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Copy pathcreateGroupVisualizations.py
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createGroupVisualizations.py
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import numpy as np
import matplotlib.pyplot as plt
import csv
from scipy.ndimage import gaussian_filter1d
import pandas as pd
import seaborn as sns
import matplotlib as mpl
from matplotlib.colors import Normalize
from matplotlib import rcParams
import matplotlib.gridspec as gridspec
from scipy.stats import ttest_ind
from statsmodels.stats.multitest import multipletests
from matplotlib.ticker import MultipleLocator
import os
from scipy.interpolate import interp1d
import scipy.stats as stats
from statsmodels.stats.multicomp import pairwise_tukeyhsd
import scipy.stats as stats
from statsmodels.stats.multicomp import pairwise_tukeyhsd
import pdb
import tools.dataAnalysis as dA
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from scipy.stats import mannwhitneyu, pearsonr
import matplotlib.gridspec as gridspec
class createGroupVisualizations:
def __init__(self, figureDir, groupAnalysisDir):
self.figureDirectory = figureDir
if not os.path.isdir(self.figureDirectory):
os.system('mkdir %s' % self.figureDirectory)
self.preprocessingDir = groupAnalysisDir
if not os.path.isdir(self.preprocessingDir):
os.system('mkdir %s' % self.preprocessingDir)
self.pawID = ['FR', 'FL', 'HL', 'HR']
def layoutOfPanel(self, ax, xLabel=None, yLabel=None, Leg=None, xyInvisible=[False, False],
xlim=None, ylim=None, xMul=None, yMul=None, yf=11, xf=11,
xt=None, yt=None):
# Set x and y axis major locators
if xMul is not None:
ax.xaxis.set_major_locator(plt.MultipleLocator(xMul))
if yMul is not None:
ax.yaxis.set_major_locator(plt.MultipleLocator(yMul))
# Set x and y axis limits
if xlim is not None:
ax.set_xlim(xlim)
if ylim is not None:
ax.set_ylim(ylim)
# Customize axis spines and grid visibility
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(False)
# Manage x-axis visibility
if xyInvisible[0]:
ax.spines['bottom'].set_visible(False)
ax.xaxis.set_visible(False)
else:
ax.spines['bottom'].set_position(('outward', 10))
ax.xaxis.set_ticks_position('bottom')
# Manage y-axis visibility
if xyInvisible[1]:
ax.spines['left'].set_visible(False)
ax.yaxis.set_visible(False)
else:
ax.spines['left'].set_position(('outward', 10))
ax.yaxis.set_ticks_position('left')
# Set axis labels
if xLabel is not None:
ax.set_xlabel(xLabel)
if yLabel is not None:
ax.set_ylabel(yLabel)
#set axis labels font size
if yf is not None:
ax.yaxis.label.set_size(yf)
if xf is not None:
ax.xaxis.label.set_size(xf)
# Customize legend
if Leg is not None:
ax.legend(loc=Leg[0], frameon=False)
if len(Leg) > 1:
legend = ax.get_legend()
ltext = legend.get_texts()
plt.setp(ltext, fontsize=Leg[1])
# Apply x-tick font size and rotation if provided
if xt is not None:
xtick_fontsize = xt[0] if len(xt) > 0 else None
xtick_rotation = xt[1] if len(xt) > 1 else None
plt.setp(ax.get_xticklabels(), fontsize=xtick_fontsize, rotation=xtick_rotation)
# Apply y-tick font size and rotation if provided
if yt is not None:
ytick_fontsize = yt[0] if len(yt) > 0 else None
ytick_rotation = yt[1] if len(yt) > 1 else None
plt.setp(ax.get_yticklabels(), fontsize=ytick_fontsize, rotation=ytick_rotation)
def plot_signal_and_trigger(self, time, signal, trigger, trigger_time, title="Signal and Trigger Plot",
signal_label="Signal", trigger_label="Trigger"):
fig, ax_signal = plt.subplots()
# Plotting the primary signal
sns.lineplot(x=time, y=signal, ax=ax_signal, color="blue", label=signal_label)
ax_signal.set_xlabel("Time")
ax_signal.set_ylabel(signal_label)
ax_signal.set_title(title)
# Creating a second y-axis for the trigger
ax_trigger = ax_signal.twinx()
sns.lineplot(x=trigger_time, y=trigger, ax=ax_trigger, color="red", label=trigger_label)
ax_trigger.set_ylabel(trigger_label)
# Creating a legend that combines elements from both axes
lines, labels = ax_signal.get_legend_handles_labels()
lines2, labels2 = ax_trigger.get_legend_handles_labels()
ax_signal.legend(lines + lines2, labels + labels2, loc='upper right')
plt.show()
def plot_fiber_photometry_raw(self, aniDic):
# Plot signals
fig, ax1 = plt.subplots() # create a plot to allow for dual y-axes plotting
plot1 = ax1.plot(aniDic['Time'], aniDic['Signal'], 'C2', label='dLight') # plot dLight on left y-axis
ax2 = plt.twinx() # create a right y-axis, sharing x-axis on the same plot
plot2 = ax2.plot(aniDic['Time'], aniDic['refSignal'], 'C1', label='ref') # plot ref on right y-axis
# Plot trigger times as ticks.
triggerPlot = ax1.plot(aniDic['Time'], aniDic['Trigger'], label='trigger',
color='w', marker="|", mec='r')
ax1.set_xlabel('Time (seconds)')
ax1.set_ylabel('dLight Signal (V)', color='C2')
ax2.set_ylabel('ref Signal (V)', color='C1')
ax1.set_title('Raw signals')
lines = plot1 + plot2 + triggerPlot # line handle for legend
labels = [l.get_label() for l in lines] # get legend labels
legend = ax1.legend(lines, labels, loc='upper right', bbox_to_anchor=(0.98, 0.93)) # add legend
plt.show()
def plot_fiber_photometry(self, aniDic, plan,experiment,trial_list_str, gen):
"""
Plot fiber photometry data over time including signal, reference signal, delta F/F, and trigger events.
Parameters:
- aniDic: Dictionary containing 'Signal', 'RefSignal', 'Time', 'Trigger', and 'dFF' keys.
