```"""
The rechunk module defines:
intersect_chunks: a function for
converting chunks to new dimensions
rechunk: a function to convert the blocks
of an existing dask array to new chunks or blockshape
"""
from __future__ import annotations

import heapq
import math
from functools import reduce
from itertools import chain, count, product
from operator import add, itemgetter, mul
from warnings import warn

import numpy as np
import tlz as toolz
from tlz import accumulate

from dask.array.core import Array, concatenate3, normalize_chunks

def cumdims_label(chunks, const):
"""Internal utility for cumulative sum with label.

>>> cumdims_label(((5, 3, 3), (2, 2, 1)), 'n')  # doctest: +NORMALIZE_WHITESPACE
[(('n', 0), ('n', 5), ('n', 8), ('n', 11)),
(('n', 0), ('n', 2), ('n', 4), ('n', 5))]
"""
return [
tuple(zip((const,) * (1 + len(bds)), accumulate(add, (0,) + bds)))
for bds in chunks
]

def _breakpoints(cumold, cumnew):
"""

>>> new = cumdims_label(((2, 3), (2, 2, 1)), 'n')
>>> old = cumdims_label(((2, 2, 1), (5,)), 'o')

>>> _breakpoints(new, old)
(('n', 0), ('o', 0), ('n', 2), ('o', 2), ('o', 4), ('n', 5), ('o', 5))
>>> _breakpoints(new, old)
(('n', 0), ('o', 0), ('n', 2), ('n', 4), ('n', 5), ('o', 5))
"""
return tuple(sorted(cumold + cumnew, key=itemgetter(1)))

def _intersect_1d(breaks):
"""
Internal utility to intersect chunks for 1d after preprocessing.

>>> new = cumdims_label(((2, 3), (2, 2, 1)), 'n')
>>> old = cumdims_label(((2, 2, 1), (5,)), 'o')

>>> _intersect_1d(_breakpoints(old, new))  # doctest: +NORMALIZE_WHITESPACE
[[(0, slice(0, 2, None))],
[(1, slice(0, 2, None)), (2, slice(0, 1, None))]]
>>> _intersect_1d(_breakpoints(old, new))  # doctest: +NORMALIZE_WHITESPACE
[[(0, slice(0, 2, None))],
[(0, slice(2, 4, None))],
[(0, slice(4, 5, None))]]

Parameters
----------

breaks: list of tuples
Each tuple is ('o', 8) or ('n', 8)
These are pairs of 'o' old or new 'n'
indicator with a corresponding cumulative sum,
or breakpoint (a position along the chunking axis).
The list of pairs is already ordered by breakpoint.
Note that an 'o' pair always occurs BEFORE
an 'n' pair if both share the same breakpoint.
Uses 'o' and 'n' to make new tuples of slices for
the new block crosswalk to old blocks.
"""
# EXPLANATION:
# We know each new chunk is obtained from the old chunks, but
# from which ones and how? This function provides the answer.
# On return, each new chunk is represented as a list of slices
# of the old chunks. Therefore,  paired with each slice is the
# index of the old chunk to which that slice refers.
# NOTE: if any nonzero-size new chunks extend beyond the total
#    span of the old chunks, then those new chunks are assumed
#    to be obtained from an imaginary old chunk that extends
#    from the end of that total span to infinity. The chunk-
#    index of this imaginary chunk follows in consecutive order
#    from the chunk-indices of the actual old chunks.

# First, let us determine the index of the last old_chunk:
o_pairs = [pair for pair in breaks if pair == "o"]
last_old_chunk_idx = len(o_pairs) - 2
last_o_br = o_pairs[-1]  # end of range spanning all old chunks

start = 0  # start of a slice of an old chunk
last_end = 0
old_idx = 0  # index of old chunk
last_o_end = 0
ret = []  # will hold the list of new chunks
ret_next = []  # will hold the list of slices comprising one new chunk
for idx in range(1, len(breaks)):  # Note start from the 2nd pair
# the interval between any two consecutive breakpoints is a potential
# new chunk:
label, br = breaks[idx]
last_label, last_br = breaks[idx - 1]
if last_label == "n":
# This always denotes the end of a new chunk or the start
# of the next new chunk or both
start = last_end
if ret_next:
ret.append(ret_next)
ret_next = []
else:
start = 0
end = br - last_br + start  # end of a slice of an old chunk
last_end = end
if br == last_br:
# Here we have a zero-size interval between the previous and
# current breakpoints. This should not result in a slice unless
# this interval's end-points (`last_label` and `label`) are both
# equal to 'n'
if label == "o":
old_idx += 1
last_o_end = end
if label == "n" and last_label == "n":
if br == last_o_br:
# zero-size new chunks located at the edge of the range
# spanning all the old chunks are assumed to come from the
# end of the last old chunk:
slc = slice(last_o_end, last_o_end)
ret_next.append((last_old_chunk_idx, slc))
continue
else:
continue
ret_next.append((old_idx, slice(start, end)))
if label == "o":
old_idx += 1
start = 0
last_o_end = end

if ret_next:
ret.append(ret_next)

return ret

def old_to_new(old_chunks, new_chunks):
"""Helper to build old_chunks to new_chunks.

