def process_folder(in_root_folder): for root, dirs, files in os.walk(in_root_folder): for text_file in files: (name, ext) = os.path.splitext(text_file) if not ext in ['.xml', '.xhtml']: continue source_name = os.path.join(root, text_file) print >>sys.stderr, 'INFO: processing file %s' % source_name process_file(source_name, name) plot.scatter(GLOBAL_WORDS_STAT['word_counts'], GLOBAL_WORDS_STAT['dominant_counts']) plot.xlim(20000) plot.title('Word count - dominant lexemes number correlation') plot.savefig('./dominant_word_count_correlation.png') plot.clf() plot.scatter(GLOBAL_SENTENCES_STAT['sentence_counts'], GLOBAL_SENTENCES_STAT['dominant_counts']) plot.xlim(2000) plot.title('Sentence count - dominant lexemes number correlation') plot.savefig('./dominant_sentence_count_correlation.png') plot.clf() plotting.make_histogram('./crirical_freq_error.png', \ 'Critical frequency error histogram', \ GLOBAL_CRITICAL_FREQUENCY_ERROR_STAT, \ in_normed = False) print 'Critical frequency error mean: ', numpy.mean(GLOBAL_CRITICAL_FREQUENCY_ERROR_STAT) print 'Critical frequency error variance: ', numpy.std(GLOBAL_CRITICAL_FREQUENCY_ERROR_STAT)
stack = make_stack(var, cuts.name, MCGroups, dataGroup, openedFiles, s, "iso", lumis, cuts.isoCutsMC, cuts.isoCutsData, "", cuts.name) stacks[var.name + s + "iso"] = stack else: for st in syst_type: stack = make_stack(var, cuts.name, MCGroups, dataGroup, openedFiles, s + st, "iso", lumis, cuts.isoCutsMC, cuts.isoCutsData, "", cuts.name) stacks[var.name + s + st + "iso"] = stack #Anti-iso region for s in systematics: if s == "Nominal": make_histogram(var, dataGroup, cuts.name, openedFiles, lumis, s, "antiiso", cuts.antiIsoCutsData) stack = make_stack(var, cuts.name + s, MCGroups, dataGroup, openedFiles, s, "antiiso", lumis, cuts.antiIsoCutsMC, cuts.antiIsoCutsData, "", cuts.name) stacks[var.name + s + "antiiso"] = stack #print "cuts",cuts.isoCutsData tdrstyle.tdrstyle() cst = TCanvas("Histogram", "Jet Systematics in final selection", 10, 10, 1000, 1000) lumibox = lumi_textbox(lumi_iso["mu"]) histos = []
stacks = {} #Iso region for s in systematics: if s == "Nominal": stack = make_stack(var, cuts.name, MCGroups, dataGroup, openedFiles, s, "iso", lumis, cuts.isoCutsMC, cuts.isoCutsData, "", cuts.name) stacks[var.name+s+"iso"] = stack else: for st in syst_type: stack = make_stack(var, cuts.name, MCGroups, dataGroup, openedFiles, s+st, "iso", lumis, cuts.isoCutsMC, cuts.isoCutsData, "", cuts.name) stacks[var.name+s+st+"iso"] = stack #Anti-iso region for s in systematics: if s == "Nominal": make_histogram(var, dataGroup, cuts.name, openedFiles, lumis, s, "antiiso", cuts.antiIsoCutsData) stack = make_stack(var, cuts.name+s, MCGroups, dataGroup, openedFiles, s, "antiiso", lumis, cuts.antiIsoCutsMC, cuts.antiIsoCutsData, "", cuts.name) stacks[var.name+s+"antiiso"] = stack #print "cuts",cuts.isoCutsData tdrstyle.tdrstyle() cst = TCanvas("Histogram","Jet Systematics in final selection",10,10,1000,1000) lumibox = lumi_textbox(lumi_iso["mu"]) histos = [] hData=dataGroup.getHistogram(var, "Nominal", "iso"+cuts.name) #hQCDShapeOrig = dataGroup.getHistogram(var, "Nominal", "antiiso") hData.Draw("e1")