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Merged
merged 3 commits into from
Feb 9, 2023
Merged

Add operation __index__ and __complex__ #1285

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f20e09c
Add operation __index__ and __complex__
antonwolfy Feb 2, 2023
566de7a
Add tests
antonwolfy Feb 2, 2023
1dfd3f1
Merge branch 'master' into index_for_dpnp_array
antonwolfy Feb 8, 2023
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10 changes: 8 additions & 2 deletions dpnp/dpnp_array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,7 +140,10 @@ def __bool__(self):
return self._array_obj.__bool__()

# '__class__',
# '__complex__',

def __complex__(self):
return self._array_obj.__complex__()

# '__contains__',
# '__copy__',
# '__deepcopy__',
Expand Down Expand Up @@ -187,7 +190,10 @@ def __gt__(self, other):
# '__imatmul__',
# '__imod__',
# '__imul__',
# '__index__',

def __index__(self):
return self._array_obj.__index__()

# '__init__',
# '__init_subclass__',

Expand Down
52 changes: 39 additions & 13 deletions tests/test_dparray.py
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Original file line number Diff line number Diff line change
@@ -1,15 +1,17 @@
import dpnp
import numpy
import pytest
from .helper import get_all_dtypes

import dpnp
import dpctl.tensor as dpt

import numpy
from numpy.testing import (
assert_array_equal
)


@pytest.mark.parametrize("res_dtype",
[numpy.float64, numpy.float32, numpy.int64, numpy.int32, numpy.bool_, numpy.complex_],
ids=['float64', 'float32', 'int64', 'int32', 'bool', 'complex'])
@pytest.mark.parametrize("arr_dtype",
[numpy.float64, numpy.float32, numpy.int64, numpy.int32, numpy.bool_, numpy.complex_],
ids=['float64', 'float32', 'int64', 'int32', 'bool', 'complex'])
@pytest.mark.parametrize("res_dtype", get_all_dtypes())
@pytest.mark.parametrize("arr_dtype", get_all_dtypes())
@pytest.mark.parametrize("arr",
[[-2, -1, 0, 1, 2], [[-2, -1], [1, 2]], []],
ids=['[-2, -1, 0, 1, 2]', '[[-2, -1], [1, 2]]', '[]'])
Expand All @@ -18,12 +20,10 @@ def test_astype(arr, arr_dtype, res_dtype):
dpnp_array = dpnp.array(numpy_array)
expected = numpy_array.astype(res_dtype)
result = dpnp_array.astype(res_dtype)
numpy.testing.assert_array_equal(expected, result)
assert_array_equal(expected, result)


@pytest.mark.parametrize("arr_dtype",
[numpy.float64, numpy.float32, numpy.int64, numpy.int32, numpy.bool_, numpy.complex_],
ids=['float64', 'float32', 'int64', 'int32', 'bool', 'complex'])
@pytest.mark.parametrize("arr_dtype", get_all_dtypes())
@pytest.mark.parametrize("arr",
[[-2, -1, 0, 1, 2], [[-2, -1], [1, 2]], []],
ids=['[-2, -1, 0, 1, 2]', '[[-2, -1], [1, 2]]', '[]'])
Expand All @@ -32,7 +32,7 @@ def test_flatten(arr, arr_dtype):
dpnp_array = dpnp.array(arr, dtype=arr_dtype)
expected = numpy_array.flatten()
result = dpnp_array.flatten()
numpy.testing.assert_array_equal(expected, result)
assert_array_equal(expected, result)


@pytest.mark.parametrize("shape",
Expand Down Expand Up @@ -68,3 +68,29 @@ def test_flags_strides(dtype, order, strides):
assert usm_array.flags == dpnp_array.flags
assert numpy_array.flags.c_contiguous == dpnp_array.flags.c_contiguous
assert numpy_array.flags.f_contiguous == dpnp_array.flags.f_contiguous


@pytest.mark.parametrize("func", [bool, float, int, complex])
@pytest.mark.parametrize("shape", [tuple(), (1,), (1, 1), (1, 1, 1)])
@pytest.mark.parametrize("dtype", get_all_dtypes(no_float16=False, no_complex=True))
def test_scalar_type_casting(func, shape, dtype):
numpy_array = numpy.full(shape, 5, dtype=dtype)
dpnp_array = dpnp.full(shape, 5, dtype=dtype)
assert func(numpy_array) == func(dpnp_array)


@pytest.mark.parametrize("method", ["__bool__", "__float__", "__int__", "__complex__"])
@pytest.mark.parametrize("shape", [tuple(), (1,), (1, 1), (1, 1, 1)])
@pytest.mark.parametrize("dtype", get_all_dtypes(no_float16=False, no_complex=True, no_none=True))
def test_scalar_type_casting_by_method(method, shape, dtype):
numpy_array = numpy.full(shape, 4.7, dtype=dtype)
dpnp_array = dpnp.full(shape, 4.7, dtype=dtype)
assert getattr(numpy_array, method)() == getattr(dpnp_array, method)()


@pytest.mark.parametrize("shape", [(1,), (1, 1), (1, 1, 1)])
@pytest.mark.parametrize("index_dtype", [dpnp.int32, dpnp.int64])
def test_array_as_index(shape, index_dtype):
ind_arr = dpnp.ones(shape, dtype=index_dtype)
a = numpy.arange(ind_arr.size + 1)
assert a[tuple(ind_arr)] == a[1]