Saravanan666/pypi-org huggingface dataset:
Package: napari-psf-simulator
napari-psf-simulatorA plugin for the simulation of the 3D Point Spread Function of an optical systen, particularly a microscope objective.Calculates the PSF using scalar and vectorial models.The following aberrations are included:phase aberration described by a Zernike polynomials with n-m coefficients.aberration induced by a slab, with a refractive index different from the one at the object.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-psf-simulatorviapip:pip install napari-psf-simulatorTo install latest development version :pip install git+https://github.com/andreabassi78/napari-psf-simulator.gitUsageLauch the plugin and select the parameters of the microscope:NA(numerical aperture),wavelenght,n(refractive index at the object),FOV xy(field of view in the transverse direction),FOV z(field of view in the axial direction),dxy(pixel size, transverse sampling),dz(voxel depth, axial sampling),lens radius(physical aperture of the lens, used in vectorial model)Select a propagation model betweenscalarandvectorial.Select an aberration type (if needed).PressCalculate PSFto run the simulator. This will create a new image layer with the 3D PSF.The optionShow Airy diskcreates a circle with radius given by the diffraction limit (Rayleigh criterion).Napari viewer with the psf-simulator widget showing the in-focus plane of an aberrated PSFSlicing through a PSF aberrated with Zernike polynomials of order N=3, M=1 (coma)Click on thePlot PSF Profile in Consolecheckbox to see the x and z profiles of the PSF.
They will show up in  the viewer console whenCalculate PSFis executed.Plot profile of the PSF, shown in the ConsoleDetailed documentationAn exhaustive documentation of the use of the plugin on scalar and vectoral propagation models can be found inthispresentation.A detailed explanation of the uses and advantages that simulating a PSF brings can be foundhere.The vectorial propagation model implements a secondary library:pyfocus. The full documentation of this library can be found atread the docsand in the paper: "PyFocus: A Python package for vectorial calculations of focused optical fields under realistic conditions. Application to toroidal foci."https://doi.org/10.1016/j.cpc.2022.108315ContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.
The plugin has been concived to be modular allowing the insertion of new aberations and pupils. Please contact the developers on github for adding new propagations and aberrations types.
Any suggestions or contributions are welcome.LicenseDistributed under the terms of theBSD-3license,
"napari-psf-simulator" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-pssr
napari-pssrA plugin for training and applying pssrThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-pssrviapip:pip install napari-pssrSome libraries need to be updated to the most recent version to get all features.
These will be updated once they are released on pypipip install git+https://github.com/bioimage-io/core-bioimage-io-python",
pip install git+https://github.com/funkey/gunpowder.git@patch-1.2.3",To install latest development version :pip install git+https://github.com/pattonw/napari-pssr.gitModel downloadA sample model can be downloaded fromhttps://github.com/pattonw/model-specs/tree/main/pssr. This model comes with some restrictive dependencies. To use follow these steps.install this plugin following the directions provided aboveinstall bioimageio.core viapip install bioimageio.coreorconda install -c conda-forge bioimageio.corepip install fastai==1.0.55 tifffile libtiff czifile scikit-imagepip uninstall torch torchvision(may need multiple runs)conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorchpip install pillow==6.1.0ContributingContributions 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-pssr" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-pymeshlab
napari-pymeshlabInterfaces betweennapariand thepymeshlablibrary to allow import, export, construction and processing of surfaces.This is a WIP and feature requests are welcome. Please checkPyMeshLabfor possible features.Feature listRead/write .3ds, .apts, .asc, .bre, .ctm, .dae, .e57, .es, .fbx, .glb, .gltf, .obj, .off, .pdb, .ply,
.ptx, .qobj, .stl, .vmi, .wrl, .x3d, .x3dvScreened Poisson Surface ReconstructionConvex hull of a surfaceLaplacian smoothing of surfacesSmoothing surfaces using Taubin's methodSurface simplification using clustering decimationcolorize_curvature_apssSome functions are shown in thedemo notebook.InstallationYou can installnapari-pymeshlabviapip:pip install napari-pymeshlabContributingContributions 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 theGNU GPL v3.0license,
"napari-pymeshlab" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-qrcode
