diff --git a/binarycpython/tests/tests_population_extensions/test__HPC.py b/binarycpython/tests/tests_population_extensions/test_HPC.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__HPC.py
rename to binarycpython/tests/tests_population_extensions/test_HPC.py
diff --git a/binarycpython/tests/tests_population_extensions/test__Moe_di_Stefano_2017.py b/binarycpython/tests/tests_population_extensions/test_Moe_di_Stefano_2017.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__Moe_di_Stefano_2017.py
rename to binarycpython/tests/tests_population_extensions/test_Moe_di_Stefano_2017.py
diff --git a/binarycpython/tests/tests_population_extensions/test__distribution_functions.py b/binarycpython/tests/tests_population_extensions/test__distribution_functions.py
index 30095a950914e85d02638d261810b80b3ed02fd3..c0c601fa7c86cd76cd191425ba61f2b2a56fd460 100644
--- a/binarycpython/tests/tests_population_extensions/test__distribution_functions.py
+++ b/binarycpython/tests/tests_population_extensions/test__distribution_functions.py
@@ -167,698 +167,698 @@ class test_powerlaw(unittest.TestCase):
# extra test for k = -1
self.assertRaises(ValueError, distribution_functions_pop.powerlaw, 1, 100, -1, 10)
-####
-class test_three_part_power_law(unittest.TestCase):
- """
- Class for unit test of three_part_power_law
- """
-
- def test_three_part_power_law(self):
- with Capturing() as output:
- self._test_three_part_power_law()
-
- def _test_three_part_power_law(self):
- """
- unittest for three_part_power_law
- """
-
- distribution_functions_pop = Population()
-
- perl_results = [
- 10.0001044752901,
- 2.03065220596677,
- 0.0501192469795434,
- 0.000251191267451594,
- 9.88540897458207e-05,
- 6.19974072148769e-06,
- ]
- python_results = []
- input_lists = []
-
- for mass in MASS_LIST:
- input_lists.append(mass)
- python_results.append(
- distribution_functions_pop.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)):
- msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(
- perl_results[i], python_results[i], str(input_lists[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), TOLERANCE, msg=msg
- )
-
- # Extra test:
- # M < M0
- self.assertTrue(
- distribution_functions_pop.three_part_powerlaw(0.05, 0.08, 0.1, 1, 300, -1.3, -2.3, -2.3) == 0,
- msg="Probability should be zero as M < M0",
- )
-
-
-class test_Kroupa2001(unittest.TestCase):
- """
- Class for unit test of Kroupa2001
- """
-
- def test_Kroupa2001(self):
- with Capturing() as output:
- self._test_Kroupa2001()
-
- def _test_Kroupa2001(self):
- """
- unittest for three_part_power_law
- """
-
- distribution_functions_pop = Population()
-
- perl_results = [
- 0, # perl value is actually 5.71196495365248
- 2.31977861075353,
- 0.143138195684851,
- 0.000717390363216896,
- 0.000282322598503135,
- 1.77061658757533e-05,
- ]
- python_results = []
- input_lists = []
-
- for mass in MASS_LIST:
- input_lists.append(mass)
- python_results.append(distribution_functions_pop.Kroupa2001(mass))
-
- # GO over the results and check whether they are equal (within tolerance)
- for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
- perl_results[i], python_results[i], str(input_lists[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), TOLERANCE, msg=msg
- )
-
- # Extra tests:
- self.assertEqual(
- distribution_functions_pop.Kroupa2001(10, newopts={"mmax": 300}),
- distribution_functions_pop.three_part_powerlaw(10, 0.1, 0.5, 1, 300, -1.3, -2.3, -2.3),
- )
-
-class TestDistributions(unittest.TestCase):
- """
- Unittest class
-
- # https://stackoverflow.com/questions/17353213/init-for-unittest-testcase
- """
-
- def __init__(self, *args, **kwargs):
- """
- init
- """
- super(TestDistributions, self).__init__(*args, **kwargs)
-
- def test_ktg93(self):
- with Capturing() as output:
- self._test_ktg93()
-
- def _test_ktg93(self):
- """
- unittest for three_part_power_law
- """
-
- perl_results = [
- 0, # perl value is actually 5.79767807698379 but that is not correct
- 2.35458895566605,
- 0.155713799148675,
- 0.000310689875361984,
- 0.000103963454405194,
- 4.02817276824841e-06,
- ]
- python_results = []
- input_lists = []
-
- for mass in self.mass_list:
- input_lists.append(mass)
- python_results.append(ktg93(mass))
-
- # GO over the results and check whether they are equal (within tolerance)
- for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
