def rmNanAndOutliers():
"""Plot without NAN and outliers from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) < 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
sd = 6
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
y = epmData['Value']
t = epmData['Timestamp']
nanPos = np.argwhere(np.isnan(y))
y = np.delete(y,nanPos)
t = np.delete(t,nanPos)
s3 = np.floor(sd * np.sqrt(y.std()))
smin = y.mean() - s3
smax = y.mean() + s3
outPos = np.argwhere(y<smin)
y = np.delete(y,outPos)
t = np.delete(t,outPos)
outPos = np.argwhere(y>smax)
y = np.delete(y,outPos)
t = np.delete(t,outPos)
res = vec2epm(t,y)
penName = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_NoOutliers'
sr.plot(penName, res)
return res
def plotGetSelection():
""" Get data range from selected pen.
"""
selData = []
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
y = epmData['Value'].copy()
x = np.arange(len(y))
fig = pl.figure(figsize=(8,6))
ax = fig.add_subplot(211, axisbg='#FFFFCC')
ax.plot(x, y, '-')
ax.set_title('Press left mouse button and drag to test')
ax2 = fig.add_subplot(212, axisbg='#FFFFCC')
line2, = ax2.plot(x, y, '-')
def onselect(xmin, xmax):
selData.append([xmin, xmax])
indmin, indmax = np.searchsorted(x, (xmin, xmax))
indmax = min(len(x)-1, indmax)
thisx = x[indmin:indmax]
thisy = y[indmin:indmax]
line2.set_data(thisx, thisy)
ax2.set_xlim(thisx[0], thisx[-1])
ax2.set_ylim(thisy.min(), thisy.max())
fig.canvas.draw()
span = SpanSelector(ax, onselect, 'horizontal', useblit=True, rectprops=dict(alpha=0.5, facecolor='red') )
pl.show()
return selData
def plotGetRetangle():
""" Area selection from selected pen.
"""
selRect = []
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
y = epmData['Value'].copy()
x = np.arange(len(y))
fig, current_ax = pl.subplots()
pl.plot(x, y, lw=2, c='g', alpha=.3)
def line_select_callback(eclick, erelease):
'eclick and erelease are the press and release events'
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
print ("\n(%3.2f, %3.2f) --> (%3.2f, %3.2f)" % (x1, y1, x2, y2))
selRect.append((int(x1), y1, int(x2), y2))
def toggle_selector(event):
if event.key in ['Q', 'q'] and toggle_selector.RS.active:
toggle_selector.RS.set_active(False)
if event.key in ['A', 'a'] and not toggle_selector.RS.active:
toggle_selector.RS.set_active(True)
toggle_selector.RS = RectangleSelector(current_ax, line_select_callback, drawtype='box', useblit=True, button=[1,3], minspanx=5, minspany=5, spancoords='pixels')
pl.connect('key_press_event', toggle_selector)
pl.show()
return selRect
def rmNanAndOutliers():
"""Plot without NAN and outliers from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) < 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
sd = 6
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
y = epmData['Value']
t = epmData['Timestamp']
nanPos = np.argwhere(np.isnan(y))
y = np.delete(y, nanPos)
t = np.delete(t, nanPos)
s3 = np.floor(sd * np.sqrt(y.std()))
smin = y.mean() - s3
smax = y.mean() + s3
outPos = np.argwhere(y < smin)
y = np.delete(y, outPos)
t = np.delete(t, outPos)
outPos = np.argwhere(y > smax)
y = np.delete(y, outPos)
t = np.delete(t, outPos)
res = vec2epm(t, y)
penName = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_NoOutliers'
sr.plot(penName, res)
return res
def main():
PreselectionRunner17 = ScriptRunner(year='2017', analysisname=analysisname, t3_base_path=t3_base_path, result_base_path=result_base_path, selectionstage='Studies', selectionname='LQDMHLTCheck', backgrounds=['LQLQToBTau_MLQ1000_MPS117_MC1100_L1p0', 'LQLQToBTauPsiChi_MLQ1000_MPS117_MC1100_L1p0', 'LQLQToPsiChi_MLQ1000_MPS117_MC1100_L1p0', 'PsiPsiToLQChi_MLQ1000_MPS2221_MC12089_L1p0'], signals=[])
# PreselectionRunner17.CalculateSelectionEfficiencies(num_and_denom=[('singlemu', 'input'), ('doublemu', 'input'), ('mutau', 'input'), ('singleele', 'input'), ('eletau', 'input'), ('elejet', 'input'), ('ditau', 'input'), ('met', 'input'), ('triggercategories', 'input')])
PreselectionRunner17.CalculateSelectionEfficiencies(num_and_denom=[('muon', 'input'), ('electron', 'input'), ('tau', 'input'), ('MET', 'input'), ('combined', 'input')])
def invCountEPMData():
""" Show count direction changes from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
n = invCount(epmData['Value'])
msg = 'Number of changes: ' + '%.0f' % (n)
sr.msgBox('EPM PyPlugin!', msg, 'Information')
def removeMean():
""" Plot removed mean data from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
rmMean = epmData.copy();
rmMean['Value'] = epmData['Value'] - epmData['Value'].mean()
penName = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_ZeroMean'
sr.plot(penName, rmMean)
return rmMean
def normalizeData():
""" Plot normalized data from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
nc = epmData.copy();
nc['Value'] = (epmData['Value'] - epmData['Value'].mean())/epmData['Value'].std()
penName = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_Normalized'
sr.plot(penName, nc)
return nc
def invCountEPMData():
""" Show count direction changes from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
n = invCount(epmData['Value'])
msg = 'Number of changes: ' + '%.0f' % (n)
sr.msgBox('EPM PyPlugin!', msg, 'Information')
def plotXY():
""" Plot XY from selected pens.
"""
import ScriptRunner as sr
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 2:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select two interpolated pens before applying this function!', 'Warning')
return 0
epmData1 = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
epmData2 = ds.EpmDatasetAnalysisPens.SelectedPens[1].values
pl.figure
pl.plot(epmData1['Value'], epmData2['Value'])
pl.show()
def integralData():
""" Show area from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
v = epmData['Value']
curveArea = integrate.simps(v)
statMsg = 'Integral: ' + '%.2f' % (curveArea)
sr.msgBox('EPM PyPlugin!', statMsg, 'Information')
return curveArea
def statsInfos():
""" Show basic statistics from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
v = epmData['Value']
vmean = v.mean()
vstd = v.std()
statMsg = 'Mean: ' + '%.2f' % (vmean) + '\nStdDev: ' + '%.2f' % (vstd)
sr.msgBox('EPM PyPlugin!', statMsg, 'Information')
return (vmean, vstd)
def integralData():
""" Show area from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
v = epmData['Value']
curveArea = integrate.simps(v)
statMsg = 'Integral: ' + '%.2f' % (curveArea)
sr.msgBox('EPM PyPlugin!', statMsg, 'Information')
return curveArea
def removeMean():
""" Plot removed mean data from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
rmMean = epmData.copy()
rmMean['Value'] = epmData['Value'] - epmData['Value'].mean()
penName = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_ZeroMean'
sr.plot(penName, rmMean)
return rmMean
def deltaVector():
""" Plot deltas from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
delta = epmData['Value'][1:] - epmData['Value'][:-1]
deltaCurves = epmData.copy();
deltaCurves['Value'][0] = 0
deltaCurves['Value'][1:] = delta
penName = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_Delta'
sr.plot(penName, deltaCurves)
return deltaCurves
def statsInfos():
""" Show basic statistics from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
v = epmData['Value']
vmean = v.mean()
vstd = v.std()
statMsg = 'Mean: ' + '%.2f' % (vmean) + '\nStdDev: ' + '%.2f' % (vstd)
sr.msgBox('EPM PyPlugin!', statMsg, 'Information')
return (vmean, vstd)
def plotXY():
""" Plot XY from selected pens.
"""
import ScriptRunner as sr
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 2:
sr.msgBox(
'EPM Python Plugin - Demo Tools',
'Please select two interpolated pens before applying this function!',
'Warning')
return 0
epmData1 = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
epmData2 = ds.EpmDatasetAnalysisPens.SelectedPens[1].values
pl.figure
pl.plot(epmData1['Value'], epmData2['Value'])
pl.show()
def normalizeData():
""" Plot normalized data from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
nc = epmData.copy()
nc['Value'] = (epmData['Value'] -
epmData['Value'].mean()) / epmData['Value'].std()
penName = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_Normalized'
sr.plot(penName, nc)
return nc
def deltaVector():
""" Plot deltas from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
delta = epmData['Value'][1:] - epmData['Value'][:-1]
deltaCurves = epmData.copy()
deltaCurves['Value'][0] = 0
deltaCurves['Value'][1:] = delta
penName = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_Delta'
sr.plot(penName, deltaCurves)
return deltaCurves
def plotGetPoints():
""" Get data points from selected pen.
"""
selPoints = []
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
y = epmData['Value'].copy()
x = np.arange(len(y))
fig = pl.figure(figsize=(8, 6))
ax = fig.add_subplot(111, axisbg='#FFFFFF')
ax.plot(x, y, '-o')
def onclick(event):
selPoints.append((event.xdata, event.ydata))
cursor = Cursor(ax, useblit=True, color='red', linewidth=1 )
cid = fig.canvas.mpl_connect('button_press_event', onclick)
pl.show()
return selPoints
def plotGetRetangle():
""" Area selection from selected pen.
"""
selRect = []
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
y = epmData['Value'].copy()
x = np.arange(len(y))
fig, current_ax = pl.subplots()
pl.plot(x, y, lw=2, c='g', alpha=.3)
def line_select_callback(eclick, erelease):
'eclick and erelease are the press and release events'
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
print("\n(%3.2f, %3.2f) --> (%3.2f, %3.2f)" % (x1, y1, x2, y2))
selRect.append((int(x1), y1, int(x2), y2))
def toggle_selector(event):
if event.key in ['Q', 'q'] and toggle_selector.RS.active:
toggle_selector.RS.set_active(False)
if event.key in ['A', 'a'] and not toggle_selector.RS.active:
toggle_selector.RS.set_active(True)
toggle_selector.RS = RectangleSelector(current_ax,
line_select_callback,
drawtype='box',
useblit=True,
button=[1, 3],
minspanx=5,
minspany=5,
spancoords='pixels')
pl.connect('key_press_event', toggle_selector)
pl.show()
return selRect
def percentTimeIn():
""" Funcao que retorna o percentual de tempo em cada intervalo informado.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
global nodes
runDialogInterval()
global nodes
t, y = rmNanAndOutliers2(epmData)
if len(nodes) < 2:
minVal = int(np.floor(np.nanmin(y)))
maxVal = int(np.ceil(np.nanmax(y)))
step = int((maxVal - minVal) / 3)
nodes = range(minVal, maxVal, step)
intervNum = np.size(nodes) + 1
totTime = np.empty(intervNum, dtype=object)
totTime.fill(dtt.timedelta(0, 0))
for i in range(1, np.size(y)):
dt = t[i] - t[i - 1]
ix = np.digitize([y[i]], nodes)
totTime[ix] += dt
nodesPercents = np.zeros([np.size(totTime), 2])
totalPeriod = totTime.sum().total_seconds()
for i in range(np.size(totTime)):
if i:
nodesPercents[i, 0] = nodes[i - 1]
else:
nodesPercents[i, 0] = -np.inf
nodesPercents[i, 1] = totTime[i].total_seconds() / totalPeriod
labels = []
for item in nodesPercents[:, 0]:
labels.append(str(item))
pl.pie(nodesPercents[:, 1], labels=labels, autopct='%1.1f%%', shadow=True)
pl.show()
return nodesPercents
def plotGetPoints():
""" Get data points from selected pen.
"""
selPoints = []
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
y = epmData['Value'].copy()
x = np.arange(len(y))
fig = pl.figure(figsize=(8, 6))
ax = fig.add_subplot(111, axisbg='#FFFFFF')
ax.plot(x, y, '-o')
def onclick(event):
selPoints.append((event.xdata, event.ydata))
cursor = Cursor(ax, useblit=True, color='red', linewidth=1)
cid = fig.canvas.mpl_connect('button_press_event', onclick)
pl.show()
return selPoints
def percentTimeIn():
""" Funcao que retorna o percentual de tempo em cada intervalo informado.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
global nodes
runDialogInterval()
global nodes
t,y = rmNanAndOutliers2(epmData)
if len(nodes) < 2:
minVal = int(np.floor(np.nanmin(y)))
maxVal = int(np.ceil(np.nanmax(y)))
step = int((maxVal-minVal)/3)
nodes = range(minVal,maxVal,step)
intervNum = np.size(nodes)+1
totTime = np.empty(intervNum, dtype=object)
totTime.fill(dtt.timedelta(0,0))
for i in range(1,np.size(y)):
dt = t[i] - t[i-1]
ix = np.digitize([y[i]], nodes)
totTime[ix] += dt
nodesPercents = np.zeros([np.size(totTime),2])
totalPeriod = totTime.sum().total_seconds()
for i in range(np.size(totTime)):
if i:
nodesPercents[i,0] = nodes[i-1]
else:
nodesPercents[i,0] = -np.inf
nodesPercents[i,1] = totTime[i].total_seconds()/totalPeriod
labels = []
for item in nodesPercents[:,0]:
labels.append(str(item))
pl.pie(nodesPercents[:,1], labels=labels, autopct='%1.1f%%', shadow=True)
pl.show()
return nodesPercents
def plotGetSelection():
""" Get data range from selected pen.
"""
selData = []
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
y = epmData['Value'].copy()
x = np.arange(len(y))
fig = pl.figure(figsize=(8, 6))
ax = fig.add_subplot(211, axisbg='#FFFFCC')
ax.plot(x, y, '-')
ax.set_title('Press left mouse button and drag to test')
ax2 = fig.add_subplot(212, axisbg='#FFFFCC')
line2, = ax2.plot(x, y, '-')
def onselect(xmin, xmax):
selData.append([xmin, xmax])
indmin, indmax = np.searchsorted(x, (xmin, xmax))
indmax = min(len(x) - 1, indmax)
thisx = x[indmin:indmax]
thisy = y[indmin:indmax]
line2.set_data(thisx, thisy)
ax2.set_xlim(thisx[0], thisx[-1])
ax2.set_ylim(thisy.min(), thisy.max())
fig.canvas.draw()
span = SpanSelector(ax,
onselect,
'horizontal',
useblit=True,
rectprops=dict(alpha=0.5, facecolor='red'))
pl.show()
return selData
def plotMinMax():
""" Plot Min-Max lines from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
minValue = epmData['Value'].min();
maxValue = epmData['Value'].max();
vmin = epmData.copy()
vmax = epmData.copy()
vmin['Value'][:] = minValue
vmax['Value'][:] = maxValue
penMin = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_Min'
penMax = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_Max'
sr.plot(penMin, vmin)
sr.plot(penMax, vmax)
return (minValue, maxValue)
def plotMinMax():
""" Plot Min-Max lines from selected pen.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
minValue = epmData['Value'].min()
maxValue = epmData['Value'].max()
vmin = epmData.copy()
vmax = epmData.copy()
vmin['Value'][:] = minValue
vmax['Value'][:] = maxValue
penMin = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_Min'
penMax = ds.EpmDatasetAnalysisPens.SelectedPens[0].name + '_Max'
sr.plot(penMin, vmin)
sr.plot(penMax, vmax)
return (minValue, maxValue)
def main():
