def single_tube(argv):
allFiles = getFiles("/Users/yumiao/Documents/Works/Simulation/PMT_Paramters/202008Updates/TTS-caimei-data-update/", ".txt")
# read matches
nnvt_id, nnvt_sn = read_match("/Users/yumiao/Documents/Works/Simulation/PMT_Paramters/202008Updates/TTS-caimei-data-update/instruction/valid_NNVT.csv")
ham_id, ham_sn = read_match("/Users/yumiao/Documents/Works/Simulation/PMT_Paramters/202008Updates/TTS-caimei-data-update/instruction/valid_HAM.csv")
graph_dyn = TGraphErrors()
graph_mcp = TGraphErrors()
dyn_arr, mcp_arr = [[] for i in range(7)], [[] for i in range(7)]
nnvt_num, ham_num = 0, 0
""" loop all files and generate pdf output """
for idx, afile in enumerate(allFiles):
print("Processing scan file No {:d} -> {} " .format(idx, afile))
data1 = pd.read_csv(afile, header=[0], nrows=24, sep="\s+") ## TTS
#data1 = pd.read_csv(afile, header=[25], nrows=24, sep='\s+') ## TT
graph_dyn.Set(0)
graph_mcp.Set(0)
""" seperate MCP and Dynode PMTs """
#scanid = int(re.sub("\D", "", afile))
pattern = re.compile(r'(?<=scan)\d+')
scanid = int(pattern.findall(afile)[0])
if scanid in nnvt_id:
print("It's a MCP PMT !!!")
nnvt_num += 1
""" average phi direction """
for itheta in range(7):
for iphi in range(24):
#print(float(np.array(data1.iloc[iphi, itheta+1])))
#mcp_arr[itheta].append( float(np.array(data1.iloc[iphi, itheta+1])) )
mcp_arr[itheta].append( np.sqrt(float(np.array(data1.iloc[iphi, itheta+1]))**2-2.1**2) )
#for itheta in range(1, 8):
# mean = np.array(data1.iloc[::, itheta]).mean()
# sigma = np.std( np.array(data1.iloc[::, itheta]) )
# graph_mcp.SetPoint(itheta-1, itheta, mean)
# graph_mcp.SetPointError(itheta-1, 0, sigma)
"""average theta direction"""
"""
for iphi in range(24):
mean = np.array(data1.iloc[iphi, 1:8]).mean()
sigma = np.std(np.array(data1.iloc[iphi, 1:8]))
graph_mcp.SetPoint(iphi, iphi*15, mean)
graph_mcp.SetPointError(iphi, 0, sigma)
#plt.plot(graph_mcp.GetX(), graph_mcp.GetY(), "o-", ms=3)
"""
elif scanid in ham_id:
print("It's a Dynode PMT !!!")
ham_num += 1
for itheta in range(7):
for iphi in range(24):
dyn_arr[itheta].append( np.sqrt(float(np.array(data1.iloc[iphi, itheta+1]))**2-2.1**2) )
""" average phi direction """
#for itheta in range(1, 8):
