DataSet¶

class minkit.DataSet(data, pars, weights=None)[source]¶

Bases: minkit.pdfs.dataset.DataObject

Definition of an unbinned data set to evaluate PDFs.

Parameters

data (darray) – data in the provided backend.
pars (Registry(Parameter)) – data parameters.
weights (darray) – possible set of weights.

Attributes Summary

`aop`	Object to do operations on arrays.
`backend`	Backend interface.
`data_pars`	Data parameters associated to this sample.
`ndim`	Number of dimensions.
`sample_type`
`values`	Values of the data set.
`weights`	Weights of the sample.

Methods Summary

`from_ndarray`(arr, data_par[, weights, backend])	Build the class from a single array.
`from_records`(arr, data_pars[, weights, backend])	Build the class from a `numpy.ndarray` object.
`make_binned`([bins])	Make a binned version of this sample.
`merge`(samples[, maximum])	Merge many samples into one.
`subset`(arg[, rescale_weights])	Get a subset of this data set.
`to_backend`(backend)	Initialize this class in a different backend.
`to_records`()	Convert this class into a `numpy.ndarray` object.

Attributes Documentation

aop¶

Object to do operations on arrays.

Type: ArrayOperations

backend¶

Backend interface.

Type: Backend

data_pars¶

Data parameters associated to this sample.

Type: Registry(Parameter)

ndim¶

Number of dimensions.

Type: int

sample_type = 'unbinned'¶

values¶

Values of the data set.

Type: darray

weights¶

Weights of the sample.

Type: darray or None

Methods Documentation

classmethod from_ndarray(arr, data_par, weights=None, backend=None)[source]¶

Build the class from a single array.

Parameters

arr (numpy.ndarray) – array of data.
data_pars (Registry(Parameter)) – data parameters.
weights (numpy.ndarray) – possible weights to use.

classmethod from_records(arr, data_pars, weights=None, backend=None)[source]¶

Build the class from a numpy.ndarray object.

Parameters

arr (numpy.ndarray) – array of data.
data_pars (Registry(Parameter)) – data parameters.
weights (numpy.ndarray) – possible weights to use.

make_binned(bins=100)[source]¶

Make a binned version of this sample.

Parameters: bins (int or tuple(int, ..)) – number of bins per data parameter.
Returns: Binned data sample.
Return type: BinnedDataSet

classmethod merge(samples, maximum=None)[source]¶

Merge many samples into one. If maximum is specified, then the last elements will be dropped.

Parameters

samples (tuple(DataSet)) – samples to merge.
maximum (int) – maximum number of entries for the final sample.

Returns

Merged sample.

Return type

DataSet

… warning:: If maximum is specified, the last elements corresponding to the: last samples might be dropped.

subset(arg, rescale_weights=False)[source]¶

Get a subset of this data set. If arg is a string, it will be considered as a range. In case it is a barray, then it is considered to be a mask array. If rescale_weights is set to True, then the weights are rescaled so their statistical weight in minimization processes is proportional to the event weights:

\[\omega^\prime_i = \omega_i \times \frac{\sum_{j = 0}^n \omega_j}{\sum_{j = 0}^n \omega_j^2}\]

Parameters

arg (str or barray) – argument to create the subset.
rescale_weights (bool) – whether to rescale the sample weights.

Returns

New data set.

Return type

DataSet

to_backend(backend)[source]¶

Initialize this class in a different backend.

Parameters: backend (Backend) – new backend.
Returns: This class in the new backend.

to_records()[source]¶

Convert this class into a numpy.ndarray object.

Returns: This object as a a numpy.ndarray object.
Return type: numpy.ndarray