code: def array(self) -> numpy.ndarray:
          
          array = numpy.full(self.shape, fillvalue, dtype=float)
          for idx, subarray in enumerate(self.arrays.values()):
              array[self.get_timeplaceslice(idx)] = subarray
          return array
docs: The aggregated data of all logged |IOSequence| objects contained
          in one single |numpy.ndarray| object.
  
          The documentation on |NetCDFVariableAgg.shape| explains how
          |NetCDFVariableAgg.array| is structured.  This first example
          confirms that, under default configuration (`timeaxis=1`),
          the first axis corresponds to the location, while the second
          one corresponds to time:
  
          >>> from hydpy.core.examples import prepare_io_example_1
          >>> nodes, elements = prepare_io_example_1()
          >>> from hydpy.core.netcdftools import NetCDFVariableAgg
          >>> ncvar = NetCDFVariableAgg('flux_nkor', isolate=False, timeaxis=1)
          >>> for element in elements:
          ...     nkor1 = element.model.sequences.fluxes.nkor
          ...     ncvar.log(nkor1, nkor1.average_series())
          >>> ncvar.array
          array([[ 12. ,  13. ,  14. ,  15. ],
                 [ 16.5,  18.5,  20.5,  22.5],
                 [ 25. ,  28. ,  31. ,  34. ]])
  
          When using the first axis as the "timeaxis", the resulting
          |NetCDFVariableAgg.array| is the transposed:
  
          >>> ncvar = NetCDFVariableAgg('flux_nkor', isolate=False, timeaxis=0)
          >>> for element in elements:
          ...     nkor1 = element.model.sequences.fluxes.nkor
          ...     ncvar.log(nkor1, nkor1.average_series())
          >>> ncvar.array
          array([[ 12. ,  16.5,  25. ],
                 [ 13. ,  18.5,  28. ],
                 [ 14. ,  20.5,  31. ],
                 [ 15. ,  22.5,  34. ]])