text: """
  Define a simple format for saving numpy arrays to disk with the full
  information about them.
  
  The ``.npy`` format is the standard binary file format in NumPy for
  persisting a *single* arbitrary NumPy array on disk. The format stores all
  of the shape and dtype information necessary to reconstruct the array
  correctly even on another machine with a different architecture.
  The format is designed to be as simple as possible while achieving
  its limited goals.
  
  The ``.npz`` format is the standard format for persisting *multiple* NumPy
  arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
  files, one for each array.
  
  Capabilities
  ------------
  
  - Can represent all NumPy arrays including nested record arrays and
    object arrays.
  
  - Represents the data in its native binary form.
  
  - Supports Fortran-contiguous arrays directly.
  
  - Stores all of the necessary information to reconstruct the array
    including shape and dtype on a machine of a different
    architecture.  Both little-endian and big-endian arrays are
    supported, and a file with little-endian numbers will yield
    a little-endian array on any machine reading the file. The
    types are described in terms of their actual sizes. For example,
    if a machine with a 64-bit C "long int" writes out an array with
    "long ints", a reading machine with 32-bit C "long ints" will yield
    an array with 64-bit integers.
  
  - Is straightforward to reverse engineer. Datasets often live longer than
    the programs that created them. A competent developer should be
    able to create a solution in his preferred programming language to
    read most ``.npy`` files that he has been given without much
    documentation.
  
  - Allows memory-mapping of the data. See `open_memmep`.
  
  - Can be read from a filelike stream object instead of an actual file.
  
  - Stores object arrays, i.e. arrays containing elements that are arbitrary
    Python objects. Files with object arrays are not to be mmapable, but
    can be read and written to disk.
  
  Limitations
  -----------
  
  - Arbitrary subclasses of numpy.ndarray are not completely preserved.
    Subclasses will be accepted for writing, but only the array data will
    be written out. A regular numpy.ndarray object will be created
    upon reading the file.
  
  .. warning::
  
    Due to limitations in the interpretation of structured dtypes, dtypes
    with fields with empty names will have the names replaced by 'f0', 'f1',
    etc. Such arrays will not round-trip through the format entirely
    accurately. The data is intact; only the field names will differ. We are
    working on a fix for this. This fix will not require a change in the
    file format. The arrays with such structures can still be saved and
    restored, and the correct dtype may be restored by using the
    ``loadedarray.view(correct_dtype)`` method.
  
  File extensions
  ---------------
  
  We recommend using the ``.npy`` and ``.npz`` extensions for files saved
  in this format. This is by no means a requirement; applications may wish
  to use these file formats but use an extension specific to the
  application. In the absence of an obvious alternative, however,
  we suggest using ``.npy`` and ``.npz``.
  
  Version numbering
  -----------------
  
  The version numbering of these formats is independent of NumPy version
  numbering. If the format is upgraded, the code in `numpy.io` will still
  be able to read and write Version 1.0 files.
  
  Format Version 1.0
  ------------------
  
  The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
  
  The next 1 byte is an unsigned byte: the major version number of the file
  format, e.g. ``\\x01``.
  
  The next 1 byte is an unsigned byte: the minor version number of the file
  format, e.g. ``\\x00``. Note: the version of the file format is not tied
  to the version of the numpy package.
  
  The next 2 bytes form a little-endian unsigned short int: the length of
  the header data HEADER_LEN.
  
  The next HEADER_LEN bytes form the header data describing the array's
  format. It is an ASCII string which contains a Python literal expression
  of a dictionary. It is terminated by a newline (``\\n``) and padded with
  spaces (``\\x20``) to make the total length of
  ``magic string + 4 + HEADER_LEN`` be evenly divisible by 16 for alignment
  purposes.
  
