content: from __future__ import division, absolute_import, print_function
  
  import sys
  import os
  import re
  import itertools
  import warnings
  import weakref
  from operator import itemgetter, index as opindex
  
  import numpy as np
  from . import format
  from ._datasource import DataSource
  from numpy.core.multiarray import packbits, unpackbits
  from ._iotools import (
      LineSplitter, NameValidator, StringConverter, ConverterError,
      ConverterLockError, ConversionWarning, _is_string_like,
      has_nested_fields, flatten_dtype, easy_dtype, _bytes_to_name
      )
  
  from numpy.compat import (
      asbytes, asstr, asbytes_nested, bytes, basestring, unicode, is_pathlib_path
      )
  
  if sys.version_info[0] >= 3:
      import pickle
  else:
      import cPickle as pickle
      from future_builtins import map
  
  loads = pickle.loads
  
  __all__ = [
      'savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt',
      'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez',
      'savez_compressed', 'packbits', 'unpackbits', 'fromregex', 'DataSource'
      ]
  
  
  class BagObj(object):
      """
      BagObj(obj)
  
      Convert attribute look-ups to getitems on the object passed in.
  
      Parameters
      ----------
      obj : class instance
          Object on which attribute look-up is performed.
  
      Examples
      --------
      >>> from numpy.lib.npyio import BagObj as BO
      >>> class BagDemo(object):
      ...     def __getitem__(self, key): # An instance of BagObj(BagDemo)
      ...                                 # will call this method when any
      ...                                 # attribute look-up is required
      ...         result = "Doesn't matter what you want, "
      ...         return result + "you're gonna get this"
      ...
      >>> demo_obj = BagDemo()
      >>> bagobj = BO(demo_obj)
      >>> bagobj.hello_there
      "Doesn't matter what you want, you're gonna get this"
      >>> bagobj.I_can_be_anything
      "Doesn't matter what you want, you're gonna get this"
  
      """
  
      def __init__(self, obj):
          # Use weakref to make NpzFile objects collectable by refcount
          self._obj = weakref.proxy(obj)
  
      def __getattribute__(self, key):
          try:
              return object.__getattribute__(self, '_obj')[key]
          except KeyError:
              raise AttributeError(key)
  
      def __dir__(self):
          """
          Enables dir(bagobj) to list the files in an NpzFile.
  
          This also enables tab-completion in an interpreter or IPython.
          """
          return object.__getattribute__(self, '_obj').keys()
  
  
  def zipfile_factory(file, *args, **kwargs):
      """
      Create a ZipFile.
  
      Allows for Zip64, and the `file` argument can accept file, str, or
      pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile
      constructor.
      """
      if is_pathlib_path(file):
          file = str(file)
      import zipfile
      kwargs['allowZip64'] = True
      return zipfile.ZipFile(file, *args, **kwargs)
  
  
  class NpzFile(object):
      """
      NpzFile(fid)
  
      A dictionary-like object with lazy-loading of files in the zipped
      archive provided on construction.
  
      `NpzFile` is used to load files in the NumPy ``.npz`` data archive
      format. It assumes that files in the archive have a ``.npy`` extension,
      other files are ignored.
  
      The arrays and file strings are lazily loaded on either
      getitem access using ``obj['key']`` or attribute lookup using
      ``obj.f.key``. A list of all files (without ``.npy`` extensions) can
      be obtained with ``obj.files`` and the ZipFile object itself using
      ``obj.zip``.
  
      Attributes
      ----------
      files : list of str
          List of all files in the archive with a ``.npy`` extension.
      zip : ZipFile instance
          The ZipFile object initialized with the zipped archive.
      f : BagObj instance
          An object on which attribute can be performed as an alternative
          to getitem access on the `NpzFile` instance itself.
      allow_pickle : bool, optional
          Allow loading pickled data. Default: True
      pickle_kwargs : dict, optional
          Additional keyword arguments to pass on to pickle.load.
          These are only useful when loading object arrays saved on
          Python 2 when using Python 3.
  
      Parameters
      ----------
      fid : file or str
          The zipped archive to open. This is either a file-like object
          or a string containing the path to the archive.
      own_fid : bool, optional
          Whether NpzFile should close the file handle.
          Requires that `fid` is a file-like object.
  
      Examples
      --------
      >>> from tempfile import TemporaryFile
      >>> outfile = TemporaryFile()
      >>> x = np.arange(10)
      >>> y = np.sin(x)
      >>> np.savez(outfile, x=x, y=y)
      >>> outfile.seek(0)
  
      >>> npz = np.load(outfile)
      >>> isinstance(npz, np.lib.io.NpzFile)
      True
      >>> npz.files
      ['y', 'x']
      >>> npz['x']  # getitem access
      array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
      >>> npz.f.x  # attribute lookup
      array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
  
      """
  
      def __init__(self, fid, own_fid=False, allow_pickle=True,
                   pickle_kwargs=None):
          # Import is postponed to here since zipfile depends on gzip, an
          # optional component of the so-called standard library.
          _zip = zipfile_factory(fid)
          self._files = _zip.namelist()
          self.files = []
          self.allow_pickle = allow_pickle
          self.pickle_kwargs = pickle_kwargs
          for x in self._files:
              if x.endswith('.npy'):
                  self.files.append(x[:-4])
              else:
                  self.files.append(x)
          self.zip = _zip
          self.f = BagObj(self)
          if own_fid:
              self.fid = fid
          else:
              self.fid = None
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
      def close(self):
          """
          Close the file.
  
          """
          if self.zip is not None:
              self.zip.close()
              self.zip = None
          if self.fid is not None:
              self.fid.close()
              self.fid = None
          self.f = None  # break reference cycle
  
      def __del__(self):
          self.close()
  
      def __getitem__(self, key):
          # FIXME: This seems like it will copy strings around
          #   more than is strictly necessary.  The zipfile
          #   will read the string and then
          #   the format.read_array will copy the string
          #   to another place in memory.
          #   It would be better if the zipfile could read
          #   (or at least uncompress) the data
          #   directly into the array memory.
          member = 0
          if key in self._files:
              member = 1
          elif key in self.files:
              member = 1
              key += '.npy'
          if member:
              bytes = self.zip.open(key)
              magic = bytes.read(len(format.MAGIC_PREFIX))
              bytes.close()
              if magic == format.MAGIC_PREFIX:
                  bytes = self.zip.open(key)
                  return format.read_array(bytes,
                                           allow_pickle=self.allow_pickle,
                                           pickle_kwargs=self.pickle_kwargs)
              else:
                  return self.zip.read(key)
          else:
              raise KeyError("%s is not a file in the archive" % key)
  
      def __iter__(self):
          return iter(self.files)
  
      def items(self):
          """
          Return a list of tuples, with each tuple (filename, array in file).
  
          """
          return [(f, self[f]) for f in self.files]
  
      def iteritems(self):
          """Generator that returns tuples (filename, array in file)."""
          for f in self.files:
              yield (f, self[f])
  
      def keys(self):
          """Return files in the archive with a ``.npy`` extension."""
          return self.files
  
      def iterkeys(self):
          """Return an iterator over the files in the archive."""
          return self.__iter__()
  
      def __contains__(self, key):
          return self.files.__contains__(key)
  
  
  def load(file, mmap_mode=None, allow_pickle=True, fix_imports=True,
           encoding='ASCII'):
      """
      Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.
  
      Parameters
      ----------
      file : file-like object, string, or pathlib.Path
          The file to read. File-like objects must support the
          ``seek()`` and ``read()`` methods. Pickled files require that the
          file-like object support the ``readline()`` method as well.
      mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
          If not None, then memory-map the file, using the given mode (see
          `numpy.memmap` for a detailed description of the modes).  A
          memory-mapped array is kept on disk. However, it can be accessed
          and sliced like any ndarray.  Memory mapping is especially useful
          for accessing small fragments of large files without reading the
          entire file into memory.
      allow_pickle : bool, optional
          Allow loading pickled object arrays stored in npy files. Reasons for
          disallowing pickles include security, as loading pickled data can
          execute arbitrary code. If pickles are disallowed, loading object
          arrays will fail.
          Default: True
      fix_imports : bool, optional
          Only useful when loading Python 2 generated pickled files on Python 3,
          which includes npy/npz files containing object arrays. If `fix_imports`
          is True, pickle will try to map the old Python 2 names to the new names
          used in Python 3.
      encoding : str, optional
          What encoding to use when reading Python 2 strings. Only useful when
          loading Python 2 generated pickled files on Python 3, which includes
          npy/npz files containing object arrays. Values other than 'latin1',
          'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
          data. Default: 'ASCII'
  
      Returns
      -------
      result : array, tuple, dict, etc.
          Data stored in the file. For ``.npz`` files, the returned instance
          of NpzFile class must be closed to avoid leaking file descriptors.
  