"""
# figure generate either a figure with only FL paw or with any desired number of paw
fig_width = 16 # width in inches
fig_height = 15 # height in inches
fig_size = [fig_width, fig_height]
params = {'axes.labelsize': 12, 'axes.titlesize': 12, 'font.size': 10, 'xtick.labelsize': 12,
'ytick.labelsize': 8,
'figure.figsize': fig_size, 'savefig.dpi': 600,
'axes.linewidth': 1.3, 'ytick.major.size': 4, # major tick size in points
'xtick.major.size': 4, 'axes.grid': False, 'axes.spines.top': False, 'axes.spines.right': False, 'ytick.direction':'in',
'xtick.direction': 'in',
# major tick size in points
# 'edgecolor' : None
# 'xtick.major.size' : 2,
# 'ytick.major.size' : 2,
}
rcParams.update(params) # update parameters
fig = plt.figure()
# Define sub-panel grid
# what='NAc mShell Dopamine release (dLight 1.3b)'
what='NAc mShell Serotonin release (grab-5-HT3.0)'
plt.figtext(0.5, 0.98, f"{what} in response to DCN-VTA opto stimulation (chrimson, 640nm, fiber on Raphe)", ha="center", va="center",
clip_on=False, color='black', size=15)
animalIds = np.unique(aniDic['animal'])
# gentoypeList = np.unique(aniDic['genotype'])
animal_number = len(animalIds)
print(animalIds)
#first goal is to plot the signal and the trigger for each stim of each animal 1Hz-1-pulse-1mW
#get the list of phases:
phases = np.unique(aniDic['phase'])
phaseNb = len(phases)
gs = gridspec.GridSpec(len(phases), 3, width_ratios=[4,4,1])
gs.update(wspace=0.2, hspace=0.3)
plt.subplots_adjust(left=0.08, right=0.96, top=0.95, bottom=0.05)
AUCArr = np.zeros((len(phases), 6))
peakRespArr = np.zeros((len(phases), 6))
riseTimeArr = np.zeros((len(phases), 6))
allAnimalSignalArray = np.zeros((len(phases), 6,19000))
triggerArray=np.zeros((len(phases), 6,19000))
timeArray=np.zeros((len(phases), 6,19000))
for p, phase in enumerate(phases):
# if p==0:
#plot 1Hz response
grid=int(animal_number/3)+1
phaseData = aniDic[aniDic['phase'] == phase].reset_index(drop=True)
phaseAnimalIds = np.unique(phaseData['animal'])
ax0 = plt.subplot(gs[p,0])
ax0b = ax0.twinx()
# ax1 = plt.subplot(gs[p, 1])
# ax1b = ax1.twinx()
ax0.text(0.2, 0.99, f'{phase}', fontsize=9, color='black', ha='center', va='center', transform=ax0.transAxes)
# pdb.set_trace()
for a, animal in enumerate(phaseAnimalIds):
# ax0 = plt.subplot(gssub[a])
# ax0b = ax0.twinx()
# ax1 = plt.subplot(gssub[1])
# ax2 = plt.subplot(gssub[2])
signal = gaussian_filter1d(phaseData['Signal'][a], 10)
dFF=gaussian_filter1d(phaseData['dFF'][a], 10)
ref_signal = gaussian_filter1d(phaseData['refSignal'][a], 5)
# pdb.set_trace()
# Extract data from the dictionary
time = phaseData['Time'][a]
# signal = aniDic['Signal']
# ref_signal = aniDic['refSignal']
# dFF = aniDic['dFF']
trigger = phaseData['Trigger'][a]
#interpolate trigger to have same length as signal
triggerTime = np.linspace(0, len(trigger), len(signal))
triggerInterp = interp1d(triggerTime, trigger, kind='nearest')
trigger = triggerInterp(np.arange(len(signal)))
signalArray=[signal, dFF]
nameArray=['rawSignal', 'dFF']
color=['orange', 'green']
# gssub = gridspec.GridSpecFromSubplotSpec(animal_number, 1, subplot_spec=gs[a], hspace=0.4,
# wspace=0.4)
#detect the triggers
triggerStart = np.where(trigger == 1)[0]
triggerStartDiff = np.diff(triggerStart)
triggerNb=np.count_nonzero(triggerStartDiff>500)+1
if triggerNb>1:
trigger1=[triggerStart[0],(triggerStart[np.where(triggerStartDiff>500)[0]][0])]
trigger2=[triggerStart[np.where(triggerStartDiff>100)[0]+1],triggerStart[-1]]
trigger1StartTimes=time[trigger1[0]]
trigger2StartTimes=time[trigger2[0]]
# pdb.set_trace()
# pdb.set_trace()
# ax0b.plot(triggerTime, trigger, label=nameArray[s] + animal, color='red', alpha=0.5)
# plt.show()
timeFilter=(time>=trigger1StartTimes-4) & (time<=trigger1StartTimes+12)
# pdb.set_trace()
signalFiltered=dFF[timeFilter]
timeFiltered=time[timeFilter]
triggerFiltered=trigger[timeFilter]
timeCentered=timeFiltered-trigger1StartTimes
# pdb.set_trace()
triggerArray[p, a, :] = triggerFiltered[:19000]
timeArray[p, a, :] = timeCentered[:19000]
allAnimalSignalArray[p, a, :] = signalFiltered[:19000]
ax0.axvline(0, ls='--', color='grey', lw=1, alpha=0.5)
ax0.axhline(0, ls='--', color='grey', lw=0.5, alpha=0.1)
if p==0 and a==0:
ax0.text(0.7, 0.99, f'n = {animal_number} WT mice', fontsize=10, color='black', ha='center', va='center',
transform=ax0.transAxes)
if a==0:
ax0b.plot(timeCentered, triggerFiltered,
color='C6', alpha=0.3, lw=0)
ax0b.fill_between(timeCentered, 0, triggerFiltered, where=(triggerFiltered > 0.5), color='C6',
alpha=0.2)
#
# ax0b.set_ylim(0.1, 1)
if gen==True:
if phaseData['genotype'][a]=='WT':
ax0.plot(timeCentered, signalFiltered, label=('WT' if a==0 else ''), color=f'C{p}', alpha=0.5)
else:
ax0.plot(timeCentered, signalFiltered, label=('HZ' if a==1 else ''), color=f'black', alpha=0.6)
else:
ax0.plot(timeCentered, signalFiltered, label=('1 mouse' if a == 0 else ''), color=f'C{p}', alpha=0.5, lw=1)
else:
continue
dt = np.diff(timeCentered)[0]
newTimeFilter=(timeCentered>=0) & (timeCentered<=5)
phases_AUC = abs(np.trapz(signalFiltered[newTimeFilter], dx=dt))
phases_picResp = np.max(signalFiltered[newTimeFilter])
indices = np.where(signalFiltered[newTimeFilter] > 0.5 * phases_picResp)[0]
if indices.size > 0:
riseTime = indices[0] * dt
else:
riseTime = np.nan # Or handle this case as needed (e.g., set to 0, np.nan, etc.)