  The dictionary contains three keys:
  
      "descr" : dtype.descr
        An object that can be passed as an argument to the `numpy.dtype`
        constructor to create the array's dtype.
      "fortran_order" : bool
        Whether the array data is Fortran-contiguous or not. Since
        Fortran-contiguous arrays are a common form of non-C-contiguity,
        we allow them to be written directly to disk for efficiency.
      "shape" : tuple of int
        The shape of the array.
  
  For repeatability and readability, the dictionary keys are sorted in
  alphabetic order. This is for convenience only. A writer SHOULD implement
  this if possible. A reader MUST NOT depend on this.
  
  Following the header comes the array data. If the dtype contains Python
  objects (i.e. ``dtype.hasobject is True``), then the data is a Python
  pickle of the array. Otherwise the data is the contiguous (either C-
  or Fortran-, depending on ``fortran_order``) bytes of the array.
  Consumers can figure out the number of bytes by multiplying the number
  of elements given by the shape (noting that ``shape=()`` means there is
  1 element) by ``dtype.itemsize``.
  
  Notes
  -----
  The ``.npy`` format, including reasons for creating it and a comparison of
  alternatives, is described fully in the "npy-format" NEP.
  
  """
  from __future__ import division, absolute_import, print_function
  
  import numpy
  import sys
  import io
  from numpy.lib.utils import safe_eval
  from numpy.compat import asbytes, isfileobj, long, basestring
  
  if sys.version_info[0] >= 3:
      import pickle
  else:
      import cPickle as pickle
  
  MAGIC_PREFIX = asbytes('\x93NUMPY')
  MAGIC_LEN = len(MAGIC_PREFIX) + 2
  BUFFER_SIZE = 2 ** 18 #size of buffer for reading npz files in bytes
  
  def magic(major, minor):
      """ Return the magic string for the given file format version.
  
      Parameters
      ----------
      major : int in [0, 255]
      minor : int in [0, 255]
  
      Returns
      -------
      magic : str
  
      Raises
      ------
      ValueError if the version cannot be formatted.
      """
      if major < 0 or major > 255:
          raise ValueError("major version must be 0 <= major < 256")
      if minor < 0 or minor > 255:
          raise ValueError("minor version must be 0 <= minor < 256")
      if sys.version_info[0] < 3:
          return MAGIC_PREFIX + chr(major) + chr(minor)
      else:
          return MAGIC_PREFIX + bytes([major, minor])
  
  def read_magic(fp):
      """ Read the magic string to get the version of the file format.
  
      Parameters
      ----------
      fp : filelike object
  
      Returns
      -------
      major : int
      minor : int
      """
      magic_str = _read_bytes(fp, MAGIC_LEN, "magic string")
      if magic_str[:-2] != MAGIC_PREFIX:
          msg = "the magic string is not correct; expected %r, got %r"
          raise ValueError(msg % (MAGIC_PREFIX, magic_str[:-2]))
      if sys.version_info[0] < 3:
          major, minor = map(ord, magic_str[-2:])
      else:
          major, minor = magic_str[-2:]
      return major, minor
  
  def dtype_to_descr(dtype):
      """
      Get a serializable descriptor from the dtype.
  
      The .descr attribute of a dtype object cannot be round-tripped through
      the dtype() constructor. Simple types, like dtype('float32'), have
      a descr which looks like a record array with one field with '' as
      a name. The dtype() constructor interprets this as a request to give
      a default name.  Instead, we construct descriptor that can be passed to
      dtype().
  
      Parameters
      ----------
      dtype : dtype
          The dtype of the array that will be written to disk.
  
      Returns
      -------
      descr : object
          An object that can be passed to `numpy.dtype()` in order to
          replicate the input dtype.
  
      """
      if dtype.names is not None:
          # This is a record array. The .descr is fine.
          # XXX: parts of the record array with an empty name, like padding bytes,
          # still get fiddled with. This needs to be fixed in the C implementation
          # of dtype().
          return dtype.descr
      else:
          return dtype.str
  
  def header_data_from_array_1_0(array):
      """ Get the dictionary of header metadata from a numpy.ndarray.
  
