334 lines
11 KiB
Python
334 lines
11 KiB
Python
import numpy as np
|
|
from .numeric import uint8, ndarray, dtype
|
|
from numpy.compat import (
|
|
os_fspath, contextlib_nullcontext, is_pathlib_path
|
|
)
|
|
from numpy.core.overrides import set_module
|
|
|
|
__all__ = ['memmap']
|
|
|
|
dtypedescr = dtype
|
|
valid_filemodes = ["r", "c", "r+", "w+"]
|
|
writeable_filemodes = ["r+", "w+"]
|
|
|
|
mode_equivalents = {
|
|
"readonly":"r",
|
|
"copyonwrite":"c",
|
|
"readwrite":"r+",
|
|
"write":"w+"
|
|
}
|
|
|
|
|
|
@set_module('numpy')
|
|
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 the memmap file.
|
|
|
|
|
|
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. |
|
|
+------+-------------------------------------------------------------+
|
|
| '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'.
|
|
|
|
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
|
|
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.
|
|
|
|
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
|
|
--------
|
|
>>> 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
|
|
|
|
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
|
|
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)
|
|
|
|
"""
|
|
|
|
__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
|
|
try:
|
|
mode = mode_equivalents[mode]
|
|
except KeyError as e:
|
|
if mode not in valid_filemodes:
|
|
raise ValueError(
|
|
"mode must be one of {!r} (got {!r})"
|
|
.format(valid_filemodes + list(mode_equivalents.keys()), mode)
|
|
) from None
|
|
|
|
if mode == 'w+' and shape is None:
|
|
raise ValueError("shape must be given")
|
|
|
|
if hasattr(filename, 'read'):
|
|
f_ctx = contextlib_nullcontext(filename)
|
|
else:
|
|
f_ctx = open(os_fspath(filename), ('r' if mode == 'c' else mode)+'b')
|
|
|
|
with f_ctx as fid:
|
|
fid.seek(0, 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,)
|
|
else:
|
|
if not isinstance(shape, tuple):
|
|
shape = (shape,)
|
|
size = np.intp(1) # avoid default choice of np.int_, which might overflow
|
|
for k in shape:
|
|
size *= k
|
|
|
|
bytes = int(offset + size*_dbytes)
|
|
|
|
if mode in ('w+', 'r+') and flen < bytes:
|
|
fid.seek(bytes - 1, 0)
|
|
fid.write(b'\0')
|
|
fid.flush()
|
|
|
|
if mode == 'c':
|
|
acc = mmap.ACCESS_COPY
|
|
elif mode == 'r':
|
|
acc = mmap.ACCESS_READ
|
|
else:
|
|
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 is_pathlib_path(filename):
|
|
# special case - if we were constructed with a pathlib.path,
|
|
# then filename is a path object, not a string
|
|
self.filename = filename.resolve()
|
|
elif hasattr(fid, "name") and isinstance(fid.name, str):
|
|
# py3 returns int for TemporaryFile().name
|
|
self.filename = os.path.abspath(fid.name)
|
|
# same as memmap copies (e.g. memmap + 1)
|
|
else:
|
|
self.filename = None
|
|
|
|
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
|
|
else:
|
|
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`.
|
|
|
|
Parameters
|
|
----------
|
|
None
|
|
|
|
See Also
|
|
--------
|
|
memmap
|
|
|
|
"""
|
|
if self.base is not None and hasattr(self.base, 'flush'):
|
|
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
|