PY: Add python bindings to allow Node based geometries to resize corr…

…ectly (#906)

Signed-off-by: Michael Jackson <[email protected]>

src/Plugins/SimplnxCore/wrapping/python/simplnxpy.cpp

@@ -689,16 +689,44 @@ PYBIND11_MODULE(simplnx, mod)
   py::class_<RectGridGeom, IGridGeometry, std::shared_ptr<RectGridGeom>> rectGridGeom(mod, "RectGridGeom");
 
   py::class_<INodeGeometry0D, IGeometry, std::shared_ptr<INodeGeometry0D>> iNodeGeometry0D(mod, "INodeGeometry0D");
+  iNodeGeometry0D.def(
+      "resize_vertices",
+      [](INodeGeometry0D& nodeGeometry0D, usize size) {
+        nodeGeometry0D.resizeVertexList(size);
+        nodeGeometry0D.getVertexAttributeMatrix()->resizeTuples({size});
+      },
+      "This will resize the shared vertex list and also resize the associated attribute matrix");
   py::class_<VertexGeom, INodeGeometry0D, std::shared_ptr<VertexGeom>> vertexGeom(mod, "VertexGeom");
 
   py::class_<INodeGeometry1D, INodeGeometry0D, std::shared_ptr<INodeGeometry1D>> iNodeGeometry1D(mod, "INodeGeometry1D");
+  iNodeGeometry1D.def(
+      "resize_edges",
+      [](INodeGeometry1D& nodeGeometry1D, usize size) {
+        nodeGeometry1D.resizeEdgeList(size);
+        nodeGeometry1D.getEdgeAttributeMatrix()->resizeTuples({size});
+      },
+      "This will resize the shared edge list and also resize the associated attribute matrix");
   py::class_<EdgeGeom, INodeGeometry1D, std::shared_ptr<EdgeGeom>> edgeGeom(mod, "EdgeGeom");
 
   py::class_<INodeGeometry2D, INodeGeometry1D, std::shared_ptr<INodeGeometry2D>> iNodeGeometry2D(mod, "INodeGeometry2D");
+  iNodeGeometry2D.def(
+      "resize_faces",
+      [](INodeGeometry2D& nodeGeometry2D, usize size) {
+        nodeGeometry2D.resizeFaceList(size);
+        nodeGeometry2D.getEdgeAttributeMatrix()->resizeTuples({size});
+      },
+      "This will resize the shared triangle list and also resize the associated attribute matrix");
   py::class_<TriangleGeom, INodeGeometry2D, std::shared_ptr<TriangleGeom>> triangleGeom(mod, "TriangleGeom");
   py::class_<QuadGeom, INodeGeometry2D, std::shared_ptr<QuadGeom>> quadGeom(mod, "QuadGeom");
 
   py::class_<INodeGeometry3D, INodeGeometry2D, std::shared_ptr<INodeGeometry3D>> iNodeGeometry3D(mod, "INodeGeometry3D");
+  iNodeGeometry3D.def(
+      "resize_polyhedra",
+      [](INodeGeometry3D& nodeGeometry3D, usize size) {
+        nodeGeometry3D.resizePolyhedraList(size);
+        nodeGeometry3D.getPolyhedraAttributeMatrix()->resizeTuples({size});
+      },
+      "This will resize the shared polyhedra list and also resize the associated attribute matrix");
   py::class_<TetrahedralGeom, INodeGeometry3D, std::shared_ptr<TetrahedralGeom>> tetrahedralGeom(mod, "TetrahedralGeom");
   py::class_<HexahedralGeom, INodeGeometry3D, std::shared_ptr<HexahedralGeom>> hexahedralGeom(mod, "HexahedralGeom");
 

src/nxrunner/src/nxrunner.cpp

@@ -481,8 +481,11 @@ std::vector<std::string> GetPythonPluginList()
   {
     return {};
   }
-
-  return StringUtilities::split(var, ';');
+#if defined(Q_OS_WIN)
+  return nx::core::StringUtilities::split(var, ';');
+#else
+  return nx::core::StringUtilities::split(var, ':');
+#endif
 }
 #endif
 } // namespace

wrapping/python/docs/source/ReleaseNotes_127.rst

@@ -14,7 +14,10 @@ Version 1.2.7
 API Changes & Additions 1.2.7
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The ColorTableParameter API has changed. Please see either the developer or user documentation for more details.
+- The ColorTableParameter API has changed. Please see either the developer or user documentation for more details.
+- A few filters have changed their name
+- DataPath has had more API take from parts of PathLib. See the documentation for the new API additions
+- Node based geometries allow the resizing of their internal data structures using the `resize_*` methods.
 
