Commit 4944fbae authored by Kruyff,D.L.W. (Dylan)'s avatar Kruyff,D.L.W. (Dylan)
Browse files

Make prototype compatible for larger data size

parent 12614e4f
import warnings
from operator import getitem
from itertools import product
from numbers import Integral
from tlz import merge, pipe, concat, partial, get
from tlz.curried import map
from . import chunk, wrap
from .core import (
Array,
map_blocks,
concatenate,
concatenate3,
reshapelist,
unify_chunks,
)
from ..highlevelgraph import HighLevelGraph
from ..base import tokenize
from ..core import flatten
from ..utils import concrete
def fractional_slice(task, axes):
"""
>>> fractional_slice(('x', 5.1), {0: 2}) # doctest: +SKIP
(getitem, ('x', 6), (slice(0, 2),))
>>> fractional_slice(('x', 3, 5.1), {0: 2, 1: 3}) # doctest: +SKIP
(getitem, ('x', 3, 5), (slice(None, None, None), slice(-3, None)))
>>> fractional_slice(('x', 2.9, 5.1), {0: 2, 1: 3}) # doctest: +SKIP
(getitem, ('x', 3, 5), (slice(0, 2), slice(-3, None)))
"""
rounded = (task[0],) + tuple(int(round(i)) for i in task[1:])
index = []
for i, (t, r) in enumerate(zip(task[1:], rounded[1:])):
depth = axes.get(i, 0)
if isinstance(depth, tuple):
left_depth = depth[0]
right_depth = depth[1]
else:
left_depth = depth
right_depth = depth
if t == r:
index.append(slice(None, None, None))
elif t < r and right_depth:
index.append(slice(0, right_depth))
elif t > r and left_depth:
index.append(slice(-left_depth, None))
else:
index.append(slice(0, 0))
index = tuple(index)
if all(ind == slice(None, None, None) for ind in index):
return task
else:
return (getitem, rounded, index)
def expand_key(k, dims, name=None, axes=None):
""" Get all neighboring keys around center
Parameters
----------
k: tuple
They key around which to generate new keys
dims: Sequence[int]
The number of chunks in each dimension
name: Option[str]
The name to include in the output keys, or none to include no name
axes: Dict[int, int]
The axes active in the expansion. We don't expand on non-active axes
Examples
--------
>>> expand_key(('x', 2, 3), dims=[5, 5], name='y', axes={0: 1, 1: 1}) # doctest: +NORMALIZE_WHITESPACE
[[('y', 1.1, 2.1), ('y', 1.1, 3), ('y', 1.1, 3.9)],
[('y', 2, 2.1), ('y', 2, 3), ('y', 2, 3.9)],
[('y', 2.9, 2.1), ('y', 2.9, 3), ('y', 2.9, 3.9)]]
>>> expand_key(('x', 0, 4), dims=[5, 5], name='y', axes={0: 1, 1: 1}) # doctest: +NORMALIZE_WHITESPACE
[[('y', 0, 3.1), ('y', 0, 4)],
[('y', 0.9, 3.1), ('y', 0.9, 4)]]
"""
def inds(i, ind):
rv = []
if ind - 0.9 > 0:
rv.append(ind - 0.9)
rv.append(ind)
if ind + 0.9 < dims[i] - 1:
rv.append(ind + 0.9)
return rv
shape = []
for i, ind in enumerate(k[1:]):
num = 1
if ind > 0:
num += 1
if ind < dims[i] - 1:
num += 1
shape.append(num)
args = [
inds(i, ind) if any((axes.get(i, 0),)) else [ind] for i, ind in enumerate(k[1:])
]
if name is not None:
args = [[name]] + args
seq = list(product(*args))
shape2 = [d if any((axes.get(i, 0),)) else 1 for i, d in enumerate(shape)]
result = reshapelist(shape2, seq)
return result
def overlap_internal(x, axes):
""" Share boundaries between neighboring blocks
Parameters
----------
x: da.Array
A dask array
axes: dict
The size of the shared boundary per axis
The axes input informs how many cells to overlap between neighboring blocks
{0: 2, 2: 5} means share two cells in 0 axis, 5 cells in 2 axis
"""
dims = list(map(len, x.chunks))
expand_key2 = partial(expand_key, dims=dims, axes=axes)
# Make keys for each of the surrounding sub-arrays
interior_keys = pipe(
x.__dask_keys__(), flatten, map(expand_key2), map(flatten), concat, list
)
name = "overlap-" + tokenize(x, axes)
getitem_name = "getitem-" + tokenize(x, axes)
interior_slices = {}
overlap_blocks = {}
for k in interior_keys:
frac_slice = fractional_slice((x.name,) + k, axes)
if (x.name,) + k != frac_slice:
interior_slices[(getitem_name,) + k] = frac_slice
else:
interior_slices[(getitem_name,) + k] = (x.name,) + k
overlap_blocks[(name,) + k] = (
concatenate3,
(concrete, expand_key2((None,) + k, name=getitem_name)),
)
chunks = []
for i, bds in enumerate(x.chunks):
depth = axes.get(i, 0)
if isinstance(depth, tuple):
left_depth = depth[0]
right_depth = depth[1]
else:
left_depth = depth
right_depth = depth
if len(bds) == 1:
chunks.append(bds)
else:
left = [bds[0] + right_depth]
right = [bds[-1] + left_depth]
mid = []
for bd in bds[1:-1]:
mid.append(bd + left_depth + right_depth)
chunks.append(left + mid + right)
dsk = merge(interior_slices, overlap_blocks)
graph = HighLevelGraph.from_collections(name, dsk, dependencies=[x])
return Array(graph, name, chunks, meta=x)
def trim_overlap(x, depth, boundary=None):
"""Trim sides from each block.