Handles missing values, as long as the dimension with the missing chunk values
is unchanged.

Notes
-----
This function expects that the arguments have been pre-processed by
:func:`dask.array.core.normalize_chunks`. In particular any ``nan`` values should
have been replaced (and are so by :func:`dask.array.core.normalize_chunks`)
by the canonical ``np.nan``. It also expects that the arguments have been validated
with `_validate_rechunk` and rechunking is thus possible.

Examples
--------
>>> old = ((10, 10, 10, 10, 10), )
>>> new = ((25, 5, 20), )
>>> old_to_new(old, new)  # doctest: +NORMALIZE_WHITESPACE
[[[(0, slice(0, 10, None)), (1, slice(0, 10, None)), (2, slice(0, 5, None))],
[(2, slice(5, 10, None))],
[(3, slice(0, 10, None)), (4, slice(0, 10, None))]]]
"""

def is_unknown(dim):
return any(math.isnan(chunk) for chunk in dim)

dims_unknown = [is_unknown(dim) for dim in old_chunks]

known_indices = []
unknown_indices = []
for i, unknown in enumerate(dims_unknown):
if unknown:
unknown_indices.append(i)
else:
known_indices.append(i)

old_known = [old_chunks[i] for i in known_indices]
new_known = [new_chunks[i] for i in known_indices]

cmos = cumdims_label(old_known, "o")
cmns = cumdims_label(new_known, "n")

sliced = [None] * len(old_chunks)
for i, cmo, cmn in zip(known_indices, cmos, cmns):
sliced[i] = _intersect_1d(_breakpoints(cmo, cmn))

for i in unknown_indices:
dim = old_chunks[i]
# Unknown dimensions are always unchanged, so old -> new is everything
extra = [
[(j, slice(0, size if not math.isnan(size) else None))]
for j, size in enumerate(dim)
]
sliced[i] = extra
assert all(x is not None for x in sliced)
return sliced

def intersect_chunks(old_chunks, new_chunks):
"""
Make dask.array slices as intersection of old and new chunks.

>>> intersections = intersect_chunks(((4, 4), (2,)),
...                                  ((8,), (1, 1)))
>>> list(intersections)  # doctest: +NORMALIZE_WHITESPACE
[(((0, slice(0, 4, None)), (0, slice(0, 1, None))),
((1, slice(0, 4, None)), (0, slice(0, 1, None)))),
(((0, slice(0, 4, None)), (0, slice(1, 2, None))),
((1, slice(0, 4, None)), (0, slice(1, 2, None))))]

Parameters
----------

old_chunks : iterable of tuples
block sizes along each dimension (convert from old_chunks)
new_chunks: iterable of tuples
block sizes along each dimension (converts to new_chunks)
"""
cross1 = product(*old_to_new(old_chunks, new_chunks))
cross = chain(tuple(product(*cr)) for cr in cross1)
return cross

def _validate_rechunk(old_chunks, new_chunks):
"""Validates that rechunking an array from ``old_chunks`` to ``new_chunks``
is possible, raises an error if otherwise.

Notes
-----
This function expects ``old_chunks`` and ``new_chunks`` to have matching
dimensionality and will not raise an informative error if they don't.
