FM / core

/usr/lib64/python2.6/site-packages/numpy/core/

/usr/lib64/python2.6/site-packages/numpy/core

Name	Size	Mode	Actions
include/	-	0755	rm
lib/	-	0755	rm
tests/	-	0755	rm
arrayprint.py	16812	0644	edit dl rm
arrayprint.pyc	15601	0644	edit dl rm
arrayprint.pyo	15601	0644	edit dl rm
defchararray.py	69952	0644	edit dl rm
defchararray.pyc	78326	0644	edit dl rm
defchararray.pyo	78326	0644	edit dl rm
defmatrix.py	28094	0644	edit dl rm
defmatrix.pyc	30970	0644	edit dl rm
defmatrix.pyo	30970	0644	edit dl rm
fromnumeric.py	71135	0644	edit dl rm
fromnumeric.pyc	75079	0644	edit dl rm
fromnumeric.pyo	75079	0644	edit dl rm
function_base.py	5201	0644	edit dl rm
function_base.pyc	5569	0644	edit dl rm
function_base.pyo	5569	0644	edit dl rm
generate_numpy_api.py	6444	0644	edit dl rm
generate_numpy_api.pyc	6202	0644	edit dl rm
generate_numpy_api.pyo	6141	0644	edit dl rm
getlimits.py	8790	0644	edit dl rm
getlimits.pyc	10068	0644	edit dl rm
getlimits.pyo	10068	0644	edit dl rm
info.py	4635	0644	edit dl rm
info.pyc	4806	0644	edit dl rm
info.pyo	4806	0644	edit dl rm
machar.py	10658	0644	edit dl rm
machar.pyc	8694	0644	edit dl rm
machar.pyo	8694	0644	edit dl rm
memmap.py	9301	0644	edit dl rm
memmap.pyc	9534	0644	edit dl rm
memmap.pyo	9534	0644	edit dl rm
multiarray.so	584824	0755	edit dl rm
multiarray_tests.so	13008	0755	edit dl rm
numeric.py	68280	0644	edit dl rm
numeric.pyc	72033	0644	edit dl rm
numeric.pyo	72033	0644	edit dl rm
numerictypes.py	25088	0644	edit dl rm
numerictypes.pyc	24189	0644	edit dl rm
numerictypes.pyo	24144	0644	edit dl rm
records.py	26838	0644	edit dl rm
records.pyc	24437	0644	edit dl rm
records.pyo	24437	0644	edit dl rm
scalarmath.so	164928	0755	edit dl rm
scons_support.py	8352	0644	edit dl rm
scons_support.pyc	9342	0644	edit dl rm
scons_support.pyo	9342	0644	edit dl rm
setup.py	32450	0644	edit dl rm
setup.pyc	23292	0644	edit dl rm
setup.pyo	23292	0644	edit dl rm
setupscons.py	4516	0644	edit dl rm
setupscons.pyc	4087	0644	edit dl rm
setupscons.pyo	4087	0644	edit dl rm
setup_common.py	10069	0644	edit dl rm
setup_common.pyc	8353	0644	edit dl rm
setup_common.pyo	8353	0644	edit dl rm
shape_base.py	6294	0644	edit dl rm
shape_base.pyc	7123	0644	edit dl rm
shape_base.pyo	7123	0644	edit dl rm
umath.so	325904	0755	edit dl rm
umath_tests.so	12192	0755	edit dl rm
_dotblas.so	20712	0755	edit dl rm
_internal.py	10368	0644	edit dl rm
_internal.pyc	10870	0644	edit dl rm
_internal.pyo	10870	0644	edit dl rm
_mx_datetime_parser.py	33252	0644	edit dl rm
_mx_datetime_parser.pyc	29029	0644	edit dl rm
_mx_datetime_parser.pyo	29029	0644	edit dl rm
_sort.so	90736	0755	edit dl rm
__init__.py	983	0644	edit dl rm
__init__.pyc	1261	0644	edit dl rm
__init__.pyo	1261	0644	edit dl rm

Edit: /usr/lib64/python2.6/site-packages/numpy/core/memmap.py (9301B)

__all__ = ['memmap']

import warnings
from numeric import uint8, ndarray, dtype
import sys

dtypedescr = dtype
valid_filemodes = ["r", "c", "r+", "w+"]
writeable_filemodes = ["r+","w+"]

mode_equivalents = {
    "readonly":"r",
    "copyonwrite":"c",
    "readwrite":"r+",
    "write":"w+"
    }

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.

