def plot_width(x, y, name, scale = 1e9):
"""Calc and plot FWHM of pulses"""
width, widthErr = calc.calcWidth(x,y)
print width, widthErr
bins = np.arange((width-8*widthErr)*scale, (width+8*widthErr)*scale, (widthErr/5.)*scale)
hist = ROOT.TH1D("%s" % name,"%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Pulse width")
hist.GetXaxis().SetTitle("FWHM (ns)")
f = calc.positive_check(y)
if f == True:
for i in range(len(y[:,0])-1):
m = max(y[i,:])
m_index = np.where(y[i,:] == m)[0][0]
thresh = m*0.5
first = calc.interpolate_threshold(x[:m_index], y[i,:m_index], thresh, rise=True)
second = calc.interpolate_threshold(x[m_index:], y[i,m_index:], thresh, rise=False)
hist.Fill((second - first)*scale)
else:
for i in range(len(y[:,0])-1):
m = min(y[i,:])
m_index = np.where(y[i,:] == m)[0][0]
thresh = m*0.5
first = calc.interpolate_threshold(x[:m_index], y[i,:m_index], thresh, rise=False)
second = calc.interpolate_threshold(x[m_index:], y[i,m_index:], thresh, rise=True)
hist.Fill((second - first)*scale)
return hist, width, widthErr
def plot_rise(x, y, name, scale=1e9):
"""Calc and plot rise time of pulses"""
rise, riseErr = calc.calcRise(x, y)
print rise, riseErr
bins = np.arange((rise - 8 * riseErr) * scale,
(rise + 8 * riseErr) * scale, (riseErr / 5.) * scale)
hist = ROOT.TH1D("%s" % name, "%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Rise time")
hist.GetXaxis().SetTitle("Rise time (ns)")
f = calc.positive_check(y)
if f == True:
for i in range(len(y[:, 0]) - 1):
m = max(y[i, :])
lo_thresh = m * 0.1
hi_thresh = m * 0.9
low = calc.interpolate_threshold(x, y[i, :], lo_thresh)
high = calc.interpolate_threshold(x, y[i, :], hi_thresh)
hist.Fill((high - low) * scale)
else:
for i in range(len(y[:, 0]) - 1):
m = min(y[i, :])
lo_thresh = m * 0.1
hi_thresh = m * 0.9
low = calc.interpolate_threshold(x, y[i, :], lo_thresh, rise=False)
high = calc.interpolate_threshold(x,
y[i, :],
hi_thresh,
rise=False)
hist.Fill((high - low) * scale)
return hist, rise, riseErr
def plot_fall(x, y, name, scale = 1e9):
"""Calc and plot fall time of pulses"""
fall, fallErr = calc.calcFall(x,y)
print fall, fallErr
bins = np.arange((fall-8*fallErr)*scale, (fall+8*fallErr)*scale, (fallErr/5.)*scale)
hist = ROOT.TH1D("%s" % name,"%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Fall time")
hist.GetXaxis().SetTitle("Fall time (ns)")
f = calc.positive_check(y)
if f == True:
for i in range(len(y[:,0])-1):
m = max(y[i,:])
m_index = np.where(y[i,:] == m)[0][0]
lo_thresh = m*0.1
hi_thresh = m*0.9
low = calc.interpolate_threshold(x[m_index:], y[i,m_index:], lo_thresh, rise=False)
high = calc.interpolate_threshold(x[m_index:], y[i,m_index:], hi_thresh, rise=False)
hist.Fill((low - high)*scale)
else:
for i in range(len(y[:,0])-1):
m = min(y[i,:])
m_index = np.where(y[i,:] == m)[0][0]
lo_thresh = m*0.1
hi_thresh = m*0.9
low = calc.interpolate_threshold(x[m_index:], y[i,m_index:], lo_thresh)
high = calc.interpolate_threshold(x[m_index:], y[i,m_index:], hi_thresh)
hist.Fill((low - high)*scale)
return hist, fall, fallErr
def plot_rise(x, y, name, scale = 1e9):
"""Calc and plot rise time of pulses"""
rise, riseErr = calc.calcRise(x,y)
print rise, riseErr
bins = np.arange((rise-8*riseErr)*scale, (rise+8*riseErr)*scale, (riseErr/5.)*scale)
hist = ROOT.TH1D("%s" % name,"%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Rise time")
hist.GetXaxis().SetTitle("Rise time (ns)")
f = calc.positive_check(y)
if f == True:
for i in range(len(y[:,0])-1):
m = max(y[i,:])
lo_thresh = m*0.1
hi_thresh = m*0.9
low = calc.interpolate_threshold(x, y[i,:], lo_thresh)
high = calc.interpolate_threshold(x, y[i,:], hi_thresh)
hist.Fill((high - low)*scale)
else:
for i in range(len(y[:,0])-1):
m = min(y[i,:])
lo_thresh = m*0.1
hi_thresh = m*0.9
low = calc.interpolate_threshold(x, y[i,:], lo_thresh, rise=False)
high = calc.interpolate_threshold(x, y[i,:], hi_thresh, rise=False)
hist.Fill((high - low)*scale)
return hist, rise, riseErr
def plot_jitter(x1, y1, x2, y2, name, scale = 1e9):
"""Calc and plot jitter of pulse pairs"""
sep, jitter, jittErr = calc.calcJitter(x1, y1, x2, y2)
bins = np.arange((sep-8*jitter)*scale, (sep+8*jitter)*scale, (jitter/5.)*scale)
hist = ROOT.TH1D("%s" % name,"%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Jitter between signal and trigger out")
hist.GetXaxis().SetTitle("Pulse separation (ns)")
p1 = calc.positive_check(y1)
p2 = calc.positive_check(y2)
for i in range(len(y1[:,0])-1):
m1 = calc.calcSinglePeak(p1, y1[i,:])
m2 = calc.calcSinglePeak(p2, y2[i,:])
time_1 = calc.interpolate_threshold(x1, y1[i,:], 0.1*m1, rise=p1)
time_2 = calc.interpolate_threshold(x2, y2[i,:], 0.1*m2, rise=p2)
hist.Fill((time_1 - time_2)*scale)
return hist, jitter, jittErr
def plot_jitter(x1, y1, x2, y2, name, scale=1e9):
"""Calc and plot jitter of pulse pairs"""
sep, jitter, jittErr = calc.calcJitter(x1, y1, x2, y2)
bins = np.arange((sep - 8 * jitter) * scale, (sep + 8 * jitter) * scale,
(jitter / 5.) * scale)
hist = ROOT.TH1D("%s" % name, "%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Jitter between signal and trigger out")
hist.GetXaxis().SetTitle("Pulse separation (ns)")
p1 = calc.positive_check(y1)
p2 = calc.positive_check(y2)
for i in range(len(y1[:, 0]) - 1):
m1 = calc.calcSinglePeak(p1, y1[i, :])
m2 = calc.calcSinglePeak(p2, y2[i, :])
time_1 = calc.interpolate_threshold(x1, y1[i, :], 0.1 * m1, rise=p1)
time_2 = calc.interpolate_threshold(x2, y2[i, :], 0.1 * m2, rise=p2)
hist.Fill((time_1 - time_2) * scale)
return hist, jitter, jittErr
def plot_peak(x, y, name):
"""Plot pulse heights for array of pulses"""
peak, peakErr = calc.calcPeak(x,y)
bins = np.arange((peak-8*peakErr), (peak+8*peakErr), (peakErr/5.))
hist = ROOT.TH1D("%s" % name,"%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Pulse hieght")
hist.GetXaxis().SetTitle("Pulse height (V)")
f = calc.positive_check(y)
if f == True:
for i in range(len(y[:,0])-1):
hist.Fill(max(y[i,:]))
else:
for i in range(len(y[:,0])-1):
hist.Fill(min(y[i,:]))
return hist, peak, peakErr
def plot_peak(x, y, name):
"""Plot pulse heights for array of pulses"""
peak, peakErr = calc.calcPeak(x, y)
bins = np.arange((peak - 8 * peakErr), (peak + 8 * peakErr),
(peakErr / 5.))
hist = ROOT.TH1D("%s" % name, "%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Pulse hieght")
hist.GetXaxis().SetTitle("Pulse height (V)")
f = calc.positive_check(y)
if f == True:
for i in range(len(y[:, 0]) - 1):
hist.Fill(max(y[i, :]))
else:
for i in range(len(y[:, 0]) - 1):
hist.Fill(min(y[i, :]))
return hist, peak, peakErr
def plot_width(x, y, name, scale=1e9):
"""Calc and plot FWHM of pulses"""
width, widthErr = calc.calcWidth(x, y)
print width, widthErr
bins = np.arange((width - 8 * widthErr) * scale,
(width + 8 * widthErr) * scale, (widthErr / 5.) * scale)
hist = ROOT.TH1D("%s" % name, "%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Pulse width")
hist.GetXaxis().SetTitle("FWHM (ns)")
f = calc.positive_check(y)
if f == True:
for i in range(len(y[:, 0]) - 1):
m = max(y[i, :])
m_index = np.where(y[i, :] == m)[0][0]
thresh = m * 0.5
first = calc.interpolate_threshold(x[:m_index],
y[i, :m_index],
thresh,
rise=True)
second = calc.interpolate_threshold(x[m_index:],
y[i, m_index:],
thresh,
rise=False)
hist.Fill((second - first) * scale)
else:
for i in range(len(y[:, 0]) - 1):
m = min(y[i, :])
m_index = np.where(y[i, :] == m)[0][0]
thresh = m * 0.5
first = calc.interpolate_threshold(x[:m_index],
y[i, :m_index],
thresh,
rise=False)
second = calc.interpolate_threshold(x[m_index:],
y[i, m_index:],
thresh,
rise=True)
hist.Fill((second - first) * scale)
return hist, width, widthErr
def plot_fall(x, y, name, scale=1e9):
"""Calc and plot fall time of pulses"""
fall, fallErr = calc.calcFall(x, y)
print fall, fallErr
bins = np.arange((fall - 8 * fallErr) * scale,
(fall + 8 * fallErr) * scale, (fallErr / 5.) * scale)
hist = ROOT.TH1D("%s" % name, "%s" % name, len(bins), bins[0], bins[-1])
hist.SetTitle("Fall time")
hist.GetXaxis().SetTitle("Fall time (ns)")
f = calc.positive_check(y)
if f == True:
for i in range(len(y[:, 0]) - 1):
m = max(y[i, :])
m_index = np.where(y[i, :] == m)[0][0]
lo_thresh = m * 0.1
hi_thresh = m * 0.9
low = calc.interpolate_threshold(x[m_index:],
y[i, m_index:],
lo_thresh,
rise=False)
high = calc.interpolate_threshold(x[m_index:],
y[i, m_index:],
hi_thresh,
rise=False)
hist.Fill((low - high) * scale)
else:
for i in range(len(y[:, 0]) - 1):
m = min(y[i, :])
m_index = np.where(y[i, :] == m)[0][0]
lo_thresh = m * 0.1
hi_thresh = m * 0.9
low = calc.interpolate_threshold(x[m_index:], y[i, m_index:],
lo_thresh)
high = calc.interpolate_threshold(x[m_index:], y[i, m_index:],
hi_thresh)
hist.Fill((low - high) * scale)
return hist, fall, fallErr
