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 File Path	alignment_shift_file_name
Selected Image Geometry	input_image_geometry_path
Mask Array	mask_array_path
Reference Slice	reference_slice
Cell Data Attribute Matrix	selected_cell_data_path
Use Reference Slice	use_reference_slice
Write Alignment Shift File	write_alignment_shifts

UI Display	Python Named Argument
DREAM3D Alignment File Format	dream3d_alignment_file
Input File	input_file
Selected Image Geometry	input_image_geometry_path

UI Display	Python Named Argument
Check Spacing	check_resolution
Destination Image Geometry	destination_image_geometry_path
Input Image Geometry	input_image_geometry_path
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
Rotation Axis-Angle	rotation
Scale	scale
Transformation Type	transformation_type_index
Translate Geometry To Global Origin Before Transformation	translate_geometry_to_global_origin
Translation	translation

simplnx Filter API

Add Bad Data

Align Geometries

Align Sections (Feature Centroid)

Align Sections (List)

Append Z Slice (Image Geometry)

Apply Transformation to Geometry

Approximate Point Cloud Hull

Attribute Array Calculator

Calculate Frequency Histogram

Find Feature Sizes

Calculate Triangle Areas

Convert Angles to Degrees or Radians

Combine Attribute Arrays

Combine STL Files

Compute Feature Corners

Compute Moment Invariants (2D)

Replace Value in Array (Conditional)

Color to GrayScale

Convert AttributeArray DataType

Copy Data Object

Create Element Array from Feature Array

Create Attribute Matrix

Create Data Array

Create Data Group

Create Feature Array from Element Array

Create Geometry

Create Geometry (Image)

Crop Geometry (Image)

Crop Geometry (Vertex)

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 DREAM.3D Pipeline To File

Extract Vertex Geometry

Find Feature Face Curvature

Fill Bad Data

Find Attribute Array Statistics

Find Biased Features (Bounding Box)

Find Boundary Cells (Image)

Find Feature Boundary Element Fractions

Find Differences Map

Find Euclidean Distance Map

Find Feature Centroids

Find Feature Clustering

Find Feature Neighbors

Find Feature Phases Binary

Find Feature Phases

Find Feature Largest Cross-Section Areas

Find Neighbor List Statistics

Find Feature Neighborhoods

Find Number of Features

Find Surface Area to Volume & Sphericity

Find Surface Features

Find Feature Centroids from Triangle Geometry

Find Feature Volumes from Triangle Geometry

Find Vertex to Triangle Distances

Find Volume Fractions of Ensembles

Surface Contour Filter (Flying Edges 3D)

Generate Color Table

Generate Python Plugin and/or Filters

Generate Vector Colors

Isolate Largest Feature (Identify Sample)

Initialize Data

Initialize Image Geometry Cell Data

Interpolate Point Cloud to Regular Grid

Iterative Closest Point

K Means

K Medoids

Label CAD Geometry

Laplacian Smoothing

Map Point Cloud to Regular Grid

Minimum Number of Neighbors

Move Data

Multi-Threshold Objects

Fuse Regular Grids (Nearest Point)