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"""
Utility functions for all Jupyter notebook plots.
"""
import
joblib
import
seaborn
as
sns
import
pandas
as
pd
import
numpy
as
np
import
matplotlib
import
matplotlib
.
pyplot
as
plt
import
matplotlib
.
gridspec
as
gridspec
from
matplotlib
.
cm
import
get_cmap
from
matplotlib
.
colors
import
Normalize
from
matplotlib
.
legend_handler
import
HandlerLine2D
import
matplotlib
.
lines
as
mlines
from
matplotlib
.
offsetbox
import
AnchoredOffsetbox
,
TextArea
,
HPacker
,
VPacker
import
glob
,
pdb
,
sys
,
os
,
pickle
,
copy
,
natsort
from
matplotlib
import
rcParams
rcParams
[
'font.family'
]
=
'arial'
from
functools
import
reduce
import
pingouin
as
pg
from
nilearn
import
plotting
as
ni_plt
from
scipy
.
signal
import
welch
from
scipy
.
fftpack
import
fft
from
scipy
.
signal
import
hann
from
tqdm
.
notebook
import
tqdm
os
.
environ
[
"CUDA_DEVICE_ORDER"
]
=
"PCI_BUS_ID"
os
.
environ
[
"CUDA_VISIBLE_DEVICES"
]
=
"0"
#Use GPU
import
tensorflow
as
tf
from
tensorflow
.
keras
.
models
import
Model
from
keras
import
backend
as
K
from
tensorflow
.
keras
.
layers
import
Input
,
Conv2D
from
mne
import
read_epochs
from
mne
.
time_frequency
import
read_tfrs
# Custom imports
from
htnet_model
import
htnet
from
model_utils
import
folds_choose_subjects
,
get_custom_motor_rois
,
proj_mats_good_rois
def
plot_model_accs
(
root_path
,
dataset
,
nb_classes
=
2
,
compare_models
=
True
,
dpi_plt
=
300
):
"""
Plot test accuracy across decoder types.
Inputs:
nb_classes : number of label types (currently set to 2: move, rest)
compare_models : if True, plot across different models; if False, plot across different HilbertNet measures
"""
# root_path = '/data2/users/stepeter/cnn_hilb_datasets/'
# dataset = 'naturalistic_v3'
replace_str
=
'___'
suffix_lp
=
'/'
+
replace_str
+
'/'
# single_sbjs, combined_sbjs
test_types
=
[
'single_sbjs'
,
'combined_sbjs'
,
'ecog2eeg'
]
suffix_lst
=
[
'Power'
,
'Log
\n
Power'
,
'Relative
\n
Power'
,
'Phase'
,
'Frequency'
]
#Needed if models used are the same (for axis label)
n_eeg_sbjs
=
15
conds
=
[
'Tailored decoder'
,
'Generalized decoder,
\n
same modality'
,
'Generalized decoder,
\n
unseen modality'
]
cond_lets
=
[
'(A)'
,
'(B)
\n
'
,
'(C)
\n
'
]
conds2
=
[
'(D)'
,
'(E)'
,
'(F)'
]
# Load variables from param file
file_pkl
=
open
(
root_path
+
dataset
+
'/combined_sbjs_power/param_file.pkl'
,
'rb'
)
params_dict
=
pickle
.
load
(
file_pkl
)
file_pkl
.
close
()
rand_seed
=
params_dict
[
'rand_seed'
]
n_folds
=
params_dict
[
'n_folds'
]
pats_ids_in
=
params_dict
[
'pats_ids_in'
]
combined_sbjs
=
params_dict
[
'combined_sbjs'
]
test_day
=
params_dict
[
'test_day'
]
n_test
=
params_dict
[
'n_test'
]
n_val
=
params_dict
[
'n_val'
]
lp
=
[
root_path
+
dataset
+
suffix_lp
[:
-
1
]
+
'_power/'
]
if
compare_models
:
models_used
=
params_dict
[
'models'
]
lp
*=
len
(
models_used
)
else
:
lp
=
[
root_path
+
dataset
+
suffix_lp
,
root_path
+
dataset
+
suffix_lp
[:
-
1
]
+
'_power_log/'
,
root_path
+
dataset
+
suffix_lp
[:
-
1
]
+
'_relative_power/'
,
root_path
+
dataset
+
suffix_lp
[:
-
1
]
+
'_phase/'
,
root_path
+
dataset
+
suffix_lp
[:
-
1
]
+
'_freqslide/'
]
models_used
=
[
'eegnet_hilb'
]
*
len
(
lp
)
model_dict
=
{
'eegnet_hilb'
:
'HTNet'
,
'eegnet'
:
'EEGNet'
,
'rf'
:
'Random
\n
Forest'
,
'riemann'
:
'Minimum
\n
Distance'
,
'phase'
:
'Phase'
,
'relative_power'
:
'Rel.
\n
Power'
,
'freqslide'
:
'Freq.'
,
'power_log'
:
'Log
\n
Power'
,
''
:
'Power'
}
# Dictionary for plot legend
# Determine test fold subject(s) using saved parameters
np
.
random
.
seed
(
rand_seed
)
sbj_inds_all_train
,
sbj_inds_all_val
,
sbj_inds_all_test
=
folds_choose_subjects
(
n_folds
,
pats_ids_in
,
n_test
=
n_test
,
n_val
=
n_val
)
sbj_inds_all_test_sm
=
[
val
[
0
]
for
val
in
sbj_inds_all_test
]
test_sbj_folds
=
np
.
asarray
(
sbj_inds_all_test_sm
)
# Create labels for ECoG and EEG participants
ecog_sbjs
,
eeg_sbjs
=
[],[]
for
i
in
range
(
len
(
pats_ids_in
)):
ecog_sbjs
.
append
(
'EC'
+
str
(
i
+
1
).
zfill
(
2
))
for
i
in
range
(
n_eeg_sbjs
):
eeg_sbjs
.
append
(
'EE'
+
str
(
i
+
1
).
zfill
(
2
))
# Create labels for model types or measures computed
col_labels
=
[
model_dict
[
val
]
for
val
in
models_used
]
if
len
(
np
.
unique
(
np
.
asarray
(
col_labels
)))
!=
len
(
col_labels
):
#If there are duplicate column labels, add different suffix strings
col_labels
=
[
suffix_lst
[
i
]
for
i
,
val
in
enumerate
(
col_labels
)]
# Plot data
title_pads
=
[
18
,
8
,
8
]
title_y
=
[
1.2
,
1.125
,
1.125
]
hspace_val
=
.8
if
compare_models
else
.6
use_asterisks
=
True
fig
,
ax
=
plt
.
subplots
(
2
,
len
(
test_types
),
dpi
=
dpi_plt
,
figsize
=
(
7.5
,
4
),
gridspec_kw
=
{
'wspace'
:
.1
,
'hspace'
:
hspace_val
})
acc_types
=
[
'Train'
,
'Val'
,
'Test'
]
test_ind
=
np
.
nonzero
(
np
.
asarray
(
acc_types
)
==
'Test'
)[
0
]
dfs_all
=
[]
for
ii
,
test_type
in
enumerate
(
test_types
):
n_accs
=
len
(
acc_types
)
n_models
=
len
(
models_used
)
if
test_type
!=
'single_sbjs'
:
if
test_type
==
'ecog2eeg'
:
accs_all
=
np
.
zeros
([
n_eeg_sbjs
,
n_accs
,
n_models
])
#middle dim is train,val,test accuracies
else
:
accs_all
=
np
.
zeros
([
n_folds
,
n_accs
,
n_models
])
accs_all
[:]
=
np
.
nan
for
i
,
model_type
in
enumerate
(
models_used
):
lp_curr
=
lp
[
i
]
if
test_type
==
'ecog2eeg'
:
lp_curr
=
'/'
.
join
(
lp_curr
.
split
(
'/'
)[:
-
2
])
+
'/'
mod_type_curr
=
''
if
model_type
==
'eegnet_hilb'
else
'_'
+
model_type
if
not
compare_models
:
lp_spl
=
lp_curr
.
split
(
'/'
)
lp_spl
[:
-
2
]
+
lp_spl
[
-
1
:]
lp_curr
=
'/'
.
join
(
lp_spl
[:
-
2
]
+
lp_spl
[
-
1
:])
suff
=
lp_spl
[
-
2
]
else
:
suff
=
'_power'
##########Use relative power instead for comparison##########
if
mod_type_curr
==
''
:
suff
=
'_relative_power'
##########Use relative power instead for comparison##########
tmp_vals
=
np
.
load
(
lp_curr
+
'accs_ecogtransfer'
+
suff
+
mod_type_curr
+
'.npy'
)
for
p
in
range
(
n_eeg_sbjs
):
accs_all
[
p
,
test_ind
,
i
]
=
np
.
mean
(
tmp_vals
[:,
p
])
else
:
lp_curr
=
lp_curr
.
replace
(
replace_str
,
test_type
)
tmp_vals
=
np
.
