Seriously, no idea. Wild amount of changes. Good luck.

python/misc_functions.py

@@ -1,5 +1,6 @@
 import numpy as np
 from scipy.stats import gaussian_kde
+from thunderhopper.filetools import crop_paths
 
 def shorten_species(name):
     genus, species = name.split('_')
@@ -9,6 +10,44 @@ def unsort_unique(array):
     values, inds = np.unique(array, return_index=True)
     return values[np.argsort(inds)]
 
+def draw_noise_segment(noise, n):
+    rng = np.random.default_rng()
+    start = rng.integers(0, noise.shape[0] - n, endpoint=True)
+    return np.take(noise, np.arange(start, start + n), axis=0)
+
+def sort_files_by_rec(paths, sources=['BM04', 'BM93', 'DJN', 'GBC', 'FTN']):
+    # Separate by source:
+    sorted_paths = {}
+    for source in sources:
+
+        # Check for any source-specific song files:
+        source_paths = [path for path in paths if source in path]
+        if not source_paths:
+            continue
+
+        # Separate by recording:
+        sorted_paths[source] = [[]]
+        for path, name in zip(source_paths, crop_paths(source_paths)):
+
+            # Find numerical ID behind source tag:
+            id_ind = name.find(source) + len(source) + 1
+            # Get segment where sub-ID would be:
+            sub_id = name[id_ind:].split('-')[1]
+            if 's' in sub_id:
+                # Found time stamp (single recording):
+                sorted_paths[source][0].append(path)
+                continue
+            sub_id = int(sub_id)
+            # Found sub-ID (multiple recordings):
+            if sub_id > len(sorted_paths[source]):
+                # Open new recording-specific slot:
+                sorted_paths[source].append([])
+            sorted_paths[source][sub_id - 1].append(path)
+    
+    # Re-sort song files by recording only (discarding source separation):
+    sorted_paths = [path for paths in sorted_paths.values() for path in paths]
+    return sorted_paths
+
 def get_kde(data, sigma, axis=None, n=1000, pad=10):
     if axis is None:
         axis = np.linspace(data.min() - pad * sigma, data.max() + pad * sigma, n)