def import_data():
ephys_path = '/Users/veronikasamborska/Desktop/neurons'
beh_path = '/Users/veronikasamborska/Desktop/data_3_tasks_ephys'
#HP_LFP,PFC_LFP, m484_LFP, m479_LFP, m483_LFP, m478_LFP, m486_LFP, m480_LFP, m481_LFP, all_sessions_LFP = ep.import_code(ephys_path,beh_path, lfp_analyse = 'True')
HP,PFC, m484, m479, m483, m478, m486, m480, m481, all_sessions = ep.import_code(ephys_path,beh_path,lfp_analyse = 'False')
exp = di.Experiment('/Users/veronikasamborska/Desktop/Veronika Backup/2018-12-12-Reversal_learning/data_pilot3')
def load_data():
ephys_path = '/Users/veronikasamborska/Desktop/neurons'
beh_path = '/Users/veronikasamborska/Desktop/data_3_tasks_ephys'
HP,PFC, m484, m479, m483, m478, m486, m480, m481, all_sessions = ep.import_code(ephys_path,beh_path,lfp_analyse = 'False')
experiment_aligned_PFC = ha.all_sessions_aligment(PFC, all_sessions)
experiment_aligned_HP = ha.all_sessions_aligment(HP, all_sessions)
data_HP = io.loadmat('/Users/veronikasamborska/Desktop/HP.mat')
data_PFC = io.loadmat('/Users/veronikasamborska/Desktop/PFC.mat')
return data_HP, data_PFC,experiment_aligned_PFC,experiment_aligned_HP
def import_data():
ephys_path = '/Users/veronikasamborska/Desktop/neurons'
beh_path = '/Users/veronikasamborska/Desktop/data_3_tasks_ephys'
HP, PFC, m484, m479, m483, m478, m486, m480, m481, all_sessions = ep.import_code(
ephys_path, beh_path, lfp_analyse='False')
HP = io.loadmat('/Users/veronikasamborska/Desktop/HP.mat')
PFC = io.loadmat('/Users/veronikasamborska/Desktop/PFC.mat')
Data_HP = HP['Data'][0]
DM_HP = HP['DM'][0]
Data_PFC = PFC['Data'][0]
DM_PFC = PFC['DM'][0]
def import_data():
ephys_path = '/Users/veronikasamborska/Desktop/neurons'
beh_path = '/Users/veronikasamborska/Desktop/data_3_tasks_ephys'
HP_LFP,PFC_LFP, m484_LFP, m479_LFP, m483_LFP, m478_LFP, m486_LFP, m480_LFP, m481_LFP, all_sessions_LFP = ep.import_code(ephys_path,beh_path, lfp_analyse = 'True')
HP = scipy.io.loadmat('/Users/veronikasamborska/Desktop/HP.mat')
PFC = scipy.io.loadmat('/Users/veronikasamborska/Desktop/PFC.mat')
Data_HP = HP['Data'][0]
DM_HP = HP['DM'][0]
Data_PFC = PFC['Data'][0]
DM_PFC = PFC['DM'][0]
def import_data():
ephys_path = '/Users/veronikasamborska/Desktop/neurons'
beh_path = '/Users/veronikasamborska/Desktop/data_3_tasks_ephys'
# HP = scipy.io.loadmat('/Users/veronikasamborska/Desktop/HP.mat')
# HP = m484 + m479 + m483
HP_LFP,PFC_LFP, m484_LFP, m479_LFP, m483_LFP, m478_LFP, m486_LFP, m480_LFP, m481_LFP, all_sessions_LFP = ep.import_code(ephys_path,beh_path, lfp_analyse = 'True')
all_times_m484_LFP, filtered_LFP_m484_LFP, peak_power_all_m484 = ripple_detect(m484_LFP)
all_times_m483_LFP, filtered_LFP_m483_LFP, peak_power_all_m483 = ripple_detect(m483_LFP)
HP = scipy.io.loadmat('/Users/veronikasamborska/Desktop/HP.mat')
PFC = scipy.io.loadmat('/Users/veronikasamborska/Desktop/PFC.mat')
Data_HP = HP['Data'][0]
DM_HP = HP['DM'][0]
Data_PFC = PFC['Data'][0]
DM_PFC = PFC['DM'][0]
all_sessions_1, all_sessions_2, all_sessions_3 = ripple_plot(peak_power_all_m484, m484_LFP,HP)
def stats_svd(data_loaded=True, d=True):
if data_loaded == False:
ephys_path = '/Users/veronikasamborska/Desktop/neurons'
beh_path = '/Users/veronikasamborska/Desktop/data_3_tasks_ephys'
HP, PFC, m484, m479, m483, m478, m486, m480, m481, all_sessions = ep.import_code(
ephys_path, beh_path, lfp_analyse='False')
