Package: cellsium
CellSium -CellSimulator formicrofluidicmicrocoloniesCellSium example simulation resultFront MatterCellSium is a cell simulator developed for the primary application of generating realistically looking images of bacterial microcolonies, which may serve as ground truth for machine learning training processes.PublicationIf you use CellSium within scientific research, we ask you to cite our publication:Sachs CC, Ruzaeva K, Seiffarth J, Wiechert W, Berkels B, Nöh K (2022)
CellSium: versatile cell simulator for microcolony ground truth generation
Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac053, doi: 10.1093/bioadv/vbac053It is available on theBioinformatics Advanceshomepage atDOI: 10.1093/bioadv/vbac053 <https://doi.org/10.1093/bioadv/vbac053>.DocumentationThe documentation to CellSium can be built usingSphinx, or be found readily built atRead the Docs.LicenseCellSium is available under the BSD license (see LICENSE.rst / license section).InstallationInstallation using pipCellSium can be installed via pip, ideally create and activate an environment beforehand to install CellSium in.>python-mpipinstallcellsiumInstallation using condaCellSium is available in the modsim Anaconda channel as well, using packages from the conda-forge channel. It can be
installed with the following commands:>condainstall-cmodsim-cconda-forge-ycellsiumUsageOnce installed, run CellSium viapython -m cellsium, specifying the desired entrypoint and options, such as outputs.
CellSium is built modular, various output modules can be activated simultaneously. To get an overview of the available options,
use the--helpswitch. Furthermore, the main mode of setting tunable parameters are so calledtunables, which can
be set from the command line using the-tswitches. A list of tunables can be shown using the--tunables-showargument.>python-mcellsium--helpusage:__main__.py[-v][-q][-cCELL][-p][-w][-oOUTPUT][-h][-mMODULE][--Output{COCOOutput,CsvOutput,FluorescenceRenderer,GenericMaskOutput,JsonPickleSerializer,MeshOutput,NoisyUnevenIlluminationPhaseContrast,PhaseContrastRenderer,PlainRenderer,PlotRenderer,QuickAndDirtyTableDumper,SvgRenderer,TiffOutput,TrackMateXML,UnevenIlluminationPhaseContrast,YOLOOutput}][--PlacementSimulation{Box2D,Chipmunk,NoPlacement}][-tTUNABLE][--tunables-show][--tunables-loadTUNABLES_LOAD][--tunables-saveTUNABLES_SAVE]optionalarguments:-h,--helpshowthishelpmessageandexit-oOUTPUT,--output-fileOUTPUT-w,--overwrite-p,--prefix-cCELL,--cellCELL-q,--quiet-v,--verbose-mMODULE,--moduleMODULE--Output{COCOOutput,CsvOutput,FluorescenceRenderer,GenericMaskOutput,JsonPickleSerializer,MeshOutput,NoisyUnevenIlluminationPhaseContrast,PhaseContrastRenderer,PlainRenderer,PlotRenderer,QuickAndDirtyTableDumper,SvgRenderer,TiffOutput,TrackMateXML,UnevenIlluminationPhaseContrast,YOLOOutput}--PlacementSimulation{Box2D,Chipmunk,NoPlacement}-tTUNABLE,--tunableTUNABLE--tunables-show--tunables-loadTUNABLES_LOAD--tunables-saveTUNABLES_SAVEYou can for example run a default simulation by just starting CellSium, the results will be shown interactively using matplotlib:>python-mcellsiumFor more in-depth usage examples, please see the examples section of the documentation.DockerAn alternative to installing CellSium locally is running it via Docker. To run CellSium without interactive (GUI) elements, the following Docker command can be used, with parameters to CellSium being appended.>dockerrun--tty--interactive--rm--volume`pwd`:/data--user`id-u`ghcr.io/modsim/cellsiumTo use interactive (GUI) elements such as thePlotRenderer, an X server must be reachable; under Linux the following command can be used:>dockerrun--tty--interactive--rm--volume`pwd`:/data--user`id-u`--envDISPLAY=$DISPLAY--volume/tmp/.X11-unix:/tmp/.X11-unixghcr.io/modsim/cellsium