def __init__(self, *paths):
self.filenames = []
for path in paths:
self.filenames.extend(glob.glob(path))
self.files = []
for filename in self.filenames:
self.files.append(io.open(filename, 'read'))
# Sum them together.
super(FileView, self).__init__(**self.files)
def __init__(self, *paths):
self.filenames = []
for path in paths:
self.filenames.extend(glob.glob(path))
self.files = []
for filename in self.filenames:
self.files.append(io.open(filename, 'read'))
# Sum them together.
super(FileView, self).__init__(**self.files)
def tree_to_table(tree):
"""Convert a ROOT TTree to an astropy Table.
Parameters
----------
Returns
-------
table : astropy.table.Table
"""
from rootpy import asrootpy
tree = asrootpy(tree)
array = tree.to_array()
from rootpy.io import open
from rootpy.root2array import tree_to_recarray
print('Reading %s' % infile)
file = open(infile)
tree_name = 'TableAllMu_WithoutNan' # 'ParTree_Postselect'
print('Getting %s' % tree_name)
tree = file.get(tree_name, ignore_unsupported=True)
print('Converting tree to recarray')
array = tree_to_recarray(tree)
print('Converting recarray to atpy.Table')
table = recarray_to_table(array)
print('Removing empty columns:')
empty_columns = []
for col in table.columns:
#if table['col'].min() == table['col'].max()
if (table['col'] == 0).all():
empty_columns.append(col)
print(empty_columns)
table.remove_columns(empty_columns)
for name in array.dtype.names:
# FITS can't save these types.
data = array[name]
if data.dtype == np.dtype('uint64'):
data = data.copy().astype('int64')
table.add_column(name, data)
return table
def tree_to_table(tree, tree_name):
"""Convert a ROOT TTree to an astropy Table.
Parameters
----------
tree : ROOT.TTree
ROOT TTree
Returns
-------
table : `~astropy.table.Table`
ROOT tree data as an astropy table.
"""
from rootpy import asrootpy
from rootpy.io import open
from rootpy.root2array import tree_to_recarray
tree = asrootpy(tree)
array = tree.to_array()
file = open(infile)
tree_name = 'TableAllMu_WithoutNan' # 'ParTree_Postselect'
tree = file.get(tree_name, ignore_unsupported=True)
array = tree_to_recarray(tree)
table = recarray_to_table(array)
# Remove empty columns
empty_columns = []
for col in table.columns:
#if table['col'].min() == table['col'].max()
if (table['col'] == 0).all():
empty_columns.append(col)
print(empty_columns)
table.remove_columns(empty_columns)
for name in array.dtype.names:
# FITS can't save these types.
data = array[name]
if data.dtype == np.dtype('uint64'):
data = data.copy().astype('int64')
table.add_column(name, data)
return table
#!/usr/bin/env python
from rootpy.tree import Tree
from rootpy.io import open
from rootpy.root2array import tree_to_recarray_py, \
tree_to_recarray, \
tree_to_ndarray
import cProfile
import time
with open('test.root') as f:
tree = f.test
branches = ["a_x", "a_y", "a_z"]
tree.SetWeight(.5)
print "Using pure Python method..."
cProfile.run('arr1 = tree_to_recarray_py(tree, branches=branches,'
'include_weight=True)')
print "time without profiler overhead:"
t1 = time.time()
arr1 = tree_to_recarray_py(tree, branches=branches,
include_weight=True)
t2 = time.time()
print "%f seconds" % (t2 - t1)
print '=' * 40
if args.verbose:
logging.basicConfig(level=logging.DEBUG, stream=sys.stderr)
else:
logging.basicConfig(level=logging.INFO, stream=sys.stderr)
#################################
###main part ############
#################################
from rootpy.plotting import views
import rootpy.io as io
import ROOT
ROOT.gROOT.SetBatch(True)
# Build view of input histograms
log.info("Merging input files")
input_files = [io.open(x) for x in args.input]
input_view = views.SumView(*input_files)
numerator = input_view.Get(args.num)
denominator = input_view.Get(args.den)
#Checks if rebinning is needed
if (args.rebinX and args.rebinX > 1) or (args.rebinY and args.rebinY > 1):
binx = args.rebinX if args.rebinX else 1
biny = args.rebinY if args.rebinY else 1
numerator.Rebin2D(binx, biny)
denominator.Rebin2D(binx, biny)
eff_map = numerator.Clone("efficiency_map")
eff_map_statUp = numerator.Clone("efficiency_map_statUp")
eff_map_statDown = numerator.Clone("efficiency_map_statDown")
worse_rel_err = numerator.Clone("worse_rel_err")
parser = argparse.ArgumentParser()
parser.add_argument("rootfile", help="ROOT file to embed label in")
parser.add_argument("labels",
metavar='label',
nargs="+",
help="Label(s) to embed. Format: key=value")
args = parser.parse_args()
for label in args.labels:
if '=' not in label:
print "Can't parse %s, you need to specify labels as key=val pars" \
% label
sys.exit(1)
# Don't import this until we know we need it
from rootpy import io
from ROOT import TText
file = io.open(args.rootfile, 'UPDATE')
file.cd()
keep = []
for label in args.labels:
key, val = tuple(label.split('='))
the_label = TText(0, 0, val)
the_label.SetName(key)
the_label.Write()
keep.append(the_label)
file.Write()
signal.SetLineColor(ROOT.EColor.kRed)
signal.SetFillStyle(0)
wz.SetTitle('WZ')
zz.SetTitle('ZZ')
fakes.SetTitle("Non-prompt")
legend.AddEntry(signal, signal.GetTitle(), "l")
legend.AddEntry(zz, 'ZZ', 'lf')
legend.AddEntry(wz, 'WZ', 'lf')
legend.AddEntry(fakes, 'Non-prompt', "lf")
# Make M_ellell
for x_title, filenamebase, units in [
("#Delta R_{min}", 'histo_drmin_afterallothercuts', None),
("|m_{l^{+}l^{-}}-m_{Z}| (GeV)", 'histo_masszmin', 'GeV')]:
mass_cut_file = io.open(filenamebase+'.root')
hZZ = mass_cut_file.Get("histo2");
hWZ = mass_cut_file.Get("histo3");
hZJets = mass_cut_file.Get("histo4");
hData = mass_cut_file.Get("histo5");
hHWW = mass_cut_file.Get("histos");
hHWW = hHWW*5.0
hZZ.decorate(zz)
hWZ.decorate(wz)
hZJets.decorate(fakes)
hData.decorate(data)
hHWW.decorate(signal)
help='Plot fit error band')
args = parser.parse_args(args[1:])
if args.verbose:
logging.basicConfig(level=logging.DEBUG, stream=sys.stderr)
else:
logging.basicConfig(level=logging.INFO, stream=sys.stderr)
from rootpy.plotting import views
import rootpy.io as io
from FinalStateAnalysis.Utilities.rootbindings import ROOT
# Build view of input histograms
log.info("Merging input files")
input_view = views.SumView(*[io.open(x) for x in args.input])
if args.rebin and args.rebin > 1:
binning = None
# if ',' in args.rebin:
# print args.rebin.split(',')
if args.rebin:
binning = eval(args.rebin)
if isinstance(binning, float) or isinstance(binning, int):
newbinning = int(binning)
elif isinstance(binning, list) and isinstance(
binning[0], float) or isinstance(binning[0], int):
newbinning = tuple(float(x) for x in binning)
elif isinstance(binning, list) and isinstance(binning[0], list):
# print args.rebin
#print binning[0]
#!/usr/bin/env python
"""
==============================
ROOT.TFile made easy by rootpy
==============================
This example demonstrates how basic file operations are made easier in rootpy.
"""
print __doc__
import shutil
from rootpy.io import open, DoesNotExist
from rootpy.plotting import Hist, Hist2D
from rootpy.utils import asrootpy
shutil.copyfile('temp.root', 'data.root')
f = open('data.root')
print f.a
print f.a.b
try:
print f.a.b.c.d.e.f
except DoesNotExist, e:
print e
for thing in f.walk():
print thing
f.close()
# supports with statements
def add_cms_blurb(sqrts, intlumi, preliminary=True, blurb=''):
latex = ROOT.TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextAlign(31)
latex.SetTextAlign(11)
label_text = "CMS"
if preliminary:
label_text += " Preliminary"
label_text += " %s TeV" % sqrts
label_text += " L=%sfb^{-1}" % (intlumi)
label_text += " " + blurb
return latex.DrawLatex(0.18, 0.97, label_text)
vh_7TeV = io.open('VHTT/cmb/common/vhtt.input_7TeV.root')
vh_8TeV = io.open('VHTT/cmb/common/vhtt.input_8TeV.root')
vh_7TeV_pf = io.open('../HiggsAnalysis/HiggsToTauTau/test/root_postfit/vhtt.input_7TeV.root')
vh_8TeV_pf = io.open('../HiggsAnalysis/HiggsToTauTau/test/root_postfit/vhtt.input_8TeV.root')
# We only care about 7 + 8 TeV
for pf_suffix, (vh7file, vh8file) in [('', (vh_7TeV, vh_8TeV)), ('_postfit', (vh_7TeV_pf, vh_8TeV_pf))]:
vh_7and8 = views.SumView(vh7file, vh8file)
for period, vh_combined in [('7+8', vh_7and8), ('7', vh7file), ('8', vh8file)]:
# Now combine sub-channels
llt_combined = views.SumView(*[
views.SubdirectoryView(vh_combined, x) for x in ['emt', 'mmt', ]])
zh_combined = views.SumView(*[
Print out signal efficiencies.
'''
import logging
import math
from rootpy import io
from uncertainties import ufloat
import sys
import ROOT
ROOT.gSystem.AddIncludePath('-IHCSaW/Higgs_CS_and_Width/include')
ROOT.gROOT.ProcessLine('.L HCSaW/Higgs_CS_and_Width/src/HiggsCSandWidth.cc++')
results_file = io.open('vhtt_shapes.root')
logging.basicConfig(stream=sys.stderr,level=logging.INFO)
log = logging.getLogger("my_efficiencies")
log.setLevel(logging.DEBUG)
from get_stat_errors import get_stat_error
def get_vhtt_yield(mass):
log.warning("Get VHTT yield %s", mass)
mmt_yield = results_file.Get('mmt_mumu_final_count/VH%i' % mass).Integral()
emt_yield = results_file.Get('emt_emu_final_count/VH%i' % mass).Integral()
_, (mmt_passed, mmt_total) = get_stat_error('VH%i' % mass, mmt_yield)
_, (emt_passed, emt_total) = get_stat_error('VH%i' % mass, emt_yield)
def data_views(files, lumifiles, styles, forceLumi=-1):
''' Builds views of files.
[files] gives an iterator of .root files with histograms to build.
[lumifiles] gives the correspond list of .lumisum files which contain
the effective integrated luminosity of the samples.
The lumi to normalize to is taken as the sum of the data file int. lumis.
'''
files = list(files)
log.info("Creating views from %i files", len(files))
# Map sample_name => root file
histo_files = dict((extract_sample(x), io.open(x)) for x in files)
# Map sample_name => lumi file
lumi_files = dict((extract_sample(x), read_lumi(x)) for x in lumifiles)
# Identify data files
datafiles = set([name for name in histo_files.keys() if 'data' in name])
log.info("Found the following data samples:")
log.info(" ".join(datafiles))
datalumi = 0
for x in datafiles:
if x not in lumi_files:
raise KeyError(
"Can't find a lumi file for %s - I have these ones: " % x +
repr(lumi_files.keys()))
datalumi += lumi_files[x]
log.warning("-> total int. lumi = %0.0fpb-1", datalumi)
if forceLumi > 0:
datalumi = forceLumi
log.warning("-> forcing lumi to = %0.0fpb-1", datalumi)
