# Licensed under a 3-clause BSD style license - see LICENSE.rst
import collections.abc
import copy
import logging
from os.path import split
import numpy as np
import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.table import Table
import matplotlib.pyplot as plt
import yaml
from gammapy.maps import Map, RegionGeom
from gammapy.modeling import Covariance, Parameter, Parameters
from gammapy.utils.scripts import make_name, make_path
__all__ = ["Model", "Models", "DatasetModels", "ModelBase"]
log = logging.getLogger(__name__)
def _set_link(shared_register, model):
for param in model.parameters:
name = param.name
link_label = param._link_label_io
if link_label is not None:
if link_label in shared_register:
new_param = shared_register[link_label]
setattr(model, name, new_param)
else:
shared_register[link_label] = param
return shared_register
def _get_model_class_from_dict(data):
"""get a model class from a dict"""
from . import (
MODEL_REGISTRY,
SPATIAL_MODEL_REGISTRY,
SPECTRAL_MODEL_REGISTRY,
TEMPORAL_MODEL_REGISTRY,
)
if "type" in data:
cls = MODEL_REGISTRY.get_cls(data["type"])
elif "spatial" in data:
cls = SPATIAL_MODEL_REGISTRY.get_cls(data["spatial"]["type"])
elif "spectral" in data:
cls = SPECTRAL_MODEL_REGISTRY.get_cls(data["spectral"]["type"])
elif "temporal" in data:
cls = TEMPORAL_MODEL_REGISTRY.get_cls(data["temporal"]["type"])
return cls
def _build_parameters_from_dict(data, default_parameters):
"""Build Parameters object from input dict and default parameter values."""
par_data = []
input_names = [_["name"] for _ in data]
for par in default_parameters:
par_dict = par.to_dict()
try:
index = input_names.index(par_dict["name"])
par_dict.update(data[index])
except ValueError:
log.warning(
f"Parameter '{par_dict['name']}' not defined in YAML file."
f" Using default value: {par_dict['value']} {par_dict['unit']}"
)
par_data.append(par_dict)
return Parameters.from_dict(par_data)
[docs]class ModelBase:
"""Model base class."""
_type = None
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)
covariance_data = kwargs.get("covariance_data", None)
if covariance_data is not None:
self.covariance = covariance_data
def __getattribute__(self, name):
value = object.__getattribute__(self, name)
if isinstance(value, Parameter):
return value.__get__(self, None)
return value
@property
def type(self):
return self._type
def __init_subclass__(cls, **kwargs):
# Add parameters list on the model sub-class (not instances)
cls.default_parameters = Parameters(
[_ for _ in cls.__dict__.values() if isinstance(_, Parameter)]
)
[docs] @classmethod
def from_parameters(cls, parameters, **kwargs):
"""Create model from parameter list
Parameters
----------
parameters : `Parameters`
Parameters for init
Returns
-------
model : `Model`
Model instance
"""
for par in parameters:
kwargs[par.name] = par
return cls(**kwargs)
def _check_covariance(self):
if not self.parameters == self._covariance.parameters:
self._covariance = Covariance(self.parameters)
@property
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
@covariance.setter
def covariance(self, covariance):
self._check_covariance()
self._covariance.data = covariance
for par in self.parameters:
pars = Parameters([par])
variance = self._covariance.get_subcovariance(pars)
par.error = np.sqrt(variance)
@property
def parameters(self):
"""Parameters (`~gammapy.modeling.Parameters`)"""
return Parameters(
[getattr(self, name) for name in self.default_parameters.names]
)
[docs] def copy(self, **kwargs):
"""A deep copy."""
