import ROI_mod
import post_analysis_core as pac
# %% Setting the directories
initialDirectory = '/Volumes/HD-SP1/Burak_data/Python_data'
alignedDataDir = os.path.join(initialDirectory,
'selected_experiments/selected')
stimInputDir = os.path.join(initialDirectory, 'stimulus_types')
saveOutputDir = os.path.join(initialDirectory, 'analyzed_data',
'200130_T4T5_goodData_TFVar')
summary_save_dir = os.path.join(initialDirectory,
'results/200116_T4T5_delay_experiments/TF_Var2/mixed')
# Plotting parameters
colors = pac.run_matplotlib_params()
color = colors[5]
color_pair = colors[2:4]
roi_plots=False
# %% Load datasets and desired variables
exp_t = '5th_exp_R64G09_mixed_CSI0p4_REL_0p4_all'
datasets_to_load = ['191206bg_fly1-TSeries-12062019-0951-002_transfer_sima_STICA.pickle',
'191206bg_fly4-TSeries-12062019-0951-002_transfer_sima_STICA.pickle',
'191210bg_fly2-TSeries-12102019-0944-007_transfer_sima_STICA.pickle',
'191212bg_fly3-TSeries-002_transfer_sima_STICA.pickle',
'191213bg_fly1-TSeries-12132019-0909-004_transfer.pickle',
'191216bg_fly2-TSeries-002_transfer_sima_STICA.pickle',
'191216bg_fly3-TSeries-002_transfer.pickle',
'200124bg_fly3-TSeries-01242020-0901-004_transfer.pickle',
'200127bg_fly3-TSeries-002_transfer.pickle',
'200127bg_fly4-TSeries-002_transfer_sima_STICA.pickle',
number=len(curr_rois_filtered),
cmap='coolwarm')
fig1.suptitle(genotype)
f1_n = 'All_STRFs_%s' % (genotype)
os.chdir(results_save_dir)
fig1.savefig('%s.png' % f1_n,
bbox_inches='tight',
transparent=False,
dpi=300)
#%% Avg STRFs
plt.close('all')
fig1 = plt.figure(figsize=(16, 12))
grid = plt.GridSpec(3, 3, wspace=0.2, hspace=0.2)
_, colors_d = pac.run_matplotlib_params()
colors = [colors_d['green2'], colors_d['magenta'], colors_d['green1']]
ax2 = plt.subplot(grid[2:, :])
for idx, genotype in enumerate(np.unique(genotypes)):
ax = plt.subplot(grid[:2, idx])
mask = genotypes == genotype
curr_rois = final_rois_all[mask]
curr_filters = np.array(map(lambda roi : \
roi.sta.T[np.where(roi.sta.T==roi.sta.max())[0]],
curr_rois))
filter_masks = curr_filters.max(axis=2) > 0.01
curr_fly_ids = np.array(flyIDs)[mask]
curr_fly_filtered = curr_fly_ids[np.array(filter_masks[:, 0])]
curr_rois_filtered = curr_rois[np.array(filter_masks[:, 0])]
saveWorkspace(saveOutputDir,pckl_save_name, varDict,
varFile='data_save_vars.txt',extension='.pickle')
print('\n\n%s saved...\n\n' % pckl_save_name)
#%% Plotting the STAs
roi_image = ROI_mod.get_masks_image(rois)
# Plotting ROIs and properties
pmc.plot_roi_masks(roi_image,mean_image,len(rois),
current_movie_ID,save_fig=True,
save_dir=figure_save_dir,alpha=0.4)
#%%
plt.close('all')
# Constructing the plot backbone, selecting colors
colors = run_matplotlib_params()
fig = plt.figure(figsize=(14, 3))
fig.suptitle(experiment_conditions['Genotype'],fontsize=12)
grid = plt.GridSpec(1, 4, wspace=0.3, hspace=0.3)
## ROIs
ax=plt.subplot(grid[0,0:1])
sns.heatmap(mean_image,cmap='gist_yarg',ax=ax,cbar=False)
sns.heatmap(roi_image,alpha=0.2,cmap = 'Dark2',ax=ax,
cbar_kws={'fraction':0.1,
'shrink' : 0,
'ticks': []})
ax.axis('off')
ax.set_title('ROIs n:%d' % len(rois))
def make_exp_summary_AB_steps(figtitle, rois, roi_image, current_movie_ID,
summary_save_dir):
""" Plots a summary of experiment with relevant information.
