def createSinglePadGraphs(numpy_arrays, max_sample):
[pulse_amplitude, gain, rise_time, time_difference_peak, time_difference_cfd, tracking] = [i for i in numpy_arrays]
# Convert pulse amplitude values from [-V] to [+mV], charge from [C] to gain, rise time from [ns] to [ps]
dm.changeIndexNumpyArray(pulse_amplitude, -1000.0)
dm.changeIndexNumpyArray(max_sample, -1000.0)
dm.changeIndexNumpyArray(gain, 1./(md.getChargeWithoutGainLayer()*10**-15))
dm.changeIndexNumpyArray(rise_time, 1000.0)
# Produce single pad plots
for chan in pulse_amplitude.dtype.names:
md.setChannelName(chan)
if (md.getSensor() != md.sensor and md.sensor != "") or md.getSensor() == "SiPM-AFP":
continue
print "Single pad", md.getSensor(), "\n"
# This function requires a ROOT file which have the center positions for each pad
tracking_chan = t_calc.changeCenterPositionSensor(np.copy(tracking))
produceTProfilePlots([np.copy(pulse_amplitude[chan]), gain[chan], rise_time[chan], time_difference_peak[chan], time_difference_cfd[chan]], max_sample[chan], tracking_chan, distance_x, distance_y)
produceEfficiencyPlot(pulse_amplitude[chan], tracking_chan, distance_x, distance_y)
def drawAndExportResults(category, category_graph, legend_graph, zoom):
# The zoom option creates plots for the region \sigma < 100 ps and gain < 100. Also
# put in a separate folder
drawOpt = "AP"
if category.find("gain") == -1:
drawOpt += "L"
category_graph.Draw(drawOpt)
positions_latex = setGraphAttributes(category_graph, category, zoom)
r_calc.canvas.Update()
legend_graph.Draw()
if category == "charge":
charge_mip = md.getChargeWithoutGainLayer()
xmin = 0
xmax = r_calc.bias_voltage_max
ymin = 0.01
ymax = category_graph.GetHistogram().GetMaximum()
new_axis = ROOT.TGaxis(xmax, ymin, xmax, ymax, ymin,
int(ymax * charge_mip), 510, "+L")
new_axis.SetTitle("Charge [fC]")
new_axis.SetLabelFont(42)
new_axis.SetTitleFont(42)
new_axis.SetLabelSize(0.035)
new_axis.SetTitleSize(0.035)
new_axis.Draw()
# Draw extra legend with information about the coloring
legend_text = ROOT.TLatex()
legend_text.SetTextSize(0.035)
legend_text.SetNDC(True)
legend_text.DrawLatex(positions_latex[0][0], positions_latex[0][1],
"Marker color")
legend_text.DrawLatex(positions_latex[1][0], positions_latex[1][1],
"#color[3]{Green} = 22 \circC")
legend_text.DrawLatex(positions_latex[2][0], positions_latex[2][1],
"#color[4]{Blue} = -30 \circC")
legend_text.DrawLatex(positions_latex[3][0], positions_latex[3][1],
"#color[6]{Purple} = -40 \circC")
if category.find("gain") != -1:
if zoom:
fileName = dm.getResultsPlotSourceDataPath(
) + "/timing_vs_gain_zoom/" + category + "_results.pdf"
else:
fileName = dm.getResultsPlotSourceDataPath(
) + "/timing_vs_gain/" + category + "_results.pdf"
else:
fileName = dm.getResultsPlotSourceDataPath(
) + "/" + category + "_results.pdf"
r_calc.canvas.Print(fileName)
def addValuesToGraph(variables):
[sensor_data, category, legend_graph, graph,
category_graph] = [i for i in variables]
doOnce = True
for DUT_pos in r_calc.availableDUTPositions(r_calc.processed_sensor):
if DUT_pos in ["3_0", "3_1", "3_3", "8_1", "7_2", "7_3"]:
continue
for temperature in md.getAvailableTemperatures():
sensor_data[temperature][DUT_pos].sort()
i = 0
for data in sensor_data[temperature][DUT_pos]:
constant = 1
if category == "charge":
constant = 1. / md.getChargeWithoutGainLayer()
graph[r_calc.processed_sensor][temperature][DUT_pos].SetPoint(
i, data[0], data[1][0] * constant)
graph[r_calc.
