def plot_crispness_for_parameters(selected_rows = None):
'''
This function plots crispness for all the selected rows motion correction states. The idea is to compare crispness results
:param selected_rows: analysis states for which crispness is required to be ploted
:return: figure that is also saved
'''
crispness_mean_original,crispness_corr_original, crispness_mean, crispness_corr = metrics.compare_crispness(selected_rows)
total_states_number = len(selected_rows)
fig, axes = plt.subplots(1,2)
axes[0].set_title('Summary image = Mean')
axes[0].plot(np.arange(1,total_states_number,1),crispness_mean_original)
axes[0].plot(np.arange(1,total_states_number,1),crispness_mean)
axes[0].legend(('Original', 'Motion_corrected'))
axes[0].set_ylabel('Crispness')
#axes[0].set_xlabel('#')
axes[1].set_title('Summary image = Corr')
axes[1].plot(np.arange(1,total_states_number,1),crispness_corr_original)
axes[1].plot(np.arange(1,total_states_number,1),crispness_corr)
axes[1].legend(('Original', 'Motion_corrected'))
axes[1].set_ylabel('Crispness')
#axes[0].set_xlabel('#')
# Get output file paths
index = selected_rows.iloc[0].name
data_dir = 'data/interim/motion_correction/'
step_index = db.get_step_index('motion_correction')
file_name = db.create_file_name(step_index, index)
output_meta_crispness = data_dir + f'meta/figures/crispness/{file_name}.png'
fig.savefig(output_meta_crispness)
return fig
def select_corr_pnr_threshold(mouse_row,parameters_source_extraction):
'''
Plots the summary images correlation and pnr. Also the pointwise product between them (used in Caiman paper Zhou
et al 2018)
:param mouse_row:
:param parameters_source_extraction: parameters that will be used for source
extraction. the relevant parameter here are min_corr and min_pnr because the source extraction algorithm is
initialized (initial cell templates) in all values that surpasses that threshold
:return: max_combined, max_pnr, max_corr: threshold for corr*pnr, and corresponding values of corr and pnr
'''
input_mmap_file_path = eval(mouse_row.loc['motion_correction_output'])['main']
# Load memory mappable input file
if os.path.isfile(input_mmap_file_path):
Yr, dims, T = cm.load_memmap(input_mmap_file_path)
# logging.debug(f'{index} Loaded movie. dims = {dims}, T = {T}.')
images = Yr.T.reshape((T,) + dims, order='F')
else:
logging.warning(f'{mouse_row.name} .mmap file does not exist. Cancelling')
# Determine output paths
step_index = db.get_step_index('motion_correction')
data_dir = 'data/interim/source_extraction/trial_wise/'
# Check if the summary images are already there
gSig = parameters_source_extraction['gSig'][0]
corr_npy_file_path, pnr_npy_file_path = fm.get_corr_pnr_path(mouse_row.name, gSig_abs=(gSig, gSig))
if corr_npy_file_path != None and os.path.isfile(corr_npy_file_path):
# Already computed summary images
logging.info(f'{mouse_row.name} Already computed summary images')
cn_filter = np.load(corr_npy_file_path)
pnr = np.load(pnr_npy_file_path)
else:
# Compute summary images
t0 = datetime.datetime.today()
logging.info(f'{mouse_row.name} Computing summary images')
cn_filter, pnr = cm.summary_images.correlation_pnr(images[::1], gSig=parameters_source_extraction['gSig'][0],
swap_dim=False)
# Saving summary images as npy files
corr_npy_file_path = data_dir + f'meta/corr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}.npy'
pnr_npy_file_path = data_dir + f'meta/pnr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}.npy'
with open(corr_npy_file_path, 'wb') as f:
np.save(f, cn_filter)
with open(pnr_npy_file_path, 'wb') as f:
np.save(f, pnr)
combination = cn_filter * pnr # this is as defined in Zhou et al 2018 (definition of R, P and L, eq 14)
max_combined = np.argmax(combination)
row = int(np.floor(max_combined / cn_filter.shape[1]))
column = int(max_combined - row * cn_filter.shape[1])
max_corr = cn_filter[row, column]
max_pnr = pnr[row, column]
return max_combined, max_corr, max_pnr
def create_corr_pnr_histogram(mouse_row,parameters_source_extraction):
'''
Returns histogram of summary images correlation and pnr
:param mouse_row:
:param parameters_source_extraction: parameters that will be used for source extraction.
