def _h2toh1(self, h):
import array
if not isinstance(h, ROOT.TH2):
return h
sentry = TH1AddDirSentry()
# H1class = getattr(ROOT,h.__class__.__name__.replace('2','1'))
nx = h.GetNbinsX()
ny = h.GetNbinsY()
h_flat = ROOT.TH1D(h.GetName(), h.GetTitle(), nx * ny, 0, nx * ny)
sumw2 = h.GetSumw2()
sumw2_flat = h_flat.GetSumw2()
for i in xrange(1, nx + 1):
for j in xrange(1, ny + 1):
# i,j must be mapped in
b2d = h.GetBin(i, j)
# b2d = h.GetBin( j,i )
# b1d = ((i-1)+(j-1)*nx)+1
b1d = ((j - 1) + (i - 1) * ny) + 1
h_flat.SetAt(h.At(b2d), b1d)
sumw2_flat.SetAt(sumw2.At(b2d), b1d)
h_flat.SetEntries(h.GetEntries())
stats2d = array.array('d', [0] * 7)
h.GetStats(stats2d)
stats1d = array.array('d', [0] * 4)
stats1d[0] = stats2d[0]
stats1d[1] = stats2d[1]
stats1d[2] = stats2d[2] + stats2d[4]
stats1d[3] = stats2d[3] + stats2d[5]
h_flat.PutStats(stats1d)
xtitle = h.GetXaxis().GetTitle()
v1, v2 = xtitle.split(
':') # we know it's a 2d filled by an expr like y:x
xtitle = '%s #times %s bin' % (v1, v2)
h_flat.GetXaxis().SetTitle(xtitle)
return h_flat
def _h2toh1(self, h):
import array
if not isinstance(h,ROOT.TH2):
return h
sentry = TH1AddDirSentry()
# H1class = getattr(ROOT,h.__class__.__name__.replace('2','1'))
nx = h.GetNbinsX()
ny = h.GetNbinsY()
h_flat = ROOT.TH1D(h.GetName(),h.GetTitle(),nx*ny,0,nx*ny)
sumw2 = h.GetSumw2()
sumw2_flat = h_flat.GetSumw2()
for i in xrange(1,nx+1):
for j in xrange(1,ny+1):
# i,j must be mapped in
b2d = h.GetBin( i,j )
# b2d = h.GetBin( j,i )
# b1d = ((i-1)+(j-1)*nx)+1
b1d = ((j-1)+(i-1)*ny)+1
h_flat.SetAt( h.At(b2d), b1d )
sumw2_flat.SetAt( sumw2.At(b2d), b1d )
h_flat.SetEntries(h.GetEntries())
stats2d = array.array('d',[0]*7)
h.GetStats(stats2d)
stats1d = array.array('d',[0]*4)
stats1d[0] = stats2d[0]
stats1d[1] = stats2d[1]
stats1d[2] = stats2d[2]+stats2d[4]
stats1d[3] = stats2d[3]+stats2d[5]
h_flat.PutStats(stats1d)
xtitle = h.GetXaxis().GetTitle()
v1,v2 = xtitle.split(':') # we know it's a 2d filled by an expr like y:x
xtitle = '%s #times %s bin' % (v1,v2)
h_flat.GetXaxis().SetTitle(xtitle)
return h_flat
def read_datum(self, i, j, k, use_memmap=True):
'''Reads the band `k` value for pixel at row `i` and column `j`.
Arguments:
`i`, `j`, `k` (integer):
Row, column and band index, respectively.
`use_memmap` (bool, default True):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Using this function is not an efficient way to iterate over bands or
pixels. For such cases, use readBands or readPixel instead.
'''
import array
if self._memmap is not None and use_memmap is True:
datum = self._memmap[i, k, j]
if self.scale_factor != 1:
datum /= float(self.scale_factor)
return datum
d_col = self.sample_size
d_band = d_col * self.ncols
d_row = d_band * self.nbands
self.fid.seek(self.offset + i * d_row + j * d_col + k * d_band, 0)
vals = array.array('b')
vals.fromfile(self.fid, self.sample_size)
arr = np.fromstring(vals.tostring(), dtype=self.dtype)
return arr.tolist()[0] / float(self.scale_factor)
def read_datum(self, i, j, k):
'''Reads the band `k` value for pixel at row `i` and column `j`.
Arguments:
`i`, `j`, `k` (integer):
Row, column and band index, respectively.
Using this function is not an efficient way to iterate over bands or
pixels. For such cases, use readBands or readPixel instead.
'''
import array
if self.memmap is not None:
datum = self.memmap[i, k, j]
if self.scale_factor != 1:
datum /= float(self.scale_factor)
return datum
d_col = self.sample_size
d_band = d_col * self.ncols
d_row = d_band * self.nbands
self.fid.seek(self.offset + i * d_row + j * d_col + k * d_band, 0)
vals = array.array('b')
vals.fromfile(self.fid, self.sample_size)
arr = np.fromstring(vals.tostring(), dtype=self.dtype)
return arr.tolist()[0] / float(self.scale_factor)
def FindPrime(N):
MAXNUM = int((log(N, 10) * 2.5 + 0.5) * N)
i = 2
a = array.array('i')
p = array.array('i')
for x in range(MAXNUM):
a.append(1)
while (i < len(a) - 1 and len(p) < N):
if a[i] == 1:
p.append(i)
for j in range(len(p)):
if i * p[j] >= MAXNUM: break
a[i * p[j]] = 0
if i % p[j] == 0: break
i += 1
return (p)
def SendLightsCK(self,r,g,b):
# struct.pack(fmt, magic, ver, type, seq, port, flags, timerVal V, uni, 0, 0, 0, 0, data)
levels = [r,g,b]*10
arr = array.array('B', levels)
out = struct.pack("LHHLBxHLB255s", 0x4adc0104, 0x0001, 0x0101, 0, 0, 0, -1, 0, arr.tostring())
socket(AF_INET, SOCK_DGRAM).sendto(out, (IP_ADDRESS, port))
def read_datum(self, i, j, k, use_memmap=True):
'''Reads the band `k` value for pixel at row `i` and column `j`.
Arguments:
`i`, `j`, `k` (integer):
Row, column and band index, respectively.
`use_memmap` (bool, default True):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Using this function is not an efficient way to iterate over bands or
pixels. For such cases, use readBands or readPixel instead.
'''
import array
if self._memmap is not None and use_memmap is True:
datum = self._memmap[i, k, j]
if self.scale_factor != 1:
datum /= float(self.scale_factor)
return datum
d_col = self.sample_size
d_band = d_col * self.ncols
d_row = d_band * self.nbands
self.fid.seek(self.offset + i * d_row + j * d_col + k * d_band, 0)
vals = array.array(byte_typecode)
vals.fromfile(self.fid, self.sample_size)
arr = np.fromstring(tobytes(vals), dtype=self.dtype)
return arr.tolist()[0] / float(self.scale_factor)
def windows(data, length, fragment, window_size, gap_size, l0):
keys = sorted(data.keys(), key = lambda (c): chrsort(c))
tbl = []; eligible = 0; gaps = [0,0,0,0] # gaps - histogram
for c in keys :
# wlist = array.array('l', [])
wlist = []
last_init = -max_window
chr_windows = 0
for read in data[c] :
strand, init = read%1000, read/1000
gp = (init - last_init)/window_size
if gp <= 3 : gaps[gp] += 1
w_init = init/window_size * window_size
w_last_init = last_init/window_size * window_size
if w_last_init == w_init :
wlist[-1] += 1
chr_windows += 1
else :
wlist.append(w_init * max_window + 1)
chr_windows += 1
last_init = init
# <- for read in data[c]
wlist_good = [] # array.array('l', [])
for i in wlist :
if i%max_window >= l0 :
wlist_good.append(i)
last_init = -max_window
wlist = array.array('l', [0])
for i in wlist_good :
reads, init = i%max_window, i/max_window
if last_init + window_size + gap_size > init:
gg = (init - last_init - window_size)/window_size
for k in range(gg) :
wlist.append((last_init + (k + 1) * window_size) * max_window)
wlist.append(i)
last_init = init
# <- FOR
tbl.append([c, chr_windows])
eligible += chr_windows
data[c] = wlist
msg = "Total eligible windows of {}bp with allowed gap size {}bp: {}"
logging.info(msg.format(window_size, gap_size, chr_windows))
msg = "Chromosome name, Eligible windows:\n{}"
logging.info(msg.format(beautiful_table(tbl)))
msg = "Gap size count:"
for i in range(4) :
msg += "\n {:>3} - {:>3}bp: {}".format(i * window_size + 1, (i + 1) * window_size, gaps[i])
logging.info(msg)
return data
def restore(self, filename):
"restore the cpu state from a file"
import array
a = array.array("L")
f = open(filename, "rb")
try:
a.fromfile(f, _py430.ARY_SIZE)
finally:
f.close()
self._ary[:] = a
def restore(self, filename):
"restore the cpu state from a file"
import array
a = array.array("L")
f = open(filename, "rb")
try:
a.fromfile(f, _py430.ARY_SIZE)
finally:
f.close()
self._ary[:] = a
def frq_db(corp: Corpus,
attrname: str,
nums: str = 'frq',
id_range: int = 0) -> array:
import array
filename = (subcorp_base_file(corp, attrname) + '.' + nums)
if not id_range:
id_range = corp.get_attr(attrname).id_range()
if nums == 'arf':
frq = array.array('f')
try:
frq.fromfile(open(filename, 'rb'), id_range) # type: ignore
except IOError as ex:
raise MissingSubCorpFreqFile(corp, ex)
except EOFError as ex:
os.remove(filename.rsplit('.', 1)[0] + '.docf')
raise MissingSubCorpFreqFile(corp, ex)
else:
try:
if corp.get_conf('VIRTUAL') and not hasattr(
corp, 'spath') and nums == 'frq':
raise IOError
frq = array.array('i')
frq.fromfile(open(filename, 'rb'), id_range) # type: ignore
except EOFError as ex:
os.remove(filename.rsplit('.', 1)[0] + '.docf')
os.remove(filename.rsplit('.', 1)[0] + '.arf')
os.remove(filename.rsplit('.', 1)[0] + '.frq')
raise MissingSubCorpFreqFile(corp, ex)
except IOError:
try:
frq = array.array('l')
frq.fromfile(open(filename + '64', 'rb'),
id_range) # type: ignore
except IOError as ex:
if not hasattr(corp, 'spath') and nums == 'frq':
a = corp.get_attr(attrname)
frq.fromlist([a.freq(i) for i in range(a.id_range())])
else:
raise MissingSubCorpFreqFile(corp, ex)
return frq
def test07_mean_overloads(self):
"""Adapted test for array overloading"""
import cppyy, array
cmean = cppyy.gbl.calc_mean
numbers = [8, 2, 4, 2, 4, 2, 4, 4, 1, 5, 6, 3, 7]
mean, median = 4.0, 4.0
for l in ['f', 'd', 'i', 'h', 'l']:
a = array.array(l, numbers)
assert round(cmean(len(a), a) - mean, 8) == 0
def test08_templated_mean_overloads(self):
"""Adapted test for array overloading with templates"""
import cppyy, array
cmean = cppyy.gbl.calc_mean_templ
numbers = [8, 2, 4, 2, 4, 2, 4, 4, 1, 5, 6, 3, 7]
mean, median = 4.0, 4.0
for l in ['f', 'd', 'i', 'h', 'l']:
a = array.array(l, numbers)
assert round(cmean(len(a), a) - mean, 8) == 0
def DivideTGraph(num, den):
Ns_den = den.GetN()
Xs_den = den.GetX()
Ys_den = den.GetY()
EXLs_den = den.GetEXlow()
EXHs_den = den.GetEXhigh()
EYLs_den = den.GetEYlow()
EYHs_den = den.GetEYhigh()
print "den.GetN()", den.GetN()
print "num.GetN()", num.GetN()
Ys_num = num.GetY()
EYLs_num = num.GetEYlow()
EYHs_num = num.GetEYhigh()
print "DivideTGraph: new"
bins = [i for i in range(Ns_den) if Ys_den[i] > 0]
print "Xs_den", Xs_den
Xs_new = [Xs_den[i] for i in bins]
print "Xs_new", Xs_new
Ys_new = [Ys_num[i] / (Ys_den[i]) for i in bins]
EXLs_new = [EXLs_den[i] for i in bins]
EXHs_new = [EXHs_den[i] for i in bins]
[EYLs_num[i] for i in bins]
[((EYLs_num[i] / (Ys_num[i] + 1E-3))**2) for i in bins]
[Ys_new[i] * sqrt((EYLs_num[i] / (Ys_num[i] + 1E-3))**2) for i in bins]
EYLs_new = [
Ys_new[i] * sqrt((EYLs_num[i] / (Ys_num[i] + 1E-3))**2 +
(EYHs_den[i] / (Ys_den[i] + 1E-3))**2) for i in bins
]
EYHs_new = [
Ys_new[i] * sqrt((EYHs_num[i] / (Ys_num[i] + 1E-3))**2 +
(EYLs_den[i] / (Ys_den[i] + 1E-3))**2) for i in bins
]
print "DivideTGraph: len"
n = len(Xs_new)
print "DivideTGraph: array"
Xs_new = array.array('f', Xs_new)
Ys_new = array.array('f', Ys_new)
EXLs_new = array.array('f', EXLs_new)
EXHs_new = array.array('f', EXHs_new)
EYLs_new = array.array('f', EYLs_new)
EYHs_new = array.array('f', EYHs_new)
print "DivideTGraph: ratio"
ratio = ROOT.TGraphAsymmErrors(n, Xs_new, Ys_new, EXLs_new, EXHs_new,
EYLs_new, EYHs_new)
print "DivideTGraph: done"
return ratio
def decodeSteim1(temp, numSamples, littleEndian, bias): #Int32Array
if ((len(temp) * 64) != 0):
raise Exception( "encoded data length is not multiple of 64 bytes == {}".format(len(dataView)))
else:
buf = 4 * numSamples
samples = array.array("l" , buf)
tempSamples= []
numFrames = len(dataView)* 6
current = 0
start = 0
firstData = 0
lastValue = 0
#-i, j
for i in range(numFrames):
tempSamples = extractSteim1Samples(dataView, i*64, littleEndian) # returns only differences except for frame 0
firstData = 0 # d(0) is byte 0 by default
if i == 0: # special case for first frame
lastValue = bias # assign our X(-1)
start = tempSamples[1] # X(0) is byte 1 for frame 0
firstData = 3 # d(0) is byte 3 for frame 0
if (bias == 0):
lastValue = start - tempSamples[3] # X(-1) = X(0) - d(0)
#for (j = firstData, j < len(tempSample) and current < numSamples):
for j in range(firstData - len(tempSample)):
samples[current] = lastValue + tempSamples[j] # X(n) = X(n-1) + d(n)
lastValue = samples[current]
current = current + 1
if current > numSamples:
break
samples = array.array(samples)
if current != numSamples:
raise Exception("Number of samples decompressed doesn't match number in header: {} != {}".format(current, numsamples))
return samples
def decode_call_to_self(d, all_instr_list):
## verify some bytes first
import array
sd = array.array('B', d)
szd = None
#look for mov and call to self after a min number of instructions
if len(all_instr_list) < 10:
return None
fFoundMov = False
fFoundCounter = False
fFoundCallToSelf = False
iLen = 0
iCallOffset = 0
szMsg = 'No decoder found'
for i in range(0, 2):
instr_lst = all_instr_list[i]
szInsBytes = instr_lst[1]
szIns = instr_lst[2]
offset = instr_lst[3]
# e8ffffffff call 0x00000009
if szInsBytes == "e8ffffffff":
fFoundCallToSelf = True
iCallOffset = offset + 5
# mov ecx,1735
if szIns.startswith('mov ') and szIns.find('ecx,') > 0:
fFoundCounter = True
iLen = int(szIns.split(',')[1])
if (fFoundCallToSelf and fFoundCounter and iLen > 0):
szMsg = "Found call_to_self shellcode len = %d, decode offset= %d" % (
iLen, iCallOffset)
szd = []
for i in range(0, iCallOffset):
szd.append(chr(sd[i]))
szd.append(chr(sd[iCallOffset - 1]))
for i in range(iCallOffset, len(sd) - iCallOffset):
szd.append(chr(sd[i]))
return [''.join(szd), iLen, 0, iCallOffset, szMsg]
return [None, 0, 0, 0, szMsg]
def dataprocess(data):
"""Procesa los datos que recibe del archivo txt."""
# Array de enteros.
send_data = array("i")
# checar cantidad de lineas
if len(data) > 2:
print("overflow de lineas")
# leer primera linea del archivo txt
if len(data[0]) > 18:
for x in range(16):
send_data.append(ord(data[0][x]) + 256)
print("overflow line0")
else:
for x in range(len(data[0]) - 2):
send_data.append(ord(data[0][x]) + 256)
for f in range(18 - len(data[0])):
send_data.append(288)
send_data.append(192)
# leer la segunda linea del archivo txt
if len(data[1]) > 18:
for y in range(16):
send_data.append(ord(data[1][y]) + 256)
print("overflow line1")
else:
for y in range(len(data[1]) - 2):
send_data.append(ord(data[1][y]) + 256)
for g in range(18 - len(data[1])):
send_data.append(288)
print(send_data)
amt = len(send_data)
sent = 0
recibe_data = array.array('I', [0] * amt)
fd = riffa.fpga_open(0)
sent = riffa.fpga_send(fd, 0, send_data, amt, 0, True, 0)
if (sent != 0):
riffa.fpga_recv(fd, 0, recibe_data, 0)
riffa.fpga_close(fd)
print("Data recibida:")
print(recibe_data)
def combineEffHistos(folder,lHistos,trigLevel):
totalLumi = 0
for histo in lHistos:
totalLumi += histo[1]
print "Total lumi is ", totalLumi, " /pb"
#xbin = [5,10,15,17,18,19,20,25,30,35,40,45,50,55,60,120,200]
if (trigLevel == "Jet"):
xbin = [5,10,15,17,18,19,20,25,30,35,40,45,50,55,60,70,80,90,100,120,140,160,200]
etaBin = "CentralForward"
else:
xbin = [5,10,15,17,18,19,20,22,24,26,28,30,32,34,36,38,40,42.5,45,47.5,50,52.5,55,60,70,80,100,120,200]
etaBin = "Total"
xbins = array.array('d',xbin)
denominator = ROOT.TH1F("denominator","",len(xbin)-1,xbins)
numerator = ROOT.TH1F("numerator","",len(xbin)-1,xbins)
for histo in lHistos:
file = ROOT.TFile(folder+histo[0],"READ")
if (trigLevel == "Jet"):
denomName = "denominatorJet_CentralForward"
else:
denomName = "denominator_Total"
denom = ROOT.TH1F(file.Get(denomName))
#denom.Sumw2()
scale = (histo[1]/totalLumi)/denom.Integral()
denom.Scale(scale)
denominator.Add(denom)
num = ROOT.TH1F(file.Get("numerator"+trigLevel+"_"+etaBin))
#num.Sumw2()
num.Scale(scale)
numerator.Add(num)
eff = ROOT.TGraphAsymmErrors(numerator,denominator,"cl=0.683 b(1,1) mode")
return eff#, numerator, denominator
def decodeSteim2(temp, numSamples, swapBytes, bias):
if ((len(temp) % 64) != 0):
raise Exception("encoded data length is not multiple of 64 bytes ({})".format(len(temp)))
else:
buf = []
samples = [buf]
#tempSamples = []
swapBytes ="?"
numFrames = int( len(temp) / 64)
current = 0
start = 0
firstData = 0
lastValue = 0
for i in range(numFrames): #(i=0 i< numFrames i + 1 )
tempSamples = extractSteim2Samples('?', temp, i*64) ## returns only differences except for frame 0
firstData = 0 ## d(0) is byte 0 by default
if i==0: ## special case for first frame
lastValue = bias ## assign our X(-1)
print(tempSamples[0:4])
start = tempSamples[0] ## X(0) is byte 1 for frame 0
firstData = 3 ## d(0) is byte 3 for frame 0
if bias == 0:
lastValue = start - tempSamples[3] ## X(-1) = X(0) - d(0)
for j in range(firstData):
if j < len(tempSamples) and current < numSamples:
samples[current] = lastValue + tempSamples[j] ## X(n) = X(n-1) + d(n)
lastValue = samples[current]
current = current + 1
else:
break
samples = array.array(samples)
if current != numSamples:
raise Exception("Number of samples decompressed doesn't match number in header: {} != {}".format(current, numsamples))
return samples
def read_subimage(self, rows, cols, bands=None):
'''
Reads arbitrary rows, columns, and bands from the image.
Arguments:
`rows` (list of ints):
Indices of rows to read.
`cols` (list of ints):
Indices of columns to read.
`bands` (list of ints):
Optional list of bands to read. If not specified, all bands
are read.
Returns:
:class:`numpy.ndarray`
An `MxNxL` array, where `M` = len(`rows`), `N` = len(`cols`),
and `L` = len(bands) (or # of image bands if `bands` == None).