# Figure out the dataset for each file, and the int lumi.
# Key = dataset name
# Value = {intlumi, rootpy file, weight, weighted view}
output = {}
has_data = False
for sample in histo_files.keys():
raw_file = histo_files[sample]
intlumi = lumi_files[sample]
weight = 1
if intlumi:
weight = datalumi / intlumi
if 'data' in sample:
has_data = True
weight = 1
log.warning(
"Building sample: %s => int lumi: %0.f pb-1. Weight => %0.2E",
sample, intlumi, weight)
view = views.ScaleView(raw_file, weight)
unweighted_view = raw_file
# Find the longest (i.e. most specific) matching style pattern
style = get_best_style(sample, styles)
if style:
log.info("Found style for %s - applying Style View", sample)
# Set style and title
# title = the name of the sample, rootpy Legend uses this.
nicename = copy.copy(style['name'])
log.debug("sample name %s", nicename)
style_dict_no_name = dict(
[i for i in style.iteritems() if i[0] != 'name'])
view = views.TitleView(views.StyleView(view, **style_dict_no_name),
nicename)
unweighted_view = views.TitleView(
views.StyleView(unweighted_view, **style_dict_no_name),
nicename)
else:
log.warning("No matching style found for %s", sample)
output[sample] = {
'intlumi': intlumi,
'file': raw_file,
'weight': weight,
'view': view,
'unweighted_view': unweighted_view
}
if not has_data:
return output
# Merge the data into just 'data'
log.info("Merging data together")
output['data'] = {
'intlumi':
datalumi,
'weight':
1,
'view':
views.SumView(*[output[x]['view'] for x in datafiles]),
'unweighted_view':
views.SumView(*[output[x]['unweighted_view'] for x in datafiles]),
}
return output
def __init__(self, outputFile, pause=False):
self.data = {}
self.file = open(outputFile, 'recreate')
self.tree = Tree("t", model=Event)
self.epoch = datetime.datetime(1995, 1, 1) #jan 1 1995 is ROOT epoch
# Build data and MC views
mc_stack = views.StackView(*[get_view(x) for x in args.mclist])
data = rebin_view(the_views['data']['view'], args.rebin)
canvas = plotting.Canvas(name='adsf', title='asdf')
keep = []
def save(name):
canvas.SetLogy(False)
canvas.SaveAs(os.path.join(outputdir, name) + '.png')
canvas.SetLogy(True)
canvas.SaveAs(os.path.join(outputdir, name) + '_log.png')
keep = []
# Walk the histogram layout
for path, subdirs, histos in io.open(files[0]).walk(class_pattern='TH1F'):
for histo in histos:
full_path = os.path.join(path, histo)
clean_path = full_path.replace('/', '-')
mc_hist = mc_stack.Get(full_path)
data_hist = data.Get(full_path)
mc_hist.Draw()
data_hist.Draw('same')
mc_hist.SetMaximum(2 * mc_hist.GetMaximum())
# Make sure we can see everything
#if data_hist.GetMaximum() > mc_hist.GetHistogram().GetMaximum():
#mc_hist.SetMaximum(2*data_hist.GetMaximum())
keep.append((mc_hist, data_hist))
save(clean_path)
if args.verbose:
logging.basicConfig(level=logging.DEBUG, stream=sys.stderr)
else:
logging.basicConfig(level=logging.INFO, stream=sys.stderr)
#################################
###main part ############
#################################
from rootpy.plotting import views
import rootpy.io as io
import ROOT
ROOT.gROOT.SetBatch(True)
# Build view of input histograms
log.info("Merging input files")
input_files = [io.open(x) for x in args.input]
input_view = views.SumView(*input_files)
numerator = input_view.Get(args.num)
denominator = input_view.Get(args.den)
#Checks if rebinning is needed
if (args.rebinX and args.rebinX > 1) or (args.rebinY and args.rebinY > 1):
binx = args.rebinX if args.rebinX else 1
biny = args.rebinY if args.rebinY else 1
numerator.Rebin2D(binx, biny)
denominator.Rebin2D(binx, biny)
eff_map = numerator.Clone("efficiency_map")
eff_map_statUp = numerator.Clone("efficiency_map_statUp")
eff_map_statDown = numerator.Clone("efficiency_map_statDown")
worse_rel_err = numerator.Clone("worse_rel_err")
#!/usr/bin/env python
from rootpy.tree import Tree, TreeModel
from rootpy.io import open
from rootpy.types import *
from random import gauss
f = open("test.root", "recreate")
# define the model
class Event(TreeModel):
x = FloatCol()
y = FloatCol()
z = FloatCol()
i = IntCol()
tree = Tree("test", model=Event)
# fill the tree
for i in xrange(10000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
tree.write()
# write tree in CSV format
#!/usr/bin/env python
from rootpy.tree import Tree
from rootpy.io import open
from time import time
from ROOT import TTreeCache
import sys
for cached in (False, True):
try:
f = open("test.root")
except IOError:
sys.exit("test.root does not exist. Please run tree_write.py first.")
tree = f.test
if cached:
TTreeCache.SetLearnEntries(1)
tree.SetCacheSize(10000000)
tree.use_cache(cached)
start_time = time()
for event in tree:
event.x
end_time = time()
print "%.2fsec to read one branch" % (end_time - start_time)
start_time = time()
for event in tree:
event.x
event.y
if __name__ == "__main__":
parser = argparse.ArgumentParser(
"Print the integrals of histograms in a ROOT file. "
"The under- and over-flow bins are included")
parser.add_argument('file', help="ROOT file")
parser.add_argument('--json',
action='store_true',
help="Write output in JSON format")
args = parser.parse_args()
# Import after, so ROOT can't mess with sys.argv
import rootpy.io as io
file = io.open(args.file)
results = {}
for path, dirs, histonames in file.walk(class_pattern='TH1*'):
for histoname in histonames:
full_path = os.path.join(path, histoname)
histo = file.Get(full_path)
int, err = get_integral(histo)
results[full_path] = (int, err)
if not args.json:
for full_path, (int, err) in results.iteritems():
sys.stdout.write(" : ".join(
[full_path, "%0.5g" % int,
"%0.5g" % err]))
import rootpy.io as io
import sys
sys.path.append(".")
from analysis_cfg import cfg
print "Run me from test/plotting"
file = io.open("../uw_old_emt_emu_vh.root")
tree = file.emt.final.Ntuple
print tree
events = [
# (166163, 21535313),
# (166380, 1570830562),
# (167284, 482525686),
# (167898, 1936669536),
# (171156, 408963773),
# (171315, 134426527),
# (176841, 121308752),
# (177074, 136387485),
# (178854, 76038953),
# (178920, 71077537),
# (180241, 731156083),
(172411, 237, 290309172),
(166408, 485, 618658267),
(166380, 1023, 1114594455),
(172949, 1103, 1547599611),
(177074, 496, 772810697),
(177074, 159, 205460982),
h = Hist(10, 0, 1)
def pyroot(tree):
tree.Draw("a_x", "", "goff", hist=h)
v1 = tree.GetV1()
return set(v1[n] for n in xrange(tree.GetEntries()))
def rootpy(tree):
return np.unique(tree_to_ndarray(tree, branches=["a_x"]))
with open("test.root") as f:
tree = f.test
print "Trees has %i entries" % tree.GetEntries()
print
print "Using the ROOT/PyROOT way..."
cProfile.run("pyroot(tree)")
print "time without profiler overhead:"
t1 = time.time()
a = pyroot(tree)
t2 = time.time()
print "%f seconds" % (t2 - t1)
print
print "=" * 40
print
print "Using compiled C extension and numpy..."
import os
import sys
import rootpy.io as io
import rootpy.plotting.views as views
import rootpy.plotting as plotting
import ROOT
from FinalStateAnalysis.PlotTools.RebinView import RebinView
if __name__ == "__main__":
if len(sys.argv) < 2:
print "Usage: WVsQCDComp.py file1 [file2] [file3]..."
if not os.path.exists("fake_study_plots"):
os.makedirs("fake_study_plots")
data_files = [io.open(x) for x in sys.argv[1:]]
data = views.SumView(*data_files)
qcd_view = views.SubdirectoryView(data, "qcd")
wjets_view = views.SubdirectoryView(data, "wjets")
ROOT.gStyle.SetPaintTextFormat("0.1f")
#canvas = plotting.Canvas(name='adsf', title='asdf')
#canvas.SetLogy(False)
#qcd_num_eta = RebinView(qcd_view, 2).Get("pt10/pfidiso02/muonAbsEta")
#qcd_denom_eta = RebinView(qcd_view, 2).Get("pt10/muonAbsEta")
#qcd_eta_eff = ROOT.TGraphAsymmErrors(qcd_num_eta, qcd_denom_eta)
#qcd_eta_eff.Draw('ape')
#canvas.SaveAs("fake_study_plots/qcd_eta_eff.png")
help='y-axis maximum')
args = parser.parse_args(args[1:])
if args.verbose:
logging.basicConfig(level=logging.DEBUG, stream=sys.stderr)
else:
logging.basicConfig(level=logging.INFO, stream=sys.stderr)
from rootpy.plotting import views
import rootpy.io as io
import ROOT
canvas = ROOT.TCanvas("asdf", "asdf", 800, 600)
workspaces = [io.open(x).Get("fit_efficiency") for x in args.workspaces]
x = workspaces[0].var('x')
frame = x.frame(ROOT.RooFit.Title("Efficiency"))
# Always Delete the frame, otherwise we get a segfault
@atexit.register
def cleanup_frame():
if frame:
frame.Delete()
ecolor = ROOT.EColor
colors = [ecolor.kAzure, ecolor.kViolet, ecolor.kOrange]
#!/usr/bin/env python
from rootpy.tree import Tree, TreeModel
from rootpy.io import open
from rootpy.types import *
from random import gauss
f = open("test.root", "recreate")
# define the model
class Event(TreeModel):
x = FloatCol()
y = FloatCol()
z = FloatCol()
i = IntCol()
tree = Tree("test", model=Event)
# fill the tree
for i in xrange(10000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
tree.write()
# write tree in CSV format
tree.csv()
def add_cms_blurb(sqrts, intlumi, preliminary=True, blurb=''):
latex = ROOT.TLatex()
latex.SetNDC();
latex.SetTextSize(0.04);
latex.SetTextAlign(31);
latex.SetTextAlign(11);
label_text = "CMS"
if preliminary:
label_text += " Preliminary"
label_text += " %s TeV" % sqrts
label_text += " L=%sfb^{-1}" % (intlumi)
label_text += " " + blurb
return latex.DrawLatex(0.18,0.97, label_text);
vh_7TeV = io.open('NEW-LIMITS/cmb/common/vhtt.input_7TeV.root')
vh_8TeV = io.open('NEW-LIMITS/cmb/common/vhtt.input_8TeV.root')
# We only care about 7 + 8 TeV
vh_combined = views.SumView(vh_7TeV, vh_8TeV)
# Now combine sub-channels
llt_combined = views.SumView(*[
views.SubdirectoryView(vh_combined, x) for x in ['emt', 'mmt', ]])
# We have to get the shape versions of these from a separate root file.
zh_combined = views.SumView(*[
views.SubdirectoryView(io.open('zh_with_shapes.root'), x) for x in [
'eeem_zh', 'eeet_zh', 'eemt_zh', 'eett_zh',
'mmme_zh', 'mmet_zh', 'mmmt_zh', 'mmtt_zh',
]])
#!/usr/bin/env python
'''
Get fake systematic.
'''
from rootpy import io
import rootpy.plotting.views as views
import sys
the_filename = sys.argv[1]
base_dirs = sys.argv[2].split(',')
syst_name = sys.argv[3]
the_file = io.open(the_filename, 'READ')
nom_view = views.SumView(
*[views.SubdirectoryView(the_file, dirname) for dirname in base_dirs])
qcd_view = views.SumView(*[
views.SubdirectoryView(the_file, dirname + '_q') for dirname in base_dirs
])
nom = nom_view.Get('fakes').Integral()
qcd = qcd_view.Get('fakes').Integral()
print "%s fakes %s %0.2f" % (','.join(base_dirs), syst_name,
1 + abs(nom - qcd) / nom)
log.info("Importing configuration")
cfg = __import__(args.cfg.replace('.py', ''))
log.info("Building views")
mu_fr_views = data_views(files, cfg.data_sample)
# Get view of double muon data
data = mu_fr_views[cfg.data_sample]['view']
# Get the different regions where we measure the stuff
log.info("Getting regions from cfg")
regions = cfg.make_regions(data)
log.info("Loading workspace")
fit_models_file = io.open(args.fit_models, 'READ')
# fit_modles is a RooWorkspace
fit_models = fit_models_file.fit_models
x = fit_models.var('x')
cut = fit_models.cat('cut')
# Wrap the views of the histograms to translate to RooDataHists
def roodatahistizer(hist):
''' Turn a hist into a RooDataHist '''
return ROOT.RooDataHist(hist.GetName(), hist.GetTitle(),
ROOT.RooArgList(x), hist)
# Now a Get() will return a RooDataHist
for type in regions.keys():
# Rebin the histograms. Make this smarter later
from rootpy.io import open
from rootpy.tree import Tree, TreeModel
from rootpy.types import FloatCol, IntCol
import random
class Model(TreeModel):
x = FloatCol(default=-1.)
