def test_missing_required_property(csvy_missing_fname):
yaml_dict, csv = csvy.load_csvy(csvy_missing_fname)
with pytest.raises(Exception):
vy = validate_dict(yaml_dict,
schemapath=os.path.join(tardis.__path__[0], 'io',
'schemas',
'csvy_model.yml'))
def from_config_dict(cls, config_dict, validate=True, config_dirname=''):
"""
Validating and subsequently parsing a config file.
Parameters
----------
config_dict : ~dict
dictionary of a raw unvalidated config file
validate: ~bool
Turn validation on or off.
Returns
-------
`tardis.config_reader.Configuration`
"""
if validate:
validated_config_dict = config_validator.validate_dict(config_dict)
else:
validated_config_dict = config_dict
validated_config_dict['config_dirname'] = config_dirname
montecarlo_section = validated_config_dict['montecarlo']
montecarlo_section['convergence_strategy'] = (
parse_convergence_section(
montecarlo_section['convergence_strategy']))
return cls(validated_config_dict)
def from_config_dict(cls, config_dict, validate=True, config_dirname=""):
"""
Validating and subsequently parsing a config file.
Parameters
----------
config_dict : dict
dictionary of a raw unvalidated config file
validate : bool
Turn validation on or off.
Returns
-------
`tardis.config_reader.Configuration`
"""
if validate:
validated_config_dict = config_validator.validate_dict(config_dict)
else:
validated_config_dict = config_dict
validated_config_dict["config_dirname"] = config_dirname
montecarlo_section = validated_config_dict["montecarlo"]
if montecarlo_section["convergence_strategy"]["type"] == "damped":
montecarlo_section[
"convergence_strategy"
] = parse_convergence_section(
montecarlo_section["convergence_strategy"]
)
elif montecarlo_section["convergence_strategy"]["type"] == "custom":
raise NotImplementedError(
'convergence_strategy is set to "custom"; '
"you need to implement your specific convergence treatment"
)
else:
raise ValueError(
'convergence_strategy is not "damped" ' 'or "custom"'
)
enable_full_relativity = montecarlo_section["enable_full_relativity"]
spectrum_integrated = (
validated_config_dict["spectrum"]["method"] == "integrated"
)
if enable_full_relativity and spectrum_integrated:
raise NotImplementedError(
"The spectrum method is set to 'integrated' and "
"enable_full_relativity to 'True'.\n"
"The FormalIntegrator is not yet implemented for the full "
"relativity mode. "
)
return cls(validated_config_dict)
def from_config_dict(cls, config_dict):
"""
Validating a config file.
Parameters
----------
config_dict : dict
dictionary of a raw unvalidated config file
Returns
-------
`tardis.config_reader.Configuration`
"""
return cls(config_validator.validate_dict(config_dict))
def from_config_dict(cls, config_dict, validate=True, config_dirname=''):
"""
Validating and subsequently parsing a config file.
Parameters
----------
config_dict : ~dict
dictionary of a raw unvalidated config file
validate: ~bool
Turn validation on or off.
Returns
-------
`tardis.config_reader.Configuration`
"""
if validate:
validated_config_dict = config_validator.validate_dict(config_dict)
else:
validated_config_dict = config_dict
validated_config_dict['config_dirname'] = config_dirname
montecarlo_section = validated_config_dict['montecarlo']
if montecarlo_section['convergence_strategy']['type'] == "damped":
montecarlo_section['convergence_strategy'] = (
parse_convergence_section(
montecarlo_section['convergence_strategy']))
elif montecarlo_section['convergence_strategy']['type'] == "custom":
raise NotImplementedError(
'convergence_strategy is set to "custom"; '
'you need to implement your specific convergence treatment')
else:
raise ValueError('convergence_strategy is not "damped" '
'or "custom"')
return cls(validated_config_dict)
def from_config_dict(cls, config_dict):
"""
Validating a config file.
Parameters
----------
config_dict : ~dict
dictionary of a raw unvalidated config file
Returns
-------
`tardis.config_reader.Configuration`
"""
return cls(config_validator.validate_dict(config_dict))
def from_config_dict(cls,
config_dict,
atom_data=None,
test_parser=False,
validate=True,
config_dirname=''):
"""
Validating and subsequently parsing a config file.
Parameters
----------
config_dict : ~dict
dictionary of a raw unvalidated config file
atom_data: ~tardis.atomic.AtomData
atom data object. if `None` will be tried to be read from
atom data file path in the config_dict [default=None]
test_parser: ~bool
switch on to ignore a working atom_data, mainly useful for
testing this reader
validate: ~bool
Turn validation on or off.
