PullAnalysis Class Reference

Inheritance diagram for PullAnalysis:

Collaboration diagram for PullAnalysis:

Public Member Functions
def	__init__ (self, quantity_name, unit=None, outlier_z_score=None, absolute=False, contact='', plot_name=None, plot_title=None, plot_name_prefix='', plot_name_postfix='', plot_title_postfix='', referenceFileName=None # if set binnings of plots will be read from corresponding histograms)
def	analyse (self, truths, estimates, variances=None, auxiliaries={}, which_plots=None, is_expert=None)
def	contact (self)
def	contact (self, contact)
def	write (self, tDirectory=None)

Public Attributes
	quantity_name
	name of the quantity the analysis is performed on
	unit
	unit the quanitity is given in
	outlier_z_score
	the outlier score defines in terms of how many std deviations a data point is considered as an outlier
	absolute
	if true only the absolute value is compared
	plot_name
	name of the plot
	plot_title
	title of the plot
	plot_name_prefix
	prefix to be prepended to the plot name
	plot_name_postfix
	post fix to be append after the plot name
	plot_title_postfix
	postfix to be appended after the title
	plots
	dictionary to store the plots
	referenceFileName
	name of the reference file, if set the binnings of plots will be read from the corresponding object in that file
	contact
	Forward the contract to all plots by reassigning the contact.

Static Public Attributes
float	default_outlier_z_score = 5.0
	default outlier z score
string	default_plot_name = "{plot_name_prefix}_{subplot_name}{plot_name_postfix}"
	default plot name
string	default_plot_title = "{subplot_title} of {quantity_name}{plot_title_postfix}"
	default plot title
list	default_which_plots
	default list of plots to be created in this analysis More...
bool	default_is_expert = True
	if true the plots created here are declared as expert plots in the validation

Private Attributes
	_contact
	contact information

Detailed Description

Performs a comparision of an estimated quantity to their truths by generating standardized validation plots.

Definition at line 27 of file pull.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			quantity_name,
			unit = None,
			outlier_z_score = None,
			absolute = False,
			contact = '',
			plot_name = None,
			plot_title = None,
			plot_name_prefix = '',
			plot_name_postfix = '',
			plot_title_postfix = '',
			referenceFileName = None  # if set binnings of plots will be read from corresponding histograms
	)

Performs a comparision of an estimated quantity to their truths by generating standardized validation plots.

Definition at line 54 of file pull.py.

    ):
        """Performs a comparision of an estimated quantity to their truths by generating standardized validation plots."""
 
        
        self.quantity_name = quantity_name
        
        self.unit = unit or get_unit(quantity_name)
 
        
        if outlier_z_score is None:
            self.outlier_z_score = self.default_outlier_z_score
        else:
            self.outlier_z_score = outlier_z_score
 
        
        self.absolute = absolute
 
        
        self.plot_name = plot_name
        
        self.plot_title = plot_title
 
        
        self.plot_name_prefix = plot_name_prefix or root_save_name(quantity_name)
        
        self.plot_name_postfix = plot_name_postfix
        
        self.plot_title_postfix = plot_title_postfix
 
        
        self._contact = contact
        
        self.plots = collections.OrderedDict()
 
        
        self.referenceFileName = referenceFileName
 

Member Function Documentation

analyse()

def analyse	(		self,
			truths,
			estimates,
			variances = None,
			auxiliaries = {},
			which_plots = None,
			is_expert = None
	)

Compares the concrete estimate to the truth and generates plots of the estimates, residuals, pulls and p-values.
Close indicates if the figure shall be closed after they are saved.

Parameters
----------
truths : array_like(float)
    Sample of the true values
estimates : array_like(float)
    Corresponding estimations
variances : array_like(float), optional
    Corresponding variance estimations
auxiliaries : Dict[name, array_like(float)], optional
    Auxiliary variable to show distribution of residuals and pull as function
selected_plots : list(str), optional
    List of analysis plots to be generated. All if not given.
    Currently valid names are
    truths, estimates, diag_profile, diag_scatter, residuals,
    sigmas, pulls, p_values

Definition at line 104 of file pull.py.

contact() [1/2]

def contact

(

self

)

 returns the contact 

Definition at line 403 of file pull.py.

contact() [2/2]

def contact	(		self,
			contact
	)

  sets the contact

  parameters:
  contact: new contact information

Definition at line 408 of file pull.py.

write()

def write	(		self,
			tDirectory = None
	)

 Write all validation plot to the given Root directory
    parameters:
    tDirectory - the root directory were to write to

Definition at line 419 of file pull.py.

Member Data Documentation

default_which_plots

list default_which_plots

static

Initial value:

=  [
        "truths",
        "estimates",
        "diag_profile",
        "diag_scatter",
        "residuals",
        "sigmas",
        "pulls",
        "p_values",
        "aux_residual_hist2d",
        "aux_residual_profile",
        "aux_pull_hist2d",
        "aux_pull_profile",
    ]

default list of plots to be created in this analysis

Definition at line 36 of file pull.py.

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

• tracking/scripts/tracking/validation/pull.py