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Figure6.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Oct 26 11:22:48 2023
@author: LABadmin
"""
import numpy as np
from matplotlib import pyplot as plt
import matplotlib as mpl
import random
import sklearn
import scipy as sp
import matplotlib.ticker as mtick
import matplotlib as mpl
from matplotlib.colors import ListedColormap
from matplotlib import cm
from scipy import signal
from sklearn.linear_model import LinearRegression
from sklearn.impute import SimpleImputer
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import ElasticNetCV
from sklearn.datasets import make_regression
from sklearn.decomposition import PCA
from sklearn.linear_model import RidgeCV
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
import os
import glob
import pickle
from FileManagement import *
# from GlmModule import *
from DataProcessing import *
from Visualisation import *
from GeneralRunners import *
from PCA import *
from StatTests import *
saveDir = "D:\\TaskProject\\EphysData\\"
if not os.path.isdir(saveDir):
os.makedirs(saveDir)
plt.close("all")
mpl.rcParams["pdf.fonttype"] = 42
mpl.rcParams["ps.fonttype"] = 42
mpl.rcParams["font.family"] = "Arial"
AllData, AllSubjects, AllSessions = LoadAllFiles(
"D:\\ProjectTask\\Ephys\\", ephys=True
)
sessId, pId, nId = GetNeuronDetails(AllData, ephys=True)
# d = list(tuple(map(tuple,tups)))
with open(saveDir + "responsiveness.pickle", "rb") as f:
res = pickle.load(f)
resp = res["Resp"]
p = res["pVals"]
z = res["zScores"]
r = p <= 0.05 / 8
def GetSscDepth(fits, AllSubjects):
dpa = {
"SS087": -298,
"SS088": -330,
"SS089": -338,
"SS091": -314,
"SS092": -310,
"SS093": -277,
}
animalId = np.vstack(fits.Id)[:, 0]
animals = np.vstack(AllSubjects.values())[animalId]
super_border = [dpa[x] for x in np.squeeze(animals)]
return super_border
#%% Panel C
tups = np.vstack((sessId, pId, nId)).T
modelsBase = GetOneFunctionVarianceDifferentModels(
dataDir="D:\\Figures\\OneFunction_ephys_raw\\",
cutoff=0.0001,
shuffles=True,
resp=tups,
)
f1 = np.vstack(modelsBase[modelsBase["bestFit"] == "F"]["Id"])
fits = modelsBase[modelsBase["bestFit"] == "F"].reset_index(drop=True)
dspf = GetDepthFromList(AllData, fits, "D:\\Figures\\OneFunction_ephys_raw\\")
dspf -= GetSscDepth(fits, AllSubjects)
n = 46
curr = f1[n, :]
tups = np.vstack((sessId, pId, nId)).T
d = AllData[curr[0]]
movOnset = d["wheelMoveIntervals"][:, 0]
choice = d["wheelDisplacement"][:, 0]
choice[choice > 0] = 1
choice[choice < 0] = -1
stimStarts = d["stimT"][:, 0]
stimEnds = d["stimT"][:, 1]
p = d["probes"][curr[1] + 1]
imagingSide = p["ImagingSide"]
scDepth = p["scDepth"][0]
spikes = MakeSpikeInfoMatrix(p, [scDepth - 1000, scDepth])
uniqueClust = np.unique(spikes[:, 1])
unitId = uniqueClust[curr[-1]]
if imagingSide == 1:
contra = d["contrastLeft"][:, 0]
ipsi = d["contrastRight"][:, 0]
choice = -choice
else:
ipsi = d["contrastLeft"][:, 0]
contra = d["contrastRight"][:, 0]
contraU = np.unique(contra)
ch = d["choice"]
beepStarts = d["goCueTimes"]
feedbackStarts = d["feedbackTimes"]
feedBackPeriod = d["posFeedbackPeriod"]
feedbackType = d["feedbackType"][:, 0]
