umami.helper_tools package#

Submodules#

umami.helper_tools.classification_tools module#

Provides methods for classification of events by flavour.

umami.helper_tools.classification_tools.get_class_label_variables(class_labels: list)#

Returns a list of the label variables used for the provided class_labels.

Parameters:

class_labels (list) – List with the class labels.

Returns:

label_var_list – List with the truth label variables needed for the classes.

Return type:

list

umami.helper_tools.classification_tools.get_class_prob_var_names(tagger_name: str, class_labels: list)#

Returns a list of the probability variable names used for the provided class_labels.

Parameters:

  • tagger_name (str) – Name of the tagger that is used e.g. dips20210729.

  • class_labels (list) – List with the class labels.

Returns:

prob_var_list – List with the tagger_name and probabilites merged e.g. [“dips20210729_pb”, “dips20210729_pc”, “dips20210729_pu”].

Return type:

list

umami.helper_tools.histogram_tools module#

Helper function for histogram handling.

umami.helper_tools.histogram_tools.hist_ratio(numerator, denominator, numerator_unc, denominator_unc, step: bool = True)#

This method calculates the ratio of the given bincounts and returns the input for a step function that plots the ratio.

Parameters:

  • numerator (array_like) – Numerator in the ratio calculation.

  • denominator (array_like) – Denominator in the ratio calculation.

  • numerator_unc (array_like) – Uncertainty of the numerator.

  • denominator_unc (array_like) – Uncertainty of the denominator.

  • step (bool) – if True duplicates first bin to match with step plotting function, by default True

Returns:

  • step_ratio (array_like) – Ratio returning 1 in case the denominator is 0.

  • step_ratio_unc (array_like) – Stat. uncertainty of the step_ratio

Raises:

AssertionError – If inputs don’t have the same shape.

umami.helper_tools.histogram_tools.hist_w_unc(arr, bins, bins_range=None, normed: bool = True)#

Computes histogram and the associated statistical uncertainty.

Parameters:

  • arr (array_like) – Input data. The histogram is computed over the flattened array.

  • bins (int or sequence of scalars or str) – bins parameter from np.histogram

  • bins_range (Tuple, optional) – range parameter from np.histogram

  • normed (bool, optional) – If True (default) the calculated histogram is normalised to an integral of 1.

Returns:

  • bin_edges (array of dtype float) – Return the bin edges (length(hist)+1)

  • hist (numpy array) – The values of the histogram. If normed is true (default), returns the normed counts per bin

  • unc (numpy array) – Statistical uncertainty per bin. If normed is true (default), returns the normed values.

  • band (numpy array) – lower uncertainty band location: hist - unc If normed is true (default), returns the normed values.

umami.helper_tools.histogram_tools.save_divide(numerator, denominator, default=1.0)#

Division using numpy divide function returning default value in cases where denominator is 0.

Parameters:

  • numerator (array_like) – Numerator in the ratio calculation.

  • denominator (array_like) – Denominator in the ratio calculation.

  • default (float) – default value which is returned if denominator is 0.

Returns:

ratio

Return type:

array_like

Module contents#