from __future__ import annotations
import builtins
import contextlib
import math
import operator
import warnings
from collections.abc import Iterable
from functools import partial
from itertools import product, repeat
from numbers import Integral, Number
import numpy as np
from tlz import accumulate, compose, drop, get, partition_all, pluck
from dask import config
from dask.array import chunk
from dask.array.blockwise import blockwise
from dask.array.core import (
Array,
_concatenate2,
asanyarray,
broadcast_to,
handle_out,
implements,
unknown_chunk_message,
)
from dask.array.creation import arange, diagonal
from dask.array.dispatch import divide_lookup, nannumel_lookup, numel_lookup
from dask.array.numpy_compat import ComplexWarning
from dask.array.utils import (
array_safe,
asarray_safe,
compute_meta,
is_arraylike,
meta_from_array,
validate_axis,
)
from dask.array.wrap import ones, zeros
from dask.base import tokenize
from dask.blockwise import lol_tuples
from dask.highlevelgraph import HighLevelGraph
from dask.utils import (
apply,
deepmap,
derived_from,
funcname,
getargspec,
is_series_like,
)
def divide(a, b, dtype=None):
key = lambda x: getattr(x, "__array_priority__", float("-inf"))
f = divide_lookup.dispatch(type(builtins.max(a, b, key=key)))
return f(a, b, dtype=dtype)
def numel(x, **kwargs):
return numel_lookup(x, **kwargs)
def nannumel(x, **kwargs):
return nannumel_lookup(x, **kwargs)
[docs]def reduction(
x,
chunk,
aggregate,
axis=None,
keepdims=False,
dtype=None,
split_every=None,
combine=None,
name=None,
out=None,
concatenate=True,
output_size=1,
meta=None,
weights=None,
):
"""General version of reductions
Parameters
----------
x: Array
Data being reduced along one or more axes
chunk: callable(x_chunk, [weights_chunk=None], axis, keepdims)
First function to be executed when resolving the dask graph.
This function is applied in parallel to all original chunks of x.
See below for function parameters.
combine: callable(x_chunk, axis, keepdims), optional
Function used for intermediate recursive aggregation (see
split_every below). If omitted, it defaults to aggregate.
If the reduction can be performed in less than 3 steps, it will not
be invoked at all.
aggregate: callable(x_chunk, axis, keepdims)
Last function to be executed when resolving the dask graph,
producing the final output. It is always invoked, even when the reduced
Array counts a single chunk along the reduced axes.
axis: int or sequence of ints, optional
Axis or axes to aggregate upon. If omitted, aggregate along all axes.
keepdims: boolean, optional
Whether the reduction function should preserve the reduced axes,
leaving them at size ``output_size``, or remove them.
dtype: np.dtype
data type of output. This argument was previously optional, but
leaving as ``None`` will now raise an exception.
split_every: int >= 2 or dict(axis: int), optional
Determines the depth of the recursive aggregation. If set to or more
than the number of input chunks, the aggregation will be performed in
two steps, one ``chunk`` function per input chunk and a single
``aggregate`` function at the end. If set to less than that, an
intermediate ``combine`` function will be used, so that any one
``combine`` or ``aggregate`` function has no more than ``split_every``
inputs. The depth of the aggregation graph will be
:math:`log_{split_every}(input chunks along reduced axes)`. Setting to
a low value can reduce cache size and network transfers, at the cost of
more CPU and a larger dask graph.
Omit to let dask heuristically decide a good default. A default can
also be set globally with the ``split_every`` key in
:mod:`dask.config`.
name: str, optional
Prefix of the keys of the intermediate and output nodes. If omitted it
defaults to the function names.
out: Array, optional
Another dask array whose contents will be replaced. Omit to create a
new one. Note that, unlike in numpy, this setting gives no performance
benefits whatsoever, but can still be useful if one needs to preserve
the references to a previously existing Array.
concatenate: bool, optional
If True (the default), the outputs of the ``chunk``/``combine``
functions are concatenated into a single np.array before being passed
to the ``combine``/``aggregate`` functions. If False, the input of
``combine`` and ``aggregate`` will be either a list of the raw outputs
of the previous step or a single output, and the function will have to
concatenate it itself. It can be useful to set this to False if the
chunk and/or combine steps do not produce np.arrays.
output_size: int >= 1, optional
Size of the output of the ``aggregate`` function along the reduced
axes. Ignored if keepdims is False.
weights : array_like, optional
Weights to be used in the reduction of `x`. Will be
automatically broadcast to the shape of `x`, and so must have
a compatible shape. For instance, if `x` has shape ``(3, 4)``
then acceptable shapes for `weights` are ``(3, 4)``, ``(4,)``,
``(3, 1)``, ``(1, 1)``, ``(1)``, and ``()``.
Returns
-------
dask array
**Function Parameters**
x_chunk: numpy.ndarray
Individual input chunk. For ``chunk`` functions, it is one of the
original chunks of x. For ``combine`` and ``aggregate`` functions, it's
the concatenation of the outputs produced by the previous ``chunk`` or
``combine`` functions. If concatenate=False, it's a list of the raw
outputs from the previous functions.
weights_chunk: numpy.ndarray, optional
Only applicable to the ``chunk`` function. Weights, with the
same shape as `x_chunk`, to be applied during the reduction of
the individual input chunk. If ``weights`` have not been
provided then the function may omit this parameter. When
`weights_chunk` is included then it must occur immediately
after the `x_chunk` parameter, and must also have a default
value for cases when ``weights`` are not provided.
axis: tuple
Normalized list of axes to reduce upon, e.g. ``(0, )``
Scalar, negative, and None axes have been normalized away.
Note that some numpy reduction functions cannot reduce along multiple
axes at once and strictly require an int in input. Such functions have
to be wrapped to cope.
keepdims: bool
Whether the reduction function should preserve the reduced axes or
remove them.
