def test_sample_powerlaw():
random_state = np.random.RandomState(seed=0)
x = sample_powerlaw(x_min=0.1,
x_max=10,
gamma=2,
size=2,
random_state=random_state)
assert_allclose(x, [0.14886601, 0.1873559])
def add_spectra(table, random_state,
mean_index_alpha=2.1, sigma_index_alpha=0.2, max_index_beta=0.4):
n_sources = len(table)
alpha = random_state.normal(mean_index_alpha, sigma_index_alpha, n_sources)
beta = random_state.uniform(0.1, max_index_beta, n_sources)
# Define the luminosity
luminosity = sample_powerlaw(
x_min=1.2e33,
x_max=5e36,
gamma=1.0,
size=n_sources,
random_state=random_state,
)
luminosity = luminosity * u.erg / u.second
distance = table['distance'].to('cm')
# integral sed between 1 and 10 TeV
energy_flux = luminosity / (4 * np.pi * distance * distance)
energy_flux = energy_flux.to('TeV cm-2 s-1')
vals = []
vals_cu = []
int_vals = []
int_vals_cu = []
name = []
for idx in range(len(table)):
val, val_cu, int_val, int_val_cu = flux_amplitude_from_energy_flux(
alpha[idx], beta[idx], energy_flux[idx],
)
vals.append(val)
vals_cu.append(val_cu)
int_vals.append(int_val)
int_vals_cu.append(int_val_cu)
source_name = 'pwn_{}'.format(idx)
name.append(source_name)
norm = u.Quantity(vals)
norm_cu = u.Quantity(vals_cu)
int_flux = u.Quantity(int_vals)
int_flux_cu = u.Quantity(int_vals_cu)
table['spec_alpha'] = Column(alpha, description='Spectral model parameter (log parabola)')
table['spec_beta'] = Column(beta, description='Spectral model parameter (log parabola)')
table['spec_norm'] = Column(norm, description='Spectral model norm parameter (log parabola)', unit='MeV-1 s-1 cm-2')
table['spec_norm_cu'] = Column(norm_cu, description='Spectral model norm parameter (log parabola) in crab units')
table['int_flux_above_1TeV'] = Column(int_flux, description='Integral flux above 1 TeV ', unit='s-1 cm-2')
table['int_flux_above_1TeV_cu'] = Column(int_flux_cu, description='Integral flux above 1 TeV in crab units')
table['luminosity'] = Column(luminosity, description='Intrinsic source luminosity', unit='erg s-1')
# print(table['int_flux_above_1TeV'], table['int_flux_above_1TeV_cu'])
return table
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
table = population.add_observed_parameters(table)
table.meta['Energy Bins'] = np.array([10, 500]) * u.GeV
# Create image
image = catalog_image(reference,
psf,
catalog='simulation',
source_type='point',
total_flux=True,
sim_table=table)
# 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
table = population.add_observed_parameters(table)
table.meta['Energy Bins'] = np.array([10, 500]) * u.GeV
# Create image
image = catalog_image(reference, psf, catalog='simulation', source_type='point',
total_flux=True, sim_table=table)
# Plot
fig = FITSFigure(image.to_fits()[0], figsize=(15, 5))
fig.show_colorscale(interpolation='bicubic', cmap='afmhot', stretch='log', vmin=1E30, vmax=1E35)
fig.tick_labels.set_xformat('ddd')
fig.tick_labels.set_yformat('dd')
ticks = np.logspace(30, 35, 6)
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(
x_min=1e34,
x_max=1e37,
gamma=1.5,
size=n_sources,
)
table['luminosity'] = luminosity
# ## Compute observable parameters
# In[5]:
table = population.add_observed_parameters(table)
table.info()
# ## Check output
# The simulation is done, you could save the simulated catalog to a file.
#
# 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)
# 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)
table['luminosity'] = luminosity
# Adds parameters to table: distance, glon, glat, flux, angular_extension
table = population.add_observed_parameters(table)
table.meta['Energy Bins'] = Quantity([10, 500], 'GeV')
# Create image
image = catalog_image(reference,
psf,
catalog='simulation',
source_type='point',
total_flux=True,
sim_table=table)
# Plot
fig = FITSFigure(image.to_fits()[0])
# 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)
# 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)
table['luminosity'] = luminosity
# Adds parameters to table: distance, glon, glat, flux, angular_extension
table = population.add_observed_parameters(table)
table.meta['Energy Bins'] = Quantity([10, 500], 'GeV')
# Create image
image = catalog_image(reference, psf, catalog='simulation', source_type='point',
total_flux=True, sim_table=table)
# Plot
fig = FITSFigure(image.to_fits()[0])
fig.show_grayscale(stretch='linear', interpolation='none')
fig.add_colorbar()
