Example #1
0
def Python_linfit(x_true, y, y_err, errors_output = True):
     
    Regression_Fit, Uncertainty_Matrix, Red_Chi_Sq, Residuals   = linfit(x_true, y, y_err, cov=True, relsigma=False, chisq=True, residuals=True)
    m_n_Matrix                                                  = [sqrt(Uncertainty_Matrix[t,t]) for t in range(2)] 
    R_Factor                                                    = Uncertainty_Matrix[0,1]/(m_n_Matrix[0]*m_n_Matrix[1])
    m, m_error                                                  = Regression_Fit[0], m_n_Matrix[0]
    n, n_error                                                  = Regression_Fit[1], m_n_Matrix[1]
    
    if errors_output: 
        return m, m_error, n, n_error
    
    else:
        return m, n    
Example #2
0
 def linfit_regression(self):
     
     fit_dict    = OrderedDict()
     
     fit_dict['methodology'] = 'Linfit'
     
     Regression_Fit, Uncertainty_Matrix, fit_dict['red_ChiSq'], fit_dict['residuals'] = linfit(x_true = self.x_array, y = self.y_array, sigmay = self.y_error, relsigma = False, cov = True, chisq = True, residuals = True)
     
     m_n_Matrix                              = [sqrt(Uncertainty_Matrix[t,t]) for t in range(2)] 
     fit_dict['R_factor']                    = Uncertainty_Matrix[0,1]/(m_n_Matrix[0]*m_n_Matrix[1])
     fit_dict['m'], fit_dict['m_error']      = Regression_Fit[0], m_n_Matrix[0]
     fit_dict['n'], fit_dict['n_error']      = Regression_Fit[1], m_n_Matrix[1]
     
     return fit_dict