ConvPDFs¶

class minkit.ConvPDFs(name, first, second, range=None)[source]¶

Bases: minkit.MultiPDF

Represent the convolution of two different PDFs.

Parameters:	name (str) – name of the PDF. first (PDF) – first PDF. second (PDF) – second PDF. range (str or None) – range of the convolution. This is needed in case part of the PDFs lie outside the evaluation range. It is set to “full” by default.
Raises:	ValueError – If an attempt to define convolution in more than one dimension is done.

Attributes Summary

`all_args`	All the argument parameters associated to this class.
`all_pars`	All the parameters associated to this class.
`all_pdfs`	Recursively get all the possible PDFs belonging to this object.
`all_real_args`	All the argument parameters that are not `ParameterFormula` instances.
`aop`	Object to do operations on arrays.
`args`	Argument parameters this object directly depends on.
`backend`	Backend interface.
`cache`	Return the cached values for this PDF.
`cache_type`	Cache type being used.
`constant`	Whether this is a constant PDF.
`conv_size`	Size of the sample used to calculate the convolution.
`data_pars`	Data parameters this object directly depends on.
`dependencies`	Registry of PDFs this instance depends on.
`interpolation_method`	Function used to do the interpolation.
`pdfs`	Get the registry of PDFs within this class.
`real_args`	Arguments that do not depend on other arguments.

Methods Summary

`__call__`(data[, range, normalized])	Call the PDF in the given set of data.
`bind`([range, normalized])	Prepare an object that will be called many times with the same set of values.
`component`(name)	Get the `PDF` object with the given name.
`convolve`([normalized])	Calculate the convolution.
`copy`([backend])	Create a copy of this PDF.
`evaluate_binned`(data[, normalized])	Evaluate the PDF over a binned sample.
`from_json_object`(obj, pars, pdfs)	Build a PDF from a JSON object.
`function`([range, normalized])
`generate`([size, mapsize, gensize, range, …])	Generate random data.
`get_values`()	Get the values of the parameters within this object.
`integral`([integral_range, range])	Calculate the integral of a `PDF`.
`max`([range, normalized, tol, min_points, …])	Calculate the maximum value of the PDF in the given range.
`norm`([range])	Calculate the normalization of the PDF.
`numerical_integral`([integral_range, range])	Calculate the integral of a `PDF`.
`numerical_normalization`([range])	Calculate a numerical normalization.
`restoring_state`()	Enter a context where the attributes of the parameters will be restored on exit.
`set_values`(**kwargs)	Set the values of the parameters associated to this PDF.
`to_backend`(backend)	Initialize this class in a different backend.
`to_json_object`()	Dump the PDF information into a JSON object.
`using_cache`(ctype)	Safe method to enable a cache of the PDF.

Attributes Documentation

all_args¶

All the argument parameters associated to this class.

Type:	Registry(Parameter)

all_pars¶

All the parameters associated to this class. This includes also any ParameterFormula and the data parameters.

Type:	Registry(Parameter)

all_pdfs¶: Recursively get all the possible PDFs belonging to this object.

all_real_args¶

All the argument parameters that are not ParameterFormula instances.

Type:	Registry(Parameter)

aop¶

Object to do operations on arrays.

Type:	ArrayOperations

args¶

Argument parameters this object directly depends on.

Type:	Registry(Parameter)

backend¶

Backend interface.

Type:	Backend

cache¶

Return the cached values for this PDF.

Type:	dict

cache_type¶

Cache type being used.

Type:	str or None

constant¶

Whether this is a constant PDF.

Type:	bool

conv_size¶: Size of the sample used to calculate the convolution. It must be an odd number.

data_pars¶

Data parameters this object directly depends on.

Type:	Registry(Parameter)

dependencies¶

Registry of PDFs this instance depends on.

Type:	Registry(PDF)

interpolation_method¶

Function used to do the interpolation.

Setter:	Set the function to use from a string.

pdfs¶

Get the registry of PDFs within this class.

Returns:	PDFs owned by this class.
Return type:	Registry(PDF)

real_args¶

Arguments that do not depend on other arguments.

Type:	Registry(Parameter)

Methods Documentation

__call__(data, range='full', normalized=True)[source]¶

Call the PDF in the given set of data.

Parameters:	data (DataSet or BinnedDataSet) – data to evaluate. range (str) – normalization range. normalized (bool) – whether to return a normalized output.
Returns:	Evaluation of the PDF.
Return type:	darray

bind(range='full', normalized=True)¶

Prepare an object that will be called many times with the same set of values. This is usefull for PDFs using a cache, to avoid creating it many times in successive calls to PDF.__call__().

