[],[],[],[],[],[],[],[],[]
-
- for i, lst in enumerate(tablerows):
-
-
-
-
- TDTFile = TDTFile + [lst[0]]
- MedFilenames = MedFilenames + [lst[1]]
- RatID = RatID + [lst[2]]
- Date = Date + [lst[3]]
- Session = Session + [lst[4]]
- Include = Include + [lst[5]]
- Licks = Licks + [lst[6]]
- Totdistractors = Totdistractors + [lst[7]]
- Distracted = Distracted + [lst[8]]
- Percentdistracted = Percentdistracted + [lst[9]]
- Note = Note + [lst[10]]
- Endcol = Endcol + [lst[11]]
-
-
- return ({'MedFilenames':MedFilenames, 'RatID':RatID, 'Date':Date, 'Session':Session, \
- 'Include':Include, 'Licks':Licks, 'PercentDistracted':Percentdistracted})
-
-
-
-def subsetter2(dictionary, dates, dis=False, verbose=False):
- '''
- SUBSETTER KP
- # Subsets data according to date, reads in dictionnary produced from metafile
- # and subsets into variable based on date(s) and drug condition
- # if distraction day argument is given as True adds the distractor type
- # to the output lists for later processing
-
- Adapted to include & include == 1 (because not all had the same date for last lick day)
- In metafile do not include 2.7 and 2.8 on the lick 3 day but do include them on lick 6
-
-
- '''
- subset = []
-
- for ind, filename in enumerate(dictionary['MedFilenames']):
- path = medfolder + filename
- onsets, offsets, med_dis_times, dis_type = medfilereader(path, ['b', 'c', 'i', 'j'], remove_var_header = True) # e onset, f offset
-
- if dis == True:
- if dictionary['Date'][ind] in dates and dictionary['Include'][ind] == '1':
- subset.append([onsets, offsets, dis_type, dictionary['RatID'][ind]])
-
- elif dis==False:
- if dictionary['Date'][ind] in dates and dictionary['Include'][ind] == '1':
- subset.append([onsets, offsets, dictionary['RatID'][ind]])
-
- if verbose: #assumes true
- print('filename, or comment ...')
- return subset
-
-
-
-def percentdisgroup(distractiondict):
- ''' Discalc_sal_M == distractiondict '''
-
- percent_dis_group = []
- for rat in distractiondict:
- percentage = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percent_dis_group.append(percentage)
- return percent_dis_group
-
-
-
-def disbygroup(dictionary):
- ''' Prodcues times of distracted and not distracted as 2 lists
- takes a dictionary of grouped rat data
- '''
-
- dis = []
- for rat in dictionary:
-
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- dis.append([distracted, notdistracted])
-
- return dis
-
-# Functions
-
-"""
-This function will create bar+scatter plots when passed a 1 or 2 dimensional
-array. Data needs to be passed in as a numpy object array, e.g.
-data = np.empty((2), dtype=np.object)
-data[0] = np.array(allData['nCasLicks'][index])
-data[1] = np.array(allData['nMaltLicks'][index])
-Various options allow specification of colors and paired/unpaired plotting.
-It can return the figures, axes, bars, and scatters for further modification.
-e.g.
-fig1, ax1, barlist1, sc1 = jmf.barscatter(data)
-for i in barlist1[1].get_children():
- i.set_color('g')
-"""
-def barscatter(data, transpose = False, unequal=False,
- groupwidth = .75,
- barwidth = .9,
- paired = False,
- spaced = False,
- barfacecoloroption = 'same', # other options 'between' or 'individual'
- barfacecolor = ['white'],
- baredgecoloroption = 'same',
- baredgecolor = [''],
- baralpha = 1,
- scatterfacecoloroption = 'same',
- scatterfacecolor = ['white'],
- scatteredgecoloroption = 'same',
- scatteredgecolor = ['grey'],
- scatterlinecolor = 'grey', # Don't put this value in a list
- scattersize = 80,
- scatteralpha = 1,
- linewidth=1,
- ylabel = 'none',
- xlabel = 'none',
- grouplabel = 'auto',
- itemlabel = 'none',
- barlabels = [],
- barlabeloffset=0.1,
- grouplabeloffset=0.2,
- yaxisparams = 'auto',
- show_legend = 'none',
- xrotation=0,
- legendloc='upper right',
- ax=[]):
-
- if unequal == True:
- dims = np.ndim(data)
- data_obj = np.ndarray((np.shape(data)), dtype=np.object)
- for i1, dim1 in enumerate(data):
- for i2, dim2 in enumerate(dim1):
- data_obj[i1][i2] = np.array(dim2, dtype=np.object)
- data = data_obj
-
- if type(data) != np.ndarray or data.dtype != np.object:
- dims = np.shape(data)
- if len(dims) == 2 or len(dims) == 1:
- data = data2obj1D(data)
-
- elif len(dims) == 3:
- data = data2obj2D(data)
-
- else:
- print('Cannot interpret data shape. Should be 2 or 3 dimensional array. Exiting function.')
- return
-
- # Check if transpose = True
- if transpose == True:
- data = np.transpose(data)
-
- # Initialize arrays and calculate number of groups, bars, items, and means
-
- barMeans = np.zeros((np.shape(data)))
- items = np.zeros((np.shape(data)))
-
- nGroups = np.shape(data)[0]
- groupx = np.arange(1,nGroups+1)
-
- if len(np.shape(data)) > 1:
- grouped = True
- barspergroup = np.shape(data)[1]
- barwidth = (barwidth * groupwidth) / barspergroup
-
- for i in range(np.shape(data)[0]):
- for j in range(np.shape(data)[1]):
- barMeans[i][j] = np.nanmean(data[i][j])
- items[i][j] = len(data[i][j])
-
- else:
- grouped = False
- barspergroup = 1
-
- for i in range(np.shape(data)[0]):
- barMeans[i] = np.nanmean(data[i])
- items[i] = len(data[i])
-
- # Calculate x values for bars and scatters
-
- xvals = np.zeros((np.shape(data)))
- barallocation = groupwidth / barspergroup
- k = (groupwidth/2) - (barallocation/2)
-
- if grouped == True:
-
- for i in range(np.shape(data)[0]):
- xrange = np.linspace(i+1-k, i+1+k, barspergroup)
- for j in range(barspergroup):
- xvals[i][j] = xrange[j]
- else:
- xvals = groupx
-
- # Set colors for bars and scatters
-
- barfacecolorArray = setcolors(barfacecoloroption, barfacecolor, barspergroup, nGroups, data)
- baredgecolorArray = setcolors(baredgecoloroption, baredgecolor, barspergroup, nGroups, data)
-
- scfacecolorArray = setcolors(scatterfacecoloroption, scatterfacecolor, barspergroup, nGroups, data, paired_scatter = paired)
- scedgecolorArray = setcolors(scatteredgecoloroption, scatteredgecolor, barspergroup, nGroups, data, paired_scatter = paired)
-
- # Initialize figure
- if ax == []:
- fig = plt.figure()
- ax = fig.add_subplot(111)
-
- # Make bars
- barlist = []
- barx = []
- for x, y, bfc, bec in zip(xvals.flatten(), barMeans.flatten(),
- barfacecolorArray, baredgecolorArray):
- barx.append(x)
- barlist.append(ax.bar(x, y, barwidth,
- facecolor = bfc, edgecolor = bec,
- zorder=-1))
-
- # Uncomment these lines to show method for changing bar colors outside of
- # function using barlist properties
- #for i in barlist[2].get_children():
- # i.set_color('r')
-
- # Make scatters
- sclist = []
- if paired == False:
- for x, Yarray, scf, sce in zip(xvals.flatten(), data.flatten(),
- scfacecolorArray, scedgecolorArray):
- for y in Yarray:
- if spaced == True:
- sclist.append(ax.scatter(x+np.random.random(size=1)*barallocation, y, s = scattersize,
- c = scf,
- edgecolors = sce,
- zorder=20))
- else:
- sclist.append(ax.scatter(x, y, s = scattersize,
- c = scf,
- edgecolors = sce,
- zorder=20))
-
- elif grouped == True:
- for x, Yarray, scf, sce in zip(xvals, data, scfacecolorArray, scedgecolorArray):
- for y in np.transpose(Yarray.tolist()):
- sclist.append(ax.plot(x, y, '-o', markersize = scattersize/10,
- color = scatterlinecolor,
- linewidth=linewidth,
- markerfacecolor = scf,
- markeredgecolor = sce,
- zorder=20))
- elif grouped == False:
- for n,_ in enumerate(data[0]):
- y = [y[n-1] for y in data]
- sclist.append(ax.plot(xvals, y, '-o', markersize = scattersize/10,
- color = scatterlinecolor,
- linewidth=linewidth,
- markerfacecolor = scfacecolorArray[0],
- markeredgecolor = scedgecolorArray[0],
- zorder=20))
-
- # Label axes
- if ylabel != 'none':
- plt.ylabel(ylabel)
-
- if xlabel != 'none':
- plt.xlabel(xlabel)
-
- # Set range and tick values for Y axis
- if yaxisparams != 'auto':
- ax.set_ylim(yaxisparams[0])
- plt.yticks(yaxisparams[1])
-
- # X ticks
- ax.tick_params(
- axis='x', # changes apply to the x-axis
- which='both', # both major and minor ticks are affected
- bottom='off', # ticks along the bottom edge are off
- top='off') # labels along the bottom edge are off
-
- if grouplabel == 'auto':
- plt.tick_params(labelbottom='off')
- else:
- if len(barlabels) > 0:
- plt.tick_params(labelbottom='off')
- yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
- offset = ax.get_ylim()[0] - yrange*grouplabeloffset
- for idx, label in enumerate(grouplabel):
- ax.text(idx+1, offset, label, va='top', ha='center')
- else:
- plt.xticks(range(1,nGroups+1), grouplabel)
-
- if len(barlabels) > 0:
- if len(barlabels) != len(barx):
- print('Wrong number of bar labels for number of bars!')
- else:
- yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
- offset = ax.get_ylim()[0] - yrange*barlabeloffset
- for x, label in zip(barx, barlabels):
- ax.text(x, offset, label, va='top', ha='center',rotation=xrotation)
-
- # Hide the right and top spines and set bottom to zero
- ax.spines['right'].set_visible(False)
- ax.spines['top'].set_visible(False)
- ax.spines['bottom'].set_position('zero')
-
- if show_legend == 'within':
- if len(itemlabel) != barspergroup:
- print('Not enough item labels for legend!')
- else:
- legendbar = []
- legendtext = []
- for i in range(barspergroup):
- legendbar.append(barlist[i])
- legendtext.append(itemlabel[i])
- plt.legend(legendbar, legendtext, loc=legendloc)
-
- return ax, barx, barlist, sclist
-
-#plt.savefig('foo.png')
-
-# To do
-# check if n's are the same for paired and if not default to unpaired
-# add color options for scatters
-# add alpha options etc
-# add axis options
-# remove white background
-# work out how to export or save as pdf, tiff, eps etc
-# work out how to return handles to scatters so can be altered outside of function
-# make help doc
-# make html file to show usage using ijupyt
-
-def setcolors(coloroption, colors, barspergroup, nGroups, data, paired_scatter = False):
-
- nColors = len(colors)
-
- if (paired_scatter == True) & (coloroption == 'within'):
- print('Not possible to make a Paired scatter plot with Within setting.')
- coloroption = 'same'
-
- if coloroption == 'within':
- if nColors < barspergroup:
- print('Not enough colors for this option! Reverting to one color.')
- coloroption = 'same'
- elif nColors > barspergroup:
- colors = colors[:barspergroup]
- coloroutput = [colors for i in data]
- coloroutput = list(chain(*coloroutput))
-
- if coloroption == 'between':
- if nColors < nGroups:
- print('Not enough colors for this option! Reverting to one color.')
- coloroption = 'same'
- elif nColors > nGroups:
- colors = colors[:nGroups]
- if paired_scatter == False:
- coloroutput = [[c]*barspergroup for c in colors]
- coloroutput = list(chain(*coloroutput))
- else:
- coloroutput = colors
-
- if coloroption == 'individual':
- if nColors < nGroups*barspergroup:
- print('Not enough colors for this color option')
- coloroption = 'same'
- elif nColors > nGroups*barspergroup:
- coloroutput = colors[:nGroups*barspergroup]
- else:
- coloroutput = colors
-
- if coloroption == 'same':
- coloroutput = [colors[0] for x in range(len(data.flatten()))]
-
- return coloroutput
-
-def data2obj1D(data):
- obj = np.empty(len(data), dtype=np.object)
- for i,x in enumerate(data):
- obj[i] = np.array(x)
- return obj
-
-def data2obj2D(data):
- obj = np.empty((np.shape(data)[0], np.shape(data)[1]), dtype=np.object)
- for i,x in enumerate(data):
- for j,y in enumerate(x):
- obj[i][j] = np.array(y)
- return obj
-
-
-
-
-################################################################################################
-################################################################################################
-
-## BEHAVIOUR SUBSETTING, PERCENT DISTRACTED AND PLOTS [THPH1, THPH2]
-
-metafile = '/Volumes/KP_HARD_DRI/kp259/THPH1AND2/THPH1&2Metafile.csv'
-extract_data = MetaExtractorTHPH(metafile)
-# Folder with all medfiles (THPH1 and THPH2)
-medfolder = '/Volumes/KP_HARD_DRI/kp259/THPH1AND2/med/'
-
-
-
-'''
- Info DPCP1(16) and DPCP2(16) males, DPCP3(24) females :
-
- THPH1 |THPH2.1 - 2.6|THPH2.7 - 2.8|CONDITION
- -----------|-------------|-------------|-----------
- 170620 |170809 |170814 |last lick
- 170621 |170810 |170815 |dis
- 170622 |170811 |170816 |hab 1
-
- All included, can later only subset the first 12 rats and ignore the
- unequal numbers from no habituation on rat 2.8
-
-'''
-
-
-last_lick = subsetter2(extract_data, ['170620', '170809', '170814'])
-distraction= subsetter2(extract_data, ['170621', '170810', '170815'], dis=True)
-habituation = subsetter2(extract_data, ['170622', '170811', '170806'], dis=True)
-
-
-discalcLick = []
-discalcDis = []
-discalcHab = []
-
-for rat in last_lick:
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- discalcLick.append([distracted, notdistracted])
-
-for rat in distraction:
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- discalcDis.append([distracted, notdistracted])
-
-for rat in habituation:
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- discalcHab.append([distracted, notdistracted])
-
-percentdisLick = []
-percentdisDis = []
-percentdisHab = []
-
-for rat in discalcLick:
- percent = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percentdisLick.append(percent)
-
-for rat in discalcDis:
- percent = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percentdisDis.append(percent)
-
-for rat in discalcHab:
- percent = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percentdisHab.append(percent)
-
-
-##########################################################################################
-
-# PERCENT DISTRACTED - THPH1 AND THPH2
-
-# For barscatter plots excluded rats 2.7 and 2.8 (as not all point on all days)
-# Make this as two separate plots
-data = [[percentdisLick[0:12], percentdisDis[0:12]]]
-col3 = ['darkturquoise','dodgerblue']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-ax, barx, barlist, sclist = barscatter(data, transpose=False, ax=ax,paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Percent distracted (%)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/PERCENT_Bar_mod_dis.pdf', bbox_inches="tight")
-
-data = [[percentdisDis[0:12], percentdisHab]]
-col3 = ['dodgerblue','lightblue']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-ax, barx, barlist, sclist = barscatter(data, transpose=False, ax=ax,paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Percent distracted (%)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/PERCENT_Bar_dis_hab.pdf', bbox_inches="tight")
-
-
-
-
-
- # Barscatters to make:
-
-## PERHAPS only care about distractORS as not many distracted trials on this habituation day?
-
-## UNEQUAL NUMBERS HERE, ISSUE FOR BARSCATTER - Maybe just index missing the LAST rat for ALL comparisons
-
-################################################################################################
-################################################################################################
-
-## Short versus long runs of licks ORANGES X 2 (ORANGE AND BRICK OR GREY AND BRICK)
-def MultBy100(list):
- output = [x*100 for x in list]
-
- return output
-shortVlongPeak = [MultBy100(peak_short_runs), MultBy100(peak_long_runs)]
-shortVlongt = [t_short_runs, t_long_runs]
-shortVlongPre = [MultBy100(pre_short_runs), MultBy100(pre_long_runs)]
-shortVlongPost = [MultBy100(post_short_runs), MultBy100(post_long_runs)]
-
-mpl.rcParams['figure.subplot.wspace'] = 0.6
-mpl.rcParams['figure.subplot.right'] = 1
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(shortVlongPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#ffb349','#ef7700'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(shortVlongt, ax=ax[1], transpose=False, paired=True, barfacecolor=['#ffb349','#ef7700'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(shortVlongPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#ffb349','#ef7700'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(shortVlongPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#ffb349','#ef7700'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/ShortVsLongPeaksBarScatter.pdf', bbox_inches="tight")
-
-################################################################################################
-################################################################################################
-
-## All runs versus all distractors (BRICK AND GREEN or ORANGE AND GREEN)
-runVdisPeak = [MultBy100(peak_runs), MultBy100(peak_distractor)]
-runVdist = [t_runs, t_distractor]
-runVdisPre = [MultBy100(pre_runs), MultBy100(pre_distractor)]
-runVdisPost = [MultBy100(post_runs), MultBy100(post_distractor)]
-
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(runVdisPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#ef7700','#4cbb17'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(runVdist, ax=ax[1], transpose=False, paired=True, barfacecolor=['#ef7700','#4cbb17'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(runVdisPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#ef7700','#4cbb17'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(runVdisPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#ef7700','#4cbb17'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/RunVsDisPeaksBarScatter.pdf', bbox_inches="tight")
-
-
-################################################################################################
-################################################################################################
-
-## Distracted vs not distracted (GREEN X 2)
-disVnotPeak = [MultBy100(peak_notdistracted),MultBy100(peak_distracted)]
-disVnott = [t_notdistracted,t_distracted]
-disVnotPre = [MultBy100(pre_notdistracted),MultBy100(pre_distracted)]
-disVnotPost = [MultBy100(post_notdistracted),MultBy100(post_distracted)]
-
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(disVnotPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(disVnott, ax=ax[1], transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(disVnotPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(disVnotPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/DisVsNotDisPeaksBarScatter.pdf', bbox_inches="tight")
-
-
-################################################################################################
-################################################################################################
-
-## Modelled versus distracion day presented distractors (GREY and GREEN)
-modVdisPeak = [MultBy100(peak_distractorMOD), MultBy100(peak_distractor)]
-modVdist = [t_distractorMOD, t_distractor]
-modVdisPre = [MultBy100(pre_distractorMOD), MultBy100(pre_distractor)]
-modVdisPost = [MultBy100(post_distractorMOD), MultBy100(post_distractor)]
-
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(modVdisPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(modVdist, ax=ax[1], transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(modVdisPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(modVdisPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/ModVsDisPeaksBarScatter.pdf', bbox_inches="tight")
-
-################################################################################################
-################################################################################################
-## Distraction day vs habituation day (GREEN, light and dark)
-disVhabPeak = [MultBy100(peak_distractor), MultBy100(peak_distractorHAB)]
-disVhabt = [t_distractor, t_distractorHAB]
-disVhabPre = [MultBy100(pre_distractor), MultBy100(pre_distractorHAB)]
-disVhabPost = [MultBy100(post_distractor), MultBy100(post_distractorHAB)]
-
-figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-
-labels = []
-ax[0], barx, barlist, sclist = barscatter(disVhabPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[1], barx, barlist, sclist = barscatter(disVhabt, ax=ax[1], transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[2], barx, barlist, sclist = barscatter(disVhabPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax[3], barx, barlist, sclist = barscatter(disVhabPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-
-ax[0].set_ylabel('Peak (% ΔF)')
-ax[1].set_ylabel('t (s)')
-ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-ax[3].set_ylabel('Post-event period (mean % ΔF)')
-
-ax[0].set_xticks([])
-#ax[0].set_ylim([0,4000])
-ax[1].set_xticks([])
-#ax[1].set_ylim([0,25])
-ax[2].set_xticks([])
-#ax[2].set_ylim(0,1200)
-ax[3].set_xticks([])
-#ax[3].set_ylim(0,1200)
-
-ax[0].spines['bottom'].set_visible(False)
-ax[1].spines['bottom'].set_visible(False)
-ax[2].spines['bottom'].set_visible(False)
-ax[3].spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/DisVsHabPeaksBarScatter.pdf', bbox_inches="tight")
-
-#???????
-
-# ANALYSIS FOR WHITE NOISE VS NON-WHITE NOISE HERE
-# (7) 4 bars --> wn vs nwn dis day vs hab day - peak, t, pre, post
-
-
-
-## Correlation between PERCENT DISTRACTED and DISTRCTOR PEAK
-import matplotlib as mpl
-from scipy import stats
-import seaborn as sn
-slope, intercept, r_value, p_value, std_err = stats.linregress(percentdisDis[2:], MultBy100(peak_distractor))
-## Plot the scatter
-plt.plot(percentdisDis[2:], MultBy100(peak_distractor),'o', color='limegreen')
-## Add line of best fit
-plt.plot(np.asarray(percentdisDis[2:]), intercept+slope*np.asarray(percentdisDis[2:]), 'limegreen')
-
-plt.legend()
-sn.despine(offset=10, trim=True);
-
-plt.xlabel('Percent distracted', fontsize=14)
-plt.ylabel('Peak response to distractors (% ΔF)', fontsize=14)
-plt.xticks(fontsize=14)
-plt.yticks(fontsize=14)
-plt.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Corr_Percent_DistractorPeak.pdf', bbox_inches="tight")
-plt.show()
-print('Linear regression, Percent distracted VS distractor peak')
-print('R squared = ',r_value**2, ', p value = ', p_value)
-
-
-## Correlation between PERCENT DISTRACTED and DISTRACTED PEAK
-slope, intercept, r_value, p_value, std_err = stats.linregress(percentdisDis[2:], MultBy100(peak_distracted))
-## Plot the scatter
-plt.plot(percentdisDis[2:], MultBy100(peak_distracted),'o', color='#257200')
-## Add line of best fit
-plt.plot(np.asarray(percentdisDis[2:]), intercept+slope*np.asarray(percentdisDis[2:]) ,'#257200')
-
-plt.legend()
-sn.despine(offset=10, trim=True);
-
-plt.xlabel('Percent distracted', fontsize=14)
-plt.ylabel('Peak response distracted (% ΔF)', fontsize=14)
-plt.xticks(fontsize=14)
-plt.yticks(fontsize=14)
-plt.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Corr_Percent_DistractedPeak.pdf', bbox_inches="tight")
-plt.show()
-print('Linear regression, Percent distracted VS distracted peak')
-print('R squared = ',r_value**2, ', p value = ', p_value)
-
-
-
-
-## Correlation between PERCENT DISTRACTED and RUNS PEAK
-slope, intercept, r_value, p_value, std_err = stats.linregress(percentdisDis[2:], MultBy100(peak_runs))
-## Plot the scatter
-plt.plot(percentdisDis[2:], MultBy100(peak_runs),'o', color='darkorange')
-## Add line of best fit
-plt.plot(np.asarray(percentdisDis[2:]), intercept+slope*np.asarray(percentdisDis[2:]), 'darkorange')
-
-plt.legend()
-sn.despine(offset=10, trim=True);
-
-plt.xlabel('Percent distracted', fontsize=14)
-plt.ylabel('Peak response runs (% ΔF)', fontsize=14)
-plt.xticks(fontsize=14)
-plt.yticks(fontsize=14)
-plt.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Corr_Percent_RunsPeak.pdf', bbox_inches="tight")
-
-plt.show()
-print('Linear regression, Percent distracted VS all runs peak')
-print('R squared = ',r_value**2, ', p value = ', p_value)
-
-
-################################################################################################
-################################################################################################
-
-
-# White noise separation - thph1 and thph2
-
-def discalc_modalities(dictionary, modalitykey):
- ''' Calculates distractors, distracted and modalities for dictionary of
- rats by group for just distraction day only
-
- Calculates a grouped percentage of each modality and how distracted
- by that modality rats are on average (by group)
-
- '''
- percent_dis_whitenoise_group = []
- percent_dis_tone_group = []
- percent_dis_combined_group = []
- percent_dis_all_non_WN_group = []
- discalcgroup = []
- ## SAL MALES - DISTRACTION DAY ONLY - DISTRACTOR TYPE ANALYSIS INCLUDED
-# Finds distracted or not (corrects for med slipping issue)
- for rat in dictionary:
- print('a')
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- print(len(distracted), len(notdistracted))
- # work out percentage and add this too
- discalcgroup.append([distracted, notdistracted])
-
- dis_numeric = []
- ndis_numeric = []
- # Modality analysis - calculates which distractors contain different features (whitenoise, tone or combination)
- # Then works out on how many of these trials rats are distracted (individual) before creating a mean
- # Tr and except because there are some cases where there are ZERO distracted or non distracted trials (or both)
- for d in distracted:
- try:
- dis_numeric.append([rat[2][idx] for idx, val in enumerate(discalc) if val == d][0])
- except IndexError:
- pass
-
-
- for nd in notdistracted:
- try:
- ndis_numeric.append([rat[2][idx] for idx, val in enumerate(discalc) if val == nd][0])
- except IndexError:
- pass
-
- # Makes the distracted trial types into integers
- dis_numeric = [int(d) for d in dis_numeric]
- # Counts to work out percentages after finding how many are each modality
- d_whitenoise_count = 0
- d_tone_count = 0
- d_combined_count = 0
-
- dis_type_text = [] #labels the distypes with text labels and adds to the counts
- for d in dis_numeric:
- if d in modalitykey['whitenoise']:
- dis_type_text.append('whitenoise')
- d_whitenoise_count += 1
- elif d in modalitykey['tone']:
- dis_type_text.append('tone')
- d_tone_count += 1
- elif d in modalitykey['combined3']:
- dis_type_text.append('combined3')
- d_combined_count += 1
- # Non-distracted trials by modality
- ndis_numeric = [int(d) for d in ndis_numeric]
- nd_whitenoise_count = 0
- nd_tone_count = 0
- nd_combined_count = 0
-
- ndis_type_text = []
- for d in ndis_numeric:
- if d in modalitykey['whitenoise']:
- ndis_type_text.append('whitenoise')
- nd_whitenoise_count += 1
- elif d in modalitykey['tone']:
- ndis_type_text.append('tone')
- nd_tone_count += 1
- elif d in modalitykey['combined3']:
- ndis_type_text.append('combined3')
- nd_combined_count += 1
-
-## DEBUGGING SECTION
-
-# Not an issue if there are zero distracted trials, because calculates 0%
-# without problem, will be an issue if both 0 dis and 0 non-dis (because no
-# trials of that modality, need to exclude)
-
-# if d_whitenoise_count < 1:
-# print('y', d_whitenoise_count, nd_whitenoise_count)
-# if d_tone_count < 1:
-# print('x', d_tone_count, nd_tone_count)
-# if d_combined_count < 1:
-# print('z', d_combined_count, nd_combined_count)
-
-# WHITENOISE
- if nd_whitenoise_count > 0:
- percent_distracted_whitenoise = d_whitenoise_count / (d_whitenoise_count + nd_whitenoise_count) *100
- elif d_whitenoise_count < 1:
- try:
- percent_distracted_whitenoise = d_whitenoise_count / (d_whitenoise_count + nd_whitenoise_count) *100
- except ZeroDivisionError:
- print('oops')
- percent_distracted_whitenoise = -1
- elif d_whitenoise_count > 1:
- percent_distracted_whitenoise = 100
-# TONE
- ## what to do if both zero
- if nd_tone_count > 0:
- percent_distracted_tone = d_tone_count / (d_tone_count + nd_tone_count) *100
- elif d_tone_count < 1:
- try:
- percent_distracted_tone = d_tone_count / (d_tone_count + nd_tone_count) *100
- except ZeroDivisionError:
- print('oopsT')
- percent_distracted_tone = -1
- elif d_tone_count > 1:
- percent_distracted_tone = 100
-
-# COMBINED
- if nd_combined_count > 0:
- percent_distracted_combined = d_combined_count / (d_combined_count + nd_combined_count) *100
-
- elif d_combined_count < 1:
- try:
- percent_distracted_combined = d_combined_count / (d_combined_count + nd_combined_count) *100
- except ZeroDivisionError:
- print('oopsC')
- percent_distracted_combined = -1
- elif d_combined_count > 1:
- percent_distracted_combined = 100
-
-# NON WHITE NOISE
-
- if nd_combined_count > 0 or nd_tone_count > 0:
- percent_distracted_all_non_WN = (d_tone_count + d_combined_count) / ((d_tone_count + d_combined_count )+ (nd_tone_count + nd_combined_count)) *100
-
- elif d_combined_count < 1 and d_tone_count < 1:
- print('all less')
- try:
- percent_distracted_all_non_WN = (d_tone_count + d_combined_count) / ((d_tone_count + d_combined_count )+ (nd_tone_count + nd_combined_count)) *100
- except ZeroDivisionError:
- print('oopsNWN')
- percent_distracted_all_non_WN = -1
- elif d_combined_count or d_tone_count > 1:
- percent_distracted_all_non_WN = 100
-
-
- percent_dis_whitenoise_group.append(percent_distracted_whitenoise)
- percent_dis_tone_group.append(percent_distracted_tone)
- percent_dis_combined_group.append(percent_distracted_combined)
- percent_dis_all_non_WN_group.append(percent_distracted_all_non_WN)
-
-## Removing the occurrences of -1 which were added to account for zero distractors
-
- percent_dis_whitenoise_group = [x for x in percent_dis_whitenoise_group if x != -1]
- percent_dis_tone_group = [x for x in percent_dis_tone_group if x != -1]
- percent_dis_combined_group = [x for x in percent_dis_combined_group if x != -1]
- percent_dis_all_non_WN_group = [x for x in percent_dis_all_non_WN_group if x != -1]
-
-
- mean_percent_WHITENOISE = np.mean(percent_dis_whitenoise_group) # the average percentage of JUST whitenoise trials that rats are distracted on
- mean_percent_TONE = np.mean(percent_dis_tone_group)
- mean_percent_COMBINED = np.mean(percent_dis_combined_group)
- mean_percent_ALL_NON_WN = np.mean(percent_dis_all_non_WN_group)
-
- return discalcgroup, percent_dis_whitenoise_group, percent_dis_tone_group, \
- percent_dis_combined_group, percent_dis_all_non_WN_group, mean_percent_WHITENOISE, mean_percent_TONE, \
- mean_percent_COMBINED, mean_percent_ALL_NON_WN
-
-
-
-################################################################################################
-################################################################################################
-
-# Distraction day analysis (including modalities)
-modalitykey = {'whitenoise':[1,4], 'tone':[2,5], 'combined3':[3,6]}
-
-
-discalcgroup, percent_dis_whitenoise_group, percent_dis_tone_group, \
-percent_dis_combined_group, percent_dis_all_non_WN_group, mean_percent_WHITENOISE, \
-mean_percent_TONE, mean_percent_COMBINED, mean_percent_ALL_NON_WN = discalc_modalities(distraction, modalitykey)
-
-
-## Report that they are no more distracting in these animals so no need to do the photometry!!!
-## Maybe report this early with the behavioural stuff?
\ No newline at end of file
diff --git a/Ch4_analysis_licking.py b/Ch4_analysis_licking.py
deleted file mode 100644
index 13d9356..0000000
--- a/Ch4_analysis_licking.py
+++ /dev/null
@@ -1,1886 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri May 25 09:08:16 2018
-
-@author: u1490431
-"""
-
-"""
-Chapter 4 - Distraction and photometry in VTA
-
-
-"""
-
-# Import modules --------------------------------------------------------
-
-
-
-import numpy as np
-import scipy.io as sio
-
-import matplotlib.pyplot as plt
-import pandas as pd
-import os
-import matplotlib as mpl
-import itertools
-import matplotlib.mlab as mlab
-#import seaborn as sb
-import statistics as stats
-
-# Functions -------------------------------------------------------------
-def mapTTLs(matdict):
- for x in ['LiA_', 'La2_']:
- try:
- licks = getattr(matdict['output'], x)
- except:
- print('File has no ' + x)
-
- lickson = licks.onset
- licksoff = licks.offset
-
- return lickson, licksoff
-
-
-def loadmatfile(file):
- a = sio.loadmat(file, squeeze_me=True, struct_as_record=False)
- print(type(a))
- sessiondict = {}
- sessiondict['bluefilt'] = a['output'].blue
- sessiondict['uv'] = a['output'].uv
- sessiondict['fs'] = a['output'].fs
-
- try:
-
- sessiondict['licks'] = a['output'].licks.onset
- sessiondict['licks_off'] = a['output'].licks.offset
- except: ## find which error it is
-
- sessiondict['licks'], sessiondict['licks_off'] = mapTTLs(a)
-
-
-
- sessiondict['distractors'] = distractionCalc2(sessiondict['licks'])
-
-# #write distracted or not to produce 2 lists of times, distracted and notdistracted
- #distracted, notdistracted= distractedOrNot(sessiondict['distractors'], sessiondict['licks'])
-#
- sessiondict['distracted'], sessiondict['notdistracted'] = distractedOrNot(sessiondict['distractors'], sessiondict['licks'])
- # sessiondict['notdistracted'] = notdistracted
-
- # ''' sessiondict['lickRuns'] = lickRunCalc(sessiondict['licks']) '''
-
- return sessiondict
-
-def distractedOrNot(distractors, licks):
- distracted = []
- notdistracted = []
- lickList = []
- for l in licks:
- lickList.append(l)
-
-
- for index, distractor in enumerate(distractors):
- if distractor in licks:
-
- ind = lickList.index(distractor)
- try:
- if (licks[ind+1] - licks[ind]) > 1:
- distracted.append(licks[ind])
- else:
- if (licks[ind+1] - licks[ind]) < 1:
- notdistracted.append(licks[ind])
- except IndexError:
- print('last lick was a distractor!!!')
- distracted.append(licks[ind])
-
- return(distracted, notdistracted)
-
-
-def remcheck(val, range1, range2):
- # function checks whether value is within range of two decimels
- if (range1 < range2):
- if (val > range1) and (val < range2):
- return True
- else:
- return False
- else:
- if (val > range1) or (val < range2):
- return True
- else:
- return False
-
-
-def distractionCalc2(licks, pre=1, post=1):
- licks = np.insert(licks, 0, 0)
- b = 0.001
- d = []
- idx = 3
-
- while idx < len(licks):
- if licks[idx]-licks[idx-2] < 1 and remcheck(b, licks[idx-2] % 1, licks[idx] % 1) == False:
- d.append(licks[idx])
- b = licks[idx] % 1
- idx += 1
- try:
- while licks[idx]-licks[idx-1] < 1:
- b = licks[idx] % 1
- idx += 1
- except IndexError:
- pass
- else:
- idx +=1
-# print(len(d))
-
-# print(d[-1])
- if d[-1] > 3599:
- d = d[:-1]
-
-# print(len(d))
-
- return d
-
-# LickCalc ============================================================
-# Looking at function from Murphy et al (2017)
-
-"""
-This function will calculate data for bursts from a train of licks. The threshold
-for bursts and clusters can be set. It returns all data as a dictionary.
-"""
-def lickCalc(licks, offset = [], burstThreshold = 0.25, runThreshold = 10,
- binsize=60, histDensity = False):
-
- # makes dictionary of data relating to licks and bursts
- if type(licks) != np.ndarray or type(offset) != np.ndarray:
- try:
- licks = np.array(licks)
- offset = np.array(offset)
- except:
- print('Licks and offsets need to be arrays and unable to easily convert.')
- return
-
- lickData = {}
-
- if len(offset) > 0:
- lickData['licklength'] = offset - licks
- lickData['longlicks'] = [x for x in lickData['licklength'] if x > 0.3]
- else:
- lickData['licklength'] = []
- lickData['longlicks'] = []
-
- lickData['licks'] = np.concatenate([[0], licks])
- lickData['ilis'] = np.diff(lickData['licks'])
- lickData['freq'] = 1/np.mean([x for x in lickData['ilis'] if x < burstThreshold])
- lickData['total'] = len(licks)
-
- # Calculates start, end, number of licks and time for each BURST
- lickData['bStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
- lickData['bInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
- lickData['bEnd'] = [lickData['licks'][i-1] for i in lickData['bInd'][1:]]
- lickData['bEnd'].append(lickData['licks'][-1])
-
- lickData['bLicks'] = np.diff(lickData['bInd'] + [len(lickData['licks'])])
- lickData['bTime'] = np.subtract(lickData['bEnd'], lickData['bStart'])
- lickData['bNum'] = len(lickData['bStart'])
- if lickData['bNum'] > 0:
- lickData['bMean'] = np.nanmean(lickData['bLicks'])
- else:
- lickData['bMean'] = 0
-
- lickData['bILIs'] = [x for x in lickData['ilis'] if x > burstThreshold]
-
- lickData['bILIs'] = [x for x in lickData['ilis'] if x > burstThreshold]
-
- # Calculates start, end, number of licks and time for each RUN
- lickData['rStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
- lickData['rInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
- lickData['rEnd'] = [lickData['licks'][i-1] for i in lickData['rInd'][1:]]
- lickData['rEnd'].append(lickData['licks'][-1])
-
- lickData['rLicks'] = np.diff(lickData['rInd'] + [len(lickData['licks'])])
- lickData['rTime'] = np.subtract(lickData['rEnd'], lickData['rStart'])
- lickData['rNum'] = len(lickData['rStart'])
-
- lickData['rILIs'] = [x for x in lickData['ilis'] if x > runThreshold]
- try:
- lickData['hist'] = np.histogram(lickData['licks'][1:],
- range=(0, 3600), bins=int((3600/binsize)),
- density=histDensity)[0]
- except TypeError:
- print('Problem making histograms of lick data')
-
- return lickData
-
-def asnumeric(s):
- try:
- x = float(s)
- return x
- except ValueError:
- return float('nan')
-
-def time2samples(self):
- tick = self.output.Tick.onset
- maxsamples = len(tick)*int(self.fs)
- if (len(self.data) - maxsamples) > 2*int(self.fs):
- print('Something may be wrong with conversion from time to samples')
- print(str(len(self.data) - maxsamples) + ' samples left over. This is more than double fs.')
-
- self.t2sMap = np.linspace(min(tick), max(tick), maxsamples)
-
-def snipper(data, timelock, fs = 1, t2sMap = [], preTrial=10, trialLength=30,
- adjustBaseline = True,
- bins = 0):
-
- if len(timelock) == 0:
- print('No events to analyse! Quitting function.')
- raise Exception('no events')
- nSnips = len(timelock)
- pps = int(fs) # points per sample
- pre = int(preTrial*pps)
-# preABS = preTrial
- length = int(trialLength*pps)
-# converts events into sample numbers
- event=[]
- if len(t2sMap) > 1:
- for x in timelock:
- event.append(np.searchsorted(t2sMap, x, side="left"))
- else:
- event = [x*fs for x in timelock]
-
- avgBaseline = []
- snips = np.empty([nSnips,length])
-
- for i, x in enumerate(event):
- start = int(x) - pre
- avgBaseline.append(np.mean(data[start : start + pre]))
-# print(x)
- try:
- snips[i] = data[start : start+length]
- except: # Deals with recording arrays that do not have a full final trial
- snips = snips[:-1]
- avgBaseline = avgBaseline[:-1]
- nSnips = nSnips-1
-
- if adjustBaseline == True:
- snips = np.subtract(snips.transpose(), avgBaseline).transpose()
- snips = np.divide(snips.transpose(), avgBaseline).transpose()
-
- if bins > 0:
- if length % bins != 0:
- snips = snips[:,:-(length % bins)]
- totaltime = snips.shape[1] / int(fs)
- snips = np.mean(snips.reshape(nSnips,bins,-1), axis=2)
- pps = bins/totaltime
-
- return snips, pps
-
-def makerandomevents(minTime, maxTime, spacing = 77, n=100):
- events = []
- total = maxTime-minTime
- start = 0
- for i in np.arange(0,n):
- if start > total:
- start = start - total
- events.append(start)
- start = start + spacing
- events = [i+minTime for i in events]
- return events
-
-def med_abs_dev(data, b=1.4826):
- median = np.median(data)
- devs = [abs(i-median) for i in data]
- mad = np.median(devs)*b
-
- return mad
-
-def findnoise(data, background, t2sMap = [], fs = 1, bins=0, method='sd'):
-
- bgSnips, _ = snipper(data, background, t2sMap=t2sMap, fs=fs, bins=bins)
-
- if method == 'sum':
- bgSum = [np.sum(abs(i)) for i in bgSnips]
- bgMAD = med_abs_dev(bgSum)
- bgMean = np.mean(bgSum)
- elif method == 'sd':
- bgSD = [np.std(i) for i in bgSnips]
- bgMAD = med_abs_dev(bgSD)
- bgMean = np.mean(bgSD)
-
- return bgMAD, bgMean
-# Distracted or not peaks
-# Manuall add the lists here
-# Start with THPH1 and 2 lick days
-# Then THPH1 and 2 distraction
-# Then THPH1 and 2 habituation
-
-# WHICH RATS DID NOT HAVE SIGNAL?
-# THPH1 AND 2
-# Lick day
-
-def zscore(snips, baseline_points=100):
-
- BL_range = range(baseline_points)
- z_snips = []
- for i in snips:
- mean = np.mean(i[BL_range])
- sd = np.std(i[BL_range])
- z_snips.append([(x-mean)/sd for x in i])
-
- return z_snips
-
-TDTfiles_thph_lick = ['thph1-1_lick6_proc', 'thph1-2_lick6_proc', 'thph1-3_lick6_proc', 'thph1-4_lick6_proc', 'thph1-5_lick6_proc',\
- 'thph1-6_lick6_proc', 'thph2-1_lick3_proc', 'thph2-2_lick3_proc','thph2-3_lick3_proc','thph2-4_lick3_proc', \
- 'thph2-5_lick3_proc','thph2-6_lick3_proc', 'thph2-7_lick6_proc', 'thph2-8_lick6_proc']
-
-# Modelled distractors change variable names for this script or script section
-# Distraction day
-TDTfiles_thph_dis = ['thph1-3_distraction1_proc', \
- 'thph1-4_distraction1_proc','thph1-5_distraction1_proc','thph1-6_distraction1_proc', \
- 'thph2-1_distraction_proc', 'thph2-2_distraction_proc', 'thph2-3_distraction_proc', \
- 'thph2-4_distraction_proc', 'thph2-5_distraction_proc', 'thph2-6_distraction_proc', \
- 'thph2-7_distraction_proc', 'thph2-8_distraction_proc'] #'thph1-1_distraction1_proc', 'thph1-2_distraction1_proc'
-
-# Habituation day
-TDTfiles_thph_hab = ['thph1-3_distraction2_proc',\
- 'thph1-4_distraction2_proc', 'thph1-5_distraction2_proc','thph1-6_distraction2_proc',\
- 'thph2-1_habituation_proc', 'thph2-2_habituation_proc', 'thph2-3_habituation_proc', \
- 'thph2-4_habituation_proc', 'thph2-5_habituation_proc', 'thph2-6_habituation_proc', \
- 'thph2-7_habituation_proc'] #['thph1-1_distraction2_proc','thph1-2_distraction2_proc',
-
-
-TDTfilepath = '/Volumes/KP_HARD_DRI/All_Matlab_Converts/BIG CONVERSION 14 AUG 2018/THPH matfiles/'
-
-#savepath = '/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures'
-
-# LICKING ANALYSIS **************************************************************************
-# Loop through files and calculate burst and run lengths
-# Assign empty lists for storing arrays of burst/run lengths
-
-# Lengths, all burst or run lengths here NOT
-allBursts = []
-allBurstsTimes = []
-allRuns = []
-allRunTimes = []
-
-allRunIndices = []
-allrILIs = []
-allbILIs = []
-allRatBlue = []
-allRatUV = []
-allRatFS = []
-allRatLicks = []
-
-
-for filename in TDTfiles_thph_lick:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlue.append(ratdata['bluefilt'])
- allRatUV.append(ratdata['uv'])
- allRatFS.append(ratdata['fs'])
- allRatLicks.append(ratdata['licks'])
- burstanalysis = lickCalc(ratdata['licks'], offset=ratdata['licks_off'])
- burstList = burstanalysis['bLicks'] # n licks per burst
- runList = burstanalysis['rLicks'] # n licks per run
- burstListTimes = burstanalysis['bStart'] # Actual times of start of runs
- runListTimes = burstanalysis['rStart'] # Actual times of start of bursts
- allBursts.append(burstList)
- allRuns.append(runList)
- allRunTimes.append(runListTimes)
- allBurstsTimes.append(burstListTimes)
-# allRunIndices.append(indexRunList)
-# allrILIs.append(runILIs)
-# allbILIs.append(burstILIs)
-
-# Make the list of lists into one long list for histogram
-MergedBurstList = list(itertools.chain.from_iterable(allBursts))
-MergedRunList = list(itertools.chain.from_iterable(allRuns))
-
-# Descriptives - aggregated data
-meanburstlength = round(np.mean(MergedBurstList))
-medburstlen = round(np.median(MergedBurstList))
-meanrunlength = round(np.mean(MergedRunList))
-medrunlen = round(np.median(MergedRunList))
-
-## Make the snips
-
-
-blueMeansBurst = []
-uvMeansBurst = []
-blueMeansRuns = []
-uvMeansRuns = []
-allbluesnips = []
-alluvsnips = []
-
-for i, val in enumerate(allRunTimes):
-
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRunTimes[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRunTimes[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
-
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
-
-# Might not need the noise index, this is just for trials fig
-
-# fig = plt.figure()
-# ax = plt.subplot(1,1,1)
-# ax.set_ylim([-0.03, 0.03])
-# #ax.set_ylim([-0.05, 0.05])
-# trialsMultShadedFig(ax, [uvSnips,blueSnips], ppsBlue, eventText='First Lick in Run')
-# plt.text(250,0.03, '{}'.format(len(allRunTimes[i])) + ' Runs' )
-#
-# fig2 = plt.figure()
-# ax2 = plt.subplot(1,1,1)
-# ax2.set_ylim([-0.2, 0.2])
-# trialsFig(ax2, blueSnips, uvSnips, ppsBlue, eventText='First Lick in Run') #noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRunTimes[i])) + ' Runs' )
-#
-# filepath ='/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/'
-# ratname = str(i+1) +'.pdf'
-#
-# fig2.savefig(filepath+ratname)
-
- # these four lines used later to define means plot (made after runs)
- blueMean = np.mean(blueSnips, axis=0)
- blueMeansRuns.append(blueMean)
- uvMean = np.mean(uvSnips, axis=0)
- uvMeansRuns.append(uvMean)
- allbluesnips.append(blueSnips)
- alluvsnips.append(uvSnips)
-
-# All runs and all bursts (representative rat, etc.)
-# Then segregate by long and short (for all rats quartiles not for each rat)
-
-
-#########################################################################################
-# Average of all runs, all rats, all trials
-## Mean of ALL runs and ALL rats on multishaded figure
-
-#linecolor=['purple', 'blue'], errorcolor=['thistle', 'lightblue']
-
-
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.03, 0.03])
-#ax.set_ylim([-0.05, 0.05])
-trialsMultShadedFig(ax, [np.asarray(uvMeansRuns[2:]),np.asarray(blueMeansRuns)], ppsBlue, eventText='First Lick in Run', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'])
-plt.text(250,0.03, '{}'.format(len(MergedRunList)) + ' Runs' ) ## Edit this to be all
-# PLOTS BLUE LINE TWICE (JAN19)
-
-
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/All_Runs_All_Rats.pdf')
-
-
-'''
-## Shows every single trial for each rat for runs - to choose representative sample
-#for index, sniplist in enumerate(allbluesnips):
-# for ind, lis in enumerate(sniplist):
-# fig = plt.figure(figsize=(6,3))
-# ax = plt.subplot(1,1,1)
-# ax = plt.plot(allbluesnips[index][ind])
-# plt.text(250,0, '{}'.format([index,ind]))
-
-#########################################################################################
-
-
-# Individual trial 1 - 13,1
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[13][1] , color='blue')
-ax.plot(alluvsnips[13][1] , color='purple')
-triallicks = nearestevents(allRunTimes[13],allRatLicks[13])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[1]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90, c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH2.8_Run2.pdf')
-
-
-# Individual trial 1 - 13,6
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[13][6] , color='blue')
-ax.plot(alluvsnips[13][6] , color='purple')
-triallicks = nearestevents(allRunTimes[13],allRatLicks[13])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[6]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90, c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH2.8_Run7.pdf', bbox_inches="tight")
-
-
-# Individual trial 1 - 12, 11
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[12][11] , color='blue')
-ax.plot(alluvsnips[12][11] , color='purple')
-triallicks = nearestevents(allRunTimes[12],allRatLicks[12])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[11]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH2.7_Run12.pdf', bbox_inches="tight")
-
-# Individual trial 1 - 11, 10
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[11][10] , color='blue')
-ax.plot(alluvsnips[11][10] , color='purple')
-triallicks = nearestevents(allRunTimes[11],allRatLicks[11])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[10]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH2.6_Run11.pdf', bbox_inches="tight")
-
-# Individual trial 1 - 4, 33
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[4][33] , color='blue')
-ax.plot(alluvsnips[4][33] , color='purple')
-triallicks = nearestevents(allRunTimes[4],allRatLicks[4])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[33]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH1.5_Run34.pdf', bbox_inches="tight")
-
-# Individual trial 1 - 3, 13
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[3][13] , color='blue')
-ax.plot(alluvsnips[3][13] , color='purple')
-triallicks = nearestevents(allRunTimes[3],allRatLicks[3])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[13]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_THPH1.4_Run14.pdf', bbox_inches="tight")
-
-# Individual trial 1 - 3, 13 ----- TESTER FOR SCALE
-f = plt.figure(figsize=(6,2))
-ax = plt.subplot(111)
-ax.plot(allbluesnips[3][13] , color='black')
-ax.plot(alluvsnips[3][13] , color='black')
-triallicks = nearestevents(allRunTimes[3],allRatLicks[3])# allRatLicks[13], allRatLicks[13])
-xvals1 = [(x+10)*10 for x in triallicks[13]]
-yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-ax.scatter(xvals1, yvals1, marker='|', s=90,c='k')
-ax.set_ylim([-0.2, 0.2])
-# Making x scale
-scale = 5
-scalebar = scale * ppsBlue
-yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
-scalebary = (yrange / 10) + ax.get_ylim()[0]
-scalebarx = [ax.get_xlim()[1] - scalebar, ax.get_xlim()[1]]
-ax.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-ax.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), str(scale) +' s', ha='center',va='top', **Calibri, **Size)
-#f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/SingleTrial_SCALE.pdf', bbox_inches="tight")
-
-'''
-###########################################################################################
-
-# Long and short runs, 25th and 75th percentiles
-
-## Find short and long run lengths
-aggregateLowerQuart = np.percentile(MergedRunList,25)
-aggregateUpperQuart = np.percentile(MergedRunList,75)
-
-allLogIndRuns = []
-for runLicksList in allRuns:
- logIndRuns = [] # so 14 empty arrays exist and then don't (add to larger)
- for item in runLicksList:
- if item < aggregateLowerQuart:
- logIndRuns.append('LOWER')
- else:
- if item > aggregateUpperQuart:
- logIndRuns.append('UPPER')
-
- else:
- logIndRuns.append('MIDDLE')
- allLogIndRuns.append(logIndRuns)
-
-# 14 lists of logical indices for whether the n licks in that run was L,M,U
-
-
-
-# Final lists of run times sorted by u.m.l and stored by rat (14 lists)
-lowerqRunTimes = []
-uppqRunTimes = []
-mid50RunTimes = []
-lowerLengths = []
-upperLengths = []
-
-# Might need the index and the value for both lists ??
-for i, listofindices in enumerate(allLogIndRuns):
- templower = []
- tempupper = []
- tempmid = []
- for j, runIndex in enumerate(listofindices):
-
- #print(i,j) i is the list index and j is the item index
-
- if runIndex == 'LOWER':
- lowrun = allRunTimes[i][j]
- templower.append(lowrun)
- lowerLengths.append(allRuns[i][j])
-
- if runIndex == 'UPPER':
- upprun = allRunTimes[i][j]
- tempupper.append(upprun)
- upperLengths.append(allRuns[i][j])
-#
- if runIndex == 'MIDDLE':
- midrun = allRunTimes[i][j]
- tempmid.append(midrun)
-
- lowerqRunTimes.append(templower)
- uppqRunTimes.append(tempupper)
- mid50RunTimes.append(tempmid)
-
- ## Figure how to also get the actual lengths? Must have done this. Line 603
- ## runLicksList inside allRuns
-# _________________________________________________________________________
-
-
-#allign to lowerqRunTimes
-#allign to uppqRunTimes
-
-uvMeans_short_run = []
-blueMeans_short_run = []
-uvMeans_long_run = []
-blueMeans_long_run = []
-# Makes tonnes of individual plots
-# Individual rats might look odd as low Ns, but mean of mean will be better
-# Repeat this with bursts if looks like might be useful
-
-for i, val in enumerate(lowerqRunTimes):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], lowerqRunTimes[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], lowerqRunTimes[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
- except:
- pass
-#
-# fig12 = plt.figure()
-# ax10 = plt.subplot(1,1,1)
-# ax10.set_ylim([-0.03, 0.03])
-# #ax.set_ylim([-0.05, 0.05])
-# trialsMultShadedFig(ax10, [uvSnips,blueSnips], ppsBlue, eventText='First Lick Short Run')
-# plt.text(250,0.03, '{}'.format(len(lowerqRunTimes[i])) + ' short runs' )
-##
-# fig13 = plt.figure()
-# ax11 = plt.subplot(1,1,1)
-# ax11.set_ylim([-0.2, 0.2])
-# trialsFig(ax11, blueSnips, uvSnips, ppsBlue, eventText='First Lick in Short Run', noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(lowerqRunTimes[i])) + ' short runs' )
-
-# # these four lines used later to define means plot (made after runs)
- blueMeanSHORT = np.mean(blueSnips, axis=0)
- blueMeans_short_run.append(blueMeanSHORT)
- uvMeanSHORT = np.mean(uvSnips, axis=0)
- uvMeans_short_run.append(uvMeanSHORT)
-
-MergedRunList_Short = list(itertools.chain.from_iterable(lowerqRunTimes))
-# Average of all SHORT runs, all rats, all trials
-## Mean of ALL SHORT runs and ALL rats on multishaded figure
-
-#linecolor=['purple', 'blue'], errorcolor=['thistle', 'lightblue']
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.03, 0.03])
-#ax.set_ylim([-0.05, 0.05])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_short_run),np.asarray(blueMeans_short_run)], ppsBlue, eventText='First Lick in Short Run', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'])
-plt.text(250,0.03, '{}'.format(len(MergedRunList_Short)) + ' Short Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Short_Runs_All_Rats.pdf', bbox_inches="tight")
-
-
-## ===============================================================
-
-# Photometry figures (individual) for LONG runs
-
-for i, val in enumerate(uppqRunTimes):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], uppqRunTimes[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], uppqRunTimes[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
- except:
- pass
-
-# fig14 = plt.figure()
-# ax12 = plt.subplot(1,1,1)
-# ax12.set_ylim([-0.03, 0.03])
-# #ax.set_ylim([-0.05, 0.05])
-# trialsMultShadedFig(ax12, [uvSnips,blueSnips], ppsBlue, eventText='First Lick Long Run')
-# plt.text(250,0.03, '{}'.format(len(uppqRunTimes[i])) + ' long runs' )
-#
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.2, 0.2])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='First Lick in Long Run', noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(uppqRunTimes[i])) + ' long runs' )
-
-# # these four lines used later to define means plot (made after runs)
- blueMeanLONG = np.mean(blueSnips, axis=0)
- blueMeans_long_run.append(blueMeanLONG)
- uvMeanLONG = np.mean(uvSnips, axis=0)
- uvMeans_long_run.append(uvMeanLONG)
-
-MergedRunList_Long = list(itertools.chain.from_iterable(uppqRunTimes))
-# Average of all SHORT runs, all rats, all trials
-## Mean of ALL SHORT runs and ALL rats on multishaded figure
-
-## Not sure how to turn the scale off here (removed ppsBlue)
-##linecolor=['purple', 'blue'], errorcolor=['thistle', 'lightblue']
-#fig = plt.figure(figsize=(6,3))
-#ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.03, 0.03])
-#ax.set_ylim([-0.05, 0.05])
-#trialsMultShadedFig(ax, [np.asarray(uvMeans_long_run),np.asarray(blueMeans_long_run)], ppsBlue, eventText='First Lick in Long Run', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-##fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Long_Runs_All_Rats.pdf', bbox_inches="tight")
-
-
-fig = plt.figure(figsize=(6,3))
-ax= plt.subplot(1,1,1)
-#ax.set_ylim([-0.05, 0.05])
-LONG_SHORTrunMultFig = trialsMultShadedFig(ax, [np.asarray(blueMeans_short_run),np.asarray(blueMeans_long_run)], ppsBlue, eventText=''
- , linecolor=['k', 'firebrick'], errorcolor=['darkgrey', 'darkorange'], scale=0)
-ax.set(ylabel = chr(916) + 'F')
-ax.yaxis.label.set_size(14)
-
-
-# Simple figure legend
-import matplotlib.patches as mpatches
-
-orange_patch = mpatches.Patch(color='darkorange', label='Long Runs')
-grey_patch = mpatches.Patch(color='darkgrey', label='Short Runs')
-plt.legend(handles=[orange_patch, grey_patch], fontsize=14)
-plt.show()
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/ShortANDLong_Runs_All_Rats.pdf', bbox_inches="tight")
-
-
-######
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# DISTRACTION FILES (minus the first 2) - this was run with all included
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-
-allRatBlue = []
-allRatUV = []
-allRatFS = []
-allRatLicks = []
-allRatDistractors = []
-allRatDistracted = []
-allRatNotDistracted = []
-blueMeans_distractor = []
-uvMeans_distractor = []
-blueMeans_distracted = []
-uvMeans_distracted = []
-blueMeans_notdistracted = []
-uvMeans_notdistracted = []
-allbluesnips = []
-alluvsnips = []
-
-for filename in TDTfiles_thph_dis:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlue.append(ratdata['bluefilt'])
- allRatUV.append(ratdata['uv'])
- allRatFS.append(ratdata['fs'])
- allRatLicks.append(ratdata['licks'])
- allRatDistractors.append(ratdata['distractors'])
- allRatDistracted.append(ratdata['distracted'])
- allRatNotDistracted.append(ratdata['notdistracted'])
- figure12 = plt.figure(figsize=(6,3))
- ax6 = plt.subplot(111)
- ax6.spines['right'].set_visible(False)
- ax6.xaxis.set_visible(False)
- ax6.spines['top'].set_visible(False)
- ax6.spines['bottom'].set_visible(False)
- ax6.set(ylabel = 'Trials')
- ax6.yaxis.label.set_size(14)
-# distractionrasterFig(ax6, ratdata['distractors'], ratdata['licks'], pre=1, post=10, sortevents='yes', sortdirection='dec')
-
-
-for i, val in enumerate(allRatDistractors):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatDistractors[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatDistractors[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
-
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
- except:
- pass
-# Individual plots to choose a representative rat
-
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distractor') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistractors[i])) + ' distractors' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_' + str(i) + '.pdf', bbox_inches="tight")
-
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractor.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractor.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractor),np.asarray(blueMeans_distractor)], ppsBlue, eventText='Distractor', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_All_Rats.pdf', bbox_inches="tight")
-
-
-
-for i, val in enumerate(allRatDistracted):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatDistracted[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatDistracted[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
- except:
- pass
-# Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistracted[i])) + ' distracted' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_' + str(i) + '.pdf', bbox_inches="tight")
-#
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distracted.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distracted.append(uvMeanDISTRACTED)
- allbluesnips.append(blueSnips)
- alluvsnips.append(uvSnips)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distracted),np.asarray(blueMeans_distracted)], ppsBlue, eventText='Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_All_Rats.pdf', bbox_inches="tight")
-
-
-
-
-for i, val in enumerate(allRatNotDistracted):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatNotDistracted[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatNotDistracted[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
- except:
- pass
-
-# Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Not Distracted') #, noiseindex=noiseindex) #, )
- # plt.text(250,0.2, '{}'.format(len(allRatNotDistracted[i])) + ' not distracted' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_' + str(i) + '.pdf', bbox_inches="tight")
-
-# Means for not distracted trials here MULT SHADED FIG
-
- blueMeanNOTDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_notdistracted.append(blueMeanNOTDISTRACTED)
- uvMeanNOTDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_notdistracted.append(uvMeanNOTDISTRACTED)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_notdistracted),np.asarray(blueMeans_notdistracted)], ppsBlue, eventText='Not Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_All_Rats.pdf', bbox_inches="tight")
-
-
-########################################################################################################
-
-
-# (1) Make plots of each trial or pick ones that look good
-
-# Individual trial Run this with [0] --> [13] for each rat all trials saved
-# Manually chose trials to run single trials for
-
-# Indices all moved by +2
-# 2.7 - 3, 6, 9
-# 2.6 - 3, 9, 14
-# 2.5 - 5
-# 2.1 - 0
-# 1.5 - 0
-
-#for ind, snip in enumerate(allbluesnips[13]):
-# print(ind)
-# f = plt.figure(figsize=(6,3))
-# ax = plt.subplot(111)
-# ax.plot(allbluesnips[13][ind] , color='blue')
-# ax.plot(alluvsnips[13][ind] , color='purple')
-#
-#
-# triallicks = nearestevents(allRatDistractors[13],allRatLicks[13])
-# trialdistractors = nearestevents(allRatDistractors[13],allRatDistractors[13])
-# trialdistracted = nearestevents(allRatDistractors[13],allRatDistracted[13])
-# trialnotdistracted = nearestevents(allRatDistractors[13],allRatNotDistracted[13])
-#
-# xvals1 = [(x+10)*10 for x in triallicks[ind]]
-# xvals1 = [(x+10)*10 for x in triallicks[ind]]
-# #xvals2 = [(x+10)*10 for x in trialdistractors[trial]]
-# xvals3 = [(x+10)*10 for x in trialdistracted[ind]]
-# xvals4 = [(x+10)*10 for x in trialnotdistracted[ind]]
-# yvals1 = [ax.get_ylim()[1]] * len(xvals1)
-# #yvals2 = [ax.get_ylim()[1] + 0.005] * len(xvals2)
-# yvals3 = [ax.get_ylim()[1] + 0.02] * len(xvals3)
-# yvals4 = [ax.get_ylim()[1] + 0.02] * len(xvals4)
-# #ax.scatter(xvals, yvals)
-# ax.scatter(xvals1, yvals1, marker='|', s=90, c='k')
-# #ax.scatter(xvals2, yvals2, marker='*')
-# ax.scatter(xvals3, yvals3, marker='o', facecolors= 'k', edgecolors='k', linewidth=2, s=60)
-# ax.scatter(xvals4, yvals4, marker='o', facecolors= 'none', edgecolors='k', linewidth=2, s=60)
-#
-# # ax.set_ylim([-0.0, 0.05])
-# ax.spines['right'].set_visible(False)
-# ax.spines['top'].set_visible(False)
-# ax.spines['left'].set_visible(False)
-# ax.spines['bottom'].set_visible(False)
-# ax.xaxis.set_visible(False)
-# ax.yaxis.set_visible(False)
-# f.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/THPH2.8_trial_' + str(ind) +'.pdf', bbox_inches="tight")
-
-### REMEMBER the axes have been removed and the scales are different
-
-### Are these just alligned to distracted or both??? Check which list you use and how many trials
-
-## Smaller time period before ? Less than a second so you know no distractors are present????
-
-'''
-
-#### Representative rat
-
-
-## repeat on LICKING DAY - with modelled distractors and modelled distraction / or not
-
-
-
-### Then look at white noise vs non white noise
-## Then white noise on habituation day AND all distractors on habituation day (not distracted trials as very few, maybe)
-## THEN compare the peaks with stats and export to SPSS for t-tests?
-
-'''
-################################################################################################
-################################################################################################
-
-## Subsetting data, to get peaks
-
-## Variables : stored blue and uv snips for different conditions (means for each rat, lists of 12 - not 14 1.1 and 1.2 removed)
-## Do this all on a uv subtracted baseline?
-
-# UV is essentially zero but check this
-
-# (1) UV signal subtracted from the blue
-# (2) Derive the peaks by following rules:
-# (3) Compare using ANOVA or t-tests
-
-## Expects list of 12 rats with mean snips in each field
-def uvSubtractor(rat_snip_means_list_blue, uv):
- subtractedSignal = []
- for ind, rat in enumerate(rat_snip_means_list_blue):
- subtractedSignal.append(rat_snip_means_list_blue[ind] - uv[ind])
-
- return subtractedSignal
-
-bkgnd_sub_Runs = uvSubtractor(blueMeansRuns, uvMeansRuns)
-bkgnd_sub_Short_Runs = uvSubtractor(blueMeans_short_run, uvMeans_short_run)
-bkgnd_sub_Long_Runs = uvSubtractor(blueMeans_long_run, uvMeans_long_run)
-bkgnd_sub_Distractor = uvSubtractor(blueMeans_distractor, uvMeans_distractor)
-bkgnd_sub_Distracted = uvSubtractor(blueMeans_distracted, uvMeans_distracted)
-bkgnd_sub_Notdistracted = uvSubtractor(blueMeans_notdistracted, uvMeans_notdistracted)
-
-## Expects list of 12 rats with mean snips in each field
-## Give it the background subtracted snips or just the blue (as list of lists with eachlist a rat)
-
-def PhotoPeaksCalc(snips_all_rats):
-
- allRat_peak = []
- allRat_t = []
- allRat_pre = []
- allRat_post = []
- allRat_base = []
-
- for rat in snips_all_rats:
- pre_event = np.mean(rat[0:50]) # Average for 5 seconds, 10 seconds before event
- peak = np.max(rat[100:300]) ## Minus the average of the first 5 seconds and after 100 points (slice)
- peak_range = rat[100:300]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- pre_event = np.mean(rat[50:100])
- post_event = np.mean(rat[100:300])
- baseline = np.mean(rat[0:50])
-
- allRat_peak.append(peak)
- allRat_t.append(t)
- allRat_pre.append(pre_event)
- allRat_post.append(post_event)
- allRat_base.append(baseline)
-
-
- return allRat_peak, allRat_t, allRat_pre, allRat_post, allRat_base
-
-
-### Photometry peak variables - remember these lists will be unequal distractors 12 rats not 14?
-### Should maybe take out the first 2 of the licks too? As they had no signal ???
-## Manually removed the first 2 rats in SPSS -- should also remove them here (check later about the photo plots)
-
-# All runs
-peak_runs, t_runs, pre_runs, post_runs, baseline_runs = PhotoPeaksCalc(bkgnd_sub_Runs[2:])
-# Short runs
-peak_short_runs, t_short_runs, pre_short_runs, post_short_runs, baseline_short_runs = PhotoPeaksCalc(bkgnd_sub_Short_Runs[2:])
-# Long runs
-peak_long_runs, t_long_runs, pre_long_runs, post_long_runs, baseline_long_runs = PhotoPeaksCalc(bkgnd_sub_Long_Runs[2:])
-# Distractors
-peak_distractor, t_distractor, pre_distractor, post_distractor, baseline_distractor = PhotoPeaksCalc(bkgnd_sub_Distractor)
-# Distracted
-peak_distracted, t_distracted, pre_distracted, post_distracted, baseline_distracted = PhotoPeaksCalc(bkgnd_sub_Distracted)
-# Not distracted
-peak_notdistracted, t_notdistracted, pre_notdistracted, post_notdistracted, baseline_notdistracted = PhotoPeaksCalc(bkgnd_sub_Notdistracted)
-
-
-## Run the distraction code on the lick day data (minus rats 1 and 2) to get modelled distractor
-## peaks
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# DISTRACTION FILES (minus the first 2) - this was run with all included
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-
-allRatBlueMOD = []
-allRatUVMOD = []
-allRatFSMOD = []
-allRatLicksMOD = []
-allRatDistractorsMOD = []
-allRatDistractedMOD = []
-allRatNotDistractedMOD = []
-blueMeans_distractorMOD = []
-uvMeans_distractorMOD = []
-blueMeans_distractedMOD = []
-uvMeans_distractedMOD = []
-blueMeans_notdistractedMOD = []
-uvMeans_notdistractedMOD = []
-allbluesnipsMOD = []
-alluvsnipsMOD = []
-
-for filename in TDTfiles_thph_lick[2:]:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlueMOD.append(ratdata['bluefilt'])
- allRatUVMOD.append(ratdata['uv'])
- allRatFSMOD.append(ratdata['fs'])
- allRatLicksMOD.append(ratdata['licks'])
- allRatDistractorsMOD.append(ratdata['distractors'])
- allRatDistractedMOD.append(ratdata['distracted'])
- allRatNotDistractedMOD.append(ratdata['notdistracted'])
-
-
-for i, val in enumerate(allRatDistractorsMOD):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueMOD[i], allRatDistractorsMOD[i], fs=allRatFSMOD[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVMOD[i], allRatDistractorsMOD[i], fs=allRatFSMOD[i], bins=300)
-
- randevents = makerandomevents(allRatBlueMOD[i][300], allRatBlueMOD[i][-300])
- bgMad, bgMean = findnoise(allRatBlueMOD[i], randevents, fs=allRatFSMOD[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
- except:
- pass
-# Individual plots to choose a representative rat
-
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distractor') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistractors[i])) + ' distractors' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_' + str(i) + '.pdf', bbox_inches="tight")
-
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractorMOD.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractorMOD.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractorMOD),np.asarray(blueMeans_distractorMOD)], ppsBlue, eventText='Modelled Distractor', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Modelled_Distractors_All_Rats.pdf', bbox_inches="tight")
-
-
-
-for i, val in enumerate(allRatDistractedMOD):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueMOD[i], allRatDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVMOD[i], allRatDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
-
- randevents = makerandomevents(allRatBlueMOD[i][300], allRatBlueMOD[i][-300])
- bgMad, bgMean = findnoise(allRatBlueMOD[i], randevents, fs=allRatFSMOD[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-# Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistracted[i])) + ' distracted' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_' + str(i) + '.pdf', bbox_inches="tight")
-#
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distractedMOD.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distractedMOD.append(uvMeanDISTRACTED)
- allbluesnipsMOD.append(blueSnips)
- alluvsnipsMOD.append(uvSnips)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractedMOD),np.asarray(blueMeans_distractedMOD)], ppsBlue, eventText='Distracted trial MOD', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_All_Rats.pdf', bbox_inches="tight")
-
-for i, val in enumerate(allRatNotDistractedMOD):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueMOD[i], allRatNotDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVMOD[i], allRatNotDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
-
- randevents = makerandomevents(allRatBlueMOD[i][300], allRatBlueMOD[i][-300])
- bgMad, bgMean = findnoise(allRatBlueMOD[i], randevents, fs=allRatFSMOD[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
- except:
- pass
-
-## Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Not Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatNotDistractedMOD[i])) + ' not distracted' )
-## fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_' + str(i) + '.pdf', bbox_inches="tight")
-
-# Means for not distracted trials here MULT SHADED FIG
-
- blueMeanNOTDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_notdistractedMOD.append(blueMeanNOTDISTRACTED)
- uvMeanNOTDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_notdistractedMOD.append(uvMeanNOTDISTRACTED)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_notdistractedMOD),np.asarray(blueMeans_notdistractedMOD)], ppsBlue, eventText='Not Distracted trial MOD', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_All_Rats.pdf', bbox_inches="tight")
-
-bkgnd_sub_Distractor_MOD = uvSubtractor(blueMeans_distractorMOD, uvMeans_distractorMOD)
-bkgnd_sub_Distracted_MOD = uvSubtractor(blueMeans_distractedMOD, uvMeans_distractedMOD)
-bkgnd_sub_Notdistracted_MOD = uvSubtractor(blueMeans_notdistractedMOD, uvMeans_notdistractedMOD)
-# Distractors (in this case only these peaks in SPSS, may decide to use DIS and NOTDIS later though)
-peak_distractorMOD, t_distractorMOD, pre_distractorMOD, post_distractorMOD, baseline_distractorMOD = PhotoPeaksCalc(bkgnd_sub_Distractor_MOD)
-# Distracted
-peak_distractedMOD, t_distractedMOD, pre_distractedMOD, post_distractedMOD, baseline_distractedMOD = PhotoPeaksCalc(bkgnd_sub_Distracted_MOD)
-# Not distracted
-peak_notdistractedMOD, t_notdistractedMOD, pre_notdistractedMOD, post_notdistractedMOD, baseline_notdistractedMOD = PhotoPeaksCalc(bkgnd_sub_Notdistracted_MOD)
-
-
-
-
-
-
-## NOW - make all photo peaks again from HABITUATION day not lick day or distraction day
-
-# Repeat everything (not the licking just distraction) and make all of the snips and peaks
-# Then transfer these to SPSS
-
-## Run the distraction code on the HABITUATION DAY data (minus rats 1 and 2)
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# HABITUATION FILES (minus the first 2)
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-allRatBlueHAB = []
-allRatUVHAB = []
-allRatFSHAB = []
-allRatLicksHAB = []
-allRatDistractorsHAB = []
-allRatDistractedHAB = []
-allRatNotDistractedHAB = []
-blueMeans_distractorHAB = []
-uvMeans_distractorHAB = []
-blueMeans_distractedHAB = []
-uvMeans_distractedHAB = []
-blueMeans_notdistractedHAB = []
-uvMeans_notdistractedHAB = []
-allbluesnipsHAB = []
-alluvsnipsHAB = []
-
-for filename in TDTfiles_thph_hab:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlueHAB.append(ratdata['bluefilt'])
- allRatUVHAB.append(ratdata['uv'])
- allRatFSHAB.append(ratdata['fs'])
- allRatLicksHAB.append(ratdata['licks'])
- allRatDistractorsHAB.append(ratdata['distractors'])
- allRatDistractedHAB.append(ratdata['distracted'])
- allRatNotDistractedHAB.append(ratdata['notdistracted'])
-
-
-for i, val in enumerate(allRatDistractorsHAB):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueHAB[i], allRatDistractorsHAB[i], fs=allRatFSHAB[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVHAB[i], allRatDistractorsHAB[i], fs=allRatFSHAB[i], bins=300)
-
- randevents = makerandomevents(allRatBlueHAB[i][300], allRatBlueHAB[i][-300])
- bgMad, bgMean = findnoise(allRatBlueHAB[i], randevents, fs=allRatFSHAB[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
- except:
- pass
-# Individual plots to choose a representative rat
-
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distractor') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistractors[i])) + ' distractors' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_' + str(i) + '.pdf', bbox_inches="tight")
-
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractorHAB.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractorHAB.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-#trialsMultShadedFig(ax, [np.asarray(uvMeans_distractorHAB),np.asarray(blueMeans_distractorHAB)], ppsBlue, eventText='Distractor', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Habituation_Distractors_All_Rats.pdf', bbox_inches="tight")
-trialsMultShadedFig(ax, [np.asarray(blueMeans_distractorHAB), np.asarray(blueMeans_distractorHAB)], ppsBlue, eventText='Distractor', linecolor = ['blue','blue'], errorcolor = ['lightblue','lightblue'], scale=0)
-
-
-
-for i, val in enumerate(allRatDistractedHAB):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueHAB[i], allRatDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVHAB[i], allRatDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
-
- randevents = makerandomevents(allRatBlueHAB[i][300], allRatBlueHAB[i][-300])
- bgMad, bgMean = findnoise(allRatBlueHAB[i], randevents, fs=allRatFSHAB[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- blueSnips = zscore(blueSnips)
- uvSnips = zscore(uvSnips)
- except:
- pass
-# Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistracted[i])) + ' distracted' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_' + str(i) + '.pdf', bbox_inches="tight")
-#
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distractedHAB.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distractedHAB.append(uvMeanDISTRACTED)
- allbluesnipsHAB.append(blueSnips)
- alluvsnipsHAB.append(uvSnips)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractedHAB),np.asarray(blueMeans_distractedHAB)], ppsBlue, eventText='Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_All_Rats.pdf', bbox_inches="tight")
-
-for i, val in enumerate(allRatNotDistractedHAB):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueHAB[i], allRatNotDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVHAB[i], allRatNotDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
-
- randevents = makerandomevents(allRatBlueHAB[i][300], allRatBlueHAB[i][-300])
- bgMad, bgMean = findnoise(allRatBlueHAB[i], randevents, fs=allRatFSHAB[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-
-## Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Not Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatNotDistractedMOD[i])) + ' not distracted' )
-## fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_' + str(i) + '.pdf', bbox_inches="tight")
-
-# Means for not distracted trials here MULT SHADED FIG
-
- blueMeanNOTDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_notdistractedHAB.append(blueMeanNOTDISTRACTED)
- uvMeanNOTDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_notdistractedHAB.append(uvMeanNOTDISTRACTED)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_notdistractedHAB),np.asarray(blueMeans_notdistractedHAB)], ppsBlue, eventText='Not Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-
-## PLOTS BLUE LINE TWICE
-#trialsMultShadedFig(ax, [np.asarray(blueMeans_notdistractedHAB), np.asarray(blueMeans_notdistractedHAB)], ppsBlue, eventText='Not Distracted trial', linecolor = ['blue', 'blue'], errorcolor = ['lightblue', 'lightblue'], scale=0)
-
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/NotDistracted_All_Rats.pdf', bbox_inches="tight")
-
-bkgnd_sub_Distractor_HAB = uvSubtractor(blueMeans_distractorHAB, uvMeans_distractorHAB)
-bkgnd_sub_Distracted_HAB = uvSubtractor(blueMeans_distractedHAB, uvMeans_distractedHAB)
-bkgnd_sub_Notdistracted_HAB = uvSubtractor(blueMeans_notdistractedHAB, uvMeans_notdistractedHAB)
-# Distractors (in this case only these peaks in SPSS, may decide to use DIS and NOTDIS later though)
-peak_distractorHAB, t_distractorHAB, pre_distractorHAB, post_distractorHAB, baseline_distractorHAB = PhotoPeaksCalc(bkgnd_sub_Distractor_HAB)
-# Distracted
-peak_distractedHAB, t_distractedHAB, pre_distractedHAB, post_distractedHAB, baseline_distractedHAB = PhotoPeaksCalc(bkgnd_sub_Distracted_HAB)
-# Not distracted
-peak_notdistractedHAB, t_notdistractedHAB, pre_notdistractedHAB, post_notdistractedHAB, baseline_notdistractedHAB = PhotoPeaksCalc(bkgnd_sub_Notdistracted_HAB)
-
-
-
-## Plots with MULTIPLE EVENTS ON SIGNLE PLOT
-'''
-lick
-np.asarray(blueMeansRuns)
-dis
-np.asarray(blueMeans_distractor)
-distractecd
-np.asarray(blueMeans_distracted)
-notdis
-np.asarray(blueMeans_notdistracted)
-mod
-np.asarray(blueMeans_distractorMOD)
-dis
-np.asarray(blueMeans_distractor)
-dis
-np.asarray(blueMeans_distractor)
-hab
-'''
-
-
-## Put this code at the bottome of ch4 licking analysis code
-
-## Licking and distractor
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(blueMeansRuns[2:]), np.asarray(blueMeans_distractor)], ppsBlue, eventText='Event', linecolor = ['darkturquoise','dodgerblue'], errorcolor = ['lightcyan','#D0E5FF'], scale=0)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Draft 1/Photo_licking_distractors.pdf', bbox_inches="tight")
-
-# Distracted and not distracted
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(blueMeans_notdistracted), np.asarray(blueMeans_distracted)], ppsBlue, eventText='Distractor', linecolor = ['darkturquoise','dodgerblue'], errorcolor = ['lightcyan','#D0E5FF'], scale=0)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Draft 1/Photo_distracted_notdis.pdf', bbox_inches="tight")
-
-# Modelled and distraction day
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(blueMeans_distractorMOD),np.asarray(blueMeans_distractor) ], ppsBlue, eventText='Distractor', linecolor = ['darkturquoise','dodgerblue'], errorcolor = ['lightcyan','#D0E5FF'], scale=0)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Draft 1/Modelled_real.pdf', bbox_inches="tight")
-
-# Distraction day and habituation day
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(blueMeans_distractorHAB),np.asarray(blueMeans_distractor[:-1])], ppsBlue, eventText='Distractor', linecolor = ['darkturquoise','dodgerblue'], errorcolor = ['lightcyan','#D0E5FF'], scale=0)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Draft 1/Dis_habituation.pdf', bbox_inches="tight")
-
-
-# Calculate AUC for variables / events (all and then means)
-## 1 second
-AUC5_all_distractors = []
-for rat in blueMeans_distractor:
- AUC = np.trapz(rat[100:150])
- AUC5_all_distractors.append(AUC)
-mean_AUC_distractors = np.mean(AUC5_all_distractors)
-
-AUC5_all_licks = []
-for rat in blueMeansRuns:
- AUC = np.trapz(rat[100:150])
- AUC5_all_licks.append(AUC)
-mean_AUC_licks = np.mean(AUC5_all_licks)
-
-AUC5_all_distracted = []
-for rat in blueMeans_distracted:
- AUC = np.trapz(rat[100:150])
- AUC5_all_distracted.append(AUC)
-mean_AUC_distracted = np.mean(AUC5_all_distracted)
-
-## AUC is higher for not distracted because it starts higher
-## distracted trials start lower (proceeding activity included)
-AUC5_all_notdistracted = []
-for rat in blueMeans_notdistracted:
- AUC = np.trapz(rat[100:150])
- AUC5_all_notdistracted.append(AUC)
-mean_AUC_notdistracted = np.mean(AUC5_all_notdistracted)
-
-AUC5_all_distractorsMOD = []
-for rat in blueMeans_distractorMOD:
- AUC = np.trapz(rat[100:150])
- AUC5_all_distractorsMOD.append(AUC)
-mean_AUC_distractorsMOD = np.mean(AUC5_all_distractorsMOD)
-
-AUC5_all_distractorsHAB = []
-for rat in blueMeans_distractorHAB:
- AUC = np.trapz(rat[100:150])
- AUC5_all_distractorsHAB.append(AUC)
-mean_AUC_distractorsHAB = np.mean(AUC5_all_distractorsHAB)
-
-
-## POST MEASURE AS AUC NOT AVERAGE ANYMORE
-## AUC all after the stimulus / all after the peak
-AUC_all_distractors20 = []
-for rat in blueMeans_distractor:
- AUC = np.trapz(rat[100:300])
- AUC_all_distractors20.append(AUC)
-mean_AUC_distractors20 = np.mean(AUC_all_distractors20)
-
-AUC_all_licks20 = []
-for rat in blueMeansRuns:
- AUC = np.trapz(rat[100:300])
- AUC_all_licks20.append(AUC)
-mean_AUC_licks20 = np.mean(AUC_all_licks20)
-
-AUC_all_distracted20 = []
-for rat in blueMeans_distracted:
- AUC = np.trapz(rat[100:300])
- AUC_all_distracted20.append(AUC)
-mean_AUC_distracted20 = np.mean(AUC_all_distracted20)
-
-AUC_all_notdistracted20 = []
-for rat in blueMeans_notdistracted:
- AUC = np.trapz(rat[100:300])
- AUC_all_notdistracted20.append(AUC)
-mean_AUC_notdistracted20 = np.mean(AUC_all_notdistracted20)
-
-AUC_all_distractorsMOD20 = []
-for rat in blueMeans_distractorMOD:
- AUC = np.trapz(rat[100:300])
- AUC_all_distractorsMOD20.append(AUC)
-mean_AUC_distractorsMOD20 = np.mean(AUC_all_distractorsMOD20)
-
-AUC_all_distractorsHAB20 = []
-for rat in blueMeans_distractorHAB:
- AUC = np.trapz(rat[100:300])
- AUC_all_distractorsHAB20.append(AUC)
-mean_AUC_distractorsHAB20 = np.mean(AUC_all_distractorsHAB20)
-
-#mean_AUC_distractors
-#mean_AUC_licks
-#mean_AUC_distracted
-#mean_AUC_notdistracted
-#mean_AUC_distractorsMOD
-#mean_AUC_distractorsHAB
-
-## Add peak calculations, max within 5 seconds (just maximum, not subtraction or zero as zscores)
-peak_all_distractors = []
-tmax_all_distractors = []
-for rat in blueMeans_distractor:
- peak = np.max(rat[100:150])
- peak_all_distractors.append(peak)
-
- peak_range = rat[100:150]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- tmax_all_distractors.append(t)
-mean_peak_distractors = np.mean(peak_all_distractors)
-
-
-peak_all_licks = []
-tmax_all_licks = []
-for rat in blueMeansRuns:
- peak = np.max(rat[100:150])
- peak_all_licks.append(peak)
-
- peak_range = rat[100:150]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- tmax_all_licks.append(t)
-mean_peak_licks = np.mean(peak_all_licks)
-
-peak_all_distracted = []
-tmax_all_distracted = []
-for rat in blueMeans_distracted:
- peak = np.max(rat[100:150])
- peak_all_distracted.append(peak)
-
- peak_range = rat[100:150]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- tmax_all_distracted.append(t)
-mean_peak_distracted = np.mean(peak_all_distracted)
-
-peak_all_notdistracted = []
-tmax_all_notdistracted = []
-for rat in blueMeans_notdistracted:
- peak = np.max(rat[100:150])
- peak_all_notdistracted.append(peak)
-
- peak_range = rat[100:150]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- tmax_all_notdistracted.append(t)
-mean_peak_notdistracted = np.mean(peak_all_notdistracted)
-
-peak_all_distractorsMOD = []
-tmax_all_distractorsMOD = []
-for rat in blueMeans_distractorMOD:
- peak = np.max(rat[100:150])
- peak_all_distractorsMOD.append(peak)
-
- peak_range = rat[100:150]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- tmax_all_distractorsMOD.append(t)
-mean_peak_distractorsMOD = np.mean(peak_all_distractorsMOD)
-
-peak_all_distractorsHAB = []
-tmax_all_distractorsHAB = []
-for rat in blueMeans_distractorHAB:
- peak = np.max(rat[100:150])
- peak_all_distractorsHAB.append(peak)
-
- peak_range = rat[100:150]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- tmax_all_distractorsHAB.append(t)
-mean_peak_distractorsHAB = np.mean(peak_all_distractorsHAB)
-
-## Add barscatter plots for the peak heights, AUC in 1 second and
-## longer AUC of all 20 seconds following events
-########################################################################
-########################################################################
-########################################################################
-
-## Modelled and distraction day
-data_peak = [peak_all_distractorsMOD[:-1], peak_all_distractors[:-1]]
-data_20sec_AUC = [AUC_all_distractorsMOD20[:-1], AUC_all_distractors20[:-1]]
-
-## Peak modelled vs distraction day
-## Make 3 plots here
-col3 = ['darkturquoise','dodgerblue']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(data_peak, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Peak (z-score)', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'k', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Peak_Bar_mod_dis.pdf', bbox_inches="tight")
-
-## Distraction and habituation day
-data_peak = [peak_all_distractors[:-1], peak_all_distractorsHAB]
-data_20sec_AUC = [AUC_all_distractors20[:-1], AUC_all_distractorsHAB20]
-
-## Peak distraction day and habituation day
-col3 = ['dodgerblue','lightblue']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(data_peak, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Peak (z-score)', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'k', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Peak_Bar_dis_hab.pdf', bbox_inches="tight")
-
-
-
-
-#col3 = ['darkturquoise','dodgerblue','lightblue']
-#labels = ['mod', 'dis', 'hab']
-#mpl.rcParams['font.size'] = 14
-#figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-#ax, barx, barlist, sclist = barscatter(data_1sec_AUC, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Percent distracted (%)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax.spines['bottom'].set_visible(False)
-
-col3 = ['darkturquoise','dodgerblue','lightblue']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-ax, barx, barlist, sclist = barscatter(data_20sec_AUC, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='AUC (unit?)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/AUC_Bar_mod_dis_hab.pdf', bbox_inches="tight")
-
-''' issue with the AUC -400? Why are 1 sec and 20 sec the same'''
-
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/PercentDisBarScatter.pdf', bbox_inches="tight")
-
-########################################################################
-########################################################################
-########################################################################
-
-## Distracted vs not distracted
-data_peak = [[ peak_all_notdistracted,peak_all_distracted]]
-data_1sec_AUC = [[AUC_all_notdistracted,AUC_all_distracted]]
-data_20sec_AUC = [[AUC_all_notdistracted20,AUC_all_distracted20]]
-# Make 3 plots here
-## Make 3 plots here
-col3 = ['darkturquoise','dodgerblue']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-#ax, barx, barlist, sclist = barscatter(data_peak, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Peak (z-score)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax, barx, barlist, sclist = barscatter(data_peak, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Peak (z-score)', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'k', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Peak_Bar_dis_notdis.pdf', bbox_inches="tight")
-
-#col3 = ['darkturquoise','dodgerblue']
-#labels = ['mod', 'dis', 'hab']
-#mpl.rcParams['font.size'] = 14
-#figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-#ax, barx, barlist, sclist = barscatter(data_1sec_AUC, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Percent distracted (%)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax.spines['bottom'].set_visible(False)
-col3 = ['dodgerblue', 'darkturquoise']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-ax, barx, barlist, sclist = barscatter(data_20sec_AUC, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='AUC (unit?)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/AUC_Bar_dis_notdis.pdf', bbox_inches="tight")
-
-########################################################################
-########################################################################
-########################################################################
-
-# Distractors vs licks
-data_peak = [peak_all_licks[2:],peak_all_distractors, ]
-data_1sec_AUC = [AUC_all_licks[2:],AUC_all_distractors]
-data_20sec_AUC = [AUC_all_licks20[2:],AUC_all_distractors20]
-# Make 3 plots here
-## Make 3 plots here
-col3 = ['darkturquoise','dodgerblue']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-ax, barx, barlist, sclist = barscatter(data_peak, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Peak (z-score)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/PercentDisBarScatter.pdf', bbox_inches="tight")
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Peak_Bar_lick_dis.pdf', bbox_inches="tight")
-
-#col3 = ['#FFE5A5','#FFE5A5','#FFE5A5']
-#labels = ['mod', 'dis', 'hab']
-#mpl.rcParams['font.size'] = 14
-#figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-#ax, barx, barlist, sclist = barscatter(data_1sec_AUC, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Percent distracted (%)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax.spines['bottom'].set_visible(False)
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,5)) ### x,y
-ax, barx, barlist, sclist = barscatter(data_20sec_AUC, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='AUC (unit?)', itemlabel=['1','2'], barlabeloffset=0.05) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/AUC_Bar_lick_dis.pdf', bbox_inches="tight")
-
-'''
-## Write list of numbers to export into excel
-## Peak heights
-peak_all_distractorsMOD,
-peak_all_distractors,
-peak_all_distractorsHAB
-peak_all_distracted,
-peak_all_notdistracted,
-peak_all_licks
-
-AUC_all_distractorsMOD,
-AUC_all_distractors,
-AUC_all_distractorsHAB
-AUC_all_distracted,
-AUC_all_notdistracted,
-AUC_all_licks
-
-AUC_all_distractorsMOD20,
-AUC_all_distractors20,
-AUC_all_distractorsHAB20
-AUC_all_distracted20,
-AUC_all_notdistracted20,
-AUC_all_licks20
-'''
-
-## measuring time to decay?
-
-# how long does it take for the snips to reach 10% of the baseline?
-
-average baseline period (10 seconds before)
-
-in the snip if > baseline * 0.9 lista.append(1)
-else if < 0.9 lista.append(0)
-
-
-slope?/??
-
-
-
-NOTES
-Snips are all zscored (as they are made) so there is an issue with AUC as it comes from the zscore values
-and not from the deltaf/f values (which the peaks do and are significant)
-
diff --git a/CompiledScriptToSeparate.py b/CompiledScriptToSeparate.py
deleted file mode 100644
index 7babe60..0000000
--- a/CompiledScriptToSeparate.py
+++ /dev/null
@@ -1,1729 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Aug 1 17:45:42 2019
-
-@author: kate
-"""
-
-# Peters, McCutcheon, Young (2019)
-
-# Distraction photometry
-
-# Extract data from saved mat files (processed files)
-# Work out and plot percentage distracted, modelled distractors and habituation
-# Calculate blue/uv snips centered aorund distractors (no licking here)
-# Plot photometry traces (not zscore, deltaF/F)
-# Calculate peak, t, post, AUC and slope / decay
-# Plot bar scatters for chosen variables
-
-# Required inports and functions
-
-# Import modules --------------------------------------------------------
-
-
-
-import numpy as np
-import scipy.io as sio
-
-import matplotlib.pyplot as plt
-import pandas as pd
-import os
-import matplotlib as mpl
-import itertools
-import matplotlib.mlab as mlab
-#import seaborn as sb
-import statistics as stats
-
-# Functions -------------------------------------------------------------
-def mapTTLs(matdict):
- for x in ['LiA_', 'La2_']:
- try:
- licks = getattr(matdict['output'], x)
- except:
- print('File has no ' + x)
-
- lickson = licks.onset
- licksoff = licks.offset
-
- return lickson, licksoff
-
-
-def loadmatfile(file):
- a = sio.loadmat(file, squeeze_me=True, struct_as_record=False)
- print(type(a))
- sessiondict = {}
- sessiondict['bluefilt'] = a['output'].blue
- sessiondict['uv'] = a['output'].uv
- sessiondict['fs'] = a['output'].fs
-
- try:
-
- sessiondict['licks'] = a['output'].licks.onset
- sessiondict['licks_off'] = a['output'].licks.offset
- except: ## find which error it is
-
- sessiondict['licks'], sessiondict['licks_off'] = mapTTLs(a)
-
-
-
- sessiondict['distractors'] = distractionCalc2(sessiondict['licks'])
-
-# #write distracted or not to produce 2 lists of times, distracted and notdistracted
- #distracted, notdistracted= distractedOrNot(sessiondict['distractors'], sessiondict['licks'])
-#
- sessiondict['distracted'], sessiondict['notdistracted'] = distractedOrNot(sessiondict['distractors'], sessiondict['licks'])
- # sessiondict['notdistracted'] = notdistracted
-
- # ''' sessiondict['lickRuns'] = lickRunCalc(sessiondict['licks']) '''
-
- return sessiondict
-
-def distractedOrNot(distractors, licks):
- distracted = []
- notdistracted = []
- lickList = []
- for l in licks:
- lickList.append(l)
-
-
- for index, distractor in enumerate(distractors):
- if distractor in licks:
-
- ind = lickList.index(distractor)
- try:
- if (licks[ind+1] - licks[ind]) > 1:
- distracted.append(licks[ind])
- else:
- if (licks[ind+1] - licks[ind]) < 1:
- notdistracted.append(licks[ind])
- except IndexError:
- print('last lick was a distractor!!!')
- distracted.append(licks[ind])
-
- return(distracted, notdistracted)
-
-
-def remcheck(val, range1, range2):
- # function checks whether value is within range of two decimels
- if (range1 < range2):
- if (val > range1) and (val < range2):
- return True
- else:
- return False
- else:
- if (val > range1) or (val < range2):
- return True
- else:
- return False
-
-
-def distractionCalc2(licks, pre=1, post=1):
- licks = np.insert(licks, 0, 0)
- b = 0.001
- d = []
- idx = 3
-
- while idx < len(licks):
- if licks[idx]-licks[idx-2] < 1 and remcheck(b, licks[idx-2] % 1, licks[idx] % 1) == False:
- d.append(licks[idx])
- b = licks[idx] % 1
- idx += 1
- try:
- while licks[idx]-licks[idx-1] < 1:
- b = licks[idx] % 1
- idx += 1
- except IndexError:
- pass
- else:
- idx +=1
-# print(len(d))
-
-# print(d[-1])
- if d[-1] > 3599:
- d = d[:-1]
-
-# print(len(d))
-
- return d
-
-# LickCalc ============================================================
-# Looking at function from Murphy et al (2017)
-
-"""
-This function will calculate data for bursts from a train of licks. The threshold
-for bursts and clusters can be set. It returns all data as a dictionary.
-"""
-def lickCalc(licks, offset = [], burstThreshold = 0.25, runThreshold = 10,
- binsize=60, histDensity = False):
-
- # makes dictionary of data relating to licks and bursts
- if type(licks) != np.ndarray or type(offset) != np.ndarray:
- try:
- licks = np.array(licks)
- offset = np.array(offset)
- except:
- print('Licks and offsets need to be arrays and unable to easily convert.')
- return
-
- lickData = {}
-
- if len(offset) > 0:
- lickData['licklength'] = offset - licks
- lickData['longlicks'] = [x for x in lickData['licklength'] if x > 0.3]
- else:
- lickData['licklength'] = []
- lickData['longlicks'] = []
-
- lickData['licks'] = np.concatenate([[0], licks])
- lickData['ilis'] = np.diff(lickData['licks'])
- lickData['freq'] = 1/np.mean([x for x in lickData['ilis'] if x < burstThreshold])
- lickData['total'] = len(licks)
-
- # Calculates start, end, number of licks and time for each BURST
- lickData['bStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
- lickData['bInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
- lickData['bEnd'] = [lickData['licks'][i-1] for i in lickData['bInd'][1:]]
- lickData['bEnd'].append(lickData['licks'][-1])
-
- lickData['bLicks'] = np.diff(lickData['bInd'] + [len(lickData['licks'])])
- lickData['bTime'] = np.subtract(lickData['bEnd'], lickData['bStart'])
- lickData['bNum'] = len(lickData['bStart'])
- if lickData['bNum'] > 0:
- lickData['bMean'] = np.nanmean(lickData['bLicks'])
- else:
- lickData['bMean'] = 0
-
- lickData['bILIs'] = [x for x in lickData['ilis'] if x > burstThreshold]
-
- lickData['bILIs'] = [x for x in lickData['ilis'] if x > burstThreshold]
-
- # Calculates start, end, number of licks and time for each RUN
- lickData['rStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
- lickData['rInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
- lickData['rEnd'] = [lickData['licks'][i-1] for i in lickData['rInd'][1:]]
- lickData['rEnd'].append(lickData['licks'][-1])
-
- lickData['rLicks'] = np.diff(lickData['rInd'] + [len(lickData['licks'])])
- lickData['rTime'] = np.subtract(lickData['rEnd'], lickData['rStart'])
- lickData['rNum'] = len(lickData['rStart'])
-
- lickData['rILIs'] = [x for x in lickData['ilis'] if x > runThreshold]
- try:
- lickData['hist'] = np.histogram(lickData['licks'][1:],
- range=(0, 3600), bins=int((3600/binsize)),
- density=histDensity)[0]
- except TypeError:
- print('Problem making histograms of lick data')
-
- return lickData
-
-def asnumeric(s):
- try:
- x = float(s)
- return x
- except ValueError:
- return float('nan')
-
-def time2samples(self):
- tick = self.output.Tick.onset
- maxsamples = len(tick)*int(self.fs)
- if (len(self.data) - maxsamples) > 2*int(self.fs):
- print('Something may be wrong with conversion from time to samples')
- print(str(len(self.data) - maxsamples) + ' samples left over. This is more than double fs.')
-
- self.t2sMap = np.linspace(min(tick), max(tick), maxsamples)
-
-def snipper(data, timelock, fs = 1, t2sMap = [], preTrial=10, trialLength=30,
- adjustBaseline = True,
- bins = 0):
-
- if len(timelock) == 0:
- print('No events to analyse! Quitting function.')
- raise Exception('no events')
- nSnips = len(timelock)
- pps = int(fs) # points per sample
- pre = int(preTrial*pps)
-# preABS = preTrial
- length = int(trialLength*pps)
-# converts events into sample numbers
- event=[]
- if len(t2sMap) > 1:
- for x in timelock:
- event.append(np.searchsorted(t2sMap, x, side="left"))
- else:
- event = [x*fs for x in timelock]
-
- avgBaseline = []
- snips = np.empty([nSnips,length])
-
- for i, x in enumerate(event):
- start = int(x) - pre
- avgBaseline.append(np.mean(data[start : start + pre]))
-# print(x)
- try:
- snips[i] = data[start : start+length]
- except: # Deals with recording arrays that do not have a full final trial
- snips = snips[:-1]
- avgBaseline = avgBaseline[:-1]
- nSnips = nSnips-1
-
- if adjustBaseline == True:
- snips = np.subtract(snips.transpose(), avgBaseline).transpose()
- snips = np.divide(snips.transpose(), avgBaseline).transpose()
-
- if bins > 0:
- if length % bins != 0:
- snips = snips[:,:-(length % bins)]
- totaltime = snips.shape[1] / int(fs)
- snips = np.mean(snips.reshape(nSnips,bins,-1), axis=2)
- pps = bins/totaltime
-
- return snips, pps
-
-def makerandomevents(minTime, maxTime, spacing = 77, n=100):
- events = []
- total = maxTime-minTime
- start = 0
- for i in np.arange(0,n):
- if start > total:
- start = start - total
- events.append(start)
- start = start + spacing
- events = [i+minTime for i in events]
- return events
-
-def med_abs_dev(data, b=1.4826):
- median = np.median(data)
- devs = [abs(i-median) for i in data]
- mad = np.median(devs)*b
-
- return mad
-
-def findnoise(data, background, t2sMap = [], fs = 1, bins=0, method='sd'):
-
- bgSnips, _ = snipper(data, background, t2sMap=t2sMap, fs=fs, bins=bins)
-
- if method == 'sum':
- bgSum = [np.sum(abs(i)) for i in bgSnips]
- bgMAD = med_abs_dev(bgSum)
- bgMean = np.mean(bgSum)
- elif method == 'sd':
- bgSD = [np.std(i) for i in bgSnips]
- bgMAD = med_abs_dev(bgSD)
- bgMean = np.mean(bgSD)
-
- return bgMAD, bgMean
-
-
-def MultBy100(list):
- output = [x*100 for x in list]
-
- return output
-
-
-
-# THPH1 AND 2
-# Lick day
-
-
-
-# THPH1 AND 2 cohorts
-# Lick day (saccharin / modelled distractors)
-#TDTfiles_thph_lick = ['thph1-1_lick6', 'thph1-2_lick6', 'thph1-3_lick6', 'thph1-4_lick6', 'thph1-5_lick6',\
-# 'thph1-6_lick6', 'thph2-1_lick3', 'thph2-2_lick3','thph2-3_lick3','thph2-4_lick3', \
-# 'thph2-5_lick3','thph2-6_lick3', 'thph2-7_lick6', 'thph2-8_lick6']
-#
-#
-## Distraction day
-#TDTfiles_thph_dis = ['thph1-3_distraction1', \
-# 'thph1-4_distraction1','thph1-5_distraction1','thph1-6_distraction1', \
-# 'thph2-1_distraction', 'thph2-2_distraction', 'thph2-3_distraction', \
-# 'thph2-4_distraction', 'thph2-5_distraction', 'thph2-6_distraction', \
-# 'thph2-7_distraction', 'thph2-8_distraction'] #'thph1-1_distraction1', 'thph1-2_distraction1'
-#
-## Habituation day
-#TDTfiles_thph_hab = ['thph1-3_distraction2',\
-# 'thph1-4_distraction2', 'thph1-5_distraction2','thph1-6_distraction2',\
-# 'thph2-1_habituation', 'thph2-2_habituation', 'thph2-3_habituation', \
-# 'thph2-4_habituation', 'thph2-5_habituation', 'thph2-6_habituation', \
-# 'thph2-7_habituation'] #['thph1-1_distraction2','thph1-2_distraction2',
-#
-#
-#TDTfilepath = '/Volumes/KP_HARD_DRI/All_Matlab_Converts/BIG CONVERSION 14 AUG 2018/THPH matfiles/'
-#
-
-
-## Processed files
-
-# THPH1 AND 2 cohorts
-# Lick day (saccharin / modelled distractors)
-TDTfiles_thph_lick = ['thph1-1_lick6_proc', 'thph1-2_lick6_proc', 'thph1-3_lick6_proc', 'thph1-4_lick6_proc', 'thph1-5_lick6_proc',\
- 'thph1-6_lick6_proc', 'thph2-1_lick3_proc', 'thph2-2_lick3_proc','thph2-3_lick3_proc','thph2-4_lick3_proc', \
- 'thph2-5_lick3_proc','thph2-6_lick3_proc', 'thph2-7_lick6_proc', 'thph2-8_lick6_proc']
-
-
-# Distraction day
-TDTfiles_thph_dis = ['thph1-3_distraction1_proc', \
- 'thph1-4_distraction1_proc','thph1-5_distraction1_proc','thph1-6_distraction1_proc', \
- 'thph2-1_distraction_proc', 'thph2-2_distraction_proc', 'thph2-3_distraction_proc', \
- 'thph2-4_distraction_proc', 'thph2-5_distraction_proc', 'thph2-6_distraction_proc', \
- 'thph2-7_distraction_proc', 'thph2-8_distraction_proc'] #'thph1-1_distraction1_proc', 'thph1-2_distraction1_proc'
-
-# Habituation day
-TDTfiles_thph_hab = ['thph1-3_distraction2_proc',\
- 'thph1-4_distraction2_proc', 'thph1-5_distraction2_proc','thph1-6_distraction2_proc',\
- 'thph2-1_habituation_proc', 'thph2-2_habituation_proc', 'thph2-3_habituation_proc', \
- 'thph2-4_habituation_proc', 'thph2-5_habituation_proc', 'thph2-6_habituation_proc', \
- 'thph2-7_habituation_proc'] #['thph1-1_distraction2_proc','thph1-2_distraction2_proc',
-
-
-TDTfilepath = '/Volumes/KP_HARD_DRI/All_Matlab_Converts/BIG CONVERSION 14 AUG 2018/THPH matfiles/'
-savepath = '/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures'
-
-
-
-######
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# DISTRACTION FILES (minus the first 2) - this was run with all included
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-
-allRatBlue = []
-allRatUV = []
-allRatFS = []
-allRatLicks = []
-allRatDistractors = []
-allRatDistracted = []
-allRatNotDistracted = []
-blueMeans_distractor = []
-uvMeans_distractor = []
-blueMeans_distracted = []
-uvMeans_distracted = []
-blueMeans_notdistracted = []
-uvMeans_notdistracted = []
-allbluesnips = []
-alluvsnips = []
-
-for filename in TDTfiles_thph_dis:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlue.append(ratdata['bluefilt'])
- allRatUV.append(ratdata['uv'])
- allRatFS.append(ratdata['fs'])
- allRatLicks.append(ratdata['licks'])
- allRatDistractors.append(ratdata['distractors'])
- allRatDistracted.append(ratdata['distracted'])
- allRatNotDistracted.append(ratdata['notdistracted'])
- figure12 = plt.figure(figsize=(6,3))
- ax6 = plt.subplot(111)
- ax6.spines['right'].set_visible(False)
- ax6.xaxis.set_visible(False)
- ax6.spines['top'].set_visible(False)
- ax6.spines['bottom'].set_visible(False)
- ax6.set(ylabel = 'Trials')
- ax6.yaxis.label.set_size(14)
-# distractionrasterFig(ax6, ratdata['distractors'], ratdata['licks'], pre=1, post=10, sortevents='yes', sortdirection='dec')
-
-
-for i, val in enumerate(allRatDistractors):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatDistractors[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatDistractors[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
-
- except:
- pass
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractor.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractor.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractor),np.asarray(blueMeans_distractor)], ppsBlue, eventText='Distractor', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-
-
-for i, val in enumerate(allRatDistracted):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatDistracted[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatDistracted[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
-
- except:
- pass
-
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distracted.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distracted.append(uvMeanDISTRACTED)
- allbluesnips.append(blueSnips)
- alluvsnips.append(uvSnips)
-
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distracted),np.asarray(blueMeans_distracted)], ppsBlue, eventText='Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-
-
-for i, val in enumerate(allRatNotDistracted):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatNotDistracted[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatNotDistracted[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
-
- except:
- pass
-
-
-# Means for not distracted trials here MULT SHADED FIG
-
- blueMeanNOTDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_notdistracted.append(blueMeanNOTDISTRACTED)
- uvMeanNOTDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_notdistracted.append(uvMeanNOTDISTRACTED)
-
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_notdistracted),np.asarray(blueMeans_notdistracted)], ppsBlue, eventText='Not Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-
-
-## Expects list of 12 rats with mean snips in each field
-def uvSubtractor(rat_snip_means_list_blue, uv):
- subtractedSignal = []
- for ind, rat in enumerate(rat_snip_means_list_blue):
- subtractedSignal.append(rat_snip_means_list_blue[ind] - uv[ind])
-
- return subtractedSignal
-
-bkgnd_sub_Distractor = uvSubtractor(blueMeans_distractor, uvMeans_distractor)
-bkgnd_sub_Distracted = uvSubtractor(blueMeans_distracted, uvMeans_distracted)
-bkgnd_sub_Notdistracted = uvSubtractor(blueMeans_notdistracted, uvMeans_notdistracted)
-
-
-def PhotoPeaksCalc(snips_all_rats):
-
- allRat_peak = []
- allRat_t = []
- allRat_pre = []
- allRat_post = []
- allRat_base = []
-
- ten_precent_baseline = []
-
- for rat in snips_all_rats:
- pre_event = np.mean(rat[0:50]) # Average for 5 seconds, 10 seconds before event
- absolutepeak = np.max(rat[100:300]) ## Added this - finds highest (the t is first)
- peak = np.max(rat[100:130]) # to 300 in origianl (can this work with both?) ## Minus the average of the first 5 seconds and after 100 points (slice)
- peak_range = rat[100:130]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- pre_event = np.mean(rat[50:100])
- post_event = np.mean(rat[100:300])
- baseline = np.mean(rat[0:50])
-
- allRat_peak.append(absolutepeak)
- allRat_t.append(t)
- allRat_pre.append(pre_event)
- allRat_post.append(post_event)
- allRat_base.append(baseline)
-
-
-
-
- return allRat_peak, allRat_t, allRat_pre, allRat_post, allRat_base
-
-
-# Distractors
-peak_distractor, t_distractor, pre_distractor, post_distractor, baseline_distractor = PhotoPeaksCalc(bkgnd_sub_Distractor)
-# Distracted
-peak_distracted, t_distracted, pre_distracted, post_distracted, baseline_distracted = PhotoPeaksCalc(bkgnd_sub_Distracted)
-# Not distracted
-peak_notdistracted, t_notdistracted, pre_notdistracted, post_notdistracted, baseline_notdistracted = PhotoPeaksCalc(bkgnd_sub_Notdistracted)
-
-
-## Run the distraction code on the lick day data (minus rats 1 and 2) to get modelled distractor
-## peaks
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# DISTRACTION FILES (minus the first 2) - this was run with all included
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-
-allRatBlueMOD = []
-allRatUVMOD = []
-allRatFSMOD = []
-allRatLicksMOD = []
-allRatDistractorsMOD = []
-allRatDistractedMOD = []
-allRatNotDistractedMOD = []
-blueMeans_distractorMOD = []
-uvMeans_distractorMOD = []
-blueMeans_distractedMOD = []
-uvMeans_distractedMOD = []
-blueMeans_notdistractedMOD = []
-uvMeans_notdistractedMOD = []
-allbluesnipsMOD = []
-alluvsnipsMOD = []
-
-for filename in TDTfiles_thph_lick[2:]:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlueMOD.append(ratdata['bluefilt'])
- allRatUVMOD.append(ratdata['uv'])
- allRatFSMOD.append(ratdata['fs'])
- allRatLicksMOD.append(ratdata['licks'])
- allRatDistractorsMOD.append(ratdata['distractors'])
- allRatDistractedMOD.append(ratdata['distracted'])
- allRatNotDistractedMOD.append(ratdata['notdistracted'])
-
-
-for i, val in enumerate(allRatDistractorsMOD):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueMOD[i], allRatDistractorsMOD[i], fs=allRatFSMOD[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVMOD[i], allRatDistractorsMOD[i], fs=allRatFSMOD[i], bins=300)
-
- randevents = makerandomevents(allRatBlueMOD[i][300], allRatBlueMOD[i][-300])
- bgMad, bgMean = findnoise(allRatBlueMOD[i], randevents, fs=allRatFSMOD[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
-
- except:
- pass
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractorMOD.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractorMOD.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractorMOD),np.asarray(blueMeans_distractorMOD)], ppsBlue, eventText='Modelled Distractor', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-
-
-for i, val in enumerate(allRatDistractedMOD):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueMOD[i], allRatDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVMOD[i], allRatDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
-
- randevents = makerandomevents(allRatBlueMOD[i][300], allRatBlueMOD[i][-300])
- bgMad, bgMean = findnoise(allRatBlueMOD[i], randevents, fs=allRatFSMOD[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distractedMOD.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distractedMOD.append(uvMeanDISTRACTED)
- allbluesnipsMOD.append(blueSnips)
- alluvsnipsMOD.append(uvSnips)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractedMOD),np.asarray(blueMeans_distractedMOD)], ppsBlue, eventText='Distracted trial MOD', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-
-for i, val in enumerate(allRatNotDistractedMOD):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueMOD[i], allRatNotDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVMOD[i], allRatNotDistractedMOD[i], fs=allRatFSMOD[i], bins=300)
-
- randevents = makerandomevents(allRatBlueMOD[i][300], allRatBlueMOD[i][-300])
- bgMad, bgMean = findnoise(allRatBlueMOD[i], randevents, fs=allRatFSMOD[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
-
- except:
- pass
-
-
-# Means for not distracted trials here MULT SHADED FIG
-
- blueMeanNOTDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_notdistractedMOD.append(blueMeanNOTDISTRACTED)
- uvMeanNOTDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_notdistractedMOD.append(uvMeanNOTDISTRACTED)
-
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_notdistractedMOD),np.asarray(blueMeans_notdistractedMOD)], ppsBlue, eventText='Not Distracted trial MOD', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-
-bkgnd_sub_Distractor_MOD = uvSubtractor(blueMeans_distractorMOD, uvMeans_distractorMOD)
-bkgnd_sub_Distracted_MOD = uvSubtractor(blueMeans_distractedMOD, uvMeans_distractedMOD)
-bkgnd_sub_Notdistracted_MOD = uvSubtractor(blueMeans_notdistractedMOD, uvMeans_notdistractedMOD)
-# Distractors (in this case only these peaks in SPSS, may decide to use DIS and NOTDIS later though)
-peak_distractorMOD, t_distractorMOD, pre_distractorMOD, post_distractorMOD, baseline_distractorMOD = PhotoPeaksCalc(bkgnd_sub_Distractor_MOD)
-# Distracted
-peak_distractedMOD, t_distractedMOD, pre_distractedMOD, post_distractedMOD, baseline_distractedMOD = PhotoPeaksCalc(bkgnd_sub_Distracted_MOD)
-# Not distracted
-peak_notdistractedMOD, t_notdistractedMOD, pre_notdistractedMOD, post_notdistractedMOD, baseline_notdistractedMOD = PhotoPeaksCalc(bkgnd_sub_Notdistracted_MOD)
-
-
-
-## Run the distraction code on the HABITUATION DAY data (minus rats 1 and 2)
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# HABITUATION FILES (minus the first 2)
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-allRatBlueHAB = []
-allRatUVHAB = []
-allRatFSHAB = []
-allRatLicksHAB = []
-allRatDistractorsHAB = []
-allRatDistractedHAB = []
-allRatNotDistractedHAB = []
-blueMeans_distractorHAB = []
-uvMeans_distractorHAB = []
-blueMeans_distractedHAB = []
-uvMeans_distractedHAB = []
-blueMeans_notdistractedHAB = []
-uvMeans_notdistractedHAB = []
-allbluesnipsHAB = []
-alluvsnipsHAB = []
-
-for filename in TDTfiles_thph_hab:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlueHAB.append(ratdata['bluefilt'])
- allRatUVHAB.append(ratdata['uv'])
- allRatFSHAB.append(ratdata['fs'])
- allRatLicksHAB.append(ratdata['licks'])
- allRatDistractorsHAB.append(ratdata['distractors'])
- allRatDistractedHAB.append(ratdata['distracted'])
- allRatNotDistractedHAB.append(ratdata['notdistracted'])
-
-
-for i, val in enumerate(allRatDistractorsHAB):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueHAB[i], allRatDistractorsHAB[i], fs=allRatFSHAB[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVHAB[i], allRatDistractorsHAB[i], fs=allRatFSHAB[i], bins=300)
-
- randevents = makerandomevents(allRatBlueHAB[i][300], allRatBlueHAB[i][-300])
- bgMad, bgMean = findnoise(allRatBlueHAB[i], randevents, fs=allRatFSHAB[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
-
- except:
- pass
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractorHAB.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractorHAB.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(blueMeans_distractorHAB), np.asarray(blueMeans_distractorHAB)], ppsBlue, eventText='Distractor', linecolor = ['blue','blue'], errorcolor = ['lightblue','lightblue'], scale=0)
-
-
-
-for i, val in enumerate(allRatDistractedHAB):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueHAB[i], allRatDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVHAB[i], allRatDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
-
- randevents = makerandomevents(allRatBlueHAB[i][300], allRatBlueHAB[i][-300])
- bgMad, bgMean = findnoise(allRatBlueHAB[i], randevents, fs=allRatFSHAB[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
-
- except:
- pass
-
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distractedHAB.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distractedHAB.append(uvMeanDISTRACTED)
- allbluesnipsHAB.append(blueSnips)
- alluvsnipsHAB.append(uvSnips)
-
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractedHAB),np.asarray(blueMeans_distractedHAB)], ppsBlue, eventText='Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_All_Rats.pdf', bbox_inches="tight")
-
-for i, val in enumerate(allRatNotDistractedHAB):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlueHAB[i], allRatNotDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUVHAB[i], allRatNotDistractedHAB[i], fs=allRatFSHAB[i], bins=300)
-
- randevents = makerandomevents(allRatBlueHAB[i][300], allRatBlueHAB[i][-300])
- bgMad, bgMean = findnoise(allRatBlueHAB[i], randevents, fs=allRatFSHAB[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-
-
-# Means for not distracted trials here MULT SHADED FIG
-
- blueMeanNOTDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_notdistractedHAB.append(blueMeanNOTDISTRACTED)
- uvMeanNOTDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_notdistractedHAB.append(uvMeanNOTDISTRACTED)
-
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_notdistractedHAB),np.asarray(blueMeans_notdistractedHAB)], ppsBlue, eventText='Not Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-
-
-bkgnd_sub_Distractor_HAB = uvSubtractor(blueMeans_distractorHAB, uvMeans_distractorHAB)
-bkgnd_sub_Distracted_HAB = uvSubtractor(blueMeans_distractedHAB, uvMeans_distractedHAB)
-bkgnd_sub_Notdistracted_HAB = uvSubtractor(blueMeans_notdistractedHAB, uvMeans_notdistractedHAB)
-# Distractors (in this case only these peaks in SPSS, may decide to use DIS and NOTDIS later though)
-peak_distractorHAB, t_distractorHAB, pre_distractorHAB, post_distractorHAB, baseline_distractorHAB = PhotoPeaksCalc(bkgnd_sub_Distractor_HAB)
-# Distracted
-peak_distractedHAB, t_distractedHAB, pre_distractedHAB, post_distractedHAB, baseline_distractedHAB = PhotoPeaksCalc(bkgnd_sub_Distracted_HAB)
-# Not distracted
-peak_notdistractedHAB, t_notdistractedHAB, pre_notdistractedHAB, post_notdistractedHAB, baseline_notdistractedHAB = PhotoPeaksCalc(bkgnd_sub_Notdistracted_HAB)
-
-
-
-## Plots with MULTIPLE EVENTS ON SIGNLE PLOT
-'''
-
-dis
-np.asarray(blueMeans_distractor)
-distractecd
-np.asarray(blueMeans_distracted)
-notdis
-np.asarray(blueMeans_notdistracted)
-mod
-np.asarray(blueMeans_distractorMOD)
-dis
-np.asarray(blueMeans_distractor)
-dis
-np.asarray(blueMeans_distractor)
-hab
-'''
-
-
-
-# Distracted and not distracted
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(blueMeans_notdistracted), np.asarray(blueMeans_distracted)], ppsBlue, eventText='Distractor', linecolor = ['darkturquoise','dodgerblue'], errorcolor = ['lightcyan','#D0E5FF'], scale=0)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Draft 1/Photo_distracted_notdis.pdf', bbox_inches="tight")
-
-# Modelled and distraction day
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(blueMeans_distractorMOD),np.asarray(blueMeans_distractor) ], ppsBlue, eventText='Distractor', linecolor = ['darkturquoise','dodgerblue'], errorcolor = ['lightcyan','#D0E5FF'], scale=0)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Draft 1/Modelled_real.pdf', bbox_inches="tight")
-
-# Distraction day and habituation day
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-#ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(blueMeans_distractorHAB),np.asarray(blueMeans_distractor[:-1])], ppsBlue, eventText='Distractor', linecolor = ['darkturquoise','dodgerblue'], errorcolor = ['lightcyan','#D0E5FF'], scale=0)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Draft 1/Dis_habituation.pdf', bbox_inches="tight")
-
-#
-## Calculate AUC for variables / events (all and then means)
-### 5 second
-#AUC5_all_distractors = []
-#for rat in blueMeans_distractor:
-# AUC = np.trapz(rat[100:150])
-# AUC5_all_distractors.append(AUC)
-#mean_AUC_distractors = np.mean(AUC5_all_distractors)
-#
-#
-#AUC5_all_distracted = []
-#for rat in blueMeans_distracted:
-# AUC = np.trapz(rat[100:150])
-# AUC5_all_distracted.append(AUC)
-#mean_AUC_distracted = np.mean(AUC5_all_distracted)
-#
-### AUC is higher for not distracted because it starts higher
-### distracted trials start lower (proceeding activity included)
-#AUC5_all_notdistracted = []
-#for rat in blueMeans_notdistracted:
-# AUC = np.trapz(rat[100:150])
-# AUC5_all_notdistracted.append(AUC)
-#mean_AUC_notdistracted = np.mean(AUC5_all_notdistracted)
-#
-#AUC5_all_distractorsMOD = []
-#for rat in blueMeans_distractorMOD:
-# AUC = np.trapz(rat[100:150])
-# AUC5_all_distractorsMOD.append(AUC)
-#mean_AUC_distractorsMOD = np.mean(AUC5_all_distractorsMOD)
-#
-#AUC5_all_distractorsHAB = []
-#for rat in blueMeans_distractorHAB:
-# AUC = np.trapz(rat[100:150])
-# AUC5_all_distractorsHAB.append(AUC)
-#mean_AUC_distractorsHAB = np.mean(AUC5_all_distractorsHAB)
-#
-#
-### POST MEASURE AS AUC NOT AVERAGE ANYMORE
-### AUC all after the stimulus / all after the peak
-#AUC_all_distractors20 = []
-#for rat in blueMeans_distractor:
-# AUC = np.trapz(rat[100:300])
-# AUC_all_distractors20.append(AUC)
-#mean_AUC_distractors20 = np.mean(AUC_all_distractors20)
-#
-#
-#AUC_all_distracted20 = []
-#for rat in blueMeans_distracted:
-# AUC = np.trapz(rat[100:300])
-# AUC_all_distracted20.append(AUC)
-#mean_AUC_distracted20 = np.mean(AUC_all_distracted20)
-#
-#AUC_all_notdistracted20 = []
-#for rat in blueMeans_notdistracted:
-# AUC = np.trapz(rat[100:300])
-# AUC_all_notdistracted20.append(AUC)
-#mean_AUC_notdistracted20 = np.mean(AUC_all_notdistracted20)
-#
-#AUC_all_distractorsMOD20 = []
-#for rat in blueMeans_distractorMOD:
-# AUC = np.trapz(rat[100:300])
-# AUC_all_distractorsMOD20.append(AUC)
-#mean_AUC_distractorsMOD20 = np.mean(AUC_all_distractorsMOD20)
-#
-#AUC_all_distractorsHAB20 = []
-#for rat in blueMeans_distractorHAB:
-# AUC = np.trapz(rat[100:300])
-# AUC_all_distractorsHAB20.append(AUC)
-#mean_AUC_distractorsHAB20 = np.mean(AUC_all_distractorsHAB20)
-#
-##mean_AUC_distractors
-##mean_AUC_distracted
-##mean_AUC_notdistracted
-##mean_AUC_distractorsMOD
-##mean_AUC_distractorsHAB
-
-# Plot the AUC - 20 seconds after (isn't this the same as post????????) Perhaps include the 5 seconds before too???
-
-
-########################################################################
-########################################################################
-########################################################################
-
-'''
-## Write list of numbers to export into excel
-## Peak heights
-peak_all_distractorsMOD,
-peak_all_distractors,
-peak_all_distractorsHAB
-peak_all_distracted,
-peak_all_notdistracted,
-peak_all_licks
-
-AUC_all_distractorsMOD,
-AUC_all_distractors,
-AUC_all_distractorsHAB
-AUC_all_distracted,
-AUC_all_notdistracted,
-AUC_all_licks
-
-AUC_all_distractorsMOD20,
-AUC_all_distractors20,
-AUC_all_distractorsHAB20
-AUC_all_distracted20,
-AUC_all_notdistracted20,
-AUC_all_licks20
-'''
-
-
-#
-#### BARSCATTER PLOTS - old code and new
-#################################################################################################
-#################################################################################################
-#
-### Distracted vs not distracted (GREEN X 2)
-#disVnotPeak = [MultBy100(peak_notdistracted),MultBy100(peak_distracted)]
-#disVnott = [t_notdistracted,t_distracted]
-#disVnotPre = [MultBy100(pre_notdistracted),MultBy100(pre_distracted)]
-#disVnotPost = [MultBy100(post_notdistracted),MultBy100(post_distracted)]
-#
-#figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-#figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-#
-#labels = []
-#ax[0], barx, barlist, sclist = barscatter(disVnotPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax[1], barx, barlist, sclist = barscatter(disVnott, ax=ax[1], transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax[2], barx, barlist, sclist = barscatter(disVnotPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax[3], barx, barlist, sclist = barscatter(disVnotPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#257200','#4cbb17'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#
-#ax[0].set_ylabel('Peak (% ΔF)')
-#ax[1].set_ylabel('t (s)')
-#ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-#ax[3].set_ylabel('Post-event period (mean % ΔF)')
-#
-#ax[0].set_xticks([])
-##ax[0].set_ylim([0,4000])
-#ax[1].set_xticks([])
-##ax[1].set_ylim([0,25])
-#ax[2].set_xticks([])
-##ax[2].set_ylim(0,1200)
-#ax[3].set_xticks([])
-##ax[3].set_ylim(0,1200)
-#
-#ax[0].spines['bottom'].set_visible(False)
-#ax[1].spines['bottom'].set_visible(False)
-#ax[2].spines['bottom'].set_visible(False)
-#ax[3].spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/DisVsNotDisPeaksBarScatter.pdf', bbox_inches="tight")
-#
-#
-#################################################################################################
-#################################################################################################
-#
-### Modelled versus distracion day presented distractors (GREY and GREEN)
-#modVdisPeak = [MultBy100(peak_distractorMOD), MultBy100(peak_distractor)]
-#modVdist = [t_distractorMOD, t_distractor]
-#modVdisPre = [MultBy100(pre_distractorMOD), MultBy100(pre_distractor)]
-#modVdisPost = [MultBy100(post_distractorMOD), MultBy100(post_distractor)]
-#
-#figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-#figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-#
-#labels = []
-#ax[0], barx, barlist, sclist = barscatter(modVdisPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax[1], barx, barlist, sclist = barscatter(modVdist, ax=ax[1], transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax[2], barx, barlist, sclist = barscatter(modVdisPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax[3], barx, barlist, sclist = barscatter(modVdisPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['lightgray','#4cbb17'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#
-#ax[0].set_ylabel('Peak (% ΔF)')
-#ax[1].set_ylabel('t (s)')
-#ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-#ax[3].set_ylabel('Post-event period (mean % ΔF)')
-#
-#ax[0].set_xticks([])
-##ax[0].set_ylim([0,4000])
-#ax[1].set_xticks([])
-##ax[1].set_ylim([0,25])
-#ax[2].set_xticks([])
-##ax[2].set_ylim(0,1200)
-#ax[3].set_xticks([])
-##ax[3].set_ylim(0,1200)
-#
-#ax[0].spines['bottom'].set_visible(False)
-#ax[1].spines['bottom'].set_visible(False)
-#ax[2].spines['bottom'].set_visible(False)
-#ax[3].spines['bottom'].set_visible(False)
-#figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/ModVsDisPeaksBarScatter.pdf', bbox_inches="tight")
-#
-#################################################################################################
-#################################################################################################
-### Distraction day vs habituation day (GREEN, light and dark)
-#disVhabPeak = [MultBy100(peak_distractor), MultBy100(peak_distractorHAB)]
-#disVhabt = [t_distractor, t_distractorHAB]
-#disVhabPre = [MultBy100(pre_distractor), MultBy100(pre_distractorHAB)]
-#disVhabPost = [MultBy100(post_distractor), MultBy100(post_distractorHAB)]
-#
-#figureA, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,4)) ### x,y
-#figureA.tight_layout(pad=3, w_pad=3, h_pad=1.0)
-#
-#labels = []
-#ax[0], barx, barlist, sclist = barscatter(disVhabPeak, ax=ax[0],transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='Peak (%)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax[1], barx, barlist, sclist = barscatter(disVhabt, ax=ax[1], transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='t (s)', barlabels=labels, baredgecolor=['']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax[2], barx, barlist, sclist = barscatter(disVhabPre, ax=ax[2],transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='Pre-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#ax[3], barx, barlist, sclist = barscatter(disVhabPost, ax=ax[3],transpose=False, paired=True, barfacecolor=['#a2e283','#257200'], barfacecoloroption='individual', ylabel='Post-event period (mean %)', barlabels=labels, baredgecolor=[''] )#,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-#
-#ax[0].set_ylabel('Peak (% ΔF)')
-#ax[1].set_ylabel('t (s)')
-#ax[2].set_ylabel('Pre-event period (mean % ΔF)')
-#ax[3].set_ylabel('Post-event period (mean % ΔF)')
-#
-#ax[0].set_xticks([])
-##ax[0].set_ylim([0,4000])
-#ax[1].set_xticks([])
-##ax[1].set_ylim([0,25])
-#ax[2].set_xticks([])
-##ax[2].set_ylim(0,1200)
-#ax[3].set_xticks([])
-##ax[3].set_ylim(0,1200)
-#
-#ax[0].spines['bottom'].set_visible(False)
-#ax[1].spines['bottom'].set_visible(False)
-#ax[2].spines['bottom'].set_visible(False)
-#ax[3].spines['bottom'].set_visible(False)
-##figureA.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/DisVsHabPeaksBarScatter.pdf', bbox_inches="tight")
-#
-#
-
-
-### Example pretty individual barscatter
-## Add barscatter plots for the peak heights, AUC in 1 second and
-## longer AUC of all 20 seconds following events
-########################################################################
-########################################################################
-########################################################################
-#
-### Modelled and distraction day
-#data_peak = [peak_distractorsMOD[:-1], peak_all_distractors[:-1]]
-#data_20sec_AUC = [AUC_all_distractorsMOD20[:-1], AUC_all_distractors20[:-1]]
-#
-#
-## Peak modelled vs distraction day
-## Make 3 plots here
-modVdisPeak = [MultBy100(peak_distractorMOD), MultBy100(peak_distractor)]
-modVdist = [t_distractorMOD, t_distractor]
-modVdisPre = [MultBy100(pre_distractorMOD), MultBy100(pre_distractor)]
-modVdisPost = [MultBy100(post_distractorMOD), MultBy100(post_distractor)]
-
-disVhabPeak = [MultBy100(peak_distractor), MultBy100(peak_distractorHAB)]
-disVhabt = [t_distractor, t_distractorHAB]
-disVhabPre = [MultBy100(pre_distractor), MultBy100(pre_distractorHAB)]
-disVhabPost = [MultBy100(post_distractor), MultBy100(post_distractorHAB)]
-
-disVnotPeak = [MultBy100(peak_notdistracted),MultBy100(peak_distracted)]
-disVnott = [t_notdistracted,t_distracted]
-disVnotPre = [MultBy100(pre_notdistracted),MultBy100(pre_distracted)]
-disVnotPost = [MultBy100(post_notdistracted),MultBy100(post_distracted)]
-
-
-
-
-## Plots for modelled vs distraction day
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['mod', 'dis']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(modVdisPeak, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Peak (% ΔF/F)', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Peak_Bar_mod_dis.pdf', bbox_inches="tight")
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['mod', 'dis']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(modVdist, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='t (seconds)', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/T_Bar_mod_dis.pdf', bbox_inches="tight")
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['mod', 'dis']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(modVdisPost, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Post (% ΔF/F)', itemlabel=['1','2'], barlabeloffset=0.7, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Post_Bar_mod_dis.pdf', bbox_inches="tight")
-
-
-## Plota for distracted bv non distracted
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['nd', 'dis']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(disVnotPeak, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Peak (% ΔF/F)', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Peak_Bar_nd_dis.pdf', bbox_inches="tight")
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['nd', 'dis']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(disVnott, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='t (seconds)', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/T_Bar_nd_dis.pdf', bbox_inches="tight")
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['nd', 'dis']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(disVnotPost, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Post (% ΔF/F)', itemlabel=['1','2'], barlabeloffset=0.7, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Post_Bar_nd_dis.pdf', bbox_inches="tight")
-
-
-### Distractiopn day vs habituation
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['test1', 'test2']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(disVhabPeak, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Peak (% ΔF/F)', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Peak_Bar_dis_hab.pdf', bbox_inches="tight")
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['test1', 'test2']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(disVhabt, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='t (seconds)', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/T_Bar_dis_hab.pdf', bbox_inches="tight")
-
-col3 = ['darkturquoise','dodgerblue']
-labels = ['test1', 'test2']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(disVhabPost, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Post (% ΔF/F)', itemlabel=['1','2'], barlabeloffset=0.7, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/Post_Bar_dis_hab.pdf', bbox_inches="tight")
-
-
-###############################################################
-
-### Percent distracted behaviour
-
-
-
-## run this script AFTER the ch4 analysis script (photometry licking days and dis, not barscatter)
-def MetaExtractorTHPH (metafile):
- f = open(metafile, 'r')
- f.seek(0)
- Metafilerows = f.readlines()[1:]
- tablerows = []
-
- for row in Metafilerows:
- items = row.split(',')
- tablerows.append(items)
-
- TDTFile, MedFilenames, RatID, Date, Session, Include, Licks, Totdistractors, Distracted, \
- Percentdistracted, Note, Endcol = [],[],[],[],[],[],[],[],[],[],[],[]
-
- for i, lst in enumerate(tablerows):
-
-
-
-
- TDTFile = TDTFile + [lst[0]]
- MedFilenames = MedFilenames + [lst[1]]
- RatID = RatID + [lst[2]]
- Date = Date + [lst[3]]
- Session = Session + [lst[4]]
- Include = Include + [lst[5]]
- Licks = Licks + [lst[6]]
- Totdistractors = Totdistractors + [lst[7]]
- Distracted = Distracted + [lst[8]]
- Percentdistracted = Percentdistracted + [lst[9]]
- Note = Note + [lst[10]]
- Endcol = Endcol + [lst[11]]
-
-
- return ({'MedFilenames':MedFilenames, 'RatID':RatID, 'Date':Date, 'Session':Session, \
- 'Include':Include, 'Licks':Licks, 'PercentDistracted':Percentdistracted})
-
-
-
-def subsetter2(dictionary, dates, dis=False, verbose=False):
- '''
- SUBSETTER KP
- # Subsets data according to date, reads in dictionnary produced from metafile
- # and subsets into variable based on date(s) and drug condition
- # if distraction day argument is given as True adds the distractor type
- # to the output lists for later processing
-
- Adapted to include & include == 1 (because not all had the same date for last lick day)
- In metafile do not include 2.7 and 2.8 on the lick 3 day but do include them on lick 6
-
-
- '''
- subset = []
-
- for ind, filename in enumerate(dictionary['MedFilenames']):
- path = medfolder + filename
- onsets, offsets, med_dis_times, dis_type = medfilereader(path, ['b', 'c', 'i', 'j'], remove_var_header = True) # e onset, f offset
-
- if dis == True:
- if dictionary['Date'][ind] in dates and dictionary['Include'][ind] == '1':
- subset.append([onsets, offsets, dis_type, dictionary['RatID'][ind]])
-
- elif dis==False:
- if dictionary['Date'][ind] in dates and dictionary['Include'][ind] == '1':
- subset.append([onsets, offsets, dictionary['RatID'][ind]])
-
- if verbose: #assumes true
- print('filename, or comment ...')
- return subset
-
-
-
-def percentdisgroup(distractiondict):
- ''' Discalc_sal_M == distractiondict '''
-
- percent_dis_group = []
- for rat in distractiondict:
- percentage = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percent_dis_group.append(percentage)
- return percent_dis_group
-
-
-
-def disbygroup(dictionary):
- ''' Prodcues times of distracted and not distracted as 2 lists
- takes a dictionary of grouped rat data
- '''
-
- dis = []
- for rat in dictionary:
-
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- dis.append([distracted, notdistracted])
-
- return dis
-
-# Functions
-
-"""
-This function will create bar+scatter plots when passed a 1 or 2 dimensional
-array. Data needs to be passed in as a numpy object array, e.g.
-data = np.empty((2), dtype=np.object)
-data[0] = np.array(allData['nCasLicks'][index])
-data[1] = np.array(allData['nMaltLicks'][index])
-Various options allow specification of colors and paired/unpaired plotting.
-It can return the figures, axes, bars, and scatters for further modification.
-e.g.
-fig1, ax1, barlist1, sc1 = jmf.barscatter(data)
-for i in barlist1[1].get_children():
- i.set_color('g')
-"""
-def barscatter(data, transpose = False, unequal=False,
- groupwidth = .75,
- barwidth = .9,
- paired = False,
- spaced = False,
- barfacecoloroption = 'same', # other options 'between' or 'individual'
- barfacecolor = ['white'],
- baredgecoloroption = 'same',
- baredgecolor = [''],
- baralpha = 1,
- scatterfacecoloroption = 'same',
- scatterfacecolor = ['white'],
- scatteredgecoloroption = 'same',
- scatteredgecolor = ['grey'],
- scatterlinecolor = 'grey', # Don't put this value in a list
- scattersize = 80,
- scatteralpha = 1,
- linewidth=1,
- ylabel = 'none',
- xlabel = 'none',
- grouplabel = 'auto',
- itemlabel = 'none',
- barlabels = [],
- barlabeloffset=0.1,
- grouplabeloffset=0.2,
- yaxisparams = 'auto',
- show_legend = 'none',
- xrotation=0,
- legendloc='upper right',
- ax=[]):
-
- if unequal == True:
- dims = np.ndim(data)
- data_obj = np.ndarray((np.shape(data)), dtype=np.object)
- for i1, dim1 in enumerate(data):
- for i2, dim2 in enumerate(dim1):
- data_obj[i1][i2] = np.array(dim2, dtype=np.object)
- data = data_obj
-
- if type(data) != np.ndarray or data.dtype != np.object:
- dims = np.shape(data)
- if len(dims) == 2 or len(dims) == 1:
- data = data2obj1D(data)
-
- elif len(dims) == 3:
- data = data2obj2D(data)
-
- else:
- print('Cannot interpret data shape. Should be 2 or 3 dimensional array. Exiting function.')
- return
-
- # Check if transpose = True
- if transpose == True:
- data = np.transpose(data)
-
- # Initialize arrays and calculate number of groups, bars, items, and means
-
- barMeans = np.zeros((np.shape(data)))
- items = np.zeros((np.shape(data)))
-
- nGroups = np.shape(data)[0]
- groupx = np.arange(1,nGroups+1)
-
- if len(np.shape(data)) > 1:
- grouped = True
- barspergroup = np.shape(data)[1]
- barwidth = (barwidth * groupwidth) / barspergroup
-
- for i in range(np.shape(data)[0]):
- for j in range(np.shape(data)[1]):
- barMeans[i][j] = np.nanmean(data[i][j])
- items[i][j] = len(data[i][j])
-
- else:
- grouped = False
- barspergroup = 1
-
- for i in range(np.shape(data)[0]):
- barMeans[i] = np.nanmean(data[i])
- items[i] = len(data[i])
-
- # Calculate x values for bars and scatters
-
- xvals = np.zeros((np.shape(data)))
- barallocation = groupwidth / barspergroup
- k = (groupwidth/2) - (barallocation/2)
-
- if grouped == True:
-
- for i in range(np.shape(data)[0]):
- xrange = np.linspace(i+1-k, i+1+k, barspergroup)
- for j in range(barspergroup):
- xvals[i][j] = xrange[j]
- else:
- xvals = groupx
-
- # Set colors for bars and scatters
-
- barfacecolorArray = setcolors(barfacecoloroption, barfacecolor, barspergroup, nGroups, data)
- baredgecolorArray = setcolors(baredgecoloroption, baredgecolor, barspergroup, nGroups, data)
-
- scfacecolorArray = setcolors(scatterfacecoloroption, scatterfacecolor, barspergroup, nGroups, data, paired_scatter = paired)
- scedgecolorArray = setcolors(scatteredgecoloroption, scatteredgecolor, barspergroup, nGroups, data, paired_scatter = paired)
-
- # Initialize figure
- if ax == []:
- fig = plt.figure()
- ax = fig.add_subplot(111)
-
- # Make bars
- barlist = []
- barx = []
- for x, y, bfc, bec in zip(xvals.flatten(), barMeans.flatten(),
- barfacecolorArray, baredgecolorArray):
- barx.append(x)
- barlist.append(ax.bar(x, y, barwidth,
- facecolor = bfc, edgecolor = bec,
- zorder=-1))
-
- # Uncomment these lines to show method for changing bar colors outside of
- # function using barlist properties
- #for i in barlist[2].get_children():
- # i.set_color('r')
-
- # Make scatters
- sclist = []
- if paired == False:
- for x, Yarray, scf, sce in zip(xvals.flatten(), data.flatten(),
- scfacecolorArray, scedgecolorArray):
- for y in Yarray:
- if spaced == True:
- sclist.append(ax.scatter(x+np.random.random(size=1)*barallocation, y, s = scattersize,
- c = scf,
- edgecolors = sce,
- zorder=20))
- else:
- sclist.append(ax.scatter(x, y, s = scattersize,
- c = scf,
- edgecolors = sce,
- zorder=20))
-
- elif grouped == True:
- for x, Yarray, scf, sce in zip(xvals, data, scfacecolorArray, scedgecolorArray):
- for y in np.transpose(Yarray.tolist()):
- sclist.append(ax.plot(x, y, '-o', markersize = scattersize/10,
- color = scatterlinecolor,
- linewidth=linewidth,
- markerfacecolor = scf,
- markeredgecolor = sce,
- zorder=20))
- elif grouped == False:
- for n,_ in enumerate(data[0]):
- y = [y[n-1] for y in data]
- sclist.append(ax.plot(xvals, y, '-o', markersize = scattersize/10,
- color = scatterlinecolor,
- linewidth=linewidth,
- markerfacecolor = scfacecolorArray[0],
- markeredgecolor = scedgecolorArray[0],
- zorder=20))
-
- # Label axes
- if ylabel != 'none':
- plt.ylabel(ylabel)
-
- if xlabel != 'none':
- plt.xlabel(xlabel)
-
- # Set range and tick values for Y axis
- if yaxisparams != 'auto':
- ax.set_ylim(yaxisparams[0])
- plt.yticks(yaxisparams[1])
-
- # X ticks
- ax.tick_params(
- axis='x', # changes apply to the x-axis
- which='both', # both major and minor ticks are affected
- bottom='off', # ticks along the bottom edge are off
- top='off') # labels along the bottom edge are off
-
- if grouplabel == 'auto':
- plt.tick_params(labelbottom='off')
- else:
- if len(barlabels) > 0:
- plt.tick_params(labelbottom='off')
- yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
- offset = ax.get_ylim()[0] - yrange*grouplabeloffset
- for idx, label in enumerate(grouplabel):
- ax.text(idx+1, offset, label, va='top', ha='center')
- else:
- plt.xticks(range(1,nGroups+1), grouplabel)
-
- if len(barlabels) > 0:
- if len(barlabels) != len(barx):
- print('Wrong number of bar labels for number of bars!')
