diff --git a/binarycpython/tests/main.py b/binarycpython/tests/main.py
index 815f86aace5ff740b062a65036c149bf23f994b3..fadaabf3a6f717dd757f985c093b05b1528ac56a 100755
--- a/binarycpython/tests/main.py
+++ b/binarycpython/tests/main.py
@@ -72,7 +72,21 @@ from binarycpython.tests.tests_population_extensions.test_distribution_functions
test_flat,
test_number,
test_const_distribution,
- test_powerlaw
+ test_powerlaw,
+ test_three_part_power_law,
+ test_Kroupa2001,
+ test_ktg93,
+ test_imf_tinsley1980,
+ test_imf_scalo1986,
+ test_imf_scalo1998,
+ test_imf_chabrier2003,
+ test_duquennoy1991,
+ test_gaussian,
+ test_Arenou2010_binary_fraction,
+ test_raghavan2010_binary_fraction,
+ test_Izzard2012_period_distribution,
+ test_flatsections,
+ test_sana12
)
from binarycpython.tests.tests_population_extensions.test_grid_options_defaults import (
test_grid_options_help,
diff --git a/binarycpython/tests/tests_population_extensions/test_distribution_functions.py b/binarycpython/tests/tests_population_extensions/test_distribution_functions.py
index c0c601fa7c86cd76cd191425ba61f2b2a56fd460..a471f72acb6aa3676de0da6b9681906d67c24b11 100644
--- a/binarycpython/tests/tests_population_extensions/test_distribution_functions.py
+++ b/binarycpython/tests/tests_population_extensions/test_distribution_functions.py
@@ -3,28 +3,15 @@ Module containing the unittests for the distribution functions.
TODO: powerlaw_constant_nocache
TODO: powerlaw_constant
-TODO: powerlaw
TODO: calculate_constants_three_part_powerlaw
-TODO: three_part_powerlaw
TODO: gaussian_normalizing_const
TODO: gaussian_func
-TODO: gaussian
-TODO: Kroupa2001
-TODO: ktg93
-TODO: imf_tinsley1980
-TODO: imf_scalo1986
-TODO: imf_scalo1998
-TODO: imf_chabrier2003
-TODO: Arenou2010_binary_fraction
-TODO: raghavan2010_binary_fraction
-TODO: duquennoy1991
TODO: sana12
TODO: interpolate_in_mass_izzard2012
-TODO: Izzard2012_period_distribution
-TODO: flatsections
TODO: cosmic_SFH_madau_dickinson2014
TODO: poisson
TODO: _poisson
+
TODO: get_max_multiplicity
TODO: merge_multiplicities
TODO: Moe_di_Stefano_2017_multiplicity_fractions
@@ -167,698 +154,781 @@ 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,
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-# 0.121764808010258,
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-# 0.121764808010258,
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-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
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-# 0.121764808010258,
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-# 0.121764808010258,
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-# 0.121764808010258,
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-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
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-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
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-# 0.121764808010258,
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-# 0.121764808010258,
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-# 0.121764808010258,
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-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
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-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 0.121764808010258,
-# 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 test_ktg93(unittest.TestCase):
+ """
+ Class for unit test of ktg93
+ """
+
+ def test_ktg93(self):
+ with Capturing() as output:
+ self._test_ktg93()
+
+ def _test_ktg93(self):
+ """
+ unittest for three_part_power_law
+ """
+
+ distribution_functions_pop = Population()
+
+ 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 MASS_LIST:
+ input_lists.append(mass)
+ python_results.append(distribution_functions_pop.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]), TOLERANCE, msg=msg
+ )
+
+ # extra test:
+ self.assertEqual(
+ distribution_functions_pop.ktg93(10, newopts={"mmax": 300}),
+ distribution_functions_pop.three_part_powerlaw(10, 0.1, 0.5, 1, 300, -1.3, -2.2, -2.7),
+ )
+
+
+class test_imf_tinsley1980(unittest.TestCase):
+ """
+ Class for unit test of imf_tinsley1980
+ """
+
+ def test_imf_tinsley1980(self):
+ with Capturing() as output:
+ self._test_imf_tinsley1980()
+
+ def _test_imf_tinsley1980(self):
+ """
+ Unittest for function imf_tinsley1980
+ """
+
+ distribution_functions_pop = Population()
+
+ m = 1.2
+ self.assertEqual(
+ distribution_functions_pop.imf_tinsley1980(m),
+ distribution_functions_pop.three_part_powerlaw(m, 0.1, 2.0, 10.0, 80.0, -2.0, -2.3, -3.3),
+ )
+
+
+class test_imf_scalo1986(unittest.TestCase):
+ """
+ Class for unit test of imf_scalo1986
+ """
+
+ def test_imf_scalo1986(self):
+ with Capturing() as output:
+ self._test_imf_scalo1986()
+
+ def _test_imf_scalo1986(self):
+ """
+ Unittest for function imf_scalo1986
+ """
+
+ distribution_functions_pop = Population()
+
+ m = 1.2
+ self.assertEqual(
+ distribution_functions_pop.imf_scalo1986(m),
+ distribution_functions_pop.three_part_powerlaw(m, 0.1, 1.0, 2.0, 80.0, -2.35, -2.35, -2.70),
+ )
+
+
+class test_imf_scalo1998(unittest.TestCase):
+ """
+ Class for unit test of imf_scalo1998
+ """
+
+ def test_imf_scalo1998(self):
+ with Capturing() as output:
+ self._test_imf_scalo1998()
+
+ def _test_imf_scalo1998(self):
+ """
+ Unittest for function imf_scalo1986
+ """
+
+ distribution_functions_pop = Population()
+
+ m = 1.2
+ self.assertEqual(
+ distribution_functions_pop.imf_scalo1998(m),
+ distribution_functions_pop.three_part_powerlaw(m, 0.1, 1.0, 10.0, 80.0, -1.2, -2.7, -2.3),
+ )
+
+
+class test_imf_chabrier2003(unittest.TestCase):
+ """
+ Class for unit test of imf_chabrier2003
+ """
+
+ def test_imf_chabrier2003(self):
+ with Capturing() as output:
+ self._test_imf_chabrier2003()
+
+ def _test_imf_chabrier2003(self):
+ """
+ Unittest for function imf_chabrier2003
+ """
+
+ distribution_functions_pop = Population()
+
+ input_1 = 0
+ self.assertRaises(ValueError, distribution_functions_pop.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 = [distribution_functions_pop.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]), TOLERANCE, msg=msg
+ )
+
+
+class test_duquennoy1991(unittest.TestCase):
+ """
+ Class for unit test of duquennoy1991
+ """
+
+ def test_duquennoy1991(self):
+ with Capturing() as output:
+ self._test_duquennoy1991()
+
+ def _test_duquennoy1991(self):
+ """
+ Unittest for function duquennoy1991
+ """
+
+ distribution_functions_pop = Population()
+
+ self.assertEqual(distribution_functions_pop.duquennoy1991(4.2), distribution_functions_pop.gaussian(4.2, 4.8, 2.3, -2, 12))
+
+
+class test_gaussian(unittest.TestCase):
+ """
+ Class for unit test of gaussian
+ """
+
+ def test_gaussian(self):
+ with Capturing() as output:
+ self._test_gaussian()
+
+ def _test_gaussian(self):
+ """
+ unittest for three_part_power_law
+ """
+
+ distribution_functions_pop = Population()
+
+ perl_results = [
+ 0.00218800520299544,
+ 0.0121641269671571,
+ 0.0657353455837751,
+ 0.104951743573429,
+ 0.16899534495487,
+ 0.0134332780385336,
+ ]
+ python_results = []
+ input_lists = []
+
+ for logper in LOGPER_LIST:
+ input_lists.append(logper)
+ python_results.append(distribution_functions_pop.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]), TOLERANCE, msg=msg
+ )
+
+ # Extra test:
+ self.assertTrue(
+ distribution_functions_pop.gaussian(15, 4.8, 2.3, -2.0, 12.0) == 0,
+ msg="Probability should be 0 because the input period is out of bounds",
+ )
+
+
+class test_Arenou2010_binary_fraction(unittest.TestCase):
+ """
+ Class for unit test of Arenou2010_binary_fraction
+ """
+
+ 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
+ """
+
+ distribution_functions_pop = Population()
+
+ perl_results = [
+ 0.123079723518677,
+ 0.178895136157746,
+ 0.541178340047153,
+ 0.838798485820276,
+ 0.838799998443204,
+ 0.8388,
+ ]
+ python_results = []
+ input_lists = []
+
+ for mass in MASS_LIST:
+ input_lists.append(mass)
+ python_results.append(distribution_functions_pop.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]), TOLERANCE, msg=msg
+ )
+
+
+class test_raghavan2010_binary_fraction(unittest.TestCase):
+ """
+ Class for unit test of raghavan2010_binary_fraction
+ """
+
+ 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
+ """
+
+ distribution_functions_pop = Population()
+
+ perl_results = [0.304872297931597, 0.334079955706623, 0.41024, 1, 1, 1]
+ python_results = []
+ input_lists = []
+
+ for mass in MASS_LIST:
+ input_lists.append(mass)
+ python_results.append(distribution_functions_pop.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]), TOLERANCE, msg=msg
+ )
+
+
+class test_Izzard2012_period_distribution(unittest.TestCase):
+ """
+ Class for unit test of Izzard2012_period_distribution
+ """
+
+ 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
+ """
+
+ distribution_functions_pop = Population()
+
+ 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 MASS_LIST:
+ for per in PER_LIST:
+ input_lists.append([mass, per])
+
+ python_results.append(distribution_functions_pop.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]), TOLERANCE, msg=msg
+ )
+
+
+class test_flatsections(unittest.TestCase):
+ """
+ Class for unit test of flatsections
+ """
+
+ def test_flatsections(self):
+ with Capturing() as output:
+ self._test_flatsections()
+
+ def _test_flatsections(self):
+ """
+ unittest for flatsections
+ """
+
+ distribution_functions_pop = Population()
+
+ perl_results = [
+ 1.01010101010101,
+ 1.01010101010101,
+ 1.01010101010101,
+ 1.01010101010101,
+ 1.01010101010101,
+ 1.01010101010101,
+ ]
+ python_results = []
+ input_lists = []
+
+ for q in Q_LIST:
+ input_lists.append(q)
+ python_results.append(
+ distribution_functions_pop.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]), TOLERANCE, msg=msg
+ )
+
+
+class test_sana12(unittest.TestCase):
+ """
+ Class for unit test of sana12
+ """
+
+ def test_sana12(self):
+ with Capturing() as output:
+ self._test_sana12()
+
+ def _test_sana12(self):
+ """
+ unittest for three_part_power_law
+ """
+
+ distribution_functions_pop = Population()
+
+ 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 MASS_LIST:
+ for q in Q_LIST:
+ for per in 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(
+ distribution_functions_pop.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]), TOLERANCE, msg=msg
+ )
if __name__ == "__main__":
diff --git a/binarycpython/tests/tmp_functions.py b/binarycpython/tests/tmp_functions.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..88581f3709a81e66accec56eb73a39a0c59517ad 100644
--- a/binarycpython/tests/tmp_functions.py
+++ b/binarycpython/tests/tmp_functions.py
@@ -0,0 +1,22 @@
+
+
+
+
+# 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)
+
+
+
+
+
+