def save_ctrl_profiles(self, path, baseline_frames=5):
""" Masks intensity profiles
"""
# MASTER MASK PROFILES + DERIVATE PROFILE
ca_deltaF = edge.deltaF(self.ca_profile())
prot_deltaF = edge.deltaF(self.prot_profile(mask=self.master_mask))
derivate_deltaF = edge.deltaF(self.derivate_profile)
plt.figure(figsize=(15,4))
plt.plot(self.time_line, ca_deltaF, label='Ca dye profile')
plt.plot(self.time_line, prot_deltaF, label='FP profile')
plt.plot(self.time_line[1:], derivate_deltaF, label='Ca dye derivate', linestyle='--')
plt.plot(np.take(self.time_line[1:], self.stim_peak), np.take(derivate_deltaF, self.stim_peak), 'v',
label='stimulation peak', markersize=10, color='red')
plt.grid(visible=True, linestyle=':')
plt.xlabel('Time (s)')
plt.ylabel('ΔF/F')
plt.xticks(np.arange(0, np.max(self.time_line)+2, step=1/self.time_scale))
plt.legend()
plt.tight_layout()
plt.suptitle(f'Master mask int profile, {self.img_name}, {self.stim_power}%', fontsize=20)
plt.tight_layout()
plt.savefig(f'{path}/{self.img_name}_profile_ca.png')
# UP REGIONS PROFILES + CA PROFILE
best_mask = self.up_diff_mask[self.best_up_mask_index]
best_mask_total = best_mask != 0
# best_mask_down = self.down_diff_mask[self.best_up_mask_index]
plt.figure(figsize=(15, 4))
plt.plot(self.time_line, edge.deltaF(self.prot_profile(mask=best_mask_total)),
label='total up mask', linestyle='--', linewidth=3, color='white')
# plt.plot(self.time_line, edge.deltaF(self.prot_profile(mask=best_mask_down)),
# label='total down mask', linestyle=':', linewidth=3, color='white')
plt.plot(np.take(self.time_line[1:], self.stim_peak), [0.5 for i in self.stim_peak], 'v',
label='stimulation peak', markersize=10, color='red')
for up_region_lab in range(1, np.max(best_mask)+1):
up_region_mask = best_mask == up_region_lab
up_region_profile = edge.deltaF(self.prot_profile(mask=up_region_mask))
plt.plot(self.time_line, up_region_profile, label=f'region {up_region_lab}', linewidth=0.75)
plt.grid(visible=True, linestyle=':')
plt.xlabel('Time (s)')
plt.ylabel('ΔF/F')
plt.xticks(np.arange(0, np.max(self.time_line)+2, step=1/self.time_scale))
plt.legend()
plt.tight_layout()
plt.suptitle(f'Best up mask regions profiles, {self.img_name}, {self.stim_power}%', fontsize=20)
plt.tight_layout()
plt.savefig(f'{path}/{self.img_name}_profile_up.png')
plt.close('all')
logging.info(f'{self.img_name} control profiles saved!')
def find_stimul_peak(self, h=0.15, d=3, l_lim=5, r_lim=18):
""" Require master_mask, results of get_master_mask!
Find peaks on deltaF/F Fluo-4 derivate profile.
h - minimal peaks height, in ΔF/F0
d - minimal distance between peaks, in frames
"""
self.stim_peak = find_peaks(edge.deltaF(self.derivate_profile), height=h, distance=d)[0]
self.stim_peak = self.stim_peak[(self.stim_peak >= l_lim) & (self.stim_peak <= r_lim)] # filter outer peaks
logging.info(f'Detected peaks: {self.stim_peak}')
def updown_mask_int(self,
up_mask,
down_mask,
delta=False,
plot_path=False):
""" Calculation mean intensity in masked area along frames series.
Require two masks, increasing and decreasing regions.
