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C++ refactoring: ak._v2.operations.convert.to_numpy is done. #1204

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Jan 5, 2022
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C++ refactoring: ak._v2.operations.convert.to_numpy is done. #1204

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208 changes: 10 additions & 198 deletions src/awkward/_v2/operations/convert/ak_to_numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,8 @@

from __future__ import absolute_import

import numpy

import awkward as ak

np = ak.nplike.NumpyMetadata.instance()
Expand All @@ -24,7 +26,8 @@ def to_numpy(array, allow_missing=True):
nested lists in a NumPy `"O"` array are severed from the array and
cannot be sliced as dimensions.

If `array` is a scalar, it is converted into a NumPy scalar.
If `array` is not an Awkward Array, then this function is equivalent
to calling `np.asarray` on it.

If `allow_missing` is True; NumPy
[masked arrays](https://docs.scipy.org/doc/numpy/reference/maskedarray.html)
Expand All @@ -34,201 +37,10 @@ def to_numpy(array, allow_missing=True):
See also #ak.from_numpy and #ak.to_cupy.
"""
layout = ak._v2.operations.convert.to_layout(
array, allow_record=False, allow_other=False
array, allow_record=True, allow_other=True
)
return layout.to_numpy(allow_missing=allow_missing)
#
# if isinstance(array, (bool, str, bytes, numbers.Number)):
# return numpy.array([array])[0]
#
# elif ak._v2._util.py27 and isinstance(array, ak._v2._util.unicode):
# return numpy.array([array])[0]
#
# elif isinstance(array, np.ndarray):
# return array
#
# elif isinstance(array, ak._v2.highlevel.Array):
# return to_numpy(array.layout, allow_missing=allow_missing)
#
# elif isinstance(array, ak._v2.highlevel.Record):
# out = array.layout
# return to_numpy(out.array[out.at : out.at + 1], allow_missing=allow_missing)[0]
#
# elif isinstance(array, ak._v2.highlevel.ArrayBuilder):
# return to_numpy(array.snapshot().layout, allow_missing=allow_missing)
#
# elif isinstance(array, ak.layout.ArrayBuilder):
# return to_numpy(array.snapshot(), allow_missing=allow_missing)
#
# elif ak._v2.operations.describe.parameters(array).get("__array__") == "bytestring":
# return numpy.array(
# [
# ak._v2.behaviors.string.ByteBehavior(array[i]).__bytes__()
# for i in range(len(array))
# ]
# )
#
# elif ak._v2.operations.describe.parameters(array).get("__array__") == "string":
# return numpy.array(
# [
# ak._v2.behaviors.string.CharBehavior(array[i]).__str__()
# for i in range(len(array))
# ]
# )
#
# elif (
# str(ak._v2.operations.describe.type(array)) == "datetime64"
# or str(ak._v2.operations.describe.type(array)) == "timedelta64"
# ):
# return array
#
# elif isinstance(array, ak.partition.PartitionedArray): # NO PARTITIONED ARRAY
# tocat = [to_numpy(x, allow_missing=allow_missing) for x in array.partitions]
# if any(isinstance(x, numpy.ma.MaskedArray) for x in tocat):
# return numpy.ma.concatenate(tocat)
# else:
# return numpy.concatenate(tocat)
#
# elif isinstance(array, ak._v2._util.virtualtypes):
# return to_numpy(array.array, allow_missing=True)
#
# elif isinstance(array, ak._v2._util.unknowntypes):
# return numpy.array([])
#
# elif isinstance(array, ak._v2._util.indexedtypes):
# return to_numpy(array.project(), allow_missing=allow_missing)
#
# elif isinstance(array, ak._v2._util.uniontypes):
# contents = [
# to_numpy(array.project(i), allow_missing=allow_missing)
# for i in range(array.numcontents)
# ]
