def bdmCue(self):
'''
'''
with open(self.FolderTracker(['bdm','cue_loc'], filename = 'plot_dict.pickle') ,'rb') as handle:
info = pickle.load(handle)
times = info['times']
files = glob.glob(self.FolderTracker(['bdm','cue_loc'], filename = 'class_*_perm-False-broad.pickle'))
bdm = []
for file in files:
print(file)
with open(file ,'rb') as handle:
bdm.append(pickle.load(handle))
# plot conditions
plt.figure(figsize = (30,20))
norm = MidpointNormalize(midpoint=1/3.0)
diag = {'partial':0,'whole':0}
for idx, cnd in enumerate(['partial','whole']):
X = np.stack([bdm[i][cnd]['standard'] for i in range(len(bdm))])
diag[cnd] = np.vstack([np.diag(x) for x in X])
cl_p_vals = clusterBasedPermutation(X,1/3.0)
X = X.mean(axis = 0)
# plot diagonal
# ax = plt.subplot(2,2, idx + 3, title = 'Diagonal-{}'.format(cnd), ylabel = 'dec acc (%)', xlabel = 'time (ms)', ylim = (0.3,0.4))
# plt.plot(times, np.diag(X))
# plt.axhline(y = 1/3.0, color = 'black', ls = '--')
# sns.despine(offset=50, trim = False)
# plot GAT
X[cl_p_vals == 1] = 1/3.0
ax = plt.subplot(2,2, idx + 1, title = 'GAT-{}'.format(cnd), ylabel = 'train time (ms)', xlabel = 'test time (ms)')
plt.imshow(X, norm = norm, aspect = 'auto', origin = 'lower',extent = [times[0],times[-1],times[0],times[-1]],
cmap = cm.bwr, interpolation = None, vmin = 1/3.0, vmax = 0.5)
plt.colorbar()
sns.despine(offset=50, trim = False)
ax = plt.subplot(2,1, 2, title = 'Diagonal', ylabel = 'dec acc (%)', xlabel = 'time (ms)')
for i, cnd in enumerate(['partial', 'whole']):
err, x = bootstrap(diag[cnd])
plt.plot(times, x, label = cnd, color = ['red','green'][i])
plt.fill_between(times, x + err, x - err, alpha = 0.2, color = ['red','green'][i])
self.clusterPlot(diag[cnd], 1/3.0, 0.05, times, ax.get_ylim()[0] + 0.01*(i+1), color = ['red','green'][i])
self.clusterPlot(diag['partial'], diag['whole'], 0.05, times, 0.3,'black', '--')
plt.axhline(y = 1/3.0, color = 'black', ls = '--')
sns.despine(offset=50, trim = False)
plt.legend(loc = 'best')
plt.tight_layout()
plt.savefig(self.FolderTracker(['bdm'], filename = 'cue_loc-decoding.pdf'))
plt.close()
def plotcrossBDM(self):
'''
'''
files = glob.glob(
self.FolderTracker(['cross_task', 'bdm'],
filename='subject-*_bdm.pickle'))
bdm = np.stack([pickle.load(open(file, 'rb')) for file in files])
#with open(self.FolderTracker(['bdm','identity'], filename = 'plot_dict.pickle') ,'rb') as handle:
# info = pickle.load(handle)
times = np.linspace(-0.2, 0.9, 128)
norm = MidpointNormalize(midpoint=1 / 8.0)
plt.figure(figsize=(20, 10))
# plot diagonal
ax = plt.subplot(1,
2,
1,
title='Diagonal',
ylabel='dec acc (%)',
xlabel='time (ms)')
diag = np.diag(np.mean(bdm, 0))
plt.plot(times, diag)
plt.axhline(y=1 / 8.0, color='black', ls='--')
sns.despine(offset=50, trim=False)
# plot gat matrix
ax = plt.subplot(1,
2,
2,
title='GAT',
ylabel='train time (ms)',
xlabel='test time (ms)')
plt.imshow(bdm.mean(0),