processed_sensor][temperature][DUT_pos].SetPointError(
i, 0, data[1][1] * constant)
i += 1
if graph[
r_calc.processed_sensor][temperature][DUT_pos].GetN() != 0:
category_graph.Add(
graph[r_calc.processed_sensor][temperature][DUT_pos])
if r_calc.oneSensorInLegend:
if r_calc.processed_sensor == "W4-S204_6e14" and doOnce:
legend_graph.AddEntry(
graph["W4-S204_6e14"]["22"]["7_0"], "W4-S204_6e14",
"p")
doOnce = False
else:
legend_graph.AddEntry(
graph[r_calc.processed_sensor][temperature]
[DUT_pos], r_calc.processed_sensor, "p")
r_calc.oneSensorInLegend = False
def importResultsValues(sensor_data, category_subcategory):
global oneSensorInLegend
if category_subcategory.endswith('gain'):
category_subcategory = category_subcategory[:-5]
gain_category = True
else:
gain_category = False
# here are imported all files, that is for each pad, temperature and bias voltage
for batchNumber in md.getAllBatchNumbers():
for chan in md.getAllChannelsForSensor(batchNumber, processed_sensor):
if batchNumber not in md.getAllBatchNumberForSensor(
processed_sensor) or omitBadData(batchNumber,
category_subcategory):
continue
md.defineRunInfo(
md.getRowForRunNumber(md.getAllRunNumbers(batchNumber)[0]))
md.setChannelName(chan)
if md.getDUTPos() in ["3_0", "3_1", "3_3", "8_1", "7_2", "7_3"]:
continue
# Define the name for the histogram, depending on type
if category_subcategory.find(
"pulse_amplitude") == -1 and category_subcategory.find(
"charge") == -1:
group = "timing"
chan2 = ""
parameter_number = 2
if category_subcategory.find("system") != -1:
if md.chan_name not in [
"chan0", "chan1", "chan2", "chan3"
]:
continue
category = "system"
chan2 = "chan" + str((int(md.chan_name[-1]) + 1) % 4)
else:
category = "normal"
if category_subcategory.endswith('cfd'):
subcategory = "cfd"
else:
subcategory = "peak"
if category_subcategory.find(
"rise_time") != -1 or category_subcategory.find(
"noise") != -1:
group = "pulse"
category = category_subcategory
subcategory = ""
chan2 = ""
# Here, import the histogram which contain the results
histogram = dm.exportImportROOTHistogram(
group, category, subcategory, chan2)
if histogram:
fit_function = histogram.GetFunction("gaus")
if category_subcategory.find(
"noise") != -1 or category_subcategory.find(
"rise_time") != -1:
parameter_number = 1
results = [
fit_function.GetParameter(parameter_number),
fit_function.GetParError(parameter_number)
]
else:
continue
# pulse and gain
else:
histogram = dm.exportImportROOTHistogram(
"pulse", category_subcategory)
if histogram:
th_name = "_" + str(
md.getBatchNumber()) + "_" + md.chan_name
function_name = "Fitfcn_" + category_subcategory + th_name
fit_function = histogram.GetFunction(function_name)
try:
fit_function.GetTitle()
except:
continue
results = [
fit_function.GetParameter(1),
fit_function.GetParError(1)
]
else:
continue
if category_subcategory.find(
"normal") != -1 or category_subcategory.find(
"system") != -1:
results[0] = np.sqrt(
np.power(results[0], 2) - np.power(md.getSigmaSiPM(), 2))
value_error = [results[0], results[1]]
voltage = md.getBiasVoltage()
# For the timing resolution vs gain, replace the bias voltage with gain
if (category_subcategory.find("system") != -1
or category_subcategory.find("normal") != -1
) and gain_category:
histogram = dm.exportImportROOTHistogram("pulse", "charge")
th_name = "_" + str(md.getBatchNumber()) + "_" + md.chan_name
function_name = "Fitfcn_" + "charge" + th_name
fit_function = histogram.GetFunction(function_name)
gain = fit_function.GetParameter(
1) / md.getChargeWithoutGainLayer()
voltage = int(
gain
) # this takes the even number of gain (to select better values)
temperature = str(md.getTemperature())
DUT_pos = md.getDUTPos()
omitRun = False
# Among the all batches, choose one with smallest error.
for index in range(0, len(sensor_data[temperature][DUT_pos])):
sensor_results = sensor_data[temperature][DUT_pos][index]
# Check if there is an earlier filled bias voltage, otherwise fill
if voltage == sensor_results[0]:
