cmomy.wrap_reduce_vals

cmomy.wrap_reduce_vals#

cmomy.wrap_reduce_vals(x, *y, mom, weight=None, axis=MISSING, mom_params=None, dim=MISSING, mom_dims=None, out=None, dtype=None, casting='same_kind', order=None, parallel=None, keep_attrs=None, apply_ufunc_kwargs=None)[source]#

Create wrapped object from 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.

  • 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

  • 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", "A", "K"}, optional) – Order argument. See numpy.asarray().

  • 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.

  • 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

reduce_vals, wrap

Examples

>>> import cmomy
>>> rng = cmomy.default_rng(0)
>>> x = rng.random((100, 3))
>>> da = cmomy.wrap_reduce_vals(x, axis=0, mom=2)
>>> da
<CentralMomentsArray(mom_ndim=1)>
array([[1.0000e+02, 5.5313e-01, 8.8593e-02],
       [1.0000e+02, 5.5355e-01, 7.1942e-02],
       [1.0000e+02, 5.1413e-01, 1.0407e-01]])