Histograms Class Reference

Public Member Functions
	__init__ (self, data, column, masks=dict(), weight_column=None, bins=100, equal_frequency=True, range_in_std=None)
	get_hist (self, name=None)
	get_summed_hist (self, names)
	get_efficiency (self, signal_names)
	get_true_positives (self, signal_names)
	get_false_positives (self, bckgrd_names)
	get_purity (self, signal_names, bckgrd_names)
	get_signal_to_noise (self, signal_names, bckgrd_names)
	get_purity_per_bin (self, signal_names, bckgrd_names)

Static Public Attributes
	hist = None
	Histogram of the full data.
	bins = None
	Binning.
tuple	bin_centers = None
	Bin centers.
tuple	bin_widths = None
	Bin widths.
	hists = None
	Dictionary of histograms for the given masks.

Detailed Description

Extracts information from a pandas.DataFrame and stores it
in a binned format.
Therefore the size independent from the size of the pandas.DataFrame.
Used by the plotting routines below.

Definition at line 42 of file histogram.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
			data,
			column,
			masks = dict(),
			weight_column = None,
			bins = 100,
			equal_frequency = True,
			range_in_std = None )

Creates a common binning of the given column of the given pandas.Dataframe,
and stores for each given mask the histogram of the column
@param data pandas.DataFrame  like object containing column and weight_column
@param column string identifying the column in the pandas.DataFrame which is binned.
@param masks dictionary of names and boolean arrays, which select the data
             used for the creation of histograms with these names
@param weight_column identifying the column in the pandas.DataFrame which is used as weight
@param bins use given bins instead of default 100
@param equal_frequency perform an equal_frequency binning
@param range_in_std show only the data in a windows around +- range_in_std * standard_deviation around the mean

Definition at line 61 of file histogram.py.

    def __init__(self, data, column, masks=dict(), weight_column=None, bins=100, equal_frequency=True, range_in_std=None):
        """
        Creates a common binning of the given column of the given pandas.Dataframe,
        and stores for each given mask the histogram of the column
        @param data pandas.DataFrame  like object containing column and weight_column
        @param column string identifying the column in the pandas.DataFrame which is binned.
        @param masks dictionary of names and boolean arrays, which select the data
                     used for the creation of histograms with these names
        @param weight_column identifying the column in the pandas.DataFrame which is used as weight
        @param bins use given bins instead of default 100
        @param equal_frequency perform an equal_frequency binning
        @param range_in_std show only the data in a windows around +- range_in_std * standard_deviation around the mean
        """
        isfinite = numpy.isfinite(data[column])
        if range_in_std is not None:
            mean, std = weighted_mean_and_std(data[column][isfinite],
                                              None if weight_column is None else data[weight_column][isfinite])
            # Everything outside mean +- range_in_std * std is considered infinite
            isfinite = isfinite & (data[column] > (mean - range_in_std * std)) & (data[column] < (mean + range_in_std * std))
 
        if equal_frequency:
            if data[column][isfinite].size > 0:
                bins = numpy.unique(numpy.percentile(data[column][isfinite], q=range(bins + 1)))
            else:
                print('Empty Array')
                bins = [1]
            # If all values are unique, we make at least one bin
            if len(bins) == 1:
                bins = numpy.array([bins[0]-1, bins[0]+1])
 
        
        self.hist, self.bins = numpy.histogram(data[column][isfinite], bins=bins,
                                               weights=None if weight_column is None else data[weight_column])
        
        self.bin_centers = (self.bins + numpy.roll(self.bins, 1))[1:] / 2.0
        
        self.bin_widths = (self.bins - numpy.roll(self.bins, 1))[1:] - 0.00001
        
        self.hists = dict()
        for name, mask in masks.items():
            self.hists[name] = numpy.histogram(data[column][mask & isfinite], bins=self.bins,
                                               weights=None if weight_column is None else data[weight_column][mask & isfinite])[0]
 

