RPCRawDataMonUtils Namespace Reference

Classes
class	Panel

Functions
	creatGraph (xs, ys, x_errs, y_errs, g_name, g_title, g_Xtitle, g_Ytitle)
	linearFit (h_temp, opt="QNS+")
	return type values: 1 : normal 0 : fit fail or (Not exist this quantity of certain panel) -1 : number of points < 2 -2 : Chi2/NDF < 0.0001 -3 : NDF == 0 except the reason number of points <2

Function Documentation

creatGraph()

RPCRawDataMonUtils.creatGraph	(		xs,
			ys,
			x_errs,
			y_errs,
			g_name,
			g_title,
			g_Xtitle,
			g_Ytitle )

Definition at line 8 of file RPCRawDataMonUtils.py.

def creatGraph(xs, ys, x_errs, y_errs, g_name, g_title, g_Xtitle, g_Ytitle):
  if len(xs) == 0:
    return ROOT.TGraph()
  
  import array
  x_arr = array.array("f",xs)
  y_arr = array.array("f",ys)
  x_err_arr = array.array("f",x_errs)
  y_err_arr = array.array("f",y_errs)
 
  g1 =  ROOT.TGraphErrors(len(x_arr),x_arr,y_arr, x_err_arr, y_err_arr)
 
  g1.SetNameTitle(g_name, g_title)
  g1.SetMarkerSize(0.5)
  g1.GetXaxis().SetTitle(g_Xtitle)
  g1.GetYaxis().SetTitle(g_Ytitle)    
  g1.GetYaxis().SetRangeUser(0.8*min(y_arr),1.2*max(y_arr))
 
  return g1
 

linearFit()

RPCRawDataMonUtils.linearFit	(		h_temp,
			opt = "QNS+" )

return type values: 1 : normal 0 : fit fail or (Not exist this quantity of certain panel) -1 : number of points < 2 -2 : Chi2/NDF < 0.0001 -3 : NDF == 0 except the reason number of points <2

Definition at line 35 of file RPCRawDataMonUtils.py.

def linearFit(h_temp, opt="QNS+"):
  default_dic = {"p0": 0., "p0_err":0., "p1": -1., "p1_err":-1., "chi2":-1., "mean":0., "mean_err":0.}
  # Qtag, un-fit-able
  # -1 -- null graph
  # 0 -- normal
  # 1 -- N points = 1
  # 2 -- N points = 2
  # 3 -- N points = 0
 
  Qtag = 0
  if not h_temp:
    Qtag = -1
    return (Qtag, default_dic)
 
  if h_temp.GetN()< 2:
    Qtag = 1
    return (Qtag, default_dic)
 
  # fit_result = h_temp.Fit("pol1", opt, "", 0, 2.2)
  fit_result = h_temp.Fit("pol1", opt)
 
  par_a   = round(fit_result.Value(0), 2)
  par_a_E = round(fit_result.ParError(0), 2)
  par_b   = round(fit_result.Value(1), 2)
  par_b_E = round(fit_result.ParError(1), 2)
 
  if fit_result.Ndf() == 0:
    Qtag = 2
    chi2  = 0
  else:
    chi2    = round(fit_result.Chi2()/fit_result.Ndf(), 2)
 
  mean    = round(h_temp.GetMean(2), 2)
  mean_rms= round(h_temp.GetRMS(2), 2)
 
  dic = {"p0": par_a, "p0_err":par_a_E, "p1": par_b, "p1_err":par_b_E, "chi2":chi2, "mean":mean, "mean_err":mean_rms}
  return (Qtag, dic)
 
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