def from_config(cls, config):
"""
Create a new Radial1DModel instance from a Configuration object.
Parameters
----------
config : tardis.io.config_reader.Configuration
Returns
-------
Radial1DModel
"""
time_explosion = config.supernova.time_explosion.cgs
structure = config.model.structure
electron_densities = None
temperature = None
if structure.type == 'specific':
velocity = quantity_linspace(structure.velocity.start,
structure.velocity.stop,
structure.velocity.num + 1).cgs
homologous_density = HomologousDensity.from_config(config)
elif structure.type == 'file':
if os.path.isabs(structure.filename):
structure_fname = structure.filename
else:
structure_fname = os.path.join(config.config_dirname,
structure.filename)
time_0, velocity, density_0, electron_densities, temperature = read_density_file(
structure_fname, structure.filetype)
density_0 = density_0.insert(0, 0)
homologous_density = HomologousDensity(density_0, time_0)
else:
raise NotImplementedError
# Note: This is the number of shells *without* taking in mind the
# v boundaries.
no_of_shells = len(velocity) - 1
if temperature:
t_radiative = temperature
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
abundances_section = config.model.abundances
isotope_abundance = pd.DataFrame()
if abundances_section.type == 'uniform':
abundance, isotope_abundance = read_uniform_abundances(
abundances_section, no_of_shells)
elif abundances_section.type == 'file':
if os.path.isabs(abundances_section.filename):
abundances_fname = abundances_section.filename
else:
abundances_fname = os.path.join(config.config_dirname,
abundances_section.filename)
index, abundance, isotope_abundance = read_abundances_file(
abundances_fname, abundances_section.filetype)
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)
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=None,
v_boundary_inner=structure.get('v_inner_boundary', None),
v_boundary_outer=structure.get('v_outer_boundary', None),
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
"""
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_config(cls, config):
"""
Create a new Radial1DModel instance from a Configuration object.
Parameters
----------
config : tardis.io.config_reader.Configuration
Returns
-------
Radial1DModel
"""
time_explosion = config.supernova.time_explosion.cgs
structure = config.model.structure
electron_densities = None
temperature = None
if structure.type == 'specific':
velocity = quantity_linspace(structure.velocity.start,
structure.velocity.stop,
structure.velocity.num + 1).cgs
homologous_density = HomologousDensity.from_config(config)
elif structure.type == 'file':
if os.path.isabs(structure.filename):
structure_fname = structure.filename
else:
structure_fname = os.path.join(config.config_dirname,
structure.filename)
time_0, velocity, density_0, electron_densities, temperature = read_density_file(
structure_fname, structure.filetype)
density_0 = density_0.insert(0, 0)
homologous_density = HomologousDensity(density_0, time_0)
else:
raise NotImplementedError
# Note: This is the number of shells *without* taking in mind the
# v boundaries.
no_of_shells = len(velocity) - 1
if temperature:
t_radiative = temperature
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
abundances_section = config.model.abundances
isotope_abundance = pd.DataFrame()
if abundances_section.type == 'uniform':
abundance, isotope_abundance = read_uniform_abundances(
abundances_section, no_of_shells)
elif abundances_section.type == 'file':
if os.path.isabs(abundances_section.filename):
abundances_fname = abundances_section.filename
else:
abundances_fname = os.path.join(config.config_dirname,
abundances_section.filename)
index, abundance, isotope_abundance = read_abundances_file(abundances_fname,
abundances_section.filetype)
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)
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=None,
v_boundary_inner=structure.get('v_inner_boundary', None),
v_boundary_outer=structure.get('v_outer_boundary', None),
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_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,
)