Details

BTVNanoCommissioning.utils.correction

BTVNanoCommissioning.utils.selection

BTVNanoCommissioning.utils.AK4_parameters

BTVNanoCommissioning.utils.array_writer

BTVNanoCommissioning.utils.histogrammer

BTVNanoCommissioning.utils.sample

BTVNanoCommissioning.utils.plot_utils

BTVNanoCommissioning.utils.plot_utils.MCerrorband(hmc, ax=None, flow=None, label='Stat. unc.', fill_opts=None, ext_error=None, clear=False)

Create a ratio plot, dividing two compatible histograms Parameters ———-

hmcHist

A single-axis histogram

axmatplotlib.axes.Axes, optional

Axes object (if None, one is created)

flowstr, optional {None, “show”, “sum”}

Whether plot the under/overflow bin. If “show”, add additional under/overflow bin. If “sum”, add the under/overflow bin content to first/last bin.

fill_optsdict, optional

A dictionary of options to pass to the matplotlib ax.fill_between call internal to this function, filling the denominator uncertainty band. Leave blank for defaults.

labelstr, optional

Associate a label to this entry (note: y axis label set by num.label)

ext_error: list of np.array[error_up,error_down], optional

External MC errors not stored in the original histogram

clearbool, optional

Whether to clear Axes before drawing (if passed); if False, this function will skip drawing the legend

Returns

axmatplotlib.axes.Axes

A matplotlib Axes object

BTVNanoCommissioning.utils.plot_utils.SFerror(collated, discr, flow=None)

BTVNanoCommissioning.utils.plot_utils.autoranger(hist, flow=None)

BTVNanoCommissioning.utils.plot_utils.clopper_pearson_interval(num, denom, coverage=0.6826894921370859)

Compute Clopper-Pearson coverage interval for a binomial distribution Parameters ———-

numnumpy.ndarray

Numerator, or number of successes, vectorized

denomnumpy.ndarray

Denominator or number of trials, vectorized

coveragefloat, optional

Central coverage interval, defaults to 68%

c.f. http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval

BTVNanoCommissioning.utils.plot_utils.compatible(self, other)

Checks if this histogram is compatible with another, i.e. they have identical binning

BTVNanoCommissioning.utils.plot_utils.normal_interval(pw, tw, pw2, tw2, coverage=0.6826894921370859)

Compute errors based on the expansion of pass/(pass + fail), possibly weighted Parameters ———-

pwnp.ndarray

Numerator, or number of (weighted) successes, vectorized

twnp.ndarray

Denominator or number of (weighted) trials, vectorized

pw2np.ndarray

Numerator sum of weights squared, vectorized

tw2np.ndarray

Denominator sum of weights squared, vectorized

coveragefloat, optional

Central coverage interval, defaults to 68%

c.f. https://root.cern.ch/doc/master/TEfficiency_8cxx_source.html#l02515

BTVNanoCommissioning.utils.plot_utils.plotratio(num, denom, ax=None, clear=True, flow=None, xerr=False, error_opts={}, denom_fill_opts={}, guide_opts={}, unc='num', label=None, ext_denom_error=None)

Create a ratio plot, dividing two compatible histograms Parameters ———-

numHist

Numerator, a single-axis histogram

denomHist

Denominator, a single-axis histogram

axmatplotlib.axes.Axes, optional

Axes object (if None, one is created)

clearbool, optional

Whether to clear Axes before drawing (if passed); if False, this function will skip drawing the legend

flowstr, optional {None, “show”, “sum”}

Whether plot the under/overflow bin. If “show”, add additional under/overflow bin. If “sum”, add the under/overflow bin content to first/last bin.

xerr: bool, optional

If true, then error bars are drawn for x-axis to indicate the size of the bin.

error_optsdict, optional

A dictionary of options to pass to the matplotlib ax.errorbar call internal to this function. Leave blank for defaults. Some special options are interpreted by this function and not passed to matplotlib: ‘emarker’ (default: ‘’) specifies the marker type to place at cap of the errorbar.

denom_fill_optsdict, optional

A dictionary of options to pass to the matplotlib ax.fill_between call internal to this function, filling the denominator uncertainty band. Leave blank for defaults.

guide_optsdict, optional

A dictionary of options to pass to the matplotlib ax.axhline call internal to this function, to plot a horizontal guide line at ratio of 1. Leave blank for defaults.

uncstr, optional

Uncertainty calculation option: ‘clopper-pearson’ interval for efficiencies; ‘poisson-ratio’ interval for ratio of poisson distributions; ‘num’ poisson interval of numerator scaled by denominator value (common for data/mc, for better or worse).

labelstr, optional

Associate a label to this entry (note: y axis label set by num.label)

ext_denom_error: list of np.array[error_up,error_down], optional

External MC errors not stored in the original histogram

Returns

axmatplotlib.axes.Axes

A matplotlib Axes object

BTVNanoCommissioning.utils.plot_utils.poisson_interval(sumw, sumw2, coverage=0.6826894921370859)

Frequentist coverage interval for Poisson-distributed observations Parameters ———-

sumwnumpy.ndarray

Sum of weights vector

sumw2numpy.ndarray

Sum weights squared vector

coveragefloat, optional

Central coverage interval, defaults to 68%

Calculates the so-called ‘Garwood’ interval, c.f. https://www.ine.pt/revstat/pdf/rs120203.pdf or http://ms.mcmaster.ca/peter/s743/poissonalpha.html For weighted data, this approximates the observed count by sumw**2/sumw2, which effectively scales the unweighted poisson interval by the average weight. This may not be the optimal solution: see https://arxiv.org/pdf/1309.1287.pdf for a proper treatment. When a bin is zero, the scale of the nearest nonzero bin is substituted to scale the nominal upper bound. If all bins zero, a warning is generated and interval is set to sumw.

BTVNanoCommissioning.utils.plot_utils.rebin_hist(h, axis_name, edges)

BTVNanoCommissioning.helpers.update_branch

BTVNanoCommissioning.helpers.func

BTVNanoCommissioning.helpers.BTA_helper

BTVNanoCommissioning.helpers.xs_scaler

BTVNanoCommissioning.helpers.xs_scaler.additional_scale(output, scale, sample_to_scale)

BTVNanoCommissioning.helpers.xs_scaler.collate(merged_output, mergemap)

BTVNanoCommissioning.helpers.xs_scaler.dict_depth(d)

BTVNanoCommissioning.helpers.xs_scaler.getSumW(accumulator)

BTVNanoCommissioning.helpers.xs_scaler.merge_output(output)

BTVNanoCommissioning.helpers.xs_scaler.scaleSumW(output, lumi, syst_shapes=True)

BTVNanoCommissioning.helpers.xs_scaler.scale_xs(hist, lumi, events)

BTVNanoCommissioning.helpers.xsection

This module provides functions to handle cross section information used to normalize data and MC when making plots.

Cross section information is crucial for normalizing data and Monte Carlo (MC) simulations to ensure accurate comparisons and analyses.

To add a new dictionary to the cross section list, you can obtain the necessary information from the [XSDB](https://cms-gen-dev.cern.ch/xsdb/).

Example of a cross section dictionary:

```python {

“id”: “61641f28294617432cff1379”, “process_name”: “QCD_Pt-30to50_EMEnriched_TuneCP5_13TeV_pythia8”, “cross_section”: “6447000.0”, “total_uncertainty”: “19870.0”, “equivalent_lumi”: “0.0001551”, “matrix_generator”: “none”, “energy”: “13”,

}

scripts.