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HASS Pyscript kernel shim for Jupyter

Pyscript provides a kernel that interfaces with the Jupyter front-ends (eg, notebook, console, lab, and also VSCode). That allows you to develop and test pyscript triggers, functions and automation logic interactively. Plus you can interact with much of HASS by looking at state variables and calling services as you experiment and develop your own logic and automations.

This repository provides a shim that sits between HASS pyscript and Jupyter. When Jupyter starts a kernel, it is configured to run the script hass_pyscript_kernel.py in this repository. This script uses the HASS web interface to do a service call to pyscript that starts the kernel. It then helps establish the various socket connections between HASS/pyscript and Jupyter.

Installation

The pyscript Jupyter kernel should be installed on the client machine that is running Jupyter notebook and the browser (not on the HASS target). To install the pyscript Jupyter kernel:

pip install hass_pyscript_kernel
jupyter pyscript install

Running jupyter pyscript install is only required on new installs, or if your old version of hass_pyscript_kernel is prior to 1.0.0.

On a new install, you'll need to edit the pyscript.conf file. The install command above will print its path. Replace these settings:

  • hass_host with the host name or IP address where your HASS instance is running
  • hass_url with the URL of your HASS httpd service
  • hass_token with a long-lived access token created via the button at the bottom of your user profile page in HASS.
  • Since you've added a HASS access token to this file, you should make sure you are comfortable with file permissions - anyone who can read this file could use the access token to use the HASS UI without being an authenticated user.
  • hass_proxy with proxy url to use if HASS is not directly reachable. e.g. when using SSH to access your HASS instance, you can open a SOCKS5 tunnel to keep your Jupyter local.

Confirm that Jupyter now recognizes the new pyscript kernel:

jupyter kernelspec list

and you can confirm the settings you added above with:

jupyter pyscript info

Running Jupyter

You can open the browser-based Jupyter clients (eg, notebook and lab) as usual, eg:

jupyter notebook

and use the Jupyter menus to start a new hass pyscript kernel.

For the Jupyter command-line console, you can run:

jupyter console --kernel=pyscript

If Jupyter can't connect look at this wiki page for suggestions.

Tutorial

There is a Jupyter notebook tutorial that covers many pyscript features. It can be downlaoded and run interactively in Jupyter notebook connected to your live HASS with pyscript. You can download the pyscript_tutorial.ipynb notebook using:

wget https://github.com/craigbarratt/hass-pyscript-jupyter/raw/master/pyscript_tutorial.ipynb

and open it with:

jupyter notebook pyscript_tutorial.ipynb

You can step through each command by hitting <Shift>Enter. There are various ways to navigate and run cells in Jupyter that you can read in the Jupyter documentation.

Work Flow

Using the tutorial as examples, you can use a Jupyter client to interactively develop and test functions, triggers and services.

Jupyter auto-completion (with <TAB>) is supported in Jupyter notebook, console and lab. It should work after you have typed at least the first character. After you hit <TAB> you should see a list of potential completions from which you can select. It's a great way to easily see available state variables, functions or services.

In a Jupyter session, one or more functions can be defined in each code cell. Every time that cell is executed (eg, <Shift>Return), those functions are redefined, and any existing trigger decorators with the same function name are canceled and replaced by the new definition. You might have other function and trigger definitions in another cell - they won't be affected (assuming those function names are different), and they will only be replaced when you re-execute that other cell.

See more documentation.

Global Context

Each Jupyter session has its own separate global context, so functions and variables defined in each interactive session are isolated from the script files and other Jupyter sessions. Pyscript provides some utility functions to switch global contexts, which allows an interactive Jupyter session to interact directly with functions and global variables created by a script file, or even another Jupyter session.

See the documentation on global contexts.

Caveats

Here are some caveats about using specific clients with the pyscript Jupyter kernel:

For Jupyter notebook:

  • Jupyter notebook supports a wide range of extensions, called nbextensions. Some of these might not work correctly with pyscript's kernel. The black and isort nbextensions do work. If you are having problems with notebooks running on the pyscript kernel, try disabling other nbextensions. Please report nbextentions that you think are useful but don't work with pyscript's kernel and we'll look at supporting them.

For Jupyter console:

  • Jupyter console allows multi-line input (eg, a function definition) and delays excution by the kernel until it is syntactically correct (ie, complete) and the indent on the last line is 0. So if you define a multi-line function or statement with indenting, you will need to hit Enter one more time so there is an empty line indicating your code block is complete.

  • Jupyter console generally assumes the kernel operates in a half-duplex manner - it sends a snippet of code to the kernel to be executed, and the result (if any) and output (if any) are then displayed. In pyscript, a trigger function runs asynchonously, so it can generate output at some future time. In Jupyter notebook and lab, the right thing happens - whenever the output messages are generated, they appear below the last cell that was executed. Jupyter notebook displays the running list of log output. However, in Jupter console, it doesn't check for any output from the kernel until you hit Enter to execute the next command. So the display of output in the console is delayed until you hit Enter. The HASS log file will show any log output in real time, subject to the logging level threshold.

For Jupyter lab:

  • In Jupyter lab, each tab starts a new session (which is same behavior with iPython - each tab will have a different iPython instance), so each tab (eg, a notebook in one and a console in another) will have different global contexts. If you wish, you can use the function pyscript.set_global_ctx() to set the context in the other tabs to be the same as the first.

For VSCode:

  • Like Jupyter console, log and print output from trigger functions won't be displayed by VSCode since their output is generated after the current code cell execution is complete. You'll need to look at the HASS log file for log output from your trigger functions.
  • Some unresolved bug causes VSCode to start two pyscript Jupyter kernels, and the second one is typically shutdown soon after it starts. This seems to be benign - it should be invisible to the user, although the global context names (eg, jupyter_0) will increment by 2 on each new session, rather than 1.

Contributing

Contributions are welcome! You are encouraged to submit PRs, bug reports, feature requests or add to the Wiki with examples and tutorials. It would be fun to hear about unique and clever applications you develop.

Developing and installing locally

From a clone of this repository run:

python -m pip install -r requirements.txt
python setup.py bdist_wheel
pip install dist/hass_pyscript_kernel-VERSION-py3-none-any.whl

where VERSION is version specified in hass_pyscript_kernel/version.py.

Useful Links

Copyright

Copyright (c) 2020 Craig Barratt. May be freely used and copied according to the terms of the Apache 2.0 License.