Python netcdftime.datetime函数代码示例

 def test_vector_lbft(self):
     field = self._field()
     field.lbtim = 21
     field.t1 = netcdftime.datetime(1970, 1, 1, 12)
     field.t2 = netcdftime.datetime(1970, 1, 1, 18)
     lbft = ([18, 15, 12], [0])
     collation = mock.Mock(fields=[field], vector_dims_shape=(3,),
                           element_arrays_and_dims={'lbft': lbft})
     metadata = convert_collation(collation)
     self._check_phenomenon(metadata)
     coords_and_dims = [(LONGITUDE, 2),
                        (LATITUDE, 1),
                        (iris.coords.DimCoord([0, 3, 6],
                                              'forecast_reference_time',
                                              units='hours since epoch'),
                         (0,))]
     coords_and_dims = [
         (iris.coords.DimCoord(15, 'time', units='hours since epoch',
                               bounds=[[12, 18]]), None),
         (iris.coords.DimCoord([15, 12, 9], 'forecast_period',
                               units='hours',
                               bounds=[[12, 18], [9, 15], [6, 12]]),
          (0,))
     ]
     self.assertEqual(metadata.aux_coords_and_dims, coords_and_dims)

 def test_vector_t2(self):
     field = self._field()
     field.lbtim = 11
     field.t1 = netcdftime.datetime(1970, 1, 1, 18)
     t2 = (
         [
             netcdftime.datetime(1970, 1, 1, 12),
             netcdftime.datetime(1970, 1, 1, 15),
             netcdftime.datetime(1970, 1, 1, 18),
         ],
         [0],
     )
     collation = mock.Mock(fields=[field], vector_dims_shape=(3,), element_arrays_and_dims={"t2": t2})
     metadata = convert_collation(collation)
     self._check_phenomenon(metadata)
     coords_and_dims = [
         (LONGITUDE, 2),
         (LATITUDE, 1),
         (iris.coords.DimCoord([12, 15, 18], "forecast_reference_time", units="hours since epoch"), (0,)),
     ]
     self.assertEqual(metadata.dim_coords_and_dims, coords_and_dims)
     coords_and_dims = [
         (iris.coords.DimCoord(18, "time", units="hours since epoch"), None),
         (iris.coords.DimCoord([6, 3, 0.0], "forecast_period", units="hours"), (0,)),
     ]
     self.assertEqual(metadata.aux_coords_and_dims, coords_and_dims)

 def test_vector_t1(self):
     field = self._field()
     field.lbtim = 11
     field.t2 = netcdftime.datetime(1970, 1, 1, 12)
     t1 = ([netcdftime.datetime(1970, 1, 1, 18),
            netcdftime.datetime(1970, 1, 2, 0),
            netcdftime.datetime(1970, 1, 2, 6)], [0])
     collation = mock.Mock(fields=[field], vector_dims_shape=(3,),
                           element_arrays_and_dims={'t1': t1})
     metadata = convert_collation(collation)
     self._check_phenomenon(metadata)
     coords_and_dims = [(LONGITUDE, 2),
                        (LATITUDE, 1),
                        (iris.coords.DimCoord([18, 24, 30], 'time',
                                              units='hours since epoch'),
                         (0,))
                        ]
     self.assertEqual(metadata.dim_coords_and_dims, coords_and_dims)
     coords_and_dims = [
         (iris.coords.DimCoord(12, 'forecast_reference_time',
                               units='hours since epoch'), None),
         (iris.coords.DimCoord([6, 12, 18], 'forecast_period',
                               units='hours'), (0,))
     ]
     self.assertEqual(metadata.aux_coords_and_dims, coords_and_dims)

 def test_vector_t1_and_t2(self):
     field = self._field()
     field.lbtim = 11
     t1 = ([netcdftime.datetime(1970, 1, 2, 6),
            netcdftime.datetime(1970, 1, 2, 9),
            netcdftime.datetime(1970, 1, 2, 12)], [1])
     t2 = ([netcdftime.datetime(1970, 1, 1, 12),
            netcdftime.datetime(1970, 1, 2, 0)], [0])
     collation = mock.Mock(fields=[field], vector_dims_shape=(2, 3),
                           element_arrays_and_dims={'t1': t1, 't2': t2})
     metadata = convert_collation(collation)
     self._check_phenomenon(metadata)
     coords_and_dims = [(LONGITUDE, 3),
                        (LATITUDE, 2),
                        (iris.coords.DimCoord([30, 33, 36], 'time',
                                              units='hours since epoch'),
                         (1,)),
                        (iris.coords.DimCoord([12, 24],
                                              'forecast_reference_time',
                                              units='hours since epoch'),
                         (0,))]
     self.assertEqual(metadata.dim_coords_and_dims, coords_and_dims)
     coords_and_dims = [
         (iris.coords.AuxCoord([[18, 21, 24], [6, 9, 12]],
                               'forecast_period', units='hours'), (0, 1))
     ]
     self.assertEqual(metadata.aux_coords_and_dims, coords_and_dims)

def harmonize_hourly_timestamp(time_range, dt):
    '''
    Adjust the ``time_range`` by setting hour value to datetime.datetime objects.
    
    :param time_range: time range selected by user   
    :type time_range: list of two datetime.datetime objects
    
    :param dt: any datetime step of input datetime vector
    :type dt: datetime.datetime object
    
    :rtype: list of two datetime.datetime objects
    
    WHY:
    if input time steps vector is from 1990-01-01 12:00 to 2000-12-31 12:00,
    and user's time_range is [datetime.datetime(1900, 1, 1), datetime.datetime(1905, 12, 31)],
    i.e. [datetime.datetime(1900, 1, 1, 0, 0), datetime.datetime(1905, 12, 31, 0, 0)],
    it will be not included in the input time steps (there will be the error message "The time range is not included in the input time steps array.").
    Thus, this function will adjust the hour of the user's time_range: [datetime.datetime(1900, 1, 1, 12, 0), datetime.datetime(1905, 12, 31, 12, 0)]

    '''

#     time_range_begin = datetime(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
#     time_range_end = datetime(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)

    time_range_begin = netcdftime.datetime(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
    time_range_end = netcdftime.datetime(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)
    
    return [time_range_begin, time_range_end]

 def test_365_day_calendar(self):
     unit = Unit('minutes since 2000-02-25 00:00:00', calendar='365_day')
     coord = AuxCoord([30, 60, 150], 'time', units=unit)
     result = _fixup_dates(coord, coord.points)
     expected_datetimes = [netcdftime.datetime(2000, 2, 25, 0, 30),
                           netcdftime.datetime(2000, 2, 25, 1, 0),
                           netcdftime.datetime(2000, 2, 25, 2, 30)]
     self.assertArrayEqual([cdt.datetime for cdt in result],
                           expected_datetimes)

 def test_t1(self):
     collation = FieldCollation([_make_field(lbyr=2013),
                                 _make_field(lbyr=2014)])
     result = collation.element_arrays_and_dims
     self.assertEqual(list(result.keys()), ['t1'])
     values, dims = result['t1']
     self.assertArrayEqual(values, [datetime(2013, 1, 1),
                                    datetime(2014, 1, 1)])
     self.assertEqual(dims, (0,))

 def test_axis_default_limits(self):
     cal = '360_day'
     unit = (cal, 'days since 2000-02-25 00:00:00')
     result = NetCDFTimeConverter().axisinfo(unit, None)
     expected_dt = [netcdftime.datetime(2000, 1, 1),
                    netcdftime.datetime(2010, 1, 1)]
     np.testing.assert_array_equal(
         result.default_limits,
         [CalendarDateTime(edt, cal) for edt in expected_dt])

 def test_data_frame_netcdftime_360(self):
     data_frame = pandas.DataFrame(
         [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]],
         index=[netcdftime.datetime(2001, 1, 1, 1, 1, 1), netcdftime.datetime(2002, 2, 2, 2, 2, 2)],
         columns=[10, 11, 12, 13, 14],
     )
     self.assertCML(
         iris.pandas.as_cube(data_frame, calendars={0: cf_units.CALENDAR_360_DAY}),
         tests.get_result_path(("pandas", "as_cube", "data_frame_netcdftime_360.cml")),
     )

 def test_data_frame_netcdftime_360(self):
     data_frame = pandas.DataFrame(
         [[0, 1, 2, 3, 4],
          [5, 6, 7, 8, 9]],
         index=[netcdftime.datetime(2001, 01, 01, 01, 01, 01),
                netcdftime.datetime(2002, 02, 02, 02, 02, 02)],
         columns=[10, 11, 12, 13, 14])
     self.assertCML(
         iris.pandas.as_cube(
             data_frame,
             calendars={0: iris.unit.CALENDAR_360_DAY}),
         tests.get_result_path(('pandas', 'as_cube',
                                'data_frame_netcdftime_360.cml')))

 def test_series_netcdftime_360(self):
     series = pandas.Series(
         [0, 1, 2, 3, 4],
         index=[netcdftime.datetime(2001, 1, 1, 1, 1, 1),
                netcdftime.datetime(2002, 2, 2, 2, 2, 2),
                netcdftime.datetime(2003, 3, 3, 3, 3, 3),
                netcdftime.datetime(2004, 4, 4, 4, 4, 4),
                netcdftime.datetime(2005, 5, 5, 5, 5, 5)])
     self.assertCML(
         iris.pandas.as_cube(series,
                             calendars={0: cf_units.CALENDAR_360_DAY}),
         tests.get_result_path(('pandas', 'as_cube',
                                'series_netcdfimte_360.cml')))

def harmonize_hourly_timestamp(time_range, calend, dt):
    '''
    Adjust the ``time_range`` by setting hour value to datetime.datetime objects.
    
    :param time_range: time range selected by user   
    :type time_range: list of two datetime.datetime objects
    
    :param dt: any datetime step of input datetime vector
    :type dt: datetime.datetime object
    
    :param calend: calendar attribute of variable "time" in netCDF file
    :type calend: str

    :rtype: list of two datetime.datetime objects
    
    WHY:
    if input time steps vector is from 1990-01-01 12:00 to 2000-12-31 12:00,
    and user's time_range is [datetime.datetime(1900, 1, 1), datetime.datetime(1905, 12, 31)],
    i.e. [datetime.datetime(1900, 1, 1, 0, 0), datetime.datetime(1905, 12, 31, 0, 0)],
    it will be not included in the input time steps (there will be the error message "The time range is not included in the input time steps array.").
    Thus, this function will adjust the hour of the user's time_range: [datetime.datetime(1900, 1, 1, 12, 0), datetime.datetime(1905, 12, 31, 12, 0)]

    '''

#     time_range_begin = datetime(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
#     time_range_end = datetime(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)

    if calend == 'noleap' or calend == '365_day':
        time_range_begin = netcdftime._netcdftime.DatetimeNoLeap(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
        time_range_end = netcdftime._netcdftime.DatetimeNoLeap(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)
    elif calend == '360_day':
        time_range_begin = netcdftime._netcdftime.Datetime360Day(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
        time_range_end = netcdftime._netcdftime.Datetime360Day(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)
    elif calend == 'gregorian':
        time_range_begin = netcdftime._netcdftime.DatetimeGregorian(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
        time_range_end = netcdftime._netcdftime.DatetimeGregorian(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)
    elif calend == 'proleptic_gregorian':
        time_range_begin = netcdftime._netcdftime.DatetimeProlepticGregorian(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
        time_range_end = netcdftime._netcdftime.DatetimeProlepticGregorian(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)
    elif calend == 'julian':
        time_range_begin = netcdftime._netcdftime.DatetimeJulian(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
        time_range_end = netcdftime._netcdftime.DatetimeJulian(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)
    elif calend == 'all_leap' or calend == '366_day':
        time_range_begin = netcdftime._netcdftime.DatetimeAllLeap(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
        time_range_end = netcdftime._netcdftime.DatetimeAllLeap(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)
    else:
        time_range_begin = netcdftime.datetime(time_range[0].year, time_range[0].month, time_range[0].day, dt.hour)
        time_range_end = netcdftime.datetime(time_range[1].year, time_range[1].month, time_range[1].day, dt.hour)

    return [time_range_begin, time_range_end]

 def test_time_360(self):
     cube = Cube(np.array([0, 1, 2, 3, 4]), long_name="ts")
     time_unit = cf_units.Unit("days since 2000-01-01 00:00",
                               calendar=cf_units.CALENDAR_360_DAY)
     time_coord = DimCoord([0, 100.1, 200.2, 300.3, 400.4],
                           long_name="time", units=time_unit)
     cube.add_dim_coord(time_coord, 0)
     expected_index = [netcdftime.datetime(2000, 1, 1, 0, 0),
                       netcdftime.datetime(2000, 4, 11, 2, 24),
                       netcdftime.datetime(2000, 7, 21, 4, 48),
                       netcdftime.datetime(2000, 11, 1, 7, 12),
                       netcdftime.datetime(2001, 2, 11, 9, 36)]
     series = iris.pandas.as_series(cube)
     self.assertArrayEqual(series, cube.data)
     self.assertArrayEqual(series.index, expected_index)

def _fixup_dates(coord, values):
    if coord.units.calendar is not None and values.ndim == 1:
        # Convert coordinate values into tuples of
        # (year, month, day, hour, min, sec)
        dates = [coord.units.num2date(val).timetuple()[0:6]
                 for val in values]
        if coord.units.calendar == 'gregorian':
            r = [datetime.datetime(*date) for date in dates]
        else:
            try:
                import nc_time_axis
            except ImportError:
                msg = ('Cannot plot against time in a non-gregorian '
                       'calendar, because "nc_time_axis" is not available :  '
                       'Install the package from '
                       'https://github.com/SciTools/nc-time-axis to enable '
                       'this usage.')
                raise IrisError(msg)

            r = [nc_time_axis.CalendarDateTime(
                 netcdftime.datetime(*date), coord.units.calendar)
                 for date in dates]
        values = np.empty(len(r), dtype=object)
        values[:] = r
    return values

 def test_all_scalar(self):
     field = self._field()
     field.lbtim = 11
     field.t1 = netcdftime.datetime(1970, 1, 1, 18)
     field.t2 = netcdftime.datetime(1970, 1, 1, 12)
     collation = mock.Mock(fields=[field], vector_dims_shape=(), element_arrays_and_dims={})
     metadata = convert_collation(collation)
     self._check_phenomenon(metadata)
     coords_and_dims = [(LONGITUDE, 1), (LATITUDE, 0)]
     self.assertEqual(metadata.dim_coords_and_dims, coords_and_dims)
     coords_and_dims = [
         (iris.coords.DimCoord(18, "time", units="hours since epoch"), None),
         (iris.coords.DimCoord(12, "forecast_reference_time", units="hours since epoch"), None),
         (iris.coords.DimCoord(6, "forecast_period", units="hours"), None),
     ]
     self.assertEqual(metadata.aux_coords_and_dims, coords_and_dims)