code: def transpose(self):
          '''
          transpose all calibration arrays
          in case different array shape orders were used (x,y) vs. (y,x)
          '''
          def _t(item):
              if type(item) == list:
                  for n, it in enumerate(item):
                      if type(it) == tuple:
                          it = list(it)
                          item[n] = it
                      if type(it) == list:
                          _t(it)
                      if isinstance(it, np.ndarray) and it.shape == s:
                          item[n] = it.T
  
          s = self.coeffs['shape']
  
          for item in self.coeffs.values():
              if type(item) == dict:
                  for item2 in item.values():
                      _t(item2)
              else:
                  _t(item)
  
          self.coeffs['shape'] = s[::-1f transpose(self):
          '''
          transpose all calibration arrays
          in case different array shape orders were used (x,y) vs. (y,x)
          '''
          def _t(item):
              if type(item) == list:
                  for n, it in enumerate(item):
                      if type(it) == tuple:
                          it = list(it)
                          item[n] = it
                      if type(it) == list:
                          _t(it)
                      if isinstance(it, np.ndarray) and it.shape == s:
                          item[n] = it.T
  
          s = self.coeffs['shape']
  
          for item in self.coeffs.values():
              if type(item) == dict:
                  for item2 in item.values():
                      _t(item2)
              else:
                  _t(item)
  
          self.coeffs['shape'] = s[::-1]
docs: transpose all calibration arrays
          in case different array shape orders were used (x,y) vs. (y,x)