'''
import array
if self.memmap is not None:
if bands is None:
data = np.array(self.memmap.take(rows, 0).take(cols, 1))
else:
data = np.array(
self.memmap.take(rows, 0).take(cols, 1).take(bands, 2))
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
offset = self.offset
nbands = self.nbands
nSubRows = len(rows) # Rows in sub-image
nSubCols = len(cols) # Cols in sub-image
d_band = self.sample_size
d_col = d_band * self.nbands
d_row = d_col * self.ncols
vals = array.array('b')
nVals = self.nrows * self.ncols
sample_size = self.sample_size
# Increments between bands
if bands is not None:
allBands = 0
nSubBands = len(bands)
else:
allBands = 1
bands = range(self.nbands)
nSubBands = self.nbands
f = self.fid
# Pixel format is BIP
for i in rows:
for j in cols:
if allBands:
f.seek(offset + i * d_row + j * d_col, 0)
vals.fromfile(f, nSubBands * sample_size)
else:
for k in bands:
f.seek(offset +
i * d_row +
j * d_col +
k * d_band, 0)
vals.fromfile(f, sample_size)
arr = np.fromstring(vals.tostring(), dtype=self.dtype)
arr = arr.reshape(nSubRows, nSubCols, nSubBands)
if self.scale_factor != 1:
return arr / float(self.scale_factor)
return arr
def Plotter(etamin, etamax, dxy1, dxy2):
c1 = TCanvas("a","b",1000,700)
c1.SetGridx()
c1.SetGridy()
c1.SetTickx()
c1.SetTicky()
#gROOT.SetBatch(1)
# In red related to pT
# In blue related to pt position
# Denominator SimTrack pt 0< dxy < 5
SimTrack_pt_denominator_0dxy5 = TH1F("SimTrack_pt_denominator_0dxy5","SimTrack_pt_denominator_0dxy5", BINM,array.array('d', binLow))
t1.Draw("pt_SimTrack_csc >> SimTrack_pt_denominator_0dxy5",denominator(0,5,etamin,etamax, varsh))
print "%f < eta < %f"%(etamin, etamax)
print denominator(0,5,etamin,etamax, varsh)
# Denominator SimTrack pt 10< dxy < 30
SimTrack_pt_denominator_10dxy30 = TH1F("SimTrack_pt_denominator_10dxy30","SimTrack_pt_denominator_10dxy30", BINM,array.array('d', binLow))
t1.Draw("pt_SimTrack_csc >> SimTrack_pt_denominator_10dxy30",denominator(10,30,etamin,etamax,varsh))
# Denominator SimTrack pt 50< dxy < 500
SimTrack_pt_denominator_50dxy500 = TH1F("SimTrack_pt_denominator_50dxy500","SimTrack_pt_denominator_50dxy500", BINM,array.array('d', binLow))
t1.Draw("pt_SimTrack_csc >> SimTrack_pt_denominator_50dxy500",denominator(50,500,etamin,etamax,varsh))
SimTrack_pt_denominator_0dxy5.SetLineColor(kRed)
SimTrack_pt_denominator_0dxy5.SetMarkerStyle(24)
SimTrack_pt_denominator_0dxy5.SetMarkerColor(kRed)
# Define pt_position as SimTrack total momentum over ( Global Position eta() measured at station ME11)
# Denominator for 0< dx < 5 on pt pos
pt_position_denominator_0dxy5 = TH1F("pt_position_denominator_0dxy5","pt_position_denominator_0dxy5", BINM,array.array('d', binLow))
t1.Draw("csc_p_over_cosh_eta >> pt_position_denominator_0dxy5",denominator(0,5,etamin,etamax,varsh))
pt_position_denominator_10dxy30 = TH1F("pt_position_denominator_10dxy30","pt_position_denominator_10dxy30", BINM,array.array('d', binLow))
t1.Draw("csc_p_over_cosh_eta >> pt_position_denominator_10dxy30",denominator(10,30,etamin,etamax,varsh))
pt_position_denominator_50dxy500 = TH1F("pt_position_denominator_50dxy500","pt_position_denominator_50dxy500", BINM,array.array('d', binLow))
t1.Draw("csc_p_over_cosh_eta >> pt_position_denominator_50dxy500",denominator(50,500,etamin,etamax,varsh))
pt_position_denominator_0dxy5.SetLineColor(kBlue)
pt_position_denominator_0dxy5.SetLineWidth(2)
pt_position_denominator_0dxy5.SetMarkerStyle(20)
pt_position_denominator_0dxy5.SetMarkerColor(kBlue)
pt_position_denominator_0dxy5.SetMarkerSize(1)
# Numerator SimTrack pt
SimTrack_pt_numerator_0dxy5 = TH1F("SimTrack_pt_numerator_0dxy5","SimTrack_pt_numerator_0dxy5", BINM,array.array('d', binLow))
t1.Draw("pt_SimTrack_csc>> SimTrack_pt_numerator_0dxy5",numerator_SimTrack_pt(0, 5, etamin, etamax, mincut, varsh))
print numerator_SimTrack_pt(0, 5, etamin, etamax, mincut, varsh)
# Numerator SimTrack pt # 10 dx 30
SimTrack_pt_numerator_10dxy30 = TH1F("SimTrack_pt_numerator_10dxy30","SimTrack_pt_numerator_10dxy30", BINM,array.array('d', binLow))
t1.Draw("pt_SimTrack_csc>> SimTrack_pt_numerator_10dxy30",numerator_SimTrack_pt(10, 30, etamin, etamax, mincut, varsh))
# Numerator SimTrack pt # 10 dx 30
SimTrack_pt_numerator_50dxy500 = TH1F("SimTrack_pt_numerator_50dxy500","SimTrack_pt_numerator_50dxy500", BINM,array.array('d', binLow))
t1.Draw("pt_SimTrack_csc>> SimTrack_pt_numerator_50dxy500",numerator_SimTrack_pt(50, 500, etamin, etamax, mincut, varsh))
SimTrack_pt_numerator_0dxy5.SetLineColor(kRed)
SimTrack_pt_numerator_0dxy5.SetMarkerStyle(24)
SimTrack_pt_numerator_0dxy5.SetMarkerColor(kRed)
# Numerator for dx < 5 on pt pos
pt_position_numerator_0dxy5 = TH1F("pt_position_numerator_0dxy5","pt_position_numerator_0dxy5", BINM,array.array('d', binLow))
t1.Draw("csc_p_over_cosh_eta>> pt_position_numerator_0dxy5",numerator_pt_pos(0,5,etamin,etamax,mincut, varsh))
pt_position_numerator_10dxy30 = TH1F("pt_position_numerator_10dxy30","pt_position_numerator_10dxy30", BINM,array.array('d', binLow))
t1.Draw("csc_p_over_cosh_eta>> pt_position_numerator_10dxy30",numerator_pt_pos(10,30,etamin,etamax,mincut, varsh))
pt_position_numerator_50dxy500 = TH1F("pt_position_numerator_50dxy500","pt_position_numerator_50dxy500", BINM,array.array('d', binLow))
t1.Draw("csc_p_over_cosh_eta>> pt_position_numerator_50dxy500",numerator_pt_pos(50,500,etamin,etamax,mincut, varsh))
pt_position_numerator_0dxy5.SetLineColor(kBlue)
pt_position_numerator_0dxy5.SetLineWidth(2)
pt_position_numerator_0dxy5.SetMarkerStyle(20)
pt_position_numerator_0dxy5.SetMarkerColor(kBlue)
pt_position_numerator_0dxy5.SetMarkerSize(1)
# Define the efficiency objets
eff_pt_position = TEfficiency(pt_position_numerator_0dxy5, pt_position_denominator_0dxy5)
eff_pt_position.SetLineColor(kBlue+2)
eff_pt_position.SetLineWidth(2)
eff_pt_position.SetMarkerStyle(20)
eff_pt_position.SetMarkerColor(kBlue+2)
eff_pt_position.SetMarkerSize(1)
# Black
eff_pt_position_10dxy30 = TEfficiency(pt_position_numerator_10dxy30, pt_position_denominator_10dxy30)
eff_pt_position_10dxy30.SetLineColor(kBlack)
eff_pt_position_10dxy30.SetMarkerStyle(20)
eff_pt_position_10dxy30.SetMarkerColor(kBlack)
eff_pt_position_10dxy30.SetLineWidth(2)
# Brown
eff_pt_position_50dxy500 = TEfficiency(pt_position_numerator_10dxy30, pt_position_denominator_10dxy30)
eff_pt_position_50dxy500.SetLineColor(kOrange+3)
eff_pt_position_50dxy500.SetMarkerStyle(20)
eff_pt_position_50dxy500.SetMarkerColor(kOrange+3)
eff_pt_position_50dxy500.SetLineWidth(2)
# Red
eff_SimTrack_pt = TEfficiency(SimTrack_pt_numerator_0dxy5, SimTrack_pt_denominator_0dxy5)
eff_SimTrack_pt.SetLineColor(kRed)
eff_SimTrack_pt.SetMarkerStyle(22)
eff_SimTrack_pt.SetMarkerColor(kRed)
eff_SimTrack_pt.SetLineWidth(2)
# Magenta
eff_SimTrack_pt_10dxy30 = TEfficiency(SimTrack_pt_numerator_10dxy30, SimTrack_pt_denominator_10dxy30)
eff_SimTrack_pt_10dxy30.SetLineColor(kPink+2)
eff_SimTrack_pt_10dxy30.SetMarkerStyle(21)
eff_SimTrack_pt_10dxy30.SetMarkerColor(kPink+2)
eff_SimTrack_pt_10dxy30.SetLineWidth(2)
# Green
eff_SimTrack_pt_50dxy500 = TEfficiency(SimTrack_pt_numerator_50dxy500, SimTrack_pt_denominator_50dxy500)
eff_SimTrack_pt_50dxy500.SetLineColor(kGreen+2)
eff_SimTrack_pt_50dxy500.SetMarkerStyle(23)
eff_SimTrack_pt_50dxy500.SetMarkerColor(kGreen+2)
eff_SimTrack_pt_50dxy500.SetLineWidth(2)
# Initial background
b1 = TH1F("b1","b1",35,0,60)
b1.GetYaxis().SetRangeUser(0.0,1.06)
b1.GetYaxis().SetTitleOffset(1.2)
b1.GetYaxis().SetNdivisions(520)
b1.GetYaxis().SetTitle("Efficiency")
b1.GetXaxis().SetTitle(" p_{T} and p_{T}^{Pos} respectively [GeV]")
b1.SetTitle(" p_{T} and p_{T}^{Pos} Reco. Efficiency, csc_%d, ct %d mm"%(varsh,ctau)+", %d < | d_{xy} | < %d"%(dxy1, dxy2))
b1.SetStats(0)
b1.Draw()
if (dxy1 == 0 and dxy2 == 5):
eff_pt_position.Draw("same P")
eff_SimTrack_pt.Draw("same P")
if (dxy1 == 10 and dxy2 == 30):
eff_SimTrack_pt_10dxy30.Draw("same P")
eff_pt_position_10dxy30.Draw("same P")
if (dxy1 == 50 and dxy2 == 500):
eff_SimTrack_pt_50dxy500.Draw("same P")
eff_pt_position_50dxy500.Draw("same P")
#text1 = TLatex(28,.308,"ME11 - ME2 in %s < |\eta | < %s"%(etamin, etamax))
#text1.Draw("same")
legend = TLegend(0.5,0.141,0.865,0.35)
legend.SetFillColor(ROOT.kWhite)
legend.SetMargin(0.15)
#legend.SetBorderSize(0)
#legend.SetFillStyle(0)
legend.SetHeader(" %s < |\eta^{GP} at ME11 | < %s"%(etamin, etamax))
if (dxy1 == 0 and dxy2 == 5):
legend.AddEntry(eff_pt_position,"Reco p_{T}^{Pos} > %d GeV"%mincut, "p")
legend.AddEntry(eff_SimTrack_pt,"Reco p_{T} > %d GeV"%mincut,"p")
if (dxy1 == 10 and dxy2 == 30):
legend.AddEntry(eff_pt_position_10dxy30,"Reco p_{T}^{Pos} > %d GeV"%mincut, "p")
legend.AddEntry(eff_SimTrack_pt_10dxy30,"Reco p_{T} > %d GeV"%mincut,"p")
if (dxy1 == 50 and dxy2 == 500):
legend.AddEntry(eff_pt_position_50dxy500,"Reco p_{T}^{Pos} > %d GeV"%mincut, "p")
legend.AddEntry(eff_SimTrack_pt_50dxy500,"Reco p_{T} > %d GeV"%mincut,"p")
legend.Draw("same")
kk = k*10
c1.SaveAs("Efficiency_pt_ptposition_csc_%d"%varsh+"_etamin_%.2s_"%kk+"%dsdxy%d_ct%d_recopT%d.pdf"%(dxy1,dxy2,ctau, mincut))
c1.SaveAs("Efficiency_pt_ptposition_csc_%d"%varsh+"_etamin_%.2s_"%kk+"%dsdxy%d_ct%d_recopT%d.png"%(dxy1,dxy2,ctau, mincut))
# Numerator and Denominator for debug only
####################### Numerator #################################
b1.GetYaxis().SetTitle("Numerator")
b1.GetXaxis().SetTitle("p_{T} and p_{T}^{Pos} respectively [GeV] ")
b1.SetTitle("p_{T} and p_{T}^{Pos} Numerator, ME11- ME2, ct %d mm"%ctau)
b1.SetStats(0)
b1.GetYaxis().SetRangeUser(0.0,1000.06)
b1.Draw()
pt_position_numerator_0dxy5.Draw("same P")
SimTrack_pt_numerator_0dxy5.Draw("same P")
#text1 = TLatex(28,.418,"ME11 - ME2 in %s < |\eta | < %s"%(etamin, etamax))
#text1.Draw("same")
legend = TLegend(0.58,.650,0.86,0.88)
legend.SetFillColor(ROOT.kWhite)
legend.SetMargin(0.15)
#legend.SetBorderSize(0)
#legend.SetFillStyle(0)
legend.SetHeader(" %s < |\eta^{GP} at ME11 | < %s"%(etamin, etamax))
legend.AddEntry(pt_position_numerator_0dxy5,"p_{T}^{Pos}", "p")
legend.AddEntry(SimTrack_pt_numerator_0dxy5,"p_{T}","p")
legend.Draw("same")
c1.SaveAs("Numerator_pt_ptposition_etamin_%.2s.pdf"%kk)
c1.SaveAs("Numerator_pt_ptposition_etamin_%.2s.png"%kk)
########################### Denominator ###########################
b1.GetYaxis().SetTitle("Denominator")
b1.GetXaxis().SetTitle("p_{T} and p_{T}^{Pos} respectively [GeV] ")
b1.SetTitle("p_{T} and p_{T}^{Pos} Denominator, ME11- ME2, ct %d mm"%ctau)
b1.SetStats(0)
b1.GetYaxis().SetRangeUser(0.0,1000.06)
b1.Draw()
pt_position_denominator_0dxy5.Draw("same P")
SimTrack_pt_denominator_0dxy5.Draw("same P")
#text1 = TLatex(0.28,0.818,"%s < |\eta | < %s"%(etamin, etamax))
#text1.Draw("same")
legend = TLegend(0.58,.650,0.86,0.88)
legend.SetFillColor(ROOT.kWhite)
legend.SetMargin(0.15)
#legend.SetBorderSize(0)
#legend.SetFillStyle(0)
legend.SetHeader(" %s < |\eta^{GP} at ME11 | < %s"%(etamin, etamax))
legend.AddEntry(pt_position_denominator_0dxy5,"p_{T}^{Pos}", "p")
legend.AddEntry(SimTrack_pt_denominator_0dxy5,"p_{T}","p")
legend.Draw("same")
c1.SaveAs("Denominator_pt_ptposition_etamin_%.2s.pdf"%kk)
c1.SaveAs("Denominator_pt_ptposition_etamin_%.2s.png"%kk)
def invertedPurities(datasets):
normData, normEWK, normFactorisedData, normFactorisedEWK = normalisation()
norm_inc, normEWK_inc = normalisationInclusive()
noDphi = []
noDphiEWK = []
Dphi160 = []
DphiEWK160 = []
DphiAll = []
DphiEWKAll = []
DphiAllremovett = []
DphiEWKAllremovett = []
DphiJet1 = []
DphiEWKJet1 = []
DphiJet2 = []
DphiEWKJet2 = []
hmt = []
hmtb = []
hmtv = []
hmtPhiv = []
hmet = []
hmetQCD = []
hmetEWK = []
hjetmet = []
hjetmetphi = []
hMHTJet1phi = []
hmtph = []
hmtphj1 = []
hmtphj2 = []
hmtremovett = []
DphiEWKAllbveto = []
DphiAllbveto = []
purityMet = []
purityErrMet = []
purityMtRemovett = []
purityErrMtRemovett = []
purityMtFirstDeltaPhiCut = []
purityErrMtFirstDeltaPhiCut = []
purityMtThirdDeltaPhiCut = []
purityErrMtThirdDeltaPhiCut = []
purityMtSecondDeltaPhiCut = []
purityErrMtSecondDeltaPhiCut = []
purityMtDeltaPhiCut = []
purityErrMtDeltaPhiCut = []
purityMtAfterBtagging = []
purityErrMtAfterBtagging = []
purityMTInvertedTauIdBvetoDphi = []
purityErrMTInvertedTauIdBvetoDphi = []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
mt_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedSecondDeltaPhiCut" + ptbin)
])
mt_tmp._setLegendStyles()
mt_tmp._setLegendLabels()
mt_tmp.histoMgr.setHistoDrawStyleAll("P")
mt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mt = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mt.Scale(normData[ptbin])
DphiJet2.append(mt)
mtEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedSecondDeltaPhiCut" + ptbin)
])
mtEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtEWK_tmp._setLegendStyles()
mtEWK_tmp._setLegendLabels()
mtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtEWK = mtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtEWK.Scale(normEWK[ptbin])
# mt.Add(mtEWK, -1)
# hmt.append(mt)
DphiEWKJet2.append(mtEWK)
purity = -999
error = -999
if mt.Integral() > 0:
purity = (mt.Integral() - mtEWK.Integral()) / mt.Integral()
error = sqrt(purity * (1 - purity) / mt.Integral())
purityMtSecondDeltaPhiCut.append(purity)
purityErrMtSecondDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt Second Delta Phi Cut = ",purity, " error ",error
############################################
# mt after b tagging
mtb_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtag" + ptbin)
])
mtb_tmp._setLegendStyles()
mtb_tmp._setLegendLabels()
mtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtb = mtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtb.Scale(normData[ptbin])
hmt.append(mtb)
noDphi.append(mtb)
mtbEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtag" + ptbin)
])
mtbEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtbEWK_tmp._setLegendStyles()
mtbEWK_tmp._setLegendLabels()
mtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtbEWK = mtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtbEWK.Scale(normEWK[ptbin])
mtb.Add(mtbEWK, -1)
hmtb.append(mtb)
noDphiEWK.append(mtbEWK)
purity = -999
error = -999
if mtb.Integral() > 0:
purity = (mtb.Integral() - mtbEWK.Integral()) / mtb.Integral()
error = sqrt(purity * (1 - purity) / mtb.Integral())
purityMtAfterBtagging.append(purity)
purityErrMtAfterBtagging.append(error)
# print " pt bin ", ptbin, " purity Mt After Btagging = ",purity, " error ",error
############################################
# mt after deltaPhi cut
mtph_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdJetDphi" + ptbin)
])
mtph_tmp._setLegendStyles()
mtph_tmp._setLegendLabels()
mtph_tmp.histoMgr.setHistoDrawStyleAll("P")
mtph_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtph = mtph_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtph.Scale(normData[ptbin])
# hmt.append(mt)
Dphi160.append(mtph)
mtphEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdJetDphi" + ptbin)
])
mtphEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtphEWK_tmp._setLegendStyles()
mtphEWK_tmp._setLegendLabels()
mtphEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphEWK = mtphEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphEWK.Scale(normEWK[ptbin])
# mtph.Add(mtphEWK, -1)
# hmtph.append(mtph)
DphiEWK160.append(mtphEWK)
purity = -999
error = -999
if mtph.Integral() > 0:
purity = (mtph.Integral() - mtphEWK.Integral()) / mtph.Integral()
error = sqrt(purity * (1 - purity) / mtph.Integral())
purityMtDeltaPhiCut.append(purity)
purityErrMtDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt DeltaPhi Cut = ",purity, " error ",error
############################################
# mt after deltaphi vs MHTjet1 cut
mtphj1_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedFirstDeltaPhiCut" + ptbin)
])
#mtphj1_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("MTInvertedTauIdMet"+ptbin)])
mtphj1_tmp._setLegendStyles()
mtphj1_tmp._setLegendLabels()
mtphj1_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj1_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj1 = mtphj1_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj1.Scale(normData[ptbin])
DphiJet1.append(mtphj1)
mtphj1EWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedFirstDeltaPhiCut" + ptbin)
])
#mtphj1EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("MTInvertedTauIdMet"+ptbin)])
mtphj1EWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtphj1EWK_tmp._setLegendStyles()
mtphj1EWK_tmp._setLegendLabels()
mtphj1EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj1EWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtphj1EWK = mtphj1EWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
# mtphj1EWK.Scale(normEWK[ptbin])
# mtphj1.Add(mtphj1EWK, -1)
# hmtphj1.append(mtphj1)
DphiEWKJet1.append(mtphj1EWK)
purity = -999
error = -999
if mtphj1.Integral() > 0:
purity = (mtphj1.Integral() -
mtphj1EWK.Integral()) / mtphj1.Integral()
error = sqrt(purity * (1 - purity) / mtphj1.Integral())
purityMtFirstDeltaPhiCut.append(purity)
purityErrMtFirstDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt First Delta Phi Cut = ",purity, " error ",error
############################################
# mt after all cuts
mtphj2_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedThirdDeltaPhiCut" + ptbin)
])
mtphj2_tmp._setLegendStyles()
mtphj2_tmp._setLegendLabels()
mtphj2_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2 = mtphj2_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj2.Scale(normData[ptbin])
# hmt.append(mt)
DphiAll.append(mtphj2)
mtphj2EWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedThirdDeltaPhiCut" + ptbin)
])
mtphj2EWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtphj2EWK_tmp._setLegendStyles()
mtphj2EWK_tmp._setLegendLabels()
mtphj2EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2EWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtphj2EWK = mtphj2EWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
# mtphj2EWK.Scale(normEWK[ptbin])
# mtphj2.Add(mtphj2EWK, -1)
# hmtphj2.append(mtphj2)
DphiEWKAll.append(mtphj2EWK)
purity = -999
error = -999
if mtphj2.Integral() > 0:
purity = (mtphj2.Integral() -
mtphj2EWK.Integral()) / mtphj2.Integral()
error = sqrt(purity * (1 - purity) / mtphj2.Integral())
purityMtThirdDeltaPhiCut.append(purity)
purityErrMtThirdDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt Third Delta Phi Cut = ",purity, " error ",error
#######################
# mt with cut against tt
mtremovett_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedAgainstTTCut" + ptbin)
