Transform Class Reference

Inheritance diagram for Transform:

Public Member Functions
def	__init__ (self, name="Original", n_bins=None)
def	fit (self, x, y=None)
def	__call__ (self, x)
def	transform (self, x, set_limits=False)
def	set_n_bins (self, n)
def	set_limits (self, x)

Public Attributes
	n_bins
	Binning in x, will be set automatically.
	max
	Maximum of the fitted distribution.
	min
	Minimum of the fitted distribution.
	is_processed
	Status flag.
	name
	Name of the transformation.

Protected Member Functions
def	_initialise (self, x)
def	_fit (self, x, y=None)
def	_transform (self, x)

Detailed Description

Base Class for the transformations.
The function _fit() is overwritten by the sub classes.

Attributes
----------
n_bins : int, optional
    Binning in x, will be set automatically
max : float
    Maximum of the fitted distribution
min : float
    Minimum of the fitted distribution
is_processed : bool
    Status flag
name : str
    Name of the transformation

Definition at line 27 of file transform.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			name = "Original",
			n_bins = None
	)

 Init function

:param name:    Name
:param n_bins:  Binning for the transformations

Reimplemented from ProTool.

Reimplemented in CDF, and ToFlat.

Definition at line 48 of file transform.py.

    def __init__(self, name="Original", n_bins=None):
        """ Init function
 
        :param name:    Name
        :param n_bins:  Binning for the transformations
        """
        
        self.n_bins = n_bins
 
        
        self.max = 0
 
        
        self.min = 0
 
        
        self.is_processed = False
 
        
        self.name = name
 
        ProTool.__init__(self, "Transform." + self.name)
 

Member Function Documentation

__call__()

def __call__	(		self,
			x
	)

 Call function calls transform
:param x:   Input data
:return:    Transformed data

Definition at line 95 of file transform.py.

    def __call__(self, x):
        """ Call function calls transform
        :param x:   Input data
        :return:    Transformed data
        """
        return self.transform(x)
 

_fit()

def _fit (   self,   x,   y = None  )

protected

This is defined in the children and overwritten.
:param x:   array x values
:param y:   class variable [1,0]

Reimplemented in CDF, and ToFlat.

Definition at line 102 of file transform.py.

    def _fit(self, x, y=None):
        """
        This is defined in the children and overwritten.
        :param x:   array x values
        :param y:   class variable [1,0]
 
        """
 

_initialise()

def _initialise (   self,   x  )

protected

Sets limits for the data.
Not called by the user.

:param x: array type

Definition at line 71 of file transform.py.

    def _initialise(self, x):
        """
        Sets limits for the data.
        Not called by the user.
 
        :param x: array type
        """
        self.io.debug("Initiating " + self.name)
        if self.n_bins is None:
            self.set_n_bins(len(x))
        self.max = np.max(x)
        self.min = np.min(x)
 

_transform()

def _transform (   self,   x  )

protected

This is defined in the children and overwritten.
In the base class it does nothing and returns the original distribution.

:param x:   Distribution to transform, array type
:return:    Transformed data

Reimplemented in CDF, and ToFlat.

Definition at line 121 of file transform.py.

    def _transform(self, x):
        """
        This is defined in the children and overwritten.
        In the base class it does nothing and returns the original distribution.
 
        :param x:   Distribution to transform, array type
        :return:    Transformed data
        """
        return x
 

fit()

def fit	(		self,
			x,
			y = None
	)

The fit function is calls the individual _fit() functions.

:param x:   Distribution to fit, array type
:param y:   optional for some transformations, sets signal class

Definition at line 84 of file transform.py.

    def fit(self, x, y=None):
        """
        The fit function is calls the individual _fit() functions.
 
        :param x:   Distribution to fit, array type
        :param y:   optional for some transformations, sets signal class
        """
        self._initialise(x)
        self._fit(x, y)
        self.is_processed = True
 

set_limits()

def set_limits	(		self,
			x
	)

Limits the data to the fitted range.
:param x:   Input data
:return:    Limited data

Definition at line 139 of file transform.py.

    def set_limits(self, x):
        """
        Limits the data to the fitted range.
        :param x:   Input data
        :return:    Limited data
        """
        try:
            _ = len(x)  # to catch exception
            x[x > self.max] = self.max
            x[x < self.min] = self.min
        except TypeError:
            if x < self.min:
                x = self.min
            if x > self.max:
                x = self.max
        return x
 
 

set_n_bins()

def set_n_bins	(		self,
			n
	)

Calculates the optimal size for the binning.
:param n:   Length of the input data

Definition at line 131 of file transform.py.

    def set_n_bins(self, n):
        """
        Calculates the optimal size for the binning.
        :param n:   Length of the input data
        """
        self.n_bins = get_optimal_bin_size(n)
        self.io.debug("Bins are set to " + str(self.n_bins) + "\t " + str(n / float(self.n_bins)) + "per bin")
 

transform()

def transform	(		self,
			x,
			set_limits = False
	)

This is defined in the children and overwritten.
:param x:           Distribution to transform, array type
:param set_limits:  Limits the range of the data to the fitted range
:return:            Transformed data

Definition at line 110 of file transform.py.

    def transform(self, x, set_limits=False):
        """
        This is defined in the children and overwritten.
        :param x:           Distribution to transform, array type
        :param set_limits:  Limits the range of the data to the fitted range
        :return:            Transformed data
        """
        if set_limits:
            self.set_limits(x)
        return self._transform(x)
 

Member Data Documentation

is_processed

is_processed

Status flag.

Definition at line 64 of file transform.py.

max

max

Maximum of the fitted distribution.

Definition at line 58 of file transform.py.

min

min

Minimum of the fitted distribution.

Definition at line 61 of file transform.py.

n_bins

n_bins

Binning in x, will be set automatically.

Definition at line 55 of file transform.py.

name

name

Name of the transformation.

Definition at line 67 of file transform.py.

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

• alignment/scripts/alignment/fancystuff/transform.py