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. High-Level Classes ------------------ .. currentmodule:: climpred.classes A primary feature of ``climpred`` is our prediction ensemble objects, :py:class:`.HindcastEnsemble` and :py:class:`.PerfectModelEnsemble`. Users can add their initialized ensemble to these classes, as well as verification products (assimilations, reconstructions, observations), control runs, and uninitialized ensembles. PredictionEnsemble ~~~~~~~~~~~~~~~~~~ :py:class:`.PredictionEnsemble` is the base class for :py:class:`.HindcastEnsemble` and :py:class:`.PerfectModelEnsemble`. :py:class:`.PredictionEnsemble` cannot be called directly, but :py:class:`.HindcastEnsemble` and :py:class:`.PerfectModelEnsemble` inherit the common base functionality. .. autosummary:: :toctree: api/ PredictionEnsemble PredictionEnsemble.__init__ ------- Builtin ------- .. autosummary:: :toctree: api/ PredictionEnsemble.__len__ PredictionEnsemble.__iter__ PredictionEnsemble.__delitem__ PredictionEnsemble.__contains__ PredictionEnsemble.__add__ PredictionEnsemble.__sub__ PredictionEnsemble.__mul__ PredictionEnsemble.__truediv__ PredictionEnsemble.__getitem__ PredictionEnsemble.__getattr__ ---------- Properties ---------- .. autosummary:: :toctree: api/ PredictionEnsemble.coords PredictionEnsemble.nbytes PredictionEnsemble.sizes PredictionEnsemble.dims PredictionEnsemble.chunks PredictionEnsemble.chunksizes PredictionEnsemble.data_vars PredictionEnsemble.equals PredictionEnsemble.identical HindcastEnsemble ~~~~~~~~~~~~~~~~ A :py:class:`.HindcastEnsemble` is a prediction ensemble that is initialized off of some form of observations (an assimilation, reanalysis, etc.). Thus, it is anticipated that forecasts are verified against observation-like products. Read more about the terminology `here `_. .. autosummary:: :toctree: api/ HindcastEnsemble HindcastEnsemble.__init__ ------------------------- Add and Retrieve Datasets ------------------------- .. autosummary:: :toctree: api/ HindcastEnsemble.add_observations HindcastEnsemble.add_uninitialized HindcastEnsemble.get_initialized HindcastEnsemble.get_observations HindcastEnsemble.get_uninitialized ------------------ Analysis Functions ------------------ .. autosummary:: :toctree: api/ HindcastEnsemble.verify HindcastEnsemble.bootstrap ------------- Generate Data ------------- .. autosummary:: :toctree: api/ HindcastEnsemble.generate_uninitialized -------------- Pre-Processing -------------- .. autosummary:: :toctree: api/ HindcastEnsemble.smooth HindcastEnsemble.remove_bias HindcastEnsemble.remove_seasonality ------------- Visualization ------------- .. autosummary:: :toctree: api/ HindcastEnsemble.plot HindcastEnsemble.plot_alignment PerfectModelEnsemble ~~~~~~~~~~~~~~~~~~~~ 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 `_. .. autosummary:: :toctree: api/ PerfectModelEnsemble PerfectModelEnsemble.__init__ ------------------------- Add and Retrieve Datasets ------------------------- .. autosummary:: :toctree: api/ PerfectModelEnsemble.add_control PerfectModelEnsemble.get_initialized PerfectModelEnsemble.get_control PerfectModelEnsemble.get_uninitialized ------------------ Analysis Functions ------------------ .. autosummary:: :toctree: api/ PerfectModelEnsemble.verify PerfectModelEnsemble.bootstrap ------------- Generate Data ------------- .. autosummary:: :toctree: api/ PerfectModelEnsemble.generate_uninitialized -------------- Pre-Processing -------------- .. autosummary:: :toctree: api/ PerfectModelEnsemble.smooth PerfectModelEnsemble.remove_seasonality ------------- Visualization ------------- .. autosummary:: :toctree: api/ PerfectModelEnsemble.plot Direct Function Calls --------------------- While not encouraged anymore, a user can directly call functions in ``climpred``. This requires entering more arguments, e.g. the initialized ensemble directly as well as a verification product. Our object :py:class:`.HindcastEnsemble` and :py:class:`.PerfectModelEnsemble` wrap most of these functions, making the analysis process much simpler. Bootstrap ~~~~~~~~~ .. currentmodule:: climpred.bootstrap .. autosummary:: :toctree: api/ bootstrap_uninit_pm_ensemble_from_control_cftime bootstrap_uninitialized_ensemble resample_skill_exclude_resample_dim_from_dim resample_skill_loop resample_skill_resample_before dpp_threshold varweighted_mean_period_threshold Prediction ~~~~~~~~~~ .. currentmodule:: climpred.prediction .. autosummary:: :toctree: api/ compute_perfect_model Reference ~~~~~~~~~ .. currentmodule:: climpred.reference .. autosummary:: :toctree: api/ compute_persistence compute_persistence_from_first_lead compute_uninitialized compute_climatology Horizon ~~~~~~~ .. currentmodule:: climpred.horizon .. autosummary:: :toctree: api/ horizon Statistics ~~~~~~~~~~ .. currentmodule:: climpred.stats .. autosummary:: :toctree: api/ decorrelation_time dpp varweighted_mean_period rm_poly rm_trend Tutorial ~~~~~~~~ .. currentmodule:: climpred.tutorial .. autosummary:: :toctree: api/ load_dataset Preprocessing ~~~~~~~~~~~~~ .. currentmodule:: climpred.preprocessing.shared .. autosummary:: :toctree: api/ load_hindcast rename_to_climpred_dims rename_SLM_to_climpred_dims set_integer_time_axis .. currentmodule:: climpred.preprocessing.mpi .. autosummary:: :toctree: api/ get_path Smoothing ~~~~~~~~~ .. currentmodule:: climpred.smoothing .. autosummary:: :toctree: api/ temporal_smoothing spatial_smoothing_xesmf Visualization ~~~~~~~~~~~~~ .. currentmodule:: climpred.graphics .. autosummary:: :toctree: api/ plot_bootstrapped_skill_over_leadyear plot_ensemble_perfect_model plot_lead_timeseries_hindcast Utils ~~~~~ .. currentmodule:: climpred.utils .. autosummary:: :toctree: api/ convert_init_lead_to_valid_time_lead convert_valid_time_lead_to_init_lead Metrics ------- For a thorough look at our metrics library, please see the `metrics `_ page. .. currentmodule:: climpred.metrics .. autosummary:: :toctree: api/ Metric Metric.__init__ Metric.__repr__ _get_norm_factor _pearson_r _pearson_r_p_value _effective_sample_size _pearson_r_eff_p_value _spearman_r _spearman_r_p_value _spearman_r_eff_p_value _mse _rmse _mae _median_absolute_error _nmse _nmae _nrmse _msess _mape _smape _uacc _std_ratio _conditional_bias _unconditional_bias _bias_slope _msess_murphy _crps _crpss _crpss_es _brier_score _threshold_brier_score _rps _discrimination _reliability _rank_histogram _contingency _roc _spread _mul_bias _less Comparisons ----------- For a thorough look at our metrics library, please see the `comparisons `_ page. .. currentmodule:: climpred.comparisons .. autosummary:: :toctree: api/ Comparison Comparison.__init__ Comparison.__repr__ _e2o _m2o _m2m _m2e _m2c _e2c Config ------ Set options analogous to `xarray `_. .. currentmodule:: climpred.options .. autosummary:: :toctree: api/ set_options