better chirpdetector

code/chirpdetection.py

@@ -886,25 +886,25 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
                 # normalize all three feature arrays to the same range to make
                 # peak detection simpler
 
-                baseline_envelope = minmaxnorm([baseline_envelope])[0]
+                # baseline_envelope = minmaxnorm([baseline_envelope])[0]
-                search_envelope = minmaxnorm([search_envelope])[0]
+                # search_envelope = minmaxnorm([search_envelope])[0]
-                baseline_frequency_filtered = minmaxnorm(
+                # baseline_frequency_filtered = minmaxnorm(
-                    [baseline_frequency_filtered]
+                #     [baseline_frequency_filtered]
-                )[0]
+                # )[0]
 
                 # PEAK DETECTION ----------------------------------------------
 
                 # detect peaks baseline_enelope
                 baseline_peak_indices, _ = find_peaks(
-                    baseline_envelope, prominence=config.prominence
+                    baseline_envelope, prominence=config.baseline_prominence
                 )
                 # detect peaks search_envelope
                 search_peak_indices, _ = find_peaks(
-                    search_envelope, prominence=config.prominence
+                    search_envelope, prominence=config.search_prominence
                 )
                 # detect peaks inst_freq_filtered
                 frequency_peak_indices, _ = find_peaks(
-                    baseline_frequency_filtered, prominence=config.prominence
+                    baseline_frequency_filtered, prominence=config.frequency_prominence
                 )
 
                 # DETECT CHIRPS IN SEARCH WINDOW ------------------------------
@@ -1097,4 +1097,4 @@ if __name__ == "__main__":
     datapath = "../data/2022-06-02-10_00/"
     # datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/"
     # datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/"
-    chirpdetection(datapath, plot="show", debug="fish")
+    chirpdetection(datapath, plot="show", debug="false")

code/chirpdetector_conf.yml

@@ -1,47 +1,41 @@
-# directory setup
+# Path setup ------------------------------------------------------------------
-dataroot: "../data/"
-outputdir: "../output/"
 
-# Duration and overlap of the analysis window in seconds
+dataroot: "../data/"      # path to data
-window: 5
+outputdir: "../output/"   # path to save plots to
-overlap: 1
-edge: 0.25
 
-# Number of electrodes to go over
+# Rolling window parameters ---------------------------------------------------
-number_electrodes: 3
-minimum_electrodes: 2
 
-# Search window bandwidth and minimal baseline bandwidth
+window: 5   # rolling window length in seconds
-minimal_bandwidth: 20
+overlap: 1  # window overlap in seconds
+edge: 0.25  # window edge cufoffs to mitigate filter edge effects
 
-# Instantaneous frequency smoothing usint a gaussian kernel of this width
+# Electrode iteration parameters ----------------------------------------------
-baseline_frequency_smoothing: 5
 
-# Baseline processing parameters
+number_electrodes: 2    # number of electrodes to go over
-baseline_envelope_cutoff: 25
+minimum_electrodes: 1   # mimumun number of electrodes a chirp must be on
-baseline_envelope_bandpass_lowf: 2
-baseline_envelope_bandpass_highf: 100
-# baseline_envelope_envelope_cutoff: 4
 
-# search envelope processing parameters
+# Feature extraction parameters -----------------------------------------------
-search_envelope_cutoff: 10
 
-# Instantaneous frequency bandpass filter cutoff frequencies
+search_df_lower: 20     # start searching this far above the baseline
-# baseline_frequency_highpass_cutoff: 0.000005
+search_df_upper: 100    # stop searching this far above the baseline
-# baseline_frequency_envelope_cutoff: 0.000005
+search_res: 1           # search window resolution
+default_search_freq: 60 # search here if no need for a search frequency
+minimal_bandwidth: 10   # minimal bandpass filter width for baseline
+search_bandwidth: 10    # minimal bandpass filter width for search frequency
+baseline_frequency_smoothing: 10 # instantaneous frequency smoothing
 
-# peak detecion parameters
+# Feature processing parameters -----------------------------------------------
-prominence: 0.7
 
-# search freq parameter
+baseline_envelope_cutoff: 25            # envelope estimation cutoff
-search_df_lower: 20
+baseline_envelope_bandpass_lowf: 2      # envelope badpass lower cutoff
-search_df_upper: 100
+baseline_envelope_bandpass_highf: 100   # envelope bandbass higher cutoff
-search_res: 1
+search_envelope_cutoff: 10              # search envelope estimation cufoff
-search_bandwidth: 20
-default_search_freq: 60
-
-# Classify events as chirps if they are less than this time apart
-chirp_window_threshold: 0.015
 
+# Peak detecion parameters ----------------------------------------------------
+baseline_prominence: 0.00005  # peak prominence threshold for baseline envelope
+search_prominence: 0.000004   # peak prominence threshold for search envelope
+frequency_prominence: 2       # peak prominence threshold for baseline freq
 
+# Classify events as chirps if they are less than this time apart
+chirp_window_threshold: 0.02
 

code/extract_chirps.py

@@ -8,7 +8,7 @@ from IPython import embed
 def main(datapaths):
 
     for path in datapaths:
-        chirpdetection(path, plot='show', debug='electrode')
+        chirpdetection(path, plot='show')
 
 
 if __name__ == '__main__':
@@ -43,6 +43,6 @@ if __name__ == '__main__':
 
     recs = pd.DataFrame(columns=['recording'], data=valid_datasets)
     recs.to_csv('../recs.csv', index=False)
-    # main(datapaths)
+    main(datapaths)
 
 # window 1524 + 244 in dataset index 4 is nice example