load
(
lp_curr
+
'acc_gen_'
+
model_type
+
'_'
+
str
(
n_folds
)
+
'.npy'
)
for
j
in
range
(
n_accs
):
for
p
in
range
(
n_folds
):
accs_all
[
p
,
j
,
i
]
=
tmp_vals
[
p
,
j
]
else
:
n_folds_sbj
=
n_folds
//
len
(
pats_ids_in
)
accs_all
=
np
.
zeros
([
len
(
pats_ids_in
),
n_accs
,
n_models
,
n_folds_sbj
])
accs_all
[:]
=
np
.
nan
for
i
,
model_type
in
enumerate
(
models_used
):
for
s
,
pat_curr
in
enumerate
(
pats_ids_in
):
lp_curr
=
lp
[
i
]
lp_curr
=
lp_curr
.
replace
(
replace_str
,
test_type
)
tmp_vals
=
np
.
load
(
lp_curr
+
'acc_'
+
model_type
+
'_'
+
pat_curr
+
'_testday_'
+
str
(
test_day
)
+
'.npy'
)
for
j
in
range
(
n_accs
):
for
p
in
range
(
n_folds_sbj
):
accs_all
[
s
,
j
,
i
,
p
]
=
tmp_vals
[
p
,
j
]
# Average results for each participant
if
test_type
==
'ecog2eeg'
:
ave_vals_test_sbj
=
accs_all
[:,
test_ind
,:].
squeeze
()
else
:
n_subjs
=
len
(
pats_ids_in
)
if
test_type
==
'combined_sbjs'
:
n_folds_sbj
=
n_folds
//
n_subjs
ave_vals_test
=
np
.
zeros
([
n_subjs
*
n_folds_sbj
,
len
(
models_used
)])
ave_vals_test_sbj
=
np
.
zeros
([
n_subjs
,
len
(
models_used
)])
if
test_type
==
'combined_sbjs'
:
for
sbj
in
range
(
len
(
pats_ids_in
)):
folds_sbj
=
np
.
nonzero
(
test_sbj_folds
==
sbj
)[
0
]
for
j
,
modtype
in
enumerate
(
models_used
):
ave_vals_test_sbj
[
sbj
,
j
]
=
round
(
np
.
mean
(
accs_all
[
folds_sbj
,
test_ind
,
j
],
axis
=
0
),
2
)
for
k
in
range
(
n_folds_sbj
):
ave_vals_test
[
sbj
+
(
k
*
n_subjs
),
j
]
=
accs_all
[
folds_sbj
[
k
],
test_ind
,
j
]
else
:
for
i
in
range
(
accs_all
.
shape
[
0
]):
for
j
in
range
(
accs_all
.
shape
[
2
]):
ave_vals_test_sbj
[
i
,
j
]
=
round
(
np
.
mean
(
accs_all
[
i
,
test_ind
,
j
,:],
axis
=
-
1
)[
0
],
2
)
for
k
in
range
(
n_folds_sbj
):
ave_vals_test
[
i
+
(
k
*
n_subjs
),
j
]
=
accs_all
[
i
,
test_ind
,
j
,
k
]
## Boxplots (top row)
if
test_type
==
'ecog2eeg'
:
curr_subjs
=
eeg_sbjs
else
:
curr_subjs
=
ecog_sbjs
patIDs_lst
=
[]
for
val
in
curr_subjs
:
patIDs_lst
.
extend
([
val
]
*
len
(
models_used
))
if
(
test_type
==
'ecog2eeg'
)
&
compare_models
:
col_labels_plt
=
[
'HilbertNet
\n
(Rel. Power)'
if
val
==
'HilbertNet'
else
val
for
val
in
col_labels
]
else
:
col_labels_plt
=
col_labels
.
copy
()
vals_np
=
np
.
asarray
([
ave_vals_test_sbj
.
flatten
().
tolist
()]
+
[
patIDs_lst
]
+
\
[
col_labels_plt
*
len
(
curr_subjs
)]).
T
df_sbj
=
pd
.
DataFrame
(
vals_np
,
columns
=
[
'Accuracy'
,
'sbj'
,
'Models'
])
df_sbj
[
'Accuracy'
]
=
pd
.
to_numeric
(
df_sbj
[
'Accuracy'
])
df
=
pd
.
DataFrame
(
ave_vals_test_sbj
,
index
=
curr_subjs
,
columns
=
col_labels_plt
)
ax
[
0
,
ii
].
axhline
(
1
/
nb_classes
,
c
=
'k'
,
linestyle
=
'--'
)
palette
=
sns
.
color_palette
(
"colorblind"
,
n_colors
=
df
.
shape
[
1
])
palette2
=
palette
.
copy
()
palette2
.
reverse
()
sns
.
boxplot
(
data
=
df
,
ax
=
ax
[
0
,
ii
],
palette
=
palette
,
showfliers
=
False
,
whis
=
0
)
# palette = sns.color_palette("Paired")
sns
.
swarmplot
(
x
=
'Models'
,
y
=
'Accuracy'
,
s
=
4
,
data
=
df_sbj
,
ax
=
ax
[
0
,
ii
],
color
=
'k'
)
#, palette=palette, hue='sbj')
# ax.legend_.remove()
ax
[
0
,
ii
].
set_ylabel
(
'Test Accuracy'
,
fontsize
=
9
)
ax
[
0
,
ii
].
set_ylim
([(
1
/
nb_classes
)
-
.05
,
1.065
])
ax
[
0
,
ii
].
set_yticks
([
.5
,
.75
,
1
])
ax
[
0
,
ii
].
spines
[
'right'
].
set_visible
(
False
)
ax
[
0
,
ii
].
spines
[
'top'
].
set_visible
(
False
)
ax
[
0
,
ii
].
spines
[
'left'
].
set_bounds
((
1
/
nb_classes
)
-
.05
,
1
)
ax
[
0
,
ii
].
tick_params
(
axis
=
'both'
,
labelsize
=
8
)
ax
[
0
,
ii
].
set_xticklabels
(
ax
[
0
,
ii
].
get_xticklabels
(),
rotation
=
40
,
ha
=
"center"
)
ax
[
0
,
ii
].
set_xlabel
(
''
)
if
ii
>
0
:
ax
[
0
,
ii
].
set_ylabel
(
''
)
ax
[
0
,
ii
].
set_yticklabels
([])
ax
[
0
,
ii
].
tick_params
(
axis
=
'x'
,
pad
=
0
)
ax
[
0
,
ii
].
set_title
(
cond_lets
[
ii
],
fontsize
=
10
,
x
=
0
,
fontweight
=
'bold'
,
pad
=
title_pads
[
ii
],
color
=
'dimgray'
)
ax
[
0
,
ii
].
text
((
len
(
lp
)
/
2
)
-
.5
,
title_y
[
ii
],
conds
[
ii
],
fontsize
=
10
,
ha
=
'center'
,
fontweight
=
'bold'
)
# # Add stats (manually created)
# star_fontsize = 18
# if (ii==0) & compare_models:
# y_start,h,w_x = 1.02,.04,1
# ax[0,ii].plot([0, 0, 2, 2, 2-w_x, 2+w_x],
# [y_start, y_start+h, y_start+h, y_start, y_start, y_start],
# lw=1.5, c='k')
# if use_asterisks:
# ax[0,ii].text(1,1.04,'*',fontsize=star_fontsize,fontweight='bold',ha='center')
# else:
# ax[0,ii].text(1,1.09,r'$p<0.05$',fontsize=7,fontweight='normal',ha='center')
# elif (ii==1) & compare_models:
# y_start,h1,h2,w_x = .95,.06,.06,1
# ax[0,ii].plot([0, 0, 2, 2, 2-w_x, 2+w_x],
# [y_start, y_start+h1, y_start+h1, y_start-h2, y_start-h2, y_start-h2],
# lw=1.5, c='k')
# if use_asterisks:
# ax[0,ii].text(1,.99,'*',fontsize=star_fontsize,fontweight='bold',ha='center')
# else:
# ax[0,ii].text(1,1.05,r'$p<0.05$',fontsize=7,fontweight='normal',ha='center')
# elif (ii==2) & compare_models:
# y_start,h1,h2,w_x = .8,.06,.1,1
# ax[0,ii].plot([0, 0, 2, 2, 2-w_x, 2+w_x],
# [y_start, y_start+h1, y_start+h1, y_start-h2, y_start-h2, y_start-h2],
# lw=1.5, c='k')
# if use_asterisks:
# ax[0,ii].text(1,.85,'***',fontsize=star_fontsize,fontweight='bold',ha='center')
# else:
# ax[0,ii].text(1,.9,r'$p<0.001$',fontsize=7,fontweight='normal',ha='center')
# if (ii==0) & (not compare_models):
# ax[0,ii].text(2,.82,'**',fontsize=star_fontsize,fontweight='bold',ha='center')
# ax[0,ii].text(3,.97,'**',fontsize=star_fontsize,fontweight='bold',ha='center')
# elif (ii==1) & (not compare_models):
# y_start,h = .92,.06
# ax[0,ii].plot([2, 2, 4, 4],
# [y_start, y_start+h, y_start+h, y_start],
# lw=1.5, c='k')
# ax[0,ii].text(3,.97,'**',fontsize=star_fontsize,fontweight='bold',ha='center')
# elif (ii==2) & (not compare_models):
# ax[0,ii].text(1,.55,'**',fontsize=star_fontsize,fontweight='bold',ha='center')
# ax[0,ii].text(2,.8,'**',fontsize=star_fontsize,fontweight='bold',ha='center')
# ax[0,ii].text(3,.65,'**',fontsize=star_fontsize,fontweight='bold',ha='center')
## Subject-by-subject lineplots (bottom row)
col_labels_plt
=
df_sbj
[
'Models'
].