omitRun = True
# For the same voltage, choose the one with smallest error.
if value_error[1] < sensor_results[1][1]:
sensor_data[temperature][DUT_pos][index] = [
voltage, value_error
]
if not omitRun:
sensor_data[temperature][DUT_pos].append(
[voltage, value_error])
oneSensorInLegend = True
def getLimitsForEachSensorAndBatch():
if md.getSensor() == "50D-GBGR2" and md.getBatchNumber() == 108:
# not used
gain_limits = [20, 70]
pulse_amplitude_limits = [100, 160]
rise_time_limits = [400, 460]
timing_res_cfd_limits = [22, 40]
timing_res_peak_limits = [27, 47]
elif md.getSensor() == "50D-GBGR2" and md.getBatchNumber() == 207:
gain_limits = [20, 55]
pulse_amplitude_limits = [80, 150]
rise_time_limits = [415, 450]
# not used
timing_res_cfd_limits = [22, 60]
timing_res_peak_limits = [25, 65]
elif md.getSensor() == "W4-LG12" and md.getBatchNumber() == 108:
# not used
gain_limits = [20, 90]
pulse_amplitude_limits = [60, 160]
rise_time_limits = [520, 560]
timing_res_cfd_limits = [18, 32]
timing_res_peak_limits = [30, 46]
elif md.getSensor() == "W4-LG12" and md.getBatchNumber() == 207:
gain_limits = [30, 90]
pulse_amplitude_limits = [60, 155]
rise_time_limits = [520, 565]
# not used
timing_res_cfd_limits = [20, 70]
timing_res_peak_limits = [25, 75]
elif md.getSensor() == "W4-RD01" and md.getBatchNumber() == 306:
gain_limits = [340, 500]
pulse_amplitude_limits = [160, 310]
rise_time_limits = [820, 870]
timing_res_peak_limits = [58, 80]
timing_res_cfd_limits = [20, 40]
elif md.getSensor() == "W4-RD01" and md.getBatchNumber() == 601:
gain_limits = [330, 440]
pulse_amplitude_limits = [140, 240]
rise_time_limits = [1220, 1300]
timing_res_peak_limits = [70, 130]
timing_res_cfd_limits = [30, 75]
elif md.getSensor() == "W4-S1022" and md.getBatchNumber() == 306:
gain_limits = [20, 38]
pulse_amplitude_limits = [35, 75]
rise_time_limits = [600, 680]
timing_res_peak_limits = [90, 160]
timing_res_cfd_limits = [30, 80]
elif md.getSensor() == "W4-S1022" and md.getBatchNumber() == 507:
gain_limits = [40, 85]
pulse_amplitude_limits = [80, 160]
rise_time_limits = [505, 545]
timing_res_peak_limits = [90, 160]
timing_res_cfd_limits = [30, 80]
elif md.getSensor() == "W4-S1022" and md.getBatchNumber() == 707:
gain_limits = [35, 85]
pulse_amplitude_limits = [80, 160]
rise_time_limits = [525, 565]
timing_res_cfd_limits = [30, 80]
timing_res_peak_limits = [90, 160]
elif md.getSensor() == "W4-S1061" and md.getBatchNumber() == 306:
gain_limits = [35, 65]
pulse_amplitude_limits = [85, 145]
rise_time_limits = [490, 530]
timing_res_cfd_limits = [30, 80]
timing_res_peak_limits = [90, 160]
elif md.getSensor() == "W4-S1061" and md.getBatchNumber() == 507:
gain_limits = [35, 65]
pulse_amplitude_limits = [80, 140]
rise_time_limits = [535, 570]
timing_res_cfd_limits = [30, 80]
timing_res_peak_limits = [90, 160]
elif md.getSensor() == "W4-S1061" and md.getBatchNumber() == 707:
gain_limits = [30, 80]
pulse_amplitude_limits = [70, 150]
rise_time_limits = [540, 575]
timing_res_cfd_limits = [30, 80]
timing_res_peak_limits = [90, 160]
elif md.getSensor() == "W4-S203" and md.getBatchNumber() == 306:
gain_limits = [40, 70]
pulse_amplitude_limits = [90, 150]
rise_time_limits = [515, 540]
timing_res_cfd_limits = [30, 80]
timing_res_peak_limits = [90, 160]
elif md.getSensor() == "W4-S203" and md.getBatchNumber() == 507:
gain_limits = [35, 70]
pulse_amplitude_limits = [50, 100]
rise_time_limits = [680, 780]
timing_res_cfd_limits = [30, 80]
timing_res_peak_limits = [90, 160]
elif md.getSensor() == "W4-S203" and md.getBatchNumber() == 707:
gain_limits = [40, 80]
pulse_amplitude_limits = [50, 110]
rise_time_limits = [725, 830]
timing_res_cfd_limits = [30, 80]
timing_res_peak_limits = [90, 160]
elif md.getSensor() == "W4-S204_6e14" and md.getBatchNumber() == 507:
gain_limits = [5, 26]
pulse_amplitude_limits = [40, 80]
rise_time_limits = [315, 390]
timing_res_cfd_limits = [36, 60]
timing_res_peak_limits = [44, 65]
elif md.getSensor() == "W4-S204_6e14" and md.getBatchNumber() == 707:
gain_limits = [8, 28]
pulse_amplitude_limits = [40, 90]
rise_time_limits = [315, 390]
timing_res_cfd_limits = [34, 48]
timing_res_peak_limits = [40, 60]
elif md.getSensor() == "W4-S215" and md.getBatchNumber() == 207:
gain_limits = [70, 130]
pulse_amplitude_limits = [160, 260]
rise_time_limits = [490, 540]
timing_res_cfd_limits = [25, 45]
timing_res_peak_limits = [25, 50]
elif md.getSensor() == "W4-S215" and md.getBatchNumber() == 507:
gain_limits = [110, 210]
pulse_amplitude_limits = [200, 330]
rise_time_limits = [520, 590]
# not used
timing_res_cfd_limits = [20, 70]
timing_res_peak_limits = [30, 80]
elif md.getSensor() == "W4-S215" and md.getBatchNumber() == 707:
gain_limits = [100, 190]
pulse_amplitude_limits = [140, 240]
rise_time_limits = [700, 825]
# not used
timing_res_cfd_limits = [20, 70]
timing_res_peak_limits = [30, 80]
elif md.getSensor() == "W9-LGA35" and md.getBatchNumber() == 108:
# not used
gain_limits = [30, 50]
pulse_amplitude_limits = [60, 110]
rise_time_limits = [415, 455]
timing_res_cfd_limits = [20, 40]
timing_res_peak_limits = [24, 46]
elif md.getSensor() == "W9-LGA35" and md.getBatchNumber() == 207:
gain_limits = [20, 45]
pulse_amplitude_limits = [50, 115]
rise_time_limits = [415, 455]
# not used
timing_res_cfd_limits = [20, 70]
timing_res_peak_limits = [25, 75]
# This method is modular to adapt for other batches
else:
th_name = "_"+str(md.getBatchNumber())+"_"+md.chan_name
pulse_amplitude_mean = dm.exportImportROOTHistogram("pulse", "pulse_amplitude").GetFunction("Fitfcn_pulse_amplitude"+th_name).GetParameter(1)
gain_mean = dm.exportImportROOTHistogram("pulse", "charge").GetFunction("Fitfcn_charge"+th_name).GetParameter(1)/md.getChargeWithoutGainLayer()
rise_time_mean = dm.exportImportROOTHistogram("pulse", "rise_time").GetFunction("gaus").GetParameter(1)
timing_res_peak_mean = np.sqrt(np.power(dm.exportImportROOTHistogram("timing", "normal", "peak").GetFunction("gaus").GetParameter(2),2) - np.power(md.getSigmaSiPM(),2))
timing_res_cfd_mean = np.sqrt(np.power(dm.exportImportROOTHistogram("timing", "normal", "cfd").GetFunction("gaus").GetParameter(2), 2) - np.power(md.getSigmaSiPM(), 2))
[pulse_amplitude_limits, gain_limits, rise_time_limits, timing_res_peak_limits, timing_res_cfd_limits] = [[max(pulse_amplitude_mean-50,0), pulse_amplitude_mean+50], [max(gain_mean-30, 0), gain_mean+30], [max(rise_time_mean-30, 0), rise_time_mean+30], [max(timing_res_peak_mean-50, 0), timing_res_peak_mean+50], [max(timing_res_cfd_mean-50, 0), timing_res_cfd_mean+50]]
limits_graph = [pulse_amplitude_limits, gain_limits, rise_time_limits, timing_res_peak_limits, timing_res_cfd_limits]
return limits_graph