from __future__ import division, absolute_import, print_function

import sys
import os
import re
import itertools
import warnings
import weakref
from operator import itemgetter

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
    )

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(*args, **kwargs):
    import zipfile
    kwargs['allowZip64'] = True
    return zipfile.ZipFile(*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 or string
        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.

    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])

    """
    import gzip

    own_fid = False
    if isinstance(file, basestring):
        fid = open(file, "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 = asbytes('PK\x03\x04')
        N = len(format.MAGIC_PREFIX)
        magic = fid.read(N)
        fid.seek(-N, 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:
                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 or str
        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, 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
    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, 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
        File name of ``.npz`` file.
    args : Arguments
        Function arguments.
    kwds : Keyword arguments
        Keywords.

    See Also
    --------
    numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
    numpy.load : Load the files created by savez_compressed.

    """
    _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'

    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.
    fd, tmpfile = tempfile.mkstemp(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)
            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.floating):
        return floatconv
    elif issubclass(typ, np.complex):
        return lambda x: complex(asstr(x))
    elif issubclass(typ, np.bytes_):
        return bytes
    else:
        return str

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 or str
        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 : 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.
    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(asbytes('|').join(comments))
    user_converters = converters
    if delimiter is not None:
        delimiter = asbytes(delimiter)
    if usecols is not None:
        usecols = list(usecols)

    fown = False
    try:
        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 = []

    def flatten_dtype(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(tp)
                types.extend(flat_dt)
                # Avoid extra nesting for subarrays
                if len(tp.shape) > 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(asbytes('\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)
        N = len(usecols or first_vals)

        dtype_types, packing = flatten_dtype(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[i] for i 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 %+.4j %.4e %+.4j %.4e %+.4j'` 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_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 or str
        File, filename, 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.
    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 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, 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 = (
                        asbytes('').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 = asbytes('')
        first_values = []
        warnings.warn('genfromtxt: Empty input file: "%s"' % fname)

    # 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 = asbytes('')
    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([asbytes('')]) 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(asbytes(","))
        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)

    # 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], np.bool)
            else:
                ddtype = [(defaultfmt % i, dt)
                          for (i, dt) in enumerate(column_types)]
                if usemask:
                    mdtype = [(defaultfmt % i, np.bool)
                              for (i, dt) in enumerate(column_types)]
        else:
            ddtype = list(zip(names, column_types))
            mdtype = list(zip(names, [np.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([('', np.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 = [(_, np.bool) for _ in dtype.names]
                else:
                    mdtype = np.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 _ != asbytes('')]
            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


#!/usr/bin/env python

"""
Train random forest classifier
Inputs: CSV from build_att_table, small area cutoff
Outputs: Packaged up Random Forest model
@authors: Kylen Solvik
Date Create: 3/17/17
"""

# Load libraries
import pandas as pd
from sklearn import model_selection
from sklearn import preprocessing
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.externals import joblib
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import *
import numpy as np
import sys
import argparse
import os
import xgboost as xgb

# Parse arguments
parser = argparse.ArgumentParser(description='Train Random Forest classifier.',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('prop_csv',
                    help='Path to attribute table (from build_att_table.py).',
                    type=str)
parser.add_argument('xgb_pkl',
                    help='Path to save random forest model as .pkl.',
                    type=str)
parser.add_argument('--area_lowbound',
                    help='Lower area bound. All regions <= in size will be ignored',
                    default=2,
                    type=int)
parser.add_argument('--path_prefix',
                    help='To be placed at beginnings of all other path args',
                    type=str,default='')
args = parser.parse_args()

def select_training_obs(full_csv_path):
    """Takes full csv and selects only the training observations.
    Writes out to csv for further use"""
    training_csv_path = full_csv_path.replace('.csv','_trainonly.csv')
    if not os.path.isfile(training_csv_path):
        dataset = pd.read_csv(full_csv_path,header=0)
        training_dataset = dataset.loc[dataset['class'] > 0]
        training_dataset.to_csv(training_csv_path,header=True,index=False)
    return(training_csv_path)

def main():
        # Set any attributes to exclude for this run
        exclude_att_patterns = []

