def next(self):
if self.start <= self.end:
self.current_cut += self.step
cut = self.incremental_cut_on + " > " + str(self.current_cut)
if self.current_cut > self.end:
raise StopIteration
if self.start > self.end:
self.current_cut -= self.step
cut = self.incremental_cut_on + " < " + str(self.current_cut)
if self.current_cut < self.end:
raise StopIteration
exp = self.pseudo_experiment
exp.add_cut(str(cut))
mean_arr = array.array('d')
for i, prob in izip(xrange(self.experiments_per_step), exp):
mean_arr.append(prob)
n = len(mean_arr)
mean_prob = TMath.Mean(n, mean_arr)
err_prob = TMath.RMS(n, mean_arr)
#err_prob /= (n - 1)
return self.current_cut, mean_prob, err_prob
def add_expected(self, cat, filename, legend, scale = 1.0,
line_color = 1, line_style = 1, line_width = 1,
marker_color = 1, marker_size = 1.2, marker_style=8,
value_type = 'median',
error_type = 'rms',
fill_style = 1002,
fill_color = ROOT.kYellow,
fill2_style = 1002,
fill2_color = ROOT.kRed,
extra_scale_name = 'unit',
smooth = None # 'single' or 'comb' or None: special tricks for smoothing expected bands
):
self.type[cat] = 'expected'
self.cat.append(cat)
self.file = filename
self.tlegend[cat] = legend
self.line_color[cat] = line_color
self.line_style[cat] = line_style
self.line_width[cat] = line_width
self.marker_color[cat] = marker_color
self.marker_size[cat] = marker_size
self.marker_style[cat] = marker_style
self.fill_style[cat] = fill_style
self.fill2_style[cat] = fill2_style
self.fill_color[cat] = fill_color
self.fill2_color[cat] = fill2_color
_x = array('d')
_y = array('d')
_exh = array('d')
_exl = array('d')
_eyh = array('d')
_eyl = array('d')
_exh2 = array('d')
_exl2 = array('d')
_eyh2 = array('d')
_eyl2 = array('d')
_d = [] # list of tuples, one tuple per mass point
with open(filename, 'r') as _file:
_mass = None
_arr = array('d')
_prev_limit = 0.0
for line in _file:
#print self.legend, line
_buf = line.split()
aMass = float(_buf[0])
aLim = float(_buf[1])*scale*get_scale(extra_scale_name, aMass)
if _mass == None:
_mass = aMass
if abs(_mass-aMass) > 0.1:
_d.append((_mass, _arr))
#print self.legend, cat, 'mass =', _mass, 'PEs per point:', len(_arr)
_mass = aMass
_arr=array('d')
#if abs(aLim - _prev_limit) < 0.000001:
# #print self.legend, 'WARNING: duplicate expected limit, ignoring...'
# continue
#_arr.append(float(_buf[1])*scale)
_arr.append(aLim)
_prev_limit = aLim
_d.append((_mass, _arr))
#print self.legend, _d
for t in _d:
_x.append(t[0])
_mean = TMath.Mean(len(t[1]), t[1])
_median = TMath.Median(len(t[1]), t[1])
_rms = TMath.RMS(len(t[1]), t[1])
_nrms = _rms/len(t[1])
#print self.legend, t[0], _mean, _rms, _nrms
if value_type == 'mean':
_value = _mean
_y.append(_mean)
elif value_type == 'median':
_value = _median
_y.append(_median)
_exh.append(0)
_exl.append(0)
_exh2.append(0)
_exl2.append(0)
if error_type == 'rms':
_eyh.append(_rms/2)
_eyl.append(_rms/2)
_eyh2.append(_rms)
_eyl2.append(_rms)
elif error_type == 'quantile':
_prob = array('d')
_prob.append(0.021)
_prob.append(0.159)
_prob.append(0.841)
_prob.append(0.979)
_nprob = 4
_quantiles = array('d')
_quantiles.append(0)
_quantiles.append(0)
_quantiles.append(0)
_quantiles.append(0)
TMath.Quantiles(len(t[1]), _nprob, t[1], _quantiles, _prob)
#print self.legend, 'Quantiles:', _quantiles
_eyh.append(abs(_value-_quantiles[2]))
_eyl.append(abs(_value-_quantiles[1]))
_eyh2.append(abs(_value-_quantiles[3]))
_eyl2.append(abs(_value-_quantiles[0]))
self.x[cat] = _x
self.y[cat] = _y
self.exh[cat] = _exh
self.exl[cat] = _exl
self.exh2[cat] = _exh2
self.exl2[cat] = _exl2
# non-smoothed bands
#self.eyh[cat] = _eyh
#self.eyl[cat] = _eyl
#self.eyh2[cat] = _eyh2
#self.eyl2[cat] = _eyl2
# smoothed bands
# ee, mumu
#self.eyh[cat] = self.smooth_band(_x,_eyh,_y,[1],300,600,'high')
