Source code for climpred.options

from .constants import GROUPBY_SEASONALITIES

    "seasonality": "month",
    "PerfectModel_persistence_from_initialized_lead_0": False,
    "warn_for_failed_PredictionEnsemble_xr_call": True,
    "warn_for_rename_to_climpred_dims": True,
    "warn_for_init_coords_int_to_annual": True,
    "climpred_warnings": True,
    "bootstrap_resample_skill_func": "default",
    "resample_iterations_func": "default",
    "bootstrap_uninitialized_from_iterations_mean": False,
}  # defaults


    "seasonality": _SEASONALITY_OPTIONS.__contains__,
    "PerfectModel_persistence_from_initialized_lead_0": lambda choice: choice
    in [True, False, "default"],
    "warn_for_failed_PredictionEnsemble_xr_call": lambda choice: choice
    in [True, False, "default"],
    "warn_for_rename_to_climpred_dims": lambda choice: choice
    in [True, False, "default"],
    "warn_for_init_coords_int_to_annual": lambda choice: choice
    in [True, False, "default"],
    "climpred_warnings": lambda choice: choice in [True, False, "default"],
    "bootstrap_resample_skill_func": lambda choice: choice
    in ["loop", "exclude_resample_dim_from_dim", "resample_before", "default"],
    "resample_iterations_func": lambda choice: choice
    in ["default", "resample_iterations", "resample_iterations_idx"],
    "bootstrap_uninitialized_from_iterations_mean": lambda choice: choice
    in [True, False],

[docs]class set_options: """ Set options for ``climpred`` in a controlled context. Analogous to :py:class:`~xarray.options.set_options`. Args: ``seasonality`` : {``"dayofyear"``, ``"weekofyear"``, ``"month"``, ``"season"``}, default: ``"month"`` # noqa: E501 Attribute to group dimension ``groupby(f"{dim}.{seasonality}"")``. Used in ``reference=climatology`` and :py:meth:`.HindcastEnsemble.remove_bias`. ``PerfectModel_persistence_from_initialized_lead_0`` : {``True``, ``False``}, default ``False`` # noqa: E501 Which persistence function to use in ``PerfectModelEnsemble.verify/bootstrap(reference="persistence")``. If ``False`` use :py:func:`~climpred.reference.compute_persistence`. If ``True`` use :py:func:`~climpred.reference.compute_persistence_from_first_lead`. ``warn_for_failed_PredictionEnsemble_xr_call`` : {``True``, ``False``}, default ``True``. # noqa: E501 Raise ``UserWarning`` when ``PredictionEnsemble.xr_call``, e.g. ``.sel(lead=[1])`` fails on one of the datasets. ``warn_for_rename_to_climpred_dims`` : {``True``, ``False``}, default ``True`` Raise ``UserWarning`` when dimensions are renamed to ``CLIMPRED_DIMS`` when :py:class:`.PredictionEnsemble` is instantiated. ``warn_for_init_coords_int_to_annual`` : {``True``, ``False``}, default ``True`` Raise ``UserWarning`` when ``init`` coordinate is of type integer and gets converted to annual cftime_range when :py:class:`.PredictionEnsemble` is instantiated. ``climpred_warnings`` : {``True``, ``False``}, default ``True`` Overwrites all options containing ``"*warn*"``. ``bootstrap_resample_skill_func`` : {"loop", "exclude_resample_dim_from_dim", "resample_before","default"} # noqa: E501 Decide which resampling method to use in PredictionEnsemble.bootstrap(). ``default`` as in code. * ``loop`` calls :py:func:`climpred.bootstrap.resample_skill_loop` which loops over iterations and calls ``verify`` every single time. Most understandable and stable, but slow. * ``exclude_resample_dim_from_dim`` calls :py:func:`climpred.bootstrap.resample_skill_exclude_resample_dim_from_dim` which calls ``verify(dim=dim_without_resample_dim)``, resamples over ``resample_dim`` and then takes a mean over ``resample_dim`` if in ``dim``. Enables ``HindcastEnsemble.bootstrap(resample_dim="init", alignment="same_verifs")``. Fast alternative for ``resample_dim="init"``. * ``resample_before`` calls :py:func:`climpred.bootstrap.resample_skill_resample_before` which resamples ``iteration`` dimension and then calls ``verify`` vectorized. Fast alternative for ``resample_dim="member"``. ``resample_iterations_func``: {``"default"``, ``"resample_iterations"``, ``"resample_iterations_idx"``} # noqa: E501 Decide which resample_iterations function to use from xskillscore. ``"default"`` as in code: * :py:func:`xskillscore.resample_iterations_idx` creates one large chunk and consumes much memory and is not recommended for large files. * :py:func:`xskillscore.resample_iterations` create many tasks but is more stable. ``bootstrap_uninitialized_from_iterations_mean``: {``True``, ``False``} Exchange ``uninitialized`` skill with the iteration mean ``uninitialized``. Defaults to False. Examples: You can use ``set_options`` either as a context manager: >>> kw = dict( ... metric="mse", ... comparison="e2o", ... dim="init", ... alignment="same_verifs", ... reference="climatology", ... ) >>> with climpred.set_options(seasonality="month"): ... HindcastEnsemble.verify(**kw).SST.sel(skill="climatology") ... <xarray.DataArray 'SST' (lead: 10)> array([0.03712573, 0.03712573, 0.03712573, 0.03712573, 0.03712573, 0.03712573, 0.03712573, 0.03712573, 0.03712573, 0.03712573]) Coordinates: * lead (lead) int32 1 2 3 4 5 6 7 8 9 10 skill <U11 'climatology' Attributes: units: (C)^2 Or to set global options: >>> climpred.set_options(seasonality="month") # doctest: +ELLIPSIS <climpred.options.set_options object at 0x...> """
[docs] def __init__(self, **kwargs): self.old = {} for k, v in kwargs.items(): if k not in OPTIONS: raise ValueError( "argument name %r is not in the set of valid options %r" % (k, set(OPTIONS)) ) if k in _VALIDATORS and not _VALIDATORS[k](v): if k == "seasonality": expected = f"Expected one of {_SEASONALITY_OPTIONS!r}" else: expected = "" raise ValueError( f"option {k!r} given an invalid value: {v!r}. " + expected ) self.old[k] = OPTIONS[k] self._apply_update(kwargs)
def _apply_update(self, options_dict): if ( "climpred_warnings" in options_dict ): # climpred_warnings == False overwrites all warnings options if not options_dict["climpred_warnings"]: for k in [o for o in OPTIONS.keys() if "warn" in o]: options_dict[k] = False OPTIONS.update(options_dict) def __enter__(self): return def __exit__(self, type, value, traceback): self._apply_update(self.old)