The NMRAspecds package provides tools for handling experimental data obtained using nuclear magnetic resonance (NMR) spectroscopy and is derived from the ASpecD framework, hence all data generated with the nmraspecds package are completely reproducible and have a complete history.
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 the following example:
format:
type: ASpecD recipe
version: '0.2'
settings:
default_package: nmraspecds
datasets:
- /path/to/first/dataset
- /path/to/second/dataset
tasks:
- kind: processing
type: Normalisation
parameters:
properties:
kind: scan_number
- kind: singleplot
type: SinglePlotter1D
properties:
filename:
- first_dataset.pdf
- second_dataset.pdf
Interested in more real-live examples? Check out the growing list of examples providing complete recipes for different needs.
Features
A list of features:
Fully reproducible processing and analysis of NMR data.
Gap-less record of each processing/analysis step, including explicit and implicit parameters.
Import of Bruker NMR data
Generic representation of NMR data, independent of the original format.
Datasets contain both, numerical data and all crucial metadata, a prerequisite for FAIR 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 (>= 3.7)
Developed mostly test-driven
Extensive user and API documentation
Target audience
The NMRAspecds package addresses scientists working with nuclear magnetic resonance (NMR) data on a daily base and concerned with reproducibility. Due to being based on the ASpecD framework, the NMRAspecds package ensures reproducibility and—as much as possible—replicability of data processing, starting from recording data and ending with their final (graphical) representation, e.g., in a peer-reviewed publication. This is achieved by automatically creating a gap-less record of each operation performed on your data. If you do care about reproducibility and are looking for a system that helps you to achieve this goal, the NMRAspecds package may well be interesting for you.
How to cite
NMRAspecds is free software. However, if you use NMRAspecds for your own research, please cite the software:
Mirjam Schröder. NMRAspecds (2024). doi:10.5281/zenodo.13293054
To make things easier, NMRAspecds 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.
Installation
To install the NMRAspecds package on your computer (sensibly within a Python virtual environment), open a terminal (activate your virtual environment), and type in the following:
pip install nmraspecds
License
This program is free software: you can redistribute it and/or modify it under the terms of the BSD License. However, if you use NMRAspecds for your own research, please cite it appropriately.
1 maintainer
Metadata
1 maintainer
Release files for nmraspecds 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nmraspecds-0.2.0.tar.gz | 29.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nmraspecds-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 51.7 kB
Release files / nmraspecds-0.2.0.tar.gz
| Download URL | nmraspecds-0.2.0.tar.gz |
|---|---|
| Size | 29.5 kB |
| Tags | Source |
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Release files / nmraspecds-0.2.0-py3-none-any.whl
| Download URL | nmraspecds-0.2.0-py3-none-any.whl |
|---|---|
| Size | 22.3 kB |
| Tags | Python 3 |
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No |
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twine/5.1.1 CPython/3.10.12
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