Various improvements to the GaussianLikelihood

1. If sigma is not given, then it will not be sampled over (or a warning raised that you didn't specify a prior for sigma).

2. Comments added to explain the logic throughout

3. Tests added for the Gaussian likelihood

test/likelihood_tests.py

+from __future__ import absolute_import
+import tupak
+from tupak.core import prior
+from tupak.core.result import Result
+import unittest
+from mock import MagicMock
+import numpy as np
+import inspect
+import os
+import copy
+
+
+class TestGaussianLikelihood(unittest.TestCase):
+
+    def setUp(self):
+        self.N = 100
+        self.sigma = 0.1
+        self.x = np.linspace(0, 1, self.N)
+        self.y = 2 * self.x + 1 + np.random.normal(0, self.sigma, self.N)
+
+        def test_function(x, m, c):
+            return m * x + c
+        self.function = test_function
+
+    def tearDown(self):
+        del self.N
+        del self.sigma
+        del self.x
+        del self.y
+        del self.function
+
+    def test_known_sigma(self):
+        likelihood = tupak.core.likelihood.GaussianLikelihood(
+            self.x, self.y, self.function, self.sigma)
+        likelihood.parameters['m'] = 2
+        likelihood.parameters['c'] = 0
+        likelihood.log_likelihood()
+        self.assertEqual(likelihood.sigma, self.sigma)
+
+    def test_known_array_sigma(self):
+        sigma_array = np.ones(self.N) * self.sigma
+        likelihood = tupak.core.likelihood.GaussianLikelihood(
+            self.x, self.y, self.function, sigma_array)
+        likelihood.parameters['m'] = 2
+        likelihood.parameters['c'] = 0
+        likelihood.log_likelihood()
+        self.assertTrue(type(likelihood.sigma) == type(sigma_array))
+        self.assertTrue(all(likelihood.sigma == sigma_array))
+
+    def test_unknown_float_sigma(self):
+        likelihood = tupak.core.likelihood.GaussianLikelihood(
+            self.x, self.y, self.function, sigma=None)
+        likelihood.parameters['m'] = 2
+        likelihood.parameters['c'] = 0
+        self.assertTrue(likelihood.sigma is None)
+        with self.assertRaises(TypeError):
+            likelihood.log_likelihood()
+        likelihood.parameters['sigma'] = 1
+        likelihood.log_likelihood()
+        self.assertTrue(likelihood.sigma is None)
+
+    def test_y(self):
+        likelihood = tupak.core.likelihood.GaussianLikelihood(
+            self.x, self.y, self.function, self.sigma)
+        self.assertTrue(all(likelihood.y == self.y))
+
+    def test_x(self):
+        likelihood = tupak.core.likelihood.GaussianLikelihood(
+            self.x, self.y, self.function, self.sigma)
+        self.assertTrue(all(likelihood.x == self.x))
+
+    def test_N(self):
+        likelihood = tupak.core.likelihood.GaussianLikelihood(
+            self.x, self.y, self.function, self.sigma)
+        self.assertTrue(likelihood.N == len(self.x))
+
+
+if __name__ == '__main__':
+    unittest.main()