def plot_correlation_matrix(res,
days,
loop_keys,
shuffle,
figure_path,
odor_end=True,
direction=0):
res = filter.filter_days_per_mouse(res, days)
res_ = _correlation(res, loop_keys, shuffle, odor_end, direction=direction)
res = reduce.new_filter_reduce(res_,
filter_keys=['Odor_A', 'Odor_B'],
reduce_key='corrcoef')
if shuffle:
s = '_shuffled'
else:
s = ''
plot.plot_weight(res,
x_key='Odor_A',
y_key='Odor_B',
val_key='corrcoef',
title='Correlation',
label='Correlation',
vmin=0,
vmax=1,
mask=True,
xticklabel=['CS+1', 'CS+2', 'CS-1', 'CS-2'],
yticklabel=['CS+1', 'CS+2', 'CS-1', 'CS-2'],
save_path=figure_path,
text=','.join([str(x) for x in days]) + s +
'_direction_' + str(direction))
return res_
def plot_max_dff_valence(res, start_days, end_days, figure_path):
res = copy.copy(res)
# list_of_days = list(zip(start_days, end_days))
list_of_days = end_days
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
start_end_day_res = filter.filter(start_end_day_res,
{'odor_valence': ['CS+', 'CS-']})
_max_dff(start_end_day_res)
start_end_day_res = reduce.new_filter_reduce(
start_end_day_res,
filter_keys=['odor_valence', 'mouse'],
reduce_key='max_dff')
add_naive_learned(start_end_day_res, start_days, end_days)
ax_args_copy = ax_args.copy()
# ax_args_copy.update({'xticks':[res['DAQ_O_ON_F'][-1], res['DAQ_W_ON_F'][-1]], 'xticklabels':['ON', 'US'],
# 'ylim':[0, .2]})
nMice = len(np.unique(res['mouse']))
# colors = ['Green'] * nMice + ['Red'] * nMice
# trace_args_copy = trace_args.copy()
# trace_args_copy.update({'linestyle':'--','alpha':.5, 'linewidth':.75})
plot.plot_results(start_end_day_res,
loop_keys='mouse',
x_key='odor_valence',
y_key='max_dff',
path=figure_path,
colors=['gray'] * 10,
legend=False,
fig_size=(2, 1.5))
def plot_overlap_water(res, start_days, end_days, figure_path):
ax_args_copy = overlap_ax_args.copy()
res = copy.copy(res)
mice = np.unique(res['mouse'])
res = filter.filter_days_per_mouse(res, days_per_mouse=end_days)
add_naive_learned(res, start_days, end_days)
ax_args_copy.update({'xlim': [-1, 2]})
y_keys = ['US/CS+', 'CS+/US']
summary_res = defaultdict(list)
for arg in y_keys:
_get_overlap_water(res, arg=arg)
new_res = reduce.new_filter_reduce(
res,
filter_keys=['mouse', 'day', 'odor_valence'],
reduce_key='Overlap')
new_res['Type'] = np.array([arg] * len(new_res['training_day']))
reduce.chain_defaultdicts(summary_res, new_res)
summary_res.pop('Overlap_sem')
summary_res.pop('Overlap_std')
summary_res = filter.filter(summary_res, {'odor_valence': 'CS+'})
mean_std_res = reduce.new_filter_reduce(summary_res,
filter_keys='Type',
reduce_key='Overlap')
types = np.unique(summary_res['Type'])
scatter_args_copy = scatter_args.copy()
scatter_args_copy.update({'s': 2, 'alpha': .6})
for i, type in enumerate(types):
reuse_arg = True
if i == 0:
reuse_arg = False
temp = filter.filter(summary_res, {'Type': type})
plot.plot_results(temp,
x_key='Type',
y_key='Overlap',
loop_keys='mouse',
colors=['Black'] * len(mice),
plot_function=plt.scatter,
path=figure_path,
plot_args=scatter_args_copy,
ax_args=ax_args_copy,
save=False,
reuse=reuse_arg,
fig_size=(1.5, 1.5),
rect=(.25, .25, .6, .6),
legend=False)
plot.plot_results(mean_std_res,
x_key='Type',
y_key='Overlap',
error_key='Overlap_sem',
path=figure_path,
plot_function=plt.errorbar,
plot_args=error_args,
ax_args=ax_args,
save=True,
reuse=True,
fig_size=(1.5, 1.5),
legend=False)
print(mean_std_res['Overlap'])
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))
def plot_bar(res,
days_per_mouse,
odor_valence,
day_pad,
save,
reuse,
figure_path,
color='black',
normalize=False):
res = copy.copy(res)
res['day_'] = np.zeros_like(res['day'])
res_ = defaultdict(list)
for i, days in enumerate(days_per_mouse):
temp = filter.filter_days_per_mouse(res, days_per_mouse=days)
temp = filter.filter(temp, {'odor_valence': odor_valence[i]})
temp['day_'] = np.array([i + day_pad] * len(temp['day_']))
reduce.chain_defaultdicts(res_, temp)
_max_dff(res_)
res_ = reduce.new_filter_reduce(
res_,
filter_keys=['odor_valence', 'mouse', 'day_'],
reduce_key='max_dff')
res_.pop('max_dff_sem')
summary = reduce.new_filter_reduce(res_,
filter_keys=['day_', 'odor_valence'],
reduce_key='max_dff')
if normalize:
_normalize_across_days(summary)
# plot.plot_results(summary, x_key='day_', y_key='max_dff', error_key='max_dff_sem',
# path=figure_path,
# colors='black', legend=False, plot_args=error_args, plot_function= plt.errorbar,
# fig_size=(2, 1.5), save=False, reuse=reuse)
line_args_copy = line_args.copy()
line_args_copy.update({'alpha': .75, 'linewidth': 1})
plot.plot_results(summary,
x_key='day_',
y_key='max_dff',
path=figure_path,
colors=color,
legend=False,
plot_args=line_args_copy,
fig_size=(2, 1.5),
save=save,
reuse=reuse,
name_str=odor_valence[-1])
def plot_summary_water(res, start_days, end_days, figure_path):
ax_args_copy = ax_args.copy()
res = copy.copy(res)
get_responsive_cells(res)
list_of_days = list(zip(start_days, end_days))
mice = np.unique(res['mouse'])
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
add_naive_learned(start_end_day_res, start_days, end_days, 'a', 'b')
odor_list = ['US']
colors = ['Turquoise']
ax_args_copy.update({'xlim': [-1, 2]})
for i, odor in enumerate(odor_list):
plot.plot_results(start_end_day_res,
select_dict={'odor_standard': odor},
x_key='training_day',
y_key='Fraction Responsive',
loop_keys='mouse',
colors=[colors[i]] * len(mice),
path=figure_path,
plot_args=line_args,
ax_args=ax_args_copy,
fig_size=(1.6, 1.5),
legend=False)
before_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'a',
'odor_standard': 'US'
})
after_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'b',
'odor_standard': 'US'
})
from scipy.stats import ranksums, wilcoxon, kruskal
print('Before PT CS+: {}'.format(np.mean(
before_csm['Fraction Responsive'])))
print('After PT CS+: {}'.format(np.mean(after_csm['Fraction Responsive'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_csm['Fraction Responsive'],
after_csm['Fraction Responsive'])))
def plot_responsive_difference_odor_and_water(res,
odor_start_days,
end_days,
use_colors=True,
figure_path=None,
normalize=False,
ylim=.6):
key = 'Change in Fraction'
if normalize:
key = 'Norm. Fraction'
def _helper(start_end_day_res):
combs, list_of_ixs = filter.retrieve_unique_entries(
start_end_day_res, ['mouse', 'odor_valence'])
for i, comb in enumerate(combs):
ixs = list_of_ixs[i]
assert len(ixs) == 2
if start_end_day_res['training_day'][0] == 'Naive':
ref = ixs[0]
test = ixs[1]
elif start_end_day_res['training_day'][0] == 'Learned':
ref = ixs[1]
test = ixs[0]
else:
raise ValueError('cannot find ref day')
if normalize:
start_end_day_res[key][test] = start_end_day_res['Fraction'][test] / \
start_end_day_res['Fraction'][ref]
start_end_day_res[key][ref] = 1
else:
start_end_day_res[key][test] = start_end_day_res['Fraction'][test] - \
start_end_day_res['Fraction'][ref]
start_end_day_res[key][ref] = 0
ax_args_copy = ax_args.copy()
res = get_compare_responsive_sig(res)
list_of_days = list(zip(odor_start_days, end_days))
mice = np.unique(res['mouse'])
res[key] = np.zeros_like(res['Fraction'])
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
start_end_day_res = filter.filter(start_end_day_res,
{'odor_valence': ['CS+', 'CS-']})
add_naive_learned(start_end_day_res, odor_start_days, end_days)
_helper(start_end_day_res)
start_end_day_res = filter.filter(start_end_day_res,
{'training_day': 'Learned'})
summary_res = reduce.new_filter_reduce(start_end_day_res,
filter_keys='odor_valence',
