algorithmic_speedups_plotting.py

import numpy as np
import pandas as pd
import polars as pl
import matplotlib.pyplot as plt

def plot_grouped_benchmark_results(results: dict):
    libraries = [
        f"Polars {pl.__version__}",
        f"Pandas {pd.__version__}",
        f"NumPy {np.__version__}",
    ]
    operations = ["Corr", "Cov"]

    data = {
        op: [results[f"{op} with {lib.split()[0]}"] for lib in libraries]
        for op in operations
    }

    x = range(len(operations))

    x = range(len(operations))

    custom_colors = ['#0075FF', '#73bfb8', '#26413c']

    plt.figure(figsize=(10, 6))

    for i, library in enumerate(libraries):
        plt.bar(
            [pos + i * 0.2 for pos in x],
            [data[operation][i] for operation in operations],
            width=0.15,
            label=library,
            color=custom_colors[i]
        )

    plt.xlabel("Operation")
    plt.ylabel("Average Time (s)")

    plt.title("Benchmarking Results Grouped by Operation")

    ax = plt.gca()
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)

    plt.xticks([pos + 0.2 for pos in x], operations)
    plt.legend()
    plt.savefig('algorithmic_speedups_results.png')
    plt.show()

plot_grouped_benchmark_results(benchmark_results)