fix fdd signature, move test in a dedicated file

distogram/__init__.py

@@ -356,7 +356,7 @@ def histogram(
     return u
 
 
-def frequency_density_distribution(h: Distogram) -> List[Tuple[float, float]]:
+def frequency_density_distribution(h: Distogram) -> Tuple[List[float], List[float]]:
     """ Returns a histogram of the distribution
 
     Args:
@@ -379,7 +379,7 @@ def frequency_density_distribution(h: Distogram) -> List[Tuple[float, float]]:
     densities = [
         (new - last) / delta 
         for new, last, delta in zip(counts[1:], counts[:-1], bin_widths)]
-    return tuple([densities, bin_bounds])
+    return (densities, bin_bounds)
 
 
 def quantile(h: Distogram, value: float) -> Optional[float]:

tests/test_frequency_density_distribution.py

@@ -0,0 +1,26 @@
+import random
+import numpy as np
+from pytest import approx
+import distogram
+import pytest
+
+
+def test_frequency_density_distribution():
+    normal = [random.normalvariate(0.0, 1.0) for _ in range(10000)]
+    h = distogram.Distogram(bin_count=64)
+
+    for i in normal:
+        h = distogram.update(h, i)
+
+    h = distogram.Distogram(bin_count=3)
+    h = distogram.update(h, 23)
+    h = distogram.update(h, 28)
+    h = distogram.update(h, 16)
+
+    hist = distogram.frequency_density_distribution(h)
+    integral = 0
+    for density, new, old in zip(hist[0], hist[1][1:], hist[1][:-1]):
+        integral += density * (new-old)
+
+    assert(hist == (approx([0.21428571428571427, 0.3]), [16.0, 23.0, 28.0]))
+    assert(integral == approx(3.0))

tests/test_histogram.py

@@ -24,18 +24,6 @@ def test_histogram():
             [16.0, 20.0, 24.0, 28]))
     assert(sum(distogram.histogram(h, bin_count=3)[0]) == approx(3.0))
 
-    hist = distogram.frequency_density_distribution(h)
-    integral = 0
-    for density, new, old in zip(hist[0], hist[1][1:], hist[1][:-1]):
-        integral += density * (new-old)
-
-    assert(hist == approx(([0.21428571428571427, 0.3], [16.0, 23.0, 28.0])))
-    assert(integral == approx(3.0))
-
-    # how to compare histograms?
-    #assert np_values == approx(d_values, abs=0.2)
-    #assert np_edges == approx(d_edges, abs=0.2)
-
 
 def test_histogram_on_too_small_distribution():
     h = distogram.Distogram(bin_count=64)