reduce_key=key)
dict = {'CS+': 'Green', 'CS-': 'Red'}
if use_colors:
colors = [
dict[key] for key in np.unique(start_end_day_res['odor_valence'])
]
else:
colors = ['Black'] * 6
ax_args_copy = ax_args_copy.copy()
n_valence = len(np.unique(summary_res['odor_valence']))
ax_args_copy.update({
'xlim': [-1, n_valence],
'ylim': [-ylim, ylim],
'yticks': np.arange(-1, 1, .2)
})
if normalize:
ax_args_copy.update({
'xlim': [-1, n_valence],
'ylim': [-.1, 1.5],
'yticks': [0, .5, 1, 1.5]
})
scatter_args_copy = scatter_args.copy()
scatter_args_copy.update({'s': 8})
odors = ['CS+', 'CS-']
for i, odor in enumerate(odors):
reuse = True
if i == 0:
reuse = False
plot.plot_results(start_end_day_res,
loop_keys='odor_valence',
select_dict={'odor_valence': odor},
x_key='odor_valence',
y_key=key,
colors=[dict[odor]] * len(mice),
path=figure_path,
plot_args=scatter_args_copy,
plot_function=plt.scatter,
ax_args=ax_args_copy,
save=False,
reuse=reuse,
fig_size=(2, 1.5),
legend=False,
name_str=','.join([str(x) for x in odor_start_days]))
if not normalize:
plt.plot(plt.xlim(), [0, 0], '--', color='gray', linewidth=1, alpha=.5)
plot.plot_results(summary_res,
x_key='odor_valence',
y_key=key,
error_key=key + '_sem',
colors='black',
path=figure_path,
plot_args=error_args,
plot_function=plt.errorbar,
ax_args=ax_args_copy,
save=True,
reuse=True,
fig_size=(2, 1.5),
legend=False)
def compare_to_shuffle(res, start, end, data_arg, figure_path):
'''
:param res:
:param start:
:param end:
:param data_arg: 'onset', 'duration', 'amplitude'
:param figure_path:
:return:
'''
n_shuffle = 1000
list_of_days = [np.arange(s, e+1) for s, e in zip(start,end)]
res_ = filter.filter_days_per_mouse(res, days_per_mouse=list_of_days)
res_ = filter.filter(res_, filter_dict={'odor_valence':'CS+'})
mice = np.unique(res_['mouse'])
real = []
shuffled = []
for mouse in mice:
res_mouse_ = filter.filter(res_, {'mouse': mouse})
days = np.unique(res_mouse_['day'])
for day in days:
res_mouse_day = filter.filter(res_mouse_, {'day': day})
assert res_mouse_day['onset'].size == 2, 'should be two entries corresponding to two CS+ per mouse'
a = res_mouse_day[data_arg][0]
b = res_mouse_day[data_arg][1]
responsive_to_both = np.array([x>-1 and y>-1 for x, y in zip(a,b)])
a_ = a[responsive_to_both]
b_ = b[responsive_to_both]
real_diff = np.abs(a_ - b_)
real.append(real_diff)
for n in range(n_shuffle):
a_shuffled = np.random.permutation(a_)
b_shuffled = np.random.permutation(b_)
shuffled_diff = np.abs(a_shuffled - b_shuffled)
shuffled.append(shuffled_diff)
flatten = lambda l: [item for sublist in l for item in sublist]
real = np.array(flatten(real), dtype=float)
shuffled = np.array(flatten(shuffled), dtype=float)
bin = 20
if data_arg == 'amplitude':
xlim = 1
hist_range = 1
else:
period = .229
xlim = np.ceil(bin * period)
hist_range = bin * period
real *= period
shuffled *= period
p = scipy.stats.ranksums(real, shuffled)[1]
fig = plt.figure(figsize=(2, 1.5))
ax = fig.add_axes([0.25, 0.25, 0.7, 0.7])
def _helper(real, label):
density, bins = np.histogram(real, bins=bin, density=True, range=[0, hist_range])
unity_density = density / density.sum()
widths = bins[:-1] - bins[1:]
ax.bar(bins[1:], unity_density, width=widths, alpha = .5, label=label)
_helper(real, 'Within Neurons')
_helper(shuffled, 'Shuffled')
ax.legend(frameon=False)
ax.set_xlabel((r'$\Delta$' + ' ' + data_arg.capitalize()))
ax.set_ylabel('Density')
plt.xlim([0, xlim])
# xticks = np.arange(xlim)
# ax.set_xticks(xticks)
# ax.set_xticklabels(xticks)
# yticks = np.array([0, .5, 1])
# ax.set_yticks(yticks)
# plt.ylim([0, 1])
ylim = ax.get_ylim()
xlim = ax.get_xlim()
x = (xlim[1] - xlim[0])/2
y = (ylim[1] - ylim[0])/2
t = 'P = {:.3e}'.format(p)
plt.text(x, y, t)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
_easy_save(os.path.join(figure_path, data_arg), 'difference_' + data_arg, dpi=300, pdf=True)
print('real mean: {}'.format(np.mean(real)))
print('shuffled mean: {}'.format(np.mean(shuffled)))
print('ranksum: {}'.format(t))
def distribution(res, start, end, data_arg, figure_path, save):
list_of_days = [np.arange(s, e+1) for s, e in zip(start,end)]
res_ = filter.filter_days_per_mouse(res, days_per_mouse=list_of_days)
res_ = filter.filter(res_, filter_dict={'odor_valence':'CS+'})
mice = np.unique(res_['mouse'])
real = []
for mouse in mice:
res_mouse_ = filter.filter(res_, {'mouse': mouse})
days = np.unique(res_mouse_['day'])
for day in days:
res_mouse_day = filter.filter(res_mouse_, {'day': day})
assert res_mouse_day['onset'].size == 2, 'should be two entries corresponding to two CS+ per mouse'
a = res_mouse_day[data_arg][0]
b = res_mouse_day[data_arg][1]
a = a[a>-1]
b = b[b>-1]
real.append(a)
real.append(b)
flatten = lambda l: [item for sublist in l for item in sublist]
real = np.array(flatten(real), dtype=float)
print('Number of points: {}'.format(real.shape))
bin = 20
if data_arg == 'amplitude':
xlim = 1.5
hist_range = 1.5
else:
period = .229
xlim = np.ceil(bin * period)
hist_range = bin * period
real *= period
if not save:
fig = plt.figure(figsize=(2, 1.5))
ax = fig.add_axes([0.2, 0.2, 0.7, 0.7])
else:
ax = plt.gca()
def _helper(real, label):
density, bins = np.histogram(real, bins=bin, density=True, range=[0, hist_range])
unity_density = density / density.sum()
widths = bins[:-1] - bins[1:]
ax.bar(bins[1:], unity_density, width=widths, alpha=.5, label=label)
_helper(real, 'Data')
ax.set_xlabel((data_arg.capitalize()))
ax.set_ylabel('Density')
if data_arg != 'amplitude':
plt.xticks([0, 2, 5], ['Odor On', 'Off', 'US'])
plt.xlim([-0.5, 5.5])
else:
plt.xlim([0, xlim])
# xticks = np.arange(xlim)
# ax.set_xticks(xticks)
# ax.set_xticklabels(xticks)
# yticks = np.array([0, .5, 1])
# ax.set_yticks(yticks)
# plt.ylim([0, 1])
mean = np.mean(real)
median = np.median(real)
ylim = ax.get_ylim()
xlim = ax.get_xlim()
x = (xlim[1] - xlim[0]) / 2
y = (ylim[1] - ylim[0]) / 2
if save:
y -= .05
t = 'Median = {:.3f}, mean = {:.3f}'.format(median, mean)
# plt.text(x, y, t)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
name = 'distribution ' + data_arg + '_' + ','.join([str(x) for x in start]) + '_' + ','.join([str(x) for x in end])
if save:
plt.legend(frameon=False)
_easy_save(os.path.join(figure_path, data_arg), name, dpi=300, pdf=True)
#statistics
if data_arg == 'onset':
odor = np.sum(real < 2) / real.size
delay = np.sum(real > 2) / real.size
print('mean, median: {}, {}'.format(mean, median))
print('Fraction of cells with onset during odor presentation: {}'.format(odor))
return real
def behavior_vs_neural_power(neural_res, behavior_res, start, end, figure_path, neural_arg ='power', behavior_arg ='onset'):
neural_res = copy.copy(neural_res)
neural_key = 'neural'
behavior_key = 'behavior'
if behavior_arg == 'onset':
behavior_data_key = 'time_first_lick_raw'
elif behavior_arg == 'magnitude':
behavior_data_key = 'lick_5s'
elif behavior_arg == 'com':
behavior_data_key = 'lick_com_raw'
else:
raise ValueError('bad key')
neural_data_key = 'max_power'
_power(neural_res, excitatory=True)
for i, p in enumerate(neural_res['Power']):
s, e = neural_res['DAQ_O_ON_F'][i], neural_res['DAQ_W_ON_F'][i]
mp = np.max(p[s:e]) - np.mean(p[:s])
neural_res[neural_data_key].append(mp)
neural_res[neural_data_key] = np.array(neural_res[neural_data_key])
list_of_days = [np.arange(s, e+1) for s, e in zip(start,end)]
print(list_of_days)
neural_res_filtered = filter.filter_days_per_mouse(neural_res, days_per_mouse=list_of_days)
neural_res_filtered = filter.filter(neural_res_filtered, filter_dict={'odor_valence':'CS+'})
