def isreadable(f):
    """
    Returns True if the file-like object can be read from.  This is a common-
    sense approximation of io.IOBase.readable.
    """

    if hasattr(f, 'closed') and f.closed:
        # This mimics the behavior of io.IOBase.readable
        raise ValueError('I/O operation on closed file')

    if not hasattr(f, 'read'):
        return False

    if hasattr(f, 'mode') and not any((c in f.mode for c in 'r+')):
        return False

    # Not closed, has a 'read()' method, and either has no known mode or a
    # readable mode--should be good enough to assume 'readable'
    return True def iswritable(f):
    """
    Returns True if the file-like object can be written to.  This is a common-
    sense approximation of io.IOBase.writable.
    """

    if hasattr(f, 'closed') and f.closed:
        # This mimics the behavior of io.IOBase.writable
        raise ValueError('I/O operation on closed file')

    if not hasattr(f, 'write'):
        return False

    if hasattr(f, 'mode') and not any((c in f.mode for c in 'wa+')):
        return False

    # Note closed, has a 'write()' method, and either has no known mode or a
    # mode that supports writing--should be good enough to assume 'writable'
    return True def isfile(f):
    """
    Returns True if the given object represents an OS-level file (that is,
    isinstance(f, file)).

    On Python 3 this also returns True if the given object is higher level
    wrapper on top of a FileIO object, such as a TextIOWrapper.
    """

    return isinstance(f, file) def fileobj_open(filename, mode):
    """
    A wrapper around the `open()` builtin.

    This exists because in Python 3, `open()` returns an `io.BufferedReader` by
    default.  This is bad, because `io.BufferedReader` doesn't support random
    access, which we need in some cases.  In the Python 3 case (implemented in
    the py3compat module) we must call open with buffering=0 to get a raw
    random-access file reader.
    """

    return open(filename, mode) def fileobj_name(f):
    """
    Returns the 'name' of file-like object f, if it has anything that could be
    called its name.  Otherwise f's class or type is returned.  If f is a
    string f itself is returned.
    """

    if isinstance(f, string_types):
        return f
    elif isinstance(f, gzip.GzipFile):
        # The .name attribute on GzipFiles does not always represent the name
        # of the file being read/written--it can also represent the original
        # name of the file being compressed
        # See the documentation at
        # https://docs.python.org/3/library/gzip.html#gzip.GzipFile
        # As such, for gzip files only return the name of the underlying
        # fileobj, if it exists
        return fileobj_name(f.fileobj)
    elif hasattr(f, 'name'):
        return f.name
    elif hasattr(f, 'filename'):
        return f.filename
    elif hasattr(f, '__class__'):
        return str(f.__class__)
    else:
        return str(type(f)) def fileobj_closed(f):
    """
    Returns True if the given file-like object is closed or if f is not a
    file-like object.
    """

    if hasattr(f, 'closed'):
        return f.closed
    elif hasattr(f, 'fileobj') and hasattr(f.fileobj, 'closed'):
        return f.fileobj.closed
    elif hasattr(f, 'fp') and hasattr(f.fp, 'closed'):
        return f.fp.closed
    else:
        return False def fileobj_mode(f):
    """
    Returns the 'mode' string of a file-like object if such a thing exists.
    Otherwise returns None.
    """

    # Go from most to least specific--for example gzip objects have a 'mode'
    # attribute, but it's not analogous to the file.mode attribute
    if hasattr(f, 'fileobj') and hasattr(f.fileobj, 'mode'):
        fileobj = f.fileobj
    elif hasattr(f, 'fp') and hasattr(f.fp, 'mode'):
        fileobj = f.fp
    elif hasattr(f, 'mode'):
        fileobj = f
    else:
        return None

    return _fileobj_normalize_mode(fileobj) def _array_from_file(infile, dtype, count, sep):
    """Create a numpy array from a file or a file-like object."""