Returns
-------
`tardis.config_reader.Configuration`
"""
if validate:
validated_config_dict = config_validator.validate_dict(config_dict)
else:
validated_config_dict = config_dict
#First let's see if we can find an atom_db anywhere:
if test_parser:
atom_data = None
elif 'atom_data' in validated_config_dict.keys():
if os.path.isabs(validated_config_dict['atom_data']):
atom_data_fname = validated_config_dict['atom_data']
else:
atom_data_fname = os.path.join(
config_dirname, validated_config_dict['atom_data'])
validated_config_dict['atom_data_fname'] = atom_data_fname
else:
raise ConfigurationError(
'No atom_data key found in config or command line')
if atom_data is None and not test_parser:
logger.info('Reading Atomic Data from %s', atom_data_fname)
atom_data = atomic.AtomData.from_hdf5(atom_data_fname)
else:
atom_data = atom_data
#Parsing supernova dictionary
validated_config_dict['supernova']['luminosity_nu_start'] = \
validated_config_dict['supernova']['luminosity_wavelength_end'].to(
u.Hz, u.spectral())
try:
validated_config_dict['supernova']['luminosity_nu_end'] = \
(validated_config_dict['supernova']
['luminosity_wavelength_start'].to(u.Hz, u.spectral()))
except ZeroDivisionError:
validated_config_dict['supernova']['luminosity_nu_end'] = (np.inf *
u.Hz)
validated_config_dict['supernova']['time_explosion'] = (
validated_config_dict['supernova']['time_explosion'].cgs)
validated_config_dict['supernova']['luminosity_requested'] = (
validated_config_dict['supernova']['luminosity_requested'].cgs)
#Parsing the model section
model_section = validated_config_dict['model']
v_inner = None
v_outer = None
mean_densities = None
abundances = None
structure_section = model_section['structure']
if structure_section['type'] == 'specific':
velocity = model_section['structure']['velocity']
start, stop, num = velocity['start'], velocity['stop'], \
velocity['num']
num += 1
velocities = quantity_linspace(start, stop, num)
v_inner, v_outer = velocities[:-1], velocities[1:]
mean_densities = parse_density_section(
model_section['structure']['density'], v_inner, v_outer,
validated_config_dict['supernova']['time_explosion']).cgs
elif structure_section['type'] == 'file':
if os.path.isabs(structure_section['filename']):
structure_fname = structure_section['filename']
else:
structure_fname = os.path.join(config_dirname,
structure_section['filename'])
v_inner, v_outer, mean_densities, inner_boundary_index, \
outer_boundary_index = read_density_file(
structure_fname, structure_section['filetype'],
validated_config_dict['supernova']['time_explosion'],
structure_section['v_inner_boundary'],
structure_section['v_outer_boundary'])
r_inner = validated_config_dict['supernova']['time_explosion'] * v_inner
r_outer = validated_config_dict['supernova']['time_explosion'] * v_outer
r_middle = 0.5 * (r_inner + r_outer)
structure_section['v_inner'] = v_inner.cgs
structure_section['v_outer'] = v_outer.cgs
structure_section['mean_densities'] = mean_densities.cgs
no_of_shells = len(v_inner)
structure_section['no_of_shells'] = no_of_shells
structure_section['r_inner'] = r_inner.cgs
structure_section['r_outer'] = r_outer.cgs
structure_section['r_middle'] = r_middle.cgs
structure_section['volumes'] = ((4. / 3) * np.pi * \
(r_outer ** 3 -
r_inner ** 3)).cgs
#### TODO the following is legacy code and should be removed
validated_config_dict['structure'] = \
validated_config_dict['model']['structure']
# ^^^^^^^^^^^^^^^^
abundances_section = model_section['abundances']
if abundances_section['type'] == 'uniform':
abundances = pd.DataFrame(columns=np.arange(no_of_shells),
index=pd.Index(np.arange(1, 120),
name='atomic_number'),
dtype=np.float64)
for element_symbol_string in abundances_section:
if element_symbol_string == 'type': continue
z = element_symbol2atomic_number(element_symbol_string)
abundances.ix[z] = float(
abundances_section[element_symbol_string])
elif abundances_section['type'] == 'file':
if os.path.isabs(abundances_section['filename']):
abundances_fname = abundances_section['filename']
else:
abundances_fname = os.path.join(config_dirname,
abundances_section['filename'])
index, abundances = read_abundances_file(
abundances_fname, abundances_section['filetype'],
inner_boundary_index, outer_boundary_index)
if len(index) != no_of_shells:
raise ConfigurationError(
'The abundance file specified has not the same number of cells'
'as the specified density profile')
abundances = abundances.replace(np.nan, 0.0)
abundances = abundances[abundances.sum(axis=1) > 0]
norm_factor = abundances.sum(axis=0)
if np.any(np.abs(norm_factor - 1) > 1e-12):
logger.warning(
"Abundances have not been normalized to 1. - normalizing")
abundances /= norm_factor
validated_config_dict['abundances'] = abundances
########### DOING PLASMA SECTION ###############
plasma_section = validated_config_dict['plasma']
if plasma_section['initial_t_inner'] < 0.0 * u.K:
luminosity_requested = validated_config_dict['supernova'][
'luminosity_requested']
plasma_section['t_inner'] = ((
luminosity_requested /
(4 * np.pi * r_inner[0]**2 * constants.sigma_sb))**.25).to('K')
logger.info(
'"initial_t_inner" is not specified in the plasma '
'section - initializing to %s with given luminosity',
plasma_section['t_inner'])
else:
plasma_section['t_inner'] = plasma_section['initial_t_inner']
if plasma_section['initial_t_rad'] > 0 * u.K:
plasma_section['t_rads'] = np.ones(no_of_shells) * \
plasma_section['initial_t_rad']
else:
plasma_section['t_rads'] = None
if plasma_section['disable_electron_scattering'] is False:
logger.debug("Electron scattering switched on")
validated_config_dict['montecarlo'][
'sigma_thomson'] = 6.652486e-25 / (u.cm**2)
else:
logger.warn('Disabling electron scattering - this is not physical')
validated_config_dict['montecarlo']['sigma_thomson'] = 1e-200 / (
u.cm**2)
if plasma_section['helium_treatment'] == 'recomb-nlte':
validated_config_dict['plasma'][
'helium_treatment'] == 'recomb-nlte'
else:
validated_config_dict['plasma']['helium_treatment'] == 'dilute-lte'
##### NLTE subsection of Plasma start
nlte_validated_config_dict = {}
nlte_species = []
