def find_fft(rawdata, peakindexes):
global FFT, FFT_PkI, FFT_Pfreq, xf, N, f1, Pwelch
FFT = []
Pwelch = []
FFT_PkI = []
FFT_Pfreq = []
preImpact = 150
postImpact = 200
sampfeq = 500
# Take the fft of each impact on a single channel
for ff in range(len(peakindexes)):
fftdata = rawdata[peakindexes[ff] - preImpact:peakindexes[ff] +
postImpact]
fft_rData = fft(fftdata)
N = len(fft_rData)
#Power Spectral Analysis using Welch method
f1, Pwelch_spec = welch(fftdata,
sampfeq,
nperseg=200,
scaling='spectrum')
# Set the length of the X vector to half of the original time vector
xf = np.linspace(0.0, sampfeq / 2, N / 2)
# Find peaks in FFT data
fftpkI = detect_peaks(abs(fft_rData[0:int(N / 2)]),
mpd=3,
edge='rising')
fftpkI = list(fftpkI)
Pwelch.insert(ii, Pwelch_spec)
FFT.insert(ii, fft_rData)
FFT_PkI.append(fftpkI[0:numFreq])
FFT_Pfreq.extend(xf[fftpkI[0:1]])
while(index < len(millis)):
time.append(interval * index)
index += 1
averageVoltage = np.mean(impact_data[0:50])
adjusted_raw = [x - averageVoltage for x in impact_data]
adjusted_smoothed = [x - averageVoltage for x in smoothed_data]
#%%Analyze the data...!
#peak voltage, total voltage displacement
total_voltage_displacement = 0;
for volt in adjusted_raw:
total_voltage_displacement += abs(volt)
total_voltage_displacement = round(total_voltage_displacement, 2)
peaks = detect_peaks(smoothed_data)
valleys = detect_peaks(smoothed_data, valley='true')
max_peak = 0
min_peak = 5
max_peak_index = 0
min_peak_index = 0
for peak in peaks:
if smoothed_data[peak] > max_peak:
max_peak = smoothed_data[peak]
max_peak_index = peak
for valley in valleys:
if smoothed_data[valley] < min_peak:
min_peak = smoothed_data[valley]
thresh = .002
if ii == 20:
Mthresh = .002
if kk == 1:
Mthresh = .001
minpkdis = 100
if ii == 5: Mthresh = .05
if ii == 8 and kk == 2: Mthresh = .22
if ii == 10 and kk == 1: Mthresh = .25
if ii == 16 and kk == 2: Mthresh = .05
if ii == 12 and kk == 4: Mthresh = .2
if ii == 11 and kk == 2: Mthresh = .15
# Find Peaks in signal
pkI = detect_peaks(fData[ii, 0:int(samples[ii]), kk + 4],
mpd=300,
mph=thresh,
edge='rising')
ind = remove_too_close(pkI)
# Once again used to remove unwanted peaks
if len(ind) > int(expectedPeaks):
ind = ind[0:int(expectedPeaks)]
reading = list(fData[ii, ind, kk + 4])
absInteg, Integ, ToPeakInt = peak_integral(
fData[ii, 0:int(samples[ii]), kk + 4], ind)
# Find the FFT and Power Spectral Analysis of the data
find_fft(rData[ii, 0:int(samples[ii]), kk + 4], ind)
# print('Test ' + str(ii) + ' Channel ' + str(kk))
FFT = np.array(FFT)
# thresh = .002
# if ii == 20:
# Mthresh = .002
# if kk == 1:
# Mthresh = .001
# minpkdis = 100
# if ii == 5: Mthresh = .05
# if ii == 8 and kk == 2: Mthresh = .22
# if ii == 10 and kk == 1: Mthresh = .25
# if ii == 16 and kk == 2: Mthresh = .05
# if ii == 12 and kk == 4: Mthresh = .2
# if ii == 11 and kk == 2: Mthresh = .15
# Find Peaks in signal
pkI = detect_peaks(rData[ii, 0:3000, 1], mpd=300, mph=thresh, edge='rising')
ind = remove_too_close(pkI)
# Once again used to remove unwanted peaks
if len(ind) > int(expectedPeaks):
ind = ind[0:int(expectedPeaks)]
#reading = list(fData[ii, ind, 1])
reading = list(rData[ii, ind, 1])
#absInteg, Integ, ToPeakInt = peak_integral(rData[ii, 0:3000, 1], ind)
# Find the FFT and Power Spectral Analysis of the data
find_fft(rData[ii, 0:3000, 1], ind)
# print('Test ' + str(ii) + ' Channel ' + str(kk))
FFT = np.array(FFT)
FFT_PkI = np.array(FFT_PkI)