Datasets

class gammapy.modeling.Datasets(datasets)

Bases: collections.abc.MutableSequence

Dataset collection.

Parameters

datasetsDataset or list of Dataset

Datasets

Attributes Summary

is_all_same_shape	Whether all contained datasets have the same data shape
is_all_same_type	Whether all contained datasets are of the same type
names
parameters	Unique parameters (Parameters).

Methods Summary

append(self, value)	S.append(value) – append value to the end of the sequence
clear(self)
copy(self)	A deep copy.
count(self, value)
extend(self, values)	S.extend(iterable) – extend sequence by appending elements from the iterable
index(self, value[, start, stop])	Raises ValueError if the value is not present.
info_table(self[, cumulative])	Get info table for datasets.
insert(self, idx, dataset)	S.insert(index, value) – insert value before index
pop(self[, index])	Raise IndexError if list is empty or index is out of range.
read(filedata, filemodel)	De-serialize datasets from YAML and FITS files.
remove(self, value)	S.remove(value) – remove first occurrence of value.
reverse(self)	S.reverse() – reverse IN PLACE
stack_reduce(self[, name])	Reduce the Datasets to a unique Dataset by stacking them together.
stat_sum(self)	Compute joint likelihood
write(self, path[, prefix, overwrite])	Serialize datasets to YAML and FITS files.

Attributes Documentation

is_all_same_shape

Whether all contained datasets have the same data shape

is_all_same_type

Whether all contained datasets are of the same type

names

parameters

Unique parameters (Parameters).

Duplicate parameter objects have been removed. The order of the unique parameters remains.

Methods Documentation

append(self, value)

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

clear(self)

copy(self)

A deep copy.

count(self, value)

extend(self, values)

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

index(self, value, start=0, stop=None)

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

info_table(self, cumulative=False)

Get info table for datasets.

Parameters

cumulativebool

Cumulate info across all observations

Returns

info_tableTable

Info table.

insert(self, idx, dataset)

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

pop(self, index=-1)

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

classmethod read(filedata, filemodel)

De-serialize datasets from YAML and FITS files.

Parameters

filedatastr

filepath to yaml datasets file

filemodelstr

filepath to yaml models file

Returns

dataset‘gammapy.modeling.Datasets’

Datasets

remove(self, value)

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

reverse(self)

S.reverse() – reverse IN PLACE

stack_reduce(self, name=None)

Reduce the Datasets to a unique Dataset by stacking them together.

This works only if all Dataset are of the same type and if a proper in-place stack method exists for the Dataset type.

Returns

dataset~gammapy.utils.Dataset

the stacked dataset

stat_sum(self)

Compute joint likelihood

write(self, path, prefix='', overwrite=False)

Serialize datasets to YAML and FITS files.

Parameters

pathpathlib.Path

path to write files

prefixstr

common prefix of file names

overwritebool

overwrite datasets FITS files