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        "author": "Matt Kafonek",
        "author_email": "kafonek@gmail.com",
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        "description": "# ipython_blocking\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/kafonek/ipython_blocking/master)\n\n`ipython_blocking` is a context manager for capturing cell execution messages in a Jupyter notebook, along with magic commands `%block` and `%blockrun` for convenience.  The primary use-case for blocking notebook execution is to wait for users to interact with [ipywidgets](https://github.com/jupyter-widgets/ipywidgets) and then reference the values entered in those Widgets.\n\n\n### Install\n`ipython_blocking` is on [PyPI](https://pypi.org/project/ipython_blocking/), install with pip.\n\n```python\npip install ipython_blocking\n```\n\n\n### Usage\nTry out the demo notebooks in Binder to see `ipython_blocking` in action.  The most common way to use `ipython_blocking` is with the `%blockrun` magic and running a notebook with \"cell -> run all\".  `%blockrun button` stops the cell execution messages from the initial \"cell -> run all\", and attaches a \"cell -> run all below\" handler to the button so that a notebook can be run in a linear fashion without callback functions after a user has filled out other Widget values.\n\n```python\n### cell #1\nimport ipywidgets as widgets\nimport ipython_blocking # enables %block and %blockrun magic\n\ntext = widgets.Text()\ndropdown = widgets.Dropdown(options=['', 'foo', 'bar', 'baz'])\nbutton = widgets.Button(description='Run')\nbox = widgets.VBox(children=[text, dropdown, button])\nbox\n\n### cell #2\n%blockrun button\n\n### cell #3 -- doesn't execute until the 'Run' button is pressed\n### This gives the user a chance to interact with the Text and Dropdown widgets\nprint(text.value)\nprint(dropdown.value)\n```\n\n*(The dropdown menu doesn't appear in this .gif because its treated as a \"separate window\" in Windows screen capture, sorry!)*\n\n![](example.gif)\n\n#### CaptureExecution\nThe way `ipython_blocking` \"blocks\" cell execution is by creating a context manager that changes the behavior of the `IPython.shell.kernel['execute_request']` handler.  When you execute a cell in a Jupyter notebook, it sends a `execute_request` comms message to the kernel with that code.  \n\nWhile the `CaptureExecution` manager is \"blocking\", it stores those messages in a list instead of actually executing them.  When the context manager exits, it resets the handler to its original behavior and then either replays the stored messages or drops them.\n\n```python\nimport ipython_blocking\nctx = ipython_blocking.CaptureExecution(replay=True)\nwith ctx:\n    while True:\n        if break_function():\n            break\n        ctx.step() # handles all other messages that aren't 'execute_request' including widget value changes\n```\n\n#### %block\nThe `%block` magic is enabled upon importing `ipython_blocking`.  It takes either a function or widget object and creates the `CaptureExecution` manager to block until that function returns True or the widget value changes.\n\n```python\n# cell 1\nimport ipywidgets as widgets\nimport ipython_blocking\ndd = widgets.Dropdown(options=['', 'foo', 'bar', baz'])\ndd\n\n# cell 2\n%block dd\n\n# cell 3\n# Won't actually be executed until the user chooses an option in the dd widget\nprint(dd.value)\n```\n\n#### %blockrun\nThe `%blockrun` magic is similar to `%block` but it only accepts an `ipywidgets.Button` target and it attaches a \"cell -> run all below\" handler to the button.  If you expect the application logic of your Notebook to be run more than once (and/or don't want to reinitialize the Widgets because the user might only change one of many options), then `%blockrun` is the better magic to use.\n\nIt is often handy to make the target `Button` unclickable when the Notebook first renders, then add `.observe` handlers on other Widgets that can make the `Button` clickable once some input validation has happened.\n\n```python\n# cell 1\nimport ipywidgets as widgets\nimport ipython_blocking\ntext = widgets.Text()\ndd = widgets.Dropdown(options=['', 'foo', 'bar', 'baz'])\nbutton = widgets.Button(description='Run', disabled=True)\n\ndef validation(ev):\n    \"make button clickable if user has put in more than 5 characters and chosen a dropdown option\"\n    if len(text.value) > 5 and dd.value:\n        button.disabled = False\n    else:\n        button.disabled = True\ntext.observe(validation)\ndd.observe(validation)\n\nbox = widgets.VBox(children=[text, dd, button])\nbox\n\n# cell 2\n%blockrun button\n\n# cell 3\nprint(text.value)\nprint(dd.value)\n```\n\n\n### Alternatives\nThe other ways to get the value of a widget after a user has interacted with the widget is to structure your notebook with [event callbacks](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Events.html) or to write your code [asynchronously](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Asynchronous.html).  \n\nI believe there are major benefits to writing the **application logic** of a Jupyter Notebook in a **linear and synchronous** fashion, with as many variables as possible in the notebook global scope.  Those benefits include:\n\n * Better introspection and comprehension of the workflow (without littering your code with `global` and `print` statements)\n * More direct debug when something goes wrong\n * Easier to break code into small blocks/cells\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
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