def test_imf_is_zero_if_no_positive_mass(available_imfs):
for imf in available_imfs:
assert select_imf(imf).for_mass(0) == 0.0
assert select_imf(imf).for_mass(-10) == 0.0
for mass in [0.015, 0.02, 0.2, 0.75, 2, 8, 35, 90]:
assert select_imf(imf).for_mass(mass) > 0.0
def test_imf_supernovae_II_is_zero_for_lower_masses():
imf = select_imf(np.random.choice(settings.valid_values["imf"]),
settings.default)
assert functions.imf_supernovae_II(np.random.sample() * constants.M_SNII,
imf) == 0
assert functions.imf_supernovae_II(constants.M_SNII, imf) == 0
def test_imf_supernovae_II_includes_binary_primaries():
imf = select_imf(np.random.choice(settings.valid_values["imf"]),
settings.default)
m = (constants.B_MIN + constants.B_MAX) / 2
assert m > constants.M_SNII
assert functions.imf_supernovae_II(m, imf) > functions.imf_zero(m, imf) / m
def test_select_imf():
strings = ["salpeter", "starburst", "chabrier", "ferrini", "kroupa2001", "kroupa2002", "miller_scalo", "maschberger"]
classes = [Salpeter, Starburst, Chabrier, Ferrini, Kroupa2001, Kroupa2002, MillerScalo, Maschberger]
for i in range(len(strings)):
imf_instance = select_imf(strings[i])
assert type(imf_instance) == classes[i]
def test_imf_binaries_are_zero_for_non_valid_masses():
imf = select_imf(np.random.choice(settings.valid_values["imf"]),
settings.default)
assert functions.imf_binary_primary(-1, imf) == 0.0
assert functions.imf_binary_primary(constants.M_MIN / 2, imf) == 0.0
assert functions.imf_binary_secondary(-1, imf) == 0.0
assert functions.imf_binary_secondary(constants.B_MAX * 2, imf) == 0.0
def test_imf_supernovae_II_non_zero_for_SNII_masses():
imf = select_imf(np.random.choice(settings.valid_values["imf"]),
settings.default)
assert functions.imf_supernovae_II(constants.M_SNII + 0.01, imf) > 0
assert functions.imf_supernovae_II(
constants.M_SNII + np.random.sample() * 100, imf) > 0
assert functions.imf_supernovae_II(constants.M_SNII + 100, imf) > 0
def test_global_imf():
imf = select_imf(np.random.choice(settings.valid_values["imf"]),
settings.default)
assert functions.global_imf(constants.M_MIN - 0.001, imf) == 0
assert functions.global_imf(100, imf) == functions.imf_zero(100, imf)
for m in [1, 4, 8, 10, 40]:
assert 0 < functions.global_imf(m, imf)
assert functions.imf_zero(100, imf) < functions.global_imf(m, imf)
def test_imf_binary_secondary_integrates_phi_secondary():
m_in_binaries_range = 5.0
m_inf = 2 * m_in_binaries_range
imf = select_imf(np.random.choice(settings.valid_values["imf"]),
settings.default)
expected = settings.default['binary_fraction'] * \
functions.newton_cotes(m_inf, constants.B_MAX, functions.phi_secondary(m_in_binaries_range, imf))
assert functions.imf_binary_secondary(m_in_binaries_range, imf) == expected
def test_return_fractions():
imf = select_imf(np.random.choice(settings.valid_values["imf"]),
settings.default)
default_yields_file = settings.default['expelled_elements_filename']
stellar_yields = elements.Expelled(default_yields_file)
expected = functions.newton_cotes(
1, 100, lambda m: (functions.global_imf(m, imf) / m) *
(m - stellar_yields.for_mass(m)['remnants']))
assert functions.return_fraction(1, 100, stellar_yields, imf) == expected
def test_imf_zero():
m_in_binaries_range = 5.0
m_lower = constants.B_MIN - 0.5
m_up = constants.B_MAX + 0.5
imf = select_imf(np.random.choice(settings.valid_values["imf"]),
settings.default)
assert functions.imf_zero(m_lower, imf) == imf.for_mass(m_lower)
assert functions.imf_zero(m_up, imf) == imf.for_mass(m_up)
imf_bin = imf.for_mass(m_in_binaries_range) * (1.0 -
constants.BIN_FRACTION)
assert functions.imf_zero(m_in_binaries_range, imf) == imf_bin
def init_variables(self):
self.initial_mass_function = select_imf(self.context["imf"],
self.context)
self.context["abundances"] = select_abundances(
self.context["sol_ab"], float(self.context["z"]))
self.context["expelled"] = elements.Expelled(
expelled_elements_filename=self.
context["expelled_elements_filename"])
self.mass_intervals = []
self.energies = []
self.sn_Ia_rates = []
self.z = self.context["z"]
self.dtd = select_dtd(self.context["dtd_sn"])
self.m_min = self.context["m_min"]
self.m_max = self.context["m_max"]
self.integration_step = self.context["integration_step"]
self.total_time_steps = 0
if "total_time_steps" in self.context:
self.total_time_steps = self.context["total_time_steps"]
self.bmaxm = constants.B_MAX / 2
def test_description_presence(available_imfs):
for imf in available_imfs:
assert select_imf(imf).description() != IMF().description()
def test_minimum_mass_value_for_kroupa_imfs():
assert select_imf("kroupa2001").for_mass(0.014) == 0.0
assert select_imf("kroupa2001").for_mass(0.015) > 0.0
assert select_imf("kroupa2002").for_mass(0.014) == 0.0
assert select_imf("kroupa2002").for_mass(0.015) > 0.0
def test_for_mass_is_normalized(available_imfs):
for imf_name in available_imfs:
imf = select_imf(imf_name)
mass = np.random.random() * 10
imf_for_mass = imf.for_mass(mass)
assert imf_for_mass == imf.m_phi(mass) * imf.normalization_factor
def test_phi_m_phi_relation(available_imfs):
for imf in available_imfs:
selected_imf = select_imf(imf)
for mass in [0.015, 0.02, 0.2, 0.75, 2, 8, 35, 90, np.random.random() * 10]:
assert selected_imf.phi(mass) == selected_imf.m_phi(mass) / mass
def test_valid_values_presence(available_imfs):
for imf in available_imfs:
assert select_imf(imf) is not None
def test_stars_per_mass_unit(available_imfs):
for imf in available_imfs:
selected_imf = select_imf(imf)
expected = selected_imf.normalization_factor * selected_imf.integrated_phi_in_mass_interval()
assert selected_imf.stars_per_mass_unit == expected