genetic-string-solver/plotter.py at main · arnavlul/genetic-string-solver · GitHub

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<<<<<<< HEAD
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
import pandas as pd

def plot_mutation_with_sliders():
    # 1. Read the CSV file
    try:
        data = pd.read_csv("time_data_multiprocessing.csv")
    except FileNotFoundError:
        print("Error: 'time_data_multiprocessing.csv' not found.")
        return

    mutation_rates = data["Mutation Rate"]
    avg_generations = data["Average Time"]

    # 2. Setup the Figure and Subplots
    # We need space on the bottom and left for sliders, so we adjust the subplot
    fig, ax = plt.subplots(figsize=(10, 7))
    plt.subplots_adjust(bottom=0.25, left=0.15)

    # 3. Initial Plot
    # We store the 'line' object so we could update data later if needed (optional here)
    line, = ax.plot(mutation_rates, avg_generations, marker='o', linestyle='-', color='b')

    # Set initial titles and labels
    ax.set_title("Impact of Mutation Rate on Convergence Speed")
    ax.set_xlabel("Mutation Rate")
    ax.set_ylabel("Generations")
    ax.grid(True, linestyle='--', alpha=0.7)

    # Calculate initial max values for limits
    max_x = max(mutation_rates)
    max_y = max(avg_generations)

    # Set initial view
    ax.set_xlim(0, max_x)
    ax.set_ylim(0, max_y)

    # 4. Create Sliders
    # Define the area for the sliders [left, bottom, width, height]
    ax_slider_x = plt.axes([0.15, 0.1, 0.65, 0.03], facecolor='lightgoldenrodyellow')
    ax_slider_y = plt.axes([0.05, 0.25, 0.03, 0.6], facecolor='lightgoldenrodyellow')

    # Create the slider objects
    # valmin=0.01 prevents crashing by trying to zoom into 0 width
    slider_x = Slider(ax_slider_x, 'Max X-Axis', valmin=0.01, valmax=1.0, valinit=max_x)

    # Vertical slider for Y-axis (orientation='vertical')
    slider_y = Slider(ax_slider_y, 'Max Y-Axis', valmin=10, valmax=max_y, valinit=max_y, orientation='vertical')

    # 5. The Update Function
    # This runs every time you move a slider
    def update(val):
        # Update the view limits based on slider values
        ax.set_xlim(0, slider_x.val)
        ax.set_ylim(0, slider_y.val)

        # Redraw the figure
        fig.canvas.draw_idle()

    # Connect the sliders to the update function
    slider_x.on_changed(update)
    slider_y.on_changed(update)

    print("Displaying interactive plot...")
    plt.show()

if __name__ == "__main__":
=======
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
import pandas as pd

def plot_mutation_with_sliders():
    # 1. Read the CSV file
    try:
        data = pd.read_csv("time_data_multiprocessing.csv")
    except FileNotFoundError:
        print("Error: 'time_data_multiprocessing.csv' not found.")
        return

    mutation_rates = data["Mutation Rate"]
    avg_generations = data["Average Time"]

    # 2. Setup the Figure and Subplots
    # We need space on the bottom and left for sliders, so we adjust the subplot
    fig, ax = plt.subplots(figsize=(10, 7))
    plt.subplots_adjust(bottom=0.25, left=0.15)

    # 3. Initial Plot
    # We store the 'line' object so we could update data later if needed (optional here)
    line, = ax.plot(mutation_rates, avg_generations, marker='o', linestyle='-', color='b')

    # Set initial titles and labels
    ax.set_title("Impact of Mutation Rate on Convergence Speed")
    ax.set_xlabel("Mutation Rate")
    ax.set_ylabel("Generations")
    ax.grid(True, linestyle='--', alpha=0.7)

    # Calculate initial max values for limits
    max_x = max(mutation_rates)
    max_y = max(avg_generations)

    # Set initial view
    ax.set_xlim(0, max_x)
    ax.set_ylim(0, max_y)

    # 4. Create Sliders
    # Define the area for the sliders [left, bottom, width, height]
    ax_slider_x = plt.axes([0.15, 0.1, 0.65, 0.03], facecolor='lightgoldenrodyellow')
    ax_slider_y = plt.axes([0.05, 0.25, 0.03, 0.6], facecolor='lightgoldenrodyellow')

    # Create the slider objects
    # valmin=0.01 prevents crashing by trying to zoom into 0 width
    slider_x = Slider(ax_slider_x, 'Max X-Axis', valmin=0.01, valmax=1.0, valinit=max_x)

    # Vertical slider for Y-axis (orientation='vertical')
    slider_y = Slider(ax_slider_y, 'Max Y-Axis', valmin=10, valmax=max_y, valinit=max_y, orientation='vertical')

    # 5. The Update Function
    # This runs every time you move a slider
    def update(val):
        # Update the view limits based on slider values
        ax.set_xlim(0, slider_x.val)
        ax.set_ylim(0, slider_y.val)

        # Redraw the figure
        fig.canvas.draw_idle()

    # Connect the sliders to the update function
    slider_x.on_changed(update)
    slider_y.on_changed(update)

    print("Displaying interactive plot...")
    plt.show()

if __name__ == "__main__":
>>>>>>> 61c30840d841667855d5aadac32653688844cec0
    plot_mutation_with_sliders()