How can I create a numpy .npy file in place on disk?

Question

Is it possible to create an .npy file without allocating the corresponding array in memory first?

I need to create and work with a large numpy array, too big to crea

Answer 1

I had the same question and was disappointed when I read Sven's reply. Seems as though numpy would be missing out on some key functionality if you couldn't have a huge array on file and work on little pieces of it at a time. Your case seems to be close to one of the use cases in the origional rational for making the .npy format (see: http://svn.scipy.org/svn/numpy/trunk/doc/neps/npy-format.txt).

I then ran into numpy.lib.format, which seems to be full useful goodies. I have no idea why this functionality is not available from the numpy root package. The key advantage over HDF5 is that this ships with 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
"""