nlte_section = plasma_section['nlte']
nlte_species_list = nlte_section.pop('species')
for species_string in nlte_species_list:
nlte_species.append(species_string_to_tuple(species_string))
nlte_validated_config_dict['species'] = nlte_species
nlte_validated_config_dict['species_string'] = nlte_species_list
nlte_validated_config_dict.update(nlte_section)
if 'coronal_approximation' not in nlte_section:
logger.debug(
'NLTE "coronal_approximation" not specified in NLTE section - defaulting to False'
)
nlte_validated_config_dict['coronal_approximation'] = False
if 'classical_nebular' not in nlte_section:
logger.debug(
'NLTE "classical_nebular" not specified in NLTE section - defaulting to False'
)
nlte_validated_config_dict['classical_nebular'] = False
elif nlte_section: #checks that the dictionary is not empty
logger.warn(
'No "species" given - ignoring other NLTE options given:\n%s',
pp.pformat(nlte_section))
if not nlte_validated_config_dict:
nlte_validated_config_dict['species'] = []
plasma_section['nlte'] = nlte_validated_config_dict
#^^^^^^^^^^^^^^ End of Plasma Section
##### Monte Carlo Section
montecarlo_section = validated_config_dict['montecarlo']
montecarlo_section['no_of_packets'] = \
int(montecarlo_section['no_of_packets'])
montecarlo_section['last_no_of_packets'] = \
int(montecarlo_section['last_no_of_packets'])
if montecarlo_section['last_no_of_packets'] < 0:
montecarlo_section['last_no_of_packets'] = \
montecarlo_section['no_of_packets']
montecarlo_section['convergence_strategy'] = (
parse_convergence_section(
montecarlo_section['convergence_strategy']))
black_body_section = montecarlo_section['black_body_sampling']
montecarlo_section['black_body_sampling'] = {}
montecarlo_section['black_body_sampling']['start'] = \
black_body_section['start']
montecarlo_section['black_body_sampling']['end'] = \
black_body_section['stop']
montecarlo_section['black_body_sampling']['samples'] = \
black_body_section['num']
virtual_spectrum_section = montecarlo_section['virtual_spectrum_range']
montecarlo_section['virtual_spectrum_range'] = {}
montecarlo_section['virtual_spectrum_range']['start'] = \
virtual_spectrum_section['start']
montecarlo_section['virtual_spectrum_range']['end'] = \
virtual_spectrum_section['stop']
montecarlo_section['virtual_spectrum_range']['samples'] = \
virtual_spectrum_section['num']
###### END of convergence section reading
validated_config_dict['spectrum'] = parse_spectrum_list2dict(
validated_config_dict['spectrum'])
return cls(validated_config_dict, atom_data)
def from_csvy(cls, config):
"""
Create a new Radial1DModel instance from a Configuration object.
Parameters
----------
config : tardis.io.config_reader.Configuration
Returns
-------
Radial1DModel
"""
if os.path.isabs(config.csvy_model):
csvy_model_fname = config.csvy_model
else:
csvy_model_fname = os.path.join(config.config_dirname,
config.csvy_model)
csvy_model_config, csvy_model_data = load_csvy(csvy_model_fname)
base_dir = os.path.abspath(os.path.dirname(__file__))
schema_dir = os.path.join(base_dir, '..', 'io', 'schemas')
csvy_schema_file = os.path.join(schema_dir, 'csvy_model.yml')
csvy_model_config = Configuration(
validate_dict(csvy_model_config, schemapath=csvy_schema_file))
time_explosion = config.supernova.time_explosion.cgs
electron_densities = None
temperature = None
if hasattr(csvy_model_config, 'v_inner_boundary'):
v_boundary_inner = csvy_model_config.v_inner_boundary
else:
v_boundary_inner = None
if hasattr(csvy_model_config, 'v_outer_boundary'):
v_boundary_outer = csvy_model_config.v_outer_boundary
else:
v_boundary_outer = None
if hasattr(csvy_model_config, 'velocity'):
velocity = quantity_linspace(csvy_model_config.velocity.start,
csvy_model_config.velocity.stop,
csvy_model_config.velocity.num +
1).cgs
else:
velocity_field_index = [
field['name'] for field in csvy_model_config.datatype.fields
].index('velocity')
velocity_unit = u.Unit(
csvy_model_config.datatype.fields[velocity_field_index]
['unit'])
velocity = csvy_model_data['velocity'].values * velocity_unit
velocity = velocity.to('cm/s')
if hasattr(csvy_model_config, 'density'):
homologous_density = HomologousDensity.from_csvy(
config, csvy_model_config)
else:
time_0 = csvy_model_config.model_density_time_0
density_field_index = [
field['name'] for field in csvy_model_config.datatype.fields
].index('density')
density_unit = u.Unit(
csvy_model_config.datatype.fields[density_field_index]['unit'])
density_0 = csvy_model_data['density'].values * density_unit
density_0 = density_0.to('g/cm^3')[1:]
density_0 = density_0.insert(0, 0)
homologous_density = HomologousDensity(density_0, time_0)
no_of_shells = len(velocity) - 1
# TODO -- implement t_radiative
#t_radiative = None
if temperature:
t_radiative = temperature
elif hasattr(csvy_model_data, 'columns'):
if 't_rad' in csvy_model_data.columns:
t_rad_field_index = [
field['name']
for field in csvy_model_config.datatype.fields
].index('t_rad')
t_rad_unit = u.Unit(
csvy_model_config.datatype.fields[t_rad_field_index]
['unit'])
t_radiative = csvy_model_data['t_rad'].iloc[
0:].values * t_rad_unit
else:
t_radiative = None
dilution_factor = None
if hasattr(csvy_model_data, 'columns'):
if 'w' in csvy_model_data.columns:
dilution_factor = csvy_model_data['w'].iloc[0:].to_numpy()
elif config.plasma.initial_t_rad > 0 * u.K:
t_radiative = np.ones(no_of_shells) * config.plasma.initial_t_rad
else:
t_radiative = None
if config.plasma.initial_t_inner < 0.0 * u.K:
luminosity_requested = config.supernova.luminosity_requested
t_inner = None
else:
luminosity_requested = None
t_inner = config.plasma.initial_t_inner
if hasattr(csvy_model_config, 'abundance'):
abundances_section = csvy_model_config.abundance
abundance, isotope_abundance = read_uniform_abundances(
abundances_section, no_of_shells)
else:
index, abundance, isotope_abundance = parse_csv_abundances(
csvy_model_data)
abundance = abundance.replace(np.nan, 0.0)
abundance = abundance[abundance.sum(axis=1) > 0]
norm_factor = abundance.sum(axis=0) + isotope_abundance.sum(axis=0)
if np.any(np.abs(norm_factor - 1) > 1e-12):
logger.warning("Abundances have not been normalized to 1."
