Python Dataset.cdm_data_type方法代码示例

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import cdm_data_type [as 别名]
def tamoc_nc_file(fname, title, summary, source):
    """
    Write the header meta data to an netCDF file for a TAMOC output
    
    The TAMOC suite stores its output by detaul in a netCDF dataset file.  
    This function writes the standard TAMOC metadata to the header of the 
    netCDF file.  
    
    Parameters
    ----------
    fname : str
        File name of the file to write
    title:  str
        String stating the TAMOC module where the data originated and the 
        type of data contained.  
    summary : str
        String summarizing what is contained in the dataset or information
        needed to interpret the dataset
    source : str
        String describing the source of the data in the dataset or of related
        datasets
    
    Returns
    -------
    nc : `netCDF4.Dataset` object
        The `netCDF4.Dataset` object containing the open netCDF4 file where
        the data should be stored.
    
    """
    
    # Create the netCDF dataset object
    nc = Dataset(fname, 'w', format='NETCDF4_CLASSIC')
    
    # Write the netCDF header data for a TAMOC suite output
    nc.Conventions = 'TAMOC Modeling Suite Output File'
    nc.Metadata_Conventions = 'TAMOC Python Model'
    nc.featureType = 'profile'
    nc.cdm_data_type = 'Profile'
    nc.nodc_template_version = \
        'NODC_NetCDF_Profile_Orthogonal_Template_v1.0'
    nc.title = title
    nc.summary = summary
    nc.source = source
    nc.creator_url = 'http://github.com/socolofs/tamoc'
    nc.date_created = datetime.today().isoformat(' ')
    nc.date_modified = datetime.today().isoformat(' ')
    nc.history = 'Creation'
    
    # Return the netCDF dataset
    return nc

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import cdm_data_type [as 别名]

#.........这里部分代码省略.........
    if fields[4] == "":
      sals[i, 0] = -9999
    else:
      sals[i, 0] = fields[4]

    if fields[5] == "":
      fco2s[i, 0] = -9999
    else:
      fco2s[i, 0] = fields[5]

    if len(fields[6]) == 0:
      fco2qcs[i, 0] = -128
    else:
      fco2qcs[i, 0] = makeqcvalue_(int(fields[6]))

  depthvar[:,:] = depths
  positionvar[:,:] = positions
  sstvar[:,:] = temps
  sssvar[:,:] = sals
  fco2var[:,:] = fco2s
  fco2qcvar[:,:] = fco2qcs
  depthdmvar[:,:] = dms
  sstdmvar[:,:] = dms
  sssdmvar[:,:] = dms
  fco2dmvar[:,:] = dms

  # Global attributes
  nc.id = filenameroot

  nc.data_type = "OceanSITES trajectory data"
  nc.netcdf_version = "netCDF-4 classic model"
  nc.format_version = "1.2"
  nc.Conventions = "CF-1.6 OceanSITES-Manual-1.2 Copernicus-InSituTAC-SRD-1.3 "\
    + "Copernicus-InSituTAC-ParametersList-3.1.0"

  nc.cdm_data_type = "Trajectory"
  nc.data_mode = "R"
  nc.area = "Global Ocean"

  nc.geospatial_lat_min = str(minlat)
  nc.geospatial_lat_max = str(maxlat)
  nc.geospatial_lon_min = str(minlon)
  nc.geospatial_lon_max = str(maxlon)
  nc.geospatial_vertical_min = "5.00"
  nc.geospatial_vertical_max = "5.00"

  nc.last_latitude_observation = lats[-1]
  nc.last_longitude_observation = lons[-1]
  nc.last_date_observation = endtime.strftime("%Y-%m-%dT%H:%M:%SZ")
  nc.time_coverage_start = starttime.strftime("%Y-%m-%dT%H:%M:%SZ")
  nc.time_coverage_end = endtime.strftime("%Y-%m-%dT%H:%M:%SZ")

  #datasetdate = datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
  #nc.date_update = datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
  #nc.history = datasetdate + " : Creation"

  nc.update_interval = "daily"

  nc.data_assembly_center = "BERGEN"
  nc.institution = "University of Bergen / Geophysical Institute"
  nc.institution_edmo_code = "4595"
  nc.institution_references = " "
  nc.contact = "[email protected]"
  nc.title = "Global Ocean - In Situ near-real time carbon observation"
  nc.author = "cmems-service"
  nc.naming_authority = "Copernicus"

  nc.platform_code = getplatformcallsign_(platform_code)
  nc.site_code = getplatformcallsign_(platform_code)

