This application is specifically designed to process data produced by Spectrium, a software product of PREVAC sp. z o.o.

MDC_cut.py provides a user-friendly GUI for handling datasets acquired with the PREVAC EA15 hemispherical electron energy analyzer.

See MDC_cut_UserManual.pdf for detailed instructions. The user manual is currently out of date. For the latest changes and new features, refer to the release notes.

• One Click to Go: Launch instantly — no setup, no friction.
• Intuitive Interface: Clean, user-friendly design with zero learning curve.
• Comprehensive Toolkit: All-in-one solution for ARPES data visualization and analysis.
  • Sample Offset Fitter: Precisely calibrate sample orientation.
  • k-plane Conversion: Transform raw data into momentum space seamlessly.
  • Volume Viewer: Explore and slice 3D k-space data with ease.
  • MDC Fitter: Analyze momentum distribution curves efficiently.
  • Spectrogram Tool: Generate and select spectrograms from your data.
  • Versatile Export Options: Export data in graph(.png, .jpg...), HDF5, CasaXPS (.vms), and OriginPro (.opj/.opju) formats.

• Installation
  • What will MDC_cut.py do?
• Basic Knowledge Overview
  • Geometry Definition
  • Coordinate Transformation: Real to Reciprocal Space
• Work Flow
  • E-Angle to E-k Conversion
• Requirements
• License
• Snapshots

• Please download MDC_cut.py and place it in the directory you want.
• Create a virtual environment (e.g., via ) with the required Python version to avoid changing your main environment, then run MDC_cut.py to automatically install the dependencies.
• If you don’t mind the environment, execute MDC_cut.py and check that it automatically installs the required dependencies.

1. Download the required files from this repository and place them into the working PATH of MDC_cut.py.
2. Try to use the pip installer to install the required python packages.
3. Start the GUI.

The PREVAC EA15 analyzer can operate in two different lens modes: Angular LensMode and Transmission LensMode.

• In Angular LensMode, the analyzer resolves the emission angles of the photoelectrons, allowing for the mapping of electronic band structures in momentum space (k-space).
• In Transmission LensMode, the analyzer focuses on the spatial distribution of photoelectrons, which is useful for imaging applications.

The choice of lens mode affects how the data is interpreted and analyzed, particularly in relation to the geometry of the sample manipulator.

The geometry of the data is defined by the angles or positions of the sample manipulator motors. The following table illustrates the relationship between motor positions and the corresponding energy and angles resolved by the EA15 analyzer.

E (Kinetic Energy)	$\theta$ (Angle resolved by EA15 lens)

$\phi$ (Rotation around Y-axis, R2 Motor)	$\psi$ (Tilt around Z-axis, R1 Motor)

The conversion from real space to reciprocal space (k-space) is essential for interpreting ARPES data. Let's see a basic case considering only $\theta$ and $\psi$ angles. The momentum components $k_x$ and $k_y$ can be calculated using the following equations:

Obviously, a larger kinetic energy E will lead to a larger momentum k. The angles $\theta$ and $\psi$ determine the direction of the momentum vector in k-space. By varying these angles during the measurement, one can map out the electronic band structure of the material under study.

The following table illustrates how changes in kinetic energy and angles affect the momentum components in k-space. The color change in viridis colormap indicates the variation in Kinetic Energy E for better visualization, and the two surfaces represent the $E-kx-ky$ map corresponding to $\psi=0°$ and $\psi=50°$, respectively.

Wave Vector in k-space	Corresponding $E-k_x-k_y$ map

Now you should have a basic understanding of how the geometry of the sample manipulator relates to the angles and positions used in ARPES measurements with the PREVAC EA15 analyzer. Let's consider if we want to obtain a $E-k_x-k_y$ map cut through a specific path in k-space, which is usually required for further analysis like MDC fitting.

The following table illustrates the $E-k_y$ cut at $k_x=1.65$ as an example. The blue line represents a hexagonal first Brillouin zone boundary as a reference. The red mesh grid indicates the desired $E-k_y$ cut. The white mesh grid indicates the $E-k_x-k_y$ map acquired at a fixed $\psi$ angle.

It's not difficult to see that we cannot obtain an orthogonal $E-k_x-k_y$ map with only one spectrum acquired at a fixed $\psi$ angle. To get an orthogonal k-space map that we can do further analysis on, we need to vary the $\psi$ angle (R1 motor) during the measurements or even change the $\phi$ angle (R2 motor) as well to cover the desired k-space region.

The application will parse the file name to extract the geometry information. R1 and R2 represent the motor of the sample manipulator, while the numbers denote the angles(in degrees) or position(in millimeters) of the respective motors.

Let's say we have some ARPES raw data files (.h5/.json format) from the PREVAC EA15 analyzer.

The typical workflow would be:

1. Launch MDC_cut.py.
2. Use the GUI to load your raw data files.
3. Open k-plane tool in Batch Master to convert raw data into k-space.

4. Set the calibration parameters(Offset angle) in the k-plane tool and export the data.

5. After exporting, you can visualize the data in the Volume Viewer tool. It allows you to slice through the 3D k-space data export the desired 2D cuts in HDF5 format.
6. Further analysis like MDC fitting can be performed in the MDC Fitter tool by loading the exported 2D cuts back into MDC_cut.py using the Load Raw Data button.

In addition to the steps mentioned above, MDC_cut.py provides a variety of tools for data visualization, exporting to CasaXPS(.vms), and generating OriginPro projects(.opj/.opju). Explore the GUI to discover more features!

You don't need to manually install the dependencies. MDC_cut.py will automatically install them via pip if they are not already present in your environment. The following are the tested Python versions and their corresponding package versions:

The only difference between Python 3.12.x and 3.13.x environments is the numpy and opencv-python versions due to pip version compatibility. You can find the full list of required packages in the beginning section of MDC_cut.py.(REQUIREMENTS) The highest tested Python version is 3.13.5.

This project is licensed under the MIT License - see the LICENSE file for details. Note that those third-party libraries used in this project may have their own licenses.