Esempio n. 1
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def kurucz_atomic_data(atomic_data_fname):
    atomic_data = AtomData.from_hdf5(atomic_data_fname)

    if atomic_data.md5 != '21095dd25faa1683f4c90c911a00c3f8':
        pytest.skip('Need default Kurucz atomic dataset '
                    '(md5="21095dd25faa1683f4c90c911a00c3f8"')
    else:
        return atomic_data
Esempio n. 2
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 def __init__(self, config_fname, atom_data=None, log_dir='./logs/'):
     self._log_dir = log_dir
     self.set_logger('startup')
     self._config = ConfigurationNameSpace.from_yaml(config_fname)
     if atom_data is None:
         self._atom_data = AtomData.from_hdf5(self._config.atom_data)
     else:
         self._atom_data = atom_data
Esempio n. 3
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def kurucz_atomic_data(atomic_data_fname):
    atomic_data = AtomData.from_hdf5(atomic_data_fname)

    if atomic_data.md5 != '21095dd25faa1683f4c90c911a00c3f8':
        pytest.skip('Need default Kurucz atomic dataset '
                    '(md5="21095dd25faa1683f4c90c911a00c3f8"')
    else:
        return atomic_data
Esempio n. 4
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 def __init__(self, *args, **kwargs):
     config_fname = args[0]
     atom_data = kwargs.pop('atom_data', None)
     self._log_dir = kwargs.pop('log_dir', './logs/')
     self.set_logger('startup')
     self._config = ConfigurationNameSpace.from_yaml(config_fname)
     if atom_data is None:
         self._atom_data = AtomData.from_hdf5(self._config.atom_data)
     else:
         self._atom_data = atom_data
Esempio n. 5
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    def from_yaml(cls, fname, resume_fit=None):
        """
        Reading the fitter configuration from a yaml file
        
        Parameters
        ----------

        fname: ~str
            filename
        
        """

        conf_dict = yaml.load(open(fname), OrderedDictYAMLLoader)
        default_config = ConfigurationNameSpace.from_yaml(
            conf_dict['tardis']['default_conf'])
        atom_data = AtomData.from_hdf5(conf_dict['tardis']['atom_data'])
        parameter_config = ParameterConfiguration.from_conf_dict(conf_dict['fitter']['parameters'])

        number_of_samples = conf_dict['fitter']['number_of_samples']
        max_iterations = conf_dict['fitter']['max_iterations']
        optimizer_dict = conf_dict['fitter'].pop('optimizer')
        optimizer_class = all_optimizer_dict[optimizer_dict.pop('name')]
        optimizer = optimizer_class(parameter_config, number_of_samples,
                                    **optimizer_dict)
        fitness_function_dict = conf_dict['fitter'].pop('fitness_function')
        fitness_function_class = all_fitness_function_dict[
            fitness_function_dict.pop('name')]
        fitness_function = fitness_function_class(**fitness_function_dict)

        resume = conf_dict['fitter'].get('resume', resume_fit)
        fitter_log = conf_dict['fitter'].get('fitter_log', None)

        spectral_store_dict = conf_dict['fitter'].get('spectral_store', None)
        if spectral_store_dict is not None:
            spectral_store_fname = spectral_store_dict['fname']
            spectral_store_mode = spectral_store_dict.get('mode', 'all')
            spectral_store_clobber = spectral_store_dict.get('clobber', False)
            spectral_store = SpectralStore(spectral_store_fname,
                                           mode=spectral_store_mode,
                                           resume=resume,
                                           clobber=spectral_store_clobber)
        else:
            spectral_store = None





        return cls(optimizer, fitness_function,
                   parameter_config=parameter_config,
                   default_config=default_config, atom_data=atom_data,
                   number_of_samples=number_of_samples,
                   max_iterations=max_iterations, fitter_log=fitter_log,
                   spectral_store=spectral_store, resume=resume)
Esempio n. 6
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    def setup(self, request, reference, data_path, atomic_data_fname):
        """
        This method does initial setup of creating configuration and performing
        a single run of integration test.
        """
        # The last component in dirpath can be extracted as name of setup.
        self.name = data_path['setup_name']

        self.config_file = os.path.join(data_path['config_dirpath'], "config.yml")