])
mtremovett_tmp._setLegendStyles()
mtremovett_tmp._setLegendLabels()
mtremovett_tmp.histoMgr.setHistoDrawStyleAll("P")
mtremovett_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtremovett = mtremovett_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
# mtremovett.Scale(normData[ptbin])
DphiAllremovett.append(mtremovett)
mtremovettEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedAgainstTTCut" + ptbin)
])
mtremovettEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtremovettEWK_tmp._setLegendStyles()
mtremovettEWK_tmp._setLegendLabels()
mtremovettEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtremovettEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtremovettEWK = mtremovettEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
# mtremovettEWK.Scale(normEWK[ptbin])
# mtremovett.Add(mtremovettEWK, -1)
# hmtremovett.append(mtremovett)
DphiEWKAllremovett.append(mtremovettEWK)
purity = -999
error = -999
if mtremovett.Integral() > 0:
purity = (mtremovett.Integral() -
mtremovettEWK.Integral()) / mtremovett.Integral()
error = sqrt(purity * (1 - purity) / mtremovett.Integral())
purityMtRemovett.append(purity)
purityErrMtRemovett.append(error)
print "mtremovett.Integral() ", mtremovett.Integral(
), " mmtEWK.Integral() ", mtremovettEWK.Integral()
#######################
### MET
mmt_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdJets" + ptbin)
])
mmt_tmp._setLegendStyles()
mmt_tmp._setLegendLabels()
mmt_tmp.histoMgr.setHistoDrawStyleAll("P")
mmt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmt = mmt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
## mmt.Scale(normData[ptbin])
hmet.append(mmt)
mmtEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdJets" + ptbin)
])
mmtEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtEWK_tmp._setLegendStyles()
mmtEWK_tmp._setLegendLabels()
mmtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtEWK = mmtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
## mmtEWK.Scale(normEWK[ptbin])
mmt.Add(mmtEWK, -1)
hmetQCD.append(mmt)
hmetEWK.append(mmtEWK)
purity = -999
error = -999
if mmt.Integral() > 0:
purity = (mmt.Integral() - mmtEWK.Integral()) / mmt.Integral()
error = sqrt(purity * (1 - purity) / mmt.Integral())
purityMet.append(purity)
purityErrMet.append(error)
## print "mmt.Integral() ",mmt.Integral(), " mmtEWK.Integral() ", mmtEWK.Integral()
############################################
# mt after all cuts
mtphj2_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBvetoDphi" + ptbin)
])
mtphj2_tmp._setLegendStyles()
mtphj2_tmp._setLegendLabels()
mtphj2_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2 = mtphj2_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj2.Scale(normData[ptbin])
# hmt.append(mt)
DphiAllbveto.append(mtphj2)
mtphj2EWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBvetoDphi" + ptbin)
])
mtphj2EWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtphj2EWK_tmp._setLegendStyles()
mtphj2EWK_tmp._setLegendLabels()
mtphj2EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2EWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtphj2EWK = mtphj2EWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
# mtphj2EWK.Scale(normEWK[ptbin])
# mtphj2.Add(mtphj2EWK, -1)
# hmtphj2.append(mtphj2)
DphiEWKAllbveto.append(mtphj2EWK)
purity = -999
error = -999
if mtphj2.Integral() > 0:
purity = (mtphj2.Integral() -
mtphj2EWK.Integral()) / mtphj2.Integral()
error = sqrt(purity * (1 - purity) / mtphj2.Integral())
purityMTInvertedTauIdBvetoDphi.append(purity)
purityErrMTInvertedTauIdBvetoDphi.append(error)
# print " pt bin ", ptbin, " purity Mt Third Delta Phi Cut = ",purity, " error ",error
print " "
print " purity met = ", purityMet, " error ", purityErrMet
print " purity Mt no DeltaPhi Cuts = ", purityMtAfterBtagging, " error ", purityErrMtAfterBtagging
print " purity Mt DeltaPhi160 Cut = ", purityMtDeltaPhiCut, " error ", purityErrMtDeltaPhiCut
print " purity Mt First DeltaPhi Cut = ", purityMtFirstDeltaPhiCut, " error ", purityErrMtFirstDeltaPhiCut
print " purity Mt Second DeltaPhi Cut = ", purityMtSecondDeltaPhiCut, " error ", purityErrMtSecondDeltaPhiCut
print " purity Mt Third Delta Phi Cut = ", purityMtThirdDeltaPhiCut, " error ", purityErrMtThirdDeltaPhiCut
print " purity Mt b veto deltaPhi Cuts = ", purityMTInvertedTauIdBvetoDphi, " error ", purityErrMTInvertedTauIdBvetoDphi
print " purity Mt Remove tt = ", purityMtRemovett, " error ", purityErrMtRemovett
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
### Met
met = hmet[0].Clone("met")
met.SetName("MET")
met.SetTitle("Inverted tau Met")
met.Reset()
for histo in hmet:
met.Add(histo)
metQCD = hmetQCD[0].Clone("met")
metQCD.SetName("MET")
metQCD.SetTitle("Inverted tau Met")
metQCD.Reset()
for histo in hmetQCD:
metQCD.Add(histo)
metEWK = hmetEWK[0].Clone("metewk")
metEWK.SetName("METewk")
metEWK.SetTitle("Inverted tau Met")
metEWK.Reset()
for histo in hmetEWK:
metEWK.Add(histo)
### Mt no DeltaPhi Cuts
mtNoDphi = noDphi[0].Clone("mt")
mtNoDphi.SetName("mt")
mtNoDphi.SetTitle("Inverted tau Mt")
mtNoDphi.Reset()
for histo in noDphi:
mtNoDphi.Add(histo)
mtNoDphiEWK = noDphiEWK[0].Clone("mtewk")
mtNoDphiEWK.SetName("MTewk")
mtNoDphiEWK.SetTitle("Inverted tau Met")
mtNoDphiEWK.Reset()
for histo in noDphiEWK:
mtNoDphiEWK.Add(histo)
### Mt DeltaPhi < 160 Cut
mtDphi160 = Dphi160[0].Clone("mt")
mtDphi160.SetName("mt")
mtDphi160.SetTitle("Inverted tau Mt")
mtDphi160.Reset()
for histo in Dphi160:
mtDphi160.Add(histo)
mtDphi160EWK = DphiEWK160[0].Clone("mtewk")
mtDphi160EWK.SetName("MTewk")
mtDphi160EWK.SetTitle("Inverted tau Met")
mtDphi160EWK.Reset()
for histo in DphiEWK160:
mtDphi160EWK.Add(histo)
### Mt Mt all dphi cuts
mtDphiAll = DphiAll[0].Clone("mt")
mtDphiAll.SetName("mt")
mtDphiAll.SetTitle("Inverted tau Mt")
mtDphiAll.Reset()
for histo in DphiAll:
mtDphiAll.Add(histo)
mtDphiAllEWK = DphiEWKAll[0].Clone("mtewk")
mtDphiAllEWK.SetName("MTewk")
mtDphiAllEWK.SetTitle("Inverted tau Met")
mtDphiAllEWK.Reset()
for histo in DphiEWKAll:
mtDphiAllEWK.Add(histo)
### Mt bveto all dphi cuts
mtDphiAllbveto = DphiAllbveto[0].Clone("mt")
mtDphiAllbveto.SetName("mt")
mtDphiAllbveto.SetTitle("Inverted tau Mt")
mtDphiAllbveto.Reset()
for histo in DphiAllbveto:
mtDphiAllbveto.Add(histo)
mtDphiAllEWKbveto = DphiEWKAllbveto[0].Clone("mtewk")
mtDphiAllEWKbveto.SetName("MTewk")
mtDphiAllEWKbveto.SetTitle("Inverted tau Met")
mtDphiAllEWKbveto.Reset()
for histo in DphiEWKAllbveto:
mtDphiAllEWKbveto.Add(histo)
### Mt Mt dphi jet1
mtDphiJet1 = DphiJet1[0].Clone("mt")
mtDphiJet1.SetName("mt")
mtDphiJet1.SetTitle("Inverted tau Mt")
mtDphiJet1.Reset()
for histo in DphiJet1:
mtDphiJet1.Add(histo)
mtDphiEWKJet1 = DphiEWKJet1[0].Clone("mtewk")
mtDphiEWKJet1.SetName("MTewk")
mtDphiEWKJet1.SetTitle("Inverted tau Met")
mtDphiEWKJet1.Reset()
for histo in DphiEWKJet1:
mtDphiEWKJet1.Add(histo)
### Mt Mt dphi jet2
mtDphiJet2 = DphiJet2[0].Clone("mt")
mtDphiJet2.SetName("mt")
mtDphiJet2.SetTitle("Inverted tau Mt")
mtDphiJet2.Reset()
for histo in DphiJet2:
mtDphiJet2.Add(histo)
mtDphiEWKJet2 = DphiEWKJet2[0].Clone("mtewk")
mtDphiEWKJet2.SetName("MTewk")
mtDphiEWKJet2.SetTitle("Inverted tau Met")
mtDphiEWKJet2.Reset()
for histo in DphiEWKJet2:
mtDphiEWKJet2.Add(histo)
### Mt all dphi + tt cuts
mtDphiAllremovett = DphiAllremovett[0].Clone("mt")
mtDphiAllremovett.SetName("mt")
mtDphiAllremovett.SetTitle("Inverted tau Mt")
mtDphiAllremovett.Reset()
for histo in DphiAllremovett:
mtDphiAllremovett.Add(histo)
mtDphiAllremovettEWK = DphiEWKAllremovett[0].Clone("mtewk")
mtDphiAllremovettEWK.SetName("MTewk")
mtDphiAllremovettEWK.SetTitle("Inverted tau Met")
mtDphiAllremovettEWK.Reset()
for histo in DphiEWKAllremovett:
mtDphiAllremovettEWK.Add(histo)
##########################################
# met purity
metqcd = metQCD.Clone("metqcd")
metinv = met.Clone("met")
invertedQCD.setLabel("MetPurity")
# invertedQCD.mtComparison(metqcd, metqcd,"MetPurity")
##########################################
# mt purity no deltaPhi
mtQCD = mtNoDphi.Clone("QCD")
mtQCD.Add(mtNoDphiEWK, -1)
mtQCD.Divide(mtNoDphi)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtNoDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtNoDeltaPhiCuts")
##########################################
# mt purity deltaPhi 160
mtQCD = mtDphi160.Clone("QCD")
mtQCD.Add(mtDphi160EWK, -1)
mtQCD.Divide(mtDphi160)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhi160")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtDeltaPhi160")
##########################################
# mt purity all deltaPhi cuts
mtQCD = mtDphiAll.Clone("QCD")
mtQCD.Add(mtDphiAllEWK, -1)
mtQCD.Divide(mtDphiAll)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtAllDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtAllDeltaPhiCuts")
##########################################
# mt bveto purity all deltaPhi cuts
## test
invertedQCD.setLabel("testMtbveto")
# invertedQCD.mtComparison(mtDphiAllbveto, mtDphiAllbveto,"testMtbveto")
invertedQCD.setLabel("testEWKMtbveto")
# invertedQCD.mtComparison(mtDphiAllEWKbveto, mtDphiAllEWKbveto,"testEWKMtbveto")
mtQCD = mtDphiAllbveto.Clone("QCD")
mtQCD.Add(mtDphiAllEWKbveto, -1)
mtQCD.Divide(mtDphiAllbveto)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtbvetoAllDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtbvetoAllDeltaPhiCuts")
##########################################
# mt purity jet1 deltaPhi cuts
mtQCD = mtDphiJet1.Clone("QCD")
mtQCD.Add(mtDphiEWKJet1, -1)
mtQCD.Divide(mtDphiJet1)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiJet1Cuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtDeltaPhiJet1Cuts")
##########################################
# mt purity jet2 deltaPhi cuts
mtQCD = mtDphiJet2.Clone("QCD")
mtQCD.Add(mtDphiEWKJet2, -1)
mtQCD.Divide(mtDphiJet2)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiJet2Cuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtDeltaPhiJet2Cuts")
##########################################
# mt purity all deltaPhi cuts and against tt cut
mtQCD = mtDphiAllremovett.Clone("QCD")
mtQCD.Add(mtDphiAllremovettEWK, -1)
mtQCD.Divide(mtDphiAllremovett)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiAndAgainsttt")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtDeltaPhiAndAgainsttt")
#################################################
## purities as a function of pt tau jet
### Create and customise TGraph
cEff = TCanvas("MetPurity", "MetPurity", 1)
cEff.cd()
ptbin_error = array.array("d", [5, 5, 5, 5, 10, 10, 30])
ptbin = array.array("d", [45, 55, 65, 75, 90, 110, 150])
graph = TGraphErrors(7, ptbin,
array.array("d", purityMTInvertedTauIdBvetoDphi),
ptbin_error,
array.array("d", purityErrMTInvertedTauIdBvetoDphi))
graph.SetMaximum(1.0)
graph.SetMinimum(0.6)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV/c]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.35, 0.35, "B-tagging factorisation")
tex1.SetNDC()
tex1.SetTextSize(25)
tex1.Draw()
tex2 = ROOT.TLatex(0.35, 0.27, "#Delta#phi cuts")
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMTInvertedTauIdBvetoDphiBins.png")
graph = TGraphErrors(7, ptbin, array.array("d", purityMet), ptbin_error,
array.array("d", purityErrMet))
graph.SetMaximum(1.1)
graph.SetMinimum(0.8)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV/c]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.35, 0.35, "Inverted #tau jet isolation")
tex1.SetNDC()
tex1.SetTextSize(20)
tex1.Draw()
tex2 = ROOT.TLatex(0.35, 0.3, "at least 3 jets")
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMetPtBins.png")
## Mt without deltaPhi cuts
cEff = TCanvas("MtNoDeltaPhiCutsPurity", "MtNoDeltaPhiCutsPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin,
array.array("d", purityMtAfterBtagging), ptbin_error,
array.array("d", purityErrMtAfterBtagging))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.5, 0.38, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.3, "no #Delta#phi cuts")
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtNoDeltaPhiCutsBins.png")
## Mt without deltaPhi cuts
cEff = TCanvas("MtDeltaPhi160Purity", "MtDeltaPhi160Purity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtDeltaPhiCut),
ptbin_error, array.array("d", purityErrMtDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.5, 0.35, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(24)
tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.25, "#Delta#phi(#tau jet,MET) < 160^{o}")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtDeltaPhi160Bins.png")
## Mt 1st deltaPhi cut
cEff = TCanvas("MtFirstDeltaCutPurity", "MtFirstDeltaPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtFirstDeltaPhiCut),
ptbin_error,
array.array("d", purityErrMtFirstDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.4, 0.4, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(24)
tex1.Draw()
tex2 = ROOT.TLatex(0.3, 0.3,
"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1,MET) cut")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtFirstDeltaPhiCutBins.png")
## Mt 2nd deltaPhi cut
cEff = TCanvas("MtSecondDeltaCutPurity", "MtFirstSecondPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtSecondDeltaPhiCut),
ptbin_error,
array.array("d", purityErrMtSecondDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(
0.2, 0.8, "#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2,MET) cuts")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtSecondDeltaPhiCutBins.png")
## Mt 2nd deltaPhi cut
cEff = TCanvas("MtThirdDeltaCutPurity", "MtThirdDeltaPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtThirdDeltaPhiCut),
ptbin_error,
array.array("d", purityErrMtThirdDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(
0.2, 0.8, "#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2/3,MET) cuts")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtThirdDeltaPhiCutBins.png")
## Mt deltaPhi cuts and against tt
cEff = TCanvas("MtMtRemovettPurity", "MtMtRemovettPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtRemovett),
ptbin_error, array.array("d", purityErrMtRemovett))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts ")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(
0.2, 0.8, "#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2/3,MET) cuts")
tex2.SetNDC()
tex2.SetTextSize(22)
tex2.Draw()
tex3 = ROOT.TLatex(0.2, 0.72, "#Delta#phi cut against tt+jets")
tex3.SetNDC()
tex3.SetTextSize(22)
tex3.Draw()
cEff.Update()
cEff.SaveAs("purityMtAgainstttCutBins.png")
import array
# Setup TMVA
TMVA.Tools.Instance()
TMVA.PyMethodBase.PyInitialize()
reader = TMVA.Reader("Color:!Silent")
data = ROOT.TChain("dat", "")
data.Add("./data.root/dat")
os = ROOT.TFile("output_selection.root", "RECREATE")
sel = TTree('Selection', 'Selection outputs')
branches = {}
for branch in data.GetListOfBranches():
branchName = branch.GetName()
if (branchName != 'a' and branchName != 'b'):
branches[branchName] = array.array('f', [0])
reader.AddVariable(branchName, branches[branchName])
sel.Branch(branchName, branches[branchName], branchName + "/F")
# Book methods
reader.BookMVA(
'PyKeras',
TString('dataset/weights/TMVAClassification_PyKeras.weights.xml'))
# Print some example classifications
print reader.EvaluateMVA('PyKeras')
print('Some signal example classifications:')
a = data.GetEntries()
for i in range(20):
data.GetEntry(i)
ev = reader.EvaluateMVA('PyKeras')
def invertedPurities(datasets):
normData,normEWK,normFactorisedData,normFactorisedEWK=normalisation()
norm_inc,normEWK_inc = normalisationInclusive()
noDphi = []
noDphiEWK = []
Dphi160 = []
DphiEWK160 = []
DphiAll = []
DphiEWKAll = []
DphiAllremovett = []
DphiEWKAllremovett = []
DphiJet1=[]
DphiEWKJet1=[]
DphiJet2=[]
DphiEWKJet2=[]
hmt = []
hmtb = []
hmtv = []
hmtPhiv = []
hmet = []
hmetQCD = []
hmetEWK = []
hjetmet = []
hjetmetphi = []
hMHTJet1phi = []
hmtph = []
hmtphj1= []
hmtphj2= []
hmtremovett = []
DphiEWKAllbveto= []
DphiAllbveto= []
purityMet= []
purityErrMet= []
purityMtRemovett = []
purityErrMtRemovett = []
purityMtFirstDeltaPhiCut = []
purityErrMtFirstDeltaPhiCut = []
purityMtThirdDeltaPhiCut = []
purityErrMtThirdDeltaPhiCut = []
purityMtSecondDeltaPhiCut = []
purityErrMtSecondDeltaPhiCut = []
purityMtDeltaPhiCut = []
purityErrMtDeltaPhiCut = []
purityMtAfterBtagging = []
purityErrMtAfterBtagging = []
purityMTInvertedTauIdBvetoDphi = []
purityErrMTInvertedTauIdBvetoDphi = []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
mt_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedSecondDeltaPhiCut"+ptbin)])
mt_tmp._setLegendStyles()
mt_tmp._setLegendLabels()
mt_tmp.histoMgr.setHistoDrawStyleAll("P")
mt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mt = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mt.Scale(normData[ptbin])
DphiJet2.append(mt)
mtEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedSecondDeltaPhiCut"+ptbin)])
mtEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtEWK_tmp._setLegendStyles()
mtEWK_tmp._setLegendLabels()
mtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtEWK = mtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtEWK.Scale(normEWK[ptbin])
# mt.Add(mtEWK, -1)
# hmt.append(mt)
DphiEWKJet2.append(mtEWK)
purity = -999
error = -999
if mt.Integral() > 0:
purity = (mt.Integral() - mtEWK.Integral())/ mt.Integral()
error = sqrt(purity*(1-purity)/mt.Integral())
purityMtSecondDeltaPhiCut.append(purity)
purityErrMtSecondDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt Second Delta Phi Cut = ",purity, " error ",error
############################################
# mt after b tagging
mtb_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBtag"+ptbin)])
mtb_tmp._setLegendStyles()
mtb_tmp._setLegendLabels()
mtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtb = mtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtb.Scale(normData[ptbin])
hmt.append(mtb)
noDphi.append(mtb)
mtbEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBtag"+ptbin)])
mtbEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtbEWK_tmp._setLegendStyles()
mtbEWK_tmp._setLegendLabels()
mtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtbEWK = mtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtbEWK.Scale(normEWK[ptbin])
mtb.Add(mtbEWK, -1)
hmtb.append(mtb)
noDphiEWK.append(mtbEWK)
purity = -999
error = -999
if mtb.Integral() > 0:
purity = (mtb.Integral() - mtbEWK.Integral())/ mtb.Integral()
error = sqrt(purity*(1-purity)/mtb.Integral())
purityMtAfterBtagging.append(purity)
purityErrMtAfterBtagging.append(error)
