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GC_mice_scripts.py
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GC_mice_scripts.py
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#*************************************************************************
#*****************************ALL TOGETHER********************************
#*************************************************************************
__author__ = 'Nuria'
import h5py
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from matplotlib import interactive
interactive(True)
import utils_cabmi as ut
import analysis_cabmi as acb
def all_learning(folder, animals=['GCP1', 'GCP2'], var='PT', sec_var='',
maxf=36000, len_days=20, smoth=1, min_bin=5, trials=150):
fpath = folder + 'raw/' + animal + '/'
folder_anal = folder + 'analysis/learning/'
flag_init = True
pc_all = np.ones((len(animals), len_days)) * np.nan
for aa, animal in enumerate(animals):
for dd, day in enumerate(os.listdir(fpath)):
if flag_init:
f = h5py.File(folder_path + 'full_' + animal + '_' + day + '_' +
sec_var + '_data.hdf5', 'r'
)
fr = f['fr']
f.close()
bins = np.arange(0, maxf/fr, 60)
hpm_all = np.ones((len(animals), len_days, bins.shape[0]))*np.nan
tth_all = np.ones((len(animals), len_days, bins.shape[0]))*np.nan
flag_init = False
[hpm, tth, pc] = acb.learning(folder, animal, day, sec_var, toplot=False)
pc_all[aa,dd] = pc
if hpm.shape[0]<hpm_all.shape[1]:
hpm_all[aa, dd, :hpm.shape[0]] = hpm
tth_all[aa, dd, :tth.shape[0]] = tth
else:
hpm_all[aa, dd, :] = hpm
tth_all[aa, dd, :] = tth
fig1 = plt.figure(figsize=(18, 5))
bx = fig1.add_subplot(131)
# make up of figure
bx.spines["top"].set_visible(False)
bx.spines["right"].set_visible(False)
bx.get_yaxis().tick_left()
bx.set_ylim(0.4, 0.95)
bx.set_xlim(0.8, 10.5)
bx.set_xlabel('day', fontsize=16)
bx.set_ylabel('percentage correct', fontsize=16)
# end of figure makeup
if smoth == 1:
bx.errorbar(np.arange(1, len_days + 1), np.nanmean(pc_all, 0),
yerr=pd.DataFrame(pc_all).sem(0).values, color='k'
)
else:
bx.errorbar(np.arange(1, len_days + 1),
sliding_mean(np.nanmean(pc_all, 0), smoth),
yerr=pd.DataFrame(pc_all).sem(0).values, color='k'
)
bx0 = fig1.add_subplot(132)
# make up of figure
bx0.spines["top"].set_visible(False)
bx0.spines["right"].set_visible(False)
bx0.get_yaxis().tick_left()
bx0.set_ylim(0.3, 7)
bx0.set_xlim(0.8, 10.5)
bx0.set_xlabel('day', fontsize=16)
bx0.set_ylabel('Hits/min', fontsize=16)
# end of figure makeup
aux_hpm = np.nanmean(hpm_all, 2)
if smoth == 1:
bx0.errorbar(np.arange(1, len_days + 1),
np.nanmean(aux_hpm, 0), yerr=pd.DataFrame(aux_hpm).sem(0).values,
color='k'
)
else:
bx0.errorbar(np.arange(1, len_days + 1),
ut.sliding_mean(np.nanmean(aux_hpm, 0), smoth),
yerr=pd.DataFrame(aux_hpm).sem(0).values, color='k'
)
bx1 = fig1.add_subplot(133)
# make up of figure
bx1.spines["top"].set_visible(False)
bx1.spines["right"].set_visible(False)
bx1.get_yaxis().tick_left()
bx1.set_ylim(0.3, 7)
bx1.set_xlim(-0.5, 25.1)
bx1.set_xlabel('Time in session (min)', fontsize=16)
bx1.set_ylabel('Hits/min', fontsize=16)
# end of figure makeup
aux_tothpm = np.reshape(hpm_all, [hpm_all.shape[0]*hpm_all.shape[1],
hpm_all.shape[2]]
)
_, p_value = stats.ttest_ind(
np.reshape(aux_tothpm[:, :min_bin],
aux_tothpm.shape[0]*min_bin),
np.reshape(aux_tothpm[:, -min_bin:], aux_tothpm.shape[0]*min_bin),
nan_policy='omit'
)
p = calc_pvalue(p_value)
bx1.errorbar(np.arange(maxf/fr), np.nanmean(aux_tothpm, 0),
yerr=pd.DataFrame(aux_tothpm).sem(0).values, color='k'
)
bx1.text(len_exper/2, 1.2, p, color='grey', alpha=0.6)
bx1.axhline(
y=1.3, xmin=0, xmax=1/maxf/fr*min_bin,
c='grey', linewidth=0.5, alpha=0.5
)
bx1.axhline(
