event time ananlysis started. tried to implement bootstrap with cupy ... memory issues ... resolve on monday

complete_analysis.py

@@ -24,47 +24,47 @@ def load_frame_times(trial_path):
     return np.array(frame_t)
 
 
-def load_and_converete_boris_events(trial_path, recording, sr, video_stated_FPS=25):
+# def load_and_converete_boris_events(trial_path, recording, sr, video_stated_FPS=25):
-    def converte_video_frames_to_grid_idx(event_frames, led_frames, led_idx):
+#     def converte_video_frames_to_grid_idx(event_frames, led_frames, led_idx):
-        event_idx_grid = (event_frames - led_frames[0]) / (led_frames[-1] - led_frames[0]) * (led_idx[-1] - led_idx[0]) + led_idx[0]
+#         event_idx_grid = (event_frames - led_frames[0]) / (led_frames[-1] - led_frames[0]) * (led_idx[-1] - led_idx[0]) + led_idx[0]
-        return event_idx_grid
+#         return event_idx_grid
-
+#
-    # idx in grid-recording
+#     # idx in grid-recording
-    led_idx = pd.read_csv(os.path.join(trial_path, 'led_idxs.csv'), header=None).iloc[:, 0].to_numpy()
+#     led_idx = pd.read_csv(os.path.join(trial_path, 'led_idxs.csv'), header=None).iloc[:, 0].to_numpy()
-    # frames where LED gets switched on
+#     # frames where LED gets switched on
-    led_frames = np.load(os.path.join(trial_path, 'LED_frames.npy'))
+#     led_frames = np.load(os.path.join(trial_path, 'LED_frames.npy'))
-
+#
-    times, behavior, t_ag_on_off, t_contact, video_FPS = load_boris(trial_path, recording)
+#     times, behavior, t_ag_on_off, t_contact, video_FPS = load_boris(trial_path, recording)
-
+#
-    contact_frame = np.array(np.round(t_contact * video_FPS), dtype=int)
+#     contact_frame = np.array(np.round(t_contact * video_FPS), dtype=int)
-    ag_on_off_frame = np.array(np.round(t_ag_on_off * video_FPS), dtype=int)
+#     ag_on_off_frame = np.array(np.round(t_ag_on_off * video_FPS), dtype=int)
-
+#
-    # led_t_GRID = led_idx / sr
+#     # led_t_GRID = led_idx / sr
-    contact_t_GRID = converte_video_frames_to_grid_idx(contact_frame, led_frames, led_idx) / sr
+#     contact_t_GRID = converte_video_frames_to_grid_idx(contact_frame, led_frames, led_idx) / sr
-    ag_on_off_t_GRID = converte_video_frames_to_grid_idx(ag_on_off_frame, led_frames, led_idx) / sr
+#     ag_on_off_t_GRID = converte_video_frames_to_grid_idx(ag_on_off_frame, led_frames, led_idx) / sr
-
+#
-    return contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames
+#     return contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames
-
-
-def load_boris(trial_path, recording):
-    boris_file = '-'.join(recording.split('-')[:3]) + '.csv'
-
-    data = pd.read_csv(os.path.join(trial_path, boris_file))
-    times = data['Start (s)']
-    behavior = data['Behavior']
-
-    t_ag_on = times[behavior == 0]
-    t_ag_off = times[behavior == 1]
-
-    t_ag_on_off = []
-    for t in t_ag_on:
-        t1 = np.array(t_ag_off)[t_ag_off > t]
-        if len(t1) >= 1:
-            t_ag_on_off.append(np.array([t, t1[0]]))
 
-    t_contact = times[behavior == 2]
 