return copy.deepcopy(self)
[docs] def to_dict(self, full_output=False):
"""Create dict for YAML serialisation"""
tag = self.tag[0] if isinstance(self.tag, list) else self.tag
params = self.parameters.to_dict()
if not full_output:
for par, par_default in zip(params, self.default_parameters):
init = par_default.to_dict()
for item in [
"min",
"max",
"error",
"interp",
"scale_method",
"is_norm",
]:
default = init[item]
if par[item] == default or (
np.isnan(par[item]) and np.isnan(default)
):
del par[item]
if par["frozen"] == init["frozen"]:
del par["frozen"]
if init["unit"] == "":
del par["unit"]
data = {"type": tag, "parameters": params}
if self.type is None:
return data
else:
return {self.type: data}
[docs] @classmethod
def from_dict(cls, data):
kwargs = {}
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__}"
)
parameters = _build_parameters_from_dict(
data["parameters"], cls.default_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"]
# TODO: same as above for temporal models
if "scale" in data:
kwargs["scale"] = data["scale"]
return cls.from_parameters(parameters, **kwargs)
def __str__(self):
string = f"{self.__class__.__name__}\n"
if len(self.parameters) > 0:
string += f"\n{self.parameters.to_table()}"
return string
@property
def frozen(self):
"""Frozen status of a model, True if all parameters are frozen"""
return np.all([p.frozen for p in self.parameters])
[docs] def freeze(self):
"""Freeze all parameters"""
self.parameters.freeze_all()
[docs] def unfreeze(self):
"""Restore parameters frozen status to default"""
for p, default in zip(self.parameters, self.default_parameters):
p.frozen = default.frozen
[docs] def reassign(self, datasets_names, new_datasets_names):
"""Reassign a model from one dataset to another
Parameters
----------
datasets_names : str or list
Name of the datasets where the model is currently defined
new_datasets_names : str or list
Name of the datasets where the model should be defined instead.
If multiple names are given the two list must have the save length,
as the reassignment is element-wise.
Returns
-------
model : `Model`
Reassigned model.
"""
model = self.copy(name=self.name)
if not isinstance(datasets_names, list):
datasets_names = [datasets_names]
if not isinstance(new_datasets_names, list):
new_datasets_names = [new_datasets_names]
if isinstance(model.datasets_names, str):
model.datasets_names = [model.datasets_names]
if getattr(model, "datasets_names", None):
for name, name_new in zip(datasets_names, new_datasets_names):
model.datasets_names = [
_.replace(name, name_new) for _ in model.datasets_names
]
return model
[docs]class Model:
"""Model class that contains only methods to create a model listed in the registries."""
[docs] @staticmethod
def create(tag, model_type=None, *args, **kwargs):
"""Create a model instance.
Examples
--------
>>> from gammapy.modeling.models import Model
>>> spectral_model = Model.create(
"pl-2", model_type="spectral", amplitude="1e-10 cm-2 s-1", index=3
)
>>> type(spectral_model)
<class 'gammapy.modeling.models.spectral.PowerLaw2SpectralModel'>
"""
data = {"type": tag}
if model_type is not None:
data = {model_type: data}
cls = _get_model_class_from_dict(data)
return cls(*args, **kwargs)
[docs] @staticmethod
def from_dict(data):
"""Create a model instance from a dict"""
cls = _get_model_class_from_dict(data)
return cls.from_dict(data)
[docs]class DatasetModels(collections.abc.Sequence):
"""Immutable models container
Parameters
----------
models : `SkyModel`, list of `SkyModel` or `Models`
Sky models
covariance_data : `~numpy.ndarray`
Covariance data
"""
def __init__(self, models=None, covariance_data=None):
if models is None:
models = []
if isinstance(models, (Models, DatasetModels)):
models = models._models
elif isinstance(models, ModelBase):
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)
# Set separataly because this trigggers the update mechanism on the sub-models
if covariance_data is not None:
self.covariance = covariance_data
def _check_covariance(self):
if not self.parameters == self._covariance.parameters:
self._covariance = Covariance.from_stack(
[model.covariance for model in self._models]
)
@property
def covariance(self):
"""Covariance (`~gammapy.modeling.Covariance`)"""
self._check_covariance()
for model in self._models:
self._covariance.set_subcovariance(model.covariance)
return self._covariance
@covariance.setter
def covariance(self, covariance):
self._check_covariance()
self._covariance.data = covariance
for model in self._models:
subcovar = self._covariance.get_subcovariance(model.covariance.parameters)
model.covariance = subcovar
@property
def parameters(self):
"""Parameters (`~gammapy.modeling.Parameters`)"""
return Parameters.from_stack([_.parameters for _ in self._models])
@property
def parameters_unique_names(self):
"""List of unique parameter names as model_name.par_type.par_name"""
names = []
for model in self:
for par in model.parameters:
components = [model.name, par.type, par.name]
name = ".".join(components)
names.append(name)
return names
@property
def names(self):
"""List of model names"""
return [m.name for m in self._models]
[docs] @classmethod
def read(cls, filename):
"""Read from YAML file."""
