# Source code for spacepy.pycdf

    #!/usr/bin/env python
    # -*- coding: utf-8 -*-

    """
    This package provides a Python interface to the Common Data Format (CDF)
    library used for many NASA missions, available at http://cdf.gsfc.nasa.gov/.
    It is targeted at Python 2.6+ and should work without change on either
    Python 2 or Python 3.

    The interface is intended to be 'pythonic' rather than reproducing the
    C interface. To open or close a CDF and access its variables, see the :class:`CDF`
    class. Accessing data within the variables is via the :class:`Var`
    class. The :data:`lib` object provides access to some routines
    that affect the functionality of the library in general. The
    :mod:`~spacepy.pycdf.const` module contains constants useful for accessing
    the underlying library.

    The CDF C library must be properly installed in order to use this package.
    The CDF distribution provides scripts meant to be called in a user's
    login scripts, ``definitions.B`` for bash and ``definitions.C`` for C-shell
    derivatives. (See the installation instructions which come with the CDF library.)
    These will set environment variables specifying the location
    of the library; pycdf will respect these variables if they are set. Otherwise
    it will search the standard system library path and the default installation
    locations for the CDF library.

    If pycdf has trouble finding the library, try setting ``CDF_LIB`` before importing
    the module, e.g. if the library is in ``CDF/lib`` in the user's home directory:

    >>> import os
    >>> os.environ["CDF_LIB"] = "~/CDF/lib"
    >>> from spacepy import pycdf

    If this works, make the environment setting permanent. Note that on OSX,
    using plists to set the environment may not carry over to Python terminal
    sessions; use ``.cshrc`` or ``.bashrc`` instead.

    Authors: Jon Niehof

    Institution: University of New Hampshire

    Contact: Jonathan.Niehof@unh.edu

    Copyright 2010-2015 Los Alamos National Security, LLC.
    """

    __contact__ = 'Jon Niehof, Jonathan.Niehof@unh.edu'

    try:
        from collections.abc import MutableMapping, MutableSequence
    except ImportError:
        from collections import MutableMapping, MutableSequence
    import ctypes
    import ctypes.util
    import datetime
    import itertools
    import operator
    import os
    import os.path
    import shutil
    import sys
    import tempfile
    import warnings
    import weakref

    import numpy
    import numpy.ma
    import spacepy.datamodel
    import spacepy.time
    try:
        import matplotlib.dates
        HAVE_MATPLOTLIB = True
    except ImportError:
        HAVE_MATPLOTLIB = False

    #Import const AFTER library loaded, so failed load doesn't leave half-imported
    #from . import const

    try:
        str_classes = (str, bytes, unicode)
    except NameError:
        str_classes = (str, bytes)

    class Library(object):
        """
        Abstraction of the base CDF C library and its state.

        Not normally intended for end-user use. An instance of this class
        is created at package load time as the :data:`~spacepy.pycdf.lib` variable, providing
        access to the underlying C library if necessary. The CDF library itself
        is described in section 2.1 of the CDF user's guide, as well as the CDF
        C reference manual.

        Calling the C library directly requires knowledge of
        :mod:`ctypes`.

        Instantiating this object loads the C library, see :doc:`/pycdf` docs
        for details.

        .. autosummary::

            ~Library.call
            ~Library.check_status
            ~Library.datetime_to_epoch
            ~Library.datetime_to_epoch16
            ~Library.datetime_to_tt2000
            ~Library.epoch_to_datetime
            ~Library.epoch_to_epoch16
            ~Library.epoch_to_num
            ~Library.epoch_to_tt2000
            ~Library.epoch16_to_datetime
            ~Library.epoch16_to_epoch
            ~Library.epoch16_to_tt2000
            ~Library.get_minmax
            ~Library.set_backward
            supports_int8
            ~Library.tt2000_to_datetime
            ~Library.tt2000_to_epoch
            ~Library.tt2000_to_epoch16
            v_datetime_to_epoch
            v_datetime_to_epoch16
            v_datetime_to_tt2000
            v_epoch_to_datetime
            v_epoch_to_tt2000
            v_epoch16_to_datetime
            v_epoch16_to_tt2000
            v_tt2000_to_datetime
            v_tt2000_to_epoch
            v_tt2000_to_epoch16
            libpath
            version

        .. automethod:: call
        .. automethod:: check_status
        .. automethod:: datetime_to_epoch
        .. automethod:: datetime_to_epoch16
        .. automethod:: datetime_to_tt2000
        .. automethod:: epoch_to_datetime
        .. automethod:: epoch_to_epoch16
        .. automethod:: epoch_to_num
        .. automethod:: epoch_to_tt2000
        .. automethod:: epoch16_to_datetime
        .. automethod:: epoch16_to_epoch
        .. automethod:: epoch16_to_tt2000
        .. automethod:: get_minmax
        .. automethod:: set_backward

        .. attribute:: supports_int8

           True if this library supports INT8 and TIME_TT2000 types; else False.

        .. automethod:: tt2000_to_datetime
        .. automethod:: tt2000_to_epoch
        .. automethod:: tt2000_to_epoch16

        .. method:: v_datetime_to_epoch(datetime)

            A vectorized version of :meth:`datetime_to_epoch` which takes a
            numpy array of datetimes as input and returns an array of epochs.

        .. method:: v_datetime_to_epoch16(datetime)

            A vectorized version of :meth:`datetime_to_epoch16` which takes a
            numpy array of datetimes as input and returns an array of epoch16.

        .. method:: v_datetime_to_tt2000(datetime)

            A vectorized version of :meth:`datetime_to_tt2000` which takes a
            numpy array of datetimes as input and returns an array of TT2000.

        .. method:: v_epoch_to_datetime(epoch)

            A vectorized version of :meth:`epoch_to_datetime` which takes a
            numpy array of epochs as input and returns an array of datetimes.

        .. method:: v_epoch_to_tt2000(epoch)

            A vectorized version of :meth:`epoch_to_tt2000` which takes a
            numpy array of epochs as input and returns an array of tt2000s.

        .. method:: v_epoch16_to_datetime(epoch0, epoch1)

            A vectorized version of :meth:`epoch16_to_datetime` which takes
            a numpy array of epoch16 as input and returns an array of datetimes.
            An epoch16 is a pair of doubles; the input array's last dimension
            must be two (and the returned array will have one fewer dimension).

        .. method:: v_epoch16_to_tt2000(epoch16)

            A vectorized version of :meth:`epoch16_to_tt2000` which takes
            a numpy array of epoch16 as input and returns an array of tt2000s.
            An epoch16 is a pair of doubles; the input array's last dimension
            must be two (and the returned array will have one fewer dimension).

        .. method:: v_tt2000_to_datetime(tt2000)

            A vectorized version of :meth:`tt2000_to_datetime` which takes
            a numpy array of tt2000 as input and returns an array of datetimes.

        .. method:: v_tt2000_to_epoch(tt2000)

            A vectorized version of :meth:`tt2000_to_epoch` which takes
            a numpy array of tt2000 as input and returns an array of epochs.

        .. method:: v_tt2000_to_epoch16(tt2000)

            A vectorized version of :meth:`tt2000_to_epoch16` which takes
            a numpy array of tt2000 as input and returns an array of epoch16.

        .. attribute:: libpath

           The path where pycdf found the CDF C library, potentially useful in
           debugging. If this contains just the name of a file (with no path
           information), then the system linker found the library for pycdf.
           On Linux, ``ldconfig -p`` may be useful for displaying the system's
           library resolution.

        .. attribute:: version

           Version of the CDF library, (version, release, increment, subincrement)
        """
        def __init__(self, libpath=None, library=None):
            """Load the CDF C library.

            Searches for the library in the order:
                1. Appropriately-named file in CDF_LIB
                2. Appropriately-named file in CDF_BASE
                3. Standard library search path
            @raise CDFError: BAD_DATA_TYPE if can't map types properly
            """

            if not 'CDF_TMP' in os.environ:
                os.environ['CDF_TMP'] = tempfile.gettempdir()

            if not library:
                if not libpath:
                    self.libpath, self._library = self._find_lib()
                    if self._library is None:
                        raise Exception((
                            'Cannot load CDF C library; checked {0}. '
                            'Try \'os.environ["CDF_LIB"] = library_directory\' '
                            'before import.').format(', '.join(self.libpath)))
                else:
                    self._library = ctypes.CDLL(libpath)
                    self.libpath = libpath
            else:
                self._library = library
                self.libpath = libpath
            self._library.CDFlib.restype = ctypes.c_long #commonly used, so set it up here
            self._library.EPOCHbreakdown.restype = ctypes.c_long
            self._library.computeEPOCH.restype = ctypes.c_double
            self._library.computeEPOCH.argtypes = [ctypes.c_long] * 7
            self._library.computeEPOCH16.restype = ctypes.c_double
            self._library.computeEPOCH16.argtypes = [ctypes.c_long] * 10 + \
                [ctypes.POINTER(ctypes.c_double * 2)]
            if hasattr(self._library, 'CDFsetFileBackward'):
                self._library.CDFsetFileBackward.restype = None
                self._library.CDFsetFileBackward.argtypes = [ctypes.c_long]
            #Map old name to the 3.7.1+ name
            if not hasattr(self._library, 'computeTT2000') \
               and hasattr(self._library, 'CDF_TT2000_from_UTC_parts'):
                self._library.computeTT2000 \
                    = self._library.CDF_TT2000_from_UTC_parts
            if hasattr(self._library, 'computeTT2000'):
                self._library.computeTT2000.restype = ctypes.c_longlong
                self._library.computeTT2000.argtypes = \
                    [ctypes.c_double] *9
            #Map old name to the 3.7.1+ name
            if not hasattr(self._library, 'breakdownTT2000') \
               and hasattr(self._library, 'CDF_TT2000_to_UTC_parts'):
                self._library.breakdownTT2000 \
                    = self._library.CDF_TT2000_to_UTC_parts
            if hasattr(self._library, 'breakdownTT2000'):
                self._library.breakdownTT2000.restype = None
                self._library.breakdownTT2000.argtypes = \
                    [ctypes.c_longlong] + [ctypes.POINTER(ctypes.c_double)] * 9
            if hasattr(self._library, 'CDF_TT2000_to_UTC_EPOCH'):
                self._library.CDF_TT2000_to_UTC_EPOCH.restype = ctypes.c_double
                self._library.CDF_TT2000_to_UTC_EPOCH.argtypes = [ctypes.c_longlong]
            if hasattr(self._library, 'CDF_TT2000_from_UTC_EPOCH'):
                self._library.CDF_TT2000_from_UTC_EPOCH.restype = ctypes.c_longlong
                self._library.CDF_TT2000_from_UTC_EPOCH.argtypes = [ctypes.c_double]
            if hasattr(self._library, 'CDF_TT2000_to_UTC_EPOCH16'):
                self._library.CDF_TT2000_to_UTC_EPOCH16.restype = ctypes.c_double
                self._library.CDF_TT2000_to_UTC_EPOCH16.argtypes = \
                    [ctypes.c_longlong, ctypes.POINTER(ctypes.c_double * 2)]
            if hasattr(self._library, 'CDF_TT2000_from_UTC_EPOCH16'):
                self._library.CDF_TT2000_from_UTC_EPOCH16.restype = \
                    ctypes.c_longlong
                self._library.CDF_TT2000_from_UTC_EPOCH16.argtypes = \
                    [ctypes.POINTER(ctypes.c_double * 2)]

            #Get CDF version information
            ver = ctypes.c_long(0)
            rel = ctypes.c_long(0)
            inc = ctypes.c_long(0)
            sub = ctypes.c_char(b' ')
            self.call(const.GET_, const.LIB_VERSION_, ctypes.byref(ver),
                      const.GET_, const.LIB_RELEASE_, ctypes.byref(rel),
                      const.GET_, const.LIB_INCREMENT_, ctypes.byref(inc),
                      const.GET_, const.LIB_subINCREMENT_, ctypes.byref(sub))
            ver = ver.value
            rel = rel.value
            inc = inc.value
            sub = sub.value
            self.version = (ver, rel, inc, sub)
            self._del_middle_rec_bug = ver < 3 or (ver == 3 and
                                                   (rel < 4 or
                                                    (rel == 4 and inc < 1)))
            self.supports_int8 = (ver > 3 or (ver == 3 and rel >=4))

            self.cdftypenames = {const.CDF_BYTE.value: 'CDF_BYTE',
                                 const.CDF_CHAR.value: 'CDF_CHAR',
                                 const.CDF_INT1.value: 'CDF_INT1',
                                 const.CDF_UCHAR.value: 'CDF_UCHAR',
                                 const.CDF_UINT1.value: 'CDF_UINT1',
                                 const.CDF_INT2.value: 'CDF_INT2',
                                 const.CDF_UINT2.value: 'CDF_UINT2',
                                 const.CDF_INT4.value: 'CDF_INT4',
                                 const.CDF_UINT4.value: 'CDF_UINT4',
                                 const.CDF_INT8.value: 'CDF_INT8',
                                 const.CDF_FLOAT.value: 'CDF_FLOAT',
                                 const.CDF_REAL4.value: 'CDF_REAL4',
                                 const.CDF_DOUBLE.value: 'CDF_DOUBLE',
                                 const.CDF_REAL8.value: 'CDF_REAL8',
                                 const.CDF_EPOCH.value: 'CDF_EPOCH',
                                 const.CDF_EPOCH16.value: 'CDF_EPOCH16',
                                 const.CDF_TIME_TT2000.value: 'CDF_TIME_TT2000',
                                 }
            self.numpytypedict = {const.CDF_BYTE.value: numpy.int8,
                                  const.CDF_CHAR.value: numpy.int8,
                                  const.CDF_INT1.value: numpy.int8,
                                  const.CDF_UCHAR.value: numpy.uint8,
                                  const.CDF_UINT1.value: numpy.uint8,
                                  const.CDF_INT2.value: numpy.int16,
                                  const.CDF_UINT2.value: numpy.uint16,
                                  const.CDF_INT4.value: numpy.int32,
                                  const.CDF_UINT4.value: numpy.uint32,
                                  const.CDF_INT8.value: numpy.int64,
                                  const.CDF_FLOAT.value: numpy.float32,
                                  const.CDF_REAL4.value: numpy.float32,
                                  const.CDF_DOUBLE.value: numpy.float64,
                                  const.CDF_REAL8.value: numpy.float64,
                                  const.CDF_EPOCH.value: numpy.float64,
                                  const.CDF_EPOCH16.value:
                                  numpy.dtype((numpy.float64, 2)),
                                  const.CDF_TIME_TT2000.value: numpy.int64,
                                  }
            self.timetypes = [const.CDF_EPOCH.value,
                              const.CDF_EPOCH16.value,
                              const.CDF_TIME_TT2000.value]
            if not self.supports_int8:
                del self.cdftypenames[const.CDF_INT8.value]
                del self.numpytypedict[const.CDF_INT8.value]
                del self.cdftypenames[const.CDF_TIME_TT2000.value]
                del self.numpytypedict[const.CDF_TIME_TT2000.value]
            elif sys.platform.startswith('linux') \
                 and os.uname()[4].startswith('arm') \
                 and hasattr(self._library, 'computeTT2000') \
                 and self._library.computeTT2000(
                     2010, 1, 1, 0, 0, 0, 0, 0, 0) != 315576066184000000:
                #TT2000 call failed, so probably need to type-pun
                #double arguments to variadic functions.
                #Calling convention for non-variadic functions with floats
                #is unique, but convention for ints is same as variadic.
                #So type-pun arguments to integers to force that calling
                #convention.
                if ctypes.sizeof(ctypes.c_longlong) != \
                   ctypes.sizeof(ctypes.c_double):
                    warnings.warn('ARM with unknown type sizes; '
                                  'TT2000 functions will not work.')
                else:
                    self._library.computeTT2000.argtypes = \
                        [ctypes.c_longlong] * 9
                    c_ll_p = ctypes.POINTER(ctypes.c_longlong)
                    if self._library.computeTT2000(
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            2010)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            1)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            1)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            0)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            0)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            0)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            0)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            0)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            0)), c_ll_p).contents) != 315576066184000000:
                        warnings.warn('ARM with unknown calling convention; '
                                      'TT2000 functions will not work.')
                    self.datetime_to_tt2000 = self._datetime_to_tt2000_typepunned

            v_epoch16_to_datetime = numpy.frompyfunc(
                self.epoch16_to_datetime, 2, 1)
            self.v_epoch16_to_datetime = \
                lambda x: v_epoch16_to_datetime(x[..., 0], x[..., 1])
            self.v_epoch_to_datetime = numpy.frompyfunc(
                self.epoch_to_datetime, 1, 1)
            self.v_tt2000_to_datetime = numpy.frompyfunc(
                self.tt2000_to_datetime, 1, 1)
            self.v_datetime_to_epoch = numpy.vectorize(
                self.datetime_to_epoch, otypes=[numpy.float64])
            v_datetime_to_epoch16 = numpy.frompyfunc(
                self.datetime_to_epoch16, 1, 2)
            #frompyfunc returns a TUPLE of the returned values,
            #implicitly the 0th dimension. We want everything from one
            #call paired, so this rolls the 0th dimension to the last
            #(via the second-to-last)
            def _v_datetime_to_epoch16(x):
                retval = numpy.require(v_datetime_to_epoch16(x),
                                          dtype=numpy.float64)
                if len(retval.shape) > 1:
                    return numpy.rollaxis(
                        numpy.rollaxis(retval, 0, -1),
                        -1, -2)
                else:
                    return retval
            self.v_datetime_to_epoch16 = _v_datetime_to_epoch16
            self.v_datetime_to_tt2000 = numpy.vectorize(
                self.datetime_to_tt2000, otypes=[numpy.int64])
            self.v_epoch_to_tt2000 = numpy.vectorize(
                self.epoch_to_tt2000, otypes=[numpy.int64])
            self.v_tt2000_to_epoch = numpy.vectorize(
                self.tt2000_to_epoch, otypes=[numpy.float64])
            v_epoch16_to_tt2000 = numpy.frompyfunc(
                self.epoch16_to_tt2000, 2, 1)
            self.v_epoch16_to_tt2000 = \
                lambda x: v_epoch16_to_tt2000(x[..., 0], x[..., 1])
            v_tt2000_to_epoch16 = numpy.frompyfunc(
                self.tt2000_to_epoch16, 1, 2)
            #frompyfunc returns a TUPLE of the returned values,
            #implicitly the 0th dimension. We want everything from one
            #call paired, so this rolls the 0th dimension to the last
            #(via the second-to-last)
            def _v_tt2000_to_epoch16(x):
                retval = numpy.require(v_tt2000_to_epoch16(x),
                                          dtype=numpy.float64)
                if len(retval.shape) > 1:
                    return numpy.rollaxis(
                        numpy.rollaxis(retval, 0, -1),
                        -1, -2)
                else:
                    return retval
            self.v_tt2000_to_epoch16 = _v_tt2000_to_epoch16
            if not self.supports_int8:
                self.datetime_to_tt2000 = self._bad_tt2000
                self.tt2000_to_datetime = self._bad_tt2000
                self.v_datetime_to_tt2000 = self._bad_tt2000
                self.v_tt2000_to_datetime = self._bad_tt2000
                self.epoch_to_tt2000 = self._bad_tt2000
                self.v_epoch_to_tt2000 = self._bad_tt2000
                self.tt2000_to_epoch = self._bad_tt2000
                self.v_tt2000_to_epoch = self._bad_tt2000
                self.epoch_16_to_tt2000 =  self._bad_tt2000
                self.v_epoch16_to_tt2000 = self._bad_tt2000
                self.tt2000_to_epoch16 = self._bad_tt2000
                self.v_tt2000_to_epoch16 = self._bad_tt2000

            #Default to V2 CDF
            self.set_backward(True)
            # User has not explicitly called set_backward
            self._explicit_backward = False

        @staticmethod
        def _find_lib():
            """
            Search for the CDF library

            Searches in likely locations for CDF libraries and attempts to load
            them. Stops at first successful load and, if fails, reports all
            the files that were tried as libraries.

