ASpecD documentation

Welcome! This is the documentation for ASpecD. For general information see its Homepage.

ASpecD is 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.

Features

A list of features, not all implemented yet but aimed at for the first public release (ASpecD 0.1):

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

• Open source project written in Python (>= 3.5)

• Developed fully test-driven

• Extensive user and API documentation

Warning

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

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.

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

Contents:

• Target audience
  • Spectroscopists aiming at reproducibility
  • Motivation and general ideas
• Introduction
  • Reproducible data analysis
  • Personal freedom vs. reproducible science
  • Scientists are not software engineers
  • Long-term storage and availability
  • The goal: concepts for reproducibility
• Concepts
  • Dataset
  • Metadata
  • History
  • Representations
  • Annotations
  • Reports
  • Tasks
• Recipe-driven data analysis
  • Reproducible and automated
  • Fully unattended
  • History
  • Human-writable
• Writing applications
  • Before you start
  • How to start
  • Datasets
  • Importer
  • Metadata
  • Processing steps
  • What’s next?
• Use cases
  • General usage
  • Conventions used
  • Create a dataset and import data
• API documentation
  • Submodules
  • Module contents

Indices and tables

• Index

• Module Index

• Search Page

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.