-    return times, behavior, np.array(t_ag_on_off), t_contact.to_numpy(), data['FPS'][0]
+# def load_boris(trial_path, recording):
+#     boris_file = '-'.join(recording.split('-')[:3]) + '.csv'
+#
+#     data = pd.read_csv(os.path.join(trial_path, boris_file))
+#     times = data['Start (s)']
+#     behavior = data['Behavior']
+#
+#     t_ag_on = times[behavior == 0]
+#     t_ag_off = times[behavior == 1]
+#
+#     t_ag_on_off = []
+#     for t in t_ag_on:
+#         t1 = np.array(t_ag_off)[t_ag_off > t]
+#         if len(t1) >= 1:
+#             t_ag_on_off.append(np.array([t, t1[0]]))
+#
+#     t_contact = times[behavior == 2]
+#
+#     return times, behavior, np.array(t_ag_on_off), t_contact.to_numpy(), data['FPS'][0]
 
 def get_baseline_freq(fund_v, idx_v, times, ident_v, idents = None, binwidth = 300):
     if not hasattr(idents, '__len__'):
@@ -179,9 +179,10 @@ def main(data_folder=None):
     sr = 20_000
     light_start_sec = 3*60*60
 
-    trial_summary = pd.DataFrame(columns=['recording', 'group', 'win_fish', 'lose_fish', 'sex_win', 'sex_lose',
+    trial_summary = pd.DataFrame(columns=['recording', 'group', 'win_fish', 'lose_fish', 'win_ID', 'lose_ID',
-                                          'size_win', 'size_lose', 'EODf_win', 'EODf_lose', 'exp_win', 'exp_lose',
+                                          'sex_win', 'sex_lose', 'size_win', 'size_lose', 'EODf_win', 'EODf_lose',
-                                          'chirps_win', 'chirps_lose', 'rises_win', 'rises_lose', 'draw'])
+                                          'exp_win', 'exp_lose', 'chirps_win', 'chirps_lose', 'rises_win', 'rises_lose',
+                                          'draw'])
     trial_summary_row = {f'{s}':None for s in trial_summary.keys()}
 
     for trial_idx in tqdm(np.arange(len(trials_meta)), desc='Trials'):
@@ -266,9 +267,9 @@ def main(data_folder=None):
         if os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
             chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy'))
             chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy'))
+            chirp_times = [chirp_t[chirp_ids == win_id], chirp_t[chirp_ids == lose_id]]
             got_chirps = True
 
-        chirp_times = [chirp_t[chirp_ids == win_id], chirp_t[chirp_ids == lose_id]]
         rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter]
         rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]
 
@@ -288,6 +289,8 @@ def main(data_folder=None):
                                   'group': trials_meta['group'][trial_idx],
                                   'win_fish': win_fish_no,
                                   'lose_fish': lose_fish_no,
+                                  'win_ID': win_id,
+                                  'lose_ID': lose_id,
                                   'sex_win': 'n',
                                   'sex_lose': 'n',
                                   'size_win': win_l,

event_time_analysis.py

@@ -0,0 +1,155 @@
+import os
+import sys
+import argparse
+import numpy as np
+import cupy as cp
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import pandas as pd
+from IPython import embed
+
+
+def load_and_converete_boris_events(trial_path, recording, sr):
+    def converte_video_frames_to_grid_idx(event_frames, led_frames, led_idx):
+        event_idx_grid = (event_frames - led_frames[0]) / (led_frames[-1] - led_frames[0]) * (led_idx[-1] - led_idx[0]) + led_idx[0]
+        return event_idx_grid
+
+    # idx in grid-recording
+    led_idx = pd.read_csv(os.path.join(trial_path, 'led_idxs.csv'), header=None).iloc[:, 0].to_numpy()
+    # frames where LED gets switched on
+    led_frames = np.load(os.path.join(trial_path, 'LED_frames.npy'))
+
+    times, behavior, t_ag_on_off, t_contact, video_FPS = load_boris(trial_path, recording)
+
+    contact_frame = np.array(np.round(t_contact * video_FPS), dtype=int)
+    ag_on_off_frame = np.array(np.round(t_ag_on_off * video_FPS), dtype=int)
+
+    # led_t_GRID = led_idx / sr
+    contact_t_GRID = converte_video_frames_to_grid_idx(contact_frame, led_frames, led_idx) / sr
+    ag_on_off_t_GRID = converte_video_frames_to_grid_idx(ag_on_off_frame, led_frames, led_idx) / sr
+
+    return contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames
+
+
+def load_boris(trial_path, recording):
+    boris_file = '-'.join(recording.split('-')[:3]) + '.csv'
+
+    data = pd.read_csv(os.path.join(trial_path, boris_file))
+    times = data['Start (s)']
+    behavior = data['Behavior']
+
+    t_ag_on = times[behavior == 0]
+    t_ag_off = times[behavior == 1]
+
+    t_ag_on_off = []
+    for t in t_ag_on:
+        t1 = np.array(t_ag_off)[t_ag_off > t]
+        if len(t1) >= 1:
+            t_ag_on_off.append(np.array([t, t1[0]]))
+
+    t_contact = times[behavior == 2]
+
+    return times, behavior, np.array(t_ag_on_off), t_contact.to_numpy(), data['FPS'][0]
+
+
+def gauss(t, shift, sigma, size, norm = False):
+    if not hasattr(shift, '__len__'):
+        g = np.exp(-((t - shift) / sigma) ** 2 / 2) * size
+        if norm:
+            g /= np.sum(g)
+        return g
+    else:
+        t = np.array([t, ] * len(shift))
+        res = np.exp(-((t.transpose() - shift).transpose() / sigma) ** 2 / 2) * size
+        return res
+
+
+def event_centered_times(centered_event_times, surrounding_event_times, max_dt = 60):
+
+
+    event_dt = []
+    for Cevent_t in centered_event_times:
+        Cdt = np.array(surrounding_event_times - Cevent_t)
+        event_dt.extend(Cdt[np.abs(Cdt) <= max_dt])
+
+    return np.array(event_dt)
+
+def kde(event_dt, max_dt = 60):
+    kernal_w = 1
+    kernal_h = 0.2
+
+    conv_t = np.arange(-max_dt, max_dt, 1)
+    conv_array = np.zeros(len(conv_t))
+
+    for e in event_dt:
+        conv_array += gauss(conv_t, e, kernal_w, kernal_h, norm=True)
+
+    plt.plot(conv_t, conv_array)
+
+
+def permulation_kde(event_dt, repetitions = 100, max_dt = 60):
+
+    embed()
+    quit()
+    kernal_w = 1
+    kernal_h = 0.2
+
+    conv_t = cp.arange(-max_dt, max_dt, 1)
+    conv_tt = cp.reshape(conv_t, (len(conv_t), 1, 1))
+
+    # array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
+    event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
+    # conv_t_perm = cp.tile(conv_tt, (1, repetitions, len(event_dt)))
+
+    gauss_3d = cp.exp(-((conv_tt - event_dt_perm) / kernal_w) ** 2 / 2) * kernal_h
+    kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
+    kde_3d_numpy = cp.asnumpy(kde_3d)
+
+
+def main(base_path):
+    trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
+
+    lose_chrips_centered_on_ag_off_t = []
+    for index, trial in trial_summary.iterrows():
+        trial_path = os.path.join(base_path, trial['recording'])
+
+        if trial['group'] < 5:
+            continue
+        if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
+            continue
+        if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
+            continue
+
+        ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy'))
+        times = np.load(os.path.join(trial_path, 'times.npy'))
+        sorter = -1 if trial['win_ID'] != ids[0] else 1
+
+        ### event times --> BORIS behavior
+        contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
+            load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000)
+
+        ### communication
+        got_chirps = False
+        if os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
+            chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy'))
+            chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy'))
+            chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]]
+            got_chirps = True
+
+        rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter]
+        rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]
+        rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
+
+        lose_chrips_centered_on_ag_off_t.append(event_centered_times(ag_on_off_t_GRID[:, 1], chirp_times[1]))
+
+    kde(np.hstack(lose_chrips_centered_on_ag_off_t))
+
+    permulation_kde(np.hstack(lose_chrips_centered_on_ag_off_t))
+
+
+    embed()
+    quit()
+    pass
+
+if __name__ == '__main__':
+    main(sys.argv[1])

