yaw.correlation.paircounts.NormalisedCounts#

class yaw.correlation.paircounts.NormalisedCounts(count: PatchedCount, total: PatchedTotal)[source]#

Bases: PatchedQuantity, BinnedQuantity, HDFSerializable

Container to store counts and the total number of objects obtained from measuring pair counts for a correlation function.

Both input containers must have the same binning and the same number of spatial patches. The container supports the same arithmetic as PatchedCount (see the listed examples), i.e. comparison, addition, multiplication by a scalar, as well as indexing. The resulting pair counts are always normalised by the number of objects stored in total.

Parameters:

count (PatchedCount) – The pair count container.
total (PatchedTotal) – The container for the total number of objects from the samples.

Methods

`__init__`(count, total)
`concatenate_bins`(*pcounts)	Concatenate pair count data containers with equal patches.
`concatenate_patches`(*pcounts)	Concatenate pair count data containers with equal redshift binning.
`from_file`(path)	Create a class instance by deserialising data from a HDF5 file.
`from_hdf`(source)	Create a class instance by deserialising data from a HDF5 group.
`get_binning`()	Get the underlying, exact redshift bin intervals.
`is_compatible`(other[, require])	Check whether this instance is compatible with another instance.
`sample`(config)	Sum the pair counts across all spatial patches and generate samples from resampling the patches.
`to_file`(path)	Serialise the class instance to a new HDF5 file.
`to_hdf`(dest)	Serialise the class instance into an existing HDF5 group.

Attributes

`auto`	Whether the stored data are from an autocorrelation measurement.
`bins`	An `Indexer` attribute that supports iteration over the bins or selecting a subset of the bins.
`closed`	Specifies on which side the redshift bin intervals are closed, can be: `left`, `right`, `both`, `neither`.
`dz`	Get the width of the redshift bins as array.
`edges`	Get the edges of the redshift bins as flat array.
`mids`	Get the centers of the redshift bins as array.
`n_bins`	Get the number of redshift bins.
`n_patches`	Get the number of spatial patches.
`patches`	An `Indexer` attribute that supports iteration over the spatial patches or selecting a subset of the patches.
`count`	The pair count container.
`total`	The container for the total number of objects from the samples.

property auto: bool#: Whether the stored data are from an autocorrelation measurement.

property bins: Indexer[int | slice | Sequence, NormalisedCounts]#

An Indexer attribute that supports iteration over the bins or selecting a subset of the bins.

The indexer always returns new container instances with the indexed data subset or the current item when iterating.

Warning

Indexing rules for a one-dimensional numpy array apply, however if the resulting binning is not contiguous or contains repeated bins, some operations on the returned container may fail.

Returns:: yaw.core.containers.Indexer

property closed: str#: Specifies on which side the redshift bin intervals are closed, can be: left, right, both, neither.

concatenate_bins(*pcounts: NormalisedCounts) → NormalisedCounts[source]#

Concatenate pair count data containers with equal patches.

The data is merged by appending the data along the redshift binning axis.

Note

Necessary condition for merging is that the patch numbers are identical and that the merged binning is contiguous and non-overlapping. Cannot merge cross- with autocorrelation containers.

Parameters:: *data – Containers of same type that are appended to the patch dimension of this container.
Returns:: New instance of this container with combined data.

concatenate_patches(*pcounts: NormalisedCounts) → NormalisedCounts[source]#

Concatenate pair count data containers with equal redshift binning.

The data is merged by extending the dimension of the patch axes. The resulting data array will be a block matrix of the input data arrays, i.e. all elements with correlations between different inputs set to zero.

Note

Necessary condition for merging is that the the redshift binning of all inputs is identical. Cannot merge cross- with autocorrelation containers.

Parameters:: *data – Containers of same type that are appended to the patch dimension of this container.
Returns:: New instance of this container with combined data.

count: PatchedCount#: The pair count container.

property dz: ndarray[Any, dtype[float64]]#: Get the width of the redshift bins as array.

property edges: ndarray[Any, dtype[float64]]#: Get the edges of the redshift bins as flat array.

classmethod from_file(path: TypePathStr) → _Thdf#

Create a class instance by deserialising data from a HDF5 file.

Parameters:: path (pathlib.Path, str) – Group in an opened HDF5 file that contains the necessary data.
Returns:: HDFSerializable

classmethod from_hdf(source: Group) → NormalisedCounts[source]#

Create a class instance by deserialising data from a HDF5 group.

Parameters:: source (h5py.Group) – Group in an opened HDF5 file that contains the serialised data.
Returns:: HDFSerializablep

get_binning() → IntervalIndex[source]#

Get the underlying, exact redshift bin intervals.

Returns:: pandas.IntervalIndex

is_compatible(other: _Tbinned, require: bool = False) → bool#

Check whether this instance is compatible with another instance.

Ensures that both objects are instances of the same class and that the redshift binning is identical.

Parameters:

other (BinnedQuantity) – Object instance to compare to.
require (bool, optional) – Raise a ValueError if any of the checks fail.

Returns:

bool

property mids: ndarray[Any, dtype[float64]]#: Get the centers of the redshift bins as array.

property n_bins: int#: Get the number of redshift bins.

property n_patches: int#: Get the number of spatial patches.

property patches: Indexer[int | slice | Sequence, NormalisedCounts]#

An Indexer attribute that supports iteration over the spatial patches or selecting a subset of the patches.

The indexer always returns new container instances with the indexed data subset or the current item when iterating.

Note

Indexing rules for a one-dimensional numpy array apply.

Returns:: yaw.core.containers.Indexer

sample(config: ResamplingConfig) → SampledData[source]#

Sum the pair counts across all spatial patches and generate samples from resampling the patches.

Calls the sample_sum() method of the counts and total containers. Returns a SampledData instance that contains the normalised pair counts and spatially resampled values. Effectively computes \(\frac{XX}{n_1 n_2}\), where :math:XX` are the number of pairs between the two samples with a total number of objects \(n_1\) and \(n_2\) respectively.

Parameters:: config (ResamplingConfig) – Specifies the resampling method and its customisation parameters.
Returns:: SampledData

to_file(path: TypePathStr) → None#

Serialise the class instance to a new HDF5 file.

Parameters:: path (pathlib.Path, str) – Path at which the HDF5 file is created.

to_hdf(dest: Group) → None[source]#

Serialise the class instance into an existing HDF5 group.

Parameters:: dest (h5py.Group) – Group in which the serialised data structures are created.

total: PatchedTotal#: The container for the total number of objects from the samples.