        # Load dataset
        training_csv = select_training_obs(args.path_prefix + args.prop_csv)
        dataset = pd.read_csv(training_csv,header=0)
        dataset_acut = dataset.loc[dataset['area'] > args.area_lowbound]

        # Exclude attributes matching user input patterns, or if they are all nans
        exclude_atts = []
        for pattern in exclude_att_patterns:
                col_list = [col for col in dataset_acut.columns if pattern in col]
                exclude_atts.extend(col_list)

        for att in dataset.columns[1:]:
                if sum(np.isfinite(dataset[att])) == 0:
                        exclude_atts.append(att)

        for att in list(set(exclude_atts)):
                del dataset_acut[att]

        (ds_y,ds_x) = dataset_acut.shape
        print(ds_y,ds_x)

        # Convert dataset to array
        feature_names = dataset_acut.columns[2:]
        array = dataset_acut.values
        X = array[:,2:ds_x].astype(float)
        Y = array[:,1].astype(int)
        Y = Y-1 # Convert from 1s and 2s to 0-1

        # Set nans to 0
        X = np.nan_to_num(X)

        # Separate test data
        test_size = 0.2
        seed = 5
        X_train, X_test, Y_train, Y_test = model_selection.train_test_split(
                X, Y, test_size=test_size,
                random_state=seed)

        # Convert data to xgboost matrices
        d_train = xgb.DMatrix(X_train,label=Y_train)
        # d_test = xgb.DMatrix(X_test,label=Y_test)

        #----------------------------------------------------------------------
        # Paramater tuning

        # Step 1: Find approximate n_estimators to use
        early_stop_rounds = 40
        n_folds = 5
        xgb_model = xgb.XGBClassifier(
            learning_rate =0.1,
            n_estimators=1000,
            max_depth=5,
            min_child_weight=1,
            gamma=0,
            subsample=0.8,
            colsample_bytree=0.8,
            objective= 'binary:logistic',
            seed=27)
        xgb_params = xgb_model.get_xgb_params()
        cvresult = xgb.cv(xgb_params, d_train,
            num_boost_round=xgb_params['n_estimators'], nfold=n_folds,
            metrics='auc', early_stopping_rounds=early_stop_rounds,
            )
        n_est_best = (cvresult.shape[0] - early_stop_rounds)
        print('Best number of rounds = {}'.format(n_est_best))

        # Step 2: Tune hyperparameters
        xgb_model = xgb.XGBClassifier()

        params = {'max_depth': range(5,10,2),
                'learning_rate': [0.1],
                'gamma':[0,0.5,1],
                'silent': [1],
                'objective': ['binary:logistic'],
                'n_estimators' : [n_est_best],
                'subsample' : [0.7, 0.8,1],
                'min_child_weight' : range(1,4,2),
                'colsample_bytree':[0.7,0.8,1],
                }
        clf = GridSearchCV(xgb_model,params,n_jobs = 1,
                cv = StratifiedKFold(Y_train,
                n_folds=5, shuffle=True),
                scoring = 'roc_auc',
                verbose = 2,
                refit = True)
        clf.fit(X_train,Y_train)

        best_parameters,score,_ = max(clf.grid_scores_,key=lambda x: x[1])
        print('Raw AUC score:',score)
        for param_name in sorted(best_parameters.keys()):
            print("%s: %r" % (param_name, best_parameters[param_name]))

        # Step 3: Decrease learning rate and up the # of trees
        #xgb_finalcv = XGBClassifier()
        tuned_params = clf.best_params_
        tuned_params['n_estimators'] = 10000
        tuned_params['learning_rate'] = 0.01
        cvresult = xgb.cv(tuned_params, d_train,
            num_boost_round=tuned_params['n_estimators'], nfold=n_folds,
            metrics='auc', early_stopping_rounds=early_stop_rounds,
            )

