import os import subprocess import json import operator import glob import sys import numpy import ROOT import Configuration import maus_cpp.globals import xboa.Common as common from xboa.Bunch import Bunch from xboa.Hit import Hit from xboa.tracking import MAUSTracking class Tracking: def amp(self, ellipse_inv, hit): arr = numpy.matrix([hit['x'], hit['px'], hit['y'], hit['py']]) amplitude = arr*ellipse_inv*arr.T return amplitude[0, 0] def amp_list(self, bunch, beta, alpha, bz): ellipse = Bunch.build_penn_ellipse(1., self.m_mu, beta, alpha, self.p, 0., bz, 1.) ellipse_inv = numpy.linalg.inv(ellipse) amplitude_list = [self.amp(ellipse_inv, hit) for hit in bunch] return amplitude_list def amp_list_6d(self, bunch): bunch.set_covariance_matrix(True) amp_list = [bunch.get_amplitude(bunch, hit, ['x', 'y', 'ct']) for hit in bunch] bunch.set_covariance_matrix(False) return amp_list def scatter_plot_mean_rms(self, x_values, y_values, xmin, xmax, xstep): nbins = 9 bin_low = 0. bin_step = 10. hist_data = [] for x, y in zip(x_values, y_values): bin = int((x-bin_low)/bin_step) while len(hist_data) - 1 < bin: hist_data.append([]) hist_data[bin].append(y) hist = ROOT.TH1D("Mean", "", nbins, bin_low, bin_low+(nbins+1)*bin_step) for i, data in enumerate(hist_data): mean, sigmaSqu = 0., 0. if len(data) > 0: mean = sum(data)/len(data) if len(data) > 1: sigmaSqu = sum([x**2 for x in data])/len(data)-mean**2 hist.SetBinContent(i+1, mean) hist.SetBinError(i+1, sigmaSqu**0.5) common._hist_persistent.append(hist) hist.SetLineWidth(2) hist.SetLineColor(8) return hist def amplitude_scatter_plot(self, bunch_in, bunch_out, predicate): """predicate takes arguments (spill_index, spills_in_bunch_out)""" spills_out = set([(hit['spill'], hit['event_number']) for hit in bunch_out]) hits_in = [hit for hit in bunch_in if (hit['spill'], hit['event_number']) in spills_out] hits_out = [hit for hit in bunch_out if (hit['spill'], hit['event_number']) in spills_out] amp_in = self.amp_list(hits_in, self.beta, 0., self.bz_us) amp_out = self.amp_list(hits_out, self.beta, 0., self.bz_ds) delta_amp = [(amp_out[i]-amp_in[i])/amp_in[i] for i, a_in in enumerate(amp_in)] if len(amp_in) == 0 or len(amp_in) != len(amp_out): print "Failed to make graph:", len(amp_in), len(amp_out) hist, graph = common.make_root_graph('amplitude x y', [0.], 'x-y amplitude in [mm]', [0.], 'x-y amplitude out [mm]') else: hist, graph = common.make_root_graph('amplitude x y', amp_in, 'x-y amplitude in [mm]', delta_amp, '(A_{out} - A_{in})/A_{in} [mm]', xmin=0., xmax=200.) hist_means = self.scatter_plot_mean_rms(amp_in, delta_amp, 0., 200., 10.) graph.SetMarkerStyle(7) return hist, hist_means, graph def amplitude_p_scatter_plot(self, bunch_in): """predicate takes arguments (spill_index, spills_in_bunch_out)""" hits_in = [hit for hit in bunch_in] amp_in = self.amp_list(hits_in, self.beta, 0., self.bz_us) p_in = [hit['p'] for hit in hits_in] if len(amp_in) == 0: print "Failed to make graph:", len(amp_in) hist, graph = common.make_root_graph('amplitude x y', [0.], 'x-y amplitude in [mm]', [0.], 'P [MeV/c]', xmin=0., xmax=200.) else: hist, graph = common.make_root_graph('amplitude x y', amp_in, 'x-y amplitude in [mm]', p_in, 'P [MeV/c]', xmin=0., xmax=200.) hist_means = self.scatter_plot_mean_rms(amp_in, p_in, 0., 200., 10.) graph.SetMarkerStyle(7) return hist, hist_means, graph def amplitude_t_scatter_plot(self, bunch_in, bunch_out, weight_cut): """predicate takes arguments (spill_index, spills_in_bunch_out)""" spills_out = set([(hit['spill'], hit['event_number']) for hit in bunch_out]) hits_in_cut = [hit for hit in bunch_in if (hit['spill'], hit['event_number']) in spills_out and hit['weight'] > weight_cut] hits_out_cut = [hit for hit in bunch_out if (hit['spill'], hit['event_number']) in spills_out and hit['weight'] > weight_cut] amp_in_cut = self.amp_list(hits_in_cut, self.beta, 0., self.bz_us) amp_out_cut = self.amp_list(hits_out_cut, self.beta, 0., self.bz_ds) delta_t_cut = [h_o['t']-h_i['t'] for h_i, h_o in zip(hits_in_cut, hits_out_cut)] if len(amp_in_cut) == 0: print "Failed to make graph:", len(amp_in_cut) hist, graph = common.make_root_graph('amplitude x y', [0.], 'x-y amplitude in [mm]', [0.], 'dt [ns]', xmin=0., xmax=200.) else: hist, graph = common.make_root_graph('amplitude x y', amp_in_cut, 'x-y amplitude in [mm]', delta_t_cut, 'dt [ns]', xmin=0., xmax=200.) hist_means = self.scatter_plot_mean_rms(amp_in_cut, delta_t_cut, 0., 200., 10.) graph.SetMarkerStyle(7) return hist, hist_means, graph def amplitude_6d_scatter_plot(self, bunch_in, bunch_out): """predicate takes arguments (spill_index, spills_in_bunch_out)""" amp_in = self.amp_list_6d(bunch_in) amp_out = self.amp_list_6d(bunch_out) hist_in = common.make_root_histogram('A_{6D} in', amp_in, 'A_{6D} [mm]', 20, xmin=0., xmax=200.) hist_out = common.make_root_histogram('A_{6D} out', amp_out, 'A_{6D} [mm]', 20, xmin=0., xmax=200.) hist_in.SetName('A_{6D} in') hist_out.SetName('A_{6D} out') return hist_in, hist_out def amplitude_6d_capture_plot(self, bunch_in, bunch_out): amp_in = sorted(self.amp_list_6d(bunch_in)) amp_out = sorted(self.amp_list_6d(bunch_out)) bins = [i*10. for i in range(11)]+[i*50. for i in range(2, 5)] contents_in = [] contents_out = [] def amplitude_scatter(self, bunch_in, bunch_out): canvas = common.make_root_canvas('amplitude 6d hist') hist_in, hist_out = self.amplitude_6d_scatter_plot(bunch_in, bunch_out) hist_out.SetLineColor(4) hist_out.Draw() hist_in.SetLineColor(2) hist_in.Draw("SAME") common.make_root_legend(canvas, [hist_in, hist_out]) canvas.Update() self.print_canvas(canvas, 'acceptance_6d_hist') spills_out = set([hit['spill'] for hit in bunch_out]) canvas = common.make_root_canvas('amplitude x y scatter') hist, hist_means, graph = self.amplitude_scatter_plot(bunch_in, bunch_out, lambda x: True) hist.Draw() graph.Draw('p') hist_means.Draw("same") canvas.Update() self.print_canvas(canvas, 'acceptance') canvas = common.make_root_canvas('amplitude x y p scatter') hist, hist_means, graph = self.amplitude_p_scatter_plot(bunch_in) hist.Draw() graph.Draw('p') hist_means.Draw("same") canvas.Update() self.print_canvas(canvas, 'acceptance_p') canvas = common.make_root_canvas('amplitude x y t scatter') hist, hist_means, graph_cut = self.amplitude_t_scatter_plot(bunch_in, bunch_out, 0.1) hist.Draw() dummy_1, dummy_2, graph_all = self.amplitude_t_scatter_plot(bunch_in, bunch_out, -0.1) graph_all.SetMarkerColor(2) graph_all.Draw('p') graph_cut.Draw('p') hist_means.Draw("same") canvas.Update() self.print_canvas(canvas, 'acceptance_t') def two_d_plots(self, bunch_list, colour, canvas_list = None): n_graphs = 4 if canvas_list == None: canvas_list = [None]*n_graphs graph_list = [None]*n_graphs