:return: histogram vector
'''
input_mmap_file_path = eval(mouse_row.loc['motion_correction_output'])['main']
# Load memory mappable input file
if os.path.isfile(input_mmap_file_path):
Yr, dims, T = cm.load_memmap(input_mmap_file_path)
# logging.debug(f'{index} Loaded movie. dims = {dims}, T = {T}.')
images = Yr.T.reshape((T,) + dims, order='F')
else:
logging.warning(f'{mouse_row.name} .mmap file does not exist. Cancelling')
# Determine output paths
step_index = db.get_step_index('motion_correction')
data_dir = 'data/interim/source_extraction/trial_wise/'
# Check if the summary images are already there
gSig = parameters_source_extraction['gSig'][0]
corr_npy_file_path, pnr_npy_file_path = fm.get_corr_pnr_path(mouse_row.name, gSig_abs=(gSig, gSig))
if corr_npy_file_path != None and os.path.isfile(corr_npy_file_path):
# Already computed summary images
logging.info(f'{mouse_row.name} Already computed summary images')
cn_filter = np.load(corr_npy_file_path)
pnr = np.load(pnr_npy_file_path)
else:
# Compute summary images
t0 = datetime.datetime.today()
logging.info(f'{mouse_row.name} Computing summary images')
cn_filter, pnr = cm.summary_images.correlation_pnr(images[::1], gSig=parameters_source_extraction['gSig'][0],
swap_dim=False)
# Saving summary images as npy files
corr_npy_file_path = data_dir + f'meta/corr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}.npy'
pnr_npy_file_path = data_dir + f'meta/pnr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}.npy'
with open(corr_npy_file_path, 'wb') as f:
np.save(f, cn_filter)
with open(pnr_npy_file_path, 'wb') as f:
np.save(f, pnr)
corr_pos, corr_histogram = np.histogram(cn_filter,100)
pnr_pos, pnr_histogram = np.histogram(pnr,100)
return corr_pos, corr_histogram, pnr_pos, pnr_histogram
def get_fig_gSig_filt_vals(row, gSig_filt_vals):
'''
Plot original cropped frame and several versions of spatial filtering for comparison
:param row: analisis state row for which the filtering is computed
:param gSig_filt_vals: array containing size of spatial filters that will be applyed
:return: figure
'''
output = row['cropping_output']
cropped_file = eval(output)['main']
m = cm.load(cropped_file)
temp = cm.motion_correction.bin_median(m)
N = len(gSig_filt_vals)
fig, axes = plt.subplots(int(math.ceil((N + 1) / 2)), 2)
axes[0, 0].imshow(temp, cmap='gray')
axes[0, 0].set_title('unfiltered')
axes[0, 0].axis('off')
for i in range(0, N):
gSig_filt = gSig_filt_vals[i]
m_filt = [high_pass_filter_space(m_, (gSig_filt, gSig_filt)) for m_ in m]
temp_filt = cm.motion_correction.bin_median(m_filt)
axes.flatten()[i + 1].imshow(temp_filt, cmap='gray')
axes.flatten()[i + 1].set_title(f'gSig_filt = {gSig_filt}')
axes.flatten()[i + 1].axis('off')
if N + 1 != axes.size:
for i in range(N + 1, axes.size):
axes.flatten()[i].axis('off')
# Get output file paths
index = row.name
data_dir = 'data/interim/motion_correction/'
step_index = db.get_step_index('motion_correction')
file_name = db.create_file_name(step_index, index)
output_meta_gSig_filt = data_dir + f'meta/figures/frame_gSig_filt/{file_name}.png'
fig.savefig(output_meta_gSig_filt)
return fig
def plot_corr_pnr_binary(mouse_row, corr_limits, pnr_limits, parameters_source_extraction, session_wise = False,
alignment = False, equalization = False):
'''
Plot 2 matrix of binary selected and not selected seeds for different corr_min and pnr_min
:param mouse_row: analysis states data
:param corr_limits: array of multiple values of corr_min to test
:param pnr_limits: arrey of multiple values of pnr_min to test
:param parameters_source_extraction: dictionary with parameters
:return: figure pointer
'''
input_mmap_file_path = eval(mouse_row.loc['motion_correction_output'])['main']
if alignment:
input_mmap_file_path = eval(mouse_row.loc['alignment_output'])['main']
if equalization:
input_mmap_file_path = eval(mouse_row['alignment_output'])['equalizing_output']['main']
# Load memory mappable input file
if os.path.isfile(input_mmap_file_path):
Yr, dims, T = cm.load_memmap(input_mmap_file_path)
