Merge branch 'main' into pandas-2.1

Author: seisman

.github/workflows/ci_docs.yml

@@ -115,7 +115,7 @@ jobs:
             sphinx-copybutton
             sphinx-design
             sphinx-gallery
-            sphinx_rtd_theme
+            sphinx_rtd_theme<3.0
 
       # Download cached remote files (artifacts) from GitHub
       - name: Download remote data from GitHub

ci/requirements/docs.yml

@@ -30,4 +30,4 @@ dependencies:
     - sphinx-copybutton
     - sphinx-design
     - sphinx-gallery
-    - sphinx_rtd_theme
+    - sphinx_rtd_theme<3.0

environment.yml

@@ -42,7 +42,7 @@ dependencies:
     - sphinx-copybutton
     - sphinx-design
     - sphinx-gallery>=0.17.0
-    - sphinx_rtd_theme
+    - sphinx_rtd_theme<3.0
     # Dev dependencies (type hints)
     - mypy
     - pandas-stubs

pygmt/tests/baseline/test_figure_shift_origin.png.dvc

@@ -0,0 +1,5 @@
+outs:
+- md5: 39b241fdd879271cf1e8cf1f73454706
+  size: 9910
+  hash: md5
+  path: test_shift_origin.png

pygmt/tests/baseline/test_shift_origin.png.dvc

@@ -0,0 +1,240 @@
+"""
+Tests for the _to_numpy function in the clib.conversion module.
+"""
+
+import sys
+
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import pytest
+from packaging.version import Version
+from pygmt.clib.conversion import _to_numpy
+
+try:
+    import pyarrow as pa
+
+    _HAS_PYARROW = True
+except ImportError:
+    _HAS_PYARROW = False
+
+
+def _check_result(result, expected_dtype):
+    """
+    A helper function to check if the result of the _to_numpy function is a C-contiguous
+    NumPy array with the expected dtype.
+    """
+    assert isinstance(result, np.ndarray)
+    assert result.flags.c_contiguous
+    assert result.dtype.type == expected_dtype
+
+
+########################################################################################
+# Test the _to_numpy function with Python built-in types.
+########################################################################################
+@pytest.mark.parametrize(
+    ("data", "expected_dtype"),
+    [
+        pytest.param(
+            [1, 2, 3],
+            np.int32
+            if sys.platform == "win32" and Version(np.__version__) < Version("2.0")
+            else np.int64,
+            id="int",
+        ),
+        pytest.param([1.0, 2.0, 3.0], np.float64, id="float"),
+        pytest.param(
+            [complex(+1), complex(-2j), complex("-Infinity+NaNj")],
+            np.complex128,
+            id="complex",
+        ),
+    ],
+)
+def test_to_numpy_python_types_numeric(data, expected_dtype):
+    """
+    Test the _to_numpy function with Python built-in numeric types.
+    """
+    result = _to_numpy(data)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, data)
+
+
+########################################################################################
+# Test the _to_numpy function with NumPy arrays.
+#
+# There are 24 fundamental dtypes in NumPy. Not all of them are supported by PyGMT.
+#
+# - Numeric dtypes:
+#   - int8, int16, int32, int64, longlong
+#   - uint8, uint16, uint32, uint64, ulonglong
+#   - float16, float32, float64, longdouble
+#   - complex64, complex128, clongdouble
+# - bool
+# - datetime64, timedelta64
+# - str_
+# - bytes_
+# - object_
+# - void
+#
+# Reference: https://numpy.org/doc/2.1/reference/arrays.scalars.html
+########################################################################################
+np_dtype_params = [
+    pytest.param(np.int8, np.int8, id="int8"),
+    pytest.param(np.int16, np.int16, id="int16"),
+    pytest.param(np.int32, np.int32, id="int32"),
+    pytest.param(np.int64, np.int64, id="int64"),
+    pytest.param(np.longlong, np.longlong, id="longlong"),
+    pytest.param(np.uint8, np.uint8, id="uint8"),
+    pytest.param(np.uint16, np.uint16, id="uint16"),
+    pytest.param(np.uint32, np.uint32, id="uint32"),
+    pytest.param(np.uint64, np.uint64, id="uint64"),
+    pytest.param(np.ulonglong, np.ulonglong, id="ulonglong"),
+    pytest.param(np.float16, np.float16, id="float16"),
+    pytest.param(np.float32, np.float32, id="float32"),
+    pytest.param(np.float64, np.float64, id="float64"),
+    pytest.param(np.longdouble, np.longdouble, id="longdouble"),
+    pytest.param(np.complex64, np.complex64, id="complex64"),
+    pytest.param(np.complex128, np.complex128, id="complex128"),
+    pytest.param(np.clongdouble, np.clongdouble, id="clongdouble"),
+]
+
+
+@pytest.mark.parametrize(("dtype", "expected_dtype"), np_dtype_params)
+def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with NumPy arrays of NumPy numeric dtypes.
+
+    Test both 1-D and 2-D arrays which are not C-contiguous.
+    """
+    # 1-D array that is not C-contiguous