y=1.3, xmin=1-1/maxf/fr*min_bin, xmax=1,
c='grey', linewidth=0.5, alpha=0.5
)
bx1.axhline(
y=1.2, xmin=1/maxf/fr*min_bin/2, xmax=1-1/maxf/fr*min_bin/2,
c='grey', linewidth=0.5, alpha=0.5
)
fig1.savefig(folder_path + 'percent_correct_min.png', bbox_inches="tight")
fig1.savefig(
folder_path + 'percent_correct_min.eps', format='eps',
bbox_inches="tight"
)
fig2 = plt.figure(figsize=(18, 5))
bx2 = fig2.add_subplot(131)
# make up of figure
bx2.spines["top"].set_visible(False)
bx2.spines["right"].set_visible(False)
bx2.get_yaxis().tick_left()
bx2.set_xlim(0.5, 10.5)
bx2.set_ylim(4, 18)
bx2.set_xlabel('Day', fontsize=16)
bx2.set_ylabel('time to hit', fontsize=16)
bx2.set_title('Averaged days')
aux_tit = np.nanmean(tth_all, 2)
if smoth == 1:
bx2.errorbar(
np.arange(1, len_days + 1), np.nanmean(aux_tit, 0),
yerr=pd.DataFrame(aux_tit).sem(0).values, color='k'
)
else:
bx2.errorbar(
np.arange(1, len_days + 1),
ut.sliding_mean(np.nanmean(aux_tit, 0), smoth),
yerr=pd.DataFrame(aux_tit).sem(0).values, color='k'
)
_, p_value = stats.ttest_ind(aux_tit[:, 0], aux_tit[:, -1], nan_policy='omit')
p = ut.calc_pvalue(p_value)
bx2.text(len_days/2, 5, p, color='grey', alpha=0.6)
bx2.axhline(
y=5, xmin=0, xmax=1/len_exper*min_bin,
c='grey', linewidth=0.5, alpha=0.5
)
bx2.axhline(
y=5, xmin=1-1/len_days, xmax=1,
c='grey', linewidth=0.5, alpha=0.5
)
bx2.axhline(
y=4.9, xmin=1/len_days, xmax=1-1/len_exper*min_bin/2,
c='grey', linewidth=0.5, alpha=0.5
)
bx3 = fig2.add_subplot(132)
# make up of figure
bx3.spines["top"].set_visible(False)
bx3.spines["right"].set_visible(False)
bx3.get_yaxis().tick_left()
bx3.set_xlabel('Trials', fontsize=16)
bx3.set_ylabel('time to hit', fontsize=16)
bx3.set_ylim(4, 18)
bx3.set_xlim(0, 100)
# end of figure makeup
aux_timhit = np.reshape(tth_all, [len_days*len(animals), trials])
sm_auxtim = ut.sliding_mean(np.nanmean(aux_timhit, 0), window=3)
sm_error = ut.sliding_mean(pd.DataFrame(aux_timhit).sem(0).values, window=3)
bx3.fill_between(
np.arange(trials), sm_auxtim - sm_error, sm_auxtim + sm_error,
color="lightgrey", alpha=0.7
)
bx3.plot(np.arange(trials), sm_auxtim, linewidth=2, c="k")
_, p_value = stats.ttest_ind(
np.reshape(aux_timhit[:, :int(trials/5)], aux_timhit.shape[0]*int(trials/5)),
np.reshape(aux_timhit[:, int(4*trials/5):], aux_timhit.shape[0]*int(trials/5)),
nan_policy='omit'
)
p = ut.calc_pvalue(p_value)
bx3.text(50, 4.9, p, color='grey', alpha=0.6)
bx3.axhline(y=4.9, xmin=0, xmax=0.2, c='grey', linewidth=0.5, alpha=0.5)
bx3.axhline(y=4.9, xmin=0.8, xmax=1, c='grey', linewidth=0.5, alpha=0.5)
bx3.axhline(y=5, xmin=0.1, xmax=0.9, c='grey', linewidth=0.5, alpha=0.5)
bx4 = fig2.add_subplot(133)
# make up of figure
bx4.spines["top"].set_visible(False)
bx4.spines["right"].set_visible(False)
bx4.get_yaxis().tick_left()
bx4.set_xlabel('Trials', fontsize=16)
bx4.set_ylabel('time to hit', fontsize=16)
bx4.set_xlim(0, 100)
# end of figure makeup
color_idx = np.linspace(0, 1, len_days)
for xx in np.arange(0, len_days):
aux_timhit = ut.sliding_mean(np.nanmean(tim_hit[:, xx, :], 0), 10)
bx4.plot(np.arange(trials), aux_timhit, color=plt.cm.copper(color_idx[xx]))
fig2.savefig(folder_path + 'tihit.png', bbox_inches="tight")
fig2.savefig(folder_path + 'tihit.eps', format='eps', bbox_inches="tight")
plt.close('all')
def all_tuning(folder, animals=['GCP1', 'GCP2'], sec_var='', len_days=20):
fpath = folder + 'raw/' + animal + '/'
folder_anal = folder + 'analysis/tunning/'
flag_init = True
pc_all = np.ones((len(animals), len_days)) * np.nan
for aa, animal in enumerate(animals):
for dd, day in np.arange(len_days):
f = h5py.File(
folder_dest + 'tunning_' + animal + '_' + day + '_' +
sec_var + '_freq.hdf5', 'w-'
)