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"))
def setup(self):
self.atom_data_filename = os.path.expanduser(
os.path.expandvars(pytest.config.getvalue('atomic-dataset')))
assert os.path.exists(
self.atom_data_filename), ("{0} atomic datafiles"
" does not seem to "
"exist".format(self.atom_data_filename))
self.config_yaml = yaml.load(
open('tardis/io/tests/data/tardis_configv1_verysimple.yml'))
self.config_yaml['atom_data'] = self.atom_data_filename
tardis_config = Configuration.from_config_dict(self.config_yaml)
self.model = Radial1DModel(tardis_config)
self.simulation = Simulation(tardis_config)
self.simulation.legacy_run_simulation(self.model)
def runner(
self, config, atomic_data_fname,
tardis_ref_data, generate_reference):
config.atom_data = atomic_data_fname
self.name = (self._name +
"_{:s}".format(config.plasma.line_interaction_type))
if config.spectrum.integrated.interpolate_shells > 0:
self.name += '_interp'
simulation = Simulation.from_config(config)
simulation.run()
if not generate_reference:
return simulation.runner
else:
simulation.runner.hdf_properties = [
'j_blue_estimator',
'spectrum',
'spectrum_integrated'
]
simulation.runner.to_hdf(
tardis_ref_data,
'',
self.name)
pytest.skip(
'Reference data was generated during this run.')
def runner(
self, config, atomic_data_fname,
tardis_ref_data, generate_reference):
config.atom_data = atomic_data_fname
self.name = (self._name +
"_{:s}".format(config.plasma.line_interaction_type))
if config.spectrum.integrated.interpolate_shells > 0:
self.name += '_interp'
simulation = Simulation.from_config(config)
simulation.run()
if not generate_reference:
return simulation.runner
else:
simulation.runner.hdf_properties = [
'j_blue_estimator',
'spectrum',
'spectrum_integrated'
]
simulation.runner.to_hdf(
tardis_ref_data,
'',
self.name)
pytest.skip(
'Reference data was generated during this run.')
def setup(self):
self.atom_data_filename = os.path.expanduser(os.path.expandvars(
pytest.config.getvalue('atomic-dataset')))
assert os.path.exists(self.atom_data_filename), ("{0} atomic datafiles"
" does not seem to "
"exist".format(
self.atom_data_filename))
self.config_yaml = yaml_load_config_file(
'tardis/io/tests/data/tardis_configv1_verysimple.yml')
self.config_yaml['atom_data'] = self.atom_data_filename
tardis_config = Configuration.from_config_dict(self.config_yaml)
self.simulation = Simulation.from_config(tardis_config)
self.simulation.run()
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 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 runner(self, config, atomic_data_fname, tardis_ref_data,
generate_reference):
config.atom_data = atomic_data_fname
self.name = self._name + f"_{config.plasma.line_interaction_type:s}"
if config.spectrum.integrated.interpolate_shells > 0:
self.name += "_interp"
simulation = Simulation.from_config(config)
simulation.run()
if not generate_reference:
return simulation.runner
else:
simulation.runner.hdf_properties = [
"j_blue_estimator",
"spectrum",
"spectrum_integrated",
]
simulation.runner.to_hdf(tardis_ref_data, "", self.name)
pytest.skip("Reference data was generated during this run.")
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.')
class TestSimpleRun():
"""
Very simple run
"""
@classmethod
@pytest.fixture(scope="class", autouse=True)
def setup(self):
self.atom_data_filename = os.path.expanduser(
os.path.expandvars(pytest.config.getvalue('atomic-dataset')))
assert os.path.exists(
self.atom_data_filename), ("{0} atomic datafiles"
" does not seem to "
"exist".format(self.atom_data_filename))
self.config_yaml = yaml.load(
open('tardis/io/tests/data/tardis_configv1_verysimple.yml'))
self.config_yaml['atom_data'] = self.atom_data_filename
tardis_config = Configuration.from_config_dict(self.config_yaml)
self.model = Radial1DModel(tardis_config)
self.simulation = Simulation(tardis_config)
self.simulation.legacy_run_simulation(self.model)
def test_j_blue_estimators(self):
j_blue_estimator = np.load(
data_path('simple_test_j_blue_estimator.npy'))
np.testing.assert_allclose(self.model.runner.j_blue_estimator,
j_blue_estimator)
def test_spectrum(self):
luminosity_density = np.load(
data_path('simple_test_luminosity_density_lambda.npy'))
luminosity_density = luminosity_density * u.Unit('erg / (Angstrom s)')
np.testing.assert_allclose(
self.model.spectrum.luminosity_density_lambda, luminosity_density)
def test_virtual_spectrum(self):
virtual_luminosity_density = np.load(
data_path('simple_test_virtual_luminosity_density_lambda.npy'))
virtual_luminosity_density = virtual_luminosity_density * u.Unit(
'erg / (Angstrom s)')
np.testing.assert_allclose(
self.model.spectrum_virtual.luminosity_density_lambda,
virtual_luminosity_density)
def test_plasma_properties(self):
pass
def test_runner_properties(self):
"""Tests whether a number of runner attributes exist and also verifies
their types
Currently, runner attributes needed to call the model routine to_hdf5
are checked.
