Update app.py

app.py

@@ -1,51 +1,16 @@
-import streamlit as st
 import numpy as np
 import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
 import random
-import time
 from scipy.stats import entropy as scipy_entropy
 
-# Константы
+# --- 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']
-lags_shown = 6
 
-st.set_page_config(layout="wide")
-st.title("🌌 Визуализация торсионных биомашин")
-
-# UI
-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)
-
-# Init
-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)
@@ -60,21 +25,10 @@ def find_local_min_runs(profile, min_run=1, max_run=2):
         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)
-
+# --- Более биологичные мутации ---
 def bio_mutate(seq):
     r = random.random()
-    if r < 0.70:
+    if r < 0.70:  # Точечная мутация
         idx = random.randint(0, len(seq)-1)
         orig = seq[idx]
         prob = random.random()
@@ -85,60 +39,133 @@ def bio_mutate(seq):
         else:
             newbase = random.choice([b for b in bases if b != orig])
         seq = seq[:idx] + newbase + seq[idx+1:]
-    elif r < 0.80:
+
+    elif r < 0.80:  # Инсерция короткого блока
         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:
+
+    elif r < 0.90:  # Делеция
         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:
+
+    else:  # Блочная перестановка (инверсия)
         if len(seq) > 10:
             start = random.randint(0, len(seq)-6)
             end = start + random.randint(3,6)
-            subseq = seq[start:end][::-1]
+            subseq = seq[start:end]
+            subseq = subseq[::-1]
             seq = seq[:start] + subseq + seq[end:]
     while len(seq) < seqlen:
         seq += random.choice(bases)
-    return seq[:seqlen]
-
-# Визуализация
-plot_area = st.empty()
+    if len(seq) > seqlen:
+        seq = seq[:seqlen]
+    return seq
 
-while st.session_state.running:
-    # Мутация
-    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)
+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
 
-    # Сохраняем последние 50
-    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]
+def compute_entropy(profile):
+    vals, counts = np.unique(profile, return_counts=True)
+    p = counts / counts.sum()
+    return scipy_entropy(p, base=2)
 
-    # График
-    fig, axs = plt.subplots(3, 1, figsize=(10, 8))
-    plt.subplots_adjust(hspace=0.5)
+# --- Дополнительный анализ стромбистов ---
+def analyze_strombists(runs, seqlen):
+    counts = len(runs)
+    lengths = [end - start + 1 for start, end, _ in runs]
+    angle_freq = {}
+    heatmap_row = np.zeros(seqlen)
+    for start, end, val in runs:
+        for pos in range(start, end + 1):
+            heatmap_row[pos] = 1
+        angle_freq[val] = angle_freq.get(val, 0) + 1
+    return counts, lengths, angle_freq, heatmap_row
+
+# --- Начальная цепь ---
+seq = ''.join(random.choices(bases, k=seqlen))
+stat_bist_counts = []
+stat_entropy = []
+stat_autocorr = []
+stat_strombists = []
+
+fig, axs = plt.subplots(4, 1, figsize=(10, 10))
+plt.subplots_adjust(hspace=0.45)
+lags_shown = 6
 
-    axs[0].plot(torsion_profile, color='royalblue')
+def draw_world(seq, axs, step, cnt_hist, ent_hist, ac_hist, st_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)
+    st_count, st_lengths, st_angle_freq, st_heatmap_row = analyze_strombists(runs, seqlen)
+    
+    axs[0].cla()
+    axs[1].cla()
+    axs[2].cla()
+    axs[3].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=4)
+        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 {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[0].set_xlabel("Позиция")
+    axs[0].set_ylabel("Торсионный угол (град.)")
+    axs[0].set_title(f"Шаг {step}: {seq}\nЧисло машин: {st_count}, энтропия: {ent_hist[-1]:.2f}")
+    axs[0].legend()
+
+    # История динамики "машин"
+    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'")
+
+    # Автокорреляция для текущего шага
+    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)
+
+    # Карта стромбистов
+    axs[3].plot(st_heatmap_row, color='orange', label="Карта стромбистов", linewidth=2)
+    axs[3].set_ylim(0, 1)
+    axs[3].set_xlabel("Позиция")
+    axs[3].set_ylabel("Стромбист (1 - стабильность)")
+    axs[3].set_title(f"Карты стромбистов на шаге {step}")
+    axs[3].legend()
+
+def animate(i):
+    global seq, stat_bist_counts, stat_entropy, stat_autocorr, stat_strombists
+    if i == 0:
+        stat_bist_counts.clear()
+        stat_entropy.clear()
+        stat_autocorr.clear()
+        stat_strombists.clear()
+    else:
+        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)
+    st_count, st_lengths, st_angle_freq, st_heatmap_row = analyze_strombists(runs, seqlen)
+    stat_strombists.append((st_count, st_lengths, st_angle_freq))
+    draw_world(seq, axs, i, stat_bist_counts, stat_entropy, stat_autocorr, stat_strombists)
+    return axs
 
-    axs[2].bar(np.arange(lags_shown), acorr[:lags_shown], color='teal')
-    axs[2].set_title("Автокорреляция")
+anim = FuncAnimation(
+    fig, animate, frames=steps, interval=600, repeat=False, blit=False
+)
 
-    plot_area.pyplot(fig)
-    st.session_state.step += 1
-    time.sleep(speed / 1000.0)
+plt.show()