SimplnxReview Filter API

This is the documentation for all filters included in the SimplnxReview module. These filters can be used by importing the appropriate module. Each filter is contained in the module:

import simplnxreview
# Instantiate and execute a filter from the module
result = simplnxreview.SomeFilterName.execute(...)

Compute Local Average C-Axis Misalignments

class SimplnxReview.ComputeLocalAverageCAxisMisalignmentsFilter

This filter finds parent average feature kernel average c-axis misalignment. Alternatively, if the “Calculate Unbiased Local Average C-Axis Misalignment” parameter is TRUE, the parent average sub-boundary misalignment is calculated.

Link to the full online documentation for ComputeLocalAverageCAxisMisalignmentsFilter

Mapping of UI display to python named argument

UI Display

Python Named Argument

Average C-Axis Mis-alignments

avg_c_axis_misalignments_path

C-Axis Mis-alignment NeighborList

c_axis_misalignment_list_path

Calculate Local C-Axis Mis-alignments

calc_biased_avg

Calculate Unbiased Local C-Axis Mis-alignments

calc_unbiased_avg

Feature Parent Ids

feature_parent_ids_path

Local C-Axis Mis-alignments Array Name

local_c_axis_misalignments_name

Neighbor List

neighbor_list_path

New Feature Attribute Matrix Name

new_cell_feature_attribute_matrix_path

Number of Features Per Parent Array Name

num_features_per_parent_name

Unbiased Local CAxis Mis-alignments Array Name

unbiased_local_c_axis_misalignments_name
Execute(data_structure, avg_c_axis_misalignments_path, c_axis_misalignment_list_path, calc_biased_avg, calc_unbiased_avg, feature_parent_ids_path, local_c_axis_misalignments_name, neighbor_list_path, new_cell_feature_attribute_matrix_path, num_features_per_parent_name, unbiased_local_c_axis_misalignments_name)
Parameters:
Returns:

Returns a nx.IFilter.ExecuteResult object that holds any warnings and/or errors that were encountered during execution.

Return type:

nx.IFilter.ExecuteResult

Compute MicroTexture Regions

class SimplnxReview.ComputeMicroTextureRegionsFilter

This filter does the following…

Link to the full online documentation for ComputeMicroTextureRegionsFilter

Mapping of UI display to python named argument

UI Display

Python Named Argument

Parent Feature Attribute Matrix

cell_feature_attribute_matrix_path

Cell Feature Ids

feature_ids_array_path

Image Geometry

image_geom_path

Micro Texture Region Fraction Occupied Array Name

micro_texture_region_fraction_occupied_array_name

Micro Texture Region Number of Cells Array Name

micro_texture_region_num_cells_array_name
Execute(data_structure, cell_feature_attribute_matrix_path, feature_ids_array_path, image_geom_path, micro_texture_region_fraction_occupied_array_name, micro_texture_region_num_cells_array_name)
Parameters:
Returns:

Returns a nx.IFilter.ExecuteResult object that holds any warnings and/or errors that were encountered during execution.

Return type:

nx.IFilter.ExecuteResult

Compute Feature Saltykov Sizes

class SimplnxReview.ComputeSaltykovSizesFilter

This filter calculates the Saltykov sizes of all Features. The filter takes the EquavalentDiameters, of assumed 2D data, and estimates the 3D equivalent diameters.The filter will continue iteratively performing the Saltykov Method of spheres, starting with an initial guess of 10 size bins, until the number of features equals the number of Saltykov equivalent diameters to be sampled. If the number of Features to be sampled from the Saltykov bins is not equal to the number of Features then the difference between the two is calculated. Then, the number of attempts is incremented and the number of bins is increased/decreased based on the “difference” and the bin arrays are resized accordingly. This approach allows for the correct number of Features to result because increasing the number of bins increases the number of SaltykovEquavalentDiameters to be sampled and decreasing the number of bins does the opposite. However, if the number of maximum attempts, hardcoded at 10, is reached, random Saltykov bins are selected to add samples to until the the number of SaltykovEquavalentDiameters to be sampled equals the number of Features. This is done because it is assumed that after 10 attempts the solution is ocsillating between the minima.The filter is applied in such a way so that SaltykovEquivalentDiameters has the correct feature-level data length. This way, a direct one-to-one comparies between EquivalentDiameters and SaltykovEquivalentDiameters is possible. However, it is important to note that the SaltykovEquivalentDiameters are not a direct transformation of EquivalentDiameters that they are matched up with.For more information, see: Joseph C. Tucker, Lisa H. Chan, Gregory S. Rohrer, Michael A. Groeber, and Anthony D. Rollett. Comparison of grain size distributions in a ni-based superalloy in three and two dimensions using the saltykov method. Scripta Materialia, 66(8):554 - 557, 2012.

Link to the full online documentation for ComputeSaltykovSizesFilter

Mapping of UI display to python named argument

UI Display

Python Named Argument

Equivalent Diameters

equivalent_diameters_array_path

Saltykov Equivalent Diameters Name

saltykov_equivalent_diameters_name

Seed

seed_value

Use Seed for Random Generation

use_seed
Execute(data_structure, equivalent_diameters_array_path, saltykov_equivalent_diameters_name, seed_value, use_seed)
Parameters:

  • data_structure (DataStructure) – The DataStructure object that holds the data to be processed.

  • equivalent_diameters_array_path (nx.ArraySelectionParameter) – Input feature based Equivalent Diameters

  • saltykov_equivalent_diameters_name (nx.DataObjectNameParameter) – Output feature based Equivalent Diameters data array

  • seed_value (nx.UInt64Parameter) – The seed fed into the random generator

  • use_seed (nx.BoolParameter) – When true the user will be able to put in a seed for random generation

Returns:

Returns a nx.IFilter.ExecuteResult object that holds any warnings and/or errors that were encountered during execution.

Return type:

nx.IFilter.ExecuteResult

Group MicroTexture Regions

class SimplnxReview.GroupMicroTextureRegionsFilter

This Filter groups neighboring Features that have c-axes aligned within a user defined tolerance. The algorithm for grouping the Features is analogous to the algorithm for segmenting the Features - only the average orientation of the Features are used instead of the orientations of the individual Cells and the criterion for grouping only considers the alignment of the c-axes. The user can specify a tolerance for how closely aligned the c-axes must be for neighbor Features to be grouped.

Link to the full online documentation for GroupMicroTextureRegionsFilter

Mapping of UI display to python named argument

UI Display

Python Named Argument

Active Array Name

active_array_name

Average Quaternions

avg_quats_array_path

C-Axis Alignment Tolerance (Degrees)

c_axis_tolerance

Cell Parent Ids Array name

cell_parent_ids_array_name

Contiguous Neighbor List

contiguous_neighbor_list_array_path

Crystal Structures

crystal_structures_array_path

Cell Feature Ids

feature_ids_array_path

Feature Parent Ids Array Name

feature_parent_ids_array_name

Feature Phases

feature_phases_array_path

Created Microtexture Feature Attribute Matrix

new_cell_feature_attribute_matrix_path

Non-Contiguous Neighbor List

non_contiguous_neighbor_list_array_path

Stored Seed Value Array Name

seed_array_name

Seed

seed_value

Use Non-Contiguous Neighbors

use_non_contiguous_neighbors

Group C-Axes With Running Average

use_running_average

Use Seed for Random Generation

use_seed

Volumes

volumes_array_path
Execute(data_structure, active_array_name, avg_quats_array_path, c_axis_tolerance, cell_parent_ids_array_name, contiguous_neighbor_list_array_path, crystal_structures_array_path, feature_ids_array_path, feature_parent_ids_array_name, feature_phases_array_path, new_cell_feature_attribute_matrix_path, non_contiguous_neighbor_list_array_path, seed_array_name, seed_value, use_non_contiguous_neighbors, use_running_average, use_seed, volumes_array_path)
Parameters:
Returns:

Returns a nx.IFilter.ExecuteResult object that holds any warnings and/or errors that were encountered during execution.

Return type:

nx.IFilter.ExecuteResult

Interpolate Values To Unstructured Grid

class SimplnxReview.InterpolateValuesToUnstructuredGridFilter

This filter will sample a point cloud’s data onto a target unstructured grid (Vertex, Edge, Triangle, Quad, Hex, Tet Geometry) via a nearest neighbor algorithm.

Link to the full online documentation for InterpolateValuesToUnstructuredGridFilter

Mapping of UI display to python named argument

UI Display

Python Named Argument

Created Vertex Attribute Matrix

created_attr_matrix_name

Interpolated Node-Based Geometry

destination_geometry_path

Vertex Attribute Matrix

existing_attr_matrix_path

Attribute Arrays to Interpolate

interpolated_array_paths

Node-Based Geometry To Interpolate

source_geometry_path

Use Existing Attribute Matrix

use_existing_attr_matrix
Execute(data_structure, created_attr_matrix_name, destination_geometry_path, existing_attr_matrix_path, interpolated_array_paths, source_geometry_path, use_existing_attr_matrix)
Parameters:
Returns:

Returns a nx.IFilter.ExecuteResult object that holds any warnings and/or errors that were encountered during execution.

Return type:

nx.IFilter.ExecuteResult

Merge Colonies

class SimplnxReview.MergeColoniesFilter

This Filter groups neighboring Features that have a special misorientation that is associated with*alpha* variants that transformed from the same beta grain in titanium. The algorithm for grouping the**Features** is analogous to the algorithm for segmenting the Features, except the average orientationof the Features are used instead of the orientations of the individual Elements and the criterionfor grouping is specific to the alpha-beta transformation. The user can specify a tolerance on both the*axis* and the angle that defines the misorientation relationship (i.e., a tolerance of 1 degree for bothtolerances would allow the neighboring Features to be grouped if their misorientation was between 59-61degrees about an axis within 1 degree of a2, as given by the 3rd special misorientation below).

Link to the full online documentation for MergeColoniesFilter

Mapping of UI display to python named argument

UI Display

Python Named Argument

Active

active_array_name

Angle Tolerance (Degrees)

angle_tolerance

Average Quaternions

avg_quats_array_path

Axis Tolerance (Degrees)

axis_tolerance

Cell Parent Ids

cell_parent_ids_array_name

Cell Phases

cell_phases_array_path

Contiguous Neighbor List

contiguous_neighbor_list_array_path

Crystal Structures

crystal_structures_array_path

Cell Feature Ids

feature_ids_array_path

Feature Parent Ids

feature_parent_ids_array_name

Feature Phases

feature_phases_array_path

Feature Attribute Matrix

new_cell_feature_attribute_matrix_path

Non-Contiguous Neighborhoods

non_contiguous_neighbor_list_array_path

Seed

seed_value

Use Non-Contiguous Neighbors

use_non_contiguous_neighbors

Use Seed for Random Generation

use_seed
Execute(data_structure, active_array_name, angle_tolerance, avg_quats_array_path, axis_tolerance, cell_parent_ids_array_name, cell_phases_array_path, contiguous_neighbor_list_array_path, crystal_structures_array_path, feature_ids_array_path, feature_parent_ids_array_name, feature_phases_array_path, new_cell_feature_attribute_matrix_path, non_contiguous_neighbor_list_array_path, seed_value, use_non_contiguous_neighbors, use_seed)
Parameters:
Returns:

Returns a nx.IFilter.ExecuteResult object that holds any warnings and/or errors that were encountered during execution.

Return type:

nx.IFilter.ExecuteResult