"""
virt_type = np.ndarray
props_required_by_modeltohdf5 = dict([
("virt_packet_last_interaction_type", virt_type),
("virt_packet_last_line_interaction_in_id", virt_type),
("virt_packet_last_line_interaction_out_id", virt_type),
("virt_packet_last_interaction_in_nu", virt_type),
("virt_packet_nus", virt_type),
("virt_packet_energies", virt_type),
])
required_props = props_required_by_modeltohdf5.copy()
for prop, prop_type in required_props.items():
assert type(getattr(self.model.runner, prop)) == prop_type, (
"wrong type of attribute '{}': expected {}, found {}".format(
prop, prop_type, type(getattr(self.model.runner, prop))))
def test_legacy_model_properties(self):
"""Tests whether a number of model attributes exist and also verifies
their types
Currently, model attributes needed to run the gui and to call the model
routine to_hdf5 are checked.
Notes
-----
The list of properties may be incomplete
"""
props_required_by_gui = dict([
("converged", bool),
("iterations_executed", int),
("iterations_max_requested", int),
("current_no_of_packets", int),
("no_of_packets", int),
("no_of_virtual_packets", int),
])
props_required_by_tohdf5 = dict([
("runner", MontecarloRunner),
("plasma_array", LegacyPlasmaArray),
("last_line_interaction_in_id", np.ndarray),
("last_line_interaction_out_id", np.ndarray),
("last_line_interaction_shell_id", np.ndarray),
("last_line_interaction_in_id", np.ndarray),
("last_line_interaction_angstrom", u.quantity.Quantity),
])
required_props = props_required_by_gui.copy()
required_props.update(props_required_by_tohdf5)
for prop, prop_type in required_props.items():
assert type(getattr(self.model, prop)) == prop_type, (
"wrong type of attribute '{}': expected {}, found {}".format(
prop, prop_type, type(getattr(self.model, prop))))
class TestSimpleRun():
"""
Very simple run
"""
@classmethod
@pytest.fixture(scope="class", autouse=True)
def setup(self):
self.atom_data_filename = os.path.expanduser(os.path.expandvars(
pytest.config.getvalue('atomic-dataset')))
assert os.path.exists(self.atom_data_filename), ("{0} atomic datafiles"
" does not seem to "
"exist".format(
self.atom_data_filename))
self.config_yaml = yaml.load(open(
'tardis/io/tests/data/tardis_configv1_verysimple.yml'))
self.config_yaml['atom_data'] = self.atom_data_filename
tardis_config = Configuration.from_config_dict(self.config_yaml)
self.model = Radial1DModel(tardis_config)
self.simulation = Simulation(tardis_config)
self.simulation.legacy_run_simulation(self.model)
def test_spectrum(self):
luminosity_density = np.load(
data_path('simple_test_luminosity_density_lambda.npy'))
luminosity_density = luminosity_density * u.Unit(
'erg / (Angstrom s)')
np.testing.assert_allclose(
self.model.spectrum.luminosity_density_lambda,luminosity_density)
def test_virtual_spectrum(self):
virtual_luminosity_density = np.load(
data_path('simple_test_virtual_luminosity_density_lambda.npy'))
virtual_luminosity_density = virtual_luminosity_density * u.Unit(
'erg / (Angstrom s)')
np.testing.assert_allclose(
self.model.spectrum_virtual.luminosity_density_lambda,
virtual_luminosity_density)
def test_plasma_properties(self):
pass
def test_runner_properties(self):
"""Tests whether a number of runner attributes exist and also verifies
their types
Currently, runner attributes needed to call the model routine to_hdf5
are checked.
"""
if self.model.runner.virt_logging > 0:
virt_type = np.ndarray
else:
virt_type = type(None)
props_required_by_modeltohdf5 = dict([
("virt_packet_last_interaction_type", virt_type),
("virt_packet_last_line_interaction_in_id", virt_type),
("virt_packet_last_line_interaction_out_id", virt_type),
("virt_packet_last_interaction_in_nu", virt_type),
("virt_packet_nus", virt_type),
("virt_packet_energies", virt_type),
])
required_props = props_required_by_modeltohdf5.copy()
for prop, prop_type in required_props.items():
assert type(getattr(self.model.runner, prop)) == prop_type, ("wrong type of attribute '{}': expected {}, found {}".format(prop, prop_type, type(getattr(self.model.runner, prop))))
def test_legacy_model_properties(self):
"""Tests whether a number of model attributes exist and also verifies
their types
Currently, model attributes needed to run the gui and to call the model
routine to_hdf5 are checked.
Notes
-----
The list of properties may be incomplete
"""
props_required_by_gui = dict([
("converged", bool),
("iterations_executed", int),
("iterations_max_requested", int),
("current_no_of_packets", int),
("no_of_packets", int),
("no_of_virtual_packets", int),
])
props_required_by_tohdf5 = dict([
("runner", MontecarloRunner),
("plasma_array", LegacyPlasmaArray),
("last_line_interaction_in_id", np.ndarray),
("last_line_interaction_out_id", np.ndarray),
("last_line_interaction_shell_id", np.ndarray),
("last_line_interaction_in_id", np.ndarray),
("last_line_interaction_angstrom", u.quantity.Quantity),
])
required_props = props_required_by_gui.copy()
required_props.update(props_required_by_tohdf5)
for prop, prop_type in required_props.items():
assert type(getattr(self.model, prop)) == prop_type, ("wrong type of attribute '{}': expected {}, found {}".format(prop, prop_type, type(getattr(self.model, prop))))