def main():
pd_obj = pu.proj_data()
pdata = pd_obj.get_data()
rois = pdata['roiLabels']
meg_subj, meg_sess = pd_obj.get_meg_metadata()
# downsamp_file = '../data/downsampled_MEG_truncated.hdf5'
# extract_phase_amp_downsamp(meg_sess, meg_subj, rois, downsamp_file)
# data_path = '../data/MEG_downsampled_phase_amp_data.hdf5'
data_path = '../data/MEG_phase_amp_data.hdf5'
output_path = '../data/MEG_phase_phase_coupling.hdf5'
for sess in meg_sess:
for subj in meg_subj:
subj_ppc = np.ndarray(shape=(len(rois), len(rois)))
data_file = h5py.File(data_path, 'r')
subj_data = data_file[subj + '/' + sess +
'/BOLD bandpass/phase_data'][...]
for r1 in range(len(rois)):
for r2 in range(len(rois)):
phase_1 = subj_data[:, r1]
phase_2 = subj_data[:, r2]
rho = circ_corr(phase_1, phase_2)
subj_ppc[r1, r2] = rho
ppc_file = h5py.File(output_path)
prog = sess + '/' + subj
if prog in ppc_file:
continue
out_group = ppc_file.require_group(prog)
out_group.create_dataset('ppc', data=subj_ppc, compression='lzf')
ppc_file.close()
def main():
pdata = pu.proj_data()
rois = pdata.roiLabels
meg_subj, meg_sess = pdata.get_meg_metadata()
phase_amp_file = '../data/MEG_phase_amp_data.hdf5'
attn_rois = ['IPS1_R', 'FEF_R', 'TPOJ1_R', 'AVI_R',
'7m_R'] # ['IPS1_R', 'FEF_R', 'TPOJ1_R', 'AVI_R']
# --Phase-phase coupling-- #
first_level_tests_ppc = first_level_ppc(phase_amp_file, meg_subj, meg_sess,
rois, attn_rois)
first_level_tests_ppc.to_excel(
'../data/attention_networks/ppc_first_level.xlsx')
first_level_tests_ppc = pd.read_excel(
'../data/attention_networks/ppc_first_level.xlsx', index_col=0)
second_level_res = second_level(first_level_tests_ppc)
cron_alpha_res = cron_alpha_test(first_level_tests_ppc, attn_rois,
meg_sess)
res = {
'first_level_tests': first_level_tests_ppc,
'second_level_tests': second_level_res,
'cron_alpha_tests': cron_alpha_res
}
pu.save_xls(res, '../data/attention_networks/ppc_second_level.xlsx')
plot_grouped_boxplot(first_level_tests_ppc,
attn_rois,
cron_alpha_df=cron_alpha_res,
fname='../figures/attention_networks/ppc_boxplot.pdf')
# --Phase-amplitude coupling-- #
first_level_tests_pac = first_level_pac(phase_amp_file, meg_subj, meg_sess,
rois, attn_rois)
first_level_tests_pac.to_excel(
'../data/attention_networks/pac_first_level.xlsx')
first_level_tests_pac = pd.read_excel(
'../data/attention_networks/pac_first_level.xlsx', index_col=0)
second_level_res = second_level(first_level_tests_pac)
cron_alpha_res = cron_alpha_test(first_level_tests_pac, attn_rois,
meg_sess)
res = {
'first_level_tests': first_level_tests_pac,
'second_level_tests': second_level_res,
'cron_alpha_tests': cron_alpha_res
}
pu.save_xls(res, '../data/attention_networks/pac_second_level.xlsx')
plot_grouped_boxplot(first_level_tests_pac,
attn_rois,
cron_alpha_df=cron_alpha_res,
fname='../figures/attention_networks/pac_boxplot.pdf')
def main():
pdir = pu.get_proj_dir()
pdata = pu.proj_data()
rois = pdata.roiLabels
meg_subj, meg_sess = pdata.get_meg_metadata()
phase_amp_file = pdir + '/data/MEG_phase_amp_data.hdf5'
attn_rois = ['IPS1_R', 'FEF_R', 'TPOJ1_R', 'AVI_R']
first_level_tests = first_level(phase_amp_file, meg_subj, meg_sess, rois,
attn_rois)
first_level_tests.to_excel(
pdir + '/figures/attention_networks/phase_amp_first_level.xlsx')
import sys
sys.path.append("..")
