def plot_brain_substates_start_end(trial_num_states, lst_start_and_end_indices,
obs, vmin, vmax, state_number, time_bins,
name, trial_type, save_figures, file, title,
kind_prepro, run):
if len(lst_start_and_end_indices) > 8:
print(
'number of substates is to large (>8), please use other function or select manually, maximal 8'
)
else:
allenMap, allenIndices = load_matrix('')
sub_state_number_list = [
x for x in range(0, len(lst_start_and_end_indices))
]
figsize = get_figsize(lst_start_and_end_indices)
fig, ax = plt.subplots(1,
len(lst_start_and_end_indices),
figsize=figsize,
tight_layout=False)
for i in range(len(lst_start_and_end_indices)):
average_obs_per_sub_state = (np.mean(
obs[lst_start_and_end_indices[i][0]:
lst_start_and_end_indices[i][1], :],
axis=0))
if i == len(lst_start_and_end_indices) - 1:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
average_obs_per_sub_state,
vmin=vmin,
vmax=vmax,
haveColorBar=True)
else:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
average_obs_per_sub_state,
vmin=vmin,
vmax=vmax,
haveColorBar=False)
ax[i].set_title('state {}, substate {} '.format(
state_number, sub_state_number_list[i]))
if save_figures:
new = name + ' plots results {} {} {}'.format(
trial_type, time_bins, trial_num_states)
subdirectory(file, new)
plt.savefig(new +
'/brainmap_sub_{}_{}_{}_{}_{}_{}_{}_run{}.png'.format(
name, trial_type, time_bins, title, get_date(),
state_number, kind_prepro, run))
plt.show()
def plot_brain_states_nan(trial_num_states, lst_average_obs_per_state, vmin,
vmax, time_bins, name, trial_type, delay,
save_figures, file, title, kind_prepro, run):
"""
function to plot the brain states. each brainstate is one image and each is saved individually, including the
possibility of empty (non existing) states
"""
allenMap, allenIndices = load_matrix('')
state_number_list = [x for x in range(0, trial_num_states)]
for i in range(len(lst_average_obs_per_state)):
sum_mean_all_roi = sum(lst_average_obs_per_state[i])
if sum_mean_all_roi == 0:
print('state {} does not exist!'.format(i))
else:
fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=False)
plot_area_values(fig,
ax,
allenMap,
allenIndices,
lst_average_obs_per_state[i],
vmin=vmin,
vmax=vmax,
haveColorBar=True)
ax.set_title(
'activity of the 27 Rois: {}, {}, {}\n{}s, {}, state {} '.
format(name, trial_type, time_bins, delay, title,
state_number_list[i]))
if save_figures:
new = name + ' plots results {} {} {}'.format(
trial_type, time_bins, trial_num_states)
subdirectory(file, new)
plt.savefig(new +
'/brainmap_{}_{}_{}_{}_{}_{}_{}_run{}.png'.format(
name, trial_type, time_bins, title, get_date(),
state_number_list[i], kind_prepro, run))
# plot the as png exported legend
plt.rcParams["axes.grid"] = False
plt.figure(figsize=(4, 2))
img = plt.imread('legend.png')
plt.imshow(img)
plt.axis('off')
if save_figures:
new = name + ' plots results {} {} {}'.format(trial_type, time_bins,
trial_num_states)
subdirectory(file, new)
plt.savefig(new + '/legend.png')
plt.show()
def plot_brain_states_1(trial_num_states, lst_average_obs_per_state, vmin,
vmax, time_bins, name, trial_type, save_figures, file,
title, run):
"""
function to plot the brainstates in one row and stored as one image, color bar only at the last map
"""
allenMap, allenIndices = load_matrix('')
state_number_list = [x for x in range(0, trial_num_states)]
if trial_num_states > 8:
print(
'number of states is to large (squeezed plot), please use other plot function'
)
else:
figsize = get_figsize(lst_average_obs_per_state)
fig, ax = plt.subplots(1,
trial_num_states,
figsize=figsize,
tight_layout=False)
for i in range(len(lst_average_obs_per_state)):
if i == len(lst_average_obs_per_state) - 1:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
lst_average_obs_per_state[i],
vmin=vmin,
vmax=vmax,
haveColorBar=True)
else:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
lst_average_obs_per_state[i],
vmin=vmin,
vmax=vmax,
haveColorBar=False)
ax[i].set_title('state {} '.format(state_number_list[i]))
if save_figures:
new = name + ' plots results {} {} {}'.format(
trial_type, time_bins, trial_num_states)
subdirectory(file, new)
plt.savefig(new + '/brainmap_1_{}_{}_{}_{}_{}_{}_run{}.png'.format(
name, trial_type, time_bins, title, get_date(),
state_number_list[i], run))
plt.show()
def plot_brain_substates_1(trial_num_states, lst_sub_states, obs, vmin, vmax,
state_number, time_bins, name, trial_type,
save_figures, file, title, run):
"""
function with a double loop for more then 8 states (does not work probably)
"""
allenMap, allenIndices = load_matrix('')
sub_state_number_list = [x for x in range(0, len(lst_sub_states))]
figsize = get_figsize(lst_sub_states)
number_of_rows = int(np.ceil(len(lst_sub_states) / 8))
print(number_of_rows)
fig, ax = plt.subplots(number_of_rows,
8,
figsize=figsize,
tight_layout=False)
for j in range(number_of_rows):
for i in range(8):
average_obs_per_sub_state = (np.mean(obs[lst_sub_states[(j * 8) +
i], :],
axis=0))
plot_area_values(fig,
ax[j, i],
allenMap,
allenIndices,
average_obs_per_sub_state,
vmin=vmin,
vmax=vmax,
haveColorBar=False)
ax[j, i].set_title('state {}, substate {} '.format(
state_number, sub_state_number_list[i]))
if save_figures:
new = name + ' plots results {} {} {}'.format(trial_type, time_bins,
trial_num_states)
subdirectory(file, new)
plt.savefig(new + '/brainmap_sub_{}_{}_{}_{}_{}_{}_run{}.png'.format(
name, trial_type, time_bins, title, get_date(), state_number, run))
plt.show()
def plot_brain_substates_2(trial_num_states, lst_sub_states, obs, vmin, vmax,
state_number, time_bins, name, trial_type,
save_figures, file, title, kind_prepro, run):
"""
function with a double loop for more then 8 states
"""
allenMap, allenIndices = load_matrix('')
sub_state_number_list = [x for x in range(0, len(lst_sub_states))]
figsize = get_figsize_2(lst_sub_states)
number_of_rows = int(np.ceil(len(lst_sub_states) / 8))
fig, ax = plt.subplots(number_of_rows,
8,
figsize=figsize,
tight_layout=False)
ax = ax.ravel()
for i in range(len(lst_sub_states)):
if len(lst_sub_states) < 8:
if i == len(lst_sub_states) - 1:
average_obs_per_sub_state = (np.mean(obs[lst_sub_states[i], :],
axis=0))
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
average_obs_per_sub_state,
vmin=vmin,
vmax=vmax,
haveColorBar=True)
else:
average_obs_per_sub_state = (np.mean(obs[lst_sub_states[i], :],
axis=0))
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
average_obs_per_sub_state,
vmin=vmin,
vmax=vmax,
haveColorBar=False)
else:
if i == 7 or i == 15 or i == 23:
average_obs_per_sub_state = (np.mean(obs[lst_sub_states[i], :],
axis=0))
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
average_obs_per_sub_state,
vmin=vmin,
vmax=vmax,
haveColorBar=True)
else:
average_obs_per_sub_state = (np.mean(obs[lst_sub_states[i], :],
axis=0))
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
average_obs_per_sub_state,
vmin=vmin,
vmax=vmax,
haveColorBar=False)
ax[i].set_title('state {}, substate {} '.format(
state_number, sub_state_number_list[i]))
if save_figures:
new = name + ' plots results {} {} {}'.format(trial_type, time_bins,
trial_num_states)
subdirectory(file, new)
plt.savefig(new +
'/brainmap_sub_{}_{}_{}_{}_{}_{}_{}_run{}.png'.format(
name, trial_type, time_bins, title, kind_prepro,
get_date(), state_number, run))
plt.show()
def plot_brain_states_2_nan(trial_num_states, lst_average_obs_per_state, vmin,
vmax, time_bins, name, trial_type, save_figures,
file, title, kind_prepro, run):
"""
function with a double loop for more then 8 states , saved as one image, including the possibility of empty states
"""
allenMap, allenIndices = load_matrix('')
state_number_list = [x for x in range(0, trial_num_states)]
figsize_2 = get_figsize_2(lst_average_obs_per_state)
number_of_rows = int(np.ceil(len(lst_average_obs_per_state) / 8))
fig, ax = plt.subplots(number_of_rows,
8,
figsize=figsize_2,
tight_layout=False)
ax = ax.ravel()
for i in range(len(lst_average_obs_per_state)):
sum_mean_all_roi = sum(lst_average_obs_per_state[i])
if sum_mean_all_roi == 0:
print('state {} does not exist!'.format(i))
else:
if len(lst_average_obs_per_state) < 8:
if i == len(lst_average_obs_per_state) - 1:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
lst_average_obs_per_state[i],
vmin=vmin,
vmax=vmax,
haveColorBar=True)
else:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
lst_average_obs_per_state[i],
vmin=vmin,
vmax=vmax,
haveColorBar=False)
else:
if i == 7 or i == 15 or i == 23:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
lst_average_obs_per_state[i],
vmin=vmin,
vmax=vmax,
haveColorBar=True)
else:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
lst_average_obs_per_state[i],
vmin=vmin,
vmax=vmax,
haveColorBar=False)
ax[i].set_title('state {} '.format(state_number_list[i]))
ax[i].set_yticks([])
ax[i].set_xticks([])
if save_figures:
new = name + ' plots results {} {} {}'.format(trial_type, time_bins,
trial_num_states)
subdirectory(file, new)
plt.savefig(new + '/brainmap_2_{}_{}_{}_{}_{}_{}_run{}.png'.format(
name, trial_type, time_bins, title, get_date(), kind_prepro, run))
plt.show()
def plot_brain_states_1_nan(trial_num_states, lst_average_obs_per_state, vmin,
vmax, time_bins, name, trial_type, save_figures,
file, title, kind_prepro, run):
"""
function to plot the brain states in one row and stored as one image, including the option of empty states
"""
allenMap, allenIndices = load_matrix('')
state_number_list = [x for x in range(0, trial_num_states)]
if trial_num_states > 8:
print(
'number of states is to large (squeezed plot), please use other plot function'
)
else:
figsize = get_figsize(lst_average_obs_per_state)
fig, ax = plt.subplots(1,
trial_num_states,
figsize=figsize,
tight_layout=False)
for i in range(len(lst_average_obs_per_state)):
sum_mean_all_roi = sum(lst_average_obs_per_state[i])
if sum_mean_all_roi == 0:
print('state {} does not exist!'.format(i))
else:
if i == trial_num_states - 1:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
lst_average_obs_per_state[i],
vmin=vmin,
vmax=vmax,
haveColorBar=True)
else:
plot_area_values(fig,
ax[i],
allenMap,
allenIndices,
lst_average_obs_per_state[i],
vmin=vmin,
vmax=vmax,
haveColorBar=False)
ax[i].set_title('state {} '.format(state_number_list[i]))
ax[i].set_yticks([])
ax[i].set_xticks([])
if save_figures:
new = name + ' plots results {} {} {}'.format(
trial_type, time_bins, trial_num_states)
subdirectory(file, new)
plt.savefig(new + '/brainmap_1_{}_{}_{}_{}_{}_{}_run{}.png'.format(
name, trial_type, time_bins, title, get_date(), kind_prepro,
run))
plt.show()
