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)