# print 'hello1'
# StudiesRunner17 = ScriptRunner(year='2017', analysisname=analysisname, t3_base_path=t3_base_path, result_base_path=result_base_path, selectionstage='Studies', selectionname='PsiPsi_LeptonIDEfficiencies', backgrounds=[], signals=['PsiPsiToLQChi_MLQ1000_MPS117_MC1100_L1p0', 'PsiPsiToLQChi_MLQ1000_MPS244_MC1214_L1p0', 'PsiPsiToLQChi_MLQ1000_MPS567_MC1457_L1p0', 'PsiPsiToLQChi_MLQ1000_MPS1051_MC1977_L1p0', 'PsiPsiToLQChi_MLQ1000_MPS2221_MC12089_L1p0'])
# StudiesRunner17.CalculateLeptonReconstructionEfficiencies()
# StudiesRunner17.CalculateLeptonIDEfficiencies()
# PreselectionRunner17 = ScriptRunner(year='2017', analysisname=analysisname, t3_base_path=t3_base_path, result_base_path=result_base_path, selectionstage='Preselection', selectionname='PsiPsi_Initial', backgrounds=['TT', 'DYJets', 'ST', 'WJets', 'QCD_Had'], signals=['PsiPsiToLQChi_MLQ1000_MPS117_MC1100_L1p0', 'PsiPsiToLQChi_MLQ1000_MPS244_MC1214_L1p0', 'PsiPsiToLQChi_MLQ1000_MPS567_MC1457_L1p0', 'PsiPsiToLQChi_MLQ1000_MPS1051_MC1977_L1p0', 'PsiPsiToLQChi_MLQ1000_MPS2221_MC12089_L1p0'])
# PreselectionRunner17.CalculateSelectionEfficiencies(num_and_denom=[('trigger', 'input'), ('base', 'input'), ('plateau', 'input'), ('tach_leptonveto', 'input'), ('tach_plateau', 'input'), ('tach_tau', 'input'), ('tach_met', 'input'), ('final', 'input')])
# LQ pair-production
# systematics17 = ['nominal', 'lumi', 'rate_tt', 'rate_dyjets', 'rate_st']
# # backgrounds17 = ['TT', 'DYJets', 'ST', 'WJets', 'QCD_Had']
# backgrounds17 = ['TT', 'DYJets', 'ST']
# signals17 = ['LQLQToBTau_MLQ1000_MPS117_MC1100_L1p0', 'LQLQToBTau_MLQ1360_MPS117_MC1100_L1p0', 'LQLQToBTau_MLQ1810_MPS117_MC1100_L1p0', 'LQLQToBTau_MLQ2170_MPS117_MC1100_L1p0', 'LQLQToBTau_MLQ2620_MPS117_MC1100_L1p0']
# channels17 = ['elch', 'much', 'elmuch', 'tach']
# histnames_in_out_per_category17 = {'cat1': ('stmet3', 'STMET'), 'cat2': ('stmet3', 'STMET')}
# limithisttag17 = 'Limits'
#
# processes_per_systematic17 = {'lumi': 'all', 'rate_tt': 'TT', 'rate_dyjets': 'DYJets', 'rate_st': 'ST', 'rate_wjets': 'WJets', 'rate_qcd': 'QCD_Had', 'pu': 'all', 'pdf': 'all', 'muid': 'all', 'muiso': 'all', 'mutrigger': 'all', 'eleid': 'all', 'elereco': 'all', 'btag_bc': 'all', 'btag_udsg': 'all', 'scale_tt': 'TT', 'scale_dyjets': 'DYJets', 'scale_st': 'ST'}
# pdf_per_systematic17 = {'lumi': 'lnN', 'pu': 'shape', 'pdf': 'shape', 'muid': 'shape', 'muiso': 'shape', 'mutrigger': 'shape', 'eleid': 'shape', 'elereco': 'shape', 'btag_bc': 'shape', 'btag_udsg': 'shape', 'rate_tt': 'lnN', 'rate_dyjets': 'lnN', 'rate_st': 'lnN', 'rate_wjets': 'lnN', 'rate_qcd': 'lnN', 'scale_tt': 'shape', 'scale_dyjets': 'shape', 'scale_st': 'shape'}
# value_per_systematic17 = {'lumi': 1.025, 'pu': 1, 'pdf': 1, 'muid': 1, 'muiso': 1, 'mutrigger': 1, 'eleid': 1, 'elereco': 1, 'btag_bc': 1, 'btag_udsg': 1, 'rate_tt': 1.056, 'rate_dyjets': 1.1, 'rate_st': 1.1, 'rate_wjets': 1.25, 'rate_qcd': 1.5, 'scale_tt': 1, 'scale_dyjets': 1, 'scale_st': 1}
# Analyzer17 = PostAnalyzer('2017', analysisname=analysisname, t3_base_path=t3_base_path, result_base_path=result_base_path, selectionname='TauTau_ElTau_MuTau_ElMu_2Jets_12LooseBTag_met50', systematics=systematics17, backgrounds=backgrounds17, signals=signals17, channels=channels17, histnames_in_out_per_category=histnames_in_out_per_category17, limithisttag=limithisttag17, processes_per_systematic=processes_per_systematic17, pdf_per_systematic=pdf_per_systematic17, value_per_systematic=value_per_systematic17)
