(c) 2016–2018 Brendon J. Brewer and Michael Rowe

AMORPH is free software, released under the GNU General Public License, version 3. See the LICENSE file for details.

• The executables are located in src/dist

The associated python package is for the postprocessing of AMORPH results.

To install, run python3 -m pip install pyamorph-ccoverstreet or python -m pip install pyamorph-ccoverstreet

To use:

import pyamorph

pyamorph.amorph_postprocess()

or

In your terminal, run python3 -m pyamorph to perform analysis on the files in your current directory

IMPORTANT: New Build Process

• git clone https://github.com/ccoverstreet/AMORPH --recursive
• cd AMORPH/src
• make. This may take a minute or two.
• ./AMORPH (make sure that all necessary config/data files are in the same directory)

AMORPH is hosted at the following URL:

https://bitbucket.org/eggplantbren/amorph

The repository includess the source code, Python scripts for viewing results, example datasets, and the Windows executable file (AMORPH.exe), which is the easiest way of using AMORPH. It is located (along with almost everything else) in the src subdirectory. The Python scripts make use of the packages Numpy, matplotlib, Pandas, and yaml, and has only been tested under Python 3. https://www.anaconda.com/download/ is a convenient distribution of Python which comes with these packages pre-installed.

The AMORPH program can be installed anywhere on the computer. All data files to be analysed by AMORPH need to be in .txt file format, space delimited, with no headers (i.e., just two columns of numbers). For simplicity, text files for processing should be located in the same folder as the AMORPH program. To configure a run, edit the file config.yaml to specify all the details of the data file and other things you might want to tweak.

Four test datasets are stored in the repository (0%glass_rhyolite_Emperyon.txt, 50% glass.02step3secdwell.txt, 90%_glass_basalt_Emperyon.txt, and easy_data.txt). The program is set to run until 10,000 saved parameter sets have been generated. Outputs may be viewed at any point, however, closing the program before reaching 10,000 will reduce the accuracy of the final calculations. After running for a while, the output can be viewed by running the Python script showresults.py:

python showresults.py

Automatically generated figures may be closed or saved. Upon closing the figures, numerical results will be displayed in Anaconda prompt window, and a CSV file of (some) output will be written to the disk. Important: make sure to save the results or copy the output file before starting another run as results will otherwise be overwritten.

Based on experimentation, several recommendations for optimization of use and accuracy may be suggested to potential users. First, the time of the analysis is dependent on the number of data points to be analyzed. Thus to optimize analysis time, we recommend an XRD instrument step of 0.02 degrees/step. While coarser steps will reduce analysis time, the peak broadening associated with it may reduce precision of the fitting. We also recommend reducing the total scan range to between 10–40 degrees (2theta). This has two advantages;

1. it will reduce the number of data points for processing and 2) outside this range the X-ray diffraction pattern is dominated by only the crystalline componentry and thus incorporation of higher 2-theta values skews measured results to higher measured crystallinities.

Background positions are currently optimized for the use of CuKa x-ray sources, with linear fits between 10–40° 2theta as described in the manuscript. For other X-ray sources, it may be necessary to adjust these fixed points to provide the best fit to the diffraction patterns. This may be modified by changing the values in control_points.in (which can be opened in any text editor). For some analyses, typically when more data points are analysed (5–50 degrees 2theta) or for highly crystalline materials, the numerical settings of the DNest4 sampler are not optimal for obtaining useful results, and need to be made more conservative (making the run slower). This is typically manifested in Python outputs as pale colours in the model curves in the output plots (showing hardly any samples from the posterior) and a low number of data points for subsequent histograms. The settings can be modified in these instances by opening the OPTIONS file in any text editor. Parameter values on line 4 (new level interval) and line 8 (Backtracking scale length) can each independently be doubled. Note, while this will improve model outputs, it significantly lengthens the time of analysis. Therefore we only recommend changing one of these two parameters at a time.

If you don't want to use AMORPH.exe and would rather compile AMORPH yourself, you'll need the dependencies yaml-cpp, boost, and DNest4.