def q5_pre(vector_len):
rand_v = lu.random_vector(vector_len)
y1 = lu.regular_fft_ifft(rand_v)
y2 = lu.conj_fft_ifft(rand_v)
if (y1 == y2).all():
return True
return False
def q1(plt, vector_len=8):
t = numpy.arange(0, vector_len, 1)
rand_v = lu.random_vector(vector_len)
y1 = lu.conj_dft_idft_with_dftmtx(rand_v)
fig, (sub1, sub2) = plt.subplots(2)
plt.subplots_adjust(hspace=0.5)
fig.suptitle("Comparison between 2 algorithms")
sub1.plot(t, numpy.abs(rand_v))
sub1.set_title("original signal")
sub2.plot(t, numpy.abs(y1))
sub2.set_title("after conj-dftmtx-conj")
plt.savefig("results/comp_2_algo")
return True
def q3_and_q4(fs,
number_of_points,
step_size_array,
filter_coeffs_length_array,
delay=1):
nt = numpy.arange(0, number_of_points / fs, 1 / fs)
s = numpy.sin(nt * 2 * numpy.pi * 200)
d = s + lu.random_vector(number_of_points) * numpy.sqrt(0.1)
x = numpy.pad(d[0:-1], (delay, 0))
for step_size in step_size_array:
plot_adaptive_filter_lms(x, d, 16, step_size)
for filter_coeffs_length in filter_coeffs_length_array:
plot_adaptive_filter_lms(x, d, filter_coeffs_length, 0.01)
return
def q5(fs,
number_of_points,
harmonic_factor_array,
step_size=0.01,
filter_coeffs_length=16,
delay=1):
nt = numpy.arange(0, number_of_points / fs, 1 / fs)
for harmonic_factor in harmonic_factor_array:
s = numpy.sin(nt * 2 * numpy.pi * 200) + numpy.sin(
nt * 2 * numpy.pi * 200 * harmonic_factor)
d = s + lu.random_vector(number_of_points) * numpy.sqrt(0.1)
x = numpy.pad(d[0:-1], (delay, 0))
plot_adaptive_filter_lms(x,
d,
filter_coeffs_length,
step_size,
harmonic=200 * harmonic_factor)
return
f"fft time:\t{t1}\n"
f"cor time:\t{t2}")
return
if __name__ == "__main__":
plt = lu.get_plt()
os.makedirs("results", exist_ok=True)
# preparation to LAB2
if q1_or_q2_pre(x=[1, 2, 3, 4], h=[4, 3, 2, 1]):
print("EQUAL for Q1-PRE")
if q1_or_q2_pre([1, 2, 3, 4], [1, 2, 3, 4]):
print("EQUAL for Q2-PRE")
q3_pre(x=[1, 2, 4], h=[1, 1, 1, 1])
q3_pre(x=[0, 1, -2, 3, -4], h=[0.5, 1, 2, 1, 1, 0.5])
q4_pre(1.5*scipy.signal.boxcar(5), 2*scipy.signal.boxcar(10), scipy.signal.boxcar(15), plt)
# LAB2
q1_and_q2(fs=2e5, number_of_points=500, plt=plt)
q3(fs=1e5, number_of_points=450, plt=plt)
q4(s1=[0, 3, 5, 5, 5, 2, 0.5, 0.25, 0],
s2=[1, 1, 1, 1, 1, 0, 0, 0, 0],
s3=[0, 9, 15, 15, 15, 6, 1.5, 0.75, 0],
s4=[2, 2, 2, 2, 2, 0, 0, 0, 0], plt=plt)
q5(s1=lu.random_vector(1000),
s2=lu.random_vector(1000))
# Unmark the command blow to show ALL the figures
# plt.show()