prevfeedback = np.append([0], feedbackType[:-1])
window = np.array([-0.5 - 0.5, 0.5 + 0.5])
stimA, t = GetRaster(spikes.copy(), stimStarts, window, dt=1 / 1000)
lenContraN = np.zeros(len(contraU) + 1)
lenContraP = np.zeros(len(contraU) + 1)
maxCount = np.zeros(len(contraU) + 1)
# contraU = contraU[contraU>0]
for ci, c in enumerate(contraU):
IndN = np.where((contra == c) & (prevfeedback == 0))[0]
IndP = np.where((contra == c) & (prevfeedback == 1))[0]
lenContraN[ci + 1] = len(IndN)
lenContraP[ci + 1] = len(IndP)
lens = [len(IndN), len(IndP)]
maxCount[ci + 1] = lens[np.argmax(lens)]
f, ax = plt.subplots(2, 2, sharex=True)
stimA_ = stimA[:, :, curr[2]].copy()
lenContra = np.zeros(len(contraU) + 1)
lenContraR = np.zeros(len(contraU) + 1)
lastI = 0
colors = np.linspace(0.8, 0, len(contraU))
# print neg
for ci, c in enumerate(contraU):
Ind = np.where((contra == c) & (prevfeedback == 0))[0]
lenContra[ci + 1] = len(Ind)
psth = GetPSTH(stimA_[:, Ind], w=30, halfGauss=True, flat=False)
m = np.nanmean(psth, 1)
sd = np.nanstd(psth, 1) / np.sqrt(psth.shape[1])
ax[1, 0].plot(t, m, color=(colors[ci], colors[ci], colors[ci]))
ax[1, 0].fill_between(
t,
m - sd,
m + sd,
color=(colors[ci], colors[ci], colors[ci]),
alpha=0.2,
)
contrastStartPoint = np.cumsum(maxCount)[ci]
for i in range(len(Ind)):
spLocal = stimA_[:, Ind[i]]
ax[0, 0].vlines(
t[spLocal],
contrastStartPoint + i,
contrastStartPoint + i + 1,
color=(colors[ci], colors[ci], colors[ci]),
)
lastI += i
lenContraR[ci + 1] = lenContra[ci + 1].copy()
lenContra[ci + 1] += lastI
# print pos
lenContra2 = np.zeros(len(contraU) + 1)
lastI = 0
for ci, c in enumerate(contraU):
Ind = np.where((contra == c) & (prevfeedback == 1))[0]
lenContra2[ci + 1] = len(Ind)
psth = GetPSTH(stimA_[:, Ind], w=30, halfGauss=True, flat=False)
m = np.nanmean(psth, 1)
sd = np.nanstd(psth, 1) / np.sqrt(psth.shape[1])
ax[1, 1].plot(t, m, color=(colors[ci], colors[ci], colors[ci]))
ax[1, 1].fill_between(
t,
m - sd,
m + sd,
color=(colors[ci], colors[ci], colors[ci]),
alpha=0.2,
)
contrastStartPoint = np.cumsum(maxCount)[ci]
for i in range(len(Ind)):
ax[0, 1].vlines(
t[stimA_[:, Ind[i]]],
contrastStartPoint + i,
contrastStartPoint + i + 1,
color=(colors[ci], colors[ci], colors[ci]),
)
lastI += i
ax[0, 0].set_xlim(-0.1, 0.5)
maxY0 = np.max(np.nanmax([np.sum(lenContra), np.sum(lenContra2)]))
maxY1 = np.max([ax[1, 0].get_ylim()[1], ax[1, 1].get_ylim()[1]])
ax[1, 0].set_ylim(0, maxY1)
ax[1, 1].set_ylim(0, maxY1)
ax[0, 1].legend(contraU, bbox_to_anchor=(1.05, 1.0), loc="upper left")
#%% Panel D
tups = np.vstack((sessId, pId, nId)).T
modelsBase = GetOneFunctionVarianceDifferentModels(
dataDir="D:\\Figures\\OneFunction_ephys_raw\\",
cutoff=0.0001,
shuffles=True,
resp=tups,
)
f1 = np.vstack(modelsBase[modelsBase["bestFit"] == "P"]["Id"])
fits = modelsBase[modelsBase["bestFit"] == "P"].reset_index(drop=True)
dspf = GetDepthFromList(AllData, fits, "D:\\Figures\\OneFunction_ephys_raw\\")
dspf -= GetSscDepth(fits, AllSubjects)
#%%
n = 15
curr = f1[n, :]
tups = np.vstack((sessId, pId, nId)).T
d = AllData[curr[0]]
movOnset = d["wheelMoveIntervals"][:, 0]
choice = d["wheelDisplacement"][:, 0]