AUCArr[p, a] = phases_AUC
peakRespArr[p, a] = phases_picResp
riseTimeArr[p, a] = riseTime
# ax0b.fill_between(timeCenteredTrigger, triggerFiltered, 0, color='red', alpha=0.5)
# ax0.plot(time, dFF, label='ΔF/F', color='green')
# ax0.set_title('Delta F/F')
# ax0.set_ylabel('ΔF/F')
print('now processing', animal, 'phase:', phase, '...')
# ax0.plot(triggerTime, trigger, label='Trigger', color='red', alpha=0.5)
ax0.set_ylim(-3,5)
# ax0b.set_ylim(-3, 5)
ax0.set_xlim(-2,1)
ax0b.set_xlim(-2, 1)
ax0.set_xlabel('Time (s)')
ax0.set_ylabel('Trigger State')
# ax0.yaxis.set_major_locator(MultipleLocator(1))
# ax0.xaxis.set_major_locator(MultipleLocator(1))
self.layoutOfPanel(ax0b, xLabel='', yLabel='',
xyInvisible=[True, True], Leg=[1, 9])
if p!=phaseNb-1:
self.layoutOfPanel(ax0, xLabel='time (s)', yLabel='dFF' , xyInvisible=[True, False], Leg=[1, 9])
else:
self.layoutOfPanel(ax0, xLabel='time (s)', yLabel='dFF', xyInvisible=[False, False], Leg=[1, 9])
# plt.show()
animalAverageSignal=np.mean(allAnimalSignalArray, axis=1)
# pdb.set_trace()
animalAverageTrigger=np.mean(triggerArray, axis=1)
animalAverageTime=np.mean(timeArray, axis=1)
animalAverageSignal_sem = stats.sem(allAnimalSignalArray, axis=1)
averageAUC = np.mean(AUCArr, axis=1)
averagePeakResp = np.mean(peakRespArr, axis=1)
averageRiseTime = np.mean(riseTimeArr, axis=1)
semAUC = stats.sem(AUCArr, axis=1)
semPeakResp = stats.sem(peakRespArr, axis=1)
semRiseTime = stats.sem(riseTimeArr, axis=1)
for p, phase in enumerate(phases):
ax1 = plt.subplot(gs[p, 1])
ax1b = ax1.twinx()
ax1.plot(animalAverageTime[p], animalAverageSignal[p], label='avg', color=f'C{p}', alpha=1)
ax1.axvline(0, ls='--', color='grey', lw=1, alpha=0.7)
ax1.fill_between(animalAverageTime[p], animalAverageSignal[p]-animalAverageSignal_sem[p], animalAverageSignal[p]+animalAverageSignal_sem[p], label='', color=f'C{p}', alpha=0.2)
ax1.text(0.2, 0.99, f'{phase}', fontsize=9, color='black', ha='center', va='center', transform=ax1.transAxes)
ax1b.plot(animalAverageTime[p], triggerArray[p,0,:], color='C6', alpha=0.2, lw=0)
ax1b.fill_between(animalAverageTime[p], 0, abs(triggerArray[p,0,:]), where=(abs(triggerArray[p,0,:]) ==1), color='C6',
alpha=0.2)
# ax1.set_ylim(-1.5, 2)
# # ax1b.set_ylim(0.1, 1)
ax1.set_xlim(-2, 1)
# ax1b.set_xlim(-4, 6)
# ax1.yaxis.set_major_locator(MultipleLocator(1))
# ax1.xaxis.set_major_locator(MultipleLocator(1))
# plt.show()
self.layoutOfPanel(ax1b, xLabel='', yLabel='',
xyInvisible=[True, True], Leg=[1, 9])
if p != phaseNb - 1:
self.layoutOfPanel(ax1, xLabel='time (s)', yLabel='mean dFF', xyInvisible=[True, False], Leg=[1, 9])
else:
self.layoutOfPanel(ax1, xLabel='time (s)', yLabel='mean dFF', xyInvisible=[False, False], Leg=[1, 9])
gssub = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=gs[0:len(phases),2], hspace=0.4,
wspace=0.4)
signalParams= [averageAUC, averagePeakResp, averageRiseTime]
signalParams_sem = [semAUC, semPeakResp, semRiseTime]
signalNames=['AUC', 'Peak response', 'Rise time']
for s in range(3):
ax2 = plt.subplot(gssub[s])
for p, phase in enumerate(phases):
ax2.bar(phase, signalParams[s][p], yerr=signalParams_sem[s][p], color=f'C{p}', alpha=0.5)
# ax2.errorbar(phase, signalParams[s][p], yerr=signalParams_sem[s][p], fmt='o', color=f'C{p}', label=phase)
# ax2.scatter(np.repeat(phases, phaseNb), sal_Y, edgecolor='black', color='white')
self.layoutOfPanel(ax2, xLabel='', yLabel=signalNames[s],
xyInvisible=([True, False] if s == 0 else [True, False]))
if s == 0:
ax2.get_xaxis().set_visible(False)
plt.setp(ax2.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor", fontsize=12)
# if s != 2:
# self.layoutOfPanel(ax2, xLabel=phases, yLabel=signalNames[s], xyInvisible=[True, False], Leg=[1, 9])
#
# else:
# self.layoutOfPanel(ax2, xLabel=phases, yLabel=signalNames[s], xyInvisible=[False, False], Leg=[1, 9])
# pdb.set_trace()
# self.layoutOfPanel(ax0, xLabel='time (s)', yLabel=nameArray[s] + animal+ gentoypeList[s], xyInvisible=[False, False], Leg=[1, 9])
# self.layoutOfPanel(ax2, xLabel='', yLabel='trigger', xyInvisible=[True, False], Leg=[1, 9])
fname = experiment
# plt.savefig(fname + '.png')
# plt.show()
plt.savefig(self.figureDirectory + '/' + fname +'/'+trial_list_str+'new_prepro'+ '.png')
plt.savefig(self.figureDirectory + '/' + fname +'/'+trial_list_str+ 'new_prepro'+'.pdf')
plt.show()
def plot_fiber_photometry_with_control_V1(self, aniDic, plan, experiment, trial_list_str, gen, array_length=600):
"""
Plot fiber photometry data over time including signal, reference signal, delta F/F, and trigger events.