behavior_res_filtered = filter.filter(behavior_res, filter_dict={'odor_valence':'CS+'})
names_neu, ixs_neu = filter.retrieve_unique_entries(neural_res_filtered, ['mouse','day','odor_standard'])
out = defaultdict(list)
for ix, names in zip(ixs_neu, names_neu):
mouse = names[0]
day = names[1]
odor_standard = names[2]
assert len(ix) == 1
neural = neural_res_filtered[neural_data_key][ix[0]]
temp = filter.filter(behavior_res_filtered, {'mouse': mouse, 'odor_standard': odor_standard})
assert len(temp[behavior_data_key]) == 1
ix_day = [x == day for x in temp['day'][0]]
lick = temp[behavior_data_key][0][ix_day]
lick = lick[lick>-1]
out['mouse'].append(mouse)
out['day'].append(day)
out['odor_standard'].append(odor_standard)
out[neural_key + neural_arg].append(np.mean(neural))
out[behavior_key + behavior_arg].append(np.mean(lick))
out['neural_raw'].append(neural)
out['lick_raw'].append(lick)
for k, v in out.items():
out[k] = np.array(v)
## versus
if behavior_arg in ['onset', 'com']:
xlim = [0, 5]
xticks = [0, 2, 5]
xticklabels = ['ON', 'OFF', 'US']
else:
xlim = [0, 35]
xticks = [0, 10, 20, 30]
xticklabels = [0, 10, 20, 30]
ax_args = {'xlim': xlim, 'ylim': [0, .1], 'xticks': xticks, 'yticks': [0, .05, .1],
'xticklabels': xticklabels}
path, name = plot.plot_results(out, x_key=behavior_key + behavior_arg, y_key=neural_key + neural_arg,
loop_keys='mouse',
plot_function=plt.scatter,
plot_args=scatter_args,
ax_args=ax_args,
colormap='jet',
path=figure_path,
save=False)
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score
scores = []
for mouse in np.unique(out['mouse']):
res = filter.filter(out, {'mouse':mouse})
regr = linear_model.LinearRegression()
x = res[behavior_key + behavior_arg].reshape(-1,1)
y = res[neural_key + neural_arg].reshape(-1,1)
regr.fit(x, y)
y_pred = regr.predict(x)
score = regr.score(x, y)
scores.append(score)
print('regression: {}'.format(scores))
xlim = plt.xlim()
ylim = plt.ylim()
plt.text((xlim[1] - xlim[0])/2, ylim[1]-.01, 'Average R = {:.2f}'.format(np.mean(scores)))
name += '_' + behavior_arg
plot._easy_save(path, name)
conditions = [
conditions.PIR, conditions.OFC, conditions.OFC_LONGTERM,
conditions.OFC_COMPOSITE, conditions.MPFC_COMPOSITE
]
save_path = os.path.join(Config.LOCAL_FIGURE_PATH, 'MISC', 'SNR')
out = defaultdict(list)
psth = False
for condition in conditions:
data_path = os.path.join(Config.LOCAL_EXPERIMENT_PATH, 'COUNTING',
condition.name)
res = fio.load_pickle(os.path.join(data_path, 'dict.pkl'))
mice = np.unique(res['mouse'])
res = filter.filter_days_per_mouse(res, len(mice) * [0])
if psth:
for i, d in enumerate(res['data']):
res['data'][i] = np.mean(d, axis=1)
for mouse in mice:
temp = filter.filter(res, {'mouse': mouse})
data = np.concatenate(temp['data'], axis=1)
out['condition'].append(condition.name)
out['mouse'].append(mouse)
out['data'].append(data)
x = data.reshape(data.shape[0], -1)
max_each_cell = np.max(x, axis=1)
def plot_max_dff_days(res,
days_per_mouse,
odor_valence,
save,
reuse,
day_pad,
ylim=.115,
colors=None,
normalize=False,
figure_path=None):
res = copy.copy(res)
res['day_'] = np.zeros_like(res['day'])
res_ = defaultdict(list)
for i, days in enumerate(days_per_mouse):
temp = filter.filter_days_per_mouse(res, days_per_mouse=days)
temp = filter.filter(temp, {'odor_valence': odor_valence[i]})
temp['day_'] = np.array([i + day_pad] * len(temp['day_']))
reduce.chain_defaultdicts(res_, temp)
_max_dff(res_)
res_ = reduce.new_filter_reduce(
res_,
filter_keys=['odor_valence', 'mouse', 'day_'],
reduce_key='max_dff')
if normalize:
_normalize_across_days(res_)
yticks = [0, 1, 2, 3, 4, 5, 6]
else:
yticks = np.arange(0, ylim, .05)
dict = {
'CS+': 'Green',
'CS-': 'Red',
'US': 'Turquoise',
'PT CS+': 'Orange'
}
n_mice = len(np.unique(res['mouse']))
ax_args_copy = ax_args.copy()
ax_args_copy.update({
'ylim': [0, ylim],
'yticks': yticks,
'xticks': list(range(20))
})
line_args_copy = line_args.copy()
line_args_copy.update({
'marker': '.',
'linestyle': '--',
'linewidth': .5,
'alpha': .5,
'markersize': 2
})
if colors is None:
colors = [dict[x] for x in odor_valence]
else:
line_args_copy.update({'marker': None})
plot.plot_results(res_,
loop_keys='mouse',
x_key='day_',
y_key='max_dff',
path=figure_path,
colors=colors * n_mice,
legend=False,
plot_args=line_args_copy,
ax_args=ax_args_copy,
fig_size=(2, 2),
save=save,
reuse=reuse)
def plot_summary_odor(res,
start_days,
end_days,
use_colors=True,
figure_path=None,
reuse=False,
excitatory=True):
ax_args_copy = ax_args.copy()
res = copy.copy(res)
get_responsive_cells(res)
list_of_days = list(zip(start_days, end_days))
mice = np.unique(res['mouse'])
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
add_naive_learned(start_end_day_res, start_days, end_days, 'a', 'b')
filter.assign_composite(start_end_day_res,
loop_keys=['odor_valence', 'training_day'])
start_end_day_res = reduce.new_filter_reduce(
start_end_day_res,
filter_keys=['training_day', 'mouse', 'odor_valence'],
reduce_key='Fraction Responsive')
odor_list = ['CS+', 'CS-']
if use_colors:
colors = ['Green', 'Red']
else:
colors = ['Black'] * 2
ax_args_copy = ax_args_copy.copy()
ax_args_copy.update({
'xlim': [-1, 8],
'ylim': [0, .4],
'yticks': [0, .1, .2, .3, .4]
})
name_str = '_E' if excitatory else '_I'
for i, odor in enumerate(odor_list):
save_arg = False
reuse_arg = True
if i == 0 and not reuse:
reuse_arg = False
if i == len(odor_list) - 1:
save_arg = True
plot.plot_results(start_end_day_res,
select_dict={'odor_valence': odor},
x_key='odor_valence_training_day',
y_key='Fraction Responsive',
loop_keys='mouse',
colors=[colors[i]] * len(mice),
path=figure_path,
plot_args=line_args,
ax_args=ax_args_copy,
save=save_arg,
reuse=reuse_arg,
fig_size=(2.5, 1.5),
legend=False,
name_str=','.join([str(x)
for x in start_days]) + name_str)
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
add_naive_learned(start_end_day_res, start_days, end_days, 'a', 'b')
filter.assign_composite(start_end_day_res,
loop_keys=['odor_valence', 'training_day'])
start_end_day_res = reduce.new_filter_reduce(
start_end_day_res,
filter_keys=['training_day', 'mouse', 'odor_standard'],
reduce_key='Fraction Responsive')
before_csp = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'a',
'odor_valence': 'CS+'
})
after_csp = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'b',
'odor_valence': 'CS+'
})
before_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'a',
'odor_valence': 'CS-'
})
after_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'b',
'odor_valence': 'CS-'
})
try:
from scipy.stats import ranksums, wilcoxon, kruskal
print('Before CS+: {}'.format(
np.mean(before_csp['Fraction Responsive'])))
print('After CS+: {}'.format(np.mean(
after_csp['Fraction Responsive'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_csp['Fraction Responsive'],
after_csp['Fraction Responsive'])))
print('Before CS-: {}'.format(
np.mean(before_csm['Fraction Responsive'])))
print('After CS-: {}'.format(np.mean(
after_csm['Fraction Responsive'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_csm['Fraction Responsive'],
after_csm['Fraction Responsive'])))
except:
print('stats didnt work')
def plot_power(res,
start_days,
end_days,
figure_path,
excitatory=True,
odor_valence=('CS+'),
naive=False,
colors_before={
'CS+': 'Green',
'CS-': 'Red'
},
colors_after={
'CS+': 'Green',
'CS-': 'Red'
},
ylim=[0, .1],
align=True,
pad=True):
res = copy.copy(res)
_power(res, excitatory)
if pad:
right_on = np.median(res['DAQ_O_ON_F'])