choice[choice > 0] = 1
choice[choice < 0] = -1
# pupil_ds = AlignSignals(d['pupil'],d['pupilTimes'],d['times'])
# pudt = np.mean(np.diff(d['pupilTimes'][:,0]))
# tdt = np.mean(np.diff(d['times'])
# actTimes = actTimes[((actTimes>=d['times'][0]) & (actTimes<=d['times'][-1]))]
# pupil_ds,put = sp.signal.resample(d['pupil'],actTimes,len(d['times']))
stimStarts = d["stimT"][:, 0]
stimEnds = d["stimT"][:, 1]
p = d["probes"][curr[1] + 1]
imagingSide = p["ImagingSide"]
scDepth = p["scDepth"][0]
spikes = MakeSpikeInfoMatrix(p, [scDepth - 1000, scDepth])
uniqueClust = np.unique(spikes[:, 1])
unitId = uniqueClust[curr[-1]]
if imagingSide == 1:
contra = d["contrastLeft"][:, 0]
ipsi = d["contrastRight"][:, 0]
choice = -choice
else:
ipsi = d["contrastLeft"][:, 0]
contra = d["contrastRight"][:, 0]
contraU = np.unique(contra)
ch = d["choice"]
beepStarts = d["goCueTimes"]
feedbackStarts = d["feedbackTimes"]
feedBackPeriod = d["posFeedbackPeriod"]
feedbackType = d["feedbackType"][:, 0]
prevfeedback = np.append([0], feedbackType[:-1])
window = np.array([-0.5 - 0.5, 0.5 + 0.5])
windowP = np.array([[-0.5, 0.5]])
pupil_ds = d["pupil"]
taskStart = np.argmin(np.abs(d["pupilTimes"] - stimStarts[0]))
taskEnd = np.argmin(np.abs(d["pupilTimes"] - stimStarts[-1]))
taskPupl = pupil_ds[taskStart:taskEnd]
# middle = np.nanmedian(pupil_ds,0)
middle = np.nanmedian(taskPupl, 0)
pu, t = AlignStim(pupil_ds, d["pupilTimes"], stimStarts, windowP)
bigTrials = pu > middle
bigTrialsSum = np.sum(bigTrials[:, :, 0], 0)
meanPupil = np.nanmean(pu[:, :, 0], 0)
largePupil = (bigTrialsSum / len(t)) > 0.5
smallPupil = (bigTrialsSum / len(t)) < 0.5
prevFeedback = largePupil
stimA, t = GetRaster(spikes.copy(), stimStarts, window, dt=1 / 1000)
# first find how much trails in each case
lenContraN = np.zeros(len(contraU) + 1)
lenContraP = np.zeros(len(contraU) + 1)
maxCount = np.zeros(len(contraU) + 1)
# contraU = contraU[contraU>0]
for ci, c in enumerate(contraU):
IndN = np.where((contra == c) & (prevfeedback == 0))[0]
IndP = np.where((contra == c) & (prevfeedback == 1))[0]
lenContraN[ci + 1] = len(IndN)
lenContraP[ci + 1] = len(IndP)
lens = [len(IndN), len(IndP)]
maxCount[ci + 1] = lens[np.argmax(lens)]
f, ax = plt.subplots(2, 2, sharex=True)
stimA_ = stimA[:, :, curr[2]].copy()
lenContra = np.zeros(len(contraU) + 1)
lenContraR = np.zeros(len(contraU) + 1)
lastI = 0
colors = np.linspace(0.8, 0, len(contraU))
# print neg
for ci, c in enumerate(contraU):
Ind = np.where((contra == c) & (prevfeedback == 0))[0]
lenContra[ci + 1] = len(Ind)
psth = GetPSTH(stimA_[:, Ind], w=30, halfGauss=True, flat=False)
m = np.nanmean(psth, 1)
sd = np.nanstd(psth, 1) / np.sqrt(psth.shape[1])
ax[1, 0].plot(t, m, color=(colors[ci], colors[ci], colors[ci]))
ax[1, 0].fill_between(
t,
m - sd,
m + sd,
color=(colors[ci], colors[ci], colors[ci]),
alpha=0.2,
)
contrastStartPoint = np.cumsum(maxCount)[ci]
for i in range(len(Ind)):
spLocal = stimA_[:, Ind[i]]
ax[0, 0].vlines(
t[spLocal],
contrastStartPoint + i,
contrastStartPoint + i + 1,
color=(colors[ci], colors[ci], colors[ci]),
)
lastI += i
lenContraR[ci + 1] = lenContra[ci + 1].copy()
lenContra[ci + 1] += lastI
