text: """
  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
  