# print " pt bin ", ptbin, " purity Mt After Btagging = ",purity, " error ",error
############################################
# mt after deltaPhi cut
mtph_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdJetDphi"+ptbin)])
mtph_tmp._setLegendStyles()
mtph_tmp._setLegendLabels()
mtph_tmp.histoMgr.setHistoDrawStyleAll("P")
mtph_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtph = mtph_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtph.Scale(normData[ptbin])
# hmt.append(mt)
Dphi160.append(mtph)
mtphEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdJetDphi"+ptbin)])
mtphEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtphEWK_tmp._setLegendStyles()
mtphEWK_tmp._setLegendLabels()
mtphEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphEWK = mtphEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphEWK.Scale(normEWK[ptbin])
# mtph.Add(mtphEWK, -1)
# hmtph.append(mtph)
DphiEWK160.append(mtphEWK)
purity = -999
error = -999
if mtph.Integral() > 0:
purity = (mtph.Integral() - mtphEWK.Integral())/ mtph.Integral()
error = sqrt(purity*(1-purity)/mtph.Integral())
purityMtDeltaPhiCut.append(purity)
purityErrMtDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt DeltaPhi Cut = ",purity, " error ",error
############################################
# mt after deltaphi vs MHTjet1 cut
mtphj1_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedFirstDeltaPhiCut"+ptbin)])
#mtphj1_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("MTInvertedTauIdMet"+ptbin)])
mtphj1_tmp._setLegendStyles()
mtphj1_tmp._setLegendLabels()
mtphj1_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj1_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj1 = mtphj1_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj1.Scale(normData[ptbin])
DphiJet1.append(mtphj1)
mtphj1EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedFirstDeltaPhiCut"+ptbin)])
#mtphj1EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("MTInvertedTauIdMet"+ptbin)])
mtphj1EWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtphj1EWK_tmp._setLegendStyles()
mtphj1EWK_tmp._setLegendLabels()
mtphj1EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj1EWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj1EWK = mtphj1EWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphj1EWK.Scale(normEWK[ptbin])
# mtphj1.Add(mtphj1EWK, -1)
# hmtphj1.append(mtphj1)
DphiEWKJet1.append(mtphj1EWK)
purity = -999
error = -999
if mtphj1.Integral() > 0:
purity = (mtphj1.Integral() - mtphj1EWK.Integral())/ mtphj1.Integral()
error = sqrt(purity*(1-purity)/mtphj1.Integral())
purityMtFirstDeltaPhiCut.append(purity)
purityErrMtFirstDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt First Delta Phi Cut = ",purity, " error ",error
############################################
# mt after all cuts
mtphj2_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedThirdDeltaPhiCut"+ptbin)])
mtphj2_tmp._setLegendStyles()
mtphj2_tmp._setLegendLabels()
mtphj2_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2 = mtphj2_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj2.Scale(normData[ptbin])
# hmt.append(mt)
DphiAll.append(mtphj2)
mtphj2EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedThirdDeltaPhiCut"+ptbin)])
mtphj2EWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtphj2EWK_tmp._setLegendStyles()
mtphj2EWK_tmp._setLegendLabels()
mtphj2EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2EWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2EWK = mtphj2EWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphj2EWK.Scale(normEWK[ptbin])
# mtphj2.Add(mtphj2EWK, -1)
# hmtphj2.append(mtphj2)
DphiEWKAll.append(mtphj2EWK)
purity = -999
error = -999
if mtphj2.Integral() > 0:
purity = (mtphj2.Integral() - mtphj2EWK.Integral())/ mtphj2.Integral()
error = sqrt(purity*(1-purity)/mtphj2.Integral())
purityMtThirdDeltaPhiCut.append(purity)
purityErrMtThirdDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt Third Delta Phi Cut = ",purity, " error ",error
#######################
# mt with cut against tt
mtremovett_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedAgainstTTCut"+ptbin)])
mtremovett_tmp._setLegendStyles()
mtremovett_tmp._setLegendLabels()
mtremovett_tmp.histoMgr.setHistoDrawStyleAll("P")
mtremovett_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtremovett = mtremovett_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtremovett.Scale(normData[ptbin])
DphiAllremovett.append(mtremovett)
mtremovettEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedAgainstTTCut"+ptbin)])
mtremovettEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtremovettEWK_tmp._setLegendStyles()
mtremovettEWK_tmp._setLegendLabels()
mtremovettEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtremovettEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtremovettEWK = mtremovettEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtremovettEWK.Scale(normEWK[ptbin])
# mtremovett.Add(mtremovettEWK, -1)
# hmtremovett.append(mtremovett)
DphiEWKAllremovett.append(mtremovettEWK)
purity = -999
error = -999
if mtremovett.Integral() > 0:
purity = (mtremovett.Integral() - mtremovettEWK.Integral())/ mtremovett.Integral()
error = sqrt(purity*(1-purity)/mtremovett.Integral())
purityMtRemovett.append(purity)
purityErrMtRemovett.append(error)
print "mtremovett.Integral() ",mtremovett.Integral(), " mmtEWK.Integral() ", mtremovettEWK.Integral()
#######################
### MET
mmt_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MET_InvertedTauIdJets"+ptbin)])
mmt_tmp._setLegendStyles()
mmt_tmp._setLegendLabels()
mmt_tmp.histoMgr.setHistoDrawStyleAll("P")
mmt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmt = mmt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
## mmt.Scale(normData[ptbin])
hmet.append(mmt)
mmtEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MET_InvertedTauIdJets"+ptbin)])
mmtEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtEWK_tmp._setLegendStyles()
mmtEWK_tmp._setLegendLabels()
mmtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtEWK = mmtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
## mmtEWK.Scale(normEWK[ptbin])
mmt.Add(mmtEWK, -1)
hmetQCD.append(mmt)
hmetEWK.append(mmtEWK)
purity = -999
error = -999
if mmt.Integral() > 0:
purity = (mmt.Integral() - mmtEWK.Integral())/ mmt.Integral()
error = sqrt(purity*(1-purity)/mmt.Integral())
purityMet.append(purity)
purityErrMet.append(error)
## print "mmt.Integral() ",mmt.Integral(), " mmtEWK.Integral() ", mmtEWK.Integral()
############################################
# mt after all cuts
mtphj2_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBvetoDphi"+ptbin)])
mtphj2_tmp._setLegendStyles()
mtphj2_tmp._setLegendLabels()
mtphj2_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2 = mtphj2_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj2.Scale(normData[ptbin])
# hmt.append(mt)
DphiAllbveto.append(mtphj2)
mtphj2EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBvetoDphi"+ptbin)])
mtphj2EWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtphj2EWK_tmp._setLegendStyles()
mtphj2EWK_tmp._setLegendLabels()
mtphj2EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2EWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2EWK = mtphj2EWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphj2EWK.Scale(normEWK[ptbin])
# mtphj2.Add(mtphj2EWK, -1)
# hmtphj2.append(mtphj2)
DphiEWKAllbveto.append(mtphj2EWK)
purity = -999
error = -999
if mtphj2.Integral() > 0:
purity = (mtphj2.Integral() - mtphj2EWK.Integral())/ mtphj2.Integral()
error = sqrt(purity*(1-purity)/mtphj2.Integral())
purityMTInvertedTauIdBvetoDphi.append(purity)
purityErrMTInvertedTauIdBvetoDphi.append(error)
# print " pt bin ", ptbin, " purity Mt Third Delta Phi Cut = ",purity, " error ",error
print " "
print " purity met = ",purityMet, " error ",purityErrMet
print " purity Mt no DeltaPhi Cuts = ",purityMtAfterBtagging, " error ",purityErrMtAfterBtagging
print " purity Mt DeltaPhi160 Cut = ",purityMtDeltaPhiCut, " error ",purityErrMtDeltaPhiCut
print " purity Mt First DeltaPhi Cut = ",purityMtFirstDeltaPhiCut, " error ",purityErrMtFirstDeltaPhiCut
print " purity Mt Second DeltaPhi Cut = ",purityMtSecondDeltaPhiCut, " error ",purityErrMtSecondDeltaPhiCut
print " purity Mt Third Delta Phi Cut = ",purityMtThirdDeltaPhiCut, " error ",purityErrMtThirdDeltaPhiCut
print " purity Mt b veto deltaPhi Cuts = ",purityMTInvertedTauIdBvetoDphi, " error ",purityErrMTInvertedTauIdBvetoDphi
print " purity Mt Remove tt = ",purityMtRemovett, " error ",purityErrMtRemovett
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
### Met
met = hmet[0].Clone("met")
met.SetName("MET")
met.SetTitle("Inverted tau Met")
met.Reset()
for histo in hmet:
met.Add(histo)
metQCD = hmetQCD[0].Clone("met")
metQCD.SetName("MET")
metQCD.SetTitle("Inverted tau Met")
metQCD.Reset()
for histo in hmetQCD:
metQCD.Add(histo)
metEWK = hmetEWK[0].Clone("metewk")
metEWK.SetName("METewk")
metEWK.SetTitle("Inverted tau Met")
metEWK.Reset()
for histo in hmetEWK:
metEWK.Add(histo)
### Mt no DeltaPhi Cuts
mtNoDphi = noDphi[0].Clone("mt")
mtNoDphi.SetName("mt")
mtNoDphi.SetTitle("Inverted tau Mt")
mtNoDphi.Reset()
for histo in noDphi:
mtNoDphi.Add(histo)
mtNoDphiEWK = noDphiEWK[0].Clone("mtewk")
mtNoDphiEWK.SetName("MTewk")
mtNoDphiEWK.SetTitle("Inverted tau Met")
mtNoDphiEWK.Reset()
for histo in noDphiEWK:
mtNoDphiEWK.Add(histo)
### Mt DeltaPhi < 160 Cut
mtDphi160 = Dphi160[0].Clone("mt")
mtDphi160.SetName("mt")
mtDphi160.SetTitle("Inverted tau Mt")
mtDphi160.Reset()
for histo in Dphi160:
mtDphi160.Add(histo)
mtDphi160EWK = DphiEWK160[0].Clone("mtewk")
mtDphi160EWK.SetName("MTewk")
mtDphi160EWK.SetTitle("Inverted tau Met")
mtDphi160EWK.Reset()
for histo in DphiEWK160:
mtDphi160EWK.Add(histo)
### Mt Mt all dphi cuts
mtDphiAll = DphiAll[0].Clone("mt")
mtDphiAll.SetName("mt")
mtDphiAll.SetTitle("Inverted tau Mt")
mtDphiAll.Reset()
for histo in DphiAll:
mtDphiAll.Add(histo)
mtDphiAllEWK = DphiEWKAll[0].Clone("mtewk")
mtDphiAllEWK.SetName("MTewk")
mtDphiAllEWK.SetTitle("Inverted tau Met")
mtDphiAllEWK.Reset()
for histo in DphiEWKAll:
mtDphiAllEWK.Add(histo)
### Mt bveto all dphi cuts
mtDphiAllbveto = DphiAllbveto[0].Clone("mt")
mtDphiAllbveto.SetName("mt")
mtDphiAllbveto.SetTitle("Inverted tau Mt")
mtDphiAllbveto.Reset()
for histo in DphiAllbveto:
mtDphiAllbveto.Add(histo)
mtDphiAllEWKbveto = DphiEWKAllbveto[0].Clone("mtewk")
mtDphiAllEWKbveto.SetName("MTewk")
mtDphiAllEWKbveto.SetTitle("Inverted tau Met")
mtDphiAllEWKbveto.Reset()
for histo in DphiEWKAllbveto:
mtDphiAllEWKbveto.Add(histo)
### Mt Mt dphi jet1
mtDphiJet1 = DphiJet1[0].Clone("mt")
mtDphiJet1.SetName("mt")
mtDphiJet1.SetTitle("Inverted tau Mt")
mtDphiJet1.Reset()
for histo in DphiJet1:
mtDphiJet1.Add(histo)
mtDphiEWKJet1 = DphiEWKJet1[0].Clone("mtewk")
mtDphiEWKJet1.SetName("MTewk")
mtDphiEWKJet1.SetTitle("Inverted tau Met")
mtDphiEWKJet1.Reset()
for histo in DphiEWKJet1:
mtDphiEWKJet1.Add(histo)
### Mt Mt dphi jet2
mtDphiJet2 = DphiJet2[0].Clone("mt")
mtDphiJet2.SetName("mt")
mtDphiJet2.SetTitle("Inverted tau Mt")
mtDphiJet2.Reset()
for histo in DphiJet2:
mtDphiJet2.Add(histo)
mtDphiEWKJet2 = DphiEWKJet2[0].Clone("mtewk")
mtDphiEWKJet2.SetName("MTewk")
mtDphiEWKJet2.SetTitle("Inverted tau Met")
mtDphiEWKJet2.Reset()
for histo in DphiEWKJet2:
mtDphiEWKJet2.Add(histo)
### Mt all dphi + tt cuts
mtDphiAllremovett = DphiAllremovett[0].Clone("mt")
mtDphiAllremovett.SetName("mt")
mtDphiAllremovett.SetTitle("Inverted tau Mt")
mtDphiAllremovett.Reset()
for histo in DphiAllremovett:
mtDphiAllremovett.Add(histo)
mtDphiAllremovettEWK = DphiEWKAllremovett[0].Clone("mtewk")
mtDphiAllremovettEWK.SetName("MTewk")
mtDphiAllremovettEWK.SetTitle("Inverted tau Met")
mtDphiAllremovettEWK.Reset()
for histo in DphiEWKAllremovett:
mtDphiAllremovettEWK.Add(histo)
##########################################
# met purity
metqcd = metQCD.Clone("metqcd")
metinv = met.Clone("met")
invertedQCD.setLabel("MetPurity")
# invertedQCD.mtComparison(metqcd, metqcd,"MetPurity")
##########################################
# mt purity no deltaPhi
mtQCD = mtNoDphi.Clone("QCD")
mtQCD.Add(mtNoDphiEWK,-1)
mtQCD.Divide(mtNoDphi)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtNoDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtNoDeltaPhiCuts")
##########################################
# mt purity deltaPhi 160
mtQCD = mtDphi160.Clone("QCD")
mtQCD.Add(mtDphi160EWK,-1)
mtQCD.Divide(mtDphi160)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhi160")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtDeltaPhi160")
##########################################
# mt purity all deltaPhi cuts
mtQCD = mtDphiAll.Clone("QCD")
mtQCD.Add(mtDphiAllEWK,-1)
mtQCD.Divide(mtDphiAll)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtAllDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtAllDeltaPhiCuts")
##########################################
# mt bveto purity all deltaPhi cuts
## test
invertedQCD.setLabel("testMtbveto")
# invertedQCD.mtComparison(mtDphiAllbveto, mtDphiAllbveto,"testMtbveto")
invertedQCD.setLabel("testEWKMtbveto")
# invertedQCD.mtComparison(mtDphiAllEWKbveto, mtDphiAllEWKbveto,"testEWKMtbveto")
mtQCD = mtDphiAllbveto.Clone("QCD")
mtQCD.Add(mtDphiAllEWKbveto,-1)
mtQCD.Divide(mtDphiAllbveto)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtbvetoAllDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtbvetoAllDeltaPhiCuts")
##########################################
# mt purity jet1 deltaPhi cuts
mtQCD = mtDphiJet1.Clone("QCD")
mtQCD.Add(mtDphiEWKJet1,-1)
mtQCD.Divide(mtDphiJet1)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiJet1Cuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtDeltaPhiJet1Cuts")
##########################################
# mt purity jet2 deltaPhi cuts
mtQCD = mtDphiJet2.Clone("QCD")
mtQCD.Add(mtDphiEWKJet2,-1)
mtQCD.Divide(mtDphiJet2)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiJet2Cuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtDeltaPhiJet2Cuts")
##########################################
# mt purity all deltaPhi cuts and against tt cut
mtQCD = mtDphiAllremovett.Clone("QCD")
mtQCD.Add(mtDphiAllremovettEWK,-1)
mtQCD.Divide(mtDphiAllremovett)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiAndAgainsttt")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtDeltaPhiAndAgainsttt")
#################################################
## purities as a function of pt tau jet
### Create and customise TGraph
cEff = TCanvas ("MetPurity", "MetPurity", 1)
cEff.cd()
ptbin_error = array.array("d",[5, 5, 5, 5, 10, 10 ,30])
ptbin = array.array("d",[45, 55, 65, 75, 90, 110 ,150])
graph = TGraphErrors(7, ptbin, array.array("d",purityMTInvertedTauIdBvetoDphi),ptbin_error,array.array("d",purityErrMTInvertedTauIdBvetoDphi))
graph.SetMaximum(1.0)
graph.SetMinimum(0.6)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV/c]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.35,0.35,"B-tagging factorisation")
tex1.SetNDC()
tex1.SetTextSize(25)
tex1.Draw()
tex2 = ROOT.TLatex(0.35,0.27,"#Delta#phi cuts" )
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMTInvertedTauIdBvetoDphiBins.png")
graph = TGraphErrors(7, ptbin, array.array("d",purityMet),ptbin_error,array.array("d",purityErrMet))
graph.SetMaximum(1.1)
graph.SetMinimum(0.8)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV/c]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.35,0.35,"Inverted #tau jet isolation")
tex1.SetNDC()
tex1.SetTextSize(20)
tex1.Draw()
tex2 = ROOT.TLatex(0.35,0.3,"at least 3 jets" )
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMetPtBins.png")
## Mt without deltaPhi cuts
cEff = TCanvas ("MtNoDeltaPhiCutsPurity", "MtNoDeltaPhiCutsPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtAfterBtagging),ptbin_error,array.array("d",purityErrMtAfterBtagging))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.5,0.38,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.3,"no #Delta#phi cuts" )
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtNoDeltaPhiCutsBins.png")
## Mt without deltaPhi cuts
cEff = TCanvas ("MtDeltaPhi160Purity", "MtDeltaPhi160Purity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtDeltaPhiCut),ptbin_error,array.array("d",purityErrMtDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.5,0.35,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(24)
tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.25,"#Delta#phi(#tau jet,MET) < 160^{o}" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtDeltaPhi160Bins.png")
## Mt 1st deltaPhi cut
cEff = TCanvas ("MtFirstDeltaCutPurity", "MtFirstDeltaPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtFirstDeltaPhiCut),ptbin_error,array.array("d",purityErrMtFirstDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.4,0.4,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(24)
tex1.Draw()
tex2 = ROOT.TLatex(0.3,0.3,"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1,MET) cut" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtFirstDeltaPhiCutBins.png")
## Mt 2nd deltaPhi cut
cEff = TCanvas ("MtSecondDeltaCutPurity", "MtFirstSecondPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtSecondDeltaPhiCut),ptbin_error,array.array("d",purityErrMtSecondDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.2,0.8,"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2,MET) cuts" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtSecondDeltaPhiCutBins.png")
## Mt 2nd deltaPhi cut
cEff = TCanvas ("MtThirdDeltaCutPurity", "MtThirdDeltaPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtThirdDeltaPhiCut),ptbin_error,array.array("d",purityErrMtThirdDeltaPhiCut ))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.2,0.8,"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2/3,MET) cuts" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtThirdDeltaPhiCutBins.png")
## Mt deltaPhi cuts and against tt
cEff = TCanvas ("MtMtRemovettPurity", "MtMtRemovettPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtRemovett),ptbin_error,array.array("d",purityErrMtRemovett))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88, "All selection cuts ")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.2,0.8,"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2/3,MET) cuts" )
tex2.SetNDC()
tex2.SetTextSize(22)
tex2.Draw()
tex3 = ROOT.TLatex(0.2,0.72,"#Delta#phi cut against tt+jets" )
tex3.SetNDC()
tex3.SetTextSize(22)
tex3.Draw()
cEff.Update()
cEff.SaveAs("purityMtAgainstttCutBins.png")
def biasScan():
biasScanResultsDir = os.path.join(biasScanDir, args.outDirName)