trial_summary.csv

@@ -1,44 +1,44 @@
-,recording,group,win_fish,lose_fish,sex_win,sex_lose,size_win,size_lose,EODf_win,EODf_lose,exp_win,exp_lose,chirps_win,chirps_lose,rises_win,rises_lose,draw
+,recording,group,win_fish,lose_fish,win_ID,lose_ID,sex_win,sex_lose,size_win,size_lose,EODf_win,EODf_lose,exp_win,exp_lose,chirps_win,chirps_lose,rises_win,rises_lose,draw
-0,2019-11-25-09_59,3,1,2,f,m,13.2,12.0,713.0544113886845,762.0273047058653,1,1,36,2657,22,165,0
+0,2019-11-25-09_59,3,1,2,10.0,6.0,f,m,13.2,12.0,713.0544113886845,762.0273047058653,1,1,36,2657,22,165,0
-1,2019-11-26-10_00,3,4,3,m,m,15.5,17.5,883.141322780704,918.0584506431281,1,1,472,1322,17,481,0
+1,2019-11-26-10_00,3,4,3,3.0,4.0,m,m,15.5,17.5,883.141322780704,918.0584506431281,1,1,472,1322,17,481,0
-2,2019-11-27-10_00,3,5,6,f,f,14.4,12.65,728.1663791991439,650.6079943890219,1,1,16,2041,14,311,0
+2,2019-11-27-10_00,3,5,6,1.0,6.0,f,f,14.4,12.65,728.1663791991439,650.6079943890219,1,1,16,2041,14,311,0
-3,2019-11-28-09_58,3,1,3,f,m,13.2,17.5,720.9491781126661,888.9029901347602,2,2,370,30,26,37,0
+3,2019-11-28-09_58,3,1,3,3.0,1.0,f,m,13.2,17.5,720.9491781126661,888.9029901347602,2,2,370,30,26,37,0
-4,2019-11-29-09_59,3,4,2,m,m,15.5,12.0,927.3677808126133,757.8912786740188,2,2,119,1232,56,161,0
+4,2019-11-29-09_59,3,4,2,11.0,0.0,m,m,15.5,12.0,927.3677808126133,757.8912786740188,2,2,119,1232,56,161,0
-5,2019-12-02-10_00,3,3,5,m,f,17.5,14.4,866.5433040990971,719.3596160671729,3,2,2,759,28,165,0
+5,2019-12-02-10_00,3,3,5,3.0,0.0,m,f,17.5,14.4,866.5433040990971,719.3596160671729,3,2,2,759,28,165,0
-6,2019-12-03-10_01,3,1,6,f,f,13.2,12.65,709.888382193265,645.345316200243,3,2,61,3191,23,230,0
+6,2019-12-03-10_01,3,1,6,3.0,4.0,f,f,13.2,12.65,709.888382193265,645.345316200243,3,2,61,3191,23,230,0
-7,2019-12-04-10_00,3,3,2,m,m,17.5,12.0,867.515070390076,732.5567785654214,4,3,2,1005,31,73,0
+7,2019-12-04-10_00,3,3,2,11.0,8.0,m,m,17.5,12.0,867.515070390076,732.5567785654214,4,3,2,1005,31,73,0
-8,2019-12-06-10_00,3,4,6,m,f,15.5,12.65,912.2881743174412,652.9837532796978,3,3,34,306,58,188,0
+8,2019-12-06-10_00,3,4,6,1.0,2.0,m,f,15.5,12.65,912.2881743174412,652.9837532796978,3,3,34,306,58,188,0
-9,2019-12-09-10_00,3,5,2,f,m,14.4,12.0,715.5845509704202,726.7506510306094,4,4,446,2345,10,180,0
+9,2019-12-09-10_00,3,5,2,5.0,9.0,f,m,14.4,12.0,715.5845509704202,726.7506510306094,4,4,446,2345,10,180,0
-10,2019-12-10-10_00,3,3,6,m,f,17.5,12.65,853.973867720756,640.9374451096469,5,4,1,205,31,165,0
