cmomy.CentralMomentsData

Contents

cmomy.CentralMomentsData#

class cmomy.CentralMomentsData(obj, *, mom_ndim=None, mom_axes=None, mom_dims=None, mom_params=None, fastpath=False)[source]#

Bases: CentralMomentsABC[DataT, MomParamsXArray]

Central moments wrapper of DataArray or Dataset objects.

Parameters:

  • obj (DataArray or Dataset) – Central moments array.

  • mom_ndim ({1, 2}, optional) – Value indicates if moments (mom_ndim = 1) or comoments (mom_ndim=2). If not specified and data is an xarray object attempt to infer mom_ndim from mom_dims. Otherwise, default to mom_ndim = 1.

  • 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_dims (hashable or tuple of hashable) – Name of moment dimensions. If specified, infer mom_ndim from mom_dims. If also pass mom_ndim, check that mom_dims is consistent with mom_dims. If not specified, defaults to data.dims[-mom_ndim:]. This is primarily used if data is a Dataset, or if mom_dims are not the last dimensions.

  • mom_params (cmomy.MomParams or cmomy.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 cmomy.MomParams object with mom_params=cmomy.MomParams(dims=("a", "b")).

  • fastpath (bool) – For internal use.

See also

cmomy.wrap, cmomy.wrap_reduce_vals, cmomy.wrap_resample_vals, cmomy.wrap_raw

Notes

Base data has the form

\[\begin{split}{\rm data}[..., i, j] = \begin{cases} \text{weight} & i = j = 0 \\ \langle x \rangle & i = 1, j = 0 \\ \langle (x - \langle x \rangle^i) (y - \langle y \rangle^j) \rangle & i + j > 0 \end{cases}\end{split}\]

Methods

as_dict()

Create a wrapped dictionary view of dataset

as_numpy()

Coerces wrapped data and coordinates into numpy arrays.

assign([variables])

Assign new variable to and return new object.

assign_attrs(*args, **kwargs)

Assign attributes to data and return new object.

assign_coords([coords])

Assign coordinates to data and return new object.

assign_moment([moment, squeeze, copy, ...])

Create object with update weight, average, etc.

astype(dtype, *[, order, casting, subok, copy])

Underlying data cast to specified type

chunk(*args, **kwargs)

Interface to xarray.DataArray.chunk() and xarray.Dataset.chunk()

cmom()

Central moments.

compute(**kwargs)

Interface to xarray.DataArray.compute() and xarray.Dataset.compute()

copy([deep])

Create a new object with copy of data.

cov()

Covariance (or variance if mom_ndim==1).

cumulative(*[, axis, dim, out, dtype, ...])

Convert to cumulative moments.

drop_vars(names, *[, errors, _reorder, ...])

Interface to xarray.DataArray.drop_vars()

isel([indexers, drop, missing_dims, ...])

Select subset of data by position.

items()

Dict like items method.

iter()

Need this for proper typing with mypy...

jackknife_and_reduce(*[, axis, dim, ...])

Jackknife resample and reduce

keys()

Dict like keys view

mean([squeeze])

Mean (first moment).

moments_to_comoments(*, mom[, mom_dims_out, ...])

Convert moments (mom_ndim=1) to comoments (mom_ndim=2).

moveaxis([axis, dest, dim, dest_dim, ...])

Generalized moveaxis

new_like([obj, copy, deep, verify, dtype, ...])

Create new object like self, with new data.

pipe(func_or_method, *args[, _reorder, ...])

Apply func_or_method to underlying data and wrap results in new wrapped object.

push_data(data, *[, scale, casting, ...])

Push data object to moments.

push_datas(datas, *[, axis, dim, mom_axes, ...])

Push and reduce multiple average central moments.

push_val(x, *y[, weight, casting, parallel, ...])

Push single sample to central moments.

push_vals(x, *y[, weight, axis, dim, ...])

Push multiple samples to central moments.

reduce([dim, by, block, use_map, group_dim, ...])

Create new object reduce along axis.

rename([new_name_or_name_dict])

Rename object.

resample_and_reduce(*, sampler[, axis, dim, ...])

Bootstrap resample and reduce.

reset_index(dims_or_levels[, drop, ...])

Interface to xarray.DataArray.reset_index().

rmom()

Raw moments.

sel([indexers, method, tolerance, drop, ...])

Select subset of data.

select_moment(name, *[, squeeze, ...])

Select specific moments.

set_index([indexes, append, _reorder, ...])

Interface to xarray.DataArray.set_index()

stack([dim, _reorder, _copy, _verify, _fastpath])

Stack dimensions.

std([squeeze])

Standard deviation (ddof=0).

swap_dims([dims_dict, _reorder, _copy, ...])

Interface to xarray.DataArray.swap_dims().

to_array([copy])

Convert to cmomy.CentralMomentsArray object if possible.

to_c([copy])

Alias to to_array().

to_dataarray([dim, name])

Convert wrapped Dataset to DataArray

to_dataset([dim, name, promote_attrs])

Convert to Dataset or wrapped Dataset.

to_numpy()

Coerce wrapped data to ndarray if possible.

to_raw(*[, weight])

Raw moments accumulation array.

transpose(*dims[, transpose_coords, ...])

Transpose dimensions of data.

unstack([dim, fill_value, sparse, _reorder, ...])

Unstack dimensions.

values()

Dict like values view

var([squeeze])

Variance (second central moment).

weight()

Weight data.

zeros(*, mom[, val_shape, dtype, order, ...])

Create a new object with specified sizes.

zeros_like()

Create new empty object like self.

Attributes

attrs

Attributes of values.

coords

Coordinates of values.

dims

Dimension names.

dtype

DType of wrapped object.

mom

Moments tuple.

mom_dims

Moments dimension names.

mom_ndim

Number of moment dimensions.

mom_params

Moments parameters object.

mom_shape

Shape of moments dimensions.

name

Name of values.

ndim

Total number of dimensions.

obj

Underlying object.

shape

Shape of wrapped object.

sizes

Sizes of values.

val_dims

Names of value (i.e., not moment) dimensions.

val_ndim

Number of values dimensions (ndim - mom_ndim).

val_shape

Shape of values dimensions.

Dunder Methods

__add__(other)

Add objects to new object.

__getitem__(key)

Access variables or coordinates of this wrapped dataset as a DataArray or a subset of variables or a indexed dataset.

__iadd__(other)

Self adder.

__imul__(scale)

Inplace multiply.

__isub__(other)

Inplace subtraction.

__mul__(scale)

New object with weight scaled by scale.

__sub__(other)

Subtract objects.
property mom_dims#

Moments dimension names.

as_dict()[source]#

Create a wrapped dictionary view of dataset

Note that this dict will only contain variables (DataArrays) which contain mom_dims. That is, non central moment arrays will be dropped.

items()[source]#

Dict like items method.

Note that only DataArrays that contain mom_dims are returned. To iterate over all items, use self.obj.items()

keys()[source]#

Dict like keys view

values()[source]#

Dict like values view

iter()[source]#

Need this for proper typing with mypy…

__getitem__(key)[source]#

Access variables or coordinates of this wrapped dataset as a DataArray or a subset of variables or a indexed dataset.

For wrapped Dataset, return a subset of variables.

The selection is wrapped with CentralMomentsData.

new_like(obj=None, *, copy=None, deep=True, verify=False, dtype=None, fastpath=False)[source]#

Create new object like self, with new data.

Parameters:

  • obj (array-like, DataArray, Dataset, or mapping) – Object for new object to wrap. Passed to self.obj.copy.

  • copy (bool, optional) – If True, copy the data. If None or False, attempt to use view. Note that False values will be converted to None for numpy versions >2.0. This will be changed to reflect the new behavior of the copy parameter to numpy.array() when the minimum numpy version >2.0.

  • deep (bool) – Parameter to copy() or copy().

  • verify (bool) – If True, make sure data is c-contiguous.

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

  • fastpath (bool) – Internal variable.

Returns:

CentralMomentsData – New CentralMomentsData object with data set to zero.

See also

xarray.DataArray.copy, xarray.Dataset.copy

copy(deep=True)[source]#

Create a new object with copy of data.

Parameters:

deep (bool) – Parameters to xarray.DataArray.copy() or xarray.Dataset.copy

Returns:

output (CentralMomentsData) – Same type as calling class. Object with same attributes as caller, but with new underlying data.

See also

new_like, zeros_like

astype(dtype, *, order=None, casting=None, subok=None, copy=False)[source]#

Underlying data cast to specified type

Parameters:

  • dtype (dtype or mapping of hashable to dtype) – If self.obj is a Dataset, passing a mapping will update only certain variables.

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

  • 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.

  • subok (bool, optional) – If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array.

  • copy (bool, optional) – By default, astype always returns a newly allocated array. If this is set to False and the dtype requirement is satisfied, the input array is returned instead of a copy.

See also

numpy.ndarray.astype

Notes

Only numpy.float32 and numpy.float64 dtype are supported.

push_data(data, *, scale=None, casting='same_kind', parallel=False, keep_attrs=True, apply_ufunc_kwargs=None)[source]#

Push data object to moments.

Parameters:

  • data (array-like or DataArray or Dataset) – Accumulation array conformable to self.obj.

  • scale (array-like) – Scaling to apply to weights of data. Optional.

  • 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.

  • 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:

output (CentralMomentsData) – Same object with pushed data.

push_datas(datas, *, axis=MISSING, dim=MISSING, mom_axes=None, casting='same_kind', parallel=None, keep_attrs=True, apply_ufunc_kwargs=None)[source]#

Push and reduce multiple average central moments.

Parameters:

  • datas (array-like or DataArray or Dataset) – Collection of accumulation arrays to push onto self.