  # For buoys -> Mooring observation.
  platform_category_code = getplatformcategorycode_(platform_code)
  nc.platform_name = getplatformname_(platform_code)
  nc.source_platform_category_code = platform_category_code
  nc.source = PLATFORM_CODES[platform_category_code]

  nc.quality_control_indicator = "6" # "Not used"
  nc.quality_index = "0"

  nc.comment = " "
  nc.summary = " "
  nc.reference = "http://marine.copernicus.eu/, https://www.icos-cp.eu/"
  nc.citation = "These data were collected and made freely available by the " \
    + "Copernicus project and the programs that contribute to it."
  nc.distribution_statement = "These data follow Copernicus standards; they " \
    + "are public and free of charge. User assumes all risk for use of data. " \
    + "User must display citation in any publication or product using data. " \
    + "User must contact PI prior to any commercial use of data."

  # Write the netCDF
  nc.close()

  # Read the netCDF file into memory
  with open(ncpath, "rb") as ncfile:
    ncbytes = ncfile.read()

  # Delete the temp netCDF file
  os.remove(ncpath)

  return [filenameroot, ncbytes]

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import cdm_data_type [as 别名]
def modify_soop_trv_netcdf(netcdf_file_path, channel_id_info):
    """
    Modify the downloaded NetCDF file so it passes both CF and IMOS checker
    input:
    netcdfFile_path(str)    : path of netcdf file to modify
    channel_id_index(tupple) : information from xml for the channel
    """
    logger = logging_aims()

    modify_aims_netcdf(netcdf_file_path, channel_id_info)
    netcdf_file_obj = Dataset(netcdf_file_path, 'a', format='NETCDF4')
    ship_code       = netcdf_file_obj.platform_code
    vessel_name     = ship_callsign(ship_code)

    if vessel_name is None:
        logger.error('   UNKNOWN SHIP - channel %s' % str(channel_id_info['channel_id']))
        netcdf_file_obj.close()
        return False

    # add gatts to net_cDF
    netcdf_file_obj.cdm_data_type = 'Trajectory'
    netcdf_file_obj.vessel_name   = vessel_name
    netcdf_file_obj.trip_id       = channel_id_info['trip_id']
    netcdf_file_obj.cdm_data_type = "Trajectory"
    coordinates_att               = "TIME LATITUDE LONGITUDE DEPTH"

    # depth
    depth                 = netcdf_file_obj.variables['depth']
    depth.positive        = 'down'
    depth.axis            = 'Z'
    depth.reference_datum = 'sea surface'
    depth.valid_max       = 30.0
    depth.valid_min       = -10.0
    netcdf_file_obj.renameVariable('depth', 'DEPTH')

    # latitude longitude
    latitude                      = netcdf_file_obj.variables['LATITUDE']
    latitude.ancillary_variables  = 'LATITUDE_quality_control'

    longitude                     = netcdf_file_obj.variables['LONGITUDE']
    longitude.ancillary_variables = 'LONGITUDE_quality_control'

    latitude_qc                   = netcdf_file_obj.variables['LATITUDE_quality_control']
    latitude_qc.long_name         = 'LATITUDE quality control'
    latitude_qc.standard_name     = 'latitude status_flag'
    longitude_qc                  = netcdf_file_obj.variables['LONGITUDE_quality_control']
    longitude_qc.long_name        = 'LONGITUDE quality control'
    longitude_qc.standard_name    = 'longitude status_flag'

    netcdf_file_obj.close()

    netcdf_file_obj = Dataset(netcdf_file_path, 'a', format='NETCDF4')
    main_var        = get_main_soop_trv_var(netcdf_file_path)
    netcdf_file_obj.variables[main_var].coordinates = coordinates_att

    netcdf_file_obj.close()

    if not convert_time_cf_to_imos(netcdf_file_path):
        return False

    remove_dimension_from_netcdf(netcdf_file_path)  # last modification to do !

    return True

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import cdm_data_type [as 别名]
def create_mhl_sst_ncfile(txtfile, site_code_short, data,
                          time, dtime, spatial_data):
    """
    create NetCDF file for MHL Wave data
    """
    site_code = site_list[site_code_short][0]
    netcdf_filename = create_netcdf_filename(site_code, data, dtime)
    netcdf_filepath = os.path.join(
        output_folder, "%s.nc") % netcdf_filename
    ncfile = Dataset(netcdf_filepath, "w", format="NETCDF4")