            Returns
            =======
            out : tuple
                 This is either (path to library, loaded library)
                 or, in the event of failure, (None, list of libraries tried)
            """
            failed = []
            for libpath in Library._lib_paths():
                try:
                    lib = ctypes.CDLL(libpath)
                except:
                    failed.append(libpath)
                else:
                    return libpath, lib
            return (failed, None)

        @staticmethod
        def _lib_paths():
            """Find candidate paths for the CDF library

            Does not check that the library is actually in any particular directory,
            just returns a list of possible locations, in priority order.

            Returns
            =======
            out : generator of str
                paths that look like the CDF library
            """
            #What the library might be named
            names = { 'win32': ['dllcdf.dll'],
                      'darwin': ['libcdf.dylib', 'cdf.dylib', 'libcdf.so'],
                      'linux2': ['libcdf.so'],
                      'linux': ['libcdf.so'],
                      }
            names = names.get(sys.platform, ['libcdf.so'])
            #All existing CDF-library-like paths within a directory
            search_dir = lambda x: \
                [os.path.join(x, fname) for fname in names
                 if os.path.exists(os.path.join(x, fname))]
            #Search the environment-specified places first
            if 'CDF_LIB' in os.environ:
                for p in search_dir(os.environ['CDF_LIB']):
                    yield p
            if 'CDF_BASE' in os.environ:
                for p in search_dir(os.path.join(os.environ['CDF_BASE'], 'lib')):
                    yield p
            ctypespath = ctypes.util.find_library(
                'dllcdf.dll' if sys.platform == 'win32' else 'cdf')
            if ctypespath:
                yield ctypespath
            #LD_LIBRARY_PATH specifies runtime library search paths
            if 'LD_LIBRARY_PATH' in os.environ:
                for d in os.environ['LD_LIBRARY_PATH'].split(os.pathsep):
                    for p in search_dir(d):
                        yield p
            #Finally, defaults places CDF gets installed uner
            #CDF_BASE is usually a subdir of these (with "cdf" in the name)
            #Searched in order given here!
            cdfdists = { 'win32': ['c:\\CDF Distribution\\', 'c:\\CDF_Distribution\\'],
                        'darwin': ['/Applications/', '/usr/local/',
                                   os.path.expanduser('~')],
                        'linux2': ['/usr/local/', os.path.expanduser('~')],
                        'linux': ['/usr/local/', os.path.expanduser('~')],
                        }
            if sys.platform in cdfdists:
                for cdfdist in cdfdists[sys.platform]:
                    if os.path.isdir(cdfdist):
                        cand = []
                        for d in os.listdir(cdfdist):
                            if d[0:3].lower() == 'cdf':
                                #checking src in case BUILT but not INSTALLED
                                for subdir in ['lib', os.path.join('src', 'lib')]:
                                    libdir = os.path.join(cdfdist, d, subdir)
                                    if os.path.isdir(libdir):
                                        cand.append(libdir)
                        #Sort reverse, so new versions are first FOR THIS cdfdist
                        for d in sorted(cand)[::-1]:
                            for p in search_dir(d):
                                yield p

        def check_status(self, status, ignore=()):
            """
            Raise exception or warning based on return status of CDF call

            Parameters
            ==========
            status : int
                status returned by the C library

            Other Parameters
            ================
            ignore : sequence of ctypes.c_long
                CDF statuses to ignore. If any of these is returned by CDF library,
                any related warnings or exceptions will *not* be raised.
                (Default none).

            Raises
            ======
            CDFError : if status < CDF_WARN, indicating an error

            Warns
            =====
            CDFWarning : if CDF_WARN <= status < CDF_OK, indicating a warning.

            Returns
            =======
            out : int
                status (unchanged)
            """
            if status == const.CDF_OK or status in ignore:
                return status
            if status < const.CDF_WARN:
                raise CDFError(status)
            else:
                warning = CDFWarning(status)
                warning.warn()
                return status

        def call(self, *args, **kwargs):
            """
            Call the CDF internal interface

            Passes all parameters directly through to the CDFlib routine of the
            CDF library's C internal interface. Checks the return value with
            :meth:`check_status`.

            Terminal NULL is automatically added to args.

            Parameters
            ==========
            args : various, see :mod:`ctypes`
                Passed directly to the CDF library interface. Useful
                constants are defined in the :mod:`~pycdf.const` module.

            Other Parameters
            ================
            ignore : sequence of CDF statuses
                sequence of CDF statuses to ignore. If any of these
                is returned by CDF library, any related warnings or
                exceptions will *not* be raised.

            Returns
            =======
            out : int
                CDF status from the library

            Raises
            ======
            CDFError : if CDF library reports an error

            Warns
            =====
            CDFWarning : if CDF library reports a warning
            """
            if 'ignore' in kwargs:
                return self.check_status(self._library.CDFlib(
                    *(args + (const.NULL_, ))
                    ), kwargs['ignore'])
            else:
                return self.check_status(self._library.CDFlib(
                    *(args + (const.NULL_, ))
                    ))

        def set_backward(self, backward=True):
            """
            Set backward compatibility mode for new CDFs

            Unless backward compatible mode is set, CDF files created by
            the version 3 library can not be read by V2.

            Parameters
            ==========
            backward : boolean
                Set backward compatible mode if True; clear it if False.

            Raises
            ======
            ValueError : if backward=False and underlying CDF library is V2
            """
            # User has explicitly chosen backward compat or not
            self._explicit_backward = True
            if self.version[0] < 3:
                if not backward:
                    raise ValueError(
                        'Cannot disable backward-compatible mode for CDF version 2.')
                else:
                    return
            self._library.CDFsetFileBackward(const.BACKWARDFILEon if backward
                                             else const.BACKWARDFILEoff)

        def epoch_to_datetime(self, epoch):
            """
            Converts a CDF epoch value to a datetime

            Parameters
            ==========
            epoch : float
                epoch value from CDF

            Returns
            =======
            out : :class:`datetime.datetime`
                date and time corresponding to epoch. Invalid values are set to
                usual epoch invalid value, i.e. last moment of year 9999.

            See Also
            ========
            v_epoch_to_datetime
            """
            yyyy = ctypes.c_long(0)
            mm = ctypes.c_long(0)
            dd = ctypes.c_long(0)
            hh = ctypes.c_long(0)
            min = ctypes.c_long(0)
            sec = ctypes.c_long(0)
            msec = ctypes.c_long(0)
            self._library.EPOCHbreakdown(ctypes.c_double(epoch),
                                         ctypes.byref(yyyy), ctypes.byref(mm),
                                         ctypes.byref(dd),
                                         ctypes.byref(hh), ctypes.byref(min),
                                         ctypes.byref(sec), ctypes.byref(msec))
            if yyyy.value <= 0:
                return datetime.datetime(9999, 12, 13, 23, 59, 59, 999000)
            else:
                return datetime.datetime(yyyy.value, mm.value, dd.value,
                                         hh.value, min.value, sec.value,
                                         msec.value * 1000)

        def datetime_to_epoch(self, dt):
            """
            Converts a Python datetime to a CDF Epoch value

            Parameters
            ==========
            dt : :class:`datetime.datetime`
                date and time to convert

            Returns
            =======
            out : float
                epoch corresponding to dt

            See Also
            ========
            v_datetime_to_epoch
            """
            if dt.tzinfo != None and dt.utcoffset() != None:
                dt = dt - dt.utcoffset()
            dt.replace(tzinfo=None)
            micro = dt.microsecond % 1000
            if micro >= 500 and dt.year < 9999:
                dt += datetime.timedelta(0, 0, 1000)
            return self._library.computeEPOCH(dt.year, dt.month, dt.day, dt.hour,
                                              dt.minute, dt.second,
                                              int(dt.microsecond / 1000))

        def epoch16_to_datetime(self, epoch0, epoch1):
            """
            Converts a CDF epoch16 value to a datetime

            .. note::
                The call signature has changed since SpacePy 0.1.2. Formerly
                this method took a single argument with two values; now it
                requires two arguments (one for each value). To convert existing
                code, replace ``epoch16_to_datetime(epoch)`` with
                ``epoch16_to_datetime(*epoch)``.

            Parameters
            ==========
            epoch0 : float
                epoch16 value from CDF, first half
            epoch1 : float
                epoch16 value from CDF, second half

            Raises
            ======
            EpochError : if input invalid

            Returns
            =======
            out : :class:`datetime.datetime`
                date and time corresponding to epoch. Invalid values are set to
                usual epoch invalid value, i.e. last moment of year 9999.

            See Also
            ========
            v_epoch16_to_datetime
            """
            yyyy = ctypes.c_long(0)
            mm = ctypes.c_long(0)
            dd = ctypes.c_long(0)
            hh = ctypes.c_long(0)
            min = ctypes.c_long(0)
            sec = ctypes.c_long(0)
            msec = ctypes.c_long(0)
            usec = ctypes.c_long(0)
            nsec = ctypes.c_long(0)
            psec = ctypes.c_long(0)
            self._library.EPOCH16breakdown((ctypes.c_double * 2)(epoch0, epoch1),
                                         ctypes.byref(yyyy), ctypes.byref(mm),
                                         ctypes.byref(dd),
                                         ctypes.byref(hh), ctypes.byref(min),
                                         ctypes.byref(sec), ctypes.byref(msec),
                                         ctypes.byref(usec), ctypes.byref(nsec),
                                         ctypes.byref(psec))
            if yyyy.value <= 0:
                return datetime.datetime(9999, 12, 13, 23, 59, 59, 999999)
            micro = int(float(msec.value) * 1000 + float(usec.value) +
                        float(nsec.value) / 1000 + float(psec.value) / 1e6 + 0.5)
            if micro < 1000000:
                return datetime.datetime(yyyy.value, mm.value, dd.value,
                                         hh.value, min.value, sec.value,
                                         micro)
            else:
                add_sec = int(micro / 1000000)
                try:
                    return datetime.datetime(yyyy.value, mm.value, dd.value,
                                             hh.value, min.value, sec.value,
                                             micro - add_sec * 1000000) + \
                                             datetime.timedelta(seconds=add_sec)
                except OverflowError:
                    return datetime.datetime(datetime.MAXYEAR, 12, 31,
                                             23, 59, 59,
                                             999999)

        def datetime_to_epoch16(self, dt):
            """
            Converts a Python datetime to a CDF Epoch16 value

            Parameters
            ==========
            dt :  :class:`datetime.datetime`
                date and time to convert

            Returns
            =======
            out : list of float
                epoch16 corresponding to dt

            See Also
            ========
            v_datetime_to_epoch16
            """
            if dt.tzinfo != None and dt.utcoffset() != None:
                dt = dt - dt.utcoffset()
            dt.replace(tzinfo=None)
            #Default to "illegal epoch"
            epoch16 = (ctypes.c_double * 2)(-1., -1.)
            if self._library.computeEPOCH16(dt.year, dt.month, dt.day, dt.hour,
                                            dt.minute, dt.second,
                                            int(dt.microsecond / 1000),
                                            dt.microsecond % 1000, 0, 0,
                                            epoch16):
                return (-1., -1.) #Failure, so illegal epoch
            return (epoch16[0], epoch16[1])

        def epoch_to_epoch16(self, epoch):
            """
            Converts a CDF EPOCH to a CDF EPOCH16 value

            Parameters
            ==========
            epoch : double
                EPOCH to convert. Lists and numpy arrays are acceptable.

            Returns
            =======
            out : (double, double)
                EPOCH16 corresponding to epoch
            """
            e = numpy.require(epoch, numpy.float64)
            s = numpy.trunc(e / 1000.0)
            #ugly numpy stuff, probably a better way....
            res = numpy.hstack((s, (e - s * 1000.0) * 1e9))
            if len(res) <= 2:
                return res
            newshape = list(res.shape[0:-2])
            newshape.append(res.shape[-1] // 2)
            newshape.append(2)
            return numpy.rollaxis(res.reshape(newshape), -1, -2)

        def epoch_to_num(self, epoch):
            """
            Convert CDF EPOCH to matplotlib number.

            Same output as :func:`~matplotlib.dates.date2num` and useful for
            plotting large data sets without converting the times through datetime.

            Parameters
            ==========
            epoch : double
                EPOCH to convert. Lists and numpy arrays are acceptable.

            Returns
            =======
            out : double
                Floating point number representing days since matplotlib
                epoch (usually 0001-01-01 as day 1, or 1970-01-01 as day 0).

            See Also
            ========
            matplotlib.dates.date2num, matplotlib.dates.num2date

            Notes
            =====
            This number is not portable between versions of matplotlib. The
            returned value is for the installed version of matplotlib. If
            matplotlib is not found, the returned value is for matplotlib 3.2
            and earlier.
            """
            #date2num day 1 is 1/1/1 00UT
            #epoch 1/1/1 00UT is 31622400000.0 (millisecond)
            # So day 0 is 31536000000.0
            baseepoch = 31536000000.0
            if HAVE_MATPLOTLIB and hasattr(matplotlib.dates, 'get_epoch'):
                # Different day 0
                baseepoch = spacepy.time.Ticktock(matplotlib.dates.get_epoch())\
                                        .CDF[0]
            return (epoch - baseepoch) / (24 * 60 * 60 * 1000.0)

        def epoch16_to_epoch(self, epoch16):
            """
            Converts a CDF EPOCH16 to a CDF EPOCH value

            Parameters
            ==========
            epoch16 : (double, double)
                EPOCH16 to convert. Lists and numpy arrays are acceptable.
                LAST dimension should be 2: the two pairs of EPOCH16

            Returns
            =======
            out : double
                EPOCH corresponding to epoch16
            """
            e = numpy.require(epoch16, numpy.float64)
            return e[..., 0] * 1000.0 + numpy.round(e[..., 1] / 1e9)

        def tt2000_to_datetime(self, tt2000):
            """
            Converts a CDF TT2000 value to a datetime

            .. note::
                Although TT2000 values support leapseconds, Python's datetime
                object does not. Any times after 23:59:59.999999 will
                be truncated to 23:59:59.999999.

            Parameters
            ==========
            tt2000 : int
                TT2000 value from CDF

            Raises
            ======
            EpochError : if input invalid

            Returns
            =======
            out : :class:`datetime.datetime`
                date and time corresponding to epoch. Invalid values are set to
                usual epoch invalid value, i.e. last moment of year 9999.

            See Also
            ========
            v_tt2000_to_datetime
            """
            yyyy = ctypes.c_double(0)
            mm = ctypes.c_double(0)
            dd = ctypes.c_double(0)
            hh = ctypes.c_double(0)
            min = ctypes.c_double(0)
            sec = ctypes.c_double(0)
            msec = ctypes.c_double(0)
            usec = ctypes.c_double(0)
            nsec = ctypes.c_double(0)
            self._library.breakdownTT2000(
                ctypes.c_longlong(tt2000),
                ctypes.byref(yyyy), ctypes.byref(mm), ctypes.byref(dd),
                ctypes.byref(hh), ctypes.byref(min), ctypes.byref(sec),
                ctypes.byref(msec), ctypes.byref(usec), ctypes.byref(nsec))
            if yyyy.value <= 0:
                return datetime.datetime(9999, 12, 13, 23, 59, 59, 999999)
            sec = int(sec.value)
            if sec >= 60:
                return datetime.datetime(
                    int(yyyy.value), int(mm.value), int(dd.value),
                    int(hh.value), int(min.value), 59, 999999)
            micro = int(msec.value * 1000 + usec.value + nsec.value / 1000 + 0.5)
            if micro < 1000000:
                return datetime.datetime(
                    int(yyyy.value), int(mm.value), int(dd.value),
                    int(hh.value), int(min.value), sec, micro)
            else:
                add_sec = int(micro / 1000000)
                try:
                    return datetime.datetime(
                        int(yyyy.value), int(mm.value), int(dd.value),
                        int(hh.value), int(min.value), sec,
                        micro - add_sec * 1000000) + \
                        datetime.timedelta(seconds=add_sec)
                except OverflowError:
                    return datetime.datetime(datetime.MAXYEAR, 12, 31,
                                             23, 59, 59, 999999)

        def datetime_to_tt2000(self, dt):
            """
            Converts a Python datetime to a CDF TT2000 value

            Parameters
            ==========
            dt :  :class:`datetime.datetime`
                date and time to convert

            Returns
            =======
            out : int
                tt2000 corresponding to dt

            See Also
            ========
            v_datetime_to_tt2000
            """
            if dt.tzinfo != None and dt.utcoffset() != None:
                dt = dt - dt.utcoffset()
            dt = dt.replace(tzinfo=None)
            if dt  == datetime.datetime.max:
                return -2**63
            return self._library.computeTT2000(
                dt.year, dt.month, dt.day, dt.hour,
                dt.minute, dt.second,
                int(dt.microsecond / 1000),
                dt.microsecond % 1000, 0)

        def _datetime_to_tt2000_typepunned(self, dt):
            """
            Converts a Python datetime to a CDF TT2000 value

            Typepunned version that passes doubles as longlongs, to get around
            ARM calling convention oddness.

            Parameters
            ==========
            dt :  :class:`datetime.datetime`
                date and time to convert

            Returns
            =======
            out : int
                tt2000 corresponding to dt

            See Also
            ========
            v_datetime_to_tt2000
            """
            c_ll_p = ctypes.POINTER(ctypes.c_longlong)
            if dt.tzinfo != None and dt.utcoffset() != None:
                dt = dt - dt.utcoffset()
            dt = dt.replace(tzinfo=None)
            if dt  == datetime.datetime.max:
                return -2**63
            return self._library.computeTT2000(
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            dt.year)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            dt.month)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            dt.day)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            dt.hour)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            dt.minute)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            dt.second)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            dt.microsecond // 1000)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            dt.microsecond % 1000)), c_ll_p).contents,
                        ctypes.cast(ctypes.pointer(ctypes.c_double(
                            0)), c_ll_p).contents)

        def epoch_to_tt2000(self, epoch):
            """
            Converts a CDF EPOCH to a CDF TT2000 value

            Parameters
            ==========
            epoch : double
                EPOCH to convert

            Returns
            =======
            out : int
                tt2000 corresponding to epoch

            See Also
            ========
            v_epoch_to_tt2000
            """
            return self._library.CDF_TT2000_from_UTC_EPOCH(epoch)

        def tt2000_to_epoch(self, tt2000):
            """
            Converts a CDF TT2000 value to a CDF EPOCH

            .. note::
                Although TT2000 values support leapseconds, CDF EPOCH values
                do not. Times during leapseconds are rounded up to beginning
                of the next day.

            Parameters
            ==========
            tt2000 : int
                TT2000 value from CDF

            Raises
            ======
            EpochError : if input invalid

            Returns
            =======
            out : double
                EPOCH corresponding to the TT2000 input time

            See Also
            ========
            v_tt2000_to_epoch
            """
            return self._library.CDF_TT2000_to_UTC_EPOCH(tt2000)

        def epoch16_to_tt2000(self, epoch0, epoch1):
            """
            Converts a CDF epoch16 value to TT2000

            .. note::
                Because TT2000 does not support picoseconds, the picoseconds
                value in epoch is ignored (i.e., truncated.)

