def profile(self, parameter_name, bins=21, bound=2, args=None, subtract_min=False):
if self.__gMinuit is None:
raise MinimizerROOTTMinuitException("Need to perform a fit before calling profile()!")
MAX_ITERATIONS = 6000
_error_code = Long(0)
_minuit_id = Long(self.parameter_names.index(parameter_name) + 1)
_par_min = Double(0)
_par_err = Double(0)
self.__gMinuit.GetParameter(_minuit_id - 1, _par_min, _par_err)
_x = np.linspace(start=_par_min - bound * _par_err, stop=_par_min + bound * _par_err, num=bins, endpoint=True)
self.__gMinuit.mnexcm("FIX", arr('d', [_minuit_id]), 1, _error_code)
_y = np.zeros(bins)
for i in range(bins):
self.__gMinuit.mnexcm("SET PAR", arr('d', [_minuit_id, Double(_x[i])]), 2, _error_code)
self.__gMinuit.mnexcm("MIGRAD", arr('d', [MAX_ITERATIONS, self.tolerance]), 2, _error_code)
_y[i] = self.get_fit_info("fcn")
self.__gMinuit.mnexcm("RELEASE", arr('d', [_minuit_id]), 1, _error_code)
self._migrad()
self.__gMinuit.mnexcm("SET PAR", arr('d', [_minuit_id, Double(_par_min)]), 2, _error_code)
return np.asarray((_x, _y))
def _recreate_gMinuit(self):
self.__gMinuit = TMinuit(self.n_pars)
self.__gMinuit.SetPrintLevel(-1)
self.__gMinuit.mncomd("SET STRATEGY {}".format(self._strategy), Long(0))
self.__gMinuit.SetFCN(self._minuit_fcn)
self.__gMinuit.SetErrorDef(self._err_def)
# set gMinuit parameters
error_code = Long(0)
for _pid, (_pn, _pv, _pe) in enumerate(zip(self._par_names, self._par_val, self._par_err)):
self.__gMinuit.mnparm(_pid,
_pn,
_pv,
0.1 * _pe,
0, 0, error_code)
err_code = Long(0)
# set fixed parameters
for _par_id, _pf in enumerate(self._par_fixed_mask):
if _pf:
self.__gMinuit.mnfixp(_par_id, err_code)
# set parameter limits
for _par_id, _pl in enumerate(self._par_limits):
if _pl is not None:
_lo_lim, _up_lim = _pl
self.__gMinuit.mnexcm("SET LIM",
arr('d', [_par_id + 1, _lo_lim, _up_lim]), 3, error_code)
def test2PassBuiltinsByNonConstRef(self):
"""Test parameter passing of builtins through non-const reference"""
SetLongThroughRef = ROOT.SetLongThroughRef
SetDoubleThroughRef = ROOT.SetDoubleThroughRef
SetIntThroughRef = ROOT.SetIntThroughRef
if sys.hexversion < 0x3000000:
l = Long(pylong(42))
SetLongThroughRef(l, 41)
self.assertEqual(l, 41)
if sys.hexversion >= 0x2050000:
import ctypes
l = ctypes.c_long(42)
SetLongThroughRef(l, 41)
self.assertEqual(l.value, 41)
d = Double(3.14)
SetDoubleThroughRef(d, 3.1415)
self.assertEqual(d, 3.1415)
if sys.hexversion < 0x3000000:
i = Long(pylong(42))
SetIntThroughRef(i, 13)
self.assertEqual(i, 13)
if sys.hexversion >= 0x2050000:
i = ctypes.c_int(42)
SetIntThroughRef(i, 13)
self.assertEqual(i.value, 13)
def test1MinuitFit(self):
"""Test minuit callback and fit"""
# setup minuit and callback
gMinuit = TMinuit(5)
gMinuit.SetPrintLevel(-1) # quiet
gMinuit.SetGraphicsMode(ROOT.kFALSE)
gMinuit.SetFCN(fcn)
arglist = array('d', 10 * [0.])
ierflg = Long()
arglist[0] = 1
gMinuit.mnexcm("SET ERR", arglist, 1, ierflg)
# set starting values and step sizes for parameters
vstart = array('d', [3, 1, 0.1, 0.01])
step = array('d', [0.1, 0.1, 0.01, 0.001])
gMinuit.mnparm(0, "a1", vstart[0], step[0], 0, 0, ierflg)
gMinuit.mnparm(1, "a2", vstart[1], step[1], 0, 0, ierflg)
gMinuit.mnparm(2, "a3", vstart[2], step[2], 0, 0, ierflg)
gMinuit.mnparm(3, "a4", vstart[3], step[3], 0, 0, ierflg)
# now ready for minimization step
arglist[0] = 500
arglist[1] = 1.
