Ejemplo n.º 1
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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
Ejemplo n.º 2
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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
Ejemplo n.º 3
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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
Ejemplo n.º 4
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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')])
Ejemplo n.º 6
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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')
Ejemplo n.º 7
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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
Ejemplo n.º 8
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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
Ejemplo n.º 9
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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')
Ejemplo n.º 10
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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()
Ejemplo n.º 11
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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
Ejemplo n.º 12
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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)
Ejemplo n.º 13
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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
Ejemplo n.º 14
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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
Ejemplo n.º 15
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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
Ejemplo n.º 16
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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)
Ejemplo n.º 17
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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()
Ejemplo n.º 18
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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
Ejemplo n.º 19
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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
Ejemplo n.º 20
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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
Ejemplo n.º 21
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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
Ejemplo n.º 22
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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
Ejemplo n.º 23
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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
Ejemplo n.º 24
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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
Ejemplo n.º 25
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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
Ejemplo n.º 26
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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)
Ejemplo n.º 27
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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)
Ejemplo n.º 28
0
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)