specsanalyzer 0.4.0

Python package for loading and converting SPECS Phoibos analyzer data.

specsanalyzer

This is the package specsanalyzer for conversion and handling of SPECS Phoibos analyzer data.

This package contains two modules: specsanalyzer is a package to import and convert MCP analyzer images from SPECS Phoibos analyzers into energy and emission angle/physical coordinates. specsscan is a Python package for loading Specs Phoibos scans acquired with the labview software developed at FHI/EPFL

Tutorials for usage and the API documentation can be found in the Documentation

Installation

Pip (for users)

• Create a new virtual environment using either venv, pyenv, conda, etc. See below for an example.

python -m venv .specs-venv

• Activate your environment:

source .specs-venv/bin/activate

• Install specsanalyzer from PyPI:

pip install specsanalyzer

• This should install all the requirements to run specsanalyzer and specsscanin your environment.

• If you intend to work with Jupyter notebooks, it is helpful to install a Jupyter kernel for your environment. This can be done, once your environment is activated, by typing:

python -m ipykernel install --user --name=specs_kernel

Configuration and calib2d file

The conversion procedures require to set up several configuration parameters in a config file. An example config file is provided as part of the package (see documentation). Configuration files can either be passed to the class constructors, or are read from system-wide or user-defined locations (see documentation).

Most importantly, conversion of analyzer data to energy/angular coordinates requires detector calibration data provided by the manufacturer. The corresponding *.calib2d file (e.g. phoibos150.calib2d) are provided together with the spectrometer software, and need to be set in the config file.

For Contributors

To contribute to the development of specsanalyzer, you can follow these steps:

1. Clone the repository:

git clone https://github.com/OpenCOMPES/specsanalyzer.git
cd specsanalyzer

2. Check out test data (optional, requires access rights):

git submodule sync --recursive
git submodule update --init --recursive

2. Install the repository in editable mode:

pip install -e .

Now you have the development version of specsanalyzer installed in your local environment. Feel free to make changes and submit pull requests.

Poetry (for maintainers)

• Prerequisites:

  • Poetry: https://python-poetry.org/docs/

• Create a virtual environment by typing:

poetry shell

• A new shell will be spawned with the new environment activated.

• Install the dependencies from the pyproject.toml by typing:

poetry install --with dev, docs

• If you wish to use the virtual environment created by Poetry to work in a Jupyter notebook, you first need to install the optional notebook dependencies and then create a Jupyter kernel for that.

  • Install the optional dependencies ipykernel and jupyter:

  poetry install -E notebook
  

  • Make sure to run the command below within your virtual environment (poetry run ensures this) by typing:

  poetry run ipython kernel install --user --name=specs_poetry
  

  • The new kernel will now be available in your Jupyter kernels list.