      Parameters
      ----------
      array : numpy.ndarray
  
      Returns
      -------
      d : dict
          This has the appropriate entries for writing its string representation
          to the header of the file.
      """
      d = {}
      d['shape'] = array.shape
      if array.flags.c_contiguous:
          d['fortran_order'] = False
      elif array.flags.f_contiguous:
          d['fortran_order'] = True
      else:
          # Totally non-contiguous data. We will have to make it C-contiguous
          # before writing. Note that we need to test for C_CONTIGUOUS first
          # because a 1-D array is both C_CONTIGUOUS and F_CONTIGUOUS.
          d['fortran_order'] = False
  
      d['descr'] = dtype_to_descr(array.dtype)
      return d
  
  def write_array_header_1_0(fp, d):
      """ Write the header for an array using the 1.0 format.
  
      Parameters
      ----------
      fp : filelike object
      d : dict
          This has the appropriate entries for writing its string representation
          to the header of the file.
      """
      import struct
      header = ["{"]
      for key, value in sorted(d.items()):
          # Need to use repr here, since we eval these when reading
          header.append("'%s': %s, " % (key, repr(value)))
      header.append("}")
      header = "".join(header)
      # Pad the header with spaces and a final newline such that the magic
      # string, the header-length short and the header are aligned on a 16-byte
      # boundary.  Hopefully, some system, possibly memory-mapping, can take
      # advantage of our premature optimization.
      current_header_len = MAGIC_LEN + 2 + len(header) + 1  # 1 for the newline
      topad = 16 - (current_header_len % 16)
      header = asbytes(header + ' '*topad + '\n')
      if len(header) >= (256*256):
          raise ValueError("header does not fit inside %s bytes" % (256*256))
      header_len_str = struct.pack('<H', len(header))
      fp.write(header_len_str)
      fp.write(header)
  
  def read_array_header_1_0(fp):
      """
      Read an array header from a filelike object using the 1.0 file format
      version.
  
      This will leave the file object located just after the header.
  
      Parameters
      ----------
      fp : filelike object
          A file object or something with a `.read()` method like a file.
  
      Returns
      -------
      shape : tuple of int
          The shape of the array.
      fortran_order : bool
          The array data will be written out directly if it is either C-contiguous
          or Fortran-contiguous. Otherwise, it will be made contiguous before
          writing it out.
      dtype : dtype
          The dtype of the file's data.
  
      Raises
      ------
      ValueError
          If the data is invalid.
  
      """
      # Read an unsigned, little-endian short int which has the length of the
      # header.
      import struct
      hlength_str = _read_bytes(fp, 2, "array header length")
      header_length = struct.unpack('<H', hlength_str)[0]
      header = _read_bytes(fp, header_length, "array header")
  
      # The header is a pretty-printed string representation of a literal Python
      # dictionary with trailing newlines padded to a 16-byte boundary. The keys
      # are strings.
      #   "shape" : tuple of int
      #   "fortran_order" : bool
      #   "descr" : dtype.descr
      try:
          d = safe_eval(header)
      except SyntaxError as e:
          msg = "Cannot parse header: %r\nException: %r"
          raise ValueError(msg % (header, e))
      if not isinstance(d, dict):
          msg = "Header is not a dictionary: %r"
          raise ValueError(msg % d)
      keys = sorted(d.keys())
      if keys != ['descr', 'fortran_order', 'shape']:
          msg = "Header does not contain the correct keys: %r"
          raise ValueError(msg % (keys,))
  
      # Sanity-check the values.
      if (not isinstance(d['shape'], tuple) or
          not numpy.all([isinstance(x, (int, long)) for x in d['shape']])):
          msg = "shape is not valid: %r"
          raise ValueError(msg % (d['shape'],))
      if not isinstance(d['fortran_order'], bool):
          msg = "fortran_order is not a valid bool: %r"
          raise ValueError(msg % (d['fortran_order'],))
      try:
          dtype = numpy.dtype(d['descr'])
      except TypeError as e:
          msg = "descr is not a valid dtype descriptor: %r"
          raise ValueError(msg % (d['descr'],))
  
      return d['shape'], d['fortran_order'], dtype
  
  def write_array(fp, array, version=(1, 0)):
      """
      Write an array to an NPY file, including a header.
  
