sp_validation.statistics

STATISTICS.

Name:

statistics.py

Description:

Cosmology-independent statistical helpers (jackknife resampling, chi2/PTE, covariance<->correlation, OneCovariance reshaping). Extracted verbatim from the former basic.py.

jackknif_weighted_average2(data, weights, remove_size=0.1, n_realization=100)[source]

Add docstring.

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corr_from_cov(cov)[source]

Correlation matrix from a covariance matrix.

Parameters:

cov (numpy.ndarray) – Covariance matrix.

Returns:

Correlation matrix.

Return type:

numpy.ndarray

chi2_and_pte(data_vector, cov, verbose=False)[source]

Chi-squared, reduced chi-squared and PTE for a data vector.

The data vector is assumed to be zero-mean under the null hypothesis, so chi2 = d^T C^-1 d.

Parameters:

  • data_vector (numpy.ndarray) – Data vector.

  • cov (numpy.ndarray) – Covariance matrix of the data vector.

  • verbose (bool, optional) – If True, print the statistics; default is False.

Returns:

(chi2, reduced_chi2, pte).

Return type:

tuple

cov_from_one_covariance(cov_one_cov, gaussian=True)[source]

Reshape a OneCovariance covariance_list table into a matrix.

Parameters:

  • cov_one_cov (numpy.ndarray) – Flat OneCovariance output (e.g. from covariance_list_..._Cell.dat), with one row per (i, j) element pair.

  • gaussian (bool, optional) – If True use the Gaussian-only column, otherwise the Gaussian+non-Gaussian column; default is True.

Returns:

Square covariance matrix.

Return type:

numpy.ndarray