def plot_overall_flu(exp_obj=exp_obj):
dff = ao.normalize_dff(exp_obj.raw)
dff_mean = np.mean(dff, axis=0)
fig, ax = plt.subplots(figsize=(20, 4))
color = 'black'
ax.plot(dff_mean, color=color, alpha=0.4, linewidth=0.5)
ax.tick_params(axis='y', labelcolor=color)
if type(exp_obj.seizure_frames) == list:
ax.axvline(x=exp_obj.seizure_frames[0], color='black')
ax.axvline(x=exp_obj.seizure_frames[-1], color='black')
# change x axis ticks to seconds
labels = [item for item in ax.get_xticks()]
for item in labels:
labels[labels.index(item)] = int(round(item / exp_obj.fps))
ax.set_xticklabels(labels)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.set_xlabel('Time (seconds)')
ax.set_title(experiment + ' - mean population Ca2+ signal of FOV',
horizontalalignment='center',
verticalalignment='top',
pad=20,
fontsize=15)
plt.show()
def _good_cells(exp_obj, min_radius_pix, max_radius_pix):
good_cells = []
radius_ = []
for i in range(len(exp_obj.cell_id)):
print('Processing cell #:', i)
raw = list(exp_obj.raw[i])
raw_dff = ao.normalize_dff(raw)
std = np.std(raw_dff, axis=0)
x = []
y = []
for j in np.arange(len(raw_dff), step=4):
avg = np.mean(raw_dff[j:j+4])
if avg > np.mean(raw_dff)+2.5*std:
x.append(j)
y.append(avg)
radius = exp_obj.radius[i]
radius_.append(radius)
if len(x) > 0 and radius > min_radius_pix and radius < max_radius_pix:
good_cells.append(exp_obj.cell_id[i])
print('# of good cells found: %s (out of %s ROIs)'% (len(good_cells), len(exp_obj.cell_id)))
exp_obj.good_cells = good_cells
to_plot = radius_
n, bins, patches = plt.hist(to_plot, 200)
plt.axvline(min_radius_pix)
plt.axvline(max_radius_pix)
plt.suptitle('radius distribution', y=0.95)
plt.show()
def plot_stim_responses_cells(title='', y_label=y_label, y_axis_range=[]):
'''plot average stim response of each cell at each stim. timepoint, along with the average dFF across all cells at each frame'''
dff = ao.normalize_dff(exp_obj.raw)
dff_mean = np.mean(dff, axis=0)
# keys = df.keys()[1:]
# response = [df["%s" % stim].mean() for stim in df.keys()[1:]]
# x = [int(i) for i in keys[pd.notna(response)]]
# y = [a for a in response if str(a) != 'nan']
fig, ax1 = plt.subplots(figsize=(20, 3))
color = 'gray'
ax1.plot(dff_mean, color=color, alpha=0.4, linewidth=0.5)
ax1.tick_params(axis='y', labelcolor=color)
if type(exp_obj.seizure_frames) == list:
ax1.axvline(x=exp_obj.seizure_frames[0], color='black')
ax1.axvline(x=exp_obj.seizure_frames[-1], color='black')
ax2 = ax1.twinx()
color = 'black'
x_all = []
y_all = []
for cell in exp_obj.good_naparm_cells_all:
stim_group = df.loc[cell, 'groups']
x = exp_obj.stim_start_frames[0][stim_group::exp_obj.n_groups]
y = [df.loc[cell, '%s' % i] for i in x]
ax2.scatter(x, y, s=5)
# ax2.plot(x, y, linewidth=0.5)
x_all.extend(x)
y_all.extend(y)
# ax2.scatter(x_all, y_all, s=5)
#ax2.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x))) # plot line of best fit through scatter plot # this best fit line is linear so not useful
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_ylabel(y_label)
ax2.set_ylim(y_axis_range)
ax1.set_title(title,
horizontalalignment='center',
verticalalignment='top',
pad=20,
fontsize=15)
ax1.spines['top'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.spines['left'].set_visible(False)
# change x axis ticks to seconds
labels = [item for item in ax1.get_xticks()]
for item in labels:
labels[labels.index(item)] = int(round(item / exp_obj.fps))
ax1.set_xticklabels(labels)
ax1.set_xlabel('Time (seconds)')
fig.show()
def plot_stim_responses_norm_cells(title='',
norm_start=0,
norm_end=exp_obj.n_frames,
