Python Dataset.contact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 contact [as 别名]
def write_nc_file(daily_results, filename, nc, anom_mode=False):
    #Grab every 4th time value to represent daily
    daily_time_var = nc.variables['time'][::4]

    nc_out = Dataset(filename, mode='w', format='NETCDF4') 
    nc_out.createDimension('lon', LONS)
    nc_out.createDimension('lat', LATS)
    nc_out.createDimension('time', None) #UNLIMITED
    nc_out.createDimension('month', MONTHS_YEAR)
    nc_out.title = ''
    nc_out.institution = ''
    nc_out.project = ''
    nc_out.contact = '[email protected]'
    nc_out.Conventions = "CF-1.6"
    
    longitude = nc_out.createVariable('lon', 'f8', ('lon',))
    longitude.standard_name = 'longitude'
    longitude.long_name = 'longitude'
    longitude.units = 'degrees_east'
    longitude.modulo = 360.0
    longitude.axis = 'X'
    longitude[:] = np.arange(0, 360.0, 2.0)
    
    latitude = nc_out.createVariable('lat', 'f8', ('lat',))
    latitude.standard_name = 'latitude'
    latitude.long_name = 'latitude'
    latitude.units = 'degrees_north'
    latitude.axis = 'Y'
    latitude[:] = np.arange(-90.0, 92.0, 2.0)
    
    time = nc_out.createVariable('time', 'f8', ('time',))
    time.units = 'hours since 1-1-1 0:0:0' 
    time.calendar = 'standard' #Gregorian
    time[:] = daily_time_var 
    
    if anom_mode:
        daily_mean = nc_out.createVariable('daily_anom', 'f8', ('time', 'lat', 'lon'))
        daily_mean.long_name = 'z500 daily anomaly vs 1981-2010'
    else:
        daily_mean = nc_out.createVariable('daily_mean', 'f8', ('time', 'lat', 'lon'))
        daily_mean.long_name = 'z500 daily mean'

    daily_mean[:] = daily_results
    nc_out.close()

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import contact [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 contact [as 别名]
    dataset.title='Daily global radiation'
    dataset.comment='Daily global radiation bias corrected (scaled distribution mapping) data of the EURO-CORDEX model. The reference period is 1981-2010, the years 2006-2010 are taken from the corresponding rcp4.5 scenario.'
    
var.grid_mapping = 'latitude_longitude'

# projection information
crs.longitude_of_prime_meridian = 0.0 
crs.semi_major_axis = 6378137.0
crs.inverse_flattening = 298.257223563
crs.comment = 'Latitude and longitude on the WGS 1984 datum'

# write data to netCDF variable
var[:] = ds[param].data
lats[:] = lat1d
lons[:] = lon1d

# fill in times
dates = [startdate+k*timedelta(days=1) for k in range(ds[param].data.shape[0])]
times[:] = date2num(dates, units=times.units, calendar=times.calendar)

# global attributes

dataset.project= "Climaproof, funded by the Austrian Development Agency (ADA) and co-funded by the United Nations Environmental Programme (UNEP)"
dataset.source = 'Bias Correction Method: Switanek et al., 2017, doi.org/10.5194/hess-21-2649-2017, Regridding Method: Higher-order patch recovery (patch) by Earth System Modelling Framework (ESMF) software ESMF_RegridWeightGen (http://www.earthsystemmodeling.org/esmf_releases/public/last/ESMF_refdoc/)'
dataset.contact = 'Maria Wind <[email protected]>, Herbert Formayer <[email protected]>'
dataset.institution = 'Institute of Meteorology, University of Natural Resources and Life Sciences, Vienna, Austria'
dataset.referencees = 'https://data.ccca.ac.at/group/climaproof'
dataset.conventions = 'CF-1.6'

# close dataset        
dataset.close()

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import contact [as 别名]
def write_netcdf(ncfile, format, domain, data):
    nc = Dataset(ncfile, 'w', format=format)
    nc.createDimension('x', domain['nx'])
    nc.createDimension('y', domain['ny'])

    nav_lon = nc.createVariable('nav_lon', 'f4', ('y','x',))
    nav_lat = nc.createVariable('nav_lat', 'f4', ('y','x',))
    soilsat_inst = nc.createVariable('soilsat_inst', 'f4', ('y','x',))
    soilsat_mean = nc.createVariable('soilsat_mean', 'f4', ('y','x',))
    soilsat_anom = nc.createVariable('soilsat_anom', 'f4', ('y','x',))
    soilsat_perc = nc.createVariable('soilsat_perc', 'f4', ('y','x',))

    nav_lon[:] = data['nav_lon']
    nav_lat[:] = data['nav_lat']
    soilsat_inst[:] = data['soilsat_inst']
    soilsat_mean[:] = data['soilsat_mean']
    soilsat_anom[:] = data['soilsat_anom']
    soilsat_perc[:] = data['soilsat_perc']

