FM / lib

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

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

Name	Size	Mode	Actions
benchmarks/	-	0755	rm
tests/	-	0755	rm
arraysetops.py	13704	0644	edit dl rm
arraysetops.pyc	13380	0644	edit dl rm
arraysetops.pyo	13380	0644	edit dl rm
arrayterator.py	6437	0644	edit dl rm
arrayterator.pyc	7022	0644	edit dl rm
arrayterator.pyo	7022	0644	edit dl rm
financial.py	20872	0644	edit dl rm
financial.pyc	21546	0644	edit dl rm
financial.pyo	21546	0644	edit dl rm
format.py	19309	0644	edit dl rm
format.pyc	17279	0644	edit dl rm
format.pyo	17279	0644	edit dl rm
function_base.py	100524	0644	edit dl rm
function_base.pyc	99169	0644	edit dl rm
function_base.pyo	99169	0644	edit dl rm
getlimits.py	8790	0644	edit dl rm
getlimits.pyc	10053	0644	edit dl rm
getlimits.pyo	10053	0644	edit dl rm
index_tricks.py	26790	0644	edit dl rm
index_tricks.pyc	28008	0644	edit dl rm
index_tricks.pyo	28008	0644	edit dl rm
info.py	6240	0644	edit dl rm
info.pyc	6425	0644	edit dl rm
info.pyo	6425	0644	edit dl rm
io.py	53226	0644	edit dl rm
io.pyc	44102	0644	edit dl rm
io.pyo	44102	0644	edit dl rm
machar.py	10658	0644	edit dl rm
machar.pyc	8688	0644	edit dl rm
machar.pyo	8688	0644	edit dl rm
polynomial.py	35562	0644	edit dl rm
polynomial.pyc	38139	0644	edit dl rm
polynomial.pyo	38139	0644	edit dl rm
recfunctions.py	32038	0644	edit dl rm
recfunctions.pyc	29451	0644	edit dl rm
recfunctions.pyo	29451	0644	edit dl rm
scimath.py	13997	0644	edit dl rm
scimath.pyc	15867	0644	edit dl rm
scimath.pyo	15867	0644	edit dl rm
setup.py	588	0644	edit dl rm
setup.pyc	918	0644	edit dl rm
setup.pyo	918	0644	edit dl rm
setupscons.py	470	0644	edit dl rm
setupscons.pyc	824	0644	edit dl rm
setupscons.pyo	824	0644	edit dl rm
shape_base.py	24387	0644	edit dl rm
shape_base.pyc	25291	0644	edit dl rm
shape_base.pyo	25291	0644	edit dl rm
stride_tricks.py	3972	0644	edit dl rm
stride_tricks.pyc	3863	0644	edit dl rm
stride_tricks.pyo	3863	0644	edit dl rm
twodim_base.py	22946	0644	edit dl rm
twodim_base.pyc	25464	0644	edit dl rm
twodim_base.pyo	25464	0644	edit dl rm
type_check.py	17075	0644	edit dl rm
type_check.pyc	18392	0644	edit dl rm
type_check.pyo	18392	0644	edit dl rm
ufunclike.py	5452	0644	edit dl rm
ufunclike.pyc	6311	0644	edit dl rm
ufunclike.pyo	6311	0644	edit dl rm
user_array.py	7475	0644	edit dl rm
user_array.pyc	15727	0644	edit dl rm
user_array.pyo	15727	0644	edit dl rm
utils.py	34034	0644	edit dl rm
utils.pyc	30422	0644	edit dl rm
utils.pyo	30422	0644	edit dl rm
_compiled_base.so	19720	0755	edit dl rm
_datasource.py	20638	0644	edit dl rm
_datasource.pyc	20986	0644	edit dl rm
_datasource.pyo	20986	0644	edit dl rm
_iotools.py	27712	0644	edit dl rm
_iotools.pyc	26916	0644	edit dl rm
_iotools.pyo	26916	0644	edit dl rm
__init__.py	936	0644	edit dl rm
__init__.pyc	1038	0644	edit dl rm
__init__.pyo	1038	0644	edit dl rm

Edit: /usr/lib64/python2.6/site-packages/numpy/lib/arrayterator.py (6437B)

"""
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

from operator import mul

__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
    --------
    >>> import numpy as np
    >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
    >>> a_itor = np.lib.arrayterator.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):
        for block in self:
            for value in block.flat:
                yield value

@property
    def shape(self):
        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]: raise StopIteration

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

while 1:
            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:
                    stop[i] = self.stop[i]  # read everything along this
                                            # dimension
                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]:
                raise StopIteration

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