# print pos
lenContra2 = np.zeros(len(contraU) + 1)
lastI = 0
for ci, c in enumerate(contraU):
Ind = np.where((contra == c) & (prevfeedback == 1))[0]
lenContra2[ci + 1] = len(Ind)
psth = GetPSTH(stimA_[:, Ind], w=30, halfGauss=True, flat=False)
m = np.nanmean(psth, 1)
sd = np.nanstd(psth, 1) / np.sqrt(psth.shape[1])
ax[1, 1].plot(t, m, color=(colors[ci], colors[ci], colors[ci]))
ax[1, 1].fill_between(
t,
m - sd,
m + sd,
color=(colors[ci], colors[ci], colors[ci]),
alpha=0.2,
)
contrastStartPoint = np.cumsum(maxCount)[ci]
for i in range(len(Ind)):
ax[0, 1].vlines(
t[stimA_[:, Ind[i]]],
contrastStartPoint + i,
contrastStartPoint + i + 1,
color=(colors[ci], colors[ci], colors[ci]),
)
lastI += i
# * np.arange(1,stimA.shape[1]+1).T
# start with negative
ax[0, 0].set_xlim(-0.1, 0.5)
# ax[0, 0].set_ylim(0, stimA.shape[1])
maxY0 = np.max(np.nanmax([np.sum(lenContra), np.sum(lenContra2)]))
maxY1 = np.max([ax[1, 0].get_ylim()[1], ax[1, 1].get_ylim()[1]])
# ax[0,0].set_ylim(0,maxY0)
# ax[0,1].set_ylim(0,maxY0)
ax[1, 0].set_ylim(0, maxY1)
ax[1, 1].set_ylim(0, maxY1)
ax[0, 1].legend(contraU, bbox_to_anchor=(1.05, 1.0), loc="upper left")
#%% Panel E
colours = np.zeros((256, 4))
red = np.ones(256)
green = np.append(np.ones(86), np.linspace(1, 0.3, 256 - 86)) # np.append(
# np.linspace(0.5, 0, 128))
blue = np.append(np.linspace(1, 0.1, 86), np.ones(256 - 86) * 0) # np.append(
alphas = np.ones_like(red)
colours[:, 0] = red
colours[: len(green), 1] = green
colours[: len(blue), 2] = blue
colours[:, -1] = alphas
newcmp = ListedColormap(colours)
plt.close("all")
tups = np.vstack((sessId, pId, nId)).T
tups = tups[r[0, :] == 1, :]
window = np.array([[-0.2, 1]])[0]
modelsBase = GetOneFunctionVarianceDifferentModels(
dataDir="D:\\Figures\\OneFunction_ephys_raw\\",
cutoff=0.01,
shuffles=True,
resp=tups,
)
f1 = np.append(
np.array(modelsBase[modelsBase["bestFit"] == "F"]["Id"]),
np.array(modelsBase[modelsBase["bestFit"] == "PF"]["Id"]),
)
f1 = np.vstack(f1)
vals, counts = np.unique(f1[:, 0], return_counts=True)
vals -= 1
prevPosResp = []
prevNegResp = []
t_list = []
for i in range(1, len(AllData) + 1):
if not (i in f1[:, 0]):
continue
print(f"Neuron {str(i)}")
relProbes = np.unique(f1[f1[:, 0] == i, 1])
for pi in relProbes:
relNeurons = f1[(f1[:, 0] == i) & (f1[:, 1] == pi), -1]
p = AllData[i]["probes"][pi + 1]
imagingSide = p["ImagingSide"]
scDepth = p["scDepth"][0]
spikes = MakeSpikeInfoMatrix(p, [scDepth - 1000, scDepth])
uniqueClust = np.unique(spikes[:, 1])
# spikesCell = spikes[spikes[:, 1] == uniqueClust[relNeurons],:]
stimStarts = AllData[i]["stimT"][:, 0]
choice = AllData[i]["choice"]
feedback = AllData[i]["feedbackType"]
prevFb = np.append(0, feedback[:-1])
if imagingSide == 1:
contra = AllData[i]["contrastLeft"][:, 0]
ipsi = AllData[i]["contrastRight"][:, 0]
choice = -choice
else:
ipsi = AllData[i]["contrastLeft"][:, 0]
contra = AllData[i]["contrastRight"][:, 0]
relevantTrial = np.where(contra > 0)[0]
choice = choice[relevantTrial]
feedback = feedback[relevantTrial]
stimStarts = stimStarts[relevantTrial]
prevFb = prevFb[relevantTrial]
contra = contra[relevantTrial]
