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_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_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_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 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_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)
res2 = scalpel(res2, keys, mouse, sessions)
print(np.unique(res2['session'][-1]))
reduce.chain_defaultdicts(res, res1)
reduce.chain_defaultdicts(res, res2)
print(np.unique(res['mouse']))
if collapse_arg:
res1 = filter.filter(res, filter_dict={'experiment': collapse_arg, 'condition':'H'})
res2 = filter.filter(res, filter_dict={'condition': 'Y'})
res = reduce.chain_defaultdicts(res1, res2, copy_dict=True)
analysis.analyze(res)
analysis.shift_discrimination_index(res)
filter.assign_composite(res, ['phase', 'odor_valence'])
res = filter.filter(res, {'phase_odor_valence': ['Pretraining_CS+', 'Discrimination_CS+', 'Discrimination_CS-']})
if 'release_of_inhibition' in plotting:
trace_args_copy = trace_args.copy()
trace_args_copy.update({'alpha': .5, 'linewidth': .75})
line_args_copy = line_args.copy()
line_args.update({'marker':',','markersize':0, 'linewidth':.5})
y_key = 'bin_ant_23_smooth'
y_key_bool = 'bin_ant_23_boolean'
phase_odor_valence = np.unique(res['phase_odor_valence'])
conditions = np.unique(res['condition'])
ax_args_cur = ax_args_copy.copy()
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 plot_stability_across_days(res, start_days_per_mouse, learned_days_per_mouse, figure_path):
strengthen_starting_threshold = .1
strengthen_absolute_threshold = .2
strengthen_relative_threshold = 2
weaken_starting_threshold = .3
weaken_absolute_threshold = .15
weaken_relative_threshold = .5
res = copy.copy(res)
res = filter.filter(res, {'odor_valence':['CS+', 'CS-']})
list_odor_on = res['DAQ_O_ON_F']
list_water_on = res['DAQ_W_ON_F']
for i in range(len(list_odor_on)):
dff_list = res['dff'][i]
dff_max = np.max(dff_list[:,list_odor_on[i]: list_water_on[i]],axis=1)
res['dff_max'].append(dff_max)
res['dff_max'] = np.array(res['dff_max'])
out = defaultdict(list)
combinations, ixs = filter.retrieve_unique_entries(res, ['mouse','odor'])
for combination, ix in zip(combinations, ixs):
odor_valence = np.unique(res['odor_valence'][ix])
mouse = np.unique(res['mouse'][ix])
days = res['day'][ix]
assert len(days) == len(np.unique(days))
assert len(mouse) == 1
sort_ix = np.argsort(days)
list_of_dff_max = res['dff_max'][ix][sort_ix]
list_of_ssig = res['sig'][ix][sort_ix]
data = [x * y for x, y in zip(list_of_ssig, list_of_dff_max)]
data = np.array(data)
strengthen_mask = data[0,:] > strengthen_starting_threshold
strengthen_overall_threshold = np.max((data[0,:] * strengthen_relative_threshold, data[0,:] + strengthen_absolute_threshold), axis=0)
strengthen_passed_mask = strengthen_overall_threshold < data
strengthen_any = np.any(strengthen_passed_mask[1:,:], axis=0)
n_strengthen_passed = np.sum(strengthen_any * strengthen_mask)
n_strengthen_denom = np.sum(strengthen_mask)
new_mask = np.invert(strengthen_mask)
n_new_passed = np.sum(strengthen_any * new_mask)
n_new_denom = np.sum(new_mask)
weaken_mask = data[0,:] > weaken_starting_threshold
weaken_overall_threshold = np.min((data[0,:] * weaken_relative_threshold, data[0,:] - weaken_absolute_threshold), axis=0)
weaken_passed_mask = weaken_overall_threshold > data
weaken_any = np.any(weaken_passed_mask[1:,:], axis=0)
n_weaken_passed = np.sum(weaken_any * weaken_mask)
n_weaken_denom = np.sum(weaken_mask)
strs = ['strengthen_denom', 'strengthen_pass', 'new_denom', 'new_pass','weaken_denom','weaken_pass']
vals = [n_strengthen_denom, n_strengthen_passed, n_new_denom, n_new_passed, n_weaken_denom, n_weaken_passed]
for k, v in zip(strs,vals):
out['Type'].append(k)
