def simulation(
self, request, atomic_data_fname,
generate_reference, tardis_ref_data):
name = request.param[0]
config = Configuration.from_yaml(request.param[1])
config['atom_data'] = atomic_data_fname
simulation = Simulation.from_config(config)
simulation.run()
self._test_name = name
if not generate_reference:
return simulation
else:
simulation.plasma.hdf_properties = [
'level_number_density',
]
simulation.model.hdf_properties = [
't_radiative'
]
simulation.plasma.to_hdf(
tardis_ref_data,
self.name,
self._test_name)
simulation.model.to_hdf(
tardis_ref_data,
self.name,
self._test_name)
pytest.skip(
'Reference data was generated during this run.')
return simulation
def setup(self):
filename = "tardis_configv1_artis_density_v_slice.yml"
self.config = Configuration.from_yaml(data_path(filename))
self.config.model.abundances.type = "file"
self.config.model.abundances.filename = "artis_abundances.dat"
self.config.model.abundances.filetype = "artis"
self.model = Radial1DModel.from_config(self.config)
def setup(self):
filename = 'tardis_configv1_artis_density_v_slice.yml'
self.config = Configuration.from_yaml(data_path(filename))
self.config.model.abundances.type = 'file'
self.config.model.abundances.filename = 'artis_abundances.dat'
self.config.model.abundances.filetype = 'artis'
self.model = Radial1DModel.from_config(self.config)
def run_tardis(
config,
atom_data=None,
packet_source=None,
simulation_callbacks=[],
virtual_packet_logging=False,
):
"""
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 or tardis.io.config_reader.Configuration
filename of configuration yaml file or dictionary or TARDIS Configuration object
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]
virtual_packet_logging : bool
option to enable virtual packet logging
[default=False]
Returns
-------
Simulation
"""
from tardis.io.config_reader import Configuration
from tardis.io.atom_data.base import AtomData
from tardis.simulation import Simulation
if atom_data is not None:
try:
atom_data = AtomData.from_hdf(atom_data)
except TypeError:
atom_data = atom_data
if isinstance(config, Configuration):
tardis_config = config
else:
try:
tardis_config = Configuration.from_yaml(config)
except TypeError:
tardis_config = Configuration.from_config_dict(config)
simulation = Simulation.from_config(
tardis_config,
packet_source=packet_source,
atom_data=atom_data,
virtual_packet_logging=virtual_packet_logging,
)
for cb in simulation_callbacks:
simulation.add_callback(*cb)
simulation.run()
return simulation
def setup(self):
filename = 'tardis_configv1_artis_density.yml'
self.config = Configuration.from_yaml(data_path(filename))
self.config.model.abundances.type = 'file'
self.config.model.abundances.filename = 'artis_abundances.dat'
self.config.model.abundances.filetype = 'artis'
self.model = Radial1DModel.from_config(self.config)
def test_ascii_reader_power_law():
filename = "tardis_configv1_density_power_law_test.yml"
config = Configuration.from_yaml(data_path(filename))
model = Radial1DModel.from_config(config)
expected_densites = [
3.29072513e-14,
2.70357804e-14,
2.23776573e-14,
1.86501954e-14,
1.56435277e-14,
1.32001689e-14,
1.12007560e-14,
9.55397475e-15,
8.18935779e-15,
7.05208050e-15,
6.09916083e-15,
5.29665772e-15,
4.61758699e-15,
4.04035750e-15,
3.54758837e-15,
3.12520752e-15,
2.76175961e-15,
2.44787115e-15,
2.17583442e-15,
1.93928168e-15,
]
assert model.no_of_shells == 20
for i, mdens in enumerate(expected_densites):
assert_almost_equal(model.density[i].to(u.Unit("g / (cm3)")).value,
mdens)
def test_ascii_reader_exponential_law():
filename = "tardis_configv1_density_exponential_test.yml"
config = Configuration.from_yaml(data_path(filename))
model = Radial1DModel.from_config(config)
expected_densites = [
5.18114795e-14,
4.45945537e-14,
3.83828881e-14,
3.30364579e-14,
2.84347428e-14,
2.44740100e-14,
2.10649756e-14,
1.81307925e-14,
1.56053177e-14,
1.34316215e-14,
1.15607037e-14,
9.95038990e-15,
8.56437996e-15,
7.37143014e-15,
6.34464872e-15,
5.46088976e-15,
4.70023138e-15,
4.04552664e-15,
3.48201705e-15,
2.99699985e-15,
]
expected_unit = "g / (cm3)"
assert model.no_of_shells == 20
for i, mdens in enumerate(expected_densites):
