time series analysis... excluded chirps in event time correlations. maybe check plots again

event_time_analysis.py

@@ -1,14 +1,102 @@
 import os
 import sys
-from numpy import numpy
+import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 import pandas as pd
 from IPython import embed
-from event_time_correlations import load_and_converete_boris_events
+from event_time_correlations import load_and_converete_boris_events, kde, gauss
 
 
 def main(base_path):
+    trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
+    female_color, male_color = '#e74c3c', '#3498db'
+
+    all_rise_times_lose = []
+    all_rise_times_win = []
+    all_chirp_times_lose = []
+    all_chirp_times_win = []
+    win_sex = []
+    lose_sex = []
+
+    for index, trial in trial_summary.iterrows():
+        print(index, len(trial_summary))
+        got_boris = False
+
+        trial_path = os.path.join(base_path, trial['recording'])
+
+        if trial['group'] < 3:
+            continue
+        if trial['draw'] == 1:
+            continue
+        if os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
+            got_boris = True
+        if os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
+            got_boris = True
+
+        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
+        if got_boris:
+            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
+        if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
+            continue
+        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']]]
+
+
+        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]]]
+
+        # iri = np.diff(rise_times[1])
+        all_rise_times_lose.append(rise_times[1])
+        all_rise_times_win.append(rise_times[0])
+
+        all_chirp_times_lose.append(chirp_times[1])
+        all_chirp_times_win.append(chirp_times[0])
+        win_sex.append(trial['sex_win'])
+        lose_sex.append(trial['sex_lose'])
+
+
+    embed()
+    quit()
+    ici_lose = []
+    ici_win = []
+    for i in range(len(all_chirp_times_lose)):
+        ici_lose.append(np.diff(all_chirp_times_lose[i]))
+        ici_win.append(np.diff(all_chirp_times_win[i]))
+
+    fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
+    gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
+    ax = fig.add_subplot(gs[0, 0])
+
+    for i in range(len(ici_lose)):
+        if win_sex[i] == 'm':
+            if lose_sex[i] == 'm':
+                color, linestyle = male_color, '-'
+            else:
+                color, linestyle = male_color, '--'
+        else:
+            if lose_sex[i] == 'm':
+                color, linestyle = female_color, '--'
+            else:
+                color, linestyle = female_color, '-'
+
+
+        conv_y_chirp_lose = np.arange(0, 30, .5)
+        kde_array = kde(ici_lose[i], conv_y_chirp_lose, kernal_w=1, kernal_h=1)
+
+        # kde_array /= np.sum(kde_array)
+        ax.plot(conv_y_chirp_lose, kde_array, zorder=2, color=color, linestyle=linestyle, lw=2)
+
+    plt.show()
 
     pass
 

event_time_correlations.py

@@ -265,6 +265,8 @@ def main(base_path):
             continue
         if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
             continue
+        if trial['draw'] == 1:
+            continue
 
         ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy'))
         times = np.load(os.path.join(trial_path, 'times.npy'))