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_fraction_value

After calculating a fraction scan, find the optimal fraction value.

Parameters:

Name	Type	Description	Default
fraction_scan	numpy.ndarray	2D array of efficiency (or rejection) scores for each fraction value.	required
fraction_space	numpy.ndarray	1D array of fraction 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_fraction_value(
    fraction_scan: np.ndarray,
    fraction_space: np.ndarray,
    rej: bool = False,
):
    """After calculating a fraction scan, find the optimal fraction value.


    Parameters
    ----------
    fraction_scan : np.ndarray
        2D array of efficiency (or rejection) scores for each fraction value.
    fraction_space : np.ndarray
        1D array of fraction 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 = fraction_scan[:, 0], fraction_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, fraction_space[opt_idx]