def make_plots(data, img_name):
"""
make_plot(rates)
"""
x_order = (0, 0.5, 1, 5)
x = array('f', x_order)
x_er = array('f', [k*0.01 for k in x_order]) # assumed errors
cu = array('f', [data[k].get_rate("cu").value for k in x_order])
cu_er = array('f', [data[k].get_rate("cu").error for k in x_order])
f = array('f', [data[k].get_rate("f").value for k in x_order])
f_er = array('f', [data[k].get_rate("f").error for k in x_order])
cu_graph = TGraphErrors(len(x), x, cu, x_er, cu_er)
set_graph_values(cu_graph, "Simulated copper muon rates", "degrader thickness (mm)", "Muons per proton")
f_graph = TGraphErrors(len(x), x, f, x_er, f_er)
set_graph_values(f_graph, "Simulated free muon rates", "degrader thickness (mm)", "Muons per proton")
canvas = make_canvas("Simulated muon rates (from direct counts)", n_x=2, n_y=1, resize=True)
canvas.cd(1)
cu_graph.Draw("ALP")
canvas.cd(2)
f_graph.Draw("ALP")
canvas.Update()
canvas.SaveAs(img_name+".svg")
canvas.SaveAs(img_name+".png")
sleep (5)
def main():
gStyle.SetOptStat(10)
gStyle.SetOptFit(111)
save_header()
root_file = TFile(img_dir + "out.root", "RECREATE");
for run in run_info:
tree = init_tree_and_hists(run)
fill_hists(tree)
for ch in channels:
hist = tree.hists[ch]
root_file.Add(hist);
print hist.GetTitle(), hist.GetEntries()
can = make_canvas(hist.GetTitle(),resize=True)
hist.Draw("E")
fit_hist(hist)
# print "\nChi^2/NDF {:.1f} / {}\n\n".format(hist.fit_func.GetChisquare(), hist.fit_func.GetNDF())
can.Update()
img_name = img_fmt.format(id=hist.file_id, ch=hist.ch)
can.SaveAs(img_name+".eps")
can.SaveAs(img_name+".svg")
if debug:
sleep(2)
save_fit_info(hist)
root_file.Write();
root_file.Close();
def make_and_save_count_hist(target, data, data_type, img_dir, out_root_file): name = "Count " if data_type == "run" else "Simulated count " name += "of muons decaying in "+target hist = make_count_hist(name, target, data) canvas = make_canvas(name, resize=True) hist.Draw() img_name = img_dir+"counts/"+data_type+"_muon_count_in_"+target canvas.SaveAs(img_name+".svg") canvas.SaveAs(img_name+".eps") return hist
def save_hist_around_zero_region(data_file, img_dir, l_bound=-200, u_bound=200):
for hist_key, hist in data_file.hists.items():
img_name = img_dir+"zoom/{}_{}".format(data_file.short_name, hist.GetName())
can = make_canvas(img_name)
hist.GetXaxis().SetRangeUser(l_bound, u_bound)
hist.Draw()
can.Update()
can.SaveAs(img_name+".eps")
can.SaveAs(img_name+".svg")
hist.GetXaxis().UnZoom() # reset the ranges to full
def draw_pretty_plots(rates):
canvas = make_canvas("Trigger Rate", 3,2, True)
# attach all the plots to the canvas to keep them in scope
canvas.plots = []
for pad_id, (file_id, data) in enumerate(rates.items(), 1):
canvas.cd(pad_id)
canvas.plots.append(get_plot(file_id, data))
canvas.plots[-1].Draw("ALP")
canvas.Update()
# Don't let the canvas go out of scope
return canvas
def main():
gStyle.SetOptStat(10)
gStyle.SetOptFit(111)
materials = {"Cu":{"Z":29,"A":63}, "Al":{"Z":13,"A":26}}
for mat, element in materials.items():
hist = get_hist(**element)
can = make_canvas(mat)
hist.Draw()
initial_settings = (("N", lambda x: 100, lambda x: 0.0 , lambda x: 1e6),
("#tau", lambda x: 100, lambda x: 0.0 , lambda x: 5000))
fit_histogram(hist, "[0]*exp(-x/[1])", initial_settings, mat,)
can.Update()
def main():
gStyle.SetOptFit()
res = {'data':{}, 'sec':{}}
for f_id in file_ids:
print "Processing", f_id
filename = file_prefix + f_id + file_suffix
