Update app.py

app.py

@@ -4,16 +4,31 @@ import matplotlib.pyplot as plt
 import random
 from scipy.stats import entropy as scipy_entropy
 import time
+import imageio
+from datetime import datetime
+
+st.set_page_config(layout="wide")
 
 # --- ПАРАМЕТРЫ ---
 seqlen = 60
 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']
-population_size = 10  # размер популяции "организмов"
-survival_rate = 0.5  # процент выживших для следующего поколения
 
 # --- 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 bio_mutate(seq):
     r = random.random()
@@ -27,24 +42,24 @@ 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 = seq[:idx] + newbase + seq[idx+1:]
+        seq = ''.join(seq[:idx]) + newbase + ''.join(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 = ''.join(seq[:idx]) + ins + ''.join(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:]
+            seq = ''.join(seq[:idx]) + ''.join(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 = seq[:start] + subseq + seq[end:]
+            seq = ''.join(seq[:start]) + ''.join(subseq) + ''.join(seq[end:])
     while len(seq) < seqlen:
         seq += random.choice(bases)
     return seq[:seqlen]
@@ -61,25 +76,6 @@ def compute_entropy(profile):
     p = counts / counts.sum()
     return scipy_entropy(p, base=2)
 
-def genetic_algorithm(population):
-    """Эволюционный алгоритм для отбора и мутации."""
-    # Отбор лучших организмов
-    population.sort(key=lambda x: x[1])  # сортируем по фитнесу (энтропия)
-    survivors = population[:int(population_size * survival_rate)]
-    
-    # Кроссовер: создаем новых организмов на основе выживших
-    offspring = []
-    for i in range(len(survivors) // 2):
-        parent1, parent2 = survivors[i], survivors[-i-1]
-        crossover_point = random.randint(0, seqlen)
-        child1 = parent1[0][:crossover_point] + parent2[0][crossover_point:]
-        child2 = parent2[0][:crossover_point] + parent1[0][crossover_point:]
-        offspring.append((bio_mutate(child1), 0))
-        offspring.append((bio_mutate(child2), 0))
-    
-    # Возвращаем новое поколение
-    return survivors + offspring
-
 # --- UI ---
 st.title("🔴 Живой эфир мутаций ДНК")
 start = st.button("▶️ Старт эфира")
@@ -88,8 +84,7 @@ stop = st.checkbox("⏹️ Остановить")
 plot_placeholder = st.empty()
 
 if start:
-    # Начальная популяция
-    population = [(random.choices(bases, k=seqlen), 0) for _ in range(population_size)]
+    seq = ''.join(random.choices(bases, k=seqlen))
     stat_bist_counts = []
     stat_entropy = []
 
@@ -99,28 +94,24 @@ if start:
             st.warning("⏹️ Эфир остановлен пользователем.")
             break
 
-        # Мутация и оценка каждого организма в популяции
-        for i in range(population_size):
-            seq, _ = population[i]
-            torsion_profile = np.array([ANGLE_MAP.get(nt, 0.0) for nt in seq])
-            ent = compute_entropy(torsion_profile)
-            population[i] = (seq, ent)
-        
-        # Применяем эволюционный алгоритм
-        population = genetic_algorithm(population)
-
-        # Статистика для отображения
-        stat_bist_counts.append(len(population))
-        ent = np.mean([ind[1] for ind in population])  # средняя энтропия
+        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(np.array([ANGLE_MAP.get(nt, 0.0) for nt in population[0][0]]))
+        acorr = compute_autocorr(torsion_profile)
 
         fig, axs = plt.subplots(3, 1, figsize=(10, 8))
         plt.subplots_adjust(hspace=0.45)
 
-        axs[0].plot([ANGLE_MAP.get(nt, 0.0) for nt in population[0][0]], color='royalblue')
+        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}: {population[0][0]}")
+        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)