"""
import matplotlib
plt.close('all')
# Constructing the plot backbone, selecting colors
colors, _ = run_matplotlib_params()
fig = plt.figure(figsize=(12, 16))
fig.suptitle(figtitle, fontsize=12)
coln = 3
grid = plt.GridSpec(4, coln, wspace=0.3, hspace=0.1)
## ROIs
ax1 = plt.subplot(grid[0:2, 0])
sns.heatmap(rois[0].source_image, cmap='gist_yarg', ax=ax1, cbar=False)
sns.heatmap(roi_image, alpha=0.3, cmap='Set2')
ax1.axis('off')
## Traces
ax2 = plt.subplot(grid[0:2, 1:])
diff_luminances = rois[0].epoch_luminance_B_steps
diff_luminances = np.delete(diff_luminances,
rois[0].stim_info['baseline_epoch'])
cmap = matplotlib.cm.get_cmap('inferno')
norm = matplotlib.colors.Normalize(vmin=np.min(diff_luminances),
vmax=np.max(diff_luminances))
all_traces = \
np.array(map(lambda roi : roi.resp_traces_interpolated,
rois))
cur_row = 1
for idx, luminance in enumerate(diff_luminances):
if np.mod(idx, coln) == 0:
cur_row += 1
curr_traces = all_traces[:, idx, :]
ax = plt.subplot(grid[cur_row, np.mod(idx, coln)])
mean_r = np.mean(curr_traces, axis=0)
std_r = np.std(curr_traces, axis=0)
ub = mean_r + std_r
lb = mean_r - std_r
x = np.linspace(0, len(mean_r), len(mean_r))
ax.plot(x, np.transpose(curr_traces), lw=1, color='k', alpha=.3)
ax.plot(x,
mean_r,
'-',
lw=3,
color=cmap(norm(luminance)),
alpha=.9,
label=luminance)
ax.fill_between(x, ub, lb, color=cmap(norm(luminance)), alpha=.3)
ax.legend()
# ax.axis('off')
ax.set_ylim(-0.5, np.max(all_traces.mean(axis=0) + 0.3))
ax2.plot(x,
mean_r,
'-',
lw=3,
color=cmap(norm(luminance)),
alpha=.6,
label=luminance)
ax2.set_ylabel('$\Delta F/F$')
ax2.set_title('Luminance responses n:%d' % len(rois))
save_name = 'Summary_%s' % (current_movie_ID)
os.chdir(summary_save_dir)
plt.savefig('%s.pdf' % save_name, bbox_inches='tight', dpi=300)
fig2 = plt.figure(figsize=(12, 5))
fig2.suptitle(figtitle, fontsize=12)
plt.subplot(121)
data = ROI_mod.data_to_list(rois, ['a_step_responses', 'b_step_responses'])
a_resps = data['a_step_responses']
b_resps = data['b_step_responses']
a_lums = rois[0].epoch_luminance_A_steps
b_lums = rois[0].epoch_luminance_B_steps
a_err = np.nanstd(a_resps, axis=0) / np.sqrt(np.shape(a_resps)[0])
b_err = np.nanstd(b_resps, axis=0) / np.sqrt(np.shape(b_resps)[0])
plt.errorbar(a_lums,
np.nanmean(a_resps, axis=0),
a_err,
fmt='s',
color=colors[2],
label='A step',
alpha=.9)
plt.errorbar(b_lums,
np.nanmean(b_resps, axis=0),
b_err,
fmt='s',
color=colors[3],
label='B step',
alpha=.9)
plt.xlabel('Luminance')
plt.ylabel('$\Delta F/F$')
plt.legend()
plt.subplot(122)
data = ROI_mod.data_to_list(rois, ['a_step_responses', 'b_step_responses'])
a_resps = data['a_step_responses']
b_resps = data['b_step_responses']
a_cont = rois[0].epoch_contrast_A_steps
b_cont = rois[0].epoch_contrast_B_steps
a_err = np.nanstd(a_resps, axis=0) / np.sqrt(np.shape(a_resps)[0])
b_err = np.nanstd(b_resps, axis=0) / np.sqrt(np.shape(b_resps)[0])
plt.errorbar(a_cont,
np.nanmean(a_resps, axis=0),
a_err,
fmt='s',
color=colors[2],
label='A step',
alpha=.9)
plt.errorbar(b_cont,
np.nanmean(b_resps, axis=0),
