def detect_modulation(data: np.ndarray,
wavelet_scale=4,
median_filter_order=11) -> str:
n_data = len(data)
data = data[np.abs(data) > 0]
if len(data) == 0:
return None
if n_data - len(data) > 3:
return "OOK"
data = data / np.abs(np.max(data))
mag_wavlt = np.abs(Wavelet.cwt_haar(data, scale=wavelet_scale))
if len(mag_wavlt) == 0:
return None
norm_mag_wavlt = np.abs(
Wavelet.cwt_haar(data / np.abs(data), scale=wavelet_scale))
var_mag = np.var(mag_wavlt)
var_norm_mag = np.var(norm_mag_wavlt)
var_filtered_mag = np.var(
c_auto_interpretation.median_filter(mag_wavlt, k=median_filter_order))
var_filtered_norm_mag = np.var(
c_auto_interpretation.median_filter(norm_mag_wavlt,
k=median_filter_order))
if all(v < 0.15 for v in (var_mag, var_norm_mag, var_filtered_mag,
var_filtered_norm_mag)):
return "OOK"
if var_mag > 1.5 * var_norm_mag:
# ASK or QAM
# todo: consider qam, compare filtered mag and filtered norm mag
return "ASK"
else:
# FSK or PSK
if var_mag > 5 * var_filtered_mag:
return "PSK"
else:
# Now we either have a FSK signal or we a have OOK single pulse
# If we have an FSK, there should be at least two peaks in FFT
fft = np.fft.fft(data[0:2**int(np.log2(len(data)))])
fft = np.abs(np.fft.fftshift(fft))
ten_greatest_indices = np.argsort(fft)[::-1][0:10]
greatest_index = ten_greatest_indices[0]
min_distance = 10
min_freq = 100 # 100 seems to be magnitude of noise frequency
if any(
abs(i -
greatest_index) >= min_distance and fft[i] >= min_freq
for i in ten_greatest_indices):
return "FSK"
else:
return "OOK"
def detect_modulation(data: np.ndarray, wavelet_scale=4, median_filter_order=11) -> str:
n_data = len(data)
data = data[np.abs(data) > 0]
if len(data) == 0:
return None
if n_data - len(data) > 3:
return "OOK"
data = data / np.abs(np.max(data))
mag_wavlt = np.abs(Wavelet.cwt_haar(data, scale=wavelet_scale))
if len(mag_wavlt) == 0:
return None
norm_mag_wavlt = np.abs(Wavelet.cwt_haar(data / np.abs(data), scale=wavelet_scale))
var_mag = np.var(mag_wavlt)
var_norm_mag = np.var(norm_mag_wavlt)
var_filtered_mag = np.var(c_auto_interpretation.median_filter(mag_wavlt, k=median_filter_order))
var_filtered_norm_mag = np.var(c_auto_interpretation.median_filter(norm_mag_wavlt, k=median_filter_order))
if all(v < 0.1 for v in (var_mag, var_norm_mag, var_filtered_mag, var_filtered_norm_mag)):
return "OOK"
if var_mag > 1.5 * var_norm_mag:
# ASK or QAM
# todo: consider qam, compare filtered mag and filtered norm mag
return "ASK"
else:
# FSK or PSK
if var_mag > 10 * var_filtered_mag:
return "PSK"
else:
# Now we either have a FSK signal or we a have OOK single pulse
# If we have an FSK, there should be at least two peaks in FFT
fft = np.fft.fft(data[0:2 ** int(np.log2(len(data)))])
fft = np.abs(np.fft.fftshift(fft))
ten_greatest_indices = np.argsort(fft)[::-1][0:10]
greatest_index = ten_greatest_indices[0]
min_distance = 10
min_freq = 100 # 100 seems to be magnitude of noise frequency
if any(abs(i - greatest_index) >= min_distance and fft[i] >= min_freq for i in ten_greatest_indices):
return "FSK"
else:
return "OOK"