norm=norm,
aspect='auto',
origin='lower',
extent=[times[0], times[-1], times[0], times[-1]],
cmap=cm.bwr,
interpolation=None,
vmin=0.09,
vmax=0.15)
plt.colorbar()
sns.despine(offset=50, trim=False)
plt.tight_layout()
plt.savefig(
self.FolderTracker(['cross_task', 'bdm'],
filename='cross-task.pdf'))
plt.close()
def plotBDM(self, elec_oi='all', task='AB'):
'''
Plots GAT matrix and diagonal for digits (top) and letters (bottom)
'''
embed()
with open(
self.FolderTracker([task, 'bdm', 'T1'],
filename='plot_dict.pickle'),
'rb') as handle:
info = pickle.load(handle)
times = info['times']
# plot conditions
# plt.figure(figsize = (30,20))
# norm = MidpointNormalize(midpoint=1/8.0)
# for idx, header in enumerate(['digit','letter']):
# bdm = []
# files = glob.glob(self.FolderTracker(['bdm',header], filename = 'class_*_perm-False-broad.pickle'))
# for file in files:
# print file
# with open(file ,'rb') as handle:
# bdm.append(pickle.load(handle))
# X = np.stack([bdm[i][header]['standard'] for i in range(len(bdm))])
# print X.shape
# X = X.mean(axis = 0)
# # plot diagonal
# ax = plt.subplot(2,2, idx + 3, title = 'Diagonal-{}'.format(header), ylabel = 'dec acc (%)', xlabel = 'time (ms)')
# plt.plot(times, np.diag(X))
# plt.axhline(y = 1/8.0, color = 'black', ls = '--')
# sns.despine(offset=50, trim = False)
# # plot GAT
# X[X <1/8.0] = 1/8.0
# ax = plt.subplot(2,2, idx + 1, title = 'GAT-{}'.format(header), ylabel = 'train time (ms)', xlabel = 'test time (ms)')
# plt.imshow(X, norm = norm, aspect = 'auto', origin = 'lower',extent = [times[0],times[-1],times[0],times[-1]],
# cmap = cm.bwr, interpolation = None, vmin = 1/8.0, vmax = 0.16)
# plt.colorbar()
# sns.despine(offset=50, trim = False)
# plt.tight_layout()
# plt.savefig(self.FolderTracker(['bdm'], filename = 'localizer.pdf'))
# plt.close()
plt.figure(figsize=(30, 20))
norm = MidpointNormalize(midpoint=1 / 8.0)
for idx, header in enumerate(['digit', 'letter']):
bdm = []
files = glob.glob(
self.FolderTracker(['bdm', header],
filename='class_*_perm-False-broad.pickle'))
for file in files:
with open(file, 'rb') as handle:
bdm.append(pickle.load(handle))
###### plot diagonal - downscaling #######
# ax = plt.subplot(1,2, idx + 1, title = 'Diagonal-{}'.format(header), ylabel = 'dec acc (%)', xlabel = 'time (ms)')
# if header == 'digit':
# label_list = ['standard','110-nrlabels','100-nrlabels','90-nrlabels','80-nrlabels','70-nrlabels']
# label_list = ['standard','100-nrlabels']
# else:
# label_list = ['standard','100-nrlabels','90-nrlabels','80-nrlabels','70-nrlabels']
# label_list = ['standard','100-nrlabels']
# for label in label_list: % use this for plotting when downscaling; loop through labels
# #for label in np.sort(bdm[0][header].keys())[::3]:
# X = np.stack([bdm[i][header][label] for i in range(len(bdm))])
# x = X.mean(axis = 0)
# plt.plot(times, x, label = label)
# plt.legend(loc = 'best')
# plt.axhline(y = 1/8.0, color = 'black', ls = '--')
# sns.despine(offset=50, trim = False)
# plt.tight_layout()
# plt.savefig(self.FolderTracker(['bdm'], filename = 'localizer-posterior-chans.pdf'))
# plt.close()
X = np.stack([bdm[i][header]['standard'] for i in range(len(bdm))])
