8.28. Compute Feature Clustering
Group (Subgroup)
Statistics (Morphological)
Description
This Filter determines the radial distribution function (RDF), as a histogram, of a given set of Features. Currently, the Features need to be of the same Ensemble (specified by the user), and the resulting RDF is stored as Ensemble data. This Filter also returns the clustering list (the list of all the inter-Feature distances) and the minimum and maximum separation distances. The algorithm proceeds as follows:
Find the Euclidean distance from the current Feature centroid to all other Feature centroids of the same specified phase
Put all caclulated distances in a clustering list
Repeat 1-2 for all Features
Sort the data into the specified number of bins, all equally sized in distance from the minimum distance to the maximum distance between Features. For example, if the user chooses 10 bins, and the minimum distance between Features is 10 units and the maximum distance is 80 units, each bin will be 8 units
Normalize the RDF by the probability of finding the Features if distributed randomly in the given box
Note: Because the algorithm iterates over all the Features, each distance will be double counted. For example, the distance from Feature 1 to Feature 2 will be counted along with the distance from Feature 2 to Feature 1, which will be identical.
Input Parameter(s)
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Selected Image Geometry |
Geometry Selection |
Image |
The target geometry |
Number of Bins for RDF |
Scalar Value |
Int32 |
Number of bins to split the RDF |
Phase Index |
Scalar Value |
Int32 |
Ensemble number for which to calculate the RDF and clustering list |
Remove Biased Features |
Bool |
Remove the biased features |
Random Number Seed Parameters
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Set Random Seed |
Bool |
When checked, allows the user to set the seed value used to randomly generate the points in the RDF |
|
Seed Value |
Scalar Value |
UInt64 |
The seed value used to randomly generate the points in the RDF |
Stored Seed Value Array Name |
DataObjectName |
Name of array holding the seed value |
Input Feature Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Phases |
Array Selection |
Allowed Types: int32 Comp. Shape: 1 |
Specifies to which Ensemble each Feature belongs |
Centroids |
Array Selection |
Allowed Types: float32 Comp. Shape: 3 |
X, Y, Z coordinates of Feature center of mass |
Biased Features |
Array Selection |
Allowed Types: uint8, boolean Comp. Shape: 1 |
Specifies which features are biased and therefor should be removed if the Remove Biased Features option is on; True values removed |
Input Ensemble Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Cell Ensemble Attribute Matrix |
AttributeMatrixSelection |
The path to the cell ensemble attribute matrix where the RDF and RDF min and max distance arrays will be stored |
Output Feature Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Clustering List |
DataObjectName |
Distance of each Feature’s centroid to every other Feature’s centroid |
Output Ensemble Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Radial Distribution Function |
DataObjectName |
A histogram of the normalized frequency at each bin |
|
Max and Min Separation Distances |
DataObjectName |
The max and min distance found between Features |
Example Pipelines
PorosityAnalysis
License & Copyright
Please see the description file distributed with this Plugin
DREAM3D-NX Help
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