yaml_str = make_path(filename).read_text()
path, filename = split(filename)
return cls.from_yaml(yaml_str, path=path)
[docs] @classmethod
def from_yaml(cls, yaml_str, path=""):
"""Create from YAML string."""
data = yaml.safe_load(yaml_str)
return cls.from_dict(data, path=path)
[docs] @classmethod
def from_dict(cls, data, path=""):
"""Create from dict."""
from . import MODEL_REGISTRY, SkyModel
models = []
for component in data["components"]:
model_cls = MODEL_REGISTRY.get_cls(component["type"])
model = model_cls.from_dict(component)
models.append(model)
models = cls(models)
if "covariance" in data:
filename = data["covariance"]
path = make_path(path)
if not (path / filename).exists():
path, filename = split(filename)
models.read_covariance(path, filename, format="ascii.fixed_width")
shared_register = {}
for model in models:
if isinstance(model, SkyModel):
submodels = [
model.spectral_model,
model.spatial_model,
model.temporal_model,
]
for submodel in submodels:
if submodel is not None:
shared_register = _set_link(shared_register, submodel)
else:
shared_register = _set_link(shared_register, model)
return models
[docs] def write(
self,
path,
overwrite=False,
full_output=False,
overwrite_templates=False,
write_covariance=True,
):
"""Write to YAML file.
Parameters
----------
path : `pathlib.Path` or str
path to write files
overwrite : bool
overwrite YAML files
full_output : bool
Store full parameter output.
overwrite_templates : bool
overwrite templates FITS files
write_covariance : bool
save covariance or not
"""
base_path, _ = split(path)
path = make_path(path)
base_path = make_path(base_path)
if path.exists() and not overwrite:
raise IOError(f"File exists already: {path}")
if (
write_covariance
and self.covariance is not None
and len(self.parameters) != 0
):
filecovar = path.stem + "_covariance.dat"
kwargs = dict(
format="ascii.fixed_width", delimiter="|", overwrite=overwrite
)
self.write_covariance(base_path / filecovar, **kwargs)
self._covar_file = filecovar
path.write_text(self.to_yaml(full_output, overwrite_templates))
[docs] def to_yaml(self, full_output=False, overwrite_templates=False):
"""Convert to YAML string."""
data = self.to_dict(full_output, overwrite_templates)
return yaml.dump(
data, sort_keys=False, indent=4, width=80, default_flow_style=False
)
[docs] def update_link_label(self):
"""update linked parameters labels used for serialization and print"""
params_list = []
params_shared = []
for param in self.parameters:
if param not in params_list:
params_list.append(param)
params_list.append(param)
elif param not in params_shared:
params_shared.append(param)
for param in params_shared:
param._link_label_io = param.name + "@" + make_name()
[docs] def to_dict(self, full_output=False, overwrite_templates=False):
"""Convert to dict."""
self.update_link_label()
models_data = []
for model in self._models:
model_data = model.to_dict(full_output)
models_data.append(model_data)
if (
hasattr(model, "spatial_model")
and model.spatial_model is not None
and "template" in model.spatial_model.tag
):
model.spatial_model.write(overwrite=overwrite_templates)
if model.tag == "TemplateNPredModel":
model.write(overwrite=overwrite_templates)
if self._covar_file is not None:
return {
"components": models_data,
"covariance": str(self._covar_file),
}
else:
return {"components": models_data}
[docs] def to_parameters_table(self):
"""Convert Models parameters to an astropy Table."""
table = self.parameters.to_table()
# Warning: splitting of parameters will break is source name has a "." in its name.
model_name = [name.split(".")[0] for name in self.parameters_unique_names]
table.add_column(model_name, name="model", index=0)
return table
[docs] def update_parameters_from_table(self, t):
"""Update Models from an astropy Table."""
