- dask.array.gufunc.apply_gufunc(func, signature, *args, axes=None, axis=None, keepdims=False, output_dtypes=None, output_sizes=None, vectorize=None, allow_rechunk=False, meta=None, **kwargs)
Apply a generalized ufunc or similar python function to arrays.
signaturedetermines if the function consumes or produces core dimensions. The remaining dimensions in given input arrays (
*args) are considered loop dimensions and are required to broadcast naturally against each other.
In other terms, this function is like
np.vectorize, but for the blocks of dask arrays. If the function itself shall also be vectorized use
Function to call like
func(*args, **kwargs)on input arrays (
*args) 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  (if this is not the case, set
vectorize=True). If this function returns multiple outputs,
output_core_dimshas to be set as well.
- signature: string
Specifies what core dimensions are consumed and produced by
func. According to the specification of numpy.gufunc signature 
Input arrays or scalars to the callable function.
- axes: List of tuples, optional, keyword only
A list of tuples with indices of axes a generalized ufunc should operate on. For instance, for a signature of
"(i,j),(j,k)->(i,k)"appropriate for matrix multiplication, the base elements are two-dimensional matrices and these are taken to be stored in the two last axes of each argument. The corresponding axes keyword would be
[(-2, -1), (-2, -1), (-2, -1)]. For simplicity, for generalized ufuncs that operate on 1-dimensional arrays (vectors), a single integer is accepted instead of a single-element tuple, and for generalized ufuncs for which all outputs are scalars, the output tuples can be omitted.
- axis: int, optional, keyword only
A single axis over which a generalized ufunc should operate. This is a short-cut for ufuncs that operate over a single, shared core dimension, equivalent to passing in axes with entries of (axis,) for each single-core-dimension argument and
()for all others. For instance, for a signature
"(i),(i)->()", it is equivalent to passing in
axes=[(axis,), (axis,), ()].
- keepdims: bool, optional, keyword only
If this is set to True, axes which are reduced over will be left in the result as a dimension with size one, so that the result will broadcast correctly against the inputs. This option can only be used for generalized ufuncs that operate on inputs that all have the same number of core dimensions and with outputs that have no core dimensions , i.e., with signatures like
"(m,m)->()". If used, the location of the dimensions in the output can be controlled with axes and axis.
- output_dtypesOptional, dtype or list of dtypes, keyword only
Valid numpy dtype specification or list thereof. If not given, a call of
funcwith a small set of data is performed in order to try to automatically determine the output dtypes.
- output_sizesdict, optional, keyword only
Optional mapping from dimension names to sizes for outputs. Only used if new core dimensions (not found on inputs) appear on outputs.
- vectorize: bool, keyword only
If set to
np.vectorizeis applied to
funcfor convenience. Defaults to
- allow_rechunk: Optional, bool, keyword only
Allows rechunking, otherwise chunk sizes need to match and core dimensions are to consist only of one chunk. Warning: enabling this can increase memory usage significantly. Defaults to
- meta: Optional, tuple, keyword only
tuple of empty ndarrays describing the shape and dtype of the output of the gufunc. Defaults to
Extra keyword arguments to pass to func
- Single dask.array.Array or tuple of dask.array.Array
>>> import dask.array as da >>> import numpy as np >>> def stats(x): ... return np.mean(x, axis=-1), np.std(x, axis=-1) >>> a = da.random.normal(size=(10,20,30), chunks=(5, 10, 30)) >>> mean, std = da.apply_gufunc(stats, "(i)->(),()", a) >>> mean.compute().shape (10, 20)
>>> def outer_product(x, y): ... return np.einsum("i,j->ij", x, y) >>> a = da.random.normal(size=( 20,30), chunks=(10, 30)) >>> b = da.random.normal(size=(10, 1,40), chunks=(5, 1, 40)) >>> c = da.apply_gufunc(outer_product, "(i),(j)->(i,j)", a, b, vectorize=True) >>> c.compute().shape (10, 20, 30, 40)