Python netCDF4.MFDataset方法代碼示例

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import MFDataset [as 別名]
def get_dataset(ncfile, dataset=None):
    """
    Utility to create a netCDF4 Dataset from a filename, list of filenames,
    or just pass it through if it's already a netCDF4.Dataset

    if dataset is not None, it should be a valid netCDF4 Dataset object,
    and it will simply be returned
    """
    if dataset is not None:
        return dataset
    if isinstance(ncfile, nc4.Dataset):
        return ncfile
    elif isinstance(ncfile, Iterable) and len(ncfile) == 1:
        return nc4.Dataset(ncfile[0])
    elif isstring(ncfile):
        return nc4.Dataset(ncfile)
    else:
        return nc4.MFDataset(ncfile) 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import MFDataset [as 別名]
def netcdf(file, aggdim=None):
    """
    Wrapper around netCDF4 to open a file as either a Dataset or an
    MFDataset.

    Parameters
    ----------
    file : string or list,
        Filename(s) to open. If the string has wildcards or is a list,
        this attempts to open an MFDataset
    aggdim : string,
        Name of dimension to concatenate along if loading a set of files.
        A value of None (default) uses the unlimited dimension.

    Returns
    -------
    netCDF4 Dataset or MFDataset
    """
    import netCDF4
    try:
        nc = netCDF4.Dataset(file)
    except (OSError, RuntimeError):
        try:
            nc = netCDF4.MFDataset(file, aggdim=aggdim)
        except IndexError:
            raise FileNotFoundError("{:s} cannot be found.".format(file))
    return nc 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import MFDataset [as 別名]
def get_index_lat(resource, variable=None):
    """
    returns the dimension index of the latiude values
    :param resource:  list of path(s) to netCDF file(s) of one Dataset
    :param variable: variable name
    :return int: index
    """

    if variable is None:
        variable = get_variable(resource)
    if type(resource) != list:
        resource = [resource]
    if len(resource) == 1:
        ds = Dataset(resource[0])
    else:
        ds = MFDataset(resource)

    var = ds.variables[variable]
    dims = list(var.dimensions)

    if 'rlat' in dims:
        index = dims.index('rlat')
    if 'lat' in dims:
        index = dims.index('lat')
    if 'latitude' in dims:
        index = dims.index('latitude')
    if 'y' in dims:
        index = dims.index('y')
    return index 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import MFDataset [as 別名]
def Convert_nc_to_tiff(input_nc, output_folder):
    """
    This function converts the nc file into tiff files

    Keyword Arguments:
    input_nc -- name, name of the adf file
    output_folder -- Name of the output tiff file
    """
    from datetime import date
    import wa.General.raster_conversions as RC

    #All_Data = RC.Open_nc_array(input_nc)

    if type(input_nc) == str:
        nc = netCDF4.Dataset(input_nc)
    elif type(input_nc) == list:
        nc = netCDF4.MFDataset(input_nc)

    Var = nc.variables.keys()[-1]
    All_Data = nc[Var]

    geo_out, epsg, size_X, size_Y, size_Z, Time = RC.Open_nc_info(input_nc)

    if epsg == 4326:
        epsg = 'WGS84'

    # Create output folder if needed
    if not os.path.exists(output_folder):
        os.mkdir(output_folder)

    for i in range(0,size_Z):
        if not Time == -9999:
            time_one = Time[i]
            d = date.fromordinal(time_one)
            name = os.path.splitext(os.path.basename(input_nc))[0]
            nameparts = name.split('_')[0:-2]
            name_out = os.path.join(output_folder, '_'.join(nameparts) + '_%d.%02d.%02d.tif' %(d.year, d.month, d.day))
            Data_one = All_Data[i,:,:]
        else:
            name=os.path.splitext(os.path.basename(input_nc))[0]
            name_out = os.path.join(output_folder, name + '.tif')
            Data_one = All_Data[:,:]


        Save_as_tiff(name_out, Data_one, geo_out, epsg)

    return() 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import MFDataset [as 別名]
def __enter__(self):
        if not self.meta_only:
            # Don't decode times since we do it anyways.
            decode_times = False

            if self._nc_files:
                try:
                    self.dataset = xarray.open_mfdataset(
                        self._nc_files,
                        decode_times=decode_times,
                        chunks=200,
                    )
                except xarray.core.variable.MissingDimensionsError:
                    # xarray won't open FVCOM files due to dimension/coordinate/variable label
                    # duplication issue, so fall back to using netCDF4.Dataset()
                    self.dataset = netCDF4.MFDataset(self._nc_files)
            else:
                try:
                    # Handle list of URLs for staggered grid velocity field datasets
                    url = self.url if isinstance(self.url, list) else [self.url]
                    # This will raise a FutureWarning for xarray>=0.12.2.
                    # That warning should be resolvable by changing to:
                    # fields = xarray.open_mfdataset(self.url, combine="by_coords", decode_times=decode_times)
                    fields = xarray.open_mfdataset(url, decode_times=decode_times)
                except xarray.core.variable.MissingDimensionsError:
                    # xarray won't open FVCOM files due to dimension/coordinate/variable label
                    # duplication issue, so fall back to using netCDF4.Dataset()
                    fields = netCDF4.Dataset(self.url)
                if getattr(self._dataset_config, "geo_ref", {}):
                    drop_variables = self._dataset_config.geo_ref.get("drop_variables", [])
                    geo_refs = xarray.open_dataset(
                        self._dataset_config.geo_ref["url"], drop_variables=drop_variables,
                    )
                    fields = fields.merge(geo_refs)
                self.dataset = fields

            if self._grid_angle_file_url:
                angle_file = xarray.open_dataset(
                    self._grid_angle_file_url,
                    drop_variables=[self._dataset_config.lat_var_key, self._dataset_config.lon_var_key]
                )
                self.dataset = self.dataset.merge(angle_file)
                angle_file.close()

            self._dataset_open = True

        return self 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import MFDataset [as 別名]
def get_timerange(resource):
    """
    returns from/to timestamp of given netcdf file(s).

    :param resource: list of path(s) to netCDF file(s)

    :returns netcdf.datetime.datetime: start, end

    """
    start = end = None

    if type(resource) != list:
        resource = [resource]
    LOGGER.debug('length of recources: %s files' % len(resource))

    try:
        resource.sort()
        if len(resource) > 1:
            # ds = MFDataset(resource)
            LOGGER.error('functon expect single file, Mulitple files found {}'.format(len(resource)))
        else:
            ds = Dataset(resource[0])
            LOGGER.debug('Dataset loaded for %s file in resource:' % len(resource))
        time = ds.variables['time']

        if (hasattr(time, 'units') and hasattr(time, 'calendar')) is True:
            s = num2date(time[0], time.units, time.calendar)
            e = num2date(time[-1], time.units, time.calendar)
        elif hasattr(time, 'units'):
            s = num2date(time[0], time.units)
            e = num2date(time[-1], time.units)
        else:
            s = num2date(time[0])
            e = num2date(time[-1])

        # TODO: include frequency
        start = '%s%s%s' % (s.year, str(s.month).zfill(2), str(s.day).zfill(2))
        end = '%s%s%s' % (e.year, str(e.month).zfill(2), str(e.day).zfill(2))
        ds.close()
    except Exception:
        msg = 'failed to get time range'
        LOGGER.exception(msg)
        ds.close()
        raise Exception(msg)
    return start, end