unique
().
tolist
().
copy
()
if
compare_models
:
col_labels_plt
.
reverse
()
ax
[
1
,
ii
].
axhline
(
1
/
nb_classes
,
c
=
'k'
,
linestyle
=
'--'
)
sns
.
lineplot
(
x
=
'sbj'
,
y
=
'Accuracy'
,
hue
=
'Models'
,
data
=
df_sbj
,
ax
=
ax
[
1
,
ii
],
marker
=
'o'
,
markersize
=
5
,
linewidth
=
1
,
hue_order
=
col_labels_plt
,
palette
=
palette2
)
leg
=
ax
[
1
,
ii
].
legend
()
leg_lines
=
leg
.
get_lines
()
for
i
in
range
(
len
(
models_used
)):
ax
[
1
,
ii
].
lines
[
i
+
1
].
set_linestyle
(
"None"
)
# 'None') #
leg_lines
[
i
].
set_linestyle
(
"None"
)
# 'None') #
# plt.xticks(np.arange(1,13))
ax
[
1
,
ii
].
set_ylim
([(
1
/
nb_classes
)
-
.05
,
1
])
ax
[
1
,
ii
].
set_ylabel
(
'Test Accuracy'
,
fontsize
=
9
)
ax
[
1
,
ii
].
set_yticks
([
.5
,
.75
,
1
])
ax
[
1
,
ii
].
spines
[
'right'
].
set_visible
(
False
)
ax
[
1
,
ii
].
spines
[
'top'
].
set_visible
(
False
)
ax
[
1
,
ii
].
tick_params
(
axis
=
'both'
,
labelsize
=
8
)
ax
[
1
,
ii
].
legend_
.
remove
()
ax
[
1
,
ii
].
set_xlabel
(
''
)
ax
[
1
,
ii
].
set_title
(
conds2
[
ii
],
fontsize
=
10
,
fontweight
=
'bold'
,
pad
=
8
,
color
=
'dimgray'
,
x
=
0
)
for
tick
in
ax
[
1
,
ii
].
get_xticklabels
():
tick
.
set_rotation
(
60
)
ax
[
1
,
ii
].
tick_params
(
axis
=
'x'
,
pad
=
0
)
if
ii
>
0
:
ax
[
1
,
ii
].
set_ylabel
(
''
)
ax
[
1
,
ii
].
set_yticklabels
([])
# Save dataframe for stats
dfs_all
.
append
(
df_sbj
)
plt
.
show
()
return
fig
,
dfs_all
def
test_acc_mod_stats
(
dfs_all
):
"""
Compute non-parametric stats for decoder accuracies.
"""
for
k
,
df_sbj
in
enumerate
(
dfs_all
):
print
(
''
)
print
(
conds
[
k
])
# Friedman test (non-parametric, repeated measures ANOVA)
print
(
pg
.
friedman
(
data
=
df_sbj
,
dv
=
'Accuracy'
,
within
=
'Models'
,
subject
=
'sbj'
)[
'p-unc'
])
# Wilcoxon tests (non-parametric t-tests)
p_vals
=
[]
col_labels
=
np
.
unique
(
df_sbj
[
'Models'
].
values
).
tolist
()
n_models
=
len
(
col_labels
)
for
i
in
range
(
n_models
):
for
j
in
range
(
i
+
1
,
n_models
):
val1
=
df_sbj
[
df_sbj
[
'Models'
]
==
col_labels
[
i
]].
iloc
[:,
0
].
values
val2
=
df_sbj
[
df_sbj
[
'Models'
]
==
col_labels
[
j
]].
iloc
[:,
0
].
values
p_vals
.
append
(
float
(
pg
.
wilcoxon
(
val1
,
val2
)[
'p-val'
]))
# Correct for multiple comparisons
_
,
p_vals
=
pg
.
multicomp
(
np
.
asarray
(
p_vals
),
alpha
=
0.05
,
method
=
'fdr_bh'
)
pval_df
=
np
.
zeros
([
n_models
,
n_models
])
q
=
0
for
i
in
range
(
n_models
):
for
j
in
range
(
i
+
1
,
n_models
):
pval_df
[
i
,
j
]
=
p_vals
[
q
]
q
+=
1
# Create output df with p_values
df_pval
=
pd
.
DataFrame
(
pval_df
,
columns
=
col_labels
,
index
=
col_labels
)
df_pval
[
df_pval
==
0
]
=
np
.
nan
print
(
df_pval
)
def
plot_weights_interp
(
root_path
,
dataset
,
roi_proj_lp
,
spec_lp
,
mod_type
=
'eegnet_hilb'
,
nb_classes
=
2
,
dat_type
=
'train'
,
temp_conv_layer
=
'conv2d'
,
weight_type
=
'mean'
,
srate
=
250
,
bode_type
=
'mag'
,
custom_rois
=
True
,
dpi_plt
=
300
,
vscale_val
=
1.5
,
epoch_times
=
[
-
1
,
1.001
]):
"""
HTNet decoder interpretability plots from analyzing convolutional layers.
Inputs:
spec_lp : path to pre-computed spectrogram data
dat_type : which accuracies to use when weighting temporal frequency response ('train' or 'test')
temp_conv_layer : which temporal convolutional layer to compute frequency response of ('conv2d' or 'separable_conv2d')
weight_type : way to average temporal frequency response across filters ('max','mean','median','weighted','weighted_percentile');
only weighted and weighted_percentile use the accuracies from dat_type input
srate : sampling rate of neural data used to train model (Hz)
bode_type : look at magnitude ('mag') or phase ('phase') frequency response
custom_rois : use predefined sensorimotor ROI's
"""
# models_compare = ['eegnet_hilb']
# root_path = '/data2/users/stepeter/cnn_hilb_datasets/'
# dataset = 'naturalistic_v3'
# roi_proj_lp = '/data1/users/stepeter/mvmt_init/ROIproj_matlab_smallROIs/'
lp
=
root_path
+
dataset
+
'/combined_sbjs_power/'
colors_plt
=
[
'b'
,
'r'
]
normalize_weighted_dat
=
False
model_dict
=
{
'eegnet_hilb'
:
'Hilbert NN'
,
'eegnet'
:
'Original NN'
,
'rf'
:
'Random Forest'
}
# Dictionary for plot legend
# Spatial filter plot parameters
zero_sub_rand_accs
=
False
# if True, set accuracies at or below random guessing to 0
rand_acc_thresh
=
0.5
# accuracy threshold for random guessing
vmin
,
vmax
=
0
,
1
## Temporal frequency response plot
fig
,
axes
=
plt
.
subplots
(
2
,
2
,
figsize
=
(
7.5
,
5
),
dpi
=
dpi_plt
,
gridspec_kw
=
{
'wspace'
:
.2
,
'hspace'
:
.47
})
pos
=
axes
[
0
,
1
].
get_position
()
width
,
height
,
x0
,
y0
=
pos
.
width
,
pos
.
height
,
pos
.
x0
,
pos
.
y0
axes
[
0
,
1
].
set_position
([
x0
-
.06
,
y0
,
width
,
height
])
print
(
temp_conv_layer
+
': '
+
weight_type
)
file_pkl
=
open
(
lp
+
'param_file.pkl'
,
'rb'
)
params_dict
=
pickle
.
load
(
file_pkl
)
file_pkl
.
close
()
n_folds
=
params_dict
[
'n_folds'
]
save_suffix
=
params_dict
[
'save_suffix'
]
combined_sbjs
=
params_dict
[
'combined_sbjs'
]
F1
=
params_dict
[
'F1'
]
D
=
params_dict
[
'D'
]
kernLength_sep
=
params_dict
[
'kernLength_sep'
]
test_day
=
params_dict
[
'test_day'
]
pats_ids_in
=
params_dict
[
'pats_ids_in'
]
if
not
combined_sbjs
:
n_folds_sbj
=
n_folds
n_folds
*=
len
(
pats_ids_in
)
weighted_pow_medians_all
=
[]
for
curr_fold
in
tqdm
(
range
(
n_folds
)):
if
weight_type
==
'weighted'
:
#Load CSV file with accuracies
df
=
pd
.
read_csv
(
lp
+
'df_acc_allbut1_'
+
str
(
curr_fold
)
+
'_'
+
mod_type
+
'_'
+
dat_type
+
'.csv'
)
#Take off first filt ind, which is temporal filt number
filts_left
=
[]
for
i
in
range
(
len
(
df
)):
exec
(
'tmp='
+
df
[
'Filt_left'
][
i
])
filts_left
.
append
(
tmp
[
0
])
filts_left
=
np
.