"""
if axis is None:
axis = tuple(range(x.ndim))
if isinstance(axis, Integral):
axis = (axis,)
axis = validate_axis(axis, x.ndim)
if dtype is None:
raise ValueError("Must specify dtype")
if "dtype" in getargspec(chunk).args:
chunk = partial(chunk, dtype=dtype)
if "dtype" in getargspec(aggregate).args:
aggregate = partial(aggregate, dtype=dtype)
if is_series_like(x):
x = x.values
# Map chunk across all blocks
inds = tuple(range(x.ndim))
args = (x, inds)
if weights is not None:
# Broadcast weights to x and add to args
wgt = asanyarray(weights)
try:
wgt = broadcast_to(wgt, x.shape)
except ValueError:
raise ValueError(
f"Weights with shape {wgt.shape} are not broadcastable "
f"to x with shape {x.shape}"
)
args += (wgt, inds)
# The dtype of `tmp` doesn't actually matter, and may be incorrect.
tmp = blockwise(
chunk, inds, *args, axis=axis, keepdims=True, token=name, dtype=dtype or float
)
tmp._chunks = tuple(
(output_size,) * len(c) if i in axis else c for i, c in enumerate(tmp.chunks)
)
if meta is None and hasattr(x, "_meta"):
try:
reduced_meta = compute_meta(
chunk, x.dtype, x._meta, axis=axis, keepdims=True, computing_meta=True
)
except TypeError:
reduced_meta = compute_meta(
chunk, x.dtype, x._meta, axis=axis, keepdims=True
)
except ValueError:
pass
else:
reduced_meta = None
result = _tree_reduce(
tmp,
aggregate,
axis,
keepdims,
dtype,
split_every,
combine,
name=name,
concatenate=concatenate,
reduced_meta=reduced_meta,
)
if keepdims and output_size != 1:
result._chunks = tuple(
(output_size,) if i in axis else c for i, c in enumerate(tmp.chunks)
)
if meta is not None:
result._meta = meta
return handle_out(out, result)
def _tree_reduce(
x,
aggregate,
axis,
keepdims,
dtype,
split_every=None,
combine=None,
name=None,
concatenate=True,
reduced_meta=None,
):
"""Perform the tree reduction step of a reduction.
Lower level, users should use ``reduction`` or ``arg_reduction`` directly.
"""
# Normalize split_every
split_every = split_every or config.get("split_every", 4)
if isinstance(split_every, dict):
split_every = {k: split_every.get(k, 2) for k in axis}
elif isinstance(split_every, Integral):
n = builtins.max(int(split_every ** (1 / (len(axis) or 1))), 2)
split_every = dict.fromkeys(axis, n)
else:
raise ValueError("split_every must be a int or a dict")
# Reduce across intermediates
depth = 1
for i, n in enumerate(x.numblocks):
if i in split_every and split_every[i] != 1:
depth = int(builtins.max(depth, math.ceil(math.log(n, split_every[i]))))
func = partial(combine or aggregate, axis=axis, keepdims=True)
if concatenate:
func = compose(func, partial(_concatenate2, axes=sorted(axis)))
for _ in range(depth - 1):
x = partial_reduce(
func,
x,
split_every,
True,
dtype=dtype,
name=(name or funcname(combine or aggregate)) + "-partial",
reduced_meta=reduced_meta,
)
func = partial(aggregate, axis=axis, keepdims=keepdims)
if concatenate:
func = compose(func, partial(_concatenate2, axes=sorted(axis)))
return partial_reduce(
func,
x,
split_every,
keepdims=keepdims,
dtype=dtype,
name=(name or funcname(aggregate)) + "-aggregate",
reduced_meta=reduced_meta,
)
def partial_reduce(
func, x, split_every, keepdims=False, dtype=None, name=None, reduced_meta=None
):
"""Partial reduction across multiple axes.
Parameters
----------
func : function
x : Array
split_every : dict
Maximum reduction block sizes in each dimension.
Examples
--------
Reduce across axis 0 and 2, merging a maximum of 1 block in the 0th
dimension, and 3 blocks in the 2nd dimension:
>>> partial_reduce(np.min, x, {0: 1, 2: 3}) # doctest: +SKIP
"""
name = (
(name or funcname(func)) + "-" + tokenize(func, x, split_every, keepdims, dtype)
)
parts = [
list(partition_all(split_every.get(i, 1), range(n)))
for (i, n) in enumerate(x.numblocks)
]
keys = product(*map(range, map(len, parts)))
out_chunks = [
tuple(1 for p in partition_all(split_every[i], c)) if i in split_every else c
for (i, c) in enumerate(x.chunks)
]
if not keepdims:
out_axis = [i for i in range(x.ndim) if i not in split_every]
getter = lambda k: get(out_axis, k)
keys = map(getter, keys)
out_chunks = list(getter(out_chunks))
dsk = {}
for k, p in zip(keys, product(*parts)):
free = {
i: j[0] for (i, j) in enumerate(p) if len(j) == 1 and i not in split_every
}
dummy = dict(i for i in enumerate(p) if i[0] in split_every)
g = lol_tuples((x.name,), range(x.ndim), free, dummy)
dsk[(name,) + k] = (func, g)
graph = HighLevelGraph.from_collections(name, dsk, dependencies=[x])
meta = x._meta
if reduced_meta is not None:
try:
meta = func(reduced_meta, computing_meta=True)
# no meta keyword argument exists for func, and it isn't required
except TypeError:
try:
meta = func(reduced_meta)
except ValueError as e:
# min/max functions have no identity, don't apply function to meta
if "zero-size array to reduction operation" in str(e):
meta = reduced_meta
# when no work can be computed on the empty array (e.g., func is a ufunc)
except ValueError:
pass
# some functions can't compute empty arrays (those for which reduced_meta
# fall into the ValueError exception) and we have to rely on reshaping
# the array according to len(out_chunks)
if is_arraylike(meta) and meta.ndim != len(out_chunks):
if len(out_chunks) == 0:
meta = meta.sum()
else:
meta = meta.reshape((0,) * len(out_chunks))
if np.isscalar(meta):
return Array(graph, name, out_chunks, dtype=dtype)
else:
with contextlib.suppress(AttributeError), warnings.catch_warnings():
if name.startswith("var") or name.startswith("moment"):
warnings.simplefilter("ignore", ComplexWarning)
meta = meta.astype(dtype)
return Array(graph, name, out_chunks, meta=meta)
[docs]@derived_from(np)
def sum(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None):
if dtype is None:
dtype = getattr(np.zeros(1, dtype=a.dtype).sum(), "dtype", object)
result = reduction(
a,
chunk.sum,
chunk.sum,
axis=axis,