      Raises
      ------
      IOError
          If the input file does not exist or cannot be read.
      ValueError
          The file contains an object array, but allow_pickle=False given.
  
      See Also
      --------
      save, savez, savez_compressed, loadtxt
      memmap : Create a memory-map to an array stored in a file on disk.
      lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
  
      Notes
      -----
      - If the file contains pickle data, then whatever object is stored
        in the pickle is returned.
      - If the file is a ``.npy`` file, then a single array is returned.
      - If the file is a ``.npz`` file, then a dictionary-like object is
        returned, containing ``{filename: array}`` key-value pairs, one for
        each file in the archive.
      - If the file is a ``.npz`` file, the returned value supports the
        context manager protocol in a similar fashion to the open function::
  
          with load('foo.npz') as data:
              a = data['a']
  
        The underlying file descriptor is closed when exiting the 'with'
        block.
  
      Examples
      --------
      Store data to disk, and load it again:
  
      >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
      >>> np.load('/tmp/123.npy')
      array([[1, 2, 3],
             [4, 5, 6]])
  
      Store compressed data to disk, and load it again:
  
      >>> a=np.array([[1, 2, 3], [4, 5, 6]])
      >>> b=np.array([1, 2])
      >>> np.savez('/tmp/123.npz', a=a, b=b)
      >>> data = np.load('/tmp/123.npz')
      >>> data['a']
      array([[1, 2, 3],
             [4, 5, 6]])
      >>> data['b']
      array([1, 2])
      >>> data.close()
  
      Mem-map the stored array, and then access the second row
      directly from disk:
  
      >>> X = np.load('/tmp/123.npy', mmap_mode='r')
      >>> X[1, :]
      memmap([4, 5, 6])
  
      """
      own_fid = False
      if isinstance(file, basestring):
          fid = open(file, "rb")
          own_fid = True
      elif is_pathlib_path(file):
          fid = file.open("rb")
          own_fid = True
      else:
          fid = file
  
      if encoding not in ('ASCII', 'latin1', 'bytes'):
          # The 'encoding' value for pickle also affects what encoding
          # the serialized binary data of NumPy arrays is loaded
          # in. Pickle does not pass on the encoding information to
          # NumPy. The unpickling code in numpy.core.multiarray is
          # written to assume that unicode data appearing where binary
          # should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'.
          #
          # Other encoding values can corrupt binary data, and we
          # purposefully disallow them. For the same reason, the errors=
          # argument is not exposed, as values other than 'strict'
          # result can similarly silently corrupt numerical data.
          raise ValueError("encoding must be 'ASCII', 'latin1', or 'bytes'")
  
      if sys.version_info[0] >= 3:
          pickle_kwargs = dict(encoding=encoding, fix_imports=fix_imports)
      else:
          # Nothing to do on Python 2
          pickle_kwargs = {}
  
      try:
          # Code to distinguish from NumPy binary files and pickles.
          _ZIP_PREFIX = b'PK\x03\x04'
          N = len(format.MAGIC_PREFIX)
          magic = fid.read(N)
          # If the file size is less than N, we need to make sure not
          # to seek past the beginning of the file
          fid.seek(-min(N, len(magic)), 1)  # back-up
          if magic.startswith(_ZIP_PREFIX):
              # zip-file (assume .npz)
              # Transfer file ownership to NpzFile
              tmp = own_fid
              own_fid = False
              return NpzFile(fid, own_fid=tmp, allow_pickle=allow_pickle,
                             pickle_kwargs=pickle_kwargs)
          elif magic == format.MAGIC_PREFIX:
              # .npy file
              if mmap_mode:
                  return format.open_memmap(file, mode=mmap_mode)
              else:
                  return format.read_array(fid, allow_pickle=allow_pickle,
                                           pickle_kwargs=pickle_kwargs)
          else:
              # Try a pickle
              if not allow_pickle:
                  raise ValueError("allow_pickle=False, but file does not contain "
                                   "non-pickled data")
              try:
                  return pickle.load(fid, **pickle_kwargs)
              except Exception:
                  raise IOError(
                      "Failed to interpret file %s as a pickle" % repr(file))
      finally:
          if own_fid:
              fid.close()
  
  
  def save(file, arr, allow_pickle=True, fix_imports=True):
      """
      Save an array to a binary file in NumPy ``.npy`` format.
  
      Parameters
      ----------
      file : file, str, or pathlib.Path
          File or filename to which the data is saved.  If file is a file-object,
          then the filename is unchanged.  If file is a string or Path, a ``.npy``
          extension will be appended to the file name if it does not already
          have one.
      allow_pickle : bool, optional
          Allow saving object arrays using Python pickles. Reasons for disallowing
          pickles include security (loading pickled data can execute arbitrary
          code) and portability (pickled objects may not be loadable on different
          Python installations, for example if the stored objects require libraries
          that are not available, and not all pickled data is compatible between
          Python 2 and Python 3).
          Default: True
      fix_imports : bool, optional
          Only useful in forcing objects in object arrays on Python 3 to be
          pickled in a Python 2 compatible way. If `fix_imports` is True, pickle
          will try to map the new Python 3 names to the old module names used in
          Python 2, so that the pickle data stream is readable with Python 2.
      arr : array_like
          Array data to be saved.
  
      See Also
      --------
      savez : Save several arrays into a ``.npz`` archive
      savetxt, load
  
      Notes
      -----
      For a description of the ``.npy`` format, see the module docstring
      of `numpy.lib.format` or the NumPy Enhancement Proposal
      http://docs.scipy.org/doc/numpy/neps/npy-format.html
  
      Examples
      --------
      >>> from tempfile import TemporaryFile
      >>> outfile = TemporaryFile()
  
      >>> x = np.arange(10)
      >>> np.save(outfile, x)
  
      >>> outfile.seek(0) # Only needed here to simulate closing & reopening file
      >>> np.load(outfile)
      array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
  
      """
      own_fid = False
      if isinstance(file, basestring):
          if not file.endswith('.npy'):
              file = file + '.npy'
          fid = open(file, "wb")
          own_fid = True
      elif is_pathlib_path(file):
          if not file.name.endswith('.npy'):
              file = file.parent / (file.name + '.npy')
          fid = file.open("wb")
          own_fid = True
      else:
          fid = file
  
      if sys.version_info[0] >= 3:
          pickle_kwargs = dict(fix_imports=fix_imports)
      else:
          # Nothing to do on Python 2
          pickle_kwargs = None
  
      try:
          arr = np.asanyarray(arr)
          format.write_array(fid, arr, allow_pickle=allow_pickle,
                             pickle_kwargs=pickle_kwargs)
      finally:
          if own_fid:
              fid.close()
  
  
  def savez(file, *args, **kwds):
      """
      Save several arrays into a single file in uncompressed ``.npz`` format.
  
      If arguments are passed in with no keywords, the corresponding variable
      names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword
      arguments are given, the corresponding variable names, in the ``.npz``
      file will match the keyword names.
  
      Parameters
      ----------
      file : str or file
          Either the file name (string) or an open file (file-like object)
          where the data will be saved. If file is a string or a Path, the
          ``.npz`` extension will be appended to the file name if it is not
          already there.
      args : Arguments, optional
          Arrays to save to the file. Since it is not possible for Python to
          know the names of the arrays outside `savez`, the arrays will be saved
          with names "arr_0", "arr_1", and so on. These arguments can be any
          expression.
      kwds : Keyword arguments, optional
          Arrays to save to the file. Arrays will be saved in the file with the
          keyword names.
  