b_err,
fmt='s',
color=colors[3],
label='B step',
alpha=.9)
plt.xlabel('Contrast')
plt.ylabel('$\Delta F/F$')
plt.legend()
save_name = 'Summary_%s_con_lum' % (current_movie_ID)
os.chdir(summary_save_dir)
plt.savefig('%s.pdf' % save_name, bbox_inches='tight', dpi=300)
return fig, fig2
import ROI_mod
import post_analysis_core as pac
# %% Setting the directories
initialDirectory = '/Volumes/Backup Plus/PhD_Archive/Data_ongoing/Python_data'
alignedDataDir = os.path.join(initialDirectory,
'selected_experiments/selected')
stimInputDir = os.path.join(initialDirectory, 'stimulus_types')
saveOutputDir = os.path.join(initialDirectory, 'analyzed_data',
'200210_T4T5_luminance', 'luminance_gratings')
summary_save_dir = os.path.join(initialDirectory,
'results/200302_luminance/T4T5')
# Plotting parameters
colors, _ = pac.run_matplotlib_params()
color = colors[5]
color_pair = colors[2:4]
roi_plots = False
# %% Load datasets and desired variables
exp_t = '200210_T4T5_luminance_gratings_contrast1'
datasets_to_load = os.listdir(saveOutputDir)
properties = ['BF', 'PD', 'SNR', 'Reliab', 'depth', 'DSI']
combined_df = pd.DataFrame(columns=properties)
all_rois = []
tfl_maps = []
# Initialize variables
flyNum = 0
for dataset in datasets_to_load:
if not (".pickle" in dataset):
def make_exp_summary_luminance_steps(figtitle, rois, roi_image,
current_movie_ID, summary_save_dir):
""" Plots a summary of experiment with relevant information.
"""
import matplotlib
plt.close('all')
# Constructing the plot backbone, selecting colors
run_matplotlib_params()
fig = plt.figure(figsize=(12, 16))
fig.suptitle(figtitle, fontsize=12)
coln = 3
grid = plt.GridSpec(6, coln, wspace=0.3, hspace=0.1)
## ROIs
ax1 = plt.subplot(grid[0:2, 0])
sns.heatmap(rois[0].source_image, cmap='gist_yarg', ax=ax1, cbar=False)
sns.heatmap(roi_image, alpha=0.3, cmap='Set2')
ax1.axis('off')
## Traces
ax2 = plt.subplot(grid[0:2, 1:])
diff_luminances = rois[0].luminances
cmap = matplotlib.cm.get_cmap('inferno')
norm = matplotlib.colors.Normalize(vmin=np.min(diff_luminances),
vmax=np.max(diff_luminances))
all_traces = \
np.array(map(lambda roi : roi.lum_resp_traces_interpolated,
rois))
cur_row = 1
for idx, luminance in enumerate(diff_luminances):
if np.mod(idx, coln) == 0:
cur_row += 1
curr_traces = all_traces[:, idx, :]
ax = plt.subplot(grid[cur_row, np.mod(idx, coln)])
mean_r = np.mean(curr_traces, axis=0)
std_r = np.std(curr_traces, axis=0)
ub = mean_r + std_r
lb = mean_r - std_r
x = np.linspace(0, len(mean_r), len(mean_r))
ax.plot(x, np.transpose(curr_traces), lw=1, color='k', alpha=.3)
ax.plot(x,
mean_r,
'-',
lw=3,
color=cmap(norm(luminance)),
alpha=.9,
label=luminance)
ax.fill_between(x, ub, lb, color=cmap(norm(luminance)), alpha=.3)
ax.legend()
# ax.axis('off')
ax.set_ylim(-0.5, np.max(all_traces.mean(axis=0) + 0.3))
ax2.plot(x,
mean_r,
'-',
lw=3,
color=cmap(norm(luminance)),
alpha=.6,
label=luminance)
ax2.set_ylabel('$\Delta F/F$')
ax2.set_title('Luminance responses n:%d' % len(rois))
save_name = 'Summary_%s' % (current_movie_ID)
os.chdir(summary_save_dir)
