#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)