      Returns
      -------
      None
  
      See Also
      --------
      save : Save a single array to a binary file in NumPy format.
      savetxt : Save an array to a file as plain text.
      savez_compressed : Save several arrays into a compressed ``.npz`` archive
  
      Notes
      -----
      The ``.npz`` file format is a zipped archive of files named after the
      variables they contain.  The archive is not compressed and each file
      in the archive contains one variable in ``.npy`` format. For a
      description of the ``.npy`` format, see `numpy.lib.format` or the
      NumPy Enhancement Proposal
      http://docs.scipy.org/doc/numpy/neps/npy-format.html
  
      When opening the saved ``.npz`` file with `load` a `NpzFile` object is
      returned. This is a dictionary-like object which can be queried for
      its list of arrays (with the ``.files`` attribute), and for the arrays
      themselves.
  
      Examples
      --------
      >>> from tempfile import TemporaryFile
      >>> outfile = TemporaryFile()
      >>> x = np.arange(10)
      >>> y = np.sin(x)
  
      Using `savez` with \\*args, the arrays are saved with default names.
  
      >>> np.savez(outfile, x, y)
      >>> outfile.seek(0) # Only needed here to simulate closing & reopening file
      >>> npzfile = np.load(outfile)
      >>> npzfile.files
      ['arr_1', 'arr_0']
      >>> npzfile['arr_0']
      array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
  
      Using `savez` with \\**kwds, the arrays are saved with the keyword names.
  
      >>> outfile = TemporaryFile()
      >>> np.savez(outfile, x=x, y=y)
      >>> outfile.seek(0)
      >>> npzfile = np.load(outfile)
      >>> npzfile.files
      ['y', 'x']
      >>> npzfile['x']
      array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
  
      """
      _savez(file, args, kwds, False)
  
  
  def savez_compressed(file, *args, **kwds):
      """
      Save several arrays into a single file in compressed ``.npz`` format.
  
      If keyword arguments are given, then filenames are taken from the keywords.
      If arguments are passed in with no keywords, then stored file names are
      arr_0, arr_1, etc.
  
      Parameters
      ----------
      file : str or file
          Either the file name (string) or an open file (file-like object)
          where the data will be saved. If file is a string or a Path, the
          ``.npz`` extension will be appended to the file name if it is not
          already there.
      args : Arguments, optional
          Arrays to save to the file. Since it is not possible for Python to
          know the names of the arrays outside `savez`, the arrays will be saved
          with names "arr_0", "arr_1", and so on. These arguments can be any
          expression.
      kwds : Keyword arguments, optional
          Arrays to save to the file. Arrays will be saved in the file with the
          keyword names.
  
      Returns
      -------
      None
  
      See Also
      --------
      numpy.save : Save a single array to a binary file in NumPy format.
      numpy.savetxt : Save an array to a file as plain text.
      numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
      numpy.load : Load the files created by savez_compressed.
  
      Notes
      -----
      The ``.npz`` file format is a zipped archive of files named after the
      variables they contain.  The archive is compressed with
      ``zipfile.ZIP_DEFLATED`` and each file in the archive contains one variable
      in ``.npy`` format. For a description of the ``.npy`` format, see
      `numpy.lib.format` or the NumPy Enhancement Proposal
      http://docs.scipy.org/doc/numpy/neps/npy-format.html
  
      When opening the saved ``.npz`` file with `load` a `NpzFile` object is
      returned. This is a dictionary-like object which can be queried for
      its list of arrays (with the ``.files`` attribute), and for the arrays
      themselves.
  
      Examples
      --------
      >>> test_array = np.random.rand(3, 2)
      >>> test_vector = np.random.rand(4)
      >>> np.savez_compressed('/tmp/123', a=test_array, b=test_vector)
      >>> loaded = np.load('/tmp/123.npz')
      >>> print(np.array_equal(test_array, loaded['a']))
      True
      >>> print(np.array_equal(test_vector, loaded['b']))
      True
  
      """
      _savez(file, args, kwds, True)
  
  
  def _savez(file, args, kwds, compress, allow_pickle=True, pickle_kwargs=None):
      # Import is postponed to here since zipfile depends on gzip, an optional
      # component of the so-called standard library.
      import zipfile
      # Import deferred for startup time improvement
      import tempfile
  
      if isinstance(file, basestring):
          if not file.endswith('.npz'):
              file = file + '.npz'
      elif is_pathlib_path(file):
          if not file.name.endswith('.npz'):
              file = file.parent / (file.name + '.npz')
  
      namedict = kwds
      for i, val in enumerate(args):
          key = 'arr_%d' % i
          if key in namedict.keys():
              raise ValueError(
                  "Cannot use un-named variables and keyword %s" % key)
          namedict[key] = val
  
      if compress:
          compression = zipfile.ZIP_DEFLATED
      else:
          compression = zipfile.ZIP_STORED
  
      zipf = zipfile_factory(file, mode="w", compression=compression)
  
      # Stage arrays in a temporary file on disk, before writing to zip.
  
      # Since target file might be big enough to exceed capacity of a global
      # temporary directory, create temp file side-by-side with the target file.
      file_dir, file_prefix = os.path.split(file) if _is_string_like(file) else (None, 'tmp')
      fd, tmpfile = tempfile.mkstemp(prefix=file_prefix, dir=file_dir, suffix='-numpy.npy')
      os.close(fd)
      try:
          for key, val in namedict.items():
              fname = key + '.npy'
              fid = open(tmpfile, 'wb')
              try:
                  format.write_array(fid, np.asanyarray(val),
                                     allow_pickle=allow_pickle,
                                     pickle_kwargs=pickle_kwargs)
                  fid.close()
                  fid = None
                  zipf.write(tmpfile, arcname=fname)
              except IOError as exc:
                  raise IOError("Failed to write to %s: %s" % (tmpfile, exc))
              finally:
                  if fid:
                      fid.close()
      finally:
          os.remove(tmpfile)
  
      zipf.close()
  
  
  def _getconv(dtype):
      """ Find the correct dtype converter. Adapted from matplotlib """
  
      def floatconv(x):
          x.lower()
          if b'0x' in x:
              return float.fromhex(asstr(x))
          return float(x)
  
      typ = dtype.type
      if issubclass(typ, np.bool_):
          return lambda x: bool(int(x))
      if issubclass(typ, np.uint64):
          return np.uint64
      if issubclass(typ, np.int64):
          return np.int64
      if issubclass(typ, np.integer):
          return lambda x: int(float(x))
      elif issubclass(typ, np.longdouble):
          return np.longdouble
      elif issubclass(typ, np.floating):
          return floatconv
      elif issubclass(typ, complex):
          return lambda x: complex(asstr(x))
      elif issubclass(typ, np.bytes_):
          return asbytes
      else:
          return asstr
  
  
  def loadtxt(fname, dtype=float, comments='#', delimiter=None,
              converters=None, skiprows=0, usecols=None, unpack=False,
              ndmin=0):
      """
      Load data from a text file.
  
      Each row in the text file must have the same number of values.
  
      Parameters
      ----------
      fname : file, str, or pathlib.Path
          File, filename, or generator to read.  If the filename extension is
          ``.gz`` or ``.bz2``, the file is first decompressed. Note that
          generators should return byte strings for Python 3k.
      dtype : data-type, optional
          Data-type of the resulting array; default: float.  If this is a
          structured data-type, the resulting array will be 1-dimensional, and
          each row will be interpreted as an element of the array.  In this
          case, the number of columns used must match the number of fields in
          the data-type.
      comments : str or sequence, optional
          The characters or list of characters used to indicate the start of a
          comment;
          default: '#'.
      delimiter : str, optional
          The string used to separate values.  By default, this is any
          whitespace.
      converters : dict, optional
          A dictionary mapping column number to a function that will convert
          that column to a float.  E.g., if column 0 is a date string:
          ``converters = {0: datestr2num}``.  Converters can also be used to
          provide a default value for missing data (but see also `genfromtxt`):
          ``converters = {3: lambda s: float(s.strip() or 0)}``.  Default: None.
      skiprows : int, optional
          Skip the first `skiprows` lines; default: 0.
  
      usecols : int or sequence, optional
          Which columns to read, with 0 being the first. For example,
          usecols = (1,4,5) will extract the 2nd, 5th and 6th columns.
          The default, None, results in all columns being read.
  
