MonitoringHist Class Reference

Public Member Functions
	__init__ (self, filename, dirname)
	sum (self, name)
	mean (self, name)
	std (self, name)
	min (self, name)
	max (self, name)

Public Attributes
dict	values = {}
	Dictionary of bin-contents for each histogram.
dict	centers = {}
	Dictionary of bin-centers for each histogram.
dict	two_dimensional = {}
	Dictionary of 2D mode for each histogram.
dict	nbins = {}
	Dictionary of number of bins for each histogram.
	valid = os.path.isfile(filename)
	Indicates if the histograms were successfully read.

Detailed Description

Reads all TH1F and TH2F from a ROOT file
and puts them into a more accessible format.

Definition at line 163 of file monitoring.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
			filename,
			dirname )

Reads histograms from the given file
@param filename the name of the ROOT file

Definition at line 169 of file monitoring.py.

    def __init__(self, filename, dirname):
        """
        Reads histograms from the given file
        @param filename the name of the ROOT file
        """
        # Always avoid the top-level 'import ROOT'.
        import ROOT  # noqa
        
        self.values = {}
        
        self.centers = {}
        
        self.two_dimensional = {}
        
        self.nbins = {}
        
        self.valid = os.path.isfile(filename)
 
        if not self.valid:
            return
 
        f = ROOT.TFile.Open(filename, 'read')
        d = f.Get(ROOT.Belle2.MakeROOTCompatible.makeROOTCompatible(dirname))
 
        for key in d.GetListOfKeys():
            name = ROOT.Belle2.MakeROOTCompatible.invertMakeROOTCompatible(key.GetName())
            hist = key.ReadObj()
            if not (isinstance(hist, ROOT.TH1D) or isinstance(hist, ROOT.TH1F) or
                    isinstance(hist, ROOT.TH2D) or isinstance(hist, ROOT.TH2F)):
                continue
            self.two_dimensional[name] = isinstance(hist, ROOT.TH2D) or isinstance(hist, ROOT.TH2F)
            if self.two_dimensional[name]:
                nbins = (hist.GetNbinsX(), hist.GetNbinsY())
                self.centers[name] = [[hist.GetXaxis().GetBinCenter(i) for i in range(nbins[0] + 2)],
                                      [hist.GetYaxis().GetBinCenter(i) for i in range(nbins[1] + 2)]]
                self.values[name] = [[hist.GetBinContent(i, j) for i in range(nbins[0] + 2)] for j in range(nbins[1] + 2)]
                self.nbins[name] = nbins
            else:
                nbins = hist.GetNbinsX()
                self.centers[name] = np.array([hist.GetBinCenter(i) for i in range(nbins + 2)])
                self.values[name] = np.array([hist.GetBinContent(i) for i in range(nbins + 2)])
                self.nbins[name] = nbins
 

Member Function Documentation

max()

max	(		self,
			name )

Calculates the maximum of a given histogram
@param name key of the histogram

Definition at line 278 of file monitoring.py.

    def max(self, name):
        """
        Calculates the maximum of a given histogram
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            tempmax = -np.inf
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    if self.values[name][i][j] > tempmax:
                        tempmax = self.centers[name][i][j]
            return tempmax
        nonzero = np.nonzero(self.values[name])[0]
        if len(nonzero) == 0:
            return np.nan
        return self.centers[name][nonzero[-1]]
 
 

mean()

mean	(		self,
			name )

Calculates the mean of a given histogram
@param name key of the histogram

Definition at line 227 of file monitoring.py.

    def mean(self, name):
        """
        Calculates the mean of a given histogram
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            tempsum = 0
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    tempsum += self.centers[name][i][j] * self.values[name][i][j]
            return tempsum / self.sum(name)
        return np.average(self.centers[name], weights=self.values[name])
 

min()

min	(		self,
			name )

Calculates the minimum of a given histogram
@param name key of the histogram

Definition at line 259 of file monitoring.py.

    def min(self, name):
        """
        Calculates the minimum of a given histogram
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            tempmin = np.inf
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    if self.values[name][i][j] < tempmin:
                        tempmin = self.centers[name][i][j]
            return tempmin
        nonzero = np.nonzero(self.values[name])[0]
        if len(nonzero) == 0:
            return np.nan
        return self.centers[name][nonzero[0]]
 

std()

std	(		self,
			name )

Calculates the standard deviation of a given histogram
@param name key of the histogram

Definition at line 242 of file monitoring.py.

    def std(self, name):
        """
        Calculates the standard deviation of a given histogram
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            avg = self.mean(name)
            tempsum = 0
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    tempsum += self.values[name][i][j] * (self.centers[name][i][j] - avg)**2
            return np.sqrt(tempsum / self.sum(name))
        avg = np.average(self.centers[name], weights=self.values[name])
        return np.sqrt(np.average((self.centers[name] - avg)**2, weights=self.values[name]))
 

sum()

sum	(		self,
			name )

Calculates the sum of a given histogram (== sum of all entries)
@param name key of the histogram

Definition at line 212 of file monitoring.py.

    def sum(self, name):
        """
        Calculates the sum of a given histogram (== sum of all entries)
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            tempsum = 0
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    tempsum += self.values[name][i][j]
            return tempsum
        return np.sum(self.values[name])
 

Member Data Documentation

centers

dict centers = {}

Dictionary of bin-centers for each histogram.

Definition at line 179 of file monitoring.py.

nbins

dict nbins = {}

Dictionary of number of bins for each histogram.

Definition at line 183 of file monitoring.py.

two_dimensional

dict two_dimensional = {}

Dictionary of 2D mode for each histogram.

Definition at line 181 of file monitoring.py.

valid

valid = os.path.isfile(filename)

Indicates if the histograms were successfully read.

Definition at line 185 of file monitoring.py.

values

dict values = {}

Dictionary of bin-contents for each histogram.

Definition at line 177 of file monitoring.py.

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The documentation for this class was generated from the following file:

• analysis/scripts/fei/monitoring.py