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/usr/local/lib64/python3.6/site-packages/numpy/core/

/usr/local/lib64/python3.6/site-packages/numpy/core

Name	Size	Mode	Actions
include/	-	0755	rm
lib/	-	0755	rm
tests/	-	0755	rm
__pycache__/	-	0755	rm
arrayprint.py	59114	0644	edit dl rm
cversions.py	347	0644	edit dl rm
defchararray.py	70041	0644	edit dl rm
einsumfunc.py	50689	0644	edit dl rm
fromnumeric.py	119216	0644	edit dl rm
function_base.py	18034	0644	edit dl rm
generate_numpy_api.py	7022	0644	edit dl rm
getlimits.py	19076	0644	edit dl rm
machar.py	10786	0644	edit dl rm
memmap.py	11562	0644	edit dl rm
multiarray.py	54410	0644	edit dl rm
numeric.py	74615	0644	edit dl rm
numerictypes.py	16563	0644	edit dl rm
overrides.py	7481	0644	edit dl rm
records.py	35083	0644	edit dl rm
setup.py	42409	0644	edit dl rm
setup_common.py	18410	0644	edit dl rm
shape_base.py	29014	0644	edit dl rm
umath.py	2040	0644	edit dl rm
umath_tests.py	389	0644	edit dl rm
_add_newdocs.py	202741	0644	edit dl rm
_asarray.py	9874	0644	edit dl rm
_dtype.py	9820	0644	edit dl rm
_dtype_ctypes.py	3673	0644	edit dl rm
_exceptions.py	6145	0644	edit dl rm
_internal.py	26293	0644	edit dl rm
_methods.py	9148	0644	edit dl rm
_multiarray_tests.cpython-36m-x86_64-linux-gnu.so	167112	0755	edit dl rm
_multiarray_umath.cpython-36m-x86_64-linux-gnu.so	4169248	0755	edit dl rm
_operand_flag_tests.cpython-36m-x86_64-linux-gnu.so	13168	0755	edit dl rm
_rational_tests.cpython-36m-x86_64-linux-gnu.so	59856	0755	edit dl rm
_string_helpers.py	2855	0644	edit dl rm
_struct_ufunc_tests.cpython-36m-x86_64-linux-gnu.so	13272	0755	edit dl rm
_type_aliases.py	8846	0644	edit dl rm
_ufunc_config.py	13821	0644	edit dl rm
_umath_tests.cpython-36m-x86_64-linux-gnu.so	27768	0755	edit dl rm
__init__.py	4335	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/numpy/core/memmap.py (11562B)

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

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