#
# if any(isinstance(x, numpy.ma.MaskedArray) for x in contents):
# try:
# out = numpy.ma.concatenate(contents)
# except Exception:
# raise ValueError(
# "cannot convert {0} into numpy.ma.MaskedArray".format(array)
#
# )
# else:
# try:
# out = numpy.concatenate(contents)
# except Exception:
# raise ValueError(
# "cannot convert {0} into np.ndarray".format(array)
#
# )
#
# tags = numpy.asarray(array.tags)
# for tag, content in enumerate(contents):
# mask = tags == tag
# out[mask] = content
# return out
#
# elif isinstance(array, ak._v2.contents.UnmaskedArray):
# content = to_numpy(array.content, allow_missing=allow_missing)
# if allow_missing:
# return numpy.ma.MaskedArray(content)
# else:
# return content
#
# elif isinstance(array, ak._v2._util.optiontypes):
# content = to_numpy(array.project(), allow_missing=allow_missing)
#
# shape = list(content.shape)
# shape[0] = len(array)
# data = numpy.empty(shape, dtype=content.dtype)
# mask0 = numpy.asarray(array.bytemask()).view(np.bool_)
# if mask0.any():
# if allow_missing:
# mask = numpy.broadcast_to(
# mask0.reshape((shape[0],) + (1,) * (len(shape) - 1)), shape
# )
# if isinstance(content, numpy.ma.MaskedArray):
# mask1 = numpy.ma.getmaskarray(content)
# mask = mask.copy()
# mask[~mask0] |= mask1
#
# data[~mask0] = content
# return numpy.ma.MaskedArray(data, mask)
# else:
# raise ValueError(
# "ak.to_numpy cannot convert 'None' values to "
# "np.ma.MaskedArray unless the "
# "'allow_missing' parameter is set to True"
#
# )
# else:
# if allow_missing:
# return numpy.ma.MaskedArray(content)
# else:
# return content
#
# elif isinstance(array, ak._v2.contents.RegularArray):
# out = to_numpy(array.content, allow_missing=allow_missing)
# head, tail = out.shape[0], out.shape[1:]
# if array.size == 0:
# shape = (len(array), 0) + tail
# else:
# shape = (head // array.size, array.size) + tail
# return out[: shape[0] * array.size].reshape(shape)
#
# elif isinstance(array, ak._v2._util.listtypes):
# return to_numpy(array.toRegularArray(), allow_missing=allow_missing)
#
# elif isinstance(array, ak._v2._util.recordtypes):
# if array.numfields == 0:
# return numpy.empty(len(array), dtype=[])
# contents = [
# to_numpy(array.field(i), allow_missing=allow_missing)
# for i in range(array.numfields)
# ]
# if any(len(x.shape) != 1 for x in contents):
# raise ValueError(
# "cannot convert {0} into np.ndarray".format(array)
#
# )
# out = numpy.empty(
# len(contents[0]),
# dtype=[(str(n), x.dtype) for n, x in zip(array.keys(), contents)],
# )
#
# mask = None
# for n, x in zip(array.keys(), contents):
# if isinstance(x, numpy.ma.MaskedArray):
# if mask is None:
# mask = numpy.ma.zeros(
# len(array), [(n, np.bool_) for n in array.keys()]
# )
# if x.mask is not None:
# mask[n] |= x.mask
# out[n] = x
#
# if mask is not None:
# out = numpy.ma.MaskedArray(out, mask)
#
# return out
#
# elif isinstance(array, ak._v2.contents.NumpyArray):
# out = ak.nplike.of(array).asarray(array)
# if type(out).__module__.startswith("cupy."):
# return out.get()
# else:
# return out
#
# elif isinstance(array, ak._v2.contents.Content):
# raise AssertionError(
# "unrecognized Content type: {0}".format(type(array))
#
# )
#
# elif isinstance(array, Iterable):
# return numpy.asarray(array)
#
# else:
# raise ValueError(
# "cannot convert {0} into np.ndarray".format(array)
#
# )

if isinstance(layout, (ak._v2.contents.Content, ak._v2.record.Record)):
return layout.to_numpy(allow_missing=allow_missing)
else:
return numpy.asarray(array)