Update app.py

app.py

@@ -1,21 +1,17 @@
-import streamlit as st
 import numpy as np
 import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
 import random
 from scipy.stats import entropy as scipy_entropy
-import time
-import imageio
-from datetime import datetime
-
-st.set_page_config(layout="wide")
+import streamlit as st
 
-# --- ПАРАМЕТРЫ ---
-seqlen = 60
-min_run, max_run = 1, 2
+# --- 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']
 
-# --- ФУНКЦИИ ---
 def find_local_min_runs(profile, min_run=1, max_run=2):
     result = []
     N = len(profile)
@@ -30,6 +26,7 @@ def find_local_min_runs(profile, min_run=1, max_run=2):
         i += run_length
     return result
 
+# --- Более биологичные мутации ---
 def bio_mutate(seq):
     r = random.random()
     if r < 0.70:
@@ -42,88 +39,104 @@ def bio_mutate(seq):
             newbase = 'G' if prob < 0.65 else random.choice(['A', 'G'])
         else:
             newbase = random.choice([b for b in bases if b != orig])
-        seq = ''.join(seq[:idx]) + newbase + ''.join(seq[idx+1:])
+        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 = ''.join(seq[:idx]) + ins + ''.join(seq[idx:])
+        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 = ''.join(seq[:idx]) + ''.join(seq[idx+dell:])
+            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]
-            seq = ''.join(seq[:start]) + ''.join(subseq) + ''.join(seq[end:])
+            subseq = seq[start:end]
+            subseq = subseq[::-1]
+            seq = seq[:start] + subseq + seq[end:]
     while len(seq) < seqlen:
         seq += random.choice(bases)
-    return seq[:seqlen]
+    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
+    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)
 
-# --- UI ---
-st.title("🔴 Живой эфир мутаций ДНК")
-start = st.button("▶️ Старт эфира")
-stop = st.checkbox("⏹️ Остановить")
-
-plot_placeholder = st.empty()
-
-if start:
-    seq = ''.join(random.choices(bases, k=seqlen))
-    stat_bist_counts = []
-    stat_entropy = []
-
-    step = 0
-    while True:
-        if stop:
-            st.warning("⏹️ Эфир остановлен пользователем.")
-            break
-
-        if step != 0:
-            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)
-
-        fig, axs = plt.subplots(3, 1, figsize=(10, 8))
-        plt.subplots_adjust(hspace=0.45)
-
-        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].set_ylim(-200, 200)
-        axs[0].set_title(f"Шаг {step}: {seq}")
-        axs[0].set_ylabel("Торсионный угол")
-
-        axs[1].plot(stat_bist_counts, '-o', color='crimson', markersize=4)
-        axs[1].set_ylabel("Биомашины")
-        axs[1].set_title("Количество машин")
-
-        axs[2].bar(np.arange(6), acorr[:6], color='teal')
-        axs[2].set_title(f"Автокорреляция / Энтропия: {ent:.2f}")
-        axs[2].set_xlabel("Лаг")
-
-        plot_placeholder.pyplot(fig)
-        plt.close(fig)
+# --- 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)
+
+def animate(i):
+    global seq, stat_bist_counts, stat_entropy, stat_autocorr
+    if i == 0:
+        stat_bist_counts.clear()
+        stat_entropy.clear()
+        stat_autocorr.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)
+    draw_world(seq, axs, i, stat_bist_counts, stat_entropy, stat_autocorr)
+    return axs
+
+# --- Запуск анимации в Streamlit ---
+if st.button("Начать анимацию"):
+    anim = FuncAnimation(
+        fig, animate, frames=steps, interval=600, repeat=False, blit=False
+    )
+    st.pyplot(fig)
 
-        step += 1
-        time.sleep(0.3)