stimA, t = GetRaster(spikes.copy(), stimStarts, window, dt=1 / 1000)
stimA = stimA[:, :, relNeurons].astype(float)
for nn in range(stimA.shape[-1]):
stimA[:, :, nn] = GetPSTH(
stimA[:, :, nn], w=30, halfGauss=True, flat=False
)
pre = np.nanmean(stimA[t <= 0, :, :], 0)
post = np.nanmean(stimA[(t > 0.2) & (t <= 1), :, :], 0)
flipInds = np.where(np.nanmean(post, 0) < np.nanmean(pre, 0))[0]
if len(flipInds) > 0:
stimA[:, :, flipInds] = -stimA[:, :, flipInds]
stimA -= np.nanmean(
stimA[(t <= 0), :, :],
0,
)
prevPosResp.append(
np.nanmean(
stimA[:, (contra == np.nanmax(contra)) & (prevFb == 1), :],
1,
)
)
prevNegResp.append(
np.nanmean(
stimA[:, (contra == np.nanmax(contra)) & (prevFb == 0), :],
1,
)
)
t_list.append(t)
slowestT = t_list[0]
prevPosResp_ = prevPosResp.copy()
prevNegResp_ = prevNegResp.copy()
prevPosResp = np.hstack(prevPosResp)
prevNegResp = np.hstack(prevNegResp)
ratio = prevPosResp / prevNegResp
meanRatio = np.nanmean(ratio[(slowestT >= 0) & (slowestT <= 0.5)], axis=0)
df = (t[1] - t[0]) / 2
colours = np.zeros((256, 4))
red = np.ones(256)
green = np.append(
np.ones(128), np.linspace(1, 0.3, 128)
) # np.append(np.ones(86), np.linspace(1, 0.3, 256 - 86)) # np.append(
# np.ones(64), np.linspace(1, 0.3, 256 - 64)
# ) # np.append(np.ones(128), np.linspace(1, 0.3, 128))##
# np.linspace(0.5, 0, 128))
blue = np.append(
np.linspace(1, 0.1, 128), np.ones(128) * 0
) # np.append(np.linspace(1, 0.1, 86), np.ones(256 - 86) * 0) # np.append(
alphas = np.ones_like(red)
colours[:, 0] = red
colours[: len(green), 1] = green
colours[: len(blue), 2] = blue
colours[:, -1] = alphas
newcmp = ListedColormap(colours)
###for 3
colours = np.zeros((256, 4))
red = np.ones(256)
green = np.append(np.ones(86), np.linspace(1, 0.3, 256 - 86)) # np.append(
# np.ones(64), np.linspace(1, 0.3, 256 - 64)
# ) # np.append(np.ones(128), np.linspace(1, 0.3, 128))##
# np.linspace(0.5, 0, 128))
blue = np.append(np.linspace(1, 0.1, 86), np.ones(256 - 86) * 0) # np.append(
alphas = np.ones_like(red)
colours[:, 0] = red
colours[: len(green), 1] = green
colours[: len(blue), 2] = blue
colours[:, -1] = alphas
newcmp = ListedColormap(colours)
### second option
negMax = np.nanmax(prevNegResp[(slowestT >= 0) & (slowestT <= 0.2)], 0)
negNorm = prevNegResp / negMax
posNorm = prevPosResp / negMax
meanRatio = np.nanmax(posNorm[(slowestT >= 0) & (slowestT <= 0.2)], axis=0)
f, ax = plt.subplots(1, 2, sharex=True)
im = ax[0].imshow(
np.flip(negNorm[:, np.argsort(meanRatio)].T, axis=0),
cmap=newcmp, # "YlOrRd",
vmin=0,
vmax=2,
extent=(slowestT[0] + df, slowestT[-1] + df, 0, ratio.shape[1]),
aspect="auto",
)
im = ax[1].imshow(
np.flip(posNorm[:, np.argsort(meanRatio)].T, axis=0),
cmap=newcmp, # "YlOrRd",
vmin=0,
vmax=2,
extent=(slowestT[0] + df, slowestT[-1] + df, 0, ratio.shape[1]),
aspect="auto",
)
ax[1].set_xlim(-0.25, 1)
f.colorbar(im)
#%% Panel F
plt.close("all")
tups = np.vstack((sessId, pId, nId)).T
tups = tups[r[0, :] == 1, :]
window = np.array([[-0.2, 1]])[0]
modelsBase = GetOneFunctionVarianceDifferentModels(
dataDir="D:\\Figures\\OneFunction_ephys_raw\\",
cutoff=0.01,
shuffles=True,
resp=tups,
)
f1 = np.append(
np.array(modelsBase[modelsBase["bestFit"] == "P"]["Id"]),