 Change Log 1.2.7
 ^^^^^^^^^^^^^^^^^^^^

wrapping/python/docs/source/Writing_A_New_Python_Filter.rst

@@ -452,4 +452,38 @@ performed.
         message_handler(nx.IFilter.Message(nx.IFilter.Message.Type.Info, f'Calculating Histogram Counts and Bin Bounds...'))
 
 
-For more Python filter examples, check out the `ExamplePlugin <https://github.com/BlueQuartzSoftware/simplnx/tree/develop/wrapping/python/plugins/ExamplePlugin>`_.
+For more Python filter examples, check out the `ExamplePlugin <https://github.com/BlueQuartzSoftware/simplnx/tree/develop/wrapping/python/plugins/ExamplePlugin>`_.
+
+10. Debugging the Python Filter.
+--------------------------------
+
+    Running the python filter through the DREAM3D-NX user interface will not allow you any opportunity to use a debugger to inspect troublesome code. For this
+    you will need to implement a separate python file that dynamically loads the python based plugin and then executes your filter with the proper arguments.
+    The below code is the bare minimum that you will need to implement.
+
+    .. code-block:: python
+
+        from typing import List
+        import simplnx as nx
+
+        # ------------------------------------------------------------------------------
+        # Replace NAME_OF_YOUR_PLUGIN with the actual name of your plugin
+        # Replace FILTER_NAME with the name of your filter that you would like to debug
+
+        import NAME_OF_YOUR_PLUGIN
+        nx.load_python_plugin(NAME_OF_YOUR_PLUGIN)
+        import NAME_OF_YOUR_PLUGIN.FILTER_NAME
+
+        # Create a Data Structure
+        data_structure = nx.DataStructure()
+        # Wrap the python filter in this "proxy" class from the target plugin so we can use it.
+        pynx_filter = nx.PyFilter(NAME_OF_YOUR_PLUGIN.FILTER_NAME())
+        # Execute the filter and check the result. We use the `execute2()` method to run the filter.
+        # Make sure to use all appropriate arguments to your filter. The named arguments are the values
+        # of each of the parameter keys that are defined at the top of the filter. For instance if you
+        # have this line:
+        #   INPUT_IMAGE_ARRAY_KEY = 'input_image_array'
+        # then you would use 'input_image_array' as the named argument in the call to `execute2()` method
+        result = pynx_filter.execute2(data_structure=data_structure, 
+                                       ..... )
+

wrapping/python/plugins/DataAnalysisToolkit/CliReaderFilter.py

@@ -57,6 +57,11 @@ def preflight_impl(self, data_structure: nx.DataStructure, args: dict, message_h
     shared_vertices_array_name: str = args[CliReaderFilter.SHARED_VERTICES_ARRAY_NAME]
     shared_edges_array_name: str = args[CliReaderFilter.SHARED_EDGES_ARRAY_NAME]
 
+
+    # Here we create the Edge Geometry (and the 2 internal Attribute Matrix to hold vertex and edge data arrays.)
+    # Because this is a "reader" type of filter we do not know (at least in this reader implementation)
+    # the number of vertices or edges at preflight time. During execute we will need to ensure that
+    # everything is sized correctly.
     output_actions = nx.OutputActions()
     output_actions.append_action(nx.CreateEdgeGeometryAction(geometry_path=output_edge_geom_path, num_edges=1, num_vertices=1, vertex_attribute_matrix_name=output_vertex_attrmat_name, edge_attribute_matrix_name=output_edge_attrmat_name, shared_vertices_name=shared_vertices_array_name, shared_edges_name=shared_edges_array_name))
 
