Struct PlotConfig 

pub struct PlotConfig<'a> { /* private fields */ }

Configuration for plotting.

Implementations

impl<'a> PlotConfig<'a>

pub fn new( title: &'a str, caption: &'a str, x_label: &'a str, y_label: &'a str, scale: Scale, ) -> PlotConfig<'a>

Crate a new PlotConfig.

Prefer using PlotConfig::default and then setting the desired values.

pub fn with_x_label(self, x_label: &'a str) -> PlotConfig<'a>

Sets the x label for the plot.

pub fn with_y_label(self, y_label: &'a str) -> PlotConfig<'a>

Sets the y label for the plot.

pub fn with_title(self, title: &'a str) -> PlotConfig<'a>

Sets the title for the plot.

Examples found in repository

examples/sorting/main.rs (line 46)

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 52)

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);
}

pub fn with_caption(self, caption: &'a str) -> PlotConfig<'a>

Sets the caption for the plot.

Examples found in repository

examples/sorting/main.rs (line 47)

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 53)

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);
}

pub fn with_scale(self, scale: Scale) -> PlotConfig<'a>

Sets the scale for the plot.

Trait Implementations

impl<'a> Default for PlotConfig<'a>

fn default() -> PlotConfig<'a>

Returns the “default value” for a type.