def make_pwn_pos(random_state, n_sources,
mean_intrinsic_extension=15, sigma_intrinsic_extension=10, ):
table = make_base_catalog_galactic(
n_sources=n_sources,
max_age=0 * u.year,
random_state=random_state,
)
size_physical = random_state.normal(mean_intrinsic_extension, sigma_intrinsic_extension, n_sources)
for iii in range(0, len(size_physical)):
if size_physical[iii] < 0:
size_physical[iii] = 0.1
size_physical = u.Quantity(size_physical, 'pc')
type = []
for iii in range(0, len(size_physical)):
type.append('isolated')
table.remove_column('age')
table.remove_column('n_ISM')
table.remove_column('v_abs')
table.remove_column('vx')
table.remove_column('vy')
table.remove_column('vz')
table.remove_column('x_birth')
table.remove_column('y_birth')
table.remove_column('z_birth')
table['type'] = Column(type, description='type of PWN')
table['size_physical'] = Column(size_physical, description='Physical size', unit='pc')
return table
def test_make_base_catalog_galactic():
table = make_base_catalog_galactic(n_sources=10, random_state=0)
d = table[0]
assert len(table) == 10
assert len(table.colnames) == 13
assert table["age"].unit == "yr"
assert_allclose(d["age"], 548813.50392732478)
assert table["n_ISM"].unit == "cm-3"
assert_allclose(d["n_ISM"], 1.0)
assert table["spiralarm"].unit is None
assert d["spiralarm"] == "Crux Scutum"
assert table["x_birth"].unit == "kpc"
assert_allclose(d["x_birth"], -5.856461, atol=1e-5)
assert table["y_birth"].unit == "kpc"
assert_allclose(d["y_birth"], 3.017292, atol=1e-5)
assert table["z_birth"].unit == "kpc"
assert_allclose(d["z_birth"], 0.049088, atol=1e-5)
assert table["x"].unit == "kpc"
assert_allclose(d["x"], -5.941061, atol=1e-5)
assert table["y"].unit == "kpc"
assert_allclose(d["y"], 3.081642, atol=1e-5)
assert table["z"].unit == "kpc"
assert_allclose(d["z"], 0.023161, atol=1e-5)
assert table["vx"].unit == "km/s"
assert_allclose(d["vx"], -150.727104, atol=1e-5)
assert table["vy"].unit == "km/s"
assert_allclose(d["vy"], 114.648494, atol=1e-5)
assert table["vz"].unit == "km/s"
assert_allclose(d["vz"], -46.193814, atol=1e-5)
assert table["v_abs"].unit == "km/s"
assert_allclose(d["v_abs"], 194.927693, atol=1e-5)
def test_add_pwn_parameters():
table = make_base_catalog_galactic(n_sources=10, random_state=0)
# To compute PWN parameters we need PSR and SNR parameters first
table = add_snr_parameters(table)
table = add_pulsar_parameters(table, random_state=0)
table = add_pwn_parameters(table)
d = table[0]
assert len(table) == 10
assert len(table.colnames) == 27
assert table["r_out_PWN"].unit == "pc"
assert_allclose(d["r_out_PWN"], 1.378224, atol=1e-4)
def test_chain_all():
# Test that running the simulation functions in chain works
table = make_base_catalog_galactic(n_sources=10, random_state=0)
table = add_snr_parameters(table)
table = add_pulsar_parameters(table, random_state=0)
table = add_pwn_parameters(table)
table = add_observed_parameters(table)
d = table[0]
# Note: the individual functions are tested above.
# Here we just run them in a chain and do very basic asserts
# on the output so that we make sure we notice changes.
assert len(table) == 10
assert len(table.colnames) == 34
assert table["r_out_PWN"].unit == "pc"
assert_allclose(d["r_out_PWN"], 1.378224, atol=1e-4)
assert table["RA"].unit == "deg"
assert_allclose(d["RA"], 244.347149, atol=1e-5)
def test_add_observed_parameters():
table = make_base_catalog_galactic(n_sources=10, random_state=0)
table = add_observed_parameters(table)
d = table[0]
assert len(table) == 10
assert len(table.colnames) == 20
assert table["distance"].unit == "pc"
assert_allclose(d["distance"], 13016.572756, atol=1e-5)
assert table["GLON"].unit == "deg"
assert_allclose(d["GLON"], -27.156565, atol=1e-5)
assert table["GLAT"].unit == "deg"
assert_allclose(d["GLAT"], 0.101948, atol=1e-5)
assert table["VGLON"].unit == "deg / Myr"
assert_allclose(d["VGLON"], 0.368166, atol=1e-5)
assert table["VGLAT"].unit == "deg / Myr"
assert_allclose(d["VGLAT"], -0.209514, atol=1e-5)
assert table["RA"].unit == "deg"
assert_allclose(d["RA"], 244.347149, atol=1e-5)
assert table["DEC"].unit == "deg"
assert_allclose(d["DEC"], -50.410142, atol=1e-5)
# Simulation Parameters
# source density at the sun (sources kpc^-1)
rho_sun = 3
# number of sources
n_sources = int(5e2)
# Spatial distribution using Lorimer (2006) model
rad_dis = 'L06'
# Velocity dispersion
vel_dis = 'F06B'
# Includes spiral arms
spiralarms = True
# Creates table
table = population.make_base_catalog_galactic(n_sources=n_sources,
rad_dis=rad_dis,
vel_dis=vel_dis,
max_age=1e6,
spiralarms=spiralarms,
random_state=0)
# Minimum source luminosity (ph s^-1)
luminosity_min = 4e34
# Maximum source luminosity (ph s^-1)
luminosity_max = 4e37
# Luminosity function differential power-law index
luminosity_index = 1.5
# Assigns luminosities to sources
luminosity = sample_powerlaw(luminosity_min,
luminosity_max,
luminosity_index,
n_sources,
# Simulation Parameters
# source density at the sun (sources kpc^-1)
rho_sun = 3
# number of sources
n_sources = int(5e2)
# Spatial distribution using Lorimer (2006) model
rad_dis = 'L06'
# Velocity dispersion
vel_dis = 'F06B'
# Includes spiral arms
spiralarms = True
# Creates table
table = population.make_base_catalog_galactic(n_sources=n_sources, rad_dis=rad_dis,
vel_dis=vel_dis, max_age=1e6,
spiralarms=spiralarms, random_state=0)
# Minimum source luminosity (ph s^-1)
luminosity_min = 4e34
# Maximum source luminosity (ph s^-1)
luminosity_max = 4e37
# Luminosity function differential power-law index
luminosity_index = 1.5
# Assigns luminosities to sources
luminosity = sample_powerlaw(luminosity_min, luminosity_max, luminosity_index,
n_sources, random_state=0)
table['luminosity'] = luminosity
# Adds parameters to table: distance, glon, glat, flux, angular_extension
import numpy as np
import astropy.units as u
from gammapy.utils.random import sample_powerlaw
from gammapy.astro import population
# ## Simulate positions
# In[3]:
# Spatial distribution using Lorimer (2006) model
n_sources = int(1e5)
table = population.make_base_catalog_galactic(
n_sources=n_sources,
rad_dis='L06',
vel_dis='F06B',
max_age=1e6 * u.yr,
spiralarms=True,
)
# ## Simulate luminosities
# Several source population models, e.g. the 1FHL paper or Strong (2007), use power-law luminosity functions.
#
# Here we implement the "reference model" from the 1FHL catalog paper section 6.2.
# In[4]:
# Source luminosity (ph s^-1)
luminosity = sample_powerlaw(