+    array = np.array([1, 2, 3, 4, 5, 6], dtype=dtype)[::2]
+    assert array.flags.c_contiguous is False
+    result = _to_numpy(array)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, array, strict=True)
+
+    # 2-D array that is not C-contiguous
+    array = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=dtype)[::2, ::2]
+    assert array.flags.c_contiguous is False
+    result = _to_numpy(array)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, array, strict=True)
+
+
+########################################################################################
+# Test the _to_numpy function with pandas.Series.
+#
+# In pandas, dtype can be specified by
+#
+# 1. NumPy dtypes (see above)
+# 2. pandas dtypes
+# 3. PyArrow types (see below)
+#
+# pandas provides following dtypes:
+#
+# - Numeric dtypes:
+#   - Int8, Int16, Int32, Int64
+#   - UInt8, UInt16, UInt32, UInt64
+#   - Float32, Float64
+# - DatetimeTZDtype
+# - PeriodDtype
+# - IntervalDtype
+# - StringDtype
+# - CategoricalDtype
+# - SparseDtype
+# - BooleanDtype
+# - ArrowDtype: a special dtype used to store data in the PyArrow format.
+#
+# References:
+# 1. https://pandas.pydata.org/docs/reference/arrays.html
+# 2. https://pandas.pydata.org/docs/user_guide/basics.html#basics-dtypes
+# 3. https://pandas.pydata.org/docs/user_guide/pyarrow.html
+########################################################################################
+@pytest.mark.parametrize(("dtype", "expected_dtype"), np_dtype_params)
+def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with pandas.Series of NumPy numeric dtypes.
+    """
+    series = pd.Series([1, 2, 3, 4, 5, 6], dtype=dtype)[::2]  # Not C-contiguous
+    result = _to_numpy(series)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, series)
+
+
+########################################################################################
+# Test the _to_numpy function with PyArrow arrays.
+#
+# PyArrow provides the following types:
+#
+# - Numeric types:
+#   - int8, int16, int32, int64
+#   - uint8, uint16, uint32, uint64
+#   - float16, float32, float64
+#
+# In PyArrow, array types can be specified in two ways:
+#
+# - Using string aliases (e.g., "int8")
+# - Using pyarrow.DataType (e.g., ``pa.int8()``)
+#
+# Reference: https://arrow.apache.org/docs/python/api/datatypes.html
+########################################################################################
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        pytest.param("int8", np.int8, id="int8"),
+        pytest.param("int16", np.int16, id="int16"),
+        pytest.param("int32", np.int32, id="int32"),
+        pytest.param("int64", np.int64, id="int64"),
+        pytest.param("uint8", np.uint8, id="uint8"),
+        pytest.param("uint16", np.uint16, id="uint16"),
+        pytest.param("uint32", np.uint32, id="uint32"),
+        pytest.param("uint64", np.uint64, id="uint64"),
+        pytest.param("float16", np.float16, id="float16"),
+        pytest.param("float32", np.float32, id="float32"),
+        pytest.param("float64", np.float64, id="float64"),
+    ],
+)
+def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with PyArrow arrays of PyArrow numeric types.
+    """
+    data = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
+    if dtype == "float16":  # float16 needs special handling
+        # Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
+        data = np.array(data, dtype=np.float16)
+    array = pa.array(data, type=dtype)[::2]
+    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("int8", np.float64, id="int8"),
+        pytest.param("int16", np.float64, id="int16"),
+        pytest.param("int32", np.float64, id="int32"),
+        pytest.param("int64", np.float64, id="int64"),
+        pytest.param("uint8", np.float64, id="uint8"),
+        pytest.param("uint16", np.float64, id="uint16"),
+        pytest.param("uint32", np.float64, id="uint32"),
+        pytest.param("uint64", np.float64, id="uint64"),
+        pytest.param("float16", np.float16, id="float16"),
+        pytest.param("float32", np.float32, id="float32"),
+        pytest.param("float64", np.float64, id="float64"),
+    ],
+)
+def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric_with_na(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with PyArrow arrays of PyArrow numeric types and NA.
+    """
+    data = [1.0, 2.0, None, 4.0, 5.0, 6.0]
+    if dtype == "float16":  # float16 needs special handling
+        # Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
+        data = np.array(data, dtype=np.float16)
+    array = pa.array(data, type=dtype)[::2]
+    result = _to_numpy(array)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, array)

