Datasets#

class gammapy.datasets.Datasets(datasets=None)[source]#

Bases: collections.abc.MutableSequence

Dataset collection.

Parameters

datasetsDataset or list of Dataset: Datasets

Attributes Summary

`contributes_to_stat`	Stat contributions
`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])	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`(time_min, time_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, nan_to_num])	Reduce the Datasets to a unique Dataset by stacking them together.
`stat_sum`()	Compute joint likelihood
`to_spectrum_datasets`(region)	Extract spectrum datasets for the given region.
`write`(filename[, filename_models, ...])	Serialize datasets to YAML and FITS files.

Attributes Documentation

contributes_to_stat#

Stat contributions

Returns

contributionsarray: Array indicating which dataset contributes to the likelihood.

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()[source]#: 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.[source]#

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

info_table(cumulative=False)[source]#

Get info table for datasets.

Parameters

cumulativebool: Cumulate info across all observations

Returns

info_tableTable: Info table.

insert(idx, dataset)[source]#: 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)[source]#

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(time_min, time_max, atol='1e-6 s')[source]#

Select datasets in a given time interval.

Parameters

time_min, time_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)[source]#

Select and slice datasets in energy range

The method keeps the current dataset names. Datasets, that do not contribute to the selected energy range are dismissed.

Parameters

energy_min, energy_maxQuantity: Energy bounds to compute the flux point for.

Returns

datasetsDatasets: Datasets

stack_reduce(name=None, nan_to_num=True)[source]#

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.

Parameters

namestr: Name of the stacked dataset.
nan_to_num: bool: Non-finite values are replaced by zero if True (default).

Returns

datasetDataset: the stacked dataset

stat_sum()[source]#: Compute joint likelihood

to_spectrum_datasets(region)[source]#

Extract spectrum datasets for the given region.

Parameters

regionSkyRegion: Region definition.

Returns

datasetsDatasets: List of SpectrumDataset

write(filename, filename_models=None, overwrite=False, write_covariance=True)[source]#

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 yaml file
overwritebool: overwrite datasets FITS files
write_covariancebool: save covariance or not