app.py

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

# --- НАСТРОЙКИ ---
seqlen = 60
steps = 10000
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']
lags_shown = 6

st.set_page_config(layout="wide")
st.title("🌌 Визуализация торсионных биомашин")

# --- ИНТЕРФЕЙС ---
col1, col2, col3 = st.columns([1,1,2])
with col1:
    if 'running' not in st.session_state:
        st.session_state.running = False
    if st.button("▶️ Старт / ⏸ Стоп"):
        st.session_state.running = not st.session_state.running

with col2:
    if st.button("🔄 Сброс"):
        st.session_state.running = False
        st.session_state.step = 0
        st.session_state.seq = ''.join(random.choices(bases, k=seqlen))
        st.session_state.stat_bist_counts = []
        st.session_state.stat_entropy = []
        st.session_state.stat_autocorr = []

with col3:
    speed = st.slider("⏱ Скорость обновления (мс)", 10, 1000, 200, step=10)

# --- ИНИЦИАЛИЗАЦИЯ ---
if 'seq' not in st.session_state:
    st.session_state.seq = ''.join(random.choices(bases, k=seqlen))
if 'step' not in st.session_state:
    st.session_state.step = 0
if 'stat_bist_counts' not in st.session_state:
    st.session_state.stat_bist_counts = []
    st.session_state.stat_entropy = []
    st.session_state.stat_autocorr = []

# --- ФУНКЦИИ ---
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 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)

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

# --- ВИЗУАЛИЗАЦИЯ ---
plot_area = st.empty()
fig, axs = plt.subplots(3, 1, figsize=(10, 8))
plt.subplots_adjust(hspace=0.5)

# --- ЦИКЛ ВИЗУАЛИЗАЦИИ ---
while st.session_state.running:
    st.session_state.seq = bio_mutate(st.session_state.seq)
    torsion_profile = np.array([ANGLE_MAP.get(nt, 0.0) for nt in st.session_state.seq])
    runs = find_local_min_runs(torsion_profile, min_run, max_run)
    ent = compute_entropy(torsion_profile)
    acorr = compute_autocorr(torsion_profile)

    st.session_state.stat_bist_counts = st.session_state.stat_bist_counts[-50:] + [len(runs)]
    st.session_state.stat_entropy = st.session_state.stat_entropy[-50:] + [ent]
    st.session_state.stat_autocorr = st.session_state.stat_autocorr[-50:] + [acorr]

    axs[0].cla()
    axs[1].cla()
    axs[2].cla()

    axs[0].plot(torsion_profile, color='royalblue')
    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=4)
    axs[0].set_ylim(-200, 200)
    axs[0].set_title(f"Шаг {st.session_state.step}: {st.session_state.seq}\nМашин: {len(runs)}, Энтропия: {ent:.2f}")

    axs[1].plot(st.session_state.stat_bist_counts, '-o', color='crimson', markersize=4)
    axs[1].set_title("Число 'биомашин'")

    axs[2].bar(np.arange(lags_shown), acorr[:lags_shown], color='teal')
    axs[2].set_title("Автокорреляция углового профиля")

    plot_area.pyplot(fig, clear_figure=True)
    st.session_state.step += 1
    time.sleep(speed / 1000.0)