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peak2equilibrium_BWData.py
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peak2equilibrium_BWData.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 28 17:32:28 2022
@author: dhruv
"""
import numpy as np
import pandas as pd
import scipy.io
import pynapple as nap
import os, sys
import time
import matplotlib.pyplot as plt
from scipy.stats import kendalltau, pearsonr, wilcoxon, mannwhitneyu
from itertools import combinations
import seaborn as sns
#%%
corrs = []
pvals = []
ratecorrs = []
ratepvals = []
allrates = []
uponset = []
peak_above_mean = []
allPETH = pd.DataFrame()
data_directory = '/media/DataDhruv/Recordings/WatsonBO'
datasets = np.genfromtxt(os.path.join(data_directory,'dataset_DM.list'), delimiter = '\n', dtype = str, comments = '#')
for s in datasets:
print(s)
name = s.split('/')[-1]
path = os.path.join(data_directory, s)
listdir = os.listdir(path)
file = [f for f in listdir if 'spikes' in f]
spikedata = scipy.io.loadmat(os.path.join(path,file[0]))
listdir = os.listdir(path)
file = [f for f in listdir if 'events' in f]
events = scipy.io.loadmat(os.path.join(path,file[0]))
listdir = os.listdir(path)
file = [f for f in listdir if 'states' in f]
states = scipy.io.loadmat(os.path.join(path,file[0]))
listdir = os.listdir(path)
file = [f for f in listdir if 'CellClass' in f]
cellinfo = scipy.io.loadmat(os.path.join(path,file[0]))
#%%
#Load EX cells
ex = cellinfo['CellClass'][0][0][1][0]
pyr = []
for i in range(len(ex)):
if ex[i] == 1:
pyr.append(i)
#Load Spikes (only EX cells)
spks = spikedata['spikes']
spk = {}
for i in range(len(spks[0][0][1][0])):
spk[i] = nap.Ts(spks[0][0][1][0][i])
spikes = nap.TsGroup(spk)
spikes = spikes[pyr]
#Load Sleep States
sleepstate = states['SleepState']
wake_ep = nap.IntervalSet(start = sleepstate[0][0][0][0][0][0][:,0], end = sleepstate[0][0][0][0][0][0][:,1])
nrem_ep = nap.IntervalSet(start = sleepstate[0][0][0][0][0][1][:,0], end = sleepstate[0][0][0][0][0][1][:,1])
rem_ep = nap.IntervalSet(start = sleepstate[0][0][0][0][0][2][:,0], end = sleepstate[0][0][0][0][0][2][:,1])
#Load UP and DOWN states
slowwaves = events['SlowWaves']
up_ep = nap.IntervalSet( start = slowwaves[0][0][2][0][0][0][:,0], end = slowwaves[0][0][2][0][0][0][:,1])
down_ep = nap.IntervalSet( start = slowwaves[0][0][2][0][0][1][:,0], end = slowwaves[0][0][2][0][0][1][:,1])
#%%
# COMPUTE EVENT CROSS CORRS
## Peak firing
### UD
cc = nap.compute_eventcorrelogram(spikes, nap.Tsd(down_ep['start'].values), binsize = 0.005, windowsize = 0.255, ep = up_ep, norm = True)
tmp = pd.DataFrame(cc)
tmp = tmp.rolling(window=8, win_type='gaussian',center=True,min_periods=1).mean(std = 2)
# plt.imshow(cc.T, aspect = 'auto', cmap = 'inferno', origin = 'lower')
#%%
### DU
cc = nap.compute_eventcorrelogram(spikes, nap.Tsd(up_ep['start'].values), binsize = 0.005, windowsize = 0.255, ep = up_ep, norm = True)
tmp = pd.DataFrame(cc)
tmp = tmp.rolling(window=8, win_type='gaussian',center=True,min_periods=1).mean(std = 2)
dd = tmp[0:0.155]
rates = spikes.restrict(up_ep)._metadata['rate'].values
if len(dd.columns) > 0:
indexplot = []
tokeep = []
peaks_keeping = []
rates_keeping = []
for i in range(len(dd.columns)):
a = np.where(dd.iloc[:,i] > 0.5)
if len(a[0]) > 0:
tokeep.append(dd.columns[i])
peaks_keeping.append(dd.iloc[:,i].max())
peak_above_mean.append(dd.iloc[:,i].max())
res = dd.iloc[:,i].index[a]
indexplot.append(res[0])
uponset.append(res[0])
rates_keeping.append(rates[i])
allrates.append(rates[i])
allPETH = pd.concat([allPETH, dd[tokeep]], axis = 1)
corr, p = kendalltau(indexplot, peaks_keeping)
corrs.append(corr)
pvals.append(p)
#%%
# plt.figure()
# plt.rc('font', size = 15)
# plt.title('Peak-mean rate ratio v/s UP onset_' + s)
# plt.scatter(indexplot,peaks_keeping, label = 'R = ' + str((round(corr,2))), color = 'cornflowerblue')
# plt.xlabel('Time from UP onset (s)')
# plt.ylabel('Peak-mean rate ratio')
# plt.legend(loc = 'upper right')
#%%
#Arrange DU by peak-to-mean
# idx = np.argsort(peaks_keeping)
# plt.imshow(dd[dd.columns[idx]].T, aspect = 'auto', cmap = 'inferno',
# extent =[0,150,len(spikes),1],
# origin = 'lower')
#%%
ratecorr, ratep = kendalltau(rates_keeping, indexplot)
ratecorrs.append(ratecorr)
ratepvals.append(ratep)
# plt.figure()
# plt.title('NREM FR v/s UP onset delay_' + s)
# plt.scatter(rates_keeping_ex,indexplot_ex, label = 'R = ' + str((round(ratecorr,2))))
# plt.xlabel('Mean NREM FR')
# plt.ylabel('UP onset delay (ms)')
# plt.legend(loc = 'upper right')
#%%
binsize = 0.005
pooledcorr, pooledp = kendalltau(uponset, peak_above_mean)
(counts,onsetbins,peakbins) = np.histogram2d(uponset,peak_above_mean,bins=[len(np.arange(0,0.155,binsize))+1,len(np.arange(0,0.155,binsize))+1],
range=[[-0.0025,0.1575],[0.5,3.6]])
masked_array = np.ma.masked_where(counts == 0, counts)
cmap = plt.cm.viridis # Can be any colormap that you want after the cm
cmap.set_bad(color='white')
plt.figure()
plt.imshow(masked_array.T, origin='lower', extent = [onsetbins[0],onsetbins[-1],peakbins[0],peakbins[-1]],
aspect='auto', cmap = cmap)
plt.colorbar(ticks = [min(counts.flatten()),max(counts.flatten())])
plt.xlabel('UP onset delay (s)')
plt.ylabel('Peak-mean rate ratio')
plt.gca().set_box_aspect(1)
y_est = np.zeros(len(uponset))
m, b = np.polyfit(uponset, peak_above_mean, 1)
for i in range(len(uponset)):
y_est[i] = m*uponset[i]
plt.plot(uponset, y_est + b, color = 'r')
r1, p1 = kendalltau(allrates,uponset)
plt.figure()
plt.rc('font', size = 15)
plt.title('NREM FR v/s UP onset: PFC pooled data')
sns.kdeplot(x = allrates, y = uponset, color = 'cornflowerblue')
plt.scatter(allrates, uponset, label = 'R = ' + str((round(r1,2))), color = 'cornflowerblue', s = 4)
plt.xlabel('NREM FR (Hz)')
plt.ylabel('UP onset delay (s)')
plt.legend(loc = 'upper right')
plt.legend(loc = 'upper right')
plt.gca().set_box_aspect(1)
#%%
#Arrange DU by peak-to-mean
idx = np.argsort(peak_above_mean)
allPETH.columns = range(allPETH.columns.size)
plt.figure()
plt.imshow(allPETH[allPETH.columns[idx]].T, aspect = 'auto', cmap = 'inferno',
extent =[0,150,allPETH.columns.size,1],
origin = 'lower', vmin = 0, vmax = 2)
plt.colorbar()
plt.plot([i * 1000 for i in uponset],np.arange(1,951,1),'o', color = 'w', markersize = 1)
#%%