parameters: (self, xvals)
return_statement: return out
docstring: Computes the function on the set of values given and returns
          the answer as a numpy array of floats
docstring_summary: Computes the function on the set of values given and returns
          the answer as a numpy array of floats
docstring_tokens: Computes the function on the set of values given and returns the answer as a numpy array of floats
function: def functionLocal(self, xvals):
          """
          Computes the function on the set of values given and returns
          the answer as a numpy array of floats
          """
          # As Fit progresses the declared parameter values will change
          # Can also be retrieve by index self.getParameterValue(0)
          height = self.getParameterValue("Height")
          peak_centre = self.getParameterValue("PeakCentre")
          sigma = self.getParameterValue("Sigma")
          weight = math.pow(1./sigma, 2)
  
          # Here you can use the NTerms attr if required by
          #   using self._nterms: see setAttributeValue below or
          #   accessing the attribute each time directly nterms = self.getAttributeValue("NTerms") but this is much slower
  
          offset_sq = np.square(xvals-peak_centre)
          out = height*np.exp(-0.5*offset_sq*weight)
          return out
function_tokens: def functionLocal ( self , xvals ) : # As Fit progresses the declared parameter values will change # Can also be retrieve by index self.getParameterValue(0) height = self . getParameterValue ( "Height" ) peak_centre = self . getParameterValue ( "PeakCentre" ) sigma = self . getParameterValue ( "Sigma" ) weight = math . pow ( 1. / sigma , 2 ) # Here you can use the NTerms attr if required by #   using self._nterms: see setAttributeValue below or #   accessing the attribute each time directly nterms = self.getAttributeValue("NTerms") but this is much slower offset_sq = np . square ( xvals - peak_centre ) out = height * np . exp ( - 0.5 * offset_sq * weight ) return out