asarray
(
filts_left
)
filts_left_r
=
[]
for
i
in
range
(
len
(
df
)):
exec
(
'tmp='
+
df
[
'Filt_left'
][
i
])
filts_left_r
.
append
(
tmp
[
1
])
filts_left_r
=
np
.
asarray
(
filts_left_r
)
#Find important frequencies in temporal filters
#Load model
if
combined_sbjs
:
loadname
=
lp
+
'checkpoint_gen_'
+
mod_type
+
'_fold'
+
str
(
curr_fold
)
+
save_suffix
+
'.h5'
else
:
loadname
=
lp
+
'checkpoint_'
+
mod_type
+
'_'
+
pats_ids_in
[
curr_fold
//
n_folds_sbj
]
+
'_testday_'
+
str
(
test_day
)
+
\
'_fold'
+
str
(
curr_fold
//
(
len
(
pats_ids_in
)))
+
save_suffix
+
'.h5'
model_curr
=
tf
.
keras
.
models
.
load_model
(
loadname
)
#Grab 2D conv layer and use it to transform white noise input
if
temp_conv_layer
==
'conv2d'
:
output_layer
=
temp_conv_layer
srate_new
=
srate
if
combined_sbjs
:
intermediate_layer_model
=
Model
(
inputs
=
[
model_curr
.
input
[
0
]],
outputs
=
[
model_curr
.
get_layer
(
output_layer
).
output
])
else
:
intermediate_layer_model
=
Model
(
inputs
=
[
model_curr
.
input
],
outputs
=
[
model_curr
.
get_layer
(
output_layer
).
output
])
elif
temp_conv_layer
==
'separable_conv2d'
:
srate_new
=
srate
/
4
# srate for data into separable convolution goes through 1 average pooling
# use longer time length for better power computation
tLen
=
int
(
model_curr
.
get_layer
(
'conv2d'
).
input
.
get_shape
()[
-
1
])
chLen
=
int
(
model_curr
.
get_layer
(
temp_conv_layer
).
input
.
get_shape
()[
-
2
])
#Create model with just convolution layer
input1
=
Input
(
shape
=
(
1
,
chLen
,
tLen
))
block1
=
Conv2D
(
int
(
F1
*
D
), (
1
,
kernLength_sep
),
padding
=
'same'
,
input_shape
=
(
1
,
chLen
,
tLen
),
use_bias
=
False
,
name
=
'conv2d'
)(
input1
)
intermediate_layer_model
=
Model
(
inputs
=
[
input1
],
outputs
=
[
block1
])
#Add in weights from separable convolution
new_w
=
np
.
moveaxis
(
model_curr
.
get_layer
(
temp_conv_layer
).
get_weights
()[
0
],
-
1
,
-
2
)
intermediate_layer_model
.
get_layer
(
'conv2d'
).
set_weights
([
new_w
])
# intermediate_layer_model.summary()
nrows
=
int
(
intermediate_layer_model
.
input
[
0
].
shape
[
-
2
])
ncols
=
int
(
intermediate_layer_model
.
input
[
0
].
shape
[
-
1
])
data_in
=
np
.
zeros
([
1
,
1
,
nrows
,
ncols
])
for
i
in
range
(
nrows
):
data_in
[...,
i
,:]
=
np
.
random
.
standard_normal
(
ncols
)
filt_dat
=
intermediate_layer_model
.
predict
(
data_in
)
#error pops up if running on CPU
#Compute spectral power density for orignal and filtered dummy data
w
=
hann
(
data_in
.
shape
[
-
1
])
pow_orig
=
fft
(
data_in
*
w
)
pow_filt
=
fft
(
filt_dat
*
w
)
f
=
np
.
fft
.
fftfreq
(
pow_orig
.
shape
[
-
1
],
d
=
1
/
srate_new
)
pow_orig
=
pow_orig
[...,
f
>
0
]
pow_filt
=
pow_filt
[...,
f
>
0
]
f
=
f
[
f
>
0
]
#Compute ratio of filtered power over original power
pow_diff
=
np
.
zeros_like
(
pow_filt
.
real
)
for
i
in
range
(
pow_filt
.
shape
[
1
]):
if
bode_type
==
'mag'
:
pow_diff
[:,
i
,...]
=
10
*
np
.
log10
(
np
.
abs
(
np
.
divide
(
pow_filt
[:,
i
,...],
pow_orig
)).
real
)
elif
bode_type
==
'phase'
:
pow_diff
[:,
i
,...]
=
np
.
angle
(
np
.
divide
(
pow_filt
[:,
i
,...],
pow_orig
),
deg
=
True
)
# Take median across channels
pow_diff_median
=
np
.
median
(
pow_diff
,
axis
=
2
)
if
weight_type
==
'weighted'
:
# Weight pow_diff by accuracies for each temporal filter
acc
=
[]
for
i
in
range
(
len
(
np
.
unique
(
filts_left
))):
#pow_diff_median.shape[1]):
for
j
in
range
(
len
(
np
.
unique
(
filts_left_r
))):
temp_filt_inds
=
np
.
nonzero
(
np
.
logical_and
(
filts_left
==
i
,
filts_left_r
==
j
))[
0
]
acc
.
append
(
df
[
'Accuracy'
][
temp_filt_inds
].
values
.
max
())
#use max accuracy
acc
=
np
.
asarray
(
acc
)
if
weight_type
==
'max'
:
weighted_pow_median
=
pow_diff_median
.
max
(
axis
=
1
)
elif
weight_type
==
'weighted_percentile'
:
perc_thresh
=
85
#percentile threshold
weighted_pow_median
=
np
.
zeros
([
1
,
pow_diff_median
.
shape
[
-
1
]])
for
i
in
range
(
pow_diff_median
.
shape
[
1
]):
perc_vals
=
np
.
percentile
(
pow_diff_median
[
0
,
i
],
perc_thresh
)
inds_perc
=
np
.
nonzero
(
pow_diff_median
[
0
,
i
]
>=
perc_vals
)[
0
]
weighted_pow_median
[
0
,
inds_perc
]
+=
1
weighted_pow_median
/=
pow_diff_median
.
shape
[
1
]
elif
weight_type
==
'weighted'
:
weighted_pow_median
=
np
.
zeros
([
1
,
pow_diff_median
.
shape
[
-
1
]])
for
i
in
range
(
pow_diff_median
.
shape
[
1
]):
weighted_pow_median
[
0
,:]
+=
pow_diff_median
[
0
,
i
]
*
acc
[
i
]
#*acc
weighted_pow_median
/=
pow_diff_median
.
shape
[
1
]
elif
weight_type
==
'mean'
:
weighted_pow_median
=
pow_diff_median
.
mean
(
axis
=
1
)
elif
weight_type
==
'median'
:
weighted_pow_median
=
np
.
median
(
pow_diff_median
,
axis
=
1
)
if
normalize_weighted_dat
:
weighted_pow_median
/=
np
.
max
(
weighted_pow_median
)
weighted_pow_medians_all
.
append
(
weighted_pow_median
)
del
intermediate_layer_model
tf
.
keras
.
backend
.
clear_session
()
weighted_pow_medians_all
=
np
.
squeeze
(
np
.
asarray
(
weighted_pow_medians_all
))
# Plot result
sns
.
lineplot
(
x
=
(
np
.
ones
([
weighted_pow_medians_all
.
shape
[
0
],
1
])
*
np
.
expand_dims
(
f
,
0
)).
flatten
(),
y
=
weighted_pow_medians_all
.
flatten
(),
ax
=
axes
[
0
,
0
],
ci
=
'sd'
,
color
=
colors_plt
[
0
])
#,estimator=np.median)
if
bode_type
==
'mag'
:
axes
[
0
,
0
].
set_ylim
([
-
8
,
-
3
])
else
:
axes
[
0
,
0
].
set_ylim
([
-
90
,
90
])
axes
[
0
,
0
].
set_xlabel
(
'Frequency (Hz)'
,
fontsize
=
9
,
fontweight
=
'normal'
)
if
bode_type
==
'mag'
:
axes
[
0
,
0
].
set_ylabel
(
'Power (dB)'
,
fontsize
=
9
,
fontweight
=
'normal'
)
else
:
axes
[
0
,
0
].
set_ylabel
(
'Phase ($^\circ$)'
,
fontsize
=
9
,
fontweight
=
'normal'
)
axes
[
0
,
0
].
set_xlim
([
0
,
srate
//
2
])
## Visualize spatial filter weights
# Load param files
file_pkl
=
open
(
lp
+
'param_file.pkl'
,
'rb'
)
params_dict
=
pickle
.
load
(
file_pkl
)
file_pkl
.