      If the array is neither C-contiguous nor Fortran-contiguous AND the
      file_like object is not a real file object, this function will have to
      copy data in memory.
  
      Parameters
      ----------
      fp : file_like object
          An open, writable file object, or similar object with a ``.write()``
          method.
      array : ndarray
          The array to write to disk.
      version : (int, int), optional
          The version number of the format.  Default: (1, 0)
  
      Raises
      ------
      ValueError
          If the array cannot be persisted.
      Various other errors
          If the array contains Python objects as part of its dtype, the
          process of pickling them may raise various errors if the objects
          are not picklable.
  
      """
      if version != (1, 0):
          msg = "we only support format version (1,0), not %s"
          raise ValueError(msg % (version,))
      fp.write(magic(*version))
      write_array_header_1_0(fp, header_data_from_array_1_0(array))
      if array.dtype.hasobject:
          # We contain Python objects so we cannot write out the data directly.
          # Instead, we will pickle it out with version 2 of the pickle protocol.
          pickle.dump(array, fp, protocol=2)
      elif array.flags.f_contiguous and not array.flags.c_contiguous:
          if isfileobj(fp):
              array.T.tofile(fp)
          else:
              fp.write(array.T.tostring('C'))
      else:
          if isfileobj(fp):
              array.tofile(fp)
          else:
              # XXX: We could probably chunk this using something like
              # arrayterator.
              fp.write(array.tostring('C'))
  
  def read_array(fp):
      """
      Read an array from an NPY file.
  
      Parameters
      ----------
      fp : file_like object
          If this is not a real file object, then this may take extra memory
          and time.
  
      Returns
      -------
      array : ndarray
          The array from the data on disk.
  
      Raises
      ------
      ValueError
          If the data is invalid.
  
      """
      version = read_magic(fp)
      if version != (1, 0):
          msg = "only support version (1,0) of file format, not %r"
          raise ValueError(msg % (version,))
      shape, fortran_order, dtype = read_array_header_1_0(fp)
      if len(shape) == 0:
          count = 1
      else:
          count = numpy.multiply.reduce(shape)
  
      # Now read the actual data.
      if dtype.hasobject:
          # The array contained Python objects. We need to unpickle the data.
          array = pickle.load(fp)
      else:
          if isfileobj(fp):
              # We can use the fast fromfile() function.
              array = numpy.fromfile(fp, dtype=dtype, count=count)
          else:
              # This is not a real file. We have to read it the memory-intensive
              # way.
              # crc32 module fails on reads greater than 2 ** 32 bytes, breaking
              # large reads from gzip streams. Chunk reads to BUFFER_SIZE bytes to
              # avoid issue and reduce memory overhead of the read. In
              # non-chunked case count < max_read_count, so only one read is
              # performed.
  
              max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, dtype.itemsize)
  
              array = numpy.empty(count, dtype=dtype)
              for i in range(0, count, max_read_count):
                  read_count = min(max_read_count, count - i)
                  read_size = int(read_count * dtype.itemsize)
                  data = _read_bytes(fp, read_size, "array data")
                  array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,
                                                           count=read_count)
  
          if fortran_order:
              array.shape = shape[::-1]
              array = array.transpose()
          else:
              array.shape = shape
  
      return array
  
  
  def open_memmap(filename, mode='r+', dtype=None, shape=None,
                  fortran_order=False, version=(1, 0)):
      """
      Open a .npy file as a memory-mapped array.
  
      This may be used to read an existing file or create a new one.
  