gMinuit.mnexcm("MIGRAD", arglist, 2, ierflg)
# verify results
amin, edm, errdef = Double(), Double(), Double()
nvpar, nparx, icstat = Long(), Long(), Long()
gMinuit.mnstat(amin, edm, errdef, nvpar, nparx, icstat)
# gMinuit.mnprin( 3, amin )
self.assertEqual(nvpar, 4)
self.assertEqual(nparx, 4)
# success means that full covariance matrix is available (icstat==3)
self.assertEqual(icstat, 3)
# check results (somewhat debatable ... )
par, err = Double(), Double()
gMinuit.GetParameter(0, par, err)
self.assertEqual(round(par - 2.15, 2), 0.)
self.assertEqual(round(err - 0.10, 2), 0.)
gMinuit.GetParameter(1, par, err)
self.assertEqual(round(par - 0.81, 2), 0.)
self.assertEqual(round(err - 0.25, 2), 0.)
gMinuit.GetParameter(2, par, err)
self.assertEqual(round(par - 0.17, 2), 0.)
self.assertEqual(round(err - 0.40, 2), 0.)
gMinuit.GetParameter(3, par, err)
self.assertEqual(round(par - 0.10, 2), 0.)
self.assertEqual(round(err - 0.16, 2), 0.)
def __init__(self, myFCN, params, **kwargs):
from ROOT import TMinuit, Long, Double
self.limits = np.zeros((len(params), 2))
self.steps = .04 * np.ones(
len(params)) # about 10 percent in log10 space
self.tolerance = .001
self.maxcalls = 10000
self.printMode = 0
self.up = 0.5
self.param_names = ['p%i' % i for i in xrange(len(params))]
self.erflag = Long()
self.npars = len(params)
self.args = ()
self.gradient = None
self.force_gradient = 0
self.strategy = 1
self.fixed = np.zeros_like(params)
self.__dict__.update(kwargs)
self.params = np.asarray(params, dtype='float')
self.fixed = np.asarray(self.fixed, dtype=bool)
self.fcn = FCN(myFCN,
self.params,
args=self.args,
gradient=self.gradient)
self.fval = self.fcn.fval
self.minuit = TMinuit(self.npars)
self.minuit.SetPrintLevel(self.printMode)
self.minuit.SetFCN(self.fcn)
if self.gradient:
self.minuit.mncomd('SET GRA %i' % (self.force_gradient),
self.erflag)
self.minuit.mncomd('SET STR %i' % self.strategy, Long())
for i in xrange(self.npars):
if self.limits[i][0] is None: self.limits[i][0] = 0.0
if self.limits[i][1] is None: self.limits[i][1] = 0.0
self.minuit.DefineParameter(i, self.param_names[i], self.params[i],
self.steps[i], self.limits[i][0],
self.limits[i][1])
self.minuit.SetErrorDef(self.up)
for index in np.where(self.fixed)[0]:
self.minuit.FixParameter(int(index))
def _migrad(self, max_calls=6000):
# need to set the FCN explicitly before every call
self._get_gMinuit().SetFCN(self._minuit_fcn)
error_code = Long(0)
self._get_gMinuit().mnexcm("MIGRAD",
arr('d', [max_calls, self.tolerance]),
2, error_code)
def runMinuit(numParameters):
minuit = TMinuit(numParameters)
minuit.SetPrintLevel(0)
minuit.SetFCN(fcn)
arglist = np.zeros(numParameters) + 0.01
internalFlag, arglist[0] = Long(0), 0.5
minuit.mnexcm("SET ERR", arglist, 1, internalFlag)
initialValues = np.zeros(numParameters) + 0.01
steps = np.zeros(numParameters) + 0.0001
for i in xrange(numParameters):
name = "epsilon%s" % i
minuit.mnparm(i, name, initialValues[i], steps[i], 0, 1, internalFlag)
# arglist[0] = 2
# minuit.mnexcm("SET STR", arglist, 1, internalFlag)
arglist[0], arglist[1] = 10000, 0.1
minuit.mnexcm("SIMPLEX", arglist, 1, internalFlag)
minuit.mnexcm("MIGRAD", arglist, 1, internalFlag)
print "FIT STATUS is " +str(minuit.GetStatus())
return ratesAndErrors(numParameters, minuit)
def test2PassBuiltinsByNonConstRef(self):
"""Test parameter passing of builtins through non-const reference"""
SetLongThroughRef = ROOT.SetLongThroughRef
SetDoubleThroughRef = ROOT.SetDoubleThroughRef
SetIntThroughRef = ROOT.SetIntThroughRef
l = Long(pylong(42))
SetLongThroughRef(l, 41)
self.assertEqual(l, 41)
d = Double(3.14)
SetDoubleThroughRef(d, 3.1415)
self.assertEqual(d, 3.1415)
i = Long(pylong(42))
SetIntThroughRef(i, 13)
self.assertEqual(i, 13)
def get_fit_info(self, info):
'''Retrieves other info from `Minuit`.
