How to use the cftime.datetime function in cftime

# and so this Gregorian date can be compared to the proleptic
        # Gregorian date d2.
        d3 = num2date(0, 'days since 3000-01-01', 'standard')
        assert d1 < d2
        assert d2 < d3

        # check all comparisons
        assert d1 != d2
        assert d1 <= d2
        assert d2 > d1
        assert d2 >= d1

        # check datetime hash
        d1 = datetimex(1995, 1, 1)
        d2 = datetime(1995, 1, 1)
        d3 = datetimex(2001, 2, 30)
        assert hash(d1) == hash(d1)
        assert hash(d1) == hash(d2)
        assert hash(d1) != hash(d3)
        assert hash(d3) == hash(d3)

        # check datetime immutability
        # using assertRaises as a context manager
        # only works with python >= 2.7 (issue #497).
        immutability_tests = {"year": 1999,
                              "month": 6,
                              "day": 5,
                              "hour": 10,
                              "minute": 33,
                              "second": 45,
                              "dayofwk": 1,
                              "dayofyr": 52,

self.assertEqual(t1+1, t2)
        # list
        t1, t2 = date2num([datetime(1582, 10, 4), datetime(1582, 10, 15)], units, calendar='gregorian')
        self.assertEqual(t1+1, t2)
        t1, t2 = date2num([datetime(1582, 10, 4), datetime(1582, 10, 15)], units, calendar='standard')
        self.assertEqual(t1+1, t2)
        # this should fail: days missing in Gregorian calendar
        try:
            t1, t2, t3 = date2num([datetime(1582, 10, 4), datetime(1582, 10, 10), datetime(1582, 10, 15)], units, calendar='standard')
        except ValueError:
            pass
        # test fix for issue #596 - julian day calculations wrong for negative years,
        # caused incorrect rountrip num2date(date2num(date)) roundtrip for dates with year
        # < 0.
        u = utime("seconds since 1-1-1",calendar='julian')
        date1 = datetimex(-1, 1, 1)
        date2 = u.num2date(u.date2num(date1))
        assert (date2.year == date1.year)
        assert (date2.month == date1.month)
        assert (date2.day == date1.day)
        assert (date2.hour == date1.hour)
        assert (date2.minute == date1.minute)
        assert (date2.second == date1.second)
        assert_almost_equal(JulianDayFromDate(date1), 1721057.5)
        # issue 596 - negative years fail in utime.num2date
        u = utime("seconds since 1-1-1", "proleptic_gregorian")
        d = u.num2date(u.date2num(datetimex(-1, 1, 1)))
        assert (d.year == -1)
        assert (d.month == 1)
        assert (d.day == 1)
        assert (d.hour == 0)
        # test fix for issue #659 (proper treatment of negative time values).

# The date d3 is well after the Julian/Gregorian transition
        # and so this Gregorian date can be compared to the proleptic
        # Gregorian date d2.
        d3 = num2date(0, 'days since 3000-01-01', 'standard')
        assert d1 < d2
        assert d2 < d3

        # check all comparisons
        assert d1 != d2
        assert d1 <= d2
        assert d2 > d1
        assert d2 >= d1

        # check datetime hash
        d1 = datetimex(1995, 1, 1)
        d2 = datetime(1995, 1, 1)
        d3 = datetimex(2001, 2, 30)
        assert hash(d1) == hash(d1)
        assert hash(d1) == hash(d2)
        assert hash(d1) != hash(d3)
        assert hash(d3) == hash(d3)

        # check datetime immutability
        # using assertRaises as a context manager
        # only works with python >= 2.7 (issue #497).
        immutability_tests = {"year": 1999,
                              "month": 6,
                              "day": 5,
                              "hour": 10,
                              "minute": 33,
                              "second": 45,

and fp_coord is not None
        and fp_coord.has_bounds()
        and clim_season_coord is not None
        and "clim_season" in cube.cell_methods[-1].coord_names
        and clim_season_coord.points[0] == "djf"
    ):
        # Climatological time mean - spanning years - djf.
        pp.lbtim.ia = 0
        pp.lbtim.ib = 3
        pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
        pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
        if pp.t1.month == 12:
            pp.t1 = cftime.datetime(pp.t1.year)
        else:
            pp.t1 = cftime.datetime(pp.t1.year - 1, 12, 1, 0, 0, 0)
        pp.t2 = cftime.datetime(pp.t2.year, 3, 1, 0, 0, 0)
        _conditional_warning(
            time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
            "modified t1 for climatological seasonal mean",
        )
        _conditional_warning(
            time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
            "modified t2 for climatological seasonal mean",
        )
        pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], "hours")

    elif (
        time_coord is not None
        and time_coord.has_bounds()
        and lower_bound_yr != upper_bound_yr
        and fp_coord is not None
        and fp_coord.has_bounds()

shape = value.shape
            value = value.reshape(-1)
            first_value = value[0]
        else:
            # Don't do anything with numeric types.
            if munits.ConversionInterface.is_numlike(value):
                return value
            first_value = value

        if not isinstance(first_value, (CalendarDateTime, cftime.datetime)):
            raise ValueError('The values must be numbers or instances of '
                             '"nc_time_axis.CalendarDateTime" or '
                             '"cftime.datetime".')

        if isinstance(first_value, CalendarDateTime):
            if not isinstance(first_value.datetime, cftime.datetime):
                raise ValueError('The datetime attribute of the '
                                 'CalendarDateTime object must be of type '
                                 '`cftime.datetime`.')

