def from_yml(cls, yml_path: str):
input_dict = load_yaml(yml_path)
cls_dict = dict()
vlsrs, Ncols = list(), list()
# make sure the number of components is the same
assert len(input_dict["vlsrs"]) == len(input_dict["Ncols"])
n_components = len(input_dict["vlsrs"])
# parse in all the different parameters
for param_list, parameter in zip([vlsrs, Ncols], ["vlsrs", "Ncols"]):
for index in range(n_components):
size_params = input_dict[parameter][index]
size_params["name"] = f"{parameter}_{index}"
if "mu" in size_params:
dist = GaussianLikelihood
elif "value" in size_params:
dist = DeltaLikelihood
else:
dist = UniformLikelihood
param_list.append(dist.from_values(**size_params))
cls_dict[parameter] = param_list
# the three stragglers
for key in ["source_size", "Tex", "dV"]:
input_dict[key]["name"] = key
if "mu" in input_dict[key]:
dist = GaussianLikelihood
elif "value" in input_dict[key]:
dist = DeltaLikelihood
else:
dist = UniformLikelihood
cls_dict[key] = dist.from_values(**input_dict[key])
# load in the observed data
cls_dict["observation"] = load(input_dict["observation"])
cls_dict["molecule"] = load(input_dict["molecule"])
return cls(**cls_dict)
def from_yml(cls, yml_path: str):
"""
Creates a composite model from a YAML input. As one can imagine,
the format of this YAML is substantially different from the
non-composite models. The structure of the YAML should be like:
```
param_indices: [[1, 2, 3,], [1, 2, 3]]
model_A:
model: TMC1FourComponent
yml_path: model_A.yml
model_B:
model: CospatialTMC1
yml_path: model_B.yml
```
The `model` subkeys should correspond to the name of a model class,
which is used to grab the correct one to instantiate. It basically
then relies on each `submodel.from_yml` method to create the submodel
and then append it to full model list.
Parameters
----------
yml_path : str
Path to the composite model YAML specification
Raises
------
KeyError
If the parameter indices are not found in the composite
YAML specification.
NameError
If the model name specified
"""
yml_data = load_yaml(yml_path)
# get the parameter indices and remove it from the dictionary
param_indices = yml_data.pop("param_indices", None)
if not param_indices:
raise KeyError("param_indices not specified in YAML file.")
models = list()
for index, subdict in enumerate(yml_data.values()):
# this tries to get the class
model_type = globals()[subdict.get("model")]
if not model_type:
raise NameError(
f"Model type for model {index + 1} is not implemented: {subdict.get('model')}"
)
# construct the submodel, and throw it in the pile
submodel = model_type.from_yml(subdict.get("yml_path"))
models.append(submodel)
return cls(param_indices, *models)
def from_yml(cls, yml_path: str):
input_dict = load_yaml(yml_path)
cls_dict = dict()
# the two stragglers
for key in input_dict.keys():
if key not in ["observation", "molecule", "nominal_vlsr"]:
if hasattr(input_dict[key], "mu"):
dist = GaussianLikelihood
else:
dist = UniformLikelihood
cls_dict[key] = dist.from_values(**input_dict[key])
else:
if key != "nominal_vlsr":
# load in the observed data
cls_dict[key] = load(input_dict[key])
else:
logger.warning(f"{key} is not recognized, and therefore ignored.")
return cls(**cls_dict)