np.array(modelsBase[modelsBase["bestFit"] == "PF"]["Id"]),
)
f1 = np.vstack(f1)
vals, counts = np.unique(f1[:, 0], return_counts=True)
vals -= 1
# count neurons
prevPosResp = []
prevNegResp = []
t_list = []
for i in range(1, len(AllData) + 1):
if not (i in f1[:, 0]):
continue
print(f"Neuron {str(i)}")
relProbes = np.unique(f1[f1[:, 0] == i, 1])
for pi in relProbes:
relNeurons = f1[(f1[:, 0] == i) & (f1[:, 1] == pi), -1]
p = AllData[i]["probes"][pi + 1]
imagingSide = p["ImagingSide"]
scDepth = p["scDepth"][0]
spikes = MakeSpikeInfoMatrix(p, [scDepth - 1000, scDepth])
uniqueClust = np.unique(spikes[:, 1])
# spikesCell = spikes[spikes[:, 1] == uniqueClust[relNeurons],:]
stimStarts = AllData[i]["stimT"][:, 0]
choice = AllData[i]["choice"]
feedback = AllData[i]["feedbackType"]
prevFb = np.append(0, feedback[:-1])
if imagingSide == 1:
contra = AllData[i]["contrastLeft"][:, 0]
ipsi = AllData[i]["contrastRight"][:, 0]
choice = -choice
else:
ipsi = AllData[i]["contrastLeft"][:, 0]
contra = AllData[i]["contrastRight"][:, 0]
middle = np.nanmedian(
AllData[i]["pupil"][AllData[i]["pupilTimes"] < stimStarts[-1]], 0
)
pu, tp = AlignStim(
AllData[i]["pupil"],
AllData[i]["pupilTimes"],
stimStarts.reshape(-1, 1),
window.reshape(1, -1),
)
bigTrials = pu > middle
bigTrialsSum = np.sum(bigTrials[:, :, 0], 0)
meanPupil = np.nanmean(pu[:, :, 0], 0)
largePupil = (bigTrialsSum / len(tp)) > 0.5
smallPupil = (bigTrialsSum / len(tp)) < 0.5
relevantTrial = np.where(contra > 0)[0]
choice = choice[relevantTrial]
feedback = feedback[relevantTrial]
stimStarts = stimStarts[relevantTrial]
prevFb = prevFb[relevantTrial]
contra = contra[relevantTrial]
largePupil = largePupil[relevantTrial]
stimA, t = GetRaster(spikes.copy(), stimStarts, window, dt=1 / 1000)
stimA = stimA[:, :, relNeurons].astype(float)
for nn in range(stimA.shape[-1]):
stimA[:, :, nn] = GetPSTH(
stimA[:, :, nn], w=30, halfGauss=True, flat=False
)
# cr = cr + stimA.shape[-1]
pre = np.nanmean(stimA[t <= 0, :, :], 0)
post = np.nanmean(stimA[(t > 0.2) & (t <= 1), :, :], 0)
# if i == 22:
# print("Ya")
flipInds = np.where(np.nanmean(post, 0) < np.nanmean(pre, 0))[0]
if len(flipInds) > 0:
stimA[:, :, flipInds] = -stimA[:, :, flipInds]
# stimA = stimA[:, :, :]
# -np.nanmean(
# stimA[(t <= 0) & (t >= -0.1), :, :][:, :, :],
# 0,
# )
stimA -= np.nanmean(
stimA[(t <= 0), :, :],
0,
)
prevPosResp.append(
np.nanmean(
stimA[:, (contra == np.nanmax(contra)) & (prevFb == 1), :],
1,
)
)
prevNegResp.append(
np.nanmean(
stimA[:, (contra == np.nanmax(contra)) & (prevFb == 0), :],
1,
)
)
t_list.append(t)
slowestT = t_list[0]
prevPosResp_ = prevPosResp.copy()
prevNegResp_ = prevNegResp.copy()
prevPosResp = np.hstack(prevPosResp)
prevNegResp = np.hstack(prevNegResp)
ratio = prevPosResp / prevNegResp
meanRatio = np.nanmean(ratio[(slowestT >= 0) & (slowestT <= 0.05)], axis=0)
#### for 2
colours = np.zeros((256, 4))
red = np.ones(256)
green = np.append(
np.ones(128), np.linspace(1, 0.3, 128)
) # np.append(np.ones(86), np.linspace(1, 0.3, 256 - 86)) # np.append(
# np.linspace(0.5, 0, 128))
blue = np.append(
np.linspace(1, 0.1, 128), np.ones(128) * 0
