original_docstring: Convert Linear scale to Logarithmic scale
          
docstring: Convert Linear scale to Logarithmic scale
docstring_tokens: Convert Linear scale to Logarithmic scale
code: def _conv_lin2log(self, search_rgn):
          """Convert Linear scale to Logarithmic scale
          """
          for i in range(search_rgn.shape[1]):
              if np.min(search_rgn[:, i]) < 0.0:
                  msg = 'search_rgn[lower_bound, upper_bound] must be positive.'
                  if i <= C.NUM:
                      raise ValueError(
                          '"C.{}": '.format(C.NAMES[i]) + msg
                      )
                  else:
                      raise ValueError(
                          '"V.{}": '.format(V.NAMES[i-C.NUM]) + msg
                      )
              elif np.min(search_rgn[:, i]) == 0 and np.max(search_rgn[:, i]) > 0:
                  msg = 'lower_bound must be larger than 0.'
                  if i <= C.NUM:
                      raise ValueError(
                          '"C.{}" '.format(C.NAMES[i]) + msg
                      )
                  else:
                      raise ValueError(
                          '"V.{}" '.format(V.NAMES[i-C.NUM]) + msg
                      )
              elif search_rgn[1, i] - search_rgn[0, i] < 0.0:
                  msg = 'lower_bound must be smaller than upper_bound.'
                  if i <= C.NUM:
                      raise ValueError(
                          '"C.{}" : '.format(C.NAMES[i]) + msg
                      )
                  else:
                      raise ValueError(
                          '"V.{}" : '.format(V.NAMES[i-C.NUM]) + msg
                      )
          difference = list(
              set(
                  np.where(
                      np.any(search_rgn != 0., axis=0)
                  )[0]
              ) ^ set(
                  np.append(
                      self.idx_params, [C.NUM + idx for idx in self.idx_initials]
                  )
              )
          )
          if len(difference) > 0:
              msg = 'in both search_idx and search_rgn'
              for idx in difference:
                  if idx <= C.NUM:
                      raise ValueError(
                          'Set "C.{}" '.format(C.NAMES[int(idx)]) + msg
                      )
                  else:
                      raise ValueError(
                          'Set "V.{}" '.format(V.NAMES[int(idx-C.NUM)]) + msg
                      )
          search_rgn = search_rgn[:, np.any(search_rgn != 0., axis=0)]
  
          return np.log10(search_rgn)
code_tokens: def _conv_lin2log ( self , search_rgn ) : for i in range ( search_rgn . shape [ 1 ] ) : if np . min ( search_rgn [ : , i ] ) < 0.0 : msg = 'search_rgn[lower_bound, upper_bound] must be positive.' if i <= C . NUM : raise ValueError ( '"C.{}": ' . format ( C . NAMES [ i ] ) + msg ) else : raise ValueError ( '"V.{}": ' . format ( V . NAMES [ i - C . NUM ] ) + msg ) elif np . min ( search_rgn [ : , i ] ) == 0 and np . max ( search_rgn [ : , i ] ) > 0 : msg = 'lower_bound must be larger than 0.' if i <= C . NUM : raise ValueError ( '"C.{}" ' . format ( C . NAMES [ i ] ) + msg ) else : raise ValueError ( '"V.{}" ' . format ( V . NAMES [ i - C . NUM ] ) + msg ) elif search_rgn [ 1 , i ] - search_rgn [ 0 , i ] < 0.0 : msg = 'lower_bound must be smaller than upper_bound.' if i <= C . NUM : raise ValueError ( '"C.{}" : ' . format ( C . NAMES [ i ] ) + msg ) else : raise ValueError ( '"V.{}" : ' . format ( V . NAMES [ i - C . NUM ] ) + msg ) difference = list ( set ( np . where ( np . any ( search_rgn != 0. , axis = 0 ) ) [ 0 ] ) ^ set ( np . append ( self . idx_params , [ C . NUM + idx for idx in self . idx_initials ] ) ) ) if len ( difference ) > 0 : msg = 'in both search_idx and search_rgn' for idx in difference : if idx <= C . NUM : raise ValueError ( 'Set "C.{}" ' . format ( C . NAMES [ int ( idx ) ] ) + msg ) else : raise ValueError ( 'Set "V.{}" ' . format ( V . NAMES [ int ( idx - C . NUM ) ] ) + msg ) search_rgn = search_rgn [ : , np . any ( search_rgn != 0. , axis = 0 ) ] return np . log10 ( search_rgn )
short_docstring: Convert Linear scale to Logarithmic scale
short_docstring_tokens: Convert Linear scale to Logarithmic scale
comment: """Convert Linear scale to Logarithmic scale
          """