putting understanding of hybridIA into code

python/tests/test_util.py

@@ -43,7 +43,7 @@ class TestUtil:
 
 
     @pytest.mark.parametrize('rv',[stats.rayleigh(),stats.weibull_min(1.2),stats.halfnorm(),stats.pareto(2.62)])
-    def test_hybridIA(self,rv):
+    def test_hybridIA_distribution(self,rv):
         bins = np.linspace(0,10,100000)
         centers = (bins[1:]+bins[:-1])/2
         N_samples = bins.shape[0]-1000
@@ -52,6 +52,21 @@ class TestUtil:
         dist_sampled = np.histogram(centers[selected],bins)[0]/N_samples*np.sum(dist)
         assert np.sqrt(((dist - dist_sampled) ** 2).mean()) < .025 and selected.shape[0]==N_samples
 
+    def test_hybridIA_constant(self):
+       N_bins = np.random.randint(20,400)
+       m = np.random.randint(1,20)
+       N_samples = m * N_bins
+       dist = np.ones(N_bins)*np.random.rand()
+       assert np.all(np.sort(util.hybrid_IA(dist,N_samples))==np.arange(N_samples).astype(int)//m)
+
+    def test_hybridIA_linear(self):
+       N_points = np.random.randint(10,200)
+       m = np.random.randint(1,20)
+       dist = np.arange(N_points)
+       N_samples = m * np.sum(dist)
+       assert np.all(np.bincount(util.hybrid_IA(dist*np.random.rand(),N_samples)) == dist*m)
+
+
     @pytest.mark.parametrize('point,direction,normalize,keepdims,answer',
                              [
                               ([1,0,0],'z',False,True, [1,0,0]),