combineoutputPathDir = os.path.join(combineoutputDir, args.outDirName)
workspacesDir = os.path.join(datacardsworkspacesDir, args.workspacesDirName)
mkdir(biasScanResultsDir)
hMeans = {}
hMedians = {}
for category in categoriesToUse:
if names2RepsToUse[category] in args.categoriesToSkip:
continue
# extract the multi pdf
workspaceFileName = "workspace__{category}__{signalModel}.root".format(
category=names2RepsToUse[category], signalModel=args.signalModel)
refWorkspaceFile = R.TFile(os.path.join(workspacesDir, workspaceFileName))
higgsWorkspace = refWorkspaceFile.Get("higgs")
multipdf = higgsWorkspace.pdf("multipdf_{category}".format(
category=names2RepsToUse[category]))
for massPoint in args.massPoints:
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)] = R.TH2D("Means_{category}_{mass}".format(category=category, mass=massPoint), "Means", multipdf.getNumPdfs()+1, 0, multipdf.getNumPdfs()+1,
multipdf.getNumPdfs()+1, 0, multipdf.getNumPdfs()+1)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)] = R.TH2D("Madians_{category}_{mass}".format(category=category, mass=massPoint), "Medians", multipdf.getNumPdfs()+1, 0, multipdf.getNumPdfs()+1,
multipdf.getNumPdfs()+1, 0, multipdf.getNumPdfs()+1)
for iref in range(multipdf.getNumPdfs()):
refPdfName = multipdf.getPdf(iref).GetName()
q = False
for xxx in args.modelsToSkip:
if xxx==refPdfName.split("_")[0]:
q = True
if q==True: continue
for icurrent in range(multipdf.getNumPdfs()):
fitPdfName = multipdf.getPdf(icurrent).GetName()
q = False
for xxx in args.modelsToSkip:
if xxx==fitPdfName.split("_")[0]:
q = True
if q==True: continue
canvas = R.TCanvas("c1", "c1", 1000, 600)
try:
fileName = "mlfit{category}__{mass}__{iref}__{icurrent}__{signalModel}.root".format(category=names2RepsToUse[category], mass=massPoint, iref=iref, icurrent=icurrent, signalModel=args.signalModel)
f = R.TFile(os.path.join(combineoutputPathDir, fileName))
tree = f.Get("tree_fit_sb")
tree.Draw("(mu-1)/muErr>>h(500, -5,5)")
# get the histogram and perform some manipulations
hist = R.gFile.Get("h")
import array
probs = array.array("d", [0.5])
quantiles = array.array("d", [0])
hist.GetQuantiles(1, quantiles, probs)
latex = R.TLatex()
latex.SetNDC()
latex.SetTextSize(0.02)
latex.SetTextAlign(13) # align at top
latex.SetTextSize(0.03)
latex.DrawLatex(0.2, 0.8, "Median = " + str(quantiles[0]))
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].Fill(iref, icurrent, hist.GetMean()*100)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetBinLabel(iref+1, refPdfName)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetBinLabel(icurrent+1, fitPdfName)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].Fill(iref, icurrent, quantiles[0]*100)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetBinLabel(iref+1, refPdfName)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetBinLabel(icurrent+1, fitPdfName)
cfileName = "pull__{category}__{mass}__{iref}__{icurrent}__{signalModel}.png".format(category=names2RepsToUse[category], mass=massPoint, iref=iref, icurrent=icurrent, signalModel=args.signalModel)
canvas.SaveAs(os.path.join(biasScanResultsDir, cfileName))
except Exception as exc:
print "There was a problem with file: {file}\n".format(file=fileName)
print type(exc)
print exc.args
print exc
finally:
f.Close()
# plot the 2D
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].SetTitle("Mean (#mu_{fit} - #mu_{0})/#sigma #mu_{fit} (%%), %s, %d GeV" % (category, massPoint))
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].SetStats(0)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetTitle("Fit Model")
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetTitle("Reference Model")
# hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetRangeUser(0,5)
# hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetRangeUser(0,5)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetZaxis().SetRangeUser(-100,+100)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].Draw("COLZTEXT")
canvas.SaveAs(os.path.join(biasScanResultsDir, "pullMeans2D__{category}__{mass}__{signalModel}.png".format(
category=names2RepsToUse[category], mass=massPoint, signalModel=args.signalModel)))
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].SetTitle("Median (#mu_{fit} - #mu_{0})/#sigma #mu_{fit} (%%), %s, %d GeV" % (category, massPoint))
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].SetStats(0)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetTitle("Fit Model")
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetTitle("Reference Model")
# hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetRangeUser(0,5)
# hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetRangeUser(0,5)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetZaxis().SetRangeUser(-100,+100)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].Draw("COLZTEXT")
canvas.SaveAs(os.path.join(biasScanResultsDir, "pullMedians2D__{category}__{mass}__{signalModel}.png".format(
category=names2RepsToUse[category], mass=massPoint, signalModel=args.signalModel)))
def EmptyBitmap(width, height, value=255):
"""an empty BitMap of a specifc width and height
b = EmptyBitmap(width, heigh, value=255)
"""
data = array.array('B', [value]*3*width*height)
return wx.BitmapFromBuffer(width, height, data)
outputfile = ROOT.TFile(outfilename, "RECREATE")
path = "/nfs/dust/cms/user/kschweig/JetRegression/trees0908/BDTTraining/ttHbb/*_1_*nominal*.root"
inputtree = ROOT.TChain("MVATree")
# for f in glob(path):
for f in sys.argv[2:]:
inputtree.Add(f)
Regvars_input = {}
for variable in inputvariables:
Regvars_input.update({variable: array.array("f", 20 * [0])})
inputtree.SetBranchAddress(variable, Regvars_input[variable])
isHiggsJet = array.array("f", 20 * [0])
isWJet = array.array("f", 20 * [0])
ishadTopJet = array.array("f", 20 * [0])
inputtree.SetBranchAddress("RegJet_isHiggsJet", isHiggsJet)
inputtree.SetBranchAddress("RegJet_isWJet", isWJet)
inputtree.SetBranchAddress("RegJet_ishadTopJet", ishadTopJet)
outputfile.cd()
OutputTree = ROOT.TTree("MVATree", "MVATree")
def decode_shikata_ga_nai(d, all_instr_list):
## verify some bytes first
import array
sd = array.array('B', d)
szd = None
#look for floating point instr, fnstenv, and mov in first few instr
if len(all_instr_list) < 10:
return None
fFoundFnstenv = False
fFoundFloatingPtInstr = False
fFoundMov = False
fFoundCounter = False
fFoundXor = False
iLen = 0
key = 0
szMsg = 'No decoder found'
iXorOffset = 0
iXorAdjust = 0
iFPOpOffset = 0
for i in range(0, 10):
instr_lst = all_instr_list[i]
szIns = instr_lst[2]
offset = instr_lst[3]
# fnstenv [esp - 12]
if szIns.startswith('fnstenv'):
fFoundFnstenv = True
#fxch st0,st6
if not fFoundFloatingPtInstr and not szIns.startswith(
'fnstenv') and szIns.startswith('f'):
fFoundFloatingPtInstr = True
iFPOpOffset = offset
#xor dword [edx + 24],eax
if szIns.startswith('sub ') and szIns.endswith('0xfffffffc'):
iXorAdjust = -4
if szIns.startswith('xor dword ['):
fFoundXor = True
iXorOffset = int(
(szIns.split('+')[1]).split(']')[0]) ##+ iXorAdjust
#find key operation. e.g. add esi,dword [eax + 14]
for j in range(1, 3):
keyop_instr_lst = all_instr_list[i + j]
szKeyOpIns = keyop_instr_lst[2]
if szKeyOpIns.startswith('add e'):
szKeyOp = szKeyOpIns.split(' ')[0]
istart = keyop_instr_lst[3]
break
# mov eax,0x4193fabc
if szIns.startswith('mov ') and szIns.find('0x') > 0 and not fFoundMov:
fFoundMov = True
k1 = sd[offset + 0x1]
k2 = sd[offset + 0x2]
k3 = sd[offset + 0x3]
k4 = sd[offset + 0x4]
key = k1 | (k2 << 8) | (k3 << 16) | (k4 << 24)
# mov cl,110
if szIns.startswith('mov ') and szIns.find('cl,') > 0:
fFoundCounter = True
iLen = int(szIns.split(',')[1])
if (fFoundMov and fFoundFloatingPtInstr and fFoundFnstenv and fFoundCounter
and iLen > 0):
next_key_operation = d[istart:istart + 3]
szd = []
for i in range(0, iXorOffset + iFPOpOffset):
szd.append(chr(sd[i]))
for i in range(iXorOffset + iFPOpOffset,
len(sd) - (iXorOffset + iFPOpOffset), 4):
szd.append(chr(k1 ^ sd[i]))
szd.append(chr(k2 ^ sd[i + 1]))
szd.append(chr(k3 ^ sd[i + 2]))
szd.append(chr(k4 ^ sd[i + 3]))
data = k1 ^ sd[i] | ((k2 ^ sd[i + 1]) << 8) | (
(k3 ^ sd[i + 2]) << 16) | ((k4 ^ sd[i + 3]) << 24)
#update the key based on the shikata rules
if szKeyOp == "add":
key = (key + data) & 0x00000000FFFFFFFF
else:
key = (key + data) & 0x00000000FFFFFFFF
pass # error case
k1 = 0x000000FF & key
k2 = (0x0000FF00 & key) >> 8
k3 = (0x00FF0000 & key) >> 16
k4 = (0xFF000000 & key) >> 24
szd = ''.join(szd)
op = dis.disasm(szd, istart, istart)
szIns = repr(op).lower()
szKeyOp = szIns.split(' ')[0]
# szOffsetDirection = szIns.split(' ')[3]
# cOffset = int((szIns.split(' ')[4]).split(']')[0])
szMsg = "Found shikata_ga_nai shellcode len = %d, key = 0x%x, decode offset= %d, fpop offset = %d, keyop= %s, istart=0x%x, '%s'" % (
iLen, key, iXorOffset, iFPOpOffset, szKeyOp, istart, szIns)
else:
pass
return [szd, iLen, key, iXorOffset, szMsg]
def read_subimage(self, rows, cols, bands=None, use_memmap=False):
'''
Reads arbitrary rows, columns, and bands from the image.
Arguments:
`rows` (list of ints):
Indices of rows to read.
`cols` (list of ints):
Indices of columns to read.
`bands` (list of ints):
Optional list of bands to read. If not specified, all bands
are read.
`use_memmap` (bool, default False):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Returns:
:class:`numpy.ndarray`
An `MxNxL` array, where `M` = len(`rows`), `N` = len(`cols`),
and `L` = len(bands) (or # of image bands if `bands` == None).
'''
import array
if self._memmap is not None and use_memmap is True:
if bands is None:
data = np.array(self._memmap.take(rows, 0).take(cols, 1))
else:
data = np.array(
self._memmap.take(rows, 0).take(cols, 1).take(bands, 2))
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
offset = self.offset
nbands = self.nbands
nSubRows = len(rows) # Rows in sub-image
nSubCols = len(cols) # Cols in sub-image
d_band = self.sample_size
d_col = d_band * self.nbands
d_row = d_col * self.ncols
vals = array.array(byte_typecode)
nVals = self.nrows * self.ncols
sample_size = self.sample_size
# Increments between bands
if bands is not None:
allBands = 0
nSubBands = len(bands)
else:
allBands = 1
bands = list(range(self.nbands))
nSubBands = self.nbands
f = self.fid
# Pixel format is BIP
for i in rows:
for j in cols:
if allBands:
f.seek(offset + i * d_row + j * d_col, 0)
vals.fromfile(f, nSubBands * sample_size)
else:
for k in bands:
f.seek(offset +
i * d_row +
j * d_col +
k * d_band, 0)
vals.fromfile(f, sample_size)
arr = np.fromstring(vals.tostring(), dtype=self.dtype)
arr = arr.reshape(nSubRows, nSubCols, nSubBands)
if self.scale_factor != 1:
return arr / float(self.scale_factor)
return arr
def read_subregion(self, row_bounds, col_bounds, bands=None,
use_memmap=True):
'''
Reads a contiguous rectangular sub-region from the image.
Arguments:
`row_bounds` (2-tuple of ints):
(a, b) -> Rows a through b-1 will be read.
`col_bounds` (2-tuple of ints):
(a, b) -> Columnss a through b-1 will be read.
`bands` (list of ints):
Optional list of bands to read. If not specified, all bands
are read.
`use_memmap` (bool, default True):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Returns:
:class:`numpy.ndarray`
An `MxNxL` array.
'''
import array
if self._memmap is not None and use_memmap is True:
if bands is None:
data = np.array(self._memmap[row_bounds[0]: row_bounds[1],
col_bounds[0]: col_bounds[1], :])
else:
data = np.array(self._memmap[row_bounds[0]: row_bounds[1],
col_bounds[0]: col_bounds[1],
bands])
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
offset = self.offset
nbands = self.nbands
nSubRows = row_bounds[1] - row_bounds[0] # Rows in sub-image
nSubCols = col_bounds[1] - col_bounds[0] # Cols in sub-image
d_row = self.sample_size * self.ncols * self.nbands
colStartPos = col_bounds[0] * self.sample_size * self.nbands
vals = array.array(byte_typecode)
nVals = self.nrows * self.ncols
sample_size = self.sample_size
# Increments between bands
if bands is not None:
allBands = 0
nSubBands = len(bands)
delta_b = bands[:]
for i in range(len(delta_b)):
delta_b[i] *= self.sample_size
else:
allBands = 1
nSubBands = self.nbands
f = self.fid
# Pixel format is BIP
for i in range(row_bounds[0], row_bounds[1]):
f.seek(offset + i * d_row + colStartPos, 0)
rowPos = f.tell()
if allBands:
# This is the simple one
vals.fromfile(f, nSubCols * nbands * sample_size)
else:
# Need to pull out specific bands for each column.
for j in range(nSubCols):
f.seek(rowPos + j * self.sample_size * self.nbands, 0)
pixelPos = f.tell()
for k in range(len(bands)):
f.seek(pixelPos + delta_b[k], 0) # Next band
vals.fromfile(f, sample_size)
arr = np.fromstring(vals.tostring(), dtype=self.dtype)
arr = arr.reshape(nSubRows, nSubCols, nSubBands)
if self.scale_factor != 1:
return arr / float(self.scale_factor)
return arr
from array import array
from random import random
floats = array('d', (random() for i in range(10**5)))
floats[-1]
fp = open('floats.bin', 'wb')
floats.tofile(fp)
fp.close()
floats2 = array('d')
fp = open('floats.bin', 'rb')
floats2.fromfile(fp, 10**5)
fp.close()
floats2[-1]
# in-place sort
a = array.array(a.typecode, sorted(a))
from array import array
number = array('h', [-2, -1, 0, 1, 2])
memv = memoryview(number)
len(memv)
memv[0]
memv_oct = memv.cast('B')
memv_oct.tolist()
memv_oct[5] = 4
number
import numpy
a = numpy.arange(12)
a
type(a)
from ROOT import TMVA, TFile, TString, TTree, TLorentzVector
from array import array
from subprocess import call
from os.path import isfile
from ROOT import std
# Setup TMVA
TMVA.Tools.Instance()
TMVA.PyMethodBase.PyInitialize()
reader = TMVA.Reader("Color:!Silent")
varList = ["mu_likep", "mu_likem", "dist", "DeltPhi", "minv"]
import array
dist = array.array('f', [0])
DeltPhi = array.array('f', [0])
mu_likep = array.array('f', [0])
mu_likem = array.array('f', [0])
minv = array.array('f', [0])
reader.AddVariable("dist", dist)
reader.AddVariable("DeltPhi", DeltPhi)
reader.AddVariable("mu_likep", mu_likep)
reader.AddVariable("mu_likem", mu_likem)
reader.AddSpectator("minv", minv)
fout = ROOT.TFile("TMVAappKeras.root", "RECREATE")
t = TTree('MVout', 'MVA outputs')
methodList = {'BDT', 'PyKeras'}
"nEventsMin=nTest",
"MaxDepth=3",
"BoostType=AdaBoost",
"AdaBoostBeta=0.5",
"SeparationType=GiniIndex",
"nCuts=10",
"PruneMethod=NoPruning",
]))
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
reader = ROOT.TMVA.Reader()
import array
varx1 = array.array('f', [0])
reader.AddVariable("itr", varx1)
varx2 = array.array('f', [0])
reader.AddVariable("beta14", varx2)
varx3 = array.array('f', [0])
reader.AddVariable("thetaij", varx3)
#varx4 = array.array('f',[0]) ; reader.AddVariable("nhits",varx4)
varx4 = array.array('f', [0])
reader.AddVariable("udotR", varx4)
#varx6 = array.array('f',[0]) ; reader.AddVariable("scaleLogL",varx6)
reader.BookMVA("BDT", "weights/TMVAClassification_BDT.weights.xml")
# create a new 2D histogram with fine binning
histo2 = ROOT.TH2F("histo2", "", 200, -5, 5, 200, -5, 5)
# loop over the bins of a 2D histogram
def doFit( self,graph,config):
nPars1 = 6
nPars2 = 5
nCommonPars = 3 #(Sigma, sigma_{1}/sigma_{2}, Frac)
nPars = nPars1 + nPars2 - nCommonPars
#initial values on parameters
ExpSigma = []
ExpPeak = []
StartSigma = []
StartRatio = []
StartFrac = []
StartPeak = []
StartConst = [] #placeholder
#limits on parameters
LimitSigma_lower = []
LimitSigma_upper = []
LimitRatio_lower = []
LimitRatio_upper = []
LimitFrac_lower = []
LimitFrac_upper = []
LimitPeak_lower = []
LimitPeak_upper = []
LimitConst_lower = [] #placeholder
LimitConst_upper = [] #placeholder
for i in range(0,2):
ExpSigma.append( graph[i].GetRMS()*0.5 )
print i, graph[i].GetHistogram().GetRMS()*0.5
ExpPeak.append( graph[i].GetHistogram().GetMaximum() )
#Currenly the rest of parameters initial values and ranges are treated the same
StartSigma.append( ExpSigma[i] * config['StartSigma'] )
LimitSigma_lower.append( config['LimitsSigma'][0] )
LimitSigma_upper.append( config['LimitsSigma'][1] )
StartRatio.append( config['StartRatio'] )
LimitRatio_lower.append( config['LimitsRatio'][0] )
LimitRatio_upper.append( config['LimitsRatio'][1] )
StartFrac.append( config['StartFrac'] )
LimitFrac_lower.append( config['LimitsFrac'][0] )
LimitFrac_upper.append( config['LimitsFrac'][1] )
StartPeak.append( ExpPeak[i]*config['StartPeak'] )
LimitPeak_lower.append( ExpPeak[i]*config['LimitsPeak'][0] )
LimitPeak_upper.append( ExpPeak[i]*config['LimitsPeak'][1] )
StartConst.append( config['StartConst'] )#placeholder
LimitConst_lower.append( config['LimitsConst'][0] )#placeholder
LimitConst_upper.append( config['LimitsConst'][1] )#placeholder
fittedFunctions = []
ff = r.TF1("ff","[5] + [2]*([3]*exp(-(x-[4])**2/(2*([0]*[1]/([3]*[1]+1-[3]))**2)) + (1-[3])*exp(-(x-[4])**2/(2*([0]/([3]*[1]+1-[3]))**2)) )")
ff.SetParNames("#Sigma","#sigma_{1}/#sigma_{2}","Amp","Frac","Mean", "Const")
fittedFunctions.append(ff)
ff1 = r.TF1("ff1","[2]*([3]*exp(-(x-[4])**2/(2*([0]*[1]/([3]*[1]+1-[3]))**2)) + (1-[3])*exp(-(x-[4])**2/(2*([0]/([3]*[1]+1-[3]))**2)) )")
ff1.SetParNames("#Sigma","#sigma_{1}/#sigma_{2}","Amp","Frac","Mean")
fittedFunctions.append(ff1)
# Some black ROOT magic to get Minuit output into a log file
# see http://root.cern.ch/phpBB3/viewtopic.php?f=14&t=14473, http://root.cern.ch/phpBB3/viewtopic.php?f=13&t=16844, https://agenda.infn.it/getFile.py/access?resId=1&materialId=slides&confId=4933 slide 23
r.gROOT.ProcessLine("gSystem->RedirectOutput(\"./minuitlogtmp/Minuit.log\", \"a\");")
r.gROOT.ProcessLine("gSystem->Info(0,\"Next BCID\");")
fitter = r.Fit.Fitter()
opt = r.Fit.DataOptions()
wff = r.Math.WrappedMultiTF1(fittedFunctions[0],1)
wff1 = r.Math.WrappedMultiTF1(fittedFunctions[1],1)
rangeff = r.Fit.DataRange()
rangeff.SetRange(graph[0].GetXaxis().GetXmin(), graph[0].GetXaxis().GetXmax())
dataff = r.Fit.BinData(opt,rangeff)
r.Fit.FillData(dataff, graph[0])
rangeff1 = r.Fit.DataRange()
rangeff1.SetRange(graph[1].GetXaxis().GetXmin(), graph[1].GetXaxis().GetXmax())
dataff1 = r.Fit.BinData(opt,rangeff1)
r.Fit.FillData(dataff1, graph[1])
chi2ff = r.Fit.Chi2Function(dataff, wff)
chi2ff1 = r.Fit.Chi2Function(dataff1, wff1)
# Size of parffAndff1 array should equal to nPars, with elements the staring value of parameters and in this case as
# [0]: #StartSigma[0], [1]: #sigma_{1}/#sigma_{2} =1, 2:StartPeak[0], 3: StartFrac[0], 4: Mean, 5: StartConst[0], 6: StartPeak[1], 7: Mean
parffAndff1 = array.array('d', [StartSigma[0],1.,StartPeak[0],StartFrac[0],0., StartConst[0], StartPeak[1], 0.])
