simplnx Filter API

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

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

Add Bad Data

class simplnx.AddBadDataFilter

This Filter adds “bad” data to an Image Geometry. This Filter is intended to add “realism” (i.e., more representative of an experimental dataset) to synthetic structures that don not have any “bad” Cells. The user can choose to add “random noise” and/or “noise” along Feature boundaries. For a given type of noise, the user must then set the volume fraction of Cells to set as “bad”. The volume fractions entered apply to only the set of Cells that the noise would affect. For example, if the user chose 0.2 for the volume fraction of boundary “noise”, then each boundary Cell would have a 20% chance of being changed to a “bad” Cell and all other Cells would have a 0% chance of being changed. In order to compute noise over the Feature boundaries, the Filter needs the Manhattan distances for each Cell from the Feature boundaries. Note that the computed Manhattan distances are floating point values, but this Filter requires an integer array. To create the necessary integer array, use the Convert Attributer Data Type Filter to cast the Manhattan distance array to an integer array.

Link to the full online documentation for AddBadDataFilter

Mapping of UI display to python named argument

UI Display	Python Named Argument
Add Boundary Noise	boundary_noise
Volume Fraction of Boundary Noise	boundary_vol_fraction
Boundary Euclidean Distances	gb_euclidean_distances_array_path
Image Geometry	input_image_geometry_path
Add Random Noise	poisson_noise
Volume Fraction of Random Noise	poisson_vol_fraction
Stored Seed Value Array Name	seed_array_name
Seed Value	seed_value
Use Seed for Random Generation	use_seed

Execute(data_structure, boundary_noise, boundary_vol_fraction, gb_euclidean_distances_array_path, input_image_geometry_path, poisson_noise, poisson_vol_fraction, seed_array_name, seed_value, use_seed)

Parameters:

data_structure (DataStructure) – The DataStructure object that holds the data to be processed.
boundary_noise (nx.BoolParameter) – If true the user may set the boundary volume fraction
boundary_vol_fraction (nx.Float32Parameter) – A value between 0 and 1 inclusive that is compared against random generation
gb_euclidean_distances_array_path (nx.ArraySelectionParameter) – This is the GB Manhattan Distances array
input_image_geometry_path (nx.GeometrySelectionParameter) – The selected image geometry
poisson_noise (nx.BoolParameter) – If true the user may set the poisson volume fraction
poisson_vol_fraction (nx.Float32Parameter) – A value between 0 and 1 inclusive that is compared against random generation
seed_array_name (nx.DataObjectNameParameter) – Name of array holding the seed value
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:

UI Display	Python Named Argument
Alignment Attribute Matrix Name	alignment_attribute_matrix_name
Alignment Centroids Data Array Name	centroids_array_name
Alignment Cumulative Shifts Data Array Name	cumulative_shifts_array_name
Selected Image Geometry	input_image_geometry_path
Mask Array	mask_array_path
Reference Slice	reference_slice
Alignment Relative Shifts Data Array Name	relative_shifts_array_name
Alignment Slices Data Array Name	slices_array_name
Store Alignment Shifts	store_alignment_shifts
Use Reference Slice	use_reference_slice

UI Display	Python Named Argument
Check Spacing	check_resolution
Destination Image Geometry	destination_image_geometry_path
Direction	direction_index
Input Image Geometries	input_image_geometries_paths
Mirror Geometry In Direction	mirror_geometry
New Image Geometry	output_image_geometry_path
Save as new geometry	save_as_new_geometry

UI Display	Python Named Argument
Cell Attribute Matrix (Image Geometry Only)	cell_attribute_matrix_path
Precomputed Transformation Matrix Path	computed_transformation_matrix_path
Selected Geometry	input_image_geometry_path
Resampling or Interpolation (Image Geometry Only)	interpolation_type_index
Transformation Matrix	manual_transformation_matrix
Transform Matrix Output Path	output_transform_matrix_path
Rotation Axis-Angle	rotation
Save Transformation Matrix	save_transform_matrix
Scale	scale
Transformation Type	transformation_type_index
Translate Geometry To Global Origin Before Transformation	translate_geometry_to_global_origin
Translation	translation

UI Display	Python Named Argument
Grid Resolution	grid_resolution
Vertex Geometry	input_vertex_geometry_path
Minimum Number of Empty Neighbors	min_empty_neighbors
Hull Vertex Geometry	output_vertex_geometry_path

simplnx Filter API

Add Bad Data

Align Geometries

Align Sections (Feature Centroid)

Align Sections (List)

Append Image Geometry

Apply Transformation to Geometry

Approximate Point Cloud Hull

Attribute Array Calculator

Convert Angles to Degrees or Radians

Combine Attribute Arrays

Combine Node Based Geometries

Combine STL Files

Compute Attribute Array Frequency Histogram

Compute Attribute Array Statistics

Compute Biased Features (Bounding Box)

Compute Boundary Cells (Image)

Compute Feature Boundary Element Fractions

Compute Differences Map

Compute Euclidean Distance Map

Compute Feature Centroids

Compute Feature Clustering

Compute Feature Neighbors

Compute Feature Phases Binary

Compute Feature Phases

Compute Feature Corners

Compute Feature Sizes

Compute K Means

Compute K Medoids

Compute Feature Largest Cross-Section Areas

Compute Moment Invariants (2D)

Compute Neighbor List Statistics

Compute Feature Neighborhoods

Compute Number of Features

Compute Surface Area to Volume & Sphericity

Compute Surface Features

Compute Triangle Areas

Compute Feature Centroids from Triangle Geometry

Compute Feature Volumes from Triangle Geometry

Generate Vector Colors

Compute Vertex to Triangle Distances

Compute Volume Fractions of Ensembles

Concatenate Data Arrays

Replace Value in Array (Conditional)

Color to GrayScale

Convert AttributeArray DataType

Copy Data Object

Create Element Array from Feature Array

Create AM Scan Paths

Create Attribute Matrix

Create Color Map

Create Data Array (Advanced)

Create Data Array

Create Data Group

Create Feature Array from Element Array

Create Geometry

Create Geometry (Image)

Create Python Plugin and/or Filters

Crop Geometry (Edge)

Crop Geometry (Image)

Crop Geometry (Vertex)

DBSCAN

Delete Data

Erode/Dilate Bad Data

Erode/Dilate Coordination Number

Erode/Dilate Mask

Execute Process

Extract/Remove Components

Extract Internal Surfaces From Triangle Geometry

Extract DREAM3D-NX Pipeline To File

Extract Vertex Geometry

Compute Feature Face Curvature

Fill Bad Data

Surface Contour Filter (Flying Edges 3D)

Isolate Largest Feature (Identify Sample)

Initialize Data

Initialize Image Geometry Cell Data

Interpolate Point Cloud to Regular Grid

Iterative Closest Point

Label CAD Geometry