Models

class gammapy.modeling.models.Models(models=None)

Bases: gammapy.modeling.models.core.DatasetModels, collections.abc.MutableSequence

Sky model collection.

Parameters

modelsSkyModel, list of SkyModel or Models

Sky models

Attributes Summary

covariance
frozen	Boolean mask, True if all parameters of a given model are frozen
names
parameters
parameters_unique_names	List of unique parameter names as model_name.par_type.par_name
positions	Positions of the models (SkyCoord)
wcs_geom	Minimum WCS geom in which all the models are contained

Methods Summary

append(value)	S.append(value) -- append value to the end of the sequence
clear()
copy(**kwargs)
count(value)
extend(values)	S.extend(iterable) -- extend sequence by appending elements from the iterable
freeze([model_type])	Freeze parameters depending on model type
from_dict(data[, path])	Create from dict.
from_yaml(yaml_str[, path])	Create from YAML string.
index(value, [start, [stop]])	Raises ValueError if the value is not present.
insert(idx, model)	S.insert(index, value) -- insert value before index
plot_positions([ax])	"Plot the centers of the spatial models on a given wcs axis
plot_regions([ax, kwargs_point, path_effect])	Plot extent of the spatial models on a given wcs axis
pop([index])	Raise IndexError if list is empty or index is out of range.
read(filename)	Read from YAML file.
read_covariance(path[, filename])	Read covariance data from file
reassign(dataset_name, new_dataset_name)	Reassign a model from one dataset to another
remove(value)	S.remove(value) -- remove first occurrence of value.
restore_status([restore_values])	Context manager to restore status.
reverse()	S.reverse() -- reverse IN PLACE
select([name_substring, datasets_names, ...])	Select models that meet all specified conditions
select_mask(mask[, margin, ...])	Check if sky models contribute within a mask map.
select_region(regions[, wcs])	Select sky models with center position contained within a given region
selection_mask([name_substring, ...])	Create a mask of models, that meet all specified conditions
set_parameters_bounds(tag, model_type[, ...])	Set bounds for the selected models types and parameters names
to_dict([full_output, overwrite_templates])	Convert to dict.
to_parameters_table()	Convert Models parameters to an astropy Table.
to_regions()	Returns a list of the regions for the spatial models
to_template_sky_model(geom[, ...])	Merge a list of models into a single SkyModel
to_yaml([full_output, overwrite_templates])	Convert to YAML string.
unfreeze([model_type])	Restore parameters frozen status to default depending on model type
update_link_label()	update linked parameters labels used for serialization and print
update_parameters_from_table(t)	Update Models from an astropy Table.
write(path[, overwrite, full_output, ...])	Write to YAML file.
write_covariance(filename, **kwargs)	Write covariance to file

Attributes Documentation

covariance

frozen

Boolean mask, True if all parameters of a given model are frozen

names

parameters

parameters_unique_names

List of unique parameter names as model_name.par_type.par_name

positions

Positions of the models (SkyCoord)

wcs_geom

Minimum WCS geom in which all the models are contained

Methods Documentation

append(value)

S.append(value) – append value to the end of the sequence

clear() → None -- remove all items from S

copy(**kwargs)

count(value) → integer -- return number of occurrences of value

extend(values)

S.extend(iterable) – extend sequence by appending elements from the iterable

freeze(model_type=None)

Freeze parameters depending on model type

Parameters

model_type{None, “spatial”, “spectral”}

freeze all parameters or only spatial or only spectral

classmethod from_dict(data, path='')

Create from dict.

classmethod from_yaml(yaml_str, path='')

Create from YAML string.

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

insert(idx, model)

S.insert(index, value) – insert value before index

plot_positions(ax=None, **kwargs)

“Plot the centers of the spatial models on a given wcs axis

Parameters

axWCSAxes

Axes to plot on. If no axes are given, an all-sky wcs is chosen using a CAR projection

**kwargsdict

Keyword arguments passed to scatter

Returns

axWcsAxes

Wcs axes

plot_regions(ax=None, kwargs_point=None, path_effect=None, **kwargs)

Plot extent of the spatial models on a given wcs axis

Parameters

axWCSAxes

Axes to plot on. If no axes are given, an all-sky wcs is chosen using a CAR projection

kwargs_pointdict

Keyword arguments passed to Line2D for plotting of point sources

path_effectPathEffect

Path effect applied to artists and lines.

**kwargsdict

Keyword arguments passed to Artist

Returns

axWcsAxes

WCS axes

pop([index]) → item -- remove and return item at index (default last).

Raise IndexError if list is empty or index is out of range.

classmethod read(filename)

Read from YAML file.

read_covariance(path, filename='_covariance.dat', **kwargs)

Read covariance data from file

Parameters

pathstr or Path

Base path

filenamestr

Filename

**kwargsdict

Keyword arguments passed to read

reassign(dataset_name, new_dataset_name)

Reassign a model from one dataset to another

Parameters

dataset_namestr or list

Name of the datasets where the model is currently defined

new_dataset_namestr 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.

remove(value)

S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

restore_status(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_valuesbool

Restore values if True, otherwise restore only frozen status and covariance matrix.

reverse()

S.reverse() – reverse IN PLACE

select(name_substring=None, datasets_names=None, tag=None, model_type=None, frozen=None)

Select models that meet all specified conditions

Parameters

name_substringstr

Substring contained in the model name

datasets_namesstr or list

Name of the dataset

tagstr or list

Model tag

model_type{None, spatial, spectral}

Type of model, used together with “tag”, if the tag is not unique.

frozenbool

Select models with all parameters frozen if True, exclude them if False.

Returns

modelsDatasetModels

Selected models

select_mask(mask, margin='0 deg', use_evaluation_region=True)

Check if sky models contribute within a mask map.

Parameters

maskWcsNDMap of boolean type

Map containing a boolean mask

marginQuantity

Add a margin in degree to the source evaluation radius. Used to take into account PSF width.

use_evaluation_regionbool

Account for the extension of the model or not. The default is True.

Returns

modelsDatasetModels

Selected models contributing inside the region where mask==True

select_region(regions, wcs=None)

Select sky models with center position contained within a given region

Parameters

regionsstr, Region or list of 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.

wcsWCS

World coordinate system transformation

Returns

modelsDatasetModels

Selected models

selection_mask(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_substringstr

Substring contained in the model name

datasets_namesstr or list of str

Name of the dataset

tagstr or list of str

Model tag

model_type{None, spatial, spectral}

Type of model, used together with “tag”, if the tag is not unique.

frozenbool

Select models with all parameters frozen if True, exclude them if False.

Returns

masknumpy.array

Boolean mask, True for selected models

set_parameters_bounds(tag, model_type, parameters_names=None, min=None, max=None, value=None)

Set bounds for the selected models types and parameters names

Parameters

tagstr or list

Tag of the models

model_type{“spatial”, “spectral”, “temporal”}

Type of model

parameters_namesstr or list

parameters names

minfloat

min value

maxfloat

max value

valuefloat

init value

to_dict(full_output=False, overwrite_templates=False)

Convert to dict.

to_parameters_table()

Convert Models parameters to an astropy Table.

to_regions()

Returns a list of the regions for the spatial models

Returns

regions: list of SkyRegion

Regions

to_template_sky_model(geom, spectral_model=None, name=None)

Merge a list of models into a single SkyModel

Parameters

geomGeom

Map geometry of the result template model.

spectral_modelSpectralModel

One of the NormSpectralMdel

namestr

Name of the new model

Returns

modelSkyModel

Template sky model.

to_yaml(full_output=False, overwrite_templates=False)

Convert to YAML string.

unfreeze(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

update_link_label()

update linked parameters labels used for serialization and print

update_parameters_from_table(t)

Update Models from an astropy Table.

write(path, overwrite=False, full_output=False, overwrite_templates=False, write_covariance=True)

Write to YAML file.

Parameters

pathpathlib.Path or str

path to write files

overwritebool

overwrite YAML files

full_outputbool

Store full parameter output.

overwrite_templatesbool

overwrite templates FITS files

write_covariancebool

save covariance or not

write_covariance(filename, **kwargs)

Write covariance to file

Parameters

filenamestr

Filename

**kwargsdict

Keyword arguments passed to write