experiment_aligned_m484 = ha.all_sessions_aligment(m484, all_sessions)
experiment_aligned_m479 = ha.all_sessions_aligment(m479, all_sessions)
experiment_aligned_m483 = ha.all_sessions_aligment(m483, all_sessions)
experiment_aligned_m478 = ha.all_sessions_aligment(m478, all_sessions)
experiment_aligned_m486 = ha.all_sessions_aligment(m486, all_sessions)
experiment_aligned_m480 = ha.all_sessions_aligment(m480, all_sessions)
experiment_aligned_m481 = ha.all_sessions_aligment(m481, all_sessions)
#average_within_HP, average_between_HP = sv.svd_plotting(experiment_aligned_HP, tasks_unchanged = True, plot_a = False, plot_b = False, HP = True, average_reward = False, diagonal = True, demean_all_tasks = False)
average_within_m484, average_between_m484 = sv.svd_plotting(
experiment_aligned_m484,
tasks_unchanged=False,
plot_a=True,
plot_b=False,
HP=True,
average_reward=False,
diagonal=d,
demean_all_tasks=False)
average_within_m479, average_between_m479 = sv.svd_plotting(
experiment_aligned_m479,
tasks_unchanged=False,
plot_a=True,
plot_b=False,
HP=True,
average_reward=False,
diagonal=d,
demean_all_tasks=False)
average_within_m483, average_between_m483 = sv.svd_plotting(
experiment_aligned_m483,
tasks_unchanged=False,
plot_a=True,
plot_b=False,
HP=True,
average_reward=False,
diagonal=d,
demean_all_tasks=False)
average_within_m478, average_between_m478 = sv.svd_plotting(
experiment_aligned_m478,
tasks_unchanged=False,
plot_a=True,
plot_b=False,
HP=False,
average_reward=False,
diagonal=d,
demean_all_tasks=False)
average_within_m481, average_between_m481 = sv.svd_plotting(
experiment_aligned_m481,
tasks_unchanged=False,
plot_a=True,
plot_b=False,
HP=False,
average_reward=False,
diagonal=d,
demean_all_tasks=False)
average_within_m486, average_between_m486 = sv.svd_plotting(
experiment_aligned_m486,
tasks_unchanged=False,
plot_a=True,
plot_b=False,
HP=False,
average_reward=False,
diagonal=d,
demean_all_tasks=False)
average_within_m480, average_between_m480 = sv.svd_plotting(
experiment_aligned_m480,
tasks_unchanged=False,
plot_a=True,
plot_b=False,
HP=False,
average_reward=False,
diagonal=d,
demean_all_tasks=False)
first, average_between_m484, average_between_y_m484, average_within_x_m484, average_within_m484 = svdu.svd_u_and_v_separately(
experiment_aligned_m484,
tasks_unchanged=True,
plot_a=False,
plot_b=False,
HP=True,
average_reward=False,
demean_all_tasks=False,
z_score=False)
first, average_between_m479, average_between_y_m479, average_within_x_m479, average_within_m479 = svdu.svd_u_and_v_separately(
experiment_aligned_m479,
tasks_unchanged=True,
plot_a=False,
plot_b=False,
HP=True,
average_reward=False,
demean_all_tasks=False,
z_score=False)
first, average_between_m483, average_between_y_m483, average_within_x_m483, average_within_m483 = svdu.svd_u_and_v_separately(
experiment_aligned_m483,
tasks_unchanged=True,
plot_a=False,
plot_b=False,
HP=True,
average_reward=False,
demean_all_tasks=False,
z_score=False)
first, average_between_m478, average_between_y_m478, average_within_x_m478, average_within_m478 = svdu.svd_u_and_v_separately(
experiment_aligned_m478,
tasks_unchanged=True,
plot_a=False,
plot_b=False,
HP=False,
average_reward=False,
demean_all_tasks=False,
z_score=False)
first, average_between_m481, average_between_y_m481, average_within_x_m481, average_within_m481 = svdu.svd_u_and_v_separately(
experiment_aligned_m481,