plt.savefig('%s.pdf' % save_name, bbox_inches='tight', dpi=300)
return fig
def make_exp_summary_luminance_edges(figtitle, rois, roi_image,
current_movie_ID, summary_save_dir):
""" Plots a summary of experiment with relevant information.
"""
import matplotlib
plt.close('all')
# Constructing the plot backbone, selecting colors
run_matplotlib_params()
fig = plt.figure(figsize=(16, 3))
fig.suptitle(figtitle, fontsize=12)
grid = plt.GridSpec(1, 3, wspace=0.3, hspace=1)
## ROIs
ax1 = plt.subplot(grid[0, 2])
sns.heatmap(rois[0].source_image, cmap='gist_yarg', ax=ax1, cbar=False)
slope_data = ROI_mod.data_to_list(rois, ['slope'])['slope']
rangecolor = np.max(np.abs([np.min(slope_data), np.max(slope_data)]))
sns.heatmap(roi_image,
alpha=0.8,
cmap='PRGn',
ax=ax1,
center=0,
vmin=-rangecolor,
vmax=rangecolor,
cbar_kws={
'fraction': 0.1,
'shrink': 1
})
ax1.axis('off')
ax1.set_title('Slope of luminance sensitivity')
## Raw traces
ax2 = plt.subplot(grid[0, 0])
ax3 = plt.subplot(grid[0, 1])
diff_luminances = rois[0].luminances
cmap = matplotlib.cm.get_cmap('inferno')
norm = matplotlib.colors.Normalize(vmin=np.min(diff_luminances),
vmax=np.max(diff_luminances))
all_traces = \
np.array(map(lambda roi : roi.edge_resp_traces_interpolated,
rois))
sensitivities = np.array(map(lambda roi: roi.edge_resps, rois))
sensitivities = np.transpose(sensitivities)
properties = ['Luminance', 'Response']
senst_df = pd.DataFrame(columns=properties)
for idx, luminance in enumerate(diff_luminances):
curr_traces = all_traces[:, idx, :]
curr_traces_aligned = list(
map(lambda trace: np.roll(trace, 20 - np.argmax(trace)),
curr_traces))
mean_t = np.mean(curr_traces_aligned, axis=0)
x = np.linspace(0, len(mean_t), len(mean_t))
ax2.plot(x,
mean_t,
'-',
lw=3,
color=cmap(norm(luminance)),
alpha=.8,
label=luminance)
curr_sensitivities = sensitivities[idx, :]
curr_luminances = np.ones(curr_sensitivities.shape) * luminance
df = pd.DataFrame.from_dict({
'Luminance': curr_luminances,
'Response': curr_sensitivities
})
rois_df = pd.DataFrame.from_dict(df)
senst_df = senst_df.append(rois_df, ignore_index=True, sort=False)
# bar_bg(curr_sensitivities, luminance, color=cmap(norm(luminance)),
# scat_s =2,ax=ax3,alpha = .8,width=0.1)
ax2.set_ylabel('$\Delta F/F$')
ax2.set_title('Aligned mean responses n:%d' % len(rois))
sns.violinplot(x="Luminance",
y="Response",
data=senst_df,
linewidth=1.5,
inner="quartile",
palette='plasma',
ax=ax3)
ax3.set_title('Luminance sensitivitiy')
ax3.set_ylabel('$\Delta F/F$')
if 'OFF' in rois[0].stim_name:
ax2.legend(title='X -> 0')
ax3.set_xlabel('Preceeding luminance')
else:
ax2.legend(title='0 -> X')
ax3.set_xlabel('Following luminance')
# Saving figure
save_name = 'Summary_%s' % (current_movie_ID)
os.chdir(summary_save_dir)
plt.savefig('%s.pdf' % save_name, bbox_inches='tight', dpi=300)
return fig
def make_exp_summary_stripes(figtitle, analysis_params, mean_image, roi_image,
roi_traces, raw_stim_trace, roi_RF, save_fig,
current_movie_ID, summary_save_dir):
""" Plots a summary of experiment with relevant information.