**info** : string
Information about the fit to retrieve.
This can be any of the following:
- ``'fcn'``: `FCN` value at minimum,
- ``'edm'``: estimated distance to minimum
- ``'err_def'``: `Minuit` error matrix status code
- ``'status_code'``: `Minuit` general status code
'''
# declare vars in which to retrieve other info
fcn_at_min = Double(0)
edm = Double(0)
err_def = Double(0)
n_var_param = Long(0)
n_tot_param = Long(0)
status_code = Long(0)
# Tell TMinuit to update the variables declared above
self.__gMinuit.mnstat(fcn_at_min,
edm,
err_def,
n_var_param,
n_tot_param,
status_code)
if info == 'fcn':
return fcn_at_min
elif info == 'edm':
return edm
elif info == 'err_def':
return err_def
elif info == 'status_code':
try:
return D_MATRIX_ERROR[status_code]
except:
return status_code
def set_err(self, up_value=1.0):
'''Sets the ``UP`` value for Minuit.
*up_value* : float (optional, default: 1.0)
This is the value by which `FCN` is expected to change.
'''
# Tell TMinuit to use an up-value of 1.0
error_code = Long(0)
# execute SET ERR command
self.__gMinuit.mnexcm("SET ERR", arr('d', [up_value]), 1, error_code)
def Get1DHisto(self, bat=False):
nbins = self.histo3D.GetNbinsZ()
nbins_merge = int(floor(nbins / 64))
print 'Merge {n} bins'.format(n=nbins_merge)
self.histo3D.RebinZ(nbins_merge)
if self.fidcut == 0:
self.CreateFidCut()
if bat: gROOT.SetBatch(True)
if len(self.fidpoints) == 5:
self.histo3D.GetXaxis().SetRangeUser(self.fidpoints[0]['x'],
self.fidpoints[2]['x'])
self.histo3D.GetYaxis().SetRangeUser(self.fidpoints[0]['y'],
self.fidpoints[2]['y'])
self.histo1D = self.histo3D.Project3D('z')
else:
self.histo1D = TH1F('hChargeVsFidCut_z', 'hChargeVsFidCut_z', 64,
0, 4096)
nbins3D = (self.histo3D.GetNbinsY() * self.histo3D.GetNbinsX() *
self.histo3D.GetNbinsZ() + 1)
for bin in xrange(1, nbins3D):
x, y, z = Long(0), Long(0), Long(0)
if self.histo3D.GetBinContent(bin) >= 1:
self.histo3D.GetBinXYZ(bin, x, y, z)
point = {
'x': self.histo3D.GetXaxis().GetBinCenter(x),
'y': self.histo3D.GetYaxis().GetBinCenter(y)
}
# if self.WindingIsPointInPoly(point):
if bool(int(self.fidcut.IsInside(point['x'], point['y']))):
self.histo1D.Fill(
self.histo3D.GetZaxis().GetBinCenter(z))
canvas_name = 'c_s_1D_{r}_{n}'.format(r=self.run, n=self.nameFid)
canvas = self.CreateCanvas(canvas_name)
gStyle.SetOptStat('nemr')
canvas.cd()
self.histo1D.SetLineWidth(3)
self.histo1D.GetYaxis().SetTitle('entries')
self.histo1D.Draw()
name = 'histo1D_charge_sel_{r}_{n}'.format(r=self.run, n=self.nameFid)
self.SaveCanvas(canvas, name)
self.bla.append(canvas)
if bat: gROOT.SetBatch(False)
def set_strategy(self, strategy_id=1):
'''Sets the strategy Minuit.
*strategy_id* : int (optional, default: 1 (optimized))
Tells ``TMinuit`` to use a certain strategy. Refer to ``TMinuit``'s
documentation for available strategies.