        # Train model with cv results and predict on test set For test accuracy
        n_est_final = int((cvresult.shape[0] - early_stop_rounds) / (1 - 1 / n_folds))
        tuned_params['n_estimators'] = n_est_final
        print(tuned_params)
        xgb_train = xgb.XGBClassifier()
        xgb_train.set_params(**tuned_params)
        xgb_train.fit(X_train,Y_train)
        bst_preds = xgb_train.predict(X_test)
        print("Xgboost Test acc = " + str(accuracy_score(Y_test, bst_preds)))
        print(confusion_matrix(Y_test, bst_preds))
        print(classification_report(Y_test, bst_preds))
        # Export cv classifier
        joblib.dump(cvresult, args.path_prefix + args.xgb_pkl + 'cv')

        # Export classifier trained on full data set
        xgb_full = xgb.XGBClassifier()
        xgb_full.set_params(**tuned_params)
        xgb_full.fit(X,Y)
        joblib.dump(xgb_full, args.path_prefix + args.xgb_pkl)

if __name__ == '__main__':
        main()


#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
"""Example of using convolutional networks over characters for DBpedia dataset.

This model is similar to one described in this paper:
   "Character-level Convolutional Networks for Text Classification"
   http://arxiv.org/abs/1509.01626

and is somewhat alternative to the Lua code from here:
   https://github.com/zhangxiangxiao/Crepe
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

import numpy as np
import pandas
import tensorflow as tf

FLAGS = None

MAX_DOCUMENT_LENGTH = 100
N_FILTERS = 10
FILTER_SHAPE1 = [20, 256]
FILTER_SHAPE2 = [20, N_FILTERS]
POOLING_WINDOW = 4
POOLING_STRIDE = 2
MAX_LABEL = 15
CHARS_FEATURE = 'chars'  # Name of the input character feature.

def char_cnn_model(features, labels, mode):
  """Character level convolutional neural network model to predict classes."""
  features_onehot = tf.one_hot(features[CHARS_FEATURE], 256)
  input_layer = tf.reshape(
      features_onehot, [-1, MAX_DOCUMENT_LENGTH, 256, 1])
  with tf.variable_scope('CNN_Layer1'):
    # Apply Convolution filtering on input sequence.
    conv1 = tf.layers.conv2d(
        input_layer,
        filters=N_FILTERS,
        kernel_size=FILTER_SHAPE1,
        padding='VALID',
        # Add a ReLU for non linearity.
        activation=tf.nn.relu)
    # Max pooling across output of Convolution+Relu.
    pool1 = tf.layers.max_pooling2d(
        conv1,
        pool_size=POOLING_WINDOW,
        strides=POOLING_STRIDE,
        padding='SAME')
    # Transpose matrix so that n_filters from convolution becomes width.
    pool1 = tf.transpose(pool1, [0, 1, 3, 2])
  with tf.variable_scope('CNN_Layer2'):
    # Second level of convolution filtering.
    conv2 = tf.layers.conv2d(
        pool1,
        filters=N_FILTERS,
        kernel_size=FILTER_SHAPE2,
        padding='VALID')
    # Max across each filter to get useful features for classification.
    pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1])

  # Apply regular WX + B and classification.
  logits = tf.layers.dense(pool2, MAX_LABEL, activation=None)

  predicted_classes = tf.argmax(logits, 1)
  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(
        mode=mode,
        predictions={
            'class': predicted_classes,
            'prob': tf.nn.softmax(logits)
        })

  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)

  eval_metric_ops = {
      'accuracy': tf.metrics.accuracy(
          labels=labels, predictions=predicted_classes)
  }
  return tf.estimator.EstimatorSpec(
      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)

