def generate_dolph_chebyshev(lobe_fraction, tolerance):
def cheb(m, x):
if np.abs(x) <= 1:
return np.cos(m * np.arccos(x))
else:
return np.cosh(m * np.arccosh(np.abs(x))).real
w = int((1 / np.pi) * (1 / lobe_fraction) * np.arccosh(1 / tolerance))
if w % 2 == 0:
w -= 1
beta = np.cosh(np.arccosh(1 / tolerance) / float(w - 1))
print('lobe_fraction = {0}, tolerance = {1}, w = {2}, beta = {3}'.format(
lobe_fraction, tolerance, w, beta))
x = np.empty(w, dtype=np.complex128)
for i in xrange(w):
x[i] = cheb(w - 1, beta * np.cos(np.pi * i / w)) * tolerance
x = fft(x, n=w)
x = fftshift(x)
x = np.real(x)
return {'x': x, 'w': w}
def generate_dolph_chebyshev(lobe_fraction, tolerance):
def cheb(m, x):
if np.abs(x) <= 1:
return np.cos(m * np.arccos(x))
else:
return np.cosh(m * np.arccosh(np.abs(x))).real
w = int((1 / np.pi) * (1 / lobe_fraction) * np.arccosh(1 / tolerance))
if w % 2 == 0:
w -= 1
beta = np.cosh(np.arccosh(1 / tolerance) / float(w - 1))
print("lobe_fraction = {0}, tolerance = {1}, w = {2}, beta = {3}".format(lobe_fraction, tolerance, w, beta))
x = np.empty(w, dtype=np.complex128)
for i in xrange(w):
x[i] = cheb(w - 1, beta * np.cos(np.pi * i / w)) * tolerance
x = fft(x, n=w)
x = fftshift(x)
x = np.real(x)
return {"x": x, "w": w}
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()
