Saravanan666/pypi-org huggingface dataset:
Package: napari-bigfish
napari-bigfishA napari-plugin providing an alternative GUI forBig-FISH. Big-FISH is a python package for the analysis of smFISH images.The plugin provides a graphical user interface for some of the functionality in Big-FISH. Currently implemented are:Gaussian-background subtractionFISH-spot detection withElimination of duplicatesAuto-detection of thresholdDense-region decompositionThe plugin further implements by itself:Counting of spots per cell, inside and outside of the nucleusBatch processing on a list of imagesYou can find the user and the api-documentation of napari-bigfishhere.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-bigfishviapip:pip install napari-bigfishContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theMITlicense,
"napari-bigfish" is free and open source softwareIssuesIf you encounter any problems, please [file an issue] along with a detailed description.
Package: napari-bigwarp
napari-bigwarpBigWarp-like interface for napariThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-bigwarpviapip:pip install napari-bigwarpTo install latest development version :pip install git+https://github.com/bkntr/napari-bigwarp.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-bigwarp" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-bil-data-viewer
DescriptionView datasets archived at theBrain Image Library.**NOTE: This plugin is under early development.  Currently, only a subset of single-channel, fMOST datasets which include projections are available to view.  An example can be foundhere.FeaturesMultiscale RenderingIn datasets that include multiple resolution representations of the data, each resolution can be combined to improve the speed of browsing and user experience.  An example of a dataset with multiple resolution projections can be foundhere.All datasets included in the current release of napari-bil-data-viewer use multi-resolution datasets.3D rendering of whole datasets.  The lowest resolution is used for rendering.  Currently, this is a limitation imposed by napari.The plugin does NOT require a BIL account as datasets are already accessible via https.Known Issues / limitationsCurrently the only datasets that are available are those which have been manually selected by the developers.  If you would like a specific dataset to be included please consider adding the dataset(s) to thedataset_info.pyfile and submitting a pull request.To inquire about this plugin please contact Brain Image Library support:bil-support@psc.eduThe plugin is still under development.  We appreciate allreports of issues / errorswhich occur during use.Thisnapariplugin was generated withCookiecutterusing with@napari'scookiecutter-napari-plugintemplate.InstallationOption #1: Install plugin via the napari plugin menuMenu: Plugins >> Install/Uninstall PluginsSearch: napari-bil-data-viewerSelect installOption #2:  Install a fresh python virtual environment# Example of venv creation using condacondacreate-y-nbil-viewerpython=3.8
condaactivatebil-viewer# Install napari-bil-data-viewerpipinstallnapari-bil-data-viewer# Run NaparinapariContributingPlease consider contributing to this project!  Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-bil-data-viewer" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.Change Log:v0.1.0:Initial release.v0.1.1 & v0.1.2:Changes to documentationv0.1.3:Added all available summary fMOST datasetsv0.2.0:Added support for SWC neuron tracingsv0.3.0:Added support for multiscale OME zarr datav0.4.0:Add scale controls for layersv0.4.2:Add URL input to visualize image stacks (tif, tiff, jp2)v0.5.0:Split the plugin into 5 widgets:Load Curated DatasetsLoad Image Stack From URLLoad Multiscale Data From URLLoad Neuron Morphology From URLLayer Scale Controlsv0.5.1:Add metadata link to curated datasetsv0.6.0:Add widget to visualize histology RGB tiffs
Package: napari-bioformats
napari-bioformatsBioformats plugin for napari usingpims-bioformatsUse this plugin as a fallback!Anyone coming to napari from the Fiji/ImageJ world will likely be aware of theincredibleBio-Formatslibrary.  A heroic effort, built over years, to readmore than a 100 file formats.  Naturally, we want some of that goodness fornapari... hence this plugin.However:it's important to note that this pluginstillrequires having a java runtime engine installed.  This is easy enough to do
(the plugin will ask to install it for you if you're in acondaenvironment), but
it definitely makes for a more complicated environment setup, it's not very
"pythonic", and the performance will likely not feel as snappy as a native "pure"
python module.So, before you reflexively install this plugin to fill that bio-formats
sized hole in your python heart, consider trying some of the other pure-python
plugins designed to read your format of interest:Zeiss (.czi):napari-aicsimageio,napari-czifile2Nikon (.nd2):napari-nikon-nd2,nd2-daskLeica (.lif):napari-aicsimageioOlympus (.oif): no plugin?  (but seeoiffile)DeltaVision (.dv, .mrc):napari-dvif you have a pure-python reader for a bio-formats-supported file format that