"""
assert len(old_chunks) == len(new_chunks)

old_shapes = tuple(map(sum, old_chunks))
new_shapes = tuple(map(sum, new_chunks))

for old_shape, old_dim, new_shape, new_dim in zip(
old_shapes, old_chunks, new_shapes, new_chunks
):
if old_shape != new_shape:
if not (
math.isnan(old_shape) and math.isnan(new_shape)
) or not np.array_equal(old_dim, new_dim, equal_nan=True):
raise ValueError(
"Chunks must be unchanging along dimensions with missing values.\n\n"
"A possible solution:\n  x.compute_chunk_sizes()"
)

[docs]def rechunk(
x,
chunks="auto",
threshold=None,
block_size_limit=None,
balance=False,
method=None,
):
"""
Convert blocks in dask array x for new chunks.

Parameters
----------
Array to be rechunked.
chunks:  int, tuple, dict or str, optional
The new block dimensions to create. -1 indicates the full size of the
corresponding dimension. Default is "auto" which automatically
determines chunk sizes.
threshold: int, optional
The graph growth factor under which we don't bother introducing an
intermediate step.
block_size_limit: int, optional
The maximum block size (in bytes) we want to produce
Defaults to the configuration value ``array.chunk-size``
balance : bool, default False
If True, try to make each chunk to be the same size.

This means ``balance=True`` will remove any small leftover chunks, so
using ``x.rechunk(chunks=len(x) // N, balance=True)``
will almost certainly result in ``N`` chunks.
Rechunking method to use.

Examples
--------
>>> x = da.ones((1000, 1000), chunks=(100, 100))

Specify uniform chunk sizes with a tuple

>>> y = x.rechunk((1000, 10))

Or chunk only specific dimensions with a dictionary

>>> y = x.rechunk({0: 1000})

Use the value ``-1`` to specify that you want a single chunk along a
dimension or the value ``"auto"`` to specify that dask can freely rechunk a
dimension to attain blocks of a uniform block size

>>> y = x.rechunk({0: -1, 1: 'auto'}, block_size_limit=1e8)

If a chunk size does not divide the dimension then rechunk will leave any
unevenness to the last chunk.

>>> x.rechunk(chunks=(400, -1)).chunks
((400, 400, 200), (1000,))

However if you want more balanced chunks, and don't mind Dask choosing a
different chunksize for you then you can use the ``balance=True`` option.

>>> x.rechunk(chunks=(400, -1), balance=True).chunks
((500, 500), (1000,))
"""
# don't rechunk if array is empty
if x.ndim > 0 and all(s == 0 for s in x.shape):
return x

if isinstance(chunks, dict):
chunks = {validate_axis(c, x.ndim): v for c, v in chunks.items()}
for i in range(x.ndim):
if i not in chunks:
chunks[i] = x.chunks[i]
elif chunks[i] is None:
chunks[i] = x.chunks[i]
if isinstance(chunks, (tuple, list)):
chunks = tuple(lc if lc is not None else rc for lc, rc in zip(chunks, x.chunks))
chunks = normalize_chunks(
chunks, x.shape, limit=block_size_limit, dtype=x.dtype, previous_chunks=x.chunks
)

# Now chunks are tuple of tuples
ndim = x.ndim
if not len(chunks) == ndim:
raise ValueError("Provided chunks are not consistent with shape")

if not balance and (chunks == x.chunks):
return x

if balance:
chunks = tuple(_balance_chunksizes(chunk) for chunk in chunks)

_validate_rechunk(x.chunks, chunks)

method = method or config.get("array.rechunk.method")

steps = plan_rechunk(
x.chunks, chunks, x.dtype.itemsize, threshold, block_size_limit
)
for c in steps:
x = _compute_rechunk(x, c)

return x

elif method == "p2p":
from distributed.shuffle import rechunk_p2p

return rechunk_p2p(x, chunks)

else:
raise NotImplementedError(f"Unknown rechunking method '{method}'")

def _number_of_blocks(chunks):
return reduce(mul, map(len, chunks))

def _largest_block_size(chunks):
return reduce(mul, map(max, chunks))

def estimate_graph_size(old_chunks, new_chunks):
"""Estimate the graph size during a rechunk computation."""
# Estimate the number of intermediate blocks that will be produced
# (we don't use intersect_chunks() which is much more expensive)
crossed_size = reduce(
mul,
(
(len(oc) + len(nc) - 1 if oc != nc else len(oc))
for oc, nc in zip(old_chunks, new_chunks)
),
)
return crossed_size

def divide_to_width(desired_chunks, max_width):
"""Minimally divide the given chunks so as to make the largest chunk
width less or equal than *max_width*.