"""
up_list = [
round(np.sum(ma.masked_where(~up_mask, img)) / np.sum(up_mask), 3)
for img in self.img_series
]
down_list = [
round(
np.sum(ma.masked_where(~down_mask, img)) / np.sum(down_mask),
3) for img in self.img_series
]
if delta: # calculate ΔF/F0
up_list = edge.deltaF(up_list)
down_list = edge.deltaF(down_list)
return np.asarray(up_list), np.asarray(down_list)
# blue/red derivate images
der_int = edge.series_derivate(cell.img_series,
mask= 'full_frame', # cell.mask_series[cell.max_frame_num],
sd_mode='cell',
sd_tolerance=False,
sigma=1, kernel_size=3,
left_w=1, space_w=0, right_w=1) # ,
# output_path=cell_path)
amp_series = [np.sum(np.abs(der_int[i])) for i in range(len(der_int))]
# control image of the cell with native image of max frame and hysteresis binary mask
cell.save_ctrl_img(path=res_path)
cell_int = cell.max_mask_int(plot_path=cell_path)
# calc relative amount in up mask
up_rel = edge.deltaF(up_int / cell_int, f_0_win=10)
down_rel = edge.deltaF(down_int / cell_int, f_0_win=10)
# calc delta F for mask series
cell_delta = edge.deltaF(cell_int, f_0_win=10)
up_delta = edge.deltaF(up_int, f_0_win=10)
down_delta = edge.deltaF(down_int, f_0_win=10)
# group all results by mask
maskres_dict = {'cell':[cell_int, cell_delta, cell_int],
'up':[up_int, up_delta, up_rel],
'down':[down_int, down_delta, down_rel]}
# saving results to CSV
if save_csv:
for val_num in range(len(cell_int)):
for maskres_key in maskres_dict.keys():
# for single_num in range(len(series_int)):
# single_int = series_int[single_num]
# df = df.append(pd.Series([int(cell_num+1), cell.exposure, cell.cycles, int(single_num+1), single_int],
# index=df.columns),
# ignore_index=True)
# PA in loading solution experiment (1 - no PA, 2 - with PA)
df = pd.DataFrame(columns=['cell', 'power', 'time', 'int'])
for cell_num in range(0, len(all_cells)):
cell = all_cells[cell_num]
logging.info('Image {} in progress'.format(cell.img_name))
series_int, mask, gauss = cell.relInt(high_lim=0.8, init_low=0.05, mask_diff=40, sigma=3, noise_size=40)
series_int = edge.deltaF(series_int)
# try: # register exceptions from lowHyst function
# series_int, mask, gauss = cell.relInt(high_lim=0.8, init_low=0.05, mask_diff=40, sigma=3, noise_size=40)
# except RuntimeError:
# logging.fatal('For image {} relative intensity DON`T calculated, RE!\n'.format(cell.img_name))
# continue
# except ValueError:
# logging.fatal('For image {} relative intensity DON`T calculated, VE!\n'.format(cell.img_name))
# continue
for single_num in range(len(series_int)):
single_int = series_int[single_num]
df = df.append(pd.Series([cell.img_name, cell.feature, int(single_num+1), single_int],
index=df.columns),
ignore_index=True)
def save_profile_df(self, id_suffix):
""" Masked regions intensity profiles data frame
"""
self.profile_df = pd.DataFrame(columns=['ID', # recording ID
'power', # 405 nm stimulation power (%)
'ch', # channel (FP or Ca dye)
'frame', # frame number
'time', # frame time (s)
'mask', # mask type (master, up, down)
'mask_region', # mask region (1 for master or down)
'mean', # mask mean intensity
'delta', # mask ΔF/F
'rel']) # mask sum / master mask sum
# Ca dye
ca_profile = self.ca_profile()
ca_profile_delta = edge.deltaF(ca_profile, f_0_win=self.baseline_frames)
for ca_val in range(0, len(self.ca_series)):
point_series = pd.Series([f'{self.img_name}{id_suffix}', # recording ID
self.stim_power, # 405 nm stimulation power (%)
'ca', # channel (FP or Ca dye)
ca_val+1, # frame number
self.time_line[ca_val], # frame time (s)
'master', # mask type (only master for Ca dye channel)
1, # mask region (1 for master or down)
ca_profile[ca_val], # mask mean intensity
ca_profile_delta[ca_val], # mask ΔF/F
1], # mask sum / master mask sum (1 for Ca dye channel)
index=self.profile_df.columns)
self.profile_df = self.profile_df.append(point_series, ignore_index=True)
# FP intensity
# FP master
fp_master_profile = self.prot_profile(mask=self.master_mask)
fp_master_profile_delta = edge.deltaF(fp_master_profile, f_0_win=self.baseline_frames)
