def compute_n_sample(self):
"""Use the fixed population and total mass sampled to generate the
fixed population to compute the number of systems in the Milky Way
realization
Parameters
----------
fixedpop : DataFrame
Contains binary parameters from an evolved population
generated by the runFixedPop executable
fixed_mass : float
Total mass sampled to generate the fixed population
gx_component : str
Milky Way component for which we are generating the population
realization; choose from 'ThinDisk', 'ThickDisk', 'Bulge'
Returns
-------
n_samp : int
The number of systems in the Milky Way realization
"""
# Compute the mass weighted number of systems
# in a realistic Milky Way pop
#######################################################################
component_mass = MC_sample.select_component_mass(self.gx_component)
n_samp = MC_sample.mass_weighted_number(self.fixed_pop,
self.fixed_mass,
component_mass)
return n_samp
def test_select_component_mass(self):
# Check that the Galactic component masses are selected properly
thin_mass = MC_samp.select_component_mass(GX_COMPONENT_THIN)
self.assertEqual(thin_mass, GX_COMPONENT_MASS_THIN)
thick_mass = MC_samp.select_component_mass(GX_COMPONENT_THICK)
self.assertEqual(thick_mass, GX_COMPONENT_MASS_THICK)
bulge_mass = MC_samp.select_component_mass(GX_COMPONENT_BULGE)
self.assertEqual(bulge_mass, GX_COMPONENT_MASS_BULGE)
def test_sample_exponential_vertical(self):
# Check that the mean & variance of the sampled distribution matches
# the analytical mean & variance of the radial exponential distribution
exp_vertical_sample = MC_samp.sample_exponential_vertical(
N_SAMP, EXP_VERT_SCALE_HEIGHT)
dist_mean = np.mean(exp_vertical_sample)
self.assertTrue(np.abs(dist_mean - (EXP_VERT_MEAN_TEST)) < 1e-3)
dist_var = np.var(exp_vertical_sample)
self.assertTrue(np.abs(dist_var - EXP_VERT_VAR_TEST) < 1e-3)
def test_sample_sech_squared(self):
# Check that the mean & variance of the sampled distribution matches
# the analytical mean & variance of the sech squared distribution
sech_squared_sample = MC_samp.sample_sech_squared(
N_SAMP, 2 * SECH_SQUARED_SCALE_HEIGHT)
dist_mean = np.mean(sech_squared_sample)
self.assertTrue(np.abs(dist_mean - SECH_SQUARED_MEAN_TEST) < 1e-3)
dist_var = np.var(sech_squared_sample)
self.assertTrue(np.abs(dist_var - SECH_SQUARED_VAR_TEST) < 1e-3)
def test_mass_weighted_number(self):
# Check that the total population number is scaled properly by mass
n_pop = MC_samp.mass_weighted_number(FIXED_POP, MASS_FIXED,
GX_COMPONENT_MASS_THIN)
self.assertEqual(n_pop, N_GX)
def test_galactic_positions(self):
# Check that the galactic positions are sampled properly
xGX, yGX, zGX, inc, OMEGA, omega = MC_samp.galactic_positions(
'ThinDisk', N_SAMP, model='McMillan')
self.assertTrue(
np.abs(
np.mean((xGX**2 + yGX**2)**0.5) -
MCMILLAN_THINDISK_MEAN_TEST_XY) < 1e-2)
self.assertTrue(
np.abs(np.mean(zGX) - MCMILLAN_THINDISK_MEAN_TEST_Z) < 1e-2)
self.assertTrue(np.abs(np.mean(inc) - 1.0) < 1e-2)
self.assertTrue(np.abs(np.mean(OMEGA) - np.pi) < 1e-2)
self.assertTrue(np.abs(np.mean(omega) - np.pi) < 1e-2)
xGX, yGX, zGX, inc, OMEGA, omega = MC_samp.galactic_positions(
'ThinDisk', N_SAMP, model='sech_squared')
self.assertTrue(
np.abs(
np.mean((xGX**2 + yGX**2)**0.5) -
SECHSQUARE_THINDISK_MEAN_TEST_XY) < 1e-2)
self.assertTrue(
np.abs(np.mean(zGX) - SECHSQUARE_THINDISK_MEAN_TEST_Z) < 1e-2)
self.assertTrue(np.abs(np.mean(inc) - 1.0) < 1e-2)
self.assertTrue(np.abs(np.mean(OMEGA) - np.pi) < 1e-2)
self.assertTrue(np.abs(np.mean(omega) - np.pi) < 1e-2)
xGX, yGX, zGX, inc, OMEGA, omega = MC_samp.galactic_positions(
'ThinDisk', N_SAMP, model='double_exp')
self.assertTrue(
np.abs(
np.mean((xGX**2 + yGX**2)**0.5) -
DOUBLEEXP_THINDISK_MEAN_TEST_XY) < 1e-2)
self.assertTrue(
np.abs(np.mean(zGX) - DOUBLEEXP_THINDISK_MEAN_TEST_Z) < 1e-2)
self.assertTrue(np.abs(np.mean(inc) - 1.0) < 1e-2)
self.assertTrue(np.abs(np.mean(OMEGA) - np.pi) < 1e-2)
self.assertTrue(np.abs(np.mean(omega) - np.pi) < 1e-2)
xGX, yGX, zGX, inc, OMEGA, omega = MC_samp.galactic_positions(
'Bulge', N_SAMP, model='exp_squared')
self.assertTrue(
np.abs(
np.mean((xGX**2 + yGX**2 + zGX**2)**0.5) -