#self.eyh2[cat] = self.smooth_band(_x,_eyh2,_y,[1],300,600,'high')
#self.eyl[cat] = self.smooth_band(_x,_eyl,_y,[],300,600)
#self.eyl2[cat] = self.smooth_band(_x,_eyl2,_y,[1],300,600)
# comb
self.eyh[cat] = self.smooth_band(_x,_eyh,_y,[],300,600,'high')
self.eyh2[cat] = self.smooth_band(_x,_eyh2,_y,[1],300,600,'high')
self.eyl[cat] = self.smooth_band(_x,_eyl,_y,[1],300,600)
self.eyl2[cat] = self.smooth_band(_x,_eyl2,_y,[1],300,600)
def add_median(self, cat, filename, legend, scale = 1.0,
line_color = 1, line_style = 1, line_width = 1,
marker_color = 1, marker_size = 1.2, marker_style=8,
dofit=False, fit_min = 100, fit_max = 3000,
var_scale = None,
fill_style = 1002,
fill_color = None,
fill2_style = 1002,
fill2_color = ROOT.kRed,
extra_scale_name = 'unit',
value_type = 'median',
error_type = 'quantile'):
#
# same as add() but for multiple values
#
if fill_color == None:
fill_color = line_color
self.type[cat] = 'observed'
self.dofit[cat] = dofit
self.fit_min[cat] = fit_min
self.fit_max[cat] = fit_max
self.cat.append(cat)
self.file = filename
self.tlegend[cat] = legend
self.line_color[cat] = line_color
self.line_style[cat] = line_style
self.line_width[cat] = line_width
self.marker_color[cat] = marker_color
self.marker_size[cat] = marker_size
self.marker_style[cat] = marker_style
self.fill_style[cat] = fill_style
self.fill2_style[cat] = fill2_style
self.fill_color[cat] = fill_color
self.fill2_color[cat] = fill2_color
_x = array('d')
_y = array('d')
_exh = array('d')
_exl = array('d')
_eyh = array('d')
_eyl = array('d')
_d = [] # list of tuples, one tuple per mass point
with open(filename, 'r') as _file:
_mass = None
_arr = array('d')
_prev_limit = 0.0
for line in _file:
#print self.legend, line
_buf = line.split()
if _buf[0][0] == '#':
continue
#_mass = float(_buf[0])
aMass = float(_buf[0])
_scale = get_scale(var_scale, aMass)
_scale_err = get_scale_err(var_scale, aMass)
if _scale != None:
_scale = _scale*scale
else:
_scale = scale
#_xval = float(_buf[0])
#_x.append(_xval)
#_yval = float(_buf[1])*_scale*get_scale(extra_scale_name, _xval)
aLim = float(_buf[1])*_scale*get_scale(extra_scale_name, aMass)
#_y.append(_yval)
if _mass == None:
_mass = aMass
if abs(_mass-aMass) > 0.1:
_d.append((_mass, _arr))
#print self.legend, cat, 'mass =', _mass, 'PEs per point:', len(_arr)
_mass = aMass
_arr=array('d')
#if abs(aLim - _prev_limit) < 0.000001:
# #print self.legend, 'WARNING: duplicate expected limit, ignoring...'
# continue
#_arr.append(float(_buf[1])*scale)
_arr.append(aLim)
_prev_limit = aLim
_d.append((_mass, _arr))
for t in _d:
_x.append(t[0])
_mean = TMath.Mean(len(t[1]), t[1])
_median = TMath.Median(len(t[1]), t[1])
_rms = TMath.RMS(len(t[1]), t[1])
_nrms = _rms/len(t[1])
#print self.legend, t[0], _mean, _rms, _nrms
if value_type == 'mean':
_value = _mean
_y.append(_mean)
elif value_type == 'median':
_value = _median
_y.append(_median)
_exh.append(0)
_exl.append(0)
if error_type == 'rms':
_eyh.append(_rms/2)
_eyl.append(_rms/2)
elif error_type == 'quantile':
_prob = array('d')
_prob.append(0.021)
_prob.append(0.159)
_prob.append(0.841)
_prob.append(0.979)
_nprob = 4
_quantiles = array('d')
_quantiles.append(0)
_quantiles.append(0)
_quantiles.append(0)
_quantiles.append(0)
TMath.Quantiles(len(t[1]), _nprob, t[1], _quantiles, _prob)
#print self.legend, 'Quantiles:', _quantiles
#_eyh.append(abs(_value-_quantiles[2]))
#_eyl.append(abs(_value-_quantiles[1]))
_eyh.append(0)
_eyl.append(0)
self.x[cat] = _x
self.y[cat] = _y
self.exh[cat] = _exh
self.exl[cat] = _exl
self.eyh[cat] = _eyh
self.eyl[cat] = _eyl
def mean(var, altname=''):
if len(altname) < 1:
altname = var
data.Draw(var, '', 'goff')
return TMath.Mean(data.GetSelectedRows(), data.GetV1())