napari-qrcodeA napari plugin to generate QR-CodesThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-qrcodeviapip:pip install napari-qrcodeTo install latest development version :pip install git+https://github.com/kephale/napari-qrcode.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-qrcode" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-quoll
napari-quollResolution estimation for electron tomographyThe Python package which does the resolution estimation isQuoll. This repository,napari-quollis just the Napari plugin.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-quollviapipinto aPython 3.7environment, replacing <env_name> with an environment name of your choice:conda -n create <env_name> python=3.7
conda activate <env_name>
pip install napari-quollTo install latest development version :pip install git+https://github.com/rosalindfranklininstitute/napari-quoll.gitNote:Due tomiplibdependencies, this plugin only works on Python 3.7 environments.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 theApache Software License 2.0license,
"napari-quoll" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-rembg
napari-rembgSegment images using a collection of fast and lightweight generalist segmentation models in Napari. This plugin is based on therembgproject.Key featuresChoose amongfive generalist segmentation models, including SAM (Segment Anything Model).Quickly annotate individual objects by drawingbounding boxesaround them.Possibility to generate predictions via a remoteweb APIand keep the installation lightweight on client machines.Compatible with 2D, RGB, 2D+t, and 3D images (slice by slice).InstallationYou can installnapari-rembgviapip. If you wish to use your local machine for the predictions (most users):pip install "napari-rembg[local]"If you wish to generate predictions from aweb api, go for a minimal install:pip install napari-rembgModelsu2net: A pre-trained model for general use cases.u2netp: A lightweight version of u2net.silueta: Same as u2net with a size reduced to 43 Mb.isnet: A pre-trained model for general use cases.sam: Segment Anything Model pre-trained for any use cases (vit_b)The models automatically get downloaded in the user's home folder in the.u2netdirectory the first time inference is run.UsageStartnapari-rembgfrom thePluginsmenu of Napari:Plugins > Napari Select Foreground > Select foregroundSegment an image loaded into NapariSelect your image in theImagedropdown and pressRun. The output segmentation appears in theLabelslayer selected in theMaskfield (if no layer is selected, a new one is created).Segment individual objects using bounding boxesClick on theAddbutton next to theROIfield. This adds aShapes layerto the viewer.Click and drag bounding boxes around objects in the image. Each time you draw a bounding box a segmentation is generated in the region selected.You can choose to auto-increment the label index to distinguish individual objects. Deselect that option to annotate a single foreground class.Running the segmentation via a web APIYou can run therembgsegmentation via a web API running in adockercontainer.AdvantagesThe segmentation can be run on a remote machine with optimization (e.g. GPU).The segmentation models will be downloaded inside the docker container instead of the user's file system.You can minimally install the package withpip install napari-rembgon the client's machine. This willnotinstall therembglibrary, which can solve potential dependency conflicts or bugs.SetupSeethese instructionson how to set up the docker container and web API.UsageStartnapari-rembgfrom thePluginsmenu of Napari:Plugins > Napari Select Foreground > Select foreground (Web API)Related projectsIf you are looking for similar generalist segmentation plugins, check out these related projects:napari-samnapari-segment-anythingContributingContributions are very welcome.LicenseDistributed under the terms of theBSD-3license,
"napari-rembg" is free and open source software.IssuesIf you encounter any problems, please file an issue along with a detailed description.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.
Package: napari-remove-objects
napari-remove-objectsRight-click on objects to remove them in Napari.InstallationYou can installnapari-remove-objectsviapip:pip install napari-remove-objectsUsageSelect the plugin from thePluginsmenu of Napari.ContributingContributions are very welcome. Please get in touch if you'd like to be involved in improving or extending the package.LicenseDistributed under the terms of theBSD-3license,
"napari-remove-objects" is free and open source softwareIssuesIf you encounter any problems, please file an issue along with a detailed description.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.
