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FPVSWORDS_Plot_Some_Figures.py
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FPVSWORDS_Plot_Some_Figures.py
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# plot some figures for individual subjects for debugging
import mne
from os import path as op
import numpy as np
from scipy import linalg
from scipy.signal import find_peaks
from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from importlib import reload
import config_fpvswords as config
reload(config)
# from mne.report import Report
plt.ion()
# sbj_ids = config.do_subjs
# sbj_ids = [99]
# sbj_ids = [99] # GM
# "bad ones"
# sbj_ids = [1, 10, 7, 12, 13, 20, 23, 24, 29]
# All
sbj_ids = [
1,
2,
3,
5,
7,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
21,
22,
23,
24,
25,
26,
27,
28,
29,
]
# sbj_ids = [29]
# 99 is GM
fig_path = "/imaging/hauk/users/olaf/FPVS2/MEG/Figures"
# conds = ['english', 'slow']
# ev_types = ['nw', 'nw']
# ATM only does one condition and one event type at a time
conds = ["english"]
ev_types = ["cn"]
# frequency range to plot
# crop = (0, 13) # Faces
crop = (0, 21) # Words
# crop = (0, 9) # Slow
scalings = None # dict(eeg=1, grad=1, mag=1)
fname = op.join(fig_path, "PSD_%s_%s_indiv.pdf" % (conds[0], ev_types[0]))
with PdfPages(fname) as pdf:
for [ss, sbj_id] in enumerate(sbj_ids): # across all subjects
for [cond, ev_type] in zip(conds, ev_types):
# path to subject's data
if sbj_id == 99: # GM
subj = "GM"
sbj_path = config.grandmean_path
else:
subj = config.map_subjects[sbj_id][0]
sbj_path = op.join(config.data_path, subj)
# PSD (non-normalised):
fname_evo =\
op.join(sbj_path, 'AVE', 'PSD_%s_%s%s' %
(cond, ev_type, '-ave.fif'))
print(fname_evo)
psd = mne.read_evokeds(fname_evo)[0].crop(crop[0], crop[1])
# # PSD (z-scored):
# fname_evo =\
# op.join(sbj_path, 'AVE', 'PSDZ_%s_%s%s' %
# (cond, ev_type, '-ave.fif'))
# print(fname_evo)
# psd_z = mne.read_evokeds(fname_evo)[0].crop(crop[0], crop[1])
# # Summed epochs, Base:
# fname_evo = op.join(
# sbj_path, "AVE", "HarmBase_%s_%s%s" % (cond, ev_type, "-ave.fif")
# )
# print(fname_evo)
# harmbase = mne.read_evokeds(fname_evo)[0]
# # Summed epochs, Odd:
# fname_evo = op.join(
# sbj_path, "AVE", "HarmOdd_%s_%s%s" % (cond, ev_type, "-ave.fif")
# )
# print(fname_evo)
# harmodd = mne.read_evokeds(fname_evo)[0]
# # fname_raw =\
# # op.join(sbj_path, 'rawavg_%s_%s.fif' % (cond, ev_type))
# # raw = mne.io.read_raw_fif(fname_raw).pick_types(meg=True, eeg=True, eog=False, ecg=False,
# # stim=False, misc=False, chpi=False)
# # Inverse Operator
# subject = config.mri_subjects[sbj_id]
# fname_inv =\
# op.join(sbj_path, '%s_EEGMEG-inv.fif' % subject)
# inv = mne.minimum_norm.read_inverse_operator(fname_inv)
# fname_fwd =\
# op.join(sbj_path, '%s_EEGMEG-fwd.fif' % subject)
# fwd = mne.read_forward_solution(fname_fwd)
# # compute inverse kernel
# K = mne.minimum_norm.resolution_matrix._get_matrix_from_inverse_operator(inv, fwd, method='MNE')
# Covariance matrix (with info from rest)
# fname_rest =\
# op.join(sbj_path, 'rest1_sss_f_ica_raw.fif')
# fname_rest =\
# op.join(sbj_path, 'rest1_sss_raw.fif')
# print(fname_rest)
# rawrest = mne.io.read_raw_fif(fname_rest)
# fname_cov =\
# op.join(sbj_path, 'rest_sss_f_raw_ica-cov.fif')
# print(fname_cov)
# cov = mne.read_cov(fname_cov)
# # SVDs
# Ui, si, Vi = linalg.svd(K, full_matrices=False)
