README.md

McSAS

Welcome to McSAS: a tool for analysis of SAS patterns. This tool can extract form-free size distributions from small-angle scattering data using the Monte-Carlo method described in:

Brian R. Pauw, Jan Skov Pedersen, Samuel Tardif, Masaki Takata, and Bo B. Iversen. “Improvements and Considerations for Size Distribution Retrieval from Small-angle Scattering Data by Monte Carlo Methods.” Journal of Applied Crystallography 46, no. 2 (February 14, 2013 ). DOI:10.1107/S0021889813001295.

The GUI and latest improvements are described in: I. Breßler, B. R. Pauw, A. F. Thünemann, "McSAS: A package for extracting quantitative form-free distributions". Journal of Applied Crystallography 48: 962-969, DOI: 10.1107/S1600576715007347

Features

Several form factors have been included in the package, including:

• Spheres

• Cylinders (spherically isotropic)

• Ellipsoids (spherically isotropic)

• Core-shell spheres and ellipsoids

• Gaussian chain

• Kholodenko worm

• Densely packed spheres (LMA-PY structure factor).

Standalone packages

Standalone packages are available in the Releases section of this page in the right pane. These are available for Mac OS X (tested on 10.6, 10.8 and 10.10), Windows and Linux. and should not require any additional software to be installed on the host computer. A quick start guide and example data is included in the "doc"-directory that comes with the distribution.

Run from source

Requirements

To run McSAS from the source code repository using an existing Python environment, there is a requirements.txt provided which contains the packages to be installed beforehand.

Get a copy of the source code

Use the green "Code" button in the top left area of this page to download a copy of the latest source code tree. Following this, McSAS can be started from a terminal window, as shown below:

1. Open a terminal window. Typically, it is opened in the current users home directory. You can change the current directory to another/path (which should exist) by entering

   cd another/path
   

2. Download a copy of the McSAS source code into the new directory McSAS using GIT:
   (on Windows, download & install GIT from here)

   git clone https://github.com/BAMresearch/McSAS.git
   

Linux/Ubuntu

1. Make sure, Python 3.11, GIT and Qt5 is installed:

   sudo apt install python3.11 python3.11-venv git libqt5widgets5
   

2. Create a python virtual environment (venv) based on Python 3.11 for McSAS, in your home dir, for example:

   python3.11 -m venv --system-site-packages --symlinks ~/.py11env
   

3. Activate the new venv:

   source ~/.py11env/bin/activate
   

4. Clone the McSAS source tree to your local home directory:

   git clone https://github.com/BAMresearch/McSAS.git ~/mcsas
   

5. Install additional Python packages needed by McSAS:

   cd ~/mcsas
   pip install -r requirements.txt
   

6. Run McSAS from its src folder:

   cd ~/mcsas/src
   python -m mcsas
   

Windows

1. Install the latest offline installer of the Qt5 series with defaults from here: https://www.qt.io/offline-installers That would be Qt 5.12.12 for Windows.

2. Install Python 3.11 (the latest supported for PySide2) via Miniconda from here, this is the installer package.

3. After installing Miniconda, run Anaconda Prompt from the Start Menu. Enter the McSAS project dir. Let's assume here, it's downloaded and extracted to C:\McSAS. Install the required packages with conda (make sure Qt was installed already for PySide2 to find the needed DLLs):

   cd /d C:\McSAS
   conda install -c conda-forge --file requirements.txt
   

4. After successful installation of the packages, from the same Anaconda Prompt, run McSAS from source dir with:

   cd /d C:\McSAS\src
   python -m mcsas
   

Screenshots: