cmomy.wrap_resample_vals

cmomy.wrap_resample_vals#

cmomy.wrap_resample_vals(x, *y, sampler, mom, weight=None, axis=MISSING, dim=MISSING, mom_dims=None, mom_axes=None, mom_params=None, rep_dim='rep', out=None, dtype=None, casting='same_kind', order=None, parallel=None, axes_to_end=False, keep_attrs=None, apply_ufunc_kwargs=None)[source]#

Create wrapped object from resampled values.

Parameters:

  • x (array-like or DataArray or Dataset) – Values to reduce.

  • *y (array-like or DataArray or Dataset) – Additional values (needed if len(mom)==2). y has same type restrictions and broadcasting rules as weight.

  • sampler (int or array-like or IndexSampler or mapping) – Passed through cmomy.resample.factory_sampler() to create an IndexSampler. Value can either be nrep (the number of replicates), freq (frequency array), a IndexSampler object, or a mapping of parameters. The mapping can have form of FactoryIndexSamplerKwargs. Allowable keys are freq, indices, ndat, nrep, nsamp, paired, rng, replace, shuffle.

  • mom (int or tuple of int) – Order or moments. If integer or length one tuple, then moments are for a single variable. If length 2 tuple, then comoments of two variables

  • weight (array-like or DataArray or Dataset) –

    Optional weight. The type of weight must be “less than” the type of x.

    In the case that weight is array-like, it must broadcast to x using usual broadcasting rules (see numpy.broadcast_to()), with the following exceptions: If weight is a 1d array of length x.shape[axis]], it will be formatted to broadcast along the other dimensions of x. For example, if x has shape (10, 2, 3) and weight has shape (10,), then weight will be converted to the broadcastable shape (10, 1, 1). If weight is a scalar, it will be broadcast to x.shape.

  • axis (int) – Axis to reduce/sample along.

  • dim (hashable) – Dimension to reduce/sample along.

  • mom_dims (hashable or tuple of hashable) – Name of moment dimensions. Defaults to ("mom_0",) for mom_ndim==1 and (mom_0, mom_1) for mom_ndim==2

  • mom_axes (int or tuple of int, optional) – Location of the moment dimensions. Default to (-mom_ndim, -mom_ndim+1, ...). If specified and mom_ndim is None, set mom_ndim to len(mom_axes). Note that if mom_axes is specified, negative values are relative to the end of the array. This is also the case for axes if mom_axes is specified.

  • mom_params (MomParams or MomParamsDict or dict, optional) – Moment parameters. You can set moment parameters axes and dims using this option. For example, passing mom_params={"dim": ("a", "b")} is equivalent to passing mom_dims=("a", "b"). You can also pass as a MomParams object with mom_params=cmomy.MomParams(dims=("a", "b")).

  • rep_dim (hashable) – Name of new ‘replicated’ dimension:

  • out (ndarray) – Optional output array. If specified, output will be a reference to this array. Note that if the output if method returns a Dataset, then this option is ignored.

  • dtype (dtype) – Optional dtype for output data.

  • casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional) –

    Controls what kind of data casting may occur.

    • ’no’ means the data types should not be cast at all.

    • ’equiv’ means only byte-order changes are allowed.

    • ’safe’ means only casts which can preserve values are allowed.

    • ’same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.

    • ’unsafe’ (default) means any data conversions may be done.

  • order ({"C", "F"}, optional) – Order argument. See numpy.zeros().

  • parallel (bool, optional) – If True, use parallel numba numba.njit or numba.guvectorized code if possible. If None, use a heuristic to determine if should attempt to use parallel method.

  • axes_to_end (bool) – If True, place sampled dimension (if exists in output) and moment dimensions at end of output. Otherwise, place sampled dimension (if exists in output) at same position as input axis and moment dimensions at same position as input (if input does not contain moment dimensions, place them at end of array).

  • keep_attrs ({"drop", "identical", "no_conflicts", "drop_conflicts", "override"} or bool, optional) –

    • ‘drop’ or False: empty attrs on returned xarray object.

    • ’identical’: all attrs must be the same on every object.

    • ’no_conflicts’: attrs from all objects are combined, any that have the same name must also have the same value.

    • ’drop_conflicts’: attrs from all objects are combined, any that have the same name but different values are dropped.

    • ’override’ or True: skip comparing and copy attrs from the first object to the result.

  • apply_ufunc_kwargs (dict-like) – Extra parameters to xarray.apply_ufunc(). One useful option is on_missing_core_dim, which can take the value "copy" (the default), "raise", or "drop" and controls what to do with variables of a Dataset missing core dimensions. Other options are join, dataset_join, dataset_fill_value, and dask_gufunc_kwargs. Unlisted options are handled internally.

Returns:

wrapped (CentralMomentsArray or CentralMomentsData) – Wrapped object. If input data is an xarray object, then return CentralMomentsData instance. Otherwise, return CentralMomentsArray instance.

See also

wrap, resample_vals, factory_sampler

Examples

>>> import cmomy
>>> rng = cmomy.default_rng(0)
>>> x = rng.random(10)
>>> cmomy.wrap_resample_vals(x, mom=2, axis=0, sampler=dict(nrep=5))
<CentralMomentsArray(mom_ndim=1)>
array([[10.    ,  0.5397,  0.0757],
       [10.    ,  0.5848,  0.0618],
       [10.    ,  0.5768,  0.0564],
       [10.    ,  0.6138,  0.1081],
       [10.    ,  0.5808,  0.0685]])
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