original_docstring: 
      NOTE: This function currently requires that the simulation
            data for A and dM/dh be obtained from the same set
            of disorder values, r
  
      Perform the fit of A, dMdh, Sigma and eta for subsets of
      the simulated r values and return the parameters for each fit
      Input:
          filenames - [A_filename, dMdh_filename]
                    - filenames to load data from
              A_filename - location where area weighted size
                           distribution is stored
              dMdh_filename - location where dM/dh data is stored
          num - size of the subsets of r to consider
          func_type - functional form to use for joint fit of Sigma and eta
          verbose - flag to print cost of fits
      Output:
          params_A - list of fit values found for A for each
                     subset of r values considered
          params_dMdh - list of fit values found for dM/dh for each
                     subset of r values considered
          params_Sigma - list of fit values found for Sigma for each
                     subset of r values considered
          params_eta - list of fit values found for eta for each
                     subset of r values considered
      
docstring: This function currently requires that the simulation
  data for A and dM/dh be obtained from the same set
  of disorder values, r
  
  
docstring_tokens: This function currently requires that the simulation data for A and dM / dh be obtained from the same set of disorder values r
code: def fit_subsets_of_r(filenames=[None, None], num=11,
                       fixed_dict=default_fixed,
                       func_type='truncated', verbose=False):
      """
      NOTE: This function currently requires that the simulation
            data for A and dM/dh be obtained from the same set
            of disorder values, r
  
      Perform the fit of A, dMdh, Sigma and eta for subsets of
      the simulated r values and return the parameters for each fit
      Input:
          filenames - [A_filename, dMdh_filename]
                    - filenames to load data from
              A_filename - location where area weighted size
                           distribution is stored
              dMdh_filename - location where dM/dh data is stored
          num - size of the subsets of r to consider
          func_type - functional form to use for joint fit of Sigma and eta
          verbose - flag to print cost of fits
      Output:
          params_A - list of fit values found for A for each
                     subset of r values considered
          params_dMdh - list of fit values found for dM/dh for each
                     subset of r values considered
          params_Sigma - list of fit values found for Sigma for each
                     subset of r values considered
          params_eta - list of fit values found for eta for each
                     subset of r values considered
      """
      dataA = load_svA(filenames[0])
      datadMdh = load_hvdMdh(filenames[1])
  
      r = dataA[0]
      params_A = []
      params_dMdh = []
      params_Sigma = []
      params_eta = []
  
      numCurves = len(r)
      for i in range(numCurves-num+1):
          dataAtemp = slice_data(dataA, i, i+num)
          datadMdhtemp = slice_data(datadMdh, i, i+num)
          pA, pM, pS, pe = perform_all_fits(data=[dataAtemp, datadMdhtemp],
                                            fixed_dict=fixed_dict,
                                            func_type=func_type,
                                            verbose=verbose,
                                            show_params=False)
          params_A.append(list(pA.values()))
          params_dMdh.append(list(pM.values()))
          params_Sigma.append(list(pS.values()))
          params_eta.append(list(pe.values()))
  
      return params_A, params_dMdh, params_Sigma, params_eta
code_tokens: def fit_subsets_of_r ( filenames = [ None , None ] , num = 11 , fixed_dict = default_fixed , func_type = 'truncated' , verbose = False ) : dataA = load_svA ( filenames [ 0 ] ) datadMdh = load_hvdMdh ( filenames [ 1 ] ) r = dataA [ 0 ] params_A = [ ] params_dMdh = [ ] params_Sigma = [ ] params_eta = [ ] numCurves = len ( r ) for i in range ( numCurves - num + 1 ) : dataAtemp = slice_data ( dataA , i , i + num ) datadMdhtemp = slice_data ( datadMdh , i , i + num ) pA , pM , pS , pe = perform_all_fits ( data = [ dataAtemp , datadMdhtemp ] , fixed_dict = fixed_dict , func_type = func_type , verbose = verbose , show_params = False ) params_A . append ( list ( pA . values ( ) ) ) params_dMdh . append ( list ( pM . values ( ) ) ) params_Sigma . append ( list ( pS . values ( ) ) ) params_eta . append ( list ( pe . values ( ) ) ) return params_A , params_dMdh , params_Sigma , params_eta
short_docstring: NOTE: This function currently requires that the simulation
  data for A and dM/dh be obtained from the same set
  of disorder values, r
short_docstring_tokens: NOTE : This function currently requires that the simulation data for A and dM / dh be obtained from the same set of disorder values r
comment: """
      NOTE: This function currently requires that the simulation
            data for A and dM/dh be obtained from the same set
            of disorder values, r
  
      Perform the fit of A, dMdh, Sigma and eta for subsets of
      the simulated r values and return the parameters for each fit
      Input:
          filenames - [A_filename, dMdh_filename]
                    - filenames to load data from
              A_filename - location where area weighted size
                           distribution is stored
              dMdh_filename - location where dM/dh data is stored
          num - size of the subsets of r to consider
          func_type - functional form to use for joint fit of Sigma and eta
          verbose - flag to print cost of fits
      Output:
          params_A - list of fit values found for A for each
                     subset of r values considered
          params_dMdh - list of fit values found for dM/dh for each
                     subset of r values considered
          params_Sigma - list of fit values found for Sigma for each
                     subset of r values considered
          params_eta - list of fit values found for eta for each
                     subset of r values considered
      """