Python netCDF4 模块，date2num() 实例源码

def append_times_to_dataset(times, dataset, time_units):
    """Appends the given list of times to the dataset. Assumes the time units
    in the NetCDF4 dataset correspond to the string time_units."""
    it_start = dataset.dimensions['time'].size
    it_end = it_start + len(times)
    if isinstance(times[0], timedelta):
        times_list = []
        for time in times:
            times_list.append(time.total_seconds())
        time_array = from_unit_to_another(
            np.array(times_list), 'seconds', time_units)
        dataset.variables['time'][it_start:it_end] = time_array[:]
    else:  # assume datetime
        dataset.variables['time'][it_start:it_end] = nc4.date2num(
            times, dataset.variables['time'].units,
            calendar='proleptic_gregorian'
        )

def test_time_from_variable():
    '''Should read date from time variable.'''

    tempdir = tempfile.gettempdir()
    nc_file = os.path.join(tempdir, 'test_time_from_variable.nc')
    date_obj = parser.parse('2017-04-29 04:00:00')
    units = 'minutes since 1970-01-01 00:00:00 UTC'
    nc_date = round(date2num(date_obj, units))
    with Dataset(nc_file, 'w') as nc:
        dim = nc.createDimension('time', 1)
        time_var = nc.createVariable('time', 'i', ('time',))
        time_var[:] = [nc_date]
        time_var.units = units
    with Dataset(nc_file, 'r') as nc:
        expected = date_obj.replace(tzinfo=pytz.utc)
        returned = nwm_data.time_from_dataset(nc)
        assert expected == returned
    os.remove(nc_file)

def create_netcdf(savef, dts, dat):
    """ Write Florida current data to netcdf file """
    dataset = Dataset(savef, 'w', format='NETCDF4_CLASSIC') 

    # Create time coordinate
    tdim = dataset.createDimension('time', None)
    time = dataset.createVariable('time',np.float64,(tdim.name,))
    time.units = 'hours since 0001-01-01 00:00:00.0'
    time.calendar = 'gregorian'
    time[:] = date2num(dts, time.units, calendar=time.calendar)

    # Create data variable
    fc = dataset.createVariable('florida_current_transport',np.float64,(tdim.name),fill_value=1.e20)
    fc.units = 'Sv'
    fc[:] = dat

    # close file
    print 'SAVING: %s' % savef 
    dataset.close()

def write_to_netcdf(self, ncfile):
        """ Write observation data to netcdf file """

        # Open ncfile and create coords
        dataset = Dataset(ncfile, 'w', format='NETCDF4_CLASSIC')
        tdim = dataset.createDimension('time', None)

        # Create time coordinate
        time = dataset.createVariable('time',np.float64,(tdim.name,))
        time.units = 'hours since 0001-01-01 00:00:00.0'
        time.calendar = 'gregorian'
        time[:] = date2num(self.dates, time.units, calendar=time.calendar)

        # Create variables
        fc = dataset.createVariable('florida_current_transport',np.float64,(tdim.name,))
        fc.units = 'Sv'
        fc[:] = self.fc

        # Close file
        print 'SAVING: %s' % ncfile
        dataset.close()

def write_to_netcdf(self, ncfile):
        """ Write observation data to netcdf file """

        # Open ncfile and create coords
        dataset = Dataset(ncfile, 'w', format='NETCDF4_CLASSIC')
        tdim = dataset.createDimension('time', None)

        # Create time coordinate
        time = dataset.createVariable('time',np.float64,(tdim.name,))
        time.units = 'hours since 0001-01-01 00:00:00.0'
        time.calendar = 'gregorian'
        time[:] = date2num(self.dates, time.units, calendar=time.calendar)

        # Create variables
        fc = dataset.createVariable('florida_current_transport',np.float64,(tdim.name,))
        fc.units = 'Sv'
        fc[:] = self.fc

        # Close file
        print 'SAVING: %s' % ncfile
        dataset.close()

def _iter (self):
    from fstd2nc.mixins import _var_type
    from datetime import datetime
    import numpy as np
    from netCDF4 import date2num

    if self._reference_date is None:
      reference_date = None
    else:
      reference_date = datetime.strptime(self._reference_date,'%Y-%m-%d')

    varlist = super(netCDF_IO,self)._iter()
    if self._unique_names:
      varlist = list(varlist)
      self._fix_names(varlist)

    for var in varlist:

      # Modify time axes to be relative units instead of datetime objects.
      if var.name in var.axes and isinstance(var,_var_type) and isinstance(var.array[0],np.datetime64):
         # Convert from np.datetime64 to datetime.datetime
        var.array = var.array.tolist()
        units = '%s since %s'%(self._time_units, reference_date or var.array[0])
        var.atts.update(units=units)
        var.array = np.asarray(date2num(var.array,units=units))

      yield var

def write_to_netcdf(self, ncfile):
        """ Write observation data to netcdf file """