This couples well with the ``map_overlap`` operation which may leave
excess data on each block.
See also
--------
dask.array.overlap.map_overlap
"""
# parameter to be passed to trim_internal
axes = coerce_depth(x.ndim, depth)
boundary2 = coerce_boundary(x.ndim, boundary)
return trim_internal(x, axes=axes, boundary=boundary2)
def trim_internal(x, axes, boundary=None):
""" Trim sides from each block
This couples well with the overlap operation, which may leave excess data on
each block
See also
--------
dask.array.chunk.trim
dask.array.map_blocks
"""
boundary = coerce_boundary(x.ndim, boundary)
olist = []
for i, bd in enumerate(x.chunks):
bdy = boundary.get(i, "none")
overlap = axes.get(i, 0)
ilist = []
for j, d in enumerate(bd):
if bdy != "none":
if isinstance(overlap, tuple):
d = d - sum(overlap)
else:
d = d - overlap * 2
else:
if isinstance(overlap, tuple):
d = d - overlap[0] if j != 0 else d
d = d - overlap[1] if j != len(bd) - 1 else d
else:
d = d - overlap if j != 0 else d
d = d - overlap if j != len(bd) - 1 else d
ilist.append(d)
olist.append(tuple(ilist))
chunks = tuple(olist)
return map_blocks(
partial(_trim, axes=axes, boundary=boundary), x, chunks=chunks, dtype=x.dtype
)
def _trim(x, axes, boundary, block_info):
"""Similar to dask.array.chunk.trim but requires one to specificy the
boundary condition.
``axes``, and ``boundary`` are assumed to have been coerced.
"""
axes = [axes.get(i, 0) for i in range(x.ndim)]
axes_front = (ax[0] if isinstance(ax, tuple) else ax for ax in axes)
axes_back = (
-ax[1]
if isinstance(ax, tuple) and ax[1]
else -ax
if isinstance(ax, Integral) and ax
else None
for ax in axes
)
trim_front = (
0 if (chunk_location == 0 and boundary.get(i, "none") == "none") else ax
for i, (chunk_location, ax) in enumerate(
zip(block_info[0]["chunk-location"], axes_front)
)
)
trim_back = (
None
if (chunk_location == chunks - 1 and boundary.get(i, "none") == "none")
else ax
for i, (chunks, chunk_location, ax) in enumerate(
zip(block_info[0]["num-chunks"], block_info[0]["chunk-location"], axes_back)
)
)
ind = tuple(slice(front, back) for front, back in zip(trim_front, trim_back))
return x[ind]
def periodic(x, axis, depth):
""" Copy a slice of an array around to its other side
Useful to create periodic boundary conditions for overlap
"""
left = (
(slice(None, None, None),) * axis
+ (slice(0, depth),)
+ (slice(None, None, None),) * (x.ndim - axis - 1)
)
right = (
(slice(None, None, None),) * axis
+ (slice(-depth, None),)
+ (slice(None, None, None),) * (x.ndim - axis - 1)
)
l = x[left]
r = x[right]
l, r = _remove_overlap_boundaries(l, r, axis, depth)
return concatenate([r, x, l], axis=axis)
def reflect(x, axis, depth):
""" Reflect boundaries of array on the same side
This is the converse of ``periodic``
"""
if depth == 1:
left = (
(slice(None, None, None),) * axis
+ (slice(0, 1),)
+ (slice(None, None, None),) * (x.ndim - axis - 1)
)
else:
left = (
(slice(None, None, None),) * axis
+ (slice(depth - 1, None, -1),)
+ (slice(None, None, None),) * (x.ndim - axis - 1)
)
right = (
(slice(None, None, None),) * axis
+ (slice(-1, -depth - 1, -1),)
+ (slice(None, None, None),) * (x.ndim - axis - 1)
)
l = x[left]
r = x[right]
l, r = _remove_overlap_boundaries(l, r, axis, depth)
return concatenate([l, x, r], axis=axis)
def nearest(x, axis, depth):
""" Each reflect each boundary value outwards
This mimics what the skimage.filters.gaussian_filter(... mode="nearest")
does.
