API Reference for Enthought Tool Suite 3.0.1

Variables

• BASE

  BASE = 'enthought.mayavi.filters'
  
  

• cell_derivatives_filter

  cell_derivatives_filter = FilterMetadata(id = 'CellDerivativesFilter', menu_name = '&CellDerivatives', class_name = (BASE + '.cell_derivatives.CellDerivatives'), tooltip = 'Calculate derivatives of input point/vector data and output these as cell data', desc = 'Calculate derivatives of input point/vector data and output these as cell data', help = 'Calculate derivatives of input point/vector data and output these as cell data', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• cell_to_point_data_filter

  cell_to_point_data_filter = FilterMetadata(id = 'CellToPointDataFilter', menu_name = '&CellToPointData', class_name = (BASE + '.cell_to_point_data.CellToPointData'), tooltip = 'Convert cell data to point data for the active data', desc = 'Convert cell data to point data for the active data', help = 'Convert cell data to point data for the active data', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'cell' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• contour_filter

  contour_filter = FilterMetadata(id = 'ContourFilter', menu_name = '&Contour', class_name = (BASE + '.contour.Contour'), tooltip = 'Compute contours of the input dataset', desc = 'Compute contours of the input dataset', help = 'Compute contours of the input dataset', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'point' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• cut_plane_filter

  cut_plane_filter = FilterMetadata(id = 'CutPlaneFilter', menu_name = '&CutPlane', class_name = (BASE + '.cut_plane.CutPlane'), tooltip = 'Slice the input dataset with a cut plane', desc = 'Slice the input dataset with a cut plane', help = 'Slice the input dataset with a cut plane', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• decimatepro_filter

  decimatepro_filter = FilterMetadata(id = 'DecimateProFilter', menu_name = '&DecimatePro', class_name = (BASE + '.decimatepro.DecimatePro'), tooltip = 'Simpilies a mesh using the DecimatePro filter', desc = 'Simpilies a mesh using the DecimatePro filter', help = 'Simpilies a mesh using the DecimatePro filter', input_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• delaunay2d_filter

  delaunay2d_filter = FilterMetadata(id = 'Delaunay2DFilter', menu_name = '&Delaunay2D', class_name = (BASE + '.delaunay2d.Delaunay2D'), tooltip = 'Perform a 2D Delaunay triangulation for the given data', desc = 'Perform a 2D Delaunay triangulation for the given data', help = 'Perform a 2D Delaunay triangulation for the given data', input_info = PipelineInfo(datasets = [ 'structured_grid', 'poly_data', 'unstructured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• delaunay3d_filter

  delaunay3d_filter = FilterMetadata(id = 'Delaunay3DFilter', menu_name = 'Delaunay&3D', class_name = (BASE + '.delaunay3d.Delaunay3D'), tooltip = 'Perform a 3D Delaunay triangulation for the given data', desc = 'Perform a 3D Delaunay triangulation for the given data', help = 'Perform a 3D Delaunay triangulation for the given data', input_info = PipelineInfo(datasets = [ 'structured_grid', 'poly_data', 'unstructured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'unstructured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• elevation_filter

  elevation_filter = FilterMetadata(id = 'ElevationFilter', menu_name = 'Elevation Filter', class_name = (BASE + '.elevation_filter.ElevationFilter'), tooltip = 'Creates scalar data from the elevation along a direction', desc = 'Creates scalar data from the elevation along a direction', help = 'Creates scalar data from the elevation along a direction', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• extract_edges_filter

  extract_edges_filter = FilterMetadata(id = 'ExtractEdgesFilter', menu_name = 'Extract Edges', class_name = (BASE + '.extract_edges.ExtractEdges'), tooltip = 'Turns edges into lines.', desc = 'Turns edges into lines.', help = 'Turns edges into lines.', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• extract_grid_filter

  extract_grid_filter = FilterMetadata(id = 'ExtractGridFilter', menu_name = 'Extract &Grid', class_name = (BASE + '.extract_grid.ExtractGrid'), tooltip = 'Extract/subsample part of any structured grid', desc = 'Extract/subsample part of any structured grid', help = 'Extract/subsample part of any structured grid', input_info = PipelineInfo(datasets = [ 'image_data', 'rectilinear_grid', 'structured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'image_data', 'rectilinear_grid', 'structured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• extract_tensor_components_filter

  extract_tensor_components_filter = FilterMetadata(id = 'ExtractTensorComponentsFilter', menu_name = 'Extract &Tensor Components', class_name = (BASE + '.extract_tensor_components.ExtractTensorComponents'), tooltip = 'Extract tensor components from tensor data', desc = 'Extract tensor components from tensor data', help = 'Extract tensor components from tensor data', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'tensors' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• extract_unstructured_grid_filter

