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Edit File: memmap.py
from contextlib import nullcontext import numpy as np from .numeric import uint8, ndarray, dtype from numpy.compat import os_fspath, 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. 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. | +------+-------------------------------------------------------------+ | '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. 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 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) """ __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 = 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().__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().__getitem__(index) if type(res) is memmap and res._mmap is None: return res.view(type=ndarray) return res