def create_mmvt_file(subject,
stim_channel,
channel_list,
fs,
time,
stim,
theta,
downsample_ratio=10):
theta -= np.min(theta)
theta /= np.max(theta)
theta *= -1
stim = utils.downsample(stim.squeeze(), downsample_ratio)
stim[stim >= 0.5] = 1
stim[stim < 0.5] = 0
theta = utils.downsample_2d(theta.T, downsample_ratio)
data = np.vstack((stim, theta))
data = data[:, :, np.newaxis]
channel_list = [stim_channel] + channel_list
meta_data_fname = op.join(MMVT_DIR, subject, 'electrodes',
'electrodes_bipolar_data.npz')
np.savez(meta_data_fname,
data=data,
names=channel_list,
conditions=['on'],
dt=1 / fs)
def create_mmvt_file(subject,
stim_channel,
channel_list,
fs,
time,
stim,
theta,
downsample_ratio=10,
downsample_using_mean=True,
smooth_win_len=101):
theta = utils.downsample_2d(theta.T, downsample_ratio,
downsample_using_mean)
theta_smooth = np.zeros((theta.shape[0], theta.shape[1]))
for k in range(theta.shape[0]):
theta_smooth[k] = scipy.signal.savgol_filter(theta[k], smooth_win_len,
5)
theta_smooth[k] -= np.min(theta_smooth[k])
theta_smooth[k] /= np.max(theta_smooth[k])
plt.plot(theta_smooth.T)
plt.show()
theta_smooth *= -1
stim = utils.downsample(stim.squeeze(), downsample_ratio)
stim[stim > 0] = 1
stim_indices = np.where(np.diff(stim) == 1)[0] + 1
print('{} stim!'.format(len(stim_indices)))
plt.plot(stim)
plt.show()
data = np.vstack((stim, theta_smooth))
data = data[:, :, np.newaxis]
channel_list = [stim_channel] + channel_list
meta_data_fname = op.join(MMVT_DIR, subject, 'electrodes',
'electrodes_bipolar_data.npz')
np.savez(meta_data_fname,
data=data,
names=channel_list,
conditions=['on'],
dt=1 / fs)
def downsample_data(data):
C, E, T = data.shape
new_data = np.zeros((C, E, int(T/2)))
for epoch in range(C):
new_data[epoch, :, :] = utils.downsample_2d(data[epoch, :, :], 2)
return new_data
def downsample_data(data):
C, E, T = data.shape
new_data = np.zeros((C, E, int(T/2)))
for epoch in range(C):
new_data[epoch, :, :] = utils.downsample_2d(data[epoch, :, :], 2)
return new_data
def read_electrodes_data_one_mat(subject, mat_file, conditions, stat, output_file_name, bipolar, electrodes_names_field,
field_cond_template, from_t=0, to_t=None, norm_by_percentile=True, norm_percs=(3, 97), threshold=0,
color_map='jet', cm_big='YlOrRd', cm_small='PuBu', flip_cm_big=False, flip_cm_small=True,
moving_average_win_size=0, downsample=2):
# load the matlab file
d = sio.loadmat(mat_file)
# get the labels names
if electrodes_names_field in d:
labels = d[electrodes_names_field]
labels = mu.matlab_cell_str_to_list(labels)
if bipolar and '-' in labels[0]:
labels = fix_bipolar_labels(labels)
# else:
# #todo: change that!!!
# if len(labels) == 1:
# labels = [str(l[0]) for l in labels[0]]
# else:
# labels = [str(l[0][0]) for l in labels]
else:
raise Exception('electrodes_names_field not in the matlab file!')
# Loop for each condition
for cond_id, cond_name in enumerate(conditions):
field = field_cond_template.format(cond_name)
if field not in d:
field = field_cond_template.format(cond_name.title())
if field not in d:
print('{} not in the mat file!'.format(cond_name))
continue
# todo: downsample suppose to be a cmdline paramter
# dims: samples * electrodes * time (single triale) or electrodes * time (evoked)
if to_t == 0 or to_t == -1:
to_t = int(d[field].shape[-1] / downsample)
# initialize the data matrix (electrodes_num x T x 2)
# times = np.arange(0, to_t*2, 2)
cond_data = d[field] # [:, times]
if cond_id == 0:
single_trials = cond_data.ndim == 3
data = {} if single_trials else np.zeros((cond_data.shape[0], to_t - from_t, 2))
if single_trials:
# Different number of trials per condition, can't save it in a matrix
# data[cond_name] = np.zeros((cond_data.shape[0], cond_data.shape[1], to_t - from_t))
cond_data_downsample = utils.downsample_3d(cond_data, downsample)
data[cond_name] = cond_data_downsample[:, :, from_t:to_t]
else:
cond_data_downsample = utils.downsample_2d(cond_data, downsample)
data[:, :, cond_id] = cond_data_downsample[:, from_t:to_t]
if bipolar:
data, labels = bipolarize_data(data, labels)
if single_trials:
output_fname = op.join(MMVT_DIR, subject, 'electrodes', 'electrodes{}_data_st.npz'.format(
'_bipolar' if bipolar else ''))
data_conds = [(data[key], key) for key in data.keys()]
data = [d[0] for d in data_conds]
conditions = [d[1] for d in data_conds]
np.savez(output_fname, data=data, names=labels, conditions=conditions)
else:
data = utils.normalize_data(data, norm_by_percentile, norm_percs)
stat_data = calc_stat_data(data, stat)
calc_colors = partial(
utils.mat_to_colors_two_colors_maps, threshold=threshold, cm_big=cm_big, cm_small=cm_small, default_val=1,
flip_cm_big=flip_cm_big, flip_cm_small=flip_cm_small, min_is_abs_max=True, norm_percs=norm_percs)
if moving_average_win_size > 0:
# data_mv[:, :, cond_id] = utils.downsample_2d(data[:, :, cond_id], moving_average_win_size)
stat_data_mv = utils.moving_avg(stat_data, moving_average_win_size)
colors_mv = calc_colors(stat_data_mv)
np.savez(output_file_name, data=data, stat=stat_data_mv, names=labels, conditions=conditions, colors=colors_mv)
else:
colors = calc_colors(stat_data)
np.savez(output_file_name, data=data, names=labels, conditions=conditions, colors=colors)
