app.py

import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
import random
from scipy.stats import entropy as scipy_entropy
import time
from matplotlib.animation import FuncAnimation

# --- НАСТРОЙКИ ---
seqlen = 60
steps = 120
min_run, max_run = 1, 2
ANGLE_MAP = {'A': 60.0, 'C': 180.0, 'G': -60.0, 'T': -180.0, 'N': 0.0}
bases = ['A', 'C', 'G', 'T']

# Функции остаются такими же
def find_local_min_runs(profile, min_run=1, max_run=2):
    result = []
    N = len(profile)
    i = 0
    while i < N:
        run_val = profile[i]
        run_length = 1
        while i + run_length < N and profile[i + run_length] == run_val:
            run_length += 1
        if min_run <= run_length <= max_run:
            result.append((i, i + run_length - 1, run_val))
        i += run_length
    return result

def bio_mutate(seq):
    r = random.random()
    if r < 0.70:
        idx = random.randint(0, len(seq)-1)
        orig = seq[idx]
        prob = random.random()
        if orig in 'AG':
            newbase = 'C' if prob < 0.65 else random.choice(['T', 'C'])
        elif orig in 'CT':
            newbase = 'G' if prob < 0.65 else random.choice(['A', 'G'])
        else:
            newbase = random.choice([b for b in bases if b != orig])
        seq = seq[:idx] + newbase + seq[idx+1:]
    elif r < 0.80:
        idx = random.randint(0, len(seq)-1)
        ins = ''.join(random.choices(bases, k=random.randint(1, 3)))
        seq = seq[:idx] + ins + seq[idx:]
        if len(seq) > seqlen:
            seq = seq[:seqlen]
    elif r < 0.90:
        if len(seq) > 4:
            idx = random.randint(0, len(seq)-2)
            dell = random.randint(1, min(3, len(seq)-idx))
            seq = seq[:idx] + seq[idx+dell:]
    else:
        if len(seq) > 10:
            start = random.randint(0, len(seq)-6)
            end = start + random.randint(3,6)
            subseq = seq[start:end]
            subseq = subseq[::-1]
            seq = seq[:start] + subseq + seq[end:]
    while len(seq) < seqlen:
        seq += random.choice(bases)
    if len(seq) > seqlen:
        seq = seq[:seqlen]
    return seq

def compute_autocorr(profile):
    profile = profile - np.mean(profile)
    result = np.correlate(profile, profile, mode='full')
    result = result[result.size // 2:]
    norm = np.max(result) if np.max(result)!=0 else 1
    return result[:10]/norm

def compute_entropy(profile):
    vals, counts = np.unique(profile, return_counts=True)
    p = counts / counts.sum()
    return scipy_entropy(p, base=2)

# --- Streamlit интерфейс ---
st.title("🧬 Эволюция ДНК-подобной последовательности")
st.markdown("Модель визуализирует мутации и анализирует структуру последовательности во времени.")

# Кнопка запуска симуляции
if st.button("▶️ Запустить симуляцию"):
    seq = ''.join(random.choices(bases, k=seqlen))
    stat_bist_counts = []
    stat_entropy = []
    stat_autocorr = []

    plot_placeholder = st.empty()

    # Создание фигуры и осей
    fig, axs = plt.subplots(3, 1, figsize=(10, 8))
    plt.subplots_adjust(hspace=0.45)

    # Начальная инициализация для графиков
    line1, = axs[0].plot([], [], color='royalblue', label="Торсионный угол")
    runs_patch = axs[0].axvspan(0, 0, color="red", alpha=0.3)
    line2, = axs[1].plot([], [], '-o', color='crimson', markersize=4)
    bar = axs[2].bar([], [], color='teal', alpha=0.7)

    axs[0].set_ylim(-200, 200)
    axs[0].set_xlabel("Позиция")
    axs[0].set_ylabel("Торсионный угол (град.)")
    axs[0].set_title(f"Шаг 0: {seq}")
    axs[0].legend()

    axs[1].set_xlabel("Шаг")
    axs[1].set_ylabel("Число машин")
    axs[1].set_ylim(0, 10)
    axs[1].set_title("Динамика: число 'биомашин'")

    axs[2].set_xlabel("Лаг")
    axs[2].set_ylabel("Автокорреляция")
    axs[2].set_title("Автокорреляция углового профиля")

    def update(frame):
        nonlocal seq
        if frame != 0:
            seq = bio_mutate(seq)
        torsion_profile = np.array([ANGLE_MAP.get(nt, 0.0) for nt in seq])
        runs = find_local_min_runs(torsion_profile, min_run, max_run)
        stat_bist_counts.append(len(runs))
        ent = compute_entropy(torsion_profile)
        stat_entropy.append(ent)
        acorr = compute_autocorr(torsion_profile)

        # Обновление графиков
        line1.set_data(range(len(torsion_profile)), torsion_profile)
        for start, end, val in runs:
            runs_patch.set_xy([(start, -200), (end, -200), (end, 200), (start, 200)])
        line2.set_data(range(len(stat_bist_counts)), stat_bist_counts)
        for i, b in enumerate(acorr[:6]):
            bar[i].set_height(b)
        
        axs[0].set_title(f"Шаг {frame}: {seq}\nЧисло машин: {len(runs)}, энтропия: {ent:.2f}")
        axs[2].text(0.70, 0.70, f"Энтропия: {ent:.2f}", transform=axs[2].transAxes)
        
        return line1, runs_patch, line2, bar

    ani = FuncAnimation(fig, update, frames=range(steps), blit=True, interval=100)

    # Показ анимации в Streamlit
    plot_placeholder.pyplot(fig)