    # generate site and deployment specific attributes
    ncfile.title = ("IMOS - ANMN New South Wales(NSW) %s "
                    "Sea water temperature (%s) -"
                    "Deployment No. %s %s to %s") % (
            site_list[site_code_short][1], site_code,
            spatial_data[0], min(dtime).strftime("%d-%m-%Y"),
            max(dtime).strftime("%d-%m-%Y"))
    ncfile.institution = 'Manly Hydraulics Laboratory'
    ncfile.keywords = ('Oceans | Ocean temperature |'
                           'Sea Surface Temperature')
    ncfile.principal_investigator = 'Mark Kulmar'
    ncfile.cdm_data_type = 'Station'
    ncfile.platform_code = site_code

    abstract_default = ("The sea water temperature is measured by a thermistor mounted in the "
                        "buoy hull approximately 400 mm below the water "
                        "surface.  The thermistor has a resolution of 0.05 "
                        "Celsius and an accuracy of 0.2 Celsius.  The "
                        "measurements are transmitted to a shore station "
                        "where it is stored on a PC before routine transfer "
                        "to Manly Hydraulics Laboratory via email.")

    if site_code_short in ['COF', 'CRH', 'EDE', 'PTK']:

        abstract_specific = ("This dataset contains sea water temperature "
                             "data collected by a wave monitoring buoy moored off %s. ") % site_list[site_code_short][1]
    else:
        abstract_specific = ("This dataset contains sea water temperature "
                             "data collected by a wave monitoring buoy moored off %s "
                             "approximately %s kilometres from the coastline. ") % (

                          site_list[site_code_short][1], site_list[site_code_short][2])

    ncfile.abstract = abstract_specific + abstract_default
    ncfile.comment = ("The sea water temperature data (SST) is routinely quality controlled (usually twice per week) "
                      "using a quality control program developed by Manly Hydraulics Laboratory.  The SST data gathered "
                      "by the buoy is regularly compared to the latest available satellite derived sea SST images available "
                      "from the Bluelink ocean forecasting web pages to ensure the integrity of the dataset.  Erroneous SST "
                      "records are removed and good quality data is flagged as \'Quality Controlled\' in the "
                      "Manly Hydraulics Laboratory SST database.") 
    ncfile.sourceFilename = os.path.basename(txtfile)
    ncfile.date_created = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
    ncfile.time_coverage_start = min(dtime).strftime("%Y-%m-%dT%H:%M:%SZ")
    ncfile.time_coverage_end = max(dtime).strftime("%Y-%m-%dT%H:%M:%SZ")
    ncfile.geospatial_lat_min = spatial_data[1]
    ncfile.geospatial_lat_max = spatial_data[1]
    ncfile.geospatial_lon_min = spatial_data[2]
    ncfile.geospatial_lon_max = spatial_data[2]
    ncfile.geospatial_vertical_max = 0.
    ncfile.geospatial_vertical_min = 0.
    ncfile.deployment_number = str(spatial_data[0])

    # add dimension and variables
    ncfile.createDimension('TIME', len(time))

    TIME = ncfile.createVariable('TIME', "d", 'TIME')
    TIMESERIES = ncfile.createVariable('TIMESERIES', "i")
    LATITUDE = ncfile.createVariable(
        'LATITUDE', "d", fill_value=99999.)
    LONGITUDE = ncfile.createVariable(
        'LONGITUDE', "d", fill_value=99999.)
    TEMP = ncfile.createVariable('TEMP', "f", 'TIME', fill_value=99999.)

    # add global attributes and variable attributes stored in config files
    config_file = os.path.join(os.getcwd(), 'global_att_sst.att')
    generate_netcdf_att(ncfile, config_file,
                        conf_file_point_of_truth=False)
    
    # replace nans with fillvalue in dataframe
    data = data.fillna(value=float(99999.))

    TIME[:] = time
    TIMESERIES[:] = 1
    LATITUDE[:] = spatial_data[1]
    LONGITUDE[:] = spatial_data[2]
    TEMP[:] = data['SEA_TEMP'].values
    ncfile.close()