+10,2019-12-10-10_00,3,3,6,3.0,2.0,m,f,17.5,12.65,853.973867720756,640.9374451096469,5,4,1,205,31,165,0
-11,2019-12-11-10_00,3,4,1,m,f,15.5,13.2,909.5564241038855,704.759181051688,4,5,44,260,48,165,0
+11,2019-12-11-10_00,3,4,1,13.0,1.0,m,f,15.5,13.2,909.5564241038855,704.759181051688,4,5,44,260,48,165,0
-12,2019-12-12-10_00,3,2,6,m,f,12.0,12.65,708.2029632781753,649.3215729301896,5,5,55,1489,26,152,0
+12,2019-12-12-10_00,3,2,6,3.0,5.0,m,f,12.0,12.65,708.2029632781753,649.3215729301896,5,5,55,1489,26,152,0
-13,2019-12-16-10_00,3,4,5,m,f,15.5,14.4,911.4475182245616,734.3463774893517,5,5,52,963,39,123,0
+13,2019-12-16-10_00,3,4,5,7.0,1.0,m,f,15.5,14.4,911.4475182245616,734.3463774893517,5,5,52,963,39,123,0
-14,2020-03-13-10_00,4,5,4,f,f,12.5,12.266666666666666,726.3470010966499,705.1654694195288,2,2,54,941,70,177,0
+14,2020-03-13-10_00,4,5,4,0.0,1.0,f,f,12.5,12.266666666666666,726.3470010966499,705.1654694195288,2,2,54,941,70,177,0
-15,2020-03-16-10_00,4,3,1,m,f,11.933333333333332,11.299999999999999,852.2318545355058,642.0347177867645,3,3,1304,724,57,154,0
+15,2020-03-16-10_00,4,3,1,3.0,2.0,m,f,11.933333333333332,11.299999999999999,852.2318545355058,642.0347177867645,3,3,1304,724,57,154,0
-16,2020-03-18-10_34,4,5,3,f,m,12.5,11.933333333333332,725.8257351336636,863.6524533012707,3,4,16557,2089,339,43,1
+16,2020-03-18-10_34,4,5,3,0.0,4.0,f,m,12.5,11.933333333333332,725.8257351336636,863.6524533012707,3,4,16557,2089,339,43,1
-17,2020-03-19-10_00,4,1,4,f,f,11.299999999999999,12.266666666666666,659.5490944255365,697.5034008357667,4,4,52,1583,36,197,0
+17,2020-03-19-10_00,4,1,4,0.0,1.0,f,f,11.299999999999999,12.266666666666666,659.5490944255365,697.5034008357667,4,4,52,1583,36,197,0
-18,2020-03-20-10_00,4,5,2,f,f,12.5,12.266666666666666,,,4,4,45,665,75,76,0
+18,2020-03-20-10_00,4,5,2,0.0,1.0,f,f,12.5,12.266666666666666,,,4,4,45,665,75,76,0
-19,2020-03-23-09_58,4,2,1,f,f,12.266666666666666,11.299999999999999,699.4914052830558,654.7533296886725,5,5,84,1158,17,67,1
+19,2020-03-23-09_58,4,2,1,0.0,2.0,f,f,12.266666666666666,11.299999999999999,699.4914052830558,654.7533296886725,5,5,84,1158,17,67,1
-20,2020-03-24-10_00,4,4,3,f,m,12.266666666666666,11.933333333333332,684.578069899078,854.0458114588357,5,5,883,2,184,86,1
+20,2020-03-24-10_00,4,4,3,2.0,1.0,f,m,12.266666666666666,11.933333333333332,684.578069899078,854.0458114588357,5,5,883,2,184,86,1
-21,2020-03-25-10_00,4,5,1,f,f,12.5,11.299999999999999,733.5001619575638,647.9874053272127,5,6,819,1831,48,70,1
+21,2020-03-25-10_00,4,5,1,1.0,0.0,f,f,12.5,11.299999999999999,733.5001619575638,647.9874053272127,5,6,819,1831,48,70,1
