def test5():
'''Test the measure_multipleX function in sp.C, which trys to locate the signal by seaching the maximum and use the period info to find others. It's used used only for calibration.'''
s1 = SigProc(nSamples=16384, nAdcCh=20, nSdmCh=19, adcSdmCycRatio=5)
data1 = (s1.ANALYSIS_WAVEFORM_BASE_TYPE * (s1.nSamples * s1.nAdcCh))()
sp = SignalProcessor()
sp.fltParam.clear()
P = 1. / 0.006 / 2500 / 1024 * 5000000
for x in [30, 50, 200, P]:
sp.fltParam.push_back(x)
sp.CF_chan_en.clear()
sp.IO_mAvg.clear()
for i in range(20):
sp.CF_chan_en.push_back(1)
sp.IO_mAvg.push_back(0.)
# inRoot = 'data/fpgaLin/Feb28t3_data_0.root'
inRoot = 'data/fpgaLin/Mar05T1a_data_0.root'
fout1 = TFile(inRoot, 'read')
tree1 = fout1.Get('tree1')
tree1.SetBranchAddress('adc', data1)
N = 2000 # 2500 Hz
Vs = array('f', [0] * (20 * 8))
# for i in range(20*8)
for ievt in range(3):
tree1.GetEntry(ievt)
sp.measure_multipleX(data1, N, Vs)
for i in range(20):
print i, ':',
for j in range(8):
# print i*8+j,
print Vs[i * 8 + j],
print
class Train(threading.Thread):
def __init__(self, cd):
threading.Thread.__init__(self)
self.cd = cd
self.tx_qs = None
self.rx_qs = None
self.on = True
self.mask = [0] * cd.nCh
self.nSig = 3
### this copy of sensorVcodes is used to save the best value find so far
self.sensorVcodes = [[v for v in cd.sensorVcodes[i]]
for i in range(cd.nCh)]
# self.bestConfigFile = 'current_best_config.json'
self.bestConfigFile = 'C3_tt6_config.json'
self.retBest = [0.] * cd.nAdcCh
### for data taking
self.sc = None
### plotting
self.axs = None
self.plot_x = None
self.pltCnt = 0
self.pltN = 20
self.sp = SignalProcessor()
apply_config(self.sp, 'Helium')
self.NVAL = 8
s1 = self.cd.sigproc
self.data1 = (s1.ANALYSIS_WAVEFORM_BASE_TYPE *
(s1.nSamples * s1.nAdcCh))()
self.ret1 = array.array('f', [0] * s1.nAdcCh)
self.par1 = array.array('f',
[0] * (self.cd.nCh * len(self.cd.inputVs)))
self.t_values = array.array('f', [0] * (s1.nAdcCh * self.NVAL))
self.keepAllData = False
self.saveT0 = -1
self.T = array.array('i', [0])
self.Tag = array.array('i', [0])
def setupOutput(self, outRootName='tt_test.root'):
s1 = self.cd.sigproc
self.fout1 = TFile(outRootName, 'recreate')
self.tree1 = TTree(
'tree1', "tune data for {0:d} channels, {1:d} samples".format(
s1.nAdcCh, s1.nSamples))
self.tree1.Branch('tag', self.Tag, 'tag/I')
self.tree1.Branch('T', self.T, 'T/i')
self.tree1.Branch('adc', self.data1,
"adc[{0:d}][{1:d}]/F".format(s1.nAdcCh, s1.nSamples))
self.tree1.Branch('ret', self.ret1, "ret[{0:d}]/F".format(s1.nAdcCh))
self.tree1.Branch(
'par', self.par1,
"par[{0:d}][{1:d}]/F".format(self.cd.nCh, len(self.cd.inputVs)))
self.tree1.Branch(
'val', self.t_values,
"val[{0:d}][{1:d}]/F".format(self.cd.nAdcCh, self.NVAL))
def plot_data(self):
# item = self.q.get()
if self.axs is None:
print("creating the plot......")
import matplotlib
matplotlib.rcParams['toolbar'] = 'None'
plt.ion()
fig, self.axs = plt.subplots(self.cd.nAdcCh, 1, sharex=True)
# Remove horizontal space between axes
plt.tight_layout(pad=0)
fig.subplots_adjust(hspace=0)
self.plot_x = [self.cd.adcDt * i for i in range(self.cd.nSamples)]
for i in range(self.cd.nAdcCh):
self.axs[i].cla()
# self.axs[i].plot(self.cd.adcData[i])
# self.axs[i].step(array.array('f', self.cd.adcData[i]), where='post')
# self.axs[i].plot(self.plot_x, array.array('f', self.cd.adcData[i]))
self.axs[i].plot(
self.plot_x,
self.data1[i * self.cd.nSamples:(i + 1) * self.cd.nSamples])
plt.draw()
# self.q.task_done()
def test_update_sensor(self, inputVs=None):
print('/' * 40)
if inputVs is None:
inputVs = [[1.379, 1.546, 1.626, 1.169, 1.357, 2.458],
[1.379, 1.546, 1.626, 1.169, 1., 2.]]
#### update sensor configurations
ss = set()
for i, p in enumerate(inputVs):
if p is None: continue
self.cd.set_sensor(i, p)
ss.add(self.sc.tms1mmX19chainSensors[
self.sc.tms1mmX19sensorInChain[i]][0])
### apply the configurations
print(ss)
for isr in ss:
self.sc.update_sensor(isr)
print('\\' * 40)
def take_data2(self, NEVT=100):
'''return an array of FOM'''
s1 = self.cd.sigproc
for ievt in range(NEVT):
self.T[0] = int(time.time())
self.cd.dataSocket.sendall(self.cd.cmd.send_pulse(1 << 2))
buf = self.cd.cmd.acquire_from_datafifo(self.cd.dataSocket,
self.cd.nWords,
self.cd.sampleBuf)
s1.demux_fifodata(buf, self.data1, self.cd.sdmData)
### measure
self.sp.measure_multipleX(self.data1, 2000, self.t_values)
### save
self.tree1.Fill()
def take_data(self):
'''return an array of FOM'''
s1 = self.cd.sigproc
NV = 8
NEVT = 100
Values = [[0.] * (NV * NEVT) for i in range(self.cd.nAdcCh)]
for ievt in range(NEVT):
self.cd.dataSocket.sendall(self.cd.cmd.send_pulse(1 << 2))
buf = self.cd.cmd.acquire_from_datafifo(self.cd.dataSocket,
self.cd.nWords,
self.cd.sampleBuf)
s1.demux_fifodata(buf, self.data1, self.cd.sdmData)
self.sp.measure_multipleX(self.data1, 2000, self.t_values)
# if ievt == 0:
# print("EVT",ievt)
# # print([self.t_values[i] for i in range(NV*self.cd.nAdcCh)])
# print([self.t_values[6*NV+i] for i in range(NV)])
for ich in range(self.cd.nAdcCh):
for ipeak in range(NV):
Values[ich][ievt * NV + ipeak] = self.t_values[ich * NV +
ipeak]
for i in range(s1.nAdcCh):
# self.ret1[i] = np.std(Values[i])/np.mean(Values[i])
self.ret1[i] = -np.mean(Values[i]) / np.std(Values[i])
# if i==6:
# # print(i,'->', Values[i][20*8:21*8])
# print(i,'->', Values[i][0*8:1*8])
# print(i, np.std(Values[i]), np.mean(Values[i]), self.ret1[i])
self.T[0] = int(time.time())
# print(self.ret1[3],self.ret1[0])
# for i in range(self.cd.nAdcCh): print(i,self.ret1[i])
self.tree1.Fill()
if self.T[0] - self.saveT0 > 200:
self.tree1.AutoSave('SaveSelf')
self.saveT0 = self.T[0]
def run(self):
nPar = len(self.cd.inputVs)
### save the initial values
for i in range(self.cd.nCh):
for kk in range(nPar):
self.par1[i * nPar + kk] = self.cd.tms1mmReg.dac_code2volt(
