d6acc5e620a5c566b3421b1c8bf2a66a064ca5bd,scanpy/preprocessing/simple.py,,normalize_per_cell_weinreb16_deprecated,#Any#Any#Any#,779

Before Change

    if max_fraction < 0 or max_fraction > 1:
        raise ValueError("Choose max_fraction between 0 and 1.")
    counts_per_cell = np.sum(X, axis=1)
    if max_fraction == 1:
        X_norm = X / counts_per_cell[:, np.newaxis]
        return X_norm
    // restrict computation of counts to genes that make up less than
    // constrain_theshold of the total reads
    tc_tiled = np.tile(counts_per_cell[:, np.newaxis], (1, X.shape[1]))
    included = np.all(X <= tc_tiled * max_fraction, axis=0)
    tc_include = np.sum(X[:, included], axis=1)
    tc_tiled = np.tile(tc_include[:, np.newaxis], (1, X.shape[1])) + 1e-6
    X_norm = X / tc_tiled
    if mult_with_mean:
        X_norm *= np.mean(counts_per_cell)
    return X_norm

After Change

    if max_fraction < 0 or max_fraction > 1:
        raise ValueError("Choose max_fraction between 0 and 1.")
        
    counts_per_cell = X.sum(1).A1 if issparse(X) else X.sum(1)
    gene_subset = np.all(X <= counts_per_cell[:, None] * max_fraction, axis=0)
    if issparse(X): gene_subset = gene_subset.A1
    tc_include = X[:, included].sum(1).A1 if issparse(X) else X[:, included].sum(1)

Instances