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from __future__ import division, absolute_import, print_function
import numpy as np
from .numeric import uint8, ndarray, dtype
from numpy.compat import long, basestring, is_pathlib_path
__all__ = ['memmap']
dtypedescr = dtype
valid_filemodes = ["r", "c", "r+", "w+"]
writeable_filemodes = ["r+", "w+"]
mode_equivalents = {
class memmap(ndarray):
"""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.
Delete the memmap instance to close.
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. |
| '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 : tuple, 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.
order : {'C', 'F'}, optional
Specify the order of the ndarray memory layout:
:term:`row-major`, C-style or :term:`column-major`,
Fortran-style. This only has an effect if the shape is
greater than 1-D. The default order is 'C'.
filename : str or pathlib.Path instance
Path to the mapped file.
offset : int
Offset position in the file.
mode : str
File mode.
Flush any changes in memory to file on disk.
When you delete a memmap object, flush is called first to write
changes to disk before removing the object.
See also
lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
The memmap object can be used anywhere an ndarray is accepted.
Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns
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.
>>> 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)
Deletion flushes memory changes to disk before removing the object:
>>> del fp
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
Copy-on-write memmap:
>>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))
>>> fpc.flags.writeable
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)
__array_priority__ = -100.0
def __new__(subtype, filename, dtype=uint8, mode='r+', offset=0,
shape=None, order='C'):
# Import here to minimize 'import numpy' overhead
import mmap
import os.path
mode = mode_equivalents[mode]
except KeyError:
if mode not in valid_filemodes:
raise ValueError("mode must be one of %s" %
(valid_filemodes + list(mode_equivalents.keys())))
if hasattr(filename, 'read'):
fid = filename
own_file = False
elif is_pathlib_path(filename):
fid = == 'c' and 'r' or mode)+'b')
own_file = True
fid = open(filename, (mode == 'c' and 'r' or mode)+'b')
own_file = True
if (mode == 'w+') and shape is None:
raise ValueError("shape must be given"), 2)
flen = fid.tell()
descr = dtypedescr(dtype)
_dbytes = descr.itemsize
if shape is None:
bytes = flen - offset
if (bytes % _dbytes):
raise ValueError("Size of available data is not a "
"multiple of the data-type size.")
size = bytes // _dbytes
shape = (size,)
if not isinstance(shape, tuple):
shape = (shape,)
size = 1
for k in shape:
size *= k
bytes = long(offset + size*_dbytes)
if mode == 'w+' or (mode == 'r+' and flen < bytes): - 1, 0)
if mode == 'c':
acc = mmap.ACCESS_COPY
elif mode == 'r':
acc = mmap.ACCESS_READ
acc = mmap.ACCESS_WRITE
start = offset - offset % mmap.ALLOCATIONGRANULARITY
bytes -= start
array_offset = offset - start
mm = mmap.mmap(fid.fileno(), bytes, access=acc, offset=start)
self = ndarray.__new__(subtype, shape, dtype=descr, buffer=mm,
offset=array_offset, order=order)
self._mmap = mm
self.offset = offset
self.mode = mode
if isinstance(filename, basestring):
self.filename = os.path.abspath(filename)
elif is_pathlib_path(filename):
self.filename = filename.resolve()
# py3 returns int for TemporaryFile().name
elif (hasattr(filename, "name") and
isinstance(, basestring)):
self.filename = os.path.abspath(
# same as memmap copies (e.g. memmap + 1)
self.filename = None
if own_file:
return self
def __array_finalize__(self, obj):
if hasattr(obj, '_mmap') and np.may_share_memory(self, obj):
self._mmap = obj._mmap
self.filename = obj.filename
self.offset = obj.offset
self.mode = obj.mode
self._mmap = None
self.filename = None
self.offset = None
self.mode = None
def flush(self):
Write any changes in the array to the file on disk.
For further information, see `memmap`.
See Also
if self.base is not None and hasattr(self.base, 'flush'):
def __array_wrap__(self, arr, context=None):
arr = super(memmap, self).__array_wrap__(arr, context)
# Return a memmap if a memmap was given as the output of the
# ufunc. Leave the arr class unchanged if self is not a memmap
# to keep original memmap subclasses behavior
if self is arr or type(self) is not memmap:
return arr
# Return scalar instead of 0d memmap, e.g. for np.sum with
# axis=None
if arr.shape == ():
return arr[()]
# Return ndarray otherwise
return arr.view(np.ndarray)
def __getitem__(self, index):
res = super(memmap, self).__getitem__(index)
if type(res) is memmap and res._mmap is None:
return res.view(type=ndarray)
return res