Package: napari-result-stack
napari-result-stackWidgets and type annotations for storing function results of any types.StoredtypeTypeStored[T]is equivalent toTfor the type checker, butmagicguiis aware of this annotation and behaves as a "storage" for theTinstances.frompathlibimportPathimportpandasaspdfrommagicguiimportmagicguifromnapari_result_stackimportStored# Returned values will be stored in a result stack.@magicguidefprovide_data(path:Path)->Stored[pd.DataFrame]:returnpd.read_csv(path)# You can choose one of the values stored in the result stack# for the argument `df` from a ComboBox widget.@magicguidefprint_data(df:Stored[pd.DataFrame]):print(df)Different types use different storage. e.g.Stored[int]andStored[str]do not share the same place.Even for the same type, you can specify the second key to split the storage. e.g.Stored[int],Stored[int, 0]andStored[int, "my-plugin-name"]use the distinct storages.Manually store variablesStored.valuesof[T]is alist-like object that returns a view of the values stored inStored[T]. This value view is useful if you want to store values outside@magicgui.frommagicgui.widgetsimportPushButtonfromdatetimeimportdatetimefromnapari_result_stackimportStoredbutton=PushButton(text="Click!")@button.changed.connectdef_record_now():Stored.valuesof[datetime].append(datetime.now())Result stack widgetnapari-result-stackprovides a plugin widget that is helpful to inspect all the stored values.Show codefromnapari_result_stackimportStoredfrommagicguiimportmagicguiimportnumpyasnpimportpandasaspd@magicguideff0()->Stored[pd.DataFrame]:returnpd.DataFrame(np.random.random((4,3)))@magicguideff1(x:Stored[pd.DataFrame])->Stored[float]:returnnp.mean(np.array(x))viewer.window.add_dock_widget(f0,name="returns a DataFrame")viewer.window.add_dock_widget(f1,name="mean of a DataFrame")Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-result-stackviapip:pip install napari-result-stackTo install latest development version :pip install git+https://github.com/hanjinliu/napari-result-stack.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-result-stack" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-rioxarray
napari-rioxarrayA rioxarray plugin for napari supporting GDAL raster datatypesThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-rioxarrayviapip:pip install napari-rioxarrayTo install latest development version :pip install git+https://github.com/AndrewAnnex/napari-rioxarray.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-rioxarray" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-roi
napari-roiSelect regions of interest (ROIs) using napariInstallationYou can install napari-roi viapip:pip install napari-roiAlternatively, you can install napari-roi viaconda:conda install -c conda-forge napari-roiUsageThenapari-roiplugin can be opened from within napari (napari -> napari-roi: regions of interest) and operates on napariShapeslayers.ROIs can be added to any napariShapeslayer, either by drawing a standard napari shape (e.g. rectangle), or by adding a rectangular ROI of specified size using theAdd ROIfunctionality in thenapari-roiwidget. Each ROI is associated with a name, a position (X/Y origin), and a size (width/height). The location of the X/Y origin of all ROIs can be chosen in thenapari-roiwidget. Note that any shape supported by napari (e.g. ellipse, rectangle, polygon, line, path) can serve as an ROI; for non-rectangular shapes,napari-roicomputes rectangular bounding boxes aligned with the napari coordinate system to determine their positions and sizes. ROIs can be edited or deleted by modifying the corresponding shapes in napari, or by editing the corresponding row in thenapari-roiwidget.All ROIs in the currentShapeslayer can be saved to a comma-separated values (CSV) file using theSavefunctionality in thenapari-roiwidget. When theAutosaveoption is checked, the file is automatically updated on every ROI change. Note that the selected file is specific to the currentShapeslayer; ROIs from differentShapeslayers cannot be saved to the same file. ROIs can be loaded from a previously saved file and added to the currentShapeslayer by opening the file in thenapari-roiwidget.CSV files saved usingnapari-roiadhere to the following format:ColumnsDescriptionNameROI nameX,YPosition (X/Y origin)W,HSize (width/height)AuthorsCreated and maintained byJonas Windhageruntil February 2023.Maintained byMilad Adibifrom February 2023.ContributingContributingChangelogChangelogLicenseMIT
Package: napari-roi-manager
napari-roi-managerA ROI Manager Widget with an UI similar to ImageJThis Plugin requiresnapari>=0.4.19.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-roi-managerviapip:pip install napari-roi-managerTo install latest development version :pip install git+https://github.com/hanjinliu/napari-roi-manager.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-roi-manager" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-roi-registration
napari-roi-registrationA Napari plugin for the registration of regions of interests (ROI) in a time lapse acquistion and processing of the intensity of the registered data.The ROI are defined using a Labels layer. Registration of multiple ROIs is supported.TheRegistrationwidget uses the user-defined labels, constructs a rectangular ROI around each of them and registers the ROIs in each time frame.TheProcessingwidget measures the ROI displacements and extracts the average intensity of the ROI, calculated on the area of the labels.TheSubtract backgroundwidget subtracts a background on each frame, calculated as the mean intensity on a Labels layer.
