napari | napari : a fast , interactive , multi-dimensional image | Data Visualization library

 by   napari Python Version: 0.4.9rc3 License: BSD-3-Clause

kandi X-RAY | napari Summary

kandi X-RAY | napari Summary

napari is a Python library typically used in Analytics, Data Visualization applications. napari has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However napari build file is not available. You can install using 'pip install napari' or download it from GitHub, PyPI.

napari: a fast, interactive, multi-dimensional image viewer for python
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            kandi-support Support

              napari has a medium active ecosystem.
              It has 1778 star(s) with 369 fork(s). There are 50 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 908 open issues and 1651 have been closed. On average issues are closed in 312 days. There are 90 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of napari is 0.4.9rc3

            kandi-Quality Quality

              napari has 0 bugs and 0 code smells.

            kandi-Security Security

              napari has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              napari code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              napari is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              napari releases are available to install and integrate.
              Deployable package is available in PyPI.
              napari has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 82056 lines of code, 6063 functions and 760 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed napari and discovered the below as its top functions. This is intended to give you an instant insight into napari implemented functionality, and help decide if they suit your requirements.
            • Visualize an image
            • Create a viewer based on add_method
            • Return a QApplication instance
            • Get napari settings
            • Removes the specified value from the set
            • Called when the cache has changed
            • Compute a precisedelta
            • Translate message
            • Return the translation for the current locale
            • Initialize from kwargs
            • Return a dictionary of source config file settings
            • Return a list of all the ideal chunks that can draw the given set
            • Setup UI
            • Move plan to dest
            • Set view slice
            • Handles click along a plane
            • Setup the UI
            • Return a list of LinkKey objects linked to the given layers
            • Select the layer
            • Opens a grid popup
            • Set keybinding
            • Blend HSV to RGB
            • Validates that a collection of objects is a sequence of length n
            • Return the package metadata
            • Creates a source of nested env_settings
            • Shade an RGB color using a cmap
            Get all kandi verified functions for this library.

            napari Key Features

            No Key Features are available at this moment for napari.

            napari Examples and Code Snippets

            napari-lazy-openslide,Usage,Using
            Pythondot img1Lines of Code : 31dot img1License : Permissive (BSD-3-Clause)
            copy iconCopy
            import dask.array as da
            import zarr
            
            from napari_lazy_openslide import OpenSlideStore
            
            store = OpenSlideStore('tumor_004.tif')
            grp = zarr.open(store, mode="r")
            
            # The OpenSlideStore implements the multiscales extension
            # https://forum.image.sc/t/mult  
            napari-animation (WIP under active development),Examples,Headless
            Pythondot img2Lines of Code : 17dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
            from napari_animation import Animation
            
            animation = Animation(viewer)
            
            viewer.dims.ndisplay = 3
            viewer.camera.angles = (0.0, 0.0, 90.0)
            animation.capture_keyframe()
            viewer.camera.zoom = 2.4
            animation.capture_keyframe()
            viewer.camera.angles = (-7.0, 1  
            napari-animation (WIP under active development),Examples,Headless
            Pythondot img3Lines of Code : 17dot img3License : Non-SPDX (NOASSERTION)
            copy iconCopy
            from napari_animation import Animation
            
            animation = Animation(viewer)
            
            viewer.dims.ndisplay = 3
            viewer.camera.angles = (0.0, 0.0, 90.0)
            animation.capture_keyframe()
            viewer.camera.zoom = 2.4
            animation.capture_keyframe()
            viewer.camera.angles = (-7.0, 1  
            CPython extension using omp freezes Qt UI
            Pythondot img4Lines of Code : 6dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            Py_BEGIN_ALLOW_THREADS
            
