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) – 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.

Attributes Summary

`BIND`
`CACHE_TYPES`
`CONST`
`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`.
`data_pars`	Data parameters this object directly depends on.
`dependencies`	Registry of PDFs this instance depends on.
`dependent`
`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.
`generate`([size, mapsize, gensize, …])	Generate random data.
`get_values`()	Get the values of the parameters within this object.
`integral`([integral_range, range])	Calculate the integral of a `PDF`.
`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

BIND = 'bind'¶

CACHE_TYPES = ('bind', 'const')¶

CONST = 'const'¶

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

data_pars¶

Data parameters this object directly depends on.

Type: Registry(Parameter)

dependencies¶

Registry of PDFs this instance depends on.

Type: Registry(PDF)

dependent = True¶

interpolation_method¶

Function used to do the interpolation.

Getter: 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 sucessive 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

copy(backend=None)¶

Create a copy of this PDF.

Parameters: backend (Backend) – 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.

generate(size=10000, mapsize=1000, gensize=None, safe_factor=1.1, range='full')¶

Generate random data. A call to PDF.bind() is implicit, since several calls will be done to the PDF with the same sets of values.

Parameters

size (int) – size (or minimum size) of the output sample.
mapsize (int) – number of points to consider per dimension (data parameter) in order to calculate the maximum value of the PDF.
gensize (int) – number of entries to generate per iteration.
safe_factor (float) – additional factor to multiply the numerically calculated maximum of the function. In general this must be modified if the function is not well-behaved.
range (str) – range of the data parameters where to generate data.

Returns

Output sample.

Return type

DataSet

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

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

Any PDF sharing parameters with this will also change its behaviour.

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

using_cache(ctype)¶

Safe method to enable a cache of the PDF. There are two types of cache:

const: it means that we plan to call the PDF consecutively in the same data set, but the value of the parameters that are not constant is allowed to change. This means that any PDF that has all its arguments as constant is assumed to have the same value in each evaluation.
bind: this is reserved for those processes where the normalization range will be the same, as well as the values of the parameters of the PDF. However, the function can be called in different data sets.

Warning

It is responsibility of the user to ensure that the conditions for the cache to be valid are preserved.

Parameters: ctype (str) – cache type.