-
Notifications
You must be signed in to change notification settings - Fork 0
/
FPVSWORDS_Plot_Figures_4Paper.py
executable file
·181 lines (134 loc) · 6.43 KB
/
FPVSWORDS_Plot_Figures_4Paper.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
### plot some figures for individual subjects for debugging
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 importlib import reload
import config_fpvswords as config
reload(config)
import mne
# from mne.report import Report
plt.ion()
subj = "GM"
sbj_path = config.grandmean_path
fig_path = "/imaging/hauk/users/olaf/FPVS2/MEG/Figures"
conds = ["english"]
# conds = ["slow", "extraslow", "extrafast"]
ev_types = ["cn", "nw", "pw"]
# ev_types = ["pw"] # extra only has pw
# for topoplots in plot_joint()
timess = {
"face": [1.2, 2.4, 3.6, 4.8], # face, odd,
"english": [2, 4, 6, 8], # english, odd
"slow": [1, 2, 3], # slow, odd
"extraslow": [1, 2, 3], # slow, odd
"extrafast": [2, 4, 6, 8], # slow, odd
}
# for base topomap
times_maps = {
"face": [6], # face, base
"english": [10], # english, base
"slow": [4], # slow, base
"extraslow": [4],
"extrafast": [10],
}
f_range = (0.5, 11) # frequency range to plot for PSDs
scalings = dict(eeg=1, grad=1, mag=1) # change units for plots
ts_args = dict(scalings=scalings) # for plot_joint()
# type of figure to save
fig_type = 'pdf'
for cond in conds:
times = timess[cond]
times_map = times_maps[cond]
for ev_type in ev_types:
# 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(f_range[0], f_range[1])
fig = psd_z.plot_joint(times=times, ts_args=ts_args)
for [sens, ff] in zip(["eeg", "mag", "grad"], fig):
fname_fig = op.join(fig_path, "GM_PSDZ_%s_%s_%s.%s" % (cond, ev_type, sens, fig_type))
ff.savefig(fname_fig, transparent=True)
# evoked = mne.read_evokeds(
# op.join(sbj_path, "AVE", "%s_f_%s%s" % (cond, ev_type, "_nch-ave.fif")), 0
# )
# evoked.plot_joint()
### Summed spectra
# Base:
fname_evo = op.join(sbj_path, "AVE", "HarmBase_%s_%s%s" % (cond, ev_type, "-ave.fif"))
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"))
harmodd = mne.read_evokeds(fname_evo)[0]
figbase = harmbase.plot(spatial_colors=True, scalings=scalings)
fname_fig = op.join(fig_path, "GM_HarmBase_%s_%s.%s" % (cond, ev_type, fig_type))
figbase.savefig(fname_fig, transparent=True)
figodd = harmodd.plot(spatial_colors=True, scalings=scalings)
fname_fig = op.join(fig_path, "GM_HarmOdd_%s_%s.%s" % (cond, ev_type, fig_type))
figodd.savefig(fname_fig, transparent=True)
times_map = 0.0
for sens in ["eeg", "grad", "mag"]:
fig = harmbase.plot_topomap(times=times_map, scalings=scalings, ch_type=sens)
fname_fig = op.join(fig_path, "GM_HarmBase_%s_%s_%s.%s" % (cond, ev_type, sens, fig_type))
fig.savefig(fname_fig, transparent=True)
for sens in ["eeg", "grad", "mag"]:
fig = harmodd.plot_topomap(times=times_map, scalings=scalings, ch_type=sens)
fname_fig = op.join(fig_path, "GM_HarmOdd_%s_%s_%s.%s" % (cond, ev_type, sens, fig_type))
fig.savefig(fname_fig, transparent=True)
# individual topomaps
fname_evo = op.join(sbj_path, "AVE", "GM_psd_sum_base_indiv_topos_%s_%s-ave.fif" % (cond, ev_type))
print(fname_evo)
evo_indiv = mne.read_evokeds(fname_evo, 0)
times_topo = evo_indiv.times
for sens in ["eeg", "grad", "mag"]:
fig = evo_indiv.plot_topomap(times=times_topo, scalings=scalings, ch_type=sens, time_format='')
fname_fig = op.join(fig_path, "GM_psd_sum_base_indiv_topos_%s_%s_%s.%s" % (cond, ev_type, sens, fig_type))
fig.savefig(fname_fig, transparent=True)
fname_evo = op.join(sbj_path, "AVE", "GM_psd_sum_odd_indiv_topos_%s_%s-ave.fif" % (cond, ev_type))
print(fname_evo)
evo_indiv = mne.read_evokeds(fname_evo, 0)
times_topo = evo_indiv.times
for sens in ["eeg", "grad", "mag"]:
fig = evo_indiv.plot_topomap(times=times_topo, scalings=scalings, ch_type=sens, time_format='')
fname_fig = op.join(fig_path, "GM_psd_sum_odd_indiv_topos_%s_%s_%s.%s" % (cond, ev_type, sens, fig_type))
fig.savefig(fname_fig, transparent=True)
# 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()
# # With topo plots
# ts_args = dict(scalings=dict(eeg=1, grad=1, mag=1))
# # psd_z.plot_joint(times=[1., 2., 3., 8.], ts_args=ts_args)
# # psd_z.plot_joint(times=[2., 4., 6., 20.], ts_args=ts_args)
# # psd_z.plot_joint(times=[1.2, 2.4, 3.6, 12.], ts_args=ts_args)
# fig = harmbase.plot_joint(times=[-0.0166, 0.], picks='grad', ts_args=ts_args)
# fig = psd_z.plot(spatial_colors=True, scalings=dict(eeg=1, grad=1, mag=1))
# 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")