Holiday syncing :)

python/save_inv_data_thresh-lp.py

@@ -8,13 +8,13 @@ from thunderhopper.model import convolve_kernels
 from IPython import embed
 
 # GENERAL SETTINGS:
-target = ['Omocestus_rufipes', '*'][1]
+target = ['Omocestus_rufipes', '*'][0]
 data_paths = search_files(target, excl='noise', dir='../data/processed/')
 noise_path = '../data/processed/white_noise_sd-1.npz'
 save_path = '../data/inv/thresh_lp/'
 
 # ANALYSIS SETTINGS:
-add_noise = True
+add_noise = False
 save_snippets = add_noise and (target == 'Omocestus_rufipes')
 plot_results = False
 example_scales = np.array([0, 1, 10, 30, 100])
@@ -62,8 +62,8 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
     thresh_abs = ref_conv[segment, :].std(axis=0, keepdims=True) * thresh_rel[:, None]
 
     # Prepare measure storage:
-    shape = (scales.size, kern_specs.shape[0], thresh_rel.size)
-    measure_feat = np.zeros(shape, dtype=float)
+    measure_inv = np.zeros((scales.size,), dtype=float)
+    measure_feat = np.zeros((scales.size, kern_specs.shape[0], thresh_rel.size), dtype=float)
     if save_snippets:
         # Prepare snippet storage:
         snip_inv = np.zeros((song.size, example_scales.size), dtype=float)
@@ -81,6 +81,9 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
         if add_noise:
             # Add noise:
             scaled_song += noise
+        
+        # Log input intensity measure:
+        measure_inv[i] = scaled_song[segment].std()
 
         # Process mixture:
         scaled_conv = convolve_kernels(scaled_song, config['kernels'], config['k_specs'])
@@ -130,6 +133,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
         data = dict(
             scales=scales,
             example_scales=example_scales,
+            measure_inv=measure_inv,
             measure_feat=measure_feat,
             thresh_rel=thresh_rel,
             thresh_abs=thresh_abs,