CDF Class Reference

Inheritance diagram for CDF:

Public Member Functions
	__init__ (self, *args)
	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
	spline = None
	Spline, fitting 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

Calculates the cumulative distribution (CDF)
Can be used for the flat transformation.

Attributes
----------
spline : InterpolatedUnivariateSpline
    Spline, fitting the CDF

Definition at line 176 of file transform.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
		*	args )

 Init function

:param args: None

Definition at line 189 of file transform.py.

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

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 199 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 CDF")
        y_ = np.linspace(0, 100, 2 * self.n_bins)
        x_ = pd.Series(np.percentile(x, list(y_)))
 
        # Count same values
        vc = x_.value_counts()
        vc = vc.sort_index()
 
        # replace same values
        for i, xi in enumerate(vc):
            if xi > 1:
                try:
                    nex_val = vc.index[i + 1]
                except IndexError:
                    nex_val = vc.index[i] + 0.01
                fill = np.linspace(vc.index[i], nex_val, xi)
                x_[x_ == vc.index[i]] = fill
        self.spline = InterpolatedUnivariateSpline(x_, y_)
 

_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 225 of file transform.py.

    def _transform(self, x):
        """
        Transforms the input data according to the cdf.
        :param x:   Input data
        :return:    Transformed data
        """
        x = self.set_limits(x)
        return self.spline(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
 

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
        """
        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()

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

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.

spline

spline = None

Spline, fitting the CDF.

Definition at line 197 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