" - normalizing")
abundance /= norm_factor
isotope_abundance /= norm_factor
#isotope_abundance = IsotopeAbundances(isotope_abundance)
isotope_abundance = IsotopeAbundances(
isotope_abundance, time_0=csvy_model_config.model_isotope_time_0)
#isotope_abundance.time_0 = csvy_model_config.model_isotope_time_0
return cls(velocity=velocity,
homologous_density=homologous_density,
abundance=abundance,
isotope_abundance=isotope_abundance,
time_explosion=time_explosion,
t_radiative=t_radiative,
t_inner=t_inner,
luminosity_requested=luminosity_requested,
dilution_factor=dilution_factor,
v_boundary_inner=v_boundary_inner,
v_boundary_outer=v_boundary_outer,
electron_densities=electron_densities)
def from_csvy(cls, config):
"""
Create a new Radial1DModel instance from a Configuration object.
Parameters
----------
config : tardis.io.config_reader.Configuration
Returns
-------
Radial1DModel
"""
CSVY_SUPPORTED_COLUMNS = {
'velocity', 'density', 't_rad', 'dilution_factor'
}
if os.path.isabs(config.csvy_model):
csvy_model_fname = config.csvy_model
else:
csvy_model_fname = os.path.join(config.config_dirname,
config.csvy_model)
csvy_model_config, csvy_model_data = load_csvy(csvy_model_fname)
base_dir = os.path.abspath(os.path.dirname(__file__))
schema_dir = os.path.join(base_dir, '..', 'io', 'schemas')
csvy_schema_file = os.path.join(schema_dir, 'csvy_model.yml')
csvy_model_config = Configuration(
validate_dict(csvy_model_config, schemapath=csvy_schema_file))
if hasattr(csvy_model_data, 'columns'):
abund_names = set([
name for name in csvy_model_data.columns
if nucname.iselement(name) or nucname.isnuclide(name)
])
unsupported_columns = set(
csvy_model_data.columns) - abund_names - CSVY_SUPPORTED_COLUMNS
field_names = set(
[field['name'] for field in csvy_model_config.datatype.fields])
assert set(csvy_model_data.columns) - field_names == set(),\
'CSVY columns exist without field descriptions'
assert field_names - set(csvy_model_data.columns) == set(),\
'CSVY field descriptions exist without corresponding csv data'
if unsupported_columns != set():
logger.warning(
"The following columns are specified in the csvy"
"model file, but are IGNORED by TARDIS: %s" %
(str(unsupported_columns)))
time_explosion = config.supernova.time_explosion.cgs
electron_densities = None
temperature = None
#if hasattr(csvy_model_config, 'v_inner_boundary'):
# v_boundary_inner = csvy_model_config.v_inner_boundary
#else:
# v_boundary_inner = None
#if hasattr(csvy_model_config, 'v_outer_boundary'):
# v_boundary_outer = csvy_model_config.v_outer_boundary
#else:
# v_boundary_outer = None
if hasattr(config, 'model'):
if hasattr(config.model, 'v_inner_boundary'):
v_boundary_inner = config.model.v_inner_boundary
else:
v_boundary_inner = None
if hasattr(config.model, 'v_outer_boundary'):
v_boundary_outer = config.model.v_outer_boundary
else:
v_boundary_outer = None
else:
v_boundary_inner = None
v_boundary_outer = None
if hasattr(csvy_model_config, 'velocity'):
velocity = quantity_linspace(csvy_model_config.velocity.start,
csvy_model_config.velocity.stop,
csvy_model_config.velocity.num +
1).cgs
else:
velocity_field_index = [
field['name'] for field in csvy_model_config.datatype.fields
].index('velocity')
velocity_unit = u.Unit(
csvy_model_config.datatype.fields[velocity_field_index]
['unit'])
velocity = csvy_model_data['velocity'].values * velocity_unit
velocity = velocity.to('cm/s')
if hasattr(csvy_model_config, 'density'):
homologous_density = HomologousDensity.from_csvy(
config, csvy_model_config)
else:
time_0 = csvy_model_config.model_density_time_0
density_field_index = [
field['name'] for field in csvy_model_config.datatype.fields
].index('density')
density_unit = u.Unit(
csvy_model_config.datatype.fields[density_field_index]['unit'])
density_0 = csvy_model_data['density'].values * density_unit
density_0 = density_0.to('g/cm^3')[1:]
density_0 = density_0.insert(0, 0)
homologous_density = HomologousDensity(density_0, time_0)
no_of_shells = len(velocity) - 1
# TODO -- implement t_radiative
#t_radiative = None
if temperature:
t_radiative = temperature
elif hasattr(csvy_model_data, 'columns'):
if 't_rad' in csvy_model_data.columns:
t_rad_field_index = [
field['name']
for field in csvy_model_config.datatype.fields
].index('t_rad')
t_rad_unit = u.Unit(
csvy_model_config.datatype.fields[t_rad_field_index]
['unit'])
t_radiative = csvy_model_data['t_rad'].iloc[
0:].values * t_rad_unit
else:
t_radiative = None
dilution_factor = None
if hasattr(csvy_model_data, 'columns'):
if 'dilution_factor' in csvy_model_data.columns:
dilution_factor = csvy_model_data['dilution_factor'].iloc[
0:].to_numpy()
elif config.plasma.initial_t_rad > 0 * u.K:
t_radiative = np.ones(no_of_shells) * config.plasma.initial_t_rad
else:
t_radiative = None
if config.plasma.initial_t_inner < 0.0 * u.K:
luminosity_requested = config.supernova.luminosity_requested
t_inner = None
else:
luminosity_requested = None
t_inner = config.plasma.initial_t_inner
if hasattr(csvy_model_config, 'abundance'):
abundances_section = csvy_model_config.abundance
abundance, isotope_abundance = read_uniform_abundances(
abundances_section, no_of_shells)
else:
index, abundance, isotope_abundance = parse_csv_abundances(
csvy_model_data)
abundance = abundance.replace(np.nan, 0.0)
abundance = abundance[abundance.sum(axis=1) > 0]
abundance = abundance.loc[:, 1:]
abundance.columns = np.arange(abundance.shape[1])
norm_factor = abundance.sum(axis=0) + isotope_abundance.sum(axis=0)
if np.any(np.abs(norm_factor - 1) > 1e-12):
logger.warning("Abundances have not been normalized to 1."
