FM / lib

/opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/

/opt/alt/python27/lib64/python2.7/site-packages/numpy/lib

Name	Size	Mode	Actions
tests/	-	0755	rm
arraypad.py	52079	0644	edit dl rm
arraypad.pyc	49560	0644	edit dl rm
arraypad.pyo	49560	0644	edit dl rm
arraysetops.py	13815	0644	edit dl rm
arraysetops.pyc	13687	0644	edit dl rm
arraysetops.pyo	13687	0644	edit dl rm
arrayterator.py	7203	0644	edit dl rm
arrayterator.pyc	7868	0644	edit dl rm
arrayterator.pyo	7868	0644	edit dl rm
financial.py	23843	0644	edit dl rm
financial.pyc	24785	0644	edit dl rm
financial.pyo	24785	0644	edit dl rm
format.py	28234	0644	edit dl rm
format.pyc	25628	0644	edit dl rm
format.pyo	25628	0644	edit dl rm
function_base.py	150516	0644	edit dl rm
function_base.pyc	136285	0644	edit dl rm
function_base.pyo	136285	0644	edit dl rm
index_tricks.py	26204	0644	edit dl rm
index_tricks.pyc	27361	0644	edit dl rm
index_tricks.pyo	27361	0644	edit dl rm
info.py	6501	0644	edit dl rm
info.pyc	6755	0644	edit dl rm
info.pyo	6755	0644	edit dl rm
nanfunctions.py	46486	0644	edit dl rm
nanfunctions.pyc	45340	0644	edit dl rm
nanfunctions.pyo	45340	0644	edit dl rm
npyio.py	71412	0644	edit dl rm
npyio.pyc	58376	0644	edit dl rm
npyio.pyo	58376	0644	edit dl rm
polynomial.py	38012	0644	edit dl rm
polynomial.pyc	40460	0644	edit dl rm
polynomial.pyo	40460	0644	edit dl rm
recfunctions.py	35012	0644	edit dl rm
recfunctions.pyc	31157	0644	edit dl rm
recfunctions.pyo	31157	0644	edit dl rm
scimath.py	14085	0644	edit dl rm
scimath.pyc	16169	0644	edit dl rm
scimath.pyo	16169	0644	edit dl rm
setup.py	379	0644	edit dl rm
setup.pyc	755	0644	edit dl rm
setup.pyo	755	0644	edit dl rm
shape_base.py	25574	0644	edit dl rm
shape_base.pyc	27079	0644	edit dl rm
shape_base.pyo	27079	0644	edit dl rm
stride_tricks.py	6827	0644	edit dl rm
stride_tricks.pyc	7096	0644	edit dl rm
stride_tricks.pyo	7096	0644	edit dl rm
twodim_base.py	26904	0644	edit dl rm
twodim_base.pyc	29858	0644	edit dl rm
twodim_base.pyo	29858	0644	edit dl rm
type_check.py	15799	0644	edit dl rm
type_check.pyc	17299	0644	edit dl rm
type_check.pyo	17299	0644	edit dl rm
ufunclike.py	4844	0644	edit dl rm
ufunclike.pyc	5643	0644	edit dl rm
ufunclike.pyo	5643	0644	edit dl rm
user_array.py	8000	0644	edit dl rm
user_array.pyc	16669	0644	edit dl rm
user_array.pyo	16669	0644	edit dl rm
utils.py	34996	0644	edit dl rm
utils.pyc	30947	0644	edit dl rm
utils.pyo	30947	0644	edit dl rm
_datasource.py	21266	0644	edit dl rm
_datasource.pyc	21830	0644	edit dl rm
_datasource.pyo	21830	0644	edit dl rm
_iotools.py	32062	0644	edit dl rm
_iotools.pyc	30226	0644	edit dl rm
_iotools.pyo	30226	0644	edit dl rm
_version.py	4867	0644	edit dl rm
_version.pyc	5781	0644	edit dl rm
_version.pyo	5781	0644	edit dl rm
__init__.py	1189	0644	edit dl rm
__init__.pyc	1324	0644	edit dl rm
__init__.pyo	1324	0644	edit dl rm

Edit: /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arrayterator.py (7203B)

"""
A buffered iterator for big arrays.

This module solves the problem of iterating over a big file-based array
without having to read it into memory. The `Arrayterator` class wraps
an array object, and when iterated it will return sub-arrays with at most
a user-specified number of elements.

"""
from __future__ import division, absolute_import, print_function

from operator import mul
from functools import reduce

from numpy.compat import long

__all__ = ['Arrayterator']

class Arrayterator(object):
    """
    Buffered iterator for big arrays.

`Arrayterator` creates a buffered iterator for reading big arrays in small
    contiguous blocks. The class is useful for objects stored in the
    file system. It allows iteration over the object *without* reading
    everything in memory; instead, small blocks are read and iterated over.

`Arrayterator` can be used with any object that supports multidimensional
    slices. This includes NumPy arrays, but also variables from
    Scientific.IO.NetCDF or pynetcdf for example.

Parameters
    ----------
    var : array_like
        The object to iterate over.
    buf_size : int, optional
        The buffer size. If `buf_size` is supplied, the maximum amount of
        data that will be read into memory is `buf_size` elements.
        Default is None, which will read as many element as possible
        into memory.

