Update app.py

app.py

@@ -2,15 +2,49 @@ 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
-from io import BytesIO
 
 # --- НАСТРОЙКИ ---
 seqlen = 60
-steps = 120
+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):
@@ -27,6 +61,18 @@ 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)
+
 def bio_mutate(seq):
     r = random.random()
     if r < 0.70:
@@ -40,102 +86,64 @@ 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:
         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]
-
+        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][::-1]
+            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
-
-def compute_entropy(profile):
-    vals, counts = np.unique(profile, return_counts=True)
-    p = counts / counts.sum()
-    return scipy_entropy(p, base=2)
+# --- ВИЗУАЛИЗАЦИЯ ---
+plot_area = st.empty()
+fig, axs = plt.subplots(3, 1, figsize=(10, 8))
+plt.subplots_adjust(hspace=0.5)
 
-def plot_step(seq, step, cnt_hist, ent_hist, ac_hist):
-    torsion_profile = np.array([ANGLE_MAP.get(nt, 0.0) for nt in seq])
+# --- 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)
-    fig, axs = plt.subplots(3, 1, figsize=(10, 8))
-    plt.subplots_adjust(hspace=0.45)
+    ent = compute_entropy(torsion_profile)
+    acorr = compute_autocorr(torsion_profile)
 
-    axs[0].plot(torsion_profile, color='royalblue', label="Торсионный угол")
+    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=5)
+        axs[0].plot(range(start, end+1), torsion_profile[start:end+1], 'ro', markersize=4)
     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(6), ac_hist[-1][:6], color='teal', alpha=0.7)
-    axs[2].set_xlabel("Лаг")
-    axs[2].set_ylabel("Автокорреляция")
-    axs[2].set_title("Автокорреляция углового профиля и энтропия")
-    axs[2].text(0.70,0.70, f"Энтропия: {ent_hist[-1]:.2f}", transform=axs[2].transAxes)
-
-    return fig
-
-# --- STREAMLIT ---
-st.set_page_config(layout="wide")
-st.title("\U0001F9EA Торсионное пространство биомашин")
+    axs[0].set_title(f"Шаг {st.session_state.step}: {st.session_state.seq}\nМашин: {len(runs)}, Энтропия: {ent:.2f}")
 
-if 'seq' not in st.session_state:
-    st.session_state.seq = ''.join(random.choices(bases, k=seqlen))
-    st.session_state.cnt_hist = []
-    st.session_state.ent_hist = []
-    st.session_state.ac_hist = []
-    st.session_state.step = 0
+    axs[1].plot(st.session_state.stat_bist_counts, '-o', color='crimson', markersize=4)
+    axs[1].set_title("Число 'биомашин'")
 
-if st.button("Следующий шаг мутации"):
-    st.session_state.seq = bio_mutate(st.session_state.seq)
-    profile = np.array([ANGLE_MAP.get(nt, 0.0) for nt in st.session_state.seq])
-    runs = find_local_min_runs(profile, min_run, max_run)
-    st.session_state.cnt_hist.append(len(runs))
-    st.session_state.ent_hist.append(compute_entropy(profile))
-    st.session_state.ac_hist.append(compute_autocorr(profile))
-    st.session_state.step += 1
+    axs[2].bar(np.arange(lags_shown), acorr[:lags_shown], color='teal')
+    axs[2].set_title("Автокорреляция углового профиля")
 
-if st.session_state.step > 0:
-    fig = plot_step(
-        st.session_state.seq,
-        st.session_state.step,
-        st.session_state.cnt_hist,
-        st.session_state.ent_hist,
-        st.session_state.ac_hist
-    )
-    st.pyplot(fig)
-else:
-    st.info("Нажмите кнопку, чтобы начать мутацию цепи и наблюдение за торсионными биомашинами.")
+    plot_area.pyplot(fig, clear_figure=True)
+    st.session_state.step += 1
+    time.sleep(speed / 1000.0)