" - normalizing")
abundance /= norm_factor
isotope_abundance /= norm_factor
#isotope_abundance = IsotopeAbundances(isotope_abundance)
isotope_abundance = IsotopeAbundances(
isotope_abundance, time_0=csvy_model_config.model_isotope_time_0)
#isotope_abundance.time_0 = csvy_model_config.model_isotope_time_0
return cls(velocity=velocity,
homologous_density=homologous_density,
abundance=abundance,
isotope_abundance=isotope_abundance,
time_explosion=time_explosion,
t_radiative=t_radiative,
t_inner=t_inner,
luminosity_requested=luminosity_requested,
dilution_factor=dilution_factor,
v_boundary_inner=v_boundary_inner,
v_boundary_outer=v_boundary_outer,
electron_densities=electron_densities)
def from_config_dict(cls, config_dict, validate=True, config_dirname=""):
"""
Validating and subsequently parsing a config file.
Parameters
----------
config_dict : dict
dictionary of a raw unvalidated config file
validate : bool
Turn validation on or off.
Returns
-------
`tardis.config_reader.Configuration`
"""
if validate:
validated_config_dict = config_validator.validate_dict(config_dict)
else:
validated_config_dict = config_dict
validated_config_dict["config_dirname"] = config_dirname
# Montecarlo Section Implementation
montecarlo_section = validated_config_dict["montecarlo"]
if montecarlo_section["convergence_strategy"]["type"] == "damped":
montecarlo_section[
"convergence_strategy"
] = parse_convergence_section(
montecarlo_section["convergence_strategy"]
)
elif montecarlo_section["convergence_strategy"]["type"] == "custom":
raise NotImplementedError(
'convergence_strategy is set to "custom"; '
"you need to implement your specific convergence treatment"
)
else:
raise ValueError(
'convergence_strategy is not "damped" ' 'or "custom"'
)
enable_full_relativity = montecarlo_section["enable_full_relativity"]
spectrum_integrated = (
validated_config_dict["spectrum"]["method"] == "integrated"
)
if enable_full_relativity and spectrum_integrated:
raise NotImplementedError(
"The spectrum method is set to 'integrated' and "
"enable_full_relativity to 'True'.\n"
"The FormalIntegrator is not yet implemented for the full "
"relativity mode. "
)
if "csvy_model" in validated_config_dict.keys():
pass
elif "model" in validated_config_dict.keys():
# Model Section Validation
model_section = validated_config_dict["model"]
if model_section["structure"]["type"] == "specific":
start_velocity = model_section["structure"]["velocity"]["start"]
stop_velocity = model_section["structure"]["velocity"]["stop"]
if stop_velocity.value < start_velocity.value:
raise ValueError(
"Stop Velocity Cannot Be Less than Start Velocity. \n"
f"Start Velocity = {start_velocity} \n"
f"Stop Velocity = {stop_velocity}"
)
elif model_section["structure"]["type"] == "file":
v_inner_boundary = model_section["structure"][
"v_inner_boundary"
]
v_outer_boundary = model_section["structure"][
"v_outer_boundary"
]
if v_outer_boundary.value < v_inner_boundary.value:
raise ValueError(
"Outer Boundary Velocity Cannot Be Less than Inner Boundary Velocity. \n"
f"Inner Boundary Velocity = {v_inner_boundary} \n"
f"Outer Boundary Velocity = {v_outer_boundary}"
)
if "density" in model_section["structure"].keys():
if (
model_section["structure"]["density"]["type"]
== "exponential"
):
rho_0 = model_section["structure"]["density"]["rho_0"]
v_0 = model_section["structure"]["density"]["v_0"]
if not rho_0.value > 0:
raise ValueError(
f"Density Specified is Invalid, {rho_0}"
)
if not v_0.value > 0:
raise ValueError(
f"Velocity Specified is Invalid, {v_0}"
)
if "time_0" in model_section["structure"]["density"].keys():
time_0 = model_section["structure"]["density"]["time_0"]
if not time_0.value > 0:
raise ValueError(
f"Time Specified is Invalid, {time_0}"
)
elif (
model_section["structure"]["density"]["type"] == "power_law"
):
rho_0 = model_section["structure"]["density"]["rho_0"]
v_0 = model_section["structure"]["density"]["v_0"]
if not rho_0.value > 0:
raise ValueError(
f"Density Specified is Invalid, {rho_0}"
)
if not v_0.value > 0:
raise ValueError(
f"Velocity Specified is Invalid, {v_0}"
)
if "time_0" in model_section["structure"]["density"].keys():
time_0 = model_section["structure"]["density"]["time_0"]
if not time_0.value > 0:
raise ValueError(
f"Time Specified is Invalid, {time_0}"
)
elif model_section["structure"]["density"]["type"] == "uniform":
value = model_section["structure"]["density"]["value"]
if not value.value > 0:
raise ValueError(
f"Density Value Specified is Invalid, {value}"
)
if "time_0" in model_section["structure"]["density"].keys():
time_0 = model_section["structure"]["density"]["time_0"]
if not time_0.value > 0:
raise ValueError(
f"Time Specified is Invalid, {time_0}"
)
# SuperNova Section Validation
supernova_section = validated_config_dict["supernova"]
time_explosion = supernova_section["time_explosion"]
luminosity_wavelength_start = supernova_section[
"luminosity_wavelength_start"
]
luminosity_wavelength_end = supernova_section[
"luminosity_wavelength_end"
]
if not time_explosion.value > 0:
raise ValueError(
f"Time Of Explosion is Invalid, {time_explosion}"
)
if (
luminosity_wavelength_start.value
> luminosity_wavelength_end.value
):
raise ValueError(
"Integral Limits for Luminosity Wavelength are Invalid, Start Limit > End Limit \n"
f"Luminosity Wavelength Start : {luminosity_wavelength_start} \n"
f"Luminosity Wavelength End : {luminosity_wavelength_end}"
)
# Plasma Section Validation
plasma_section = validated_config_dict["plasma"]
initial_t_inner = plasma_section["initial_t_inner"]
initial_t_rad = plasma_section["initial_t_rad"]
if not initial_t_inner.value >= -1:
raise ValueError(
f"Initial Temperature of Inner Boundary Black Body is Invalid, {initial_t_inner}"
)
if not initial_t_rad.value >= -1:
raise ValueError(
f"Initial Radiative Temperature is Invalid, {initial_t_inner}"
)
# Spectrum Section Validation
spectrum_section = validated_config_dict["spectrum"]
start = spectrum_section["start"]
stop = spectrum_section["stop"]
if start.value > stop.value:
raise ValueError(
"Start Value of Spectrum Cannot be Greater than Stop Value. \n"
f"Start : {start} \n"
f"Stop : {stop}"
)
return cls(validated_config_dict)
def from_config_dict(cls, config_dict, atom_data=None, test_parser=False,
validate=True,
config_dirname=''):
"""
Validating and subsequently parsing a config file.
