Loop live update julia plot

When working with Julia, it is often necessary to update plots in real-time. This can be particularly useful when visualizing data that is constantly changing or when creating animations. In this article, we will explore three different ways to achieve live updates in Julia plots.

Option 1: Using the Plots.jl Package

The Plots.jl package provides a high-level interface for creating and manipulating plots in Julia. To achieve live updates, we can make use of the `plot!` function, which allows us to add new data points to an existing plot.

using Plots

# Create an empty plot
plot()

# Generate some random data
x = 1:10
y = rand(10)

# Add initial data to the plot
plot!(x, y)

# Update the plot in a loop
for i in 1:10
    y = rand(10)
    plot!(x, y)
    display(plot!)
    sleep(1)  # Add a delay to slow down the updates
end

This code snippet creates an empty plot and adds initial data to it. Then, it enters a loop where it generates new random data, updates the plot using `plot!`, displays the updated plot using `display`, and adds a delay using `sleep`. This process is repeated 10 times, resulting in a live update of the plot.

Option 2: Using the Gaston.jl Package

The Gaston.jl package provides a Julia interface to the Gnuplot plotting program. To achieve live updates, we can make use of the `replot` function, which allows us to update an existing plot with new data.

using Gaston

# Create an empty plot
g = plot()

# Generate some random data
x = 1:10
y = rand(10)

# Add initial data to the plot
plot!(g, x, y)

# Update the plot in a loop
for i in 1:10
    y = rand(10)
    replot!(g, x, y)
    sleep(1)  # Add a delay to slow down the updates
end

This code snippet is similar to the previous one, but it uses the Gaston.jl package instead. It creates an empty plot, adds initial data to it, and enters a loop where it generates new random data, updates the plot using `replot!`, and adds a delay using `sleep`. This process is repeated 10 times, resulting in a live update of the plot.

Option 3: Using the Makie.jl Package

The Makie.jl package provides a high-performance plotting library for Julia. To achieve live updates, we can make use of the `scatter!` function, which allows us to add new data points to an existing scatter plot.

using Makie

# Create an empty scatter plot
scatterplot = scatter()

# Generate some random data
x = 1:10
y = rand(10)

# Add initial data to the scatter plot
scatter!(scatterplot, x, y)

# Update the scatter plot in a loop
for i in 1:10
    y = rand(10)
    scatter!(scatterplot, x, y)
    sleep(1)  # Add a delay to slow down the updates
end

This code snippet creates an empty scatter plot and adds initial data to it. Then, it enters a loop where it generates new random data, updates the scatter plot using `scatter!`, and adds a delay using `sleep`. This process is repeated 10 times, resulting in a live update of the scatter plot.

After exploring these three options, it is clear that the Plots.jl package provides the most straightforward and intuitive way to achieve live updates in Julia plots. It offers a high-level interface and a wide range of plotting functionalities, making it a versatile choice for real-time visualization. However, the choice ultimately depends on the specific requirements of your project and your familiarity with the different packages.