Gap ratio return update for uncertainty.

Author: msuzen

NEWS

@@ -1,3 +1,8 @@
+v0.2.33
+
+* `mean_adjacent_gap_ratio`, return ratios matrix as well.
+   This is to quantify uncertainty on `adjacent_gap_ratio`.
+
 v0.2.32
 
 * Addition of `mean_adjacent_gap_ratio` and test.

leymosun/spectral.py

@@ -200,10 +200,12 @@ def mean_adjacent_gap_ratio(eigenvalues):
 
     """
     adjacent_gap_ratios = []
+    adjacent_gap_ratios_mean = []
     for eigens in  eigenvalues:
         eigens_sorted = np.sort(eigens)
         spacings = np.diff(eigens_sorted)
         adjacent_gap_ratio = np.minimum(spacings[:-1], spacings[1:]) /(np.maximum(spacings[:-1], spacings[1:])+1e-9)
-        adjacent_gap_ratios.append(np.mean(adjacent_gap_ratio))
-    bar_adjacent_gap_ratio = np.mean(adjacent_gap_ratios)
-    return bar_adjacent_gap_ratio
+        adjacent_gap_ratios.append(adjacent_gap_ratio)
+        adjacent_gap_ratios_mean.append(np.mean(adjacent_gap_ratio))
+    bar_adjacent_gap_ratio = np.mean(adjacent_gap_ratios_mean)
+    return bar_adjacent_gap_ratio, adjacent_gap_ratios

leymosun/version.py

@@ -1 +1 @@
-__version__="0.2.32"
+__version__="0.2.33"

tests/test_spectral.py

@@ -64,5 +64,5 @@ def test_nnsd():
 
 def test_mean_adjacent_gap_ratio():
     sim_e2 = np.array([normal(size=1000) for _ in range(10)])
-    r = mean_adjacent_gap_ratio(sim_e2)
+    r, _ = mean_adjacent_gap_ratio(sim_e2)
     assert r > 0.0