Correlation Class Reference

Inheritance diagram for Correlation:

Collaboration diagram for Correlation:

Public Member Functions
def	__init__ (self, figure=None)
def	add (self, data, column, cut_column, quantiles, signal_mask=None, bckgrd_mask=None, weight_column=None)
def	finish (self)

Public Attributes
	figure
	create figure
	axis
	define first subplot
	axis_d1
	define second subplot
	axis_d2
	define third subplot

Static Public Attributes
None	figure = None
	figure which is used to draw
None	axis = None
	Main axis which is used to draw.
None	axis_d1 = None
	Axis which shows shape of signal.
None	axis_d2 = None
	Axis which shows shape of background.

Detailed Description

Plots change of a distribution of a quantity depending on the cut on a classifier

Definition at line 1064 of file plotting.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			figure = None
	)

Creates a new figure if None is given, sets the default plot parameters
@param figure default draw figure which is used

Reimplemented from Plotter.

Definition at line 1077 of file plotting.py.

    def __init__(self, figure=None):
        """
        Creates a new figure if None is given, sets the default plot parameters
        @param figure default draw figure which is used
        """
        if figure is None:
            
            self.figure = matplotlib.figure.Figure(figsize=(32, 18))
            self.figure.set_tight_layout(True)
        else:
            self.figure = figure
 
        gs = matplotlib.gridspec.GridSpec(3, 2)
        
        self.axis = self.figure.add_subplot(gs[0, :])
        
        self.axis_d1 = self.figure.add_subplot(gs[1, :], sharex=self.axis)
        
        self.axis_d2 = self.figure.add_subplot(gs[2, :], sharex=self.axis)
 
        super().__init__(self.figure, self.axis)
 

Member Function Documentation

add()

def add	(		self,
			data,
			column,
			cut_column,
			quantiles,
			signal_mask = None,
			bckgrd_mask = None,
			weight_column = None
	)

Add a new correlation plot.
@param data pandas.DataFrame containing all data
@param column which is used to calculate distribution histogram
@param cut_column which is used to calculate cut on the other quantity defined by column
@param quantiles list of quantiles between 0 and 100, defining the different cuts
@param weight_column column in data containing the weights for each event

Reimplemented from Plotter.

Definition at line 1099 of file plotting.py.

    def add(self, data, column, cut_column, quantiles, signal_mask=None, bckgrd_mask=None, weight_column=None):
        """
        Add a new correlation plot.
        @param data pandas.DataFrame containing all data
        @param column which is used to calculate distribution histogram
        @param cut_column which is used to calculate cut on the other quantity defined by column
        @param quantiles list of quantiles between 0 and 100, defining the different cuts
        @param weight_column column in data containing the weights for each event
        """
        if len(data[cut_column]) == 0:
            b2.B2WARNING("Ignore empty Correlation.")
            return self
 
        axes = [self.axis, self.axis_d1, self.axis_d2]
 
        for i, (l, m) in enumerate([('.', signal_mask | bckgrd_mask), ('S', signal_mask), ('B', bckgrd_mask)]):
 
            if weight_column is not None:
                weights = numpy.array(data[weight_column][m])
            else:
                weights = numpy.ones(len(data[column][m]))
 
            xrange = numpy.percentile(data[column][m], [5, 95])
 
            colormap = plt.get_cmap('coolwarm')
            tmp, x = numpy.histogram(data[column][m], bins=100,
                                     range=xrange, density=True, weights=weights)
            bin_center = ((x + numpy.roll(x, 1)) / 2)[1:]
            axes[i].plot(bin_center, tmp, color='black', lw=1)
 
            for quantil in numpy.arange(5, 100, 5):
                cut = numpy.percentile(data[cut_column][m], quantil)
                sel = data[cut_column][m] >= cut
                y, x = numpy.histogram(data[column][m][sel], bins=100,
                                       range=xrange, density=True, weights=weights[sel])
                bin_center = ((x + numpy.roll(x, 1)) / 2)[1:]
                axes[i].fill_between(bin_center, tmp, y, color=colormap(quantil / 100.0))
                tmp = y
 
            axes[i].set_ylim(bottom=0)
 
            flatness_score = basf2_mva_util.calculate_flatness(data[column][m], data[cut_column][m], weights)
            axes[i].set_title(r'Distribution for different quantiles: $\mathrm{{Flatness}}_{} = {:.3f}$'.format(l, flatness_score))
        return self
 

finish()

def finish

(

self

)

Sets limits, title, axis-labels and legend of the plot

Reimplemented from Plotter.

Definition at line 1144 of file plotting.py.

    def finish(self):
        """
        Sets limits, title, axis-labels and legend of the plot
        """
        return self
 
 

Member Data Documentation

axis [1/2]

None axis = None

static

Main axis which is used to draw.

Definition at line 1071 of file plotting.py.

axis [2/2]

axis

define first subplot

Definition at line 1091 of file plotting.py.

axis_d1 [1/2]

None axis_d1 = None

static

Axis which shows shape of signal.

Definition at line 1073 of file plotting.py.

axis_d1 [2/2]

axis_d1

define second subplot

Definition at line 1093 of file plotting.py.

axis_d2 [1/2]

None axis_d2 = None

static

Axis which shows shape of background.

Definition at line 1075 of file plotting.py.

axis_d2 [2/2]

axis_d2

define third subplot

Definition at line 1095 of file plotting.py.

figure [1/2]

None figure = None

static

figure which is used to draw

Definition at line 1069 of file plotting.py.

figure [2/2]

figure

create figure

Definition at line 1084 of file plotting.py.

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

• mva/scripts/basf2_mva_evaluation/plotting.py