- else:
- yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
- offset = ax.get_ylim()[0] - yrange*barlabeloffset
- for x, label in zip(barx, barlabels):
- ax.text(x, offset, label, va='top', ha='center',rotation=xrotation)
-
- # Hide the right and top spines and set bottom to zero
- ax.spines['right'].set_visible(False)
- ax.spines['top'].set_visible(False)
- ax.spines['bottom'].set_position('zero')
-
- if show_legend == 'within':
- if len(itemlabel) != barspergroup:
- print('Not enough item labels for legend!')
- else:
- legendbar = []
- legendtext = []
- for i in range(barspergroup):
- legendbar.append(barlist[i])
- legendtext.append(itemlabel[i])
- plt.legend(legendbar, legendtext, loc=legendloc)
-
- return ax, barx, barlist, sclist
-
-#plt.savefig('foo.png')
-
-# To do
-# check if n's are the same for paired and if not default to unpaired
-# add color options for scatters
-# add alpha options etc
-# add axis options
-# remove white background
-# work out how to export or save as pdf, tiff, eps etc
-# work out how to return handles to scatters so can be altered outside of function
-# make help doc
-# make html file to show usage using ijupyt
-
-def setcolors(coloroption, colors, barspergroup, nGroups, data, paired_scatter = False):
-
- nColors = len(colors)
-
- if (paired_scatter == True) & (coloroption == 'within'):
- print('Not possible to make a Paired scatter plot with Within setting.')
- coloroption = 'same'
-
- if coloroption == 'within':
- if nColors < barspergroup:
- print('Not enough colors for this option! Reverting to one color.')
- coloroption = 'same'
- elif nColors > barspergroup:
- colors = colors[:barspergroup]
- coloroutput = [colors for i in data]
- coloroutput = list(chain(*coloroutput))
-
- if coloroption == 'between':
- if nColors < nGroups:
- print('Not enough colors for this option! Reverting to one color.')
- coloroption = 'same'
- elif nColors > nGroups:
- colors = colors[:nGroups]
- if paired_scatter == False:
- coloroutput = [[c]*barspergroup for c in colors]
- coloroutput = list(chain(*coloroutput))
- else:
- coloroutput = colors
-
- if coloroption == 'individual':
- if nColors < nGroups*barspergroup:
- print('Not enough colors for this color option')
- coloroption = 'same'
- elif nColors > nGroups*barspergroup:
- coloroutput = colors[:nGroups*barspergroup]
- else:
- coloroutput = colors
-
- if coloroption == 'same':
- coloroutput = [colors[0] for x in range(len(data.flatten()))]
-
- return coloroutput
-
-def data2obj1D(data):
- obj = np.empty(len(data), dtype=np.object)
- for i,x in enumerate(data):
- obj[i] = np.array(x)
- return obj
-
-def data2obj2D(data):
- obj = np.empty((np.shape(data)[0], np.shape(data)[1]), dtype=np.object)
- for i,x in enumerate(data):
- for j,y in enumerate(x):
- obj[i][j] = np.array(y)
- return obj
-
-
-
-
-################################################################################################
-################################################################################################
-
-## BEHAVIOUR SUBSETTING, PERCENT DISTRACTED AND PLOTS [THPH1, THPH2]
-
-metafile = '/Volumes/KP_HARD_DRI/kp259/THPH1AND2/THPH1&2Metafile.csv'
-extract_data = MetaExtractorTHPH(metafile)
-# Folder with all medfiles (THPH1 and THPH2)
-medfolder = '/Volumes/KP_HARD_DRI/kp259/THPH1AND2/med/'
-
-
-
-'''
- Info DPCP1(16) and DPCP2(16) males, DPCP3(24) females :
-
- THPH1 |THPH2.1 - 2.6|THPH2.7 - 2.8|CONDITION
- -----------|-------------|-------------|-----------
- 170620 |170809 |170814 |last lick
- 170621 |170810 |170815 |dis
- 170622 |170811 |170816 |hab 1
-
- All included, can later only subset the first 12 rats and ignore the
- unequal numbers from no habituation on rat 2.8
-
-'''
-
-
-last_lick = subsetter2(extract_data, ['170620', '170809', '170814'])
-distraction= subsetter2(extract_data, ['170621', '170810', '170815'], dis=True)
-habituation = subsetter2(extract_data, ['170622', '170811', '170806'], dis=True)
-
-
-discalcLick = []
-discalcDis = []
-discalcHab = []
-
-for rat in last_lick:
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- discalcLick.append([distracted, notdistracted])
-
-for rat in distraction:
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- discalcDis.append([distracted, notdistracted])
-
-for rat in habituation:
- discalc = distractionCalc2(rat[0])
- distracted, notdistracted = distractedOrNot(discalc, rat[0])
- discalcHab.append([distracted, notdistracted])
-
-percentdisLick = []
-percentdisDis = []
-percentdisHab = []
-
-for rat in discalcLick:
- percent = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percentdisLick.append(percent)
-
-for rat in discalcDis:
- percent = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percentdisDis.append(percent)
-
-for rat in discalcHab:
- percent = len(rat[0]) / (len(rat[0])+len(rat[1])) * 100
- percentdisHab.append(percent)
-
-
-##########################################################################################
-
-# PERCENT DISTRACTED - THPH1 AND THPH2
-
-# For barscatter plots excluded rats 2.7 and 2.8 (as not all point on all days)
-# Make this as two separate plots
-
-data = [[percentdisLick[0:12], percentdisDis[0:12], percentdisHab]]
-col3 = ['darkturquoise','dodgerblue', 'darkblue']
-labels = ['mod', 'dis', 'hab']
-mpl.rcParams['font.size'] = 14
-figureA, ax = plt.subplots(nrows=1, ncols=1, figsize=(1.5,3)) ### x,y
-ax, barx, barlist, sclist = barscatter(data, transpose=False, ax=ax, paired=True, barfacecolor=col3, barlabels=labels,barfacecoloroption='individual', ylabel='Distraction probability', itemlabel=['1','2'], barlabeloffset=0.05, scatterlinecolor = 'gray', scatteredgecolor='k', baredgecolor = ['black']) #,grouplabel=['Sal', 'Pcp', 'day -2', 'day -1'])
-ax.spines['bottom'].set_visible(False)
-figureA.savefig('/Volumes/KP_HARD_DRI/distraction_paper/ProbabilityDistraction.pdf', bbox_inches="tight")
-
-
diff --git a/CumulativePlots.py b/CumulativePlots.py
deleted file mode 100644
index 3faa051..0000000
--- a/CumulativePlots.py
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Oct 31 17:29:57 2018
-
-@author: u1490431
-"""
-
-
-
-## these are created in RATSER PLOT
-##pdp_day_list = [pdps_lickday, pdps_disday, pdps_habday]
-
-## PDPS on licking day
-
-fig = plt.figure()
-#plt.title('Lickday SAL_M', **Calibri, **Size)
-ax = fig.add_subplot(111)
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-
-for index, licklist in enumerate(pdps_lickday):
- plot = cumulativelickFig(ax, pdps_lickday[index], normed=True, color='lightgrey', log=True)
-avg = [item for rat in pdps_lickday for item in rat]
-cumulativelickFig(ax, avg, normed=True, color='dimgrey', log=True)
-ax.set(ylabel = 'Probability')
-ax.yaxis.label.set_size(16)
-ax.set(xlabel = 'post-distraction pause log(s)')
-ax.xaxis.label.set_size(16)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/figs/CumulativeLickDay.pdf', bbox_inches='tight')
-
-
-## PDPS on distraction day
-
-fig = plt.figure()
-#plt.title('Lickday SAL_M', **Calibri, **Size)
-ax = fig.add_subplot(111)
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-
-for index, licklist in enumerate(pdps_disday):
- plot = cumulativelickFig(ax, pdps_disday[index], normed=True, color='lightgrey', log=True)
-avg = [item for rat in pdps_disday for item in rat]
-cumulativelickFig(ax, avg, normed=True, color='darkblue', log=True)
-ax.set(ylabel = 'Probability')
-ax.yaxis.label.set_size(16)
-ax.set(xlabel = 'post-distraction pause log(s)')
-ax.xaxis.label.set_size(16)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/figs/CumulativeDisDay.pdf', bbox_inches='tight')
-
-
-
-## PDPS on habituation day
-
-fig = plt.figure()
-#plt.title('Lickday SAL_M', **Calibri, **Size)
-ax = fig.add_subplot(111)
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-
-for index, licklist in enumerate(pdps_habday):
- plot = cumulativelickFig(ax, pdps_habday[index], normed=True, color='lightgrey', log=True)
-avg = [item for rat in pdps_habday for item in rat]
-cumulativelickFig(ax, avg, normed=True, color='green', log=True)
-ax.set(ylabel = 'Probability')
-ax.yaxis.label.set_size(16)
-ax.set(xlabel = 'post-distraction pause log(s)')
-ax.xaxis.label.set_size(16)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/figs/CumulativeHabDay.pdf', bbox_inches='tight')
-
-
-## Plot all three lines on one plot (NOT AVERAGES, but the cPDPs for rat 1.4, index 3)
-fig = plt.figure()
-#plt.title('Lickday SAL_M', **Calibri, **Size)
-ax = fig.add_subplot(111)
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-
-cumulativelickFig(ax, pdps_lickday[3], normed=True, color='dimgrey', log=True)
-cumulativelickFig(ax, pdps_disday[1], normed=True, color='green', log=True)
-cumulativelickFig(ax, pdps_habday[1], normed=True, color='black', log=True)
-ax.set(ylabel = 'Probability')
-ax.yaxis.label.set_size(16)
-ax.set(xlabel = 'post-distraction pause log(s)')
-ax.xaxis.label.set_size(16)
-fig.savefig('/Volumes/KP_HARD_DRI/distraction_paper/figs/Cumulative1.4Rep.pdf', bbox_inches='tight')
-
-make the long course potometry plots with the zoomed
-
diff --git a/JM_custom_figs.py b/JM_custom_figs.py
index 32c717a..3dc2397 100644
--- a/JM_custom_figs.py
+++ b/JM_custom_figs.py
@@ -9,6 +9,9 @@
import matplotlib.pyplot as plt
from itertools import chain, count
from collections import Sequence
+import matplotlib.cbook as cbook
+import matplotlib.mlab as mlab
+import JM_general_functions as jmf
"""
This function will create bar+scatter plots when passed a 1 or 2 dimensional
array. Data needs to be passed in as a numpy object array, e.g.
@@ -32,6 +35,8 @@ def barscatter(data, transpose = False, unequal=False,
barwidth = .9,
paired = False,
spaced = False,
+ yspace = 20,
+ xspace = 0.1,
barfacecoloroption = 'same', # other options 'between' or 'individual'
barfacecolor = ['white'],
baredgecoloroption = 'same',
@@ -44,6 +49,7 @@ def barscatter(data, transpose = False, unequal=False,
scatterlinecolor = 'grey', # Don't put this value in a list
scattersize = 80,
scatteralpha = 1,
+ spreadscatters = False,
linewidth=1,
xlim=[],
ylim=[],
@@ -53,10 +59,12 @@ def barscatter(data, transpose = False, unequal=False,
itemlabel = 'none',
barlabels = [],
barlabeloffset=0.025,
- grouplabeloffset=0.11,
+ grouplabeloffset=0,
yaxisparams = 'auto',
show_legend = 'none',
legendloc='upper right',
+ xfontsize=8,
+ barbaseline=0,
ax=[]):
if unequal == True:
@@ -150,8 +158,9 @@ def barscatter(data, transpose = False, unequal=False,
for x, y, bfc, bec in zip(xvals.flatten(), barMeans.flatten(),
barfacecolorArray, baredgecolorArray):
barx.append(x)
- barlist.append(ax.bar(x, y, barwidth,
+ barlist.append(ax.bar(x, y-barbaseline, barwidth,
facecolor = bfc, edgecolor = bec,
+ bottom=barbaseline,
zorder=-1))
# Uncomment these lines to show method for changing bar colors outside of
@@ -164,13 +173,14 @@ def barscatter(data, transpose = False, unequal=False,
if paired == False:
for x, Yarray, scf, sce in zip(xvals.flatten(), data.flatten(),
scfacecolorArray, scedgecolorArray):
- for y in Yarray:
- if spaced == True:
- sclist.append(ax.scatter(x+np.random.random(size=1)*barallocation, y, s = scattersize,
+ if spaced == True:
+ xVals, yVals = xyspacer(ax, x, Yarray, bindist=yspace, space=xspace)
+ sclist.append(ax.scatter(xVals, yVals, s = scattersize,
c = scf,
edgecolors = sce,
- zorder=20))
- else:
+ zorder=20))
+ else:
+ for y in Yarray:
sclist.append(ax.scatter(x, y, s = scattersize,
c = scf,
edgecolors = sce,
@@ -202,41 +212,39 @@ def barscatter(data, transpose = False, unequal=False,
if xlabel != 'none':
ax.set_xlabel(xlabel)
- xrange = ax.get_xlim()[1] - ax.get_xlim()[0]
- yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
-
# Set range and tick values for Y axis
if yaxisparams != 'auto':
ax.set_ylim(yaxisparams[0])
- plt.yticks(yaxisparams[1])
+ ax.set_yticks(yaxisparams[1])
# X ticks
ax.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
- bottom='off', # ticks along the bottom edge are off
- top='off') # labels along the bottom edge are off
+ bottom=False, # ticks along the bottom edge are off
+ top=False,
+ labelbottom=False) # labels along the bottom edge are off
if len(xlim) > 0:
ax.set_xlim(xlim)
if len(ylim) > 0:
ax.set_ylim(ylim)
+ xrange = ax.get_xlim()[1] - ax.get_xlim()[0]
+ yrange = ax.get_ylim()[1] - ax.get_ylim()[0]
+
if grouplabel == 'auto':
ax.tick_params(labelbottom='off')
else:
- if len(barlabels) > 0:
- ax.tick_params(labelbottom='off')
+ ax.tick_params(labelbottom='off')
- groupx = np.arange(1, len(grouplabel)+1)
- xpos = (groupx - ax.get_xlim()[0])/xrange
- ypos = (-ax.get_ylim()[0]/yrange) - grouplabeloffset
+ groupx = np.arange(1, len(grouplabel)+1)
+ if len(xlim) > 0:
+ groupx = [x for x in groupx]
+ xpos = (groupx - ax.get_xlim()[0])/xrange
- for x, label in zip(xpos, grouplabel):
- ax.text(x, ypos, label, va='top', ha='center', transform=ax.transAxes)
- else:
- ax.set_xticks(range(1,nGroups+1))
- ax.set_xticklabels(grouplabel)
+ for x, label in zip(xpos, grouplabel):
+ ax.text(x, -0.05+grouplabeloffset, label, va='top', ha='center', fontsize=xfontsize, transform=ax.transAxes)
if len(barlabels) > 0:
if len(barlabels) != len(barx):
@@ -244,9 +252,10 @@ def barscatter(data, transpose = False, unequal=False,
else:
xpos = (barx - ax.get_xlim()[0])/xrange
ypos = (-ax.get_ylim()[0]/yrange) - barlabeloffset
+
for x, label in zip(xpos, barlabels):
- ax.text(x, ypos, label, va='top', ha='center', transform=ax.transAxes)
-
+ ax.text(x, ypos, label, va='top', ha='center', fontsize=xfontsize, transform=ax.transAxes)
+
# Hide the right and top spines and set bottom to zero
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
@@ -335,11 +344,33 @@ def data2obj2D(data):
obj[i][j] = np.array(y)
return obj
+def xyspacer(ax, x, yvals, bindist=20, space=0.1):
+
+ histrange=[]
+ histrange.append(min(ax.get_ylim()[0], min(yvals)))
+ histrange.append(max(ax.get_ylim()[1], max(yvals)))
+
+ yhist = np.histogram(yvals, bins=bindist, range=histrange)
+
+ xvals=[]
+ for ybin in yhist[0]:
+ if ybin == 1:
+ xvals.append(x)
+ elif ybin > 1:
+ temp_vals = np.linspace(x-space, x+space, num=ybin)
+ for val in temp_vals:
+ xvals.append(val)
+
+ yvals.sort()
+
+ return xvals, yvals
+
def depth(seq):
for level in count():
if not seq:
return level
seq = list(chain.from_iterable(s for s in seq if isinstance(s, Sequence)))
+
# Arguments to include in function
@@ -704,12 +735,12 @@ def invisible_axes(ax):
for sp in ['left', 'right', 'top', 'bottom']:
ax.spines[sp].set_visible(False)
-def shadedError(ax, yarray, linecolor='black', errorcolor = 'xkcd:silver'):
+def shadedError(ax, yarray, linecolor='black', errorcolor = 'xkcd:silver', linewidth=1):
yarray = np.array(yarray)
- y = np.mean(yarray)
+ y = np.mean(yarray, axis=0)
yerror = np.std(yarray)/np.sqrt(len(yarray))
x = np.arange(0, len(y))
- ax.plot(x, y, color=linecolor)
+ ax.plot(x, y, color=linecolor, linewidth=linewidth)
ax.fill_between(x, y-yerror, y+yerror, color=errorcolor, alpha=0.4)
return ax
@@ -743,4 +774,16 @@ def lighten_color(color, amount=0.5):
except:
c = color
c = colorsys.rgb_to_hls(*mc.to_rgb(c))
- return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
\ No newline at end of file
+ return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
+
+def get_violinstats(dataset, points=100, bw_method=None):
+
+ def _kde_method(X, coords):
+ # fallback gracefully if the vector contains only one value
+ if np.all(X[0] == X):
+ return (X[0] == coords).astype(float)
+ kde = mlab.GaussianKDE(X, bw_method)
+ return kde.evaluate(coords)
+
+ vpstats = cbook.violin_stats(dataset, _kde_method, points=points)
+ return vpstats
\ No newline at end of file
diff --git a/JM_general_functions.py b/JM_general_functions.py
index e32036e..7a46087 100644
--- a/JM_general_functions.py
+++ b/JM_general_functions.py
@@ -487,6 +487,30 @@ def distractedOrNot(distractors, licks, delay=1):
return firstlick, distractedArray
+# This revised version of distracted_or_not combined two different iterations of this
+# function: one written by Jaime and one written by Kate. This newer version outputs
+# pdps and the Boolean array as well as times of distracted and non distracted trials
+
+def distracted_or_not(distractors, licks, delay=1):
+ pdp = [] # post-distraction pause
+ distractedArray = []
+
+ for d in distractors:
+ try:
+ pdp.append([i-d for i in licks if (i > d)][0])
+ except IndexError:
+ pdp.append(np.NaN)
+
+ distracted_boolean_array = np.array([i>delay for i in pdp], dtype=bool)
+
+ distracted = [d for d,l in zip(distractors, distracted_boolean_array) if l]
+ notdistracted = [d for d,l in zip(distractors, distracted_boolean_array) if not l]
+
+ if np.isnan(pdp)[-1] == 1:
+ distracted_boolean_array[-1] = True
+
+ return [distracted, notdistracted], distracted_boolean_array, pdp
+
def calcDistractors(licks):
# disStart = [licks[idx-2] for idx, val in enumerate(licks) if (val - licks[idx-2]) < 1]
# disEnd = [val for idx, val in enumerate(licks) if (val - licks[idx-2]) < 1]
diff --git a/LongTimeCourse Distraction.py b/LongTimeCourse Distraction.py
deleted file mode 100644
index 7b406f0..0000000
--- a/LongTimeCourse Distraction.py
+++ /dev/null
@@ -1,302 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Jan 10 17:37:57 2019
-
-@author: kate
-"""
-
-######
-##############################################################################################################
-##############################################################################################################
-##############################################################################################################
-
-# DISTRACTION FILES (minus the first 2) - this was run with all included
-### Distractors, distracted and not distracted, licks and blue / uv signals
-
-
-allRatBlue = []
-allRatUV = []
-allRatFS = []
-allRatLicks = []
-allRatDistractors = []
-allRatDistracted = []
-allRatNotDistracted = []
-blueMeans_distractor = []
-uvMeans_distractor = []
-blueMeans_distracted = []
-uvMeans_distracted = []
-blueMeans_notdistracted = []
-uvMeans_notdistracted = []
-allbluesnips = []
-alluvsnips = []
-
-for filename in TDTfiles_thph_dis:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlue.append(ratdata['blue'])
- allRatUV.append(ratdata['uv'])
- allRatFS.append(ratdata['fs'])
- allRatLicks.append(ratdata['licks'])
- allRatDistractors.append(ratdata['distractors'])
- allRatDistracted.append(ratdata['distracted'])
- allRatNotDistracted.append(ratdata['notdistracted'])
- figure12 = plt.figure(figsize=(6,3))
- ax6 = plt.subplot(111)
- ax6.spines['right'].set_visible(False)
- ax6.xaxis.set_visible(False)
- ax6.spines['top'].set_visible(False)
- ax6.spines['bottom'].set_visible(False)
- ax6.set(ylabel = 'Trials')
- ax6.yaxis.label.set_size(14)
-# distractionrasterFig(ax6, ratdata['distractors'], ratdata['licks'], pre=1, post=10, sortevents='yes', sortdirection='dec')
-
-
-for i, val in enumerate(allRatDistractors):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatDistractors[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatDistractors[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-# Individual plots to choose a representative rat
-
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distractor') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistractors[i])) + ' distractors' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_' + str(i) + '.pdf', bbox_inches="tight")
-
-
- blueMeanDISTRACTOR = np.mean(blueSnips, axis=0)
- blueMeans_distractor.append(blueMeanDISTRACTOR)
- uvMeanDISTRACTOR = np.mean(uvSnips, axis=0)
- uvMeans_distractor.append(uvMeanDISTRACTOR)
-
-
-# Means for distractORS trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distractor),np.asarray(blueMeans_distractor)], ppsBlue, eventText='Distractor', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distractors_All_Rats.pdf', bbox_inches="tight")
-
-
-
-for i, val in enumerate(allRatDistracted):
- try:
- # make a blue and uv snip for all 14, and noise remover / index
- blueSnips, ppsBlue = snipper(allRatBlue[i], allRatDistracted[i], fs=allRatFS[i], bins=300)
- uvSnips, ppsUV = snipper(allRatUV[i], allRatDistracted[i], fs=allRatFS[i], bins=300)
-
- randevents = makerandomevents(allRatBlue[i][300], allRatBlue[i][-300])
- bgMad, bgMean = findnoise(allRatBlue[i], randevents, fs=allRatFS[i], method='sum', bins=300)
- threshold = 1
- sigSum = [np.sum(abs(i)) for i in blueSnips]
- noiseindex = [i > bgMean + bgMad*threshold for i in sigSum]
- # Might not need the noise index, this is just for trials fig
- except:
- pass
-# Individual plots to choose a representative rat
-# fig14 = plt.figure()
-# ax13 = plt.subplot(1,1,1)
-# ax13.set_ylim([-0.15, 0.15])
-# trialsFig(ax13, blueSnips, uvSnips, ppsBlue, eventText='Distracted') #, noiseindex=noiseindex) #, )
-# plt.text(250,0.2, '{}'.format(len(allRatDistracted[i])) + ' distracted' )
-# fig14.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_' + str(i) + '.pdf', bbox_inches="tight")
-#
-
- blueMeanDISTRACTED = np.mean(blueSnips, axis=0)
- blueMeans_distracted.append(blueMeanDISTRACTED)
- uvMeanDISTRACTED = np.mean(uvSnips, axis=0)
- uvMeans_distracted.append(uvMeanDISTRACTED)
- allbluesnips.append(blueSnips)
- alluvsnips.append(uvSnips)
-# Means for distracted trials here MULT SHADED FIG
-fig = plt.figure(figsize=(6,3))
-ax = plt.subplot(1,1,1)
-ax.set_ylim([-0.04, 0.04])
-trialsMultShadedFig(ax, [np.asarray(uvMeans_distracted),np.asarray(blueMeans_distracted)], ppsBlue, eventText='Distracted trial', linecolor = ['purple','blue'], errorcolor = ['thistle','lightblue'], scale=0)
-# EDIT THIS TEXT TO SHOW NUMBER OF TOTAL DISTRACTORS OR TRIALS ON THE AVERAGED PLOT
-#plt.text(250,0.03, '{}'.format(len(MergedRunList_Long)) + ' Long Runs' ) ## Edit this to be all
-#fig.savefig('/Volumes/KPMSB352/Thesis/Chapter 4 - Photometry VTA/Figures/Distracted_All_Rats.pdf', bbox_inches="tight")
-
-
-
-# RAT3 - 10 minutes (value zero is rat3 here as 1 and 2 deleted in distraciton)
-fig9 = plt.figure(figsize=(12,2))
-ax7 = plt.subplot(1,1,1)
-plt.plot(allRatBlue[10], color='royalblue')
-plt.plot(allRatUV[10] + 150, color='darkorchid') ### OFFSET THE UV
-ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-ax7.set_xticklabels([0,10,20,30,40,50,60])
-ax7.set_xlabel('Mins', fontsize=14)
-#ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-
-## FIRSRT 10 MINS
-#ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-# Second 10 mins
-ax7.set_xlim([122070.31494140625,1220703.1494140625]) # 2 mins to 12 mins, a 10 min snip without noise at start
-
-ax7.set_ylim([500,900])
-
-multipliedLicks = []
-for element in allRatLicks[10]:
- multElement = element*allRatFS[0]
- multipliedLicks.append([multElement])
-
-multipliedDistractors = []
-for element in allRatDistractors[10]:
- multElement = element*allRatFS[0]
- multipliedDistractors.append([multElement])
-
-multipliedDistracted = []
-for element in allRatDistracted[10]:
- multElement = element*allRatFS[0]
- multipliedDistracted.append([multElement])
-
-xvals = multipliedLicks
-yvals = [ax7.get_ylim()[1] - 10] * len(xvals)
-ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-ax7.set(ylabel = '∆F')
-ax7.yaxis.label.set_size(14)
-ax7.xaxis.set_visible(False)
-
-
-xvals = multipliedDistractors
-yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-ax7.scatter(xvals, yvals, marker='o', color='k', linewidth=0.2)
-ax7.set(ylabel = '∆F')
-ax7.yaxis.label.set_size(14)
-ax7.xaxis.set_visible(False)
-
-xvals = multipliedDistracted
-yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-ax7.scatter(xvals, yvals, marker='o', color='r', linewidth=0.2)
-ax7.set(ylabel = '∆F')
-ax7.yaxis.label.set_size(14)
-ax7.xaxis.set_visible(False)
-
-
-
-scalebar = 1*allRatFS[0]*60 # 1 minute
-
-yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-scalebary = (yrange / 10) + ax7.get_ylim()[0]
-scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-ax7.spines['right'].set_visible(False)
-ax7.spines['top'].set_visible(False)
-ax7.spines['bottom'].set_visible(False)
-#fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/LongTimeCourseDISRat10_20min.pdf', bbox_inches="tight")
-
-
-
-### Short time course
-
-# RAT3 - 10 minutes (value zero is rat3 here as 1 and 2 deleted in distraciton)
-fig9 = plt.figure(figsize=(4,2))
-ax7 = plt.subplot(1,1,1)
-plt.plot(allRatBlue[10], color='royalblue')
-plt.plot(allRatUV[10] + 160, color='darkorchid') ### OFFSET THE UV
-ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-ax7.set_xticklabels([0,10,20,30,40,50,60])
-ax7.set_xlabel('Mins', fontsize=14)
-#ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-
-## FIRSRT 10 MINS
-#ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-# Second 10 mins
-ax7.set_xlim([396728.5235595703,518798.83850097656]) # 2 mins to 12 mins, a 10 min snip without noise at start
-
-ax7.set_ylim([500,800])
-
-multipliedLicks = []
-for element in allRatLicks[10]:
- multElement = element*allRatFS[0]
- multipliedLicks.append([multElement])
-
-multipliedDistractors = []
-for element in allRatDistractors[10]:
- multElement = element*allRatFS[0]
- multipliedDistractors.append([multElement])
-
-multipliedDistracted = []
-for element in allRatDistracted[10]:
- multElement = element*allRatFS[0]
- multipliedDistracted.append([multElement])
-
-xvals = multipliedLicks
-yvals = [ax7.get_ylim()[1] - 10] * len(xvals)
-ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-ax7.set(ylabel = '∆F')
-ax7.yaxis.label.set_size(14)
-ax7.xaxis.set_visible(False)
-
-
-xvals = multipliedDistractors
-yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-ax7.scatter(xvals, yvals, marker='o', color='k', linewidth=0.2)
-ax7.set(ylabel = '∆F')
-ax7.yaxis.label.set_size(14)
-ax7.xaxis.set_visible(False)
-
-xvals = multipliedDistracted
-yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-ax7.scatter(xvals, yvals, marker='o', color='r', linewidth=0.2)
-ax7.set(ylabel = '∆F')
-ax7.yaxis.label.set_size(14)
-ax7.xaxis.set_visible(False)
-
-
-
-scalebar = 1*allRatFS[0]*60 # 1 minute
-
-yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-scalebary = (yrange / 10) + ax7.get_ylim()[0]
-scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-ax7.spines['right'].set_visible(False)
-ax7.spines['top'].set_visible(False)
-ax7.spines['bottom'].set_visible(False)
-#fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/LongTimeCourseDISRat10_2min.pdf', bbox_inches="tight")
-
-
-
-## then individual trials
-
-
-## Converts snips to z-scores based on baseline period before event
-# Default is 10 seconds as snips set up to be 30 seconds with 10 min
-# before the event
-
-def zscore(snips, baseline_points=100):
-
- BL_range = range(baseline_points)
- z_snips = []
- for i in snips:
- mean = np.mean(i[BL_range])
- sd = np.std(i[BL_range])
- z_snips.append([(x-mean)/sd for x in i])
-
- return z_snips
-
-
-
-
-
diff --git a/LongTimeCoursePhotoLicking.py b/LongTimeCoursePhotoLicking.py
deleted file mode 100644
index d77e3d4..0000000
--- a/LongTimeCoursePhotoLicking.py
+++ /dev/null
@@ -1,320 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Jan 9 19:09:07 2019
-
-@author: kate
-"""
-
-
-'''
-
-MAKE
-SURE
-THAT
-YOU HAVE RUN
-THE CORRECT BLUE AND UV
-SIGNALS (LICKING - 14 RATS)
-AND
-NOT
-DISTRACTION / MODELLED OR HABITUATION
-
-'''
-
-## Functions and code to creat the long time course figures for distraction paper
-
-# Step 1 - run the all functions file
-# Step 2 - run the Ch4_analysis_licking and Ch4_analysis_distraction files
-# Step 3 - change the snipper function to make longer snips and add this to this file
-# Step 4 - copy over only the code that is needed from the licking and distraciton file
-# - to make the long time course figures
-
-#! ! ! Decide if you are going to use data from one representative rats or all of the animals
- # Check back to MMIN18 and see whether you used group or individual data
- # Maybe only need the blue signal or maybe also violet is useful
-
-# Step 5 - make and save long timecourse figures
-
-
-# Step 6 - subset the required data for the barscatter plots, decide which info is important
-
-# Step 7 - calculate peaks within time region and look at these statistically
-
-
-# Step 8 - check which figures and statistics from thesis / and corrections (time bins)
-# - will be used
-
-# Step 9 - make barscatter of 'probability' of distraction in 10 min time bins
-# - averaged for all of the rats (make the percent for each and then average)
-# - show statistically the lack of habituaiton effect within the session
-
-
-
-## Step 10 - is it possible to make barscatters for average post distraction pauses
-# - or other measure median then bin these into 10 mins as with percent distracted?
-
-
-### Could plot all the rats as 1hr then as 30 mins to see which rat to use for long time course
-
-# RAT3 - 10 minutes
-fig9 = plt.figure(figsize=(12,2))
-ax7 = plt.subplot(1,1,1)
-plt.plot(allRatBlue[3], color='royalblue')
-plt.plot(allRatUV[3] - 130, color='darkorchid') ### OFFSET THE UV
-ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-ax7.set_xticklabels([0,10,20,30,40,50,60])
-ax7.set_xlabel('Mins', fontsize=14)
-#ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-ax7.set_ylim([400,800])
-
-multipliedLicks = []
-for element in allRatLicks[3]:
- multElement = element*allRatFS[0]
- multipliedLicks.append([multElement])
-
-xvals = multipliedLicks
-yvals = [ax7.get_ylim()[1] - 10] * len(xvals)
-ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-ax7.set(ylabel = '∆F')
-ax7.yaxis.label.set_size(14)
-ax7.xaxis.set_visible(False)
-
-scalebar = 1*allRatFS[0]*60 # 1 minute
-
-yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-scalebary = (yrange / 10) + ax7.get_ylim()[0]
-scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-ax7.spines['right'].set_visible(False)
-ax7.spines['top'].set_visible(False)
-ax7.spines['bottom'].set_visible(False)
-#fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/PLongTimeCourseRat3_10min.pdf', bbox_inches="tight")
-
-
-## RAT 12
-#fig9 = plt.figure(figsize=(12,2))
-#ax7 = plt.subplot(1,1,1)
-#plt.plot(allRatBlue[12], color='royalblue')
-#plt.plot(allRatUV[12], color='darkorchid')
-#ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-#ax7.set_xticklabels([0,10,20,30,40,50,60])
-#ax7.set_xlabel('Mins', fontsize=14)
-##ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-#ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-#ax7.set_ylim([300,800])
-#
-#
-#
-## Adding the scatter to long time course plot of photo signals
-##allRatLicks.append(ratdata['licks'])
-#multipliedLicks = []
-#for element in allRatLicks[12]:
-# multElement = element*allRatFS[0]
-# multipliedLicks.append([multElement])
-#
-#xvals = multipliedLicks
-#yvals = [ax7.get_ylim()[1] - 50] * len(xvals)
-#ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-#
-## Get rid of the spines and add labels and ticks to plot
-## Add a 1 minute scale bar OR tick labels for mins
-#ax7.set(ylabel = '∆F')
-#ax7.yaxis.label.set_size(14)
-#ax7.xaxis.set_visible(False)
-#
-#scalebar = 1*allRatFS[0]*60 # 1 minute
-#
-#yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-#scalebary = (yrange / 10) + ax7.get_ylim()[0]
-#scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-#ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-#ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-#ax7.spines['right'].set_visible(False)
-#ax7.spines['top'].set_visible(False)
-#ax7.spines['bottom'].set_visible(False)
-##fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-#
-
-
-## RAT 3 ZOOMED SECITON 30 seconds
-fig9 = plt.figure(figsize=(6,2))
-ax7 = plt.subplot(1,1,1)
-plt.plot(allRatBlue[3], color='royalblue')
-#plt.plot(allRatUV[3], color='darkorchid')
-ax7.set_xticks([0,(20*60*allRatFS[0]),(30*60*allRatFS[0])])
-ax7.set_xticklabels([0,20,30])
-ax7.set_xlabel('Mins', fontsize=14)
-#ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-ax7.set_xlim([228881.84051513672, 259399.41925048828]) # 2 mins to 12 mins, a 10 min snip without noise at start
-ax7.set_ylim([500,800])
-
-# 3.75 (45 seconds) and 4.25
-
-## 4- 5
-# Adding the scatter to long time course plot of photo signals
-#allRatLicks.append(ratdata['licks'])
-multipliedLicks = []
-for element in allRatLicks[3]:
- multElement = element*allRatFS[0]
- multipliedLicks.append([multElement])
-
-xvals = multipliedLicks
-yvals = [ax7.get_ylim()[1] - 50] * len(xvals)
-ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.6)
-
-# Get rid of the spines and add labels and ticks to plot
-# Add a 1 minute scale bar OR tick labels for mins
-ax7.set(ylabel = '∆F')
-ax7.yaxis.label.set_size(14)
-ax7.xaxis.set_visible(False)
-
-scalebar = 1*allRatFS[0]*60 # 1 minute
-
-yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-scalebary = (yrange / 10) + ax7.get_ylim()[0]
-scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-#ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-#ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '30 Sec', ha='center',va='top', **Calibri, **Size)
-ax7.spines['right'].set_visible(False)
-ax7.spines['top'].set_visible(False)
-ax7.spines['bottom'].set_visible(False)
-#fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/PLongTimeCourseRat3_30sec(1).pdf', bbox_inches="tight")
-### RAT 3 ZOOMED SECITON
-#fig9 = plt.figure(figsize=(4,2))
-#ax7 = plt.subplot(1,1,1)
-#plt.plot(allRatBlue[3], color='royalblue')
-##plt.plot(allRatUV[3], color='darkorchid')
-#ax7.set_xticks([0,(20*60*allRatFS[0]),(30*60*allRatFS[0])])
-#ax7.set_xticklabels([0,20,30])
-#ax7.set_xlabel('Mins', fontsize=14)
-##ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-#ax7.set_xlim([228881.84051513672, 289916.99798583984]) # 2 mins to 12 mins, a 10 min snip without noise at start
-#ax7.set_ylim([300,800])
-#
-## 3.75 (45 seconds) and 4.75
-#
-### 4- 5
-## Adding the scatter to long time course plot of photo signals
-##allRatLicks.append(ratdata['licks'])
-#multipliedLicks = []
-#for element in allRatLicks[3]:
-# multElement = element*allRatFS[0]
-# multipliedLicks.append([multElement])
-#
-#xvals = multipliedLicks
-#yvals = [ax7.get_ylim()[1] - 50] * len(xvals)
-#ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-#
-## Get rid of the spines and add labels and ticks to plot
-## Add a 1 minute scale bar OR tick labels for mins
-#ax7.set(ylabel = '∆F')
-#ax7.yaxis.label.set_size(14)
-#ax7.xaxis.set_visible(False)
-#
-#scalebar = 1*allRatFS[0]*60 # 1 minute
-#
-#yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-#scalebary = (yrange / 10) + ax7.get_ylim()[0]
-#scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-#ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-#ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-#ax7.spines['right'].set_visible(False)
-#ax7.spines['top'].set_visible(False)
-#ax7.spines['bottom'].set_visible(False)
-##fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-#
-
-
-
-## RAT 3 ZOOMED SECITON 2 - 5.30 TO 6 - 30 seconds
-fig9 = plt.figure(figsize=(6,2))
-ax7 = plt.subplot(1,1,1)
-plt.plot(allRatBlue[3], color='royalblue')
-#plt.plot(allRatUV[3], color='darkorchid')
-ax7.set_xticks([0,(20*60*allRatFS[0]),(30*60*allRatFS[0])])
-ax7.set_xticklabels([0,20,30])
-ax7.set_xlabel('Mins', fontsize=14)
-#ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-ax7.set_xlim([335693.3660888672, 366210.94482421875]) # 2 mins to 12 mins, a 10 min snip without noise at start
-ax7.set_ylim([500,800])
-
-
-## 4- 5
-# Adding the scatter to long time course plot of photo signals
-#allRatLicks.append(ratdata['licks'])
-multipliedLicks = []
-for element in allRatLicks[3]:
- multElement = element*allRatFS[0]
- multipliedLicks.append([multElement])
-
-xvals = multipliedLicks
-yvals = [ax7.get_ylim()[1] - 50] * len(xvals)
-ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.6) # Altered line width
-
-# Get rid of the spines and add labels and ticks to plot
-# Add a 1 minute scale bar OR tick labels for mins
-ax7.set(ylabel = '∆F')
-ax7.yaxis.label.set_size(14)
-ax7.xaxis.set_visible(False)
-
-scalebar = 1*allRatFS[0]*60 # 1 minute
-
-yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-scalebary = (yrange / 10) + ax7.get_ylim()[0]
-scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-#ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-#ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-ax7.spines['right'].set_visible(False)
-ax7.spines['top'].set_visible(False)
-ax7.spines['bottom'].set_visible(False)
-fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/PLongTimeCourseRat3_30sec(2).pdf', bbox_inches="tight")
-### RAT 12 ZOOMED SECTION
-#
-## RAT 12
-#fig9 = plt.figure(figsize=(4,2))
-#ax7 = plt.subplot(1,1,1)
-#plt.plot(allRatBlue[12], color='royalblue')
-##plt.plot(allRatUV[12], color='darkorchid')
-#ax7.set_xticks([0,(20*60*allRatFS[0]),(30*60*allRatFS[0])])
-#ax7.set_xticklabels([0,20,30])
-#ax7.set_xlabel('Mins', fontsize=14)
-##ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-#
-#ax7.set_xlim([213623.05114746094,274658.20861816406]) # 2 mins to 12 mins, a 10 min snip without noise at start
-#ax7.set_ylim([300,800])
-#
-#
-#
-## Adding the scatter to long time course plot of photo signals
-##allRatLicks.append(ratdata['licks'])
-#multipliedLicks = []
-#for element in allRatLicks[12]:
-# multElement = element*allRatFS[0]
-# multipliedLicks.append([multElement])
-#
-#xvals = multipliedLicks
-#yvals = [ax7.get_ylim()[1] - 50] * len(xvals)
-#ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-#
-## Get rid of the spines and add labels and ticks to plot
-## Add a 1 minute scale bar OR tick labels for mins
-#ax7.set(ylabel = '∆F')
-#ax7.yaxis.label.set_size(14)
-#ax7.xaxis.set_visible(False)
-#
-#scalebar = 1*allRatFS[0]*60 # 1 minute
-#
-#yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-#scalebary = (yrange / 10) + ax7.get_ylim()[0]
-#scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-#ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-#ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-#ax7.spines['right'].set_visible(False)
-#ax7.spines['top'].set_visible(False)
-#ax7.spines['bottom'].set_visible(False)
-##fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-#
diff --git a/PhotoPeaksCalc_slope_edit.py b/PhotoPeaksCalc_slope_edit.py
deleted file mode 100644
index 38e8b22..0000000
--- a/PhotoPeaksCalc_slope_edit.py
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Aug 2 11:03:30 2019
-
-@author: kate
-"""
-
-def PhotoPeaksCalc(snips_all_rats):
-
- allRat_peak = []
- allRat_t = []
- allRat_pre = []
- allRat_post = []
- allRat_base = []
-
- allRatten_percent_baseline= []
-
- for rat in snips_all_rats:
- pre_event = np.mean(rat[0:50]) # Average for 5 seconds, 10 seconds before event
- absolutepeak = np.max(rat[100:300]) ## Added this - finds highest (the t is first)
- peak = np.max(rat[100:130]) # to 300 in origianl (can this work with both?) ## Minus the average of the first 5 seconds and after 100 points (slice)
- peak_range = rat[100:130]
- a = peak_range.tolist()
- peak_index = a.index(peak)
- t = peak_index / 10
- pre_event = np.mean(rat[50:100])
- post_event = np.mean(rat[100:300])
- baseline = np.mean(rat[0:50])
-
- allRat_peak.append(absolutepeak)
- allRat_t.append(t)
- allRat_pre.append(pre_event)
- allRat_post.append(post_event)
- allRat_base.append(baseline)
-
-# Calculate the slope (time to decay to 10% of baseline)
-
- total_points = 0
- pointindices = []
- for index, point in enumerate(rat[peak_index:300]):
- if point < baseline*0.9:
- pointindices.append(index)
- total_points += 1
- allRatten_percent_baseline.append(pointindices[0]) # the first time there is a point below 10%
-
-
- return allRat_peak, allRat_t, allRat_pre, allRat_post, allRat_base, allRatten_percent_baseline
-
-
-# Distractors
-peak_distractor, t_distractor, pre_distractor, post_distractor, baseline_distractor, slope_distractor = PhotoPeaksCalc(bkgnd_sub_Distractor)
-peak_distracted, t_distracted, pre_distracted, post_distracted, baseline_distracted, slope_distracted = PhotoPeaksCalc(bkgnd_sub_Distracted)
-# Not distracted
-peak_notdistracted, t_notdistracted, pre_notdistracted, post_notdistracted, baseline_notdistracted, slope_notdistracted = PhotoPeaksCalc(bkgnd_sub_Notdistracted)
-
-
-print(np.mean(slope_distractor))
-print(np.mean(slope_distracted))
-print(np.mean(slope_notdistracted))
diff --git a/README.md b/README.md
index 08de62e..af53893 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,31 @@
-# Distraction-Paper
\ No newline at end of file
+# Distraction-Paper
+
+This is forked from Kate Peters' code to analyse distraction data from PhD experiments conducted c. 2017 at Uni. of Leicester. Jaime made the fork in Feb 2020 to add to analysis and prepare results and figures for publication.
+
+## Order of analysis
+
+Main input files are TDT fiber photometry tanks. The analysis pipeline is designed to enable relevant data to be extracted from tanks and saved in a way that individual analyses can be conducted rapidly without necessarily having access to all of the tanks and metadata (which are large). The process is as follows:
+
+### 1. Assemble data
+The script assemble_data.py is executed, which reads in data from an Excel metafile (distraction_photo_metafile.xlsx) and uses functions in fx4assembly.py to construct dictionaries for each day of analysis (3 used - Mod(elled), Dis(traction) and Hab(ituation). Each dictionary is constructed with sub-dictionaries for each rat. Each sub-dictionary includes data from the metafile and tdtfile including the full photometry signals.
+
+This script saves resulting dictionaries in a .pickle file, which is approximately 2GB.
+
+This script will need to be re-run if you do not have access to this 2GB file or if you want to make changes to the metadata that are captured or the processing of the signals.
+
+### 2. Make snips
+Next, the script makesnips.py is executed, in which 'snips' are made (short strectches of binned photometry data aligned to the event of interest - the distractors). This script saves .pickle files both with all data (~2GB) and with the streamed photometry data removed (<100MB).
+
+This will need to be re-run if changes to the snip procedure occur (e.g. different trial length).
+
+### 3. ROC analysis
+ROC analysis of photometry and behavioural data is performed. First, prepare_roc_data.py is executed, which removes trials from rat dictionaries and flattens into 1-D lists which can be analysed using trial-based ROC analysis. This script saves two .pickle files - one with behavioural data, data4roc_licks.pickle, and one for photometry data, data4roc_photometry.pickle. Next, run_roc_analysis.py is executed (which relies on ROC functions in fx4roc.py). This script performs ROC analyses - which take some time, approx 5 min per comparsison - and thus has the option of commenting out. The results of ROC analyses are saved as .pickle files with appropriate names.
+
+### 4. Plotting of figures using Jupyter Notebooks
+Two main notebooks are used - 01_dis-behavior.ipynb and 02_dis-roc-all-figs.ipynb - and should be self-explanatory. Other notebooks for testing data ands for looking at data from individual rats are also included.
+
+### Note on environment file
+There is an environment.yml file which can be installed to run all code using standard Anaconda installations.
+
+### Note on folders
+Most of the scripts and notebooks will require the destination and source folders for data to be renamed to match your computer, e.g. datafolder = "C:\\YOURNAME\\Github\\Distraction-Paper\\data\\"
diff --git a/RasterPlots.py b/RasterPlots.py
deleted file mode 100644
index f8f7bd7..0000000
--- a/RasterPlots.py
+++ /dev/null
@@ -1,279 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Sat Oct 20 21:17:27 2018
-
-@author: u1490431
-"""
-
-## CURRENTLY NEED TO RUN CH4_ANALYSIS_LICKING FIRST to extract the data
-## And AllFunctions
-
-
-def distractionrasterFig(ax, timelock, events,
- pre = 1, post = 1,
- sortevents=None, sortdirection='ascending'):
-
- if sortevents != None:
- if len(timelock) != len(sortevents):
- print('Length of sort events does not match timelock events; no sorting')
-
- ## Rats (like 6) where the last distractor is the last lick (so no pdp at the end)
- ## Repeated code here which could be turned into a "sortevents" function if wanted
- if len(timelock) == (len(sortevents) + 1):
- sortevents.append(0)
-
- if sortdirection == 'ascending':
- sortOrder = np.argsort(sortevents)
- else:
- sortOrder = np.argsort(sortevents)[::-1]
-
- timelock = [timelock[i] for i in sortOrder]
-
-
-
- else:
- if sortdirection == 'ascending':
- sortOrder = np.argsort(sortevents)
- else:
- sortOrder = np.argsort(sortevents)[::-1]
-
- timelock = [timelock[i] for i in sortOrder]
-
- rasterData = [[] for i in timelock]
-
- for i,x in enumerate(timelock):
- rasterData[i] = [j-x for j in events if (j > x-pre) & (j < x+post)]
-
-
- for ith, trial in enumerate(rasterData):
-
- xvals = [x for x in trial]
- yvals = [1+ith] * len(xvals)
-
- pdplist = [lick for lick in xvals if lick > 0 and lick < 1]
- if len(pdplist) > 0:
- ax.scatter(xvals, yvals, marker=',', s=1, color='k')
- # print('there is a pdp')
- else:
- ax.scatter(xvals, yvals, marker=',', s=1, color='r')
- # print('no pdp')
-
-
-### SORTED PLOTS HERE - MODELLED DAY, DISTRACTION DAY, HABITUATION DAY
-
-## lick day (modelled distractors)
-allRatBlue = []
-allRatUV = []
-allRatFS = []
-allRatLicks = []
-allRatDistractors = []
-allRatDistracted = []
-allRatNotDistracted = []
-blueMeans_distractor = []
-uvMeans_distractor = []
-blueMeans_distracted = []
-uvMeans_distracted = []
-blueMeans_notdistracted = []
-uvMeans_notdistracted = []
-allbluesnips = []
-alluvsnips = []
-pdps_lickday = []
-
-for filename in TDTfiles_thph_lick:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlue.append(ratdata['blue'])
- allRatUV.append(ratdata['uv'])
- allRatFS.append(ratdata['fs'])
- allRatLicks.append(ratdata['licks'])
- burstanalysis = lickCalc(ratdata['licks'], offset=ratdata['licks_off'])
- burstList = burstanalysis['bLicks'] # n licks per burst
- runList = burstanalysis['rLicks'] # n licks per run
- burstListTimes = burstanalysis['bStart'] # Actual times of start of runs
- runListTimes = burstanalysis['rStart'] # Actual times of start of bursts
- allBursts.append(burstList)
- allRuns.append(runList)
- allRunTimes.append(runListTimes)
- allBurstsTimes.append(burstListTimes)
-
- allRatDistractorsMOD.append(ratdata['distractors'])
- allRatDistractedMOD.append(ratdata['distracted'])
- allRatNotDistractedMOD.append(ratdata['notdistracted'])
- # print(ratdata['distractors'])
-
- indices1 = []
- for index, value in enumerate(ratdata['distractors']):
- a = np.where(ratdata['licks'] == value)
- indices1.append(a)
-
- pdps = []
- for tupl in indices1:
- i = tupl[0][0]
- if i+1 < len(ratdata['licks']):
-
- pdp = (ratdata['licks'][i+1] - ratdata['licks'][i])
- pdps.append(pdp)
- pdps_lickday.append(pdps)
- # pdps.append(0) ## Add in a check, if PDPs len does not equal len(distractors)
- figure12 = plt.figure(figsize=(6,6))
- ax6 = plt.subplot(111)
- ax6.spines['right'].set_visible(False)
- ax6.xaxis.set_visible(False)
- ax6.spines['top'].set_visible(False)
- ax6.spines['bottom'].set_visible(False)
- ax6.set(ylabel = 'Trials')
- ax6.yaxis.label.set_size(14)
-
- scale = 1
- scalebar = 1
- yrange = ax6.get_ylim()[1] - ax6.get_ylim()[0]
- scalebary = (yrange / 10) + ax6.get_ylim()[0]
- scalebarx = [ax6.get_xlim()[1] - scalebar, ax6.get_xlim()[1]]
- ax6.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
- ax6.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), str(scale) +' s', ha='center',va='top', **Calibri, **Size)
-
- rasterPlot = distractionrasterFig(ax6, ratdata['distractors'], ratdata['licks'], pre=1, post=10, sortevents=None, sortdirection='dec') # sortevents = pdps
-
- figure12.savefig('/Volumes/KP_HARD_DRI/distraction_paper/figs/Raster_' + filename + '.pdf', bbox_inches='tight')
-
-#
-#Make plots for every rat on lick day, distraction day and habituation day
-#Choose the best lookng representative rat for the ordered, non ordered (by time)
-#Make the dots where it is distracted a different colour (this was added into a previous script)
-#Sort out the spacing issues (talk to Jaime perhaps) - replace with a different marker or something
-# Could make the plots as large as possible
-# Think about issues of different scales on (1) Different days and (2) Different rats
-# Consider using ',' rather than '.' (a pixel and not a dot)
-#
-
-allRatBlue = []
-allRatUV = []
-allRatFS = []
-allRatLicks = []
-allRatDistractors = []
-allRatDistracted = []
-allRatNotDistracted = []
-blueMeans_distractor = []
-uvMeans_distractor = []
-blueMeans_distracted = []
-uvMeans_distracted = []
-blueMeans_notdistracted = []
-uvMeans_notdistracted = []
-allbluesnips = []
-alluvsnips = []
-pdps_disday = []
-
-for filename in TDTfiles_thph_dis:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlue.append(ratdata['blue'])
- allRatUV.append(ratdata['uv'])
- allRatFS.append(ratdata['fs'])
- allRatLicks.append(ratdata['licks'])
- allRatDistractors.append(ratdata['distractors'])
- allRatDistracted.append(ratdata['distracted'])
- allRatNotDistracted.append(ratdata['notdistracted'])
- indices1 = []
-
- for index, value in enumerate(ratdata['distractors']):
- a = np.where(ratdata['licks'] == value)
- indices1.append(a)
-
- pdps = []
- for tupl in indices1:
- i = tupl[0][0]
- if i+1 < len(ratdata['licks']):
-
- pdp = (ratdata['licks'][i+1] - ratdata['licks'][i])
- pdps.append(pdp)
- pdps_disday.append(pdps)
-
- # pdps.append(0) ## Add in a check, if PDPs len does not equal len(distractors)
- figure12 = plt.figure(figsize=(6,6))
- ax6 = plt.subplot(111)
- ax6.spines['right'].set_visible(False)
- ax6.xaxis.set_visible(False)
- ax6.spines['top'].set_visible(False)
- ax6.spines['bottom'].set_visible(False)
- ax6.set(ylabel = 'Trials')
- ax6.yaxis.label.set_size(14)
-
- scale = 1
- scalebar = 1
- yrange = ax6.get_ylim()[1] - ax6.get_ylim()[0]
- scalebary = (yrange / 10) + ax6.get_ylim()[0]
- scalebarx = [ax6.get_xlim()[1] - scalebar, ax6.get_xlim()[1]]
- ax6.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
- ax6.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), str(scale) +' s', ha='center',va='top', **Calibri, **Size)
-
- rasterPlot = distractionrasterFig(ax6, ratdata['distractors'], ratdata['licks'], pre=1, post=10, sortevents=None, sortdirection='dec')
-
- figure12.savefig('/Volumes/KP_HARD_DRI/distraction_paper/figs/Raster_' + filename + '.pdf', bbox_inches='tight')
-
-allRatBlue = []
-allRatUV = []
-allRatFS = []
-allRatLicks = []
-allRatDistractors = []
-allRatDistracted = []
-allRatNotDistracted = []
-blueMeans_distractor = []
-uvMeans_distractor = []
-blueMeans_distracted = []
-uvMeans_distracted = []
-blueMeans_notdistracted = []
-uvMeans_notdistracted = []
-allbluesnips = []
-alluvsnips = []
-pdps_habday = []
-
-for filename in TDTfiles_thph_hab:
-
- file = TDTfilepath + filename
- ratdata = loadmatfile(file)
- allRatBlue.append(ratdata['blue'])
- allRatUV.append(ratdata['uv'])
- allRatFS.append(ratdata['fs'])
- allRatLicks.append(ratdata['licks'])
- allRatDistractors.append(ratdata['distractors'])
- allRatDistracted.append(ratdata['distracted'])
- allRatNotDistracted.append(ratdata['notdistracted'])
- indices1 = []
-
- for index, value in enumerate(ratdata['distractors']):
- a = np.where(ratdata['licks'] == value)
- indices1.append(a)
-
- pdps = []
- for tupl in indices1:
- i = tupl[0][0]
- if i+1 < len(ratdata['licks']):
-
- pdp = (ratdata['licks'][i+1] - ratdata['licks'][i])
- pdps.append(pdp)
- pdps_habday.append(pdps)
- # pdps.append(0) ## Add in a check, if PDPs len does not equal len(distractors)
- figure12 = plt.figure(figsize=(6,6))
- ax6 = plt.subplot(111)
- ax6.spines['right'].set_visible(False)
- ax6.xaxis.set_visible(False)
- ax6.spines['top'].set_visible(False)
- ax6.spines['bottom'].set_visible(False)
- ax6.set(ylabel = 'Trials')
- ax6.yaxis.label.set_size(14)
-
- scale = 1
- scalebar = 1
- yrange = ax6.get_ylim()[1] - ax6.get_ylim()[0]
- scalebary = (yrange / 10) + ax6.get_ylim()[0]
- scalebarx = [ax6.get_xlim()[1] - scalebar, ax6.get_xlim()[1]]
- ax6.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
- ax6.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), str(scale) +' s', ha='center',va='top', **Calibri, **Size)
-
- rasterPlot = distractionrasterFig(ax6, ratdata['distractors'], ratdata['licks'], pre=1, post=10, sortevents=None, sortdirection='dec')
-
- figure12.savefig('/Volumes/KP_HARD_DRI/distraction_paper/figs/Raster_' + filename + '.pdf', bbox_inches='tight')
-
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diff --git a/analyze_roc_data.py b/analyze_roc_data.py
new file mode 100644
index 0000000..41ef03c
--- /dev/null
+++ b/analyze_roc_data.py
@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 27 11:45:46 2020
+
+@author: admin
+"""
+
+from fx4roc import *
+import time
+import dill
+
+def run_roc_comparison(data, n4shuf=10, timer=True, savedata=""):
+ """ Function to run ROC analysis with option for timing and saving resulting data
+ Args
+ data: list or array with two distributions to be compared. Normally should
+ be of the shape (2,x,y) where x is number of trials and can be different
+ between each array and y is bins and should be identical.
+ Example, data[0] can be 300x20 list of lists or array and data[1] can
+ be 360x20.
+ n4shuf: number of times to repeat roc with shuffled values to calculate ps
+ default=10, so that it is fast to run, but for accurate p-vals should
+ run 2000 times
+ timer: Boolean, prints time taken if True
+ savedata: insert complete filename here to save the results
+
+ Returns
+ a: list of ROC values (between 0 and 1) corresponding to bins provided
+ (e.g. y in description above)
+ p: list of p-vals that correspond to each ROC value in a
+
+ """
+
+ if timer: start_time = time.time()
+
+ a, p = nanroc(data[0], data[1], n4shuf=n4shuf)
+
+ if timer: print(f"--- Total ROC analysis took {(time.time() - start_time)} seconds ---")
+
+ if len(savedata)>0:
+ try:
+ pickle_out = open(savedata, 'wb')
+ dill.dump([a, p, data], pickle_out)
+ pickle_out.close()
+ except:
+ print("Cannot save. Check filename.")
+
+ return a, p
+
+
+# Loads in data
+datafolder = "C:\\Github\\Distraction-Paper\\data\\"
+figfolder = "C:\\Github\\Distraction-Paper\\figs\\"
+outputfolder = "C:\\Github\\Distraction-Paper\\output\\"
+
+# Loads data for ROC analysis on licking
+pickle_in = open(outputfolder+"data4roc_licks.pickle", 'rb')
+[mod_dis_hist, mod_notdis_hist, dis_dis_hist, dis_notdis_hist, hab_dis_hist, hab_notdis_hist] = dill.load(pickle_in)
+
+# list of comparisons
+
+# Uncomment to run lick comparisons
+n4shuf = 2000 # change to 2000 for proper comparison
+
+# ### Comparison of lick data between distracted and non-distracted trials on distraction day
+# a, p = run_roc_comparison([dis_notdis_hist, dis_dis_hist], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_licks_disday_disVnondis.pickle")
+
+# # # Comparison of lick data between modelled and distraction day for distracted trials
+# a, p = run_roc_comparison([mod_dis_hist, dis_dis_hist], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_licks_distrials_modVdis.pickle")
+
+# # # Comparison of lick data between modelled and distraction day for NOT distracted trials
+# a, p = run_roc_comparison([mod_notdis_hist, dis_notdis_hist], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_licks_notdistrials_modVdis.pickle")
+
+# # Comparison of lick data between distraction and habituation day for ALL trials
+
+# dis_all_hist = dis_notdis_hist + dis_dis_hist
+# hab_all_hist = hab_notdis_hist + hab_dis_hist
+# a, p = run_roc_comparison([dis_all_hist, hab_all_hist], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_licks_alltrials_disVhab.pickle")
+
+
+# Loads data for ROC analysis on photometry snips
+pickle_in = open(outputfolder+"data4roc_photo.pickle", 'rb')
+[mod_dis_photo_snips_flat, mod_notdis_photo_snips_flat, dis_dis_photo_snips_flat, dis_notdis_photo_snips_flat, dis_dis_filt_photo_snips_flat, dis_notdis_filt_photo_snips_flat, hab_dis_photo_snips_flat, hab_notdis_photo_snips_flat] = dill.load(pickle_in)
+
+# makes lists of all snips for each day (distracted and not distracted trials)
+mod_all_photo_snips_flat = mod_dis_photo_snips_flat + mod_notdis_photo_snips_flat
+dis_all_photo_snips_flat = dis_dis_photo_snips_flat + dis_notdis_photo_snips_flat
+hab_all_photo_snips_flat = hab_dis_photo_snips_flat + hab_notdis_photo_snips_flat
+
+# ### Comparison of photometry data between modelled, distraction, and hab day for ALL trials
+# a, p = run_roc_comparison([mod_all_photo_snips_flat, dis_all_photo_snips_flat], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_photo_alltrials_modVdis.pickle")
+
+# a, p = run_roc_comparison([mod_all_photo_snips_flat, hab_all_photo_snips_flat], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_photo_alltrials_modVhab.pickle")
+
+# a, p = run_roc_comparison([dis_all_photo_snips_flat, hab_all_photo_snips_flat], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_photo_alltrials_disVhab.pickle")
+
+# ### Comparison of photometry data between distracted and non-distracted trials for each day in turn
+# a, p = run_roc_comparison([mod_notdis_photo_snips_flat, mod_dis_photo_snips_flat], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_photo_modday_disVnotdis.pickle")
+
+# a, p = run_roc_comparison([dis_notdis_photo_snips_flat, dis_dis_photo_snips_flat], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_photo_disday_disVnotdis.pickle")
+
+# a, p = run_roc_comparison([hab_notdis_photo_snips_flat, hab_dis_photo_snips_flat], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_photo_habday_disVnotdis.pickle")
+
+# a, p = run_roc_comparison([dis_dis_photo_snips_flat, hab_dis_photo_snips_flat], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_photo_distrials_disVhab.pickle")
+
+# a, p = run_roc_comparison([dis_notdis_photo_snips_flat, hab_notdis_photo_snips_flat], n4shuf=n4shuf,
+# savedata=outputfolder+"roc_photo_notdistrials_disVhab.pickle")
+
+
+# ### Comparison of photometry data between distracted and non-distracted trials for each day in turn USING non-Zscored SIGNAL
+a, p = run_roc_comparison([dis_notdis_filt_photo_snips_flat, dis_dis_filt_photo_snips_flat], n4shuf=n4shuf,
+ savedata=outputfolder+"roc_photo_disday_disVnotdis_filt.pickle")
\ No newline at end of file
diff --git a/assemble_data.py b/assemble_data.py
new file mode 100644
index 0000000..e7fe48a
--- /dev/null
+++ b/assemble_data.py
@@ -0,0 +1,149 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Feb 5 11:28:04 2020
+
+@author: admin
+"""
+
+import tdt
+import xlrd
+import csv
+import numpy as np
+import trompy as tp
+
+import dill
+
+from fx4assembly import *
+
+
+def loaddata(tdtfile, SigBlue, SigUV):
+
+ tdtfile=raw_datafolder+tdtfile
+
+ try:
+ tmp = tdt.read_block(tdtfile, evtype=['streams'], store=[SigBlue])
+ data = getattr(tmp.streams, SigBlue)['data']
+ fs = getattr(tmp.streams, SigBlue)['fs']
+
+ tmp = tdt.read_block(tdtfile, evtype=['streams'], store=[SigUV])
+ dataUV = getattr(tmp.streams, SigUV)['data']
+
+ ttls = tdt.read_block(tdtfile, evtype=['epocs']).epocs
+ except:
+ print('Unable to load data properly.')
+ data=[]
+ dataUV=[]
+ ttls=[]
+ fs=[]
+
+ return data, dataUV, fs, ttls
+
+def process_rat(row_data, sessiontype='dis', get_trialtype=False):
+ print('yo there', row_data[2])
+ ratdata={}
+ ratdata['rat'] = row_data[2]
+
+ # gets photometry signals from TDT file and performs correction
+ blue, uv, fs, ttls = loaddata(row_data[0], row_data[12], row_data[13])
+
+ # sets start and stop of good signal from values in metafile (entered by hand)
+ datarange=[int(np.float(row_data[19])), int(np.float(row_data[20]))]
+
+ # first line uses Vaibhav correction, second uses Lerner correction
+ # filt = correctforbaseline(blue, uv)
+ filt, filt_sd = lerner_correction(blue, uv)
+
+ rms = np.sqrt(np.mean(np.square(filt[datarange[0]:datarange[1]])))
+ ratdata["rms"] = rms
+ print(rms)
+
+ # assigns photometry data to output dictionary
+ ratdata['blue'] = blue
+ ratdata['uv'] = uv
+ ratdata['fs'] = fs
+ ratdata['filt'] = filt
+# ratdata['z'] = convert2zscore(filt, zrange=[int(np.float(row_data[19])), int(np.float(row_data[20]))])
+ ratdata['deltaF'] = filt / rms
+ ratdata['tick'] = ttls.Tick.onset
+
+ try:
+ ratdata['filt_sd'] = filt_sd
+ except: pass
+
+ # gets licks from ttls
+ lick = getattr(ttls, row_data[15])
+ ratdata['licks'] = lick.onset
+ ratdata['licks_off'] = lick.offset
+
+ # Calculates distractors and whether distracted or not
+ ratdata['distractors'] = distractionCalc2(ratdata['licks'])
+ [ratdata['distracted'], ratdata['notdistracted']], ratdata['d_bool_array'], ratdata['pdp'], ratdata['pre_dp'] = distracted_or_not(ratdata['distractors'], ratdata['licks'])
+
+ if get_trialtype == True:
+ medfile = medfilefolder + row_data[1]
+ ratdata["trialtype"] = tp.medfilereader(medfile, varsToExtract=["j"])[1:]
+ else:
+ ratdata["trialtype"] = []
+
+ return ratdata
+
+# declares locations for data and required files
+raw_datafolder = 'D:\\DA_and_Reward\\kp259\\THPH1AND2\\tdtfiles\\'
+folder = "C:\\Github\\Distraction-Paper\\"
+datafolder = folder+"data\\"
+medfilefolder = datafolder + "medfiles\\"
+xlfile = datafolder+"distraction_photo_metafile.xlsx"
+metafile = datafolder+"metafile"
+sheetname="THPH1&2Metafile"
+
+# extracts data from metafile
+tp.metafilemaker(xlfile, metafile, sheetname=sheetname, fileformat='txt')
+rows, header = tp.metafilereader(metafile + '.txt')
+
+# creates lists that include row indexes for last lick, distraction, and hab days
+rows_mod = []
+rows_dis = []
+rows_hab = []
+
+for idx, row in enumerate(rows):
+ if row[5] == 'D0':
+ rows_mod.append(idx)
+ elif row[5] == 'D1':
+ rows_dis.append(idx)
+ elif row[5] == 'D2':
+ rows_hab.append(idx)
+
+# makes dictionaries that include data for individual rats for each day (e.g.
+# modelled day, distraction day, and habituation day)
+disDict = {}
+modDict = {}
+habDict = {}
+
+for idx in rows_mod:
+ ratdata = process_rat(rows[idx])
+ modDict[ratdata['rat']] = ratdata
+
+for idx in rows_dis:
+ ratdata = process_rat(rows[idx], get_trialtype=True)
+ disDict[ratdata['rat']] = ratdata
+
+for idx in rows_hab:
+ ratdata = process_rat(rows[idx], get_trialtype=True)
+ habDict[ratdata['rat']] = ratdata
+
+# Saves pickle file with three dictionaries that can be used by Kate's old script
+# or new scripts for further analysis
+
+savefile=True
+outputfile=datafolder+'distraction_data.pickle'
+
+if savefile == True:
+ pickle_out = open(outputfile, 'wb')
+ dill.dump([modDict, disDict, habDict], pickle_out)
+ pickle_out.close()
+
+
+
+
+
+
\ No newline at end of file
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..3ce550f
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,335 @@
+name: distraction
+channels:
+ - pvlib
+ - conda-forge
+ - anaconda
+ - defaults
+dependencies:
+ - _anaconda_depends=2020.02=py37_0
+ - _ipyw_jlab_nb_ext_conf=0.1.0=py37_0
+ - alabaster=0.7.12=py37_0
+ - altgraph=0.17=py_0
+ - anaconda=custom=py37_1
+ - anaconda-client=1.7.2=py37_0
+ - anaconda-navigator=1.9.7=py37_0
+ - anaconda-project=0.8.4=py_0
+ - argh=0.26.2=py37_0
+ - asn1crypto=1.3.0=py37_0
+ - astroid=2.4.0=py37_0
+ - astropy=3.2.3=py37he774522_0
+ - atomicwrites=1.4.0=py_0
+ - attrs=19.3.0=py_0
+ - autopep8=1.4.4=py_0
+ - babel=2.8.0=py_0
+ - backcall=0.1.0=py37_0
+ - backports=1.0=py_2
+ - backports.shutil_get_terminal_size=1.0.0=py37_2
+ - bcrypt=3.1.7=py37he774522_0
+ - beautifulsoup4=4.9.0=py37_0
+ - bitarray=1.2.1=py37he774522_0
+ - bkcharts=0.2=py37_0
+ - blas=1.0=mkl
+ - bleach=3.1.4=py_0
+ - blosc=1.16.3=h7bd577a_0
+ - bokeh=2.0.2=py37_0
+ - boto=2.49.0=py37_0
+ - bottleneck=1.3.2=py37h2a96729_0
+ - bzip2=1.0.8=he774522_0
+ - ca-certificates=2020.1.1=0
+ - certifi=2020.4.5.1=py37_0
+ - cffi=1.14.0=py37h7a1dbc1_0
+ - chardet=3.0.4=py37_1003
+ - click=7.1.2=py_0
+ - cloudpickle=1.4.1=py_0
+ - clyent=1.2.2=py37_1
+ - colorama=0.4.3=py_0
+ - comtypes=1.1.7=py37_0
+ - conda=4.8.3=py37_0
+ - conda-build=3.17.8=py37_0
+ - conda-env=2.6.0=1
+ - conda-package-handling=1.6.1=py37h62dcd97_0
+ - conda-verify=3.1.1=py37_0
+ - console_shortcut=0.1.1=3
+ - contextlib2=0.6.0.post1=py_0
+ - cryptography=2.9.2=py37h7a1dbc1_0
+ - curl=7.69.1=h2a8f88b_0
+ - cycler=0.10.0=py37_0
+ - cython=0.29.17=py37ha925a31_0
+ - cytoolz=0.10.1=py37he774522_0
+ - dask=2.16.0=py_0
+ - dask-core=2.16.0=py_0
+ - decorator=4.4.2=py_0
+ - defusedxml=0.6.0=py_0
+ - diff-match-patch=20181111=py_0
+ - dill=0.3.0=py37_0
+ - distributed=2.16.0=py37_0
+ - docutils=0.16=py37_0
+ - entrypoints=0.3=py37_0
+ - ephem=3.7.7.0=py37he774522_0
+ - et_xmlfile=1.0.1=py37_0
+ - fastcache=1.1.0=py37he774522_0
+ - ffmpeg=4.2.2=he774522_0
+ - filelock=3.0.12=py_0
+ - flake8=3.7.9=py37_0
+ - flask=1.1.2=py_0
+ - freetype=2.9.1=ha9979f8_1
+ - fsspec=0.7.1=py_0
+ - future=0.18.2=py37_0
+ - get_terminal_size=1.0.0=h38e98db_0
+ - gevent=1.4.0=py37he774522_0
+ - glob2=0.7=py_0
+ - greenlet=0.4.15=py37hfa6e2cd_0
+ - h5py=2.10.0=py37h5e291fa_0
+ - hdf5=1.10.4=h7ebc959_0
+ - heapdict=1.0.1=py_0
+ - html5lib=1.0.1=py37_0
+ - hypothesis=5.11.0=py_0
+ - icc_rt=2019.0.0=h0cc432a_1
+ - icu=58.2=ha925a31_3
+ - idna=2.9=py_1
+ - imageio=2.8.0=py_0
+ - imagesize=1.2.0=py_0
+ - importlib_metadata=1.5.0=py37_0
+ - intel-openmp=2020.1=216
+ - intervaltree=3.0.2=py_0
+ - ipykernel=5.1.4=py37h39e3cac_0
+ - ipython=7.13.0=py37h5ca1d4c_0
+ - ipython_genutils=0.2.0=py37_0
+ - ipywidgets=7.5.1=py_0
+ - isort=4.3.21=py37_0
+ - itsdangerous=1.1.0=py37_0
+ - jdcal=1.4.1=py_0
+ - jedi=0.15.2=py37_0
+ - jinja2=2.11.2=py_0
+ - joblib=0.14.1=py_0
+ - jpeg=9c=hfa6e2cd_1001
+ - json5=0.9.4=py_0
+ - jsonschema=3.2.0=py37_0
+ - jupyter=1.0.0=py37_7
+ - jupyter_client=6.1.3=py_0
+ - jupyter_console=6.1.0=py_0
+ - jupyter_core=4.6.3=py37_0
+ - jupyterlab=1.2.6=pyhf63ae98_0
+ - jupyterlab_server=1.1.1=py_0
+ - keyring=21.1.1=py37_2
+ - kiwisolver=1.2.0=py37h74a9793_0
+ - krb5=1.17.1=hc04afaa_0
+ - lazy-object-proxy=1.4.3=py37he774522_0
+ - libarchive=3.3.3=h0643e63_5
+ - libblas=3.8.0=10_mkl
+ - libcblas=3.8.0=10_mkl
+ - libcurl=7.69.1=h2a8f88b_0
+ - libiconv=1.15=h1df5818_7
+ - liblapack=3.8.0=10_mkl
+ - liblapacke=3.8.0=10_mkl
+ - liblief=0.10.1=ha925a31_0
+ - libpng=1.6.37=h2a8f88b_0
+ - libsodium=1.0.16=h9d3ae62_0
+ - libspatialindex=1.9.3=h33f27b4_0
+ - libssh2=1.9.0=h7a1dbc1_1
+ - libtiff=4.1.0=h56a325e_0
+ - libwebp=1.0.1=he774522_0
+ - libxml2=2.9.9=h464c3ec_0
+ - libxslt=1.1.33=h579f668_0
+ - llvmlite=0.32.1=py37ha925a31_0
+ - locket=0.2.0=py37_1
+ - lxml=4.5.0=py37h1350720_0
+ - lz4-c=1.8.1.2=h2fa13f4_0
+ - lzo=2.10=he774522_2
+ - m2w64-gcc-libgfortran=5.3.0=6
+ - m2w64-gcc-libs=5.3.0=7
+ - m2w64-gcc-libs-core=5.3.0=7
+ - m2w64-gmp=6.1.0=2
+ - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
+ - macholib=1.11=py_0
+ - markupsafe=1.1.1=py37he774522_0
+ - matplotlib=3.1.3=py37_0
+ - matplotlib-base=3.1.3=py37h64f37c6_0
+ - mccabe=0.6.1=py37_1
+ - menuinst=1.4.16=py37he774522_0
+ - mistune=0.8.4=py37he774522_0
+ - mkl=2019.3=203
+ - mkl-service=2.0.2=py37he774522_0
+ - mkl_fft=1.0.12=py37h14836fe_0
+ - mkl_random=1.0.2=py37h343c172_0
+ - mock=4.0.2=py_0
+ - more-itertools=8.2.0=py_0
+ - mpmath=1.1.0=py37_0
+ - msgpack-python=1.0.0=py37h74a9793_1
+ - msys2-conda-epoch=20160418=1
+ - multipledispatch=0.6.0=py37_0
+ - navigator-updater=0.2.1=py37_0
+ - nbconvert=5.6.1=py37_0
+ - nbformat=5.0.6=py_0
+ - networkx=2.4=py_0
+ - nltk=3.4.5=py37_0
+ - nose=1.3.7=py37_2
+ - notebook=6.0.3=py37_0
+ - numba=0.49.1=py37h47e9c7a_0
+ - numexpr=2.6.9=py37hdce8814_0
+ - numpy=1.16.4=py37h19fb1c0_0
+ - numpy-base=1.16.4=py37hc3f5095_0
+ - numpydoc=0.9.2=py_0
+ - olefile=0.46=py37_0
+ - opencv=4.1.1=py37h6afde12_1
+ - openpyxl=3.0.3=py_0
+ - openssl=1.1.1g=he774522_0
+ - packaging=20.3=py_0
+ - pandas=1.0.3=py37h47e9c7a_0
+ - pandoc=2.2.3.2=0
+ - pandocfilters=1.4.2=py37_1
+ - paramiko=2.7.1=py_0
+ - parso=0.5.2=py_0
+ - partd=1.1.0=py_0
+ - path=13.1.0=py37_0
+ - path.py=12.4.0=0
+ - pathlib2=2.3.5=py37_0
+ - pathtools=0.1.2=py_1
+ - patsy=0.5.1=py37_0
+ - pefile=2019.4.18=py_0
+ - pep8=1.7.1=py37_0
+ - pexpect=4.8.0=py37_0
+ - pickleshare=0.7.5=py37_0
+ - pillow=7.1.2=py37hcc1f983_0
+ - pip=20.0.2=py37_3
+ - pkginfo=1.5.0.1=py37_0
+ - pluggy=0.13.1=py37_0
+ - ply=3.11=py37_0
+ - powershell_shortcut=0.0.1=2
+ - prometheus_client=0.7.1=py_0
+ - prompt-toolkit=3.0.4=py_0
+ - prompt_toolkit=3.0.4=0
+ - psutil=5.7.0=py37he774522_0
+ - pvlib=0.7.2=py_0
+ - py=1.8.1=py_0
+ - py-lief=0.10.1=py37ha925a31_0
+ - pycodestyle=2.5.0=py37_0
+ - pycosat=0.6.3=py37he774522_0
+ - pycparser=2.20=py_0
+ - pycrypto=2.6.1=py37hfa6e2cd_9
+ - pycurl=7.43.0.5=py37h7a1dbc1_0
+ - pydocstyle=4.0.1=py_0
+ - pyflakes=2.1.1=py37_0
+ - pygments=2.6.1=py_0
+ - pyinstaller=3.5=py37h7602738_0
+ - pylint=2.5.0=py37_1
+ - pynacl=1.3.0=py37h62dcd97_0
+ - pyodbc=4.0.30=py37ha925a31_0
+ - pyopenssl=19.1.0=py37_0
+ - pyparsing=2.4.7=py_0
+ - pyqt=5.9.2=py37h6538335_2
+ - pyreadline=2.1=py37_1
+ - pyrsistent=0.16.0=py37he774522_0
+ - pysocks=1.7.1=py37_0
+ - pytables=3.6.1=py37h1da0976_0
+ - pytest=5.4.1=py37_0
+ - pytest-arraydiff=0.3=py37h39e3cac_0
+ - pytest-astropy=0.8.0=py_0
+ - pytest-astropy-header=0.1.2=py_0
+ - pytest-doctestplus=0.5.0=py_0
+ - pytest-openfiles=0.5.0=py_0
+ - pytest-remotedata=0.3.2=py37_0
+ - python=3.7.3=h8c8aaf0_0
+ - python-dateutil=2.8.1=py_0
+ - python-jsonrpc-server=0.3.4=py_0
+ - python-language-server=0.31.10=py37_0
+ - python-libarchive-c=2.9=py_0
+ - pytz=2020.1=py_0
+ - pywavelets=1.1.1=py37he774522_0
+ - pywin32=227=py37he774522_1
+ - pywin32-ctypes=0.2.0=py37_1000
+ - pywinpty=0.5.7=py37_0
+ - pyyaml=5.3.1=py37he774522_0
+ - pyzmq=18.1.1=py37ha925a31_0
+ - qdarkstyle=2.8.1=py_0
+ - qt=5.9.7=vc14h73c81de_0
+ - qtawesome=0.7.0=py_0
+ - qtconsole=4.7.3=py_0
+ - qtpy=1.9.0=py_0
+ - requests=2.23.0=py37_0
+ - rope=0.17.0=py_0
+ - rtree=0.9.4=py37h21ff451_1
+ - ruamel_yaml=0.15.87=py37he774522_0
+ - scikit-image=0.16.2=py37h47e9c7a_0
+ - scikit-learn=0.21.1=py37h6288b17_0
+ - scipy=1.2.1=py37h29ff71c_0
+ - seaborn=0.10.1=py_0
+ - send2trash=1.5.0=py37_0
+ - simplegeneric=0.8.1=py37_2
+ - singledispatch=3.4.0.3=py37_0
+ - sip=4.19.8=py37h6538335_0
+ - six=1.14.0=py37_0
+ - snappy=1.1.7=h777316e_3
+ - snowballstemmer=2.0.0=py_0
+ - sortedcollections=1.1.2=py37_0
+ - sortedcontainers=2.1.0=py37_0
+ - soupsieve=2.0=py_0
+ - sphinxcontrib=1.0=py37_1
+ - sphinxcontrib-applehelp=1.0.2=py_0
+ - sphinxcontrib-devhelp=1.0.2=py_0
+ - sphinxcontrib-htmlhelp=1.0.3=py_0
+ - sphinxcontrib-jsmath=1.0.1=py_0
+ - sphinxcontrib-qthelp=1.0.3=py_0
+ - sphinxcontrib-serializinghtml=1.1.4=py_0
+ - sphinxcontrib-websupport=1.2.1=py_0
+ - spyder=4.1.3=py37_0
+ - spyder-kernels=1.9.1=py37_0
+ - sqlalchemy=1.3.16=py37he774522_0
+ - sqlite=3.31.1=h2a8f88b_1
+ - statsmodels=0.11.0=py37he774522_0
+ - sympy=1.5.1=py37_0
+ - tbb=2020.0=h74a9793_0
+ - tblib=1.6.0=py_0
+ - terminado=0.8.3=py37_0
+ - testpath=0.4.4=py_0
+ - tk=8.6.8=hfa6e2cd_0
+ - toml=0.10.0=py37h28b3542_0
+ - toolz=0.10.0=py_0
+ - tornado=6.0.4=py37he774522_1
+ - tqdm=4.46.0=py_0
+ - traitlets=4.3.3=py37_0
+ - typed-ast=1.4.1=py37he774522_0
+ - typing_extensions=3.7.4.1=py37_0
+ - ujson=1.35=py37hfa6e2cd_0
+ - unicodecsv=0.14.1=py37_0
+ - urllib3=1.25.8=py37_0
+ - vc=14.1=h0510ff6_4
+ - vs2015_runtime=14.16.27012=hf0eaf9b_1
+ - watchdog=0.10.2=py37_0
+ - wcwidth=0.1.9=py_0
+ - webencodings=0.5.1=py37_1
+ - werkzeug=1.0.1=py_0
+ - wheel=0.34.2=py37_0
+ - widgetsnbextension=3.5.1=py37_0
+ - win_inet_pton=1.1.0=py37_0
+ - win_unicode_console=0.5=py37_0
+ - wincertstore=0.2=py37_0
+ - winpty=0.4.3=4
+ - wrapt=1.11.2=py37he774522_0
+ - xlrd=1.2.0=py37_0
+ - xlsxwriter=1.2.8=py_0
+ - xlwings=0.19.0=py37_0
+ - xlwt=1.3.0=py37_0
+ - xz=5.2.5=h62dcd97_0
+ - yaml=0.1.7=hc54c509_2
+ - yapf=0.28.0=py_0
+ - zeromq=4.3.1=h33f27b4_3
+ - zict=2.0.0=py_0
+ - zipp=3.1.0=py_0
+ - zlib=1.2.11=h62dcd97_4
+ - zstd=1.3.7=h508b16e_0
+ - pip:
+ - imageio-ffmpeg==0.3.0
+ - julian==0.14
+ - moviepy==1.0.1
+ - opencv-contrib-python==4.1.2.30
+ - paranoid-scientist==0.2.2
+ - proglog==0.1.9
+ - readme-renderer==25.0
+ - requests-toolbelt==0.9.1
+ - setuptools==49.2.0
+ - sphinx==3.0.2
+ - tdt==0.3.6
+ - trompy==0.12.2
+ - twine==3.1.1
+prefix: C:\ProgramData\Anaconda3
diff --git a/figs4roc.py b/figs4roc.py
new file mode 100644
index 0000000..591cee2
--- /dev/null
+++ b/figs4roc.py
@@ -0,0 +1,85 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Feb 26 13:44:02 2020
+
+@author: admin
+"""
+
+import numpy as np
+
+import matplotlib.gridspec as gridspec
+from matplotlib.colors import LinearSegmentedColormap
+
+from fx4roc import logical_subset
+from JM_custom_figs import shadedError
+
+
+def plot_ROC_and_line(f, a, p, snips1, snips2,
+ cdict=['grey', 'white', 'red'],
+ colors=['grey', 'red'],
+ labels=["", ""],
+ labeloffset=0,
+ ylabel=''
+ ):
+
+ ax=[]
+
+ gs=gridspec.GridSpec(2,2, figure=f, height_ratios=[0.25, 1], width_ratios=[1, 0.05], wspace=0.05,
+ bottom=0.2, right=0.75, left=0.15)
+
+ ax.append(f.add_subplot(gs[0, 0]))
+
+ # Creates colormap for ROC
+
+ heatmap_color_scheme = LinearSegmentedColormap.from_list('rocmap', cdict)
+
+ roc_for_plot = a + [0]
+ xlen=len(snips1[0])
+ xvals=np.arange(-0.5,xlen+0.5)
+ yvals=[1, 2]
+ xx, yy = np.meshgrid(xvals, yvals)
+
+ mesh = ax[0].pcolormesh(xx, yy, [roc_for_plot, roc_for_plot], cmap=heatmap_color_scheme, shading = 'flat')
+ mesh.set_clim([0, 1])
+
+ threshold = 0.05/len(p)
+ sigpoints = np.array([pval < threshold for pval in p], dtype=bool)
+
+ if sum(sigpoints) > 0:
+ xdata = [x for x, L in zip(range(len(sigpoints)), sigpoints) if L]
+ ydata = logical_subset(a, sigpoints)
+ ax[0].scatter(xdata, [2.5]*len(xdata), color='k', marker='.', clip_on=False)
+ else:
+ ax[0].scatter(2, 2.5, color='white', marker='.', clip_on=False)
+
+ ax[0].text(-1, 2.5, 'p<0.05', va='center', ha='right')
+
+ ax[0].spines['top'].set_visible(False)
+ ax[0].spines['right'].set_visible(False)
+ ax[0].spines['bottom'].set_visible(False)
+ ax[0].spines['left'].set_visible(False)
+ ax[0].set_xticks([])
+ ax[0].set_yticks([])
+
+ cbar_ax = f.add_subplot(gs[0,1])
+ cbar = f.colorbar(mesh, cax=cbar_ax, ticks=[0, 1], label='ROC')
+
+ ax.append(f.add_subplot(gs[1, 0]))
+
+ shadedError(ax[1], snips1, linecolor=colors[0])
+ shadedError(ax[1], snips2, linecolor=colors[1])
+
+ ax[1].set_ylabel(ylabel)
+ ax[1].spines['top'].set_visible(False)
+ ax[1].spines['right'].set_visible(False)
+
+ ax[1].set_xticks([0, 5, 10, 15])
+ ax[1].set_xticklabels(['-5', '0', '5', '10'])
+ ax[1].set_xlabel('Time from distractor (s)')
+
+ ax[1].text(20, np.mean(snips1, axis=0)[-1]-labeloffset, labels[0], color=colors[0], ha='left', va='center')
+ ax[1].text(20, np.mean(snips2, axis=0)[-1]+labeloffset, labels[1], color=colors[1], ha='left', va='center')
+
+ ax[0].set_xlim(ax[1].get_xlim())
+
+ return f, ax
\ No newline at end of file
diff --git a/fx4assembly.py b/fx4assembly.py
new file mode 100644
index 0000000..b65326d
--- /dev/null
+++ b/fx4assembly.py
@@ -0,0 +1,117 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Feb 5 12:39:35 2020
+
+@author: admin
+"""
+import numpy as np
+import scipy.signal as sig
+
+def correctforbaseline(blue, uv):
+ pt = len(blue)
+ X = np.fft.rfft(uv, pt)
+ Y = np.fft.rfft(blue, pt)
+ Ynet = Y-X
+
+ datafilt = np.fft.irfft(Ynet)
+
+ datafilt = sig.detrend(datafilt)
+
+ b, a = sig.butter(9, 0.012, 'low', analog=True)
+ datafilt = sig.filtfilt(b, a, datafilt)
+
+ return datafilt
+
+def lerner_correction(blue, uv):
+ x = np.array(uv)
+ y = np.array(blue)
+ bls = np.polyfit(x, y, 1)
+ Y_fit_all = np.multiply(bls[0], x) + bls[1]
+ Y_dF_all = y - Y_fit_all
+ dFF = np.multiply(100, np.divide(Y_dF_all, Y_fit_all))
+ std_dFF = np.std(dFF)
+
+ return [dFF, std_dFF]
+
+def convert2zscore(signal, zrange=[600000, 1200000]):
+ mean = np.mean(signal[zrange[0]:zrange[1]])
+ sd = np.std(signal[zrange[0]:zrange[1]])
+ return (signal-mean) / sd
+
+def convert2deltaF(signal, zrange=[600000, 1200000]):
+ mean = np.mean(signal[zrange[0]:zrange[1]])
+ return signal / mean
+
+def remcheck(val, range1, range2):
+ # function checks whether value is within range of two decimels
+ if (range1 < range2):
+ if (val > range1) and (val < range2):
+ return True
+ else:
+ return False
+ else:
+ if (val > range1) or (val < range2):
+ return True
+ else:
+ return False
+
+def distractionCalc2(licks, pre=1, post=1):
+ """
+ Works out from list of lick timestamps when distractors should occur
+ """
+
+ licks = np.insert(licks, 0, 0)
+ b = 0.001
+ d = []
+ idx = 3
+
+ while idx < len(licks):
+ if licks[idx]-licks[idx-2] < 1 and remcheck(b, licks[idx-2] % 1, licks[idx] % 1) == False:
+ d.append(licks[idx])
+ b = licks[idx] % 1
+ idx += 1
+ try:
+ while licks[idx]-licks[idx-1] < 1:
+ b = licks[idx] % 1
+ idx += 1
+ except IndexError:
+ pass
+ else:
+ idx +=1
+
+ if d[-1] > 3599:
+ d = d[:-1]
+
+ return d
+
+def distracted_or_not(distractors, licks, delay=1):
+ pdp = [] # post-distraction pause
+ pre_dp = [] # pre-distraction pause
+ distractedArray = []
+
+ for d in distractors:
+ try:
+ pdp.append([lick - d for lick in licks if (lick > d)][0])
+ except IndexError:
+ pdp.append(3600-d) # designates end of session as max pdp
+
+ distractor_index = [idx for idx, lick in enumerate(licks) if lick == d][0]
+ try:
+ pre_dp.append(-[ili for ili in np.diff(licks[distractor_index::-1]) if ili < -1][0])
+ except IndexError:
+ pre_dp.append(licks[0]) # if it is at the start of session then uses time from start
+
+ distracted_boolean_array = np.array([i>delay for i in pdp], dtype=bool)
+
+ distracted = [d for d,l in zip(distractors, distracted_boolean_array) if l]
+ notdistracted = [d for d,l in zip(distractors, distracted_boolean_array) if not l]
+
+ if np.isnan(pdp)[-1] == 1:
+ distracted_boolean_array[-1] = True
+
+ if len(pre_dp) == len(pdp) == len(distracted_boolean_array):
+ pass
+ else:
+ print('Numbers of pdps, pre-dps and distractors do not match. Something might be wrong')
+
+ return [distracted, notdistracted], distracted_boolean_array, pdp, pre_dp
\ No newline at end of file
diff --git a/fx4roc.py b/fx4roc.py
new file mode 100644
index 0000000..e034362
--- /dev/null
+++ b/fx4roc.py
@@ -0,0 +1,107 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Feb 18 15:50:43 2020
+
+@author: admin
+"""
+
+import numpy as np
+
+# import JM_custom_figs.py"
+
+def rocN(x,y,N=100):
+ """ Function to calculate ROC based on MATLAB function"""
+ if len(x) > 0 and len(y) >0:
+ pass
+ else:
+ print("x and/or y are incorrect form")
+ return np.NaN
+
+ if len(x)<3 or len(y)<3:
+ print('Too few trials for roc analysis!')
+ return np.NaN
+
+ zlo = np.min([np.min(x), np.min(y)])
+ zhi = np.max([np.max(x), np.max(y)])
+ z = np.linspace(zlo, zhi, N)
+
+ fa, hit = [], []
+
+ for i in range(N):
+ fa.append(np.count_nonzero(y > z[i])) # faster than np.sum
+ hit.append(np.count_nonzero(x > z[i]))
+
+ fa.reverse()
+ hit.reverse()
+
+ fa = np.divide(fa, len(y))
+ hit = np.divide(hit, len(x))
+
+ fa[0], fa[-1] = 0, 1
+ hit[0], hit[-1] = 0, 1
+
+ a = np.trapz(fa, hit)
+
+ return a
+
+def logical_subset(data, logical, condition=True):
+ if condition:
+ return [d for d, L in zip(data, logical) if L]
+ else:
+ return [d for d, L in zip(data, logical) if not L]
+
+def rocshuf(x,y,nsims=10):
+ z = x + y
+ b = [True for val in x] + [False for val in y]
+ n0 = len(b)
+
+ roc0 = rocN(logical_subset(z, b), logical_subset(z, b, condition=False))
+
+ a=[]
+ for sim in range(nsims):
+ I = np.random.permutation(n0)
+ B = [b[idx] for idx in I]
+ a.append(rocN(logical_subset(z, B), logical_subset(z, B, condition=False)))
+
+ absa = np.abs(roc0 -0.5)
+ p1 = len([val for val in a if val >= 0.5+absa])
+ p2 = len([val for val in a if val <= 0.5-absa])
+
+ p = p1/nsims + p2/nsims
+
+ return roc0, p
+
+def nanroc(x, y, N=100, min4roc=4, n4shuf=100):
+
+ # checks dimensions of matrices
+ if np.shape(x)[1] != np.shape(y)[1]:
+ print('nanroc: matrices must have the same number of columns')
+
+ a=[]
+ p=[]
+
+ for idx in range(np.shape(x)[1]):
+ print(f"Analysing column {idx}")
+ x0 = [val[idx] for val in x]
+ x1 = [val[idx] for val in y]
+
+ a0, p0 = rocshuf(x0, x1, nsims=n4shuf)
+
+ a.append(a0)
+ p.append(p0)
+
+ return a, p
+
+
+def flatten_list(listoflists):
+ try:
+ flat_list = [item for sublist in listoflists for item in sublist]
+ return flat_list
+ except:
+ print('Cannot flatten list. Maybe is in the wrong format. Returning empty list.')
+ return []
+
+ # Makes lick snips for all distractors and then flattens (e.g. pools snips from all rats)
+
+
+
diff --git a/ipython.html b/ipython.html
deleted file mode 100644
index 99cbb2a..0000000
--- a/ipython.html
+++ /dev/null
@@ -1,3062 +0,0 @@
-
-
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: scalebar = 1*allRatFS[0]*60 # 1 minute
-Â Â Â Â ...:
-Â Â Â Â ...: yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-Â Â Â Â ...: scalebary = (yrange / 10) + ax7.get_ylim()[0]
-Â Â Â Â ...: scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-Â Â Â Â ...: ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-Â Â Â Â ...: ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-Â Â Â Â ...: ax7.spines['right'].set_visible(False)
-Â Â Â Â ...: ax7.spines['top'].set_visible(False)
-Â Â Â Â ...: ax7.spines['bottom'].set_visible(False)
-Â Â Â Â ...: #fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-Â Â Â Â ...: #fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/PLongTimeCourseRat3_10min.pdf', bbox_inches="tight")
-
-
-
-In [ 73 ]: allRatFS[0] * 6.5 * 60
-Out[ 73 ]: 396728.5235595703
-
-In [ 74 ]: allRatFS[0] * 9.5 * 60
-Out[ 74 ]: 579833.9959716797
-
-In [ 75 ]: fig9 = plt.figure(figsize=(4,2))
-Â Â Â Â ...: ax7 = plt.subplot(1,1,1)
-Â Â Â Â ...: plt.plot(allRatBlue[10], color='royalblue')
-Â Â Â Â ...: plt.plot(allRatUV[10] + 150, color='darkorchid') ### OFFSET THE UV
-Â Â Â Â ...: ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-Â Â Â Â ...: ax7.set_xticklabels([0,10,20,30,40,50,60])
-Â Â Â Â ...: ax7.set_xlabel('Mins', fontsize=14)
-Â Â Â Â ...: #ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-Â Â Â Â ...:
-Â Â Â Â ...: ## FIRSRT 10 MINS
-Â Â Â Â ...: #ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...: # Second 10 mins
-Â Â Â Â ...: ax7.set_xlim([396728.5235595703,579833.9959716797]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...:
-Â Â Â Â ...: ax7.set_ylim([500,900])
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedLicks = []
-Â Â Â Â ...: for element in allRatLicks[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedLicks.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistractors = []
-Â Â Â Â ...: for element in allRatDistractors[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistractors.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistracted = []
-Â Â Â Â ...: for element in allRatDistracted[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistracted.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedLicks
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 10] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistractors
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistracted
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='r', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: scalebar = 1*allRatFS[0]*60 # 1 minute
-Â Â Â Â ...:
-Â Â Â Â ...: yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-Â Â Â Â ...: scalebary = (yrange / 10) + ax7.get_ylim()[0]
-Â Â Â Â ...: scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-Â Â Â Â ...: ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-Â Â Â Â ...: ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-Â Â Â Â ...: ax7.spines['right'].set_visible(False)
-Â Â Â Â ...: ax7.spines['top'].set_visible(False)
-Â Â Â Â ...: ax7.spines['bottom'].set_visible(False)
-Â Â Â Â ...: #fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-Â Â Â Â ...: #fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/PLongTimeCourseRat3_10min.pdf', bbox_inches="tight")
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: ## then individual trials
-
-
-
-In [ 76 ]: allRatFS[0] * 8.5 * 60
-Out[ 76 ]: 518798.83850097656
-
-In [ 77 ]: fig9 = plt.figure(figsize=(4,2))
-Â Â Â Â ...: ax7 = plt.subplot(1,1,1)
-Â Â Â Â ...: plt.plot(allRatBlue[10], color='royalblue')
-Â Â Â Â ...: plt.plot(allRatUV[10] + 150, color='darkorchid') ### OFFSET THE UV
-Â Â Â Â ...: ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-Â Â Â Â ...: ax7.set_xticklabels([0,10,20,30,40,50,60])
-Â Â Â Â ...: ax7.set_xlabel('Mins', fontsize=14)
-Â Â Â Â ...: #ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-Â Â Â Â ...:
-Â Â Â Â ...: ## FIRSRT 10 MINS
-Â Â Â Â ...: #ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...: # Second 10 mins
-Â Â Â Â ...: ax7.set_xlim([396728.5235595703,518798.83850097656]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...:
-Â Â Â Â ...: ax7.set_ylim([500,900])
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedLicks = []
-Â Â Â Â ...: for element in allRatLicks[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedLicks.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistractors = []
-Â Â Â Â ...: for element in allRatDistractors[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistractors.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistracted = []
-Â Â Â Â ...: for element in allRatDistracted[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistracted.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedLicks
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 10] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistractors
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistracted
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='r', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: scalebar = 1*allRatFS[0]*60 # 1 minute
-Â Â Â Â ...:
-Â Â Â Â ...: yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-Â Â Â Â ...: scalebary = (yrange / 10) + ax7.get_ylim()[0]
-Â Â Â Â ...: scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-Â Â Â Â ...: ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-Â Â Â Â ...: ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-Â Â Â Â ...: ax7.spines['right'].set_visible(False)
-Â Â Â Â ...: ax7.spines['top'].set_visible(False)
-Â Â Â Â ...: ax7.spines['bottom'].set_visible(False)
-Â Â Â Â ...: #fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-Â Â Â Â ...: #fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/PLongTimeCourseRat3_10min.pdf', bbox_inches="tight")
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: ## then individual trials
-
-
-
-In [ 78 ]: fig9 = plt.figure(figsize=(4,2))
-Â Â Â Â ...: ax7 = plt.subplot(1,1,1)
-Â Â Â Â ...: plt.plot(allRatBlue[10], color='royalblue')
-Â Â Â Â ...: plt.plot(allRatUV[10] + 150, color='darkorchid') ### OFFSET THE UV
-Â Â Â Â ...: ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-Â Â Â Â ...: ax7.set_xticklabels([0,10,20,30,40,50,60])
-Â Â Â Â ...: ax7.set_xlabel('Mins', fontsize=14)
-Â Â Â Â ...: #ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-Â Â Â Â ...:
-Â Â Â Â ...: ## FIRSRT 10 MINS
-Â Â Â Â ...: #ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...: # Second 10 mins
-Â Â Â Â ...: ax7.set_xlim([396728.5235595703,518798.83850097656]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...:
-Â Â Â Â ...: ax7.set_ylim([500,800])
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedLicks = []
-Â Â Â Â ...: for element in allRatLicks[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedLicks.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistractors = []
-Â Â Â Â ...: for element in allRatDistractors[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistractors.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistracted = []
-Â Â Â Â ...: for element in allRatDistracted[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistracted.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedLicks
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 10] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistractors
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistracted
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='r', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: scalebar = 1*allRatFS[0]*60 # 1 minute
-Â Â Â Â ...:
-Â Â Â Â ...: yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-Â Â Â Â ...: scalebary = (yrange / 10) + ax7.get_ylim()[0]
-Â Â Â Â ...: scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-Â Â Â Â ...: ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-Â Â Â Â ...: ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-Â Â Â Â ...: ax7.spines['right'].set_visible(False)
-Â Â Â Â ...: ax7.spines['top'].set_visible(False)
-Â Â Â Â ...: ax7.spines['bottom'].set_visible(False)
-Â Â Â Â ...: #fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-Â Â Â Â ...: #fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/PLongTimeCourseRat3_10min.pdf', bbox_inches="tight")
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: ## then individual trials
-
-
-
-In [ 79 ]: fig9 = plt.figure(figsize=(4,2))
-Â Â Â Â ...: ax7 = plt.subplot(1,1,1)
-Â Â Â Â ...: plt.plot(allRatBlue[10], color='royalblue')
-Â Â Â Â ...: plt.plot(allRatUV[10] + 160, color='darkorchid') ### OFFSET THE UV
-Â Â Â Â ...: ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-Â Â Â Â ...: ax7.set_xticklabels([0,10,20,30,40,50,60])
-Â Â Â Â ...: ax7.set_xlabel('Mins', fontsize=14)
-Â Â Â Â ...: #ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-Â Â Â Â ...:
-Â Â Â Â ...: ## FIRSRT 10 MINS
-Â Â Â Â ...: #ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...: # Second 10 mins
-Â Â Â Â ...: ax7.set_xlim([396728.5235595703,518798.83850097656]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...:
-Â Â Â Â ...: ax7.set_ylim([500,800])
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedLicks = []
-Â Â Â Â ...: for element in allRatLicks[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedLicks.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistractors = []
-Â Â Â Â ...: for element in allRatDistractors[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistractors.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistracted = []
-Â Â Â Â ...: for element in allRatDistracted[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistracted.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedLicks
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 10] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistractors
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistracted
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='r', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: scalebar = 1*allRatFS[0]*60 # 1 minute
-Â Â Â Â ...:
-Â Â Â Â ...: yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-Â Â Â Â ...: scalebary = (yrange / 10) + ax7.get_ylim()[0]
-Â Â Â Â ...: scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-Â Â Â Â ...: ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-Â Â Â Â ...: ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-Â Â Â Â ...: ax7.spines['right'].set_visible(False)
-Â Â Â Â ...: ax7.spines['top'].set_visible(False)
-Â Â Â Â ...: ax7.spines['bottom'].set_visible(False)
-Â Â Â Â ...: #fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-Â Â Â Â ...: #fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/PLongTimeCourseRat3_10min.pdf', bbox_inches="tight")
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: ## then individual trials
-
-
-
-In [ 80 ]: fig9 = plt.figure(figsize=(12,2))
-Â Â Â Â ...: ax7 = plt.subplot(1,1,1)
-Â Â Â Â ...: plt.plot(allRatBlue[10], color='royalblue')
-Â Â Â Â ...: plt.plot(allRatUV[10] + 150, color='darkorchid') ### OFFSET THE UV
-Â Â Â Â ...: ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-Â Â Â Â ...: ax7.set_xticklabels([0,10,20,30,40,50,60])
-Â Â Â Â ...: ax7.set_xlabel('Mins', fontsize=14)
-Â Â Â Â ...: #ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-Â Â Â Â ...:
-Â Â Â Â ...: ## FIRSRT 10 MINS
-Â Â Â Â ...: #ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...: # Second 10 mins
-Â Â Â Â ...: ax7.set_xlim([122070.31494140625,1220703.1494140625]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...:
-Â Â Â Â ...: ax7.set_ylim([500,900])
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedLicks = []
-Â Â Â Â ...: for element in allRatLicks[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedLicks.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistractors = []
-Â Â Â Â ...: for element in allRatDistractors[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistractors.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistracted = []
-Â Â Â Â ...: for element in allRatDistracted[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistracted.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedLicks
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 10] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistractors
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistracted
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='r', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: scalebar = 1*allRatFS[0]*60 # 1 minute
-Â Â Â Â ...:
-Â Â Â Â ...: yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-Â Â Â Â ...: scalebary = (yrange / 10) + ax7.get_ylim()[0]
-Â Â Â Â ...: scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-Â Â Â Â ...: ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-Â Â Â Â ...: ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-Â Â Â Â ...: ax7.spines['right'].set_visible(False)
-Â Â Â Â ...: ax7.spines['top'].set_visible(False)
-Â Â Â Â ...: ax7.spines['bottom'].set_visible(False)
-Â Â Â Â ...: #fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-Â Â Â Â ...: fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/LongTimeCourseDISRat10_20min.pdf', bbox_inches="tight")
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: ### Short time course
-Â Â Â Â ...:
-Â Â Â Â ...: # RAT3 - 10 minutes (value zero is rat3 here as 1 and 2 deleted in distraciton)
-Â Â Â Â ...: fig9 = plt.figure(figsize=(4,2))
-Â Â Â Â ...: ax7 = plt.subplot(1,1,1)
-Â Â Â Â ...: plt.plot(allRatBlue[10], color='royalblue')
-Â Â Â Â ...: plt.plot(allRatUV[10] + 160, color='darkorchid') ### OFFSET THE UV
-Â Â Â Â ...: ax7.set_xticks([0,(10*60*allRatFS[0]),(20*60*allRatFS[0]),(30*60*allRatFS[0]),(40*60*allRatFS[0]),(50*60*allRatFS[0]),(60*60*allRatFS[0])] )
-Â Â Â Â ...: ax7.set_xticklabels([0,10,20,30,40,50,60])
-Â Â Â Â ...: ax7.set_xlabel('Mins', fontsize=14)
-Â Â Â Â ...: #ax7.set_xlim([500000,700000]) # looks really nice scale wise, approx 3 mins
-Â Â Â Â ...:
-Â Â Â Â ...: ## FIRSRT 10 MINS
-Â Â Â Â ...: #ax7.set_xlim([122070.31494140625,732421.8896484375]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...: # Second 10 mins
-Â Â Â Â ...: ax7.set_xlim([396728.5235595703,518798.83850097656]) # 2 mins to 12 mins, a 10 min snip without noise at start
-Â Â Â Â ...:
-Â Â Â Â ...: ax7.set_ylim([500,800])
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedLicks = []
-Â Â Â Â ...: for element in allRatLicks[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedLicks.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistractors = []
-Â Â Â Â ...: for element in allRatDistractors[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistractors.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: multipliedDistracted = []
-Â Â Â Â ...: for element in allRatDistracted[10]:
-Â Â Â Â ...: multElement = element*allRatFS[0]
-Â Â Â Â ...: multipliedDistracted.append([multElement])
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedLicks
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 10] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='|', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistractors
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='k', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...: xvals = multipliedDistracted
-Â Â Â Â ...: yvals = [ax7.get_ylim()[1] - 40] * len(xvals)
-Â Â Â Â ...: ax7.scatter(xvals, yvals, marker='o', color='r', linewidth=0.2)
-    ...: ax7.set(ylabel = '∆F')
-Â Â Â Â ...: ax7.yaxis.label.set_size(14)
-Â Â Â Â ...: ax7.xaxis.set_visible(False)
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: scalebar = 1*allRatFS[0]*60 # 1 minute
-Â Â Â Â ...:
-Â Â Â Â ...: yrange = ax7.get_ylim()[1] - ax7.get_ylim()[0]
-Â Â Â Â ...: scalebary = (yrange / 10) + ax7.get_ylim()[0]
-Â Â Â Â ...: scalebarx = [ax7.get_xlim()[1] - scalebar, ax7.get_xlim()[1]]
-Â Â Â Â ...: ax7.plot(scalebarx, [scalebary, scalebary], c='k', linewidth=2)
-Â Â Â Â ...: ax7.text((scalebarx[0] + (scalebar/2)), scalebary-(yrange/50), '1 Min', ha='center',va='top', **Calibri, **Size)
-Â Â Â Â ...: ax7.spines['right'].set_visible(False)
-Â Â Â Â ...: ax7.spines['top'].set_visible(False)
-Â Â Â Â ...: ax7.spines['bottom'].set_visible(False)
-Â Â Â Â ...: #fig9.savefig('/Volumes/KPMSB352/PHOTOMETRY MMIN18/PDF figures/LongTimeCourse.pdf', bbox_inches="tight")
-Â Â Â Â ...: fig9.savefig('/Users/kate/Desktop/Peters, McCutcheon & Young, 2019/Draft 1/LongTimeCourseDISRat10_2min.pdf', bbox_inches="tight")
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...:
-Â Â Â Â ...: ## then individual trials
-
-
-
-
-
-In [ 81 ]: 396728.5235595703 / 60
-Out[ 81 ]: 6612.142059326172
-
-In [ 82 ]: 396728.5235595703 / 60 / allRatFS[0]
-Out[ 82 ]: 6.5
-
-In [ 83 ]:
\ No newline at end of file
diff --git a/lick_roc_analysis.py b/lick_roc_analysis.py
new file mode 100644
index 0000000..2b66e7d
--- /dev/null
+++ b/lick_roc_analysis.py
@@ -0,0 +1,199 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Feb 18 15:50:43 2020
+
+@author: admin
+"""
+
+import dill
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import matplotlib.transforms as transforms
+from matplotlib.backends.backend_pdf import PdfPages
+import numpy as np
+
+# import JM_custom_figs.py"
+
+def rocN(x,y,N=100):
+ """ Function to calculate ROC based on MATLAB function"""
+ if len(x) > 0 and len(y) >0:
+ pass
+ else:
+ print("x and/or y are incorrect form")
+ return np.NaN
+
+ if len(x)<3 or len(y)<3:
+ print('Too few trials for roc analysis!')
+ return np.NaN
+
+ zlo = min([min(x), min(y)])
+ zhi = max([max(x), max(y)])
+ z = np.linspace(zlo, zhi, N)
+
+ fa, hit = [], []
+
+ for i in range(N):
+ fa.append(sum(y > z[i]))
+ hit.append(sum(x > z[i]))
+
+ fa.reverse()
+ hit.reverse()
+
+ fa = [f/len(y) for f in fa]
+ hit = [h/len(x) for h in hit]
+
+ fa[0], fa[-1] = 0, 1
+ hit[0], hit[-1] = 0, 1
+
+ a = np.trapz(fa, hit)
+
+ return a
+
+def logical_subset(data, logical, condition=True):
+ if condition:
+ return [d for d, L in zip(data, logical) if L]
+ else:
+ return [d for d, L in zip(data, logical) if not L]
+
+def rocshuf(x,y,nsims=100):
+ z = x + y
+ b = [True for val in x] + [False for val in y]
+ n0 = len(b)
+
+ roc0 = rocN(logical_subset(z, b), logical_subset(z, b, condition=False))
+
+ a=[]
+ for sim in range(nsims):
+ I = np.random.permutation(n0)
+ B = [b[idx] for idx in I]
+ a.append(rocN(logical_subset(z, B), logical_subset(z, B, condition=False)))
+
+ absa = np.abs(roc0 -0.5)
+ p1 = len([val for val in a if val > 0.5+absa])
+ p2 = len([val for val in a if val < 0.5-absa])
+
+ p = p1/nsims + p2/nsims
+
+ return roc0, p
+
+def nanroc(x, y, N=100, min4roc=4, n4shuf=100):
+
+ # checks dimensions of matrices
+ if np.shape(x)[1] != np.shape(y)[1]:
+ print('nanroc: matrices must have the same number of columns')
+
+ a=[]
+ p=[]
+
+ for idx in range(np.shape(x)[1]):
+ print(f"Analysing column {idx}")
+ x0 = [val[idx] for val in x]
+ x1 = [val[idx] for val in y]
+
+ a0, p0 = rocshuf(x0, x1)
+
+ a.append(a0)
+ p.append(p0)
+
+ return a, p
+
+
+def flatten_list(listoflists):
+ try:
+ flat_list = [item for sublist in listoflists for item in sublist]
+ return flat_list
+ except:
+ print('Cannot flatten list. Maybe is in the wrong format. Returning empty list.')
+ return []
+
+ # Makes lick snips for all distractors and then flattens (e.g. pools snips from all rats)
+
+def make_lick_snips(d, pre=10, post=20):
+ snips = []
+ for dis in d['distractors']:
+ snips.append([lick-dis for lick in d['licks'] if (lick-dis>-pre) and (lick-disx1 and d 0:
+ return True
+ else:
+ return False
+
+dis_snips = [snip for snip in all_lick_snips_flat if not check_val_between(snip)]
+notdis_snips = [snip for snip in all_lick_snips_flat if check_val_between(snip)]
+
+# turns snips into binned data
+
+bins = np.arange(-10, 20, 1)
+dis_hist = [np.histogram(snip, bins=bins)[0] for snip in dis_snips]
+notdis_hist = [np.histogram(snip, bins=bins)[0] for snip in notdis_snips]
+
+# a, p = nanroc(dis_hist, notdis_hist)
+
+f, ax = plt.subplots(nrows=3)
+
+ax[0].plot(np.mean(notdis_hist, axis=0), color='grey')
+ax[0].plot(np.mean(dis_hist, axis=0), color='red')
+
+ax[0].set_ylabel('Licks (Hz)')
+ax[0].set_xticks([])
+
+ax[0].text(0.99, 0.95, 'Distracted', color='red', ha='right', va='top', transform=ax[0].transAxes)
+ax[0].text(0.99, 0.80, 'Not distracted', color='grey', ha='right', va='top', transform=ax[0].transAxes)
+
+ax[1].plot(a)
+ax[1].set_ylabel('ROC value')
+ax[1].set_xticks([])
+
+threshold = 0.05/len(p)
+sigpoints = np.array([pval < threshold for pval in p], dtype=bool)
+
+xdata = [x for x, L in zip(range(len(sigpoints)), sigpoints) if L]
+ydata = logical_subset(a, sigpoints)
+ax[1].scatter(xdata, ydata, color='red')
+
+ax[1]. set_ylim([-0.1, 1.1])
+
+ax[2].plot(p)
+ax[2].set_ylim([-0.1, 1.1])
+ax[2].set_ylabel('p')
+
+ax[2].set_xticks([0, 10, 20])
+ax[2].set_xticklabels(['-10', '0', '10'])
+ax[2].set_xlabel('Time from distractor (s)')
+
+f.savefig(outputfolder + "licks_roc_analysis.png")
+
+
+
+
diff --git a/makesnips.py b/makesnips.py
new file mode 100644
index 0000000..966be26
--- /dev/null
+++ b/makesnips.py
@@ -0,0 +1,439 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 6 10:09:19 2020
+
+@author: admin
+"""
+
+import dill
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+"""
+this function makes 'snips' of a data file ('data' single scalar) aligned to an
+event of interest ('event', list of times in seconds).
+
+If a timelocked map is needed to align data precisely (e.g. with TDT equipment)
+then it is necessary to pass a t2sMap to the function.
+
+preTrial and trialLength are in seconds.
+
+Will put data into bins if requested.
+
+"""
+def time2samples(data, tick, fs):
+ maxsamples = len(tick)*int(fs)
+ if (len(data) - maxsamples) > 2*int(fs):
+ print('Something may be wrong with conversion from time to samples')
+ print(str(len(data) - maxsamples) + ' samples left over. This is more than double fs.')
+ t2sMap = np.linspace(min(tick), max(tick), maxsamples)
+ else:
+ t2sMap = np.linspace(min(tick), max(tick), maxsamples)
+
+ return t2sMap
+
+def event2sample(EOI):
+ idx = (np.abs(t2sMap - EOI)).argmin()
+ return idx
+
+def lickCalc(licks, offset = [], burstThreshold = 0.25, runThreshold = 10,
+ binsize=60, histDensity = False, adjustforlonglicks='none'):
+ # makes dictionary of data relating to licks and bursts
+ if type(licks) != np.ndarray or type(offset) != np.ndarray:
+ try:
+ licks = np.array(licks)
+ offset = np.array(offset)
+ except:
+ print('Licks and offsets need to be arrays and unable to easily convert.')
+ return
+
+ lickData = {}
+
+ if len(offset) > 0:
+ lickData['licklength'] = offset - licks[:len(offset)]
+ lickData['longlicks'] = [x for x in lickData['licklength'] if x > 0.3]
+ else:
+ lickData['licklength'] = []
+ lickData['longlicks'] = []
+
+ if adjustforlonglicks != 'none':
+ if len(lickData['longlicks']) == 0:
+ print('No long licks to adjust for.')
+ else:
+ lickData['median_ll'] = np.median(lickData['licklength'])
+ lickData['licks_adj'] = int(np.sum(lickData['licklength'])/lickData['median_ll'])
+ if adjustforlonglicks == 'interpolate':
+ licks_new = []
+ for l, off in zip(licks, offset):
+ x = l
+ while x < off - lickData['median_ll']:
+ licks_new.append(x)
+ x = x + lickData['median_ll']
+ licks = licks_new
+
+ lickData['licks'] = licks
+ lickData['ilis'] = np.diff(np.concatenate([[0], licks]))
+ lickData['shilis'] = [x for x in lickData['ilis'] if x < burstThreshold]
+ lickData['freq'] = 1/np.mean([x for x in lickData['ilis'] if x < burstThreshold])
+ lickData['total'] = len(licks)
+
+ # Calculates start, end, number of licks and time for each BURST
+ lickData['bStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
+ lickData['bInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > burstThreshold)]
+ lickData['bEnd'] = [lickData['licks'][i-1] for i in lickData['bInd'][1:]]
+ lickData['bEnd'].append(lickData['licks'][-1])
+
+ lickData['bLicks'] = np.diff(lickData['bInd'] + [len(lickData['licks'])])
+ lickData['bTime'] = np.subtract(lickData['bEnd'], lickData['bStart'])
+ lickData['bNum'] = len(lickData['bStart'])
+ if lickData['bNum'] > 0:
+ lickData['bMean'] = np.nanmean(lickData['bLicks'])
+ lickData['bMean-first3'] = np.nanmean(lickData['bLicks'][:3])
+ else:
+ lickData['bMean'] = 0
+ lickData['bMean-first3'] = 0
+
+ lickData['bILIs'] = [x for x in lickData['ilis'] if x > burstThreshold]
+
+ # Calculates start, end, number of licks and time for each RUN
+ lickData['rStart'] = [val for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
+ lickData['rInd'] = [i for i, val in enumerate(lickData['licks']) if (val - lickData['licks'][i-1] > runThreshold)]
+ lickData['rEnd'] = [lickData['licks'][i-1] for i in lickData['rInd'][1:]]
+ lickData['rEnd'].append(lickData['licks'][-1])
+
+ lickData['rLicks'] = np.diff(lickData['rInd'] + [len(lickData['licks'])])
+ lickData['rTime'] = np.subtract(lickData['rEnd'], lickData['rStart'])
+ lickData['rNum'] = len(lickData['rStart'])
+
+ lickData['rILIs'] = [x for x in lickData['ilis'] if x > runThreshold]
+ try:
+ lickData['hist'] = np.histogram(lickData['licks'][1:],
+ range=(0, 3600), bins=int((3600/binsize)),
+ density=histDensity)[0]
+ except TypeError:
+ print('Problem making histograms of lick data')
+
+ return lickData
+
+def snipper(data, timelock, fs = 1, t2sMap = [], preTrial=10, trialLength=30,
+ adjustBaseline = True,
+ bins = 0):
+
+ if len(timelock) == 0:
+ print('No events to analyse! Quitting function.')
+ raise Exception('no events')
+
+ pps = int(fs) # points per sample
+ pre = int(preTrial*pps)
+# preABS = preTrial
+ length = int(trialLength*pps)
+# converts events into sample numbers
+ event=[]
+ if len(t2sMap) > 1:
+ for x in timelock:
+ event.append(np.searchsorted(t2sMap, x, side="left"))
+ else:
+ event = [x*fs for x in timelock]
+
+ new_events = []
+ for x in event:
+ if int(x-pre) > 0:
+ new_events.append(x)
+ event = new_events
+
+ nSnips = len(event)
+ snips = np.empty([nSnips,length])
+ avgBaseline = []
+
+ for i, x in enumerate(event):
+ start = int(x) - pre
+ avgBaseline.append(np.mean(data[start : start + pre]))
+ try:
+ snips[i] = data[start : start+length]
+ except ValueError: # Deals with recording arrays that do not have a full final trial
+ snips = snips[:-1]
+ avgBaseline = avgBaseline[:-1]
+ nSnips = nSnips-1
+
+ if adjustBaseline == True:
+ snips = np.subtract(snips.transpose(), avgBaseline).transpose()
+ snips = np.divide(snips.transpose(), avgBaseline).transpose()
+
+ if bins > 0:
+ if length % bins != 0:
+ snips = snips[:,:-(length % bins)]
+ totaltime = snips.shape[1] / int(fs)
+ snips = np.mean(snips.reshape(nSnips,bins,-1), axis=2)
+ pps = bins/totaltime
+
+ return snips, pps
+
+def mastersnipper(data, dataUV, data_filt, data_deltaF,
+ t2sMap, fs, bgMAD,
+ events,
+ bins=300,
+ baselinebins=100,
+ preTrial=10,
+ trialLength=30,
+ threshold=8,
+ peak_between_time=[0, 1],
+ latency_events=[],
+ latency_direction='pre',
+ max_latency=30,
+ verbose=True):
+
+ if len(events) < 1:
+ print('Cannot find any events. All outputs will be empty.')
