def _hist(res, save_path):
## distribution
def histogram(real, label, bin, range, ax):
density, bins = np.histogram(real, bins=bin, density=True, range=range)
unity_density = density / density.sum()
widths = bins[:-1] - bins[1:]
ax.bar(bins[1:], unity_density, width=widths, alpha=.5, label=label)
for mouse in np.unique(res['mouse']):
pt_csp = filter.filter(res, {'mouse': mouse, 'odor_valence': 'PT CS+'})
csp = filter.filter(res, {'mouse': mouse})
csm = filter.filter(res, {'mouse': mouse})
data = pt_csp['velocity']
start = pt_csp['on'][0]
end = pt_csp['end'][0]
data_before = data[:, :start].flatten()
data_during = data[:, start:end].flatten()
data_after = data[:, end:].flatten()
bins = 50
range = [-70, 70]
fig = plt.figure(figsize=(2, 1.5))
ax = fig.add_axes([0.2, 0.2, 0.7, 0.7])
histogram(data_before, 'before', bin=bins, range=range, ax=ax)
histogram(data_during, 'during', bin=bins, range=range, ax=ax)
plt.xlim([range[0] - 0.5, range[1] + .5])
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
rs = ranksums(data_before, data_during)[-1]
xlim = plt.xlim()
ylim = plt.ylim()
x = xlim[0] + .7 * (xlim[1] - xlim[0])
y = ylim[0] + .7 * (ylim[1] - ylim[0])
plot.significance_str(x, y, rs)
name = 'before_during_mouse_{}'.format(mouse)
plot._easy_save(save_path, name=name)
fig = plt.figure(figsize=(2, 1.5))
ax = fig.add_axes([0.2, 0.2, 0.7, 0.7])
histogram(data_during, 'before', bin=bins, range=range, ax=ax)
histogram(data_after, 'during', bin=bins, range=range, ax=ax)
plt.xlim([range[0] - 0.5, range[1] + .5])
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
rs = ranksums(data_during, data_after)[-1]
xlim = plt.xlim()
ylim = plt.ylim()
x = xlim[0] + .7 * (xlim[1] - xlim[0])
y = ylim[0] + .7 * (ylim[1] - ylim[0])
plot.significance_str(x, y, rs)
name = 'during_after_mouse_{}'.format(mouse)
plot._easy_save(save_path, name=name)
def distribution_dff(res,
start_days,
end_days,
arg,
valence,
figure_path,
hist_range=(-.05, 1.2)):
list_of_days = list(zip(start_days, end_days))
res = filter.filter_days_per_mouse(res, days_per_mouse=list_of_days)
res = filter.filter(res, {'odor_valence': valence})
new = _compare_dff(res, loop_keys=['mouse', 'odor'], arg=arg)
#
def _helper(real, label, bin=20):
density, bins = np.histogram(real,
bins=bin,
density=True,
range=hist_range)
unity_density = density / density.sum()
widths = bins[:-1] - bins[1:]
ax.bar(bins[1:], unity_density, width=widths, alpha=.5, label=label)
fig = plt.figure(figsize=(2, 1.5))
ax = fig.add_axes([0.2, 0.2, 0.7, 0.7])
x = np.concatenate(new['day_0'])
y = np.concatenate(new['day_1'])
sr = wilcoxon(x, y)[-1]
_helper(x, 'Before')
_helper(y, 'After')
ax.set_xlabel('Amplitude')
ax.set_ylabel('Density')
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.legend(frameon=False)
ylim = plt.ylim()
xlim = plt.xlim()
sig_str = plot.significance_str(x=(xlim[-1] - ylim[0]) * .7,
y=.7 * (ylim[-1] - ylim[0]),
val=sr)
_easy_save(os.path.join(figure_path, 'dff_distribution'),
valence,
dpi=300,
pdf=True)
print('Before mean: {}'.format(np.mean(x)))
print('After mean: {}'.format(np.mean(y)))
print('wilcoxon: {}'.format(sr))
select_dict={'phase_odor_valence': phase},
ax_args=ax_args_cur,
plot_function=plt.errorbar,
plot_args=error_args,
fig_size=[2, 1.5],
path=save_path, reuse=True, save=False)
plt.xlim(-1, 2)
test = filter.filter(res_, {'phase_odor_valence': phase})
ixs = test['condition'] == 'Y'
x = test[keyword][ixs]
y = test[keyword][np.invert(ixs)]
ylim = plt.gca().get_ylim()
rs = ranksums(x, y)
sig_str = plot.significance_str(x=.4, y= .7 * (ylim[-1] - ylim[0]), val= rs[-1])
plot._easy_save(path, name, pdf=True)
print('Phase: {}'.format(phase))
print('YFP: {}'.format(np.mean(x)))
print('YFP: {}'.format(x))
print('HALO: {}'.format(np.mean(y)))
print('HALO: {}'.format(y))
print(rs)
print(summary_res[keyword])
print(summary_res['odor_valence_condition'])
print(summary_res['phase_odor_valence'])
if 'trials_per_day' in plotting:
rect=(.3, .3, .6, .6),
plot_function=plt.scatter,
plot_args=scatter_args_copy,
ax_args=ax_args_local,
colors=color,
save=False,
path=save_path)
plt.plot([0, 5], [0, 5], '--', color='gray')
try:
stat_res = filter.filter(res_use, {'odor_valence': valence})
a, b = stat_res[before_key], stat_res[after_key]
stat = wilcoxon(a, b)
ylim = plt.gca().get_ylim()
sig_str = plot.significance_str(x=.4,
y=.7 * (ylim[-1] - ylim[0]),
val=stat[-1])
plot._easy_save(path, name)
print('Before: {}, After: {}'.format(np.mean(a), np.mean(b)))
print(stat)
except:
print('no stats')
if 'summary_mouse_line' in experiments:
_collapse_conditions(all_res, control_condition='YFP', str=collapse_arg)
ykey = reduce_key_raw + '_mean'
for i, v in enumerate(all_res[reduce_key_raw]):
all_res[ykey].append(np.mean(v[v > 0]))
all_res[ykey] = np.array(all_res[ykey])
res_modified = reduce.new_filter_reduce(