assert_almost_equal(model.density[i].value, mdens)
assert model.density[i].unit == u.Unit(expected_unit)
def test_model_decay(simple_isotope_abundance):
filename = "tardis_configv1_verysimple.yml"
config = Configuration.from_yaml(data_path(filename))
model = Radial1DModel.from_config(config)
model.raw_isotope_abundance = simple_isotope_abundance
decayed = simple_isotope_abundance.decay(model.time_explosion).as_atoms()
norm_factor = 1.4
assert_almost_equal(
model.abundance.loc[8][0],
model.raw_abundance.loc[8][0] / norm_factor,
decimal=4,
)
assert_almost_equal(
model.abundance.loc[14][0],
(model.raw_abundance.loc[14][0] + decayed.loc[14][0]) / norm_factor,
decimal=4,
)
assert_almost_equal(
model._abundance.loc[12][5],
(model.raw_abundance.loc[12][5] + decayed.loc[12][5]) / norm_factor,
decimal=4,
)
assert_almost_equal(
model.abundance.loc[6][12],
(decayed.loc[6][12]) / norm_factor,
decimal=4,
)
def test_compare_models(model_config_fnames):
"""Compare identical models produced by .from_config and
.from_csvy to check that velocities, densities and abundances
(pre and post decay) are the same"""
csvy_config_file, old_config_file = model_config_fnames
tardis_config = Configuration.from_yaml(csvy_config_file)
tardis_config_old = Configuration.from_yaml(old_config_file)
csvy_model = Radial1DModel.from_csvy(tardis_config)
config_model = Radial1DModel.from_config(tardis_config_old)
csvy_model_props = csvy_model.get_properties().keys()
config_model_props = config_model.get_properties().keys()
npt.assert_array_equal(csvy_model_props, config_model_props)
for prop in config_model_props:
csvy_model_val = csvy_model.get_properties()[prop]
config_model_val = config_model.get_properties()[prop]
if prop == "homologous_density":
npt.assert_array_almost_equal(
csvy_model_val.density_0.value, config_model_val.density_0.value
)
npt.assert_array_almost_equal(
csvy_model_val.time_0.value, config_model_val.time_0.value
)
else:
if hasattr(config_model_val, "value"):
config_model_val = config_model_val.value
csvy_model_val = csvy_model_val.value
npt.assert_array_almost_equal(csvy_model_val, config_model_val)
assert csvy_model.raw_abundance.shape == config_model.raw_abundance.shape
assert (
csvy_model.raw_isotope_abundance.shape
== config_model.raw_isotope_abundance.shape
)
assert csvy_model.abundance.shape == config_model.abundance.shape
npt.assert_array_almost_equal(
csvy_model.raw_abundance.to_numpy(),
config_model.raw_abundance.to_numpy(),
)
npt.assert_array_almost_equal(
csvy_model.raw_isotope_abundance.to_numpy(),
config_model.raw_isotope_abundance.to_numpy(),
)
npt.assert_array_almost_equal(
csvy_model.abundance.to_numpy(), config_model.abundance.to_numpy()
)
def test_plasma_vboundary(config_init_trad_fname, v_inner_boundary,
v_outer_boundary, atomic_data_fname):
tardis_config = Configuration.from_yaml(config_init_trad_fname)
tardis_config.atom_data = atomic_data_fname
tardis_config.model.structure.v_inner_boundary = (v_inner_boundary * u.km /
u.s)
tardis_config.model.structure.v_outer_boundary = (v_outer_boundary * u.km /
u.s)
simulation = Simulation.from_config(tardis_config)
def __init__(self, configFile, gridFrame):
try:
tardis_config = Configuration.from_yaml(configFile)
except TypeError:
tardis_config = Configuration.from_config_dict(configFile)
self.config = tardis_config
self.grid = gridFrame
return
def config(request):
config = Configuration.from_yaml(
'tardis/io/tests/data/tardis_configv1_verysimple.yml')
config["plasma"]["line_interaction_type"] = request.param
config["montecarlo"]["no_of_packets"] = 4.0e+4
config["montecarlo"]["last_no_of_packets"] = 1.0e+5
config["montecarlo"]["no_of_virtual_packets"] = 0
config["spectrum"]["method"] = "integrated"
return config
def test_csvy_abundance():
csvypath = os.path.join(DATA_PATH, 'config_v_filter.yml')
config = Configuration.from_yaml(csvypath)
csvy_model = Radial1DModel.from_csvy(config)
csvy_abund = csvy_model.abundance
ref_abund = pd.DataFrame(
np.array([[0.35, 0.3, 0.6, 0.4], [0.65, 0.7, 0.4, 0.6]]))
ref_abund.index.name = 'atomic_number'
ref_abund.index = np.array([1, 2])
assert csvy_abund.equals(ref_abund)
def base_config(request):
config = Configuration.from_yaml(
