# Licensed under a 3-clause BSD style license - see LICENSE.rst
import collections.abc
import copy
import html
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
from gammapy.maps import Map, RegionGeom
from gammapy.modeling import Covariance, Parameter, Parameters
from gammapy.modeling.covariance import CovarianceMixin
from gammapy.utils.scripts import from_yaml, make_name, make_path, to_yaml, write_yaml
__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 dictionary."""
from . import (
MODEL_REGISTRY,
PRIOR_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"])
elif "prior" in data:
cls = PRIOR_REGISTRY.get_cls(data["prior"]["type"])
return cls
def _build_parameters_from_dict(data, default_parameters):
"""Build a `~gammapy.modeling.Parameters` object from input dictionary 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)
def _check_name_unique(model, names):
"""Check if a model is not duplicated"""
if model.name in names:
raise (
ValueError(
f"Model names must be unique. Models named '{model.name}' are duplicated."
)
)
return
def _write_models(
models,
path,
overwrite=False,
full_output=False,
overwrite_templates=False,
write_covariance=True,
checksum=False,
extra_dict=None,
):
"""Write models to YAML file with additionnal informations using an `extra_dict`"""
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 models.covariance is not None
and len(models.parameters) != 0
):
filecovar = path.stem + "_covariance.dat"
kwargs = dict(format="ascii.fixed_width", delimiter="|", overwrite=overwrite)
models.write_covariance(base_path / filecovar, **kwargs)
models._covar_file = filecovar
yaml_str = ""
if extra_dict is not None:
yaml_str += to_yaml(extra_dict)
yaml_str += models.to_yaml(full_output, overwrite_templates)
write_yaml(yaml_str, path, overwrite=overwrite, checksum=checksum)
[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).data
par.error = np.sqrt(variance[0][0])
@property
def parameters(self):
"""Parameters as a `~gammapy.modeling.Parameters` object."""
return Parameters(
[getattr(self, name) for name in self.default_parameters.names]
)
@property
def parameters_unique_names(self):
return self.parameters.unique_parameters.names
[docs]
def copy(self, **kwargs):
"""Deep copy."""
return copy.deepcopy(self)
[docs]
def to_dict(self, full_output=False):
"""Create dictionary 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",
]:
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
)
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
def _repr_html_(self):
try:
return self.to_html()
except AttributeError:
return f"<pre>{html.escape(str(self))}</pre>"
@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 dictionary."""
cls = _get_model_class_from_dict(data)
return cls.from_dict(data)
[docs]
class DatasetModels(collections.abc.Sequence, CovarianceMixin):
"""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)):
if covariance_data is None and models.covariance is not None:
covariance_data = models.covariance.data
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:
_check_name_unique(model, names=unique_names)
unique_names.append(model.name)
self._models = models
self._covar_file = None
self._covariance = Covariance(self.parameters)
# Set separately because this triggers the update mechanism on the sub-models
if covariance_data is not None:
self.covariance = covariance_data
@property
def parameters(self):
"""Parameters as a `~gammapy.modeling.Parameters` object."""
return Parameters.from_stack([_.parameters for _ in self._models])
@property
def parameters_unique_names(self):
"""List of unique parameter names. Return formatted as model_name.par_type.par_name."""
names = []
for model in self._models:
for par_name in model.parameters_unique_names:
components = [model.name, 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, checksum=False):
"""Read from YAML file.
Parameters
----------
filename : str
input filename
checksum : bool, optional
Whether to perform checksum verification. Default is False.
"""
yaml_str = make_path(filename).read_text()
path, filename = split(filename)
return cls.from_yaml(yaml_str, path=path, checksum=checksum)
[docs]
@classmethod
def from_yaml(cls, yaml_str, path="", checksum=False):
"""Create from YAML string.
Parameters
----------
yaml_str : str
yaml str
path : str
base path of model files
checksum : bool, optional
Whether to perform checksum verification. Default is False.
"""
data = from_yaml(yaml_str, checksum=checksum)
# TODO : for now metadata are not kept. Add proper metadata creation.
data.pop("metadata", None)
return cls.from_dict(data, path=path)
[docs]
@classmethod
def from_dict(cls, data, path=""):
"""Create from dictionary."""
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,
checksum=False,
):
"""Write to YAML file.
Parameters
----------
path : `pathlib.Path` or str
Path to write files.
overwrite : bool, optional
Overwrite existing file. Default is False.
full_output : bool, optional
Store full parameter output. Default is False.
overwrite_templates : bool, optional
Overwrite templates FITS files. Default is False.
write_covariance : bool, optional
Whether to save the covariance. Default is True.
checksum : bool, optional
When True adds a CHECKSUM entry to the file.
Default is False.
"""
_write_models(
self,
path,
overwrite,
full_output,
overwrite_templates,
write_covariance,
checksum,
)
[docs]
def to_yaml(self, full_output=False, overwrite_templates=False):
"""Convert to YAML string.
Parameters
----------
full_output : bool, optional
Store full parameter output. Default is False.
overwrite_templates : bool, optional
Overwrite templates FITS files. Default is False.
"""
data = self.to_dict(full_output, overwrite_templates)
return to_yaml(data)
[docs]
def update_link_label(self):
"""Update linked parameters labels used for serialisation 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 dictionary."""
