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rsnemmen/scatterfit.py

Created April 1, 2015 19:16
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Computes the mean deviation of the data about the linear model
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scatterfit.py
def scatterfit(x,y,a=None,b=None):
"""
Compute the mean deviation of the data about the linear model given if A,B
(y=ax+b) provided as arguments. Otherwise, compute the mean deviation about
the best-fit line.
x,y assumed to be Numpy arrays. a,b scalars.
Returns the float sd with the mean deviation.
Author: Rodrigo Nemmen
"""
if a==None:
# Performs linear regression
a, b, r, p, err = scipy.stats.linregress(x,y)
# Std. deviation of an individual measurement (Bevington, eq. 6.15)
N=numpy.size(x)
sd=1./(N-2.)* numpy.sum((y-a*x-b)**2); sd=numpy.sqrt(sd)
return sd
@Gabriel-p
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Is there some indentation missing here perhaps?

  if a==None:   
      # Performs linear regression  
      a, b, r, p, err = scipy.stats.linregress(x,y)  

  # Std. deviation of an individual measurement (Bevington, eq. 6.15)  
  N=numpy.size(x)  
  sd=1./(N-2.)* numpy.sum((y-a*x-b)**2); sd=numpy.sqrt(sd)

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