# mean = np.array(data1.iloc[::, itheta]).mean()
# sigma = np.std( np.array(data1.iloc[::, itheta]) )
# print("It's a Dynode PMT !!!")
# graph_dyn.SetPoint(itheta-1, itheta, mean)
# graph_dyn.SetPointError(itheta-1, 0, sigma)
"""average theta direction"""
"""
for iphi in range(24):
mean = np.array(data1.iloc[iphi, 1:8]).mean()
sigma = np.std(np.array(data1.iloc[iphi, 1:8]))
graph_dyn.SetPoint(iphi, iphi*15, mean)
graph_dyn.SetPointError(iphi, 0, sigma)
plt.plot(graph_dyn.GetX(), graph_dyn.GetY(), "o-", ms=3)
"""
dyn_pos = [13, 28, 41, 55, 66, 79, 85]
mcp_pos = [14, 30, 42.5, 55, 67, 77.5, 85]
dyn_pos = [1, 2, 3, 4, 5, 6, 7]
print([np.array(dyn_arr[i]).mean() for i in range(7)])
def draw_plot(data, pos, edge_color, fill_color):
bp = ax.boxplot(data, positions=pos, patch_artist=True, sym="")
for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:
plt.setp(bp[element], color=edge_color)
for patch in bp['boxes']:
patch.set(facecolor=fill_color)
return bp
fig, ax = plt.subplots()
bp1 = draw_plot(dyn_arr, dyn_pos, 'red', 'tan')
bp2 = draw_plot(mcp_arr, dyn_pos, 'blue', 'cyan')
ax.set_ylim(0, 10)
ax.legend([bp1["boxes"][0], bp2["boxes"][0]], ["dynode", "mcp"], loc='best')
ax.set_xlabel("theta/deg")
ax.set_ylabel("TTS mean/ns")
plt.show()
#plt.violinplot(dyn_arr, dyn_pos, points=1000, widths=0.8, showmeans=True, showextrema=True, showmedians=True)
#plt.violinplot(mcp_arr, mcp_pos, points=1, widths=0.3, showmeans=True, showextrema=True, showmedians=True)
#plt.violinplot(, pos, points=20, widths=0.3,
# showmeans=True, showextrema=True, showmedians=True)
#plt.set_title('Custom violinplot 1', fontsize=fs)
#plt.title("Dyn PMT average all theta")
#plt.xlabel("phi")
#plt.ylabel("hittime/ns")
#plt.show()
#plt.savefig(argv[1])
print(" Dynode PMT Number: %d" % ham_num)
print(" MCP PMT Number: %d" % nnvt_num)
def main():
args = parser()
filename = args.filename
os.system('mkdir -p rootfiles')
os.system('mkdir -p plots')
f = tables.open_file(filename)
print(f"Successfully opened file: {filename}.")
data = f.get_node('/data')
print("Read /data")
event_ids = getDataCol('event_id', data)
waveforms = getDataCol('waveform', data)
times = getDataCol('time', data)
timestamps = getDataCol('timestamp', data)
thresFlags = getDataCol('thresholdFlags', data)
i_1V1 = getDataCol('i_1V1', data)
i_1V35 = getDataCol('i_1V35', data)
i_1V8 = getDataCol('i_1V8', data)
i_2V5 = getDataCol('i_2V5', data)
i_3V3 = getDataCol('i_3V3', data)
v_1V1 = getDataCol('v_1V1', data)
v_1V35 = getDataCol('v_1V35', data)
v_1V8 = getDataCol('v_1V8', data)
v_2V5 = getDataCol('v_2V5', data)
v_3V3 = getDataCol('v_3V3', data)
nsamples = len(waveforms[0])
fq = np.linspace(0, 240, nsamples)
gmean = np.mean(waveforms[0])
globalmean = np.mean(waveforms)
print(np.mean(waveforms[0]))
c = TCanvas("c", "c", 800, 600)
c.Draw()
c.SetGrid()
of = TFile('rootfiles/{0}.root'.format(filename.split('.h', 1)[0]),
"RECREATE")
h = TH1D('qdist', 'qdist;ADC count;Entry', 500, -100, 900)
hpk = TH1D('peak', 'peak;ADC count;Entry', 500, -100, 900)
havg = TH1D('avgwf', 'Averaged Waveform;Sampling Bin;ADC count', nsamples,
