Python Dataset.delncattr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 delncattr [as 别名]
    def updateNCattrs_single(self, ncName):
        """on a single file: run when ONLY nc METADATA needs updating, NOT any data

        :param str ncName: filename of netCDF to be made, with path

        .. note::
            Function uses metaDict variables set in various levels of __init__.

        .. todo::
            - Shouldn't delete global attributes set in ``NCtimeMeta`` :
            time_coverage_duration, date_issued, date_modified, time_coverage_start, time_coverage_end
            - Except SASS object now sets ``metaDict`` in ``createNCshell``. Therefore,
            these global attributes won't work with this function.
            These include: title, date_created, history, geospatial_lat/lon/vertical_min/max,
            institution, comment.
        """
        print ncName
        ncfile = Dataset(ncName, 'a', format='NETCDF4')
        #print ncfile.variables.keys()
        #print ncfile.ncattrs()
        print ncfile.__dict__
        ncfile.setncatts(self.metaDict)
        print "EDITED"
        #print ncfile.__dict__.keys()
        #take out attributes that are no longer in the meta dictionary
        for k in ncfile.__dict__.keys():
            if k not in self.metaDict:
                print 'DELETED', k
                ncfile.delncattr(k)
        self.NCtimeMeta(ncfile)
        print "DONE"
        print ncfile.__dict__#.keys()
        ncfile.close()

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import delncattr [as 别名]
def set_dynamic_md(resource):
  """
  Dynamic meta data like time frequency, spatial extent, start/end time, etc.
  :param resource: netCDF file where basic meta data should be set
  """
  from flyingpigeon.utils import get_timerange, get_time
  frequency = get_frequency(resource)
  
  time_coverage_start, time_coverage_end = get_timerange(resource)
  time_number_steps = len(get_time(resource))
  
  # max_lat, min_lat, max_lon, min_lat = get_extent(resource)
  
  ds = Dataset(resource, mode='a')
  
  try:
    driving_experiment = ds.driving_experiment
    ds.delncattr('driving_experiment')
  except Exception as e: 
    logger.error(e)
    driving_experiment = ''

  try:
    driving_experiment_name = ds.driving_experiment_name
    ds.delncattr('driving_experiment_name')
  except Exception as e: 
    logger.error(e)
    driving_experiment_name = ''

  try:
    driving_model_ensemble_member = ds.driving_model_ensemble_member
    ds.delncattr('driving_model_ensemble_member')
  except Exception as e: 
    logger.error(e)
    driving_model_ensemble_member = ''    
    
  try:
    experiment = ds.experiment
    ds.delncattr('experiment')
  except Exception as e: 
    logger.error(e)
    experiment = ''
  try:
    tracking_id = ds.tracking_id
    ds.delncattr('tracking_id')
  except Exception as e: 
    logger.error(e)
    tracking_id = ''    
    
  try:
    experiment_id = ds.experiment_id
    ds.delncattr('experiment_id')
  except Exception as e: 
    logger.error(e)
    experiment_id = ''
  
  try:
    project_id = ds.project_id
    ds.delncattr('project_id')
  except Exception as e: 
    logger.error(e)
    project_id = ''
    
  try:
    institution_id = ds.institution_id
    ds.delncattr('institution_id')
  except Exception as e: 
    logger.error(e)
    institution_id = ''
 
  try:
    model_version_id = ds.model_version_id
    ds.delncattr('model_version_id')
  except Exception as e: 
    logger.error(e)
    model_version_id = ''
    
  try:
    driving_model_id = ds.driving_model_id
    ds.delncattr('driving_model_id')
  except Exception as e: 
    logger.error(e)
    driving_model_id = ''

  try:
    driving_ensemble_member = ds.driving_ensemble_member
    ds.delncattr('driving_ensemble_member')
  except Exception as e: 
    logger.error(e)
    driving_ensemble_member = '' 
    
  try:
    driving_model_id = ds.driving_model_id
    ds.delncattr('driving_model_id')
  except Exception as e: 
    logger.error(e)
    driving_model_id = ''
  
  try:
    model_id = ds.model_id
#.........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 delncattr [as 别名]
        
#    except:
#        print "not a mask_rho?"
#        sys.exit()



if args.field:
    if not args.outf:
        nc = Dataset(args.inf+"_vert", 'w', format='NETCDF3_64BIT')
    else:
        nc = Dataset(args.outf, 'w', format='NETCDF3_64BIT')
    for i in f.ncattrs():
        print i, f.getncattr(i)
        nc.setncattr(i, f.getncattr(i))
    nc.delncattr('history')
    nc.setncattr('history', f.getncattr('history')+'\n Modified by '+str(os.path.basename(__file__))+' '+str(tm.strftime("%c")))


    for i in f.dimensions.keys():
        print i
        nc.createDimension(i, len(f.dimensions[i]))

    for i in f.variables.keys():
        print i, f.variables[i].dimensions
        w = nc.createVariable(i, 'f8',  f.variables[i].dimensions)

        for j in f.variables[i].ncattrs():
       #     print j, ": ", f.variables[i].getncattr(j), f.variables[i].dimensions
            w.setncattr(j, f.variables[i].getncattr(j))

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import delncattr [as 别名]
    img_file = sys.argv[2]
    dst_file = sys.argv[3]

    timestamp_str = dst_file.split("-")[0] + "Z"
    timestamp = datetime.strptime(timestamp_str, DATE_TIME_FORMAT)

    shutil.copy(src_file, dst_file)
    ds_out = Dataset(dst_file, mode='r+')

    for attr in time_attributes:
        print "Setting time attribut '%s' -> '%s'" % (attr, timestamp_str)
        ds_out.setncattr(attr, timestamp_str)

    for attr in attrs_remove:
        print "Removing attribute '%s'" % attr
        ds_out.delncattr(attr)

    for attr in attrs_add:
        print "Setting attribute '%s' -> '%s'" % (attr[0], attr[1])
        ds_out.setncattr(attr[0], attr[1])

    print "Plainting image '%s'" % img_file
    img_data = np.full((image_height, image_width), fill_value=target_variable_fill_value, dtype=target_variable_type)

    image = np.asarray(Image.open(img_file))

    iTransparent = np.all(image == [0, 0, 0, 0], axis=2)
    img_data[~iTransparent] = target_variable_value

    ds_out['l2p_flags'].delncattr('flag_masks') # Make CF compliant!