API Reference

This page provides an auto-generated summary of climpred’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.


compute_hindcast(hind, reference[, metric, …]) Compute a predictability skill score against a reference
compute_perfect_model(ds, control[, metric, …]) Compute a predictability skill score for a perfect-model framework simulation dataset.
compute_persistence(hind, reference[, …]) Computes the skill of a persistence forecast from a simulation.
compute_uninitialized(uninit, reference[, …]) Compute a predictability score between an uninitialized ensemble and a reference.


bootstrap_compute(hind, reference[, hist, …]) Bootstrap compute with replacement.
bootstrap_hindcast(hind, hist, reference[, …]) Bootstrap compute with replacement. Wrapper of
bootstrap_perfect_model(ds, control[, …]) Bootstrap compute with replacement. Wrapper of
bootstrap_uninit_pm_ensemble_from_control(ds, …) Create a pseudo-ensemble from control run.
bootstrap_uninitialized_ensemble(hind, hist) Resample uninitialized hindcast from historical members.
dpp_threshold(control[, sig, bootstrap, dim]) Calc DPP significance levels from re-sampled dataset.
varweighted_mean_period_threshold(control[, …]) Calc the variance-weighted mean period significance levels from re-sampled dataset.


autocorr(ds[, lag, dim, return_p]) Calculate the lagged correlation of time series.
corr(x, y[, dim, lag, return_p]) Computes the Pearson product-moment coefficient of linear correlation.
decorrelation_time(da[, r, dim]) Calculate the decorrelaton time of a time series.
dpp(ds[, m, chunk]) Calculates the Diagnostic Potential Predictability (dpp)
rm_poly(ds, order[, dim]) Returns xarray object with nth-order fit removed.
rm_trend(da[, dim]) Remove linear trend from time series.
varweighted_mean_period(ds[, time_dim]) Calculate the variance weighted mean period of time series.


load_dataset([name, cache, cache_dir, …]) Load example data or a mask from an online repository.