y = FloatCol()
i = IntCol()
f = open('test.root', 'recreate')
tree = Tree(name='test', model=Model)
for i in xrange(100):
tree.x = random.gauss(4, 3)
tree.y = random.gauss(2, 3)
tree.i = i
tree.fill()
tree.write()
f.close()
def __init__(self, outputFile, pause=False):
self.data = {}
self.file = open(outputFile,'recreate')
self.tree = Tree("t", model=Event)
self.epoch = datetime.datetime(1995, 1, 1) #jan 1 1995 is ROOT epoch
file_groups[(kind, method)] = []
file_groups[(kind, method)].append(tfile_path)
for info, paths in file_groups.iteritems():
store = {}
kind, method = info
channel = paths[0].split('/')[-3]
store['kind'] = kind
store['method'] = method
store['channel'] = channel
store['limits'] ={}
for path in paths:
print path
mass = path.split('/')[-2]
store['limits'][mass]={}
tfile = io.open(path)
limit_tree = tfile.Get('limit')
limit_map = tree_to_quantile_map(limit_tree)
if kind == 'expected':
store['limits'][mass]['+2sigma'] = limit_map['0.975']
store['limits'][mass]['+1sigma'] = limit_map['0.840']
store['limits'][mass]['median'] = limit_map['0.500']
store['limits'][mass]['-1sigma'] = limit_map['0.160']
store['limits'][mass]['-2sigma'] = limit_map['0.025']
else:
store['limits'][mass]['median'] = limit_map['-1.000']
tfile.Close()
outfilename = '%s_%s_%s_limit.json' % (channel, method, kind)
with open( os.path.join(input_dir,outfilename),'w') as outfile:
outfile.write(prettyjson.dumps(store))
#!/usr/bin/env python
from rootpy.io import open, DoesNotExist
from rootpy.plotting import Hist, Hist2D
from rootpy.utils import asrootpy
f = open('data.root')
print f.a
print f.a.b
try:
print f.a.b.c.d.e.f
except DoesNotExist, e:
print e
for thing in f.walk():
print thing
f.close()
# supports with statements
with open('data.root', 'update') as f:
print f
# write some histograms
h1 = Hist(100, 0, 100, name='h1', type='I')
# variable bin widths
h2 = Hist2D((0, 3, 5, 20, 50), (10, 20, 30, 40, 1000), name='h2')
h1.Write()
def get_view(sample_pattern):
for sample, sample_info in the_views.iteritems():
if fnmatch.fnmatch(sample, sample_pattern):
return rebin_view(sample_info['view'])
raise KeyError("I can't find a view that matches %s, I have: %s" % (
sample_pattern, " ".join(the_views.keys())))
wz_view = get_view('WZ*')
zz_view = get_view('ZZ*')
data = rebin_view(the_views['data']['view'])
corrected_view = int_view(
SubtractionView(data, wz_view, zz_view, restrict_positive=True))
output = io.open(args.outputfile, 'RECREATE')
output.cd()
corr_numerator = corrected_view.Get(args.numerator)
corr_denominator = corrected_view.Get(args.denom)
log.info("Corrected: %0.2f/%0.2f = %0.1f%%",
corr_numerator.Integral(),
corr_denominator.Integral(),
100*corr_numerator.Integral()/corr_denominator.Integral()
if corr_denominator.Integral() else 0
)
uncorr_numerator = data.Get(args.numerator)
uncorr_denominator = data.Get(args.denom)
content = ret.GetBinContent(i+1) / bin1
content = content if content <= 1 else 0.
ret.SetBinContent(i+1,
content)
return ret
jobid = os.environ['jobid']
channels = ['mmt', 'emt', 'eet']
public = os.environ['pub']
chan_hists = {}
for ch, color in zip(channels, ['darkgreen','blue','red']):
view = views.TitleView(
views.StyleView(
views.SumView(
# io.open('results/%s/WHAnalyze%s/VH_120_HWW.root' % (jobid, ch.upper()) ),
io.open('results/%s/WHAnalyze%s/VH_H2Tau_M-120.root' % (jobid, ch.upper()) )
),
drawstyle = 'hist TEXT00',
linecolor = color,
linewidth = 2,
fillstyle = 'hollow',
legendstyle = 'l',
),
ch
)
chan_hists[ch] = view.Get('ss/CUT_FLOW')
canvas = plotting.Canvas(name='adsf', title='asdf')
canvas.SetGridx(True)
canvas.SetGridy(True)
canvas.SetLogy(True)
for sample, sample_info in the_views.iteritems():
if fnmatch.fnmatch(sample, sample_pattern):
return rebin_view(sample_info['view'])
raise KeyError("I can't find a view that matches %s, I have: %s" % (
sample_pattern, " ".join(the_views.keys())))
#from pdb import set_trace; set_trace()
wz_view = get_view('WZ*')
zz_view = get_view('ZZ*')
data = rebin_view(the_views['data']['view'])
corrected_view = int_view(
SubtractionView(data, wz_view, zz_view, restrict_positive=True))
log.debug('creating output file')
output = io.open(args.outputfile, 'RECREATE')
output.cd()
log.debug('getting from corrected view')
corr_numerator = corrected_view.Get(args.numerator)
corr_denominator = corrected_view.Get(args.denom)
log.info("Corrected: %0.2f/%0.2f = %0.1f%%",
corr_numerator.Integral(),
corr_denominator.Integral(),
100*corr_numerator.Integral()/corr_denominator.Integral()
if corr_denominator.Integral() else 0
)
uncorr_numerator = data.Get(args.numerator)
uncorr_denominator = data.Get(args.denom)
#!/usr/bin/env python
"""
Get fake systematic.