        ut = cftime.utime(cls.standard_unit, calendar=first_value.calendar)

        if isinstance(value, (CalendarDateTime, cftime.datetime)):
            value = [value]

        if isinstance(first_value, CalendarDateTime):
            result = ut.date2num([v.datetime for v in value])
        else:
            result = ut.date2num(value)

        if shape is not None:
            result = result.reshape(shape)

time_coord is not None
        and time_coord.has_bounds()
        and lower_bound_yr != upper_bound_yr
        and fp_coord is not None
        and fp_coord.has_bounds()
        and clim_season_coord is not None
        and "clim_season" in cube.cell_methods[-1].coord_names
        and clim_season_coord.points[0] == "mam"
    ):
        # Climatological time mean - spanning years - mam.
        pp.lbtim.ia = 0
        pp.lbtim.ib = 3
        # TODO: wut?
        pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
        pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
        pp.t1 = cftime.datetime(pp.t1.year, 3, 1, 0, 0, 0)
        pp.t2 = cftime.datetime(pp.t2.year, 6, 1, 0, 0, 0)
        _conditional_warning(
            time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
            "modified t1 for climatological seasonal mean",
        )
        _conditional_warning(
            time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
            "modified t2 for climatological seasonal mean",
        )
        pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], "hours")

    elif (
        time_coord is not None
        and time_coord.has_bounds()
        and lower_bound_yr != upper_bound_yr
        and fp_coord is not None

def axisinfo(unit, axis):
        """
        Returns the :class:`~matplotlib.units.AxisInfo` for *unit*.

        *unit* is a tzinfo instance or None.
        The *axis* argument is required but not used.
        """
        calendar, date_unit, date_type = unit

        majloc = NetCDFTimeDateLocator(4, calendar=calendar,
                                       date_unit=date_unit)
        majfmt = NetCDFTimeDateFormatter(majloc, calendar=calendar,
                                         time_units=date_unit)
        if date_type is CalendarDateTime:
            datemin = CalendarDateTime(cftime.datetime(2000, 1, 1),
                                       calendar=calendar)
            datemax = CalendarDateTime(cftime.datetime(2010, 1, 1),
                                       calendar=calendar)
        else:
            datemin = date_type(2000, 1, 1)
            datemax = date_type(2010, 1, 1)
        return munits.AxisInfo(majloc=majloc, majfmt=majfmt, label='',
                               default_limits=(datemin, datemax))

"""
        Returns the :class:`~matplotlib.units.AxisInfo` for *unit*.

        *unit* is a tzinfo instance or None.
        The *axis* argument is required but not used.
        """
        calendar, date_unit, date_type = unit

        majloc = NetCDFTimeDateLocator(4, calendar=calendar,
                                       date_unit=date_unit)
        majfmt = NetCDFTimeDateFormatter(majloc, calendar=calendar,
                                         time_units=date_unit)
        if date_type is CalendarDateTime:
            datemin = CalendarDateTime(cftime.datetime(2000, 1, 1),
                                       calendar=calendar)
            datemax = CalendarDateTime(cftime.datetime(2010, 1, 1),
                                       calendar=calendar)
        else:
            datemin = date_type(2000, 1, 1)
            datemax = date_type(2010, 1, 1)
        return munits.AxisInfo(majloc=majloc, majfmt=majfmt, label='',
                               default_limits=(datemin, datemax))

and time_coord.has_bounds()
        and lower_bound_yr != upper_bound_yr
        and fp_coord is not None
        and fp_coord.has_bounds()
        and clim_season_coord is not None
        and "clim_season" in cube.cell_methods[-1].coord_names
        and clim_season_coord.points[0] == "mam"
    ):
        # Climatological time mean - spanning years - mam.
        pp.lbtim.ia = 0
        pp.lbtim.ib = 3
        # TODO: wut?
        pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
        pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
        pp.t1 = cftime.datetime(pp.t1.year, 3, 1, 0, 0, 0)
        pp.t2 = cftime.datetime(pp.t2.year, 6, 1, 0, 0, 0)
        _conditional_warning(
            time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
            "modified t1 for climatological seasonal mean",
        )
        _conditional_warning(
            time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
            "modified t2 for climatological seasonal mean",
        )
        pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], "hours")

    elif (
        time_coord is not None
        and time_coord.has_bounds()
        and lower_bound_yr != upper_bound_yr
        and fp_coord is not None
        and fp_coord.has_bounds()

primary_elements,
            vector_element_arrays_and_dims,
        ) = optimal_array_structure(ordering_definitions, element_definitions)

        # Replace time tuples in the result with real datetime-like values.
        # N.B. so we *don't* do this on the whole (expanded) input arrays.
        for name in ("t1", "t2"):
            if name in vector_element_arrays_and_dims:
                arr, dims = vector_element_arrays_and_dims[name]
                arr_shape = arr.shape[:-1]
                extra_length = arr.shape[-1]
                # Flatten out the array apart from the last dimension,
                # convert to cftime objects, then reshape back.
                arr = np.array(
                    [
                        cftime.datetime(*args)
                        for args in arr.reshape(-1, extra_length)
                    ]
                ).reshape(arr_shape)
                vector_element_arrays_and_dims[name] = (arr, dims)

        # Write the private cache values, exposed as public properties.
        self._vector_dims_shape = dims_shape
        self._primary_dimension_elements = primary_elements
        self._element_arrays_and_dims = vector_element_arrays_and_dims
        # Do all this only once.
        self._structure_calculated = True