'tardis/io/tests/data/tardis_configv1_verysimple.yml')
config["plasma"]["line_interaction_type"] = request.param
config["montecarlo"]["no_of_packets"] = 4.0e+4
config["montecarlo"]["last_no_of_packets"] = 1.0e+5
config["montecarlo"]["no_of_virtual_packets"] = 0
config["spectrum"]["method"] = "integrated"
config["spectrum"]["integrated"]["points"] = 200
return config
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
def test_compare_models(filename):
fn, fnold = filename
tardis_config = Configuration.from_yaml(fn)
tardis_config_old = Configuration.from_yaml(fnold)
csvy_model = Radial1DModel.from_csvy(tardis_config)
config_model = Radial1DModel.from_config(tardis_config_old)
csvy_model_props = csvy_model.get_properties().keys()
config_model_props = config_model.get_properties().keys()
npt.assert_array_equal(csvy_model_props, config_model_props)
for prop in config_model_props:
csvy_model_val = csvy_model.get_properties()[prop]
config_model_val = config_model.get_properties()[prop]
if prop == 'homologous_density':
npt.assert_array_almost_equal(csvy_model_val.density_0.value,
config_model_val.density_0.value)
npt.assert_array_almost_equal(csvy_model_val.time_0.value,
config_model_val.time_0.value)
else:
if hasattr(config_model_val, 'value'):
config_model_val = config_model_val.value
csvy_model_val = csvy_model_val.value
npt.assert_array_almost_equal(csvy_model_val, config_model_val)
def base_config(request):
config = Configuration.from_yaml(
"tardis/io/tests/data/tardis_configv1_verysimple.yml")
config["plasma"]["line_interaction_type"] = request.param
config["montecarlo"]["no_of_packets"] = 4.0e4
config["montecarlo"]["last_no_of_packets"] = 1.0e5
config["montecarlo"]["no_of_virtual_packets"] = 0
config["spectrum"]["method"] = "integrated"
config["spectrum"]["integrated"]["points"] = 200
print("config", config)
return config
def test_logging_config(self, atomic_data_fname, caplog, log_state,
specific):
config = Configuration.from_yaml(
"tardis/io/tests/data/tardis_configv1_verysimple_logger.yml")
config["atom_data"] = atomic_data_fname
caplog.clear()
run_tardis(config=config, log_state=log_state, specific=specific)
for record in caplog.records:
if specific == True:
assert record.levelno == LOGGING_LEVELS[log_state.upper()]
else:
assert record.levelno >= LOGGING_LEVELS[log_state.upper()]
def test_run_tardis_from_config_obj(atomic_data_fname):
"""
Tests whether the run_tardis function can take in the Configuration object
as arguments
"""
config = Configuration.from_yaml(
"tardis/io/tests/data/tardis_configv1_verysimple.yml")
config["atom_data"] = atomic_data_fname
try:
sim = run_tardis(config)
except Exception as e:
pytest.fail(str(e.args[0]))
def run_final_models_plus_pickle(params, fname='blondin_model_compare_ddc25.yml'):
model_config = Configuration.from_yaml(fname)
model_config.model.v_inner_boundary = params[2]
model_config.model.v_outer_boundary = 35000*u.km/u.s
model_config.supernova.luminosity_requested = params[1]
model_config.supernova.time_explosion = params[0]
sim = Simulation.from_config(model_config)
print(sim.model.v_boundary_inner)
sim.run()
import pickle
dump = 'Output/ddc25/ddc25_t{}_v{}.pickle'.format(params[0].value, params[2].value)
with open(dump, 'wb') as dumpfile:
pickle.dump(sim, dumpfile)
return 1
def test_logging_simulation(atomic_data_fname, caplog):
"""
Testing the logs for simulations runs
"""
config = Configuration.from_yaml(
"tardis/io/tests/data/tardis_configv1_verysimple.yml")
config["atom_data"] = atomic_data_fname
simulation = Simulation.from_config(config)
simulation.run()
for record in caplog.records:
assert record.levelno >= logging.INFO
def runner(self, atomic_data_fname, tardis_ref_data, generate_reference):
config = Configuration.from_yaml(
'tardis/io/tests/data/tardis_configv1_verysimple.yml')
config['atom_data'] = atomic_data_fname
simulation = Simulation.from_config(config)
simulation.run()
if not generate_reference:
return simulation.runner
else:
simulation.runner.hdf_properties = [
'j_blue_estimator', 'spectrum', 'spectrum_virtual'
]
simulation.runner.to_hdf(tardis_ref_data, '', self.name)
pytest.skip('Reference data was generated during this run.')