            Parameters
            ==========
            epoch0 : float
                epoch16 value from CDF, first half
            epoch1 : float
                epoch16 value from CDF, second half

            Raises
            ======
            EpochError : if input invalid

            Returns
            =======
            out : long
                TT2000 corresponding to epoch.

            See Also
            ========
            v_epoch16_to_tt2000
            """
            return self._library.CDF_TT2000_from_UTC_EPOCH16(
                (ctypes.c_double * 2)(epoch0, epoch1))

        def tt2000_to_epoch16(self, tt2000):
            """
            Converts a CDF TT2000 value to a CDF EPOCH16

            .. note::
                Although TT2000 values support leapseconds, CDF EPOCH16 values
                do not. Times during leapseconds are rounded up to beginning
                of the next day.

            Parameters
            ==========
            tt2000 : int
                TT2000 value from CDF

            Raises
            ======
            EpochError : if input invalid

            Returns
            =======
            out : double, double
                EPOCH16 corresponding to the TT2000 input time

            See Also
            ========
            v_tt2000_to_epoch16
            """
            #Default to "illegal epoch" if isn't populated
            epoch16 = (ctypes.c_double * 2)(-1., -1.)
            if self._library.CDF_TT2000_to_UTC_EPOCH16(tt2000, epoch16):
                return (-1., -1.) #Failure; illegal epoch
            return (epoch16[0], epoch16[1])

        def _bad_tt2000(*args, **kwargs):
            """Convenience function for complaining that TT2000 not supported"""
            raise NotImplementedError(
                'TT2000 functions require CDF library 3.4.0 or later')

        def get_minmax(self, cdftype):
            """Find minimum, maximum possible value based on CDF type.

            This returns the processed value (e.g. datetimes for Epoch
            types) because comparisons to EPOCH16s are otherwise
            difficult.

            Parameters
            ==========
            cdftype : int
                CDF type number from :mod:`~spacepy.pycdf.const`

            Raises
            ======
            ValueError : if can't match the type

            Returns
            =======
            out : tuple
                minimum, maximum value supported by type (of type matching the
                CDF type).

            """
            try:
                cdftype = cdftype.value
            except:
                pass
            if cdftype == spacepy.pycdf.const.CDF_EPOCH.value:
                return (datetime.datetime(1, 1, 1),
                    #Can get asymptotically closer, but why bother
                    datetime.datetime(9999, 12, 31, 23, 59, 59))
            elif cdftype == spacepy.pycdf.const.CDF_EPOCH16.value:
                return (datetime.datetime(1, 1, 1),
                    datetime.datetime(9999, 12, 31, 23, 59, 59))
            elif cdftype == spacepy.pycdf.const.CDF_TIME_TT2000.value:
                inf = numpy.iinfo(numpy.int64)
                #Using actual min results in invalid/fill value
                return (self.tt2000_to_datetime(inf.min + 2),
                        self.tt2000_to_datetime(inf.max))
            dtype = spacepy.pycdf.lib.numpytypedict.get(cdftype, None)
            if dtype is None:
                raise ValueError('Unknown data type: {}'.format(cdftype))
            if numpy.issubdtype(dtype, numpy.integer):
                inf = numpy.iinfo(dtype)
            elif numpy.issubdtype(dtype, numpy.floating):
                inf = numpy.finfo(dtype)
            else:
                raise ValueError('Unknown data type: {}'.format(cdftype))
            return (inf.min, inf.max)

    def download_library():
        """Download and install the CDF library"""
        if sys.platform != 'win32':
            raise NotImplementedError(
                'CDF library install only supported on Windows')
        try:
            import html.parser as HTMLParser
        except ImportError:
            import HTMLParser
        #https://stackoverflow.com/questions/1713038/super-fails-with-error-typeerror-argument-1-must-be-type-not-classobj
        class LinkParser(HTMLParser.HTMLParser, object):
            def __init__(self, *args, **kwargs):
                self.links_found = []
                super(LinkParser, self).__init__(*args, **kwargs)
            def handle_starttag(self, tag, attrs):
                if tag != 'a' or attrs[0][0] != 'href':
                    return
                self.links_found.append(attrs[0][1])
        import re
        import subprocess
        try:
            import urllib.request as u
        except ImportError:
            import urllib as u
        import spacepy
        if spacepy.config.get('user_agent', None):
            class AppURLopener(u.FancyURLopener):
                version = spacepy.config['user_agent']
            u._urlopener = AppURLopener()
        baseurl = 'https://spdf.sci.gsfc.nasa.gov/pub/software/cdf/dist/'
        url = u.urlopen(baseurl)
        listing = url.read()
        url.close()
        p = LinkParser()
        p.feed(listing)
        cdfdist = [l for l in p.links_found if re.match('^cdf3\d_\d(?:_\d)?/$', l)]
        if not cdfdist:
            raise RuntimeError(
                "Couldn't find CDF distribution directory to download")
        cdfdist.sort(key=lambda x: x.rstrip('/').split('_'))
        cdfverbase = cdfdist[-1].rstrip('/')
        instfname = cdfverbase + ('_0' if cdfverbase.count('_') == 1 else '') + \
                    '-setup-{0}.exe'.format(len('%x' % sys.maxsize)*4)
        insturl = baseurl + cdfverbase + '/windows/' + instfname
        tmpdir = tempfile.mkdtemp()
        try:
            fname, status = u.urlretrieve(insturl, os.path.join(tmpdir, instfname))
            subprocess.check_call([fname, '/install', '/q1'], shell=False)
        finally:
            shutil.rmtree(tmpdir)

    _libpath, _library = Library._find_lib()
    if _library is None:
        raise Exception(('Cannot load CDF C library; checked {0}. '
                         'Try \'os.environ["CDF_LIB"] = library_directory\' '
                         'before import.').format(', '.join(_libpath)))
    from . import const
    lib = Library(_libpath, _library)
    """Module global library object.

    Initalized at module load time so all classes have ready
    access to the CDF library and a common state. E.g:
        >>> from spacepy import pycdf
        >>> pycdf.lib.version
            (3, 3, 0, ' ')
    """

    class CDFException(Exception):
        """
        Base class for errors or warnings in the CDF library.

        Not normally used directly, but in subclasses :class:`CDFError`
        and :class:`CDFWarning`.

        Error messages provided by this class are looked up from the underlying
        C library.
        """
        def __init__(self, status):
            """
            Create a CDF Exception

            Uses CDF C library to look up an appropriate error message.

            Parameters
            ==========
            status : ctypes.c_long
                CDF status
            """
            self.status = status
            self.string = 'CDF error ' + repr(status) + ', unable to get details.'
            message = ctypes.create_string_buffer(const.CDF_STATUSTEXT_LEN + 1)
            try:
                retval = lib._library.CDFlib(const.SELECT_, const.CDF_STATUS_,
                                             ctypes.c_long(status),
                                             const.GET_, const.STATUS_TEXT_, message,
                                             const.NULL_)
                if retval == const.CDF_OK:
                    if isinstance(message.value, str):
                        self.string = message.value
                    elif isinstance(message.value, bytes):
                        self.string = message.value.decode()
            except:
                pass

        def __str__(self):
            """
            Error string associated with the library error.

            Returns
            =======
            out : str
                Error message from the CDF library.
            """
            return self.string

    class CDFError(CDFException):
        """Raised for an error in the CDF library."""
        pass

    class CDFWarning(CDFException, UserWarning):
        """Used for a warning in the CDF library."""

        def warn(self, level=4):
            """
            Issues a warning based on the information stored in my exception

            Intended for use in check_status or similar wrapper function.

            Other Parameters
            ================
            level : int
                optional (default 3), how far up the stack the warning should
                be reported. Passed directly to :class:`warnings.warn`.
            """
            warnings.warn(self, self.__class__, level)

    class EpochError(Exception):
        """Used for errors in epoch routines"""
        pass

    def _compress(obj, comptype=None, param=None):
        """Set or check the compression of a :py:class:`pycdf.CDF` or :py:class:`pycdf.Var`

        @param obj: object on which to set or check compression
        @type obj: :py:class:`pycdf.CDF` or :py:class:`pycdf.Var`
        @param comptype: type of compression to change to, see CDF C reference
                         manual section 4.10. Constants for this parameter
                         are in :py:mod:`pycdf.const`. If not specified, will not change
                         compression.
        @type comptype: ctypes.c_long
        @param param: Compression parameter, see CDF CRM 4.10 and :py:mod:`pycdf.const`.
                      If not specified, will choose reasonable default (5 for
                      gzip; other types have only one possible parameter.)
        @type param: ctypes.c_long
        @return: (comptype, param) currently in effect
        @rtype: tuple
        """
        if isinstance(obj, CDF):
            COMPRESSION_ = const.CDF_COMPRESSION_
        elif isinstance(obj, Var):
            COMPRESSION_ = const.zVAR_COMPRESSION_
        else:
            raise ValueError('Must specify a CDF or Var type.')

        validparams = {const.NO_COMPRESSION.value: [ctypes.c_long(0)],
                       const.RLE_COMPRESSION.value: [const.RLE_OF_ZEROs],
                       const.HUFF_COMPRESSION.value:
                           [const.OPTIMAL_ENCODING_TREES],
                       const.AHUFF_COMPRESSION.value:
                           [const.OPTIMAL_ENCODING_TREES],
                       const.GZIP_COMPRESSION.value: [ctypes.c_long(5),
                                                      ctypes.c_long(1),
                                                      ctypes.c_long(2),
                                                      ctypes.c_long(3),
                                                      ctypes.c_long(4),
                                                      ctypes.c_long(6),
                                                      ctypes.c_long(7),
                                                      ctypes.c_long(8),
                                                      ctypes.c_long(9),
                                                      ],
                       }
        comptypes = [const.NO_COMPRESSION, const.RLE_COMPRESSION,
                     const.HUFF_COMPRESSION, const.AHUFF_COMPRESSION,
                     const.GZIP_COMPRESSION]
        comptypevalues = [i.value for i in comptypes]

        if comptype != None:
            if not hasattr(comptype, 'value'):
                comptype = ctypes.c_long(comptype)
            if param is None:
                if not comptype.value in validparams:
                    raise CDFError(const.BAD_COMPRESSION)
                param = validparams[comptype.value][0]
            paramlist = (ctypes.c_long * 1)(param)
            obj._call(const.PUT_, COMPRESSION_,
                       comptype, paramlist)
        params = (ctypes.c_long *
                  const.CDF_MAX_PARMS)(*([0] * const.CDF_MAX_PARMS))
        comptype = ctypes.c_long(0)
        percent = ctypes.c_long(0)
        obj._call(const.GET_, COMPRESSION_,
                   ctypes.byref(comptype), ctypes.byref(params),
                   ctypes.byref(percent))
        param = params[0]
        if not comptype.value in comptypevalues:
            raise CDFError(const.BAD_COMPRESSION)
        validparamvalues = [i.value for i in validparams[comptype.value]]
        if not param in validparamvalues:
            raise CDFError(const.BAD_COMPRESSION_PARM)
        comptype = comptypes[comptypevalues.index(comptype.value)]
        if comptype in (const.RLE_COMPRESSION, const.HUFF_COMPRESSION,
                        const.AHUFF_COMPRESSION):
            param = validparams[comptype.value][validparamvalues.index(param)]
        return (comptype, param)

    class CDF(MutableMapping, spacepy.datamodel.MetaMixin):
        """
        Python object representing a CDF file.

        Open or create a CDF file by creating an object of this class.

        Parameters
        ==========
        pathname : string
            name of the file to open or create
        masterpath : string
            name of the master CDF file to use in creating
            a new file. If not provided, an existing file is
            opened; if provided but evaluates to ``False``
            (e.g., ``''``), an empty new CDF is created.
        create : bool
            Create a new CDF even if masterpath isn't provided
        readonly : bool
            Open the CDF read-only. Default True if opening an
            existing CDF; False if creating a new one. A readonly
            CDF with many variables may be slow to close. See
            :meth:`readonly`.

        Raises
        ======
        CDFError
            if CDF library reports an error

        Warns
        =====
        CDFWarning
            if CDF library reports a warning and interpreter
            is set to error on warnings.

        Examples
        ========
        Open a CDF by creating a CDF object, e.g.:

            >>> cdffile = pycdf.CDF('cdf_filename.cdf')

        Be sure to :meth:`close` or :meth:`save` when
        done.

        .. note::
            Existing CDF files are opened read-only by default, see
            :meth:`readonly` to change.

        CDF supports the `with
        <http://docs.python.org/tutorial/inputoutput.html#methods-of-file-objects>`_
        keyword, like other file objects, so:

            >>> with pycdf.CDF('cdf_filename.cdf') as cdffile:
            ...     #do brilliant things with the CDF

        will open the CDF, execute the indented statements, and close the CDF when
        finished or when an error occurs. The `python docs
        <http://docs.python.org/reference/compound_stmts.html#with>`_ include more
        detail on this 'context manager' ability.

        CDF objects behave like a python `dictionary
        <http://docs.python.org/tutorial/datastructures.html#dictionaries>`_,
        where the keys are names of variables in the CDF, and the values,
        :class:`Var` objects. As a dictionary, they are also `iterable
        <http://docs.python.org/tutorial/classes.html#iterators>`_ and it is easy
        to loop over all of the variables in a file. Some examples:

            #. List the names of all variables in the open CDF ``cdffile``:

                   >>> cdffile.keys()
                   >>> for k in cdffile: #Alternate
                   ...     print(k)

            #. Get a :class:`Var` object for the variable named ``Epoch``:

                   >>> epoch = cdffile['Epoch']

            #. Determine if a CDF contains a variable named ``B_GSE``:

                   >>> if 'B_GSE' in cdffile:
                   ...     print('B_GSE is in the file')
                   ... else:
                   ...     print('B_GSE is not in the file')

            #. Find how many variables are in the file:

                   >>> print(len(cdffile))

            #. Delete the variable ``Epoch`` from the open CDF file ``cdffile``:

                  >>> del cdffile['Epoch']

            #. Display a summary of variables and types in open CDF file ``cdffile``:

                  >>> print(cdffile)

            #. Open the CDF named ``cdf_filename.cdf``, read *all* the data from
               all variables into dictionary ``data``, and close it when done or
               if an error occurs:

                   >>> with pycdf.CDF('cdf_filename.cdf') as cdffile:
                   ...     data = cdffile.copy()

        This last example can be very inefficient as it reads the entire CDF.
        Normally it's better to treat the CDF as a dictionary and access only
        the data needed, which will be pulled transparently from disc. See
        :class:`Var` for more subtle examples.

        Potentially useful dictionary methods and related functions:

          - `in <http://docs.python.org/reference/expressions.html#in>`_
          - `keys <http://docs.python.org/tutorial/datastructures.html#dictionaries>`_
          - :py:func:`len`
          - `list comprehensions
            <http://docs.python.org/tutorial/datastructures.html#list-comprehensions>`_
          - :py:func:`sorted`
          - :py:func:`~spacepy.toolbox.dictree`

        The CDF user's guide section 2.2 has more background information on CDF
        files.

        The :attr:`~CDF.attrs` Python attribute acts as a dictionary
        referencing CDF attributes (do not confuse the two); all the
        dictionary methods above also work on the attribute dictionary.
        See :class:`gAttrList` for more on the dictionary of global
        attributes.

        Creating a new CDF from a master (skeleton) CDF has similar syntax to
        opening one:

            >>> cdffile = pycdf.CDF('cdf_filename.cdf', 'master_cdf_filename.cdf')

        This creates and opens ``cdf_filename.cdf`` as a copy of
        ``master_cdf_filename.cdf``.

        Using a skeleton CDF is recommended over making a CDF entirely from
        scratch, but this is possible by specifying a blank master:

            >>> cdffile = pycdf.CDF('cdf_filename.cdf', '')

        When CDFs are created in this way, they are opened read-write, see
        :py:meth:`readonly` to change.

        By default, new CDFs (without a master) are created in version 2
        (backward-compatible) format. To create a version 3 CDF, use
        :meth:`Library.set_backward`:

            >>> pycdf.lib.set_backward(False)
            >>> cdffile = pycdf.CDF('cdf_filename.cdf', '')

        Add variables by direct assignment, which will automatically set type
        and dimension based on the data provided:

            >>> cdffile['new_variable_name'] = [1, 2, 3, 4]

        or, if more control is needed over the type and dimensions, use
        :py:meth:`new`.

        Although it is supported to assign Var objects to Python variables
        for convenience, there are some minor pitfalls that can arise when
        changing a CDF that will not affect most users. This is only a
        concern when assigning a zVar object to a Python variable, changing the
        CDF through some other variable, and then trying to use the zVar
        object via the originally assigned variable.

        Deleting a variable:

            >>> var = cdffile['Var1']
            >>> del cdffile['Var1']
            >>> var[0] #fail, no such variable

        Renaming a variable:

            >>> var = cdffile['Var1']
            >>> cdffile['Var1'].rename('Var2')
            >>> var[0] #fail, no such variable

        Renaming via the same variable works:

            >>> var = cdffile['Var1']
            >>> var.rename('Var2')
            >>> var[0] #succeeds, aware of new name

        Deleting a variable and then creating another variable with the same name
        may lead to some surprises:

            >>> var = cdffile['Var1']
            >>> var[...] = [1, 2, 3, 4]
            >>> del cdffile['Var1']
            >>> cdffile.new('Var1', data=[5, 6, 7, 8]
            >>> var[...]
            [5, 6, 7, 8]

        .. autosummary::

            ~CDF.attr_num
            ~CDF.attrs
            ~CDF.add_attr_to_cache
            ~CDF.add_to_cache
            ~CDF.backward
            ~CDF.checksum
            ~CDF.clear_attr_from_cache
            ~CDF.clear_from_cache
            ~CDF.clone
            ~CDF.close
            ~CDF.col_major
            ~CDF.compress
            ~CDF.copy
            ~CDF.from_data
            ~CDF.new
            ~CDF.raw_var
            ~CDF.readonly
            ~CDF.save
            ~CDF.var_num
            ~CDF.version

        .. attribute:: CDF.attrs

           Global attributes for this CDF in a dict-like format.
           See :class:`gAttrList` for details.

        .. attribute:: CDF.backward

           True if this CDF was created in backward-compatible mode
           (for opening with CDF library before 3.x)

        .. automethod:: add_to_cache
        .. automethod:: add_attr_to_cache
        .. automethod:: attr_num
        .. automethod:: checksum
        .. automethod:: clear_from_cache
        .. automethod:: clear_attr_from_cache
        .. automethod:: clone
        .. automethod:: close
        .. automethod:: col_major
        .. automethod:: compress
        .. automethod:: copy
        .. automethod:: from_data
        .. automethod:: new
        .. automethod:: raw_var
        .. automethod:: readonly
        .. automethod:: save
        .. automethod:: var_num
        .. automethod:: version

        """
        def __init__(self, pathname, masterpath=None, create=None, readonly=None):
            """Open or create a CDF file.

            Parameters
            ==========
            pathname : string
                name of the file to open or create
            masterpath : string
                name of the master CDF file to use in creating
                a new file. If not provided, an existing file is
                opened; if provided but evaluates to ``False``
                (e.g., ``''``), an empty new CDF is created.
            create : bool
                Create a new CDF even if masterpath isn't provided
            readonly : bool
                Open the CDF read-only. Default True if opening an
                existing CDF; False if creating a new one.