Tipically useful when ambient light affects the measurement.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-roi-registrationviapip:pip install napari-roi-registrationTo install latest development version :pip install git+https://github.com/GiorgiaTortora/napari-roi-registration.gitUsageA detailed guide which shows how to use the widgets of the napari-roi-registration plugin and how to properly choose the parameters can be foundhere. A demo video is available at thislink.Sample datasetsare available.Registration WidgetCreate a new Labels layer and draw one or more labels where you want to select a ROI (Region Of Interest). Each color in the same Labels layer represents a different label which will correspond to a different ROI.Push theRegister ROIsbutton: registration of the entire stack will be performed. When the registration is finished two new layers will appear in the viewer. One layer contains the centroids of the drawn labels while the other contains the bounding boxes enclosing the ROIs.
The registration starts from the currently selected frame. Ifregister entire stackis selected, the registration will create a new layer for each label, with the registered ROI stacks.Processing WidgetPushing theProcess registered ROIsbutton, the registered ROIs will be analyzed. The intensity of the registered ROIs (measured on the area of the selected label) and the displacement of the ROIs will be calculated.
Ifplot resultsis selected the plot of displacement vs time index and mean intensity vs time index will appear in the Console.
Choosing thesave resultsoption, an excel file containing ROIs positions, displacements and intensities, will be saved.Background WidgetCreate a new Labels layer and draw a label on the area where you want to calculate the background.Push theSubtract backgroundbutton. A new image layer will appear in the viewer. This layer contains the image to which the background was subtracted.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-roi-registration" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-s3zarr
napari-s3zarrload zarr stores from s3 into napari via daskThisnapariplugin was generated withCookiecutterusing with@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-s3zarrviapip:pip install napari-s3zarrContributingContributions 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-s3zarr" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-sairyscan
napari-sairyscanAiryscan image reconstructionThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-sairyscanviapip:pip install napari-sairyscanTo install latest development version :pip install git+https://github.com/sylvainprigent/napari-sairyscan.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-sairyscan" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-sam
Segment Anything Model (SAM) in NapariSegment anything with ourNapariintegration of Meta AI's newSegment Anything Model (SAM)!SAM is the new segmentation system from Meta AI capable ofone-click segmentation of any object, and now, our plugin neatly integrates this into Napari.We have alreadyextendedSAM's click-based foreground separation to fullclick-based semantic segmentation and instance segmentation!At last, our SAM integration supports both2D and 3D images!Everything modeClick-based semantic segmentation modeClick-based instance segmentation modeSAM in Napari demohttps://user-images.githubusercontent.com/3471895/236152620-0de983db-954b-4480-97b9-901ee82f8edd.mp4InstallationThe plugin requirespython>=3.8, as well aspytorch>=1.7andtorchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.Install Napari viapip:pip install napari[all]You can installnapari-samviapip:pip install git+https://github.com/facebookresearch/segment-anything.git
pip install napari-samTo install latest development version :pip install git+https://github.com/MIC-DKFZ/napari-sam.gitUsageStart Napari from the console with:napariThen navigate toPlugins -> Segment Anything (napari-sam)and drag & drop an image into Napari. At last create, a labels layer that will be used for the SAM predictions, by clicking in the layer list on the third button.You can then auto-download one of the available SAM models (this can take 1-2 minutes),  activate one of the annotations & segmentation modes, and you are ready to go!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 theApache Software License 2.0license,
"napari-sam" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.Acknowledgementsnapari-sam is developed and maintained by the Applied Computer Vision Lab (ACVL) ofHelmholtz Imagingand theDivision of Medical Image Computingat theGerman Cancer Research Center (DKFZ).