  • axis (int, optional) – Axis to reduce/sample along. Note that negative values are relative to data.ndim - mom_ndim. It is assumed that the last dimensions are for moments. For example, if data.shape == (1,2,3) with mom_ndim=1, axis = -1 `` would be equivalent to ``axis = 1. Defaults to axis=-1.

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

  • 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.

  • 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.

  • 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:

output (CentralMomentsData) – Same object with pushed data.

push_val(x, *y, weight=None, casting='same_kind', parallel=False, keep_attrs=True, apply_ufunc_kwargs=None)[source]#

Push single sample to central moments.

Parameters:

  • x (array-like or DataArray or Dataset) – Values to push onto self.

  • *y (array-like or DataArray or Dataset) – Additional values (needed if mom_ndim > 1)

  • weight (int, float, array-like or DataArray or Dataset) – Weight of each sample. If scalar, broadcast w.shape to x0.shape.

  • 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.

  • 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:

output (CentralMomentsData) – Same object with pushed data.

Notes

Array x0 should have same shape as self.val_shape.

push_vals(x, *y, weight=None, axis=MISSING, dim=MISSING, casting='same_kind', parallel=None, keep_attrs=True, apply_ufunc_kwargs=None)[source]#

Push multiple samples to central moments.

Parameters:

  • x (array) – Value to reduce.

  • *y (array-like) – Additional array (if self.mom_ndim == 2).

  • weight (int, float, array-like, optional) – Weight of each sample. If scalar, broadcast to x0.shape

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

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

  • 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.

  • 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:

output (CentralMomentsData) – Same object with pushed data.

reduce(dim=MISSING, *, by=None, block=None, use_map=None, group_dim=None, groups=None, axis=MISSING, out=None, dtype=None, casting='same_kind', order=None, keepdims=False, parallel=None, axes_to_end=False, coords_policy='first', keep_attrs=None, apply_ufunc_kwargs=None)[source]#

Create new object reduce along axis.

Parameters:

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

  • by (ndarray or DataArray or str or iterable of str, optional) – If None, reduce over entire dim. Otherwise, reduce by group. If ndarray, use the unique values. If DataArray, use unique values and rename dimension by.name. If str or Iterable of str, Create grouper from these named coordinates.

  • block (int, optional) – If specified, perform block average reduction with blocks of this size. Negative values are transformed to all data.

  • use_map (bool, optional) – See reduction.reduce_data()

  • group_dim (str, optional) – Name of the output group dimension. Defaults to dim.

  • groups (sequence, optional) – Sequence of length by.max() + 1 to assign as coordinates for group_dim.

  • axis (int, optional) – Axis to reduce/sample along. Note that negative values are relative to data.ndim - mom_ndim. It is assumed that the last dimensions are for moments. For example, if data.shape == (1,2,3) with mom_ndim=1, axis = -1 `` would be equivalent to ``axis = 1. Defaults to axis=-1.

  • 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().

  • keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

  • 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).

  • coords_policy ({'first', 'last', 'group', None}) –

    Policy for handling coordinates along dim if by is specified for DataArray data. If no coordinates do nothing, otherwise use:

    • ’first’: select first value of coordinate for each block.

    • ’last’: select last value of coordinate for each block.

    • ’group’: Assign unique groups from group_idx to dim

    • None: drop any coordinates.

    Note that if coords_policy is one of first or last, parameter groups will be ignored.

  • 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:

output (CentralMomentsData) – If by is None, reduce all samples along axis, optionally keeping axis with size 1 if keepdims=True. Otherwise, reduce for each unique value of by. In this case, output will have shape (..., shape[axis-1], ngroup, shape[axis+1], ...) where ngroups = np.max(by) + 1 is the number of unique positive values in by.

See also

reduction.reduce_data, grouped.reduce_data_grouped, grouped.reduce_data_indexed

Notes

This is a new feature, and subject to change. In particular, the current implementation is not smart about the output coordinates and dimensions, and is inconsistent with xarray.DataArray.groupby(). It is up the the user to manipulate the dimensions/coordinates. output dimensions and coords simplistically.

Examples

>>> import cmomy
>>> rng = cmomy.default_rng(0)
>>> data = xr.DataArray(rng.random((6, 3)), dims=["rec", "mom"]).assign_coords(
...     rec=range(6)
... )
>>> da = CentralMomentsData(data)
>>> da
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (rec: 6, mom: 3)> Size: 144B
array([[0.637 , 0.2698, 0.041 ],
       [0.0165, 0.8133, 0.9128],
       [0.6066, 0.7295, 0.5436],
       [0.9351, 0.8159, 0.0027],
       [0.8574, 0.0336, 0.7297],
       [0.1757, 0.8632, 0.5415]])
Coordinates:
  * rec      (rec) int64 48B 0 1 2 3 4 5
Dimensions without coordinates: mom

Reduce along a single dimension

>>> da.reduce(dim="rec")
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (mom: 3)> Size: 24B
array([3.2283, 0.4867, 0.4535])
Dimensions without coordinates: mom

Reduce by group:

>>> by = [0, 0, 0, 1, 1, 1]
>>> da.reduce(dim="rec", by=by, coords_policy="first")
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (rec: 2, mom: 3)> Size: 48B
array([[1.2601, 0.4982, 0.3478],
       [1.9681, 0.4793, 0.521 ]])
Coordinates:
  * rec      (rec) int64 16B 0 3
Dimensions without coordinates: mom

Note the coordinate rec. Using the coords_policy="first" uses the first coordinate a group is observed.