keepdims=keepdims,
dtype=dtype,
split_every=split_every,
out=out,
)
return result
[docs]@derived_from(np)
def prod(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None):
if dtype is not None:
dt = dtype
else:
dt = getattr(np.ones((1,), dtype=a.dtype).prod(), "dtype", object)
return reduction(
a,
chunk.prod,
chunk.prod,
axis=axis,
keepdims=keepdims,
dtype=dt,
split_every=split_every,
out=out,
)
[docs]@implements(np.min, np.amin)
@derived_from(np)
def min(a, axis=None, keepdims=False, split_every=None, out=None):
return reduction(
a,
chunk_min,
chunk.min,
combine=chunk_min,
axis=axis,
keepdims=keepdims,
dtype=a.dtype,
split_every=split_every,
out=out,
)
def chunk_min(x, axis=None, keepdims=None):
"""Version of np.min which ignores size 0 arrays"""
if x.size == 0:
return array_safe([], x, ndmin=x.ndim, dtype=x.dtype)
else:
return np.min(x, axis=axis, keepdims=keepdims)
[docs]@implements(np.max, np.amax)
@derived_from(np)
def max(a, axis=None, keepdims=False, split_every=None, out=None):
return reduction(
a,
chunk_max,
chunk.max,
combine=chunk_max,
axis=axis,
keepdims=keepdims,
dtype=a.dtype,
split_every=split_every,
out=out,
)
def chunk_max(x, axis=None, keepdims=None):
"""Version of np.max which ignores size 0 arrays"""
if x.size == 0:
return array_safe([], x, ndmin=x.ndim, dtype=x.dtype)
else:
return np.max(x, axis=axis, keepdims=keepdims)
[docs]@derived_from(np)
def any(a, axis=None, keepdims=False, split_every=None, out=None):
return reduction(
a,
chunk.any,
chunk.any,
axis=axis,
keepdims=keepdims,
dtype="bool",
split_every=split_every,
out=out,
)
[docs]@derived_from(np)
def all(a, axis=None, keepdims=False, split_every=None, out=None):
return reduction(
a,
chunk.all,
chunk.all,
axis=axis,
keepdims=keepdims,
dtype="bool",
split_every=split_every,
out=out,
)
[docs]@derived_from(np)
def nansum(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None):
if dtype is not None:
dt = dtype
else:
dt = getattr(chunk.nansum(np.ones((1,), dtype=a.dtype)), "dtype", object)
return reduction(
a,
chunk.nansum,
chunk.sum,
axis=axis,
keepdims=keepdims,
dtype=dt,
split_every=split_every,
out=out,
)
[docs]@derived_from(np)
def nanprod(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None):
if dtype is not None:
dt = dtype
else:
dt = getattr(chunk.nansum(np.ones((1,), dtype=a.dtype)), "dtype", object)
return reduction(
a,
chunk.nanprod,
chunk.prod,
axis=axis,
keepdims=keepdims,
dtype=dt,
split_every=split_every,
out=out,
)
[docs]@derived_from(np)
def nancumsum(x, axis, dtype=None, out=None, *, method="sequential"):
"""Dask added an additional keyword-only argument ``method``.
method : {'sequential', 'blelloch'}, optional
Choose which method to use to perform the cumsum. Default is 'sequential'.
* 'sequential' performs the cumsum of each prior block before the current block.
* 'blelloch' is a work-efficient parallel cumsum. It exposes parallelism by
first taking the sum of each block and combines the sums via a binary tree.
This method may be faster or more memory efficient depending on workload,
scheduler, and hardware. More benchmarking is necessary.
"""
return cumreduction(
chunk.nancumsum,
operator.add,
0,
x,
axis,
dtype,
out=out,
method=method,
preop=np.nansum,
)
[docs]@derived_from(np)
def nancumprod(x, axis, dtype=None, out=None, *, method="sequential"):
"""Dask added an additional keyword-only argument ``method``.
method : {'sequential', 'blelloch'}, optional
Choose which method to use to perform the cumprod. Default is 'sequential'.
* 'sequential' performs the cumprod of each prior block before the current block.
* 'blelloch' is a work-efficient parallel cumprod. It exposes parallelism by first
taking the product of each block and combines the products via a binary tree.
This method may be faster or more memory efficient depending on workload,
scheduler, and hardware. More benchmarking is necessary.
"""
return cumreduction(
chunk.nancumprod,
operator.mul,
1,
x,
axis,
dtype,
out=out,
method=method,
preop=np.nanprod,
)
[docs]@derived_from(np)
def nanmin(a, axis=None, keepdims=False, split_every=None, out=None):
if np.isnan(a.size):
raise ValueError(f"Arrays chunk sizes are unknown. {unknown_chunk_message}")
if a.size == 0:
raise ValueError(
"zero-size array to reduction operation fmin which has no identity"
)
return reduction(
a,
_nanmin_skip,
_nanmin_skip,
axis=axis,
keepdims=keepdims,
dtype=a.dtype,
split_every=split_every,
out=out,
)
def _nanmin_skip(x_chunk, axis, keepdims):
if x_chunk.size > 0:
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", "All-NaN slice encountered", RuntimeWarning
)
return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)
else:
return asarray_safe(
np.array([], dtype=x_chunk.dtype), like=meta_from_array(x_chunk)
)
[docs]@derived_from(np)
def nanmax(a, axis=None, keepdims=False, split_every=None, out=None):
if np.isnan(a.size):
raise ValueError(f"Arrays chunk sizes are unknown. {unknown_chunk_message}")
if a.size == 0:
raise ValueError(
"zero-size array to reduction operation fmax which has no identity"
)
return reduction(
a,
_nanmax_skip,
_nanmax_skip,
axis=axis,
keepdims=keepdims,
dtype=a.dtype,
split_every=split_every,
out=out,
)
def _nanmax_skip(x_chunk, axis, keepdims):
if x_chunk.size > 0:
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", "All-NaN slice encountered", RuntimeWarning
)
return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
else:
return asarray_safe(
np.array([], dtype=x_chunk.dtype), like=meta_from_array(x_chunk)
)
def mean_chunk(
x, sum=chunk.sum, numel=numel, dtype="f8", computing_meta=False, **kwargs
):
if computing_meta:
return x
n = numel(x, dtype=dtype, **kwargs)
total = sum(x, dtype=dtype, **kwargs)
return {"n": n, "total": total}
def mean_combine(
pairs,
sum=chunk.sum,
numel=numel,
dtype="f8",
axis=None,
computing_meta=False,
