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Given any number of Dataset and/or DataArray objects, returns new
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Array from the aligned objects are suitable as input to mathematical
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Missing values (if ``join != 'inner'``) are filled with NaN.
Parameters
----------
*objects : Dataset or DataArray
Objects to align.
join : {'outer', 'inner', 'left', 'right'}, optional
Method for joining the indexes of the passed objects along each
dimension:
- 'outer': use the union of object indexes
- 'inner': use the intersection of object indexes
- 'left': use indexes from the first object with each dimension
- 'right': use indexes from the last object with each dimension
copy : bool, optional
If ``copy=True``, the returned objects contain all new variables. If
``copy=False`` and no reindexing is required then the aligned objects
will include original variables.
Returns
-------
aligned : same as *objects
Tuple of objects with aligned coordinates.
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|��r@|| }n|�rN|j!� n|}||| < �qW |S )aw Conform a dictionary of aligned variables onto a new set of variables,
filling in missing values with NaN.
WARNING: This method is not public API. Don't use it directly.
Parameters
----------
variables : dict-like
Dictionary of xarray.Variable objects.
indexes : dict-like
Dictionary of xarray.Coordinate objects associated with variables.
indexers : dict
Dictionary with keys given by dimension names and values given by
arrays of coordinates tick labels. Any mis-matched coordinate values
will be filled in with NaN, and any mis-matched dimension names will
simply be ignored.
method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional
Method to use for filling index values in ``indexers`` not found in
this dataset:
* None (default): don't fill gaps
* pad / ffill: propgate last valid index value forward
* backfill / bfill: propagate next valid index value backward
* nearest: use nearest valid index value
tolerance : optional
Maximum distance between original and new labels for inexact matches.
The values of the index at the matching locations most satisfy the
equation ``abs(index[indexer] - target) <= tolerance``.
copy : bool, optional
If `copy=True`, the returned dataset contains only copied
variables. If `copy=False` and no reindexing is required then
original variables from this dataset are returned.
Returns
-------
reindexed : OrderedDict
Another dict, with the items in variables but replaced indexes.
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a� Explicitly broadcast any number of DataArray or Dataset objects against
one another.
xarray objects automatically broadcast against each other in arithmetic
operations, so this function should not be necessary for normal use.
Parameters
----------
*args: DataArray or Dataset objects
Arrays to broadcast against each other.
Returns
-------
broadcast: tuple of xarray objects
The same data as the input arrays, but with additional dimensions
inserted so that all data arrays have the same dimensions and shape.
Raises
------
ValueError
If indexes on the different objects are not aligned.
Examples
--------
Broadcast two data arrays against one another to fill out their dimensions:
>>> a = xr.DataArray([1, 2, 3], dims='x')
>>> b = xr.DataArray([5, 6], dims='y')
>>> a
<xarray.DataArray (x: 3)>
array([1, 2, 3])
Coordinates:
* x (x) int64 0 1 2
>>> b
<xarray.DataArray (y: 2)>
array([5, 6])
Coordinates:
* y (y) int64 0 1
>>> a2, b2 = xr.broadcast(a, b)
>>> a2
<xarray.DataArray (x: 3, y: 2)>
array([[1, 1],
[2, 2],
[3, 3]])
Coordinates:
* x (x) int64 0 1 2
* y (y) int64 0 1
>>> b2
<xarray.DataArray (x: 3, y: 2)>
array([[5, 6],
[5, 6],
[5, 6]])
Coordinates:
* y (y) int64 0 1
* x (x) int64 0 1 2
Fill out the dimensions of all data variables in a dataset:
>>> ds = xr.Dataset({'a': a, 'b': b})
>>> ds2, = xr.broadcast(ds) # use tuple unpacking to extract one dataset
>>> ds2
<xarray.Dataset>
Dimensions: (x: 3, y: 2)
Coordinates:
* x (x) int64 0 1 2
* y (y) int64 0 1
Data variables:
a (x, y) int64 1 1 2 2 3 3
b (x, y) int64 5 6 5 6 5 6
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