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import numpy as np |
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import matplotlib.pyplot as plt |
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import random |
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from scipy.stats import entropy as scipy_entropy |
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import streamlit as st |
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seqlen = st.slider("Длина последовательности", 10, 100, 60) |
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steps = st.slider("Количество шагов", 10, 200, 120) |
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min_run, max_run = st.slider("Длина машин", 1, 10, 1), st.slider("Максимальная длина машин", 2, 10, 2) |
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ANGLE_MAP = {'A': 60.0, 'C': 180.0, 'G': -60.0, 'T': -180.0, 'N': 0.0} |
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bases = ['A', 'C', 'G', 'T'] |
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def find_local_min_runs(profile, min_run=1, max_run=2): |
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result = [] |
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N = len(profile) |
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i = 0 |
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while i < N: |
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run_val = profile[i] |
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run_length = 1 |
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while i + run_length < N and profile[i + run_length] == run_val: |
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run_length += 1 |
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if min_run <= run_length <= max_run: |
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result.append((i, i + run_length - 1, run_val)) |
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i += run_length |
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return result |
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def bio_mutate(seq): |
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r = random.random() |
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if r < 0.70: |
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idx = random.randint(0, len(seq)-1) |
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orig = seq[idx] |
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prob = random.random() |
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if orig in 'AG': |
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newbase = 'C' if prob < 0.65 else random.choice(['T', 'C']) |
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elif orig in 'CT': |
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newbase = 'G' if prob < 0.65 else random.choice(['A', 'G']) |
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else: |
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newbase = random.choice([b for b in bases if b != orig]) |
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seq = seq[:idx] + newbase + seq[idx+1:] |
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elif r < 0.80: |
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idx = random.randint(0, len(seq)-1) |
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ins = ''.join(random.choices(bases, k=random.randint(1, 3))) |
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seq = seq[:idx] + ins + seq[idx:] |
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if len(seq) > seqlen: |
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seq = seq[:seqlen] |
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elif r < 0.90: |
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if len(seq) > 4: |
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idx = random.randint(0, len(seq)-2) |
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dell = random.randint(1, min(3, len(seq)-idx)) |
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seq = seq[:idx] + seq[idx+dell:] |
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else: |
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if len(seq) > 10: |
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start = random.randint(0, len(seq)-6) |
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end = start + random.randint(3,6) |
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subseq = seq[start:end] |
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subseq = subseq[::-1] |
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seq = seq[:start] + subseq + seq[end:] |
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while len(seq) < seqlen: |
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seq += random.choice(bases) |
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if len(seq) > seqlen: |
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seq = seq[:seqlen] |
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return seq |
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def compute_autocorr(profile): |
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profile = profile - np.mean(profile) |
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result = np.correlate(profile, profile, mode='full') |
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result = result[result.size // 2:] |
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norm = np.max(result) if np.max(result)!=0 else 1 |
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return result[:10]/norm |
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def compute_entropy(profile): |
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vals, counts = np.unique(profile, return_counts=True) |
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p = counts / counts.sum() |
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return scipy_entropy(p, base=2) |
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seq = ''.join(random.choices(bases, k=seqlen)) |
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stat_bist_counts = [] |
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stat_entropy = [] |
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stat_autocorr = [] |
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fig, axs = plt.subplots(3, 1, figsize=(10, 8)) |
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plt.subplots_adjust(hspace=0.45) |
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lags_shown = 6 |
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def draw_world(seq, axs, step, cnt_hist, ent_hist, ac_hist): |
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torsion_profile = np.array([ANGLE_MAP.get(nt, 0.0) for nt in seq]) |
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runs = find_local_min_runs(torsion_profile, min_run, max_run) |
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axs[0].cla() |
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axs[1].cla() |
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axs[2].cla() |
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axs[0].plot(torsion_profile, color='royalblue', label="Торсионный угол") |
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for start, end, val in runs: |
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axs[0].axvspan(start, end, color="red", alpha=0.3) |
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axs[0].plot(range(start, end+1), torsion_profile[start:end+1], 'ro', markersize=5) |
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axs[0].set_ylim(-200, 200) |
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axs[0].set_xlabel("Позиция") |
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axs[0].set_ylabel("Торсионный угол (град.)") |
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axs[0].set_title(f"Шаг {step}: {seq}\nЧисло машин: {len(runs)}, энтропия: {ent_hist[-1]:.2f}") |
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axs[0].legend() |
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axs[1].plot(cnt_hist, '-o', color='crimson', markersize=4) |
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axs[1].set_xlabel("Шаг") |
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axs[1].set_ylabel("Число машин") |
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axs[1].set_ylim(0, max(10, max(cnt_hist)+1)) |
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axs[1].set_title("Динамика: число 'биомашин'") |
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axs[2].bar(np.arange(lags_shown), ac_hist[-1][:lags_shown], color='teal', alpha=0.7) |
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axs[2].set_xlabel("Лаг") |
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axs[2].set_ylabel("Автокорреляция") |
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axs[2].set_title("Автокорреляция углового профиля (структурность) и энтропия") |
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axs[2].text(0.70,0.70, f"Энтропия: {ent_hist[-1]:.2f}", transform=axs[2].transAxes) |
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if st.button("Начать анимацию"): |
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chart_placeholder = st.empty() |
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for step in range(steps): |
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seq = bio_mutate(seq) |
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torsion_profile = np.array([ANGLE_MAP.get(nt, 0.0) for nt in seq]) |
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runs = find_local_min_runs(torsion_profile, min_run, max_run) |
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stat_bist_counts.append(len(runs)) |
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ent = compute_entropy(torsion_profile) |
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stat_entropy.append(ent) |
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acorr = compute_autocorr(torsion_profile) |
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stat_autocorr.append(acorr) |
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draw_world(seq, axs, step, stat_bist_counts, stat_entropy, stat_autocorr) |
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chart_placeholder.pyplot(fig) |
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