# xarray.apply_ufunc¶

xarray.apply_ufunc(func : Callable, *args : Any, input_core_dims : Optional[Sequence[Sequence]] = None, output_core_dims : Optional[Sequence[Sequence]] = ((), ), exclude_dims : Collection = frozenset(), vectorize : bool = False, join : str = 'exact', dataset_join : str = 'exact', dataset_fill_value : Any = _NO_FILL_VALUE, keep_attrs : bool = False, kwargs : Mapping = None, dask : str = 'forbidden', output_dtypes : Optional[Sequence] = None, output_sizes : Optional[Mapping[Any, int]] = None)

Apply a vectorized function for unlabeled arrays on xarray objects.

The function will be mapped over the data variable(s) of the input arguments using xarray’s standard rules for labeled computation, including alignment, broadcasting, looping over GroupBy/Dataset variables, and merging of coordinates.

Parameters: func : callable Function to call like func(*args, **kwargs) on unlabeled arrays (.data) that returns an array or tuple of arrays. If multiple arguments with non-matching dimensions are supplied, this function is expected to vectorize (broadcast) over axes of positional arguments in the style of NumPy universal functions [R35] (if this is not the case, set vectorize=True). If this function returns multiple outputs, you must set output_core_dims as well. *args : Dataset, DataArray, GroupBy, Variable, numpy/dask arrays or scalars Mix of labeled and/or unlabeled arrays to which to apply the function. input_core_dims : Sequence[Sequence], optional List of the same length as args giving the list of core dimensions on each input argument that should not be broadcast. By default, we assume there are no core dimensions on any input arguments. For example ,input_core_dims=[[], [‘time’]] indicates that all dimensions on the first argument and all dimensions other than ‘time’ on the second argument should be broadcast. Core dimensions are automatically moved to the last axes of input variables before applying func, which facilitates using NumPy style generalized ufuncs [R36]. output_core_dims : List[tuple], optional List of the same length as the number of output arguments from func, giving the list of core dimensions on each output that were not broadcast on the inputs. By default, we assume that func outputs exactly one array, with axes corresponding to each broadcast dimension. Core dimensions are assumed to appear as the last dimensions of each output in the provided order. exclude_dims : set, optional Core dimensions on the inputs to exclude from alignment and broadcasting entirely. Any input coordinates along these dimensions will be dropped. Each excluded dimension must also appear in input_core_dims for at least one argument. vectorize : bool, optional If True, then assume func only takes arrays defined over core dimensions as input and vectorize it automatically with numpy.vectorize(). This option exists for convenience, but is almost always slower than supplying a pre-vectorized function. Using this option requires NumPy version 1.12 or newer. join : {‘outer’, ‘inner’, ‘left’, ‘right’, ‘exact’}, optional Method for joining the indexes of the passed objects along each dimension, and the variables of Dataset objects with mismatched data variables: ‘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 ‘exact’: raise ValueError instead of aligning when indexes to be aligned are not equal dataset_join : {‘outer’, ‘inner’, ‘left’, ‘right’, ‘exact’}, optional Method for joining variables of Dataset objects with mismatched data variables. ‘outer’: take variables from both Dataset objects ‘inner’: take only overlapped variables ‘left’: take only variables from the first object ‘right’: take only variables from the last object ‘exact’: data variables on all Dataset objects must match exactly dataset_fill_value : optional Value used in place of missing variables on Dataset inputs when the datasets do not share the exact same data_vars. Required if dataset_join not in {'inner', 'exact'}, otherwise ignored. keep_attrs: boolean, Optional Whether to copy attributes from the first argument to the output. kwargs: dict, optional Optional keyword arguments passed directly on to call func. dask: ‘forbidden’, ‘allowed’ or ‘parallelized’, optional How to handle applying to objects containing lazy data in the form of dask arrays: ‘forbidden’ (default): raise an error if a dask array is encountered. ‘allowed’: pass dask arrays directly on to func. ‘parallelized’: automatically parallelize func if any of the inputs are a dask array. If used, the output_dtypes argument must also be provided. Multiple output arguments are not yet supported. output_dtypes : list of dtypes, optional Optional list of output dtypes. Only used if dask=’parallelized’. output_sizes : dict, optional Optional mapping from dimension names to sizes for outputs. Only used if dask=’parallelized’ and new dimensions (not found on inputs) appear on outputs. Single value or tuple of Dataset, DataArray, Variable, dask.array.Array or numpy.ndarray, the first type on that list to appear on an input.

References

Examples

For illustrative purposes only, here are examples of how you could use apply_ufunc to write functions to (very nearly) replicate existing xarray functionality:

Calculate the vector magnitude of two arguments:

def magnitude(a, b):
func = lambda x, y: np.sqrt(x ** 2 + y ** 2)
return xr.apply_func(func, a, b)


Compute the mean (.mean) over one dimension:

def mean(obj, dim):
# note: apply always moves core dimensions to the end
return apply_ufunc(np.mean, obj,
input_core_dims=[[dim]],
kwargs={'axis': -1})


Inner product over a specific dimension:

def _inner(x, y):
result = np.matmul(x[..., np.newaxis, :], y[..., :, np.newaxis])
return result[..., 0, 0]

def inner_product(a, b, dim):
return apply_ufunc(_inner, a, b, input_core_dims=[[dim], [dim]])


Stack objects along a new dimension (like xr.concat):

def stack(objects, dim, new_coord):
# note: this version does not stack coordinates
func = lambda *x: np.stack(x, axis=-1)
result = apply_ufunc(func, *objects,
output_core_dims=[[dim]],
join='outer',
dataset_fill_value=np.nan)
result[dim] = new_coord
return result


If your function is not vectorized but can be applied only to core dimensions, you can use vectorize=True to turn into a vectorized function. This wraps numpy.vectorize(), so the operation isn’t terribly fast. Here we’ll use it to calculate the distance between empirical samples from two probability distributions, using a scipy function that needs to be applied to vectors:

import scipy.stats

def earth_mover_distance(first_samples,
second_samples,
dim='ensemble'):
return apply_ufunc(scipy.stats.wasserstein_distance,
first_samples, second_samples,
input_core_dims=[[dim], [dim]],
vectorize=True)


Most of NumPy’s builtin functions already broadcast their inputs appropriately for use in apply. You may find helper functions such as numpy.broadcast_arrays helpful in writing your function. apply_ufunc also works well with numba’s vectorize and guvectorize. Further explanation with examples are provided in the xarray documentation [3].