+ blueTrials, uvTrials, filtTrials, filtTrials_z, filtTrials_z_adjBL, filt_avg, filt_avg_z, deltaFTrials, noiseindex, peak, latency = ([] for i in range(11))
+ else:
+ if verbose: print('{} events to analyze.'.format(len(events)))
+
+ blueTrials,_ = snipper(data, events,
+ t2sMap=t2sMap,
+ fs=fs,
+ bins=bins,
+ preTrial=preTrial,
+ trialLength=trialLength)
+ uvTrials,_ = snipper(dataUV, events,
+ t2sMap=t2sMap,
+ fs=fs,
+ bins=bins,
+ preTrial=preTrial,
+ trialLength=trialLength)
+ filtTrials,_ = snipper(data_filt, events,
+ t2sMap=t2sMap,
+ fs=fs,
+ bins=bins,
+ preTrial=preTrial,
+ trialLength=trialLength,
+ adjustBaseline=False)
+
+ deltaFTrials,_ = snipper(data_deltaF, events,
+ t2sMap=t2sMap,
+ fs=fs,
+ bins=bins,
+ preTrial=preTrial,
+ trialLength=trialLength,
+ adjustBaseline=False)
+
+ filtTrials_z = zscore(filtTrials, baseline_points=baselinebins)
+ filtTrials_z_adjBL = zscore(filtTrials, baseline_points=50)
+
+ sigSum = [np.sum(abs(i)) for i in filtTrials]
+ sigSD = [np.std(i) for i in filtTrials]
+
+ noiseindex = [i > bgMAD*threshold for i in sigSum]
+
+ # do i need to remove noise trials first before averages
+ filt_avg = np.mean(removenoise(filtTrials, noiseindex), axis=0)
+ if verbose: print('{} noise trials removed'.format(sum(noiseindex)))
+ filt_avg_z = zscore(filt_avg)
+
+
+ bin2s = bins/trialLength
+ peakbins = [int((preTrial+peak_between_time[0])*bin2s),
+ int((preTrial+peak_between_time[1])*bin2s)]
+ peak = [np.mean(trial[peakbins[0]:peakbins[1]]) for trial in filtTrials_z]
+
+ latency = []
+
+ if len(latency_events) > 1:
+ for event in events:
+ if latency_direction == 'pre':
+ try:
+ latency.append(np.abs([lat-event for lat in latency_events if lat-event<0]).min())
+ except ValueError:
+ latency.append(np.NaN)
+
+ elif latency_direction == 'post':
+ try:
+ latency.append(np.abs([lat-event for lat in latency_events if lat-event>0]).min())
+ except ValueError:
+ latency.append(np.NaN)
+
+ latency = [x if (x total:
+ start = start - total
+ events.append(start)
+ start = start + spacing
+ events = [i+minTime for i in events]
+ return events
+
+def med_abs_dev(data, b=1.4826):
+ median = np.median(data)
+ devs = [abs(i-median) for i in data]
+ mad = np.median(devs)*b
+
+ return mad
+
+# Load assembled files from pickle file
+# Gets dictionaries with ratadata for modelled, distraction and hab days
+
+datafolder = "C:\\Github\\Distraction-Paper\\data\\"
+
+try:
+ pickle_in = open(datafolder + "distraction_data.pickle", 'rb')
+except FileNotFoundError:
+ print('Cannot access pickled file')
+
+[modDict, disDict, habDict] = dill.load(pickle_in)
+
+for d, s in zip([modDict, disDict, habDict],
+ ['modelled', 'distraction', 'habituation']):
+
+ for rat in d.keys():
+
+ ratdata = d[rat]
+
+ print('Analysing rat {} in {} session'.format(rat, s))
+
+ data = ratdata['blue']
+ dataUV = ratdata['uv']
+ data_filt = ratdata['filt']
+ data_deltaF = ratdata["deltaF"]
+ fs = ratdata['fs']
+ tick = ratdata['tick']
+
+ ratdata['lickdata'] = lickCalc(ratdata['licks'],
+ offset=ratdata['licks_off'])
+
+ bins=200
+ baselinebins=40
+ preTrial=5
+ trialLength=20
+
+ t2sMap = time2samples(data, tick, fs)
+
+ randomevents = makerandomevents(120, max(tick)-120)
+
+ bgTrials, pps = snipper(data, randomevents,
+ t2sMap = t2sMap, fs=fs, bins=bins)
+
+ bgMAD = findnoise(data_filt, randomevents, t2sMap=t2sMap, fs=fs,
+ bins=bins, method='sum')
+
+ ratdata['snips_distractors'] = mastersnipper(data, dataUV, data_filt, data_deltaF,
+ t2sMap, fs, bgMAD,
+ ratdata['distractors'],
+ bins=200,
+ baselinebins=40,
+ preTrial=5,
+ trialLength=20,
+ latency_events=ratdata['licks'],
+ latency_direction='post')
+
+ ratdata['snips_distracted'] = mastersnipper(data, dataUV, data_filt, data_deltaF,
+ t2sMap, fs, bgMAD,
+ ratdata['distracted'],
+ bins=200,
+ baselinebins=40,
+ preTrial=5,
+ trialLength=20)
+
+ ratdata['snips_not-distracted'] = mastersnipper(data, dataUV, data_filt, data_deltaF,
+ t2sMap, fs, bgMAD,
+ ratdata['notdistracted'],
+ bins=200,
+ baselinebins=40,
+ preTrial=5,
+ trialLength=20)
+
+
+save_total_file=False
+save_reduced_file=True
+
+# saves pickled file including full dictionaries
+
+if save_total_file == True:
+ outputfile=datafolder+'distraction_data_with_snips.pickle'
+ pickle_out = open(outputfile, 'wb')
+ dill.dump([modDict, disDict, habDict], pickle_out)
+ pickle_out.close()
+
+# removes large, streaming variables and saves reduced file
+if save_reduced_file == True:
+ for d in [modDict, disDict, habDict]:
+ for rat in d.keys():
+ for item_to_be_removed in ['blue', 'uv', 'filt']:
+ del d[rat][item_to_be_removed]
+
+ outputfile=datafolder+'distraction_data_only_snips.pickle'
+
+ pickle_out = open(outputfile, 'wb')
+ dill.dump([modDict, disDict, habDict], pickle_out)
+ pickle_out.close()
+
+
+
diff --git a/notebooks/00_dis-photometry-individual rats.ipynb b/notebooks/00_dis-photometry-individual rats.ipynb
new file mode 100644
index 0000000..f27956e
--- /dev/null
+++ b/notebooks/00_dis-photometry-individual rats.ipynb
@@ -0,0 +1,687 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "import numpy as np\n",
+ "\n",
+ "%run \"..//JM_custom_figs.py\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_with_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['rat', 'blue', 'uv', 'fs', 'filt', 'tick', 'filt_sd', 'licks', 'licks_off', 'distractors', 'distracted', 'notdistracted', 'd_bool_array', 'pdp', 'pre_dp', 'lickdata', 'snips_distractors', 'snips_distracted', 'snips_not-distracted'])"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rat = 'thph1.1'\n",
+ "d = modDict[rat]\n",
+ "d.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_trials(ax, d, rat, key, signal):\n",
+ " trials = d[rat][key][signal]\n",
+ " for trial in trials:\n",
+ " ax.plot(trial, color='k', alpha=0.1)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_avg_of_multiple_days(ax, ds, rat, key, signal):\n",
+ " \"\"\"Plots the average of the same signal from multiple days (contained in separate dictionaries)\"\"\"\n",
+ " \n",
+ " colors=['grey', 'blue', 'xkcd:light blue']\n",
+ " \n",
+ " for d, color in zip(ds, colors):\n",
+ " trials = d[rat][key][signal]\n",
+ " ax.plot(np.mean(trials, axis=0), color=color)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_bars(ax, ds, rat, key, signal, bins=[100, 130], calctype='auc'):\n",
+ " for idx, d in enumerate(ds):\n",
+ " \n",
+ " trials = d[rat][key][signal]\n",
+ "\n",
+ " if calctype == 'auc':\n",
+ " trials_out = [np.trapz(trial[bins[0]:bins[1]])*0.1 for trial in trials]\n",
+ "\n",
+ " ax.bar(idx, np.mean(trials_out))\n",
+ "\n",
+ " ax.scatter([idx]*len(trials_out), trials_out)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_raster(ax, d, rat, pre = 5, post = 10,\n",
+ " sortevents=None, sortdirection='ascending',\n",
+ " title=''):\n",
+ " \n",
+ " timelock = d[rat]['distractors']\n",
+ " events = d[rat]['licks']\n",
+ " sortevents = d[rat]['pdp']\n",
+ " \n",
+ " if sortevents != None:\n",
+ " if len(timelock) != len(sortevents):\n",
+ " print('Length of sort events does not match timelock events; no sorting')\n",
+ "\n",
+ " if len(timelock) == (len(sortevents) + 1):\n",
+ " sortevents.append(0)\n",
+ " \n",
+ " if sortdirection == 'ascending':\n",
+ " sortOrder = np.argsort(sortevents)\n",
+ " else:\n",
+ " sortOrder = np.argsort(sortevents)[::-1]\n",
+ " \n",
+ " timelock = [timelock[i] for i in sortOrder] \n",
+ " else:\n",
+ " if sortdirection == 'ascending':\n",
+ " sortOrder = np.argsort(sortevents)\n",
+ " else:\n",
+ " sortOrder = np.argsort(sortevents)[::-1]\n",
+ " \n",
+ " timelock = [timelock[i] for i in sortOrder]\n",
+ " \n",
+ " rasterData = [[] for i in timelock]\n",
+ " \n",
+ " for i,x in enumerate(timelock):\n",
+ " rasterData[i] = [j-x for j in events if (j > x-pre) & (j < x+post)]\n",
+ " \n",
+ " for ith, trial in enumerate(rasterData):\n",
+ "\n",
+ " xvals = [x for x in trial] \n",
+ " yvals = [1+ith] * len(xvals)\n",
+ " \n",
+ " pdplist = [lick for lick in xvals if lick > 0 and lick < 1]\n",
+ " if len(pdplist) > 0:\n",
+ " ax.scatter(xvals, yvals, marker=',', s=1, color='k')\n",
+ " else:\n",
+ " ax.scatter(xvals, yvals, marker=',', s=1, color='xkcd:light blue')\n",
+ " \n",
+ " ax.set_title(title)\n",
+ " set_raster_axes(ax)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def set_raster_axes(ax, length=1, offset=1, ypos=0):\n",
+ " \"\"\"Prepares axes for raster plots including adding scale bar\"\"\"\n",
+ " \n",
+ " # Turns off spines and x ticks\n",
+ " ax.spines['right'].set_visible(False)\n",
+ " ax.spines['top'].set_visible(False)\n",
+ " ax.spines['bottom'].set_visible(False)\n",
+ " ax.set_xticks([])\n",
+ " \n",
+ " # Gets x coordinates for scale bar\n",
+ " xmin=ax.get_xlim()[0]\n",
+ " x0=xmin+offset\n",
+ " x1=xmin+offset+length\n",
+ " \n",
+ " # Sets up a transform for x in data coords and y in axis coords\n",
+ " trans = transforms.blended_transform_factory(\n",
+ " ax.transData, ax.transAxes)\n",
+ "\n",
+ " # Plots line using x and y and transform\n",
+ " line = mpl.lines.Line2D([x0, x1], [ypos, ypos], lw=2., color='k', transform=trans)\n",
+ " line.set_clip_on(False)\n",
+ " ax.add_line(line)\n",
+ " \n",
+ " # Adds text below line\n",
+ " ax.text((x1-length/2), ypos-0.02, '{} s'.format(length), va='top', ha='center', transform=trans)\n",
+ " \n",
+ " # Adds triangle to show distractor\n",
+ " ax.plot(0, 1., marker=\"v\", markersize=6, color='grey', clip_on=False, transform=trans)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_trials_and_avg(ax, d, rat, key, signal, plot_avg=True, plot_trials=False):\n",
+ " \"\"\"Plots all trials and/or the average from a single day.\"\"\"\n",
+ " \n",
+ " trials = d[rat][key][signal]\n",
+ " \n",
+ " if plot_trials:\n",
+ " for trial in trials:\n",
+ " ax.plot(trial, color='k', alpha=0.1)\n",
+ " \n",
+ " if plot_avg:\n",
+ " mean = np.mean(trials, axis=0)\n",
+ " ax.plot(mean, color='k')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def shadedError(ax, yarray, linecolor='black', errorcolor = 'xkcd:silver', linewidth=1):\n",
+ " yarray = np.array(yarray)\n",
+ " y = np.mean(yarray, axis=0)\n",
+ " yerror = np.std(yarray)/np.sqrt(len(yarray))\n",
+ " x = np.arange(0, len(y))\n",
+ " ax.plot(x, y, color=linecolor, linewidth=1)\n",
+ " ax.fill_between(x, y-yerror, y+yerror, color=errorcolor, alpha=0.4)\n",
+ " \n",
+ " return ax"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def set_photometry_axes(ax):\n",
+ " \"\"\"Alters axes to match photometry style.\"\"\"\n",
+ "# ax.set_xlabel('Time from distractor (s)')\n",
+ "# ax.set_xticks([0, 100, 200, 300])\n",
+ "# ax.set_xticklabels(['-10', '0', '10', '20'])\n",
+ "\n",
+ " ax.spines['right'].set_visible(False)\n",
+ " ax.spines['top'].set_visible(False)\n",
+ " ax.spines['bottom'].set_visible(False)\n",
+ " \n",
+ " ax.set_xticks([])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_single_avg(ax, d, rat, key, signal):\n",
+ " \"\"\"Plots average of photometry data using single condition\"\"\"\n",
+ " \n",
+ " trials = d[rat][key][signal]\n",
+ " if len(trials) > 0:\n",
+ " shadedError(ax, trials)\n",
+ " \n",
+ " set_photometry_axes(ax)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_multiple_avgs(ax, d, rat, keys, signal):\n",
+ " \"\"\"Plots averages from multiple conditions taken from same day (dictionary)\"\"\"\n",
+ " \n",
+ " ypos = 1\n",
+ " \n",
+ " for key, color, legend in zip(keys,\n",
+ " ['grey', 'red'],\n",
+ " ['not distracted', 'distracted']):\n",
+ " trials = d[rat][key][signal]\n",
+ " if len(trials) > 0:\n",
+ " shadedError(ax, trials, linecolor=color)\n",
+ " text = f\"{legend} ({len(trials)})\"\n",
+ " ax.text(1, ypos, text, color=color, ha='right', va='top', transform=ax.transAxes)\n",
+ " ypos=ypos-0.1\n",
+ " \n",
+ " set_photometry_axes(ax)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def time2samples(d):\n",
+ " \"\"\"Creates time2samples (t2s) map allowing events to be easily converted to sample number in photometry data\"\"\"\n",
+ " \n",
+ " d['t2sMap'] = np.linspace(1, len(d['blue']), len(d['blue'])) / d['fs']\n",
+ "\n",
+ "def event2sample(EOI, t2sMap):\n",
+ " \"\"\"Uses map from photometry data to transfrom timestamps into sample numbers\"\"\"\n",
+ " idx = (np.abs(t2sMap - EOI)).argmin() \n",
+ " return idx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_full_signal(ax, d, rat, sigs, colors, buffer=0.1, plot_events=True):\n",
+ " \"\"\"\n",
+ " This function plots the full photometry signal(s) from the session.\n",
+ " \n",
+ " \"\"\"\n",
+ " bounds=[]\n",
+ " \n",
+ " for idx, sig in enumerate(sigs):\n",
+ " data=d[rat][sig]\n",
+ " ax.plot(data, color=colors[idx])\n",
+ " \n",
+ " n_points=len(data)\n",
+ " middle_portion = data[int(n_points*0.1):int(n_points*0.9)]\n",
+ " bounds.append([np.max(middle_portion), np.min(middle_portion)])\n",
+ " \n",
+ " min_val, max_val = np.min(bounds), np.max(bounds)\n",
+ " ax.set_ylim([min_val - np.abs(min_val)*buffer, max_val + np.abs(max_val)*buffer])\n",
+ " \n",
+ " ax.set_xticks([])\n",
+ " \n",
+ " ax.spines['right'].set_visible(False)\n",
+ " ax.spines['top'].set_visible(False)\n",
+ " ax.spines['bottom'].set_visible(False)\n",
+ " \n",
+ " # Sets up a transform for x in data coords and y in axis coords\n",
+ " trans = transforms.blended_transform_factory(\n",
+ " ax.transData, ax.transAxes)\n",
+ " \n",
+ " if plot_events:\n",
+ " for marker, color, ypos in zip(['distracted', 'notdistracted'],\n",
+ " ['red', 'grey'],\n",
+ " [1, 1.1]):\n",
+ " events = [event*d[rat]['fs'] for event in d[rat][marker]]\n",
+ " ax.scatter(events, [ypos]*len(events), s=15, edgecolors=color, facecolors='none', clip_on=False, transform=trans)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def make_heatmap(ax, d, rat, key, signal, events=[], sortevents=[], event_direction='post', ylabel='Trials'):\n",
+ " \n",
+ " data = d[rat][key][signal]\n",
+ " \n",
+ " if len(sortevents)> 0:\n",
+ " sort_order = np.argsort(sortevents)\n",
+ " data = [data[i] for i in sort_order]\n",
+ " events = [events[i] for i in sort_order]\n",
+ " \n",
+ " if event_direction == 'pre':\n",
+ " events = [-event for event in events]\n",
+ " \n",
+ " ntrials = np.shape(data)[0]\n",
+ " xvals = np.linspace(-4.9,15,200)\n",
+ " yvals = np.arange(1, ntrials+2)\n",
+ " xx, yy = np.meshgrid(xvals, yvals)\n",
+ " \n",
+ " mesh = ax.pcolormesh(xx, yy, data, cmap=heatmap_color_scheme, shading = 'flat')\n",
+ " \n",
+ " if len(events) > 0:\n",
+ " ax.vlines(events, yvals[:-1], yvals[1:], color='w')\n",
+ " else:\n",
+ " print('No events')\n",
+ " \n",
+ " ax.set_ylabel(ylabel)\n",
+ " ax.set_yticks([1, ntrials])\n",
+ " ax.set_xticks([])\n",
+ " ax.invert_yaxis()\n",
+ " ax.spines['bottom'].set_visible(False)\n",
+ " \n",
+ " ax.set_xlim([-4.9,15])\n",
+ " mesh.set_clim(clims)\n",
+ " \n",
+ " return ax, mesh"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "'thph2.8'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodDict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdisDict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhabDict\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mtime2samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrat\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 21\u001b[0m \u001b[0mplot_full_signal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_signals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'blue'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'xkcd:pink'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'thph2.8'"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
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+ ""
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+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
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+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
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+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
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+ ""
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+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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HgXsxK8a59Unlt8N/lu4j9ll6Bf+jaAi+20Z9Mg3fxSgq/v34Oz4zxKn4SWOuAN4xsz7OuWhe3qfwP0wOCJ4/gJ+m/tCwFSjA8ZsP0Ql0xuHcQgDMnsRn4elcU5USyVInfMNOvPn4uKEjsWxJG5hZU2L5+qM2wl9xFakprYE5OTuHOOfq9Q2fDsqVlpa6MMDfBgwIVdy5yy5zrl0755Yt88/PPtu51q2da9rUL+vc2bkddnCubVvn1q71t969nTv5ZL/8yCNj+yovd27XXZ078MCQLy7Vcsop/n+xdm1s2RlnONepk/9fJPvpJ//h+M9/Ystmz3auUSPnhg/P9Eo1fhxkewOuAj5Ps87wJ81L4pY1xecsPiN4vg0+FeOucWV2C5b1yaIeVTp+wbnRoxOfv/56qF1UX8eO/gVbtIgt69zZL2vatECVkByq8eMx17fgODyikjLfAJcmLRsSbNspzTZXEUvHqptutenWNVfHT71vcc6700+H22+HffeFv/wF1qyBZcugSxd48UXo3RtGj4aBA+Hpp+Hxx+HHH+HJJ2HaNDjtNH/bZx944QUYNw7eqG8Nl7XU2WfDE0/AQQfBySfDp5/CAw/A9deDpeie2rMnHHkk/O1vMHcubLyx/9+3bw/HH1/4+ufflmY2B1iNz7t7mXPuB/zUyZ2Iu4zrnFttZqPwrfj34y/zljrnxsWVGWtmpUGZr1O9YIoWq9ZVrfz6pOsF/rxeAHffDccdBytXQnGx/yxFKxOJFKgSItU2D3+cx9sE3+1oUcXiANyAz8IR1RqYNXPmTNq0aZNmE5H8Wbp0Kd27d4cczlCqwDmN0CfZXr3g7bfhvPPgD3+A1q3hd7+Dr7/2wVjbtjBsGEydCr//PWyzjQ+od9kFdt4ZVqzwgdojj/h9PfkkHHBApS8rObDzzjBiBPz97/5/06EDXHONf57OY4/BBRf4cmVlMHQoPPMMtKt3XXfHASfjW502xXfFGBP0Y46eTFNdxu0ZPO6E7+ef7BcqnozjXYqfOKTaysszP8+bY4+FDz6A++9PfNEDDoBL69PcRlLPfULFblX7A+NdmixCzrnV+B/aAFjQANGmTRsFzlJv1PusGmbWBigtLS0NdeBGGxr794eJE7N8seXLoVkzaNTIR97Ll0Pz5v75+vU+SG7dumJrZnm5L5tqneRf9H/VooVvIQxjzRpYuxZatgxTus7/U82sJX4yn5uAsfgp47s45+bGlXkQ6O6cO8DMLgNOcc71SdrPt8DDzrl/pXmdVC3Os7I9fgHeeQf2i5vQ/aWX4LDDKt1F7jgH48f7z8qgQVCksdh1VJ0/fgHMD27uHTydhB+j8AGw2Dk3w8xuwF/OPjko3wuYir+C9CD+KtJ9wPEuZDq6bM+/Irm2dOlS2rZtC9DWObc0F/tUi3MaVfo90apV7LGZD4Sjiosh3RdHUVH6dZJ/yf+rMJo08bcGwjm3wsym4GfDjKaw6kTiAKFNiLVCz8O3VCfbmIot1fGvk7LFqipqrMU5ysxf1RCpHQbiA+WoaJeKx/CDfDvjU3MC4Jz70cwOAm4DzsbPSfDXsEGzSH2lwFlEKhW0BG+Dzzv+Iz4w3g/fcoWZNQGGApcEm3wCtDWzXVyQccbMdsWnZhtTiDqn6+P897/DpEl+KEEjfQNKA+GcG0mG1nPn3Kkplo0CdspfrUTqHp020qjnPVhEMjKzm/Gp9mbgW5KvwGe4eMw558zsduCyoOvFt8BlwEp8Cjqcc1+Z2ZvAg2Z2RrDbB4BXnXMpBwbmWnLgHG1x/ve//f3bb/txoSIiImEpcE5DgbM0cN2A/+HztS7A92vezTn3c7D+JqA5fsKf9vjBhPu7WA5ngBOBO4ll33gZOCf/Vfcq66qxbh0iIiJZUeCchgJnacicc8dVst7hc7ZelaHMYvzMmjWissBZ43BFRCRbGuadhgJnkbotOVBOPqYVOIuISLYUOItIvZSuj3OUAmcREcmWAuc0Ctni7ByMHesnHBSR3FDgXPssX17TNRARqR4FzmlMm1a413ryST8/wpAhhXtNkfoueRb0gudxlgR33OHTpT/xRE3XRESk6hQ4Z1Co4Pm///X3U6YU5vVEGiL1ca5Z553n73//+5qth4hIdShwzuCDDyovkwsaiCiSf+qqISIi1aXAOQOdWEXqjyefTHyu41tERLKlwDmDc86Brwswx5lanEXy7733Eo81Bc4iIpItBc6VKMSUvBq0JFIY8ceaAmcREcmWAudK/PBD/l9DgbNIYehYExGR6lDgXAvoZC5SGGpxFhGR6ihI4Gxml5qZM7Pb45Y1NbO7zGyhma0ws5fNrFvSdj3M7JVg/UIzu9PMmhSizoWkPs4ihREfOBep2UBERLKU91OHme0M/BmYnLTqduBI4Dhgd6AV8KqZFQfbFQOvAS2D9ccBRwG35LvOyVatyu/+P/oov/sXES8+cN5nH9/qvG5dzdVHRETqlrwGzmbWCngS+BPwa9zytsAfgQudc+865yYBJwHbA/sGxfYHtgVOcs5Ncs69C1wI/MnM2uSz3snatfOtwqtXw5lnwiuvFPLVRSRXUnWLuvXWwtdDRETqpny3ON8DvBYEvfEGAI2Bt6MLnHNzgKnA4GDRIGBqsDzqLaBpsH3BrFnjT7jDh8N998FhhxXy1UUkV1J1i7rkksLXQ0RE6qZG+dqxmR2HD3AHpljdCVjjnPs1afn8YF20zPz4lc65X81sTVyZVK/bFB9cR7XOsuopOQezZuViTyKSL506wbx56ddrIK6IiFRHXlqczaw7cAdwonOuLJtNgfg2oVTD5pLLJLsUKI275STcnTMnf6PwG+Xt54tIw1LZgD8FziIiUh356qoxANgEmGBm68xsHTAU+GvweD7QxMzaJ223CbFW5nkktSwH5RuT1BKd5AagbdytW4ayoR1zDHz8cS72VFGXLvnZr0hDU9mP2/vvr7isd+/81EVEROqffAXO7+EH+vWLu43HDxSMPl4L7BfdwMw6A9sBY4JFnwDbBcuj9gdWAxPSvbBzbrVzbmn0BizLxR80bhyMHZuLPVWkdHRS29WXlJKXXVZx2ZZbFr4eIiJSN+UlcHbOLXPOTY2/ASuARcHzUuBh4BYz28fM+gNPAFOA6EDCt4EvgcfNrL+Z7QPcDDwYBMRSxzgHN98Mo0fXdE0kG/UhpWQmLVvCokVw7rkwcWJN10ZERGqzmpwC4HxgBPAs8DGwEjjUObceILg/GCgL1j8blL+oRmor1fb003DxxbDnnjVdk+o74ww4//yarkX+1ZeUkpn89BOcfDLcfTcMCPL1lJbWaJUalJkzYeHCmq6FiEg4BQucnXPDnHPnxT0vc86d65zr4Jxr4Zw71Dk3M2mbGc65Q4L1HYLyqwtV51wZOxZ+9zt/ggbfuvXnP8OYMRXL1uduGyecUNM1yI2ff4YHHoDbb/e5veu5gqaUDLp/tIneyFFWnEz69oXXX489f/xxn7v9ppvy/cqyeDH06AEbb1zTNRERCUeTzhbAoEHw/POxvpQXXAAPPghDhlQs26kTjBpV2PpJdsqyyRNTh8WllLw0xeoqp5QEMqWUrFZWnG6tytij55JsNuGxxxKfn3yyv1d+5/z76quaroGISHYUOCdp3Dh/+163zqfDevrpxOXxrcy//AIHH5y/OtSU9etruga5E5/SrL5eIajBlJLVyooz9oRxfHjaF/TrlJMxwaxbV78+u7VNfT1+RKT+UuCcpLI8sFFVzQfbr5+fiTBe8sQq9THX7O9/X9M1yJ34k309PvHXSErJXGXF2blrbgLnzTeHbbet1//nGlUfv+tEpH5T4JykuDhcuap+4U+ZUnmZ+jghyv/+V9M1yJ2G0OJMDaaUzIWiHE1WNHMmfPMNrFyZm/1JIgXOIlLX1MMQrXratw93kgz7hZ8qb2xl6mPgnGzNGmhSY9l8qyf+0n19DZydc8vwA/02MLMNKSWD59GUkouAxfh0kelSSl4MbESBUkoWWT39x9QzCpxFpK5Ri3OSbFqcKwuanIMbbshfHeqyu+7ymQz+8pe6N9iugbQ4h1FrU0rmqsU5KtsA77nnfCadZSF6jCxdCltvDS+9VLW61WUNpNuTiNQjCpyTnNBnDs8fO40mxZnPlH/4A/Tpk7l1OsygolRB4+LFlW9X1330kR8Eef/9Pq1bsokT4ZlnCl+vMOJP8A2pxawupZSMb3G+KAdheqqgbvRo+OKL1OWPOcZn0gnzw3nXXeHrr+GII6pXx7pIP0JFpK5pAJ0CsnPD7t8CcNpO87jvsy5py0UzYzz/fPqBbzfeWPnr9exZcVlDCMbis5fMnJm4bsmS2EQUm24Kw4YVrFqhNNTAuS5p1TL2eIstqr+/5P/zrFmxiXwyBXxz5qRfF9WiRdXrVdclH0thB2eLiNQUfU2l0bbpulDl0gVOS5bAFVdUvv0vv2RRqToqVWARHzhb3GX1667z/cyjjjkmf/Wqqvj/uQLn2umUk2Mfulz0pf/888Tn//xnuO3Wr4d774UPPkhfpkv63+f1nlqcRaSuUeCchlWzj+Tjj+emHvXB8OEVl8X34/7+ez9L2/LlFX9sLFiQ37pVhVqca7/4FudctGLOnp34/NFHY48zBXwjR8JZZ8Hee6cv05ADxvg+4DqWCsPMzjKzH82szMwmmNkeGcqeamYuxa1ZIessUpuoq0YaYUflr17tW0n33x923jm2/MMP81SxOuiOOyouiz9Jvvmmv/38c+HqVB1qca4D4g7fXEznnOmzuX59+kw4yTna4/3wg28NT27NbghefdW/p+ecE1vWkH9AFIqZHQvcDpyFH7B7BvCGmW3rnJuRZrOlQJ/4BVlOiiRSryhwTiNsg/MZZ/j7K67QF386K1ZUXPb99xWXjR6d/7rkQvygTwXOtZXjySdhwgQ46KDq7239ev8juWnTisd5psA5nWXLctP3uq469NCKy3QsFcQFwMPOuYeC5+eZ2W+AM/HT3afinHPzClI7kTpAXTXSOPMv1Ws1VhAdkypwrm5XmJqkwLn2MwcnnAC33JKbz9p770GzZn5fRUXQunVs3bpwwyESJHf9kMq/M596Cl55pTB1qY/MrAl+RtC3k1a9DQzOsGkrM/vZzGaZ2atm1j/DazQ1szbRG9A6XVmRukqBc1qOPfbwuVirYtq03NamListrbhs220LX4/KzJnjp1iuLBuKAuc6IMe/XEeNSnwe3zd3w+fhi3lw9Si49zPaNVubcX/ZBPNLl8J558HYseG3qYsyHUtz5sCJJ8JhhxWuPvVQR6CYitPdzwc6pdlmOnAqcBhwPEFOdjPbMk35S4HSuFuGzkoidZMC5zSi57U774QhQ8Jvt3atP2cnp1iTRKla6Wq6FToSgR9/hH/8I3O5SZNijxU4S4s7RvuA+aWv/YIFK/n10jG0aJyUyH1t7HnjcTO479BvSOiMncbll/txAoMG5bDS1VG2DtaESFKfpUy/dRYtij3WMVdtye+0pVjmCzo31jn3hHPuC+fcaOAY4Bvg3DT7vgFoG3frlpsqi9Qe6uNcic6d/WQdYYK6RYugVy/fp3J1NaZ4iOaHrc/efLPislRdOgrpoYcqLwM+U0KUTuK1kxWor9S5u86ikUv9IVhxxUd0u2U3Fq1sxKp/fuRDCoBj+rL5Tz9yxkD4v2kdefeHjQDH/lv8CktawIQ50K4ZDOgCa9bTccFiios60qH5WqCA89TPLIW2zeD2oKl7p84wcW5s/fm7Qeum/vH3i+HJKXBqP+jYgonTGzNggJ8Rcdq0cJlNwkwYBTB+POyyS3Z/igCwEFhPxdblTajYCp2Sc67czD4DUrY4B5MbbTj7WU23hojkgQLnHHriCX8Jt7oz3m2Z7iJYPTI/xdd0qgGDYZWVwQsvwL77wiabVH0/YZxwQqyvpQLn2qnVoiUFeZ07D8r8oZ11YYr+Fc/G+nG9c8oUBty3Ex1brOWtk6fAnXHlXvOTMUW29VdDAHirK/ymdzVrnUa5gznLYMUaeCZFX7P4oBngthR/26M+RciXX2wCbMP06T6H9T5D1jHzgs94aGJnSkZulvLlX3/dd8dIdvbZiT9qv/lGgXNVOOfWmNkEYD/gxbhV+wGhJrS1ghEAACAASURBVHw3Hwn3A6bkvoYidYO6aqRThRarTNNvZ0M/0mMGZxqyEueqq/xJtxCt9erjXPu1XhTXsf6G0biSUQzqXoorGcXdB3+7YVWv9qtwJaM23M7ceTavnDCF4iLH33abxaF9Fua9rhP+MtEHzWGMm+27hVw9quI652Lr1pX75w9PhB9+rVh24Upf7qMZsHiVf3zth/CfSamD5iydtOMv9GjrM5atX+vgpo/p1nYNV+31M1t1SP1Fma6L1PDhsGZN7Pknn1S7eg3ZrcDpZnaamW1jZrcBPYD7AMzsv2a2YaJ4M4uY2W/MbHMz6wc8jA+c76uJyovUBmpxTqMqsetll+XmtcNesmwIhg4NV+7FoP3k669Tr//iC5/+q1Wr6tdpbdy4LwXOtVhScDnmdN8aevYuc/h6YXPWrC/ivkO/TSgz/JDvAFgXiaXUsUjsQ9i4uJz2zdbx/LHT2Kh5FdJp5NLVo2BQN/gkxfirN7+Dz+f5VuQnJseWD+kOH8cNwHj/R3/Lg58vGOcffJq4/JjtFnDTR90pLnKsWhubCWnuHAdL10Cbphn3u9VWua5pw+Gce8bMOgBXAp2BqcBBzrlopvIeQPy3WjvgAXz3jlJgErCncy7pvyrScOQtcDazM/G5ITcLFk0DrnbOvRGsbwrcjB+p2xx4DzjLOTcrbh89gHuAvYFVwFPARc65uPaH+kfBWEzY96JphnPtY4/Bqaf6x7no+hofOPfpAz161J3JW8SrrItFPFcyius+7MHXC5vz39+m+WUWdeXQ1K3B+ZIqaIaK3SqiPq7iqOVj+sKSMh+Iv/tD1fYRuGbvn7hm7582PH/s80257ZNufH7mBD81B0CxwRFbw7Ybk9yM0bqSBGe//grt2+MHMDYphk9nw/pyGNS9WvWuL5xzw4EU87mCc25Y0vPzgfMLUC2ROiOfLc6zgH8A3wXPTwFeMrP+zrlp+K/IQ4HjgEXALcCrZjbAObfezIqB14AFwO5AB+Ax/LdouhG9uVMDiZgPPhhee00tzvHCvheZAudo0Jwr99+f+HzGDJg3DzqlS+gkdd7le6abVC3OEVvnvyKFdMqOMHc57JaUGGFwEIA6B1N+ga07wtvfw+p1PrievazivjK9TL/5nNIvadDDegfPfwXPf0WPtrsyozR6gFvFjDxLyqBtU3Dw6ovr+e2xRay5MsVsSn03qbQ1W0SkMnkLnJ1zyanqLw9aoXczs1nAH4HfO+feBTCzk4CZwL7AW8D+wLZAd+fcnKDMhcCjZna5c25pvupeU/bd1wfOanGOCRs4FxdXXiZXJkyouEw/dhq4Tq1gh00BmDS3Ff07L69Q5OmOu3Dcwlp0hfuQreDVb/zjvXvB7j0qlunZLv32Zhv+Zg4J+k88mOLgqKYNXT4CI1btCKtawoKV8N1i3087cAiw5so0O4pmBzl3F1zbZliRBpOISPYKMjjQzIrN7DigJfAJfvaixsTNYBQEx1OJzWA0CJgaDZoDbwFNg+3TvVZOZi5K/kq9666q7CW8ww+PpWxSEBYT9kdErgdUfvZZbvcn9VyX2NfMTR+l7hJw/NnNQ+3q/f4FSti8U2ffteTKoamD5qoowMjmI0q/gH+P8Rk8PgpxJSDZXZ9i135I+ctf+64nV4+CzzSVo4iEk9fA2cy2N7Pl+LyO9wFHOue+xA80WOOcSx7uHT+DUSeScksG5deQfpYjyNXMRUk9Nc46C04/vUp7CmXEiFirqVqcY2rqR4TSXUlW1sUO2hHTO2x4vM1dAwE49tltAOh442C2uTfzh2vhqiYc99w2lJYVc+arW9Lm+iF0+FeI9DIXh0xBA9A5B6Nks1B+eO3rxlL0+TyfSQTgje8yFxYRCeS7xflrfOqa3YB7gcfMLNNky8kzGKXqaJx2lqNAjmYuSnyJoiJ48MGq7Sms2triPGUKXH89rFqV/bazq9mQU50fERMn5mfQ3u67V1xW337smNmZZjbZzJYGt0/M7MC49U3N7C4zW2hmK8zsZTPrlrSPHmb2SrB+oZndaWYFnMGjgNbHPgBl64ppVLInFtmT6QtbYpGhPDvNJxdftLIx0+c1Z4+Hd0y7q+nT4Zmpm9D+X0O477MuLFvdiMWrGqd/7T4d4J97QvP0ZdwFg+DIuOD1t9uE/9uysV1iEvX3fmjH5e9txmMTN+XKMT7/9A7D014wrFnL6/WYcxHJkbymowuyX0R/yo83s52BvwHPAE3MrH1Sq/MmwJjg8Txg1/j9mVl7fBePtLMc5XvmojZtYGmeelfX1hbnHXbw9ytWwHXXhd9u1izoXs2B7FX9EfHTTzCgmufn00+Hu++GZs0Sl7dvX7FsqinE67i6Pbi30JK6Oqwvz/y989GMdnT69yAO7L2Yt75vjwHvn/oFV4/sSVkwAZJziftods0elN0wFg7sDV3bwKS5MLALtGoS6yKx7+YVsl5sc9dA7hzUhP322hhenO4HyHVoUa0/N60WseB9yzt25rvFwet8CF27duWat7oC8MnM1gzqnt0gwmxM+6UFfbuvhtVZfIHkYRpxEal/Cj0BiuH7KE8A1uJnLPIrzDoD2xELnD8BtguWR+2PD4pzPwIlWZo27auvzt9L1rYW50gksctitjMijspBVq6qvheTJ1deJt6FF1Zc9vDDcMcdictGj47NGhivvgXOzrlXnHOvO+e+CW6XA8vxg3vb4gf3Xuice9c5Nwk4CdgeP7gXYoN7T3LOTQoGAV8I/CkYe1Bv/LfbHrBprOvDNiEbc+cvb8Kjn3di7rKmzFnWlK3v2oWnpmzKCy+kLr96XRFcNNhnh2jXDPbq5ae8jj9IkzJgHP/cNkxf2JIvvwQaFfn+zOftluVfmIW+G7O8W0cufGvzWNAciE9UtPvD/Tc8HvJQP9rdMCQhX3Z5OTw7deNwr3nRYP93BeYvb8x29+yc+Hf26ZBiwyQtM7Tqi4gE8pnH+XrgDXymjNb4lqlhwAHOuVIzexi4xcwWAYvxOZ2nAO8Gu3gb+BJ43MwuBjYKyjxYiIwa6dqLXnst96+1aJG/bxT8N6ZVf+KunEj+kdCzZ3bbF6X4WXbbbXB+FllBCzE4cPlyuPXW1OveegsuuST2PF1qu/jczvVN0Hp8NJUM7jWz6ODet6h8cO8Hhal9Zut27c7K75bRphpTdJetS/yg77cffPVVdWtWRUXGhQt2Z/nY+bw8vQPzlvv0awWbjbS4iFGb9uXWMRVXxR/L5XGt6Y2KHKVl/stv5/v707HFWt78zge6xz7ncCUfks7sIdvSNWjlHvzmUD4d52Kt/fFZM4Zu5luff0rzfx7aE5pqPjARqVw+W5w3BR7H93N+D9/t4gDn3DvB+vOBEcCzwMfASuBQ59x6gOD+YKAsWP9sUP6iPNY5Tuom53HjUi6ulo028vdPPOHvf6je/AIxS1f7Zp7ySnJS5yNntXNY0nt4+OHwXZZjcJKnt778ct/qW5l588K/xk8/pV83fnzi8zVpukHWtxZnqJnBvbnKipPg9J1gSPcNA/XiNdqvF23O3hHOzW406JCH+vHA+M78+eUt+fhj/6Py//7Pr8v2Kkm/fuHLzplTeZl1RcU8ML7LhqAZ8tP9q6wMHn0U5ibNtfLmm6nLJ9dh3jIf8E6aF2utHz+nzYag2TMsMpR/vNOLt76L9ZG6fPzWWGRPtjky1ipdVJTURaY47vRWZCw5ZAce+HVbNr4xRdaSoZulrrSISJJ85nH+YyXry/B9HdP2d3TOzcCn5qw1Mk20UV3vvRd7vG5drAU6o5+X+JaSTq1g2WqYtRQ2ag5jZ8EXcXFLl9YwJ+hT+Ntt4OMZ0KMtfBaciTdrB0dt43OjtmwMGzVnk5brOHKbhWy78Uru/awzp282H1Z2T+jHyPI18NJ02KMndG/jm7acg8cnM/AHgB2Itt/vsgvMzHLisvgg5NZb/SDF66+vGOsnt6iFDRTKymD77dOvX5bUDTM5SIiqpy3O0cG97YCj8IN7M02CnovBvZcCkSzrmVr7ZnBuMEyiS2umL4Tt7xnAKydMZbP2q+EP/WKtku2bwwnbM/Ph77hm4lYcdEIzFr/4M3d/2oWFKxsz7ezx3DC6O13brKFxkWPMzDaMmdnWbxt0HDv6aP+5zDZwvu8+2C2uV0Hz5ukH4p59dmx6+XRStS5fdBGcd1529arM6afDk0/6x/HH4557+rEByZKPyZ637UbTRuUsW135F92NH/Xgxo96cO6us+jYYi3Xf+DzR8cfn598krRRkbG+e1t+nbOOpUtb8K+bjAcf9IH2/eM7c8bANAeziEgGujaVRrorm9UJnJ97DvbaC0aOhN/9ruL6li39ADyoJHB2Dm75BFZmEa3NiTvDvBBcR56/IrbspyV+n3Hm/z32+K+7Bekxbo6LfI/bDp6e6h9/HzQ+7t7DB/A/LaF3Efzvd19x2kt9WLW2mCOPhE8/9YFCWPEtufEnY+cSA4T4cosWhfzRAXz7beb1yftJFxQ99FD1ByPWNjUxuBefFSe+40xrskgpOXd5Ezq3WsOK9q1peXb/Cuun/tKKXrfvlvoiS++N6Hb9LtyPD07/+FKfDavaXD8EMI4/Hv73v8x1yObzDdC3b+LzTNlrpk6tfJbKVF2k8jFuIho0JxsxIvXy5Pd8zfoi1qzP7qLnXePSJ0lK9WP5yu925IYboFGJMTDugsNfXtmKWX234JpTfoWebbOqg4g0bIUeHFh3pGkTS5VRIYzevX2w3KEDHHVUbPkhce3phx4ae5xwopu1FOYv9wHpDaPhmg+zC5rzJRo0x/toRkJL93HbL2DlFR/xzNFf0qsXNM5y/E1814j41t7kLhPJgXPY16msZfq3vw23n2yDpToq74N7nXOrnXNLozegSqkXZm6/ZWIf15DM/O2QQxJbgaM/pbfOcTri22/PbtbL776Dzp19cD12rJ/uPVnB+jOn8dRTqZfXRLagDz4wnDPWrq34nXHtv4r9dOEZ0viJiCRT4JyGpen3e/31udn/Bx/4APr++2PLhsZdBHfzgkB50Ur4zyS4fwLcNhbW1rJcdSEds90CilevoUmILL7vvgt/+pN/vCRuLE+buFwMy5bB6FEOF/Tfjj8pNmoUPnCurHt3uj7N9Z2ZXW9me5jZZkFf5+vwg3ufdM6VAtHBvfuYWX/gCdIP7u1vZvtQwMG91dWkSYpL//hj9phjcvc6fftWbbr4hx6CQYMqDthdsyZ1MF0oK1akX/drco/4Aoj/ETEh/7mYRKQBUFeNdNJEVF27Vm13W26Z+HxY/zKG3boaOreByb9A2To6dOjKpq3WMO/iTyDNZdCsDNsMBneH0jLfar1pK3ggOHt0bgVzl8fKHtAb3szv7FmN7/iELTr15pCtmtKskeO5Y7/csM4ie9KySTknngD77FPM3Ll+wpn33/cn3PbTZrDg3B8BaHv9EM7dazH/O/orGAVcNJgpR49h+WFFXPzOFnT+YCEbF/XEz3/jHbjlIr5f3BxITJHV843xuJIVdLtlN2YvrdgPJ6GPs3Mkd+L57TYLeP64Lzng8e3xiV/qjejg3s74GTgnU3Fw7zr8oN3m+AHAp8YP7jWzg4Hh+MG9q4CnKNjg3vzo29dfNUrn6KMTn/fokTmQLS4O360o3l//GntcXg4LFsCmm/r+xfkYwBxWfODcv78P7NN13QC44gr4+mvfja2qkrttRa1fD2NSZPeIt2YNoX7Mi4hEKXBOI12Lc7qWzP79YdIk/7hlk/Vs1WEl68qNc69pxciR8O8bk4KuOyqe3Y7q+gtHX5xFY9xhffylxjnLoFsbaJKm6apDi9iEB1cOTTzTrCv3+V0Bdon9Klj+v69p9W3q1BTrWjalUdsmcOhWviW8XTNYUhYrsHsP32Ujhf7zvuOVEysuT0g5tXoITZpEP5qOJf+ZTvsVv2xYXXrZx4kb3+zPjq2alnPvId/CbNiPX7nlN125+O0tWH9VbN//vmgAF7eZAMf0ha060H6tP9PPunAsuz7Qn+O3/4VvFzfnoC0Xc+qLfVj1zSq4+vMN2z93TEfOfHVL/r3/DzwyqRPPH+eD/zd/PwXING6ubqmPg3uvu85nZYlezaiqYcPg3ntTr4tm1ogaMwa6ZZi7tKgodZ/kbBxzDDz/vB87UZNBM+DzRQeaNfO54DMFztdcAyUl1Quc338f9tmn4vILLqh826ZN85NUSETqLwXOaRStT90lolEjOG2nuRSb48EJXZg9Gz7/HA480KeR27ZPOcsv/2hD+R/7DeRPJzWBez6D3hvBEVunnaGqaHbIoPmSIYk5RzfPsuN1fPNMo9Rn7bFNe7IvPnC+dlQPtt14Jf94txffLmrBjz/CZpsFBaMTD6wvh+tGw06dYe9esNdmvi92Vdz4MX069uLwrVsw4vhpkOHybyYXDJ7NBYMT5/y+uE3Q4v5sxWTZ4/48KeH5gksqXqv/Xd+F/K7vQgBO7Z9pjJvUNpdeCkccUf1+ykcf7QeVXnFF5WW7doVjj00/eVBVumkke/55f3/bbdXfV3XttVfscVGRzxBSmep2h7rtttSB8513htu+vLz6P15EpOFQ4JzGRnN+AZKmAFu6mqbrjIcP/waAp6ZsSpcpP9Jl/GwYD1t0b8PVeyeO0O71xng/DQzA5Pnw6yqYWfUunr+saMwmBUjU33WbZlw3vAfryo2rPtgsYd3cuX4q7YSTfnFRwuxd1R2htOPCHxlxfLV2IZLADLbdNrtt/vUv+Mc/Ku7n8ssrD5yj2TIyzbiZy4AtUxCeKeVivpilzkJUXJw4+DlMcJ3Jjz9Wb/sVK6B19bOFi0gDod/ZmTw8Ea4eFbvdPpZeL8RaIZdf/hGMi2vRnLmUS3avJFFxNYJmgLNG9q28UBrr1sFZZ8Gzz1ZedvVquOK9XhWCZoDBg/3saJVt35D0uHXXygtJnZNqKvaoVIMH44WZATQa7O6+e/g6pZMp5Vyhjsf4NJtFRan7DycH+Oem7ewTTnz3kKq4+ebqbS8iDYsC50xmVykTVk40uXqPDY9PG7EVfe8eyGFP9eX5TxNbtN94w7fs3HJL5ft88knfN/PYYytvpXn11czrP8gwYfKCBb5/Y669uHKL3O80R2aW5uEPlhrXqBH06uUfn3NO4rrEdHUVbbdd5fuPBpEjR2ZdtQoy5ST/5hvfsjtggA8Uq9Ovd+JEPxV9KkOTLjqlanF+4glfl2hXirYh0ihnak3v0SPzD5zKXH111bcVkYZHgXMt9P3iZqxdX0TPW3dl5/v788ikzny5oCWvfN0RSDzpHXSQv78oyFUQvy65lSl+ut7NN/f399zjLymPHAkrV8bW//Ofldcz+eS7cKHPQnHXXZVvm+DQrdIPbASGPbIjjUr25JERNRicnpPdlMxSf3z8sU//duON2W03fLi/nzw5fZloV41c9HWurOW1rMwHvRdfXLH7STYGDIADDki9LjlXc6rAefBg/z2RTUvz2WenXzdjhp9VVESkEBQ410KXvuubuGaUNmP8nDYV1qe77Nq3rz8Rv/givPKKb/W9/fbY+lSXls85x59w99or+5nvFi3y9/Pm+T7PG2/scy3HB+gAwz/t4gc0nrxj6h317wz/SH+tetRP7Vhfbrz7Q/pBkDOXx4LqL39pUWH94U8ldXHZrRsPth6ERYZy9cgeafcLsOPwAaxYlbrP9vBPu7DbgxVnqJP6o3Nn+OMfoUXFj1VGW23l77ffPn3u50wtv/nMO3zTTZnXl5X57COvvJK4PF19P/oI9t8fpkyJLRs1KnVXjaKi7H8o3HQTvPxydttk44478rdvEalfFDjn0p92ysluWjXJPD/u3Ln+pJTcKhxtcfrtb+Gww/zj88+H8eN9cJt8EoyfbQ9g+vTs6hkNnK+9FmbFTYrcpYu/33H4AP72xhb89Y3ePgvIZu2yewHguWkdNzxetbaYqfNj0csBj2/PVwtasOPwAWx120CGf9qFg57YjpNfTEybMPihfrz8dceEZev32YLXRvmzeuSDXtwxtivjyzvD5XsklFu8shGT57fik6kVIwCLDOXs17Zk3Kw2OZsYR+qWyy9Pvy5+4N8zz8Dee1csMzVu8s34LgsrVsBOO0HHjhW3KYSbbvKt7NHvkahZaSY/32MPeOcdv01UcXHq9J1VGTfctGnizKq5dt55+du3iNQvCpxz5a+7QufWsGX1J8Ho03FVxvWbb+5zyV57bbj97byzbzVLliqf7dq1iV02Mtl6a3+Cv+eexOU7Bb8fJs9vxZ1ju7G+PO5MWRw8PrVfxR02r5gt5KTnEzOb7HDvQN77oR2Pf7EJb323EdvevTOT57eibF0xZ7+2JW9824EJc1ontDB/MtNHJF1v3o2ZpU35zX+357zzYnm3AYoO7M3Aq7by2UFOi7UgzwwmRXnuhSJaXhtrFX95euIsGJdeWvHPkfrv9dfTr0sOEFMFwfGp2MaO9bMBvvlmrHW7R+aLIXnz1VfV38dOO6UOksNmErn9dt/CrTzLIlKbKB1dLpy4vZ8EBOD47f03/eT58NLXlW+7VQc/q9/KtRsWjZ1ZsXtGPjz6aMVlqS6tLlvml19xBfz734nrWrWqWP7IIzO86IWDYfka6NgiMX0d+AlYRv2csGjN+sSzrHPGvo+l6fIRp7io4tl2zrKm9LjVj+h6++7EdQl9M7vF3v8L3/IDEh94AKCYgffvxLDNlnDLmO6V1kHqv1dfTT+baPIMg6edVjGjzbBhscdbb11xprtBg3y/5EJatQqefjr1urKy1MtTSZcCL1UwPW4c7JqUmOZvfwv/WiIihaIW51zYIqmV2SxxYpF/7glnpOhAfPpOcNx2cNHgDX18l65txMtfZ5jTt8D69PHBcZMm4QYMpnLUUXFPmjXyQXMqg7vDgBRN41Xw/o/ZTQozL3mSxPN2Y9/HduC9HxK7l0yY01pBs2zQpQvcd1/qdckBYnKA/cgjsOWWmfd/992Z11dHeXnFwXwAL7yQ+PyDD+C77/wYiGi/7TDSpXlL1X1jl6SxtwMHhn+dXFHLtoiEocA5yYKVaebUjtemaaxV8ndpZlRYEWtBxgyap9hvl7is+02K4cqhFF0yhHJXvclDcqmkJPa4qpME/Oc/IQs2LoaDt4ILBlXtheKUljWi/Q2DaX5NuAS5FTIftGnKez+0J2Ga9DjHa3IWCZxxRrhAr29fn0ni2mt9kHbqqeH2n6+W1+228/2QP//cj4Uw87eTTkost/fePsDPtuW7ffDbtVHSdc3k56kcUsWJ2p96qmrbAfz0U9W3FZGGQ4FzkpXr/HDvxV03gbN2hv02j62MtiLv08v3g71yKGy7ceodbZHU4tmmqU+7VolWrRIHHB1wAPTunc1fkFtHHJH4PNMkC+m0ybbnSasmcOVQzp4/tPKyGSwpa0xZ8P/cdNPMZXfYIbt9h+1fLg3DO+8kPk/V2mrmcxdnGlCYSrSvb7LPP89uP8mi/Zj790/MvpNra9cmPk+XUePww/399ddXfczA8cdXfUKVFSuqtp2INCzm6vn1KTNrA5SWlpbSJkQE9/NF4+jZpoyvd+9Pn72TyjvnW5JbpegInMrMUmjZBDaKm1PWOfi/L2HjljBss9B/B/h+gJVNuhDGGWfA/feHK5vq4/Hzz7DZZuFfr6ofsbVrU/e5jnr7bZ8CqzJz5vjBkZlG8y9cWLFP6sSJ6VP0rVgBLVvGnlfyN9aeSwh1TLbH79y/f0LnVmuYvudObD2ssPMoRz9f550Ht92W+/3Pnu1zSUfzpDvnBxd26+YnHYrq3Nln0hk/3rfc5iJHdFXEHxPxx155edUya4D/sdA/Q/ZH53wf7WxTB4IfFJ1m+m8dv1WU7fErkmtLly6lrU9Z1NY5V72pmwNqcU6S8RvSLHzQDNC9bWLQHN3H0X2zDprBD55xLjaV76hRvgX4llv8Zd+pU+HAAxO3Sc6pfPPNvk/m0BCNuemC1p49fcvZiScmLv/++8TnW2wRS1lXFY0bp55R7bvv/Puw337hpjWOZhR57rnYRDHJkoNmiGUHSXbFFVU7MUv9Nm8efPhhfoJm8H2kb73VX4W67DK/rEmTiini5szxfa8POyx8BotcS27Bjq9jVYNmgH79EruMxHcDGz/e36cJfjfUo3dv/z141lmJ6zJtJyKygXMuLzfgUuAzYBnwCzAC6JNUpilwF7AQWAG8DHRLKtMDeCVYvxC4E2iSRT3aAK60tNSF8fOFY50rGemmvxeufF2wZo1zy5c7V16euPx//3Puq6/848WLnWvd2rlrrslu3+Xlzs2b59z69bFl69Y5N3Nm9eocv69Bg6JJqfwtWfy65NvkyeHKpzN+fPqyixY5N3Soc598UumfkbfjrL7fsj1+51w8xrmSke6rD5aGKl9fZPos33pr5mMk21ujRrHHI0bEHi9ZEnu8bl3FeiR//1THlVc6d/fd6ddH61Va6tykSf7x668nlhk1Ktx3gKsFx0FdvWV7/IrkWmlpqQMc0Mbl6HOdz/aIocA9wG7AfvjUd2+bWdwFbm4HjgSOA3YHWgGvmlkxQHD/GtAyWH8ccBRwS74qbVb/uq40buy7FSS39Bx3nE+BBX4gz9KlvjU1G2a+/3B8y1Zxsb98nAvFxT5FV3m5H91fWlqxTHRA0GOPxaYgB3/JNlVKrORW50yDngYM8KfV8nJYvDjx8vNGG/kW8Vx0nxGpjnnz/LH8888V151/vv/8zpzpj6FMtt3WH9NXXum3mTwZRozwkyWtWOE//2vX+smS3n/f90uOhp5t28Yep+oeUp2W5mQlJZmn4Y7Wq00b30rtXMWrcXvskXpbEZFM8pbH2Tl3QPxzM/sDvuV5APChmbUF/gj83jn3blDmJGAmsC/wFrA/sC3Q3Tk3JyhzIfComV3uctRfRWo/s8Sct/GOP97njm7WDIYMgfnz4e9/989TufxyP/34ppvC58GyMgAAIABJREFU44+H63ZhFssSIFLbbLpp5klLzPyP2W7dfBA8ezbMmOFTRf7yi/9xOihFMpvtt4/9+Iw/Tvr08be6zMwPCp48uepZPESk4SnkBCjRCWUXB/cDgMbA29ECzrk5ZjYVGIwPnAcBU6NBc+AtfBePAUCF9hMzaxqsj8pqhJBVeCB1QTRI3mKLWF/HdNq1g48+yn+dRGqj4mI/I2GPHv5HZkP2xRewfHnqiZxERFIpyNARMzPgVuAj59zUYHEnYI1z7tek4vODddEyCV/tQfk1cWWSXQqUxt1mpSknIiINXEMLms3sLDP70czKzGyCmWXstGJmR5nZl2a2OrjPNDesSL1XqDHXdwM7AGGmjTB8R+6oVJ2Ok8vEuwHfuh29ZdXbNtrQ7NTkLCIi9YiZHYsfW3Qd0B8YDbxhZj3SlB8EPAM8DuwY3D9rZrumKi/SEOQ9cDazu4DDgL2cc/Gtv/OAJmaW3HN0E2KtzPNIalkOyjcmqSU6yjm32jm3NHrDZ/UQERFp6C4AHnbOPeSc+8o5dx5+XNGZacqfB7zjnLvBOTfdOXcD8F6wXKRBylsf56B7xl34rBnDnHM/JhWZAKzFZ9x4NtimM7Ad8PegzCfA5WbW2Tk3N1i2P7A62F5EREQqYWZN8GOD/pW06m38uKJUBgHJmcnfIk3gnG6M0dKlGscvNSMfn718Dg68BzgBOBxYZmbRluNS59wq51ypmT0M3GJmi/CDBm8GpgDvBmXfBr4EHjezi4GNgjIPZptRo23btqFmjen6b59bbOtsdi4ieRX2+O18k08NoeNXpIKOQDEVr9bGjytKVmGcUSXlLwUiyQu7d+8evpYi+bERkJMoOp+Bc/TSz8ik5X8AHg0enw+sw7c4N8dfAjrVObcewDm33swOBoYDHwOrgKeANPO/pbQM39dZXTZE6h4dvyK5lTw+KNOYoWzL34BPBBDVGj9Avxs6hqtK72H1RN+/xZUVDCufeZwrHV3nnCsDzg1u6crMAKqcZdM558jRrwwRKSwdvyI5sxBYT8XW4vhxRckqjDPKVN45txrflRIAi816s0zzLlSN3sPqsVzOvBQoVFYNERERqSHOuTX4sUH7Ja3aDxiTZrNPUpTfP0N5kXqvkBOgiIiISM25FT9maDw+KP4z0AO4D8DM/gvMds5dGpS/Az/T7yXAS/gxS/sCuxe64iK1hQJnERGRBsA594yZdQCuBDoDU4GDnHM/B0V6AOVx5ceY2XHAtcA1wPfAsc65cSFfcjVQQlz3Dcma3sPqyfn7Z74LoYiIiIiIZKI+ziIiIiIiIShwFhEREREJQYGziIiIiEgICpxFREREREJQ4CwiIiJVYmZnmdmPZlZmZhPMbI9Kyh9lZl+a2erg/shC1bU2yub9M7NTzcyluDUrZJ1rCzPb08xeMbM5wftwRIhthgbvc5mZ/WBmf8n2dRU4i4iISNbM7FjgduA6oD8wGnjDzHqkKT8IeAZ4HNgxuH/WzHYtTI1rl2zfv8BSfCrBDbdgFuaGqCXwBXBOmMJm1gt4Hf8+9weuB+40s6OyeVGloxMREZGsmdk4YKJz7sy4ZV8BI+ImUYkv/wzQxjl3YNyyN4FfnXPHF6LOtUkV3r9Tgdudc+0KV8u6wcwccKRzbkSGMjcChznntolbdh+wo3NuUNjXUouziIiIZMXMmgADgLeTVr0NDE6z2aAU5d/KUL7equL7B9DKzH42s1lm9qqZ9c9bJeufdJ+/gWbWOOxOFDiLiIhItjoCxcD8pOXzgU5ptumUZfn6rCrv33TgVOAw4HigDPjYzLbMUx3rm3Sfv0b4/0comnJbREREqiq5v6elWFad8vVd6PfDOTcWGLuhoNnHwETgXOCv+apgPZPq/U61PC21OIuIiEi2FgLrqdg6ugkVW/Wi5mVZvj6ryvuXwDlXDnwGqMU5nHSfv3XAorA7UeAsIiIiWXHOrQEmAPslrdoPGJNms09SlN8/Q/l6q4rvXwIzM6AfMDe3tau30n3+xjvn1obdibpqiIiISFXcCjxuZuPxQcmfgR7AfQBm9l9gdlyGiDuAD83sEuAl4HBgX2D3Qle8lsjq/TOzCL6rxrdAG3z3jH7A2YWves0zs1ZA77hFvcysH7DYOTfDzG4AujrnTg7W3wecY2a3Ag/iBwv+Ed9fPDQFziIiIpI19//s3XucHFWd9/HPL/cLuZCBQICEBAh3k0BwBVYBhaCiIogK4q7iXtxV0EV31cVHDayrrLeIuvFBd30iuuqCKHgFBlEuCkFICIEAzgiEBEISmJDJkGQmycx5/qjuTHV1VXVVd/X9+369+jXTVadOna6Z6j596le/49z1ZtYBfAYvp/CjwDnOuWdyRWYBQ77y95rZRcC/A58FngQudM7dX9uWN4a0xw+YCnwbL9ygF3gIOM0598fatbqhnAT8zvd8Se7ndXg3Uc7AO4YAOOeeNrNzgK/ifdnYAHzYOfeTNDtVHmcRERERkQQU4ywiIiIikoA6ziIiIiIiCajjLCIiIiKSgDrOIiIiIiIJqOMsIiIiIpKAOs4iIiIiIgmo4ywiIiIikoA6ziIiIiIiCajjLCIiIiKSgDrOIiIiIiIJqOMsIiIiIpKAOs4iIiIiIgmo4ywiIiIikoA6ziIiIiIiCajjLCIiIiKSgDrOIiIiIiIJqOMsIiIiIpKAOs4iIiIiIgmo4ywiItLizOw0M/uFmW0wM2dm5yXY5nQzW2Fm/Wb2lJn9Yy3aKtLI1HEWERFpfROBh4HLkhQ2sznAr4F7gBOAzwNfN7MLqtZCkSZgzrl6t0FERERqxMwccL5z7uaYMl8AznXOHeNbdi0w3zl3Sg2aKdKQRlWrYjM7DfgYsBCYQeAkNTMDFgPvB/YF7gcudc6t8ZXZF/g6cG5u0c+BDznntqZohwGTgD6nbwkiTUXnr0jdnAJ0BpbdBvytmY12zu0ObmBmY4GxgcXTgC3VaaJIIpOADVl9hlSt48zwZaFlwE9C1n8c+ChwCdAFfAq43cyOcs715cr8EDgEeEPu+beB7wNvSdGOSUBvb29vstI/NO/n9DPgrN+l2I1IJKt3A5pYeecvwMXqZ0sm2vX8PRDYFFi2Ca/fsB/wfMg2V+ANiIk0mkOA57KoqGodZ+fcLcAtAN6g0bDcKNLlwOeccz/NLXsv3kl5MfAtMzsGr8N8snPu/lyZvwfuy3Wu/1SttgOw+c6qVi8iItLggt8+LWJ53tXAEt/zScCz69evZ/LkyVm3TaSkbdu2MXPmTIC+UmWTquaIc5w5eN9m914Gcs4NmNldwKnAt/AuE/XmO825MsvNrDdXprodZxERkfa1Ee9z2m86sAfoCdvAOTcADOSf5wfNJk+erI6ztIx6dZzzJ2PYZaBDfWU2h2y7meKTea+QGKtJZbZRRESkXd1HcVjk2cCDYfHNIu2i3unowi4DuZj1YWWCrgB6fY9nK2mgiDShkeO8n87B40vghT/Utz0idWZm+5jZAjNbkFs0J/d8Vm791Wb2Pd8m1wKHmtkSMzvGzP4G+FvgyzVuukhDqVfHeWPuZ9hloE2+MgeEbLs/xSPVflcDU3yPQ8pvpog0pQPO9H6u+zE89M9w+6thSINk0tZOAh7KPcCLRX4I+Lfc8xnArHxh59zTwDnAGcAq4NPAh51zYTf7i7SNeoVqPI3XMV5E7iQ2szHA6cAncmXuA6aY2V845/6YK/MqvM7wvVEVR8VYiUg7yV2U6rl/eNEz/wtz/ro+zRGpM+fcncRkCHHOXRKy7C7gxOq1SqT5VG3EOe6yUC6X3jXAJ83sfDM7HvgusAMvBR3OuceBW4H/MrOTzexk4L+AX1Y9o4aINDfnvMcTvhv81/6wfu0REZGWUM0R55MAfyLk/CfYdXi5m78IjAe+yfAEKGf7cjgDvBtvApR89o2fk3C6UBFpc71rCp8P9tenHSIi0jKqmcf5TuIvCzngytwjqswW4K8ybpqItDq3B/ZsDywcqktTRESkddQ7q4aISPY23g47NxQuK+pIi4iIpKOOs4i0pud+Ufh8cGd92iEiIi1DHWcRaU0HLip8vmdHfdohIiItQx1nEWlNo6cUPleohoiIVEgdZxFpUYGbAfOhGkOD6kSLiEhZ1HEWkdbkAh3nfGf5lnlwwz6wa2vt2yQiIk1NHWcRaU3BjnN+NsHex7yfm++qaXNERKT5qeMsIi2qVN7myDTzIiIiodRxFpHW5AZLFFDHWURE0lHHOY5z9W6BiJSrKFQjwNRxFhGRdNRxjvPcz+vdAhEpV6mOs97+ak+DESLS5PTJEWf7+trtS+mxRLK18/n49Rpxrq11N8KN02Djb+rdEhGRsqnjHKdWH6yb7/HSY638l9rsT6QdPPTPJQqo41xTv38H7N4Kv11UuqyISINSxznOg5fBi/dXfz8Pfdz7+cRXqr8vEclRx1lERNJRx7mUO15b7xaISFb8MbYK1RARkZTUcS4lP01vNekDXKRG/Den6bwTEZF01HFuCPoAF6kJf25nfWEVEZGU1HFuBEMD9W6BSGpmdqWZucBjo2+95cpsMLOdZnanmR1XzzYXpqjT25+IiKSjT46knPM+dLu+CS+tyrbuLSuyrU+kdtYAM3yPV/jWfRz4KHAZ8EpgI3C7mU2qdSP38necf3smrPrXujVFRESajzrOSfzQ4KaDYOVH4cFL4ZYTsq1/zLRs6xOpnT3OuY2+xwvgjTYDlwOfc8791Dn3KPBeYAJwcf2aG5gU5bEv1KcZIiLSlNRxDrJR4cv7N8KfvladfY4/qDr1ilTf3FwoxtNm9r9mdlhu+RzgQKAzX9A5NwDcBZxatdaMOyB+fdhsgpqQQ0REElLHOcjqcEjc7trvU6Ry9wPvAV4P/D1eR/leM+vI/Q6wKbDNJt+6ImY21swm5x9AyrCOEjf8hXWcn78t3S5ERKRt1a3j3Lg3FtXhTvshdZyl+TjnbnHO/cQ594hz7jfAm3Kr3usvFtjMQpb5XQH0+h7PZtVeT0jHec+ObHchIiItq94jzs11Y1G1qOMsLcA5tx14BJiLd75C8ejydIpHof2uBqb4Hodk1sCx+8HQYPHy9TdmtgsREWlt9e44N9mNRRVyQ3DHWXDvX9e7JSKZM7OxwDHA88DTeJ3nRb71Y4DTgXuj6nDODTjntuUfQF9mDRx4Edb/uHj56KmZ7UJERFpbvTvOmd9YVHmMZBX1roFNd8Da//EydawL+RDfnV0/QarIDYXHy7YRM/uymZ1uZnPM7FXAjcBk4DrnnAOuAT5pZueb2fHAd4EdwA/r1ugHPli8bMy+hVNxS20N7gq/EiAi0oDq2XHO/MainCrHSJbh2Z/Br14BL60uXP77d+Z+8cVV/+r4mjWrLlqhgzI0CL+eB52ntsbrKd8hwI+APwE/BXYBJzvnnsmt/yJe5/mbwIPAwcDZzrkqfjss4+/Rc7+XYnLlP0Pfn7NvkkTbswOuHwv/G5HNSESkwdTt3co5d4vv6SNmdh/wJF5IxvJ8scBmpW4sAi9Gconv+SSq0Xl2LvmUvXef5/2876/C1/vr2bGusnY1sl0vwY3T4Jh/gRO+VO/WlO/lp7yrB+DN+jhyXH3bUyfOuYtKrHfAlblHY9v6sPd4Ygm8fSus/QHMvADGl0hvJ5VZcXm9WyAikkq9QzX2yujGospjJBN3gqo40rjloerVXU835iZ6efzL9W1HxfwhGnXIwiLVdeNUb6Kj376u3i1pfS/cXe8WiIik0jAd5yxuLMrE2P2TlXMJY/J2bU1QKND5uvXEZHU3kxf/WO8WZMf/t2/zOOem8Ip/K2+73se8qwvbnyldVsrk+wjSuSQiTaCeeZyb78Yiv6Rv8jfuW7rM9rUVNaUpdL6q3i3ITsHfXh/2DW/k2PK3/fnh8LPZunmtWmzk8O9Du+rXjjZiZh/M3ZDfb2YrzOw1MWUvCZlvwZlZe8aniVDfEecGvLEohXU3wMOfjr85LMmNY+16c1n/i9HrdvfBL4+Fhz5eu/ak4e84t+vfr5lYBrdyDO6svA4p5u84Dw7Urx1twswuxPtc/RxwAnAPcIuZzYrZbBuF8y3McM71V7utIo2qnjcHNveNRfe9x/s5/TUw4+zi9c7BjxJ8L2nXy5MP/Quc8t3i5ZvugjvO8H7f9jjM/zyMaLQ77v0dZ41ENrws/n/cnsrrkGL+G6OH1HGugY8C33HO/Xfu+eVm9nrgA3gZqcI459zGiHUibadhYpyb1s7nw5cPvJBs+3bteO15GZ74GtyyEAZ6hpfnO815+YwkjaRglLlNv/g0rJArAFmMOPcH7kl2Q15KwuV/E15+w23wxw/AngQj1Ts3we2nwdPfr7ydTcf3EaRR/arK3Se0EN/8CDmdxM+PsI+ZPWNmz5rZL83shJh9NO48CiIZUce5SMpL73teDl++dXX48qD72nQWwXHTYeXl8NJKWPP56HIbflW7NiXmH3FWx7khvfKb2dY3IhAn/fiXYOsj8NSy8PJ3vgH+fG2yDDIPXwEv3DN8Faud+EM19myvXzvaw37ASNLNj/AEcAlwLvAuoB/4g5nNjSjfePMoiGRMHedKPXgZbFkBu3oLl6/612Tbr7sh+zY1Bd+/3oZb4N73xMc9NxKnUI2Gt/+rh38fNTGDCgNfkB79XLLNdiTIyLFrS/rmtIotDwz/ro5zrSSeH8E5t9w59z/OuYedc/cA7wS6gA9F1H01MMX3OCSbJos0jkYLHm0cZ/waXloFk47wzfAX4daTYMIhcN764WX9m6vbvmayZ0fxMv9I07bHvceIkcXlGtHQ7uHfNeLc+Pb/y8rr2LMT/vxtmLoAxkyFPb57lOMmQxrsh9tOhumnwQlfDC/TjjeY3vse6A+EuYW9T0iWXgQGKWN+hDzn3JCZPYA330LY+gFgb7C6JZ0kTKSJqOMcZfRUOC53r8RZd8FvTo8vvyNwRWrs/rBjfXjZdvPLo4qXWcjFjt7Hq9+WLPjTZqnj3Lgu6PHuNZh0ROV1/fr46HVuMDqOeu0PvJ899xd3nB/7AozaB577eeXtazZrQ+K5B2M6zm4IfpT7Yn1xG37RyIBzbpeZrcCbH+Em36pFwM+S1GFeT3gB3mRlIm1JHeciIW/K009LX81R/wTL31teEyZFhY81qeCXCiB0xr2e+6velFjOwfO3wr4LYPyM6HIF+WbVcW5MBmOneY9qG+yHEfuk22b7uuThXAC7t8GoSdEj260gLlTjxeW1a0drWwJ838weBO4D3g/MAq4FMLPvAc85567IPV8MLAe68eZZ+DBex/nS2jddpDEoxjlLbghW/jOs+0mKqbtDHHBmdm1qVEMNmAb0mevhznPg54fFlysYcVaMc9uLukEwzu7e0mXyNv8efjwF/vgP6ffTTKI6zkN7CsOjtjxUm/a0IOfc9cDlwGeAVcBpwDm++RNm4eVqzpsKfBt4HC/7xsHAac65FpoKViQdjThnad2N8MQSYAmc+qMKKmqDUczu/1vvFhR79mbv52CJTr1CNcSvnJCsNF+4HrnS+/nkf8Grvp1+X/Wyq9fLOjTh4GTld24IX/6/owufb38GpkVmRJMSnHPfxJtYLGzdGYHnHwE+UoNmiTQNjThnyX858enryq9nSJMt7DXzgmTltj7qzTa47sby97Xu+mTlBtVxblj1uNGunBnv0pzjYfcDNLqtj8CNU+HmQ2BnwrkzHv5k8bKwv6cmoxGROmrCd+QaKSeW8E9fHf79+VvL37cu/w+bcmyycve9x8vM8ft3VLc9AM532dh/CVnaU0HMe0LPpLgi1Ywd59+cMfx7TwVX9cPeCzU1t4jUURO+I1dZPVNDnZCbLEEd52FJj0XURDTV4O8o/eoYuK/Mm0ClNQT/R6PykfuvTjyxJMUOynib3v1yfd/L/Lmp8zcHT5ydvp6wL6ZxX1R29Xo5tl9+Kv2+REQSUMe5keRTWulS5LDEXyJirhAM7vIyGGz6XSZN4vnbC58//T2NgjW6BRE5lLNw8JsLn//x78LLJflfDk6s8vT34flb0rWn93H48ST4w7vSbZeVYPjSw1eELw8TPEfD4p7jsg7d8zZY/Sn4+eHRZf7wLi+9qMKsRKQM6jgndehF1d9HflIQxTgPS3osgqE1Q4Nw17nw8P+Brq97OXPveF02bQqLhR5oklkP20bg/+HYj1VvV8EO2LMRKXGTdJxXf8obKV75L/D4V8qbhvtPX/N+Jo3Zz1rwS2T+eZLXv+1Phc/DXv+IMdHbb/pt6X0887+w+W5vgisRkZSUVSOpIz/kveFW04j8iLNCNfZKHEMc+A646Q547hfeY/Tk8vc9YnTpctLeksY49/0Z1v8UjiyRAvfF5fDEVypoUJ0nCAmmlRvKd6QTjPA+8AHoeQBO/g788R/hxXuLy7iI94RS2XCCNEAhImXQiHOkwIjV/qfCCV+q3u5O+ErrhmpMnVf+tkmPRfAGqsGdw7/v3pZ+vz80uGEfL/VVsgak34e0hqRf7m57FTyyGJa/L77c7q2Vt6mewnJU7+pNHhrx1P/zyv75W+Hrozq8wTCXUjRAISJlUMe5SMxozTH/AvNTvjkndcxHh0M1Wu0NfdTE8rctt+OcRfzi0C54POQmrskhU4jXe5RPchL+HWa9M8NdJuw456eUfu6X8eUqvdHVf1Pg6ivhtlNgT8x01rXwp2vSva+FpabLe+w/wpdv7EzXplZ7nxWRmlDHOa0x+1av7nyoRqtdQhzTUf62iY9FcMQ3ZUf2wQ9FVBuod/u64jhMaL2rBK3ulOvgvDImLgmTOiVhif/NLSvLbkpR/Y9eBT3LvRtYayUsdOWRK9N9mX3sC9Hrnr8V+p6E318IPQ/6VqT8OFPHWUTKoI5zWtWIcz7nEe+ntViMs3Ow6grYUGKELbaOQId0z86ID+BABzdNKq6hPdD1n+HrNv6m8PmDl0XXIc3DRsGEQwqvhpQbC7/57sLnlX65TpPjOalyck2Xyx8m5ZdlFou7z4V1N8BtrxxeNmJk/DbbuuFnc4afR03xLSISQx3ntHrXVLb98Z+B+Z8vXDb1eO/n9rXez013VLaPrHV9E352mDfKk8bG3xReVp3xRjjiH9LV4e+Q7uqFGybArQuLy1UySUTUBz0U/70HesLLaSKU5pL/f3njKnjFv8F5z8HbNg2vP/YTyeta+z+Fz+dcUlnb4uLqy83NXE6cf7kiO84ZDgj0Pla8LPRKkIMX7/c6yQ9eNvweCzCU8mZCERHUcU4vLhVSEge9EY67InxdXFxfPay/CX4+Fx68FLY/DQ/9c7rt+zcXPu9dA/suSFeHP350/U+9n2FppPxxoUMpPqCdK5zlrJSoDnItR/SakJl90MyeNrN+M1thZq+p8g5LrM+99U06Al7xaZhwEIwcN7x+zLTw7eZ/DibOKVx2/Kdh8++HszqkDdtZEBGzGyZJyEXYl8jVn06+j6TcELzwh+L46cgvolXOmxyWEvLJ70DnyV4qyuA5mvjGXxGRYeo4F8mP6ER88I47sPyqX3sb7Hdy4TL/h9zUVwz/PtgAHbF73gYv/3n4ediITpxg52X/v0z/xcM/4vyna6LL+UeCh/qTT5m+7XF4KSam9OBzC59HjZolvUGsDZnZhcA1wOeAE4B7gFvMbFZdGxbmlP+Bhd+InuXOOTgwkA/80c/Cb14Dt5+WK5Oy43zE+5OXffTfhkedh/ZEjEDX6G39T1+H218Nd72lcHnUjYj1CEF76v95P3v+CJvvLFy38qM1b46INL+m6DjXfLQqzrH/Wt52+78aZpw9/Py0n3vL3tI9vOyAs4Z/b8Q4521PpNwg0HmdciyMGJtgsxEwcoL3u78T4g+bCMZLjvTVO9if/JJ2qZHiF35f+DyqU9R9bbL9taePAt9xzv23c+5x59zlwHrgA3VuV7E574ajLou+MrLfKdHn5pYHvJ/d/zf5/k79QbJzIu/lp+BHI7ywpZtmwB9CJmaqJGwpja6l3k//pCNuCO5+a3j5eszUF5Yaz++5X9emHSLSMhq+49xwo1WhqchizHoHzHg9nBK4xHrIW2DRPbDPYcPLJh46/Hvww9m52sYpZiE4icPsv4KRCUac3zUIr8x9KD/3i+HlB/q+WOzcGOgc+/6V00yEUOoLin+fAFtXh5d78r+T77ONmNkYYCEQzBXWCZwaUn6smU3OP4BJ6faYUVrAyUfC2fcVLz/wdV54QlbGdAzfFJzG6s94oQnrbvCe58+FdT+G7m9m1760eh6IXhf2JXXev1e+zxf/GLF8eXgstN9dbyo/blxE2lLDd5xptNGqtKEGx34CXnsr7DOndFn/3fjBDt2Dl8KPp3ixlLUQ9WGyKsWI+5YVhc/Hz0h+/Mx3h3y+ozLjjcPLbj7YG3nLx1H7yw/tju4QP99ZeJPj87fHt6Oa6Qfbw37ASGBTYPkmICzu6Qqg1/d4tqqtixMMq8o7LWJK7ShTjo9eN2Jk4f9uUl1fH/798SVw8yHezIS/zzA/dTn8Xyz3OxXOuqe4jH9E/Pj/U/k+/eFkfre/Otn2QwOly4iI5DR0xzntaFVN+EeH9l0Ar7sDjvoneOcO7/egNB3tmecP/z60G3ZthXv/Cm46aPjy7yOLy2t3WlGjto99ofzJFEaMLn08Tv2B9/P524aXvfzU8PZBPz3A+9nxF8PLhnZFh1T87vXwiyOGnz8ccaNmnn/2skaIO29ewW9iFrIM4Gpgiu9xSGYtOD2XFnHhN5JvExayMeUYeOND4eUfCmTjOPfJ+C/NNrLy0IqH/hl2boCHPlZZPVl49LPDv48YBWNDbrK0wHl88FuKy6QxGNHxTRrutvG3pcuIiOSUcY2wptKOVmFmYwF/0GC6S735kdaom8tG+A7ZPod7l27zNwsFbxqCdPGLo31NfWoZrPp4SKEaTe0cFxayawuMmpC+ThsRfVn61B/AzLcPh3Ks/cGYFXAkAAAgAElEQVTwunxHPWpkaNdLhc+HdmWbHm5ot9dpr0YO79b3IjBI8fk6neLzGufcALD3D21Jb/IsErLdwW+Ci1Neln/Vd8LTH+67wOvw+UOJAB7/4vDvY/fzQrFGxbwF2cjkN7KW0giZXXb4JpWxUYWZSvJGjC48l2e+rfg4puH2wO4KZlu8q4z/CxFpWw094uyTdLQKqn2pt+CyakgTTv1B4fNy09eFdpqp3Y0/22JiA5OO5Mx4Q7JyNsrLXuGPf57m66y8nAutiBpZunFaYZuGdsGzNyXbdxL5Dsnzt2ZXZ5twzu0CVgCLAqsWAffWvkUpTTsxet1JEZPm5OWvkDzzw+gy5cQ3RykVq7u7L/ocqgYbGT5wELxyNOc9peuKex99+JPw8wShcCIiGWj0jnOq0aqc6l3qhcL8rmEhC7MvhnOfGn4eFl5QkRqNOMd9oCedJa9UR/OMX3shLu/YBqP3KVz3F74Qice/5HUK4kbU/KEZfX+GZxPGoU5IcI/pnlxe2mrM6NYelgB/Z2Z/Y2bHmNlXgVlAc6QiWZCb/vmsuwqXTyzxv5OkU5z/Ij7vs/HlkugrkS7yx5Ph+nFw28leJ7oajvfli47qOJ+4xPt55Idz5RJ8DJ12c/S6gRfDczin8fJTpcuIiNDgHedyRquccwPOuW35B5DtJ4S/g9cXcVOK/4MgTahGEv5LoeXYcEuyGQDjPsx+cQQ89sXo9XH809zOeAOMGu89gvY9ofD5jfvGh0r4O/M9EXfZB226C3asK12unlkKWoBz7nrgcuAzwCrgNOAc51xzzEBx7Me9S/nTT0u3XZIvzfmO8/GfSt+uoKSdv5774eeHlS4X5dmfe7OJhl1xG7vf8O/9mwvTRObNfDu87QVYGJOXPeigN8KFVZzp75dHV69uEWkpDd1xzmnc0aq/iMjX6r9kmiT9WhrbHi983n0t/NDg9xeW3nbTXXDnOV7Ht9Rl3ed+Fb9+VcyUxIO74J63Fy5bkOtoH3CGd9l13xPjYzuDHffdvcWv3c8/Gp0kpMI52HxX8fI3Ply87JHF0V+SJBHn3Dedc7Odc2Odcwudc3fXu01Vt/Br3s+40dJahV4FJRmh3b4u/IbYu9/qZfl5OfAF3A0V52oOGziwETBuv2Sx3fP+HU75vvd7WCc8K1neEyEiLa3hO861H60qMXMgwEW74bz1xTl+80b5RqVHhNwcUyn/lNIP5LLyrbvB6wz2+z4Qgx96L/ryz96Wy0KxZwdsXVPckX7s6gTt8E9O8gTcfR489iUvpnP9TwrLHpObrnvMVLigB16fcFQ4qU2+jCb+iVIiy/+uMIc2eJPS7DvP6zwfeHbhuvUZxkxL9dQ6J+/Cr0evy8fpH/LW6JkI43KOv3N79Lpqe+Fe+NmhxSnd9kRMp337afCjkd4VrbxR48NH3ZOm3zt+sZeubs5fJStfKeVzFpEEGr7jDA04WjViFEyICZ0etx8c+SE45uPZjzgD7HnZy3oRfKP/0Qj46f7w7C+8FEs3jPdyvOb1PDj8+5YHve1vmAi/Pt7b9pGr0rUjPyvXprvgV8d4ccWrPh4+QYR/ZG30Pl7+2nr67Zlw318PP59/tTcpDXid59fdVlh+4IX4+k7+bqbNkyYxGJea0fc//9anwydU8XecF/yH9/OQt3qhIaMmwOx3Z9LMImHZLvzueZv3c0tgQpOomfheyOVr3ujLHHr8Z8JHlcNG2c8NCTOZd2V8G7OWzwkvIhKj0dPRNa+TYkaiKnXj1Pj1d5/rTVnthrwcr1OO9eIag5kmgvG9j1wJr1icPE/z/X8Pp/3US53nN/6gwuev+Wmy+uqpVCqvUlNqH/be7NoiGajRTbS7tkavC97wGnZe+bPBHPMxmH56Ye7okSHx/1mYeGjuipErHhXe9ifoj7j3+rlfJt/HjLMjVoR0nIO5rt8ace/BnPfA098LXwfw6hvKnwRm4EUYf0B524pI21DHudHs/5fZTOnrHwm7843hZX42u3jZfZfAC4HZCed+0LvUHEyR9+xNXnx10ONfKnweFdLSSErNHrYncI/pjNcXTtIi7Wn+5+Gx/whfN2pi4XP/JD37v8b7ecAZw8tsRPFshX/x7fKmc+84GXqWR68f2u2FYuzc4N1/YDY8gcqkIwvLPvJv0PWN8jNXTJwN29cOPy8V133Cl2DizPB1p1wHo6cWzpzoN+sdMHU+bA25VyHodXd4V57yxnaU3kZE2l5ThGq0ldf9pr77f/q64pt+XrkUjv3Y8MxrpQwG4iCDHYikjvpIedsF5dOJxdkVcgk6Ki4VvGnURcy8sIqOiOm5/UbvA2/f6sUuL7rbe5TKvGEGb1rjhT1ctAsOvXh43buG4MjLwrc763fx4UMvP+V1msH7QuyfdbCvq7DsI4srS/f2li6YNHf4edRNga/7jXfOH/mh+PpOKHE+R83qCHDqj7w0gXM/CGOmFK4bHzqnlohIAXWci5SYObDaRo7zYqPB63C+ts6jmkf90/DvB7+pvDrKzRzwik+HLz/kPLhoT7LZvg45z0sn9vatcNbd4VMoQ/iH5jmPhJc945bw5dK+zryj8Lk/LZvfmCnpZ92ccizMu8rrZB92yfByMzjpG3D+xsLy52/w3kcOe693E3O9jRid7MvzgWfCwiWls2eMHOfdoP2uQe+15kPD3pH78hv33j37InhnnzcYMHZ6svaLiPio49yITviC1yl858tenOBFu+F1t8MR/1CHtnyl8PlFCSc/ycKYfcNH4KefMXxz4Zm/i68jf+l7zBSY/pro0ahjPla8LBijmndQwhkRpX0EO8Nv6a7Ofg48yxuVPfm64WXBuNzxM4Z/j7uJuZqO+MfC52femW39I0Z5X8jHz4Dzn/PeL0dPHl6fv9Hy3Ke8kfkz7/RG7GH4xsiocBARkRjqODeDEaO8D8y/uNb7EKjUSUtT7Htk8fOLapjz9IDXejdM+R15qW/9GdHbHv534Zd9j72i8Pmrb4we5XplRK5u8C65n/AVePuW6DJSY3VMKfb6P8IJX/a+XI4pcQNvucy8UdnDIqapnhgy9fTbt8LY/b3fz3062/ac6ptN802PDf9+0jcKy42Z4nVuk1wlysKxn/D2tc8c75gdcHp4WMx5z8Lhfw9vUZ52EUnGXIvnrjSzyUBvb28vkydPLlmem2fCjmfhDQ8O52FtRIP94SmlXn4Kur8FW1bAcZ+E/U+F63135p98nfehu/5muOf88LqP/JB3M9C5TxXf7e7nnHcH/q+O8WI8X3+fN5Xvtj95NzQt/Hp26fj6X/RS7R11OSz8auG6ngfhtlfContz6fByNzdGfUjvfhl+PQ+2Pw1n/tbrnMfZvc2btKWvG6YcX04YT53ifppf6vP3xmmw6yV40+MwRbPBhdrWBb88ysussT2XDn/8wd65MHJsbmZR54VObLjNy2Jx6EWwawsc8DqYcLB3s9+ODTD91XF7ahU6f8uU+vwVydi2bduYMmUKwJTcbNIVU8c5qFk6zuVwQ4XxxoO7vFGYfEdwW7fXGW/mS5g7noP7/87rYB/0+nq3Jk8fvGUqu+P85idg8lFVb5+0BZ2/ZVLHWeqtGh1npaML2vtFogXfK4M36QVHgyfPpelNOBheq5v3REREJHuKcRYREWkTZvZBM3vazPrNbIWZvaZE+QvM7DEzG8j9jIjxE2kP6jiLiIi0ATO7ELgG+BxwAnAPcIuZzYoofwpwPfB9YH7u5w1m9qratFik8ajjLCIi0h4+CnzHOfffzrnHnXOXA+uBD0SUvxy43Tl3tXPuCefc1cAdueUibUkxziIiIi3OzMYAC4HgHPGdwKkRm50CBNIYcRsRHWczGwv4c3tOAu8GLZF6qMb/Xtt0nKdMmZLsjsrzn61Ba0QkjcTnr3Jqi0TZDxgJbAos3wREzTd+YMryVwCLgwtnzmziTE3SKqYByqqRUB8wJfdTRJqLzl+RbAVz0FrIsnLLXw0s8T2fBDwLHILO4XLpGFYmf/wyG1Vp+Y6z8xJV6zqRSBPS+SuSmReBQYpHi6dTPKqctzFNeefcADCQf27Dk0X1ZZVDt93oGFbG0k9YVpJuDhQREWlxzrldwApgUWDVIuDeiM3uCyl/dkx5kZbX8iPOIiIiAnhhFN83swfxOsXvB2YB1wKY2feA55xzV+TKfw2428w+AfwMeCtwFtAWc62LhFHHWUREpA045643sw7gM8AM4FHgHOfcM7kis4AhX/l7zewi4N+BzwJPAhc65+5PuMsB4Cp84RuSmo5hZTI/fuZc3D0BIiIiIiICinEWEREREUlEHWcRERERkQTUcRYRERERSUAdZxERERGRBNRxFhERkbKY2QfN7Gkz6zezFWb2mhLlLzCzx8xsIPfz/Fq1tRGlOX5mdomZuZDHuFq2uVGY2Wlm9gsz25A7Ducl2Ob03HHuN7OnzOwf0+5XHWcRERFJzcwuBK4BPgecANwD3GJmsyLKnwJcD3wfmJ/7eYOZvao2LW4saY9fzja8VIJ7H865/mq3tUFNBB4GLktS2MzmAL/GO84nAJ8Hvm5mF6TZqdLRiYiISGpmdj+w0jn3Ad+yx4GbfZOo+MtfD0x2zr3Rt+xW4CXn3Ltq0eZGUsbxuwS4xjk3tXatbA5m5oDznXM3x5T5AnCuc+4Y37JrgfnOuVOS7ksjziIiIpKKmY0BFgKdgVWdwKkRm50SUv62mPItq8zjB7CPmT1jZs+a2S/N7ISqNbL1RP3/nWRmo5NWoo6ziIiIpLUfMBLYFFi+CTgwYpsDU5ZvZeUcvyeAS4BzgXcB/cAfzGxuldrYaqL+/0bh/T0S0ZTbIiIiUq5gvKeFLKukfKtLfDycc8uB5XsLmv0BWAl8CPhwtRrYYsKOd9jySBpxFhERkbReBAYpHh2dTvGoXt7GlOVbWTnHr4Bzbgh4ANCIczJR/397gJ6klajjLCIiIqk453YBK4BFgVWLgHsjNrsvpPzZMeVbVpnHr4CZGbAAeD7b1rWsqP+/B51zu5NWolANERERKccS4Ptm9iBep+T9wCzgWgAz+x7wnC9DxNeAu83sE8DPgLcCZwGvrnXDG0Sq42dmi/FCNbqByXjhGQuAS2vf9Pozs32AI3yL5pjZAmCLc26dmV0NHOyce09u/bXAZWa2BPgvvJsF/xYvXjwxdZxFREQkNefc9WbWAXwGL6fwo8A5zrlnckVmAUO+8vea2UXAvwOfBZ4ELnTO3V/bljeGtMcPmAp8Gy/coBd4CDjNOffH2rW6oZwE/M73fEnu53V4N1HOwDuGADjnnjazc4Cv4n3Z2AB82Dn3kzQ7VR5nEREREZEEFOMsIiIiIpKAOs4iIiIiIgmo4ywiIiIikoA6ziIiIiIiCajjLCIiIiKSgDrOIiIiIiIJqOMsIiIiIpKAOs4iIiIiIgmo4ywiIiIikoA6ziIiIiIiCajjLCIiIiKSgDrOIiIiIiIJqOMsIiIiIpKAOs4iIiIiIgmo4ywiIiIikoA6ziIiIiIiCajjLCIiIiKSwKh6N0DSu+qqq2YC+8cU2bx48eJna9UeEUlO56+ISPMy51y92yApXHXVVWOBZ4ADYoptBGYvXrx4oDatEpEkdP5KvZjZacDHgIXADOB859zNJbY5HVgCHAdsAL7onLu22m0VaWQK1Wg+u4B1wFDE+iFgfa6ciDQWnb9SLxOBh4HLkhQ2sznAr4F7gBOAzwNfN7MLqtZCkSagEecmdNVVV70euDWmyBsWL158W63aIyLJ6fyVejMzR4kRZzP7AnCuc+4Y37JrgfnOuVNq0EyRhtTyMc5mZsAkoM+1zreETuAB4ERgpG/5ILAyt16k6en8FambUyj+X7wN+FszG+2c2x3cwMzGAmMDi6cBW6rTRJFEJgEbsvoMafkRZzObDPT29vYyefLkejcnM3/+85/5wQ9+ULT83e9+N0cccUQdWiQxrN4NaFY6f6UBtNz5m3DEuQv4rnPu875lpwJ/AA5yzj0fss2VwOLsWyxSsUOcc89lUVHLjzi3qsMPP5yDDjqI559/HuccZsaMGTM4/PDD6900ESlB5680ieDImkUsz7sa72bCvEnAs+vXr2+pL77SPLZt28bMmTMB+rKqUx3nJmVmvPa1r907auWc47WvfS3elW0RaWQ6f6UJbAQODCybDuwBesI2cM4NAHuzweT/nydPnqyOs7QMdZwz1N3dzZo1awCYOHEiHR0dTJo0iblz59Ld3U1fX9/e52nr7evro6enh46Ojr0/X3zxRcaPH8/OnTsZNWoUjzzyCM65ov0BRe3K1xFsX09PD9u3b2fDhg0cdNBBHHfccaHrAY477jiA0P3k1/mf5+vxt8W/r/xzf/msBI9h1N8h6jhk+feU9hN2/hx77LFMmzaNLVu2MH78eIaGohJtpKt7w4YNALHnVNT/b9T5ma8vrI6wc6XUeZ1227C6ouqNe3/Jv/fFvS7Z6z7gLYFlZwMPhsU3i7QN51xLP4DJgOvt7XXV1NXV5fAuXxU9Ojs7C553dXVlUi/gDjvsMHfppZe6ww47LHJ/pR6lyqetL6t60hynco5hsP5Sx7qCv2fdz4NmfdTq/K2muP+r4Pmb9n8+6f+s/381uE1+n0nrCqujmu8Dtao35tjX/TzI4gHsAyzIPRzwkdzvs3Lrrwa+5ys/B9iOF3pxDPA3eGkSL0ixz6Y/f6W59fb25s/xyS6jc0kjzhnp64sOn9m8eXPismnqBXjqqadYunRp7P5KKVU+bX1Z1ZPmOJVTT3B50v1V8veU9hP3/xE8f9P+L2VRPr8saV2V/r9n9X6Sdb1tcB6fBPzO9zwfi3wdcAnepCiz8iudc0+b2TnAV4FL8SZA+bBz7ic1aa1Ig1LHOSP5UIUw06dPT1w2Tb1Rdu9OdxVt/fr1seu7u7tTtyHM8uXLU5cvdSk3eOk4eJkWYOvWraH1r1u3ruDSeVS5oODfs6cnNNxPBKju+Z5F+fyypHWV857kFzx/slJpvZW+rkbnnLuTmAwhzrlLQpbdhZc2UURylI4uQ9WMcV6zZg3nn39+wfKrr76aK664InE9ixcv5qqrrkq170ol2efixYtZsGABEydOpLu7m0svvTTVPrq6ugA48sgjy25nEjfddNPe2O3gvrq6uuL+rrrjq0ytko4u7h6B4H0AldatGOfMY5x1/papVc5faV7btm1jypQpAFOcc9uyqFMjzhmaO3du5JtvJTeezJ07N/QyYi7FSqp6ai3JPs8991xOPNEb1Ojo6Ei9j1pdYp01axZz585l5cqVdWuDNKe486DS87Kc7ePep5LWV+l7WjWkab+ISDlG1LsBkkzYZcS0lyardYm00n36X1s5l0snTZpUk8uscZe0W/0ybxgzO83MfmFmG8zMmdl5gfVmZlfm1u80szvN7Lh6tVdERKRSbT/iHBZe4b+cGnZpME0aI3/9laQ8mjt3Ll1dXaxZs2bv/rdv386yZct44oknWLt2LZMnT2bBggUsWrQIYG9ZfzvzdYS9ns2bN9Pf38+4ceOYPn16ZExxsO5Sl0/97S6Vdi6ujaVinMO2C6vDn0Yvyd86uJ9gG9s4ldVE4GFgGRB2w9DHgY/i3XjUBXwKuN3MjnLOaYheRESaTlvHOHd3d2cSFxsV3xpWf4lY2JKStDnpPuLqqrSdUqDlYySDU/iaN/PBBuAa59wXcsvGApuATzjnvpWw3rrESFYzJjlfv/9LcJIbX1s1Jjmu3uDrCsupnuSYhv29kuZ2pw3O32pRjLPUm2KcM1brdGdZ7DPJ9lmklVLMrlRoDt6sY535Bc65ATO7CzgVCO045zrXY32Lah4Dk+YLdTlfMNPWD4U3o+b3mbSeSm+e7ezs5Oyzzy5r22rXW86xiCuvAQMRKaWtO85ZxaVG1RMXCxuc2S9qNClYJknqs56eHm6++eaikafgfuLq6unpYeXKlZGhDGGjN8FwjBKjONLa8lP1bgos3wQcGrPdFcDiqrQooSzzrFejfuVdHlbusUgz2CEi4tfWHeeweNosY5yjYmGThEjElens7NzbjmDqtmXLliUaxSk12pOkjqSjPRrFaWvBWDALWeZ3NcMTM4A34vxs1o2KU+0865XWr7zLw8o9FmkGO0RE/OracTazK4C3AUcDO4F78eIf/+QrMxb4MvAuYDxwB/BB51wmH6bVTl8UVn+SEIm4Mh0dHSxatCg0Ldro0aMTtauWoz0axWlLG3M/DwSe9y2fTvEo9F7OuQFgIP/cC5WurfwX3mrFOIfd6JvkxtfgValSN/tG1VFOjHPabdPcOJxFjHOpYxr8ewX3ratjIpJUvUecTweWAg/k2vI5oNPMjnXObc+VuQZ4C3AR0AN8BfilmS10zg3Woc0VixvVSDKCElcm6ShOLUd7NIrTlp7G6zwvAh4CMLMxeOf8J+rYrkSqmXc5X0eaelo573KWdWVxTEVE4jRUVg0z2x/YDJzunLvbzKYALwB/7Zy7PlfmIGA9cI5z7rYEdTbkXb2VxDjnR0cAbr/9dlatWoWZMX/+/L2p6PLLt23bxuzZszn66KPZsWMH69atY9asWUXlzIwZM2awYMGCyHRtW7du5YknnuCll15i/vz5zJ07t2BdT08Pa9euZeLEiYwbN27vbIBJ08kFX3fUDF9QOh1eXP1h+6nyiFNL3pVvZvsAR+SePoSXeu53wBbn3Doz+wRezPL7gG7gk8AZQOJ0dI16/kpbacnztxZ0/kq9tUNWjSm5n1tyPxcCoym8M3+DmT2Kd2d+Uce5Ee7KTyJJBy2sQ7lw4cKS23V2diaatjqqXFhMclap+8L491eN/aR5PYrHTuUkvI5yXj42+Tq83M1fxAuv+iawL3A/cLZyOIuISLNqmI5zLu/rEuD3zrlHc4sPBHY5514KFN/E8F37QXW/K79aksYKJ41fjipXjTR6cfx1V2M/aV6P4rGTc87dScxonPMuZ12Ze1RFWB7jqN+Txs+GxdPmr0oEY57L+ZIVlSM6LGczkPj1hb2eYL3lHquwCZDy7Ybwq2GlJjEqZ53/ylDY5FLBZf62Rb1exTeLSBoN03EG/hOYB7w6Qdm4O/NT35UfnFwgLLVakpRslSoVOpA0Vjhp/PL69etDl9d6SulKp9xOU3+p/Sgeu3lU8ypIUmmvUJTKqAPl51uOUq166yksK1CleaF1tUlEkmiIjrOZfQM4FzgtkC1jIzDGzPYNjDpPx8vAUSTtXfnBD7K0b75ZvdkmCR2YO3duovbNnj07Ubkrrrii4PnSpUtZtGhR6OspNXV2WCq/uHjkqBjkqDvtIdsYZ91V3/wa4epA2jbUY9KhRjhOWQu7WlZppqBWPE4ikr16p6Mz4BvA+cAZzrmnA0VWALvx7sy/IbfNDOB44ONZtCH4Zpn2zbfWsw92dHQkqitJuaCTTz65ZDaBctNvZV0+q8wG0rwa4epA2jYkyZaTtUY4TlkLu6pWaaagVjxOIpK9eo84LwUuBt4K9JlZPm651zm30znXa2bfAb5iZj14Nw1+GXgE+E0WDQi+WaZ986317INJ9ldum/TBIc0kKo9x1O+NEOMclyM6LGczZBfjXMmxasQY57DJpYLL/G1TjLOIZKGu6ejMLGrn73POfTdXZhzwJbwOtn8ClPAA3eJ9lEyH06gxzsFJAYIfWhMnTqS7u5tVq1YBsGDBgr1p5tasWcOqVauYNGkSu3fvZubMmUyfPp3Zs2eHfnhm/Vr8r8l/LKu1nwandFZlUjoraQA6f8uk81fqrRrp6Boqj3M1NOuJm+TGp3JukKnlDTBK+baXPnjL1Kznr7QUnb9l0vkr9VaNjvOILCqR7CWJnS7nBpla3gCjlG8iIiLSSuod49xwKgktiMq/GpUJwh87WU7que7u7qJlUSnm8np6erj55puBwhhCIPT1huWcDYsLDIa7xL2G5cuXs27duoLcq2GhMsE2BsNmwnLU+l9LXN7XJLGNYXW2aahJWwr7n86qPiiOCy41a6jicEVEGoBzrqUfwGTA9fb2ulK6urocXn7ogkdXV1fZ26Z5BPfT1dXlbrrpJrd06VJ30003ua6urr3L0tS7bNkyt2LFCtfZ2ZmqHUleU75N5W4fbFPSNsbtP8l+Sv194+pM8v8QUPfzoFkfac7fLEX9T2dVX7nnXaXtkLLU/Txo1ke9zl+RvN7e3vz752SX0f+1Rpx9KgktyCL8IFhHVPq3tPuaN28eJ554IitXrkzVjnJfd5rtg6El1UoHGFdv2pkSFWrS+oJ/40r/5mnOJf3viYg0LnWcfSqZTS6LVG5J6yg3d2za+st93Wm2D6b/q1Y6wLh6086UqLR9ra+cNJBp6osro/89EZHGpawaOcF41s2bNzNp0iQ6OjqYN29eaNxyMD537dq1rF69mr6+PqZPn87mzZvp7+9n1qxZTJgwgZ6eHvr6+liwYAFbt25l9OjRrF+/ntGjR+9dniSGtru7m9tvv51169axfft2Bga8iRIPOuggAPr7+9m+fTsTJ05k1qxZzJ07tyCW2f/apk6dund5nj+Wt5IY5+BxjYpRbvQY51WrVrFhwwamTZvG0Ucfzbx589Lm89Vd+WWq5135inGWHJ2/ZVJWDak3paMrQ9I8zqVSv4VJOz130u3i0rWV29ZS+wqrtw3TxhVJerxLHCt98JZJH7zSAHT+lknnr9Sb0tFVSblxg2njcZNuV8sYx7i4SsVTJj8GOlYiIiKtTx1nyo8bTBuPm3S7WsY4xsVVKp6yenHnIiIi0nx0cyBe9oqurq6ivMvBGEQojnHu6uoqyt0cNVW3P5Yxv13YtNqVhEcsXbo0cprwYA5pf8xk2DFo9zANKD4u/vzb1Z6uXOpr64Bjy07H9j2OiaONaeOMqWMru2qfrxODUSNgzyBgVFT31gHH7kHYOejYM8je9o4aAeNHGqNHUnG7S+0/7DVlvf9q/D1ERNfzINsAACAASURBVNJSjHOTWblyJQsXLoxcv2LFCk488cQatkgS0Kd7mep1/m4dcPzwiaGi5RcfPaKiDm5YnZXUXarOSupupP1X4++Rgs7fMrXa5680H8U4S8mQAIUMiFRu92C65ZXUWUndSbeppN2NsP9q/D1ERMqhUI2UskxRlaYuf9l86MCGDRvYvHkzGzZswMyYP3/+3rJR04b708uFpZQLSwMHxKaCK9XuNCngyhFMmRfW5mD4TFzYStJ9KqyldY0emW55JXVWUnfSbSppdyPsvxp/DxGRcihUI4VgarJK0rWlqSusLJAqLV3cNuXUl98u7vXHpXLLKtVdlun5krapjNR9utRbpnpe6lWMc/L9t3iMs87fMilUQ+qtGqEaGnFOIctpeNPUlcV+q5HirtR2tUirl2UauEpSzykdXeuZOjb7jlm16vTUp39XjddUz/20OjP7IPAxYAawBrjcOXdPRNlLgGUhq8Y75/qr1kiRBtb2Hee4me38l/EB1q1bV7Dt6tWr9y4Ly2JR6ne/np6eyDYG45Z7enro7u5O9TpXr17Njh07Qtf19PQwe/bsVPUBLF++nOXLl8dm8YjS09PDzTffDKTLYhKcUW358uWp2x0lKj48GG4Sduzj/n4iIo3AzC4ErgE+CPwB+AfgFjM71jm3LmKzbcBR/gXqNEs7a+tQjaxn4Utr2bJlvO9979v7vFS4Rr5jmXS2wssuu4z//M//TFQ2H66R/xJx6aWXJtoujWXLlu2drroaMy5GWbx4MVdddVXk+ptuuikyTjnN/0jM30/DZGXSpV5pAC1z/prZ/cBK59wHfMseB252zl0RUv4S4Brn3NQy96fzV+pKoRoZq/fl9dGjRxc8j2tPvkO2cuXKxPWffPLJiTvOfX19nHjiicydOzfVPtKYN28eJ554Ytn1lztTY6m45VmzZkWWSfM/Uu//J2ke1YyfzjLGuVbxy2n2rzzO5TGzMcBC4D8CqzqBU2M23cfMngFGAquATzvnHqpOK0UaX1t3nOudui04g2CS9qRpc5qZDf31Vuu4xM1SmESjz9RY7/8naQ7FOYkd4DLOEe0KflYnR3T5dVe+/8qPWRvaD6/zuymwfBNwYMQ2TwCXAI8Ak4F/Av5gZvOdc0Uxa2Y2FhjrW6Q3RWk5bd1x9s8KlyTG2Z/yLL++3N/DZh5MktFh7ty5dHZ2cscddwAwdepURo8evXe0c8OGDUybNo0zzzyTRYsWFc1Q6H9dYSnp/MclyeuNOm5RMyYGj3v+WPiPbzkzNYI3It3f38+4ceOYPn16QSq+uJkawRvJz+872IalS5eyatUqpk2bVnC88x3yLGZ8lPahHNHZ1as8zmUJxmdayDKvoHPLgb03kpjZH4CVwIeAD4dscgWwOJtmijSmtu44g9eJq2eHJ+2+u7u7E8X5fuELX6go5Vu1j0nS4x4sE/Y8Kg7Z//qTpvorR1ap9aQ9KEd0dvUqj3MqLwKDFI8uT6d4FDqUc27IzB4Aot7wrgaW+J5PAp5N2U6Rhtb2Hedmo5jbYlGvM8nrz+IYtctxlmxMHWtcfPSITGOcw+qsNA7ZX2c9Ypyj9q8Y5/I453aZ2QpgEXCTb9Ui4GdJ6jAzAxbghW6E7WMAGPCVL7u9Io1KHecaq3QmvTRpz/zp8tKGEkSl6QsLzYiamTBJ+bhtwuoNHqvu7u6iNIFhxyq4ff71rVq1KvExiaJUdNHM7EqKL91ucs5FxVS2hWbKEV3Pzmm999+ClgDfN7MHgfuA9wOzgGsBzOx7wHP5DBtmthgvVKMbL8b5w3gd5+zTLok0C+dc3R7AacAvgA14MVbnBdYbcGVu/U7gTuC4lPuYDLje3l5Xb11dXY78XS2BR1dXV0XbJ3kk2Ue5++nq6kq1Xb4tpbYJqzfptlHbd3Z2Jm5nZ2enW7p0aaXHtq7nWT0fufP3UbzLw/nH/im2b5jzV9pW3c+jLB94OZzX4o0MrwBO8627E/iu7/lXgWdyZTcDtwGnpNiXzl+pq97e3vzn9GSX0TlU7xHnicDDeDMT/SRk/ceBj+Ld1dsFfAq43cyOcs413fXxSmfSqzQkoJKZ8bLeJl++nNkHk24bVSZNWruOjg46OjrK2o/stcc5t7HejRARcM59E/hmxLozAs8/AnykBs0SaRp17Tg7524BboHiWKhcLNXlwOeccz/NLXsv3k0MFwPfyqodUWEApcoCRduF1eUPRYhSKpVZXEhCUkn20dfXx+rVqzOvO6p8qe3C1qdJaxdWptw0fVmUa1NzzWwD3qjV/cAnnXNP1boR9c4JHJZnOR8znNX+g6+xFrmWRUTaSb1HnOPMwbus25lf4JwbMLO78JK1h3ac0+aRDGZVKDV7X1QGhvzMe8G6gsvKUW7mh2XLljF1qjfhU6kY56h9lJp9cOnSpSxatGhv3VFp36JinEuligvWG5bWLrgtFKeIC24flYYwbnt/bHpUO6XI/cB78K4YHYB31eheMzvOOVf0TbJaeWDrnRM4Os/y8PNK9x/+Gquba1lEpN00csc5f/NQWLL2Q2O2S5VHMniJvdxwiriQgnLaUW49fvmZ+irZf6nZB08++eSiHNBplZOWLu3+wtLYpWlrvdMWNqvcVaW8R8zsPuBJ4L0Upq3Kq0oe2HrnBK7F/uO2V75jEZFsjKh3AxJInKw952pgiu9xSFzlwUvs5c4iN2nSpNC6srjUX24YQBaz3lUy655IkHNuO14qq7g8sInP36TqnRO4FvuP2175jkVEstHII875m4kOBJ73LY9N1u5S5pFMM3tf2Mx1we3C6oraJmz7JPtdu3Ytq1evZvz48UyYMIHRo0eze/du5s2bx+rVqxk9ejTTp09PPaLa2dnJ5s2bWb9+PTNnzmT37t10dHSwbNkyenp69o5K9/f3M2vWLBYtWgR4s+4FZ/qLmp2wVOq54HoonFkwbEbB4HN/url8W6JmPFSYRW3lQjGOAe4JW5/2/E2q3jmBo/IsZxnjHPYaFeMsIpItcy5u8LZ2zMwB5zvnbs49N7w0dF91zn0xt2wMXkqcTzjnEt0caGaTgd7e3l4mT55cncbXUFy8c2dnZ8GsgmlmtCsnjjq4v0rLp60vq3pqMPNf2/ZazOzLeCkn1+F96f0UcDrwCufcMwm2b6nzV5pS256/ldL5K/W2bds2pkyZAjDFObctizrrOuJsZvsAR/gWzTGzBcAW59w6M7sG+KSZdeMlYP8ksAP4Ye1b2xji4p2DadaqPctgmrRuScqnrS+repRKrqoOAX4E7Ae8gDeZwslJOs0iIiKNpt6hGicBv/M9z98sdB1e7uYvAuPxck7ui3eH/tnNkMM5Lm1dOenv8iEPcSntgvHIPT09RdtH7bOcWOXu7u5U5UvFS6dJE5dlPYrTrh7n3EX1boOIiEhWGiZUo1rqcakoq3CKqHo6Ozv3xuleeunwzKddXV2sXbu2ZJhC1D79eZzf9773xdYR1qbZs2cnjnEO20eS9gf30yQxzrrUW6ZS52818haH5XvOIg65Fnmc4/a9exB2DrqC2O6s46CDr7EW+bFroOlfQL0oVEPqreVCNVpVVuEUUes6OjpYtGgRK1euLCpfySx3lXQgOzo6EqVsi1ufpP1R+6k03Zw0n2rkLS6V7zm7erPP45x838E2ZJPrOfw1Vjc/tohIranjXAVxl/6DYQTlpKGLmjkvq1nuygldKHfmwLR1KKxC8qqRt7jUdtWqt5K6s6q3WnmklUNaRFqJOs5QcFk/i0v3YWnr8vXPnj27YBa6/P6jYqDDZrjr6+uju7u7IIWcP/2cfza97u5udu7cSUdHx95ZBMP2GbR06VLWrVvHrFmzmDBhAj09Paxdu5bZs2fT0dHBjh07WLVqFdOmTePMM88sqiN/TPMz8QF7258P4Vi6dCmrVq3CzJg/fz5r165l+/btLF68mP7+fsaNG7f3i4ZSx0lQNfIWl9quWvVWUndW9VYrj7RySItIK2n7GOewOOKs05MF9xGXLi24Lk1Zf7vTpJcrd7us60izjyaj69RlUoxzNhTjXJGmfwH1ohhnqTfFOFdBpVNll7OPuHRpwXVpyvr3U24qunJfexZ1pNmHCHidvaw7ZtWos5r1Jt23p7r7r+drFBGphbbvOJcba1vJPuLSpQXXxZXdNW6/yP2keQ19NpEXdjh2Djp2j9on8XZZ7DuNl9xEHnlxMHQkK2pELapccITNP1IGhI7GBbf1j0y2yMiaiIiIxGj7UA3IPsY5ah9RccwQn+c5WLZrYy8PvLQP0w+dy+Znuunf0ce4CZO4/PVHFXTegq8LYEPPNn63eSLA3u2mH1r4ejc/082GJ9ew7YXnmbL/DMaMn8jAzu1se+F53nLCwUyfuk/JlG4r1nTxrc5H2PbC8Gzp+bomTu1g+9aevXXm173hqEnYrh08ue45HtjoCsqHtTN/t358JoTockm3B3jzYcYvn3KRz/31hVCPuky61CsNQOdvmXT+Sr0pVKNKapG6rFT6tDRlXccQz6z3Om3+zmTw7vWw1zX1YMea7vhO4vRD5xZ1UvPOnjuC/SeU/hyZNWcuC153eMlyfq/O1f3CDsf4Em2E4debNBNCsFzS7QH6d8c/T1qPCDRfLHCztTdOK70WEak9dZyb0LjR4ctrced+Ne/Qz2+Tdh9JMyEEy6XZX/CYh/0NlD1Akmi2fMfN1t44rfRaRKQ+1HEOiLqjPvh78E70qHjXsPqCd9WnHfmYPMZ4zUGOl3d7o5yTxkLH+GTbTh1rXHz0iL3xwONHWujP3gEvVngQ2LUHBoZgv/GwbVd4ZoCwTAFvPsyKjl/cawf2xlq/4VArijcOO1b5uOM3H2Z72x51TP2v3f+3yy8vFeN88dGFMc4XH60YZ0mv2fIdN1t747TSaxGR+lDH2SdJrOuw4dm2gKJRjPDlxdsXPi898hE3C9jFR7vEnWdP+M+tAxTF74a3O+w1FEo6khP1uuK2L45ZNvafMCJ2P1F1JckGEFyvDAJSjmbLd9xs7Y3TSq9FROpDHWefckYdsh7BqGTmsqxGTbIcfal01rI0r1ejRtIMglc4Gj3OttnaG6eVXouI1Ic6zj6VxOVmUVfcdv50a2m3zaoN5fDaW5jKDSgKl4ja5wv9Q8CI0O2C2+wcdDy1tTjcIh+64V8WNulDWEq7vGAYSi0nr5DW02xXK5qtvXFa6bWISO0pHV1AI8Y4B0MS3nyYsWcIbl2bOB1aalsHHFv6XcE+Fh4AKzaFlz+uA9b0hK8LS92W52/zur6hmBCR8O38nd2k20bVkzxMJ7qeCPqULpPSWUkD0PlbJp2/Um9KR1cD5Y5GRG2XxehGMARh/EiDkRCMKc4yVGHqWMvVN7yPfcdY0T7zZkww1vSErwtL3Zbnb/P4kdH1R22XP7Yv7Chsa1JpUtIlqUdERERalzrOTSAqjVqpclnvNyoNXiXr/PtI0/6kxyRpPbVK0yfV0Wy5eZutvVGCV9TCQqAqqdcfYlXpMYq7+tesx19Eak+hGgH+S/9ZfgiETQed5oMmGCOc/1DZuQdGG3RMMGZNis8oUUnbw6adBjJZF7a/YIq8Utvltw1LKdcAMc76RC5TkvO3nIws9dRs7Y0SF95UyWupRr1JQrE082f2FKoh9aZQjSoLT45frQ8BV/Cz1D78MdOVpqNLIywFW9KySdeFlwv+TLZtJa+/eN/SDJotN2+ztTdKtTL8VKPeJNs12/EXkfrIfoiyiVXjA63SdGxpyumNX9pRs+Xmbbb2RolrbyWvpRr11mJWVRFpDxpxztk64OjbFR62snPQsXWA0HCLUpfps56iulofViLNqtly8zZbe6P4X0eWMc5RM3lWcozC2qoYZxEphzrOlI5/89KchXWqS8/2F5ziutwYZxGJ1my5eZutvVGq9TqqUW+rHHMRqS91nKl+KrIsY2cVqiEiIiJSH4pxprlSkSlUQ0RERKQ+NOKc84ZDbW9qMaAgJZo/FdrOQUfvgKNvACaNhSm5iULW9Q0VlA2bRTCNsJkI8+nS3nyY0TvghX0MApNHG+NGK9xDpFK1yPUbTHuoaduzF5VfGip/bxaR9tYUHWcz+yDwMWAGsAa43Dl3TxZ1B+ObLz7aYkMrtg4QmNrZH/8cHgedNp1dWFq8uGmrh1PaZZ+OTqRdxN/rUPp+hsr2UXnd4gk/xtm8N7eCtJ+nZnYB8FngcOBJ4P84526qRVtFGlHDh2qY2YXANcDngBOAe4BbzGxWFvUH44JLxQmXE0ecdpuw8nHTVpe7HxEZVotcv7pHofrSHMd2O+ZpP0/N7BTgeuD7wPzczxvM7FW1abFI42n4jjPwUeA7zrn/ds497py7HFgPfCCLytNO3VxOHHHabcLKx01bXe5+RGRYLXL96h6F6ktzHNvwmKf9PL0cuN05d7Vz7gnn3NXAHbnlIm2poafcNrMxwA7gHf5LQ2b2NWCBc+70kG3GAmN9iyYBz5aasjduKue48hAeD13NGOeo/bbbJccmoj9MmWo9Za9inFtDxjHOLfGHKfPzdB3wVefcV33LPoIX3nFogn1qym2pq3accns/YCSwKbB8E3BgxDZXAIvT7OSsv3wlGzduTFz+wAMP5MEHHwwsTT41dRJhOUfD62yJ93SRipx00kmJz+Hw89dTi1y/mtq9+uL+jm38BaWcz9MD05SPGLhi27ZM+isiqVXjf6/RO855wWFxC1mWdzWwxPd8EvDslClTIr9trFix4lng4KSNee65554DDklaXkQqE3f+QrpzWOevtLk0n6dpy4cOXM2cOTNx40SqZBrQFiPOL+JlXAt+u51O8bdgAJxzA8BA/rmZ9QFTgL6Y/SQfbi6vvIiUJ8n5C+nOSZ2/0o5Sf57inStpyocOXOF9US11Dks4HcPK5I/flqwqbOiOs3Nul5mtABYB/vQ3i4CfJazDUeJbhnPupLIbKSJVk+T8zZXTOSwSo8zP0/ty67/qW3Y2cG/EPoIDV/lf+7KKL203OoaV8R2/zDRDVo0lwN+Z2d+Y2TFm9lVgFnBtJZWa2Wlm9gsz22BmzszOy6S1IlJ1On9FyhL7eWpm3zOzq33lvwacbWafMLOjzewTwFl4Ke1E2lJDjzgDOOeuN7MO4DN4CdsfBc5xzj1TYdUTgYeBZcBPKqxLRGpL569ISgk+T2cBQ77y95rZRcC/402C8iRwoXPu/tq2XKRxNHQ6uloxMwec75y7uUS5+XjftE/CuzmiG/gH51z4LfoiUnU6f0UaUy7LxhXA1bkwDklJx7Ay1Th+DT/i3GB+ADyElyx+EFgAJJjTT0QagM5fkRrKdVSurHc7mpmOYWWqcfzUcU5nFvAl59wTuefd9WyMiKSi81dERCrSDDcHNpIlwH+b2W/M7F/N7PB6N0hEEtP5KyIiFVHHOQXn3JXAccCvgNcBj5nZ+XVtlIgkovNXREQqpZsDSX5zUch2PwImOufOrU7LRKQUnb8iIlIrbTvibGb7mNkCM1uQWzQn93xWRPnxZvafZnaGmR1qZn8JvBJ4vGaNFhFA569IozCzD5rZ02bWb2YrzOw1JcpfYGaPmdlA7mdbX/VJc/zM7JJc3vrgY1wt29woysnnb2an545zv5k9ZWb/mHa/bdtxxktJ9VDuAV7840PAv0WUHwQ6gO8BXcANwC3A4uo2U0RC6PwVqTMzuxAvxePngBOAe4BbYr7AngJcD3wfmJ/7eYOZvao2LW4saY9fzja8HNx7H865/mq3tUHl8/lflqSwmc0Bfo13nE8APg983cwuSLNThWqIiIhIamZ2P7DSOfcB37LHgZudc1eElL8emOyce6Nv2a38f/buO06K+n78+OuzM9turwMHaDAkakDj14bGYCyxkESssYGxxI5dbFE0iqIGE40axYq9YhdEscVubIiJ2H7RxAQNXL+9rTOzM/P5/TG36x3X9vre3ef5eOxDd27KZ5fdnfd85vN5v6FJSnnYYLS5kPTi/TsauF5KWT54rRwe8hmyJ4T4I7CflHKzVstuBbaSUk7P91ijucdZURRFUZReEEIEgGnAi+v96UVgx042m97B+i90sf6I1cv3D6BYCPFfIcS3QojlQohtBqyRI09nn7/thBD+fHeiAmdFURRFUXpqLKABNestrwEmdLLNhB6uP5L15v37Ajga2A84DDCAt4UQmw5QG0eazj5/Ot6/R15UARRFURRFUXpr/fGeooNlfVl/pMv7/ZBSvgu8m1tRiLeBVcDpwBkD1cARpqP3u6PlnVI9zoqiKIqi9FQ93qTb9XtHq2jfq5dV3cP1R7LevH9tSCld4ANA9Tjnp7PPnw005LsTFTgriqIoitIjUkoL+BCYsd6fZgB/62SzdzpY/xddrD9i9fL9a0MIIYCtgXX927oRq7PP30opZSbfnaihGoqiKIqi9Ma1wP1CiJV4QcmJwEbArQBCiPuA/7XKEPEX4A0hxPnAUmB/YE9gp8FueIHo0fsnhJiPN1TjS6AUb3jG1sCpg9/0oSeEKAY2abXoBy25/RullGuEEAuBDaWUR7X8/VbgNCHEtcBivMmCx+GNF8+bCpwVRVEURekxKeUjQogxwCV4OYU/AWZKKf/bsspGgNtq/b8JIWYDVwCXA/8CZkkp3xvclheGnr5/QDlwO95wg2a83PW7SCnfH7xWF5TtgFdbPb+25b/34k2inIj3HgIgpfxaCDETuA7vYmMtcIaU8omeHFTlcVYURVEURVGUPKgxzoqiKIqiKIqSBxU4K4qiKIqiKEoeVOCsKIqiKIqiKHlQgbOiKIqiKIqi5EEFzoqiKIqiKIqSBxU4K4qiKIqiKEoeVOCsKIqiKIqiKHlQgbOiKIqiKIqi5EEFzoqiKIqiKIqSBxU4K4qiKIqiKEoeVOCsKIqiKIqiKHlQgbOiKIqiKIqi5EEFzoqiKIqiKIqSBxU4K4qiKIqiKEoeVOCsKIqiKIqiKHlQgbOiKIqiKIqi5EEFzoqiKIqiKIqSBxU4K4qiKIqiKEoeVOCsKIqiKCOcEGIXIcQzQoi1QggphDggj212FUJ8KIQwhBD/FkKcNBhtVZRCpgJnRVEURRn5IsA/gNPyWVkI8QPgOeBNYBvgD8ANQoiDBqyFijIMCCnlULdBURRFUZRBIoSQwK+llE93sc4fgf2klJu1WnYrsJWUcvogNFNRCpI+1A0YaEIIAZQAcamuEhRlWFHfX0UZMtOBF9db9gJwnBDCL6XMrL+BECIIBNdbXAk0DkwTFSUvJcDa/jqHjPjAGe8Na25ubh7qdiijlxjqBgxj6vurDLXR+v2dANSst6wGL24YC6zrYJt5wPwBbpei9Mb3gP/1x45GQ+CsKIqiKErPrd9DJzpZnrUQuLbV8xLg22+++YbS0tL+bpuidCsWizFp0iSAeH/tUwXOiqIoiqKsrxqv17m1KsAGGjraQEppAmb2uTfSCkpLS1XgrIwYKqtGAbJtG9u2h7oZiqL0s0wmg+O6Q90MRcnHO8CM9Zb9AljZ0fhmRRktVOBcYNKGwbqaatbVVJNMpYa6OYqi9JNkKsW62hrq6urUhbEy6IQQxUKIrYUQW7cs+kHL841a/r5QCHFfq01uBb4vhLhWCLGZEOJY4DjgmkFuuqIUFBU4FxApJfF4zOuVchwamxrJZNSFvaIMd7Zt0xRtwnUcUukUzTE12VEZdNsBH7U8wBuL/BGwoOX5RGCj7MpSyq+BmcDPgb8DFwNnSCmfGKT2KkpBUmOcC4hlWaSNNMFgEJ/PRzqdJhaPMaZyzFA3TVGUPkilU1iWRTgcxnEcEskUJcUlBAKBoW6aMkpIKV+jiwwhUsqjO1j2OrDtwLVKUYYf1eNcQAzTwHFcNE1DCEEgECCRTGFaZvcbK4pSkKSUJBLJ3Pda0zQcx1ZDsRRFUYYhFTgXCCklyWQKTdNyy3Rdx3Fs4vH2WVQcx0HVg1CUwpfJZMjYFrru3eATQqDpGql0CldNFFQURRlWVOBcIEzLxMp8d3LNCgQCJFNJ0ul0blk6naa6poZEIjHYzVQUpYesTAbXdfH5vvu59et+rIyl7iYpiqIMMypwLhCGYeC6bpseZ/B6naUraYpGsSwL13VpikZJppNEY82jana+99qbiDY3t+upiyfirKutxrKsIWqdonTMsiyQIpfTFsDn8yElJJNquIaiKMpwogLnAiClJJVKtwuas4KhEIZlUt/QQGO0CcM0KI4Uk7EzpA1jkFs7NKSUNDY10tjURGNTI8lkMvc3wzBobGoiEU/Q2NSkhrAoBcUwTXxa+5/agN9PMpXCNFWvs6IoynChAucCYFlWmzGQ6xNCEG4JnmOxGLrfj8/nwyd8JFPJUREoxuIxYokEoVAITdNojsewHRvXdYnGvB7oSCRC2kiTajWsRVGGkuM42I7dZphGlq7rOK5Nc6x5VHyHFUVRRgIVOBcA07Jy2TQ6I4SgKBwmHA4T8PsB78RrmhYZe2TnerYyGZpjMXRNQ9M0AoEAlmURbW4m2txMKvVdCj8hIBZrHpTqbFJKGhobOhw6oiiAd3HnOJ3fTQoESaZSxBPtJwAriqIohUcFzgUgbaQ77JHqSOtxkpqm4bg2hjGyb/UmEgls28bfcsGQTdUXi8WINkcJBPy59y8QCJI2DVKthnLkw3ZsHMfp0TbxRILmWIyGpgaaotEebauMTIZhUFdfnxt+Yds2rivbfG9b01ouBhubmojFY6rnWVEUpcCpwHmI2baNaZpoeue9zZ0RQuATPtLpkTvByLIsEskEfr+/TfCh6zpFRUUUFRW1GeLi8/nQfBqxRDyvXmDLsqiurWFt9TrWVa8jmcov4HYch1g8hqZp6Jo+4rMjCCHmCSE+EELEhRC1QoinhRBTutnmaCGE7OARGqx2D6ZEMkltfR3N8RgNjQ04joNlZYDOA2fwMuf4NB9NTU2jZs6CoijKcKUC5yFmWiaOY6NrBs0pgQAAIABJREFUvSviqOs6pmWNqNLcjuNQ39BAdW0N9Y0N2LbT6fjvjvj9fsyWKoxdkVISbY6STCURCGzXoaGxEcNsH7yYpollWUgpaY7FqK2vx7Ks0VT5bVfgJuCnwAy8qqMvCiEi3WwXwyvlm3tIKUdcdJhIJmhobEBKSaSoCMM0SaVSGIbR5RCsrIA/4H22mtV4Z0VRlEKmSm4PMcMw2qWq6glN03L5YLNDGYa75niMWDyWG34RCgV79P74fD4EXqqvSFHncV22xHko6E049OMnnU7T2NhIJBIhY9uEQ2FMyyQejyGEj0AgQDqdQggfoVCo1/9uw42U8letnwshjgFqgWnAG11vKqsHsm1DLZlK0tjYBEAwGATITWDtKMVkZwLBIIZpkEp3/blVFEVRho4KnIeQ47qk00avhmlkCSEQCFLpNMWR4n5s3dDI2DaJhDc0oy8XAn6/n7RhYGUsAv6Oe4XTRhrHcQkGv3v/Q6EQhmFgWCZIQTweR0qJP+AH6W0TCATzDoZGsLKW/zZ2s16xEOK/gAb8HbhYSvlRZysLIYJAsNWikj61coB41QBtHMemKRrFRRIKftds7wIrjRC+vD/HPp8PBMRiccKhcN7zHpT8SClz8xh6cgdLURSlNfXrMYRM0yBjZ3K9VL2l+3UMwyCTyQz7XmfTMLAdr6e3LzRNw7IsUqkUMuyVM/f7dQKBID7h9fAnEkm09U6gQgjC4e+Onb1tnu1ZHu7vb38Q3ptxLfCWlPKTLlb9AjgaWA2UAmcCbwshtpJSftnJNvOA+f3Y3H4hpSSVTpNKp8hkMti23VL2HjTN1yZohpYsOEVFPT5OMJDtdU5THFG9zv3Ftm3qmxqwTAsElBaXUFZaNmruGCmK0n9U4DyEUqk0Evrcs6RrOulMmrSRLqjATkqJlbFAej1w+ZykUkYaQe+HrmQJIdB1nVg8TiKZwLQyCLwgR+DDp/mwMlabILmz/SjtLAK2BHbqaiUp5bvAu9nnQoi3gVXA6cAZnWy2EC8ozyoBvu1LY/tDc6yZpuYoSO/76vN5Q3Wgfz8jXkpF705HpKhIff76geu6RJujpJIp/AE/0pVEm5vRdb+6OFEUpcdU4DxEMrZNKp3C3w+3DIUQaD6NRCJBcaS4IG7xZiv9JZIJpISicJgxlWO6HOLgui6mafbbbVS/349lWdiOpKglQJZS4rouruuOqjHK/UUIcSOwH7CLlLJHAa2U0hVCfABs2sU6JpBLUVII/z6GYbTkEdcH5cI0EAhgWAaGYXR7Yae0lb0z4LoO4VCYeCJOMpnEynh39rK/P6Zp0tDYiJSSkuLhP8RNUZTBM/QR1ihlGOlOs0X0Zla93+/HsKxO01lZlkUqnR60GfupdIpYIo7WEmwkkkmaY7Eut8lkMjhdFIvoKSEEwWCQUNCbXCiEwOfzoeu6lwKsAC4whgvhWQQcCOwupfy6N/sAtgbW9Xf7BoqUkmjcK3DT16D5mzVreOjBh7rNgOPz+ZASkqmRm2ZyILiuS3Osmdr6Ourq66muqaapOYorZa7iaFYwGAThXdynRnA6T0VR+p+KHIaAlN6YW5+v7ZAEKSUXzpvHD74/mXPPObdHBTmymSTSHZSbtiyL2ro6autqiScS/fESuuS6LrFYHPCGS2ia1hI8J8jYdqfbWZkMrnRVQFuYbgKOAH4DxIUQE1oeuS5RIcR9QoiFrZ7PF0L8UgjxQyHE1sCdeIHzrYPd+HxIKdtcWHqBWIx0KtXneQj/+c9/2HOPPTnn7LM54bjju72A9ft1UukU1ghKMzmQbNumtq6OxqYmdE0jHA7jSEkoGOr0IjkYCHoXRlFV+VNRlPypCGUIZDIZTMtEX68H629vv83dd93NjF/MYMnDD3PN1df0aL+6rrdkimgbcMficayMhRCCWCyG3UXw2h8SyQRpM02wVY5jXdexHdtLv9eJdMv4ZqUgnYyXSeM1vB7j7GNWq3U2wsvVnFUO3A58DrwIbIg3xOP9QWhvj6TSKdbVVFNTW4uVsbAyGWrrWwIx3d/ni7kL582jrKyMGxbdyAsvvMCypUu7XF/XdGzbpjnW7GXwyGS8C0sV4HUonkiQSqcIhUK5YknBPO4qBQIBDNPMu/CRoihKQQfOvalWNhwYpuENSVjvR/3OO+9iypQp3L54MaefcQa33nIL//vf//Ler67rZGwbw/yuil0mk/HGUgf8BAIBLNvq91vA2d4z13Vpija19ProbU5a2aESrascSimxbTv3X8MwVJqoAiWlFJ087mm1zs+llEe3en6WlPL7UsqglLJKSvlLKeU7Q9H+riRTKerqGzAtk1Q6RW19HTW1taRSKUKhYJ+HaPzzn//k1Vde5YJ58zjkkEPYbffdWLTopi57nbPDjOLxOGurq3OVLatra7ot7DPaOI5DIplE1/UeX+D4fD40zUdzLNajO3yKooxeBR040/tqZQXru2EavjbDNKLRKC+/9BK/OfxwhBCcetqpRCIRrv7Tn/Let5fTmTbBabolvZuu6d4kQk0nnoh3e5JwXZd0Ou3lNDZN4ok4qXSqzck+W0VvbfVaqmtrqKmro6k5iqZrHVbU8+t+DNNsyX/rUFNXmwsI6hvqsW1bBc5Kv5JSYlkWhmlgWV5PcuvPcCaToSnahJQu4VDYu8VvO97zcP/kUn70kUeoHFPJ3vvsDcDxJ5zAJ6tXs+rDVV1up7UMOdB1DV3XvSqhpkldQ32Xd25Gm1Q6Tca2en2B4/f7sW0bWwXOiqLkoaCjlD5UKytYacPAsAyCgbZjJp9fsQLbttlv//0AKC4u5pxzz+GiCy/i5JNPZsrUqXntX/frpFJprBILXddJJBL4fFouSA/4/aTTBslUktKS0g734bou9Q31JFsCcIHAlS5CCAL+AKFgkHC4CNM0icai+ISPTMYb/hEKhjoNNrK5ldPpNJmMl2M5EAiQsW1c6RIM9qxCoKJ0JZFIEI014zgOrpT4BICPkuII5WXlADRFm7Cs79ISZnt6+9Pbb73Nz3++W26/u+66KxMmTOCpp55i2nbTutzWu9j9blJbOBz2qltGmxg/rmrUF+JxXdf7jRM+9duhKMqgKPQe5/V1W61MCBEUQpRmHxRQ5TEpJbFELJcLtrWnn17K9OnTmTBhQm7Z4Uccwfe+9z2uvOLKXC+Z4zhcNO9C9t9v/w4nAvp1P7Zr0xyLEU/EMSyTQKueGO9E7CMeT3TY6yylJNocJZFMEQwEc5NrwqEwAX8A23Fojseoqa2hKeoNyQgGg4RCIUKhzoPm746tEY1FiSXiBAIBdN3bPhwKj/ogQOk/hmnQ2NSE7XiZa4KBALruR9N8RGPNNDQ2UFNXRyKZHNC0hMlkktWrV7PDT3fILdM0jX33249lS5diWVaP95mtbhmNNQ9alpxCYts28bh3ByyRTGCYRod3uBRFUQbCsAmce1CtbB7Q3Oox5MUTstJGmnQ63a5Hq76ujrfefJP9f31Am+WBQIBLF1zGSy+9xB2L7wDgsUcf5a677uL9997jxhtu6PA4wUCQeCJOY7QJTdPaBbOBQADT6nhCTDKVIhaPEQj4c8NJsv/VNI1gIEBRuIhQKEQ4HO7x7dFAIIB0JX7d3+NhGf/+979Z+IeFfPPNN22WNzc3c/HvL869R8roY9s2yWSSZCpFKp0mGm3GcR1CLbl7vbGsWksQHSSRTGKaRrcXe3216sNVOI7DDjvs0Gb54Yf/hrq6Op5Z9kyP9ymEIBAIEIvFuk3xOJI4rkssHqe6poa6hnpq6mppbGrKFaRRFEUZDAU9VGM9eVUro0ArjwEkkskOe5tfeuklXNdl5syZ7baZOXMmJ518EpdcfDGpVIqHH3qIvWbOZMKE8Tz80MOcc+657XpqNU3LVTXr6ISSraoXjcUIBoO5YSO2bdPcHAXh6zao7W0PXW9vhX/99dfsM3NvmpqaePzxx3nzrTdzJY1/d+55LFu2DICNN9mY3XbbrVdtU4anjG1TX1/vTZoT3tAiiZeKrCPZscOD4dNPPyEcDrPppm1rvkyZOpXddt+Na//8Z/beZ+/c9zVfuq57aR/jMYrC4RHd45otatLc3IxhGWg+798vmz5Q3alSFGUwDYvL9FbVynbrrlqZlNKUUsayDyA+KI3shje21+iwh/bll//KtGnTGDt2bIfbXjJ/PscdfzxXLVxIbW0t8y6cx0EHHUx1dTXv/K3jJAXd9cL4/X6cltyn9Q0N1Dc00BhtwrSsNmnkCkEymeSY3x5NRUUFK154nrraWm679TYAXnvtNZYtW8YNi27kp9N/yh+u/MOovH09Wpmm6QXNZppwOJy7G1IULiqIXsjPP/+cqVOndtiWSy+7jG+++abTO0fdCQQC2LZNLF4QP3EDwpuA3ExdfR1WxiIcCufmQmTvIiiKogymoT+zdKE/qpUVimQqhePY7X7oTdPk9ddeY88ZMzrdVgjB5VdczpNPP8WK51ew6aabsu20bZk8eTJPPvFEr9ojhCAUCiGlJJ6I50rTFuIEvYV/WMi3337LXffczdZbb82xxx3Lohtv5KUXX+T8837H9B2nc/DBB3PeeefxyerVPPnEk4PSrs8+/YwrL7+i3dARZfBk8/eGQ+Hc53YoP7/Zcu5Zn332OZtttlmH6/7oRz/itNNP48YbbuTVV1/t1fECgQDJZKLD+Q7DnWGa1DU00NQy5Gwgx6IriqLkq6ADZ/KoVlbovPRzSeLxOHpLYv7W3n33XZLJJHvO2LPL/QghmD59ei67hhCCAw86kOXLl/c6NVV2rGQ4HM49Cq0HZ+3atdx/332cdvrpTJnipfCee9ZZTNxgA4468ihsx+H6669HCMGOP/sZ++y7D1dcfvmAp+tKJZMce8wxLFq0iF/vfwBf/vPLAT2e0jGJxKcVRkaF//7nv/xizxn88he/pLa2lkwmw5f//CdTOwmcAc6cO5ddd92V3x55FC++8GKPj6nrOhJJY5N3t2ikSKaS1NbVkkjGCQT6nktbURSlvxR64JxPtbKCZZgGNbW11DbU4Uq3TXaLrJdfepkNNtiAzTffvMf732///YnH47z15pv90dyCdMNf/kIkEuH4E47PLSsvL+ell19i6TPLeO3119jo+9/P/W3ehRdSW1vLww89lNf+H37oIQ456GA232xzzjnrbDJ5lji+5557WLt2LcuffZZxVVV89vlnPXthyojy97//nV/98pckk0mq163j0vmX8snq1ZimybbbbtvpdsFgkLvuuZvdd9+duXPn0tDQ0ONjB4MhzIxFXV0dyVRq2A9Vsm2bpmgUV7oUhYsK7mJeUZTRraAD53yqlRUqy7Kor28gbaS9tG4dTP6RUvL88yuY8YsZveox+9GPfsTGG2/MiudW9EeTC8aHKz9kzgkncvbcs7j/vvs59bRTKS4ubrNOOBzmJz/5CSUlbbMN/vCHP2Sfffbhrrvu7jaAWPnBSs45+xySySSHHTabxx57jAvOv6Ddeq7rUlNTA0BtbS3XXH011117HYfOmsW07abxzPJn2H///fv4qpXh6pNPPuGwWbO97+ILz/O788/n6aee4s477yIcDrPlVlt2ub3f7+fqa64hY1ncevMtPT6+EIJwKITt2NTV13lBZx6lubMBarrAiqnE4nEsy2qX615RFKUQDKesGsOGlwu5Gcu22oy9XN/q1av59ptvmTlz714dRwjBr/b6FUseXsKfHGdE9MzU1NRw6CGHMGHCBNLpNLNmzeKkk0/u0T4OP+IIZh16KCtXrmT77bfvdL1FixYxdepUnnl2OZqmsemmm3LW3LPYYIOJ6LqfyZO/z4+32IK5Z5zJhx9+yO577M6X//ySuro69po5k/mXzgdQ1Q5HsS8+/5xDDzmUjb6/EQ8+/BBlZWXMPmw2t9x8M088/ji77LJLXsMMxlWN46jfHsW9997L6WeeQWlpx8WJOpOds2DbNtFYFICK8vI2vz1WJkMs1oztOESKIiRaxofrup+S4mJCoWCXv1eDwTAN4ok4/kD7YW2KoiiFQJ3xB0BzLEYilSQY6Hqi3XPPPkd5eTnTd5ze62PttddMblp0E++//z7Tp/d+P4Xihuv/QjAY5NkV3nvTGzvtvBOTJk3ioQce7DRwjkajvPLXv3LxJZfkLjhmH3YYq1Z9xDVXX0MwGMQ0TQAmTZrEgssv54EH7mfy5Mk88dSTTJo0qXcvUBnWUskka9as4bkVK2hsaOTpp55ig4kTWfLII5SVefWZ/H4/C/94FQ898CCnnXF63vs+cc4c7rzjTu65+x7OOPOMXrUvexEXjTUDkmAgiJWxMC0Ly7KwbRvhE6RSKYSAoqKiXLAt4oKSSAkV5eVDchHuOE6ut7y/qzc6jsPrr7+OYRj86Ec/YpNNNunX/SuKMnqowLmfxRNxos1R/Lre7cnnueee5Ze/+mWfJr5ss+02TJgwgeXLnhn2gbPjOCxdupTZh83uddAMXiq+2b85jEU33MjF8y/hg/c/4Ouv/80hhx7KmDFjALj3nnsRQrD/AW2HWPzxT3/k2GOPYeIGG7D2f2v56qsv+fluu1FSUsIJJ57Qp9enDF9/ffllrlp4FZ984tVeKi0tJRKJsN3223PNn6+hoqKizfq77roru+66a4+OMX78eA6dNYvFt9/OCSee0Otc07ngubkZWq7bfT4NzecjHPZ6lKWUuYt6v9/vpad0HGLxGK7rMHbM2D6l85NSkjYMMpkMPiEIF4XRtc5PN1bGIhptJp1O9zindXdc1+WkOXNY/sxywMvjfdPNN7H/AQd0s6WiKEp7KnDuR8lUiqZoFOHzdRsMf/H553z5zy+56KLf9+mYPp+Pgw4+iIcefIhLLp3f7z01fSGlJBqNUr7eLePOvPfuuzQ0NLDvvvv2+dhHHXkkN924iB1/Op3m5mb8fj8LLluA3+9n62224ZPVqzniiCOoqqpqs50QIpcFoaysjM027zwjgjI6fPzxx/z2qN+y44478pcb/sKECRPZ/ifbD0gRlVNOPYUHH3iAR5Ys4ehjjun1fnRd73IIUUffx2zKt0Qyhe5vprK8ooMt8xOLx3IT/JAQTAQpjkRaCpboFEciuTak0mkaGhvIZDL9XsmxurqaO++4k+XPLOemm29mp5134uKLfs95557HxptswhZbbNFvx1IUZXQo6MmBhc6yLBLJBJZl0RyLUd9Q791mzKOAyIMPPsSYMWPYbfe+V7mbNXs2TU1NvPjCC33eV3/58ssv2WO33dl86mb8ZPvtefutt7rd5q233qJyTCVbb7NNn48/dtw4Lr3sMkpLS/n9xb9n1d8/4rrrr2PeRRcyZswYZs+ezQUXzuvzcZSRLZPJcPbcs5g6dSoPPvwQh86axS677jJglQcnT57Mfvvtx8033Yxt2wNyjK74fD4CAT+xWIxUL3NDJ5NJos1RfJpGUbiIcDiMbds0NDXR2NREfWM98UQC8PLYNzY2YjsO4XC4z0HzK6+8wkG/PpAbb7iRa66+mm233oZbbr6Z887/HQcedCBVVVVcc+2f+eHGP+SgXx+Y1+/SSCOEOEUI8bUQwhBCfCiE2LmLdY8WQsgOHv17W0BRhhEx3FMXdUcIUQo0Nzc35zXhxivvmiKVTiNdl+LiEsKhEKZlIhDofj+maZJKJUml09i2ja7pOK43OS+f0reZTIatttyK2bNnccn8+f3wKmHfvfehpLSEhx5+uMO/r169milTpgxKaV4pJfvtsy+NjY2cfc45PPTgg3z88ce89sbrbLjhhp1ud+jBh1BUVMQ999074G3sT6Zp4vf72WDCxM5WGTGznIQQpwDnAROBT4G5UspO8yEKIQ4CLgc2Bv4FXCSlfKoHx+vy+1vXUE8imSAcGphA9vrrrueaq6/muedXsOWWXWfH6C+ffvope+6+B7fceisH/HpohhMY6TSBQJDy8nLCeRYesR2bWMwrpgR0evfLsiykhNKSYhLJJLZt97m4SSaT4Z6772HBZZcxdepUvvjiC1zX5exzzubwI45gwoQJbdaPxWIcf+xxfPLpp7z815eprKxk4oSJnXV6jKTv7yzgfuAU4G1gDnA8sLmUck0H6x8N/AWY0nq5lLI6z+P16PyrKP0tFotl55+UtVST7jPV49xCSulVqqqvo7aujkQiQTKVoq6+jpraGqpraqiuqWbturXU1NUSjye8NFDhMD7NRyAQyDsofeONN2hqbOSggw/ut/bPPuwwXn/tddauXdvub1decSW/2HMGe8/ce1AqjL3w/AusXLmShVddxUEHH8Rd99xNKBTipkWLOt3Gtm1WrVrF9j/pPAuGMrRaTrrXA1cC2wBvAiuEEBt1sv504BG8E/VWLf99VAixw+C0OH+GYfDhyg9xHAfwxtsvvn0x1117LSefckq/BM1SSkzT7DZX+I9//GN22mknFt9++5DlZA6GQpiWRU1dDY1NjTiui5Wxcu/P+kzTpK6u3utp9vm6HDIWCARA0DKUQ/Y5aJZSctKck7h0/nwOnTWLFS88z2tvvM6zK57jnHPPbRc0gzdG/dbbb8MnBH/64x97fexh6GzgTinlHVLKz6WUc4Fv8GomdEZKKatbPwanqYpSmFTgjNdT0tDYQE1tNYlkimAwmKuk5/P5SLf0KOot45YDfj/hojD+lkqAeh4TAVtb+vTTbLLpph0WPZFSYhgGaSOdVy7WrP32349AMMhjjz7WZvlXX33FzTfdxJFHHcXnn33Gfffel/c+eyOZTHLllVey8847s8uuuwDeWOGDDj6I5c8s7/TE+/7775NMJtlxxx0HtH1Kn/T0pDsXeElKuVBK+YWUciHw15blBePvf/87u+68C/vsvTe/3v8A/vOf//Cb2Ycx/5JLOOKIIzj3vHP75TiGYaBpGrbjdBs8n3bG6axatYp5F1zA0qef5pElS3jh+RcGvCJmltcpEMKv+2mOxVi3bi3rqqtZV1Pdrg3xRIKaulrSpkE4HM5rsnMwEKCoqIhgINDntHPvvvsuzz37LDffcjN/vvbP6LrOxhtvzNZbb93ldpWVlZx9ztk89uhj/L//9//61IbhQAgRAKYB65eofBHo6oe3WAjxXyHEt0KI5UKITsfSCSGCQojS7AMo6WxdRRmuRnXg7OVbjlJdU0MsHkfTdIqK2o6z8/v9hEMhNE1D0zT8fn+fUjWl02lWPLeCAw7Yv8MThmkY+AMBQoEghmnk3eNUUlLCPvvszWOPPtpmm9tuvY2qqiouv+JyZs2axaIbbySVTPa6/V35Zs0aZh1yKNXr1nHZggVt/rbvvvtRV1fHe+++2+G2K55bwcSJE9mqm5OdMjR6edKd3sH6L3Sx/qD7Zs0afjP7MCorK7nt9tv49NNPmb7DT/nk00949LHHuHLhH/plwq1t2/h8GmPHjKG8tJRMJtPld3vXXXflsgULePSRRzlpzknMPXMuR//2t+yx2+589dVXfW5PvnRdJxQKIVv+P5PJUN/YQCqdwnVdDMOgqakJiaQoPDQ5oO++6y423mQT9utFEaIjjjySjTbaiD9dNSp6nccCGlCz3vIaoH23vOcL4GhgP+AwwADeFkJs2sn684DmVo9v+9ZkRSk8ozqrhuM6xOJxnJaJKYPxo//Siy+SSCQ6TIXkui4SKC8tJeAPUF1bi5XJ5DXZEODAgw7i8cceZ/Xq1Wy55Zak02mWPv00xx1/PMFgkLlnzeWxxx7j7rvv5tTTTutyX6lUioaGBsaPH8+aNWtY+7+17TIJRKNRrrziSqrXrWPjTTbmicefIBwO88ijj7bLRrHttG3ZYMMNeeaZZ9jxZz/j1Vdf5ZKLL0G6LttO25ZnnlnOkUce2a8z6pV+1ZuT7oQero8QIgi0jlR73WMVjUb5+B8f8/nnn+P360QiETbd9EdsO80rgW1ZFieecCIlJSU8tORhKioqmDJ1Kv/4xz/YY489cqkL+4OVyVBaXEIoGELXdOLJJBnbJtBF7+yJc07kqN8ehW3bBINBvvryK046aQ6HHnwIf331lXYp8AaKz+fLfS9DoRCGYVJTV4tf94MEx3UJh4dmrtjatWt57tnnWHD5gl79fgcCARZccTmNDY3DvlR5D6z/QkUHy7wVpXwXyPV2CCHeBlYBpwMdJRtfCFzb6nkJKnhWRphRHThnZYdcDIbFty/mp9N/2mECfrvlRBoOeb3eZaUl1Dc2IGV+7dt5552pqqrinrvu5trrr2PZ0qXE43EOnXUoAJM22ohZs2dzy823cMyxx1JUVNThft577z2OP/Y46uvr2yzfYMMN2X333fnVXr9i8uTJnHTiHL799lu22nprnnv2OXbeeWcWXHE5Y8eObbdPIQT77rsPTzz+BCfOmcMpJ53M1M2mMmXKFF595VW23247zr/g/HzeQmVo5X3S7eX684A+z5hd+cFKDps9m0QiQSgcxrHt3PCICy+6iFNOPYVLLr6YTz/9lGXLn8kFoVOmTGHKlCld7brHXNdFCIi0fN90Xae4KEK0Odpl4Ay0yWm82eabseSRR9ht159z4QXzuOW2W/u1nfnIDuFwXTeX9SMUGroUmItvv51wOMyhs2b1eh977LEHlmWNhkqF9YBD+wvXKtpf4HZISukKIT4AOuxxllKagJl9PgreU2UUUoHzIHrj9TdYuXIl997X8Thj23YoKS/O9e5EiiLEEwlMyyKUx+1iXdc55dRTuXzBAo448khuufkWZsyYwQ9+8IPcOqeffhoPP/QQ9993P3NOmtNuH9+sWcOxxxzDppv+iOuuv46GhgbKyyvY8Hsbcu899/LOO+/wwP33AzBu3DieevqpXN7j7hx51FHcsfgOdvzpdCZOnMhdd989aL1mSp/15qRb3cP1oR96rFKpFHPmzGGzzTfnuuuvY/LkyQghcF2Xa//8Z/5w5ZXcftttNDQ0sPCqhd2Ohe0rx3HQNL3N5OFwOExzPIbvGEn9AAAgAElEQVTruj26yzJx4kQWXnUVp5x8Mtttvx3HHX/8QDS5W17auoHP0LO+uto6Fi1axBdffMFuu+/G3XfdzWmnn0ZxcfGgt2W4kVJaQogPgRlA68w2M4Cl+exDeJHw1sDq/m+hogwPozodne3YrF23Dp/P12WxgP4gpWTmr/bC7/ez9Jll7a7EXdfFtCwmVo1v08sUTySoq68nFArmTrDZ2fkdjbc2DIN999mXT1avJhQK8dTTT7XLizz3zDN59ZVX+du77xCJRHLLbdtm75kziTZFWfHC81RWVnb4Oj7++GMaGhqYNm1arsxwvm5atIhXX3mVeRdeyLTtpvVo20I1WtLRCSHeAz6UUp7SatlnwFIpZbuk2EKIR4ASKeXMVstWAFEp5WF5HrPbdHT1DfXcevOtrPnvf6kaP55PVq9m5cqVvP7mG0yePLnN+lJKnl2+nFWrVrHffvv1S87w7hiGQTgUZnyrYjuu61JdU02mZRhGT82/ZD6Lb7+dpc8s67Ss/EhiGAZ3LF7MX67/C7quM3Wzqbz7zrtst912LHn0kTa/Yz3lui6WZY22dHQnAe8AJwInAD+WUv5XCHEf8L/s91kIMR9vqMaXQCne8IwjgZ9JKd/P43gqHZ0ypAYiHZ0KnAcpcF714Sr2njmTBx58gD323LPd303TRNd1NpgwsU1Q7bouNbW1GKaRS9uUSqcJ+P1YltXh2Oz6ujqefPIpdvjpDmy11VbtjvXNmjXssvMuHHnUUSy4/LtJfLfdehuXXXopzz73LNtsu20/vvr+4zgOQoh2vXTZW8eDOewmaxQFzj096e4IvAFchNejtT9wBbCTlPK9PI/Z5Yn3m/99y+G/+Q0ffLCSbbbZmtraOnw+wSWXXMKeM2b0+TX3h1Q6xZiKSspK215kxuIx6hsbCId6Pr/CcRz222df0uk0L/315T5NWC50H3/8MXNOOJFvv/2Wo485hrPPOZuKigq+/fZbqqqq+tzzPZoCZ8jlYv8dXi72T4CzpJRvtPztNeA/UsqjW55fBxyId+eoGfgIuFRK+U6ex1KBszKkBiJwVkM1Bsm9997DpEmT+PluHVcKdFyH0nBpuxOoz+ejoqKCuvp6DMPE79fRfILy0jIao025YLG1sePGceKcEztty6SNNuKCefO4dP58GhsbmTz5+wSDIa6/7jqOOeaYgg2aM5kMtm0jpSQQCOQudrKz+3VdxzTNNj32Sv+RUj4ihBgDXMJ3J92ZUsr/tqyyEeC2Wv9vQojZeMHy5XgFUGblGzR356OPPuKII4/k3//6F/c/8AA77bxTf+y2X0kpEYCutx/LXBQuIqDHsSyrx73Omqax4PLL2WfvvXnyySc55JBD+qnFheXRRx/l/PN+x5SpU7jvgfvZdNPvhtZ+73vfG8KWDV9SypuBmzv528/Xe34WcNYgNEtRhg0VOA+CxsZGlj69lHPPO7fDniFv8pCPcCcBXygYZNyYMdQ11GNZJqUlZUQiEQzTIJ5I5JU3dX0nzjmRQMDPnXfexZtvvEEsHvcm6M27oMf7Ggy2bWPbNuVl5TiOTSwRR9M0hBBYlkk4FKK42JtM6Y0pHbk9cEOpJyfdlmWPA48PRFuKioooLSnh0ccfK9jhCtkxzP4O7mjpuk5ZWSn1DQ09HusMMG27aeyz7z4suPQyfr7rzxlXNa6/mj3kPv30Uy6bfylvvvkmhx12GH+4aqG6IFYUpSCooRoDPFTDNE3mnnEmL7/8Mu+8926HGScsy8Ln87HBhIldnjxNy8Q0TSKRYjSfj2QqRW1dbZ8rbxU6KSXpdJrS0lLGVFSSyWSorq0BAT7hw7ZtqsaNIxwKU1tfRyqVapM2byA5LcUsAoHAiB+qMRSGuuR2b7QOgrOZPDacuEGH323XdaltqCOVTBEKhXocPNfW1jJjjz2pqqri/gcf6LBK3nDz73//m733msnYceO48MIL+dVevxrQ37fRNlRjMKmhGspQUyW3hxEpJUsefpgdp+/IsmXL+NPVV3cYNIMXfBWFw92eNIOBIKUlpWjZnKrBoFeUwO66CtlwZ2UyBPwBysvKEEIQCAQoLSnJBa2lJSW5caJlJaX4fL5cqqyBZNs2pml5+bdH+AWo0j3vAs/ANM1cdT3HddF1vdPvts/nY0zFGMLhMIaRf8GjrKqqKh5esoSGhgZ+OeMXvPXmW31+HUNJSsn55/2O8vJylj+7nL1m7jWiOwUURRl+VOA8QK5aeBVnzT2LadO25bU3XufXB/66w/WklEjo1cx6TdMoCodx7I7LWI8EUkoc26a0tARd++6uQFlpGePHVVE1roqK8orcyTUUChGJRLAsK+/9ZzIZ0kYawzDyLnMupcSyLEqKiyktKR3wyaVKYcveFQkE/FS2pFh0HAfXdbpNJenXdcaNGUsoGMI0zS7X7cjmP96c5198gY032ZhDDj6YmxYt6nTdmpoaXNcllUpx9tyz+OUvfsFnn37W42MOlOeee4633nqLK668oscZexRFUQbDsAichRCnCCG+FkIYQogPhRA7D3WburLqw1Xc8Je/cNHvL+L2xYvbTGhZn+M46JpGINC7IgKhUBiQI67H024pWGEYBoFAgEhR23RTQgiKwkVEiora9UiVFBejaVpevc6maSKlJBIuIhgI5tXrl721q+s6ZaWljB0zhqqxI2d8qdIzUkrSRppwKMy4seMoLSmlKFyEaZkIyOu7nR3vLKXM++KttaqqKh5/4gnmnnUWV1x+BYtvX9ymff/617848YQT2HrLrdj5Zzsx+9BZPP3006TTaWbutRcH/fpAXn7ppR4ftz8lEgkunT+fPfbco8PMQ4qiKIWg4LvJWlJgXQ+cArwNzAFWCCE2l1KuGdLGdSAWizF37plsscUWnHzKKd2ub9s24XCow8lD+QgGA2i6v8PsGsOVlcngtkzw8+s6FeUVPZrsF/AHCIdCJFOpLnuCs0MsKisqKY5EsG2b2vo6DMMgGAziSonm83mBsmmBIBeQ+/1+ysvKcqmw1O3k0ckLmg1CgSBjKitzlQBLS0tIGwY+n+i2OmBWUbiIUCiMYaZ7NWbb5/Nx/gXnY1kml1x8Me+//x7JZJL33n2PVCrF2LFjueLKK3jj9Teoqa3hgQcfYNtp07jrzjt5+eWXOfKIIznttNO44MJ5gz65VkrJ+b87n6bGJq688g+DemxFUZSeKPjAGTgbuFNKeUfL87lCiF8CJ+OV5y0YmUyGE447ntqaWpY/92y3Jx8pJa50CYc6Ln2dD13TCYdCJHqZXaPQSCmxMxkqKyooKSkFKXt8EhdCECmKkEylvHRgnQS1mZYS50UtEwl1XaeyorIleDbRNA3LMRFCUFxSjHRdDNOkorzcG2uuMneMOq7r4rquNwzDcXClJBQKMXbM2Db5hEPBEGPHVKL5tLy/l0IISktKMMx0nzLD/P7ii5kwYSLPLFtGUSTC3LPOYrPNpjJ9xx2JRCLtqg2ecuqpnHzKKdx2221cftkChM/HhRdd2KtjZ7322mvcufgODMNgs80356yzz+q0Sug3a9Zw0YUX8dJLL3Hrbbfy/cnf79OxO2PbNo7jgAC/7u/xRExFURQo8MBZCBEApgFXrfenF4EdO9kmCLS+N1oyMK1ry7Ztzjn7bN555x2WPLKETTbZpNttHMdB82l5ldPuSjgUJh5PdBkkDhe2baP7/RS3ZA7prVAohF/3k8lk8Pv9uUwH2fcnN3a6vKLNCTQUDDJ+7Dhsx8Gv6ximgebTKCryLm4cx1HjmUch13UxTAMhfGgt6eVCxd5PS3FxhIC/fTaG9YcX5aMoHCYcCpNOp3udGUYIwQknnsAJJ57Qo21OOukkpOuy4LIFbLPNNuw1c69eHf/tt97iyMOPYMstt2TSpEksefhhHn/sMSZPnkwqleJ3F5zPzJleMcmvv/6affbem1AwxF13393rY3Ynk8lgO3bu38kwjF5lMVEURSn0CGAsoAE16y2vwatk1JF5wPyBbFTWV199hZSSqqoq5pw4h7ffeosbbryRHX/2s7y2t23bC/D62FMcCgXx+/VcD+pwZts2JcXFfQ5ONU0jEonQGG3Edmw0n4bjOiAFQniBgq7rHZbqDQaDuSuv9auSqaB59JFSYhgGkUiEspaJoNkc4v1NCEFpcQnpdLpXuZ376qSTT+aDD1Zyzjnn8JMdfsKYMWN6tP3fP/qI4487nh133JEHHnoQv99PTU0Nd9y+mJraGv7z9X849eRTeGrp01RVVXH4Yb+hoqKSZc8so7Kyst9fT7aiqO3YVJRVUF5W5qUArKsj3cshMYqijG7DJQpYf7aW6GBZ1kLg2lbPS4Bv8z2Qbds8+eSTvPu3d9ji/7bgt0cfDUB9fT1VVVU4jsPzK1Zw/33388YbbwBeIQa/38/DS5bkXb0sOwmoo8ltPaVrOpGiIqLNzQUVONu2jZXJIKRE9/u7vUDwMozIlgmPfVdSXEzaSCElVFZU4NgOtuMVUjEti9KS0oJ6v5TCZLZU9htTWdkms8tACYVCBANBMplMr7Lt9IUQgj/+8Y/ssvPOHHLQwZxz3rnsvffe3W5nmiaXXXop995zL1tttRW3Lb49930