def test_csvy_abundance():
csvypath = os.path.join(DATA_PATH, 'config_v_filter.yml')
config = Configuration.from_yaml(csvypath)
csvy_model = Radial1DModel.from_csvy(config)
csvy_abund = csvy_model.abundance
ref_abund = pd.DataFrame(
np.array([[0.35, 0.3, 0.6, 0.4], [0.65, 0.7, 0.4, 0.6]]))
ref_abund.index.name = 'atomic_number'
ref_abund.index = np.array([1, 2])
assert csvy_abund.equals(ref_abund)
def test_compare_models(model_config_fnames):
"""Compare identical models produced by .from_config and
.from_csvy to check that velocities, densities and abundances
(pre and post decay) are the same"""
csvy_config_file, old_config_file = model_config_fnames
tardis_config = Configuration.from_yaml(csvy_config_file)
tardis_config_old = Configuration.from_yaml(old_config_file)
csvy_model = Radial1DModel.from_csvy(tardis_config)
config_model = Radial1DModel.from_config(tardis_config_old)
csvy_model_props = csvy_model.get_properties().keys()
config_model_props = config_model.get_properties().keys()
npt.assert_array_equal(csvy_model_props, config_model_props)
for prop in config_model_props:
csvy_model_val = csvy_model.get_properties()[prop]
config_model_val = config_model.get_properties()[prop]
if prop == "homologous_density":
npt.assert_array_almost_equal(
csvy_model_val.density_0.value, config_model_val.density_0.value
)
npt.assert_array_almost_equal(
csvy_model_val.time_0.value, config_model_val.time_0.value
)
else:
if hasattr(config_model_val, "value"):
config_model_val = config_model_val.value
csvy_model_val = csvy_model_val.value
npt.assert_array_almost_equal(csvy_model_val, config_model_val)
assert csvy_model.raw_abundance.shape == config_model.raw_abundance.shape
assert (
csvy_model.raw_isotope_abundance.shape
== config_model.raw_isotope_abundance.shape
)
assert csvy_model.abundance.shape == config_model.abundance.shape
npt.assert_array_almost_equal(
csvy_model.raw_abundance.to_numpy(),
config_model.raw_abundance.to_numpy(),
)
npt.assert_array_almost_equal(
csvy_model.raw_isotope_abundance.to_numpy(),
config_model.raw_isotope_abundance.to_numpy(),
)
npt.assert_array_almost_equal(
csvy_model.abundance.to_numpy(), config_model.abundance.to_numpy()
)
def test_compare_models(full_filename):
tardis_config = Configuration.from_yaml(full_filename)
csvy_model = Radial1DModel.from_csvy(tardis_config)
config_model = Radial1DModel.from_config(tardis_config)
csvy_model_props = csvy_model.get_properties().keys()
config_model_props = config_model.get_properties().keys()
npt.assert_array_equal(csvy_model_props, config_model_props)
for prop in config_model_props:
csvy_model_val = csvy_model.get_properties()[prop]
config_model_val = config_model.get_properties()[prop]
if prop == 'homologous_density':
npt.assert_array_almost_equal(csvy_model_val.density_0.value, config_model_val.density_0.value)
npt.assert_array_almost_equal(csvy_model_val.time_0.value, config_model_val.time_0.value)
else:
if hasattr(config_model_val, 'value'):
config_model_val = config_model_val.value
csvy_model_val = csvy_model_val.value
npt.assert_array_almost_equal(csvy_model_val, config_model_val)
def csvy_model_test_abundances():
"""Returns Radial1DModel to use to test abundances dataframes"""
csvypath = os.path.join(DATA_PATH, "csvy_model_to_test_abundances.yml")
config = Configuration.from_yaml(csvypath)
csvy_model_test_abundances = Radial1DModel.from_csvy(config)
return csvy_model_test_abundances
def from_config(cls,
config,
packet_source=None,
virtual_packet_logging=False,
**kwargs):
"""
Create a new Simulation instance from a Configuration object.
Parameters
----------
config : tardis.io.config_reader.Configuration
**kwargs
Allow overriding some structures, such as model, plasma, atomic data
and the runner, instead of creating them from the configuration
object.
