我有同样的问题，当我阅读Sven的答复时感到失望。如果您无法在文件上拥有大量数组并且一次只处理其中的小块，似乎numpy会缺少某些关键功能。您的案例似乎与采用.npy格式的原始用例之一接近（请参阅：http ://svn.scipy.org/svn/numpy/trunk/doc/neps/npy-format.txt ）。
然后，我遇到了numpy.lib.format，它似乎是完全有用的东西。我不知道为什么从numpy根软件包中无法使用此功能。与HDF5相比，关键优势在于它附带了numpy。
>>> print numpy.lib.format.open_memmap.__doc__"""Open a .npy file as a memory-mapped array.This may be used to read an existing file or create a new one.Parameters----------filename : str The name of the file on disk. This may not be a filelike object.mode : str, optional The mode to open the file with. In addition to the standard file modes, 'c' is also accepted to mean "copy on write". See `numpy.memmap` for the available mode strings.dtype : dtype, optional The data type of the array if we are creating a new file in "write" mode.shape : tuple of int, optional The shape of the array if we are creating a new file in "write" mode.fortran_order : bool, optional Whether the array should be Fortran-contiguous (True) or C-contiguous (False) if we are creating a new file in "write" mode.version : tuple of int (major, minor) If the mode is a "write" mode, then this is the version of the file format used to create the file.Returns-------marray : numpy.memmap The memory-mapped array.Raises------ValueError If the data or the mode is invalid.IOError If the file is not found or cannot be opened correctly.See Also--------numpy.memmap"""
