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
condition.name)
# retrieving relevant days
learned_day_per_mouse, last_day_per_mouse = get_days_per_mouse(
data_path, condition)
if condition_config.start_at_training and hasattr(condition,
'training_start_day'):
start_days_per_mouse = condition.training_start_day
else:
start_days_per_mouse = [0] * len(condition_config.condition.paths)
training_start_day_per_mouse = condition.training_start_day
#behavior
lick_res = behavior.behavior_analysis.get_licks_per_day(data_path, condition)
analysis.add_odor_value(lick_res, condition)
lick_res = filter.filter(lick_res, {'odor_valence': ['CS+', 'CS-', 'PT CS+']})
lick_res = reduce.new_filter_reduce(lick_res, ['odor_valence', 'day', 'mouse'],
reduce_key='lick_boolean')
temp_res = behavior.behavior_analysis.analyze_behavior(data_path, condition)
if condition.name == 'OFC' or condition.name == 'BLA':
if condition.name == 'OFC':
last_day_per_mouse = [5, 5, 3, 4, 4]
res = statistics.analyze.analyze_data(save_path,
condition_config,
m_threshold=0.04)
# naive_config = statistics.analyze.OFC_LONGTERM_Config()
# data_path_ = os.path.join(Config.LOCAL_DATA_PATH, Config.LOCAL_DATA_TIMEPOINT_FOLDER, naive_config.condition.name)
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)