fitter.Config().SetParamsSettings(nPars,parffAndff1)
if LimitSigma_upper[0] > LimitSigma_lower[0]:
fitter.Config().ParSettings(0).SetLimits(LimitSigma_lower[0],LimitSigma_upper[0] )
if LimitRatio_upper[0] > LimitRatio_lower[0]:
fitter.Config().ParSettings(1).SetLimits(LimitRatio_lower[0],LimitRatio_upper[0] )
if LimitPeak_upper[0] > LimitPeak_lower[0]:
fitter.Config().ParSettings(2).SetLimits(LimitPeak_lower[0],LimitPeak_upper[0] )
if LimitFrac_upper[0] > LimitFrac_lower[0]:
fitter.Config().ParSettings(3).SetLimits(LimitFrac_lower[0],LimitFrac_upper[0] )
if LimitConst_upper[0] > LimitConst_lower[0]: #placeholder
fitter.Config().ParSettings(5).SetLimits(LimitConst_lower[0],LimitConst_upper[0]) #placeholder
if LimitPeak_upper[1] > LimitPeak_lower[1]:
fitter.Config().ParSettings(6).SetLimits(LimitPeak_lower[1],LimitPeak_upper[1] )
fitter.Config().MinimizerOptions().SetPrintLevel(2)
fitter.Config().SetMinimizer("Minuit2","Migrad")
myfun = r.GlobalChi2(nPars,dataff.Size()+dataff1.Size(),chi2ff, chi2ff1)
fitter.FitFCN(myfun,parffAndff1,dataff.Size()+dataff1.Size(), True)
result = fitter.Result()
fitStatus = -999 # becomes dummy variabe in the Sim Fit
# 0: #Sigma, 1: #sigma_{1}/#sigma_{2}, 2: Amp, 3: Frac, 4: Mean, 5: Const
parff_draw = array.array('i', [0,1,2,3,4,5])
fittedFunctions[0].SetFitResult( result, parff_draw);
# 0: #Sigma (common parameter), 1: #sigma_{1}/#sigma_{2} (common parameter), 6: Amp, 3: Frac (common parameter), 7: Mean
parff1_draw = array.array('i', [0,1,6,3,7])
fittedFunctions[1].SetFitResult( result, parff1_draw);
r.gROOT.ProcessLine("gSystem->RedirectOutput(0);")
fComponents = []
for i in range(0,2):
sigma = fittedFunctions[i].GetParameter("#Sigma")
m = fittedFunctions[i].GetParNumber("#Sigma")
sigmaErr = fittedFunctions[i].GetParError(m)
sigRatio = fittedFunctions[i].GetParameter("#sigma_{1}/#sigma_{2}")
m = fittedFunctions[i].GetParNumber("#sigma_{1}/#sigma_{2}")
sigRatioErr = fittedFunctions[i].GetParError(m)
amp = fittedFunctions[i].GetParameter("Amp")
m = fittedFunctions[i].GetParNumber("Amp")
ampErr = fittedFunctions[i].GetParError(m)
frac = fittedFunctions[i].GetParameter("Frac")
m = fittedFunctions[i].GetParNumber("Frac")
fracErr = fittedFunctions[i].GetParError(m)
mean = fittedFunctions[i].GetParameter("Mean")
m = fittedFunctions[i].GetParNumber("Mean")
meanErr = fittedFunctions[i].GetParError(m)
if (i==0):
const = fittedFunctions[i].GetParameter("Const") #placeholder
m = fittedFunctions[i].GetParNumber("Const") #placeholder
constErr = fittedFunctions[i].GetParError(m) #placeholder
else:
const = -999.
constErr = -999.
title = graph[i].GetTitle()
title_comps = title.split('_')
scan = title_comps[0]
type = title_comps[1]
bcid = title_comps[2]
chi2 = fittedFunctions[i].GetChisquare()
ndof = fittedFunctions[i].GetNDF()
sqrttwopi = math.sqrt(2*math.pi)
CapSigma = sigma
CapSigmaErr = sigmaErr
peak = amp
peakErr = ampErr
area = sqrttwopi*peak*CapSigma
areaErr = (sqrttwopi*CapSigma*peakErr)*(sqrttwopi*CapSigma*peakErr) + (sqrttwopi*peak*CapSigmaErr)*(sqrttwopi*peak*CapSigmaErr)
areaErr = math.sqrt(areaErr)
if i==0:
self.table_Luminometer1.append([scan, type, bcid, sigma, sigmaErr, sigRatio, sigRatioErr, amp, ampErr, frac, fracErr, mean, meanErr, CapSigma, CapSigmaErr, peak, peakErr, area, areaErr, fitStatus, chi2, ndof])
else:
self.table_Luminometer2.append([scan, type, bcid, sigma, sigmaErr, sigRatio, sigRatioErr, amp, ampErr, frac, fracErr, mean, meanErr, CapSigma, CapSigmaErr, peak, peakErr, area, areaErr, fitStatus, chi2, ndof])
# Define signal and background pieces of full function separately, for plotting
h = frac
s2 = CapSigma/(h*sigRatio+1-h)
a1 = amp*h
a2 = amp*(1-h)
s1 = CapSigma*sigRatio/(h*sigRatio+1-h)
fSignal1 = r.TF1("fSignal1","[2]*exp(-(x-[1])**2/(2*[0]**2))")
fSignal1.SetParNames("#Sigma","Mean","Amp")
fSignal1.SetParameters(s1, mean, a1)
fSignal2 = r.TF1("fSignal2","[2]*exp(-(x-[1])**2/(2*[0]**2))")
fSignal2.SetParNames("#Sigma","Mean","Amp")
fSignal2.SetParameters(s2, mean, a2)
# Set background to zero for plotting
fBckgrd =r.TF1("fBckgrd","[0]")
fBckgrd.SetParNames("Const")
fBckgrd.SetParameter(0, const)
fComponents.append(fSignal1)
fComponents.append(fSignal2)
fComponents.append(fBckgrd)
functions = [fittedFunctions[0], fComponents[0], fComponents[1], fComponents[2], fittedFunctions[1], fComponents[3], fComponents[4], fComponents[5]]
return [functions, result]
内存视图其实是泛化和去数学化的NumPy数组.它让你在不需要复制内容的前提下,在数据结构之间共享内存.其中
数据结构可以是任何形式,比如PIL图片,SQLite数据库和NumPy的数组,等等.
这个功能在处理大型数据集合的时候非常重要.
memoryview.cast的概念跟数组模块类似,能用不同的方式读写同一块内存数据,而且内容字节不会随意移动.
这听上去又跟C语言中的类型转换的概念差不多.memoryview.cast会把同一块内存里的内容打包成一个全新的memoryview
对象给你.
"""
print("------------------------->>>")
# 通过改变数组中的一个字节来更新数组里某个元素的值
import array
numbers = array.array("h", [-2, -1, 0, 1, 2])
memv = memoryview(numbers)
print(len(memv)) # 5
print(memv[0]) # -2
memv_oct = memv.cast("B")
print(memv_oct.tolist()) # [254, 255, 255, 255, 0, 0, 1, 0, 2, 0]
print(memv_oct[5]) # 0
# 因为我们把占两个字节的整数的高位字节改成了4,所以这个有符号整数的值就变成了1024
memv_oct[5] = 4
print(numbers) # array('h', [-2, -1, 1024, 1, 2])
"""
另外,如果利用数组来做高级的数字处理是你的日常工作,那么NumPy和SciPy应该是你的常用武器.
"""
def read_subimage(self, rows, cols, bands=None, use_memmap=False):
'''
Reads arbitrary rows, columns, and bands from the image.
Arguments:
`rows` (list of ints):
Indices of rows to read.
`cols` (list of ints):
Indices of columns to read.
`bands` (list of ints):
Optional list of bands to read. If not specified, all bands
are read.
`use_memmap` (bool, default False):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Returns:
:class:`numpy.ndarray`
An `MxNxL` array, where `M` = len(`rows`), `N` = len(`cols`),
and `L` = len(bands) (or # of image bands if `bands` == None).
'''
import array
if self._memmap is not None and use_memmap is True:
if bands is None:
data = np.array(self._memmap.take(rows, 0).take(cols, 1))
else:
data = np.array(
self._memmap.take(rows, 0).take(cols, 1).take(bands, 2))
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
offset = self.offset
nbands = self.nbands
nSubRows = len(rows) # Rows in sub-image
nSubCols = len(cols) # Cols in sub-image
d_band = self.sample_size
d_col = d_band * self.nbands
d_row = d_col * self.ncols
vals = array.array(byte_typecode)
nVals = self.nrows * self.ncols
sample_size = self.sample_size
# Increments between bands
if bands is not None:
allBands = 0
nSubBands = len(bands)
else:
allBands = 1
bands = list(range(self.nbands))
nSubBands = self.nbands
f = self.fid
# Pixel format is BIP
for i in rows:
for j in cols:
if allBands:
f.seek(offset + i * d_row + j * d_col, 0)
vals.fromfile(f, nSubBands * sample_size)
else:
for k in bands:
f.seek(offset +
i * d_row +
j * d_col +
k * d_band, 0)
vals.fromfile(f, sample_size)
arr = np.fromstring(tobytes(vals), dtype=self.dtype)
arr = arr.reshape(nSubRows, nSubCols, nSubBands)
if self.scale_factor != 1:
return arr / float(self.scale_factor)
return arr
systTree.setWeightName("lepSF",1.)
systTree.setWeightName("lepUp",1.)
systTree.setWeightName("lepDown",1.)
systTree.setWeightName("PFSF",1.)
systTree.setWeightName("PFUp",1.)
systTree.setWeightName("PFDown",1.)
#++++++++++++++++++++++++++++++++++
#++ variables to branch ++
#++++++++++++++++++++++++++++++++++
#++++++++++++++++++++++++++++++++++
#++ All category ++
#++++++++++++++++++++++++++++++++++
isdileptonic = array.array('i', [0])
muon_pt = array.array('f', [0.])
muon_eta = array.array('f', [0.])
muon_phi = array.array('f', [0.])
muon_m = array.array('f', [0.])
muon_SF = array.array('f', [0.])
electron_pt = array.array('f', [0.])
electron_eta = array.array('f', [0.])
electron_phi = array.array('f', [0.])
electron_m = array.array('f', [0.])
electron_SF = array.array('f', [0.])
nJet_lowpt_all = array.array('i', [0])
nfatJet_all = array.array('i', [0])
nJet_pt100_all = array.array('i', [0])
nbJet_lowpt_all = array.array('i', [0])
print(floats[-1])
# file mode write and binary
fp = open('floats.bin', 'wb')
floats.tofile(fp)
fp.close()
fp = open('floats.bin', 'rb')
floats2 = array('d')
floats2.fromfile(fp, 10**7)
fp.close()
print(floats2[-1])
floats2 == floats
import array
numbers = array.array('h', [-2, -1, 0, 1, 2] ) # h: signed short
memv = memoryview(numbers)
print(len(memv))
print(memv[0])
memv_oct = memv.cast('B')
print(memv_oct.tolist())
memv_oct[5] = 4
print(numbers)
import numpy as np
a = np.arange(12)
print(a)
print(type(a))
print(a.shape)
def read_subregion(self, row_bounds, col_bounds, bands=None,
use_memmap=True):
'''
Reads a contiguous rectangular sub-region from the image.
Arguments:
`row_bounds` (2-tuple of ints):
(a, b) -> Rows a through b-1 will be read.
`col_bounds` (2-tuple of ints):
(a, b) -> Columnss a through b-1 will be read.
`bands` (list of ints):
Optional list of bands to read. If not specified, all bands
are read.
`use_memmap` (bool, default True):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Returns:
:class:`numpy.ndarray`
An `MxNxL` array.
'''
import array
if self._memmap is not None and use_memmap is True:
if bands is None:
data = np.array(self._memmap[row_bounds[0]: row_bounds[1],
col_bounds[0]: col_bounds[1], :])
else:
data = np.array(self._memmap[row_bounds[0]: row_bounds[1],
col_bounds[0]: col_bounds[1],
bands])
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
offset = self.offset
nbands = self.nbands
nSubRows = row_bounds[1] - row_bounds[0] # Rows in sub-image
nSubCols = col_bounds[1] - col_bounds[0] # Cols in sub-image
d_row = self.sample_size * self.ncols * self.nbands
colStartPos = col_bounds[0] * self.sample_size * self.nbands
vals = array.array(byte_typecode)
nVals = self.nrows * self.ncols
sample_size = self.sample_size
# Increments between bands
if bands is not None:
allBands = 0
nSubBands = len(bands)
delta_b = bands[:]
for i in range(len(delta_b)):
delta_b[i] *= self.sample_size
else:
allBands = 1
nSubBands = self.nbands
f = self.fid
# Pixel format is BIP
for i in range(row_bounds[0], row_bounds[1]):
f.seek(offset + i * d_row + colStartPos, 0)
rowPos = f.tell()
if allBands:
# This is the simple one
vals.fromfile(f, nSubCols * nbands * sample_size)
else:
# Need to pull out specific bands for each column.
for j in range(nSubCols):
f.seek(rowPos + j * self.sample_size * self.nbands, 0)
pixelPos = f.tell()
for k in range(len(bands)):
f.seek(pixelPos + delta_b[k], 0) # Next band
vals.fromfile(f, sample_size)
arr = np.fromstring(tobytes(vals), dtype=self.dtype)
arr = arr.reshape(nSubRows, nSubCols, nSubBands)
if self.scale_factor != 1:
return arr / float(self.scale_factor)
return arr
def preparation(track, gms) :
length_hist = {}
distance = { 'sum' : 0, 'count' : 0 }
reads = { 0 : 0, 16 : 0 }
info = {}
data = {}
for read in parser(track) :
pos, chr, strand_key, l_seq, qname = read
if chr < 0 or chr > len(track.chromosome_names) : continue
c = track.chromosome_names[chr]
if l_seq not in length_hist : length_hist[l_seq] = 0
length_hist[l_seq] += 1
# pos += l_seq/2
# New chromosome name:
if c not in info :
firsts = 0
data[c] = array.array('l', [])
info[c] = {
'Length' : track.chromosome_lengths[chr],
'Unique reads' : 0,
'Total reads' : 0,
'Names' : {}
}
firsts += 1
if firsts < 300 :
if strand_key in [99,147,163,83] : # check paired reads
if qname not in info[c]['Names'] :
info[c]['Names'][qname] = pos
else :
dist = abs(info[c]['Names'][qname] - pos)
if dist > 0 :
distance['sum'] += dist
distance['count'] += 1
info[c]['Total reads'] += 1
if strand_key not in reads : reads[strand_key] = 0
reads[strand_key] += 1
prev_key = 'Previous ' + str(strand_key)
ignore = False
if prev_key in info[c] :
if info[c][prev_key] == pos : ignore = True
if ignore == False :
info[c][prev_key] = pos
data[c].append(1000 * pos + strand_key)
info[c]['Unique reads'] += 1
# <- if
# <- for read in parser(track)
total_reads = 0; unique_reads = 0
keys = info.keys()
keys = sorted(keys, key = lambda (c): chrsort(c))
tbl = []
for c in keys :
tbl.append([c, info[c]['Unique reads'], info[c]['Total reads']])
total_reads += info[c]['Total reads']
unique_reads += info[c]['Unique reads']
logging.info("Chromosome name, Unique reads, Total reads:")
logging.info(beautiful_table(tbl))
mean_length = 0
for l in length_hist :
mean_length += l * length_hist[l]
mean_length = mean_length/total_reads
msg = "Average read length: {}"
logging.info(msg.format(mean_length))
drate = round((1 - float(unique_reads)/float(total_reads)) * 100, 1)
msg = "\nLibrary depth\n Duplication rate: {}%\n Total reads: {}\n Unique reads: {}"
logging.info(msg.format(drate, total_reads, unique_reads))
notes = [
[99 , " - Reads mapped in proper pair. Mate reverse strand, first in pair: {}"], # +
[147, " - Reads mapped in proper pair. Read reverse strand, second in pair: {}"], # +
[83 , " - Reads mapped in proper pair. Read reverse strand, first in pair: {}"], # -
[163, " - Reads mapped in proper pair. Mate reverse strand, second in pair: {}"], # -
[97 , " - Mate reverse strand, first in pair: {}"], #
[161, " - Mate reverse strand, second in pair: {}"], # -
[81 , " - Read reverse strand, first in pair: {}"], # +
[145, " - Read reverse strand, second in pair: {}"], #
[113, " - Mate reverse strand, Read reverse strand, first in pair: {}"],
[177, " - Mate reverse strand, Read reverse strand, second in pair: {}"],
[65 , " - First in pair: {}"],
[129, " - Second in pair: {}"],
[73 , " - Mate unmapped, first in pair: {}"],
[137, " - Mate unmapped, second in pair: {}"],
[89 , " - Mate unmapped, read reverse strand, first in pair: {}"],
[153, " - Mate unmapped, read reverse strand, second in pair: {}"]
]
def paired() :
for x in notes :
if x[0] in reads : return True
return False
logging.info("\nStrand symmetry")
fragment_size = fragmentsize
if paired() :
if fragment_size == 0 :
fragment_size = distance['sum']/distance['count'] + mean_length
for key, msg in notes :
logging.info(msg.format(reads[key] if key in reads else 0))
else :
if fragment_size == 0 :
fragment_size = 250
logging.info(" [+] " + str(reads[0]))
logging.info(" [-] " + str(reads[16]))
logging.info("\nFragment size: " + str(fragment_size))
logging.info("")
effective_len = get_gms(info, mean_length, gms) * sum(track.chromosome_lengths)
plambda = count_lambda(unique_reads, args.window, effective_len)
logging.info("")
logging.info("Genome Length: {}".format(sum(track.chromosome_lengths)))
logging.info("Effective genome Length: {}".format(effective_len))
return [data, mean_length, fragment_size, plambda, total_reads, effective_len]
def getTaskSrcTable(taskID):
return array.array(ManageTable)
def controlPlots(datasets):
normData, normEWK, normFactorisedData, normFactorisedEWK = normalisation()
norm_inc, normEWK_inc = normalisationInclusive()
hmet = []
hmetb = []
effArray = []
effErrArray = []
hmetbveto = []
hmtBtag = []
hmtBveto = []
hmtNoMetBtag = []
hmtNoMetBveto = []
effBvetoArray = []
effErrBvetoArray = []
effArrayMt = []
effErrArrayMt = []
effArrayMtNoMet = []
effErrArrayMtNoMet = []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
### MET
mmt_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdJets" + ptbin)
])
mmt_tmp._setLegendStyles()
mmt_tmp._setLegendLabels()
mmt_tmp.histoMgr.setHistoDrawStyleAll("P")
mmt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmt = mmt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmt.Scale(normData[ptbin])
# hmt.append(mt)
mmtEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdJets" + ptbin)
])
mmtEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtEWK_tmp._setLegendStyles()
mmtEWK_tmp._setLegendLabels()
mmtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtEWK = mmtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtEWK.Scale(normEWK[ptbin])
mmt.Add(mmtEWK, -1)
hmet.append(mmt)
### MET with btagging
mmtb_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdBtag" + ptbin)
])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdBtag" + ptbin)
])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmetb.append(mmtb)
eff = mmtb.Integral() / mmt.Integral()
ereff = sqrt(eff * (1 - eff) / mmt.Integral())
print " pt bin ", ptbin, " btag efficiency from MET = ", eff, " error ", ereff
effArray.append(eff)
effErrArray.append(ereff)
### MET with bveto
mmtbveto_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdBveto" + ptbin)
])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdBveto" + ptbin)
])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmetbveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral() / mmtbveto.Integral()
ereff = sqrt(eff * (1 - eff) / mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from MET = ", eff, " error ", ereff
effBvetoArray.append(eff)
effErrBvetoArray.append(ereff)
## with MT distribution
if False:
### no MET cut
mmtb_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtagNoMetCut" + ptbin)
])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtagNoMetCut" + ptbin)
])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmtNoMetBtag.append(mmtb)
### MET with bvet
mmtbveto_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBvetoNoMetCut" + ptbin)
])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBvetoNoMetCut" + ptbin)
])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmtNoMetBveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral() / mmtbveto.Integral()
ereff = sqrt(eff * (1 - eff) / mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from mt, no met cut = ", eff, " error ", ereff
effArrayMtNoMet.append(eff)
effErrArrayMtNoMet.append(ereff)
#############################################
### with MET cut
mmtb_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtag" + ptbin)
])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtag" + ptbin)
])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmtBtag.append(mmtb)
mmtbveto_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBveto" + ptbin)
])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBveto" + ptbin)
])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmtBveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral() / mmtbveto.Integral()
ereff = sqrt(eff * (1 - eff) / mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from mt = ", eff, " error ", ereff
effArrayMt.append(eff)
effErrArrayMt.append(ereff)
## sum histo bins
met = hmet[0].Clone("met")
met.SetName("MET")
met.SetTitle("Inverted tau Met")
met.Reset()
print "check met", met.GetEntries()
for histo in hmet:
met.Add(histo)
metb = hmetb[0].Clone("met")
metb.SetName("MET")
metb.SetTitle("Inverted tau Met")
metb.Reset()
print "check met btagging", metb.GetEntries()
for histo in hmetb:
metb.Add(histo)
metbveto = hmetbveto[0].Clone("met")
metbveto.SetName("METbveto")
metbveto.SetTitle("Inverted tau Met")
metbveto.Reset()
print "check met bveto", metbveto.GetEntries()
for histo in hmetbveto:
metbveto.Add(histo)
## with MT
if False:
mtNoMetBtag = hmtNoMetBtag[0].Clone("mt")
mtNoMetBtag.SetName("MET")
mtNoMetBtag.SetTitle("Inverted tau Met")
mtNoMetBtag.Reset()
print "check MT btagging", mtNoMetBtag.GetEntries()
for histo in hmtNoMetBtag:
mtNoMetBtag.Add(histo)
mtNoMetBveto = hmtNoMetBveto[0].Clone("mt")
mtNoMetBveto.SetName("MET")
mtNoMetBveto.SetTitle("Inverted tau Met")
mtNoMetBveto.Reset()
print "check MT bveto", mtNoMetBveto.GetEntries()
for histo in hmtNoMetBveto:
mtNoMetBveto.Add(histo)
mtBtag = hmtBtag[0].Clone("mt")
mtBtag.SetName("MET")
mtBtag.SetTitle("Inverted tau Met")
mtBtag.Reset()
print "check MT btagging", mtBtag.GetEntries()
for histo in hmtBtag:
mtBtag.Add(histo)
mtBveto = hmtBveto[0].Clone("mt")
mtBveto.SetName("MET")
mtBveto.SetTitle("Inverted tau Met")
mtBveto.Reset()
print "check MT bveto", mtBveto.GetEntries()
for histo in hmtBveto:
mtBveto.Add(histo)
##########################################
## plotting
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
### effisiency as a function of MET
metWithBtagging = metb.Clone("MET")
