Python cftime.datetime方法代碼示例

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def datetime_or_default(date, default):
    """Return a datetime-like object or a default.

    Parameters
    ----------
    date : `None` or datetime-like object or str
    default : The value to return if `date` is `None`

    Returns
    -------
    `default` if `date` is `None`, otherwise returns the result of
    `utils.times.ensure_datetime(date)`

    """
    if date is None:
        return default
    else:
        return ensure_datetime(date) 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def ensure_datetime(obj):
    """Return the object if it is a datetime-like object

    Parameters
    ----------
    obj : Object to be tested.

    Returns
    -------
    The original object if it is a datetime-like object

    Raises
    ------
    TypeError if `obj` is not datetime-like
    """
    _VALID_TYPES = (str, datetime.datetime, cftime.datetime,
                    np.datetime64)
    if isinstance(obj, _VALID_TYPES):
        return obj
    raise TypeError("datetime-like object required.  "
                    "Type given: {}".format(type(obj))) 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
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)) 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def test_cftime_transform_noleap_warn(self):
        try:
            import cftime
        except:
            raise SkipTest('Test requires cftime library')
        gregorian_dates = [cftime.DatetimeNoLeap(2000, 2, 28),
                           cftime.DatetimeNoLeap(2000, 3, 1),
                           cftime.DatetimeNoLeap(2000, 3, 2)]
        curve = Curve((gregorian_dates, [1, 2, 3]))
        plot = bokeh_renderer.get_plot(curve)
        xs = plot.handles['cds'].data['x']
        self.assertEqual(xs.astype('int64'),
                         np.array([951696000000, 951868800000, 951955200000]))
        substr = (
            "Converting cftime.datetime from a non-standard calendar "
            "(noleap) to a standard calendar for plotting. This may "
            "lead to subtle errors in formatting dates, for accurate "
            "tick formatting switch to the matplotlib backend.")
        self.log_handler.assertEndsWith('WARNING', substr) 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def cftime_to_timestamp(date, time_unit='us'):
    """Converts cftime to timestamp since epoch in milliseconds

    Non-standard calendars (e.g. Julian or no leap calendars)
    are converted to standard Gregorian calendar. This can cause
    extra space to be added for dates that don't exist in the original
    calendar. In order to handle these dates correctly a custom bokeh
    model with support for other calendars would have to be defined.

    Args:
        date: cftime datetime object (or array)

    Returns:
        time_unit since 1970-01-01 00:00:00
    """
    import cftime
    utime = cftime.utime('microseconds since 1970-01-01 00:00:00')
    if time_unit == 'us':
        tscale = 1
    else:
        tscale = (np.timedelta64(1, 'us')/np.timedelta64(1, time_unit))
    return utime.date2num(date)*tscale 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def ensure_cftime_array(time: Sequence):
    """Convert an input 1D array to an array of cftime objects. Python's datetime are converted to cftime.DatetimeGregorian.

    Raises ValueError when unable to cast the input.
    """
    if isinstance(time, xr.DataArray):
        time = time.indexes["time"]
    elif isinstance(time, np.ndarray):
        time = pd.DatetimeIndex(time)
    if isinstance(time[0], cftime.datetime):
        return time
    if isinstance(time[0], pydt.datetime):
        return np.array(
            [cftime.DatetimeGregorian(*ele.timetuple()[:6]) for ele in time]
        )
    raise ValueError("Unable to cast array to cftime dtype") 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def cfindex_start_time(cfindex, freq):
    """
    Get the start of a period for a pseudo-period index. As we are using
    datetime indices to stand in for period indices, assumptions regarding the
    period are made based on the given freq. IMPORTANT NOTE: this function
    cannot be used on greater-than-day freq that start at the beginning of a
    month, e.g., 'MS', 'QS', 'AS' -- this mirrors pandas behavior.

