I find the documentation of the histplot function [0] slightly confusing/contradictive

Histplot errorbars about mplhep HOT 3 OPEN

meisonlikesicecream commented on July 29, 2024

Histplot errorbars

from mplhep.

Comments (3)

nsmith- commented on July 29, 2024

For sure the sqrt method should be using variances (sumw2), so that's a bug.
As for the rest of the plumbing, there is some attempt to do scaled Poisson in

mplhep/src/mplhep/error_estimation.py

Lines 11 to 31 in 9b47e92

    
           def poisson_interval(sumw, sumw2, coverage=_coverage1sd): 
        
               """Frequentist coverage interval for Poisson-distributed observations 
        
               Parameters 
        
               ---------- 
        
                   sumw : numpy.ndarray 
        
                       Sum of weights vector 
        
                   sumw2 : numpy.ndarray 
        
                       Sum weights squared vector 
        
                   coverage : float, 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``. 
        
               # Taken from Coffea 
        
               """

for the case where sumw2 is available. So in practice, it will be very close to sqrt(N) for reasonably large N. Nevertheless, one may prefer to be using exactly sqrt(sumw2) the documentation and the code should be synchronized, and I'm sure the maintainers would be happy to receive a PR to correct this.

In the meantime, you can pass a custom error function to mplhep.histplot(..., w2method=mymethod) where

def mymethod(sumw, sumw2):
    return np.array([sumw - np.sqrt(sumw2), sumw + np.sqrt(sumw2)])

from mplhep.

Recommend Projects

Histplot errorbars about mplhep HOT 3 OPEN

Comments (3)

Related Issues (20)

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent

	def poisson_interval(sumw, sumw2, coverage=_coverage1sd):
	"""Frequentist coverage interval for Poisson-distributed observations
	Parameters
	----------
	sumw : numpy.ndarray
	Sum of weights vector
	sumw2 : numpy.ndarray
	Sum weights squared vector
	coverage : float, 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``.
	# Taken from Coffea
	"""