Parameters
----------
config_dict : ~dict
dictionary of a raw unvalidated config file
atom_data: ~tardis.atomic.AtomData
atom data object. if `None` will be tried to be read from
atom data file path in the config_dict [default=None]
test_parser: ~bool
switch on to ignore a working atom_data, mainly useful for
testing this reader
validate: ~bool
Turn validation on or off.
Returns
-------
`tardis.config_reader.Configuration`
"""
if validate:
validated_config_dict = config_validator.validate_dict(config_dict)
else:
validated_config_dict = config_dict
#First let's see if we can find an atom_db anywhere:
if test_parser:
atom_data = None
elif 'atom_data' in validated_config_dict.keys():
if os.path.isabs(validated_config_dict['atom_data']):
atom_data_fname = validated_config_dict['atom_data']
else:
atom_data_fname = os.path.join(config_dirname,
validated_config_dict['atom_data'])
validated_config_dict['atom_data_fname'] = atom_data_fname
else:
raise ConfigurationError('No atom_data key found in config or command line')
if atom_data is None and not test_parser:
logger.info('Reading Atomic Data from %s', atom_data_fname)
atom_data = atomic.AtomData.from_hdf5(atom_data_fname)
else:
atom_data = atom_data
#Parsing supernova dictionary
validated_config_dict['supernova']['luminosity_nu_start'] = \
validated_config_dict['supernova']['luminosity_wavelength_end'].to(
u.Hz, u.spectral())
try:
validated_config_dict['supernova']['luminosity_nu_end'] = \
(validated_config_dict['supernova']
['luminosity_wavelength_start'].to(u.Hz, u.spectral()))
except ZeroDivisionError:
validated_config_dict['supernova']['luminosity_nu_end'] = (
np.inf * u.Hz)
validated_config_dict['supernova']['time_explosion'] = (
validated_config_dict['supernova']['time_explosion'].cgs)
validated_config_dict['supernova']['luminosity_requested'] = (
validated_config_dict['supernova']['luminosity_requested'].cgs)
#Parsing the model section
model_section = validated_config_dict['model']
v_inner = None
v_outer = None
mean_densities = None
abundances = None
structure_section = model_section['structure']
if structure_section['type'] == 'specific':
velocity = model_section['structure']['velocity']
start, stop, num = velocity['start'], velocity['stop'], \
velocity['num']
num += 1
velocities = quantity_linspace(start, stop, num)
v_inner, v_outer = velocities[:-1], velocities[1:]
mean_densities = parse_density_section(
model_section['structure']['density'], v_inner, v_outer,
validated_config_dict['supernova']['time_explosion']).cgs
elif structure_section['type'] == 'file':
if os.path.isabs(structure_section['filename']):
structure_fname = structure_section['filename']
else:
structure_fname = os.path.join(config_dirname,
structure_section['filename'])
v_inner, v_outer, mean_densities, inner_boundary_index, \
outer_boundary_index = read_density_file(
structure_fname, structure_section['filetype'],
validated_config_dict['supernova']['time_explosion'],
structure_section['v_inner_boundary'],
structure_section['v_outer_boundary'])
r_inner = validated_config_dict['supernova']['time_explosion'] * v_inner
r_outer = validated_config_dict['supernova']['time_explosion'] * v_outer
r_middle = 0.5 * (r_inner + r_outer)
structure_section['v_inner'] = v_inner.cgs
structure_section['v_outer'] = v_outer.cgs
structure_section['mean_densities'] = mean_densities.cgs
no_of_shells = len(v_inner)
structure_section['no_of_shells'] = no_of_shells
structure_section['r_inner'] = r_inner.cgs
structure_section['r_outer'] = r_outer.cgs
structure_section['r_middle'] = r_middle.cgs
structure_section['volumes'] = ((4. / 3) * np.pi * \
(r_outer ** 3 -
r_inner ** 3)).cgs
#### TODO the following is legacy code and should be removed
validated_config_dict['structure'] = \
validated_config_dict['model']['structure']
# ^^^^^^^^^^^^^^^^
abundances_section = model_section['abundances']
if abundances_section['type'] == 'uniform':
abundances = pd.DataFrame(columns=np.arange(no_of_shells),
index=pd.Index(np.arange(1, 120), name='atomic_number'), dtype=np.float64)
for element_symbol_string in abundances_section:
if element_symbol_string == 'type': continue
z = element_symbol2atomic_number(element_symbol_string)
abundances.ix[z] = float(abundances_section[element_symbol_string])
elif abundances_section['type'] == 'file':
if os.path.isabs(abundances_section['filename']):
abundances_fname = abundances_section['filename']
else:
abundances_fname = os.path.join(config_dirname,
abundances_section['filename'])
index, abundances = read_abundances_file(abundances_fname,
