def _calculate_confidence_band(root_obj: TF1,
ci: float,
steps: int = 1000) -> dict:
"""
Calculate confidence bands from a fit of a
TF1 to a histogram.
Parameters
----------
root_obj : TH1
ROOT TF1 pdf used for the fit
ci : float
Confidence interval
steps : int
Number of points in band
Returns
-------
dict
Dictionary with x, y, xerr, yerr points
"""
assert(isinstance(root_obj, TF1))
band = TGraphErrors(steps)
lowb, highb = root_obj.GetXaxis().GetXmin(), root_obj.GetXaxis().GetXmax()
for i in range(steps):
x = (lowb + (
(highb - lowb) / steps) * i)
y = 0
band.SetPoint(i, x, y)
(TVirtualFitter.GetFitter()).GetConfidenceIntervals(band, ci)
return _convert_tgrapherrors(band)
def _convert_tf1(root_obj: TF1) -> dict:
"""
Takes values from a fit function and puts them into an array
to be plotted.
Parameters
----------
root_obj : TF1
ROOT TF1
Returns
-------
dict
Dictionary with x, y points
"""
assert(isinstance(root_obj, TF1))
x = np.linspace(root_obj.GetXaxis().GetXmin(),
root_obj.GetXaxis().GetXmax(),
1000)
y = []
for p in x:
y.append(root_obj.Eval(p))
return {'x': x, 'y': np.array(y)}