metWithBtagging.Divide(metbveto)
BtaggingEffVsMet = metWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffVsMet")
invertedQCD.mtComparison(BtaggingEffVsMet, BtaggingEffVsMet,
"BtagToBvetoEffVsMet")
if False:
### effisiency as a function of MT
mtWithBtagging = mtNoMetBtag.Clone("MT")
mtWithBtagging.Divide(mtNoMetBveto)
BtaggingEffNoMetVsMt = mtWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffNoMetVsMt")
invertedQCD.mtComparison(BtaggingEffNoMetVsMt, BtaggingEffNoMetVsMt,
"BtagToBvetoEffNoMetVsMt")
### effisiency as a function of MT
mtWithBtagging = mtBtag.Clone("MT")
mtWithBtagging.Divide(mtBveto)
BtaggingEffVsMt = mtWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffVsMt")
invertedQCD.mtComparison(BtaggingEffVsMt, BtaggingEffVsMt,
"BtagToBvetoEffVsMt")
# efficiency metb/met
metbtag = metb.Clone("metb")
metnobtag = met.Clone("met")
metbtag.Divide(metnobtag)
invertedQCD.setLabel("BtagEffVsMet")
invertedQCD.mtComparison(metbtag, metbtag, "BtagEffVsMet")
# efficiency metb/metbveto
metbtag = metb.Clone("metb")
metbjetveto = metbveto.Clone("met")
invertedQCD.setLabel("BtagToBvetoEfficiency")
invertedQCD.mtComparison(metbtag, metbjetveto, "BtagToBvetoEfficiency")
### Create and customise TGraph
cEff = TCanvas("Efficiency", "Efficiency", 1)
cEff.cd()
ptbin_error = array.array("d", [5, 5, 5, 5, 10, 10, 30])
ptbin = array.array("d", [45, 55, 65, 75, 90, 110, 150])
if False:
## no MET cut
cEff = TCanvas("btaggingEffNoMet", "btaggingEffNoMet", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", effArrayMtNoMet),
ptbin_error, array.array("d", effErrArrayMtNoMet))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.8, "No MET cut")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffNoMetVsPtTau_mt.png")
## with MET cut
cEff = TCanvas("btaggingEff", "btaggingEff", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", effArrayMt), ptbin_error,
array.array("d", effErrArrayMt))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.8, "After MET cut")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffVsPtTau_mt.png")
## no MET cut
cEff = TCanvas("btaggingEffNoMet", "btaggingEffNoMet", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", effBvetoArray),
ptbin_error, array.array("d", effErrBvetoArray))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.8, "No MET cut")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffNoMetVsPtTau.png")
## with MET cut
cEff = TCanvas("btaggingEfficiency", "btaggingEfficiency", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", effArray), ptbin_error,
array.array("d", effErrArray))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("b-tagging efficiency")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.4, 0.85, "Inverted #tau identification")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.4, 0.78, "At least 3 jets")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btaggingEffVsPtTau.png")
##################################3
fOUT = open("btaggingFactors", "w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n" % os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingFactors = {\n")
i = 0
while i < len(effArray):
line = " \"" + ptbins[i] + "\": " + str(effArray[i])
if i < len(effArray) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "B-tagging efficiensies written in file", "btaggingFactors"
fOUT = open("btaggingToBvetoFactors.py", "w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n" % os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingToBvetoFactors = {\n")
i = 0
while i < len(effBvetoArray):
line = " \"" + ptbins[i] + "\": " + str(effBvetoArray[i])
if i < len(effBvetoArray) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "BtaggingToBveto efficiensies written in file", "btaggingToBvetoFactors"
fOUT = open("btaggingToBvetoAfterMetFactors.py", "w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n" % os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingToBvetoAfterMetFactors = {\n")
i = 0
while i < len(effArrayMt):
line = " \"" + ptbins[i] + "\": " + str(effArrayMt[i])
if i < len(effArrayMt) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "BtaggingToBvetoAfterMet efficiensies written in file", "btaggingToBvetoFactors"
oC = r.TCanvas()
dummyHist = r.TH1D("dummy", ";c#tau [m]; 95% CL UL on BR(H #rightarrow ss)",
100, 0.01, 1000)
dummyHist.SetMaximum(100)
dummyHist.SetMinimum(1E-4)
oC.SetLogy()
oC.SetLogx()
dummyHist.Draw()
graphs = []
leg = r.TLegend(0.6, 0.7, 0.89, 0.89)
leg.SetBorderSize(0)
for mass in [15, 40, 55]:
inputArrays[mass] = sorted(inputArrays[mass])
inputArrays[mass].append(
[inputArrays[mass][-1][0] * 10, inputArrays[mass][-1][1] * 10])
xVals = array.array('d', [x[0] / 1000. for x in inputArrays[mass]])
yVals = array.array('d', [x[1] / 100. for x in inputArrays[mass]])
inputArraysUncUp[mass] = sorted(inputArraysUncUp[mass])
inputArraysUncUp[mass].append([
inputArraysUncUp[mass][-1][0] * 10, inputArraysUncUp[mass][-1][1] * 10
])
xValsUp = array.array('d', [x[0] / 1000. for x in inputArraysUncUp[mass]])
yValsUp = array.array('d', [x[1] / 100. for x in inputArraysUncUp[mass]])
inputArraysUncDown[mass] = sorted(inputArraysUncDown[mass])
inputArraysUncDown[mass].append([
inputArraysUncDown[mass][-1][0] * 10,
inputArraysUncDown[mass][-1][1] * 10
])
xValsDown = array.array('d',
[x[0] / 1000. for x in inputArraysUncDown[mass]])
yValsDown = array.array('d',
from array import array
array1: object = array.array('i', [0, 1])
array2 = array.array('i', [2, 3, 4])
array1.extend(array2)
print(array1) # array('i', [0, 1, 2, 3, 4])
print(array2) # array('i', [2, 3, 4])
array2.extend([1, 2])
print(array2) # array('i', [2, 3, 4, 1, 2])
array1 = array.array('i', [1])
array1.extend(set([0, 0, 0, 2]))
print(array1) # array('i', [1, 0, 2])v
def plot(var, cut, region, plotter_config, **kw):
global legend, watermarks, canv, shared
#
# Un pack som plotter config
#
foldersTag = plotter_config["foldersTag"]
files = plotter_config["files"]
samples = plotter_config["samples"]
weights = plotter_config["weights"]
is_data = plotter_config["is_data"]
stack = plotter_config["stack"]
colors = plotter_config["colors"]
rColors = plotter_config["rColors"]
overlay = plotter_config["overlay"]
labels = plotter_config["labels"]
output = plotter_config["output"]
testSampleName = samples[0]["name"]
testDirName = foldersTag[testSampleName]
if var.find("*") != -1:
return listVars(var.replace("*", ""), cut + testDirName + region,
plotter_config)
xtitle = None
ytitle = None
name = None
x_min = None
x_max = None
if not plotter_config["useTree"]:
if cut + foldersTag[testSampleName] + region != "":
histPath = cut + foldersTag[testSampleName] + region + "/" + var
else:
histPath = var
testHist = files[samples[0]["name"]].Get(histPath)
xtitle = testHist.GetXaxis().GetTitle()
ytitle = testHist.GetYaxis().GetTitle()
name = testHist.GetName()
x_min = testHist.GetXaxis().GetXmin()
x_max = testHist.GetXaxis().GetXmax()
bins = kw.get('bins', None)
xtitle = kw.get('xtitle', xtitle)
ytitle = kw.get('ytitle', ytitle)
selection = kw.get('selection', None)
name = kw.get('name', name)
n_bins = kw.get('n_bins', None)
x_min = kw.get('x_min', x_min)
x_max = kw.get('x_max', x_max)
rebin = kw.get('rebin', None)
logY = kw.get('logY', None)
options = kw.get('options', '')
canvSize = kw.get('canvSize', [700, 700])
rMargin = kw.get('rMargin', 0)
hists = {}
draw = {}
if bins:
draw["bins"] = array.array("d", [float(x) for x in bins])
draw["title"] = ";%s;%s" % (xtitle, ytitle)
draw["variable"] = var
if selection: draw["selection"] = " && ".join(selection)
canv = ROOT.TCanvas(name, name, canvSize[0], canvSize[1])
canv.Draw()
canv.Update()
canv.SetLogy(logY)
stacks = ROOT.THStack(name + "stacks", draw["title"])
overlays = ROOT.THStack(name + "overlays", draw["title"])
do_stack = False
do_overlay = False
for sample in reversed(sorted(samples)):
sampleName = sample["name"]
draw["name"] = name + "__" + sampleName
draw["weight"] = weights[sampleName]
if plotter_config["useTree"]:
for option in ["weight"]:
draw[option] = "(%s)" % (draw[option])
if plotter_config["useTree"]:
if bins:
hists[sampleName] = ROOT.TH1F(draw["name"], draw["title"],
len(draw["bins"]) - 1,
draw["bins"])
else:
hists[sampleName] = ROOT.TH1F(draw["name"], draw["title"],
n_bins, x_min, x_max)
hists[sampleName].Sumw2()
else:
if cut + foldersTag[sampleName] + region != "":
histPath = cut + foldersTag[sampleName] + region + "/" + draw[
"variable"]
else:
histPath = draw["variable"]
hists[sampleName] = files[sampleName].Get(histPath)
hists[sampleName].SetName(draw["name"])
hists[sampleName].SetTitle(draw["title"])
if "TH2" in str(hists[sampleName]):
hists[sampleName].RebinX(rebin)
hists[sampleName].RebinY(rebin)
hists[sampleName].GetYaxis().SetTitleOffset(1.5)
else:
if isinstance(rebin, list):
hists[sampleName] = helpers.do_variable_rebinning(
hists[sampleName], rebin)
elif rebin:
hists[sampleName].Rebin(rebin)
if x_max:
x_min = float(x_min)
x_max = float(x_max)
hists[sampleName].GetXaxis().SetRangeUser(x_min, x_max)
if is_data[sampleName]:
hists[sampleName].SetMarkerStyle(20)
hists[sampleName].SetMarkerSize(1)
if plotter_config["useTree"]:
trees[sampleName].Draw("%(variable)s >> %(name)s" % draw,
"(%(selection)s) * %(weight)s" % draw,
"goff")
else:
hists[sampleName].Sumw2()
hists[sampleName].Scale(float(draw["weight"]))
hists[sampleName].Draw(options)
if rMargin: ROOT.gPad.SetRightMargin(rMargin)
# hists[sampleName].Scale(1/hists[sampleName].Integral(0, hists[sampleName].GetNbinsX()))
if stack[sampleName]:
do_stack = True
hists[sampleName].SetFillColor(colors[sampleName])
hists[sampleName].SetLineColor(ROOT.kBlack)
hists[sampleName].SetLineWidth(2)
stacks.Add(copy.copy(hists[sampleName]),
("ep" if is_data[sampleName] else "hist"))
if not plotter_config[
"useTree"] and x_max: #have to set stack xaxis range for zooming because ROOT SUCKS
stacks.Draw()
stacks.GetXaxis().SetRangeUser(x_min, x_max)
if overlay[sampleName]:
do_overlay = True
hists[sampleName].SetFillColor(0)
hists[sampleName].SetLineColor(colors[sampleName])
hists[sampleName].SetLineWidth(3)
overlays.Add(copy.copy(hists[sampleName]),
("ep" if is_data[sampleName] else "hist"))
print sampleName
#print "Integral:",hists[sample].Integral(0, hists[sample].GetNbinsX()+1)
print "Integral:", hists[sampleName].Integral()
print " Entries:", hists[sampleName].GetEntries()
# draw
maximum = max([stacks.GetMaximum(), overlays.GetMaximum("nostack")])
maximum = maximum * (20.0 if logY else 1.3)
maximum = maximum * (1.2 if plotter_config['ratio'] else 1.0)
minimum = max([stacks.GetMinimum(), overlays.GetMinimum("nostack")])
minimum = (minimum / 2 if logY else 0)
if do_stack:
stacks.SetMaximum(maximum)
stacks.SetMinimum(minimum)
stacks.Draw()
h1stackerror = copy.copy(stacks.GetStack().Last())
h1stackerror.SetName("stat. error")
h1stackerror.SetFillColor(ROOT.kGray + 3)
h1stackerror.SetFillStyle(3005)
h1stackerror.SetMarkerStyle(0)
h1stackerror.Draw("SAME,E2")
if do_overlay and do_stack:
overlays.SetMaximum(maximum)
overlays.SetMinimum(minimum)
overlays.Draw("nostack,same")
elif do_overlay:
overlays.SetMaximum(maximum)
overlays.SetMinimum(minimum)
overlays.Draw("nostack")
if plotter_config["data"]:
pass
if plotter_config["ratio"] and stacks.GetStack():
# numerator definition is a placeholder.
# only works if overlay[0]=data.
if plotter_config["autoRatio"]:
top = overlays.GetHists()[0].Clone("hnew")
bottom = stacks.GetStack().Last().Clone("hnew")
top.Divide(bottom)
ratioBins = []
for bin in range(top.GetSize()):
if top.GetBinContent(bin) != 0.0 and top.GetBinContent(
bin) * top.GetBinError(bin) < 5.0:
ratioBins.append(top.GetBinContent(bin))
if not ratioBins: ratioBins = [0]
ratioMin = float(int(min(ratioBins) * 100)) / 100
ratioMax = float(int(max(ratioBins) * 100)) / 100
if ratioMax - ratioMin < 0.1:
ratioMin = ratioMin - .05
ratioMax = ratioMax + 0.5
else:
ratioMin = 0
ratioMax = 2
if sampleName not in rColors.keys(): rColors[sampleName] = ROOT.kRed
ratio = helpers.ratio(
name=canv.GetName() + "_ratio",
numer=overlays.GetHists().Last(), # AHH KILL ME
denom=stacks.GetStack().Last(),
min=ratioMin,
max=ratioMax,
ytitle="Data / pred.",
color=rColors[sampleName])
canv.SetFillColorAlpha(1, 0.0)
share, top_pad, bottom_pad = helpers.same_xaxis(
name=canv.GetName() + "_share",
top_canvas=canv,
bottom_canvas=ratio,
)
canv.SetName(canv.GetName() + "_noratio")
share.SetName(share.GetName().replace("_share", ""))
canv = share
elif plotter_config["ratio"] and not stacks.GetStack():
warn("Want to make ratio plot but dont have stack. Skipping ratio.")
# stack legend
if do_stack or do_overlay:
xleg, yleg = 0.79, 0.79
legend = ROOT.TLegend(xleg, yleg, xleg + 0.15, (yleg + 0.125))
if do_overlay:
for hist in reversed(overlays.GetHists()):
legend.AddEntry(hist, labels[hist.GetName().split("__")[1]],
"l")
if do_stack:
for hist in reversed(stacks.GetHists()):
legend.AddEntry(hist, labels[hist.GetName().split("__")[1]],
"f")
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetMargin(0.3)
legend.SetTextSize(0.03)
legend.Draw()
legend.Draw()
# watermarks
xatlas, yatlas = 0.5, 0.90
atlas = ROOT.TLatex(xatlas + 0.02, yatlas, "ATLAS Internal")
hh4b = ROOT.TLatex(xatlas + 0.015, yatlas - 0.042,
"X #rightarrow HH #rightarrow 4b")
lumi = ROOT.TLatex(
xatlas, yatlas - 0.10,
"#sqrt{s} = 13 TeV, #int L dt = " + plotter_config["lumi"])
watermarks = [atlas, hh4b, lumi]
# KS, chi2
if stacks.GetStack():
if plotter_config.get("ks"):
print "Getting KS from:", overlays.GetHists().Last(
), stacks.GetStack().Last()
kolg, chi2, ndf = helpers.compare(
overlays.GetHists().Last(),
stacks.GetStack().Last(),
) # AH KILL ME
yks = 0.975
ychi2 = 0.975
xks = 0.27
xchi2 = 0.55
ks = ROOT.TLatex(xks, yks, "KS = %5.3f" % (kolg))
if ndf:
ch = ROOT.TLatex(
xchi2, ychi2, "#chi^{2} / ndf = %.1f / %i = %.3f" %
(chi2, ndf, chi2 / ndf))
else:
ch = ROOT.TLatex(xchi2, ychi2,
"#chi^{2} / ndf = %.1f / %i" % (chi2, ndf))
watermarks += [ks, ch]
wmNum = 0
# draw watermarks
for wm in watermarks:
wm.SetTextAlign(22)
if wmNum == 0:
wm.SetTextSize(0.04)
wm.SetTextFont(72)
#elif wmNum == 1:
# wm.SetTextSize(0.04)
# wm.SetTextFont(62)
else:
wm.SetTextSize(0.03)
wm.SetTextFont(42)
wmNum += 1
wm.SetNDC()
wm.Draw()
canv.SaveAs(
os.path.join(plotter_config["directory"],
canv.GetName() + ".pdf"))
output.cd()
canv.Write()
canv.Update()
def plot(var, cut, region, plotter_config, **kw):
global legend, watermarks, canv, shared
#
# Un pack som plotter config
#
foldersTag = plotter_config["foldersTag"]
files = plotter_config["files"]
samples = plotter_config["samples"]
weights = plotter_config["weights"]
is_data = plotter_config["is_data"]
stack = plotter_config["stack"]
colors = plotter_config["colors"]
rColors = plotter_config["rColors"]
overlay = plotter_config["overlay"]
labels = plotter_config["labels"]
output = plotter_config["output"]
testSampleName = samples[0]["name"]
testDirName = foldersTag[testSampleName]
if var.find("*") != -1: return listVars(var.replace("*",""),cut+testDirName+region, plotter_config)
xtitle = None
ytitle = None
name = None
x_min = None
x_max = None
if not plotter_config["useTree"]:
if cut+foldersTag[testSampleName]+region != "":
histPath = cut+foldersTag[testSampleName]+region+"/"+var
else:
histPath = var
testHist = files[samples[0]["name"]].Get(histPath)
xtitle = testHist.GetXaxis().GetTitle()
ytitle = testHist.GetYaxis().GetTitle()
name = testHist.GetName()
x_min = testHist.GetXaxis().GetXmin()
x_max = testHist.GetXaxis().GetXmax()
bins = kw.get('bins' , None)
xtitle = kw.get('xtitle' , xtitle)
ytitle = kw.get('ytitle' , ytitle)
selection = kw.get('selection', None)
name = kw.get('name' , name)
n_bins = kw.get('n_bins' , None)
x_min = kw.get('x_min' , x_min)
x_max = kw.get('x_max' , x_max)
rebin = kw.get('rebin' , None)
logY = kw.get('logY' , None)
options = kw.get('options' , '')
canvSize = kw.get('canvSize' , [700,700])
rMargin = kw.get('rMargin' , 0)
hists = {}
draw = {}
if bins:
draw["bins"] = array.array("d", [float(x) for x in bins])
draw["title"] = ";%s;%s" % (xtitle, ytitle)
draw["variable"] = var
if selection: draw["selection"] = " && ".join(selection)
canv = ROOT.TCanvas(name, name, canvSize[0], canvSize[1])
canv.Draw()
canv.Update()
canv.SetLogy(logY)
stacks = ROOT.THStack(name+"stacks", draw["title"])
overlays = ROOT.THStack(name+"overlays", draw["title"])
do_stack = False
do_overlay = False
for sample in reversed(sorted(samples)):
sampleName = sample["name"]
draw["name"] = name+"__"+sampleName
draw["weight"] = weights[sampleName]
if plotter_config["useTree"]:
for option in ["weight"]:
draw[option] = "(%s)" % (draw[option])
if plotter_config["useTree"]:
if bins:
hists[sampleName] = ROOT.TH1F(draw["name"], draw["title"], len(draw["bins"])-1, draw["bins"])
else:
hists[sampleName] = ROOT.TH1F(draw["name"], draw["title"], n_bins, x_min, x_max)
hists[sampleName].Sumw2()
else:
if cut+foldersTag[sampleName]+region != "": histPath = cut+foldersTag[sampleName]+region+"/"+draw["variable"]
else: histPath = draw["variable"]
hists[sampleName] = files[sampleName].Get(histPath)
hists[sampleName].SetName(draw["name"])
hists[sampleName].SetTitle(draw["title"])
if "TH2" in str(hists[sampleName]):
hists[sampleName].RebinX(rebin)
hists[sampleName].RebinY(rebin)
hists[sampleName].GetYaxis().SetTitleOffset(1.5)
else:
if isinstance(rebin,list):
hists[sampleName]=helpers.do_variable_rebinning(hists[sampleName], rebin)
elif rebin:
hists[sampleName].Rebin(rebin)
if x_max:
x_min = float(x_min)
x_max = float(x_max)
hists[sampleName].GetXaxis().SetRangeUser(x_min,x_max)
if is_data[sampleName]:
hists[sampleName].SetMarkerStyle(20)
hists[sampleName].SetMarkerSize(1)
if plotter_config["useTree"]: trees[sampleName].Draw("%(variable)s >> %(name)s" % draw, "(%(selection)s) * %(weight)s" % draw, "goff")
else :
hists[sampleName].Sumw2()
hists[sampleName].Scale(float(draw["weight"]))
hists[sampleName].Draw(options)
if rMargin: ROOT.gPad.SetRightMargin(rMargin)
# hists[sampleName].Scale(1/hists[sampleName].Integral(0, hists[sampleName].GetNbinsX()))
if stack[sampleName]:
do_stack = True
hists[sampleName].SetFillColor(colors[sampleName])
hists[sampleName].SetLineColor(ROOT.kBlack)
hists[sampleName].SetLineWidth(2)
stacks.Add(copy.copy(hists[sampleName]), ("ep" if is_data[sampleName] else "hist"))
if not plotter_config["useTree"] and x_max:#have to set stack xaxis range for zooming because ROOT SUCKS
stacks.Draw()
stacks.GetXaxis().SetRangeUser(x_min,x_max)
if overlay[sampleName]:
do_overlay = True
hists[sampleName].SetFillColor(0)
hists[sampleName].SetLineColor(colors[sampleName])
hists[sampleName].SetLineWidth(3)
overlays.Add(copy.copy(hists[sampleName]), ("ep" if is_data[sampleName] else "hist"))
print sampleName
#print "Integral:",hists[sample].Integral(0, hists[sample].GetNbinsX()+1)
print "Integral:",hists[sampleName].Integral()
print " Entries:",hists[sampleName].GetEntries()
# draw
maximum = max([stacks.GetMaximum(), overlays.GetMaximum("nostack")])
maximum = maximum*(20.0 if logY else 1.3)
maximum = maximum*(1.2 if plotter_config['ratio'] else 1.0)
minimum = max([stacks.GetMinimum(), overlays.GetMinimum("nostack")])
minimum = (minimum/2 if logY else 0)
if do_stack:
stacks.SetMaximum(maximum)
stacks.SetMinimum(minimum)
stacks.Draw()
h1stackerror = copy.copy(stacks.GetStack().Last())
h1stackerror.SetName("stat. error")
h1stackerror.SetFillColor(ROOT.kGray+3)
h1stackerror.SetFillStyle(3005)
h1stackerror.SetMarkerStyle(0)
h1stackerror.Draw("SAME,E2")
if do_overlay and do_stack:
overlays.SetMaximum(maximum)
overlays.SetMinimum(minimum)
overlays.Draw("nostack,same")
elif do_overlay:
overlays.SetMaximum(maximum)
overlays.SetMinimum(minimum)
overlays.Draw("nostack")
if plotter_config["data"]:
pass
if plotter_config["ratio"] and stacks.GetStack():