"""
left = (
(slice(None, None, None),) * axis
+ (slice(0, 1),)
+ (slice(None, None, None),) * (x.ndim - axis - 1)
)
right = (
(slice(None, None, None),) * axis
+ (slice(-1, -2, -1),)
+ (slice(None, None, None),) * (x.ndim - axis - 1)
)
l = concatenate([x[left]] * depth, axis=axis)
r = concatenate([x[right]] * depth, axis=axis)
l, r = _remove_overlap_boundaries(l, r, axis, depth)
return concatenate([l, x, r], axis=axis)
def constant(x, axis, depth, value):
""" Add constant slice to either side of array """
chunks = list(x.chunks)
chunks[axis] = (depth,)
try:
c = wrap.full_like(
getattr(x, "_meta", x),
value,
shape=tuple(map(sum, chunks)),
chunks=tuple(chunks),
dtype=x.dtype,
)
except TypeError:
c = wrap.full(
tuple(map(sum, chunks)), value, chunks=tuple(chunks), dtype=x.dtype
)
return concatenate([c, x, c], axis=axis)
def _remove_overlap_boundaries(l, r, axis, depth):
lchunks = list(l.chunks)
lchunks[axis] = (depth,)
rchunks = list(r.chunks)
rchunks[axis] = (depth,)
l = l.rechunk(tuple(lchunks))
r = r.rechunk(tuple(rchunks))
return l, r
def boundaries(x, depth=None, kind=None):
""" Add boundary conditions to an array before overlaping
See Also
--------
periodic
constant
"""
if not isinstance(kind, dict):
kind = dict((i, kind) for i in range(x.ndim))
if not isinstance(depth, dict):
depth = dict((i, depth) for i in range(x.ndim))
for i in range(x.ndim):
d = depth.get(i, 0)
if d == 0:
continue
this_kind = kind.get(i, "none")
if this_kind == "none":
continue
elif this_kind == "periodic":
x = periodic(x, i, d)
elif this_kind == "reflect":
x = reflect(x, i, d)
elif this_kind == "nearest":
x = nearest(x, i, d)
elif i in kind:
x = constant(x, i, d, kind[i])
return x
def overlap(x, depth, boundary):
""" Share boundaries between neighboring blocks
Parameters
----------
x: da.Array
A dask array
depth: dict
The size of the shared boundary per axis
boundary: dict
The boundary condition on each axis. Options are 'reflect', 'periodic',
'nearest', 'none', or an array value. Such a value will fill the
boundary with that value.
The depth input informs how many cells to overlap between neighboring
blocks ``{0: 2, 2: 5}`` means share two cells in 0 axis, 5 cells in 2 axis.
Axes missing from this input will not be overlapped.
Examples
--------
>>> import numpy as np
>>> import dask.array as da
>>> x = np.arange(64).reshape((8, 8))
>>> d = da.from_array(x, chunks=(4, 4))
>>> d.chunks
((4, 4), (4, 4))
>>> g = da.overlap.overlap(d, depth={0: 2, 1: 1},
... boundary={0: 100, 1: 'reflect'})
>>> g.chunks
((8, 8), (6, 6))
>>> np.array(g)
array([[100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
[ 0, 0, 1, 2, 3, 4, 3, 4, 5, 6, 7, 7],
[ 8, 8, 9, 10, 11, 12, 11, 12, 13, 14, 15, 15],
[ 16, 16, 17, 18, 19, 20, 19, 20, 21, 22, 23, 23],
[ 24, 24, 25, 26, 27, 28, 27, 28, 29, 30, 31, 31],
[ 32, 32, 33, 34, 35, 36, 35, 36, 37, 38, 39, 39],
[ 40, 40, 41, 42, 43, 44, 43, 44, 45, 46, 47, 47],
[ 16, 16, 17, 18, 19, 20, 19, 20, 21, 22, 23, 23],
[ 24, 24, 25, 26, 27, 28, 27, 28, 29, 30, 31, 31],
[ 32, 32, 33, 34, 35, 36, 35, 36, 37, 38, 39, 39],
[ 40, 40, 41, 42, 43, 44, 43, 44, 45, 46, 47, 47],
[ 48, 48, 49, 50, 51, 52, 51, 52, 53, 54, 55, 55],
[ 56, 56, 57, 58, 59, 60, 59, 60, 61, 62, 63, 63],
[100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100]])
"""
depth2 = coerce_depth(x.ndim, depth)
boundary2 = coerce_boundary(x.ndim, boundary)