out['Count'].append(v)
out['mouse'].append(mouse[0])
out['odor_valence'].append(odor_valence[0])
for k, v in out.items():
out[k] = np.array(v)
def _helper(res):
out = defaultdict(list)
strs = [['strengthen_denom', 'strengthen_pass'], ['new_denom', 'new_pass'], ['weaken_denom', 'weaken_pass']]
out_strs = ['up', 'new', 'down']
for i, keys in enumerate(strs):
ix_denom = res['Type'] == keys[0]
ix_numer = res['Type'] == keys[1]
numer = np.sum(res['Count'][ix_numer])
denom = np.sum(res['Count'][ix_denom])
fraction = numer/ denom
out['Type'].append(out_strs[i])
out['Fraction'].append(fraction)
out['numer'].append(numer)
out['denom'].append(denom)
for k, v in out.items():
out[k] = np.array(v)
return out
ax_args_copy = ax_args.copy()
ax_args_copy.update({'ylim':[0, 0.5], 'yticks':[0, .1, .2, .3, .4, .5]})
overall = copy.copy(out)
csm_res = filter.filter(overall, {'odor_valence':'CS-'})
csp_res = filter.filter(overall, {'odor_valence':'CS+'})
csp_stats = _helper(csp_res)
csp_stats['Condition'] = np.array(['CS+'] * len(csp_stats['Type']))
csm_stats = _helper(csm_res)
csm_stats['Condition'] = np.array(['CS-'] * len(csm_stats['Type']))
overall_stats = reduce.chain_defaultdicts(csp_stats, csm_stats, copy_dict=True)
filter.assign_composite(overall_stats, ['Type', 'Condition'])
bar_args = {'alpha': 1, 'edgecolor':'black','linewidth':1}
colors = ['Green','Red']
save_path, name = plot.plot_results(overall_stats, x_key='Type_Condition', y_key='Fraction', sort=True,
colors=colors,
path=figure_path,
plot_function=plt.bar, plot_args=bar_args, ax_args=ax_args_copy,
save=False, reuse=False)
strs = ['down','new','up']
fontsize = 4
for i in range(3):
ix = csp_stats['Type'] == strs[i]
numer = csp_stats['numer'][ix][0]
denom = csp_stats['denom'][ix][0]
x = i * 2
y = numer/denom
y_offset = .025
plt.text(x, y + y_offset, str(numer) + '/' + str(denom), horizontalalignment = 'center', fontsize= fontsize)
strs = ['down','new','up']
for i in range(3):
ix = csm_stats['Type'] == strs[i]
numer = csm_stats['numer'][ix][0]
denom = csm_stats['denom'][ix][0]
x = i * 2 + 1
y = numer/denom
y_offset = .025
plt.text(x, y + y_offset, str(numer) + '/' + str(denom), horizontalalignment = 'center', fontsize= fontsize)
plot._easy_save(save_path, name, pdf=True)
overall_stats_no_distinction = _helper(overall)
save_path, name = plot.plot_results(overall_stats_no_distinction, x_key='Type', y_key='Fraction', sort=True,
colors=['Grey']*10,
path=figure_path,
plot_function=plt.bar, plot_args=bar_args, ax_args=ax_args_copy,
save=False, reuse=False)
strs = ['down','new','up']
for i in range(3):
ix = overall_stats_no_distinction['Type'] == strs[i]
numer = overall_stats_no_distinction['numer'][ix][0]
denom = overall_stats_no_distinction['denom'][ix][0]
x = i
y = numer/denom
y_offset = .025
plt.text(x, y + y_offset, str(numer) + '/' + str(denom), horizontalalignment = 'center', fontsize= fontsize)
plot._easy_save(save_path, name, pdf=True)
else:
raise ValueError('wtf')
if 'summary_raw' in experiments:
line_args_local = line_args.copy()
line_args_local.update({
'marker': '.',
'markersize': .5,
'linewidth': .75,
'alpha': .5
})
all_res_ = filter.filter(all_res,
{'odor_valence': ['CS+', 'CS-', 'PT CS+']})
_collapse_conditions(all_res_, control_condition='YFP', str=collapse_arg)
filter.assign_composite(all_res_, [collapse_arg, 'odor_valence'])
composite_arg = collapse_arg + '_' + 'odor_valence'
valences = np.unique(all_res_['odor_valence'])
for valence in valences:
color = [color_dict_valence[valence]]
for i in range(len(color)):
color.append('black')
if 'PT CS+' in valence or 'PT Naive' in valence:
ax_args = ax_args_pt
elif 'PT CS+' in all_res_['odor_valence']:
ax_args = ax_args_dt
else:
ax_args = ax_args_mush
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()