# logging.debug(f'{index} Loaded movie. dims = {dims}, T = {T}.')
images = Yr.T.reshape((T,) + dims, order='F')
else:
logging.warning(f'{mouse_row.name} .mmap file does not exist. Cancelling')
# Determine output paths
step_index = db.get_step_index('motion_correction')
data_dir = 'data/interim/source_extraction/trial_wise/'
# Check if the summary images are already there
gSig = parameters_source_extraction['gSig'][0]
corr_npy_file_path, pnr_npy_file_path = fm.get_corr_pnr_path(mouse_row.name, gSig_abs=(gSig, gSig))
if corr_npy_file_path != None and os.path.isfile(corr_npy_file_path):
# Already computed summary images
logging.info(f'{mouse_row.name} Already computed summary images')
cn_filter = np.load(corr_npy_file_path)
pnr = np.load(pnr_npy_file_path)
else:
# Compute summary images
t0 = datetime.datetime.today()
logging.info(f'{mouse_row.name} Computing summary images')
cn_filter, pnr = cm.summary_images.correlation_pnr(images[::1], gSig=parameters_source_extraction['gSig'][0],
swap_dim=False)
# Saving summary images as npy files
corr_npy_file_path = data_dir + f'meta/corr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}.npy'
pnr_npy_file_path = data_dir + f'meta/pnr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}.npy'
with open(corr_npy_file_path, 'wb') as f:
np.save(f, cn_filter)
with open(pnr_npy_file_path, 'wb') as f:
np.save(f, pnr)
fig1 = plt.figure(figsize=(50, 50))
combined_image = cn_filter * pnr
fig1, axes1 = plt.subplots(len(corr_limits), len(pnr_limits), sharex=True)
fig2, axes2 = plt.subplots(len(corr_limits), len(pnr_limits), sharex=True)
fig3, axes3 = plt.subplots(len(corr_limits), len(pnr_limits), sharex=True)
ii=0
for min_corr in corr_limits:
min_corr = round(min_corr,2)
jj=0
for min_pnr in pnr_limits:
min_pnr = round(min_pnr,2)
# binary
limit = min_corr * min_pnr
axes1[ii, jj].imshow(combined_image> limit, cmap='binary')
axes1[ii, jj].set_title(f'{min_corr}')
axes1[ii, jj].set_ylabel(f'{min_pnr}')
axes2[ii, jj].imshow(cn_filter > min_corr, cmap='binary')
axes2[ii, jj].set_title(f'{min_corr}')
axes2[ii, jj].set_ylabel(f'{min_pnr}')
axes3[ii, jj].imshow(pnr> min_pnr, cmap='binary')
axes3[ii, jj].set_title(f'{min_corr}')
axes3[ii, jj].set_ylabel(f'{min_pnr}')
jj=jj+1
ii=ii+1
fig_dir = 'data/interim/source_extraction/trial_wise/meta/'
if session_wise:
fig_dir = 'data/interim/source_extraction/session_wise/meta/'
fig_name= fig_dir + f'figures/min_corr_pnr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}_comb.png'
fig1.savefig(fig_name)
fig_name= fig_dir + f'figures/min_corr_pnr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}_corr.png'
fig2.savefig(fig_name)
fig_name= fig_dir + f'figures/min_corr_pnr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}_pnr.png'
fig3.savefig(fig_name)
return fig1,fig2,fig3
def plot_corr_pnr(mouse_row, parameters_source_extraction):
'''
Plots the summary images correlation and pnr. Also the pointwise product between them (used in Caiman paper Zhou
et al 2018)
:param mouse_row:
:param parameters_source_extraction: parameters that will be used for source
extraction. the relevant parameter here are min_corr and min_pnr because the source extraction algorithm is
initialized (initial cell templates) in all values that surpasses that threshold
:return: figure
'''
input_mmap_file_path = eval(mouse_row.loc['motion_correction_output'])['main']
# Load memory mappable input file
if os.path.isfile(input_mmap_file_path):
Yr, dims, T = cm.load_memmap(input_mmap_file_path)
# logging.debug(f'{index} Loaded movie. dims = {dims}, T = {T}.')
images = Yr.T.reshape((T,) + dims, order='F')
else:
logging.warning(f'{mouse_row.name} .mmap file does not exist. Cancelling')
# Determine output paths
step_index = db.get_step_index('motion_correction')
data_dir = 'data/interim/source_extraction/trial_wise/'
# Check if the summary images are already there
gSig = parameters_source_extraction['gSig'][0]
corr_npy_file_path, pnr_npy_file_path = fm.get_corr_pnr_path(mouse_row.name, gSig_abs=(gSig, gSig))
if corr_npy_file_path != None and os.path.isfile(corr_npy_file_path):
# Already computed summary images
logging.info(f'{mouse_row.name} Already computed summary images')
cn_filter = np.load(corr_npy_file_path)
pnr = np.load(pnr_npy_file_path)
else:
# Compute summary images
t0 = datetime.datetime.today()
logging.info(f'{mouse_row.name} Computing summary images')
cn_filter, pnr = cm.summary_images.correlation_pnr(images[::1], gSig=parameters_source_extraction['gSig'][0],
swap_dim=False)
# Saving summary images as npy files
corr_npy_file_path = data_dir + f'meta/corr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}.npy'
pnr_npy_file_path = data_dir + f'meta/pnr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}.npy'
with open(corr_npy_file_path, 'wb') as f:
np.save(f, cn_filter)
with open(pnr_npy_file_path, 'wb') as f:
np.save(f, pnr)
fig = plt.figure(figsize=(15, 15))
min_corr = round(parameters_source_extraction['min_corr'], 2)
min_pnr = round(parameters_source_extraction['min_pnr'], 1)
max_corr = round(cn_filter.max(), 2)
max_pnr= 20
# continuous
cmap = 'viridis'
fig, axes = plt.subplots(1, 3, sharex=True)
corr_fig = axes[0].imshow(np.clip(cn_filter,min_corr,max_corr), cmap=cmap)
axes[0].set_title('Correlation')
fig.colorbar(corr_fig, ax=axes[0])
pnr_fig = axes[1].imshow(np.clip(pnr,min_pnr,max_pnr), cmap=cmap)
axes[1].set_title('PNR')
fig.colorbar(pnr_fig, ax=axes[1])
combined = cn_filter*pnr
max_combined = 10
min_combined = np.min(combined)
corr_pnr_fig = axes[2].imshow(np.clip(cn_filter*pnr,min_combined,max_combined), cmap=cmap)
axes[2].set_title('Corr * PNR')
fig.colorbar(corr_pnr_fig, ax=axes[2])
fig_dir = 'data/interim/source_extraction/trial_wise/meta/'
fig_name= fig_dir + f'figures/corr_pnr/{db.create_file_name(3, mouse_row.name)}_gSig_{gSig}.png'
fig.savefig(fig_name)
return fig