parameters_dict = [dict(zip(t.colnames, row)) for row in t]
for k, data in enumerate(parameters_dict):
self.parameters[k].update_from_dict(data)
[docs] def read_covariance(self, path, filename="_covariance.dat", **kwargs):
"""Read covariance data from file
Parameters
----------
path : str or `Path`
Base path
filename : str
Filename
**kwargs : dict
Keyword arguments passed to `~astropy.table.Table.read`
"""
path = make_path(path)
filepath = str(path / filename)
t = Table.read(filepath, **kwargs)
t.remove_column("Parameters")
arr = np.array(t)
data = arr.view(float).reshape(arr.shape + (-1,))
self.covariance = data
self._covar_file = filename
[docs] def write_covariance(self, filename, **kwargs):
"""Write covariance to file
Parameters
----------
filename : str
Filename
**kwargs : dict
Keyword arguments passed to `~astropy.table.Table.write`
"""
names = self.parameters_unique_names
table = Table()
table["Parameters"] = names
for idx, name in enumerate(names):
values = self.covariance.data[idx]
table[str(idx)] = values
table.write(make_path(filename), **kwargs)
def __str__(self):
self.update_link_label()
str_ = f"{self.__class__.__name__}\n\n"
for idx, model in enumerate(self):
str_ += f"Component {idx}: "
str_ += str(model)
return str_.expandtabs(tabsize=2)
def __add__(self, other):
if isinstance(other, (Models, list)):
return Models([*self, *other])
elif isinstance(other, ModelBase):
if other.name in self.names:
raise (ValueError("Model names must be unique"))
return Models([*self, other])
else:
raise TypeError(f"Invalid type: {other!r}")
def __getitem__(self, key):
if isinstance(key, np.ndarray) and key.dtype == bool:
return self.__class__(list(np.array(self._models)[key]))
else:
return self._models[self.index(key)]
[docs] def index(self, key):
if isinstance(key, (int, slice)):
return key
elif isinstance(key, str):
return self.names.index(key)
elif isinstance(key, ModelBase):
return self._models.index(key)
else:
raise TypeError(f"Invalid type: {type(key)!r}")
def __len__(self):
return len(self._models)
def _ipython_key_completions_(self):
return self.names
[docs] def copy(self, copy_data=False):
"""A deep copy.
Parameters
----------
copy_data : bool
Whether to copy data attached to template models
Returns
-------
models: `Models`
Copied models.
"""
models = []
for model in self:
model_copy = model.copy(name=model.name, copy_data=copy_data)
models.append(model_copy)
return self.__class__(
models=models, covariance_data=self.covariance.data.copy()
)
[docs] def select(
self,
name_substring=None,
datasets_names=None,
tag=None,
model_type=None,
frozen=None,
):
"""Select models that meet all specified conditions
Parameters
----------
name_substring : str
Substring contained in the model name
datasets_names : str or list
Name of the dataset
tag : str or list
Model tag
model_type : {None, spatial, spectral}
Type of model, used together with "tag", if the tag is not unique.
frozen : bool
Select models with all parameters frozen if True, exclude them if False.
Returns
-------
models : `DatasetModels`
Selected models
"""
mask = self.selection_mask(
name_substring, datasets_names, tag, model_type, frozen
)
return self[mask]
[docs] def selection_mask(
self,
name_substring=None,
datasets_names=None,
tag=None,
model_type=None,
frozen=None,
):
"""Create a mask of models, that meet all specified conditions
Parameters
----------
name_substring : str
Substring contained in the model name
datasets_names : str or list of str
Name of the dataset
tag : str or list of str
Model tag
model_type : {None, spatial, spectral}
Type of model, used together with "tag", if the tag is not unique.
frozen : bool
Select models with all parameters frozen if True, exclude them if False.
Returns
-------
mask : `numpy.array`
Boolean mask, True for selected models
"""
selection = np.ones(len(self), dtype=bool)
if tag and not isinstance(tag, list):
tag = [tag]
if datasets_names and not isinstance(datasets_names, list):
datasets_names = [datasets_names]
for idx, model in enumerate(self):
if name_substring:
selection[idx] &= name_substring in model.name
if datasets_names:
selection[idx] &= model.datasets_names is None or np.any(
[name in model.datasets_names for name in datasets_names]
)
if tag:
if model_type is None:
sub_model = model
else:
sub_model = getattr(model, f"{model_type}_model", None)
if sub_model:
selection[idx] &= np.any([t in sub_model.tag for t in tag])
else:
selection[idx] &= False
if frozen is not None:
if frozen:
selection[idx] &= model.frozen
else:
selection[idx] &= ~model.frozen
return np.array(selection, dtype=bool)
[docs] def select_mask(self, mask, margin="0 deg", use_evaluation_region=True):
"""Check if sky models contribute within a mask map.
