Datasets

class gammapy.datasets.Datasets(datasets=None)

Bases: collections.abc.MutableSequence

Dataset collection.

Parameters

datasetsDataset or list of Dataset

Datasets

Attributes Summary

energy_axes_are_aligned	Whether all contained datasets have aligned energy axis
energy_ranges	Get global energy range of datasets.
gti	GTI table
is_all_same_energy_shape	Whether all contained datasets have the same data shape
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
meta_table	Meta table
models	Unique models (Models).
names
parameters	Unique parameters (Parameters).

Methods Summary

append(value)	S.append(value) – append value to the end of the sequence
clear()
copy()	A deep copy.
count(value)
extend(values)	S.extend(iterable) – extend sequence by appending elements from the iterable
index(value, [start, [stop]])	Raises ValueError if the value is not present.
info_table([cumulative, region])	Get info table for datasets.
insert(idx, dataset)	S.insert(index, value) – insert value before index
pop([index])	Raise IndexError if list is empty or index is out of range.
read(filename[, filename_models, lazy, cache])	De-serialize datasets from YAML and FITS files.
remove(value)	S.remove(value) – remove first occurrence of value.
reverse()	S.reverse() – reverse IN PLACE
select_time(t_min, t_max[, atol])	Select datasets in a given time interval.
slice_by_energy(energy_min, energy_max)	Select and slice datasets in energy range
stack_reduce([name])	Reduce the Datasets to a unique Dataset by stacking them together.
stat_sum()	Compute joint likelihood
write(filename[, filename_models, …])	Serialize datasets to YAML and FITS files.

Attributes Documentation

energy_axes_are_aligned

Whether all contained datasets have aligned energy axis

energy_ranges

Get global energy range of datasets.

The energy range is derived as the minimum / maximum of the energy ranges of all datasets.

Returns

energy_min, energy_maxQuantity

Energy range.

gti

GTI table

is_all_same_energy_shape

Whether all contained datasets have the same data shape

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

meta_table

Meta table

models

Unique models (Models).

Duplicate model objects have been removed. The order of the unique models remains.

names

parameters

Unique parameters (Parameters).

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

Methods Documentation

append(value)

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

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

copy()

A deep copy.

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

extend(values)

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

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.

info_table(cumulative=False, region=None)

Get info table for datasets.

Parameters

cumulativebool

Cumulate info across all observations

Returns

info_tableTable

Info table.

insert(idx, dataset)

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

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, filename_models=None, lazy=True, cache=True)

De-serialize datasets from YAML and FITS files.

Parameters

filenamestr or Path

File path or name of datasets yaml file

filename_modelsstr or Path

File path or name of models fyaml ile

lazybool

Whether to lazy load data into memory

cachebool

Whether to cache the data after loading.

Returns

datasetgammapy.datasets.Datasets

Datasets

remove(value)

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

reverse()

S.reverse() – reverse IN PLACE

select_time(t_min, t_max, atol='1e-6 s')

Select datasets in a given time interval.

Parameters

t_min, t_maxTime

Time interval

atolQuantity

Tolerance value for time comparison with different scale. Default 1e-6 sec.

Returns

datasetsDatasets

Datasets in the given time interval.

slice_by_energy(energy_min, energy_max)

Select and slice datasets in energy range

Parameters

energy_min, energy_maxQuantity

Energy bounds to compute the flux point for.

Returns

datasetsDatasets

Datasets

stack_reduce(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()

Compute joint likelihood

write(filename, filename_models=None, overwrite=False, write_covariance=True)

Serialize datasets to YAML and FITS files.

Parameters

filenamestr or Path

File path or name of datasets yaml file

filename_modelsstr or Path

File path or name of models fyaml ile

overwritebool

overwrite datasets FITS files

write_covariancebool

save covariance or not