PyPI 官网下载 | imjoy-jupyter-extension-0.2.13.tar.gz

ImJoy Jupyter Extension ============ Try the demo here: [](https://mybinder.org/v2/gh/imjoy-team/imjoy-binder-image/master?filepath=imjoy-jupyter-tutorial.ipynb)  ## Using ImJoy with Jupyter Notebook and JupyterLab This extension utilizes the [imjoy-rpc](https://github.com/imjoy-team/imjoy-rpc) library to enable bidirectional RPC calls between the ImJoy core and plugins. The library has an abstract transport interface that can support different types of transport. For now, we support [Jupyter comms message](https://jupyter-notebook.readthedocs.io/en/stable/comms.html) which is a custom message protocol used in Jupyter notebooks (for example powers jupyter widgets). To use it, you need to install the `imjoy-jupyter-extension` in Python which will also include the imjoy-rpc as a dependency (see below). ### Install the extension for Jupyter Notebook or JupyterLab For Jupyter Notebook, run: ```bash pip install -U imjoy-jupyter-extension ``` For Jupyter Lab, run: ```bash pip install -U imjoy-jupyter-extension jupyter labextension install imjoy-jupyter-extension ``` ### Use ImJoy plugins inside Jupyter notebooks Now you can start a jupyter notebook via for example `jupyter notebook` command, you should be able to see an ImJoy icon in the toolbar if everything goes well.  For JupyterLab, start it via `jupyter lab` command, then create a notebook and you should see an ImJoy button in the toolbar:  Now run ImJoy plugins in a cell, see an example that uses itk-vtk-viewer to visualize images: ```python import imageio import numpy as np from imjoy_rpc import api class ImJoyPlugin(): def setup(self): api.log('plugin initialized') async def run(self, ctx): viewer = await api.showDialog(src="https://kitware.github.io/itk-vtk-viewer/app/") # show a 3D volume image_array = np.random.randint(0, 255, [10,10,10], dtype='uint8') # show a 2D image # image_array = imageio.imread('imageio:chelsea.png') await viewer.setImage(image_array) api.export(ImJoyPlugin()) ``` With the above code, you created an ImJoy plugin. To run it, click the Run button with the ImJoy icon. It will then call the run function of your plugin. * GIF Demo: [Visualizing 2D image](https://ibb.co/XDFF5bQ) * GIF Demo: [Visualizing 3D volume](https://ibb.co/QXR63XM) * GIF Demo: [Load ImageAnnotator](https://ibb.co/0Zyfxkr) You can also do the reverse by running a notebook inside ImJoy, to do that, please first create an empty jupyter notebook. Then copy and paste the url into the "+ PLUGINS" dialog, press enter and install the plugin. Click the newly installed plugin and you will get a notebook page open in ImJoy. Try to copy and paste the itk-vtk-viewer example to a cell and execute it. Similarily, if you now click the run ImJoy button in the toolbar, you will get the viewer open insided ImJoy. ### Load plugin dynamically in a notebook There are different ways to load a plugin in a notebook, you can load from URL for the source code of the plugin (e.g. gist or github file url) or a hosted web app (e.g. the itk-vtk-viewer app used above). Besides that, you can also write a window plugin directly in a notebook. This is practical especially for debugging window plugin for your GUI. You can directly pass a string with the plugin source code to `api.getPlugin`, `api.createWindow(src=...)` or `api.showDialog(src=...)`, however, that doesn't give you syntax highliting in a Jupyter notebook cell. A workaround solution (which also works for Google Colab) is to write plugin code in a seperate cell startting with `## ImJoy Plugin` and wrap the source code with `IPython.display.HTML()`. For example: ```html ## ImJoy Plugin from IPython.display import HTML my_plugin_source = HTML(''' <docs lang="markdown"> [TODO: write documentation for this plugin.] </docs> <config lang="json"> { "name": "Untitled Plugin", "type": "window", "tags": [], "ui": "", "version": "0.1.0", "cover": "", "description": "[TODO: describe this plugin with one sentence.]", "icon": "extension", "inputs": null, "outputs": null, "api_version": "0.1.8", "env": "", "permissions": [], "requirements": [], "dependencies": [], "defaults": {"w": 20, "h": 10} } </config> <script lang="javascript"> class ImJoyPlugin { async setup() { api.log('initialized') } async run(ctx) { } } api.export(new ImJoyPlugin()) </script> <window lang="html"> <div> <p> Hello World </p> </div> </window> <style lang="css"> </style> ''') ``` Then in another cell, you can passing the plugin source code to `api.getPlugin`, `api.createWindow(src=...)` or `api.showDialog(src=...)` to make an actually plugin: ```python from imjoy_rpc import api class ImJoyPlugin(): async def setup(self): pass async def run(self, ctx): # for regular plugin # p = await api.getPlugin(my_plugin_source) # or for window plugin # await api.createWindow(src=my_plugin_source) await api.showDialog(src=my_plugin_source) api.export(ImJoyPlugin()) ``` ## Development To develop the JupyterLab extension, open a terminal and run: ```bash cd imjoy_jupyterlab_extension npm run watch ``` In another terminal window run: ``` jupyter labextension install ./imjoy_jupyterlab_extension ```