def eff_kkpi_comp(opts, tabname, label):
tab = DHadTable()
tabprefix = 'dir_' + label
mode = 205
tab.column_append_from_dict('Mode', 'fname,fnamebar', mode=mode)
tab.column_append([20000, 20000], 'Generated')
tab.column_append_from_files('Yield',
'N1,N2',
fitbase,
tabprefix,
's',
's',
'txt',
rnd='1.',
mode=mode)
tab.column_append_by_divide('Eff(%)', 'Yield', 'Generated', 'Efcy', '.01',
100)
tab.columns_delete(['Generated', 'Yield'])
def get_eff(subdir):
logname = 'signal_Single_%s.txt' % modes[mode]['fname']
logpath = os.path.join(attr.logpath, label, subdir, 'yld')
logfile = tools.set_file(extbase=logpath, comname=logname)
for line in file(logfile):
if 'selected' in line:
total = int(line.split()[-1].replace('.', ''))
break
t = DHadTable()
t.column_append([total, total], 'Generated')
t.column_append_from_files('Yield',
'N1,N2',
fitbase,
'%s/%s' % (tabprefix, subdir),
's',
's',
'txt',
rnd='1.',
mode=mode)
t.column_append_by_divide('%s(%%)' % subdir, 'Yield', 'Generated',
'Efcy', '.01', 100)
return t.column_get('%s(%%)' % subdir)
for subdir in ['phipi', 'k0star', 'phsp']:
tab.column_append(get_eff(subdir))
#head_diff = subdir
#col = '%s(%%)' % subdir
#std = 'Eff(%)'
#tab.column_append_by_diff_pct(head_diff, col, std)
tab.output(tabname, label=label)
def bkg_names(opts, args):
debug_ = opts.debug
test_ = opts.test
set_ = opts.set
prefix = None
if args[0] == 'peak':
prefix = 'peak'
args = args[1:]
parsed = yld.parse_args(args)
datatype = parsed[0]
tag = parsed[1]
modes = parsed[2]
label = parsed[3]
limit = None
if set_ != None and 'min' in set_:
min_ = set_.split('=')[1]
limit = '>%s' %min_
bkgmodes = 20
if set_ != None and 'bkgmodes' in set_:
bkgmodes = int(set_.split('=')[1])
for mode in modes:
tabname = 'bkg_names_%s_%s_%s' %(
datatype, mode, label)
if prefix != None:
tabname = 'bkg_names_%s_%s_%s_%s' %(
prefix, datatype, mode, label)
tabname = tabname.replace('/', '_')
tab = DHadTable()
modename = get_orgname_from_fname(mode)
bkg_names, bkg_numbers = bkg_names_single_mode(
datatype, mode, label, limit, bkgmodes, debug_, prefix)
tab.column_append(bkg_names, title='Backgrounds for %s' %modename)
tab.column_append(bkg_numbers, title='Number of Events')
tab.sort_by_column('Number of Events', reverse=True)
orgfooter = ': '+sys.argv[0].split('/')[-1]+\
' tab backgrounds names %s %s %s ' %(
datatype, mode, label)
if prefix != None:
orgfooter = ': '+sys.argv[0].split('/')[-1]+\
' tab backgrounds names %s %s %s %s ' %(
prefix, datatype, mode, label)
tab.output(tabname, orgfooter=orgfooter, test=test_)
def mctruth_names(opts, args):
debug_ = opts.debug
test_ = opts.test
set_ = opts.set
prefix = None
if args[0] == 'peak':
prefix = 'peak'
args = args[1:]
parsed = yld.parse_args(args)
datatype = parsed[0]
tag = parsed[1]
modes = parsed[2]
label = parsed[3]
limit = None
if set_ != None and 'min' in set_:
min_ = set_.split('=')[1]
limit = '>%s' % min_
mctruthmodes = 50
if set_ != None and 'mctruthmodes' in set_:
mctruthmodes = int(set_.split('=')[1])
for mode in modes:
tabname = 'mctruth_names_%s_%s_%s' % (datatype, mode, label)
if prefix != None:
tabname = 'mctruth_names_%s_%s_%s_%s' % (prefix, datatype, mode,
label)
tabname = tabname.replace('/', '_')
tab = DHadTable()
modename = get_orgname_from_fname(mode)
mctruth_names, mctruth_numbers = mctruth_names_single_mode(
datatype, mode, label, limit, mctruthmodes, debug_, prefix)
tab.column_append(mctruth_names, title='MC Truth for %s' % modename)
