clib.conversion: Remove the unused array_to_datetime function

pygmt/clib/conversion.py

@@ -344,81 +344,3 @@ def strings_to_ctypes_array(strings: Sequence[str] | np.ndarray) -> ctp.Array:
     ['first', 'second', 'third']
     """
     return (ctp.c_char_p * len(strings))(*[s.encode() for s in strings])
-
-
-def array_to_datetime(array: Sequence[Any] | np.ndarray) -> np.ndarray:
-    """
-    Convert a 1-D datetime array from various types into numpy.datetime64.
-
-    If the input array is not in legal datetime formats, raise a ValueError exception.
-
-    Parameters
-    ----------
-    array
-        The input datetime array in various formats.
-
-        Supported types:
-
-        - str
-        - numpy.datetime64
-        - pandas.DateTimeIndex
-        - datetime.datetime and datetime.date
-
-    Returns
-    -------
-    array
-        1-D datetime array in numpy.datetime64.
-
-    Raises
-    ------
-    ValueError
-        If the datetime string is invalid.
-
-    Examples
-    --------
-    >>> import datetime
-    >>> # numpy.datetime64 array
-    >>> x = np.array(
-    ...     ["2010-06-01", "2011-06-01T12", "2012-01-01T12:34:56"],
-    ...     dtype="datetime64[ns]",
-    ... )
-    >>> array_to_datetime(x)
-    array(['2010-06-01T00:00:00.000000000', '2011-06-01T12:00:00.000000000',
-           '2012-01-01T12:34:56.000000000'], dtype='datetime64[ns]')
-
-    >>> # pandas.DateTimeIndex array
-    >>> import pandas as pd
-    >>> x = pd.date_range("2013", freq="YS", periods=3)
-    >>> array_to_datetime(x)
-    array(['2013-01-01T00:00:00.000000000', '2014-01-01T00:00:00.000000000',
-           '2015-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
-
-    >>> # Python's built-in date and datetime
-    >>> x = [datetime.date(2018, 1, 1), datetime.datetime(2019, 1, 1)]
-    >>> array_to_datetime(x)
-    array(['2018-01-01T00:00:00.000000', '2019-01-01T00:00:00.000000'],
-          dtype='datetime64[us]')
-
-    >>> # Raw datetime strings in various format
-    >>> x = [
-    ...     "2018",
-    ...     "2018-02",
-    ...     "2018-03-01",
-    ...     "2018-04-01T01:02:03",
-    ... ]
-    >>> array_to_datetime(x)
-    array(['2018-01-01T00:00:00', '2018-02-01T00:00:00',
-           '2018-03-01T00:00:00', '2018-04-01T01:02:03'],
-          dtype='datetime64[s]')
-
-    >>> # Mixed datetime types
-    >>> x = [
-    ...     "2018-01-01",
-    ...     np.datetime64("2018-01-01"),
-    ...     datetime.datetime(2018, 1, 1),
-    ... ]
-    >>> array_to_datetime(x)
-    array(['2018-01-01T00:00:00.000000', '2018-01-01T00:00:00.000000',
-           '2018-01-01T00:00:00.000000'], dtype='datetime64[us]')
-    """
-    return np.asarray(array, dtype=np.datetime64)

pygmt/clib/session.py

@@ -17,7 +17,6 @@
 import pandas as pd
 import xarray as xr
 from pygmt.clib.conversion import (
-    array_to_datetime,
     dataarray_to_matrix,
     sequence_to_ctypes_array,
     strings_to_ctypes_array,
@@ -934,11 +933,6 @@ def _check_dtype_and_dim(self, array: np.ndarray, ndim: int) -> int:
             msg = f"Expected a numpy {ndim}-D array, got {array.ndim}-D."
             raise GMTInvalidInput(msg)
 
-        # For 1-D arrays, try to convert unknown object type to np.datetime64.
-        if ndim == 1 and array.dtype.type is np.object_:
-            with contextlib.suppress(ValueError):
-                array = array_to_datetime(array)
-
         # 1-D arrays can be numeric or text, 2-D arrays can only be numeric.
         valid_dtypes = DTYPES if ndim == 1 else DTYPES_NUMERIC
         if (dtype := array.dtype.type) not in valid_dtypes:
@@ -993,7 +987,7 @@ def put_vector(
         gmt_type = self._check_dtype_and_dim(vector, ndim=1)
         if gmt_type in {self["GMT_TEXT"], self["GMT_DATETIME"]}:
             if gmt_type == self["GMT_DATETIME"]:
-                vector = np.datetime_as_string(array_to_datetime(vector))
+                vector = np.datetime_as_string(vector)
             vector_pointer = strings_to_ctypes_array(vector)
         else:
             vector_pointer = vector.ctypes.data_as(ctp.c_void_p)