  # Prepare training and testing data
  dbpedia = tf.contrib.learn.datasets.load_dataset(
      'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data, size='large')
  x_train = pandas.DataFrame(dbpedia.train.data)[1]
  y_train = pandas.Series(dbpedia.train.target)
  x_test = pandas.DataFrame(dbpedia.test.data)[1]
  y_test = pandas.Series(dbpedia.test.target)

  # Process vocabulary
  char_processor = tf.contrib.learn.preprocessing.ByteProcessor(
      MAX_DOCUMENT_LENGTH)
  x_train = np.array(list(char_processor.fit_transform(x_train)))
  x_test = np.array(list(char_processor.transform(x_test)))

  x_train = x_train.reshape([-1, MAX_DOCUMENT_LENGTH, 1, 1])
  x_test = x_test.reshape([-1, MAX_DOCUMENT_LENGTH, 1, 1])

  # Build model
  classifier = tf.estimator.Estimator(model_fn=char_cnn_model)

  # Train.
  train_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={CHARS_FEATURE: x_train},
      y=y_train,
      batch_size=128,
      num_epochs=None,
      shuffle=True)
  classifier.train(input_fn=train_input_fn, steps=100)

  # Predict.
  test_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={CHARS_FEATURE: x_test},
      y=y_test,
      num_epochs=1,
      shuffle=False)
  predictions = classifier.predict(input_fn=test_input_fn)
  y_predicted = np.array(list(p['class'] for p in predictions))
  y_predicted = y_predicted.reshape(np.array(y_test).shape)

  # Score with tensorflow.
  scores = classifier.evaluate(input_fn=test_input_fn)
  print('Accuracy: {0:f}'.format(scores['accuracy']))

if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--test_with_fake_data',
      default=False,
      help='Test the example code with fake data.',
      action='store_true')
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)


#!/usr/bin/python
# Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.

import os
import subprocess
import sys
import tempfile
import time

script_dir = os.path.dirname(__file__)
sys.path.append(os.path.join(script_dir,
                             '../../tools/browser_tester'))

import browser_tester
import browsertester.browserlauncher

# This script extends browser_tester to check for the presence of
# Breakpad crash dumps.

# This reads a file of lines containing 'key:value' pairs.
# The file contains entries like the following:
#   plat:Win32
#   prod:Chromium
#   ptype:nacl-loader
#   rept:crash svc
def ReadDumpTxtFile(filename):
  dump_info = {}
  fh = open(filename, 'r')
  for line in fh:
    if ':' in line:
      key, value = line.rstrip().split(':', 1)
      dump_info[key] = value
  fh.close()
  return dump_info

def StartCrashService(browser_path, dumps_dir, windows_pipe_name,
                      cleanup_funcs, crash_service_exe,
                      skip_if_missing=False):
  # Find crash_service.exe relative to chrome.exe.  This is a bit icky.
  browser_dir = os.path.dirname(browser_path)
  crash_service_path = os.path.join(browser_dir, crash_service_exe)
  if skip_if_missing and not os.path.exists(crash_service_path):
    return
  proc = subprocess.Popen([crash_service_path,
                           '--v=1',  # Verbose output for debugging failures
                           '--dumps-dir=%s' % dumps_dir,
                           '--pipe-name=%s' % windows_pipe_name])

  def Cleanup():
    # Note that if the process has already exited, this will raise
    # an 'Access is denied' WindowsError exception, but
    # crash_service.exe is not supposed to do this and such
    # behaviour should make the test fail.
    proc.terminate()
    status = proc.wait()
    sys.stdout.write('crash_dump_tester: %s exited with status %s\n'
                     % (crash_service_exe, status))

  cleanup_funcs.append(Cleanup)

def ListPathsInDir(dir_path):
  if os.path.exists(dir_path):
    return [os.path.join(dir_path, name)
            for name in os.listdir(dir_path)]
  else:
    return []

