original_docstring: A k-nearest mean filter is a type of edge-preserving smoothing filter.
  
          Keyword arguments:
  
          i -- Input raster file. 
          output -- Output raster file. 
          filterx -- Size of the filter kernel in the x-direction. 
          filtery -- Size of the filter kernel in the y-direction. 
          k -- k-value in pixels; this is the number of nearest-valued neighbours to use. 
          callback -- Custom functon for handling tool text outputs.
          
docstring: A k-nearest mean filter is a type of edge-preserving smoothing filter.
  Keyword arguments.
  
  - Input raster file.
  output -- Output raster file.
  filterx -- Size of the filter kernel in the x-direction.
  filtery -- Size of the filter kernel in the y-direction.
  k -- k-value in pixels; this is the number of nearest-valued neighbours to use.
  callback -- Custom functon for handling tool text outputs.
docstring_tokens: A k - nearest mean filter is a type of edge - preserving smoothing filter . Keyword arguments . - Input raster file . output -- Output raster file . filterx -- Size of the filter kernel in the x - direction . filtery -- Size of the filter kernel in the y - direction . k -- k - value in pixels ; this is the number of nearest - valued neighbours to use . callback -- Custom functon for handling tool text outputs .
code: def k_nearest_mean_filter(self, i, output, filterx=11, filtery=11, k=5, callback=default_callback):
          """A k-nearest mean filter is a type of edge-preserving smoothing filter.
  
          Keyword arguments:
  
          i -- Input raster file. 
          output -- Output raster file. 
          filterx -- Size of the filter kernel in the x-direction. 
          filtery -- Size of the filter kernel in the y-direction. 
          k -- k-value in pixels; this is the number of nearest-valued neighbours to use. 
          callback -- Custom functon for handling tool text outputs.
          """
          args = []
          args.append("--input='{}'".format(i))
          args.append("--output='{}'".format(output))
          args.append("--filterx={}".format(filterx))
          args.append("--filtery={}".format(filtery))
          args.append("-k={}".format(k))
          return self.run_tool('k_nearest_mean_filter', args, callback)
code_tokens: def k_nearest_mean_filter ( self , i , output , filterx = 11 , filtery = 11 , k = 5 , callback = default_callback ) : args = [ ] args . append ( "--input='{}'" . format ( i ) ) args . append ( "--output='{}'" . format ( output ) ) args . append ( "--filterx={}" . format ( filterx ) ) args . append ( "--filtery={}" . format ( filtery ) ) args . append ( "-k={}" . format ( k ) ) return self . run_tool ( 'k_nearest_mean_filter' , args , callback )
short_docstring: A k-nearest mean filter is a type of edge-preserving smoothing filter.
short_docstring_tokens: A k - nearest mean filter is a type of edge - preserving smoothing filter .
comment: """A k-nearest mean filter is a type of edge-preserving smoothing filter.
  
          Keyword arguments:
  
          i -- Input raster file. 
          output -- Output raster file. 
          filterx -- Size of the filter kernel in the x-direction. 
          filtery -- Size of the filter kernel in the y-direction. 
          k -- k-value in pixels; this is the number of nearest-valued neighbours to use. 
          callback -- Custom functon for handling tool text outputs.
          """