for i, odor_on in enumerate(res['DAQ_O_ON_F']):
if np.abs(odor_on - right_on) > 2:
diff = (right_on - odor_on).astype(int)
if diff > 0:
p = res['Power'][i]
newp = np.zeros_like(p)
newp[:diff] = p[0]
newp[diff:] = p[:-diff]
res['Power'][i] = newp
print('early odor time. mouse: {}, day: {}'.format(
res['mouse'][i], res['day'][i]))
else:
p = res['Power'][i]
newp = np.zeros_like(p)
newp[:diff] = p[-diff:]
newp[diff:] = p[-1]
res['Power'][i] = newp
print('late odor time. mouse: {}, day: {}'.format(
res['mouse'][i], res['day'][i]))
if align:
nF = [len(x) for x in res['Power']]
max_frame = np.max(nF)
for i, p in enumerate(res['Power']):
if len(p) < max_frame:
newp = np.zeros(max_frame)
newp[:len(p)] = p
newp[len(p):] = p[-1]
res['Power'][i] = newp
res['Time'][i] = np.arange(0, max_frame)
print('pad frames. mouse: {}, day: {}'.format(
res['mouse'][i], res['day'][i]))
list_of_days = list(zip(start_days, end_days))
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
add_naive_learned(start_end_day_res, start_days, end_days)
if naive:
start_end_day_res = filter.exclude(start_end_day_res, {
'odor_standard': 'PT CS+',
'training_day': 'Naive'
})
ix = start_end_day_res['odor_valence'] == 'PT Naive'
start_end_day_res['odor_valence'][ix] = 'PT CS+'
start_end_day_sum_res = reduce.new_filter_reduce(
start_end_day_res,
filter_keys=['training_day', 'odor_valence'],
reduce_key='Power')
ax_args_copy = trace_ax_args.copy()
if excitatory:
yticks = np.arange(0, .2, .05)
else:
yticks = -1 * np.arange(0, .2, .025)
ax_args_copy.update({
'xticks': [res['DAQ_O_ON_F'][-1], res['DAQ_W_ON_F'][-1]],
'xticklabels': ['ON', 'US'],
'ylim':
ylim,
'yticks':
yticks
})
colors_b = [colors_before[x] for x in odor_valence]
colors = [colors_after[x] for x in odor_valence]
strr = ','.join([str(x) for x in start_days]) + '_' + ','.join(
[str(x) for x in end_days])
if excitatory:
strr += '_E'
else:
strr += '_I'
plot.plot_results(start_end_day_sum_res,
select_dict={
'odor_valence': odor_valence,
'training_day': 'Naive'
},
x_key='Time',
y_key='Power',
loop_keys='odor_valence',
error_key='Power_sem',
path=figure_path,
plot_function=plt.fill_between,
plot_args=fill_args,
ax_args=ax_args_copy,
colors=colors_b,
fig_size=(2, 1.5),
rect=(.3, .2, .6, .6),
save=False)
plot.plot_results(start_end_day_sum_res,
select_dict={
'odor_valence': odor_valence,
'training_day': 'Naive'
},
x_key='Time',
y_key='Power',
loop_keys='odor_valence',
path=figure_path,
plot_args=trace_args,
ax_args=ax_args_copy,
colors=colors_b,
fig_size=(2, 1.5),
reuse=True,
save=False)
plot.plot_results(start_end_day_sum_res,
select_dict={
'odor_valence': odor_valence,
'training_day': 'Learned'
},
x_key='Time',
y_key='Power',
loop_keys='odor_valence',
error_key='Power_sem',
path=figure_path,
plot_function=plt.fill_between,
plot_args=fill_args,
ax_args=ax_args_copy,
colors=colors,
fig_size=(2, 1.5),
reuse=True,
save=False)
plot.plot_results(start_end_day_sum_res,
select_dict={
'odor_valence': odor_valence,
'training_day': 'Learned'
},
x_key='Time',
y_key='Power',
loop_keys='odor_valence',
path=figure_path,
plot_args=trace_args,
ax_args=ax_args_copy,
colors=colors,
fig_size=(2, 1.5),
reuse=True,
name_str=strr)
for i, x in enumerate(start_end_day_res['Power']):
on, off = [
start_end_day_res['DAQ_O_ON_F'][i],
start_end_day_res['DAQ_W_ON_F'][i]
]
y = np.max(x[on:off]) - np.min(x)
start_end_day_res['stat'].append(y)
start_end_day_res['stat'] = np.array(start_end_day_res['stat'])
for valence in odor_valence:
before_odor = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'Naive',
'odor_valence': valence
})
after_odor = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'Learned',
'odor_valence': valence
})
try:
from scipy.stats import ranksums, wilcoxon, kruskal
print(before_odor['odor_valence'])
# print('Before: {}'.format(before_odor['stat']))
print('Before: {}'.format(np.mean(before_odor['stat'])))
# print('After: {}'.format(after_odor['stat']))
print('After: {}'.format(np.mean(after_odor['stat'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_odor['stat'], after_odor['stat'])))
except:
print('stats didnt work')
return before_odor['stat'], after_odor['stat']
def _helper(lick_res, summary_res, mouse, odor_valence):
behavior_colors = [colors_dict[x] for x in odor_valence]
colors = [colors_dict[x] for x in odor_valence]
if 'COMPOSITE' in condition.name:
days = behavior.behavior_analysis._get_days_per_condition(
data_path, condition)
res_dt_naive = filter.filter_days_per_mouse(
summary_res, [x[0] for x in days])
res_dt_train = filter.filter_days_per_mouse(
summary_res, [x[1:] for x in days])
list_of_res = [res_dt_naive, res_dt_train]
lick_dt_naive = filter.filter_days_per_mouse(
lick_res, [x[0] for x in days])
lick_dt_train = filter.filter_days_per_mouse(
lick_res, [x[1:] for x in days])
list_of_lick_res = [lick_dt_naive, lick_dt_train]
else:
list_of_res = [summary_res]
list_of_lick_res = [lick_res]
# plot.plot_results(summary_res, x_key='day', y_key='PCA Distance',
# select_dict={'mouse': mouse, 'shuffle': 1, 'odor_valence': odor_valence},
# colors='grey',
# ax_args=ax_args, plot_args=line_args,
# path=figure_path, reuse=False, save=False)
#
# plot.plot_results(summary_res, x_key='day', y_key='PCA Distance', error_key='PCA Distance_sem',
# select_dict={'mouse': mouse, 'shuffle': 1, 'odor_valence': odor_valence},
# colors='gray', plot_function=plt.fill_between,
# ax_args=ax_args, plot_args=fill_args,
# path=figure_path, reuse=True, save=False)
for i, res in enumerate(list_of_res):
reuse = True
if i == 0:
reuse = False
save = False
if i == len(list_of_res) - 1:
save = True
l_res = list_of_lick_res[i]
plot.plot_results(l_res,
x_key='day',
y_key='lick_boolean',
loop_keys='odor_valence',
select_dict={
'mouse': mouse,
'odor_valence': odor_valence
},
colors=behavior_colors,
ax_args=ax_args,
plot_args=behavior_line_args,
path=figure_path,
save=False,
sort=False,
reuse=reuse)
plot.plot_results(res,
x_key='day',
y_key='PCA Distance',
loop_keys='odor_valence',
select_dict={
'mouse': mouse,
'shuffle': 0,
'odor_valence': odor_valence
},
colors=colors,
ax_args=ax_args,
plot_args=line_args,
path=figure_path,
reuse=True,
sort=False,
save=save)
def organizer_test_fp_fn(condition, decodeConfig, data_path, save_path):
'''
:param condition:
:param decodeConfig:
:param data_path:
:param save_path:
:return:
'''
data_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.mat_ext)))
config_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.cons_ext)))
list_of_all_data = np.array([Config.load_mat_f(d) for d in data_pathnames])
list_of_all_cons = np.array(
[Config.load_cons_f(d) for d in config_pathnames])
mouse_names_per_file = np.array(
[cons.NAME_MOUSE for cons in list_of_all_cons])
mouse_names, list_of_mouse_ix = np.unique(mouse_names_per_file,
return_inverse=True)
if mouse_names.size != len(condition.paths):
raise ValueError(
"res has {0:d} mice, but filter has {1:d} mice".format(
mouse_names.size, len(condition.paths)))
import copy
counting_res = copy.copy(decodeConfig.res_counting)
start_days = decodeConfig.start_day
end_days = decodeConfig.end_day
days_per_mouse = []
for x, y in zip(start_days, end_days):
days_per_mouse.append(np.arange(x, y + 1))
print(days_per_mouse)
for i, mouse_name in enumerate(mouse_names):
start_time = time.time()
ix = mouse_name == mouse_names_per_file
list_of_cons_ = list_of_all_cons[ix]
list_of_data_ = list_of_all_data[ix]
for cons in list_of_cons_:
assert cons.NAME_MOUSE == mouse_name, 'Wrong mouse file!'