>>> da.reduce(dim="rec", by=by, coords_policy="group", groups=["a", "b"])
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (rec: 2, mom: 3)> Size: 48B
array([[1.2601, 0.4982, 0.3478],
       [1.9681, 0.4793, 0.521 ]])
Coordinates:
  * rec      (rec) <U1 8B 'a' 'b'
Dimensions without coordinates: mom

Reduce by coord:

>>> dag = da.assign_coords(group_coord=("rec", by))
>>> dag
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (rec: 6, mom: 3)> Size: 144B
array([[0.637 , 0.2698, 0.041 ],
       [0.0165, 0.8133, 0.9128],
       [0.6066, 0.7295, 0.5436],
       [0.9351, 0.8159, 0.0027],
       [0.8574, 0.0336, 0.7297],
       [0.1757, 0.8632, 0.5415]])
Coordinates:
  * rec          (rec) int64 48B 0 1 2 3 4 5
    group_coord  (rec) int64 48B 0 0 0 1 1 1
Dimensions without coordinates: mom

>>> dag.reduce(dim="rec", by="group_coord")
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (rec: 2, mom: 3)> Size: 48B
array([[1.2601, 0.4982, 0.3478],
       [1.9681, 0.4793, 0.521 ]])
Coordinates:
  * rec          (rec) int64 16B 0 3
    group_coord  (rec) int64 16B 0 1
Dimensions without coordinates: mom

Block averaging:

>>> da.reduce(block=3, dim="rec")
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (rec: 2, mom: 3)> Size: 48B
array([[1.2601, 0.4982, 0.3478],
       [1.9681, 0.4793, 0.521 ]])
Coordinates:
  * rec      (rec) int64 16B 0 3
Dimensions without coordinates: mom

Note that this is the same as the group reduction above. For finer control of how block is transformed to by, see block_by().

classmethod zeros(*, mom, val_shape=None, dtype=None, order=None, dims=None, mom_dims=None, attrs=None, coords=None, name=None, template=None)[source]#

Create a new object with specified sizes.

This can only be used to create a wrapped DataArray object. To create a new wrapped Dataset, use xarray.zeros_like().

Parameters:

  • 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

  • val_shape (tuple) – Shape of values part of data. That is, the non-moment dimensions.

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

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

  • dims (hashable or sequence of hashable) –

    Dimension of resulting xarray.DataArray.

    • If len(dims) == self.ndim, then dims specifies all dimensions.

    • If len(dims) == self.val_ndim, dims = dims + mom_dims

    Default to ('dim_0', 'dim_1', ...)

  • attrs (mapping) – Attributes of output

  • coords (mapping) – Coordinates of output

  • name (hashable) – Name of output

  • indexes (Any) – indexes attribute. This is ignored.

  • template (DataArray) – If present, output will have attributes of template. Overrides other options.

Returns:

CentralMomentsData – New instance with zero values.

See also

cmomy.CentralMomentsArray.zeros, cmomy.CentralMomentsArray.to_x

to_dataset(dim=None, *, name=None, promote_attrs=False)[source]#

Convert to Dataset or wrapped Dataset.

Parameters:

  • dim (Hashable, optional) – Name of the dimension on this array along which to split this array into separate variables. If not provided, this array is converted into a Dataset of one variable.

  • name (Hashable, optional) – Name to substitute for this array’s name. Only valid if dim is not provided.

  • promote_attrs (bool, default: False) – Set to True to shallow copy attrs of DataArray to returned Dataset.

Returns:

CentralMomentsData – Object wrapping Dataset.

See also

xarray.DataArray.to_dataset

to_dataarray(dim='variable', *, name=None)[source]#

Convert wrapped Dataset to DataArray

The data variables of this dataset will be broadcast against each other and stacked along the first axis of the new array. All coordinates of this dataset will remain coordinates.

Parameters:

  • dim (Hashable, default: "variable") – Name of the new dimension.

  • name (Hashable or None, optional) – Name of the new data array.

Returns:

CentralMomentsData – New object wrapping DataArray

See also

xarray.Dataset.to_dataarray

property attrs#

Attributes of values.

property dims#

Dimension names.

