def initialize(self, args, **cfg_args):
self.configure(**cfg_args)
(grid_file, data_file, interface, varname, grid_coords,
var_proc_op) = args
self.gf = cdms2.open(grid_file) if grid_file else None
self.df = cdms2.open(data_file)
self.var = self.getProcessedVariable(varname, var_proc_op)
self.extractMetadata()
self.grid = self.var.getGrid()
self.lev = self.getLevel(self.var)
lon, lat = self.getLatLon(grid_coords)
if (id(lon) <> id(None)) and (id(lat) <> id(None)):
self.time = self.var.getTime()
z_scale = 0.5
self.missing_value = self.var.attributes.get('missing_value', None)
if self.lev == None:
domain = self.var.getDomain()
for axis in domain:
if PlotType.isLevelAxis(axis[0].id.lower()):
self.lev = axis[0]
break
self.computePoints()
self.setPointHeights(height_var=grid_coords[3], z_scale=z_scale)
np_var_data_block = self.getDataBlock(self.var).flatten()
if self.missing_value:
var_data = numpy.ma.masked_equal(np_var_data_block,
self.missing_value, False)
else:
var_data = np_var_data_block
self.point_data_arrays[self.defvar] = var_data
self.vrange = (var_data.min(), var_data.max())
self.var_data_cache[self.iTimeStep] = var_data
self.metadata['vbounds'] = self.vrange
def initialize( self, args, **cfg_args ):
self.configure( **cfg_args )
( grid_file, data_file, interface, varname, grid_coords, var_proc_op ) = args
self.gf = cdms2.open( grid_file ) if grid_file else None
self.df = cdms2.open( data_file )
self.var = self.getProcessedVariable( varname, var_proc_op )
self.extractMetadata()
self.grid = self.var.getGrid()
self.lev = self.getLevel(self.var)
lon, lat = self.getLatLon( grid_coords )
if ( id(lon) <> id(None) ) and ( id(lat) <> id(None) ):
self.time = self.var.getTime()
z_scale = 0.5
self.missing_value = self.var.attributes.get( 'missing_value', None )
if self.lev == None:
domain = self.var.getDomain()
for axis in domain:
if PlotType.isLevelAxis( axis[0].id.lower() ):
self.lev = axis[0]
break
self.computePoints()
self.setPointHeights( height_var=grid_coords[3], z_scale=z_scale )
np_var_data_block = self.getDataBlock(self.var).flatten()
if self.missing_value: var_data = numpy.ma.masked_equal( np_var_data_block, self.missing_value, False )
else: var_data = np_var_data_block
self.point_data_arrays[ self.defvar ] = var_data
self.vrange = ( var_data.min(), var_data.max() )
self.var_data_cache[ self.iTimeStep ] = var_data
self.metadata['vbounds'] = self.vrange