def config(self, request):
config_path = os.path.join('tardis', 'plasma', 'tests', 'data',
'plasma_base_test_config.yml')
config = Configuration.from_yaml(config_path)
hash_string = ''
for prop, value in request.param.items():
hash_string = '_'.join((hash_string, prop))
if prop == 'nlte':
for nlte_prop, nlte_value in request.param[prop].items():
config.plasma.nlte[nlte_prop] = nlte_value
if nlte_prop != 'species':
hash_string = '_'.join((hash_string, nlte_prop))
else:
config.plasma[prop] = value
hash_string = '_'.join((hash_string, str(value)))
setattr(config.plasma, 'save_path', hash_string)
return config
def config(self, request):
config_path = os.path.join(
'tardis', 'plasma', 'tests', 'data', 'plasma_base_test_config.yml')
config = Configuration.from_yaml(config_path)
hash_string = ''
for prop, value in request.param.items():
hash_string = '_'.join((hash_string, prop))
if prop == 'nlte':
for nlte_prop, nlte_value in request.param[prop].items():
config.plasma.nlte[nlte_prop] = nlte_value
if nlte_prop != 'species':
hash_string = '_'.join((hash_string, nlte_prop))
else:
config.plasma[prop] = value
hash_string = '_'.join((hash_string, str(value)))
setattr(config.plasma, 'save_path', hash_string)
return config
def run_tardis(config,
atom_data=None,
packet_source=None,
simulation_callbacks=[]):
"""
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.io.atom_data.base import AtomData
from tardis.simulation import Simulation
if atom_data is not None:
try:
atom_data = AtomData.from_hdf(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,
packet_source=packet_source,
atom_data=atom_data)
for cb in simulation_callbacks:
simulation.add_callback(*cb)
simulation.run()
return simulation
def test_ascii_reader_power_law():
filename = 'tardis_configv1_density_power_law_test.yml'
config = Configuration.from_yaml(data_path(filename))
model = Radial1DModel.from_config(config)
expected_densites = [3.29072513e-14, 2.70357804e-14, 2.23776573e-14,
1.86501954e-14, 1.56435277e-14, 1.32001689e-14,
1.12007560e-14, 9.55397475e-15, 8.18935779e-15,
7.05208050e-15, 6.09916083e-15, 5.29665772e-15,
4.61758699e-15, 4.04035750e-15, 3.54758837e-15,
3.12520752e-15, 2.76175961e-15, 2.44787115e-15,
2.17583442e-15, 1.93928168e-15]
assert model.no_of_shells == 20
for i, mdens in enumerate(expected_densites):
assert_almost_equal(model.density[i].to(u.Unit('g / (cm3)')).value,
mdens)
def test_ascii_reader_exponential_law():
filename = 'tardis_configv1_density_exponential_test.yml'
config = Configuration.from_yaml(data_path(filename))
model = Radial1DModel.from_config(config)
expected_densites = [5.18114795e-14, 4.45945537e-14, 3.83828881e-14,
3.30364579e-14, 2.84347428e-14, 2.44740100e-14,
2.10649756e-14, 1.81307925e-14, 1.56053177e-14,
1.34316215e-14, 1.15607037e-14, 9.95038990e-15,
8.56437996e-15, 7.37143014e-15, 6.34464872e-15,
5.46088976e-15, 4.70023138e-15, 4.04552664e-15,
3.48201705e-15, 2.99699985e-15]
expected_unit = 'g / (cm3)'
assert model.no_of_shells == 20
for i, mdens in enumerate(expected_densites):
assert_almost_equal(model.density[i].value, mdens)
assert model.density[i].unit == u.Unit(expected_unit)
def test_model_decay(simple_isotope_abundance):
filename = 'tardis_configv1_verysimple.yml'
config = Configuration.from_yaml(data_path(filename))
model = Radial1DModel.from_config(config)
model.raw_isotope_abundance = simple_isotope_abundance
decayed = simple_isotope_abundance.decay(
model.time_explosion).as_atoms()
norm_factor = 1.4
assert_almost_equal(
model.abundance.loc[8][0], model.raw_abundance.loc[8][0] / norm_factor, decimal=4)
assert_almost_equal(model.abundance.loc[14][0], (
model.raw_abundance.loc[14][0] + decayed.loc[14][0]) / norm_factor, decimal=4)
assert_almost_equal(model._abundance.loc[12][5], (
model.raw_abundance.loc[12][5] + decayed.loc[12][5]) / norm_factor, decimal=4)
assert_almost_equal(