self.sensorVcodes[i][kk])
while self.on:
cnt = 0
# cnt1 = 0
ss = set()
for i, q in enumerate(self.rx_qs):
if q is None: continue
cnt += 1
# x = q.get()
# self.pars[i] = x
if not q.empty():
# cnt1 += 1
x = q.get()
self.mask[i] = 1
# print('--- {0:d} get'.format(i)+' ['+','.join([str(a) for a in x])+']')
# self.pars[i] = x
# print(i,x)
self.cd.set_sensor(i, x)
for kk in range(nPar):
self.par1[i * nPar + kk] = x[kk]
ss.add(self.sc.tms1mmX19chainSensors[
self.sc.tms1mmX19sensorInChain[i]][0])
if cnt == 0: self.on = False
if len(ss) == 0: continue
# if cnt1 == 0: continue
#### update sensor configurations
# ss = set()
# for i,p in enumerate(self.pars):
# if self.mask[i] == 1:
# self.cd.set_sensor(i,p)
# ss.append(self.sc.tms1mmX19chainSensors[self.sc.tms1mmX19sensorInChain[i]][0])
### apply the configurations
for isr in ss:
self.sc.update_sensor(isr, quiet=1)
### take data
self.pltCnt += 1
t = None
if self.pltCnt % self.pltN == 0:
self.plot_data()
# t = threading.Thread(target=self.plot_data)
# t.daemon = True
# t.start()
# self.q.put('run')
time.sleep(30)
if t is not None: t.join()
self.take_data()
ret = self.ret1
### return
needUpdate = False
for i, t in enumerate(self.tx_qs):
if self.mask[i] == 1:
t.put(ret[i])
self.mask[i] = 0
### save the values if it's the best so far
if ret[i] < self.retBest[i]:
print("find better parameters for channel", i)
print('old:', self.sensorVcodes[i], self.retBest[i])
self.retBest[i] = ret[i]
self.sensorVcodes[i] = [
a for a in self.cd.sensorVcodes[i]
]
print('new:', self.sensorVcodes[i], self.retBest[i])
needUpdate = True
# print("--- {0:d} {1:g}".format(i, self.meas[i]))
if needUpdate:
self.sc.write_config_fileX(self.bestConfigFile,
self.sensorVcodes)
print('Stopping the train.....')
plt.close('all')
self.tree1.Write()
self.fout1.Close()
def tune_check():
'''The same configuration as the tune'''
be1 = bkgEstimator()
# be1.show_data()
s1 = SigProc(nSamples=16384, nAdcCh=20, nSdmCh=19, adcSdmCycRatio=5)
data1 = (s1.ANALYSIS_WAVEFORM_BASE_TYPE * (s1.nSamples * s1.nAdcCh))()
data1 = array('f', [0] * (16384 * 20))
# pTag = 'Feb09b'
pTag = 'Feb25a'
tagA = 'data/fpgaLin/' + pTag + '_data_'
inRoot = 'data/fpgaLin/' + pTag + '_data_1138.root'
if len(sys.argv) > 1:
if os.path.exists(sys.argv[1]):
inRoot = sys.argv[1]
elif os.path.exists(tagA + sys.argv[1] + '.root'):
inRoot = tagA + sys.argv[1] + '.root'
else:
files = sorted([f for f in glob(tagA + '*.root')],
key=lambda f: os.path.getmtime(f))
a = -1
try:
a = int(sys.argv[1])
except TypeError:
pass
if time.time() - os.path.getmtime(files[-1]) < 10:
print "dropping the latest file, which probably is still being written:", files[
-1]
if a != 0: files.pop()
else: a = -1
if abs(a) < len(files):
inRoot = files[a]
else:
print "Index {0:d} out of range:{1:d}".format(a, len(files))
return
print "Using file:", inRoot
fout1 = TFile(inRoot, 'read')
tree1 = fout1.Get('tree1')
tree1.SetBranchAddress('adc', data1)