"""
chunks = []
for c in desired_chunks:
nb_divides = int(np.ceil(c / max_width))
for i in range(nb_divides):
n = c // (nb_divides - i)
chunks.append(n)
c -= n
assert c == 0
return tuple(chunks)

def merge_to_number(desired_chunks, max_number):
"""Minimally merge the given chunks so as to drop the number of
chunks below *max_number*, while minimizing the largest width.
"""
if len(desired_chunks) <= max_number:
return desired_chunks

distinct = set(desired_chunks)
if len(distinct) == 1:
# Fast path for homogeneous target, also ensuring a regular result
w = distinct.pop()
n = len(desired_chunks)
total = n * w

desired_width = total // max_number
width = w * (desired_width // w)
adjust = (total - max_number * width) // w

return (width + w,) * adjust + (width,) * (max_number - adjust)

desired_width = sum(desired_chunks) // max_number
nmerges = len(desired_chunks) - max_number

heap = [
(desired_chunks[i] + desired_chunks[i + 1], i, i + 1)
for i in range(len(desired_chunks) - 1)
]
heapq.heapify(heap)

chunks = list(desired_chunks)

while nmerges > 0:
# Find smallest interval to merge
width, i, j = heapq.heappop(heap)
# If interval was made invalid by another merge, recompute
# it, re-insert it and retry.
if chunks[j] == 0:
j += 1
while chunks[j] == 0:
j += 1
heapq.heappush(heap, (chunks[i] + chunks[j], i, j))
continue
elif chunks[i] + chunks[j] != width:
heapq.heappush(heap, (chunks[i] + chunks[j], i, j))
continue
# Merge
assert chunks[i] != 0
chunks[i] = 0  # mark deleted
chunks[j] = width
nmerges -= 1

return tuple(filter(None, chunks))

def find_merge_rechunk(old_chunks, new_chunks, block_size_limit):
"""
Find an intermediate rechunk that would merge some adjacent blocks
together in order to get us nearer the *new_chunks* target, without
violating the *block_size_limit* (in number of elements).
"""
ndim = len(old_chunks)

old_largest_width = [max(c) for c in old_chunks]
new_largest_width = [max(c) for c in new_chunks]

graph_size_effect = {
dim: len(nc) / len(oc)
for dim, (oc, nc) in enumerate(zip(old_chunks, new_chunks))
}

block_size_effect = {
dim: new_largest_width[dim] / (old_largest_width[dim] or 1)
for dim in range(ndim)
}

# Our goal is to reduce the number of nodes in the rechunk graph
# by merging some adjacent chunks, so consider dimensions where we can
# reduce the # of chunks
merge_candidates = [dim for dim in range(ndim) if graph_size_effect[dim] <= 1.0]

# Merging along each dimension reduces the graph size by a certain factor
# and increases memory largest block size by a certain factor.
# We want to optimize the graph size while staying below the given
# block_size_limit.  This is in effect a knapsack problem, except with
# multiplicative values and weights.  Just use a greedy algorithm
# by trying dimensions in decreasing value / weight order.
def key(k):
gse = graph_size_effect[k]
bse = block_size_effect[k]
if bse == 1:
bse = 1 + 1e-9
return (np.log(gse) / np.log(bse)) if bse > 0 else 0

sorted_candidates = sorted(merge_candidates, key=key)

largest_block_size = reduce(mul, old_largest_width)

chunks = list(old_chunks)
memory_limit_hit = False

for dim in sorted_candidates:
# Examine this dimension for possible graph reduction
new_largest_block_size = (
largest_block_size * new_largest_width[dim] // (old_largest_width[dim] or 1)
)
if new_largest_block_size <= block_size_limit:
# Full replacement by new chunks is possible
chunks[dim] = new_chunks[dim]
largest_block_size = new_largest_block_size
else:
# Try a partial rechunk, dividing the new chunks into
# smaller pieces
largest_width = old_largest_width[dim]
chunk_limit = int(block_size_limit * largest_width / largest_block_size)
c = divide_to_width(new_chunks[dim], chunk_limit)
if len(c) <= len(old_chunks[dim]):
# We manage to reduce the number of blocks, so do it
chunks[dim] = c
largest_block_size = largest_block_size * max(c) // largest_width

memory_limit_hit = True

assert largest_block_size == _largest_block_size(chunks)
assert largest_block_size <= block_size_limit
return tuple(chunks), memory_limit_hit

def find_split_rechunk(old_chunks, new_chunks, graph_size_limit):
"""
Find an intermediate rechunk that would split some chunks to
get us nearer *new_chunks*, without violating the *graph_size_limit*.