"""
from rootpy import io
import rootpy.plotting.views as views
import sys
the_filename = sys.argv[1]
base_dirs = sys.argv[2].split(",")
syst_name = sys.argv[3]
the_file = io.open(the_filename, "READ")
nom_view = views.SumView(*[views.SubdirectoryView(the_file, dirname) for dirname in base_dirs])
qcd_view = views.SumView(*[views.SubdirectoryView(the_file, dirname + "_q") for dirname in base_dirs])
nom = nom_view.Get("fakes").Integral()
qcd = qcd_view.Get("fakes").Integral()
print "%s fakes %s %0.2f" % (",".join(base_dirs), syst_name, 1 + abs(nom - qcd) / nom)
import sys
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('path', help='Path to histogram')
parser.add_argument('files', nargs='+', help='Input root files')
parser.add_argument('--above', default=None, type=float, help='Cut above')
parser.add_argument('--below', default=None, type=float, help='Cut below')
args = parser.parse_args()
from rootpy.io import open
from FinalStateAnalysis.StatTools.efficiencies import efficiency
# Get histogram
for a_file in args.files:
file = open(a_file)
if not file:
sys.stderr.write("Can't open file: %s\n" % args.file)
sys.exit(2)
histo = file.Get(args.path)
if not histo:
sys.stderr.write("Can't get histogram: %s\n" % args.path)
sys.exit(3)
total = 0
below_below = 0
below_above = 0
if histo.GetEntries():
total = histo.GetIntegral()[histo.GetNbinsX() + 1]
postfix = '_TEST_'
ROOT.gStyle.SetPaintTextFormat('.2g')
jobid = os.environ['jobid']
channels = ['eet', 'emt', 'mmt']
public = os.environ['pub']
chan_hists = {}
for ch, color in zip(channels, ['darkgreen', 'blue', 'red']):
view = views.TitleView(
views.StyleView(
views.SumView(
# io.open('results/%s/WHAnalyze%s/VH_120_HWW.root' % (jobid, ch.upper()) ),
io.open('results/%s/WHAnalyze%s/VH_H2Tau_M-120.root' %
(jobid, ch.upper()))),
drawstyle='hist TEXT00',
linecolor=color,
linewidth=2,
fillstyle='hollow',
legendstyle='l',
),
ch)
chan_hists[ch] = view.Get('ss/CUT_FLOW')
chan_hists[ch].SetLabelSize(0.035)
canvas = plotting.Canvas(name='adsf', title='asdf')
canvas.SetGridx(True)
canvas.SetGridy(True)
canvas.SetLogy(True)
legend = plotting.Legend(len(channels),
data.SetLineWidth(2)
legend.AddEntry(data, "Observed", "lp")
signal.SetLineStyle(1)
signal.SetLineWidth(3)
signal.SetTitle("m_{H}=120 (#times 5)")
signal.SetLineColor(ROOT.EColor.kRed)
signal.SetFillStyle(0)
wz.SetTitle('WZ')
zz.SetTitle('ZZ')
fakes.SetTitle("Non-prompt")
legend.AddEntry(signal, signal.GetTitle(), "l")
#legend.AddEntry(wz, 'WZ', 'lf')
legend.AddEntry(zz, 'ZZ', 'lf')
legend.AddEntry(fakes, 'Non-prompt', "lf")
zz_file = io.open('file_ZZ.root')
signal_file = io.open('file_Signal.root')
data_file = io.open('file_Data.root')
fake_file = io.open('file_Fake.root')
hZZ = zz_file.Get('zz')
hZJets = fake_file.Get('fake')
hData = data_file.Get('data')
hSignal = signal_file.Get('signal')
hHWW = hSignal
canvas.cd()
hHWW = hHWW*5.0
#print hZZ, hZJets
parser.add_option("--zz_err", type="float", default=0.40,
help="WZ normalization error")
parser.add_option("--triboson_err", type="float", default=1.00,
help="Error on triboson cross section")
parser.add_option("--mu_fake_err", type="float", default=0.3,
help="Error on muon fake rate")
def quad(*xs):
return math.sqrt(sum(x*x for x in xs))
if __name__ == "__main__":
(options, args) = parser.parse_args()
file = io.open(options.file, 'read')
emt_folder = file.Get('emt_emu_final_count')
mmt_folder = file.Get('mmt_mumu_final_count')
results = {}
mass = options.mass
results['mass'] = mass
results['emt_data'] = emt_folder.Get("data_obs").Integral()
results['emt_wz'] = emt_folder.Get("WZ").Integral()
results['emt_zz'] = emt_folder.Get("ZZ").Integral()
results['emt_triboson'] = emt_folder.Get("tribosons").Integral()
results['emt_fakes'] = emt_folder.Get("fakes").Integral()
results['emt_fakes_err'] = emt_folder.Get("fakes").GetBinError(1)
action='store_true', help='Plot fit error band')
args = parser.parse_args(args[1:])
if args.verbose:
logging.basicConfig(level=logging.DEBUG, stream=sys.stderr)
else:
logging.basicConfig(level=logging.INFO, stream=sys.stderr)
from rootpy.plotting import views
import rootpy.io as io
from FinalStateAnalysis.Utilities.rootbindings import ROOT
# Build view of input histograms
log.info("Merging input files")
input_view = views.SumView(*[io.open(x) for x in args.input])
if args.rebin and args.rebin > 1:
binning = None
# if ',' in args.rebin:
# print args.rebin.split(',')
if args.rebin:
binning = eval(args.rebin)
if isinstance(binning,float) or isinstance(binning,int) :
newbinning = int(binning)
elif isinstance(binning,list) and isinstance(binning[0],float) or isinstance(binning[0],int) :
newbinning= tuple(float(x) for x in binning)
elif isinstance(binning,list) and isinstance(binning[0],list):
# print args.rebin
#print binning[0]
binningx= tuple(float(x) for x in binning[0])
file_groups[(kind, method)] = []
file_groups[(kind, method)].append(tfile_path)
for info, paths in file_groups.iteritems():
store = {}
kind, method = info
channel = paths[0].split('/')[-3]
store['kind'] = kind
store['method'] = method
store['channel'] = channel
store['limits'] = {}
for path in paths:
print path
mass = path.split('/')[-2]
store['limits'][mass] = {}
tfile = io.open(path)
limit_tree = tfile.Get('limit')
limit_map = tree_to_quantile_map(limit_tree)
if kind == 'expected':
store['limits'][mass]['+2sigma'] = limit_map['0.975']
store['limits'][mass]['+1sigma'] = limit_map['0.840']
store['limits'][mass]['median'] = limit_map['0.500']
store['limits'][mass]['-1sigma'] = limit_map['0.160']
store['limits'][mass]['-2sigma'] = limit_map['0.025']
else:
store['limits'][mass]['median'] = limit_map['-1.000']
tfile.Close()
outfilename = '%s_%s_%s_limit.json' % (channel, method, kind)
with open(os.path.join(input_dir, outfilename), 'w') as outfile:
outfile.write(prettyjson.dumps(store))
data.SetLineWidth(2)
legend.AddEntry(data, "Observed", "lp")
signal.SetLineStyle(1)
signal.SetLineWidth(3)
signal.SetTitle("(5#times) m_{H}=120 GeV")
signal.SetLineColor(ROOT.EColor.kRed)
signal.SetFillStyle(0)
wz.SetTitle('WZ')
zz.SetTitle('ZZ')
fakes.SetTitle("Non-prompt")
legend.AddEntry(signal, signal.GetTitle(), "l")
legend.AddEntry(wz, 'WZ', 'lf')
legend.AddEntry(zz, 'ZZ', 'lf')
legend.AddEntry(fakes, 'Non-prompt', "lf")
whtt_file = io.open('../vhtt_shapes.root')
def get_total(histo):
return whtt_file.Get('mmt_mumu_final_MuTauMass/%s' % histo) + \
whtt_file.Get('emt_emu_final_SubleadingMass/%s' % histo)
hWZ = get_total('WZ')
hZZ = get_total('ZZ')
hZJets = get_total('fakes')
hData = get_total('data_obs')
hSignal = get_total('VH120') + get_total('VH120WW')
hHWW = hSignal
canvas.cd()
hHWW = hHWW*5.0
#!/usr/bin/env python
'''
Examples of using the TreeChain class, a replacement for TChain
'''
from random import gauss
from rootpy.io import open
from rootpy.tree import Tree, TreeChain
# Make two files, each with a Tree called "test"
print "Creating test tree in chaintest1.root"
f = open("chaintest1.root", "recreate")
tree = Tree("test")
tree.create_branches([('x', 'F'), ('y', 'F'), ('z', 'F'), ('i', 'I')])
for i in xrange(10000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
# Make a histogram of x when y > 1
hist1 = tree.Draw('x', 'y > 1', min=-10, max=10, bins=100).Clone()
hist1.SetName('hist1')
hist1.SetDirectory(0) # memory resident
print "The first tree has %f entries where y > 1" % hist1.Integral()
def data_views(files, lumifiles):
''' Builds views of files.
