pysteps.verification

Methods for verification of deterministic, probabilistic and ensemble forecasts.

pysteps.verification.interface

Interface for the verification module.

get_method(name[, type])

Return a callable function for the method corresponding to the given verification score.

pysteps.verification.detcatscores

Forecast evaluation and skill scores for deterministic categorial (dichotomous) forecasts.

det_cat_fct(pred, obs, thr[, scores, axis])	Calculate simple and skill scores for deterministic categorical (dichotomous) forecasts.
det_cat_fct_init(thr[, axis])	Initialize a contingency table object.
det_cat_fct_accum(contab, pred, obs)	Accumulate the frequency of “yes” and “no” forecasts and observations in the contingency table.
det_cat_fct_compute(contab[, scores])	Compute simple and skill scores for deterministic categorical (dichotomous) forecasts from a contingency table object.

pysteps.verification.detcontscores

Forecast evaluation and skill scores for deterministic continuous forecasts.

det_cont_fct(pred, obs[, scores, axis, …])	Calculate simple and skill scores for deterministic continuous forecasts.
det_cont_fct_init([axis, conditioning])	Initialize a verification error object.
det_cont_fct_accum(err, pred, obs)	Accumulate the forecast error in the verification error object.
det_cont_fct_compute(err[, scores])	Compute simple and skill scores for deterministic continuous forecasts from a verification error object.

pysteps.verification.ensscores

Evaluation and skill scores for ensemble forecasts.

ensemble_skill(X_f, X_o, metric, \*\*kwargs)	Compute mean ensemble skill for a given skill metric.
ensemble_spread(X_f, metric, \*\*kwargs)	Compute mean ensemble spread for a given skill metric.
rankhist(X_f, X_o[, X_min, normalize])	Compute a rank histogram counts and optionally normalize the histogram.
rankhist_init(num_ens_members[, X_min])	Initialize a rank histogram object.
rankhist_accum(rankhist, X_f, X_o)	Accumulate forecast-observation pairs to the given rank histogram.
rankhist_compute(rankhist[, normalize])	Return the rank histogram counts and optionally normalize the histogram.

pysteps.verification.lifetime

Estimation of precipitation lifetime from a decaying verification score function (e.g. autocorrelation function).

lifetime(X_s, X_t[, rule])	Compute the average lifetime by integrating the correlation function as a function of lead time.
lifetime_init([rule])	Initialize a lifetime object.
lifetime_accum(lifetime, X_s, X_t)	Compute the lifetime by integrating the correlation function and accumulate the result into the given lifetime object.
lifetime_compute(lifetime)	Compute the average value from the lifetime object.

pysteps.verification.plots

Methods for plotting verification results.

plot_intensityscale(iss[, fig, vmin, vmax, …])	Plot a intensity-scale verification table with a color bar and axis labels.
plot_rankhist(rankhist[, ax])	Plot a rank histogram.
plot_reldiag(reldiag[, ax])	Plot a reliability diagram.
plot_ROC(ROC[, ax, opt_prob_thr])	Plot a ROC curve.

pysteps.verification.probscores

Evaluation and skill scores for probabilistic forecasts.

CRPS(X_f, X_o)	Compute the continuous ranked probability score (CRPS).
CRPS_init()	Initialize a CRPS object.
CRPS_accum(CRPS, X_f, X_o)	Compute the average continuous ranked probability score (CRPS) for a set of forecast ensembles and the corresponding observations and accumulate the result to the given CRPS object.
CRPS_compute(CRPS)	Compute the averaged values from the given CRPS object.
reldiag(P_f, X_o, X_min[, n_bins, min_count])	Compute the x- and y- coordinates of the points in the reliability diagram.
reldiag_init(X_min[, n_bins, min_count])	Initialize a reliability diagram object.
reldiag_accum(reldiag, P_f, X_o)	Accumulate the given probability-observation pairs into the reliability diagram.
reldiag_compute(reldiag)	Compute the x- and y- coordinates of the points in the reliability diagram.
ROC_curve(P_f, X_o, X_min[, n_prob_thrs, …])	Compute the ROC curve and its area from the given ROC object.
ROC_curve_init(X_min[, n_prob_thrs])	Initialize a ROC curve object.
ROC_curve_accum(ROC, P_f, X_o)	Accumulate the given probability-observation pairs into the given ROC object.
ROC_curve_compute(ROC[, compute_area])	Compute the ROC curve and its area from the given ROC object.

pysteps.verification.spatialscores

Skill scores for spatial forecasts.

intensity_scale(X_f, X_o, name, thrs[, …])	Compute an intensity-scale verification score.
intensity_scale_init(name, thrs[, scales, …])	Initialize an intensty-scale verification object.
intensity_scale_accum(intscale, X_f, X_o)	Compute and update the intensity-scale verification scores.
intensity_scale_compute(intscale)	Return the intensity scale matrix.
binary_mse(X_f, X_o, thr[, wavelet])	Compute an intensity-scale verification as the MSE of the binary error.
fss(X_f, X_o, thr, scale)	Compute the fractions skill score (FSS) for a deterministic forecast field and the corresponding observation.