      Parameters
      ----------
      filename : str
          The name of the file on disk.  This may *not* be a file-like
          object.
      mode : str, optional
          The mode in which to open the file; the default is 'r+'.  In
          addition to the standard file modes, 'c' is also accepted to
          mean "copy on write."  See `memmap` for the available mode strings.
      dtype : data-type, optional
          The data type of the array if we are creating a new file in "write"
          mode, if not, `dtype` is ignored.  The default value is None,
          which results in a data-type of `float64`.
      shape : tuple of int
          The shape of the array if we are creating a new file in "write"
          mode, in which case this parameter is required.  Otherwise, this
          parameter is ignored and is thus optional.
      fortran_order : bool, optional
          Whether the array should be Fortran-contiguous (True) or
          C-contiguous (False, the default) if we are creating a new file
          in "write" mode.
      version : tuple of int (major, minor)
          If the mode is a "write" mode, then this is the version of the file
          format used to create the file.  Default: (1,0)
  
      Returns
      -------
      marray : memmap
          The memory-mapped array.
  
      Raises
      ------
      ValueError
          If the data or the mode is invalid.
      IOError
          If the file is not found or cannot be opened correctly.
  
      See Also
      --------
      memmap
  
      """
      if not isinstance(filename, basestring):
          raise ValueError("Filename must be a string.  Memmap cannot use" \
                           " existing file handles.")
  
      if 'w' in mode:
          # We are creating the file, not reading it.
          # Check if we ought to create the file.
          if version != (1, 0):
              msg = "only support version (1,0) of file format, not %r"
              raise ValueError(msg % (version,))
          # Ensure that the given dtype is an authentic dtype object rather than
          # just something that can be interpreted as a dtype object.
          dtype = numpy.dtype(dtype)
          if dtype.hasobject:
              msg = "Array can't be memory-mapped: Python objects in dtype."
              raise ValueError(msg)
          d = dict(
              descr=dtype_to_descr(dtype),
              fortran_order=fortran_order,
              shape=shape,
          )
          # If we got here, then it should be safe to create the file.
          fp = open(filename, mode+'b')
          try:
              fp.write(magic(*version))
              write_array_header_1_0(fp, d)
              offset = fp.tell()
          finally:
              fp.close()
      else:
          # Read the header of the file first.
          fp = open(filename, 'rb')
          try:
              version = read_magic(fp)
              if version != (1, 0):
                  msg = "only support version (1,0) of file format, not %r"
                  raise ValueError(msg % (version,))
              shape, fortran_order, dtype = read_array_header_1_0(fp)
              if dtype.hasobject:
                  msg = "Array can't be memory-mapped: Python objects in dtype."
                  raise ValueError(msg)
              offset = fp.tell()
          finally:
              fp.close()
  
      if fortran_order:
          order = 'F'
      else:
          order = 'C'
  
      # We need to change a write-only mode to a read-write mode since we've
      # already written data to the file.
      if mode == 'w+':
          mode = 'r+'
  
      marray = numpy.memmap(filename, dtype=dtype, shape=shape, order=order,
          mode=mode, offset=offset)
  
      return marray
  
  
  def _read_bytes(fp, size, error_template="ran out of data"):
      """
      Read from file-like object until size bytes are read.
      Raises ValueError if not EOF is encountered before size bytes are read.
      Non-blocking objects only supported if they derive from io objects.
  
      Required as e.g. ZipExtFile in python 2.6 can return less data than
      requested.
      """
      data = bytes()
      while True:
          # io files (default in python3) return None or raise on would-block,
          # python2 file will truncate, probably nothing can be done about that.
          # note that regular files can't be non-blocking
          try:
              r = fp.read(size - len(data))
              data += r
              if len(r) == 0 or len(data) == size:
                  break
          except io.BlockingIOError:
              pass
      if len(data) != size:
          msg = "EOF: reading %s, expected %d bytes got %d"
          raise ValueError(msg %(error_template, size, len(data)))
      else:
          return data
  