app.py

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

# --- НАСТРОЙКИ ---
seqlen = st.slider("Длина последовательности", 10, 100, 60)
steps = st.slider("Количество шагов", 10, 200, 120)
min_run, max_run = st.slider("Длина машин", 1, 10, 1), st.slider("Максимальная длина машин", 2, 10, 2)
ANGLE_MAP = {'A': 60.0, 'C': 180.0, 'G': -60.0, 'T': -180.0, 'N': 0.0}
bases = ['A', 'C', 'G', 'T']

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

# --- Более биологичные мутации ---
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  # только лаги 0..9

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

# --- Начальная цепь ---
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)
lags_shown = 6

def draw_world(seq, axs, step, cnt_hist, ent_hist, ac_hist):
    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)
    axs[0].cla()
    axs[1].cla()
    axs[2].cla()

    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_xlabel("Позиция")
    axs[0].set_ylabel("Торсионный угол (град.)")
    axs[0].set_title(f"Шаг {step}: {seq}\nЧисло машин: {len(runs)}, энтропия: {ent_hist[-1]:.2f}")
    axs[0].legend()

    axs[1].plot(cnt_hist, '-o', color='crimson', markersize=4)
    axs[1].set_xlabel("Шаг")
    axs[1].set_ylabel("Число машин")
    axs[1].set_ylim(0, max(10, max(cnt_hist)+1))
    axs[1].set_title("Динамика: число 'биомашин'")

    axs[2].bar(np.arange(lags_shown), ac_hist[-1][:lags_shown], color='teal', alpha=0.7)
    axs[2].set_xlabel("Лаг")
    axs[2].set_ylabel("Автокорреляция")
    axs[2].set_title("Автокорреляция углового профиля (структурность) и энтропия")
    axs[2].text(0.70,0.70, f"Энтропия: {ent_hist[-1]:.2f}", transform=axs[2].transAxes)

# --- Запуск анимации в Streamlit ---
if st.button("Начать анимацию"):
    chart_placeholder = st.empty()  # создаем пустое место для графиков
    for step in range(steps):
        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)
        stat_autocorr.append(acorr)
        draw_world(seq, axs, step, stat_bist_counts, stat_entropy, stat_autocorr)
        
        chart_placeholder.pyplot(fig)  # обновляем график
        # После каждого шага Streamlit перерисует график