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Updates PROTOs pdarray_creation_test #3393

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merged 4 commits into from
Jul 18, 2024
Merged

Updates PROTOs pdarray_creation_test #3393

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4f118da
Updates PROTOs pdarray_creation_test
drculhane Jul 8, 2024
3356e77
Remade after master update.
drculhane Jul 9, 2024
09e7375
Merge branch 'Bears-R-Us:master' into Closes-3389
drculhane Jul 9, 2024
96c41ce
Addresses arange comments.
drculhane Jul 11, 2024
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33 changes: 24 additions & 9 deletions PROTO_tests/tests/pdarray_creation_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -206,6 +206,10 @@ def test_randint_num_dtype(self, dtype):

@pytest.mark.parametrize("size", pytest.prob_size)
def test_randint_misc(self, size):

# Test that int_scalars covers uint8, uint16, uint32
ak.randint(low=np.uint8(1), high=np.uint16(100), size=np.uint32(100))
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# test resolution of modulus overflow - issue #1174
assert (ak.randint(-(2**63), 2**63 - 1, size) != ak.full(size, -(2**63))).any()

Expand Down Expand Up @@ -343,11 +347,12 @@ def test_ones_misc(self):
@pytest.mark.parametrize("size", pytest.prob_size)
@pytest.mark.parametrize("dtype", [ak.int64, ak.float64, ak.bool_, ak.bigint])
def test_ones_like(self, size, dtype):
ones_arr = ak.ones(size, dtype)
ones_like_arr = ak.ones_like(ones_arr)
ran_arr = ak.array(ak.arange(size, dtype=dtype))
ones_like_arr = ak.ones_like(ran_arr)
assert isinstance(ones_like_arr, ak.pdarray)
assert dtype == ones_like_arr.dtype
assert (1 == ones_like_arr).all()
assert ones_like_arr.size == ran_arr.size

@pytest.mark.parametrize("size", pytest.prob_size)
@pytest.mark.parametrize("dtype", [int, ak.int64, ak.uint64, float, ak.float64, bool, ak.bool_])
Expand Down Expand Up @@ -389,20 +394,22 @@ def test_full_misc(self):
@pytest.mark.parametrize("size", pytest.prob_size)
@pytest.mark.parametrize("dtype", [int, ak.int64, ak.uint64, float, ak.float64, bool, ak.bool_])
def test_full_like(self, size, dtype):
full_arr = ak.full(size, 1, dtype)
full_like_arr = ak.full_like(full_arr, 1)
ran_arr = ak.full(size, 5, dtype)
full_like_arr = ak.full_like(ran_arr, 1)
assert isinstance(full_like_arr, ak.pdarray)
assert dtype == full_like_arr.dtype
assert (full_like_arr == 1).all()
assert full_like_arr.size == ran_arr.size

@pytest.mark.parametrize("size", pytest.prob_size)
@pytest.mark.parametrize("dtype", [int, ak.int64, ak.uint64, float, ak.float64, bool, ak.bool_])
def test_zeros_like(self, size, dtype):
zeros_arr = ak.zeros(size, dtype)
zeros_like_arr = ak.zeros_like(zeros_arr)
ran_arr = ak.array(ak.arange(size, dtype=dtype))
zeros_like_arr = ak.zeros_like(ran_arr)
assert isinstance(zeros_like_arr, ak.pdarray)
assert dtype == zeros_like_arr.dtype
assert (zeros_like_arr == 0).all()
assert zeros_like_arr.size == ran_arr.size

def test_linspace(self):
pda = ak.linspace(0, 100, 1000)
Expand Down Expand Up @@ -560,6 +567,9 @@ def test_random_strings_lognormal_errors(self):
with pytest.raises(TypeError):
ak.random_strings_lognormal(2, 0.25, 100, 1000000)

# Test that int_scalars covers uint8, uint16, uint32
ak.random_strings_lognormal(np.uint8(2), 0.25, np.uint16(100))
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def test_random_strings_lognormal_with_seed(self):
randoms = [
"VWHJEX",
Expand Down Expand Up @@ -612,11 +622,11 @@ def test_from_series_dtypes(self, size, dtype):
assert isinstance(p_array, ak.pdarray if dtype != str else ak.Strings)
assert dtype == p_array.dtype

p_i_objects_array = ak.from_series(
p_objects_array = ak.from_series(
pd.Series(np.random.randint(0, 10, size), dtype="object"), dtype=dtype
)
assert isinstance(p_i_objects_array, ak.pdarray if dtype != str else ak.Strings)
assert dtype == p_i_objects_array.dtype
assert isinstance(p_objects_array, ak.pdarray if dtype != str else ak.Strings)
assert dtype == p_objects_array.dtype

def test_from_series_misc(self):
p_array = ak.from_series(pd.Series(["a", "b", "c", "d", "e"]))
Expand Down Expand Up @@ -658,6 +668,11 @@ def test_fill(self, size, dtype):
ones.fill(dtype(2))
assert (dtype(2) == ones).all()

# Test that int_scalars covers uint8, uint16, uint32
ones.fill(np.uint8(2))
ones.fill(np.uint16(2))
ones.fill(np.uint32(2))
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def test_endian(self):
a = np.random.randint(1, 100, 100)
aka = ak.array(a)
Expand Down
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