"""
plt.close('all')
# Constructing the plot backbone, selecting colors
colors, _ = run_matplotlib_params()
fig = plt.figure(figsize=(18, 12))
fig.suptitle(figtitle, fontsize=12)
grid = plt.GridSpec(2, 2, wspace=0.3, hspace=0.3)
## ROIs
ax = plt.subplot(grid[0:1, 0:1])
sns.heatmap(mean_image, cmap='gist_yarg', ax=ax, cbar=False)
sns.heatmap(roi_image,
alpha=0.3,
cmap='Dark2',
ax=ax,
cbar_kws={
'fraction': 0.1,
'shrink': 0,
'ticks': []
})
ax.axis('off')
ax.set_title('ROIs n:%d' % np.shape(roi_traces)[0])
## Raw traces
ax = plt.subplot(grid[1, :])
factor = np.abs(np.max(roi_traces) - np.min(roi_traces))
adder = np.linspace(0,
np.shape(roi_traces)[0] * 1.5,
np.shape(roi_traces)[0])[:, None]
scaled_responses = roi_traces + adder
# Finding stimulus
stim_frames = raw_stim_trace[:, 7] # Frame information
stim_vals = raw_stim_trace[:, 3] # Stimulus value
uniq_frame_id = np.unique(stim_frames, return_index=True)[1]
stim_vals = stim_vals[uniq_frame_id]
# Make normalized values of stimulus values for plotting
stim_vals = (stim_vals/np.max(np.unique(stim_vals))) \
*np.max(scaled_responses)/3
stim_df = pd.DataFrame(stim_vals + np.max(scaled_responses),
columns=['Stimulus'],
dtype='float')
resp_df = pd.DataFrame(np.transpose(scaled_responses), dtype='float')
resp_df.plot(legend=False, alpha=0.8, lw=2, ax=ax, cmap='Dark2')
stim_df.plot(dashes=[2, 0.5], ax=ax, color='k', alpha=.6, lw=2)
ax.get_legend().remove()
ax.axis('off')
ax.set_title('Raw traces')
## RF responses
ax = plt.subplot(grid[0, 1:])
if (analysis_params['analysis_type'] == 'stripes_ON_vertRF_transfer')or\
(analysis_params['analysis_type'] == 'stripes_OFF_vertRF_transfer'):
sns.heatmap(np.transpose(roi_RF),
cmap='coolwarm',
center=0,
ax=ax,
yticklabels=10,
xticklabels=2,
cbar_kws={'label': '$\Delta F/F$'})
ax.set_title('Receptive fields')
ax.set_ylabel('Position ($^\circ$)')
ax.set_xlabel('ROI #')
else:
sns.heatmap(roi_RF,
cmap='coolwarm',
center=0,
ax=ax,
yticklabels=2,
xticklabels=10,
cbar_kws={'label': '$\Delta F/F$'})
ax.set_title('Receptive fields')
ax.set_xlabel('Position ($^\circ$)')
ax.set_ylabel('ROI #')
if save_fig:
# Saving figure
save_name = 'Summary_%s_%s' % (current_movie_ID)
os.chdir(summary_save_dir)
plt.savefig('%s.pdf' % save_name, bbox_inches='tight', dpi=300)
return fig
def make_exp_summary_TF(figtitle, extraction_type, mean_image, roi_image,
roi_traces, rawStimData, bf_image, rois_df, rois,
stimulus_information, save_fig, current_movie_ID,
summary_save_dir):
""" Plots a summary of experiment with relevant information.
