Adding tests on different kind of sampleable distributions

cara/tests/test_sampleable_distribution.py

@@ -0,0 +1,81 @@
+import numpy as np
+import numpy.testing as npt
+import pytest
+
+from cara.monte_carlo import sampleable
+
+@pytest.mark.parametrize(
+    "mean, std",[
+        [1., 0.5],
+    ]
+)
+def test_normal(mean, std):
+    # test that the sample has approximately the right mean,
+    # std deviation and distribution function.
+    sample_size = 2000000
+    samples = sampleable.Normal(mean, std).generate_samples(sample_size)
+    histogram, bins = np.histogram(samples,bins=100, density=True)
+    x = (bins[1:]+bins[:-1])/2
+    exact_dist = 1/(np.sqrt(2*np.pi)*std) * np.exp(-((x-mean)/std)**2/2)
+
+    assert len(samples) == sample_size
+    npt.assert_allclose([samples.mean(), samples.std()], [mean, std], atol=mean/100)
+    npt.assert_allclose(histogram, exact_dist, atol=exact_dist.mean()/20)
+
+
+@pytest.mark.parametrize(
+    "mean_gaussian, std_gaussian",[
+        [-0.6872121723362303, 0.10498338229297108],
+    ]
+)
+def test_lognormal(mean_gaussian, std_gaussian):
+    # test that the sample has approximately the right mean,
+    # std deviation and distribution function.
+    sample_size = 2000000
+    samples = sampleable.LogNormal(mean_gaussian, std_gaussian
+                                ).generate_samples(sample_size)
+    histogram, bins = np.histogram(samples,bins=50, density=True)
+    x = (bins[1:]+bins[:-1])/2
+    exact_dist = ( 1/(x*np.sqrt(2*np.pi)*std_gaussian) * 
+                   np.exp(-((np.log(x)-mean_gaussian)/std_gaussian)**2/2) )
+    exact_mean = np.exp(mean_gaussian + std_gaussian**2/2)
+    exact_std = np.sqrt( (np.exp(std_gaussian**2)-1) *
+                         np.exp(2*mean_gaussian + std_gaussian**2) )
+
+    assert len(samples) == sample_size
+    npt.assert_allclose([samples.mean(), samples.std()],
+                        [exact_mean, exact_std], atol=exact_mean/100)
+    npt.assert_allclose(histogram, exact_dist, atol=exact_dist.mean()/20)
+
+
+@pytest.mark.parametrize(
+    "use_kernel",
+    [False, True],
+)
+def test_custom(use_kernel):
+    # test that the sample has approximately the right distribution
+    # function, with both Custom and CustomKernel method. The latter
+    # is less accurate for smooth functions.
+    # the distribution function is an inverted parabola, with maximum 0.15,
+    # which is 0 at the bounds (0,10) (normalized)
+    norm = 500/3.
+    function = lambda x: (-(5 - x)**2 + 25)/norm
+    max_function = 0.15
+    sample_size = 2000000
+
+    if use_kernel:
+        variable = np.linspace(0.1,9.9,100) 
+        frequencies = function(variable)
+        samples = sampleable.CustomKernel(variable, frequencies,
+                                    kernel_bandwidth=0.1
+                                    ).generate_samples(sample_size)
+        tolerance = max_function/10
+    else:
+        samples = sampleable.Custom((0, 10), function, max_function
+                                    ).generate_samples(sample_size)
+        tolerance = max_function/50
+
+    histogram, bins = np.histogram(samples, bins=100, density=True)
+    correct_dist = function((bins[1:]+bins[:-1])/2)
+    assert len(samples) == sample_size
+    npt.assert_allclose(histogram, correct_dist, atol=tolerance)