def do_upper_limits(verbose=False, prefix='upperlim'):
from scharmfit import utils
utils.load_susyfit()
from ROOT import ConfigMgr, Util
mgr = ConfigMgr.getInstance()
mgr.m_outputFileName = prefix + '.root'
mgr.m_nToys = 1
mgr.m_calcType = 2
mgr.m_testStatType = 3
mgr.m_useCLs = True
mgr.m_nPoints = -1
if verbose:
mgr.doUpperLimitAll()
else:
with OutputFilter():
mgr.doUpperLimitAll()
def do_histfitter_magic(self, input_workspace, verbose=False):
"""
Here we break into histfitter voodoo. The functions here are pulled
out of the HistFitter.py script.
"""
from scharmfit import utils
utils.load_susyfit()
from ROOT import ConfigMgr, Util
mgr = ConfigMgr.getInstance()
mgr.initialize()
mgr.setNToys(1)
# the fit configs seem to need unique names, use random numbers
import random
fc_number = 0
# such a hack... but this is to check if the fit config is unique
with OutputFilter():
while mgr.getFitConfig(str(fc_number)):
fc_number += 1
fc = mgr.addFitConfig(str(fc_number))
fc.m_inputWorkspaceFileName = input_workspace
fc.m_signalSampleName = basename(input_workspace).split('.')[0]
for chan in self.channels:
if chan == 'signal':
continue
fc.m_bkgConstrainChannels.push_back(chan)
fc.m_signalChannels.push_back('signal')
accept_strings = {'ERROR:','WARNING:'} if not verbose else {''}
# this snapshot error appears to be a hackish check, can ignore
veto = {'snapshot_paramsVals_initial'}
with OutputFilter(accept_strings=accept_strings, veto_strings=veto):
Util.GenerateFitAndPlot(
fc.m_name,
"ana_name",
False, #drawBeforeFit,
False, #drawAfterFit,
False, #drawCorrelationMatrix,
False, #drawSeparateComponents,
False, #drawLogLikelihood,
False, #runMinos,
"", #minosPars
)
def do_histfitter_magic(self, ws_dir, verbose=False):
"""
Here we break into histfitter voodoo. The functions here are pulled
out of the HistFitter.py script. The input workspace is the one
produced by the `save_workspace` function above.
"""
from scharmfit import utils
utils.load_susyfit()
from ROOT import ConfigMgr, Util
ws_name = self._get_ws_name()
ws_path = join(ws_dir, ws_name)
# The histfitter authors somehow thought that creating a
# singleton configuration manager was good design. Maybe it is
# when you're wrapping ROOT code (with all its global variable
# glory). In any case, most of the hacking below is to work
# around this.
mgr = ConfigMgr.getInstance()
mgr.initialize()
mgr.setNToys(1) # make configurable?
# such a hack... but this is to check if the fit config is unique
fc_number = 0
with OutputFilter():
# name fig configs '0', '1', '2', '3', etc...
while mgr.getFitConfig(str(fc_number)):
fc_number += 1
fc = mgr.addFitConfig(str(fc_number))
# had to dig pretty deep into the HistFitter code to find this
# stuff, but this seems to be how it sets things up.
fc.m_inputWorkspaceFileName = ws_path
# HistFitter name convention seems to be that the background only fit
# is called "Bkg" or "" (empty string).
fc.m_signalSampleName = self._signal_point or ''
# HistFitter doesn't seem to distinguish between background
# and signal channels. The only possible difference is a
# commented out line that sets 'lumiConst' to true if there
# are no signal channels. May be worth looking into...
for chan in self._channels:
if chan not in self._non_fit_regions:
fc.m_bkgConstrainChannels.push_back(chan)
# fc.m_signalChannels.push_back(chan)
accept_strings = {'ERROR:','WARNING:'} if not verbose else {''}
# this snapshot error appears to be a hackish check, can ignore
veto = {'snapshot_paramsVals_initial'}
with OutputFilter(accept_strings=accept_strings, veto_strings=veto):
Util.GenerateFitAndPlot(
fc.m_name,
"ana_name",
False, #drawBeforeFit,
False, #drawAfterFit,
False, #drawCorrelationMatrix,
False, #drawSeparateComponents,
False, #drawLogLikelihood,
False, #runMinos,
"", #minosPars
)
