Package: data-slicer
Data slicerThedata-slicerpackage offers fast tools for inspection, visualization,
slicing and analysis of 3(+) dimensional datasets at a general level.
It also provides a framework and building blocks for users to easily create
more specialized and customized tools for their individual use cases.data-slicerwas originally developed to deal with the high data throughput of
modern measurement instruments, where quick visualizations and preliminary
analyses are necessary to guide the direction of a measurement session.
However, the package is designed to be agnostic of the concrete use-case and
all scientific, engineering, medical, artistic or other data driven
disciplines where inspection and slicing of (hyper)cubes is required could
potentially benefit fromdata-slicer.DocumentationThis README just gives a minimal overview.
For more information, guides, examples and more, visit the documentation which is hosted
by the friendly people over at ReadTheDocs.org:https://data-slicer.readthedocs.io/en/latest/Installationdata-slicershould run on all platforms that support python and has been
shown to run on Windows, macOS ans Linux.The package can be installed fromPyPIusingpip install data_slicer.
It is recommended to do this from within some sort of virtual environment.
Visit the documentation for more detailed instructions:https://data-slicer.readthedocs.io/en/latest/installation.htmlDependenciesThis software is built upon on a number of other open-source frameworks.
The complete list of packages can be found in the filerequirements.txt.
Most notably, this includesmatplotlib,numpyandpyqtgraph.CitingIf you usedata-slicerin your work, please credit it by citing the following publication:Kramer et al., (2021). Visualization of Multi-Dimensional Data -- The data-slicer Package. Journal of Open Source Software, 6(60), 2969,https://doi.org/10.21105/joss.02969ContributingYou are welcome to help making this software package more useful!
You can do this by giving feedback, reporting bugs, issuing feature requests
or fixing bugs and adding new features yourself.
Furthermore, you can create and share your own plugins (refer to thedocumentation).Feedback, bugs and feature requestsThe most straightforward and organized way to help improve this software is
by opening anissueonthe github
repository.
To do this, navigate to theIssuestab and clickNew issue.
Please try to describe the bug you encountered or the new feature you would
like to see as detailed as possible.
If you have several bugs/ideas please open a separate issue for each of them
in order to keep the discussions focused.If you have anything to tell me that does not seem to warrant opening an
issue or you simply prefer to contact me directly you can do this via e-mail:kevin.pasqual.kramer@protonmail.chContributing codeIf you have fixed a bug or created a new feature in the source code yourself,
it can be merged into this project.
Code contributions will be acknowledged in this README or, if the number of
contributors grows too large, in a separate file.
If you are familiar with the workflow on github, please go ahead and create a
pull request.
If you are unsure you can always contact me via e-mail (see above).PluginsIf you have created a PIT plugin, feel free to add it to the list below,
either via pull request or through an e-mail (see above).
Also check thedocumentationfor a guide on how to create a plugin.Plugin Name (link)Descriptionds-example-pluginexists as a minimal example that can be used for guidance when creating your own plugins. A step-by-step tutorial on how it was made can be found inthe documentationds-arpes-plugintools for angle-resolved photoelectron spectroscopy (ARPES)