Function datetime_to_numeric

xarray/core/duck_array_ops.py:619–676

Convert an array containing datetime-like data to numerical values. Convert the datetime array to a timedelta relative to an offset. Parameters ---------- array : array-like Input data offset : None, datetime or cftime.datetime Datetime offset. If None, this is se

(array, offset=None, datetime_unit=None, dtype=float)

def datetime_to_numeric(array, offset=None, datetime_unit=None, dtype=float):
    """Convert an array containing datetime-like data to numerical values.
    Convert the datetime array to a timedelta relative to an offset.
    Parameters
    ----------
    array : array-like
        Input data
    offset : None, datetime or cftime.datetime
        Datetime offset. If None, this is set by default to the array's minimum
        value to reduce round off errors.
    datetime_unit : {None, Y, M, W, D, h, m, s, ms, us, ns, ps, fs, as}
        If not None, convert output to a given datetime unit. Note that some
        conversions are not allowed due to non-linear relationships between units.
    dtype : dtype
        Output dtype.
    Returns
    -------
    array
        Numerical representation of datetime object relative to an offset.
    Notes
    -----
    Some datetime unit conversions won't work, for example from days to years, even
    though some calendars would allow for them (e.g. no_leap). This is because there
    is no `cftime.timedelta` object.
    """
    # Set offset to minimum if not given
    if offset is None:
        if dtypes.is_datetime_like(array.dtype):
            offset = _datetime_nanreduce(array, min)
        else:
            offset = min(array)

    # Compute timedelta object.
    # For np.datetime64, this can silently yield garbage due to overflow.
    # One option is to enforce 1970-01-01 as the universal offset.

    # This map_blocks call is for backwards compatibility.
    # dask == 2021.04.1 does not support subtracting object arrays
    # which is required for cftime
    if is_duck_dask_array(array) and dtypes.is_object(array.dtype):
        array = array.map_blocks(lambda a, b: a - b, offset, meta=array._meta)
    else:
        array = array - offset

    # Scalar is converted to 0d-array
    if not hasattr(array, "dtype"):
        array = np.array(array)

    # Convert timedelta objects to float by first converting to microseconds.
    if dtypes.is_object(array.dtype):
        return py_timedelta_to_float(array, datetime_unit or "ns").astype(dtype)

    # Convert np.NaT to np.nan
    elif dtypes.is_datetime_like(array.dtype):
        # Convert to specified timedelta units.
        if datetime_unit:
            array = array / np.timedelta64(1, datetime_unit)
        return np.where(isnull(array), np.nan, array.astype(dtype))