model.abundance.loc[6][12], (decayed.loc[6][12]) / norm_factor, decimal=4)
def runner(self, atomic_data_fname, tardis_ref_data, generate_reference):
config = Configuration.from_yaml(
"tardis/io/tests/data/tardis_configv1_verysimple.yml")
config["atom_data"] = atomic_data_fname
simulation = Simulation.from_config(config)
simulation.run()
if not generate_reference:
return simulation.runner
else:
simulation.runner.hdf_properties = [
"j_blue_estimator",
"spectrum",
"spectrum_virtual",
]
simulation.runner.to_hdf(tardis_ref_data, "", self.name)
pytest.skip("Reference data was generated during this run.")
def config(self, request):
config_path = os.path.join("tardis", "plasma", "tests", "data",
"plasma_base_test_config.yml")
config = Configuration.from_yaml(config_path)
hash_string = ""
for prop, value in request.param.items():
hash_string = "_".join((hash_string, prop))
if prop == "nlte":
for nlte_prop, nlte_value in request.param[prop].items():
config.plasma.nlte[nlte_prop] = nlte_value
if nlte_prop != "species":
hash_string = "_".join((hash_string, nlte_prop))
else:
config.plasma[prop] = value
hash_string = "_".join((hash_string, str(value)))
hash_string = os.path.join("plasma_unittest", hash_string)
setattr(config.plasma, "save_path", hash_string)
return config
def run_tardis_model(params):
model_config = Configuration.from_yaml('blondin_model_compare_ddc25.yml')
model_config.model.v_inner_boundary = params[2]
model_config.model.v_outer_boundary = 35000*u.km/u.s
model_config.supernova.luminosity_requested = params[1]
model_config.supernova.time_explosion = params[0]
sim = Simulation.from_config(model_config)
print(sim.model.v_boundary_inner)
sim.run()
fname = 'Output/ddc25/ddc25_t{}_v{}.hdf'.format(params[0].value, params[2].value)
with pd.HDFStore(fname) as hdf:
hdf.put('wavelength', pd.Series(sim.runner.spectrum.wavelength.value))
hdf.put('lum', pd.Series(sim.runner.spectrum_integrated.luminosity_density_lambda.value))
hdf.put('w', pd.Series(sim.plasma.w))
hdf.put('t_electrons', pd.Series(sim.plasma.t_electrons))
hdf.put('ion_num_dens', sim.plasma.ion_number_density)
hdf.put('electron_dens', sim.plasma.electron_densities)
return 1
def test_logging_both_specified(self, atomic_data_fname, caplog, log_level,
specific_log_level):
config = Configuration.from_yaml(
"tardis/io/tests/data/tardis_configv1_verysimple_logger.yml")
config["atom_data"] = atomic_data_fname
config["debug"]["log_level"] = log_level
config["debug"]["specific_log_level"] = specific_log_level
caplog.clear()
run_tardis(
config=config,
log_level=log_level,
specific_log_level=specific_log_level,
)
for record in caplog.records:
if specific_log_level == True:
assert record.levelno == LOGGING_LEVELS[log_level.upper()]
else:
assert record.levelno >= LOGGING_LEVELS[log_level.upper()]
def run_tardis(config, atom_data=None, simulation_callbacks=[]):
"""
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.io.atom_data.base import AtomData
from tardis.simulation import Simulation
if atom_data is not None:
try:
atom_data = AtomData.from_hdf(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)
for cb in simulation_callbacks:
simulation.add_callback(cb)
simulation.run()
return simulation
def simulation(self, request, atomic_data_fname, generate_reference,
tardis_ref_data):
name = request.param[0]
config = Configuration.from_yaml(request.param[1])
config['atom_data'] = atomic_data_fname
simulation = Simulation.from_config(config)
simulation.run()
self._test_name = name
if not generate_reference:
return simulation
else:
simulation.plasma.hdf_properties = [
'level_number_density',
]
simulation.model.hdf_properties = ['t_radiative']
simulation.plasma.to_hdf(tardis_ref_data, self.name,
self._test_name)
simulation.model.to_hdf(tardis_ref_data, self.name,
self._test_name)
pytest.skip('Reference data was generated during this run.')
