def __init__(self, parent):
from gammapy.datasets import Dataset, Datasets
if isinstance(parent, Dataset):
self._datasets = [parent]
self._is_dataset = True
elif isinstance(parent, Datasets):
self._datasets = parent._datasets
self._is_dataset = False
else:
raise TypeError(f"Invalid type: {type(parent)!r}")
unique_models = []
for d in self._datasets:
if d._models is not None:
for model in d._models:
if model not in unique_models:
if (model.datasets_names is None
or d.name in model.datasets_names):
unique_models.append(model)
else:
d._models = Models([])
self._models = unique_models
if self._is_dataset == False:
self.force_models_consistency()
self._covar_file = None
self._covariance = Covariance(self.parameters)
def covariance(self):
for par in self.parameters:
pars = Parameters([par])
covar = Covariance(pars, data=[[par.error ** 2]])
self._covariance.set_subcovariance(covar)
return self._covariance
def covariance_diagonal():
x = Parameter("x", 1, error=0.1)
y = Parameter("y", 2, error=0.2)
z = Parameter("z", 3, error=0.3)
parameters = Parameters([x, y, z])
return Covariance(parameters=parameters)
def covariance(self):
self._check_covariance()
for par in self.parameters:
pars = Parameters([par])
error = np.nan_to_num(par.error**2, nan=1)
covar = Covariance(pars, data=[[error]])
self._covariance.set_subcovariance(covar)
return self._covariance
def __init__(self, **kwargs):
# Copy default parameters from the class to the instance
default_parameters = self.default_parameters.copy()
for par in default_parameters:
value = kwargs.get(par.name, par)
if not isinstance(value, Parameter):
par.quantity = u.Quantity(value)
else:
par = value
setattr(self, par.name, par)
self._covariance = Covariance(self.parameters)
def __init__(self, models=None):
if models is None:
models = []
if isinstance(models, (Models, DatasetModels)):
models = models._models
elif isinstance(models, Model):
models = [models]
elif not isinstance(models, list):
raise TypeError(f"Invalid type: {models!r}")
unique_names = []
for model in models:
if model.name in unique_names:
raise (ValueError("Model names must be unique"))
unique_names.append(model.name)
self._models = models
self._covar_file = None
self._covariance = Covariance(self.parameters)
def _check_covariance(self):
if not self.parameters == self._covariance.parameters:
self._covariance = Covariance(self.parameters)
def covariance(covariance_diagonal):
x = covariance_diagonal.parameters["x"]
y = covariance_diagonal.parameters["y"]
parameters = Parameters([x, y])
data = np.ones((2, 2))
return Covariance(parameters=parameters, data=data)