How to use the pyroofit.models.Gauss function in pyroofit

def test_fit():
    import ROOT
    x = ROOT.RooRealVar('mbc', '', 0, 0, 1)
    pdf = Gauss(x)
    df = get_test_df(100)
    pdf.fit(df)
    assert isinstance(pdf.roo_pdf, ROOT.RooAbsPdf)

def test_fit_bins():
    import ROOT
    x = ROOT.RooRealVar('mbc', '', 0, 0, 1)
    pdf = Gauss(x)
    df = get_test_df(100)
    pdf.fit(df, nbins=5)
    assert isinstance(pdf.roo_pdf, ROOT.RooAbsPdf)

def test_AddPdf():
    import ROOT
    df = get_test_df()
    assert isinstance(df, pd.DataFrame)
    bkg = Chebychev(('mbc', 0, 1))
    sig = Gauss(('mbc', 0, 1))

    pdf = sig+bkg

    #pdf.fit(df)
    #pdf.plot('test2.pdf')

    assert isinstance(pdf, AddPdf)
    assert isinstance(pdf.roo_pdf, ROOT.RooAbsPdf)

def test_AddPdf_fit():
    import ROOT
    df = get_test_df()
    assert isinstance(df, pd.DataFrame)
    bkg = Chebychev(('mbc', 0, 1))
    sig = Gauss(('mbc', 0, 1))

    pdf = sig+bkg

    pdf.fit(df)
    #pdf.plot('test2.pdf')

    assert isinstance(pdf, AddPdf)
    assert isinstance(pdf.roo_pdf, ROOT.RooAbsPdf)

def test_Convolution():
    import ROOT
    df = get_test_df()
    assert isinstance(df, pd.DataFrame)
    bkg = Chebychev(('mbc', 0, 1))
    sig = Gauss(('mbc', 0, 1))

    pdf = Convolution(bkg, sig)

    assert isinstance(pdf, Convolution)
    assert isinstance(pdf.roo_pdf, ROOT.RooAbsPdf)

def test_ProdPdf():
    import ROOT
    df = get_test_df()
    assert isinstance(df, pd.DataFrame)

    bkg = Chebychev(('mbc', 0, 1))
    sig = Gauss(('mbc', 0, 1))

    pdf = sig*bkg

    assert isinstance(pdf, ProdPdf)
    assert isinstance(pdf.roo_pdf, ROOT.RooAbsPdf)

def test_PDF_Gauss():
    import ROOT
    x = ROOT.RooRealVar('mbc', '', 0, 0, 1)
    pdf = Gauss(x)
    assert isinstance(pdf.roo_pdf, ROOT.RooAbsPdf)

from pyroofit.models import Gauss, Chebychev
from pyroofit.composites import SimFit
import numpy as np
import pandas as pd
import ROOT


df_mixed = {'mass': np.append(np.random.random_sample(1000)*7 - 3.5, np.random.normal(0, 0.5, 1000))}
df_mixed = pd.DataFrame(df_mixed)

df_bkg = {'mass': np.random.random_sample(2000)*7 - 3.5}
df_bkg = pd.DataFrame(df_bkg)

x = ROOT.RooRealVar('mass', 'M', 0, -3, 3, 'GeV')

pdf_sig = Gauss(x, mean=(-1, 1))
pdf_bkg = Chebychev(x, n=1)

pdf = pdf_sig + pdf_bkg

sf = SimFit(pdf, pdf_bkg)
sf.use_extended = True  # bug
sf.use_minos = True
sf.fit([(pdf, df_mixed), (pdf_bkg, df_bkg)])

#pdf_bkg.plot('simultaneous_fit_bkg.pdf', df_bkg)
pdf.plot('simultaneous_fit.pdf',
         df_mixed,
         nbins=20,
         extra_info=[["Legend"], ["More Legend"], ['#mu', *pdf_sig.get('mean')], ['#sigma', *pdf_sig.get('sigma')]])
pdf.get()

x = ('x', -3, 3)
x = ('mass', -3, 0.02, 3)

Parameters are initialised with a tuple: sigma=(0,1) or again including a starting parameter: sigma=(0.01, 0, 1)
The order here is not important.

All parameters and observables can also be initialised by a ROOT.RooRealVar.

"""

from pyroofit.models import Gauss
import numpy as np

data = np.random.normal(0, 1, 1000)

pdf = Gauss(('x', -3, 3), mean=(-1, 0, 1))
pdf.fit(data)
pdf.plot('simple_fit.pdf',)

pdf.get()

x = ('x', -3, 3)
x = ('mass', -3, 0.02, 3)

Parameters are initialised with a tuple: sigma=(0,1) or again including a starting parameter: sigma=(0.01, 0, 1)
The order here is not important.

All parameters and observables can also be initialised by a ROOT.RooRealVar.

"""

from pyroofit.models import Gauss
import numpy as np

data = np.random.normal(0, 1, 1000)

pdf = Gauss(('x', -3, 3), mean=(-1, 0, 1))
pdf.fit(data, nbins=10)
pdf.plot('simple_fit_binning.pdf',)

pdf.get()