if len(days_per_mouse[i]):
list_of_cons = list_of_cons_[days_per_mouse[i]]
list_of_data = list_of_data_[days_per_mouse[i]]
if counting_res is not None:
temp = filter.filter(counting_res, {
'mouse': i,
'odor_valence': 'US'
})
temp = filter.filter_days_per_mouse(
temp, days_per_mouse=[days_per_mouse[i]])
list_of_neural_ixs = temp['sig']
else:
list_of_neural_ixs = None
cons = list_of_cons[0]
cons_dict = cons.__dict__
for key, value in cons_dict.items():
if isinstance(value, list) or isinstance(value, np.ndarray):
pass
else:
setattr(decodeConfig, key, value)
if hasattr(condition, 'odors'):
odor = condition.odors[i]
else:
odor = condition.dt_odors[i]
cons_odors = [cons.ODOR_UNIQUE for cons in list_of_cons]
ix = [
i for i, unique_odors in enumerate(cons_odors)
if odor[0] in unique_odors
]
list_of_cons = [list_of_cons[i] for i in ix]
list_of_data = [list_of_data[i] for i in ix]
if decodeConfig.decode_style == 'identity':
csp = None
else:
if hasattr(condition, 'odors'):
csp = condition.csp[i]
else:
csp = condition.dt_csp[i]
scores_res = decoding.test_fp_fn(list_of_cons, list_of_data, odor,
csp, decodeConfig,
list_of_neural_ixs)
name = cons.NAME_MOUSE
save_config = copy.copy(decodeConfig)
save_config.res_counting = ''
fio.save_json(save_path=save_path,
save_name=name,
config=save_config)
fio.save_pickle(save_path=save_path,
save_name=name,
data=scores_res)
print("Analyzed: {0:s} in {1:.2f} seconds".format(
name,
time.time() - start_time))
def plot_compare_dff(res,
start_days,
end_days,
arg,
valence,
more_stats,
figure_path,
lim=(-.05, 1.2),
ticks=(0, .5, 1)):
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)
#analysis
xs, ys = new['day_0'], new['day_1']
a, b = [], []
for x, y in zip(xs, ys):
a.append(np.sum(x > y))
b.append(np.sum(y > x))
fraction = np.sum(a) / (np.sum(a) + np.sum(b))
ax_args_copy = {}
ax_args_copy.update({
'ylim': lim,
'xlim': lim,
'yticks': ticks,
'xticks': ticks
})
scatter_args_copy = scatter_args.copy()
scatter_args_copy.update({
'marker': ',',
's': 1,
'alpha': .2,
'facecolors': 'none'
})
colors = ['Black']
# if valence == 'CS+':
# colors = ['Green']
# if valence == 'CS-':
# colors = ['darkred']
colors *= 300
path, name = plot.plot_results(new,
select_dict={'odor_valence': valence},
x_key='day_0',
y_key='day_1',
loop_keys=['mouse', 'odor'],
path=figure_path,
plot_args=scatter_args_copy,
ax_args=ax_args_copy,
plot_function=plt.scatter,
colors=colors,
legend=False,
fig_size=(2, 1.5),
rect=(.25, .25, .6, .6),
save=False)
plt.xlim(lim)
plt.ylim(lim)
plt.plot(lim, lim, '--', color='red', alpha=.5, linewidth=1)
plt.title(valence)
if valence == 'CS+':
plt.text(0,
lim[1] - .1,
'{:.1f}% diminished'.format(fraction * 100),
fontsize=5)
if valence == 'CS-':
plt.text(0,
lim[1] - .1,
'{:.1f}% increased'.format(100 - fraction * 100),
fontsize=5)
if more_stats:
new = _compare_dff(res, loop_keys=['mouse', 'odor'], arg='first')
new = filter.filter(new, {'odor_valence': valence})
xs, ys = new['day_0'], new['day_1']
a, b, c = [], [], []
for x, y in zip(xs, ys):
a.append(np.sum(x > y))
b.append(np.sum(y > x))
c.append(np.sum(y < 0.1))
lost_fraction = np.sum(c) / (np.sum(a) + np.sum(b))
plt.text(0,
lim[1] - .2,
'Of those, {:.1f}% are unresponsive'.format(100 *
lost_fraction),
fontsize=5)
_easy_save(path, name, pdf=True)
def plot_correlation_across_days(res,
days,
loop_keys,
shuffle,
figure_path,
reuse,
save,
analyze,
plot_bool,
odor_end=True):
if analyze:
res_ = defaultdict(list)
for day_list in days:
d = list(zip(day_list[0], day_list[1]))
res_temp = filter.filter_days_per_mouse(res, d)
corr_res = _correlation(res_temp,
loop_keys,
shuffle,
odor_end=odor_end)
reduce.chain_defaultdicts(res_, corr_res)
res_ = filter.filter(res_, {'Odor_A': 0, 'Odor_B': 1})
res_ = reduce.new_filter_reduce(res_,
filter_keys=['mouse', 'odor_standard'],
reduce_key='corrcoef')
res_.pop('corrcoef_sem')
return res_
if plot_bool:
res_ = res
if shuffle:
s = '_shuffled'
else:
s = ''
ax_args_copy = ax_args.copy()
ax_args_copy.update({
'xlim': [-.5, 2.5],
'ylim': [0, 1.05],
'yticks': np.arange(0, 1.1, .2)
})
swarm_args_copy = swarm_args.copy()
swarm_args_copy.update({'palette': ['green', 'red', 'gray']})
plot.plot_results(res_,
x_key='odor_valence',
y_key='corrcoef',
path=figure_path,
plot_args=swarm_args_copy,
plot_function=sns.stripplot,
ax_args=ax_args_copy,
reuse=reuse,
save=False,
sort=True,
name_str=s)
summary = reduce.new_filter_reduce(res_,
filter_keys=['odor_valence'],
reduce_key='corrcoef')
plot.plot_results(summary,
x_key='odor_valence',
y_key='corrcoef',
error_key='corrcoef_sem',
colors='black',
path=figure_path,
plot_args=error_args,
plot_function=plt.errorbar,
save=save,
reuse=True,
legend=False,
name_str=s)
from scipy.stats import ranksums
print(summary['corrcoef'])
def plot_summary_odor_and_water(res,
odor_start_days,
water_start_days,
end_days,
use_colors=True,
excitatory=True,
arg='odor_valence',
figure_path=None):
include_water = True
ax_args_copy = ax_args.copy()
res = copy.copy(res)
get_responsive_cells(res)
mice = np.unique(res['mouse'])
list_of_days = list(zip(odor_start_days, end_days))
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
start_end_day_res = filter.exclude(start_end_day_res,
{'odor_valence': 'US'})
add_naive_learned(start_end_day_res, odor_start_days, end_days, 'a', 'b')
if include_water:
list_of_days = list(zip(water_start_days, end_days))
start_end_day_res_water = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
start_end_day_res_water = filter.filter(start_end_day_res_water,
{'odor_valence': 'US'})
add_naive_learned(start_end_day_res_water, water_start_days, end_days,
'a', 'b')
reduce.chain_defaultdicts(start_end_day_res, start_end_day_res_water)
ax_args_copy = ax_args_copy.copy()
if arg == 'odor_valence':
start_end_day_res = reduce.new_filter_reduce(
start_end_day_res,
filter_keys=['training_day', 'mouse', 'odor_valence'],
reduce_key='Fraction Responsive')
odor_list = ['CS+', 'CS-']
ax_args_copy.update({
'xlim': [-1, 6],
'ylim': [0, .6],
'yticks': [0, .1, .2, .3, .4, .5]
})
colors = ['Green', 'Red']
elif arg == 'naive':
arg = 'odor_valence'
start_end_day_res = reduce.new_filter_reduce(
start_end_day_res,
filter_keys=['training_day', 'mouse', 'odor_valence'],
reduce_key='Fraction Responsive')
odor_list = ['CS+']
ax_args_copy.update({
'xlim': [-1, 4],
'ylim': [0, .6],
'yticks': [0, .1, .2, .3, .4, .5]
})
colors = ['GoldenRod']
else:
odor_list = ['CS+1', 'CS+2', 'CS-1', 'CS-2']
colors = ['Green', 'Green', 'Red', 'Red']
ax_args_copy.update({
'xlim': [-1, 10],
'ylim': [0, .6],
'yticks': [0, .1, .2, .3, .4, .5]
})
filter.assign_composite(start_end_day_res, loop_keys=[arg, 'training_day'])
if not use_colors:
colors = ['Black'] * 4
name_str = '_E' if excitatory else '_I'
for i, odor in enumerate(odor_list):
reuse_arg = True
if i == 0:
reuse_arg = False
plot.plot_results(start_end_day_res,
select_dict={arg: odor},
x_key=arg + '_training_day',
y_key='Fraction Responsive',
loop_keys='mouse',
colors=[colors[i]] * len(mice),
path=figure_path,
plot_args=line_args,
ax_args=ax_args_copy,
save=False,
reuse=reuse_arg,
fig_size=(2.5, 1.5),
legend=False,
name_str=','.join([str(x) for x in odor_start_days]))
plot.plot_results(start_end_day_res,
select_dict={'odor_standard': 'US'},
x_key='training_day',
y_key='Fraction Responsive',
loop_keys='mouse',
colors=['Turquoise'] * len(mice),
path=figure_path,
plot_args=line_args,
ax_args=ax_args_copy,
fig_size=(1.6, 1.5),
legend=False,
reuse=True,
save=True,
name_str=name_str)
before_odor = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'a',
'odor_valence': ['CS+', 'CS-']
})
after_odor = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'b',
'odor_valence': ['CS+', 'CS-']
})
before_csp = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'a',
'odor_valence': 'CS+'
})
after_csp = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'b',
'odor_valence': 'CS+'
})
before_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'a',
'odor_valence': 'CS-'
})
after_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'b',
'odor_valence': 'CS-'
})
before_water = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'a',
'odor_valence': 'US'
})
after_water = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'b',
'odor_valence': 'US'
})
try:
from scipy.stats import ranksums, wilcoxon, kruskal
print('Before Odor: {}'.format(
np.mean(before_odor['Fraction Responsive'])))
print('After Odor: {}'.format(
np.mean(after_odor['Fraction Responsive'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_odor['Fraction Responsive'],
after_odor['Fraction Responsive'])))
