xskillscore.discrimination¶
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xskillscore.discrimination(observations, forecasts, dim=None, probability_bin_edges=array([0.0, 0.2, 0.4, 0.60000001, 0.80000001, 1.00000001]))¶ Returns the data required to construct the discrimination diagram for an event; the histogram of forecasts likelihood when observations indicate an event has occurred and has not occurred.
- Parameters
observations (xarray.Dataset or xarray.DataArray) – The observations or set of observations of the event. Data should be boolean or logical (True or 1 for event occurance, False or 0 for non-occurance).
forecasts (xarray.Dataset or xarray.DataArray) – The forecast likelihoods of the event. Data should be between 0 and 1.
dim (str or list of str, optional) – Dimension(s) over which to compute the histograms Defaults to None meaning compute over all dimensions.
probability_bin_edges (array_like, optional) – Probability bin edges used to compute the histograms. Bins include the left most edge, but not the right. Defaults to 6 equally spaced edges between 0 and 1+1e-8
- Returns
Array with added dimension “event” containing the histograms of forecast probabilities when the event was observed and not observed
- Return type
Examples
>>> observations = xr.DataArray(np.random.normal(size=(30,30)), ... coords=[('x', np.arange(30)), ... ('y', np.arange(30))]) >>> forecasts = xr.DataArray(np.random.normal(size=(30,30,10)), ... coords=[('x', np.arange(30)), ... ('y', np.arange(30)), ... ('member', np.arange(10))]) >>> forecast_event_likelihood = (forecasts > 0).mean('member') >>> observed_event = observations > 0 >>> disc = discrimination(observed_event, forecast_event_likelihood, dim=['x','y'])
Notes