csv_name = '/Trial_table_final_level_3_moving_light_ephys_shaders.csv'
np.savetxt(results_dir + csv_name, final_table,delimiter=',', fmt='%s')
#################################################################################
trial_table = 'F:/Videogame_Assay/Trial_table_final_level_2_touching_light.csv'
table_open =np.genfromtxt(trial_table, delimiter = ',')
for r, rat in enumerate(rat_summary_table_path):
try:
Level_2_pre= prs.Level_2_pre_paths(rat)
sessions_subset = Level_2_pre
rat_pos_at_touch, before_touch, after_touch = trial.rat_event_crop_pos_finder(sessions_subset, event=2, offset = 120)
rat_pos_at_start_flat = [val for sublist in rat_pos_at_start for val in sublist]
rat_pos_at_touch_flat = [val for sublist in rat_pos_at_touch for val in sublist]
rat_pos_before_touch = [val for sublist in before_touch for val in sublist]
rat_pos_after_touch = [val for sublist in after_touch for val in sublist]
print(len(rat_pos_at_start_flat))
print(len(rat_pos_at_touch_flat))
print(len(rat_pos_before_touch))
print(len(rat_pos_after_touch))
import matplotlib.pyplot as plt
import behaviour_library as behaviour
import parser_library as prs
from matplotlib.colors import PowerNorm
from matplotlib.colors import LogNorm
from pylab import *
from matplotlib.ticker import LogFormatterExponent
import DLC_parser_library as DLC
import scipy.signal
import seaborn as sns
from matplotlib.lines import Line2D
rat_summary_table_path = 'F:/Videogame_Assay/AK_33.2_Pt.csv'
hardrive_path = r'F:/'
Level_2_pre = prs.Level_2_pre_paths(rat_summary_table_path)
sessions_subset = Level_2_pre
x_centroid_te, y_centroid_te = create_tracking_snippets_touch_to_end_centroid(sessions_subset,end_snippet_idx = 1,mid_snippet_idx = 2)
x = len(sessions_subset)
sessions_dst_te = [[] for _ in range(x)]
for count in np.arange(x):
try:
x_snippets = np.copy(x_centroid_te[count])
y_snippets = np.copy(y_centroid_te[count])
l =len(x_snippets)