"""
plt.close('all')
# Constructing the plot backbone, selecting colors
colors, _ = run_matplotlib_params()
fig = plt.figure(figsize=(18, 9))
fig.suptitle(figtitle, fontsize=12)
grid = plt.GridSpec(2, 6, wspace=1, hspace=0.3)
## BF masks
ax = plt.subplot(grid[0, :2])
sns.heatmap(mean_image, cmap='gray', ax=ax, cbar=False)
sns.heatmap(bf_image,
cmap='plasma',
cbar_kws={
'ticks': np.unique(bf_image[~np.isnan(bf_image)]),
'fraction': 0.1,
'shrink': 1,
'label': 'Hz',
},
alpha=0.5,
vmin=0.1,
vmax=1.5)
ax.axis('off')
ax.set_title('BF map')
## Histogram
ax = plt.subplot(grid[0, 2])
chart = sns.countplot('BF', data=rois_df, palette='plasma')
chart.set_xticklabels(chart.get_xticklabels(),
rotation=45,
fontweight='bold')
leg = chart.legend()
leg.remove()
## Tuning curve
ax = plt.subplot(grid[0, 3:])
# Plot tuning curves
tunings = np.squeeze(list(map(lambda roi: roi.TF_curve_resp, rois)))
mean_t = np.mean(tunings, axis=0)
std_t = np.std(tunings, axis=0)
ub = mean_t + std_t
lb = mean_t - std_t
# Tuning curve
TF_stim = rois[0].TF_curve_stim
ax.fill_between(TF_stim, ub, lb, color=colors[0], alpha=.2)
ax.plot(TF_stim, mean_t, '-o', lw=4, color=colors[0], markersize=10)
#ax.plot(TF_stim,tunings.T,alpha=0.3,lw=1)
ax.set_xscale('log')
ax.set_title('Frequency tuning curve')
ax.set_xlabel('Hz')
ax.set_ylabel('$\Delta F/F$')
ax.set_xlim((ax.get_xlim()[0], 10))
## Plotting all tuning curves
if len(rois) > 100:
ax = plt.subplot(grid[1, :])
elif len(rois) > 75:
ax = plt.subplot(grid[1, :4])
elif len(rois) > 50:
ax = plt.subplot(grid[1, :3])
elif len(rois) > 25:
ax = plt.subplot(grid[1, :2])
else:
ax = plt.subplot(grid[1, 1])
non_edge_stims = (stimulus_information['stim_type'] != 50)
uniq_freq_nums = len(
np.where(
np.unique(stimulus_information['epoch_frequency'][non_edge_stims])
> 0)[0])
bfs = np.squeeze(list(map(lambda roi: roi.BF, rois)))
sorted_indices = np.argsort(bfs)
tf_tunings = tunings[sorted_indices, :]
plot_tf_array = np.zeros(shape=(np.shape(tf_tunings)[0],
np.shape(tf_tunings)[0] *
np.shape(tf_tunings)[1]))
plot_tf_array[:] = np.nan
for i in range(np.shape(tf_tunings)[0]):
curr_data = tf_tunings[i, :]
curr_data = curr_data + np.mod(i, 9)
curve_start = i * np.shape(tf_tunings)[1] - (i * (uniq_freq_nums - 1))
plot_tf_array[i, curve_start:curve_start + uniq_freq_nums] = curr_data
ax.plot(np.transpose(plot_tf_array),
'-o',
linewidth=2.0,
alpha=.8,
color=colors[0],
markersize=0.4)
ax.axis('off')
ax.set_title('ROI tuning curves N: %s' % len(rois))
if save_fig:
# Saving figure
save_name = 'Summary_%s_%s' % (current_movie_ID, extraction_type)
os.chdir(summary_save_dir)
plt.savefig('%s.pdf' % save_name, bbox_inches='tight', dpi=300)
return fig
def make_exp_summary_FFF(figtitle, mean_image, roi_image, roi_traces,
raw_stim_trace, stim_trace, roi_conc_traces, save_fig,
current_movie_ID, summary_save_dir):
""" Plots a summary of experiment with relevant information.
