/
ak_unflatten.py
205 lines (170 loc) · 8.57 KB
/
ak_unflatten.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE
import awkward as ak
np = ak.nplike.NumpyMetadata.instance()
def unflatten(array, counts, axis=0, highlevel=True, behavior=None):
raise NotImplementedError
# """
# Args:
# array: Data to create an array with an additional level from.
# counts (int or array): Number of elements the new level should have.
# If an integer, the new level will be regularly sized; otherwise,
# it will consist of variable-length lists with the given lengths.
# axis (int): The dimension at which this operation is applied. The
# outermost dimension is `0`, followed by `1`, etc., and negative
# values count backward from the innermost: `-1` is the innermost
# dimension, `-2` is the next level up, etc.
# highlevel (bool): If True, return an #ak.Array; otherwise, return
# a low-level #ak.layout.Content subclass.
# behavior (None or dict): Custom #ak.behavior for the output array, if
# high-level.
# Returns an array with an additional level of nesting. This is roughly the
# inverse of #ak.flatten, where `counts` were obtained by #ak.num (both with
# `axis=1`).
# For example,
# >>> original = ak.Array([[0, 1, 2], [], [3, 4], [5], [6, 7, 8, 9]])
# >>> counts = ak.num(original)
# >>> array = ak.flatten(original)
# >>> counts
# <Array [3, 0, 2, 1, 4] type='5 * int64'>
# >>> array
# <Array [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] type='10 * int64'>
# >>> ak.unflatten(array, counts)
# <Array [[0, 1, 2], [], ... [5], [6, 7, 8, 9]] type='5 * var * int64'>
# An inner dimension can be unflattened by setting the `axis` parameter, but
# operations like this constrain the `counts` more tightly.
# For example, we can subdivide an already divided list:
# >>> original = ak.Array([[1, 2, 3, 4], [], [5, 6, 7], [8, 9]])
# >>> print(ak.unflatten(original, [2, 2, 1, 2, 1, 1], axis=1))
# [[[1, 2], [3, 4]], [], [[5], [6, 7]], [[8], [9]]]
# But the counts have to add up to the lengths of those lists. We can't mix
# values from the first `[1, 2, 3, 4]` with values from the next `[5, 6, 7]`.
# >>> print(ak.unflatten(original, [2, 1, 2, 2, 1, 1], axis=1))
# Traceback (most recent call last):
# ...
# ValueError: structure imposed by 'counts' does not fit in the array at axis=1
# Also note that new lists created by this function cannot cross partitions
# (which is only possible at `axis=0`, anyway).
# See also #ak.num and #ak.flatten.
# """
# nplike = ak.nplike.of(array)
# layout = ak._v2.operations.convert.to_layout(
# array, allow_record=False, allow_other=False
# )
# if isinstance(counts, (numbers.Integral, np.integer)):
# current_offsets = None
# else:
# counts = ak._v2.operations.convert.to_layout(
# counts, allow_record=False, allow_other=False
# )
# ptr_lib = ak._v2.operations.convert.kernels(array)
# counts = ak._v2.operations.convert.to_kernels(counts, ptr_lib, highlevel=False)
# if ptr_lib == "cpu":
# counts = ak._v2.operations.convert.to_numpy(counts, allow_missing=True)
# mask = ak.nplike.numpy.ma.getmask(counts)
# counts = ak.nplike.numpy.ma.filled(counts, 0)
# elif ptr_lib == "cuda":
# counts = ak._v2.operations.convert.to_cupy(counts)
# mask = False
# else:
# raise AssertionError(
# "unrecognized kernels lib"
# )
# if counts.ndim != 1:
# raise ValueError(
# "counts must be one-dimensional"
# )
# if not issubclass(counts.dtype.type, np.integer):
# raise ValueError(
# "counts must be integers"
# )
# current_offsets = [nplike.empty(len(counts) + 1, np.int64)]
# current_offsets[0][0] = 0
# nplike.cumsum(counts, out=current_offsets[0][1:])
# def doit(layout):
# if isinstance(counts, (numbers.Integral, np.integer)):
# if counts < 0 or counts > len(layout):
# raise ValueError(
# "too large counts for array or negative counts"
#
# )
# out = ak._v2.contents.RegularArray(layout, counts)
# else:
# position = (
# nplike.searchsorted(
# current_offsets[0], nplike.array([len(layout)]), side="right"
# )[0]
# - 1
# )
# if position >= len(current_offsets[0]) or current_offsets[0][
# position
# ] != len(layout):
# raise ValueError(
# "structure imposed by 'counts' does not fit in the array or partition "
# "at axis={0}".format(axis)
# )
# offsets = current_offsets[0][: position + 1]
# current_offsets[0] = current_offsets[0][position:] - len(layout)
# out = ak._v2.contents.ListOffsetArray64(ak._v2.contents.Index64(offsets), layout)
# if not isinstance(mask, (bool, np.bool_)):
# index = ak._v2.index.Index8(nplike.asarray(mask).astype(np.int8))
# out = ak._v2.contents.ByteMaskedArray(index, out, valid_when=False)
# return out
# if axis == 0 or layout.axis_wrap_if_negative(axis) == 0:
# if isinstance(layout, ak.partition.PartitionedArray): # NO PARTITIONED ARRAY
# outparts = []
# for part in layout.partitions:
# outparts.append(doit(part))
# out = ak.partition.IrregularlyPartitionedArray(outparts) # NO PARTITIONED ARRAY
# else:
# out = doit(layout)
# else:
# def transform(layout, depth, posaxis):
# # Pack the current layout. This ensures that the `counts` array,
# # which is computed with these layouts applied, aligns with the
# # internal layout to be unflattened (#910)
# layout = _pack_layout(layout)
# posaxis = layout.axis_wrap_if_negative(posaxis)
# if posaxis == depth and isinstance(layout, ak._v2._util.listtypes):
# # We are one *above* the level where we want to apply this.
# listoffsetarray = layout.toListOffsetArray64(True)
# outeroffsets = nplike.asarray(listoffsetarray.offsets)
# content = doit(listoffsetarray.content[: outeroffsets[-1]])
# if isinstance(content, ak._v2.contents.ByteMaskedArray):
# inneroffsets = nplike.asarray(content.content.offsets)
# elif isinstance(content, ak._v2.contents.RegularArray):
# inneroffsets = nplike.asarray(
# content.toListOffsetArray64(True).offsets
# )
# else:
# inneroffsets = nplike.asarray(content.offsets)
# positions = (
# nplike.searchsorted(inneroffsets, outeroffsets, side="right") - 1
# )
# if not nplike.array_equal(inneroffsets[positions], outeroffsets):
# raise ValueError(
# "structure imposed by 'counts' does not fit in the array or partition "
# "at axis={0}".format(axis)
# )
# positions[0] = 0
# return ak._v2.contents.ListOffsetArray64(
# ak._v2.index.Index64(positions), content
# )
# else:
# return ak._v2._util.transform_child_layouts(
# transform, layout, depth, posaxis
# )
# if isinstance(layout, ak.partition.PartitionedArray): # NO PARTITIONED ARRAY
# outparts = []
# for part in layout.partitions:
# outparts.append(transform(part, depth=1, posaxis=axis))
# out = ak.partition.IrregularlyPartitionedArray(outparts) # NO PARTITIONED ARRAY
# else:
# out = transform(layout, depth=1, posaxis=axis)
# if current_offsets is not None and not (
# len(current_offsets[0]) == 1 and current_offsets[0][0] == 0
# ):
# raise ValueError(
# "structure imposed by 'counts' does not fit in the array or partition "
# "at axis={0}".format(axis)
# )
# return ak._v2._util.maybe_wrap_like(out, array, behavior, highlevel)