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calc_ali.py
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from my_settings import *
import mne
import numpy as np
import pandas as pd
epochs = mne.read_epochs(epochs_folder + "0005_target-epo.fif",
preload=False)
times = epochs.times
from_time = np.abs(times + 0.1).argmin()
to_time = np.abs(times - 0.1).argmin()
sides = ["left", "right"]
conditions = ["ctl", "ent"]
rois = ["lh", "rh"]
corr = ["correct", "incorrect"]
phase = ["in_phase", "out_phase"]
columns_keys = ["subject", "type", "side",
"correct", "phase", "ALI_pow"]
df = pd.DataFrame(columns=columns_keys)
for subject in subjects_select:
print("Working on subject: %s\n" % subject)
for cor in corr:
for p in phase:
ctl_lc_lr = np.load(tf_folder +
"%s_pow_ctl_left_MNE_%s_%s" %
(subject, cor, p) +
"_Brodmann.17-lh_target.npy")
ctl_lc_rr = np.load(tf_folder +
"%s_pow_ctl_left_MNE_%s_%s" %
(subject, cor, p) +
"_Brodmann.17-rh_target.npy")
ctl_lc_lr = ctl_lc_lr.mean(axis=0).mean(axis=0)
ctl_lc_rr = ctl_lc_rr.mean(axis=0).mean(axis=0)
ali_ctl_left = ((ctl_lc_lr - ctl_lc_rr) /
(ctl_lc_lr + ctl_lc_rr))
ctl_rc_lr = np.load(tf_folder +
"%s_pow_ctl_right_MNE_%s_%s" %
(subject, cor, p) +
"_Brodmann.17-lh_target.npy")
ctl_rc_rr = np.load(tf_folder +
"%s_pow_ctl_right_MNE_%s_%s" %
(subject, cor, p) +
"_Brodmann.17-rh_target.npy")
ctl_rc_lr = ctl_rc_lr.mean(axis=0).mean(axis=0)
ctl_rc_rr = ctl_rc_rr.mean(axis=0).mean(axis=0)
ali_ctl_right = ((ctl_rc_lr - ctl_rc_rr) /
(ctl_rc_lr + ctl_rc_rr))
ent_lc_lr = np.load(tf_folder +
"%s_pow_ent_left_MNE_%s_%s" %
(subject, cor, p) +
"_Brodmann.17-lh_target.npy")
ent_lc_rr = np.load(tf_folder +
"%s_pow_ent_left_MNE_%s_%s" %
(subject, cor, p) +
"_Brodmann.17-rh_target.npy")
ent_lc_lr = ent_lc_lr.mean(axis=0).mean(axis=0)
ent_lc_rr = ent_lc_rr.mean(axis=0).mean(axis=0)
ali_ent_left = ((ent_lc_lr - ent_lc_rr) /
(ent_lc_lr + ent_lc_rr))
ent_rc_lr = np.load(tf_folder +
"%s_pow_ent_right_MNE_%s_%s" %
(subject, cor, p) +
"_Brodmann.17-lh_target.npy")
ent_rc_rr = np.load(tf_folder +
"%s_pow_ent_right_MNE_%s_%s" %
(subject, cor, p) +
"_Brodmann.17-rh_target.npy")
ent_rc_lr = ent_rc_lr.mean(axis=0).mean(axis=0)
ent_rc_rr = ent_rc_rr.mean(axis=0).mean(axis=0)
ali_ent_right = ((ent_rc_lr - ent_rc_rr) /
(ent_rc_lr + ent_rc_rr))
# ent right
row = pd.DataFrame([{"subject": subject,
"type": "ent",
"side": "right",
"correct": cor,
"phase": p,
"ALI_pow": ali_ent_righty[from_time:to_time].mean()}])
df = df.append(row, ignore_index=True)
# ent left
row = pd.DataFrame([{"subject": subject,
"type": "ent",
"side": "left",
"correct": cor,
"phase": p,
"ALI_pow": ali_ent_left[from_time:to_time].mean()}])
df = df.append(row, ignore_index=True)
# ctl right
row = pd.DataFrame([{"subject": subject,
"type": "ctl",
"side": "right",
"correct": cor,
"phase": p,
"ALI_pow": ali_ctl_right[from_time:to_time].mean()}])
df = df.append(row, ignore_index=True)
# ctl left
row = pd.DataFrame([{"subject": subject,
"type": "ctl",
"side": "left",
"correct": cor,
"phase": p,
"ALI_pow": ali_ctl_left[from_time:to_time].mean()}])
df = df.append(row, ignore_index=True)
df.to_csv(data_path + "alpha_ali_mean_data_extracted_phase_target.csv", index=False)