canvas_list[0], hist, graph_list[0] = bunch_list[0].root_scatter_graph("t", "energy", "ns", "MeV", include_weightless=False, canvas=canvas_list[0]) canvas_list[1], hist, graph_list[1] = bunch_list[-1].root_scatter_graph("t", "energy", "ns", "MeV", include_weightless=False, canvas=canvas_list[1]) canvas_list[2], hist, graph_list[2] = bunch_list[0].root_scatter_graph("r", "pt", "mm", "MeV/c", include_weightless=False, canvas=canvas_list[2]) canvas_list[3], hist, graph_list[3] = bunch_list[-1].root_scatter_graph("r", "pt", "mm", "MeV/c", include_weightless=False, canvas=canvas_list[3]) for i in range(n_graphs): canvas_list[i].cd() graph_list[i].SetMarkerStyle(6) graph_list[i].SetMarkerColor(colour) graph_list[i].Draw("p") canvas_list[i].Update() self.print_canvas(canvas_list[0], "time_energy_at_start") self.print_canvas(canvas_list[1], "time_energy_at_end") self.print_canvas(canvas_list[2], "r_pt_at_start") self.print_canvas(canvas_list[3], "r_pt_at_end") return canvas_list def cut(self, bunch_list): print " weights in " print " no cut:", bunch_list[0].bunch_weight(), bunch_list[-1].bunch_weight() for bunch in bunch_list: bunch.transmission_cut(bunch_list[-1], global_cut=True) bunch.transmission_cut(bunch_list[-1], global_cut=True, test_variable = ['spill', 'event_number', 'particle_number', 'pid']) bunch.cut({'pid':-13}, operator.ne, global_cut=True) print ' transmission cut:', bunch_list[0].bunch_weight(), bunch_list[-1].bunch_weight(), "**" bunches = [bunch_list[0], bunch_list[-1]] cut_stream = ['upstream', 'downstream'] for i in [0, 1]: print ' ', cut_stream[i] print ' ', 'cut ', ' u/s', ' d/s' bunches[i].cut({'r':self.r_cut[i]}, operator.gt, global_cut=True) print ' cut r: ', self.r_cut[i], str(bunches[0].bunch_weight()).rjust(10), str(bunches[1].bunch_weight()).rjust(10) bunches[i].cut({'amplitude x y':self.amplitude_trans_cut[i]}, operator.gt, global_cut=True) print ' cut amp_trans: ', str(self.amplitude_trans_cut[i]).rjust(5), str(bunches[0].bunch_weight()).rjust(10), str(bunches[1].bunch_weight()).rjust(10) bunches[i].cut({'amplitude x y':self.amplitude_trans_cut[i]}, operator.gt, global_cut=True) print ' cut amp_trans: ', str(self.amplitude_trans_cut[i]).rjust(5), str(bunches[0].bunch_weight()).rjust(10), str(bunches[1].bunch_weight()).rjust(10) if self.do_long_cut: bunches[i].cut({'amplitude ct':self.amplitude_ct_cut[i]}, operator.gt, global_cut=True) print ' cut amp_long: ', str(self.amplitude_ct_cut[i]).rjust(5), str(bunches[0].bunch_weight()).rjust(10), str(bunches[1].bunch_weight()).rjust(10) bunches[i].cut({'amplitude ct':self.amplitude_ct_cut[i]}, operator.gt, global_cut=True) print ' cut amp_long: ', str(self.amplitude_ct_cut[i]).rjust(5), str(bunches[0].bunch_weight()).rjust(10), str(bunches[1].bunch_weight()).rjust(10) mean_p = bunches[i].mean(['p'])['p'] bunches[i].cut({'p':mean_p+self.p_cut[i][1]}, operator.gt, global_cut=True) bunches[i].cut({'p':mean_p-self.p_cut[i][0]}, operator.lt, global_cut=True) print ' cut', round(mean_p-self.p_cut[i][0], 1), '< p <', round(mean_p+self.p_cut[i][1], 1), ' ', str(bunches[0].bunch_weight()).rjust(10), str(bunches[1].bunch_weight()).rjust(10) def z_plot(self, bunch_list): print "Start MOMENTS" print bunch_list[0].moment(['t', 't']) print bunch_list[0].moment(['pz', 't']) print bunch_list[0].moment(['pz', 'pz']) """ for item in ['t', 'pz', 'energy']: canvas = common.make_root_canvas('sigma_'+item) z_list = [bunch.mean(['z'])['z'] for bunch in bunch_list] item_list = [bunch.moment([item, item])**0.5 for bunch in bunch_list] hist, graph = common.make_root_graph('sigma_'+item, z_list, 'mean z [mm]', item_list, '#sigma('+item+')') hist.Draw() graph.Draw('l') self.print_canvas(canvas, 's_'+item+'_vs_z') """ canvas, hist, graph = Bunch.root_graph(bunch_list, 'mean', ['z'], 'mean', ['p'], 'mm', 'MeV/c') self.print_canvas(canvas, 'momentum_vs_z') canvas, hist, graph = Bunch.root_graph(bunch_list, 'mean', ['z'], 'beta', ['x', 'y'], 'mm', 'mm', ymin=0., ymax=2000.) hist.GetXaxis().SetTitle("z [mm]") hist.GetYaxis().SetTitle("Transverse #beta [mm]") self.set_style(graph) self.get_verticals(canvas) self.print_canvas(canvas, 'beta_trans_vs_z') canvas, hist, graph = Bunch.root_graph(bunch_list, 'mean', ['z'], 'emittance', ['x', 'y'], 'mm', 'mm') hist.GetXaxis().SetTitle("z [mm]") hist.GetYaxis().SetTitle("Transverse emittance [mm]") self.set_style(graph) self.get_verticals(canvas) self.print_canvas(canvas, 'emittance_trans_vs_z') canvas, hist, graph = Bunch.root_graph(bunch_list, 'mean', ['z'], 'beta', ['t'], 'mm', 'mm') self.print_canvas(canvas, 'beta_long_vs_z') canvas, hist, graph = Bunch.root_graph(bunch_list, 'mean', ['z'], 'emittance', ['ct'], 'mm', 'mm') self.print_canvas(canvas, 'emittance_long_vs_z') canvas, hist, graph = Bunch.root_graph(bunch_list, 'mean', ['z'], 'emittance', ['ct', 'x', 'y'], 'mm', 'mm') self.print_canvas(canvas, 'emittance_6d_vs_z') ref_bunch_list = [Bunch.new_from_hits([bunch[0]]) for bunch in bunch_list] canvas, hist, graph = Bunch.root_graph(ref_bunch_list, 'mean', ['z'], 'mean', ['bz'], 'mm', 'T') hist.GetXaxis().SetTitle("z [mm]") hist.GetYaxis().SetTitle("B_{z} [T]") self.set_style(graph) self.get_verticals(canvas) self.print_canvas(canvas, 'bz_vs_z') def grok_filename(self, filename): base = os.path.basename(filename)[12:-5] emittance = base.split('_')[0] emittance = float(emittance.split('=')[1]) a_dir = os.path.dirname(filename).split('/') index = a_dir[-1].split('_')[-1] base += '_'+index a_dir = "/".join(a_dir[:-1]) print " directory", a_dir, "name", base self.current = { "dir":a_dir, "name":base, "full":filename, "emittance":emittance, "index":index } return self.current def print_canvas(self, canvas, name): for format in ["png", "root"]: canvas.Print(self.current["dir"]+"/"+name+"_"+self.current["name"]+"."+format) def single_analysis(self, filename): print "Loading data", filename metadata = self.grok_filename(filename) bunch_list = Bunch.new_list_from_read_builtin('maus_root_virtual_hit', filename) len_cut = max([len(bunch) for bunch in bunch_list])/20 print " Discarding bunches with fewer than", len_cut, "particles" bunch_list = [bunch for bunch in bunch_list if len(bunch) > len_cut] if self.z_max != None: bunch_list = [bunch for bunch in bunch_list if bunch[0]['z'] < self.z_max] print " Printing transmission (before cut)" canvas, hist, graph = Bunch.root_graph(bunch_list, 'mean', ['z'], 'bunch_weight', [], 'mm', '') self.print_canvas(canvas, 'weight_vs_z') print " Making cuts" #canvas_list = self.two_d_plots(bunch_list, 2) self.cut(bunch_list) #self.amplitude_scatter(bunch_list[0], bunch_list[-1]) print " Making 2d plots" #self.two_d_plots(bunch_list, 1, canvas_list) print " Making z plots" self.z_plot(bunch_list) print " Making acceptance plots" self.data.append((metadata, bunch_list)) Bunch.clear_global_weights() def multi_analysis(self): multi_data = open(self.data[0][0]["dir"]+"/multi_analysis_"+self.current["index"]+".json", "w") delta_transmission = [] delta_emittance_trans = [] nominal_emittance_trans = [] actual_emittance_trans = [] actual_emittance_long = [] delta_emittance_long = [] actual_emittance_6d = [] delta_emittance_6d = [] sigma_energy = [] for item in self.data: self.cut(item[1]) try: delta_trans = float(len(item[1][-1]))/float(len(item[1][0])) emit_in = item[1][0].get_emittance(['x', 'y']) emit_out = item[1][-1].get_emittance(['x', 'y']) delta_emit_t = emit_out/emit_in-1. s_energy = (item[1][0].moment(['energy', 'energy']))**0.5 emit_long_in = item[1][0].get_emittance(['ct']) emit_long_out = item[1][-1].get_emittance(['ct']) delta_emit_l = emit_long_out/emit_long_in-1. emit_6d_in = item[1][0].get_emittance(['x', 'y', 'ct']) emit_6d_out = item[1][-1].get_emittance(['x', 'y', 'ct']) delta_emit_6d = emit_6d_out/emit_6d_in-1. sigma_energy.append(s_energy) actual_emittance_long.append(emit_long_in) delta_emittance_long.append(delta_emit_l) actual_emittance_trans.append(emit_in) delta_emittance_trans.append(delta_emit_t) actual_emittance_6d.append(emit_6d_in) delta_emittance_6d.append(delta_emit_6d) delta_transmission.append(delta_trans) nominal_emittance_trans.append(item[0]['emittance']) except Exception: sys.excepthook(*sys.exc_info()) Bunch.clear_global_weights() json_doc = { "delta_transmission":delta_transmission, "nominal_emittance_trans":nominal_emittance_trans, "delta_emittance_trans":delta_emittance_trans, "actual_emittance_trans":actual_emittance_trans, "delta_emittance_long":delta_emittance_long, "actual_emittance_long":actual_emittance_long, "delta_emittance_6d":delta_emittance_6d, "actual_emittance_6d":actual_emittance_6d, "sigma_energy":sigma_energy, } print >> multi_data, json.dumps(json_doc) def set_style(self, graph, is_alternative = None): if is_alternative == None: is_alternative = self.current["full"].find("alternative") > -1 if is_alternative: graph.SetLineColor(ROOT.kBlue-4) graph.SetLineStyle(2) graph.SetLineWidth(1) graph.SetMarkerStyle(ROOT.kFullSquare) graph.SetMarkerSize(1) else: graph.SetLineColor(1) graph.SetLineStyle(1) graph.SetLineWidth(1) graph.SetMarkerStyle(ROOT.kFullCircle) graph.SetMarkerSize(1) def get_verticals(self, canvas, is_alternative = None): print "get_verticals", self.current["full"] if is_alternative == None: is_alternative = self.current["full"].find("alternative") > -1 canvas.cd() if is_alternative: afc_centre = [-1090., +1090.] absorber_centre = [0.] else: afc_centre = [-862.75, +862.75] absorber_centre = [0.] canvas.cd() for z in afc_centre: dummy, graph = common.make_root_graph("afc", [z]*2, "", [-1.e6, 1.e6], "") graph.SetLineColor(ROOT.kRed) graph.SetLineStyle(3) graph.SetLineWidth(1) graph.Draw('l') for z in absorber_centre: dummy, graph = common.make_root_graph("abs", [z]*2, "", [-1.e6, 1.e6], "") graph.SetLineColor(ROOT.kRed+2) graph.SetLineStyle(3) graph.SetLineWidth(1) graph.Draw('l') canvas.Update() def __init__(self, momentum, amplitude): self.data = [] self.current = {} self.p = momentum self.m_mu = common.pdg_pid_to_mass[13] self.bz_us = 4.e-3 self.bz_ds = -4.e-3 self.beta = self.p/self.bz_us*2./common.constants['c_light'] self.z_max = 4050.1 self.scraped = {'__any__':[]} self.r_cut = [150., 150.] self.amplitude_trans_cut = [72., 72.] self.do_long_cut = True self.amplitude_ct_cut = [150., 150.] self.p_cut = [[50., 50.], [50., 50.]] file_list = glob.glob("output/output_reference/output*_13/*.root") batch = len(file_list) > 2 ROOT.gROOT.SetBatch(batch) print file_list for filename in sorted(file_list): try: self.single_analysis(filename) except Exception: sys.excepthook(*sys.exc_info()) if batch: self.multi_analysis() print "Done\n\n" if not batch: raw_input() def main(): Tracking(200., 1.) if __name__ == "__main__": main()