## normalize activity within trial and for each neuron
activity_normalized = np.zeros((activity.shape))
for j in range(activity.shape[0]):
for i in range(0, len(timeline_1) - 1):
activity_segment = activity[j,
int(timeline_1[i]):int(timeline_1[i +
1])]
activity_segment = activity_segment - min(activity_segment)
if max(activity_segment):
activity_segment_normalized = activity_segment / max(
activity_segment)
activity_normalized[j,
int(timeline_1[i]):int(timeline_1[
i + 1])] = activity_segment_normalized
neural_activity1 = activity[1:, :]
corr_matrix1 = stats.corr_matrix(neural_activity=neural_activity1)
neural_activity1 = activity_normalized[1:, :]
sparceness_mean = []
sparceness_std = []
for re_sf in bin_vector:
reshape_neural_activity = np.reshape(
neural_activity1[:, :int(
int(neural_activity1.shape[1] / re_sf) * re_sf)],
(neural_activity1.shape[0], int(
neural_activity1.shape[1] / re_sf), re_sf))
resample_neural_activity = np.mean(reshape_neural_activity, axis=2)
denominator = np.mean(np.multiply(resample_neural_activity,
resample_neural_activity),
axis=1)
reshape_behaviour = np.reshape(
behaviour[:int(int(behaviour.shape[0] / re_sf) * re_sf)],
(int(behaviour.shape[0] / re_sf), re_sf))
resample_beh1 = np.reshape(
scipy.stats.mode(reshape_behaviour, axis=1)[0], reshape_behaviour.shape[0])
resample_timeline = timeline_1 / re_sf
#%%
vector = resample_neural_activity_mean[:, :int(timeline_1[40])]
vector_beh = resample_beh1[:int(resample_timeline[40])]
neural_activity_new = []
corr_matrix = []
for i in range(6):
neural_activity_new.append(vector[:, np.where(vector_beh == i)])
corr_matrix.append(
stats.corr_matrix(neural_activity=neural_activity_new[i][:, 0, :]))
#corr_matrix.append(stats.cov_matrix(neural_activity = neural_activity_new[i][:,0,:]))
#%%
correlation_behaviour_path = os.environ['PROJECT_DIR'] + 'neural_analysis/data/process/figures/' \
'correlation_matrix_task_mouse_'+f'{mouse}'+'_session_'+f'{session}'+\
'_binsize_'+f'{re_sf}'+'.png'
figs.plot_correlation_matrix_behaviour(corr_matrix_list=corr_matrix,
path_save=correlation_behaviour_path,
title=task)
#%% Statistics of correlation values
correlation_stats_path = os.environ['PROJECT_DIR'] + 'neural_analysis/data/process/figures/' \
'correlation_statistics_mouse_'+f'{mouse}'+'_session_'\
+f'{session}'+'_binsize_'+f'{re_sf}'+'.png'
neural_activity1_days = []
time_length = np.diff(timeline_1)
for i in range(0,42,2):
trial_matrix = neural_activity1[:,int(timeline_1[i]):int(timeline_1[i]) + int(time_length[i])]
neural_activity1_days.append(trial_matrix)
neural_activity1_resting_days = []
for i in range(1,42,2):
trial_matrix = neural_activity1[:,int(timeline_1[i]):int(timeline_1[i]) + int(time_length[i])]
neural_activity1_resting_days.append(trial_matrix)
#%%
corr_matrix_days = []
for i in range(21):
corr_matrix_days.append(stats.corr_matrix(neural_activity = neural_activity1_days[i]))
#corr_matrix_days.append(stats.cov_matrix(neural_activity = neural_activity1_days[i]))
corr_matrix_resting_days = []
for i in range(21):
corr_matrix_resting_days.append(stats.corr_matrix(neural_activity = neural_activity1_resting_days[i]))
#corr_matrix_resting_days.append(stats.cov_matrix(neural_activity = neural_activity1_resting_days[i]))
#%%
objects_fig_path = os.environ['PROJECT_DIR'] + 'neural_analysis/data/process/figures/' \
'correlation_with_object_position_'+f'{mouse}'+\
'_session_'+f'{session}'+'_binsize_'+f'{re_sf}'+'.png'
figs.plot_correlation_statistics_objects(corr_matrix1=corr_matrix_days, corr_matrix2=corr_matrix_resting_days,
