remove deprecated correct_analytical_cov_eigenspectrum_ratio_gp

pspipe_utils/covariance.py

@@ -368,92 +368,6 @@ def skew(cov, dir=1):
         return corrected_cov
 
 
-
-def correct_analytical_cov_eigenspectrum_ratio_gp(lb, an_full_cov, mc_full_cov,
-                                                  var_eigenspectrum_ratios_by_block=None,
-                                                  idx_arrs_by_block=None, return_all=False):
-    """Correct an analytical covariance matrix with a monte carlo covariance
-    matrix. Assumes the following rotated monte carlo matrix is diagonal:
-
-    mc_rot = (ana**-.5) @ mc @ (ana**-.5).T
-
-    and then fits the diagonal in that basis with a Gaussian process (GP) on
-    the observed values:
-
-    ana_corrected = (ana**.5) @ np.diag(GP(np.diag(mc_rot))) @ (ana**.5).T
-
-    The GP is applied to each block diagonal separately.
-
-    Parameters
-    ----------
-    lb : (nbin,) np.ndarray
-        The locations in ell of the bins.
-    an_full_cov : (nblock*nbin, nblock*nbin) np.ndarray
-        Analytic covariance matrix to be corrected.
-    mc_full_cov : (nblock*nbin, nblock*nbin) np.ndarray
-        Noisy monte carlo matrix to use to correct the analytic matrix.
-    var_eigenspectrum_ratios_by_block : (nblock, nbin) np.ndarray, optional
-        The variance of the diagonal elements of mc_rot, by default None. These
-        would need to be precomputed in the rotated basis. If None, the
-        noise level in the diagonal elements of mc_rot is estimated from the  
-        elements themselves by the Gaussian process.
-    idx_arrs_by_block : (nblock,) list, optional
-        A list of np.ndarrays, each of which may have between 0 and nbin elements,
-        that gives for each block the bin indices that should actually be used
-        in the Gaussian Process, by default None. If the list is None, or if any
-        element of the list is None, all elements for all blocks (or for the 
-        specific block) are used in the Gaussian process. If an element of the list
-        is an empty array, then no Gaussian process is run on that block; its 
-        observed values are used without any smoothing applied to them.
-    return_all : bool, optional
-        If True, in addition to returning ana_corrected, also return the
-        diagonal of the mc_rot matrix, the smoothed diagonal, and a list
-        of the Gaussian process regression objects for each block,
-        by default False.
-
-    Returns
-    -------
-    (nblock*nbin, nblock*nbin) np.ndarray, {(nblock*nbin,) np.ndarray, 
-    (nblock*nbin,) np.ndarray, (nblock,) list of sklearn.GaussianProcessRegressor
-    objects}
-        ana_corrected as above, and if return_all, np.diag(mc_rot),
-        GP(np.diag(mc_rot)), and a list of the GPs for each block. If for any
-        block the idx_arrs_by_block array is empty, the returned GP is None 
-        since no GP was actually used for that block.
-    """
-    sqrt_an_full_cov = utils.eigpow(an_full_cov, 0.5)
-    inv_sqrt_an_full_cov = np.linalg.inv(sqrt_an_full_cov)
-    res = inv_sqrt_an_full_cov @ mc_full_cov @ inv_sqrt_an_full_cov.T # res should be close to the identity if an_full_cov is good
-    res_diag = np.diag(res)
-
-    n_spec = len(res_diag) // len(lb)
-
-    res_diags = np.split(res_diag, n_spec)
-
-    if var_eigenspectrum_ratios_by_block is None:
-        var_eigenspectrum_ratios_by_block = [None] * n_spec
-
-    if idx_arrs_by_block is None:
-        idx_arrs_by_block = [None] * n_spec
-
-    smoothed_res_diags = []
-    gprs = []
-    for i in range(n_spec):
-        smoothed_gp_diag, gpr = smooth_gp_diag(lb, res_diags[i], var_eigenspectrum_ratios_by_block[i],
-                                               idx_arr=idx_arrs_by_block[i], return_gpr=True)
-        smoothed_res_diags.append(smoothed_gp_diag)
-        gprs.append(gpr)
-
-    smoothed_res_diag = np.hstack(smoothed_res_diags)
-
-    corrected_cov = sqrt_an_full_cov @ np.diag(smoothed_res_diag) @ sqrt_an_full_cov.T
-
-    if return_all:
-        return corrected_cov, res_diag, smoothed_res_diag, gprs
-    else:
-        return corrected_cov
-
-
 def correct_analytical_cov_block_diag_gp(lb, an_full_cov, mc_full_cov,
                                          var_mc_cov_anaflat=None,
                                          idx_arrs_by_block=None, return_all=False):