def write(self, path, prefix="", overwrite=False, write_covariance=True):
"""Serialize datasets to YAML and FITS files.
Parameters
----------
path : `pathlib.Path`
path to write files
prefix : str
common prefix of file names
overwrite : bool
overwrite datasets FITS files
write_covariance : bool
save covariance or not
"""
path = make_path(path).resolve()
datasets_dictlist = []
for dataset in self._datasets:
filename = f"{prefix}_data_{dataset.name}.fits"
dataset.write(path / filename, overwrite)
datasets_dictlist.append(dataset.to_dict(filename=filename))
datasets_dict = {"datasets": datasets_dictlist}
write_yaml(datasets_dict,
path / f"{prefix}_datasets.yaml",
sort_keys=False)
self.models.write(
path / f"{prefix}_models.yaml",
overwrite=overwrite,
write_covariance=write_covariance,
)
def write(
self, filename, filename_models=None, overwrite=False, write_covariance=True
):
"""Serialize datasets to YAML and FITS files.
Parameters
----------
filename : str or `Path`
File path or name of datasets yaml file
filename_models : str or `Path`
File path or name of models yaml file
overwrite : bool
overwrite datasets FITS files
write_covariance : bool
save covariance or not
"""
path = make_path(filename)
data = {"datasets": []}
for dataset in self._datasets:
d = dataset.to_dict()
filename = d["filename"]
dataset.write(path.parent / filename, overwrite=overwrite)
data["datasets"].append(d)
write_yaml(data, path, sort_keys=False)
if filename_models:
self.models.write(
filename_models, overwrite=overwrite, write_covariance=write_covariance,
)
def test_absorption_io(tmp_path):
dominguez = Absorption.read_builtin("dominguez")
model = AbsorbedSpectralModel(
spectral_model=Model.create("PowerLawSpectralModel"),
absorption=dominguez,
parameter=0.5,
parameter_name="redshift",
)
assert len(model.parameters) == 5
model_dict = model.to_dict()
parnames = [_["name"] for _ in model_dict["parameters"]]
assert parnames == ["redshift", "alpha_norm"]
new_model = AbsorbedSpectralModel.from_dict(model_dict)
assert new_model.parameter == 0.5
assert new_model.parameter_name == "redshift"
assert new_model.alpha_norm.name == "alpha_norm"
assert new_model.alpha_norm.value == 1
assert new_model.spectral_model.tag == "PowerLawSpectralModel"
assert_allclose(new_model.absorption.energy, dominguez.energy)
assert_allclose(new_model.absorption.param, dominguez.param)
assert len(new_model.parameters) == 5
test_absorption = Absorption(
u.Quantity(range(3), "keV"),
u.Quantity(range(2), ""),
u.Quantity(np.ones((2, 3)), ""),
)
model = AbsorbedSpectralModel(
spectral_model=Model.create("PowerLawSpectralModel"),
absorption=test_absorption,
parameter=0.5,
parameter_name="redshift",
)
model_dict = model.to_dict()
new_model = AbsorbedSpectralModel.from_dict(model_dict)
assert_allclose(new_model.absorption.energy, test_absorption.energy)
assert_allclose(new_model.absorption.param, test_absorption.param)
write_yaml(model_dict, tmp_path / "tmp.yaml")
read_yaml(tmp_path / "tmp.yaml")
def test_absorption_io(tmp_path):
dominguez = EBLAbsorptionNormSpectralModel.read_builtin("dominguez",
redshift=0.5)
assert len(dominguez.parameters) == 2
model_dict = dominguez.to_dict()
parnames = [_["name"] for _ in model_dict["parameters"]]
assert parnames == [
"alpha_norm",
"redshift",
]
new_model = EBLAbsorptionNormSpectralModel.from_dict(model_dict)
assert new_model.redshift.value == 0.5
assert new_model.alpha_norm.name == "alpha_norm"
assert new_model.alpha_norm.value == 1
assert_allclose(new_model.energy, dominguez.energy)
assert_allclose(new_model.param, dominguez.param)
assert len(new_model.parameters) == 2
model = EBLAbsorptionNormSpectralModel(
u.Quantity(range(3), "keV"),
u.Quantity(range(2), ""),
u.Quantity(np.ones((2, 3)), ""),
redshift=0.5,
alpha_norm=1,
)
model_dict = model.to_dict()
new_model = EBLAbsorptionNormSpectralModel.from_dict(model_dict)
assert_allclose(new_model.energy, model.energy)
assert_allclose(new_model.param, model.param)
assert_allclose(new_model.data, model.data)
write_yaml(model_dict, tmp_path / "tmp.yaml")
read_yaml(tmp_path / "tmp.yaml")
def write(self, path, prefix="", overwrite=False):
"""Serialize datasets to YAML and FITS files.
Parameters
----------
path : `pathlib.Path`
path to write files
prefix : str
common prefix of file names
overwrite : bool
overwrite datasets FITS files
"""
from .io import datasets_to_dict
path = make_path(path)
datasets_dict, components_dict = datasets_to_dict(
self, path, prefix, overwrite)
write_yaml(datasets_dict,
path / f"{prefix}_datasets.yaml",
sort_keys=False)
write_yaml(components_dict,
path / f"{prefix}_models.yaml",
sort_keys=False)
def to_yaml(self, filename):
"""Write to YAML file."""
from gammapy.modeling.serialize import models_to_dict
components_dict = models_to_dict(self.skymodels)
write_yaml(components_dict, filename, sort_keys=False)
def to_yaml(self, filename, selection="all"):
"""Write to yaml file."""
from ..utils.serialization import models_to_dict
components_dict = models_to_dict(self.models, selection)
write_yaml(components_dict, filename)
