ToFlat Class Reference

Inheritance diagram for ToFlat:

Public Member Functions
	__init__ (self, *args)
	get_flat_bins (self)
	get_x (self, x_flat)
	fit (self, x, y=None)
	__call__ (self, x)
	transform (self, x, set_limits=False)
	set_n_bins (self, n)
	set_limits (self, x)
	io (self)

Public Attributes
	cdf = CDF(*args)
	Transformation with the CDF.
	n_bins = n_bins
	Binning in x, will be set automatically.
int	max = 0
	Maximum of the fitted distribution.
int	min = 0
	Minimum of the fitted distribution.
bool	is_processed = False
	Status flag.
	name = name
	Name of the class.

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

Detailed Description

This transformation uses the CDF to transform input data to a
flat transformation.

Attributes
----------
cdf : Transform.CDF
    Transformation with the CDF

Definition at line 238 of file transform.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
		*	args )

 Init function

:param args: None

Definition at line 251 of file transform.py.

    def __init__(self, *args):
        """ Init function
 
        :param args: None
        """
        Transform.__init__(self, "Flat", *args)
 
        
        self.cdf = CDF(*args)
 

Member Function Documentation

__call__()

__call__ ( self, x )

inherited

 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()

_fit ( self, x, y = None )

protected

Fit function calculates the cumulative distribution with numpy percentile.

:param x:   Input distribution
:param y:   Will not be used in this transformation

Reimplemented from Transform.

Definition at line 261 of file transform.py.

    def _fit(self, x, y=None):
        """
        Fit function calculates the cumulative distribution with numpy percentile.
 
        :param x:   Input distribution
        :param y:   Will not be used in this transformation
        """
        self.io.debug("Fitting Flat")
        self.cdf.fit(x)
 

_initialise()

_initialise ( self, x )

protectedinherited

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()

_transform ( self, x )

protected

Transforms the input data according to the cdf.
:param x:   Input data
:return:    Transformed data

Reimplemented from Transform.

Definition at line 271 of file transform.py.

    def _transform(self, x):
        """
        Transforms the input data according to the cdf.
        :param x:   Input data
        :return:    Transformed data
        """
        if not self.is_processed:
            self.fit(x)
        return self.cdf.transform(x)
 

fit()

fit ( self, x, y = None )

inherited

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
 

get_flat_bins()

get_flat_bins

(

self

)

Returns the binning of the CDF
:return: Binning for a flat distribution

Definition at line 281 of file transform.py.

    def get_flat_bins(self):
        """
        Returns the binning of the CDF
        :return: Binning for a flat distribution
        """
        return self.cdf.x
 

get_x()

get_x	(		self,
			x_flat )

Dirty version for getting the original x value out of a flat x value.
:param x_flat:  x value in the flat distribution
:return:        x value on the original axis (approx)

Definition at line 288 of file transform.py.

    def get_x(self, x_flat):
        """
        Dirty version for getting the original x value out of a flat x value.
        :param x_flat:  x value in the flat distribution
        :return:        x value on the original axis (approx)
        """
        x_cumul = np.linspace(self.min, self.max, self.n_bins * 50)
        for xx in x_cumul:
            if self.cdf.spline(xx) > x_flat:
                return xx

io()

io ( self )

inherited

Logging function
:return: logger

Definition at line 49 of file settings.py.

    def io(self):
        """
        Logging function
        :return: logger
        """
        return logging.getLogger(self.name)
 
 

set_limits()

set_limits ( self, x )

inherited

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
        """
        x_min = self.min
        x_max = self.max
 
        try:
            _ = len(x)  # to catch exception
            x[x > x_max] = x_max
            x[x < x_min] = x_min
        except TypeError:
            if x < x_min:
                x = x_min
            if x > x_max:
                x = x_max
        return x
 
 

set_n_bins()

set_n_bins ( self, n )

inherited

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()

transform ( self, x, set_limits = False )

inherited

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

cdf

cdf = CDF(*args)

Transformation with the CDF.

Definition at line 259 of file transform.py.

is_processed

bool is_processed = False

inherited

Status flag.

Definition at line 64 of file transform.py.

max

int max = 0

inherited

Maximum of the fitted distribution.

Definition at line 58 of file transform.py.

min

int min = 0

inherited

Minimum of the fitted distribution.

Definition at line 61 of file transform.py.

n_bins

n_bins = n_bins

inherited

Binning in x, will be set automatically.

Definition at line 55 of file transform.py.

name

name = name

inherited

Name of the class.

Definition at line 46 of file settings.py.

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

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