def plot_correction_graph(calib_file, eta_min, eta_max, oDir, oFormat='pdf'):
"""Plot the graph of correction value vs pT"""
gname = generate_eta_graph_name(eta_min, eta_max)
gr = cu.get_from_file(calib_file, gname)
c = generate_canvas()
gr.Draw("ALP")
y_min = ROOT.TMath.MinElement(gr.GetN(), gr.GetY())
y_max = ROOT.TMath.MaxElement(gr.GetN(), gr.GetY())
if y_max > 5:
y_max = 5
gr.GetYaxis().SetRangeUser(y_min * 0.7, y_max * 1.1)
c.SaveAs('%s/%s.%s' % (oDir, gname, oFormat))
def get_functions_graphs_params_rootfile(root_filename):
"""Get function parameters from ROOT file
Gets object based on name in runCalibration.py
Parameters
----------
root_filename : str
Name of ROOT file to get things from
Returns
-------
all_fits : list[TF1]
Collection of TF1 objects, one per line of file ( = 1 eta bin)
all_fit_params : list[list[float]]
Collection of fit parameters, one per line of file ( = 1 eta bin)
all_graphs : list[TGraphErrors]
Colleciton of correction graphs, one per eta bin
"""
print 'Reading functions from ROOT file'
in_file = cu.open_root_file(root_filename)
all_fit_params = []
all_fits = []
all_graphs = []
# Get all the fit functions from file and their corresponding graphs
etaBins = binning.eta_bins
for i, (eta_min, eta_max) in enumerate(izip(etaBins[:-1], etaBins[1:])):
print "Eta bin:", eta_min, "-", eta_max
# get the fitted TF1
try:
fit_func = cu.get_from_file(in_file, "fitfcneta_%g_%g" % (eta_min, eta_max))
fit_params = [fit_func.GetParameter(par) for par in range(fit_func.GetNumberFreeParameters())]
print "Fit fn evaluated at 5 GeV:", fit_func.Eval(5)
except IOError:
print "No fit func"
fit_func = None
fit_params = []
all_fits.append(fit_func)
all_fit_params.append(fit_params)
# print "Fit parameters:", fit_params
# get the corresponding fit graph
fit_graph = cu.get_from_file(in_file, generate_eta_graph_name(eta_min, eta_max))
all_graphs.append(fit_graph)
in_file.Close()
return all_fits, all_fit_params, all_graphs
def process_file(filename, eta_bins=binning.eta_bins_forward):
"""Process a ROOT file with graphs, print a mean & mean histogram for each.
Parameters
----------
filename : str
Name of ROOT file to process (from runCalibration.py)
eta_bins : list[[float, float]]
Eta bin edges.
"""
f = cu.open_root_file(filename)
for eta_min, eta_max in binning.pairwise(eta_bins):
gr = cu.get_from_file(f, generate_eta_graph_name(eta_min, eta_max))
if not gr:
raise RuntimeError("Can't get graph")
xarr, yarr = cu.get_xy(gr)
xarr, yarr = np.array(xarr), np.array(
yarr) # use numpy array for easy slicing
# Loop over all possible subgraphs, and calculate a mean for each
end = len(yarr)
means = []
while end > 0:
start = 0
while start < end:
means.append(yarr[start:end].mean())
start += 1
end -= 1
# Jackknife means
jack_means = [np.delete(yarr, i).mean() for i in range(len(yarr))]
# Do plotting & peak finding in both ROOT and MPL...not sure which is better?
# peak = plot_find_peak_mpl(means, eta_min, eta_max, os.path.dirname(os.path.realpath(filename)))
peak = plot_find_peak_root(means, eta_min, eta_max,
os.path.dirname(os.path.realpath(filename)))
jackpeak = plot_jacknife_root(
jack_means, eta_min, eta_max,
os.path.dirname(os.path.realpath(filename)))
print 'Eta bin:', eta_min, '-', eta_max
print peak
print 'jackknife mean:'
print np.array(jack_means).mean()
f.Close()
def process_file(filename, eta_bins=binning.eta_bins_forward):
"""Process a ROOT file with graphs, print a mean & mean histogram for each.
Parameters
----------
filename : str
Name of ROOT file to process (from runCalibration.py)
eta_bins : list[[float, float]]
Eta bin edges.
"""
f = cu.open_root_file(filename)
for eta_min, eta_max in binning.pairwise(eta_bins):
gr = cu.get_from_file(f, generate_eta_graph_name(eta_min, eta_max))
if not gr:
raise RuntimeError("Can't get graph")
xarr, yarr = cu.get_xy(gr)
xarr, yarr = np.array(xarr), np.array(yarr) # use numpy array for easy slicing
# Loop over all possible subgraphs, and calculate a mean for each
end = len(yarr)
means = []
while end > 0:
start = 0
while start < end:
means.append(yarr[start:end].mean())
start += 1
end -= 1
# Jackknife means
jack_means = [np.delete(yarr, i).mean() for i in range(len(yarr))]
# Do plotting & peak finding in both ROOT and MPL...not sure which is better?
# peak = plot_find_peak_mpl(means, eta_min, eta_max, os.path.dirname(os.path.realpath(filename)))
peak = plot_find_peak_root(means, eta_min, eta_max, os.path.dirname(os.path.realpath(filename)))
jackpeak = plot_jacknife_root(jack_means, eta_min, eta_max, os.path.dirname(os.path.realpath(filename)))
print 'Eta bin:', eta_min, '-', eta_max
print peak
print 'jackknife mean:'
print np.array(jack_means).mean()
f.Close()