            Raises
            ======
            CDFError
                if CDF library reports an error
            CDFWarning
                if CDF library reports a warning and interpreter
                is set to error on warnings.

            Examples
            ========
            Open a CDF by creating a CDF object, e.g.:
                >>> cdffile = pycdf.CDF('cdf_filename.cdf')
            Be sure to :py:meth:`pycdf.CDF.close` or :py:meth:`pycdf.CDF.save`
            when done.
            """
            if masterpath is not None: #Looks like we want to create
                if create is False:
                    raise ValueError('Cannot specify a master CDF without creating a CDF')
                if readonly is True:
                    raise ValueError('Cannot create a CDF in readonly mode')
            if create and readonly:
                raise ValueError('Cannot create a CDF in readonly mode')
            try:
                self.pathname = pathname.encode()
            except AttributeError:
                raise ValueError(
                    'pathname must be string-like: {0}'.format(pathname))
            self._handle = ctypes.c_void_p(None)
            self._opened = False
            if masterpath is None and not create:
                self._open(True if readonly is None else readonly)
            elif masterpath:
                self._from_master(masterpath.encode())
            else:
                self._create()
            lib.call(const.SELECT_, const.CDF_zMODE_, ctypes.c_long(2))
            self._attrlistref = weakref.ref(gAttrList(self))
            self.backward = self.version()[0] < 3
            self._var_nums = {}
            """Cache of name-to-number mappings for variables in this CDF"""
            self._attr_info = {}
            """Cache of name-to-(number, global) mappings for attributes
            in this CDF"""

        def __del__(self):
            """Destructor; called when CDF object is destroyed.

            Close CDF file if there is still a valid handle.
            .. note::
                To avoid data loss, explicitly call :py:meth:`pycdf.CDF.close`
                or :py:meth:`pycdf.CDF.save`.
            """
            if self._opened:
                self.close()

        def __delitem__(self, name):
            """Delete a zVariable in this CDF, by name or number

            Parameters
            ==========
            name : string or int
                Name or number of the CDF variable
            .. note:
                Variable numbers may change if variables are added or removed.

            Examples
            ========
            Delete the variable ``Epoch`` from the open CDF file ``cdffile``.
                >>> del cdffile['Epoch']
            """
            self[name]._delete()

        def __enter__(self):
            """Context manager entrance function."""
            return self

        def __exit__(self, type, value, traceback):
            """Context manager exit function.

            Close CDF file.
            """
            self.close()

        def __getitem__(self, name):
            """Gets a zVariable in this CDF, by name or number

            The CDF acts like a dict

            @param name: Name or number of the CDF variable
            @type name: string or int
            @return: CDF variable named or numbered L{name}
            @rtype: :py:class:`pycdf.Var`
            @raise KeyError: for pretty much any problem in lookup
            @note: variable numbers may change if variables are added or removed.
            """
            try:
                return Var(self, name)
            except CDFException as e:
                raise KeyError('{0}: {1}'.format(name, e))

        def __setitem__(self, name, data):
            """Writes data to a zVariable in this CDF

            If the zVariable does not exist, will create one matching
            L{data}. If it does exist, will attempt to write L{data}
            to it without changing the type or dimensions.

            @param name: name or number of the variable to write
            @type name: str or int
            @param data: data to write, or a :py:class:`pycdf.Var` to copy
            """
            if isinstance(data, Var):
                self.clone(data, name)
            elif name in self:
                self[name][...] = data
                if hasattr(data, 'attrs'):
                    self[name].attrs.clone(data.attrs)
            else:
                self.new(name, data)

        def __iter__(self, current = 0):
            """Iterates over zVars in CDF

            Iterators for dicts return keys
            @note: Returned in variable-number order
            """
            while current < self.__len__():
                name = self[current].name()
                value = (yield name)
                if value is None:
                    current += 1
                else:
                    current = self[value]._num()
                    current += 1

        def __len__(self):
            """Implements 'length' of CDF (number of zVars)

            @return: number of zVars in the CDF
            @rtype: int
            """
            count = ctypes.c_long(0)
            self._call(const.GET_, const.CDF_NUMzVARS_, ctypes.byref(count))
            return count.value

        def __contains__(self, key):
            """Determines whether a particular variable name is in the CDF

            @note: Essentially an efficiency function; L{__iter__} is called
                   if this isn't defined
            @param key: key/variable name to check
            @type key: string
            @return: True if L{key} is the name of a variable in CDF, else False
            @rtype: Boolean
            """
            if str is not bytes and isinstance(key, str):
                key = key.encode('ascii')
            key = key.rstrip()
            if key in self._var_nums:
                return True
            status = self._call(const.CONFIRM_, const.zVAR_EXISTENCE_, key,
                                ignore=(const.NO_SUCH_VAR,))
            return status != const.NO_SUCH_VAR

        def __repr__(self):
            """Returns representation of CDF

            Cannot return anything that can be eval'd to create a copy of the
            CDF, so just wrap the informal representation in angle brackets.
            @return: all the data in this list of attributes
            @rtype: str
            """
            return '<CDF:\n' + str(self) + '\n>'

        def __str__(self):
            """Returns a string representation of the CDF

            This is an 'informal' representation in that it cannot be evaluated
            directly to create a :py:class:`pycdf.CDF`, just the names, types, and sizes of all
            variables. (Attributes are not listed.)

            @return: description of the variables in the CDF
            @rtype: str
            """
            if self._opened:
                return '\n'.join([key + ': ' + str(value)
                                  for (key, value) in sorted(self.items())])
                #can get away with this sort because second value in tuple isn't
                #compared unless first are different, and variable name is unique.
            else:
                if isinstance(self.pathname, str):
                    return 'Closed CDF {0}'.format(self.pathname)
                else:
                    return 'Closed CDF {0}'.format(self.pathname.decode('ascii'))

        def _open(self, readonly=True):
            """Opens the CDF file (called on init)

            Will open an existing CDF file read/write.

            Raises
            ======
            CDFError : if CDF library reports an error
            CDFWarning : if CDF library reports a warning and interpreter
                         is set to error on warnings.
            .. note:
                Not intended for direct call; pass parameters to
                :py:class:`pycdf.CDF` constructor.
            """

            lib.call(const.OPEN_, const.CDF_, self.pathname, ctypes.byref(self._handle))
            self._opened = True
            if readonly: #Default is RW
                self.readonly(readonly)

        def _create(self):
            """Creates (and opens) a new CDF file

            Created at ``pathname``.
            Assumes zero-dimension r variables

            Raises
            ======
            CDFError : if CDF library reports an error
            CDFWarning : if CDF library reports a warning and interpreter
                         is set to error on warnings.
            .. note:
                Not intended for direct call; pass parameters to
                :py:class:`pycdf.CDF` constructor.
            """
            if not lib._explicit_backward:
                warnings.warn(
                    'spacepy.pycdf.lib.set_backward not called;'
                    ' making backward-compatible CDF.'
                    ' This default will change in the future.',
                    DeprecationWarning)
            lib.call(const.CREATE_, const.CDF_, self.pathname, ctypes.c_long(0),
                                  (ctypes.c_long * 1)(0), ctypes.byref(self._handle))
            self._opened = True

        def _from_master(self, master_path):
            """Creates a new CDF from a master CDF file

            ``master_path`` is copied to ``pathname`` and opened.

            Parameters
            ==========
            master_path : string
                location of the master CDF file

            Raises
            ======
            CDFError : if CDF library reports an error
            CDFWarning : if CDF library reports a warning and interpreter
                         is set to error on warnings.
            .. note:
                Not intended for direct call; pass parameters to
                :py:class:`pycdf.CDF` constructor.
            """

            if os.path.exists(self.pathname):
                raise CDFError(const.CDF_EXISTS)
            shutil.copy2(master_path, self.pathname)
            self._open(False)

        @classmethod
        def from_data(cls, filename, sd):
            """Create a new CDF file from a SpaceData object or similar

            The CDF named ``filename`` is created, opened, filled with the
            contents of ``sd`` (including attributes), and closed.

            ``sd`` should be a dictionary-like object; each key will be made
            into a variable name. An attribute called ``attrs``, if it exists,
            will be made into global attributes for the CDF.

            Each value of ``sd`` should be array-like and will be used as
            the contents of the variable; an attribute called ``attrs``, if
            it exists, will be made into attributes for that variable.

            Parameters
            ----------
            filename : string
                name of the file to create
            sd : spacepy.datamodel.SpaceData
                data to put in the CDF. This structure cannot be nested,
                i.e., it must contain only :class:`~spacepy.datamodel.dmarray`
                and no :class:`~spacepy.datamodel.Spacedata` objects.
            """
            with cls(filename, '') as cdffile:
                for k in sd:
                    cdffile[k] = sd[k]
                cdffile.attrs.clone(sd.attrs)

        def _call(self, *args, **kwargs):
            """Select this CDF as current and call the CDF internal interface

            Adds call to select this CDF to L{args} and passes all parameters
            directly through to the CDFlib routine of the CDF library's C internal
            interface. Checks the return value with L{Library.check_status}.

            Parameters
            ==========
            args : various, see :py:mod:`ctypes`.
                Passed directly to the CDF library interface. Useful
                constants are defined in the :doc:`const <pycdf_const>`
                module of this package.

            Returns
            =======
            out : ctypes.c_long
                CDF status from the library

            .. note:
                Terminal NULL_ is automatically added to ``args``.
            Raises
            ======
            CDFError : if CDF library reports an error
            CDFWarning : if CDF library reports a warning and interpreter
                         is set to error on warnings.
            """
            return lib.call(const.SELECT_, const.CDF_, self._handle,
                            *args, **kwargs)

        def clone(self, zVar, name=None, data=True):
            """
            Clone a zVariable (from another CDF or this) into this CDF

            Parameters
            ==========
            zVar : :py:class:`Var`
                variable to clone

            Other Parameters
            ================
            name : str
                Name of the new variable (default: name of the original)
            data : boolean (optional)
                Copy data, or only type, dimensions, variance, attributes?
                (default: True, copy data as well)

            Returns
            =======
            out : :py:class:`Var`
                The newly-created zVar in this CDF
            """
            if name is None:
                name = zVar.name()
            if name in self:
                del self[name]
            self.new(name, type=zVar.type(), recVary=zVar.rv(),
                     dimVarys=zVar.dv(), dims=zVar._dim_sizes(),
                     n_elements=zVar.nelems())
            self[name].compress(*zVar.compress())
            self[name].attrs.clone(zVar.attrs)
            if data:
                r = zVar._raw
                zVar._raw = True
                self.raw_var(name)[...] = zVar[...]
                zVar._raw = r
            return zVar

        def col_major(self, new_col=None):
            """
            Finds the majority of this CDF file

            Other Parameters
            ================
            new_col : boolean
                Specify True to change to column-major, False to change to
                row major, or do not specify to check the majority
                rather than changing it.
                (default is check only)

            Returns
            =======
            out : boolean
                True if column-major, false if row-major
            """
            if new_col != None:
                new_maj = const.COLUMN_MAJOR if new_col else const.ROW_MAJOR
                self._call(const.PUT_, const.CDF_MAJORITY_, new_maj)
            maj = ctypes.c_long(0)
            self._call(const.GET_, const.CDF_MAJORITY_, ctypes.byref(maj))
            if not maj.value in (const.ROW_MAJOR.value, const.COLUMN_MAJOR.value):
                raise CDFError(const.BAD_MAJORITY)
            return maj.value == const.COLUMN_MAJOR.value

        def readonly(self, ro=None):
            """
            Sets or check the readonly status of this CDF

            If the CDF has been changed since opening, setting readonly mode
            will have no effect.

            .. note::
                Closing a CDF that has been opened readonly, or setting readonly
                False, may take a substantial amount of time if there are many
                variables in the CDF, as a (potentially large) cache needs to
                be cleared. Consider specifying ``readonly=False`` when opening
                the file if this is an issue. However, this may make some reading
                operations slower.

            Other Parameters
            ================
            ro : Boolean
                True to set the CDF readonly, False to set it read/write,
                or leave out to check only.

            Returns
            =======
            out : Boolean
                True if CDF is read-only, else False

            Raises
            ======
            CDFError : if bad mode is set
            """
            if ro == True:
                self._call(const.SELECT_, const.CDF_READONLY_MODE_,
                           const.READONLYon)
            elif ro == False:
                self._call(const.SELECT_, const.CDF_READONLY_MODE_,
                           const.READONLYoff)
            mode = ctypes.c_long(0)
            self._call(const.CONFIRM_, const.CDF_READONLY_MODE_,
                       ctypes.byref(mode))
            if mode.value == const.READONLYon.value:
                return True
            elif mode.value == const.READONLYoff.value:
                return False
            else:
                raise CDFError(const.BAD_READONLY_MODE.value)

        def checksum(self, new_val=None):
            """
            Set or check the checksum status of this CDF. If checksums
            are enabled, the checksum will be verified every time the file
            is opened.

            Other Parameters
            ================
            new_val : boolean
                True to enable checksum, False to disable, or leave out
                to simply check.

            Returns
            =======
            out : boolean
                True if the checksum is enabled or False if disabled
            """
            if new_val != None:
                self._call(const.PUT_, const.CDF_CHECKSUM_,
                           const.MD5_CHECKSUM if new_val else const.NO_CHECKSUM)
            chk = ctypes.c_long(0)
            self._call(const.GET_, const.CDF_CHECKSUM_, ctypes.byref(chk))
            if not chk.value in (const.MD5_CHECKSUM.value,
                                 const.NO_CHECKSUM.value):
                raise CDFError(const.BAD_CHECKSUM)
            return chk.value == const.MD5_CHECKSUM.value

        def close(self):
            """
            Closes the CDF file

            Although called on object destruction (:meth:`~CDF.__del__`),
            to ensure all data are saved, the user should explicitly call
            :meth:`~CDF.close` or :meth:`~CDF.save`.

            Raises
            ======
            CDFError : if CDF library reports an error

            Warns
            =====
            CDFWarning : if CDF library reports a warning
            """

            self._call(const.CLOSE_, const.CDF_)
            self._opened = False

        def compress(self, comptype=None, param=None):
            """
            Set or check the compression of this CDF

            Sets compression on entire *file*, not per-variable.

            See section 2.6 of the CDF user's guide for more information on
            compression.

            Other Parameters
            ================
            comptype : ctypes.c_long
                type of compression to change to, see CDF C reference manual
                section 4.10. Constants for this parameter are in
                :mod:`~spacepy.pycdf.const`. If not specified, will not change
                compression.
            param : ctypes.c_long
                Compression parameter, see CDF CRM 4.10 and
                :mod:`~spacepy.pycdf.const`.
                If not specified, will choose reasonable default (5 for gzip;
                other types have only one possible parameter.)

            Returns
            =======
            out : tuple
                (comptype, param) currently in effect

            See Also
            ========
            :meth:`Var.compress`

            Examples
            ========
            Set file ``cdffile`` to gzip compression, compression level 9:
                >>> cdffile.compress(pycdf.const.GZIP_COMPRESSION, 9)
            """
            return _compress(self, comptype, param)

        def new(self, name, data=None, type=None, recVary=None, dimVarys=None,
                dims=None, n_elements=None, compress=None, compress_param=None):
            """Create a new zVariable in this CDF

            .. note::
                Either ``data`` or ``type`` must be specified. If type is not
                specified, it is guessed from ``data``.

            Parameters
            ==========
            name : str
                name of the new variable

            Other Parameters
            ================
            data
                data to store in the new variable. If this has a an
                ``attrs`` attribute (e.g.,
                :class:`~spacepy.datamodel.dmarray`), it will be used to
                populate attributes of the new variable. Similarly the CDF
                type, record variance, etc. will, by default, be taken
                from `data` if it is a
                :class:`~spacepy.pycdf.VarCopy`. This can be overridden by
                specifying other keywords.
            type : ctypes.c_long
                CDF type of the variable, from :mod:`~spacepy.pycdf.const`.
                See section 2.5 of the CDF user's guide for more information on
                CDF data types.
            recVary : boolean
                record variance of the variable (default True)
            dimVarys : list of boolean
                dimension variance of each dimension, default True for all
                dimensions.
            dims : list of int
                size of each dimension of this variable, default zero-dimensional.
                Note this is the dimensionality as defined by CDF, i.e., for
                record-varying variables it excludes the leading record dimension.
                See :py:class:`Var`.
            n_elements : int
                number of elements, should be 1 except for CDF_CHAR,
                for which it's the length of the string.
            compress : ctypes.c_long
                Compression to apply to this variable, default None.
                See :py:meth:`Var.compress`.
            compress_param : ctypes.c_long
                Compression parameter if compression used; reasonable default
                is chosen. See :py:meth:`Var.compress`.

            Returns
            =======
            out : :py:class:`Var`
                the newly-created zVariable

            Raises
            ======
            ValueError : if neither data nor sufficient typing information
                         is provided.

            Warns
            =====
            DeprecationWarning
                if no type is provided and data is datetime, warning that
                the default will change in the future.

                .. versionadded:: 0.2.2

            Notes
            =====
            Any given data may be representable by a range of CDF types; if
            the type is not specified, pycdf will guess which
            the CDF types which can represent this data. This breaks down to:

                #. If input data is a numpy array, match the type of that array
                #. Proper kind (numerical, string, time)
                #. Proper range (stores highest and lowest number provided)
                #. Sufficient resolution (EPOCH16 required if datetime has
                   microseconds or below.)

            If more than one value satisfies the requirements, types are returned
            in preferred order:

                #. Type that matches precision of data first, then
                #. integer type before float type, then
                #. Smallest type first, then
                #. signed type first, then
                #. specifically-named (CDF_BYTE) vs. generically named (CDF_INT1)

            So for example, EPOCH_16 is preferred over EPOCH if ``data`` specifies
            below the millisecond level (rule 1), but otherwise EPOCH is preferred
            (rule 2).

            In the future, CDF_TIME_TT2000 will be the preferred EPOCH type if
            not specified.

            For floats, four-byte is preferred unless eight-byte is required:

                #. absolute values between 0 and 3e-39
                #. absolute values greater than 1.7e38

            This will switch to an eight-byte double in some cases where four bytes
            would be sufficient for IEEE 754 encoding, but where DEC formats would
            require eight.

