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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:
1format: 2 type: ASpecD recipe 3 version: '0.2' 4 5datasets: 6 - /path/to/first/dataset 7 - /path/to/second/dataset 8 9tasks: 10 - kind: processing 11 type: BaselineCorrection 12 properties: 13 parameters: 14 kind: polynomial 15 order: 0 16 - kind: singleplot 17 type: SinglePlotter1D 18 properties: 19 filename: 20 - first-dataset.pdf 21 - 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.
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
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.
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.
This program is free software: you can redistribute it and/or modify it under the terms of the BSD License. However, if you use ASpecD for your own research, please cite it appropriately. See How to cite for details.
A note on the logo¶
The snake (obviously a python, look at how it’s holding the magnifying glass) is well familiar with the scientific method and illustrates the basic idea of the ASpecD framework: reproducible data analysis. The copyright of the logo belongs to J. Popp.