print "Entries in the tree:", tree1.GetEntries()
run = -1
runPattern = '.*_data_(\d+).root'
if runPattern is not None:
m = re.match(runPattern, inRoot)
if m:
try:
run = int(m.group(1))
except ValueError:
print "Run number not exatracted for file", iRoot
i = 56
ich = 1
sp1 = SignalProcessor()
print "using configuration:", apply_config(sp1, 'Helium')
for i in range(sp1.nAdcCh):
sp1.CF_chan_en[i] = 1 if i == ich else 0
plt.ion()
plt.show()
# fig, ax1 = plt.subplots(1, 1, figsize=(28, 10))
fig, ax1 = plt.subplots(1, 1, figsize=(14, 5))
# fig.set_size_inches(11,8)
ax1.set_xlabel('time index')
ax1.set_ylabel('U [V]', color='b')
ax1.tick_params('y', colors='b')
ax2 = ax1.twinx()
ax2.set_ylabel('U [V]', color='r')
ax2.tick_params('y', colors='r')
# for ievt in range(tree1.GetEntries()):
NMax = tree1.GetEntries()
ievt = 0
NVAL = 8
t_values = array('f', [0] * (sp1.nAdcCh * NVAL))
while ievt < NMax:
print "Event:", ievt
tree1.GetEntry(ievt)
va = data1[ich * sp1.nSamples:(ich + 1) * sp1.nSamples]
# be1.correct(data1, ich)
# apply_wiener_filter(data1, ich)
# sp1.measure_pulse2(data1, ich)
sp1.measure_multipleX(data1, 2000, t_values)
vx = np.array([sp1.scrAry[i] for i in range(sp1.nSamples)])
vo = data1[ich * sp1.nSamples:(ich + 1) * sp1.nSamples]
ax1.clear()
ax2.clear()
ax1.plot(va, label='Raw', color='b')
# ax1.plot(vo, label='Wiener', color='g')
ax2.plot(vx, label='Filtered', color='r')
# ax2.plot([vo[i]-va[i] for i in range(sp1.nSamples)], label='Correction', color='k')
ylim1 = ax1.get_ylim()
ylim2 = ax2.get_ylim()
x1 = min(ylim1[0], ylim2[0] + vo[0])
x2 = max(ylim1[1], ylim2[1] + vo[0])
# print x1,x2
ax1.set_ylim(x1, x2)
ax2.set_ylim(x1 - vo[0], x2 - vo[0])
# print sp1.signals[ich].size()
x1 = []
y1 = []
iss = 0
for x in t_values[ich * NVAL:(ich + 1) * NVAL]:
print x
# if len(sp1.signals[ich])>0:
# print "idx: iMax iMidian A w0 w1 w2"
# print '-'*30
# for s in sp1.signals[ich]:
# print iss,':', s.idx, s.im, s.Q, s.w0, s.w1, s.w2
# x1.append(s.im)
# y1.append(s.Q)
# plt.axvline(x=s.im, linestyle='--', color='black')
# iss += 1
plt.text(0.04,
0.1,
'run {0:d} event {1:d}, ch {2:d}'.format(run, ievt, ich),
horizontalalignment='center',
verticalalignment='center',
transform=ax2.transAxes)
plt.xlim(auto=False)
if x1: ax2.scatter(x1, y1, c="g", marker='o', s=220, label='Analysis')
fig.tight_layout()
plt.draw()
plt.legend()
plt.grid(True)
plt.pause(0.001)
while True:
x = raw_input("Next:")
if x == 'q': sys.exit()
elif len(x) > 0 and x[0] == 's':
for name in x.split()[1:]:
dirx = os.path.dirname(name)
if not os.path.exists(dirx): os.makedirs(dirx)
plt.savefig(name)
print "saved figure to", name
elif len(x) > 2 and x[:2] == 'ch':
try:
ich = int(x[2:])
for i in range(sp1.nAdcCh):
sp1.CF_chan_en[i] = 1 if i == ich else 0
print "Switching to channel:", ich
break
except ValueError:
continue
elif len(x) > 0 and x[0] == 'p':
ievt -= 1
break
else:
try:
ievt = int(x)
except ValueError:
ievt += 1
break