yaw.RedshiftData#

class yaw.RedshiftData(binning: Binning, data: ArrayLike, samples: ArrayLike)[source]#

Bases: CorrData

Container for a clustering redshift estimate.

Implements conveniences methods to estimate the standard error, covariance and correlation matrix, normalisation, and plotting methods. Additionally implements len(), comparison with the == operator and addition with +/-.

Parameters:

binning – The redshift Binning applied to the data.
data – Array containing the values in each of the N redshift bin.
samples – 2-dim array containing M jackknife samples of the data, expected to have shape (num_samples, num_bins).

Methods

`__init__`(binning, data, samples)
`from_corrdata`(cross_data[, ref_data, unk_data])	Compute a redshift estimate from a set of cross- and autocorrelation functions.
`from_corrfuncs`(cross_corr[, ref_corr, unk_corr])	Compute a redshift estimate from a set of cross- and autocorrelation pair counts.
`from_files`(path_prefix)	Restore the class instance from a set of ASCII files.
`is_compatible`(other, *[, require])	Checks if two containers have compatible binning and number of jackknife samples.
`normalised`([target])	Attempts to normalise the redshift estimate to a probability density.
`plot`(*[, color, label, style, ax, xoffset, ...])	Plot the data with its uncertainty against the redshift bin centers.
`plot_corr`(*[, redshift, cmap, ax])	Plot the correlation matrix of the data.
`to_files`(path_prefix)	Serialise the class instance into a set of ASCII files.

Attributes

`binning`	Accessor for the redshift `Binning` attribute.
`data`	Array containing the values in each of the N redshift bins.
`samples`	2-dim array containing M jackknife samples of the data, expected to have shape (M, N).
`bins`	Indexing helper to create a new instance from a subset of patches.
`correlation`	Correlation matrix computed from the Gaussian covariance matrix with shape (N, N).
`covariance`	Gaussian covariance matrix estimated from the jackknife samples with shape (N, N).
`error`	Standard error estimated from the jackknife samples.
`num_bins`	The number of redshift bins.
`num_samples`	The number of jackknife samples.

binning: Binning#: Accessor for the redshift Binning attribute.

property bins: Indexer[TypeSliceIndex, Self]#

Indexing helper to create a new instance from a subset of patches.

The indexer supports indexing, slicing, and iteration over individual patches.

Caution

Indixing a non-contiguous subset of bins will result in expanding the previous bin to encompass all omitted bins, e.g. selecting the first and third bin of (0, 1], (1, 2], (2, 3] will result in a binning with edges (0, 2], (2, 3].

Slicing is unaffected since it always results in a contiguous subset of bins.

property correlation: NDArray#: Correlation matrix computed from the Gaussian covariance matrix with shape (N, N).

property covariance: NDArray#: Gaussian covariance matrix estimated from the jackknife samples with shape (N, N).

data: NDArray#: Array containing the values in each of the N redshift bins.

property error: NDArray#: Standard error estimated from the jackknife samples.

classmethod from_corrdata(cross_data: CorrData, ref_data: CorrData | None = None, unk_data: CorrData | None = None) → Self[source]#

Compute a redshift estimate from a set of cross- and autocorrelation functions.

Computes the clustering redshift estimate with optional correction for galaxy sample biases:

\[n(z) = \frac{w_{sp}(z)}{\sqrt{\Delta z^2 \, w_{ss}(z) \, w_{pp}(z)}}\]

Parameters:

cross_data – The cross-correlation function amplitude (\(w_{sp}\)), must be a CorrData instance.

Keyword Arguments:

ref_data – Optional autocorrelation function amplitude of the reference sample (\(w_{ss}\)), must be a CorrData nstance.
unk_data – Optional autocorrelation function amplitude of the unknown sample (\(w_{pp}\)), must be a CorrData instance. Typically unknown quantity.