    Parameters
    __________
    cfindex : CFTimeIndex
        CFTimeIndex as a proxy representation for CFPeriodIndex
    freq : str
        String specifying the frequency/offset such as 'MS', '2D', 'H', or '3T'

    Returns
    _______
    CFTimeIndex
        The starting datetimes of periods inferred from dates and freq
    """
    return CFTimeIndex([cftime_start_time(date, freq) for date in cfindex]) 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def cfindex_end_time(cfindex, freq):
    """
    Get the start of a period for a pseudo-period index. As we are using
    datetime indices to stand in for period indices, assumptions regarding the
    period are made based on the given freq. IMPORTANT NOTE: this function
    cannot be used on greater-than-day freq that start at the beginning of a
    month, e.g., 'MS', 'QS', 'AS' -- this mirrors pandas behavior.

    Parameters
    __________
    cfindex : CFTimeIndex
        CFTimeIndex as a proxy representation for CFPeriodIndex
    freq : str
        String specifying the frequency/offset such as 'MS', '2D', 'H', or '3T'

    Returns
    _______
    CFTimeIndex
        The ending datetimes of periods inferred from dates and freq
    """
    return CFTimeIndex([cftime_end_time(date, freq) for date in cfindex]) 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def apply_time_offset(time, years=0, months=0, days=0, hours=0):
    """Apply a specified offset to the given time array.

    This is useful for GFDL model output of instantaneous values.  For example,
    3 hourly data postprocessed to netCDF files spanning 1 year each will
    actually have time values that are offset by 3 hours, such that the first
    value is for 1 Jan 03:00 and the last value is 1 Jan 00:00 of the
    subsequent year.  This causes problems in xarray, e.g. when trying to group
    by month.  It is resolved by manually subtracting off those three hours,
    such that the dates span from 1 Jan 00:00 to 31 Dec 21:00 as desired.

    Parameters
    ----------
    time : xarray.DataArray representing a timeseries
    years, months, days, hours : int, optional
        The number of years, months, days, and hours, respectively, to offset
        the time array by.  Positive values move the times later.

    Returns
    -------
    pandas.DatetimeIndex

    Examples
    --------
    Case of a length-1 input time array:

    >>> times = xr.DataArray(datetime.datetime(1899, 12, 31, 21))
    >>> apply_time_offset(times)
    Timestamp('1900-01-01 00:00:00')

    Case of input time array with length greater than one:

    >>> times = xr.DataArray([datetime.datetime(1899, 12, 31, 21),
    ...                       datetime.datetime(1899, 1, 31, 21)])
    >>> apply_time_offset(times) # doctest: +NORMALIZE_WHITESPACE
    DatetimeIndex(['1900-01-01', '1899-02-01'], dtype='datetime64[ns]',
                  freq=None)
    """
    return (pd.to_datetime(time.values) +
            pd.DateOffset(years=years, months=months, days=days, hours=hours)) 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def average_time_bounds(ds):
    """Return the average of each set of time bounds in the Dataset.

    Useful for creating a new time array to replace the Dataset's native time
    array, in the case that the latter matches either the start or end bounds.
    This can cause errors in grouping (akin to an off-by-one error) if the
    timesteps span e.g. one full month each.  Note that the Dataset's times
    must not have already undergone "CF decoding", wherein they are converted
    from floats using the 'units' attribute into datetime objects.

    Parameters
    ----------
    ds : xarray.Dataset
        A Dataset containing a time bounds array with name matching
        internal_names.TIME_BOUNDS_STR.  This time bounds array must have two
        dimensions, one of which's coordinates is the Dataset's time array, and
        the other is length-2.

    Returns
    -------
    xarray.DataArray
        The mean of the start and end times of each timestep in the original
        Dataset.

    Raises
    ------
    ValueError
        If the time bounds array doesn't match the shape specified above.

