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Version: Java (Groovy)

How to use Matplotlib and Seaborn

This guide shows you how to use Matplotlib and its relative [Seaborn]((https://seaborn.pydata.org/) to create plots.

By default, Deephaven does not come with Matplotlib or Seaborn, so you can either access the specific Matplotlib Deephaven example or extend the Deephaven Dockerized set up. Both options are documented below.

Quickstart​

To get up and running with Matplotlib and Seaborn, clone the Matplotlib Deephaven base repo, enter its directory, and then run docker-compose up -d as usual:

git clone https://github.com/deephaven-examples/deephaven-matplotlib-base.git
cd deephaven-matplotlib-base
docker-compose up --build -d

This starts the Deephaven IDE with all the needed packages.

Now, you're ready to use these plotting libraries. Open the IDE to try our sample queries.

Extend Deephaven​

If instead you wish to extend the Deephaven build, you can follow the Launch Deephaven from pre-built images steps from the Quickstart guide.

To launch the IDE in a Python session, run:

compose_file=https://raw.githubusercontent.com/deephaven/deephaven-core/main/containers/python-examples/base/docker-compose.yml
curl  -O "${compose_file}"

Once you have the docker-compose.yml file pulled down, define your own web Docker image in web/Dockerfile that includes the plug-ins you would like to use. In this case, we are installing Matplotlib.

  1. Create subdirectory web in the same folder as your docker-compose.yml: mkdir web

  2. Create the Dockerfile for web and open for editing: vi web/Dockerfile

  3. Paste the following into the web/Dockerfile and save:

    # Pull the web-plugin-packager image
    FROM ghcr.io/deephaven/web-plugin-packager:main as build

    # Specify the plugins you wish to use. You can specify multiple plugins separated by a space, and optionally include the version number, e.g.
    # RUN ./pack-plugins.sh <js-plugin-name>[@version] ...
    # For a list of published plugins, see https://www.npmjs.com/search?q=keywords%3Adeephaven-js-plugin

    # Here is how you would install the matplotlib and table-example plugins
    RUN ./pack-plugins.sh @deephaven/js-plugin-matplotlib @deephaven/js-plugin-table-example

    # Copy the packaged plugins over
    FROM ghcr.io/deephaven/web:${VERSION:-latest}
    COPY --from=build js-plugins/ /usr/share/nginx/html/js-plugins/

Many plug-ins will also require a server side component. To define the plug-ins used on the server, create a server/Dockerfile similar to above:

  1. Create subdirectory server in the same folder as your docker-compose.yml: mkdir server

  2. Create the Dockerfile for server and open for editing: vi server/Dockerfile

  3. Paste the following into the server/Dockerfile and save:

    FROM ghcr.io/deephaven/server:${VERSION:-latest}
    # pip install any of the plugins required on the server
    RUN pip install deephaven-plugin-matplotlib

After building, you need to specify using that build in your docker-compose. Do this by modifying the existing docker-compose.yml file and replace the web and server definitions with the following:

services:
  server:
    # Comment out the image name
    # image: ghcr.io/deephaven/server:${VERSION:-latest}
    # Build from your local Dockerfile you just created
    build: ./server

    ...

  web:
    # Comment out the image name
    # image: ghcr.io/deephaven/web:${VERSION:-latest}
    # Build from your local Dockerfile you just created
    build: ./web

When you're done, your directory structure should look like:

.
├── docker-compose.yml
├── server
│   └── Dockerfile
└── web
    └── Dockerfile

Everything's ready to go! Now after running docker-compose up as normal, you can use your custom image with your JS plug-ins installed. See what you can create!

Matplotlib examples​

Here is the basic usage of Matplotlib to show one figure:

img

The full functionaly of Matplotlib is avilable inside the Deephaven IDE:

img

Here are some 3D examples from Rashida Nasrin Sucky. The data, available from Kaggle, needs to be placed in the data directory. For more information see our guide on Docker data volumes.

img

img

img

Seaborn examples​

Here is the basic usage of Seaborn:

img