          .. versionadded:: 1.11.0
  
          Also when a single column has to be read it is possible to use
          an integer instead of a tuple. E.g ``usecols = 3`` reads the
          fourth column the same way as `usecols = (3,)`` would.
  
      unpack : bool, optional
          If True, the returned array is transposed, so that arguments may be
          unpacked using ``x, y, z = loadtxt(...)``.  When used with a structured
          data-type, arrays are returned for each field.  Default is False.
      ndmin : int, optional
          The returned array will have at least `ndmin` dimensions.
          Otherwise mono-dimensional axes will be squeezed.
          Legal values: 0 (default), 1 or 2.
  
          .. versionadded:: 1.6.0
  
      Returns
      -------
      out : ndarray
          Data read from the text file.
  
      See Also
      --------
      load, fromstring, fromregex
      genfromtxt : Load data with missing values handled as specified.
      scipy.io.loadmat : reads MATLAB data files
  
      Notes
      -----
      This function aims to be a fast reader for simply formatted files.  The
      `genfromtxt` function provides more sophisticated handling of, e.g.,
      lines with missing values.
  
      .. versionadded:: 1.10.0
  
      The strings produced by the Python float.hex method can be used as
      input for floats.
  
      Examples
      --------
      >>> from io import StringIO   # StringIO behaves like a file object
      >>> c = StringIO("0 1\\n2 3")
      >>> np.loadtxt(c)
      array([[ 0.,  1.],
             [ 2.,  3.]])
  
      >>> d = StringIO("M 21 72\\nF 35 58")
      >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
      ...                      'formats': ('S1', 'i4', 'f4')})
      array([('M', 21, 72.0), ('F', 35, 58.0)],
            dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])
  
      >>> c = StringIO("1,0,2\\n3,0,4")
      >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
      >>> x
      array([ 1.,  3.])
      >>> y
      array([ 2.,  4.])
  
      """
      # Type conversions for Py3 convenience
      if comments is not None:
          if isinstance(comments, (basestring, bytes)):
              comments = [asbytes(comments)]
          else:
              comments = [asbytes(comment) for comment in comments]
  
          # Compile regex for comments beforehand
          comments = (re.escape(comment) for comment in comments)
          regex_comments = re.compile(b'|'.join(comments))
      user_converters = converters
      if delimiter is not None:
          delimiter = asbytes(delimiter)
  
      if usecols is not None:
          # Allow usecols to be a single int or a sequence of ints
          try:
              usecols_as_list = list(usecols)
          except TypeError:
              usecols_as_list = [usecols]
          for col_idx in usecols_as_list:
              try:
                  opindex(col_idx)
              except TypeError as e:
                  e.args = (
                      "usecols must be an int or a sequence of ints but "
                      "it contains at least one element of type %s" %
                      type(col_idx),
                      )
                  raise
          # Fall back to existing code
          usecols = usecols_as_list
  
      fown = False
      try:
          if is_pathlib_path(fname):
              fname = str(fname)
          if _is_string_like(fname):
              fown = True
              if fname.endswith('.gz'):
                  import gzip
                  fh = iter(gzip.GzipFile(fname))
              elif fname.endswith('.bz2'):
                  import bz2
                  fh = iter(bz2.BZ2File(fname))
              elif sys.version_info[0] == 2:
                  fh = iter(open(fname, 'U'))
              else:
                  fh = iter(open(fname))
          else:
              fh = iter(fname)
      except TypeError:
          raise ValueError('fname must be a string, file handle, or generator')
      X = []
  
      # not to be confused with the flatten_dtype we import...
      def flatten_dtype_internal(dt):
          """Unpack a structured data-type, and produce re-packing info."""
          if dt.names is None:
              # If the dtype is flattened, return.
              # If the dtype has a shape, the dtype occurs
              # in the list more than once.
              shape = dt.shape
              if len(shape) == 0:
                  return ([dt.base], None)
              else:
                  packing = [(shape[-1], list)]
                  if len(shape) > 1:
                      for dim in dt.shape[-2::-1]:
                          packing = [(dim*packing[0][0], packing*dim)]
                  return ([dt.base] * int(np.prod(dt.shape)), packing)
          else:
              types = []
              packing = []
              for field in dt.names:
                  tp, bytes = dt.fields[field]
                  flat_dt, flat_packing = flatten_dtype_internal(tp)
                  types.extend(flat_dt)
                  # Avoid extra nesting for subarrays
                  if tp.ndim > 0:
                      packing.extend(flat_packing)
                  else:
                      packing.append((len(flat_dt), flat_packing))
              return (types, packing)
  
      def pack_items(items, packing):
          """Pack items into nested lists based on re-packing info."""
          if packing is None:
              return items[0]
          elif packing is tuple:
              return tuple(items)
          elif packing is list:
              return list(items)
          else:
              start = 0
              ret = []
              for length, subpacking in packing:
                  ret.append(pack_items(items[start:start+length], subpacking))
                  start += length
              return tuple(ret)
  
      def split_line(line):
          """Chop off comments, strip, and split at delimiter.
  
          Note that although the file is opened as text, this function
          returns bytes.
  
          """
          line = asbytes(line)
          if comments is not None:
              line = regex_comments.split(asbytes(line), maxsplit=1)[0]
          line = line.strip(b'\r\n')
          if line:
              return line.split(delimiter)
          else:
              return []
  
      try:
          # Make sure we're dealing with a proper dtype
          dtype = np.dtype(dtype)
          defconv = _getconv(dtype)
  
          # Skip the first `skiprows` lines
          for i in range(skiprows):
              next(fh)
  
          # Read until we find a line with some values, and use
          # it to estimate the number of columns, N.
          first_vals = None
          try:
              while not first_vals:
                  first_line = next(fh)
                  first_vals = split_line(first_line)
          except StopIteration:
              # End of lines reached
              first_line = ''
              first_vals = []
              warnings.warn('loadtxt: Empty input file: "%s"' % fname, stacklevel=2)
          N = len(usecols or first_vals)
  
          dtype_types, packing = flatten_dtype_internal(dtype)
          if len(dtype_types) > 1:
              # We're dealing with a structured array, each field of
              # the dtype matches a column
              converters = [_getconv(dt) for dt in dtype_types]
          else:
              # All fields have the same dtype
              converters = [defconv for i in range(N)]
              if N > 1:
                  packing = [(N, tuple)]
  
          # By preference, use the converters specified by the user
          for i, conv in (user_converters or {}).items():
              if usecols:
                  try:
                      i = usecols.index(i)
                  except ValueError:
                      # Unused converter specified
                      continue
              converters[i] = conv
  
          # Parse each line, including the first
          for i, line in enumerate(itertools.chain([first_line], fh)):
              vals = split_line(line)
              if len(vals) == 0:
                  continue
              if usecols:
                  vals = [vals[j] for j in usecols]
              if len(vals) != N:
                  line_num = i + skiprows + 1
                  raise ValueError("Wrong number of columns at line %d"
                                   % line_num)
  
              # Convert each value according to its column and store
              items = [conv(val) for (conv, val) in zip(converters, vals)]
              # Then pack it according to the dtype's nesting
              items = pack_items(items, packing)
              X.append(items)
      finally:
          if fown:
              fh.close()
  
      X = np.array(X, dtype)
      # Multicolumn data are returned with shape (1, N, M), i.e.
      # (1, 1, M) for a single row - remove the singleton dimension there
      if X.ndim == 3 and X.shape[:2] == (1, 1):
          X.shape = (1, -1)
  
      # Verify that the array has at least dimensions `ndmin`.
      # Check correctness of the values of `ndmin`
      if ndmin not in [0, 1, 2]:
          raise ValueError('Illegal value of ndmin keyword: %s' % ndmin)
      # Tweak the size and shape of the arrays - remove extraneous dimensions
      if X.ndim > ndmin:
          X = np.squeeze(X)
      # and ensure we have the minimum number of dimensions asked for
      # - has to be in this order for the odd case ndmin=1, X.squeeze().ndim=0
      if X.ndim < ndmin:
          if ndmin == 1:
              X = np.atleast_1d(X)
          elif ndmin == 2:
              X = np.atleast_2d(X).T
  
      if unpack:
          if len(dtype_types) > 1:
              # For structured arrays, return an array for each field.
              return [X[field] for field in dtype.names]
          else:
              return X.T
      else:
          return X
  
  
  def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='',
              footer='', comments='# '):
      """
      Save an array to a text file.
  
