ak_to_numpy.py

# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE

import numpy

import awkward as ak

np = ak.nplike.NumpyMetadata.instance()


def to_numpy(array, allow_missing=True):
    """
    Converts `array` (many types supported, including all Awkward Arrays and
    Records) into a NumPy array, if possible.

    If the data are numerical and regular (nested lists have equal lengths
    in each dimension, as described by the #type), they can be losslessly
    converted to a NumPy array and this function returns without an error.

    Otherwise, the function raises an error. It does not create a NumPy
    array with dtype `"O"` for `np.object_` (see the
    [note on object_ type](https://docs.scipy.org/doc/numpy/reference/arrays.scalars.html#arrays-scalars-built-in))
    since silent conversions to dtype `"O"` arrays would not only be a
    significant performance hit, but would also break functionality, since
    nested lists in a NumPy `"O"` array are severed from the array and
    cannot be sliced as dimensions.

    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)
    are a possible result; otherwise, missing values (None) cause this
    function to raise an error.

    See also #ak.from_numpy and #ak.to_cupy.
    """
    with ak._v2._util.OperationErrorContext(
        "ak._v2.to_numpy",
        dict(array=array, allow_missing=allow_missing),
    ):
        return _impl(array, allow_missing)


def _impl(array, allow_missing):
    layout = ak._v2.operations.convert.to_layout(
        array, allow_record=True, allow_other=True
    )

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