Returns
-------
Simulation
"""
# Allow overriding some config structures. This is useful in some
# unit tests, and could be extended in all the from_config classmethods.
if "model" in kwargs:
model = kwargs["model"]
else:
if hasattr(config, "csvy_model"):
model = Radial1DModel.from_csvy(config)
else:
model = Radial1DModel.from_config(config)
if "plasma" in kwargs:
plasma = kwargs["plasma"]
else:
plasma = assemble_plasma(config,
model,
atom_data=kwargs.get("atom_data", None))
if "runner" in kwargs:
if packet_source is not None:
raise ConfigurationError(
"Cannot specify packet_source and runner at the same time."
)
runner = kwargs["runner"]
else:
runner = MontecarloRunner.from_config(
config,
packet_source=packet_source,
virtual_packet_logging=virtual_packet_logging,
)
luminosity_nu_start = config.supernova.luminosity_wavelength_end.to(
u.Hz, u.spectral())
if u.isclose(config.supernova.luminosity_wavelength_start,
0 * u.angstrom):
luminosity_nu_end = np.inf * u.Hz
else:
luminosity_nu_end = (
const.c / config.supernova.luminosity_wavelength_start).to(
u.Hz)
last_no_of_packets = config.montecarlo.last_no_of_packets
if last_no_of_packets is None or last_no_of_packets < 0:
last_no_of_packets = config.montecarlo.no_of_packets
last_no_of_packets = int(last_no_of_packets)
return cls(
iterations=config.montecarlo.iterations,
model=model,
plasma=plasma,
runner=runner,
no_of_packets=int(config.montecarlo.no_of_packets),
no_of_virtual_packets=int(config.montecarlo.no_of_virtual_packets),
luminosity_nu_start=luminosity_nu_start,
luminosity_nu_end=luminosity_nu_end,
last_no_of_packets=last_no_of_packets,
luminosity_requested=config.supernova.luminosity_requested.cgs,
convergence_strategy=config.montecarlo.convergence_strategy,
nthreads=config.montecarlo.nthreads,
)
def from_config(cls, config, **kwargs):
"""
Create a new Simulation instance from a Configuration object.
Parameters
----------
config : tardis.io.config_reader.Configuration
**kwargs
Allow overriding some structures, such as model, plasma, atomic data
and the runner, instead of creating them from the configuration
object.
Returns
-------
Simulation
"""
# Allow overriding some config structures. This is useful in some
# unit tests, and could be extended in all the from_config classmethods.
if 'model' in kwargs:
model = kwargs['model']
else:
if hasattr(config, 'csvy_model'):
model = Radial1DModel.from_csvy(config)
else:
model = Radial1DModel.from_config(config)
if 'plasma' in kwargs:
plasma = kwargs['plasma']
else:
plasma = assemble_plasma(config, model,
atom_data=kwargs.get('atom_data', None))
if 'runner' in kwargs:
runner = kwargs['runner']
else:
runner = MontecarloRunner.from_config(config)
luminosity_nu_start = config.supernova.luminosity_wavelength_end.to(
u.Hz, u.spectral())
try:
luminosity_nu_end = config.supernova.luminosity_wavelength_start.to(
u.Hz, u.spectral())
except ZeroDivisionError:
luminosity_nu_end = np.inf * u.Hz
last_no_of_packets = config.montecarlo.last_no_of_packets
if last_no_of_packets is None or last_no_of_packets < 0:
last_no_of_packets = config.montecarlo.no_of_packets
last_no_of_packets = int(last_no_of_packets)
return cls(iterations=config.montecarlo.iterations,
model=model,
plasma=plasma,
runner=runner,
no_of_packets=int(config.montecarlo.no_of_packets),
no_of_virtual_packets=int(
config.montecarlo.no_of_virtual_packets),
luminosity_nu_start=luminosity_nu_start,
luminosity_nu_end=luminosity_nu_end,
last_no_of_packets=last_no_of_packets,
luminosity_requested=config.supernova.luminosity_requested.cgs,
convergence_strategy=config.montecarlo.convergence_strategy,
nthreads=config.montecarlo.nthreads)