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_log-hp.py

@@ -26,7 +26,21 @@ search_path = '../data/inv/log_hp/'
 save_path = '../data/inv/log_hp/condensed/'
 
 # ANALYSIS SETTINGS:
-compute_ratios = True
+mode = 'noise'
+normalization = [
+    'none',
+    'min',
+    'max',
+    'base',
+    'range',
+    ][3]
+suffix = dict(
+    none='_unnormed',
+    min='_norm-min',
+    max='_norm-max',
+    base='_norm-base',
+    range='_norm-range'
+)[normalization]
 plot_overview = True
 
 # PREPARATION:
@@ -44,7 +58,7 @@ for i, species in enumerate(target_species):
         axes[0, i].set_title(shorten_species(species))
 
     # Fetch all species-specific song files:
-    all_paths = search_files(species, incl='noise', 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)
@@ -57,10 +71,6 @@ for i, species in enumerate(target_species):
             data, config = load_data(path, ['scales', 'measure_inv'])
             scales, measure = data['scales'], data['measure_inv']
 
-            # Relate to noise:
-            if compute_ratios:
-                measure /= measure[0]
-
             if k == 0:
                 # Prepare song file-specific storage:
                 file_data = np.zeros((scales.size, len(rec_paths)), dtype=float)
@@ -70,6 +80,21 @@ for i, species in enumerate(target_species):
                     rec_sd = np.zeros((scales.size, len(sorted_paths)), 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:
@@ -85,8 +110,9 @@ for i, species in enumerate(target_species):
                                     rec_mean[:, j] + rec_sd[:, j], color='k', alpha=0.2)
 
     # Save condensed recording data for current species:
+    save_name = save_path + species + '_' + mode + suffix
     archive = dict(scales=scales, mean_inv=rec_mean, sd_inv=rec_sd)
-    save_data(save_path + species, archive, config, overwrite=True)
+    save_data(save_name, archive, config, overwrite=True)
 
     if plot_overview:
         spec_mean = rec_mean.mean(axis=1)