[files] gives an iterator of .root files with histograms to build.
[lumifiles] gives the correspond list of .lumisum files which contain
the effective integrated luminosity of the samples.
The lumi to normalize to is taken as the sum of the data file int. lumis.
'''
files = list(files)
log.info("Creating views from %i files", len(files))
# Map sample_name => root file
histo_files = dict((extract_sample(x), io.open(x)) for x in files)
# Map sample_name => lumi file
lumi_files = dict((extract_sample(x), read_lumi(x)) for x in lumifiles)
# Identify data files
datafiles = set([name for name in histo_files.keys() if 'data' in name])
log.info("Found the following data samples:")
log.info(" ".join(datafiles))
datalumi = 0
for x in datafiles:
if x not in lumi_files:
raise KeyError(
"Can't find a lumi file for %s - I have these ones: " % x
+ repr(lumi_files.keys()))
datalumi += lumi_files[x]
log.info("-> total int. lumi = %0.0fpb-1", datalumi)
# Figure out the dataset for each file, and the int lumi.
# Key = dataset name
# Value = {intlumi, rootpy file, weight, weighted view}
output = {}
for sample in histo_files.keys():
raw_file = histo_files[sample]
intlumi = lumi_files[sample]
log.info("Building sample: %s => int lumi: %0.f pb-1", sample, intlumi)
weight = 1
if intlumi:
weight = datalumi/intlumi
if 'data' in sample:
weight = 1
log.debug("Weight: %0.2f", weight)
view = views.ScaleView(raw_file, weight)
unweighted_view = raw_file
# Find the longest (i.e. most specific) matching style pattern
best_pattern = ''
for pattern, style_dict in data_styles.iteritems():
log.debug("Checking pattern: %s against %s", pattern, sample)
if fnmatch.fnmatch(sample, pattern):
log.debug("-> it matches!")
if len(pattern) > len(best_pattern):
best_pattern = pattern
log.info("Found new best style for %s: %s",
sample, pattern)
if best_pattern:
style_dict = data_styles[best_pattern]
log.info("Found style for %s - applying Style View", sample)
# Set style and title
# title = the name of the sample, rootpy Legend uses this.
nicename = copy.copy(style_dict['name'])
view = views.TitleView(
views.StyleView(view, **style_dict), nicename
)
unweighted_view = views.TitleView(
views.StyleView(unweighted_view, **style_dict), nicename
)
output[sample] = {
'intlumi': intlumi,
'file' : raw_file,
'weight' : weight,
'view' : view,
'unweighted_view' : unweighted_view
}
# Merge the data into just 'data'
log.info("Merging data together")
output['data'] = {
'intlumi' : datalumi,
'weight' : 1,
'view' : views.SumView(*[output[x]['view'] for x in datafiles]),
'unweighted_view' : views.SumView(*[output[x]['unweighted_view']
for x in datafiles]),
}
return output
return False
var.setVal(value)
log.info("Setting var %s to %f", name, value)
return True
if __name__ == "__main__":
parser.add_argument('input', help='Input file w/ fit results RooWorkspace')
parser.add_argument('output', help='Output directory to store plots in')
args = parser.parse_args(args[1:])
log.info("Opening input file %s", args.input)
file = io.open(args.input, 'r')
# Always close the file before exiting, otherwise RooPlot will segfault
atexit.register(file.Close)
log.info("Getting workspace")
ws = file.Get("fit_results")
x = ws.var('x')
cut = ws.cat('cut')
# Put everything in python form
data = {}
fake_rate_datas = ws.allData()
# Plot the results of each one
for datum in fake_rate_datas:
data[datum.GetName()] = datum
log.info("Importing configuration")
cfg = __import__(args.cfg.replace('.py', ''))
log.info("Building views")
mu_fr_views = data_views(files, cfg.data_sample)
# Get view of double muon data
data = mu_fr_views[cfg.data_sample]['view']
# Get the different regions where we measure the stuff
log.info("Getting regions from cfg")
regions = cfg.make_regions(data)
log.info("Loading workspace")
fit_models_file = io.open(args.fit_models, 'READ')
# fit_modles is a RooWorkspace
fit_models = fit_models_file.fit_models
x = fit_models.var('x')
cut = fit_models.cat('cut')
# Wrap the views of the histograms to translate to RooDataHists
def roodatahistizer(hist):
''' Turn a hist into a RooDataHist '''
return ROOT.RooDataHist(hist.GetName(), hist.GetTitle(),
ROOT.RooArgList(x), hist)
# Now a Get() will return a RooDataHist
for type in regions.keys():
# Rebin the histograms. Make this smarter later
from rootpy.io import open
from rootpy.tree import Tree, TreeModel
from rootpy.types import FloatCol, IntCol
import random
class Model(TreeModel):
x = FloatCol(default=-1.)
y = FloatCol()
i = IntCol()
f = open('test.root', 'recreate')
tree = Tree(name='test', model=Model)
for i in xrange(100):
tree.x = random.gauss(4, 3)
tree.y = random.gauss(2, 3)
tree.i = i
tree.fill()
tree.write()
f.close()
=============================================
The TreeChain class, a replacement for TChain
=============================================
This example demonstrates how to use the TreeChain class, a more Python-friendly
TChain replacement.