close
()
n_folds
=
params_dict
[
'n_folds'
]
save_suffix
=
params_dict
[
'save_suffix'
]
combined_sbjs
=
params_dict
[
'combined_sbjs'
]
n_chans_all
=
params_dict
[
'n_chans_all'
]
pats_ids_in
=
params_dict
[
'pats_ids_in'
]
rem_bad_chans
=
params_dict
[
'rem_bad_chans'
]
dipole_dens_thresh
=
params_dict
[
'dipole_dens_thresh'
]
# Find ROI x,y,z positions
if
custom_rois
:
custom_roi_inds
=
get_custom_motor_rois
()
# load custom roi's from precentral, postcentral, and inf parietal (AAL2)
else
:
custom_roi_inds
=
None
print
(
"Determining ROIs"
)
proj_mat_out
,
good_ROIs
,
chan_ind_vals_all
=
proj_mats_good_rois
(
pats_ids_in
,
n_chans_all
=
n_chans_all
,
rem_bad_chans
=
rem_bad_chans
,
dipole_dens_thresh
=
dipole_dens_thresh
,
custom_roi_inds
=
custom_roi_inds
,
chan_cut_thres
=
n_chans_all
,
roi_proj_loadpath
=
roi_proj_lp
)
roi_pos_df
=
pd
.
read_csv
(
roi_proj_lp
+
'none_EC01_ROIcentroids_Lside.csv'
)
#file with ROI positions
roi_pos_good_df
=
roi_pos_df
.
iloc
[
good_ROIs
,:]
print
(
"ROIs found"
)
# Retain only the electrodes with nonzero data (initially padded because number of electrodes varies across subjects)
# proj_mat_out : (len(pats_ids_in) x len(good_ROIs) x n_chans_all)
n_chans_all
=
len
(
np
.
nonzero
(
proj_mat_out
.
reshape
(
-
1
,
proj_mat_out
.
shape
[
-
1
]).
mean
(
axis
=
0
))[
0
])
proj_mat_out
=
proj_mat_out
[...,:
n_chans_all
]
#For each spatial filter, plot absolute value of weights on Nilearn's glass brain (weighted by node removal accuracies)
colors_all
,
colorvals_all
=
[],[]
for
curr_fold
in
tqdm
(
range
(
n_folds
)):
if
weight_type
==
'weighted'
:
#Load CSV file with accuracies
df
=
pd
.
read_csv
(
lp
+
'df_acc_allbut1_'
+
str
(
curr_fold
)
+
'_'
+
mod_type
+
'_'
+
dat_type
+
'.csv'
)
if
zero_sub_rand_accs
:
df
[
'Accuracy'
].
values
[
df
[
'Accuracy'
].
values
<=
rand_acc_thresh
]
=
0
filts_left
=
[]
#save filts left info to list
for
val
in
df
[
'Filt_left'
].
tolist
():
exec
(
'tmp='
+
val
)
filts_left
.
append
(
tmp
[
0
]
*
10
+
tmp
[
1
])
filts_left
=
np
.
asarray
(
filts_left
)
#Load model
if
combined_sbjs
:
loadname
=
lp
+
'checkpoint_gen_'
+
mod_type
+
'_fold'
+
str
(
curr_fold
)
+
save_suffix
+
'.h5'
else
:
loadname
=
lp
+
'checkpoint_'
+
mod_type
+
'_fold'
+
str
(
curr_fold
)
+
save_suffix
+
'.h5'
model_curr
=
tf
.
keras
.
models
.
load_model
(
loadname
)
spatial_weights
=
copy
.
deepcopy
(
model_curr
.
get_layer
(
'depthwise_conv2d'
).
get_weights
()[
0
])
w_abs_accweight
=
np
.
zeros
([
spatial_weights
.
shape
[
0
]])
nrows
,
ncols
=
spatial_weights
.
shape
[
-
2
],
spatial_weights
.
shape
[
-
1
]
for
i
in
range
(
nrows
):
for
j
in
range
(
ncols
):
if
weight_type
==
'weighted'
:
filt_ind
=
np
.
nonzero
(
filts_left
==
(
i
*
10
+
j
))[
0
]
acc_val
=
df
[
'Accuracy'
][
filt_ind
].
values
else
:
acc_val
=
1
# do mean
w_abs_accweight
+=
np
.
abs
(
np
.
squeeze
(
spatial_weights
[...,
i
,
j
]))
*
acc_val
w_abs_accweight
/=
(
nrows
*
ncols
)
#Generate color for each value from colormap
cmap
=
get_cmap
(
'OrRd'
)
norm
=
Normalize
(
vmin
=
w_abs_accweight
.
min
(),
vmax
=
w_abs_accweight
.
max
())
norm2
=
Normalize
(
vmin
=
vmin
,
vmax
=
vmax
)
colors
,
colorvals
=
list
(),
list
()
for
val
in
w_abs_accweight
.
tolist
():
curr_val
=
norm2
(
norm
(
val
))
colors
.
append
(
cmap
(
curr_val
)[
0
:
3
])
colorvals
.
append
(
curr_val
)
tf
.
keras
.
backend
.
clear_session
()
colors_all
.
append
(
colors
)
colorvals_all
.
append
(
colorvals
)
colors_plt
=
np
.
asarray
(
colors_all
).
mean
(
axis
=
0
).
tolist
()
colorvals_all
=
np
.
asarray
(
colorvals_all
).
mean
(
axis
=
0
)
colors_plt
=
[
tuple
(
val
)
for
val
in
colors_plt
]
#Plot results onto Nilearn's glass brain
node_size
=
15
sides_2_display
=
'l'
_plot_electrodes
(
roi_pos_good_df
,
node_size
,
colors_plt
,
axes
[
0
,
1
],
sides_2_display
,
1
,
node_edge_colors
=
'silver'
,
alpha
=
1
,
edge_linewidths
=
.5
)
## Plot difference spectrograms
chan_ind_2_plot
=
0
for
ii
,
loadname
in
enumerate
([
'diff_spec_eegnet_hilb_tfr.h5'
,
'diff_spec_eegnet_hilb_eeg_tfr.h5'
]):
# Load power
power
=
read_tfrs
(
spec_lp
+
loadname
)[
0
]
freq_lims
=
[
power
.
freqs
[
0
],
125
]
# Plot spectrograms
plot_spectrogram_subplots
(
2
,
2
,
ii
+
2
,
axes
[
1
,
ii
],
fig
,
power
,
[
chan_ind_2_plot
],
epoch_times
,
vscale_val
,
freq_lims
=
freq_lims
,
y_tick_step
=
25
,
axvlines
=
[
0
],
pad_val
=
0
,
cmap
=
'RdBu_r'
)
# Specific plot updates
axes
[
1
,
0
].
set_ylabel
(
'Frequency (Hz)'
,
fontsize
=
9
,
fontweight
=
'normal'
)
axes
[
1
,
0
].
set_xlabel
(
'Time (sec)'
,
fontsize
=
9
,
fontweight
=
'normal'
)
axes
[
1
,
1
].
set_xlabel
(
'Time (sec)'
,
fontsize
=
9
,
fontweight
=
'normal'
)
axes
[
0
,
0
].
set_ylabel
(
'Power (dB)'
,
fontsize
=
9
,
fontweight
=
'normal'
)
axes
[
0
,
0
].
tick_params
(
axis
=
'both'
,
labelsize
=
8
)
axes
[
0
,
0
].
spines
[
'right'
].
set_visible
(
False
)
axes
[
0
,
0
].
spines
[
'top'
].
set_visible
(
False
)
axes
[
1
,
0
].
tick_params
(
axis
=
'both'
,
labelsize
=
8
)
axes
[
1
,
1
].
tick_params
(
axis
=
'both'
,
labelsize
=
8
)
axes
[
0
,
0
].
set_title
(
'(A)'
,
fontsize
=
10
,
x
=
0
,
fontweight
=
'bold'
,
pad
=
15
,
color
=
'dimgray'
)
axes
[
0
,
0
].
text
(
125
/
2
+
5
,
-
2.8
,
'Temporal convolution
\n
frequency response'
,
fontsize
=
10
,
ha
=
'center'
,
fontweight
=
'bold'
)
axes
[
0
,
1
].
set_title
(
'(B)'
,
fontsize
=
10
,
x
=
0
,
fontweight
=
'bold'
,
pad
=
15
,
color
=
'dimgray'
)
axes
[
0
,
1
].
text
(
0.5
,
1.04
,
'Depthwise convolution
\n
weights'
,
fontsize
=
10
,
ha
=
'center'
,
fontweight
=
'bold'
)
# fig.text(0.5,0.47,'(C) Move \N{MINUS SIGN} rest difference spectrograms',fontsize=10,ha='center',fontweight='bold')
axes
[
1
,
0
].
set_title
(
'(C)'
,
fontsize
=
10
,
x
=
0
,
fontweight
=
'bold'
,
pad
=
12
,
color
=
'dimgray'
)
axes
[
1
,
0
].
text
(
0
,
138
,
'ECoG difference spectrogram'
,
fontsize
=
10
,
ha
=
'center'
,
fontweight
=
'bold'
)
axes
[
1
,
0
].
text
(
0
,
128
,
'(n=12)'
,
fontsize
=
8
,
ha
=
'center'
,
fontweight
=
'normal'
)
axes
[
1
,
1
].
set_title
(
'(D)'
,
fontsize
=
10
,
x
=
0
,
fontweight
=
'bold'
,
pad
=
12
,
color
=
'dimgray'
)
axes
[
1
,
1
].