"""
ndim = len(old_chunks)

chunks = list(old_chunks)

for dim in range(ndim):
graph_size = estimate_graph_size(chunks, new_chunks)
if graph_size > graph_size_limit:
break
if len(old_chunks[dim]) > len(new_chunks[dim]):
# It's not interesting to split
continue
# Merge the new chunks so as to stay within the graph size budget
max_number = int(len(old_chunks[dim]) * graph_size_limit / graph_size)
c = merge_to_number(new_chunks[dim], max_number)
assert len(c) <= max_number
# Consider the merge successful if its result has a greater length
# and smaller max width than the old chunks
if len(c) >= len(old_chunks[dim]) and max(c) <= max(old_chunks[dim]):
chunks[dim] = c

return tuple(chunks)

def plan_rechunk(
old_chunks, new_chunks, itemsize, threshold=None, block_size_limit=None
):
"""Plan an iterative rechunking from *old_chunks* to *new_chunks*.
The plan aims to minimize the rechunk graph size.

Parameters
----------
itemsize: int
The item size of the array
threshold: int
The graph growth factor under which we don't bother
introducing an intermediate step
block_size_limit: int
The maximum block size (in bytes) we want to produce during an
intermediate step

Notes
-----
No intermediate steps will be planned if any dimension of ``old_chunks``
is unknown.
"""
threshold = threshold or config.get("array.rechunk.threshold")
block_size_limit = block_size_limit or config.get("array.chunk-size")
if isinstance(block_size_limit, str):
block_size_limit = parse_bytes(block_size_limit)

has_nans = (any(math.isnan(y) for y in x) for x in old_chunks)

if len(new_chunks) <= 1 or not all(new_chunks) or any(has_nans):
# Trivial array / unknown dim => no need / ability for an intermediate
return [new_chunks]

# Make it a number of elements
block_size_limit /= itemsize

# Fix block_size_limit if too small for either old_chunks or new_chunks
largest_old_block = _largest_block_size(old_chunks)
largest_new_block = _largest_block_size(new_chunks)
block_size_limit = max([block_size_limit, largest_old_block, largest_new_block])

# The graph size above which to optimize
graph_size_threshold = threshold * (
_number_of_blocks(old_chunks) + _number_of_blocks(new_chunks)
)

current_chunks = old_chunks
first_pass = True
steps = []

while True:
graph_size = estimate_graph_size(current_chunks, new_chunks)
if graph_size < graph_size_threshold:
break

if first_pass:
chunks = current_chunks
else:
# We hit the block_size_limit in a previous merge pass =>
# accept a significant increase in graph size in exchange for
# 1) getting nearer the goal 2) reducing the largest block size
# to make place for the following merge.
# To see this pass in action, make the block_size_limit very small.
chunks = find_split_rechunk(
current_chunks, new_chunks, graph_size * threshold
)
chunks, memory_limit_hit = find_merge_rechunk(
chunks, new_chunks, block_size_limit
)
if (chunks == current_chunks and not first_pass) or chunks == new_chunks:
break
if chunks != current_chunks:
steps.append(chunks)
current_chunks = chunks
if not memory_limit_hit:
break
first_pass = False

return steps + [new_chunks]

def _compute_rechunk(x, chunks):
"""Compute the rechunk of *x* to the given *chunks*."""
if x.size == 0:
# Special case for empty array, as the algorithm below does not behave correctly
return empty(x.shape, chunks=chunks, dtype=x.dtype)

ndim = x.ndim
crossed = intersect_chunks(x.chunks, chunks)
x2 = dict()
intermediates = dict()
token = tokenize(x, chunks)
merge_name = "rechunk-merge-" + token
split_name = "rechunk-split-" + token
split_name_suffixes = count()