      Parameters
      ----------
      fname : filename or file handle
          If the filename ends in ``.gz``, the file is automatically saved in
          compressed gzip format.  `loadtxt` understands gzipped files
          transparently.
      X : array_like
          Data to be saved to a text file.
      fmt : str or sequence of strs, optional
          A single format (%10.5f), a sequence of formats, or a
          multi-format string, e.g. 'Iteration %d -- %10.5f', in which
          case `delimiter` is ignored. For complex `X`, the legal options
          for `fmt` are:
              a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted
                  like `' (%s+%sj)' % (fmt, fmt)`
              b) a full string specifying every real and imaginary part, e.g.
                  `' %.4e %+.4ej %.4e %+.4ej %.4e %+.4ej'` for 3 columns
              c) a list of specifiers, one per column - in this case, the real
                  and imaginary part must have separate specifiers,
                  e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns
      delimiter : str, optional
          String or character separating columns.
      newline : str, optional
          String or character separating lines.
  
          .. versionadded:: 1.5.0
      header : str, optional
          String that will be written at the beginning of the file.
  
          .. versionadded:: 1.7.0
      footer : str, optional
          String that will be written at the end of the file.
  
          .. versionadded:: 1.7.0
      comments : str, optional
          String that will be prepended to the ``header`` and ``footer`` strings,
          to mark them as comments. Default: '# ',  as expected by e.g.
          ``numpy.loadtxt``.
  
          .. versionadded:: 1.7.0
  
  
      See Also
      --------
      save : Save an array to a binary file in NumPy ``.npy`` format
      savez : Save several arrays into an uncompressed ``.npz`` archive
      savez_compressed : Save several arrays into a compressed ``.npz`` archive
  
      Notes
      -----
      Further explanation of the `fmt` parameter
      (``%[flag]width[.precision]specifier``):
  
      flags:
          ``-`` : left justify
  
          ``+`` : Forces to precede result with + or -.
  
          ``0`` : Left pad the number with zeros instead of space (see width).
  
      width:
          Minimum number of characters to be printed. The value is not truncated
          if it has more characters.
  
      precision:
          - For integer specifiers (eg. ``d,i,o,x``), the minimum number of
            digits.
          - For ``e, E`` and ``f`` specifiers, the number of digits to print
            after the decimal point.
          - For ``g`` and ``G``, the maximum number of significant digits.
          - For ``s``, the maximum number of characters.
  
      specifiers:
          ``c`` : character
  
          ``d`` or ``i`` : signed decimal integer
  
          ``e`` or ``E`` : scientific notation with ``e`` or ``E``.
  
          ``f`` : decimal floating point
  
          ``g,G`` : use the shorter of ``e,E`` or ``f``
  
          ``o`` : signed octal
  
          ``s`` : string of characters
  
          ``u`` : unsigned decimal integer
  
          ``x,X`` : unsigned hexadecimal integer
  
      This explanation of ``fmt`` is not complete, for an exhaustive
      specification see [1]_.
  
      References
      ----------
      .. [1] `Format Specification Mini-Language
             <http://docs.python.org/library/string.html#
             format-specification-mini-language>`_, Python Documentation.
  
      Examples
      --------
      >>> x = y = z = np.arange(0.0,5.0,1.0)
      >>> np.savetxt('test.out', x, delimiter=',')   # X is an array
      >>> np.savetxt('test.out', (x,y,z))   # x,y,z equal sized 1D arrays
      >>> np.savetxt('test.out', x, fmt='%1.4e')   # use exponential notation
  
      """
  
      # Py3 conversions first
      if isinstance(fmt, bytes):
          fmt = asstr(fmt)
      delimiter = asstr(delimiter)
  
      own_fh = False
      if is_pathlib_path(fname):
          fname = str(fname)
      if _is_string_like(fname):
          own_fh = True
          if fname.endswith('.gz'):
              import gzip
              fh = gzip.open(fname, 'wb')
          else:
              if sys.version_info[0] >= 3:
                  fh = open(fname, 'wb')
              else:
                  fh = open(fname, 'w')
      elif hasattr(fname, 'write'):
          fh = fname
      else:
          raise ValueError('fname must be a string or file handle')
  
      try:
          X = np.asarray(X)
  
          # Handle 1-dimensional arrays
          if X.ndim == 1:
              # Common case -- 1d array of numbers
              if X.dtype.names is None:
                  X = np.atleast_2d(X).T
                  ncol = 1
  
              # Complex dtype -- each field indicates a separate column
              else:
                  ncol = len(X.dtype.descr)
          else:
              ncol = X.shape[1]
  
          iscomplex_X = np.iscomplexobj(X)
          # `fmt` can be a string with multiple insertion points or a
          # list of formats.  E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
          if type(fmt) in (list, tuple):
              if len(fmt) != ncol:
                  raise AttributeError('fmt has wrong shape.  %s' % str(fmt))
              format = asstr(delimiter).join(map(asstr, fmt))
          elif isinstance(fmt, str):
              n_fmt_chars = fmt.count('%')
              error = ValueError('fmt has wrong number of %% formats:  %s' % fmt)
              if n_fmt_chars == 1:
                  if iscomplex_X:
                      fmt = [' (%s+%sj)' % (fmt, fmt), ] * ncol
                  else:
                      fmt = [fmt, ] * ncol
                  format = delimiter.join(fmt)
              elif iscomplex_X and n_fmt_chars != (2 * ncol):
                  raise error
              elif ((not iscomplex_X) and n_fmt_chars != ncol):
                  raise error
              else:
                  format = fmt
          else:
              raise ValueError('invalid fmt: %r' % (fmt,))
  
          if len(header) > 0:
              header = header.replace('\n', '\n' + comments)
              fh.write(asbytes(comments + header + newline))
          if iscomplex_X:
              for row in X:
                  row2 = []
                  for number in row:
                      row2.append(number.real)
                      row2.append(number.imag)
                  fh.write(asbytes(format % tuple(row2) + newline))
          else:
              for row in X:
                  try:
                      fh.write(asbytes(format % tuple(row) + newline))
                  except TypeError:
                      raise TypeError("Mismatch between array dtype ('%s') and "
                                      "format specifier ('%s')"
                                      % (str(X.dtype), format))
          if len(footer) > 0:
              footer = footer.replace('\n', '\n' + comments)
              fh.write(asbytes(comments + footer + newline))
      finally:
          if own_fh:
              fh.close()
  
  
  def fromregex(file, regexp, dtype):
      """
      Construct an array from a text file, using regular expression parsing.
  
      The returned array is always a structured array, and is constructed from
      all matches of the regular expression in the file. Groups in the regular
      expression are converted to fields of the structured array.
  
      Parameters
      ----------
      file : str or file
          File name or file object to read.
      regexp : str or regexp
          Regular expression used to parse the file.
          Groups in the regular expression correspond to fields in the dtype.
      dtype : dtype or list of dtypes
          Dtype for the structured array.
  
      Returns
      -------
      output : ndarray
          The output array, containing the part of the content of `file` that
          was matched by `regexp`. `output` is always a structured array.
  
      Raises
      ------
      TypeError
          When `dtype` is not a valid dtype for a structured array.
  
      See Also
      --------
      fromstring, loadtxt
  
      Notes
      -----
      Dtypes for structured arrays can be specified in several forms, but all
      forms specify at least the data type and field name. For details see
      `doc.structured_arrays`.
  