tasks_unchanged=True,
plot_a=False,
plot_b=False,
HP=False,
average_reward=False,
demean_all_tasks=False,
z_score=False)
first, average_between_m486, average_between_y_m486, average_within_x_m486, average_within_m486 = svdu.svd_u_and_v_separately(
experiment_aligned_m486,
tasks_unchanged=True,
plot_a=False,
plot_b=False,
HP=False,
average_reward=False,
demean_all_tasks=False,
z_score=False)
first, average_between_m480, average_between_y_m480, average_within_x_m480, average_within_m480 = svdu.svd_u_and_v_separately(
experiment_aligned_m480,
tasks_unchanged=True,
plot_a=False,
plot_b=False,
HP=False,
average_reward=False,
demean_all_tasks=False,
z_score=False)
m484 = (np.trapz(average_within_m484) -
np.trapz(average_between_y_m484)) / average_within_m484.shape[0]
m479 = (np.trapz(average_within_m479) -
np.trapz(average_between_y_m479)) / average_within_m479.shape[0]
m483 = (np.trapz(average_within_m483) -
np.trapz(average_between_y_m483)) / average_within_m483.shape[0]
HP_area = [m484, m479, m483]
m478 = (np.trapz(average_within_m478) -
np.trapz(average_between_y_m478)) / average_within_m478.shape[0]
m481 = (np.trapz(average_within_m481) -
np.trapz(average_between_y_m481)) / average_within_m481.shape[0]
m486 = (np.trapz(average_within_m486) -
np.trapz(average_between_y_m486)) / average_within_m486.shape[0]
m480 = (np.trapz(average_within_m480) -
np.trapz(average_between_y_m480)) / average_within_m480.shape[0]
PFC_area = [m478, m481, m486, m480]
s = stats.ttest_ind(PFC_area, HP_area)
plt.figure()
sns.barplot(data=[HP_area, PFC_area],
capsize=.1,
ci="sd",
palette="Blues_d")
return HP_area, PFC_area, s
'/Users/veronikasamborska/Desktop/ephys_beh_analysis/regressions')
sys.path.append(
'/Users/veronikasamborska/Desktop/ephys_beh_analysis/modelling')
import scipy.io
import pylab as plt
import heatmap_aligned as ha
import RW_model_fitting as mfit
import create_data_arrays_for_tim as cda
import forced_trials_extract_data as ft
import ephys_beh_import as ep
import numpy as np
ephys_path = '/Users/veronikasamborska/Desktop/neurons'
beh_path = '/Users/veronikasamborska/Desktop/data_3_tasks_ephys'
HP, PFC, m484, m479, m483, m478, m486, m480, m481, all_sessions = ep.import_code(
ephys_path, beh_path, lfp_analyse='False')
experiment_aligned_PFC = ha.all_sessions_aligment(PFC, all_sessions)
experiment_aligned_HP = ha.all_sessions_aligment(HP, all_sessions)
#PFC_forced = ft.all_sessions_aligment_forced(PFC,all_sessions)
#HP_forced = ft.all_sessions_aligment_forced(HP,all_sessions)
# experiment_sim_Q1_HP, experiment_sim_Q4_HP, experiment_sim_Q1_value_a_HP ,experiment_sim_Q1_value_b_HP, experiment_sim_Q4_values_HP,\
# experiment_sim_Q1_PFC, experiment_sim_Q4_PFC, experiment_sim_Q1_value_a_PFC, experiment_sim_Q1_value_b_PFC, experiment_sim_Q4_values_PFC = mfit.run(experiment_aligned_HP,experiment_aligned_PFC)
# data_PFC = cda.tim_create_mat(experiment_aligned_PFC,experiment_sim_Q1_PFC, experiment_sim_Q4_PFC, experiment_sim_Q1_value_a_PFC, experiment_sim_Q1_value_b_PFC, experiment_sim_Q4_values_PFC, 'PFC_RPE')
# data_HP = cda.tim_create_mat(experiment_aligned_HP, experiment_sim_Q1_HP, experiment_sim_Q4_HP, experiment_sim_Q1_value_a_HP, experiment_sim_Q1_value_b_HP, experiment_sim_Q4_values_HP, 'HP_RPE')
#data_PFC = cda.tim_create_mat(experiment_aligned_PFC,'PFC')
#data_HP = cda.tim_create_mat(experiment_aligned_HP, 'HP')