tab.column_append(mctruth_numbers, title='Number of Events')
tab.sort_by_column('Number of Events', reverse=True)
orgfooter = ': '+sys.argv[0].split('/')[-1]+\
' tab mctruth names %s %s %s ' %(
datatype, mode, label)
if prefix != None:
orgfooter = ': '+sys.argv[0].split('/')[-1]+\
' tab mctruth names %s %s %s %s ' %(
prefix, datatype, mode, label)
tab.output(tabname, orgfooter=orgfooter, test=test_)
def brf_data_results(opts, tabname, args):
'''
-----------------------------
Results of the Data Fit
-----------------------------
'''
label_A = args[0]
label_B = args[1]
verbose = opts.verbose
bffilename = 'bf_stat_sys'
labels = []
labels.append(label_A)
labels.append(label_B)
tab = DHadTable()
paras = False
for label in labels:
if '281ipb' in label:
factor = 0.000001
elif '537ipb' in label:
factor = 0.000001*281/537
elif '818ipb' in label or 'v13' in label:
factor = 0.000001*281/818
else:
raise NameError(label)
if '818ipb' in label_A and '818ipb' in label_B:
factor = 0.000001
if '818ipb' in label_A and 'v13' in label_B:
factor = 0.000001
bffile = os.path.join(attr.brfpath, label, bffilename)
if not paras:
tab.column_append(tools.parse_result(bffile, 'paras'),
'Parameters')
paras = True
tab.column_append(tools.parse_result(bffile, 'value'), 'value', rnd='.00001' )
tab.column_append(tools.parse_result(bffile, 'stat'), 'stat')
tab.column_append(tools.parse_result(bffile, 'syst'), 'syst')
tab.columns_join3('Fitted Value', 'value', 'stat', 'syst')
tab.column_trim('Fitted Value', row=['ND0D0Bar', 'ND+D-'],
rnd='.0001', factor=factor, opt='(cell)x1E6')
tab.column_trim('Fitted Value', rnd='.00001',
except_row=['ND0D0Bar', 'ND+D-'])
tab.column_append(tools.parse_result(bffile, 'err_frac'),
'Frac. Err', rnd='.1', opt='(cell%)')
tab.columns_join(label, 'Fitted Value','Frac. Err', str=' ')
tab.column_append_by_diff_sigma('Difference', label_B,label_A)
tab.output(tabname, test=opts.test)
def fitResultsMC(opts, tabname, label):
bffilename = 'bf_stat'
bffile = tools.set_file(extbase=attr.brfpath,
prefix='dir_' + label,
comname=bffilename)
if '281ipbv0' in label and '/generic' in label:
generated = attr.Generic281v0_NBF
power = '10E6'
factor = 0.000001
elif '281ipbv12' in label and '/generic' in label:
generated = attr.Generic281_NBF
power = '10E7'
factor = 0.0000001
elif '818ipbv12' in label and '/generic' in label:
generated = attr.Generic818_NBF
power = '10E7'
factor = 0.0000001
else:
raise NameError(label)
tab = DHadTable()
tab.column_append(parse_result(bffile, 'paras'), 'Parameters')
tab.column_append(parse_result(bffile, 'value_err'), 'Fitted Value')
tab.column_append(generated, 'Generated Value')
tab.column_append_by_diff_sigma('Difference', 'Fitted Value',
'Generated Value')
tab.column_trim('Fitted Value',
row=['ND0D0Bar', 'ND+D-'],
rnd='.001',
factor=factor,
opt='(cell)x%s' % power)
tab.column_trim('Fitted Value',
rnd='.00001',
except_row=['ND0D0Bar', 'ND+D-'])
tab.column_append(parse_result(bffile, 'err_frac'),
'Frac. Err',
rnd='.1',
opt='(cell%)')
tab.columns_join('Fitted Value', 'Fitted Value', 'Frac. Err', str=' ')
tab.column_trim('Generated Value',
row=['ND0D0Bar', 'ND+D-'],
rnd='.001',
factor=factor,
opt='cellx%s' % power)
tab.output(tabname, label=label)
def brf_data_syst(args):
print args
label_A = args[0]
label_B = args[1]
bffilename = 'bf_stat_sys'
tab = DHadTable()
bffileA = os.path.join(attr.brfpath, label_A, bffilename)
