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# ASpecD documentation¶

Welcome! This is the documentation for ASpecD – a framework for handling spectroscopic data focussing on reproducibility.

In short: Each and every processing step applied to your data will be recorded and can be traced back. Additionally, for each representation of your data (e.g., figures, tables) you can easily follow how the data shown have been processed and where they originate from.

What is even better: Actual data processing and analysis no longer requires programming skills, but is as simple as writing a text file summarising all the steps you want to have been performed on your dataset(s) in an organised way. Curious? Have a look at recipe-driven data analysis – or at the following example:

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 datasets: - /path/to/first/dataset - /path/to/second/dataset tasks: - kind: processing type: BaselineCorrection properties: parameters: kind: polynomial order: 0 - kind: singleplot type: SinglePlotter1D properties: filename: - first-dataset.pdf - second-dataset.pdf 

For more general information on the ASpecD framework see its Homepage, and for how to use it, carry on reading. Interested in more real-live examples? Check out the use cases section.

## Features¶

A list of features:

• Framework for writing applications handling spectroscopic data

• Consistent handling of numeric data and corresponding metadata

• History of each processing step, automatically generated, aiming at full reproducibility

• Undo and redo of processing steps

• Import and export of data

• Generic plotting capabilities, easily extendable

• Report generation using pre-defined templates

• Recipe-driven data analysis, allowing tasks to be performed fully unattended in the background and without programming skills

And to make it even more convenient for users and future-proof:

• Open source project written in Python (>= 3.7)

• Developed fully test-driven

• Extensive user and API documentation

Warning

The ASpecD framework is currently under active development and still considered in Beta development state. Therefore, expect changes in features and public APIs that may break your own code. Nevertheless, feedback as well as feature requests are highly welcome.

## How to cite¶

ASpecD is free software. However, if you use ASpecD for your own research, please cite it appropriately:

Till Biskup. ASpecD (2021). doi:10.5281/zenodo.4717937

To make things easier, ASpecD has a DOI provided by Zenodo, and you may click on the badge below to directly access the record associated with it. Note that this DOI refers to the package as such and always forwards to the most current version.

## Where to start¶

Users new to ASpecD should probably start at the beginning, those familiar with its underlying concepts may jump straight to the section explaining how to write applications based on the ASpecD framework.

If you are interested in how working with the ASpecD framework looks like, particularly recipe-driven data analysis, have a look at the use cases section.

The API documentation is the definite source of information for developers, besides having a look at the source code.

## Installation¶

To install the ASpecD framework on your computer (sensibly within a Python virtual environment), open a terminal (activate your virtual environment), and type in the following:

pip install aspecd


Have a look at the more detailed installation instructions as well.