# User Documentation¶

MCALF is an open-source Python package for accurately constraining velocity information from spectral imaging observations using machine learning techniques.

This software package is intended to be used by solar physicists trying to extract line-of-sight (LOS) Doppler velocity information from spectral imaging observations (Stokes I measurements) of the Sun. A ‘toolkit’ is provided that can be used to define a spectral model optimised for a particular dataset.

This package is particularly suited for extracting velocity information from spectral imaging observations where the individual spectra can contain multiple spectral components. Such multiple components are typically present when active solar phenomenon occur within an isolated region of the solar disk. Spectra within such a region will often have a large emission component superimposed on top of the underlying absorption spectral profile from the quiescent solar atmosphere.

A sample model is provided for an IBIS Ca II 8542 Å spectral imaging sunspot dataset. This dataset typically contains spectra with multiple atmospheric components and this package supports the isolation of the individual components such that velocity information can be constrained for each component. Using this sample model, as well as the separate base (template) model it is built upon, a custom model can easily be built for a specific dataset.

The custom model can be designed to take into account the spectral shape of each particular spectrum in the dataset. By training a neural network classifier using a sample of spectra from the dataset labelled with their spectral shapes, the spectral shape of any spectrum in the dataset can be found. The fitting algorithm can then be adjusted for each spectrum based on the particular spectral shape the neural network assigned it.

This package is designed to run in parallel over large data cubes, as well as in serial. As each spectrum is processed in isolation, this package scales very well across many processor cores. Numerous functions are provided to plot the results in a clearly. The MCALF API also contains many useful functions which have the potential of being integrated into other Python packages.

## Installation¶

For easier package management we recommend using Miniconda (or Anaconda) and creating a new conda environment to install MCALF inside. To install MCALF using Miniconda, run the following commands in your system’s command prompt, or if you are using Windows, in the ‘Anaconda Prompt’:

$conda config --add channels conda-forge$ conda config --set channel_priority strict
$conda install mcalf  MCALF is updated to the latest version by running: $ conda update mcalf


Alternatively, you can install MCALF using pip:



### Running Tests¶

Tests should be run within the virtual environment where MCALF and its testing dependencies were installed. Run the following command to test your installation,

$pytest --pyargs mcalf  ### Editing the Code¶ If you are planning on making changes to your local version of the code, it is recommended to run the test suite to help ensure that the changes do not introduce problems elsewhere. Before making changes, you’ll need to set up a development environment. The SunPy Community have compiled an excellent set of instructions and is available in their documentation. You can mostly replace sunpy with mcalf, and install with $ pip install -e .[tests,docs]


After making changes to the MCALF source, run the MCALF test suite with the following command (while in the same directory as setup.py),

\$ pytest --pyargs mcalf --cov


The tox package has also been configured to run the MCALF test suite.

## Getting Started¶

The following examples provide the key details on how to use this package. For more details on how to use the particular classes and function, please consult the Code Reference. We plan to expand this section with more examples of this package being used.

## Contributing¶

Code of Conduct

If you find this package useful and have time to make it even better, you are very welcome to contribute to this package, regardless of how much prior experience you have. Types of ways you can contribute include, expanding the documentation with more use cases and examples, reporting bugs through the GitHub issue tracker, reviewing pull requests and the existing code, fixing bugs and implementing new features in the code.

You are encouraged to submit any bug reports and pull requests directly to the GitHub repository. If you have any questions regarding contributing to this package please contact Conor MacBride.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

## Citation¶

If you have used this package in work that leads to a publication, we would be very grateful if you could acknowledge your use of this package in the main text of the publication. Please cite,

MacBride CD, Jess DB, Grant SDT, Khomenko E, Keys PH, Stangalini M. 2020 Accurately constraining velocity information from spectral imaging observations using machine learning techniques. Philosophical Transactions of the Royal Society A. 379, 2190. (doi:10.1098/rsta.2020.0171)

Please also cite the Zenodo DOI for the package version you used. Please also consider integrating your code and examples into the package.