Welcome to the Xtra Library for
Thermo Scientific Amira, Avizo and PerGeos Software
Below you will find a collection of add-ons (recipes, scripts, demos,…) that will help you improve your day-to-day use of Amira-Avizo and PerGeos Software and make you gain both time and efficiency.
Use the Search field to look for specific keywords related to your domain of interest. The different filters also help you target specific resources.
Porosity and Permeability by blocks
Tcl
3 Jul 2020 - Splits a binary pore space into blocks and output one porosity and permeability value for every block
Load a Numpy array into PerGeos/Amira-Avizo using the Python API
Python (pyscro)
21 Oct 2019 - This script simply reads and loads numpy arrays in a user specified directory into PerGeos/Amira-Avizo as an image.
Random Forests for Multiclass Segmentation using Python API in PerGeos
Python (pyscro), Project (hx, pgo), Data
21 Oct 2019 - Simple demonstration of feature computation where the feature vector contains intensity and 2D eigenvalues (X & Y). This example uses random forests implementation from the sklearn package.
Eagleford Shale FIB-SEM Segmentation
Recipe (hxrecipe)
17 Oct 2019 - This recipe guided workflow will result in a 5-phase segmentation applicable to the included dataset and most typical FIB-SEM data. Input Data, the Recipe, Output data are included here.
PerGeos TCL script for processing all files in a folder with a recipe
Tcl
16 Oct 2019 - Process all files in a folder by applying the same recipe to all of them individually.
Tcl
14 Oct 2019 - This module allows storing and retrieving camera settings for viewer windows to be able to switch easily between different view presets.
Berea Pore Size Distribution Tutorial
Recipe (hxrecipe), Project (hx, pgo), Demo, Data
11 Oct 2019 - Create a semi-automatic workflow for Pore Size Distribution on Berea sandstone
Getting Started with Deep Learning Training
Deep learning model, Data
5 Sep 2019 - Model and companion project for the Deep Learning tutorial.
Deep learning model
21 Jun 2019 - U-Net model for denoising back-scattered SEM images.