print('Before CS+: {}'.format(
np.mean(before_csp['Fraction Responsive'])))
print('After CS+: {}'.format(np.mean(
after_csp['Fraction Responsive'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_csp['Fraction Responsive'],
after_csp['Fraction Responsive'])))
print('Before CS-: {}'.format(
np.mean(before_csm['Fraction Responsive'])))
print('After CS-: {}'.format(np.mean(
after_csm['Fraction Responsive'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_csm['Fraction Responsive'],
after_csm['Fraction Responsive'])))
print('Before US: {}'.format(
np.mean(before_water['Fraction Responsive'])))
print('After US: {}'.format(np.mean(
after_water['Fraction Responsive'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_water['Fraction Responsive'],
after_water['Fraction Responsive'])))
except:
print('stats didnt work')
def plot_overlap_odor(res,
start_days,
end_days,
delete_non_selective=False,
figure_path=None,
excitatory=True):
ax_args_copy = overlap_ax_args.copy()
res = copy.copy(res)
res = _get_overlap_odor(res, delete_non_selective)
list_of_days = list(zip(start_days, end_days))
mice = np.unique(res['mouse'])
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
start_end_day_res = reduce.new_filter_reduce(
start_end_day_res,
filter_keys=['mouse', 'day', 'condition'],
reduce_key='Overlap')
add_naive_learned(start_end_day_res, start_days, end_days)
filter.assign_composite(start_end_day_res,
loop_keys=['condition', 'training_day'])
odor_list = ['+:+', '-:-', '+:-']
colors = ['Green', 'Red', 'Gray']
name_str = '_E' if excitatory else '_I'
ax_args_copy.update({'xlim': [-1, 6]})
for i, odor in enumerate(odor_list):
save_arg = False
reuse_arg = True
if i == 0:
reuse_arg = False
if i == len(odor_list) - 1:
save_arg = True
temp = filter.filter(start_end_day_res, {'condition': odor})
name = ','.join([str(x) for x in start_days]) + '_' + ','.join(
[str(x) for x in end_days])
name += name_str
plot.plot_results(temp,
x_key='condition_training_day',
y_key='Overlap',
loop_keys='mouse',
colors=[colors[i]] * len(mice),
path=figure_path,
plot_args=line_args,
ax_args=ax_args_copy,
save=save_arg,
reuse=reuse_arg,
name_str=name,
fig_size=(2, 1.5),
legend=False)
b = filter.filter(temp, {'training_day': 'Learned'})
print(odor)
print(np.mean(b['Overlap']))
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
add_naive_learned(start_end_day_res,
start_days,
end_days,
str1='0',
str2='1')
start_end_day_res.pop('Overlap_sem', None)
summary_res = reduce.new_filter_reduce(start_end_day_res,
filter_keys='training_day',
reduce_key='Overlap')
ax_args_copy.update({
'xlim': [-1, 2],
'ylim': [0, .5],
'yticks': [0, .1, .2, .3, .4, .5]
})
plot.plot_results(summary_res,
x_key='training_day',
y_key='Overlap',
path=figure_path,
plot_args=bar_args,
ax_args=ax_args_copy,
plot_function=plt.bar,
fig_size=(2, 1.5),
legend=False,
reuse=False,
save=False)
plot.plot_results(summary_res,
x_key='training_day',
y_key='Overlap',
error_key='Overlap_sem',
path=figure_path,
plot_function=plt.errorbar,
plot_args=error_args,
ax_args=ax_args,
save=True,
reuse=True,
name_str=name_str)
before_odor = filter.filter(start_end_day_res,
filter_dict={
'training_day': '0',
'condition': '+:+'
})
after_odor = filter.filter(start_end_day_res,
filter_dict={
'training_day': '1',
'condition': '+:+'
})
before_csp = filter.filter(start_end_day_res,
filter_dict={
'training_day': '0',
'condition': '+:-'
})
after_csp = filter.filter(start_end_day_res,
filter_dict={
'training_day': '1',
'condition': '+:-'
})
before_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': '0',
'condition': '-:-'
})
after_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': '1',
'condition': '-:-'
})
from scipy.stats import ranksums, wilcoxon, kruskal
print('Before ++: {}'.format(np.mean(before_odor['Overlap'])))
print('After ++: {}'.format(np.mean(after_odor['Overlap'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_odor['Overlap'], after_odor['Overlap'])))
print('Before +-: {}'.format(np.mean(before_csp['Overlap'])))
print('After +-: {}'.format(np.mean(after_csp['Overlap'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_csp['Overlap'], after_csp['Overlap'])))
print('Before --: {}'.format(np.mean(before_csm['Overlap'])))
print('After --: {}'.format(np.mean(after_csm['Overlap'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_csm['Overlap'], after_csm['Overlap'])))
def plot_responsive_difference_odor_and_water(res,
odor_start_days,
water_start_days,
end_days,
use_colors=True,
figure_path=None,
include_water=True,
normalize=False,
pt_start=None,
pt_learned=None,
average=True,
ylim=.22,
reuse_arg=False,
save_arg=True):
key = 'Change in Fraction Responsive'
if normalize:
key = 'Norm. Fraction Responsive'
def _helper(start_end_day_res):
combs, list_of_ixs = filter.retrieve_unique_entries(
start_end_day_res, ['mouse', 'odor_standard'])
for i, comb in enumerate(combs):
ixs = list_of_ixs[i]
assert len(ixs) == 2
if start_end_day_res['training_day'][0] == 'Naive':
ref = ixs[0]
test = ixs[1]
elif start_end_day_res['training_day'][0] == 'Learned':
ref = ixs[1]
test = ixs[0]
else:
raise ValueError('cannot find ref day')
if normalize:
start_end_day_res[key][test] = start_end_day_res['Fraction Responsive'][test] / \
start_end_day_res['Fraction Responsive'][ref]
start_end_day_res[key][ref] = 1
else:
start_end_day_res[key][test] = start_end_day_res['Fraction Responsive'][test] - \
start_end_day_res['Fraction Responsive'][ref]
start_end_day_res[key][ref] = 0
ax_args_copy = ax_args.copy()
res = copy.copy(res)
get_responsive_cells(res)
list_of_days = list(zip(odor_start_days, end_days))
mice = np.unique(res['mouse'])
res[key] = np.zeros_like(res['Fraction Responsive'])
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
start_end_day_res = filter.filter(
start_end_day_res, {'odor_valence': ['CS+', 'CS-', 'Naive']})
add_naive_learned(start_end_day_res, odor_start_days, end_days)
odors = ['CS+', 'CS-', 'Naive']
if 'PT CS+' in np.unique(res['odor_valence']):
odors = ['PT CS+'] + odors
list_of_days = list(zip(pt_start, pt_learned))
start_end_day_res_pt = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
start_end_day_res_pt = filter.filter(start_end_day_res_pt,
{'odor_valence': 'PT CS+'})
add_naive_learned(start_end_day_res_pt, pt_start, pt_learned)
reduce.chain_defaultdicts(start_end_day_res, start_end_day_res_pt)
if include_water:
odors += ['US']
list_of_days = list(zip(water_start_days, end_days))
start_end_day_res_water = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
start_end_day_res_water = filter.filter(start_end_day_res_water,
{'odor_valence': 'US'})
add_naive_learned(start_end_day_res_water, water_start_days, end_days)
reduce.chain_defaultdicts(start_end_day_res, start_end_day_res_water)
filter.assign_composite(start_end_day_res,
loop_keys=['odor_standard', 'training_day'])
if average:
start_end_day_res = reduce.new_filter_reduce(
start_end_day_res,
filter_keys=['odor_valence', 'mouse', 'training_day'],
reduce_key=key)
start_end_day_res.pop(key + '_sem')
_helper(start_end_day_res)
start_end_day_res = filter.filter(start_end_day_res,
{'training_day': 'Learned'})
summary_res = reduce.new_filter_reduce(start_end_day_res,
filter_keys='odor_valence',
reduce_key=key)
dict = {
'CS+': 'Green',
'CS-': 'Red',
'US': 'Turquoise',
'PT CS+': 'Orange',
'Naive': 'Gray'
}
if use_colors:
colors = [
dict[key] for key in np.unique(start_end_day_res['odor_valence'])
]
else:
colors = ['Black'] * 6
ax_args_copy = ax_args_copy.copy()
n_valence = len(np.unique(summary_res['odor_valence']))
ax_args_copy.update({
'xlim': [-.5, 3.5],
'ylim': [-ylim, ylim],
'yticks': [-.3, -.2, -.1, 0, .1, .2, .3]
})
if normalize:
ax_args_copy.update({
'xlim': [-.5, 3.5],
'ylim': [-.1, 1.5],
'yticks': [0, .5, 1, 1.5]
})
error_args_ = {
'fmt': '.',
'capsize': 2,
'elinewidth': 1,
'markersize': 2,
'alpha': .75
}
scatter_args_copy = scatter_args.copy()
scatter_args_copy.update({'s': 3})
for i, odor in enumerate(odors):
reuse = True
if i == 0:
reuse = reuse_arg
plot.plot_results(start_end_day_res,
loop_keys='odor_valence',
select_dict={'odor_valence': odor},
x_key='odor_valence',
y_key=key,
colors=[dict[odor]] * len(mice),
path=figure_path,
plot_args=scatter_args_copy,
plot_function=plt.scatter,
ax_args=ax_args_copy,
save=False,
reuse=reuse,
fig_size=(2, 1.5),
rect=(.25, .2, .6, .6),
legend=False,
name_str=','.join([str(x) for x in odor_start_days]))
if not normalize:
plt.plot(plt.xlim(), [0, 0], '--', color='gray', linewidth=1, alpha=.5)
plot.plot_results(summary_res,
x_key='odor_valence',
y_key=key,
error_key=key + '_sem',