            // computation goes here
            
            Py_END_ALLOW_THREADS
            
            Adding blobs from blob_dog to a 3D image stack using skimage and napari
            Pythondot img5Lines of Code : 18dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            viewer.add_points(
                blobs[:, :-1], size=blobs[:, -1], name='points', scale=spacing
            )
            
            from skimage.util import label_points
            
            labels = label_points(blobs[:, :-1], bblobs.shape)
            viewer.add_labels(labels, scale=spac
            How best to create a filled volume of Lorenz Attractor in python for volume rendering
            Pythondot img6Lines of Code : 13dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from scipy import ndimage
            
            vol = np.zeros((512, 512, 512), dtype=states_int.dtype)
            # add data to vol
            vol[tuple(np.split(states_int, vol.ndim, axis=1))] = values[:, np.newaxis]
            # apply gaussian filter, sigma=5 in this case
            vol = ndimage.gau
            3D nrrd image visualization (matplotlib)?
            Pythondot img7Lines of Code : 19dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import numpy as np
            import napari
            from skimage import data, filters  # Just to generate some test data (3D blobs).
            
            with napari.gui_qt():
            
                # Generate some test data (smooth 3D blob shapes)
                imgarray = filters.gaussian(np.squeeze(np.s

            Community Discussions

            QUESTION

            CPython extension using omp freezes Qt UI
            Asked 2022-Mar-22 at 15:43

            I am working on a scientific algorithm (image processing), which is written in C++, and uses lots of parallelization, handled by OpenMP. I need it to be callable from Python, so I created a CPython package, which handles the wrapping of the algorithm. Now I need some UI, as user interaction is essential for initializing some stuff. My problem is that the UI freezes when I run the algorithm. I start the algorithm in a separate thread, so this shouldn't be a problem (I even proved it by replacing the function call with time.sleep, and it works fine, not causing any freeze). For testing I reduced the UI to two buttons: one for starting the algorithm, and another just to print some random string to console (to check UI interactions).
            I also experienced something really weird. If I started moving the mouse, then pressed the button to start the computation, and after that kept moving the mouse continuously, the UI did not freeze, so hovering over the buttons gave them the usual blueish Windows-style tint. But if I stopped moving my mouse for a several seconds over the application window, clicked a button, or swapped to another window, the UI froze again. It's even more strange that the UI stayed active if I rested my mouse outside of the application window.
            Here's my code (unfortunately I cannot share the algorithm for several reasons, but I hope I manage to get some help even like this):

            ...

            ANSWER

            Answered 2022-Mar-22 at 15:43

            Because of Python's "Global Interpreter Lock", only one thread can run Python code at a time. However, other threads can do I/O at the same time.

            If you want to allow other threads to run (just like I/O does) you can surround your code with these macros:

            Source https://stackoverflow.com/questions/71574663

            QUESTION

            Adding blobs from blob_dog to a 3D image stack using skimage and napari
            Asked 2021-Nov-04 at 03:47

            I'm trying to use blob log or blog dog for blob detection in a 3D image using skimage. I'm using napari and binary blob (3D) images as a sample (but this will not be the image I will be using later this just has clear-cut blobs). However, I'm having trouble applying the blobs to the image/adding it to the viewer.

            Skimage has a 2D image example using matplotlib adding circles to the image, but I would like to use this to identify blobs on the 3D image and create either a binary image (a mask essentially) or labels.

            This is what I have, but I'm not sure where to go from here:

            ...

            ANSWER

            Answered 2021-Nov-04 at 03:47

            What are you trying to do after? Do you need the blob sizes or only the positions? The answer depends a lot on the question. Here's three answers:

            1. Just visualise the blobs as points:

            Source https://stackoverflow.com/questions/69819105

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install napari

            It is recommended to install napari into a virtual environment, like this:. If you prefer conda over pip, you can replace the last line with: conda install -c conda-forge napari. See here for the full installation guide, including how to install napari as a bundled app.

            Support

            Contributions are encouraged! Please read our contributing guide to get started. Given that we're in an early stage, you may want to reach out on our Github Issues before jumping in.
            Find more information at:

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            Install
          • PyPI

            pip install napari

          • CLONE
          • HTTPS

            https://github.com/napari/napari.git

          • CLI

            gh repo clone napari/napari

          • sshUrl

            git@github.com:napari/napari.git

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