ijazz.ScaleAndSmearing

Classes

`IJazZSAS`	Scale-and-smearing fitter with category handling and plotting helpers.
`IJazZEnergyCorrector`	Apply IJazZ scale/smear corrections to data and MC dataframes.

Functions

`ijazz_sas_cmd`()	entry point for the ijazz_sas command
`ijazz_sas`([config, do_fit, save_corrlib, ...])	Perform the scale and smearing fit, plot the results and save the results.
`compute_sas`(→ IJazZSAS)	Wrapper to compute the scale and smearing parameters

Module Contents

ijazz.ScaleAndSmearing.ijazz_sas_cmd()[source]: entry point for the ijazz_sas command

ijazz.ScaleAndSmearing.ijazz_sas(config: dict = None, do_fit=True, save_corrlib=True, correct_files=True, suffixe_plots: str = '')[source]

Perform the scale and smearing fit, plot the results and save the results. The configuration file should contain the following sections:

file_dt: data file (parquet format)
file_mc: mc file (parquet format)
dir_results: directory where to save the results
fitter: configuration for the fitter
sas: configuration for the scale and smearing fit
minimizer: configuration for the minimizer
syst: configuration for the systematic computation
corrlib: add description and version number to the correctionlib file
cat_latex: dictionary to convert the category name to latex
scale_flat_syst: flat systematic for scale
smear_flat_syst: flat systematic for smearing
dset_name: name of the dataset eg 2023
cset_name: name of the correction set eg EtaR9

See an example sas_config.yaml

Parameters:

config (dict) – configuration dictionary containing the parameters for the fit
do_fit – if True, perform the fit
save_corrlib – if True, save the correctionlib file
correct_files – if True, apply the correction to the input files and save them

class ijazz.ScaleAndSmearing.IJazZSAS(dt: pandas.DataFrame, mc: pandas.DataFrame, config: dict, categories: dict = None)[source]

Scale-and-smearing fitter with category handling and plotting helpers.

categories_def = None[source]

cfg[source]

verbose[source]

double_gaussian[source]

mll_name[source]

pt_name[source]

cat_name[source]

name_weights[source]

cut = ''[source]

has_pt = False[source]

categories_mc[source]

categories_dt[source]

fitter = None[source]

resp = None[source]

reso = None[source]

resp_corr = None[source]

reso_uncorr = None[source]

eresp = None[source]

ereso = None[source]

eresp_mc = None[source]

ereso_mc = None[source]

pt_mean = None[source]

fit(optimizer=tf.keras.optimizers.Adam) → numpy.ndarray[source]

Actually performs the fit

Parameters:

optimizer (class) – tensorflow compatible optimizer for instance tf.keras.optimizers.Adam (this is not the optimizer the the class)
learning_rate (float, optional) – learning rate to pass to the optimizer. Defaults to 0.01.
dnll_tol (float, optional) – delta likelihood tolerance to stop the fit. Defaults to 0.01.
max_epochs (int, optional) – maximum number of iteration. Defaults to 1000.
init_rand (bool, optional) – random starting poin. Defaults to False.
batch_size (int, optional) – can perform the fit in batch size (usefull for large number of parameters). Defaults to -1.
device (str, optional) – _description_. Defaults to ‘CPU’.
batch_training (bool, optional) – use the batch training mode. Defaults to False.
hess (str, optional) – computation of the hession (None, ‘numerical’, or ‘analytical’), it can take a while. Defaults to ‘numerical’.
cats (_type_, optional) – subset of categories to perform the fit. Defaults to slice(None).

Returns:

array of the value of the -2 * log likelihood during the fit

Return type:

np.ndarray

calc_err_stat(force=False)[source]

calc_err_mc()[source]: compute the error due to MC statistical fluctuations, this may take a while

correct_data(response: numpy.ndarray, prefix: str = None)[source]

correct the data dataframe after the fit

Parameters:

response (np.ndarray) – array with the responses to apply for correction
prefix (str, optional) – prefix used for naming the categorisation. Defaults to None.

correct_mc(resolution: numpy.ndarray, prefix: str = None)[source]

correct the mc dataframe

Parameters:

resolution (np.ndarray) – array with the relative resolution to smear the MC
prefix (str, optional) – prefix used for naming the categorisation. Defaults to None.

recast_pt_bins(prefix='cat')[source]

when doing a fit that contains pt bining with the option use_r_pt, one has to recast the relative pt in real pt value (in GeV)

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

dump_json(outfile: pathlib.Path | str = None)[source]

Dump the resulst to a json file

Parameters:: outfile (Union[Path, str], optional) – output file name. Defaults to None.

ijazz.ScaleAndSmearing.compute_sas(dt, mc, cfg, categories=None) → IJazZSAS[source]

Wrapper to compute the scale and smearing parameters

Parameters:

dt (pd.DataFrame) – input dataframe for data
mc (pd.DataFrame) – input dataframe for mc
cfg (Union[str, Path]) – config file to control the sas fit
categories (dict, optional) – categories to do the fit (can be specified in config). Defaults to None.

Returns:

return the ScaleAndSmearing fitter

Return type:

IJazZSAS

class ijazz.ScaleAndSmearing.IJazZEnergyCorrector(dt: pandas.DataFrame, mc: pandas.DataFrame, json_file: str | pathlib.Path | Dict, mll_name, pt_name)[source]