    if isfile(infile):

        global CHUNKED_FROMFILE
        if CHUNKED_FROMFILE is None:
            if sys.platform == 'darwin' and LooseVersion(platform.mac_ver()[0]) < LooseVersion('10.9'):
                CHUNKED_FROMFILE = True
            else:
                CHUNKED_FROMFILE = False

        if CHUNKED_FROMFILE:
            chunk_size = int(1024 ** 3 / dtype.itemsize)  # 1Gb to be safe
            if count < chunk_size:
                return np.fromfile(infile, dtype=dtype, count=count, sep=sep)
            else:
                array = np.empty(count, dtype=dtype)
                for beg in range(0, count, chunk_size):
                    end = min(count, beg + chunk_size)
                    array[beg:end] = np.fromfile(infile, dtype=dtype, count=end - beg, sep=sep)
                return array
        else:
            return np.fromfile(infile, dtype=dtype, count=count, sep=sep)
    else:
        # treat as file-like object with "read" method; this includes gzip file
        # objects, because numpy.fromfile just reads the compressed bytes from
        # their underlying file object, instead of the decompressed bytes
        read_size = np.dtype(dtype).itemsize * count
        s = infile.read(read_size)
        return np.fromstring(s, dtype=dtype, count=count, sep=sep) def _array_to_file(arr, outfile):
    """
    Write a numpy array to a file or a file-like object.

    Parameters
    ----------
    arr : `~numpy.ndarray`
        The Numpy array to write.
    outfile : file-like
        A file-like object such as a Python file object, an `io.BytesIO`, or
        anything else with a ``write`` method.  The file object must support
        the buffer interface in its ``write``.

    If writing directly to an on-disk file this delegates directly to
    `ndarray.tofile`.  Otherwise a slower Python implementation is used.
    """


    if isfile(outfile):
        write = lambda a, f: a.tofile(f)
    else:
        write = _array_to_file_like

    # Implements a workaround for a bug deep in OSX's stdlib file writing
    # functions; on 64-bit OSX it is not possible to correctly write a number
    # of bytes greater than 2 ** 32 and divisible by 4096 (or possibly 8192--
    # whatever the default blocksize for the filesystem is).
    # This issue should have a workaround in Numpy too, but hasn't been
    # implemented there yet: https://github.com/astropy/astropy/issues/839
    #
    # Apparently Windows has its own fwrite bug:
    # https://github.com/numpy/numpy/issues/2256

    if (sys.platform == 'darwin' and arr.nbytes >= _OSX_WRITE_LIMIT + 1 and
            arr.nbytes % 4096 == 0):
        # chunksize is a count of elements in the array, not bytes
        chunksize = _OSX_WRITE_LIMIT // arr.itemsize
    elif sys.platform.startswith('win'):
        chunksize = _WIN_WRITE_LIMIT // arr.itemsize
    else:
        # Just pass the whole array to the write routine
        return write(arr, outfile)

    # Write one chunk at a time for systems whose fwrite chokes on large
    # writes.
    idx = 0
    arr = arr.view(np.ndarray).flatten()
    while idx < arr.nbytes:
        write(arr[idx:idx + chunksize], outfile)
        idx += chunksize def _write_string(f, s):
    """
    Write a string to a file, encoding to ASCII if the file is open in binary
    mode, or decoding if the file is open in text mode.
    """

    # Assume if the file object doesn't have a specific mode, that the mode is
    # binary
    binmode = fileobj_is_binary(f)

    if binmode and isinstance(s, text_type):
        s = encode_ascii(s)
    elif not binmode and not isinstance(f, text_type):
        s = decode_ascii(s)
    elif isinstance(f, StringIO) and isinstance(s, np.ndarray):
        # Workaround for StringIO/ndarray incompatibility
        s = s.data

    f.write(s) def encode_ascii(s):
    """
    In Python 2 this is a no-op.  Strings are left alone.  In Python 3 this
    will be replaced with a function that actually encodes unicode strings to
    ASCII bytes.
    """

    return s class classproperty(property):
    """
    Similar to `property`, but allows class-level properties.  That is,
    a property whose getter is like a `classmethod`.

