Metadata-Version: 2.1
Name: ipyannotations
Version: 0.4.1
Summary: Create rich adata annotations in jupyter notebooks.
Home-page: https://github.com/janfreyberg/ipyannotations
Author: Jan Freyberg
Author-email: jan.freyberg@gmail.com
License: MIT
Description: 
        # ipyannotations
        
        [![Coverage Status](https://coveralls.io/repos/github/janfreyberg/ipyannotations/badge.svg?branch=main)](https://coveralls.io/github/janfreyberg/ipyannotations?branch=main)
        [![Build](https://github.com/janfreyberg/ipyannotations/actions/workflows/build.yml/badge.svg)](https://github.com/janfreyberg/ipyannotations/actions/workflows/build.yml)
        [![Unit tests and linting](https://github.com/janfreyberg/ipyannotations/actions/workflows/test.yml/badge.svg)](https://github.com/janfreyberg/ipyannotations/actions/workflows/test.yml)
        [![PyPI version](https://badge.fury.io/py/ipyannotations.svg)](https://badge.fury.io/py/ipyannotations)
        
        Create rich adata annotations in jupyter notebooks.
        
        ipyannotations provides interactive UI elements, based on ipywidgets, to allow
        developers and scientists to label data right in the notebook.
        
        ipyannotations supports many common data labelling tasks, such as image and text
        classification and annotation. It also supports custom data presentation by
        leveraging the Jupyter ecosystem.
        
        [![interface](https://user-images.githubusercontent.com/4092425/132008979-2fa43ec2-1add-4376-aba9-7836509b8e8f.png)](https://user-images.githubusercontent.com/4092425/132008979-2fa43ec2-1add-4376-aba9-7836509b8e8f.png)
        
        ## Installation
        
        You can install using `pip`:
        
        ```bash
        pip install ipyannotations
        ```
        
        If you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
        the nbextension:
        ```bash
        jupyter nbextension enable --py [--sys-prefix|--user|--system] ipyannotations
        ```
        
        ## Development Installation
        
        Create a dev environment:
        ```bash
        conda create -n ipyannotations-dev -c conda-forge nodejs yarn python jupyterlab
        conda activate ipyannotations-dev
        ```
        
        Install the python. This will also build the TS package.
        ```bash
        pip install -e ".[test, examples]"
        ```
        
        When developing your extensions, you need to manually enable your extensions with the
        notebook / lab frontend. For lab, this is done by the command:
        
        ```
        jupyter labextension develop --overwrite .
        yarn run build
        ```
        
        For classic notebook, you need to run:
        
        ```
        jupyter nbextension install --sys-prefix --symlink --overwrite --py ipyannotations
        jupyter nbextension enable --sys-prefix --py ipyannotations
        ```
        
        Note that the `--symlink` flag doesn't work on Windows, so you will here have to run
        the `install` command every time that you rebuild your extension. For certain installations
        you might also need another flag instead of `--sys-prefix`, but we won't cover the meaning
        of those flags here.
        
        ### How to see your changes
        #### Typescript:
        If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different
        terminals to watch for changes in the extension's source and automatically rebuild the widget.
        
        ```bash
        # Watch the source directory in one terminal, automatically rebuilding when needed
        yarn run watch
        # Run JupyterLab in another terminal
        jupyter lab
        ```
        
        After a change wait for the build to finish and then refresh your browser and the changes should take effect.
        
        #### Python:
        If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.
        
Keywords: Jupyter,Widgets,IPython
Platform: Linux
Platform: Mac OS X
Platform: Windows
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Framework :: Jupyter
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: dev
Provides-Extra: examples
Provides-Extra: doc
