Overview

scripton.lyra is a plotting toolkit based on the Plotly JavaScript library. Here's a quick example:

from scripton.lyra import plot
import numpy as np

# Evenly spaced values between 0 and 2π
x = np.linspace(0, 2 * np.pi)

# Plot sin(x)
plot({'x': x, 'y': np.sin(x)})

Try running that snippet in Scripton (the Scratchpad might be handy here), and you should see an interactive plot pop up in the visualizations tab.

`lyra.plot`

As shown in the example above, the plot function is used for creating plots. Its signature is:

def plot(
    *traces: dict,
    key: str | None = None,
    **options: dict
):
    ...

The traces positional arguments represent data to be plotted on a single figure. Each trace is a single dictionary.

# This creates a single figure containing two line plots plot( { 'x': x, 'y': np.sin(x), 'name' : 'sin' }, { 'x': x, 'y': np.cos(x), 'name' : 'cos' } )
The plot function is not limited to just line plots (which is just the default). For instance, you can use it to create a histogram:

plot( { 'x': np.random.randn(1000), 'type': 'histogram' } )
The optional key keyword argument can be used to update an existing figure instead of creating a new one.

# Display an animated version of the plot by updating # the same figure in a loop. for t in range(1000): plot( { 'x': x, 'y': np.cos(x + t / 10) }, # Using a fixed key here causes the same figure to be updated. key='oscillator' ) time.sleep(0.01)
Optional keyword arguments can be used to control various attributes of the plot like its layout:

plot( {'x': x, 'y': np.sin(x), 'name': 'sin'}, {'x': x, 'y': np.cos(x), 'name': 'cos', 'xaxis': 'x2', 'yaxis': 'y2'}, grid={'rows': 2, 'columns': 1, 'pattern': 'independent'} )

Pandas Support

While the examples above use NumPy arrays, you can also pass in Pandas Series instances as well.

import pandas as pd
from scripton.lyra import plot

df = pd.read_csv('iris.csv')

plot(
    {
        'x': df['sepal_width'],
        'y': df['sepal_length'],
        'mode': 'markers',
    }
)