Member Function Documentation

get_efficiency()

get_efficiency	(		self,
			signal_names )

Return the cumulative efficiency in each bin of the sum of the histograms with the given names.
@param  signal_names of the histograms
@return numpy.array with hist data, numpy.array with corresponding binomial errors

Definition at line 125 of file histogram.py.

    def get_efficiency(self, signal_names):
        """
        Return the cumulative efficiency in each bin of the sum of the histograms with the given names.
        @param  signal_names of the histograms
        @return numpy.array with hist data, numpy.array with corresponding binomial errors
        """
        signal, _ = self.get_summed_hist(signal_names)
        cumsignal = (signal.sum() - signal.cumsum()).astype('float')
 
        efficiency = 0
        efficiency_error = 0
        if signal.sum() > 0:
            efficiency = cumsignal / signal.sum()
            efficiency_error = binom_error(cumsignal, signal.sum())
        return efficiency, efficiency_error
 

get_false_positives()

get_false_positives	(		self,
			bckgrd_names )

Return the cumulative false positives in each bin of the sum of the histograms with the given names.
@param names names of the histograms
@return numpy.array with hist data, numpy.array with corresponding binomial errors

Definition at line 152 of file histogram.py.

    def get_false_positives(self, bckgrd_names):
        """
        Return the cumulative false positives in each bin of the sum of the histograms with the given names.
        @param names names of the histograms
        @return numpy.array with hist data, numpy.array with corresponding binomial errors
        """
        background, _ = self.get_summed_hist(bckgrd_names)
        cumbackground = (background.sum() - background.cumsum()).astype('float')
        background_error = poisson_error(cumbackground)
        return cumbackground, background_error
 

get_hist()

get_hist	(		self,
			name = None )

Return histogram with the given name. If none returns histogram of the full data.
@param name name of the histogram
@return numpy.array with hist data, numpy.array with corresponding poisson errors

Definition at line 104 of file histogram.py.

    def get_hist(self, name=None):
        """
        Return histogram with the given name. If none returns histogram of the full data.
        @param name name of the histogram
        @return numpy.array with hist data, numpy.array with corresponding poisson errors
        """
        if name is None:
            return self.hist, poisson_error(self.hist)
        return self.get_summed_hist([name])
 

get_purity()

get_purity	(		self,
			signal_names,
			bckgrd_names )

Return the cumulative purity in each bin of the sum of the histograms with the given names.
@param names names of the histograms
@return numpy.array with hist data, numpy.array with corresponding binomial errors

Definition at line 163 of file histogram.py.

    def get_purity(self, signal_names, bckgrd_names):
        """
        Return the cumulative purity in each bin of the sum of the histograms with the given names.
        @param names names of the histograms
        @return numpy.array with hist data, numpy.array with corresponding binomial errors
        """
        signal, _ = self.get_summed_hist(signal_names)
        bckgrd, _ = self.get_summed_hist(bckgrd_names)
        cumsignal = (signal.sum() - signal.cumsum()).astype('float')
        cumbckgrd = (bckgrd.sum() - bckgrd.cumsum()).astype('float')
 
        with numpy.errstate(divide='ignore', invalid='ignore'):
            purity = cumsignal / (cumsignal + cumbckgrd)
            purity_error = binom_error(cumsignal, cumsignal + cumbckgrd)
        return purity, purity_error
 

get_purity_per_bin()

get_purity_per_bin	(		self,
			signal_names,
			bckgrd_names )

Return the purity in each bin of the sum of the histograms with the given names.
@param names names of the histograms
@return numpy.array with hist data, numpy.array with corresponding binomial errors

Definition at line 195 of file histogram.py.