    nav_lon.units = 'degrees_east'
    nav_lat.units = 'degrees_north'
    soilsat_inst.units = '-'
    soilsat_mean.units = '-'
    soilsat_anom.units = '-'
    soilsat_perc.units = '-'

    nav_lon.long_name = 'Longitude'
    nav_lat.long_name = 'Latitude'
    soilsat_inst.long_name = 'Soil saturation'
    soilsat_mean.long_name = 'Soil saturation mean'
    soilsat_anom.long_name = 'Soil saturation anomaly'
    soilsat_perc.long_name = 'Soil saturation percentile'

    nav_lon.FillValue = default_fillvals['f4']
    nav_lat.FillValue = default_fillvals['f4']
    soilsat_inst.FillValue = default_fillvals['f4']
    soilsat_mean.FillValue = default_fillvals['f4']
    soilsat_anom.FillValue = default_fillvals['f4']
    soilsat_perc.FillValue = default_fillvals['f4']

    nav_lon.missing_value = 1.e+20
    nav_lat.missing_value = 1.e+20
    soilsat_inst.missing_value = 1.e+20
    soilsat_mean.missing_value = 1.e+20
    soilsat_anom.missing_value = 1.e+20
    soilsat_perc.missing_value = 1.e+20

    nav_lon.axis = 'YX'
    nav_lat.axis = 'YX'
    soilsat_inst.axis = 'YX'
    soilsat_mean.axis = 'YX'
    soilsat_anom.axis = 'YX'
    soilsat_perc.axis = 'YX'

    nav_lon.description = 'Longitude of grid cell center'
    nav_lat.description = 'Latitude of grid cell center'
    soilsat_inst.description = ('Simulated total column soil saturation for '
                              'a specific date')
    soilsat_mean.description = ('Long-term mean simulated total colunm soil '
                              'saturation for a specific day of the year. '
                              'The long-term mean is calculated as the 5 day '
                              'moving average centered on the current day. '
                              'The averaging pariod is 1916-2004')
    soilsat_anom.description = ('Total column soil saturation anomaly. Calculated '
                              'as soilsat_inst - soilsat_anom')
    soilsat_perc.description = ('Total column soil saturation percentile. This '
                              'value shows how often during the 1916-2004 '
                              'reference period the soil saturation on this '
                              'day of the year (using a 5 day centered '
                              'window) was less than soilsat_inst')

    nav_lon.valid_min = -180.
    nav_lat.valid_min = -90.
    soilsat_inst.valid_min = 0.
    soilsat_mean.valid_min = 0.
    soilsat_anom.valid_min = -1.
    soilsat_perc.valid_min = 0.

    nav_lon.valid_max = 180.
    nav_lat.valid_max = 90.
    soilsat_inst.valid_max = 1.
    soilsat_mean.valid_max = 1.
    soilsat_anom.valid_max = 1.
    soilsat_perc.valid_max = 1.

    nav_lon.modulo = 360.

    nc.history = 'Created ' + time.ctime(time.time())
    nc.description = ('Soil saturation data from the Variable Infiltration Model '
                      'as part of the operational surface water monitor. Note '
                      'that since this data is from the operational surface '
                      'water monitor, there may be occassional data problems. '
                      'Please check the data carefully')
    nc.source = ('Surface Water Monitor, Surface Water Hydrology Group, '
                 'University of Washington, Seattle, Washington, USA')
    nc.website = 'http://www.hydro.washington.edu/forecast/monitor'
    nc.contact = 'Bart Nijssen, email: [email protected]'
    nc.history = ' '.join(sys.argv)
    nc.projection = 'Geographic'
    nc.resolution = ('Spatial resolution: Longitude ({} degrees), latitude ({} '
#.........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 contact [as 别名]
        # Create output NetCDF file
        # --------------------------
        if os.path.exists(outfilename): os.remove(outfilename)
        f1 = Dataset(outfilename, mode='w', format='NETCDF4_CLASSIC')

        # Global attributes
        f1.Conventions = "CF-1.6"
        f1.title = "Monthly-mean (full water column) fields"
        f1.history = "Simulations were done using a 8x8 km polar stereographic grid projection, however the final " \
                     "data are presented using a reference grid. Conversion between grid " \
                     "projectionsby grid2lonlatZ.py"
        f1.source = "IMR, ROMSv3.7, IS4DVAR, NorthSea-8km reanalysis"
        f1.institution = "Institute of Marine Research, Norway"
        f1.references = "http://www.imr.no"
        f1.product_version = "1.0"
        f1.contact = "[email protected]"
        f1.netcdf_version_id = "netCDF-4 Classic"
        # Define dimensions
        f1.createDimension('time', None)
        f1.createDimension('depth', len(outlevels))
        f1.createDimension('longitude', len(lon))
        f1.createDimension('latitude', len(lat))

        v = f1.createVariable('time', 'd', ('time',))
        v0 = f0.variables['time']
        v.long_name = 'time'
        v.units = "Days since 1948-01-01 00:00:00"
        v.calendar = "Gregorian"

        ntimes = len(f0.dimensions['time'])
        v[:ntimes] = v0[:]