        # Load atom data file separately, pass it for forming tardis config.
        self.atom_data = AtomData.from_hdf5(atomic_data_fname)

        # Check whether the atom data file in current run and the atom data
        # file used in obtaining the reference data are same.
        # TODO: hard coded UUID for kurucz atom data file, generalize it later.
        kurucz_data_file_uuid1 = "5ca3035ca8b311e3bb684437e69d75d7"
        assert self.atom_data.uuid1 == kurucz_data_file_uuid1

        # Create a Configuration through yaml file and atom data.
        tardis_config = Configuration.from_yaml(
            self.config_file, atom_data=self.atom_data)

        # Check whether current run is with less packets.
        if request.config.getoption("--less-packets"):
            less_packets = request.config.integration_tests_config['less_packets']
            tardis_config['montecarlo']['no_of_packets'] = (
                less_packets['no_of_packets']
            )
            tardis_config['montecarlo']['last_no_of_packets'] = (
                less_packets['last_no_of_packets']
            )

        # We now do a run with prepared config and get radial1d model.
        self.result = Radial1DModel(tardis_config)

        # If current test run is just for collecting reference data, store the
        # output model to HDF file, save it at specified path. Skip all tests.
        # Else simply perform the run and move further for performing
        # assertions.
        if request.config.getoption("--generate-reference"):
            run_radial1d(self.result, hdf_path_or_buf=os.path.join(
                data_path['gen_ref_dirpath'], "{0}.h5".format(self.name)
            ))
            pytest.skip("Reference data saved at {0}".format(
                data_path['gen_ref_dirpath']
            ))
        else:
            run_radial1d(self.result)

        # Get the reference data through the fixture.
        self.reference = reference
Esempio n. 7
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    def setup(self):
        """
        This method does initial setup of creating configuration and performing
        a single run of integration test.
        """
        self.config_file = data_path("config_w7.yml")
        self.abundances = data_path("abundancies_w7.dat")
        self.densities = data_path("densities_w7.dat")

        # First we check whether atom data file exists at desired path.
        self.atom_data_filename = os.path.expanduser(os.path.expandvars(
                                    pytest.config.getvalue('atomic-dataset')))
        assert os.path.exists(self.atom_data_filename), \
            "{0} atom data file does not exist".format(self.atom_data_filename)

        # The available config file doesn't have file paths of atom data file,
        # densities and abundances profile files as desired. We load the atom
        # data seperately and provide it to tardis_config later. For rest of
        # the two, we form dictionary from the config file and override those
        # parameters by putting file paths of these two files at proper places.
        config_yaml = yaml.load(open(self.config_file))
        config_yaml['model']['abundances']['filename'] = self.abundances
        config_yaml['model']['structure']['filename'] = self.densities

        # Load atom data file separately, pass it for forming tardis config.
        self.atom_data = AtomData.from_hdf5(self.atom_data_filename)

        # Check whether the atom data file in current run and the atom data
        # file used in obtaining the baseline data for slow tests are same.
        # TODO: hard coded UUID for kurucz atom data file, generalize it later.
        kurucz_data_file_uuid1 = "5ca3035ca8b311e3bb684437e69d75d7"
        assert self.atom_data.uuid1 == kurucz_data_file_uuid1

        # The config hence obtained will be having appropriate file paths.
        tardis_config = Configuration.from_config_dict(config_yaml, self.atom_data)

        # We now do a run with prepared config and get radial1d model.
        self.obtained_radial1d_model = Radial1DModel(tardis_config)
        simulation = Simulation(tardis_config)
        simulation.legacy_run_simulation(self.obtained_radial1d_model)

        # The baseline data against which assertions are to be made is ingested
        # from already available compressed binaries (.npz). These will return
        # dictionaries of numpy.ndarrays for performing assertions.
        self.slow_test_data_dir = os.path.join(os.path.expanduser(
                os.path.expandvars(pytest.config.getvalue('slow-test-data'))), "w7")

        self.expected_ndarrays = np.load(os.path.join(self.slow_test_data_dir,
                                                      "ndarrays.npz"))
        self.expected_quantities = np.load(os.path.join(self.slow_test_data_dir,
                                                        "quantities.npz"))
Esempio n. 8
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def run_tardis(config, atom_data=None):
    """
    This function is one of the core functions to run TARDIS from a given
    config object.