    def get_purity_per_bin(self, signal_names, bckgrd_names):
        """
        Return the purity in each bin of the sum of the histograms with the given names.
        @param names names of the histograms
        @return numpy.array with hist data, numpy.array with corresponding binomial errors
        """
        signal, _ = self.get_summed_hist(signal_names)
        bckgrd, _ = self.get_summed_hist(bckgrd_names)
        signal = signal.astype('float')
        bckgrd = bckgrd.astype('float')
 
        with numpy.errstate(divide='ignore', invalid='ignore'):
            purity = signal / (signal + bckgrd)
            purity_error = binom_error(signal, signal + bckgrd)
        return purity, purity_error

get_signal_to_noise()

get_signal_to_noise	(		self,
			signal_names,
			bckgrd_names )

Return the cumulative signal to noise ratio in each bin of the sum of the histograms with the given names.
@param names names of the histograms
@return numpy.array with hist data, numpy.array with corresponding binomial errors

Definition at line 179 of file histogram.py.

    def get_signal_to_noise(self, signal_names, bckgrd_names):
        """
        Return the cumulative signal to noise ratio in each bin of the sum of the histograms with the given names.
        @param names names of the histograms
        @return numpy.array with hist data, numpy.array with corresponding binomial errors
        """
        signal, _ = self.get_summed_hist(signal_names)
        bckgrd, _ = self.get_summed_hist(bckgrd_names)
        cumsignal = (signal.sum() - signal.cumsum()).astype('float')
        cumbckgrd = (bckgrd.sum() - bckgrd.cumsum()).astype('float')
 
        with numpy.errstate(divide='ignore', invalid='ignore'):
            signal2noise = cumsignal / (cumsignal + cumbckgrd)**0.5
            signal2noise_error = numpy.sqrt(cumsignal * cumbckgrd) / (cumsignal + cumbckgrd)
        return signal2noise, signal2noise_error
 

get_summed_hist()

get_summed_hist	(		self,
			names )

Return the sum of histograms with the given names.
@param names names of the histograms
@return numpy.array with hist data, numpy.array with corresponding poisson errors

Definition at line 114 of file histogram.py.

    def get_summed_hist(self, names):
        """
        Return the sum of histograms with the given names.
        @param names names of the histograms
        @return numpy.array with hist data, numpy.array with corresponding poisson errors
        """
        default = numpy.zeros(len(self.bin_centers))
        hist = numpy.sum(self.hists.get(v, default) for v in names)
        hist_error = poisson_error(hist)
        return hist, hist_error
 

get_true_positives()

get_true_positives	(		self,
			signal_names )

Return the cumulative true positives in each bin of the sum of the histograms with the given names.
@param names names of the histograms
@return numpy.array with hist data, numpy.array with corresponding binomial errors

Definition at line 141 of file histogram.py.

    def get_true_positives(self, signal_names):
        """
        Return the cumulative true positives in each bin of the sum of the histograms with the given names.
        @param names names of the histograms
        @return numpy.array with hist data, numpy.array with corresponding binomial errors
        """
        signal, _ = self.get_summed_hist(signal_names)
        cumsignal = (signal.sum() - signal.cumsum()).astype('float')
        signal_error = poisson_error(cumsignal)
        return cumsignal, signal_error
 

Member Data Documentation

bin_centers

tuple bin_centers = None

static

Bin centers.

bin centers

Definition at line 55 of file histogram.py.

bin_widths

tuple bin_widths = None

static

Bin widths.

bin centers

Subtract a small number from the bin width, otherwise the errorband plot is unstable.

Definition at line 57 of file histogram.py.

bins

bins = None

static

Binning.

Definition at line 53 of file histogram.py.

hist

hist = None

static

Histogram of the full data.

create histogram

Definition at line 51 of file histogram.py.

hists

hists = None

static

Dictionary of histograms for the given masks.

bin centers

Subtract a small number from the bin width, otherwise the errorband plot is unstable.

initialize empty dictionary for histograms

Definition at line 59 of file histogram.py.

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

• mva/scripts/basf2_mva_evaluation/histogram.py