y_axis_range=[]):
'''plot average stim response of each cell at each stim. timepoint, along with the average dFF across all cells at each frame'''
dff = ao.normalize_dff(exp_obj.raw)
dff_mean = np.mean(dff, axis=0)
keys = df.keys()[1:]
response = [df["%s" % stim].mean() for stim in df.keys()[1:]]
x = [int(i) for i in keys[pd.notna(response)]]
y = [a for a in response if str(a) != 'nan']
fig, ax1 = plt.subplots(figsize=(20, 3))
color = 'gray'
ax1.plot(dff_mean, color=color, alpha=0.4, linewidth=0.5)
ax1.tick_params(axis='y', labelcolor=color)
if type(exp_obj.seizure_frames) == list:
ax1.axvline(x=exp_obj.seizure_frames[0], color='black')
ax1.axvline(x=exp_obj.seizure_frames[-1], color='black')
ax2 = ax1.twinx()
color = 'black'
for cell in exp_obj.good_naparm_cells_all:
stim_group = df.loc[cell, 'groups']
x = exp_obj.stim_start_frames[0][stim_group::exp_obj.n_groups]
y = [df.loc[cell, '%s' % i] for i in x]
mean_ = np.mean(
[y[i] for i in range(len(x)) if norm_end > x[i] > norm_start])
y_ = [i / mean_ for i in y] # normalized response of this cell
ax2.scatter(x, y_, s=5)
ax2.plot(x, y_, linewidth=0.5)
ax2.set_ylim(y_axis_range)
ax2.tick_params(axis='y', labelcolor=color)
ax1.set_title((title),
horizontalalignment='center',
verticalalignment='top',
pad=20,
fontsize=15)
fig.show()
def plot_flu_trace(exp_obj=exp_obj, idx=idx, slm_group=group, to_plot='raw'):
raw = exp_obj.raw[idx]
raw_ = np.delete(raw, exp_obj.photostim_frames)
raw_dff = ao.normalize_dff(raw_)
std_dff = np.std(raw_dff, axis=0)
std = np.std(raw_, axis=0)
if to_plot == 'raw':
to_plot_ = raw
to_thresh = std
else:
to_plot_ = raw_dff
to_thresh = std_dff
plt.figure(figsize=(20, 3))
plt.plot(to_plot_, linewidth=0.1)
# if to_plot == 'raw':
# plt.suptitle(('raw flu for cell #%s' % exp_obj.cell_id[idx]), horizontalalignment='center',
# verticalalignment='top',
# fontsize=15, y=1.00)
# else:
# plt.scatter(x, y=[0] * len(x), c='r', linewidth=0.10)
# plt.axhline(y=np.mean(to_plot_) + 2.5 * to_thresh, c='green')
# plt.suptitle(('%s flu for cell #%s' % (to_plot, exp_obj.cell_id[idx])), horizontalalignment='center',
# verticalalignment='top',
# fontsize=15, y=1.00)
for i in exp_obj.stim_start_frames[0][slm_group::exp_obj.n_groups]:
plt.axvline(x=i - 1, c='gray', alpha=0.1)
if exp_obj.seizure_frames:
plt.scatter(exp_obj.seizure_frames,
y=[-20] * len(exp_obj.seizure_frames),
c='g',
linewidth=0.10)
# plt.ylim(0, 300)
plt.show()
def plot_stim_responses_stimgroups(title=''):
'''plot average stim response of each cell at each stim. timepoint, along with the average dFF across all cells at each frame'''
dff = ao.normalize_dff(exp_obj.raw)
dff_mean = np.mean(dff, axis=0)
# # use for troubleshooting gathering responses from pandas df
# keys = df.keys()[1:]
# response = [df["%s" % stim].mean() for stim in df.keys()[1:]]
# x = [int(i) for i in keys[pd.notna(response)]]
# y = [a for a in response if str(a) != 'nan']
# #
fig, ax1 = plt.subplots(figsize=(20, 3))
color = 'gray'
ax1.plot(dff_mean, color=color, alpha=0.4, linewidth=0.5)
ax1.tick_params(axis='y', labelcolor=color)
if type(exp_obj.seizure_frames) == list:
ax1.axvline(x=exp_obj.seizure_frames[0], color='black')
ax1.axvline(x=exp_obj.seizure_frames[-1], color='black')
ax2 = ax1.twinx()
color = 'black'
for stim_group in range(exp_obj.n_groups):
stims = exp_obj.stim_start_frames[0][stim_group::exp_obj.n_groups]
response = [df["%s" % stim].mean() for stim in stims]
x = [int(i) for i in stims[pd.notna(response)]]
y = [a for a in response if str(a) != 'nan']
ax2.scatter(x, y, s=5)
ax2.plot(x, y, linewidth=0.5)