DOUBLEEXP_BULGE_MEAN_TEST_XYZ) < 1e-2)
self.assertTrue(np.abs(np.mean(inc) - 1.0) < 1e-2)
self.assertTrue(np.abs(np.mean(OMEGA) - np.pi) < 1e-2)
self.assertTrue(np.abs(np.mean(omega) - np.pi) < 1e-2)
xGX, yGX, zGX, inc, OMEGA, omega = MC_samp.galactic_positions(
'Bulge', 50000, model='McMillan')
self.assertTrue(
np.abs(
np.mean((xGX**2 + yGX**2)**0.5) -
MCMILLAN_BULGE_MEAN_TEST_XY) < 1e-1)
self.assertTrue(np.abs(np.mean(zGX) - 0.0 < 1e-2))
self.assertTrue(np.abs(np.mean(inc) - 1.0) < 1e-2)
self.assertTrue(np.abs(np.mean(OMEGA) - np.pi) < 1e-2)
self.assertTrue(np.abs(np.mean(omega) - np.pi) < 1e-2)
xGX, yGX, zGX, inc, OMEGA, omega = MC_samp.galactic_positions(
'ThickDisk', N_SAMP, model='McMillan')
self.assertTrue(
np.abs(
np.mean((xGX**2 + yGX**2)**0.5) -
MCMILLAN_THICKDISK_MEAN_TEST_XY) < 1e-2)
self.assertTrue(
np.abs(np.mean(zGX) - MCMILLAN_THICKDISK_MEAN_TEST_Z) < 1e-2)
self.assertTrue(np.abs(np.mean(inc) - 1.0) < 1e-2)
self.assertTrue(np.abs(np.mean(OMEGA) - np.pi) < 1e-2)
self.assertTrue(np.abs(np.mean(omega) - np.pi) < 1e-2)
xGX, yGX, zGX, inc, OMEGA, omega = MC_samp.galactic_positions(
'ThinDisk', N_SAMP, model='double_exp')
self.assertTrue(
np.abs(
np.mean((xGX**2 + yGX**2)**0.5) -
DOUBLEEXP_THICKDISK_MEAN_TEST_XY) < 1e-2)
self.assertTrue(
np.abs(np.mean(zGX) - DOUBLEEXP_THICKDISK_MEAN_TEST_Z) < 1e-2)
self.assertTrue(np.abs(np.mean(inc) - 1.0) < 1e-2)
self.assertTrue(np.abs(np.mean(OMEGA) - np.pi) < 1e-2)
self.assertTrue(np.abs(np.mean(omega) - np.pi) < 1e-2)
def sample_population(self):
"""Once the fixed population is evolved, we Monte-Carlo
sample the binary parameters to generate a Milky Way realization.
Parameters
----------
fixedpop : DataFrame
Contains binary parameters from an evolved population
generated by the runFixedPop executable
n_samp : int
The number of systems in the Milky Way realization
gx_component : str
Milky Way component for which we are generating the population
realization; choose from 'ThinDisk', 'ThickDisk', 'Bulge'
gx_model : str
Model for spatial distribution of binaries in the Galactic
component; Default='McMillan'
dat_list : list
List containing the parameters to MC sample to generate the
Galactic realization
Returns
-------
realization : DataFrame
Milky Way population realization of size n_samp
"""
if not hasattr(self, 'n_samp'):
self.n_samp = self.compute_n_sample()
# Based on the user supplied filter flags, filter the
# population to reduce the sample to only the relevant population
#######################################################################
# Transform the fixed population to have limits between 0 and 1
# then transform to logit space to maintain the population boundaries
# and create a KDE using knuth's rule to select the bandwidth
#######################################################################
dat_kde = utils.dat_transform(self.fixed_pop, self.dat_list)
bw = utils.knuth_bw_selector(dat_kde)
dat_kernel = stats.gaussian_kde(dat_kde, bw_method=bw)
# Sample from the KDE
#######################################################################
binary_dat_trans = dat_kernel.resample(self.n_samp)
binary_dat = utils.dat_un_transform(binary_dat_trans, self.fixed_pop,
self.dat_list)
# Sample positions and orientations for each sampled binary
#######################################################################
xGx, yGx, zGx, inc, OMEGA, omega = MC_sample.galactic_positions(
self.gx_component, size=len(binary_dat[0]), model=self.gx_model)
binary_sample_positions = np.vstack([xGx, yGx, zGx, inc, OMEGA, omega])
# Create a single DataFrame for the Galactic realization
#######################################################################
if 'ecc' not in self.dat_list:
full_sample = np.vstack(
[binary_dat,
np.zeros(self.n_samp), binary_sample_positions]).T
column_list = self.dat_list + [
'ecc', 'xGx', 'yGx', 'zGx', 'inc', 'OMEGA', 'omega'
]
else:
full_sample = np.concatenate([binary_dat,
binary_sample_positions]).T
column_list = self.dat_list + [
'xGx', 'yGx', 'zGx', 'inc', 'OMEGA', 'omega'
]
realization = pd.DataFrame(full_sample,\
columns = column_list)
return realization