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import cdm_data_type [as 别名]
fid.references = "Painter, T. H., Brodzik, M. J., A. Racoviteanu, R. Armstrong. 2012. Automated mapping of Earth's annual minimum exposed snow and ice with MODIS. Geophysical Research Letters, 39(20):L20501, doi:10.1029/2012GL053340."
fid.summary = ["An improved, enhanced-resolution, gridded passive microwave Earth System Data Record \n",
               "for monitoring cryospheric and hydrologic time series\n" ]fid.title = "MEaSUREs Calibrated Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR"
fid.institution = ["National Snow and Ice Data Center\n",
                   "Cooperative Institute for Research in Environmental Sciences\n",
                   "University of Colorado at Boulder\n",
                   "Boulder, CO"]
fid.publisher = ["National Snow and Ice Data Center\n",
                   "Cooperative Institute for Research in Environmental Sciences\n",
                   "University of Colorado at Boulder\n",
                   "Boulder, CO"]
fid.publisher_url = "http://nsidc.org/charis"
fid.publisher_email = "[email protected]"
fid.project = "CHARIS"
fid.standard_name_vocabulary = "CF Standard Name Table (v27, 28 September 2013)"
fid.cdm_data_type = "grid"
fid.keywords = "EARTH SCIENCE > SPECTRAL/ENGINEERING > MICROWAVE > BRIGHTNESS TEMPERATURE" 
fid.keywords_vocabulary = "NASA Global Change Master Directory (GCMD) Earth Science Keywords, Version 8.1"
fid.platform = "TBD"
fid.sensor = "TBD"
fid.naming_authority = "org.doi.dx"
fid.id = "10.5067/MEASURES/CRYOSPHERE/nsidc-0630.001"
fid.date_created = "TBD"
fid.acknowledgement = ["This data set was created with funding from NASA MEaSUREs Grant #NNX13AI23A.\n",
                       "Data archiving and distribution is supported by the NASA NSIDC Distributed Active Archive Center (DAAC)."]
fid.license = "No constraints on data access or use"
fid.processing_level = "Level 3"
fid.creator_name = "Mary J. Brodzik"
fid.creator_email = "[email protected]"
fid.creator_url = "http://nsidc.org/charis"
fid.contributor_name = "T. H. Painter, M. J. Brodzik, R. L. Armstrong"

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import cdm_data_type [as 别名]
def create_mhl_wave_ncfile(txtfile, site_code_short, data,
                           time, dtime, spatial_data):
    """
    create NetCDF file for MHL Wave data
    """
    site_code = site_list[site_code_short][0]
    netcdf_filename = create_netcdf_filename(site_code, data, dtime)
    netcdf_filepath = os.path.join(
        output_folder, "%s.nc") % netcdf_filename
    ncfile = Dataset(netcdf_filepath, "w", format="NETCDF4")

    # add IMOS1.4 global attributes and variable attributes stored in config
    # files
    config_file = os.path.join(os.getcwd(),'mhl_wave_library', 'global_att_wave.att')
    generate_netcdf_att(ncfile, config_file,
                        conf_file_point_of_truth=False)
    # Additional attribute either retrieved from original necdtf file
    # (if exists) or defined below
    original_netcdf_file_path = os.path.join(
        input_folder, "%s.nc") % netcdf_filename

    if os.path.exists(original_netcdf_file_path):
        # get glob attributes from original netcdf files.
        parse_nc_attribute(original_netcdf_file_path, ncfile)
    else:
        # generate site and deployment specific attributes
        ncfile.title = ("IMOS - ANMN New South Wales(NSW) %s"
                        "Offshore Wave Data (% s) -"
                        "Deployment No. %s %s to %s") % (
            site_list[site_code_short][1], site_code,
            spatial_data[0], min(dtime).strftime("%d-%m-%Y"),
            max(dtime).strftime("%d-%m-%Y"))
        ncfile.institution = 'Manly Hydraulics Laboratory'
        ncfile.keywords = ('Oceans | Ocean Waves |'
                           'Significant Wave Height, Oceans | Ocean Waves'
                           '| Wave Period, Oceans | Ocean Waves |'
                           'Wave Spectra, Oceans | Ocean Waves |'
                           'Wave Speed / direction')
        ncfile.principal_investigator = 'Mark Kulmar'
        ncfile.cdm_data_type = 'Station'
        ncfile.platform_code = site_code
        ncfile.site_name = site_list[site_code_short][1]
        if site_code in ['WAVEPOK', 'WAVECOH', 'WAVECRH', 'WAVEEDN']:
            config_file = os.path.join(
                os.getcwd(), 'common', 'abstract_WAVE_default.att')
        elif site_code == 'WAVEBAB':
            config_file = os.path.join(os.getcwd(),'common', 'abstract_WAVEBAB.att')
        elif site_code == 'WAVEBYB':
            config_file = os.path.join(os.getcwd(), 'common', 'abstract_WAVEBYB.att')
        else:  # WAVESYD
            config_file = os.path.join(os.getcwd(), 'common', 'abstract_WAVESYD.att')

