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.
climpred.classes
A primary feature of climpred is our prediction ensemble objects, :py~climpred.classes.HindcastEnsemble and :py~climpred.classes.PerfectModelEnsemble. Users can append their initialized ensemble to these classes, as well as an arbitrary number of verification products (assimilations, reconstructions, observations), control runs, and uninitialized ensembles.
A HindcastEnsemble is a prediction ensemble that is initialized off of some form of observations (an assimilation, renanalysis, etc.). Thus, it is anticipated that forecasts are verified against observation-like products. Read more about the terminology here.
HindcastEnsemble
HindcastEnsemble.__init__ HindcastEnsemble.add_observations HindcastEnsemble.add_uninitialized HindcastEnsemble.get_initialized HindcastEnsemble.get_observations HindcastEnsemble.get_uninitialized
HindcastEnsemble.verify HindcastEnsemble.compute_persistence HindcastEnsemble.compute_uninitialized
HindcastEnsemble.smooth
A PerfectModelEnsemble is a prediction ensemble that is initialized off of a control simulation for a number of randomly chosen initialization dates. Thus, forecasts cannot be verified against real-world observations. Instead, they are compared to one another and to the original control run. Read more about the terminology here.
PerfectModelEnsemble
PerfectModelEnsemble.__init__ PerfectModelEnsemble.add_control PerfectModelEnsemble.get_initialized PerfectModelEnsemble.get_control PerfectModelEnsemble.get_uninitialized
PerfectModelEnsemble.bootstrap PerfectModelEnsemble.compute_metric PerfectModelEnsemble.compute_persistence PerfectModelEnsemble.compute_uninitialized
PerfectModelEnsemble.generate_uninitialized
A user can directly call functions in climpred. This requires entering more arguments, e.g. the initialized ensemble :py~xarray.core.dataset.Dataset/:pyxarray.core.dataarray.DataArray directly as well as a verification product. Our object :py~climpred.classes.HindcastEnsemble and :py~climpred.classes.PerfectModelEnsemble wrap most of these functions, making the analysis process much simpler. Once we have wrapped all of the functions in their entirety, we will likely depricate the ability to call them directly.
climpred.bootstrap
bootstrap_compute bootstrap_hindcast bootstrap_perfect_model bootstrap_uninit_pm_ensemble_from_control_cftime bootstrap_uninitialized_ensemble dpp_threshold varweighted_mean_period_threshold
climpred.prediction
compute_hindcast compute_perfect_model
climpred.reference
compute_persistence compute_uninitialized
climpred.metrics
Metric _get_norm_factor
climpred.comparisons
Comparison
climpred.stats
autocorr corr decorrelation_time dpp rm_poly rm_trend varweighted_mean_period
climpred.tutorial
load_dataset
climpred.preprocessing.shared
load_hindcast rename_to_climpred_dims rename_SLM_to_climpred_dims
climpred.preprocessing.mpi
get_path