Added Python script showing how to use advanced Create Data Array fil…

…ter.

Signed-off-by: Joey Kleingers <[email protected]>

wrapping/python/examples/scripts/create_data_array_advanced.py

@@ -0,0 +1,168 @@
+"""
+Important Note
+==============
+
+This python file can be used as an example of how to execute the Create Data Array
+(Advanced) filter using all the various initialization options.
+
+If you plan to use the codes below (and you are welcome to), there are a few things
+that you, the developer, should take note of:
+
+Import Statements
+-----------------
+
+You will most likely *NOT* need to include the following code:
+
+   .. code:: python
+      
+      import simplnx_test_dirs as nxtest
+
+Filter Error Detection
+----------------------
+
+In each section of code a filter is created and executed immediately. This may or
+may *not* be what you want to do. You can also preflight the filter to verify the
+correctness of the filters before executing the filter **although** this is done
+for you when the filter is executed. As such, you will want to check the 'result'
+variable to see if there are any errors or warnings. If there **are** any then
+you, as the developer, should act appropriately on the errors or warnings. 
+More specifically, this bit of code:
+
+   .. code:: python
+
+      nxtest.check_filter_result(nxor.ReadAngDataFilter, result)
+
+is used by the simplnx unit testing framework and should be replaced by your own
+error checking code. You are welcome to look up the function definition and use
+that.
+
+"""
+import simplnx as nx
+
+import simplnx_test_dirs as nxtest
+
+import numpy as np
+
+# Create a Data Structure
+data_structure = nx.DataStructure()
+
+#------------------------------------------------------------------------------
+# Create Data Array With Fill Value
+#------------------------------------------------------------------------------
+output_array_path = nx.DataPath(["Fill Value Array"])
+array_type = nx.NumericType.float32
+tuple_dims = [[10]]
+component_dims = [[3, 2]]
+create_array_nx_filter = nx.CreateDataArrayAdvancedFilter()
+result  = create_array_nx_filter.execute(data_structure=data_structure, 
+                                         component_dimensions=component_dims, 
+                                         data_format="",
+                                         init_type_index=0, # Fill value initialization type
+                                         init_value="1;4;7;2;1;5", 
+                                         numeric_type_index=array_type, 
+                                         output_array_path=output_array_path, 
+                                         tuple_dimensions=tuple_dims)
+nxtest.check_filter_result(nx.CreateDataArrayFilter, result)
+
+
+# We can check the output of the filter by simply printing the array
+npdata = data_structure[output_array_path].npview()
+print(npdata)
+
+#------------------------------------------------------------------------------
+# Create Data Array With Incremental Values
+#------------------------------------------------------------------------------
+output_array_path = nx.DataPath(["Incremental Value Array"])
+array_type = nx.NumericType.int16
+tuple_dims = [[3, 3]]
+component_dims = [[3]]
+create_array_nx_filter = nx.CreateDataArrayAdvancedFilter()
+result  = create_array_nx_filter.execute(data_structure=data_structure, 
+                                         component_dimensions=component_dims, 
+                                         data_format="",
+                                         init_type_index=1, # Initialization type = Incremental/Decremental
+                                         starting_fill_value="1;4;7",
+                                         step_operation_index=0,  # Step type = Incrementing
+                                         step_value="1;2;3",
+                                         numeric_type_index=array_type, 
+                                         output_array_path=output_array_path, 
+                                         tuple_dimensions=tuple_dims)
+nxtest.check_filter_result(nx.CreateDataArrayFilter, result)
+
+
+# We can check the output of the filter by simply printing the array
+npdata = data_structure[output_array_path].npview()
+print(npdata)
+
+#------------------------------------------------------------------------------
+# Create Data Array With Decremental Values
+#------------------------------------------------------------------------------
+output_array_path = nx.DataPath(["Decremental Value Array"])
+array_type = nx.NumericType.int32
+tuple_dims = [[3, 3]]
+component_dims = [[3]]
+create_array_nx_filter = nx.CreateDataArrayAdvancedFilter()
+result  = create_array_nx_filter.execute(data_structure=data_structure, 
+                                         component_dimensions=component_dims, 
+                                         data_format="",
+                                         init_type_index=1, # Initialization type = Incremental/Decremental
+                                         starting_fill_value="1;4;7",
+                                         step_operation_index=1,  # Step type = Decrementing
+                                         step_value="1;2;3",
+                                         numeric_type_index=array_type, 
+                                         output_array_path=output_array_path, 
+                                         tuple_dimensions=tuple_dims)
+nxtest.check_filter_result(nx.CreateDataArrayFilter, result)
+
+
+# We can check the output of the filter by simply printing the array
+npdata = data_structure[output_array_path].npview()
+print(npdata)
+
+#------------------------------------------------------------------------------
+# Create Data Array With Random Values
+#------------------------------------------------------------------------------
+output_array_path = nx.DataPath(["Random Value Array"])
+array_type = nx.NumericType.int8
+tuple_dims = [[4, 4]]
+component_dims = [[2]]
+create_array_nx_filter = nx.CreateDataArrayAdvancedFilter()
+result  = create_array_nx_filter.execute(data_structure=data_structure, 
+                                         component_dimensions=component_dims, 
+                                         data_format="",
+                                         init_type_index=2, # Initialization type = Random
+                                         numeric_type_index=array_type, 
+                                         output_array_path=output_array_path, 
+                                         tuple_dimensions=tuple_dims,
+                                         seed_array_name="Seed Value Array")
+nxtest.check_filter_result(nx.CreateDataArrayFilter, result)
+
+
+# We can check the output of the filter by simply printing the array
+npdata = data_structure[output_array_path].npview()
+print(npdata)
+
+#------------------------------------------------------------------------------
+# Create Data Array With Random Values In A Range
+#------------------------------------------------------------------------------
+output_array_path = nx.DataPath(["Random Values In A Range Array"])
+array_type = nx.NumericType.int8
+tuple_dims = [[4, 4]]
+component_dims = [[2]]
+create_array_nx_filter = nx.CreateDataArrayAdvancedFilter()
+result  = create_array_nx_filter.execute(data_structure=data_structure, 
+                                         component_dimensions=component_dims, 
+                                         data_format="",
+                                         init_type_index=3, # Initialization type = Random With Range
+                                         init_start_range="-2;4",
+                                         init_end_range="4;7",
+                                         numeric_type_index=array_type, 
+                                         output_array_path=output_array_path, 
+                                         tuple_dimensions=tuple_dims,
+                                         seed_array_name="Seed Value Array 2")
+nxtest.check_filter_result(nx.CreateDataArrayFilter, result)
+
+
+# We can check the output of the filter by simply printing the array
+npdata = data_structure[output_array_path].npview()
+print(npdata)