treename = "ts" if f_id == "448" else "Scaler"
tree = get_tree_from_file(treename, filename)
print tree.filename, tree
load_branch(tree, f_id)
# tfile, tree, branch = get_tfile_tree_and_branch(name, **file_data)
key = int(f_id)
res['data'][key] = get_gain_stability_dict(tree)
res['sec'][key] = get_sec_dict(tree)
# tfile.Close()
canvas = make_canvas("Gain Stability", 3,2, True)
# add the histograms to the dictionary
res.update({'gain_hist':{}, 'count_hist':{}})
for pad_id, (file_id, data) in enumerate(res['data'].items(), 1):
gain_hist = get_gain_stability_hist_for_file(data, file_id)
# count_hist = get_trigger_count_hist_for_file(data, file_id)
# potential_hist = get_potential_trigger_count_hist_for_file(data, file_id)
sec_hist = get_sec_count_hist_for_file(res['sec'][int(file_id)], file_id)
# c1 = make_canvas('foobar', 1, 1, True)
# c1.cd()
# sec_hist.Draw()
# c1.SaveAs("foobar%i.png"%file_id)
# continue
full_range = max(data.keys())[0] - min(data.keys())[0]
# Fit the central 80%
fit_min = full_range * 0.05
fit_max = full_range * 0.95
print fit_min, fit_max
# gain_hist.Fit("pol0") # attempt to fit the gain with a flat function
gain_hist.Fit("pol0", "", "", fit_min, fit_max) # attempt to fit the gain with a flat function
# draw_gain_and_count_hist(gain_hist, count_hist, canvas, pad_id)
# draw_gain_and_count_hist(gain_hist, potential_hist, canvas, pad_id)
draw_gain_and_count_hist(gain_hist, sec_hist, canvas, pad_id)
res['gain_hist'][file_id] = gain_hist
res['count_hist'][file_id] = sec_hist
canvas.SaveAs("images/gain_stability.svg")
canvas.SaveAs("images/gain_stability.eps")
def fit_histogram(hist, func_fmt, initial_settings, func_name, fit_options="MER",img_name=""):
func = get_func_with_named_initialised_param(func_name, hist, func_fmt, initial_settings)
if img_name:
canvas = make_canvas(img_name, resize=True)
hist.Draw()
hist.Fit(func, fit_options)
canvas.Update()
canvas.SaveAs(img_name+".eps")
canvas.SaveAs(img_name+".svg")
else:
hist.Fit(func, fit_options+"N")
hist.func = func
hist.fit_param = get_fit_parameters(func)
def main():
gStyle.SetOptStat(10)
gStyle.SetOptFit(111)
for run in run_info:
tree = init_tree(run)
fill_hists(tree)
for hist in tree.hists.values():
print hist.GetTitle(), hist.GetEntries()
can = make_canvas(hist.GetTitle(),resize=True)
hist.Draw()
fit_hist(hist)
print "\nChi^2/NDF {:.1f} / {}".format(hist.fit_func.GetChisquare(), hist.fit_func.GetNDF())
can.Update()
can.SaveAs("images/noise_fit_zoom.png")
if debug:
sleep(5)
def fit_data(data_file, fit_type, data_type, inc_phase, inc_sin, l_bound, u_bound, img_dir="", fit_options="ILMER"):
data_file.sum_integrals = {'f':0.0, 'cu':0.0}
short_name = data_file.short_name
bin_width = data_file.bin_width
for hist_key, hist in data_file.hists.items():
func_fmt, initial_settings = get_fit_func_and_settings(data_type, hist, fit_type, inc_phase, inc_sin)
func_name = "{}_{}".format(short_name, hist.GetName())
if img_dir:
can = make_canvas(func_name, resize=True)
hist.Draw()
fit_histogram(hist, func_fmt, initial_settings, func_name, fit_options, l_bound, u_bound)
can.Update()
img_name = img_dir+"fits/"+func_name
can.SaveAs(img_name+".eps")
can.SaveAs(img_name+".svg")
else:
fit_histogram(hist, func_fmt, initial_settings, func_name, fit_options+"N")
calculate_exp_integrals(hist, l_bound, u_bound)
for k in data_file.sum_integrals:
data_file.sum_integrals[k] += hist.integrals[k]