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import streamlit as st
import numpy as np
import random
from scipy.stats import entropy as scipy_entropy
import time
# --- 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']
# --- БИОЛОГИЧЕСКИЕ 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()
if r < 0.70:
idx = random.randint(0, len(seq)-1)
orig = seq[idx]
prob = random.random()
if orig in 'AG':
newbase = 'C' if prob < 0.65 else random.choice(['T', 'C'])
elif orig in 'CT':
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:]
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:]
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]
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)
# --- STREAMLIT ИНТЕРФЕЙС ---
st.title("🧬 Эволюция ДНК-подобной последовательности")
st.markdown("Модель визуализирует мутации и анализирует структуру последовательности во времени.")
if st.button("▶️ Запустить симуляцию"):
seq = ''.join(random.choices(bases, k=seqlen))
stat_bist_counts = []
stat_entropy = []
stat_autocorr = []
# Симуляция изменения последовательности
for step in range(steps):
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)
# Используем Streamlit для отображения графиков
st.subheader(f"Шаг {step}: {seq}")
st.write(f"Число машин: {len(runs)}, энтропия: {ent:.2f}")
# График динамики числа 'биомашин'
st.line_chart(stat_bist_counts)
# График автокорреляции
st.bar_chart(acorr[:6])
# График энтропии
st.line_chart(stat_entropy)
time.sleep(0.5)
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