**kwargs,
):
if not isinstance(pairs, list):
pairs = [pairs]
ns = deepmap(lambda pair: pair["n"], pairs) if not computing_meta else pairs
n = _concatenate2(ns, axes=axis).sum(axis=axis, **kwargs)
if computing_meta:
return n
totals = deepmap(lambda pair: pair["total"], pairs)
total = _concatenate2(totals, axes=axis).sum(axis=axis, **kwargs)
return {"n": n, "total": total}
def mean_agg(pairs, dtype="f8", axis=None, computing_meta=False, **kwargs):
ns = deepmap(lambda pair: pair["n"], pairs) if not computing_meta else pairs
n = _concatenate2(ns, axes=axis)
n = np.sum(n, axis=axis, dtype=dtype, **kwargs)
if computing_meta:
return n
totals = deepmap(lambda pair: pair["total"], pairs)
total = _concatenate2(totals, axes=axis).sum(axis=axis, dtype=dtype, **kwargs)
with np.errstate(divide="ignore", invalid="ignore"):
return divide(total, n, dtype=dtype)
[docs]@derived_from(np)
def mean(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None):
if dtype is not None:
dt = dtype
elif a.dtype == object:
dt = object
else:
dt = getattr(np.mean(np.zeros(shape=(1,), dtype=a.dtype)), "dtype", object)
return reduction(
a,
mean_chunk,
mean_agg,
axis=axis,
keepdims=keepdims,
dtype=dt,
split_every=split_every,
combine=mean_combine,
out=out,
concatenate=False,
)
[docs]@derived_from(np)
def nanmean(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None):
if dtype is not None:
dt = dtype
else:
dt = getattr(np.mean(np.ones(shape=(1,), dtype=a.dtype)), "dtype", object)
return reduction(
a,
partial(mean_chunk, sum=chunk.nansum, numel=nannumel),
mean_agg,
axis=axis,
keepdims=keepdims,
dtype=dt,
split_every=split_every,
out=out,
concatenate=False,
combine=partial(mean_combine, sum=chunk.nansum, numel=nannumel),
)
def moment_chunk(
A,
order=2,
sum=chunk.sum,
numel=numel,
dtype="f8",
computing_meta=False,
implicit_complex_dtype=False,
**kwargs,
):
if computing_meta:
return A
n = numel(A, **kwargs)
n = n.astype(np.int64)
if implicit_complex_dtype:
total = sum(A, **kwargs)
else:
total = sum(A, dtype=dtype, **kwargs)
with np.errstate(divide="ignore", invalid="ignore"):
u = total / n
d = A - u
if np.issubdtype(A.dtype, np.complexfloating):
d = np.abs(d)
xs = [sum(d**i, dtype=dtype, **kwargs) for i in range(2, order + 1)]
M = np.stack(xs, axis=-1)
return {"total": total, "n": n, "M": M}
def _moment_helper(Ms, ns, inner_term, order, sum, axis, kwargs):
M = Ms[..., order - 2].sum(axis=axis, **kwargs) + sum(
ns * inner_term**order, axis=axis, **kwargs
)
for k in range(1, order - 1):
coeff = math.factorial(order) / (math.factorial(k) * math.factorial(order - k))
M += coeff * sum(Ms[..., order - k - 2] * inner_term**k, axis=axis, **kwargs)
return M
def moment_combine(
pairs,
order=2,
ddof=0,
dtype="f8",
sum=np.sum,
axis=None,
computing_meta=False,
**kwargs,
):
if not isinstance(pairs, list):
pairs = [pairs]
kwargs["dtype"] = None
kwargs["keepdims"] = True
ns = deepmap(lambda pair: pair["n"], pairs) if not computing_meta else pairs
ns = _concatenate2(ns, axes=axis)
n = ns.sum(axis=axis, **kwargs)
if computing_meta:
return n
totals = _concatenate2(deepmap(lambda pair: pair["total"], pairs), axes=axis)
Ms = _concatenate2(deepmap(lambda pair: pair["M"], pairs), axes=axis)
total = totals.sum(axis=axis, **kwargs)
with np.errstate(divide="ignore", invalid="ignore"):
if np.issubdtype(total.dtype, np.complexfloating):
mu = divide(total, n)
inner_term = np.abs(divide(totals, ns) - mu)
else:
mu = divide(total, n, dtype=dtype)
inner_term = divide(totals, ns, dtype=dtype) - mu
xs = [
_moment_helper(Ms, ns, inner_term, o, sum, axis, kwargs)
for o in range(2, order + 1)
]
M = np.stack(xs, axis=-1)
return {"total": total, "n": n, "M": M}
def moment_agg(
pairs,
order=2,
ddof=0,
dtype="f8",
sum=np.sum,
axis=None,
computing_meta=False,
**kwargs,
):
if not isinstance(pairs, list):
pairs = [pairs]
kwargs["dtype"] = dtype
# To properly handle ndarrays, the original dimensions need to be kept for
# part of the calculation.
keepdim_kw = kwargs.copy()
keepdim_kw["keepdims"] = True
keepdim_kw["dtype"] = None
ns = deepmap(lambda pair: pair["n"], pairs) if not computing_meta else pairs
ns = _concatenate2(ns, axes=axis)
n = ns.sum(axis=axis, **keepdim_kw)
if computing_meta:
return n
totals = _concatenate2(deepmap(lambda pair: pair["total"], pairs), axes=axis)
Ms = _concatenate2(deepmap(lambda pair: pair["M"], pairs), axes=axis)
mu = divide(totals.sum(axis=axis, **keepdim_kw), n)
with np.errstate(divide="ignore", invalid="ignore"):
if np.issubdtype(totals.dtype, np.complexfloating):
inner_term = np.abs(divide(totals, ns) - mu)
else:
inner_term = divide(totals, ns, dtype=dtype) - mu
M = _moment_helper(Ms, ns, inner_term, order, sum, axis, kwargs)
denominator = n.sum(axis=axis, **kwargs) - ddof
# taking care of the edge case with empty or all-nans array with ddof > 0
if isinstance(denominator, Number):
if denominator < 0:
denominator = np.nan
elif denominator is not np.ma.masked:
denominator[denominator < 0] = np.nan
return divide(M, denominator, dtype=dtype)
[docs]def moment(
a, order, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None
):
"""Calculate the nth centralized moment.
Parameters
----------
a : Array
Data over which to compute moment
order : int
Order of the moment that is returned, must be >= 2.
axis : int, optional
Axis along which the central moment is computed. The default is to
compute the moment of the flattened array.
dtype : data-type, optional
Type to use in computing the moment. For arrays of integer type the
default is float64; for arrays of float types it is the same as the
array type.
keepdims : bool, optional
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 original array.
ddof : int, optional
"Delta Degrees of Freedom": the divisor used in the calculation is
N - ddof, where N represents the number of elements. By default
ddof is zero.
