def analyze_pulser_data(outputfile='temp.root', force_overwrite=False):
server = SoudanServer()
file_list = server.get_accepted_runs()
time_list = []
mean_list = []
sigma_list = []
for id in file_list:
print id
rundoc = server.get_run(id.id)
if len(time_list) == 0:
first_time = rundoc.time_of_start_of_run
time_list.append(rundoc.time_of_start_of_run - first_time)
pd = rundoc.pulser_data
mean_list.append((pd.mean, \
pd.mean_err))
sigma_list.append((pd.sigma, \
pd.sigma_err))
file_to_output = TFile(outputfile, 'recreate');
objects_to_write = []
# generate final plots
list_to_analyze = [ ("Mean of pulser signal", "Mean (keV)", mean_list),\
("Sigma of pulser signal", "Sigma (keV)", sigma_list) ]
for name, axis_name, data_list in list_to_analyze:
file_to_output.cd()
new_graph = TGraphErrors(len(data_list))
new_graph.SetNameTitle(name.replace(' ' , ''),\
name.replace(' ' , ''))
new_hist = TH1F(name.replace(' ', '') + "hist",\
name, 100, time_list[0].days, \
time_list[len(time_list)-1].days + 1)
new_hist.GetXaxis().SetTitle("Run start time (days)")
new_hist.GetYaxis().SetTitle(axis_name)
new_hist.GetYaxis().SetTitleOffset(1.17)
maximum = data_list[0][0]
minimum = data_list[0][0]
for i in range(len(data_list)):
new_graph.SetPoint(i, time_list[i].days + time_list[i].seconds/(24*3600.),\
data_list[i][0])
new_graph.SetPointError(i, 0,\
data_list[i][1])
if minimum > data_list[i][0]:
minimum = data_list[i][0]
if maximum < data_list[i][0]: maximum = data_list[i][0]
ten_percent = (maximum - minimum)*0.1
new_hist.SetMaximum(maximum + ten_percent)
new_hist.SetMinimum(minimum - ten_percent)
#new_graph.SetHistogram(new_hist)
objects_to_write.append(new_graph)
objects_to_write.append(new_hist)
file_to_output.cd()
for object in objects_to_write:
object.Write(object.GetName(), TObject.kOverwrite)
file_to_output.Close()
def analyze_risetime_vs_energy(outputfile='temp.root'):
channels_to_analyze = {0 : ("*0.0125749", "low_energy", 80), \
8 : ("*0.602356 -0.303815", "high_energy", 2800)}
histograms = {}
server = SoudanServer()
file_list = server.get_accepted_runs()
hist_2d = None
file_to_output = TFile(outputfile, 'recreate');
objects_to_write = []
for chan, tmp_tuple in channels_to_analyze.items():
energy_scale = tmp_tuple[0]
maximum = tmp_tuple[2]
histograms[chan] = TH2D("RiseTimeVsEnergyChannel%i" % chan, "RiseTimeVsEnergyChannel%i" % chan, 400, 0, maximum, 100, 0, 10)
hist_2d = histograms[chan]
hist_2d.GetXaxis().SetTitle("Energy (keV)")
hist_2d.GetYaxis().SetTitle("10-90 Risetime (#mus)")
objects_to_write.append(hist_2d)
for id in file_list:
print id
rundoc = server.get_run(id.id)
real_value = rundoc.baseline_dict.average_fit_constant
real_value_rms = rundoc.baseline_dict.average_fit_rms
for chan, tmp_tuple in channels_to_analyze.items():
(energy_scaler, pfn_dir, max_energy) = tmp_tuple
file_name = eval("rundoc.output_data_file_tier_3.%s.pfn" % pfn_dir)
open_file = TFile(file_name)
main_tree = open_file.Get("wf_analysis")
coinc_tree = open_file.Get("event_coincidence")
main_tree.AddFriend(coinc_tree)
file_to_output.cd()
hist_2d = histograms[chan]
main_tree.Draw("(endRiseTime-startRiseTime)/100:(-energy%s)>> +%s" % \
(energy_scaler,hist_2d.GetName()), \
"channel==%i && !(event_coincidence.coincidence & 0x280) && \
lastInhibitTimeDif > 1e5 && abs(fitConstant-%f) <= 3*%f" % \
(chan, real_value, real_value_rms), "goff")
open_file.Close()
file_to_output.cd()
for object in objects_to_write:
object.Write(object.GetName(), TObject.kOverwrite)
file_to_output.Close()
from management.soudan_database import SoudanServer
server = SoudanServer()
# loop over all the accepted runs
for record in server.get_accepted_runs():
doc = server.get_run(record.id)
print "Run number: %s, gretina file: %s" %(doc._get_id(), doc.root_data_file_tier_1.pfn)
def analyze_channel_rates(outputfile='temp.root', force_overwrite=False):
server = SoudanServer()
file_list = server.get_accepted_runs()
channel_to_analyze = { 0 : "Low-energy channel",\
1 : "Low-energy channel trigger",\