text
(
0
,
138
,
'EEG difference spectrogram'
,
fontsize
=
10
,
ha
=
'center'
,
fontweight
=
'bold'
)
axes
[
1
,
1
].
text
(
0
,
128
,
'(n=15)'
,
fontsize
=
8
,
ha
=
'center'
,
fontweight
=
'normal'
)
plt
.
setp
(
axes
[
1
,
0
].
get_xticklabels
(),
fontweight
=
"normal"
)
plt
.
setp
(
axes
[
1
,
0
].
get_yticklabels
(),
fontweight
=
"normal"
)
plt
.
setp
(
axes
[
1
,
1
].
get_xticklabels
(),
fontweight
=
"normal"
)
plt
.
setp
(
axes
[
1
,
1
].
get_yticklabels
(),
fontweight
=
"normal"
)
axes
[
1
,
0
].
set_yticks
([
1
,
25
,
50
,
75
,
100
,
125
])
axes
[
1
,
1
].
set_yticks
([
1
,
25
,
50
,
75
,
100
,
125
])
# Add colorbars
add_colorbar
(
fig
,
vmin
,
vmax
,
cmap
,
label_name
=
'Normalized
\n
Value'
,
vert_pos
=
.65
,
horiz_pos
=
.8
,
fontname
=
'arial'
,
tick_fontsize
=
8
,
label_fontsize
=
9
,
fontweight
=
'normal'
,
width
=
0.02
,
height
=
0.12
,
label_pad
=
-
15
,
label_y
=
1.5
)
vscale_val
=
1.5
add_colorbar
(
fig
,
-
vscale_val
,
vscale_val
,
plt
.
cm
.
RdBu_r
,
label_name
=
'dB'
,
label_fontsize
=
9
,
tick_fontsize
=
8
,
label_pad
=
-
25
,
label_y
=
1.2
,
fontname
=
'arial'
,
width
=
0.03
,
height
=
0.15
,
fontweight
=
'normal'
,
vert_pos
=
.2
,
horiz_pos
=
.85
)
return
fig
def
plot_overlap_trainnum
(
lp
,
dpi_plt
=
300
):
"""
Plot the effects of electrode overlap and number of training participants on test accuracy.
"""
# Load in results
df_sbj_ntra
=
pd
.
read_csv
(
lp
+
'ntra_df.csv'
)
df_sbj_fo
=
pd
.
read_csv
(
lp
+
'frac_overlap_df.csv'
)
# Plot results on separate subplots
fig
,
ax
=
plt
.
subplots
(
1
,
2
,
figsize
=
(
7.5
,
2
),
dpi
=
dpi_plt
,
gridspec_kw
=
{
'wspace'
:
.1
})
for
mod_name
in
df_sbj_ntra
[
'Models'
].
unique
().
tolist
():
g0
=
sns
.
regplot
(
x
=
'n_tra'
,
y
=
'Accuracy'
,
data
=
df_sbj_ntra
[
df_sbj_ntra
[
'Models'
]
==
mod_name
],
scatter
=
False
,
logx
=
True
,
ax
=
ax
[
0
])
ax
[
0
].
set_xlabel
(
'Number of Training Participants'
,
fontsize
=
9
)
ax
[
0
].
set_ylabel
(
'Test Accuracy'
,
fontsize
=
9
)
ax
[
0
].
set_ylim
([
.45
,
.9
])
ax
[
0
].
set_yticks
(
np
.
arange
(
.5
,
1
,
.1
))
ax
[
0
].
set_xticks
(
np
.
arange
(
10
)
+
1
)
ax
[
0
].
axhline
(
.5
,
c
=
'k'
,
linestyle
=
'--'
)
leg
=
ax
[
0
].
legend
(
df_sbj_ntra
[
'Models'
].
unique
().
tolist
(),
loc
=
'upper left'
,
frameon
=
False
,
prop
=
{
'size'
:
9
})
ax
[
0
].
tick_params
(
axis
=
'both'
,
labelsize
=
8
)
ax
[
0
].
spines
[
'right'
].
set_visible
(
False
)
ax
[
0
].
spines
[
'top'
].
set_visible
(
False
)
# Move legend up slightly
bb
=
leg
.
get_bbox_to_anchor
().
inverse_transformed
(
ax
[
0
].
transAxes
)
yOffset
=
0.07
bb
.
y0
+=
yOffset
bb
.
y1
+=
yOffset
leg
.
set_bbox_to_anchor
(
bb
,
transform
=
ax
[
0
].
transAxes
)
for
mod_name
in
df_sbj_fo
[
'Model'
].
unique
().
tolist
():
g1
=
sns
.
regplot
(
x
=
'Frac'
,
y
=
'Acc'
,
data
=
df_sbj_fo
[
df_sbj_fo
[
'Model'
]
==
mod_name
],
scatter
=
False
,
ax
=
ax
[
1
])
ax
[
1
].
set_xlabel
(
'Electrode Fraction Overlap'
,
fontsize
=
9
)
ax
[
1
].
set_ylabel
(
''
,
fontsize
=
9
)
ax
[
1
].
set_xlim
([
0.3
,
1
])
ax
[
1
].
set_ylim
([
.45
,
.9
])
ax
[
1
].
set_yticks
(
np
.
arange
(
.5
,
1
,
.1
))
ax
[
1
].
set_yticklabels
([
''
]
*
len
(
ax
[
1
].
get_yticklabels
()))
ax
[
1
].
axhline
(
.5
,
c
=
'k'
,
linestyle
=
'--'
)
ax
[
1
].
tick_params
(
axis
=
'both'
,
labelsize
=
8
)
ax
[
1
].
spines
[
'right'
].
set_visible
(
False
)
ax
[
1
].
spines
[
'top'
].
set_visible
(
False
)
ax
[
0
].
set_title
(
'(A)'
,
fontsize
=
10
,
x
=
0
,
fontweight
=
'bold'
,
pad
=
8
,
color
=
'dimgray'
)
ax
[
0
].
text
(
5.5
,
.935
,
'Training participants v. test accuracy'
,
fontsize
=
10
,
ha
=
'center'
,
fontweight
=
'bold'
)
ax
[
1
].
set_title
(
'(B)'
,
fontsize
=
10
,
x
=
0
,
fontweight
=
'bold'
,
pad
=
8
,
color
=
'dimgray'
)
ax
[
1
].
text
(
.65
,
.935
,
'Electrode overlap v. test accuracy'
,
fontsize
=
10
,
ha
=
'center'
,
fontweight
=
'bold'
)
plt
.
show
()
return
fig
def
plot_spectrogram_subplots
(
n_rows
,
n_cols
,
subplot_num
,
ax1
,
fig
,
power_ave_masked
,
curr_ind
,
epoch_times
=
[
-
2.5
,
2.5
],
vscale_val
=
3
,
freq_lims
=
[
8
,
120
],
y_tick_step
=
25
,
axvlines
=
[
0
],
pad_val
=
0.5
,
log_freq_scale
=
[],
cmap
=
'bwr'
,
scale_one_dir
=
False
,
fontweight
=
'bold'
,
xtick_vals
=
None
,
axvline_w
=
3
):
"""
Helper function to plot spectrograms in specific subplots.
"""
if
scale_one_dir
:
vscale_val_min
=
0
else
:
vscale_val_min
=
-
vscale_val
power_ave_masked
.
plot
(
curr_ind
,
baseline
=
None
,
colorbar
=
False
,
title
=
""
,
yscale
=
'linear'
,
tmin
=
epoch_times
[
0
],
tmax
=
epoch_times
[
1
],
vmin
=
vscale_val_min
,
vmax
=
vscale_val
,
cmap
=
cmap
,
verbose
=
False
,
axes
=
ax1
,
show
=
False
)
for
vals
in
axvlines
:
ax1
.
axvline
(
vals
,
linewidth
=
axvline_w
,
color
=
"black"
,
linestyle
=
"--"
)
# event
ax1
.
spines
[
"top"
].
set_visible
(
False
)
ax1
.
spines
[
"right"
].
set_visible
(
False
)
plt
.
setp
(
ax1
.
get_xticklabels
(),
fontsize
=
12
,
fontweight
=
fontweight
)
#, fontweight="bold")
plt
.
setp
(
ax1
.
get_yticklabels
(),
fontsize
=
12
,
fontweight
=
fontweight
)
#, fontweight="bold")
ax1
.
set_ylabel
(
'Frequency (Hz)'
,
fontsize
=
14
,
fontweight
=
fontweight
)
ax1
.
set_xlabel
(
'Time (s)'
,
fontsize
=
14
,
fontweight
=
fontweight
)
ax1
.
set_ylim
(
freq_lims
[
0
],
freq_lims
[
1
])
#8,120)
ax1
.
set_xlim
([
epoch_times
[
0
]
+
pad_val
,
epoch_times
[
1
]
-
pad_val
])
if
len
(
log_freq_scale
)
==
0
:
y_tick_list
=
list
(
np
.
arange
(
0
,
freq_lims
[
1
]
+
y_tick_step
,
y_tick_step
))
y_tick_list
[
0
]
=
freq_lims
[
0
]
y_tick_list
[
-
1
]
=
freq_lims
[
1
]
ax1
.