'''
error_code = Long(0)
# execute SET STRATEGY command
self.__gMinuit.mnexcm("SET STRATEGY",
arr('d', [strategy_id]), 1, error_code)
def test1CheckEnumCalls(self):
"""Be able to pass enums as function arguments"""
gROOT.LoadMacro("Till.C+")
a = ROOT.Monkey()
self.assertEqual(ROOT.fish, a.testEnum1(ROOT.fish))
self.assertEqual(ROOT.cow, a.testEnum2(ROOT.cow))
self.assertEqual(ROOT.bird, a.testEnum3(ROOT.bird))
self.assertEqual(ROOT.marsupilami, a.testEnum4(ROOT.marsupilami))
self.assertEqual(ROOT.marsupilami, a.testEnum4(Long(ROOT.marsupilami)))
def fix_parameter(self, parameter_number):
'''
Fix parameter number <`parameter_number`>.
**parameter_number** : int
Number of the parameter to fix.
'''
error_code = Long(0)
logger.info("Fixing parameter %d in Minuit" % (parameter_number,))
# execute FIX command
self.__gMinuit.mnexcm("FIX",
arr('d', [parameter_number+1]), 1, error_code)
def unlimit(self, parameter_name):
# set local flag
_par_id = self.parameter_names.index(parameter_name)
if self._par_limits[_par_id] is None:
return # parameter is already unlimited
self._par_limits[_par_id] = None
if self.__gMinuit is not None:
# also update Minuit instance
error_code = Long(0)
self.__gMinuit.mnexcm("SET LIM",
arr('d', [_par_id+1]), 1, error_code)
self._min_result_stale = True
def SimulateScan(self, c, pos, nbins=95):
"""Generate a toy BI scan.
c: Index of the position parameter of the BI scan steps.
pos: Lists of the scan steps of the beam.
Returns: Histogram.
"""
p = array(pos)
nevents = Long(0)
result = RootToyGenerator.GenerateToys(self, len(pos), c, p, nbins,
nevents)
return result, float(nevents)
def release_parameter(self, parameter_number):
'''
Release parameter number <`parameter_number`>.
**parameter_number** : int
Number of the parameter to release.
'''
error_code = Long(0)
logger.info("Releasing parameter %d in Minuit" % (parameter_number,))
# execute RELEASE command
self.__gMinuit.mnexcm("RELEASE",
arr('d', [parameter_number+1]), 1, error_code)
def Get2DMapFiducial(self):
if self.fidcut == 0:
self.CreateFidCut()
self.histo3D.GetXaxis().SetRangeUser(self.sel_old['x_low'],
self.sel_old['x_high'])
self.histo3D.GetYaxis().SetRangeUser(self.sel_old['y_low'],
self.sel_old['y_high'])
if self.map == 0:
self.map = self.histo3D.Project3DProfile('yx')
self.map.GetZaxis().SetTitle('charge/ADC')
self.map.GetZaxis().SetRangeUser(0, 3000)
gStyle.SetOptStat('n')
self.map_fid = self.map.Clone(
'{n}_FidRegion'.format(n=self.map.GetName()))
self.map_fid.SetTitle(self.map_fid.GetName())
nbinsMap = (self.map.GetNbinsY() * self.map.GetNbinsX() + 1)
for bin in xrange(1, nbinsMap):
x, y, z = Long(0), Long(0), Long(0)
if self.map.GetBinContent(bin) >= 0:
self.map.GetBinXYZ(bin, x, y, z)
point = {
'x': self.map.GetXaxis().GetBinCenter(x),
'y': self.map.GetYaxis().GetBinCenter(y)
}
# if not self.WindingIsPointInPoly(point):
if not bool(int(self.fidcut.IsInside(point['x'], point['y']))):
self.map_fid.SetBinContent(bin, 0)
canvas_name = 'c_s_2D_{r}_fid_{n}'.format(r=self.run, n=self.nameFid)
canvas = self.CreateCanvas(canvas_name)
gStyle.SetOptStat('n')
canvas.cd()
self.map_fid.GetXaxis().SetTitle('Silicon X/ch')
self.map_fid.GetYaxis().SetTitle('Silicon Y/ch')
self.map_fid.Draw('colz')
self.fidcut.Draw('same')
self.bla.append(canvas)
name = 'histo2D_charge_sel_{r}_fid_{n}'.format(r=self.run,
n=self.nameFid)
self.SaveCanvas(canvas, name)
def fix(self, parameter_name):
# set local flag
_par_id = self.parameter_names.index(parameter_name)
if self._par_fixed_mask[_par_id]:
return # par is already fixed
self._par_fixed_mask[_par_id] = True
if self.__gMinuit is not None:
# also update Minuit instance
err_code = Long(0)
self.__gMinuit.mnfixp(_par_id, err_code)
# self.__gMinuit.mnexcm("FIX",
# arr('d', [_par_id+1]), 1, error_code)
self._min_result_stale = True
def minos_errors(self, log_print_level=1):
'''
Get (asymmetric) parameter uncertainties from MINOS
algorithm. This calls `Minuit`'s algorithms ``MINOS``,
which determines parameter uncertainties using profiling
of the chi2 function.