Parameters:
- aniDic: Dictionary containing 'Signal', 'RefSignal', 'Time', 'Trigger', 'dFF', 'animal', 'phase', and 'condition' keys.
- array_length: Length of arrays to initialize (default is 600).
"""
from matplotlib.lines import Line2D
import matplotlib.colors as mcolors
import colorsys
# Function to adjust color brightness
def adjust_color_lightness(color, amount=0.5):
try:
c = mcolors.cnames[color]
except:
c = color
c = mcolors.to_rgb(c)
c = colorsys.rgb_to_hls(*c)
new_c = colorsys.hls_to_rgb(c[0], max(0, min(1, amount * c[1])), c[2])
return new_c
# Figure parameters
fig_width, fig_height = 5, 5
fig_size = [fig_width, fig_height]
params = {
'axes.labelsize': 12, 'axes.titlesize': 12, 'font.size': 8, 'xtick.labelsize': 12,
'ytick.labelsize': 8, 'figure.figsize': fig_size, 'savefig.dpi': 600,
'axes.linewidth': 1.3, 'ytick.major.size': 4, 'xtick.major.size': 4,
'axes.grid': False, 'axes.spines.top': False, 'axes.spines.right': False,
'ytick.direction': 'in', 'xtick.direction': 'in'
}
rcParams.update(params)
fig = plt.figure()
plt.figtext(0.5, 0.95,
"NAc Dopamine release (dLight 1.3b) \n in response to DCN-VTA stimulation (Chrimson in DCN, 640nm, fiber on VTA)",
ha="center", va="center", clip_on=False, color='black', size=8)
# Get unique animals and phases
animalIds = np.unique(aniDic['animal'])
phases = np.unique(aniDic['phase'])
phaseNb = len(phases)
# The two conditions are TdTomato and Chrimson and they are stored in aniDic['condition']
# Get the id of the control animal
controlId = np.unique(aniDic['animal'][aniDic['condition'] == 'dLight-NAc-stim-VTA-DCN-TdTomato'])
# Get id of Chrimson animal
chrimsonId = np.unique(aniDic['animal'][aniDic['condition'] == 'dLight-NAc-stim-VTA-DCN-Chrimson'])
num_controls = len(controlId)
num_chrimson = len(chrimsonId)
# pdb.set_trace()
gs = gridspec.GridSpec(1, 2, width_ratios=[2, 0.5])
gs.update(wspace=0.3, hspace=0.3)
plt.subplots_adjust(left=0.15, right=0.95, top=0.85, bottom=0.2)
# Initialize arrays for control group
AUCArr_control = np.zeros((len(phases), num_controls))
allAnimalSignalArray_control = np.zeros((len(phases), num_controls, array_length))
timeArray_control = np.zeros((len(phases), num_controls, array_length))
# Initialize arrays for chrimson group
AUCArr_chrimson = np.zeros((len(phases), num_chrimson))
allAnimalSignalArray_chrimson = np.zeros((len(phases), num_chrimson, array_length))
timeArray_chrimson = np.zeros((len(phases), num_chrimson, array_length))
for p, phase in enumerate(phases):
phaseData = aniDic[aniDic['phase'] == phase].reset_index(drop=True)
# Separate the data into control and chrimson
phaseData_control = phaseData[phaseData['animal'].isin(controlId)].reset_index(drop=True)
phaseData_chrimson = phaseData[phaseData['animal'].isin(chrimsonId)].reset_index(drop=True)
# Process control group
for c, animal in enumerate(phaseData_control['animal']):
signal = gaussian_filter1d(phaseData_control['Signal'][c], 10)
dFF = gaussian_filter1d(phaseData_control['dFF'][c], 10)
time = phaseData_control['Time'][c]
trigger = phaseData_control['Trigger'][c]
# Interpolate trigger to match signal length
triggerTime = np.linspace(0, len(trigger), len(signal))
triggerInterp = interp1d(triggerTime, trigger, kind='nearest')
trigger = triggerInterp(np.arange(len(signal)))
# Detect triggers
triggerStart = np.where(trigger > 0.5)[0]
# Get the time of the first trigger
try:
trigger1StartTimes = time[triggerStart[0]]
except:
print('Error in trigger detection for control animal', animal)
continue # Skip this animal
# Select the time window
timeFilter = (time >= trigger1StartTimes - 5) & (time <= trigger1StartTimes + 5)
signalFiltered = dFF[timeFilter]
timeFiltered = time[timeFilter]
timeCentered = timeFiltered - trigger1StartTimes
# Store the data
allAnimalSignalArray_control[p, c, :] = signalFiltered[:array_length]
timeArray_control[p, c, :] = timeCentered[:array_length]
# Calculate AUC
dt = np.diff(timeCentered)[0]
newTimeFilter = (timeCentered >= 0) & (timeCentered <= 1)
phases_AUC = abs(np.trapz(signalFiltered[newTimeFilter], dx=dt))
AUCArr_control[p, c] = phases_AUC
print('Processing control animal', animal, 'phase:', phase)
# Process chrimson group
for c, animal in enumerate(phaseData_chrimson['animal']):
signal = gaussian_filter1d(phaseData_chrimson['Signal'][c], 3)
dFF = gaussian_filter1d(phaseData_chrimson['dFF'][c], 3)
time = phaseData_chrimson['Time'][c]
trigger = phaseData_chrimson['Trigger'][c]
# Interpolate trigger to match signal length
triggerTime = np.linspace(0, len(trigger), len(signal))
triggerInterp = interp1d(triggerTime, trigger, kind='nearest')
trigger = triggerInterp(np.arange(len(signal)))
# Detect triggers
triggerStart = np.where(trigger > 0.5)[0]
# Get the time of the first trigger
try:
trigger1StartTimes = time[triggerStart[0]]
except:
print('Error in trigger detection for chrimson animal', animal)
continue # Skip this animal
# Select the time window
timeFilter = (time >= trigger1StartTimes - 5) & (time <= trigger1StartTimes + 5)
signalFiltered = dFF[timeFilter]
timeFiltered = time[timeFilter]
timeCentered = timeFiltered - trigger1StartTimes