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 model parameters to a `~astropy.table.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 a `~astropy.table.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 _repr_html_(self):
try:
return self.to_html()
except AttributeError:
return f"<pre>{html.escape(str(self))}</pre>"
def __add__(self, other):
if isinstance(other, (Models, list)):
return Models([*self, *other])
elif isinstance(other, ModelBase):
_check_name_unique(other, self.names)
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):
"""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()
)
def _slice_by_energy(self, energy_min, energy_max, sum_over_energy_groups=False):
"""Copy models and slice TemplateNPredModel in energy range.
Parameters
----------
energy_min, energy_max : `~astropy.units.Quantity`
Energy bounds of the slice
sum_over_energy_groups : bool
Whether to sum over the energy groups or not. Default is False.
Returns
-------
models : `Models`
Sliced models.
"""
models_sliced = Models(self.copy())
for k, m in enumerate(models_sliced):
if m.tag == "TemplateNPredModel":
m_sliced = m.slice_by_energy(energy_min, energy_max)
if sum_over_energy_groups:
m_sliced.map = m_sliced.map.sum_over_axes(keepdims=True)
models_sliced[k] = m_sliced
return models_sliced
[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, optional
Substring contained in the model name. Default is None.
datasets_names : str or list, optional
Name of the dataset. Default is None.
tag : str or list, optional
Model tag. Default is None.
model_type : {'None', 'spatial', 'spectral'}
Type of model, used together with "tag", if the tag is not unique. Default is None.
frozen : bool, optional
If True, select models with all parameters frozen; if False, exclude them. Default is None.
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, optional
Substring contained in the model name. Default is None.
datasets_names : str or list of str, optional
Name of the dataset. Default is None.
tag : str or list of str, optional
Model tag. Default is None.
model_type : {'None', 'spatial', 'spectral'}
Type of model, used together with "tag", if the tag is not unique. Default is None.
frozen : bool, optional
Select models with all parameters frozen if True, exclude them if False. Default is None.
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`, optional
Add a margin in degree to the source evaluation radius.
Used to take into account PSF width. Default is "0 deg".
use_evaluation_region : bool, optional
Account for the extension of the model or not. 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_from_geom(self, geom, **kwargs):
"""Select models that fall inside a given geometry.
Parameters
----------
geom : `~gammapy.maps.Geom`
Geometry to select models from.
**kwargs : dict
Keyword arguments passed to `~gammapy.modeling.models.DatasetModels.select_mask`.
Returns
-------
models : `DatasetModels`
Selected models.
"""
mask = Map.from_geom(geom=geom, data=True, dtype=bool)
return self.select_mask(mask=mask, **kwargs)
[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`, optional
World coordinate system transformation. Default is None.
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, optional
Restore values if True, otherwise restore only frozen status and covariance matrix.
Default is True.
"""
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, optional
Parameters names. Default is None.
min : float, optional
Minimum value. Default is None.
max : float, optional
Maximum value. Default is None.
value : float, optional
Initial value. Default is None.
"""
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.
Default is None.
"""
for m in self:
m.freeze(model_type)
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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.
Default is None.
"""
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])
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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`, optional
One of the NormSpectralModel. Default is None.
name : str, optional
Name of the new model. Default is None.
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
)
[docs]
def to_template_spectral_model(self, geom, mask=None):
"""Merge a list of models into a single `~gammapy.modeling.models.TemplateSpectralModel`.
For each model the spatial component is integrated over the given geometry where the mask is true
and multiplied by the spectral component value in each energy bin.
Parameters
----------
geom : `~gammapy.maps.Geom`
Map geometry on which the template model is computed.
mask : `~gammapy.maps.Map` with bool dtype.
Evaluate the model only where the mask is True.
Returns
-------
model : `~gammapy.modeling.models.TemplateSpectralModel`
Template spectral model.
"""
from . import TemplateSpectralModel
energy = geom.axes[0].center
if mask is None:
mask = Map.from_geom(geom, data=True)
elif mask.geom != geom:
mask = mask.interp_to_geom(geom, method="nearest")
values = 0
for m in self:
dnde = m.spectral_model(energy)
spatial_integ = m.spatial_model.integrate_geom(geom)
masked_integ = spatial_integ.data * np.isfinite(spatial_integ.data) * mask
spatial_integ.data[~np.isfinite(spatial_integ.data)] = np.nan
dnde *= masked_integ.sum(axis=(1, 2)) * spatial_integ.unit
values += dnde
return TemplateSpectralModel(energy, values)
@property
def positions(self):
"""Positions of the models as a `~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)
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def to_regions(self):
"""Return 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")
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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`, optional
Axes to plot on. If no axes are given, an all-sky WCS
is chosen using a CAR projection. Default is None.
kwargs_point : dict, optional
Keyword arguments passed to `~matplotlib.lines.Line2D` for plotting
of point sources. Default is None.
path_effect : `~matplotlib.patheffects.PathEffect`, optional
Path effect applied to artists and lines. Default is None.
**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`, optional
Axes to plot on. If no axes are given, an all-sky WCS
is chosen using a CAR projection. Default is None.
**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
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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,
TemplateNPredModel,
)
if isinstance(model, (SkyModel, FoVBackgroundModel, TemplateNPredModel)):
self._models[self.index(key)] = model
else:
raise TypeError(f"Invalid type: {model!r}")
[docs]
def insert(self, idx, model):
_check_name_unique(model, self.names)
self._models.insert(idx, model)
[docs]
def set_prior(self, parameters, priors):
for parameter, prior in zip(parameters, priors):
parameter.prior = prior
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
