plotplot 1.0.8

Drag and drop plotting, data selection, and filtering

Plotplot

Drag and drop plotting, data selection, and filtering.

Developed by the Deverman lab.

Live demo

• Demo server with example data
• Demo -- bring your own .csv (deletes your sessions after 24 hours)

Installation

pip install plotplot

Main Features

• Drag-and-drop to graph
• "Google Maps style" pan-and-zoom controls
• Scatter plots, heatmaps, histograms, and rank plots
• Group data into multiple subsets
  • Refine, rename, and export subsets
• Large data:
  • Millions of rows supported
    • Streaming of plot tiles for large plots
    • Automatic switching to density plots when plotting huge numbers of points
  • Thousands of columns
• Polygon selection of points
• Categorical filtering
• Sequence filtering
• Native NaN support
• Interactive column-based math
• User accounts and sharing sessions (for server deployments)

Screenshots

Polygon selection

Drag and drop to make a plot

Create subsets of data via polygon, string, or categorical selection

Filter on string columns

Supported files

• .csv files that are pivot tables (columns are measurements, rows are values):

Sequence	Binding	Transduction
SAQAQAQ	0.1	0.231
TTTQQQA	5.12	4.1212
AAATAAT	0.32	0.5423

or

Month	Savings
January	250
February	80
March	450

• .h5ad files also have experimental support. If you try them, please file any issues you experience.

Installation

On a single computer

You can install Plotplot from pip and run it yourself:

pip install plotplot
plotplot

Configration

See plotplot.ini and plotplot/plotplot_config.py for list of configuration options.

Deployment to a server

Plotplot works well on a high-powered server, espeically when colocated with your data.

• Streams data to the user as needed (avoids large transfers if colocated with data)
• Generate plots very quickly
• Open large files when lots of RAM is available

A few features are specifically for shared systems:

• Support for hot-linking from other tools directly into Plotplot
• Share sessions among users
• User authentication with Google accounts
• User whitelist

To deploy on a server, use Docker.

Step 1: Clone

git clone [e-mail]:vector-engineering/plotplot.git

Step 2: Build docker image

DOCKER_BUILDKIT=1 docker build -f Dockerfile -t plotplot .

• pass --build-arg URL_PREFIX=/my-custom-plotplot if you want to change the URL_PREFIX
• you must also change frontend/package.json's homepage line to match your URL_PREFIX

Step 3: Run docker image

# This will run on port 9042
docker run --restart=unless-stopped -p 0.0.0.0:9042:9042 -d plotplot

• The docker image defaults to port 9042, you can change that in the dockerfile.
• To use a custom plotplot.ini file, you should mount the file and set the enrionment variable at runtime:

docker run --restart=unless-stopped -p 0.0.0.0:9042:9042 -d -v /my/dir/plotplot.ini:/app/plotplot.ini -e PLOTPLOT_CONFIG_PATH=/app/plotplot.ini plotplot

Then navigate to your-server.com:9042 and you should see Plotplot.

Step 4: Nginx / reverse proxy

A reverse proxy like Nginx is well supported.

Run with a Docker command like this:

docker run --restart=unless-stopped -p 127.0.0.1:9042:9042 -d plotplot

Example Nginx configuration:

	location = /plotplot/ {
		proxy_pass http://localhost:9042/plotplot/index.html;
		proxy_set_header Host $http_host;
		proxy_redirect default;
		proxy_http_version 1.1;
		proxy_set_header Upgrade $http_upgrade;
		proxy_set_header Connection "upgrade";
		add_header xv-nginx-remote_user $remote_user;

	}

	location /plotplot/ {
		proxy_pass http://localhost:9042/plot/;
		proxy_set_header Host $http_host;
		proxy_redirect default;
		proxy_http_version 1.1;
		proxy_set_header Upgrade $http_upgrade;
		proxy_set_header Connection "upgrade";
		add_header xv-nginx-remote_user $remote_user;
	}

Development setup

Development is done with 2 processes:

1. React
2. Flask

This is so you can live-reload the frontend while working.

Step 1: Clone repo

git clone [e-mail]:vector-engineering/plotplot.git

Step 2: Install React dependencies

cd frontend
npm install

Step 3: Install Python dependencies

cd plotplot
pip install -r requirements.txt

Step 4: Run frontend and backend

cd plotplot
flask run --no-debugger --cert=adhoc

# In a new terminal
cd frontend
npm start

Creating the Python wheel

cd frontend
npm run build
cd ..
poetry build

Building custom-plotly

Plotly has a bug that causes heatmaps with repeated values to be very slow.

The best way to generate this yourself is to use the Docker image that creates it on build.

If you really want to do it yourself:

cd plotly.js

# I used node 18.18.0
npm install
npm install regl-scatter2d@2.1.17 # <--- this is the key step
npm run build

# Then copy the dist/plotly[.min].js file into ./custom-plotly.js
# then in this repo
cd ../plotplot
cp -r ../plotly.js/dist/plotly.min.js frontend/custom-plotly.js
npm install ./custom-plotly.js