Note that for a wrapped Dataset, self.obj.dims is a mapping, while self.dims is always a tuple of names.

property val_dims#

Names of value (i.e., not moment) dimensions.

property coords#

Coordinates of values.

property name#

Name of values.

property sizes#

Sizes of values.

compute(**kwargs)[source]#

Interface to xarray.DataArray.compute() and xarray.Dataset.compute()

chunk(*args, **kwargs)[source]#

Interface to xarray.DataArray.chunk() and xarray.Dataset.chunk()

as_numpy()[source]#

Coerces wrapped data and coordinates into numpy arrays.

assign(variables=None, **variables_kwargs)[source]#

Assign new variable to and return new object.

assign_coords(coords=None, **coords_kwargs)[source]#

Assign coordinates to data and return new object.

assign_attrs(*args, **kwargs)[source]#

Assign attributes to data and return new object.

rename(new_name_or_name_dict=None, **names)[source]#

Rename object.

stack(dim=None, *, _reorder=True, _copy=False, _verify=False, _fastpath=False, **dimensions_kwargs)[source]#

Stack dimensions.

Returns:

output (CentralMomentsData) – With dimensions stacked.

See also

pipe, xarray.DataArray.stack

Examples

>>> import cmomy
>>> rng = cmomy.default_rng(0)
>>> vals = xr.DataArray(rng.random((10, 2, 3)), dims=["rec", "dim_0", "dim_1"])
>>> da = cmomy.wrap_reduce_vals(vals, mom=2, dim="rec")
>>> da
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (dim_0: 2, dim_1: 3, mom_0: 3)> Size: 144B
array([[[10.    ,  0.5205,  0.0452],
        [10.    ,  0.4438,  0.0734],
        [10.    ,  0.5038,  0.1153]],

       [[10.    ,  0.5238,  0.1272],
        [10.    ,  0.628 ,  0.0524],
        [10.    ,  0.412 ,  0.0865]]])
Dimensions without coordinates: dim_0, dim_1, mom_0
>>> da_stack = da.stack(z=["dim_0", "dim_1"])
>>> da_stack
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (z: 6, mom_0: 3)> Size: 144B
array([[10.    ,  0.5205,  0.0452],
       [10.    ,  0.4438,  0.0734],
       [10.    ,  0.5038,  0.1153],
       [10.    ,  0.5238,  0.1272],
       [10.    ,  0.628 ,  0.0524],
       [10.    ,  0.412 ,  0.0865]])
Coordinates:
  * z        (z) object 48B MultiIndex
  * dim_0    (z) int64 48B 0 0 0 1 1 1
  * dim_1    (z) int64 48B 0 1 2 0 1 2
Dimensions without coordinates: mom_0

And unstack

>>> da_stack.unstack("z")
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (dim_0: 2, dim_1: 3, mom_0: 3)> Size: 144B
array([[[10.    ,  0.5205,  0.0452],
        [10.    ,  0.4438,  0.0734],
        [10.    ,  0.5038,  0.1153]],

       [[10.    ,  0.5238,  0.1272],
        [10.    ,  0.628 ,  0.0524],
        [10.    ,  0.412 ,  0.0865]]])
Coordinates:
  * dim_0    (dim_0) int64 16B 0 1
  * dim_1    (dim_1) int64 24B 0 1 2
Dimensions without coordinates: mom_0
unstack(dim=None, fill_value=nan, *, sparse=False, _reorder=True, _copy=False, _verify=False, _fastpath=False)[source]#

Unstack dimensions.

Returns:

output (CentralMomentsData) – With dimensions unstacked

See also

stack, xarray.DataArray.unstack

__add__(other)[source]#

Add objects to new object.

__iadd__(other)[source]#

Self adder.

__imul__(scale)[source]#

Inplace multiply.

__isub__(other)[source]#

Inplace subtraction.

__mul__(scale)[source]#

New object with weight scaled by scale.

__sub__(other)[source]#

Subtract objects.

assign_moment(moment=None, *, squeeze=True, copy=True, dim_combined=None, keep_attrs=None, apply_ufunc_kwargs=None, **moment_kwargs)[source]#

Create object with update weight, average, etc.

Parameters:

  • moment (mapping of str to array-like) –

    Mapping from moment name to new value. Allowed moment names are:

    • "weight" : weights

    • "ave" : Averages.

    • "var": Variance.

    • "cov": Covariance if mom_ndim == 2, or variance if mom_ndim == 1.

    • "xave": Average of first variable.

    • "xvar": Variance of first variable.

    • "yave": Average of second variable (if mom_ndim == 2).

    • "yvar": Variance of second variable (if mom_ndim == 2).

    • "xmom_n", "ymom_n": All values with first (second) variable moment == n.

    • "all": All values.

    Names "weight", "xave", "yave", "xvar", "yvar", "cov" imply shape data.shape[:-mom_ndim]. Names "ave", "var" imply shape (*data.shape[:-mom_ndim], mom_ndim), unless mom_ndim == 1 and squeeze = True.

  • squeeze (bool, default False) – If True, squeeze last dimension if name is one of ave or var and mom_ndim == 1.

  • copy (bool, default True) – If True (the default), return new array with updated weights. Otherwise, return the original array with weights updated inplace. Note that a copy is always created for a dask backed object.