print(X.shape)
X = X.mean(axis=0)
# plot diagonal
ax = plt.subplot(2,
2,
idx + 3,
title='Diagonal-{}'.format(header),
ylabel='dec acc (%)',
xlabel='time (ms)')
plt.plot(times, np.diag(X))
plt.axhline(y=1 / 8.0, color='black', ls='--')
sns.despine(offset=50, trim=False)
# plot GAT
X[X < 1 / 8.0] = 1 / 8.0
ax = plt.subplot(2,
2,
idx + 1,
title='GAT-{}'.format(header),
ylabel='train time (ms)',
xlabel='test time (ms)')
plt.imshow(X,
norm=norm,
aspect='auto',
origin='lower',
extent=[times[0], times[-1], times[0], times[-1]],
cmap=cm.bwr,
interpolation=None,
vmin=1 / 8.0,
vmax=0.16)
plt.colorbar()
sns.despine(offset=50, trim=False)
plt.tight_layout()
plt.savefig(self.FolderTracker(['bdm'], filename='localizer.pdf'))
plt.close()
def plotBDMwithin(self, to_plot='T1'):
'''
THIS FUNCTION PLOTS WITHIN TASK DECODING
'''
# set plotting parameters
plt.figure(figsize=(30, 20))
norm = MidpointNormalize(midpoint=1 / 8.0)
with open(
self.FolderTracker(['bdm', 'identity'],
filename='plot_dict.pickle'),
'rb') as handle:
info = pickle.load(handle)
times = info['times']
for task in ['localizer', 'AB']:
files = glob.glob(
self.FolderTracker([task, 'bdm'], filename='*_*_dec.pickle'))
bdm = [pickle.load(open(file, 'rb')) for file in files]
# collapse data
if task == 'localizer':
for idx, cnd in enumerate(['digit', 'letter']):
X = np.stack(
[bdm[i][cnd]['standard'] for i in range(len(bdm))])
# plot diagonal
ax = plt.subplot(3,
2, [2, 4][idx],
title='Diagonal-{}'.format(cnd),
ylabel='dec acc (%)',
xlabel='time (ms)')
diag = np.diag(np.mean(X, 0))
plt.plot(times, diag)
plt.axhline(y=1 / 8.0, color='black', ls='--')
sns.despine(offset=50, trim=False)
# plot gat matrix
ax = plt.subplot(3,
2, [1, 3][idx],
title='GAT-{}'.format(cnd),
ylabel='train time (ms)',
xlabel='test time (ms)')
plt.imshow(
X.mean(0),
norm=norm,
aspect='auto',
origin='lower',
extent=[times[0], times[-1], times[0], times[-1]],
cmap=cm.bwr,
interpolation=None,
vmin=0.09,
vmax=0.15)
plt.colorbar()
sns.despine(offset=50, trim=False)
elif task == 'AB':
X = np.stack(
[bdm[i]['all']['standard'] for i in range(len(bdm))])
# plot diagonal
ax = plt.subplot(3,
2,
6,
title='Diagonal-{}'.format(to_plot),
ylabel='dec acc (%)',
xlabel='time (ms)')
diag = np.diag(np.mean(X, 0))
plt.plot(times, diag)
plt.axhline(y=1 / 8.0, color='black', ls='--')
sns.despine(offset=50, trim=False)
# plot gat matrix
ax = plt.subplot(3,
2,
5,
title='GAT-{}'.format(to_plot),
ylabel='train time (ms)',
xlabel='test time (ms)')
plt.imshow(X.mean(0),
norm=norm,
aspect='auto',
origin='lower',
extent=[times[0], times[-1], times[0], times[-1]],
cmap=cm.bwr,
interpolation=None,