def GetDumpFiles(dumps_dirs):
  all_files = [filename
               for dumps_dir in dumps_dirs
               for filename in ListPathsInDir(dumps_dir)]
  sys.stdout.write('crash_dump_tester: Found %i files\n' % len(all_files))
  for dump_file in all_files:
    sys.stdout.write('  %s (size %i)\n'
                     % (dump_file, os.stat(dump_file).st_size))
  return [dump_file for dump_file in all_files
          if dump_file.endswith('.dmp')]

def Main(cleanup_funcs):
  parser = browser_tester.BuildArgParser()
  parser.add_option('--expected_crash_dumps', dest='expected_crash_dumps',
                    type=int, default=0,
                    help='The number of crash dumps that we should expect')
  parser.add_option('--expected_process_type_for_crash',
                    dest='expected_process_type_for_crash',
                    type=str, default='nacl-loader',
                    help='The type of Chromium process that we expect the '
                    'crash dump to be for')
  # Ideally we would just query the OS here to find out whether we are
  # running x86-32 or x86-64 Windows, but Python's win32api module
  # does not contain a wrapper for GetNativeSystemInfo(), which is
  # what NaCl uses to check this, or for IsWow64Process(), which is
  # what Chromium uses.  Instead, we just rely on the build system to
  # tell us.
  parser.add_option('--win64', dest='win64', action='store_true',
                    help='Pass this if we are running tests for x86-64 Windows')
  options, args = parser.parse_args()

  temp_dir = tempfile.mkdtemp(prefix='nacl_crash_dump_tester_')
  def CleanUpTempDir():
    browsertester.browserlauncher.RemoveDirectory(temp_dir)
  cleanup_funcs.append(CleanUpTempDir)

  # To get a guaranteed unique pipe name, use the base name of the
  # directory we just created.
  windows_pipe_name = r'\\.\pipe\%s_crash_service' % os.path.basename(temp_dir)

  # This environment variable enables Breakpad crash dumping in
  # non-official builds of Chromium.
  os.environ['CHROME_HEADLESS'] = '1'
  if sys.platform == 'win32':
    dumps_dir = temp_dir
    # Override the default (global) Windows pipe name that Chromium will
    # use for out-of-process crash reporting.
    os.environ['CHROME_BREAKPAD_PIPE_NAME'] = windows_pipe_name
    # Launch the x86-32 crash service so that we can handle crashes in
    # the browser process.
    StartCrashService(options.browser_path, dumps_dir, windows_pipe_name,
                      cleanup_funcs, 'crash_service.exe')
    if options.win64:
      # Launch the x86-64 crash service so that we can handle crashes
      # in the NaCl loader process (nacl64.exe).
      # Skip if missing, since in win64 builds crash_service.exe is 64-bit
      # and crash_service64.exe does not exist.
      StartCrashService(options.browser_path, dumps_dir, windows_pipe_name,
                        cleanup_funcs, 'crash_service64.exe',
                        skip_if_missing=True)
    # We add a delay because there is probably a race condition:
    # crash_service.exe might not have finished doing
    # CreateNamedPipe() before NaCl does a crash dump and tries to
    # connect to that pipe.
    # TODO(mseaborn): We could change crash_service.exe to report when
    # it has successfully created the named pipe.
    time.sleep(1)
  elif sys.platform == 'darwin':
    dumps_dir = temp_dir
    os.environ['BREAKPAD_DUMP_LOCATION'] = dumps_dir
  elif sys.platform.startswith('linux'):
    # The "--user-data-dir" option is not effective for the Breakpad
    # setup in Linux Chromium, because Breakpad is initialized before
    # "--user-data-dir" is read.  So we set HOME to redirect the crash
    # dumps to a temporary directory.
    home_dir = temp_dir
    os.environ['HOME'] = home_dir
    options.enable_crash_reporter = True

  result = browser_tester.Run(options.url, options)