returns : tuple
A tuple of [err+, err-, parabolic error, global correlation]
'''
# Set the FCN again. This HAS to be done EVERY
# time the minimize method is called because of
# the implementation of SetFCN, which is not
# object-oriented but sets a global pointer!!!
logger.debug("Updating current FCN")
self.__gMinuit.SetFCN(self.FCN_wrapper)
# save the old stdout stream
if (log_print_level >= 0):
old_out_stream = os.dup(sys.stdout.fileno())
os.dup2(self.out_file.fileno(), sys.stdout.fileno())
self.__gMinuit.SetPrintLevel(log_print_level)
logger.debug("Running MINOS")
error_code = Long(0)
self.__gMinuit.mnexcm("MINOS", arr('d', [self.max_iterations]), 1,
error_code)
# return to normal print level
self.__gMinuit.SetPrintLevel(self.print_level)
# restore the previous output stream
if (log_print_level >= 0):
os.dup2(old_out_stream, sys.stdout.fileno())
output = []
errpos = Double(0) # positive parameter error
errneg = Double(0) # negative parameter error
err = Double(0) # parabolic error
gcor = Double(0) # global correlation coefficient
for i in xrange(0, self.number_of_parameters):
self.__gMinuit.mnerrs(i, errpos, errneg, err, gcor)
output.append(
[float(errpos),
float(errneg),
float(err),
float(gcor)])
return output
def Get1DHisto(self):
if self.fidcut_1 == 0 or self.fidcut_2 == 0:
self.fidcut_1, self.fidcut_2 = 0, 0
self.CreateFidCut()
self.histo1D_1 = TH1F('hChargeTransp1OutOf10VsFidCut_z',
'hChargeTransp1OutOf10VsFidCut_z', 64, 0, 4096)
nbins2D_1 = (self.histo2D_1.GetNbinsY() * self.histo2D_1.GetNbinsX() +
1)
for bin in xrange(1, nbins2D_1):
x, y, z = Long(0), Long(0), Long(0)
self.histo2D_1.GetBinXYZ(bin, x, y, z)
point = {
'x': self.histo2D_1.GetXaxis().GetBinCenter(x),
'y': self.histo2D_1.GetYaxis().GetBinCenter(y)
}
# if self.WindingIsPointInPoly(point):
if bool(int(self.fidcut_1.IsInside(point['x'], point['y']))):
self.histo1D_1.Fill(self.histo2D_1.GetBinContent(bin))
canvas_name_1 = 'c_t_1D_1_out_of_10_{r}_{n}'.format(r=self.run,
n=self.nameFid)
canvas_1 = self.CreateCanvas(canvas_name_1)
gStyle.SetOptStat('nemr')
canvas_1.cd()
self.histo1D_1.SetLineWidth(3)
self.histo1D_1.GetYaxis().SetTitle('entries')
self.histo1D_1.Draw()
name_1 = 'histo1D_charge_transp_1_out_of_10_{r}_{n}'.format(
r=self.run, n=self.nameFid)
self.SaveCanvas(canvas_1, name_1)
self.bla.append(canvas_1)
self.histo1D_2 = TH1F('hChargeTransp2OutOf10VsFidCut_z',
'hChargeTransp2OutOf10VsFidCut_z', 64, 0, 4096)
nbins2D_2 = (self.histo2D_2.GetNbinsY() * self.histo2D_2.GetNbinsX() +
1)
for bin in xrange(1, nbins2D_2):
x, y, z = Long(0), Long(0), Long(0)
self.histo2D_2.GetBinXYZ(bin, x, y, z)
point = {
'x': self.histo2D_2.GetXaxis().GetBinCenter(x),
'y': self.histo2D_2.GetYaxis().GetBinCenter(y)
}
# if self.WindingIsPointInPoly(point):
if bool(int(self.fidcut_2.IsInside(point['x'], point['y']))):
self.histo1D_2.Fill(self.histo2D_2.GetBinContent(bin))
canvas_name_2 = 'c_t_1D_2_out_of_10_{r}_{n}'.format(r=self.run,
n=self.nameFid)
canvas_2 = self.CreateCanvas(canvas_name_2)
gStyle.SetOptStat('nemr')
canvas_2.cd()
self.histo1D_2.SetLineWidth(3)
self.histo1D_2.GetYaxis().SetTitle('entries')
self.histo1D_2.Draw()
name_2 = 'histo1D_charge_transp_2_out_of_10_{r}_{n}'.format(
r=self.run, n=self.nameFid)
self.SaveCanvas(canvas_2, name_2)
self.bla.append(canvas_2)
def decode_larcv2_evimage2d(io, producername, imgdata_np, imgmeta_np):
# make evcontainer
evout = io.get_data("image2d", producername)
# make meta
nimgs = imgdata_np.shape[1]
for i in xrange(nimgs):
nrows = Long(imgmeta_np[0, i, 0, 1])
ncols = Long(imgmeta_np[0, i, 0, 0])