-22,2020-03-31-09_59,4,3,2,m,f,11.933333333333332,12.266666666666666,860.5459022492297,692.2978867242133,6,6,10,225,26,50,1
+22,2020-03-31-09_59,4,3,2,0.0,3.0,m,f,11.933333333333332,12.266666666666666,860.5459022492297,692.2978867242133,6,6,10,225,26,50,1
-23,2020-05-11-10_00,5,1,2,m,f,12.333333333333334,13.166666666666666,834.369973908149,667.9762847453638,1,1,4,631,25,230,0
+23,2020-05-11-10_00,5,1,2,3.0,4.0,m,f,12.333333333333334,13.166666666666666,834.369973908149,667.9762847453638,1,1,4,631,25,230,0
-24,2020-05-12-10_00,5,5,3,f,m,19.0,10.666666666666666,697.6088902440882,818.2108387976053,1,1,1,117,8,429,0
+24,2020-05-12-10_00,5,5,3,7.0,6.0,f,m,19.0,10.666666666666666,697.6088902440882,818.2108387976053,1,1,1,117,8,429,0
-25,2020-05-13-10_00,5,4,2,m,f,17.5,13.166666666666666,885.2957289220773,681.372424868242,1,2,34,614,22,98,0
+25,2020-05-13-10_00,5,4,2,4.0,7.0,m,f,17.5,13.166666666666666,885.2957289220773,681.372424868242,1,2,34,614,22,98,0
-26,2020-05-14-10_00,5,5,1,f,m,19.0,12.333333333333334,703.5828000211009,840.457519990521,2,2,83,316,10,232,0
+26,2020-05-14-10_00,5,5,1,4.0,5.0,f,m,19.0,12.333333333333334,703.5828000211009,840.457519990521,2,2,83,316,10,232,0
-27,2020-05-15-10_00,5,4,3,m,m,17.5,10.666666666666666,875.2647282681933,824.4852744512042,2,2,98,745,27,255,0
+27,2020-05-15-10_00,5,4,3,18.0,19.0,m,m,17.5,10.666666666666666,875.2647282681933,824.4852744512042,2,2,98,745,27,255,0
-28,2020-05-18-10_00,5,2,3,f,m,13.166666666666666,10.666666666666666,677.7516154017525,837.794665426305,3,3,338,530,28,270,0
+28,2020-05-18-10_00,5,2,3,3.0,6.0,f,m,13.166666666666666,10.666666666666666,677.7516154017525,837.794665426305,3,3,338,530,28,270,0
-29,2020-05-19-10_00,5,5,4,f,m,19.0,17.5,699.3246023368515,881.0368775083901,3,3,628,1457,2,256,0
+29,2020-05-19-10_00,5,5,4,6.0,7.0,f,m,19.0,17.5,699.3246023368515,881.0368775083901,3,3,628,1457,2,256,0
-30,2020-05-21-10_00,5,5,2,f,f,19.0,13.166666666666666,702.20947265625,684.967041015625,4,4,86,671,43,257,0
+30,2020-05-21-10_00,5,5,2,1434.0,1420.0,f,f,19.0,13.166666666666666,702.20947265625,684.967041015625,4,4,86,671,43,257,0
-31,2020-05-25-10_00,5,4,1,m,m,17.5,12.333333333333334,880.891870115058,842.1688052017244,4,4,125,165,37,122,0
+31,2020-05-25-10_00,5,4,1,30.0,19.0,m,m,17.5,12.333333333333334,880.891870115058,842.1688052017244,4,4,125,165,37,122,0
-32,2020-05-27-10_00,6,3,1,f,m,13.5,9.0,686.4001347696975,815.713300982056,1,1,17,92,8,330,0
+32,2020-05-27-10_00,6,3,1,10.0,17.0,f,m,13.5,9.0,686.4001347696975,815.713300982056,1,1,17,92,8,330,0
