diff --git a/code/chirpdetection.py b/code/chirpdetection.py
index a10608e..083fc62 100644
--- a/code/chirpdetection.py
+++ b/code/chirpdetection.py
@@ -183,19 +183,6 @@ def main(datapath: str) -> None:
     chirps = []
     fish_ids = []
 
-    baseline_ts = [[[
-        [] for el in range(config.number_electrodes)]
-        for tr in range(len(data.ids))]
-        for wi in range(nwindows)]
-    search_ts = [[[
-        [] for el in range(config.number_electrodes)]
-        for tr in range(len(data.ids))]
-        for wi in range(nwindows)]
-    freq_ts = [[[
-        [] for el in range(config.number_electrodes)]
-        for tr in range(len(data.ids))]
-        for wi in range(nwindows)]
-
     for st, start_index in enumerate(window_starts[: nwindows]):
 
         # make t0 and dt
@@ -208,7 +195,7 @@ def main(datapath: str) -> None:
         # calucate median of fish frequencies in window
         median_freq = []
         track_ids = []
-        for el, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
+        for _, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
             window_idx = np.arange(len(data.idx))[
                 (data.ident == track_id) & (data.time[data.idx] >= t0) & (
                     data.time[data.idx] <= (t0 + dt))
@@ -333,7 +320,7 @@ def main(datapath: str) -> None:
 
             # iterate through electrodes
             for el, electrode in enumerate(best_electrodes):
-
+                print(el)
                 # load region of interest of raw data file
                 data_oi = data.raw[start_index:stop_index, :]
                 time_oi = raw_time[start_index:stop_index]
@@ -454,47 +441,6 @@ def main(datapath: str) -> None:
                     prominence=prominence
                 )
 
-                # DETECT CHIRPS IN SEARCH WINDOW -------------------------------
-
-                baseline_ts = time_oi[baseline_peaks]
-                search_ts = time_oi[search_peaks]
-                freq_ts = baseline_freq_time[inst_freq_peaks]
-
-                # check if one list is empty
-                if len(baseline_ts) == 0 or len(search_ts) == 0 or len(freq_ts) == 0:
-                    continue
-
-                # get index for each feature
-                baseline_idx = np.zeros_like(baseline_ts)
-                search_idx = np.ones_like(search_ts)
-                freq_idx = np.ones_like(freq_ts) * 2
-
-                timestamps_features = np.hstack(
-                    [baseline_idx, search_idx, freq_idx])
-                timestamps = np.hstack([baseline_ts, search_ts, freq_ts])
-
-                # sort timestamps
-                timestamps_idx = np.arange(len(timestamps))
-                timestamps_features = timestamps_features[np.argsort(timestamps)]
-                timestamps = timestamps[np.argsort(timestamps)]
-
-                # # get chirps
-                # diff = np.empty(timestamps.shape)
-                # diff[0] = np.inf  # always retain the 1st element
-                # diff[1:] = np.diff(timestamps)
-                # mask = diff < config.chirp_window_threshold
-                # shared_peak_indices = timestamp_idx[mask]
-
-                current_chirps = []
-                for tt in timestamps:
-                    cm = timestamps_idx[(timestamps >= tt) & (
-                        timestamps <= tt + config.chirp_window_threshold)]
-                    if all([0, 1, 2]) in timestamps_features[cm]:
-                        chirps.append(np.mean(timestamps[cm]))
-                        current_chirps.append(np.mean(timestamps[cm]))
-                        fish_ids.append(track_id)
-
-
                 # # SAVE DATA ---------------------------------------------------
 
                 # PLOT --------------------------------------------------------
@@ -503,9 +449,6 @@ def main(datapath: str) -> None:
                 plot_spectrogram(
                     axs[0, el], data_oi[:, electrode], data.raw_rate, t0)
 
-                for ct in current_chirps:
-                    axs[0, el].axvline(ct, color='r', lw=1)
-
                 # plot baseline instantaneos frequency
                 axs[1, el].plot(baseline_freq_time, baseline_freq -
                                 np.median(baseline_freq))
@@ -569,6 +512,68 @@ def main(datapath: str) -> None:
                 axs[5, el].set_title("Search envelope")
                 axs[6, el].set_title(
                     "Filtered absolute instantaneous frequency")
+                print(el)
+                # DETECT CHIRPS IN SEARCH WINDOW -------------------------------
+
+                baseline_ts = time_oi[baseline_peaks]
+                search_ts = time_oi[search_peaks]
+                freq_ts = baseline_freq_time[inst_freq_peaks]
+
+                # check if one list is empty
+                if len(baseline_ts) == 0 or len(search_ts) == 0 or len(freq_ts) == 0:
+                    continue
+
+                # get index for each feature
+                baseline_idx = np.zeros_like(baseline_ts)
+                search_idx = np.ones_like(search_ts)
+                freq_idx = np.ones_like(freq_ts) * 2
+
+                timestamps_features = np.hstack(
+                    [baseline_idx, search_idx, freq_idx])
+                timestamps = np.hstack([baseline_ts, search_ts, freq_ts])
+
+                # sort timestamps
+                timestamps_idx = np.arange(len(timestamps))
+                timestamps_features = timestamps_features[np.argsort(
+                    timestamps)]
+                timestamps = timestamps[np.argsort(timestamps)]
+
+                # # get chirps
+                # diff = np.empty(timestamps.shape)
+                # diff[0] = np.inf  # always retain the 1st element
+                # diff[1:] = np.diff(timestamps)
+                # mask = diff < config.chirp_window_threshold
+                # shared_peak_indices = timestamp_idx[mask]
+
+                current_chirps = []
+                for tt in timestamps:
+                    cm = timestamps_idx[(timestamps >= tt) & (
+                        timestamps <= tt + config.chirp_window_threshold)]
+                    if set([0, 1, 2]).issubset(timestamps_features[cm]):
+                        chirps.append(np.mean(timestamps[cm]))
+                        current_chirps.append(np.mean(timestamps[cm]))
+                        fish_ids.append(track_id)
+
+                for ct in current_chirps:
+                    axs[0, el].axvline(ct, color='r', lw=1)
+
+                axs[0, el].scatter(
+                    baseline_freq_time[inst_freq_peaks],
+                    np.ones_like(baseline_freq_time[inst_freq_peaks]) * 600,
+                    c=ps.red,
+                )
+                axs[0, el].scatter(
+                    (time_oi)[search_peaks],
+                    np.ones_like((time_oi)[search_peaks]) * 600,
+                    c=ps.red,
+                )
+
+                axs[0, el].scatter(
+                    (time_oi)[baseline_peaks],
+                    np.ones_like((time_oi)[baseline_peaks]) * 600,
+                    c=ps.red,
+                )
+
 
             plt.show()