set_yticks
(
y_tick_list
)
#[freq_lims[0],20,40,60,80,100,freq_lims[1]]) #[8,20,40,60,80,100,120])
else
:
spaced_y_ticks
=
np
.
linspace
(
log_freq_scale
[
0
],
log_freq_scale
[
-
1
],
len
(
log_freq_scale
)
+
1
)[:
-
1
]
half_step
=
np
.
mean
(
np
.
diff
(
spaced_y_ticks
))
/
2
y_tick_list
=
list
(
spaced_y_ticks
+
half_step
)
ax1
.
set_yticks
(
y_tick_list
)
log_freq_scale_str
=
[
str
(
int
(
round
(
val
)))
for
val
in
log_freq_scale
]
ax1
.
set_yticklabels
(
log_freq_scale_str
)
if
xtick_vals
is
None
:
ax1
.
set_xticks
(
np
.
arange
(
epoch_times
[
0
]
+
pad_val
,
epoch_times
[
1
]
-
pad_val
).
tolist
())
#[-2,-1,0,1,2])
else
:
ax1
.
set_xticks
(
xtick_vals
)
ax1
.
spines
[
"bottom"
].
set_linewidth
(
1.5
)
ax1
.
spines
[
"left"
].
set_linewidth
(
1.5
)
for
item
in
[
fig
,
ax1
]:
item
.
patch
.
set_visible
(
False
)
if
subplot_num
//
n_cols
==
(
n_rows
-
1
):
#//n_cols][i%n_cols
#Time info in last row
plt
.
setp
(
ax1
.
get_xticklabels
(),
fontsize
=
12
,
fontweight
=
fontweight
)
ax1
.
set_xlabel
(
''
)
#Time (s)',fontsize=14, fontweight="bold")
else
:
ax1
.
set_xlabel
(
''
)
if
subplot_num
%
n_cols
==
0
:
#(n_cols-1):
#Frequency info in first column
ax1
.
set_ylabel
(
''
)
#Frequency (Hz)',fontsize=14, fontweight="bold")
plt
.
setp
(
ax1
.
get_yticklabels
(),
fontsize
=
12
,
fontweight
=
fontweight
)
else
:
ax1
.
set_ylabel
(
''
)
def
_plot_electrodes
(
locs
,
node_size
,
colors
,
axes
,
sides_2_display
,
N
,
node_edge_colors
,
alpha
,
edge_linewidths
,
marker
=
'o'
):
"""
Low-level function that handles electrode plotting.
"""
if
N
==
1
:
ni_plt
.
plot_connectome
(
np
.
eye
(
locs
.
shape
[
0
]),
locs
,
output_file
=
None
,
node_kwargs
=
{
'alpha'
:
alpha
,
'edgecolors'
:
node_edge_colors
,
'linewidths'
:
edge_linewidths
,
'marker'
:
marker
},
node_size
=
node_size
,
node_color
=
colors
,
axes
=
axes
,
display_mode
=
sides_2_display
)
elif
sides_2_display
==
'yrz'
or
sides_2_display
==
'ylz'
:
colspans
=
[
5
,
6
,
5
]
#different sized subplot to make saggital view similar size to other two slices
current_col
=
0
total_colspans
=
int
(
np
.
sum
(
np
.
asarray
(
colspans
)))
for
ind
,
colspan
in
enumerate
(
colspans
):
axes
[
ind
]
=
plt
.
subplot2grid
((
1
,
total_colspans
), (
0
,
current_col
),
colspan
=
colspan
,
rowspan
=
1
)
ni_plt
.
plot_connectome
(
np
.
eye
(
locs
.
shape
[
0
]),
locs
,
output_file
=
None
,
node_kwargs
=
{
'alpha'
:
alpha
,
'edgecolors'
:
node_edge_colors
,
'linewidths'
:
edge_linewidths
,
'marker'
:
marker
},
node_size
=
node_size
,
node_color
=
colors
,
axes
=
axes
[
ind
],
display_mode
=
sides_2_display
[
ind
])
current_col
+=
colspan
else
:
for
i
in
range
(
N
):
ni_plt
.
plot_connectome
(
np
.
eye
(
locs
.
shape
[
0
]),
locs
,
output_file
=
None
,
node_kwargs
=
{
'alpha'
:
alpha
,
'edgecolors'
:
node_edge_colors
,
'linewidths'
:
edge_linewidths
,
'marker'
:
marker
},
node_size
=
node_size
,
node_color
=
colors
,
axes
=
axes
[
i
],
display_mode
=
sides_2_display
[
i
])
def
add_colorbar
(
f_in
,
vmin
,
vmax
,
cmap
,
width
=
0.025
,
height
=
0.16
,
horiz_pos
=
0.85
,
border_width
=
1.5
,
tick_len
=
0
,
adjust_subplots_right
=
0.84
,
label_name
=
''
,
tick_fontsize
=
14
,
label_fontsize
=
18
,
label_pad
=
15
,
label_y
=
0.6
,
label_rotation
=
0
,
fontweight
=
'bold'
,
fontname
=
'Times New Roman'
,
vert_pos
=
None
):
'''
Adds colorbar to existing plot based on vmin, vmax, and cmap
'''
f12636
,
a14u3u43
=
plt
.
subplots
(
1
,
1
,
figsize
=
(
0.01
,
0.01
))
im
=
a14u3u43
.
imshow
(
np
.
random
.
random
((
10
,
10
)),
vmin
=
vmin
,
vmax
=
vmax
,
cmap
=
cmap
)
plt
.
close
(
f12636
)
f_in
.
subplots_adjust
(
right
=
adjust_subplots_right
)
if
vert_pos
==
None
:
vert_pos
=
(
1
-
height
)
/
2
cbar_ax
=
f_in
.
add_axes
([
horiz_pos
,
vert_pos
,
width
,
height
])
cbar
=
f_in
.
colorbar
(
im
,
cax
=
cbar_ax
)
cbar
.
set_ticks
([
vmin
,
0
,
vmax
])
cbar
.
ax
.
set_yticklabels
([
vmin
,
0
,
vmax
],
fontsize
=
tick_fontsize
,
weight
=
fontweight
,
fontname
=
fontname
)
cbar
.
ax
.
tick_params
(
length
=
tick_len
)
cbar
.
outline
.
set_linewidth
(
border_width
)
cbar
.
set_label
(
label_name
,
rotation
=
label_rotation
,
fontsize
=
label_fontsize
,
weight
=
fontweight
,
labelpad
=
label_pad
,
y
=
label_y
,
fontname
=
fontname
)
def
plot_hyptuning_results
(
lp
):
"""
Plots hyperparameter tuning results (code is specific to hyperparameter tuning from manuscript)
"""
PARAM_TO_NAME
=
{
'F1'
:
'Temporal Filter Count'
,
'dropoutRate'
:
'Dropout Rate'
,
'kernLength'
:
'Kernel Length'
,
'kernLength_sep'
:
'Seperable Kernel Length'
,
'dropoutType'
:
'Dropout Type'
,
'modeltype'
:
'Model Type'
,
'n_estimators'
:
'# of Estimators'
,
'max_depth'
:
'Maximum Depth'
}
PARAM_TO_XLIM
=
{
'dropoutRate'
: (
.2
,
.8
),
'kernLength'
: (
24
,
136
),
'F1'
: (
4
,
20
),
'kernLength_sep'
: (
24
,
136
),
'n_estimators'
: (
25
,
250
),
'max_depth'
: (
2
,
12
)}
PARAM_TO_XTICKS
=
{
'dropoutRate'
: [
.2
,
.4
,
.6
,
.8
],
'kernLength'
: [
24
,
52
,
80
,
108
,
136
],
'F1'
: [
4
,
8
,
12
,
16
,
20
],
'kernLength_sep'
: [
24
,
52
,
80
,
108
,
136
],
'n_estimators'
: [
25
,
100
,
175
,
250
],
'max_depth'
: [
2
,
4
,
6
,
8
,
10
,
12
]}
delta
=
.001
ylim
=
(
.45
,
1
)
yticks
=
[
.45
,
.5
,
.55
,
.6
,
.65
,
.7
,
.75
,
.8
,
.85
,
.9
,
.95
,
1
]
eegnet_params
=
[
'dropoutRate'
,
'kernLength'
,
'F1'
,
'kernLength_sep'
]
categorical_params
=
[
'dropoutType'
,
'modeltype'
]
rf_params
=
[
'n_estimators'
,
'max_depth'
]
study_eegnet
=
joblib
.
load
(
lp
+
'optuna_study_[
\'
eegnet
\'
,
\'
eegnet_hilb
\'
].pkl'
)
study_rf
=
joblib
.
load
(
lp
+
'optuna_study_[
\'
rf
\'
].pkl'
)
study_eegnet_df
=
pd
.
read_csv
(
'eegnet_trial_data_sp.csv'
)
study_rf_df
=
pd
.
read_csv
(
'rf_trial_data_sp.csv'
)
colors
=
sns
.