@@ -87,6 +92,7 @@ def execute_impl(self, data_structure: nx.DataStructure, args: dict, message_han
     output_edge_geom_path: nx.DataPath = args[CliReaderFilter.OUTPUT_EDGE_GEOM_PATH]
     output_edge_attrmat_name: str = args[CliReaderFilter.OUTPUT_EDGE_ATTRMAT_NAME]
     output_feature_attrmat_name: str = args[CliReaderFilter.OUTPUT_FEATURE_ATTRMAT_NAME]
+    output_vertex_attrmat_name: str = args[CliReaderFilter.OUTPUT_VERTEX_ATTRMAT_NAME]
 
     layer_features = []
 
@@ -124,33 +130,43 @@ def execute_impl(self, data_structure: nx.DataStructure, args: dict, message_han
 
     edge_geom: nx.EdgeGeom = data_structure[output_edge_geom_path]
 
+    # Tell the Edge Geometry to resize the shared vertex list so that we can 
+    # copy in the vertices.
     message_handler(nx.IFilter.Message(nx.IFilter.Message.Type.Info, f'Saving Vertex List...'))
     vertex_list = [item for pair in zip(start_vertices, end_vertices) for item in pair]
+    edge_geom.resize_vertices(len(vertex_list))
+
     vertices_array = edge_geom.vertices
-    vertices_array.resize_tuples([len(vertex_list)])
     vertices_view = vertices_array.store.npview()
     vertices_view[:] = vertex_list
 
+    # Tell the Edge Geometry to resize the shared edge list so that we can
+    # copy in the edge list and also copy in all the edge arrays
     message_handler(nx.IFilter.Message(nx.IFilter.Message.Type.Info, f'Saving Edges...'))
+    edge_geom.resize_edges(num_of_hatches)
     edges_array = edge_geom.edges
-    edges_array.resize_tuples([num_of_hatches])
     edges_view = edges_array.store.npview()
     edges_view[:] = [[i, i+1] for i in range(0, len(vertex_list), 2)]
 
+    # Get the nx.DataPath to the Edge Attribute Matrix
     edge_attr_mat_path = output_edge_geom_path.create_child_path(output_edge_attrmat_name)
+    # Copy the all the edge data into the edge attribute matrix
     for array_name, values in data_arrays.items():
       message_handler(nx.IFilter.Message(nx.IFilter.Message.Type.Info, f"Saving Cell Array '{array_name}'..."))
       array_path = edge_attr_mat_path.create_child_path(array_name)
       array: nx.IDataArray = data_structure[array_path]
-      array.resize_tuples([num_of_hatches])
       values_arr = np.array(values)
       values_arr = values_arr.reshape([len(values)] + array.cdims)
       array_view = array.store.npview()
       array_view[:] = values_arr
 
+
+    # Save the feature level data
     feature_attr_mat_path = output_edge_geom_path.create_child_path(output_feature_attrmat_name)
     label_feature_array_path = feature_attr_mat_path.create_child_path(self.LABEL_ARRAY_NAME)
     label_feature_array: nx.StringArray = data_structure[label_feature_array_path]
     message_handler(nx.IFilter.Message(nx.IFilter.Message.Type.Info, f"Saving Feature Array '{self.LABEL_ARRAY_NAME}'..."))
     label_feature_array.initialize_with_list(list(hatch_labels.values()))
+
+    # Filter is complete, return the results.
     return nx.Result()

wrapping/python/plugins/ExamplePlugin/ExampleFilter1.py

@@ -79,7 +79,7 @@ def clone(self):
     """
     return ExampleFilter1()
 