ax2.tick_params(axis='y', labelcolor=color)
ax1.set_title((title),
horizontalalignment='center',
verticalalignment='top',
pad=20,
fontsize=15)
fig.show()
expobj,
background=expobj.meanFluImg_registered,
title='SLM targets location w/ registered mean Flu img')
#%%#####################################################################################################################
########################################################################################################################
########################################################################################################################
########################################################################################################################
##### ------------------- processing for SLM targets Flu traces ########################################################
########################################################################################################################
########################################################################################################################
########################################################################################################################
########################################################################################################################
# collect SLM photostim individual targets -- individual, full traces, dff normalized
expobj.dff_SLMTargets = aoutils.normalize_dff(np.array(expobj.raw_SLMTargets))
expobj.save()
# collect and plot peri- photostim traces for individual SLM target, incl. individual traces for each stim
expobj.pre_stim = int(0.5 * expobj.fps)
expobj.post_stim = int(4 * expobj.fps)
expobj.SLMTargets_stims_dff, expobj.SLMTargets_stims_dffAvg, expobj.SLMTargets_stims_dfstdF, \
expobj.SLMTargets_stims_dfstdF_avg, expobj.SLMTargets_stims_raw, expobj.SLMTargets_stims_rawAvg = \
expobj.get_alltargets_stim_traces_norm(pre_stim=expobj.pre_stim, post_stim=expobj.post_stim)
# %% photostim. SUCCESS RATE MEASUREMENTS and PLOT - SLM PHOTOSTIM TARGETED CELLS
# measure, for each cell, the pct of trials in which the dF_stdF > 20% post stim (normalized to pre-stim avgF for the trial and cell)
# can plot this as a bar plot for now showing the distribution of the reliability measurement
SLMtarget_ids = list(range(len(expobj.SLMTargets_stims_dfstdF)))
labels[labels.index(item)] = int(round(item / exp_obj.fps))
ax.set_xticklabels(labels)
ax.set_title(title, horizontalalignment='center', verticalalignment='top', pad=20, fontsize=15)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# ax.spines['left'].set_visible(False)
ax.set_xlabel(x_label)
if save_path:
plt.savefig(save_path)
plt.show()
rois = [95, 16]
exp_obj.dff = ao.normalize_dff(exp_obj.raw[:10, :], threshold=50)
plot_flu_overlap_plots(dff_array=exp_obj.dff[:10, 10000:22000], title=(experiment + ' - flu of %s cells' % len(exp_obj.dff)), alpha=0.5)
# save_path="/Users/prajayshah/OneDrive - University of Toronto/UTPhD/Proposal/2020/Figures/%s: 10 cells.svg" % experiment)
#%% (quick) plot suite2p traces ###############################################################################
plt.figure(figsize=(50, 3))
for i in range(200,250):
plt.plot(exp_obj.raw[i][exp_obj.good_frames], linewidth=0.2, alpha=0.2)
plt.plot(exp_obj.spks[i][exp_obj.good_frames], linewidth=0.2, alpha=0.2)
# plt.xlim(0, exp_obj.spks.shape[1])
plt.xlim(0, len(exp_obj.good_frames))
plt.show()
#%% Gaussian filter of spks data and then plotting
def fwhm2sigma(fwhm):
return fwhm / np.sqrt(8 * np.log(2))