title: Using the absolute_sigma parameter in scipy.optimize.curve_fit
body: <p>To perform a fit, I am currently using the <a href="http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.optimize.curve_fit.html" rel="noreferrer"><code>curve_fit</code></a> from <code>scipy.optimize</code>. </p>
  
  <p>I have calculated the error associated with each of my <code>ydata</code> and I would like to add the calculated <code>sigma = y_errors</code> present in the data to the fit, </p>
  
  <p>i.e. minimising <code>sum( ((f(xdata, *popt) - ydata) / sigma)**2 )</code> instead of just <code>sum( (f(xdata, *popt) - ydata))</code>. </p>
  
  <p>I can see that it can be done assigning the parameter <strong><code>sigma</code></strong> in the documentation. What I don't clearly understand is the <strong><code>absolute_sigma</code></strong> parameter. The explanation given in the documentation is quite confusing to me. </p>
  
  <p><strong>Should I set <code>absolute_sigma= = True</code> ? Or should it be set to <code>False</code> if I need to consider the <code>y_errors</code> associated with each of my <code>ydata</code>?</strong>  </p>