      Examples
      --------
      >>> f = open('test.dat', 'w')
      >>> f.write("1312 foo\\n1534  bar\\n444   qux")
      >>> f.close()
  
      >>> regexp = r"(\\d+)\\s+(...)"  # match [digits, whitespace, anything]
      >>> output = np.fromregex('test.dat', regexp,
      ...                       [('num', np.int64), ('key', 'S3')])
      >>> output
      array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],
            dtype=[('num', '<i8'), ('key', '|S3')])
      >>> output['num']
      array([1312, 1534,  444], dtype=int64)
  
      """
      own_fh = False
      if not hasattr(file, "read"):
          file = open(file, 'rb')
          own_fh = True
  
      try:
          if not hasattr(regexp, 'match'):
              regexp = re.compile(asbytes(regexp))
          if not isinstance(dtype, np.dtype):
              dtype = np.dtype(dtype)
  
          seq = regexp.findall(file.read())
          if seq and not isinstance(seq[0], tuple):
              # Only one group is in the regexp.
              # Create the new array as a single data-type and then
              #   re-interpret as a single-field structured array.
              newdtype = np.dtype(dtype[dtype.names[0]])
              output = np.array(seq, dtype=newdtype)
              output.dtype = dtype
          else:
              output = np.array(seq, dtype=dtype)
  
          return output
      finally:
          if own_fh:
              file.close()
  
  
  #####--------------------------------------------------------------------------
  #---- --- ASCII functions ---
  #####--------------------------------------------------------------------------
  
  
  def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
                 skip_header=0, skip_footer=0, converters=None,
                 missing_values=None, filling_values=None, usecols=None,
                 names=None, excludelist=None, deletechars=None,
                 replace_space='_', autostrip=False, case_sensitive=True,
                 defaultfmt="f%i", unpack=None, usemask=False, loose=True,
                 invalid_raise=True, max_rows=None):
      """
      Load data from a text file, with missing values handled as specified.
  
      Each line past the first `skip_header` lines is split at the `delimiter`
      character, and characters following the `comments` character are discarded.
  
      Parameters
      ----------
      fname : file, str, pathlib.Path, list of str, generator
          File, filename, list, or generator to read.  If the filename
          extension is `.gz` or `.bz2`, the file is first decompressed. Note
          that generators must return byte strings in Python 3k.  The strings
          in a list or produced by a generator are treated as lines.
      dtype : dtype, optional
          Data type of the resulting array.
          If None, the dtypes will be determined by the contents of each
          column, individually.
      comments : str, optional
          The character used to indicate the start of a comment.
          All the characters occurring on a line after a comment are discarded
      delimiter : str, int, or sequence, optional
          The string used to separate values.  By default, any consecutive
          whitespaces act as delimiter.  An integer or sequence of integers
          can also be provided as width(s) of each field.
      skiprows : int, optional
          `skiprows` was removed in numpy 1.10. Please use `skip_header` instead.
      skip_header : int, optional
          The number of lines to skip at the beginning of the file.
      skip_footer : int, optional
          The number of lines to skip at the end of the file.
      converters : variable, optional
          The set of functions that convert the data of a column to a value.
          The converters can also be used to provide a default value
          for missing data: ``converters = {3: lambda s: float(s or 0)}``.
      missing : variable, optional
          `missing` was removed in numpy 1.10. Please use `missing_values`
          instead.
      missing_values : variable, optional
          The set of strings corresponding to missing data.
      filling_values : variable, optional
          The set of values to be used as default when the data are missing.
      usecols : sequence, optional
          Which columns to read, with 0 being the first.  For example,
          ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.
      names : {None, True, str, sequence}, optional
          If `names` is True, the field names are read from the first valid line
          after the first `skip_header` lines.
          If `names` is a sequence or a single-string of comma-separated names,
          the names will be used to define the field names in a structured dtype.
          If `names` is None, the names of the dtype fields will be used, if any.
      excludelist : sequence, optional
          A list of names to exclude. This list is appended to the default list
          ['return','file','print']. Excluded names are appended an underscore:
          for example, `file` would become `file_`.
      deletechars : str, optional
          A string combining invalid characters that must be deleted from the
          names.
      defaultfmt : str, optional
          A format used to define default field names, such as "f%i" or "f_%02i".
      autostrip : bool, optional
          Whether to automatically strip white spaces from the variables.
      replace_space : char, optional
          Character(s) used in replacement of white spaces in the variables
          names. By default, use a '_'.
      case_sensitive : {True, False, 'upper', 'lower'}, optional
          If True, field names are case sensitive.
          If False or 'upper', field names are converted to upper case.
          If 'lower', field names are converted to lower case.
      unpack : bool, optional
          If True, the returned array is transposed, so that arguments may be
          unpacked using ``x, y, z = loadtxt(...)``
      usemask : bool, optional
          If True, return a masked array.
          If False, return a regular array.
      loose : bool, optional
          If True, do not raise errors for invalid values.
      invalid_raise : bool, optional
          If True, an exception is raised if an inconsistency is detected in the
          number of columns.
          If False, a warning is emitted and the offending lines are skipped.
      max_rows : int,  optional
          The maximum number of rows to read. Must not be used with skip_footer
          at the same time.  If given, the value must be at least 1. Default is
          to read the entire file.
  
          .. versionadded:: 1.10.0
  
      Returns
      -------
      out : ndarray
          Data read from the text file. If `usemask` is True, this is a
          masked array.
  
      See Also
      --------
      numpy.loadtxt : equivalent function when no data is missing.
  
      Notes
      -----
      * When spaces are used as delimiters, or when no delimiter has been given
        as input, there should not be any missing data between two fields.
      * When the variables are named (either by a flexible dtype or with `names`,
        there must not be any header in the file (else a ValueError
        exception is raised).
      * Individual values are not stripped of spaces by default.
        When using a custom converter, make sure the function does remove spaces.
  
      References
      ----------
      .. [1] NumPy User Guide, section `I/O with NumPy
             <http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
  
      Examples
      ---------
      >>> from io import StringIO
      >>> import numpy as np
  
      Comma delimited file with mixed dtype
  
      >>> s = StringIO("1,1.3,abcde")
      >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
      ... ('mystring','S5')], delimiter=",")
      >>> data
      array((1, 1.3, 'abcde'),
            dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
  
      Using dtype = None
  
      >>> s.seek(0) # needed for StringIO example only
      >>> data = np.genfromtxt(s, dtype=None,
      ... names = ['myint','myfloat','mystring'], delimiter=",")
      >>> data
      array((1, 1.3, 'abcde'),
            dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
  
      Specifying dtype and names
  
      >>> s.seek(0)
      >>> data = np.genfromtxt(s, dtype="i8,f8,S5",
      ... names=['myint','myfloat','mystring'], delimiter=",")
      >>> data
      array((1, 1.3, 'abcde'),
            dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
  
      An example with fixed-width columns
  
      >>> s = StringIO("11.3abcde")
      >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
      ...     delimiter=[1,3,5])
      >>> data
      array((1, 1.3, 'abcde'),
            dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')])
  
      """
      if max_rows is not None:
          if skip_footer:
              raise ValueError(
                      "The keywords 'skip_footer' and 'max_rows' can not be "
                      "specified at the same time.")
          if max_rows < 1:
              raise ValueError("'max_rows' must be at least 1.")
  
      # Py3 data conversions to bytes, for convenience
      if comments is not None:
          comments = asbytes(comments)
      if isinstance(delimiter, unicode):
          delimiter = asbytes(delimiter)
      if isinstance(missing_values, (unicode, list, tuple)):
          missing_values = asbytes_nested(missing_values)
  
      #
      if usemask:
          from numpy.ma import MaskedArray, make_mask_descr
      # Check the input dictionary of converters
      user_converters = converters or {}
      if not isinstance(user_converters, dict):
          raise TypeError(
              "The input argument 'converter' should be a valid dictionary "
              "(got '%s' instead)" % type(user_converters))
  
      # Initialize the filehandle, the LineSplitter and the NameValidator
      own_fhd = False
      try:
          if is_pathlib_path(fname):
              fname = str(fname)
          if isinstance(fname, basestring):
              if sys.version_info[0] == 2:
                  fhd = iter(np.lib._datasource.open(fname, 'rbU'))
              else:
                  fhd = iter(np.lib._datasource.open(fname, 'rb'))
              own_fhd = True
          else:
              fhd = iter(fname)
      except TypeError:
          raise TypeError(
              "fname must be a string, filehandle, list of strings, "
              "or generator. Got %s instead." % type(fname))
  
      split_line = LineSplitter(delimiter=delimiter, comments=comments,
                                autostrip=autostrip)._handyman
      validate_names = NameValidator(excludelist=excludelist,
                                     deletechars=deletechars,
                                     case_sensitive=case_sensitive,
                                     replace_space=replace_space)
  
      # Skip the first `skip_header` rows
      for i in range(skip_header):
          next(fhd)
  
      # Keep on until we find the first valid values
      first_values = None
      try:
          while not first_values:
              first_line = next(fhd)
              if names is True:
                  if comments in first_line:
                      first_line = (
                          b''.join(first_line.split(comments)[1:]))
              first_values = split_line(first_line)
      except StopIteration:
          # return an empty array if the datafile is empty
          first_line = b''
          first_values = []
          warnings.warn('genfromtxt: Empty input file: "%s"' % fname, stacklevel=2)
  
