import tkinter as tk
from tkinter import ttk
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

matplotlib.use('TkAgg')

def generate_data(mu, sigma, n=1000):
    return np.random.normal(loc=mu, scale=sigma, size=n)

def draw_plot(ax, mu, sigma):
    ax.clear()
    data = generate_data(mu, sigma)
    ax.hist(data, bins=30, density=True, alpha=0.6, color='steelblue')
    x = np.linspace(mu - 4 * sigma, mu + 4 * sigma, 300)
    pdf = (1 / (sigma * np.sqrt(2 * np.pi)))
    pdf = (1 / (sigma * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - mu) / sigma) ** 2)
    ax.plot(x, pdf, color='darkred', lw=2)
    ax.set_title(f'Normal Distribution (μ={mu:.2f}, σ={sigma:.2f})')
    ax.set_xlabel('Value')
    ax.set_ylabel('Density')
    ax.figure.canvas.draw_idle()

root = tk.Tk()
root.title("Normal Distribution Explorer")

fig, ax = plt.subplots(figsize=(5, 3), dpi=100)
canvas = FigureCanvasTkAgg(fig, master=root)
canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)

slider_frame = ttk.Frame(root, padding=10)
slider_frame.pack(fill=tk.X)

mu_label = ttk.Label(slider_frame, text="μ (mean):")
mu_label.grid(row=0, column=0, sticky="w")
mu_var = tk.DoubleVar(value=0.0)
mu_slider = ttk.Scale(
    slider_frame,
    from_=-10, to=10, orient="horizontal",
    variable=mu_var, command=lambda e: draw_plot(ax, mu_var.get(), sigma_var.get())
)
mu_slider.grid(row=0, column=1, sticky="ew", padx=5)
slider_frame.columnconfigure(1, weight=1)

sigma_label = ttk.Label(slider_frame, text="σ (std dev):")
sigma_label.grid(row=1, column=0, sticky="w")
sigma_var = tk.DoubleVar(value=1.0)
sigma_slider = ttk.Scale(
    slider_frame,
    from_=0.1, to=5, orient="horizontal",
    variable=sigma_var, command=lambda e: draw_plot(ax, mu_var.get(), sigma_var.get())
)
sigma_slider.grid(row=1, column=1, sticky="ew", padx=5)

draw_plot(ax, mu_var.get(), sigma_var.get())

root.mainloop()