示例#1
0
    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)
示例#2
0
    def from_config_dict(cls, config_dict, atom_data=None, test_parser=False,
                         config_definition_file=None, validate=True):
        """
        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

        config_definition_file: ~str
            path to config definition file, if `None` will be set to the default
            in the `data` directory that ships with TARDIS

        validate: ~bool
            Turn validation on or off. 


        Returns
        -------

        `tardis.config_reader.Configuration`

        """

        if config_definition_file is None:
            config_definition_file = default_config_definition_file

        config_definition = yaml.load(open(config_definition_file))
        if validate:
            validated_config_dict = ConfigurationValidator(config_definition,
                                       config_dict).get_config()
        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():
            atom_data_fname = 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':
            start, stop, num = model_section['structure']['velocity']
            num += 1
            velocities = np.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':
            v_inner, v_outer, mean_densities, inner_boundary_index, \
            outer_boundary_index = read_density_file(
                structure_section['filename'], 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_validated_config_dict = {}
        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':
            index, abundances = read_abundances_file(abundances_section['filename'], 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']

        plasma_section['t_rads'] = np.ones(no_of_shells) * \
                                   plasma_section['initial_t_rad']
        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)




        ##### 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']
        if montecarlo_section['last_no_of_packets'] < 0:
            montecarlo_section['last_no_of_packets'] = \
                montecarlo_section['no_of_packets']

        default_convergence_section = {'type': 'damped',
                                      'lock_t_inner_cycles': 1,
                                      't_inner_update_exponent': -0.5,
                                      'damping_constant': 0.5}



        if montecarlo_section['convergence_strategy'] is None:
            logger.warning('No convergence criteria selected - '
                           'just damping by 0.5 for w, t_rad and t_inner')
            montecarlo_section['convergence_strategy'] = (
                parse_convergence_section(default_convergence_section))
        else:
            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[0]
        montecarlo_section['black_body_sampling']['end'] = \
            black_body_section[1]
        montecarlo_section['black_body_sampling']['samples'] = \
            black_body_section[2]
        virtual_spectrum_section = montecarlo_section['virtual_spectrum_range']
        montecarlo_section['virtual_spectrum_range'] = {}
        montecarlo_section['virtual_spectrum_range']['start'] = \
            virtual_spectrum_section[0]
        montecarlo_section['virtual_spectrum_range']['end'] = \
            virtual_spectrum_section[1]
        montecarlo_section['virtual_spectrum_range']['samples'] = \
            virtual_spectrum_section[2]

        ###### END of convergence section reading


        validated_config_dict['spectrum'] = parse_spectrum_list2dict(
            validated_config_dict['spectrum'])

        return cls(validated_config_dict, atom_data)
示例#3
0
    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)
示例#4
0
文件: base.py 项目: kush789/tardis
    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
        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 = 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 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
        if abundances_section.type == 'uniform':
            abundance = 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)
                abundance.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.config_dirname,
                                                abundances_section.filename)

            index, 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)

        if np.any(np.abs(norm_factor - 1) > 1e-12):
            logger.warning("Abundances have not been normalized to 1."
                           " - normalizing")
            abundance /= norm_factor


        return cls(velocity=velocity,
                   homologous_density=homologous_density,
                   abundance=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))