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