# Store the data
allAnimalSignalArray_chrimson[p, c, :] = signalFiltered[:array_length]
timeArray_chrimson[p, c, :] = timeCentered[:array_length]
# Calculate AUC
dt = np.diff(timeCentered)[0]
newTimeFilter = (timeCentered >= 0) & (timeCentered <= 1)
phases_AUC = abs(np.trapz(signalFiltered[newTimeFilter], dx=dt))
AUCArr_chrimson[p, c] = phases_AUC
print('Processing chrimson animal', animal, 'phase:', phase)
# Averages after processing all phases
animalAverageSignal_control = np.mean(allAnimalSignalArray_control, axis=1)
animalAverageSignal_sem_control = stats.sem(allAnimalSignalArray_control, axis=1)
animalAverageTime_control = np.mean(timeArray_control, axis=1)
animalAverageSignal_chrimson = np.mean(allAnimalSignalArray_chrimson, axis=1)
animalAverageSignal_sem_chrimson = stats.sem(allAnimalSignalArray_chrimson, axis=1)
animalAverageTime_chrimson = np.mean(timeArray_chrimson, axis=1)
# Generate shades of colors for each phase
control_colors = [adjust_color_lightness('C7', amount=0.7 + 0.3 * i / (len(phases) - 1)) for i in
range(len(phases))]
chrimson_colors = [adjust_color_lightness('C2', amount=0.7 + 0.3 * i / (len(phases) - 1)) for i in
range(len(phases))]
alphas=np.arange(0.2, 1, 0.8/len(phases))
# Plot average response for each phase on a single plot
ax0 = plt.subplot(gs[0, 0])
for p, phase in enumerate(phases):
# Plot control average response
ax0.plot(animalAverageTime_control[p], animalAverageSignal_control[p],
color=control_colors[p], label=f'TdTomato - {phase}', alpha=alphas[p])
ax0.fill_between(animalAverageTime_control[p],
animalAverageSignal_control[p] - animalAverageSignal_sem_control[p],
animalAverageSignal_control[p] + animalAverageSignal_sem_control[p],
color=control_colors[p], alpha=0.2)
# Plot chrimson average response
ax0.plot(animalAverageTime_chrimson[p], animalAverageSignal_chrimson[p],
color=chrimson_colors[p], label=f'Chrimson - {phase}', alpha=alphas[p])
ax0.fill_between(animalAverageTime_chrimson[p],
animalAverageSignal_chrimson[p] - animalAverageSignal_sem_chrimson[p],
animalAverageSignal_chrimson[p] + animalAverageSignal_sem_chrimson[p],
color=chrimson_colors[p], alpha=0.2)
ax0.fill_betweenx([7.8,8],0,1, alpha=0.1, color='C3')
# Plot settings
ax0.axvline(0, ls='--', color='grey', lw=1, alpha=0.5)
ax0.axhline(0, ls='--', color='grey', lw=0.5, alpha=0.1)
ax0.set_ylim(-4, 8)
ax0.set_xlim(-5, 5)
ax0.set_xlabel('Time (s)')
ax0.set_ylabel('dF/F - Zscore')
ax0.text(-4, 7.5, f'n={num_controls} TdTomato \nn={num_chrimson} Chrimson', ha='left', fontsize=6)
ax0.legend(loc='upper right', fontsize=5, ncol=1, frameon=False)
# Plot bar graph with scatter plots and Mann-Whitney U-test p-value for 50Hz condition
ax1 = plt.subplot(gs[0, 1])
# Find the index of the 50Hz condition
try:
phase_50Hz_index = [i for i, phase in enumerate(phases) if '50Hz' in phase][0]
except IndexError:
print('50Hz condition not found in phases')
return
# Extract data for the 50Hz condition
data_control_50Hz = AUCArr_control[phase_50Hz_index, :]
data_chrimson_50Hz = AUCArr_chrimson[phase_50Hz_index, :]
# Compute means and SEMs
mean_control = np.mean(data_control_50Hz)
sem_control = stats.sem(data_control_50Hz)
mean_chrimson = np.mean(data_chrimson_50Hz)
sem_chrimson = stats.sem(data_chrimson_50Hz)
# Positions
bar_width = 0.01
positions = np.array([0, 0.011]) # Positions for control and chrimson
# Plot the bars
ax1.bar(positions[0], mean_control, bar_width, yerr=sem_control, label='TdTomato', color='C7', alpha=0.5)
ax1.bar(positions[1], mean_chrimson, bar_width, yerr=sem_chrimson, label='Chrimson', color='C2', alpha=0.5)
# Plot individual data points
ax1.scatter(np.repeat(positions[0], len(data_control_50Hz)), data_control_50Hz,
color='C7', edgecolor='black', alpha=0.2, s=20)
ax1.scatter(np.repeat(positions[1], len(data_chrimson_50Hz)), data_chrimson_50Hz,
color='C2', edgecolor='black', alpha=0.2, s=20)
# Perform Mann-Whitney U-test and annotate p-value
stat, p_value = stats.mannwhitneyu(data_control_50Hz, data_chrimson_50Hz)
# Place the p-value above the bars
max_height = max(mean_control + sem_control, mean_chrimson + sem_chrimson)
ax1.text(0.005, max_height + max_height * 0.3, f'p = {p_value:.3f}', ha='center')
#remove spline
ax1.spines['bottom'].set_visible(False)
# Plot settings
ax1.set_xticks(positions)
ax1.set_xticklabels(['Control', 'Chrimson'], rotation=45, ha="right")
ax1.set_ylabel('AUC (0-1s)')
ax1.set_title('50Hz Stimulation', fontsize=8)
#add the test used as text on bottom right of the figure not subplot
plt.figtext(0.95, 0.02, 'Mann-Whitney U-test', ha='right', fontsize=8, color='black', va='center')
# ax1.text(0.5, 0.01, 'Mann-Whitney U-test', ha='center', fontsize=6, color='black', va='center')
# Adjust layout and save the figure
plt.tight_layout()
fname = experiment
plt.savefig(f'{self.figureDirectory}/{fname}/{trial_list_str}_new_prepro.png')
plt.savefig(f'{self.figureDirectory}/{fname}/{trial_list_str}_new_prepro.pdf')
plt.show()
def plot_fiber_photometry_without_control_V1(self, aniDic, plan, experiment, region, gen, array_length=2400):
"""
Plot fiber photometry data over time including signal, reference signal, delta F/F, and trigger events.
