Remove the array_to_datetime function

Author: seisman

pygmt/clib/conversion.py

@@ -320,86 +320,3 @@ 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.
-
-    .. deprecated:: 0.14.0
-
-       The function is no longer used in the PyGMT project, but we keep this function
-       to docuemnt and test the supported datetime types.
-
-    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)