Parameters
----------
mask : `~gammapy.maps.WcsNDMap` of boolean type
Map containing a boolean mask
margin : `~astropy.unit.Quantity`
Add a margin in degree to the source evaluation radius.
Used to take into account PSF width.
use_evaluation_region : bool
Account for the extension of the model or not. The default is True.
Returns
-------
models : `DatasetModels`
Selected models contributing inside the region where mask==True
"""
models = []
if not mask.geom.is_image:
mask = mask.reduce_over_axes(func=np.logical_or)
for model in self.select(tag="sky-model"):
if use_evaluation_region:
contributes = model.contributes(mask=mask, margin=margin)
else:
contributes = mask.get_by_coord(model.position, fill_value=0)
if np.any(contributes):
models.append(model)
return self.__class__(models=models)
[docs] def select_region(self, regions, wcs=None):
"""Select sky models with center position contained within a given region
Parameters
----------
regions : str, `~regions.Region` or list of `~regions.Region`
Region or list of regions (pixel or sky regions accepted).
A region can be defined as a string ind DS9 format as well.
See http://ds9.si.edu/doc/ref/region.html for details.
wcs : `~astropy.wcs.WCS`
World coordinate system transformation
Returns
-------
models : `DatasetModels`
Selected models
"""
geom = RegionGeom.from_regions(regions, wcs=wcs)
models = []
for model in self.select(tag="sky-model"):
if geom.contains(model.position):
models.append(model)
return self.__class__(models=models)
[docs] def restore_status(self, restore_values=True):
"""Context manager to restore status.
A copy of the values is made on enter,
and those values are restored on exit.
Parameters
----------
restore_values : bool
Restore values if True,
otherwise restore only frozen status and covariance matrix.
"""
return restore_models_status(self, restore_values)
[docs] def set_parameters_bounds(
self, tag, model_type, parameters_names=None, min=None, max=None, value=None
):
"""Set bounds for the selected models types and parameters names
Parameters
----------
tag : str or list
Tag of the models
model_type : {"spatial", "spectral", "temporal"}
Type of model
parameters_names : str or list
parameters names
min : float
min value
max : float
max value
value : float
init value
"""
models = self.select(tag=tag, model_type=model_type)
parameters = models.parameters.select(name=parameters_names, type=model_type)
n = len(parameters)
if min is not None:
parameters.min = np.ones(n) * min
if max is not None:
parameters.max = np.ones(n) * max
if value is not None:
parameters.value = np.ones(n) * value
[docs] def freeze(self, model_type=None):
"""Freeze parameters depending on model type
Parameters
----------
model_type : {None, "spatial", "spectral"}
freeze all parameters or only spatial or only spectral
"""
for m in self:
m.freeze(model_type)
[docs] def unfreeze(self, model_type=None):
"""Restore parameters frozen status to default depending on model type
Parameters
----------
model_type : {None, "spatial", "spectral"}
restore frozen status to default for all parameters or only spatial or only spectral
"""
for m in self:
m.unfreeze(model_type)
@property
def frozen(self):
"""Boolean mask, True if all parameters of a given model are frozen"""
return np.all([m.frozen for m in self])
[docs] def reassign(self, dataset_name, new_dataset_name):
"""Reassign a model from one dataset to another
Parameters
----------
dataset_name : str or list
Name of the datasets where the model is currently defined
new_dataset_name : str or list
Name of the datasets where the model should be defined instead.
If multiple names are given the two list must have the save length,
as the reassignment is element-wise.
"""
models = [m.reassign(dataset_name, new_dataset_name) for m in self]
return self.__class__(models)
[docs] def to_template_sky_model(self, geom, spectral_model=None, name=None):
"""Merge a list of models into a single `~gammapy.modeling.models.SkyModel`
Parameters
----------
geom : `Geom`
Map geometry of the result template model.
spectral_model : `~gammapy.modeling.models.SpectralModel`
One of the NormSpectralMdel
name : str
Name of the new model
Returns
-------
model : `SkyModel`
Template sky model.