        # Open ncfile and create coords
        dataset = Dataset(ncfile, 'w', format='NETCDF4_CLASSIC')
        zdim = dataset.createDimension('depth', self.z.size)
        tdim = dataset.createDimension('time', None)

        # Create time coordinate
        time = dataset.createVariable('time',np.float64,(tdim.name,))
        time.units = 'hours since 0001-01-01 00:00:00.0'
        time.calendar = 'gregorian'
        time[:] = date2num(self.dates, time.units, calendar=time.calendar)

        # Create depth coordinate 
        z = dataset.createVariable('depth',np.float64,(zdim.name,))
        z.units = 'm'
        z[:] = self.z

        # Create streamfunction variable
        sf = dataset.createVariable('stream_function_mar',np.float64,(tdim.name, zdim.name))
        sf.units = 'Sv'
        sf[:] = self.sf

        # Close file
        print 'SAVING: %s' % ncfile
        dataset.close()

def write_to_netcdf(self, ncfile):
        """ Write observation data to netcdf file """

        # Open ncfile and create coords
        dataset = Dataset(ncfile, 'w', format='NETCDF4_CLASSIC')
        tdim = dataset.createDimension('time', None)

        # Create time coordinate
        time = dataset.createVariable('time',np.float64,(tdim.name,))
        time.units = 'hours since 0001-01-01 00:00:00.0'
        time.calendar = 'gregorian'
        time[:] = date2num(self.dates, time.units, calendar=time.calendar)

        # Create variables
        ek = dataset.createVariable('t_ek10',np.float64,(tdim.name,))
        ek.units = 'Sv'
        ek[:] = self.ekman

        umo = dataset.createVariable('t_umo10',np.float64,(tdim.name,))
        umo.units = 'Sv'
        umo[:] = self.umo

        fc = dataset.createVariable('t_gs10',np.float64,(tdim.name,))
        fc.units = 'Sv'
        fc[:] = self.fc

        moc = dataset.createVariable('t_moc_mar_hc10',np.float64,(tdim.name,))
        moc.units = 'Sv'
        moc[:] = self.moc

        # Close file
        print 'SAVING: %s' % ncfile
        dataset.close()

def write_to_netcdf(self, ncfile):
        """ Write observation data to netcdf file """

        # Open ncfile and create coords
        dataset = Dataset(ncfile, 'w', format='NETCDF4_CLASSIC')
        zdim = dataset.createDimension('depth', self.z.size)
        tdim = dataset.createDimension('time', None)

        # Create time coordinate
        time = dataset.createVariable('time',np.float64,(tdim.name,))
        time.units = 'hours since 0001-01-01 00:00:00.0'
        time.calendar = 'gregorian'
        time[:] = date2num(self.dates, time.units, calendar=time.calendar)

        # Create depth coordinate 
        z = dataset.createVariable('depth',np.float64,(zdim.name,))
        z.units = 'm'
        z[:] = self.z

        # Create streamfunction variable
        sf = dataset.createVariable('stream_function_mar',np.float64,(tdim.name, zdim.name))
        sf.units = 'Sv'
        sf[:] = self.sf

        # Close file
        print 'SAVING: %s' % ncfile
        dataset.close()

def load_data(file, varname, extent=None, period=None, **kwargs):
    """
    Loads netCDF files and extracts data given a spatial extend and time period
    of interest.
    """
    # Open either single or multi-file data set depending if list of wildcard
    if "*" in file or isinstance(file, list):
        ds = xr.open_mfdataset(file, decode_times=False)
    else:
        ds = xr.open_dataset(file, decode_times=False)

    # Construct condition based on spatial extents
    if extent:
        n, e, s, w = extent
        ds = ds.sel(lat=(ds.lat >= s) & (ds.lat <= n))
        # Account for extent crossing Greenwich
        if w > e:
            ds = ds.sel(lon=(ds.lon >= w) | (ds.lon <= e))
        else:
            ds = ds.sel(lon=(ds.lon >= w) & (ds.lon <= e))

    # Construct condition base on time period
    if period:
        t1 = date2num(datetime(*period[0]), ds.time.units, ds.time.calendar)
        t2 = date2num(datetime(*period[1]), ds.time.units, ds.time.calendar)
        ds = ds.sel(time=(ds.time >= t1) & (ds.time <= t2))

    # Extra keyword arguments to select from additional dimensions (e.g. plev)
    if kwargs:
        ds = ds.sel(**kwargs)

    # Load in the data to a numpy array
    dates = num2date(ds.time, ds.time.units, ds.time.calendar)
    arr = ds[varname].values
    lat = ds.lat.values
    lon = ds.lon.values

    # Convert pr units to mm/day
    if ds[varname].units == 'kg m-2 s-1':
        arr *= 86400
    # Convert tas units to degK
    elif ds[varname].units == 'K':
        arr -= 273.15

    return arr, lat, lon, dates