hepstats.splot.sweights module#

hepstats.splot.sweights.is_sum_of_extended_pdfs(model)[source]#

Checks if the input model is a sum of extended models.

Parameters:

model – the input model/pdf

Return type:

`bool`

Returns:

True if the model is a sum of extended models, False if not.

hepstats.splot.sweights.compute_sweights(model, x)[source]#

Computes sWeights from probability density functions for different components/species in a fit model (for instance signal and background) fitted on some data x.

i.e. model = Nsig * pdf_signal + Nbkg * pdf_bkg

Parameters:
• model – sum of extended pdfs.

• x (`ndarray`) – data on which model is fitted

Return type:

`dict`[`Any`, `ndarray`]

Returns:

dictionary with yield parameters as keys, and sWeights for correspoind species as values.

Example with zfit:

Imports:

```>>> import numpy as np
>>> import zfit
>>> from zfit.loss import ExtendedUnbinnedNLL
>>> from zfit.minimize import Minuit
```

Definition of the bounds and yield of background and signal species:

```>>> bounds = (0.0, 3.0)
>>> nbkg = 10000
>>> nsig = 5000
>>> obs = zfit.Space('x', limits=bounds)
```

Generation of data:

```>>> bkg = np.random.exponential(0.5, nbkg)
>>> peak = np.random.normal(1.2, 0.1, nsig)
>>> data = np.concatenate((bkg, peak))
>>> data = data[(data > bounds[0]) & (data < bounds[1])]
>>> N = data.size
>>> data = zfit.data.Data.from_numpy(obs=obs, array=data)
```

Model definition:

```>>> mean = zfit.Parameter("mean", 1.2, 0.5, 2.0)
>>> sigma = zfit.Parameter("sigma", 0.1, 0.02, 0.2)
>>> lambda_ = zfit.Parameter("lambda", -2.0, -4.0, -1.0)
>>> Nsig = zfit.Parameter("Nsig", nsig, 0., N)
>>> Nbkg = zfit.Parameter("Nbkg", nbkg, 0., N)
>>> signal = zfit.pdf.Gauss(obs=obs, mu=mean, sigma=sigma).create_extended(Nsig)
>>> background = zfit.pdf.Exponential(obs=obs, lambda_=lambda_).create_extended(Nbkg)
>>> tot_model = zfit.pdf.SumPDF([signal, background])
```

Loss construction and minimization:

```>>> loss = ExtendedUnbinnedNLL(model=signal + background, data=data)
>>> minimizer = Minuit()
>>> minimum = minimizer.minimize(loss)
```

sWeights computation:

```>>> from hepstats.splot import compute_sweights
>>> sweights = compute_sweights(tot_model, data)
>>> print(sweights)
{<zfit.Parameter 'Nsig' floating=True value=4985>: array([-0.09953299, -0.09953299, -0.09953299, ...,
0.78689884, 1.08823111,  1.05948873]),
<zfit.Parameter 'Nbkg' floating=True value=9989>: array([ 1.09953348,  1.09953348,  1.09953348, ...,
0.21310097, -0.08823153, -0.05948912])}
```