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clib.conversion._to_numpy: Add tests for PyArrow's timestamp type#3621

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Merged
seisman merged 9 commits into from
Nov 28, 2024
Merged

clib.conversion._to_numpy: Add tests for PyArrow's timestamp type #3621

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ce2b830
clib.conversion._to_numpy: Add tests for PyArrow's timestamp type
seisman Nov 11, 2024
214f060
Add a dummy function to mimin pyarrow
seisman Nov 16, 2024
bf16e97
Merge branch 'main' into to_numpy/pyarrow_timestamp
seisman Nov 21, 2024
e0c2e7b
Merge branch 'main' into to_numpy/pyarrow_timestamp
seisman Nov 25, 2024
95ac538
Merge branch 'main' into to_numpy/pyarrow_timestamp
seisman Nov 26, 2024
7cbfeb8
Merge branch 'main' into to_numpy/pyarrow_timestamp
seisman Nov 28, 2024
c622438
Explicitly check each array element since np.assert_array_equal needs…
seisman Nov 28, 2024
7bc1f6f
Apply suggestions from code review
seisman Nov 28, 2024
5c2eb46
Improve comments about timestamp types
seisman Nov 28, 2024
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54 changes: 54 additions & 0 deletions pygmt/tests/test_clib_to_numpy.py
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Expand Up@@ -18,6 +18,18 @@

_HAS_PYARROW = True
except ImportError:

class pa: # noqa: N801

@seismanseismanNov 21, 2024

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pa.timestamp() with tz doesn't have string aliases, so we can't use strings like timestamp[s, UTC]. So we have to define a dummy function here.

@weiji14weiji14Nov 26, 2024

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Could we avoid this dummy class by starting from a pandas.Timestamp instead of Python datetime? The timezone info can be carried over:

importpyarrowaspaimportpandasaspdfromzoneinfoimportZoneInfoarray=pa.array(
[
pd.Timestamp(2024, 1, 2, 3, 4, 5, tzinfo=ZoneInfo("America/New_York")),
pd.Timestamp(2024, 1, 2, 3, 4, 6, tzinfo=ZoneInfo("America/New_York")),
]
)
print(array.type)
# timestamp[us, tz=America/New_York]

@seismanseismanNov 27, 2024

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We can, but I feel it will make the pytest parametrize more complicated.

@weiji14weiji14Nov 28, 2024

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Ok, let's keep this dummy class then.

"""
A dummy class to mimic pyarrow.
"""

@staticmethod
def timestamp(unit: str, tz: str | None = None):
"""
A dummy function to mimic pyarrow.timestamp.
"""

_HAS_PYARROW = False


Expand DownExpand Up@@ -244,6 +256,7 @@ def test_to_numpy_pandas_series_pyarrow_dtypes_date(dtype, expected_dtype):
# - Date types:
# - date32[day]
# - date64[ms]
# - Timestamp types: timestamp[unit], timestamp[unit, tz]
#
# In PyArrow, array types can be specified in two ways:
#
Expand DownExpand Up@@ -366,3 +379,44 @@ def test_to_numpy_pyarrow_array_pyarrow_dtypes_date(dtype, expected_dtype):
result,
np.array(["2024-01-01", "2024-01-02", "2024-01-03"], dtype=expected_dtype),
)


@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
@pytest.mark.parametrize(
("dtype", "expected_dtype"),
[
pytest.param(None, "datetime64[us]", id="None"),
pytest.param("timestamp[s]", "datetime64[s]", id="timestamp[s]"),
pytest.param("timestamp[ms]", "datetime64[ms]", id="timestamp[ms]"),
pytest.param("timestamp[us]", "datetime64[us]", id="timestamp[us]"),
pytest.param("timestamp[ns]", "datetime64[ns]", id="timestamp[ns]"),
pytest.param(
pa.timestamp("s", tz="UTC"), "datetime64[s]", id="timestamp[s, tz=UTC]"
), # pa.timestamp with tz has no string alias.
pytest.param(
pa.timestamp("s", tz="America/New_York"),
"datetime64[s]",
id="timestamp[s, tz=America/New_York]",
),
pytest.param(
pa.timestamp("s", tz="+07:30"),
"datetime64[s]",
id="timestamp[s, tz=+07:30]",
),
],
)
def test_to_numpy_pyarrow_timestamp(dtype, expected_dtype):
"""
Test the _to_numpy function with PyArrow arrays of PyArrow timestamp types.

pyarrow.timestamp(unit, tz=None) can accept units "s", "ms", "us", and "ns".

Reference: https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html
"""
data = [datetime(2024, 1, 2, 3, 4, 5), datetime(2024, 1, 2, 3, 4, 6)]
array = pa.array(data, type=dtype)
result = _to_numpy(array)
_check_result(result, np.datetime64)
assert result.dtype == expected_dtype
assert result[0] == np.datetime64("2024-01-02T03:04:05")
assert result[1] == np.datetime64("2024-01-02T03:04:06")