vmin=0.09,
vmax=0.15)
plt.colorbar()
sns.despine(offset=50, trim=False)
plt.tight_layout()
plt.savefig(
self.FolderTracker(['bdm'],
filename='within_dec_{}.pdf'.format(to_plot)))
plt.close()
def plotBDM(self, header):
'''
'''
# read in data
with open(
self.FolderTracker(['bdm', '{}_type'.format(header)],
filename='plot_dict.pickle'),
'rb') as handle:
info = pickle.load(handle)
times = info['times']
files = glob.glob(
self.FolderTracker(['bdm', '{}_type'.format(header)],
filename='class_*_perm-False.pickle'))
bdm = []
for file in files:
with open(file, 'rb') as handle:
bdm.append(pickle.load(handle))
# plot diagonal decoding (all conditions in one plot)
plt.figure(figsize=(15, 10))
diff = []
for i, cnd in enumerate(['DTsim', 'DTdisP', 'DTdisDP']):
ax = plt.subplot(1, 1, 1, ylim=(0.3, 0.45))
X = np.stack([bdm[j][cnd]['standard'] for j in range(len(bdm))])
X_diag = np.stack([np.diag(x) for x in X])
diff.append(X_diag)
self.clusterPlot(X_diag,
1 / 3.0,
p_val=0.05,
times=times,
y=plt.ylim()[0] + i * 0.002,
color=['red', 'purple', 'darkblue'][i])
err_diag, X_diag = bootstrap(X_diag)
plt.plot(times,
X_diag,
label=cnd,
color=['red', 'purple', 'darkblue'][i])
plt.fill_between(info['times'],
X_diag + err_diag,
X_diag - err_diag,
alpha=0.2,
color=['red', 'purple', 'darkblue'][i])
self.clusterPlot(diff[-1],
diff[-2],
p_val=0.05,
times=times,
y=plt.ylim()[0] + 3 * 0.002,
color='black')
plt.legend(loc='best')
self.beautifyPlot(y=1 / 3.0,
xlabel='Time (ms)',
ylabel='Decoding accuracy (%)')
sns.despine(offset=50, trim=False)
plt.tight_layout()
plt.savefig(
self.FolderTracker(['bdm', '{}_type'.format(header)],
filename='cnd-dec.pdf'))
plt.close()
plt.figure(figsize=(30, 20))
norm = MidpointNormalize(midpoint=1 / 3.0)
plt_idx = 1
for i, cnd in enumerate(['DTsim', 'DTdisP', 'DTdisDP']):
for plot in ['matrix', 'diag']:
ax = plt.subplot(
3, 2, plt_idx, title=cnd
) #, title = cnd, ylabel = 'Time (ms)', xlabel = 'Time (ms)')
X = np.stack(
[bdm[j][cnd]['standard'] for j in range(len(bdm))])
p_vals = signedRankArray(X, 1 / 3.0)
h, _, _, _ = FDR(p_vals)
dec = np.mean(X, 0)
dec[~h] = 1 / 3.0
X_diag = np.stack([np.diag(x) for x in X])
if plot == 'matrix':
plt.imshow(
dec,
norm=norm,
cmap=cm.bwr,
interpolation='none',
aspect='auto',
origin='lower',
extent=[times[0], times[-1], times[0], times[-1]],
vmin=0.3,
vmax=0.40)
plt.colorbar()
elif plot == 'diag':
plt.ylim(0.3, 0.45)
self.clusterPlot(X_diag,
1 / 3.0,
p_val=0.05,
times=times,
y=(0.31),
color='blue')
err_diag, X_diag = bootstrap(X_diag)
plt.plot(times, X_diag)
plt.fill_between(times,
X_diag + err_diag,
X_diag - err_diag,
alpha=0.2)
plt.axhline(y=0.33, color='black', ls='--')
plt.axvline(x=-0.2, color='black', ls='--')
plt.axvline(x=0, color='black', ls='--')
sns.despine(offset=50, trim=False)
plt_idx += 1
plt.tight_layout()
plt.savefig(
self.FolderTracker(['bdm', '{}_type'.format(header)],
filename='dec.pdf'))
plt.close()