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Modular data analysis code for angle resolved photoemission spectroscopy (ARPES)

Project description

Documentation
Documentation
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PyARPES

docs/static/video/intro-video.gif

PyARPES simplifies the analysis and collection of angle-resolved photoemission spectroscopy (ARPES) and emphasizes

  • modern, best practices for data science
  • support for a standard library of ARPES analysis tools mirroring those available in Igor Pro
  • interactive and extensible analysis tools

It supports a variety of data formats from synchrotron and laser-ARPES sources including ARPES at the Advanced Light Source (ALS), the data produced by Scienta Omicron GmbH’s “SES Wrapper”, data and experiment files from Igor Pro, NeXuS files, and others.

To learn more about installing and using PyARPES in your analysis or data collection application, visit the documentation site.

PyARPES is currently developed by Conrad Stansbury of the Lanzara Group at the University of California, Berkeley.

Installation

PyARPES can be installed from source, or using either pip or conda into a Python 3.6 or 3.7 environment. conda is preferred as a package manager in order to facilitate installing the libraries for reading HDF and NetCDF files.

Pip installation

pip install arpes

Conda installation

PyARPES is distributed through the arpes Anaconda channel, but includes dependencies through conda-forge. Please make sure not to put conda-forge above the main channel priority, as this can cause issues with installing BLAS. A minimal install looks like

conda config --apend channels conda-forge
conda install -c arpes -c conda-forge arpes

Local installation from source

If you want to modify the source for PyARPES as you use it, you might prefer a local installation from source. Details can be found on the documentation site.

Suggested steps

  1. Clone or duplicate the folder structure in the repository arpes-analysis-scaffold, skipping the example folder and data if you like
  2. Install and configure standard tools like Jupyter or Jupyter Lab. Notes on installing and configuring Jupyter based installations can be found in jupyter.md
  3. Explore the documentation and example notebooks at the documentation site.

Contact

Questions, difficulties, and suggestions can be directed to Conrad Stansbury (chstan@berkeley.edu) or added to the repository as an issue. In the case of trouble, also check the FAQ.

Copyright © 2018-2019 by Conrad Stansbury, all rights reserved.

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