abundances_section['filetype'],
inner_boundary_index, outer_boundary_index)
if len(index) != no_of_shells:
raise ConfigurationError('The abundance file specified has not the same number of cells'
'as the specified density profile')
abundances = abundances.replace(np.nan, 0.0)
abundances = abundances[abundances.sum(axis=1) > 0]
norm_factor = abundances.sum(axis=0)
if np.any(np.abs(norm_factor - 1) > 1e-12):
logger.warning("Abundances have not been normalized to 1. - normalizing")
abundances /= norm_factor
validated_config_dict['abundances'] = abundances
########### DOING PLASMA SECTION ###############
plasma_section = validated_config_dict['plasma']
if plasma_section['initial_t_inner'] < 0.0 * u.K:
luminosity_requested = validated_config_dict['supernova']['luminosity_requested']
plasma_section['t_inner'] = ((luminosity_requested /
(4 * np.pi * r_inner[0] ** 2 *
constants.sigma_sb)) ** .25).to('K')
logger.info('"initial_t_inner" is not specified in the plasma '
'section - initializing to %s with given luminosity',
plasma_section['t_inner'])
else:
plasma_section['t_inner'] = plasma_section['initial_t_inner']
if plasma_section['initial_t_rad'] > 0 * u.K:
plasma_section['t_rads'] = np.ones(no_of_shells) * \
plasma_section['initial_t_rad']
else:
plasma_section['t_rads'] = None
if plasma_section['disable_electron_scattering'] is False:
logger.debug("Electron scattering switched on")
validated_config_dict['montecarlo']['sigma_thomson'] = 6.652486e-25 / (u.cm ** 2)
else:
logger.warn('Disabling electron scattering - this is not physical')
validated_config_dict['montecarlo']['sigma_thomson'] = 1e-200 / (u.cm ** 2)
if plasma_section['helium_treatment'] == 'recomb-nlte':
validated_config_dict['plasma']['helium_treatment'] == 'recomb-nlte'
else:
validated_config_dict['plasma']['helium_treatment'] == 'dilute-lte'
##### NLTE subsection of Plasma start
nlte_validated_config_dict = {}
nlte_species = []
nlte_section = plasma_section['nlte']
nlte_species_list = nlte_section.pop('species')
for species_string in nlte_species_list:
nlte_species.append(species_string_to_tuple(species_string))
nlte_validated_config_dict['species'] = nlte_species
nlte_validated_config_dict['species_string'] = nlte_species_list
nlte_validated_config_dict.update(nlte_section)
if 'coronal_approximation' not in nlte_section:
logger.debug('NLTE "coronal_approximation" not specified in NLTE section - defaulting to False')
nlte_validated_config_dict['coronal_approximation'] = False
if 'classical_nebular' not in nlte_section:
logger.debug('NLTE "classical_nebular" not specified in NLTE section - defaulting to False')
nlte_validated_config_dict['classical_nebular'] = False
elif nlte_section: #checks that the dictionary is not empty
logger.warn('No "species" given - ignoring other NLTE options given:\n%s',
pp.pformat(nlte_section))
if not nlte_validated_config_dict:
nlte_validated_config_dict['species'] = []
plasma_section['nlte'] = nlte_validated_config_dict
#^^^^^^^^^^^^^^ End of Plasma Section
##### Monte Carlo Section
montecarlo_section = validated_config_dict['montecarlo']
montecarlo_section['no_of_packets'] = \
int(montecarlo_section['no_of_packets'])
montecarlo_section['last_no_of_packets'] = \
int(montecarlo_section['last_no_of_packets'])
if montecarlo_section['last_no_of_packets'] < 0:
montecarlo_section['last_no_of_packets'] = \
montecarlo_section['no_of_packets']
montecarlo_section['convergence_strategy'] = (
parse_convergence_section(
montecarlo_section['convergence_strategy']))
black_body_section = montecarlo_section['black_body_sampling']
montecarlo_section['black_body_sampling'] = {}
montecarlo_section['black_body_sampling']['start'] = \
black_body_section['start']
montecarlo_section['black_body_sampling']['end'] = \
black_body_section['stop']
montecarlo_section['black_body_sampling']['samples'] = \
black_body_section['num']
virtual_spectrum_section = montecarlo_section['virtual_spectrum_range']
montecarlo_section['virtual_spectrum_range'] = {}
montecarlo_section['virtual_spectrum_range']['start'] = \
virtual_spectrum_section['start']
montecarlo_section['virtual_spectrum_range']['end'] = \
virtual_spectrum_section['stop']
montecarlo_section['virtual_spectrum_range']['samples'] = \
virtual_spectrum_section['num']
###### END of convergence section reading
validated_config_dict['spectrum'] = parse_spectrum_list2dict(
validated_config_dict['spectrum'])
return cls(validated_config_dict, atom_data)
def from_csvy(cls, config):
"""
Create a new Radial1DModel instance from a Configuration object.
Parameters
----------
config : tardis.io.config_reader.Configuration
Returns
-------
Radial1DModel
"""
CSVY_SUPPORTED_COLUMNS = {
"velocity",
"density",
"t_rad",
"dilution_factor",
}
if os.path.isabs(config.csvy_model):
csvy_model_fname = config.csvy_model
else:
csvy_model_fname = os.path.join(
config.config_dirname, config.csvy_model
)
csvy_model_config, csvy_model_data = load_csvy(csvy_model_fname)
base_dir = os.path.abspath(os.path.dirname(__file__))
schema_dir = os.path.join(base_dir, "..", "io", "schemas")
csvy_schema_file = os.path.join(schema_dir, "csvy_model.yml")
csvy_model_config = Configuration(
validate_dict(csvy_model_config, schemapath=csvy_schema_file)
)
if hasattr(csvy_model_data, "columns"):
abund_names = set(
[
name
for name in csvy_model_data.columns
if is_valid_nuclide_or_elem(name)
]
)
unsupported_columns = (
set(csvy_model_data.columns)
- abund_names
- CSVY_SUPPORTED_COLUMNS
)
field_names = set(
[field["name"] for field in csvy_model_config.datatype.fields]
)
assert (
set(csvy_model_data.columns) - field_names == set()
), "CSVY columns exist without field descriptions"
assert (
field_names - set(csvy_model_data.columns) == set()
), "CSVY field descriptions exist without corresponding csv data"
if unsupported_columns != set():
logger.warning(
"The following columns are "
"specified in the csvy model file,"
f" but are IGNORED by TARDIS: {str(unsupported_columns)}"
)
time_explosion = config.supernova.time_explosion.cgs
electron_densities = None
temperature = None
# if hasattr(csvy_model_config, 'v_inner_boundary'):
# v_boundary_inner = csvy_model_config.v_inner_boundary
# else:
# v_boundary_inner = None
# if hasattr(csvy_model_config, 'v_outer_boundary'):
# v_boundary_outer = csvy_model_config.v_outer_boundary
# else:
# v_boundary_outer = None
if hasattr(config, "model"):
if hasattr(config.model, "v_inner_boundary"):
v_boundary_inner = config.model.v_inner_boundary
else:
v_boundary_inner = None
if hasattr(config.model, "v_outer_boundary"):
v_boundary_outer = config.model.v_outer_boundary
else:
v_boundary_outer = None
else:
v_boundary_inner = None
v_boundary_outer = None
if hasattr(csvy_model_config, "velocity"):
velocity = quantity_linspace(
csvy_model_config.velocity.start,
csvy_model_config.velocity.stop,
csvy_model_config.velocity.num + 1,
).cgs
else:
velocity_field_index = [
field["name"] for field in csvy_model_config.datatype.fields
].index("velocity")
velocity_unit = u.Unit(
csvy_model_config.datatype.fields[velocity_field_index]["unit"]
)
velocity = csvy_model_data["velocity"].values * velocity_unit
velocity = velocity.to("cm/s")
if hasattr(csvy_model_config, "density"):
homologous_density = HomologousDensity.from_csvy(
config, csvy_model_config
)
else:
time_0 = csvy_model_config.model_density_time_0
density_field_index = [
field["name"] for field in csvy_model_config.datatype.fields
].index("density")
density_unit = u.Unit(
csvy_model_config.datatype.fields[density_field_index]["unit"]
)
density_0 = csvy_model_data["density"].values * density_unit
density_0 = density_0.to("g/cm^3")[1:]
density_0 = density_0.insert(0, 0)
homologous_density = HomologousDensity(density_0, time_0)
no_of_shells = len(velocity) - 1
# TODO -- implement t_radiative
# t_radiative = None
if temperature:
t_radiative = temperature
elif hasattr(csvy_model_data, "columns"):
if "t_rad" in csvy_model_data.columns:
t_rad_field_index = [
field["name"] for field in csvy_model_config.datatype.fields
].index("t_rad")
t_rad_unit = u.Unit(
csvy_model_config.datatype.fields[t_rad_field_index]["unit"]
)
t_radiative = (
csvy_model_data["t_rad"].iloc[0:].values * t_rad_unit
)
else:
t_radiative = None
dilution_factor = None
if hasattr(csvy_model_data, "columns"):
if "dilution_factor" in csvy_model_data.columns:
dilution_factor = (
csvy_model_data["dilution_factor"].iloc[0:].to_numpy()
)
elif config.plasma.initial_t_rad > 0 * u.K:
t_radiative = np.ones(no_of_shells) * config.plasma.initial_t_rad
else:
t_radiative = None
if config.plasma.initial_t_inner < 0.0 * u.K:
luminosity_requested = config.supernova.luminosity_requested
t_inner = None
else:
luminosity_requested = None
t_inner = config.plasma.initial_t_inner
if hasattr(csvy_model_config, "abundance"):
abundances_section = csvy_model_config.abundance
abundance, isotope_abundance = read_uniform_abundances(
abundances_section, no_of_shells
)
else:
index, abundance, isotope_abundance = parse_csv_abundances(
csvy_model_data
)
abundance = abundance.loc[:, 1:]
abundance.columns = np.arange(abundance.shape[1])
isotope_abundance = isotope_abundance.loc[:, 1:]
isotope_abundance.columns = np.arange(isotope_abundance.shape[1])
abundance = abundance.replace(np.nan, 0.0)
abundance = abundance[abundance.sum(axis=1) > 0]
isotope_abundance = isotope_abundance.replace(np.nan, 0.0)
isotope_abundance = isotope_abundance[isotope_abundance.sum(axis=1) > 0]
norm_factor = abundance.sum(axis=0) + isotope_abundance.sum(axis=0)
if np.any(np.abs(norm_factor - 1) > 1e-12):
logger.warning(
"Abundances have not been normalized to 1." " - normalizing"
)
abundance /= norm_factor
isotope_abundance /= norm_factor
# isotope_abundance = IsotopeAbundances(isotope_abundance)
isotope_abundance = IsotopeAbundances(
isotope_abundance, time_0=csvy_model_config.model_isotope_time_0
)
# isotope_abundance.time_0 = csvy_model_config.model_isotope_time_0
return cls(
velocity=velocity,
homologous_density=homologous_density,
abundance=abundance,
isotope_abundance=isotope_abundance,
time_explosion=time_explosion,
t_radiative=t_radiative,
t_inner=t_inner,
luminosity_requested=luminosity_requested,
dilution_factor=dilution_factor,
v_boundary_inner=v_boundary_inner,
v_boundary_outer=v_boundary_outer,
electron_densities=electron_densities,
)
