def ROC_curve_init(X_min, n_prob_thrs=10):
    """
    Initialize a ROC curve object.

    Parameters
    ----------
    X_min: float
      Precipitation intensity threshold for yes/no prediction.
    n_prob_thrs: int
      The number of probability thresholds to use.
      The interval [0,1] is divided into n_prob_thrs evenly spaced values.

    Returns
    -------
    out: dict
      The ROC curve object.
    """
    ROC = {}

    ROC["X_min"] = X_min
    ROC["hits"] = np.zeros(n_prob_thrs, dtype=int)
    ROC["misses"] = np.zeros(n_prob_thrs, dtype=int)
    ROC["false_alarms"] = np.zeros(n_prob_thrs, dtype=int)
    ROC["corr_neg"] = np.zeros(n_prob_thrs, dtype=int)
    ROC["prob_thrs"] = np.linspace(0.0, 1.0, int(n_prob_thrs))

    return ROC
Initialize a ROC curve object.

Parameters
----------
X_min: float
Precipitation intensity threshold for yes/no prediction.
n_prob_thrs: int
The number of probability thresholds to use.
The interval [0,1] is divided into n_prob_thrs evenly spaced values.

Returns
-------
out: dict
The ROC curve object.
pysteps/verification/probscores.py