      # Should we take the first values as names ?
      if names is True:
          fval = first_values[0].strip()
          if fval in comments:
              del first_values[0]
  
      # Check the columns to use: make sure `usecols` is a list
      if usecols is not None:
          try:
              usecols = [_.strip() for _ in usecols.split(",")]
          except AttributeError:
              try:
                  usecols = list(usecols)
              except TypeError:
                  usecols = [usecols, ]
      nbcols = len(usecols or first_values)
  
      # Check the names and overwrite the dtype.names if needed
      if names is True:
          names = validate_names([_bytes_to_name(_.strip())
                                  for _ in first_values])
          first_line = b''
      elif _is_string_like(names):
          names = validate_names([_.strip() for _ in names.split(',')])
      elif names:
          names = validate_names(names)
      # Get the dtype
      if dtype is not None:
          dtype = easy_dtype(dtype, defaultfmt=defaultfmt, names=names,
                             excludelist=excludelist,
                             deletechars=deletechars,
                             case_sensitive=case_sensitive,
                             replace_space=replace_space)
      # Make sure the names is a list (for 2.5)
      if names is not None:
          names = list(names)
  
      if usecols:
          for (i, current) in enumerate(usecols):
              # if usecols is a list of names, convert to a list of indices
              if _is_string_like(current):
                  usecols[i] = names.index(current)
              elif current < 0:
                  usecols[i] = current + len(first_values)
          # If the dtype is not None, make sure we update it
          if (dtype is not None) and (len(dtype) > nbcols):
              descr = dtype.descr
              dtype = np.dtype([descr[_] for _ in usecols])
              names = list(dtype.names)
          # If `names` is not None, update the names
          elif (names is not None) and (len(names) > nbcols):
              names = [names[_] for _ in usecols]
      elif (names is not None) and (dtype is not None):
          names = list(dtype.names)
  
      # Process the missing values ...............................
      # Rename missing_values for convenience
      user_missing_values = missing_values or ()
  
      # Define the list of missing_values (one column: one list)
      missing_values = [list([b'']) for _ in range(nbcols)]
  
      # We have a dictionary: process it field by field
      if isinstance(user_missing_values, dict):
          # Loop on the items
          for (key, val) in user_missing_values.items():
              # Is the key a string ?
              if _is_string_like(key):
                  try:
                      # Transform it into an integer
                      key = names.index(key)
                  except ValueError:
                      # We couldn't find it: the name must have been dropped
                      continue
              # Redefine the key as needed if it's a column number
              if usecols:
                  try:
                      key = usecols.index(key)
                  except ValueError:
                      pass
              # Transform the value as a list of string
              if isinstance(val, (list, tuple)):
                  val = [str(_) for _ in val]
              else:
                  val = [str(val), ]
              # Add the value(s) to the current list of missing
              if key is None:
                  # None acts as default
                  for miss in missing_values:
                      miss.extend(val)
              else:
                  missing_values[key].extend(val)
      # We have a sequence : each item matches a column
      elif isinstance(user_missing_values, (list, tuple)):
          for (value, entry) in zip(user_missing_values, missing_values):
              value = str(value)
              if value not in entry:
                  entry.append(value)
      # We have a string : apply it to all entries
      elif isinstance(user_missing_values, bytes):
          user_value = user_missing_values.split(b",")
          for entry in missing_values:
              entry.extend(user_value)
      # We have something else: apply it to all entries
      else:
          for entry in missing_values:
              entry.extend([str(user_missing_values)])
  
      # Process the filling_values ...............................
      # Rename the input for convenience
      user_filling_values = filling_values
      if user_filling_values is None:
          user_filling_values = []
      # Define the default
      filling_values = [None] * nbcols
      # We have a dictionary : update each entry individually
      if isinstance(user_filling_values, dict):
          for (key, val) in user_filling_values.items():
              if _is_string_like(key):
                  try:
                      # Transform it into an integer
                      key = names.index(key)
                  except ValueError:
                      # We couldn't find it: the name must have been dropped,
                      continue
              # Redefine the key if it's a column number and usecols is defined
              if usecols:
                  try:
                      key = usecols.index(key)
                  except ValueError:
                      pass
              # Add the value to the list
              filling_values[key] = val
      # We have a sequence : update on a one-to-one basis
      elif isinstance(user_filling_values, (list, tuple)):
          n = len(user_filling_values)
          if (n <= nbcols):
              filling_values[:n] = user_filling_values
          else:
              filling_values = user_filling_values[:nbcols]
      # We have something else : use it for all entries
      else:
          filling_values = [user_filling_values] * nbcols
  
      # Initialize the converters ................................
      if dtype is None:
          # Note: we can't use a [...]*nbcols, as we would have 3 times the same
          # ... converter, instead of 3 different converters.
          converters = [StringConverter(None, missing_values=miss, default=fill)
                        for (miss, fill) in zip(missing_values, filling_values)]
      else:
          dtype_flat = flatten_dtype(dtype, flatten_base=True)
          # Initialize the converters
          if len(dtype_flat) > 1:
              # Flexible type : get a converter from each dtype
              zipit = zip(dtype_flat, missing_values, filling_values)
              converters = [StringConverter(dt, locked=True,
                                            missing_values=miss, default=fill)
                            for (dt, miss, fill) in zipit]
          else:
              # Set to a default converter (but w/ different missing values)
              zipit = zip(missing_values, filling_values)
              converters = [StringConverter(dtype, locked=True,
                                            missing_values=miss, default=fill)
                            for (miss, fill) in zipit]
      # Update the converters to use the user-defined ones
      uc_update = []
      for (j, conv) in user_converters.items():
          # If the converter is specified by column names, use the index instead
          if _is_string_like(j):
              try:
                  j = names.index(j)
                  i = j
              except ValueError:
                  continue
          elif usecols:
              try:
                  i = usecols.index(j)
              except ValueError:
                  # Unused converter specified
                  continue
          else:
              i = j
          # Find the value to test - first_line is not filtered by usecols:
          if len(first_line):
              testing_value = first_values[j]
          else:
              testing_value = None
          converters[i].update(conv, locked=True,
                               testing_value=testing_value,
                               default=filling_values[i],
                               missing_values=missing_values[i],)
          uc_update.append((i, conv))
      # Make sure we have the corrected keys in user_converters...
      user_converters.update(uc_update)
  
      # Fixme: possible error as following variable never used.
      #miss_chars = [_.missing_values for _ in converters]
  
      # Initialize the output lists ...
      # ... rows
      rows = []
      append_to_rows = rows.append
      # ... masks
      if usemask:
          masks = []
          append_to_masks = masks.append
      # ... invalid
      invalid = []
      append_to_invalid = invalid.append
  
      # Parse each line
      for (i, line) in enumerate(itertools.chain([first_line, ], fhd)):
          values = split_line(line)
          nbvalues = len(values)
          # Skip an empty line
          if nbvalues == 0:
              continue
          if usecols:
              # Select only the columns we need
              try:
                  values = [values[_] for _ in usecols]
              except IndexError:
                  append_to_invalid((i + skip_header + 1, nbvalues))
                  continue
          elif nbvalues != nbcols:
              append_to_invalid((i + skip_header + 1, nbvalues))
              continue
          # Store the values
          append_to_rows(tuple(values))
          if usemask:
              append_to_masks(tuple([v.strip() in m
                                     for (v, m) in zip(values,
                                                       missing_values)]))
          if len(rows) == max_rows:
              break
  
      if own_fhd:
          fhd.close()
  
      # Upgrade the converters (if needed)
      if dtype is None:
          for (i, converter) in enumerate(converters):
              current_column = [itemgetter(i)(_m) for _m in rows]
              try:
                  converter.iterupgrade(current_column)
              except ConverterLockError:
                  errmsg = "Converter #%i is locked and cannot be upgraded: " % i
                  current_column = map(itemgetter(i), rows)
                  for (j, value) in enumerate(current_column):
                      try:
                          converter.upgrade(value)
                      except (ConverterError, ValueError):
                          errmsg += "(occurred line #%i for value '%s')"
                          errmsg %= (j + 1 + skip_header, value)
                          raise ConverterError(errmsg)
  