- perl_results[i], python_results[i], str(input_lists[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
- )
-
- # extra test:
- self.assertEqual(
- ktg93(10, newopts={"mmax": 300}),
- three_part_powerlaw(10, 0.1, 0.5, 1, 300, -1.3, -2.2, -2.7),
- )
-
- def test_imf_tinsley1980(self):
- with Capturing() as output:
- self._test_imf_tinsley1980()
-
- def _test_imf_tinsley1980(self):
- """
- Unittest for function imf_tinsley1980
- """
-
- m = 1.2
- self.assertEqual(
- imf_tinsley1980(m),
- three_part_powerlaw(m, 0.1, 2.0, 10.0, 80.0, -2.0, -2.3, -3.3),
- )
-
- def test_imf_scalo1986(self):
- with Capturing() as output:
- self._test_imf_scalo1986()
-
- def _test_imf_scalo1986(self):
- """
- Unittest for function imf_scalo1986
- """
-
- m = 1.2
- self.assertEqual(
- imf_scalo1986(m),
- three_part_powerlaw(m, 0.1, 1.0, 2.0, 80.0, -2.35, -2.35, -2.70),
- )
-
- def test_imf_scalo1998(self):
- with Capturing() as output:
- self._test_imf_scalo1998()
-
- def _test_imf_scalo1998(self):
- """
- Unittest for function imf_scalo1986
- """
-
- m = 1.2
- self.assertEqual(
- imf_scalo1998(m),
- three_part_powerlaw(m, 0.1, 1.0, 10.0, 80.0, -1.2, -2.7, -2.3),
- )
-
- def test_imf_chabrier2003(self):
- with Capturing() as output:
- self._test_imf_chabrier2003()
-
- def _test_imf_chabrier2003(self):
- """
- Unittest for function imf_chabrier2003
- """
-
- input_1 = 0
- self.assertRaises(ValueError, imf_chabrier2003, input_1)
-
- masses = [0.1, 0.2, 0.5, 1, 2, 10, 15, 50]
- perl_results = [
- 5.64403964849588,
- 2.40501495673496,
- 0.581457346702825,
- 0.159998782068074,
- 0.0324898485372181,
- 0.000801893469684309,
- 0.000315578044662863,
- 1.97918170035704e-05,
- ]
- python_results = [imf_chabrier2003(m) for m in masses]
-
- # GO over the results and check whether they are equal (within tolerance)
- for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
- perl_results[i], python_results[i], str(masses[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
- )
-
- def test_duquennoy1991(self):
- with Capturing() as output:
- self._test_duquennoy1991()
-
- def _test_duquennoy1991(self):
- """
- Unittest for function duquennoy1991
- """
-
- self.assertEqual(duquennoy1991(4.2), gaussian(4.2, 4.8, 2.3, -2, 12))
-
- def test_gaussian(self):
- with Capturing() as output:
- self._test_gaussian()
-
- 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 = []
- input_lists = []
-
- for logper in self.logper_list:
- input_lists.append(logper)
- 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)):
- msg = "Error: Value perl: {} Value python: {} for logper: {}".format(
- perl_results[i], python_results[i], str(input_lists[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
- )
-
- # Extra test:
- self.assertTrue(
- gaussian(15, 4.8, 2.3, -2.0, 12.0) == 0,
- msg="Probability should be 0 because the input period is out of bounds",
- )
-
- def test_Arenou2010_binary_fraction(self):
- with Capturing() as output:
- self._test_Arenou2010_binary_fraction()
-
- 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 = []
- input_lists = []
-
- for mass in self.mass_list:
- input_lists.append(mass)
- 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)):
- msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
- perl_results[i], python_results[i], str(input_lists[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
- )
-
- def test_raghavan2010_binary_fraction(self):
- with Capturing() as output:
- self._test_raghavan2010_binary_fraction()
-
- 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 = []
- input_lists = []
-
- for mass in self.mass_list:
- input_lists.append(mass)
- 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)):
- msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
- perl_results[i], python_results[i], str(input_lists[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
- )
-
- def test_Izzard2012_period_distribution(self):
- with Capturing() as output:
- self._test_Izzard2012_period_distribution()
-
- 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 = []
- input_lists = []
-
- for mass in self.mass_list:
- for per in self.per_list:
- input_lists.append([mass, per])
-
- 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)):
- msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(
- perl_results[i], python_results[i], str(input_lists[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
- )
-
- def test_flatsections(self):