# Analyzer17.ProduceCombineHistograms(signal_scaled_by=0.01)
# Analyzer17.CreateDatacards()
# Analyzer17.CombineChannels()
# Analyzer17.ExecuteCombineCombination()
# Analyzer17.PlotLimits(signal_scaled_by=0.01, draw_observed=False)
# LQ t-channel
PreselectionRunner17_tach = ScriptRunner(year='2017', analysisname=analysisname, t3_base_path=t3_base_path, result_base_path=result_base_path, selectionstage='Preselection', selectionname='TChannelTauNu_tau', backgrounds=['TT', 'VV', 'DYJets', 'ST', 'WJets', 'QCD_Had'], signals=['LQTChannelTauNu_DynamicScale_MLQ1000_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1360_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1810_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2620_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2980_MPS117_MC1100_L1p0'])
# PreselectionRunner17_tach.CalculateSelectionEfficiencies(num_and_denom=[('trigger', 'input'), ('base', 'input'), ('plateau', 'input'), ('tach_leptonveto', 'input'), ('tach_plateau', 'input'), ('tach_tau', 'input'), ('tach_met', 'input'), ('final', 'input')])
PreselectionRunner17_much = ScriptRunner(year='2017', analysisname=analysisname, t3_base_path=t3_base_path, result_base_path=result_base_path, selectionstage='Preselection', selectionname='TChannelTauNu_muon', backgrounds=['TT', 'VV', 'DYJets', 'ST', 'WJets', 'QCD_Had'], signals=['LQTChannelTauNu_DynamicScale_MLQ1000_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1360_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1810_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2620_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2980_MPS117_MC1100_L1p0'])
# PreselectionRunner17_much.CalculateSelectionEfficiencies(num_and_denom=[('trigger', 'input'), ('plateau', 'input'), ('much_leptonveto', 'input'), ('much_lepton', 'input'), ('much_plateau', 'input'), ('much_dphi', 'input'), ('much_ptratio', 'input'), ('final', 'input')])
FullselectionRunner17_tach = ScriptRunner(year='2017', analysisname=analysisname, t3_base_path=t3_base_path, result_base_path=result_base_path, selectionstage='Fullselection', selectionname='TChannelTauNu_tau', backgrounds=['TT', 'VV', 'DYJets', 'ST', 'WJets', 'QCD_Had'], signals=['LQTChannelTauNu_DynamicScale_MLQ1000_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1360_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1810_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2620_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2980_MPS117_MC1100_L1p0'])
# FullselectionRunner17_tach.CalculateSelectionEfficiencies(num_and_denom=[('tach_trigger', 'input'), ('tach_taucleaner', 'input'), ('tach_tauselection', 'input'), ('tach_dphi', 'input'), ('tach_ptratio', 'input'), ('tach_cat1_nominal', 'input')])
# FullselectionRunner17_tach.CalculateSelectionEfficiencies(num_and_denom=[('tach_trigger', 'input'), ('tach_taucleaner', 'input'), ('tach_tauselection', 'input'), ('tach_cat1jets_initial', 'input'), ('tach_cat1jets_dphi', 'input'), ('tach_cat1jets_ptratio', 'input'), ('tach_cat1jets_1btagloose', 'input'), ('tach_cat1jets_1btagloose_nominal', 'input'), ('tach_cat1jets_0btagloose', 'input'), ('tach_cat1jets_0btagloose_nominal', 'input'), ('tach_cat1jets_nominal', 'input'), ('tach_cat0jets_initial', 'input'), ('tach_cat0jets_dphi', 'input'), ('tach_cat0jets_ptratio', 'input'), ('tach_cat0jets_nominal', 