      # Check that we don't have invalid values
      nbinvalid = len(invalid)
      if nbinvalid > 0:
          nbrows = len(rows) + nbinvalid - skip_footer
          # Construct the error message
          template = "    Line #%%i (got %%i columns instead of %i)" % nbcols
          if skip_footer > 0:
              nbinvalid_skipped = len([_ for _ in invalid
                                       if _[0] > nbrows + skip_header])
              invalid = invalid[:nbinvalid - nbinvalid_skipped]
              skip_footer -= nbinvalid_skipped
  #
  #            nbrows -= skip_footer
  #            errmsg = [template % (i, nb)
  #                      for (i, nb) in invalid if i < nbrows]
  #        else:
          errmsg = [template % (i, nb)
                    for (i, nb) in invalid]
          if len(errmsg):
              errmsg.insert(0, "Some errors were detected !")
              errmsg = "\n".join(errmsg)
              # Raise an exception ?
              if invalid_raise:
                  raise ValueError(errmsg)
              # Issue a warning ?
              else:
                  warnings.warn(errmsg, ConversionWarning, stacklevel=2)
  
      # Strip the last skip_footer data
      if skip_footer > 0:
          rows = rows[:-skip_footer]
          if usemask:
              masks = masks[:-skip_footer]
  
      # Convert each value according to the converter:
      # We want to modify the list in place to avoid creating a new one...
      if loose:
          rows = list(
              zip(*[[conv._loose_call(_r) for _r in map(itemgetter(i), rows)]
                    for (i, conv) in enumerate(converters)]))
      else:
          rows = list(
              zip(*[[conv._strict_call(_r) for _r in map(itemgetter(i), rows)]
                    for (i, conv) in enumerate(converters)]))
  
      # Reset the dtype
      data = rows
      if dtype is None:
          # Get the dtypes from the types of the converters
          column_types = [conv.type for conv in converters]
          # Find the columns with strings...
          strcolidx = [i for (i, v) in enumerate(column_types)
                       if v in (type('S'), np.string_)]
          # ... and take the largest number of chars.
          for i in strcolidx:
              column_types[i] = "|S%i" % max(len(row[i]) for row in data)
          #
          if names is None:
              # If the dtype is uniform, don't define names, else use ''
              base = set([c.type for c in converters if c._checked])
              if len(base) == 1:
                  (ddtype, mdtype) = (list(base)[0], bool)
              else:
                  ddtype = [(defaultfmt % i, dt)
                            for (i, dt) in enumerate(column_types)]
                  if usemask:
                      mdtype = [(defaultfmt % i, bool)
                                for (i, dt) in enumerate(column_types)]
          else:
              ddtype = list(zip(names, column_types))
              mdtype = list(zip(names, [bool] * len(column_types)))
          output = np.array(data, dtype=ddtype)
          if usemask:
              outputmask = np.array(masks, dtype=mdtype)
      else:
          # Overwrite the initial dtype names if needed
          if names and dtype.names:
              dtype.names = names
          # Case 1. We have a structured type
          if len(dtype_flat) > 1:
              # Nested dtype, eg [('a', int), ('b', [('b0', int), ('b1', 'f4')])]
              # First, create the array using a flattened dtype:
              # [('a', int), ('b1', int), ('b2', float)]
              # Then, view the array using the specified dtype.
              if 'O' in (_.char for _ in dtype_flat):
                  if has_nested_fields(dtype):
                      raise NotImplementedError(
                          "Nested fields involving objects are not supported...")
                  else:
                      output = np.array(data, dtype=dtype)
              else:
                  rows = np.array(data, dtype=[('', _) for _ in dtype_flat])
                  output = rows.view(dtype)
              # Now, process the rowmasks the same way
              if usemask:
                  rowmasks = np.array(
                      masks, dtype=np.dtype([('', bool) for t in dtype_flat]))
                  # Construct the new dtype
                  mdtype = make_mask_descr(dtype)
                  outputmask = rowmasks.view(mdtype)
          # Case #2. We have a basic dtype
          else:
              # We used some user-defined converters
              if user_converters:
                  ishomogeneous = True
                  descr = []
                  for i, ttype in enumerate([conv.type for conv in converters]):
                      # Keep the dtype of the current converter
                      if i in user_converters:
                          ishomogeneous &= (ttype == dtype.type)
                          if ttype == np.string_:
                              ttype = "|S%i" % max(len(row[i]) for row in data)
                          descr.append(('', ttype))
                      else:
                          descr.append(('', dtype))
                  # So we changed the dtype ?
                  if not ishomogeneous:
                      # We have more than one field
                      if len(descr) > 1:
                          dtype = np.dtype(descr)
                      # We have only one field: drop the name if not needed.
                      else:
                          dtype = np.dtype(ttype)
              #
              output = np.array(data, dtype)
              if usemask:
                  if dtype.names:
                      mdtype = [(_, bool) for _ in dtype.names]
                  else:
                      mdtype = bool
                  outputmask = np.array(masks, dtype=mdtype)
      # Try to take care of the missing data we missed
      names = output.dtype.names
      if usemask and names:
          for (name, conv) in zip(names or (), converters):
              missing_values = [conv(_) for _ in conv.missing_values
                                if _ != b'']
              for mval in missing_values:
                  outputmask[name] |= (output[name] == mval)
      # Construct the final array
      if usemask:
          output = output.view(MaskedArray)
          output._mask = outputmask
      if unpack:
          return output.squeeze().T
      return output.squeeze()
  
  
  def ndfromtxt(fname, **kwargs):
      """
      Load ASCII data stored in a file and return it as a single array.
  
      Parameters
      ----------
      fname, kwargs : For a description of input parameters, see `genfromtxt`.
  
      See Also
      --------
      numpy.genfromtxt : generic function.
  
      """
      kwargs['usemask'] = False
      return genfromtxt(fname, **kwargs)
  
  
  def mafromtxt(fname, **kwargs):
      """
      Load ASCII data stored in a text file and return a masked array.
  
      Parameters
      ----------
      fname, kwargs : For a description of input parameters, see `genfromtxt`.
  
      See Also
      --------
      numpy.genfromtxt : generic function to load ASCII data.
  
      """
      kwargs['usemask'] = True
      return genfromtxt(fname, **kwargs)
  
  
  def recfromtxt(fname, **kwargs):
      """
      Load ASCII data from a file and return it in a record array.
  
      If ``usemask=False`` a standard `recarray` is returned,
      if ``usemask=True`` a MaskedRecords array is returned.
  
      Parameters
      ----------
      fname, kwargs : For a description of input parameters, see `genfromtxt`.
  
      See Also
      --------
      numpy.genfromtxt : generic function
  
      Notes
      -----
      By default, `dtype` is None, which means that the data-type of the output
      array will be determined from the data.
  
      """
      kwargs.setdefault("dtype", None)
      usemask = kwargs.get('usemask', False)
      output = genfromtxt(fname, **kwargs)
      if usemask:
          from numpy.ma.mrecords import MaskedRecords
          output = output.view(MaskedRecords)
      else:
          output = output.view(np.recarray)
      return output
  
  
  def recfromcsv(fname, **kwargs):
      """
      Load ASCII data stored in a comma-separated file.
  
      The returned array is a record array (if ``usemask=False``, see
      `recarray`) or a masked record array (if ``usemask=True``,
      see `ma.mrecords.MaskedRecords`).
  
      Parameters
      ----------
      fname, kwargs : For a description of input parameters, see `genfromtxt`.
  
      See Also
      --------
      numpy.genfromtxt : generic function to load ASCII data.
  
      Notes
      -----
      By default, `dtype` is None, which means that the data-type of the output
      array will be determined from the data.
  
      """
      # Set default kwargs for genfromtxt as relevant to csv import.
      kwargs.setdefault("case_sensitive", "lower")
      kwargs.setdefault("names", True)
      kwargs.setdefault("delimiter", ",")
      kwargs.setdefault("dtype", None)
      output = genfromtxt(fname, **kwargs)
  
      usemask = kwargs.get("usemask", False)
      if usemask:
          from numpy.ma.mrecords import MaskedRecords
          output = output.view(MaskedRecords)
      else:
          output = output.view(np.recarray)
      return output
  