def from_dict(cls, data):
kwargs = {}
par_data = []
key0 = next(iter(data))
if key0 in ["spatial", "temporal", "spectral"]:
data = data[key0]
if data["type"] not in cls.tag:
raise ValueError(
f"Invalid model type {data['type']} for class {cls.__name__}"
)
input_names = [_["name"] for _ in data["parameters"]]
for par in cls.default_parameters:
par_dict = par.to_dict()
try:
index = input_names.index(par_dict["name"])
par_dict.update(data["parameters"][index])
except ValueError:
log.warning(
f"Parameter '{par_dict['name']}' not defined in YAML file. Using default value: {par_dict['value']} {par_dict['unit']}"
)
par_data.append(par_dict)
parameters = Parameters.from_dict(par_data)
# TODO: this is a special case for spatial models, maybe better move to `SpatialModel` base class
if "frame" in data:
kwargs["frame"] = data["frame"]
return cls.from_parameters(parameters, **kwargs)
def from_dict(cls, data):
kwargs = {}
par_data = []
input_names = [_["name"] for _ in data["parameters"]]
for par in cls.default_parameters:
par_dict = par.to_dict()
try:
index = input_names.index(par_dict["name"])
par_dict.update(data["parameters"][index])
except ValueError:
log.warning(
f"Parameter {par_dict['name']} not defined. Using default value: {par_dict['value']} {par_dict['unit']}"
)
par_data.append(par_dict)
parameters = Parameters.from_dict(par_data)
# TODO: this is a special case for spatial models, maybe better move to `SpatialModel` base class
if "frame" in data:
kwargs["frame"] = data["frame"]
return cls.from_parameters(parameters, **kwargs)
def from_dict(cls, data):
if "filename" in data:
bkg_map = Map.read(data["filename"])
elif "map" in data:
bkg_map = data["map"]
else:
# TODO: for now create a fake map for serialization,
# uptdated in MapDataset.from_dict()
axis = MapAxis.from_edges(np.logspace(-1, 1, 2),
unit=u.TeV,
name="energy")
geom = WcsGeom.create(skydir=(0, 0),
npix=(1, 1),
frame="galactic",
axes=[axis])
bkg_map = Map.from_geom(geom)
parameters = Parameters.from_dict(data["parameters"])
return cls.from_parameters(
parameters=parameters,
map=bkg_map,
name=data["name"],
datasets_names=data.get("datasets_names"),
filename=data.get("filename"),
)
def from_dict(cls, data):
init = cls.read(data["filename"],
normalize=data.get("normalize", True))
init.parameters = Parameters.from_dict(data)
for parameter in init.parameters.parameters:
setattr(init, parameter.name, parameter)
return init
def from_dict(cls, data):
kwargs = {}
parameters = Parameters.from_dict(data["parameters"])
# TODO: this is a special case for spatial models, maybe better move to `SpatialModel` base class
if "frame" in data:
kwargs["frame"] = data["frame"]
return cls.from_parameters(parameters, **kwargs)
def from_dict(cls, data):
m = Map.read(data["filename"])
if m.unit == "":
m.unit = "sr-1"
parameters = Parameters.from_dict(data["parameters"])
return cls.from_parameters(parameters=parameters,
map=m,
filename=data["filename"],
normalize=data.get("normalize", True))
def from_dict(cls, data):
if "filename" in data:
map = Map.read(data["filename"])
elif "map" in data:
map = data["map"]
else:
raise ValueError("Requires either filename or `Map` object")
init = cls(map=map, name=data["name"])
init.parameters = Parameters.from_dict(data)
for parameter in init.parameters.parameters:
setattr(init, parameter.name, parameter)
return init
def from_dict(cls, data):
m = Map.read(data["filename"])
if not m.unit.is_equivalent("sr-1"):
m.unit = "sr-1"
log.warning(
"Spatial template unit is not equivalent to sr^-1, unit changed to sr^-1"
)
parameters = Parameters.from_dict(data["parameters"])
return cls.from_parameters(
parameters=parameters,
map=m,
filename=data["filename"],
normalize=data.get("normalize", True),
)
def from_dict(cls, data):
kwargs = {}
par_data = []
for par, par_yaml in zip(cls.default_parameters, data["parameters"]):
par_dict = par.to_dict()
par_dict.update(par_yaml)
par_data.append(par_dict)
parameters = Parameters.from_dict(par_data)
# TODO: this is a special case for spatial models, maybe better move to `SpatialModel` base class
if "frame" in data:
kwargs["frame"] = data["frame"]
return cls.from_parameters(parameters, **kwargs)
def from_dict(cls, data):
parameters = Parameters.from_dict(data["parameters"])
filename = data["filename"]
map_ = cls.read(filename).map
apply_irf = data.get("apply_irf", cls._apply_irf_default)
datasets_names = data.get("datasets_names")
name = data.get("name")
return cls.from_parameters(parameters=parameters,
map=map_,
apply_irf=apply_irf,
datasets_names=datasets_names,
filename=filename,
name=name)
def test_parameters_s():
pars = Parameters([
Parameter("", 20, scale_method="scale10"),
Parameter("", 20, scale_method=None),
])
pars_dict = pars.to_dict()
pars.autoscale()
assert_allclose(pars[0].factor, 2)
assert_allclose(pars[0].scale, 10)
assert pars_dict[0]["scale_method"] == "scale10"
assert pars_dict[1]["scale_method"] is None
pars = Parameters.from_dict(pars_dict)
pars.autoscale()
assert_allclose(pars[0].factor, 2)
assert_allclose(pars[0].scale, 10)
assert pars[1].scale_method is None
pars.autoscale()
assert_allclose(pars[1].factor, 20)
assert_allclose(pars[1].scale, 1)
def from_dict(cls, data):
init = cls.read(data["filename"])
init.parameters = Parameters.from_dict(data)
for parameter in init.parameters.parameters:
setattr(init, parameter.name, parameter)
return init
def from_dict(cls, data):
parameters = Parameters.from_dict(data["parameters"])
return cls.from_parameters(parameters)