you'd like to see added to this list, please open an issueInstallationThe easiest way to installnapari-bioformatsis viaconda, from theconda-forgechannel:conda install -c conda-forge napari-bioformatsIt is also possible to install viapip, but you will need to have a working
JVM installed, and may need to set theJAVA_HOMEenvironment variablepip install napari-bioformatsFirst UsageThe first time you attempt to open a file with napari-bioformats, you will
likely notice a long delay as pims downloads theloci_tools.jar(speed will
depend on your internet connection). Subsequent files should open more quickly.LicenseDistributed under the terms of theGPLv3license,
"napari-bioformats" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.Thisnapariplugin was generated withCookiecutterusing with@napari'scookiecutter-napari-plugintemplate.
Package: napari-biomag-annotator
napari-biomag-annotatorAn annotator tool collection by the BIOMAG group.This plugin allows object annotation on 2/3D images using 4 assisted annotation methods arising from two napari plugins:napari-annotatorjnapari-nD-annotatorThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-biomag-annotatorviapip:pip install napari[all]
pip install napari-biomag-annotatorTo install latest development version :pip install git+https://github.com/biomag-lab/napari-biomag-annotator.gitOn Linux distributions, the following error may arise upon napari startup after the installation of the plugin:Could not load the Qt platform plugin “xcb” in “” even though it was found. In this case, the manual install oflibxcb-xinerama0for Qt is required:sudo apt install libxcb-xinerama0Bundled napari appThe bundled application version ofnapariallows the pip install of plugins in the .zip distribution. After installation of this release, napari-annotatorj can be installed from thePlugins --> Install/Uninstall plugins...menu by searching for its name and clicking on theInstallbutton next to it.ScriptSingle-file install is supported onWindowsandLinux(currently). It will create a virtual environment namednapariAnnotatorEnvin the parent folder of the cloned repository, install the package via pip and start napari. It requires a valid Python install.WindowsTo start it, run in the Command promptgit clone https://github.com/biomag-lab/napari-biomag-annotator.git
cd napari-biomag-annotator
install.batOr downloadinstall.batand run it from the Command prompt.After install, you can usestartup_napari.batto activate your installed virtual environment and run napari. Run it from the Command prompt with:startup_napari.batLinuxTo start it, run in the Terminalgit clone https://github.com/biomag-lab/napari-biomag-annotator.git
cd napari-annotatorj
install.shOr downloadinstall.shand run it from the Terminal.After install, you can usestartup_napari.shto activate your installed virtual environment and run napari. Run it from the Terminal with:startup_napari.shContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-biomag-annotator" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-bio-sample-data
napari-bio-sample-dataa sample data plugin for bio-related demosThis plugin contains 5 sample datasets with additional napari layer types:(1) 3D EM dataset (image + points + vectors)Image credit: Alister BurtTheoriginal datais down-sampled to have smaller file size.(2) 2D skin RGB dataset (image + shape)Image credit: skimage.data.skin(3) 3D nuclei dataset (image + label + surface)Image credit: skimage.data.cells3d(4) 2D timelapse dataset (image + points + tracks)Image credit:Cell Tracking ChallengeThe original data is cropped to have smaller file size.(5) large multi-resolution 3D EM datasetImage credit:Janelia Open OrganelleThis plugin only accesses 2 lower resolution levels.Datasets (1)-(4) are stored locally.Dataset (5) is downloaded and temporarily stored on RAM when accessed.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-bio-sample-dataviapip:pip install napari-bio-sample-dataTo install latest development version :pip install git+https://github.com/chili-chiu/napari-bio-sample-data.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-bio-sample-data" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-bleach-correct
napari-bleach-correctBleach correction for napariThis plugin is a python implementation of three different algorithms for bleach correction and can be used
to correct time-lapse images that lose intensity due to photobleaching. The implementation is based on the ImageJ
plugin Bleach Corrector by Miura et al. All methods work with 2D and 3D time series.Napari Bleach correction is easy to use:Ratio MethodThis is the simplest method. Every pixel in a frame is multiplied by the ratio from the mean intensity of the
first frame to that of thei-thframe.Assumptions:the mean intensity is constant through the time-lapsethe background fluorescence is the same for every pixel and frameParameters:Background Intensity: Must be estimatedExponential Curve FittingDrift estimation of fluorescence signal by fitting the mean intensity to an exponential curve.