fP348F138e8Dr6T3wgF8z+9BZSCkpLinhqaee7LegOXtnAPAulh2HgN9PeWk5ZaVeZVZN0xhTWUl9Qz1pI02wlxOzFUUZnQo9cK4HHNr3LlfRvhcaACmlCeRyOuUblLquyzPLlnH1n67mX//6F5tvvjmPPvoozy5/lm+//ZY1a9aw4fc2xHVc1q1bx7Rp07jl1luxMhaff/YZRxx5JBtvvHHeLyw7JKG/ejyKiiLEE/GCqZrnOA5WxqKspBQhfMTjMSzoMlB1XRfN5+u3YFbXdSZUeR8ddUtW6Q3XdZGu66UcHISgGbzPanFxhPqGhiEZfjWuahxPPvUUF1xwAccfexxnzp3LDj/dgXVr1zFjxox2FQq//PJLzp57Fh9//DEXz7+EY489tt2dmqxQKMQDDz3IohtvRPNpnHzqKf0WNNu2jWVZFEWKCPoDJJJJSoqLKSsta/ebGAgEGDd2HHX19RimoX4fFEXJW0EHzlJKSwjxITADeKrVn2YAS/vrOKs+/JAL513Ip59+yh577sEtt93K//3f//HKK69w+WUL2Gzzzblg3jw+/vgfOI7LwYcczJZbbtmnY2Zsm9Likn679R8MBAiFwqTSKcJa22A8e6tZSkkwGByUwNqyLIqLIlRWVCKEwOcTNDY14df1TgMBL+jX+3WSozohKvnKpi/MfiezgVgkEqG4gyE9AykcLkL3Nw/Z8KvNNt+Mp5c+zXXXXcc1f7oaeb13g2+jjTZi2fJnGD9+PAArP1jJoYccwrhx43jq6afZdtq23e67srKSS+b372g6bwyzQ3lZGaWl3kVOSUkJmq/zITV+v5/y8nJq6mr7tS2KooxsBV9yuyUd3f3AScA7wInACcCPpZT/zWP7Tkt+vvf+e9x0080sefhhtthiCy5dcBk/+clP+v9FrMd1XUzTZHxVFUXh3mfUWF88EaeuvoFwuG0JbtM00TWdQMBPPJEgHA4PaC9WtqLf+KrxhEMhoKW8eXU1AjoNjNPpNCXFJYzt4bjKYWB4z9gcQoNRcjubUk7XNC+Hcva7IaXXY1lWNmi9za01RaM0RaP4tO8u/jTNh18f3ED666+/9uZjBIPsv9/+RIqLOf2M0/npDjtwwP4H8L1Jk1jyyJLcBNqhkEqlKC8rp6K8vEe/bVJKqmtrsCyLCePHdzZsQ31/e0mV3FaG2kCU3C7oHmcAKeUjQogxwCXAROATYGY+QXN3br75Zp5/fgVnzp3L6Wec3untxf5m2zZ+3U8wGOrX/QaDITRNa5P5QUqJ4zpUVlQQDIZIG8aA5nx2HAfTNCktKWmTLUTXdIpCIeKdpM2TUiKldytXUQaT0SoPcyaTobG5CZ/wUV5WPug9za15Y3IhbRi5YNA0LdKZNKFQaNCGcPzgBz/I/f+SRx/hgvPP58zTzwCgoqKCW269ZUiDZtu2WyYD93y+iBCC8tIyos1RVHysKEo+Cr7Hua+6uuKtq68jkUzi9/sHNVtCKp2ivLSMyor+n0VeU1dDKv3dbHErk0EAG0yYiKZpNEabiEajPe517mysZXbWuhDCC9Idm+LiEio7KFqSTCWpraslFPKObWUsHNvBp/mQUuLz+dhg/MSRmLlCnZF7qS89zlLKXO7e3OdXeAVJskN4rIwFLlRVjSPUciGbsW18LZPICo1hGtQ3NOQy8gyVb775hr+9/Ta77b47VVVVQ9YO8O5URYqKGDd2XK8vJqxMpqthZOr720uqx1kZaqOyx3kgVVRUkDaMQT2m4zgI4RuwNEhF4baFP+xMhvKy7ybHFEciJBKJbnudrUwGO5Npl79E03V0Tcv1bJum6VXZc12EEFSUV+Zmr68vGAyi6Tq2bSORSNe7FW5aFo7jUF5aNhKDZmUISClJp9P4/X7CoRCapuO4Dq7jepkUgsFcLu/Kispc0Az0uornYAgFQ1SWV1BbX+dNMB6itk6aNIlZs2cPybFby5Ynj0SK+9QDr7LrKIqSr8I9Q4xQGcsiHAoPWG9RUThM0B/Asiwv36zP1+Y2asAfoDgSIRpr7jRwzrSUvK4oL8fn0wCJrutYVoZUOoVtZzBNM1fprLKi0outu6nyp2s6ReEiotEoPp+PyorKXF5V13VV0Kz0G8uyCASCVI0b1yYocl2XxqZG4skEPuGjrNSrAjmchMNhSktKiTZHc+nVRgspZe5OFUIgXZdIcSRX/VNRFGWgqUhlEDmOgxRQWlIyYOMTNU2jtLSsJUepQWlxSbsJL8WRYuKJJBk7026ikTexz6ai3Jto01qkCMrLyrAsC8uy8Glez3lPMleUlpRimSa6309piVd5zefzqewXBUwIMRm4GNgdLzXkWuAB4EoppdXFdq8Bu663+BEpZb93VVoZK5cVAwnC52NMafs83T6fjzGVY4hEivH5xLDM4SuEoLysDNu2vWEqAzzZt1BkMpmWSqAByivLvEwatkNlWcWoeP2KohQGFTgPIivj9Tb3tpRuvoojEZAS23E6DNIDgQAlxRGizc3o2nfj+qSUmKZJcaSYcm9MUDtCCK+yXi8LMwT8fiZOmKhOdMPLVMAHzAG+ArYAFgMR4Nxutl2MN7E3K93fjctkMggEFS0BlOu6BAIBIp1MWBNC5LK9DFc+n4+K8nJMyySTyQzaxOahkC1qoms6FeXllPRjGk9FUZSeUr8+g0RKiXS93t6BDhqFEJS09OZ2pqSklLRhYBhGbsiG4zjofp3ysrIBbaMKmocXKeXzwPOtFv1bCDEFOJnuA+eUlLJ6oNrmui7BQJDK8opuP/Mjjd/vp6S4mMZoE37pH5HfK8dxMEyTSFERFeXlw/IOgaIoI4u6Pz5IMpkMgZaJSoXAr+uMqRxDKBjCdVxcx5vcV1ZSNqJ7r5R+UwY05rHe4UKIeiHEp0KIa4QQXUa3QoigEKI0+wC6XL+8rIzxVVWjLmjOKikuIRQI5spMjxRe0aY0VsaitKSEcWPGqqBZUZSCoHqcB0E2LVZFBynahlIoGGTC+PHYjuMtkHLA8jsrI4cQYmPgdOCcblZ9EPgaqMYb3rEQ2Aqv8mdn5gF5l5Ub7cGUpmlUlFdQ11CPYRgEg8ER0fNsGAbBQJCysjKKRskYbkVRhgfV4zyApJRYltWSVzmUmwxXSIQQ+HXde6igeVQRQlwqhJDdPLZbb5sN8IZtPCalvKOr/UspF0spX5ZSfiKlXAIcDOwphOiqLvNCvN7s7ON7fXqRo0A4HGZs5Rg0TSdtpHMp2vJRiHn8M5kMmk+jsrKSSFHPi5ooiqIMJNXjPEC8Mr5pdN3vjb8sLi6o3mZFARYBS7pZ5z/Z/2kJml8F3gFO7MXxVgEZYNOW/29HSmkCZqtj9uIwo09RURG6rtPY3EQqmfJypnfwe+O6LrIlbaRlWbliL4NZibArUkoymQzl5eVtKo8qiqIUChU4DxDTNAn6g4wbO1aNGVYKkpSyHqjPZ10hxIZ4QfOHwDFSyvy7Nb/zY8APrOvFtko3AoEAVWPG0ehrJBaPEwqF8Pm8qpyu65KxM0jXq9BpmgaapjGmooJUOk06bYCAYCAwpBf4pmURCAQoLS68u3OKoiigAucBkS3xW16mJtopw19LT/NrwBq8LBrjWqUwrG5ZZ0Pgr8BRUsr3W8ZBHw48hxecbw78GfgIeHuQX8Ko4aWpq8B1XRLJVK5YtObz4df9lLVU5zSMNH6/l7KvpLgEw/z/7N15mBTVufjx76nq6mV69p19VWQRUMANBTQiiFEhGteoaGKCS6JJjN6Ym0SNiUn8XRPj1asxJphco/EqgogIqKCsCogSEESRfZl9pqf3rqrz+6O6m5lhZpgZWYbhfJ5nHpjqqurqmj5db596z3tihEIhgqEQbrdxTMq9maaJlJK8nFxVbk5RlE5LfTodZrZtE4vFyM7KbjRjn6Icxy4CBiZ/djd5LHV/3wAGAak3fRz4GnAXkAnsAuYBD0oprSN9wCcyXdcpLCgkIyPslJh0GRiGgeE6ULO9YRqEruv4MzLI8PkwDBe1dXVfeUbC1OdgakZRV4MxFE1TQqSUxOIxpO10NqjPTUVROjMVOB9GTgmlKJn+TPLz1GxWStcgpZwJzDzEOts5EEQjpdzFwbMGKkeJpmlk+jPbtY0zI2EulmUTqA90eEbC1Odghj+DzIxMLMukrr6eSDQCUgAynUYCEI3GMAwXufm5ajCgoiidngqcDxPbtolGo/i8PvLz89QU0oqiHHeEEOTl5hKPx4nGnM+z9kjN8uf1einMK0inXGT4MojH4yAE9fX1hCNhfD4f0VgM3aVTmF+At5PUuFcURWmNCpybIaVM5ym7GtzebPp4IpHgQDUnSabfT35ePi5dnVZFUY5Puq6Tm5tDZVUV4XAEj6dtAwbjiQRmIoHP66MgP79RnrJhGOlUDbdhYFVaRCIR3G43BXn5KmhWFOW4oSK8JhKJBIlEwvnQFxCJRHC5XMlJQiSa0LBtG1134c/wk+HzEU2OUM/JzlE9zYqiHPcyfBkUF2rUB+upDwZxuVzY0kYTGobReHpvKSXRWBRNaOTn5ZOdldXq56BhGJQUFRONxfB6PGogoKIoxxX1iZVkmiaJeBxNd5GXm0em3w9ATW0N0Xic3JwcBBCPJ3C7DfwZ/nTFjMzM9uUSKoqidHZerxe3242uuwiHQxiGF9NMEItF8SZTOKSURCIRvB4vubk5ZPjaNrDP5XKRqQJmRVGOQ+qTC4jGYrh0nazsbDL9fryeA7cNiwqLkj3MavISRVFOLJqmkZ+XR26OczctEo1QkUzhcBkuLMvE43bq1auZRxVFORGc0HkFAqfkks/rpaiwyBmg4mmca/dVyzIpiqIc71KpFz6vj+KCQrKzshCAS3eRl5ergubjiBDidiHENiFEVAixVghx3iHWv0II8akQIpb8d9rROlZF6YxO6B5nXdcpLixC13WVm6woitIGXq8Xr9frDKBGqsHQxxEhxNXAH4HbcSYi+h4wXwgxREq5s5n1zwb+BfwceA2YBrwshDhXSvnB0TtyRek8Tvho0TAMFTQriqK0k67rKmg+/vwIeE5K+Rcp5SYp5d04kxPd1sL6dwOLpJSPSCk3SykfwZkh9O6jdLyK0umoTz1FURRF6eKEEG5gFPDbJg8tBM5pYbOzgT80WbaAFgJnIYQH8DRYlAUQCATae7iKclgciffeCRM45+Tk5EgpVetVlOOQar+K8pUVAjpQ1mR5GVDawjal7Vz/p8Avmy7s1atX249SUY6MfOCwXENOhMC5HshJ/qsoyvFFtV9FObxkk99FM8s6uv4jwGMNfs8CdgM9UW24o9Q5/GpS56/6cO2wywfOUkrJYfqWoSjK0aXar6IcNpWAxcG9xcUc3Kucsr8960spY0As9XuDiXLq1R2jjlHn8KtpOvPz4aBGxSmKoihKFyeljANrgYlNHpoIrGhhs5XNrH9RK+srSpfX5XucFUVRFEUBnDSKfwgh1uAExd8FegNPAwgh/g7skVL+NLn+48D7Qoj7gDnA5cCFwLlH+8AVpbNQgbOiKIqinACklP8SQhQAvwC6ARuAKVLKHclVegN2g/VXCCGuAR4GfgVsBa5uRw3nGPAgDdI3lHZT5/CrOeznTzgphIqiKIqiKIqitEblOCuKoiiKoihKG6jAWVEURVEURVHaQAXOiqIoiqIoitIGKnBWFEVRFEVRlDZQgbOiKIqiKB0ihLhdCLFNCBEVQqwVQpx3iPWvEEJ8KoSIJf+ddrSOtTNqz/kTQkwXQshmfrxH85g7CyHEOCHEXCHE3uR5mNqGbcYnz3NUCPGlEGJGe59XBc6KoiiKorSbEOJq4I/Ar4HTgKXAfCFE7xbWPxv4F/APYETy35eFEGcenSPuXNp7/pICOKUE0z9SyuiRPtZOyg98AtzZlpWFEP2AN3HO82nAb4A/CSGuaM+TqnJ0iqIoiqK0mxDiA+AjKeVtDZZtAmY3mESl4fr/ArKllBc3WPYWUCOlvPZoHHNn0oHzNx34o5Qy9+gd5fFBCCGBaVLK2a2s8zvgMinl4AbLngZGSCnPbutzqR5nRVEURVHaRQjhBkYBC5s8tBA4p4XNzm5m/QWtrN9ldfD8AWQKIXYIIXYLId4QQpx2xA6y62np/TdaCGG0dScqcFYURVEUpb0KAR0oa7K8DChtYZvSdq7flXXk/G0GpgOXAdcCUWC5EOKkI3SMXU1L7z8Xzt+jTdSU24qiKIqidFTTfE/RzLKvsn5X1+bzIaVcBaxKryjEcuAj4PvAD47UAXYxzZ3v5pa3SPU4K4qiKIrSXpWAxcG9o8Uc3KuXsr+d63dlHTl/jUgpbWA1oHqc26al958JVLV1JypwVjq0PbwAACAASURBVBRFURSlXaSUcWAtMLHJQxOBFS1strKZ9S9qZf0uq4PnrxEhhABGAvsO79F1WS29/9ZIKRNt3YlK1VAURVEUpSMeA/4hhFiDE5R8F+gNPA0ghPg7sKdBhYjHgfeFEPcBc4DLgQuBc4/2gXcS7Tp/Qohf4qRqfA5k46RnjATuOPqHfuwJITKBgQ0W9RNCjASqpZQ7hRCPAD2klDcmH38auFMI8RjwLM5gwW/j5Iu3mQqcFUVRFEVpNynlv4QQBcAvcGoKbwCmSCl3JFfpDdgN1l8hhLgGeBj4FbAVuFpK+cHRPfLOob3nD8gF/oyTblAHrAPGSSk/PHpH3amMBhY3+P2x5L/P4wyi7IZzDgGQUm4TQkwB/oDzZWMv8AMp5avteVJVx1lRFEVRFEVR2kDlOCuKoiiKoihKG6jAWVEURVEURVHaQAXOiqIoiqIoitIGKnBWFEVRFEVRlDZQgbOiKIqiKIqitIEKnBVFURRFURSlDVTgrCiKoiiKoihtoAJnRVEURVEURWkDFTgriqIoiqIoShuowFlRFEVRFEVR2kAFzoqiKIqiKIrSBipwVhRFURRFUZQ2UIGzoiiKoiiKorSBCpwVRVEURVEUpQ1U4KwoiqIoiqIobaACZ0VRFEVRFEVpAxU4K4qiKIqiKEobqMBZURRFURRFUdpABc6KoiiK0sUJIcYJIeYKIfYKIaQQYmobthkvhFgrhIgKIb4UQsw4GseqKJ2ZCpwVRVEUpevzA58Ad7ZlZSFEP+BNYClwGvAb4E9CiCuO2BEqynFASCmP9TEoiqIoinKUCCEkME1KObuVdX4HXCalHNxg2dPACCnl2UfhMBWlU3Id6wM40oQQAsgC6qX6lqAoxxXVfhXlmDkbWNhk2QLg20IIQ0qZaLqBEMIDeJoszgeqj8whKkqbZAF7D9c1pMsHzjgnrK6uru5YH4dy4hLH+gCOY6r9Ksfaidp+S4GyJsvKcOKGQmBfM9v8FPjlET4uRemInsCew7GjEyFwPqHNnj2baDTKNddcA8DMmTMpLS1l8uTJx/jIjpzt27fz/PPPc9999+H1eltcb9u2bcybN4877rgDp2Pz8Hj55Zfp1asXZ5+t7mYqR55q44e3jav220jTHjrRwvKUR4DHGvyeBezetWsX2dnZh/vYFOWQAoEAvXr1Aqg/XPtUgfNxYMmSJWzevJkZM776gOarr74aTWvbmNCjeQFu64XwcFq0aBHnnXde+oJaX1/PwoUL2bdvH1VVVZx55pnNvvZVq1axZs0a6urqyMjIYPDgwVx44YW4XE5zGj9+PM8//zynn346Hk/Tu5aKcjDVxo+Mpm28oZ07dzJz5kyKi4sbnXfVftP24/Q6N1QMmEBVcxtIKWNALPV76rxnZ2erwFnpMlRVjROMz+c7rBcDKSW2bR+2/R0tu3btorq6mqFDh6aXWZZFRkYG5513HqWlTa8XjvXr1/P2228zfvx47rjjDi677DI2btzI22+/nV6npKSE3Nxc1q9ff8Rfh6I0pdq4o7k2nhKNRpk9ezb9+/c/6DHVftNWAhObLLsIWNNcfrOinChUj/MRNnPmTEpKSnC5XHz00Ufous7o0aOZMGFCep26ujrmz5/Pl19+iRCCgQMHcvHFF5OZmcnHH3/Me++9B8CDDz4IwOWXX87IkSMPei7btlm0aBHr1q1D0zROO+20Zo+nYQ/T6tWrWbVqFXV1dXi9Xnr37s1VV13F7Nmz2bFjBzt27OCDDz4A4K677qK2tpbnn3+e66+/nnfffZeysjK+9a1vkZOTw8KFC9m9ezfxeJyioiK+9rWvNbowmabJ4sWL2bBhA6FQiJycHMaOHUv//v15/vnnAfjd734HwKBBg5g6bSoet4cVK1awZs0agsEgBQUFjBs3jiFDhqT3+/nnn/PWW28RCATo2bMnI0aMOOTfZcOGDQwYMCDdSwyQm5vLxRdfDMC6deua3W737t307t2bU089Nb3NsGHD2Lt3b6P1Tj75ZDZs2MCYMWMOeSzK8a2tbXzevHls274dAfTr359Lv/71E7qNjxgxgqlTpyKlPGptPOWNN95g2LBhaJrG5s2bD3q8K7ZfIUQmMLDBon5CiJFAtZRypxDiEaCHlPLG5ONPA3cKIR4DnsUZLPht4NqjedyK0tmowPko+OSTTzjrrLP4zne+w+7du5k9eza9evViwIABSCl56aWXcLvdTJ8+Hdu2efPNN3nllVeYPn06Q4cOpby8nC+++IIbb3Q+z1rqTVq5ciXr1q3jsssuo6ioiJUrV7Jp0yb69evX7Pp79+5l/vz5TJs2jV69ehGJRNi5cycAkydPpqqqiuLiYs4//3wAMjIyqK2tBeDtt99m4sSJ5OXl4fV6CQQCDBw4kPPPPx+Xy8Unn3zCiy++yJ133klOTg7g5GLu2rWLyZMnU1paSk1NDeFwmOzsbK666ipefvllbv3ud6kLBABJZVUVmzZ+ymeffcakSZMoLi5m27ZtzJo1i4yMDPr27UtdXR3/+te/GD16NKNHj2bv3r0sXNh0IPjBduzYwbBhw9r1dwTo3bs369evZ8+ePfTo0YOamhq++OKLgy7kPXr0YNmyZZim2eyFW+laGrbxXbt2MWfOnHQbN02TF154AU3TuGjSRUgkH678gJdffplbbrnlhGvjd955Jx6PJ90u3n33XTZv3swll1xCQUEBO3bsOKJtfN26ddTU1PCNb3yD999/v9ltu2j7HQ0sbvB7Khf5eWA60A3onXpQSrlNCDEF+ANwB7AX+IGU8tWjcrSK0kl1mU+EzqykpCTd+1RQUMCHH37Itm3bGDBgAF9++SVlZWXcdddd6YvPtGnTeOqpp9LBmdvtRtM0MjMzW32eVatWce6556Z7ar7+9a+zdevWFtevq6vD7XZz8skn4/F4yM3NpVu3bgB4vV50XccwjGafd8KECQwYMCD9e0ZGRqP0hgsuuIDNmzfz2WefccYZZ1BVVcXGjRu54YYb0j1UeXl56fV9Ph8AUtp4vG58Xh/1gQAffPABk6dcjM+fQSwRp6ikmAEDBvDhhx/St29f1qxZQ15eHpMmTUIIQWFhIeXl5SxfvrzVc1VbW0tWVlar6zRn2LBhhEIh/vrXvwJOD+Do0aM599xzG62XnZ2NZVkEg0Fyc3Pb/TzK8SXVxsORMEUlxRQXF/Pll18yYMAA/v3vf1NZWcmVV32TvLw8hBC4XQZzZs9h9+7d9OzZ84Rq436/P53jHI/HWbVqFTfeeGNqAA95eXns3LmTtWvXHvY2XlVVxTvvvMPNN9/cah54V2y/UsoltFIhREo5vZll7wGnH7mjUpTjjwqcj4Li4uJGv2dlZREKhQCorKwkJycnHTQDFBUV4fV6qayspEePHm16jmg0SjAYTF98ADRNo3v37rRUurB///7k5OTwpz/9iYEDBzJgwAAGDx6MYRiHfL7u3bs3+j0ej/Pee++xZcsW6uvrsW0b0zRJlRHbv38/Qgj69OnT6n7D0ShejwchBJFoFMuyWDD/rUbr2LZNQWEhlmVRWVlJt27dqKisxDBc5GTn0LNnz0Mef0d7krZv387SpUu55JJL6NGjB9XV1bz11ltkZmYyfvz49HqpfScSKhXwRFBcXEwoHKKquhrTNHF7PNTW1hKNRdlfVobf7yc/P//A+iUluN1udu7a1ab3K3SdNt5QRUUFpmnyj3/8o9Fyy7LSAX5lZSU9e/ZsNMCvI23ctm1mzZrFhAkTKCgoaHVb1X4VRWmJCpyPAl3XD1qWutC1dME7GnM9eDwevve977F9+3a2bt3KkiVLeO+997j11lvTPUKWbREKhRrdWgVwu92N9rVo0SK2bt3KxIkTyc/PxzAM/vWvfxEMhSgrLyPexguQbVq4/I3flhdfMgW/339gneQFu6Kqklgs5vQMJb+IuPS2vaUzMjKIRqNtWrehxYsXM3z4cE4/3emEKSkpIZFIMHfuXMaNG5e+uEciEYBGx610XVJKqqqrsaUkIyMDTRPE4rHkMvugqg6p3s5wJEJ1bQ2WZR2R42pLG29JW9r4yy+/nD72jnwRTX3OXXfddQdVXUh9bnb0s7BpG4/H4+zdu5d9+/bx5ptvNtr3Qw89xA033JBOeVHtV1GUlhzTqhpCiHFCiLlCiL1CCCmEmNrkcSGEeCD5eEQIsUQIcfAQ6eNYUVERdXV1NJzgoaKiglgsRlFREeBcQA518fB6vWRmZrJ79+70Mtu2Dxq01pSmafTv35+JEycyY8YMamtr2bZtW/rxUChMWUU5ZRWtB787d+5kxIgRDB48mJKSEvx+P7W1tcTjccLRKLrLeQ2p28qWZRGORNIX3dRFsmGQkZeXh67rBOuD6V75nJwc8vLyKCgoIBqN4s/MpKKigowMH7quEagPsGvXrlZfM0BpaSkVFRWHXK+pRCJxUBCU+r3h36i8vJzs7GwyMjLa/RxK52VZFpFIhEgkku6NtG2baCyGbVl4k7nJmtDQhJYeRBcMBgkGg+n91FRXE4/HKcjPo7a2lnAkTDwepz4YbLGtH4k2rut6mytmNG3jmZmZ6XxocL5ESinZsWNHs9un23iD5ysqKkLXderq6sjPz2/0k7oLV1RU1Og1Awf93pymbdzj8XDbbbcxY8aM9M/o0aMpKChgxowZje7uqfarKEpLjnWPsx/4BPgb0NyAg3uBH+EMXNgC/CewSAgxSEp52IpZH0v9+/enpKSEWbNmMXnyZGzbZt68efTp0yd9qzQ3N5eamhr2799PdnY2bre72d6dM888k2XLlpGfn58eONRar+qWLVuoqanhtB/+EDliBOtvuQUpJSdPmoT5gx/g6dmT8vJyzIRJNBrF5TJavKjn5+ezefNmBg0aBDgDi6SU6LqOz+vF6/EwYMAA5s6dy5QpU3C5DSorKrEsi1GjTsfrc3q/9u3dh9frxeVy4Xa7GT5iBCtXrEBKSWm3UhLxBPvL9mO4DAadMojhI4bz6caNrFi+glMGn8K+ffv45JNPDnnez9y0iZ7/9V8QDjdavn//fsDpnQqHw9Q99hi+11/HvWQJ4Iy2X7lyJd26dUunaixevJhBgwYdyJm88kqMrCz6T230PVA5zkVjUaqrq4nF44ATCLrdbuKJBLZl4WnSgys0gc/no1evXhQUFPDO2+9wzthzkLbN0qXL6Na9G92SaRbZOTnU19ez5fMt9OzRk4L8/Da38W888QTRQYMgOQHKlffcw64rroDJk9NtvE+fPni9Xj7//HOklOlUhdzcXPbs2UNtbS1utzudh9ycpm188eLFjT4PcnNzGTlyJHPmzOHiiy+mpKSEuro6QqEQQ4cOTQfCW7Zs4aSTTsIwDDweD+eccw4LFixASknv3r2JxWLs2rULt9vNyJEjGT16NCtXrmTBggWMGjWKffv2Yf7lL9w3ezbcd1+LxztgwACiTz4Jjz0GCxcihDgobc7v9+NyuRovHzOGxAUX0P+cc1rct6IoJ65jGjhLKecD84HmevEEcDfwaynlrOSym3Cm/LwOeOaoHuwRIoTgmmuuYf78+fztb39rVI4uZfDgwWzatInnn3+eaDTaYqmqc845h2AwyJw5cxBCMHLkSAYPHtxi8Oz1etm0aRPd9u9nP/DR2rVcccUV6LfdRnkkwuCaGlauXMn/vfwypmnyjSuvwKU1STvp2xfuvptJ06czZ84cnnvuOTIyMhgxYgTBYDD9dxVCMPa8c1m7eg1vvPEG0WiUzMxMhp06jLLycud4TxvJmjVrWPr++5w86GTOv+ACxpwxBp/Px8fr1tH3j+8xaf58PnjySU473SnDlZWVxUWTJrFixXI+3biRgsICRo8enS7v1ZJevXphS0llZSWFhYXp5c88c+BtVb5rFxc+/jizb7qJq5LLxo0bhysYxL7tNoyPP6ZPNMotpaW4n3givZ15//0MPPdc8u+9t9VjUI4f8XicyqoqEolEOsXBNJ0vlEKA7nK1ODOdEIKLJk9i+bLlvD7baZu9evdibHJAaarN79i+g4VvLSAej3PxxRdzxhlnHLSv5tq4PyODhi187i9+QUHv3gzmQBtfsmQJpmlSUFDAFVdckQ4Up9xxB6vPOYcny8sxTZO77rqrxXMwadKkRm187NixxGKxRutccsklvPPOO8ybN49IJEJOTk564Gz2rFn87NFHeTwzkzlz5qTL0Z1//vn4/X6WLVtGTU0NXq+Xbt26cd555wGQk5PDVVddxYIFC1i9ejU9evTggsGDYfbsVv9mwwcNwpw9m9oXXiA9vG/CBGjw2TAh+cPcuTBvHgDW/fdz0q230uOOO1rdv6IoJyZxNHJp20IIIYFpUsrZyd/7A1uB06WU6xqsNweolVLe1Mb9ZgN1dXV1auailkyYACNHwh//CEAsHmd/2X50XW/U6xWLxRBCUJCfjz8jmfuXDJy5+24g2VMbCVNTV4vbcDu3Zy0LhABNIxKJIKXEZRi4kwOUbNtGSommaa1Oi+t76SWyfv4Lyj/f0uI6tm0TjUbJy80lLzevxfWYOZPEnXfy1osvcumllza/zj//CQ8+CJ99dmBZPA5jx0JxMdx/P/TsCbt2QVYWJEvSffjhhwy4+moK7r0XbrsNWhnJrrSuM7RfKSUVlRUEwyF8Xt9hnZ69OdFoBLfbQ2lxSdtmAGzSftulSfttVoP2+5XMnOk8T4P0jiO6r3/+k9B99/HuU08daOPV1U4bTqmqctrtX/4C06cD8OHKlZw6eTK+l14CpwNDtd8O6gztVzmxBQKB1N2uHCll4HDsszPPHJiqe1TWZHkZB08DmiaE8AghslM/QPtrjnVloRDceCNkZkK3bvBf/3XQKq6BA/E/+2w6aM589FGKTh9F74En0X3UaOw773R6miZMgB074Ic/dC6sQlBWUU782Wfpc+oIMt55h8LzzqOkV2/0XbtwrVtH9xtvos/po+g5eAj5U6fiWr8eTdPQdR0hBKKujuwf30PR0GGU9O5DwbjxeBYuxL18OTl33Y0WCFBaUkppSSmZjz7qHHA8TuZDD1E0YiSl/QfQY9o3iC1cRCzeoDds5kzo3RsyMmDaNKiqwuVykZub23KO50svwWWXNV721786F9/Zs50Auk8fOPfcdNAMzi1871VXwYsvfoU/lNJZRKIRQpEwHrfniAfNAG63h1gsSjg5QK2RNrRf+vZtHEQ/8IDz3vd4oHt3+MEPnOXNtF/AaSu5ufDGGzBkiLPdjh2wejVMnAiFhZCTA+PHw0cfNX7u2lr47nehpAS8Xhg2zNnPkiVw881QV3fguR54wNkmHod774UePcDvhzPPdNZvqJn2e0gvvYTnyisbt/H8fCgtPfCzaJGzz29+M72Z7nYjpkxR7VdRlGZ15sA5pWmXuGhmWUM/Beoa/Bx6FMmJ5Cc/gcWL4bXXYOFC5wK1dm36YdMyndzkZO+SZ+5cMp75M3WP/p6KlSuoe34m0ZNPpqa2FuuVV5A9exL/+c+p2fQpu9asxrIs3IYbEYng/9MT1D32GJXvv4ddWIgWDBG5+iqqX59D1ZvzMPv1J++66xGpQVO2Td6112GsXk3dU09S+f77BP/zZ0hdJz5mDIFf/Qo7K4vyf6+n/N/rCd1+OwA5d92N+8PV1D7zNFVLFhO7/DKKb7iBcCrX+YMP4JZb4Pbb4eOP4fzz4eGHEcB5553Xcq/e0qUwenTjZa+/DmefDXfc4QQHw4bBb37j9MoljRo1Cv+ECfDhh9DkVrZyfJFSOoP6ZPPVcY4E586Lli751sgh2u9BXnkF/vAHeOYZ+Pxz5wtfctZLZs1y7pg89BDs2+f8pITD8MgjTk/sxo3OHZb6erjpJqddrFoFJ50EU6Y4ywFs2+mhXbEC/vd/4dNP4be/BV2Hc85xgvns7APPdc89znY33wzLlztfVNevd4LYyZOd44UW2+8hLV2K66yzWm/jzz3n5IY3qJ4xatQovOPGOa9TURSliWM9OLA1+5P/lgINPtEp5uBe6IYe4cCMSOD0OHe54DmeSBCNRsj0Z7btdi5AMOhcKP7+d6fnCOD5552LZ1IkEsErJVoySND37MEuLiY+bhwYBnbPntgjRxKNhKnOzCRfCOqBOp8PIysLr2E4HUqJBIHf/RZz6IEiKPHzGk8SEvh/j1J88iDcK1YQu+gi3O+/j7FuHZXLlmIlJ16w+h6oCSuzs0AI7AYDefTt2/G+9hoVH6/DTk7OEL79dtzvvIv2/N+JjxiJ+/HHYdIk+I//cDY6+WTn4v5W4/rQjdTWOj9Natny5Zfw7rtw/fXw5pvOxf2OO8A04Re/OLBejx5O0Lx/v9MrrRxXorEYgUAAy7aIRKN4mpRmO9LcbjeRaIS6QB25OblOT3cb2u9Bdu50elYvvBAMw+m1TeVO5+c7QW1WlrNOQ4kEPPVUozspXHBB43WeeQby8pyc4a9/Hd5+2/myuGmT08YAGkzHTU6O09Pc8Lm2bnV6dnfvPtDW7rnHaZt/+5vzpfRwtt+GPvwQNmxwzmlTPXo45862v3qKiqIoXUpn/kTYhhM8T0wtEEK4gfHAipY2klLGpJSB1A/QJapvNGTbNpVVlVRWVVFdW9OmOqfxeJzAxx9DPI4866wDD+TnQ3KUvNO7FnIC32RaX/TSSxHRKEVnnEn2j36M58030Wwbj8dDfTCIbdvoLp0Mn6/RpArS7cZMzm6WolVUkP2Teyk8+xyKB55E8cCTEKEQ2p49ABgbNmB375YOmtvCtX49Qkpnn/36p388q1ahb99OoD6A3LTJ6SVuqOnvTaVukzetdWvbTu/bn/8Mo0Y5vVU/+xn8z/80Xi9VnaBJ1Q6l87Ftm3AkTGV1FZXVVdQH652c5lCIWCyG2zCOWm9ziqZpuN1uautqqUm18a1bnbSGhu/dBu23Wd/8pvNe7t8fbr3V6ak2zUMfgNsNw4c3XlZeDjNmOIFrTo7zEww6ASY4vcE9ex4Imtvio49ASmebzMwDP++957xecALxw9V+G3ruOeeOUTODMPH5nLau7hgpitLEMe1xFkJkAgMbLOonhBgJVEspdwoh/gjcL4T4HPgcuB8IA/88+kfbecTicWLxGIZhEAoG8Wf48bVygZBSUl1bQ6y2hmygqrqKrNJSXC4XCdPEg5P/Eo1GicZjNBwLY/foQcXyZXjeex/3+++Tfd9/YD35FNWzX0tP9KCJg79/Sa/3QM5kUs5dd6FVVRH41UPYPXsiPR4KLrkEEU8c2KadhG0jdZ2qRQud3rMGTK+XSH2QXDOBnUjQrj7DggLn+GtqGi/v1s3puWv4XIMHOz3L8bgTcICTBw2QrMWtHHu2bROJRJwycV4fUkqisSiBQD2RaCSZ/yVBgqZr+Hzeo5LT3JLUGIPaQABN08ntyEDuXr2cwa2LFjk9wrffDo8+6gSmrc0e6PMd1H6ZPh0qKpyUiz59nNzns88+MNiulVJ2LbJtpy2tXXtQ+yU1DXhHXndL7TclHHZSQx56qPnHq6ud3OeOvCZFUbq0Y52qMRpY3OD3VIrF8zi1m38P+ICngDzgA+CirlLDuaOi0SjSBsNrEDFN6usD6WmqmxOJRolEI3gHD0YaBtaKFZQVFiE0gayupudnnxE/+2wC9fVIKQ+6XuLzEZs8idjkSYRvuZmisefi2rQJc/hwpOEGu22znhmrPiDwu98Sv/BCALQ9e9CqqtOPJ4YMQdu7D33r1mZ7naXhbpRLDJA49VSEZaFVVpJo2JOOE/67TZNo//7oy5cTjUYPzJa2alXrB+t2OwOjPv0ULrrowPKxY51qGw1v4W7Z4gTUDW/nb9jg9L41KHWnHF2WZaUn2InFY4QjEUKhMEITZPkzicWi6Ul9PB5POuXJaQOdo5CCy+XClpJAfQBvr554DcN57/bu7axQU+O8/xpM934Qn88Z5HrZZU5a0SmnwL//Daef7rxn2zpr4dKlTvrGlCnO77t2QWXlgceHD3dSLrZsab7XubnnOu00Z1l5OSTLzx1kyJCD22tH22/Kyy87vcnf+lbz22/Y4JwfRVGUJo5pqoaUcomUUjTzMz35uJRSPiCl7Cal9Eopx0spNxzLYz7WpJREohF03fnTud1uwpHIQfVUG4pGo04wkJVF5LprKfztb/GuWI6xeTNFP/kJaBrRaIRQOJSe+SzF99JL+F74J65Nm9C378D3f68gfT6sZF6l1asX7pWr0PbtQxxipLvVrx++/3sFfcsWjLUfkXv7HcgGPTqJc84hftZZ5N7ybdzvvYe+Ywfud97B/e67zva9e6GFQrjfX+o8VziMNWAAkSuuIOfO7+OZNw99xw5c69bhf+IJ3G+/jcvlIva9GXiXLMF85DfORf2//7v1/MiUSZNg2bLGy267zRnRf9ddzr7mzXPyMJvWfF26tPkLtnJURCIR9pXtS/7sp6KqknA4gtfrwXC5qA/WY1oWHo8Hn8/XaJxAZwmaU9yGgWVZVMcTWNOnOwME33nHCe6mT289B3fmTCclYcMGJz//H/9wAulU3n3fvvD++7BnT+MguDkDBzrbb9rkDNi7/vrGPbLjx8O4cXDFFU4P97ZtMH/+gbbWt6+T2vHOO85zhcNOgH399U6lkFmznG1Wr4bf/c4ZQwBOFZC33oLf//6rt9+U556DqVOdnunmqParKEoLOnOOs9KMhGmSSJjoydu4uq47PVLB+mZznVP5my7dWb/+l78kftbZFN58C0XXXkfizDMxRwzH5XIdFEAA2Nk5+P73f8m/9DIKzj8f99Kl1Pz978j8fACC996LvmsXRWeeRcmQ1mdDr/vjH9Dqaim8cCI5d95J6Dvfxi5sfOGq/etzJEaOJHfGbRSOG0/WQ79CJHupEmPGEL7pRnK/9z1Khgwl88knnf0+/keiV32TrF8+QOHYc8m78SaMj9Zhd3em0E2MHkXN/3sU77N/QY4c6VQj+M//oGjf+gAAIABJREFUPPTJvvVW5+LdYDp0evVytl+92ulh+8EPnCA6NXAJIBp1cklvvfXQz6EcNpZlEY1FqQvUUVFVScK0cLkM573t9eHzedE0Lf1eb9jL3Nl5vV5i8Rj77vkx5tixTu/xhRc6pRBHjWp5w9xcePZZ507J8OFO0Dp37oGA8aGHYPt2GDDg0GlFf/2r08N92mlwww3Oe7/JTHy8+iqMGQPXXuv0+N5774Fe5nPOcXKkr77aea7f/95Z/re/OYHzj3/s5GtfdpkTmPfq5Tx+1llOdY8nnnDqVX+V9gtO8L1sGXz7281vt2ePM/jw5psP/RyKopxwOs0EKEdKVyvAHgwFKa+owOc7MBGDZVkkEglKiooPmjI3HIlQVl52XAUJR0o4HCY3J4f8vPy2b3TVVU6g8NOftn2bJ5+EOXOcC7yjc3VhHkfa0n5t2yZQHyAQDGJZFtK201O2dyWpnGzDZVBcWNTlXt9XYVkW4UgknZpjGM6XJe2aa9rffn/yEyfY/vOfU0tU++2grnb9VY4/R2IClGOd46y0UzgcdiYKaXA7Wdd1EvEENbU16Lre6IIaDoeRcMIHzeCktdQH6/F4vPgzMtq20aOPOrWb28MwnN4x5YgL1NdTXx8gFo/jMlx43O5O+V6XUvLpxo2s/2Q9Obk5jB8/Hn9q8FsbCSHwerxEIhGqa2ooKiw86tU+jhTnS0GMRCKObdvJzzfnM07TNDTNee3Nvd6EaVJVU0U4HOZAmX+B22Xgue8+fIsWEa12pkvXNI0Mrw+3x43hMpp/rxQXH6gxrSiK0oTqcT6OxONx9pWVoetao6mwIZX77NSbLSosxG24icVj7C8rb3b9TisWw//EE1h9+xG94hsHj+z/iqJRp8eupLj4aJ4T1WPVQYdqvxVVldTW1eLP8HeagLnp4MLNmzazds0agsEgAwcOZN++fXh9XqZOm9ah96Bt20SiUbL8mRQUFKQnKzpeRWMxamtricSiSLvB9UiAQCKTxTFdLgO/PwPDZSCljRAatm1RHwwSj8fxer3p94Bt25imiS3t9D41TUNKiS1tdKGh6TqGy4XH48FwGckqQc2eS9V+O6grXX+V45PqcT7BhcJhLMvE7T64RJIQAp832RtVXUNhYQF1dXXYlonH08be1U4g55578L46C2FZBCorCM+Y4ZSjSgUiDf/fAR6Ph0gkQqC+nvy8vMN01Mqx5HK5OkXQHI1GWTD/LWpraxk3YTz9+vVjz+49vLdkCf0HDOBrEy+ktLSUqspKZr06i08+/oRRo1vJT26Bpmn4vF6CoSCaplGQn9/pBjS2xDRNTMtET465iETC1NbVOTOOut0t9qBLKUkkEtTW1QIi2ansBMSuSITSh3+Nd/5biPp67OJiolOmEJ1yMXbPnliFhWg1Neh792EOOhmZnY1lWdi2TTQeJxQJ49INDLf7qE90oyjK8UcFzscJy7IIhkK4XK4WL5JCCLxeL+FImLJyi1g8dqD82nHAtWkT3v97hcDvfotr+3ayHngQz4KFuNeuxTxlEFb3HnjefZfwjTdS/6uHOhRACyEwDIP6YD2G4cLj9qQHjClKR0UiEebOeZ1wOExOTg7vvv0Ol0+dyocffEBxSQkXTrww3W4LCgs5dfiprPvoI/r160d+QTty7pM0TcPj8RAI1uNy6eRk53Sq4FlKmS4HaNk2tmURjcUIR8JYttPjC2DaJkYsTsH/vYL3tddwff45JBLIjAzI8CF9PmRGBonBQ4hefhnxc89tVH9a37qV3O9+D337dsK33opdXIT+5Ta8s2bhf/bZg49LCMwhQ4ifMYbEmDOInzEGs3v3dFlCRVGUQ1HRwnEiHImQMOP4vK0X5Nc0Da/XSzwRTweFxwvvq7OQ+XlErrsOdB07Owf3ihUEf/B9PO8vxbX1CyLXXI3/2Wcxhw0lcs01ICXe11/HWLeO8E03YfXrd8jnMQyDWCxGRVUluqahaTp5uXlk+v1H4VUqXY1lWSxcsJBIJMLUadPwZ/p5ffYcXn3lFQAuvfwyhBDYto1lW+iazugxY9i1cxdzX3+dM88+i969e5PR1rz7JD2ZalBTV4sQgpzsnCPx8g5JSollW9iWTcI0sSyTcCRCPO7kK0sp0zeKXC4nD922bQCyP/qEvNtuRysvJ3bxZEKTJyM9HkQkggiHnX+DQdyrVpHx4ovYeXnYBQVOcO31YHy2BatXT6rnzsUcMjh9TPW/+DmuL79E278frbIKmZ2FVVqKsWEDxgcf4nl/Kf6/zQTAKi4mMu48eOGEnldLUZQ2UjnOnVDTHEnbttlfXpbO4zuatD17wOXCLilp97bG6jX4XngB85RTiF4yBTtVXqoFBeMnYA4/lbpDDKzLvusuvHPfIPTDuzE+XI134ULs7GzsggIq31vizGjWBlJKbNu52GtCUFxUhNdzRM5v5+kKPM60Jcc5GAoe8gvl4SClpKysjFAwiNvjoXv37pimyZJ3F7Nz504uvfwySktLAWdQ7prVaygsLGDI0KGYpkkw5FT90HUdr9eHbVksW7qMbV9+CUBJaQkTzj+f3Nzcdh1XPBFHWjb5+QVktXPAYXuk0iXAyZQwzQSmaRIKRzDNRLI9WUgp0DSRTqERQiDicYy1a/G+MQ8Au6QE1xef4311FokxY6j70+NYqdrSzT85ro0b8c6bhwiFkS4dEYmQOP10opdeCl4vtm2nA3Jd15vtgbdMi3gijqbpeAMB3GvW4PrkYxKZmfj/8+ctpWqo9ttBx+P1V+lajkSOswqcOxEpJXWBOoKhIBk+P9nZWQgEwVCIqppqvEezpJxtk/XAg/ifeQbpchF4+FdE2lHX1LV+PQWXXoZVXIy+fz8iHqf+5/9J6M47ARChEJgm0nlDo2/dStE5Y6n5y7PELr201X2LYJCcGbfhWbYMs08fQj+8G/OUUyi44GvUP/wrwrfc0u6XG4lE8Hl9FBcVHYlzrC68HdRZAudEIsE7i95mx44d6WVerzf95euCC79G3759m91WSkmgvp5EIoHLpWPZzoA1j9tNRkaGM1nL3n2sXbMG0zS5fNpUsrKy2nV8sXgcadtkZ2WT6fe3qVSdbdvEEwmkbTtl/JDYtkRKiUvXMQzDmV3UlsTiMYKhUDJwdq4ZliVBSDRNx5UMVDVNQwuH8SxahGv9elyff4Hriy/Qd+xAWBZWzx7YmVno5eXY+fmEb7iB8He+DS5X+lw27ThIVRFqKRVFSkksFkMi0TU9eWxOWTpN19GEQNp28q6diZQSXdfJ8PnSf8N4PE630m4qcD7Mjqfrr9I1qcC5A46XhmvbNjW1NdTV16Mlb+u6DAOQmAkLl0s/qnVbsx58kIz/edq55bltO74XX6Ry8btYJ5106I2lJP+yyxHBIFXz30QkEmT++tdk/PNFKpYvQy8rJ+/KKxFA4JHfELn2WrJ+8Ut8L79M+cfroIO96rnf/jb6519Q9d6Sduc/27ZNNBqjqKCg3UFLG6gLbwd1hsBZSsmC+W+xZ88eLvja1+jeozvBYJAtn21B0zSGDhtKZis9vdFYjOjWrRT//e9Ij4e6668nXlKMaZpOz6fHgzc5sHfOa7NxuVx848or2p13n0gkksG5i4L8fPwZ/oMeD4VDRGMxkBLLskkke4pTJdxkMigm2WsshEgHtKkqFCmapqHV1OB5dzH6zp3ou3fj2rgRY9MmRCyG2asX1sknYQ48CXPgQMyhQ0mcNvKgmQ5TgWuqN955TkgF6E6qh/N/wzDS1TFS6S/xRALDZZCfl4vH7dxtSk2xnojHSVgmkWiURNwpR0cqtcSWuA2DDL8fpFSB8xFwvFx/la5LBc4dcLw03Nq6WqpranC73biSvS+maR7oxTmKucqujZ9ScOGFBO//KaHvfx+iUQonnI/drZTqV19tfYpfwL10GflXXknNP/5OLDltrQiFKBxzBvEJEzD+vR7p9WGedBK+V1/FKilBLysjeNddBO9vx0QFLTxv7dNPE502td3bx2IxNE2ntLgYo8EApMNAXXg7qDMEzls++4zF7y5m0sWTW+xVbomUkuD2bfS9fBoiHkfYNiISoeaWm6m643ZMtxvLspyyaG4PoVCI116dxcjTTmPMGWMa7cepSGFhWxZC09A1DaFpGE0GDEdjMXRNo6TIeR+nBhYHgvWYiQSariFwguLUYOOmvbmpYDUVoLoqq/C+MRd965fI7Cz0PXtwrf83ri1bnJ7kwkLskhLMYUNJDDuV2ORJWL17H/L8WJZFLBbD4/aQnZWFx+NJz4aajJ6xpZ18DWGi0Ui6zrNzbBo+r4ecnJx00NzwNQTqA9QFAiQSiXS5OtM0nUmjzATRaBTd5SLLn0lJUXFLnROq/XbQ8XL9VbouVY6ui0qYJoFgEJfhSvcypao/HAv+/3kKq0cPQjNmOAu8XgK//z15V11F9o/vIfD/HoUWykbpn39O5sMPkxgxnNjEienl0u8ndOcdZD/4ENIwqHznbaxBg4hcfRXu5Suw+vUlcvXVX+m44+edS+SyS8m+/37i547FPtQUwk243W4ikQh1gToK8gs6VZUC5chLBWRNxxesWb2Gfv37txg0N8ytTW2f+qIbj8cpfOS3iHicnXPnYGVmkv/cX8l7+hmy3pxP2cMPET77bCcwDIdwuw2GDB3Cv9ev59Thp+JN5u6GIxEnHSHdOwwk6xvrLteBnmAh0DWNcCTMzt27cRtOzWMrOZuiPxRC370bhMCzbBmuDRvRKirQ9+5FJOJolVXYmZmYgwdjnnIK0uPB+Phj3MuXg65j9euLCNRjlZaSGD2a8He+Q+yiidhNp95u5hyZloVpJpzjlk7fttAEWVlZ5OXkNuphb+7TJcOXkQ56UykkqYo4Df9mluVUFIpGY9TV16FrOhkZGel1DMPAMAy8eMnwZaRrYh+rz1tFUY4vqse5E6gLBKiqrmo0jfaxolVUUHT6KOr/4z7Cd9zR6DHvK6+Q8/0fEJ4+nfpHftN4wwY50XZ2NrUz/0Z87NjG60iJ5+23sfPySXSgfm1bj79w/HiiEycSePzxdm9vWc7gocL8ArIym0/ZcG6xt+suwAkfgQshbgd+AnQDNgJ3SymXtmG7VttveWUFgfoAhssJetI9sUKQSCSIJ+JO+TNNw6W7MAyj0cCxVJpANBZzAjIBhuF2Ak5g86ZPWbViFRddPIniImfSHE3XkLYkYSYwTafkWupz1NmtSJc6THz2Gf0nTabiZz+l9sYb08dtfPklJT/7ORmrV1M3bSpVP/whiW6lJEyTWCTK/HlvMnjIEEaNGU08HicWj2G4XGhSIkwL6XGnj99MTjOOEOmYWiKxTNPJ8dU0MnfvpuSpp/HOno1IHqv0ekmMGIFdVIjVuzfSZWAX5KMFArg+3eSUhovHsfr2JXrppUS/fgkyv/XSeam0DtM0sWyL1Ex+Qmi4dB1/RgZuw518DNyGG6/Xe9g+90zLpLKqinAkAkgMl3HIgDgWi6HrOt1KSluqI33Ct9+OOh6uv0rXpnqcuyDbtgmFgi2OAj/afC/8EzTNKQnXRPTKKxH19eT8x0+Jnz/BScOIx3EvX47/z3/G8+5iAg/8kvDNNzefpyxEo17oI8EuKiJ49w/JeuABQj/6Uesj9Zuh6zq6pVNVXY1l2eRkZ6dvC0eiEcLhMOFIBLfHTWFegar/3AZCiKuBPwK3A8uB7wHzhRBDpJQ7v8q+w+Ew4XA4+SUmNUWzACGwLRsEaEJgmSYxGUMkA2gtGWDHzQRmwkSIA9PSB+uDrP/kYyrKK4hEIvTq3ZucnBwi0Wjy9ZAMlEV6O13X08GzM1gtTiwWp/vTT2Pl5VH7zW86tYKdzbD79mX3C/8g+5VXKXr0UbLemEfdtddQffttGAUFDBg4gE2bPqWouJj8/DyKXnuN7Dlz8K7/N1osRqJbNxJ9+hAZOYK6q6/CKi4BKdMBNVJi7N+PZ+1H+N97n+w338QsKqLqwQdIjBmDgSBx0kBMr7fV2vCtccrQ2ZhmAts68MVB0zQMl0G2LxuXy+WUfNSdZUdqivBUr3wwGCQcCTeaRfBQDMMgHo9jJnOsFUVRWqN6nI+xSCTC/oqydtdcdn38MTI/v015hG1mmhSNOYPYhAkE/vBY8+tISe63bsBYt47gT39K5uN/RN+1G7NvX+p/9VA6p/mYCocpGj2G2NcvIfD733doF4lEAsuyKMjPx224CYZDBOrrITki3zRNfF4fPp8Pw3Dh9Xhbu+ge+29Ex5AQ4gPgIynlbQ2WbQJmSylbTWo/VPv9cvs2AvWB9N2ahrm5mpR4N3+GNFzE+/cHw0jWUnYqN5DM343s3Ufk9ddJ7N0Hpw7j07w86uoCDOjTm26ffUbfggLsYUNJ9OmDbDCDpVFWjmfDBnzr1qGFw1jZ2djZ2cQHDiA0diyu3bvpN+XrVP74R5TdeCMuw4XX7SGeiBOPx9MBtxYKkff3f5D3l+fAsqi9eTo1Ey9kwc7dhCormb5mNcXz3iR4/vlEzjwDKzcXY8cO3Nu2k7F8OXp9ffp8WJmZmD16oFdX46qoACA2YAC1111L1RVXYLsNhHDyolO1lw3jwHTT6XPX5I6KZVkkEglsW6anwgaBpum4DQOfz5tMmzAOVOQ4Sh0BlmVRVV1FMBxGA9ztrD5k27aqqnGEdPbrr9L1qcGBHdDZG251TTW1gToyfG2f/MA7eza535uBnZVF1bvvtCl4NlatQquuITbpohbzkz1vvEHet79D5duLME89tcV9icpK8q+4EmPzZuKjRhH43W8xhw37SlNhH27+Pz1B5qOPUvneEqz+/Tu0j1gshi0lGmDZdnrgJhy42NrSBgl+v5+SohbzPDvPiTnKhBBuIAx8U0r5WoPljwMjpZTjm6zvARqO8soCdjfbfsvKqHh+JtqqVfg/24JVUEC8fz9iJ5+Me+dOsmbPwSgrA8B2uwkOOpkvxo3DNWUKnn59Ce7ajee/n+SkBQvwxOPYQqBJSU1hIdbIkeR+8kk6+ASwcnOJnHYaVkE+Gas+wNi9G4BEt27Y2dlo9fVodXXooRCJkhLsrCy0SIStb76B7fGQlZmFYRhOj3Q8TjgSxrasdL1jraaGgv9+kuxXXkUPhYh364YZDGJEIuz65S8wr73m4PMbCuF/fykiGkVIG72yCmPPHqysLKKjTicyciR2g6nlnTQKZ7ppJwAmWc3CSWNJlbx0vlzYIJ23rq4J3G5Pk1xhFy7d1eEe645KtT3n7oFOZXUVoVCoXb3Mze1PBc6HX2e//ipdnwqcO6AzN1zLsti7fx8ScLdjYEr+5VMR9fVo1dUkRo6kdubfWl0/VW0CIDF0KIHf/JrEWWcdtF7etG8gTJPqua8f+iCiUVzbtmGedBJ0wnQFEQpRcP4FSL+f4D0/JjZpUoeOM1UPtrVbuLFYDMMw6F7arcXDafcTdxFCiO7AHmCslHJFg+X3AzdJKQc1Wf8B4JdN99Ns+12wAHnxxUQHDiQ+Yjh6TS3urVtxb9+OlZ1NxYVfo/bii6kNBNDWrSP3w9WctGWLk3UrnAFqcbebnZdeCnd9H7u0FPeqD8h9fS7uHTuIDzqZ2quvwioqwrNpM9516/CtXYteFyA6Yjihc88lNmQwZvfuB740Soln0yZy//cFXHv3Ufmju6k/5RR8Xh/+JjNTmqZJNBYlFosjcWona5qGiMXxrVpFxooV2FLyenExkV69ueDCC9K96giBlnzOtgStqbxjJwdcd1IqhJbuGU5Nj53hy6CwsCCZw20mK1c4uchut/uYp5NFo1FqamuJxmNoQqDrenpiqI5WHlKB85HTma+/yolBBc4d0JkbbjAYpLyyol2DArXyCoqGDyfw2H8h3R5y77iDqjffJDHq9Ba3yZ9yCUhJ/cMPk3X//bg//pj42Wfh2rQZEYsR+u53iZ91JvnXXkftM08Tndr+Um6dkWvjRnK/NwPX558Tvu66ltNPviIVOLesQeB8jpRyZYPlPwNukFKe0mT9tvc4x2Js/2wzdZrA5ztQjs6sD/Lu0qVUVdekl2Xn5NC/fz8G+f3sWbAQ1769FPfsiWvqVGRhweF7wU2kBg5mZ2U3mw+fmo0vGoulJxdp2oNbUV7OO4veoUfPnow+c4wz2E2mSsbZjap4wIFc7VS1j1TZNrfbjc/rbXE8hVPLPEpmZhaF+flHtQRmW0SjUcqrKrFME4/Hgy1lox77jlKB85HTma+/yolBBc4d0FkbrpSS/WVlxOKx/8/efcdZcl4F3v89FW/d3GGiNEE5WdaMcrKyZOMgMAZMNH7BGGxsY96FNV6wjYFdY3bXGNaAX8zywTavQWbBkmwsWcZWztnKWSNNns43VX72j7r3qrune6a7p9NMn+/nMx+p69atW7e669a5T53nnFm10c5985tUP/ox9jz1JLqnh/5LLyPevJmRf/z6lOubr25j1XnnMfyVvyW49lpIU7x//mfcm24mPukk0CmFv/vfKN8nPOsshm64Ho6wskzeV79K5T9/goHv3Uy8Zcu8b18C5+nNNlVjiucfNMe5Vq/hOA7DQ0MMDQ3z/LPP4fs+F1x0IaAplssU2jm809UsTpJO+kJWzq0TiCVJQqo1Crp5v+ObgmT/7XyGtmf+kU1QNE2TKIpw3dxBW2F3AuhmK2tfPb5+exzHvP76dh5+4EF6ent5xzvfgWVZpDolTdKsIkiaZCPpujNx8Y1KFpZlYdn2fvWep9Kpq1wulenr7V3yEeaOOInZu3cvQRhO+JI0HyRwXjjL9forVg6pqnEEabaa+IGP67oHX3kc9447iE4/Hd3fD0D9479F9Tc/gvXEE1PmJeduvJHU8wivvDJb0K6YMb5qRvMDH8B+6CHCSy894oJmgNYv/iKFv/prCn/zZUb/vy8v9e6sKFrrUCn1MHA18K1xD10N3HDI20ezZ88eHnv4UVqtFihYs2YNF1x8IaVSKZt8Z5jo9uhrmqYow+imOXSCX8uycV23XZ4uao/+gmla5BybNEmIk4QoiruvbBhZaoXr2pimlVW1QHcDsTCKsEwTbwZfjJVS3Rz6MIqyjndxTBxnlR5OPeUUjlq3jm/f+G1uu/VWrnnrWzGVCRYTmnbodlc8NO33PruRWNM0cV2XsVoN0zSoVqpLHjxrrRkZGcEPgnkPmoUQYrYkcF4CaZoyNlbb7xbrDJ6Ic8edtH7qPd1F/k/8BPH/+J+UPvc5hr/xjf2ekrvxRoKrr0ZPyq+csNl16wje9a5ZvYfDimnS/PUPUvrUp6n9we+Tbtiw1Hu00nwB+LpS6iHgXuCDwEbgkL7FbNu2jW/fcCOjI6OsW7+OK6+6kmpvD4Yy2i3rLWzrjRbNnRrdWXWIFK3BsrKKKOPzd5Nc0s1tH58G0GnioduNUg6UIuDlPKIowmyP+M6UYRhZC27XbVcASTDbAfqq1au58qqruPmmm3jowQc559xzgYk5zrP+TJmCaZrYjsXw6CitVotqtYf8EgWsWmtGRkeo1Wu4rrvkQfyRYDY11ZVS7wemmkTjaa39BdtJIZax5ZXEtoz4QcDo2BhhFM77thvNBi3fn66967TsBx7A3LNnYi1ky6L2qT/A/cEPyf/9309Y33zpJewnnsD/8WvnY7cPa62f/Vl0pUL5s5/ttvIVi0NrfR3wceDTwGPAJcDbtdbbDmW7xWKRDRs2cunll/Gua6/lqKOPppAv4HnZRLzxJR47gW7ey1MpV6hWqlQrFcql8n4BmWmaOE42GW5y7rBjZyPTkx+bLBuJdicEzWmatmuBt7oj2gfSqYc8/nU2bd7EeeefxyMPP8Kzzzw7q+M1G7aVVdgIwpDBoUGCIJj1Njql7eYqSRKGR4YZHh3Fth2psTwPxtVU/6/AVuBOsprqByrNNEYWZHf/SdAsVjIZcZ5Ea029Xmd4dIQwjMjnPXqqPd3apHMRx3F35nrL97M2sOasOs8B4H3zX0g2HE103nkTlgdvfzuND36Q8if/C6QpzQ98AIDC//oSSX8/QSdNYwXThQKjf/Z5ej7wa3jf+Cdav7B/gxexcLTWfw389Xxus6+vj0suvYR6oz7rkcipcp0XitYav908xct5uK7D6FiWajeXz5QztmyhNlbj9ttuw/M8Nm2eXZOfmTIMg1wuh+/77BscoFQsUSjkscwDXza01jRbTcbGaiRpQs7NUS6XZ1U5KAgCBoYGCIJwQhlIccj+X+B/a63/rv3zx5VSbwU+BExXU11rrXcvyt4JcRhY0Z9GaZqyZ+8ePM/Dy3mkWlNv1KnXs05+hUKelt9i7749KGVQKpaoVioT2vXC9OWg4jhmrFaj3qiDUuhUkyRxN49wNtTwMN6//Rv1j34EJgfcSlH7o8+CoSh96tPEJ55EWiriXXcdtc9+Fg6zvMDObfX5vlgG73oXzZ//eUp/8AcEV19FunraustCzEkUZfnRylDk3KyVdMv38dwc5VIJz/O6X5iHR0bmVANZKcXFl7yFZrPJD/7jP/jxd/8EfX0LUxlEKUUulyNsjzw3m01W9fdPeW4maUrg+9SbTRrNOoosbWSsNkYQBPT19s5oInSapgwNDxMG0awqDokDa0/UPQv400kP3QJceICnFpVS2wCT7I7Rp7TWj07zGlNVxRHiiLKyA2edEsUxzaGhrGUuGp1qXNft3hb0cl53UtHw6DDNZgPVroOaxNlEoVy7ba1htDtmAS2/Ra1RJ47i7qiSYRo4ztwuBPmvfR3SlNb73jf1CkpR+/SnsZ55luqv/ApoTXz6m2j+yv8zp2OzVLTWtFqtbkWCXC43rxfO2qc/Re6736X8id9j5Mt/A7P8AiPEdMIoJEkSyuUyge8TBD6WZWMaBj09PeTG/a2VS2VarRZ+4OPlZv/FVinFFVddyY3X38B3bvw273jnO+lf1T+fb2fCa3UmTrZaLYaGh+jt6e0Gz1prms0mo2Oj+GGIIpuh4RBAAAAgAElEQVSw2PkMtW0b3w/YO7CPUrFEqVQ84Kh1s9mkFfjkcpLTPM/6yYLfPZOW7wHWTvOcZ4H3A08AZeC3gLuVUmdorV+YYv1PMkUddiGOJJLjDN2cxZybI5/PT8ilU+0i+1lXrRxJmhK1W+amQKo1tXqdoeERBgYH2L13N7v27mZ4ZASFardktrFte+4dtsKQ/N//Pa2f+inSVaumX880Gfmbvya86CLiE09k+KtfXZbNSQ4kDENc16W/rx/TsvB9H9/3abWyvNDOz3PNndQ9PYz++Rdwv/991px0MqtPPoXyf/odCOc/l12sHFHcbtHe00tfTy+9Pb0YhokfBBQKhQlBM2R51NVqD6Zh0mq1upMRZ8O2bd7xrndSKpX49o03smf35HhofnVGn+uNBrv37qHeaBDFMfsGBtg7sI8wivByOTzP2+8z1PNyoBTDI8Ps3buPcJrzLUkSxmpjGMahT3KcatuPPPwIt/7wVoaHhw/+hCPX5A9PNcWybEWt79Na/6PW+vH2BMKfAZ4HPjrNtj8HVMb9O3p+dlmI5ePwiqoW0Ew+pA3DOOiEvoOlb8xF7sZvY+7eTfODv3bQdXVfHyNf/Yd5e+3F1EnR6K32UGi39h0eGc46hLWDaNdxSXV2Szg3vulFkmDMsKJA8Pa3M3D7bbi33IIxNEzhb/4GbVvU/nTyHUwhJur8jcIbdZ3jOCaKInoqVUrFUjtQ9FjV108r8KmUpq5fm/c8+vv6GR0dJQwDgiDAsqxZTRrO5XK8413v5Obv3sS/f+c7nHPuOZx8yilzno9xMIZh4HkeQRiyb3Aflmm1a1W7B52857RrSbf8FgODA1SrVSzTmlA2b7Q2tiBl5154/gXuvusuwjDLmd7++utc/dZrWLt2uoHWI9IAkLD/6PJq9h+FnpLWOlVKPQicMM3jAdCdSSp3DMSRSALneXYoHxRqYIDyH34WHYYMf/L3UJs2Yfg+hS99ieCyy4hPPvngG1lmOp3TputWNnld07Rw23mQec+bUAM3jmMsy6Ll++wb2EsYhpimSRCGmIZBmCQzuoADJMcdR/NDH8r+/6j1VD7xewRvexvhZZfN/c2KI1ocx4RhmKUoKEUUBuhUYxgG1XKFSrky4W/c87yDBoCdv/EwCgmCgJGRUYIgmNUcCNd1efs738Edt9/Bfffex1NPPsW73/OTs55HMVNKKXKu220aM5s8ZKUUXs7D9wP27NuLgUKZJma76kmz1ZrX1t5aa5595lnuuP12jj/+eM48+yxybo7v3XwzN3zrerZs3crZ55w9L6+13M1HTXWV/WK2kKVuCLEirejOgXESs3PXLgzDWPJZ2+a2bfS892dRg4OkroNRbxCcdiruCy+iooih679FfMYZi7pPWmuCwCdNs/xsd5YXtDAMu3Vo4yTGUArXnT5n2Q98XNtl7Zo1B32dkdFRxmpjaK1xHYdqtUqtXqdWq5HP52f1PtGanp98D+bOnQzcduusJ1NK58CFc7DOY/sGB6g36nPKE56NJEkIwpBquUK5XEKhCMKQKApxbGfecvEbjQYDQwPd+RKzNTw8zA3fup6169bx1re9dVmP+KVpCrzx5TpN06zL4Tx9Fg8MDHDLzd+jVqtx0sknc+lll3aPR5qmPP7Y4zxw//1ccuklHHPssSuic2C7HN3Xgd/gjZrqvwacprXeppT6GrBDa/3J9vqfAe4DXiDLcf4Y8EvARVrrB2bwetI5UCwp6Rx4hFJDQ/T81E+TAq9+85+hr5/er30N+5VXqJ9/AeG178R68+IHzX6rheu6FApFxmqjhFGI68xsFKvTLKK/txcv5xEEAUOjI0RRhOM4hGFInMRYZnZrWmtNmqQUqoUZXeyrlQqFfB6tdTYhUykUimaz0R65nkXNV6UY+7PP03/5FRT/4i+p/94nZvzUJElIkvRIbLgo2rIvkAHlUome6hud9PKeN+8VawqFAmEUMTwyPKc5ET09PVx+xRXcfNNN3HXnnZx55pnkJuUcLxed9Iz5zmXetWsXe3bv4bFHH6VULnPFVVful5JhGAZbz9zK8PAw999/P+vWr5/XfViutNbXKaX6yGqqrwOeZGJN9Y1AOu4pVeBvydI7RoFHgUtmEjQLcaSSwHkZ8P7qrzAGB3nphuth00aUUgz91scAurdD834rG1VbpLGPMAyxHYf+/lU4to1hKAYGB0jTdEYXujAM8dxcN+fTtm3iJGFoeAjDNLPqA8UyzVaTZquJIpvsNJvcxskjco7j4Lo5Wq3WrHMkkxNOoPHRj1D4X1+i9ZPvJjnxxAM/QWeVU1q+T5zExHF84PXFYSuKYyzb3i8VY6GUSyUazQZhFE03AnpAmzZv4i2XXsLdd97F0089jWVZnH/B+Zx62mnLegR6Pjz26KPcf9/9KKVYu24tb33b2w6YsnL+Beez7dVXefThRzhm8zGLuKdL50A11bXWl036+beB316E3RLisCGB81LRWapINDLMqn/4KkPv/RnYtInJ17XOSFGz1cQwslSHBd+1ziS9np5u04JCvkCjkQW5B8tpzNoZa0ql0oT1isUC9UYd329RKVfo6+2lGBZpNOokaUqpUMQ+hNu0SikqpawUWCcfejbqH/sYuX/7FpXf+V2GrruO2DJJUo3WKWmatNsn26RpShiF+C0fZRqgNamefVUEsfxprYmjiGq1umAT7iYzTZNSscTg0CC6fTdltk499VQ2bdzIvn0DvP7aa9x15128/NLL9PT2csYZb6ZQLLJzxw5My2LdumlTjA4bu3bu4qmnnuKlF19k65lncs6558zouOXzec4971xGR8cOqcuhEGLlkMB5CaRpSrPVIgwDyjfciFmvM/ZLv4jWKc89+zy7du7ENC1OPvVkVq9endWY1tBotlCGOasOXHPRSafIe2/kChuGQW9vL+lg2h3RnXxh0loT+AGaFC+Xz25lj2OZFv29ffiBT7FY7E4ymlyq61B4nkepVGJkdHRGExI7kjQlUop9n/9T1rzvlyn/1HvY9qUvkXTy8qKI8q23Uvk//4qzbRs7/uizmBdeiGEaMtp8BIrjmCgMSbXGdVxKheKivn6xUKDeqBH4Pu4c86cLxSKFYpHNx2xm7bp1vPTSS7zy8ss89+yzuK5Lo9EAmFWguRz96PHHufeee6lUKlz8lotnPbJ+yqmnEobhYfv+hRCLSyYHLvLkwDAMaTZbxEmEZVps/KX3gWHw6j/8PffceTc7d+5k3fp1tJotRkZGOPucszn+xKzyTxRlrbuzmtMOljX/AXSnyUFvTw/VSnW/x6MoYu++fURxNKELWJqm+L5PznWplCvkcrkly6tMkoRde3aTpMlBc7I73c6CMGiXGVN4jz3Kpg/9JsHpp7Pj7/4W56WXWPt7nyT3xJM0t24FyyL3ox8x+PHfIjjxBIbPPBO7UODkE06a7mXkijxHizE5UGtNHL+RbmMYBkmaUi4WsW0bx3EnVHdZLFnZtiHieP4aAQVBwBM/eoIg8DnhxBPZsX0HD9x/P/39/bz5jDM44cQpq4wtKyPDIwwPD7N27Vpefvll7rrzTrZs3cq55507p2OUpilhGK6IyYGLTSYHiqUmkwMPZ+2c2GarBYBtOzivv0b+gQd49U/+mB98/z8YGx3jkssvZf369aRpyiMPP8KDDzyI7/ucdvqbsG2LJEnw/VaWQ5zLzXvec5IkWKY1bWUK27apVivsGxjoBhphGKIMg7yXp7e3B8eefV7mfJp4q1tPezFNkoRavU4cR+0qBk7WEfLcc9n9F1/kqF/9AMedcx5mrUa4eTOv/cs38bduQQUBaz75X+j7n1/AiGNW5/Ns/71PwKc+vcjvVMyG1jprh93Oue8EzFEUYdkWpWIRrSEIfCrlCpVyeUlHIb2cx6r+fgYGBwiCYEbtqg/Gdd0J5ddWr15Nf38/Tz75BD/8wQ+o1cY486yzDvl1FkIYhtx+2+28/NJLQPYFJ01T3nT66XMOmoUQYrYkcF5Imiw/tj0rv9VqYZhGdyS2+H/+lTjv8c04RrV8rrzmKnp7e4HsonD2OWfjeR4/euxxNHD6m0/HNE1M08T3A0bGRonjiGKhiJrBhD2tpw8iO6IoolQsHjD4zXt5KuUyI2OjKBTVSoW8l8d1l0+L3EIhz1it1k07mSxJEhqNBnEcdQPm8ZoXX8T2f/wahdtup37amxi9+ELc9ohJYtt875d+iWe2bOW4OGbTvfdCucKxi/HGxIx1SpxBdncpiRNs2yZNsxbRAJZlUSmXKZcr2JaVPUdrzHmu9DBXOdelp9rDvoF9c8rbn4kNGzewYeMGHn7oIR584EEKhSInnTzt3ZMlkaYp37vpZgYGBrjs8stZf9R6Xnn5ZfL5Ascdf9yy+dwRQhz5JHBeAFprwjDED3ySJLtwp2k2kmuY2SjJ8888y6Xf+Cd+dPIprN68mbPPOQfH3T/AO+1NpwHwo8cex/M8jj/heNI0bbcBt6g3mySpppjPYx0g99kPAnSaotE4tjPlBThNU1CQzxcO+P6UUlQrVVzH7bbhXW4XLsu0qJRLDAwOdjuTKaW6TSz8MCBNknYpu+w5nUAr8AOef/559uwbJDj+BBpDQ3Djd1i7bh2el2Pf3n3U63W2nH0Wp5x6Cs2fe+8hTWoU8y+KIqIowjSzANi2HIr5AuVSmbj9pSnL4/cmnAtKKcxl9rec9zzK7bx9YMHSys486yzq9QZ33H47hUKeozdsWJDXmYsHH3iQXbt28c5r38X6dum4Ny9yXXshhAAJnOeVTlOCMMRvlyhTyuiOXJntUc3RkRHuvfte1t5zD5WREXIf/QgXXnzRxO1oAA0olIJTTzuVVrPJQw88iGEoNmzciGmalEtl1NhYu0lJguO4FDxvv9HnrMFASk+1hziOaTQbRFHUzuXMGpR0aiu7M8znVErNvtHIPJiqpbnWGt/38QMfDVimSc7NUSwUCcKQRqNB1E4riaIwK6lnZqkZoNm7Zw8/evxH7Nu7r7tN27Y5esPR2WTDcgmt4eUXX2I0DFi7fh3HHLOZ/lWrgOzuwHKsk7vSdKrBxHGMRtNT7Wn/jWZfFjt/M7ZtL0nO8lx1vqimWjNWq3Vrly/E67zlkrfQbDS45Xu38PZ3vmNZtKTe9uo2Hnv0Uc47/7xu0CyEEEtFAuc56DTrSHSKTlNAkaYJQRBkHfIMY8rb/88/9zyPPvwIpUKBd91/H423vAXrLReP2252S1mnWUqF1ml3lPrMs88iDCPuv/d+nn7qabaeeSbVU6p4nkecJCgUvt8iTROKheKEWstRHOM6uW7OZrFQYGR0lDRNKOWKtFp+u9ydQblYWnajx5BVORirjdFsNYFslDvn5kDrdi3oFqlu1+3XWZ6z53nkc16WljE0SBTH5BwXy8n+7EdHR7nnrnsYGR6mp6eHs845u9u5bO26tfuleBx3/HGL/K7FjGjwfT9r7W6YWRWMUolCPr8s/5bnwjAM+np6MQ2T4dGRBfuyZhgGV11zNf/+ne9ww7eu5+ijj+aat7110UrxTbZr5y7+4/vfZ9PmzZyxZcuS7IMQQox3WATOSqkPA79L1unoKeDjWus75+0FNCRxQpJmI7NaZ6MvyjBQgEaTxNlIVqrTrE5xqtFao8dtZPwEswmb15ofPfY4Tz/1NCeedCKXbtuG9+o2tn3xz8etA3EUZQFfMev0FYYBrZaPRmOaJhdefCEnnHgCT/zoCe647XZGR0Y597xzsaysoYht24RhyFhaI+e67QlQEPgB+Vwum/hnWeRyOdaMy0dOygmNZhOlFIXCgdM0lkIQhgwODeL7fvs2taY2VmNM1YBsbqTjOBMCiTiOaTQa1Ot1AFw3R6pbKCN7z61Wi9t+eCuWZXHZlZezdu3aOQVZR3hRmsNCmqbkXJdqpYrjOHPquHc4yEaeKyRJzFitRi6Xm/eue5CNyL/r2mt58YUXufuuu/iP73+ft77tbbN+rZnMqTjQc1968UXuuP0OVq9ezZVXXXlE/k6FEIefZR84K6XeC3wR+DBwN/DrwE1KqVO11q8dyra11oRRSBTH6DQlTTUKjdaQf+hBdKGIf+qp7eBYo5SBUtkFzGjXCJ7us7xWq7H99e20mk2Gh4bZu3cvW8/cyinHHceq3/ldam+9huBNb+ruRxRFWKZNsVjo5jBapoVSBs1WC60TLMukt6+Xt1x6Cdtfe4377r2PfXv3cuHFF3UDeNt2SJKYeqPenXUeJwlGzcAPAoqFApVyZUKQmaV9lA7lUM67zu/G9wPGamNEcTyxdvRBCnd0Ro470jQliiPiOGb3rl089shj6FRz2RWXz/nLQtZuO8lyxyWAXhKlQpGcm8Pzcljmsv84O2RKKXqqPURxQmsGzYjmyjRNTjr5JPL5PDd997vc8K3rOf3Np3Pc8ccf9PWyOzl3s337dkqlEscdfxxnnX32jAPvNE25+aabeP211zn22GO57IrLl2zEWwghJlv2dZyVUvcDj2itPzRu2TPA9VrrT87g+dPWkQyCgOdffpE0TbsT3cx6ndWf+hTl7/w7ALv+7POMvfvd0wbIk42NjvHM00/zysuvYBgGhWIBz/M46eST2JDP0/+FP6fyzX9h279/h+D440mSmDRNcWyHQr6AaU26/arBD3wazaxzYJKmuLZDqVRix/Yd3PrDHxJFEVvP2spRGzZ065Bq/UZ9Wtu2KBVLJElCFGU1YXuq1UOqfdvR6RLYmXyXvXb2RSBJk/aXC5V13ZtBYKPbqRdjtTphmE2utCyrPYnvwN0KoyjCsqwpb2FrrXn++ed49OFHGR0dZc3atZxz3jmUDvCFoXMMs7sQGkMZGKbRzaXVOpu4laQJtmWxacOm6TYlQ2VzJHVgpxZFEfsGB/CDAG+BJ+fu3LmThx98iJ07d3LGli2cf8H5+62jtWbPnj089+yzvPjCi3iex2lvOo3R0TGefeYZ1q9fz1XXXD2jknqPPvIIDz34EFe/9Ro2b968AO9oIqnjvHDk/BVLbSHqOC/rwFkp5QBN4Ke11t8at/wvgC1a60uneI4LjO96UQK2T9kA5TOfZuxHT7D3E/8Zs5DHffpp1n7ik5gDA+z9w89QuPtuytffQOp5NM8/j93//c9Iq/s3BdFaM7BvgGefeZbtr7+O53mcfOopnGpZrP3853Gff4Gkrxf3qacB2PPZP2Top3+KNNXZRLZc7sCl3DQ0Wy0azQYaTaVUxm132/N9n7vvuosXX3iRvv5+LnrLReTbuZ3ZqG2Ujcq1L1idiXSGYWRluEpZ3nOaptlI+gzL2vlBQLPZoNVqkQJWe4IhZPVWozgkTTUohQIM06RUyKoaTJebmSQJwyMj1BpZCoZjv5F+kaYpO7bvoFYbo1gqsW7dOmzbptVq8ewzz/Lkk0/QbGQlxvr6+zj55FM44cQTcByHba9u4+GHHmJgYIDVa9ZwyqmnsP6oiZOMtM4qn3ROB90uY6YMBUphqqwpRtr+MmAaWQ6167gEYYBt26xfO23rYrnwzpFceKcXhAF79+0jSZJ5qfF8MJ0Ofeedfx5nbNnS/bwaHh7mrjvuZOfOnRRLRU4++RTefMabu6PEO3fs4Pu3fB/Lsjjv/PMPWD7O933+6f//BieedBIXTZo0vVAkcF44cv6KpbYSA+f1wA7gIq31PeOW/xfgl7XW+xUbVUr9IfCZycunOnGjv/sKxm99HKPVQrWPg3/aaez6iy8Sbd4EWlO6/nrsnbuo/sNXiR2HoTPOoGEo1NAwuYEBRioVXj/2WFppyvDJJ3P0+edxvG2z6stfpnTTzURr11D/sR/D2rmT1vnnMXbFlQTVCpZp4XkejuPMbLRIw8jYCGEYUiqWcF13QpC7c+dObrn5e3iex9nnncuqVf3EcYwyDCql8n4Bcadcl+u6KBRxGqPIAkLbtnBdF8dxcdv7FycxfivrsOcHAWEUZZMXLQtDGSRJ0q2Z2+nEaBhGtwpGZ7Tby3lUqxVybg7f92kFPkmSBaO+72eVPVx3QnBdr9e7NVw7DMOgWq0yMjKCUooTTjyBo48+miiKeG3ba7z66qvZiH+hwNjYGOvWr+Psc85h9erVjNVqKEW3lXm27wmGYWKoLKvdNG3cnIPZXqYMI8tzb0/+tEyzW70kCCRwXihy4T2wZqvJwOAgqdbz2rp+KlprHnzgQR595BFWrV7FFVdcySuvvMJDDz5IsVTkggsvZNOmTVN+ntXGxrj7rrvZtm0bJ5x4IpddftmUX9Lvv+8+nnziSX7+F34BL3/od8RmQgLnhSPnr1hqKzlwvlBrfe+45b8P/JLW+uQpnjPjEecgDHjmzjvhW9czFPgM2A6vrF9PYho4toPjONiOTbPZxN72Gpfedhs9w8Pkooi0UMBfu5ae11+n8tr+qdbRmjUM/+qvMPpzP4v2sgtAlhObksu5eDlvVrPitc6aNnRGpoMwwLbsCbl/g4ODfO+mm6nVavT29bFq1SrGRkcJgoDevj42bdo0YbQny/uNQet2EKm7kx/RWU634zg4tk3L94niqD16bGG3A+PZSNOUIAgxFJiWleWW62ykG52NSjvjUjK01uzYsYNbf/BDDNPgyquuYs2aNYyOjLJjxw6GhgapVKqceNKJ+424NRoNnn/uOer1OscedxxHHXVU97Fms0mz1cSyLOI4xjSzSgxuLpeVD8wqAc6YBM4LRy68B9doNhgYHEQpNWWzn/m2e/dubvvhrYy260pv2bqVs84+a0b1pV984UV++IMftKtknEEul6NSqaCUYu+ePdxw/Q1s2bqVc849Z0H2PUkSwijCNIzusZLAeeHI+SuW2koMnGedqjHFNqY9cW+++WYeeOABAFavWUO1WsFt5wtGYUgYRYRBiOfl6OntodrTk92en5RWoYIQFfh4jzyCMTJKWirRvOhC9LhgLo4TtNbkPW9ObbLjOCZJUtatWYNlWYyOjTEyNorRvlh2K2TEMS+88ALPPvsc9VqNvr4+isUi+wb2sW/vPtavX89lV1x+wNzejk5N3Dcarhw4z3imOpPqOl0QJ2s1Wzz11FO8+MILjI6Osm7dOq665up5qxudpiljtRotv4Vt2eTz+UPKE5XAeeHIhXdmavU6Q8ND2XyJSRVmFkIURbzwwgv09PSwbt20f/dTeuWVV7j91tsIgqC7rFKpUK/X6evv59ofv3be9z9LUQtAaXJujiiMSHWK67rdhlUSOM8/OX/FUltxgTN0Jwc+rLX+8LhlTwM3HOrkwMcee4xXt21j06aNFErFed3vNElJkoSsmF2WO1zI57u5ybPVarUoFous6usHsgtBo9FgaHSEOI7IuRNLU3VylscHg9u3b+9esC66+CJOPOmkOQeLaZqy/fXXGRoawvM8yuUK+UKeVqvFnt272bVrN2mS4OXz5PN51qxZw6bNU9/GHb/NRx5+mMcfexylFMcceyzHn3A8Rx999LxNfupMXAzDENo1rdMkwQ8DvNzcKhRI4Lxw5MI7c81Wi7GxMVpBq9vUaLkKw5B6rU6j2aA2NsbQ0BCmaXHW2WfN+36naYrv++RyHtVKGS/n0Wy1GB4ZJgxDHMchjmMJnBeAnL9iqa3UwPm9wNeB3wDuBT4I/BpwmtZ62wyeP31VjTDg+RdfxDQMLHt+Sll10jEMQ+HYDrZtAQrLtPavmDGFNE276QOdUZfO7cW1q1bjeRPz/sIwZGhkmGaziWmaB82ZDoKAe+6+h+efe45NmzfzpjedRqvl02jUswtLtUJvX9+UF684jtmzew/bt2/n+eefo9lodjsOjmdZFmvXrsWyLVrNbFJjvVZn46aNvOWSSygWi933WqvVMJSi0Wxy/733sWfPHs7YsqV7G3emOscNsgl9WSm/id0FwzAkSRIcx6FYKFAsFrFMiyiK2Duwr1txZLYkcF44cuGdHa01tXqNwaGhRRl5Xu78IECnCTk3R19fH479xudaFMcMDw8xVqthWZYEzgtAzl+x1BYicF72hU+11tcppfqAT5M1QHkSePtMguaF3S8AjW5Xjkh1SppkE8w8L4frOFimPauP3CTJug86jkMQht0R5CRJKBUKUwZ1juOwun8VjUaDkbGx9sjK9GkHruty+RWXs/mYzdx5+x38+6uvdrczPgDuX7WKjRs3svmYzQRBwLPPPMurr7xCkiS4rstxxx/HKaecSn97EmKtVqPRaOB5HpVKZb98x1dfeYU777yTf7num2zZupU4jnn2mWdoNpvddSqVCu+69lrWrd8/AO3Udc5G8VX2ZceyshHkOAJNN5UkSRNafgtDGdi23U05cRyH3p4e8l5+QkBh2zbVSoW9A/u6KSRCHI6UUpSKpawRUq3WnUS8kpqHdMpwdgYgevtWkc/n95uTYVsWPT29BGHnc0UIIQ5u2Y84H6qZjjib7SAMrdvJFardZS6rspC0uwV2Hs+2/cYFSSlwHQfHcWc0SWayOEkIg5ByqURPtUqj2aTeaKAU5Fz3gGXcxr+fgcHB7ghoZz+mKzWXJAn1Wg0vn+/erhwdGWHfvgF2bN/Oa6+91g2mq9VqVot640Z6e3vnnNJw37338cLzz7crYZzIscceA0phmSar16yZcsJhdqs1wHFsCu0851bLzyY2kh33UqmIl/MwDIM4iWm1fOr1OmEUYhomxWKRUrE47THUWrNvcJB6vTbrphIy4rxwZMRqbpIkYXB4mFazQdrucJqbVInnSNRJy7BsG9Mw6Kn2kPcOXJ1jZHSEeqPOqr5V06XSyfk7R3L+iqW2IlM1DtVMAuc0zUYZs4uK6sTK7VJqWYkF01CYlo2hFIZpZGXKDKNbcq1TsmwutNa0Wi3K5TJ9PXMLSjvCKGJsbIxmq0mcxHRGZ7XW3Ul+My2BlyQJe/fsxbIt+vv7523UKkmSGY2CdUaOoigi7+Xp6+3tVhHpPAZM22K5k8883STEycIwZPe+vYpzAFAAACAASURBVKD1rPIsJXBeOHLhnbtuelKaMjo2RqvVxDCM7kTfNE27nwmH+4h051yP44h8vkB/b9+M31eaptQbdYqF4nRfLA7vg7OE5PwVS21FpmosJKWMbs3hvOdhmRZGu+FFJ9DsBs4LeHGJ4xjLtqi0m5EcCse26e/rI4rKBGGIUlkbbp2m+EGQVZNotcjlcgcdfTJNc8q0iZnIOu7p/SYodrZ7IEmSEIYRkGKaFtVKlUp54oi7UuqgbXhnW57LcRyq5TIDg4PdOtRCHK6UUt0RVNdxqNVrhFGE3/KzFKn2nZ4wDGf0ebBcdUaZbduhWumhXCrN6q6fYRiUSxLUCSFmZkUHzoaRVVUwDXO/yYEz7aI3H+IoolKpHDQQnA3btvfbnuu6eF6O4ZERGo0mlm3hzPE1Oy2ukyTtDtJ3jlfWhrvddU/rbj74wVpna60JwhCtUwr5PIVCAXeOqS9zVSwUabZaNJvNKYOJTiOXzqjdStaup/4OYAsQaq33a6uplNoI/BVwBdACvgH8jtY6nLyuWDimaVKtZL+eIAho+T62bWFbNsOjIzSaDYx23fblHkB3Wt53PqN938/uSPX1YS/iZ4UQYmVa8Z8ySqklvRGXpinKMMh781Oj+GAc22FVXz+uU2O0ndIBCtXO3O7kdpumOaEyRedilSQJSZqiyEZoK+U8hmESxxFBOx/asW1ybg7TMtFplhMehiFj9ax2cqd0XqcSRre7YJpgWw491T4K7bbhi80wDPp7+xhSinqj0Q2ekyQhCINu44QwDEl1OmGW/grkAP9CVu3mVyc/qJQygX8H9gEXA33AV8nOuI8u3m6K8VzXnZDL29/bR97LU6/Xs/NzGY8+x3FMGIZYpoUmJUlSvJxHf1/fon7BFkKsXPJJs8TCKMJxnDnXd54LwzCoVirkci7NZgut0yydQ6fd0ndhFNLy/e5IcqfahG3ZlHI5cq47qwts3svjeXmGR4dpNVvdCZiO7WCaBqnWlNwSxWJhyYNRy7Lo6+0jTTWNVrPdhhtKhRLlUgnHcag36gyPjuAHPkmcrMiLttb6MwBKqfdPs8o1wKnABq31zva6/wn4B6XU789Xvpk4NKZpUioW8bwcAwODNFvNWU+QXQyd+Q7VSpVSsUjSvuvl5XIr8vwTQiwN+bRZQlpr0iShWK0uyUUq5+bIuVPXLU6SBD/w29UrsgYrhXz+kLoHuo7D6r5VNPMt4jjCth28ZTq6ZZomfX19WKMmoPC8HHnvjVHwUrFELue1R8CCA29s5boAeLITNLd9D3CBs4BbJz9BKeW2H+84eItLMS8s06Kvt5d0IKHl+4fUTXO+ddpiV6tVeirZ56VNVilECCEWkwTOSygbxbXm1HRjoZmmSSFfoJAvzOt2DcOgWJjfbS4U27Lob3dqnO5x27LwluHvb5lYC+wZv0BrPayUCtuPTeWTwGcWesfE1Gzbpq+vn30D+w5aE36xaK3xgyyPuVquLPn+CCFWtuU31LeCdG4zLnVqghAdSqk/VErpg/w7exabnKrepZpmOcDngMq4f0fP7h2IQ+U6Dn09vdiWTavVIoqiJdsXrTWB7+NYWfOi5Xh3SgixssiI8xJJ06waRaf9tBDLxJeAfz7IOq/OcFu7gfPGL1BK9QA2k0aiO7TWARCMW3+GLyXmk+d5rFm9up3LP4pSatHziNM0JQiCbM5BX9+sSksKIcRCkcB5iQRBgOd5cptfLCta6wFgYJ42dy/w+0qpdVrrXe1l15AFxg/P02uIBZK1oq+SthuoxEmCY9szGvUNo4g4itqlKrN66rNpZd+pnpHL5eit9izLdDYhxMokgfMBdBp4zLdODdL5aHgixFJp12juBTYCplJqS/uhF7XWdeAW4Gng60qp322v+z+Ar0hFjcODUoqeag+2nVWS6eQ9TxU8dzt9xhGWabWfZ9NsNak36ji2M6NR607Hw0q5TE9V0jOEEMuLBM5T0Frj+37WStsw5nW0Q2tNEAQUiyUZRRGHuz8Cfnncz4+2/3s5cJvWOlFKvQP4a+BuxjVAWdS9FIck66xXopDPs29ogGZjYnMgrTVRHBFHMZZtUy6WKRWL3RKbna6so7VR4jg+aJOVMAyzLp6VqgTNQohlRwLnSTq3CL2cRz7vZU1Cmk0MY25dtTpllJRS3Y56OdelWpHZ4eLwprV+P/D+g6zzGvDOxdgfsbBM06Svpw80NJstLMvEsix838eyLXp7eigVS/ulZBiGQU+1ius6jI6N4fs+rutOmbqRpilJmtBT7ZlVaocQQiyWFR04G8rAsW2arRZhFAFZaka5XKanUsU0TRzbodFsEscxzVYTy8q66XUCYcguKJ2AuhMMdwJmrVNyuTxojWka5PMFcu7itpEWQoj5YFsWq/tXUavXGR0bxfcDCoUCPdXqAasDKaUo5At4OY+h4SHGajXsdr60YRjdz80gCLKa8YdJyUohxMqzoqM3wzBY1b+qGxgbhoFt2+THdc3yPA/P80jTlJHRUZqtBqkGnaaYZhYsx3FEqjU61e1CWwqlsgYjxWKRQj4vtxyFEEcEwzColMvkci5xnMyqiZFhGPT29GIYBvVGgziOSZJsAEKTdTDtrfZgyuflglFKfRj4XWAd8BTwca31nQdY/z3AHwPHAS8Bv6+1/tZi7KsQy9GKDpwhGy0ulw7enCz7wO+hp1ol1Rra+c+Q1WNO06xddRTHgMa2bbycJwGzEOKI5Dou7hwqxHWC53KpTKpT4jghSbJ/uZw7bTdTceiUUu8Fvgh8mGzewa8DNymlTm2nVU1e/wLgOuBTwLeAdwPfVEpdrLW+f/H2XIjlQ2k9XR+CI4NSqgyMjo6OUi6Xl3p3xMokyexzJOevWAaOmPNXKXU/8IjW+kPjlj0DXK+1/uQU618HlLXWPzZu2c3AsNb652bwenL+iiU1NjZGpVIBqMxXNacVP+IshBBCHOmUUg5wFvCnkx66BbhwmqddAPz5pGXfAz4+zWu4gDtuUQmy4EWIpbAQf3srJnCuVCrz9m1DCLG45PwV4pD1Ayb7d+3cA6yd5jlrZ7n+J4HPTF64YcOGme+lEAujF5AR5xmqAZX2f4UQhxc5f4WYX5PzM9UUy+a6/ueAL4z7uQRsB45GzuG5kmN4aDrHb2i+NnjEB846S+KWkSohDkNy/goxbwaAhP1Hi1ez/6hyx+7ZrK+1DoCg8/O4XgU1uWM0N3IMD81C9MuQkg9CCCHEEU5rHQIPA1dPeuhq4J5pnnbvFOtfc4D1hTjiHfEjzkIIIYQAsjSKryulHiILij8IbAS+DKCU+hqwY1yFjb8A7lBKfQK4Afhx4Crg4sXecSGWCwmchRBCiBVAa32dUqoP+DRZA5Qngbdrrbe1V9kIpOPWv0cp9bPAn5A1QXkJeO8sajgHwGcZl74hZk2O4aGZ9+N3xNdxFkIIIYQQYj5IjrMQQgghhBAzIIGzEEIIIYQQMyCBsxBCCCGEEDMggbMQQgghhBAzIIGzEEIIIeZEKfVhpdQrSilfKfWwUuotB1n/PUqpp5VSQfu/716sfV2OZnP8lFLvV0rpKf7lFnOflwul1CVKqW8rpXa2j8NPzOA5l7aPs6+Uelkp9RuzfV0JnIUQQggxa0qp9wJfBP4rsBW4E7hJKbVxmvUvAK4Dvg6c0f7vN5VS5y3OHi8vsz1+bWNkpQS7/7TW/kLv6zJVAB4HPjKTlZVSxwDfJTvOW4H/BvylUuo9s3lRKUcnhBBCiFlTSt0PPKK1/tC4Zc8A149rojJ+/euAstb6x8YtuxkY1lr/3GLs83Iyh+P3fuCLWuvq4u3l4UEppYF3a62vP8A6nweu1VqfMm7Zl4EztNYXzPS1ZMRZCCGEELOilHKAs4BbJj10C3DhNE+7YIr1v3eA9Y9Yczx+AEWl1Dal1Hal1HeUUlsXbCePPNP9/Z2tlLJnuhEJnIUQQggxW/2ACeyZtHwPsHaa56yd5fpHsrkcv2eB9wPXAj8H+MDdSqkTFmgfjzTT/f1ZZL+PGZGW20IIIYSYq8n5nmqKZYey/pFuxsdDa30fcF93RaXuBh4BPgp8bKF28Agz1fGeavm0ZMRZCCGEELM1ACTsPzq6mv1H9Tp2z3L9I9lcjt8EWusUeBCQEeeZme7vLwYGZ7oRCZyFEEIIMSta6xB4GLh60kNXA/dM87R7p1j/mgOsf8Sa4/GbQCmlgC3ArvnduyPWdH9/D2mto5luRFI1hBBCCDEXXwC+rpR6iCwo+SCwEfgygFLqa8COcRUi/gK4Qyn1CeAG4MeBq4CLF3vHl4lZHT+l1GfIUjVeAMpk6RlbgN9c/F1fekqpInD8uEXHKKW2AENa69eUUp8DjtJav6/9+JeBjyilvgB8hWyy4K+S5YvPmATOQgghhJg1rfV1Sqk+4NNkNYWfBN6utd7WXmUjkI5b/x6l1M8CfwL8MfAS8F6t9f2Lu+fLw2yPH1AF/pYs3WAUeBS4RGv9wOLt9bJyNnDruJ+/0P7vV8kmUa4jO4YAaK1fUUq9Hfhzsi8bO4GPaa3/dTYvKnWchRBCCCGEmAHJcRZCCCGEEGIGJHAWQgghhBBiBiRwFkIIIYQQYgYkcBZCCCGEEGIGJHAWQgghhBBiBiRwFkIIIYQQYgYkcBZCCCGEEGIGJHAWQgghhBBiBiRwFkIIIYQQYgYkcBZCCCGEEGIGJHAWQgghhBBiBiRwFkIIIYQQYgYkcBZCCCGEEGIGJHAWQgghhBBiBiRwFkIIIYQQYgYkcBZCCCGEEGIGJHAWQgghhBBiBiRwFkIIIYQQYgaWfeCslLpEKfVtpdROpZRWSv3EUu+TEGJm5PwVYnmYy7molLpUKfWwUspXSr2slPqNxdhXIZazZR84AwXgceAjS70jQohZk/NXiOVhVueiUuoY4LvAncBW4L8Bf6mUes+C7aEQhwGltV7qfZgxpZQG3q21vn6p90UIMTty/gqxPMzkXFRKfR64Vmt9yrhlXwbO0FpfsAi7KcSydDiMOAshhBBicV0A3DJp2feAs5VS9hLsjxDLgrXUOzDflFIu4E5a3AsMLcHuCAFQAnbqw+n2zhKR81csQyv1/F0L7Jm0bA9Z3NAP7Jr8BDl/xTI1r+fwERc4A58EPrPUOyHEJEcDO5Z6Jw4Dcv6K5Wilnr+TAw01zfIOOX/FcjVv5/CRGDh/DvjCuJ9LwPaP/PeXKFYq5FwT0wDLUpgW5ByFY4FhaApuyvryGIbSpFqhlMZRMTEmCo1DREkP48ZNjDjEin2MOCCxciROHnSK0xwGILVzhLkyr1vHUg89RpoOIw2TWkOTanAcRc6G912W7eRXb4UohmYrZfeeFrXRgNpwndGBERrDY4t8CMV8ieMmD//gpwFqS70vh4kpz98Pff5FCqUKhoJCwcKyFDlH0VtJyVkay0hx7ARTaRwzJmeF6PY5bJFgqRiDFFNFKK0xdIqT+jhREyMJAVBpgpFGAJiRjzZMwlyF1HKIzBxmmj1XK0WqTCIrz4Zjjwfg9ZdfJEGhUZSCIfLDr2MM7UE3G+goBKUwvAJ4Htg5tG2j4hi0hjQBw6R+1Mk07CqxsrB0jKVDYuUQKwswCLRLPfYY9T2GmxZ7hyAIUnw/odmMCcOYseEWY4M1GiNjBE0fnaaL+ss70qzw83c32ajzeKuBGBic5jlTnr+vv/465XJ5/vdQiIMYGxtjw4YNMI/n8BEXOGutAyDo/KxU9gXZ9UrkvDK53BuBs2VBzn0jcM7nUopFMFRKqo0scDYiYt0OnFVEKY1wYwMzDrBiEyMySWyP2MmjdIqjspdOHY8gV6RoldGhR2g4BJhEOgucXUeRc6DzWeIVwIohVSmuZxMEPrarsJwYy04W/TgKsRSmPX9zJVyvjKEgl88CZ89V5AspOTsLnN1xgbNnBW8EzuqNwNnqBs4JTmLhRAozydI1JwTOoZEFzl6BxHKnDpztfDcYKJWKJBhZ4GwHFMI8hp9Dk6BDIwuc87kscHZyaNtBxdGEwFkVixhOkVjZWDrCSkNiwyFWNhoDW7voKE9keQTKIueDMlO0Skh0DEaM41rYjsayUxLblMBZHIp7gXdNWnYN8JDWOprqCdOdv+VyWQJnccRY9oGzUqoIHD9u0TFKqS3AkNb6tZluJ0k0QZBdRAxT4WFg2waVQkrRTSg4IXkrpGzVcHULN27ihA2MOMTQCSoOMZKIyKsSWy6hU6Th9THIKhqRR5iYxKlBsRJQsHwKZp2yP8DJg3egDTPbiQKoXIQ5vBeiBJQLfACAn1x7D02nTNMsM3pamSDJESQFwng9jdAkThRRrAgiGK1l7yWKUsIwpdmMiOOUKEiIwpj6aJMojEiihDiKSOOUVKfoVKPTlDiKSeM3gvE0SbBcp3uRNS2TJE5QhpGtH4SH8isUK9h8nb9CiENzsHNRKfU54Cit9fvaj38Z+IhS6gvAV8gmC/4q8HOLud9CLDfLPnAGzgZuHfdz5zbQV4H3L/reCCFmQ85fIZaHg52L64CNnQe11q8opd4O/Dnwm8BO4GNa639dlL0VYpla9oGz1vo23piQIIQ4jMj5K8TycLBzUWv9/imW3Q6cuXB7JcThZ9kHzvPlnNNtvIKFacBo0ySIwLHANGCwbvFay0brApbVQ87RpGn2+aIUFHMJRTfGsRIcI6bPGSGXNvCiGhuTIaywQWq6pKaFChMIwdvxHMnr26i//Bq7Hn2VHbfu3W+fzLzH20azVI0HLvowSbM15b57k35eN69H5vDnHeXSe2KFfF8Bt5QjqPmMvj7C3vuHl3rXaOqEn1nqnRBCCCHEvFgxgXNHnE78wm0ozfgv4e6ksu6mMbHqjmcFEx+P/P1ew4zfWLbr0VfZede+Oe6tmA23lMOwzKXeDbEAVq1yWbXKwbbAMjVlL2V1sU7JaqLQWComlzYAMHSCF4wx6B3NSFShlngkqYFjJmgUiVYMN12G6yZxkp3zlqkJIsXGfp83G49TfPpBwt17CPdmX76K6/qx+nox+lahC2VUmmQT+447EYDq2Gv8ILqcwZqJYx1HyTubaI1irGkQx9l7eOyxYV587CX8emOad9lq/xNCCLFcSedAIYSYiyTGv+krAJS3XoUpn6ZCCHHEk496IYQQQgghZmDFpGo88WJKvqjbNZxTHFuhFDRaJgUPVlcTCm6Ca8WYSmOqBMeMMVSKpRI81cRJfUIjh1Ka2HBoOFWMNMYKm5hREytIslu4QNy7FtWzmuPPOI/jLCdrkmI5pMpE6RSFBvXG95bL/vW3SW0XbTskuRKxlUMbJqkyabg9hCpHrC1CbTMaFkhTRaIN4lQx0rRp+opaQzNWT2jUY+I4xW9FRGFCqxGw6+WdtMbqi3rMc8UCjueiDIVONa16EwDLzv7sij1lyr0lSlWPciWH6xoopYjjlGYzJgoTbMfEsgws2yDvmZimwjRAGar9uwTTULgOrK1GuHZWTTfRBi5QDyy0znLZLSPFaKfeaK0wDU2SKvwoq7/rWGlWiVeBbWal+SwjxTKy/0812d8EkGhFlJoEsYUfmwSxgaEgTqAZGNSbmkZTMzY6Ah/o+7/s3XmcbGlZ4Pnf854ttozc7l4bVVAUW7PIJtgCI6KI3SjyaRGnbarbT6vQaqPTjtuMthuoMx/EtpuhXVpbtEec1tGmESx1RGhQkGKvgqIoarv7vblFxnaW933mjxM3K+tyb1Vm3ryZee99vp9PfCLinBNxnjhx3sw3znnO8+7odjfGGGPM5WFHnK8Bq0s722HeK87PZzfGGGOMuRTWcTbGGGOMMWYDrONsjDHGGGPMBlwzOc5veO4XYfoGBr5V5xdPNKMxQR2C4iTQK6dYLTMGeQMfYHVUl6yKo7qmsw91FSofIC8gL25lqi20G0qaKM/ffy/7Tt9F/MA9DO/7Mv2jZyiHdQk7cUKVVyw9sMTZjy9P6jh/CoAPvPZXLlrH+XzN6zKmb56ie2Sa9oFpsuk2cbtJNNUharepnvUcqqxD5AuCROSNabxLUJljFHUYhjZ5SNdydMsQcXI5pTdQxmNlMPQcP7rKaJCzdHqZUW+AiyM0BPLh6FHDdT+WcX9wwdJb5eRjjnp9zjwILo7IWk1mDszRnm7R6TboTGXMzWVEkVCUgapUqkqJIuHQfuH62THPDB+nufAQsnia8t7jHP3Ap3joA8epetXauh5VXTARnvKdt5G0G0C93tFCjyqvKIcF/dMDzn58GQXOFR18dPHBC3M8Ums7A9rA/snzoW5sWxljjDFm77tmOs7XsipKEdXHX9CYPeqrb+3R6tSPl0YtqiCc6HU4G7cZl45xIYwLwQmkiTIYCeNcaTWFfV1PM/WUISJygUZUcX23Rztt4ZzSTnIiUWJXsY/TNBdPU5w8xej0EuOlVUZLQ05++kGSZsLyQyssf+6RawbqH7//EoD05c/l8Lofvyn1j6hzbtiB7XRO87qM1sEGi5/toeX2tP2ZZ9RfQHOuyfwT9xFlKeVgTHO+y+DUEvf/fw+Rnyq2ZV17hQ1gZIw5n3WcjTFmi/xwxHuT24C6E/3N5T0AvH/62Rs+g3QlixvJ4y9kjDFXEctxNsYYY4wxZgOumSPO46hFE2VUZYzKmNxHFJXjxIIjcpCldQ5z5GA8OduYxDDO6+lhUgv48GzBXGNEOx7SkgGdfBHvEuJqjKDkvkM+dYDmoRHJ6dOc+tynOfOJ5bXTpa0nNOgcbPKEV11HNj+9Ft9Lf/2fQlkgzSY6f4iQZuRTB8iTDnEoiHyB8xXO58TjPiFKqBpdfJxSxC1GLiYOJWk5oLF0nHjYQ0XQtEG3GjNuzTNKuyiOWCoKEmIJuKQg8jHOJbSbQrspzM86brtlFlUY5YcZjpVGJqjCcKTkhTIaedLUcXBfxIGZipu6Cxwoj9I9cTdy6jjD+76MiMOlMXG3S3Td9fj5w/iszbgxi4owdeY+3OJJ/JnTVMsr+LxAK49fqAgnKorVuu7zuWG0i359BG+8MqJ3fJW//cTKpvYBLZW7/vMXLnVXMsYYY8w1yo44XwNClO52CMYYY4wxVzzrOBtjjDHGGLMB10yqxnvvegJzc9PMdpU0VmKntNKKr3rCmMR5nNTDKnuNqIIjqJs8r0efK3zMsIg5vZJyYilFdZpmpjz1wDRd16OtgeZokfbRu2F1hTAc4BoNnvytL+S2b6vfI5QVoSwBiLIU0sZafFoWCID3SH+ZKG3QKAuytEHZ6BLilCLtUMT7KGaaeGIqYgQlpsQTI5EiyRzj5q2oCokraeqAZrlKc7RIY3AW50vURfikLqAW532i3gIvyMcwHlEtLlCtrFINRoyXVlk9vsRwccji3SsUi+VFt+9Dk9v5JBGiZvSoEnHXAkmE6dvazMxn8Ff37XY4xhhjjNkGdsT5GpCUw90OwRhjjDHminfNHHE2xly5/vKTbQ4c7JKlwnRHEakHIuqPBREljmC6XZ81ihzsmyqJXX1BbiSBqXRMKxqRuXF9EW014hbXJxn3iBZ7uP4yRDEkKb7ZYfDqf4l3MUhMJhENlCiUzPtxfaGuekTD2gW7ripw5SM1jF/2x/8GQlUHGc4bBCcfo6s9ytOnUe8JRYXPC1wa12eixIEG4m4XyVIkTZFGC53dh0ZJfbWyq495qIsIcYqPG/ikwTidonQZ3sVUmqDF9NpAR6MyZmUYkZf14E2jUWBxqaS3MqbfG1OVHg1KWZRUZYUvPb2FZULl8eV5Z5tOXOBLes62fd2b5uKIRqdNa6pNe7rNdTfNMT2T0mk7pqeEW/YP6KZDZmWRueX7kY99gHKlh3pP1GggTvCjvP4O2m3iQ4fRmTmWSOHl37F7H8wYs+fYEWdjjLlEEjwEz+iDfwhA4+X/FJwdlzDGmKvNNfOX/fVPupOZRkJz8SiyvFofBRIhTM3hmx2K5gzjdIrVeJZRaDCqMgZFyqisN1HQ+uDR/m5ZD7mtgg/C3af2Ufn9+FAPw90vlMIpVUOpYiVPPONxRVUGPGFtDGgNiqsCvzaJ75fD/0IVBC0Uv6I4V+dFex/IsoigUBaeqgrEsUMD+FAfYev3cqrS4339fNQf40uPOCFOGkTJE3FO8FU9X5ygQYmTCHFC8IFRf0xVVlRlhXOOEALBe8JUwLc80U3R2rYUJ+STwR2iJCFOYtJGtvZeZV5QjHJU6/WVebHhYbovlThHnKU0Oy2iJMY5h4gQJzFJFuOi+jO3pxrMzrd48hMbTKrdEUc6+Z7rbV956A+VcnLg8Fy5wiiifs8Ibj5Y0E4KIudRFXKfUPiIwjvOeGGw2oO/mtuRz26MMcaYy+uSjzhL7Ski0n78pc1uiKJr88SCc9fm594Ma7/GGGPMxm26ZyEivywit08eO+CvgLuB4yLyNdsbnjFmO1n7NcYYY7ZuK4fkvgO4a/L4m4GnAc8G/iPwi9sUlzHm8rD2a4wxxmzRVnKcD/DINdXfDPyhqn5GRN4JfO+2RbbNfuPTz8JFHaanv4Zux9FoQZYoR2ZGzGWrZJKjCCk5LVaJooos6pPli0T5ECnHII7QaK/VQc7bM5yevZ5h1WS1zFgdJ7Qyh3NC5etbt+VopUInLWnGJVNRn0Mr95AunUBPnwJ+BIDX3/FPiJ0n7TSYf+7TcfsOQKOJZi2q9jQ+aTFuTNNPZjhbzjEoM5aGTcpKgBaVFyKnOAerQ6GqoNmAbiuQxoHKC8Pc1fM8dNt15YF9rQFTcZ8Ih5OIKCiC0h2cpnnsHlb+5kPc/cefYfjA+FHb82nfdRuN2Q7pVIt0fpbk4EGYP0DZ3UeIUvqdg+RRC09Mrhljn7FSNFkdJ4xLx7gQFleUPA9r76mTPPKiCJRlwPvAcFhS5nV+9K23zXDTYeW6bp+5dIWZ4jQuVCCC4hilU3iJibQiSMRQ25wZz7A8Sjm1FLHaDwyGnrJSNCjihAceLogiod2KmJtx3HJgxE3NYxw8+nHkaHNxsAAAIABJREFUxP2EwQBJU1yrDd0Z8v03MWzto5/MMgpNjg9mON5rUVb1933slGc8DlRVnWPebe+5dJErsv0aY4wxe8FW/qufBm6bnOZ9JfCXk+kNQLcrMGMu1aBq7XYIe5G1X2OMMWaLtnLE+V3Au4Fjk9ffMZn+fOCebYrLGHN5XJHt9wVP80x3xzinJJMKJpU6iipieZQwGDvGueADOAGRFIA4hjiC+amEJGqTRIFGVJE4TxU7QtvhOoGnTd9J48wDsLyAO3GU2fvuRtIU4qSuqwz1GwMUBdXiAlpWiJO6znJUl2Yp+yOaL/l2AP7H7W+nWOiTziV0rmvSmM6AuiqNiyMW71tidCwnVIqWSjqXkM7FRKnDJRFThzqEyjNeyRkv5xSLFa0jGc25Jq25Fge/6klkB/aRTHVJWm3ImjQ6M6iL1m43DHtIVSD5CMZDyOszZ3VpGAeHm3Cdq0vGiIOlM4R+Hz8coWUJt9VVaQCq4aguLzTZDlGzQTzVQbIGOEGSFFpt6K2QHzvO4Nhp+ieX6B1fqTdfHOEioTnbWqvskzQTOtftI5uZwjUaaFnWNa3jiGKlT77cxxclUZoQNzM0BIZnVihHJeWoREP9Wy9fGlM+XJ+ZihKHBmXlS4O1EUtXJ7djm9zvhroz1YCMMVeOTXecVfUnReTzwA3AH6jquXP4MfB/bGdw2+nrnj3m8GxJV5cAKKMMVaGbLxCNCxRBUBShjJuMkw695hwL7pn004SlQcKpBXjwrgGjQUlVeeI44km3TnPkgHBwOuf6bo990Rk64wXicoQLJcnn72Tl03fzpTvu5uTdA04C905iilpNXvn6OlXj/vc8hJ+UeOM3PrcWd9R0HH7xfqYOdWnOd5mbnWJ/lq79wzg3hHfcaRN3p3Bz84T91yH5EPFKGKVo2kDKAjfsoYtnGD/4MA/+9Wc5e/cyp08VnN7C9rz7XZvrY7We0OC2V95G+8g+0rlZom4X9h2EjoMQIHjC0Qcpl1YIecF4cYXxUp+k3SBKYxpz0zTjZ8JSgg4yyplDPDD1LE6PpjndS1kdCiurgX6/YnW1YDyqKMYFw9UV8nFBqAJxUu/uaTMlbSQ02xnd6Yz5+Yx9c452I5BFJQUZJ69/Ps2DT2H2gY+jZ09TPPwwZe/zjJfuIJQVujSgOt3nADCfVyzf26dYLHnGS/Yzc8MM4hzDhT5husGPbmH7Xi5Xavvd89xkP64KHn7Tazlzz6lH2vP5i2YN/sGf1r9X/uLQVxF6V+7InpI1djsEY4zZUVuq46yqv3eBab916eFcW/xwxPunn80rVz7FK1c+xfunn33Rf7aXQsUh2/6ue19ZVLsdwp5k7dcYY4zZmg11nEXkezb6hqr661sPxxiz3az9GmOMMdtjo0ecf2aDyylg/3iN2Vus/RpjjDHbYEMdZ1U9fLkDudzuvK9Dp9slSw8RuTolESCEm+vhrEvwQZnuCNMtTzOpSGNPNx3STQZc3wk886ASP70ilpLE58ShpDn+DJEvkNwjwxJXjBFfgQbQgOw/yMwrDvO8V7y8XmFVwmCVMBxAVdUXH018zVu+iajTxB25kdCaomjNMmrOccLdwPGqQb9IGBYxi6uOwVAZjuryaqeOr9I/PmD1rj6j1T754ELpHtPAQeDW+mn2OnjO5dziF/DA5HZRT/rKSWfWPf6782d+/tLi2ZBbWdtm52utu3/BuukLjzysTg+A912WyDbqami/xhhjzF6w54rMGmOMMcYYsxdt6eJAETlIPXjCjUC6fp6q/sQ2xGWMuUys/RpjjDFbs+mOs4i8FHgP9UAKN1FXV7sB8MDd2xqdMWZbXant96v0Y8yOIB72kPEAkpSQNNA4Ju/sx3czirjJKOqQa4PVqsW4Sil8RF45VoYRlY9RhchBEisi4KS+f6B6KVX6MqpZyNswGD4yoqWcq1vs6rrQSUtoHYI4UuJIiVxd3jiEumb03DfkzDd6PE0XiEKFaMC7GO/qtKxzdZEBnvahdxH5giiUKI7gInRSA6c5XiZdPYMb9ur6y1UFaQpJupbipSIoIGUBwz7R8kI9/GacTIpYJxBFaJpB1kRGgzpdLHhwEXpudNJGhyLrMrjlxZSSUpHgNapvkxOTiVSkUhBJRRxKRAOCEqSOOUhEwKGT5WNK2n7EXNFfK/UJIChRVQAQXAQiFFFK5dK1z1+5lEoSHIG51YeJxz2i8QApc2YBnKs/e5SAq2toa5wQ4hQfNyjTNlEokeBRF5HkfWSyToCQZEjwuKpAqrKep/V3Lt5DWYBz9IZjeO0PbOeuvOtE5E3UQ9YeBu4C3qyqH7rIsrcDv32BWc11pSyNuaZs5YjzLwLvUNUfE5FV4B8Bi8DvA3+8ncFtp3+R/T7NfTexPHszALlrsuKnuevEHGeXlYWFAlVYbNR/hLMsodVMmWq3ONAtCQi9YcTh6TFT6ZhWNGJOT+GjlBAlJPkqPpvi/rkXMSwbtJMRc5wlSES7WKZz6l70ofuoFhYZLyzjxwV3vfuzVKsRr3zD/w7Ah3/ifV9Rju7AC2fZ9+QDNFdGHG6nZN0WM097IvH8HCQZXDfNA1/79axWXYQpOvGAJ3zs9znxFx/m+CePMXV4ivlbDzL95JuIn3ALYWY/PmvzpennUfqETjxgxp9lqneU5NSDhFMnGD18nHvf9xna+9t0DkxRjgru/a/37+wXdpUYqufbdzuIR7si2+9eF8LjL3OOqvLgffdy0xNv5bpbn86pez59+QK7zPSaLHR55RKR1wFvB94EfBj4XuB9IvI0VX3oIi/rAbetn2CdZnMt20qO89OB35w8rqh/eS4D/xvwk9sVmNk+jdmp3Q7B7B3Wfo25dv0w8Fuq+puq+nlVfTPwMPDGx3iNqurJ9bedCdWYvWkrHecRcK4UxAnglsnjCjiwHUEZYy4ba7/GXIOkHof+ucAd5826A3jxY7y0IyIPishREfnvIrLT9ZiM2VO2kqrxUeBF1LXA3g/8sog8GfgnwN9vY2zb6uGbX8YT4mX23/cR8PWIctfHCU+ePUQ106F6UoPgYobpNJ6IsTbJQ0rhY4oQUwVHNuVZzVOWRymlnyYvjzAuoCihqhS/7nStahsN8/gAISghPJcqCvh5JTsS0Z1yzHyLMNP2a6+Z+eiH8EHqYb+9IyAMRDlbxuSVo/TCKHcsrtTr8l7xS4r75COnS0Wmmd3/o2RvgNb3Bsp2jmusMMUKAEEiKhJkMmT32XyaY9U8fX0641lH3hHCzTB6IYxzrdfhlZk3ROybCcy1C7rpmG68SkSFiCKqJCEn8TlJNSIdLRP3l5Cls4TVFaqFRcr+EBdFRM2MqN0m6nbRwzcSsiY+a5NnXZYbhyg1Iaz7PRdLVedR4vAaUYSEIsQEdfV34yMK7ygrYVw6VCEvhXEOlddHnUL3of6eslRot4Q0fmS6KojUj8/lwIYgjPK6dKFIvUxVQV4oRamT71ypSiXPPcErLhKazQgXCcErw/4KvHH/du/Ol+KKbL/GmEu2D4iAU+dNPwUcushrvgDcDnwW6AL/GviwiDxLVe89f2ERyYBs3SQ73WmuOlvpOP8I0Jk8/mlghjpP6kvA1XUVxTYIkw7qbmrJoB7aYgfJoLezK7yAzeSdbpc42fMVHq39GnNtO/+/gVxgWr2g6t+xroK+iHwY+AT134ofvMBLfpz674oxV61Nd5xV9Z51j1eBf7GtERljLhtrv8Zcs85SV885/+jyAb7yKPQFqWoQkb/noqNC8VbgbeueTwFHNxmnMXvanj88ZowxxphLo6oFcCfwivNmvQL4yEbeQ+pais+mvj7iQuvIVbV37gasXkLIxuxJGzriLCLHgX+gqgsicoLHOPGvqke2K7jt9KnT13FX9jQGvJjg6hSKcgzj+8Nafmu75bjxYKCdebK4InGeWALNqERiJRJPqzOioUOa5SpJNSIZrdS1ZYu6Ok/ImoSsRUib5FmXUdqlcA0KzRiHjLFPKcOk7qgKkVPOjd1cBUcVHKMyxqugCj4Iw9zhg1D5Os82SQRKBQS3rhpU0DqHd6UXaDYdo9xxdrnJF3giPrC2bFBIE/AeyqrOA46i+r1DUFRhOAr4oPXI4ao8fKzigYcCUeRwUYskbhNCPUz5+hjiuP4tlueeqgpUVSCkitsneK9oUPwgoKuKO/Xo321l7hEnRJEQRY4krbeT94Gy8FRlmGw3T1XWNVk1KN4HVLWO/bzUGHGCBqUqK4IPlHlJVVZr8xutBlESESfx2rJ1DI4oquNzsSNJY7JGXNf+dYKIEMeONHUEhWYzot2KuOkINNO6mq0IjIdb3GG30dXQfsu0zer0HOP5NiNtrdUF9hoxLUtk1ZCkymkUqyjCVDZDiCO8iyk0w09HpFKQ6Yi0GpHHLUbSZuQbrOQt7nq4wdJKxaBfMRqWLC8M8D7gy4qq9HTnOqSNhEYzptVKaLVigtb57ap1vvtoVAJ1O0qTLkeOHKDZENLJpZjnroFopEorU256Yv38jpPPYrmvDEf115LngcGgpDOVcP2hmIP7S2abI2bT3lq+f1DH0DdRFUSUMkSMqoQTyxnjQohcvZ6yEjrNQLdREruAVyEv63bVTCqm0jGxqyb7q5JJTkxJIgURESKBdrlCc7yM8zmuzHH5EFcVkI/RhdNUi0uoKmWvTyhKhqeXCFVAnFAEpbcyQkPAl4FiUNSPi0C+WrB6z+M3kLgbk0xFhEqpehVRcxL/oZTmXLNeT7+o223qWPrCKsViuaX9bOYZHZpzTYZnhwSvlI2r7tjS24B3icjHgb8Fvod6IKR3AojI7wLHVPXHJ89/mjpV417qHOcfpO44/6udD92YvWGjqRo/A/Qnj//t5QnFXC66+2nWZndZ+73MisI//kLr+ADv/ojyuhcL3/VS+L/ef5kCu8zKhcVHDehi9jZVfbeIzAM/RT0AyueAV6nqg5NFbgTWXx0yA/w6dXrHCvBJ4CWq+rGdi9qYvWVDHWdV/Y8AIhIDy8Bfq+rpyxmYMWZ7WPs1xpyjqu8A3nGReS877/kPAT+0A2EZc8XY1HkoVa2A3wGalyUaY8xlY+3XGGOMuTRbKUf398AzgQcfb8G95B8v/xbdRlYPOd3soFFCcAnLnSNUkiCiqArD0KbSiCLE+BAxCnWCYhUcpXeodvEqFJUjL4W8FMoK8krxHvLVOkc4cnUusWpdb3k4quv8xrFQVXXuhIuEJIavfVod48fuaVL6R2oGi9R5yS4CJ7r2foOBJ8/9Wk5ynDhazYh2y7Fv1rF/uqIRl2Rxncsb1OFV8KHOx3UCkQu04oJuMqBDj/0nPoM7fj/l6TMQAsmBA9CdRtvTa7WWy7SNdykAzf4p4v4icvYU5YkTVP0hLo2RKEKSBIki1Nc5y5IkSBITRiPGZxYYL/YYLQ04c88Z8pWCYrFCy8DMU6do72/TmG6STTVoHZglmarzv0NR4fOCcjAiXxkwWhoyXBxy5s5F/GgX6s49Dp3cKt3cKfwdcEW2X2OMMWYv2ErH+VeA/1NEDlJfoTtYP1NVv7gdgRljLgtrv8YYY8wWbaXj/EeT+1+f3J+79OxcEfXoUoMyxlw21n6NMcaYLdpKx/mp2x7FDnj7yu00yi6N0pEmQrMhtBrQKTyKMC6EygvO1aWiYqeksedIe5nUFWSMiUOBIsShxKlHNBD5gsgXiCoSKlyVI8HjfImUBYQKOXWcarxc138TRygKCAFJEogT4C0AvO5D/4xiucdwoU/wStpOSdsZ5aigHBb4MpA0E+afch3NQ/uJZ6aR7gzlkVvQKAENdZmoUlEfIeOAhDpVYDx1gLzRYRzX5byODeY5sdrhwWqKsjrM6cUnkxOo5pTx2HPm/j6jQc64P6YsSopRTpkXeO/xZUmoOtQD0N249S/l8OR2vt7kduxxXt8BXvrYi+y74TBJlqyVpCuLgqqsqPKCqqjw5dbKVm1UVQ7gz7/5sq5jk67I9muMMcbsBRvuOIvIfwL+9fqRx8zjk/HosReoShZ+9nuoisB1b/s9AO7+9lfB8HFetwkhaWzbe11JWjPd3Q5hz7jS2+8773wGWbNL8HW97m434YbDEYemc8pmTBR5NBJ8iKjU1eOjUddKV4RmnCMoXiKGSYdKYzQITgKHWws878DniDo9XDkG7wnNDiFtoK4ucK5RjARPNOrXy+RjcA6SFI0S1EV1Dfc4rX9EVwXRcAVRJUQNQtZk1DnAIJtlwBQj32BQNjh3neaNBwKjWSESJYuVOIropCNiCYjUJwWWiu7a9QpBHargnNKMSjpxXef5hlb9wZX6czd0iIojiENUGWqbshGjWpeQG1YNfJXRjEqmkx77hg+R5H3El2ufW11EiDJ83KBK2/juIYq4yThuU972KlJyAAKORAtaxcraRRpl1KDtC6JQEFUFLpT1j3kNiK9wvkRFUBfXBx7GA6S3NClKX6JVSRgMqFZWKZZ7AFSjHF9UqPcU/TG+9HAAQhUIlaf51U3yfrFW011DoPfg4FG1nS90XUTzuoxq7BmeHVIOPS4W8l6x/TuzMeaKtpmqGm/ArsY35kpl7fdyijaf4RIU3v+pgvfcWa0NjhI5eNVXJXzDs9JHDSy0V8nFx9Ixxpir0mY6zlfAn3FjzEVY+zXGGGMu0WZznK/Ywwv75mLaUzFpUh/VyUs4dTbwubMFSRox042ZnxX2dytaaUknGZO5gmldxHmPlxhF6IwXkOCJyxHRaBUAqQrQgG9Nc2b/0yhJKTRlMZ/i6FILNwOVh5W+MBoFzi4UlGU9hLT3gXFUElzBf5rE+mPTv8hK1ccPS1g/Im0EFMBnJrcLulhaRgksTW5b1+x2mD+8n+tu2T8Zcrg+VLa6XAfqnOAix5EbZrjx+ozr91dcN7XMTaPPI+/5LwAks9PE+/czvO0FjLNpyihjLC3O5HNE4plKhkzrIvs/+WcM7v4Cw9NLuCQmm+4QNTOiLEW9R4MStZpErfpA6sN3fJSzXzzL8uf6Fw5+YuYZHbrXdekcmKI536UcjPBFRTkqeeAvH9rycL0XMlTPt2/bu12yK7b9GmOMMXvBZjvOX5RzCXcXoapzlxCPMebysfZrjDHGXILNdpx/mnq8emPMlcfarzHGGHMJNttx/gNVPX1ZIjHGXG7Wfo0xxphLsJmO8xWdH/n8WxY4Mn2WPDQYhwyRumDT2NdDSAd1DMuUkyspJ6sEaNXDW48OUpSsDZPdatY1oA/OVHTnCg41FpjLT5COe4h69i19iXh1AVk6S1he5Emf/SKhrMh7Q87ee5alu3p0n9xheHJMfurRpY7e+1aIWk3+28qnAHj/9LPxGyhLl84lzD5liva+DrO3HGT6m76R4YEncqJxC/ev7OO+YxEf/usHOPPwSar80sorjXp9Rr0+J7/88GMu96U7LzT1O857Hnh0zvWXz5v//Mlto14O11PfNuLM5LbeCzaxug3YQ3Wcr+j2a4wxxuwFm+k421X5V6gHHhw8/kLmandFt98D+xO63ZQkBpG6THBZwYnljFO9FFVZq3esKlQe4ggaaaCRBJ4002O6WqA1PEuc94kXT9ZvnKSErEXVmWU8c5gi7VBEDZx6gkRULsUTUxFTaUw1E1GFmDODNoWvixI5AZF60KR2WnCkcZqDC3cT7v0CVa9HuTokeE9j/xytdotD0zMwPUux73rKbIqi0aKImwyYIkgEHASgHY9QhUojxj5lWKaIKInzdNMh7WhIQoFTj3cxQSMaYUBWDhH1lHGTXjyH14gITzv0eMLCXUioCGmLMm3Tb+5j4LoMfZNT4zk+ungDwzH0+oGVlZLTJ/sMeiOGvSHFOMdFEXESEycx4gQXBSCeDJI0ohjnDJfX/9hf/1iAlKn5WeIsIWtkpM0UFzl8WeF9oNHK+NqXHubgjKeVVqRRhSKUPiLoI7vwpEQzWeRpxCVZVBLUUakjqMNJYDrpsy8/RrN3kri3UO8baQPfaBOSDFFFXYQipMsnkKqCqoTg0XYXdRG9UQ7fdPu278/GmCvXhjvOqrqZ0nXGmD3kam6/qrv/m0C2KQRV+My9G8umiagm6xaeeMstABz90ue3J5ANiuIIX/kdXacxxuymrQy5fUX6m7tmaU1Nc2AOOo3ATLPgSPMMTz79wfoogzg0jsmn91MmLUbJFCNps1R0GVUJwyJmVDjyUhiO4b5jEVXVQNz17J+9gRvmRhxoLnN4dB8qgmt14cjNTD/1ObjRKjIacF1vhfL0aU7f+QXyG8YsfnmJlbsffTS4edMj5eRe9LOvIJlqIbc8hXJqjippgQhBIgQlLoYISpU0CRJRJG1GcYseKaUm5CFlrjnmW14WMfqa66nCjQxyxygXzmVsnDtyMxoFxAntpjAzpaSxEhRCgKCCE8UHIY6UJFJWR9HayGVOoKiEEGA4VoYjparqW557xuMK75V2OyGOHVEkVFUgaH207tx9kjrKIjAeefK8YjwqERFUlar05OOS4AMiQhQ7ZNJbGQ9zfOlpdBqkaUySxcRx3U9cXhhQlRVhMsLEYOWRUnUalGI8JgRFQ1gbaQxANSDiHndIbhdHRElCqPzaa1rdDuIEDYpKa2s7rDHGGGP2nGum47xbJGzuaEwYjXj4Ta+luW+GpNsBWuAius/9RgAWP/eRTcdQhs2PanapfNj9lNo8r3Z8nUmW7vg6jTHGGLMzrtrTt8YYY4wxxmwn6zgbY4wxxhizAddMqsbtN36A6WYKqriqRFY8uhpRNqfxSQPvEnyUMkinKTWl0pgyxAhKFnmiTGkmjlEZ0W44ikooK8dgBKtD+Eyvgeoh8uIg3is+KMErZVXfh6A4J8gUxF/v1uZ7HyiKOp1DFbwPhEn+b6gC/kQgWxD+w0vqz/ELH3gGla9ze88NeV0WVZ1PO0mPSBsJUSSIE8QFYEyowuQ19fsEH9bW5X39Hucu8lGdVCcIivd+sownTB4HDfhicmGSE8S5tRxhcYKLI5w4xNVxRpOr8OMkflQesVuXp6yqj/oMQevYzsVyLtc5TNYTvMf7Ot5Q1bFVD1dr6zwXf5zGk9fX8cRJsjY/aCAEXctPDl9xkdPjp9mEyj/qdeP+gHH/kbz1qrSKJsYYY8zV4prpOF/JihK+75eWiCLBxQ533hX8aQLv+F/3A/B9b7HxLc6R8zeUMcYYY8wlsI6zMWbPU4WiVCovRK4u/zbdUW6c7XND/DD7jn4SGa7CeFSXgpmeI0RtqmSavDHDModYjA5wvHUDRSOm38zwQdB117CGSqhyYVQ4xoUgAlmiTDU9U1lBLAERpQwR3UZBFpVkri5Pk4eUlbzJsEx4OBxieXqGA193C4KSuyaFZqyUHVaLBoM8oj+OeOhez3BY0evlrC6PWDq1zGB59VFnLMQ5oiRGnCNOYoL3lHnxqLMcjczxl//1VgBu/7GzjPMwmTPm3ABDcZaSNhvADWtnWLJWk69++a3sn49pNurPKgJZCnMzjrmZjJtvylBlsi1gqulxAj5ANdl+zsF8K+dAc5kpXaaVj1FxlHGTImrgXf1vJmh9kfJ0fgYXBjj1SPDkWZcgESpC5XJyKYkpSf2IxOeoOPKsRSENxtpgpejw0GKLwVgY50qeZwxHAe+VanJmrdtNSJIuzew6kgTi9NwZPdA+jPPJma0wiX8aGh0ljQMOJSCEIBSyvN27sjHmCmcdZ2PMFW1m5aHdDoFRlTz+QpfJOA/8w3/8N18xve5Qfy0Ar/yfP0r4iiUuXVDB7fCglEujbEfXZ4wx610zHee3fPQFzO3fx/75hCwVWg2YaXu6aUHsAqmrSKOCNNR1e1MpaMmAqdQRiKg0JteUUZJRhogqOHxwuBmlEVdkUUk7GnLjwp1Eg2VkeRF/5jTLd3+J3tFF+qdW6Z8aMXxgfFk+33t/sh6u+52bHK57L7n5H13P1OGZtVJ86ZFDSNaARhNtTzPcdxN50mEYTzEMbc6Mu4zKmLISxqXj7DLkuZIXSlEERsOKs6dWJ/WfH52vLO6R62Jd5Or86nWHH13kyBoJ+w9NkWURWeZoNR3zM0Kn4WkkgdgF2mmBk4CjHkA8cR6ZdCScBFb7fV7y5zux9YwxxhhzuVlVDXPVGg6L3Q7BGGOMMVcR6zgbY4wxxhizAddMqsbBgy3anRjnJhcaVbCwGtEbNVCtL3JZG/o5VtpZoJlUzDf7ZK4glYI2q7T9ImnRx5VjXFUgZYFGMRonhKTBcOow1cxN6PWTig7/E8z5gnlVRD2RL8j6Z3DjATIeQTFJ3XARxAkkKeQjyMdQlYTBgHJxeW0IZzTgGg0kinBZCiKo90iSIM322ud92bu/HwBNMzRp4BttEAcaHrlXRSb3GsWEKEVdXK8jlIgq6iKCS/BxShU9kltYxE1EA4nPiXyBCxXqIhTBqcf5CgmTknWT1IUqbuCjlOBiqihlHLfJQ4NcU6oQs+RjlqjTHdKoYEaWSP2YKJT1dhuvkI57dKuCaLTKk+7/ItXyCj4vqAYjhqeXEOfWSuQxKXWHKhI5klZG0u0Qt5r1Nkxi3FSX+uqhdTmqIUDw9XZqNNEkRbMWIUpwvkQrQUMMIrjeGKmK+sK0QZ8wGlItLRPyAp8XLC2uXJ4deg8QkZcAPwI8FzgMvEZV/2Td/G8Dvncyfx54jqp+ajdiNcYYY7bDNdNx3i1uk0NuXxJfMv6jX4GsUXcao/rr1XjnL1w612neEUnK/p9/BwAP/+Drodq9FI0wurLyyi9RG/g08NvAH11k/oeB/wf4jR2MyxhjjLksrONsjNkSVX0f8D5gbSCb8+a/azLvCTsZlzHGGHO5WMfZGLNniEgGrK83NgXwXcd+junZLm56hurwzQy6R1hMD7FUdPnb5Os4FaWMM6giqLxSjqClQiNAI1fmq4IkqguyeRWC1nWIS19f5hEUOlnFfHNMOx6TuRwARQjq6GidclO5FEHp6xSDqsFq2UJEUYQOwLKrAAAgAElEQVTeOKGZeGYaA/brSeYf/iQyGtTFg52DtEHI6tQfiZTq1g5lNsUgm2XopkgkIQpdKpkn1pLZ3kOPpDklTdCCpBjgiiEuHyFVWacUlUWd2uU9pCmIQ1d7lIvLQF2O7rcO/XvKlR6L9y9w4oNnHtm679ra9xQB6brnQ+CBrb3VpjlgenLbjH3Pm6F7pEtjukn3CYdIuh1cI8M1mzDzdMp4mjJtU8ZNTrgbGFYNFssdPGNojLki7GrHWUR+HPg24CnACPgI8KOqes8FlhXgz4BXcl4u5Ua8sfN7dKe7hDhF0wYhTglRStHoUrmUMsrIXYt+aFP6hJFPKKqIL56do/JQeaGs6vTXNKkHADj3/zAfKnlRZ0cc2Q/dRkUrrUijimac40NEpY4qxLgkcP3+o8STsneNfIXWiS/Wb1aM8cceoveFL1MOxvRPrdA7vsrCJx47T/bAC2eRSFCvJK2EbKpBMcgZLY0ZLeaMjuVo+ZW1VtO5BEmEqOnITxX40eWo9Lp1xza4XNRq8sp/Uz9uHZ4njgWJIiRJSJ78FHx3nrw9z6A5z0n2MfYZwypFFcoQkVeOvHJUXnCi+FAfPY0jpZEEIlGcU5zA0YWU4VjXBs6YagutRiDtKJFT/M1CUCF29QKrvR786h9u85a5qv048NObeUE/3/3f/6NyF2LQx6mfXBWc/anvJpQlWuQ7E9NE1Gryyiu4NKYxxlzMblfVeCnwH4CvBl5B3ZG/Q0TaF1j2zbDDlfavEhfqNF9N/HDE373wBRz9gW+vj75NxDObPSZl9oC38sgBxWng+t0NxxhjjHnErh6qUdVXrn8uIv8cOE19Ff4H101/FvDDwPOBEzsZozFm56hqDqwdHr1Q7rQxxhizW3b/HOejnTtEuHhugoi0gP8b+H5VPfl4/0gvliNpjDHGGGPMpdgzHedJDvPbgP+hqp9bN+tXgI+o6p9u8K0umCNZzh5iuO8QJ5pPpClDnHrG0mLgW3z2+BzHTnrmZyN6/UBeBNqtiDSBWw5VzLWGdOIRjkAqOSUpjkAz9Gnly6RFnyppUsZNPls+g88/nLLad5RlTKPR5oVPyXlm+DjJ3/w3PvpL7+ezp7a3XNrpjy5t6XXFYrmtceymhU+ssPCJT2/ptQ5oPsZ8BfzkdnAD77VeqlfvxUUi0gGetG7SzSLybGBRVR8SkTngRuDIZP5tkx++J1X15M5Ga4wBEJE3UddfPwzcBbxZVT/0GMu/Fvg54InAfcBPqur/uxOxGrMX7XaO83r/Hngm8PpzE0Tk1cDXUec3b5TlSBqzM54HfHJyg/qH7yeBn508f/Xk+Xsnz/9g8vz7djBGY8yEiLwOeDvwC8BzgA8B7xORGy+y/IuAd1PXX3nW5P4PReSFOxOxMXvPnjjiLCK/Rv1P9iWqenTdrK+j/pW7fF6Kxh+JyIdU9WXnv5flSBqzM1T1A8BFG5iq/g7wOzsUjjHm8f0w8Fuq+puT528WkW8E3kh9tvZ8bwb+QlXfOnn+VhF56WT66y+wvDFXvd0uRyfArwGvAV6mqveft8gvAr953rTPAj8EvOfyR2iMMcZc+UQkpb7w/hfPm3UH8OKLvOxF1OmS6/05FzkLfLFrjHq93mbDNWZbXI59T/TxaoFeRiLyDuA7gW8B1tduXlHVCxb+FBFlE3WcRaQLrADTqmqt1+wo2/8ujW0/s5uupv1PRI5Ql8f/GlX9yLrpPwG8QVVvu8BrCuB2Vf0v66Z9J/DbqppdYPl/yybrsBuzQ25W1Qe24412O1XjjZP7D5w3/Z+zfad4V6lznVe36f2M2Qzb/y6NbT+zm67G/e/8o2VygWlbXf6t1Nc6nDMFHKW+1uhq2oY7ybbhpTm3/RYfb8GN2u06zptOQN7sa7Q+pH5FHykwVy7b/y6NbT+zm66y/e8sdXGgQ+dNPwCcushrTm5m+ce4xmj1Sj9iv1tsG16ay3Gd216qqmGMMcaYy0BVC+BO6lF613sF8JGvfAUAf3uB5b/hMZY35qq326kaxhhjjNkZbwPeJSIfp+4Ufw91rfV3AojI7wLHVPVchY1fBT4oIj8K/Cn19UhfD/zDnQ7cmL3COs7GGGPMNUBV3y0i88BPUQ+A8jngVar64GSRG4GwbvmPiMh3AD9PPQjKfcDrVPWjG1xlDvwM69I3zKbZNrw02779drWqhjHGGGOMMVcKy3E2xhhjjDFmA6zjbIwxxhhjzAZYx9kYY4wxxpgNsI6zMcYYY4wxG2AdZ2OMMcZsiYi8SUTuF5GxiNwpIl/7OMu/VkTuFpF8cv+anYp1L9rM9hOR20VEL3Br7GTMe4WIvERE3iMixyfb4Vs38JqXTrbzWES+LCLft9n1WsfZGGOMMZsmIq8D3g78AvAc4EPA+0Tkxoss/yLg3cC7gGdN7v9QRF64MxHvLZvdfhM96lKCazdVHV/uWPeoNvBp4Ps3srCI3Az8GfV2fg7wFuDfichrN7NSK0dnjDHGmE0TkY8Cn1DVN66b9nngT9YNorJ++XcDXVX9pnXT3g8sqerrdyLmvWQL2+924O2qOrNzUV4ZRESB16jqnzzGMr8EvFpVn7pu2juBZ6nqiza6LjvibIwxxphNEZEUeC5wx3mz7gBefJGXvegCy//5Yyx/1dri9gPoiMiDInJURP67iDznsgV59bnY/vc8EUk2+ibWcTbGGGPMZu0DIuDUedNPAYcu8ppDm1z+araV7fcF4Hbg1cDrgTHwYRG59TLFeLW52P4XU38fG2JDbhtjjDFmq87P95QLTLuU5a92G94eqvp3wN+tLSjyYeATwA8AP3i5ArzKXGh7X2j6RdkRZ2OMMcZs1lnA85VHRw/wlUf1zjm5yeWvZlvZfo+iqgH4e8COOG/Mxfa/CljY6JtYx9kYY4wxm6KqBXAn8IrzZr0C+MhFXva3F1j+Gx5j+avWFrffo4iIAM8GTmxvdFeti+1/H1fVcqNvYqkaxhhjjNmKtwHvEpGPU3dKvge4EXgngIj8LnBsXYWIXwU+KCI/Cvwp8C3A1wP/cKcD3yM2tf1E5KepUzXuBbrU6RnPBv7Vzoe++0SkAzxp3aSbReTZwKKqPiQibwWuU9V/Npn/TuD7ReRtwG9QXyz43dT54htmHWdjjDHGbJqqvltE5oGfoq4p/DngVar64GSRG4GwbvmPiMh3AD8P/BxwH/A6Vf3ozka+N2x2+wEzwK9TpxusAJ8EXqKqH9u5qPeU5wF/ve752yb3/5n6IsrD1NsQAFW9X0ReBfwK9Y+N48APquofbWalVsfZGGOMMcaYDbAcZ2OMMcYYYzbAOs7GGGOMMcZsgHWcjTHGGGOM2QDrOBtjjDHGGLMB1nE2xhhjjDFmA6zjbIwxxhhjzAZYx9kYY4wxxpgNsI6zMcYYY4wxG2AdZ2OMMcYYYzbAOs7GGGOMMcZsgHWcjTHGGGOM2QDrOBtjjDHGGLMB1nE2xhhjjDFmA6zjbIwxxhhjzAZYx9kYY4wxxpgNsI6zMcYYY4wxG2AdZ2OMMcYYYzbAOs7GGGOMMcZswJ7vOIvIS0TkPSJyXERURL51t2MyxmyMtV9j9oattEUReamI3CkiYxH5soh8307Easxetuc7zkAb+DTw/bsdiDFm06z9GrM3bKotisjNwJ8BHwKeA7wF+Hci8trLFqExVwBR1d2OYcNERIHXqOqf7HYsxpjNsfZrzN6wkbYoIr8EvFpVn7pu2juBZ6nqi3YgTGP2pHi3A9huIpIB2XmT54DFXQjHGIAp4LheSb9Sd4m1X7MHXavt90XAHedN+3Pgu0UkUdXy/BdY+zV71La24auu4wz8OPDTux2EMee5Hji220FcAaz9mr3oWmy/h4BT5007Rd1v2AecuMBrrP2avWrb2vDV2HF+K/C2dc+ngKM/9Kv346I23W7CVNvRyCBNlMPTI2bTPpnkKEJMQawlkVZkxYB0uERUDJEyBxFCo426CB83KBrTnEmuY+gb9MuM/jhhWDiSGF7zfAHgvZ8IpHFFJy35/9m782jLsruw7989nOmOb6i5B3W3WurWEE0IMTgGbEUEI3ACLAwOcaLYKya2CRZaixVjJ8FOlg0mWeC1SFiMMUEhBhzFwQSDFEBCSIBAQo2G1tBqqceqrukN993hnLOHX/44t15Vl6q6q6pfVb2q2p+1zrr3nnPuPfuee/d7++7z279d2oahnnFo8hj51knk5LMsnnyaM596EgBbWvJBydobXoVePwBFhRQVvjcmZBVNOWJqx2z4VWauYGuR43x3nBAUWgtaw3Su8B6qEoZVJLcRHxTzVnfbIox6sD5wrPVmDM0MQ0CpgIkBhTCcn6Q68RiTD/0hn/2NT7N4sn7eSX7ou19BuTIgH1Zkqytkhw7B2kHccJ1oMmaDQzS6ImJppKCOOZO2Ylpn1F7TtIrNidA0cfc1RbrFuYhzkRAii4XDNQGAl79ihXuOCMeGU1azCWN3Gh0DKBA0i2xIUAYjgag0Z/wBFj5ne5FzasswnUVmi4D3gkRBaYVWoI2iXxlWx5r7Di64pzzOoeMfRx1/kjifo/IMXfVhOKY5cA+L3jozu8JCKk7Mxkwbi/OKEBTHTwWaJuJ9RGnFsLfgh99xL8DOdfq+324uWX//7fs/TW8wBGBr0cNHhQ+KzMru96luu88zz4TZQtG0QlUq1oeBMg9oBUZHSuPJjWOr7qG1MMgb3vyKMQDPPv45xptfJHz496hPb1Fv7VBvLWimNVmZsf3MhO1Hpzf8pFyt8lhB71DJ5qcniNubjtLxqwfda6+WrN2/jilyJASyXsXs1CZPfvAZ2lPtnhxrv5hL5B3xS3Dn1t+LvzzqMuvPuWT9ffrppxmNRntdtiR5UZPJhHvuuQf2sA7fdg1nEWmA5txjpbp6XlRDtBlQ9jKqZcO5yIT+IGNQKAqVIygyWmxslw1nKHSDaUA5DUoTy17XcM4qmnLAIh+ifYW4gmgzaLqG82jUHbc3iBTW088dlW0YGM0o9sldhVQlWZlTZ93HkOUZeZ4xqgp0r4KyQooevt8jZD3qcoDKhjRuBK6g1ecbzj4ozLLhHNT5hnOvd77hTKO7bQGqPvQGjkHPMLAaQ0CrgIkehTDSU6pehRQ5fW1QyjzvPA/zjLLIyIucvCrIeiX0K9ygRzQ5ejAgNz0ClkwKbCgIbUW0GdppdKZYeEGZL2846zaibddwDuJQqms4l70Rvb7QH2gGuTBsF+joQSkEjc0HBGUx4onKULsR2uc4k1M5g5dIIOAuajgboyh7hqqv6Q8yhtUOo34P1SuJElB5jl6+v2bQx/a6z8HEir4aEW3XcPZBUfYC6IhZNpzLXnb9vuy3ocvV3/5gSG/Q/eNtzfMbzue+Tyo733COWqGsUFWK3iBQXdRwLozD2fMN53P/1CfDAaO2RyhzsiIjyzNs5sisJcss3hjcRXVhP6q0oWcMjTKI2puGc99077uylmGeYfIMCZqsyNB5Rl8b7C1wbpIr9hxdr/OFDgEeOHupJ1yu/o5Go9RwTm4bt13D+XIOrmX0hwV5BkZD4+DMhvDIpxVZvsrKyLK+qjmy4ujljkFWU5Qt42KMlkBQ3aka1GdRMWDdgsHZLzHgSyjfgkRCb8zpo6/GqwK4B4C6VSzajDM7GdvTPovFCmfOHsK5gHeR0IvUb3Aspg2udRij2fjgBvXOnOAuDiGbLJenbtBZe6hbHqZbgGo0YHxglbseOEgUwbsINez8+RwArRXaaI7ds8K9dxfcfdBz13CLB5pPoX7j/wQgWx1jDx5k/tBbqIsxzhTUqsfpZg2jAsNszlg2OPjxf8fs+GeZn9pEZ5ZiNsBUxW5Pl0TB9CpMrwJg830f4cznz7D1qef3CGZ012jOWXntgNFdIwaHhlTrI9xsQWg9buF44nee4tENx6NXcZZ6F9xfv2jbXMJVvFKSJMm+8UfAt1607huBj14qvjlJ7hT7vuGslBoAD16w6n6l1BuADRG5US3IJEmuQaq/SbI/vFhdVEr9CHCXiPxny+0/DXyfUurHgZ+jGyz4t4C/fiPLnST7zb5vOANvBt5/weNz8VP/O/COG16aJEmuRqq/SbI/vFhdPArce26jiHxJKfXNwE8Afw84Dny/iLznhpQ2Sfapfd9wFpEPcH5AQpIkt5BUf5Nkf3ixuigi77jEut8H3nT9SpUkt55933DeK60Tci9orRCBzMKBNc3aSkUUcA5mC+GMyRj3NK035LbE5RajIlpFFMKiN8AqRxYa7NBR1ZuY0KJiQAXH4eMfR4nAA12M8380/yWIywFwBigdrOwQ5zPwHoCmOQMjsL0KszJGv+1eYm9I21tlUa1xQt/D3JdM24x5a9nY0czmwnwRmc0DJ4/vMN2asbM5ZbEzpZktrtt5XEymLCZTnvvi0y+43xc+dqm1333R4whsXvD4ixdt/8rlcqXe2gUz3/2iO3ZOL5cLveUqDncFvJvBe9++ty+aJEmSJMlNccc0nJMkuXX98WMjDh4ck1mwRhhVkaOjKUM7RyFY5SnjDAAtgaqdcLa6my03pg6WEDW5CQgKL5rTkxGbU4MP0C9K3vJQd5zH6vt57WDC4Mjh3WOXq0N6Rw9g19fQ6weR/ggVA3gHroUQeOw138kXNg5wdseQWxhWARcUk7k+9/uYRx7Z5AuPPE49nd24E/fW6/S6l8rg+8brdKybKP3wTZLkYvpmFyBJkiRJkiRJbgWp4ZwkSZIkSZIkV+COCdX4m/oXGPmCODhIqAaIyYg6Y2twDK8ylBJEFPPYx4uhjZYQDZO2y9Lro8YFjYgiiKL1msYpGqdwHppWCKG7tRretTzuT+38DepWmC8CMQjWKrzvJiTQZjk5y30aoxVm+VhNQLZBKXZnttMK4nKCkNnM0zQBEYhROHxsyP0vX6Hf0wz7ioNjT2kDhe2uEUfRBFGEqInC7kQQPdsyymYMmHDwxCfQx7+EO3UaYuxmAhyNkf6YWFSEoo/L+wSdA1BNT2KnG6gzJ3EnTuCnc3RuUcagsgxlDBICSiuUzVCZJS4W1CfPUm9MWGzOOP250zTbLe2GR1xk5VVD+gf7lOOKYljSO7RKNuzOf2w9oWlxswXN9ozF5pz5xpzTH9sgLM5PorLfzCXw1252IZIkSZIk2ROpxzlJkiRJkiRJrkBqOCdJkiRJkiTJFbhjQjXaIw/gqhxE0L5FtQtEG1ajJ2QlQWcEk6NywUlOri1OLLXPCWIQJYgWFq4L2Wi9wgdF3UCIEEIXRiECIcjucXdmnqYRYuxS4YUgZJkmRCEGIYTIbNoC554biT4SBWKIhBBRSmGMxhiFthqtupCOc1Neu9YjUZDYHTcvM4xRXZiEVkAg+rh8Tleuc68dvSGEMa79GoJ/y7IcAk+BRCGE7rkhBOLyfpRIaA8Dh1H61SitkRiRKCit0NaglV4eG0xmsctFonRp+Q6APqJRy/ciIs97D1Ei8UxETnePlVKICDFGRAlxHAiDAPdC9F3ZfOt3jwld+W1ul8/vymOzbHd7lEi7aIg+IBKJfu+nx06j8pMkSZLk9nHHNJxvlMzAu77NAPDP/pXD+ZtcoDvYhY3oJEmSJEmSlyo1nJMk2fc2txzzpiWG7urNaJRxz9ExR8Yla9UMowIzeoRo8KK7v2wORBRWR4b5glw5tOquvKwVFllRoBSve/AgAPMPvYevaWYQAvG+hzCvfD19bUAEMZYQAyymaFdDU4PWUPURk/Hykx/ifpsTV3KUCMq3mMU2CiFWJbGo+I5vOcTsO1aZcReLULLTljTB7A46boPCKKGwEWuEQd5gVUSp7qqLj3p3oG8UjQhoLVTGUZqGTDsM3VUTQSEoSpkjShOVRokwlz5OLCLdj8q5LwmiqIxjnE04OH+SrJmigtt936IN0RSIUog2BJPT2ora9vGSkdFdMYtoMmnptdvd5TOlcKbEhhYTW4xv0dF1ObAlooJHB7d8XYuKHl3PUJPN7tKYd4h3xNkMv71DuzXpzsOiIbQeCYF2WhNc956j764aSRSaabt79UpiZPLkjHbD7X6fLjWguLqrwFQak2vcPKCtYhYC6XpRkiQXSjHOSZIkSZIkSXIF7pge5/fWf4lMxswWXWdGjILzUNfxXOcI/Z7m3sORftGlcst0wKqIUZ7CCEYFev0Fpcyp3A6ZX5CV29j5BNXWYCzlW/8GAJNHfpf/8nUDFvmIVpe0UlDHgjrkuNiFcogolBIyLUTpUsVFyVg4S5BuavAQFfNGE6LCL+OoFw04J4TIbq8KLDtpvJBniqrS2K6zDOjisM9FLkSBPOvisp0XYgSz3DfG7na+iIQoSOzij5umi4k2RqONIrOKGCFE4cKICGu732JNE/A+4n18Xnx3FzfdxUNr+/zfba7p0tcZ08V0Z3l3nkKIuDbgXVyeN8Eve5l2X0+6nsgLzwd04RoSBe88MURc4/DL+BkDDFdGmMxgM7u7b1cGjTFd+bTVZLmlKG2XIlArlFJYq8nz7nPLrKLfM7zsGFR5RCMoBfV8m29/70v55iZJkiRJsl+kHuc7gMiL75MkSZIkSZK8sNRwTpIkSZIkSZIrkBrOSZIkSZIkSXIF7pgY52+d/TLVysvYOnY/AI2u2A5jPn1ijTNbwtmzLdMpbE8MoCmKkl6lGPYVh0aOiGIyNxwd1wzzPr1swJo6SVSGtrdK1uwQs4pyebwnBq9jRc4QlWHcnGZw8jHkqcfxZzeoz24R6pZP/+onaU62lyyvWi5HvmqVA688RL29IO/nFKMeK69+OXZ9DbIChmOeeOA/YMcPUAgDO+O+P/llTvy/H+b4x59leHTI+isOM37ly7D3PUBcOUgo+nxh/GZcyBjYGSvhDMPJM2QnnySeOsHi6eM89lufoH+wz+DQELdoeez/+tKN+JhuG7JcvOx9bugkSZIkSW6O1ON8ByhXhze7CEmSJEmSJLe8O6bHOUmSW9dfeJ0wHgW0FjIdEPF40bTe8MWNFWa13p3FU3fpmQGwFqyB9WFFZiKZiZSmy5gT0Xz1w92Pyq1PfJBysQOTTWSxQEnE5jnYDNSyf+Fc+pi2xW+cRVw3U6XKc1SWoWMkbm2z9YWnOf25k5x+ZJOwCORrGYO7KspxAXSZXgprmD2+Cc82GC+IE8ZrGfmaxeQanRmGRwZEH6i3G+qthnbD0ztWMFir6K31OPymBykOHUAPR9DrQ1ERBiuINruLnU9QvkU1C6jnXf5ppbs0OkpDWXX5qJXqHm+eJk6nhPkCcQ50N7unAfx8gYqC1QoLDKuS7MCB3XOjsrwrx2Sb5tnjzJ49xfS5TSbHt7tdrEEbRbXa281gk1UZg7sOUKwM0WVJcI7QtGhraLenNFtTQusweYatCiRG5qe3cQuHW7jdLDrNpMbV3dUdk2kkCttfmOEnVz4D1eLZ5svWzdMVoyRJLnLHNJyndz2MLTMOPPvnqPkOxMDdSvGq4Rrh2ID25SvU+ZAds8oilix8wazNWTi7nHigSzNWe8vCDQgy5PPxMPNG44MixC604h3L4733kTUWzSoiwmTS4t2rCGGZdH+9S6Pmv69L1TZerSgKs/vPJIQufRt0qdiKwhCFLiWbj1irkU0IMcJzMP1Yg3du+foZi+l3E4bfifoGhc0Mxln0c4rwTHf87jgNNvMobYjhIIvpEO9egXcerTXxDXF3iu1gA+Z7zO65VFrRzBcAmCzDZpa8LFBaLVO+tbimJYbun45r2usynfWlKK2xRU416GEyi17+47eZJSss2nTnuT8sWV3v8cqXl9jlW7NGiMLu5BA+wHTepS08l7LQnGtzKIU1cP/hln7WYnRAlhNTzFxOG7rvRbszgbeu3ZD3niRJkiTJ9fWSQzVU52GlVH8vCnS7adsvn6HqRjuXj/hGujif8s2g9Y1/3+dydN8qUv1NkiRJkit31S0LpdSPKaXesbyvgd8FHgWOK6X+wt4WL0mSvZTqb5IkSZJcu2vpkvtu4NPL+28HXg28AfgZ4Ef3qFxJklwfqf4mSZIkyTW6lhjnQ8CJ5f23A78mIp9QSv008L17VrI99u7Pvon+YERRKKxRZEUXr5opQRYKmXdTUWsFmRX6RaTKPHePtih0S65acqnp1xvk7RTta7RvUb5FjEXyjFj0gbcB8D2v/wwigo4BLQElgpKACS3F9DS6nqHqBbR1V0BtuoFIWQ7NohvE4x1xNsOd3tqNf0YiuixRxqCLHJRCsoDKsm6QUlFCUS4Dci2SF0hWEsp+N/hH4vlbEdTyVowlmhzRtjtGdCgRRBuizgg2x5ti93y2doCSSBYaTJii41Y3IAmFloBx9e6+ii5sw9uSYHKitniTU9s+TSxpJMdHSxu6r2OmA7lpWVGb5KHGRNedt7obZKR8i1nsIF/6PH5rm9C0+NmC+alNlNbdgK1zA56ge59oMlOQjQbYXrU8hxZ9dgRZ1p37c2KEGHYHT0mVI0WPaDJ0cIhS3XlSCj2pu8FX8x2YTfEbZ4l1Q2xaQtOyubF9nb7R1+yWrL9JkiRJsh9cS4/zKeCh5WXebwJ+Z7m+BG5+YGvyZeTCRmFyp0v1N0mSJEmu0bX0OL8b+FXg2eXz37dc/5XA5/aoXEmSXB+p/iZJkiTJNbrqhrOI/COl1GeAe4BfEZFz1+Qt8D/tZeGSJNlbt2r9/ao//eeMV0fo8Qr+6P3MRsfYKI6w2Y7YabLdNIEi4EOXQrBXdikDy1wobSAzXYabOljmPudtr8sB+PWPRlz49xkc/GrGd9f0bU2hu5y+QpdicCBdyI3XOQphKkNmviREg1LCpCmYt5YqCxz51m0OyXO86pmPoxYzCKHLlZyXxKJCshwlwsvKAa4YMitWmeshmWox0eNVhhXH6uSp82FOWQUiZO0M3c7RzQLl2i7kyrVdaNd8hplNQGlkZ4I7dYr51g7ROdppjV80bHzpLCc+ePomfIIv5LEbcpQDb2RC2qkAACAASURBVF5hdGxEOa4Y3XeEbDRAlwW6quDB1+CqMS7v42zFCX0Pc18StmfwtYdvSPmSJLk1XFMeZxH5Py6x7hdeenGun50dBzoQosFowVqFtVAWityC1sKojNw9mqBVJIpGKSHTnlYynFicyjC5Q7TB2BLra7StCVmFLwasv+arAZj+yW8ysDlNOeYp+yA7bcXmPGdzaphMu1zBRd4d3y/DcI3u/unPdiInnlsw2aqZbEzZOr3J7IbHyZrlciG3XM6ZXuJ5F6beu1R4SATqCx5vXGW5qgtux8DdV/n862H0glu9mwG/dmOKcoVuxfqbJEmSJPvBFTWclVJ/+0pfUER+9tqLkyTJXkv1N0mSJEn2xpX2OP+TK9xPgPSPN0n2l1R/kyRJkmQPXFHDWUSOXu+CXG8/8OrfpzpwhBPVy6nUHC2BWvWYhR6fPL7G089G1lcNn39qTNNG+j1DnsEDRzxrvTkD200xXZs+O2YVXUSqOKXXbJG30y6t29K7N76Zze2Ic5HJpOVbvw6+vvxDso/8Wz7yz3+b5mT7gmV91XU9E8mNNJfAX7vJZbgd6m+SJEmS7Ac3fk7i25HWrL32awH4vU82+HB+06FD1WWelCRJkiRJktxKrmlwoFLqMN3kCfcC+YXbROQf7kG5kiS5TlL9TZIkSZJrc9UNZ6XU1wO/QTeRwsvocgndAwTg0T0tXZIkeyrV3yRJkiS5dtfS4/yjwE+JyD9QSu0A30KXV+yXgf97Lwu3l36ft1ItRshCMas1jYPcwqgXsEY4sGYQgaMHNUopYuzyxG3NLY0fMChKchsY5zN6ekElc+55RReNPPn477D1yAeIxvLGGHjzkZr85JOEp5/ksV/6IMc/dJo/cmlStuuluqtg7ZVjVu9bR1vDYnPG9tNbnPrI5s0u2n50S9bfn+z/Qw6trlLkirET1EaXvjFE1aWNtEKZd3XMaOjlHquXj1VkmNf0zIJC19joKEIDvA6Aty3eg56cBWMhywnVgO31BwnaEpQlKkNQGSY6em6CCS0jOYWSiA4eHRpM2+VVVvMAmx4VlvFaWQFmeT8G9GIKWzWyM4FTp7AhMGw9vaZF5xZT5N1U7xKxoxGqyFF5Tl72kNUDiMlAKSQvu0Ubos0JtiRkJXU+xOmCoC1eMjbaMS4aGm9ZOMv23NA4aFpYLCIbm47Jds10UuNdQKLgWod3nuACk7NbRB8Izl38kewr2hrKQZ/esE9/3Oeul60xXskZ9DXjoeKBgzOKfE5PbbC29SXUn3wAtz3BT2eYskSd/QPioiHLLWW/z+qRo8jKGpvPvyBzW1BK/V3gB4GjwKeBd4rIH1xm33cA//ISm6oLcsAnyR3lWmKcXwP8/PK+p6tAW8B/C/yjvSpYkiTXRaq/SXKHUkp9F/AvgH8KvBH4A+C3lFL3vsDTJnSN7N0lNZqTO9m1NJwXnJ/d4gTwwPK+Bw7tRaGSJLluUv1NkjvXu4BfEJGfF5HPiMg7gaeBv/MCzxERee7C5cYUNUn2p2sJ1fgI8DXAZ4DfBn5MKfVK4DuBP93DsiVJsvdS/U2SO5BSKge+gi5c60LvA772BZ46UEo9STed7CPAfyciH7/MMQqguGDV8NpLnCT707U0nH8QGCzv/zCwAnwv8AXgv96jcu25V689RTVY43SzxqKwNMHQes2zZwxGQ5GDWk5/vWi6O5mFyUyxMzecNd1+d69b3va6gwA88cXH6ddnCdU61tcohLocUbYTsqpPrGvc/Pmxgb37SgaHK3rrPfJBSTmuWP+K16CqCqKgqgpZP0LMC5rhIZpsgI0tJrS78ZS2nhJNhi9HBJvT2h5BW2x05G5GuXkcFQOyjIWMNqfurbPIRzS6h5OMeaiIoolAGyxPbfRw5+b/BqpCEOnOxbwWykIhAvOF0LTCYhHIc83hA4ZDK56Xjc5yyD3D6MSjqJPHmT/+RZTS6NxiRyPMXXcT1o8Sij51uYooxfD04+iN5winT+G3tglNi/hAcJ7YetqdOdDFLwK002Uu7e0Fk+M7nP2zbiryxbMNzz57imc5dd2+P7eRW7L+Jknykh2ga/yevGj9SeDIZZ7zWeAdwCeBEfD3gQ8rpV4vIo9dYv8fovu7kiS3ratuOIvI5y64vwP8zT0tUbLnorn9Brgk1ybV3yS54108Ul1dYl23o8gfA3+8u6NSHwb+jO5H9vdf4ik/Avz4BY+HwDMvpbBJst9cUx7nJEmSJEluKWfo0k5e3Lt8iC/vhb4kEYlKqT8FXnGZ7Q3QnHuslLrUbklyS7uihrNS6jjw74nIWaXUCS7z6xRARI7tVeH20k4cMVSOtXyb2hYoJSiEY6OuNzaKZu5yntvOsaar7DGC80LrwPvuLddNxtu6LFY8XR/lUJGz1pwga2coCeT1BLtzFrV5BjMc8ODb38TRN5ymmcw589gZNj89oRjnnPjY6fNTb//cp17Se8vXMlYfHtI/MGD1gcNUf+U/ZH7o5ZwoH+BL2wd4/BnDh9//BKeffg7fXO9whnuXy1e/wD6z5e3h5XINDgHfdG1PvZG8m8F7335Ty3A71N8kSV4aEWmVUh8D3gb8mws2vQ349St5DdW1hN9AF7qRJHekK+1x/ifAdHn/H1+fouxvmYF3fVuXhOQX3w/rI8+ofPHnLU7d/FzCTzw5e/GdktvZLV9//9brP0E1XqO2fRbSQyEIiiCGsdqk8HN0DJjYIijmxUqXf1lbHCUPP3AMWOWpxz9P5hfUtsdnHn+WRSjZXvkWPr1TsrkZmE09i7lj6+yMECLBebwLjNYG5GVGWVl6vYxezxJF8E4QEeZzTwgR6PJL55nh2LGSqlTky6FSy82UfaE4LLj7uh/ojVNM58J80f2eaZrIbOYYDDPuPmI5PHasVgtW8wkKIaK7H/qhQqTLY+2iYeEzTpwsqFuF0VDmgvOKQRUZlY5R2dIvFI3rxgxUmWeY11jt0SiUMhSqwRIQpYmSoThE321T1Vvo0KBdg27maN9CU9N+/rMopRAR3GRKbB3zU5tEH1FaIVGotxdIjAQXaWdtd7+NNDstO5+bv+hnb0eWbGiIXvATj6mW5T+SU61VKK1opy1KK0yu2fzsDu3G88eW7CyXZ1/kWCuvHVCtVczPzIlBcOW1JJ7a134ceLdS6qPAHwF/m66n46cBlFK/BDwrIj+0fPzDdKEaj9HFOH8/XcP57934oifJ/nBFDWcR+RkApZQFtoD3i0gaiZUkt4BUf5MkARCRX1VKrQP/PV1O5k8B3ywiTy53uReIFzxlBfhZuvCObeDjwNeJyJ/cuFInyf5yVTHOIuKVUr8IPHx9ipMkyfWS6m+SJCLyU8BPXWbbN1z0+AeAH7gBxUqSW8a1DA78U7q5ap98sR33k4e+9JuMxiOizXdTtEWT05YjvM5xtqDJe6xXfVzIWISM1hvOznJ8AOT8JTuj4eSW5XiwfF73aNq7adound2xgzAae3oHPbnxVH+xIUSDF42PlpGK3G2fwcbuUmLZbNM78XkIAdqacPoUk89+ETermZ7cfl7atctZeUWXXWx6codmp2bzi79EO2tYbNYMNhq+8dkGucSU3/lahsoUptI0J1vCIn7ZPreK9TeNufst91EdXMFWBcoYVJaRvfJhwmidpr/OrFpnkwPUoWDuc0RYTkesabzGB4VWQlhOt26NUGYRowStBa3gmbM587pL1Qcw7Ct6RSS3gtHdc6Oo3emedyYTPvLem3VWLumWrL9JkiRJsh9cS8P5J4D/WSl1GPgY50d6ASAin9+Lgt0sWsFbHh4D8Luf3B0cjI/wc7/TDRjMs8s9e3+6VKP5TmBXxje7CPvRbV1/kyRJkuR6upaG83uWtz+7vD3XKjuXC9K81EIlSXLdpPqbJEmSJNfoWhrOr9rzUiRJcqOk+pskSZIk1+iKG85Kqf8N+PsXzjx2K/nZ+X9Kz4w5tAYDG1mpWo5Vp7n7uT8F70BpxFqawUFOfqbHIhtyrNensiMWPmPeWhatpnGaeQ2LWvBeUFpxcFVxzz0LDlVbHF08jm1n6FkLIujgUM0ctZjBZBt36hSnPvZZpjs1G1/cZPvR56eK691X8uDbHqR3aJXxg/dw39oK6oGHccM1fNYDpYjKoBBsO0ch+KwiKkOb9WlsD0eOlYwsFgSfQzAsnMVHzazRLBpFs0whHZf9jYtFlz6qXylWhkJuhShdaEqU87G/1giZEXYWBhF2Y39br4gR5rUsp+WOSISmCdS1JwSh38+wVmOMwvtIlC405txtlmtcG6kXgabx1Au3m+rKu0BTO2KIKKUwVu8m16/nDcEFykFJLpYsWKzScBK2Hp3hnScuc4HNts/n+ZcotHVNjILEiMTzIS0iEaU0wT0/rdXFtDWYLCP6gEgkL0uyIt9NxSWqecHn3yi3ev1NkiRJkv3ganqc/3PgH9Clw0xuIS7emVffm8bf7CLsJ7d0/X3Pk2+k6o3IM8X3fmO37l//sXDXeMr9sz/rJh2a70C9gBgZjNeIZR/fH9OUKzz2uKORgjocpBXLdFYQotod5PnAEU88ovAhY9EW1O0QpaDIhGEVGBYtVsVlzuSAUZ7COArd/QLdavu0IcNHRWECw7zmkH4ChdDoilYKtt2AnbZk1himteGp44H53DOZNOxsLdg8ucVsa4d6ev7HtNIak1mU1tjMEkPANS3Rh6s6f7bIyasu8fy5H3lFr+Kr3/oqDq5bqrJ7rz4ofDifcxq6vNTduYBhFdD9brvvK8Jbuh/T672GQ9UWQ9mi32whSuNsRWtKct39m4liKIFxcxodHVoCKgaaYkRUBlEKr3MaVWFx5GFBFhpEaRrbo1UltZQ8sb3OzsIwqxV1IzSNMF9EQhC87wo+GmVkmaIqFFkG1nTvI8Tutm66Dz5G0BrWx13e600b2UaIKGJUtPMt+PoDV/t1TZLkNnY12d3T3JlJcutK9TdJkiRJXqKrjXG+ZdMz/JXXHuc++yj9Zx6FybIn0ma41SP4wQG8LYnaMs/HBAy1VDQh70IidKSXe0qrWDjLoAQXFI3T1C3szOETk5IQj9Dlie96NSQKIUKMQozgQySsCMU3G0ZDzcpQMawCq71mtzdLRPEkChc0EYVRwsJZGq9xc8Wi0Wxsd68bghCCoPX5NpFSUOSK1ZGiV0YO9BuO9TcY0qW0i8rgyZjFHm3IqYOl8ZZpY6mdonEwrxVnm643pztGZGVkOLASWeu3jPKa0doOBt9NXS5CFhuy0JD5BfliCzvdRG2eIfptfL2Bm87RE4OpCky/jxmNkKP3EouKUPRpihFb5RGcZMQLfs9Z5XdnSwtiaGNBGy1RNG2wtMHQBo3zitppRLqZ2OoGfOjO+zkhdlOnF7mi31Pk9vz6c71qIXbpBjMrxKhYNF2PlFLdPt5D0wqt60J1Quxmj2uaQAyCNoqqMmijiEGYT7d57y9ez2/2Vbll62+SJEmS7AdX23D+vFLqBf/5isjaSyjPbSfGG99W6VXP71zsqdkNbzKp2eTGHvAS4k1IS22zfT1Fb6q/SZIkSfISXG3D+YeBF56NI0mS/SrV3yRJkiR5Ca624fwrInLqupQkSZLrLdXfJEmSJHkJrqbhfEvHR264McVgjdHLVwFwpkBEMWrOYmKLkogNDcPFaZytMFnAmIqFX2HhLJuzjJNn4cmnZixmDu8D1hoefMWYY4cUh8cN43zBAXOaQX0W6xbo6Mg+8zG2//xRvvC+R78s9dw50xcot6k0x772IMMjI6r1EcXqEFPku6nT4jJdmh30saMhem2dWN3VpcCbCrHuphhXrkXPJ8jGaeonn2b2/k8yeXSL5mSXFSBfLi+mXi5X2/rq3Vfyim96iP6xA5h+HwDlGkxwmOkWeXyK/jO/idvcJjYt9cY29eaUrF9icku5NqZ6/evAZkhe4FaO8MTw9ZxajJnMDTtzxfZOZDr17Oy01AtPWzvmOwuauiX6iM26r3te5eRlRtUvGI0L1tcLDq1r+mXk7vGUtXybjJbK7bD6xEeRU6dwp07hJlPqzR2i8yw2Z0xPdZ9caDxbj01pNxxHv+4gK/esoLRmfnZKHJf82FWeq+vklq6/SZIkSbIfpKwadwBR+zru9rpxbUpHd4FUf5MkSZLkJbriHmcRuTNbX0lyG7jV6+93H/0QqznY+YT6t2eQ5bw9K5HNbtKi+vAara1YmAGNlOz4HrXPaYOhqTXbGwYfurzN57KmKAVadbfOq90cxk0Ls/n5kaVKG6DCaLAWMqvolWCNYI1gNJxLq2wNlL1AYVqUCCZ6+mGbUlv62Q5kQB8EBcfAxhYbHSY6BE3UBmEMQFVvke88h55PoKm7lC55DlkONgNAlpMAKdd2Oazbpkv/YrOusDYDY5AsB6W7iZi8gxhAG2TwCFL08OWAthgxK9dwKseTEcR0y7J/JVOeXLUY5bHRoSQuy6ARuomZWlXSVMe6c7HMxTyoz6KQ7j0DCsH47kpX1IbczQgmx+scS4vB4XVObfq0pmJt52kG9dOYeoZyDa8A0BrRChlkMOry1IvNiDYn2BKX9zHRoWJAtCFrpqjlMQHiaoGKAe1blHfd+Wu796NCANeC1kzm9d5+kZMkueXd0v9MkyRJkiRJkuRGudrBgbes33ukpDcYY/RBrFXkmUIpyLP76VfdjFj9IlBYj4mCaQO58fSzBeN8yoPDOfmhmvY15W7uYi2BYf0ZbDtH1y1q3s2EBV0vjJJIePC19B9+E6/9npJg8266bImoZcipDo7qzJPgHbHsI1lOKId4WyLaEJVhVqzSqpJNsbSSsd32iVERROOjYmueMa8VOzNhMg3MHvV4H6kXDtcGFrOGE188zmIyha6/Borvgjde33NeDvrkVbE7/fTi5BxOshtrPFgdMVobMlypGI1LikKjjnbTcc/XPK4NZLnBWo3NNL2ZwRiF0aAmarcH0GhFkcNr7w8UWURjCJIBFdPG7vYyWh3RujvvIgqju2nEaycIkdxGNhcVW3VFZrreJ3v3Q9h7u/tRIDceDQRRuGhovKX2hsZrtIIvBJg3mulcmM2FyfYWvHv9+p7oJEmSJEluiNTjfAfY2Xyh4Ye3Lx9TWG+SJEmSJHsnNZyTJEmSJEmS5ArcMaEag75lbdWyOhJy202jndvAuKjJdECr7nJ8EIOPmiia2ucE6XotT4YV5q1la2Z2p2iuCuFVh1YZVRP67RbVYoP81JOws02cz5C6IS4WEJeTSDuPWqaPM8VysI5WhOEAlWUom6HzHNPrk+clsaiQvCQrZ0Sb401Ja0tGZUXA4rHdlOBDR1jeVwgei4sZmXZUMqNyO1SLDZRv0cEh2hCyCgDbTDGTs93go3qB3ziL397BzxbUmzvsHN9kvjFn49Ft2g131eddZQpTGfzk+me4ELpUeRfKLtoeLnh8bvhXftE+AjTLxw0vTgPV8n4B9IFDmWL8UB/WC/7ZFbxGkiRJkiT7X+pxvs7EhxffaY95ef7voczNb3gZkiRJkiRJbjep4ZwkSZIkSZIkV+COCdVIkuTW9RN/9pX0RytoBYOBxVpFVSjWx5GyFayPFFnAKCE3nso25NqjlGBVwK54NBGr3G5GnDzU5G6GCV1+XxUDOi5DqdoFog1ttUKwBc6UXV5gEUQtcxbbCq+7YCAbHVEZBMWwPkP/xFPosyeQ+QxpW1AK3etD1YO8RLIc5V0X87XMqbxz92uo7QivMqw4Qs/iB8fwKkPQNFKw43ps1RWbM8tzZ6FpIotFYD73NI1nsjFn+8yE6eaEZr5AYrzsOf1yLfDcHn9yl3Nu4O658i2Wy+X2HS+XK3Vh0FagC6aqLrFf/wVfxbtLz/aaJMmd645pOH/Hw4/C+B5mobebCg6gMjVRNApBq8jEDZn7nFmTESLsLAw+dBMbKNWlJIPudmOi+L0zBxj2D9IvhTwTvvLBxzhw6tOYJx9j/vSzTJ85jZt3kbJKK3zj2XxikzMf3brm91LdVTC+f8jo2Jj+oTHFuI/tV5jhANPv4x96I74YYEJLVIamHFMXY0QpFmbAPPZpYt6lU8ssrmd4bitnYoXaCrNB4PgzOyykYbPdYpHP0HcbJEaa+YK4x+En2hqKXsXKoTX64x6DUclgWDAaZRijaF3EO8EYRVFo7jqsuHu15nXxo1Rnn0JtnMIdP84zH3iEpz5w/LLx1CpTvPo/eYisX+4ed3F2gm88bt4yPTV7SZ/LhcQJW5+aMpftPXm9JEmSJEluvhSqcQfwJn/xnZIkSZIkSZIXlBrOSZIkSZIkSXIFUsM5SZJropT6OqXUbyiljiulRCn1H1+0/duVUu9VSp1Zbn/DzSprkiRJkuyFOybG+Wc+9HKq/pii0GijqApNVWkOrUYGRaCft/Rsy0q2zWG7oMjm5G6Gzlq0hN0cyK5awdsCb0ucKTjLQWauog1d/ufj7ijbB1fpH3kDo9efYX37OKLNbjlUcNy7eQpCAGMgK3Crh3HliHmxwtyM2PYjmpDRBEPrDbPW4IPCeUXjYHtHaJqIc5G2jcznDu8jbjvgTnumfz7HtY7gAt45oo9EiUgUJG7g3annxSnHELBFvjuQyFhD8AGlNRIjvmmv62cTfWAxmS6nBL9ady+XNwHfAl/7IruffIFtB4BvuoYivADvZvDet+/ti+4ffeDPgX8JvOcy2z8M/Gvg525guZIkSZLkurhjGs5JkuwtEfkt4LcAlPry6c1F5N3LbffdyHIlSZIkyfWSGs5JkuwbSqmCbgLGc4Y3qyxJkiRJcrGb2nBWSv0Q8O3Aw3RJPP8Q+G9E5HOX2FcB/47uYvq3icj/czXHahpPf6RYGVuKXNErYaUfGJVtN/229ljt8WLxDGmyCpuNiGgiBi+WRnIWvsBFg3ea0Gi0EkrrGeQNfTNn3Z1g9PSjqK0NwulTtBubPPfRx5ie3GF6csH8iYsnhX5xF2cfPXrVr3BruP9b7mZ4dIXqwArZaEB+7AiqKKGskP6Y+YGX0WQD5nbIPPY5XY9YOIvzitppZrViazvStELbRhZzz5mTOzS1I4bnp9BT+nx4vza6C2MRed66osw4eGRIURiKQtOrNOsrikEZKLO4O217YRyaLiOtBqzu0uFpFdmZTvm6996Ak3f7+CHghy9eeexYwWiUk9kuLeS5j6puNY0DMCjV/TkTUfgwxBoo80iZRR5cOcnQb9Kbn8E2U+zGMl9xlhOLHn6wircldbVKa0q0BKIyeJ3vTm/vxeLF4KPl9KxPG7rvkFagtWCU0M9bjpWasjiDnDyJn0xwO3NiCJQH1zD9Hma8ghqv0h64G1cMaW2P1lbMGBIwSFz23itAwEdDHXLmLkctj3G4P+EtRydoIloCQVuiGMo4o3BzlAScHTOxawQxGBXoxwmrZ7+Aip6Y93B5n2l1gJkeMQ8VM1fwxJk+8xom08j2tuPUc1NmkwXzyZy2btDGYDOLzSxKK7IiI/iAax3toqWtG+Zbkxf8gIfrq9gioygL8ipHG01wnhAiZa/gL379UQ6vBHq5JzceQeGCIcr5qxrn0oIWJlBaR2EcUTReNFE0WkXG2ZQDzbNUk+ewk7PddyMvCWWfmBVdTm7d5d7Ot06gvAfvIAakP0K0YbJoOJrqb5IkF7jZgwO/Hvhfga8G3kbXkH+fUupSWenfCRckYN6n+pPjz3s8f+7sTSrJnWdRP//rMZ9f39hsgEw/v0Fu9I2fYv028yOcn+1iTBfAniRJkiT7wk3tcRaR5w3FUkr9F8Ap4CuAD16w/vXAu4CvBE7cyDImSXLjiEgDNOceXyp2OkmSJElulpvd43yxc3OqbpxboZTqAf8K+D4RuVHzwSZJkiRJkiTJ8+ybwYHLGOYfBz4kIp+6YNNPAH8oIr9+ha9zycFFKysleaZpWkGk68lSyhBijqCoW4UPCq2FXiFYLeQ2cKy/Ra5bKjVnKFuIVdjouhR1EjGhxbgW1QoqekRpQm+MLnrotUOs3/My1rc38dtbXQo6pYltCzGisgyAdnMbP1vgZjVu3jA/OyUGIe/n5P0Ct2hx85bgIlmVsf7wXVRHDmJXxqjRCu7YA4jJQCLat7CM3VMSUbELHaiHh2iyAbXts5Aez87WmTaW1ndp7k5tdCnuvBfqOnD65JTFrKGe1sv4xQbXtIQQCM7t+bTbAHjg6eVySQ7YXC5X5sA9R8mKDImCdx7Xtnjn8U2Lbz3BuRd8/pcF218l72Yv8RX2L6XUAHjwglX3L3M1b4jIU0qpNeBe4Nhy+0PLHuTn0o/gJLk5lFJ/F/hBuuEynwbeKSJ/8AL7fwfwPwIvBx4H/pGI/JsbUdYk2Y/2U4/z/wK8Dvjr51Yopf4q8Jfp4puv1A8B2xcsz+xhGW9JMStvdhFuit7K6GYX4Xb3ZuDjywW6H74fB/6H5eO/unz8m8vHv7J8/F/dwDImSbKklPou4F8A/xR4I/AHwG8ppe69zP5fA/wq8G7g9cvbX1NKfdWNKXGS7D/7ouGslPpJun+yf0lELmzo/mW6X7lbSimvlPLL9e9RSn3gMi+XBhclyQ0gIh8QEXWJ5R3L7b94me3/+OaWPEnuWO8CfkFEfl5EPiMi76S7xvd3LrP/O4H/T0R+REQ+KyI/AvwuV9eZlSS3lZudjk4BPwl8G/ANIvKli3b5UeDnL1r3SeAHgN+41GumwUVJkiRJ8nxKqZxu4P2PXrTpfVx+ztWvoQuXvNB7uUzD+XKhkpPJC6coTP7/9u4l1K7qjuP490etFbRRUCQ+CARapCMTKpTU18QIZuADB1EHbUpBWl9T0YFVfOEkPgYSdGBrQIggKC3VOlEUr6ZNLAVRQdRGjDWgFpuBMWr+DvaJnNycc9373HvPOff0+4HLIev8196Lf1bgf1fWXlvLZTnmXvrPrh23JA8D1wCXceR20s+r6oshfYoO5zgnWUWzZePEqvJfr8bK+bc45k+TNEvzL8npCREfVwAABIVJREFUwF7g3Kqa62u/Ffh1VZ01oM9BYEtVPdHXdg3wWFX9aED87Qw4h12aAmur6t9LcaFJPxx4+L+HXpzX/hvgj0t0j/00Wzb2L9H1pC6cf4tj/jRJszj/5q+WZUDbqPH30jzrcNiPaZ4zOpPZyuE4mcPFOZy/z74vsK1Jn+PceR9F1z7VLKmv6JUCrVzOv8Uxf5qkGZt/nwDfAKvntZ8K7BvS5+Mu8Qtsldy/0lfsJ8UcLs5ybNediocDJUnS8qmqg8Bumrf09tsIzB3dA4BXB8RfvEC8NPMmvVVDkiSNx1Zge5JdNEXxtTRnrW8DSPI4sLeqbunFPwi8lORm4Bma55EuAs4b98ClaWHhLEnS/4Gq2pHkZOA2mhegvAFsqqo9vZA1wKG++LkkVwF30bwE5V1gc1XtbHnLL4E76Nu+oc7M4eIsef4meqqGJEmStFK4x1mSJElqwcJZkiRJasHCWZIkSWrBwlmSJElqwcJZkiSNJMl1Sd5PciDJ7iTnf0/8lUneTPJl7/OKcY11GnXJX5ItSWrAz3HjHPO0SHJBkj8n+aiXh8tb9Lmwl+cDSd5L8ruu97VwliRJnSXZDDwA3A2sB14Gnk2yZkj8BmAHsB04u/f5ZJJfjGfE06Vr/nr+R3OU4Hc/VXVgucc6pY4H/gXc0CY4yVrgrzR5Xg/cAzyU5MouN/U4OkmS1FmSncDrVfX7vra3gKf7XqLSH78DWFVVl/S1PQf8t6quHseYp8kI+dsCPFBVJ41vlCtDkgKuqKqnF4i5D7i0qn7W17YNOLuqNrS9lyvOkiSpkyTHAj8Hnp/31fPAL4d02zAg/m8LxM+sEfMHcEKSPUk+TPKXJOuXbZCzZ9j8OyfJD9texMJZkiR1dQrwA2DfvPZ9wOohfVZ3jJ9lo+TvbWALcClwNXAAeCXJT5dpjLNm2Pw7hubvoxVfuS1JkkY1f79nBrQtJn7Wtc5HVb0GvPZdYPIK8DpwI3DTcg1wxgzK96D2oVxxliRJXX0CfMPRq6OncvSq3mEfd4yfZaPk7whVdQj4B+CKczvD5t/XwKdtL2LhLEmSOqmqg8BuYOO8rzYCc0O6vTog/uIF4mfWiPk7QpIA64D/LO3oZtaw+berqr5qexG3akiSpFFsBbYn2UVTlFwLrAG2ASR5HNjbd0LEg8BLSW4GngEuAy4Czhv3wKdEp/wl+QPNVo13gFU02zPWAdePf+iTl+QE4Cd9TWuTrAM+q6oPktwLnFFVv+p9vw24IclW4FGahwV/S7NfvDULZ0mS1FlV7UhyMnAbzZnCbwCbqmpPL2QNcKgvfi7JVcBdwJ3Au8Dmqto53pFPh675A04CHqHZbvA58E/ggqr6+/hGPVXOAV7o+/PW3uefaB6iPI0mhwBU1ftJNgH30/yy8RFwU1U91eWmnuMsSZIkteAeZ0mSJKkFC2dJkiSpBQtnSZIkqQULZ0mSJKkFC2dJkiSpBQtnSZIkqQULZ0mSJKkFC2dJkiSpBQtnSZIkqQULZ0mSJKkFC2dJkiSpBQtnSZIkqYVvAU5jBL7hYke6AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# rat = 'thph1.1'\n",
+ "\n",
+ "heatmap_color_scheme = 'coolwarm'\n",
+ "clims = [-3,4]\n",
+ "\n",
+ "rats = modDict.keys()\n",
+ "# rats = ['thph1.1']\n",
+ "\n",
+ "for rat in rats:\n",
+ " full_signals = ['blue', 'uv']\n",
+ " key='snips_distractors'\n",
+ " keys = ['snips_not-distracted', 'snips_distracted']\n",
+ " signal='filt_z'\n",
+ "\n",
+ " f, ax =plt.subplots(nrows=7, ncols=3, figsize=(8.27, 11.69), dpi=100)\n",
+ " f.subplots_adjust(wspace=0.5, hspace=0.5)\n",
+ " f.suptitle(rat)\n",
+ "\n",
+ " for idx, d in enumerate([modDict, disDict, habDict]):\n",
+ " time2samples(d[rat])\n",
+ " plot_full_signal(ax[0][idx], d, rat, full_signals, ['blue', 'xkcd:pink'])\n",
+ "\n",
+ " plot_full_signal(ax[1][idx], d, rat, ['filt'], ['orange'], buffer=0.5, plot_events=False)\n",
+ "\n",
+ " plot_raster(ax[2][idx], d, rat, sortdirection='dec')\n",
+ "\n",
+ " plot_single_avg(ax[3][idx], d, rat, key, signal)\n",
+ "\n",
+ " plot_multiple_avgs(ax[4][idx], d, rat, keys, signal)\n",
+ " \n",
+ " make_heatmap(ax[5][idx], d, rat, key, signal, events=d[rat]['pdp'], sortevents=d[rat]['pdp'])\n",
+ " \n",
+ " make_heatmap(ax[6][idx], d, rat, key, signal, events=d[rat]['pre_dp'], sortevents=d[rat]['pre_dp'], event_direction='pre')\n",
+ "\n",
+ " ax[0][0].set_ylabel('Raw signals (mV)')\n",
+ " ax[1][0].set_ylabel('Filtered signal')\n",
+ " ax[2][0].set_ylabel('Trials')\n",
+ " ax[3][0].set_ylabel('Z-score')\n",
+ " ax[4][0].set_ylabel('Z-score')\n",
+ "\n",
+ " pad=30\n",
+ " ax[0][0].set_title('Modelled', pad=pad)\n",
+ " ax[0][1].set_title('Distraction', pad=pad)\n",
+ " ax[0][2].set_title('Habituation', pad=pad)\n",
+ "\n",
+ " f.savefig(outputfolder+rat+\".pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rat = 'thph1.1'\n",
+ "pdf_pages = PdfPages(outputfolder + rat + '.pdf')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pdf_pages.savefig(photoFig)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pdf_pages.close()\n",
+ "plt.close('all')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/01_dis-behavior.ipynb b/notebooks/01_dis-behavior.ipynb
new file mode 100644
index 0000000..621581c
--- /dev/null
+++ b/notebooks/01_dis-behavior.ipynb
@@ -0,0 +1,1110 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "import numpy as np\n",
+ "import csv\n",
+ "from scipy import stats\n",
+ "import trompy as tp\n",
+ "\n",
+ "# %run ..//JM_custom_figs.py\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fig settings\n",
+ "scattersize=50\n",
+ "colors = ['darkturquoise','dodgerblue', 'darkblue']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "statsfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\stats\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# thph2.8 removed from analysis because no data on habituation day\n",
+ "\n",
+ "modDict.pop('thph2.8')\n",
+ "disDict.pop('thph2.8')\n",
+ ";"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d = disDict[\"thph2.1\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# adds parameters used for figures - no. of distractors, distracted, not distracted and calculates probability of distraction\n",
+ "\n",
+ "for day in [modDict, disDict, habDict]:\n",
+ " rats = day.keys()\n",
+ " for rat in rats:\n",
+ " d = day[rat]\n",
+ " # check that numbers add up\n",
+ " d ['#ds'] =len(d['distractors'])\n",
+ " d ['#distracted'] =len(d['distracted'])\n",
+ " d ['#not-dis'] =len(d['notdistracted']) \n",
+ " if d['#not-dis'] + d['#distracted'] == d['#ds']:\n",
+ " d['prob-distracted'] = d['#distracted'] / d['#ds']\n",
+ " else:\n",
+ " print(\"Something wrong in the number of distracted and non-distracted trials\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# makes lists with probability of distraction for each day\n",
+ "\n",
+ "probdisMod, probdisDis, probdisHab = [], [], []\n",
+ "for day, output in zip([modDict, disDict, habDict],\n",
+ " [probdisMod, probdisDis, probdisHab]):\n",
+ " rats = day.keys()\n",
+ " for rat in rats:\n",
+ " d = day[rat]\n",
+ " output.append(d['prob-distracted'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# makes panel for figure 1 showing probability of distraction\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(2,3))\n",
+ "f.subplots_adjust(left=0.4)\n",
+ "tp.barscatter([probdisMod, probdisDis, probdisHab], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " barlabels=['Mod', 'Dis', 'Hab'],\n",
+ " barlabeloffset=-0.04,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax)\n",
+ "\n",
+ "ax.set_ylabel('Probability of distraction')\n",
+ "ax.set_xticks([])\n",
+ "# ax.set_xticks([1,2,3])\n",
+ "# ax.set_xticklabels(['Mod', 'Dis', 'Hab'])\n",
+ "ax.set_ylim([-0.1, 1.1])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig1_probability of distraction.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean: 0.035736657778564794, SEM: 0.010063763058217283\n",
+ "Mean: 0.48438394306147653, SEM: 0.0686893248420231\n",
+ "Mean: 0.23832920617079603, SEM: 0.06087197101162473\n",
+ "Mod vs. Dis - t-stat: 6.4, corrected p: 0.0001\n",
+ "Dis vs. Hab - t-stat: 5.5, corrected p: 0.0004\n",
+ "Mod vs. Hab - t-stat: 3.4, corrected p: 0.0170\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Stats for probability of distraction across days\n",
+ "def mean_and_sem(data):\n",
+ " print(f\"Mean: {np.mean(data)}, SEM: {np.std(data)/(np.sqrt(np.size(data)))}\")\n",
+ "\n",
+ "def bonferroni_corrected_ttest(data1, data2, comps=3, string_prefix=\"\"):\n",
+ " t, p = stats.ttest_rel(data1, data2)\n",
+ " print(f\"{string_prefix}t-stat: {np.abs(t):03.1f}, corrected p: {p*comps:03.4f}\")\n",
+ "\n",
+ "with open(statsfolder+'percent_distracted.csv', 'w', newline='') as csvfile:\n",
+ " writer = csv.writer(csvfile, delimiter=',')\n",
+ " for m, d, h in zip(probdisMod, probdisDis, probdisHab):\n",
+ " writer.writerow([m, d, h])\n",
+ "\n",
+ "# One-way ANOVA conducted in SPSS with this csv file\n",
+ "\n",
+ "# these lines print mean and SEM for reporting and calculate Bonferroni posthocs\n",
+ "for data in [probdisMod, probdisDis, probdisHab]:\n",
+ " mean_and_sem(data)\n",
+ " \n",
+ "bonferroni_corrected_ttest(probdisMod, probdisDis, string_prefix=\"Mod vs. Dis - \")\n",
+ "bonferroni_corrected_ttest(probdisDis, probdisHab, string_prefix=\"Dis vs. Hab - \")\n",
+ "bonferroni_corrected_ttest(probdisMod, probdisHab, string_prefix=\"Mod vs. Hab - \")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# function for rasters\n",
+ "\n",
+ "def distractionrasterFig(ax, timelock, events,\n",
+ " pre = 1, post = 1,\n",
+ " sortevents=None, sortdirection='ascending',\n",
+ " title=''):\n",
+ "\n",
+ " if sortevents != None:\n",
+ " if len(timelock) != len(sortevents):\n",
+ " print('Length of sort events does not match timelock events; no sorting')\n",
+ "\n",
+ " if len(timelock) == (len(sortevents) + 1):\n",
+ " sortevents.append(0)\n",
+ " \n",
+ " if sortdirection == 'ascending':\n",
+ " sortOrder = np.argsort(sortevents)\n",
+ " else:\n",
+ " sortOrder = np.argsort(sortevents)[::-1]\n",
+ " \n",
+ " timelock = [timelock[i] for i in sortOrder] \n",
+ " else:\n",
+ " if sortdirection == 'ascending':\n",
+ " sortOrder = np.argsort(sortevents)\n",
+ " else:\n",
+ " sortOrder = np.argsort(sortevents)[::-1]\n",
+ " \n",
+ " timelock = [timelock[i] for i in sortOrder]\n",
+ " \n",
+ " rasterData = [[] for i in timelock]\n",
+ " \n",
+ " for i,x in enumerate(timelock):\n",
+ " rasterData[i] = [j-x for j in events if (j > x-pre) & (j < x+post)]\n",
+ " \n",
+ " for ith, trial in enumerate(rasterData):\n",
+ "\n",
+ " xvals = [x for x in trial] \n",
+ " yvals = [1+ith] * len(xvals)\n",
+ " \n",
+ " pdplist = [lick for lick in xvals if lick > 0 and lick < 1]\n",
+ " if len(pdplist) > 0:\n",
+ " ax.scatter(xvals, yvals, marker='|', s=1, linewidths=0.5, color='k')\n",
+ " else:\n",
+ " ax.scatter(xvals, yvals, marker='|', s=1, color='xkcd:light blue')\n",
+ " \n",
+ " ax.set_title(title)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# makes scalebars for rasters and sets up axes\n",
+ "\n",
+ "def scalebar4raster(ax, length=1, offset=1, ypos=0):\n",
+ " \n",
+ " # Turns off bottom axis and x ticks\n",
+ " ax.spines['bottom'].set_visible(False)\n",
+ " ax.set_xticks([])\n",
+ " \n",
+ " # Gets x coordinates for scale bar\n",
+ " xmin=ax.get_xlim()[0]\n",
+ " x0=xmin+offset\n",
+ " x1=xmin+offset+length\n",
+ " \n",
+ " # Sets up a transform for x in data coords and y in axis coords\n",
+ " trans = transforms.blended_transform_factory(\n",
+ " ax.transData, ax.transAxes)\n",
+ "\n",
+ " # Plots line using x and y and transform\n",
+ " line = mpl.lines.Line2D([x0, x1], [ypos, ypos], lw=2., color='k', transform=trans)\n",
+ " line.set_clip_on(False)\n",
+ " ax.add_line(line)\n",
+ " \n",
+ " # Adds text below line\n",
+ " ax.text((x1-length/2), ypos-0.02, '{} s'.format(length), va='top', ha='center', transform=trans)\n",
+ " \n",
+ " # Adds triangle to show distractor\n",
+ " ax.plot(0, 1., marker=\"v\", markersize=6, color='grey', clip_on=False, transform=trans)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# makes raster plots for each day, change rat to see different rats and set savefig to True to save\n",
+ "\n",
+ "rat='thph1.4'\n",
+ "savefig=True\n",
+ "\n",
+ "f, ax = plt.subplots(ncols=3, figsize=(7,2))\n",
+ "f.subplots_adjust(left=0.1)\n",
+ "\n",
+ "d=modDict[rat]\n",
+ "rasterPlot = distractionrasterFig(ax[0],\n",
+ " d['distractors'],\n",
+ " d['licks'],\n",
+ " pre=5, post=10, sortevents=d['pdp'], sortdirection='dec',\n",
+ " title='Modelled')\n",
+ "\n",
+ "d=disDict[rat]\n",
+ "rasterPlot = distractionrasterFig(ax[1],\n",
+ " d['distractors'],\n",
+ " d['licks'],\n",
+ " pre=5, post=10, sortevents=d['pdp'], sortdirection='dec',\n",
+ " title='Distraction')\n",
+ "\n",
+ "d=habDict[rat]\n",
+ "rasterPlot = distractionrasterFig(ax[2],\n",
+ " d['distractors'],\n",
+ " d['licks'],\n",
+ " pre=5, post=10, sortevents=d['pdp'], sortdirection='dec',\n",
+ " title='Habituation')\n",
+ "\n",
+ "ax[0].set_ylabel('Sorted trials')\n",
+ "\n",
+ "for axis in ax:\n",
+ " #axis.set_xlabel('Time from distractor (s)')\n",
+ " axis.spines['right'].set_visible(False)\n",
+ " axis.spines['top'].set_visible(False)\n",
+ " scalebar4raster(axis)\n",
+ "\n",
+ "if savefig:\n",
+ " f.savefig(figfolder+\"fig1_raster plots.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# makes lists of PDPs (post-distraction pause) - can ccomment out a line to give raw PDPs rather than log PDPs\n",
+ "\n",
+ "pdpsMod, pdpsDis, pdpsHab = [], [], []\n",
+ "pdpsMod_all, pdpsDis_all, pdpsHab_all = [], [], []\n",
+ "for day, output, output_all in zip([modDict, disDict, habDict],\n",
+ " [pdpsMod, pdpsDis, pdpsHab],\n",
+ " [pdpsMod_all, pdpsDis_all, pdpsHab_all]):\n",
+ " rats = day.keys()\n",
+ " for rat in rats:\n",
+ " d = day[rat]\n",
+ " \n",
+ " # raw PDPs\n",
+ " pdps = d['pdp']\n",
+ " \n",
+ " # log PDPs - comment out for raw PDPs\n",
+ " pdps = np.log10(d['pdp'])\n",
+ " \n",
+ " \n",
+ " output.append(np.mean(pdps))\n",
+ " output_all.append(pdps)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(-0.9749821261009044, 1.486913129975438)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(-1.1, 1.5)"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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jnHt8iuXwSoqLi1m5ciUAlZWVZGdnA66dL0NER0eTkZFBR0cHg4ODNDQ0OL0fHByMSqVy2kzg7+8/woCbasa6Z39FCPEusPCO+/VRwHui6KYAV3lJ33v/fRYvXowQgk8++QSdTkd4eDjf/e53UalUpKens2nTJtLT05UhfuiIjo7m0Ucf5b333uO7/3NkubLReveKFStGGHBTyVj37BNAE7AA+Odh53sAz1sT08hohV76+vow9vUR+y9W4kzl9Fmqid/7HywfOMYV7Wr+1PwHkv16WJyeRGlFLTXGQN7T7kYK5/5TYW7gM/X/qPjQh9BoNCQkJFBTU6NM2VQqFcuWLePChQvKiDKVjHXPvubwln0NODXsfl2GfUP+PcNobtLLly8rQ2q66TyXdetASjTSTKT1Osl+Pbz4/B625efx4vN7SPLrJtZSPeLa7T6xlJeX884774zosenp6VRVVTn50mNiYujo6KCkpGTKMzG5c8/+DbfDkQCs2GuE3DOMViWgvr6eiooKAHTSSI86lChLHc2aROZbm1ic7uwuXZKezHxr86jXNxgM+Pj48MEHHzgpXAjBAw88wNmzZ5VzNpuNpqYmjh8/7rTIMhUKd0fZGimlEhrpeD76vphZzPBpVH9/P/39/TQ2NhJsbWNA2K30aEstjZokOtRRXC6voby8nGPHjlFeXk5JeTUd6tE3AprNZtLS0jAajXz88cdOCg8LC8NoNNLX1wfYtwMbjUaeffZZp0WW6uqRo8Z4cUfZbUKIB4deCCEewr6Tc0oYrfrudDKaMVRRUcH8+fOxWq0sHjxJpXYlamnGKtRIoeKGOonuXiOHDx/GZDJx+PBhenr7uaF2vX09OzubwcFBEhISRih87dq1nDp1Chg7E9NkcUfZz2GvHlAvhLiOvTDqs5Nu+TavMLL67rQxWq2ulpYWgoPtqVlDrG00+KSSYC7nmo99MSTGWsu8QF+ef/558vPzef7555kXoCPGWuuyHV9fX2JjY6mpqWHx4sVOCvf19WXevHk0NjYSFRXlcpFlsrjjG6+RUq4FMoFMKeV6KeXkx5Tb1y8EOqbqeuPlTuNsYGCA/v5+4uPj0ev12LD35vnWFjrVdnfqfEsTmekpTr0vMyONWPPYOVVWr15Na2srAQEBLFmyxEnhy5cv5+LFiyQnJxMYGEhBQQGHDx+moKCAoKCgKcnEJNyZ0wkhPsfIPGh/O+nWb18/EXhPSjmqW2p4lV1fX98HhuKxL5aUYJnkToucnBw+/fRThFrN8qVLiYmJISQkhLKyMvR6PywWM5cuXWLJkiXKcuT69et5+OGHeeGFF5Q0G3v37uXMmTP89re/xWKxOLWh8dGyfJk9wVRGRgZSSn7zmzcJDZ3vpPS4uDhCQkK4fPkyqampREZG0tzcTFVV1Zhzb43Gh+XL7dHdZ8+ePSilHHWkvKuyhRA/B/yAXKAAe/aFYinlHrf+m25wN2UPJzs7Ww5FcQoh4NzFu3xibHLLr3A0IxOyliOl5PDhwwgh2Lp1K7/61a/40pe+RElJCYsXL1bSWr7//vvU1dWh0WhITk5WjKfdu3dz8eJFNm/e7LK9np4e/vCHP/DMM88wMPB3hIfbWLrUwscf+yClIC/PRGGhDybTeNJef3f4j8HlB925Z6+XUn4d6HQEH64D4sYhyazBZDIpIcJmsxmr1Yq/vz89PT2Koo1GI01NTTz11FPk5uYqS5w5OTk0NDQwf/586urqXLYRGBhIWFgYq1atAqC1VUVJiYYtW8wIITl1SsOaNZ7ZxO9Omo2huBmjECIau6t0oUekmWb0pkH6fW7PIquqqggICGDBggVUVFQQFhY2YunznXfeITc3F51OR1pamlOu0uLiYsLDw6moqCAqKgqdTjdqu2vWrCE+Ph6tVmIyCUXhW7eaOXLEB7MZsrLMBARImptVVFWpkXLyCe7d6dnvCiFCsNcFOYc9K/Ebk27ZgYvqu9NCZFc3zcG3CyI0NTUpIcNXrlxhxYoVlJWVkZGRAdhDiG02m+LevJNVq1ZRW1tLZmYmn3zyict2582bx61bt3jggds9uLVVxaVLGrZuNbFkieDrXw/k+99fxTPPBPDkk1aEmLy/fExlCyFUwBEp5S0p5e+ABCBDSvmjSbfsQEr5FSlllCOrQ6yU8j+m6tp3I7L7Fs1BdmVrNBo0Go2yKNLb20tUVBRWqxWtVktvby+XLl1ixQrXq7tCCDZv3szly5cJDQ0dM+NhcXExSUk2NJrbSmxtVdHVJUhMFOTnb0Gn0/K5z+WRkeFPaqrV5bXcZUxlSyltDFsEkVIOSinvmX1fvmYzA44AgpSUFBITE9FoNFy/fp3AwEDq6upYuHAhNpuN48ePExAQcNcMSCqVii1btnDjxg1qa2udIkqH09bWRk+PYMUK5/uzXi8JDPRTYtILCwvR61VERnpY2Q4+EkJ8SdwrVVFcEBMTg81mIzY2lpKSEjIyMqirqyMxMZFTp06xZMkStFqtWyUadTodGzduVH4krjh9WkNamg2Vanjokv0HM9xdat+SNPnv6M4lvo194WNQCNF9r2RJ0litWFQOp4jVitVq5caNG8TExNDe3k5CQoLSy3U6Ha2trS6jU0YjKCiIrKwsBgYGXG4GaGpS098PS5bc7t1SCtLT050cNvadKNNgoA3LlqSVUgbdK1mSwnq6aXVMp5La26itrcVsNtPd3Y1Op1P2Z1VUVLBy5UqXucfHIjIykszMTEpKSujpGT1F+9mzPmRm3jbAmppUXLpU4eQuvXSpgubmyXdtd/Z6HXHn3GwjqquL5mC7Tzyu4yaNjY2o1WpKS0uJjo6mtbWVK1eukJOTc9eEOmORmppKcnIy77///qhesPp6NRaLICPD7nWrqlJTVtbL3r37+OijQ+zdu4+ysl6qqiZf4XessCRfIcR8YIEQYp4QYr7jSASiJ93yDBPcb6RLr0dlsyEdYUQxMTE0NTURFxdHZ2cny5Ytw9fX12n6NRFWr16Nv78/hYWFo75//ryGpUstgERKwS9/qebll3v5yU9O8/LLvfzyl56fZz8LnAUyHI9Dx9vAv0+6ZW9ACBLb26kLDSM+Pp6QkBAsFoviFImOjsZkMqFSqUYUchtvXa9du3ZRXV09IvgQoKZGDQhSUuy9W0pBZaWGwkItlZWaKVE0jB2W9FMp5ULgL6WUSVLKhY5juZTyZ1PS+gwhpEQ6XMiJN9u5FhqKVqvl6tWrBAcHc/36dTZs2ABAaWmpkghniKG6Xndu2R1L4UIIvvrVr/Luu++OknlBcOmSmpUrJz+9Ggt37vrNjo34CCF+IIT4vRAiy6NSeZh5fX10+vsjpASH2q1WKw0NDXR0dJCdna1ErrS3t48IRhxe12s80SR6vZ7t27fzxhtvjNhgUFGhQa2GhATPKdwdZf9QStkjhNiAvQLQq8A+j0k0DUR2d9EcFEzCzXbqQxcQfauT1tZWGhsbCQoKYulS+3Kkqy1Bk4kmSUpKIjo6ms9+9rNO56UUlJaqnVyoU407CyFDP7XPAfuklG8LIX7sMYmmgbCeHsojo9hUWU5hWgZra6pRh4Xh4+NDcHCwsm33ypUrynA+nKioKI4ePUpubu6E6nrl5eXx+utvkJ19wCnpTmmpiqVL/4ToaAONjY1uf5+ICPeCfd1R9g0hxH4gD/i/QggdszwNpkraLXCVlFhVKvxMJpIzM4mKilIWOcxmM0KIUfdrp6Sk8NFHH/Gzn/2MoKAguru7EUK4XVBdpVLxz//8TxgMBpYuXerkgj179iwPP/wwX/jC1Ofvd0dpjwAHgR1SylvAfOC7Uy7JNBPb2cH1efNRWa3E3WxX6nskJCQA9l6dmZk56mevXbuGTqdDp9MRGxuLTqcjNDSUTz75hK4u95YOFixYoGwSGN6LV6xYQW9vL62trZP/kncw1jx7yEvmCxiAm4559yBwxtXnvB3/gQH6tDqSW1upDQsnp6qCm4GBDAwMEBQUpBhOY5VSLioqQkrJ008/TX5+Pk8//TTd3d34+flx6NAh6uvr3aoiMLR3rKSkRPmRqNVqkpKSeOutt3j11Vc5evToiDCniTLWMP5rYBf2ubXEOdxFMkvLPtmNsyBSW1uZZ+wj+lYn7QGB6DUapSe3tbW5TGl57tw5QkJCCA8PH5GVuKenh8zMTI4dO0Z4eLiyJfdOy1ulUuHn54efnx/x8fHU1dVhMBjIz89Hq9VSVFSEyWQiIiKCCxcucP78eV588cVJF213+Wkp5S7H4z0RlTJERHcXjcHzaAkKYtXVqzQHBhEyMIDFYlEUXFpayvr160d8tq+vj97eXmJiYvj0009HNdDS0tLIyMjgyJEjZGZmjnoft1qt9Pf3YzQaMRqNSpK9X//616hUKmw2GyEhIcTHx1NbW0tXVxeFhYVs2TK5GvUulX23ubSU8txY73srOouFhI6baC1mPk1OYWNVJTqzmZr6esCeRMdms41aDGYozcann35KSEgIBQUFJCcnU1NT4xTuq1KpyMvL4+OPP8ZqtSqb9IdQq9VKPhWwb9A/ePAgu3bt4rXXXsPf359nnnlG+SHt37+fyspKzymb20ELvkA2cBH7UL4MOAWMnJPMBqQksruTcwkL8Teb0FotaC1mZRmyrKxsVMPsxo0bBAUFceLECfLz81Gr1Rw/fpz6+nrS0tLYuHGjUxpMIQRbtmzh2LFjWK1W4uJcx2gKIdiwYQPFxcUEBQWNmMOnpqZy48aNSX/1sdyluVLKXOAakCWlzJZSPgCsBKZsk8B04mOxENzfj1WoqIqIJOFmOxaVwKLWKBElo+3olFJy4cIFWltbyc3NxcfHhzfffJOqqipiYmKoqqrizTffHOEuFUKwadMm6urquHp17GxiAQEBREZGkpycTEWF8xJneXm5Eo06Gdy542dIKUuGXkgpLwshZmWajahbt4jt7KBg4yYA5vX1EjgwSLlj+dJVRuJz587R39/P9u3b8fPzo7KyUnGXDq/FWV1d7RRtCnaFb9y4kaKiIqxW65g7OzIzM7l06RL9/f3s37+f1NRUqqqqGBgYcCtC5m64M88uE0IUCCE2CyE2CSF+gX2P9qxjQ3UlDfPmM6DVIaRkfp8RvdnEhTj73Hq0XCl9fX2cP3+erVu3KrHjE3GXrlu3js7OzrsWhwkLC2P58uXk5eWh0+nIy8tjxYoVUzLvdkfZTwCl2CsK/AVwhVmY9C6jqRFhs1G7wL6oEdHdhVUIzGo1VrVa6aHDY72llBw4cIDt27c77d+OjIykoqLCacvuUJz5WKxatQqj0Tgi++Fw4uLiqK2tJTk5mZycHCWb4nRt7BuQUr4kpfwfwN85nk9fws1JIKQkrbmJbZdLWHyjAbWUVDqG7NTmZrQWC6XR9pyk6enpTm5LKSXvv/8+iYmJI4yr5uZmBgYGOHLkCCaTiSNHjjA4OEhNTc1de2BWVhZWq9Upme1wkpKS6O/vZ9++fRw6dIh9+/ZhNBpH5DefCOP1cRfc/U+8AyElT54p5pm6Wv4yMpx1FjOxPT0Mauy+7thbHegsFs7FJwL26NLhU6QTJ05gNBpH7Ns6deoUQggCAwN57rnnyM/P57nnnsPPz0+Zht2tcNuyZcvQaDROGReGqK2txc/Pz2kY1+v11Na63g7sLuNV9qwJJ05taWaR1cJfPPMM+fn5fP3LXyZI60NqSzNCSvwGBxn08cGmVtvXtzs7FU9XcXExRqORdevWOU2nzp8/ryTAW7hwodM9OyUlheLiYgYGBmhpaaGgoICjR49SUVFBW1vbCPfp4sWLCQgIGJExacgeUKlUSlWDqdqMP17/299MusVpIrKri6XDCq1VVVWxZNEiIru76NPq0FqslDmG8CU3GigusU84Lly4gF6vp7u722n4Hirk0tnZSU9PD21tbcrukTs9aIASwdLS0oLJZKK8vNzJx+3j46PYAYWFhWzcuFFZOXvvvfeUzIgGg4Hu7m527do16f+JO3W9jkgptwJIKf9w5zlvpTk4mJKqKrY6XJp9fX2UX7tGc2ISK+vrsAm4GJeAymZDa7UwODhIaWkpKpWKjo4O1q5dq1yrqqqK6upqfH19yc7OZs2aNRw4cMClBw3syszPz6e2tiWhHowAAAvGSURBVJbq6mo2btyIXq9X3jeZTHR2dqJWq2ltbeVXv/oVcXFx3Lx5E19fX6dp3c9//vMp+Z+M5S71xb4ve4EQYh63h/AgZkF0aVVEJGXX69n78sukxsVRVl5OVVAwVRGRbK4oo9fXlz6djoymRioiIklNTWVgYIC4uDgGBgYUV+a5c+e4cOECW7ZsITExUbn+ww8/zPHjx7l+/fqoHrQhkpKSiIqK4vjx4yQnJyvr5VqtloiICCIiIsjIyOD69evU1NQQHh7OvHnzRmwSaG1tnVSEK9zD0aVSCH6ZvZqXE5M4UN/A+YBAfpm9GqTEb9BEY4jdeRLX0YHGaiU0NJSsrCzOnj1LdnY2PT09vP3221RUVPCNb3zDSdE2m03xoEVHR7v0oA2h1+vZtm0b/f39HD16dNTlz6Gsxq2trR7LqTLWqtdPgZ8KIV6QUv7bpFuaQfTmQZpC7PfHpQ3XsahUVERGEWw0ApK4zk6OnzzJlStXSE1NpaioiN7eXnQ6HQ8++OCIJcrhAYd386ANZ8mSJXR3d3P48GGWL18+YoEkKiqKjRs38rvf/Y6XX36ZlJQUqqurCQ4OnpKcKvdsdOnQ1OvZulp2Z2aS19fHk2eKyb52lQ5/f5pCQlhbU4UAjqfa00FfvHiR2tpa4uPjsdlsbNu2bdTST42NjRMOOAwKCmLHjh3cuHGDEydOjBgNwsPDCQgIYGBggIaGBreCINzlno0uHZp6fdMx9Xrx2WfIsJgJ7e2lJTiY+b09xHZ2cihzCbGdnTz22GOsXLmSDRs2cOnSJfLz80fcg6WUSi2v4SWdhqrsDqWrvhtCCFatWkVqaioffvghHR23k0VVV1czODjIiy++yBNPPMGf/dmf0dPTM21J70ZElzILMhzeOfVSq9UkRUejkpIuvZ78K6UUJaewtayU+JvtlJaWkpSUxNGjR8nLyxsRFdLU1MTBgwdRqVQ88sgjBAUFOaWvCgkJwWQy8emnn7odRhQWFsb27dspLS3l/PnzSCk9mvTuno0uvXPqZbVaKSsvp1unI+bWLXRmE/OMfRxLTWdrRRlHz5/nyJEjSr6UIbq6ujh9+jQLFixg27ZtSm/fvXs31dXVNDc3k5ubS0pKijJtO3ToEJmZmUrw4lgMlWiur6/n4MGDREVFcfLkSWJjY5W49erq6kkHLoB7qbH8sGcgLJFSVgkhooClUsqPJt36BHA3NZaQkidPn2KJxczitDRKKyu52dFBr48PNeGRdOt9OZaRSXpTI1YhSPnXvRw4cEBJGj8wMEBxcTEajYbs7OxRI1dcIaWkpKSEtrY21q9f7zS/HovBwUEKCwu5ePEier1eKcpuMpl44YUX3I1Bc+nldKcWp1EIUQNsF0JsB47PlKLHg5CS6O4ubGo1169fx9TXh8ZmoyY8go6AAM7HJ6CxWklob0eqVEqUiNVq5dy5c/T09LB69Wplvj2utoVg2bJlGI1GTpw4QUREhJKkfix0Op0SXvz000+jVqvZsmULBQUF1NbWjmnpu4M7+7O/CbwOhDuO/xJCvDCpVqeBnIpywvR6du7cqfixtVotNiHQm00YdTrW1FSjQnIpNo6Ojg6uXLnC4cOHSUxMZMuWLRNS9HD8/PzYunUrAQEBHDx40KlMhCuamppISUnxyD3bnXvvHmCNlPJHjixJa4GnJ92yh4m/2Y5KCAoLC7FY7O5QrVZLbMdNasIiCOnrI6GjncsxsfhYLezYsQM/Pz+2b99+13Xp8ZKYmEheXh6lpaWcPHnSKZn8nXgyUa07NwHBbYscx3OvX/3q0/kiTQPs2bOH1tZWbt68SXt7O+qBAeoWLOCxE59QEhPLoqZGmoJDOHjwoJOXbKrRaDRs2LCBtrY2PvroI5YuXaqUaB5OSkoKZ8+eHdPvPmEZ3Pib/wROCSHecrz+AjBtucomSntgIGmh8dTU1HD48GGWLVvG/PnzOVldzY6Si6htNrQ2K0czMrGo1TBN5Y3DwsLYsWMHFy9epKqqivXr1ztZ/yqVyqWlP1nczUqchT10WACFUsqRxaqmCXet8fSmRh6/fAk/X18sFgtqtZr+wUEqdb4EmAYp2LiZ/uHpJh2JaqeT3t5eioqKiI2NJSMj464GnJu4vIhLZTtWvZ4DUoAS4D+klFOz6WgSDFd2ZEICLY7g/jvJyMjgiSeeIDg4mKSkJK5evcqtW7eoqanht7/9rZPXCiAiPp7ma9c8Lv9oVFdXU1NTw9q1a5Wk9pNgQlOvVwEzcBz4LLAIe8Ch1zCWcl555RWMRiNPPfWU086KzMxMbt70rrJkKSkpJCQkUFRUhF6vZ+XKldTW1irloaZqGB/rCplSykellPux5xjPmXRr00h/fz+pd7hLU1NTR91v7Q34+PiQk5NDTEwM+/bt4+OPP57W6j/Kcos3DN/jZSiN9PApjDvhvjPN0NLq008/PeXVf8YaxpcPS1spAL3jtQCkt2c5DA0N5erVq+zbt4/09HQqKiowGo0ut+J6C2MthHjMgyalVDvSVw6lsNTMpnSWNpsNKSUWi4Xr169jtVqRUo7p0PAGZtqpMiuprKzE39+f559/XjHQ9u3bR2VlJXl5eTMtnktm2qkyKzEajSxfvnxE4J6rnRjegiedKl6/Lj1REhISRjXQ3FljnmlUKhVpaWnk5OSQlpY2JYoGNz1o3sRwp8pYmEwmXnrpJfz9/RUDra+vj29961vjWpuehYzfg+atuKtssCv8nXfeUZLGP/jgg/e6omEywQuzGa1Wy5e//OWZFsNruGfv2XOMxKPKFkLsEEJUCCGqhRB/Ncr7OUKIc0IIixBirgt6GI8pWwihxr5N6LPYK/R+RQhxZxqieuBx7An25vAwnrxnrwaqpZS1AEKI/wYewp6mAwApZZ3jvcl7+ee4K54cxmOA68NeNzjOjRshxDNCiDNCiDNtbW0j3jcYDGg0GiXFxenTpxFCjFkAFeDHP/4xBoPBbTkMBgMJCQls3bqVzZs388Ybb9Dc3MxPfvKT8XydEdf8wQ9+oLx+/PHHR130eOWVVygomFziC08qe7QpwITmeVLKlx152LJdrVqtWLGCt99+G4C33npLSQI71Tz22GMcOXKEP/7xj7z++us0Njby/e9/3yNtTTWeVHYDzqWXYwH3M6aPky1btnDkiL0C1VBdj66uLnbt2kVOTg4vvvgiAB0dHeTm5rJz504uXLgw4fb0ej3f+c53ePfdd3n00UcxmUzs2rWL3NxcHnnkkUl9l1u3brFp0ybWrl3LP/zDPyjnP/roI7Zv385DDz2EaQJF4j2p7NNAqhBioRBCC/wJ8I6nGtNqtfj6+nLy5EkWLVoE2P85u3fvprCwEKPRyKlTpygoKOCpp57igw8+oL+/f1JtRkdHc/LkSQDq6+tZsGABR48e5cCBA+O6zmuvvcbmzZvZvHkzH374ITqdDoPBwMmTJzl06JAiZ3BwMAcPHmT9+vX8/ve/H7e8HlO2I+Dhz7Enpi8DfiOlLBVC/K0Q4kEAIcQqIUQD8DCwXwjhOkGYG+zcuZPnnnuOL37xiwAcOXKErCz77uLs7Gyqq6upra1l5cqVAMp7E+XGjRusW7cOsK9WLV26lK997Wu89NJL47rOY489hsFgwGAwsGPHDqSU7Ny5k02bNlFWVqbYIkNyr1ixYkLBDB6dZ0spP5BSpkkpk6WUP3Gc+5GU8h3H89OOMsr+UspQKeXisa84Njt37uSBBx5Q8nxu2bJFST915swZkpOTWbhwobLydf78xINkBwYG2Lt3L5///OcB+z6tb33rW7z++ut8+OGHtLS0TPja3/ve9/je977HsWPHSElJUaJeh+S+ePGiyxreYyKlnFUH8OEo5zYDf3/HuVeAFcAH2IMm/91xPhR7ZYQ/Yk8ZsnkcbW/Gnrj3iOMaXwUSgf8C0hztFGHfLiXGcc2/v0PuPwcuA78BPna08TjwW+Aj7LdD3Xj/d7NuIWSOiTPnG7+PmFP2fcScsu8j5pR9HzGn7PuIOWXfR8wp+z7i/wP5VOqy+2KsSAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plots panel for figure 1 showing PDPs\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(2,3))\n",
+ "f.subplots_adjust(left=0.4)\n",
+ "tp.barscatter([pdpsMod, pdpsDis, pdpsHab], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " barlabels=['Mod', 'Dis', 'Hab'],\n",
+ " barlabeloffset=0.35,\n",
+ " scattersize=scattersize,\n",
+ "# barbaseline=-1.1,\n",
+ " ax=ax)\n",
+ "\n",
+ "ax.set_ylabel('Post-distraction pause (s)')\n",
+ "ax.set_xticks([])\n",
+ "print(ax.get_ylim())\n",
+ "# ax.spines['bottom'].set_visible(False)\n",
+ "# ax.plot(ax.get_xlim(), [-1.1, -1.1])\n",
+ "\n",
+ "ax.set_yticks([-1, 0, 1, 2])\n",
+ "ax.set_yticklabels(['0.1', '1', '10', '100'])\n",
+ "ax.set_ylim([-1.1, 1.5])\n",
+ "\n",
+ "# f.savefig(figfolder+\"fig1_pdps.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plots panel for figure 1 showing PDPs - with baseline at 0\n",
+ "\n",
+ "pdps_adj = []\n",
+ "for p_in in [pdpsMod, pdpsDis, pdpsHab]:\n",
+ " p_out = [pdp +1 for pdp in p_in]\n",
+ " pdps_adj.append(p_out)\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(2,3))\n",
+ "f.subplots_adjust(left=0.4)\n",
+ "tp.barscatter(pdps_adj, paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " barlabels=['Mod', 'Dis', 'Hab'],\n",
+ " barlabeloffset=-0.04,\n",
+ " scattersize=scattersize,\n",
+ "# barbaseline=-1.1,\n",
+ " ax=ax)\n",
+ "\n",
+ "ax.set_ylabel('Post-distractor pause (s)')\n",
+ "ax.set_xticks([])\n",
+ "# ax.spines['bottom'].set_visible(False)\n",
+ "# ax.plot(ax.get_xlim(), [-1.1, -1.1])\n",
+ "\n",
+ "ax.set_yticks([0, 1, 2, 3])\n",
+ "ax.set_yticklabels(['0.1', '1', '10', '100'])\n",
+ "ax.set_ylim([-0.24, 2.6])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig1_pdps_adjBL.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean: -0.7467827540034992, SEM: 0.030228347249829764\n",
+ "Mean: 0.13144915966881537, SEM: 0.1435405525481379\n",
+ "Mean: -0.2538688896811673, SEM: 0.14487316625592184\n",
+ "Mod vs. Dis - t-stat: 5.8, corrected p: 0.0002\n",
+ "Dis vs. Hab - t-stat: 4.9, corrected p: 0.0010\n",
+ "Mod vs. Hab - t-stat: 3.3, corrected p: 0.0177\n"
+ ]
+ }
+ ],
+ "source": [
+ "# stats for PDPs - ANOVA conducted in SPSS with .csv file\n",
+ "\n",
+ "with open(statsfolder+'log_pdp.csv', 'w', newline='') as csvfile:\n",
+ " writer = csv.writer(csvfile, delimiter=',')\n",
+ " for m, d, h in zip(pdpsMod, pdpsDis, pdpsHab):\n",
+ " writer.writerow([m, d, h])\n",
+ " \n",
+ "# One-way ANOVA conducted in SPSS with this csv file\n",
+ "\n",
+ "# these lines print mean and SEM for reporting and calculate Bonferroni posthocs\n",
+ "for data in [pdpsMod, pdpsDis, pdpsHab]:\n",
+ " mean_and_sem(data)\n",
+ " \n",
+ "bonferroni_corrected_ttest(pdpsMod, pdpsDis, string_prefix=\"Mod vs. Dis - \")\n",
+ "bonferroni_corrected_ttest(pdpsDis, pdpsHab, string_prefix=\"Dis vs. Hab - \")\n",
+ "bonferroni_corrected_ttest(pdpsMod, pdpsHab, string_prefix=\"Mod vs. Hab - \")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plots panel with cumulative PDPs for Fig 1\n",
+ "\n",
+ "def plot_cumulative_pdps(ax, pdps, bins, plot_all_data=True, color='k'):\n",
+ " \n",
+ " cumulative_sum = []\n",
+ " for data in pdps:\n",
+ " binned_data, _ = np.histogram(data, bins=bins, density=True)\n",
+ " binned_data = np.divide(binned_data, 10) # divides to get probability from 0 to 1 (because bins are only 0.1s)\n",
+ " cumsum_data = np.cumsum(binned_data)\n",
+ " cumulative_sum.append(cumsum_data)\n",
+ " if plot_all_data:\n",
+ " ax.plot(bins[:-1], cumsum_data, color=color, alpha=0.1)\n",
+ " \n",
+ " ax.plot(bins[:-1], np.mean(cumulative_sum, axis=0), color=color)\n",
+ " \n",
+ "# shadedError(ax, cumulative_sum, linecolor=color)\n",
+ "\n",
+ " ax.set_xlim([-1.5, 2.3])\n",
+ " ax.set_xticks([-1, 0, 1, 2])\n",
+ " ax.set_xticklabels(['0.1', '1', '10', '100'])\n",
+ " ax.set_xlabel('Post-distractor pause (s)')\n",
+ " \n",
+ " ax.set_ylabel('Probability')\n",
+ " ax.set_yticks([0, 1])\n",
+ " ax.set_yticklabels(['0', '1'])\n",
+ " \n",
+ " ax.spines['top'].set_visible(False)\n",
+ " ax.spines['right'].set_visible(False)\n",
+ " \n",
+ "\n",
+ "colors = ['darkturquoise','dodgerblue', 'darkblue'] \n",
+ "bins = np.arange(-2.1, 3.1, 0.1)\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(2.6, 2.1))\n",
+ "f.subplots_adjust(left=0.15, bottom=0.2, right=0.95, top=0.93)\n",
+ "\n",
+ "plot_cumulative_pdps(ax, pdpsMod_all, bins, color=colors[0])\n",
+ "plot_cumulative_pdps(ax, pdpsDis_all, bins, color=colors[1])\n",
+ "plot_cumulative_pdps(ax, pdpsHab_all, bins, color=colors[2])\n",
+ "\n",
+ "ax.text(1, 0.4, 'Modelled', transform=ax.transAxes, ha='right', color=colors[0])\n",
+ "ax.text(1, 0.3, 'Distraction', transform=ax.transAxes, ha='right', color=colors[1])\n",
+ "ax.text(1, 0.2, 'Habituation', transform=ax.transAxes, ha='right', color=colors[2])\n",
+ "\n",
+ "ax.axvline(0, color='grey', linewidth=0.75, alpha=0.75, linestyle='dashed', zorder=-20)\n",
+ "\n",
+ "ax.text(-0.08, 1.1, 'not distracted', fontsize=8, ha='right')\n",
+ "ax.text(0.1, 1.1, 'distracted', fontsize=8, ha='left')\n",
+ " \n",
+ "f.savefig(figfolder+\"fig1_cumulative_pdps.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\envs\\dis\\lib\\site-packages\\scipy\\stats\\stats.py:3233: RuntimeWarning: invalid value encountered in true_divide\n",
+ " msb = ssbn / dfbn\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "F_onewayResult(statistic=array([nan, nan, nan, nan, nan, nan, nan, nan, nan]), pvalue=array([nan, nan, nan, nan, nan, nan, nan, nan, nan]))"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "
analyze_roc_data.py to make ROC and bar graphs for figs 2, 3, 4, and 5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "\n",
+ "import numpy as np\n",
+ "import trompy as tp\n",
+ "\n",
+ "from scipy import stats\n",
+ "import pandas as pd\n",
+ "from statsmodels.stats.anova import AnovaRM\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# figure settings\n",
+ "scattersize=50\n",
+ "\n",
+ "#colors for distracted vs not distracted\n",
+ "colors_dis = ['grey', 'red']\n",
+ "\n",
+ "# colors for different days\n",
+ "colors_days = ['darkturquoise','dodgerblue', 'darkblue']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# thph2.8 removed from analysis because no data on habituation day\n",
+ "\n",
+ "modDict.pop('thph2.8')\n",
+ "disDict.pop('thph2.8')\n",
+ ";"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# loads in lick trials\n",
+ "pickle_in = open(outputfolder+\"data4roc_licks.pickle\", 'rb')\n",
+ "[mod_dis_hist, mod_notdis_hist, dis_dis_hist, dis_notdis_hist, hab_dis_hist, hab_notdis_hist] = dill.load(pickle_in)\n",
+ "\n",
+ "# loads in photo snips\n",
+ "pickle_in = open(outputfolder+\"data4roc_photo.pickle\", 'rb')\n",
+ "[mod_dis_photo_snips_flat, mod_notdis_photo_snips_flat, dis_dis_photo_snips_flat, dis_notdis_photo_snips_flat, dis_dis_filt_photo_snips_flat, dis_notdis_filt_photo_snips_flat, hab_dis_photo_snips_flat, hab_notdis_photo_snips_flat] = dill.load(pickle_in)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Analysis for Fig 2 - lick rate between distracted and not distracted trials"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Loads in ROC data for modelled vs distraction day ROC\n",
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_licks_disday_disVnondis.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ " \n",
+ "[a, p, data] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = tp.plot_ROC_and_line(f, a, p, data[0], data[1],\n",
+ " cdict=[colors_dis[0], 'white', colors_dis[1]],\n",
+ " colors = colors_dis,\n",
+ " labels=['Not distracted', 'Distracted'],\n",
+ " ylabel='Licks (Hz)')\n",
+ "\n",
+ "# ax[1].set_yticks([-1, 0, 1])\n",
+ "\n",
+ "ax[1].set_xlabel(\"Time from distractor (s)\")\n",
+ "ax[1].set_xticks([0, 5, 10, 15])\n",
+ "ax[1].set_xticklabels([\"-5\", \"0\", \"5\", \"10\", \"15\"])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig2_roc-lickrate.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# find licks before and after distractor for all rats\n",
+ "\n",
+ "def check_val_between(data, x1=0.001, x2=1.000):\n",
+ " \"\"\" Checks if there is a value in a list between two numbers\"\"\"\n",
+ " \n",
+ " vals = [1 for d in data if d>x1 and d 0:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ "pickle_in = open(outputfolder+\"data4epochs_licks.pickle\", 'rb')\n",
+ "[mod_lick_snips, dis_lick_snips, hab_lick_snips] = dill.load(pickle_in)\n",
+ "\n",
+ "pre_lickrate_dis, pre_lickrate_notdis = [], []\n",
+ "post_lickrate_dis, post_lickrate_notdis = [], []\n",
+ "\n",
+ "for snips in dis_lick_snips:\n",
+ " \n",
+ " pre_dis, pre_notdis = [], []\n",
+ " post_dis, post_notdis = [], []\n",
+ " \n",
+ " for snip in snips:\n",
+ " \n",
+ " nlicks_before_distractor = len([lick for lick in snip if lick<-2])\n",
+ " nlicks_after_distractor = len([lick for lick in snip if lick>1])\n",
+ " \n",
+ " if check_val_between(snip):\n",
+ " pre_notdis.append(nlicks_before_distractor)\n",
+ " post_notdis.append(nlicks_after_distractor)\n",
+ " else:\n",
+ " pre_dis.append(nlicks_before_distractor)\n",
+ " post_dis.append(nlicks_after_distractor)\n",
+ "\n",
+ " pre_lickrate_dis.append(np.mean(pre_dis)/10) # dividing by 10 puts this into Hz as a 10s predistractor period is used\n",
+ " pre_lickrate_notdis.append(np.mean(pre_notdis)/10)\n",
+ " \n",
+ " post_lickrate_dis.append(np.mean(post_dis)/10)\n",
+ " post_lickrate_notdis.append(np.mean(post_notdis)/10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pre distractor 2.307220313459323 0.03967546647845937\n",
+ "Post distractor 7.06055533088144 1.3180323000523508e-05\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plots panel for fig 2 showing bar graphs of pre and post lick rate and calculates stats\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(3.4,2.5), ncols=2)\n",
+ "f.subplots_adjust(left=0.2, wspace=0.8)\n",
+ "\n",
+ "tp.barscatter([pre_lickrate_notdis, pre_lickrate_dis], paired=True,\n",
+ " barfacecolor=['xkcd:light grey', colors_dis[1]], barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[0])\n",
+ "\n",
+ "tp.barscatter([post_lickrate_notdis, post_lickrate_dis], paired=True,\n",
+ " barfacecolor=['xkcd:light grey', colors_dis[1]], barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[1])\n",
+ "\n",
+ "for axis in ax:\n",
+ " axis.set_xticks([1,2])\n",
+ " axis.set_xticklabels(['Not', 'Dis'])\n",
+ " \n",
+ "ax[0].set_ylabel('Lick rate (Hz)')\n",
+ "ax[0].set_yticks([0, 0.5, 1, 1.5])\n",
+ "ax[0].set_ylim([-0.11, 1.1])\n",
+ "ax[0].set_title('Pre')\n",
+ "\n",
+ "# ax[1].set_ylabel('Mean post-distractor lick rate (Hz)')\n",
+ "ax[1].set_yticks([0, 5, 10])\n",
+ "ax[1].set_ylim([-1, 10])\n",
+ "ax[1].set_title('Post')\n",
+ "\n",
+ "f.savefig(figfolder+\"fig2_pre-and-post-distractor-lickrate.pdf\")\n",
+ "\n",
+ "from scipy import stats\n",
+ "\n",
+ "t, p = stats.ttest_rel(pre_lickrate_notdis, pre_lickrate_dis)\n",
+ "print('Pre distractor', t, p)\n",
+ "\n",
+ "t, p = stats.ttest_rel(post_lickrate_notdis, post_lickrate_dis)\n",
+ "print('Post distractor', t, p)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Analysis for Fig 3 - photometry comparison between modelled and distraction day"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Loads in ROC data for modelled vs distraction day ROC\n",
+ "\n",
+ "pickle_in = open(outputfolder+\"roc_photo_alltrials_modVdis.pickle\", 'rb')\n",
+ " \n",
+ "[a, p, data] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = tp.plot_ROC_and_line(f, a, p, data[0], data[1],\n",
+ " cdict = [(0, colors_days[0]), (0.25, colors_days[0]), (0.5, 'white'), (0.75, colors_days[1]), (1, colors_days[1])],\n",
+ " colors = [colors_days[0], colors_days[1]],\n",
+ " labels=['Modelled', 'Distraction'],\n",
+ " ylabel='Z-score')\n",
+ "\n",
+ "# ax[1].set_yticks([-1, 0, 1])\n",
+ "\n",
+ "ax[1].set_xlabel(\"Time from distractor (s)\")\n",
+ "ax[1].set_xticks([0, 5, 10, 15])\n",
+ "ax[1].set_xticklabels([\"-5\", \"0\", \"5\", \"10\", \"15\"])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig3_roc-photo-modVdis.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loads in data to make bar graphs and combines all trials (distracted and not distracted) as first comparison is modelled vs. distraction ALL trials\n",
+ "\n",
+ "pickle_in = open(outputfolder+\"data4epochs_photo.pickle\", 'rb')\n",
+ "[ mod_dis_photo_snips, mod_notdis_photo_snips, dis_dis_photo_snips, dis_notdis_photo_snips, dis_dis_filt_photo_snips, dis_notdis_filt_photo_snips, hab_dis_photo_snips, hab_notdis_photo_snips] = dill.load(pickle_in)\n",
+ "\n",
+ "dis_all_photo_snips, mod_all_photo_snips, hab_all_photo_snips = [], [], []\n",
+ "\n",
+ "for dis, nondis in zip(mod_dis_photo_snips, mod_notdis_photo_snips):\n",
+ " mod_all_photo_snips.append(dis + nondis)\n",
+ " \n",
+ "for dis, nondis in zip(dis_dis_photo_snips, dis_notdis_photo_snips):\n",
+ " dis_all_photo_snips.append(dis + nondis)\n",
+ " \n",
+ "for dis, nondis in zip(hab_dis_photo_snips, hab_notdis_photo_snips):\n",
+ " hab_all_photo_snips.append(dis + nondis)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# divides data into 3 separate epochs\n",
+ "\n",
+ "epoch1_mod, epoch1_dis = [], []\n",
+ "epoch2_mod, epoch2_dis = [], []\n",
+ "epoch3_mod, epoch3_dis = [], []\n",
+ "\n",
+ "for day, epoch1, epoch2, epoch3 in zip([mod_all_photo_snips, dis_all_photo_snips],\n",
+ " [epoch1_mod, epoch1_dis],\n",
+ " [epoch2_mod, epoch2_dis],\n",
+ " [epoch3_mod, epoch3_dis]):\n",
+ " for rat in day:\n",
+ " e1, e2, e3 = [], [], []\n",
+ " for snip in rat:\n",
+ " e1.append(np.mean(snip[0:5]))\n",
+ " e2.append(np.mean(snip[6:9]))\n",
+ " e3.append(np.mean(snip[9:]))\n",
+ " epoch1.append(np.mean(e1))\n",
+ " epoch2.append(np.mean(e2))\n",
+ " epoch3.append(np.mean(e3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(4,2), ncols=3)\n",
+ "f.subplots_adjust(left=0.15, wspace=0.3)\n",
+ "\n",
+ "colors=[colors_days[0], colors_days[1]]\n",
+ "\n",
+ "tp.barscatter([epoch1_mod, epoch1_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[0])\n",
+ "\n",
+ "tp.barscatter([epoch2_mod, epoch2_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[1])\n",
+ "\n",
+ "tp.barscatter([epoch3_mod, epoch3_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[2])\n",
+ "\n",
+ "for axis in ax:\n",
+ " axis.set_xticks([])\n",
+ " axis.set_ylim([-4, 3.5])\n",
+ " axis.set_yticks([-3, 0, 3])\n",
+ " axis.set_yticklabels([])\n",
+ " axis.text(1, -3.9, 'Mod', ha='center')\n",
+ " axis.text(2, -3.9, 'Dis', ha='center')\n",
+ " \n",
+ "ax[0].set_ylabel('AUC (Z-score)')\n",
+ "\n",
+ "ax[0].set_yticklabels([\"-3\", '0', '3'])\n",
+ "\n",
+ "ax[0].set_title('Pre')\n",
+ "ax[1].set_title('Dist.')\n",
+ "ax[2].set_title('Post')\n",
+ "\n",
+ "f.savefig(figfolder+\"fig3_photo_modVdis_epochs.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Anova\n",
+ "==========================================\n",
+ " F Value Num DF Den DF Pr > F\n",
+ "------------------------------------------\n",
+ "variable 3.0105 2.0000 24.0000 0.0681\n",
+ "day 3.3725 1.0000 12.0000 0.0912\n",
+ "variable:day 10.9642 2.0000 24.0000 0.0004\n",
+ "==========================================\n",
+ "\n",
+ "Epoch 1 - pre 0.4437902564302567 0.6650905923021398 Sidak: 0.9624351169160664\n",
+ "Epoch 2 - dis -3.0633974822528196 0.00983674509410371 Sidak: 0.029220902438914065\n",
+ "Epoch 3 - post -0.10424863573716375 0.9186941607447985 Sidak: 0.9994625164076835\n"
+ ]
+ }
+ ],
+ "source": [
+ "rats = disDict.keys()\n",
+ "data = np.array([epoch1_mod, epoch2_mod, epoch3_mod], dtype=\"float\").T\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"e1\", \"e2\", \"e3\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df1 = df.melt(id_vars = \"ratid\")\n",
+ "df1.insert(1, \"day\", \"mod\")\n",
+ "\n",
+ "data = np.array([epoch1_dis, epoch2_dis, epoch3_dis], dtype=\"float\").T\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"e1\", \"e2\", \"e3\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df2 = df.melt(id_vars = \"ratid\")\n",
+ "df2.insert(1, \"day\", \"dis\")\n",
+ "\n",
+ "df3 = pd.concat([df1, df2])\n",
+ "\n",
+ "aovrm = AnovaRM(df3, \"value\", \"ratid\", within=[\"variable\", \"day\"])\n",
+ "res = aovrm.fit()\n",
+ "\n",
+ "print(res)\n",
+ "\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch1_mod, epoch1_dis)\n",
+ "print('Epoch 1 - pre', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch2_mod, epoch2_dis)\n",
+ "print('Epoch 2 - dis', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch3_mod, epoch3_dis)\n",
+ "print('Epoch 3 - post', t, p, \"Sidak:\", tp.sidakcorr(p))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Analysis for Fig 4 - photometry comparison between distracted and not distracted trials (distraction day)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Loads in ROC data for modelled vs distraction day ROC\n",
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_photo_disday_disVnotdis.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ " \n",
+ "[a, p, data] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = tp.plot_ROC_and_line(f, a, p, data[0], data[1],\n",
+ " cdict=[colors_dis[0], 'white', colors_dis[1]],\n",
+ " colors = colors_dis,\n",
+ " labels=['Not distracted', 'Distracted'],\n",
+ " ylabel='Z-score')\n",
+ "\n",
+ "ax[1].set_yticks([-1, 0, 1])\n",
+ "\n",
+ "ax[1].set_xlabel(\"Time from distractor (s)\")\n",
+ "ax[1].set_xticks([0, 5, 10, 15])\n",
+ "ax[1].set_xticklabels([\"-5\", \"0\", \"5\", \"10\", \"15\"])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig4_roc-photo-disVnondis.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# organises data into epochs for bar graph panel\n",
+ "\n",
+ "epoch1_notdis, epoch1_dis = [], []\n",
+ "epoch2_notdis, epoch2_dis = [], []\n",
+ "epoch3_notdis, epoch3_dis = [], []\n",
+ "\n",
+ "for day, epoch1, epoch2, epoch3 in zip([dis_notdis_photo_snips, dis_dis_photo_snips],\n",
+ " [epoch1_notdis, epoch1_dis],\n",
+ " [epoch2_notdis, epoch2_dis],\n",
+ " [epoch3_notdis, epoch3_dis]):\n",
+ " for rat in day:\n",
+ " e1, e2, e3 = [], [], []\n",
+ " for snip in rat:\n",
+ " e1.append(np.mean(snip[0:5]))\n",
+ " e2.append(np.mean(snip[6:9]))\n",
+ " e3.append(np.mean(snip[9:]))\n",
+ " epoch1.append(np.mean(e1))\n",
+ " epoch2.append(np.mean(e2))\n",
+ " epoch3.append(np.mean(e3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['rat', 'rms', 'fs', 'deltaF', 'tick', 'filt_sd', 'licks', 'licks_off', 'distractors', 'distracted', 'notdistracted', 'd_bool_array', 'pdp', 'pre_dp', 'trialtype', 'lickdata', 'snips_distractors', 'snips_distracted', 'snips_not-distracted'])"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "disDict[\"thph2.1\"].keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(4,2), ncols=3)\n",
+ "f.subplots_adjust(left=0.15, wspace=0.3)\n",
+ "\n",
+ "colors=['xkcd:light grey', colors_dis[1]]\n",
+ "\n",
+ "tp.barscatter([epoch1_notdis, epoch1_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[0])\n",
+ "\n",
+ "tp.barscatter([epoch2_notdis, epoch2_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[1])\n",
+ "\n",
+ "tp.barscatter([epoch3_notdis, epoch3_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[2])\n",
+ "\n",
+ "for axis in ax:\n",
+ " axis.set_xticks([])\n",
+ " axis.set_ylim([-0.3, 1.0]) # changed from [-5.5, 4]\n",
+ " axis.set_yticks([-4, -2, 0, 2, 4])\n",
+ " axis.set_yticklabels([])\n",
+ " axis.text(1, -4.9, 'Not', ha='center')\n",
+ " axis.text(2, -4.9, 'Dis', ha='center')\n",
+ " \n",
+ "ax[0].set_ylabel('AUC (Z-score)')\n",
+ "\n",
+ "ax[0].set_yticklabels([\"-4\", \"-2\", '0', '2', '4'])\n",
+ "\n",
+ "ax[0].set_title('Pre')\n",
+ "ax[1].set_title('Dist.')\n",
+ "ax[2].set_title('Post')\n",
+ "\n",
+ "f.savefig(figfolder+\"fig4_photo_disVnotdis_epochs.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Anova\n",
+ "============================================\n",
+ " F Value Num DF Den DF Pr > F\n",
+ "--------------------------------------------\n",
+ "variable 8.6733 2.0000 24.0000 0.0015\n",
+ "trial 15.0014 1.0000 12.0000 0.0022\n",
+ "variable:trial 10.8537 2.0000 24.0000 0.0004\n",
+ "============================================\n",
+ "\n",
+ "Epoch 1 - pre -0.2427519078322366 0.8122971795245387 Sidak: 0.9933867887555538\n",
+ "Epoch 2 - dis -2.53313426068498 0.026267685005721655 Sidak: 0.07675120566396854\n",
+ "Epoch 3 - post -5.565975721698865 0.00012260677228993286 Sidak: 0.0003677752214510388\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = np.array([epoch1_notdis, epoch2_notdis, epoch3_notdis], dtype=\"float\").T\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"e1\", \"e2\", \"e3\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df1 = df.melt(id_vars = \"ratid\")\n",
+ "df1.insert(1, \"trial\", \"mod\")\n",
+ "\n",
+ "data = np.array([epoch1_dis, epoch2_dis, epoch3_dis], dtype=\"float\").T\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"e1\", \"e2\", \"e3\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df2 = df.melt(id_vars = \"ratid\")\n",
+ "df2.insert(1, \"trial\", \"dis\")\n",
+ "\n",
+ "df3 = pd.concat([df1, df2])\n",
+ "\n",
+ "aovrm = AnovaRM(df3, \"value\", \"ratid\", within=[\"variable\", \"trial\"])\n",
+ "res = aovrm.fit()\n",
+ "\n",
+ "print(res)\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch1_notdis, epoch1_dis)\n",
+ "print('Epoch 1 - pre', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch2_notdis, epoch2_dis)\n",
+ "print('Epoch 2 - dis', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch3_notdis, epoch3_dis)\n",
+ "print('Epoch 3 - post', t, p, \"Sidak:\", tp.sidakcorr(p))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Analysis for Fig 5 - photometry comparison between distraction day and habituation day - all trials then distracted and not distracted separately"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Loads in ROC data for distraction vs habituation day ROC - ALL trials\n",
+ "pickle_in = open(outputfolder+\"roc_photo_alltrials_disVhab.pickle\", 'rb')\n",
+ " \n",
+ "[a, p, data] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = tp.plot_ROC_and_line(f, a, p, data[0], data[1],\n",
+ " cdict = [(0, colors_days[1]), (0.25, colors_days[1]), (0.5, 'white'), (0.75, colors_days[2]), (1, colors_days[2])],\n",
+ " colors = [colors_days[1], colors_days[2]],\n",
+ " labels=['Distraction', 'Habituation'],\n",
+ " ylabel='Z-score')\n",
+ "\n",
+ "ax[1].set_xlabel(\"Time from distractor (s)\")\n",
+ "ax[1].set_xticks([0, 5, 10, 15])\n",
+ "ax[1].set_xticklabels([\"-5\", \"0\", \"5\", \"10\", \"15\"])\n",
+ "\n",
+ "ax[1].set_yticks([-1, 0, 1])\n",
+ "# ax[1].set_ylim([-1.1, 1.3])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig5_roc-photo-disVhab.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Loads in ROC data for distraction vs habituation day ROC - ONLY DISTRACTED TRIALS\n",
+ "pickle_in = open(outputfolder+\"roc_photo_distrials_disVhab.pickle\", 'rb')\n",
+ " \n",
+ "[a, p, data] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = tp.plot_ROC_and_line(f, a, p, data[0], data[1],\n",
+ " cdict = [(0, colors_days[1]), (0.25, colors_days[1]), (0.5, 'white'), (0.75, colors_days[2]), (1, colors_days[2])],\n",
+ " colors = [colors_days[1], colors_days[2]],\n",
+ " labels=['Distraction', 'Habituation'],\n",
+ " ylabel='Z-score')\n",
+ "\n",
+ "ax[1].set_xlabel(\"Time from distractor (s)\")\n",
+ "ax[1].set_xticks([0, 5, 10, 15])\n",
+ "ax[1].set_xticklabels([\"-5\", \"0\", \"5\", \"10\", \"15\"])\n",
+ "\n",
+ "ax[1].set_yticks([-1, 0, 1])\n",
+ "# ax[1].set_ylim([-1.1, 1.7])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig5_roc-photo-disVhab-onlyDIS.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Loads in ROC data for distraction vs habituation day ROC - ONLY NOT DISTRACTED TRIALS\n",
+ "pickle_in = open(outputfolder+\"roc_photo_notdistrials_disVhab.pickle\", 'rb')\n",
+ " \n",
+ "[a, p, data] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = tp.plot_ROC_and_line(f, a, p, data[0], data[1],\n",
+ " cdict = [(0, colors_days[1]), (0.25, colors_days[1]), (0.5, 'white'), (0.75, colors_days[2]), (1, colors_days[2])],\n",
+ " colors = [colors_days[1], colors_days[2]],\n",
+ " labels=['Distraction', 'Habituation'],\n",
+ " ylabel='Z-score')\n",
+ "\n",
+ "ax[1].set_xlabel(\"Time from distractor (s)\")\n",
+ "ax[1].set_xticks([0, 5, 10, 15])\n",
+ "ax[1].set_xticklabels([\"-5\", \"0\", \"5\", \"10\", \"15\"])\n",
+ "\n",
+ "ax[1].set_yticks([-1, 0, 1])\n",
+ "# ax[1].set_ylim([-1.1, 1.2])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig5_roc-photo-disVhab-onlyNOTDIS.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Analysis for Extended Data Fig - photometry comparison between distracted and non-distracted trials USING NON-ZSCORED SIGNAL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Loads in ROC data for modelled vs distraction day ROC\n",
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_photo_disday_disVnotdis_filt.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ " \n",
+ "[a, p, data] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = tp.plot_ROC_and_line(f, a, p, data[0], data[1],\n",
+ " cdict=[colors_dis[0], 'white', colors_dis[1]],\n",
+ " colors = colors_dis,\n",
+ " labels=['Not distracted', 'Distracted'],\n",
+ " ylabel='Delta F')\n",
+ "\n",
+ "ax[1].set_yticks([0, 2, 4])\n",
+ "ax[1].set_ylim([-0.2, 5.5])\n",
+ "\n",
+ "ax[1].set_xlabel(\"Time from distractor (s)\")\n",
+ "ax[1].set_xticks([0, 5, 10, 15])\n",
+ "ax[1].set_xticklabels([\"-5\", \"0\", \"5\", \"10\", \"15\"])\n",
+ "\n",
+ "f.savefig(figfolder+\"extfig_roc-photo-disVnondis.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# organises data into epochs for bar graph panel\n",
+ "\n",
+ "epoch1_notdis, epoch1_dis = [], []\n",
+ "epoch2_notdis, epoch2_dis = [], []\n",
+ "epoch3_notdis, epoch3_dis = [], []\n",
+ "\n",
+ "for day, epoch1, epoch2, epoch3 in zip([dis_notdis_filt_photo_snips, dis_dis_filt_photo_snips],\n",
+ " [epoch1_notdis, epoch1_dis],\n",
+ " [epoch2_notdis, epoch2_dis],\n",
+ " [epoch3_notdis, epoch3_dis]):\n",
+ " for rat in day:\n",
+ " e1, e2, e3 = [], [], []\n",
+ " for snip in rat:\n",
+ " e1.append(np.mean(snip[0:5]))\n",
+ " e2.append(np.mean(snip[6:9]))\n",
+ " e3.append(np.mean(snip[9:]))\n",
+ " epoch1.append(np.mean(e1))\n",
+ " epoch2.append(np.mean(e2))\n",
+ " epoch3.append(np.mean(e3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(4,2), ncols=3, sharey=True)\n",
+ "f.subplots_adjust(left=0.15, wspace=0.3)\n",
+ "\n",
+ "colors=['xkcd:light grey', colors_dis[1]]\n",
+ "\n",
+ "tp.barscatter([epoch1_notdis, epoch1_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[0])\n",
+ "\n",
+ "tp.barscatter([epoch2_notdis, epoch2_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[1])\n",
+ "\n",
+ "tp.barscatter([epoch3_notdis, epoch3_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax[2])\n",
+ "\n",
+ "\n",
+ "for axis in ax:\n",
+ " trans = transforms.blended_transform_factory(\n",
+ " axis.transData, axis.transAxes)\n",
+ " axis.set_xticks([])\n",
+ "# axis.set_ylim([-0.3, 1.0]) # changed from [-5.5, 4]\n",
+ " axis.set_yticks([-10, 0, 10])\n",
+ "# axis.set_yticklabels([])\n",
+ " axis.text(1, -0.1, 'Not', ha='center', transform=trans)\n",
+ " axis.text(2, -0.1, 'Dis', ha='center', transform=trans)\n",
+ " \n",
+ "ax[0].set_ylabel('AUC (Delta F)')\n",
+ "\n",
+ "# ax[0].set_yticklabels([\"-10\", \"-5\", '0', '5', '10'])\n",
+ "\n",
+ "ax[0].set_title('Pre')\n",
+ "ax[1].set_title('Dist.')\n",
+ "ax[2].set_title('Post')\n",
+ "\n",
+ "f.savefig(figfolder+\"extfig_photo_disVnotdis_epochs.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Anova\n",
+ "============================================\n",
+ " F Value Num DF Den DF Pr > F\n",
+ "--------------------------------------------\n",
+ "variable 7.5682 2.0000 24.0000 0.0028\n",
+ "trial 9.2899 1.0000 12.0000 0.0101\n",
+ "variable:trial 7.3914 2.0000 24.0000 0.0032\n",
+ "============================================\n",
+ "\n",
+ "Epoch 1 - pre -2.1731398150008903 0.05051032916815173 Sidak: 0.14400597411347804\n",
+ "Epoch 2 - dis -2.8733523012798576 0.014002377714153023 Sidak: 0.04142167879584302\n",
+ "Epoch 3 - post -3.88253730341847 0.0021778470301866922 Sidak: 0.006519322367066205\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = np.array([epoch1_notdis, epoch2_notdis, epoch3_notdis], dtype=\"float\").T\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"e1\", \"e2\", \"e3\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df1 = df.melt(id_vars = \"ratid\")\n",
+ "df1.insert(1, \"trial\", \"mod\")\n",
+ "\n",
+ "data = np.array([epoch1_dis, epoch2_dis, epoch3_dis], dtype=\"float\").T\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"e1\", \"e2\", \"e3\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df2 = df.melt(id_vars = \"ratid\")\n",
+ "df2.insert(1, \"trial\", \"dis\")\n",
+ "\n",
+ "df3 = pd.concat([df1, df2])\n",
+ "\n",
+ "aovrm = AnovaRM(df3, \"value\", \"ratid\", within=[\"variable\", \"trial\"])\n",
+ "res = aovrm.fit()\n",
+ "\n",
+ "print(res)\n",
+ "t, p = stats.ttest_rel(epoch1_notdis, epoch1_dis)\n",
+ "print('Epoch 1 - pre', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch2_notdis, epoch2_dis)\n",
+ "print('Epoch 2 - dis', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch3_notdis, epoch3_dis)\n",
+ "print('Epoch 3 - post', t, p, \"Sidak:\", tp.sidakcorr(p))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Stuff below this here is not used"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "IndexError",
+ "evalue": "list index out of range",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3.4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m f, ax = tp.plot_ROC_and_line(f, a, p, data[0], data[1],\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mcdict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.25\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'white'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.75\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Modelled'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Habituation'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mIndexError\u001b[0m: list index out of range"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Loads in ROC data for modelled vs distraction day ROC\n",
+ "pickle_in = open(outputfolder+\"roc_photo_alltrials_modVhab.pickle\", 'rb')\n",
+ " \n",
+ "[a, p, data] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = tp.plot_ROC_and_line(f, a, p, data[0], data[1],\n",
+ " cdict = [(0, colors[0]), (0.25, colors[0]), (0.5, 'white'), (0.75, colors[2]), (1, colors[2])],\n",
+ " colors = [colors[0], colors[2]],\n",
+ " labels=['Modelled', 'Habituation'],\n",
+ " ylabel='Z-score')\n",
+ "\n",
+ "\n",
+ "ax[1].set_yticks([-1, 0, 1])\n",
+ "ax[1].set_ylim([-1.1, 1.3])\n",
+ "\n",
+ "# f.savefig(figfolder+\"fig5_roc-photo-modVhab.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loads in ROC data for modelled vs distraction day ROC\n",
+ "pickle_in = open(outputfolder+\"roc_licks_alltrials_disVhab.pickle\", 'rb')\n",
+ " \n",
+ "[a, p, data] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = plot_ROC_and_line(f, a, p, data[0], data[1],\n",
+ " cdict = [(0, 'darkturquoise'), (0.25, 'darkturquoise'), (0.5, 'white'), (0.75, 'dodgerblue'), (1, 'dodgerblue')],\n",
+ " colors = ['darkturquoise', 'dodgerblue'],\n",
+ " labels=['Distraction', 'Habituation'],\n",
+ " labeloffset=0.3,\n",
+ " ylabel='Licks (Hz)')\n",
+ "\n",
+ "# ax[1].set_yticks([-1, 0, 1])\n",
+ "\n",
+ "# f.savefig(figfolder+\"fig3_roc-photo-modVdis.pdf\")\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# code using original data for epochs rather than ROC data - returns very similar result\n",
+ "\n",
+ "def get_auc(daydict, epoch=[60, 90], signal=\"filt_z\"):\n",
+ " \n",
+ " dis_auc, notdis_auc = [], []\n",
+ " \n",
+ " rats = daydict.keys()\n",
+ " for rat in rats:\n",
+ " d = daydict[rat]\n",
+ " \n",
+ " dis = [np.mean(snip[epoch[0]:epoch[1]]) for snip in d[\"snips_distracted\"][signal]]\n",
+ " dis_auc.append(np.mean(dis))\n",
+ " \n",
+ " notdis = [np.mean(snip[epoch[0]:epoch[1]]) for snip in d[\"snips_not-distracted\"][signal]]\n",
+ " notdis_auc.append(np.mean(notdis))\n",
+ " \n",
+ " return dis_auc, notdis_auc\n",
+ " \n",
+ "epoch1_notdis, epoch1_dis = get_auc(disDict, epoch=[0, 50])\n",
+ "epoch2_notdis, epoch2_dis = get_auc(disDict, epoch=[60, 90])\n",
+ "epoch3_notdis, epoch3_dis = get_auc(disDict, epoch=[90, 199])\n",
+ " "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/mm_Distraction data modeling.ipynb b/notebooks/mm_Distraction data modeling.ipynb
new file mode 100644
index 0000000..a2eaab3
--- /dev/null
+++ b/notebooks/mm_Distraction data modeling.ipynb
@@ -0,0 +1,4599 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "# import matplotlib as mpl\n",
+ "# import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "import numpy as np\n",
+ "import math\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'thph1.1': {'rat': 'thph1.1',\n",
+ " 'blue': array([ 0.46441472, 0.4676711 , 0.47095278, ..., 139.68901 ,\n",
+ " 139.68954 , 139.68993 ], dtype=float32),\n",
+ " 'uv': array([ 0.16405275, 0.16475719, 0.16543539, ..., 100.403625 ,\n",
+ " 100.40226 , 100.4009 ], dtype=float32),\n",
+ " 'fs': 1017.2526245117188,\n",
+ " 'filt': array([-99.86142 , -99.86044 , -99.85947 , ..., -63.90548 , -63.905273,\n",
+ " -63.90511 ], dtype=float32),\n",
+ " 'tick': array([1.63840000e-04, 1.00024320e+00, 2.00032256e+00, ...,\n",
+ " 3.84830554e+03, 3.84930562e+03, 3.85030570e+03]),\n",
+ " 'filt_sd': 14.177406,\n",
+ " 'licks': array([ 212.24751104, 212.77048832, 212.86649856, ..., 3079.83302656,\n",
+ " 3079.98097408, 3080.1420288 ]),\n",
+ " 'licks_off': array([ 212.2964992 , 212.81456128, 212.9174528 , ..., 3079.90003712,\n",
+ " 3080.03897344, 3080.18495488]),\n",
+ " 'distractors': [212.86649856,\n",
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+ " 'distracted': [],\n",
+ " 'notdistracted': [212.86649856,\n",
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+ " 3062.1442048,\n",
+ " 3065.51128064,\n",
+ " 3069.7971712,\n",
+ " 3079.57694464],\n",
+ " 'd_bool_array': array([False, False, False, False, False, False, False, False, False,\n",
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+ " 1.0190848000002006]},\n",
+ " 'thph1.2': {'rat': 'thph1.2',\n",
+ " 'blue': array([1.20873615e-01, 1.18781075e-01, 1.16634928e-01, ...,\n",
+ " 1.98440140e+02, 1.98445190e+02, 1.98450226e+02], dtype=float32),\n",
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+ " 'fs': 1017.2526245117188,\n",
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+ " 'tick': array([1.63840000e-04, 1.00024320e+00, 2.00032256e+00, ...,\n",
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+ " 2404.0513536 , 2404.17849344, 2404.54647808, 2404.67345408,\n",
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+ " 3381.81128192, 3382.0942336 , 3382.25922048, 3383.8563328 ,\n",
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+ " 3704.99354624, 3705.2506112 , 3705.37152512, 3705.4906368 ,\n",
+ " 3706.03065344, 3706.17565184, 3706.69764608, 3706.84854272,\n",
+ " 3706.97551872, 3707.0995456 , 3707.23258368, 3707.38446336,\n",
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+ " 3713.0264576 , 3714.47136256, 3714.62144 , 3715.8633472 ]),\n",
+ " 'distractors': [458.60831232,\n",
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+ " 3381.77523712,\n",
+ " 3384.83331072,\n",
+ " 3704.66865152,\n",
+ " 3709.09954048],\n",
+ " 'distracted': [510.26739200000003,\n",
+ " 612.44571648,\n",
+ " 707.82910464,\n",
+ " 891.41690368,\n",
+ " 3384.83331072,\n",
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+ " 'notdistracted': [458.60831232,\n",
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+ " 3378.90738176,\n",
+ " 3381.77523712,\n",
+ " 3704.66865152],\n",
+ " 'd_bool_array': array([False, False, False, False, True, False, False, False, True,\n",
+ " False, False, True, False, True, False, False, False, False,\n",
+ " False, False, False, True, False, True]),\n",
+ " 'pdp': [0.11714560000001484,\n",
+ " 0.1690828799999622,\n",
+ " 0.15007744000001821,\n",
+ " 0.22593535999999403,\n",
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+ " 0.1269760000000133,\n",
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+ " 0.2719744000000901,\n",
+ " 312.5044838399999,\n",
+ " 0.16203776000020298,\n",
+ " 2.354053120000117],\n",
+ " 'pre_dp': [132.23165952,\n",
+ " 1.3729792000000316,\n",
+ " 1.115914240000052,\n",
+ " 7.142932480000013,\n",
+ " 6.347816959999989,\n",
+ " 1.3561036799999897,\n",
+ " 1.43687680000005,\n",
+ " 81.86363904000007,\n",
+ " 3.208970239999985,\n",
+ " 82.29257216000008,\n",
+ " 2.0268646399999852,\n",
+ " 1.4560460799999646,\n",
+ " 10.264739840000061,\n",
+ " 172.31609856,\n",
+ " 2.522972159999995,\n",
+ " 1464.3693158400001,\n",
+ " 1.5440281599999253,\n",
+ " 2.3039180800001304,\n",
+ " 22.09955839999975,\n",
+ " 1.1398348800003077,\n",
+ " 1.1979980799997065,\n",
+ " 1.6159539199998108,\n",
+ " 1.179975679999643,\n",
+ " 1.2150374399998327]}}"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "modDict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d = modDict[\"thph1.1\"]\n",
+ "blue = d[\"blue\"]\n",
+ "filt = d[\"filt\"]\n",
+ "fs = d[\"fs\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3917568"
+ ]
+ },
+ "execution_count": 137,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(blue)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "38511.259696974805\n"
+ ]
+ }
+ ],
+ "source": [
+ "nbins = 10 * len(blue) / d[\"fs\"]\n",
+ "print(nbins)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nsamples = np.int(len(blue) / nbins)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "101"
+ ]
+ },
+ "execution_count": 140,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nsamples"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "maxindex = int(math.floor(len(blue) / 1000.0)) * 1000\n",
+ "\n",
+ "new_blue = np.mean(np.reshape(blue[:maxindex], (1000, -1)), axis=0)\n",
+ "new_filt = np.mean(np.reshape(filt[:maxindex], (1000, -1)), axis=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3917"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(new_blue)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "licks = d[\"licks\"] * fs/1000"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "binned_licks = np.histogram(licks, bins=range(len(new_blue)+1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 144,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(binned_licks[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn import linear_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "Expected 2D array, got 1D array instead:\narray=[0 0 0 ... 0 0 0].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mreg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mreg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbinned_licks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_blue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 461\u001b[0m \u001b[0mn_jobs_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 462\u001b[0m X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[1;32m--> 463\u001b[1;33m y_numeric=True, multi_output=True)\n\u001b[0m\u001b[0;32m 464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 465\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m 717\u001b[0m \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 718\u001b[0m \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 719\u001b[1;33m estimator=estimator)\n\u001b[0m\u001b[0;32m 720\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 721\u001b[0m y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m 519\u001b[0m \u001b[1;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 520\u001b[0m \u001b[1;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 521\u001b[1;33m \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[0;32m 522\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 523\u001b[0m \u001b[1;31m# in the future np.flexible dtypes will be handled like object dtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[0 0 0 ... 0 0 0].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
+ ]
+ }
+ ],
+ "source": [
+ "reg = linear_model.LinearRegression()\n",
+ "reg.fit(binned_licks[0], new_blue)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(new_blue, binned_licks[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(3917,)"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_blue.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(3917,)"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "binned_licks[0].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def make_design_matrix(licks, nback):\n",
+ " M = np.ones((len(licks), nback))\n",
+ " \n",
+ " for i in range(len(licks)):\n",
+ " try:\n",
+ " M[i, :] = licks[i:i+nback]\n",
+ " except: pass\n",
+ " \n",
+ " return M\n",
+ "\n",
+ "M = make_design_matrix(binned_licks[0], 5)\n",
+ "\n",
+ "M = make_design_matrix(new_filt, 5)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+ ]
+ },
+ "execution_count": 168,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reg = linear_model.LinearRegression()\n",
+ "reg.fit(M, binned_licks[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-10622.63770279, 18149.04950004, -18400.77670058, 24858.12958023,\n",
+ " -13983.67449226])"
+ ]
+ },
+ "execution_count": 169,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reg.coef_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 170,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 170,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(reg.coef_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 171,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "shuff_licks = binned_licks[0]\n",
+ "np.random.shuffle(shuff_licks)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 172,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[3 0 0 ... 0 0 0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(binned_licks[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 173,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 173,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "reg = linear_model.LinearRegression()\n",
+ "reg.fit(M, shuff_licks)\n",
+ "plt.plot(reg.coef_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "a = [0, 0, 0, 10, 40, 6]\n",
+ "b = np.random.shuffle(a)\n",
+ "print(b)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/old-and-unused/dis-photometry_early analysis.ipynb b/notebooks/old-and-unused/dis-photometry_early analysis.ipynb
new file mode 100644
index 0000000..6fc8a1e
--- /dev/null
+++ b/notebooks/old-and-unused/dis-photometry_early analysis.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Github\\Distraction-Paper\\JM_custom_figs.py:11: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
+ " from collections import Sequence\n"
+ ]
+ }
+ ],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "import numpy as np\n",
+ "\n",
+ "%run ..//JM_custom_figs.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def average_snips(d, key, signal):\n",
+ " avg_snips_out =[]\n",
+ " rats = d.keys()\n",
+ " for rat in rats:\n",
+ " avg_snips_out.append(d[rat][key][signal])\n",
+ " \n",
+ " return avg_snips_out"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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1UlV7br5Zi4Btw4wZeknHsvRrZs/WF9nhw0tf87GP6bv4+fP1OaZP19tnzoRvfEOLgG3r5aM779SO7k9/Wi+TFZznoK2AO+7QloDnwYIFWuh6ypVXaoti1iw9j4UL9fbZs/W5Z87Uvp8TT+w4/7o6/X77MzIQb+jnz5+vlg2mYOx21O6oJVAKu02cbqEPuuu6zJ0znIcfaWTipIBUKoWIhes62LbNh84dzhe+GGfBwjg1o2qoiEb78J0YDAOTz35WC8wnP9nXMymh1+vVB7UT3dB7lFLaArFK/7Qighd45HI+dXU2h4wJCIKAkBvikJoaPM8jCAJGjgqor7cQwPf3IODeYDB0ybHH6qWugvO9P2MEZICh/RaKdDrNf1xxJcl8HSwRAQU7diqGDg0IhcDzfSKRCNFolBHDhpPL5fQSVoMFJhfEYNgvLF+uo7cKDvr+jBGQAYbne6CEJx5/goceeog77rijzV5h2zadcQ66rEkkEgGgoqICEWH4cC0gOvnQCIjBYOgcIyADDN/3USgefPBBPnfVVfzvbf9brIOFKLZvE8aO09FVIhDKtyS0bZuKigqGDfNoqLewLYucSSY0GAxdYARkgJHNZmluamb5smVc9fmrmD59OkuWLAFABYrt223GjdOlTpQCp00QesgNMXy4R0ODhYgQBIEJ5TUYDJ1iBGSAkclm2bJ1C5MnT6aiooKzzj6LP//pz8X9O2ptxozRjaZs2ykJ4XUcm+EjAhoaCtvECIjBYOgUIyADCKUU2WyW2u3bGT9hAgBnnHkmT//tb6TTaSzL0hFYhwQESuE6peV4LctmRN4HAiAo7VMxGAyGMhgBGUDkPF1+ZNu27UwYPx6A0aNHM3nKFFb9exUiQkuLUD1UoYKgZPkKwLYsho/wiwKiBBOJZTAYOsUIyACicLHfunUrEyZOKG4/6qjprF+/HoCWmM3QIdoCaS8glm0zYoTOA1EKE4llMBi6xAjIACKdzmBZFtu2bWN83gIBmD59OuvWrUNEiLVYDKn2oZyAiBCtANtWxGKCJZYp624wGDrFCMgAIp3RArJ161YmTGi1QKZNn876detQShGL2wwZEoDoToVtEREc22bCBJ+tW2xdP8v4QAwGQycYARkgKKXwvFzeAtladKIDTJs2rbiEFYvZVA3xEMCyO/75Xcdh/ASfrVt1KK8KFH5g2tsaDIaOGAEZIHiehwikkkmSiSSjRrX23Ro3bhypVIrduxtIJiwqKzyU6miBANi2w4QJHlu3tu4zobwGg6EcRkAGCDoDHbZu28a4ceNKiimKCEceeSQrX9tMJBKg8EDoUHARwHUdxo3PsWVLQUAk3yLXYDAYSjECMkDIeR4o2LxpM4cedmiH/ZMmT2bjxnqGDPHxfR/bcsp2G7Qtm/Hj/TYWiDIWiMFgKIsRkAFCNpvFsiw2btzA1KlTO+yfNGkSb77ZyJDqAM/zcZyOy1cAYllFJzrkOxmaSCyDwVAGIyADhGwulxeQjUydekSH/RMnTmTLlhjV1QG+Csr6PwAsS5gw3isuYelOhiYSy2AwdKSve6L/WkR2iciqTvaLiNwqIhtFZKWIzDvQc+wP6CZS2gLZtHETU4/QFojnebo/OlpAamtTDBnig+rYl7qAJRY1o33icSGZzAtINnug3orBYOhH9LUFcidwehf7zwCOyD8uA247AHPqd+h2tfr3jRs3MvXwwwHtWPd9XThx4qSJ7K7LMWRIgFIBnXWvtCxLWyETfd5+y9adDH3drdBgMBja0qcCopR6DmjoYsj7gbuU5kVgmIiMPTCz6z/oEF5F/e7dAIwcNYogCLBtm2gkiud5jB8/nqZmoarKQxD8oLxj3LIslFJMPdxj46a8o10oWjIGg8FQoK8tkO4YD2xp83xrfpuhDZ7voxS8/vrrTJ06VVsNnseQqiqqq4fo3uehEBXRMVhWC2JZ+J0Igo7MEg473GPTxryfROWjvAwGg6ENB7uAlFtnUWUHilwmIstEZFldXd1+ntbBRTaXQ0R49dVXmT1nDgAKRTgUJhwKY1kWQRBQUTke369HhE7LtIsItm1x+OEemzY5xW1Z4wcxGAztONgFZCswsc3zCcD2cgOVUrcrpeYrpebX1NQckMkdLOSyugbW8uWvcOyxx+qNSrBs7cOorKjE8zzC4UPIZnchCEHQeX6HbVkcdpjHxg1aQHRV3syBejsGg6GfcLALyKPARflorOOBZqVUbV9P6mCjYIEsX7aM+fPzAoLCzmeaV0SjKBSuM5JEYjtYgojV6bKUbdscPjXHpk22Lnli22SyWZQqa/wZDIZBitP9kP2HiNwPvBsYJSJbgWsBF0Ap9b/AY8CZwEYgCVzSNzM9eClEWu3auROlFBMmTkQphYgUS5U4joMAtl1NS8t2LLFAFLlcjkg43OGYlm0zfHgW24bddRY1o3UP9VwuRygUOsDv0GAwHKz0qYAopS7oZr8CPnOAptMvyeVyCLBixQrmzZuHiO5j7jitpUps2wYEkUoaGrYhlu71kcmkGVJV1eGYtmWjlOLQQz02v2FTM1qH8GZzWSMgBoOhyMG+hGXohkzeuf36668zbfp0QCcWOnbrvYGI4LouSlWwe/fbWIhelsqUd4zbthaQSZOCkoz0dMb4QQwGQytGQPo5mUway7bZuGFjMQM9CAJct9S4DIdC+L5Lc1Mtnu9hWRae75ft9WHbNghMnOTz9tt2cVs6nd7/b8hgMPQbjID0Y5RSpDMZbNtm48aNHHHEEfnt4DhuyVjXcUlnbIaPiLBr5y69URRermOhRNu2ECVMmuSz5a1WC6TgbzEYDAYwAtKvyeZ0ZJRSik2bNrVW4RWFY5cWS3Rch3TKYtzYYWzbtq3NMcoIiGWDKCZO8tv0BdFJOZ3ljxgMhsGHEZB+TCqVRhC2bd3KsGHDqGrjELfbCYhl2WQywvgJI9i6RSf3Fxzp7dE+EJjUZgkLdAan5xkLxGAwaIyA9FOUUiQSCWzHZsPGjaU9QJQO3W2LIKTTFodPHceGDRuBvF+jjGNcF1S0GDfOY+dOi0K6iMlINxgMbTEC0k/JZrN4vqf9Hxta/R9BEGDnBaAtnieIwJHTDmXD668DXfs1HMfBcQJGjQqo3W4Vx5uMdIPBUMAISD9EKUVTc1NRJDZs2FAagVUmVyOZVEQiAYcfPpXX8wICgJT3g7iOQxAETJzk89ZbrZFY2ZyxQAwGg8YISD8kk82SzmRwXR1ppdvYtlogIdft8JpEQhGOKMaNG0dTUxMtLS2AXtrKlFnGch0XpRRHHunx+uutRRUDZXqkGwwGjRGQfkgiES9mmQMlOSBKqbLZ4smUIhpViCUcccQRvL5eWyG2bZNKpzqMd1wHpRRHHeWxZk0bf4oSIyAGgwEwAtLvCIKARDJZtD4aGxtJp9OMGTMmP0I6RGABJJMQjSpQcOS0aaxfvx7I+zVy2Q4dB23bRhCOmuGxdk2pQ94zAmIwGDAC0u9IZzLFYomQb2GbbyKlUbhO+SWsSERX050xYwZrVq8G9LKUKMh5pX4Qx3ZQaAtk/XqHVn1RpjuhwWAAjID0O5LJZEmE1cZ2DnTHdspaIKkURCMKBGbNmsXKlSuL+3R+R6ko2LaNCFRXBwwdpor5IAWLxWAwGIyA9COCICCZSpbkeKxZs5Zp06YBurR7JNKxPDtAMgHRaIAgzJo9i9WrVxdFo1x+h4jgOC5BEDDjKI/Vq1ubS+XKRG0ZDIbBhxGQfkAul6O5uZn6hvqS5SuAV5YvZ16+C2EQBEQikbLHSCZ1FJZlCZWVlYwbN44Nr28A6LQyb8jVAjL32ByvLNfLYgUBMc2lDAaDEZCDHKUU9Y31NLY0l4TuAmQyGdauXcuc2bOL28r5PwASeSe67ejoKr2M9RrQuizVXhRCoRBBEHDcwiwvv6SPKyIoE8prMBgwAnLQk85kSGeyRMJhXNct8X+s+vcqDp86lYrKyuLFv30JkwKJhA7jdWybIAiYNXsWK1/TfhBt0XQUhUJF37lzc6xe7ZLKR/sKmGUsg8FgBORgJplKUbe7rkNl3QLLli3j2GPnAdpSsW2nQwmTAqmkIhLWEVpBEHRwpKM6Vtp18p0MKyph2jSP117LWzciprmUwWAwAnKwks1lqavfjW3bnVoV//zHPzj++BOAzjPQCyRTEKkIip0Kjz7mGNasWVN0pCvoYIHoaC5t2cxfkC36QTpLPjQYDIOLPhUQETldRNaLyEYRuabM/k+ISJ2IrMg/PtUX8+wLGhobsaR8UiBAPB7nxRdfZNHiRUBeQLroV55M6jBe29HdBqurqxkzZgwbN2hHuoiQy5VaIJZlQd7ncdRRHuvWlUZiletmaDAYBg99JiAiYgM/A84AZgAXiMiMMkN/q5Sak3/88oBOso/wfI9MJtOp5QGw5JlnmL9gAdXV1QAoVImDvT2pFEUfSCGGa/bs2cVlLO1IL/VriAhOvj/69OmtAlKIAsuaZSyDYVDTIwERkSNF5GkRWZV/PktEvrmX514IbFRKbVZKZYEHgPfv5TEHBIWQ2rbhuu354x8f4owzzyg+FwWOU95aAUglhUhER2GBtipmzZ7FayteK57L88pV5dU+kyOn+Wzc4FBY5RJLSKaSe/DuDAbDQKGnFsgvgK8BOQCl1Erg/L0893hgS5vnW/Pb2vMhEVkpIr8XkYmdHUxELhORZSKyrK6ubi+n1rckkgksu/M/zauvvMJrK1Zw3nnnFbcpKPo3ypHMWyCWWLiuLtU+79hjWbpsGZBflvK8DqG8hbFVVYqRo4JiRrpjOyRTKZMPYjAMYnoqIBVKqZfbbdvbgkjlbq/bX43+BExRSs0C/gb8prODKaVuV0rNV0rNr6mp2cup9R2+75NOpbsUgx/84Ad84YtfpKKiAtARWGJ17i8BvYQViepEwpAbJggCZs+ezaaNG4nFYtraUXQoqujky7oDTJ/msW5tqx8kCIKyvUQMBsPgoKcCsltEDid/gReR84DavTz3VqCtRTEB2N52gFKqXilVWGj/BXDsXp6zW7LZLA2NDQe0davv+9Tu3EFjUxOJZAIlqtPlq23btvHqK69y3odbrY8gCAg5nTvQAVIpIRrRfdALCYLhcJhZs2exLG+FFObSFsdp9ZlMa+MHKVBu2ctgMAwOeiognwH+D5guItuAzwNX7OW5lwJHiMihIhJCL4k92naAiIxt8/QcYO1enrNLgiBgd0M9LfEYO+t2HbAoo5znkc1maYm10NDY2KX18Yff/4Gzzj6baDRa3OZ3E4GllCKVFKLRAERwHado/x133PG89OJLhZEdckFsyy6OnTrVY/PmVivHsizSaeNINxgGK90KiIhYwHyl1ClADTBdKfVOpdRbe3NipZQHfBb4K1oYHlRKrRaR60XknPywz4nIahF5Dfgc8Im9OWd3JJJJcrkskXCEIAiIx2P783RFcrkcCITz2eZdLUX94Q+/58P/78Ml21Q3OSBKKVJp7UTXRRKd4mLhggXzWZ63QEQ6did0HAel9DEOPcznjXYCkjE90g2GQUvnt7p5lFKBiHwWfYFP7MuTK6UeAx5rt+3bbX7/Gtp5v99RStHS0lIs3+G6Lk3NTQBkszksSxg2dFiXF/c9JZvNYInW8q6Ov3nzZpqbmpk/f36HfY7b+Z9SKUUyIVRVKUQKpdp1JNaMmTNZs3ZNPpPd7pBhbllWXkQUhx3qs/kNp2RfNpsjCIJOM+ANBsPApaff+qdE5MsiMlFERhQe+3VmBwClFOlMhkw2QyweJ+d7xQu4ZVm4boim5mbSmTSxeJzmWMt+mUcmm+3RBfivTzzBe089tcxY6XLZSylFMmlRURnoBlIihEIh/CBgzJgxqEBRV1dXIghtCYdC+L7PqJqAXBaamvSalj6WaTBlMAxWurVA8lya//mZNtsUcNi+nc6BpbmlheaWJvQivyLklvoRLMsiHNb9NRzHoaWlBddxiEaiXSb59YYgCMjlcl36MF5++WVeWb6c++67n+9859qSfbq8e0fLxfM8JJ/J3iogCsk7NMLhMNlYDMe2mX7UdNauXcvo0aMpdBxsO59QKEwymUSE4jLW3Hn5Eij5boZdzd9gMAxMemSBKKUOLfPo1+IBOuPbsnPm2B4AACAASURBVGzC4TDhcKRLK6Bw197Q2Mi22u0kEvtmNU9f6DtPGnzxxRf55CWXsnXrNi644HzeddJJJft93ycUCnV4fWNTI3X1u1FKoZQikRAqK1rPEw6FiuG5R00/irVrW+MT2kegua5bjK8+7FCfzZtLl7HS6fQevXeDwdC/6dFttIi4wJVA4er1LPB/SqlBFcNZsEh836c51kJFRUWX2eI9QSfvdb7/h9//AV//xte54KMfLbvfD3yGRKpKtgVBQCqdJggCnZQoLrmcEA63hgc7bfqGTD/qKJYv145027ZJpJJUVbUe03UcCm/z0MM83nij1drRhRWNI91gGIz01AdyGzoH4+f5x7H5bYMS27bJ5XJk9kGuSDabKStCnufxrW9+i927d3Pehz9c5pV5lF5iKj1mtsQpHovrXiCWBQUl0KJQKJR4FOvWrSu+t0wmXeIH0U53nTg4fbrHqlWlFojve6bBlMEwCOmpgCxQSl2slHom/7gEWLA/J3awY9s2DY0NHRzOvSWTyWKViby66cYbWbN6NQ8/+kiXRRL1XCx27NpJ7c4d5HI5Eslk0f+RyWRobMxRUREAqmhJiAhuvmXtlEOn8PZbbxe3K0VJeK6IEAlH8H2fBQtzLF0aamc1CdncgUu8NBgMBwc9FRA/n4kOgIgcBgzqW07HccjlcrTE9jxXRClFNpfFbud7efnll3nkkUf5xa9+ybBhwzp9fRDo/h6pVJp0JkMul6Ouvo5YPI7rurpAou+xuz5NZaWOepA2FWQKojBy5Eiy2SzNzc2AFoxUqtSvEY3o3Jhx4wIqKxQbN7aKnggdxhsMhoFPTwXkamCJiDwrIn8HngG+tP+m1T9wXZeWWMseL9/4vo9SHR3ojzz0MBdffDEjRnQdKe15Ho7j0NTcRMh1CYVCeDkf13WK4boAzU0elVUKaXeucDhUPP/kyZN56y2dG+o4DslUsqRQYigUKmakLzwuy8svtUZdOY6jS7CYwooGw6Cip1FYTwNHoLPBPwdMU0ot2Z8T6w9YlqUjnJK6rLnne+ys20VzS0uPLqapdJr29SOVUjz51JOceup7u3297/skkgkcp7WVbSgc6hDSm0haVFYqbYG0ERDXcRHR5580eTJv5wVE+zX8kvwO13Uhn5G+8LgcL77YuqxWLKx4AOuHGQyGvqen/UA+A0SVUiuVUq8BFSLyH/t3av0Dx3GIxeP5UNkkqVSKxubGbkNblVI0tzR3yCdZu2YtllhMmz69y9cXKuHajtNl9rpt2aSSNpUVCtpYJYW5F3qDTJ48qWiBACCUBAm09Zm8++QMS54J09bwEpG8IBoMhsFCT5ewPq2Uaio8UUo1Ap/eP1PqX9i2jeflyOVyxOIxQqEQruPqooyxWKeWSCqdxvf9Dhf/Rx55hDPOPKPb8OBMJoMgXdbAAi0SmYxDZWWA1e6YIkI4pMOS9RLW28V9llgkU6V9zwtVfCdNDhg92mf58tZzO45DImGWsQyGwURPBcSSNle0fDvaAZl6fM/d9/DUk0/2KrpKEHbt3o3v+1iWhW3bWJZFQ2MD8US87GtisVhRPO69516+ds01rFu7lt8+8AAfOu+8Li/EQRDg+T6hsNujPJREQnQWepmx0WiEwA+Y1MYHAloQ0ulUyecQDoUIAj2v007P8NcnWsOHLcvC8z1T1sRgGET0VED+CjwoIotFZBFwP/DE/ptW37BkyRJ+8uMfc/31/8Wvf/XrHr/ODbmAKpY9AW2ZhEIhGpuaihfVQlZ4LpcjnUkXBeTBBx8kFotz1vvOYty4cYwdN7ZLf0I2l0UQXKdr66NAIiFUdiIgoVAYJYopU6bwxubNxe2FHJG283AdV/fOBRYvzvLc3zveQ6TSqQ7bDAbDwKSnBZ2+ClyGzkYX4Engl/trUgeawkX9K1dfzU9uvplRo0Zxwfnnc8FHL6CysrLb1xdLpLfDsiwUipZYC8OGDmN3Qz2+7xP4unqtiBAEAWtWr+aOO+/gU5/6FIlEHMdxSKXTHUqU6JpWSRLJJEOqqnpcGTiRECoqgmLF37YUlsAmT55MY2MjjY2NDB8+vPi+UukUkUgEQPdTV3o+M4/OsXGTQzYLhTJYruvS2NyE67qE3I7OfIPBMLDokYAopQLgf0Xk18BMYJtSakDkgSiliCfi/OnRRxk7dhzvOPEdOI7Dye8+mc9c+R+cetppbNq0kcWLF/OOE0/s9fEtsdi+YwdNLS35rPHWcvEAb7/9NtVDhzJixAiqqqqIxeN6OSjn0dTcDCgqohWEw2EymQzJVJJwOFTSUKo7EgmLykofsTpaIJZlEXJDxRa3r776KosWLQK0FdU2GMCxbUT0ZxaNCpMn+axf73DMMV7xWLZls6uujpAbYuyYMb3+vAwGQ/+hyyUsEflfEZmZ/30osAK4C3hVRC44APPbr8TjcV555RV2767nV7/8NR+/8OPE4nGCIOCHP/oR48aP46mnnqKyspLLPn0Zu3fvBnSWdjweJ5PJdPBVKKVIpVJks1ldiyqfT5FIxMlk08X+GgVWr17N0TNnArqqbeEi77it9adS6TRKKe04z2eF94Zkvhtheyd6gXAojB/4zJ07lxWvvlrcblkW2ZxX9IMULK3C82Nm5fj3ytJ7EMdxdKXfXFY3yjIYDAOW7iyQdymlCq1rLwFeV0p9QETGAI+jfSH9kg0bNrBo0SKy2SyNjY188EMf5L2nvhelAtKZNBXRCm767neL45uamvnCVZ/n5ltvwbK17mayWULZLJWVlcU8DM/3SKZ0Xohl2aggIBzWazye59PU3ExlRUXRX7J61SpmHp0XkJxXcpEvHDPneaQz6aKTvrvSJu1JFn0g5e8XwqEQsbhi7rHzuP/e+4rbRQSktLx7yHVJZdLY2Bx9tMeqVS5QJnxXhGQqxdBeztVgMPQfunOit/Xkvhd4GEAptWO/zegAceihh3LrT2/l6Wef4V8vvcD1N/wXtm3n60dlO1gW13ztGiZPmcKxc+dx0ccvyudF6HImqTbhrrlsro1PRGE7rX4Ax7GxbYt4IlGsNbV61WpmzJyJ7/tFgWiPJRaJRAJfBcUIr96QSAjRis67Bjr5artz587llVdfKY1AU1rACuhcEP3ZlLNAise0bRLJfdrA0mAwHGR0dyVqEpGzRGQucCL5yCsRcYCeL8IfhDiOw5y5cwGorq4ubtfRR0GHMN6KigpuuPEGlq94hUQizvP/fB4A27FJZzP5siRKdxfMt4wttI5ti96ucyyUUqxavZqjjz5aFyPsJCJXqQDHcYmEI0SjFd2+t3Qa/ve2imKiXyIpVFQGWGV8IIXPQgFjx45l+PAR/Pvf/26dr1XaJ911XAqVFGfN8lizRjvS22NZFrlcDn8vi00aDIaDl+4E5HLgs8AdwOfbWB6Lgb/s7clF5HQRWS8iG0XkmjL7wyLy2/z+l0Rkyt6esycoRcn6fTH81ssBwsc//nHuufvuwhwRhHRGi0igOvc1FLAsi8APqKuro6W5mYkTJ5LOZDoUVQQIAr30NLS6miFVVd0mDgL847kQ131nCDf/REeQJRJCZVSVjcIC7Sy38uXaFy9ezDNPP9O6z7JLKvPajl3UuaoqxWGH+az6d0crpCCcOVOl12AYsHQpIEqp15VSpyul5iil7myz/a9Kqb0qpphPRvwZcAYwA7hARGa0G/ZJoFEpNRX4CfC9vTlnT7Fti0y+p4bv+zQ1N9HQ1EgsFsOyhLPOOZsNr2/g2SVLiuPTmTTJVLJb8ShgWcKrK1YwY+YMgiAohva2RSmFH/hURKO9alz11FNhrrgywc9/VkEyUQjj9ZEulr7C+SzzRYsX8czTT7eZp7YkCkt6ju2UVO+avyDHy0vL55QKQjZrHOkGw0Cld4vpgIi8so/OvRDYqJTarJTKAg8A72835v3Ab/K//x5YLL25ku4hlmXheR7ZbJZ4vnWtm685Zds2kUiE7//w+3zz698kHovnrRDI5nI99k/Yts2a1as56qgZWnjaLS8ppZ3uldEKwu0aRrVHKdi1y8L39e9/eyrMxz6eYuoRPmvXujQ2WgypVl3Ozc2H8h5//PGsX7+ehoYGoLU/iJdfDyvkrxQEZcGCHMuWlreKLNsibRILDYYBS68FhE5X6nvNeGBLm+db89vKjlFKeUAzMLLspEQuE5FlIrKsrq5urydn2xaxRLykXlVb7Zq/YAHHHX8c99+vA9Ecxyl2+esp69et4/DDD8crUxPL9z2i0ahO4uvmkN/65hCOXziKc98/nMceC1NZpZg61efomR5LloSIx4RJk7wu56b7nuts+ne84x38/dm/l+wvZNOLCCE3VCxhv2BBlpdfcinn6tDtbtMlTniDwTBw6C4P5LQym/+S39dFn9UeUe5q1r4AVE/G6I1K3a6Umq+Uml9TU7OXU9N32q7j4DidZ1N/+vLL+M0ddxadzA/98SF+8qMfs6O2+yA1z/N4+aWXmXvsXNx2Wez65l6IRrrP91i92uHhhyIsXV5H1RDFlZcP5fs/aAFgxswcd98VZcGCHLYlXQqIY9vFLPP3LF7E022WsYRSn5Cun6UFZOKkgKHDFK+tKO8HsURoaGigsanJFFo0GAYY3Vkgj4nIEhEpWgZKqW/mf/3aXp57KzCxzfMJwPbOxuQjv4YCDXt53h7TnTUxffp0Dj3sMP7x3HNkMhm+/9/fY+u2bVz95au7vVg+8/TTjBk7liOPPLLDviDwix0Fu2LTJpuLLhzGd66PMXKk4tb/aeaXv2rihBP0xX7m0R67dtksPE47srvyz7Rtq7to0SKeXbKkNYHQErJtHOnhULhExU8/PcMTT5RfZnNcl0w2Q3NLc0k0l8Fg6P90JyArgfuAF8tYHHu7lLUUOEJEDhWREHA+8Gi7MY8CF+d/Pw94Rh1kt7Fnvu9M/vLnv/DXJ/7KzJkz+d73v0dLSzPf/+/vsX17ez1s5e677uHCiz5edp9SinCo62LHSsEXPl/NlVcmOO88ncg3apTi1NNao55mzNBLRwuP04LSrQWSl4WJEycyctRIXluxAiiUNMkUBUUnMrb6QU4/PcNfnyhvLYkIoVAIy7aJm7wQg2FA0Z2AKKXUL9Bhu18RkTtEpJCIsFcX8rxP47PoSr9rgQeVUqtF5HoROSc/7FfASBHZCHwR6BDq29ecdtppPPf35/jRD37IBR/7KI7jcMv/3Eo8HufST1xS9q57/fr1vPnmG5x6WscVwoI8tl/Was/z/3RpqLe45NLOndTV1Yprr4sxZ06uQzfC9liWVeywCOhw3meeKe7zA5+WmO60aNs2rtta0mTuvByNjcIbb3S+3Oc6Dslksldl8g0Gw8FNT1vavg6cAOxE18E6bl+cXCn1mFLqSKXU4UqpG/Pbvq2UejT/e1op9WGl1FSl1EKl1Oauj7gv5gSBH9BTO2fEyBH851Wf47vf+y6nvPcUAKZMmcJ/3XgDRx45jdt+fluH19x79z185PzziyVJdMSVRxAogsAn5Ia6DLkF+OlPK/nPzyXoruDtFVcki9Vyu1sScxy3eIFftGgxT/+t1Q8SCoVobmlh566dKKWorKjA9wqRWfDeU0v7g7RHREDpUvQGg2Fg0J2AFK84SilPKXUNOrnwfnSP9AGH7+toJc/zeiwin7jkE2Ur9X7lmq9w/333kUql2PD6Bs5+39nceMONPPXkU3zk/I8Uxyml8nWz4L57h3LP3WUDzYps2WKxcqXL+z/Quxay3QlIqI1VsfC4hWzatKlYQFJEdEXgrC6SGI1EUW2M0K78IK0TUGTSxg9iMAwUuhOQ69pvUEo9CxwL3Lg/JtSXBH6AbTsMrR6aL3G+dxXrJ0yYwJw5c3jwtw/yrW98k3e84wRSySQPPPhb2kaKBUFAyA3x8EOH8OADI7nl5moaG8tf7N9+y+J/bq3kg+em6UGQVgndCkgoTKCC/O8h5h17LMuXLe8wLpPN4rquzqjPC84735Vl3VqH9eu76M9uOyRNXojBMGDoLhP94U62Nyql/nv/TOnAohSsXBkmnRYCFVAZjYJARTSKUoogUMUmUHvivv/ABz7NT360lfETJvHVr13DDTfdyOQpk9vPAsexeebpCFd/Jcmpp2W49ZZKmpqEt99q/RPd8esoZ5wxktrtNp++LNnbd9oDC8QtNBwEYNasWaxc+VrJGMu2SKXTiAgV0WgxHyQSgW9fG+PyTw/jV7+MkkiU7z2SzWZNfSyDYYDQ046EAxKl4K7fjOSXv6gh8OGYWWl+9rMkh4wJsB2bqsoqEokEruviBz5B0DHhryuyWbj77nMZOvTDNDTkyOW2Ew53okLK5uWXXX78kxaOOcbjP/+zmjmzagiHFS8t3Y3nwXdvquJvT9czafKeXYC7ExCdTKiX1ESEWbNn8cB9pRX7Hdshk9GFIEOhcDFTH+CCj6aJxy0efDCKCFz6yVJro3D+WCzGsKFD9+g9GAyGg4c9yUQfEKxaBdOmTua+e0fwuz9s5s+Pb+awwxU33VRVHBMKhRg+bDhVVVVUVVahlOKtNx1OXTyZc86axO9/V93BKrnzjmF86tJxPP5YFTfeUEN1tc/Tz77J0KE+V3/xEBob29e70j/Xrgsx+pCAmtEBk6f4PPqnRja/uYtTT8tw7z1R/vKXCIsXZ/dIPAqC0J2AFHqNFJaltAWysmSMbsOrnf4h1y0J5haByy5PcvXVcR5+uPz6WigUoqm5mUTChPQaDP2dQSsgM2bAG2/W8fwLOzhqeiWHH1bFt7+d5O/Phli/ro2Vkb9A2ra2SO67r5p3vivO17+xi3vuGsoN19cURUApuPfuobzznUlu/slIXn6pgh/+aCeOA//9/Z0MH+lz5mmTeW1Fq7O5EBb74gthTjihNELJsvQF+bafV/KL2yt67TRve47OKvG2JxLW3QlB+3ByuRw7dpRm1gsKz/d0z5MyBtVJJ2fZuNFh69aO59R5IS519fUkU8Yf0t/wPM90mjQUGbQCYlkQidrYllVs0lRVpTjn/Wkef7z83XMQhHj04eFcdlma+Qvi3H3fNl74V5Tn/q5TY15/PYTvCxdf0sQjf3qb3/5uC1VD9N18JKK47vo6vvu9nVxx2Tjq6rRIKRXg2A6PPRbmlFM6Rigdc4zHz29r5rjjsrxn0Z5HMJXrh16OSCSKyjeMEhGOmTWLVW36gxTI5XL54pJOh9yOUAhOOSXDM8+Uj8rSbX1tWmLNvXwXhr4kl8uxY9dOduzaSTaXw/M8WmItJrdnEDNoBaQzFp+S5em/lc8C/92DUWbPyXHkkTYiFlVVPlf8RyO//MVwAP72VBWL3xtHRAtGdXXHL9a735PkjDPj3HXnMEBbB7Xbw2zc6PDu95TPkTjp5Cw/+nGMcDdRsp3RGwtEZ8C3ZpkfddR01q1bVzLGsm3SGT3XSLi1sGJb5i/IsXxZ571LCp0fTV7IwUkmk+lQjqexuYlABYgIO3buYOeundQ3NrJrHxQvNfRPjIC04/jjs6xf71Bfr+/Ym5qE5//p8vrrNrfeWskXvpgAgWgkgu/7nHFmjK1bXJa+HOEPv6/m7LNj3Z7jkk828uCDQ4nF9Mf/8