LArHVGainsPredictor.py File Reference

Go to the source code of this file.

Classes
class	LArHVGainsPredictor.OracleGainReader
class	LArHVGainsPredictor.GainPredictor

Namespaces
namespace	LArHVGainsPredictor

Variables
	LArHVGainsPredictor.parser = OptionParser(add_help_option=False)
	configure options
	LArHVGainsPredictor.action
	LArHVGainsPredictor.type
	LArHVGainsPredictor.dest
	LArHVGainsPredictor.default
	LArHVGainsPredictor.options
	LArHVGainsPredictor.args
int	LArHVGainsPredictor.error_code = 2
	LArHVGainsPredictor.hv_input_new = PlotCalibrationHV.L1CaloHVReader(options.hv_input_new)
	LArHVGainsPredictor.hv_input_ref = PlotCalibrationHV.L1CaloHVReader(options.hv_input_ref)
	LArHVGainsPredictor.geometry_convertor = PlotCalibrationGains.L1CaloGeometryConvertor()
	create instance of geometry convertor and load receiver-PPM map
	LArHVGainsPredictor.gain_predictor = GainPredictor(geometry_convertor)
	create instance of gain predictor
	LArHVGainsPredictor.h_orig_gains_em = PlotCalibrationGains.L1CaloMap("Em: orig. gains"," #eta bin","#phi bin")
	initialise histograms
	LArHVGainsPredictor.h_orig_gains_had = PlotCalibrationGains.L1CaloMap("Had: orig. gains"," #eta bin","#phi bin")
	LArHVGainsPredictor.h_pred_gains_em = PlotCalibrationGains.L1CaloMap("Em: pred. gains"," #eta bin","#phi bin")
	LArHVGainsPredictor.h_pred_gains_had = PlotCalibrationGains.L1CaloMap("Had: pred. gains"," #eta bin","#phi bin")
	LArHVGainsPredictor.h_diff_gains_em = PlotCalibrationGains.L1CaloMap("Em: (pred. gain - orig. gain) / orig. gain","#eta bin","#phi bin")
	LArHVGainsPredictor.h_diff_gains_had = PlotCalibrationGains.L1CaloMap("Had: (pred. gain - orig. gain) / orig. gain","#eta bin","#phi bin")
	LArHVGainsPredictor.output_text = open(options.output_files+".txt","w")
	initialise output .txt file
dict	LArHVGainsPredictor.pred_gains = {}
	initialise empty dictionary for predicted gains
	LArHVGainsPredictor.channel_list = open(options.channel_list)
dict	LArHVGainsPredictor.orig_gains = {}
	LArHVGainsPredictor.parts = channel.split()
dict	LArHVGainsPredictor.receiver_list = orig_gains.keys()
	LArHVGainsPredictor.coolid = geometry_convertor.getPPMfromReceiver(receiver)
	check if channel list is empty
	LArHVGainsPredictor.eta_bin = geometry_convertor.getEtaBin(coolid)
	LArHVGainsPredictor.phi_bin = geometry_convertor.getPhiBin(coolid)
tuple	LArHVGainsPredictor.num_layers = (hv_input_ref.GetNLayers())[receiver]
	retrieve num layers and layer names (from new or ref hv input)
list	LArHVGainsPredictor.layer_names = [-1,-1,-1,-1]
list	LArHVGainsPredictor.layer_corr_new = [1.,1.,1.,1.]
	retrieve hv corrections from new hv input
list	LArHVGainsPredictor.layer_corr_ref = [1.,1.,1.,1.]
	retrieve hv corrections from ref hv input
dict	LArHVGainsPredictor.orig_gain = orig_gains[receiver]
	LArHVGainsPredictor.pred_gain = gain_predictor.GetGain(receiver,orig_gain,layer_corr_ref,layer_corr_new,layer_names)
	calculate new gain for this receiver
tuple	LArHVGainsPredictor.diff_gain = ((pred_gain - orig_gain) / orig_gain) * 100.0
str	LArHVGainsPredictor.layer = "EM"
	fill histograms
	LArHVGainsPredictor.end
	print output to screen
	LArHVGainsPredictor.file
	write output to text file
	LArHVGainsPredictor.canvas = ROOT.TCanvas("canvas","",200,10,700,500)
	write output to .sqlite file