utils

Documentation for functions in the utilities.utils module:

utilities.utils.default_rootpaths()[source]

Returns the default root file paths of the package, where background is pions and signal is kaons.

Returns:

Three element tuple containing the paths of the pion MC, the kaon MC and the mixed data root files, respectively.

Return type:

tuple[str]

utilities.utils.default_txtpaths()[source]

Returns the .txt file paths containing the training MC array and the data array, to be used in DNN or DTC analyses.

Returns:

Tuple containing the paths of the MC training array (50/50 signal/background for unbiased training) and the path of the data array, respectively.

Return type:

tuple[str]

utilities.utils.default_vars()[source]

Returns default variables used by the package in the pi-K analysis.

Returns:

13 element tuple containing the names of the default variables to use.

Return type:

tuple[str]

utilities.utils.default_figpath(figname, dir='fig', figtype='pdf')[source]

Returns the path to the figure folder with respect to the cwd in which to then save figures.

Parameters:

  • figname (str) – Name with which to save the figure.

  • dir (str) – Directory relative to cwd where to save the figure.

  • figtype (str) – Type of figure save file.

Returns:

Path where to save the figure.

Return type:

str

utilities.utils.default_resultsdir(dir='outputs-PiKclassifier')[source]

Returns the path where to store the outputs of the package.

Parameters:

dir (str) – Directory where to save the outputs.

Returns:

The figure path.

Return type:

str

utilities.utils.find_cut(pi_array, k_array, efficiency, specificity_mode=False, inverse_mode=False)[source]

Finds where to cut a certain variable to obtain a certain sensitivity/specificity in a hypothesis test between two given species’ arrays.

Parameters:

  • pi_array (numpy.array[float]) – Array containing the background species.

  • k_array (numpy.array[float]) – Array containing the signal species.

  • efficiency (float) – Sensitivity required from the test (specificity if specificity_mode = True).

  • specificity_mode (bool) – If set to True the efficiency given is taken to be the intended specificity.

  • inverse_mode (bool) – Set to True if the signal events tend to have lower values.

Returns:

Two element tuple containing cut value and misidentification probability for the negative species (or sensitivity if specificity_mode = True)

Return type:

tuple[double]

utilities.utils.plot_rocs(rocx_arrays, rocy_arrays, roc_labels, roc_linestyles, roc_colors, x_pnts=(), y_pnts=(), point_labels='', eff=0, figtitle='ROC', figname='')[source]

Draws superimposed roc curves and/or points

Parameters:

  • rocx_arrays (list[numpy.array[float]] or tuple[numpy.array[float]]) – List or tuple of numpy arrays, each containing the respective x points of different roc curves to be plotted.

  • rocy_arrays (list[numpy.array[float]] or tuple[numpy.array[float]]) – List or tuple of numpy arrays, each containing the respective y points of different roc curves to be plotted.

  • roc_labels (list[str] or tuple[str]) – Names of the respective species whose roc coordinates were given.

  • roc_linestyles (list[str] or tuple[str]) – Linestyles of the respective species whose roc coordinates were given.

  • roc_colors (list[str] or tuple[str]) – Colors of the respective species whose roc coordinates were given.

  • x_pnts (list[double] or tuple[double]) – List or tuple of the respective x coordinates of points to be plotted.

  • y_pnts (list[double] or tuple[double]) – List or tuple of the respective y coordinates of points to be plotted.

  • point_labels (list[str] or tuple[str]) – List or tuple of names of the respective species whose point coordinates were given.

  • eff (double) – If different than 0., draws a green dashed line at y = eff on the plot.

  • figtitle (str) – Title to be given to the figure.

  • figname (str) – If different than ‘’, saves the figure as a pdf with name figname.

utilities.utils.roc(pi_array, k_array, inverse_mode=False, makefig=False, eff=0, name='ROC')[source]

Returns the roc curve’s x and y coordinates given two arrays of values for two different species. optionally draws the roc curve using plot_rocs().

Parameters:

  • pi_array (numpy.array[float]) – Array containing the “negative” species.

  • k_array (numpy.array[float]) – Array containing the “positive” species.

  • inverse_mode (bool) – To activate if the “positive” events tend to have lower values

  • makefig (bool) – If set to True draws the roc curve

  • eff (double) – If different than 0. and makefig = True , draws a green dashed line at y = eff on the plot.

  • name (str) – If makefig = True , name of the saved figure.

Returns:

Three element tuple containing: numpy array of floats of x coordinates of the roc curve, numpy array of floats of y coordinates of the roc curve, AUC of the ROC curve.

Return type:

tuple[numpy.array[float], numpy.array[float], float]

utilities.utils.stat_error(fraction, data_size, eff, misid)[source]

Evaluates the statistical error on fraction estimate due to the finite sample of the data set, using the variance of sum of two binomials (of signal and background events respectively).

Parameters:

  • fraction (float) – Estimated fraction by the algorithm.

  • data_size – Size of the data set.

  • eff (float) – Estimated efficiency of the algorithm.

  • misid (float) – Estimated misidentification probability (false positive) of the algorithm.

Returns:

The statistical error associated to the fraction.

Return type:

float

utilities.utils.syst_error(fraction, template_sizes, eff, misid)[source]

Evaluates the systematic error on fraction estimate due to the finite sample used to evaluate the “efficiency” and “misid” parameters.

Parameters:

  • fraction (float) – Estimated fraction by the algorithm.

  • template_sizes (list[int] tuple[int]) – Two element list or tuple of sizes of the evaluation arrays (background and signal dataset, in this order).

  • eff (float) – Estimated efficiency of the algorithm.

  • misid (float) – Estimated misidentification probability (false positive) of the algorithm

Returns:

The systematic error associated to the fraction.

Return type:

float