Parameters:	range (str) – normalization range. normalized (bool) – whether to return a normalized output.

component(name)¶

Get the PDF object with the given name.

Parameters:	name (str) – name of the `PDF`.
Returns:	Component with the given name.
Return type:	PDF

convolve(normalized=True)[source]¶

Calculate the convolution.

Parameters:	range (str) – normalization range. normalized (bool) – whether to return a normalized output.
Returns:	Data and result of the evaluation.
Return type:	numpy.ndarray, numpy.ndarray
Raises:	RuntimeError – If the bounds of the parameter in the convolution range are disjointed.

copy(backend=None)¶

Create a copy of this PDF.

Parameters:	backend (Backend or None) – new backend.
Returns:	A copy of this PDF.

evaluate_binned(data, normalized=True)[source]¶

Evaluate the PDF over a binned sample.

Parameters:	data (BinnedDataSet) – input data. normalized (bool) – whether to normalize the output array or not.
Returns:	Values from the evaluation.
Return type:	farray

classmethod from_json_object(obj, pars, pdfs)[source]¶

Build a PDF from a JSON object. This object must represent the internal structure of the PDF.

Parameters:	obj (dict) – JSON object. pars (Registry) – parameter to build the PDF. backend (Backend) – backend to build the PDF.
Returns:	This type of PDF constructed together with its parameters.

See also

PDF.max()

get_values()¶

Get the values of the parameters within this object.

Returns:	Dictionary with the values of the parameters.
Return type:	dict(str, float)

integral(integral_range='full', range='full')¶

Calculate the integral of a PDF.

Parameters:	integral_range (str) – range of the integral to compute. range (str) – normalization range to consider.
Returns:	Integral of the PDF in the range defined by “integral_range” normalized to “range”.
Return type:	float

max(range='full', normalized=True, tol=1e-07, min_points=4, max_call=None, sampling_size=None)¶

Calculate the maximum value of the PDF in the given range.

Parameters:	range (str) – range to consider. normalized (bool) – if set to True, the result corresponds to the maximum of the normalized PDF. sampling_size (int) – size of the samples to use to determine the maximum. tol (float) – relative dispersion that is allowed for convergence. min_points (int) – minimum number of points to estimate the maximum. It must be greater than four. max_call (int) – maximum number of iterations to perform. Must be greater than two. sampling_size – number of elements to generate in each call.
Returns:	Maximum value.
Return type:	float

The maximum is calculated from successive random samples of size equal to sampling_size. Afterwards, a fit is done in order to polish the minimum. Once the minimization was done at least min_points times, a final fit to the function

\[f(n) = \alpha e^{-\beta n} + \delta\]

is done, where n is the iteration number and \(f(n)\) is the global maximum in that iteration. The value of \(\delta\) is returned as long as the error satisfies the given tolerance. To improve the performance, only the iterations where the achieved maximum is within the tolerance with respect to the global maximum are considered. The parameter \(\delta\) is minimized in such a way that it is always greater or equal to the global maximum achieved from the random samples.

Note

For two-dimensional PDFs, the process of calculating the maximum can turn really slow, so you might want to consider reducing the precision of the maximum value.

norm(range='full')[source]¶

Calculate the normalization of the PDF.

Parameters:	range (str) – normalization range to consider.
Returns:	Value of the normalization.
Return type:	float

numerical_integral(integral_range='full', range='full')[source]¶

Calculate the integral of a PDF.

Parameters:	integral_range (str) – range of the integral to compute. range (str) – normalization range to consider.
Returns:	Integral of the PDF in the range defined by “integral_range” normalized to “range”.
Return type:	float

numerical_normalization(range='full')[source]¶

Calculate a numerical normalization.

Parameters:	range (str) – normalization range.
Returns:	Normalization.
Return type:	float

restoring_state()¶: Enter a context where the attributes of the parameters will be restored on exit.

set_values(**kwargs)¶

Set the values of the parameters associated to this PDF.

Parameters:	kwargs (dict(str, float)) – keyword arguments with “name”/”value”.

Note

Parameters of this PDF might be shared with others.

to_backend(backend)[source]¶

Initialize this class in a different backend.

Parameters:	backend (Backend) – new backend.
Returns:	An instance of this class in the new backend.

to_json_object()[source]¶

Dump the PDF information into a JSON object. The PDF can be constructed at any time by calling PDF.from_json_object().

Returns:	Object that can be saved into a JSON file.
Return type:	dict