MNDOOecNN1UL+kVuVwOr81SQ2fdCNtj2zbhUKsoTJ8+nbVr2wmIZRVrY4XDkWLob1sWzO9aQAo+mVi8+8/LcGDJeR51u+tKlqoKrZtdx8VxnGIXy2gkQjaXMRWXBylGQNoRDsPJ787yxz9EyOXgYxcM4zvXDuETFw1j4cIsCxboL1UkHMG2HSzL53Ofr+eznxnHIYd4zJrd/TLTxIke73xXggfuH0oyKdz1m0ouvHDf+gOCQBEoiq12pYcWCEBlZUWrgBx1FOvbWyCWpXu4BwGhUKhYxbctR07z2LXLKgpxOVzXJR5PkEwlzbp6H+P5Hg2NjSilSCTipDMZ0m3K8MQT8ZJADL0MqYM4lYJMJo1SiobGRhqbGkkmk6b68iBgUIfxdsYXvpDg/I8MY9nSEMNHKO66u5EOlUUEqioqaWpp5pz3x/jD76r59OWNPT7Hpz/dyKc+OZ6tW2yOPyHHjJn77g7O930c22bo0KHUN9Tn61z1vPZl2wZWRx55JJs2b8bzvOIFo0A2mymOLUR6FbBtXSNr2dIQp51eXlRFBNuyqNtdDyhGjRxJZUVlL96pYV+RTqVpam7CdV1aYjHC4TDJZILqIUNQShFPJDr8/QvYtk0ymUTEIhZvQcSiOWhh7Jgx3TZDM/RvjAVShhkzPc79YJqRowL+7/bmjuKRx3ZsQm4I8Ln7vm2cdHISz/MJgu7vvKYfleV9ZzWTSjl885t7v4zj+36xa6Dv+1RWVpZUze1N/krbLPOKigrGjhnD5s2lZcgsyyKRTGJZVrEdbnvOPTfNz35a0WUCpuO6hMMhXNelvrGRIAjI5XJs3baVdGbPos4MvSeeTGDZNvUN9YgIjuOQyWbxfZ9sNksQdGy5XMC2bZLpFHW763Acl1Ao1OOgDUP/xghIJ1x3fZybvhujsrJrMYhGw/m7b/ADfecfBEGPzPerv7KLn/60eY8TAwv4vq+/4AjZjO7lXhGN5iv6Kl0ePtzzuiciQrRNlvlRM2aw9OWlJWOcNtV1o5EIfpk18P/3kTTZnPCek0fyP7dWdNjfFsuyUEFAY1MTW7ZtZXdDA5veeINksrcZ94be4vs+mUyGcChEKBQq6UWTyWZIppJd5hCJCJFwhFAo1KsbFUP/xwjIXuLYbmuPcKWoqKigsqISz/O7FJEgUNiWjeN07mjuCUopcjmPkcNHMHp0DWJZupZXKKTXqW1HZ427vTtPNBItWhWXX3E5P/zBD4jFWi0lXV1XsWt3HW7ILVvb37Lg7rubuOrzCX79qwrKBGuV4LousUScbC5LJBxGoajdWUvC9BHZrxQi4USkxMqwLIt4PEEs3vnyVVs6s1AMAxfzF99bRPsMvFwOy7JxbIdwKEQ0EsUPAgK/vHURBH6P+p13hVKKbDbDkCFVRKNRHNth7CGHMLS6ujgmHA4Vo2Z6QzgcKp5j4cKFvO997+Pkd51U7BECEAqHyWayxOLxksKKbakZHXDuB9PUjA741/Ndh5kV2ggHQYBYFrZl4fk+dbt3646G2UyxN7uhc8qFVXdFNls+lNpxHFLpFEp1vnxlGNyY/4p9QDgUJhKJMqSqSguCQEVFlOqqIfhBxyKMhZ7nob2M2/U8j2ikghHDhheXGBzHKVluCIfCPeqt3h7HdgiFWsua3HDTjXzxS1/irjt/UzLODbmk0mlcx9EXdwWqjJB88ENpHn6o+3noi5kgApZl43l6eWVn3S6279jBttrttMRaev1+BgupdIodu3bg+T0X2kwmW3bpqe3/lMFQDiMg+wDbsamsrOzwJXQch3Cb0iC+7+N5Pp7vEQmHu61N1RW6UnDA0KFDuzxORUUFQ6v3rHBhZUVV0TEPcMp7T+Gll14qsTREBMe2yeSyqEART8ZpamnWYblthHPRogz/+Ec3gqkgnUlj21b+2PozVErheT7pdIpkMsn2HbVkOrlrHuy0tLSQzeZoaOh5RGAmm+nUwgiHw8avYegUIyD7mUg4DPmy8KCLCUbDEaLR6F4d1/d9IuFIt73Tbdve4zvIcDhcIgJjxoxh+PDhrGuXWOg4DipQxJJxMplMPpwzRlNzI01NzSSTKaZO9UinYcvbnf/LZbKZDuHAIrqEvOPocjOWbeHlPN54682y7YIHM9lslnQmQyQSIZlKleRxdIYOwFBmicqwR5j/mv2MYzvY+cisyopKqiorqaio6NRq8DyPdDrV7Tq27/sMGTJkf0y5iOs4IKokGOCEE07gxRdf6DA2FArhOm4x58RxHMSyEAvSmRSxeAsnvCPL8+38IL7vE4vHaG5pJpFMdHm3W0hkC4VD5HJZNmzexNtbt5BOm3Bf0OVHkHx+jW3R0FhP7c4dNDY1dhrQkUqlKFsR02DoAX0iICIyQkSeEpEN+Z/DOxnni8iK/OPRAz3PfYJAddUQhg8b1q3PQyndvGpo9bBOncVBEJDNZrEsS1s3+xHt1HZLlqwWLFzAsjJdCi3LIhqJEAStglO44DuOg+8HzF8Q5+/PllYijsXi5PLtgy3L7vGyXiikl1aaWprZWrvtoI3UUkoRi8fZXb97v2dmJ1MpbEsLcOEzD4KA5paWks9HKUVzSws7du2kvrE+ny9kMPSevrJArgGeVkodATydf16OlFJqTv5xzv6ajO/75HK5/fYFz3lejyJjCstSQ6urscQil8uRy+WKF/DCPKuHDKFm1KgDsuwQDoVLBGTm0UezZvXqsmNdVyeReZ6H55W+X9u2WbSokX+94PLCCy4oSCQTBErnzliW9CpbXgRs2yLkuqTTGbZs29ZnnQ49z+v8Dj+dor6hnngiQUvLvnf+x2Ix6nbv1qVHspmiBSciuK6Lbdu4rktTc7OOCgwC6nbvprGpCc/zcN1Qr/6Pamtr9/l7MPRf+kpA3g8Uwnl+A3ygj+aBH/j5C3eYXC5HtgfrxgUHdk/IZrPFnIzOwiULBH5ANBrFsixqamoIh8OE3FCxTpTneYwcMYJhQ4cR6UVi4N4QCZcKyBFHHMFbb71V1v8gIlRWVDCkagiFBMbWfTByFHznulquuHwof/+HtqRse+8ifHQTLIdsNkNzU1On45RS++UGIQgCduzaybba7WX/vi2xlmIwRVNLc4cxjU1NZLJ75ssJgoCmlmZS6RQ7d+0EVb5tcWEJtXZHLdt31JJKp4lEwjiO0yvx+PWvfsWxc+dx289/DsCzzz7Lm2++uUdzNwwM+kpADlFK1QLkf47uZFxERJaJyIsi0qXIiMhl+bHL6npYXjrshoiEItSMHEXNqBomjBuP47jd5hpks1m8fPZu+7FBEJBKpchkMvmlJpuakaM4pGY0jm0XM8XLo4pO8Ug4zOhRNdSMGpXvAhgg6AS/A4mTTHi93AAAIABJREFUL4dSIBKJMHnyZDZs2FB2vGVZhEIhQm7H8iaWJbz7PXGuv2Ebl3+qhnTaYS8C0YoUlsl21dd1WpSxsamJxi4EZk9JpVr9VXX1u0usoGwuVwyRFREs26K+oaH49w+CgJZYC41NTXskbolkgiBf0LKQQd4ZoVComChYyPHp6j01NpZGca1bu5abf3IzDz3yMHf95i4uvvAi/vMzn+XMM87kxRdf7PXcDQOD/SYgIvI3EVlV5vH+XhxmklJqPvBR4GYRObyzgUqp25VS85VS82tqanp08CFDhjDmkEOKEVGWZTF8+LAul5uCIMC2bSaOG88how/Jh5i2iojneQytHsromhocx2H0qFH5xks2o0ePprKqkkwZEVFKofLLDm2xLIvqIdVkMhmi0egBD6ksF8E1Y8aMTpexCoTCobIXRdu2ec97MixYmOaxP1eXeeWeYds2uVyOt7a+zZtb3iKTa72rT6VStMRaiMVb9ml9LaUULfFYMdLN87ySVr2pVBKhtYKt67hkc9miP6JgjWQymV5bIUopWlpixb9P+yzychQap3XH9dddx9Vf/nLJttv/73YuufRSjjvuOP761JMcdvhhPPi73/GFL3yBBx/4ba/mbhg47LcMIaXUKZ3tE5GdIjJWKVUrImOBXZ0cY3v+52YReRaYC2zaH/MtEAlHiqVJyi0H5Lwcw6qH5ev/hDmkZjS1O3cU7zKVUlRURImEI0THlFoLju0wcvgIbMumuaVZh8nm8TyPiki07Bd8aHU1kUi46CA9kOiOjVZJMb0ZM2ewZs2aLl/n2LrdbWef4/kXNPPjH47kwx9p2WdWiOu6pNNpAqXIZrIcOnkKmWyWrf+/vfOOl6K8/vBzZmdn++6tVBUwokTAXsGKaOwFu4iaGI0tGhNjibHFGDV2McYuGntiF1SM3WABK1gQ9SeKdC7cunXm/f0xs8vt9+7lAvfC+/DZz53deWfmnZ1lzrzvOed7fvoRECLhCHV1dd02/ZdKpUinMwSD7nV0lWxriEWjiAh19Q34zKbXzO/3s3zFCsKhMA2pZEESJp3OFNWvTDZDzs41+Q2tCo8/9hgjRo5kgw024OmnnvZUDtzp16qqKqZMmcI70/4HQDwe57LLLwcgGApy28SJKKWYM2cOzzz9NCNGjmT48OH069O3W/qm6bmsrSms54ATveUTgWebNxCRUhEJeMsVwGig/btWNyAihIKhVqexXL0rIRJZKQxoWRZBr73jOBgiHUpYx2OxFtIfjuMQjbQuZZ4Xq1tb0TLNp6O22WYb3n23ZShvYwyvVHBbUzOjd2kgkxHeeqt9kcVicP0hfgJWgFQmzYJFC1mydAm245BzbLJ2jmQy2S2+kFwuR9WK5Zj+lc9g+TopyVTKLeiVy7YYMeave01tLXV1da4fwucremRUX9++wGEx2LbNVX+9iisuu5y77ryTMWPGsMkmm/Deu+7U1EsvvsSuu+1KRUVFi22HDBlCLBbjs88+4/e/O5e5c3/g4Yce4rJLL+2Wvml6NmtLo+Aa4AkRORn4ATgSQES2A05TSv0a+Dlwp4g4uIbuGqXUajcgAJFwmIbkShXY/FSD4zgk4nH36boRiXichYsXoRxFLBbt8D92flpqRfUKAp6T2hCj254mu5tAwCKZThV+LDvsuCM/zfuJH+bOZaNBg9rczvT73SznVtYZBpx1dhV/v7qC6e/Xc+z4agYOdI32G6+/jhgGu+++e9F9zX/3lt+iuroaBHw+E9NnkEwmyflNstlsIaQ6/6RdzHdv2zYLFi0EpfA3C832+dxa7wErQFtDK9M0qa5ZgYiBYRiISKGEbGeMQs7OUVdXh9/qngeKGTNmUFZWxty5c/nm2295YfILPP7Y47zyyivstvtuPP/88xx77DFtbn/wIQczYfzx9O3Xj4m3TcQwjC4HBmh6F7IuVg3bbrvt1IwZM7q8vW3b/LRgvjevbbujDE+SpCSeaNUPsXjpEhoa6hnYf2CnRgpNjpHNkognSCS6JjmyumlIurUeGt9kz/vDefzsZxtz+hlntLldJpOhtr7Ok5VviePAnXeUUlXlY/LzMR55fB6DBmXYd599WbZ0KX+//jrG7DWmy/3OZ/+Ll1yXr5zXr29/+nhP024E02LKSkqJxzvnk6mrr2dZ1bI2jU4+Qi3vuG6NXC5XmPbMbzOgX/8Wv51szh01NSQbsG2bspJSUukUNXW13Vas6dJLLiUSibDTzjtRVlbGiBEj+Pzzzznl5F8z5aUX2XH7Hfj400+ItDFCVkrx6SefUFJayuDBg93zyaTp16evLijVuyh6SKtV0lrB5/ORiMepWl6F5beoKC9rMepoTmmihEAHkTDNjxGPxahasQLTNIlGo93R9dVCa470cYeP47RTf8PQoUMZu/ferW6XN7RtPVkbBpx+hhvtU1LicOc/yzj40JdZtvQqRozchptvOmOVDEjzvBIR1zdTtbyKyvJyAJYvX4HP9LG8egXhcLhTsi8dZcz7fD5oVP61NVo7TiqdbvL7SaVSLFrqugd9hrvPRUsXI7QMtlgV3nn7ba648i+M3GIkju1g2zbDhw8nnU7zt6v+xti99y4Yj0wmg0I1MQwiwlZbb91t/dH0HrSUSRvEojHKSsvp17dfh8YDXOdosaKFsWiMyvIK+lX26dGCdX4vj6FxiOyoUaO44647Oeecc/j6669b3c7n82H5/Z4icfsj3eMnrOC1VyOccdpubLDhMIaPqGDO1w/yytQfu/VcfKZJJpMhmUxS39BANpfFb/pBoL4Txats2yaVSrd7vUzTxCzyerrJfitYtryKnxYsYEV1NUuWLfXKAwQK+wwGggQCgTajqYqdUUgmk3z33XcMHTrU3achJFNJcrkce40dy0P/+hcnn3wy4I6a6urraKhv6HQelGbdRhuQNjAMg0Q8vlpv7D6fj1g0usqy7qsbEaGspLTgL8gzatQofv/733PB+ecXblzNQ6DDoTCiIJdtO1sbIJFwuPJv/wfqOO66J8kfz69izJjXOOe3I7nk4mf4soOor86fi/tavHQJy73RH7ghtrV1tR3egPPaUW2NLtKZNPUN9UXXLTEMw609XlcHKGpqazxNq+J+f0cfeRT33nNPp9vP/uorhgwZguXlhvgMg2wmS21tLXvuNYZRo0ezzbbb4DhOoe6LIv89tE2+SqZm3UYbEE2nsCyL/n374WsWPXbSL3/JsmXLeOutt5gzZw5bbbEl115zbcGQ+Hw+SkpKvBK57d9Qqpc/zs6jhD593Dyemyduy29O/5jnnj2N446ZzUH7v8ExR17LRx99tErnYnqjEFhZKz4fQZVXsE1n0qyoribTLDGxpq62yfRTPsNdKUU6naauzlUkrquvL3o0YFlWQT7dsqyiVZQ/nPEh3377LTfeeCOfd5Cnk2fmzJkM+/mwJhIopt/E9JuMGrUzd997N6l0iuqaGhTKi64zSLVT3CuXy1Hf0KCLf60HaCe6piiqlldR11CP5V85anrqyae44frrqaur48yzzuKVqVOZN28eg4cMYciQwZx44on8bJNNqK6pwe9v/aaYTqc57OBDueCiC9h9jz2arPvyC4uH/mXxww8/MPOzkVjWXtx2+1nssOMOXToHpRTKgZKSplOO2WwWy29RWVHB/IULyOVyWFaA/n37IiJkMhkWLFpYcJ67yXw15BwbQwRHKXyGG1mVzeYIh0IEg8FuC7ftiNN/cxpbb7MNpunjjdff4MGH/tXhNn88749suOEGnPSrX7a63vH0swzvvPLYto2IQSwaLSRx1jc0IN66bC7LxoOHeLI2ml5C0T9UbUA0RdGQbGDJ0qVNIpBs2+bWW25lzF5j2HLLLVFK8fFHH1NVtYyZM2dxzz13c80117Dr7rsDTWtPTJk8hRenTKGuro5oNMqtt01s94Y76f4Snn06w+LF23LIYQcxcuRI9hq7V9HTgLlcjtJECdKoL0opt55GIEAqnSIUDJFKpykvLSMWjbJ02TIakg2FY2UyGTeXw282USDO78vO2SgBy+8nGuk4vHtVyGazDP/55rz7/ntEIhFG7bQze40dSzAYZPz44xj285+32CaTyTB2zF5ccukl7LjzTkUf0x1lihvmnWxcCExIZzIM2WiQNiC9C21AQBuQ1Ukul2P+wvlYRYRnzpo1i6OPOopnn3uOsvKywtTM5Bde4KYbbuKMs86kqqqKcYePo6ysrN19ZbNwyskDqa+vZqut72fmp/+htKyMWybeUpQRyeVs4rEops9PQ7IBpRShUIi6+npSqSSmzyQSiXiy6DaV5RUsXrqkSWhuTU0NtmO366eora1l4q0TOfKII9h2u+063b9imT59On+68CJeefW/AEx9eSozZ35GOp1hyuTJvPHWm4XvfemSJdxxxx18/NHHRKIRbrz5pi774RzHjdpqXko5lU5rA9L70AYEtAFZ3cyb/1OLKY2OuOnGm5j+wQdMvP02/H4/c7+f6zp877+P4SOGF3X8XA7uurOUByaVcvU18/jXg8ex66678qtfn1zEPmzCnrZYTV0tAhhi4CgH0zRxHIVj25h+k2AgSDKZxPL7CYXdzPlsNktNbW3hqbu170IpxbhDDqO8vJzvv/+el16ZSllpq6VvukRDQwOXXPxn9t5nH7744nPq6uq49LLLmhy/rr6eCePHc9z48Rx11FF8+umnnHD8BA466CA2HbYZ+/ziF4RC3T/Npg1Ir6ToH4F2omuKxrJaKu12xFm/PYvFSxbzyEMPM2/ePM793bmccdaZRRsPANOEM85czq0TF3DpJQM5+OCrue+++8mkO18n3RAhk82QSqUwPDVfhSrIyxuG60x2HcL1ZLJZ6lPJgnO4vqEBwxCeePwJdt5hJ+66864Wx/jwww9pSCa5+7572GnUzlxx+eVNvrcff/iB66+7rs0+5qs1tvVdP/zQw3z11Vf8/dprueXmW9hl112brM/lcmQyGS688CIuv+xyzv7tbxl/7HFcc+21/PVvV3H88cfjt/xrzEejWffQBkRTNAEr0KkCWY3x+/1MnHgbzzz9DAcfcBCjR49iwgkTVqkfO+6U5A/nLeWWm3emrPRc7rqr5U28LcQwvBtsFsMTqXQzw5u2y9fMsCw/ftMknUpRU1NTcCw/OOkBLrr4Ip547HGmTJ7SZNvHH32MY489BhHh/AvO5/VXX2PatP8V/CVPPfU0N1x/A9OnT2+1j5lMhnQ6TW1dXYuIrnQ6zT9vv52/XXM1L019mauvuYbRo0e3aOM4DlttvRUvTH6B4cNH8OTTT7Hf/vsBLUOuO0Ip1yi5Idl4r3VvBkPTefQUlqZokp60idUF7a5sNsuK6mqsbnzyfe/dEJdcXEbOHsYGA/tz8KGHcORRRwLuDa6qajnLq/qxydDOj1A6w7vvvstVf/krz095gZkzZ3L6qafx+ptvYAUsGhoa2HXULrzy2n8Lfp3nnn2Oe++5h+eef55oNMpBBxzIhhtuSNXy5Tz+RFNJdKUUK6qrEXELjcVisSbZ5y88/zz333c/Tz79VKt9cxynsL3PZxKPtZxKqm+oJ51OtxourFRLKa9cziYQsBAgmUp7691GZjPVYT2F1SvRU1ia1Y9pml34qbn4/X7C4TC5nN1tT6877pQkFjc47fSpHH/CBK7/+3UsWriQZDLJ787+HbvvcikH7DeIN9/sPuVfpRQTb7mVX578K0SELbbYgmHDhjFlymTAlQcZucXIJkEBBx18EKWlpUyaNIlv/+87Zs+ezZ/+fDHTP/iAVKqpGq9t2zjKHeWIIaTSKTeyy7bJ5XI8/vgTHH3M0W32z7btQt5GayV3lSch31oAgFKQzTatWZNfDAdDhEMRopEIlhWgNJFAGq3XrF9oA6IpGveJVbpsAMKhEOFQCDtX3BRKW4jABRcu5R8Tf8a8eUfzs00e4C+X38y999xLMpmkX787KSl5gj9fVEk63T2jnuefe57amloOPWxlocwJJ07gwQf+hVKKV6a+wt777NOsn8J55/+R+++9j8cfeZTd99iDaCzKkI03ZubMmU3aZrPZgo02vOzwqhXLqa6p5tvvvuX9997jF/vu22b/Mt72IoJCNZmuyisKNNYoU8od6YBrfEyfv0niZ77ssxgGCAQCAaKRCGIY+C0Lx3H379hOQcRSs+6jDYimaEQEy28VPYfeePtAIADSfXPoO+6U5JBDa5n+QYg+fXbl7Xcu5+477+fAA2/A8A1g/wMmU1v7Be+8verHm3T/JG68/gauuvqqJk/wu+2+O3W1tbz26mu8+cabjB27V4ttR4wYwUaDBnHvPfdy7h/OxefzscUWW/DBBx80aeeWQ16ZV2H6XS0s0zR57NHH2G///dsYPaiC89xotD6fFZ5KpVhRXU1dfX0hggzAcWxsJ3/zV16RrMbflWpTWdey/CilcBzljnpEyP/TrNtoNV5Nl4hEIlQtrypabiOPYRhYfotMLttCeFApUI6DGEZR1Qr/8Mdlhe2PP24AgcCbPProMM48q4qDD7mYzz6dxdVXpdhzzLZFhSA37ZviwUkPcMddd7RIzjMMg+NPmMA5vz2b48aPp2+/fq3u44ILL2Du3O/ZcMMNAdhyqy156803C+sdrwCW6fPxzZxvmDZtGp9+8gmDBg1i/ITjeezRR3ni3/8mk800qRTpOI4bMZZxRx+FiDIRMlm3uFV9Qz2maRbWNT6vQMAilUoRDoUIBAI0JJMFR7khvjbzXfw+d0Rq2zlikRhWwCKdTndbvRJNz0WPQDRdIhR0y6+uyggiGAwWdKQaoxwHRylsu2taSiJw863VRCObkE4JBxxYi4hwwZ+2ZtGirfhwxoeFtm+8HubdaaF29taUWbNm4ff72WzYsFbXjxs3jr332Zuzf3d2m/sYPmI4+x9wQOH9VltvxUcfflSoI5JOp0Ep/viHP/LLk07imzlzGDV6NB+8/wF77rYHRx99NIMGDwLl1jPJk0wlyWaymKavRaXEXM4tQtW4BkkepeCBSWVMeaGcaCRKOBRxSwSbpnstHIdAwGrT7yWe8Gg00kgYVA8+1gv0CETTJUzTxLIsstksfn/XIqpM03RHIZmMFy3k3twc5RANR0mmkjiOalHXozNUVtrc+o+FTT7bZpscIoN54YX72X6H7amrNbj4or7kbOH2O+az7bYdl5V96cWX2He/fds832gsyk233FxUX4cMGcKgwYO5+uqrmTP7a36++eakUkm++/ZbXn3ttYJS7iGHHkJDQ0Oh8JXP9JFKpVGezyGdyWCaLQ1Evu66+FpP/ly2VLjj9goqKxWptMEJJ7hGybIs6hvqC8vt4fO1PULRrLvoEYimy1SWVxBsViekWCLhMPFYjEAg4EULAQh+y08wGCw4Z7sD04Rdd1vI1JfcqaV77y1h9C4NXPSnJdx6c3mH29u2zfPPPsf+Bx7QYdtiEBEuvuRi7rvnXn4+fHOqq1dQVVXFjbfcVDAebv/NJlUTRQTT9JHNZcnmsq0aj8K2frPNabtHHk6w/wEN3HhzDf/8R5i8a8vvd30bftPE5/Mx93sf++xdxtzvtaHQuKyVEYiIHAlcjlv3fAelVKtJGyKyL3AL4APuUUpds8Y6qekQ0zQpLytn/sIF2Hb7mlBtkZdEMU2TbDZHLpt1a6mnXbl1N3nN9tqt+rzIaWcYHHX4UXz84VweeWhjnnr2ByorbK7+WyXz5plssEHb02bvTnuXispKNttss1XuR3OGDRvGux+8RzRanOhi85ohjqMaaVN1vL1S8MzTCe69fzlbbAHxhOLCC2JstlmOAw5ME4n6CQZD1NQIJ5xQQiLhcNllUSY9UN2V09SsY6ytEcgsYBzwVlsNRMQH/APYD9gcOFZENl8z3dN0Fp/PR2lJaSHKJ5t1s6eL9Y2ICKFgENuxiUYiDOjfn1jUHZkELAsUroHJ2V2O/gIYOTJLWdkiTjpxe8YdXsPAgTmsgOKgg2p56j/t10R/8j//4fDDx7W5ftasAAcfuBHHHzuQ+vrijV0sFlvl5ErHsT0ByM75j2bPthCBESMcRODv19VQVuYwa5afgw4sI5uJk8v5Oe03CUaNyvDv/yzn69kmr77a9pTW7Nk+Lruk+zS/ND2XtWJAlFJfKqVmd9BsB+AbpdR3SqkM8BhwyOrvnaZYIuEwPp+PdCqNIT7isXhBhiOTTnf6hm8YBqUlpQzo1x/L76estJSyklJ8plujPhIOFwQQc6uQQ3LSr/7NbrvfwFlnLyt8dvQx1TzxeIL6+hyzZ9fwm1MGcNop/Ukm3Rv6jz/8wLT/TePgQ1f+BNNpYcb0IF99ZfG/d8Kc/psB/Ork5VRU2txz95q/gSovfTzmjWI6Y8Rf+2+EPcbU4fOmt7bcMsdFf6rn5ltqOPjgFAcdUM4eu5VTWupw5V9rCQTgyr/WcumfYzTOfcxkYOlS4aefDE75dQnDR3Rv1r+mZ9KTnegDgcYFsecBO7bVWEROBU4F2GijjVZvzzRNyJe8TWfSxKIxTNOVQk972dO1tW6VvsZS6HnyeQuOUihH0a9vn0IbEaG0pASfz8eK6hWIGIAiEo5QV++WjTXEKDrcd4stRvLqKzcSiRzJV199xUsvvsSoUaMYMHBv9h5zD9XVG7PJJhkqK4fyz3+U8fvzlnH33fdwzLHHEGskCXLdtRW8/XYYn6EQgcuvWMxeY+vZYcckhx2yERUVNttum2SzYZmi+tdVbNsh6NVLDwVDboEnkXan/15/PcIfzlsG0jLk9s+X1LHf/mkE2G77lX6uvcZmePLJLCf/qoS771nBt9+anH5agiWLDXwmTJjQwFFH1wPR1XSmmp7CajMgIvJfoLVA+IuVUs92ZhetfNbmI5VS6i7gLnC1sDrVSU23EQ6HCYdXSoVYfj+Wp90UjbjFmFLpNFaz3IB0JkM4FMLyWxiGQTAQbLJeREjE4wSDARzHzaBeUb2cWDRKOp0ml8uSyebadSA3Z/iI4cyePZtsNsslF/+ZTTfdlLPP+i2wO+n0IwSDJkM3PYs333yTTz75hC23/oKpL73MlJdeLOyjvl547tkYzz7/A/0HNJ0uGjAgx4MPzeOm68v55+1lnHJKFcefULUGopRUQS8rEAiQzeXwGQbpTAbHVhhNEgcVDUnFnK8D7NRGLSnDgO23bz1A4pZbazjvD3H2/UU5VcsMrvxrLYeNSxU0tIoQRtb0YlabAVFKjV3FXcwDNmz0fgNg/iruU7MWMAyDstJSFixc0CTxLZPJEgoGqSyv6PDmn8+CDgYCpFJJ0pkMoVAICJHJZKitq+u04zgajdK/f38ee/RRlixewmNPPM6+++3Hjz/+yKjRi/hmjsXYvc/j3WmjOP03f+Lcs4/jt2efQVn5Sl2r556Ns/0OyRbGI89mm2W44+4FfPuNyXHHbMiee9axwYbZJjfx7sBxFEo5BUVhs1Ft80g4im270VS1dbUY3oy1nbNB4JuvYwwdmiMeLz7hz++Hm2+p4T//CTJ0kxxbbZ3zjttNJ6bpFfTkMN7pwFARGSIiFnAM8Nxa7pOmi5imSWlpGZms+2jq1tSG8rKyoqOOysrKMU2zIFduWRaW319UyO/pZ57BDdfdwBFHHYnP52PX3XbluPHHMXhwlrF7u7kPO48axeQXD2Xo0BBPPnkJM2a4o6OGBuGf/yjllFOXA5DN5RqFIDdl0OAUZ59dy7m/24CGZMukScd22vRV1NcL7bmPlHKd5q5PyM00b1yi9+q/RfnrlVH8ph/TdGub5HI5DJ9BIp7giy/ibLdd15I1wTUWRx6ZKhgPzfrHWpFzF5HDgIlAJbAC+EQp9QsRGYAbrru/125/4GbcMN77lFJXdWb/Ws69Z6KUYmnVMhoaGjAMg8qKihZTVp3FcRxq62pZvqKaQMDCtnNU19R2ehQCsHTpUuKxeJNci9b7DVNfjvLXKyvZc896Viw38Ftww00LcWxXcsVvmiRTKfyNMsBd9VyHkkQJZ52RALG58qofC22UohAt1ZokzPHHDiRR4nDTLQtoLY/PFT00icViZDKuYc4n/OVysO3WFZSWKt54a5lb7z2VcnXILAsxDE49JcE+v0hzxBEdJ1AWSzqTpl+fvm3qZ2l6JL1Dzl0p9bRSagOlVEAp1Vcp9Qvv8/l54+G9n6KU2lQp9bPOGg9Nz0VEqCyvYGD/AQzsP6DLxgPcabFEPEFJIk46nUYwCAUD5LxQ3848F1VUVHRoPNx+wy/2rWPyi3MJhx023SzDJZctBsBRDqFgkHAojOU3m0ScrXRqC9ffWMPsrwI8+nB5QfVWKeVl3xstFGznfu/nu+8sUilpM6JLKVWQlLEsq0m2+NtvW/Tt5zB/vsHSpeKGSYdCBINBxDBQCmZM97PNNl1PAtVoevIUlmYdJV/lrztIxBNUlJeTs3OEgmFisSiW31+4kSul3PyRVcgdyROPO1z4p6WcdXYVJSWucq0hhuu4FgiFQs2mo1Thph4Ow333V3PnHeVMmRKhvl5YtEgwJOAFCDTt39NPxzjwoFrOv3Apjz5SQvNkf1fg0MA0W/dfPP9skCOOSLH9Dlnee6+lkfxwhp9IVDFkSPdl+mvWP7QB0fRqRIRoJEosGiWby2JZViEazA0RdhMTlaNWKXekNRzHJhQKF3w4ps+Pz/B5jm231obZSPV20GCbu++t5p67Ktlxu40Zd8gQ9t93ALlMSyPw4pQYBx1Sy2abZRg8KMOdd5Tx/nshHnk4wVNPxli2jIIkfnOUgtdes9h7nzSjRmV4b1pLA/LM00EOOyylnd6aVUIbEM06QTQaKyj7GoZBJBwim80SCLhTO8FAkFwu16RIUldRCnLZHH7TcrPk8wgF/S7bdp37zW/wO+2UZeorS5jx8Ve8P+MHttgyx3V/TzRp8+OPJrU1BsOHu+q8F1+6hM9nBbjhunK+/CLAa69GOeaowfiDixIiAAAQe0lEQVR8rU+/ff65STjsji523jnDtGkrDVQqBS+/FODpZ4Icelj3+z406xc9OZFQo+k0lt9PPB5nRfUKN7lQDMKhMIFgkGwuRyzmjlIWLl6EYbRMaCwGx7Hx+y1i0WgLA2FZbj10hWrTx2OafqIR1x/xlytr2XnHCk75jUk84Y5a3nk7wuhdGsjP8m22WZqJt88DpfD5XD/LMUcO5p13QowZ0zThYurLFk/+Z+XnI7fI8eOPPqqqhDlzTCaML2HYsBzXXlvDxhvr6SvNqqFHIJp1hkQsTkm8hL6VfQgEApimH0ME5TiUxBOUlCQIh4KrpKUFK4svtTZ9JCIEQyFCoXDbiYPiJl4ahkF5uWL0Lhleey2B47ijo7ffCrPrbg2FLH3lKAKWRTAQwrZzCHD8hBQPPtC0jskVl0e54vIY0ZjDseNdSXa/380inzI5yJlnJLj9n9U898JyDjwovUrfgUYD2oBo1iFcLa0SgsGgl18C6bRbYc+yLPymH8sKeFNdXTtGfjt/O5UYQ8FgITqqM4wbl+KF5+M4SvHhDItPPw2y62515GybSChMSaKESDhCOBwiEUtQkkhw+BEZZs3y8+p/3WmsV/9rMWVygBdfruKGG2sZPnxlbsbOO2c4/48xjjkmydi9dYq4pvvQU1iadRLTZ9K3T19q62rdqSby2dlh6hsa3JK5PqPwlO/mj3Q8rZWfvpJuiiIDGLt3miv/EuOxRyp44P4EV1zhRnn5zSCBQLDJSMdnuqOaSEQx8bZqTji+hD59HBYtMrh/UjXxeEvLePgRKSorHY49rm2fh23bZLJZLL9fF4bSdJq1kki4utGJhJq2aEgm+WnBT2Sz2UJ2tt/0k8lmOmVEcrkcsWisoDnVXXz0ocnh48r48yU1nPzrzju3ly4Rlq8w6NPHIZHo+v/ldDpNPBajpra2VdHLPPn7RUffk04k7JUU7RjUIxDNekUwEHANRiaD4ziIGEQiEVQD2Llcu0/fhdDcdqavuso22+b4cvZiipj5AqCiUlFRuWo+nbw+WSKeQCmoq69rtYStUopMxvWdBFYhCVSz7qB9IJr1CsMwiMfiOI6DbduEQkFPBTiA08Fo3LZtgoFgu0/f+VDirlCs8egucrkc0UgEwzCIRqMopbxaLpkm55LJZImEI1jWqpUx1qw7aAOiWe+Ix2JYVsDThQq4wo64ch95wUFXHHHlzTNfqz0QaHtKJn/jzWazhSf13oBSylM2dsOh+/XtS9/KPkQiYTKZTGHkYVl+ykrLKC8tw3HaFoFUSoECKX5GRNPL0FNYmvUO0zTpU1FJTW0NjuPgOA6mz8RAsB1X/LC+oZ50Ybom4I0+Ai0kWLLZbGFqy3ZsEvEE8Xic+QvmN5Gu76nk+97YV5FftiyLbC5HOp0mFAxRVlaKYRhYlpsDU1tf12S7fFnj/Iimu/1Emp6HNiCa9ZJgIEBtrXuzKy8tIxQKIYaQTCZRXp7G4I0G8c3/fUcmk8XnM1qE5uZHLrFYjGw2SzgcIuxJm5SWlLKsahkigs/n67GRTblcjnAo1Oq0nGEY9K3sUzCEjdsk4glS6TSZTAa/308mnSYQDCAIkUiURDfUd9f0fLQB0ayXWJblRmHZNiGvznq/Pn2prqlh+YrlVJSXY1kWGwwYyKLFi9ybY7PRRC6Xo6yktEmZ2zzRSAS/adKQSlFbU1MoLdvTcByHYDvOl7wBbI7P56NvZR+WVi0lmUxRWlJKXBuN9Q5tQDTrJT6fj359+5HNZpvcIBPxOOFwuFDZLxaNkkwlSaebhtYqpRCkSRnf5gQCATcj3uejqmoZViCwxm+wnZlGs/wdS9q3hs/no09FH7LZbKtRW5p1n573SKTRrCEMw2jVKe5vlA8iIpSXlmFZAdLpVEFuJJvJEIvHOjU1FY1ECIUjZIqMXHILUnU9RNdxHJLJZKHPre0fVi0sWUS08ViP0QZEo+kA0zTpW9mHkkQp2WyOVDqN4fMRj7acumoNEaG8rAy/zywq/DUfDZbNdC1kNpfNEQwG26yF4pYD9vfIqTVN70D/cjSaTiAiJOJxBvbvT2V5Bf379ivKMe4zDCoqKlBKtTkiaI7jOJSXlaO85WJRKK8WSuvb2rZNKBhqdZ1G0xm0AdFoisA0TSLhdpR228Hy+ykvKyOTzbSZb5In77uIhMPEY7FCiGxnsW0b0/QTDkfaLBrl5n/ojHJN11krBkREjhSRz0XEEZHt2mn3vYjMFJFPRESLW2l6PZFwhL4VffD7/ViWhc/wkcm2VMjN5XJEwhFXADISQSnIZDKk02k3SzyTaTfj3bZzRMJh/KaJYfhajGAcx8Hn83XZga7RwNqLwpoFjAPu7ETbPZVSS1dzfzSaNUYoFCpkftuOw4KFC7Btu8mopvHowG+a9OvTh4ZkA6bpquXW19eRTKXadGArRSE8NxQM0pBMYhgr1Ydtx6Y0UaLDbjWrxFoxIEqpL6FjRU+NZl3HZxiUJBIsW15VMCBuiDBYjbPDvZDgPKZp0pBcUMgkz2PbNrlcDkOkUG43FAxRX19fkFqJhCNEI5F28z80ms7Q0/NAFDBVRBRwp1LqrrYaisipwKkAG2200Rrqnkaz6oSCIVArZUVs28YKBPC1Ex1l+f0EAha5nF0Iw837UyrKy/H5fAXD4rcsFJDOZIjH4pSVlq6J09KsB6w2AyIi/wX6tbLqYqXUs53czWil1HwR6QO8IiJfKaXeaq2hZ1zuArceSJc6rdGsBXw+H6FgkHQm49UosSkvLetwu5JECYsWLy6E4dq27dbgaJbbYvp8mKaJ3++nJJFYLeegWT9ZbQZEKTW2G/Yx3/u7WESeBnYAWjUgGk1vJhFPsHDxItLpDLFYtOAjaY9gIEhJooSa2mpASCQSrSZGigiVFZWYPp/O+dB0Kz12CktEIoChlKr1lvcB/rKWu6XRrBYCgQClJSXYtk1JoqTT2yXicRLxeIftLK2Mq1kNrK0w3sNEZB6wMzBZRF72Ph8gIlO8Zn2Bd0TkU+ADYLJS6qW10V+NZk0Qj8UpLSnVwSWaXoOuia7RaDQa6EJNdD0hqtFoNJouoQ2IRqPRaLqENiAajUaj6RLagGg0Go2mS2gDotFoNJouoQ2IRqPRaLqENiAajUaj6RLrZB6IiCwB5naiaQWgpeJ7D/p69S709epdBJVSI4rZoMdKmawKSqnKzrQTkRlKqTYLWml6Fvp69S709epddKVon57C0mg0Gk2X0AZEo9FoNF1ifTcgbRao0vRI9PXqXejr1bso+nqtk050jUaj0ax+1vcRiEaj0Wi6iDYgGo1Go+kS65UBEZFhIvKuiKRF5Lxm6/YVkdki8o2IXLi2+qhpHX19ejYicp+ILBaRWY0+KxORV0Rkjve3dG32UbMSEdlQRF4XkS9F5HMROcf7vKhrtl4ZEKAKOBu4vvGHIuID/gHsB2wOHCsim6/57mlaQ1+fXsEkYN9mn10IvKqUGgq86r3X9AxywB+UUj8HdgLO9P5PFXXN1isDopRarJSaDmSbrdoB+EYp9Z1SKgM8BhyyxjuoaQt9fXo4Sqm3cB/QGnMI8IC3/ABw6BrtlKZNlFILlFIfecu1wJfAQIq8ZuuVAWmHgcCPjd7P8z7T9Az09emd9FVKLQD3hgX0Wcv90bSCiAwGtgbep8hrpg2IS2u1gHV8c89BXx+NZjUgIlHgSeB3SqmaYrdf5w2IiJwpIp94rwFtNJsHbNjo/QbA/NXfO00n0dend7JIRPoDeH8Xr+X+aBohIn5c4/GwUuop7+Oirtk6b0CUUv9QSm3lvdq66UwHhorIEBGxgGOA59ZcLzUdoK9P7+Q54ERv+UTg2bXYF00jRESAe4EvlVI3NlpV1DVbrzLRRaQfMAOIAw5QB2yulKoRkf2BmwEfcJ9S6qq111NNc/T16dmIyKPAHrgS7ouAy4BngCeAjYAfgCOVUs0d7Zq1gIjsArwNzMS9FwL8CdcP0ulrtl4ZEI1Go9F0H+v8FJZGo9FoVg/agGg0Go2mS2gDotFoNJouoQ2IRqPRaLqENiAajUaj6RLagGjWOiJS3ijZc6GI/NTo/bTVdMxHReQzETl3dey/jWPuISIveMsHt6cqLCJbeaHL3XXsk9pJpC1mP78TkRPaWX+giFyxqsfR9A50GK+mRyEilwN1SqnrO2q7CsfoB7yvlBrUyjpTKZVbTcfdAzhPKXVgJ9qeBGynlDqrlXVF91FE3vCOPaOIbXxKKbvxcYGPgG3aOr6XoPYRMFop1VBMHzW9Dz0C0fRoRKTO+7uHiLwpIk+IyNcico2IjBeRD0Rkpoj8zGtXKSJPish07zW6ld1OBfp4I5xdReQNEfmbiLwJnCMig0TkVW+E8qqIbOTte5KI/NOro/CdiOzu1cH4UkQmtdH/fUXkKxF5BxjX6POTROQ2b/lIEZklIp+KyFtetv1fgKO9Ph4tIpeLyF0iMhV4UEQGi8jbIvKR9xrVaN/ne9/Jp973dASwHfCwt7+QiOwlIh977e4TkYC37fcicqnX3yObnc4Y4KO88RCRs0XkC+97egxAuU+kbwAdGknNOoBSSr/0q8e8gMtxn5Tz7+u8v3sAK4D+QAD4CbjCW3cOcLO3/Aiwi7e8Ea5UQ/NjDAZmNXr/BnB7o/fPAyd6y78CnvGWJ+FKyQuu7HUNMBL3QexDYKtmxwniqggP9bZ5AnjBW3cScJu3PBMY6C2XNF/f6Hv5EAh578NA0FseCszwlvcDpgFh731Zo3Pcrlm/NvXeP4grpgfwPXB+G9fmCuC3jd7PBwKN++0tjwcmru3fkn6t/pcegWh6E9OVW8cgDXyLO5IA9wY82FseC9wmIp/g6vrERSTWiX0/3mh5Z1xDBPAvYJdG655X7l1yJrBIKTVTKeUAnzfqQ55hwP8ppeZ42zzUxrH/B0wSkVNwpVra4jmlVNJb9gN3i8hM4N+4hbbAPf/7lTd9pFqXodjM69fX3vsHgN0arX+85SaAa7yXNHr/Ge6o5njcAkV5FgOr7G/R9HzMtd0BjaYI0o2WnUbvHVb+lg1g50Y32s5S3866xo7Cxsds3p/W/j916GRUSp0mIjsCBwCfiMhWnejjubiaU1vinnPK+1w6cczW5PHbOk5jkrijlzwH4Bqeg4FLRGS4cqe3gl5bzTqOHoFo1jWmAgXHczs34/aYhqv4C+50zDtd7MtXwJC8fwY4trVGIvIzpdT7SqlLgaW40vW1QHsjpwSwwBv9TGDlyGUq8CsRCXv7LvM+b7y/r4DBIrKJ934C8GYnzudLYBNvvwawoVLqdeB8oASIeu02BWa1ugfNOoU2IJp1jbOB7TzH7hfAaV3cxy9F5DPcm+s5XemIUioFnApM9pzSc9toep3nzJ4FvAV8CrwObJ53oreyze3AiSLyHu4Nu9475ku4U3czvGm887z2k4A7vM8E+CXwb28KzAHu6MQpvcjKqS4f8JC3/cfATUqpFd66PYHJndifppejw3g1Gk2nEZGncZ3sc9pY3xd4RCm115rtmWZtoA2IRqPpNCKyGW7d7LfaWL89kFVKfbJme6ZZG2gDotFoNJouoX0gGo1Go+kS2oBoNBqNpktoA6LRaDSaLqENiEaj0Wi6hDYgGo1Go+kS/w8Q3dFjCmxRKwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# difference between modelled day and distraction day\n",
+ "\n",
+ "f, ax = plt.subplots()\n",
+ "\n",
+ "shadedError(ax, average_snips(modDict, 'snips_distractors', 'filt_avg_z'))\n",
+ "shadedError(ax, average_snips(disDict, 'snips_distractors', 'filt_avg_z'), linecolor='blue')\n",
+ "\n",
+ "ax.set_xlabel('Time from distractor (s)')\n",
+ "ax.set_xticks([0, 100, 200, 300])\n",
+ "ax.set_xticklabels(['-10', '0', '10', '20'])\n",
+ "\n",
+ "ax.set_ylabel('Z-Score')\n",
+ "\n",
+ "ax.spines['right'].set_visible(False)\n",
+ "ax.spines['top'].set_visible(False)\n",
+ "\n",
+ "ax.text(0.9, 0.9, 'Modelled day', color='black', ha='right', transform=ax.transAxes)\n",
+ "ax.text(0.9, 0.8, 'Distraction day', color='blue', ha='right', transform=ax.transAxes)\n",
+ "\n",
+ "f.savefig(figfolder+\"fig3_dis-vs-model.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# difference between distracted and not-distracted trials\n",
+ "\n",
+ "f, ax = plt.subplots()\n",
+ "\n",
+ "shadedError(ax, average_snips(disDict, 'snips_distracted', 'filt_avg_z'), linecolor='xkcd:dark blue')\n",
+ "shadedError(ax, average_snips(disDict, 'snips_not-distracted', 'filt_avg_z'), linecolor='xkcd:light blue',)\n",
+ "\n",
+ "ax.set_xlabel('Time from distractor (s)')\n",
+ "ax.set_xticks([0, 100, 200, 300])\n",
+ "ax.set_xticklabels(['-10', '0', '10', '20'])\n",
+ "\n",
+ "ax.set_ylabel('Z-Score')\n",
+ "\n",
+ "ax.spines['right'].set_visible(False)\n",
+ "ax.spines['top'].set_visible(False)\n",
+ "\n",
+ "ax.text(0.9, 0.9, 'Distracted trials', color='xkcd:dark blue', ha='right', transform=ax.transAxes)\n",
+ "ax.text(0.9, 0.8, 'Non-distracted trials', color='xkcd:light blue', ha='right', transform=ax.transAxes)\n",
+ "\n",
+ "f.savefig(figfolder+\"fig4_dis-vs-non-dis.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# aligned to distrator on all three days\n",
+ "\n",
+ "f, ax = plt.subplots(ncols=3, figsize=(14,4))\n",
+ "\n",
+ "# sets space between plots\n",
+ "f.subplots_adjust(wspace=0.1)\n",
+ "\n",
+ "shadedError(ax[0], average_snips(modDict, 'snips_distractors', 'filt_avg_z'))\n",
+ "shadedError(ax[1], average_snips(disDict, 'snips_distractors', 'filt_avg_z'), linecolor='blue')\n",
+ "shadedError(ax[2], average_snips(habDict, 'snips_distractors', 'filt_avg_z'), linecolor='xkcd:light blue')\n",
+ "\n",
+ "# adds y label to left hand plot\n",
+ "ax[0].set_ylabel('Z-Score')\n",
+ "\n",
+ "# applies settings to all axes\n",
+ "for axis in ax:\n",
+ " axis.set_xlabel('Time from distractor (s)')\n",
+ " axis.set_xticks([0, 100, 200, 300])\n",
+ " axis.set_xticklabels(['-10', '0', '10', '20'])\n",
+ " \n",
+ " axis.spines['right'].set_visible(False)\n",
+ " axis.spines['top'].set_visible(False)\n",
+ " \n",
+ " axis.set_ylim(ax[1].get_ylim())\n",
+ "\n",
+ "# turns of axis and ticks from middle and right plots\n",
+ "for axis in [ax[1], ax[2]]:\n",
+ " axis.spines['left'].set_visible(False)\n",
+ " axis.set_yticks([])\n",
+ "\n",
+ "# adds titles to plots \n",
+ "ax[0].set_title('Modelled')\n",
+ "ax[1].set_title('Distraction')\n",
+ "ax[2].set_title('Habituation')\n",
+ "\n",
+ "\n",
+ "f.savefig(figfolder+\"fig3_distractor on all days.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_auc_or_peak(d, key, signal, bins=[100, 130], calctype='auc'):\n",
+ " \n",
+ " all_trials = []\n",
+ " mean = []\n",
+ " \n",
+ " rats = d.keys()\n",
+ " for rat in rats:\n",
+ " snip = d[rat][key][signal]\n",
+ " rat_trials = []\n",
+ " for trial in snip:\n",
+ " if calctype == 'auc':\n",
+ " rat_trials.append(np.trapz(trial[bins[0]:bins[1]])*0.1) # multiplies by 0.1 because of 10 Hz sample rate, output is Z per second\n",
+ " elif calctype == 'peak':\n",
+ " rat_trials.append(np.max(trial[bins[0]:bins[1]]))\n",
+ " else:\n",
+ " print('Not a valid calculation type. Try peak or auc')\n",
+ " return [], []\n",
+ " \n",
+ " all_trials.append(rat_trials)\n",
+ " mean.append(np.mean(rat_trials))\n",
+ "\n",
+ " return all_trials, mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# To make peak and AUC comparison for modelled and distractor day\n",
+ "\n",
+ "colors = ['grey','blue']\n",
+ "\n",
+ "bins=[100, 130]\n",
+ "# For use with shorter snip length\n",
+ "bins=[50, 80]\n",
+ "\n",
+ "f, ax = plt.subplots(ncols=2)\n",
+ "f.subplots_adjust(wspace=0.4)\n",
+ "\n",
+ "avg_auc_mod = get_auc_or_peak(modDict, 'snips_distractors', 'filt_z', bins=bins)[1]\n",
+ "avg_auc_dis = get_auc_or_peak(disDict, 'snips_distractors', 'filt_z', bins=bins)[1]\n",
+ "\n",
+ "barscatter([avg_auc_mod, avg_auc_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " ax=ax[0])\n",
+ "\n",
+ "avg_peak_mod = get_auc_or_peak(modDict, 'snips_distractors', 'filt_z', calctype='peak', bins=bins)[1]\n",
+ "avg_peak_dis = get_auc_or_peak(disDict, 'snips_distractors', 'filt_z', calctype='peak', bins=bins)[1]\n",
+ "\n",
+ "barscatter([avg_peak_mod, avg_peak_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " ax=ax[1])\n",
+ "\n",
+ "ax[0].set_title('AUC')\n",
+ "ax[0].set_ylabel('AUC of Z-score')\n",
+ "\n",
+ "ax[1].set_title('Peak')\n",
+ "ax[1].set_ylabel('Average peak of Z-score')\n",
+ "#ax[1].set_ylim([-0.3, 3.9])\n",
+ "\n",
+ "for axis in ax:\n",
+ " \n",
+ " trans = transforms.blended_transform_factory(axis.transData, axis.transAxes)\n",
+ " \n",
+ " axis.set_xticks([1,2])\n",
+ " axis.set_xticklabels(['Mod', 'Dis'], va='top', transform=trans)\n",
+ " \n",
+ "f.savefig(figfolder+\"fig3_distractor_auc and peak.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Ttest_relResult(statistic=1.6171641328069282, pvalue=0.12983888233644703)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# To make peak and AUC comparison for distracted and non-distracted trials\n",
+ "\n",
+ "colors = ['xkcd:dark blue','xkcd:light blue']\n",
+ "\n",
+ "bins=[100, 130]\n",
+ "# For use with shorter snip length\n",
+ "bins=[50, 80]\n",
+ "\n",
+ "f, ax = plt.subplots(ncols=2)\n",
+ "f.subplots_adjust(wspace=0.4)\n",
+ "\n",
+ "avg_auc_d = get_auc_or_peak(disDict, 'snips_distracted', 'filt_z', bins=bins)[1]\n",
+ "avg_auc_nd = get_auc_or_peak(disDict, 'snips_not-distracted', 'filt_z', bins=bins)[1]\n",
+ "\n",
+ "barscatter([avg_auc_d, avg_auc_nd], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " ax=ax[0])\n",
+ "\n",
+ "avg_peak_d = get_auc_or_peak(disDict, 'snips_distracted', 'filt_z', calctype='peak', bins=bins)[1]\n",
+ "avg_peak_nd = get_auc_or_peak(disDict, 'snips_not-distracted', 'filt_z', calctype='peak', bins=bins)[1]\n",
+ "\n",
+ "barscatter([avg_peak_mod, avg_peak_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " ax=ax[1])\n",
+ "\n",
+ "ax[0].set_title('AUC')\n",
+ "ax[0].set_ylabel('AUC of Z-score')\n",
+ "\n",
+ "ax[1].set_title('Peak')\n",
+ "ax[1].set_ylabel('Average peak of Z-score')\n",
+ "#ax[1].set_ylim([-0.3, 3.9])\n",
+ "\n",
+ "for axis in ax:\n",
+ " \n",
+ " trans = transforms.blended_transform_factory(axis.transData, axis.transAxes)\n",
+ " \n",
+ " axis.set_xticks([1,2])\n",
+ " axis.set_xticklabels(['Distracted', 'Not-distracted'], va='top', transform=trans)\n",
+ " \n",
+ "f.savefig(figfolder+\"fig4_distracted vs nd_auc and peak.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rat = 'thph1.1'\n",
+ "d = disDict[rat]\n",
+ "\n",
+ "snips \n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/old-and-unused/dis-roc-licks-all-days.ipynb b/notebooks/old-and-unused/dis-roc-licks-all-days.ipynb
new file mode 100644
index 0000000..78d0765
--- /dev/null
+++ b/notebooks/old-and-unused/dis-roc-licks-all-days.ipynb
@@ -0,0 +1,455 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "import numpy as np\n",
+ "\n",
+ "%run ../JM_custom_figs.py\n",
+ "%run ../fx4roc.py\n",
+ "%run ../figs4roc.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Makes lick snips for all distractors and then flattens (e.g. pools snips from all rats)\n",
+ "\n",
+ "def make_lick_snips(d, pre=5, post=15):\n",
+ " snips = []\n",
+ " for dis in d['distractors']:\n",
+ " snips.append([lick-dis for lick in d['licks'] if (lick-dis>-pre) and (lick-disx1 and d 0:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ " \n",
+ "mod_dis_snips = [snip for snip in mod_lick_snips_flat if not check_val_between(snip)]\n",
+ "mod_notdis_snips = [snip for snip in mod_lick_snips_flat if check_val_between(snip)]\n",
+ "\n",
+ "dis_dis_snips = [snip for snip in dis_lick_snips_flat if not check_val_between(snip)]\n",
+ "dis_notdis_snips = [snip for snip in dis_lick_snips_flat if check_val_between(snip)]\n",
+ "\n",
+ "print(f\"Number of DISTRACTED trials on MODELLED day is {len(mod_dis_snips)}\")\n",
+ "print(f\"Number of NOT DISTRACTED trials on MODELLED day is {len(mod_notdis_snips)}\")\n",
+ "print(f\"Number of DISTRACTED trials on DISTRACTION day is {len(dis_dis_snips)}\")\n",
+ "print(f\"Number of NOT DISTRACTED trials on DISTRACTION day is {len(dis_notdis_snips)}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "741"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Cell to test that code using check_val_between is selecting correct number of distracted and non-distracted snips\n",
+ "snips=[]\n",
+ "\n",
+ "for rat in rats:\n",
+ " d = modDict[rat]\n",
+ " snips.append(len(d['snips_not-distracted']['filt_z']))\n",
+ "sum(snips)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# turns snips into binned data\n",
+ "\n",
+ "# bins = np.arange(-10, 20, 1)\n",
+ "bins = np.arange(-5, 15, 1)\n",
+ "mod_dis_hist = [np.histogram(snip, bins=bins)[0] for snip in mod_dis_snips]\n",
+ "mod_notdis_hist = [np.histogram(snip, bins=bins)[0] for snip in mod_notdis_snips]\n",
+ "\n",
+ "dis_dis_hist = [np.histogram(snip, bins=bins)[0] for snip in dis_dis_snips]\n",
+ "dis_notdis_hist = [np.histogram(snip, bins=bins)[0] for snip in dis_notdis_snips]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Analysing column 0\n",
+ "Analysing column 1\n",
+ "Analysing column 2\n",
+ "Analysing column 3\n",
+ "Analysing column 4\n",
+ "Analysing column 5\n",
+ "Analysing column 6\n",
+ "Analysing column 7\n",
+ "Analysing column 8\n",
+ "Analysing column 9\n",
+ "Analysing column 10\n",
+ "Analysing column 11\n",
+ "Analysing column 12\n",
+ "Analysing column 13\n",
+ "Analysing column 14\n",
+ "Analysing column 15\n",
+ "Analysing column 16\n",
+ "Analysing column 17\n",
+ "Analysing column 18\n",
+ "--- Total ROC analysis took 119.07851552963257 seconds ---\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Cell to run ROC analysis - takes a while so commented out unless needed\n",
+ "import time\n",
+ "start_time = time.time()\n",
+ "\n",
+ "a, p = nanroc(mod_dis_hist, dis_dis_hist, n4shuf=2000)\n",
+ "\n",
+ "print(f\"--- Total ROC analysis took {(time.time() - start_time)} seconds ---\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, 'Time from distractor (s)')"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(nrows=3)\n",
+ "\n",
+ "ax[0].plot(np.mean(mod_dis_hist, axis=0), color='red')\n",
+ "ax[0].plot(np.mean(dis_dis_hist, axis=0), color='grey')\n",
+ "\n",
+ "ax[0].set_ylabel('Licks (Hz)')\n",
+ "ax[0].set_xticks([])\n",
+ "\n",
+ "ax[0].text(0.99, 0.95, 'Modelled - distracted', color='red', ha='right', va='top', transform=ax[0].transAxes)\n",
+ "ax[0].text(0.99, 0.80, 'Distraction - distracted', color='grey', ha='right', va='top', transform=ax[0].transAxes)\n",
+ "\n",
+ "ax[1].plot(a)\n",
+ "ax[1].set_ylabel('ROC value')\n",
+ "ax[1].set_xticks([])\n",
+ "\n",
+ "threshold = 0.05/len(p)\n",
+ "sigpoints = np.array([pval < threshold for pval in p], dtype=bool)\n",
+ "\n",
+ "xdata = [x for x, L in zip(range(len(sigpoints)), sigpoints) if L]\n",
+ "ydata = logical_subset(a, sigpoints)\n",
+ "ax[1].scatter(xdata, ydata, color='red')\n",
+ "\n",
+ "ax[1]. set_ylim([-0.1, 1.1])\n",
+ "\n",
+ "ax[2].plot(p)\n",
+ "ax[2].set_ylim([-0.1, 1.1])\n",
+ "ax[2].set_ylabel('p')\n",
+ "\n",
+ "ax[2].set_xticks([0, 10, 20])\n",
+ "ax[2].set_xticklabels(['-10', '0', '10'])\n",
+ "ax[2].set_xlabel('Time from distractor (s)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "savefile=True\n",
+ "if savefile:\n",
+ " pickle_out = open(outputfolder+\"roc_results_licks_mod-disVdis-dis\", 'wb')\n",
+ " dill.dump([a, p], pickle_out)\n",
+ " pickle_out.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Analysing column 0\n",
+ "Analysing column 1\n",
+ "Analysing column 2\n",
+ "Analysing column 3\n",
+ "Analysing column 4\n",
+ "Analysing column 5\n",
+ "Analysing column 6\n",
+ "Analysing column 7\n",
+ "Analysing column 8\n",
+ "Analysing column 9\n",
+ "Analysing column 10\n",
+ "Analysing column 11\n",
+ "Analysing column 12\n",
+ "Analysing column 13\n",
+ "Analysing column 14\n",
+ "Analysing column 15\n",
+ "Analysing column 16\n",
+ "Analysing column 17\n",
+ "Analysing column 18\n",
+ "--- Total ROC analysis took 301.8006203174591 seconds ---\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Cell to run ROC analysis - takes a while so commented out unless needed\n",
+ "import time\n",
+ "start_time = time.time()\n",
+ "\n",
+ "a, p = nanroc(mod_notdis_hist, dis_notdis_hist, n4shuf=2000)\n",
+ "\n",
+ "print(f\"--- Total ROC analysis took {(time.time() - start_time)} seconds ---\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "savefile=True\n",
+ "if savefile:\n",
+ " pickle_out = open(outputfolder+\"roc_results_licks_mod-notdisVdis-notdis\", 'wb')\n",
+ " dill.dump([a, p], pickle_out)\n",
+ " pickle_out.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%run ../figs4roc.py\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_results_licks_mod-notdisVdis-notdis\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[a, p] = dill.load(pickle_in)\n",
+ "\n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = plot_ROC_and_line(f, a, p, mod_notdis_hist, dis_notdis_hist,\n",
+ " cdict = [(0, 'darkturquoise'), (0.25, 'darkturquoise'), (0.5, 'white'), (0.75, 'dodgerblue'), (1, 'dodgerblue')],\n",
+ " colors = ['darkturquoise', 'dodgerblue'],\n",
+ " labels=['Modelled', 'Distraction'],\n",
+ " labeloffset=0.5,\n",
+ " ylabel='Licks (Hz)')\n",
+ "\n",
+ "ax[1].set_yticks([0, 5, 10])\n",
+ "ax[0].set_title('Not distracted trials')\n",
+ "\n",
+ "f.savefig(figfolder+\"fig4_mod-v-dis_notdistracted trials.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_results_licks_mod-disVdis-dis\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[a, p] = dill.load(pickle_in)\n",
+ "\n",
+ "\n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = plot_ROC_and_line(f, a, p, mod_dis_hist, dis_dis_hist,\n",
+ " cdict = [(0, 'darkturquoise'), (0.25, 'darkturquoise'), (0.5, 'white'), (0.75, 'dodgerblue'), (1, 'dodgerblue')],\n",
+ " colors = ['darkturquoise', 'dodgerblue'],\n",
+ " labels=['Modelled', 'Distraction'],\n",
+ " labeloffset=0,\n",
+ " ylabel='Licks (Hz)')\n",
+ "\n",
+ "ax[1].set_yticks([0, 1, 2, 3])\n",
+ "ax[0].set_title('Distracted trials')\n",
+ "\n",
+ "f.savefig(figfolder+\"fig4_mod-v-dis_distracted trials.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "19"
+ ]
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(mod_dis_hist[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/old-and-unused/dis-roc-licks.ipynb b/notebooks/old-and-unused/dis-roc-licks.ipynb
new file mode 100644
index 0000000..d1b2a36
--- /dev/null
+++ b/notebooks/old-and-unused/dis-roc-licks.ipynb
@@ -0,0 +1,548 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "import matplotlib.gridspec as gridspec\n",
+ "from matplotlib.colors import LinearSegmentedColormap\n",
+ "import numpy as np\n",
+ "\n",
+ "%run ../JM_custom_figs.py\n",
+ "%run ../fx4roc.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_results_licks\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[a, p] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['rat', 'fs', 'tick', 'filt_sd', 'licks', 'licks_off', 'distractors', 'distracted', 'notdistracted', 'd_bool_array', 'pdp', 'pre_dp', 'lickdata', 'snips_distractors', 'snips_distracted', 'snips_not-distracted'])"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rats = disDict.keys()\n",
+ "rat= 'thph1.2'\n",
+ "d = disDict[rat]\n",
+ "d.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Makes lick snips for all distractors and then flattens (e.g. pools snips from all rats)\n",
+ "\n",
+ "def make_lick_snips(d, pre=5, post=15):\n",
+ " snips = []\n",
+ " for dis in d['distractors']:\n",
+ " snips.append([lick-dis for lick in d['licks'] if (lick-dis>-pre) and (lick-disx1 and d 0:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ " \n",
+ "dis_snips = [snip for snip in all_lick_snips_flat if not check_val_between(snip)]\n",
+ "notdis_snips = [snip for snip in all_lick_snips_flat if check_val_between(snip)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "344"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Test to ensure that code using check_val_between is selecting correct number of distracted and non-distracted snips\n",
+ "number_of_distracted_snips=[]\n",
+ "\n",
+ "for rat in rats:\n",
+ " d = disDict[rat]\n",
+ " number_of_distracted_snips.append(len(d['snips_distracted']['filt_z']))\n",
+ "sum(number_of_distracted_snips)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# turns snips into binned data\n",
+ "\n",
+ "# bins = np.arange(-10, 20, 1)\n",
+ "bins = np.arange(-5, 15, 1)\n",
+ "dis_hist = [np.histogram(snip, bins=bins)[0] for snip in dis_snips]\n",
+ "notdis_hist = [np.histogram(snip, bins=bins)[0] for snip in notdis_snips]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Analysing column 0\n",
+ "Analysing column 1\n",
+ "Analysing column 2\n",
+ "Analysing column 3\n",
+ "Analysing column 4\n",
+ "Analysing column 5\n",
+ "Analysing column 6\n",
+ "Analysing column 7\n",
+ "Analysing column 8\n",
+ "Analysing column 9\n",
+ "Analysing column 10\n",
+ "Analysing column 11\n",
+ "Analysing column 12\n",
+ "Analysing column 13\n",
+ "Analysing column 14\n",
+ "Analysing column 15\n",
+ "Analysing column 16\n",
+ "Analysing column 17\n",
+ "Analysing column 18\n",
+ "--- Total ROC analysis took 207.96642017364502 seconds ---\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Cell to run ROC analysis - takes a while so commented out unless needed\n",
+ "import time\n",
+ "start_time = time.time()\n",
+ "\n",
+ "a, p = nanroc(notdis_hist, dis_hist, n4shuf=2000)\n",
+ "\n",
+ "print(f\"--- Total ROC analysis took {(time.time() - start_time)} seconds ---\")\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "savefile=True\n",
+ "if savefile:\n",
+ " pickle_out = open(outputfolder+\"roc_results_licks\", 'wb')\n",
+ " dill.dump([a, p], pickle_out)\n",
+ " pickle_out.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(nrows=3)\n",
+ "\n",
+ "ax[0].plot(np.mean(dis_hist, axis=0), color='red')\n",
+ "ax[0].plot(np.mean(notdis_hist, axis=0), color='grey')\n",
+ "\n",
+ "ax[0].set_ylabel('Licks (Hz)')\n",
+ "ax[0].set_xticks([])\n",
+ "\n",
+ "ax[0].text(0.99, 0.95, 'Distracted', color='red', ha='right', va='top', transform=ax[0].transAxes)\n",
+ "ax[0].text(0.99, 0.80, 'Not distracted', color='grey', ha='right', va='top', transform=ax[0].transAxes)\n",
+ "\n",
+ "ax[1].plot(a)\n",
+ "ax[1].set_ylabel('ROC value')\n",
+ "ax[1].set_xticks([])\n",
+ "\n",
+ "threshold = 0.05/len(p)\n",
+ "sigpoints = np.array([pval < threshold for pval in p], dtype=bool)\n",
+ "\n",
+ "xdata = [x for x, L in zip(range(len(sigpoints)), sigpoints) if L]\n",
+ "ydata = logical_subset(a, sigpoints)\n",
+ "ax[1].scatter(xdata, ydata, color='red')\n",
+ "\n",
+ "ax[1]. set_ylim([-0.1, 1.1])\n",
+ "\n",
+ "ax[2].plot(p)\n",
+ "ax[2].set_ylim([-0.1, 1.1])\n",
+ "ax[2].set_ylabel('p')\n",
+ "\n",
+ "ax[2].set_xticks([0, 10, 20])\n",
+ "ax[2].set_xticklabels(['-10', '0', '10'])\n",
+ "ax[2].set_xlabel('Time from distractor (s)')\n",
+ "\n",
+ "# f.savefig(outputfolder + \"licks_roc_analysis.png\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.gridspec as gridspec\n",
+ "\n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "\n",
+ "gs=gridspec.GridSpec(2,2, figure=f, height_ratios=[0.25, 1], width_ratios=[1, 0.05], wspace=0.05,\n",
+ " bottom=0.2, right=0.75, left=0.15)\n",
+ "\n",
+ "ax[0] = f.add_subplot(gs[0, 0])\n",
+ "\n",
+ "# Creates colormap for ROC\n",
+ "cdict=['grey', 'white', 'red']\n",
+ "heatmap_color_scheme = LinearSegmentedColormap.from_list('rocmap', cdict)\n",
+ "\n",
+ "roc_for_plot = a\n",
+ "xvals=np.arange(-0.5,19.)\n",
+ "yvals=[1, 2]\n",
+ "xx, yy = np.meshgrid(xvals, yvals)\n",
+ " \n",
+ "mesh = ax[0].pcolormesh(xx, yy, [roc_for_plot, roc_for_plot], cmap=heatmap_color_scheme, shading = 'flat')\n",
+ "mesh.set_clim([0, 1])\n",
+ "\n",
+ "\n",
+ "xdata = [x for x, L in zip(range(len(sigpoints)), sigpoints) if L]\n",
+ "ydata = logical_subset(a, sigpoints)\n",
+ "ax[0].scatter(xdata, [2.5]*len(xdata), color='k', marker='.', clip_on=False)\n",
+ "ax[0].text(-1, 2.5, 'p<0.05', va='center', ha='right')\n",
+ "\n",
+ "ax[0].spines['top'].set_visible(False)\n",
+ "ax[0].spines['right'].set_visible(False)\n",
+ "ax[0].spines['bottom'].set_visible(False)\n",
+ "ax[0].spines['left'].set_visible(False)\n",
+ "ax[0].set_xticks([])\n",
+ "ax[0].set_yticks([])\n",
+ "\n",
+ "cbar_ax = f.add_subplot(gs[0,1]) \n",
+ "cbar = f.colorbar(mesh, cax=cbar_ax, ticks=[0, 1], label='ROC')\n",
+ "\n",
+ "ax[1] = f.add_subplot(gs[1, 0])\n",
+ "\n",
+ "shadedError(ax[1], notdis_hist, linecolor='grey')\n",
+ "shadedError(ax[1], dis_hist, linecolor='red')\n",
+ "\n",
+ "ax[1].set_ylabel('Lick rate (Hz)')\n",
+ "ax[1].spines['top'].set_visible(False)\n",
+ "ax[1].spines['right'].set_visible(False)\n",
+ "\n",
+ "# ax[1].set_xticks([0, 10, 20])\n",
+ "# ax[1].set_xticklabels(['-10', '0', '10'])\n",
+ "# ax[1].set_xlabel('Time from distractor (s)')\n",
+ "\n",
+ "ax[1].set_xticks([0, 5, 10, 15])\n",
+ "ax[1].set_xticklabels(['-5', '0', '5', '10'])\n",
+ "ax[1].set_xlabel('Time from distractor (s)')\n",
+ "\n",
+ "ax[1].text(19, np.mean(dis_hist, axis=0)[-1], 'Distracted', color='red', ha='left', va='center')\n",
+ "ax[1].text(19, np.mean(notdis_hist, axis=0)[-1], 'Not distracted', ha='left', va='center')\n",
+ "\n",
+ "ax[0].set_xlim(ax[1].get_xlim())\n",
+ "\n",
+ "f.savefig(figfolder+\"fig3_roc-lickrate.pdf\")\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "pre_lickrate_dis, pre_lickrate_notdis = [], []\n",
+ "post_lickrate_dis, post_lickrate_notdis = [], []\n",
+ "\n",
+ "for snips in all_lick_snips:\n",
+ " \n",
+ " pre_dis, pre_notdis = [], []\n",
+ " post_dis, post_notdis = [], []\n",
+ " \n",
+ " for snip in snips:\n",
+ " \n",
+ " nlicks_before_distractor = len([lick for lick in snip if lick<0])\n",
+ " nlicks_after_distractor = len([lick for lick in snip if lick>1])\n",
+ " \n",
+ " if check_val_between(snip):\n",
+ " pre_notdis.append(nlicks_before_distractor)\n",
+ " post_notdis.append(nlicks_after_distractor)\n",
+ " else:\n",
+ " pre_dis.append(nlicks_before_distractor)\n",
+ " post_dis.append(nlicks_after_distractor)\n",
+ "\n",
+ " pre_lickrate_dis.append(np.mean(pre_dis)/10) # dividing by 10 puts this into Hz as a 10s predistractor period is used\n",
+ " pre_lickrate_notdis.append(np.mean(pre_notdis)/10)\n",
+ " \n",
+ " post_lickrate_dis.append(np.mean(post_dis)/10)\n",
+ " post_lickrate_notdis.append(np.mean(post_notdis)/10)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pre distractor 2.626631250085044 0.020920903320542816\n",
+ "Post distractor 7.614424692690707 3.82409586364871e-06\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ " \n",
+ "f, ax = plt.subplots(figsize=(3.4,2.5), ncols=2)\n",
+ "f.subplots_adjust(left=0.2, wspace=0.8)\n",
+ "\n",
+ "barscatter([pre_lickrate_notdis, pre_lickrate_dis], paired=True,\n",
+ " barfacecolor=['xkcd:light grey', 'red'], barfacecoloroption='individual',\n",
+ " ax=ax[0])\n",
+ "\n",
+ "barscatter([post_lickrate_notdis, post_lickrate_dis], paired=True,\n",
+ " barfacecolor=['xkcd:light grey', 'red'], barfacecoloroption='individual',\n",
+ " ax=ax[1])\n",
+ "\n",
+ "for axis in ax:\n",
+ " axis.set_xticks([1,2])\n",
+ " axis.set_xticklabels(['Not', 'Dis'])\n",
+ "# axis.set_xticks([])\n",
+ " \n",
+ "ax[0].set_ylabel('Lick rate (Hz)')\n",
+ "ax[0].set_yticks([0, 0.5, 1, 1.5])\n",
+ "ax[0].set_ylim([-0.15, 1.5])\n",
+ "ax[0].set_title('Pre')\n",
+ "\n",
+ "# ax[1].set_ylabel('Mean post-distractor lick rate (Hz)')\n",
+ "ax[1].set_yticks([0, 5, 10])\n",
+ "ax[1].set_ylim([-1, 10])\n",
+ "ax[1].set_title('Post')\n",
+ "\n",
+ "f.savefig(figfolder+\"fig3_predistractor-lickrate.pdf\")\n",
+ "\n",
+ "from scipy import stats\n",
+ "\n",
+ "t, p = stats.ttest_rel(pre_lickrate_notdis, pre_lickrate_dis)\n",
+ "print('Pre distractor', t, p)\n",
+ "\n",
+ "t, p = stats.ttest_rel(post_lickrate_notdis, post_lickrate_dis)\n",
+ "print('Post distractor', t, p)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Note on time taken to run ROC analysis\n",
+ "\n",
+ "On 19 Feb 2020 ran tests to see if ROC analysis was quicker running outside of Notebook.\n",
+ "\n",
+ "Running inside Notebook took 413s .\n",
+ "\n",
+ "(1) Used the magic command to pass arguments to separate script\n",
+ "\n",
+ " %run ../run_roc_analysis.py \"hist_licks\" dis_hist notdis_hist 5 \"roc_results_licks\"\n",
+ "\n",
+ "Results show 409s (i.e. no different to running in Notebook).\n",
+ "\n",
+ "(2) Tried running completely separately within Spyder but outside Notebook (411 s)\n",
+ "\n",
+ "(3) Tried running from a command prompt\n",
+ "\n",
+ " python run_roc_analysis.py\n",
+ " \n",
+ "Took 389s so marginally faster but not a huge difference. Conclusion is to continue running within notebook. Will save [a, p] for easy analysis.\n",
+ "\n",
+ "\n",
+ "NB for (2) also necessary to run the following lines:\n",
+ "\n",
+ " pickle_out = open(\"hist_licks\", 'wb')\n",
+ " dill.dump([dis_hist, notdis_hist], pickle_out)\n",
+ " pickle_out.close()\n",
+ " \n",
+ "NB2 Later speeded up the code by ~100x by vectorizing using Numpy functions rather than inbuilt Python methods\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "bins = np.arange(-10, 20, 0.5)\n",
+ "data = np.histogram(flatten_list(flatten_list(all_lick_snips)), bins=bins)\n",
+ "\n",
+ "plt.bar(data[1][1:], data[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/old-and-unused/dis-roc-photo-all-days.ipynb b/notebooks/old-and-unused/dis-roc-photo-all-days.ipynb
new file mode 100644
index 0000000..619204e
--- /dev/null
+++ b/notebooks/old-and-unused/dis-roc-photo-all-days.ipynb
@@ -0,0 +1,348 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "import numpy as np\n",
+ "\n",
+ "%run ../JM_custom_figs.py\n",
+ "%run ../fx4roc.py\n",
+ "%run ../figs4roc.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def resample_snips(snips, factor=0.1):\n",
+ " if len(snips)>0:\n",
+ " n_bins = len(snips[0])\n",
+ " out_bins = int(n_bins * factor)\n",
+ "\n",
+ " snips_out = []\n",
+ " for snip in snips:\n",
+ " snips_out.append(np.mean(np.reshape(snip, (out_bins, -1)), axis=1))\n",
+ " \n",
+ " return snips_out\n",
+ " else:\n",
+ " return []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mod_dis_photo_snips, mod_notdis_photo_snips, = [], []\n",
+ "dis_dis_photo_snips, dis_notdis_photo_snips, = [], []\n",
+ "\n",
+ "rats=disDict.keys()\n",
+ "\n",
+ "for rat in rats:\n",
+ " d = disDict[rat]\n",
+ " \n",
+ " snips_dis = resample_snips(d['snips_distracted']['filt_z'])\n",
+ " dis_dis_photo_snips.append(snips_dis)\n",
+ " \n",
+ " snips_notdis = resample_snips(d['snips_not-distracted']['filt_z'])\n",
+ " dis_notdis_photo_snips.append(snips_notdis)\n",
+ " \n",
+ " d = modDict[rat]\n",
+ " \n",
+ " snips_dis = resample_snips(d['snips_distracted']['filt_z'])\n",
+ " mod_dis_photo_snips.append(snips_dis)\n",
+ " \n",
+ " snips_notdis = resample_snips(d['snips_not-distracted']['filt_z'])\n",
+ " mod_notdis_photo_snips.append(snips_notdis)\n",
+ "\n",
+ "mod_dis_photo_snips_flat = flatten_list(mod_dis_photo_snips)\n",
+ "mod_notdis_photo_snips_flat = flatten_list(mod_notdis_photo_snips)\n",
+ "\n",
+ "dis_dis_photo_snips_flat = flatten_list(dis_dis_photo_snips)\n",
+ "dis_notdis_photo_snips_flat = flatten_list(dis_notdis_photo_snips)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%run ../fx4roc.py\n",
+ "import time\n",
+ "start_time = time.time()\n",
+ "\n",
+ "a, p = nanroc(mod_dis_photo_snips_flat, dis_dis_photo_snips_flat, n4shuf=2000)\n",
+ "\n",
+ "print(f\"--- Total ROC analysis took {(time.time() - start_time)} seconds ---\")\n",
+ "\n",
+ "savefile=True\n",
+ "if savefile:\n",
+ " pickle_out = open(outputfolder+\"roc_results_photo_mod-disVdis-dis\", 'wb')\n",
+ " dill.dump([a, p], pickle_out)\n",
+ " pickle_out.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Analysing column 0\n",
+ "Analysing column 1\n",
+ "Analysing column 2\n",
+ "Analysing column 3\n",
+ "Analysing column 4\n",
+ "Analysing column 5\n",
+ "Analysing column 6\n",
+ "Analysing column 7\n",
+ "Analysing column 8\n",
+ "Analysing column 9\n",
+ "Analysing column 10\n",
+ "Analysing column 11\n",
+ "Analysing column 12\n",
+ "Analysing column 13\n",
+ "Analysing column 14\n",
+ "Analysing column 15\n",
+ "Analysing column 16\n",
+ "Analysing column 17\n",
+ "Analysing column 18\n",
+ "Analysing column 19\n",
+ "--- Total ROC analysis took 327.40357780456543 seconds ---\n"
+ ]
+ }
+ ],
+ "source": [
+ "%run ../fx4roc.py\n",
+ "import time\n",
+ "start_time = time.time()\n",
+ "\n",
+ "a, p = nanroc(mod_notdis_photo_snips_flat, dis_notdis_photo_snips_flat, n4shuf=2000)\n",
+ "\n",
+ "print(f\"--- Total ROC analysis took {(time.time() - start_time)} seconds ---\")\n",
+ "\n",
+ "savefile=True\n",
+ "if savefile:\n",
+ " pickle_out = open(outputfolder+\"roc_results_photo_mod-notdisVdis-notdis\", 'wb')\n",
+ " dill.dump([a, p], pickle_out)\n",
+ " pickle_out.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0.2455,\n",
+ " 0.48,\n",
+ " 0.125,\n",
+ " 0.588,\n",
+ " 0.627,\n",
+ " 0.0655,\n",
+ " 0.0,\n",
+ " 0.028,\n",
+ " 0.5925,\n",
+ " 0.3355,\n",
+ " 0.0625,\n",
+ " 0.0505,\n",
+ " 0.024,\n",
+ " 0.022,\n",
+ " 0.0795,\n",
+ " 0.014,\n",
+ " 0.010499999999999999,\n",
+ " 0.0055,\n",
+ " 0.0,\n",
+ " 0.005]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_results_photo_mod-disVdis-dis\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ " \n",
+ "[a, p] = dill.load(pickle_in)\n",
+ "\n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = plot_ROC_and_line(f, a, p, mod_dis_photo_snips_flat, dis_dis_photo_snips_flat,\n",
+ " cdict = [(0, 'darkturquoise'), (0.25, 'darkturquoise'), (0.5, 'white'), (0.75, 'dodgerblue'), (1, 'dodgerblue')],\n",
+ " colors = ['darkturquoise', 'dodgerblue'],\n",
+ " labels=['Modelled', 'Distraction'],\n",
+ " labeloffset=0,\n",
+ " ylabel='Z-score')\n",
+ "\n",
+ "ax[1].set_yticks([0, 2, 4])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig4_mod-v-dis_distracted trials_photo.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_results_photo_mod-notdisVdis-notdis\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ " \n",
+ "[a, p] = dill.load(pickle_in)\n",
+ " \n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "f, ax = plot_ROC_and_line(f, a, p, mod_notdis_photo_snips_flat, dis_notdis_photo_snips_flat,\n",
+ " cdict = [(0, 'darkturquoise'), (0.25, 'darkturquoise'), (0.5, 'white'), (0.75, 'dodgerblue'), (1, 'dodgerblue')],\n",
+ " colors = ['darkturquoise', 'dodgerblue'],\n",
+ " labels=['Modelled', 'Distraction'],\n",
+ " labeloffset=0,\n",
+ " ylabel='Z-score')\n",
+ "\n",
+ "ax[1].set_yticks([-1, 0, 1])\n",
+ "\n",
+ "f.savefig(figfolder+\"fig4_mod-v-dis_notdistracted trials_photo.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0.07400000000000001,\n",
+ " 0.7725,\n",
+ " 0.025500000000000002,\n",
+ " 0.952,\n",
+ " 0.178,\n",
+ " 0.0575,\n",
+ " 0.643,\n",
+ " 0.2615,\n",
+ " 0.5985,\n",
+ " 0.158,\n",
+ " 0.271,\n",
+ " 0.047,\n",
+ " 0.05550000000000001,\n",
+ " 0.058499999999999996,\n",
+ " 0.052000000000000005,\n",
+ " 0.175,\n",
+ " 0.353,\n",
+ " 0.4195,\n",
+ " 0.347,\n",
+ " 0.033]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/old-and-unused/dis-roc-photo-analysis.ipynb b/notebooks/old-and-unused/dis-roc-photo-analysis.ipynb
new file mode 100644
index 0000000..67a9eee
--- /dev/null
+++ b/notebooks/old-and-unused/dis-roc-photo-analysis.ipynb
@@ -0,0 +1,665 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Github\\Distraction-Paper\\JM_custom_figs.py:11: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
+ " from collections import Sequence\n"
+ ]
+ }
+ ],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "import numpy as np\n",
+ "\n",
+ "%run ../JM_custom_figs.py\n",
+ "%run ../fx4roc.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['rat', 'fs', 'tick', 'filt_sd', 'licks', 'licks_off', 'distractors', 'distracted', 'notdistracted', 'd_bool_array', 'pdp', 'pre_dp', 'lickdata', 'snips_distractors', 'snips_distracted', 'snips_not-distracted'])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rats = disDict.keys()\n",
+ "rat= 'thph1.2'\n",
+ "d = disDict[rat]\n",
+ "d.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def resample_snips(snips, factor=0.1):\n",
+ " if len(snips)>0:\n",
+ " n_bins = len(snips[0])\n",
+ " out_bins = int(n_bins * factor)\n",
+ "\n",
+ " snips_out = []\n",
+ " for snip in snips:\n",
+ " snips_out.append(np.mean(np.reshape(snip, (out_bins, -1)), axis=1))\n",
+ " \n",
+ " return snips_out\n",
+ " else:\n",
+ " return []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "photo_snips_dis, photo_snips_notdis, = [], []\n",
+ "\n",
+ "rats=disDict.keys()\n",
+ "\n",
+ "for rat in rats:\n",
+ " d = disDict[rat]\n",
+ " \n",
+ " snips_dis = resample_snips(d['snips_distracted']['filt_z'])\n",
+ " photo_snips_dis.append(snips_dis)\n",
+ " \n",
+ " snips_notdis = resample_snips(d['snips_not-distracted']['filt_z'])\n",
+ " photo_snips_notdis.append(snips_notdis)\n",
+ " \n",
+ "photo_snips_dis_flat = flatten_list(photo_snips_dis)\n",
+ "photo_snips_notdis_flat = flatten_list(photo_snips_notdis)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_results_photo_disVnotdis.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[a, p, photo_snips_dis, photo_snips_notdis] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Analysing column 0\n",
+ "Analysing column 1\n",
+ "Analysing column 2\n",
+ "Analysing column 3\n",
+ "Analysing column 4\n",
+ "Analysing column 5\n",
+ "Analysing column 6\n",
+ "Analysing column 7\n",
+ "Analysing column 8\n",
+ "Analysing column 9\n",
+ "Analysing column 10\n",
+ "Analysing column 11\n",
+ "Analysing column 12\n",
+ "Analysing column 13\n",
+ "Analysing column 14\n",
+ "Analysing column 15\n",
+ "Analysing column 16\n",
+ "Analysing column 17\n",
+ "Analysing column 18\n",
+ "Analysing column 19\n",
+ "--- Total ROC analysis took 219.40415692329407 seconds ---\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Cell to run ROC analysis on DISTRACTED vs. NON-DISTRACTED - takes a while so commented out unless needed\n",
+ "%run ../fx4roc.py\n",
+ "import time\n",
+ "start_time = time.time()\n",
+ "\n",
+ "# a, p = nanroc(photo_snips_notdis_flat, photo_snips_dis_flat, n4shuf=2000)\n",
+ "\n",
+ "print(f\"--- Total ROC analysis took {(time.time() - start_time)} seconds ---\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "savefile=True\n",
+ "if savefile:\n",
+ " pickle_out = open(outputfolder+\"roc_results_photo_disVnotdis.pickle\", 'wb')\n",
+ " dill.dump([a, p, photo_snips_dis, photo_snips_notdis], pickle_out)\n",
+ " pickle_out.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(nrows=3)\n",
+ "\n",
+ "ax[0].plot(np.mean(photo_snips_dis_flat, axis=0), color='red')\n",
+ "ax[0].plot(np.mean(photo_snips_notdis_flat, axis=0), color='grey')\n",
+ "\n",
+ "ax[0].set_ylabel('Z-score')\n",
+ "ax[0].set_xticks([])\n",
+ "\n",
+ "ax[0].text(0.99, 0.95, 'Distracted', color='red', ha='right', va='top', transform=ax[0].transAxes)\n",
+ "ax[0].text(0.99, 0.80, 'Not distracted', color='grey', ha='right', va='top', transform=ax[0].transAxes)\n",
+ "\n",
+ "ax[1].plot(a)\n",
+ "ax[1].set_ylabel('ROC value')\n",
+ "ax[1].set_xticks([])\n",
+ "\n",
+ "threshold = 0.05/len(p)\n",
+ "sigpoints = np.array([pval < threshold for pval in p], dtype=bool)\n",
+ "\n",
+ "xdata = [x for x, L in zip(range(len(sigpoints)), sigpoints) if L]\n",
+ "ydata = logical_subset(a, sigpoints)\n",
+ "ax[1].scatter(xdata, ydata, color='red')\n",
+ "\n",
+ "ax[1]. set_ylim([-0.1, 1.1])\n",
+ "\n",
+ "ax[2].plot(p)\n",
+ "ax[2].set_ylim([-0.1, 1.1])\n",
+ "ax[2].set_ylabel('p')\n",
+ "\n",
+ "ax[2].set_xticks([0, 5, 10, 15])\n",
+ "ax[2].set_xticklabels(['-5', '0', '5', '10'])\n",
+ "ax[2].set_xlabel('Time from distractor (s)')\n",
+ "\n",
+ "f.savefig(outputfolder + \"photo_roc_analysis.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_results_photo_modVdis\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[a, p, photo_snips_mod_day_flat, photo_snips_dis_day_flat] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "photo_snips_mod_day, photo_snips_dis_day, = [], []\n",
+ "\n",
+ "rats=['thph1.1']\n",
+ "rats=disDict.keys()\n",
+ "\n",
+ "for rat in rats:\n",
+ " \n",
+ " d = modDict[rat] \n",
+ " snips = resample_snips(d['snips_distractors']['filt_z'])\n",
+ " photo_snips_mod_day.append(snips)\n",
+ " \n",
+ " d = disDict[rat]\n",
+ " snips = resample_snips(d['snips_distractors']['filt_z'])\n",
+ " photo_snips_dis_day.append(snips)\n",
+ " \n",
+ "photo_snips_mod_day_flat = flatten_list(photo_snips_mod_day)\n",
+ "photo_snips_dis_day_flat = flatten_list(photo_snips_dis_day)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "IndexError",
+ "evalue": "tuple index out of range",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnanroc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mphoto_snips_mod_day\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphoto_snips_dis_day\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn4shuf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"--- Total ROC analysis took {(time.time() - start_time)} seconds ---\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mC:\\Github\\Distraction-Paper\\fx4roc.py\u001b[0m in \u001b[0;36mnanroc\u001b[1;34m(x, y, N, min4roc, n4shuf)\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;31m# checks dimensions of matrices\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 77\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 78\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'nanroc: matrices must have the same number of columns'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mIndexError\u001b[0m: tuple index out of range"
+ ]
+ }
+ ],
+ "source": [
+ "# Cell to run ROC analysis - takes a while so commented out unless needed\n",
+ "%run ../fx4roc.py\n",
+ "import time\n",
+ "start_time = time.time()\n",
+ "\n",
+ "# a, p = nanroc(photo_snips_mod_day_flat, photo_snips_dis_day_flat, n4shuf=2000)\n",
+ "\n",
+ "print(f\"--- Total ROC analysis took {(time.time() - start_time)} seconds ---\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "savefile=True\n",
+ "if savefile:\n",
+ " pickle_out = open(outputfolder+\"roc_results_photo_modVdis\", 'wb')\n",
+ " dill.dump([a, p, photo_snips_mod_day, photo_snips_dis_day], pickle_out)\n",
+ " pickle_out.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, 'Time from distractor (s)')"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(nrows=3)\n",
+ "\n",
+ "ax[0].plot(np.mean(photo_snips_mod_day_flat, axis=0), color='red')\n",
+ "ax[0].plot(np.mean(photo_snips_dis_day_flat, axis=0), color='grey')\n",
+ "\n",
+ "ax[0].set_ylabel('Z-score')\n",
+ "ax[0].set_xticks([])\n",
+ "\n",
+ "ax[0].text(0.99, 0.95, 'Modelled', color='red', ha='right', va='top', transform=ax[0].transAxes)\n",
+ "ax[0].text(0.99, 0.80, 'Distraction', color='grey', ha='right', va='top', transform=ax[0].transAxes)\n",
+ "\n",
+ "ax[1].plot(a)\n",
+ "ax[1].set_ylabel('ROC value')\n",
+ "ax[1].set_xticks([])\n",
+ "\n",
+ "threshold = 0.05/len(p)\n",
+ "sigpoints = np.array([pval < threshold for pval in p], dtype=bool)\n",
+ "\n",
+ "xdata = [x for x, L in zip(range(len(sigpoints)), sigpoints) if L]\n",
+ "ydata = logical_subset(a, sigpoints)\n",
+ "ax[1].scatter(xdata, ydata, color='red')\n",
+ "\n",
+ "ax[1]. set_ylim([-0.1, 1.1])\n",
+ "\n",
+ "ax[2].plot(p)\n",
+ "ax[2].set_ylim([-0.1, 1.1])\n",
+ "ax[2].set_ylabel('p')\n",
+ "\n",
+ "ax[2].set_xticks([0, 5, 10, 15])\n",
+ "ax[2].set_xticklabels(['-5', '0', '5', '10'])\n",
+ "ax[2].set_xlabel('Time from distractor (s)')\n",
+ "\n",
+ "# f.savefig(outputfolder + \"photo_roc_analysis.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.gridspec as gridspec\n",
+ "from matplotlib.colors import LinearSegmentedColormap\n",
+ "\n",
+ "f = plt.figure(figsize=(3.4,2))\n",
+ "\n",
+ "gs=gridspec.GridSpec(2,2, figure=f, height_ratios=[0.25, 1], width_ratios=[1, 0.05], wspace=0.05,\n",
+ " bottom=0.2, right=0.75, left=0.15)\n",
+ "\n",
+ "ax[0] = f.add_subplot(gs[0, 0])\n",
+ "\n",
+ "# Creates colormap for ROC\n",
+ "\n",
+ "cdict = [(0, 'darkturquoise'), (0.25, 'darkturquoise'), (0.5, 'white'), (0.75, 'dodgerblue'), (1, 'dodgerblue')]\n",
+ "heatmap_color_scheme = LinearSegmentedColormap.from_list('rocmap', cdict)\n",
+ "\n",
+ "roc_for_plot = a + [0]\n",
+ "xvals=np.arange(-0.5,20.5)\n",
+ "yvals=[1, 2]\n",
+ "xx, yy = np.meshgrid(xvals, yvals)\n",
+ " \n",
+ "mesh = ax[0].pcolormesh(xx, yy, [roc_for_plot, roc_for_plot], cmap=heatmap_color_scheme, shading = 'flat')\n",
+ "mesh.set_clim([0, 1])\n",
+ "\n",
+ "\n",
+ "xdata = [x for x, L in zip(range(len(sigpoints)), sigpoints) if L]\n",
+ "ydata = logical_subset(a, sigpoints)\n",
+ "ax[0].scatter(xdata, [2.5]*len(xdata), color='k', marker='.', clip_on=False)\n",
+ "ax[0].text(-1, 2.5, 'p<0.05', va='center', ha='right')\n",
+ "\n",
+ "ax[0].spines['top'].set_visible(False)\n",
+ "ax[0].spines['right'].set_visible(False)\n",
+ "ax[0].spines['bottom'].set_visible(False)\n",
+ "ax[0].spines['left'].set_visible(False)\n",
+ "ax[0].set_xticks([])\n",
+ "ax[0].set_yticks([])\n",
+ "\n",
+ "\n",
+ "cbar_ax = f.add_subplot(gs[0,1]) \n",
+ "cbar = f.colorbar(mesh, cax=cbar_ax, ticks=[0, 1], label='ROC')\n",
+ "\n",
+ "ax[1] = f.add_subplot(gs[1, 0])\n",
+ "\n",
+ "shadedError(ax[1], photo_snips_mod_day_flat, linecolor='darkturquoise')\n",
+ "shadedError(ax[1], photo_snips_dis_day_flat, linecolor='dodgerblue')\n",
+ "\n",
+ "ax[1].set_ylabel('Z-score')\n",
+ "ax[1].spines['top'].set_visible(False)\n",
+ "ax[1].spines['right'].set_visible(False)\n",
+ "\n",
+ "ax[1].set_xticks([0, 5, 10, 15])\n",
+ "ax[1].set_xticklabels(['-5', '0', '5', '10'])\n",
+ "ax[1].set_xlabel('Time from distractor (s)')\n",
+ "\n",
+ "ax[1].text(20, np.mean(photo_snips_mod_day_flat, axis=0)[-1], 'Modelled', color='darkturquoise', ha='left', va='center')\n",
+ "ax[1].text(20, np.mean(photo_snips_dis_day_flat, axis=0)[-1], 'Distraction', color='dodgerblue', ha='left', va='center')\n",
+ "\n",
+ "ax[0].set_xlim(ax[1].get_xlim())\n",
+ "\n",
+ "f.savefig(figfolder+\"fig3_roc-photo-modVdis.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "epoch1_mod, epoch1_dis = [], []\n",
+ "epoch2_mod, epoch2_dis = [], []\n",
+ "epoch3_mod, epoch3_dis = [], []\n",
+ "\n",
+ "for day, epoch1, epoch2, epoch3 in zip([photo_snips_mod_day, photo_snips_dis_day],\n",
+ " [epoch1_mod, epoch1_dis],\n",
+ " [epoch2_mod, epoch2_dis],\n",
+ " [epoch3_mod, epoch3_dis]):\n",
+ " for rat in day:\n",
+ " e1, e2, e3 = [], [], []\n",
+ " for snip in rat:\n",
+ " e1.append(np.mean(snip[0:5]))\n",
+ " e2.append(np.mean(snip[6:9]))\n",
+ " e3.append(np.mean(snip[9:]))\n",
+ " epoch1.append(np.mean(e1))\n",
+ " epoch2.append(np.mean(e2))\n",
+ " epoch3.append(np.mean(e3))\n",
+ "\n",
+ " \n",
+ "# print(e1)\n",
+ "# epoch1.append(np.mean(e1))\n",
+ " \n",
+ "# pre_dis, pre_notdis = [], []\n",
+ "# post_dis, post_notdis = [], []\n",
+ " \n",
+ "# for snip in snips:\n",
+ " \n",
+ "# nlicks_before_distractor = len([lick for lick in snip if lick<0])\n",
+ "# nlicks_after_distractor = len([lick for lick in snip if lick>1])\n",
+ " \n",
+ "# if check_val_between(snip):\n",
+ "# pre_notdis.append(nlicks_before_distractor)\n",
+ "# post_notdis.append(nlicks_after_distractor)\n",
+ "# else:\n",
+ "# pre_dis.append(nlicks_before_distractor)\n",
+ "# post_dis.append(nlicks_after_distractor)\n",
+ "\n",
+ "# pre_lickrate_dis.append(np.mean(pre_dis)/10) # dividing by 10 puts this into Hz as a 10s predistractor period is used\n",
+ "# pre_lickrate_notdis.append(np.mean(pre_notdis)/10)\n",
+ " \n",
+ "# post_lickrate_dis.append(np.mean(post_dis)/10)\n",
+ "# post_lickrate_notdis.append(np.mean(post_notdis)/10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1 - pre 0.7430495132300441 0.4706672019396775\n",
+ "Epoch 2 - dis -2.7938530643336805 0.015207585946736157\n",
+ "Epoch 3 - post 0.4336395958584519 0.6716593062202525\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ " \n",
+ "f, ax = plt.subplots(figsize=(4,2), ncols=3)\n",
+ "f.subplots_adjust(left=0.15, wspace=0.3)\n",
+ "\n",
+ "colors=['darkturquoise', 'dodgerblue']\n",
+ "\n",
+ "barscatter([epoch1_mod, epoch1_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " ax=ax[0])\n",
+ "\n",
+ "barscatter([epoch2_mod, epoch2_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " ax=ax[1])\n",
+ "\n",
+ "barscatter([epoch3_mod, epoch3_dis], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " ax=ax[2])\n",
+ "\n",
+ "for axis in ax:\n",
+ " axis.set_xticks([])\n",
+ " axis.set_ylim([-4, 3.5])\n",
+ " axis.set_yticks([-3, 0, 3])\n",
+ " axis.set_yticklabels([])\n",
+ " axis.text(1, -3.9, 'Mod', ha='center')\n",
+ " axis.text(2, -3.9, 'Dis', ha='center')\n",
+ " \n",
+ "ax[0].set_ylabel('Z-score')\n",
+ "\n",
+ "\n",
+ "ax[0].set_yticklabels([\"-3\", '0', '3'])\n",
+ "\n",
+ "ax[0].set_title('Pre')\n",
+ "ax[1].set_title('Dist.')\n",
+ "ax[2].set_title('Post')\n",
+ "\n",
+ "\n",
+ "# # ax[1].set_ylabel('Mean post-distractor lick rate (Hz)')\n",
+ "# ax[1].set_yticks([0, 5, 10])\n",
+ "# ax[1].set_ylim([-1, 15])\n",
+ "# ax[1].set_title('Post')\n",
+ "\n",
+ "f.savefig(figfolder+\"fig3_photo_modVdis_epochs.pdf\")\n",
+ "\n",
+ "from scipy import stats\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch1_mod, epoch1_dis)\n",
+ "print('Epoch 1 - pre', t, p)\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch2_mod, epoch2_dis)\n",
+ "print('Epoch 2 - dis', t, p)\n",
+ "\n",
+ "t, p = stats.ttest_rel(epoch3_mod, epoch3_dis)\n",
+ "print('Epoch 3 - post', t, p)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0.3875,\n",
+ " 0.374,\n",
+ " 0.2945,\n",
+ " 0.5985,\n",
+ " 0.17149999999999999,\n",
+ " 0.2115,\n",
+ " 0.0,\n",
+ " 0.0,\n",
+ " 0.002,\n",
+ " 0.14850000000000002,\n",
+ " 0.4575,\n",
+ " 0.905,\n",
+ " 0.692,\n",
+ " 0.6675,\n",
+ " 0.8765000000000001,\n",
+ " 0.39,\n",
+ " 0.169,\n",
+ " 0.0695,\n",
+ " 0.029,\n",
+ " 0.0375]"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/rr_noise comparisons.ipynb b/notebooks/rr_noise comparisons.ipynb
new file mode 100644
index 0000000..9e7d706
--- /dev/null
+++ b/notebooks/rr_noise comparisons.ipynb
@@ -0,0 +1,405 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "import numpy as np\n",
+ "import csv\n",
+ "from scipy import stats\n",
+ "import trompy as tp\n",
+ "import pandas as pd\n",
+ "from statsmodels.stats.anova import AnovaRM\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fig settings\n",
+ "scattersize=50\n",
+ "colors = ['darkturquoise','dodgerblue', 'darkblue']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "statsfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\stats\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# thph2.8 removed from analysis because no data on habituation day\n",
+ "\n",
+ "modDict.pop('thph2.8')\n",
+ "disDict.pop('thph2.8')\n",
+ ";"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_rms(daydict):\n",
+ " \n",
+ " \"\"\" Gets rms for all rats\"\"\"\n",
+ " rms = []\n",
+ "\n",
+ " rats = daydict.keys()\n",
+ " for rat in rats:\n",
+ " d = daydict[rat]\n",
+ " rms.append(d[\"rms\"])\n",
+ " \n",
+ " return rms\n",
+ "\n",
+ "rms_mod = get_rms(modDict)\n",
+ "rms_dis = get_rms(disDict)\n",
+ "rms_hab = get_rms(habDict)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_epoch_auc(daydict, signal=\"filt\", epoch=[0, 40], predp_threshold=5):\n",
+ " \"\"\"Gets average baseline on trials from filtered, non-zscored signal\"\"\"\n",
+ " \n",
+ " baseline_auc = []\n",
+ " \n",
+ " rats = daydict.keys()\n",
+ " for rat in rats:\n",
+ " d = daydict[rat]\n",
+ " \n",
+ " bl = [np.mean(snip[epoch[0]:epoch[1]]) for snip, predp in zip(d[\"snips_distractors\"][signal], d[\"pre_dp\"]) if (predp > predp_threshold)]\n",
+ " baseline_auc.append(np.mean(bl))\n",
+ " \n",
+ " return baseline_auc\n",
+ "\n",
+ "threshold = 0\n",
+ "\n",
+ "epoch = [0, 40]\n",
+ "\n",
+ "mod_bl = get_epoch_auc(modDict, predp_threshold=threshold, epoch=epoch)\n",
+ "dis_bl = get_epoch_auc(disDict, predp_threshold=threshold, epoch=epoch)\n",
+ "hab_bl = get_epoch_auc(habDict, predp_threshold=threshold, epoch=epoch)\n",
+ "\n",
+ "epoch = [60, 90]\n",
+ "\n",
+ "mod_ep2 = get_epoch_auc(modDict, predp_threshold=threshold, epoch=epoch)\n",
+ "dis_ep2 = get_epoch_auc(disDict, predp_threshold=threshold, epoch=epoch)\n",
+ "hab_ep2 = get_epoch_auc(habDict, predp_threshold=threshold, epoch=epoch)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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VSoVer2dsbIzAwEBfm+OXrMbiyqWQkpKyqmef7sQv/g24LIT4hmMWcQH4qRAiGFgx1SSutHqMRiPh4eHLZsPQ0BCnTp2itLR0loMAu0NIS0vzS00hIUSOEOJVIUS5EOL01MOnRi2RuTSyioqKuHNn3hbva5bVXFy5WDIzM2loaPC1GV7Dneymbwohfo294loAn5dSXne8/PHFnNShJPsZ7Km01cCnpJTjizmWu0zX6pn6p4aHh9Pf38/169cpKSnxasy/q6uLqqoqDh8+jEYz95/dlZ1+0k/iZ8APgGOA1ce2LJn51n4iIiIYGBhY1SGEJbAmiisXwtSsf7U2I5rzaiWECJNSDjmakzc5HlOvRUkpn2w96BZCiETsi54FjpDVf2Cfuv5kMcdzl/n6NHR2dvLOO++wadMm4uPjPX7uxsZGWlpaOHTo0FMvOn7cT2JSSnnU10Z4iqfVzSQkJNDe3k5iYqKvTfUrXDkIIUQkkCylvC2lNAMrvrhyoUy1Ns3NzX36ziuM+a48/+b4eQO4Pu0x9XwpaIBAIYQGCALal3i8BfFkBpLBYODIkSO0t7dz5swZJiY8t9RSXV1NX18f+/fvd/uu1E/7Sfy3EOJ/CiHihRBRUw9fG7VYnqaRtdbkoBeKEKJCCBHmGAO3sDcj+3tf27UUliLRn5ycTGtrqxet8x1zXn2klO9z/EyXUmZMe6QvRQFWSvkQ+FugBejArg1V/uR+QoiXhRDXhRDXe3p6Fns6J0+T5VCpVGzevJmtW7dy7tw57t275zKd1V2klFy6dAmNRsPWratCD/GTwB9iT3u9gWduFnyGqzWquro6ZyqjRqNBpVKtqeYyCyRcSjkEPA/8WEq5GTjoY5sWzVJle4QQ6HQ6j95g+gtPvUUVQux2LFIjhPhtIcTfCyEWHXN0TE2fA9KxZ0YECyF++8n9PN3Hdnp44eDBg7z00ksMDQ1RXz9zZhwSEsKzzz6LXq/n+PHj9Pf3L/hcNpuNM2fOkJiYSH5+/pJt9wccNwdPPlasXLyrXuIBAQE0NDTQ29sLQGFhIXfv3vWxpX6LxtFi9CPAm742ZqnMdX2oqalx+2YxLy+Pmpoar9rpi4Zk7qTAHgU2CCE2AH+EXTr8n4F9izznQaBJStkDIIR4A9gF/Msij+cWc8lyXL58mfDwcGJjY2eEgzIzM0lNTeXKlSuoVCq2bdvm1qKU2Wzm1KlTbN26lZiYGK99nuVCCFEmpTwthHje1etSyjeW2yZPMNfaj5SSq1evYjQa2b59O1VVK0ZUYLn5c+y1MuellNeEEBlAnY9tWjRzXR9u3LhBd7f7rbabm5sxm83odLp5HxqNZsFJEb4qtHXHSUxKKaUQ4jngO1LKHwkhPvnUd81NC7BDCBGEPb/6AMsQtphLlmPPnj10d3dTXV2NXq8nPz/feXHXaDTs3r2b3t5eysvLKSgomDcXemRkhLNnz7Jv377VJBK3DzgNvN/FaxJYkU4CHq/9PFl/snPnTvr6+igvL0ev19Pd3U1sbKyPrPRPpJQ/w57xNvW8kRXc1tgd2Z6nIaVEo9GQmprq7IM99RgeHp7xfErqx9UxgBkORKPRoNPp6O/v90mhrXjaVEoIcRZ4B/gU9irrHqBKSrno3n1CiD8DPgpMAjeBz0gp5wzmbdmyRV6/vjQ/MuWFh4aGZqSWTvfC4+Pj1NTU0NvbS1BQEPn5+c56Bikl1dXVdHd3s2vXrlkdqfr6+rh69SoHDhxAp9MtyVYvsiryOT0xHtxBSkllZSW3b9/mYx/7GHq93uvnXGZW/Hjw1Fhw5/rgDoODg9TW1rJt27Yl2wT2MTg5OYnZbOb8+fOo1WoOHny89HPy5El0Oh1793qkNMXleHBnJvFR4GPAS1LKTsd6xLeWYomU8hvYVUWXDXdSSwMCAti4cSMAo6Oj3L9/n/7+fkJCQigoKKC4uJixsTEuXbpEVFQUGzZsQAhBa2srRqORw4cP+0smkseYQ+DRiZRyRWe0zIcQgs2bN9PT00NFRQVpaWmrMsVRwXOp5+Hh4QwNDXnMLiEEWq0WrVZLenr6rNnOcjQkc6eYrhN7+9Kp5y3AP3nTKG8xV3jBFUFBQWzevBmA4eFh7t+/z9DQEOHh4Wzbto3+/n7eeecdoqKisNlslJWVrdbCq1BfG+BrNmzYQG9vL0IIjh8/zq5duwgNXfN/llXHQq4P8xEZGcmjR4+IivJshrivCm3dmUmseUJDQ53Tx8HBQaqrqzGZTIyOjjIyMoLBYGByctLrfSd8gZTyz3xtg6+Jj4/nzp07PPvss6SlpXHx4kXCwsLYtGnTar0xWBBCiNNSyhXVhMwVnpLoLygo4MaNG+zZs8ej9vmq0HZ1xUaegifSx8LDw9mxYwdarZbMzEwSEhIYGhri9ddf5/79+35jp6dxaDedEkLccTwvFkL8ia/tWi6m7g51Oh379+8nLi6Ot99+25kuu1YQQtx+4lEN7J567mv7Fosn2xsHBgYyPj6+pDqrufBFoe2amUl4Kn1scnKSU6dOsWHDhhmie93d3Zw/f57z58+zadMmiouLF7WA7cf9JH6IvZjuFQAp5W0hxL8Bf+FLo5aL9evXc/XqVecCYWJiIgaDgatXr1JbW8uOHTtWpW6PC5qBIez/9zHsi53v4jr7bcXg6fbG8fHxdHR0kJCQ4AVrl5f5tJuqsac4ukRKWewVi7yEq0Hwwx/+kNu3b5Oeno7NZkNK6fzp6vfx8XFu3LjB+vXrGRsbo6GhYcZ+BQUFTExMcOvWLW7cuEFoaCiRkZGsW7cOlUo177Gnfvb29jIwMMDLL7/sb/0kgqSUV58Ir7jO41uFBAQEYLFYZoi4qdVqdu7cyaNHjygvL6ewsJCUlNWtbSel/IAQ4jeBV4G/lVL+SghhkVI+8LVtS2E+mZbFfO9ycnK4cOHC6nYSwPscP7/o+PnPjp8fB1ZcY9e5BkFNTQ02mw2VSoUQwvlz+u8qlcqZ7bRp0yb0ej0qlQq1Wj1rPyEE8fHx9PX1ce/ePcLCwujt7cVms5GUlERqaqpT8sHV+d59913Cw8P9rp8E0CuEyMRx4yCE+BB2WZU1Q3Z2NnV1dc5+JFNERUVx5MgRbt++TX19Pbt27SIgIMBHVnofKeUvhBDlwDeFEJ8B/Dbn211c1UnU1dVRVra4pRatVovVanVeW1YyczqJqTsDIcRuKeXuaS99VQhxAXvF5YphKcUyHR0dNDc38+EPf3heme/pxMbGkpeXx82bNzGbzezYsYPu7m5u3LiBlJL09HTS0tJmDSBXdi5HmpsbfBH73WOeEOIhdlXgWXIqq5nk5GROnjw5y0mAPVVxw4YNmEwm3n33XZKTk13utxj8see5lNIE/L5DiWGnT43xAK4yhwICAmhubiYjI2NRSSlpaWnO969k3CmmqwK+JKU873i+C/i+lHLjMtgHLF8xnSumvpx79uxZdCbLyMgIly5dcmo5SSlpamqiubkZlUpFZmYmycnJCCGw2Wz8+7//Oz09PYSFhTE0NMS6det44YUXPHFhWHIqjkPHSyWlHF7qsRbLchXTueLy5csUFBQQFhY27351dXU0NjYuOV32yTWqxsZGQkNDPbVG5ZHULCHEn0op/9QTx1oonhwLU864s7MTg8FAVlYWJpOJc+fOsWvXLpeNwp52vDNnznDgwAGP2LcMLLqY7iXgNSFEOPZQwyDwaQ8atiwsJn2sqqoKq9XKM888s6RzT4kGNjY2cvz4cbZv305mZiaZmZnOmcLp06edoSUAvV5PUlKS33S8EkL8LvBjYBj4oRCiBPiqKwXf1UxxcTE3b95k9+7d8+6XnZ1Namoqly5dIiQkhJKSkgXfZEgpqaqq8tee59P5APCnvjZiqbiqkwgNDeXIkSNUVFSQnp6+oFmBSqVCpVIxOTnpdgTCH3GnmO4GdoG/MOwzj0Hvm+Ud3C2WkVJy8eJFYmJiPFphm5GRQUpKCleuXEEIwfbt21Gr1U6bLBYLZ8+eZWBggM997nOo1WrKysr85aLwaSnld4QQh4FY7DItPwbWlJMICgpibGzMra51Op2Offv20d7ezttvv83WrVuZS9HYZrPR19dHR0cHfX19zuN3dXX5Zc/zJ1jVxSJqtZoDBw5w8+ZNrl69ytatW912+Dk5ORiNRgoKCrxspfd4qpMQQsQB/wdIkFL+hhCiANgppfyR163zMO7Edq1WK2fOnCEvL4+kpCSP2zAlGjglIDddNHCq/D4nJ8cfLwpT34r3YO8fcEus0UqyqbDP1KzvaSQkJBAXF8e1a9cwGo1s3ryZ/v5+Ojo6GBgYAOw3MNHR0SQkJLB+/XrnuDQajf66RjWdzb42YDnYtGkTra2tnDhxgtLSUrfWKRISErh///6KdhLuBDV/gl0SeCqXywh8xVsGeQt3imUmJiYoLy+npKTEKw5iOtHR0Rw5coShoSFOnjzJ6Kg9YcxVM5yGhoYZNRk+4oYjo+U9wHEhRCjg+yo/H5Cenk5TU9PTd8QuHd/S0sL169cZHR1lcHCQf/mXf6G+vp6MjAxKS0spKytj//79FBUVERMTM+PGxVXfCz/pee5ESmkDEEL8b1/bshTcKWJNTk5m586dlJeXOx38fAgh0Ov1jI+Pe8PkZcGdQFmMlPI/hBBfA5BSTgohrF62a17ik9PobFtYWnZOTg4vv/wyX/nKV5x3ZN/5znfIz8/HaDQSEhLC3r17qaiocF6w58OQlEpHa/MiP4EdIQRFRUVkZWU5RQOLioq4fv06r776qrNvbnh4uD9cFF4CNgKNUspRIUQ09pDTkhFCNGNf67Bil6bf4onjegshBIGBgZhMJoKDg53bR0dH6ezspLOz03lR0Ol0GAwGCgsLnfLxU4rClZWVT02X9eOe5674DB7IevTFeJhKGOnt7SU0NJRbt24RExPjMmEkNDSUw4cPc/bsWTIyMkhPT5/32AUFBc70+ZWIO07C5LggTOXH78C+eO0zOtsekPLthZW8546fpajYMiOMU1RUROknvkaJOpoQ6wCXg44Qc8i9LngtX/FcpCUwMJCysjLa2to4fvw4ZrMZi8VCW1sbFovFY+dZClJKmxCiCcgRQnijCKBUSrkiNC6mUphPnTpFRESEszdAUFAQBoOBkpKSeS/8QgiKi4ud6bJJSUnzdjD0lPCcJxBCzCVxKoBAD55qWceD0Wikvb2dsLAwkpOTaWxspL29HaPR6DKVWaPRUFZWxs2bN+nt7WXLli1zrlNER0dz8+ZNb38Er+GOk/h94FdApqM+Yh3wIa9a5QUeqeO5W1vBgbLHsd3q2kbatIVobGbuBu7AMNlClrkagWRIFUmnJo1hVSQsU+g9KSmJkZERHjx4wBe/+EW/ymZxFE39LpAEVAE7gEvAihV2c2eNSkrpXD/o6elxhiDCwsKYnJxkx44di+4fEhwczLPPPkt9fT3Hjx9n586dT02t9QMGgK1Syq4nXxBCtPrAHo9w9+5dgoKCZigyvPLKK1RWVs5Z7yKEoKSkhJaWFk6ePMn+/fvnXKcIDQ1laGhoJfx/ZzGvkxBCqIAA7N3JcrHfLdRKKZd0eyuEiACOAeuxz1A+LaW8tJRjPo02TRYNpuv8w9FXKczN4m5tPe0jGqxCQ3XgbhCCQY1jFiElobYBDJPNZNuqEEhGVBF0aFIZUkV71Wl0dXWRnZ3tjwvXvwtsBS5LKUuFEHmApxRiJVAuhJDAK1LKV5/cQQjxMvAy4BHpi6nwwlTdzOnTp7l+/TqlpaV0d3fz6NEjZ4ZRZGQk8fHx5OXlzdBnCg0NpbOzc8n2ZGVlkZKSsqR02WXkn4BUYJaTAP7NQ+eYdzx4eiw4jjnre5ednU1LSwsnTpxg3bp1FBYWunQCKSkpREREUF5ezp49ewgPD5+1T0FBAXfv3mXHjh0esXc5mddJOEIMfyel3Al4siP8d4B3pJQfEkLogKCnvcEjSMnYqIk7d+4yYbGALYz7QdtmX/SFYFgdybD6cfFMsG2A+MkHZJntQpdxW7fS3d3NunXrPPqF9uOK63Ep5bhDQkQvpawRQngqP3i3lLJdCBELnBBC1Egpz03fwXGheBXsBVRLPaHRaKS/v5/Pf/7zzlTjo0ePUntEm7YAACAASURBVF1dzebNmykuLn7q/zU7O5szZ8545EL1ZLrsli1b/LJlqpRyTuVfKeUfe+g0844HT48FsF/ET506RVlZmfN7V1tby8GDB8nLy3MKeAohKCwsnJXKHBYWxuHDh6moqHDWyEwnNDSUkZERT5i67LgTbioXQvwW8Ib0gPato95iL/A/AKSUZsC81OM+jSSLkWR1B5HhYc4URjE4RJLFSKvu6fIJJlUE9boI5/P+mhq6urq4c+cOYA8dpKamEhcXtySn4avGIm7Q5pgB/hf2L24/0O6JA0sp2x0/u4UQvwC2Aefmf9fSuHfv3qxU49zcXIaHh4mOjnbrGCqVCq1Wy/j4uMe0mhISEjAYDM502R07dqBSqfxGlkMIkSalbJ7ndQEkSinbFnsOX4yHnJwcKisrZySMBAQE0NLSQmpqKrGxscTGxmI2m7l79y5VVVUYDAby8/OdhXIajYYDBw5QWVlJT08PmzdvnnEtiI6Opre3l5iYGG9+FI/jjizHMBCMPdNgShpYSikXFVwTQmzEfhdwD9gA3AB+16EFM32/6VPKzQ8ePJj+2oIXrvea/pOdEV3OIrWpmOOlAQPngp9f8Odo+YqYoRc/tZbQ3d2NlJLAwEDS0tKIi4tb8BfalTyAhy4KnpJh2AeEY58NLsnBT5f5cPx+AvhzKeU7c73HE1IM//mf/0lX1+zxsG7dOj784Q+7fZz+/n4aGhrYssXzCTj9/f1cunSJrq4uLBaLX8hyCCF+hj11/pfYv7s92EPSWUApcAD4hpTyxKIMWuB48LYsx8TEBBcuXCA5OXlWYW1HRwf37993JsFM70T34MED6urq2L9/v9OJjI+Pc+3atSUrOHiRxclySCk93adRA5QAvyOlvCKE+A7wVeB/PXFeD08pXcccL13zjARRSEgIhYWFFBYWAmAymWhpaaGmpgYpJQEBAaSmphIfH//UL7c/ZbM8iRAiCDAB95bqIBzEAb9w3HFpgH+bz0F4isLCQpqammbM2EwmE/Hx8U6JlOTkZJKTk+ctmoqMjKS/v98rNkZGRpKZmcmDBw/8RjpeSvlhR0Htx7HL88RjV4W+D/wa+Esp5VKKAnwyHqYz/eYvMDCQgwcPYjQaOXHiBLt37yYoyB4dj4+PJz4+nomJCaqrq7l+/TpJSUnk5uaSmppKREQEx48f55lnniEsLIyAgAAmJibcqtb3J9ypuBbYB0S6lPKbQohkIF5KeXWR52wD2qSUVxzPf47dSXiVJm0B92tnxhzv19TSpDnolfNlZubR1fV4xh0YGEhKSorTSUxMTNDS0kJ7e7uzcG6KqUU0g8FAZ2cndXV1i+pyFReXRGfn0hJOhBAfAL4LPAL+BPhH7IuWaUKIP5ZS/t+lHF9K2Yh9Rrms5OTkkJCQQE9PD62trYyPj5OYmMhzzz3n1NtpbW3l0qVLWCwWdDodqampJCYmzmouFB8fT3t7u1d6B3R2dvpdIoOU8h7wdS8d2yfj4WnNvnJyckhOTubChQskJSXNyHjS6/Vs2bIFKSUPHz6koqICnU5HUVERhw4doqKigtzcXFJSUkhMTOThw4deL9b1JO6sSXwfe2VtGfBNYAT7hWLrYk4opewUQrQKIXKllLXYp6f3FnOshdCmzcFoqpyR3dRiCiZOtvJQZmETnhXgsjuIbzmfj41Bba39AaDXS1JSrOzZY0OtBrMZWlvVPHwo+OQnbeTnh1BUlEt1dS3374/w2mtqpFzY3UdX1x964qN8EziEPbx0BiiWUjY6FhVPAUtyEr5CpVLxkY98hHfffZfW1laKi4t55plnnLM8jUZDenq6s1BqqnL63XffxWq1EhAQQFpamjPr6dy5c15xEn6cyLCqcKcz3dSsoq6ubtasAuw3d0lJSSQlJTE2NkZ1dTWDg4MkJyc7U6iLi4s5f/78qnMS26WUJUKImwBSyn5HRtJS+B3gXx3HacRDlbvzIYWKN/UvkDRez+nKTh6pD9AWkEWQHGLbWDk3A/YzoVqeJCuAiQlBXZ2Gujr7c51Okpxs5f3vt7B+fQS/8ztfcAiLlfHtbx8lO3sEo9EnSpI2KaURQAjR5LjTm1pUXLGd6Ww2Gz/72c+cd451dXV0dnbOGevX6XRkZWU5EwjGxsZ48OABRqMRKSXt7e20traSlJTk0VCCHycy+CWLUWMA2Lt3L1//+tdnzNjS09P57Gc/y7lzs9fMAwIC2L17Nw8fPqSmpmbGa1NqDNu2bUNKSUtLC21tbYyNjdHV1YVWq11RzYjcuepYhBBqHldcr2OJmj1Syipg2aUXpFDRqs2hVft4mm4SEdwIPEDJ2Glq9FsYVLtXce1pzGZBQ4OGxEQbBQW5T1SG52IwXMNo9IlpKiFEJPbFSpvj96mr4MoY5S5Yak/jwMBA8vLynGGHBw8ecPv2bWdoMCwsjPT0dKKjo5fkNFaYLIfPWYwaA4DVYuSusYIDBx7P2O4ZG0g79CWif+N/M6yKoFOTxpAqypkyXwckWer4rckmbuv3OG8yp6sxCCFITU0lNTWV0dFRrly5QnV1NQAHDx5cEWsT7jiJ7wK/AGKFEH+Jvdr6f83/lpWFRei5GniYDRPn6LYl0651T93TG3R2qqiuruXAgcdrJ9XVtXR2+uyiEI49i2VqNFdOe80jOeq+wFU724yMDNrb2xcV609NTcVoNDrbXQ4NDdHc3Mzt27eRUhIZGUl6evqCG9eAfyUyOKTiQ6WUP39i+8eB7sVmNfmah+oMBkfe4ujRo+Tm5lJbW8vAiIVzQb+JTaWZVid1C4AxVQgdmjTaNFl0aZIpHr9ArzqeB7q51V6DgoIoLS1l586dHDt2jOHhYQwGA8XFxU5dL3/EneymfxVC3MC+diCAD0op73vdsmVGChVVAfvJnqgkZ6ISo77EJ3bU1am5f3+Eb3/76Iw1ibo69dPf7AWklGk+ObGXiYuL49SpU5SWljqdsdFoZN26dVy9epVNmzYtuGVlTEwMPT09rFu3jrCwMIqLi52v9ff309TU5NTwiYmJIS0tbSXKNPwZ8H4X209hv5lckU4i0dpIaFAgBQU5tLW1UVBQQOWdWhLNjbSqcmbVSQXahjFMPiDDbK+TGhPBBNqG2TZ6nN6g+cPWAQEBFBYWOmtrbt++zejoKJmZmaSlpfnd7MKd7KZ/llJ+AqhxsW1FIaSNpMl6oqwdPFLH06bJQoqZd+h1+hISLI1sGjtDVcC+Wa97GykFr72mJjt7BIPhGp2dKurqFr5o7SmWo3jKV4yNjc2I9Y+NjVFcXExsbCznzp0jODiYkpISt7WZCgsLuXjxIvv375/1WmRkpHMWIaWkr6+P2tpahoftKdhxcXGkpaXNUJWdws96XAdJKXue3OhISJlt/AoharIdfaD9RiEzMxOj0UiA2kbUZMeM8PQUY6pQmnTrmRKMD7CZiJtsIdg2xAsvvMAbb7zB1q1bSUhImJUNB5CXl0dfXx9BQUE0NTWxb98+mpubOXnyJKGhoRQXF89YFPcl7oSbCqc/caxPrLgmI0LaeJ/5dTKDhinMzeBubQUNozd4U/fRWY6gXZuBSRXGtrHjVAaWYRH6ZbVVSoHRqPHVGsSTfMuh4TVv8RT21OYVQ1dXFxs2bCA1NdUZ658qhszLy+PAgQMMDAxw/vx5AgIC2Lx5M3r9/ONAp9Nhs9me2q5SCEFMTIyz8lZKSVdXF9XV1YyOjiKEID4+nrS0NHQ63bypmT4gQAihkVLOSFoQQmjxrArssqLCBlI628SWlZXx/aNHUeFeV4RxVTAPdPk80OXT8toh/uAP/oCLFy8SGRmJRqNBq9WSnJxMUlISGo0Gg8HA3bt3OXjwIJGRkZSXl7N3716ys7MZGhqisrKS8fFxZ+rt1OzCFzcMc45kR/+I/wcIdMgDT93KmnEUua0kkibryQwa5stfsC9UHiiz8p2jx0gar3d5pzCojqEqYB+bx05xR7+TEfXCY8mrgWUonvIJ8fHxnDlzhuTkZKSUzoYzhYWFzmKniIgIysrKGBwc5OLFi+h0OjZv3jyvBEdeXh41NTWsX7/ebVuEEBgMBmdjKZvNRkdHB5WVlTx8+JCBgQG/KaYD3sDe4/xLUyoJjhnEdx2vrUhCbf3k5OTQ0NDgvADn5uTQcukmQ6ooRlVhTAodk0KLBZ3z90m0LgU/8/PzycjI4MKFC8TFxZGdnU1raysXL1503kSMjo4yMDBAZGQkhw4d4syZMxQUFJCUlMSePXucY/LkyZNERERQWFjIf/3Xf80SpXTV88KTzOkkpJR/BfyVEOKvpJRf85oFy0SUtYPC3JkLlUW5GRivXCTc2kuPJpFedQJW8TgOPaEK4mrgYTaNn6FVm0u3JtlX5vsUbxZP+YqMjAzeeustTp06RU5ODqdOncJsNpOQkMCJEyecjYLUajXh4eGUlpYyNDTE5cuX0Wg0bN68mcDA2TfO8fHx3L17d0FO4klUKhWJiYkkJiZy9uxZIiMj/amY7k+AvwAeCCEeYL95TAZ+xEpOaJGSW7du0dzc7JyxDQ4OYkNDpvk2fSoDXdo0gplEI81oMaORFjTSgj1/Y8pRSDL276eiogKw3wDU1tZy+fJlkpKSCAoKIjg4GLVajclk4uc//zlRUVHO2UVNTQ3t7e2UlJSgVqudGXQDAwO88847PHr0iC984QvO2c4PfvCDOXteeIr5ZhJTK7c/m/a7Eyll5ZPb/Jm5+kncCSilS51CjPUhhROXHf90GFRH06NOYkgVxY2AAxSYrxJiG6BRV+TjT6IwncXmxU91KpyuAvvtb3+b97///RiNRuLj4yksLGRwcJCqqqoZzZ9CQkLYvHkzNpuNGzduMDo66syNn5qB9Pf3LyqTadbn87NiOkeY6atCiD/DHnIEqJdSjvnEIA8xrI4iMLBzRkr0948epWEynw5dJjkTlRRMXOFa4LP0a+ZvJdxSUcaZM2dmbJvSgAoPDyczMxOLxUJqairj4+Ns3LiR8fFxuru7nVpgFy9eJCEhgYiICOeaWFdXF7m5M9Pjc3JyuHfvnm+cBPB387wmWWHNZto0WTSM3uA7R4+xPjeTO7UNNI6G0aazL163qzIfp75KSZjtEbHWNjLNtxFIJoWOMNnHxrEKqgL2+fbDKDhZbF68q06FxcUbyPvUq4wH2v+/RiDU+oj3Pl+FRegw6kqcufBN2BcrD7z3BgCnv/ke57GLioo8JuTmb8V0Qogn1TAlECGEqJJSekYIzQdIVLMuwHm5uVRfaaOdLM4HPUeUtZNt4+VMouNc0HNYVO4vwej1esrKymhsbOT8+fPs3r2biIgIkpKSiIqKIjw8fEZ3wr6+Pk6fPo1er2dychIhBDqdjtra2lly5vHx8R7/e0xnvnDTqqr7l0LFm7qPkjReT0VlJ4/UpU4HMQshGFJHM6R+LBmtkWZirO2kmGt438iPMB48SGVlJUlJSbOa1yv4P65mlndq64m2hpNiqaFFk+voKxJFZWAZAbYR8szXUUkrRv0mTKoIxlXB3ArYi942yqZNmzh79iwlJSWEhoZiNps9UlXrh8V0rtJfo4BiIcRLUsrTy22QJ+jTJHCntoKyGeOhkRr9HjTSQvHEu6illTZNNmah58jIP9Oniedq4CEmF5DYkpGRQWJiIhcuXCA2Npb8/Hyqq6vZuXPnjP2io6N57rnnOHPmDOvXrychIQGTyURNTc2sjDxPzFjnY06pcCFEmZTytIs7BwCklMu2SPWkHPBipMI9SaBtmMyTL/Dqq6/S399PT08PUkrUajXx8fEkJiY6imO+9dRjeZc/fFIYcDHS0H5XPDV9PCx2LAhp430T/05W8KBTy6veFM6b+heIn2wm2VJLk279rHUorZwg23yTINsIDboi+tVxgL3KdnR0lMrKSsxmM3Fxcc4eFX6Mx/KqhRCpwH9IKbd76pju4Klrw1T2Y0bQ0IxIw5PZjwE2E/GTzURau4iydhBu7eOhNpObAaUzKq7dEeRsamrCaDRitVp5z3ve43IfKSVXrlwhMDCQ/v5+zGYzaWlpTjnz5uZm9Ho9+/Z5JLqxYKnwvcBpXN85SFZwJsNSGVOFcuLECe7cucOGDRuci5STk