Added multi-thresh simulation to "full" and "short" (currently running).

Added complete "rect-lp" analysis except figure.
Added multiple appendix figs.
Overhauled normalization options across all condense scripts.

Co-authored-by: Copilot <copilot@github.com>

python/condense_inv_data_thresh-lp.py

@@ -26,7 +26,21 @@ search_path = '../data/inv/thresh_lp/'
 save_path = '../data/inv/thresh_lp/condensed/'
 
 # ANALYSIS SETTINGS:
-with_noise = False
+mode = ['pure', 'noise'][1]
+normalization = [
+    'none',
+    'min',
+    'max',
+    'base',
+    'range',
+    ][0]
+suffix = dict(
+    none='_unnormed',
+    min='_norm-min',
+    max='_norm-max',
+    base='_norm-base',
+    range='_norm-range'
+)[normalization]
 plot_overview = False
 thresh_rel = np.array([0.5, 1, 3])
 
@@ -53,8 +67,7 @@ for i, species in enumerate(target_species):
             all_axes[thresh][0, i].set_title(shorten_species(species))
 
     # Fetch all species-specific song files:
-    incl = 'noise' if with_noise else 'pure'
-    all_paths = search_files(species, incl=incl, ext='npz', dir=search_path)
+    all_paths = search_files(species, incl=mode, ext='npz', dir=search_path)
     
     # Sort song files by recording (one or more per source):
     sorted_paths = sort_files_by_rec(all_paths, sources)
@@ -78,6 +91,21 @@ for i, species in enumerate(target_species):
                     rec_sd = np.zeros(shape, dtype=float)
 
             # Log song file data:
+            if normalization == 'min':
+                # Minimum normalization:
+                measure /= measure.min(axis=0, keepdims=True)
+            elif normalization == 'max':
+                # Maximum normalization:
+                measure /= measure.max(axis=0, keepdims=True)
+            elif normalization == 'base':
+                # Noise baseline normalization:
+                measure /= measure[0]
+            elif normalization == 'range':
+                # Min-max normalization:
+                min_measure = measure.min(axis=0, keepdims=True)
+                max_measure = measure.max(axis=0, keepdims=True)
+                measure = (measure - min_measure) / (max_measure - min_measure)
+
             file_data[..., k] = measure
 
             if plot_overview:
@@ -100,11 +128,7 @@ for i, species in enumerate(target_species):
                     axes[1, i].fill_between(scales, *spread, color=c, alpha=0.2)
 
     # Save condensed recording data:
-    save_name = save_path + species
-    if with_noise:
-        save_name += '_noise'
-    else:
-        save_name += '_pure'
+    save_name = save_path + species + '_' + mode + suffix
     archive = dict(
         scales=scales,
         mean_feat=rec_mean,