- with Capturing() as output:
- self._test_flatsections()
-
- 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 = []
- input_lists = []
-
- for q in self.q_list:
- input_lists.append(q)
- 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)):
- msg = "Error: Value perl: {} Value python: {} for q: {}".format(
- perl_results[i], python_results[i], str(input_lists[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
- )
-
- def test_sana12(self):
- with Capturing() as output:
- self._test_sana12()
-
- 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 = []
- input_lists = []
-
- 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)
-
- input_lists.append([mass, mass_2, per])
-
- 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)):
- msg = "Error: Value perl: {} Value python: {} for mass, mass2, per: {}".format(
- perl_results[i], python_results[i], str(input_lists[i])
- )
- self.assertLess(
- np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
- )
+# ####
+# class test_three_part_power_law(unittest.TestCase):
+# """
+# Class for unit test of three_part_power_law
+# """
+
+# def test_three_part_power_law(self):
+# with Capturing() as output:
+# self._test_three_part_power_law()
+
+# def _test_three_part_power_law(self):
+# """
+# unittest for three_part_power_law
+# """
+
+# distribution_functions_pop = Population()
+
+# perl_results = [
+# 10.0001044752901,
+# 2.03065220596677,
+# 0.0501192469795434,
+# 0.000251191267451594,
+# 9.88540897458207e-05,
+# 6.19974072148769e-06,
+# ]
+# python_results = []
+# input_lists = []
+
+# for mass in MASS_LIST:
+# input_lists.append(mass)
+# python_results.append(
+# distribution_functions_pop.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)):
+# msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(
+# perl_results[i], python_results[i], str(input_lists[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), TOLERANCE, msg=msg
+# )
+
+# # Extra test:
+# # M < M0
+# self.assertTrue(
+# distribution_functions_pop.three_part_powerlaw(0.05, 0.08, 0.1, 1, 300, -1.3, -2.3, -2.3) == 0,
+# msg="Probability should be zero as M < M0",
+# )
+
+
+# class test_Kroupa2001(unittest.TestCase):
+# """
+# Class for unit test of Kroupa2001
+# """
+
+# def test_Kroupa2001(self):
+# with Capturing() as output:
+# self._test_Kroupa2001()
+
+# def _test_Kroupa2001(self):
+# """
+# unittest for three_part_power_law
+# """
+
+# distribution_functions_pop = Population()
+
+# perl_results = [
+# 0, # perl value is actually 5.71196495365248
+# 2.31977861075353,
+# 0.143138195684851,
+# 0.000717390363216896,
+# 0.000282322598503135,
+# 1.77061658757533e-05,
+# ]
+# python_results = []
+# input_lists = []
+
+# for mass in MASS_LIST:
+# input_lists.append(mass)
+# python_results.append(distribution_functions_pop.Kroupa2001(mass))
+
+# # GO over the results and check whether they are equal (within tolerance)
+# for i in range(len(python_results)):
+# msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+# perl_results[i], python_results[i], str(input_lists[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), TOLERANCE, msg=msg
+# )
+
+# # Extra tests:
+# self.assertEqual(
+# distribution_functions_pop.Kroupa2001(10, newopts={"mmax": 300}),
+# distribution_functions_pop.three_part_powerlaw(10, 0.1, 0.5, 1, 300, -1.3, -2.3, -2.3),
+# )
+
+# class TestDistributions(unittest.TestCase):
+# """
+# Unittest class
+
+# # https://stackoverflow.com/questions/17353213/init-for-unittest-testcase
+# """
+
+# def __init__(self, *args, **kwargs):
+# """
+# init
+# """
+# super(TestDistributions, self).__init__(*args, **kwargs)
+
+# def test_ktg93(self):
+# with Capturing() as output:
+# self._test_ktg93()
+
+# def _test_ktg93(self):
+# """
+# unittest for three_part_power_law
+# """
+
+# perl_results = [
+# 0, # perl value is actually 5.79767807698379 but that is not correct
+# 2.35458895566605,
+# 0.155713799148675,
+# 0.000310689875361984,
+# 0.000103963454405194,
+# 4.02817276824841e-06,
+# ]
+# python_results = []
+# input_lists = []
+
+# for mass in self.mass_list:
+# input_lists.append(mass)
+# python_results.append(ktg93(mass))
+
+# # GO over the results and check whether they are equal (within tolerance)
+# for i in range(len(python_results)):
+# msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+# perl_results[i], python_results[i], str(input_lists[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+# )