color_palette
(
"hls"
,
8
)
color_num
=
0
plt
.
rcParams
.
update
({
'font.size'
:
7.5
})
fig
,
axs
=
plt
.
subplots
(
nrows
=
2
,
ncols
=
8
,
gridspec_kw
=
{
'wspace'
:
0.15
,
'hspace'
:
-
.62
})
col_count
=
0
row_count
=
0
plt
.
suptitle
(
'Same Participant'
,
va
=
'bottom'
,
y
=
.7
,
fontsize
=
10
)
for
param
in
eegnet_params
:
if
col_count
==
0
:
axs
[
row_count
,
col_count
].
set
(
title
=
PARAM_TO_NAME
[
param
],
xlim
=
PARAM_TO_XLIM
[
param
],
xticks
=
PARAM_TO_XTICKS
[
param
],
ylim
=
ylim
,
yticks
=
yticks
)
else
:
axs
[
row_count
,
col_count
].
set
(
title
=
PARAM_TO_NAME
[
param
],
xlim
=
PARAM_TO_XLIM
[
param
],
xticks
=
PARAM_TO_XTICKS
[
param
],
ylim
=
ylim
,
yticks
=
[])
axs
[
row_count
,
col_count
].
spines
[
'left'
].
set_visible
(
False
)
sns
.
regplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
order
=
2
,
scatter
=
True
,
scatter_kws
=
{
"color"
:
"gray"
,
"alpha"
:
.25
,
"s"
:
15
},
color
=
colors
[
color_num
],
ax
=
axs
[
row_count
,
col_count
])
if
col_count
==
0
:
axs
[
row_count
,
col_count
].
set
(
xlabel
=
""
)
else
:
axs
[
row_count
,
col_count
].
set
(
ylabel
=
""
,
xlabel
=
""
)
axs
[
row_count
,
col_count
].
spines
[
'right'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'top'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
set_aspect
(
1.0
/
axs
[
row_count
,
col_count
].
get_data_ratio
(),
adjustable
=
'box'
)
color_num
+=
1
col_count
+=
1
for
param
in
categorical_params
:
if
param
==
"dropoutType"
:
sns
.
boxplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
color
=
colors
[
color_num
],
ax
=
axs
[
row_count
,
col_count
],
order
=
[
"Dropout"
,
"SpatialDropout2D"
])
sns
.
swarmplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
color
=
"gray"
,
alpha
=
.4
,
s
=
5
,
ax
=
axs
[
row_count
,
col_count
],
order
=
[
"Dropout"
,
"SpatialDropout2D"
])
else
:
sns
.
boxplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
color
=
colors
[
color_num
],
ax
=
axs
[
row_count
,
col_count
])
sns
.
swarmplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
color
=
"gray"
,
alpha
=
.4
,
s
=
5
,
ax
=
axs
[
row_count
,
col_count
])
axs
[
row_count
,
col_count
].
set
(
ylabel
=
""
,
xlabel
=
""
,
title
=
PARAM_TO_NAME
[
param
],
ylim
=
ylim
,
yticks
=
[])
axs
[
row_count
,
col_count
].
spines
[
'right'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'top'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'left'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
set_aspect
(
1.0
/
axs
[
row_count
,
col_count
].
get_data_ratio
(),
adjustable
=
'box'
)
color_num
+=
1
col_count
+=
1
for
param
in
rf_params
:
axs
[
row_count
,
col_count
].
set
(
title
=
PARAM_TO_NAME
[
param
],
xlim
=
PARAM_TO_XLIM
[
param
],
xticks
=
PARAM_TO_XTICKS
[
param
],
ylim
=
ylim
,
yticks
=
[])
sns
.
regplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_rf_df
,
order
=
2
,
scatter
=
True
,
scatter_kws
=
{
"color"
:
"gray"
,
"alpha"
:
.25
,
"s"
:
15
},
color
=
colors
[
color_num
],
ax
=
axs
[
row_count
,
col_count
])
axs
[
row_count
,
col_count
].
set
(
ylabel
=
""
,
xlabel
=
""
)
axs
[
row_count
,
col_count
].
spines
[
'right'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'top'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'left'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
set_aspect
(
1.0
/
axs
[
row_count
,
col_count
].
get_data_ratio
(),
adjustable
=
'box'
)
color_num
+=
1
col_count
+=
1
row_count
+=
1
col_count
=
0
color_num
=
0
study_eegnet_df
=
pd
.
read_csv
(
'eegnet_trial_data.csv'
)
study_rf_df
=
pd
.
read_csv
(
'rf_trial_data.csv'
)
plt
.
figtext
(
0.5
,
0.48
,
'Unseen Participant'
,
ha
=
'center'
,
va
=
'bottom'
,
fontsize
=
10
)
for
param
in
eegnet_params
:
if
col_count
==
0
:
axs
[
row_count
,
col_count
].
set
(
xlim
=
PARAM_TO_XLIM
[
param
],
xticks
=
PARAM_TO_XTICKS
[
param
],
ylim
=
ylim
,
yticks
=
yticks
)
else
:
axs
[
row_count
,
col_count
].
set
(
xlim
=
PARAM_TO_XLIM
[
param
],
xticks
=
PARAM_TO_XTICKS
[
param
],
ylim
=
ylim
,
yticks
=
[])
axs
[
row_count
,
col_count
].
spines
[
'left'
].
set_visible
(
False
)
sns
.
regplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
order
=
2
,
scatter
=
True
,
scatter_kws
=
{
"color"
:
"gray"
,
"alpha"
:
.25
,
"s"
:
15
},
color
=
colors
[
color_num
],
ax
=
axs
[
row_count
,
col_count
])
if
col_count
==
0
:
axs
[
row_count
,
col_count
].
set
(
xlabel
=
""
)
else
:
axs
[
row_count
,
col_count
].
set
(
ylabel
=
""
,
xlabel
=
""
)
axs
[
row_count
,
col_count
].
spines
[
'right'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'top'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
set_aspect
(
1.0
/
axs
[
row_count
,
col_count
].
get_data_ratio
(),
adjustable
=
'box'
)
color_num
+=
1
col_count
+=
1
for
param
in
categorical_params
:
if
param
==
"dropoutType"
:
sns
.
boxplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
color
=
colors
[
color_num
],
ax
=
axs
[
row_count
,
col_count
],
order
=
[
"Dropout"
,
"SpatialDropout2D"
])
sns
.
swarmplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
color
=
"gray"
,
alpha
=
.4
,
s
=
5
,
ax
=
axs
[
row_count
,
col_count
],
order
=
[
"Dropout"
,
"SpatialDropout2D"
])
else
:
sns
.
boxplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
color
=
colors
[
color_num
],
ax
=
axs
[
row_count
,
col_count
])
sns
.
swarmplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_eegnet_df
,
color
=
"gray"
,
alpha
=
.4
,
s
=
5
,
ax
=
axs
[
row_count
,
col_count
])
axs
[
row_count
,
col_count
].
set
(
ylabel
=
""
,
xlabel
=
""
,
ylim
=
ylim
,
yticks
=
[])
axs
[
row_count
,
col_count
].
spines
[
'right'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'top'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'left'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
set_aspect
(
1.0
/
axs
[
row_count
,
col_count
].
get_data_ratio
(),
adjustable
=
'box'
)
color_num
+=
1
col_count
+=
1
for
param
in
rf_params
:
axs
[
row_count
,
col_count
].
set
(
xlim
=
PARAM_TO_XLIM
[
param
],
xticks
=
PARAM_TO_XTICKS
[
param
],
ylim
=
ylim
,
yticks
=
[])
sns
.
regplot
(
x
=
param
,
y
=
'Validation Accuracy'
,
data
=
study_rf_df
,
order
=
2
,
scatter
=
True
,
scatter_kws
=
{
"color"
:
"gray"
,
"alpha"
:
.25
,
"s"
:
15
},
color
=
colors
[
color_num
],
ax
=
axs
[
row_count
,
col_count
])
axs
[
row_count
,
col_count
].
set
(
ylabel
=
""
,
xlabel
=
""
)
axs
[
row_count
,
col_count
].
spines
[
'right'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'top'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
spines
[
'left'
].
set_visible
(
False
)
axs
[
row_count
,
col_count
].
set_aspect
(
1.0
/
axs
[
row_count
,
col_count
].
get_data_ratio
(),
adjustable
=
'box'
)
color_num
+=
1
col_count
+=
1
plt
.
figtext
(
0.415
,
0.492
,
'Eegnet'
,
ha
=
'center'
,
va
=
'bottom'
,
fontsize
=
9
)
plt
.
figtext
(
0.81
,
0.492
,
'Random Forest'
,
ha
=
'center'
,
va
=
'bottom'
,
fontsize
=
9
)
plt
.
figtext
(
0.415
,
0.28
,
'Eegnet'
,
ha
=
'center'
,
va
=
'bottom'
,
fontsize
=
9
)
plt
.
figtext
(
0.81
,
0.28
,
'Random Forest'
,
ha
=
'center'
,
va
=
'bottom'
,
fontsize
=
9
)
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