Parameters:
- aniDic: Dictionary containing 'Signal', 'RefSignal', 'Time', 'Trigger', 'dFF', 'animal', 'phase', and 'condition' keys.
- array_length: Length of arrays to initialize (default is 600).
"""
from matplotlib.lines import Line2D
import matplotlib.colors as mcolors
import colorsys
# Function to adjust color brightness
def adjust_color_lightness(color, amount=0.5):
try:
c = mcolors.cnames[color]
except:
c = color
c = mcolors.to_rgb(c)
c = colorsys.rgb_to_hls(*c)
new_c = colorsys.hls_to_rgb(c[0], max(0, min(1, amount * c[1])), c[2])
return new_c
# Figure parameters
fig_width, fig_height = 7, 7
fig_size = [fig_width, fig_height]
params = {
'axes.labelsize': 7, 'axes.titlesize': 7, 'font.size': 7, 'xtick.labelsize': 6,
'ytick.labelsize': 6, 'figure.figsize': fig_size, 'savefig.dpi': 600,
'axes.linewidth': 1.3, 'ytick.major.size': 2, 'xtick.major.size': 2,
'axes.grid': False, 'axes.spines.top': False, 'axes.spines.right': False,
'ytick.direction': 'in', 'xtick.direction': 'in'
}
rcParams.update(params)
fig = plt.figure()
# Get unique animals and phases
animalIds = np.unique(aniDic['animal'])
phases = np.unique(aniDic['phase'])
phaseNb = len(phases)
# The two conditions are TdTomato and Chrimson and they are stored in aniDic['condition']
# Get the id of the control animal
if region=='DR':
condition='5HT3-NAc-stim-DR-DCN-Chrimson'
elif region=='MR':
condition='5HT3-NAc-stim-MR-DCN-Chrimson'
if condition=='5HT3-NAc-stim-DR-DCN-Chrimson':
plt.figtext(0.5, 0.95,
"NAc mShell 5HT release (grab-5HT3.0) \n in response to DCN-DR stimulation (Chrimson in DCN, 640nm, fiber on DR)",
ha="center", va="center", clip_on=False, color='black', size=8)
elif condition=='5HT3-NAc-stim-MR-DCN-Chrimson':
plt.figtext(0.5, 0.95,
"NAc mShell 5HT release (grab-5HT3.0) \n in response to DCN-MR stimulation (Chrimson in DCN, 640nm, fiber on MR)",
ha="center", va="center", clip_on=False, color='black', size=8)
# controlId = np.unique(aniDic['animal'][aniDic['condition'] == '5HT3-NAc-stim-DR-DCN-Chrimson'])
# Get id of Chrimson animal
chrimsonId = np.unique(aniDic['animal'][aniDic['condition'] == condition])
# num_controls = len(controlId)
num_chrimson = len(chrimsonId)
gs = gridspec.GridSpec(3, 2, width_ratios=[2,1])
gs.update(wspace=0.6, hspace=0.5)
plt.subplots_adjust(left=0.15, right=0.95, top=0.85, bottom=0.1)
# Initialize arrays for chrimson group
AUCArr_chrimson = np.zeros((len(phases), num_chrimson))
allAnimalSignalArray_chrimson = np.zeros((len(phases), num_chrimson, array_length))
timeArray_chrimson = np.zeros((len(phases), num_chrimson, array_length))
allAnimalVelocityArray_chrimson = np.zeros((len(phases), num_chrimson, array_length))
triggerArray = np.zeros((len(phases), num_chrimson, array_length))
# pdb.set_trace()
phases=['01Hz-01mW', '01Hz-10mW', '01Hz-20mW-10x', '01Hz-20mW',
'50Hz-10mW', '50Hz-20mW-10x']
# phases = ['01Hz-20mW','50Hz-10mW',
# ]
phases = ['01Hz-01mW', '01Hz-10mW',
'50Hz-10mW']
for p, phase in enumerate(phases):
phaseData = aniDic[aniDic['phase'] == phase].reset_index(drop=True)
# Separate the data into control and chrimson
# phaseData_control = phaseData[phaseData['animal'].isin(controlId)].reset_index(drop=True)
phaseData_chrimson = phaseData[phaseData['animal'].isin(chrimsonId)].reset_index(drop=True)
# Process chrimson group
# pdb.set_trace()
ax0 = plt.subplot(gs[p, 0])
plotSingle=True
plotVelocity=True
for c, animal in enumerate(np.unique(phaseData_chrimson['animal'])):
signal = gaussian_filter1d(phaseData_chrimson['Signal'][c], 2)
dFF = gaussian_filter1d(phaseData_chrimson['dFF'][c], 2)
time = phaseData_chrimson['Time'][c]
trigger = phaseData_chrimson['Trigger'][c]
if plotVelocity:
velocity = phaseData_chrimson['velocity'][c]
velocity_time=phaseData_chrimson['velocity_time'][c]
zscore_velocity=abs((velocity-np.mean(velocity))/np.std(velocity))
# interpolate velocity to match signal length
# Detect triggers
# interpolateTrigger = True
# if interpolateTrigger:
# triggerTime = np.linspace(0, len(trigger), len(signal))
# triggerInterp = interp1d(triggerTime, trigger, kind='nearest')
# trigger = triggerInterp(np.arange(len(signal)))
triggerStart = np.where(trigger > 0.5)[0]
# Get the time of the first trigger
try:
trigger1StartTimes = time[triggerStart[0]]
except:
print('Error in trigger detection for chrimson animal', animal)
continue # Skip this animal
# Select the time window
timeFilter = (time >= trigger1StartTimes - 20) & (time <= trigger1StartTimes + 20)
timeFilterbefore = (time >= trigger1StartTimes - 20) & (time <= trigger1StartTimes)
signalFiltered = dFF[timeFilter]
timeFiltered = time[timeFilter]
timeCentered = timeFiltered - trigger1StartTimes
triggerFiltered = trigger[timeFilter]
