Discrete colorbar with plotlyjs

When working with plotlyjs in Julia, you may come across the need to create a discrete colorbar. By default, plotlyjs generates a continuous colorbar, but there are several ways to achieve a discrete colorbar. In this article, we will explore three different approaches to solve this problem.

Approach 1: Using a Color Scale

One way to create a discrete colorbar is by using a color scale. Plotlyjs allows you to define a custom color scale with specific colors for each value in your data. Here’s an example:

using PlotlyJS

# Define your data
x = 1:10
y = 1:10
z = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1]

# Define your custom color scale
colors = ["red", "green", "blue"]

# Create a scatter plot with a color scale
scatter = scatter(x=x, y=y, mode="markers", marker_color=z, marker_colorscale=colors)

# Create a layout with a colorbar
layout = Layout(coloraxis_colorbar=dict(
    title="My Colorbar",
    tickvals=[1, 2, 3],
    ticktext=["Value 1", "Value 2", "Value 3"]
))

# Create the plot
plot(scatter, layout)

This approach allows you to define specific colors for each value in your data, creating a discrete colorbar. However, it requires manually defining the color scale and tick values, which can be time-consuming for large datasets.

Approach 2: Using a Categorical Color Scale

Another way to create a discrete colorbar is by using a categorical color scale. Plotlyjs provides a built-in categorical color scale that automatically assigns a different color to each unique value in your data. Here’s an example:

using PlotlyJS

# Define your data
x = 1:10
y = 1:10
z = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1]

# Create a scatter plot with a categorical color scale
scatter = scatter(x=x, y=y, mode="markers", marker_color=z, marker_colorscales="Viridis")

# Create the plot
plot(scatter)

This approach automatically assigns a different color to each unique value in your data, creating a discrete colorbar. It is more convenient than the previous approach as it does not require manually defining the color scale and tick values.

Approach 3: Using a Color Discretization Function

If you prefer a more flexible approach, you can use a color discretization function to map your data values to specific colors. Here’s an example:

using PlotlyJS

# Define your data
x = 1:10
y = 1:10
z = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1]

# Define your color discretization function
function discretize_color(value)
    if value == 1
        return "red"
    elseif value == 2
        return "green"
    elseif value == 3
        return "blue"
    end
end

# Create a scatter plot with a color discretization function
scatter = scatter(x=x, y=y, mode="markers", marker_color=z, marker_colorscale=discretize_color)

# Create the plot
plot(scatter)

This approach allows you to define a custom color discretization function that maps your data values to specific colors. It provides the most flexibility but requires manual coding of the function.

After exploring these three approaches, it is clear that using a categorical color scale (Approach 2) is the most convenient and efficient way to create a discrete colorbar in plotlyjs. It automatically assigns colors to each unique value in your data, saving you time and effort compared to the other approaches.