  extract_unstructured_grid_filter = FilterMetadata(id = 'ExtractUnstructuredGridFilter', menu_name = 'Extract &Unstructured Grid', class_name = (BASE + '.extract_unstructured_grid.ExtractUnstructuredGrid'), tooltip = 'Extract part of an unstructured grid', desc = 'Extract part of an unstructured grid', help = 'Extract part of an unstructured grid', input_info = PipelineInfo(datasets = [ 'unstructured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'unstructured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• extract_vector_components_filter

  extract_vector_components_filter = FilterMetadata(id = 'ExtractVectorComponentsFilter', menu_name = 'Extract &Vector Components', class_name = (BASE + '.extract_vector_components.ExtractVectorComponents'), tooltip = 'Extract vector components from vector data', desc = 'Extract vector components from vector data', help = 'Extract vector components from vector data', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'vectors' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• extract_vector_norm_filter

  extract_vector_norm_filter = FilterMetadata(id = 'ExtractVectorNormFilter', menu_name = 'Extract Vector &Norm', class_name = (BASE + '.extract_vector_norm.ExtractVectorNorm'), tooltip = 'Compute the vector norm for the current vector data', desc = 'Compute the vector norm for the current vector data', help = 'Compute the vector norm for the current vector data', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'vectors' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• filters

  Now collect all the filters for the mayavi registry.

  filters = [ cell_derivatives_filter, cell_to_point_data_filter, contour_filter, cut_plane_filter, decimatepro_filter, delaunay2d_filter, delaunay3d_filter, elevation_filter, extract_edges_filter, extract_grid_filter, extract_tensor_components_filter, extract_unstructured_grid_filter, extract_vector_norm_filter, extract_vector_components_filter, gaussian_splatter_filter, greedy_terrain_decimation_filter, image_data_probe_filter, mask_points_filter, point_to_cell_data_filter, poly_data_normals_filter, quadric_decimation_filter, select_output_filter, set_active_attribute_filter, transform_data_filter, threshold_filter, triangle_filter, tube_filter, user_defined_filter, vorticity_filter, warp_scalar_filter, warp_vector_filter ]
  
  

• gaussian_splatter_filter

  gaussian_splatter_filter = FilterMetadata(id = 'GaussianSplatterFilter', menu_name = 'Gaussian Splatter', class_name = (BASE + '.gaussian_splatter.GaussianSplatter'), tooltip = 'Builds a structured set of points from a cloud of points, the local density defining the scalar', desc = 'Builds a structured set of points from a cloud of points, the local density defining the scalar', help = """Builds a structured set of points from a cloud of points,
      the local density defining the scalar.  It is essentially equivalent
      to a 3D Gaussian kernel density estimate.""", input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'image_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• greedy_terrain_decimation_filter

  greedy_terrain_decimation_filter = FilterMetadata(id = 'GreedyTerrainDecimationFilter', menu_name = 'Greedy Terrain Decimation', class_name = (BASE + '.greedy_terrain_decimation.GreedyTerrainDecimation'), tooltip = 'Simplifies image data and performs a triangulation', desc = 'Simplifies image data and performs a triangulation', help = 'Simplifies image data and performs a triangulation', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• image_data_probe_filter

  image_data_probe_filter = FilterMetadata(id = 'ImageDataProbeFilter', menu_name = '&Probe data onto image data', class_name = (BASE + '.image_data_probe.ImageDataProbe'), tooltip = 'Samples arbitrary datasets onto an image dataset (cube of data)', desc = 'Samples arbitrary datasets onto an image dataset (cube of data)', help = 'Samples arbitrary datasets onto an image dataset (cube of data)', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'image_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• mask_points_filter

  mask_points_filter = FilterMetadata(id = 'MaskPointsFilter', menu_name = '&Mask Points', class_name = (BASE + '.mask_points.MaskPoints'), tooltip = 'Mask the input points in the data', desc = 'Mask the input points in the data', help = 'Mask the input points in the data', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• point_to_cell_data_filter

  point_to_cell_data_filter = FilterMetadata(id = 'PointToCellDataFilter', menu_name = '&PointToCellData', class_name = (BASE + '.point_to_cell_data.PointToCellData'), tooltip = 'Convert point data to cell data for the active data', desc = 'Convert point data to cell data for the active data', help = 'Convert point data to cell data for the active data', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'point' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'cell' ], attributes = [ 'any' ]))
  
  

• poly_data_normals_filter

  poly_data_normals_filter = FilterMetadata(id = 'PolyDataNormalsFilter', menu_name = 'Compute &Normals', class_name = (BASE + '.poly_data_normals.PolyDataNormals'), tooltip = 'Compute normals and smooth the appearance', desc = 'Compute normals and smooth the appearance', help = 'Compute normals and smooth the appearance', input_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• quadric_decimation_filter

  quadric_decimation_filter = FilterMetadata(id = 'QuadricDecimationFilter', menu_name = 'Quadric Decimation', class_name = (BASE + '.quadric_decimation.QuadricDecimation'), tooltip = 'Simplifies a triangular mesh', desc = 'Simplifies a triangular mesh', help = 'Simplifies a triangular mesh', input_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• select_output_filter

  select_output_filter = FilterMetadata(id = 'SelectOutputFilter', menu_name = '&Select Output', class_name = (BASE + '.select_output.SelectOutput'), tooltip = 'Choose the output of the source that should be used', desc = 'Choose the output of the source that should be used', help = 'Choose the output of the source that should be used', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• set_active_attribute_filter

  set_active_attribute_filter = FilterMetadata(id = 'SetActiveAttributeFilter', menu_name = '&SetActiveAttribute', class_name = (BASE + '.set_active_attribute.SetActiveAttribute'), tooltip = 'Set the active attribute (scalar/vector/tensor) to use', desc = 'Set the active attribute (scalar/vector/tensor) to use', help = 'Set the active attribute (scalar/vector/tensor) to use', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• threshold_filter

  threshold_filter = FilterMetadata(id = 'ThresholdFilter', menu_name = '&Threshold', class_name = (BASE + '.threshold.Threshold'), tooltip = 'Threshold input data based on scalar values', desc = 'Threshold input data based on scalar values', help = 'Threshold input data based on scalar values', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data', 'unstructured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• transform_data_filter

  transform_data_filter = FilterMetadata(id = 'TransformDataFilter', menu_name = 'T&ransform Data', class_name = (BASE + '.transform_data.TransformData'), tooltip = 'Transform (rotate/translate/scale) non ImageData datasets', desc = 'Transform (rotate/translate/scale) non ImageData datasets', help = 'Transform (rotate/translate/scale) non ImageData datasets', input_info = PipelineInfo(datasets = [ 'poly_data', 'structured_grid', 'unstructured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data', 'structured_grid', 'unstructured_grid' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• triangle_filter

  triangle_filter = FilterMetadata(id = 'TriangleFilterFilter', menu_name = 'TriangleFilter', class_name = (BASE + '.triangle_filter.TriangleFilter'), tooltip = 'Convert input polygons and triangle strips to triangles', desc = 'Convert input polygons and triangle strips to triangles', help = 'Convert input polygons and triangle strips to triangles', input_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• tube_filter

  tube_filter = FilterMetadata(id = 'TubeFilter', menu_name = 'Tu&be', class_name = (BASE + '.tube.Tube'), tooltip = 'Turns lines into tubes', desc = 'Turns lines into tubes', help = 'Turns lines into tubes', input_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'poly_data' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• user_defined_filter

  user_defined_filter = FilterMetadata(id = 'UserDefinedFilter', menu_name = '&UserDefined', factory = make_user_defined_filter, tooltip = 'Create a UserDefined filter (will popup a selection dialog)', desc = 'Create a UserDefined filter (will popup a selection dialog)', help = 'Create a UserDefined filter (will popup a selection dialog)', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• vorticity_filter

  vorticity_filter = FilterMetadata(id = 'VorticityFilter', menu_name = '&Vorticity', class_name = (BASE + '.vorticity.Vorticity'), tooltip = 'Calculate the vorticity (curl) of input vector field', desc = 'Calculate the vorticity (curl) of input vector field', help = 'Calculate the vorticity (curl) of input vector field', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'vectors' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• warp_scalar_filter

  warp_scalar_filter = FilterMetadata(id = 'WarpScalarFilter', menu_name = 'Warp S&calar', class_name = (BASE + '.warp_scalar.WarpScalar'), tooltip = 'Move points of data along normals by the scalar data', desc = 'Move points of data along normals by the scalar data', help = 'Move points of data along normals by the scalar data', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'scalars' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

• warp_vector_filter

  warp_vector_filter = FilterMetadata(id = 'WarpVectorFilter', menu_name = 'Warp &Vector', class_name = (BASE + '.warp_vector.WarpVector'), tooltip = 'Move points of data along the vector data at point', desc = 'Move points of data along the vector data at point', help = 'Move points of data along the vector data at point', input_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'vectors' ]), output_info = PipelineInfo(datasets = [ 'any' ], attribute_types = [ 'any' ], attributes = [ 'any' ]))
  
  

Function summary

• make_user_defined_filter()

Functions

• make_user_defined_filter()

Imported Names

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