# Pre-allocate old block references, to allow re-use and reduce the
# graph's memory footprint a bit.
old_blocks = np.empty([len(c) for c in x.chunks], dtype="O")
for index in np.ndindex(old_blocks.shape):
old_blocks[index] = (x.name,) + index

# Iterate over all new blocks
new_index = product(*(range(len(c)) for c in chunks))

for new_idx, cross1 in zip(new_index, crossed):
key = (merge_name,) + new_idx
old_block_indices = [[cr[i] for cr in cross1] for i in range(ndim)]
subdims1 = [len(set(old_block_indices[i])) for i in range(ndim)]

rec_cat_arg = np.empty(subdims1, dtype="O")
rec_cat_arg_flat = rec_cat_arg.flat

# Iterate over the old blocks required to build the new block
for rec_cat_index, ind_slices in enumerate(cross1):
old_block_index, slices = zip(*ind_slices)
name = (split_name, next(split_name_suffixes))
old_index = old_blocks[old_block_index][1:]
if all(
slc.start == 0 and slc.stop == x.chunks[i][ind]
for i, (slc, ind) in enumerate(zip(slices, old_index))
):
rec_cat_arg_flat[rec_cat_index] = old_blocks[old_block_index]
else:
intermediates[name] = (getitem, old_blocks[old_block_index], slices)
rec_cat_arg_flat[rec_cat_index] = name

assert rec_cat_index == rec_cat_arg.size - 1

# New block is formed by concatenation of sliced old blocks
if all(d == 1 for d in rec_cat_arg.shape):
x2[key] = rec_cat_arg.flat
else:
x2[key] = (concatenate3, rec_cat_arg.tolist())

del old_blocks, new_index

layer = toolz.merge(x2, intermediates)
graph = HighLevelGraph.from_collections(merge_name, layer, dependencies=[x])
return Array(graph, merge_name, chunks, meta=x)

class _PrettyBlocks:
def __init__(self, blocks):
self.blocks = blocks

def __str__(self):
runs = []
run = []
repeats = 0
for c in self.blocks:
if run and run[-1] == c:
if repeats == 0 and len(run) > 1:
runs.append((None, run[:-1]))
run = run[-1:]
repeats += 1
else:
if repeats > 0:
assert len(run) == 1
runs.append((repeats + 1, run[-1]))
run = []
repeats = 0
run.append(c)
if run:
if repeats == 0:
runs.append((None, run))
else:
assert len(run) == 1
runs.append((repeats + 1, run[-1]))

parts = []
for repeats, run in runs:
if repeats is None:
parts.append(str(run))
else:
parts.append("%d*[%s]" % (repeats, run))
return " | ".join(parts)

__repr__ = __str__

def format_blocks(blocks):
"""
Pretty-format *blocks*.

>>> format_blocks((10, 10, 10))
3*
>>> format_blocks((2, 3, 4))
[2, 3, 4]
>>> format_blocks((10, 10, 5, 6, 2, 2, 2, 7))
2* | [5, 6] | 3* | 
"""
assert isinstance(blocks, tuple) and all(
isinstance(x, int) or math.isnan(x) for x in blocks
)
return _PrettyBlocks(blocks)

def format_chunks(chunks):
"""
>>> format_chunks((10 * (3,), 3 * (10,)))
(10*, 3*)
"""
assert isinstance(chunks, tuple)
return tuple(format_blocks(c) for c in chunks)

def format_plan(plan):
"""
>>> format_plan([((10, 10, 10), (15, 15)), ((30,), (10, 10, 10))])
[(3*, 2*), (, 3*)]
"""
return [format_chunks(c) for c in plan]

def _get_chunks(n, chunksize):
leftover = n % chunksize
n_chunks = n // chunksize

chunks = [chunksize] * n_chunks
if leftover:
chunks.append(leftover)
return tuple(chunks)

def _balance_chunksizes(chunks: tuple[int, ...]) -> tuple[int, ...]:
"""
Balance the chunk sizes

Parameters
----------
chunks : tuple[int, ...]

Returns
-------
new_chunks : tuple[int, ...]
New chunks for Dask array with balanced sizes.
"""
median_len = np.median(chunks).astype(int)
n_chunks = len(chunks)
eps = median_len // 2
if min(chunks) <= 0.5 * max(chunks):
n_chunks -= 1

new_chunks = [
_get_chunks(sum(chunks), chunk_len)
for chunk_len in range(median_len - eps, median_len + eps + 1)
]
possible_chunks = [c for c in new_chunks if len(c) == n_chunks]
if not len(possible_chunks):
warn(
"chunk size balancing not possible with given chunks. "
"Try increasing the chunk size."
)
return chunks

diffs = [max(c) - min(c) for c in possible_chunks]
best_chunk_size = np.argmin(diffs)
return possible_chunks[best_chunk_size]
```