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figures.py
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figures.py
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import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import time
import scipy
import h5py
import pandas as pd
import copy
import seaborn as sns
import utils_cabmi as uc
from scipy import stats
from matplotlib import interactive
interactive(True)
def fig1(folder, animal, day, df_e2):
#animal = 'IT2'
#day = '181002'
file_template = "full_{}_{}__data.hdf5"
file_name = os.path.join(folder, animal, file_template.format(animal, day))
folder_proc = os.path.join(folder, 'processed')
f = h5py.File(file_name, 'r')
ens_neur = np.asarray(f['ens_neur'])
e2_neur = np.asarray(df_e2[animal][day])
e1_neur = copy.deepcopy(ens_neur)
online_data = np.asarray(f['online_data'])[:,2:]
dff = np.asarray(f['dff'])
cursor = np.asarray(f['cursor'])
startBMI = np.asarray(f['trial_start'])[0]
f.close()
if np.isnan(np.sum(ens_neur)):
print('Only ' + str(4 - np.sum(np.isnan(ens_neur))) + ' ensemble neurons')
if np.isnan(np.sum(e2_neur)):
print('Only ' + str(2 - np.sum(np.isnan(e2_neur))) + ' e2 neurons')
if np.nansum(e2_neur)>0:
for i in np.arange(len(e2_neur)):
e1_neur[np.where(ens_neur==e2_neur[i])[0]] = np.nan
e1_neur = np.int16(e1_neur[~np.isnan(e1_neur)])
ens_neur = np.int16(ens_neur[~np.isnan(ens_neur)])
e2_neur = np.int16(e2_neur[~np.isnan(e2_neur)])
dff_e1 = np.zeros((2, dff.shape[1]))
dff_e2 = np.zeros((2, dff.shape[1]))
dff_e1[0,:] = uc.sliding_mean(dff[e1_neur[0],:],2)
dff_e1[1,:] = uc.sliding_mean(dff[e1_neur[1],:],2)
dff_e2[0,:] = uc.sliding_mean(dff[e2_neur[0],:],2)
dff_e2[1,:] = uc.sliding_mean(dff[e2_neur[1],:],2)
fig1 = plt.figure(figsize=(16,6))
ax1 = fig1.add_subplot(3, 1, 1)
ax2 = fig1.add_subplot(3, 1, 2)
ax3 = fig1.add_subplot(3, 1, 3)
window = np.arange(8000,10000)
ax1.plot(dff_e1[:,window].T)
ax2.plot(dff_e2[:,window].T)
ax3.plot(np.nansum(dff_e2[:,window],0)-np.nansum(dff_e1[:,window],0))
fig2 = plt.figure(figsize=(16,6))
ax3 = fig2.add_subplot(2, 1, 1)
ax4 = fig2.add_subplot(2, 1, 2)
window = np.arange(14000,14900)
ax3.plot((online_data[window, 2:]-np.nanmean(online_data[:, 2:],0))/np.nanmean(online_data[:, 2:],0))
ax4.plot((online_data[window, :2]-np.nanmean(online_data[:, :2],0))/np.nanmean(online_data[:, :2],0))
def fig2():
folder_main = 'I:/Nuria_data/CaBMI/Layer_project'
out = 'I:/Nuria_data/CaBMI/Layer_project/learning_stats'
file_csv = os.path.join(out, 'learning_stats_summary_bin_1.csv')
file_csv_hpm = os.path.join(out, 'learning_stats_HPM_bin_1.csv')
file_csv_PC = os.path.join(out, 'learning_stats_cumuPC_bin_1.csv')