    The wrapped method may explicitly use the `classmethod` decorator (which
    must become before this decorator), or the `classmethod` may be omitted
    (it is implicit through use of this decorator).

    .. note::

        classproperty only works for *read-only* properties.  It does not
        currently allow writeable/deleteable properties, due to subtleties of how
        Python descriptors work.  In order to implement such properties on a class
        a metaclass for that class must be implemented.

    Parameters
    ----------
    fget : callable
        The function that computes the value of this property (in particular,
        the function when this is used as a decorator) a la `property`.

    doc : str, optional
        The docstring for the property--by default inherited from the getter
        function.

    lazy : bool, optional
        If True, caches the value returned by the first call to the getter
        function, so that it is only called once (used for lazy evaluation
        of an attribute).  This is analogous to `lazyproperty`.  The ``lazy``
        argument can also be used when `classproperty` is used as a decorator
        (see the third example below).  When used in the decorator syntax this
        *must* be passed in as a keyword argument.

    Examples
    --------

    ::

        >>> class Foo(object):
        ...     _bar_internal = 1
        ...     @classproperty
        ...     def bar(cls):
        ...         return cls._bar_internal + 1
        ...
        >>> Foo.bar
        2
        >>> foo_instance = Foo()
        >>> foo_instance.bar
        2
        >>> foo_instance._bar_internal = 2
        >>> foo_instance.bar  # Ignores instance attributes
        2

    As previously noted, a `classproperty` is limited to implementing
    read-only attributes::

        >>> class Foo(object):
        ...     _bar_internal = 1
        ...     @classproperty
        ...     def bar(cls):
        ...         return cls._bar_internal
        ...     @bar.setter
        ...     def bar(cls, value):
        ...         cls._bar_internal = value
        ...
        Traceback (most recent call last):
        ...
        NotImplementedError: classproperty can only be read-only; use a
        metaclass to implement modifiable class-level properties

    When the ``lazy`` option is used, the getter is only called once::

        >>> class Foo(object):
        ...     @classproperty(lazy=True)
        ...     def bar(cls):
        ...         print("Performing complicated calculation")
        ...         return 1
        ...
        >>> Foo.bar
        Performing complicated calculation
        1
        >>> Foo.bar
        1

    If a subclass inherits a lazy `classproperty` the property is still
    re-evaluated for the subclass::

        >>> class FooSub(Foo):
        ...     pass
        ...
        >>> FooSub.bar
        Performing complicated calculation
        1
        >>> FooSub.bar
        1
    """

    def __new__(cls, fget=None, doc=None, lazy=False):
        if fget is None:
            # Being used as a decorator--return a wrapper that implements
            # decorator syntax
            def wrapper(func):
                return cls(func, lazy=lazy)

            return wrapper

        return super(classproperty, cls).__new__(cls)

    def __init__(self, fget, doc=None, lazy=False):
        self._lazy = lazy
        if lazy:
            self._cache = {}
        fget = self._wrap_fget(fget)

        super(classproperty, self).__init__(fget=fget, doc=doc)

        # There is a buglet in Python where self.__doc__ doesn't
        # get set properly on instances of property subclasses if
        # the doc argument was used rather than taking the docstring
        # from fget
        if doc is not None:
            self.__doc__ = doc

    def __get__(self, obj, objtype=None):
        if self._lazy and objtype in self._cache:
            return self._cache[objtype]

        if objtype is not None:
            # The base property.__get__ will just return self here;
            # instead we pass objtype through to the original wrapped
            # function (which takes the class as its sole argument)
            val = self.fget.__wrapped__(objtype)
        else:
            val = super(classproperty, self).__get__(obj, objtype=objtype)

        if self._lazy:
            if objtype is None:
                objtype = obj.__class__

            self._cache[objtype] = val

        return val

    def getter(self, fget):
        return super(classproperty, self).getter(self._wrap_fget(fget))

    def setter(self, fset):
        raise NotImplementedError(
            "classproperty can only be read-only; use a metaclass to "
            "implement modifiable class-level properties")

    def deleter(self, fdel):
        raise NotImplementedError(
            "classproperty can only be read-only; use a metaclass to "
            "implement modifiable class-level properties")