if plotVelocity:
velocityTimeFilter = (velocity_time >= trigger1StartTimes - 20) & (velocity_time <= trigger1StartTimes + 20)
velocityFiltered = zscore_velocity[velocityTimeFilter]
allAnimalVelocityArray_chrimson[p, c, :] = velocityFiltered[:array_length]
# Store the data
allAnimalSignalArray_chrimson[p, c, :] = signalFiltered[:array_length]
timeArray_chrimson[p, c, :] = timeCentered[:array_length]
triggerArray[p, c, :] = triggerFiltered[:array_length]
# Calculate AUC
dt = np.diff(timeCentered)[0]
AUCrange=1
newTimeFilter = (timeCentered >= 0) & (timeCentered <= AUCrange)
phases_AUC = np.trapz(signalFiltered[newTimeFilter], dx=dt)
AUCArr_chrimson[p, c] = phases_AUC
print('Processing chrimson animal', animal, 'phase:', phase)
if plotSingle:
ax0.plot(timeCentered, signalFiltered, color=f'C{p}', label=(f'Chrimson - {animal}' if c>10 else ''), alpha=0.2, lw=0.5)
#plot only the animals where average signal is <0 in the time window [0,1]
# if np.mean(signalFiltered[timeCentered>=0])<0:
# ax0.plot(timeCentered, signalFiltered, color=f'C{c}', label=(f'Chrimson - {animal}' if c>0 else ''), alpha=0.5, lw=0.5)
# # Averages after processing all phases
# animalAverageSignal_control = np.mean(allAnimalSignalArray_control, axis=1)
# animalAverageSignal_sem_control = stats.sem(allAnimalSignalArray_control, axis=1)
# animalAverageTime_control = np.mean(timeArray_control, axis=1)
animalAverageSignal_chrimson = np.mean(allAnimalSignalArray_chrimson, axis=1)
animalAverageSignal_sem_chrimson = stats.sem(allAnimalSignalArray_chrimson, axis=1)
animalAverageTime_chrimson = np.mean(timeArray_chrimson, axis=1)
animalAverageTrigger_chrimson = np.mean(triggerArray, axis=1)
if plotVelocity:
animalAverageVelocity_chrimson = np.mean(allAnimalVelocityArray_chrimson, axis=1)
velocity_sem_chrimson = stats.sem(allAnimalVelocityArray_chrimson, axis=1)
# Generate shades of colors for each phase
# control_colors = [adjust_color_lightness('C7', amount=0.7 + 0.3 * i / (len(phases) - 1)) for i in
# range(len(phases))]
if len(phases)==1:
chrimson_colors = ['C2']
else:
chrimson_colors = [adjust_color_lightness('C1', amount=0.7 + 0.3 * i / (len(phases) - 1)) for i in
range(len(phases))]
chrimson_colors=['C0','C1', 'C2', 'C3', 'C4']
alphas=np.arange(0.2, 1, 0.8/len(phases))
# Plot average response for each phase on a single plot
phases = ['01Hz-01mW', '01Hz-10mW',
'50Hz-10mW']
for p, phase in enumerate(phases):
ax0 = plt.subplot(gs[p, 0])
# Plot chrimson average response
if phase in ['01Hz-01mW', '01Hz-10mW',
'50Hz-10mW']:
# if phase in [ '01Hz-01mW','01Hz-20mW',
# '50Hz-10mW']:
ax0.plot(animalAverageTime_chrimson[p], animalAverageSignal_chrimson[p],
color=chrimson_colors[p], label=f'Chrimson - {phase}',lw=0.5)
ax0.fill_between(animalAverageTime_chrimson[p],
animalAverageSignal_chrimson[p] - animalAverageSignal_sem_chrimson[p],
animalAverageSignal_chrimson[p] + animalAverageSignal_sem_chrimson[p],
color=chrimson_colors[p], alpha=0.2)
ax0.fill_betweenx([2.3,2.4],0,1, alpha=0.1, color='C3')
# ax0.plot(animalAverageTime_chrimson[p], animalAverageTrigger_chrimson[p],
# color='C3', alpha=0.3)
#plot average velocity as twinx
if plotVelocity:
ax0b = ax0.twinx()
ax0b.plot(animalAverageTime_chrimson[p], animalAverageVelocity_chrimson[p], color='k', lw=0.5)
ax0b.fill_between(animalAverageTime_chrimson[p],
animalAverageVelocity_chrimson[p] - velocity_sem_chrimson[p],
animalAverageVelocity_chrimson[p] + velocity_sem_chrimson[p],
color='k', alpha=0.2)
#ax0b ylabel = 'Velocity (cm/s)'
ax0b.set_ylabel('Velocity Z-score', fontsize=5)
#remove x axis ans spine if p!=3
if p!=2:
ax0.get_xaxis().set_visible(False)
ax0.spines['bottom'].set_visible(False)
ax0.axvline(0, ls='--', color='grey', lw=0.5, alpha=0.5)
ax0.axhline(0, ls='--', color='grey', lw=0.5, alpha=0.5)
if condition=='5HT3-NAc-stim-DR-DCN-Chrimson':
ax0.set_ylim(-3, 4)
elif condition=='5HT3-NAc-stim-MR-DCN-Chrimson':
ax0.set_ylim(-2, 3)
ax0.set_xlim(-5, 5)
ax0.set_xlabel('Time (s)')
ax0.set_ylabel('dF/F - Z-score')
if p==0:
ax0.text(0, 4, f'n={num_chrimson} Chrimson', ha='left', fontsize=5)
ax0.legend(loc='upper right', fontsize=5, ncol=1, frameon=False)
# Create a new subplot for the AUC comparison across all phases
ax1 = plt.subplot(gs[0:2, 1])
ax2= plt.subplot(gs[2:4, 1])
# Prepare data for each phase
bar_width = 0.01
positions = np.arange(len(phases)) * 0.02 # Positions for each phase
colors = ['C' + str(i % 10) for i in range(len(phases))] # Colors for each phase
# Loop over each phase to plot bars and scatter points
for i, phase in enumerate(phases):
data_chrimson_phase = AUCArr_chrimson[i, :] # AUC data for current phase
velocity_chrimson_phase = allAnimalVelocityArray_chrimson[i, :, :] # AUC data for current phase