+
+# # extra test:
+# self.assertEqual(
+# ktg93(10, newopts={"mmax": 300}),
+# three_part_powerlaw(10, 0.1, 0.5, 1, 300, -1.3, -2.2, -2.7),
+# )
+
+# def test_imf_tinsley1980(self):
+# with Capturing() as output:
+# self._test_imf_tinsley1980()
+
+# def _test_imf_tinsley1980(self):
+# """
+# Unittest for function imf_tinsley1980
+# """
+
+# m = 1.2
+# self.assertEqual(
+# imf_tinsley1980(m),
+# three_part_powerlaw(m, 0.1, 2.0, 10.0, 80.0, -2.0, -2.3, -3.3),
+# )
+
+# def test_imf_scalo1986(self):
+# with Capturing() as output:
+# self._test_imf_scalo1986()
+
+# def _test_imf_scalo1986(self):
+# """
+# Unittest for function imf_scalo1986
+# """
+
+# m = 1.2
+# self.assertEqual(
+# imf_scalo1986(m),
+# three_part_powerlaw(m, 0.1, 1.0, 2.0, 80.0, -2.35, -2.35, -2.70),
+# )
+
+# def test_imf_scalo1998(self):
+# with Capturing() as output:
+# self._test_imf_scalo1998()
+
+# def _test_imf_scalo1998(self):
+# """
+# Unittest for function imf_scalo1986
+# """
+
+# m = 1.2
+# self.assertEqual(
+# imf_scalo1998(m),
+# three_part_powerlaw(m, 0.1, 1.0, 10.0, 80.0, -1.2, -2.7, -2.3),
+# )
+
+# def test_imf_chabrier2003(self):
+# with Capturing() as output:
+# self._test_imf_chabrier2003()
+
+# def _test_imf_chabrier2003(self):
+# """
+# Unittest for function imf_chabrier2003
+# """
+
+# input_1 = 0
+# self.assertRaises(ValueError, imf_chabrier2003, input_1)
+
+# masses = [0.1, 0.2, 0.5, 1, 2, 10, 15, 50]
+# perl_results = [
+# 5.64403964849588,
+# 2.40501495673496,
+# 0.581457346702825,
+# 0.159998782068074,
+# 0.0324898485372181,
+# 0.000801893469684309,
+# 0.000315578044662863,
+# 1.97918170035704e-05,
+# ]
+# python_results = [imf_chabrier2003(m) for m in masses]
+
+# # GO over the results and check whether they are equal (within tolerance)
+# for i in range(len(python_results)):
+# msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+# perl_results[i], python_results[i], str(masses[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+# )
+
+# def test_duquennoy1991(self):
+# with Capturing() as output:
+# self._test_duquennoy1991()
+
+# def _test_duquennoy1991(self):
+# """
+# Unittest for function duquennoy1991
+# """
+
+# self.assertEqual(duquennoy1991(4.2), gaussian(4.2, 4.8, 2.3, -2, 12))
+
+# def test_gaussian(self):
+# with Capturing() as output:
+# self._test_gaussian()
+
+# 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 = []
+# input_lists = []
+
+# for logper in self.logper_list:
+# input_lists.append(logper)
+# 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)):
+# msg = "Error: Value perl: {} Value python: {} for logper: {}".format(
+# perl_results[i], python_results[i], str(input_lists[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+# )
+
+# # Extra test:
+# self.assertTrue(
+# gaussian(15, 4.8, 2.3, -2.0, 12.0) == 0,
+# msg="Probability should be 0 because the input period is out of bounds",
+# )
+
+# def test_Arenou2010_binary_fraction(self):
+# with Capturing() as output:
+# self._test_Arenou2010_binary_fraction()
+
+# 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 = []
+# input_lists = []
+
+# for mass in self.mass_list:
+# input_lists.append(mass)
+# 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)):
+# msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+# perl_results[i], python_results[i], str(input_lists[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+# )
+
+# def test_raghavan2010_binary_fraction(self):
+# with Capturing() as output:
+# self._test_raghavan2010_binary_fraction()
+
+# 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 = []
+# input_lists = []
+
+# for mass in self.mass_list:
+# input_lists.append(mass)
+# 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)):
+# msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+# perl_results[i], python_results[i], str(input_lists[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+# )
+
+# def test_Izzard2012_period_distribution(self):
+# with Capturing() as output:
+# self._test_Izzard2012_period_distribution()
+
+# 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 = []
+# input_lists = []
+
+# for mass in self.mass_list:
+# for per in self.per_list:
+# input_lists.append([mass, per])
+
+# 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)):
+# msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(