# is depth larger than chunk size?
depth_values = [depth2.get(i, 0) for i in range(x.ndim)]
for d, c in zip(depth_values, x.chunks):
maxd = max(d) if isinstance(d, tuple) else d
if maxd > min(c):
raise ValueError(
"The overlapping depth %d is larger than your\n"
"smallest chunk size %d. Rechunk your array\n"
"with a larger chunk size or a chunk size that\n"
"more evenly divides the shape of your array." % (d, min(c))
)
x2 = boundaries(x, depth2, boundary2)
x3 = overlap_internal(x2, depth2)
trim = dict(
(k, v * 2 if boundary2.get(k, "none") != "none" else 0)
for k, v in depth2.items()
)
x4 = chunk.trim(x3, trim)
return x4
def add_dummy_padding(x, depth, boundary):
"""
Pads an array which has 'none' as the boundary type.
Used to simplify trimming arrays which use 'none'.
>>> import dask.array as da
>>> x = da.arange(6, chunks=3)
>>> add_dummy_padding(x, {0: 1}, {0: 'none'}).compute() # doctest: +NORMALIZE_WHITESPACE
array([..., 0, 1, 2, 3, 4, 5, ...])
"""
for k, v in boundary.items():
d = depth.get(k, 0)
if v == "none" and d > 0:
empty_shape = list(x.shape)
empty_shape[k] = d
empty_chunks = list(x.chunks)
empty_chunks[k] = (d,)
try:
empty = wrap.empty_like(
getattr(x, "_meta", x),
shape=empty_shape,
chunks=empty_chunks,
dtype=x.dtype,
)
except TypeError:
empty = wrap.empty(empty_shape, chunks=empty_chunks, dtype=x.dtype)
out_chunks = list(x.chunks)
ax_chunks = list(out_chunks[k])
ax_chunks[0] += d
ax_chunks[-1] += d
out_chunks[k] = tuple(ax_chunks)
x = concatenate([empty, x, empty], axis=k)
x = x.rechunk(out_chunks)
return x
def map_overlap(
func, *args, depth=None, boundary=None, trim=True, align_arrays=True, **kwargs
):
""" Map a function over blocks of arrays with some overlap
We share neighboring zones between blocks of the array, map a
function, and then trim away the neighboring strips.
Parameters
----------
func: function
The function to apply to each extended block.
If multiple arrays are provided, then the function should expect to
receive chunks of each array in the same order.
args : dask arrays
depth: int, tuple, dict or list
The number of elements that each block should share with its neighbors
If a tuple or dict then this can be different per axis.
If a list then each element of that list must be an int, tuple or dict
defining depth for the corresponding array in `args`.
Asymmetric depths may be specified using a dict value of (-/+) tuples.
Note that asymmetric depths are currently only supported when
``boundary`` is 'none'.
The default value is 0.
boundary: str, tuple, dict or list
How to handle the boundaries.
Values include 'reflect', 'periodic', 'nearest', 'none',
or any constant value like 0 or np.nan.
If a list then each element must be a str, tuple or dict defining the
boundary for the corresponding array in `args`.
The default value is 'reflect'.
trim: bool
Whether or not to trim ``depth`` elements from each block after
calling the map function.
Set this to False if your mapping function already does this for you
align_arrays: bool
Whether or not to align chunks along equally sized dimensions when
multiple arrays are provided. This allows for larger chunks in some
arrays to be broken into smaller ones that match chunk sizes in other
arrays such that they are compatible for block function mapping. If
this is false, then an error will be thrown if arrays do not already
have the same number of blocks in each dimension.
**kwargs:
Other keyword arguments valid in ``map_blocks``
Examples
--------