5N0dnZSXV3tiBmbMZkEbW0qOjtV2Gz+VSTjJquyeGoKs9lCa2sbZvPjNYd2bQbtmnQyLHdIG7tHrW4zg2p7uqpF6Lmn34FKTpJpribLfIsWbS4t2KU6du/ezfj4ODdu3KCmpgaDweCyneVqQ0r5wJEGuyJxN9IwrgqmSVdIk6MyIMLaxbaxE7xv+EeMqYKp1xXT4yKhwRXp6ekkJibyxhtvOENQTxbSCSHYsWMH9fX11NXV0dvbS0pKijMjr7Gx0Vnl7y3mcxJTIvk/klKe96oVKxCLxcKhQ4e4cOECg4OD5ObmotFonCqQ9oWr9xASYiMx0UZ2tgWVCqSEri4VbW0qhoddhwyEkGRnWzEYbD4vpmOVFk8lTdaTGTzCl7/wRWd44R+OvkrRyAWadfmMqUJp1K6nWZtPrrmSTPNt7uu3Mqayt1exCQ11+k0gJSmWWg4fPkxNTQ25ublO8TcpJTdv3sRqtVJSUrLosICfFdO5xNHKdsLXdiwFV9puT2NAHUd5yG8TNdlB3vhV4iytfOhDH+LNN98kJCSE5ORkUlJS5qze1+l0PP/887z99tuUl5eza9cuQkNnt/DJysoiLCyMH//4x5w8eZLc3FxnRl5GRsaiP7M7zOckPoW9F/V3sTcJUngCIQR79uyhurqaK1eusG3btll3AiMjKmprVU6JcJVKEhtrIzfXSmiovR5pfNw+2+joUGG1wqc/bfWIVLiHWJXFU65Sogtys7l27REx1naCLCNo5eNrnpBW9pnewCbUPNDmMqyKYkwVwqgItc8kjh8nMDCQEydOsG7dOoqKiti0aRNVVVXs2rWLyspKTCYTmzZtmtHB7Gk8rc/BciOE+G9ma3ZFYa+fWXEqDJ7ikSaeS8HvJ8dcyWRbGzExMQwODjI4OOjsSaLX60lPT5/VeEyn0xEcHMwzzzzDpUuXiIqKoqioaNa1ZGBggIiICD7zmc84M/KOHTtGY2OjV9Oh53MS94UQzcA6IcTtadun2pcWu37b2qOoqIiWlhZOnz7N/v37XZbhT2GzCTo71XR2Pt4nMFCSmGhl504LsbE2f5MKX5XFU64Xrht4oCud904y1NpH4cRlwm2P6FUlEE8TetsYGfv309jYiFar5eHDh1RVVTkXsNPT0yksLESn03Hr1i2GhobYuHGjW72O6+vrGRoaYt++fXR1dbF3714qKip8WUz3t088l0AfUOehToUrFilU1Oq30H/9OkNDQ6SkpGA2mxkfHycvL4/Y2Fiam5sxGo3YbDZCQ0PJyMggOjqalJQUurq62L9/P83NzRw/fpzdu3fPmFW0t7e7FKXs6OjwjZOQUr4ohDAAx4EPeM2CVUJKSgqhoaEcP37cpXbPfIyNCerrNdTXw969Zn+TCl+VxVPzpUTPx7A6mstB7yXc2kuOuZI+dTz3ArbPyo23Wq20t7dz9uxZTpw4QWZmJlarFZvNhhCC48ePMz4+jsFgIDY2lpCQEEJCQhBCYDKZ6OvrY2JigubmZiwWC+Xl5YSGhnLr1i1UKpXXLwxzIaU862q7EGK3EOJjUsovLrdN/sbw8DCHDh2irq6OlpYWduzY4RwLGRkZ7N+/HyEEQ0NDNDU1cevWLaSU9Pb2EhkZSVpaGgkJCVy8eHHGrEJK6VQanp4CO9Uy2VvMe2sqpezES60EHRpQ14GHUsr3eeMcy01kZCRlZfaLRXR0NH19Cz+Gv0mFr9biKSlUvKX9MBtG3qXmaivd6hxu6Z9xW9BxUB3DtcBDxE62sH3sHTRPxIWnemR//OMf55133kEIgdVqZePGjURFRTE2NkZnZyeVlZXcvHmT8PBwNBoNQgi0Wi0BAQFoNBqklIyNjREeHk5ycjKNjY0MDg7OannrC4QQG4GPAR/BXjqyYmeW3iA7O5u0tDQuXbpEQEAABw8epLm5mfLycpKSksjPz2fDBvvlVUrJW2+9xd27dzGZTKhUKmenw1/+8pekpqbS2tqKlJLvfe97hIWFMTQ0hFqt9ql2039j12h6R0ppeeK1DOB/AM1SytcWee7fxa7/s+K0kucjICCAw4cPU1RUxIMHVpqaFibx7W9S4au1eEpIG++1/IzMkCnBxzqSRjtdCj7OR7cmhW51MoFaLe+88w4bN27EYDA4s0/Gx8exWCxYrVbGxsb45S9/ycTEBLGxsSQkJFBQUIBaraaxsZH+/n6Sk5MJDQ3FarVitVqxWCwEBQXNqAR+5ZVX6O/vf7pxXkAIkQO8ALyIPcz0OvZU+lVVV+UptFote/fupauri/LycjZs2MDhw4dpa2vj5MmTrFu3zikpn5ycTHd3t7OArqamhqGhIVQqFdevXyc0NJSxsTGCg4NJSkqirq4Ok8lEXFycVz/DfDOJzwK/D3xbCPGIx+qfaUAD8D0p5S8Xc1IhRBLwXuAvHedYVahUKioqKigpOUREhI2bN93PDPQ3qXBWafGUK8HH733/FfYO/YIh9dTC8mM9npnPZzOUkIDVauVXv/oVJpOJkJAQdDodGo0GvV5Pa2sr+fn5TkXP9vZ2Hjx44MyGmwopNDY20tzcTH5+PomJiTQ3NxMRETEj/Jidne2UGPcBNcC7wPullPUAQojf85UxK4W4uDiOHDnC9evXqaysJCEhAb1ez8OHD7l58yYBAQHk5+czNjZGaWnpLBVhs9nMr3/9awIDA/nc5z7nXLg+evSoWzUZS2G+NYlO4I+APxJCpGHPXhgDjFLK0SWe99uOY8/O9XIghHgZeBns8f6VSGWllszMSfbtM3PunNbtC70/SYVLKV32H58qngKWtXjKU7jKbsrPy+FUpY5bAXvnfqOUBMlhoqxdRFh7nBlQNpuN5ORkNm/eTHh4ODdv3sRsNrNt2zaCgoI4ceIEmzZtcmasFBUVIaWkvr6empoaZ/ghISEBm83G3bt3OX/+PAaDgVu3bs2KQx84cMDrf6M5+C3sM4kzQoh3gH/Hg0V5vsSdfjPuYjab6enpobe3l/7+fueFXKPRkJiYyF//9be4c+cW9+7dA+yh6o0bN5KYmMgnPvEJhoaGZh3z+eefd2qNAc5iza9//ev84he/WLCNcXFJdHa2PnU/t9JlHE1nmhdshQuEEO/DXql7Qwixf55zvopDknzLli0rtk1mQ4OGoSEbhw+bOX1ah9m8Kr5PK7546pEqjpraUxwoe9yZrqa2lkeqxxdfnW2MSFs3UdYuAm0jSMe1cFQVxiN1LLX6zc5eIy1ny2Z0otu1axejo6NcvXoVvV5PfHw8bW1tJCc/ruAWQpCdnU12drYz/BAdHU1xcTFFRUUUFhZy8uTJWc2RxsfHfda9TEr5C+AXjgy3DwK/B8QJIY4Cv5BSlvvEsCXyuAJ/iMLcTO7WnqbedJ039S/M6yh0tnHCbT1EWHsIsQ0CkFlayrVr14iJiSE5OZmioqJZ6wb/8R8/JTPzrzh82Mrly1r6+1WcOQNpaVaOHMmls1NFZaWGkZHH75NynLq6uhk3DHV1ddgrFHYt+DN3df2hW/v5IqdyN/ABIcR7sIevwoQQ/yKl/G0f2LIs9PSoOHdOx8GDZi5c0DI46F+FUIthNRRPze5MN0rGRDUx1g4AJkQgA+pYmrQFjItgl30D5iMoKIj9+/fT39/PtWvXuHXrFi+++KLLhcapsFNvby/nzp0jMDCQTZs2odfr2bhx45zNkXyFIx36X4F/FUJEAR8GvgqsSCeRZDGSE9zPF7/wWBX4H4/+gKQxI626PAJsJsJtvURYewiyPQ71mUUAA+p1tGszMYkwEIKWM2Xs3r37qedsaNDw4IGaHTssWCyCa9c0NDerycqycu2alpISCzod3LypYWBAxb17arZvN80Ys8PDJu7d8+565bI7CUdviq8BOGYSf7CaHcQUo6OC48d1lJZaqKlR09bmm4XohbJai6eibF1s3LSB9LTHF9+m5gdcqNRxK9CzKr9TTWXeeust3n77bbKyssjJyXE5G4iJieHAgQMMDQ1x9epVent76ezspKysjJycHKxWKydOnPBluGkWUspHwCuOx4ok3XKP/I05M8OPuTmUXD5DjLWDcVUQA6p1tGhzGRMhC75hmIvJScH58zpiYuzRhupqDSaTQKWCCxd06HSSjRsnCQ2d5M4dFSaTBa3WREtLCyaTCZPJQl2ddy/jbh/dEVpYjz1ltdt7Jq1erFbByZNaduyYJDxccveuT4rjFsqqLJ56pI7nnrGCgwceX3zfLD/DI413knQMhmQsllHS09Pp77d3Qbt79y6trfPHhAsLC/nEJz4x4+5xZGSED37wg9ROlfG7ibsx6LWJnBXKMdbV0a82cDPQ+4lbvb0q3nlHx4YNk4SH2ygutnDpkj08ffWqFrVa8uyzZqKiwjhy5Fm6u7uJjY3lzTdPkJVlwmj0QWc6IcQPgH+QUt4VQoQDlwArECWE+AMp5U+XenIpZQVQsdTjrCwEly9ryc+fZNcuMxcvavHndb/VWjy12GK6xdLV1QZ8i5ISMzdu6GhqkhQW7iM/30plpZbeXtdf8uhoMxs3biI9Pe3xjKepmbg4K7W1ugXa4F4Mei3SpC1kvalphjMeGhpmkiSEtC16AXshSCmoqtISFCR58cVxhoYmuXtXDQisVsHoqKCgIM/ZrQ6gpaXV64W2833yZ6SUdx2/fwp7VlMRsBl7ZpLCErh/X0NTk5pnn7Wg0ayMdXkhxEYhxN845Fr+Ans65Ipkqpju3EgOP7/6kHMjObyl/bDXLwadnSoMBitSCu7c0XDqlI70dCtlZWZCQmwu979zx0hmZiZ79+4lMzOTO3eMPiuuXK20aXNotSUwaBqnpaWVQdM4D0Uy9/Tb2TZ2fAt7egAAGNVJREFUnOjJ9mWzZXRUUFWlQQjJkSNmIiPt42Kq0HaqkHK5Cm3ni3dMDyU8C/wMnOqfXjVqrdDRoWZkRHDokD1Fdnomg7+wWounPFVMt1BqatQ884zFqd1ltQquXdOi10u2brXXrF67pmViwv4d87fiytWKFCre1L9gT4Ht6eSR2kCb3p4Ce0V9hBzzTVIsRqoDdjEpFjaDWwz37mkoKbFw4oSO7dst2GyC69ftY+G73/0+69fncedODffvm7w+FuZzEgOOdNWH2DOSXgIQQmhYweqf/sbwsIoTJ3SUlZmpqtLS1eV3jmJVFk+5Kqb7ztFjJI3XL0gqeqFMTgqkBK1WYrE8vtmamLAvYIaG2ti928LwsKCyUoPVKvjxj1Xs3TvI7duXaWlRLajmRsF95pQKFwKjvoRA2zCbxivo0KTR5sUxAnY9t8BAsFrh4kUd0dE2Dh40ExCgcvRAaXX0QPF+FGI+J/E57EqfBuArjuI6gAPAW942bC1hsQjKy3Xs2WMhLExFfb3an/pJrMriqShrBwU56TQ0NDj7NBTmZFBxs9OrTgLsd4kFBZPcujW7zGR4WMXp0/ZslwMHzLS3q9i+XZCfH+6cSWRmjvDaa0JxFMvMmCqUa4GHSLbUsnWsnDv6XYypQrx2vo4OleMaoKavT0Vjo4ZDh0L48pe/4FxcXw6F6Pkqro3AERfbj2NXhlXwIFIK3n1XR3Gxma98RZKYGOoX/SRWa/HUI1UcVbfe5EFzk7NPw8DAIKGTWehs45hVAV47d3e3iuLiyXn36e1VUV6uZ9cuM0VFoXzpS34jHb/madXm0qFJo2j8EiZVGEbdJo+lxE7HaFSza9fj0KTBYKOwcPkVoufLbvrufG+UUn7Z8+YojI+rSE5+fLfgLxcFbxZPCSGOYG9wpQaOSSn/eqnHdIfAwMAZwnn/ePQHdI6mUThxCYvQUavf4qyo9jR9fYKYGNucWU1TaDSQn+9X0vFex1fjYSFMCj03A/cTM/mQHWNvc1+/lUH1Oo+ew2IRaDT2TpVSCjo7Vdy+XUNKSjJdXV3ExcVx+3aN1xeu5zv654E9QDt2Se8bTzwUvMDcdwuzM198hZTykZTyFSnlkpvrOiTj/xH4DaAAeFEIUbDU4z6NKFsXebkz/855ubkEy2FuBpbSpFtP0fgFCscvofFCOcjdu/9/e2ceE+d95vHPM5cx4TTEnD5gOGwMA3bsHHZimxhc7ypRtFKkeFfVbhU5jaymu67a7mq3f2yi1a72Urd7SWnqTdttu6obbbWp0sQYjHHtxEkcsDkMGHPFxuEImAA21xy//WNgMoMBg83MO8DvIyHNyzsz7zPwzPv8juf5Pha2bZt/NgHejJbm5sCMlubm4N8YjMIof7hf+i1pfLT2K6S52igYfx+Tuvf/dDF0dprYtMn73W9tNTE8fJuKigomJyepqKhgePg2ra3GBYkUvNpJX8FbWWsFfqOU+qlS6qdBtWoVY1Sam4E8irc/RftUcd4vgeeCfVFvZ7r2gL9z49VrJLi6SXJd547EULP2aT61bqVw/Bx5Ex9hDlTMfyAmJwWzGczm+TceTSblkw+pqKjg+PHjjI2NI7I80qbvA0P84UFQYqZxzeN0WvPYNVbBpk2bluy9OzrMZGZ6fTQry0NsbDRHjx6ltLSUo0ePEhsbTVZWcAeQ8+1JDACvA6+LSBreFMgrIvIXSqmfBdWqVcwqTHlMA/zLgLuYRVnWXxU4IiKCnTt3AmCx2rh+bPHrwTdE2PLii/zgByMUFBRQX19PU1MT//3jH5OTk8OWjRvp6uqiqamJRqWIj49nx44dDA8Pc+nSJVyuwBGjxWrz2TQbFosVlyuwmK25OZXc3DifEqg/MTExOBwOsrKyKCo6TEZGhl8xXQdJSX/H1au/W9RntlisATZWV1efVErdte9oMPf0h7l8Ae7fH5aKK0BhURFf/epXaWtrw+mcfWAxmz/MhscDHs9+zOZzJCfvoaDgezNWGXJITv5bWloW5wvTNizEH+65yC0iO/AGiFLgPfRSU1AJw34SwWa2D3bXMHmmKvAnn3zywBf2eDy0trbS09PDs88+y7e+9S2OHz/uO3/jxg2am5uJi4ujsLAQm83GwMAANTU1Pmnnmbr/i0EpRUVFBaWlpb7jtrY2Ojo6iI6OxuFw0NXVRVVVFSUlJT75kDNnzvCjH/1oKdqXhluAgAX4QzB8YakZHR3lwoULJCUlsW3btgdS7b1x4wYtLS309/fT1taG2/1lX/aOjo6l8gWYwx/m27h+DXgGb/e4XwJ/OdXKUhNkwqmfRAjowtsze5p0vPtgQcdkMpGTkzPnF2zDhg1s2LCBW7du8f7772OxWNi+fTulpaV8/vnnvja1hYWFvtHdYhAR4uLifA2Ibt++jd1up6SkxHdTycrKorq6OkAuIiYmhqys4MiHhAGG+cNSEhkZyYEDB+jo6KCsrIzHH3+cuLi4Rb3H6OgodXV1DA8PMzIywvPPP8+vfvWrkPuCzNXVSEQ8QDveRkMQ2J5LKaUcs74wCMwcLYgIG39g7Jrs9WMyb0co75f8n0Jn0Kx8d6aNYTcdmSrObMFbf3MTuAj8kZ8kzF0YNXocHR3l0qVLjI+P43A4ePjhh+nt7aW2tpb169fjcDgW3G9YKcX169dpamqiv7+f5557jujo2Xtw+c94kpOTycrKWqq+xsveH8J1JuGP0+nkww8/xGazsWvXrnv+77q7u2lsbMRms1FYWEhMTAznzp1j165d2Gy2YPkCzOEP882VM5bqyuHCUnae0iwNSimXiLyCt/bGDLw5X4AwksjISPbs2YPT6aSuro5Lly6Rm5tLaWkpPT09lJeXk5KSQn5+/pxf3PHxcerr6xkcHGTTpk0cPHiQyspKHnrooTmve68Zz0piOfnDQrFarTz11FP09fVRVlZGUVERKSkpAc9xuVw0NjbS09NDSkoK+/fvD5idbt26laamJrZv3x5yX5hv4/rT2X4/laJ2GJj1fLgiysMzkyewR05r9VTRNloddK0ezb1RSr0LvGu0HQvFarXyyCOPoJTi6tWrnDp1ivT0dEpLS+nu7qa8vJy0tLSAteju7m6ampqwWCwUFBQQHx/ve7+srCxaW1tXRRBYCMvNHxbK+vXrOXToEDU1NbS0tPDEE08wNjZGXV0dk5OT5OXlBXQ39CcxMZHLly+H2GIv8+1JxADfwJtt8BugHHgF+A5wGW9h1bJhNq2efwuBVo9m5SIiPtnmrq4uTp8+TWxsLMXFxfT29vLee++hlMJqtZKamsrevXtn3ejetGkTFRUVOkisAkSEHTt20NjYyE9+8hNSUlIoKSlh7dp7y+FFRUUxMjIy57JksJhvCP0zIBeoB47grax9HnhOKXXfecsiskFEzohIk4hcEZE/u9/3WgwJrs/Iz80ISB/Lz80kwdUdistrVjjTM4msrCzKy8uprKzEZDKRlpaGx+PBarXOubktIr4bgGblMjExwcWLFykvL8dms/HSSy+RnZ3NuXPnuH379j1fv23btlnTpYPNfHsSmVP9IxCR40A/sFEp9aCe7AK+rZSqEZFooFpEypVSQf30guLq1ZaAzlPNV6+S4nqITvdWvjCvD+blF4WICieBP80CcLvdNDU10d3dTWpqKnv27OHKlSsMDAywY8cO7ty5Q1lZGXa7naysrLtSIgsKCqirq2P37sU3tNeEN/39/dTX1yMiOBwO1q1b5zu3ZcsWMjIy+OCDD4iLi6OoqGjOdNno6GhDBhLzBQlfFYhSyi0iHUsQIFBKdQPdU49HRKQJ75JWUIOEB0GEgPQxkwhDso5E92dkTdYyZEqkw7YtJHrxcyGiePFFN1u3RoWFwJ9mfgYHB6mvr8flcrFlyxby8/N95/bs2YPL5aKuro7+/n5fnUNZWRnZ2dlkZmb6bghRUVHcuXMHpdQD5dRrwgOPx0NLSwvXr18nMTGRJ598Eqv1btVfgDVr1lBcXExXVxcnT55k165dJCYmzvrchIQEBgYGSEhICKb5AcwXJApFZHjqsQBrp46nU2BjHvTiIrIZ2A58NMs5X1Xlxo0bH/RS3DIn4/I0cGDfPvr6+ti3bx9lpypwmiKId/fRZ97AkGkd+RMXMCk316259JtT76nuqJTC4/Hgdrt9Py6Xi4SEBMxmD2azwmz2CrVNP57veP16D/n5cXzzm+El8Kf5Eo/Hw7Vr1/j000+Ji4vj0UcfJSJidtVYi8XCjh07UMrbQ7mjo4O0tDScTidlZWVs2bKFzZs3A5CRkUFnZycZGSsusXDVcOfOHWpraxkdHSUnJ4fS0tIFB/309HRSUlL4+OOPuXr1Ko899thde1i5ubmcOnWK+Ph4UlJSljoFdlbmy24Kqg6EiEQB/4u3V8XwzPMzqyqX4prj4+OcPXsWu93O2bNnmRgfI9I9wrA5gXRnC/meAZzYGDStp2D8AyLVMJMSwaBpPW4JHAVk7t9PVVUV4E1RNJvNmM1mLBYLZrOZ1NRU3G4Pbre3cYjLJUxOiu/x9O9nHu/Z4yQvb3Wpfi4XRkZGqKurY2xsjOzs7EXdAETEl7p48+ZNGhsbiY+PZ2RkhJMnT5KXl0dmZiaVlZU6SCxDbt68SVNTE2vXrsXhcNz35rLZbOaJJ57g1q1blJeXs23bNt8g2ePx8PbbbzM4OEhUVBRVVVVUV1fzwgsvBDVQGDI0FREr3gDxC6XUr0NxzXWeXoq2F5KxedOXGjidn3L+0hpqI/b6nveQZ4isyct8oRKptj2NBxOZzgasaoKb1ix6zJtAhOtVxZw5c2bO69XX13M/f95pgb8DB77cO1nhAn9hjVKKjo4O2traiIqKoqioaN6ahoWQlpZGWloag4OD1NbWsnbtWvr6+mhqasLlcjE6OkpkZOQSfQJNsHC5XDQ0NNDX10daWhrFxcX3VXk/G+vWrePQoUPU19fT2trK7t27uX79OiMjI7z88su+e8Px48eDnj4d8iAh3qHXfwFNSqnvh+q6t8wpNLZUUXLgad/a8DunznDLHNiq+Y4pltqIfVjUJPbJOmI8t+iyeINDmruNneMVjEkUAw94o5iLVSjwZxjTlczTnen8p+7T+evDw8NkZGQESGUsFfHx8ezfv5+xsTEuXbqEyWRizZo1vPXWW5SWlpKamnpPOzWh54svvqC2tha32822bdsoKioKynWmN7pHR0c5f/48g4ODbN68OaCbYmZmJj09PUENEnPKcgTtgiJP4u2ZXA9Ma9z+1VQBzawshSzHdDFdZuQw+bl2Gq620T4ac+9iOqVId7WS4urgC1Mi7bYCbGqcdW99hddee43Nmzdjt9vv+tI+iCzH0mU3hb8sx/2wFFIMHo+HEydOMDIyQmZmJu3t7URHR7N7926uXbtGREQEDoeDmJgH3npbMC6Xi/r6ei5evEhGRgZutxuHw0FVVRX9/f2+7JbExEQOHz68FIFi2ftDqGQ5lFK0t7fT1tZGXFwcDodjzn2oYHH27FkuXLhAfHw8drud9vZ2hoeHeeaZZ9iyZctSXGLRshxBQSl1HgOcU4mJd2wvkD7eSlVND7fMxXTZFiDLIUKXNZsuazZx7j4Kx8/hEitnGxooKSmhs7OTyspKIiIiKCgoIDY29sFtXV0Cf4bQ2trKyMhIQGe6H/7wh7S2ti7pssFimBYQXLNmDUNDQwwNDVFRUUFvby+xsbFs2LCB9vZ2PvvsM1paWpbqxqCZYrYZ2+TkJHV1dQwNDZGZmbmofailJikpiejoaI4cOeLz2ddffz3o111V6TJKTNyw5tx3hfUX5vXUrH2aNZ5R8vPzOX36NHl5eZSUlDA6OkpDQwNDQ0Okp6djMpnwhE8zOc0Muru7yczMDEgQyMnJmbfoLVTk5uZSVVXFoUOH+PnPf05kZORdwayxsVEHiSVk5szyzJkzvo3jwsLCABkVo+jt7SV3RjfF3Nxc+vr6guoLemHzPpgwRXLhwgWKi4sZGBjg1KlTdHZ2snPnTkpKSoiOjmb//v3s2zdJQoKOFOFISkoK7e2Bnena2tpITk422DJ8WXITExNERkaSk5MTcGPIzs422MKVh//MsqSkhCNHjmCxWEhNTQ2LAAHG+awOEg+ANz21gIMHDxITE8Pp06f56KOPSExMpLKykgsXrGza5Ka0dBKHw4XFsmJbTi47srKyiI6ODmgLGk59GvLz82loaCAvL4+WlpaAG0NLSwt5eWHb9nlZMtvM0m6309PTY7BlX2KUz66q5aZgkp6eTnp6OkNDQ3z88ccUFxdTV6eoqfHWVyQleXjqKW8Re2Ojmd5ena1kJCaTiRdeeMGnzV9cXBxWWUMJCQnU1NSwfft2ampqeOONN3xqsfHx8VoMcIlJSUmhqqoqoOtbW1sbxcXF935xiDDKZ3WQWGJiY2PZu3cv58+fx+Eo5ZFHnLS2WmhvN9Hba8NiUWzd6sbhmGRgQLhyxcLExLJPMlmWhHufhqSkJPr6+jh8+LDvxnDgwIGwCmYrheXSAdAIn9VBIkg4nU6qq62IKOx2NwcPTvL55ybq6y1TP7BunYfHHnNisUBLi5muLhMgWuBPA3gbzZw/f57k5OSwDmYrgXCfWRqJDhJBRimhtdVCa6uFhx/2sHevE6cTLl+2cOuWid/9zobJpMjJcVNS4mR4GAoLTeTmaoG/1c60IJzT6ZxTHE6zdIT7zNIodJgMIZ9/bqKy0sYnn1jJz3dTUjJJaqobj0dobrZQUWFjYkLIy4vi2LGjHDxYyrFjR9m6NYrsbLfR5msMIC8vj6amJqPN0Kxi9EzCAMbGhAsXrJhMirw8NwUFE9y4Yaa52UxsrNICfxofSUlJ1NbWEhERoWU5NIagPc1APB6hocFCWdkahoeFAwecxMZ6aG6+GpDy2NzcrAX+Vikej4fOzk7OnDmD0+mkqqqKEydO4NGVmpoQoWcSYUJXl5muLjOFhZOMjY0FZFmMjY0jomssViOtra0opQKkGEKh/KnRTKOHp2FGbCwUFRVSXFyMzWajuLiYoqJCkpJ0kFiNdHd3Y7fbw7rIS7Oy0UEizOjpMdHQ0ILdbmfv3r3Y7XYaGlr0ctMqJZzlQzSrA73cFGbofhIaf5ZLkZdm5aKDRJihlPDmm2ays2+TnHxRF9OtcnSRl8ZojGpfegj4V8AMHFdK/b0RdoQrup+Exh9d5KUxkpAPR0TEDPwn8HtAHvCHIqIlLTUajSYMMWLO+ijQqpRqV0pNAr8EnjPADo1Go9HcAyOWm9KAG37HXcBjM58kIl8Hvg4QERHBzp07fecsVhvXjxm7Rm+x2gJsuuu8xYrL9d0QWjS7Df42VldXn1RKHTLQJI1Gs8wwIkjMdne/qwhAKfUG8AaErtn5KkAHCI1GsyiMWG7qAjb4HacDnxlgh0aj0WjugRFB4iKQLSIZImIDDgO/McAOjcGIyKsiclNELk/9/L7RNmmMQ/tDeBLy5SallEtEXgHK8KbAvqmUuhJqOzRhw78opf7ZaCM0YYP2hzDDkDoJpdS7wLtGXFuj0Wg0C0eUCn/hOBHRWTkrEBF5FfgaMAx8AnxbKTU4x3N92W5AhFIqPxQ2akLHQv1B+0JoWRZBQrN8EZEKYDY1uu8BHwL9eLPb/gZIUUq9GELzNCFG+8PyQwcJTVggIpuBd/SoUAPaH8IJrRKmMQwRSfE7/AOgwShbNMaj/SE80SqwGiP5RxEpwru80Am8bKw5GoPR/hCGrNjlJhFxA/WAFXABPwV+oJTyiMhO4I+VUn9qoH23lVJRfsdfA3YqpV6Z5zWvArd1iuDi0f6g8Sec/SHcfGElzyTGlFJFACKyHvgfIBb4a6XUJ3izJzSrB+0PGn+0PyyQVbEnoZTqw5sy94p42S8i7wCIyD6/Cs9LIhJtrLUgIs+KyEdT9lSISJLf6UIRqRSRayLykmFGLmO0P2j8WU7+YIQvrOSZRABKqXYRMQHrZ5z6DvANpdT7IhIFjIfIpLUictnveB1fypOcBx5XSikROQL8OfDtqXMO4HHgIeCSiPxWKaW1rxaJ9geNP2HmD2HlC6smSEwxmwLt+8D3ReQXwK+VUl0hssU33YUv1x2nDtOBE1PZHjagw+91byulxoAxETmDtz/H/4XG5BWH9geNP+HiD2HlC6tiuQlARDIBN9Dn//up1qlHgLXAhyKyxQDzZvLvwH8opQrwZnhE+J2bmWmwMjMPgoz2B40/y8gfQu4LqyJIiMjDwOt4/7hqxjm7UqpeKfUPeDerjHYC8G6g3Zx6/Cczzj0nIhEikgDsx6uqq1kE2h80/iwzfwi5L6zk5abpdb3pFLefAd+f5XnHRKQY7yiiEXgvdCbOyavAWyJyE69UQYbfuY+B3wIbgb/R688LRvuDxp/l6g+vEmJfWLF1EhqNRqN5cFbFcpNGo9Fo7g8dJDQajUYzJzpIaDQajWZOdJDQaDQazZzoIKHRaDSaOdFBQqPRaDRzooOERqPRaObk/wHxv0T7H+AwVwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, [ax1, ax2, ax3] = plt.subplots(figsize=(6,3), ncols=3)\n",
+ "f.subplots_adjust(left=0.1, wspace=0.6)\n",
+ "\n",
+ "_, barx, _, _ = tp.barscatter([rms_dis, rms_hab], paired=True,\n",
+ " barfacecolor=colors[1:], barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax1)\n",
+ "\n",
+ "ax1.set_ylabel('RMS (filtered signal)')\n",
+ "ax1.set_ylim([-0.5, 13])\n",
+ "\n",
+ "tp.barscatter([dis_bl, hab_bl], paired=True,\n",
+ " barfacecolor=colors[1:], barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax2)\n",
+ "\n",
+ "ax2.set_ylabel('AUC (Baseline)')\n",
+ "ax2.set_yticks([-5, 0, 5, 10, 15])\n",
+ "\n",
+ "tp.barscatter([dis_ep2, hab_ep2], paired=True,\n",
+ " barfacecolor=colors[1:], barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax3)\n",
+ "\n",
+ "ax3.set_ylabel('AUC (1-4 s)')\n",
+ "ax3.set_yticks([-5, 0, 5, 10, 15])\n",
+ "\n",
+ "barlabels=['Dis', 'Hab']\n",
+ "\n",
+ "for axis in [ax1, ax2, ax3]:\n",
+ " trans = transforms.blended_transform_factory(\n",
+ " axis.transData, axis.transAxes)\n",
+ " for x, label in zip(barx, barlabels):\n",
+ " axis.text(x, -0.07, label, ha=\"center\", transform=trans)\n",
+ "\n",
+ "f.savefig(figfolder+\"figs6_rms_and_bl.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RMS (Dis) vs RMS (Hab) 1.5850746150417214 0.13893468636891437 Sidak: 0.36157735288204806\n",
+ "BL (Dis) vs BL (Hab) 1.9840900717804237 0.07059629188121051 Sidak: 0.19718920673261564\n",
+ "E2 (Dis) vs E2 (Hab) 2.2776439210309 0.04185609230875025 Sidak: 0.12038580858307979\n"
+ ]
+ }
+ ],
+ "source": [
+ "t, p = stats.ttest_rel(rms_dis, rms_hab)\n",
+ "print('RMS (Dis) vs RMS (Hab)', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(dis_bl, hab_bl)\n",
+ "print('BL (Dis) vs BL (Hab)', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(dis_ep2, hab_ep2)\n",
+ "print('E2 (Dis) vs E2 (Hab)', t, p, \"Sidak:\", tp.sidakcorr(p))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Old stuff below here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, [ax1, ax2] = plt.subplots(figsize=(6,3), ncols=2)\n",
+ "f.subplots_adjust(left=0.4, wspace=0.4)\n",
+ "tp.barscatter([rms_mod, rms_dis, rms_hab], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " barlabels=['Mod', 'Dis', 'Hab'],\n",
+ " barlabeloffset=0.05,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax1)\n",
+ "\n",
+ "ax1.set_ylabel('RMS (filtered signal)')\n",
+ "ax1.set_xticks([])\n",
+ "# ax.set_xticks([1,2,3])\n",
+ "# ax.set_xticklabels(['Mod', 'Dis', 'Hab'])\n",
+ "# ax.set_ylim([-0.1, 1.1])\n",
+ "\n",
+ "tp.barscatter([mod_bl, dis_bl, hab_bl], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " barlabels=['Mod', 'Dis', 'Hab'],\n",
+ " barlabeloffset=0.05,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax2)\n",
+ "\n",
+ "# f.savefig(figfolder+\"figs5_rms.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Anova\n",
+ "======================================\n",
+ " F Value Num DF Den DF Pr > F\n",
+ "--------------------------------------\n",
+ "variable 0.7556 2.0000 24.0000 0.4806\n",
+ "======================================\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "rats = disDict.keys()\n",
+ "data = np.array([rms_mod, rms_dis, rms_hab], dtype=\"float\").T\n",
+ "\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"mod\", \"dis\", \"hab\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df = df.melt(id_vars = \"ratid\")\n",
+ "\n",
+ "aovrm = AnovaRM(df, \"value\", \"ratid\", within=[\"variable\"])\n",
+ "res = aovrm.fit()\n",
+ "\n",
+ "print(res)\n",
+ "\n",
+ "# print(\"Mean of WN is\", np.mean(wn_prob))\n",
+ "# print(\"Mean of Tone is\", np.mean(tone_prob))\n",
+ "# print(\"Mean of Comb is\", np.mean(combined_prob))\n",
+ "\n",
+ "# t, p = stats.ttest_rel(wn_prob, tone_prob)\n",
+ "# print('WN vs Tone', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "# t, p = stats.ttest_rel(tone_prob, combined_prob)\n",
+ "# print('Tone vs Comb', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "# t, p = stats.ttest_rel(wn_prob, combined_prob)\n",
+ "# print('WN vs Comb', t, p, \"Sidak:\", tp.sidakcorr(p))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Anova\n",
+ "======================================\n",
+ " F Value Num DF Den DF Pr > F\n",
+ "--------------------------------------\n",
+ "variable 3.4163 2.0000 24.0000 0.0495\n",
+ "======================================\n",
+ "\n",
+ "WN vs Tone 1.9840900717804237 0.07059629188121051 Sidak: 0.19718920673261564\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from statsmodels.stats.anova import AnovaRM\n",
+ "from scipy import stats\n",
+ "\n",
+ "rats = disDict.keys()\n",
+ "data = np.array([mod_bl, dis_bl, hab_bl], dtype=\"float\").T\n",
+ "\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"mod\", \"dis\", \"hab\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df = df.melt(id_vars = \"ratid\")\n",
+ "\n",
+ "aovrm = AnovaRM(df, \"value\", \"ratid\", within=[\"variable\"])\n",
+ "res = aovrm.fit()\n",
+ "\n",
+ "print(res)\n",
+ "\n",
+ "# print(\"Mean of WN is\", np.mean(wn_prob))\n",
+ "# print(\"Mean of Tone is\", np.mean(tone_prob))\n",
+ "# print(\"Mean of Comb is\", np.mean(combined_prob))\n",
+ "\n",
+ "t, p = stats.ttest_rel(dis_bl, hab_bl)\n",
+ "print('WN vs Tone', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "# t, p = stats.ttest_rel(tone_prob, combined_prob)\n",
+ "# print('Tone vs Comb', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "# t, p = stats.ttest_rel(wn_prob, combined_prob)\n",
+ "# print('WN vs Comb', t, p, \"Sidak:\", tp.sidakcorr(p))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/ss_tercile analysis.ipynb b/notebooks/ss_tercile analysis.ipynb
new file mode 100644
index 0000000..62cb863
--- /dev/null
+++ b/notebooks/ss_tercile analysis.ipynb
@@ -0,0 +1,589 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "import numpy as np\n",
+ "import csv\n",
+ "from scipy import stats\n",
+ "import trompy as tp\n",
+ "\n",
+ "# %run ..//JM_custom_figs.py\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fig settings\n",
+ "scattersize=50\n",
+ "colors = ['darkturquoise','dodgerblue', 'darkblue']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "statsfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\stats\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# thph2.8 removed from analysis because no data on habituation day\n",
+ "\n",
+ "modDict.pop('thph2.8')\n",
+ "disDict.pop('thph2.8')\n",
+ ";"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# adds parameters used for figures - no. of distractors, distracted, not distracted and calculates probability of distraction\n",
+ "\n",
+ "for day in [modDict, disDict, habDict]:\n",
+ " rats = day.keys()\n",
+ " for rat in rats:\n",
+ " d = day[rat]\n",
+ " # check that numbers add up\n",
+ " d ['#ds'] =len(d['distractors'])\n",
+ " d ['#distracted'] =len(d['distracted'])\n",
+ " d ['#not-dis'] =len(d['notdistracted']) \n",
+ " if d['#not-dis'] + d['#distracted'] == d['#ds']:\n",
+ " d['prob-distracted'] = d['#distracted'] / d['#ds']\n",
+ " else:\n",
+ " print(\"Something wrong in the number of distracted and non-distracted trials\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def find_tercile_prob(d, verbose=False):\n",
+ " \"\"\" Finds probability distracted for each tercile from distraction data dictionary\n",
+ " \n",
+ " Args\n",
+ " d - dictionary with distraction data\n",
+ " \n",
+ " Returns\n",
+ " tercile_prob_distracted - list of 3 values\"\"\"\n",
+ " \n",
+ " tercile_length = int(np.round((d[\"#ds\"] / 3)))\n",
+ " tercile_length_array = [tercile_length, d[\"#ds\"] - tercile_length*2, tercile_length]\n",
+ "\n",
+ " dba = d[\"d_bool_array\"]\n",
+ " t1 = sum(dba[:tercile_length_array[0]])\n",
+ " t2 = sum(dba[tercile_length_array[0]:tercile_length_array[1]+tercile_length_array[0]])\n",
+ " t3 = sum(dba[tercile_length_array[1]+tercile_length_array[0]:])\n",
+ "\n",
+ " tercile_prob_distracted = []\n",
+ " for t_sum, t_len in zip([t1, t2, t3], tercile_length_array):\n",
+ " tercile_prob_distracted.append(t_sum / t_len)\n",
+ "\n",
+ "\n",
+ " if sum([t1, t2, t3]) != len(d[\"distracted\"]):\n",
+ " print(\"Check tercile calculation\")\n",
+ " \n",
+ " if verbose:\n",
+ " print(d[\"rat\"], sum([t1, t2, t3]), len(d[\"distracted\"]), len(d[\"notdistracted\"]), print(sum(dba)))\n",
+ " print(\"Tercile length array\", tercile_length_array)\n",
+ " print(\"Number of distractors\", d[\"#ds\"])\n",
+ " print(dba)\n",
+ " print(\"Tercile probability of distraction\", tercile_prob_distracted)\n",
+ " print(\"Number of Trues in each tercile\", t1, t2, t3)\n",
+ " print(\"\\n\")\n",
+ " \n",
+ " return tercile_prob_distracted\n",
+ "\n",
+ "rats = modDict.keys()\n",
+ "\n",
+ "all_tercile_probs = []\n",
+ "for rat in rats:\n",
+ " t_probs = []\n",
+ " t_probs.append(find_tercile_prob(modDict[rat]))\n",
+ " t_probs.append(find_tercile_prob(disDict[rat]))\n",
+ " t_probs.append(find_tercile_prob(habDict[rat]))\n",
+ " \n",
+ " all_tercile_probs.append(tp.flatten_list(t_probs))\n",
+ "\n",
+ "# d = disDict['thph2.3']\n",
+ "# aaa = find_tercile_prob(d, verbose=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\envs\\dis\\lib\\site-packages\\scipy\\stats\\stats.py:3233: RuntimeWarning: invalid value encountered in true_divide\n",
+ " msb = ssbn / dfbn\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "F_onewayResult(statistic=array([nan, nan, nan, nan, nan, nan, nan, nan, nan]), pvalue=array([nan, nan, nan, nan, nan, nan, nan, nan, nan]))"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "all_tercile_probs_array = np.array(all_tercile_probs)\n",
+ "\n",
+ "colors_triple = [colors[0], colors[0], colors[0],\n",
+ " colors[1], colors[1], colors[1],\n",
+ " colors[2], colors[2], colors[2]]\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(4,4))\n",
+ "f.subplots_adjust(left=0.15)\n",
+ "_, barx, _, _ = tp.barscatter(all_tercile_probs_array.T, paired=True,\n",
+ " barfacecolor=colors_triple, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax)\n",
+ "\n",
+ "ax.set_ylabel(\"Probility of distraction\")\n",
+ "ax.set_ylim([-0.05, 1.1])\n",
+ "\n",
+ "ax.set_xticks(barx)\n",
+ "ax.set_xticklabels([\"1\", \"2\", \"3\", \"1\", \"2\", \"3\", \"1\", \"2\", \"3\"])\n",
+ "\n",
+ "f.savefig(figfolder + \"tercile_analysis_prob.pdf\")\n",
+ "\n",
+ "from scipy.stats import f_oneway\n",
+ "\n",
+ "f_oneway(all_tercile_probs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def find_tercile_aucs(d, epoch=[60, 90], signal=\"filt_z\"):\n",
+ " \"\"\" Finds probability distracted for each tercile from distraction data dictionary\n",
+ " \n",
+ " Args\n",
+ " d - dictionary with distraction data from single rat\n",
+ " \n",
+ " Returns\n",
+ " tercile_prob_distracted - list of 3 values\"\"\"\n",
+ " \n",
+ " tercile_length = int(np.round((d[\"#ds\"] / 3)))\n",
+ " tercile_length_array = [tercile_length, d[\"#ds\"] - tercile_length*2, tercile_length]\n",
+ "\n",
+ " snips = d[\"snips_distractors\"][signal]\n",
+ " t1 = [np.mean(snip[epoch[0]:epoch[1]]) for snip in snips[:tercile_length_array[0]]]\n",
+ " t2 = [np.mean(snip[epoch[0]:epoch[1]]) for snip in snips[tercile_length_array[0]:tercile_length_array[1]+tercile_length_array[0]]]\n",
+ " t3 = [np.mean(snip[epoch[0]:epoch[1]]) for snip in snips[tercile_length_array[1]+tercile_length_array[0]:]]\n",
+ " \n",
+ " \n",
+ " tercile_aucs = []\n",
+ " for t in [t1, t2, t3]:\n",
+ " tercile_aucs.append(np.mean(t))\n",
+ " \n",
+ " return tercile_aucs\n",
+ "\n",
+ "rats = modDict.keys()\n",
+ "\n",
+ "all_tercile_aucs = []\n",
+ "for rat in rats:\n",
+ " t_aucs= []\n",
+ " t_aucs.append(find_tercile_aucs(modDict[rat]))\n",
+ " t_aucs.append(find_tercile_aucs(disDict[rat]))\n",
+ " t_aucs.append(find_tercile_aucs(habDict[rat]))\n",
+ " \n",
+ " all_tercile_aucs.append(tp.flatten_list(t_aucs))\n",
+ "\n",
+ "# d = disDict['thph2.3']\n",
+ "# find_tercile_aucs(d)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\envs\\dis\\lib\\site-packages\\scipy\\stats\\stats.py:3233: RuntimeWarning: invalid value encountered in true_divide\n",
+ " msb = ssbn / dfbn\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "F_onewayResult(statistic=array([nan, nan, nan, nan, nan, nan, nan, nan, nan]), pvalue=array([nan, nan, nan, nan, nan, nan, nan, nan, nan]))"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "all_tercile_aucs_array = np.array(all_tercile_aucs)\n",
+ "\n",
+ "colors_triple = [colors[0], colors[0], colors[0],\n",
+ " colors[1], colors[1], colors[1],\n",
+ " colors[2], colors[2], colors[2]]\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(4,4))\n",
+ "f.subplots_adjust(left=0.15)\n",
+ "_, barx, _, _ = tp.barscatter(all_tercile_aucs_array.T, paired=True,\n",
+ " barfacecolor=colors_triple, barfacecoloroption='individual',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax)\n",
+ "\n",
+ "ax.set_ylabel(\"Z-score (AUC)\")\n",
+ "# ax.set_ylim([-0.05, 1.1])\n",
+ "\n",
+ "# ax.set_xticks(barx)\n",
+ "# ax.set_xticklabels([\"1\", \"2\", \"3\", \"1\", \"2\", \"3\", \"1\", \"2\", \"3\"])\n",
+ "\n",
+ "f.savefig(figfolder + \"tercile_analysis_aucs.pdf\")\n",
+ "\n",
+ "from scipy.stats import f_oneway\n",
+ "\n",
+ "f_oneway(all_tercile_probs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dis_probs = all_tercile_probs_array[:,3:6]\n",
+ "dis_aucs = all_tercile_aucs_array[:,3:6]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, [ax1, ax2] = plt.subplots(ncols=2, figsize=(5.5,4))\n",
+ "f.subplots_adjust(left=0.15, wspace=0.4)\n",
+ "\n",
+ "\n",
+ "_, barx, _, _ = tp.barscatter(dis_probs.T, paired=True,\n",
+ " barfacecolor=[colors[1]], barfacecoloroption='same',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax1)\n",
+ "\n",
+ "tp.barscatter(dis_aucs.T, paired=True,\n",
+ " barfacecolor=[colors[1]], barfacecoloroption='same',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax2)\n",
+ "\n",
+ "ax1.set_ylabel(\"Probability of distraction\")\n",
+ "ax2.set_ylabel(\"Z-score (AUC)\")\n",
+ "\n",
+ "ax1.set_ylim([-0.05, 1.1])\n",
+ "\n",
+ "barlabels=['1st', '2nd', '3rd']\n",
+ "\n",
+ "for axis in [ax1, ax2]:\n",
+ " trans = transforms.blended_transform_factory(\n",
+ " axis.transData, axis.transAxes)\n",
+ " for x, label in zip(barx, barlabels):\n",
+ " axis.text(x, -0.05, label, ha=\"center\", transform=trans)\n",
+ " axis.text(barx[1], -0.13, \"Tercile\", ha=\"center\", transform=trans)\n",
+ " \n",
+ "f.savefig(figfolder + \"within_session_hab.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Anova\n",
+ "======================================\n",
+ " F Value Num DF Den DF Pr > F\n",
+ "--------------------------------------\n",
+ "variable 5.0127 2.0000 24.0000 0.0152\n",
+ "======================================\n",
+ "\n",
+ "1 v 2 2.8236282257268974 0.015356220065884196 Sidak: 0.04536484091743631\n",
+ "1 v 3 2.29752885513493 0.04037776743507989 Sidak: 0.11630804045870269\n",
+ "2 v 3 0.637804302216334 0.5355821448177593 Sidak: 0.8998325252317831\n",
+ " Anova\n",
+ "======================================\n",
+ " F Value Num DF Den DF Pr > F\n",
+ "--------------------------------------\n",
+ "variable 0.7818 2.0000 24.0000 0.4689\n",
+ "======================================\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from statsmodels.stats.anova import AnovaRM\n",
+ "\n",
+ "rats = disDict.keys()\n",
+ "data = np.array(dis_probs, dtype=\"float\")\n",
+ "\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"1\", \"2\", \"3\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df = df.melt(id_vars = \"ratid\")\n",
+ "\n",
+ "aovrm = AnovaRM(df, \"value\", \"ratid\", within=[\"variable\"])\n",
+ "res = aovrm.fit()\n",
+ "\n",
+ "print(res)\n",
+ "t, p = stats.ttest_rel(dis_probs.T[0], dis_probs.T[1])\n",
+ "print('1 v 2', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(dis_probs.T[0], dis_probs.T[2])\n",
+ "print('1 v 3', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(dis_probs.T[1], dis_probs.T[2])\n",
+ "print('2 v 3', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "data = np.array(dis_aucs, dtype=\"float\")\n",
+ "\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"1\", \"2\", \"3\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df = df.melt(id_vars = \"ratid\")\n",
+ "\n",
+ "aovrm = AnovaRM(df, \"value\", \"ratid\", within=[\"variable\"])\n",
+ "res = aovrm.fit()\n",
+ "\n",
+ "print(res)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.59259259, 0.92 , 0.66666667, 0.65 , 0.41935484,\n",
+ " 0.25 , 0.94736842, 0.125 , 0.81818182, 0.33333333,\n",
+ " 0.5 , 0.83333333, 0.625 ])"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dis_probs.T[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dis_terciles = []\n",
+ "for rat in all_tercile_probs:\n",
+ " dis_terciles.append(rat[3:6])\n",
+ " \n",
+ "dis_terciles\n",
+ "\n",
+ "import pandas as pd\n",
+ "df = pd.DataFrame(dis_terciles)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def get_rms(daydict):\n",
+ " \n",
+ " \"\"\" Gets rms for all rats\"\"\"\n",
+ " rms = []\n",
+ "\n",
+ " rats = daydict.keys()\n",
+ " for rat in rats:\n",
+ " d = daydict[rat]\n",
+ " rms.append(d[\"rms\"])\n",
+ " \n",
+ " return rms\n",
+ "\n",
+ "rms_mod = get_rms(modDict)\n",
+ "rms_dis = get_rms(disDict)\n",
+ "rms_hab = get_rms(habDict)\n",
+ "\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(2,3))\n",
+ "f.subplots_adjust(left=0.4)\n",
+ "tp.barscatter([rms_mod, rms_dis, rms_hab], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " barlabels=['Mod', 'Dis', 'Hab'],\n",
+ " barlabeloffset=0.05,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax)\n",
+ "\n",
+ "ax.set_ylabel('RMS (filtered signal)')\n",
+ "ax.set_xticks([])\n",
+ "# ax.set_xticks([1,2,3])\n",
+ "# ax.set_xticklabels(['Mod', 'Dis', 'Hab'])\n",
+ "# ax.set_ylim([-0.1, 1.1])\n",
+ "\n",
+ "f.savefig(figfolder+\"figs5_rms.pdf\")\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/uu_trialtypes.ipynb b/notebooks/uu_trialtypes.ipynb
new file mode 100644
index 0000000..075cad6
--- /dev/null
+++ b/notebooks/uu_trialtypes.ipynb
@@ -0,0 +1,438 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "import numpy as np\n",
+ "\n",
+ "from scipy.stats import linregress\n",
+ "\n",
+ "import trompy as tp\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fig settings\n",
+ "scattersize=50\n",
+ "colors = ['darkturquoise','dodgerblue', 'darkblue']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# thph2.8 removed from analysis because no data on habituation day\n",
+ "\n",
+ "modDict.pop('thph2.8')\n",
+ "disDict.pop('thph2.8')\n",
+ ";"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "11.324645"
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d = modDict[\"thph2.2\"]\n",
+ "np.shape(d['snips_distractors'][\"filt_z\"])\n",
+ "d.keys()\n",
+ "d[\"rms\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modalitykey = {'whitenoise':[1,4], 'tone':[2,5], 'combined3':[3,6]}\n",
+ "\n",
+ "\n",
+ "def get_trialtype_prob(daydict):\n",
+ " \"\"\" Gets probility of being distracted divided into different trial types\"\"\"\n",
+ " wn_prob, tone_prob, combined_prob = [], [], []\n",
+ "\n",
+ " rats = daydict.keys()\n",
+ " for rat in rats:\n",
+ " d = daydict[rat]\n",
+ "\n",
+ " wn = [L for L, tt in zip(d[\"d_bool_array\"], d[\"trialtype\"]) if (tt in [1,4])]\n",
+ " wn_prob.append(sum(wn)/len(wn))\n",
+ "\n",
+ " tn = [L for L, tt in zip(d[\"d_bool_array\"], d[\"trialtype\"]) if (tt in [2,5])]\n",
+ " tone_prob.append(sum(tn)/len(tn))\n",
+ "\n",
+ " comb = [L for L, tt in zip(d[\"d_bool_array\"], d[\"trialtype\"]) if (tt in [3,6])]\n",
+ " combined_prob.append(sum(comb)/len(comb))\n",
+ " \n",
+ " return wn_prob, tone_prob, combined_prob\n",
+ "\n",
+ "wn_prob, tone_prob, combined_prob = get_trialtype_prob(disDict)\n",
+ "\n",
+ "def get_trialtype_auc(daydict, epoch=[60, 90], signal=\"filt_z\"):\n",
+ " \n",
+ " wn_auc, tone_auc, combined_auc = [], [], []\n",
+ " \n",
+ " rats = daydict.keys()\n",
+ " for rat in rats:\n",
+ " d = daydict[rat]\n",
+ " \n",
+ " wn = [np.mean(snip[epoch[0]:epoch[1]]) for snip, tt in zip(d[\"snips_distractors\"][signal], d[\"trialtype\"]) if (tt in [1,4])]\n",
+ " wn_auc.append(np.mean(wn))\n",
+ " \n",
+ " tone = [np.mean(snip[epoch[0]:epoch[1]]) for snip, tt in zip(d[\"snips_distractors\"][signal], d[\"trialtype\"]) if (tt in [2,5])]\n",
+ " tone_auc.append(np.mean(tone))\n",
+ " \n",
+ " combined = [np.mean(snip[epoch[0]:epoch[1]]) for snip, tt in zip(d[\"snips_distractors\"][signal], d[\"trialtype\"]) if (tt in [3,6])]\n",
+ " combined_auc.append(np.mean(combined))\n",
+ " \n",
+ " return wn_auc, tone_auc, combined_auc\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "f, [ax1, ax2] = plt.subplots(ncols=2, figsize=(5,4), sharey=True)\n",
+ "f.subplots_adjust(left=0.15)\n",
+ "\n",
+ "wn_prob, tone_prob, combined_prob = get_trialtype_prob(disDict)\n",
+ "\n",
+ "tp.barscatter([wn_prob, tone_prob, combined_prob], paired=True,\n",
+ " barfacecolor=[colors[1]], barfacecoloroption='same',\n",
+ " barlabels=['WN', 'Tone', 'Comb'],\n",
+ " barlabeloffset=0,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax1)\n",
+ "\n",
+ "wn_prob, tone_prob, combined_prob = get_trialtype_prob(habDict)\n",
+ "\n",
+ "tp.barscatter([wn_prob, tone_prob, combined_prob], paired=True,\n",
+ " barfacecolor=[colors[2]], barfacecoloroption='same',\n",
+ " barlabels=['WN', 'Tone', 'Comb'],\n",
+ " barlabeloffset=0,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax2)\n",
+ "\n",
+ "ax1.set_ylabel(\"Probability of distraction\")\n",
+ "ax1.set_ylim([-0.05, 1.1])\n",
+ "\n",
+ "f.savefig(figfolder + \"trialtype_prob.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, [ax1, ax2] = plt.subplots(ncols=2, figsize=(5,4), sharey=True)\n",
+ "f.subplots_adjust(left=0.15)\n",
+ "\n",
+ "wn_auc, tone_auc, combined_auc = get_trialtype_auc(disDict)\n",
+ "\n",
+ "tp.barscatter([wn_auc, tone_auc, combined_auc], paired=True,\n",
+ " barfacecolor=[colors[1]], barfacecoloroption='same',\n",
+ " barlabels=['WN', 'Tone', 'Comb'],\n",
+ " barlabeloffset=0,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax1)\n",
+ "\n",
+ "wn_auc, tone_auc, combined_auc = get_trialtype_prob(habDict)\n",
+ "\n",
+ "tp.barscatter([wn_auc, tone_auc, combined_auc], paired=True,\n",
+ " barfacecolor=[colors[2]], barfacecoloroption='same',\n",
+ " barlabels=['WN', 'Tone', 'Comb'],\n",
+ " barlabeloffset=0,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax2)\n",
+ "\n",
+ "ax1.set_ylabel(\"Z-Score (AUC)\")\n",
+ "# ax1.set_ylim([-0.05, 1.1])\n",
+ "\n",
+ "f.savefig(figfolder + \"trialtype_auc.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, [ax1, ax2] = plt.subplots(ncols=2, figsize=(5.5,4))\n",
+ "f.subplots_adjust(left=0.15, wspace=0.4)\n",
+ "\n",
+ "wn_prob, tone_prob, combined_prob = get_trialtype_prob(disDict)\n",
+ "\n",
+ "_, barx, _, _ = tp.barscatter([wn_prob, tone_prob, combined_prob], paired=True,\n",
+ " barfacecolor=[colors[1]], barfacecoloroption='same',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax1)\n",
+ "\n",
+ "wn_auc, tone_auc, combined_auc = get_trialtype_auc(disDict)\n",
+ "\n",
+ "tp.barscatter([wn_auc, tone_auc, combined_auc], paired=True,\n",
+ " barfacecolor=[colors[1]], barfacecoloroption='same',\n",
+ " scattersize=scattersize,\n",
+ " ax=ax2)\n",
+ "\n",
+ "ax1.set_ylabel(\"Probability of distraction\")\n",
+ "ax2.set_ylabel(\"Z-score (AUC)\")\n",
+ "\n",
+ "barlabels=['WN', 'Tone', 'Comb']\n",
+ "\n",
+ "for axis in [ax1, ax2]:\n",
+ " trans = transforms.blended_transform_factory(\n",
+ " axis.transData, axis.transAxes)\n",
+ " for x, label in zip(barx, barlabels):\n",
+ " axis.text(x, -0.05, label, ha=\"center\", transform=trans)\n",
+ " \n",
+ "f.savefig(figfolder + \"trialtype_bars.pdf\")\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Anova\n",
+ "======================================\n",
+ " F Value Num DF Den DF Pr > F\n",
+ "--------------------------------------\n",
+ "variable 4.4024 2.0000 24.0000 0.0235\n",
+ "======================================\n",
+ "\n",
+ "Mean of WN is 0.5572229987361565\n",
+ "Mean of Tone is 0.42211786189518985\n",
+ "Mean of Comb is 0.5119659580185897\n",
+ "WN vs Tone 3.1341212090815302 0.00862558003004453 Sidak: 0.025654179946165256\n",
+ "Tone vs Comb -1.7840386374512658 0.09970305089660463 Sidak: 0.27027817556988554\n",
+ "WN vs Comb 0.9994255111728306 0.3373159620730357 Sidak: 0.708982215863079\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from statsmodels.stats.anova import AnovaRM\n",
+ "from scipy import stats\n",
+ "\n",
+ "rats = disDict.keys()\n",
+ "data = np.array([wn_prob, tone_prob, combined_prob], dtype=\"float\").T\n",
+ "\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"wn\", \"tone\", \"comb\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df = df.melt(id_vars = \"ratid\")\n",
+ "\n",
+ "aovrm = AnovaRM(df, \"value\", \"ratid\", within=[\"variable\"])\n",
+ "res = aovrm.fit()\n",
+ "\n",
+ "print(res)\n",
+ "\n",
+ "print(\"Mean of WN is\", np.mean(wn_prob))\n",
+ "print(\"Mean of Tone is\", np.mean(tone_prob))\n",
+ "print(\"Mean of Comb is\", np.mean(combined_prob))\n",
+ "\n",
+ "t, p = stats.ttest_rel(wn_prob, tone_prob)\n",
+ "print('WN vs Tone', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(tone_prob, combined_prob)\n",
+ "print('Tone vs Comb', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(wn_prob, combined_prob)\n",
+ "print('WN vs Comb', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Anova\n",
+ "======================================\n",
+ " F Value Num DF Den DF Pr > F\n",
+ "--------------------------------------\n",
+ "variable 0.2839 2.0000 24.0000 0.7554\n",
+ "======================================\n",
+ "\n",
+ "Mean of WN is 0.9453013977383533\n",
+ "Mean of Tone is 0.6952480697819904\n",
+ "Mean of Comb is 0.6958461301892815\n",
+ "WN vs Tone 0.5689667357568912 0.5798721290685419 Sidak: 0.9258443100987529\n",
+ "Tone vs Comb -0.0016214042447824937 0.9987329500217592 Sidak: 0.9999999979658581\n",
+ "WN vs Comb 0.7511779905071346 0.4670219433844821 Sidak: 0.848599263852535\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from statsmodels.stats.anova import AnovaRM\n",
+ "\n",
+ "rats = disDict.keys()\n",
+ "data = np.array([wn_auc, tone_auc, combined_auc], dtype=\"float\").T\n",
+ "\n",
+ "\n",
+ "df = pd.DataFrame(data, columns=[\"wn\", \"tone\", \"comb\"], index=rats)\n",
+ "df.insert(0, \"ratid\", rats)\n",
+ "df = df.melt(id_vars = \"ratid\")\n",
+ "\n",
+ "aovrm = AnovaRM(df, \"value\", \"ratid\", within=[\"variable\"])\n",
+ "res = aovrm.fit()\n",
+ "\n",
+ "print(res)\n",
+ "\n",
+ "print(\"Mean of WN is\", np.mean(wn_auc))\n",
+ "print(\"Mean of Tone is\", np.mean(tone_auc))\n",
+ "print(\"Mean of Comb is\", np.mean(combined_auc))\n",
+ "\n",
+ "t, p = stats.ttest_rel(wn_auc, tone_auc)\n",
+ "print('WN vs Tone', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(tone_auc, combined_auc)\n",
+ "print('Tone vs Comb', t, p, \"Sidak:\", tp.sidakcorr(p))\n",
+ "\n",
+ "t, p = stats.ttest_rel(wn_auc, combined_auc)\n",
+ "print('WN vs Comb', t, p, \"Sidak:\", tp.sidakcorr(p))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/ww_PDPs and activity.ipynb b/notebooks/ww_PDPs and activity.ipynb
new file mode 100644
index 0000000..3f31430
--- /dev/null
+++ b/notebooks/ww_PDPs and activity.ipynb
@@ -0,0 +1,462 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "import numpy as np\n",
+ "\n",
+ "from scipy.stats import linregress\n",
+ "\n",
+ "import trompy as tp\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['rat', 'rms', 'fs', 'deltaF', 'tick', 'filt_sd', 'licks', 'licks_off', 'distractors', 'distracted', 'notdistracted', 'd_bool_array', 'pdp', 'pre_dp', 'trialtype', 'lickdata', 'snips_distractors', 'snips_distracted', 'snips_not-distracted'])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rat = 'thph1.1'\n",
+ "d = disDict[rat]\n",
+ "d.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "epoch = [60, 90]\n",
+ "pdp_threshold = 1\n",
+ "\n",
+ "PDPs = []\n",
+ "AUCs = []\n",
+ "\n",
+ "rats = disDict.keys()\n",
+ "for rat in rats:\n",
+ " d = disDict[rat]\n",
+ " snips = d[\"snips_distractors\"][\"filt_z\"]\n",
+ " \n",
+ " PDPs.append([pdp for pdp in d[\"pdp\"] if pdp > pdp_threshold])\n",
+ " AUCs.append([np.sum(snip[epoch[0]:epoch[1]]) for snip, pdp in zip(snips, d[\"pdp\"]) if pdp > pdp_threshold])\n",
+ " \n",
+ "PDPs = tp.flatten_list(PDPs)\n",
+ "AUCs = tp.flatten_list(AUCs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "f, [ax1, ax2] = plt.subplots(figsize=(5, 3), ncols=2, sharey=True)\n",
+ "f.subplots_adjust(left=0.2, bottom=0.2, wspace=0.3)\n",
+ "\n",
+ "trans = transforms.blended_transform_factory(\n",
+ " ax1.transData, ax1.transAxes)\n",
+ "\n",
+ "ax1.scatter(PDPs, AUCs, marker=\"o\", color=\"none\", edgecolor=\"k\")\n",
+ "\n",
+ "ax1.set_ylabel(\"AUC (1-4 s)\")\n",
+ "ax1.set_xlabel(\"Post-distraction pause (s)\")\n",
+ "\n",
+ "ax1.set_xscale(\"log\")\n",
+ "\n",
+ "slope, intercept, r, p, _ = linregress(PDPs, AUCs)\n",
+ "\n",
+ "x1, x2 = ax1.get_xlim()\n",
+ "ax1.plot([x1, x2], [x1*slope + intercept, x2*slope+intercept], \"--\", color=\"xkcd:light blue\")\n",
+ "\n",
+ "\n",
+ "stats_str = f\"r={r:2.2}\\np={p:2.3}\" \n",
+ "ax1.text(300, 0.9, stats_str, transform=trans, ha=\"left\")\n",
+ "\n",
+ "\n",
+ "median_pdp = np.median(PDPs)\n",
+ "ax1.axvline(median_pdp, linestyle=\"--\", color=\"orange\", alpha=0.7)\n",
+ "ax1.text(median_pdp, 1.03, \"Median PDP\", ha=\"center\", transform=trans)\n",
+ "\n",
+ "\n",
+ "\n",
+ "AUC_from_shPDPs = [auc for auc, pdp in zip(AUCs, PDPs) if pdp <= median_pdp]\n",
+ "AUC_from_loPDPs = [auc for auc, pdp in zip(AUCs, PDPs) if pdp >= median_pdp]\n",
+ "\n",
+ "\n",
+ "ax2.boxplot([AUC_from_shPDPs, AUC_from_loPDPs],\n",
+ " widths=0.7,\n",
+ " patch_artist =True,\n",
+ " notch=True)\n",
+ "\n",
+ "ax2.set_xticklabels([\"Short\", \"Long\"])\n",
+ "ax2.set_xlabel(\"Post-distraction pause\")\n",
+ "\n",
+ "for axis in [ax1, ax2]:\n",
+ " axis.spines[\"top\"].set_visible(False)\n",
+ " axis.spines[\"right\"].set_visible(False)\n",
+ "\n",
+ "f.savefig(figfolder+\"figs5_pdp_and_auc.pdf\")\n",
+ "\n",
+ "# ax2.set_yscale(\"log\")\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "757.9331344547738"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "max(AUC_from_loPDPs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Median AUC on short PDP trials is 5.937955018634052 and mean is 22.276669291787826\n",
+ "Median AUC on long PDP trials is 26.714134466129565 and mean is 37.14036832311489\n",
+ "Ttest_indResult(statistic=-1.566305743532648, pvalue=0.11820182862620787)\n",
+ "Ttest_indResult(statistic=nan, pvalue=nan)\n",
+ "Ttest_indResult(statistic=-1.0912381721623996, pvalue=0.2759362761061925)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\envs\\dis\\lib\\site-packages\\ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in sqrt\n",
+ " if __name__ == '__main__':\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "array([ 97.66934822, 154.1993562 , 82.58856965, 123.126783 ,\n",
+ " 101.82879442, 116.2658916 , 83.09137563, 191.5758055 ,\n",
+ " 219.65406648, 98.58649217, 164.8120904 , 116.95174922,\n",
+ " 59.93995619, 144.17034189, 148.90716144, 113.37687842,\n",
+ " 133.08024387, 112.01061545, 206.97950161, 185.1434157 ,\n",
+ " 222.97157474, 408.49256515, 82.69957315, 164.40795782,\n",
+ " 115.79393589, 74.56733606, 108.10617911, 124.47287 ,\n",
+ " 130.4112578 , 130.95472752, 110.01194811, 158.0026843 ,\n",
+ " 91.51213532, 166.34225584, 139.36576554, 134.1992023 ,\n",
+ " 113.18222526, 163.94834019, 112.61204475, 127.90533508,\n",
+ " 115.96395525, 171.26211541, 114.64319026, 141.78697771,\n",
+ " 86.63215419, 110.09982071, 83.01792569, 102.52844018,\n",
+ " 112.5470395 , 90.55329598, 137.27819182, 227.85410118,\n",
+ " 71.71894582, 134.41647033, 259.36155244, 206.77737521,\n",
+ " 179.60861921, 139.65622206, 180.99735671, 101.45349572,\n",
+ " 46.35728648, 203.68818711, 51.39511431, 368.9548363 ,\n",
+ " 191.45529704, 148.3840386 , 340.55176669, 129.15673978,\n",
+ " 50.77904858, 115.44104383, 71.87548568, 273.93018696,\n",
+ " 326.33638305, 114.8808766 , 106.53071831, 173.9146889 ,\n",
+ " 139.0512835 , 149.05245332, 148.75724979, 166.70671959,\n",
+ " 202.93704749, 207.63987383, 163.78784285, 214.67315729,\n",
+ " 213.42659926, 206.68887 , 176.86388689, 247.35089048,\n",
+ " 157.81964626, 296.71134034, 237.53531961, 263.89169097,\n",
+ " 134.84727762, 210.83598933, 169.45748978, 149.75962606,\n",
+ " 128.19808369, 141.8080772 , 176.94318784, 86.36516821,\n",
+ " 129.97535519, 147.42662896, 153.28210147, 192.897844 ,\n",
+ " 176.54439857, 324.74081743, 144.19355419, 194.65242089,\n",
+ " 262.7224028 , 268.45228942, 169.57400569, 193.40461295,\n",
+ " 184.59682002, 70.45278977, 103.40382495, 129.41169301,\n",
+ " 212.65315965, 131.66144837, 520.24249299, 206.57836658,\n",
+ " 84.5956215 , 152.24434379, 193.60056738, 160.97235118,\n",
+ " 329.21844977, 184.39034431, 211.82024549, 85.72727024,\n",
+ " 169.22638309, 115.06605047, 183.02659599, 249.99774759,\n",
+ " 215.09831489, 202.97688472, 102.18037433, 120.09196061,\n",
+ " 246.55718371, 176.11258107, 265.80369806, 157.39856366,\n",
+ " 560.90951542, 314.10691563, 160.95218144, 172.51274857,\n",
+ " 148.16443407, 111.61469584, 81.95383909, 159.69497571,\n",
+ " 246.29374037, 314.88177424, 172.51584978, 220.81884223,\n",
+ " 347.0681515 , 156.79994228, 167.23635277, 326.06602091,\n",
+ " 181.31372113, 338.29345842, 131.40018848, 157.94613524,\n",
+ " 141.99469613, 142.37385278, 497.63687968, 368.17727426,\n",
+ " 150.2939243 , 144.40713688, 174.71369652, 88.84438493,\n",
+ " 183.32387426, 257.80135594, 150.84378626, 130.58739687])"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from scipy.stats import ttest_rel as ttest\n",
+ "from scipy.stats import ttest_ind as ttest_ind\n",
+ "\n",
+ "print(\"Median AUC on short PDP trials is\", np.median(AUC_from_shPDPs), \"and mean is\", np.mean(AUC_from_shPDPs))\n",
+ "print(\"Median AUC on long PDP trials is\", np.median(AUC_from_loPDPs), \"and mean is\", np.mean(AUC_from_loPDPs))\n",
+ "\n",
+ "print(ttest_ind(AUC_from_shPDPs, AUC_from_loPDPs))\n",
+ "\n",
+ "print(ttest_ind(np.sqrt(AUC_from_shPDPs), np.sqrt(AUC_from_loPDPs)))\n",
+ "\n",
+ "# print(\"The proportion of trials in which the last distractor was within the baseline period is approximately:\")\n",
+ "# print(len([L for L in pre_dps_notdistracted_all if L < 5]) / len(pre_dps_notdistracted_all), \"for nondistracted trials.\")\n",
+ "# print(len([L for L in pre_dps_distracted_all if L < 5]) / len(pre_dps_notdistracted_all), \"for distracted trials.\")\n",
+ "\n",
+ "minval = np.abs(min(min(AUC_from_shPDPs), min(AUC_from_loPDPs)))\n",
+ "\n",
+ "tr_shPDPs = AUC_from_shPDPs + (minval + 1)\n",
+ "tr_loPDPs = AUC_from_loPDPs + (minval + 1)\n",
+ "\n",
+ "print(ttest_ind(np.log(tr_shPDPs), np.log(tr_loPDPs)))\n",
+ "\n",
+ "tr_shPDPs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\envs\\dis\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in sqrt\n",
+ " \"\"\"Entry point for launching an IPython kernel.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "array([ nan, 1.74300265, nan, nan, nan,\n",
+ " nan, nan, 6.35724056, 8.2760358 , nan,\n",
+ " 3.69469788, nan, nan, nan, nan,\n",
+ " nan, nan, nan, 7.47115812, 5.8294183 ,\n",
+ " 8.47409445, 16.04154815, nan, 3.63959611, nan,\n",
+ " nan, nan, nan, nan, nan,\n",
+ " nan, 2.61560439, nan, 3.89627487, nan,\n",
+ " nan, nan, 3.5758974 , nan, nan,\n",
+ " nan, 4.48339352, nan, nan, nan,\n",
+ " nan, nan, nan, nan, nan,\n",
+ " nan, 8.75744273, nan, nan, 10.40193513,\n",
+ " 7.45761874, 5.33360303, nan, 5.46223935, nan,\n",
+ " nan, 7.24754366, nan, 14.75782973, 6.34775544,\n",
+ " nan, 13.76192097, nan, nan, nan,\n",
+ " nan, 11.08011232, 13.2353725 , nan, nan,\n",
+ " 4.77005146, nan, nan, nan, 3.94276827,\n",
+ " 7.19553678, 7.51522294, 3.55338499, 7.96943281, 7.89083654,\n",
+ " 7.4516825 , 5.06977208, 9.8076293 , 2.58037755, 12.06441223,\n",
+ " 9.29376251, 10.61745699, nan, 7.72493957, 4.2774048 ,\n",
+ " nan, nan, nan, 5.07758701, nan,\n",
+ " nan, nan, 1.45629788, 6.46038281, 5.03816441,\n",
+ " 13.17495804, nan, 6.59477998, 10.56224904, 10.83009656,\n",
+ " 4.29100312, 6.49948575, 5.78234572, nan, nan,\n",
+ " nan, 7.84167467, nan, 19.21148602, 7.44426414,\n",
+ " nan, 1.04069488, 6.51454292, 3.13226008, 13.34380575,\n",
+ " 5.7644641 , 7.78838543, nan, 4.25030412, nan,\n",
+ " 5.64493561, 9.94165226, 7.99606259, 7.19830444, nan,\n",
+ " nan, 9.76708174, 4.99512594, 10.70711913, 2.49745184,\n",
+ " 20.24223845, 12.76501538, 3.12903875, 4.62076299, nan,\n",
+ " nan, nan, 2.92124593, 9.75358613, 12.79533025,\n",
+ " 4.62109855, 8.34610953, 13.99667295, 2.37458298, 4.00937088,\n",
+ " 13.22515493, 5.49112221, 13.67962574, nan, 2.60477202,\n",
+ " nan, nan, 18.61385456, 14.73146212, nan,\n",
+ " nan, 4.85308135, nan, 5.67120589, 10.32666732,\n",
+ " nan, nan])"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.sqrt(AUC_from_shPDPs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.022107900050024165 28.80617658954732 0.03157991037341743 0.5593994324098989\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(slope, intercept, r, p)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD4CAYAAAD4k815AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAL8ElEQVR4nO3dX2jd5R3H8c/HtDutnQQ7CxUT/4yIxPRm9LCLTgYdY7hBcBcDLTIYhhQvDBuFgiO72E0KK7SgIISOBhnYWNnGtoJjNw1IQIap7KIuiMVae+iodZasixZr+O6if0izVNOek99z+j3vFxzoeXr+fMP5nk9+PHl+z88RIQBAHneULgAA0FoEOwAkQ7ADQDIEOwAkQ7ADQDJrShcgSffcc088+OCDpcsAgNvKsWPHPo6ITUvHiwa77UFJg319fZqZmSlZCgDcdmyfWm686FRMRByJiJ3d3d0lywCAVJhjB4BkCHYASIZgB4BkCHYASIZgT2RyclJbtmxRV1eXtmzZosnJydIlAZLozaq1xTp2NG9yclKjo6M6ePCgHnvsMU1PT2toaEiStGPHjsLVoZPRmwVERPHb1q1bA80ZGBiIo0ePXjd29OjRGBgYKFQRcBm9uXokzcQymepog/3Y6/V6cIJSc7q6unTx4kWtXbv22tilS5e0bt06LSwsFKwMnY7eXD22j0VEfel40Tl224O2D8zNzZUsI4X+/n5NT09fNzY9Pa3+/v5CFQGX0ZvVKzrHHhFHJB2p1+vDJevIYHR0VE8++aQ2bNigU6dO6YEHHtD8/LxeeOGF0qWhw9Gb1WNVTEK2S5cALIverAbBnsTY2JgOHz6skydPamFhQSdPntThw4c1NjZWujR0OHqzegR7ErOzs2o0GtetFW40GpqdnS1dGjocvVk9VsUk0dvbq4WFBb3yyivX1go//fTT6urq0unTp0uXhw5Gb66etlwVg9Za+ku6HX5pAxK9WTWCPYkzZ85o7969GhkZ0bp16zQyMqK9e/fqzJkzpUtDh6M3q8eWAkn09/erp6dHx48fvzY2NTXFWmEUR29WjyP2JEZHRzU0NKSpqSldunRJU1NTGhoa0ujoaOnS0OHozepxxJ7E1c2URkZGNDs7q/7+fo2NjbHJEoqjN6vHqhgAuE2xKgYAOgTBDgDJsLsjACRTNNgj4khE7Ozu7i5ZBgCkwlQMACRDsANAMgR7IlwJHu2K3qwWJyglwZXg0a7ozQKWu8J11betW7e2+NrdnYcrwaNd0ZurR9JMLJOpnHmaBFeCR7uiN1cPZ54mx5Xg0a7ozeoR7Emwgx7aFb1ZPf54mgQ76KFd0ZvVY44dAG5TzLF3gKuXHrN97RJkQDugN6tFsCcxMjKi8fFx7dmzR/Pz89qzZ4/Gx8f5AqE4erOA5dZAVn1jHXvzarVa7Nu377qxffv2Ra1WK1QRcBm9uXrEOvbcbGt+fl533nnntbFPP/1UGzZsUDt8xuhc9ObqYY49uVqtpvHx8evGxsfHVavVClUEXEZvVo9gT2J4eFi7d+/W5s2bdccdd2jz5s3avXu3hoeHS5eGDkdvVo9gT2Lbtm2q1Wo6e/asIkJnz55VrVbTtm3bSpeGDkdvVq/oHLvtQUmDfX19w++9916xOjLo7e3VhQsXdPfdd+vDDz/U/fffr/Pnz+uuu+7S6dOnS5eHDkZvrp62nGMPLo3XMo1GQ+vXr9fExIQuXryoiYkJrV+/Xo1Go3Rp6HD0ZvWYiklk165d2r59u9auXavt27dr165dpUsCJNGbVSPYE9m/f/91Gy3t37+/dEmAJHqzamwClkRPT48uXLigZ5555to85meffaaenp7SpaHD0ZvVI9hvU7aXHZ+bm5MkffDBB9fuL34sJ4SgCsv1J71ZHaZiblPLnUZ86NAhDQwMSJIGBgZ06NCh/3sMUAV6syy2FEjINl8UtCV6s7XacrkjAKD1CHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkWh7stn9s+7e2/2z7B61+fQDAl1tRsNuesP2R7eNLxh+3/a7tE7afl6SI+FNEDEv6maQnW14xAOBLrfSI/WVJjy8esN0l6SVJP5T0qKQdth9d9JBfXfl/AECFVhTsEfGGpE+WDH9b0omIeD8iPpf0qqQnfNlvJP01It6+0Wva3ml7xvbMuXPnbrV+AMASzcyx3yfp9KL7jStjI5K+L+kntp+90ZMj4kBE1COivmnTpibKAAAstqaJ53qZsYiIFyW92MTrAgCa0MwRe0NS76L7PZLONFcOAKBZzQT7W5Ietv2Q7a9JekrSX1pTFgDgVq10ueOkpDclPWK7YXsoIr6Q9Jykv0malfRaRLxzM29ue9D2gbm5uZutGwBwA46I0jWoXq/HzMxM6TLSsK12+FyBpejN1rJ9LCLqS8fZUgAAkiHYASAZgh0AkiHYASCZosHOqhgAaL2iwR4RRyJiZ3d3d8kyACAVpmIAIBmCHQCSIdgBIBmCHQCSYVUMACTDqhgASIapGABIhmAHgGQIdgBIhmAHgGQIdgBIhuWOAJAMyx0BIBmmYgAgGYIdAJIh2AEgGYIdAJIh2Nvcxo0bZfumbpJu6vEbN24s/FPidkRvtq81pQvAlzt//rwiYlXf4+oXDrgZ9Gb7Yh07ACTDOnYASIY5dgBIhmAHgGQIdgBIhmAHgGQIdgBIhmAHgGQIdgBIhmAHgGQIdgBIhi0FACAZthQAgGSYigGAZAh2AEiGYAeAZAh2AEiGYAeAZAh2AEiGYAeAZAh2AEiGYAeAZAh2AEiGYAeAZNgEDACSYRMwAEiGqRgASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZL4wFAMlwaDwCSYSoGAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJIh2AEgGYIdAJJpebDb/qbtg7Z/3+rXBgB8tRUFu+0J2x/ZPr5k/HHb79o+Yft5SYqI9yNiaDWKBQB8tZUesb8s6fHFA7a7JL0k6YeSHpW0w/ajLa0OAHDTVhTsEfGGpE+WDH9b0okrR+ifS3pV0hMtrg8AcJOamWO/T9LpRfcbku6z/Q3b45K+ZfuXN3qy7Z22Z2zPnDt3rokyAACLrWniuV5mLCLi35Ke/aonR8QBSQckqV6vRxN1AAAWaeaIvSGpd9H9HklnmisHANCsZoL9LUkP237I9tckPSXpL60pCwBwq1a63HFS0puSHrHdsD0UEV9Iek7S3yTNSnotIt5ZvVIBACuxojn2iNhxg/HXJb1+q29ue1DSYF9f362+BABgiaJbCkTEkYjY2d3dXbIMAEiFvWIAIBmCHQCSIdgBIJmiwW570PaBubm5kmUAQCr88RQAkmEqBgCSIdgBIBmCHQCSIdgBIBlWxQBAMqyKAYBkmIoBgGQIdgBIhmAHgGQIdgBIhmAHgGRY7ggAyTgiSteger0eMzMzpctoT7+uaCnor/nliptEbxZn+1hE1P9vnGBvb7a12p9RFe+BfOjN8m4U7MyxA0AyBDsAJEOwA0AyBDsAJEOwA0AyBDsAJMMJSgCQDPuxA0AybXGCku1zkk6VriOReyR9XLoIYBn0Zms9EBGblg62RbCjtWzPLHc2GlAavVkN/ngKAMkQ7ACQDMGe04HSBQA3QG9WgDl2AEiGI3YASIZgB4BkCPYkbE/Y/sj28dK1AIvZ7rU9ZXvW9ju2f166puyYY0/C9ncl/VfS7yJiS+l6gKts3yvp3oh42/Zdko5J+nFE/LNwaWlxxJ5ERLwh6ZPSdQBLRcS/IuLtK/++IGlW0n1lq8qNYAdQGdsPSvqWpL+XrSQ3gh1AJWx/XdIfJP0iIv5Tup7MCHYAq872Wl0O9Vci4o+l68mOYAewqmxb0kFJsxGxv3Q9nYBgT8L2pKQ3JT1iu2F7qHRNwBXfkfRTSd+z/Y8rtx+VLiozljsCQDIcsQNAMgQ7ACRDsANAMgQ7ACRDsANAMgQ7ACRDsANAMv8DFyp5NN12oF8AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "median_pdp = np.median(PDPs)\n",
+ "\n",
+ "AUC_from_shPDPs = [auc for auc, pdp in zip(AUCs, PDPs) if pdp < median_pdp]\n",
+ "AUC_from_loPDPs = [auc for auc, pdp in zip(AUCs, PDPs) if pdp > median_pdp]\n",
+ "\n",
+ "f, ax = plt.subplots()\n",
+ "\n",
+ "ax.boxplot([AUC_from_shPDPs, AUC_from_shPDPs])\n",
+ "\n",
+ "ax.set_yscale(\"log\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "can't multiply sequence by non-int of type 'numpy.float64'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mslope\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'numpy.float64'"
+ ]
+ }
+ ],
+ "source": [
+ "slope*x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.019734111992620314"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "slope"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/xx_dis-testing notebook.ipynb b/notebooks/xx_dis-testing notebook.ipynb
new file mode 100644
index 0000000..845360d
--- /dev/null
+++ b/notebooks/xx_dis-testing notebook.ipynb
@@ -0,0 +1,487 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Github\\Distraction-Paper\\JM_custom_figs.py:11: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
+ " from collections import Sequence\n"
+ ]
+ }
+ ],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "import numpy as np\n",
+ "\n",
+ "%run \"..//JM_custom_figs.py\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cannot access pickled file\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'pickle_in' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot access pickled file'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphoto_snips_dis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphoto_snips_notdis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdill\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpickle_in\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[0minputfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdill\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpickle_in\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'pickle_in' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(outputfolder+\"roc_results_photo_disVnondis\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[a, p, photo_snips_dis, photo_snips_notdis] = dill.load(pickle_in)\n",
+ "inputfile = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['rat', 'fs', 'tick', 'filt_sd', 'licks', 'licks_off', 'distractors', 'distracted', 'notdistracted', 'd_bool_array', 'pdp', 'pre_dp', 'lickdata', 'snips_distractors', 'snips_distracted', 'snips_not-distracted'])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rat = 'thph1.1'\n",
+ "d = disDict[rat]\n",
+ "d.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# to test new zscore method - using sd from lerner correction\n",
+ "\n",
+ "def zscore_with_constant_sd(snips, sd, baseline_points=100):\n",
+ " \n",
+ " BL_range = range(baseline_points)\n",
+ " z_snips = []\n",
+ " try:\n",
+ " for i in snips:\n",
+ " mean = np.mean(i[BL_range])\n",
+ " z_snips.append([(x-mean)/sd for x in i])\n",
+ " except IndexError:\n",
+ " mean = np.mean(snips[BL_range])\n",
+ " z_snips = [(x-mean)/sd for x in snips]\n",
+ "\n",
+ " return z_snips\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "old_snips = d['snips_distractors']['filt']\n",
+ "\n",
+ "new_snips = zscore_with_constant_sd(old_snips, d['filt_sd'], baseline_points=50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "11.535198"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots()\n",
+ "ax.plot(np.mean(new_snips, axis=0)*100)\n",
+ "ax.plot(np.mean(d['snips_distractors']['filt_z'], axis=0))\n",
+ "\n",
+ "d['filt_sd']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(nrows=2)\n",
+ "\n",
+ "new_snips_peak=[]\n",
+ "for x, snip in enumerate(new_snips):\n",
+ " ax[0].scatter(x, np.mean(snip[50:80]))\n",
+ " new_snips_peak.appoend()\n",
+ " \n",
+ "for x, snip in enumerate(d['snips_distractors']['filt_z']):\n",
+ " ax[1].scatter(x, np.mean(snip[50:80]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# This figure shows that, largely, whether we use SD from entire session or SD from individual trials we get a similar result\n",
+ "\n",
+ "f, ax = plt.subplots()\n",
+ "\n",
+ "for x, y in zip(new_snips, d['snips_distractors']['filt_z']):\n",
+ " ax.scatter(np.mean(x[50:80]), np.mean(y[50:80]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def remcheck(val, range1, range2):\n",
+ " # function checks whether value is within range of two decimels\n",
+ " if (range1 < range2):\n",
+ " if (val > range1) and (val < range2):\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ " else:\n",
+ " if (val > range1) or (val < range2):\n",
+ " return True\n",
+ " else:\n",
+ " return False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def distractionCalc2(licks, pre=1, post=1):\n",
+ " \"\"\"\n",
+ " Works out from list of lick timestamps when distractors should occur\n",
+ " \"\"\"\n",
+ " \n",
+ " licks = np.insert(licks, 0, 0)\n",
+ " b = 0.001\n",
+ " d = []\n",
+ " idx = 3\n",
+ " \n",
+ " while idx < len(licks):\n",
+ " if licks[idx]-licks[idx-2] < 1 and remcheck(b, licks[idx-2] % 1, licks[idx] % 1) == False:\n",
+ " d.append(licks[idx])\n",
+ " b = licks[idx] % 1\n",
+ " idx += 1\n",
+ " try:\n",
+ " while licks[idx]-licks[idx-1] < 1:\n",
+ " b = licks[idx] % 1\n",
+ " idx += 1\n",
+ " except IndexError:\n",
+ " pass\n",
+ " else:\n",
+ " idx +=1\n",
+ " \n",
+ " if d[-1] > 3599:\n",
+ " d = d[:-1]\n",
+ " \n",
+ " return d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def distracted_or_not(distractors, licks, delay=1): \n",
+ " pdp = [] # post-distraction pause\n",
+ " pre_dp = [] # pre-distraction pause\n",
+ " distractedArray = []\n",
+ "\n",
+ " for d in distractors: \n",
+ " try:\n",
+ " pdp.append([lick - d for lick in licks if (lick > d)][0])\n",
+ " except IndexError:\n",
+ " pdp.append(3600-d) # designates end of session as max pdp\n",
+ " \n",
+ " distractor_index = [idx for idx, lick in enumerate(licks) if lick == d][0]\n",
+ " try:\n",
+ " pre_dp.append(-[ili for ili in np.diff(licks[distractor_index::-1]) if ili < -1][0])\n",
+ " except IndexError:\n",
+ " pre_dp.append(licks[0])\n",
+ " \n",
+ "\n",
+ " distracted_boolean_array = np.array([i>delay for i in pdp], dtype=bool)\n",
+ " \n",
+ " distracted = [d for d,l in zip(distractors, distracted_boolean_array) if l]\n",
+ " notdistracted = [d for d,l in zip(distractors, distracted_boolean_array) if not l] \n",
+ " \n",
+ " if np.isnan(pdp)[-1] == 1: \n",
+ " distracted_boolean_array[-1] = True\n",
+ " \n",
+ " return [distracted, notdistracted], distracted_boolean_array, pdp, pre_dp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dis = distractionCalc2(d['licks'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "59 59\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(len(dis), len(d['distractors']))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "59"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(distracted_or_not(dis, d['licks'])[3])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[4, 12, 15, 20, 23, 26, 29, 32, 35]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "distractor_indices = []\n",
+ "for dis in ds:\n",
+ " distractor_indices.append([idx for idx, lick in enumerate(licks) if lick == dis][0])\n",
+ " \n",
+ "distractor_indices"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4\n",
+ "12\n",
+ "15\n",
+ "20\n",
+ "23\n",
+ "26\n",
+ "29\n",
+ "32\n",
+ "35\n"
+ ]
+ }
+ ],
+ "source": [
+ "pre_dp=[]\n",
+ "for d_ind in distractor_indices:\n",
+ " pre_dp.append(-[ili for ili in np.diff(licks[d_ind::-1]) if ili < -1][0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[85.26757888,\n",
+ " 71.28481791999997,\n",
+ " 36.02726912000003,\n",
+ " 26.54355456000002,\n",
+ " 26.075299840000014,\n",
+ " 4.6609203200000024,\n",
+ " 4.217896959999962,\n",
+ " 101.19430144,\n",
+ " 6.447923199999991]"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pre_dp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pre_dp=[]\n",
+ "for d_ind in distractor_indices:\n",
+ " [lick for idx, lick in enumerate(licks) if lick]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/yy_dis-heatmaps.ipynb b/notebooks/yy_dis-heatmaps.ipynb
new file mode 100644
index 0000000..d40be79
--- /dev/null
+++ b/notebooks/yy_dis-heatmaps.ipynb
@@ -0,0 +1,283 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Github\\Distraction-Paper\\JM_custom_figs.py:11: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
+ " from collections import Sequence\n"
+ ]
+ }
+ ],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "import numpy as np\n",
+ "\n",
+ "%run \"..//JM_custom_figs.py\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['rat', 'fs', 'tick', 'filt_sd', 'licks', 'licks_off', 'distractors', 'distracted', 'notdistracted', 'd_bool_array', 'pdp', 'pre_dp', 'lickdata', 'snips_distractors', 'snips_distracted', 'snips_not-distracted'])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d = disDict['thph1.1']\n",
+ "d.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def makeheatmap(ax, data, events=[], sortevents=[], event_direction='post', ylabel='Trials'):\n",
+ " \n",
+ " if len(sortevents)> 0:\n",
+ " sort_order = np.argsort(sortevents)\n",
+ " data = [data[i] for i in sort_order]\n",
+ " events = [events[i] for i in sort_order]\n",
+ " \n",
+ " if event_direction == 'pre':\n",
+ " events = [-event for event in events]\n",
+ " \n",
+ " ntrials = np.shape(data)[0]\n",
+ " xvals = np.linspace(-4.9,15,200)\n",
+ " yvals = np.arange(1, ntrials+2)\n",
+ " xx, yy = np.meshgrid(xvals, yvals)\n",
+ " \n",
+ " mesh = ax.pcolormesh(xx, yy, data, cmap=heatmap_color_scheme, shading = 'flat')\n",
+ " \n",
+ " if len(events) > 0:\n",
+ " ax.vlines(events, yvals[:-1], yvals[1:], color='w')\n",
+ " else:\n",
+ " print('No events')\n",
+ " \n",
+ " ax.set_ylabel(ylabel)\n",
+ "# ax.set_yticks([1, ntrials])\n",
+ " ax.set_yticks([])\n",
+ " ax.set_xticks([])\n",
+ " ax.invert_yaxis()\n",
+ " ax.spines['bottom'].set_visible(False)\n",
+ " ax.spines['top'].set_visible(False)\n",
+ " ax.spines['right'].set_visible(False)\n",
+ " ax.spines['left'].set_visible(False)\n",
+ " \n",
+ " return ax, mesh"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rat = 'thph1.1'\n",
+ "d = disDict[rat]\n",
+ "heatmap_color_scheme = 'coolwarm'\n",
+ "clims = [-3,4]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(-4.9, 15)"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plots heathaps sorted by post-distraction pause (left) and pre-distraction pause (right)\n",
+ "\n",
+ "f, ax = plt.subplots(ncols=2)\n",
+ "ax[0], mesh = makeheatmap(ax[0], d['snips_distractors']['filt_z'], events=d['pdp'], sortevents=d['pdp'])\n",
+ "mesh.set_clim(clims)\n",
+ "ax[0].set_xlim([-4.9,15])\n",
+ "\n",
+ "ax[1], mesh = makeheatmap(ax[1], d['snips_distractors']['filt_z'], events=d['pre_dp'], sortevents=d['pre_dp'], event_direction='pre')\n",
+ "mesh.set_clim(clims)\n",
+ "ax[1].set_xlim([-4.9,15])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No events\n",
+ "No events\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rat = 'thph2.6'\n",
+ "d_mod = modDict[rat]\n",
+ "d_dis = disDict[rat]\n",
+ "heatmap_color_scheme = 'coolwarm'\n",
+ "clims = [-3,4]\n",
+ "\n",
+ "import matplotlib.gridspec as gridspec\n",
+ "\n",
+ "f = plt.figure(figsize=(4,2))\n",
+ "\n",
+ "gs=gridspec.GridSpec(1,3, figure=f, width_ratios=[1, 1, 0.05], wspace=0.05,\n",
+ " bottom=0.1, right=0.85, left=0.1)\n",
+ "\n",
+ "ax[0] = f.add_subplot(gs[0, 0])\n",
+ "\n",
+ "ax[0], mesh = makeheatmap(ax[0], d_mod['snips_distractors']['filt_z'])\n",
+ "mesh.set_clim(clims)\n",
+ "ax[0].set_xlim([-4.9,15])\n",
+ "ax[0].set_title('Modelled')\n",
+ "ax[0].plot([0,0], ax[0].get_ylim(), color='white', linestyle='--')\n",
+ "\n",
+ "ax[1] = f.add_subplot(gs[0, 1])\n",
+ "\n",
+ "ax[1], mesh = makeheatmap(ax[1], d_dis['snips_distractors']['filt_z'], ylabel='')\n",
+ "mesh.set_clim(clims)\n",
+ "ax[1].set_xlim([-4.9,15])\n",
+ "ax[1].set_title('Distraction')\n",
+ "ax[1].plot([0,0], ax[1].get_ylim(), color='white', linestyle='--')\n",
+ "\n",
+ "\n",
+ "cbar_ax = f.add_subplot(gs[0,2]) \n",
+ "cbar = f.colorbar(mesh, cax=cbar_ax, ticks=[-2, 0, 2], label='Z-score')\n",
+ "\n",
+ "f.savefig(figfolder+\"fig3_heatplot_modVdis.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.225, 0.675, 1.125, 1.35 , 0.9 , -1.575, 1.35 , 0.675,\n",
+ " 1.125])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "a = [4,7,2,3,2,3,56,7,8,9,8,7]\n",
+ "b = [-1, 3, 5, 6, 4, -7, 6, 3, 5]\n",
+ "\n",
+ "def scale_y(data):\n",
+ " mean = np.mean([abs(d) for d in data])\n",
+ " return data/mean\n",
+ "\n",
+ "scale_y(b)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/zz_dis-long-pre_dps.ipynb b/notebooks/zz_dis-long-pre_dps.ipynb
new file mode 100644
index 0000000..62817a1
--- /dev/null
+++ b/notebooks/zz_dis-long-pre_dps.ipynb
@@ -0,0 +1,42313 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This notebook examines whether the pre-distraction pause has an effect on either photometry response to distractors or if it is related to the post-distraction pause. The first figures show, for each rat, the average photometry response on distraction trials when the pre-distraction pause was >5s (left) vs those when the pre-distraction pause was >5s (right). There does not appear to be a large difference - at least based on the mean. (NB all this analysis is perfomred on distraction day when distractors were present.\n",
+ "\n",
+ "Below, I have regressed pre-distraction pause against post-distraction pause (collapsing all trials from all rats) and find no relationship.\n",
+ "\n",
+ "At the end is a comparison of the pre-distraction pause on distracted and not distracted trials. There is no statistical difference when assessed with a paired t-text."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dill\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.transforms as transforms\n",
+ "from matplotlib.backends.backend_pdf import PdfPages\n",
+ "import numpy as np\n",
+ "\n",
+ "import trompy as tp\n",
+ "\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# %run \"..//JM_custom_figs.py\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fig settings\n",
+ "scattersize=50\n",
+ "\n",
+ "colors = ['darkturquoise','dodgerblue', 'darkblue']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datafolder = \"C:\\\\Github\\\\Distraction-Paper\\\\data\\\\\"\n",
+ "figfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\figs\\\\\"\n",
+ "outputfolder = \"C:\\\\Github\\\\Distraction-Paper\\\\output\\\\\"\n",
+ "\n",
+ "try:\n",
+ " pickle_in = open(datafolder + \"distraction_data_only_snips.pickle\", 'rb')\n",
+ "except FileNotFoundError:\n",
+ " print('Cannot access pickled file')\n",
+ "\n",
+ "[modDict, disDict, habDict] = dill.load(pickle_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# thph2.8 removed from analysis because no data on habituation day\n",
+ "\n",
+ "modDict.pop('thph2.8')\n",
+ "disDict.pop('thph2.8')\n",
+ ";"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['rat', 'rms', 'fs', 'deltaF', 'tick', 'filt_sd', 'licks', 'licks_off', 'distractors', 'distracted', 'notdistracted', 'd_bool_array', 'pdp', 'pre_dp', 'trialtype', 'lickdata', 'snips_distractors', 'snips_distracted', 'snips_not-distracted'])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rat = 'thph1.1'\n",
+ "d = disDict[rat]\n",
+ "d.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "long_pre_dp_snips = []\n",
+ "short_pre_dp_snips = []\n",
+ "\n",
+ "rats = disDict.keys()\n",
+ "for rat in rats:\n",
+ " d = disDict[rat]\n",
+ " L_predp = np.array([pre_dp > 5 for pre_dp in d['pre_dp']], dtype=bool)\n",
+ "\n",
+ " long_pre_dp_snips.append(np.mean([snip for snip, L in zip(d['snips_distractors']['filt_z'],\n",
+ " L_predp) if L], axis=0))\n",
+ " short_pre_dp_snips.append(np.mean([snip for snip, L in zip(d['snips_distractors']['filt_z'],\n",