    @staticmethod
    def _wrap_fget(orig_fget):
        if isinstance(orig_fget, classmethod):
            orig_fget = orig_fget.__func__

        # Using stock functools.wraps instead of the fancier version
        # found later in this module, which is overkill for this purpose

        @functools.wraps(orig_fget)
        def fget(obj):
            return orig_fget(obj.__class__)

        # Set the __wrapped__ attribute manually for support on Python 2
        fget.__wrapped__ = orig_fget

        return fget
import gzip
import mmap
import os
import sys
import tempfile
import warnings
import zipfile
import bz2
import numpy as np
from numpy import memmap as Memmap
from .extern.six import b, string_types
from .extern.six.moves import urllib, reduce
from .util import (isreadable, iswritable, isfile, fileobj_open, fileobj_name,
                   fileobj_closed, fileobj_mode, _array_from_file,
                   _array_to_file, _write_string, encode_ascii, classproperty)
from __future__ import division, with_statement






# Maps PyFITS-specific file mode names to the appropriate file modes to use
# for the underlying raw files
PYFITS_MODES = {
    'readonly': 'rb',
    'copyonwrite': 'rb',
    'update': 'rb+',
    'append': 'ab+',
    'ostream': 'wb',
    'denywrite': 'rb'}

# This is the old name of the PYFITS_MODES dict; it is maintained here for
# backwards compatibility and should be removed no sooner than PyFITS 3.4
PYTHON_MODES = PYFITS_MODES

# Maps OS-level file modes to the appropriate PyFITS specific mode to use
# when given file objects but no mode specified; obviously in PYFITS_MODES
# there are overlaps; for example 'readonly' and 'denywrite' both require
# the file to be opened in 'rb' mode.  But 'readonly' is the default
# behavior for such files if not otherwise specified.
# Note: 'ab' is only supported for 'ostream' which is output-only.
FILE_MODES = {
    'rb': 'readonly', 'rb+': 'update',
    'wb': 'ostream', 'wb+': 'update',
    'ab': 'ostream', 'ab+': 'append'}


# readonly actually uses copyonwrite for mmap so that readonly without mmap and
# with mmap still have to same behavior with regard to updating the array.  To
# get a truly readonly mmap use denywrite
# the name 'denywrite' comes from a deprecated flag to mmap() on Linux--it
# should be clarified that 'denywrite' mode is not directly analogous to the
# use of that flag; it was just taken, for lack of anything better, as a name
# that means something like "read only" but isn't readonly.
MEMMAP_MODES = {'readonly': 'c', 'copyonwrite': 'c', 'update': 'r+',
                'append': 'c', 'denywrite': 'r'}

# TODO: Eventually raise a warning, and maybe even later disable the use of
# 'copyonwrite' and 'denywrite' modes unless memmap=True.  For now, however,
# that would generate too many warnings for too many users.  If nothing else,
# wait until the new logging system is in place.

GZIP_MAGIC = b('\x1f\x8b\x08')
PKZIP_MAGIC = b('\x50\x4b\x03\x04')
BZIP2_MAGIC = b('\x42\x5a')


class _File(object):
    """
    Represents a FITS file on disk (or in some other file-like object).
    """

    def __init__(self, fileobj=None, mode=None, memmap=None, clobber=False):
        self.strict_memmap = bool(memmap)
        memmap = True if memmap is None else memmap

        if fileobj is None:
            self._file = None
            self.closed = False
            self.binary = True
            self.mode = mode
            self.memmap = memmap
            self.compression = None
            self.readonly = False
            self.writeonly = False
            self.simulateonly = True
            return
        else:
            self.simulateonly = False

        # Holds mmap instance for files that use mmap
        self._mmap = None

        if mode is None:
            if _is_random_access_file_backed(fileobj):

                fmode = fileobj_mode(fileobj)