support for fake recs

code/chirpdetection.py

@@ -1176,13 +1176,13 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
     purged_chirps = purged_chirps[np.argsort(purged_chirps)]
 
     # save them into the data directory
-    np.save(datapath + "chirps.npy", purged_chirps)
-    np.save(datapath + "chirp_ids.npy", purged_ids)
+    np.save(datapath + "chirp_times_gp.npy", purged_chirps)
+    np.save(datapath + "chirp_ids_gp.npy", purged_ids)
 
 
 if __name__ == "__main__":
     # datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-05-13-10_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/"
-    datapath = "../data/2022-06-02-10_00/"
+    datapath = "../../chirpdetector-cnn/testing_data/"
     chirpdetection(datapath, plot="save", debug="false")

code/chirpdetector_conf.yml

@@ -38,9 +38,9 @@ search_envelope_cutoff: 10  # search envelope estimation cufoff
 # search_prominence: 0.000004   # peak prominence threshold for search envelope
 # frequency_prominence: 2       # peak prominence threshold for baseline freq
 
-baseline_prominence: 0.3  # peak prominence threshold for baseline envelope
-search_prominence: 0.3  # peak prominence threshold for search envelope
-frequency_prominence: 0.3  # peak prominence threshold for baseline freq
+baseline_prominence: 0.00005  # peak prominence threshold for baseline envelope
+search_prominence: 0.000005  # peak prominence threshold for search envelope
+frequency_prominence: 1  # peak prominence threshold for baseline freq
 
 # Classify events as chirps if they are less than this time apart
 chirp_window_threshold: 0.02

code/modules/filehandling.py

@@ -1,9 +1,9 @@
 import os
 
-import yaml
-import numpy as np
-from thunderfish.dataloader import DataLoader
 import matplotlib.pyplot as plt
+import numpy as np
+import yaml
+from thunderfish.dataloader import DataLoader
 
 
 class ConfLoader:
@@ -38,8 +38,12 @@ class LoadData:
         # load raw data
         self.datapath = datapath
         self.file = os.path.join(datapath, "traces-grid1.raw")
-        self.raw = DataLoader(self.file, 60.0, 0, channel=-1)
-        self.raw_rate = self.raw.samplerate
+        if os.path.isfile(self.file) == False:
+            self.raw = np.load(os.path.join(datapath, "raw.npy"))
+            self.raw_rate = 20000.0
+        else:
+            self.raw = DataLoader(self.file, 60.0, 0, channel=-1)
+            self.raw_rate = self.raw.samplerate
 
         # load wavetracker files
         self.time = np.load(datapath + "times.npy", allow_pickle=True)