  • dim_combined (str, optional) – Name of dimensions for multiple values. Must supply if passing in multiple values for name="ave" etc.

  • mom_dims (hashable or tuple of hashable) – Name of moment dimensions. If specified, infer mom_ndim from mom_dims. If also pass mom_ndim, check that mom_dims is consistent with mom_dims. If not specified, defaults to data.dims[-mom_ndim:]. This is primarily used if data is a Dataset, or if mom_dims are not the last dimensions.

  • 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.

  • **moment_kwargs (Union[Buffer, _SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[bool | int | float | complex | str | bytes], DataArray, Dataset]) – Keyword argument form of moment. Must provide either moment or moment_kwargs.

Returns:

output (object) – Same type as self with updated data.

See also

utils.assign_moment

cmom()[source]#

Central moments.

Strict central moments of the form

\[\text{cmom[..., n, m]} = \langle (x - \langle x \rangle)^n (y - \langle y \rangle)^m \rangle\]

where

\[\langle x \rangle = \sum_i w_i x_i / \sum_i w_i\]
Returns:

output (ndarray or DataArray)

cov()[source]#

Covariance (or variance if mom_ndim==1).

cumulative(*, axis=MISSING, dim=MISSING, out=None, dtype=None, casting='same_kind', order=None, parallel=None, axes_to_end=False, keep_attrs=None, apply_ufunc_kwargs=None)[source]#

Convert to cumulative moments.

Parameters:

  • axis (int, optional) – Axis to reduce/sample along. Note that negative values are relative to data.ndim - mom_ndim. It is assumed that the last dimensions are for moments. For example, if data.shape == (1,2,3) with mom_ndim=1, axis = -1 `` would be equivalent to ``axis = 1. Defaults to axis=-1.

  • 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).

  • 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.

  • mom_dims (hashable or tuple of hashable) – Name of moment dimensions. If specified, infer mom_ndim from mom_dims. If also pass mom_ndim, check that mom_dims is consistent with mom_dims. If not specified, defaults to data.dims[-mom_ndim:]. This is primarily used if data is a Dataset, or if mom_dims are not the last dimensions.

  • 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:

output (numpy.ndarray or xarray.DataArray or xarray.Dataset) – Same type as self.obj, with moments accumulated over axis.

See also

convert.cumulative

property dtype#

DType of wrapped object.

jackknife_and_reduce(*, axis=MISSING, dim=MISSING, data_reduced=None, rep_dim='rep', axes_to_end=False, out=None, dtype=None, casting='same_kind', order=None, parallel=None, keep_attrs=None, apply_ufunc_kwargs=None)[source]#

Jackknife resample and reduce

Parameters:

  • axis (int, optional) – Axis to reduce/sample along. Note that negative values are relative to data.ndim - mom_ndim. It is assumed that the last dimensions are for moments. For example, if data.shape == (1,2,3) with mom_ndim=1, axis = -1 `` would be equivalent to ``axis = 1. Defaults to axis=-1.

  • 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.

  • data_reduced (array or object) – Data reduced along axis. Array of same type as self.obj or same type as self.

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

  • 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).

  • 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 – 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:

object – Instance of calling class with jackknife resampling along axis.

See also

jackknife_data

mean(squeeze=True)[source]#

Mean (first moment).

property mom#

Moments tuple.

property mom_ndim#

Number of moment dimensions.

property mom_params#

Moments parameters object.

property mom_shape#

Shape of moments dimensions.

moments_to_comoments(*, mom, mom_dims_out=None, dtype=None, order=None, keep_attrs=None, apply_ufunc_kwargs=None)[source]#

Convert moments (mom_ndim=1) to comoments (mom_ndim=2).

Parameters:

  • mom (tuple of int) –

    Moments for comoments array. Pass a negative value for one of the moments to fill all available moments for that dimensions. For example, if original array has moments m (i.e., values.shape=(..., m + 1)), and pass in mom = (2, -1), then this will be transformed to

    mom = (2, m - 2).

  • mom_dims_out (tuple of str) – Moments dimensions for output (mom_ndim=2) data. Defaults to ("mom_0", "mom_1").

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

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

  • 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:

output (object) – Same type as self with mom_ndim=2.

See also

convert.moments_to_comoments

moveaxis(axis=MISSING, dest=MISSING, *, dim=MISSING, dest_dim=MISSING, axes_to_end=False)[source]#

Generalized moveaxis

Parameters:
Returns:

output (object) – Object with moved axes. This is a view of the original data.

See also

utils.moveaxis, numpy.moveaxis

property ndim#

Total number of dimensions.

property obj#

Underlying object.

pipe(func_or_method, *args, _reorder=True, _copy=None, _verify=False, _fastpath=False, **kwargs)[source]#

Apply func_or_method to underlying data and wrap results in new wrapped object.

This is useful for calling any not implemented methods on numpy.ndarray or xarray.DataArray data.

Note that a ValueError will be raised if last dimension(s) do not have the same shape as self.mom_shape.