  # Find crash dump results.
  if sys.platform.startswith('linux'):
    # Look in "~/.config/*/Crash Reports".  This will find crash
    # reports under ~/.config/chromium or ~/.config/google-chrome, or
    # under other subdirectories in case the branding is changed.
    dumps_dirs = [os.path.join(path, 'Crash Reports')
                  for path in ListPathsInDir(os.path.join(home_dir, '.config'))]
  else:
    dumps_dirs = [dumps_dir]
  dmp_files = GetDumpFiles(dumps_dirs)

  failed = False
  msg = ('crash_dump_tester: ERROR: Got %i crash dumps but expected %i\n' %
         (len(dmp_files), options.expected_crash_dumps))
  if len(dmp_files) != options.expected_crash_dumps:
    sys.stdout.write(msg)
    failed = True

  for dump_file in dmp_files:
    # Sanity check: Make sure dumping did not fail after opening the file.
    msg = 'crash_dump_tester: ERROR: Dump file is empty\n'
    if os.stat(dump_file).st_size == 0:
      sys.stdout.write(msg)
      failed = True

    # On Windows, the crash dumps should come in pairs of a .dmp and
    # .txt file.
    if sys.platform == 'win32':
      second_file = dump_file[:-4] + '.txt'
      msg = ('crash_dump_tester: ERROR: File %r is missing a corresponding '
             '%r file\n' % (dump_file, second_file))
      if not os.path.exists(second_file):
        sys.stdout.write(msg)
        failed = True
        continue
      # Check that the crash dump comes from the NaCl process.
      dump_info = ReadDumpTxtFile(second_file)
      if 'ptype' in dump_info:
        msg = ('crash_dump_tester: ERROR: Unexpected ptype value: %r != %r\n'
               % (dump_info['ptype'], options.expected_process_type_for_crash))
        if dump_info['ptype'] != options.expected_process_type_for_crash:
          sys.stdout.write(msg)
          failed = True
      else:
        sys.stdout.write('crash_dump_tester: ERROR: Missing ptype field\n')
        failed = True
    # TODO(mseaborn): Ideally we would also check that a backtrace
    # containing an expected function name can be extracted from the
    # crash dump.

  if failed:
    sys.stdout.write('crash_dump_tester: FAILED\n')
    result = 1
  else:
    sys.stdout.write('crash_dump_tester: PASSED\n')

  return result

def MainWrapper():
  cleanup_funcs = []
  try:
    return Main(cleanup_funcs)
  finally:
    for func in cleanup_funcs:
      func()

if __name__ == '__main__':
  sys.exit(MainWrapper())


#!/usr/bin/env python
import csv
import sys

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')

# arg 1 = w init
# arg 2 = n periods
# arg 3 = n ratio

# time step
dt = np.float64(0.00010)

# constants
L_0 = np.float64(1.0)  # unstretched length
g = np.float64(9.81)   # gravitation
n = np.float64(sys.argv[3])
K_over_M = (n*n - 1)*g/L_0

# initial conditions
theta = np.float64(0)
L = L_0 + g/K_over_M  # equilibrium length with gravity

# 2mgl = 1/2 m l^2 w^2
w_sep = np.sqrt(4.*g/L)
w_0 = np.float64(sys.argv[1])
w = w_0
#
v_l_0 = 0
v_l = v_l_0

# periods
T_p = 2.*np.pi/np.sqrt(g/L)
T_k = 2.*np.pi/np.sqrt(K_over_M)
# record some stuff
print "Tp = {} T/dt = {}".format(T_p, T_p/dt)
print "Tk = {} T/dt = {}".format(T_k, T_k/dt)
print "Tk/Tp = {}".format(T_k/T_p)
print "w_esc = {}".format(w_sep)

t = np.float64(0.0)
theta_last = theta

# keep some records
data = []
t_s = []

theta += w*dt/2.
L += v_l*dt/2.

