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Closes #3782: flip function to match numpy
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Original file line number | Diff line number | Diff line change |
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@@ -96,3 +96,4 @@ | |
from arkouda.numpy.rec import * | ||
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from ._numeric import * | ||
from ._manipulation_functions import * |
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# from __future__ import annotations | ||
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from typing import Optional | ||
from typing import Tuple | ||
from typing import Union | ||
from typing import cast | ||
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from arkouda.client import generic_msg | ||
from arkouda.pdarrayclass import create_pdarray | ||
from arkouda.pdarrayclass import pdarray | ||
from arkouda.strings import Strings | ||
from arkouda.categorical import Categorical | ||
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__all__ = ["flip"] | ||
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def flip( | ||
x: Union[pdarray, Strings, Categorical], /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None | ||
) -> Union[pdarray, Strings, Categorical]: | ||
""" | ||
Reverse an array's values along a particular axis or axes. | ||
Parameters | ||
---------- | ||
x : pdarray, Strings, or Categorical | ||
Reverse the order of elements in an array along the given axis. | ||
The shape of the array is preserved, but the elements are reordered. | ||
axis : int or Tuple[int, ...], optional | ||
The axis or axes along which to flip the array. If None, flip the array along all axes. | ||
Returns | ||
------- | ||
pdarray, Strings, or Categorical | ||
An array with the entries of axis reversed. | ||
Note | ||
---- | ||
This differs from numpy as it actually reverses the data, rather than presenting a view. | ||
""" | ||
axisList = [] | ||
if axis is not None: | ||
axisList = list(axis) if isinstance(axis, tuple) else [axis] | ||
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if isinstance(x, pdarray): | ||
try: | ||
return create_pdarray( | ||
cast( | ||
str, | ||
generic_msg( | ||
cmd=( | ||
f"flipAll<{x.dtype},{x.ndim}>" | ||
if axis is None | ||
else f"flip<{x.dtype},{x.ndim}>" | ||
), | ||
args={ | ||
"name": x, | ||
"nAxes": len(axisList), | ||
"axis": axisList, | ||
}, | ||
), | ||
) | ||
) | ||
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except RuntimeError as e: | ||
raise IndexError(f"Failed to flip array: {e}") | ||
elif isinstance(x, Categorical): | ||
if isinstance(x.permutation, pdarray): | ||
return Categorical.from_codes( | ||
codes=flip(x.codes), | ||
categories=x.categories, | ||
permutation=flip(x.permutation), | ||
segments=x.segments, | ||
) | ||
else: | ||
return Categorical.from_codes( | ||
codes=flip(x.codes), | ||
categories=x.categories, | ||
permutation=None, | ||
segments=x.segments, | ||
) | ||
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elif isinstance(x, Strings): | ||
rep_msg = generic_msg( | ||
cmd="flipString", args={"objType": x.objType, "obj": x.entry, "size": x.size} | ||
) | ||
return Strings.from_return_msg(cast(str, rep_msg)) | ||
else: | ||
raise TypeError("flip only accepts type pdarray, Strings, or Categorical.") |
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,56 @@ | ||
import numpy as np | ||
import pytest | ||
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import arkouda as ak | ||
from arkouda.categorical import Categorical | ||
from arkouda.testing import assert_equal | ||
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seed = pytest.seed | ||
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class TestNumpyManipulationFunctions: | ||
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@pytest.mark.parametrize("size", pytest.prob_size) | ||
@pytest.mark.parametrize("dtype", [int, ak.int64, ak.uint64, float, ak.float64, bool, ak.bool_]) | ||
def test_flip_pdarray(self, size, dtype): | ||
a = ak.arange(size, dtype=dtype) | ||
f = ak.flip(a) | ||
assert_equal(f, a[::-1]) | ||
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@pytest.mark.skip_if_max_rank_less_than(3) | ||
@pytest.mark.parametrize("size", pytest.prob_size) | ||
@pytest.mark.parametrize("dtype", [ak.int64, ak.uint64, ak.float64]) | ||
def test_flip_multi_dim(self, size, dtype): | ||
a = ak.arange(size * 4, dtype=dtype).reshape((2, 2, size)) | ||
f = ak.flip(a) | ||
assert_equal(f, (size * 4 - 1) - a) | ||
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@pytest.mark.skip_if_max_rank_less_than(3) | ||
@pytest.mark.parametrize("size", pytest.prob_size) | ||
def test_flip_multi_dim_bool(self, size): | ||
shape = (size, 2) | ||
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vals = ak.array([True, False]) | ||
segs = ak.array([0, size]) | ||
perm = ak.concatenate([ak.arange(size) * 2, ak.arange(size) * 2 + 1]) | ||
a = ak.broadcast(segments=segs, values=vals, permutation=perm).reshape(shape) | ||
f = ak.flip(a) | ||
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vals2 = ak.array([False, True]) | ||
f2 = ak.broadcast(segments=segs, values=vals2, permutation=perm).reshape(shape) | ||
assert_equal(f, f2) | ||
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@pytest.mark.parametrize("size", pytest.prob_size) | ||
def test_flip_string(self, size): | ||
s = ak.random_strings_uniform(1, 2, size, seed=seed) | ||
assert_equal(ak.flip(s), s[::-1]) | ||
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@pytest.mark.parametrize("size", pytest.prob_size) | ||
def test_flip_categorical(self, size): | ||
s = ak.random_strings_uniform(1, 2, size, seed=seed) | ||
c = Categorical(s) | ||
assert_equal(ak.flip(c), c[::-1]) | ||
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# test case when c.permutation = None | ||
c2 = Categorical(c.to_pandas()) | ||
assert_equal(ak.flip(c2), c2[::-1]) |