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deephaven-ipywidgets

Deephaven Community IPython Widget Library

Installation

You can install using pip.

If running with the embedded server (deephaven-server), install with the following:

pip install "deephaven-ipywidgets[server]"

This installs the embedded server.

If connecting to a server running elsewhere (pydeephaven), install with the following:

pip install "deephaven-ipywidgets[client]"

If you are using Jupyter Notebook 5.2 or earlier, you may also need to enable the nbextension:

jupyter nbextension enable --py [--sys-prefix|--user|--system] deephaven-ipywidgets

Usage

Starting the server

First, if you are using the embedded server, you'll need to start the Deephaven server.

# Start up the Deephaven Server on port 8080 with token `iris`
from deephaven_server import Server
s = Server(port=8080, jvm_args=["-Dauthentication.psk=iris"])
s.start()

Display Tables

Pass the table into a DeephavenWidget to display a table:

# Create a table and display it
from deephaven import empty_table
from deephaven_ipywidgets import DeephavenWidget
t = empty_table(1000).update("x=i")
display(DeephavenWidget(t))

You can also pass in the size you would like the widget to be:

# Specify a size for the table
display(DeephavenWidget(t, width=100, height=250))

Connecting to a Remote Server

If you are using the client to connect to an already running server, create a pydeephaven session. See the pydeephaven documentation for more information.

from pydeephaven import Session

client_session = Session(
    host="deephaven.local",
    port=10000,
    auth_type="io.deephaven.authentication.psk.PskAuthenticationHandler",
    auth_token="YOUR_PASSWORD_HERE",
)

Displaying Tables from a Remote Server

The session can be used to create objects such as tables on the server and then display them in the widget.

t = client_session.time_table("PT1s")
from deephaven_ipywidgets import DeephavenWidget
display(DeephavenWidget(t))

You can also reference objects already created on the server. This code assumes a table named t exists on the server.

display(DeephavenWidget("t", session=client_session))

Alternate Deephaven Server URL

By default, the Deephaven server is located at http://localhost:{port}, where {port} is the port set in the Deephaven server creation call. If the server is not there, such as when running a Jupyter notebook in a Docker container, modify the DEEPHAVEN_IPY_URL environmental variable to the correct URL before creating a DeephavenWidget.

import os
os.environ["DEEPHAVEN_IPY_URL"] = "http://localhost:1234"

Development Installation

Before starting, you will need python3, node, and yarn installed.

Create and source a dev python venv environment:

export JAVA_HOME=/Library/Java/JavaVirtualMachines/openjdk-11.jdk/Contents/Home
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip setuptools
pip install deephaven-server jupyter jupyterlab jupyter-packaging

After initial installation/creation, you can just do

source .venv/bin/activate

Install the python. This will also build the TS package.

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 deephaven_ipywidgets
jupyter nbextension enable --sys-prefix --py deephaven_ipywidgets

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.

For running in VS Code, you need to run the classic notebook steps, as well as set up the VS Code environment:

  1. Create a .env file with your JAVA_HOME variable set, e.g.
JAVA_HOME=/Library/Java/JavaVirtualMachines/openjdk-11.jdk/Contents/Home
  1. Create a new notebook (.ipynb) or open an existing notebook file (such as example.ipynb)
  2. In the notebook, make sure your .venv Python environment is selected - either use the dropdown menu in the top right, or hit Ctrl + P then type > Select Kernel and select the Notebook: Select Notebook Kernel option and choose .venv.

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.

# 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.

Testing your changes

There are separate test suites for the python and TypeScript code.

  • Python: To run the python tests, run pytest in the root directory of the repository.
  • TypeScript: To run the TypeScript tests, run yarn run lint:check in the root directory of the repository to run the eslint tests. Then run yarn run test to run the rest of the unit tests.

Releasing your initial packages:

  • Add tests
  • Ensure tests pass locally and on CI. Check that the coverage is reasonable.
  • Make a release commit, where you remove the , 'dev' entry in _version.py.
  • Update the version in package.json
  • Relase the npm packages: 
    npm login
npm publish
  • Install publish dependencies:
pip install build twine
  • Build the assets and publish 
    python -m build .
twine check dist/*
twine upload dist/*
  • Tag the release commit (git tag <python package version identifier>)
  • Update the version in _version.py, and put it back to dev (e.g. 0.1.0 -> 0.2.0.dev). Update the versions of the npm packages (without publishing).
  • Commit the changes.
  • git push and git push --tags.