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import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
import random
from scipy.stats import entropy as scipy_entropy
from io import BytesIO
# --- 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']
# --- 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:]
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]
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)
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])
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)
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(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")
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
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
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.")
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