        generate_netcdf_att(ncfile, config_file,
                            conf_file_point_of_truth=False)

    ncfile.sourceFilename = os.path.basename(txtfile)
    ncfile.date_created = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
    ncfile.time_coverage_start = min(dtime).strftime("%Y-%m-%dT%H:%M:%SZ")
    ncfile.time_coverage_end = max(dtime).strftime("%Y-%m-%dT%H:%M:%SZ")
    ncfile.geospatial_lat_min = spatial_data[1]
    ncfile.geospatial_lat_max = spatial_data[1]
    ncfile.geospatial_lon_min = spatial_data[2]
    ncfile.geospatial_lon_max = spatial_data[2]
    ncfile.geospatial_vertical_max = 0.
    ncfile.geospatial_vertical_min = 0.
    ncfile.deployment_number = str(spatial_data[0])

    # add dimension and variables
    ncfile.createDimension('TIME', len(time))

    TIME = ncfile.createVariable('TIME', "d", 'TIME')
    TIMESERIES = ncfile.createVariable('TIMESERIES', "i")
    LATITUDE = ncfile.createVariable(
        'LATITUDE', "d", fill_value=99999.)
    LONGITUDE = ncfile.createVariable(
        'LONGITUDE', "d", fill_value=99999.)
    WHTH = ncfile.createVariable('WHTH', "f", 'TIME', fill_value=99999.)
    WMSH = ncfile.createVariable('WMSH', "f", 'TIME', fill_value=99999.)
    HRMS = ncfile.createVariable('HRMS', "f", 'TIME', fill_value=99999.)
    WHTE = ncfile.createVariable('WHTE', "f", 'TIME', fill_value=99999.)
    WMXH = ncfile.createVariable('WMXH', "f", 'TIME', fill_value=99999.)
    TCREST = ncfile.createVariable('TCREST', "f", 'TIME', fill_value=99999.)
    WPMH = ncfile.createVariable('WPMH', "f", 'TIME', fill_value=99999.)
    WPTH = ncfile.createVariable('WPTH', "f", 'TIME', fill_value=99999.)
    YRMS = ncfile.createVariable('YRMS', "f", 'TIME', fill_value=99999.)
    WPPE = ncfile.createVariable('WPPE', "f", 'TIME', fill_value=99999.)
    TP2 = ncfile.createVariable('TP2', "f", 'TIME', fill_value=99999.)
    M0 = ncfile.createVariable('M0', "f", 'TIME', fill_value=99999.)
    WPDI = ncfile.createVariable('WPDI', "f", 'TIME', fill_value=99999.)

    # add global attributes and variable attributes stored in config files
    config_file = os.path.join(os.getcwd(),'mhl_wave_library', 'global_att_wave.att')
    generate_netcdf_att(ncfile, config_file,
                        conf_file_point_of_truth=True)

    for nc_var in [WPTH, WPPE, WPMH, WPDI, WMXH,WMSH, WHTH, WHTE, TP2, TCREST]:
        nc_var.valid_max = np.float32(nc_var.valid_max)
        nc_var.valid_min = np.float32(nc_var.valid_min)

    # replace nans with fillvalue in dataframe
#.........这里部分代码省略.........

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import cdm_data_type [as 别名]
def initialize_output(filename, id_dim_name, time_len,
                      id_len, time_step_seconds):
    """Creates netCDF file with CF dimensions and variables, but no data.