    It will return a model object containing

    Parameters
    ----------

    config: ~str or ~dict
        filename of configuration yaml file or dictionary

    atom_data: ~str or ~tardis.atomic.AtomData
        if atom_data is a string it is interpreted as a path to a file storing
        the atomic data. Atomic data to use for this TARDIS simulation. If set to None, the
        atomic data will be loaded according to keywords set in the configuration
        [default=None]
    """
    from tardis.io.config_reader import Configuration
    from tardis.simulation import Simulation
    from tardis.atomic import AtomData

    if atom_data is not None:
        try:
            atom_data = AtomData.from_hdf5(atom_data)
        except TypeError:
            atom_data = atom_data

    try:
        tardis_config = Configuration.from_yaml(config)
    except TypeError:
        tardis_config = Configuration.from_config_dict(config)

    simulation = Simulation.from_config(tardis_config, atom_data=atom_data)
    simulation.run()

    return simulation
Esempio n. 9
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def run_tardis(config, atom_data=None):
    """
    This function is one of the core functions to run TARDIS from a given
    config object.

    It will return a model object containing

    Parameters
    ----------

    config: ~str or ~dict
        filename of configuration yaml file or dictionary

    atom_data: ~str or ~tardis.atomic.AtomData
        if atom_data is a string it is interpreted as a path to a file storing
        the atomic data. Atomic data to use for this TARDIS simulation. If set to None, the
        atomic data will be loaded according to keywords set in the configuration
        [default=None]
    """
    from tardis.io.config_reader import Configuration
    from tardis.simulation import Simulation
    from tardis.atomic import AtomData

    if atom_data is not None:
        try:
            atom_data = AtomData.from_hdf5(atom_data)
        except TypeError:
            atom_data = atom_data

    try:
        tardis_config = Configuration.from_yaml(config)
    except TypeError:
        tardis_config = Configuration.from_config_dict(config)

    simulation = Simulation.from_config(tardis_config, atom_data=atom_data)
    simulation.run()

    return simulation
Esempio n. 10
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def atomic_data():
    atomic_db_fname = os.path.join(tardis.__path__[0], 'tests', 'data',
                                   'chianti_he_db.h5')
    return AtomData.from_hdf5(atomic_db_fname)
Esempio n. 11
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def atomic_data(selected_atoms):
    atomic_db_fname = os.path.join(tardis.__path__[0], 'tests', 'data',
                                   'chianti_he_db.h5')
    atom_data = AtomData.from_hdf5(atomic_db_fname)
    atom_data.prepare_atom_data(selected_atoms)
    return atom_data
Esempio n. 12
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    def setup(self, request, reference, data_path):
        """
        This method does initial setup of creating configuration and performing
        a single run of integration test.
        """
        # The last component in dirpath can be extracted as name of setup.
        self.name = data_path['setup_name']

        self.config_file = os.path.join(data_path['config_dirpath'], "config.yml")

        # A quick hack to use atom data per setup. Atom data is ingested from
        # local HDF or downloaded and cached from a url, depending on data_path
        # keys.
        atom_data_name = yaml.load(open(self.config_file))['atom_data']

        # Get the path to HDF file:
        if 'atom_data_url' in data_path:
            # If the atom data is to be ingested from url:
            atom_data_filepath = download_file(urlparse.urljoin(
                base=data_path['atom_data_url'], url=atom_data_name), cache=True
            )
        else:
            # If the atom data is to be ingested from local file:
            atom_data_filepath = os.path.join(
                data_path['atom_data_dirpath'], atom_data_name
            )

        # Load atom data file separately, pass it for forming tardis config.
        self.atom_data = AtomData.from_hdf5(atom_data_filepath)