Parameters:

  • func_or_method (str or callable()) – If callable, then apply values = func_or_method(self.to_values(), *args, **kwargs). If string is passed, then values = getattr(self.to_values(), func_or_method)(*args, **kwargs).

  • *args (Any) – Extra positional arguments to func_or_method

  • _reorder (bool, default True) – If True, reorder the data such that mom_dims are last. Only applicable for DataArray like underlying data.

  • _copy (bool, default False) – If True, copy the resulting data. Otherwise, try to use a view.

  • _verify (bool, default False)

  • **kwargs (Any) – Extra keyword arguments to func_or_method

Returns:

output (object) – New object after func_or_method is applies to self.to_values()

Notes

Use leading underscore for (_copy, etc) to avoid possible name clashes with func_or_method.

resample_and_reduce(*, sampler, axis=MISSING, dim=MISSING, rep_dim='rep', axes_to_end=False, out=None, dtype=None, casting='same_kind', order=None, parallel=None, keep_attrs=None, apply_ufunc_kwargs=None)[source]#

Bootstrap resample and reduce.

Parameters:

  • sampler (int or array-like or IndexSampler or mapping) – Passed through 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.

  • axis (int, optional) – Axis to reduce/sample along. Note that negative values are relative to data.ndim - mom_ndim. It is assumed that the last dimensions are for moments. For example, if data.shape == (1,2,3) with mom_ndim=1, axis = -1 `` would be equivalent to ``axis = 1. Defaults to axis=-1.

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

  • 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).

  • 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:

output (object) – Instance of calling class. Note that new object will have (...,shape[axis-1], nrep, shape[axis+1], ...), where nrep is the number of replicates.

See also

reduce, factory_sampler

resample_data

method to perform resampling

Examples

>>> import cmomy
>>> rng = cmomy.default_rng(0)
>>> da = cmomy.wrap_reduce_vals(
...     rng.random((10, 3)),
...     mom=3,
...     axis=0,
... ).to_x(dims="rec")
>>> da
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (rec: 3, mom_0: 4)> Size: 96B
array([[ 1.0000e+01,  5.2485e-01,  1.1057e-01, -4.6282e-03],
       [ 1.0000e+01,  5.6877e-01,  6.8876e-02, -1.2745e-02],
       [ 1.0000e+01,  5.0944e-01,  1.1978e-01, -1.4644e-02]])
Dimensions without coordinates: rec, mom_0

>>> sampler = cmomy.resample.factory_sampler(data=da.obj, dim="rec", nrep=5)
>>> da_resamp = da.resample_and_reduce(
...     dim="rec",
...     sampler=sampler,
... )
>>> da_resamp
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (rep: 5, mom_0: 4)> Size: 160B
array([[ 3.0000e+01,  5.0944e-01,  1.1978e-01, -1.4644e-02],
       [ 3.0000e+01,  5.3435e-01,  1.0038e-01, -1.2329e-02],
       [ 3.0000e+01,  5.2922e-01,  1.0360e-01, -1.6009e-02],
       [ 3.0000e+01,  5.5413e-01,  8.3204e-02, -1.1267e-02],
       [ 3.0000e+01,  5.4899e-01,  8.6627e-02, -1.5407e-02]])
Dimensions without coordinates: rep, mom_0
rmom()[source]#

Raw moments.

\[\text{rmom[..., n, m]} = \langle x^n y^m \rangle\]

where

\[\langle x \rangle = \sum_i w_i x_i / \sum_i w_i\]
Returns:

raw_moments (ndarray or DataArray)

See also

to_raw, convert.moments_type, cmom

select_moment(name, *, squeeze=True, dim_combined='variable', coords_combined=None, keep_attrs=None, apply_ufunc_kwargs=None)[source]#

Select specific moments.

Parameters:

  • moment ({"weight", "ave", "var", "cov", "xave", "xvar", "yave", "yvar", "xmom_0", "xmom_1", "ymom_0", "ymom_1"}) –

    Name of moment(s) to select.

    • "weight" : weights

    • "ave" : Averages.

    • "var": Variance.

    • "cov": Covariance if mom_ndim == 2, or variance if mom_ndim == 1.

    • "xave": Average of first variable.

    • "xvar": Variance of first variable.

    • "yave": Average of second variable (if mom_ndim == 2).

    • "yvar": Variance of second variable (if mom_ndim == 2).

    • "all": All values.

    Names "weight", "xave", "yave", "xvar", "yvar", "cov" imply shape data.shape[:-mom_ndim]. Names "ave", "var" imply shape (*data.shape[:-mom_ndim], mom_ndim), unless mom_ndim == 1 and squeeze = True.

  • squeeze (bool, default False) – If True, squeeze last dimension if name is one of ave or var and mom_ndim == 1.

  • dim_combined (str, optional) – Name of dimension for options that produce multiple values (e.g., name="ave").

