diff --git a/event_time_analysis.py b/event_time_analysis.py
index 3601250..31d573d 100644
--- a/event_time_analysis.py
+++ b/event_time_analysis.py
@@ -2,11 +2,16 @@ import os
 import sys
 import argparse
 import numpy as np
-import cupy as cp
+try:
+    import cupy as cp
+except ImportError:
+    import numpy as cp
+
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 import pandas as pd
 from IPython import embed
+from tqdm import tqdm
 
 
 def load_and_converete_boris_events(trial_path, recording, sr):
@@ -86,30 +91,76 @@ def kde(event_dt, max_dt = 60):
     plt.plot(conv_t, conv_array)
 
 
-def permulation_kde(event_dt, repetitions = 100, max_dt = 60):
+def permulation_kde(select_event_dt, repetitions = 2000, max_dt = 60, max_mem_use_GB = 1):
+    def chunk_permutation(select_event_dt, conv_tt, n_chuck, max_jitter, kernal_w, kernal_h):
+        # array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
+        # event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
+        event_dt_perm = cp.tile(select_event_dt, (len(conv_tt), n_chuck, 1))
+        jitter = np.random.uniform(-max_jitter, max_jitter, size=(event_dt_perm.shape[1], event_dt_perm.shape[2]))
+        jitter = np.expand_dims(jitter, axis=0)
+
+        event_dt_perm += jitter
+        # 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()
+
+        try:
+            kde_3d_numpy = cp.asnumpy(kde_3d)
+            del event_dt_perm, gauss_3d, kde_3d
+            return kde_3d_numpy
+
+        except AttributeError:
+            del event_dt_perm, gauss_3d
+            return kde_3d
+
     embed()
     quit()
-
     kernal_w = 1
     kernal_h = 0.2
 
+    max_jitter = 30
+    select_event_dt = event_dt[np.abs(event_dt) <= max_dt + max_jitter*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)))
+    chunk_size = int(np.floor(max_mem_use_GB / (select_event_dt.nbytes * conv_t.size / 1e9)))
+    chunk_collector =[]
+
+    # for _ in range(repetitions // chunk_size):
+    for _ in range(3):
+        chunk_boot_KDE = chunk_permutation(select_event_dt, conv_tt, chunk_size, max_jitter, kernal_w, kernal_h)
+        chunk_collector.extend(chunk_boot_KDE)
+        # # array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
+        # # event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
+        # event_dt_perm = cp.tile(event_dt, (len(conv_t), chunk_size, 1))
+        # jitter = np.random.uniform(-max_jitter, max_jitter, size=(event_dt_perm.shape[1], event_dt_perm.shape[2]))
+        # jitter = np.expand_dims(jitter, axis=0)
+        #
+        # event_dt_perm += jitter
+        # # 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()
+        # try:
+        #     kde_3d_numpy = cp.asnumpy(kde_3d)
+        #     chunk_collector.extend(kde_3d_numpy)
+        # except AttributeError:
+        #     chunk_collector.extend(kde_3d)
+        # del event_dt_perm, gauss_3d, kde_3d
+    chunk_boot_KDE = chunk_permutation(select_event_dt, conv_tt, repetitions % chunk_size, max_jitter, kernal_w, kernal_h)
+    chunk_collector.extend(chunk_boot_KDE)
+
+
 
-    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():
+    for index, trial in tqdm(trial_summary.iterrows()):
         trial_path = os.path.join(base_path, trial['recording'])
 
         if trial['group'] < 5:
@@ -128,12 +179,12 @@ def main(base_path):
             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
+        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))]