The image is corrected by the decay in the normalized exponential function.Assumptions:time intervals between frames are equalParameters:Exponential Curve: Bleaching can be modelled as a mono- or bi-exponential curveHistogram MatchingBleaching correction by matching histograms to a reference image.
The correct pixel values can be calculated by the cumulative distribution function
of a frame and its reference frame. This method introduced by Miura et al.Parameters:Reference Frame: Match the frame's histogram with the first our neighbor frameThe Histogram Matching method using the neighbor frame as reference is a good start to correct bleaching.All methods are described in detail in Miura et al.ReferencesMiura K.Bleach correction ImageJ plugin for compensating the photobleaching of time-lapse sequences.F1000Res. 2020 Dec 21;9:1494. doi: 10.12688/f1000research.27171.1Documentation of the ImageJ pluginThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-bleach-correctviapip:pip install napari-bleach-correctTo install latest development version :pip install git+https://github.com/marx-alex/napari-bleach-correct.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theMITlicense,
"napari-bleach-correct" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-blender-bridge
napari-blender-bridgeTransfer surface layers between Napari and Blender. This plugin is young and has just limited functionality. Contributions are welcome.UsageThis plugin has its own submenu with all functionality underTools > Blender. You can start up Blender, send a surface layer to Blender, retrieve all meshes back as one surface layer and shut down Blender.Installation instructionsDownload and installBlender 3.5.Start Blender and click the menuEdit > Preferences. ActivateDeveloper extras.It is recommended to run this plugin in a conda environment together withdevbio-napari,vedoandnapari-process-points-and-surfaces.To install these, please run these commands line-by-line:mamba create --name napari-blender-env python=3.9 devbio-napari vedo -c conda-forge

mamba activate napari-blender-env

pip install napari-process-points-and-surfaces napari-blender-bridgeSimilar and related pluginsThere are other plugins for working with surface meshes:napari-stressnapari-pymeshlabnapari-process-points-and-surfacesContributingContributions are very welcome. Tests can be run withtox, please ensurethe coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theGNU GPL v3.0license,"napari-blender-bridge" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-blob-detection
napari-blob-detectionDetects blobs in imagesThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.This plugin consists of two widgets:Detects blobs on imagesConvert points layer to labels layerDetects blobs on imagesThis widget usesscikit-image's blob detection algorithmsto detect bright blobs on dark backgrounds.Parametersmethod: Laplacian of Gaussian (most accurate) or Difference of Gaussian (faster approximation)image: Image layer for blob detection. Can be a 2D, 3D, or higher dimensionality image.dimensionality: users can specify if the image is 2D(+t) or 3D(+t).min sigma: the smallest blob size to detectmax sigma: the largest blob size to detectthreshold: the lower the threshold, the more low intensity blobs are detected.OutputBlobs are represented by the Points layer.
The size of each blob is proportional toPoints.feature['sigma'],
which signifies the scale at which the feature point was found.Convert points layer to labels layerThis widget takes a points layer and converts it into a labels layer, with the image dimension matching the selected image layer.