pygmt/tests/test_clib_to_numpy.py

@@ -292,26 +292,6 @@ def test_figure_show():
     fig.show()
 
 
-@pytest.mark.mpl_image_compare
-def test_figure_shift_origin():
-    """
-    Test if fig.shift_origin works.
-    """
-    kwargs = {"region": [0, 3, 0, 5], "projection": "X3c/5c", "frame": 0}
-    fig = Figure()
-    # First call shift_origin without projection and region.
-    # Test issue https://github.com/GenericMappingTools/pygmt/issues/514
-    fig.shift_origin(xshift="2c", yshift="3c")
-    fig.basemap(**kwargs)
-    fig.shift_origin(xshift="4c")
-    fig.basemap(**kwargs)
-    fig.shift_origin(yshift="6c")
-    fig.basemap(**kwargs)
-    fig.shift_origin(xshift="-4c", yshift="6c")
-    fig.basemap(**kwargs)
-    return fig
-
-
 def test_figure_show_invalid_method():
     """
     Test to check if an error is raised when an invalid method is passed to show.
@@ -407,22 +387,6 @@ def test_invalid_method(self):
             set_display(method="invalid")
 
 
-def test_figure_unsupported_xshift_yshift():
-    """
-    Raise an exception if X/Y/xshift/yshift is used.
-    """
-    fig = Figure()
-    fig.basemap(region=[0, 1, 0, 1], projection="X1c/1c", frame=True)
-    with pytest.raises(GMTInvalidInput):
-        fig.plot(x=1, y=1, style="c3c", xshift="3c")
-    with pytest.raises(GMTInvalidInput):
-        fig.plot(x=1, y=1, style="c3c", X="3c")
-    with pytest.raises(GMTInvalidInput):
-        fig.plot(x=1, y=1, style="c3c", yshift="3c")
-    with pytest.raises(GMTInvalidInput):
-        fig.plot(x=1, y=1, style="c3c", Y="3c")
-
-
 class TestGetDefaultDisplayMethod:
     """
     Test the _get_default_display_method function.

pygmt/tests/test_figure.py

@@ -0,0 +1,43 @@
+"""
+Test Figure.shift_origin.
+"""
+
+import pytest
+from pygmt.exceptions import GMTInvalidInput
+from pygmt.figure import Figure
+
+
+@pytest.mark.mpl_image_compare
+def test_shift_origin():
+    """
+    Test if fig.shift_origin works.
+    """
+    kwargs = {"region": [0, 3, 0, 5], "projection": "X3c/5c", "frame": 0}
+    fig = Figure()
+    # First call shift_origin without projection and region.
+    # Test issue https://github.com/GenericMappingTools/pygmt/issues/514
+    fig.shift_origin(xshift="2c", yshift="3c")
+    fig.basemap(**kwargs)
+    fig.shift_origin(xshift="4c")
+    fig.basemap(**kwargs)
+    fig.shift_origin(yshift="6c")
+    fig.basemap(**kwargs)
+    fig.shift_origin(xshift="-4c", yshift="6c")
+    fig.basemap(**kwargs)
+    return fig
+
+
+def test_shift_origin_unsupported_xshift_yshift():
+    """
+    Raise an exception if X/Y/xshift/yshift is used.
+    """
+    fig = Figure()
+    fig.basemap(region=[0, 1, 0, 1], projection="X1c/1c", frame=True)
+    with pytest.raises(GMTInvalidInput):
+        fig.plot(x=1, y=1, style="c3c", xshift="3c")
+    with pytest.raises(GMTInvalidInput):
+        fig.plot(x=1, y=1, style="c3c", X="3c")
+    with pytest.raises(GMTInvalidInput):
+        fig.plot(x=1, y=1, style="c3c", yshift="3c")
+    with pytest.raises(GMTInvalidInput):
+        fig.plot(x=1, y=1, style="c3c", Y="3c")