def main():
code_1 = ca_code.generate(prn=1) | np.roll(ca_code.generate(prn=2), 100)
code_2 = ca_code.generate(prn=1)
# code_2 = np.roll(code_2, 100)
# fftshift(ifft(fft(a,corrLength).*conj(fft(b,corrLength))))
q = 2
code_1 = sfft_aliasing.execute(code_1, q)
code_2 = sfft_aliasing.execute(code_2, q)
code_1_fft = fourier_transforms.fft(code_1)
code_2_fft = fourier_transforms.fft(code_2)
multiplied = code_1_fft * np.conj(code_2_fft)
print multiplied.size
params = Parameters(
n=multiplied.size/2,
k=1,
B_k_location=2,
B_k_estimation=2,
estimation_loops=8,
location_loops=5,
loop_threshold=4,
tolerance_location=1e-8,
tolerance_estimation=1e-8
)
result = sfft_inverse.execute(params=params, x=multiplied)
result = fourier_transforms.fftshift(result)
result_actual = fourier_transforms.ifft(multiplied)
result_actual = fourier_transforms.fftshift(result_actual)
print 'sfft size', result.size
print 'original size', result_actual.size
fig = plt.figure()
ax = fig.gca()
ax.plot(np.linspace(-1, 1, result.size), np.abs(result))
ax.plot(np.linspace(-1, 1, result_actual.size), np.abs(result_actual))
plt.show()
def make_multiple(x, w, n, b):
print("w = {0}, n = {1}, b = {2}".format(w, n, b))
assert b <= n
assert w <= n
g = np.zeros(n, dtype=np.complex128)
h = np.zeros(n, dtype=np.complex128)
g[0 : w - (w / 2)] = x[(w / 2) :]
g[n - (w / 2) :] = x[: (w / 2)]
g = fft(g, n=n)
s = 0
for i in xrange(b):
s += g[i]
max = 0
offset = int(b / 2)
for i in xrange(n):
h[(i + n + offset) % n] = s
max = np.maximum(max, np.abs(s))
s += g[(i + b) % n] - g[i]
h /= max
offsetc = 1
step = np.exp(-2 * np.pi * 1j * (w / 2) / n)
for i in xrange(n):
h[i] *= offsetc
offsetc *= step
x = ifft(h, n=n)
return {"time": x, "size": w, "freq": h}
def make_multiple(x, w, n, b):
print('w = {0}, n = {1}, b = {2}'.format(w, n, b))
assert b <= n
assert w <= n
g = np.zeros(n, dtype=np.complex128)
h = np.zeros(n, dtype=np.complex128)
g[0:w - (w / 2)] = x[(w / 2):]
g[n - (w / 2):] = x[:(w / 2)]
g = fft(g, n=n)
s = 0
for i in xrange(b):
s += g[i]
max = 0
offset = int(b / 2)
for i in xrange(n):
h[(i + n + offset) % n] = s
max = np.maximum(max, np.abs(s))
s += (g[(i + b) % n] - g[i])
h /= max
offsetc = 1
step = np.exp(-2 * np.pi * 1j * (w / 2) / n)
for i in xrange(n):
h[i] *= offsetc
offsetc *= step
x = ifft(h, n=n)
return {'time': x, 'size': w, 'freq': h}
def generate_input(self):
"""Generate time domain signal"""
self.x = fourier_transforms.ifft(self.x_f)
def generate_input(self):
"""Generate time domain signal"""
self.x = fourier_transforms.ifft(self.x_f)