Quick Start

This quick start will show how to do the following:

  • Install everything needed to use Bowtie.
  • Write an app connecting a slider to a plot.
  • How to deploy to Heroku.

Install Bowtie

If you use conda, you can install with:

conda install -c conda-forge bowtie-py

If you use pip, you can install with:

pip install bowtie

To install bleeding edge you can use flit, to install:

flit install

Install Yarn

Bowtie uses Yarn to manage the Javascript libraries We need to install it before we can use Bowtie. If you installed Bowtie with conda, Yarn was installed as a dependency and you can move on to Creating Your First App.

Conda Install

Yarn is available through conda-forge:

conda install -c conda-forge yarn

MacOS Install

Yarn is available through Homebrew:

brew install yarn

Other Environments

For other environments please follow the install instructions on the official website.

Creating Your First App

First we’ll import the components we plan on using:

from bowtie.visual import Plotly
from bowtie.control import Nouislider
import numpy as np
import plotlywrapper as pw

I imported Plotlywrapper and Numpy to make the generation of Plotly plots easier. Now we instantiate the components and configure them:

sine_plot = Plotly()
freq_slider = Nouislider(caption='frequency', minimum=1, maximum=10, start=5)

Next we’ll create a listener that generates a plot on slider changes:

def listener(freq):
    freq = float(freq[0])
    t = np.linspace(0, 10, 100)
    sine_plot.do_all(pw.line(t, np.sin(freq * t)).to_json())

If you prefer not to use plotlywrapper:

def listener(freq):
    freq = float(freq[0])
    t = np.linspace(0, 10, 100)
        'data': [{
            'type': 'scatter',
            'mode': 'lines+markers',
            'x': t,
            'y': np.sin(freq * t)

The bowtie.control.Nouislider component sends its values as a list of strings so we had to cast it to a float.

Lastly we need to build the application by laying out the components and connecting listeners to events. The App class handles this and we put this logic into a function. Bowtie provides a decorator, command, which we’ll use to make a simple command line interface. To finish, we simply wrap the function with the command decorator:

from bowtie import command
def main():
    from bowtie import App
    app = App()
    app.subscribe(listener, freq_slider.on_change)
    return app

Now take a look at the CLI we just created by running this script:

python app.py

The output should look something like this:

Usage: app.py [--help] COMMAND [ARGS]...

  Bowtie CLI to help build and run your app.

  --help  Show this message and exit.

  build  Writes the app, downloads the packages, and...
  dev    Recompiles the app for development.
  prod   Recompiles the app for production.
  run    Build the app and serve it.
  serve  Serves the Bowtie app locally.

To construct the app, we run the script with the build command:

python app.py build

This will construct the app, install the JavaScript libraries and compile your project. Once it’s done you should be able to run the following to launch your app:

python app.py serve

That will launch the app locally and you should be able to access it at http://localhost:9991.

Deploy to Heroku

This isn’t streamlined right now but you can try the following approach. For example, this was done to create bowtie-demo so you may refer to that.

  • Create the Procfile, try the following:

    web: python app.py serve -p $PORT
  • Create requirements files, again see bowtie-demo for an example.

  • Rebuild with production settings with webpack, by default Bowtie makes a development build:

    python app.py prod
  • Commit the following files to your repo:

  • Finally push your repo to Heroku!