# Calculate mean and SEM
mean_chrimson = np.mean(data_chrimson_phase)
sem_chrimson = stats.sem(data_chrimson_phase)
# Plot the bar
ax1.bar(positions[i], mean_chrimson, bar_width, yerr=sem_chrimson,
label=phase, color=colors[i], alpha=0.7)
# Plot individual data points
ax1.scatter(np.repeat(positions[i], len(data_chrimson_phase)), data_chrimson_phase,
color=colors[i], edgecolor='black', alpha=0.6, s=20)
# Perform pairwise Mann-Whitney U-tests and annotate significant differences
# for i in range(len(phases)):
# for j in range(i + 1, len(phases)):
# Perform Mann-Whitney U-test between phases i and j
# stat, p_value = stats.mannwhitneyu(AUCArr_chrimson[i, :], AUCArr_chrimson[j, :])
#
# # Annotate significant differences (adjust y position as needed)
# max_height = max(np.mean(AUCArr_chrimson[i, :]) + sem_chrimson,
# np.mean(AUCArr_chrimson[j, :]) + sem_chrimson)
# ax1.text((positions[i] + positions[j]) / 2, max_height + 0.05,
# f'p = {p_value:.3f}' if p_value >= 0.001 else 'p < 0.001',
# ha='center', fontsize=6, color='black')
# Customize plot settings
ax1.set_xticks(positions)
ax1.set_xticklabels(phases, rotation=45, ha="right")
ax1.set_ylabel(f'AUC (0-{AUCrange}s)')
ax1.set_title('AUC')
ax1.spines['bottom'].set_visible(False)
ax1.get_xaxis().set_visible(False)
# Add a legend and show plot
ax1.legend(loc='upper right', fontsize=5, ncol=1, frameon=False)
# Adding comparison of velocity 5s before and after trigger in ax2
velocity_means_before = []
velocity_means_after = []
velocity_sems_before = []
velocity_sems_after = []
for i, phase in enumerate(phases):
velocity_chrimson_phase = allAnimalVelocityArray_chrimson[i, :, :] # Velocity data for current phase
velocity_time_phase = timeArray_chrimson[i, :, :] # Corresponding time data
# Lists to store individual animal velocities before and after for current phase
phase_velocity_before = []
phase_velocity_after = []
for animal_index in range(velocity_chrimson_phase.shape[0]):
# Apply the mask for each animal's velocity and time data separately
before_mask = (velocity_time_phase[animal_index] < 0) & (velocity_time_phase[animal_index] >= -AUCrange)
after_mask = (velocity_time_phase[animal_index] > 0) & (velocity_time_phase[animal_index] <= AUCrange)
# Calculate mean velocity for 5s before and after for this animal
velocity_before = np.mean(velocity_chrimson_phase[animal_index, before_mask])
velocity_after = np.mean(velocity_chrimson_phase[animal_index, after_mask])
# Store the results for this phase and animal
phase_velocity_before.append(velocity_before)
phase_velocity_after.append(velocity_after)
ax2.plot([i - 0.1, i + 0.1], [velocity_before, velocity_after], color=colors[i], alpha=0.5, lw=0.8)
# Calculate mean and SEM across all animals in this phase
velocity_means_before.append(np.mean(phase_velocity_before))
velocity_means_after.append(np.mean(phase_velocity_after))
velocity_sems_before.append(stats.sem(phase_velocity_before))
velocity_sems_after.append(stats.sem(phase_velocity_after))
# Plot the comparison for this phase
# ax2.bar(i - 0.1, velocity_means_before[-1], 0.2, yerr=velocity_sems_before[-1],
# color=colors[i], alpha=0.7, label=f'{phase} - Before')
# ax2.bar(i + 0.1, velocity_means_after[-1], 0.2, yerr=velocity_sems_after[-1],
# color=adjust_color_lightness(colors[i], 1.2), alpha=0.7, label=f'{phase} - After')
# Scatter plot for individual animal values (before and after)
ax2.scatter(np.repeat(i - 0.1, len(phase_velocity_before)), phase_velocity_before,
color=colors[i], edgecolor='black', alpha=0.4, s=15,lw=0.2, label=f'{phase} Individual Before')
ax2.scatter(np.repeat(i + 0.1, len(phase_velocity_after)), phase_velocity_after,
color=adjust_color_lightness(colors[i], 1.2), edgecolor='black', alpha=0.9, s=15,lw=0.2,
label=f'{phase} Individual After')
# Customize ax2 plot settings
ax2.set_xticks(range(len(phases)))
ax2.set_xticklabels(phases, rotation=45, ha="right")
ax2.set_ylabel('Mean Velocity Z-score')
ax2.set_title(f'Velocity ({AUCrange}s Before vs {AUCrange}s After)')
# ax2.legend(loc='upper right', fontsize=5, ncol=1, frameon=False)
plt.tight_layout()
fname = experiment
plt.savefig(f'{self.figureDirectory}/{fname}/{region}_new_prepro.png')
plt.savefig(f'{self.figureDirectory}/{fname}/{region}_new_prepro.pdf')
plt.show()
def plot_fiber_photometry_fluoxetine_exp(self, aniDic, plan,experiment, gen):
"""
Plot fiber photometry data over time including signal, reference signal, delta F/F, and trigger events.
Parameters:
- aniDic: Dictionary containing 'Signal', 'RefSignal', 'Time', 'Trigger', and 'dFF' keys.
"""
# figure generate either a figure with only FL paw or with any desired number of paw
fig_width = 20 # width in inches