        # Check whether the atom data file in current run and the atom data
        # file used in obtaining the reference data are same.
        # TODO: hard coded UUID for kurucz atom data file, generalize it later.
        # kurucz_data_file_uuid1 = "5ca3035ca8b311e3bb684437e69d75d7"
        # assert self.atom_data.uuid1 == kurucz_data_file_uuid1

        # Create a Configuration through yaml file and atom data.
        tardis_config = Configuration.from_yaml(
            self.config_file, atom_data=self.atom_data)

        # Check whether current run is with less packets.
        if request.config.getoption("--less-packets"):
            less_packets = request.config.integration_tests_config['less_packets']
            tardis_config['montecarlo']['no_of_packets'] = (
                less_packets['no_of_packets']
            )
            tardis_config['montecarlo']['last_no_of_packets'] = (
                less_packets['last_no_of_packets']
            )

        # We now do a run with prepared config and get radial1d model.
        self.result = Radial1DModel(tardis_config)

        # If current test run is just for collecting reference data, store the
        # output model to HDF file, save it at specified path. Skip all tests.
        # Else simply perform the run and move further for performing
        # assertions.
        if request.config.getoption("--generate-reference"):
            run_radial1d(self.result, hdf_path_or_buf=os.path.join(
                data_path['gen_ref_dirpath'], "{0}.h5".format(self.name)
            ))
            pytest.skip("Reference data saved at {0}".format(
                data_path['gen_ref_dirpath']
            ))
        else:
            run_radial1d(self.result)

        # Get the reference data through the fixture.
        self.reference = reference
Esempio n. 13
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def included_he_atomic_data(test_data_path):
    atomic_db_fname = os.path.join(test_data_path, 'chianti_he_db.h5')
    return AtomData.from_hdf5(atomic_db_fname)
Esempio n. 14
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def atomic_data(selected_atoms):
    atomic_db_fname = os.path.join(tardis.__path__[0], 'tests', 'data',
                                   'chianti_he_db.h5')
    atom_data = AtomData.from_hdf5(atomic_db_fname)
    atom_data.prepare_atom_data(selected_atoms)
    return atom_data
Esempio n. 15
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    def setup(self, request, reference, data_path, pytestconfig):
        """
        This method does initial setup of creating configuration and performing
        a single run of integration test.
        """
        # Get capture manager
        capmanager = pytestconfig.pluginmanager.getplugin('capturemanager')

        # The last component in dirpath can be extracted as name of setup.
        self.name = data_path['setup_name']

        self.config_file = os.path.join(data_path['config_dirpath'], "config.yml")

        # A quick hack to use atom data per setup. Atom data is ingested from
        # local HDF or downloaded and cached from a url, depending on data_path
        # keys.
        atom_data_name = yaml.load(open(self.config_file))['atom_data']

        # Get the path to HDF file:
        atom_data_filepath = os.path.join(
            data_path['atom_data_path'], atom_data_name
        )

        # Load atom data file separately, pass it for forming tardis config.
        self.atom_data = AtomData.from_hdf5(atom_data_filepath)

        # Check whether the atom data file in current run and the atom data
        # file used in obtaining the reference data are same.
        # TODO: hard coded UUID for kurucz atom data file, generalize it later.
        # kurucz_data_file_uuid1 = "5ca3035ca8b311e3bb684437e69d75d7"
        # assert self.atom_data.uuid1 == kurucz_data_file_uuid1

        # Create a Configuration through yaml file and atom data.
        tardis_config = Configuration.from_yaml(self.config_file)

        # Check whether current run is with less packets.
        if request.config.getoption("--less-packets"):
            less_packets = request.config.integration_tests_config['less_packets']
            tardis_config['montecarlo']['no_of_packets'] = (
                less_packets['no_of_packets']
            )
            tardis_config['montecarlo']['last_no_of_packets'] = (
                less_packets['last_no_of_packets']
            )