for i in range(int(sys.argv[2])*int(T_p/dt)):
    w  += -dt*g*np.sin(theta)/L
    v_l += -K_over_M*(L-L_0) + g*np.cos(theta) + w*w*L

    theta += w*dt
    theta = np.fmod(theta, 2.*np.pi)
    L += v_l*dt

    t += dt
    data.append([t, theta, w, L, v_l])
    if theta_last < 0 and theta > 0:
        t_s.append(t)
    theta_last = theta

# periods by measure
t_s = [t_s[i] - t_s[i-1] for i in range(1,len(t_s)) ]
print "avg period = {} std periods = {}".format(np.average(t_s), np.std(t_s))

# plots
df = pd.DataFrame().from_records(data)
df.columns = ["t", "theta", "omega", "l", "v_l"]
df.set_index("t")

ax = df.plot(kind="scatter", x="theta", y="omega", marker=".")
fig = ax.get_figure()
fig.savefig("phase1.png")

ax = df.plot(kind="scatter", x="l", y="v_l", marker=".")
fig = ax.get_figure()
fig.savefig("phase2.png")

# config space
df["y_c"] = -df["l"]
df["x_c"] = df["l"] * np.sin(df["theta"])
ax = df.plot(kind="scatter", x="x_c", y="y_c", marker=".")
fig = ax.get_figure()
fig.savefig("config.png")


############################################################
'''
    testForks.py
    Written by: Brian O'Dell, Spetember 2017

    A program to run each program a 500 times per thread count.
    Then uses the data collected to make graphs and tables that
    are useful to evaluate the programs running time.
'''
############################################################

from subprocess import *
from numba import jit
import numpy as np
import csv as csv
import pandas as pd
from pandas.plotting import table
import matplotlib.pyplot as plt

'''
    Call the C program multiple times with variable arguments to gather data
    The name of the executable should exist before running
'''
@jit
def doCount(name):
    j = 0
    while (j < 1025):
        for i in range(0,501):
            call([name,"-t",str(j), "-w"])
        if (j == 0):
            j = 1

        else:
            j = 2*j;

'''
    Turn the data into something meaningful.
    Takes all the data gets the average and standard deviation for each
    number of threads. Then plots a graph based on it. Also, makes
    a csv with the avg and stddev
'''
@jit
def exportData(name):
    DF = pd.read_csv("data/"+name+".csv")
    f = {'ExecTime':['mean','std']}

    #group by the number of threads in the csv and
    #apply the mean and standard deviation functions to the groups
    avgDF = DF.groupby('NumThreads').agg(f)
    avgTable = DF.groupby('NumThreads', as_index=False).agg(f)

    #When the data csv was saved we used 0 to indicate serial execution
    #this was so the rows would be in numerical order instead of Alphabetical
    #Now rename index 0 to Serial to be an accurate representation
    indexList = avgDF.index.tolist()
    indexList[0] = 'Serial'
    avgDF.index = indexList

    #make the bar chart and set the axes
    avgPlot = avgDF.plot(kind='bar',
                 title=('Run Times Using '+ name), legend='False', figsize=(15,8))
    avgPlot.set_xlabel("Number of Forks")
    avgPlot.set_ylabel("Run Time (seconds)")

    #put the data values on top of the bars for clarity
    avgPlot.legend(['mean','std deviation'])
    for p in avgPlot.patches:
        avgPlot.annotate((str(p.get_height())[:6]),
                  (p.get_x()-.01, p.get_height()), fontsize=9)

    #save the files we need
    plt.savefig('data/'+name+'Graph.png')
    avgTable.to_csv('data/'+name+'Table.csv', index=False, encoding='utf-8')

def main():
    doCount("./forkedSemaphor")
    doCount("./forkedPrivateCount")
    doCount("./forkedPrivateCount32")
    exportData("forkedSemaphor")
    exportData("forkedPrivateCount")
    exportData("forkedPrivateCount32")

if __name__ == '__main__':
    main()