overlapping_matrix=overlapping_matrix, path_save=objects_fig_path,
if max(activity_segment):
activity_segment_normalized = activity_segment / max(
activity_segment)
activity_normalized[j,
int(timeline_3[i]):int(timeline_3[
i + 1])] = activity_segment_normalized
neural_activity3 = activity_normalized[1:, :]
#neural_activity2 = activity[1:,:]
delete_list = []
sum_activity = np.sum(neural_activity3, axis=1)
for i in range(neural_activity3.shape[0]):
if sum_activity[i] == 0:
delete_list.append(i)
for i in delete_list:
neural_activity3 = np.delete(neural_activity3, i, 0)
corr_matrix3 = stats.corr_matrix(neural_activity=neural_activity3)
eigenvalues3, eigenvectors3 = stats.compute_PCA(corr_matrix=corr_matrix3)
proj3 = stats.PCA_projection(neural_activity=neural_activity3,
eigenvalues=eigenvalues3,
eigenvectors=eigenvectors3,
n_components=6)
behaviour = np.load(behaviour_dir + beh_file_name_2)
c = np.linspace(0, 20, len(behaviour))
elements3 = []
testing_3 = []
color3 = []
for i in range(6):
vector = proj3[:, :int(timeline_3[40])]
vector_beh = behaviour[:int(timeline_3[40])]
elements3.append(vector[:, np.where(vector_beh == i)])
):int(resample_timeline[i]) +
int(time_length[i])]
neural_activity1_days.append(trial_matrix)
neural_activity1_resting_days = []
for i in range(1, 42, 2):
trial_matrix = neural_activity1[:,
int(resample_timeline[i]
):int(resample_timeline[i]) +
int(time_length[i])]
neural_activity1_resting_days.append(trial_matrix)
corr_matrix_days = []
for i in range(21):
corr_matrix_days.append(
stats.corr_matrix(neural_activity=neural_activity1_days[i]))
corr_matrix_resting_days = []
for i in range(21):
corr_matrix_resting_days.append(
stats.corr_matrix(neural_activity=neural_activity1_resting_days[i]))
#%% plotting
corr_evol_path = os.environ['PROJECT_DIR'] + 'neural_analysis/data/process/figures/' \
'correlation_with_resting_evoluction_'+f'{mouse}'+\
'_session_'+f'{session}'+'_binsize_'+f'{re_sf}'+'.png'
figs.plot_correlation_with_resting_evolution(
corr_matrix1=corr_matrix_days,
corr_matrix2=corr_matrix_resting_days,
time_file_session_1 = 'mouse_'+ f'{mouse}'+'_session_'+ f'{session}' +'_trial_1_v'+ f'{decoding_v}'+'.1.'+f'{1}'+\
'.'+f'{0}'+ '.pkl'
beh_file_name_1 = 'mouse_' + f'{mouse}' + '_session_' + f'{session}' + '_event_' + f'{re_sf}' + '.npy'
##load activity and timeline
activity = np.load(file_directory + file_name_session_1)
timeline_file = open(timeline_file_dir + time_file_session_1, 'rb')
timeline_info = pickle.load(timeline_file)
##normalize neural activity
neural_activity1, timeline_1 = stats.normalize_neural_activity(
activity=activity, timeline=timeline_info)
##downsample neural activity
resample_neural_activity_mean, resample_neural_activity_std = stats.resample_matrix(
neural_activity=neural_activity1, re_sf=re_sf)
correlation_matrix.append(
stats.corr_matrix(neural_activity=resample_neural_activity_mean))
#cov_matrix.append(stats.cov_matrix(neural_activity = resample_neural_activity_mean))
task_list.append(task)
#%% plotting corr matrix for all conditions
correlation_fig_path = os.environ['PROJECT_DIR'] + 'neural_analysis/data/process/figures/' \
'correlation_matrix_mouse_'+f'{mouse}'+'_session_'+f'{session}'+\
'_binsize_'+f'{re_sf}'+'.png'
figs.plot_correlation_matrix_conditions(matrix_list=correlation_matrix,
save_path=correlation_fig_path,
title='CorrelationMatrix',
conditions=task_list)
#cov_fig_path = os.environ['PROJECT_DIR'] + 'neural_analysis/data/process/figures/' \
# 'cov_matrix_mouse_'+f'{mouse}'+'_session_'+f'{session}'+\
# '_binsize_'+f'{re_sf}'+'.png'
