Merge branch 'main' into feature/scatter

.github/workflows/benchmarks.yml

@@ -45,7 +45,7 @@ jobs:
 
       # Install Micromamba with conda-forge dependencies
       - name: Setup Micromamba
-        uses: mamba-org/[email protected].0
+        uses: mamba-org/[email protected].1
         with:
           environment-name: pygmt
           condarc: |

.github/workflows/cache_data.yaml

@@ -43,7 +43,7 @@ jobs:
 
       # Install Micromamba with conda-forge dependencies
       - name: Setup Micromamba
-        uses: mamba-org/[email protected].0
+        uses: mamba-org/[email protected].1
         with:
           environment-name: pygmt
           condarc: |

.github/workflows/ci_docs.yml

@@ -80,7 +80,7 @@ jobs:
 
       # Install Micromamba with conda-forge dependencies
       - name: Setup Micromamba
-        uses: mamba-org/[email protected].0
+        uses: mamba-org/[email protected].1
         with:
           environment-name: pygmt
           condarc: |

.github/workflows/ci_doctests.yaml

@@ -42,7 +42,7 @@ jobs:
 
       # Install Micromamba with conda-forge dependencies
       - name: Setup Micromamba
-        uses: mamba-org/[email protected].0
+        uses: mamba-org/[email protected].1
         with:
           environment-name: pygmt
           condarc: |

.github/workflows/ci_tests.yaml

@@ -114,7 +114,7 @@ jobs:
 
       # Install Micromamba with conda-forge dependencies
       - name: Setup Micromamba
-        uses: mamba-org/[email protected].0
+        uses: mamba-org/[email protected].1
         with:
           environment-name: pygmt
           condarc: |

.github/workflows/ci_tests_dev.yaml

@@ -57,7 +57,7 @@ jobs:
 
       # Install Micromamba with conda-forge dependencies
       - name: Setup Micromamba
-        uses: mamba-org/[email protected].0
+        uses: mamba-org/[email protected].1
         with:
           environment-name: pygmt
           condarc: |

.github/workflows/ci_tests_legacy.yaml

@@ -51,7 +51,7 @@ jobs:
 
       # Install Micromamba with conda-forge dependencies
       - name: Setup Micromamba
-        uses: mamba-org/[email protected].0
+        uses: mamba-org/[email protected].1
         with:
           environment-name: pygmt
           condarc: |

.github/workflows/publish-to-pypi.yml

@@ -75,10 +75,10 @@ jobs:
         ls -lh dist/
 
     - name: Publish to Test PyPI
-      uses: pypa/[email protected].0
+      uses: pypa/[email protected].2
       with:
         repository-url: https://test.pypi.org/legacy/
 
     - name: Publish to PyPI
       if: startsWith(github.ref, 'refs/tags')
-      uses: pypa/[email protected].0
+      uses: pypa/[email protected].2

pygmt/clib/conversion.py

@@ -157,9 +157,9 @@ def _to_numpy(data: Any) -> np.ndarray:
         The C contiguous NumPy array.
     """
     # Mapping of unsupported dtypes to the expected NumPy dtype.
-    dtypes: dict[str, type] = {
-        "date32[day][pyarrow]": np.datetime64,
-        "date64[ms][pyarrow]": np.datetime64,
+    dtypes: dict[str, str | type] = {
+        "date32[day][pyarrow]": "datetime64[D]",
+        "date64[ms][pyarrow]": "datetime64[ms]",
     }
 
     if (

pygmt/tests/test_clib_to_numpy.py

@@ -3,6 +3,7 @@
 """
 
 import sys
+from datetime import date, datetime
 
 import numpy as np
 import numpy.testing as npt
@@ -48,11 +49,12 @@ def _check_result(result, expected_dtype):
             np.complex128,
             id="complex",
         ),
+        pytest.param(["abc", "defg", "12345"], np.str_, id="string"),
     ],
 )
-def test_to_numpy_python_types_numeric(data, expected_dtype):
+def test_to_numpy_python_types(data, expected_dtype):
     """
-    Test the _to_numpy function with Python built-in numeric types.
+    Test the _to_numpy function with Python built-in types.
     """
     result = _to_numpy(data)
     _check_result(result, expected_dtype)
@@ -121,6 +123,17 @@ def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):
     npt.assert_array_equal(result, array, strict=True)
 
 
+@pytest.mark.parametrize("dtype", [None, np.str_, "U10"])
+def test_to_numpy_ndarray_numpy_dtypes_string(dtype):
+    """
+    Test the _to_numpy function with NumPy arrays of string types.
+    """
+    array = np.array(["abc", "defg", "12345"], dtype=dtype)
+    result = _to_numpy(array)
+    _check_result(result, np.str_)
+    npt.assert_array_equal(result, array)
+
+
 ########################################################################################
 # Test the _to_numpy function with pandas.Series.
 #
@@ -161,6 +174,28 @@ def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, expected_dtype):
     npt.assert_array_equal(result, series)
 
 
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        pytest.param("date32[day][pyarrow]", "datetime64[D]", id="date32[day]"),
+        pytest.param("date64[ms][pyarrow]", "datetime64[ms]", id="date64[ms]"),
+    ],
+)
+def test_to_numpy_pandas_series_pyarrow_dtypes_date(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with pandas.Series of PyArrow date32/date64 types.
+    """
+    series = pd.Series(pd.date_range(start="2024-01-01", periods=3), dtype=dtype)
+    result = _to_numpy(series)
+    _check_result(result, np.datetime64)
+    assert result.dtype == expected_dtype  # Explicitly check the date unit.
+    npt.assert_array_equal(
+        result,
+        np.array(["2024-01-01", "2024-01-02", "2024-01-03"], dtype=expected_dtype),
+    )
+
+
 ########################################################################################
 # Test the _to_numpy function with PyArrow arrays.
 #
@@ -170,6 +205,9 @@ def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, expected_dtype):
 #   - int8, int16, int32, int64
 #   - uint8, uint16, uint32, uint64
 #   - float16, float32, float64
+# - Date types:
+#   - date32[day]
+#   - date64[ms]
 #
 # In PyArrow, array types can be specified in two ways:
 #
@@ -238,3 +276,35 @@ def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric_with_na(dtype, expected_d
     result = _to_numpy(array)
     _check_result(result, expected_dtype)
     npt.assert_array_equal(result, array)
+
+
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        pytest.param("date32[day]", "datetime64[D]", id="date32[day]"),
+        pytest.param("date64[ms]", "datetime64[ms]", id="date64[ms]"),
+    ],
+)
+def test_to_numpy_pyarrow_array_pyarrow_dtypes_date(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with PyArrow arrays of PyArrow date types.
+
+    date32[day] and date64[ms] are stored as 32-bit and 64-bit integers, respectively,
+    representing the number of days and milliseconds since the UNIX epoch (1970-01-01).
+
+    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),
+    ]
+    array = pa.array(data, type=dtype)
+    result = _to_numpy(array)
+    _check_result(result, np.datetime64)
+    assert result.dtype == expected_dtype  # Explicitly check the date unit.
+    npt.assert_array_equal(
+        result,
+        np.array(["2024-01-01", "2024-01-02", "2024-01-03"], dtype=expected_dtype),
+    )