By converting points to labels, users can leverage feature extraction functions that are available to labels to the detected points.InstallationYou can installnapari-blob-detectionviapip:pip install napari-blob-detectionTo install latest development version :pip install git+https://github.com/andy-sweet/napari-blob-detection.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-blob-detection" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-blossom
napari-blossomSegmentation of blossom apple tree imagesThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.This plugin was written by Herearii Metuarea, student intern at LARIS (French laboratory located in Angers, France) in Imhorphen, french scientific team lead by David Rousseau (Full professor). This plugin was designed as part of the european project INVITE.InstallationYou can installnapari-blossomviapip:pip install napari-blossomTo install latest development version :pip install git+https://github.com/hereariim/napari-blossom.gitHow does it worksThis module offers a plugin that allows you to segment the images of the apple tree flowers. As input, you can enter asingle imagewith the image selection widget. Once the image is entered in the napari window, you can segment the apple blossoms with the image segmentation widget by running the run button. The segmented image will appear in the napari window.ContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-blossom" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-boids
napari-boidsA plugin to look at boidsThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-boidsviapip:pip install napari-boidsTo install latest development version :pip install git+https://github.com/leoguignard/napari-boids.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-boids" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-boxmanager
napari-boxmanagerParticle selection tool for cryo-emThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationHere is how to install napari together with the boxmanager plugin:mamba env create -n napari -f https://raw.githubusercontent.com/MPI-Dortmund/napari-boxmanager/main/conda_env.yml
conda activate napariContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theMozilla Public License 2.0license,
"napari-boxmanager" is free and open source softwareIssuesIf you encounter any problems, please [file an issue] along with a detailed description.
Package: napari-brainbow-diagnose
napari-brainbow-diagnoseExplore image in channel coordinate space.
Brainbow dataset have unique features that need to be addressed by specialized tools. This plugin aims at visualize and diagnose brainbow dataset.
In particular we want to interact with the distribution of the dataset in the channel space.UsageYou can also look at thedemo notebookFind all menus underPlugins > napari-brainbow-diagnose > Diagnose Brainbow ImageChoose your datasetIf you want to use your dataset, you have to format it such as each channel is in one distinctnapari.LayersYou can open test dataset to try this plugin inFile > Open Sample > napari-brainbow-diagnose.The RGB Cube is an array with shape (3x256x256x256) cube : Great to check how the plugin work when all color are representedChrom Cortex Sample is an array with shape (3x256x256x256) #Hugo : Real life brainbow image (Cortex E18 Emx1Cre) !Once you have your layers you can use the dropdown and select the corresponding layer. It is advised to match thered, green, blueorder so the ratio you see on the napari viewer corresponds to the Hue-Saturation Wheel of the plugin.Get Channel Ratio Density of the imageWhen you click onCompute brainbow image densityyou will populate the Hue-Saturation density Wheel.
This should allow you to quickly see which ratio is more present in your image. You can see the corresponding ratio according to the "HS Color wheel" on the right.
For example here on this screenshot we can see that:there is a high number of non saturated red-only ratio. (2)there is not a high number of non saturated magenta ratio. (3)Create a selection of ratio on the channel coordinate system and apply it on the original imageCreate a selection of pixel in the image and show where they are in the channel coordinate systemInstallationYou can installnapari-brainbow-diagnoseviapip:pip install napari-brainbow-diagnoseTo install latest development version :pip install git+https://github.com/LaboratoryOpticsBiosciences/napari-brainbow-diagnose.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-brainbow-diagnose" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.
Package: napari-brainreg-standard
napari-brainreg-standardVisualisebrainregregistration output innapariBased on thenapari cookiecutter plugin templateandnapari-ndtiffsby@tlambert03InstallationAssuming you already havenapariinstalled, you can installnapari-brainreg-standardvia pip:pip install napari-brainreg-standardUsageOpen napari and drag yourbrainregoutput directory (the one with the log file) onto the napari window.Various images should then open, including:Image (downsampled)- the image used for registrationAnnotations- the atlas labelsIf you downsampled additional channels, these will also be loaded.