2 : "Muon veto",\
7 : "Pulser channel",\
8 : "High-energy channel",\
9 : "Reset inhibit"}
#c1 = TCanvas()
graph_list = {}
time_list = []
for channel in channel_to_analyze.keys(): graph_list[channel] = []
for id in file_list:
print id
rundoc = server.get_run(id.id)
open_file = TFile(rundoc.output_data_file_tier_2.pfn)
if len(time_list) == 0:
first_time = rundoc.time_of_start_of_run
time_list.append(rundoc.time_of_start_of_run - first_time)
main_tree = open_file.Get("wf_analysis")
for channel in channel_to_analyze.keys():
num_events = main_tree.Draw(">> eventList", "channel==%i" % channel, "goff")
graph_list[channel].append(num_events/float(rundoc.livetime.run_seconds))
open_file.Close()
file_to_output = TFile(outputfile, 'recreate');
objects_to_write = []
# generate final plots
last_time = time_list[len(time_list)-1]
for channel, data_list in graph_list.items():
file_to_output.cd()
new_graph = TGraph(len(data_list))
new_graph.SetNameTitle(channel_to_analyze[channel].replace(' ' , ''),\
channel_to_analyze[channel].replace(' ' , ''))
new_hist = TH1D(channel_to_analyze[channel].replace(' ', '') + "hist",\
channel_to_analyze[channel], 100, 0, \
last_time.days + 1)
new_hist.GetXaxis().SetTitle("Run start time (days)")
new_hist.GetYaxis().SetTitle("Rate (Hz)")
maximum = data_list[0]
minimum = data_list[0]
for i in range(len(data_list)):
new_graph.SetPoint(i, time_list[i].days + time_list[i].seconds/(24*3600.),\
data_list[i])
if minimum > data_list[i]: minimum = data_list[i]
if maximum < data_list[i]: maximum = data_list[i]
ten_percent = (maximum - minimum)*0.1
new_hist.SetMaximum(maximum + ten_percent)
new_hist.SetMinimum(minimum - ten_percent)
#new_graph.SetHistogram(new_hist)
objects_to_write.append(new_graph)
objects_to_write.append(new_hist)
file_to_output.cd()
for object in objects_to_write:
object.Write(object.GetName(), TObject.kOverwrite)
file_to_output.Close()
def analyze_trigger_efficiency(outputfile='temp.root', force_overwrite=False):
server = SoudanServer()
file_list = server.get_accepted_runs()
time_list = []
scaling = []
offset = []
for id in file_list:
print id
rundoc = server.get_run(id.id)
if len(time_list) == 0:
first_time = rundoc.time_of_start_of_run
time_list.append(rundoc.time_of_start_of_run - first_time)
scaling.append((rundoc.trigger_efficiency.scaling, \
rundoc.trigger_efficiency.scaling_err))
offset.append((rundoc.trigger_efficiency.offset, \
rundoc.trigger_efficiency.offset_err))
file_to_output = TFile(outputfile, 'recreate');
objects_to_write = []
# generate final plots
first_data_point = 0
for i in range(len(scaling)):
if scaling[i][0]:
first_data_point = i
break
time_list = time_list[first_data_point:len(time_list)]
list_to_analyze = [ ("Scaling", "Scaling (keV^{-1})", scaling[first_data_point:len(scaling)]),\
("Offset", "Offset (keV)", offset[first_data_point:len(offset)]) ]
for name, axis_name, data_list in list_to_analyze:
file_to_output.cd()
new_graph = TGraphErrors(len(data_list))
new_graph.SetNameTitle(name.replace(' ' , ''),\
name.replace(' ' , ''))
new_hist = TH1F(name.replace(' ', '') + "hist",\
name, 100, time_list[0].days, \
time_list[len(time_list)-1].days + 1)
new_hist.GetXaxis().SetTitle("Run start time (days)")
new_hist.GetYaxis().SetTitle(axis_name)
new_hist.GetYaxis().SetTitleOffset(1.17)
maximum = data_list[0][0]
minimum = data_list[0][0]
for i in range(len(data_list)):
new_graph.SetPoint(i, time_list[i].days + time_list[i].seconds/(24*3600.),\
data_list[i][0])
new_graph.SetPointError(i, 0,\
data_list[i][1])
if minimum > data_list[i][0]: minimum = data_list[i][0]
if maximum < data_list[i][0]: maximum = data_list[i][0]
ten_percent = (maximum - minimum)*0.1
new_hist.SetMaximum(maximum + ten_percent)
new_hist.SetMinimum(minimum - ten_percent)
#new_graph.SetHistogram(new_hist)
objects_to_write.append(new_graph)
objects_to_write.append(new_hist)
file_to_output.cd()
for object in objects_to_write:
object.Write(object.GetName(), TObject.kOverwrite)
file_to_output.Close()