+# perl_results[i], python_results[i], str(input_lists[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+# )
+
+# def test_flatsections(self):
+# with Capturing() as output:
+# self._test_flatsections()
+
+# 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 = []
+# input_lists = []
+
+# for q in self.q_list:
+# input_lists.append(q)
+# 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)):
+# msg = "Error: Value perl: {} Value python: {} for q: {}".format(
+# perl_results[i], python_results[i], str(input_lists[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+# )
+
+# def test_sana12(self):
+# with Capturing() as output:
+# self._test_sana12()
+
+# 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 = []
+# input_lists = []
+
+# 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)
+
+# input_lists.append([mass, mass_2, per])
+
+# 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)):
+# msg = "Error: Value perl: {} Value python: {} for mass, mass2, per: {}".format(
+# perl_results[i], python_results[i], str(input_lists[i])
+# )
+# self.assertLess(
+# np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+# )
if __name__ == "__main__":
diff --git a/binarycpython/tests/tests_population_extensions/test__analytics.py b/binarycpython/tests/tests_population_extensions/test_analytics.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__analytics.py
rename to binarycpython/tests/tests_population_extensions/test_analytics.py
diff --git a/binarycpython/tests/tests_population_extensions/test__cache.py b/binarycpython/tests/tests_population_extensions/test_cache.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__cache.py
rename to binarycpython/tests/tests_population_extensions/test_cache.py
diff --git a/binarycpython/tests/tests_population_extensions/test__condor.py b/binarycpython/tests/tests_population_extensions/test_condor.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__condor.py
rename to binarycpython/tests/tests_population_extensions/test_condor.py
diff --git a/binarycpython/tests/tests_population_extensions/test__dataIO.py b/binarycpython/tests/tests_population_extensions/test_dataIO.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__dataIO.py
rename to binarycpython/tests/tests_population_extensions/test_dataIO.py
diff --git a/binarycpython/tests/tests_population_extensions/test__grid_logging.py b/binarycpython/tests/tests_population_extensions/test_grid_logging.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__grid_logging.py
rename to binarycpython/tests/tests_population_extensions/test_grid_logging.py
diff --git a/binarycpython/tests/tests_population_extensions/test__grid_options_defaults.py b/binarycpython/tests/tests_population_extensions/test_grid_options_defaults.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__grid_options_defaults.py
rename to binarycpython/tests/tests_population_extensions/test_grid_options_defaults.py
diff --git a/binarycpython/tests/tests_population_extensions/test__gridcode.py b/binarycpython/tests/tests_population_extensions/test_gridcode.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__gridcode.py
rename to binarycpython/tests/tests_population_extensions/test_gridcode.py
diff --git a/binarycpython/tests/tests_population_extensions/test__metadata.py b/binarycpython/tests/tests_population_extensions/test_metadata.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__metadata.py
rename to binarycpython/tests/tests_population_extensions/test_metadata.py
diff --git a/binarycpython/tests/tests_population_extensions/test__slurm.py b/binarycpython/tests/tests_population_extensions/test_slurm.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__slurm.py
rename to binarycpython/tests/tests_population_extensions/test_slurm.py
diff --git a/binarycpython/tests/tests_population_extensions/test__spacing_functions.py b/binarycpython/tests/tests_population_extensions/test_spacing_functions.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__spacing_functions.py
rename to binarycpython/tests/tests_population_extensions/test_spacing_functions.py
diff --git a/binarycpython/tests/tests_population_extensions/test__version_info.py b/binarycpython/tests/tests_population_extensions/test_version_info.py
similarity index 100%
rename from binarycpython/tests/tests_population_extensions/test__version_info.py
rename to binarycpython/tests/tests_population_extensions/test_version_info.py