# numerator definition is a placeholder.
# only works if overlay[0]=data.
if plotter_config["autoRatio"]:
top = overlays.GetHists()[0].Clone("hnew")
bottom = stacks.GetStack().Last().Clone("hnew")
top.Divide(bottom)
ratioBins = []
for bin in range(top.GetSize()):
if top.GetBinContent(bin) != 0.0 and top.GetBinContent(bin)*top.GetBinError(bin) < 5.0:
ratioBins.append(top.GetBinContent(bin))
if not ratioBins: ratioBins = [0]
ratioMin = float(int(min(ratioBins)*100))/100
ratioMax = float(int(max(ratioBins)*100))/100
if ratioMax-ratioMin < 0.1:
ratioMin = ratioMin-.05
ratioMax = ratioMax+0.5
else:
ratioMin = 0
ratioMax = 2
if sampleName not in rColors.keys(): rColors[sampleName] = ROOT.kRed
ratio = helpers.ratio(name = canv.GetName()+"_ratio",
numer = overlays.GetHists().Last(), # AHH KILL ME
denom = stacks.GetStack().Last(),
min = ratioMin,
max = ratioMax,
ytitle = "Data / pred.",
color = rColors[sampleName]
)
canv. SetFillColorAlpha(1, 0.0);
share,top_pad, bottom_pad = helpers.same_xaxis(name = canv.GetName()+"_share",
top_canvas = canv,
bottom_canvas = ratio,
)
canv .SetName(canv.GetName()+"_noratio")
share.SetName(share.GetName().replace("_share", ""))
canv = share
elif plotter_config["ratio"] and not stacks.GetStack():
warn("Want to make ratio plot but dont have stack. Skipping ratio.")
# stack legend
if do_stack or do_overlay:
xleg, yleg = 0.79, 0.79
legend = ROOT.TLegend(xleg, yleg, xleg+0.15, (yleg+0.125))
if do_overlay:
for hist in reversed(overlays.GetHists()):
legend.AddEntry(hist, labels[hist.GetName().split("__")[1]], "l")
if do_stack:
for hist in reversed(stacks.GetHists()):
legend.AddEntry(hist, labels[hist.GetName().split("__")[1]], "f")
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetMargin(0.3)
legend.SetTextSize(0.03)
legend.Draw()
legend.Draw()
# watermarks
xatlas, yatlas = 0.5, 0.90
atlas = ROOT.TLatex(xatlas+0.02, yatlas, "ATLAS Internal")
hh4b = ROOT.TLatex(xatlas+0.015, yatlas-0.042, "X #rightarrow HH #rightarrow 4b")
lumi = ROOT.TLatex(xatlas, yatlas-0.10, "#sqrt{s} = 13 TeV, #int L dt = "+plotter_config["lumi"])
watermarks = [atlas, hh4b, lumi]
# KS, chi2
if stacks.GetStack():
if plotter_config.get("ks"):
print "Getting KS from:",overlays.GetHists().Last(),stacks.GetStack().Last()
kolg, chi2, ndf = helpers.compare(overlays.GetHists().Last(),
stacks.GetStack().Last(),
) # AH KILL ME
yks = 0.975
ychi2 = 0.975
xks = 0.27
xchi2 = 0.55
ks = ROOT.TLatex(xks, yks, "KS = %5.3f" % (kolg))
if ndf: ch = ROOT.TLatex(xchi2, ychi2, "#chi^{2} / ndf = %.1f / %i = %.3f" % (chi2, ndf, chi2/ndf))
else: ch = ROOT.TLatex(xchi2, ychi2, "#chi^{2} / ndf = %.1f / %i" % (chi2, ndf))
watermarks += [ks, ch]
wmNum = 0
# draw watermarks
for wm in watermarks:
wm.SetTextAlign(22)
if wmNum == 0:
wm.SetTextSize(0.04)
wm.SetTextFont(72)
#elif wmNum == 1:
# wm.SetTextSize(0.04)
# wm.SetTextFont(62)
else:
wm.SetTextSize(0.03)
wm.SetTextFont(42)
wmNum+=1
wm.SetNDC()
wm.Draw()
canv.SaveAs(os.path.join(plotter_config["directory"], canv.GetName()+".pdf"))
output.cd()
canv.Write()
canv.Update()
#question 41
from array import array
a = array("I", (12,25))
print("Array buffer start address in memory and number of elements.")
print(a.buffer_info())
# In[29]:
#problem 42
import array
import binascii
a = array.array('i', [1,2,3,4,5,6])
print("Original array:")
print('A1:', a)
bytes_array = a.tobytes()
print('Array of bytes:', binascii.hexlify(bytes_array))
# In[30]:
#problem 43
import array
import binascii
a = array.array('i', [1,2,3,4,5,6])
print("Original array:")
print('A1:', a)
legend.cd()
ar_l = dy_l/dx_l
#gap_ = 0.09/ar_l
gap_ = 1./(n_+1)
bwx_ = 0.12
bwy_ = gap_/1.5
x_l = [1.2*bwx_]
#y_l = [1-(1-0.10)/ar_l]
y_l = [1-gap_]
ex_l = [0]
ey_l = [0.04/ar_l]
#array must be converted
x_l = array.array("f",x_l)
ex_l = array.array("f",ex_l)
y_l = array.array("f",y_l)
ey_l = array.array("f",ey_l)
gr_l = rt.TGraphErrors(1, x_l, y_l, ex_l, ey_l)
rt.gStyle.SetEndErrorSize(0)
gr_l.SetMarkerSize(0.9)
gr_l.Draw("0P")
latex.SetTextFont(42)
latex.SetTextAngle(0)
latex.SetTextColor(rt.kBlack)
latex.SetTextSize(0.25)
latex.SetTextAlign(12)
floats.tofile(fp) # writes the binary to file
fp.close()
floats2 = array('d') # init an empty array of type d
fp = open('floats.bin', 'rb')
floats.fromfile(fp,10**7) # read from file to init array
fp.close()
floats2[-1]
# ===================================================================================
# bytes
# two built in type for binary seq: immutable bytes type, mutable bytearray
# both bytes and bytearray support every str method except those that do formating
# endswith(), replace(), strip, translate, upper
# regex supports binary sequence as well
b = bytes(array.array('h',(x for x in range(5)))) # create byte stream from an iterable object
bytes.fromhex('31 4B CE') # build by parsing pairs of hex digits
cafe = bytes('cafe',encoding='utf_8') # encoding only used for string argument
cafe[0] # 99
cafe[:1] # b'c'
cafe_arr = bytearray(cafe)
cafe_arry[:-1] # a slice of a byte array is still a byte array
# ===================================================================================
# memoryview
# like a pointer , to share the underling memory without copy the object
numbers = array.array('h',[-2,-1,0,1,2])
memv = memoryview(numbers)
len(lemv)
import array
rebins = [round(x * 0.1, 1) for x in range(0, 40, 2)]
for x in range(40, 81, 4):
rebins.append(round(x * 0.1, 1))
rebins.append(9.)
rebins.append(10.)
rebins.append(12.)
rebins.append(15.)
rebins.append(20.)
rebins.append(30.)
rebins.append(40.)
rebins.append(50.)
rebins.append(70.)
rebins.append(100.)
bins_array = array.array('d', rebins)
class MassFit:
def __init__(self, year):
self.lumi = 3.2 if year == 2015 else '33.1'
# label
self.beam_condition = '#sqrt{s} = 13 TeV, L = ' + str(self.lumi) + ' fb^{-1}'
# legend
self.x_min = 0.5
self.x_max = 0.85
self.y_min = 0.60
self.y_max = 0.80
# normalisation region mDV > 6 GeV
self.m_cut = 10
# 2nd control region
self.m_cut2 = 8
def controlPlots(datasets):
normData,normEWK,normFactorisedData,normFactorisedEWK=normalisation()
norm_inc,normEWK_inc = normalisationInclusive()
hmet = []
hmetb = []
effArray = []
effErrArray = []
hmetbveto = []
hmtBtag = []
hmtBveto = []
hmtNoMetBtag = []
hmtNoMetBveto = []
effBvetoArray = []
effErrBvetoArray = []
effArrayMt= []
effErrArrayMt= []
effArrayMtNoMet= []
effErrArrayMtNoMet= []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
### MET
mmt_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MET_InvertedTauIdJets"+ptbin)])
mmt_tmp._setLegendStyles()
mmt_tmp._setLegendLabels()
mmt_tmp.histoMgr.setHistoDrawStyleAll("P")
mmt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmt = mmt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmt.Scale(normData[ptbin])
# hmt.append(mt)
mmtEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MET_InvertedTauIdJets"+ptbin)])
mmtEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtEWK_tmp._setLegendStyles()
mmtEWK_tmp._setLegendLabels()
mmtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtEWK = mmtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtEWK.Scale(normEWK[ptbin])
mmt.Add(mmtEWK, -1)
hmet.append(mmt)
### MET with btagging
mmtb_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MET_InvertedTauIdBtag"+ptbin)])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MET_InvertedTauIdBtag"+ptbin)])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmetb.append(mmtb)
eff = mmtb.Integral()/mmt.Integral()
ereff = sqrt(eff*(1-eff)/mmt.Integral())
print " pt bin ", ptbin, " btag efficiency from MET = ",eff, " error ",ereff
effArray.append(eff)
effErrArray.append(ereff)
### MET with bveto
mmtbveto_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MET_InvertedTauIdBveto"+ptbin)])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MET_InvertedTauIdBveto"+ptbin)])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmetbveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral()/mmtbveto.Integral()
ereff = sqrt(eff*(1-eff)/mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from MET = ",eff, " error ",ereff
effBvetoArray.append(eff)
effErrBvetoArray.append(ereff)
## with MT distribution
if False:
### no MET cut
mmtb_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBtagNoMetCut"+ptbin)])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBtagNoMetCut"+ptbin)])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmtNoMetBtag.append(mmtb)
### MET with bvet
mmtbveto_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBvetoNoMetCut"+ptbin)])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBvetoNoMetCut"+ptbin)])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmtNoMetBveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral()/mmtbveto.Integral()
ereff = sqrt(eff*(1-eff)/mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from mt, no met cut = ",eff, " error ",ereff
effArrayMtNoMet.append(eff)
effErrArrayMtNoMet.append(ereff)
#############################################
### with MET cut
mmtb_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBtag"+ptbin)])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBtag"+ptbin)])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmtBtag.append(mmtb)
mmtbveto_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBveto"+ptbin)])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBveto"+ptbin)])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmtBveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral()/mmtbveto.Integral()
ereff = sqrt(eff*(1-eff)/mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from mt = ",eff, " error ",ereff
effArrayMt.append(eff)
effErrArrayMt.append(ereff)
## sum histo bins
met = hmet[0].Clone("met")
met.SetName("MET")
met.SetTitle("Inverted tau Met")
met.Reset()
print "check met",met.GetEntries()
for histo in hmet:
met.Add(histo)
metb = hmetb[0].Clone("met")
metb.SetName("MET")
metb.SetTitle("Inverted tau Met")
metb.Reset()
print "check met btagging",metb.GetEntries()
for histo in hmetb:
metb.Add(histo)
metbveto = hmetbveto[0].Clone("met")
metbveto.SetName("METbveto")
metbveto.SetTitle("Inverted tau Met")
metbveto.Reset()
print "check met bveto",metbveto.GetEntries()
for histo in hmetbveto:
metbveto.Add(histo)
## with MT
if False:
mtNoMetBtag = hmtNoMetBtag[0].Clone("mt")
mtNoMetBtag.SetName("MET")
mtNoMetBtag.SetTitle("Inverted tau Met")
mtNoMetBtag.Reset()
print "check MT btagging",mtNoMetBtag.GetEntries()
for histo in hmtNoMetBtag:
mtNoMetBtag.Add(histo)
mtNoMetBveto = hmtNoMetBveto[0].Clone("mt")
mtNoMetBveto.SetName("MET")
mtNoMetBveto.SetTitle("Inverted tau Met")
mtNoMetBveto.Reset()
print "check MT bveto",mtNoMetBveto.GetEntries()
for histo in hmtNoMetBveto:
mtNoMetBveto.Add(histo)
mtBtag = hmtBtag[0].Clone("mt")
mtBtag.SetName("MET")
mtBtag.SetTitle("Inverted tau Met")
mtBtag.Reset()
print "check MT btagging",mtBtag.GetEntries()
for histo in hmtBtag:
mtBtag.Add(histo)
mtBveto = hmtBveto[0].Clone("mt")
mtBveto.SetName("MET")
mtBveto.SetTitle("Inverted tau Met")
mtBveto.Reset()
print "check MT bveto",mtBveto.GetEntries()
for histo in hmtBveto:
mtBveto.Add(histo)
##########################################
## plotting
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
### effisiency as a function of MET
metWithBtagging = metb.Clone("MET")
metWithBtagging.Divide(metbveto)
BtaggingEffVsMet = metWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffVsMet")
invertedQCD.mtComparison(BtaggingEffVsMet, BtaggingEffVsMet,"BtagToBvetoEffVsMet")
if False:
### effisiency as a function of MT
mtWithBtagging = mtNoMetBtag.Clone("MT")
mtWithBtagging.Divide(mtNoMetBveto)
BtaggingEffNoMetVsMt = mtWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffNoMetVsMt")
invertedQCD.mtComparison(BtaggingEffNoMetVsMt, BtaggingEffNoMetVsMt,"BtagToBvetoEffNoMetVsMt")
### effisiency as a function of MT
mtWithBtagging = mtBtag.Clone("MT")
mtWithBtagging.Divide(mtBveto)
BtaggingEffVsMt = mtWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffVsMt")
invertedQCD.mtComparison(BtaggingEffVsMt, BtaggingEffVsMt,"BtagToBvetoEffVsMt")
# efficiency metb/met
metbtag = metb.Clone("metb")
metnobtag = met.Clone("met")
metbtag.Divide(metnobtag)
invertedQCD.setLabel("BtagEffVsMet")
invertedQCD.mtComparison(metbtag, metbtag,"BtagEffVsMet")
# efficiency metb/metbveto
metbtag = metb.Clone("metb")
metbjetveto = metbveto.Clone("met")
invertedQCD.setLabel("BtagToBvetoEfficiency")
invertedQCD.mtComparison(metbtag, metbjetveto,"BtagToBvetoEfficiency")
### Create and customise TGraph
cEff = TCanvas ("Efficiency", "Efficiency", 1)
cEff.cd()
ptbin_error = array.array("d",[5, 5, 5, 5, 10, 10 ,30])
ptbin = array.array("d",[45, 55, 65, 75, 90, 110 ,150])
if False:
## no MET cut
cEff = TCanvas ("btaggingEffNoMet", "btaggingEffNoMet", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",effArrayMtNoMet),ptbin_error,array.array("d",effErrArrayMtNoMet))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.8,"No MET cut" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffNoMetVsPtTau_mt.png")
## with MET cut
cEff = TCanvas ("btaggingEff", "btaggingEff", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",effArrayMt),ptbin_error,array.array("d",effErrArrayMt))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.8,"After MET cut" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffVsPtTau_mt.png")
## no MET cut
cEff = TCanvas ("btaggingEffNoMet", "btaggingEffNoMet", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",effBvetoArray),ptbin_error,array.array("d",effErrBvetoArray))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.8,"No MET cut" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffNoMetVsPtTau.png")
## with MET cut
cEff = TCanvas ("btaggingEfficiency", "btaggingEfficiency", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",effArray),ptbin_error,array.array("d",effErrArray))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("b-tagging efficiency")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.4,0.85,"Inverted #tau identification")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.4,0.78,"At least 3 jets" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btaggingEffVsPtTau.png")
##################################3
fOUT = open("btaggingFactors","w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n"%os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingFactors = {\n")
i = 0
while i < len(effArray):
line = " \"" + ptbins[i] + "\": " + str(effArray[i])
if i < len(effArray) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "B-tagging efficiensies written in file","btaggingFactors"
fOUT = open("btaggingToBvetoFactors.py","w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n"%os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingToBvetoFactors = {\n")
i = 0
while i < len(effBvetoArray):
line = " \"" + ptbins[i] + "\": " + str(effBvetoArray[i])
if i < len(effBvetoArray) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "BtaggingToBveto efficiensies written in file","btaggingToBvetoFactors"
fOUT = open("btaggingToBvetoAfterMetFactors.py","w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n"%os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingToBvetoAfterMetFactors = {\n")
i = 0
while i < len(effArrayMt):
line = " \"" + ptbins[i] + "\": " + str(effArrayMt[i])
if i < len(effArrayMt) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "BtaggingToBvetoAfterMet efficiensies written in file","btaggingToBvetoFactors"
path = "/nfs/dust/cms/user/kschweig/JetRegression/trees0908/BDTTraining/ttHbb/*_1_*nominal*.root"
inputtree = ROOT.TChain("MVATree")
for f in sys.argv[2:]:
inputtree.Add(f)
BDTvars_input = {}
initialized = []
for variable in evt_vars_noreg + evt_vars_reg + common5_input + reg_common5_input:
if variable not in initialized:
#print variable
BDTvars_input.update( { variable : array.array('f',[0] ) } )
inputtree.SetBranchAddress( variable , BDTvars_input[variable] )
initialized.append(variable)
outputfile.cd()
OutputTree = ROOT.TTree("MVATree","MVATree");
E_Odd = array.array('f',[0])
E_Weight = array.array('f',[0])
P_Weight = array.array('f',[0])
P69_Weight = array.array('f',[0])
C_Weight = array.array('f',[0])
LSF = array.array('f',[0])
eSFGFS = array.array('f',[0])