Returns:

The redshift estimate as RedshiftData.

classmethod from_corrfuncs(cross_corr: CorrFunc, ref_corr: CorrFunc | None = None, unk_corr: CorrFunc | None = None) → Self[source]#

Compute a redshift estimate from a set of cross- and autocorrelation pair counts.

Computes the correlation functions from the input pair counts and calls from_corrdata().

Parameters:

cross_corr – The cross-correlation function pair counts (\(w_{sp}\)), must be a CorrFunc instance.

Keyword Arguments:

ref_corr – Optional autocorrelation function pair counts of the reference sample (\(w_{ss}\)), must be a CorrFunc instance.
unk_corr – Optional autocorrelation function pair counts of the unknown sample (\(w_{pp}\)), must be a CorrFunc instance. Typically an unknown quantity.

Returns:

The redshift estimate as RedshiftData.

classmethod from_files(path_prefix: Path | str) → Self#

Restore the class instance from a set of ASCII files.

Parameters:: path_prefix – A path (str or pathlib.Path) prefix used as [path_prefix].{dat,smp,cov}, pointing to the ASCII files to restore from, see also to_files().

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

Checks if two containers have compatible binning and number of jackknife samples.

Parameters:

other – Another instance of this class to compare to, returns False if instance types do not match.

Keyword Arguments:

require – Whether to raise exceptions if any of the checks fail.

Returns:

Whether the containers have identical binning and number of jackknife samples if require=False.

Raises:

TypeError – If require=True and type of other does match this class.
ValueError – If require=True the binning or number of samples is not identical.

normalised(target: TypeCorrData | None = None) → Self[source]#

Attempts to normalise the redshift estimate to a probability density.

By default rescales data and jackknife samples by computing a normalisation factor obtained from integrating the data over the redshift range of the binning. Alternatively, the normalisation may be optained by fitting to another data container to achieve a relative normalisation.

Warning

Both approaches are inaccuarte due to noise fluctions (in particular negative correlation amplitudes).

Keyword Arguments:: target – Optional, when provided used as reference to fit the normalisation factor, must be a subclass of CorrData.
Returns:: A new instance with a normalisation factor applied to the data and jackknife samples.

property num_bins: int#: The number of redshift bins.

property num_samples: int#: The number of jackknife samples.

Plot the data with its uncertainty against the redshift bin centers.

Keyword Arguments:

color – Matplotlib color to use for the line or error bars.
label – Matplotlib-compatible label for plot.
style –
Plotting style, must be either of
- point: point with error bar,
- line: line with transparent shade marking the errors, or
- step: same as line, but using a step-plot to emulate a histgram-style visualisation.
ax – Matplotlib axis to plot onto.
xoffset – Offset to apply to the redshift bin centers.
plot_kwargs – Keyword arguments passed on to the primary matplotlib plotting function (plot, errorbar, stairs).
indicate_zero – Whether to draw a thin black line at y=0.
scale_dz – Whether to scale the data and uncertainty by the inverse of the redshift bin width.

plot_corr(*, redshift: bool = False, cmap: str = 'RdBu_r', ax: Axis | None = None) → Axis#

Plot the correlation matrix of the data.

Keyword Arguments:

redshift – Whether to plot the correlation on a axes scales by redshift instead of bin indices.
cmap – Name or other matplotlib-compatible color map for the matrix elements.
ax – Matplotlib axis to plot onto.

samples: NDArray#: 2-dim array containing M jackknife samples of the data, expected to have shape (M, N).

to_files(path_prefix: Path | str) → None#

Serialise the class instance into a set of ASCII files.

This method creates three files, which are all readable with numpy.loadtxt:

[path_prefix].dat: File with header and four columns, the left and right redshift bin edges, data, and errors.
[path_prefix].smp: File containing the jackknife samples. The first two columns are the left and right redshift bin edges, the remaining columns each represent one jackknife sample.
[path_prefix].cov: File storing the covariance matrix.

Parameters:: path_prefix – A path (str or pathlib.Path) prefix [path_prefix].{dat,smp,cov} pointing to the ASCII files to serialise into, see also from_files().