0, nsamples)
havgfft = TH1D('avgfft',
'FFT Averaged Waveform;Frequency [MHz];Amplitude [LSB]',
int(len(fq) / 2) + 1, 0, 120 + 240 / (nsamples - 1))
hifft = TH1D('Subtwf', 'Averaged Waveform;Sampling Bin;ADC count',
nsamples, 0, nsamples)
hsfft = TH1D('Subtfft',
'FFT Averaged Waveform;Frequency [MHz];Amplitude [LSB]',
int(len(fq) / 2) + 1, 0, 120 + 240 / (nsamples - 1))
hsspe = TH1D('qdist_subt', 'qdist_subt;ADC count;Entry', 500, -100, 900)
hmax = TH1D('hmax', 'hmax;ADC count;Entry', 500, -100, 900)
bsfluc = TH1D('bsfluc', 'bsfluc;ADC count;Entry', 100,
int(globalmean) - 50,
int(globalmean) + 50)
bsshift = TGraphErrors()
bsshift.SetName('bsshift')
bsshift.SetTitle('bsshift;Event Number;Baseline [LSB]')
nrshift = TGraphErrors()
nrshift.SetName('nrshift')
nrshift.SetTitle('NoiseRateShift;Event Number;Noise Rate [Hz]')
rmsshift = TGraphErrors()
rmsshift.SetName('rmsshift')
rmsshift.SetTitle('RMSNoiseShift;Event Number;RMS Noise [LSB]')
rmstime = TGraphErrors()
rmstime.SetName('rmstime')
rmstime.SetTitle('rmstime;Unix time [s];RMS Noise [LSB]')
powerc = TGraphErrors()
powerc.SetName('powerc')
powerc.SetTitle('powerc;Event Number;Power Consumption [W]')
h_powerc = TH1D('h_powerc', 'h_powerc;Power Consumption [W];Entry', 100, 0,
5)
winmin = args.minimum
winmax = args.minimum + args.window
bsstart = args.baselineEst
print(f'Total #Events: {len(waveforms)}')
print(
f'Setting... Window [{winmin}:{winmax}] and Pedestal start from {bsstart}.\n'
)
topdir = gDirectory.GetDirectory(gDirectory.GetPath())
subdir = topdir.mkdir("Waveforms")
subdir2 = topdir.mkdir("FFT")
subdir3 = topdir.mkdir("proj")
thrdir = topdir.mkdir("thresFlags")
fltwfs = []
starttimestamp = timestamps[0]
procN = len(waveforms)
if args.nevents > 0:
procN = args.nevents
for i in tqdm(range(procN)):
if i < args.nskip:
continue
waveform = waveforms[i]
timestamp = timestamps[i]
bsfluc.Fill(np.mean(waveform))
n = bsshift.GetN()
bsshift.Set(n + 1)
bsshift.SetPoint(n, i, np.mean(waveform))
bsshift.SetPointError(n, 0, np.std(waveform))
n = rmsshift.GetN()
rmsshift.Set(n + 1)
rmsshift.SetPoint(n, i, np.std(waveform))
rmsshift.SetPointError(n, 0, 0)
hmax.Fill(np.max(waveform) - np.mean(waveform))
timeinterval = ((timestamp - starttimestamp - i * nsamples) / 240.e6 -
i * 0.501)
nr = 0
nrerr = 0
if timeinterval > 0:
nr = (i + 1) / timeinterval
nrerr = np.sqrt(i + 1) / timeinterval
n = nrshift.GetN()
nrshift.Set(n + 1)
nrshift.SetPoint(n, i, nr)
nrshift.SetPointError(n, 0, nrerr)
# FFT & IFFT ###
F_abs_amp = doFFT(waveform)
F = np.fft.fft(waveform)
F2 = np.copy(F)
fc = 60
df = 2
F2[(fq > fc - df) & (fq < fc + df)] = 0
F2[(fq > 2 * fc - df) & (fq < 2 * fc + df)] = 0
F2[(fq > 240 - fc - df) & (fq < 240 - fc + df)] = 0
F2_abs = np.abs(F2)
F2_abs_amp = F2_abs / len(waveform) * 2
F2_abs_amp[0] = F_abs_amp[0] / 2
F2_ifft = np.fft.ifft(F2)
F2_ifft_real = F2_ifft.real
######
baseline_mean = np.mean(waveform[bsstart:nsamples])
if args.fixbs:
baseline_mean = globalmean
center = int(baseline_mean)
proj = TH1D(f'proj{i}', f'Projection waveform{i};ADC count;Entry', 300,
center - 150, center + 150)
