Interactive Scatter Plot

This notebook shows usage examples of interactive Scatter Plot. Detailed API description can be found here.

Import functions

from bokeh.plotting import output_file, save, output_notebook
from ESMBenchmarkViz import scatter_plot

# Enable Bokeh output in the notebook
output_notebook()

Loading BokehJS ...

1: Basic usage

Example 1 show basic usage of the function.

x = [1, 2, 3]
y = [6, 7, 2]
names = ["Point A", "Point B", "Point C"]

# Create the plot layout
p = scatter_plot(x, y, names)

2: Basic usage with images

Example 2 exapands to include images.

x = [1, 2, 3]
y = [6, 7, 2]
names = ["Point A", "Point B", "Point C"]
images = [
    "images/image1.jpg",  # Example of a valid image URL
    None,  # Example of no image (None value)
    "images/image3.jpg",  # Example of another valid image URL
]

# Create the plot layout
p = scatter_plot(x, y, names, images=images)

3: Practical usage example

x = [1.26, 0.93, 0.83]  # RMSE
y = [0.87, 0.91, 0.93]  # Correlation
names = ["ACCESS-CM2", "E3SM-1-0", "GFDL-CM4"]
images = [
    "images/model_sample1.png",
    "images/model_sample2.png",
    "images/model_sample3.png",
]

# Create the plot layout
p = scatter_plot(x, y, names, images=images, width=460, height=600)

Save the plot

# set output to static HTML file
output_file(filename="interactive_scatter_plot.html", title="Interactive Scatter Plot")

# save the results to a file
save(p)

# Result: [`interactive_scatter_plot.html`](interactive_scatter_plot.html)

'/pscratch/sd/l/lee1043/git/ESMBenchmarkViz/docs/examples/interactive_scatter_plot.html'