"""
from . import PowerLawNormSpectralModel, SkyModel, TemplateSpatialModel
unit = u.Unit("1 / (cm2 s sr TeV)")
map_ = Map.from_geom(geom, unit=unit)
for m in self:
map_ += m.evaluate_geom(geom).to(unit)
spatial_model = TemplateSpatialModel(map_, normalize=False)
if spectral_model is None:
spectral_model = PowerLawNormSpectralModel()
return SkyModel(
spectral_model=spectral_model, spatial_model=spatial_model, name=name
)
@property
def positions(self):
"""Positions of the models (`~astropy.coordinates.SkyCoord`)"""
positions = []
for model in self.select(tag="sky-model"):
if model.position:
positions.append(model.position.icrs)
else:
log.warning(
f"Skipping model {model.name} - no spatial component present"
)
return SkyCoord(positions)
[docs] def to_regions(self):
"""Returns a list of the regions for the spatial models
Returns
-------
regions: list of `~regions.SkyRegion`
Regions
"""
regions = []
for model in self.select(tag="sky-model"):
try:
region = model.spatial_model.to_region()
regions.append(region)
except AttributeError:
log.warning(
f"Skipping model {model.name} - no spatial component present"
)
return regions
@property
def wcs_geom(self):
"""Minimum WCS geom in which all the models are contained"""
regions = self.to_regions()
try:
return RegionGeom.from_regions(regions).to_wcs_geom()
except IndexError:
log.error("No spatial component in any model. Geom not defined")
[docs] def plot_regions(self, ax=None, kwargs_point=None, path_effect=None, **kwargs):
"""Plot extent of the spatial models on a given wcs axis
Parameters
----------
ax : `~astropy.visualization.WCSAxes`
Axes to plot on. If no axes are given, an all-sky wcs
is chosen using a CAR projection
kwargs_point : dict
Keyword arguments passed to `~matplotlib.lines.Line2D` for plotting
of point sources
path_effect : `~matplotlib.patheffects.PathEffect`
Path effect applied to artists and lines.
**kwargs : dict
Keyword arguments passed to `~matplotlib.artists.Artist`
Returns
-------
ax : `~astropy.visualization.WcsAxes`
WCS axes
"""
regions = self.to_regions()
geom = RegionGeom.from_regions(regions=regions)
return geom.plot_region(
ax=ax, kwargs_point=kwargs_point, path_effect=path_effect, **kwargs
)
[docs] def plot_positions(self, ax=None, **kwargs):
""" "Plot the centers of the spatial models on a given wcs axis
Parameters
----------
ax : `~astropy.visualization.WCSAxes`
Axes to plot on. If no axes are given, an all-sky wcs
is chosen using a CAR projection
**kwargs : dict
Keyword arguments passed to `~matplotlib.pyplot.scatter`
Returns
-------
ax : `~astropy.visualization.WcsAxes`
Wcs axes
"""
from astropy.visualization.wcsaxes import WCSAxes
if ax is None or not isinstance(ax, WCSAxes):
ax = Map.from_geom(self.wcs_geom).plot()
kwargs.setdefault("marker", "*")
kwargs.setdefault("color", "tab:blue")
path_effects = kwargs.get("path_effects", None)
xp, yp = self.positions.to_pixel(ax.wcs)
p = ax.scatter(xp, yp, **kwargs)
if path_effects:
plt.setp(p, path_effects=path_effects)
return ax
[docs]class Models(DatasetModels, collections.abc.MutableSequence):
"""Sky model collection.
Parameters
----------
models : `SkyModel`, list of `SkyModel` or `Models`
Sky models
"""
def __delitem__(self, key):
del self._models[self.index(key)]
def __setitem__(self, key, model):
from gammapy.modeling.models import FoVBackgroundModel, SkyModel
if isinstance(model, (SkyModel, FoVBackgroundModel)):
self._models[self.index(key)] = model
else:
raise TypeError(f"Invalid type: {model!r}")
[docs] def insert(self, idx, model):
if model.name in self.names:
raise (ValueError("Model names must be unique"))
self._models.insert(idx, model)
class restore_models_status:
def __init__(self, models, restore_values=True):
self.restore_values = restore_values
self.models = models
self.values = [_.value for _ in models.parameters]
self.frozen = [_.frozen for _ in models.parameters]
self.covariance_data = models.covariance.data
def __enter__(self):
pass
def __exit__(self, type, value, traceback):
for value, par, frozen in zip(self.values, self.models.parameters, self.frozen):
if self.restore_values:
par.value = value
par.frozen = frozen
self.models.covariance = self.covariance_data