Fraction Scan

puma.fraction_scan.get_fx_values

Source code in puma/fraction_scan.py

def get_fx_values(resolution=100):
    return np.concatenate((
        np.logspace(-3, -1, resolution // 2),
        np.linspace(0.1, 1.0, resolution // 2),
    ))

puma.fraction_scan.get_efficiency

Source code in puma/fraction_scan.py

def get_efficiency(scores, fx):
    return np.sum(scores > fx) / len(scores)

puma.fraction_scan.get_optimal_fc

After calculating an fc scan, find the optimal value of fc.

Parameters:

Name	Type	Description	Default
fc_scan	numpy.ndarray	2D array of efficiency (or rejection) scores for each value of fc.	required
fc_space	numpy.ndarray	1D array of fc values.	required
rej	bool	If True, find the maximum rejection values else find the minimum efficiency values, by default False.	False

Source code in puma/fraction_scan.py

def get_optimal_fc(fc_scan: np.ndarray, fc_space: np.ndarray, rej=False):
    """After calculating an fc scan, find the
    optimal value of fc.


    Parameters
    ----------
    fc_scan : np.ndarray
        2D array of efficiency (or rejection) scores
        for each value of fc.
    fc_space : np.ndarray
        1D array of fc values.
    rej : bool, optional
        If True, find the maximum rejection values else
        find the minimum efficiency values, by default False.
    """
    # normalise x- and y-axes
    xs, ys = fc_scan[:, 0], fc_scan[:, 1]
    xs = xs / max(xs)
    ys = ys / max(ys)

    # if rej=True get maximum distance to origin
    opt_idx = np.argmax(xs**2 + ys**2) if rej else np.argmin(xs**2 + ys**2)

    return opt_idx, fc_space[opt_idx]