code: def pointspace(self, **kwargs):
          
          scale_array = numpy.array([
              [prefix_factor(self.independent)**(-1)],
              [prefix_factor(self.dependent)**(-1)]
          ])
  
          linspace = numpy.linspace(self.limits[0], self.limits[1], **kwargs)
          return {
            'data': self.data.array * scale_array,
            'fit': numpy.array([linspace, self.fitted_function(linspace)]) * scale_array
          }
docs: Returns a dictionary with the keys `data` and `fit`.
  
          `data` is just `scipy_data_fitting.Data.array`.
  
          `fit` is a two row [`numpy.ndarray`][1], the first row values correspond
          to the independent variable and are generated using [`numpy.linspace`][2].
          The second row are the values of `scipy_data_fitting.Fit.fitted_function`
          evaluated on the linspace.
  
          For both `fit` and `data`, each row will be scaled by the corresponding
          inverse prefix if given in `scipy_data_fitting.Fit.independent`
          or `scipy_data_fitting.Fit.dependent`.
  
          Any keyword arguments are passed to [`numpy.linspace`][2].
  
          [1]: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html
          [2]: http://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html