-33,2020-05-28-10_00,6,2,4,m,f,11.0,11.0,774.6150067187118,728.8412253286924,1,1,69,684,84,342,0
+33,2020-05-28-10_00,6,2,4,7.0,12.0,m,f,11.0,11.0,774.6150067187118,728.8412253286924,1,1,69,684,84,342,0
-34,2020-05-29-10_00,6,5,3,m,f,17.5,13.5,805.4542233630881,681.7640419584177,1,2,373,478,18,58,0
+34,2020-05-29-10_00,6,5,3,10.0,12.0,m,f,17.5,13.5,805.4542233630881,681.7640419584177,1,2,373,478,18,58,0
-35,2020-06-02-10_00,6,1,4,m,f,9.0,11.0,820.4496652837709,723.7667250846596,2,2,485,1253,69,309,0
+35,2020-06-02-10_00,6,1,4,7.0,8.0,m,f,9.0,11.0,820.4496652837709,723.7667250846596,2,2,485,1253,69,309,0
-36,2020-06-03-10_10,6,5,2,m,m,17.5,11.0,810.7042669363011,783.6640529162586,2,2,54,182,16,74,0
+36,2020-06-03-10_10,6,5,2,14.0,5.0,m,m,17.5,11.0,810.7042669363011,783.6640529162586,2,2,54,182,16,74,0
-37,2020-06-04-10_00,6,3,4,f,f,13.5,11.0,695.5929553333448,714.6541711375795,3,3,44,994,34,291,0
+37,2020-06-04-10_00,6,3,4,6.0,7.0,f,f,13.5,11.0,695.5929553333448,714.6541711375795,3,3,44,994,34,291,0
-38,2020-06-05-10_00,6,2,1,m,m,11.0,9.0,804.8998142492978,827.5225072258723,3,3,117,425,41,143,0
+38,2020-06-05-10_00,6,2,1,10.0,14.0,m,m,11.0,9.0,804.8998142492978,827.5225072258723,3,3,117,425,41,143,0
-39,2020-06-08-10_00,6,5,3,m,f,17.5,13.5,816.1812754102803,691.6736840654672,3,4,1087,170,8,14,1
+39,2020-06-08-10_00,6,5,3,4.0,0.0,m,f,17.5,13.5,816.1812754102803,691.6736840654672,3,4,1087,170,8,14,1
-40,2020-06-09-10_00,6,3,2,f,m,13.5,11.0,691.8529359583595,798.4298849024372,5,4,18,632,21,391,0
+40,2020-06-09-10_00,6,3,2,10.0,12.0,f,m,13.5,11.0,691.8529359583595,798.4298849024372,5,4,18,632,21,391,0
-41,2020-06-10-10_00,6,5,1,m,m,17.5,9.0,815.498890219021,828.5259822280207,4,4,66,269,1,14,0
+41,2020-06-10-10_00,6,5,1,5.0,8.0,m,m,17.5,9.0,815.498890219021,828.5259822280207,4,4,66,269,1,14,0
-42,2020-06-11-10_00,6,5,4,m,f,17.5,11.0,817.6355361855158,730.7609124893474,5,4,144,1100,2,54,0
+42,2020-06-11-10_00,6,5,4,10.0,13.0,m,f,17.5,11.0,817.6355361855158,730.7609124893474,5,4,144,1100,2,54,0

trial_summary_eval.py

@@ -181,8 +181,10 @@ def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_ke
     ax.set_xlabel('experience [trials]', fontsize=12)
     ax.set_ylabel(ylabel, fontsize=12)
     ax.tick_params(labelsize=10)
+
+
 def main():
-    beh_per_exp = []
+
     trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
     chirp_notes = pd.read_csv('chirp_notes.csv', index_col=0)
     trial_summary = trial_summary[chirp_notes['good'] == 1]