updated tests to include everything in main.py and created tests for the distributions

binarycpython/tests/main.py

 # Main file for the tests. This file imports all the combined_test functions from all files.
 import unittest
 
-from test_c_bindings import *
-from test_functions import *
-
-
+from binarycpython.tests.test_c_bindings import *
+from binarycpython.tests.test_custom_logging import *
+from binarycpython.tests.test_distributions import *
+from binarycpython.tests.test_functions import *
+from binarycpython.tests.test_grid import *
+from binarycpython.tests.test_hpc_functions import *
+from binarycpython.tests.test_plot_functions import *
+from binarycpython.tests.test_run_system_wrapper import *
+from binarycpython.tests.test_spacing_functions import *
+from binarycpython.tests.test_useful_funcs import *
 
 if __name__ == '__main__':
     unittest.main()

binarycpython/tests/test_distributions.py

+"""
+Module containing the unittests for the distribution functions. 
+"""
+import unittest
+
 from binarycpython.utils.distribution_functions import *
+from binarycpython.utils.useful_funcs import calc_sep_from_period
+
+class TestDistributions(unittest.TestCase):
+    """
+    Unittest class
+
+    # https://stackoverflow.com/questions/17353213/init-for-unittest-testcase
+    """
+
+    def __init__(self, *args, **kwargs):
+        super(TestDistributions, self).__init__(*args, **kwargs)
+        # self.gen_stubs()
+
+        self.mass_list = [0.1, 0.2, 1, 10, 15, 50]
+        self.logper_list = [-2, -0.5, 1.6, 2.5, 5.3, 10]
+        self.q_list = [0.01, 0.2, 0.4, 0.652, 0.823, 1]
+        self.per_list = [10**logper for logper in self.logper_list]
+
+        self.tolerance = 1e-5
+
+    def test_powerlaw(self):
+        """
+        unittest for the powerlaw test
+        """
+
+        perl_results = [0, 0, 1.30327367546194, 0.00653184128064016, 0.00257054805572128, 0.000161214690242696]
+        python_results = []
+
+        for mass in self.mass_list:
+            python_results.append(powerlaw(1, 100, -2.3, mass))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+    def test_three_part_power_law(self):
+        """
+        unittest for three_part_power_law
+        """
+
+        perl_results = [10.0001044752901, 2.03065220596677, 0.0501192469795434, 0.000251191267451594, 9.88540897458207e-05, 6.19974072148769e-06]
+        python_results = []
+
+        for mass in self.mass_list:
+            python_results.append(three_part_powerlaw (mass, 0.08, 0.1, 1, 300, -1.3, -2.3, -2.3))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+    def test_Kroupa2001(self):
+        """
+        unittest for three_part_power_law
+        """
+
+        perl_results = [5.71196495365248, 2.31977861075353, 0.143138195684851, 0.000717390363216896, 0.000282322598503135, 1.77061658757533e-05]
+        python_results = []
+
+        for mass in self.mass_list:
+            python_results.append(Kroupa2001(mass))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+    def test_ktg93(self):
+        """
+        unittest for three_part_power_law
+        """
+
+        perl_results = [5.79767807698379, 2.35458895566605, 0.155713799148675, 0.000310689875361984, 0.000103963454405194, 4.02817276824841e-06]
+        python_results = []
+
+        for mass in self.mass_list:
+            python_results.append(ktg93(mass))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+
+    def test_gaussian(self):
+        """
+        unittest for three_part_power_law
+        """
+
+        perl_results = [0.00218800520299544, 0.0121641269671571, 0.0657353455837751, 0.104951743573429, 0.16899534495487, 0.0134332780385336]
+        python_results = []
+
+        for logper in self.logper_list:
+            python_results.append(gaussian(logper, 4.8, 2.3, -2.0, 12.0))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+
+    def test_Arenou2010_binary_fraction(self):
+        """
+        unittest for three_part_power_law
+        """
+
+        perl_results = [0.123079723518677, 0.178895136157746, 0.541178340047153, 0.838798485820276, 0.838799998443204, 0.8388]
+        python_results = []
+
+        for mass in self.mass_list:
+            python_results.append(Arenou2010_binary_fraction(mass))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+    def test_raghavan2010_binary_fraction(self):
+        """
+        unittest for three_part_power_law
+        """
+
+        perl_results = [0.304872297931597, 0.334079955706623, 0.41024, 1, 1, 1]
+        python_results = []
+
+        for mass in self.mass_list:
+            python_results.append(raghavan2010_binary_fraction(mass))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+    def test_Izzard2012_period_distribution(self):
+        """
+        unittest for three_part_power_law
+        """
+
+        perl_results = [0, 0.00941322840619318, 0.0575068231479569, 0.0963349886047932, 0.177058537292581, 0.0165713385659234, 0, 0.00941322840619318, 0.0575068231479569, 0.0963349886047932, 0.177058537292581, 0.0165713385659234, 0, 0.00941322840619318, 0.0575068231479569, 0.0963349886047932, 0.177058537292581, 0.0165713385659234, 0, 7.61631504133159e-09, 0.168028727846997, 0.130936282216512, 0.0559170865520968, 0.0100358604460285, 0, 2.08432736869149e-21, 0.18713622563288, 0.143151383185002, 0.0676299576972089, 0.0192427864870784, 0, 1.1130335685003e-24, 0.194272603987661, 0.14771508552257, 0.0713078479280884, 0.0221093965810181]
+        python_results = []
+
+        for mass in self.mass_list:
+            for per in self.per_list:
+                python_results.append(Izzard2012_period_distribution(per, mass))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+    def test_flatsections(self):
+        """
+        unittest for three_part_power_law
+        """
+
+        perl_results = [1.01010101010101, 1.01010101010101, 1.01010101010101, 1.01010101010101, 1.01010101010101, 1.01010101010101]
+        python_results = []
+
+        for q in self.q_list:
+            python_results.append(flatsections(q, [{'min': 0.01, 'max': 1.0, 'height': 1.0}]))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+
+    def test_sana12(self):
+        """
+        unittest for three_part_power_law
+        """
+
+        perl_results = [0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.121764808010258, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805, 0.481676471294883, 0.481676471294883, 0.131020615300798, 0.102503482445846, 0.0678037785559114, 0.066436408359805]
+        python_results = []
+
+        for mass in self.mass_list:
+            for q in self.q_list:
+                for per in self.per_list:
+                    mass_2 = mass * q
+
+                    sep = calc_sep_from_period(mass, mass_2, per)
+                    sep_min = calc_sep_from_period(mass, mass_2, 10**0.15)
+                    sep_max = calc_sep_from_period(mass, mass_2, 10**5.5)
+                    python_results.append(sana12(mass, mass_2, sep, per, sep_min, sep_max, 0.15, 5.5, -0.55))
+
+        # GO over the results and check whether they are equal (within tolerance)
+        for i in range(len(python_results)):
+            self.assertLess(np.abs(python_results[i]-perl_results[i]), self.tolerance)
+
+if __name__ == '__main__':
+    unittest.main()