# file_csv_hpm = os.path.join(out, 'learning_stats_HPM_bin_5.csv')
# file_csv_PC = os.path.join(out, 'learning_stats_PC_bin_5.csv')
to_load_df = os.path.join(folder_main, 'df_all.hdf5')
df = pd.read_hdf(to_load_df)
df_hpm = pd.read_csv(file_csv_hpm)
df_PC = pd.read_csv(file_csv_PC)
df_results = pd.read_csv(file_csv)
bins = np.arange(1,51,1)
days = np.arange(1,16)
#convert df_hpm to matrix same for PC
folder = os.path.join(folder_main, 'processed')
folder_plots = os.path.join(folder_main, 'plots', 'figures')
animals = os.listdir(folder)
hpm_5bin = np.zeros((len(animals), 76)) + np.nan
PC_5bin = np.zeros((len(animals), 76)) + np.nan
hpm_smoo = np.zeros((len(animals), 76)) + np.nan
PC_smoo = np.zeros((len(animals), 76)) + np.nan
hpm_gsmoo = np.zeros((len(animals), 76)) + np.nan
PC_gsmoo = np.zeros((len(animals), 76)) + np.nan
hpm_ses = np.zeros((len(animals), 15)) + np.nan
PC_ses = np.zeros((len(animals), 15)) + np.nan
hpm_g = np.zeros((len(animals), 76)) + np.nan
PC_g = np.zeros((len(animals), 76)) + np.nan
for aa, animal in enumerate(animals):
# per session
aux_hpm = df_results[df_results['animal']==animal].iloc[:,6].values
aux_PC = df_results[df_results['animal']==animal].iloc[:,5].values
if len(aux_hpm)<=hpm_ses.shape[1]:
hpm_ses[aa,:len(aux_hpm)] = aux_hpm
else:
hpm_ses[aa,:] = aux_hpm[:hpm_ses.shape[1]]
if len(aux_PC)<=PC_ses.shape[1]:
PC_ses[aa,:len(aux_PC)] = aux_PC
else:
PC_ses[aa,:] = aux_PC[:PC_ses.shape[1]]
# per timebin
hpm_5bin[aa,:] = np.nanmean(df_hpm[df_hpm['animal']==animal].iloc[:,3:].values,0)
PC_5bin[aa,:] = np.nanmean(df_PC[df_PC['animal']==animal].iloc[:,3:].values,0)
# per timebin smoothed
hpm_smoo[aa,:] = uc.sliding_mean(hpm_5bin[aa,:], window=2)
PC_smoo[aa,:] = uc.sliding_mean(PC_5bin[aa,:], window=2)
# relative % increase
hpm_g[aa,:] = (hpm_5bin[aa,:] - np.nanmean(hpm_5bin[aa,:]))/np.nanmean(hpm_5bin[aa,:])*100 - \
(hpm_5bin[aa,0] - np.nanmean(hpm_5bin[aa,:]))/np.nanmean(hpm_5bin[aa,:])*100
PC_g[aa,:] = (PC_5bin[aa,:] - np.nanmean(PC_5bin[aa,:]))/np.nanmean(PC_5bin[aa,:])*100 - \
(PC_5bin[aa,0] - np.nanmean(PC_5bin[aa,:]))/np.nanmean(PC_5bin[aa,:])*100
hpm_gsmoo[aa,:] = (hpm_smoo[aa,:] - np.nanmean(hpm_smoo[aa,:]))/np.nanmean(hpm_smoo[aa,:])*100 - \
(hpm_smoo[aa,0] - np.nanmean(hpm_smoo[aa,:]))/np.nanmean(hpm_smoo[aa,:])*100
PC_gsmoo[aa,:] = (PC_smoo[aa,:] - np.nanmean(PC_smoo[aa,:]))/np.nanmean(PC_smoo[aa,:])*100 - \
(PC_smoo[aa,0] - np.nanmean(PC_smoo[aa,:]))/np.nanmean(PC_smoo[aa,:])*100
#5 bin
# fig1 = plt.figure(figsize=(8,4))
# ax1 = fig1.add_subplot(1, 2, 1)
# ax2 = fig1.add_subplot(1, 2, 2)
# ax1.plot(np.nanmean(hpm_5bin[:9,:11],0))
# ax1.plot(np.nanmean(hpm_5bin[9:,:11],0))
# ax2.plot(np.nanmean(PC_5bin[:9,:11],0))
# ax2.plot(np.nanmean(PC_5bin[9:,:11],0))
fig1 = plt.figure(figsize=(8,8))