planeid = Long(imgmeta_np[0, i, 0, 6])
lcvmeta = larcv.ImageMeta(imgmeta_np[0, i, 0, 2], imgmeta_np[0, i, 0,
3],
imgmeta_np[0, i, 0, 4], imgmeta_np[0, i, 0,
5], nrows,
ncols, planeid)
# convert image
outarr = np.flip(imgdata_np[0, i, :, :].transpose((1, 0)), 0)
lcvimg = larcv.as_image2d_meta(outarr, lcvmeta)
evout.append(lcvimg)
return
def limit(self, parameter_name, parameter_bounds):
assert len(parameter_bounds) == 2
# set local flag
_par_id = self.parameter_names.index(parameter_name)
if self._par_limits[_par_id] == parameter_bounds:
return # same limits already set
self._par_limits[_par_id] = parameter_bounds
if self.__gMinuit is not None:
_lo_lim, _up_lim = self._par_limits[_par_id]
# also update Minuit instance
error_code = Long(0)
self.__gMinuit.mnexcm("SET LIM",
arr('d', [_par_id+1, _lo_lim, _up_lim]), 3, error_code)
self._min_result_stale = True
def _calculate_asymmetric_parameter_errors(self):
self._get_gMinuit().mnmnos()
_asymm_par_errs = np.zeros(shape=(self.num_pars, 2))
for _n in range(self.num_pars):
_number = Long(_n)
_eplus = ctypes.c_double(0)
_eminus = ctypes.c_double(0)
_eparab = ctypes.c_double(0)
_gcc = ctypes.c_double(0)
self._get_gMinuit().mnerrs(_number, _eplus, _eminus, _eparab, _gcc)
_asymm_par_errs[_n, 0] = _eminus.value
_asymm_par_errs[_n, 1] = _eplus.value
self.minimize()
return _asymm_par_errs
def minimize(self, final_fit=True, log_print_level=2):
'''Do the minimization. This calls `Minuit`'s algorithms ``MIGRAD``
for minimization and, if `final_fit` is `True`, also ``HESSE``
for computing/checking the parameter error matrix.'''
# Set the FCN again. This HAS to be done EVERY
# time the minimize method is called because of
# the implementation of SetFCN, which is not
# object-oriented but sets a global pointer!!!
logger.debug("Updating current FCN")
self.__gMinuit.SetFCN(self.FCN_wrapper)
# Run minimization algorithm (MIGRAD + HESSE)
error_code = Long(0)
prefix = "Minuit run on" # set the timestamp prefix
# insert timestamp
self.out_file.write('\n')
self.out_file.write('#' * (len(prefix) + 4 + 20))
self.out_file.write('\n')
self.out_file.write("# %s " % (prefix, ) +
strftime("%Y-%m-%d %H:%M:%S #\n", gmtime()))
self.out_file.write('#' * (len(prefix) + 4 + 20))
self.out_file.write('\n\n')
self.out_file.flush()
# save the old stdout stream
if (log_print_level >= 0):
old_out_stream = os.dup(sys.stdout.fileno())
os.dup2(self.out_file.fileno(), sys.stdout.fileno())
self.__gMinuit.SetPrintLevel(log_print_level) # set Minuit print level
logger.debug("Running MIGRAD")
self.__gMinuit.mnexcm("MIGRAD",
arr('d', [self.max_iterations, self.tolerance]),
2, error_code)
if (final_fit):
logger.debug("Running HESSE")
self.__gMinuit.mnexcm("HESSE", arr('d', [self.max_iterations]), 1,
error_code)
# return to normal print level
self.__gMinuit.SetPrintLevel(self.print_level)
# restore the previous output stream
if (log_print_level >= 0):
os.dup2(old_out_stream, sys.stdout.fileno())
os.close(old_out_stream)
def get_chi2_probability(self, n_deg_of_freedom):
'''
Returns the probability that an observed :math:`\chi^2` exceeds
the calculated value of :math:`\chi^2` for this fit by chance,
even for a correct model. In other words, returns the probability that
a worse fit of the model to the data exists. If this is a small value
(typically <5%), this means the fit is pretty bad. For values below
this threshold, the model very probably does not fit the data.
n_def_of_freedom : int
The number of degrees of freedom. This is typically
:math:`n_\text{datapoints} - n_\text{parameters}`.
'''
chi2 = Double(self.get_fit_info('fcn'))
ndf = Long(n_deg_of_freedom)
return TMath.Prob(chi2, ndf)
def update_parameter_data(self, show_warnings=False):
"""
(Re-)Sets the parameter names, values and step size on the
C++ side of Minuit.
"""
error_code = Long(0)
try:
# Set up the starting fit parameters in TMinuit
for i in xrange(0, self.number_of_parameters):
self.__gMinuit.mnparm(i, self.parameter_names[i],
self.current_parameters[i],
0.1 * self.parameter_errors[i], 0, 0,
error_code)
# use 10% of the par. 1-sigma errors as the initial step size
except AttributeError, e:
if show_warnings:
logger.warn("Cannot update Minuit data on the C++ side. "
"AttributeError: %s" % (e, ))
X_predictions_for_fit = np.column_stack([x for x in X_predicted_all])
mpvs=[]
print 'here'
fit_quantile=[]
gMinuit = TMinuit(4)
gMinuit.SetPrintLevel(-1)
gMinuit.SetFCN( fcn )
arglist = np.array( 10*[0.] )
ierflg = 0
arglist[0] = 1
gMinuit.mnexcm( "SET ERR", arglist, 1, Long(ierflg) )
arglist[0] = 0
gMinuit.mnexcm("SET PRINT", arglist ,0,Long(ierflg));
vstart = np.array( [ -2 , 4,-1, 0.5] )
step = np.array( [ 0.001, 0.001, 0.01, 0.01 ] )
gMinuit.mnparm( 0, "a1", vstart[0], step[0], 0, 0, Long(ierflg) )
gMinuit.mnparm( 1, "a2", vstart[1], step[1], 0, 0, Long(ierflg) )
gMinuit.mnparm( 2, "a3", vstart[2], step[2], 0, 0, Long(ierflg) )
gMinuit.mnparm( 3, "a4", vstart[3], step[3], 0, 0, Long(ierflg) )
arglist[0] = 500
arglist[1] = 1.
fParamVal = Double(0.)
fParamErr = Double(0.)
for i in range(5):
changeme(X_predictions_for_fit[i])
gMinuit.mnexcm( "MIGRAD", arglist, 2, Long(ierflg) )
def fit(self):
numberOfParameters = len(self.samples)
gMinuit = TMinuit(numberOfParameters)
if self.method == 'logLikelihood': # set function for minimisation
gMinuit.SetFCN(self.logLikelihood)
gMinuit.SetMaxIterations(1000000000000)
# set Minuit print level
# printlevel = -1 quiet (also suppress all warnings)
# = 0 normal
# = 1 verbose
# = 2 additional output giving intermediate results.
# = 3 maximum output, showing progress of minimizations.
gMinuit.SetPrintLevel(-1)
# Error definition: 1 for chi-squared, 0.5 for negative log likelihood
# SETERRDEF<up>: Sets the value of UP (default value= 1.), defining parameter errors.
# Minuit defines parameter errors as the change in parameter value required to change the function value by UP.
# Normally, for chisquared fits UP=1, and for negative log likelihood, UP=0.5.
gMinuit.SetErrorDef(0.5)
# error flag for functions passed as reference.set to as 0 is no error
errorFlag = Long(2)
N_min = 0
N_max = self.fit_data_collection.max_n_data() * 2
param_index = 0
# MNPARM
# Implements one parameter definition:
# mnparm(k, cnamj, uk, wk, a, b, ierflg)
# K (external) parameter number
# CNAMK parameter name
# UK starting value
# WK starting step size or uncertainty
# A, B lower and upper physical parameter limits
# and sets up (updates) the parameter lists.
# Output: IERFLG =0 if no problems
# >0 if MNPARM unable to implement definition
for sample in self.samples: # all samples but data
if self.n_distributions > 1:
gMinuit.mnparm(
param_index, sample,
self.normalisation[self.distributions[0]][sample], 10.0,
N_min, N_max, errorFlag)
else:
gMinuit.mnparm(param_index, sample, self.normalisation[sample],
10.0, N_min, N_max, errorFlag)
param_index += 1
arglist = array('d', 10 * [0.])
# minimisation strategy: 1 standard, 2 try to improve minimum (a bit slower)
arglist[0] = 2
# minimisation itself
# SET STRategy<level>: Sets the strategy to be used in calculating first and second derivatives and in certain minimization methods.