) # np.append(np.linspace(1, 0.1, 86), np.ones(256 - 86) * 0) # np.append(
alphas = np.ones_like(red)
colours[:, 0] = red
colours[: len(green), 1] = green
colours[: len(blue), 2] = blue
colours[:, -1] = alphas
newcmp = ListedColormap(colours)
negMax = np.nanmax(prevNegResp[(slowestT >= 0) & (slowestT <= 0.2)], 0)
negNorm = prevNegResp / negMax
posNorm = prevPosResp / negMax
meanRatio = np.nanmax(posNorm[(slowestT >= 0) & (slowestT <= 0.2)], axis=0)
f, ax = plt.subplots(1, 2, sharex=True)
im = ax[0].imshow(
np.flip(negNorm[:, np.argsort(meanRatio)].T, axis=0),
cmap=newcmp,
vmin=0,
vmax=2,
extent=(slowestT[0] + df, slowestT[-1] + df, 0, ratio.shape[1]),
aspect="auto",
)
im = ax[1].imshow(
np.flip(posNorm[:, np.argsort(meanRatio)].T, axis=0),
cmap=newcmp,
vmin=0,
vmax=2,
extent=(slowestT[0] + df, slowestT[-1] + df, 0, ratio.shape[1]),
aspect="auto",
)
ax[1].set_xlim(-0.25, 1)
f.colorbar(im)
#%% Panels G-J
nullProps1 = GetNullDistribution(allFit, 4)
nullProps2 = GetNullDistribution(allFit, 1)
def CreateNullIntervalPlot(ax, dist):
ys = []
f, axt = plt.subplots(1)
# sns.histplot(data= dist.T,legend=False,common_norm=False,cumulative=True, stat='proportion',element='poly',fill=False,binrange=(-2,2),binwidth=0.3,ax=axt)
sns.kdeplot(
data=dist,
legend=False,
common_norm=False,
common_grid=True,
cumulative=True,
ax=axt,
clip=(-2, 2),
bw_adjust=0.1,
cut=0,
)
for i in range(len(axt.lines)):
line = axt.lines[i]
x, y = line.get_data()
y /= np.nanmax(y)
ys.append(y)
plt.close(f)
ys = np.vstack(ys)
# ax.plot(x,np.nanmean(ys,0),'grey')
ax.fill_between(
x,
np.nanpercentile(ys, 2.5, axis=0),
np.nanpercentile(ys, 97.5, axis=0),
color="grey",
alpha=0.4,
)
return None
tups = np.vstack((sessId, pId, nId)).T
tups = tups[r[0, :] == 1, :]
modelsBase = GetOneFunctionVarianceDifferentModels(
dataDir="D:\\Figures\\OneFunction_ephys_raw\\",
cutoff=0.01,
shuffles=True,
resp=tups,
)
plt.close("all")
depthList = -GetDepthFromList(
AllData, modelsBase, "D:\\Figures\\OneFunction_ephys_raw\\"
)
# Find the R for pupil and feedback
f1 = np.array(modelsBase[modelsBase["bestFit"] == "F"]["fit"])
fp1 = np.array(modelsBase[modelsBase["bestFit"] == "PF"]["fit"])
p1 = np.array(modelsBase[modelsBase["bestFit"] == "P"]["fit"])
nf1 = np.array(modelsBase[modelsBase["bestFit"] != "F"]["fit"])
np1 = np.array(modelsBase[modelsBase["bestFit"] != "P"]["fit"])
nfp1 = np.array(modelsBase[modelsBase["bestFit"] != "PF"]["fit"])
g1 = np.array(modelsBase[modelsBase["bestFit"] == "G"]["fit"])
c1 = np.array(modelsBase[modelsBase["bestFit"] == "C"]["fit"])
ng1 = np.array(modelsBase[modelsBase["bestFit"] != "G"]["fit"])
nc1 = np.array(modelsBase[modelsBase["bestFit"] != "C"]["fit"])
allFit = np.array(modelsBase["fit"])
fr = GetRFromFitList(list(f1), "F")
fpr = GetRFromFitList(list(fp1), "PF")
nfr = GetRFromFitList(list(nf1), "F")
pr = GetRFromFitList(list(p1), "P")
npr = GetRFromFitList(list(np1), "P")
nfpr = GetRFromFitList(list(nfp1), "PF")
gr = GetRFromFitList(list(g1), "G")
ngr = GetRFromFitList(list(ng1), "G")
cr = GetRFromFitList(list(c1), "C")
ncr = GetRFromFitList(list(nc1), "C")
frall = GetRFromFitList(list(allFit), "F")