ax1 = fig1.add_subplot(2, 2, 1)
ax2 = fig1.add_subplot(2, 2, 2)
ax3 = fig1.add_subplot(2, 2, 3)
ax4 = fig1.add_subplot(2, 2, 4)
for aa, animal in enumerate(animals):
if animal[:2]=='IT':
ax1.plot(bins, hpm_smoo[aa,:50], 'r', linewidth=0.5, alpha=0.5)
ax3.plot(bins, PC_5bin[aa,:50], 'r', linewidth=0.5, alpha=0.5)
else:
ax2.plot(bins, hpm_smoo[aa,:50], 'b', linewidth=0.5, alpha=0.5)
ax4.plot(bins, PC_5bin[aa,:50], 'b', linewidth=0.5, alpha=0.5)
ax1.plot(bins, np.nanmean(hpm_smoo[:9,:50],0),'r', linewidth=2)
ax2.plot(bins, np.nanmean(hpm_smoo[9:,:50],0),'b', linewidth=2)
ax3.plot(bins, np.nanmean(PC_5bin[:9,:50],0),'r', linewidth=2)
ax4.plot(bins, np.nanmean(PC_5bin[9:,:50],0),'b', linewidth=2)
ax1.set_ylim([0.3,1.6])
ax2.set_ylim([0.3,1.6])
ax3.set_ylim([0.1,0.5])
ax4.set_ylim([0.1,0.5])
ax1.set_ylabel('hpm')
ax2.set_ylabel('hpm')
ax3.set_ylabel('PC')
ax4.set_ylabel('PC')
ax3.set_xlabel('IT')
ax4.set_xlabel('PT')
fig1.tight_layout(pad=0.4, w_pad=1.0, h_pad=1.0)
hpm_ci_IT = stats.norm.interval(0.90, loc=np.nanmean(hpm_smoo[:9,:50],0),scale=np.nanstd(hpm_smoo[:9,:50],0)/np.sqrt(hpm_smoo[:9,:50].shape[0]))
hpm_ci_PT = stats.norm.interval(0.90, loc=np.nanmean(hpm_smoo[9:,:50],0),scale=np.nanstd(hpm_smoo[9:,:50],0)/np.sqrt(hpm_smoo[9:,:50].shape[0]))
PC_ci_IT = stats.norm.interval(0.90, loc=np.nanmean(PC_smoo[:9,:50],0),scale=np.nanstd(PC_smoo[:9,:50],0)/np.sqrt(PC_smoo[:9,:50].shape[0]))
PC_ci_PT = stats.norm.interval(0.90, loc=np.nanmean(PC_smoo[9:,:50],0),scale=np.nanstd(PC_smoo[9:,:50],0)/np.sqrt(PC_smoo[9:,:50].shape[0]))
#all minutes
fig2 = plt.figure(figsize=(8,8))
bx1 = fig2.add_subplot(2, 2, 1)
bx2 = fig2.add_subplot(2, 2, 2)
bx3 = fig2.add_subplot(2, 2, 3)
bx4 = fig2.add_subplot(2, 2, 4)
# bx1.errorbar(bins[:16]+0.5, np.nanmean(PC_5bin[:9,:16],0), label='IT', yerr=pd.DataFrame(PC_5bin[:9,:16]).sem(0), c='r')
# bx1.errorbar(bins[:16], np.nanmean(PC_5bin[9:,:16],0), label='PT', yerr=pd.DataFrame(PC_5bin[9:,:16]).sem(0), c='b')
bx1.plot(bins[:50], np.nanmean(hpm_smoo[:9,:50],0), label='IT', c='r')
bx1.fill_between(bins[:50], hpm_ci_IT[0][:50], hpm_ci_IT[1][:50], color='r', alpha=0.3)
bx2.plot(bins[:50], np.nanmean(hpm_smoo[9:,:50],0), label='PT', c='b')
bx2.fill_between(bins[:50], hpm_ci_PT[0][:50], hpm_ci_PT[1][:50], color='b', alpha=0.3)
bx1.legend(loc=4)
bx1.set_ylabel('HPM')
bx1.set_xlabel('Time (min)')
bx2.legend(loc=4)
bx2.set_ylabel('HPM')
bx2.set_xlabel('Time (min)')
bx3.plot(bins[:50], np.nanmean(PC_smoo[:9,:50],0), label='IT', c='r')
bx3.fill_between(bins[:50], PC_ci_IT[0][:50], PC_ci_IT[1][:50], color='r', alpha=0.3)
bx4.plot(bins[:50], np.nanmean(PC_smoo[9:,:50],0), label='PT', c='b')
bx4.fill_between(bins[:50], PC_ci_PT[0][:50], PC_ci_PT[1][:50], color='b', alpha=0.3)
