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/misc_functions.py

@@ -1,6 +1,7 @@
 import numpy as np
 from scipy.stats import gaussian_kde
 from thunderhopper.filetools import crop_paths
+from IPython import embed
 
 def shorten_species(name):
     genus, species = name.split('_')
@@ -48,6 +49,40 @@ def sort_files_by_rec(paths, sources=['BM04', 'BM93', 'DJN', 'GBC', 'FTN']):
     sorted_paths = [path for paths in sorted_paths.values() for path in paths]
     return sorted_paths
 
+def get_thresholds(data=None, path=None, perc=None, factor=None,
+                   direct=False, which=None):
+
+    def get_inds(nearest, which):
+        if which == 'floor':
+            nearest[nearest < 0] = np.inf
+            return nearest.argmin(axis=0)
+        elif which == 'ceil':
+            nearest[nearest > 0] = -np.inf
+            return nearest.argmax(axis=0)
+        return np.abs(nearest).argmin(axis=0)
+    
+    if data is None:
+        # Load threshold data:
+        data = dict(np.load(path))
+
+    # From SD scaling factor:
+    if factor is not None:
+        if direct:
+            # Scale SDs directly by factor:
+            return data['sds'] * factor, factor, None
+
+        # Link to supra-thresh proportion:
+        nearest = np.atleast_2d(factor) - data['factors'][:, None]
+        inds = get_inds(nearest, which)
+        factors = data['factors'][inds]
+        return data['sds'] * factors, factors, data['percs'][inds, :]
+
+    # From supra-thresh proportion:
+    nearest = perc - data['percs']
+    inds = get_inds(nearest, which)
+    factors = data['factors'][inds]
+    return data['sds'] * factors, factors, data['percs'][inds, :]
+
 def get_histogram(data, edges=None, nbins=50, pad=0.1, shared=True):
     if edges is None:
         axis = None if shared else 0