# Uf, sf, Vf = linalg.svd(fwd['sol']['data'], full_matrices=False)
# fig1 = plt.figure()
# plt.plot(si)[0]
# fig1.suptitle(str(sbj_id) + ': ' + subj)
# fig2 = plt.figure()
# plt.plot(sf)[0]
# fig2.suptitle(str(sbj_id) + ': ' + subj)
# print('Largest s forward: %d %f' % (sbj_id, sf[0]))
# #### INSPECT TRIGGERS
# fname_raw =\
# op.join(sbj_path, 'rawavg_%s_%s%s' % (cond, ev_type, '_0.fif'))
# rawstim = mne.io.read_raw_fif(fname_raw).pick_types(
# meg=False, eeg=False, eog=False, ecg=False, stim=True, misc=False, chpi=False)
# x = rawstim.get_data()[2, :] # trigger value 4
# p = find_peaks(x, height=(0, 7), width=(1,3))[0] # peaks of certain triggers with certain width
# diffs = []
# all_diffs = []
# for i in np.arange(len(p)-1):
# diff = p[i+1]-p[i]
# all_diffs.append(diff)
# if diff not in [166, 167, 332, 333, 334]:
# diffs.append(diff)
# print('\n\n')
# print(diffs)
# print('\n\n')
# print(np.mean(all_diffs))
# fig = mne.viz.plot_cov(cov, rawrest.info)
# for f in fig:
# f.suptitle(str(sbj_id) + ': ' + subj)
# fig = psd.plot(spatial_colors=True, scalings=dict(eeg=1, grad=1, mag=1))
# fig.suptitle(str(sbj_id) + ': ' + subj)
# fig = psd_z.plot(spatial_colors=True, scalings=dict(eeg=1, grad=1, mag=1))
# fig.suptitle(str(sbj_id) + ': ' + subj)
# fig = harmbase.plot(spatial_colors=True, scalings=dict(eeg=1, grad=1, mag=1))
# fig.suptitle(str(sbj_id) + ': ' + subj)
# figodd = harmodd.plot(spatial_colors=True,
# scalings=dict(eeg=1, grad=1, mag=1))
# figodd.suptitle(str(sbj_id) + ": " + subj)
# figbase = harmbase.plot(
# spatial_colors=True, scalings=dict(eeg=1, grad=1, mag=1)
# )
# figbase.suptitle(str(sbj_id) + ": " + subj)
# mne.viz.plot_raw_psd()
# fig = harmbase.plot_joint(times=[-0.0166, 0.], picks='grad', ts_args=ts_args)
# psd.crop(tmin=0., tmax=21.)
psd.crop(tmin=0.5, tmax=21.)
# # With topo plots
ts_args = dict(scalings=scalings)
# fig = psd_z.plot_joint(times=[1., 2., 3., 8.], ts_args=ts_args)
fig = psd.plot_joint(times=[2., 4., 6., 20.], ts_args=ts_args)
# fig3 = psd_z.plot_joint(times=[1.2, 2.4, 3.6, 12.], ts_args=ts_args)
for i in [0, 1, 2]:
fname = op.join(fig_path, "PSDtopo_%s_%s_%s_%s.jpg" % (str(sbj_id),
cond, ev_type, str(i)))
fig[i].savefig(fname)
fig = psd.plot(spatial_colors=True,
scalings=scalings)
fig.suptitle('PSD, %s %s %s' % (cond, ev_type, str(
sbj_id)), fontweight='bold', fontsize=14)
fname = op.join(fig_path, "PSD_%s_%s_%s.jpg" % (str(sbj_id),
cond, ev_type))
fig.savefig(fname)
pdf.savefig(fig)
plt.close('all')
# fname = "/imaging/hauk/users/olaf/FPVS2/MEG/Figures/%s_%s_harmodd_%s.jpg" % (
# cond,
# ev_type,
# str(sbj_id),
# )
# figodd.savefig(fname)
# fname = "/imaging/hauk/users/olaf/FPVS2/MEG/Figures/%s_%s_harmbase_%s.jpg" % (
# cond,
# ev_type,
# str(sbj_id),
# )
# figbase.savefig(fname)
# plt.close(fig)
# # summed topos Base
# harmbase.plot_topomap(times=[0.0], scalings=dict(eeg=1, grad=1, mag=1), ch_type="eeg")
# harmbase.plot_topomap(times=[0.0], scalings=dict(eeg=1, grad=1, mag=1), ch_type="grad")
# harmbase.plot_topomap(times=[0.0], scalings=dict(eeg=1, grad=1, mag=1), ch_type="mag")
# # summed topos Odd
# harmodd.plot_topomap(times=[0.0], scalings=dict(
# eeg=1, grad=1, mag=1), ch_type="eeg")
# harmodd.plot_topomap(times=[0.0], scalings=dict(
# eeg=1, grad=1, mag=1), ch_type="grad")
# harmodd.plot_topomap(times=[0.0], scalings=dict(
# eeg=1, grad=1, mag=1), ch_type="mag")