bx3.legend(loc=4)
bx1.set_ylim([0.4,1.1])
bx2.set_ylim([0.4,1.1])
bx3.set_ylim([0.2,0.45])
bx4.set_ylim([0.2,0.45])
bx3.set_ylabel('PC')
bx3.set_xlabel('Time (min)')
bx4.legend(loc=4)
bx4.set_ylabel('PC')
bx4.set_xlabel('Time (min)')
fig2.tight_layout(pad=0.4, w_pad=1.0, h_pad=1.0)
# first 10min
fig2 = plt.figure(figsize=(8,4))
bx1 = fig2.add_subplot(1, 2, 1)
bx2 = fig2.add_subplot(1, 2, 2)
bx1.plot(bins[:10], np.nanmean(hpm_smoo[:9,:10],0), label='IT', c='r')
bx1.plot(bins[:10]-0.1, hpm_smoo[:9,:10].T, 'r.', alpha=0.3)
bx1.plot(bins[:10], np.nanmean(hpm_smoo[9:,:10],0), label='PT', c='b')
bx1.plot(bins[:10]+0.1, hpm_smoo[9:,:10].T, 'b.', alpha=0.3)
bx1.legend(loc=4)
bx1.set_ylabel('HPM')
bx1.set_xlabel('Time (min)')
bx2.plot(bins[:10], np.nanmean(PC_smoo[:9,:10],0), label='IT', c='r')
bx2.plot(bins[:10]-0.1, PC_smoo[:9,:10].T, 'r.', alpha=0.3)
bx2.plot(bins[:10], np.nanmean(PC_smoo[9:,:10],0), label='PT', c='b')
bx2.plot(bins[:10]+0.1, PC_smoo[9:,:10].T, 'b.', alpha=0.3)
bx2.legend(loc=4)
bx1.set_ylim([0.3,1.3])
bx2.set_ylim([0.1,0.45])
bx2.set_ylabel('PC')
bx2.set_xlabel('Time (min)')
fig2.tight_layout(pad=0.4, w_pad=1.0, h_pad=1.0)
fig3 = plt.figure(figsize=(8,8))
cx1 = fig3.add_subplot(2, 2, 1)
cx2 = fig3.add_subplot(2, 2, 2)
cx3 = fig3.add_subplot(2, 2, 3)
cx4 = fig3.add_subplot(2, 2, 4)
for aa, animal in enumerate(animals):
if animal[:2]=='IT':
cx1.plot(days, hpm_ses[aa,:], 'r', linewidth=0.5, alpha=0.5)
cx3.plot(days, PC_ses[aa,:], 'r', linewidth=0.5, alpha=0.5)
else:
cx2.plot(days, hpm_ses[aa,:], 'b', linewidth=0.5, alpha=0.5)
cx4.plot(days, PC_ses[aa,:], 'b', linewidth=0.5, alpha=0.5)
cx1.plot(days, np.nanmean(hpm_ses[:9,:],0),'r', linewidth=2)
cx2.plot(days, np.nanmean(hpm_ses[9:,:],0),'b', linewidth=2)
cx3.plot(days, np.nanmean(PC_ses[:9,:],0),'r', linewidth=2)
cx4.plot(days, np.nanmean(PC_ses[9:,:],0),'b', linewidth=2)
cx1.set_ylim([0,4])
cx2.set_ylim([0,4])
cx3.set_ylim([0,1])
cx4.set_ylim([0,1])
cx1.set_ylabel('hpm')
cx2.set_ylabel('hpm')
cx3.set_ylabel('PC')
cx4.set_ylabel('PC')
cx3.set_xlabel('IT')
cx4.set_xlabel('PT')
fig3.tight_layout(pad=0.4, w_pad=1.0, h_pad=1.0)
fig4 = plt.figure(figsize=(8,4))
dx1 = fig4.add_subplot(1, 2, 1)
dx2 = fig4.add_subplot(1, 2, 2)
dx1.errorbar(days+0.1, np.nanmean(hpm_ses[:9,:],0), label='IT', yerr=pd.DataFrame(hpm_ses[:9,:]).sem(0), c='r')
dx1.errorbar(days, np.nanmean(hpm_ses[9:,:],0), label='PT', yerr=pd.DataFrame(hpm_ses[9:,:]).sem(0), c='b')
dx1.legend(loc=2)
dx1.set_ylabel('HPM')
dx1.set_xlabel('Time (min)')
dx2.errorbar(days+0.1, np.nanmean(PC_ses[:9,:],0), label='IT', yerr=pd.DataFrame(PC_ses[:9,:]).sem(0), c='r')
dx2.errorbar(days, np.nanmean(PC_ses[9:,:],0), label='PT', yerr=pd.DataFrame(PC_ses[9:,:]).sem(0), c='b')
