def LoRaFilterBank(dataI, dataQ, fs, Bw, fMix, downSamplingRate):
n = np.arange(dataI.size)
cosMix = np.cos((n * 2 * np.pi * fMix / fs) + 0.06287)
sinMix = np.sin((n * 2 * np.pi * fMix / fs) + 0.06287)
dataMixI = np.multiply(dataI, cosMix) - np.multiply(dataQ, sinMix)
dataMixQ = np.multiply(dataQ, cosMix) + np.multiply(dataI, sinMix)
fftPlot(dataMixI + 1j * dataMixQ, n=1, fs=fs)
d = signal.firwin(301,
cutoff=(1.2 * Bw) / (fs / 2.),
window='blackmanharris')
dataFltI = np.convolve(d, dataMixI)
dataFltQ = np.convolve(d, dataMixQ)
dataFltI = dataFltI[::downSamplingRate]
dataFltQ = dataFltQ[::downSamplingRate]
fs /= downSamplingRate
fftPlot(dataFltI + 1j * dataFltQ, n=1, fs=fs)
#specPlot(dataFltI+1j*dataFltQ, fs=fs)
return dataFltI, -dataFltQ, fs
while True:
if msvcrt.kbhit():
c = msvcrt.getch().decode("utf-8")
print(c)
c = c.lower()
if c == 'r':
[fs, dataI, dataQ] = wav.readWaveFile(filename)
print('Sampling freq = ', fs)
print(dataI.shape, dataQ.shape)
dataI = dataI[int(0.0e6):int(8.5e6)]
dataQ = dataQ[int(0.0e6):int(8.5e6)]
data = dataI + 1j * dataQ
fp.fftPlot(data, n=1, fs=fs)
fp.specPlot(data, fs=fs)
elif c == 'b':
# fileName_local = path + 'SDRSharp_20210704_200704Z_868000000Hz_IQ.wav'
fileName_local = path + 'RKE/' + 'SDRSharp_20210815_142820Z_868000266Hz_IQ.wav'
[fs, dataI, dataQ] = wav.readWaveFile(fileName_local)
print('Sampling freq = ', fs)
print(dataI.shape, dataQ.shape)
dataI = dataI[int(0.7e6):int(5.4e6)]
dataQ = dataQ[int(0.7e6):int(5.4e6)]
data = dataI + 1j * dataQ
fp.fftPlot(data, n=1, fs=fs)
fp.specPlot(data, fs=fs)
print(
f'ADC DataI Min/Max (in selected range): {dataI.min()}, {dataI.max()} , {type(dataI[0])}'
)
print(
f'ADC DataI Min/Max (in selected range): {dataQ.min()}, {dataQ.max()} , {type(dataQ[0])}'
)
fs = 30720000 * 2
data = dataI #+1j*dataQ
#filename_local = '../Samples/Wpc/' + 'Wpc_adc2_20211020_130755.bttraw'
#adcData = rawFile.readRawFile(filename_local, 13)
#adcData = rawFile.readRawFile(filename1, 13)
#data = adcData[int(0.0e6):int(26.0e6)].astype('float')
#print(f'ADC Data Min/Max (in selected range): {data.min()}, {data.max()} , {type(data[0])}')
fp.fftPlot(data, fs=fs)
plt.plot(data[1000:10000])
#plt.plot(data)
plt.show()
elif c == 'm':
n_bit = 4000
n_loop = 5
errorlist = []
for snr_dB in range(5, 10, 1):
error = 0
run = 0
while (run < n_loop):
print(f'---------run:{run:2d}-----------')
error_run = WpcBER.AskBERtest(n_bit // 2,
127.0e3,
print('>> ')
filename = '../Samples/Sound/' + 'test1.wav'
while True:
if msvcrt.kbhit():
c = msvcrt.getch().decode("utf-8")
print(c)
c = c.lower()
if c == 's':
[fs, dataI, dataQ] = readWaveFile(filename)
specPlot(dataI+1j*dataQ, fs=fs)
elif c == 'a':
[fs, dataI, dataQ] = readWaveFile(filename)
fftPlot(dataI+1j*dataQ, fs=fs)
elif c == 'c':
fs = 1000
t = np.arange(0,1, 1/fs)
x = 2*np.cos(2*np.pi*100*t)+0.01*np.cos(2*np.pi*200*t)+0.005*np.cos(2*np.pi*300*t)
#plt.plot(x)
#plt.grid()
#plt.show()
#mLib.fftPlot(x, fs=1000)
Thd(x, fs)
ThdN(x, fs)
elif c == 'w':
while True:
if msvcrt.kbhit():
c = msvcrt.getch().decode("utf-8")
print(c)
c = c.lower()
if c == 'r':
[fs, dataI, dataQ] = wav.readWaveFile(filename)
print('Sampling freq = ', fs)
print(dataI.shape, dataQ.shape)
dataI = dataI[int(0.0e6):int(8.5e6)]
dataQ = dataQ[int(0.0e6):int(8.5e6)]
data = dataI+1j*dataQ
fp.fftPlot(data, n=1, fs=fs)
fp.specPlot(data, fs=fs)
elif c == 'k':
fileName_local = path + 'SDRSharp_20210612_204730Z_433200000Hz_IQ.wav'
[fs, dataI, dataQ] = wav.readWaveFile(fileName_local)
print('Sampling freq = ', fs)
print(dataI.shape, dataQ.shape)
dataI = dataI[int(3.0e6):int(5.0e6)]
dataQ = dataQ[int(3.0e6):int(5.0e6)]
data = dataI+1j*dataQ
fp.fftPlot(data, n=1, fs=fs)
fp.specPlot(data, fs=fs)
print('X: Exit')
print('>> ')
# mPluto = Pluto(0)
mPluto = pl(0)
while True:
if msvcrt.kbhit():
c = msvcrt.getch().decode("utf-8")
print(c)
c = c.lower()
if c == 'r':
[samples, fs] = mPluto.Read(5.0e6, 5.0e6, 868.0e6, 50.0, int(2e6))
print('sample freq: ', fs, 'sample size: ', samples.size, 'sample min/max: ', samples.min(), samples.max())
fp.fftPlot(samples.real, n=1, fs=fs)
np.save('testPluto', samples)
elif c == 't':
t = np.arange(10000)/10.0e6
samples = 0.5*np.exp(2.0j*np.pi*100e3*t)
samples *= 2**14 # The PlutoSDR expects samples to be between -2^14 and +2^14, not -1 and +1 like some SDRs
fs = mPluto.Write(10.0e6, 10.0e6, 868.0e6, -30.0, samples, 3)
fp.fftPlot(samples.real, n=1, fs=fs)
elif c == 'w':
t = np.arange(10000)/3.0e6
samples = 0.99*np.exp(2.0j*np.pi*120e3*t)
#samples = np.array((np.random.rand(2000) >= 0.5)*0.5)
#samples = samples.repeat(200)