return simulation
def runner(
self, atomic_data_fname,
tardis_ref_data, generate_reference):
config = Configuration.from_yaml(
'tardis/io/tests/data/tardis_configv1_verysimple.yml')
config['atom_data'] = atomic_data_fname
simulation = Simulation.from_config(config)
simulation.run()
if not generate_reference:
return simulation.runner
else:
simulation.runner.hdf_properties = [
'j_blue_estimator',
'spectrum',
'spectrum_virtual'
]
simulation.runner.to_hdf(
tardis_ref_data,
'',
self.name)
pytest.skip(
'Reference data was generated during this run.')
def setup(self):
filename = "paper1_tardis_configv1.yml"
self.config = Configuration.from_yaml(data_path(filename))
self.model = Radial1DModel.from_config(self.config)
def setup(self):
filename = "tardis_configv1_ascii_density_abund.yml"
self.config = Configuration.from_yaml(data_path(filename))
self.config.model.structure.filename = "density.dat"
self.config.model.abundances.filename = "abund.dat"
self.model = Radial1DModel.from_config(self.config)
def setup(self):
filename = "tardis_configv1_uniform_density.yml"
self.config = Configuration.from_yaml(data_path(filename))
self.config.plasma.initial_t_inner = 2508 * u.K
self.model = Radial1DModel.from_config(self.config)
def csvy_model_test_abundances():
"""Returns Radial1DModel to use to test abundances dataframes"""
csvypath = os.path.join(DATA_PATH, "csvy_model_to_test_abundances.yml")
config = Configuration.from_yaml(csvypath)
csvy_model_test_abundances = Radial1DModel.from_csvy(config)
return csvy_model_test_abundances
def setup(self):
filename = 'tardis_configv1_uniform_density.yml'
self.config = Configuration.from_yaml(data_path(filename))
self.config.plasma.initial_t_inner = 2508 * u.K
self.model = Radial1DModel.from_config(self.config)
def config_verysimple():
filename = 'tardis_configv1_verysimple.yml'
path = os.path.abspath(os.path.join('tardis/io/tests/data/', filename))
config = Configuration.from_yaml(path)
return config
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_hdf(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['reference_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(file_path=ref_data_path)
pytest.skip("Reference data saved at {0}".format(
data_path['reference_path']
))
capmanager.resumecapture()
# Get the reference data through the fixture.
self.reference = reference
def setup(self):
filename = "tardis_configv1_artis_density.yml"
self.config = Configuration.from_yaml(data_path(filename))
self.model = Radial1DModel.from_config(self.config)
def setup(self):
filename = 'paper1_tardis_configv1.yml'
self.config = Configuration.from_yaml(data_path(filename))
self.model = Radial1DModel.from_config(self.config)
def setup(self):
filename = 'tardis_configv1_ascii_density_abund.yml'
self.config = Configuration.from_yaml(data_path(filename))
self.config.model.structure.filename = 'density.dat'
self.config.model.abundances.filename = 'abund.dat'
self.model = Radial1DModel.from_config(self.config)
def setup(self):
filename = 'tardis_configv1_artis_density.yml'
self.config = Configuration.from_yaml(data_path(filename))
self.model = Radial1DModel.from_config(self.config)
def isotope_uniform_abundance():
config_path = os.path.join(
data_path, 'tardis_configv1_isotope_uniabund.yml')
config = Configuration.from_yaml(config_path)
return config.model.abundances
def tardis_model_density_config():
filename = 'tardis_configv1_tardis_model_format.yml'
return Configuration.from_yaml(os.path.join(data_path, filename))
def config():
return Configuration.from_yaml(
'tardis/io/tests/data/tardis_configv1_verysimple.yml')