Returns
-------
moment : Array
References
----------
.. [1] Pebay, Philippe (2008), "Formulas for Robust, One-Pass Parallel
Computation of Covariances and Arbitrary-Order Statistical Moments",
Technical Report SAND2008-6212, Sandia National Laboratories.
"""
if not isinstance(order, Integral) or order < 0:
raise ValueError("Order must be an integer >= 0")
if order < 2:
reduced = a.sum(axis=axis) # get reduced shape and chunks
if order == 0:
# When order equals 0, the result is 1, by definition.
return ones(
reduced.shape, chunks=reduced.chunks, dtype="f8", meta=reduced._meta
)
# By definition the first order about the mean is 0.
return zeros(
reduced.shape, chunks=reduced.chunks, dtype="f8", meta=reduced._meta
)
if dtype is not None:
dt = dtype
else:
dt = getattr(np.var(np.ones(shape=(1,), dtype=a.dtype)), "dtype", object)
implicit_complex_dtype = dtype is None and np.iscomplexobj(a)
return reduction(
a,
partial(
moment_chunk, order=order, implicit_complex_dtype=implicit_complex_dtype
),
partial(moment_agg, order=order, ddof=ddof),
axis=axis,
keepdims=keepdims,
dtype=dt,
split_every=split_every,
out=out,
concatenate=False,
combine=partial(moment_combine, order=order),
)
[docs]@derived_from(np)
def var(a, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None):
if dtype is not None:
dt = dtype
else:
dt = getattr(np.var(np.ones(shape=(1,), dtype=a.dtype)), "dtype", object)
implicit_complex_dtype = dtype is None and np.iscomplexobj(a)
return reduction(
a,
partial(moment_chunk, implicit_complex_dtype=implicit_complex_dtype),
partial(moment_agg, ddof=ddof),
axis=axis,
keepdims=keepdims,
dtype=dt,
split_every=split_every,
combine=moment_combine,
name="var",
out=out,
concatenate=False,
)
[docs]@derived_from(np)
def nanvar(
a, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None
):
if dtype is not None:
dt = dtype
else:
dt = getattr(np.var(np.ones(shape=(1,), dtype=a.dtype)), "dtype", object)
implicit_complex_dtype = dtype is None and np.iscomplexobj(a)
return reduction(
a,
partial(
moment_chunk,
sum=chunk.nansum,
numel=nannumel,
implicit_complex_dtype=implicit_complex_dtype,
),
partial(moment_agg, sum=np.nansum, ddof=ddof),
axis=axis,
keepdims=keepdims,
dtype=dt,
split_every=split_every,
combine=partial(moment_combine, sum=np.nansum),
out=out,
concatenate=False,
)
def _sqrt(a):
if isinstance(a, np.ma.masked_array) and not a.shape and a.mask.all():
return np.ma.masked
return np.sqrt(a)
def safe_sqrt(a):
"""A version of sqrt that properly handles scalar masked arrays.
To mimic ``np.ma`` reductions, we need to convert scalar masked arrays that
have an active mask to the ``np.ma.masked`` singleton. This is properly
handled automatically for reduction code, but not for ufuncs. We implement
a simple version here, since calling `np.ma.sqrt` everywhere is
significantly more expensive.
"""
if hasattr(a, "_elemwise"):
return a._elemwise(_sqrt, a)
return _sqrt(a)
[docs]@derived_from(np)
def std(a, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None):
result = safe_sqrt(
var(
a,
axis=axis,
dtype=dtype,
keepdims=keepdims,
ddof=ddof,
split_every=split_every,
out=out,
)
)
if dtype and dtype != result.dtype:
result = result.astype(dtype)
return result
[docs]@derived_from(np)
def nanstd(
a, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None
):
result = safe_sqrt(
nanvar(
a,
axis=axis,
dtype=dtype,
keepdims=keepdims,
ddof=ddof,
split_every=split_every,
out=out,
)
)
if dtype and dtype != result.dtype:
result = result.astype(dtype)
return result
def _arg_combine(data, axis, argfunc, keepdims=False):
"""Merge intermediate results from ``arg_*`` functions"""
if isinstance(data, dict):
# Array type doesn't support structured arrays (e.g., CuPy),
# therefore `data` is stored in a `dict`.
assert data["vals"].ndim == data["arg"].ndim
axis = (
None
if len(axis) == data["vals"].ndim or data["vals"].ndim == 1
else axis[0]
)
else:
axis = None if len(axis) == data.ndim or data.ndim == 1 else axis[0]
vals = data["vals"]
arg = data["arg"]
if axis is None:
local_args = argfunc(vals, axis=axis, keepdims=keepdims)
vals = vals.ravel()[local_args]
arg = arg.ravel()[local_args]
else:
local_args = argfunc(vals, axis=axis)
inds = list(np.ogrid[tuple(map(slice, local_args.shape))])
inds.insert(axis, local_args)
inds = tuple(inds)
vals = vals[inds]
arg = arg[inds]
if keepdims:
vals = np.expand_dims(vals, axis)
arg = np.expand_dims(arg, axis)
return arg, vals
def arg_chunk(func, argfunc, x, axis, offset_info):
arg_axis = None if len(axis) == x.ndim or x.ndim == 1 else axis[0]
vals = func(x, axis=arg_axis, keepdims=True)
arg = argfunc(x, axis=arg_axis, keepdims=True)
if x.ndim > 0:
if arg_axis is None:
offset, total_shape = offset_info
ind = np.unravel_index(arg.ravel()[0], x.shape)
total_ind = tuple(o + i for (o, i) in zip(offset, ind))
arg[:] = np.ravel_multi_index(total_ind, total_shape)
else:
arg += offset_info
if isinstance(vals, np.ma.masked_array):
if "min" in argfunc.__name__:
fill_value = np.ma.minimum_fill_value(vals)
else:
fill_value = np.ma.maximum_fill_value(vals)
vals = np.ma.filled(vals, fill_value)
try:
result = np.empty_like(
vals, shape=vals.shape, dtype=[("vals", vals.dtype), ("arg", arg.dtype)]
)
except TypeError:
# Array type doesn't support structured arrays (e.g., CuPy)
result = dict()
result["vals"] = vals
result["arg"] = arg
return result
def arg_combine(argfunc, data, axis=None, **kwargs):
arg, vals = _arg_combine(data, axis, argfunc, keepdims=True)
try:
result = np.empty_like(
vals, shape=vals.shape, dtype=[("vals", vals.dtype), ("arg", arg.dtype)]
)
except TypeError:
# Array type doesn't support structured arrays (e.g., CuPy).
result = dict()
result["vals"] = vals
result["arg"] = arg
return result
def arg_agg(argfunc, data, axis=None, keepdims=False, **kwargs):
return _arg_combine(data, axis, argfunc, keepdims=keepdims)[0]
def nanarg_agg(argfunc, data, axis=None, keepdims=False, **kwargs):
arg, vals = _arg_combine(data, axis, argfunc, keepdims=keepdims)
if np.any(np.isnan(vals)):
raise ValueError("All NaN slice encountered")
return arg
def arg_reduction(
x, chunk, combine, agg, axis=None, keepdims=False, split_every=None, out=None
):
"""Generic function for argreduction.
Parameters
----------
x : Array
chunk : callable
Partialed ``arg_chunk``.
combine : callable
Partialed ``arg_combine``.
agg : callable
Partialed ``arg_agg``.
axis : int, optional
split_every : int or dict, optional
"""
if axis is None:
axis = tuple(range(x.ndim))
ravel = True
elif isinstance(axis, Integral):
axis = validate_axis(axis, x.ndim)
axis = (axis,)
ravel = x.ndim == 1
else:
raise TypeError(f"axis must be either `None` or int, got '{axis}'")
for ax in axis:
chunks = x.chunks[ax]
if len(chunks) > 1 and np.isnan(chunks).any():
raise ValueError(
"Arg-reductions do not work with arrays that have "
"unknown chunksizes. At some point in your computation "
"this array lost chunking information.\n\n"
"A possible solution is with \n"
" x.compute_chunk_sizes()"
)
# Map chunk across all blocks
name = f"arg-reduce-{tokenize(axis, x, chunk, combine, split_every)}"
old = x.name
keys = list(product(*map(range, x.numblocks)))
offsets = list(product(*(accumulate(operator.add, bd[:-1], 0) for bd in x.chunks)))
if ravel:
offset_info = zip(offsets, repeat(x.shape))
else:
offset_info = pluck(axis[0], offsets)
chunks = tuple((1,) * len(c) if i in axis else c for (i, c) in enumerate(x.chunks))
dsk = {
(name,) + k: (chunk, (old,) + k, axis, off)
for (k, off) in zip(keys, offset_info)
}
dtype = np.argmin(asarray_safe([1], like=meta_from_array(x)))
meta = None
if is_arraylike(dtype):
# This case occurs on non-NumPy types (e.g., CuPy), where the returned
# value is an ndarray rather than a scalar.
meta = dtype
dtype = meta.dtype
graph = HighLevelGraph.from_collections(name, dsk, dependencies=[x])
tmp = Array(graph, name, chunks, dtype=dtype, meta=meta)
result = _tree_reduce(
tmp,
agg,
axis,
keepdims=keepdims,
dtype=dtype,
split_every=split_every,
combine=combine,
)
return handle_out(out, result)
def _nanargmin(x, axis, **kwargs):
try:
return chunk.nanargmin(x, axis, **kwargs)
except ValueError:
return chunk.nanargmin(np.where(np.isnan(x), np.inf, x), axis, **kwargs)
def _nanargmax(x, axis, **kwargs):
try:
return chunk.nanargmax(x, axis, **kwargs)
except ValueError:
return chunk.nanargmax(np.where(np.isnan(x), -np.inf, x), axis, **kwargs)
[docs]@derived_from(np)
def argmax(a, axis=None, keepdims=False, split_every=None, out=None):
return arg_reduction(
a,
partial(arg_chunk, chunk.max, chunk.argmax),
partial(arg_combine, chunk.argmax),
partial(arg_agg, chunk.argmax),
axis=axis,
keepdims=keepdims,
split_every=split_every,
out=out,
)
[docs]@derived_from(np)
def argmin(a, axis=None, keepdims=False, split_every=None, out=None):
return arg_reduction(
a,
partial(arg_chunk, chunk.min, chunk.argmin),
partial(arg_combine, chunk.argmin),
partial(arg_agg, chunk.argmin),
axis=axis,
keepdims=keepdims,
split_every=split_every,
out=out,
)
[docs]@derived_from(np)
def nanargmax(a, axis=None, keepdims=False, split_every=None, out=None):
return arg_reduction(
a,
partial(arg_chunk, chunk.nanmax, _nanargmax),
partial(arg_combine, _nanargmax),
partial(nanarg_agg, _nanargmax),
axis=axis,
keepdims=keepdims,
split_every=split_every,
out=out,
)
[docs]@derived_from(np)
def nanargmin(a, axis=None, keepdims=False, split_every=None, out=None):
return arg_reduction(
a,
partial(arg_chunk, chunk.nanmin, _nanargmin),
partial(arg_combine, _nanargmin),
partial(nanarg_agg, _nanargmin),
axis=axis,
keepdims=keepdims,
split_every=split_every,
out=out,
)
def _prefixscan_combine(func, binop, pre, x, axis, dtype):
"""Combine results of a parallel prefix scan such as cumsum
Parameters
----------
func : callable
Cumulative function (e.g. ``np.cumsum``)
binop : callable
Associative function (e.g. ``add``)
pre : np.array
The value calculated in parallel from ``preop``.
For example, the sum of all the previous blocks.
x : np.array
Current block
axis : int
dtype : dtype
Returns
-------
np.array
"""
# We could compute this in two tasks.
# This would allow us to do useful work (i.e., func), while waiting on `pre`.
# Using one task may guide the scheduler to do better and reduce scheduling overhead.
return binop(pre, func(x, axis=axis, dtype=dtype))
def _prefixscan_first(func, x, axis, dtype):
"""Compute the prefix scan (e.g., cumsum) on the first block
Parameters
----------
func : callable
Cumulative function (e.g. ``np.cumsum``)
x : np.array
Current block
axis : int
dtype : dtype
Returns
-------
np.array
"""
return func(x, axis=axis, dtype=dtype)
def prefixscan_blelloch(func, preop, binop, x, axis=None, dtype=None, out=None):
"""Generic function to perform parallel cumulative scan (a.k.a prefix scan)
The Blelloch prefix scan is work-efficient and exposes parallelism.