dx2.legend(loc=2)
# dx1.set_ylim([0.4,1.1])
# dx2.set_ylim([0.25,0.45])
dx2.set_ylabel('PC')
dx2.set_xlabel('Time (min)')
fig4.tight_layout(pad=0.4, w_pad=1.0, h_pad=1.0)
## relative percentage increase of hpm and pc
# the whole session
fig5 = plt.figure(figsize=(8,8))
ex1 = fig5.add_subplot(2, 2, 1)
ex2 = fig5.add_subplot(2, 2, 2)
ex3 = fig5.add_subplot(2, 2, 3)
ex4 = fig5.add_subplot(2, 2, 4)
for aa, animal in enumerate(animals):
if animal[:2]=='IT':
ex1.plot(bins-1, hpm_g[aa,:50], 'r.', alpha=0.1)
ex3.plot(bins-1, PC_g[aa,:50], 'r.', alpha=0.1)
else:
ex2.plot(bins-1, hpm_g[aa,:50], 'b.', alpha=0.1)
ex4.plot(bins-1, PC_g[aa,:50], 'b.', alpha=0.1)
# ex1.plot(bins, np.nanmean(hpm_g[:9,:50],0),'r', linewidth=2)
# ex2.plot(bins, np.nanmean(hpm_g[9:,:50],0),'b', linewidth=2)
# ex3.plot(bins, np.nanmean(PC_g[:9,:50],0),'r', linewidth=2)
# ex4.plot(bins, np.nanmean(PC_g[9:,:50],0),'b', linewidth=2)
ex1.plot(bins, uc.sliding_mean(np.nanmean(hpm_g[:9,1:51],0),2),'r', linewidth=2)
ex2.plot(bins, uc.sliding_mean(np.nanmean(hpm_g[9:,1:51],0),2),'b', linewidth=2)
ex3.plot(bins, uc.sliding_mean(np.nanmean(PC_g[:9,1:51],0),2),'r', linewidth=2)
ex4.plot(bins, uc.sliding_mean(np.nanmean(PC_g[9:,1:51],0),2),'b', linewidth=2)
ex1.set_ylim([-60,160])
ex2.set_ylim([-60,160])
ex3.set_ylim([-100,150])
ex4.set_ylim([-100,150])
ex1.set_ylabel('hpm (% increase)')
ex2.set_ylabel('hpm (% increase)')
ex3.set_ylabel('PC (% increase)')
ex4.set_ylabel('PC (% increase)')
ex3.set_xlabel('IT')
ex4.set_xlabel('PT')
fig5.tight_layout(pad=0.4, w_pad=1.0, h_pad=1.0)
fig5 = plt.figure(figsize=(8,4))
ex1 = fig5.add_subplot(1, 2, 1)
ex2 = fig5.add_subplot(1, 2, 2)
# ex1.errorbar(bins[0:10]-0.1, uc.sliding_mean(np.nanmean(hpm_g[:9,1:11],0),2), yerr= pd.DataFrame(hpm_g[:9,1:11]).sem(0),c='r', linewidth=2)
# ex1.errorbar(bins[0:10]+0.1, uc.sliding_mean(np.nanmean(hpm_g[9:,1:11],0),2), yerr= pd.DataFrame(hpm_g[9:,1:11]).sem(0),c='b', linewidth=2)
# ex1.plot(bins[0:10], uc.sliding_mean(np.nanmean(hpm_g[:9,1:11],0),2),'r', linewidth=2)
# ex1.plot(bins[0:10], uc.sliding_mean(np.nanmean(hpm_g[9:,1:11],0),2),'b', linewidth=2)
# ex2.plot(bins[0:10], uc.sliding_mean(np.nanmean(PC_g[:9,1:11],0),2),'r', linewidth=2)
# ex2.plot(bins[0:10], uc.sliding_mean(np.nanmean(PC_g[9:,1:11],0),2),'b', linewidth=2)
ex1.plot(bins[0:10], np.nanmean(hpm_gsmoo[:9,1:11],0),'r', linewidth=2)
ex1.plot(bins[0:10], np.nanmean(hpm_gsmoo[9:,1:11],0),'b', linewidth=2)
ex2.plot(bins[0:10], np.nanmean(PC_gsmoo[:9,1:11],0),'r', linewidth=2)
ex2.plot(bins[0:10], np.nanmean(PC_gsmoo[9:,1:11],0),'b', linewidth=2)
ex1.plot(bins[0:11]-1.1, hpm_gsmoo[:9,0:11].T, 'r.', alpha=0.3)