prall = GetRFromFitList(list(allFit), "P")
grall = GetRFromFitList(list(allFit), "G")
crall = GetRFromFitList(list(allFit), "C")
bins = np.arange(-1, 1.1, 0.03)
ymax = 80
##### Feedback Plotting #########
f, ax = plt.subplots(1)
ax.scatter(nfr[:, 0] + nfr[:, 1], nfr[:, 0], s=30, c=["w"], edgecolors="grey")
ax.scatter(fr[:, 0] + fr[:, 1], fr[:, 0], s=30, c=["k"], edgecolors="k")
ax.scatter(fpr[:, 0] + fpr[:, 2], fpr[:, 0], s=30, c=["k"], edgecolors="k")
ax.set_aspect("equal", adjustable="box")
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.plot(np.arange(0, 0.7, 0.1), np.arange(0, 0.7, 0.1), "k--")
ax.set_title("R (gain) feedback")
ax.set_xlabel("Positive Feedback")
ax.set_ylabel("Negative Feedback")
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
f.set_size_inches(8, 8)
# Extra histogram to put diagonally
f, ax = plt.subplots(1)
valsPos_ns = nullProps1[:, :, [0, 4]]
valsPos_ns = valsPos_ns[:, :, 1] / (
0.5 * (2 * valsPos_ns[:, :, 0] + valsPos_ns[:, :, 1])
)
valsPos = (frall[:, 0] + frall[:, 1] - frall[:, 0]) / (
0.5 * (frall[:, 0] + frall[:, 1] + frall[:, 0])
)
ff, axt = plt.subplots(1)
sns.kdeplot(
data=[valsPos],
palette=["black"],
ax=axt,
legend=False,
common_norm=False,
cumulative=True,
clip=(-2, 2),
bw_adjust=0.1,
cut=0,
)
line = axt.lines[0]
x, y = line.get_data()
y /= np.max(y)
plt.close(ff)
plt.plot(x, y, "k")
sorted_data = np.sort(valsPos)
y = np.arange(sorted_data.size)
y = y / np.max(y)
CreateNullIntervalPlot(ax, valsPos_ns)
plt.grid(False)
ax.set_xlabel("Respone Modulation (%)")
ax.set_ylabel("Proportion of Neurons")
ax.set_xlim(-2, 2)
ax.set_ylim(0, 1)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
f.set_size_inches(np.sqrt(8), np.sqrt(8))
ax.vlines(0, 0, 1, "k")
##### Pupil Plotting #########
f, ax = plt.subplots(1)
ax.scatter(npr[:, 0] + npr[:, 1], npr[:, 0], s=30, c=["w"], edgecolors="grey")
ax.scatter(pr[:, 0] + pr[:, 1], pr[:, 0], s=30, c=["k"], edgecolors="k")
ax.scatter(fpr[:, 0] + fpr[:, 1], fpr[:, 0], s=30, c=["k"], edgecolors="k")
ax.set_aspect("equal", adjustable="box")
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.plot(np.arange(0, 0.7, 0.1), np.arange(0, 0.7, 0.1), "k--")
ax.set_title("R (gain) Pupil")
ax.set_xlabel("Large Pupil")
ax.set_ylabel("Small Pupil")
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
f.set_size_inches(8, 8)
# Extra histogram to put diagonally
f, ax = plt.subplots(1)
valsPos_ns = nullProps2[:, :, [0, 1]]
valsPos_ns = valsPos_ns[:, :, 1] / (
0.5 * (2 * valsPos_ns[:, :, 0] + valsPos_ns[:, :, 1])
)
valsPos = (prall[:, 1]) / (0.5 * (2 * prall[:, 0] + prall[:, 1]))
ff, axt = plt.subplots(1)
sns.kdeplot(
data=[valsPos],
palette=["black"],
ax=axt,
legend=False,
common_norm=False,
cumulative=True,
clip=(-2, 2),
bw_adjust=0.1,
cut=0,
)
line = axt.lines[0]
x, y = line.get_data()
y /= np.max(y)
plt.close(ff)
plt.plot(x, y, "k")
CreateNullIntervalPlot(ax, valsPos_ns)
plt.grid(False)
ax.set_xlabel("Respone Modulation (%)")
ax.set_ylabel("Proportion of Neurons")
ax.set_xlim(-2, 2)
ax.set_ylim(0, 1)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
f.set_size_inches(np.sqrt(8), np.sqrt(8))
ax.vlines(0, 0, 1, "k")
#%% Panel K