    Arguments:
        filename -- full path and filename for output netCDF file
        id_dim_name -- name of Id dimension and variable, e.g., COMID
        time_len -- (integer) length of time dimension (number of time steps)
        id_len -- (integer) length of Id dimension (number of time series)
        time_step_seconds -- (integer) number of seconds per time step
    """

    cf_nc = Dataset(filename, 'w', format='NETCDF3_CLASSIC')

    # Create global attributes
    log('    globals', 'DEBUG')
    cf_nc.featureType = 'timeSeries'
    cf_nc.Metadata_Conventions = 'Unidata Dataset Discovery v1.0'
    cf_nc.Conventions = 'CF-1.6'
    cf_nc.cdm_data_type = 'Station'
    cf_nc.nodc_template_version = (
        'NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1')
    cf_nc.standard_name_vocabulary = ('NetCDF Climate and Forecast (CF) ' +
                                      'Metadata Convention Standard Name ' +
                                      'Table v28')
    cf_nc.title = 'RAPID Result'
    cf_nc.summary = ("Results of RAPID river routing simulation. Each river " +
                     "reach (i.e., feature) is represented by a point " +
                     "feature at its midpoint, and is identified by the " +
                     "reach's unique NHDPlus COMID identifier.")
    cf_nc.time_coverage_resolution = 'point'
    cf_nc.geospatial_lat_min = 0.0
    cf_nc.geospatial_lat_max = 0.0
    cf_nc.geospatial_lat_units = 'degrees_north'
    cf_nc.geospatial_lat_resolution = 'midpoint of stream feature'
    cf_nc.geospatial_lon_min = 0.0
    cf_nc.geospatial_lon_max = 0.0
    cf_nc.geospatial_lon_units = 'degrees_east'
    cf_nc.geospatial_lon_resolution = 'midpoint of stream feature'
    cf_nc.geospatial_vertical_min = 0.0
    cf_nc.geospatial_vertical_max = 0.0
    cf_nc.geospatial_vertical_units = 'm'
    cf_nc.geospatial_vertical_resolution = 'midpoint of stream feature'
    cf_nc.geospatial_vertical_positive = 'up'
    cf_nc.project = 'National Flood Interoperability Experiment'
    cf_nc.processing_level = 'Raw simulation result'
    cf_nc.keywords_vocabulary = ('NASA/Global Change Master Directory ' +
                                 '(GCMD) Earth Science Keywords. Version ' +
                                 '8.0.0.0.0')
    cf_nc.keywords = 'DISCHARGE/FLOW'
    cf_nc.comment = 'Result time step (seconds): ' + str(time_step_seconds)

    timestamp = datetime.utcnow().isoformat() + 'Z'
    cf_nc.date_created = timestamp
    cf_nc.history = (timestamp + '; added time, lat, lon, z, crs variables; ' +
                     'added metadata to conform to NODC_NetCDF_TimeSeries_' +
                     'Orthogonal_Template_v1.1')

    # Create dimensions
    log('    dimming', 'DEBUG')
    cf_nc.createDimension('time', time_len)
    cf_nc.createDimension(id_dim_name, id_len)

    # Create variables
    log('    timeSeries_var', 'DEBUG')
    timeSeries_var = cf_nc.createVariable(id_dim_name, 'i4', (id_dim_name,))
    timeSeries_var.long_name = (
        'Unique NHDPlus COMID identifier for each river reach feature')
    timeSeries_var.cf_role = 'timeseries_id'

    log('    time_var', 'DEBUG')
    time_var = cf_nc.createVariable('time', 'i4', ('time',))
    time_var.long_name = 'time'
    time_var.standard_name = 'time'
    time_var.units = 'seconds since 1970-01-01 00:00:00 0:00'
    time_var.axis = 'T'

    log('    lat_var', 'DEBUG')
    lat_var = cf_nc.createVariable('lat', 'f8', (id_dim_name,),
                                   fill_value=-9999.0)
    lat_var.long_name = 'latitude'
    lat_var.standard_name = 'latitude'
    lat_var.units = 'degrees_north'
    lat_var.axis = 'Y'

    log('    lon_var', 'DEBUG')
    lon_var = cf_nc.createVariable('lon', 'f8', (id_dim_name,),
                                   fill_value=-9999.0)
    lon_var.long_name = 'longitude'
    lon_var.standard_name = 'longitude'
    lon_var.units = 'degrees_east'
    lon_var.axis = 'X'

    log('    z_var', 'DEBUG')
    z_var = cf_nc.createVariable('z', 'f8', (id_dim_name,),
                                 fill_value=-9999.0)
    z_var.long_name = ('Elevation referenced to the North American ' +
                       'Vertical Datum of 1988 (NAVD88)')
    z_var.standard_name = 'surface_altitude'
    z_var.units = 'm'
#.........这里部分代码省略.........