+ " L_predp) if not L], axis=0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'barscatter' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[0mpeak_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msnip\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m80\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msnip\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlong_pre_dp_snips\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[0mpeak_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msnip\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m80\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msnip\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mshort_pre_dp_snips\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mbarscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpeak_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpaired\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbarfacecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'white'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'xkcd:light grey'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbarfacecoloroption\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'individual'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'barscatter' is not defined"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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22zgzNZvNbnoR9k7m3k5wQojjsVvlemPQ2m3WdTAYpNFoyG5/J8ReY/g8wWAQ27bNzpvVahVAKtdCCEPC9QnltYVUq1WmpqYYHx83J/FgMGj+TQhxfHarXG8MWrvNut5tkog4Hhvnk99PIBBAa02hUNjUuifhWgjhkXB9QnkLGtPptDmBp9Np8+/hcFjCtRDHbGvl2nEcKpUKExMT5vm526xrCdcnh+u6NBqNfYVr7+dWKBSIxWK0tLQA0hYihPgRudQ+obzK9crKCk1NTTQaDUqlkvn3SCRCsVg8xiMUQnjheuO22NlslkgkQrlc5plnntkUoiORiPlYCdcnx04LUXez8X0ikQh9fX10dHTs62OFEKeDVK5PIO8F27ZtbNsmHo+TSCSoVqvmRSAcDmPbtkwMEeIYbd1EJp/P02g0SKVS1Ot1SqWSqVxvXdQYCARQSkm4PgF26pXfzcb3SSaTKKWIxWJHdmxCiMePhOsTyAvX3m3leDxupoJ41WvvZO4tqhFCPHqu624a3bayskIgEGBwcBClFLlcDsuydp11vduYPvFoPUi4tiyLSCRCR0cHqVTqqA9NCPEYknB9AnnVsEqlgt/vJxKJEI1GUUpRLpcBiEajgIRrIY7Txqk+AKurqySTSSzLIplMUigUgN3H8YVCIVk7cQI8SLgGuHTpEv39/Ud5SEKIx5iE6xPIq1xv7NFUShEOh0249kK3hGshjs/GzZ5qtRp37txhaWmJRqNBIpGgVqth2/au4/ii0SiVSsU858XxsG0bpZRM/BBCHAoJ1yeQ90LbaDS2LZ7ZONs6FotJuBbiGHlTfQAmJyepVCqEw2FWV1c3tXLt1v4Rj8fRWpuLZnE8bNuWaR9CiEMj4foEajQaaK1pNBqEQiEymQzz8/OEQqFNixhjsRiNRkNuKwvxiLmui9Z6U+U6m82ilCIej1MoFLAsi0ajwerq6q6zrr21ExsnAYlHz3EcqVoLIQ6NnE1OINd1cRwHpRTBYJCpqSngR33WlUqFRCKxaVGjt2ujEOJo5fN57t69Szwe3zQbOZfLEYvFaG1tZXJykmw2y8rKCvF4nO7ubmD7OD7LsgiHw+RyOTo7O1FKHcvXdNptvEgSQoiDkrPJCdRoNKjX6/h8vk0n/EajQTabZXJyEtd1CYfD+P1+aQ0R4hFKp9O4rkuxWKRWq5nnqBeuk8kk2WzWXCCvrq6a99mpNaSnp4dyuUwmk3mkX4f4EQnXQojDtK+ziVLqvFLq75RS7937+xWl1L842kM7vVzXxbZtfD4fWmtgfdpALpcjm80yMzNDoVBAKUU0GpVwLcQj4vVHJxIJ4Ec91/V6nXK5TDweJxqNUq1WCYfDRKNRSqWSmUax08SQ5uZmEokECwsLsrDxmEi4FkIcpv2eTf4N8LuADaC1vgp86qgO6rTb2Bbihevm5maKxSI+nw/btk2PZiKRoFwub+vlFEIcPm+yR3t7u2nFCgaDFItFHMchHo+b5208HieVSlEul6lWq7vOuob16rXjOKysrDzKL0fcs3WkohBCHMR+e66jWuvvb+kHlK0Bj4gXrr3ZuEopUqkUa2trRKNRgsEgq6urAKaCtrq6SlNT03EethBPPO8uUSwW48KFC9i2TSQSYXJyEq21WWSslMLn8xGLxVBKmYkhO1WuARPKd/t3cbS2bgYkhBAHsd9L9axS6iygAZRSfx9YOLKjOuVc1zULpWq1GqFQiFKpxNraGpFIBMuyqFQqOI5DLBbD5/OZsC2EODrVahW/308wGDQ79cF6u4e3DqJWqxGPx3Ech0gkYtZF7LUbo2VZZhKQeLSkLUQIcZj2ezb5DPB/AheVUnPArwH/3ZEd1SmntUZrjd/vN5Xr0dFR5ufniUajhMNhHMehVCqhlCKRSJDL5UwLiRDiaOw2D9lrF4nFYiZce33X4XCYarVqFirvxrIs05stHi0J10KIw7Tn2UQp5QNe0lr/DNAOXNRa/7jWeurIj+6U8mboegulVldXKZVKZkfGSCRi5ucCdHR0YNs2S0tLErCFOEK2be84D7larZrRmV64tm2bcDhMJBKhVqvhOM6Os649gUBAKtfHwFtEKuFaCHFY9jybaK1d4B/f+/81rbX0Hxwx13VND6DjOKytraGUor293VSrLcsy4TqZTBKLxZidnWVsbGzTLo5CiMPjOM6OletqtUogEEAphW3bZudFr4XEW0cBO4/jA2kLOS4SroUQh22/Z5OvKqX+R6VUv1KqxftzpEd2inltIY1GA8dxzHQQv99PqVTCcRzC4TCVSsUsgLpw4QJDQ0M4jsOtW7fue/tZCPFw7tcW4u3ICD/aedG2bUKhED6fzwTn3bY6l3B9PCRcCyEO237PJv81633X3wLevvfnraM6qNPOW9CotaZarZLP5031OpfLUalUzBSCdDoNgFKKlpYWzp8/j+u6zM7OHvNXIcSTxXte7tQWUqvVsCzLBDVvik+9XiccDuPz+czui7stPg4EAjQaDZl1/Yh5F0QyLUQIcVj2Fa611kM7/Bk+6oM7TTYuZEqn00xOTlIqlbh58yaLi4ukUinOnz9PqVSiVCqhtaa1tZVsNmtmXgOEw2G6urpMCBdCHA6vqrxT5doL1977bAzXkUgEn8+H67pYlkWxWNzx8b3QLtXrR0sq10KIw7bfHRoDSqlfVUr9xb0//1gptf0VRjyUcrnMtWvXmJ+fB2BxcRGAq1evUiqV6OrqYmhoiIsXL6KUIpPJUKvV6OvrIxgMcvfu3U0LGdva2gB2fREXQjw47wJ4a7j2Fip6leeNo/q8cB2JRKjX6/j9fmzb5s6dO9tmWku4Ph4SroUQh22/Z5PPAy8C//renxfvvU0cgqmpKbTWpNNpKpWKmaW7vLxsFjJGo1FisRipVIpcLmdeqM+cOUO9XieTyZjHCwQChMNhmX0txCHyQu/WtpBarUaj0SAYDNJoNAiFQgBmrvXGtpBgMEhvby/FYpEbN25sagHxQruM43u0JFwLIQ7bfs8mL2utf0Fr/R/v/fkl4OWjPLDTolqtUi6X6ejowHVd7ty5Q6PRoL29nXq9juu6KKXMVsudnZ0UCgVs28ZxHJLJJPF4fFO4hvXb0l77iBDi4HarXFcqFROuHcfZFK5rtRrhcBilFH6/n3K5TFdXF+fOncNxHHK5nHkcqVwfDwnXQojDtt+zSePeDo0AKKWGgZ2HtYoHUigUgPXQnEwmqdVqBAIBYrEY9XodrbWpeAH09PTQaDQoFApmIkhLSwvVapVqtWoeN5FI0Gg0zHbNQoiD2S1cexfBXlvI1sq1UspMDGk0GlSrVRKJBOFwmGw2ax5HwvXxkHAthDhs+z2b/BbwdaXUN5RS3wT+I/CbB/nE98b6fV0pNaaUuq6U+icHebzHVaFQIBKJEAwGaW9vByCVSpmKtdYay7IIBALmRcB1XbLZrOnZbGpqAthUBUsmk2a6iBDi4BzHwe/3UygUmJubM3eFNt5h8oI0QCgUMvOtw+GwWWTsXfC2tLSY0Zrwo2kVu20yI46GTAsRQhy27TOldqC1/jul1AhwAVDADa11bY8P24sD/KbW+gdKqQTwtlLqq1rr0QM+7mNDa83a2ppZgNjU1MTly5d57bXXmJ2dJZfLmWqZZVkmUK+trZHNZk3lOhAIEI/HWV5epru7G1h/oUilUuTzefr6+swYMCHEw/FmXM/NzZk7RcPDw9RqNXPh6/f7CYVC3Lp1i89+9rOEQiEuXrxIOp1maWkJpRRNTU20traSSqWYn5+nUCjQ2toKyKzr4yCVayHEYdtXuFZKfQb491rrq/f+3qyU+m+01v/6YT+x1noBWLj3/6tKqTGgFzg14bpSqeC6LvF43LwtFArhOA7ZbBbHcahUKpRKJQKBALOzs4TDYcLhMJlMZlMbSFtbG5OTk6yurpoxYK2treTzeVZWVsyLtxDi4di2TaPRwLZtkskk+Xye8fFxqtWqqWL7fD6q1So//uM/btZBDA0NcfbsWTo7OykWi0xOTnL58mWi0SiBQGBbuLZtm0KhQCwW23GmtjhcEq6FEIdtv2eTf6S1znt/0VrngH90WAehlBoEngfe2OHfPq2Ueksp9dbWRXuPO+/2sLebm8cL1ZFIhGg0yujoKO+++y7lcplz586RSqUoFotmAxmA5uZmLMtiamrKVLubmpqIRqMsLCw8ui/qhCoUCjL3+xF4nJ6vWmsmJye3jcTbjeM45jk7NDTEwMAAxWKRubk508Ll8/n4wz/8w00LjP/gD/6AarVKOBymo6ODQqFgxmQ2NTVRKBRMa4JlWSwuLjI+Ps57770nVexHwHVdM4f8NG7g8zg9Z4V4XOw3XPvUhr4CpZQfCB7GASil4sCXgF/TWm8bzKy1/mOt9Uta65e8nuQnxdraGoFAwCxWhPUX/FqtRr1eJ5FIsLa2xm//9m/z4Q9/mF/91V8llUrR09ODbdtMTk4yMzMDrFddzp07h23b3L5927wod3Z2UqvVTvVYvlKpxPj4ODdv3iSXy7G4uMjY2BjvvvvupgVl4uAep+fr2toay8vL3L17d1/v703oCYfDWJZFW1sbiUSCfD6PUsrsqvr5z69PKf3a175GMBhkenqaRCLB8vKyuavk7aDa2tqK67pmbYS366r3ePl8fueDEYfGdV0qlQrXrl3j2rVrp26dyuP0nBXicbHfcP3/AX+mlPqIUuqngT8F/uagn/zeRjRfYr3l5D8c9PEeN2tra9uq1l4ftrcZxZ/+6Z+aoPz666/z2c9+lp6eHmKxGPl8nqWlJVMli8VinD17lmq1ys2bN6nX6zQ1NeHz+VhZWXnkX99JMTs7azb1mJiYMJXGaDTK1NTUtjGG4nTwqsX7mSvtbX3uuq4ZiwkQj8cplUr4/X4cx+G1115jZWWFZ599lve9730MDg4CMDExgVKKcrlMPB43F8WxWIxIJMLMzAwTExPMzs5i2zbnz58nHA6zvLx8+F+42KRarTI3N4dlWQSDQSYnJze13AkhxIPab7j+beDvgP8e+My9//+nB/nE9yrh/xcwprX+3w/yWI8jx3GoVqvbwrXrupTLZbTWjI6OsrCwQHt7O7/+678OwO///u8Ti8Voa2vDsixWVlbMjo6wPiXk/Pnz2LbNrVu3gPWpBMvLy5TL5Uf3BT5Ct27dYnR0lImJCWZmZjaFJe9WfltbG08//TQjIyM888wzXLx4kZGREVKpFDMzM/Jiegp5vyf7ab1wHAettalce+LxOLZtY9s2ruvyla98BYBf+qVfYnp6mpGREQDeeustUqkUCwsLdHZ2sry8bNqUvN/DcrlMvV6nra2NeDxOc3MzpVJJpoccMW9n3HPnznHu3DmUUkxPTx/zUQkhHmf7Ctdaa1dr/X9orf8+673W39NaH/SM/0HgvwR+Win17r0/P3fAx3xseEF3t8q13+/nu9/9LgC/+Iu/yCc+8QlefPFFVlZW+OpXv0pTUxOJRIJKpWJ2bPTE43EzxWBlZYXe3l4sy+LOnTub3u9JkMvlWF1dpV6vU6lUyGQyjI+Pm95Jrx3GG02YTCZNG45SisHBQXw+n7lNL04PL1zvp9fWC9CWZW0K17FYDMdxqNfrrK2t8d3vfhelFJ/85CdZXFzkueeeA+DrX/86w8PDVCoVM1bTa0cJBAIMDw9z+fJlnnvuOZqamnBd17SQnOaWrqNWLBYpFAp0dHQQCoUIBAL09vayuroqLTlCiIe2r3B9b751UinVArwL/Ful1IGqzVrr72itldb6itb6uXt//uogj/k48RZGRaNR87ZqtcrMzAy1Wo1arcbk5CTBYJBXX32V5uZmPvWpTwHw1a9+lWAwSCQSIRQKkU6nt70AJ5NJIpEImUwGy7I4d+4cjUbjiQuRS0tLhMNhnn32WZ5++mmGh4cpl8umGlUsFvH7/Zu+zxtZlkV7ezvFYlEqhKfMxgvNvRa72rZNvV7fFq79fj9+vx/btnnzzTep1+t88IMfpKmpiWKxyAsvvEA8HmdiYgKtNZFIhGw2SzKZ5M6dO9t2UN046zoWi6GUolQqHeJXLTyu6zI9PU0gEGBjr3FbW5tp1TmNCxyFEAe337aQ1L3Fhv858G+11i8CP3N0h7+yce8AACAASURBVPXkK5VKRCIR82K6urrK9evXWVhYoNFomAkfly9fpqmpiaamJl588UX8fj/f//73TSWtp6eHer3O9evXt7V9tLW1US6XqVarRKNRUqnUE1UFc12XtbU1mpqazBzvpqYm2traSKfTpNNpVlZWSKVS953z3dTUhNba7JYpHn/lcnnPiyXbts34tb3agmq1GtVqdVu41loTDAapVqt85zvfAeBjH/sYxWKRer1OX18fzz//PADf+MY3GBkZIZ/Pk0qlKBQKTExMbPo83ig+x3Hw+XzE4/En6jl7kiwuLlKr1ejq6tq0gYxSiv7+fur1+hNXjBBCPBr7DdeWUqob+HngK0d4PE8srTXZbNb0U5dKJTPf2qtSh0IhnnrqKeLxODdu3ADgueeeIxgM0tzcTDKZ5OWXX8ZxHEZHR3Fdl2AwyNDQEJlMhrGxsU1j97ydG73QmEgkTK/34yqdTjM+Pk4ul2NtbQ2t9aY54QD9/f0kk0lmZ2dxXZeurq77PqY3T/h+4XpxcZEf/vCHTE5OHsaXIY6Q4ziMjY0xNTV13/ezbdu0Ze3VLlWtVmk0GkSj0U1BrNFoEIlE8Pl85vM988wzrKys4PP5aGpq4uWXXwbWp4d0dnYSj8cJBAJEIhF+8IMfmPDsOA63b99mfHzcjNmMx+P7ulAQD6ZSqbC4uEhrayvRaHTTRZZt2yQSCTo7O8lkMpvWtAghxH7sN1z/z6xPDBnXWr+plBoGbh/dYT15FhYWmJqaYmxsjKtXr3Lnzh0zBmx0dJRGo8HQ0BDBYJBcLmfaGoaHh/H7/aYF5JVXXgHgxo0bZh720NCQCZTz8/Om5cRrHfF6Bx/3Hs6VlRVmZ2dZW1tjYmLCTEDZGq69sYQDAwP09/cTiUT2fGxv3vDW2/Sw/oI7Pz+P3+9neXlZJjiccN7F414XkbZtEwqFCAaDe8669jaK2fq7ZNs2SilSqRTLy8vEYjFaW1vJ5XIkk0ni8bh5zn75y182bUi2bdPd3Y3jOHzjG9/AcRyz6DiTyfDVr36Vd955h0wmY/q5PY1GA8dxSKfTlEolZmZmuH79ujlniL2Njo7i8/no6+tDa41SCtd1GR0d5erVq5RKJXp7e2lpaWFubo7p6ekdzw1CCLGT/S5o/PN7vdH/w72/T2itP3G0h/bksG2bdDpNMpmko6ODlZUVLMui0WiY3dkuXbpELBYzI782bjIxNTVFo9GgubmZS5cuAXDz5k201mZkn9/vp7W1Fb/fz9LSkvncTU1NlEolEyQCgcBj28OZz+cJBAJcuHABWK8mb2yt2UgpRVtbGx0dHft67FQqRaPR2PF7480dvnDhAuFw+FSPNXwceP3TG+fHb6W1NtuZh0KhPcO195hbw7X3cbdvr9cafuzHfgzLsswYzHg8ztDQEM8//zyrq6v87d/+LeFwmFQqRSgUoru7m2w2y2uvvcbs7Cy5XI7u7m4sy2J6epr33nuPb37zm+aOydzcHO+88w5f//rXmZ6e5ubNmywtLeHz+VhYWGBubs6EQNmAZmc/+MEPGBsbM+M5vU1kcrmc+d4tLCyYBc9dXV1kMhlu3boldxCEEPvywHvrKqV+oLV+4SgO5km1srKC67r09/cTDoep1Wp0dHSYoLyxH9hxHDP9w7Isuru7aTQazM3N0draylNPPWVmsZZKJbPgRilFpVKhtbWVTCZDf38/lmXR0tLCwsICuVyOjo4OEomEqVx74eJxoLWmWCzS3NxMOBwmFAoxMzNDd3f3oTx+Mpk0L7Behd+zurpKMpkkEAiQTCbJZrOm2iVOHm/twf22s/aCp7eJk3cx63FdF6UUSikzztFr5dhodXWVmZkZxsbGAHjllVe4cuUK8/PzZLNZwuEwgUCAj3/847zzzjt88Ytf5HOf+xy5XI7+/n7m5uYIh8O8+eabBINBnn32Wdrb26lWq7S2tlKr1VheXubv/u7vCAQCpkUknU7T2dnJwMAAPp+PRCLB1NQUi4uLLC0tmUWWwWCQ8+fPEwqFDvNbfKSq1SrLy8vYtk0kEqG1tfXQtoFfXl7mxo0bRKNRs+mPF66Xl5cJhULmnOmdH3t7e4lGo9y9e5e7d+9y7tw5XNc159VkMkkqlSIYDBIOh+W8IITYd1vIRnLmeEArKyvEYjHC4fCmfmvvxXujUqlk2g6SySStra20trZSKpWIxWIkEgmefvpptNaMj4+jtaZcLhOLxUwVXGttdhkLh8NEo1EymYzpT7Ztm4WFBa5evbrpdvNJ5oWKVCoFQCgU2nETnofl8/lobm5meXl5U3XKtm1qtZppPYnH42YW+VHy7mCIB+dVme/3/fPCtWVZhEIhM6ca1i/kxsbGzKzjarVKtVolFAptC9czMzM4jmPe98qVKwQCAfx+P0opgsEggUCAj3zkI8D6pJ9QKESj0cDn83H58mVefPFFAO7evUtTUxPJZJJSqURPTw9dXV385E/+JI1Gg7fffptKpYLW2rSUhMNhczE4MDDAyMgIra2tJJNJent7cRxn195z13VZXFzcc1LKo7SwsMD169dJp9MUi0VmZ2d57733Hnon1UajwdTUFO+++y5vv/027777Ln6/n5dffpl6vW6q1Y7jsLq6SktLCy0tLQCbdmpsbm6mt7eXQqHA9PQ0t27dYnFxkWAwSDabZXx8nNHR0UP5HgghHn/3DddKqf9khzf/v/f+7b84kiN6wtRqNcrlsjlhVyoVGo3Gtuqop1gsmpO6Vw2JRqNUKhVc16WpqYlnnnkGgLGxMXw+H6VSiebmZiqVCj6fj1AotGlGa09PD9VqldnZWRKJBFprs2DycZnl6vXPbgw3Wuv7VicfVGdnJ67rcvXqVaanp3Fd17SJbAzXwJG21pTLZcbGxmTx5EPyWjX2G6696R9e9XppaYlqtWoWuFYqFXMht/H3b+NUH++/V65cMcfgVYsDgQAtLS28733vo1Kp8PrrrxONRllYWMB1XUZGRvjwhz9MX18fd+7coVAoMDU1xZe//GVGR0dZW1vjgx/8IBcvXqRarTI+Pm4W6W5dbJdMJjlz5oxpZ/BmNu+0zuLmzZt87Wtf4+23395Wud+JF0IPm3exeufOHebn52ltbeXKlStcuXKFS5cumZ1UNy7W3o9Go8GtW7dYXl6mqamJrq4uUqkUIyMj9PX1EQ6HSafTZuoQYO6MhcPhbd+Tjo4O8vk8r7/+OvV6neHhYc6fP89zzz3HhQsXOHv2rFSthRDA3pXrv1JKfV0p1eu9QWv9L+797+8e3WE9ObwXaK/iujWsbVUul03g9T7Gq86Wy2Wam5vNC/jt27dNZdV7n5WVFZqbm1ldXTUvhKlUio6ODpaWlsw85/n5eRYXF7lz585j0YNdrVZNJRDWXzgDgcC+tq7er0gkwtmzZ2lubiaTyZDJZLbNyfZ6dI/qe5bL5bhx4waNRmPT7F2xf16ovl+49n5vAoEATU1NRCIRpqamKJVKvPPOO8zMzLCwsMDa2hrVatW0XG20vLxMtVolkUiQz+exLIuzZ88CPwrX3p2qYrHIxz/+cWB9YWN/fz+u63L9+nVzofuRj3yEYrHI9773PdMH7oU/13WJxWI0NzfT3d1NvV4nmUyyvLxMrVYjl8sxPT1NPp9neXnZhGlvJ9etITybzTI9PU1LSwu2bZt1HfczPT3NtWvXHmja0OzsLFevXmVqampbMNdak06nuXr1KmNjYxQKBfr6+hgcHDRtIJFIxFTj5+fnN1WTG43GrsfcaDS4ffs2lUqFs2fPMjg4SG9vL6FQyIzu7OzspFwuMzs7a1p4vIunZDLJ6urqpkWM9XodpRSRSIT+/n6am5sBzMhEbzqTEELs1ch2FfgC8LpS6je01n++4d/kEn0fCoWC6RGG9R5Nb0LBTtbW1swLo1ft9oLd2toaHR0dvPDCesv7nTt3WFtbM3N2U6kUmUyG4eFhFhcXTZsIYOa23rx5E5/PR3d3Nz6fj+npaUKhEE8//fSugf8k8G7Le6v6K5UKzc3Nm9paNlYjH5Y3U7xarZLNZnEcx+zu6EkkEpte5O/H6+fcr8XFRcLhMOfPnz+0PtPTRGu9rwV9G39XlFIMDw9z8+ZNvvnNb5LP5xkZGeHWrVtcvXrVLJpNJpPm46vVKouLi/h8PnMB3dfXZ35mtVqNaDTKzZs3WVxcJJvN8uyzzwLr4fpzn/scvb29ZDIZAHp7e+nq6iKdTvPOO++YCUCzs7NMTU3hui6ZTIaLFy9y8eJFbt68aVpPbt++Ta1Ww+fzmceD9UlDzc3NdHZ2Mjc3R7FYJJlMUi6XmZmZQSlFR0cHPp/PzHQeGBjY8ftVr9dZXl5Ga83k5CQXL17c82exuLhIOp0mkUiwvLxMPp83owxt2zZ38VKpFC0tLSSTyR1/55VSDAwMUK1WmZqaMptjeQs5vYXL3jm2VqsxMTFhgrVXpKhWq9TrdVP1b2tro16vc+PGDYLBIIODg+ZzJpNJlpaWzHoL7+tJpVI0NzeTTqdNuBZCiK32evXWWut/o5T6JvDv721P/hmtdRmQuUR7cF2X1dXVTRMrSqWSOdl70um0eZEoFoumKtrZ2YnWmkAggGVZpno7PDzM4OAgk5OT/PCHP+RDH/oQxWKRrq4ubt68SaFQMCP9Nlbb4vE4c3NzWJbF+9//fpLJpFnYMzMzw1NPPXWoX79XmYpGo5uCycOoVqvm9r23kLOrq4tCoUCj0WBtbY3x8XEAhoaGDvzC197ebtoytlak4vE42WyWSqVCMBikVCptC+CwfhdhcnKS4eHhfVW16vU65XJ5U0gTD2bjAt/9tIV4k2bC4TAjIyOk02meeeYZrly5Qq1WM7PpI5GI+R0ul8tmOkgqlTJV4f7+fvPYhUKBQqFAMplkeHiYRqNBT08PZ86cYXp6mi984Qu89NJLwHrI6+zsBKCrq4uhoSFyuRy1Wo0rV65g2zbJZJJGo8GZM2eIRqMEg0FGR0d5/vnnyeVytLW10d/fTyaTIRwOMz09be5idXZ2moV8Z86cIZPJmDUG3oVfW1sb2WzWzNPfqNFosLS0hNaajo4O0un0prYXT6lUMps61Wo15ubmaGlpYWhoyMyV9nafDQaDtLS0mB7zvSilGBoa4saNG1y/fh3AnNs2Bm0vuHvjODc+9ta7iADd3d2cO3eO7u7uTes3EokElmUxPz9PrVajUqmQzWbp6Ogw39/V1VXT3pfP58lms9IaIoQA9jktRGt9Syn1AeB/Ad5RSv1XR3tYTwZvkxPvBFypVHAcZ1O/9cYqUqVSoVAomMVyXV1dps8xGAyajS6am5t5+eWXmZyc5Dvf+Q5Xrlwxm8m0t7eTTqfNzN2hoSH8fj/z8/MsLCxw+fJlfD4f6XTajPHzNqrwboHPzc2Z3s2D9DRPTk6asXWDg4Pbbqvvl9aaWq1mAmoul8Pn83H27Flu3LjBxMQEa2trhMNhs5mHt1HHw/ImNRQKhW0XQ16FP5PJmO9bJBLhqaeeMi+s3sZAWmvm5ub23CUSftT/LreXH54XrgOBAPV6fdc7B47jmKq1x7ZtWltbTfW2q6uLWq3GjRs3uHz5svl9mpycxOfzcf78eebn5zdVn2E9xM3NzXHu3DlGRkbMpJtz587x6quv8q/+1b/iC1/4Ap/85CdZW1tjcXERrTU9PT04jsP73vc+bNvm+9//PtPT07S2ttLV1cXS0hLpdJqenh6zNiOTyZiKOPxo3UAqlSKbzZqvXynF6Ogoy8vL9Pf309fXx+TkJE1NTeYivFQqcffuXYaGhswFQrVapVQqMTo6SltbG4FAgBs3blAoFEzVu6mpiXq9bp7r3q6G4XDYfC8jkQhDQ0MH+tmGQiEuXLjA4uIiyWTS3Nnr6emhUChQq9VMu1hbW9u2u4PeXcSNb3dd1yxq3cjn89He3m5ag2D9Qsy747e4uMjU1BTnzp0jm82STqeJxWI0Gg25MBZC7BmuzSuP1toBfkcp9TfAnwLSELoHr71j6yK4je0X169fZ3x8nHPnzrGwsGA2koD128zBYJDV1VXT+gHrVZWf/dmf5c///M954403+MxnPkOpVKJardLf3080GsV1XSYmJpifn6fRaLC8vExbWxtnzpxBKcXU1JRpewiFQlSrVWZmZkxIXV5exrIs+vr6zLHm83nu3LljxoR1dnZuqxC7rmtupxaLRfPCNzc3t6lS9iC8DTzC4TCu65LL5WhubiYWi5nQEYvFGBwcNH2s6XR607E/jJ6eHnp6era9PRQKmZGHSina29vJZDIUCgUTjDdWM2dmZiiVSrsuYvXk8/lNLUTiwXnh2rsY9aZybGXb9rYQlM/nN7V/RCIR4vG4uTPiXfxUKhUGBwfN5jPebopeu4F3sXzp0iUSiYSZxmHbNr/5m7/J5z//eb71rW/xla98hVdffRWfz8f8/Dz5fB6lFN3d3YRCIZaWlpiYmODMmTNmbcUbb7zB8PAw0WiUlpYWJicnuXDhAnNzc5TLZcLhMLlcDsdxzMVhsVjk3XffNS1OPT09TE5Oks/nGRwcJJfL0Wg0OHv2LLdu3TJVecuyCAaDrK2tsbS0RKFQoKuryxxPIpHAtm3zu97d3W020IH1uz+1Wo2xsTEsy2JwcPDAd7DC4fCm9g3vZ73X+oRSqcTq6uq257P3+7LT70hnZydKKZqamrAsC621+Z0ZGhri1q1bpore3t5Of3+/VK2FEMDe4fp/2voGrfU3lFIvAv/t0RzSk2N1dXXTdsleSPbCU7FY5NatW6RSKbPxRD6fNy/GQ0NDBAIBCoUCrusyPj5OPB6np6eHn/qpnzLzWMfGxhgZGWF1dZVwOExbWxuhUIiFhQXeeOMNent7GRgY2PTC0tfXR6FQIJvNkkgkSCaTTExMEIlEePbZZ5mdnWVpaYmOjg4T8H/4wx8yOztLIBAgHA4zMTFBR0cHPT095mucmppiZWWFQCBAV1cX3d3dxONxs2r/YRbpeRcb4XDYXCx4VfDe3l5TMfS0tLSQyWTo7u7ecYOZw+DNEW9tbSUcDpvbwl6/tncx09rayszMzKZbyDtxHIdSqWTaA04Lr0f6sKa+eK0gXpXZq2Ru5TjOtrcXi0USiYQJSJFIhFqtRnd3N/39/dy5cwfAtDTUarVN4bKnp4dGo0E2myUej5s2A69SWq/X6ezs5Pd+7/f49Kc/zac//WleeeUVhoeHCQaDLC0t0dXVZc4PL7zwArOzs9y6dYtyuUxTUxOxWIzl5WWee+45kskk3/ve9xgfH6dSqWBZlhk1d+HCBa5du8bY2BjlcplgMEhvby/lcpm7d++STqfJ5XLmgt22bVKpFJcuXaJUKpnZ2dPT0yileO655/D7/biuy8DAANls1lwEVCoVMpkMHR0dWJZlLjJc1+XatWvcvXsXy7JMmO/q6rrvBj9beT30DxtcK5UKd+/eJRAIbHt+eY+90++fV6neSTwe59KlSxSLReLxuFkXI4QQsMe0EK31/7PL23Na6//1aA7pyWDb9rb+am++NWDG4VmWxeXLl3Fdl1AoZCYDhMNhmpubaW1tZXl5mcnJScrlMktLS9y+fZumpiY+9rGPAfClL32JarW6adxWIpHg8uXL2LaNbdvbKjZ+v9+EgevXrzM5OWmCaCaTMe/vzceempoin89z+fJlRkZGSCaTtLW1sbS0ZLZibzQa5HI52tvbuXLligm9iUSCWCxGOp1+qC2E19bWqNfr3Lp1i3Q6TVtb232DakdHh6lwHxW/309fXx+RSASlFK2trRQKBWzbZnp6Gp/PZy46otHontNF8vk8WutT1RKitWZ0dNRswHIYNraFwO4TQ7y2EE+tVjMTODzez82yLF566SVCoZCZZewtqnUcx2w73tfXx8rKCpVKhZaWFhPY/H6/WTMB8Mu//Mt88pOfpFKp8M//+T8HMBtEbbwT5F3olkolMpkMZ8+e5UMf+hBdXV3Mz8+bufVvvPEG09PTNBoNOjs7aWtrMxOC7t69Sz6f59KlSwwODpJKpeju7mZgYIBUKmWeI970FMuyWF1d5fbt21y/fp1sNksgEKC9vZ3m5mZu377N9PQ009PTzM7O0mg0mJ6eJpPJmFnfnvn5eSYnJxkcHGRkZIRSqcT8/Dyjo6MPNNLvxo0bvPPOO+YOwf1UKpVNM+iLxSI3btxAa83Zs2e3heiNPfoPKhwO09HRIcFaCLHN4Q0JFpusrKyY7clXVlYol8usrKyYPuzl5WVWVlbo6uqir68PpRQrKyumauVtGNPX10dHRwe9vb0MDw9z5swZKpUKpVKJf/gP/yGWZfGtb32LdDrN0tKSuZVcKpUYGBjgJ37iJ4jFYjvOuW1ra+O5554jGAyyuLhIPB6ns7OT+fl5s2jp9ddf57vf/S6zs7M0NTXR399v+jP7+/s5f/481WqVhYUFExC9XsiNOjs7zW3qB7W2tkaxWMTn83HhwoVdJxp4vA17HnbjiYfR1tYGrM8eX11d3bQoMZFIsLa2Zl7IG42G+X9YD5kLCwtEo9FD2xTncTA3N2c2aHmYi66dbGwLge3hulqtUi6Xt7WFeDONN4brcDhMpVJhYGDABMjFxUWmp6f59re/zezsrHlue20a3s6CW58D4XDYhGulFL//+79PKBTii1/8Im+++eauX8/IyAgXL1401fJkMsmVK1doa2sjn88TDAYpl8v09/czMDDA+973Prq6usjn8/zYj/0Yg4ODZvJIT08PwWCQTCbDnTt3SKVSZg6/F66npqa4evUq1WqV69evUy6XzcWBbducOXOGWCyGZVm8+eab/NVf/RVLS0skk0lyuZwJtvl8nsnJSWKxGOfOnePChQv09PRgWRYLCwtm/OBevLDsrV3w5l3Pz88zMTFhxuWVy2XS6bS5WMtkMtTrdSYmJgiFQjz11FM7Prfu1xYihBAPS1ZeHAFv5zOvJxfWw3Y6nSYUCjE/P29W33d2dpqFVRsnhcTjcVMV7e/vNxuLhEIh2tvbyWaz9PX18eEPf5ivfe1r/Nmf/ZnZyjcQCHDt2jWzqMfbrOLll1/edqw9PT189KMfJZPJkM1mqdfrNDU18dZbb5nRd3fv3qVQKPDSSy+Z/sNMJsPq6iqpVMr0H3stLzuN9GtqaiIUCpFOpx+oOluv183xey0m+9He3s7MzAyZTOaRzIsOhUJma/l4PG7CNqwHNm/HuWQyydjYmKmkRaNRpqenqdfrnDlz5siP8yRpb2+nWCxSqVSo1+uH0mu+V+X65s2bpmq68fewWCxuatmC9WruwMCAmT1fKpUYHh4mn8+zurrKO++8YybKeKPgVlZWsCxrWzXTax3ynDlzhl/7tV/j937v9/jUpz7Fz/3czxEKhXj11Vd57rnnzPv5/X6Ghob4/ve/b9qq/H4/Z86cMaGyo6PD3EmC9Sr45OSkec5XKhVzp+fMmTP89V//NXNzczz77LMEAgFWVlbo7u5mfn6eGzdukM1mWVxcNOsustksfr+fXC5nAv4LL7zAxMSEmQgyODho5lnDeiuX4zh0dnaatrfh4WEmJyep1Wpcu3aN9vb2PdugvO/Z008/zczMDPl8ftOkl1wut2mxt7dJ1vz8vClwnD17dtfFzfdrCxFCiIclZ5QjsLi4yN27d80iu/PnzxMOh81ILq8v0nuh8hYcOo5jVqbH43Ezeg4292329PSY2bS/8Au/AMC3v/1tU01uamoyM2R9Ph/lctlM1dhJKBSir6+Pc+fOmb5vrzWlq6vLbF6hlDK9mH6/37zw9fb2YlkWtVpt10WE3kxdb1zXfjiOw7e+9S0WFhbo6+t7oJDc3t5uekbHxsYOdbOZ3Zw9e5aLFy9y/vz5TW/3xnqtrKwwOztLrVbDdV3u3r3LwsIC2WzW7B53moRCIXNB4e2qeFBbw/XW9gPv76FQyFSptdab5hl71tbWWFtbw7Iss7i1o6MDrTWXL182F4uA+d30wp4X0r0Lbdd1cRxn0/H8zu/8Dh0dHUxMTPBHf/RH/MEf/AEf+MAH+KM/+qNN79fd3Y1lWczMzJi3KaUYHBzkAx/4AB/5yEdYW1vj9u3bXL16lcnJSarVqmkV2fh7VSwWzVxpb5JIo9FgYmKC27dvmzUV+XyeUCjE4uIisViMvr4+s4h4ZWXFbEjlTTvyeudHR0fN5JOts6u9qvtP//RPEw6HuX79+p7tISsrK+ZcODIywrPPPsvzzz/Ps88+y5UrV2hubjYXIBcuXGBkZMRs0OOdD+930XaQthAhhNjNExmutdaPJEzt9rnHxsZQSnHx4kVaW1uJRqNEo1GamprI5/NmIwPLsiiXy1y/ft0s0lteXgbWZ7FuDNeWZeHz+ajValiWRW9vL41GgytXrvDRj34U27b5d//u3zE/P0+xWOTy5cvmNujw8DC2bXPt2rVN7QhbxeNxzp8/T3t7Ox/60IcYGBigs7OTn/iJn+DDH/6w6cdUSpFMJk2LRyAQ4Pz58wwMDNy3Kt3W1obf79+2W9xuxsbGSKfTXLp0adOYu/1QSnHu3Dmz+cTNmzeP/HfC7/cTi8W2HadSipaWFnK5HDMzM8TjcXNc8/PzNDc3b1uUeVp4F42HHa69x90a3izLIhAIbBoLVy6XaTQa28J1qVSiVCoRi8VMhTOVSpFIJHBdlwsXLpiKZ0dHB47jmBYRL9CNj48zNzfH/Pw8juOYxcqwXjl/7bXX+NjHPsbP//zP84lPfIJqtcqv/Mqv8MILL/CNb3wDWO/9TqVSpuVk69fjzbj2Fi5Go1GzM+To6Kh5njqOw8rKCh0dHbS3t5vzi9aaN954g3fffZdIJEJPTw8f+MAH+Af/4B/wMz/zM/y9v/f3GBoa4pVXXuHFF18kHo+bEZVdXV3cuXOHb3/72+RyObTWnDlzxrSR7HRBnEwmGRoaYnFxkffee89UnbcqFotUq9Vtj+Hz+cz5I9yt/AAAIABJREFUcHh4mEuXLtHS0kI8HkcpRTQa5ZlnnmFkZGTTHaSdSOVaCHEUnqi2kIWFBarVqtlYxNvuduu4ONu2mZ2dRSlFPB431Y/D4FV1Ll68aBbWXbt2jRs3bjAwMEBrayuu65JOpykUCmitSaVSPP3002Y+Nay/AG0M18Cm259tbW1kMhls2+a3fuu3eO2117h69Sr/7J/9Mz73uc9x6dIlM+c3k8mYnujp6elto6w2isfjpvVi4wuT9zjedBFvPu7a2prpcd56vFv5fD7T0z05ObnjZileQHFdl7m5Obq6unj66acfanastzFPJBLh1q1bTE5OMjIysu39bNvGcRyz9fFR8EYSeq1BgUDABIKHnf/9JPBaog4rXHttIF742tgW4s2M7+np2dR/u7KyglJq2yLZYrFIo9GgubmZ1dVVAoEAjuMwMDCAbds0NTVx69YtYP3nOzc3B2Aq17Zts7q6SmtrK0tLSywuLjI4OLjp85w9e5a//Mu/BNafY1/+8pf5jd/4Da5du8ZP/dRP8alPfYo/+ZM/obW11ZwfvGkcG3V0dJjNqrxNT1KpFPV6ncnJSZ555hkTfoPBIGfOnDHV3Y2z6M+fP0+9Xicej9Pe3k57ezuu65rdYb3FlN7XOjAwwPj4OLOzs7z//e9neXmZ1157jdbWViKRyK6bOT311FNkMhmzQPTpp582i7q978Xc3ByBQOChNoSyLGtfY/+kci2EOApPzOW667rcvn2b1157jWKxyNLSEl/60pf42te+tqnX0etdzufzFAoFpqameO+99w7txX18fNxscAIwMTHBnTt3aG9v56mnnqK/v5+7d/9/9t49PM67vPP+jOZ8ntFxRmdpdJYd+Ygd50BCIEmhEG84tHm5aLfLbmg26dZAWqCFAG0vaAtZoAllFxJgr6YJSVm4kt00iTGOTZw4tuP4JMmypNHMSDPSaEbSnM+jmfcPXc+NZTspkPTlJej7T3Ilmmdmnnme53f/7vt78PHKK68IZ7m3t1cWRqWrW19f/7rFtUqlor29XRb7Bx98EKPRyMGDB/nGN74hf7+8vEwikaC5uZlKpbLOmSGdTjMzM8PCwsJrdo8UKIluimhRSSS8+Nz+InC73bhcLlZWVpiamlonZCuVSkxMTDA1NYXX6yWbzdLe3v5L2XZdCWazmZaWFpLJ5GWft1gscv78ecbHxy9zO3izkEwmJZ3O4/Hgcrnks+j1+t/artnCwgJjY2Notdo3vXOtdDcv7lxfHHmuuPJUq1VWVlZES6CgVCpJt9lqtZJOp1Gr1VKcKvqHc+fOAWvToVgsxsrKCtPT00xPT4ugtqmpiZaWFvGSj0QiXLhwgcnJSdlgj4+Pc+rUKbZv387Y2Bh//dd/jdFo5Ac/+AG33347dXV1rK6uMjU1ta77fSXo9Xrq6+sl6EUJhInFYvJMqaurk02Gy+Viz549/O7v/q78/cWpsheHYWk0GiwWCx6Ph6uuuoqOjg6cTqfEkpdKJaLRKBqNhv7+fvG4V3ymp6am8Pv9aLVa9uzZQ0dHB6FQiP3793PmzBnZQBw+fFhcPv49J05vRNC4urpKKBSSqPUNbGADG1DwlulcK3xgxVKrubmZQCDA7Owsx44d48YbbxSPV41Gw8DAAEajkXQ6zdTUFHNzc/T09Pyb76P4w9bX1182clQifxWBUTgcZnFxUbiGBoOBQCBAtVqV4BNlEdPpdKhUKukItbS0oNVqCYVCLC0tYTKZhEaiwGKxCN3glltu4W//9m+59957+c53voPNZuOTn/ykcEKr1SpOp1M66zabjampKVQqFbFYjPn5efR6PS6Xi3K5zNLSEvX19eu6ZErqmxKIooyHf1lKQ0tLC0ajEZ/Px9LSkox95+bmJOo5Ho/T0tLypvk+K8mVihd1NBqVsA+VSiXfze12v6Fkx0uhBO/A2gLe2dkp5/nChQv4/X6GhoZ+K1PdLBaLFLBv1vevVCrym15aXCtFWqFQ4OzZs1QqFXQ6HeVyWa7BarXK5OQkFy5cYGxsjJaWFi5cuIBKpUKv13P48GE++9nPriumWlpaMBgMDA8Pi9/z6uoq4+PjNDU1YTQacblc1NbWSqdbSfObnp6WoBmDwUA0GiWdTvOJT3yCm266ife9733s37+fH//4x9xwww1cuHCB+fl5rr766tcVwLpcLpaWlshkMmi1WsLhMNlsFpfLRTgcRqfT4XK5sFqttLW1EQwGRTxsMBjWddeVRFRlotXW1ib2niqVivr6ekZHRzl06BBDQ0OMjIxQKBRECA0/bxrodDpx/lGpVJRKJRFz19TUkEwmSSaTcvxKpYLX62VwcPDNuDwuw69KC8lkMszMzEjUeiKRoKGh4d/VW38DG9jAbw7eMi0zlUolCYVOp5O+vj5cLhe5XI7nn3+eo0eP4vP5MBgMDA4OCgVACWVROjuvh0wmQygUIpfLMTs7K9ZagMQk53I5cRQIhUKUy2Xq6+txOBx4vV7hJ6fTaVZXV9Hr9bzyyisSvLKyskJNTQ1tbW34/X4RFKVSKZaXl1laWhI6BiA2frFYjI985CN8/OMfR61Wc//99/P5z38es9mMVqsVizyFEz46Okq1WmVoaIjh4WHa2tpQq9UEAgEp8EOhkEQ7A9KtVhZMRTj5q3Qda2trsVqtzM/Pk8vlmJubIxaLUSwWZaE1m81vONFNgUqlwul0kkwmSSQSjI2Ncf78efx+Pz09PbS2topF4puJ5eVltFotV111FcPDwzL21mg0dHV1USqViEQib+p7/qbAarVitVqJx+Pr7qU3govjzq/Uua5WqyLaU0JP2tvbJWnR5/NJvLfD4WDPnj0SthSLxfjiF79ILpeT54dKpeKOO+4Qjr/SNW5qaiISiYhriFqtZuvWrbhcLjo6OhgcHGR4eJi6ujomJibI5/MMDQ3h8XgolUqcP38enU4nPtif//zncTgc3HTTTZjNZo4ePSr+2leCTqejrq5OaFypVEq68PBzwadCy5mfn+enP/0pk5OTGAwGiWxXeNoOh0OKRsUaVKVSSepjS0uLpJTCmivLiRMnsFgsbNmyhY6ODtra2hgaGqKxsZFoNEokEqG2tpaGhgauv/56HA4HJ0+eZGZmhuHhYa677jqcTqdsQv8tlMtlAoGA2Cr+oteL8jteeqxcLifFt5KAC2vTT2WT1N/fz/DwMGq1mrGxsdfVtGxgAxv47cFbpl1WrVblAb+wsEAmk8Hn89HZ2SkP+s2bN7Nr167LumSNjY2srKyIyv5KqVyLi4scP34cm83G2972Ni5cuEAoFMLj8Uh6omKVVSgUmJmZQaPRYDabaWhokICRwcFBxsfHmZ+f58CBA/zn//yfyWazWCwW7rnnHgkSUbo4bW1t4igwMTHB8vIyRqMRvV4vYS6dnZ34fD5mZma4/vrrSafT/I//8T/4zne+w0033cSNN94olAeTySQKf7fbLa4gSiBCKpVCo9FgNBrFPcBsNmMymcQpJJFI0NraisPhIBgMMj8/v04gdqXf5kqcxra2NqFkwM8Lo6amJmw225vOgXY6nSwuLko8upKyqNVqxUIwFotdkdP6WqhWq+RyuSsGSayurspo/krdcJPJhM1mY3l5Gbfb/VvJ+2xubsbn8xGJRFhcXFyXbHgpAoEA5XKZtrY2ALnfLr6fK5UKq6urwuu/uANbKpXIZDKSVqiIb5VNrFarpVQqYTQaqa2tpa6ujp6eHgKBAOl0mn/8x38kmUzyjne8g5/85Cc8/vjjuFwuotEo0WgUp9NJoVCgWq1iMBhYXV1dR3uy2WzY7XbhgSs2mydOnCCfz0vUtslkYmVlRSZbzzzzDPv37+ed73wn/f39IvKbmJjg3nvvvex8VatVjh49KhZ8+XyepaUlmQopYsf5+XkymQxer5eZmRnxrY/FYhw+fBir1UpNTQ2VSuU1hYHJZBKTycT27dvFXjQej5PJZCgUCsRiMXK5nLxesVw0Go1ks1nMZjPlclnO+/T0NG63m2w2Sy6XI5FIkMvlGBsbo6OjQ/j5FzuArK6uMjc3x/j4OMVikYaGBkZGRqTTfvGG61Jc2rmuVCosLS0RDAblWaxQhxQBZTAYRKPRMDg4KNeeTqejUqn8Vk6gNrCBDVyOt9STwG63Uy6XiUaj+P1+SSxTqVQcOXJEKAhtbW3U19eTTCZZXFyko6NDHprz8/Oo1ep1nENY44emUilqamrI5XLkcjm8Xi/5fB6TySSx5blcjkwmg9PppFgsUq1WcblczMzMYDAYMJlM+P1+/vmf/1k6vjabjWQyyd/+7VropdPplAVa+Ry1tbWkUiny+Tzt7e3E43Hm5uYYHh7G6XRiMBgkirevr4/bbruNH//4x9x999288MILsmDHYjFUKhWtra1YrVYmJyfFKhBYV4x0dnYyMTHB5OQkra2t2O12dDod8/PzhMNh6urqcLlcLCwsYLPZrijMi8fj+Hw+uru7L7OaMxqN9Pf3y+ja5/PhcDhe087vjcJsNssIu66ujq1btzI5OUkqlUKv12O32wmFQpRKpV+YGhKJRAgGg1gsFnp7e8nlcszMzFAul9Hr9VSr1dcVLNbX10voj3LuFS7wW328rMRSh8NhSRxUCkqTySQWdIDEisNaF1EJaEokEvT29sq5yuVynD17lunpafF437p1K3q9nnK5TDwep62tTa7FpaUljh07htVqpaenB61WK9xqq9VKqVTCYrEwPT3NE088AcDf//3fU1NTwx133AHA97//fTQaDQaDAZVKJUW18u8KtFotBoOBVColGzgl1lytVktHXKFswNpk7Ytf/CKnT58mEAiIjzSsJbMeOXKEJ598UjZ3+Xyej370ozz66KMA3HjjjeL20dXVRTAYxOfz0d/fv87Rw2azsXv3bhoaGjhz5gzBYJCdO3eKA0m5XCYSiWA0GsWVA35eXJtMJiwWCxqNRp67yWRS9CxKOuSxY8ek6FasGI1Go6TQKk5EY2NjQuNpb2/H5/MRjUbJZDJEo1EGBgZkquX1epmbm6NcLstzUomgn5mZkdTYK9FoLu5ch0IhIpEIlUoFm82G2WwmHA4Ti8Wkk63QDzs7O6UgTyQSnDlzBqvVSrlcflNpZRvYwAZ+M/GWKa5VKpVQLfr6+jh9+rRYvymLbDgc5tlnn6Wnp0c6LTU1NWLN5fF4mJycJBQKUV9fLwVOMpkkEAhIGtirr76K0+mkpqaG2dlZfD4fNTU1xGIxmpub6erqolAosLKyIoViOp2mqamJbDbLpz71KQqFAv39/XzhC1/ghhtu4MMf/jAHDx4EEE/pi23tLBYLpVIJnU6HWq2mqamJ2dlZ6eIYjUbi8ThGo5Hh4WHhAR48eJDrrruOJ598kr6+Pubm5hgbG+Pd7343Q0NDIqy6Eu9XobD4fD5Z1AuFgnCj6+vr6erqwmaz4ff7MRgM67popVIJn89HpVIhGAwKreRimM1mzGYzXq+XmpoaGdErm4s3G62trQSDQVpaWkSglUqlJDI6FAqRSCRe18KrUCig1WqpqakRGkk6ncbr9YqrhMPhkATO1+vAK+cklUphtVqJxWIEAgEqlYpsBN+qOHLkCMeOHaO2tpaZmRl0Op1MLTKZjBTOer2eVColAUtKAEpLSwtzc3OcO3cOh8NBe3s7gUCARCIh4lufz4fX62VoaEg2vo2NjZIMeezYMVQqFUNDQywvL8tvm8vlCIfDrKyskEgk+O///b9TLpf54Ac/yPDwsFjRATK5KJVKQqnIZDJYLJbLrACtVivLy8vSMVWOo1KpiEajlxWAKpWKkZERHnvsMQ4dOkRLSwvNzc0cPHiQb3/72xw4cIA77riDz372s2i1Wr7yla9IYQ3w/PPPMzU1xS233MIdd9xBPB7nlVdewe/3o9Fo6OvrY+/evVy4cEE2dcrELZfLoVar8fv9zM3Nif7BbDbT398vXGmr1crq6ioajQa73c7ExATNzc10dnYyPT1NPp9ndHRUnreAaEfm5ubo7OwUseOuXbtobm7m/PnzHDp0iMbGRj7wgQ8QDAbFmrBQKBCJRNi5cycmk4lEIkFNTQ19fX3yTI7FYmi1Wimyo9EoLpfrMnG0wtFfXl4mHA7jdDppbGyUrndTUxNTU1NkMhkJxTGbzVy4cIG5uTlGRkZYXl5GrVazffv2jcJ6AxvYAPAWKq5hbZFT0u+q1SqdnZ0sLy9LCIkyultYWODo0aNUKhXh/Z47d46tW7dSW1vL0aNHmZ6epru7G5PJRDQaJRAISIqZ4iNts9lkZOx0OmlqasLj8TA9Pc3s7CxWq5WtW7eytLREtVrFYrHwkY98hJmZGerr6/nDP/xDKfwfeOAB7rnnHlKpFAMDA5dZSeXzeYxGI5VKhVQqJUVXJpNBr9dTKBSEg9nY2Mji4iJf/vKX+au/+iuefvppbrzxRv7rf/2vXH311UKBaWxspKenh+PHj/Pyyy9z7bXXXnZO9Xo9AwMDpFIpcrkcWq0WjUYjdnIzMzM0NDQQiUREnKcU0NFolEqlQnNzM/Pz8+u6sxejUCgQj8dpbm4mFosxOzsri+W/FQe+urrKxMQEarUah8MhSXtut/uK4RErKysYDAYRsClOEIB0DV+vuE4kEni9XgwGA62treRyOdra2sjlciwtLYmHr+JF/m85najVakwmk8TTh0IhdDodBoPhLT9ibmpqIpFI0N7eTl1dHZlMhvb2dgku8nq9YmE5OjrK+fPnKRQKIrStra0lnU5z9uxZuS+j0Sg2m43Nmzfzk5/8hEwmw/T0NENDQ5I0aLfbOXXqFLOzsywtLXHLLbdgNpsZHx/H7/fT19fHv/7rv0rxqCQaut1u9u3bx9TUlHjVNzc3k8/n5fMr9+jS0hJNTU3kcrl1tASbzUY0GmVyclKuO6fTKSFDra2tl1EYjEYj7e3tfOADH2Dz5s2oVCre+973MjIywl133cVTTz3FU089JX+v0Wi47777yOfzPPjggwSDQR5++GFeeOEFGhsbOXLkyLrj33XXXXz0ox+VqV8+n2f79u2cP3+epaUlDAYDU1NT7N+/H71ez6c//WmsVqtsKJQY9WQyicPhwOl00tbWhlarJZPJyD2aTCYZHBzE4XDgcrkIBAKEw2GxAzQYDNxwww2k02kWFxcJBALybNixYwdPPvmk+P+fOXNGKHyxWAy73U5bWxvJZBKDwUCxWOTs2bOUSiUGBgYAZLN7MZQpQygUwmaz0d3dLf99bGyMuro6Hn30UR566CGKxSI//OEP6evrY3FxkUKhwKuvvkp9fT2tra2/1ZaaG9jABtbj17p6q1SqW4FvAGrgoWq1+rdv5HiKJ6vX66Wrq0vcQ2CN3zsyMsLi4iJTU1PS/Wpra+Ohhx6S7qHT6eTll1+WIjIcDpNIJNYJ0ILBIIcPH6a+vp6amhpMJhO1tbW0trYyMjLC1NQUdrsdu91OIBCQce8999zDj370IwwGA3/+53/Otm3bCAaDJJNJKpUKjzzyCE888QQTExPo9Xp0Oh2zs7OYTCbK5bJ0xdLptFi4ZbNZamtrRUikRJYr7hc/+tGPuOeee3jooYf4xje+wY9+9CM++tGP4vV6WV5eZvv27VQqFfHCfa2UQEV8Bmt2ektLS3g8HgKBgCw0o6OjIuZSfLHtdjsNDQ3C77xSca0IkJxOJ1NTU8LD9Hq9DAwMvG6BqiQeKs4qBoOBbDYrUwS1Wk00GpWo+Wg0islkkqJd6RYrEwAlrONKPPF8Pi/0nmKxyNTUlCRtKsK4i1/zi1oIWq1W8T1XRujKePqtjO9+97ucOHFCqAmhUIjDhw+TTqcZHBzEYDCwsrLC888/z+joKCaTiVOnTtHd3U1LSwtms5lSqURrayuhUIiXXnqJXC6Hy+Vi//79FItFzGYzPp+PU6dOMTc3J040Dz30EI8//jitra3imrG0tMThw4f56le/is/nW/dZBwYGeOCBB+RaU54NSmFpNpvJZDJy7cPaMyccDpPL5eR6czgcmM1m2ZDb7XYcDgfZbFZoW1cq0hoaGpienhahJaxRPv7yL/+S559/nvn5eel8/+mf/ilNTU14vV7+5E/+hEQiwWOPPcbk5CSTk5OoVCq2bdtGQ0MD+/fv51vf+hZms5k//MM/pFAoYLFYcLlc4qaj1+v5yEc+Ig4pDQ0N/Kf/9J+w2+3CT1c6/eFwmNraWkmytNvt5HI5Nm/ejNlspq6ujvr6erRarXTx5+bmSKfTNDQ0kM/nKZfL7Nmzh9bWVpLJJLOzs2zatAmn08nq6ioul0u66YqWRa1Wy/PRaDRiMpmYmprCZDIxPz+PVqtdp6dQwoEqlYrwqZ1OJ5VKhR/+8Ifce++96xIxFXzwgx/kH//xH9HpdOzatYvZ2VlsNtu6JMoNbGADG/i1PQ1UKpUa+CbwLiAInFCpVE9Vq9XxX+V4Fy5c4I/+6I84fvw4lUqF3bt3MzIygsPhoKWlhfr6ekkcrK+v5+WXXyYSiVAulyVY5eTJk+IaUS6XxRPaYrFgt9uZmZmhvb0dv99PTU0NRqORaDQqxVY8HmdsbEx4iqVSiVOnTpHJZHj22Wd59NFH0ev1/Mf/+B/Zu3cvDQ0NEsyijKwVpwyTycTs7CzJZFICLhwOB6VSiVwuRzabxWg0ymui0ShGo5G6ujqCwSD19fWMj4+TyWS4++67ueGGG7jvvvvwer089dRTfPKTn0SlUnHq1ClxHFHoITqd7nXHmzabjUgkQqFQoK+vj3K5TCqV4uDBg7z88svcfPPNxONxyuWyJFEaDAbp1F2KVCqFTqcjn89TLBZpa2vDYDCI53VfX98VP48SG19fX097e7twpYvFIoFAgGAwCKwJB1UqlTgOXCy+VIr9i3nX0Wj0sjhsJYCnpqaG3t5estksMzMzdHR0yKL6qwoSrVYr4XCY2dlZ4d5ms1nxSn8rolqt8tRTT+Hz+XjppZe4/vrr2bFjB0tLS4yOjorQ8eTJk0J3UsJZgsEgr7zyCn19fSwvL9PR0SHpl7FYjAcffJCZmRmcTid33XUX8/PzHD16FK1Wy44dO/jqV7/K9773PQAmJyf54Ac/CKwVvqlUStIad+7cSSwWY2hoiL1797Jp0yZefPFFoTJNTU1JsEy5XGZubo729nYaGhpIp9NotVqmp6dJp9N0dXUJLUHxxG5sbJRrxmq1YjQaWVhYoLa29rJryWazodfrCYVC2O12SqUSJpOJa6+9lj179ojzj9vtZnx8nJMnTwrX+LbbbmPv3r08/PDDBINBbr31Vt71rndRKBTYtWsXX/ziF7n//vtlc7ywsIBKpWLz5s285z3v4a677iKXy8nz6pFHHmFkZIRrr72WgYEBSUX0eDzEYjGhtqhUKvr6+pifn8fhcFzm/FNXV4fb7Safz8v3y+fz4p2dyWRIpVIsLi6STqfFXjGXy+HxeMQ/XuHG//SnP8XpdDI9PY1arRaamVIkp9Npenp6mJ+fZ2lpiebmZorFIl6vF7fbTTgc5i//8i959NFHRSuxurpKuVzmT//0T3nyyScZHR3l7rvv5nvf+x49PT1Uq1XRwnz729/mzjvv/He9bzawgQ38ZuDXudV+GzBdrVZnAFQq1Q+A24Bfqbh2uVwcO3ZMuIxHjx7l6NGjwBrP9hOf+IQUo7OzszzyyCMEg0HhRNbW1nLddddht9tpbW2lWCwSCoVoaWmRiN/R0VEaGhpYWVlh69athMNhTCYTxWKRbDZLb28vRqORcDhMS0sLGo2Gb3zjGzz++OPA2rj2c5/7HB6Ph5aWFkwmE/X19czNzcnCubS0hEajEaqAxWIhnU6TTCapr68nHo9LLLNSlE1NTUnn1Gg0EgqFhCby4x//GKvVSnd3N5/+9Ke59957efXVV7nzzjtpbm7mlltuYXV1VegsxWKR+vp6+vv7X/NcKy4CiURCOjZOp5Pt27fz8ssvc+LECXK5HD09PcJdNJvN0l2/FMp3iUQi6HQ64aD29PQwNTXFzMwMfX19lxUcSuKcQuFQCnCdTifiwkqlIvHV8XgcnU63jmpiMBjQarXCu770uwHi2WswGOjo6ECr1WK329myZcub4vChfJ6FhQV6enpYWlrC6XS+bpT8WwEPPvggX/rSl3jxxRd54YUXcLvdbNq0iUwmw8LCAidOnMDn82Gz2WhoaOD48eNYrVYWFhbIZrMYDAb8fj9nzpyR8JLTp08zMzMDrF0fjz76KLt378ZsNlNTU8NDDz3Ed7/7XQBuuOEGlpaWGB8fp1KpSMDQrl27uPXWW+nt7RXf4sXFRREzK8mLigh3eXmZZDIpoVBdXV3EYjERD66srGCz2YTypXDLL0VLS4uEz1wa+a04i0xOTnL8+HERIxqNRhGDKhvDWCzG9PQ0TU1NNDU1iRvHV77yFXw+HwcOHODkyZM4nU7e9ra3sW/fPr7+9a/z7LPPrnvPAwcO8LWvfQ2Avr4+Hn30UT7/+c/z9NNPc+LECfbu3btOT6BM6y6G8ly6ElQqFbt27cJms1EsFmlqalrnrONyuUilUkL58Xg8xONxisUibrebCxcukM/n6e/vJxKJ0NTUJFHzs7Oz3HbbbWzbtg29Xo/X6yUajco/LRYLi4uLRKNRkskk27dvZ9++fbz00kuoVCo+9KEPcddddxEKhdBoNNTV1bFz50727duH3+/n3nvvlc3EqVOneOCBB5iYmEClUvFf/st/+YXvgQ1sYANvTfw6i+sW4OK5WxDY9asezG638+1vf1tEOYcPH0alUnHo0CGCwSCf+MQn+Ku/+isphBWo1WrhOz711FO89NJLaDQaWltb6e3tpVQq8S//8i+cPn163fs99thjXH/99bz//e+XQiyXyxGNRjl//jzHjh3jhRde4NChQ6jVaq6//no++9nPkslkxOpOiWBfXFzE4/GIKNNsNotjQXNzMwsLC1QqFTpkvgyGAAAgAElEQVQ6OsjlcqRSKVKplPjqKgWmUugNDQ3JOHVubo6Ojg7MZjMej4fPf/7z/M3f/I0kJM7NzXHPPfdw4403kslk8Pv9LCwsYDabhTKRz+dxOBzivHKxJd/Fgju3201XV5eMay8NlymXy5fZaOXzeUqlkvwGin8urE0MlEmBsnheDIUPeiUbPGDdwq/4XF8JClVA4cYq303hksfjcVQqFR6PZx3V482yzlN+a0Uwu7q6eplbzVsNKpWKTZs2sW/fPsxmM/v37+fxxx8XEdvp06fp7u5Gr9fz0ksvyeZPeW04HOb06dN0dXXJfbq6uiqF9a233soLL7yA3+9Hr9eTTCapVqvs37+f1dVVNm/ezOc+9zkymYzoBM6dO0dvby/z8/MsLi4yMDBAuVwmGAyKUE6ZLCh0M6V7Cmsb9Hw+z7lz56TwHRgYkE7r9PS0CPiuBLvdjsViYWFhgbq6usu413a7nWQySSwWY/fu3fj9fgKBAHa7HY1Gw/HjxwFEnLh161YikQhzc3PkcjnK5bJEjOdyOTo7O7HZbHz0ox/F6XRy6tQpWlpa2LFjBzU1NTz33HP87Gc/Y2hoiH/4h3+gt7eXP/7jP+bpp5/mwIED3H333XR3d7+h+0CtVrNp06Yr/r+mpiaWl5fR6/U4HA5xYTKbzeh0OuHbx+NxrFYrKpUKs9ks4lXl/LW1tZHJZDh37hyHDh3C6XTS398vbiYWi4W7776bEydOYLfb2bdvH9u2bRNnF2U61draype//GX27dvHxMTEZcE2mzdv5vbbb/+Vz8UGNrCBtw5+ncX1lZ7I1cv+SKW6E7gTeN1EMoAtW7YQDodRq9Xceuut5PN5tm7dyvHjx3nuueekM6VwAjs6Orjpppu47rrr+MxnPsOhQ4ckNEXpnClQ4nyVcePp06c5ePAgU1NTGI1GNBoNHo+HEydOyGILawXiN7/5TW677TZUKhX/5//8H6666irpyCiCScWqSqVSkclkKBaLqNVqsdZTYDabWV5eForFlYR3im/1zTffzNmzZ9m9ezcmk4lqtcp1113Hn/zJn/DSSy/x3/7bf+PMmTM8/PDDqNVq/vzP/5xoNMrY2JgkxylcxlAoRDweFxrI+Pg4iUQCt9tNXV0d1WpVCn1lTHz69GlaWlpEMJTP54UvriCdTksipEajuez71NXVSYKkMo5XNiGZTEZoPG9kgbfb7SwsLLCysiKuISsrK7z66qtUKhUCgQBXX331G45hfz0oHdF0Oi12Z7+J+GXu19bWVlZXV2VT8dJLL7G4uMji4iKpVIpNmzbxD//wD1QqFUZHR694jAsXLhCNRvm93/s9Tp8+LRuT/v5+urq6+J//839y4cIFLly4IK/Ztm0bH/7wh2lqaqJarZLNZimXy4yMjIh7THNzM9lsltHRURKJBIODg+Je4vV6MRqNpFIp8XZ2OBwMDAxgMpmEGrG8vIzdbhfrQEVoXFdXd0XtASCJkNFo9LLNZCKRoFKpyMRLq9Vy5MgRgsGgeED39fVRKBRobm6mt7eXuro6/H4/pVKJyclJzGazFNUNDQ1Uq1UKhQK33XYb73vf+9iyZYu83x/8wR9c9vne8573cPXVV3P06FGuv/56PvShD/F3f/d3r7lheC3Mzs5KN17hLV86qVEoWIFAQLIAdDodNTU1QilRq9UUCgXa29spFotoNBoaGhqwWCysrKzIeTQYDOJCNDg4SKFQYGpqitnZWebn5zlx4gQtLS1861vfYmRkRJIwBwYGZLM3ODhIf38/Dz/8MF/96ld59dVXcbvddHR00Nvby3333fcbKWr8Ze7ZDWxgA78Yfp3FdRC42GesFbgscqxarX4b+DbAjh07Liu+L4aySNbV1bF9+3a8Xi8HDhxg79693HvvvdTX15PL5di/fz8TExPceOONeDwe3G43n/vc5+js7KRUKuHxeGR0qhTN27Zto62tjZWVFSnCDh48uE70ooSheDwePvCBD4jK32q1Mjs7Kwlx7e3tvPTSSyQSCTo6OoC1Yt7j8TA0NMT09DQqlQqXy3VF6zq1Wk25XBa7r9dCfX29FKdKUap0c6699loeeOABPvaxj3H+/Hn+7u/+jrNnz/Iv//IvIsBqamoSWsqJEyd4+eWXefnll2lqapKC98CBA+zatYuVlRV0Oh25XE5cAebm5njllVfEEjGZTJLJZMT+ymAwcO7cOSKRCL29vXR2dl5RFNTR0cHExIQUSLlcToqUpaUlFhcXJRDHbDb/0oVpOBwmFAqRTqfp7OwkHo/j9/tlg6P428LPQyfejK51uVzG7/ezuLhIOBymWq0SDofZuXMngUAArVZLc3PzG36f/y/xy9yvF1MyvvKVr3Do0CEOHjzI008/jd/v50tf+hIAIyMjeL1e1Go1H/jAB9i/fz+FQoHV1VVisRgnT57k5MmTctyOjg5JJL3nnnt4+umnJYK+qamJkZERJicnyeVybNmyhVgsRltbGwMDAxw6dIhyucz27dsJhUJotVoaGhoYHBwUoavP5xO7y7m5Obq6uhgeHqa1tVUmPSaTiUAggMPhkECbYrFILpcjEAgwNDR0xWATi8UiLkTxeByDwUB7ezv5fF4cforFoqQmKvQYo9Eo6alNTU2YTCbRECgbX7vdTrFYxOl00tPTIxZ2ynNmfn7+dQNXYO26//73v8/27dtJp9M88cQTHDt2jBdffPGySRUgHPixsTH0ej1Op5OTJ09y5MgR9Ho9Tz75JPX19USjUTZt2nSZw49er6evr498Pr/OYcXn86HVarnuuuuYnZ1ldXWVq666SmLVlWtjYmKCTCYjbiTNzc10dHTw8ssv4/f7KZfL7N+/H1hLwWxra6OpqYkXX3yRSCRCZ2cnHR0dnDt3jlwux/bt29m0aRMej4disSjpjL/J+GXu2Q1sYAO/GH6dxfUJoFelUnUBIeD3gf/njRwwkUhIdLfH48Fut1NXV0ehUEClUhGPx5mZmSEYDLJp0yZuv/12XnjhBWKxGF1dXezevRtYEzZt3bpV+I+KwEdZaNVqNVu2bOHtb38709PT7NmzB61WyzPPPIPRaOT973+/dB8XFhbEIlCv18uoc2lpSWLQVSoVMzMzOBwOLBaLCPSuxMtU0hLz+TyJROJ1C0m1Wi3x0pcGs9TU1LBlyxa+853v8Pzzz/M3f/M3PPPMMwwMDPD7v//7/M7v/I44DigLi8J3VUbObrebM2fOsLq6Snt7O9FolJqaGnQ6nYgd5+fnmZmZkejgqakpiU3X6/VCBbk42ONSaLVahoeHWVlZYWZmhkAggE6nI5vNCvdVSaAE6O7uRqPRCDXl9QrhVCpFNpvF7XZLoet2u9m1axdqtZoXX3wRtVotqXM+n49CoUBdXR3t7e2vW4i8HhRXm0QiIVx0g8FALpcTDu8vkxT5mwjFu9pkMomo12Aw8Bd/8Rfcf//9ZLNZNm/ezAc/+EFyuRybNm1ix44d3HnnnXLufD4fzzzzDKOjo1SrVW6++Wa6urpkE7dp0yY0Gg2vvPIKsMbHX15eFis4JQ1Vo9GQzWYlLfPMmTNoNBpqa2vR6XTYbDby+TxTU1NS4DU3N9Pa2kpjY6OEiiguL8pm2uVySax6bW0tsEaFWlhYuGIxCmsFpc/no1wuU1NTw8zMDFarFZ1OJ171KysrRCIRtFotW7ZsESGmIvC12WzodDpWV1fFi7q2tpb5+XlxPlpZWWFubo54PC6frVgs/pv+8n19fczMzHDkyBHuu+8+RkdH2bp1K9/97ncZHBzkwIEDqFQq8vk8X/rSl1hcXLzicQqFAu9+97vp6enh5ptv5jOf+cwVN5PVahWv10u1WqW/v59wOMz09DQajUYE6rOzs+RyOXkeKgmQ8Xicn/70p2SzWXbs2IHf70er1RIIBLDZbExOThKPx2lvb2d4eJjV1VVxATKbzeTzecLhsEw5FG1GW1sbExMTYvO6gQ1sYAMX49dWXFer1bJKpboHeI41K77vVqvVsTdwPBwOB8vLyzICNZlMXH/99Zw6dYpYLEYmk2F8fByTyYTT6cTv91OpVFhcXKRYLLJz506OHDlCKpXiPe95j/hBm81mmpubMRgMlEolfud3fger1UoqlaKvr4/W1lZaW1tpaWmRTqparaatrY2Ojg4MBgMXLlygXC5jNBo5e/asFJtK0TA6OiqLoM1mY3Bw8IpFodK5VqvVV+Q1Xwqn00kgECCTyVzmGW21Wqmrq+O2227j3e9+N3v37mVubo6vfvWrfO1rX2P79u285z3vYefOnVx99dW43W58Ph8+n4+GhgY8Hg/VapXJyUkqlQrlchmPx4NarZbIZIVnHYlEmJ+fp1AoYLPZCIVCmM1m2trahM+qCCMv/V0nJydl1KvRaNizZw8ul4tnnnmGVCrFTTfdRCKRQK/XEwgEmJmZQaVSUa1W0Wq1V9ykKFDoKFdffbW4gCjXSqVSoa2tjVKpxNmzZ5mZmaGrq0tcE/L5PD09Pb+UBVc+nycej7OysiLFpMvlore3V1IIE4mEeHW/ldHQ0MDAwAChUIhTp06RTCZpamriXe96Fx/72Md47rnnqKur4+DBg9KNVIpHJUVVq9Xyzne+k4MHD8o1PjAwQDKZJJfL0d7ejl6vZ3l5mXw+T6FQYPPmzXR1dcn9Pzw8LMI2xWFCcZxRhHbBYJDm5mZxAbHZbFx11VVMTEyg0WioqakhmUxSLBbFKx3WKGiKjZ5C/9Lr9SwuLuJ0Oi/TC8TjcaLRKFdddRV2u51gMIjf7yefz7Nnzx4MBgO1tbX4/X7xmFfEz4olZy6Xw+12iwNPoVBYR59yOp2o1WoaGhowGAx4vV4RXnZ1db1ucV2tViXg6KabbsJms/HFL36RF154gfe+971XfM0111zDRz7yESYmJmTz8bGPfYwnn3ySr3/962IROD4+zh//8R+zbdu2dU45S0tLcq/lcjmhp12qlVCEisprFC/yRCLB0tKSBMX4/X62bdtGX18f3/rWtwDYs2cPJ06coKenh4aGBpqbm9m5cyfpdJqFhQUJzVIciHQ6HbW1tUQiEeHap9PpDVrFBjawAeDX7HNdrVb/FfjXN+NYCg83nU6j0+moVqtcddVVqNVq0uk0kUhEUuD6+vqora0lmUzS09NDPp9nfHyc3t5etFotwWCQAwcOsLKygt1up6+vjy1btpBIJAgEAmg0GjQaDZ2dnSQSCebn5zl+/Pi6DqvNZhOHipGREem66vV6MpmMBI+kUimhbJRKJUqlEg6H4zWt8JQAl9XVVXK5HOl0+nW7106nUxLLlJhhxfYK1rpvU1NTNDc3c/z4cR588EGeffZZTp06xfHjxzl+/Ditra3ceeed/MEf/AGdnZ0ShWy1Wtm+fbt4/MZiMUwmE3V1dahUKrESrK+vZ3h4mGAwyPLyMtlsVrjlGo1GfIEVwdLFWFlZEV620WikpqaG+vp6lpeX10VjK69TopKVMI+FhYXXLK6r1ar4BtfU1KyLmp+amiKVSrF9+3bi8Tjj4+MinNq6dSs2m0266FarlWAwKAK21+qUJxIJZmZmqFQqGI1G3G4358+fp6enRzi4DoeDYrFITU3Nr9wV/02BVquVDYwiTFX8ntvb23nf+97Hs88+K/7INTU14nyh0WjEx1zRT4RCIZaXlxkYGMDpdHL06FH8fj8dHR3s2LGDfD5Pc3Mzq6urDA4Ootfrxft4enqaZDLJ9ddfLxud06dPs7y8LC4UMzMzZDIZDAYDbrcbi8UiFBBY68SrVCp6e3sZG1vrExiNRhwOB3V1dUQiEVKpFG63W4KpLr1e5ufnMRgM8t+bm5sZHh7G6/UyPz+P3W6ntrZWvJk9Ho+4kJTLZfL5PLFYDJVKRS6XkyLfZrMRi8Vwu90SlAJrG+z+/n5mZ2eZmZlhfHyca6655orXcKVSYXJykkKhgMfjwWaz0d7ezv33389jjz3Gd7/7XZLJJLfddptQUJRJmOL64vF4yOfzzM3N8ZnPfIabb76Zw4cP88ADD3Dw4EFJqf3KV77CvffeS7lcZn5+XuxQp6am0Ov1IoKcn58X+0LluTs7O8v4+LjYa7pcLkZGRpibm8Pv96PT6RgeHubMmTPiAtTf38/c3BwejwetVovJZKK9vZ1gMMjo6Chut1v8tJW0WpPJhFqt5uDBgxQKBcrlMm63eyOlcQMb2MBbJ6GxUqlQKpWwWq1kMhmOHj1KQ0MD/f39YnOnPDw7Ojqoq6ujtraW2tpaUqkUp0+f5tSpU5jNZhobG4lGo3g8Hurr69m9e7c8ME+dOoXX68XhcJDP5yXZLZVK0dvbKwmBir1bPB7H4XBw9uxZSX10Op1SgF5zzTUSZJDL5YjFYusEjKVSSfxdFZjNZlZXV4UnefHfXwolkU4pUjOZjHh/BwIBamtrcTqdhMNhNm/ezL59+3jf+95HNBrlxRdf5Ac/+AE+n4/77ruP++67j1tuuYWvf/3rGI1GvF4vWq0Wo9EokcXKiFmx0QqFQhSLRbq7u2Vxstvt6HQ6vF4v2WwWrVaLTqcjk8lc9vkjkYhQelZWViSOuFqt0tDQQE1NDYFAQNxWzGazLLwKvSKfz1+xG6ekx11KR7HZbJIqmc1m0ev1jIyMSAd+cXFROoahUEg6eUrXWxmxKwJahfcaCATQ6/Xij/uzn/2MSCRyme3hv6dw8v9PWFlZQa1WMzAwwObNmwGkMIpEIrJxHRgYoL+/n+7ubmZnZ8lkMnR1dQnXVUnzbG9vZ2xsjObmZmpra2VqVS6Xuemmm+jt7RUervJ7RSIRwuGwbKz0ej2Tk5MEg0Hi8Th1dXV0dnaKMHBpaYmenh7xtVfuxWg0KgmNBoOBwcFBSqUSsLZhUtwuIpEI7e3ttLe3MzMzw8LCglAhFhcXyeVydHV1rStuzWYzbreb2dlZ2Uwr15winlZcTorFIqlUSr63YiOnUESi0SiRSEQcN2BtA9Df38/CwgKpVOqKVoCwpk3IZDJiQbhp0ya5l++++24+9KEPUVtbK8+jXC7HuXPnOH36NDU1NYyMjDA0NCS0mBMnTuByufj4xz/Oddddx/3330+pVOLIkSP82Z/9Ga+88gr9/f1YrVbuuOMOvva1r/HNb36T5uZmmawpvtxarRa1Ws2hQ4fI5/O0trbS2dlJOBzG5XLxxBNPiH7khz/8IX//938vNqzXXXcd73znO4VaoxTWygSuo6ODvr4+AoEATqcTl8tFPB4nFAqhUqmE/tPd3f2W3xBvYAMb+MXwlimua2pqxENWp9Pxv//3/+b//t//y+zsrHR2PR4PDQ0N4m+tjHFhzb7OYDDQ3d2Ny+ViYmJCnDou7kS0trZy4cIFcrkcJpMJrVZLJBJhZGSE97///UxNTeH3+2XRUzpgr776qhxD8VZWq9US4PK2t72Nn/3sZ5TL5XUL2/nz5ymVSmzbtk0WXJPJxMrKCo2NjRLmcqkQKJ1OS4S2w+GQsbfRaCSRSEjSYzqdpq6ujkqlIl06pZBQFr5Tp07x/e9/nx//+Mc899xzbNu2jU996lP8h//wH0Q4pXTFUqmUBGlUq1UWFhYIhUK8+OKL2Gw2LBYLLS0tYveXSqUoFots2bKF5eVlZmdnWVlZkU1ONpulqamJY8eOYbFYqFarMjWANd7m7OwsPp9PipJKpSLCsovPRTweJ5fLUVtbi16vF4u9S8MtYK3wg7WCQqfT4XQ6xVd8YmKCUCgkLiiZTIZdu3bh8/lYXFyUqcjFIjq1Wi2jZZ/PRzqdJh6PY7FYpAj7bYPb7ZYIbeUeczqdsnFROsxut1voDMq9c6XNkjIBUYruxsZGbrzxRvL5vFi1NTQ0iCNNMpnEbDazuLhIU1MT11xzDYuLi5TLZbEKtFgscs12dHTw9NNPU1tbS7FYZHZ2VkKSFM6/cu9eTPdQXICGhoY4e/Yso6Oj3HDDDTidThYWFsTJwuv1YrFYUKlUZLPZdceoq6tjfn6eubk5iYZX+OOFQoFisUipVJIiWtnY19XV0dbWRj6fZ2VlRcR9JpNpnY0mrAmgFXFtfX298OEVAbVyzyq+/36/X+6TwcFBcdZRvOKDwSCBQIBSqURbW5vQnAwGA1u2bCEUCtHT04Pdbmfnzp38xV/8BVarlZ/85Cd84QtfkHwAgD/7sz+Tf/d6vVxzzTXccccdfPrTn5Y0WoPBgMViYefOnUKXGx8f59prr5UU2EvR2trKpz71Ka699lqWl5fx+/00NTVhNBopFAoidlXE8Mr0QaVSiRtJd3c3mzZtolKp/MaLGzewgQ28OXjLFNcAPT09UoDu3buX5557jvPnz9PU1MTQ0BBtbW2cO3dOrJuUkSPA29/+dvR6vRSkSgKj0oVUUFdXh81mIxqN0traysmTJ+nu7mbr1q3Y7XY6OjqIRqOcOnUKu90uXcnu7m7MZjMjIyOYzWbpyCmf1+l0Mjg4iNfrFYGM0o0H1vk8K9xppaCOxWI0NTXh8/lwOBxUq1VJkRwaGpKumaJun5mZER/ncrnM8vIymUyGaDRKY2MjlUpF3BhCoRA33HADN998M8vLy+zbt49HHnmEL3zhC3znO9/h4Ycf5h3veAdnz54V2z6F32o2m8Xya2lpidXVVXQ6HePj4+h0Oq699lomJyclBEMRf7rdbpLJJMeOHSMajUrxpXjbKnHVSiGeSCQIBoOkUik6OzuZm5sTH2KFo5vJZFhaWgKQBTEajQr/9GJks1ny+Tz19fUsLS3J9GBpaYlkMsn8/Dw2m41rr70Wk8lEoVDg0KFDDAwMMD8/L/QERQinCLqcTifLy8vyG7a2toow87cRoVCISCQCID7q5XKZcDiMVquls7MTnU4ngSnlcvk1Lezg58X1xd1DZbN9MZR7TonvrqmpIZ/Po9FoGBoaIp1Ok8vlaGtrY25ujmw2K3+r8Ph7e3tlk2qxWIQWoFKp8Pl8dHZ2yvtoNBquuuoqNBoN5XKZyclJjhw5gsFgwGazce7cOYLBoIgWZ2Zm5F7ZvHmz2H92dnYyMTHB6dOn0Wq1DA0Nkc1mCYVCnDlzRlIVU6kUO3fuRK/XC5UtlUqxvLxMLBYTX/BLoQQ4JZNJjh49il6vx2g00tfXh8/nY2Jigt27d2O32yWaXvmOKysrYmN45MgRmpubRXC5detWGhsb121iOzs7yWQy4mOdz+dZXFykvr6ez372s9x888088cQTJJNJxsbGOHr0KN3d3ezdu5dQKMQPfvADvv/97/PYY4/x1FNP4XA4ePDBB/lf/+t/0d3dzfDwsOhDkskkvb292Gw2tFotv/d7v4fH42F+fl5+s0qlQm1tLeFwWBIllRwC5bmr0JcUQWtfXx8dHR1kMhlxMtq6detG93oDG9jAW6u4vniU2trayu23347X6+XChQsUi0WMRiMul0tEabDGg1Xs+y7GawkFFZ7jiRMnmJqaYn5+nt27d8sDVQmTUez7uru7WV1dFcFjY2MjgUCAiYmJdd03hVdqt9tlRH1xXLhCicjlcmg0GhlHms1mYrGYdI8VL1xY4xRHIhHpUGWz2XU2XIpPrEqlIplMkkgkSCaTQh+pVCqkUin8fj89PT3U1dXxT//0T/zRH/0Rn/nMZzh+/Di33nort99+Ox//+McxGAzMzs6KS0s6naahoYGhoSHC4TDHjx+nVCrJ+FvZJBiNRpaWltZxQpUUvObmZtrb2+no6JCNUENDA3NzcywtLQm9Z3p6mnPnzonoUInGnpubw+v1YrfbcblcwqdWFsMrhbUo9BS3200ul2NycpK6ujpaWlpYXV3F7/dLx9zhcNDZ2cn4+DivvvoqS0tLRKNRCoUCb3/722lqahLKS21trTjTKAVcQ0MDi4uL/6YF2lsRiqtFNpslHA4LjSaRSODxePB4PJw+fVqExKVSSe6ZarXKysrKuqjw1dVVgF/4PNbX12MymbDb7dINVuhMarVaOrnKBKRarWI0GuXaVZx4FKrP0tKSUIMUbrQCpTNvs9lwu90Eg0FisZh0m5uamti6datY6p07d46xsTGq1Sq9vb1YLBZMJpMILvv7+3E4HIyNjYk7jlKEl8tlLBaLFH6Ki0l/fz/nzp2TWPRLoQgjI5EI+Xxe+NzKprJarcrzQqGRKN33lZUVcQ1KJpNUKhWKxSIdHR309PTIexSLRdk4uN1u5ufnicVilMtlseoDGB4e5pOf/KR0uzOZDIcPHyadTjM4OMinP/1pvvzlL/P4449z++2343a7JWRIEUgq2L59Oz/5yU/k2JlMhomJCerq6lhYWACQdE1FtD01NUUymaSlpWXd5qyhoeGyc1cul0Xo+mYFS21gAxv4zcZbqri+FE6nkx07dogzQCgUkrGzgteyf3s9dHV1MT09LRzalpYWkskkq6urBINBmpqaaGlpobm5GZvNJtxkJeFQo9FQrVbFMxmQjlxjY6MUnUpx3dTUxOLiIvF4XKgG8HMBYDAYZHZ2VoSclUqFrq4uUckri7xi9XZpTHF7e7t0f5XodIVTrsQpj42N0d7ejt1u5x3veAcvvvgi999/P3/913/Nj370Iw4ePMh73/te9uzZQ3d3N2q1mlAoJClmLpeLlpYWJiYmaG9vZ3FxkdHRUSqVinA4nU4nFouF5eVlFhYWMJlMdHR0yBjfYDBgtVopFArSZVc2Dm1tbQQCAbHpq6mpwWazUSgUmJyc5JZbbpENk1IYJJNJpqam6OzsXBdgoQgtla6nWq2mu7tbFvrV1VWhFZXLZbHtu3DhAqlUSjp0CiWpVCphsViIRCLCOZ+bm5NEvks3G78tMBqNGI1G7HY7DQ0NUghqNBqsVqvcH0pgy8Xd1mQyid/vR61Wy293pc7168FkMokrRaFQQK1Wi+2mYq2nFNOwVhharVbK5bIIYRVes0ajweVycfbsWWBtOnLp1AvWrmG1Wk1PTw+lUonz58/j8XgYGUwVftcAACAASURBVBmRjenCwoJQxpaWlqhUKtK9n5ycZPv27VIYKkW1cs2PjY0JlUl5lnR2dkoGQG9vL5FIhFAoxMDAgHwuZXObSqUwmUwSRjM7O4tWqxX6nAKNRiP3nsPhENpFW1sbP/vZz6S7nUqlpMMdDodlSqNWq+nr68PpdMrvXltbK7Z4Xq9XNlSK8FuJQFccXP75n/8ZrVbLI488wvT0NE6nkyeeeAJYs/xsb29Ho9Fw0003XaZZUWz2FKFsIpHAbrfjdDqJxWLE43Hq6+tf12no4nNxaQDOBjawgd9uvKWLawVKgqDiFvBGodPp6Orqwm63Cy9PoR3E43G6urpIpVKEw2HsdjuJRILGxkbpaiiL/8XFtcFguCxEQeE8Op1OFhcX1xXWSohMR0eHcMg7Ozupra2lXC6j1Woxm83E43EmJyel8xWPx9fFgsPaQudyuVheXmZqagqLxUJ/fz8qlYpCoSD88OnpaRmvajQaPvWpT/HhD3+YO++8k2eeeYZ/+qd/4oknnuB3f/d32bJlCw0NDZRKJeLxOOl0Go/HQ7lcxmQyYTabqampkXG6wtWMRqOkUinK5bKIHRUoC7ESPKJYo9XU1GAymejq6sLpdMoIG9aKsFKptO531+v16PV66YR6vV5qa2ux2WxS3IyOjqLX68lmswwMDEhh3d3dTVdXF4uLi5RKJXF96O/vF2/hhYUFbDYbJ06c4OzZs3R0dIg/eGdnp/B6a2tr5bf4bSyuL4ZGo5ENn9FoFBoIIN3qi4trZbqQy+V+5eIa1jbXy8vLqNVqtFqtXPfKfWgwGKRrWyqVMJvNFItFwuGwdGEVTYeSsqlQBy7mkitQOu0Oh4PV1VUKhQItLS1ks1nxUV9eXpYwk3w+L93gdDot/so6nU7sPXU6HeVymb6+PkZHRzEajajVapaXl2ltbUWv1zM3N8fq6irNzc00Njbi9/tJJpPYbDYqlQqhUEiSUldXV/F4PMTjcebm5jh//jzVahWn0ynTgUu/k3L9Kt14JbJcrVbj8/nkXCpUqGAwyNTUFENDQ1LQajQaisUioVBIfutoNEqxWBSv7+7ublKplCQvfu973+PDH/4w0WiUd73rXeINf8MNNwgFy+/3U61WaW1tlfutr6+P2dlZseJUNgcqlUosRje60BvYwAZ+VfxWFNdw5ZjwNwKFS51IJBgfHxfluMPhoKGhAavVyuTkJKOjoyKkUqCMV5XiQcHFhXW1WiWTycjo2mAwkM/ncblcZLNZKe4UQVSlUhFqi7Kg6/V6uru7iUQiWCwWkskkKysrV/RPVqKZFWqI9v9l782jG7uvO8/vD/tOgAAIEARJcN+qpJJUsbxkZEfyaBzHUSZjO52cpNtyfFpxT9zt9ExO4p4kfez0OXMiZ2In03Z3xt0n407SihNLie14SSw7smpkLbYlq1RVZHFfsBA7QCzERuA3fwC/n0AS3KpYJMi6n3N4COA9PFw8vN97993fvd+rVkOtVsPtdmN9fX2bXNfo6KgstvJ6vfjGN76BH/zgB/j0pz+Nv/3bv8UzzzyDZ555BkD9xiaZTIJzjl//9V/H448/jnK5jM7OTvT29kKpVCIQCGB5eVmqd4yNjcl9s7i4iOHhYSwsLMgp+kKhgP7+fszOzuL3fu/3MDs7i97eXmg0GoyMjODxxx9HR0cHDAYDyuWyVCVp3r+JRAJmsxkjIyNSIlAUZ125cgUKhQJf+cpXpNRgc9FouVxGV1cXFAqFTIXx+/1IpVKw2Wwyh1w04QmHwyiVStJ5vHHjBi5evCgbE4mGG82/fTabBYCWxZbnHbVaLY8FADIq28q5bt5vt+JcWywWJBIJOavT7FgDdYeQc45yuYxyuSxbdsfjcal5LH5XYVNPTw8WFxeRyWR2pZsxxmREW6FQyBv0fD4vmxU5HA7Z6nt1dVU6fKJ4OpfL4cUXX4RCoZBygELuz+fzQa/Xw2w2Y2NjA16vF8ViUY4noc3v9/uxurqKCxcuIJlMSicdqNdwZDIZeDwedHR04Ec/+hGuXr2KYrGIGzduyLEmUCqV6Ovrg9/vx8bGBoaHh+VvICRMRUMr4bDqdDrcvHkTc3NzqFar8rcV9QlqtRoXL15EsViUDbbuu+8+2O122do8FAohm83C6XTC5XJBrVZLp1iMabEtANKZFzU1ogZC9EdoVhU6qmNdq9WQz+f3rQkgCOLu4a5xro8bUQTX2dmJ+++/H0DdgR8aGkImk5G5mKVSCU6nc9sFW6RmiOnmVmxubqJWq0n1gImJCdkkI5lMIh6Py86HHR0dUKvVUvavmeYUENGZbKcSAVC/0Is8zlAohNnZWbjdbpkTnEwmMTw8jJs3b8puk6KrImMMDz74IJ555hm8/vrr+MpXvoIXXngBL730EhKJhLxQff7zn4fFYsH73/9+2O12mR4j7Bb5jSLdolarYWZmRhYlhcNhrK6uIhgM4tVXX5X5kgCkrjAA/PEf/zEefPBBvP/978cv/MIvYHFxEdFodFvOZalUkioUGo0GDocDjDF897vfxcrKCi5fvox0Oi0jhrOzs5iamkKpVJJFsl6vFw6HA6FQCDdu3MDY2JiU8BPFcR6PB3//938Ph8OBrq4ubG1tYWlpCdlsFuvr67Db7bDZbNvyep999lmZm/2Wt7zlaAfmOUCtVm+LXKtUKvmaYD/n+iiKDeK40+v1WFhYQLlc3uVci88TKQ0ejwdutxtzc3NSGQOoj2vhMKvVainLuJ+jZrVaEQ6HZZ50X18fxsfHZYObeDwuZ47EPjCbzcjlctBqtVCr1UgmkzIiK9br7OxEIBBAPp+XbdxFtFyhUMDlciEajeLq1aswGo1Qq9VSf1ukxYibgFKphHQ6jWq1imQyiZs3b2JkZGTbDJiYxXE4HPB4PLh+/TosFot0Ynei1+sxNDSEYDAIjUaDwcFBlEolbGxsIBwO46GHHpKSfeVyGW95y1tkgEKr1aKzsxPxeByMMQwODiISichovOiC29XVhe7ubqhUKtklNxAISKUhcfPW2dmJRCKBjY0N6HQ62XBGFCTvvEHaSaFQkBKRFy5cuGukNAmC2Btyro8BkeYgpofX1tZknuTQ0JCMNonlorCwUCjsOf0o8q3FexQKhYyK2O12VCoVWdDndrtlJGe/3D+r1SoVNnY610DdkRAa4CsrK1hdXYVer4fBYJAXq4mJCaRSKcRiMaysrGBqampbPuOlS5dw6dIlAPUL7tWrV1Gr1fDcc8/hE5/4BD772c9ic3MTDz74oFTbKJfLmJycRCqVkoogYvv/9E//hOXlZZmDuXO/P/LII/jgBz+IUCgEjUaD559/Ht/4xjfw4osv4sUXX8Tv/u7v4sEHH4RWq5WzDUId5Utf+hK+/vWv4/nnn4dSqcR73vMeXLx4EbOzszCZTHjPe94jC7OuXLmC73znO6hWqxgfH5f7jzEGnU4nC2NDoRAuX74so4yi5b3dbpcNg9xuN7RarZQ/jEajGB0dBecc3/zmN/Haa6+d++6M+yGaywjFl53OtVCyUalULZ3ro0QdFQoF3G63zCkulUrboo9i1mh5eRmhUAhdXV2yQ+rU1NS2bYnIp4hOi1Su5oK+nXR2dsLv92NhYQHd3d1wu92y4Ywo8FSr1SiVSvJm/L777oPNZkM0GkU0GkUikZCOrugQabPZEAwGMTs7C8YYvF4vqtWqlITknOPGjRvQ6/UYHh6G2+2G3+9Hd3c3zGbztht/j8cDu90uGzNVq1XMzc1hZGREjoNYLAbOOdxut2zJ3pz21gqLxbJtZsZkMmFqagp+v182WxK55zuLCHt7e2GxWGTzIVFYGQqFpHKJaDIE1J15IV3q8XikZKFYJsawyWSS573mfPFW+fMAZB2MQqHAwMAAOdYEQQAg5/rYEFHqaDQKzjn6+/uxuroq86RFaoaIyHV2diKXy6FYLO7KgS6VSlJTtlW3L8YYuru7ceHCBdnxTalUwu/379kwBXgzrzWZTKKnp2dPJ8RkMuHChQvY2NjAysqKVLa4efMmHA4HvF4vzGYzpqenEYlEZKFgqVSSkWCg7hRcvnwZpVIJ8XgcDz30EK5cuYI/+ZM/AVCf7v/Yxz6Gn/7pn4bFYsErr7yCp556Sqou+P3+bd95amoKb33rW/Hggw/innvuwfDwsMzPXVhYkMWFv/Ebv4Fvfetb+OpXv4rZ2Vl873vfw8svv4xXX30VH/3oR/F3f/d3ePLJJ+UUtODpp5/G008/DQB47rnn8Ed/9EeYnJzE6urqNuUWAPjABz6AL33pS7h+/Tr+6q/+CrVaTTobnZ2dWF5eRqVSQSqVwvDwMH7iJ35CqgpYrVapwpDNZrG2tobZ2VkEAgEZAb///vvv2k5v4vgRDp4o2BPRauEIWywWJJNJmbIjorK3kisr0nOEQy9QKBQYGRmRHRo3Njb2HGPFYlHeDIvxJVKChH3r6+tyvAJ1x04UzopmTEC9wFlIAJrNZlQqFfncarXCbDbL4srFxcVtKUtiez09PQgEAhgYGEA6nUY8HodCoUC5XEYkEoFGo0E4HIbdbodarYbZbJYpWM3ykAaDAZOTkwAgZ20SiQRmZ2fR19cnI7/NnV8ZY7f0O7hcLqlJL8ZSq5kIpVK5y+Ht7u5GrVZDOBxGT0/PrvcJ5zoSiaC3t1em+ajVaumcLy4uQqVSYWxsDEqlEjdu3JDa9TsROveiw2NzkIEgiLsbOhscExqNBuVyWba2djgcSKfTcspYFEWJi5aQqtvc3ATnHGtra+jp6YHZbMb6+jo459vaFLfCbDbLzo5WqxV+vx/JZHLfok1RkZ/L5Q7MD+zo6MDk5KR0FLVaLSKRCBQKBZxOJzY2NrC5uSmnkwOBAJRKpdTmFQQCATgcDvz2b/82BgYGMD09jVKphDfeeAOf+cxn8JnPfGbP7/fxj38c73znO/HAAw/AZrOhVqtJGb4XX3xRyv75fD5Eo1E5NX/PPffgfe97H65evYq//Mu/xCuvvIInn3wSTz75pNz+5OQkHn74YZmD+uUvfxmJRAJjY2NwuVx49tlnZbrJxMQE3G438vk8fvSjH+Hpp5+Wjr3g29/+Nn7+538ewWBQdnN75zvfifvuu2+b6kC1WkUoFMLq6qrUG4/FYrIpiUhPcbvd6O3tvesk+sRNxebmplTX0Wg0SKVSsrAQgMy9FakctyNnKHKtdzrXQH1s+3w+LC0toVwuy7zjrq4uJJNJ6HQ66HQ6lMvlbTm7oo4hHo9LR1c0O2muARkYGADnHJOTk/JGW+QQ+3w+me6g0WikYo6gv79f3tADdedapIGJGROVSgWbzSYlI0XRocfjQSqVksfdwMCAbNDTfDMpbma8Xi9u3ryJSCSCCxcuYHl5GdevX0dHRwe2trakPv/tslPN6CgIudNWN6YajUamk3R3d8sbEbEsEAjAZDLh3nvv3daIqLkzZjOi+6uQQiQIghDQGeGYEOkM5XJZpmaI6eB0Oo1UKoVQKCSjR8IZzufzspI/Ho/DYDDIXM2DphjFlKrIM7RarbI1d6uLiyjYE0oChym+UavVGBwclMoler0e6+vrSCQSUKvVWFhYkKkRGo0GlUoF0WhUpjVsbGxIea3JyUmZq2m1WvGpT30KTz/9tHTcNRoNHnvsMTz44INSRcHr9SKfz2NpaUkWc2YyGXR2dmJxcRGMMej1eoTDYakGk8vlEAgEYDQacfnyZQwNDeH555/Hs88+i5mZGRgMBnzgAx/Ahz/8YaTTaXzrW9+Cy+XCH/zBHwAAHn74YRQKBZnrPTU1te1G58tf/jI+8pGPIJvNSoWJ9fV1XL16FVevXt22/0SXuYceegjvfve7cePGDTz77LPytwDqEbf3v//9sNls+P3f/33Z7c9gMOyKmN8NiGNXaLoD9Zmh5sJC4M1mSsLZvl2tcI1G09K5BiCLBpuPr46ODqmE0d/fDwDbItpqtRoGgwH5fF7WFgDY9Rni+zSPdyHdCdQddaF4IWocBDqdTqaJicLA5pzxZqdPFFavrq5CqVTKgsV8Po+LFy/K84FoECP2p8h9Fzr6sVgMhUIBXV1dmJubky3nxXlnpxMK1NWNgsEgPB7PHZ+R2W/7QhVJtIoXTZyEZv3W1ta2Y0h0xhRt1LPZLEwmE8xmMxKJBMnwEQTREnKujwkx7QtgV5qHyA0Mh8Oy4YRKpZK5zOIilsvlkEqlUKvVWjZ52ImYSt7Y2JDa2hsbG/D7/fD5fDLaIpQWhCPAOZeFS16v98ACMJVKJVU/Lly4gFwuB8YYRkdH8eqrr+L73/8+Lly4gEceeQTBYBCRSER2evT7/dDpdOjq6kK5XMbg4CA455iensZ73/te3HvvvXj44YelusrAwICcgg0EAohEIlLWS7SU7+/vR7lcRnd3N9RqNbRaLfL5PCwWC7q6uhAOhzE+Pi4LpFQqFS5cuICf+ZmfwejoqFRbWF5eRjgcllKEPp8PWq1WFjcKXeOdSjMf/OAHYbPZ8MILL8DpdMo27UtLS5iZmYFer8db3/pWLC8v45lnnkEoFMKVK1dw5coVuY3+/n709PTIAs3Pfe5zctnU1BQuXbokG3bcbQjnSHRDBN5U0hGtvpVKpXztuJxrrVaLXC63rZASgFSsAOozP2azGZlMBqurq/K9q6ur2+oiBELrWehMA9jVkbNSqUChUOw5Djs7O6WT3ko9RuSjizSaneefZhwOB8LhMB544AEwxqRufLPdzftazM4oFAqkUikplxkIBFCr1cAYky3WRaF1V1cXvF7vtmM3kUjIdC+hfX8aiJuRubk51Go1WK1WKfn59re/HcvLy1I/vaOjAwqFAna7Xc4yAZCKMel0Gk6n864cowRB7A8518dEs0bxzoub6OoYDAbR09Mjo1tOpxPLy8tQKpWya5twJlsVHLbCYrEgGo2iVqtBp9PB4/EgGAyiUCjI3NDJyUnZ+lur1SKZTMomKlqtVmrDFgoFZDIZ6HS6XdOyIiq+sLAgZQRF9zwxdV8sFtHd3Q2/34/XXntNSmN5vV5cv35dthFXKpWoVqu4fPkykskk3njjDfh8PqloIPB6vXC5XNLJTCaTMBqN0Gq1uHbtGrq7u6HRaOSNgmhDXKvV0NfXh2KxiGq1Cr/fL4tKC4WC1AUulUoolUr4qZ/6KfT398vcS3GxHBgYwOzsLBYWFuD1erd1c5ycnIRSqUQymcRP/uRP4sc//jF0Oh1GRkakisiv/dqv4dOf/jQymQyeeuopXL9+HRMTE7J5htlsxsDAAD7/+c/jqaeeQqFQwGOPPYYnnngCqVTqrtW9Fo6mKFoEtjt8oquh6Eh4XM51R0cHYrEYrl+/jmq1ip6eHjidTlkUODIyIjXSNRqNnLno6uqSDap2pgdotVokEgnpUJtMpl3OdXN6wl6I1JO9lsXjcTnLsd+5Q6FQ4MKFCzInOhwOIxgMymJHsT1gu3MtZhH0er2USlxbW0OhUEBfXx8eeOABqZcdjUaRz+cxODgov5eI2m9ubqJQKOx7A3AnqdVqSKfTcgZMaHqL88/Q0BDm5uawsLAgZVWFtJ/NZoPb7UYqlcIrr7wCs9m8q6iVIAgCIOf62DCZTDJns9WFQ6fTyW5wAlFEYzKZ0NHRgUAgILWsD0tHRwcikYhs3ytapCeTSZjNZmSzWeTzeVlw5PF4UCqVoFKpEI1G4XQ64Xa7kcvlMD8/v03OzGw2w2QywWQyoVarYXl5WRY9CQd7ZGQE8XgckUgEzz//PPR6PXK5HOLxOB544AF0d3djdXUVnHP4fD4Ui0Wpc202mzE+Pi4d04sXL+5ykJqneIUkVjKZRKVSgdPplLJnjDGZkiIawlgsFjidTkxNTWFxcVG2fu7t7cW9994rb27uu+++likyoovcysoK/H4/0uk0fD4fNBoN7HY7MpkM+vr60NXVhfvuuw8vvfSSlGacn5+XkXiLxYInnngCW1tbSCaTCAaDMkq6urqKhx56CA899BAUCgVWV1fx/e9/H16vFxcvXjz0cXDeUKvVqFar8vfXaDSy4LBcLm97XaRViG6Jt4roKlgsFqHVahGLxaDVamWDluZjxOfzSXUep9OJUqnUctwKR3VjY0Pq4AcCAeRyOSmn1/x9btVuUagnlFX2o3l8NRdCivOWeG1zcxNWqxWVSgWZTAZer1fmV9tsNilF2Kzb3dvbKxU3bty4AbvdLou3RUOtYrEIlUqFWq22LYXlJNjc3JQ3TqLDqyjIBupjfmxsDOFwGLFYDOl0Gmtra6hUKrBarXI2pVAoSEWgnVF6giAIcq6PkYmJiW15ogch9KtFFEl08jtIV7UZkfYh2vcKjVqXywWtVovXX38d0WgUpVIJLpcLRqMRo6OjGBwcxGuvvYbV1VW43W4pZTc8PCyjOtlsVkacgHpEzOFwSIezu7sbw8PDmJmZwYsvvoi1tTUYDAa87W1vw+rqqpSey+VyUuJvJ2NjYwgGg6jVaggEAlhZWYHBYIDX68XW1tauKH6lUoHf74der5dT5M2ydUIyrHkfG41G3HPPPXC5XFhYWIBer8eFCxfg9/tl58q9UKlUGB4eRjweh9/vx/T0NJxOJwqFArxeLwYGBsAYg8vlws/+7M+Ccw6VSoXBwUGsrq5ienpaptSItCGr1YrBwUHcvHlTdsnTaDSIx+PweDzS+alUKndtoZRGo5FOmECr1aJYLKJSqcjfrFmiTxwvt4PQWy6Xy7JwVjRracZsNktpR+DNnOudCHuEhrJoMjQ7OwvgTVWNo4z5nZjNZnmze9SmQyKy3OxcixvrcDgMq9WKfD6Pcrksi7TT6TQsFgvGxsZgMBh25ZDbbDYYjUaEQiEkEgnZUXJ8fFw61yKaL5opnRRi1sBoNCKfz7cMhCgUCng8HnR3dyOZTMr9YDQaZX3Jo48+ikwmIyUTOzs7USqVDixCJwji7uDuvHLfIZRKZctinv1ojiI5HI4jd5IUeZOifa94TTgfoiUxAJlyISJwQ0NDeOGFF3D16lXZREKkRojGD4lEAsvLy/D5fPD5fHj99dfx0ksvSSms+fl5FItFGI1GWVglpltjsRgqlQr0ev2ezoNKpcLDDz+M9fV1VKtVGTGcm5uT+2d4eFja7Pf7UavVMDg42PKi3MpRLpVKsnGGmOK9fv06arVaS8muVjgcDpjNZqytrSESich89eb88Hw+j76+PqnOYDQasbKyglgsho6ODtlUw263yzQDoZ0sCsEMBgPUajVu3LiBQCCAkZGRA207j4joa3MU1mQyIZVKoVqtbpN7FBJ9rdqNHxW9Xg+9Xi/1szOZzK4iwqOwsyGNRqOBx+ORknyBQEC2Fr9VhDRnNpvdpkpzGMT+2qkhPzg4iOvXr0stbbVaDbvdjmAwKHXixY1roVDYNWsgFFY8Hg/+4R/+QTq1Go0GhUIB2WxWzoZtbm7C7XbD4XDIFvBqtRojIyPHrpQj8quHhoZkUepeCAUZt9uNzs7ObUXIkUgEo6OjsFgsWF9fRzgchsFguO3ZE4Igzgd0FjgHiOnW5hbdgs7OTmxubsJisexyPHw+H4LBoMxdzeVysukFYwzValW2dd/Y2IDFYkEsFsPW1hYcDsc2Z72zsxM9PT0oFArI5XLo7e2FzWaDyWSSTu1eqFQq9Pb2yudOpxO5XA4KhQJ+v182q8lms0ilUvB4PHvmn+5ENHnQ6/WYnJxEV1cXDAYDotEozGbzoQpHBVqtFiMjI7sa/4TDYSlRuLCwgKmpKSgUCmg0GoyOju6pi6xSqdDX1yef2+122cFRTMnfrbRyrs1ms6wdEBFatVotJTBF46bjQOQ4C5WQW0WpVMooqTgGXC4Xurq6wBiTWutHvSnficvlOrJjDby5f0VqDeccW1tbmJubk10XhVa7Wq2GXq+Xeu3C7ng8jnK53HLfb25uwuVyoVqtYmFhQRZGAnXnNZVKQaFQIB6Pw+FwyFm2UqmEWCx2S99pP0R3WrVajYGBgQPXz2azAOpNayqVCiqVijwP1mo1KRvYnLNOEARBzvU5QDgaGxsb24rugLpO687XmvF4PDIi43A4EI/HsbCwgN7eXuRyOVQqFRm1SafTmJqakk5ONpuFx+OREfdmHWJRaNbX13fki46Qt9rY2IBWq8XS0hIqlQqUSiX0ev2RLrihUAhAvVhTXFhFHvmt0uxYb21tYX19HVarVUqT7XQKDnsjUKvVZAOPg9pmn3eE09fssDVLxYliT41GA865VMo4Tpm3yclJcM5vO3o6OjqKeDy+LTotflufz7dnl9aTQDRREelW0WgUjDFwzqW0aK1Wk6lXOp1OSiI2z5AVCoWWs0bRaBQGgwHDw8OYn5+X0WK9Xi/PMZxz2QxIKHCIRlqlUknOijUTj8exvr4OxhgmJycP9RuJLpdHmSVIp9MwmUwyPQyoO9yiaFMck+RYEwTRDDnX5wCtVgu9Xo94PH5kaSi73Y5CoQCj0Yj+/n5oNBpEIhGsrKxArVZDo9FgYGAAOp0Om5ubGBgYkE1kqtWqdOz7+vrQ3d0tWy4LDepSqSSLCz0eDzjnKJVKMBgM+9opVAyAelRtZmYGk5OTGBoaOrSzI1QO3G63nN72+XyydbzT6bztSGckEpHpJTqdDhaLBeFwGA6H40gXXM45lpeXkc/nMTQ0JIun7lbEDUnzTIxarYbT6dxWWChyZkVa1HFOyd9ql8GdKBSKPW9wj5ojfScQ0X+ROy1avNdqNczNzcmCa7Gu2CeiqY1o8GO325FOp7GxsSHVerLZLHp6eqDX6zE2NoZMJoNIJIJ7770Xa2trMlLOGJMpX6Jj5Pz8PGKxGGKxGEwmkyxqTSQSWF1dlXaHw+F9G2cJRDrXYVLvtra2MDs7i2KxKJvjiPOOyWQCYwzZbPZQvQIIgrj7IOf6nNDd3Y2lpSVEIpEjqY1YLBbZ2lhsR61WSw1fkTbRXDQIoKVMy9ZPcQAAIABJREFUnOgoZ7FY8IMf/ADXr19HOByW6RjpdBq1Wg1bW1tStm6nBNnW1hbi8TiCwSA6OzvR39+PCxcu4Pr169BqtUeKTApd2s7OTnDOEYlEYDQaEYlEZEt2IaknKBQKWF9fRzablUofe1Gr1RCLxWCz2aQz2NPTg5mZGUQikUNd8AWBQEA22xFpCHdz5NpiseDixYu7jo/mNBrgzRx78VvfrS3jbwfRWr5SqaCnp0eePzjn0qkVMz1CV16k4YhizPX1daTTafj9fhnVFjNYwpnVarW4dOkSFhYWkEqloNPp4PV6MT8/j0QiAaVSCbVaLc8tU1NTKJVKmJmZwfLyMkqlEmw2G9LpNIxGI8bGxrC0tCQLgQ8ilUrBZrMd6hhJpVKyoHZnpFupVMJgMMiUEYIgiJ3cXX2VzzE2mw1WqxXBYFBGhI5KIpGA3++HwWCQaQm3UmjV2dmJRx99FO9973sxMTEhu6CFw2GoVCr09/ejUqlgbm5O5noC9SnYN954A8FgEBaLBf39/VAoFDLXuVAoIBAIHNqOTCYj80TdbjdUKpXcN729vSiXy0gkEgDqTvXi4iKmp6eRyWSgUCiwsrIiG3+0IhQKoVqtbksBMRgMsFqtiEajUh1kJ5xzrKysSJUUMc3c1dV1pBzw885B2s9A3dHR6XSyqJGKyY5Os+JKc7qUx+PBxMTEttkiMXvQLJPodDqh1WqxuLiIcrkMo9EoZex2zg6ZTCaMj49jYGAAExMTUj60VquhWCzuKjDUarXweDzI5/PY2tqSNR9C/s5kMqFSqew7ToH6TfvW1ta2oMDW1tauQk6BcP7vvffelseh2WxGPp9HNpvFtWvX4Pf77+qZJoIgtkNXonPE4OCg7JCoUCjkdOZhyOVyWFlZAVDXkR4dHYXP57vl6KlCocDk5CQMBgNCoRDW19el/rZomrO+vo7l5WWMjY2hWq1idXUVer0ePp+vZZdLl8uFSCQCi8VyYJEZ5xyZTEbeHIgujUKHXDSliUajAIClpSXodDpYrVYUi0U4HA7Z1KdZBxeo524uLi7KFtA7o/jd3d1Ip9MIhULbCjUFkUhEOvUir1Wr1e76HOJwGAwGqe5BzvXRaU69Oah5lUqlkjfIzUWlU1NTSCaTsnNqpVIB57ylYypSSYSmvcfjQTgcRigUwj333LNrfafTKQt8GWPQ6/Vyu+I8USgU9k3REDn5YoYpm81ibm5O5mw310VUq1Vks9lds3XNiPQvoWoUjUZlATdBEARdic4RQh6uVqshEolIObiDEFFbrVaLwcFBzM3NYXp6WnaWvJ280P7+fpjNZumov/HGGzK3WCiUJJNJFItF2ZRmr+5tPT09yGazWFlZkbJ2brcbOp0OpVIJs7OzCAaDGBwcRFdXF6rVqmykk0gkpGSZuGHo7u7GSy+9hH/8x39EsVjEvffeKxvnJJNJdHZ2IhqNgnMOs9mMjo4OcM4xPz+ParWKvr6+lvmbBoMBXV1diEaj0nlIJBLIZrMyp13IHa6srEi5weOWHbtbaJZJo314dBwOBxQKBVQq1YH7r7nQtNlx3qnVfVDqhejuODExAaVSKWs5mmeymrft8/labuewzrW4+RJOdCwWA1C/CY9Go9vSjYRs4H7bMxqNUgXG6/XKxkDkXBMEAZBzfS7p6elBIpFALBY70LkulUqYn5/f1t55amoKqVQK0WgUfr8fk5OTtxzB3nnRrVarsitlLpdDPp9HNBqVkmf7Rc4YYxgcHMTa2hpyuZxsZdzX14fl5WUpuSfaslerVanEoFQqZTHl6OgoKpUKZmdnZYTfarXKrnn33HMPNjc35cWzWq0iEonIDn6iWcR+KTNerxfValXK9InObkKLua+vD/F4XHaRNBgMh2qDTeymo6MDDofjwNQAojVKpfLQ6Uh76WIfFaHeI6LXQqkoGAyio6Pj0DMQarUaarUam5ubMhXL6XRia2sLoVAI/f39yGQyCAaDUh5za2sL6XQaXV1dqNVqSCQS8Hg88jOFc71fi3aFQoHx8XE5nsXN81HqXQiCOL+Qc30OUSqVsNvtSCQS6O3t3Ve1YmVlBbVaDePj43J6WK1Wy8YmS0tLSKfTt9XkQjinCoUCNpsN8XhcNpeJxWKYmZlBT0/PvtOwApF/DdRVRG7evInl5WVEo1H09vZiYmICi4uLCIVCsNlscLlcMJvNMJvNSCaTWFtbQzQaxfT0NAKBgGyPPjk5iUwmg9XVVZTLZeh0OsRiMdkAR6fTIRKJYHp6Gnq9Hvfffz845wgGg0in0xgaGtrVGdLn8yEcDmNlZQVvf/vb0dfXh1KpBMYYYrEYQqEQVCrVti6YOp0O4+PjJO11RPbqkEgcL8IBvV3nWiiTlEolaDQaKBQK9PX1YXZ2FrOzsxgaGjq0hKXBYMDGxoZMtQLq+dSpVAqVSgW5XA5ra2sYHx8HACn/Z7PZoFQqEY/HEY/HpWO8ubkJjUZzKAdf3GyYTCYkEolTlVUkCKJ9IOf6nOJwOBCLxZBIJFrKgFUqFTmV2d/f3/JCZrVaodVqEY1GZb7jrbC6uiobR4jPBiDbQcfjcRiNxl1R61gshnw+D6fT2TICr9FoMDk5iVAoJIucrFYr7rnnHgwMDMjuic37JB6P47XXXkMikcDk5KRUBFEoFLBarVCr1bh586Zs9V4ulxGNRjExMQG73Y5AIIBUKoUbN27AYDAglUpBqVTKNBqj0Si7NK6vr6NSqUCj0cjvplAosL6+jlgsBrvdLiPYW1tbUCqVCAQCCAaDu1QxCKIdYIxBpVLdlnO9tbUli303NzcxPT0Nu90On8+H4eFhLC0tYWZmBqOjo4dKaxNNtASiuBWAbHYjxpdYLjS6FQoFLBaLLChWKBRSD/8o2Gw2KBQK1Go1ujEmCOJ0nGvG2B8C+FkAZQCLAD7MOU/v/y7iKBgMBlm1b7fbt53whbyVaA6xl+4rYwxdXV3w+/24du2alNk7CplMRnZVtNvtyOVyCAaDKBQKqNVqcLlcUi+3mUgkgkAgAIVCgXQ6jbGxsZbTtGK52WyWkW+1Wi07U2azWSknBgButxvXrl1Db28vLl68uCs31Gg0yhxKs9ks86VnZmbkdHGpVMLVq1eh0WgwPj6O0dFRJJNJbG1tIZlMYnZ2FpxzuX/FxVun08mGOF1dXVLxoPnmR0gEHrYtO0GcNM3qIreCiFoDkNHmRCIBr9cLs9mMiYkJqZU/NTV14DgQ5ySNRgOz2SwdbavVCrvdjnK5jIWFBenQ5/N56VgDdVWUmzdvYmVlRRbHHlW1R8yOEQRBAKcXuX4WwL/jnG8xxp4E8O8A/PYp2XJucbvdWFxcxPXr16Vzabfbkc1mwTnfVSXfiq6uLmg0GoRCISwuLuLixYv7Tpdubm5ia2sLFotFpk1oNBq43W4wxmQ0eXl5GUajEYlEAmazGZlMRuZfFotFBINBWK1W9Pb2YmZmBmtraxgbG9v1eaL1slKplI0sRJFSMBjcJY/FGMP4+DgmJib2LLpyuVwoFAoIhUIwmUzo7e2VOei5XA6XLl2S08ClUgnFYlE69g6HAz/84Q8Rj8fxrne9SxbbLS8vY3Z2Vkbb98rnFDMOqVTqUM0uCOKk0Wq1skDwVhDOtUKh2KYVnUgk5M324OAgbt68eSi9eK1WKwuRRf4zUHd4rVYrCoUCqtWqVDDJ5/PbxpbRaITb7UY4HEYqlYLRaCRJTIIgbotTca45599uevoygA+chh3nHavVirGxMcRiMVSrVWxtbcHv9wOA7Ch42O1otVpMT0/LC2Arcrkc5ufnUavVZFGS6OrYnFJitVqhVCqlQkEikUC5XEYgEIDFYoHf74dSqUR/fz9UKhU8Hg9ee+01ZLNZTExMyKniWq2GcDiMXC4HxpjMHy8UCojFYrBarfB6vdJxZ4xhc3NT5pPvR19fH3K5HFZXV2WuZldXFyYmJtDT0yMb5kxPT2NtbQ02mw0ajQZdXV0wmUwIBoNIJpOwWCzo7OyUyijd3d37RuIMBgPUajVmZmYwNja2b+t6gjgNdDodNjY2bjm/WDjXoqMrYwwajQa5XE6eW4xGI2w2G6LRKFwu14HR69HRUQBvSu4Bb6p9aLVa2QlSFFHuTDfp6emBy+U6VPdYgiCIg2iHnOtfBfDXey1kjD0B4Algd3c24mBMJtM2eSiRI3zU9A69Xg+z2YxIJLJna2+/3w+1Wg2Hw4H19XWZ9rEz91m0Tw+HwxgaGkIwGMTKygpmZmbgcDigUqkwMjIiI+QqlQqpVEoWP4kuhvF4HIVCAUqlUuZHr62tAahHn4XO961EoRQKBXw+H+bn53Ht2jV5Qe7v75cXXlG0KAoqOecyUj46OopMJoMbN25gdHQUVqv1QG1uoC4Nlk6nsba2hu7u7jPnXNN4Pf/o9XpwzlEsFvdV1NiLcrkMlUoFt9uNSqUCo9GIjY2NXR0P3W43UqkU4vH4njf0rWwbHx+HWq3epryjUqmwtbUlc7BbpXAIDe+7DRqzBHH83LEzCWPsOwBa6RL9Duf8q411fgfAFoD/vtd2OOdfAPAFALh8+TK1wLpNbkf1w+v14ubNm1hdXd0Vjc5kMtjc3ER/fz8cDgecTic453terFwuF6LRqGxBLprNdHd3w+12b9PbFQ69Xq9HrVbD4uIiDAYDCoUCOOdSV1qpVGJ5eVl2fbtdTCaTzKfW6XSw2+27IlpGoxEXLlwA5xyzs7Oyg+TFixdRrVYxPz+PxcVFqee7F5VKBZlMBslkEmq1WuafnjVovJ5/mrWlb8W53trakucF0WRJ1Cs0y1EaDAaYzWasr6/LmaGd1Go1hEIhJBIJaLVadHZ2YmNjQ2r+C0lOjUaDcrmMbDYLnU53qBbodws0Zgni+LljzjXn/N37LWeMfQjA+wA8wqlv7JnAYDCgp6dHOpADAwMol8soFovw+/3QaDQySn3QNK5I9wgEAkgkEujv70c+n0cikYDT6ZTOdTKZRD6fx8jICOLxOLxeLzjnSCQSsNls2NzclF3bALTMy74dRLOIgxA64YFAYFsHucHBQczOziIWi7XUwOWcIxQKSS1spVKJ4eFhJBIJZDKZY7lJIIjjRKfTgTF2y3nXzc61QMyuZTKZbfnQ/f39mJmZwdLSEsbGxnbd0AvpTJvNhnw+L89DtVoN8/PzGBkZkSlxoVAIjLGWXVMJgiCOk9NSC3kP6gWM7+Scb56GDcStIZw9IeMnVAOEc3mUDnkul0u2Ce/s7ITdbseVK1ewurqKqakp6PV6hMNhGAwG9PX1IZ1OY2NjA0NDQ3A6nSiVSrh+/XrbtA0XeeLNmEymXVJfzcRiMYTDYdjtdrhcLum4WK3Wu3KKmmh/GGPQarXb8puPQrVa3dZyHajfuBsMBkQikW0zRFqtFv39/VhaWkIwGJSpXsViEUtLS1Cr1RgbG5POuUgdKxaLmJ+fx40bN1AoFKQUaKVS2Zb6UKlUUCwWEQqFUK1WMTQ0tMs2giCIo3JaV+/PAdACeLZxEn2Zc/7RU7KFOCIul0vqXzscDnR0dECj0dzSVKtINVlaWkIul0NXVxc2Nzfx6quvolQqwWazYWhoCJlMBul0GtPT0+Ccw+fzSVWAnTnd7UZ3dzdmZ2fh9/vR29srHQcRTbNYLLvaO9O0NdHO6PV6KU15VLa2tlrOBrlcLiwvLyOfz2+rE7HZbHA6nYhEIgDq4315eVne0DeniwjHWOReJ5NJZLNZVKtV2Y1R6Oonk0kp26dUKsEYw9LSEiYmJm7pexEEQQhOSy1k+DQ+lzg+DlugdxBGoxHd3d1YX1+HTqfDO97xDqRSKYTDYZTLZVitVuTzeSwsLIBzDrPZjHA4jGKxKJe3e8twk8kEl8uFSCSCZDIJxphsNsE5pyIi4syh1+uRSqWkfOZRaJUWAryZGlIoFLY510A9N5tzjkgkgkgk0tKx3omQANVqtchkMtDr9RgcHEQmk8Hy8jIUCgW6u7tlbncqlcLq6ipyudyuzycIgjgKNO9MnDoejwcWiwVGoxGMMbhcrm25xrVaDel0Gnq9HjMzM9Dr9bJT3M6Ib7vi9XrR0dGBdDotc6tFTjVNQxNnDVHjUCwWj9TNsFqt7lnorNFooFQqW+ZyM8bQ398Pl8uFfD4Ps9l86JvqWq0GABgeHoZGowHnHLlcDgaDYVttSGdnJwKBAKLR6IHOdblclk1plEpl29/gEwRxspBzTbQF+13MFAqFTP3o6OhAPp/HxYsXz5wW7c4ubu2SK04QR0Vo5BcKhSM511tbWwCwZz2BTqfbN5dbp9MdWp9f0OwEA3VHvZUSj5AIjUQi21RLmhFyn83t1oF6zrjT6ZSa9gRB3N3QWYA4U9jtdlQqFaTT6dM2hSDuWrRaLRhjRy5qPIxzfTvdH1shnOvDOL1CQjQcDu9aFo/HcePGDWSzWXg8HgwODmJwcFCmrPj9/l0dYQmCuDuhyDVxpujo6IBOp8P6+jqsVuuZi14TxHlAKIaUy+Ujve8g51qv1yORSOyZl30rVKtVKBSKQ50rNBqNrI+wWq2wWCzgnGN1dRWJRAJmsxk+n29XVLurqwulUulACVKCIO4OKHJNnCkYY/B4PCgUCojH46dtDkHctYjGLIdBRI8Pcq6F0yokPo+DarV6JKfX4/FAp9NhdXUVW1tbWFtbQyKRgMfjwejo6J751VQ7QRCEgJxr4sxhs9lgsVgQCARQKpVO2xyCuCvZy7muVCrbxmUsFsPrr7+OjY2NA51r8bpY7zg4qnOtUCjg8/lQqVTwxhtvIB6Pw+12o7u7+9hsIgjifEPONXEm6e/vh0KhwNLSklQDIAji5NBoNKhUKrvG39raGubm5gDUHdu1tTUAQCqVQqFQgFqt3tPZbQfnGqhLhI6Pj8PhcGBgYICKjwmCOBLkXBNnEo1GA5/Ph83NTdmOnSCIk6NVCgfnHNlsFuVyGYVCYVtxYi6XQz6fb9lARiCcYJFGchzcav626Azb7k2qCIJoP8i5Js4sHR0dcLvdiMViiMVip20OQdxVCOe6OTWkWCxKx3hjY0OmhzidTpRKJRSLxX2d63aJXBMEQdwO5FwTZ5qenh5YLBYEg8FjvSATBLE/wrluzq/OZrMAALVajUwmI5c1N4Xaz7lWKBRQKBQtx3I+n8ePf/xj5HK5I9l5nMojBEEQh4Gca+LM09vbi2q1ikgkckc/JxaLYW5ujqLkBIG6c61QKLZpXW9ubkKtVsNmsyGfz6NYLEKj0UCr1WJiYgJ2u31f5xqoR69bOdcrKyuo1Wq7GrjsB+ecItcEQZw45FwTZx6dTger1YpEInHHmjiEw2Gsra2hUChgbW1NRugI4m6FMQaj0YhsNouNjQ1wzlEsFqHT6WA0GqUjLCTqDAYDfD7fgc1clErlLue6OX/7KE1mRIoKRa4JgjhJyLkmzgUOhwOVSuVIUa3DkslkEAwG0dnZiQsXLkCr1bbsxsY5RygUwsLCAubn5xEKhUjJhDjXmM1mFAoFLCwsIBaLSedatESvVqtH1n9WqVS7Chrz+TyAepOZo3SF3Nn6nCAI4iQg55o4F1gsFqjV6mNvLFOtVrG6ugq9Xo/+/n4olUr09PSgUCggkUhsW3dpaQnr6+uoVCrY2trC+vo6KZkQ5xqTySQfh0IhVKtV6PV66HQ6+fpR1TZapYXk83kolUrYbDaUSqVDq4kcpKtNEARxJ6AzDnEuYIzB4XBgfX0d5XJ5zy5qRyUSiaBcLmN8fFxOZ9tsNpjNZgQCARgMBhgMBqRSKaTTafT09MDtdgOo6/3G43G4XC7q3kacS0wmE3p6eqDT6bC4uAgA0rEeHR2FQqE4MMd6J3s510ajUUbED1IdEVDkmiCI04Ai18S5wW63A6g3qzgOqtUqotEobDbbrgt5f38/GGOYmZnB9PQ0lpeXYTAYtqkiiI5u0Wj0WOwhiHaDMQa32w2r1QqLxQLgTefabDYf2bEGdjvXxWIRhUIBJpOppfzfflDkmiCI04DOOMS5QavVyihys5N7VJLJJDQaDbLZLKrVasttabVaTE1NIR6PI5fLwWAwoLe3F4wxuY5QTUgkEvB4PBQ9I841fX19yGQyUKvVt7Ud8f5KpYJAIIBkMgnGGJxOp1ynuXHNflDkmiCI04Cca+JcYbPZEAwGbzk1JJlMYnl5edv29oq+qVQqmQKyF11dXUgmk/D7/fD5fEe2hyDOClqtdpsDfDvbAeqpIMlkEkBdz15EnxljFLkmCKKtobQQ4lxhtVoBAOl0+sjvrVarCAQC0Ol06O/vh81mQ29v723ZYzQa0d3djUQigcXFxTsmFUgQ5wXhXAvd+omJiW2zR2q1+sDIdS6Xg9/vR6VSgUKh2DajRBAEcaeh23niXKHT6aDT6ZBOp9HV1dVyneamEpVKBblcTiqNVCoVDA8Pw2AwwOFwHItNHo8HCoUCwWAQ0Wj0tlJWCOK8o9FowBhDLpeDRqORRYzNy/dyrsvlMm7evCmXCy1ugiCIk4Sca+LcYbPZ9lQNWV9fRygUgkajgcPhQDQa3VY85XK5dl3MjwO3241cLodQKAS73U7T1ASxB4wxaLVaFItFORPVjFqt3lPrOpVKoVKpwOVyIZlMolKpyEJngiCIk4LSQohzh8PhAGMM4XB42+vCubVarVCpVAiFQlCpVBgdHcXQ0BDGxsbg9XrvmF1erxe1Wu2Ot2kniLOOSA2x2Wy7lqnV6j1zrlOpFAwGA7xeL7q7u6FSqVpugyAI4k5C4TPi3KHRaGC32xGLxVCpVODxeKDX6xEOh6FSqTAwMADGGCqVyrHpYR8GnU4Hu92OSCQCs9kspcsIgtiOyWTaU8tarVajVqttS+8C6sWL+XwePT09AACn03ksBZYEQRBHhSLXxLmkt7cX3d3dyGazmJ2dRTgcxsbGBlwulyxwOknHutkurVaL+fl5rK2tSS3t2dnZXR0fCeJuxe12Y2pqqmUhotDRzuVy214vFosAcEfSugiCII4CRa6Jc4lCoYDH44HD4cDNmzcRDAah1Wr3LHI8KZRKJcbHx7G+vo5IJIJ4PA7OOdRqNVZWVqDVare1lCaIu5W9FD46OjqgVqsRjUbR0dEhXxfONXVDJQjitCHnmjjXaDQaTExMIJ1Ow2QyyRbmp4lSqYTX64XVakUymYTFYoHFYsG1a9cQDocxPDx82iYSRNvCGIPD4cD6+vq21JBSqXRqM1IEQRDNnKqnwRj7TcYYZ4wdj+YZQbRArVbD6XRCr9eftinbMJlM6Ovrg9VqhUKhQFdXFzY2NvZUQiAIoo5I/RDRavFYq9WSpjVBEKfOqTnXjLFeAP8jgLXTsoEg2gmn0wmFQrFL5YQgiO2IvOtSqSRfK5VK8nWCIIjT5DQj158F8FsAqGUdQaDeolm0S4/H46dtDkG0LSKvulXkmiAI4rQ5FeeaMfYYgCDn/OppfD5BtCsejwcWiwWrq6tYWVk5bXMIoi1pbjQD1GX4OOeUb00QRFtwxwoaGWPfAeBuseh3APwfAB495HaeAPAEAPT19R2bfQTRjjDGMDw8jEgksk3D96xA45U4KbRarUwLEU1l1Gr1aZp0JqExSxDHzx2LXHPO3805v7DzD8ASgAEAVxljKwC8AF5jjLVyxME5/wLn/DLn/DI1BCDuBhhjcLvdZ7IBBo1X4qRodq4rlQoAUOT6FqAxSxDHz4lL8XHOrwGQYsMNB/sy55ySTAmCIIhDoVarUa1WwTmXzjVFrgmCaAdOX/SXIAiCII6ISlWPDW1tbVFaCEEQbcWpN5HhnPtO2waCIAjibCFqEqrVKiqVCtRqNWlcEwTRFlDkmiAIgjhz7IxcU9SaIIh2gZxrgiAI4szR7FxXKhUqZiQIom0g55ogCII4c1DkmiCIdoWca4IgCOLMIZzrcrmMarVKkWuCINoGcq4JgiCIM4dCoQBjDIVCAQAphRAE0T6Qc00QBEGcSVQqlXSuKXJNEES7QM41QRAEcSZRqVSySyNFrgmCaBfIuSYIgiDOJCLvGqDINUEQ7QM51wRBEMSZRDjUjDEoFHQ5IwiiPaCzEUEQBHEmsdvtAADO+SlbQhAE8SbkXBMEQRBnErPZDAAwGAynbAlBEMSbqA5ehSAIgiDak0uXLoExdtpmEARBSMi5JgiCIM4sSqXytE0gCILYBqWFEARBEARBEMQxQc41QRAEQRAEQRwT5FwTBEEQBEEQxDFBzjVBEARBEARBHBPkXBMEQRAEQRDEMUHONUEQBEEQBEEcE+RcEwRBEARBEMQxQc41QRAEQRAEQRwT5FwTBEEQBEEQxDFBzjVBEARBEARBHBOMc37aNhwaxlgMwOoBqzkAxE/AnOOEbD4ZzqPNcc75e07KmKNA47WtIJtPhjM7XgGAMZYFMHvaduxBOx8P7Wwb0N72tbNtADDGOTcf9U2qO2HJnYJz7jxoHcbYjzjnl0/CnuOCbD4ZyOaThcZr+0A2nwxn0eYdzLar/e28b9vZNqC97Wtn24C6fbfyPkoLIQiCIAiCIIhjgpxrgiAIgiAIgjgmzqNz/YXTNuAWIJtPBrK5/TiL349sPhnI5pOnne0n226ddravnW0DbtG+M1XQSBAEQRAEQRDtzHmMXBMEQRAEQRDEqXBmnWvG2LsYYxuMsdcbf/9+j/UGGGOvMMbmGWN/zRjTnLStTba8hzE2yxhbYIx9osVybcPGhYbNvpO3cpdNK4yxa419vKtqltX5vxs2v8EYu/8UbPwzxliUMXa96bVOxtizjd/9WcaYbY/3fqixzjxj7EOnbPMnGWPBpmP6vXu8d9/jqB2h8Xoy0Hg9UZvP5Hht9+P6EPb9b4yx6cbx+13GWH+72Na03gcYY5wxdmIqGIexjTH2C419d4Mx9tRJ2XYY+xhjfYyx5xhjP278ti3H0x2ybdf43rH86OdNzvmZ/APwLgBfP8R6fwPgFxvXTcIRAAAKyklEQVSP/xTAvzole5UAFgEMAtAAuApgcsc6/yuAP208/kUAf90G+3kFgGOf5e8F8C0ADMBbAbxyCjY+BOB+ANebXvs0gE80Hn8CwJMt3tcJYKnx39Z4bDtFmz8J4Ddv9zhqxz8arydmN43Xk7P5zI3Xdj+uD2nfTwEwNB7/q5Oy77C/JQAzgCsAXgZwuV1sAzAC4MdizADoarPf9QvifA9gEsDKCdq3a3zvWH7k8+aZjVwfBsYYA/AwgKcbL/03AP/zKZnzFgALnPMlznkZwJcA/NyOdX4OdRuBus2PNL5DO/NzAP6c13kZgJUx1n2SBnDOrwBItrBL7Mu9fvf/CcCznPMk5zwF4FkAJ9LgYQ+bD8NhjqMzCY3XE4HG6y1wjsZrux/XB9rHOX+Oc77ZePoyAG+72NbgP6B+s1g8IbsOa9u/BPD5xtgB5zzaZvZxAJbG4w4AoZMy7hDj+8jnzbPuXL+NMXaVMfYtxthUi+V2AGnO+VbjeQBAz8mZt40eAP6m561skes0bN5A/TucJhzAtxljrzLGnmix/DDf6zRwcc7XAaDxv6vFOu1o+8ca005/tsfUeDvafFhovN55aLyeLGdtvLb7cX3U/fUR1COKJ8GBtjHG7gPQyzn/+gnZJDjMfhsFMMoY+z5j7GXG2El2Cj2MfZ8E8CuMsQCAbwL41ydj2qE48jg+y871awD6Oef3AviPAL7SYp1Wd9unJY9yGFvayV7BOzjn9wP4aQC/zhh7aMfydrT5sLSb7f8ZwBCASwDWAfxRi3XazebDQuP1ZKDxenKcxfHa7sf1oT+bMfYrAC4D+MM7alHTR7Z4TdrGGFMA+CyA//2E7GnmMPtNhXpqyLsA/BKA/8oYs95huwSHse+XAHyRc+5FPQ3jLxr7tB048phoF8MPBWPs10XxCAAT5zwHAJzzbwJQM8YcO94SRz18L9q8e3GCUw07CADobXreyha5TsPmDtzaVOSxwTkPNf5HAfwd6tM7zRzme50GETFt0/jfagqsrWznnEc451XOeQ3Af8HufQ20mc37QeP15KHxenKc0fHa7sf1ofYXY+zdAH4HwGOc81Kb2GYGcAHA9xhjK6jn5n7thIoaD/u7fpVzXuGcLwOYRd3ZPgkOY99HUK+5Aef8JQA6ADuvEafFkcfxmXKuOeef55xf4pxfAlATeWCMsbeg/l0SO9bnAJ4D8IHGSx8C8NUTNLmZHwIYYXU1BA3qhSJf27HO11C3Eajb/E+N73AqMMaMjDGzeAzgUQA7q2m/BuBfNKpp3wpgQ0zvnjLN+3Kv3/0fATzKGLM1pnQfbbx2KuzI4fp57N7XwOGOo7aAxuvJQuP1ZDmj47Xdj+sD7WukXvw/qDvWJ5k3vK9tnPMNzrmDc+7jnPtQzwd/jHO+S7XnpG1r8BXUi0HRCGyMol4UfBIcxr41AI807JtA3bmOnZB9B3H08+ZBFY/t+gfgYwBuoF51+jKAtzct+yYAT+PxIIAfAFgA8GUA2lO0+b0A5lCvmv2dxmu/j/oABOoH05cbtv4AwOAp7+PBxv692tjXwuaPAvho4zED8PnGd7qGE6qO3mHnX6E+LVtB/Q7zI6jnCH4XwHzjf2dj3csA/mvTe3+1sb8XAHz4lG3+i8Y+fKMxmLsb63oAfHO/46jd/2i8noi9NF5P1uYzOV7b/bg+hH3fARAB8Hrj72vtYtuOdb93kuPrEPuNAfgMgOnGcfuLbfa7TgL4fuP89TqAR0/Qtlbj+7bOm9ShkSAIgiAIgiCOiTOVFkIQBEEQBEEQ7Qw51wRBEARBEARxTJBzTRAEQRAEQRDHBDnXBEEQBEEQBHFMkHNNEARBEARBEMcEOdcEQRAEQRAEcUyQc31MMMbsohsdYyzMGAs2PX/xDn3mXzHG3mCM/ds7sf09PvNdjLGvNx4/xhj7xD7rXmKMvfcYP/txxpjnGLbzG4yxf7HP8vcxxj51u59DtC80XluuS+OVIAjiGCCd6zsAY+yTAHKc8//rDn6GG8ArnPP+FstUnPOtO/S57wLwm5zz9x1i3cdRF1v/WItlR7aRMfa9xmcfuuMVY0zJOa82fy6A1wDcv9fnNzoJvgbgHZzzzaPYSJw9aLzKdR8HjVeCIIjbhiLXJwBjLNf4/y7G2POMsb9hjM0xxv6AMfbLjLEfMMauMcaGGus5GWPPMMZ+2Ph7R4vNfhtAVyPS9j8wxr7HGPs/GWPPA/g4Y6yfMfbdRqTsu4yxvsa2v8gY+8+MsecYY0uMsXcyxv6MMTbDGPviHva/hzF2kzH2AoD/pen1xxljn2s8/iBj7Dpj7Cpj7EqjxenvA/hnDRv/GWPsk4yxLzDGvg3gzxljPsbY/8cYe63x9/ambf9WY59cbeynD6Dere2/N7anZ4w9whj7cWO9P2OMaRvvXWGM/fuGvR/c8XUeBvCauFAzxv4NY2y6sZ++BMg23N8DcKBDQpw/aLzSeCUIgrgtTrL95d3yB+CTqEdsxPNc4/+7AKQBdAPQAggC+FRj2ccB/HHj8VMAfrLxuA/ATIvP8AG43vT8ewD+U9PzvwfwocbjXwXwlcbjLwL4EurtPH8OQAbARdRvtF4FcGnH5+gA+AGMNN7zNwC+3lj2OIDPNR5fA9DTeGzdubxpv7wKQN94bgCgazweAfCjxuOfBvAiAEPjeWfTd7y8w67RxvM/B/AbjccrAH5rj9/mUwD+ddPzEBottoXdjce/DOA/nvaxRH93/o/GK41X+qM/+qO/4/yjyPXJ80PO+TrnvIR6n/pvN16/hvoFGADeDeBzjLHXAXwNgIUxZj7Etv+66fHbUL/oA8BfAPjJpmV/zznnjc+McM6vcc5rAG402SAYB7DMOZ9vvOcv9/js7wP4ImPsXwJQ7mPj1zjnhcZjNYD/whi7BuDLACYbr78bwP/LG1O8nPNki+2MNeyaazz/bwAealr+17vfAqDuKMWanr+BenTtVwA0TztHAdx2vihx5qHxSuOVIAjiSKhO24C7kFLT41rT8xre/D0UAN7WdFE7LPl9ljUn1zd/5k57Wh0TBybmc84/yhh7EMDPAHidMXbpEDb+WwARAPei/p2LjdfZIT6THbB8r31RQD2KJvgZ1C/yjwH4PcbYFK9PQesa6xJ3NzRe34TGK0EQxCGgyHV78m0AsqhonwvffrwI4Bcbj38ZwAu3aMtNAAMivxTAL7VaiTE2xDl/hXP+7wHEAfQCyALYL4LXAWC9EYX753gzgvZtAL/KGDM0tt3ZeL15ezcB+Bhjw43n/xzA84f4PjMAhhvbVQDo5Zw/B+C3AFgBmBrrjQK4fojtEQSNVxqvBEEQEnKu25N/A+Byo2hnGsBHb3EbH2aMvYH6hezjt2II57wI4AkA32gUHK3useofNgqVrgO4AuAqgOcATIoCqRbv+U8APsQYexn1i2O+8Zn/gPr0+o8aU+2/2Vj/iwD+tPEaA/BhAF9uTFPXAPzpIb7St/DmdLQSwF823v9jAJ/lnKcby34KwDcOsT2CoPFK45UgCEJCUnzEXQdj7O9QL6Ca32O5C8BTnPNHTtYygiB2QuOVIIizBjnXxF0HY2wMgItzfmWP5T8B4P9v145NAACBIAj23539GLy58SIIMxVc8rDB75lZb5cBN/cK/EZcAwBAxM81AABExDUAAETENQAARMQ1AABExDUAAEQO4P1e0Z7/H1sAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(ncols=3, figsize=(12,5), sharey=True)\n",
+ "for avgsnip in long_pre_dp_snips:\n",
+ " ax[0].plot(avgsnip, color='k', alpha=0.2)\n",
+ "ax[0].plot(np.mean(long_pre_dp_snips, axis=0), color='k', linewidth=2)\n",
+ "\n",
+ "for avgsnip in short_pre_dp_snips:\n",
+ " ax[1].plot(avgsnip, color='k', alpha=0.2)\n",
+ "ax[1].plot(np.mean(short_pre_dp_snips, axis=0), color='k', linewidth=2)\n",
+ "\n",
+ "ax[0].set_ylabel('Z-score')\n",
+ "\n",
+ "for axis in [ax[0], ax[1]]:\n",
+ " xticks=[0, 50, 100, 150, 200]\n",
+ " axis.set_xticks(xticks)\n",
+ " axis.set_xticklabels([str(x/10-5) for x in xticks])\n",
+ " axis.set_xlabel('Time from distractor (s)')\n",
+ " axis.spines['top'].set_visible(False)\n",
+ " axis.spines['right'].set_visible(False)\n",
+ "\n",
+ "peak_data = []\n",
+ "peak_data.append([np.max(snip[50:80]) for snip in long_pre_dp_snips])\n",
+ "peak_data.append([np.max(snip[50:80]) for snip in short_pre_dp_snips])\n",
+ "barscatter(peak_data, ax=ax[2], paired=True, barfacecolor=['white', 'xkcd:light grey'], barfacecoloroption = 'individual')\n",
+ "\n",
+ "ax[2].set_xticks([1,2])\n",
+ "ax[2].set_xticklabels(['Long', 'Short'])\n",
+ "ax[2].set_title('Peak Z-score')\n",
+ " \n",
+ "ax[0].set_title('Long pre-dps')\n",
+ "ax[1].set_title('Short pre-dps')\n",
+ "\n",
+ "f.savefig(outputfolder + \"short-vs-long predps.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_pre_dps = []\n",
+ "all_post_dps =[]\n",
+ "\n",
+ "rats = disDict.keys()\n",
+ "for rat in rats:\n",
+ " d = disDict[rat]\n",
+ " all_pre_dps.append(d['pre_dp'])\n",
+ " all_post_dps.append(d['pdp'])\n",
+ " \n",
+ "all_pre_dps = tp.flatten_list(all_pre_dps)\n",
+ "all_post_dps = tp.flatten_list(all_post_dps)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'all_pre_dps' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_pre_dps\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_post_dps\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mcoef\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mpoly1d_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoly1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcoef\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'all_pre_dps' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "x = all_pre_dps\n",
+ "y = all_post_dps\n",
+ "\n",
+ "coef = np.polyfit(x,y,1)\n",
+ "poly1d_fn = np.poly1d(coef) \n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(6,6))\n",
+ "\n",
+ "#ax.scatter(x, y)\n",
+ "ax.plot(x,y, 'yo', x, poly1d_fn(x), '--k')\n",
+ "# ax.set_yscale('log')\n",
+ "# ax.set_xscale('log')\n",
+ "\n",
+ "ax.set_xlabel('Pre-distraction pauses (s)')\n",
+ "ax.set_ylabel('Post-distraction pauses (s)')\n",
+ "\n",
+ "f.savefig(outputfolder + \"predps-vs-postdps.png\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'x' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlinregress\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mlinregress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "from scipy.stats import linregress\n",
+ "linregress(x,y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pre_dps_distracted = []\n",
+ "pre_dps_notdistracted =[]\n",
+ "\n",
+ "pre_dps_distracted_all = []\n",
+ "pre_dps_notdistracted_all =[]\n",
+ "\n",
+ "rats = disDict.keys()\n",
+ "for rat in rats:\n",
+ " d = disDict[rat]\n",
+ " \n",
+ " pre_dps = [x for x, L in zip(d['pre_dp'], d[\"d_bool_array\"]) if not L]\n",
+ " pre_dps_notdistracted.append(np.mean(pre_dps))\n",
+ " pre_dps_notdistracted_all.append(pre_dps)\n",
+ " \n",
+ " pre_dps = [x for x, L in zip(d['pre_dp'], d[\"d_bool_array\"]) if L]\n",
+ " pre_dps_distracted.append(np.mean(pre_dps))\n",
+ " pre_dps_distracted_all.append(pre_dps)\n",
+ "\n",
+ "pre_dps_notdistracted_all = tp.flatten_list(pre_dps_notdistracted_all)\n",
+ "pre_dps_distracted_all = tp.flatten_list(pre_dps_distracted_all)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(2,3))\n",
+ "f.subplots_adjust(left=0.4)\n",
+ "_, barx, _, _ = tp.barscatter([pre_dps_notdistracted, pre_dps_distracted], paired=True,\n",
+ " barfacecolor=[\"grey\", \"red\"], barfacecoloroption='individual',\n",
+ " barlabels=['Not dis', 'Dis'],\n",
+ "# barlabeloffset=-0.04,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax)\n",
+ "\n",
+ "ax.set_ylabel(\"Pre-distraction pause\")\n",
+ "# ax.set_ylim([-0.05, 1.1])\n",
+ "\n",
+ "f.savefig(figfolder + \"pre-dps_bar.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\envs\\dis\\lib\\site-packages\\numpy\\core\\_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
+ " return array(a, dtype, copy=False, order=order)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dis_x, dis_sem = tp.mean_and_sem(pre_dps_distracted_all)\n",
+ "notdis_x, notdis_sem = tp.mean_and_sem(pre_dps_notdistracted_all)\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(2,3))\n",
+ "f.subplots_adjust(left=0.3)\n",
+ "\n",
+ "boxes = ax.boxplot([pre_dps_notdistracted_all, pre_dps_distracted_all],\n",
+ " widths=0.7,\n",
+ " patch_artist =True,\n",
+ " notch=True)\n",
+ "\n",
+ "colors = [\"grey\", \"red\"]\n",
+ "for box, color in zip([0, 1], colors):\n",
+ " for item in ['boxes']:\n",
+ " plt.setp(boxes[item][box], color=color)\n",
+ "\n",
+ "ax.set_yscale(\"log\")\n",
+ "\n",
+ "ax.set_ylabel(\"Pre-distraction pause (s)\")\n",
+ "ax.set_xticklabels([\"Not dis.\", \"Dis.\"])\n",
+ "\n",
+ "ax.axhline(5, linestyle=\"--\", color=\"k\")\n",
+ "\n",
+ "ax.spines[\"top\"].set_visible(False)\n",
+ "ax.spines[\"right\"].set_visible(False)\n",
+ "\n",
+ "ax.text(ax.get_xlim()[1], 5, \"5 s\", ha=\"left\", va=\"center\")\n",
+ "\n",
+ "f.savefig(figfolder + \"pre-dps_box.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Median Pre-distraction pause on not distracted trials is 3.265822720000017\n",
+ "Median Pre-distraction pause on distracted trials is 5.56580864\n",
+ "Ttest_indResult(statistic=1.3737255699774387, pvalue=0.16994974160237256)\n",
+ "Ttest_indResult(statistic=5.240128364556177, pvalue=2.1038028537723913e-07)\n",
+ "The proportion of trials in which the last distractor was within the baseline period is approximately:\n",
+ "0.6361323155216285 for nondistracted trials.\n",
+ "0.4071246819338422 for distracted trials.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from scipy.stats import ttest_rel as ttest\n",
+ "from scipy.stats import ttest_ind as ttest_ind\n",
+ "\n",
+ "print(\"Median Pre-distraction pause on not distracted trials is \", np.median(pre_dps_notdistracted_all))\n",
+ "print(\"Median Pre-distraction pause on distracted trials is \", np.median(pre_dps_distracted_all))\n",
+ "\n",
+ "print(ttest_ind(pre_dps_distracted_all, pre_dps_notdistracted_all))\n",
+ "\n",
+ "print(ttest_ind(np.log(pre_dps_distracted_all), np.log(pre_dps_notdistracted_all)))\n",
+ "\n",
+ "print(\"The proportion of trials in which the last distractor was within the baseline period is approximately:\")\n",
+ "print(len([L for L in pre_dps_notdistracted_all if L < 5]) / len(pre_dps_notdistracted_all), \"for nondistracted trials.\")\n",
+ "print(len([L for L in pre_dps_distracted_all if L < 5]) / len(pre_dps_notdistracted_all), \"for distracted trials.\")\n",
+ "\n",
+ "\n",
+ "### Ideally I need to calculate this in a more rigorous way - could do it by calculating all inter-distractor intervals and looking at how many were <5s.\n",
+ "### Or just by looking at how predps are calculated - it is probably from the first lick rather than from the distractor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.6361323155216285\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(len([L for L in pre_dps_notdistracted_all if L < 5]) / len(pre_dps_notdistracted_all), \"for nondistracted trials.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def get_auc_from_long_predp(daydict, epoch=[60, 90], signal=\"filt\", predp_threshold=5):\n",
+ " \n",
+ " auc_list = []\n",
+ "\n",
+ " rats = daydict.keys()\n",
+ " for rat in rats:\n",
+ " d = daydict[rat]\n",
+ " \n",
+ " aucs = [np.mean(snip[epoch[0]:epoch[1]]) for snip, predp in zip(d[\"snips_distractors\"][signal], d[\"pre_dp\"]) if (predp > predp_threshold)]\n",
+ "# print(len(aucs))\n",
+ " auc_list.append(np.mean(aucs))\n",
+ " \n",
+ " return auc_list\n",
+ "\n",
+ "epoch = [0, 50]\n",
+ "predp = 5\n",
+ "\n",
+ "aucs_mod = get_auc_from_long_predp(modDict, epoch=epoch, predp_threshold=predp)\n",
+ "aucs_dis = get_auc_from_long_predp(disDict, epoch=epoch, predp_threshold=predp)\n",
+ "aucs_hab = get_auc_from_long_predp(habDict, epoch=epoch, predp_threshold=predp)\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(2,3))\n",
+ "f.subplots_adjust(left=0.4)\n",
+ "\n",
+ "tp.barscatter([aucs_mod, aucs_dis, aucs_hab], paired=True,\n",
+ " barfacecolor=colors, barfacecoloroption='individual',\n",
+ " barlabels=['Mod', 'Dis', 'Hab'],\n",
+ " barlabeloffset=0.24,\n",
+ " scattersize=scattersize,\n",
+ " ax=ax)\n",
+ "\n",
+ "ax.set_ylabel(\"Delta F (AUC)\")\n",
+ "ax.set_yticks([-5, 0, 5, 10])\n",
+ "\n",
+ "f.savefig(figfolder + \"aucs_from_pre-dps_bar.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pre-distraction pause on not distracted trials is (3.249225503300094, 0.8397900376065762)\n",
+ "Pre-distraction pause on distracted trials is (1.2004600463997994, 0.6870884098920103)\n",
+ "Ttest_relResult(statistic=1.9734472163184558, pvalue=0.07192309673947567)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from scipy.stats import ttest_rel as ttest\n",
+ "\n",
+ "\n",
+ "print(\"Pre-distraction pause on not distracted trials is \", tp.mean_and_sem(aucs_dis))\n",
+ "print(\"Pre-distraction pause on distracted trials is \", tp.mean_and_sem(aucs_hab))\n",
+ "\n",
+ "print(ttest(aucs_dis, aucs_hab))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "df = pd.DataFrame([aucs_mod, aucs_mod, aucs_hab])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1.65475 \n",
+ " -3.168365 \n",
+ " 10.166318 \n",
+ " 3.895167 \n",
+ " 6.105994 \n",
+ " 3.731229 \n",
+ " 3.970470 \n",
+ " 12.568251 \n",
+ " 4.263988 \n",
+ " 6.769931 \n",
+ " 5.345059 \n",
+ " 2.627283 \n",
+ " 3.946402 \n",
+ " 12.903111 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1.65475 \n",
+ " -3.168365 \n",
+ " 10.166318 \n",
+ " 3.895167 \n",
+ " 6.105994 \n",
+ " 3.731229 \n",
+ " 3.970470 \n",
+ " 12.568251 \n",
+ " 4.263988 \n",
+ " 6.769931 \n",
+ " 5.345059 \n",
+ " 2.627283 \n",
+ " 3.946402 \n",
+ " 12.903111 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 3.08103 \n",
+ " 0.088397 \n",
+ " -3.752339 \n",
+ " 5.069833 \n",
+ " 6.047863 \n",
+ " 1.008006 \n",
+ " 1.275895 \n",
+ " 9.663872 \n",
+ " 3.310868 \n",
+ " 5.935280 \n",
+ " 2.636910 \n",
+ " 3.077659 \n",
+ " 0.556681 \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def halfviolin(v, half='right', fill_color='k', alpha=1,\n",
+ " line_color='k', line_width=0):\n",
+ " import numpy as np\n",
+ "\n",
+ " for b in v['bodies']:\n",
+ " V = b.get_paths()[0].vertices\n",
+ "\n",
+ " mean_vertical = np.mean(V[:, 0])\n",
+ " mean_horizontal = np.mean(V[:, 1])\n",
+ "\n",
+ " if half == 'right':\n",
+ " V[:, 0] = np.clip(V[:, 0], mean_vertical, np.inf)\n",
+ " elif half == 'left':\n",
+ " V[:, 0] = np.clip(V[:, 0], -np.inf, mean_vertical)\n",
+ " elif half == 'bottom':\n",
+ " V[:, 1] = np.clip(V[:, 1], -np.inf, mean_horizontal)\n",
+ " elif half == 'top':\n",
+ " V[:, 1] = np.clip(V[:, 1], mean_horizontal, np.inf)\n",
+ "\n",
+ " b.set_color(fill_color)\n",
+ " b.set_alpha(alpha)\n",
+ " b.set_edgecolor(line_color)\n",
+ " b.set_linewidth(line_width)\n",
+ "\n",
+ "default_violinplot_kwargs = {'widths':0.9, 'vert':True,\n",
+ " 'showextrema':False, 'showmedians':True}\n",
+ "\n",
+ "violinplot_kwargs = default_violinplot_kwargs\n",
+ " \n",
+ "f, ax = plt.subplots(figsize=(3,3))\n",
+ "f.subplots_adjust(left=0.4)\n",
+ "\n",
+ "v = ax.violinplot(pre_dps_notdistracted_all,\n",
+ " positions=[1],\n",
+ " **violinplot_kwargs)\n",
+ "\n",
+ "# halfviolin_alpha=0.7\n",
+ "# halfviolin(v, fill_color=\"grey\", alpha=0.8)\n",
+ "\n",
+ "v = ax.violinplot(pre_dps_distracted_all,\n",
+ " positions=[2],\n",
+ " **violinplot_kwargs)\n",
+ "\n",
+ "# v = ax.violinplot(current_bootstrap[~np.isinf(current_bootstrap)],\n",
+ "# positions=[tick],\n",
+ "# **violinplot_kwargs)\n",
+ "\n",
+ "\n",
+ "\n",
+ "# halfviolin_alpha=0.7\n",
+ "# halfviolin(v, fill_color=\"red\", alpha=0.8)\n",
+ "\n",
+ "ytick_color=\"black\"\n",
+ "es_marker_size=4\n",
+ "\n",
+ "ax.set_yscale(\"log\")\n",
+ "\n",
+ "ax.set_ylim([0, 500])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import seaborn as sns\n",
+ "\n",
+ "f, ax = plt.subplots(figsize=(3,3))\n",
+ "f.subplots_adjust(left=0.4)\n",
+ "\n",
+ "bins = np.arange(0,50,0.2)\n",
+ "\n",
+ "notdis_hist = np.histogram(pre_dps_notdistracted_all, bins=bins, density=True)\n",
+ "dis_hist = np.histogram(pre_dps_distracted_all, bins=bins, density=True)\n",
+ "\n",
+ "# ax.plot(notdis_hist[0][:50], color=\"grey\")\n",
+ "# ax.plot(dis_hist[0][:50], color=\"red\")\n",
+ "\n",
+ "\n",
+ "# sns.kdeplot(notdis_hist[0][:100]\n",
+ "# , color=\"grey\")\n",
+ "\n",
+ "# sns.kdeplot(dis_hist[0][:100]\n",
+ "# , color=\"red\")\n",
+ "\n",
+ "\n",
+ "cumsum_notdis = np.cumsum(notdis_hist[0])\n",
+ "cumsum_dis = np.cumsum(dis_hist[0])\n",
+ "\n",
+ "ax.plot(cumsum_notdis)\n",
+ "ax.plot(cumsum_dis)\n",
+ "\n",
+ "ax.set_xscale(\"log\")\n",
+ "\n",
+ "ax.axvline(5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'rat': 'thph2.7',\n",
+ " 'rms': 8.365806,\n",
+ " 'fs': 1017.2526245117188,\n",
+ " 'deltaF': array([-11.944731 , -11.944609 , -11.9444895 , ..., -0.69005847,\n",
+ " -0.6901559 , -0.6902379 ], dtype=float32),\n",
+ " 'tick': array([1.63840000e-04, 1.00024320e+00, 2.00032256e+00, ...,\n",
+ " 3.51627919e+03, 3.51727927e+03, 3.51827935e+03]),\n",
+ " 'filt_sd': 16.342562,\n",
+ " 'licks': array([ 186.81249792, 186.92456448, 187.047936 , ..., 3420.13018112,\n",
+ " 3420.26010624, 3496.9182208 ]),\n",
+ " 'licks_off': array([ 186.85001728, 186.9774848 , 187.10757376, ..., 3420.17622016,\n",
+ " 3420.31712256, 3496.91838464]),\n",
+ " 'distractors': [209.31919872,\n",
+ " 348.63169536,\n",
+ " 360.31938560000003,\n",
+ " 398.09482752,\n",
+ " 418.40345088000004,\n",
+ " 432.75616256,\n",
+ " 457.11491072,\n",
+ " 596.47131648,\n",
+ " 608.24223744,\n",
+ " 659.14535936,\n",
+ " 668.93316096,\n",
+ " 680.15685632,\n",
+ " 753.81768192,\n",
+ " 756.80759808,\n",
+ " 800.51093504,\n",
+ " 810.50566656,\n",
+ " 814.2536704,\n",
+ " 824.73549824,\n",
+ " 838.60013056,\n",
+ " 863.95568128,\n",
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+ " 4.403940019977489],\n",
+ " 'latency': []}}"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['rat', 'rms', 'fs', 'deltaF', 'tick', 'filt_sd', 'licks', 'licks_off', 'distractors', 'distracted', 'notdistracted', 'd_bool_array', 'pdp', 'pre_dp', 'trialtype', 'lickdata', 'snips_distractors', 'snips_distracted', 'snips_not-distracted'])"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[209.31919872,\n",
+ " 348.63169536,\n",
+ " 360.31938560000003,\n",
+ " 398.09482752,\n",
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+ " 432.75616256,\n",
+ " 457.11491072,\n",
+ " 596.47131648,\n",
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+ " 753.81768192,\n",
+ " 863.95568128,\n",
+ " 892.51528704,\n",
+ " 925.90063616,\n",
+ " 1023.5559936]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d[\"distracted\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_percent_predps_in_BL(daydict):\n",
+ "\n",
+ " predp_yes = 0\n",
+ " predp_no = 0\n",
+ " \n",
+ " rats = daydict.keys()\n",
+ " for rat in rats:\n",
+ " d = daydict[rat]\n",
+ " \n",
+ " predp_bool = 0\n",
+ " for ds in d[\"distracted\"]:\n",
+ " if len([lick for lick in d[\"licks\"] if (lick < ds-1) and (lick > ds-5)]) > 0:\n",
+ " predp_yes += 1\n",
+ " else:\n",
+ " predp_no += 1\n",
+ " \n",
+ " dis_pc = predp_yes / (predp_yes + predp_no)\n",
+ " \n",
+ " predp_yes = 0\n",
+ " predp_no = 0\n",
+ " \n",
+ " for rat in rats:\n",
+ " d = daydict[rat]\n",
+ " \n",
+ " predp_bool = 0\n",
+ " for ds in d[\"notdistracted\"]:\n",
+ " if len([lick for lick in d[\"licks\"] if (lick < ds-1) and (lick > ds-5)]) > 0:\n",
+ " predp_yes += 1\n",
+ " else:\n",
+ " predp_no += 1\n",
+ " \n",
+ " notdis_pc = predp_yes / (predp_yes + predp_no)\n",
+ " \n",
+ " return dis_pc, notdis_pc\n",
+ "\n",
+ "d, n = get_percent_predps_in_BL(disDict)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.49554896142433236"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.6259541984732825"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/prepare_roc_data.py b/prepare_roc_data.py
new file mode 100644
index 0000000..dd0d29b
--- /dev/null
+++ b/prepare_roc_data.py
@@ -0,0 +1,230 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 27 10:51:40 2020
+
+@author: admin
+"""
+
+import numpy as np
+import dill
+
+
+def make_lick_snips(d, pre=5, post=15):
+ """ Makes snips of licks aligned to each distractor
+
+ Args
+ d: dictionary with lick data (key='licks') and distractor timestamps (key='distractors')
+ pre: time before distractor (in s, default=5)
+ post: time after each distractor (in s, default=15)
+
+ Returns
+ snips: list of lick snips aligned to distractor
+
+ """
+ snips = []
+ for dis in d['distractors']:
+ snips.append([lick-dis for lick in d['licks'] if (lick-dis>-pre) and (lick-disx1 and d 0:
+ return True
+ else:
+ return False
+
+def resample_snips(snips, factor=0.1):
+ """ Resamples snips to collapse data into larger bins (e.g. for ROC analysis)
+ Args
+ snips: array of snips (list of lists)
+ factor: constant to decide how to bin data (default=0.1)
+
+ Returns
+ snips: resamples snips
+
+ """
+ if len(snips)>0:
+ n_bins = len(snips[0])
+ out_bins = int(n_bins * factor)
+
+ snips_out = []
+ for snip in snips:
+ snips_out.append(np.mean(np.reshape(snip, (out_bins, -1)), axis=1))
+
+ return snips_out
+ else:
+ return []
+
+# Loads in data
+datafolder = "C:\\Github\\Distraction-Paper\\data\\"
+figfolder = "C:\\Github\\Distraction-Paper\\figs\\"
+outputfolder = "C:\\Github\\Distraction-Paper\\output\\"
+
+signal2use = "filt_z"
+signal2use_noBLadj = "filt"
+
+try:
+ pickle_in = open(datafolder + "distraction_data_only_snips.pickle", 'rb')
+except FileNotFoundError:
+ print('Cannot access pickled file')
+
+[modDict, disDict, habDict] = dill.load(pickle_in)
+
+# removes rat 2.8 because no data on hab day
+modDict.pop('thph2.8')
+disDict.pop('thph2.8')
+
+# gets list of rats
+rats = disDict.keys()
+
+# goes through dict from each test day nd makes lick snips
+mod_lick_snips, dis_lick_snips, hab_lick_snips = [], [], []
+
+for rat in rats:
+ d = modDict[rat]
+ mod_lick_snips.append(make_lick_snips(d))
+
+ d = disDict[rat]
+ dis_lick_snips.append(make_lick_snips(d))
+
+ d = habDict[rat]
+ hab_lick_snips.append(make_lick_snips(d))
+
+pickle_out = open(outputfolder+"data4epochs_licks.pickle", 'wb')
+dill.dump([mod_lick_snips, dis_lick_snips, hab_lick_snips], pickle_out)
+pickle_out.close()
+
+# flattens lists so that trials from all rats are pooled
+mod_lick_snips_flat = flatten_list(mod_lick_snips)
+dis_lick_snips_flat = flatten_list(dis_lick_snips)
+hab_lick_snips_flat = flatten_list(hab_lick_snips)
+
+# divides snips into distracted and non-distracted trials for each day
+mod_dis_snips = [snip for snip in mod_lick_snips_flat if not check_val_between(snip)]
+mod_notdis_snips = [snip for snip in mod_lick_snips_flat if check_val_between(snip)]
+
+dis_dis_snips = [snip for snip in dis_lick_snips_flat if not check_val_between(snip)]
+dis_notdis_snips = [snip for snip in dis_lick_snips_flat if check_val_between(snip)]
+
+hab_dis_snips = [snip for snip in hab_lick_snips_flat if not check_val_between(snip)]
+hab_notdis_snips = [snip for snip in hab_lick_snips_flat if check_val_between(snip)]
+
+print("\nFor lick data - should match numbers for photometry data...")
+print(f"Number of DISTRACTED trials on MODELLED day is {len(mod_dis_snips)}")
+print(f"Number of NOT DISTRACTED trials on MODELLED day is {len(mod_notdis_snips)}")
+print(f"Number of DISTRACTED trials on DISTRACTION day is {len(dis_dis_snips)}")
+print(f"Number of NOT DISTRACTED trials on DISTRACTION day is {len(dis_notdis_snips)}")
+print(f"Number of DISTRACTED trials on HABITUATION day is {len(hab_dis_snips)}")
+print(f"Number of NOT DISTRACTED trials on HABITUATION day is {len(hab_notdis_snips)}")
+
+# Puts lick data into histograms
+bins = np.arange(-5, 16, 1)
+mod_dis_hist = [np.histogram(snip, bins=bins)[0] for snip in mod_dis_snips]
+mod_notdis_hist = [np.histogram(snip, bins=bins)[0] for snip in mod_notdis_snips]
+
+dis_dis_hist = [np.histogram(snip, bins=bins)[0] for snip in dis_dis_snips]
+dis_notdis_hist = [np.histogram(snip, bins=bins)[0] for snip in dis_notdis_snips]
+
+hab_dis_hist = [np.histogram(snip, bins=bins)[0] for snip in hab_dis_snips]
+hab_notdis_hist = [np.histogram(snip, bins=bins)[0] for snip in hab_notdis_snips]
+
+# Saves lick histograms for each day for further ROC analysis
+
+pickle_out = open(outputfolder+"data4roc_licks.pickle", 'wb')
+dill.dump([mod_dis_hist, mod_notdis_hist, dis_dis_hist, dis_notdis_hist, hab_dis_hist, hab_notdis_hist], pickle_out)
+pickle_out.close()
+
+
+
+# resamples snips from each dictionary
+mod_dis_photo_snips, mod_notdis_photo_snips, = [], []
+dis_dis_photo_snips, dis_notdis_photo_snips, = [], []
+hab_dis_photo_snips, hab_notdis_photo_snips, = [], []
+
+# These lists are for exploring the non-Z-scored signals on distraction day
+dis_dis_filt_photo_snips, dis_notdis_filt_photo_snips, = [], []
+
+rats=disDict.keys()
+
+for rat in rats:
+ d = disDict[rat]
+
+ snips_dis = resample_snips(d['snips_distracted'][signal2use])
+ dis_dis_photo_snips.append(snips_dis)
+
+ snips_notdis = resample_snips(d['snips_not-distracted'][signal2use])
+ dis_notdis_photo_snips.append(snips_notdis)
+
+ snips_dis_filt = resample_snips(d['snips_distracted'][signal2use_noBLadj])
+ dis_dis_filt_photo_snips.append(snips_dis_filt)
+
+ snips_notdis_filt = resample_snips(d['snips_not-distracted'][signal2use_noBLadj])
+ dis_notdis_filt_photo_snips.append(snips_notdis_filt)
+
+ d = modDict[rat]
+
+ snips_dis = resample_snips(d['snips_distracted'][signal2use])
+ mod_dis_photo_snips.append(snips_dis)
+
+ snips_notdis = resample_snips(d['snips_not-distracted'][signal2use])
+ mod_notdis_photo_snips.append(snips_notdis)
+
+ d = habDict[rat]
+
+ snips_dis = resample_snips(d['snips_distracted'][signal2use])
+ hab_dis_photo_snips.append(snips_dis)
+
+ snips_notdis = resample_snips(d['snips_not-distracted'][signal2use])
+ hab_notdis_photo_snips.append(snips_notdis)
+
+pickle_out = open(outputfolder+"data4epochs_photo.pickle", 'wb')
+dill.dump([ mod_dis_photo_snips, mod_notdis_photo_snips, dis_dis_photo_snips, dis_notdis_photo_snips, dis_dis_filt_photo_snips, dis_notdis_filt_photo_snips, hab_dis_photo_snips, hab_notdis_photo_snips], pickle_out)
+pickle_out.close()
+
+# flattens all lists
+mod_dis_photo_snips_flat = flatten_list(mod_dis_photo_snips)
+mod_notdis_photo_snips_flat = flatten_list(mod_notdis_photo_snips)
+
+dis_dis_photo_snips_flat = flatten_list(dis_dis_photo_snips)
+dis_notdis_photo_snips_flat = flatten_list(dis_notdis_photo_snips)
+
+dis_dis_filt_photo_snips_flat = flatten_list(dis_dis_filt_photo_snips)
+dis_notdis_filt_photo_snips_flat = flatten_list(dis_notdis_filt_photo_snips)
+
+hab_dis_photo_snips_flat = flatten_list(hab_dis_photo_snips)
+hab_notdis_photo_snips_flat = flatten_list(hab_notdis_photo_snips)
+
+print("\nFor photometry data - should match numbers for lick data...")
+print(f"Number of DISTRACTED trials on MODELLED day is {len(mod_dis_photo_snips_flat)}")
+print(f"Number of NOT DISTRACTED trials on MODELLED day is {len(mod_notdis_photo_snips_flat)}")
+print(f"Number of DISTRACTED trials on DISTRACTION day is {len(dis_dis_photo_snips_flat)}")
+print(f"Number of NOT DISTRACTED trials on DISTRACTION day is {len(dis_notdis_photo_snips_flat)}")
+print(f"Number of DISTRACTED trials on HABITUATION day is {len(hab_dis_photo_snips_flat)}")
+print(f"Number of NOT DISTRACTED trials on HABITUATION day is {len(hab_notdis_photo_snips_flat)}")
+
+# Saves resampled and flattened photo data for each day for further ROC analysis
+
+pickle_out = open(outputfolder+"data4roc_photo.pickle", 'wb')
+dill.dump([mod_dis_photo_snips_flat, mod_notdis_photo_snips_flat, dis_dis_photo_snips_flat, dis_notdis_photo_snips_flat, dis_dis_filt_photo_snips_flat, dis_notdis_filt_photo_snips_flat, hab_dis_photo_snips_flat, hab_notdis_photo_snips_flat], pickle_out)
+pickle_out.close()
+
+
\ No newline at end of file
diff --git a/quick_look_at_photometry.py b/quick_look_at_photometry.py
new file mode 100644
index 0000000..09e1bad
--- /dev/null
+++ b/quick_look_at_photometry.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Nov 12 13:21:40 2020
+
+@author: admin
+"""
+
+import tdt
+import matplotlib.pyplot as plt
+
+folder = 'D:\\DA_and_Reward\\kp259\\THPH1AND2\\tdtfiles\\'
+tdtfile = "Kate-170620-090315"
+
+
+SigBlue1 = "Dv1A"
+
+# SigBlue1 = "Dv1B"
+# SigBlue2 = "Dv3B"
+
+tmp = tdt.read_block(folder+tdtfile, evtype=['streams'], store=[SigBlue1])
+data = getattr(tmp.streams, SigBlue1)['data']
+
+plt.plot(data)
+
+try:
+ tmp = tdt.read_block(folder+tdtfile, evtype=['streams'], store=[SigBlue2])
+ data = getattr(tmp.streams, SigBlue2)['data']
+
+ plt.plot(data)
+except:
+ print("No second channel")
+# plt.plot(data[87328:3650000])
\ No newline at end of file
diff --git a/run_roc_analysis.py b/run_roc_analysis.py
new file mode 100644
index 0000000..1aef0ec
--- /dev/null
+++ b/run_roc_analysis.py
@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Feb 19 11:55:06 2020
+
+@author: admin
+"""
+
+import time
+start_time = time.time()
+
+from fx4roc import *
+import numpy as np
+import sys
+import dill
+
+# args = sys.argv
+
+args = [ [], "C:\\Github\\Distraction-Paper\\notebooks\\hist_licks", "dis_hist", "notdis_hist", 5, "roc_results_licks"]
+
+print("Loading data")
+
+try:
+ pickle_in = open(args[1], 'rb')
+except FileNotFoundError:
+ print('Cannot access pickled file')
+
+# [args[2], args[3]] = dill.load(pickle_in)
+[dis_hist, notdis_hist] = dill.load(pickle_in)
+
+print("Running ROC analysis")
+
+# a, p = nanroc(args[2], args[3], n4shuf=args[4])
+
+a, p = nanroc(dis_hist, notdis_hist, n4shuf=args[4])
+
+
+pickle_out = open(args[5], 'wb')
+dill.dump([a, p], pickle_out)
+pickle_out.close()
+
+print(f"--- Total ROC analysis took {(time.time() - start_time)} seconds ---")
\ No newline at end of file
