Source code for proteopy.utils.array

import numpy as np
from scipy import sparse


def _is_log_transformed_array(
    X,
    neg_frac_thresh=5e-3,
    p95_thresh=100.0,
):
    """Heuristic log-transform check on a dense float array.

    Parameters
    ----------
    X : np.ndarray
        Dense 2-D float matrix. Non-finite values are ignored.
    neg_frac_thresh : float
        Fraction of negative values above which the data are
        considered log-transformed.
    p95_thresh : float
        If the 95th percentile is at or below this value the
        data are considered log-transformed.

    Returns
    -------
    is_log : bool
        ``True`` if the matrix looks log-transformed.
    stats : dict
        Diagnostic statistics used for the decision.
    """
    finite = np.isfinite(X)
    vals = X[finite]
    if vals.size == 0:
        raise ValueError("No finite values found.")

    frac_negative = float(np.mean(vals < 0))
    p95 = float(np.nanpercentile(vals, 95))
    p5 = float(np.nanpercentile(vals, 5))
    dr_ratio = float(
        (p95 - p5) / max(abs(p5), 1e-12)
    )

    is_log = (
        frac_negative >= neg_frac_thresh
        or p95 <= p95_thresh
    )

    stats = dict(
        frac_negative=frac_negative,
        p95=p95,
        p5=p5,
        dynamic_range_ratio=dr_ratio,
        n_finite=int(vals.size),
    )

    return bool(is_log), stats




def is_log_transformed(
        adata,
        layer=None,
        neg_frac_thresh=5e-3,
        p95_thresh=100.0,
):
    """
    Heuristic detector for log-transformed matrices.

    Returns
    -------
    is_log : bool
        True if the matrix looks log-transformed.
    stats : dict
        {'frac_negative', 'p95', 'p5', 'dynamic_range_ratio',
         'n_finite'}
    """
    Xsrc = (
        adata.layers[layer] if layer is not None
        else adata.X
    )
    X = (
        Xsrc.toarray() if sparse.issparse(Xsrc)
        else np.asarray(Xsrc)
    )
    X = X.astype(float, copy=False)

    return _is_log_transformed_array(
        X,
        neg_frac_thresh=neg_frac_thresh,
        p95_thresh=p95_thresh,
    )