-  def preflight_impl(self, data_structure: nx.DataStructure, args: dict, message_handler: nx.IFilter.MessageHandler, should_cancel: nx.AtomicBoolProxy) -> nx.IFilter.PreflightResult:
+  def parameters(self) -> nx.Parameters:
     """This function defines the parameters that are needed by the filter. Parameters collect the values from the user
        or through a pipeline file.
     """

wrapping/python/plugins/debugging_helper.py

@@ -0,0 +1,99 @@
+"""
+This code can be used to debug your python based DREAM3D-NX filter. There are a
+number of bits of code that you will need to change in order for you to be able
+to debug.
+
+
+"""
+from typing import List
+import simplnx as nx
+
+# ------------------------------------------------------------------------------
+# This NEEDS to be executed here so that we can load the python based plugin
+#
+# You will need to REPLACE the name of the plugin and the name of the filter
+# that you are trying to debug in the next 3 lines of code
+# ------------------------------------------------------------------------------
+import DataAnalysisToolkit
+nx.load_python_plugin(DataAnalysisToolkit)
+import DataAnalysisToolkit.CliReaderFilter
+
+
+"""
+The below are convenience functions that you can use to check the result of running
+preflight or execute on the filter or execute on a loaded pipeline. You should
+NOT have to change anything these functions.
+"""
+# ------------------------------------------------------------------------------
+# check_filter_execute_result
+# ------------------------------------------------------------------------------
+def check_filter_preflight_result(filter: nx.IFilter, result: nx.IFilter.PreflightResult) -> None:
+    """This function will check the result of a filter's preflight method"""
+    has_errors = len(result.get_result()) != 0 
+    if has_errors:
+        print(f'{filter.name()} :: Errors: {result.get_result()}')
+        raise RuntimeError(result)
+
+    print(f"{filter.name()} :: No errors preflighting the filter")
+
+# ------------------------------------------------------------------------------
+# check_filter_execute_result
+# ------------------------------------------------------------------------------
+def check_filter_execute_result(filter: nx.IFilter, result: nx.IFilter.ExecuteResult) -> None:
+    """This function will check the result of a filter's execute method."""
+    if len(result.warnings) != 0:
+        print(f'{filter.name()} ::  Warnings: {result.warnings}')
+
+    has_errors = len(result.errors) != 0 
+    if has_errors:
+        print(f'{filter.name()} :: Errors: {result.errors}')
+        raise RuntimeError(result)
+
+    print(f"{filter.name()} :: No errors running the filter")
+
+# ------------------------------------------------------------------------------
+# check_pipeline_execute_result
+# ------------------------------------------------------------------------------
+def check_pipeline_execute_result(result: nx.IFilter.ExecuteResult) -> None:
+    """This method will check the result of a pipeline's execute method"""
+    if len(result.warnings) != 0:
+        print(f'{filter.name()} ::  Warnings: {result.warnings}')
+
+    has_errors = len(result.errors) != 0 
+    if has_errors:
+        print(f'{filter.name()} :: Errors: {result.errors}')
+        raise RuntimeError(result)
+
+    print(f"Pipeline :: No errors running the pipeline")  
+
+
+# *****************************************************************************
+# This section is where you will need to programmatically execute what ever 
+# needs to be done to prep your filter to run. This may involve programmatically
+# running filters one after another or loading a pipeline to prep the DataStructure
+# and then running your filter. Take a look at the Examples/scripts and 
+# Examples/pipelines for examples to do that.
+# *****************************************************************************
+
+
+# Create a Data Structure
+data_structure = nx.DataStructure()
+
+# Wrap the python filter in this "proxy" class from the target plugin so we can use it.
+pynx_filter = nx.PyFilter(DataAnalysisToolkit.CliReaderFilter())
+
+# Execute the filter and check the result. We use the `execute2()` method to
+# run the filter.
+result = pynx_filter.execute2(data_structure=data_structure, 
+                              cli_file_path="/paht/to/input/file.cli")
+check_filter_execute_result(pynx_filter, result)
+
+# ------------------------------------------------------------------------------
+# If we want to check the results of the filter, we can save this file to a 
+# dream3d file and load the .dream3d file directly into DREAM3D-NX to see the
+# immediate results.
+# ------------------------------------------------------------------------------
+result = nx.WriteDREAM3DFilter.execute(data_structure=data_structure, 
+                                       export_file_path="/path/to/output/file.dream3d",
+                                       write_xdmf_file=False)
+check_filter_execute_result(nx.WriteDREAM3DFilter, result)