From ad888734dd273f9ee23f5411e937eddd123b1bf3 Mon Sep 17 00:00:00 2001
From: wendtalexander <wendtalexander@protonmail.com>
Date: Wed, 18 Jan 2023 10:25:48 +0100
Subject: [PATCH] saved chirps

---
 code/chirpdetection.py     | 22 ++++++++++++++++------
 code/modules/timestamps.py |  3 ++-
 2 files changed, 18 insertions(+), 7 deletions(-)

diff --git a/code/chirpdetection.py b/code/chirpdetection.py
index 26a9b52..9e71f5e 100644
--- a/code/chirpdetection.py
+++ b/code/chirpdetection.py
@@ -527,7 +527,10 @@ def main(datapath: str) -> None:
                 if len(baseline_ts) == 0 or len(search_ts) == 0 or len(freq_ts) == 0:
                     continue
 
+                #current_chirps = group_timestamps_v2(
+                #    [list(baseline_ts), list(search_ts), list(freq_ts)], 3)
 
+               
                 # get index for each feature
                 baseline_idx = np.zeros_like(baseline_ts)
                 search_idx = np.ones_like(search_ts)
@@ -561,7 +564,7 @@ def main(datapath: str) -> None:
                         current_chirps.append(np.mean(timestamps[cm]))
                         electrodes_of_chirps.append(el)
                     bool_timestamps[cm] = False
-
+                
                 # for checking if there are chirps on multiple electrodes
 
                 chirps_electrodes.append(current_chirps)
@@ -610,11 +613,16 @@ def main(datapath: str) -> None:
                 cm = index_vector[(sort_chirps_electrodes >= seoc) & (
                     sort_chirps_electrodes <= seoc + config.chirp_window_threshold)]
 
+                
+                chirps_unique = []
                 for combination in combinations_best_elctrodes:
                     if set(combination).issubset(sort_electrodes[cm]):
-                        the_real_chirps.append(
-                            np.mean(sort_chirps_electrodes[cm]))
+                        chirps_unique.append(np.mean(sort_chirps_electrodes[cm]))
+
+                the_real_chirps.append(np.mean(chirps_unique))
+
 
+                """
                 if set([0,1]).issubset(sort_electrodes[cm]):
                     the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
                 elif set([1,0]).issubset(sort_electrodes[cm]):
@@ -623,12 +631,14 @@ def main(datapath: str) -> None:
                     the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
                 elif set([1,2]).issubset(sort_electrodes[cm]):
                     the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
-
+                """
                 bool_vector[cm] = False
+            chirps.append(the_real_chirps)
             for ct in the_real_chirps:
                 axs[0, el].axvline(ct, color='b', lw=1)
-            embed()
-            plt.show()
+    embed()
+
+
 
 
 if __name__ == "__main__":
diff --git a/code/modules/timestamps.py b/code/modules/timestamps.py
index 1f3aa8d..9ad6add 100644
--- a/code/modules/timestamps.py
+++ b/code/modules/timestamps.py
@@ -82,7 +82,8 @@ def group_timestamps_v2(sublists: List[List[Union[int, float]]], n: int, time_th
     current_group = []
     # Create a set to store the timestamps that occur in at least n of the sublists
     common_timestamps = set.intersection(*[set(lst) for lst in sublists])
-    embed()
+    # convert the set to a list
+    common_timestamps = list(common_timestamps)
     # Iterate through the timestamps
     for i in range(len(common_timestamps)):
         # If the current timestamp is less than 50 milliseconds away from the previous timestamp