import os
import h5py
import datetime
import numpy as np
import pandas as pd
import proj_utils as pu
import matplotlib.pyplot as plt
from scipy.signal import coherence
from nipype.algorithms import icc
#Load data
pdObj = pu.proj_data()
pdir = pu._get_proj_dir()
pData = pdObj.get_data()
rois = pData['roiLabels']
database = pData['database']
#Getting fmri info
fmri_subj_path = pdir + '/data/timeseries_rs-fMRI'
files = os.listdir(fmri_subj_path)
fmri_subj = sorted(list(set([f.split('_')[0] for f in files])))
fmri_sess = list(set([f.split('_')[2].replace('.mat', '') for f in files]))
badSubjects = ['104012', '125525', '151526', '182840', '200109', '500222']
for subject in badSubjects:
def calc_phase_amp_power(how='location'):
p_data = pu.proj_data()
subjects, sessions = p_data.get_meg_metadata()
rois = p_data.roiLabels
fs = 500
if how is 'location':
df_list = []
for session in sessions:
session_df = pd.DataFrame(index=subjects)
for subject in subjects:
prog = "%s - %s" % (session, subject)
print('%s: Calculating infraslow power for %s with location' % (pu.ctime(), prog))
database = h5py.File('../data/multimodal_HCP.hdf5', 'r+')
dset = database[subject + '/MEG/' + session + '/timeseries'][...]
for ROIindex in range(len(rois)):
data = dset[:, ROIindex]
label = rois[ROIindex]
# Get real amplitudes of FFT (only in postive frequencies)
# Squared to get power
fft_power = np.absolute(np.fft.rfft(data)) ** 2
# Get frequencies for amplitudes in Hz
fft_freq = np.fft.rfftfreq(len(data), 1.0 / fs)
infraslow_band = (.01, .1) # ('BOLD bandpass', (.01, .1))
freq_ix = np.where((fft_freq >= infraslow_band[0]) &
(fft_freq <= infraslow_band[1]))[0]
colname = '%s %s' % (session, label)
if colname not in session_df:
session_df[colname] = np.nan
avg_power = np.mean(fft_power[freq_ix])
session_df.loc[subject][colname] = avg_power
database.close()
df_list.append(session_df)
grand_df = pd.concat(df_list, axis=1)
return grand_df
elif how is 'bandpass':
df_list = []
for sess in sessions:
session_colnames = ['%s %s' % (sess, r) for r in rois]
session_df = pd.DataFrame(index=subjects, columns=session_colnames)
for subj in subjects:
prog = "%s - %s" % (sess, subj)
print('%s: Calculating infraslow power for %s with bandpass' % (pu.ctime(), prog))
f = h5py.File('../data/multimodal_HCP.hdf5', 'r')
data = f[subj + '/MEG/' + sess + '/timeseries'][...]
f.close()
data = _butter_filter(data, fs=500, cutoffs=[.01, .1])
fft_power = np.absolute(np.fft.rfft(data, axis=0)) ** 2
average_power = np.mean(fft_power, axis=0)
session_df.loc[subj] = average_power
df_list.append(session_df)
grand_df = pd.concat(df_list, axis=1)
return grand_df
elif how is 'truncated':
df_list = []
for sess in sessions:
session_colnames = ['%s %s' % (sess, r) for r in rois]
session_df = pd.DataFrame(index=subjects, columns=session_colnames)
for subj in subjects:
f = h5py.File('../data/downsampled_MEG_truncated.hdf5', 'r')
data = f[subj + '/MEG/' + sess + '/resampled_truncated'][...]
f.close()
data = _butter_filter(data, fs=500, cutoffs=[.01, .1])
fft_power = np.absolute(np.fft.rfft(data, axis=0)) ** 2
average_power = np.mean(fft_power, axis=0)
session_df.loc[subj] = average_power
df_list.append(session_df)
grand_df = pd.concat(df_list, axis=1)
return grand_df
print('%s: Finished' % pu.ctime())
continue
else:
val = conj_res.loc[idx_label]['LV_1']
heatmap_data.loc[roi2][roi1] = val
fig, ax = plt.subplots(figsize=(8, 6))
heatmap(data=heatmap_data,
cmap='coolwarm',
center=0.0,
annot=True,
fmt='.2f',
cbar=True,
square=True,
ax=ax)
fig.savefig(fig_dir + '/heatmap.svg')
# plt.show()
logging.info('%s: Finished' % pu.ctime())
if __name__ == "__main__":
meg_subj, meg_sess = pu.proj_data.get_meg_metadata()
rois = pu.proj_data().roiLabels
pls_psqi_with_power(meg_sess,
rois,
fig_dir='../figures/PLS/card_sort/power',
run_check=True)
pls_psqi_with_bold_alpha_pac(
fig_dir='../figures/PLS/card_sort/pac_bold_alpha', run_check=True)
# pls_psqi_with_ppc_roi_version(fig_dir='../figures/PLS/card_sort/ppc_network_rois', run_check=True)