A parallel cumsum works by first taking the sum of each block, then do a binary tree
merge followed by a fan-out (i.e., the Brent-Kung pattern). We then take the cumsum
of each block and add the sum of the previous blocks.
When performing a cumsum across N chunks, this method has 2 * lg(N) levels of dependencies.
In contrast, the sequential method has N levels of dependencies.
Floating point operations should be more accurate with this method compared to sequential.
Parameters
----------
func : callable
Cumulative function (e.g. ``np.cumsum``)
preop : callable
Function to get the final value of a cumulative function (e.g., ``np.sum``)
binop : callable
Associative function (e.g. ``add``)
x : dask array
axis : int
dtype : dtype
Returns
-------
dask array
"""
if axis is None:
x = x.flatten().rechunk(chunks=x.npartitions)
axis = 0
if dtype is None:
dtype = getattr(func(np.ones((0,), dtype=x.dtype)), "dtype", object)
assert isinstance(axis, Integral)
axis = validate_axis(axis, x.ndim)
name = f"{func.__name__}-{tokenize(func, axis, preop, binop, x, dtype)}"
base_key = (name,)
# Right now, the metadata for batches is incorrect, but this should be okay
batches = x.map_blocks(preop, axis=axis, keepdims=True, dtype=dtype)
# We don't need the last index until the end
*indices, last_index = full_indices = [
list(
product(
*[range(nb) if j != axis else [i] for j, nb in enumerate(x.numblocks)]
)
)
for i in range(x.numblocks[axis])
]
prefix_vals = [[(batches.name,) + index for index in vals] for vals in indices]
dsk = {}
n_vals = len(prefix_vals)
level = 0
if n_vals >= 2:
# Upsweep
stride = 1
stride2 = 2
while stride2 <= n_vals:
for i in range(stride2 - 1, n_vals, stride2):
new_vals = []
for index, left_val, right_val in zip(
indices[i], prefix_vals[i - stride], prefix_vals[i]
):
key = base_key + index + (level, i)
dsk[key] = (binop, left_val, right_val)
new_vals.append(key)
prefix_vals[i] = new_vals
stride = stride2
stride2 *= 2
level += 1
# Downsweep
# With `n_vals == 3`, we would have `stride = 1` and `stride = 0`, but we need
# to do a downsweep iteration, so make sure stride2 is at least 2.
stride2 = builtins.max(2, 2 ** math.ceil(math.log2(n_vals // 2)))
stride = stride2 // 2
while stride > 0:
for i in range(stride2 + stride - 1, n_vals, stride2):
new_vals = []
for index, left_val, right_val in zip(
indices[i], prefix_vals[i - stride], prefix_vals[i]
):
key = base_key + index + (level, i)
dsk[key] = (binop, left_val, right_val)
new_vals.append(key)
prefix_vals[i] = new_vals
stride2 = stride
stride //= 2
level += 1
if full_indices:
for index in full_indices[0]:
dsk[base_key + index] = (
_prefixscan_first,
func,
(x.name,) + index,
axis,
dtype,
)
for indexes, vals in zip(drop(1, full_indices), prefix_vals):
for index, val in zip(indexes, vals):
dsk[base_key + index] = (
_prefixscan_combine,
func,
binop,
val,
(x.name,) + index,
axis,
dtype,
)
if len(full_indices) < 2:
deps = [x]
else:
deps = [x, batches]
graph = HighLevelGraph.from_collections(name, dsk, dependencies=deps)
result = Array(graph, name, x.chunks, batches.dtype)
return handle_out(out, result)
def cumreduction(
func,
binop,
ident,
x,
axis=None,
dtype=None,
out=None,
method="sequential",
preop=None,
):
"""Generic function for cumulative reduction
Parameters
----------
func: callable
Cumulative function like np.cumsum or np.cumprod
binop: callable
Associated binary operator like ``np.cumsum->add`` or ``np.cumprod->mul``
ident: Number
Associated identity like ``np.cumsum->0`` or ``np.cumprod->1``
x: dask Array
axis: int
dtype: dtype
method : {'sequential', 'blelloch'}, optional
Choose which method to use to perform the cumsum. Default is 'sequential'.
* 'sequential' performs the scan of each prior block before the current block.
* 'blelloch' is a work-efficient parallel scan. It exposes parallelism by first
calling ``preop`` on each block and combines the values via a binary tree.
This method may be faster or more memory efficient depending on workload,
scheduler, and hardware. More benchmarking is necessary.
preop: callable, optional
Function used by 'blelloch' method,
like ``np.cumsum->np.sum`` or ``np.cumprod->np.prod``
Returns
-------
dask array
See also
--------
cumsum
cumprod
"""
if method == "blelloch":
if preop is None:
raise TypeError(
'cumreduction with "blelloch" method required `preop=` argument'
)
return prefixscan_blelloch(func, preop, binop, x, axis, dtype, out=out)
elif method != "sequential":
raise ValueError(
f'Invalid method for cumreduction. Expected "sequential" or "blelloch". Got: {method!r}'
)
if axis is None:
x = x.flatten().rechunk(chunks=x.npartitions)
axis = 0
if dtype is None:
dtype = getattr(func(np.ones((0,), dtype=x.dtype)), "dtype", object)
assert isinstance(axis, Integral)
axis = validate_axis(axis, x.ndim)
m = x.map_blocks(func, axis=axis, dtype=dtype)
name = f"{func.__name__}-{tokenize(func, axis, binop, ident, x, dtype)}"
n = x.numblocks[axis]
full = slice(None, None, None)
slc = (full,) * axis + (slice(-1, None),) + (full,) * (x.ndim - axis - 1)
indices = list(
product(*[range(nb) if i != axis else [0] for i, nb in enumerate(x.numblocks)])
)
dsk = dict()
for ind in indices:
shape = tuple(x.chunks[i][ii] if i != axis else 1 for i, ii in enumerate(ind))
dsk[(name, "extra") + ind] = (
apply,
np.full_like,
(x._meta, ident, m.dtype),
{"shape": shape},
)
dsk[(name,) + ind] = (m.name,) + ind
for i in range(1, n):
last_indices = indices
indices = list(
product(
*[range(nb) if ii != axis else [i] for ii, nb in enumerate(x.numblocks)]
)
)
for old, ind in zip(last_indices, indices):
this_slice = (name, "extra") + ind
dsk[this_slice] = (
binop,
(name, "extra") + old,
(operator.getitem, (m.name,) + old, slc),
)
dsk[(name,) + ind] = (binop, this_slice, (m.name,) + ind)
graph = HighLevelGraph.from_collections(name, dsk, dependencies=[m])
result = Array(graph, name, x.chunks, m.dtype, meta=x._meta)
return handle_out(out, result)
def _cumsum_merge(a, b):
if isinstance(a, np.ma.masked_array) or isinstance(b, np.ma.masked_array):
values = np.ma.getdata(a) + np.ma.getdata(b)
return np.ma.masked_array(values, mask=np.ma.getmaskarray(b))
return a + b
def _cumprod_merge(a, b):
if isinstance(a, np.ma.masked_array) or isinstance(b, np.ma.masked_array):
values = np.ma.getdata(a) * np.ma.getdata(b)
return np.ma.masked_array(values, mask=np.ma.getmaskarray(b))
return a * b
[docs]@derived_from(np)
def cumsum(x, axis=None, dtype=None, out=None, method="sequential"):
"""Dask added an additional keyword-only argument ``method``.