  • coords_combined (str or sequence of str, optional) – Coordinates to assign to dim_combined. Defaults to names of moments dimension(s)

  • 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:

output (ndarray or DataArray or Dataset) – Same type as self.obj. If name is ave or var, the last dimensions of output has shape mom_ndim with each element corresponding to the ith variable. If squeeze=True and mom_ndim==1, this last dimension is removed. For all other name options, output has shape of input with moment dimensions removed.

See also

utils.select_moment

set_index(indexes=None, append=False, *, _reorder=True, _copy=False, _verify=False, _fastpath=False, **indexes_kwargs)[source]#

Interface to xarray.DataArray.set_index()

Returns:

output (CentralMomentsData) – With new index.

See also

stack, unstack, reset_index

property shape#

Shape of wrapped object.

std(squeeze=True)[source]#

Standard deviation (ddof=0).

to_numpy()[source]#

Coerce wrapped data to ndarray if possible.

to_raw(*, weight=None)[source]#

Raw moments accumulation array.

\[\begin{split}\text{raw[..., n, m]} = \begin{cases} \text{weight} & n = m = 0 \\ \langle x^n y ^m \rangle & \text{otherwise} \end{cases}\end{split}\]

where

\[\langle x \rangle = \sum_i w_i x_i / \sum_i w_i\]
Returns:

raw (ndarray or DataArray)

See also

wrap_raw, convert.moments_type

property val_ndim#

Number of values dimensions (ndim - mom_ndim).

property val_shape#

Shape of values dimensions.

var(squeeze=True)[source]#

Variance (second central moment).

weight()[source]#

Weight data.

zeros_like()[source]#

Create new empty object like self.

Returns:

output (object) – Object with same attributes as caller, but with data set to zero.

See also

new_like

reset_index(dims_or_levels, drop=False, *, _reorder=True, _copy=False, _verify=False, _fastpath=False)[source]#

Interface to xarray.DataArray.reset_index().

drop_vars(names, *, errors='raise', _reorder=True, _copy=False, _verify=False, _fastpath=False)[source]#

Interface to xarray.DataArray.drop_vars()

swap_dims(dims_dict=None, _reorder=True, _copy=False, _verify=False, _fastpath=False, **dims_kwargs)[source]#

Interface to xarray.DataArray.swap_dims().

sel(indexers=None, method=None, tolerance=None, drop=False, *, _reorder=False, _copy=False, _verify=False, _fastpath=False, **indexers_kws)[source]#

Select subset of data.

Returns:

output (CentralMomentsData) – With dimensions unstacked

See also

xarray.DataArray.sel

Examples

>>> import cmomy
>>> rng = cmomy.default_rng(0)
>>> da = cmomy.wrap_reduce_vals(rng.random((10, 3)), axis=0, mom=2).to_x(
...     dims="x", coords=dict(x=list("abc"))
... )
>>> da
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (x: 3, mom_0: 3)> Size: 72B
array([[10.    ,  0.5248,  0.1106],
       [10.    ,  0.5688,  0.0689],
       [10.    ,  0.5094,  0.1198]])
Coordinates:
  * x        (x) <U1 12B 'a' 'b' 'c'
Dimensions without coordinates: mom_0

Select by value

>>> da.sel(x="a")
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (mom_0: 3)> Size: 24B
array([10.    ,  0.5248,  0.1106])
Coordinates:
    x        <U1 4B 'a'
Dimensions without coordinates: mom_0
>>> da.sel(x=["a", "c"])
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (x: 2, mom_0: 3)> Size: 48B
array([[10.    ,  0.5248,  0.1106],
       [10.    ,  0.5094,  0.1198]])
Coordinates:
  * x        (x) <U1 8B 'a' 'c'
Dimensions without coordinates: mom_0

Select by position

>>> da.isel(x=0)
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (mom_0: 3)> Size: 24B
array([10.    ,  0.5248,  0.1106])
Coordinates:
    x        <U1 4B 'a'
Dimensions without coordinates: mom_0
>>> da.isel(x=[0, 1])
<CentralMomentsData(mom_ndim=1)>
<xarray.DataArray (x: 2, mom_0: 3)> Size: 48B
array([[10.    ,  0.5248,  0.1106],
       [10.    ,  0.5688,  0.0689]])
Coordinates:
  * x        (x) <U1 8B 'a' 'b'
Dimensions without coordinates: mom_0
isel(indexers=None, drop=False, missing_dims='raise', *, _reorder=False, _copy=False, _verify=False, _fastpath=False, **indexers_kws)[source]#

Select subset of data by position.

Returns:

output (CentralMomentsData) – With dimensions unstacked

See also

sel, xarray.DataArray.isel

transpose(*dims, transpose_coords=True, missing_dims='raise', _copy=False, _verify=False, _fastpath=False)[source]#

Transpose dimensions of data.

Notes

mom_dims will always be put at the end of the array.

See also

pipe, xarray.DataArray.transpose, xarray.Dataset.transpose

to_array(copy=False)[source]#

Convert to cmomy.CentralMomentsArray object if possible.

to_c(copy=False)[source]#

Alias to to_array().

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