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clib.conversion._to_numpy: Add tests for Python sequence of datetime-like objects (#3758) #3776

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clib.conversion._to_numpy: Add tests for Python sequence of datetime-like objects (#3758) #3776

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4 changes: 2 additions & 2 deletions pygmt/tests/baseline/test_plot_datetime.png.dvc
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
outs:
- md5: 583947facaa873122f0bf18137809cd4
size: 12695
- md5: 0a2eae0da1e3d5b71d7392de1c081346
size: 13124
path: test_plot_datetime.png
hash: md5
77 changes: 72 additions & 5 deletions pygmt/tests/test_clib_to_numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,8 @@
Tests for the _to_numpy function in the clib.conversion module.
"""

import datetime
import sys
from datetime import date, datetime

import numpy as np
import numpy.testing as npt
Expand Down Expand Up @@ -80,6 +80,70 @@ def test_to_numpy_python_types(data, expected_dtype):
npt.assert_array_equal(result, data)


@pytest.mark.parametrize(
"data",
[
pytest.param(
["2018", "2018-02", "2018-03-01", "2018-04-01T01:02:03"], id="iso8601"
),
pytest.param(
[
datetime.date(2018, 1, 1),
datetime.datetime(2018, 2, 1),
datetime.date(2018, 3, 1),
datetime.datetime(2018, 4, 1, 1, 2, 3),
],
id="datetime",
),
pytest.param(
[
np.datetime64("2018"),
np.datetime64("2018-02"),
np.datetime64("2018-03-01"),
np.datetime64("2018-04-01T01:02:03"),
],
id="np_datetime64",
),
pytest.param(
[
pd.Timestamp("2018-01-01"),
pd.Timestamp("2018-02-01"),
pd.Timestamp("2018-03-01"),
pd.Timestamp("2018-04-01T01:02:03"),
],
id="pd_timestamp",
),
pytest.param(
[
"2018-01-01",
np.datetime64("2018-02-01"),
datetime.datetime(2018, 3, 1),
pd.Timestamp("2018-04-01T01:02:03"),
],
id="mixed",
),
],
)
def test_to_numpy_python_datetime(data):
"""
Test the _to_numpy function with Python sequence of datetime types.
"""
result = _to_numpy(data)
assert result.dtype.type == np.datetime64
npt.assert_array_equal(
result,
np.array(
[
"2018-01-01T00:00:00",
"2018-02-01T00:00:00",
"2018-03-01T00:00:00",
"2018-04-01T01:02:03",
],
dtype="datetime64[s]",
),
)


########################################################################################
# Test the _to_numpy function with NumPy arrays.
#
Expand Down Expand Up @@ -466,9 +530,9 @@ def test_to_numpy_pyarrow_date(dtype, expected_dtype):
Here we explicitly check the dtype and date unit of the result.
"""
data = [
date(2024, 1, 1),
datetime(2024, 1, 2),
datetime(2024, 1, 3),
datetime.date(2024, 1, 1),
datetime.datetime(2024, 1, 2),
datetime.datetime(2024, 1, 3),
]
array = pa.array(data, type=dtype)
result = _to_numpy(array)
Expand Down Expand Up @@ -512,7 +576,10 @@ def test_to_numpy_pyarrow_timestamp(dtype, expected_dtype):

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)]
data = [
datetime.datetime(2024, 1, 2, 3, 4, 5),
datetime.datetime(2024, 1, 2, 3, 4, 6),
]
array = pa.array(data, type=dtype)
result = _to_numpy(array)
_check_result(result, np.datetime64)
Expand Down
7 changes: 6 additions & 1 deletion pygmt/tests/test_plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -467,9 +467,14 @@ def test_plot_datetime():
fig.plot(x=x, y=y, style="a0.2c", pen="1p")

# the Python built-in datetime and date
x = [datetime.date(2018, 1, 1), datetime.datetime(2019, 1, 1)]
x = [datetime.date(2018, 1, 1), datetime.datetime(2019, 1, 1, 0, 0, 0)]
y = [8.5, 9.5]
fig.plot(x=x, y=y, style="i0.2c", pen="1p")

# Python sequence of pd.Timestamp
x = [pd.Timestamp("2018-01-01"), pd.Timestamp("2019-01-01")]
y = [5.5, 6.5]
fig.plot(x=x, y=y, style="d0.2c", pen="1p")
return fig


Expand Down
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