# In general, low values of <level> mean fewer function calls and high values mean more reliable minimization.
# Currently allowed values are 0, 1 (default), and 2.
gMinuit.mnexcm("SET STR", arglist, 1, errorFlag)
gMinuit.Migrad()
gMinuit.mnscan(
) # class for minimization using a scan method to find the minimum; allows for user interaction: set/change parameters, do minimization, change parameters, re-do minimization etc.
gMinuit.mnmatu(1) # prints correlation matrix (always needed)
self.module = gMinuit
self.performedFit = True
if not self.module:
raise Exception(
'No fit results available. Please run fit method first')
results = {}
param_index = 0
for sample in self.samples:
temp_par = Double(0)
temp_err = Double(0)
self.module.GetParameter(param_index, temp_par, temp_err)
if (math.isnan(temp_err)):
self.logger.warning(
'Template fit error is NAN, setting to sqrt(N).')
temp_err = math.sqrt(temp_par)
# gMinuit.Command("SCAn %i %i %i %i" % ( param_index, 100, N_min, N_total ) );
# scan = gMinuit.GetPlot()
# results[sample] = ( temp_par, temp_err, scan )
results[sample] = (temp_par, temp_err)
param_index += 1
# # gMinuit.Command("CONtour 1 2 3 50")
# gMinuit.SetErrorDef(1)
# results['contour'] = [gMinuit.Contour(100, 0, 1)]
# gMinuit.SetErrorDef(4)
# results['contour'].append(gMinuit.Contour(100, 0, 1))
self.results = results
def main():
#The first part of the script is to input the root files#
canvas = TCanvas("Canvas_d", "Canvas_d", 533, 76, 1383, 852)
input_datafile = TFile(args["input_data"])
EV_data = input_datafile.Get("EventCategorizer subtask 0 stats/ExpecValue")
entries_data = EV_data.GetSize()
print "# of Entries in Data file:", entries_data
print "Experimental Data Inputed"
data_arr = []
canvas = TCanvas("Canvas_mc", "Canvas_mc", 533, 76, 1383, 852)
input_mcfile = TFile(args["input_mc"])
EV_mc = input_mcfile.Get(
"EventCategorizer subtask 0 stats/ExpecValue_Smeared")
entries_mc = EV_mc.GetSize()
print "# of Entries in MC file:", entries_mc
print "MC Data Inputed"
mc_arr = []
bin_arr = []
for x in range(entries_data):
Data_i = EV_data.GetBinContent(x)
MC_i = EV_mc.GetBinContent(x)
data_arr.append(Data_i)
mc_arr.append(MC_i)
Bin_i = EV_data.GetBinCenter(x)
bin_arr.append(Bin_i)
#print Data_i
#print MC_i
#print data_arr
#print mc_arr
#print bin_arr
# --> Set parameters and function to f i t
name = ["c", "d"] #variable names
vstart = arr('d', (1.0, 1.0)) #the initial values
step = arr('d', (0.001, 0.001)) #the initial step size
npar = len(name)
# --> Defining the Chi-Square function to be minimized#
def Chi_Induced(Data, MC, BinCenter, C, D):
chi = 0.
for i in range(0, entries_data):
#num1 = Data[i]
#num2 = D*((1-(C*BinCenter[i]))*MC[i])
#num = (num1 - num2)**2
#num = 0.
#den1 = ((Data[i])**(1/2))**2
#den2 = (D*((1-(C*BinCenter[i])))*((MC[i])**(1/2)))**2
num = 2
den = 2
#den = den1 + den2
chi = chi + num / den
return chi
# --> set up MINUIT
myMinuit = TMinuit(
npar) # initialize TMinuit with maximum of npar parameters
myMinuit.SetFCN(Chi_Induced) # set function to minimize
ierflg = Long(0)
arglist = arr('d', 2 * [0.01]) # set error definition
arglist[0] = 6000 # Number of calls for FCN before gving up
arglist[1] = 0.3 # Toleranceierflg = Long(0)
myMinuit.mnexcm("SET ERR", arglist, 1, ierflg)
for i in range(0, npar):
myMinuit.mnparm(i, name[i], vstart[i], step[i], 0, 0, ierflg)
myMinuit.mnexcm("MIGRAD", arglist, 1, ierflg) # execute the minimisation
# --> check TMinuit status
amin, edm, errdef = Double(0.), Double(0.), Double(0.)
nvpar, nparx, icstat = Long(0), Long(0), Long(0)
myMinuit.mnstat(amin, edm, errdef, nvpar, nparx, icstat)