Reactor-Performance-Analyzer/main.py at main · MatthewNguyen865/Reactor-Performance-Analyzer · GitHub

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import numpy as np
from core.metrics import compute_conversion, compute_reaction_rate
from plotting import plot_conversion, plot_reaction_rate, plot_average_conversion, set_plot_style
from core.data_extractor import load_data
from core.comparisons import summarize_scenarios, export_summary_csv
from data_generator import generate_dataset
from core.statistics import percent_improvement, rank_scenarios

def main():
    #generate and load data
    #Note: plots in README.md and data in data.csv use seed = 1
    generate = input("Generate new dataset? (y/n): ")
    if generate.lower() == "y":
        seed = input("Enter random seed (integer) or leave blank: ")
        if seed == "":
            seed = None
        else:
            seed = int(seed)

        generate_dataset(seed=seed)

    time, CA_in, CA_out_base, CA_out_fast, CA_out_slow = load_data("data.csv")

    #compute conversion, reactor performance
    # baseline
    conversion_base = compute_conversion(CA_in, CA_out_base)

    # fast reaction scenario
    conversion_fast = compute_conversion(CA_in, CA_out_fast)

    # slow reaction scenario
    conversion_slow = compute_conversion(CA_in, CA_out_slow)

    conversions = [conversion_base, conversion_fast, conversion_slow]

    #compute reaction rates
    reaction_rate_base = compute_reaction_rate(CA_out_base, time)
    reaction_rate_fast = compute_reaction_rate(CA_out_fast, time)
    reaction_rate_slow = compute_reaction_rate(CA_out_slow, time)

    reaction_rates = [reaction_rate_base, reaction_rate_fast, reaction_rate_slow]

    #plot results
    labels = [
        "Base",
        "Fast Reaction",
        "Slow Reaction"
    ]
    set_plot_style()
    all_values = np.concatenate(conversions)
    plot_conversion(time,
                    np.max(all_values),
                    conversions,
                    labels)

    plot_reaction_rate(time,
                    reaction_rates,
                    labels)

    plot_average_conversion(
        labels,
        conversions
    )

    #summarize results
    summarize_scenarios(
        labels,
        conversions,
        reaction_rates
    )

    export_summary_csv(
        labels,
        conversions,
        reaction_rates
    )

    #ranking scenarios
    ranking = rank_scenarios(labels, conversions)

    print("\nScenario Ranking (by Avg Conversion):")
    for name, value in ranking:
        print(f"{name}: {value:.3f}")

    print("\nPercent Improvement vs Base:")

    for i in range(len(labels)):
        if labels[i] != "Base":
            improvement = percent_improvement(conversions[0], conversions[i])
            print(f"{labels[i]}: {improvement:.2f}%")

if __name__ == "__main__":
    main()