pyarrow: Check compatibility of pyarrow.array and pyarrow.table with numeric and timestamp types #2864
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Install pyarrow as an optional dependency, and ensure that pyarrow.array objects with int64 and timestamp types can be read by pygmt.info.
weiji14
changed the title
Ensure that pyarrow.table objects can be passed into pygmt functions like blockm, info, nearneighbor, project, triangulate and xyz2grd.
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/format |
weiji14
commented
Dec 9, 2023
pygmt/tests/test_info.py
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| pytest.param( | ||
| getattr(pa, "table", None), | ||
| "vector memory", | ||
| marks=td.skip_if_no(package="pyarrow"), | ||
| ), | ||
| ], | ||
| ) | ||
| def test_info_2d_array(array_func, expected_memory): | ||
| """ | ||
| Make sure info works on 2-D numpy.ndarray inputs. | ||
| Make sure info works on 2-D numpy.ndarray and pyarrow.table inputs. | ||
| """ | ||
| table = np.loadtxt(POINTS_DATA) | ||
| table = array_func(pd.read_csv(POINTS_DATA, sep=" ", header=None)) | ||
| output = info(data=table) | ||
| expected_output = ( | ||
| "<matrix memory>: N = 20 <11.5309/61.7074> <-2.9289/7.8648> <0.1412/0.9338>\n" | ||
| ) | ||
| expected_output = f"<{expected_memory}>: N = 20 <11.5309/61.7074> <-2.9289/7.8648> <0.1412/0.9338>\n" |
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Note that pyarrow.table goes through put_vector rather than put_matrix, because pyarrow.table is more akin to pandas.DataFrame (which allows for columns with different dtypes) than a numpy.array object (which only allows for one dtype).
Done by casting the pyarrow.TimestampScalar to a string first, before letting pd.to_datetime do the formatting. Have added a new parametrized unit test to test_solar_set_terminator_datetime with pyarrow and pd.Timestamp array_func to test this.
CodSpeed Performance ReportMerging #2864 will degrade performances by 6.62%Falling back to comparing Summary
Benchmarks breakdown
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weiji14
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Description of proposed changes
Check that
pyarrow.arrayandpyarrow.tableobjects of various types (uint/int/timestamp) can be passed to PyGMT modules/functions.TODO:
pyarrow.arrayby adding parametrized tests topygmt.infopyarrow.tableto unit tests that already have the@pytest.mark.parametrize("array_func", ...)pyarrow.arrayobjects can be used in place ofnumpy.arraysolar's terminator_datetime parameter to accept apyarrow.TimestampScalarinputAddresses #2800 (comment), part of #2800.
Reminders
make formatandmake checkto make sure the code follows the style guide.doc/api/index.rst.Slash Commands
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/format: automatically format and lint the code/test-gmt-dev: run full tests on the latest GMT development version