selected = waveform[waveform < np.mean(waveform) + 10]
for j in range(len(selected)):
proj.Fill(selected[j])
#proj.Fit("gaus")
#if len(selected) > 0:
# f = proj.GetFunction('gaus')
# norm = f.GetParameter(0)
# mean = f.GetParameter(1)
# sigma = f.GetParameter(2)
reduced_waveform = waveform - baseline_mean
scale = (nsamples - bsstart) / (winmax - winmin)
#h.Fill(sum(waveform[winmin:winmax])-sum(waveform[bsstart:nsamples])/scale)
h.Fill(sum(reduced_waveform[winmin:winmax]))
hsspe.Fill(
np.sum(F2_ifft_real[winmin:winmax]) - baseline_mean *
(winmax - winmin))
hpk.Fill(
max(waveform[winmin:winmax]) - np.mean(waveform[bsstart:nsamples]))
hfft = TH1D(f'FFT{i}', 'FFT{i};Frequency [MHz];Amplitude [LSB]',
int(len(fq) / 2) + 1, 0, 120 + 240 / (nsamples - 1))
hfft2 = TH1D(f'FFT2{i}', 'FFT2{i};Frequency [MHz];Amplitude [LSB]',
int(len(fq) / 2) + 1, 0, 120 + 240 / (nsamples - 1))
for j in range(int(len(F_abs_amp) / 2. + 1)):
hfft.Fill(fq[j], F_abs_amp[j])
hfft2.Fill(fq[j], F2_abs_amp[j])
h2 = TH1D(f'w{i}', 'Waveform{i};Sampling Bin;ADC count', nsamples, 0,
nsamples)
for j in range(len(waveform)):
h2.Fill(j, waveform[j])
#h2.Draw("hist")
#if np.max(F2_abs_amp[1:int(len(F2_abs_amp)/2.+1)]) > 0.5:
# c.SetLogy(0)
# h2.SetLineColor(4)
# h2.Draw("hist")
# c.Print(f"plots/w{i}.pdf(")
# c.SetLogy(1)
# hfft.SetLineColor(4)
# hfft.Draw("hist")
# c.Print(f"plots/w{i}.pdf)")
htot = TH1D(f'thresflags{i}',
'ThresholdFlags{i};Sampling Bin;Threshold Flag', nsamples,
0, nsamples)
for j in range(len(thresFlags[i])):
htot.Fill(j, thresFlags[i][j])
if max(waveform) - baseline_mean < args.threshold:
continue
fltwfs.append(waveform)
if args.silent:
del hfft
del hfft2
del h2
del htot
del proj
del waveform
del timestamp
del F
del F2
gc.collect()
continue
subdir2.cd()
hfft.Write()
subdir.cd()
h2.Write()
subdir3.cd()
proj.Write()
thrdir.cd()
htot.Write()
print('')
for i in range(len(i_1V1)):
internal_power = v_1V1[i] * i_1V1[i] + v_1V35[i] * i_1V35[i] + v_1V8[
i] * i_1V8[i] + v_2V5[i] * i_2V5[i] + v_3V3[i] * i_3V3[i]
n = powerc.GetN()
powerc.Set(n + 1)
powerc.SetPoint(n, i, internal_power * 1e-3)
powerc.SetPointError(n, 0, 0)
h_powerc.Fill(internal_power * 1e-3)
avgfltwfs = np.mean(fltwfs, axis=0)
for i in range(len(avgfltwfs)):
havg.Fill(i, avgfltwfs[i])
Favg_abs_amp = doFFT(avgfltwfs)
Favg = np.fft.fft(avgfltwfs)
Favg2 = np.copy(Favg)
Favg2[(fq > 59) & (fq < 61)] = 0
Favg2[(fq > 119) & (fq < 121)] = 0
Favg2[(fq > 179) & (fq < 181)] = 0
Favg2_abs = np.abs(Favg2)
Favg2_abs_amp = Favg2_abs / len(avgfltwfs) * 2
Favg2_abs_amp[0] = Favg_abs_amp[0] / 2
Favg2_ifft = np.fft.ifft(Favg2)
Favg2_ifft_real = Favg2_ifft.real
for i in range(int(len(Favg_abs_amp) / 2. + 1)):
havgfft.Fill(fq[i], Favg_abs_amp[i])
for i in range(int(len(Favg2_abs_amp) / 2. + 1)):
hsfft.Fill(fq[i], Favg2_abs_amp[i])
for i in range(len(Favg2_ifft_real)):
hifft.Fill(i, Favg2_ifft_real[i])
topdir.cd()
h.Write()
hpk.Write()
havg.Write()
havgfft.Write()
hsfft.Write()
hifft.Write()
hsspe.Write()
hmax.Write()
bsfluc.Write()
bsshift.Write()
nrshift.Write()
rmsshift.Write()
powerc.Write()
h_powerc.Write()
of.Close()
f.close()