Parameters
    ----------
    filename : str or file-like object
        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 be a multiple of the byte-size of
        `dtype`. Requires ``shape=None``. The default is 0.
    shape : tuple, optional
        The desired shape of the array. 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: C (row-major) or
        Fortran (column-major).  This only has an effect if the shape is
        greater than 1-D.  The default order is 'C'.

Methods
    -------
    close
        Close the memmap file.
    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.

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

Memory-mapped arrays use the Python memory-map object which
    (prior to Python 2.5) does not allow files to be larger than a
    certain size depending on the platform. This size is always < 2GB
    even on 64-bit systems.

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)

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
        try:
            mode = mode_equivalents[mode]
        except KeyError:
            if mode not in valid_filemodes:
                raise ValueError("mode must be one of %s" % \
                                 (valid_filemodes + mode_equivalents.keys()))

if hasattr(filename,'read'):
            fid = filename
        else:
            fid = file(filename, (mode == 'c' and 'r' or mode)+'b')

if (mode == 'w+') and shape is None:
            raise ValueError, "shape must be given"

fid.seek(0, 2)
        flen = fid.tell()
        descr = dtypedescr(dtype)
        _dbytes = descr.itemsize

if shape is None:
            bytes = flen - offset
            if (bytes % _dbytes):
                fid.close()
                raise ValueError, "Size of available data is not a "\
                      "multiple of data-type size."
            size = bytes // _dbytes
            shape = (size,)
        else:
            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):
            fid.seek(bytes - 1, 0)
            fid.write(chr(0))
            fid.flush()

if mode == 'c':
            acc = mmap.ACCESS_COPY
        elif mode == 'r':
            acc = mmap.ACCESS_READ
        else:
            acc = mmap.ACCESS_WRITE

if sys.version_info[:2] >= (2,6):
            # The offset keyword in mmap.mmap needs Python >= 2.6
            start = offset - offset % mmap.ALLOCATIONGRANULARITY
            bytes -= start
            offset -= start
            mm = mmap.mmap(fid.fileno(), bytes, access=acc, offset=start)
        else:
            mm = mmap.mmap(fid.fileno(), bytes, access=acc)

self = ndarray.__new__(subtype, shape, dtype=descr, buffer=mm,
            offset=offset, order=order)
        self._mmap = mm
        return self

def __array_finalize__(self, obj):
        if hasattr(obj, '_mmap'):
            self._mmap = obj._mmap
        else:
            self._mmap = 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._mmap is not None:
            self._mmap.flush()

def sync(self):
        """This method is deprecated, use `flush`."""
        warnings.warn("Use ``flush``.", DeprecationWarning)
        self.flush()

def _close(self):
        """Close the memmap file.  Only do this when deleting the object."""
        if self.base is self._mmap:
            # The python mmap probably causes flush on close, but
            # we put this here for safety
            self._mmap.flush()
            self._mmap.close()
            self._mmap = None

def close(self):
        """Close the memmap file. Does nothing."""
        warnings.warn("``close`` is deprecated on memmap arrays.  Use del",
                      DeprecationWarning)

def __del__(self):
        # We first check if we are the owner of the mmap, rather than
        # a view, so deleting a view does not call _close
        # on the parent mmap
        if self._mmap is self.base:
            try:
                # First run tell() to see whether file is open
                self._mmap.tell()
            except ValueError:
                pass
            else:
                self._close()

cmd: