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
def _get_days_per_condition(data_path, condition, odor_valence=None):
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
lick_res = convert(res, condition)
if odor_valence is not None:
add_odor_value(lick_res, condition)
lick_res = filter.filter(lick_res, {'odor_valence': odor_valence})
days = []
list_of_dates = lick_res['day']
list_of_mice = lick_res['mouse']
_, mouse_ixs = np.unique(list_of_mice, return_inverse=True)
for mouse_ix in np.unique(mouse_ixs):
mouse_dates = list_of_dates[mouse_ixs == mouse_ix]
counts = np.unique(mouse_dates)
days.append(counts)
return days
def get_days_per_mouse(data_path, condition, odor_valence='CS+'):
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
days_per_mouse = np.array(
_get_days_per_condition(data_path, condition, odor_valence))
first_day_per_mouse = np.array([x[0] for x in days_per_mouse])
last_day_per_mouse = np.array([x[-1] for x in days_per_mouse])
if hasattr(condition, 'csp') or hasattr(condition, 'pt_csp'):
res_behavior = analyze_behavior(data_path, condition)
res_behavior_csp = filter.filter(res_behavior,
{'odor_valence': odor_valence})
res_behavior_summary = reduce.filter_reduce(res_behavior_csp,
filter_keys='mouse',
reduce_key='learned_day')
mice, ix = np.unique(res_behavior_summary['mouse'],
return_inverse=True)
temp = res_behavior_summary['learned_day'][ix]
temp[temp == None] = last_day_per_mouse[temp == None]
temp[np.isnan(temp)] = last_day_per_mouse[np.isnan(temp)]
learned_days_per_mouse = np.ceil(temp)
else:
learned_days_per_mouse = last_day_per_mouse
return learned_days_per_mouse, last_day_per_mouse
else:
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)
def _rolling_window(a, window):
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1], )
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
if __name__ == '__main__':
config = psth.psth_helper.PSTHConfig()
condition_config = PIR_NAIVE_Config()
condition = condition_config.condition
data_path = os.path.join(Config.LOCAL_DATA_PATH,
Config.LOCAL_DATA_TIMEPOINT_FOLDER,
condition.name)
save_path = os.path.join(Config.LOCAL_EXPERIMENT_PATH, 'COUNTING',
condition.name)
figure_path = os.path.join(Config.LOCAL_FIGURE_PATH, 'OTHER', 'PSTH',
condition.name, 'CELL')
res = analysis.load_data(data_path)
analysis.add_indices(res, arg_plane=True)
analysis.add_time(res)
odor_res = convert(res, condition_config)
analysis.add_odor_value(odor_res, condition_config.condition)
parse_data(odor_res, excitatory=False)
# odor_res = fio.load_pickle(pickle_path=os.path.join(save_path,'dict_backup.pkl'))
# analyze_data(odor_res, condition_config)
