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

# Настройки
seqlen = 60
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']

# --- Логика ---
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:]
        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][::-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 UI ---
st.title("🧬 Эволюция ДНК-подобной цепи с мутациями")
steps = st.slider("Число шагов симуляции", 1, 200, 60)

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

    fig, axs = plt.subplots(3, 1, figsize=(10, 8))
    plt.subplots_adjust(hspace=0.45)

    for i in range(steps):
        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))
        stat_entropy.append(compute_entropy(torsion_profile))
        stat_autocorr.append(compute_autocorr(torsion_profile))
        seq = bio_mutate(seq)

    # Отрисовка последнего состояния
    axs[0].plot(torsion_profile, color='royalblue', label="Торсионный угол")
    for start, end, val in runs:
        axs[0].axvspan(start, end, color="red", alpha=0.3)
        axs[0].plot(range(start, end+1), torsion_profile[start:end+1], 'ro', markersize=5)
    axs[0].set_ylim(-200, 200)
    axs[0].set_title(f"Шаг {steps}: {seq}\nЧисло машин: {len(runs)}, энтропия: {stat_entropy[-1]:.2f}")
    axs[0].legend()

    axs[1].plot(stat_bist_counts, '-o', color='crimson', markersize=4)
    axs[1].set_title("Динамика: число 'биомашин'")

    axs[2].bar(np.arange(6), stat_autocorr[-1][:6], color='teal', alpha=0.7)
    axs[2].set_title("Автокорреляция и энтропия")
    axs[2].text(0.7, 0.7, f"Энтропия: {stat_entropy[-1]:.2f}", transform=axs[2].transAxes)

    st.pyplot(fig)