colors='black',
path=figure_path,
plot_args=error_args_,
plot_function=plt.errorbar,
ax_args=ax_args_copy,
save=save_arg,
reuse=True,
fig_size=(2, 1.5),
legend=False)
'marker': 'o',
'markersize': 1
}
summary_res.pop('PCA Distance_std')
summary_res.pop('PCA Distance_sem')
summary_res = filter.filter(summary_res, filter_dict={'shuffle': 0})
if plot_cs:
select_dict = {'odor_valence': 'CS+'}
else:
select_dict = None
if condition.name == 'OFC_LONGTERM':
dt_learned_day, dt_last_day = get_days_per_mouse(
data_path, condition)
dt_learned_day = [3, 2, 2, 3]
filtered_res = filter.filter_days_per_mouse(
summary_res, list(zip(dt_learned_day, dt_last_day)))
analysis.add_naive_learned(filtered_res, dt_learned_day,
dt_last_day, 'Learned', 'Over-trained')
mean_std_res = reduce.new_filter_reduce(
filtered_res,
reduce_key='PCA Distance',
filter_keys=['training_day', 'odor_valence'])
plot.plot_results(filtered_res,
x_key='training_day',
y_key='PCA Distance',
loop_keys=['odor_valence', 'mouse'],
select_dict=select_dict,
colors=['Green'] * mice.size +
['Red'] * mice.size,
ax_args=summary_ax_args,
plot_function=plt.plot,
def plot_summary_odor_pretraining(res,
start_days,
end_days,
arg_naive,
figure_path,
save,
excitatory=True):
ax_args_copy = ax_args.copy()
res = copy.copy(res)
list_of_days = list(zip(start_days, end_days))
mice = np.unique(res['mouse'])
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
get_responsive_cells(start_end_day_res)
if arg_naive:
day_start = filter.filter(start_end_day_res,
{'odor_standard': 'PT Naive'})
day_start['odor_standard'] = np.array(['PT CS+'] *
len(day_start['odor_standard']))
day_end = filter.filter_days_per_mouse(start_end_day_res,
days_per_mouse=end_days)
day_end = filter.filter(day_end, {'odor_standard': 'PT CS+'})
reduce.chain_defaultdicts(day_start, day_end)
start_end_day_res = day_start
else:
start_end_day_res = filter.exclude(start_end_day_res,
{'odor_standard': 'PT Naive'})
add_naive_learned(start_end_day_res, start_days, end_days, 'a', 'b')
filter.assign_composite(start_end_day_res,
loop_keys=['odor_standard', 'training_day'])
odor_list = ['PT CS+']
colors = ['Orange']
ax_args_copy = ax_args_copy.copy()
ax_args_copy.update({
'xlim': [-1, 10],
'ylim': [0, .4],
'yticks': [0, .1, .2, .3, .4]
})
for i, odor in enumerate(odor_list):
save_arg = False
reuse_arg = True
if i == 0:
reuse_arg = False
if save and i == len(odor_list) - 1:
save_arg = True
plot.plot_results(start_end_day_res,
select_dict={'odor_standard': odor},
x_key='odor_standard_training_day',
y_key='Fraction Responsive',
loop_keys='mouse',
colors=[colors[i]] * len(mice),
path=figure_path,
plot_args=line_args,
ax_args=ax_args_copy,
save=save_arg,
reuse=reuse_arg,
fig_size=(2.5, 1.5),
legend=False,
name_str='_E' if excitatory else '_I')
before_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'a',
'odor_standard': 'PT CS+'
})
after_csm = filter.filter(start_end_day_res,
filter_dict={
'training_day': 'b',
'odor_standard': 'PT CS+'
})
from scipy.stats import ranksums, wilcoxon, kruskal
print('Before PT CS+: {}'.format(np.mean(
before_csm['Fraction Responsive'])))
print('After PT CS+: {}'.format(np.mean(after_csm['Fraction Responsive'])))
from scipy.stats import sem
print('After PT CS+ STD: {}'.format(sem(after_csm['Fraction Responsive'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_csm['Fraction Responsive'],
after_csm['Fraction Responsive'])))
def behavior_vs_neural_onset(neural_res, behavior_res, start, end, figure_path, neural_arg ='onset', behavior_arg ='onset'):
neural_key = 'neural'
behavior_key = 'behavior'
if behavior_arg == 'onset':
behavior_data_key = 'time_first_lick_raw'
elif behavior_arg == 'magnitude':
behavior_data_key = 'lick_5s'
elif behavior_arg == 'com':
behavior_data_key = 'lick_com_raw'
else:
raise ValueError('bad key')
if neural_arg == 'onset':
neural_data_key = 'onset'
elif neural_arg == 'magnitude':
neural_data_key = 'dff'
else:
raise ValueError('bad key')
list_of_days = [np.arange(s, e+1) for s, e in zip(start,end)]
neural_res_filtered = filter.filter_days_per_mouse(neural_res, days_per_mouse=list_of_days)
neural_res_filtered = filter.filter(neural_res_filtered, filter_dict={'odor_valence':'CS+'})
behavior_res_filtered = filter.filter(behavior_res, filter_dict={'odor_valence':'CS+'})
names_neu, ixs_neu = filter.retrieve_unique_entries(neural_res_filtered, ['mouse','day','odor_standard'])
out = defaultdict(list)
for ix, names in zip(ixs_neu, names_neu):
mouse = names[0]
day = names[1]
odor_standard = names[2]
assert len(ix) == 1
neural = neural_res_filtered[neural_data_key][ix[0]]
#test
ixs = np.where(neural > -1)[0]
for a in ixs:
plt.plot(np.transpose(neural_res_filtered['data'][ix[0]][a]))
plt.title(neural[a])
plt.show()
neural = neural[neural > -1] * .229
temp = filter.filter(behavior_res_filtered, {'mouse': mouse, 'odor_standard': odor_standard})
assert len(temp[behavior_data_key]) == 1
ix_day = [x == day for x in temp['day'][0]]
lick = temp[behavior_data_key][0][ix_day]
lick = lick[lick>-1]
out['mouse'].append(mouse)
out['day'].append(day)
out['odor_standard'].append(odor_standard)
out[neural_key + neural_arg].append(np.mean(neural))
out[behavior_key + behavior_arg].append(np.mean(lick))
out['neural_raw'].append(neural)
out['lick_raw'].append(lick)
for k, v in out.items():
out[k] = np.array(v)
## distribution
def _helper(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)
all_neural = np.concatenate(out['neural_raw'])
all_licks = np.concatenate(out['lick_raw'])
bins = 20
range = [0, 5]
xticks = [0, 2, 5]
xticklabels = ['Odor ON', 'Odor OFF', 'US']
fig = plt.figure(figsize=(2, 1.5))
ax = fig.add_axes([0.2, 0.2, 0.7, 0.7])
_helper(all_neural, neural_key + neural_arg, bins, range, ax)
_helper(all_licks, behavior_key + behavior_arg, bins, range, ax)
plt.xticks(xticks, xticklabels)
plt.xlim([range[0]-0.5, range[1] + .5])
plt.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')
folder = 'onset_distribution_lick_neural_'
name = behavior_arg + '_' + ','.join([str(x) for x in start]) + '_' + ','.join([str(x) for x in end])
plt.legend(frameon=False)
_easy_save(os.path.join(figure_path, folder), name, dpi=300, pdf=True)
print('lick mean: {}'.format(np.mean(all_licks)))
print('neural mean: {}'.format(np.mean(all_neural)))
## versus
if behavior_arg in ['onset', 'com']:
xlim = [0, 5]
xticks = [0, 2, 5]
xticklabels = ['ON','OFF', 'US']
else:
xlim = [0, 35]
xticks = [0, 10, 20, 30]
xticklabels = [0, 10, 20, 30]
ax_args = {'xlim':xlim, 'ylim':[0, 3.5], 'xticks':xticks, 'yticks':[0, 2, 5],
'xticklabels':xticklabels, 'yticklabels': ['ON','OFF','US']}
path, name = plot.plot_results(out, x_key=behavior_key + behavior_arg, y_key=neural_key + neural_arg, loop_keys='mouse',
plot_function=plt.scatter,
plot_args= scatter_args,
ax_args=ax_args,
colormap='jet',
path = figure_path,
save=False)
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score
scores = []
for mouse in np.unique(out['mouse']):
res = filter.filter(out, {'mouse':mouse})
regr = linear_model.LinearRegression()
x = res[behavior_key + behavior_arg].reshape(-1,1)
y = res[neural_key + neural_arg].reshape(-1,1)
regr.fit(x, y)
y_pred = regr.predict(x)
score = regr.score(x, y)
scores.append(score)
print('average regression: {}'.format(np.mean(scores)))
xlim = plt.xlim()
ylim = plt.ylim()
plt.text((xlim[1] - xlim[0])/2, ylim[1]-.5, 'Average R = {:.2f}'.format(np.mean(scores)))
name += '_' + behavior_arg
plot._easy_save(path, name)
def plot_reversal(res, start_days, end_days, figure_path):
ax_args_copy = ax_args.copy()
ax_args_copy.update({'ylim': [0, .6]})
res = copy.copy(res)
list_of_days = list(zip(start_days, end_days))
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
reversal_res, stats_res = get_reversal_sig(start_end_day_res)
filter.assign_composite(reversal_res, loop_keys=['day', 'odor_valence'])
import seaborn as sns
swarm_args = {
'marker': '.',
'size': 8,
'facecolors': 'none',
'alpha': .5,
'palette': ['green', 'red'],
'jitter': .1
}
mean_res = reduce.new_filter_reduce(reversal_res,
filter_keys=['day', 'odor_valence'],
reduce_key='Fraction')
plot.plot_results(mean_res,
x_key='day_odor_valence',
y_key='Fraction',
error_key='Fraction_sem',
path=figure_path,
plot_function=plt.errorbar,
plot_args=error_args,
ax_args=ax_args_copy,
fig_size=(2, 1.5),
save=False)
plt.plot([1.5, 1.5], plt.ylim(), '--', color='gray', linewidth=2)
plot.plot_results(
reversal_res,
x_key='day_odor_valence',
y_key='Fraction',
path=figure_path,
colors=['Green', 'Red', 'Green', 'Red'],
# plot_function=plt.scatter, plot_args=scatter_args,
plot_function=sns.stripplot,
