Merge branch 'main' into to_numpy/pandas_numeric

Author: seisman

examples/gallery/images/cross_section.py

@@ -37,7 +37,7 @@
 
 # Add a colorbar for the elevation
 fig.colorbar(
-    # Place the colorbar inside the plot (lower-case "j") in the Botton Right (BR)
+    # Place the colorbar inside the plot (lower-case "j") in the Bottom Right (BR)
     # corner with an offset ("+o") of 0.7 centimeters and 0.3 centimeters in x or y
     # directions, respectively; move the x label above the horizontal colorbar ("+ml")
     position="jBR+o0.7c/0.8c+h+w5c/0.3c+ml",

examples/tutorials/advanced/cartesian_histograms.py

@@ -133,7 +133,7 @@
 # ----------------------------
 #
 # By default, a histogram showing the counts in each bin is created (``histtype=0``).
-# To show the frequency percent set the ``histtpye`` parameter to ``1``. For further
+# To show the frequency percent set the ``histtype`` parameter to ``1``. For further
 # options please have a look at the documentation of :meth:`pygmt.Figure.histogram`.
 
 fig = pygmt.Figure()

pygmt/tests/test_clib_to_numpy.py

@@ -118,9 +118,9 @@ def test_to_numpy_python_types(data, expected_dtype):
 
 
 @pytest.mark.parametrize(("dtype", "expected_dtype"), np_dtype_params)
-def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):
+def test_to_numpy_numpy_numeric(dtype, expected_dtype):
     """
-    Test the _to_numpy function with NumPy arrays of NumPy numeric dtypes.
+    Test the _to_numpy function with NumPy arrays of numeric dtypes.
 
     Test both 1-D and 2-D arrays which are not C-contiguous.
     """
@@ -140,9 +140,9 @@ def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):
 
 
 @pytest.mark.parametrize("dtype", [None, np.str_, "U10"])
-def test_to_numpy_ndarray_numpy_dtypes_string(dtype):
+def test_to_numpy_numpy_string(dtype):
     """
-    Test the _to_numpy function with NumPy arrays of string types.
+    Test the _to_numpy function with NumPy arrays of string dtypes.
     """
     array = np.array(["abc", "defg", "12345"], dtype=dtype)
     result = _to_numpy(array)
@@ -213,8 +213,7 @@ def test_to_numpy_ndarray_numpy_dtypes_string(dtype):
 )
 def test_to_numpy_pandas_numeric(dtype, expected_dtype):
     """
-    Test the _to_numpy function with pandas.Series of NumPy/pandas/PyArrow numeric
-    dtypes.
+    Test the _to_numpy function with pandas.Series of numeric dtypes.
     """
     data = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
     if dtype == "float16[pyarrow]" and Version(pd.__version__) < Version("2.2"):
@@ -294,9 +293,9 @@ def test_to_numpy_pandas_series_pandas_dtypes_numeric_with_na(dtype, expected_dt
         ),
     ],
 )
-def test_to_numpy_pandas_series_pandas_dtypes_string(dtype):
+def test_to_numpy_pandas_string(dtype):
     """
-    Test the _to_numpy function with pandas.Series of pandas string types.
+    Test the _to_numpy function with pandas.Series of string dtypes.
 
     In pandas, string arrays can be specified in multiple ways.
 
@@ -316,7 +315,7 @@ def test_to_numpy_pandas_series_pandas_dtypes_string(dtype):
         pytest.param("date64[ms][pyarrow]", "datetime64[ms]", id="date64[ms]"),
     ],
 )
-def test_to_numpy_pandas_series_pyarrow_dtypes_date(dtype, expected_dtype):
+def test_to_numpy_pandas_date(dtype, expected_dtype):
     """
     Test the _to_numpy function with pandas.Series of PyArrow date32/date64 types.
     """
@@ -369,9 +368,9 @@ def test_to_numpy_pandas_series_pyarrow_dtypes_date(dtype, expected_dtype):
         pytest.param("float64", np.float64, id="float64"),
     ],
 )
-def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric(dtype, expected_dtype):
+def test_to_numpy_pyarrow_numeric(dtype, expected_dtype):
     """
-    Test the _to_numpy function with PyArrow arrays of PyArrow numeric types.
+    Test the _to_numpy function with PyArrow arrays of numeric types.
     """
     data = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
     if dtype == "float16":  # float16 needs special handling
@@ -400,9 +399,9 @@ def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric(dtype, expected_dtype):
         pytest.param("float64", np.float64, id="float64"),
     ],
 )
-def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric_with_na(dtype, expected_dtype):
+def test_to_numpy_pyarrow_numeric_with_na(dtype, expected_dtype):
     """
-    Test the _to_numpy function with PyArrow arrays of PyArrow numeric types and NA.
+    Test the _to_numpy function with PyArrow arrays of numeric types and NA.
     """
     data = [1.0, 2.0, None, 4.0, 5.0, 6.0]
     if dtype == "float16":  # float16 needs special handling
@@ -426,9 +425,9 @@ def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric_with_na(dtype, expected_d
         "string_view",
     ],
 )
-def test_to_numpy_pyarrow_array_pyarrow_dtypes_string(dtype):
+def test_to_numpy_pyarrow_string(dtype):
     """
-    Test the _to_numpy function with PyArrow arrays of PyArrow string types.
+    Test the _to_numpy function with PyArrow arrays of string types.
     """
     array = pa.array(["abc", "defg", "12345"], type=dtype)
     result = _to_numpy(array)
@@ -444,9 +443,9 @@ def test_to_numpy_pyarrow_array_pyarrow_dtypes_string(dtype):
         pytest.param("date64[ms]", "datetime64[ms]", id="date64[ms]"),
     ],
 )
-def test_to_numpy_pyarrow_array_pyarrow_dtypes_date(dtype, expected_dtype):
+def test_to_numpy_pyarrow_date(dtype, expected_dtype):
     """
-    Test the _to_numpy function with PyArrow arrays of PyArrow date types.
+    Test the _to_numpy function with PyArrow arrays of date32/date64 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).