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# numpy.memmap

class numpy.memmap(filename, dtype=<class 'numpy.ubyte'>, mode='r+', offset=0, shape=None, order='C')

  Create a memory-map to an array stored in a binary file on disk.

  Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory. NumPy’s memmap’s are array-like objects. This differs from Python’s `mmap` module, which uses file-like objects.

  This subclass of ndarray has some unpleasant interactions with some operations, because it doesn’t quite fit properly as a subclass. An alternative to using this subclass is to create the `mmap` object yourself, then create an ndarray with ndarray.__new__ directly, passing the object created in its ‘buffer=’ parameter.

  This class may at some point be turned into a factory function which returns a view into an mmap buffer.

  Flush the memmap instance to write the changes to the file. Currently there is no API to close the underlying `mmap`. It is tricky to ensure the resource is actually closed, since it may be shared between different memmap instances.

  Parameters:
    filename str, file-like object, or pathlib.Path instance

      The file name or file object to be used as the array data buffer.

    dtype data-type, optional

      The data-type used to interpret the file contents. Default is `uint8`.

    mode{‘r+’, ‘r’, ‘w+’, ‘c’}, optional

      The file is opened in this mode:

      ‘r’ | Open existing file for reading only.
      ‘r+’ | Open existing file for reading and writing.
      ‘w+’ | Create or overwrite existing file for reading and writing. If `mode == 'w+'` then `shape` must also be specified.
      ‘c’ | Copy-on-write: assignments affect data in memory, but changes are not saved to disk. The file on disk is read-only.

      Default is ‘r+’.

    offset int, optional

      In the file, array data starts at this offset. Since offset is measured in bytes, it should normally be a multiple of the byte-size of `dtype`. When `mode != 'r'`, even positive offsets beyond end of file are valid; The file will be extended to accommodate the additional data. By default, `memmap` will start at the beginning of the file, even if `filename` is a file pointer `fp` and `fp.tell() != 0`.

    shape int or sequence of ints, optional

      The desired shape of the array. If `mode == 'r'` and the number of remaining bytes after offset is not a multiple of the byte-size of `dtype`, you must specify `shape`. By default, the returned array will be 1-D with the number of elements determined by file size and data-type.

      Changed in version 2.0: The shape parameter can now be any integer sequence type, previously types were limited to tuple and int.

    order{‘C’, ‘F’}, optional

      Specify the order of the ndarray memory layout: row-major, C-style or column-major, Fortran-style. This only has an effect if the shape is greater than 1-D. The default order is ‘C’.

  Attributes:
    filename str or pathlib.Path instance

      Path to the mapped file.

    offset int

      Offset position in the file.

    mode str

      File mode.

  Methods

  `flush`() | Write any changes in the array to the file on disk.

  See also

  `lib.format.open_memmap`

    Create or load a memory-mapped `.npy` file.

  Notes

  The memmap object can be used anywhere an ndarray is accepted. Given a memmap `fp`, `isinstance(fp, numpy.ndarray)` returns `True`.

  Memory-mapped files cannot be larger than 2GB on 32-bit systems.

  When a memmap causes a file to be created or extended beyond its current size in the filesystem, the contents of the new part are unspecified. On systems with POSIX filesystem semantics, the extended part will be filled with zero bytes.

  Examples

      >>> import numpy as np
      >>> data = np.arange(12, dtype='float32')
      >>> data.resize((3,4))

  This example uses a temporary file so that doctest doesn’t write files to your directory. You would use a ‘normal’ filename.

      >>> from tempfile import mkdtemp
      >>> import os.path as path
      >>> filename = path.join(mkdtemp(), 'newfile.dat')

  Create a memmap with dtype and shape that matches our data:

      >>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))
      >>> fp
      memmap([[0., 0., 0., 0.],
              [0., 0., 0., 0.],
              [0., 0., 0., 0.]], dtype=float32)

  Write data to memmap array:

      >>> fp[:] = data[:]
      >>> fp
      memmap([[  0.,   1.,   2.,   3.],
              [  4.,   5.,   6.,   7.],
              [  8.,   9.,  10.,  11.]], dtype=float32)

      >>> fp.filename == path.abspath(filename)
      True

  Flushes memory changes to disk in order to read them back

      >>> fp.flush()

  Load the memmap and verify data was stored:

      >>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
      >>> newfp
      memmap([[  0.,   1.,   2.,   3.],
              [  4.,   5.,   6.,   7.],
              [  8.,   9.,  10.,  11.]], dtype=float32)

  Read-only memmap:

      >>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
      >>> fpr.flags.writeable
      False

  Copy-on-write memmap:

      >>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))
      >>> fpc.flags.writeable
      True

  It’s possible to assign to copy-on-write array, but values are only written into the memory copy of the array, and not written to disk:

      >>> fpc
      memmap([[  0.,   1.,   2.,   3.],
              [  4.,   5.,   6.,   7.],
              [  8.,   9.,  10.,  11.]], dtype=float32)
      >>> fpc[0,:] = 0
      >>> fpc
      memmap([[  0.,   0.,   0.,   0.],
              [  4.,   5.,   6.,   7.],
              [  8.,   9.,  10.,  11.]], dtype=float32)

  File on disk is unchanged:

      >>> fpr
      memmap([[  0.,   1.,   2.,   3.],
              [  4.,   5.,   6.,   7.],
              [  8.,   9.,  10.,  11.]], dtype=float32)

  Offset into a memmap:

      >>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)
      >>> fpo
      memmap([  4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.], dtype=float32)