#plotting scatter summary_folder = 'F:/Videogame_Assay/LFP_summary/' event_folder = ['catch_level_3/', 'trigger_level_3/', 'reward_level_3/'] events_str = ['catch', 'trigger', 'reward'] band = ['delta', 'theta', 'beta', 'alpha', 'gamma', 'h_gamma'] for e in range(len(event_folder)): for b in range(len(band)): #for r in range(len(rat_summary_table_path)): #retrieve bad channel frim the last session of each rat Level_3_post = prs.Level_3_moving_post_paths(rat_summary_table_path[r]) sessions_subset = Level_3_post session = sessions_subset[-1] session_path = os.path.join(hardrive_path, session) csv_bad_ch = os.path.join(session_path + '/bad_channels.csv') bad_ch = np.genfromtxt(csv_bad_ch, delimiter=',', dtype=int) #session_path = os.path.join(hardrive_path,session) csv_dir_path = os.path.join(summary_folder + event_folder[e]) offset_folder = 'pre/' csv_to_path = os.path.join(csv_dir_path + offset_folder) matching_files_before = np.array( glob.glob(csv_to_path + "*" + RAT_ID[r] + "*" + "*" + band[b] + "*"))
# create raw downsampld chunks around event for each channel all the trial and save a cube ch x samples x trials #generate random snippets of the same lenght of the trail for each session and stack in a big array for each rat RAT_ID = RAT_ID_ephys #[0] #[0] rat_summary_table_path = rat_summary_ephys #[0]#[0] probe_map_flatten = ephys.probe_map.flatten() len(probe_map_flatten) for r, rat in enumerate(rat_summary_table_path): #rat = rat_summary_table_path #Level_2_post = prs.Level_2_post_paths(rat) #sessions_subset = Level_2_post Level_3_post = prs.Level_3_moving_post_paths(rat) sessions_subset = Level_3_post[:5] N = 121 tot_sessions = len(sessions_subset) offset = 1500 final_array = np.zeros(( 121, offset * 2, )) tot_trial = [] for s, session in enumerate(sessions_subset): session_path = os.path.join(hardrive_path, session)