"""
print __doc__
from random import gauss
from rootpy.io import open
from rootpy.tree import Tree, TreeChain
# Make two files, each with a Tree called "test"
print "Creating test tree in chaintest1.root"
f = open("chaintest1.root", "recreate")
tree = Tree("test")
tree.create_branches([('x', 'F'),
('y', 'F'),
('z', 'F'),
('i', 'I')])
for i in xrange(10000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
# Make a histogram of x when y > 1
# Build data and MC views
mc_stack = views.StackView(*[get_view(x) for x in args.mclist])
data = rebin_view(the_views['data']['view'], args.rebin)
canvas = plotting.Canvas(name='adsf', title='asdf')
keep = []
def save(name):
canvas.SetLogy(False)
canvas.SaveAs(os.path.join(outputdir, name) + '.png')
canvas.SetLogy(True)
canvas.SaveAs(os.path.join(outputdir, name) + '_log.png')
keep = []
# Walk the histogram layout
for path, subdirs, histos in io.open(files[0]).walk(class_pattern='TH1F'):
for histo in histos:
full_path = os.path.join(path, histo)
clean_path = full_path.replace('/', '-')
mc_hist = mc_stack.Get(full_path)
data_hist = data.Get(full_path)
mc_hist.Draw()
data_hist.Draw('same')
mc_hist.SetMaximum(2*mc_hist.GetMaximum())
# Make sure we can see everything
#if data_hist.GetMaximum() > mc_hist.GetHistogram().GetMaximum():
#mc_hist.SetMaximum(2*data_hist.GetMaximum())
keep.append( (mc_hist, data_hist) )
save(clean_path)
for _ in range(nentries):
new.Fill(center)
return new
def int_view(x):
return views.FunctorView(x, round_to_ints)
def get_view(sample_pattern):
for sample, sample_info in the_views.iteritems():
if fnmatch.fnmatch(sample, sample_pattern):
return rebin_view(sample_info["view"])
raise KeyError("I can't find a view that matches %s, I have: %s" % (sample_pattern, " ".join(the_views.keys())))
data = rebin_view(the_views["data"]["view"])
output = io.open(args.outputfile, "RECREATE")
output.cd()
log.info("numerator = %s", args.numerator)
uncorr_numerator = data.Get(args.numerator)
uncorr_denominator = data.Get(args.denom)
uncorr_numerator.SetName("numerator_uncorr")
uncorr_denominator.SetName("denominator_uncorr")
uncorr_numerator.Write()
uncorr_denominator.Write()
log.info(
"Uncorrected: %0.2f/%0.2f = %0.1f%%",
uncorr_numerator.Integral(),
with open(args.meta) as meta_file:
meta_info = json.load(meta_file)
log.info("Importing configuration")
cfg = __import__(args.cfg.replace('.py', ''))
log.info("Building views")
data_views = data_views.data_views( args.files, cfg.data_sample)
# We just need to figure out the directory structure from any old file
layout_filename = data_views.values()[0]['subsamples'].values()[0]['filename']
log.info("Getting file layout from %s", layout_filename)
keys_to_plot = []
with io.open(layout_filename, 'r') as layout_file:
for path, subdirs, histos in layout_file.walk(class_pattern='TH1F'):
# Skip folders w/o histograms
if not histos:
continue
for histo in histos:
key = [
x for x in path.split('/')
if x != 'all' and x != 'pass' ] + [ histo ]
keys_to_plot.append(tuple(key))
for key in keys_to_plot:
path = os.path.join(*key)
sys.stdout.write("Fake rates for %s\n" % path)
for sample, sample_info in data_views.iteritems():
num_view = views.PathModifierView(
help='pattern of shape to be excluded',dest='excluded')
parser.add_option('-n','--nuisance', metavar='pattern', type=str, default = '*CMS_*',
help='pattern of nuisances to be excluded',dest='nuisances')
parser.add_option('-x','--x-title', metavar='name', type=str, default = '',
help='x axis title',dest='xtitle')
parser.add_option('-y','--y-title', metavar='name', type=str, default = '',
help='y axis title',dest='ytitle')
parser.add_option('-d','--differential', type=int, default = 0,
help='makes a differential plot',dest='differential')
(options,arguments) = parser.parse_args()
tfile_name = arguments.pop(0)
categories = arguments
tfile = io.open(tfile_name)
logging.info("Opened file %s" % tfile_name)
#get a directory and look into that
keys = [i.GetName() for i in tfile.Get(categories[0]).GetListOfKeys() if not fnmatch(i.GetName(), options.nuisances)]
if options.excluded:
keys = [ i for i in keys if not fnmatch(i, options.excluded)]
data = [i for i in keys if i.startswith('data')][0]
keys = [i for i in keys if not i.startswith('data')]
logging.debug("These shapes will be plotted: %s" % keys.__repr__())
input_view = views.SumView(
*[ views.SubdirectoryView( tfile, category )
for category in categories]
)
'''
Stupid tests of the th1fmorph tool
'''
from FinalStateAnalysis.StatTools.morph import morph
from rootpy.io import open, DoesNotExist
file = open('$fsa/VHiggs/test/plotting/wh_shapes.root')
hist1 = file.get('mmt_mumu_final_140_MuTauMass/VH140')
hist2 = file.get('mmt_mumu_final_120_MuTauMass/VH120')
hist130true = file.get('mmt_mumu_final_130_MuTauMass/VH130')
print '140', hist1.Integral(), hist1.GetMean()
print '130 true', hist130true.Integral(), hist130true.GetMean()
print '120', hist2.Integral(), hist2.GetMean()
# Try to morph to 130
m130 = morph('130', '130', 130, hist1, 140, hist2, 120)
print m130.Integral(), m130.GetMean()
