Python Dataset.summary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 summary [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 summary [as 别名]
def generate_nc(parser_context):
    parser = XLSParser()
    with open(parser_context.filepath, 'r') as f:
        doc = f.read()
    info = parser.extract_worksheets(doc)
    nccl = info[parser_context.worksheet]
    #header_line = 3
    #columns = nccl[header_line]
    #data_range = (4, 66)
    data_rows = nccl[parser_context.data_range[0]:parser_context.data_range[1]]
    print 'Generating',parser_context.output_file
    nc = Dataset(parser_context.output_file, 'w')
    nc.createDimension('time', len(data_rows)*12)
    nc.GDAL = "GDAL 1.9.2, released 2012/10/08"
    nc.history = "Created dynamically in IPython Notebook 2013-11-14"
    nc.title = nccl[0][0]
    nc.summary = nccl[1][0]
    nc.naming_authority = 'GLOS'
    nc.source = 'GLERL'
    nc.standard_name_vocabulary = "http://www.cgd.ucar.edu/cms/eaton/cf-metadata/standard_name.html"
    nc.project = 'GLOS'
    nc.Conventions = "CF-1.6"
    time = nc.createVariable('time', 'f8', ('time',))
    time.standard_name = 'time'
    time.units = 'seconds since 1970-01-01'
    time.long_name = 'Time'
    time.axis = 'T'
    precip = nc.createVariable(parser_context.variable, 'f8', ('time',), fill_value=parser_context.fill_value)
    #precip.standard_name = 'precipitation_amount'
    precip.standard_name = parser_context.standard_name

    precip.units = parser_context.units
    for i,row in enumerate(data_rows):
        for j in xrange(12):
            the_date = datetime(row[0], j+1, 1)
            timestamp = calendar.timegm(the_date.utctimetuple())
            time[i*12 + j] = timestamp
            try:
                value = float(row[j+1])
            except ValueError:
                continue
            except TypeError:
                continue

            precip[i*12 + j] = value
    nc.close() 

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import summary [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 summary [as 别名]
# Assume that /projects/CHARIS is sshfs mounted on this machine, and
# that the user has write permission
fid = Dataset('~/projects/CHARIS/snow_cover/modice.v0.4/min05yr_nc/MODICE.v0.4.1test.nc', 'w', format='NETCDF4')
fid.Conventions = "CF-1.6"
fid = Dataset('/home/vagrant/measures-byu/src/prod/cetb_file/templates/cetb_global_template.nc', 'w', format='NETCDF4')
fid.Conventions = "CF-1.6"
fid.title = "MODICE mask for a minimum number of years"
fid.product_version = "v0.4"
#fid.software_version_id = "TBD"
#fid.software_repository = "[email protected]:nsidc/measures-byu.git"
fid.source = "MODICE"
fid.source_version_id = "v04"
fid.history = ""
fid.comment = "Mask locations with 2 indicate MODICE for >= min_years."
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"

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import summary [as 别名]
	nc_lat_var.long_name = 'Latitude'
	#nc_lat_var.standard_name = 'latitude'
	
	# lon variable attributes
	nc_lon_var = nc_file.createVariable('lon', 'f4',('station_nm',))
	nc_lon_var.units = 'degree_east'
	nc_lon_var.long_name = 'Longitude'
	#nc_lon_var.standard_name = 'longitude'
	
	# Create ncdf attributes
	nc_file.WML_Conventions = 'CF-1.6'
	nc_file.WML_featureType = 'timeSeries'
	nc_file.WML_cdm_data_type = 'Station'
	nc_file.WML_standard_name_vocabulary = 'CF-1.6'
	nc_file.title = nc_title
	nc_file.summary = nc_summary
	nc_file.id = 'testing_id'
	nc_file.naming_authory = 'testing_authority'
	nc_file.WML_date_created = nc_date_create
	nc_file.WML_creator_name = nc_creator_name
	nc_file.creator_email = nc_creator_email
	nc_file.project = nc_project
	nc_file.processing_level = nc_proc_level
	nc_file.WML_profile = 'single variable'
	
	# data
	dates = [datetime(2001,3,1)+n*timedelta(hours=12) for n in range(12)]
	nc_time[:] = date2num(dates,units=nc_time.units,calendar=nc_time.calendar)
	#nc_station_names[:] = [stringtoarr("aaaa",4),stringtoarr("bbbb",4)]
	dummy = [stringtoarr("aaaa",4),stringtoarr("bbbb",4)]
	nc_station_names[:] = dummy

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