'input'), ('tach_cat1_nominal', 'input')])
FullselectionRunner17_much = ScriptRunner(year='2017', analysisname=analysisname, t3_base_path=t3_base_path, result_base_path=result_base_path, selectionstage='Fullselection', selectionname='TChannelTauNu_muon_wprime', backgrounds=['TT', 'VV', 'DYJets', 'ST', 'WJets', 'QCD_Had'], signals=['LQTChannelTauNu_DynamicScale_MLQ1000_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1360_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1810_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2620_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2980_MPS117_MC1100_L1p0'])
# FullselectionRunner17_much.CalculateSelectionEfficiencies(num_and_denom=[('much_jetveto5', 'input'), ('much_btagveto1', 'input'), ('much_dphi25', 'input'), ('much_ptratio0415', 'input'), ('much_ptmu50', 'input'), ('much_cat1_nominal', 'input')])
systematics17 = ['nominal', 'lumi', 'rate_tt', 'rate_dyjets', 'rate_st', 'rate_vv', 'rate_wjets', 'rate_qcd']
backgrounds17 = ['TT', 'DYJets', 'ST', 'WJets', 'QCD_Had']
# backgrounds17 = ['TT', 'DYJets', 'ST', 'WJets']
signals17 = ['LQTChannelTauNu_DynamicScale_MLQ1000_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1360_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ1810_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2620_MPS117_MC1100_L1p0', 'LQTChannelTauNu_DynamicScale_MLQ2980_MPS117_MC1100_L1p0']
channels17 = ['tach']
histnames_in_out_per_category17 = {
'cat1': ('mt_taumet_rebinlimit2', 'MT'),
# 'cat1jets_0btagloose': ('mt_taumet_rebinlimit2', 'MT'),
# 'cat1jets_1btagloose': ('mt_taumet_rebinlimit2', 'MT'),
# 'cat0jets': ('mt_taumet_rebinlimit2', 'MT'),
}
# histnames_in_out_per_category17 = {'cat1': ('mt_mumet_rebinlimit2', 'MT')}
limithisttag17 = 'General'
processes_per_systematic17 = {'lumi': 'all', 'rate_tt': 'TT', 'rate_dyjets': 'DYJets', 'rate_st': 'ST', 'rate_wjets': 'WJets', 'rate_qcd': 'QCD_Had', 'rate_vv': 'VV', 'pu': 'all', 'pdf': 'all', 'muid': 'all', 'muiso': 'all', 'mutrigger': 'all', 'eleid': 'all', 'elereco': 'all', 'btag_bc': 'all', 'btag_udsg': 'all', 'scale_tt': 'TT', 'scale_dyjets': 'DYJets', 'scale_st': 'ST'}
pdf_per_systematic17 = {'lumi': 'lnN', 'pu': 'shape', 'pdf': 'shape', 'muid': 'shape', 'muiso': 'shape', 'mutrigger': 'shape', 'eleid': 'shape', 'elereco': 'shape', 'btag_bc': 'shape', 'btag_udsg': 'shape', 'rate_tt': 'lnN', 'rate_dyjets': 'lnN', 'rate_st': 'lnN', 'rate_vv': 'lnN', 'rate_wjets': 'lnN', 'rate_qcd': 'lnN', 'scale_tt': 'shape', 'scale_dyjets': 'shape', 'scale_st': 'shape'}
value_per_systematic17 = {'lumi': 1.025, 'pu': 1, 'pdf': 1, 'muid': 1, 'muiso': 1, 'mutrigger': 1, 'eleid': 1, 'elereco': 1, 'btag_bc': 1, 'btag_udsg': 1, 'rate_tt': 1.056, 'rate_dyjets': 1.1, 'rate_st': 1.1, 'rate_vv': 1.2, 'rate_wjets': 1.25, 'rate_qcd': 1.5, 'scale_tt': 1, 'scale_dyjets': 1, 'scale_st': 1}
Analyzer17 = PostAnalyzer('2017', analysisname=analysisname, t3_base_path=t3_base_path, result_base_path=result_base_path, selectionstage='Fullselection', selectionname='TChannelTauNu_wprime_tau', systematics=systematics17, backgrounds=backgrounds17, signals=signals17, channels=channels17, histnames_in_out_per_category=histnames_in_out_per_category17, limithisttag=limithisttag17, processes_per_systematic=processes_per_systematic17, pdf_per_systematic=pdf_per_systematic17, value_per_systematic=value_per_systematic17)