plot_args=swarm_args,
ax_args=ax_args_copy,
fig_size=(2, 1.5),
reuse=True,
save=True,
legend=False)
print(mean_res['day_odor_valence'])
print(mean_res['Fraction'])
from scipy.stats import wilcoxon
ix_before_p = reversal_res['day_odor_valence'] == 'Lrn_CS+'
ix_after_p = reversal_res['day_odor_valence'] == 'Rev_CS+'
ix_before_m = reversal_res['day_odor_valence'] == 'Lrn_CS-'
ix_after_m = reversal_res['day_odor_valence'] == 'Rev_CS-'
stat_csp = wilcoxon(reversal_res['Fraction'][ix_before_p],
reversal_res['Fraction'][ix_after_p])
stat_csm = wilcoxon(reversal_res['Fraction'][ix_before_m],
reversal_res['Fraction'][ix_after_m])
print('CS+ to CS-: {}'.format(stat_csp))
print('CS- to CS+: {}'.format(stat_csm))
titles = ['', 'CS+', 'CS-', 'None']
conditions = [['none-p', 'p-m', 'p-none', 'p-p'], ['p-m', 'p-none', 'p-p'],
['m-m', 'm-none', 'm-p'], ['none-m', 'none-none', 'none-p']]
labels = [['Added', 'Reversed', 'Lost', 'Retained'],
['Reversed', 'Lost',
'Retained'], ['Retained', 'Lost', 'Reversed'],
['to CS-', 'Retained', 'to CS+']]
for i, title in enumerate(titles):
mean_stats = reduce.new_filter_reduce(stats_res,
filter_keys='condition',
reduce_key='Fraction')
ax_args_copy.update({
'ylim': [-.1, 1],
'yticks': [0, .5, 1],
'xticks': [0, 1, 2, 3],
'xticklabels': labels[i]
})
plot.plot_results(mean_stats,
select_dict={'condition': conditions[i]},
x_key='condition',
y_key='Fraction',
loop_keys='mouse',
error_key='Fraction_sem',
sort=True,
path=figure_path,
colors=['Black'] * 10,
plot_function=plt.errorbar,
plot_args=error_args,
ax_args=ax_args_copy,
fig_size=(2, 1.5),
save=False)
plt.title(title)
plot.plot_results(stats_res,
select_dict={'condition': conditions[i]},
x_key='condition',
y_key='Fraction',
loop_keys='mouse',
sort=True,
path=figure_path,
colors=['Black'] * 10,
plot_function=plt.scatter,
plot_args=scatter_args,
ax_args=ax_args_copy,
fig_size=(2, 1.5),
legend=False,
save=True,
reuse=True)
print(mean_stats['Fraction'])
print(mean_res['Fraction'])
def plot_correlation(res,
start_days,
end_days,
figure_path,
odor_end=True,
linestyle='-',
direction=0,
arg=False,
save=False,
reuse=False,
color='black'):
def _get_ixs(r, arg):
A = r['Odor_A']
B = r['Odor_B']
l = []
for i, a in enumerate(A):
b = B[i]
if arg == 'opposing':
if a < 2 and b > 1:
l.append(i)
elif arg == 'CS+':
if a == 0 and b == 1:
l.append(i)
elif arg == 'CS-':
if a == 2 and b == 3:
l.append(i)
return np.array(l)
res = filter.filter(res, {'odor_valence': ['CS+', 'CS-']})
res_before = filter.filter_days_per_mouse(res, start_days)
corr_before = _correlation(res_before, ['mouse'],
shuffle=False,
odor_end=odor_end,
direction=direction)
corr_before['day'] = np.array(['A'] * len(corr_before['Odor_A']))
res_after = filter.filter_days_per_mouse(res, end_days)
corr_after = _correlation(res_after, ['mouse'],
shuffle=False,
odor_end=odor_end,
direction=direction)
corr_after['day'] = np.array(['B'] * len(corr_after['Odor_A']))
corr = defaultdict(list)
reduce.chain_defaultdicts(corr, corr_before)
reduce.chain_defaultdicts(corr, corr_after)
ix_same = np.equal(corr['Odor_A'], corr['Odor_B'])
ix_different = np.invert(ix_same)
for k, v in corr.items():
corr[k] = v[ix_different]
if arg is not False:
ixs = _get_ixs(corr, arg)
for k, v in corr.items():
corr[k] = v[ixs]
mouse_corr = reduce.new_filter_reduce(corr,
filter_keys=['mouse', 'day'],
reduce_key='corrcoef')
mouse_corr.pop('corrcoef_sem')
mouse_corr.pop('corrcoef_std')
average_corr = reduce.new_filter_reduce(mouse_corr,
filter_keys=['day'],
reduce_key='corrcoef')
error_args = {'capsize': 2, 'elinewidth': 1, 'markersize': 2, 'alpha': .5}
error_args.update({'linestyle': linestyle})
plot.plot_results(average_corr,
x_key='day',
y_key='corrcoef',
error_key='corrcoef_sem',
plot_args=error_args,
plot_function=plt.errorbar,
colors=color,
ax_args={
'ylim': [0, 1],
'xlim': [-.5, 1.5]
},
save=save,
reuse=reuse,
path=figure_path,
name_str=str(direction) + '_' + str(arg))
#stats
before_odor = filter.filter(corr, filter_dict={'day': 'A'})
after_odor = filter.filter(corr, filter_dict={'day': 'B'})
from scipy.stats import ranksums, wilcoxon, kruskal
print('direction: {}'.format(direction))
# print('Data Before: {}'.format(before_odor['corrcoef']))
# print('Data After: {}'.format(after_odor['corrcoef']))
print('Before: {}'.format(np.mean(before_odor['corrcoef'])))
print('After: {}'.format(np.mean(after_odor['corrcoef'])))
print('Wilcoxin:{}'.format(
wilcoxon(before_odor['corrcoef'], after_odor['corrcoef'])))
def plot_consistency_within_day(res, start, end, shuffle, pretraining,
figure_path):
d = list(zip(start, end))
res_temp = filter.filter_days_per_mouse(res, d)
if pretraining:
res_temp = filter.filter(res_temp, {'odor_valence': ['PT CS+']})
else:
res_temp = filter.filter(res_temp, {'odor_valence': ['CS+', 'CS-']})
corr_res = _correlation(res_temp)
corr_res.pop('data')
analysis.add_naive_learned(corr_res, start, end, '0', '1')
res_ = reduce.new_filter_reduce(
corr_res,
filter_keys=['mouse', 'odor_standard', 'training_day'],
reduce_key='consistency_corrcoef')
res_.pop('consistency_corrcoef_sem')
filter.assign_composite(res_, loop_keys=['training_day', 'odor_valence'])
if shuffle:
s = '_shuffled'
else:
s = ''
ax_args_copy = ax_args.copy()
ax_args_copy.update({
'xlim': [-.5, 2.5],
'ylim': [0, .55],
'yticks': np.arange(0, 1.1, .1)
})
swarm_args_copy = swarm_args.copy()
if pretraining:
swarm_args_copy.update({'palette': ['gray', 'orange', 'green', 'red']})
else:
swarm_args_copy.update({'palette': ['gray', 'gray', 'green', 'red']})
ix = res_['training_day_odor_valence'] == '1_PT CS+'
res_['training_day_odor_valence'][ix] = '1_APT CS+'
plot.plot_results(res_,
x_key='training_day_odor_valence',
y_key='consistency_corrcoef',
path=figure_path,
plot_args=swarm_args_copy,
plot_function=sns.stripplot,
ax_args=ax_args_copy,
reuse=False,
save=False,
sort=True,
name_str=s)
summary = reduce.new_filter_reduce(res_,
filter_keys='training_day_odor_valence',
reduce_key='consistency_corrcoef')
plot.plot_results(summary,
x_key='training_day_odor_valence',
y_key='consistency_corrcoef',
error_key='consistency_corrcoef_sem',
colors='black',
path=figure_path,
plot_args=error_args,
plot_function=plt.errorbar,
save=True,
reuse=True,
legend=False,
name_str=s)
print(summary['consistency_corrcoef'])
ix_a = res_['training_day_odor_valence'] == '0_CS+'
ix_b = res_['training_day_odor_valence'] == '0_CS-'
ix_c = res_['training_day_odor_valence'] == '1_CS+'
ix_d = res_['training_day_odor_valence'] == '1_CS-'
a = res_['consistency_corrcoef'][ix_a]
b = res_['consistency_corrcoef'][ix_b]
c = res_['consistency_corrcoef'][ix_c]
d = res_['consistency_corrcoef'][ix_d]
from scipy.stats import ranksums, wilcoxon, kruskal
import scikit_posthocs
print(kruskal(a, b, c))
x = scikit_posthocs.posthoc_dunn(a=[a, b, c, d], p_adjust=None)
print(x)
def magnitude_histogram(res,
learned_days,
end_days,
use_colors=True,
figure_path=None,
reuse=False):
def _helper(res):
list_odor_on = res['DAQ_O_ON_F']
list_water_on = res['DAQ_W_ON_F']
list_of_dff = res['dff']
for i, dff in enumerate(list_of_dff):
s = list_odor_on[i]
e = list_water_on[i]
max = np.max(dff[:, s:e], axis=1)
res['max_dff'].append(max)
res['max_dff'] = np.array(res['max_dff'])
def _helper1(res):
for data in res['amplitude']:
res['max_dff'].append(data[data > 0])
res['max_dff'] = np.array(res['max_dff'])
res = copy.copy(res)
# list_of_days = list(zip(learned_days, end_days))
list_of_days = end_days
start_end_day_res = filter.filter_days_per_mouse(
res, days_per_mouse=list_of_days)
_helper(start_end_day_res)
add_naive_learned(start_end_day_res, learned_days, end_days)
filter.assign_composite(start_end_day_res,
loop_keys=['odor_standard', 'training_day'])
csp = filter.filter(start_end_day_res, filter_dict={'odor_valence': 'CS+'})
csm = filter.filter(start_end_day_res, filter_dict={'odor_valence': 'CS-'})
csp_data = np.hstack(csp['max_dff'].flat)
csm_data = np.hstack(csm['max_dff'].flat)
fig = plt.figure(figsize=(3, 2))
ax = fig.add_axes([.2, .2, .7, .7])
x_upper = 1.01
y_upper = .5
bins = 20
xrange = [0, x_upper]
yrange = [0, y_upper]
xticks = np.arange(0, x_upper, .2)
yticks = np.arange(0, y_upper, 3)
legends = ['CS+', 'CS-']
colors = ['green', 'red']
for i, data in enumerate([csp_data, csm_data]):
plt.hist(data,
bins=bins,
range=xrange,
density=True,
color=colors[i],
alpha=.5)
plt.legend(legends, loc=1, bbox_to_anchor=(1.05, .4), fontsize=5)
ax.set_xlabel('DF/F')
ax.set_ylabel('Fraction')
ax.set_xticks(xticks)
# ax.set_yticks(yticks)
plt.xlim(xrange)
# plt.ylim(yrange)
plt.show()