"""
plt.close('all')
# Constructing the plot backbone, selecting colors
colors, _ = run_matplotlib_params()
fig = plt.figure(figsize=(7, 7))
fig.suptitle(figtitle, fontsize=12)
grid = plt.GridSpec(2, 2, wspace=0.3, hspace=0.3)
## ROIs
ax = plt.subplot(grid[0:1, 0:1])
sns.heatmap(mean_image, cmap='gist_yarg', ax=ax, cbar=False)
sns.heatmap(roi_image,
alpha=0.3,
cmap='Dark2',
ax=ax,
cbar_kws={
'fraction': 0.1,
'shrink': 0,
'ticks': []
})
ax.axis('off')
ax.set_title('ROIs n:%d' % np.shape(roi_traces)[0])
## Raw traces
ax = plt.subplot(grid[1, :])
adder = np.linspace(0,
np.shape(roi_traces)[0] * 1.5,
np.shape(roi_traces)[0])[:, None]
scaled_responses = roi_traces + adder
# Finding stimulus
stim_frames = raw_stim_trace[:, 7] # Frame information
stim_vals = raw_stim_trace[:, 3] # Stimulus value
uniq_frame_id = np.unique(stim_frames, return_index=True)[1]
stim_vals = stim_vals[uniq_frame_id]
# Make normalized values of stimulus values for plotting
stim_vals = (stim_vals/np.max(np.unique(stim_vals))) \
*np.max(scaled_responses)/6
stim_df = pd.DataFrame(stim_vals + np.max(scaled_responses),
columns=['Stimulus'],
dtype='float')
resp_df = pd.DataFrame(np.transpose(scaled_responses), dtype='float')
resp_df.plot(legend=False, alpha=0.8, lw=2, ax=ax, cmap='Dark2')
stim_df.plot(dashes=[2, 0.5], ax=ax, color='k', alpha=.6, lw=2)
ax.get_legend().remove()
ax.axis('off')
ax.set_title('Raw traces')
## Conc responses
ax = plt.subplot(grid[0, 1:])
ax.plot(np.transpose(roi_conc_traces), color='k', alpha=.4, lw=1)
mean_r = np.mean(roi_conc_traces, axis=0)
std_r = np.std(roi_conc_traces, axis=0)
ub = mean_r + std_r
lb = mean_r - std_r
ax.fill_between(range(len(mean_r)), ub, lb, color=colors[3], alpha=.4)
ax.plot(range(len(mean_r)), mean_r, lw=3, color=colors[3])
scaler = np.abs(np.max(mean_r) - np.min(mean_r))
plot_stim = np.array(stim_trace).astype(int) / (scaler * 5) + np.max(
roi_conc_traces) + (0.1 * scaler)
ax.plot(plot_stim, 'k', lw=2.5)
ax.set_title('5sFFF response')
ax.set_xlabel('Frames')
ax.set_ylabel('$\Delta F/F$')
if save_fig:
# Saving figure
save_name = 'Summary_%s' % (current_movie_ID)
os.chdir(summary_save_dir)
plt.savefig('%s.pdf' % save_name, bbox_inches='tight', dpi=300)
return fig