    """
    bounds = ds[TIME_BOUNDS_STR]
    new_times = bounds.mean(dim=BOUNDS_STR, keep_attrs=True)
    new_times = new_times.drop_vars(TIME_STR).rename(TIME_STR)
    new_times[TIME_STR] = new_times
    return new_times 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def get_time_decoded(self, midpoint=True):
        """Return time decoded.
        """
        # to compute a time midpoint, we need a time_bound variable
        if midpoint and self.time_bound is None:
            raise ValueError('cannot compute time midpoint w/o time bounds')

        if midpoint:
            time_data = self.time_bound.mean(self.tb_dim)

        else:
            # if time has already been decoded and there's no year_offset,
            # just return the time as is
            if self.isdecoded(self.time):
                if self.year_offset is None:
                    return self.time

                # if we need to un-decode time to apply the year_offset,
                time_data = self.get_time_undecoded()

            # time has not been decoded
            else:
                time_data = self.time

        if self.year_offset is not None:
            time_data += cftime.date2num(
                datetime(int(self.year_offset), 1, 1),
                units=self.time_attrs['units'],
                calendar=self.time_attrs['calendar'],
            )
        time_out = self.time.copy()
        time_out.data = xr.CFTimeIndex(
            cftime.num2date(
                time_data,
                units=self.time_attrs['units'],
                calendar=self.time_attrs['calendar'],
                only_use_cftime_datetimes=True,
            )
        )
        return time_out 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def time_year_to_midyeardate(self):
        """Set the time coordinate to the mid-point of the year.
        """
        ds = self._ds_time_computed.copy(True)
        ds[self.time_coord_name].data = np.array(
            [cftime.datetime(entry.year, 7, 2) for entry in ds[self.time_coord_name].data]
        )
        return ds 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def climatology(dset, freq, time_coord_name=None):
    """Compute climatologies for a specified time frequency

    Parameters
    ----------
    dset : xarray.Dataset
           The data on which to operate

    freq : str
        Frequency alias. Accepted alias:

        - ``mon``: for monthly climatologies

    time_coord_name : str
            Name for time coordinate to use


    Returns
    -------
    computed_dset : xarray.Dataset
                    The computed climatology data

    """

    accepted_freq = {'mon'}
    if freq not in accepted_freq:
        raise ValueError(f'{freq} is not among supported frequency aliases={accepted_freq}')

    else:
        ds = dset.esmlab.set_time(time_coord_name=time_coord_name).compute_mon_climatology()
        new_history = f'\n{datetime.now()} esmlab.climatology(<DATASET>, freq="{freq}")'
        ds.attrs['history'] = new_history
        return ds 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def anomaly(dset, clim_freq, slice_mon_clim_time=None, time_coord_name=None):
    """Compute anomalies for a specified time frequency

    Parameters
    ----------
    dset : xarray.Dataset
           The data on which to operate

    clim_freq : str
        Climatology frequency alias. Accepted alias:

        - ``mon``: for monthly climatologies

    slice_mon_clim_time : slice, optional
                          a slice object passed to
                          `dset.isel(time=slice_mon_clim_time)` for subseting
                          the time-period overwhich the climatology is computed
    time_coord_name : str
            Name for time coordinate to use


    Returns
    -------
    computed_dset : xarray.Dataset
                    The computed anomaly data

    """

    accepted_freq = {'mon'}
    if clim_freq not in accepted_freq:
        raise ValueError(f'{clim_freq} is not among supported frequency aliases={accepted_freq}')
    else:
        ds = dset.esmlab.set_time(time_coord_name=time_coord_name).compute_mon_anomaly(
            slice_mon_clim_time=slice_mon_clim_time
        )
        new_history = f'\n{datetime.now()} esmlab.anomaly(<DATASET>, clim_freq="{clim_freq}", slice_mon_clim_time="{slice_mon_clim_time}")'
        ds.attrs['history'] = new_history
        return ds 

# 需要導入模塊: import cftime [as 別名]
# 或者: from cftime import datetime [as 別名]
def __init__(self, datetime, calendar):
        self.datetime = datetime
        self.calendar = calendar