def main():
N = 4096
# get frequency data for each tone
#A_data, B_data, C_data = get_tones()
A_data, B_data = get_tones()
a_fft = fourier.fft(A_data, 1, N)
b_fft = fourier.fft(B_data, 1, N)
#c_fft = fourier.fft(C_data, 1, N)
a_psd = fourier.psd(a_fft)
b_psd = fourier.psd(b_fft)
#c_psd = fourier.psd(c_fft)
fs = 44100
bins = []
for k in range(0, N):
bins.append((k * fs) / N)
max_a = max(a_psd)
max_b = max(b_psd)
#max_c = max(c_psd)
# for i in a_psd:
# print(i / max_a)
for i in b_psd:
print(i / max_b)
"""
def main():
signal_x = []
signal_y = []
samples = 512
for i in range(0, 512):
t = i / samples
signal_x.append(add_sin(t, 4, 0.1))
signal_y.append(mult_sin(t, 4, 0.1))
x_fft = fourier.fft(signal_x, 1, 512)
y_fft = fourier.fft(signal_y, 1, 512)
x_psd = []
y_psd = []
for i in x_fft:
temp = i * i.conjugate()
x_psd.append(temp.real)
for i in y_fft:
temp = i * i.conjugate()
y_psd.append(temp.real)
with open("Q2_results.txt", 'w') as file:
file.write("x(t) PSD DATA" + "\n" + "\n")
for i in x_psd:
file.write(str(i) + "\n")
file.write("\n" + "y(t) PSD DATA" + "\n")
for i in y_psd:
file.write(str(i) + "\n")
#q3a()
q3b()
def q3a():
test_signal1 = gen_signal(0)
test_signal2 = gen_signal(10)
test_signal3 = gen_signal(500)
sig1_fft = fourier.fft(test_signal1, 1, 1024)
sig2_fft = fourier.fft(test_signal2, 1, 1024)
sig3_fft = fourier.fft(test_signal3, 1, 1024)
sig1_psd = fourier.psd(sig1_fft)
sig2_psd = fourier.psd(sig2_fft)
sig3_psd = fourier.psd(sig3_fft)
with open("Q3a_results.txt", 'w') as file:
file.write("PSD Data for Signal 1 with pulse index = 0 " + "\n" + "\n")
for i in sig1_psd:
file.write(str(i) + "\n")
file.write("\n" + "PSD Data for Signal 2 with pulse index = 10 " +
"\n" + "\n")
for i in sig2_psd:
file.write(str(i) + "\n")
file.write("\n" + "PSD Data for Signal 3 with pulse index = 500 " +
"\n" + "\n")
for i in sig3_psd:
file.write(str(i) + "\n")
def smooth_filter(og_signal, filter_signal):
conv_fft = fourier.fft(og_signal, 1, 1024)
filtered_fft = fourier.fft(filter_signal, 1, 1024)
temp_list = []
for i in range(0, 1024):
t = conv_fft[i] * filtered_fft[i]
temp_list.append(t)
return fourier.fft(temp_list, -1, 1024)
def main():
pulse, response = get_data()
for i in range(0, 972):
pulse.append(0)
pulse_fft = fourier.fft(pulse, 1, 1024)
resp_fft = fourier.fft(response, 1, 1024)
corr_list = fast_correlation(pulse_fft, resp_fft)
fcc_final = fourier.fft(corr_list, -1, 1024)
find_distance(fcc_final)
filtered_response = create_filter(response, 6)
final = smooth_filter(fcc_final, filtered_response)
max_final = max(final)
def q3b():
h_0 = gen_sinusoidal(0)
h_1 = gen_sinusoidal(0.1)
h_2 = gen_sinusoidal(0.25)
h_3 = gen_sinusoidal(0.75)
fft_0 = fourier.fft(h_0, 1, 512)
fft_1 = fourier.fft(h_1, 1, 512)
fft_2 = fourier.fft(h_2, 1, 512)
fft_3 = fourier.fft(h_3, 1, 512)
alpha = 0.9999
#for i in fft_2:
#print(i)
psd_0 = fourier.psd(fft_0)
psd_1 = fourier.psd(fft_1)
psd_2 = fourier.psd(fft_2)
psd_3 = fourier.psd(fft_3)
def main():
fs_signal = generate_fs()
fs_fft = fourier.fft(fs_signal, 1, 512)
samples = 512
S = 50
low_pass(fs_fft, samples)
high_pass(fs_fft, samples)
band_pass(fs_fft, samples)
notch_filter(fs_fft, samples)
"""
def main():
N = 4096
# get frequency data for each tone
A_data, B_data, C_data = get_tones()
a_fft = fourier.fft(A_data, 1, N)
b_fft = fourier.fft(B_data, 1, N)
c_fft = fourier.fft(C_data, 1, N)
a_psd = fourier.psd(a_fft)
b_psd = fourier.psd(b_fft)
c_psd = fourier.psd(c_fft)
fs = 44100
bins = []
for k in range(0, N):
bins.append((k * fs) / N)
max_a = max(a_psd)
max_b = max(b_psd)
max_c = max(c_psd)
"""
def notch_filter(fs_fft, samples):
"""
:param x:
:param S:
:return:
"""
filter_sig = [1] * samples
nf_filter = []
for i in range(3, 8):
filter_sig[i] = 0
for i in range(0, samples):
nf_filter.append(fs_fft[i] * filter_sig[i])
final = fourier.fft(nf_filter, -1, samples)
with open("notch_filter.txt", 'w') as file:
for i in final:
file.write(str(i) + "\n")
def band_pass(fs_fft, samples):
"""
:param x:
:param S:
:return:
"""
filter_sig = [0] * samples
bp_filter = []
for i in range(3, 8):
filter_sig[i] = 1
for i in range(0, samples):
bp_filter.append(fs_fft[i] * filter_sig[i])
final = fourier.fft(bp_filter, -1, samples)
with open("band_pass.txt", 'w') as file:
for i in final:
file.write(str(i) + "\n")