tab.column_append(tools.parse_result(bffileA, 'paras'), 'Parameters')
tab.column_append(tools.parse_result(bffileA, 'value'), 'value_A')
tab.column_append(tools.parse_result(bffileA, 'syst'), label_A)
bffileB = os.path.join(attr.brfpath, label_B, bffilename)
tab.column_append(tools.parse_result(bffileB, 'value'), 'value_B')
tab.column_append(tools.parse_result(bffileB, 'syst'), label_B)
#tab.column_append_by_diff_sigma_pct('diff(%)', label_B,label_A, rnd=rnd)
tab.output()
def get_eff(subdir):
logname = 'signal_Single_%s.txt' % modes[mode]['fname']
logpath = os.path.join(attr.logpath, label, subdir, 'yld')
logfile = tools.set_file(extbase=logpath, comname=logname)
for line in file(logfile):
if 'selected' in line:
total = int(line.split()[-1].replace('.', ''))
break
t = DHadTable()
t.column_append([total, total], 'Generated')
t.column_append_from_files('Yield',
'N1,N2',
fitbase,
'%s/%s' % (tabprefix, subdir),
's',
's',
'txt',
rnd='1.',
mode=mode)
t.column_append_by_divide('%s(%%)' % subdir, 'Yield', 'Generated',
'Efcy', '.01', 100)
return t.column_get('%s(%%)' % subdir)
def brf_data_syst(args):
print args
label_A = args[0]
label_B = args[1]
bffilename = 'bf_stat_sys'
tab = DHadTable()
bffileA = os.path.join(attr.brfpath, label_A, bffilename)
tab.column_append(tools.parse_result(bffileA, 'paras'), 'Parameters')
tab.column_append(tools.parse_result(bffileA, 'value'), 'value_A')
tab.column_append(tools.parse_result(bffileA, 'syst'), label_A)
bffileB = os.path.join(attr.brfpath, label_B, bffilename)
tab.column_append(tools.parse_result(bffileB, 'value'), 'value_B')
tab.column_append(tools.parse_result(bffileB, 'syst'), label_B)
#tab.column_append_by_diff_sigma_pct('diff(%)', label_B,label_A, rnd=rnd)
tab.output()
def pdg2009(args):
'''
--------------------------------------------------------
Results of the Data Fit Compare with PDG 2009
--------------------------------------------------------
'''
label = args[0]
verbose = opts.verbose
bffilename = 'bf_stat_sys'
bffile = os.path.join(attr.brfpath, label, bffilename)
tab = DHadTable()
tab.column_append(tools.parse_result(bffile, 'paras'), 'Parameters')
tab.column_append(tools.parse_result(bffile, 'value'), 'value')
tab.column_append(tools.parse_result(bffile, 'stat'), 'stat')
tab.column_append(tools.parse_result(bffile, 'syst'), 'syst')
tab.columns_join3('Fitted Value', 'value', 'stat', 'syst')
tab.column_trim('Fitted Value', row = ['ND0D0Bar', 'ND+D-'],
rnd = '.001', factor = 0.000001, opt = '(cell)x10E6')
tab.column_trim('Fitted Value', rnd = '.0001',
except_row = ['ND0D0Bar', 'ND+D-'])
tab.column_append(tools.parse_result(bffile, 'err_frac'),
'Frac. Err', rnd = '.1', opt = '(cell%)')
tab.columns_join('Fitted Value','Fitted Value','Frac. Err', str=' ')
tab.column_append(attr.PDG2009_NBF, 'PDG 2009')
tab.column_append_by_diff_sigma('Difference', 'Fitted Value', 'PDG 2009')
tab.output(_tabname)
def get_brs_from_file(label):
tab = DHadTable()
bffilename = 'bf_stat_sys'
bffile = os.path.join(attr.brfpath, label, bffilename)
tab.column_append(parse_result(bffile, 'paras'), 'Parameters')
tab.column_append(parse_result(bffile, 'value'), 'value')
tab.column_append(parse_result(bffile, 'stat'), 'stat')
tab.column_append(parse_result(bffile, 'syst'), 'syst')
tab.rows_delete(['Parameters', 'ND0D0Bar', 'ND+D-'])
content = []
n = 0
for row in tab.data:
n += 1
line = 'exec addData %s %s %s %s \n' %(
n, row[1], row[2], row[3])
content.append(line)
return content