Function time_plot 

pub fn time_plot(
    file_name: &str,
    measurements: Measurements,
    config: &PlotConfig<'_>,
)

Plots the data from the Measurements using plotters. The plot is saved to the file specified by file_name, the file created will be an SVG file.

Arguments

• file_name - The name of the file to save the plot to
• measurements - The measurements to plot
• builder - The builder that was used to generate the measurements

Examples found in repository

examples/sorting/main.rs (line 49)

fn main() {
    // Create a distribution for the length of the vectors
    // Here we use an exponential distribution with a minimum of 1000 and a maximum of 500_000
    let length_distribution = Exponential::new(1000..=500_000);

    // Create the builder for the vectors
    let vector_builder = InputBuilder::new(length_distribution, ());

    // Build the vectors
    // Here we build 2000 vectors, 10 of each length
    let mut vectors = vector_builder.build_with_repetitions(200, 10);

    // Create a slice of the algorithms we want to measure
    let algorithms: &[(fn(&mut input::InputVec), &str); 2] = &[
        (merge_sort_input, "Merge sort"),
        (quick_sort_input, "Quick sort"),
    ];

    // Measure the algorithms on the vectors, given a relative error of 0.001
    let results = measure_mut(&mut vectors, algorithms, 0.001);

    let result_clone = results.clone();
    // Serialize the results to a json file
    result_clone.serialize_json("results.json");

    let file_name = "results/sorting.svg";

    // Plot the results
    let config = PlotConfig::default()
        .with_title("Sorting algorithms")
        .with_caption("The time plot of sorting algorithms");

    time_plot(file_name, results, &config);
}

examples/search/main.rs (line 56)

fn main() {
    // Create a distribution for the length of the vectors
    // Here we use an uniform distribution with a minimum of 10 and a maximum of 100_000
    let length_distribution = Uniform::new(10..=100_000);

    // Create the builder for the vectors
    // Here we choose to use the fast generator method in order to generate ordered vectors
    let vector_builder = InputBuilder::new(length_distribution, Generator::Fast);

    // Build 200 vectors
    let vectors = vector_builder.build(200);

    // Create a slice of the algorithms we want to measure
    let algorithms: &[(fn(&input::SearchInput) -> Option<usize>, &str); 2] = &[
        (linear_search_input, "Linear search"),
        (binary_search_input, "Binary search"),
    ];

    // Measure the algorithms on the vectors, given a relative error of 0.001
    let results = measure(&vectors, algorithms, 0.001);

    let file_name = "results/search.svg";

    // Here we print the linear regression of the log-log scale of the results
    for result in results.clone().measurements {
        let log_linear_regression = result.log_log_scale().linear_regression();
        println!(
            "{}: {} * x + {}",
            result.algorithm_name, log_linear_regression.0, log_linear_regression.1
        )
    }

    let config = PlotConfig::default()
        .with_title("Search in an ordered vector")
        .with_caption("The time plot of searching algorithms in an ordered vector");

    // Plot the results
    time_plot(file_name, results, &config);
}