method : {'sequential', 'blelloch'}, optional
Choose which method to use to perform the cumsum. Default is 'sequential'.
* 'sequential' performs the cumsum of each prior block before the current block.
* 'blelloch' is a work-efficient parallel cumsum. It exposes parallelism by
first taking the sum of each block and combines the sums via a binary tree.
This method may be faster or more memory efficient depending on workload,
scheduler, and hardware. More benchmarking is necessary.
"""
return cumreduction(
np.cumsum,
_cumsum_merge,
0,
x,
axis,
dtype,
out=out,
method=method,
preop=np.sum,
)
[docs]@derived_from(np)
def cumprod(x, axis=None, dtype=None, out=None, method="sequential"):
"""Dask added an additional keyword-only argument ``method``.
method : {'sequential', 'blelloch'}, optional
Choose which method to use to perform the cumprod. Default is 'sequential'.
* 'sequential' performs the cumprod of each prior block before the current block.
* 'blelloch' is a work-efficient parallel cumprod. It exposes parallelism by first
taking the product of each block and combines the products via a binary tree.
This method may be faster or more memory efficient depending on workload,
scheduler, and hardware. More benchmarking is necessary.
"""
return cumreduction(
np.cumprod,
_cumprod_merge,
1,
x,
axis,
dtype,
out=out,
method=method,
preop=np.prod,
)
[docs]def topk(a, k, axis=-1, split_every=None):
"""Extract the k largest elements from a on the given axis,
and return them sorted from largest to smallest.
If k is negative, extract the -k smallest elements instead,
and return them sorted from smallest to largest.
This performs best when ``k`` is much smaller than the chunk size. All
results will be returned in a single chunk along the given axis.
Parameters
----------
x: Array
Data being sorted
k: int
axis: int, optional
split_every: int >=2, optional
See :func:`reduce`. This parameter becomes very important when k is
on the same order of magnitude of the chunk size or more, as it
prevents getting the whole or a significant portion of the input array
in memory all at once, with a negative impact on network transfer
too when running on distributed.
Returns
-------
Selection of x with size abs(k) along the given axis.
Examples
--------
>>> import dask.array as da
>>> x = np.array([5, 1, 3, 6])
>>> d = da.from_array(x, chunks=2)
>>> d.topk(2).compute()
array([6, 5])
>>> d.topk(-2).compute()
array([1, 3])
"""
axis = validate_axis(axis, a.ndim)
# chunk and combine steps of the reduction, which recursively invoke
# np.partition to pick the top/bottom k elements from the previous step.
# The selection is not sorted internally.
chunk_combine = partial(chunk.topk, k=k)
# aggregate step of the reduction. Internally invokes the chunk/combine
# function, then sorts the results internally.
aggregate = partial(chunk.topk_aggregate, k=k)
return reduction(
a,
chunk=chunk_combine,
combine=chunk_combine,
aggregate=aggregate,
axis=axis,
keepdims=True,
dtype=a.dtype,
split_every=split_every,
output_size=abs(k),
)
[docs]def argtopk(a, k, axis=-1, split_every=None):
"""Extract the indices of the k largest elements from a on the given axis,
and return them sorted from largest to smallest. If k is negative, extract
the indices of the -k smallest elements instead, and return them sorted
from smallest to largest.
This performs best when ``k`` is much smaller than the chunk size. All
results will be returned in a single chunk along the given axis.
Parameters
----------
x: Array
Data being sorted
k: int
axis: int, optional
split_every: int >=2, optional
See :func:`topk`. The performance considerations for topk also apply
here.
Returns
-------
Selection of np.intp indices of x with size abs(k) along the given axis.
Examples
--------
>>> import dask.array as da
>>> x = np.array([5, 1, 3, 6])
>>> d = da.from_array(x, chunks=2)
>>> d.argtopk(2).compute()
array([3, 0])
>>> d.argtopk(-2).compute()
array([1, 2])
"""
axis = validate_axis(axis, a.ndim)
# Generate nodes where every chunk is a tuple of (a, original index of a)
idx = arange(a.shape[axis], chunks=(a.chunks[axis],), dtype=np.intp)
idx = idx[tuple(slice(None) if i == axis else np.newaxis for i in range(a.ndim))]
a_plus_idx = a.map_blocks(chunk.argtopk_preprocess, idx, dtype=object)
# chunk and combine steps of the reduction. They acquire in input a tuple
# of (a, original indices of a) and return another tuple containing the top
# k elements of a and the matching original indices. The selection is not
# sorted internally, as in np.argpartition.
chunk_combine = partial(chunk.argtopk, k=k)
# aggregate step of the reduction. Internally invokes the chunk/combine
# function, then sorts the results internally, drops a and returns the
# index only.
aggregate = partial(chunk.argtopk_aggregate, k=k)
if isinstance(axis, Number):
naxis = 1
else:
naxis = len(axis)
meta = a._meta.astype(np.intp).reshape((0,) * (a.ndim - naxis + 1))
return reduction(
a_plus_idx,
chunk=chunk_combine,
combine=chunk_combine,
aggregate=aggregate,
axis=axis,
keepdims=True,
dtype=np.intp,
split_every=split_every,
concatenate=False,
output_size=abs(k),
meta=meta,
)
[docs]@derived_from(np)
def trace(a, offset=0, axis1=0, axis2=1, dtype=None):
return diagonal(a, offset=offset, axis1=axis1, axis2=axis2).sum(-1, dtype=dtype)