1 лр / lab1_2

import numpy as np
import matplotlib.pyplot as plt

def invert(data):
return np.logical_not(data).astype(int)

def Wfun(data, r):
data_bin = np.binary_repr(data)

if r > len(data_bin):
data_ = np.concatenate((np.zeros(r - len(data_bin)), np.array(list(map(int, data_bin)))))
else:
data_ = np.array(list(map(int, data_bin)))

W = [data_[0]]
for i in range(2, len(data_)): #правка с 1 -- 2
if data_[i] == 1:
W = np.concatenate((W, np.logical_not(W)))
else:
W = np.concatenate((W, W))

W = np.where(W == 0, 1, -1)

return W

def Bpfun(W, r):
while r < len(W):
for i in range(1, len(W), r * 2): #правка с 0 --1
for j in range(i, i + r):
if j + r < len(W): # Добавлено условие для предотвращения выхода за пределы массива
x = W[j]
y = W[j + r]
W[j] = x + y
W[j + r] = x - y
r = r * 2

Wprev = np.copy(W)
Wnext = np.zeros_like(W)

for i in range(0, r - 1):
step_wing = len(W) // (2 ** i)
for j in range(len(W)):
butterfly_size = step_wing // 2
if step_wing == 0: # Добавлена проверка на ноль
cost = 0
else:
cost = j % step_wing
if cost == 0:
cost = butterfly_size + 1
if j + butterfly_size < len(W): # Добавлено условие для предотвращения выхода за пределы массива
if cost <= butterfly_size:
Wnext[j] = Wprev[j] + Wprev[j + butterfly_size]
else:
Wnext[j] = Wprev[j - butterfly_size] - Wprev[j]

Wprev = np.copy(Wnext)

return Wprev



def Ishod_data(Bpfun_result):
ish_data = []
absBpfunres = np.abs(Bpfun_result)

t = []

for i in range(len(absBpfunres)):
max_value = np.max(absBpfunres)
if max_value != 0 and absBpfunres[i] / max_value > 0.7:
t.append(i)

print('Найденные числа:')
for j in range(len(t)):
if Bpfun_result[t[j] + 1] < 0:
f = np.flip(np.binary_repr(abs(t[j])))
f = [1] + list(map(int, f))
else:
f = np.flip(np.binary_repr(abs(t[j])))
f = list(map(int, f))
ish_data += [int(np.packbits(f))]

return ish_data

data1 = 3
data2 = 121
r = 10


W1 = Wfun(data1, r)
W2 = Wfun(data2, r)

Wsum = W1 + W2

plt.figure(1)
plt.subplot(2, 1, 1)
plt.plot(W1)
plt.title('W1')

plt.subplot(2, 1, 2)
plt.plot(W2)
plt.title('W2')

plt.figure(2)
plt.plot(Wsum)
plt.title('Wsum')

BPU1 = Bpfun(W1, r)
plt.figure(3)
plt.subplot(2, 1, 1)
plt.plot(BPU1)
plt.title('BPU1')
IdataW1 = Ishod_data(Bpfun(W1, r))

BPU2 = Bpfun(W2, r)
plt.subplot(2, 1, 2)
plt.plot(BPU2)
plt.title('BPU2')
IdataW2 = Ishod_data(Bpfun(W2, r))

plt.figure(4)
plt.subplot(2, 1, 1)
BPU = Bpfun(Wsum, r)
plt.plot(BPU)
plt.title('BPU')
IdataWsum = Ishod_data(Bpfun(Wsum, r))

plt.subplot(2, 1, 2)
Shum = 11 - 2 * 11 * np.random.rand(len(Wsum))
Wshum = Wsum + Shum
IdataWshum = Ishod_data(Bpfun(Wshum, r))

fwhdWshum = Bpfun(Wshum, r)
plt.plot(fwhdWshum)
plt.title('fwhdWshum')

plt.show()