ex1.plot(bins[0:11]-0.9, hpm_gsmoo[9:,0:11].T, 'b.', alpha=0.3)
ex2.plot(bins[0:11]-1.1, PC_gsmoo[:9,0:11].T, 'r.', alpha=0.3)
ex2.plot(bins[0:11]-0.9, PC_gsmoo[9:,0:11].T, 'b.', alpha=0.3)
# ex1.set_ylim([0,75])
# ex2.set_ylim([0,45])
ex1.set_ylabel('hpm gain')
ex2.set_ylabel('PC gain')
ex1.set_xlabel('IT')
ex2.set_xlabel('PT')
fig5.tight_layout(pad=0.4, w_pad=1.0, h_pad=1.0)
fig6 = plt.figure(figsize=(8,4))
fx1 = fig6.add_subplot(1, 2, 1)
fx2 = fig6.add_subplot(1, 2, 2)
fx1.bar([0,1], [np.nanmean(hpm_ses[:9,:]), np.nanmean(hpm_ses[9:,:])])
fx1.errorbar([0,1], [np.nanmean(hpm_ses[:9,:]), np.nanmean(hpm_ses[9:,:])], \
yerr=[pd.DataFrame(np.nanmean(hpm_ses[:9,:],1)).sem(0)[0], \
pd.DataFrame(np.nanmean(hpm_ses[9:,:],1)).sem(0)[0]], c='k')
_, p_value = stats.ttest_ind(np.nanmean(hpm_ses[:9,:],1), np.nanmean(hpm_ses[9:,:],1))
p = uc.calc_pvalue(p_value)
fx1.text(0.45, 0.8, p)
fx1.set_ylabel('HPM')
fx1.set_xticks([0,1])
fx1.set_xticklabels(['IT', 'PT'])
fx1.set_ylim([0,1])
fx2.bar([0,1], [np.nanmean(PC_ses[:9,:]), np.nanmean(PC_ses[9:,:])])
fx2.errorbar([0,1], [np.nanmean(PC_ses[:9,:]), np.nanmean(PC_ses[9:,:])], \
yerr=[pd.DataFrame(np.nanmean(PC_ses[:9,:],1)).sem(0)[0], \
pd.DataFrame(np.nanmean(PC_ses[9:,:],1)).sem(0)[0]], c='k')
_, p_value = stats.ttest_ind(np.nanmean(PC_ses[:9,:],1), np.nanmean(PC_ses[9:,:],1))
p = uc.calc_pvalue(p_value)
fx2.text(0.45, 0.4, p)
fx2.set_ylabel('PC')
fx2.set_xticks([0,1])
fx2.set_xticklabels(['IT', 'PT'])
fx2.set_ylim([0,0.45])
fig6.tight_layout(pad=0.4, w_pad=1.0, h_pad=1.0)
# plots evaluating learning on different datasets
# requires dfs for each dataset columns = animals, rows = sessions
to_load_koralek = os.path.join(folder_main, 'df_koralek.hdf5')
to_load_clancy = os.path.join(folder_main, 'df_clancy.hdf5')
to_load_neely = os.path.join(folder_main, 'df_neely.hdf5')
to_load_df = os.path.join(folder_main, 'df_all.hdf5')
df_k = pd.read_hdf(to_load_koralek)
df_k_early = df_k.loc[0:1].mean()
df_k_late = df_k.loc[7:8].mean()
df_c = pd.read_hdf(to_load_clancy)
df_c_early = df_c.loc[0:1].mean()
df_c_late = df_c.loc[7:8].mean()
df_n = pd.read_hdf(to_load_neely)
df_n_early = df_n.loc[0:1].mean()
df_n_late = df_n.loc[7:8].mean()
df_v = pd.read_hdf(to_load_df)
df_v_IT = df_v[df_v['ITPTlabel'] == 0]
df_v_PT = df_v[df_v['ITPTlabel'] == 1]
df_v_IT_early_aux = df_v_IT[df_v_IT['session'] <= 1]['totalPC']
df_v_IT_late_aux = df_v_IT[np.logical_and(df_v_IT['session']<=8, df_v_IT['session']>=7)]['totalPC']
aux_early_IT = df_v_IT[df_v_IT['session'] <= 1]['animal']
aux_late_IT = df_v_IT[np.logical_and(df_v_IT['session'] <= 8, df_v_IT['session'] >= 7)]['animal']
df_v_IT_early = df_v_IT_early_aux.groupby(aux_early_IT).mean()
df_v_IT_late = df_v_IT_late_aux.groupby(aux_late_IT).mean()
