def load_results_train_test_scores(data_path):
res = defaultdict(list)
experiment_dirs = sorted(
[os.path.join(data_path, d) for d in os.listdir(data_path)])
keys = [
'DAQ_O_ON_F', 'DAQ_O_OFF_F', 'DAQ_W_ON_F', 'DAQ_O_ON', 'DAQ_O_OFF',
'DAQ_W_ON', 'TRIAL_FRAMES', 'TRIAL_PERIOD', 'NAME_MOUSE', 'NAME_PLANE',
'NAME_DATE', 'decode_style', 'neurons', 'shuffle', 'split_style'
]
for exp_dir in experiment_dirs:
data_dirs = sorted(glob.glob(os.path.join(exp_dir, '*' + '.pkl')))
config_dirs = sorted(glob.glob(os.path.join(exp_dir, '*.json')))
for data_dir, config_dir in zip(data_dirs, config_dirs):
config = tools.file_io.load_json(config_dir)
cur_res = tools.file_io.load_pickle(data_dir)
for k in keys:
cur_res[k] = np.array([config[k]] * len(cur_res['scores']))
reduce.chain_defaultdicts(res, cur_res)
add_indices(res)
add_time(res)
for i, _ in enumerate(res['scores']):
res['top_score'].append(np.max(res['scores'][i]))
res['top_score'] = np.array(res['top_score'])
return res
def analyze_behavior(data_path, condition):
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
analysis.add_time(res)
lick_res = convert(res, condition)
days_per_mouse = _get_days_per_condition(data_path, condition)
last_day_per_mouse = np.array([x[-1] for x in days_per_mouse])
plot_res = agglomerate_days(lick_res, condition,
condition.training_start_day,
last_day_per_mouse)
add_odor_value(plot_res, condition)
add_behavior_stats(plot_res, condition)
return plot_res
def get_licks_per_day(data_path, condition, return_raw=False):
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
analysis.add_time(res)
lick_res = convert(res, condition)
lick_res['lick_boolean'] = np.array([y > 0 for y in lick_res['lick']])
out = reduce.new_filter_reduce(lick_res, ['odor', 'day', 'mouse'],
'lick_boolean')
if return_raw:
add_odor_value(lick_res, condition)
return lick_res
else:
return out
def custom_analyze_behavior(data_path, condition, start_time, end_time):
import analysis
from behavior import behavior_analysis
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
analysis.add_time(res)
lick_res = custom_convert(res, condition, start_time, end_time)
days_per_mouse = behavior_analysis._get_days_per_condition(
data_path, condition)
last_day_per_mouse = np.array([x[-1] for x in days_per_mouse])
plot_res = behavior_analysis.agglomerate_days(lick_res, condition,
condition.training_start_day,
last_day_per_mouse)
analysis.add_odor_value(plot_res, condition)
behavior_analysis.add_behavior_stats(plot_res, condition)
return plot_res
raise ValueError('sorting method is not recognized')
sorted_list_of_psths = [x[ixs, :] for x in list_of_psths]
psth = psth[ixs, :]
else:
sorted_list_of_psths = list_of_psths
images.append(psth)
odor_on_times.append(odor_on)
water_on_times.append(water_on)
list_of_odor_names.append(odors_copy)
return images, odor_on_times, water_on_times, list_of_odor_names, sorted_list_of_psths
res = analysis.load_data(data_path)
analysis.add_indices(res)
analysis.add_time(res)
if condition_config.plot_big:
mice = np.unique(res['mouse'])
list_of_days_per_mouse = condition_config.plot_big_days
list_of_images = []
list_of_list_of_psths = []
odor_on_times = []
water_on_times = []
days = [int(not condition_config.plot_big_naive)]
for i, day in enumerate(list_of_days_per_mouse):
mouse = mice[i]
if day != -1:
images, odor_on_times, water_on_times, list_of_odor_names, list_of_psth = helper(
res, mouse, [day], condition_config)
list_of_list_of_psths.append(list_of_psth)
list_of_images.append(images[0])