Static plotting for spatial data.
Project description
spatialdata-plot: rich static plotting from SpatialData objects
The spatialdata-plot
package extends spatialdata
with a declarative plotting API that enables to quickly visualize spatialdata
objects and their respective elements (i.e. images
, labels
, points
and shapes
).
SpatialData’s plotting capabilities allow to quickly visualise all contained modalities. The user can specify which elements should be rendered (images, labels, points, shapes) and specify certain parameters for each layer, such as for example the intent to color shapes by a gene’s expression profile or which color to use for which image channel. When the plot is then eventually displayed, all transformations, alignments and coordinate systems are internally processed to form the final visualisation. In concordance with the general SpatialData philosophy, all modalities of the major spatial technologies are supported out of the box.
Getting started
For more information on the spatialdata-plot
library, please refer to the documentation. In particular, the
- API documentation.
- Example notebooks (section "Visiualizations")
Installation
You need to have Python 3.8 or newer installed on your system. If you don't have Python installed, we recommend installing Mambaforge.
There are several alternative options to install spatialdata-plot:
- Install the latest development version:
pip install git+https://github.com/scverse/spatialdata-plot.git@main
Contact
For questions and help requests, you can reach out in the scverse discourse. If you found a bug, please use the issue tracker.
Citation
If you use spatialdata
in your work, please cite the spatialdata
publication as follows:
SpatialData: an open and universal data framework for spatial omics
Luca Marconato, Giovanni Palla, Kevin A. Yamauchi, Isaac Virshup, Elyas Heidari, Tim Treis, Marcella Toth, Rahul Shrestha, Harald Vöhringer, Wolfgang Huber, Moritz Gerstung, Josh Moore, Fabian J. Theis, Oliver Stegle
bioRxiv 2023.05.05.539647; doi: https://doi.org/10.1101/2023.05.05.539647
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