            """
            if hasattr(data, 'compress'):
                try:
                    c, cp = data.compress()
                except: #numpy arrays have "compress" and it behaves differently
                    pass
                else:
                    if compress is None:
                        compress = c
                    if compress_param is None:
                        compress_param = cp
            #Get defaults from VarCopy if data looks like a VarCopy
            if recVary is None:
                recVary = data.rv() if hasattr(data, 'rv') else True
            #Use dimension variance from the copy if it matches # of dims
            if dimVarys is None and hasattr(data, 'dv') and hasattr(data, 'shape'):
                dv = data.dv()
                if len(dv) + int(recVary) == len(data.shape):
                    dimVarys = dv
            if type in (const.CDF_EPOCH16, const.CDF_INT8, const.CDF_TIME_TT2000) \
                    and self.backward:
                raise ValueError('Cannot use EPOCH16, INT8, or TIME_TT2000 '
                                 'in backward-compatible CDF')
            if not lib.supports_int8 and \
                    type in (const.CDF_INT8, const.CDF_TIME_TT2000):
                raise ValueError('INT8 and TIME_TT2000 require CDF library 3.4.0')
            if data is None:
                if type is None:
                    raise ValueError('Must provide either data or a CDF type.')
                if dims is None:
                    dims = []
                if n_elements is None:
                    n_elements = 1
            else:
                #This supports getting the type straight from a VarCopy
                (guess_dims, guess_types, guess_elements) = _Hyperslice.types(data)
                if dims is None:
                    if recVary:
                        if guess_dims == ():
                            raise ValueError(
                                'Record-varying data cannot be scalar. '
                                'Specify NRV with CDF.new() or put data in array.')
                        dims = guess_dims[1:]
                    else:
                        dims = guess_dims
                if type is None:
                    type = guess_types[0]
                    if type == const.CDF_EPOCH16.value and self.backward:
                        type = const.CDF_EPOCH
                    if type in lib.timetypes and len(guess_types) > 1:
                        warnings.warn(
                            'No type specified for time input; assuming {}. This'
                            ' will change to TT2000 in the future, on systems'
                            ' which support it.'.format(lib.cdftypenames[type]),
                            DeprecationWarning)
                if n_elements is None:
                    n_elements = guess_elements
            if dimVarys is None:
                dimVarys = [True for i in dims]
            recVary = const.VARY if recVary else const.NOVARY
            dimVarys = [const.VARY if dimVary else const.NOVARY
                        for dimVary in dimVarys]
            if not hasattr(type, 'value'):
                type = ctypes.c_long(type)
            if type.value == const.CDF_INT8.value and not lib.supports_int8:
                raise ValueError(
                    '64-bit integer support require CDF library 3.4.0')
            if type.value in (const.CDF_EPOCH16.value, const.CDF_INT8.value,
                        const.CDF_TIME_TT2000.value) \
                    and self.backward:
                raise ValueError('Data requires EPOCH16, INT8, or TIME_TT2000; '
                                 'incompatible with backward-compatible CDF')
            new_var = Var(self, name, type, n_elements, dims, recVary, dimVarys)
            if compress != None:
                new_var.compress(compress, compress_param)
            if data is not None:
                new_var[...] = data
                if hasattr(data, 'attrs'):
                    new_var.attrs.clone(data.attrs)
            return new_var

        def raw_var(self, name):
            """
            Get a "raw" :class:`Var` object.

            Normally a :class:`Var` will perform translation of values for
            certain types (to/from Unicode for CHAR variables on Py3k,
            and to/from datetime for all time types). A "raw" object
            does not perform this translation, on read or write.

            This does *not* affect the data on disk, and in fact it
            is possible to maintain multiple Python objects with access
            to the same zVariable.

            Parameters
            ==========
            name : str
                name or number of the zVariable
            """
            v = self[name]
            v._raw = True
            return v

        def save(self):
            """
            Saves the CDF file but leaves it open.

            If closing the CDF, :meth:`close` is sufficient;
            there is no need to call
            :meth:`save` before :meth:`close`.

            .. note::
                Relies on an undocumented call of the CDF C library, which is
                also used in the Java interface.

            Raises
            ======
            CDFError : if CDF library reports an error

            Warns
            =====
            CDFWarning : if CDF library reports a warning
            """

            self._call(const.SAVE_, const.CDF_)

        def copy(self):
            """
            Make a copy of all data and attributes in this CDF

            Returns
            =======
            out : :py:class:`CDFCopy`
                :class:`~spacepy.datamodel.SpaceData`-like object of all data
            """
            return CDFCopy(self)

        def version(self):
            """
            Get version of library that created this CDF

            Returns
            =======
            out : tuple
                version of CDF library, in form (version, release, increment)
            """
            ver = ctypes.c_long(0)
            rel = ctypes.c_long(0)
            inc = ctypes.c_long(0)
            self._call(const.GET_, const.CDF_VERSION_, ctypes.byref(ver),
                       const.GET_, const.CDF_RELEASE_, ctypes.byref(rel),
                       const.GET_, const.CDF_INCREMENT_, ctypes.byref(inc))
            return (ver.value, rel.value, inc.value)

        def _get_attrs(self):
            """Get attribute list

            Provide access to the CDF's attribute list without holding a
            strong reference, as the attribute list has a (strong)
            back-reference to its parent.

            Either deref a weak reference (to try and keep the object the same),
            or make a new AttrList instance and assign it to the weak reference
            for next time.
            """
            al = self._attrlistref()
            if al is None:
                al = gAttrList(self)
            self._attrlistref = weakref.ref(al)
            return al

        def _set_attrs(self, value):
            """Assign to the attribute list

            Clears all elements of the attribute list and copies from value
            """
            self.attrs.clone(value)

        attrs = property(
            _get_attrs, _set_attrs, None,
            """Global attributes for this CDF in a dict-like format.
            See :class:`gAttrList` for details.
            """)

        def var_num(self, varname):
            """Get the variable number of a particular variable name

            This maintains a cache of name-to-number mappings for zVariables
            to keep from having to query the CDF library constantly. It's mostly
            an internal function.

            Parameters
            ==========
            varname : bytes
                name of the zVariable. Not this is NOT a string in Python 3!

            Raises
            ======
            CDFError : if variable is not found

            Returns
            =======
            out : int
                Variable number of this zvariable.
            """
            num = self._var_nums.get(varname, None)
            if num is None: #Copied from Var._get, which can hopefully be thinned
                varNum = ctypes.c_long(0)
                self._call(const.GET_, const.zVAR_NUMBER_, varname,
                           ctypes.byref(varNum))
                num = varNum.value
                self._var_nums[varname] = num
            return num

        def attr_num(self, attrname):
            """Get the attribute number and scope by attribute name

            This maintains a cache of name-to-number mappings for attributes
            to keep from having to query the CDF library constantly. It's mostly
            an internal function.

            Parameters
            ==========
            attrname : bytes
                name of the attribute. Not this is NOT a string in Python 3!

            Raises
            ======
            CDFError : if attribute is not found

            Returns
            =======
            out : tuple
                attribute number, scope (True for global) of this attribute
            """
            res = self._attr_info.get(attrname, None)
            if res is None: #Copied from Var._get, which can hopefully be thinned
                attrNum = ctypes.c_long(0)
                self._call(const.GET_, const.ATTR_NUMBER_, attrname,
                           ctypes.byref(attrNum))
                scope = ctypes.c_long(0)
                self._call(const.SELECT_, const.ATTR_, attrNum,
                           const.GET_, const.ATTR_SCOPE_, ctypes.byref(scope))
                if scope.value == const.GLOBAL_SCOPE.value:
                    scope = True
                elif scope.value == const.VARIABLE_SCOPE.value:
                    scope = False
                else:
                    raise CDFError(const.BAD_SCOPE)
                res = (attrNum.value, scope)
                self._attr_info[attrname] = res
            return res

        def clear_attr_from_cache(self, attrname):
            """Mark an attribute deleted in the name-to-number cache

            Will remove an attribute, and all attributes with higher numbers,
            from the attribute cache.

            Does NOT delete the variable!

            This maintains a cache of name-to-number mappings for attributes
            to keep from having to query the CDF library constantly. It's mostly
            an internal function.

            Parameters
            ==========
            attrname : bytes
                name of the attribute. Not this is NOT a string in Python 3!
            """
            num, scope = self.attr_num(attrname)
            #All numbers higher than this are renumbered
            for a, n in list(self._attr_info.items()):
                if n[0] >= num:
                    del self._attr_info[a]

        def clear_from_cache(self, varname):
            """Mark a variable deleted in the name-to-number cache

            Will remove a variable, and all variables with higher numbers,
            from the variable cache.

            Does NOT delete the variable!

            This maintains a cache of name-to-number mappings for zVariables
            to keep from having to query the CDF library constantly. It's mostly
            an internal function.

            Parameters
            ==========
            varname : bytes
                name of the zVariable. Not this is NOT a string in Python 3!
            """
            num = self.var_num(varname)
            #All numbers higher than this are renumbered
            for v, n in list(self._var_nums.items()):
                if n >= num:
                    del self._var_nums[v]

        def add_attr_to_cache(self, attrname, num, scope):
            """Add an attribute to the name-to-number cache

            This maintains a cache of name-to-number mappings for attributes
            to keep from having to query the CDF library constantly. It's mostly
            an internal function.

            Parameters
            ==========
            varname : bytes
                name of the zVariable. Not this is NOT a string in Python 3!
            num : int
                number of the variable
            scope : bool
                True if global scope; False if variable scope.
            """
            self._attr_info[attrname] = (num, scope)

        def add_to_cache(self, varname, num):
            """Add a variable to the name-to-number cache

            This maintains a cache of name-to-number mappings for zVariables
            to keep from having to query the CDF library constantly. It's mostly
            an internal function.

            Parameters
            ==========
            varname : bytes
                name of the zVariable. Not this is NOT a string in Python 3!
            num : int
                number of the variable
            """
            self._var_nums[varname] = num

        #Note there is no function for delete, currently handled in Var.rename
        #and Attr.rename by just deleting from the dict directly. Maybe this
        #should be differen (maybe should be possible to follow a variable across
        #a rename...)

    class CDFCopy(spacepy.datamodel.SpaceData):
        """
        A dictionary-like copy of all data and attributes in a :py:class:`CDF`

        Data are :class:`VarCopy` objects, keyed by variable name.
        CDF attributes are in :attr:`attrs`. (I.e.,
        data are accessed much like from a :class:`CDF`).

        Do not instantiate this class directly; use :meth:`~CDF.copy`
        on an existing :class:`CDF`.

        Examples
        --------
        >>> from spacepy import pycdf
        >>> with pycdf.CDF('test.cdf') as cdffile:
        ...     data = cdffile.copy()

        .. attribute:: attrs

           Python dictionary containing attributes copied from the CDF.
        """

        def __init__(self, cdf):
            """Copies all data and attributes from a CDF

            @param cdf: CDF to take data from
            @type cdf: :py:class:`pycdf.CDF`
            """
            super(CDFCopy, self).__init__(((key, var.copy())
                                          for (key, var) in cdf.items()),
                                          attrs = cdf.attrs.copy())

    def concatCDF(cdfs, varnames=None, raw=False):
        """Concatenate data from multiple CDFs

        Reads data from all specified CDFs in order and returns as if they
        were from a single CDF. The assumption is that the CDFs all have the
        same structure (same variables, each with the same dimensions and
        variance.)

        Parameters
        ----------
        cdfs : list of :class:`~spacepy.pycdf.Var`
            Open CDFs, will be read from in order. Must be a list (cannot
            be an iterable, as all files need to be open).
        varnames : list of str
            Names of variables to read (default: all variables in first CDF)
        raw : bool
            If True, read variables as raw (don't convert epochs, etc.)
            Default False.

        Returns
        -------
        :class:`~spacepy.datamodel.SpaceData`
            data concatenated from each CDF, with all attributes from first.
            Non-record-varying data is also only from first, and record
            variance is only checked on the first!

        Examples
        --------
        Read all data from all CDFs in the current directory. Note that
        CDFs are closed when their variable goes out of scope.

        >>> import glob
        >>> import spacepy.pycdf
        >>> data = spacepy.pycdf.concatCDF([
        ...     spacepy.pycdf.CDF(f) for f in glob.glob('*.cdf')])
        """
        if varnames is None:
            varnames = list(cdfs[0].keys()) #Iterate over this CDF only once
        vargetter = lambda f, v: f.raw_var(v) if raw else f[v]
        return spacepy.datamodel.SpaceData(
            {v:
             spacepy.datamodel.dmarray(
                 numpy.concatenate([vargetter(f, v)[...] for f in cdfs]),
                 attrs=vargetter(cdfs[0], v).attrs.copy())
             if cdfs[0][v].rv() else vargetter(cdfs[0], v).copy()
            for v in varnames},
            attrs=cdfs[0].attrs.copy())

    class Var(MutableSequence, spacepy.datamodel.MetaMixin):
        """
        A CDF variable.

        This object does not directly store the data from the CDF; rather,
        it provides access to the data in a format that much like a Python
        list or numpy :class:`~numpy.ndarray`.
        General list information is available in the python docs:
        `1 <http://docs.python.org/tutorial/introduction.html#lists>`_,
        `2 <http://docs.python.org/tutorial/datastructures.html#more-on-lists>`_,
        `3 <http://docs.python.org/library/stdtypes.html#typesseq>`_.

        The CDF user's guide, section 2.3, provides background on variables.

        .. note::
            Not intended to be created directly; use methods of :class:`CDF` to gain access to a variable.

        A record-varying variable's data are viewed as a hypercube of dimensions
        n_dims+1 (the extra dimension is the record number). They are indexed in
        row-major fashion, i.e. the last index changes most frequently / is
        contiguous in memory. If the CDF is column-major, the data are
        transformed to row-major before return.

        Non record-varying variables are similar, but do not have the extra
        dimension of record number.

        Variables can be subscripted by a multidimensional index to return the
        data. Indices are in row-major order with the first dimension
        representing the record number. If the CDF is column major,
        the data are reordered to row major. Each dimension is specified
        by standard Python
        `slice <http://docs.python.org/tutorial/introduction.html#strings>`_
        notation, with dimensions separated by commas. The ellipsis fills in
        any missing dimensions with full slices. The returned data are
        lists; Python represents multidimensional arrays as nested lists.
        The innermost set of lists represents contiguous data.

        .. note::
            numpy 'fancy indexing' is *not* supported.

        Degenerate dimensions are 'collapsed', i.e. no list of only one
        element will be returned if a single subscript is specified
        instead of a range. (To avoid this, specify a slice like 1:2,
        which starts with 1 and ends before 2).

        Two special cases:

          1. requesting a single-dimension slice for a
             record-varying variable will return all data for that
             record number (or those record numbers) for that variable.
          2. Requests for multi-dimensional variables may skip the record-number
             dimension and simply specify the slice on the array itself. In that
             case, the slice of the array will be returned for all records.

        In the event of ambiguity (e.g., single-dimension slice on a one-dimensional
        variable), case 1 takes priority.
        Otherwise, mismatch between the number of dimensions specified in
        the slice and the number of dimensions in the variable will cause
        an :exc:`~exceptions.IndexError` to be thrown.

        This all sounds very complicated but it is essentially attempting
        to do the 'right thing' for a range of slices.

        An unusual case is scalar (zero-dimensional) non-record-varying variables.
        Clearly they cannot be subscripted normally. In this case, use the
        ``[...]`` syntax meaning 'access all data.':

        >>> from spacepy import pycdf
        >>> testcdf = pycdf.CDF('test.cdf', '')
        >>> variable = testcdf.new('variable', recVary=False,
        ...     type=pycdf.const.CDF_INT4)
        >>> variable[...] = 10
        >>> variable
        <Var:
        CDF_INT4 [] NRV
        >
        >>> variable[...]
        10

        Reading any empty non-record-varying variable will return an empty
        with the same *number* of dimensions, but all dimensions will be
        of zero length. The scalar is, again, a special case: due to the
        inability to have a numpy array which is both zero-dimensional and empty,
        reading an NRV scalar variable with no data will return an empty
        one-dimensional array. This is really not recommended.

        As a list type, variables are also `iterable
        <http://docs.python.org/tutorial/classes.html#iterators>`_; iterating
        over a variable returns a single complete record at a time.

        This is all clearer with examples. Consider a variable ``B_GSM``, with
        three elements per record (x, y, z components) and fifty records in
        the CDF. Then:

          1. ``B_GSM[0, 1]`` is the y component of the first record.
          2. ``B_GSM[10, :]`` is a three-element list, containing x, y, and z
             components of the 11th record. As a shortcut, if only one dimension
             is specified, it is assumed to be the record number, so this
             could also be written ``B_GSM[10]``.
          3. ``B_GSM[...]`` reads all data for ``B_GSM`` and returns it as a
             fifty-element list, each element itself being a three-element
             list of x, y, z components.

        Multidimensional example: consider fluxes stored as a function of
        pitch angle and energy. Such a variable may be called Flux and
        stored as a two-dimensional array, with the first dimension
        representing (say) ten energy steps and the second, eighteen
        pitch angle bins (ten degrees wide, centered from 5 to 175 degrees).
        Assume 100 records stored in the CDF (i.e. 100 different times).

          1. ``Flux[4]`` is a list of ten elements, one per energy step,
             each element being a list of 18 fluxes, one per pitch bin.
             All are taken from the fifth record in the CDF.
          2. ``Flux[4, :, 0:4]`` is the same record, all energies, but
             only the first four pitch bins (roughly, field-aligned).
          3. ``Flux[..., 0:4]`` is a 100-element list (one per record),
             each element being a ten-element list (one per energy step),
             each containing fluxes for the first four pitch bins.

        This slicing notation is very flexible and allows reading
        specifically the desired data from the CDF.

        .. note::
            The C CDF library allows reading records which have not been
            written to a file, returning a pad value. pycdf checks the
            size of a variable and will raise `IndexError` for most
            attempts to read past the end. If these checks fail, a value
            is returned with a warning ``VIRTUAL_RECORD_DATA``. Please
            `open an issue
            <https://github.com/spacepy/spacepy/issues/new>`_ if this
            occurs. See pg. 39 and following of the `CDF User's Guide
            <https://cdf.gsfc.nasa.gov/html/cdf_docs.html>`_ for more on
            virtual records.

        All data are, on read, converted to appropriate Python data
        types; EPOCH, EPOCH16, and TIME_TT2000 types are converted to
        :class:`~datetime.datetime`. Data are returned in numpy arrays.

        .. note::
            Although pycdf supports TIME_TT2000 variables, the Python
            :class:`~datetime.datetime` object does not support leap
            seconds. Thus, on read, any seconds past 59 are truncated
            to 59.999999 (59 seconds, 999 milliseconds, 999 microseconds).

        Potentially useful list methods and related functions:
          - `count <http://docs.python.org/tutorial/datastructures.html#more-on-lists>`_
          - `in <http://docs.python.org/reference/expressions.html#in>`_
          - `index <http://docs.python.org/tutorial/datastructures.html#more-on-lists>`_
          - `len <http://docs.python.org/library/functions.html#len>`_
          - `list comprehensions
            <http://docs.python.org/tutorial/datastructures.html#list-comprehensions>`_
          - `sorted <http://docs.python.org/library/functions.html#sorted>`_

        The topic of array majority can be very confusing; good background material
        is available at `IDL Array Storage and Indexing
        <http://www.idlcoyote.com/misc_tips/colrow_major.html>`_. In brief,
        *regardless of the majority stored in the CDF*, pycdf will always present
        the data in the native Python majority, row-major order, also known as
        C order. This is the default order in `NumPy
        <http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html
        #internal-memory-layout-of-an-ndarray>`_.
        However, packages that render image data may expect it in column-major
        order. If the axes seem 'swapped' this is likely the reason.

        The :attr:`~Var.attrs` Python attribute acts as a dictionary referencing
        zAttributes (do not confuse the two); all the dictionary methods above
        also work on the attribute dictionary. See :class:`zAttrList` for more on
        the dictionary of attributes.

        With writing, as with reading, every attempt has been made to match the
        behavior of Python lists. You can write one record, many records, or even
        certain elements of all records. There is one restriction: only the record
        dimension (i.e. dimension 0) can be resized by write, as all records
        in a variable must have the same dimensions. Similarly, only whole
        records can be deleted.

        .. note::
            Unusual error messages on writing data usually mean that pycdf is
            unable to interpret the data as a regular array of a single type
            matching the type and shape of the variable being written.
            A 5x4 array is supported; an irregular array where one row has
            five columns and a different row has six columns is not. Error messages
            of this type include:

              - ``Data must be well-formed, regular array of number, string, or datetime``
              - ``setting an array element with a sequence.``
              - ``shape mismatch: objects cannot be broadcast to a
                single shape``

        For these examples, assume Flux has 100 records and dimensions [2, 3].

        Rewrite the first record without changing the rest:

        >>> Flux[0] = [[1, 2, 3], [4, 5, 6]]

        Writes a new first record and delete all the rest:

        >>> Flux[...] = [[1, 2, 3], [4, 5, 6]]

        Write a new record in the last position and add a new record after:

        >>> Flux[99:] = [[[1, 2, 3], [4, 5, 6]],
        ...              [[11, 12, 13], [14, 15, 16]]]

        Insert two new records between the current number 5 and 6:

        >>> Flux[5:6] = [[[1, 2, 3], [4, 5, 6]],  [[11, 12, 13],
        ...               [14, 15, 16]]]

        This operation can be quite slow, as it requires reading and
        rewriting the entire variable. (CDF does not directly support
        record insertion.)

        Change the first element of the first two records but leave other
        elements alone:

        >>> Flux[0:2, 0, 0] = [1, 2]

        Remove the first record:

        >>> del Flux[0]

        Removes record 5 (the sixth):

        >>> del Flux[5]

        Due to the need to work around a bug in the CDF library, this operation
        can be quite slow.

        Delete *all data* from ``Flux``, but leave the variable definition intact:

        >>> del Flux[...]

        .. note::
            Although this interface only directly supports zVariables, zMode is
            set on opening the CDF so rVars appear as zVars. See p.24 of the
            CDF user's guide; pyCDF uses zMode 2.

        .. autosummary::

            ~Var.attrs
            ~Var.compress
            ~Var.copy
            ~Var.dtype
            ~Var.dv
            ~Var.insert
            ~Var.name
            ~Var.nelems
            ~Var.rename
            ~Var.rv
            ~Var.shape
            ~Var.type
        .. attribute:: Var.attrs

           zAttributes for this zVariable in a dict-like format.
           See :class:`zAttrList` for details.
        .. automethod:: compress
        .. automethod:: copy
        .. autoattribute:: dtype
        .. automethod:: dv
        .. automethod:: insert
        .. automethod:: name
        .. automethod:: nelems
        .. automethod:: rename
        .. automethod:: rv
        .. autoattribute:: shape
        .. automethod:: type
        """
        def __init__(self, cdf_file, var_name, *args):
            """Create or locate a variable

            Parameters
            ==========
            cdf_file : :py:class:`pycdf.CDF`
                CDF file containing this variable
            var_name : string
                name of this variable

            Other Parameters
            ================
            args
                additional arguments passed to :py:meth:`_create`. If none,
                opens an existing variable. If provided, creates a
                new one.

            Raises
            ======
            CDFError
                if CDF library reports an error

            Warns
            =====
            CDFWarning
                if CDF library reports a warning
            """
            self.cdf_file = cdf_file
            #This is the definitive "identify" of variable
            self._name = None
            self._type = None #CDF type (long)
            self._raw = False #Raw access (skip all conversions)
            if len(args) == 0:
                self._get(var_name)
            else:
                self._create(var_name, *args)
            #Weak reference to attribute list (use attrs instead)
            #This avoids a reference loop
            self._attrlistref = weakref.ref(zAttrList(self))

        def __getitem__(self, key):
            """Returns a slice from the data array. Details under :py:class:`pycdf.Var`.

            @return: The data from this variable
            @rtype: list-of-lists of appropriate type.
            @raise IndexError: if L{key} is out of range, mismatches dimensions,
                               or simply unparseable.
            @raise CDFError: for errors from the CDF library
            """
            hslice = _Hyperslice(self, key)
            #Hyperslice mostly catches this sort of thing, but
            #an empty variable is a special case, since we might want to
            #WRITE to 0th record (which Hyperslice also supports) but
            #can't READ from it, and iterating over tries to read from it.
            if hslice.rv:
                if hslice.dimsizes[0] == 0 and hslice.degen[0] and \
                   hslice.starts[0] == 0:
                    raise IndexError('record index out of range')
            #For NRV, again hslice will assume 0th record exists since we might
            #want to write. So ANY degenerate dim other than the glued-on 0th
            #suggests an explicit index that should fail. None degenerate suggests
            #make an empty array.
            #Note this is pulling a lot of hyperslice stuff into getitem!
            elif hslice.dimsizes[0] == 0:
                if len(hslice.degen) > 1 and max(hslice.degen[1:]):
                    raise IndexError('record index out of range')
                else:
                    #The zero-length dimension is degenerate so it gets chopped,
                    #and you can't have a zero-length numpy array that still
                    #maintains the size of all other dimensions. So just force
                    #a zero-dim array and the rest will follow
                    hslice.counts[...] = 0
                    #If this is a scalar, need to make a single non-degenerate
                    #dimension so it can be empty.
                    if len(hslice.counts) == 1:
                        hslice.degen[0] = False
            result = hslice.create_array()
            if hslice.counts[0] != 0:
                hslice.select()
                lib.call(const.GET_, const.zVAR_HYPERDATA_,
                         result.ctypes.data_as(ctypes.c_void_p))
            return hslice.convert_input_array(result)

        def __delitem__(self, key):
            """Removes a record (or set of records) from the CDF

            Only whole records can be deleted, so the del call must either specify
            only one dimension or it must specify all elements of the non-record
            dimensions. This is *not* a way to resize a variable!

            Deleting records from the middle of a variable may be very slow in
            some circumstances. To work around a bug in CDF library versions
            3.4.0 and before, all the data must be read in, the requested deletions
            done, and then all written back out.

            @param key: index or slice to delete
            @type key: int or slice
            @raise TypeError: if an attempt is made to delete from a non
                              record-varying variable, or to delete below
                              the record level
            """
            if not self.rv():
                raise TypeError('Cannot delete records from non-record-varying '
                                'variable.')
            hslice = _Hyperslice(self, key)
            if hslice.dims > 1 and (hslice.counts[1:] != hslice.dimsizes[1:]).any():
                raise TypeError('Can only delete entire records.')
            if hslice.counts[0] == 0:
                return
            start = hslice.starts[0]
            count = hslice.counts[0]
            interval = hslice.intervals[0]
            dimsize = hslice.dimsizes[0]

            self._call()
            dangerous_delete = False
            if lib._del_middle_rec_bug and \
                   (interval != 1 or (start != 0 and start + count < dimsize)):
                #delete from middle is dangerous if only have one index entry
                entries = ctypes.c_long(0)
                lib.call(const.GET_, const.zVAR_nINDEXENTRIES_,
                         ctypes.byref(entries))
                dangerous_delete = (entries.value == 1)

            if dangerous_delete:
                data = self[...]
                data = numpy.delete(
                    data,
                    numpy.arange(start, start + count * interval, interval),
                    0)
                self[0:dimsize - count] = data
                first_rec = dimsize - count
                last_rec = dimsize - 1
                lib.call(const.DELETE_, const.zVAR_RECORDS_,
                         ctypes.c_long(first_rec), ctypes.c_long(last_rec))
            elif interval == 1:
                first_rec = ctypes.c_long(start)
                last_rec = ctypes.c_long(start + count - 1)
                lib.call(const.DELETE_, const.zVAR_RECORDS_,
                         first_rec, last_rec)
            else:
                self._call()
                #delete from end to avoid renumbering of records
                for recno in range(start + (count - 1) * interval,
                                   start - 1, -1 * interval):
                    lib.call(const.DELETE_, const.zVAR_RECORDS_,
                             ctypes.c_long(recno), ctypes.c_long(recno))

        def __setitem__(self, key, data):
            """Puts a slice into the data array. Details under :py:class:`pycdf.Var`.

            @param key: index or slice to store
            @type key: int or slice
            @param data: data to store
            @type data: numpy.array
            @raise IndexError: if L{key} is out of range, mismatches dimensions,
                               or simply unparseable. IndexError will
            @raise CDFError: for errors from the CDF library
            """
            hslice = _Hyperslice(self, key)
            n_recs = hslice.counts[0]
            hslice.expand(data)
            cdf_type = self.type()
            if cdf_type == const.CDF_EPOCH16.value:
                if not self._raw:
                    try:
                        data = lib.v_datetime_to_epoch16(data)
                    except AttributeError:
                        pass
                data = numpy.require(data, requirements=('C', 'A', 'W'),
                                     dtype=numpy.float64)
            elif cdf_type == const.CDF_EPOCH.value:
                if not self._raw:
                    try:
                        data = lib.v_datetime_to_epoch(data)
                    except AttributeError:
                        pass
                data = numpy.require(data, requirements=('C', 'A', 'W'),
                                     dtype=numpy.float64)
            elif cdf_type == const.CDF_TIME_TT2000.value:
                if not self._raw:
                    try:
                        data = lib.v_datetime_to_tt2000(data)
                    except AttributeError:
                        pass
                data = numpy.require(data, requirements=('C', 'A', 'W'),
                                     dtype=numpy.int64)
            else:
                 data = numpy.require(data, requirements=('C', 'A', 'W'),
                                      dtype=self._np_type())
            if cdf_type == const.CDF_EPOCH16.value:
                datashape = data.shape[:-1]
            else:
                datashape = data.shape
            #Check data sizes
            if datashape != tuple(hslice.expected_dims()):
                 raise ValueError('attempt to assign data of dimensions ' +
                                  str(datashape) + ' to slice of dimensions ' +
                                  str(tuple(hslice.expected_dims())))
            #Flip majority and reversed dimensions, see convert_input_array
            data = hslice.convert_output_array(data)
            #Handle insertions and similar weirdness
            if hslice.counts[0] > n_recs and \
                   hslice.starts[0] + n_recs < hslice.dimsizes[0]:
                #Specified slice ends before last record, so insert in middle
                saved_data = self[hslice.starts[0] + n_recs:]
            if hslice.counts[0] > 0:
                hslice.select()
                lib.call(const.PUT_, const.zVAR_HYPERDATA_,
                         data.ctypes.data_as(ctypes.c_void_p))
            if hslice.counts[0] < n_recs:
                first_rec = hslice.starts[0] + hslice.counts[0]
                last_rec = hslice.dimsizes[0] - 1
                lib.call(const.DELETE_, const.zVAR_RECORDS_,
                         ctypes.c_long(first_rec), ctypes.c_long(last_rec))
            elif hslice.counts[0] > n_recs and \
                   hslice.starts[0] + n_recs < hslice.dimsizes[0]:
                #Put saved data in after inserted data
                self[hslice.starts[0] + hslice.counts[0]:] = saved_data

        def extend(self, data):
            """
            Append multiple values to the end of this variable

            This is an efficiency function which overrides the base implementation
            in MutableSequence.

            Parameters
            ----------
            data :
                the data to append
            """
            self[len(self):] = data

        def insert(self, index, data):
            """
            Inserts a *single* record before an index

            Parameters
            ----------
            index : int
                index before which to insert the new record
            data :
                the record to insert
            """
            self[index:index] = [data]

        def _create(self, var_name, datatype, n_elements = 1, dims = (),
                   recVary = const.VARY, dimVarys = None):
            """Creates a new zVariable

            @param var_name: name of this variable
            @type var_name: string
            @param datatype: CDF data type
            @type datatype: ctypes.c_long
            @param n_elements: number of elements (should be 1 except for
                               CDF_CHAR variables).
            @type n_elements: long
            @param dims: size of each dimension for multi-dimensional variable,
                         or empty for a zero-dimensional
            @type dims: sequence of long
            @param recVary: record variance for this variable (VARY/NOVARY)
            @type recVary: long
            @param dimVarys: array of VARY or NOVARY, variance for each dimension
            @type dimVarys: sequence of long
            @return: new variable with this name
            @rtype: :py:class:`pycdf.Var`
            @raise CDFError: if CDF library reports an error
            @raise CDFWarning: if CDF library reports a warning and interpreter
                               is set to error on warnings.
            @note: Not intended to be used directly; use L{CDF.new}.
            """

            dim_array = (ctypes.c_long * len(dims))(*dims)
            enc_name = var_name.encode('ascii')
            if dimVarys is None:
                dim_vary_array = (ctypes.c_long * (len(dims) if len(dims) > 0 else 1))(const.VARY)
            else:
                dim_vary_array = (ctypes.c_long * len(dims))(*dimVarys)
            varNum = ctypes.c_long(0)
            self.cdf_file._call(const.CREATE_, const.zVAR_, enc_name, datatype,
                     ctypes.c_long(n_elements), ctypes.c_long(len(dims)), dim_array,
                     recVary, dim_vary_array, ctypes.byref(varNum))
            self._name = enc_name
            self.cdf_file.add_to_cache(enc_name, varNum.value)

        def _delete(self):
            """Removes this zVariable from the CDF

            @raise CDFError: if CDF library reports an error
            @raise CDFWarning: if CDF library reports a warning and interpreter
                               is set to error on warnings.
            """
            self._call(const.DELETE_, const.zVAR_)
            self.cdf_file.clear_from_cache(self._name)
            self._name = None

        def _get(self, var_name):
            """Gets an existing zVariable

            @param var_name: name of this variable
            @type var_name: string
            @return: variable with this name
            @rtype: :py:class:`pycdf.Var`
            @raise CDFError: if CDF library reports an error
            @raise CDFWarning: if CDF library reports a warning and interpreter
                               is set to error on warnings.
            @note: Not intended to be used directly; use L{CDF.__getitem__}.
            """

            if isinstance(var_name, str_classes):
                try:
                    enc_name = var_name.encode('ascii').rstrip()
                except AttributeError:
                    enc_name = var_name.rstrip() #already in ASCII
                #'touch' CDF to cause an error if the name isn't there; get number
                varNum = ctypes.c_long(0)
                self.cdf_file._call(const.GET_, const.zVAR_NUMBER_, enc_name, ctypes.byref(varNum))
                self._name = enc_name
                self.cdf_file.add_to_cache(enc_name, varNum.value)
            else: #Looking up by number
                name = ctypes.create_string_buffer(const.CDF_VAR_NAME_LEN256+1)
                self.cdf_file._call(const.SELECT_, const.zVAR_, ctypes.c_long(var_name),
                         const.GET_, const.zVAR_NAME_, name)
                self._name = name.value.rstrip()
                self.cdf_file.add_to_cache(self._name, var_name)

        def _num(self):
            """Returns the zVar number for this variable

            @return: number of this zVar
            @rtype: int
            """
            return self.cdf_file.var_num(self._name)

        def __len__(self):
            """Get number of records for this variable in this file

            @return: Number of records
            @rtype: long
            @raise CDFError: if CDF library reports an error
            @raise CDFWarning: if CDF library reports a warning and interpreter
                               is set to error on warnings.
            """
            count = ctypes.c_long(0)
            self._call(const.GET_, const.zVAR_MAXREC_, ctypes.byref(count))
            return (count.value + 1)

        def __repr__(self):
            """Returns representation of the variable

            Cannot return anything that can be eval'd to create a copy,
            so just wrap the informal representation in angle brackets.
            @return: info on this zVar
            @rtype: str
            """
            return '<Var:\n' + str(self) + '\n>'

        def __str__(self):
            """Returns a string representation of the variable

            This is an 'informal' representation in that it cannot be evaluated
            directly to create a :py:class:`pycdf.Var`.

            @return: info on this zVar, CDFTYPE [dimensions] NRV
                     (if not record-varying)
            @rtype: str
            """
            if self.cdf_file._opened:
                cdftype = self.type()
                chartypes = (const.CDF_CHAR.value, const.CDF_UCHAR.value)
                rv = self.rv()
                typestr = lib.cdftypenames[cdftype] + \
                          ('*' + str(self.nelems()) if cdftype in chartypes else '' )
                if rv:
                    sizestr = str([len(self)] + self._dim_sizes())
                else:
                    sizestr = str(self._dim_sizes())
                return typestr + ' ' + sizestr + ('' if rv else ' NRV')
            else:
                if isinstance(self._name, str):
                    return 'zVar "{0}" in closed CDF {1}'.format(
                        self._name, self.cdf_file.pathname)
                else:
                    return 'zVar "{0}" in closed CDF {1}'.format(
                        self._name.decode('ascii'),
                        self.cdf_file.pathname.decode('ascii'))

        def _n_dims(self):
            """Get number of dimensions for this variable

            @return: the number of dimensions
            @rtype: long
            """
            n_dims = ctypes.c_long(0)
            self._call(const.GET_, const.zVAR_NUMDIMS_, ctypes.byref(n_dims))
            return n_dims.value

        def _dim_sizes(self):
            """Get the dimension sizes for this variable

            @return: sequence of sizes
            @rtype: sequence of long
            @note: This will always be in Python order (i.e. row major, last index
            iterates most quickly), *regardless* of the majority of the CDF.
            """
            sizes = (ctypes.c_long * const.CDF_MAX_DIMS)(0)
            self._call(const.GET_, const.zVAR_DIMSIZES_, sizes)
            sizes = sizes[0:self._n_dims()]
            return sizes

        def rv(self, new_rv=None):
            """
            Gets or sets whether this variable has record variance

            If the variance is unknown, True is assumed
            (this replicates the apparent behavior of the CDF library on
            variable creation).

            Other Parameters
            ================
            new_rv : boolean
                True to change to record variance, False to change to NRV,
                unspecified to simply check variance.

            Returns
            =======
            out : Boolean
                True if record varying, False if NRV
            """
            if new_rv != None:
                self._call(const.PUT_, const.zVAR_RECVARY_,
                           const.VARY if new_rv else const.NOVARY)
            vary = ctypes.c_long(0)
            self._call(const.GET_, const.zVAR_RECVARY_, ctypes.byref(vary))
            return vary.value != const.NOVARY.value

        def dv(self, new_dv=None):
            """
            Gets or sets dimension variance of each dimension of variable.

            If the variance is unknown, True is assumed
            (this replicates the apparent behavior of the
            CDF library on variable creation).

            Parameters
            ==========
            new_dv : list of boolean
                Each element True to change that dimension to dimension
                variance, False to change to not dimension variance.
                (Unspecified to simply check variance.)

            Returns
            =======
            out : list of boolean
                True if that dimension has variance, else false.
            """
            ndims = self._n_dims()
            if new_dv != None:
                if len(new_dv) != ndims:
                    raise ValueError('Must specify variance for ' +
                                     str(ndims) + 'dimensions.')
                varies = (ctypes.c_long * ndims)(
                    *[const.VARY if dv else const.NOVARY for dv in new_dv])
                self._call(const.PUT_, const.zVAR_DIMVARYS_,
                           varies)
            if ndims == 0:
                return []
            varies = (ctypes.c_long * const.CDF_MAX_DIMS)()
            self._call(const.GET_, const.zVAR_DIMVARYS_, varies)
            return [dv != const.NOVARY.value for dv in varies[0:ndims]]

        def _call(self, *args, **kwargs):
            """Select this CDF and variable and call the CDF internal interface

            Adds call to select this CDF to L{args} and passes all parameters
            directly through to the CDFlib routine of the CDF library's C internal
            interface. Checks the return value with L{Library.check_status}.

            @param args: Passed directly to the CDF library interface. Useful
                         constants are defined in the :py:mod:`pycdf.const` module of this package.
            @type args: various, see :py:mod:`ctypes`.
            @return: CDF status from the library
            @rtype: ctypes.c_long
            @note: Terminal NULL_ is automatically added to L{args}.
            @raise CDFError: if CDF library reports an error
            @raise CDFWarning: if CDF library reports a warning and interpreter
                               is set to error on warnings.
            """
            return self.cdf_file._call(
                const.SELECT_, const.zVAR_,
                ctypes.c_long(self.cdf_file.var_num(self._name)), *args, **kwargs)

        def _np_type(self):
            """Returns the numpy type of this variable

            This is the numpy type that will come directly out of the CDF;
            see :meth:`dtype` for the representation post-conversion.

            Raises
            ======
            CDFError : for library-reported error or failure to find numpy type

            Returns
            =======
            out : dtype
                numpy dtype that will hold value from this variable

            """
            cdftype = self.type()
            if cdftype == const.CDF_CHAR.value or cdftype == const.CDF_UCHAR.value:
                return numpy.dtype('S' + str(self.nelems()))
            try:
                return lib.numpytypedict[cdftype]
            except KeyError:
                raise CDFError(const.BAD_DATA_TYPE)

        def type(self, new_type=None):
            """
            Returns or sets the CDF type of this variable

            Parameters
            ==========
            new_type : ctypes.c_long
                the new type from :mod:`~spacepy.pycdf.const`

            Returns
            =======
            out : int
                CDF type
            """
            if new_type != None:
                if not hasattr(new_type, 'value'):
                    new_type = ctypes.c_long(new_type)
                n_elements = ctypes.c_long(self.nelems())
                self._call(const.PUT_, const.zVAR_DATASPEC_,
                           new_type, n_elements)
                self._type = None
            if self._type is None:
                cdftype = ctypes.c_long(0)
                self._call(const.GET_, const.zVAR_DATATYPE_,
                           ctypes.byref(cdftype))
                self._type = cdftype.value
            return self._type

        def nelems(self):
            """Number of elements for each value in this variable

            This is the length of strings for CHAR and UCHAR,
            should be 1 otherwise.

            Returns
            =======
            int
                length of strings
            """
            nelems = ctypes.c_long(0)
            self._call(const.GET_, const.zVAR_NUMELEMS_, ctypes.byref(nelems))
            return nelems.value

        def _nelems(self):
            """Number of elements for each value in this variable

            .. deprecated:: 0.2.2
                This method will be removed in the future. Use the public
                interface `nelems` instead.
            """
            warnings.warn("_nelems is deprecated and will be removed. Use nelems.",
                          DeprecationWarning)
            return self.nelems()

        def name(self):
            """
            Returns the name of this variable

            Returns
            =======
            out : str
                variable's name
            """
            if isinstance(self._name, str):
                return self._name
            elif isinstance(self._name, bytes):
                return self._name.decode()

        def compress(self, comptype=None, param=None):
            """
            Set or check the compression of this variable

            Compression may not be changeable on variables with data already
            written; even deleting the data may not permit the change.

            See section 2.6 of the CDF user's guide for more information on
            compression.

            Other Parameters
            ================
            comptype : ctypes.c_long
                type of compression to change to, see CDF C reference
                manual section 4.10. Constants for this parameter
                are in :mod:`~spacepy.pycdf.const`. If not specified, will not
                change compression.
            param : ctypes.c_long
                Compression parameter, see CDF CRM 4.10 and
                :mod:`~spacepy.pycdf.const`.
                If not specified, will choose reasonable default (5 for
                gzip; other types have only one possible parameter.)

            Returns
            =======
            out : tuple
                the (comptype, param) currently in effect
            """
            return _compress(self, comptype, param)

        def copy(self):
            """
            Copies all data and attributes from this variable

            Returns
            =======
            out : :class:`VarCopy`
                list of all data in record order
            """
            return VarCopy(self)

        def rename(self, new_name):
            """
            Renames this variable

            Parameters
            ==========
            new_name : str
                the new name for this variable
            """
            try:
                enc_name = new_name.encode('ascii')
            except AttributeError:
                enc_name = new_name
            if len(enc_name) > const.CDF_VAR_NAME_LEN256:
                raise CDFError(const.BAD_VAR_NAME)
            self._call(const.PUT_, const.zVAR_NAME_, enc_name)
            self.cdf_file.add_to_cache(
                enc_name,
                self.cdf_file.var_num(self._name)) #Still in cache
            del self.cdf_file._var_nums[self._name]
            self._name = enc_name

        @property
        def shape(self):
            """
            Provides the numpy array-like shape of this variable.

            Returns a tuple; first element is number of records (RV variable
            only) And the rest provide the dimensionality of the variable.

            .. note::
                Assigning to this attribute will not change the shape.
            """
            if self.rv():
                return tuple([len(self)] + self._dim_sizes())
            else:
                return tuple(self._dim_sizes())

        @property
        def dtype(self):
            """
            Provide the numpy dtype equivalent to the CDF type of this variable.

            Data from this variable will be returned in numpy arrays of this type.

            See Also
            --------
            type
            """
            cdftype = self.type()
            if cdftype in (const.CDF_CHAR.value, const.CDF_UCHAR.value) and \
               str is not bytes and not self._raw:
                return numpy.dtype('U' + str(self.nelems()))
            if cdftype in (const.CDF_EPOCH.value, const.CDF_EPOCH16.value,
                           const.CDF_TIME_TT2000.value) and not self._raw:
                return numpy.dtype('O')
            return self._np_type()

        def _get_attrs(self):
            """Get attribute list

            Provide access to the zVar's attribute list without holding a
            strong reference, as the attribute list has a (strong)
            back-reference to its parent.

            Either deref a weak reference (to try and keep the object the same),
            or make a new AttrList instance and assign it to the weak reference
            for next time.
            """
            al = self._attrlistref()
            if al is None:
                al = zAttrList(self)
            self._attrlistref = weakref.ref(al)
            return al

        def _set_attrs(self, value):
            """Assign to the attribute list

            Clears all elements of the attribute list and copies from value
            """
            self.attrs.clone(value)

        attrs = property(
            _get_attrs, _set_attrs, None,
            """zAttributes for this zVariable in a dict-like format.
            See :class:`zAttrList` for details.
            """)

    class VarCopy(spacepy.datamodel.dmarray):
        """A list-like copy of the data and attributes in a :class:`Var`

        Data are in the list elements. CDF attributes are in a dict,
        accessed through :attr:`attrs`. (I.e.,
        data and attributes are accessed like in a :class:`Var`.)

        Do not instantiate this class directly; use :meth:`~Var.copy`
        on an existing :class:`Var`.

        Several methods provide access to details about how the original
        variable was constructed. This is mostly for making it easier to
        reproduce the variable by passing it to
        :meth:`~spacepy.pycdf.CDF.new`. Operations that e.g. change the
        dimensionality of the copy may make this (or any) metadata out of
        date; see :meth:`set` to update.

        .. autosummary::

            compress
            dv
            nelems
            rv
            set
            type

        .. attribute:: attrs

           Python dictionary containing attributes copied from the zVar

        .. automethod:: compress
        .. automethod:: dv
        .. automethod:: nelems
        .. automethod:: rv
        .. automethod:: set
        .. automethod:: type

        """
        Allowed_Attributes = spacepy.datamodel.dmarray.Allowed_Attributes \
                             + ['_cdf_meta']

        def __new__(cls, zVar):
            """Copies all data and attributes from a zVariable

            @param zVar: variable to take data from
            @type zVar: :py:class:`pycdf.Var`
            """
            obj = super(VarCopy, cls).__new__(cls, zVar[...], zVar.attrs.copy())
            obj._cdf_meta = {
                'compress': zVar.compress(),
                'dv': zVar.dv(),
                'nelems': zVar.nelems(),
                'rv': zVar.rv(),
                'type': zVar.type(),
                }
            return obj

        def compress(self, *args, **kwargs):
            """Gets compression of the variable this was copied from.

            For details on CDF compression, see
            :meth:`spacepy.pycdf.Var.compress`.

            If any arguments are specified, calls
            :meth:`numpy.ndarray.compress` instead (as the names conflict)

            Returns
            =======
            tuple
                compression type, parameter currently in effect.

            """
            if args or kwargs:
                return super(VarCopy, self).compress(*args, **kwargs)
            else:
                return self._cdf_meta['compress']

        def dv(self):
            """Gets dimension variance of the variable this was copied from.

            Each dimension other than the record dimension may either vary
            or not.

            Returns
            =======
            list of boolean
                True if that dimension has variance, else False

            """
            return self._cdf_meta['dv']

        def nelems(self):
            """Gets number of elements of the variable this was copied from.

            This is usually 1 except for strings, where it is the length of the
            string.

            Returns
            =======
            int
                Number of elements in parent variable
            """
            return self._cdf_meta['nelems']

        def rv(self):
            """Gets record variance of the variable this was copied from.

            Returns
            =======
            boolean
                True if parent variable was record varying, False if NRV
            """
            return self._cdf_meta['rv']

        def set(self, key, value):
            """Set CDF metadata

            Set the metadata describing the original variable this was
            copied from. Can be used to update the metadata if
            transformation of the copy has made it out of date (e.g. by
            removing dimensions.) There is very little checking done and
            this function should only be used with care.

            Parameters
            ==========
            key : str
                Which metadata to set; this matches the name of the method
                used to retrieve it (e.g. use ``type`` to set the CDF type, which
                is returned by :meth:`type`).
            value
                Value to assign to `key`.
            """
            if key not in self._cdf_meta:
                raise KeyError('Invalid CDF metadata key {}'.format(key))
            self._cdf_meta[key] = value

        def type(self):
            """Returns CDF type of the variable this was copied from.

            Returns
            =======
            int
                CDF type
            """
            return self._cdf_meta['type']

    class _Hyperslice(object):
        """Represents a CDF 'slice' used for the hyper CDF functions

        For internal module use only.

        @ivar dims: number of dimensions to this slice, usually
                    number of dimensions to the variable plus one
                    for the record, which represents the 0th
                    (least rapidly varying) dimension.
        @type dims: int
        @ivar dimsizes: size of each dimension (0th is number of records)
        @type dimsizes: list of int
        @ivar starts: index of the start value for each dimension
                      ('dimension indices' in CDF speak)
        @type starts: list of int
        @ivar counts: number of values to get from each dimension.
                      Final result will be the product of everything
                      in counts.
                      ('dimension counts' in CDF speak)
        @type counts: numpy.array
        @ivar intervals: interval between successive indices
                         to use for each dimension.
                         ('dimension invervals' in CDF speak)
        @type intervals: list of int
        @ivar degen: is this dimension degenerate, i.e. should be
                     removed in the returned dataset. A 3D array
                     with one dimension degenerate will be returned
                     as a 2D array (i.e. list-of-lists.)
        @type degen: numpy.array
        @ivar rev: should this dimension be returned in reverse order?
        @type rev: numpy.array
        @ivar column: is this slice in column-major mode (if false, row-major)
        @type column: boolean
        @ivar zvar: what CDF variable this object slices on
        @type zvar: :py:class:`pycdf.Var`
        @ivar expanded_key: fully-expanded version of the key passed to the
                            constructor (all dimensions filled in)
        @type expanded_key: tuple
        @note: All dimension-related variables are stored row-major
               (Python order)
        """

        def __init__(self, zvar, key):
            """Create a Hyperslice

            @param zvar: zVariable that this slices
            @type zvar: :py:class:`pycdf.Var`
            @param key: Python multi-dimensional slice as passed to
                        __getitem__
            @type key: tuple of slice and/or int
            @raise IndexError: if slice is out of range, mismatches dimensions, or
                               otherwise unparsable.
            @raise ValueError: if slice has invalid values
            """

            self.zvar = zvar
            self.rv = self.zvar.rv()
            #dim of records, + 1 record dim (NRV always is record 0)
            self.dims = zvar._n_dims() + 1
            self.dimsizes = [len(zvar)] + \
                            zvar._dim_sizes()
            self.starts = [0] * self.dims
            self.counts = numpy.empty((self.dims,), dtype=numpy.int32)
            self.counts.fill(1)
            self.intervals = [1] * self.dims
            self.degen = numpy.zeros(self.dims, dtype=numpy.bool)
            self.rev = numpy.zeros(self.dims, dtype=numpy.bool)
            #key is:
            #1. a single value (integer or slice object) if called 1D
            #2. a tuple (of integers and/or slice objects) if called nD
            #3. Each item is either a single value (degenerate dim)
            #   or a slice object.

            if not hasattr(key, '__len__'): #Not a container object, pack in tuple
                key = (key, )
            if not self.rv:
                key = (0, ) + key #NRV, so always get 0th record (degenerate)
            key = self.expand_ellipsis(key, self.dims)
            if self.rv: #special-cases for RV variables
                if len(key) == 1: #get all data for this record(s)
                    key = self.expand_ellipsis(key + (Ellipsis, ), self.dims)
                elif len(key) == self.dims - 1: #get same slice from each record
                    key = (slice(None, None, None), ) + key
            if len(key) == self.dims:
                self.expanded_key = key
                for i in range(self.dims):
                    idx = key[i]
                    if hasattr(idx, 'start'): #slice
                        (self.starts[i], self.counts[i],
                         self.intervals[i], self.rev[i]) = \
                         self.convert_range(idx.start, idx.stop,
                                                  idx.step, self.dimsizes[i])
                    else: #Single degenerate value
                        if idx < 0:
                            idx += self.dimsizes[i]
                        if idx != 0 and (idx >= self.dimsizes[i] or idx < 0):
                            raise IndexError('list index out of range')
                        self.starts[i] = idx
                        self.degen[i] = True
            else:
                raise IndexError('Slice does not match dimensions for zVar ' +
                                 str(zvar._name))

            self.column = zvar.cdf_file.col_major()

        def expected_dims(self, data=None):
            """Calculate size of non-degenerate dimensions

            Figures out size, in each dimension, of expected input data

            @return: size of each dimension for this slice, excluding degenerate
            @rtype: list of int
            """
            return [self.counts[i] for i in range(self.dims) if not self.degen[i]]

        def expand(self, data):
            """Expands the record dimension of this slice to hold a set of data

            If the length of data (outermost dimension) is larger than the record
            count (counts[0]) for this slice, expand the slice to hold all the data.
            This requires that the record dimension of the slice not be degenerate,
            and also that it not have been completely specified when the hyperslice
            was created (i.e. record dimension either ellipsis or no specified
            stop.)

            Does *not* expand any other dimension, since that's Very Hard in CDF.

            @param data: the data which are intended to be stored in this slice
            @type data: list
            """
            rec_slice = self.expanded_key[0]
            if not self.rv or isinstance(data, str_classes) or self.degen[0] or \
                   not hasattr(rec_slice, 'stop'):
                return
            if len(data) < self.counts[0]: #Truncate to fit data
                if rec_slice.stop is None and rec_slice.step in (None, 1):
                    self.counts[0] = len(data)
            elif len(data) > self.counts[0]: #Expand to fit data
                if rec_slice.step in (None, 1):
                    self.counts[0] = len(data)

        def create_array(self):
            """Creates a numpy array to hold the data from this slice

            Returns
            =======
            out : numpy.array
                array sized, typed, and dimensioned to hold data from
                this slice
            """
            counts = self.counts
            degen = self.degen
            if self.column:
                counts = self.reorder(counts)
                degen = self.reorder(degen)
            #TODO: Forcing C order for now, revert to using self.column later
            array = numpy.empty(
                [counts[i] for i in range(len(counts)) if not degen[i]],
                self.zvar._np_type(), order='C')
            return numpy.require(array, requirements=('C', 'A', 'W'))

        def convert_input_array(self, buffer):
            """Converts a buffer of raw data from this slice

            EPOCH(16) variables always need to be converted.
            CHAR need converted to Unicode if py3k

            Parameters
            ==========
            buffer : numpy.array
                data as read from the CDF file

            Returns
            =======
            out : numpy.array
                converted data
            """
            result = self._flip_array(buffer)

            #Convert to derived types
            cdftype = self.zvar.type()
            if not self.zvar._raw:
                if cdftype in (const.CDF_CHAR.value, const.CDF_UCHAR.value) and \
                        str != bytes:
                    dt = numpy.dtype('U{0}'.format(result.dtype.itemsize))
                    result = numpy.require(numpy.char.array(result).decode(),
                                           dtype=dt)
                elif cdftype == const.CDF_EPOCH.value:
                    result = lib.v_epoch_to_datetime(result)
                elif cdftype == const.CDF_EPOCH16.value:
                    result = lib.v_epoch16_to_datetime(result)
                elif cdftype == const.CDF_TIME_TT2000.value:
                    result = lib.v_tt2000_to_datetime(result)
            if getattr(result, 'shape', None) == ():
                result = result.item()
            return result

        def convert_output_array(self, buffer):
            """Convert a buffer of data that will go into this slice

            Parameters
            ==========
            buffer : numpy.array
            data to go into the CDF file

            Returns
            =======
            out : numpy.array
            input with majority flipped and dimensions reversed to be
            suitable to pass directly to CDF library.
            """
            buffer = self._flip_array(buffer)
            return numpy.require(buffer, requirements=('C', 'A', 'W'))

        def _flip_array(self, data):
            """
            Operations for majority, etc. common between convert_input and _output
            """
            cdftype = self.zvar.type()
            #Flip majority if any non-degenerate dimensions exist
            if self.column and not min(self.degen):
                #Record-number dim degen, swap whole thing
                if self.degen[0]:
                    if cdftype == const.CDF_EPOCH16.value:
                        #Maintain last dimension
                        data = data.transpose(
                            list(range(len(data.shape) - 2, 0, -1)) +
                            [len(data.shape) - 1]
                            )
                    else:
                        data = data.transpose()
                #Record-number dimension is not degenerate, so keep it first
                else:
                    if cdftype == const.CDF_EPOCH16.value:
                        data = data.transpose(
                            [0] + list(range(len(data.shape) - 2, 0, -1)) +
                            [len(data.shape) - 1]
                            )
                    else:
                        data = data.transpose(
                            [0] + list(range(len(data.shape) - 1, 0, -1)))
            #Reverse non-degenerate dimensions in rev
            #Remember that the degenerate indices are already gone!
            if self.rev.any():
                sliced = [(slice(None, None, -1) if self.rev[i] else slice(None))
                          for i in range(self.dims) if not self.degen[i]]
                if cdftype == const.CDF_EPOCH16.value: #don't reverse last dim
                    sliced.extend(slice(None))
                data = operator.getitem(data, tuple(sliced))
            return data

        def select(self):
            """Selects this hyperslice in the CDF

            Calls the CDF library to select the CDF, variable, records, and
            array elements corresponding to this slice.
            """
            args = (const.SELECT_, const.zVAR_RECNUMBER_, ctypes.c_long(self.starts[0]),
                    const.SELECT_, const.zVAR_RECCOUNT_, ctypes.c_long(self.counts[0]),
                    const.SELECT_, const.zVAR_RECINTERVAL_,
                    ctypes.c_long(self.intervals[0]))
            if self.dims > 1:
                dims = self.dims - 1
                args += (const.SELECT_, const.zVAR_DIMINDICES_,
                         (ctypes.c_long * dims)(*self.starts[1:]),
                         const.SELECT_, const.zVAR_DIMCOUNTS_,
                         (ctypes.c_long * dims)(*self.counts[1:]),
                         const.SELECT_, const.zVAR_DIMINTERVALS_,
                         (ctypes.c_long * dims)(*self.intervals[1:]))
            self.zvar._call(*args)

        @staticmethod
        def expand_ellipsis(slices, n_dims):
            """Expands any ellipses into correct number of full-size slices

            @param slices: tuple of slices, integers, or ellipse objects
            @type slices: tuple
            @param n_dims: number of dimensions this slice is over
            @type n_dims: int
            @return: L{slices} with ellipses replaced by appropriate number of
                     full-dimension slices
            @rtype: tuple
            @raise IndexError: if ellipses specified when already have enough
                               dimensions
            """
            if slices is Ellipsis:
                return tuple([slice(None, None, None)
                              for i in range(n_dims)])
            #Elements might be numpy arrays, so can't use in/index
            idx = [i for i, v in enumerate(slices) if v is Ellipsis]
            if not idx: #no ellipsis
                return slices
            if len(idx) > 1: #multiples!
                raise IndexError('Ellipses can only be used once per slice.')
            idx = idx[0]

            #how many dims to expand ellipsis to
            #remember the ellipsis is in len(slices) and must be replaced!
            extra = n_dims - len(slices) + 1
            if extra < 0:
                raise IndexError('too many indices')
            result = slices[0:idx] + (slice(None), ) * extra + slices[idx+1:]
            return result

        @staticmethod
        def check_well_formed(data):
            """Checks if input data is well-formed, regular array"""
            d = numpy.asanyarray(data)
            if d.dtype == numpy.object: #this is probably going to be bad
                if d.shape != () and not len(d):
                    #Completely empty, so "well-formed" enough
                    return
                try:
                    len(d.flat[0])
                except TypeError: #at least it's not a list
                    pass
                else:
                    raise ValueError(
                        'Data must be well-formed, regular array of number, '
                        'string, or datetime')

        @staticmethod
        def dimensions(data):
            """Finds the dimensions of a nested list-of-lists

            @param data: data of which dimensions are desired
            @type data: list (of lists)
            @return: dimensions of L{data}, in order outside-in
            @rtype: list of int
            @raise ValueError: if L{data} has irregular dimensions
            """
            d = numpy.asanyarray(data)
            _Hyperslice.check_well_formed(d)
            return d.shape

        @staticmethod
        def types(data, backward=False):
            """Find dimensions and valid types of a nested list-of-lists

            Any given data may be representable by a range of CDF types; infer
            the CDF types which can represent this data. This breaks down to:
              1. Proper kind (numerical, string, time)
              2. Proper range (stores highest and lowest number)
              3. Sufficient resolution (EPOCH16 required if datetime has
                 microseconds or below.)