fp_master_sum = np.asarray([round(np.sum(ma.masked_where(~self.master_mask, img)), 3) for img in self.prot_series]) # intensity sum for master mask
for fp_val in range(0, len(self.prot_series)):
point_series = pd.Series([f'{self.img_name}{id_suffix}', # recording ID
self.stim_power, # 405 nm stimulation power (%)
'fp', # channel (FP or Ca dye)
fp_val+1, # frame number
self.time_line[fp_val], # frame time (s)
'master', # mask type (only master for Ca dye channel)
1, # mask region (1 for master or down)
fp_master_profile[fp_val], # mask mean intensity
fp_master_profile_delta[fp_val], # mask ΔF/F
1], # mask sum / master mask sum (1 for Ca dye channel)
index=self.profile_df.columns)
self.profile_df = self.profile_df.append(point_series, ignore_index=True)
# FP down regions
fp_down_profile = self.prot_profile(mask=self.down_diff_mask[self.best_up_mask_index])
fp_down_profile_delta = edge.deltaF(fp_down_profile, f_0_win=self.baseline_frames)
fp_down_sum = np.asarray([round(np.sum(ma.masked_where(~self.down_diff_mask[self.best_up_mask_index], img)), 3) for img in self.prot_series])
for fp_val in range(0, len(self.prot_series)):
point_series = pd.Series([f'{self.img_name}{id_suffix}', # recording ID
self.stim_power, # 405 nm stimulation power (%)
'fp', # channel (FP or Ca dye)
fp_val+1, # frame number
self.time_line[fp_val], # frame time (s)
'down', # mask type (only master for Ca dye channel)
1, # mask region (1 for master or down)
fp_down_profile[fp_val], # mask mean intensity
fp_down_profile_delta[fp_val], # mask ΔF/F
fp_down_sum[fp_val]/fp_master_sum[fp_val]], # mask sum / master mask sum (1 for Ca dye channel)
index=self.profile_df.columns)
self.profile_df = self.profile_df.append(point_series, ignore_index=True)
# FP up regions
best_up_mask = self.up_diff_mask[self.best_up_mask_index]
best_up_mask_prop = measure.regionprops(best_up_mask)
fp_up_profile_dict = {}
for i in best_up_mask_prop: # calculate profiles for each up region
best_up_mask_region = best_up_mask == i.label
fp_up_profile = self.prot_profile(mask=best_up_mask_region)
fp_up_profile_delta = edge.deltaF(fp_up_profile, f_0_win=self.baseline_frames)
fp_up_sum = np.asarray([round(np.sum(ma.masked_where(~best_up_mask_region, img)), 3) for img in self.prot_series])
fp_up_profile_dict.update({i.label: [fp_up_profile, fp_up_profile_delta, fp_up_sum]})
for fp_val in range(0, len(self.prot_series)):
for up_region_key in fp_up_profile_dict.keys():
up_region = fp_up_profile_dict.get(up_region_key)
point_series = pd.Series([f'{self.img_name}{id_suffix}', # recording ID
self.stim_power, # 405 nm stimulation power (%)
'fp', # channel (FP or Ca dye)
fp_val+1, # frame number
self.time_line[fp_val], # frame time (s)
'up', # mask type (only master for Ca dye channel)
up_region_key, # mask region (1 for master or down)
up_region[0][fp_val], # mask mean intensity
up_region[1][fp_val], # mask ΔF/F
up_region[2][fp_val]/fp_master_sum[fp_val]], # mask sum / master mask sum (1 for Ca dye channel)
index=self.profile_df.columns)
self.profile_df = self.profile_df.append(point_series, ignore_index=True)
logging.info(f'Recording profile data frame {self.profile_df.shape} created')
return self.profile_df
sd_mode='cell',
sd_tolerance=False,
sigma=1,
kernel_size=3,
left_w=2,
space_w=1,
right_w=2) #,
# output_path=cell_path)
amp_series = [np.sum(np.abs(der_int[i])) for i in range(len(der_int))]
# control image of the cell with native image of max frame and hysteresis binary mask
cell.save_ctrl_img(path=res_path)
cell_int = cell.max_mask_int(plot_path=cell_path)
# calc delta F for mask series
cell_delta = edge.deltaF(cell_int, f_0_win=max_frame_number - 1)
# group all results by mask
maskres_dict = {'cell': [cell_int, cell_delta]}
# saving results to CSV
# NOT READY!
if save_csv:
for val_num in range(len(cell_int)):
int_val = cell_int[val_num]
delta_val = cell_delta[val_num]
df = df.append(
pd.Series(
[
cell.img_name, # cell file name
cell.feature, # 405 nm power