        # We now do a run with prepared config and get the simulation object.
        self.result = Simulation.from_config(tardis_config,
                                             atom_data=self.atom_data)

        capmanager.suspendcapture(True)
        # If current test run is just for collecting reference data, store the
        # output model to HDF file, save it at specified path. Skip all tests.
        # Else simply perform the run and move further for performing
        # assertions.
        self.result.run()
        if request.config.getoption("--generate-reference"):
            ref_data_path = os.path.join(
                data_path['gen_ref_path'], "{0}.h5".format(self.name)
            )
            if os.path.exists(ref_data_path):
                pytest.skip(
                    'Reference data {0} does exist and tests will not '
                    'proceed generating new data'.format(ref_data_path))
            self.result.to_hdf(path_or_buf=ref_data_path,
                               suffix_count=False)
            pytest.skip("Reference data saved at {0}".format(
                data_path['gen_ref_path']
            ))
        capmanager.resumecapture()

        # Get the reference data through the fixture.
        self.reference = reference
Esempio n. 16
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def included_he_atomic_data():
    import os, tardis
    atomic_db_fname = os.path.join(tardis.__path__[0], 'tests', 'data',
                                   'chianti_he_db.h5')
    return AtomData.from_hdf5(atomic_db_fname)
Esempio n. 17
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import os
import sys
import argparse
import numpy as np
import pandas as pd

from tardis.atomic import AtomData

atomic_dataset = AtomData.from_hdf5()


def get_atomic_number(element):
    index = -1
    for atomic_no, row in atomic_dataset.atom_data.iterrows():
        if element in row["name"]:
            index = atomic_no
            break
    return index


def extract_file_block(f):
    qty = []

    for line in f:
        items = line.split()
        if items:
            qty.extend(np.array(items).astype(np.float64))
        else:
            break

    qty = np.array(qty)
Esempio n. 18
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def included_he_atomic_data(test_data_path):
    atomic_db_fname = os.path.join(test_data_path, 'chianti_he_db.h5')
    return AtomData.from_hdf5(atomic_db_fname)
Esempio n. 19
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def included_he_atomic_data():
    import os, tardis
    atomic_db_fname = os.path.join(tardis.__path__[0], 'tests', 'data',
                                   'chianti_he_db.h5')
    return AtomData.from_hdf5(atomic_db_fname)
Esempio n. 20
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with h5py.File(fpath, "w") as f:
    f["basic_atom_data"] = atom_data_h0
    f["ionization_data"] = ionization_data_h0.to_records(index=False)
    f["lines_data"] = lines_cut.to_records(index=False)
    f["levels_data"] = levels_cut.to_records(index=False)
    f["macro_atom_data"] = macro_atom_data_cut.to_records(index=False)
    f["macro_atom_references"] = macro_atom_references_cut.to_records(index=False)
    f["synpp_refs"] = synpp_refs_h0.to_records(index=False)
    f["zeta_data"] = zeta_data_h0
    f['zeta_data'].attrs['t_rad'] = np.arange(2000, 42000, 2000)
    f['zeta_data'].attrs['source'] = 'Used with kind permission from Knox Long'
    
    f.attrs['data_sources'] = data_sources
    f.attrs['database_version'] = 'v0.9'

    md5_hash = hashlib.md5()
    for dataset in f.values():
        md5_hash.update(dataset.value.data)
    uuid1 = uuid.uuid1().hex
    f.attrs['md5'] = md5_hash.hexdigest()
    f.attrs['uuid1'] = uuid1

ad = AtomData.from_hdf5(fpath)
print ad.atom_data
print ad.ionization_data
print ad.levels
print ad.lines
print ad.macro_atom_data_all
print ad.macro_atom_references_all
print ad.synpp_refs
Esempio n. 21
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def atomic_data():
    atomic_db_fname = os.path.join(tardis.__path__[0], 'tests', 'data',
                                   'chianti_he_db.h5')
    return AtomData.from_hdf5(atomic_db_fname)
Esempio n. 22
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import os
import sys
import argparse
import numpy as np
import pandas as pd

from tardis.atomic import AtomData


atomic_dataset = AtomData.from_hdf5()


def get_atomic_number(element):
    index = -1
    for atomic_no, row in atomic_dataset.atom_data.iterrows():
        if element in row['name']:
            index = atomic_no
            break
    return index


def extract_file_block(f):
    qty = []

    for line in f:
        items = line.split()
        if items:
            qty.extend(np.array(items).astype(np.float64))
        else:
            break