Attributes
    ----------
    var
    buf_size
    start
    stop
    step
    shape
    flat

See Also
    --------
    ndenumerate : Multidimensional array iterator.
    flatiter : Flat array iterator.
    memmap : Create a memory-map to an array stored in a binary file on disk.

Notes
    -----
    The algorithm works by first finding a "running dimension", along which
    the blocks will be extracted. Given an array of dimensions
    ``(d1, d2, ..., dn)``, e.g. if `buf_size` is smaller than ``d1``, the
    first dimension will be used. If, on the other hand,
    ``d1 < buf_size < d1*d2`` the second dimension will be used, and so on.
    Blocks are extracted along this dimension, and when the last block is
    returned the process continues from the next dimension, until all
    elements have been read.

Examples
    --------
    >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
    >>> a_itor = np.lib.Arrayterator(a, 2)
    >>> a_itor.shape
    (3, 4, 5, 6)

Now we can iterate over ``a_itor``, and it will return arrays of size
    two. Since `buf_size` was smaller than any dimension, the first
    dimension will be iterated over first:

>>> for subarr in a_itor:
    ...     if not subarr.all():
    ...         print(subarr, subarr.shape)
    ...
    [[[[0 1]]]] (1, 1, 1, 2)

"""

def __init__(self, var, buf_size=None):
        self.var = var
        self.buf_size = buf_size

self.start = [0 for dim in var.shape]
        self.stop = [dim for dim in var.shape]
        self.step = [1 for dim in var.shape]

def __getattr__(self, attr):
        return getattr(self.var, attr)

def __getitem__(self, index):
        """
        Return a new arrayterator.

"""
        # Fix index, handling ellipsis and incomplete slices.
        if not isinstance(index, tuple):
            index = (index,)
        fixed = []
        length, dims = len(index), len(self.shape)
        for slice_ in index:
            if slice_ is Ellipsis:
                fixed.extend([slice(None)] * (dims-length+1))
                length = len(fixed)
            elif isinstance(slice_, (int, long)):
                fixed.append(slice(slice_, slice_+1, 1))
            else:
                fixed.append(slice_)
        index = tuple(fixed)
        if len(index) < dims:
            index += (slice(None),) * (dims-len(index))

# Return a new arrayterator object.
        out = self.__class__(self.var, self.buf_size)
        for i, (start, stop, step, slice_) in enumerate(
                zip(self.start, self.stop, self.step, index)):
            out.start[i] = start + (slice_.start or 0)
            out.step[i] = step * (slice_.step or 1)
            out.stop[i] = start + (slice_.stop or stop-start)
            out.stop[i] = min(stop, out.stop[i])
        return out

def __array__(self):
        """
        Return corresponding data.

"""
        slice_ = tuple(slice(*t) for t in zip(
                self.start, self.stop, self.step))
        return self.var[slice_]

@property
    def flat(self):
        """
        A 1-D flat iterator for Arrayterator objects.

This iterator returns elements of the array to be iterated over in
        `Arrayterator` one by one. It is similar to `flatiter`.

See Also
        --------
        Arrayterator
        flatiter

Examples
        --------
        >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
        >>> a_itor = np.lib.Arrayterator(a, 2)

>>> for subarr in a_itor.flat:
        ...     if not subarr:
        ...         print(subarr, type(subarr))
        ...
        0 <type 'numpy.int32'>

"""
        for block in self:
            for value in block.flat:
                yield value

@property
    def shape(self):
        """
        The shape of the array to be iterated over.

For an example, see `Arrayterator`.

"""
        return tuple(((stop-start-1)//step+1) for start, stop, step in
                zip(self.start, self.stop, self.step))

def __iter__(self):
        # Skip arrays with degenerate dimensions
        if [dim for dim in self.shape if dim <= 0]:
            return

start = self.start[:]
        stop = self.stop[:]
        step = self.step[:]
        ndims = len(self.var.shape)

while True:
            count = self.buf_size or reduce(mul, self.shape)

# iterate over each dimension, looking for the
            # running dimension (ie, the dimension along which
            # the blocks will be built from)
            rundim = 0
            for i in range(ndims-1, -1, -1):
                # if count is zero we ran out of elements to read
                # along higher dimensions, so we read only a single position
                if count == 0:
                    stop[i] = start[i]+1
                elif count <= self.shape[i]:
                    # limit along this dimension
                    stop[i] = start[i] + count*step[i]
                    rundim = i
                else:
                    # read everything along this dimension
                    stop[i] = self.stop[i]
                stop[i] = min(self.stop[i], stop[i])
                count = count//self.shape[i]

# yield a block
            slice_ = tuple(slice(*t) for t in zip(start, stop, step))
            yield self.var[slice_]

# Update start position, taking care of overflow to
            # other dimensions
            start[rundim] = stop[rundim]  # start where we stopped
            for i in range(ndims-1, 0, -1):
                if start[i] >= self.stop[i]:
                    start[i] = self.start[i]
                    start[i-1] += self.step[i-1]
            if start[0] >= self.stop[0]:
                return

cmd: