"""
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
from binarycpython.utils.functions import (
Capturing,
temp_dir
)
TMP_DIR = temp_dir("tests", "test_distributions")
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)
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_setopts(self):
with Capturing() as output:
self._test_setopts()
def _test_setopts(self):
"""
Unittest for function set_opts
"""
default_dict = {"m1": 2, "m2": 3}
output_dict_1 = set_opts(default_dict, {})
self.assertTrue(output_dict_1 == default_dict)
new_opts = {"m1": 10}
output_dict_2 = set_opts(default_dict, new_opts)
updated_dict = default_dict.copy()
updated_dict["m1"] = 10
self.assertTrue(output_dict_2 == updated_dict)
def test_flat(self):
with Capturing() as output:
self._test_flat()
def _test_flat(self):
"""
Unittest for the function flat
"""
output_1 = flat()
self.assertTrue(isinstance(output_1, float))
self.assertEqual(output_1, 1.0)
def test_number(self):
with Capturing() as output:
self._test_number()
def _test_number(self):
"""
Unittest for function number
"""
input_1 = 1.0
output_1 = number(input_1)
self.assertEqual(input_1, output_1)
def test_const(self):
with Capturing() as output:
self._test_const()
def _test_const(self):
"""
Unittest for function const
"""
output_1 = const(min_bound=0, max_bound=2)
self.assertEqual(
output_1, 0.5, msg="Value should be 0.5, but is {}".format(output_1)
)
output_2 = const(min_bound=0, max_bound=2, val=3)
self.assertEqual(
output_2, 0, msg="Value should be 0, but is {}".format(output_2)
)
def test_powerlaw(self):
with Capturing() as output:
self._test_powerlaw()
def _test_powerlaw(self):
"""
unittest for the powerlaw test
"""
perl_results = [
0,
0,
1.30327367546194,
0.00653184128064016,
0.00257054805572128,
0.000161214690242696,
]
python_results = []
input_lists = []
for mass in self.mass_list:
input_lists.append(mass)
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)):
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)
# extra test for k = -1
self.assertRaises(ValueError, powerlaw, 1, 100, -1, 10)
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
"""
perl_results = [
10.0001044752901,
2.03065220596677,
0.0501192469795434,
0.000251191267451594,
9.88540897458207e-05,
6.19974072148769e-06,
]
python_results = []
input_lists = []
for mass in self.mass_list:
input_lists.append(mass)
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)):
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
)
# Extra test:
# M < M0
self.assertTrue(
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",
)
def test_Kroupa2001(self):
with Capturing() as output:
self._test_Kroupa2001()
def _test_Kroupa2001(self):
"""
unittest for three_part_power_law
"""
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 self.mass_list:
input_lists.append(mass)
python_results.append(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]), self.tolerance, msg=msg
)
# Extra tests:
self.assertEqual(
Kroupa2001(10, newopts={"mmax": 300}),
three_part_powerlaw(10, 0.1, 0.5, 1, 300, -1.3, -2.3, -2.3),
)
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__":
unittest.main()