df_v_PT_early_aux = df_v_PT[df_v_PT['session'] <= 1]['totalPC']
df_v_PT_late_aux = df_v_PT[np.logical_and(df_v_PT['session'] <= 8, df_v_PT['session'] >= 7)]['totalPC']
aux_early_PT = df_v_PT[df_v_PT['session'] <= 1]['animal']
aux_late_PT = df_v_PT[np.logical_and(df_v_PT['session'] <= 8, df_v_PT['session'] >= 7)]['animal']
df_v_PT_early = df_v_PT_early_aux.groupby(aux_early_PT).mean()
df_v_PT_late = df_v_PT_late_aux.groupby(aux_late_PT).mean()
means = np.asarray([df_k_early.mean(), df_k_late.mean(),
df_n_early.mean(), df_n_late.mean(),
df_c_early.mean(), df_c_late.mean(),
df_v_IT_early.mean(), df_v_IT_late.mean(),
df_v_PT_early.mean(), df_v_PT_late.mean()])
sem = np.asarray([df_k_early.std() / np.sqrt(df_k_early.shape[0]), df_k_late.std() / np.sqrt(df_k_late.shape[0]),
df_n_early.std() / np.sqrt(df_n_early.shape[0]), df_n_late.std() / np.sqrt(df_n_late.shape[0]),
df_c_early.std() / np.sqrt(df_c_early.shape[0]), df_c_late.std() / np.sqrt(df_c_late.shape[0]),
df_v_IT_early.std() / np.sqrt(df_v_IT_early.shape[0]),
df_v_IT_late.std() / np.sqrt(df_v_IT_late.shape[0]),
df_v_PT_early.std() / np.sqrt(df_v_PT_early.shape[0]),
df_v_PT_late.std() / np.sqrt(df_v_PT_late.shape[0])])
fig7 = plt.figure()
plt.errorbar(np.arange(10), means, yerr=sem, ecolor='k', elinewidth=2, linewidth=0)
plt.bar(np.arange(10), means)
_, p_value_k = stats.ttest_rel(df_k_early, df_k_late)
p = calc_pvalue(p_value_k)
plt.text(0.4, 0.05+np.nanmean(df_k_late), p, color='grey', alpha=0.6)
_, p_value_n = stats.ttest_ind(df_n_early, df_n_late[~np.isnan(df_n_late)])
p = calc_pvalue(p_value_n)
plt.text(2.4, 0.05+np.nanmean(df_n_late), p, color='grey', alpha=0.6)
_, p_value_c = stats.ttest_ind(df_c_early, df_c_late[~np.isnan(df_c_late)])
p = calc_pvalue(p_value_c)
plt.text(4.4, 0.05+np.nanmean(df_c_late), p, color='grey', alpha=0.6)
_, p_value_IT = stats.ttest_rel(df_v_IT_early, df_v_IT_late)
p = calc_pvalue(p_value_IT)
plt.text(6.4, 0.05+np.nanmean(df_v_IT_late), p, color='grey', alpha=0.6)
_, p_value_PT = stats.ttest_rel(df_v_PT_early, df_v_PT_late)
p = calc_pvalue(p_value_PT)
plt.text(8.4, 0.05+np.nanmean(df_v_PT_late), p, color='grey', alpha=0.6)
_, p_value_PT_k = stats.ttest_ind(df_k_early, df_v_PT_early)
p = calc_pvalue(p_value_PT_k)
plt.text(8, 0.7, p, color='grey', alpha=0.6)
_, p_value_PT_n = stats.ttest_ind(df_n_early, df_v_PT_early)
p = calc_pvalue(p_value_PT_n)
plt.text(8, 0.65, p, color='grey', alpha=0.6)
_, p_value_PT_c = stats.ttest_ind(df_c_early, df_v_PT_early)
p = calc_pvalue(p_value_PT_c)
plt.text(8, 0.6, p, color='grey', alpha=0.6)
def calc_pvalue(p_value):
if p_value <= 0.0005:
p = '***'
elif p_value <= 0.005:
p = '**'
elif p_value <= 0.05:
p = '*'
else:
p = 'ns'
return p