ijazz.categorize

Functions

categorize(→ numpy.ndarray)	Create the categories for both lepton based on a dataframe containing the categorisation variable per lepton
categorize_mmg(→ numpy.ndarray)	Create the categories for both lepton based on a dataframe containing the categorisation variable per lepton

Module Contents

ijazz.categorize.categorize(df: pandas.DataFrame, category_dict: dict, cut='', prefix='cat', var_prefixes=None, var_suffixes=['1', '2']) → numpy.ndarray

Create the categories for both lepton based on a dataframe containing the categorisation variable per lepton under the form var1, var2 for lepton 1 and lepton 2, the name of the variables to categorize upon are specified in the the category dictionnary along with the corresponding bining.

Parameters:

• df (pd.DataFrame) – input dataframe containing the variable to categorize

• category_dict (dict) – dictionary for categorisation, e.g. {‘pt’: [25, 50, 100], ‘abs_eta’: [0, 1, 2]}

• cut (str, optional) – cut to be use if df.eval(cut) to apply a selection. Defaults to “”.

• prefix (str, optional) – prefix used for the categorisation. Defaults to ‘cat’.

Returns:

array with the category numbers that have been created

Return type:

np.ndarray

ijazz.categorize.categorize_mmg(df: pandas.DataFrame, category_dict: dict, cut='', prefix='cat', var_prefixes=None, var_suffixes='g') → numpy.ndarray

Create the categories for both lepton based on a dataframe containing the categorisation variable per lepton under the form var1, var2 for lepton 1 and lepton 2, the name of the variables to categorize upon are specified in the the category dictionnary along with the corresponding bining.

Parameters:

• df (pd.DataFrame) – input dataframe containing the variable to categorize

• category_dict (dict) – dictionary for categorisation, e.g. {‘pt’: [25, 50, 100], ‘abs_eta’: [0, 1, 2]}

• cut (str, optional) – cut to be use if df.eval(cut) to apply a selection. Defaults to “”.

• prefix (str, optional) – prefix used for the categorisation. Defaults to ‘cat’.

Returns:

array with the category numbers that have been created

Return type:

np.ndarray