            If more than one value satisfies the requirements, types are returned
            in preferred order:
              1. Type that matches precision of data first, then
              2. integer type before float type, then
              3. Smallest type first, then
              4. signed type first, then
              5. specifically-named (CDF_BYTE) vs. generically named (CDF_INT1)
            So for example, EPOCH_16 is preferred over EPOCH if L{data} specifies
            below the millisecond level (rule 1), but otherwise EPOCH is preferred
            (rule 2).

            For floats, four-byte is preferred unless eight-byte is required:
              1. absolute values between 0 and 3e-39
              2. absolute values greater than 1.7e38
            This will switch to an eight-byte double in some cases where four bytes
            would be sufficient for IEEE 754 encoding, but where DEC formats would
            require eight.

            @param data: data for which dimensions and CDF types are desired
            @type data: list (of lists)
            @param backward: limit to pre-CDF3 types
            @type backward: bool
            @return: dimensions of L{data}, in order outside-in;
                     CDF types which can represent this data;
                     number of elements required (i.e. length of longest string)
            @rtype: 3-tuple of lists ([int], [ctypes.c_long], [int])
            @raise ValueError: if L{data} has irregular dimensions
            """
            d = numpy.asanyarray(data)
            dims = d.shape
            elements = 1
            types = []

            _Hyperslice.check_well_formed(d)
            if d.dtype.kind in ('S', 'U'): #it's a string
                types = [const.CDF_CHAR, const.CDF_UCHAR]
                elements = d.dtype.itemsize
                if d.dtype.kind == 'U': #UTF-8 uses 4 bytes per
                    elements //= 4
            elif d.size and hasattr(numpy.ma.getdata(d).flat[0], 'microsecond'):
                if max((dt.microsecond % 1000 for dt in d.flat)) > 0:
                    types = [const.CDF_EPOCH16, const.CDF_EPOCH,
                             const.CDF_TIME_TT2000]
                else:
                    types = [const.CDF_EPOCH, const.CDF_EPOCH16,
                             const.CDF_TIME_TT2000]
                if backward:
                    del types[types.index(const.CDF_EPOCH16)]
                    del types[-1]
                elif not lib.supports_int8:
                    del types[-1]
            elif d is data or isinstance(data, numpy.generic):
                #numpy array came in, use its type (or byte-swapped)
                types = [k for k in lib.numpytypedict
                         if (lib.numpytypedict[k] == d.dtype
                             or lib.numpytypedict[k] == d.dtype.newbyteorder())
                         and not k in lib.timetypes]
                if (not lib.supports_int8 or backward) \
                   and const.CDF_INT8.value in types:
                    del types[types.index(const.CDF_INT8.value)]
                #Maintain priority to match the ordered lists below:
                #float/double (44, 45) before real (21/22), and
                #byte (41) before int (1) before char (51). So hack.
                #Consider making typedict an ordered dict once 2.6 is dead.
                types.sort(key=lambda x: x % 50, reverse=True)

            if not types: #not a numpy array, or can't parse its type
                if d.dtype.kind == 'O': #Object. Try to make it numeric
                    if d.shape != () and not len(d):
                        raise ValueError(
                            'Cannot determine CDF type of empty object array.')
                    #Can't do safe casting from Object, so try and compare
                    #Basically try most restrictive to least restrictive
                    trytypes = (numpy.uint64, numpy.int64, numpy.float64)
                    for t in trytypes:
                        try:
                            newd = d.astype(dtype=t)
                        except: #Failure to cast, try next type
                            continue
                        if (newd == d).all(): #Values preserved, use this type
                            d = newd
                            #Continue with normal guessing, as if a list
                            break
                    else:
                        #fell through without a match
                        raise ValueError(
                            'Cannot convert generic objects to CDF type.')
                if d.dtype.kind in ('i', 'u'): #integer
                    minval = numpy.min(d)
                    maxval = numpy.max(d)
                    if minval < 0:
                        types = [const.CDF_BYTE, const.CDF_INT1,
                                 const.CDF_INT2, const.CDF_INT4, const.CDF_INT8,
                                 const.CDF_FLOAT, const.CDF_REAL4,
                                 const.CDF_DOUBLE, const.CDF_REAL8]
                        cutoffs = [2 ** 7, 2 ** 7, 2 ** 15, 2 ** 31, 2 ** 63,
                                   1.7e38, 1.7e38, 8e307, 8e307]
                    else:
                        types = [const.CDF_BYTE, const.CDF_INT1, const.CDF_UINT1,
                                 const.CDF_INT2, const.CDF_UINT2,
                                 const.CDF_INT4, const.CDF_UINT4,
                                 const.CDF_INT8,
                                 const.CDF_FLOAT, const.CDF_REAL4,
                                 const.CDF_DOUBLE, const.CDF_REAL8]
                        cutoffs = [2 ** 7, 2 ** 7, 2 ** 8,
                                   2 ** 15, 2 ** 16, 2 ** 31, 2 ** 32, 2 ** 63,
                                   1.7e38, 1.7e38, 8e307, 8e307]
                    types = [t for (t, c) in zip(types, cutoffs) if c > maxval
                             and (minval >= 0 or minval >= -c)]
                    if (not lib.supports_int8 or backward) \
                           and const.CDF_INT8 in types:
                        del types[types.index(const.CDF_INT8)]
                else: #float
                    if dims == ():
                        if d != 0 and (abs(d) > 1.7e38 or abs(d) < 3e-39):
                            types = [const.CDF_DOUBLE, const.CDF_REAL8]
                        else:
                            types = [const.CDF_FLOAT, const.CDF_REAL4,
                                     const.CDF_DOUBLE, const.CDF_REAL8]
                    else:
                        absolutes = numpy.abs(d[d != 0])
                        if len(absolutes) > 0 and \
                               (numpy.max(absolutes) > 1.7e38 or
                                numpy.min(absolutes) < 3e-39):
                            types = [const.CDF_DOUBLE, const.CDF_REAL8]
                        else:
                            types = [const.CDF_FLOAT, const.CDF_REAL4,
                                     const.CDF_DOUBLE, const.CDF_REAL8]
            types = [t.value if hasattr(t, 'value') else t for t in types]
            #If data has a type, might be a VarCopy, prefer that type
            if hasattr(data, 'type'):
                try:
                    t = data.type()
                except:
                    t = None
                    pass
                if t in types:
                    types = [t]
                #If passed array, types prefers its dtype, so try for compatible
                #and let type() override
                elif d is data:
                    try:
                        _ = data.astype(dtype=lib.numpytypedict[t])
                    except:
                        pass
                    finally:
                        types = [t]
            #And if the VarCopy specifies a number of elements, use that
            #if compatible
            if hasattr(data, 'nelems'):
                ne = data.nelems()
                if ne > elements:
                    elements = ne
            return (dims, types, elements)

        @staticmethod
        def reorder(seq):
            """Reorders seq to switch array majority

            Used to take an array of subscripts between row
            and column majority. First element is not touched,
            being the record number.

            @param seq: a sequence of *subscripts*
            @type seq: sequence of integers
            @return: seq with all but element 0 reversed in order
            @rtype: sequence of integers
            """
            return numpy.concatenate((seq[0:1],
                                      numpy.flipud(seq)[:-1]))

        @staticmethod
        def convert_range(start, stop, step, size):
            """Converts a start/stop/step range to start/count/interval

            (i.e. changes from Python-style slice to CDF-style)
            @param start: index to start a slice at, may be none or negative
            @type start: int
            @param stop: index at end of slice (one-past, standard Python),
                         may be none or negative
            @type stop: int
            @param step: interval for stepping through stlice
            @type step: int
            @param size: size of list to slice
            @type size: int
            @return: (start, count, interval, rev) where:
                       1. start is the start index, normalized to be within
                          the size of the list and negatives handled
                       2. count is the number of records in the slice,
                          guaranteed to stop before the end
                       3. interval is the skip between records
                       4. rev indicates whether the sequence should be reversed
            @rtype: (int, int, int, boolean)
            """
            (start, stop, step) = slice(start, stop, step).indices(size)
            if step < 0:
                step *= -1
                count = int((start - stop + step - 1) / step)
                start = start - (count - 1) * step
                rev = True
            else:
                count = int((stop - start + step - 1) / step)
                rev = False
            if count < 0:
                count = 0
                start = 0
            return (start, count, step, rev)

    class Attr(MutableSequence):
        """An attribute, g or z, for a CDF

        .. warning::
            This class should not be used directly, but only in its
            subclasses, :class:`gAttr` and :class:`zAttr`. The methods
            listed here are safe to use in the subclasses.

        Represents a CDF attribute, providing access to the Entries in a format
        that looks like a Python
        list. General list information is available in the python docs:
        `1 <http://docs.python.org/tutorial/introduction.html#lists>`_,
        `2 <http://docs.python.org/tutorial/datastructures.html#more-on-lists>`_,
        `3 <http://docs.python.org/library/stdtypes.html#typesseq>`_.

        An introduction to CDF attributes can be found in section 2.4 of
        the CDF user's guide.

        Each element of the list is a single Entry of the appropriate type.
        The index to the elements is the Entry number.

        Multi-dimensional slicing is *not* supported; an Entry with multiple
        elements will have all elements returned (and can thus be sliced itself).
        Example:

            >>> first_three = attribute[5, 0:3] #will fail
            >>> first_three = attribute[5][0:3] #first three elements of 5th Entry

        .. autosummary::

            ~Attr.append
            ~Attr.has_entry
            ~Attr.insert
            ~Attr.max_idx
            ~Attr.new
            ~Attr.number
            ~Attr.rename
            ~Attr.type

        .. automethod:: append
        .. automethod:: has_entry
        .. automethod:: insert
        .. automethod:: max_idx
        .. automethod:: new
        .. automethod:: number
        .. automethod:: rename
        .. automethod:: type
        """

        def __init__(self, cdf_file, attr_name, create=False):
            """Initialize this attribute

            @param cdf_file: CDF file containing this attribute
            @type cdf_file: :py:class:`pycdf.CDF`
            @param attr_name: Name of this attribute
            @type attr_name: str
            @param create: True to create attribute, False to look up existing.
            @type create: bool
            """
            self._cdf_file = cdf_file
            self._raw = False
            if isinstance(attr_name, str_classes):
                try:
                    self._name = attr_name.encode('ascii')
                except AttributeError:
                    self._name = attr_name
                attrno = ctypes.c_long()
                if create:
                    self._cdf_file._call(const.CREATE_, const.ATTR_,
                                         self._name, self.SCOPE,
                                         ctypes.byref(attrno))
                    self._cdf_file.add_attr_to_cache(
                        self._name, attrno.value, self.SCOPE == const.GLOBAL_SCOPE)
                else: #Ensure exists, and populate cache. See scope note below
                    attrno, scope = self._cdf_file.attr_num(self._name)
            else:
                name = ctypes.create_string_buffer(const.CDF_ATTR_NAME_LEN256 + 1)
                scope = ctypes.c_long(0)
                self._cdf_file._call(const.SELECT_, const.ATTR_,
                                     ctypes.c_long(attr_name))
                #Because it's possible to create a gAttr Python object
                #referencing an Attribute with variable scope, and vice-versa,
                #do NOT assume the scope matches
                #(Higher level code checks for that being a bad thing.)
                self._cdf_file._call(
                    const.GET_, const.ATTR_NAME_, name,
                    const.GET_, const.ATTR_SCOPE_, ctypes.byref(scope))
                self._name = name.value.rstrip()
                if scope.value == const.GLOBAL_SCOPE.value:
                    scope = True
                elif scope.value == const.VARIABLE_SCOPE.value:
                    scope = False
                else:
                    raise CDFError(const.BAD_SCOPE)
                self._cdf_file.add_attr_to_cache(self._name, attr_name, scope)

        def __getitem__(self, key):
            """Return a slice of Entries.

            Because Attributes may be sparse, a multi-element slice will return
            None for those elements which do not have associated Entries.

            @param key: index or range of Entry number to return
            @type key: slice or int
            @return: a list of entries, appropriate type.
            @raise IndexError: if L{key} is an int and that Entry number does not
                               exist.
            """
            if key is Ellipsis:
                key = slice(None, None, None)
            if hasattr(key, 'indices'):
                idx = range(*key.indices(self.max_idx() + 1))
                return [self._get_entry(i) if self.has_entry(i) else None
                        for i in idx]
            else:
                if self.has_entry(key):
                    return self._get_entry(key)
                else:
                    raise IndexError('list index ' + str(key) + ' out of range.')

        def _check_other_entries(self, types):
            """Try to get the type of this entry from others in the Attribute

            For zAttrs, checks if all other Entries are the same type, and at
            least one doesn't match its zVar, i.e. Entry type dominates (otherwise
            assumption is the Var type dominates).

            For gAttrs, checks all other Entries, and gives priority to the
            one that's earliest in the possible type list and exists in other
            Entries.

            This is only one component of Entry type guessing!

            :param list types: CDF types that are candidates (match the data)
            :return: The type discerned from other Entries, or None
            """
            if self.ENTRY_ == const.zENTRY_:
                #If everything else is the same entry type,
                #and one is not the same as its var, probably
                #all entries should be of that type
                cand_et = None #The Entry type that might work
                one_var_diff = False #One Var has a type different from Entry
                for num in range(self.max_idx() + 1):
                    if not self.has_entry(num):
                        continue
                    vartype = self._cdf_file[num].type()
                    entrytype = self.type(num)
                    if vartype != entrytype:
                        one_var_diff = True
                    if cand_et is None:
                        if not entrytype in types:
                            return None #One var has Entry with "impossible" type
                        cand_et = entrytype
                    elif cand_et != entrytype:
                        return None #Two vars have Entries with different types
                if one_var_diff and cand_et is not None:
                    return cand_et
            else:
                # Of those types which exist in other entries,
                # find the one which is earliest
                # in types, i.e. the preferred type
                entrytypes = [self.type(num) for num in
                              range(self.max_idx() + 1)
                              if self.has_entry(num)]
                entrytypes = [et for et in entrytypes if et in types]
                if entrytypes:
                    return types[
                        min([types.index(et) for et in entrytypes])]
            return None

        def __setitem__(self, key, data):
            """Set a slice of Entries.

            @param key: index or range of Entry numbers to set
            @type key: slice or int
            @param data: the data to set these entries to. Normally each entry should
            be a sequence; if a scalar is provided, it is treated
            as a single-element list.
            @type data: scalar or list
            @raise ValueError: if size of {data} does not match size of L{key}
            @note: Attributes do not 'grow' or 'shrink' as entries are added
                   or removed. Indexes of entries never change and there is no
                   way to 'insert'.
            """
            if key is Ellipsis:
                key = slice(None, None, None)
            if not hasattr(key, 'indices'):
                #Single value, promote everything a dimension
                idx = (key, key + 1, 1)
                data = [data]
            else:
                idx = key.indices(self.max_idx() + 1)
                if key.step is None or key.step > 0:
                    #Iterating forward, extend slice to match data
                    if len(data) > len(range(*idx)):
                        idx = (idx[0], idx[0] + idx[2] * len(data), idx[2])

            #get, and check, types and sizes for all data
            #checks first so don't have error after changing half the Entries
            data_idx = -1
            typelist = []
            for i in range(*idx):
                data_idx += 1
                if data_idx >= len(data):
                    continue
                datum = data[data_idx]
                if datum is None:
                    typelist[i] = (None, None, None)
                    continue
                (dims, types, elements) = _Hyperslice.types(
                    datum, backward=self._cdf_file.backward)
                if len(types) <= 0:
                    raise ValueError('Cannot find a matching CDF type.')
                if len(dims) > 1:
                    raise ValueError('Entries must be scalar or 1D.')
                elif len(dims) == 1 and isinstance(datum[0], str_classes):
                    raise ValueError('Entry strings must be scalar.')
                entry_type = None
                if self.has_entry(i): #If the entry already exists, match its type
                    entry_type = self.type(i)
                    if not entry_type in types:
                        entry_type = None
                if entry_type is None: #Check other entries for this attribute
                    entry_type = self._check_other_entries(types)
                if entry_type is None and self.ENTRY_ == const.zENTRY_:
                    #Fall back to zVar type
                    vartype = self._cdf_file[i].type()
                    if vartype in types:
                        entry_type = vartype
                if entry_type is None:
                    entry_type = types[0]
                    if entry_type in lib.timetypes and len(types) > 1:
                        warnings.warn(
                            'Assuming {} for time input. This will change to'
                            ' TT2000 in the future, on systems which support it.'
                            .format(lib.cdftypenames[entry_type]),
                            DeprecationWarning)
                if not entry_type in lib.numpytypedict:
                    raise ValueError('Cannot find a matching numpy type.')
                typelist.append((dims, entry_type, elements))

            data_idx = -1
            for i in range(*idx):
                data_idx += 1
                if data_idx >= len(data) or data[data_idx] is None:
                    if self.has_entry(i):
                        del self[i]
                    continue
                datum = data[data_idx]
                (dims, entry_type, elements) = typelist[data_idx]
                self._write_entry(i, datum, entry_type, dims, elements)

        def __delitem__(self, key):
            """Delete a slice of Entries.

            @param key: index or range of Entry numbers to delete
            @type key: slice or int
            @note: Attributes do not 'grow' or 'shrink' as entries are added
                   or removed. Indexes of entries never change and there is no
                   way to 'insert'.
            """
            if key is Ellipsis:
                key = slice(None, None, None)
            if not hasattr(key, 'indices'):
                idx = (key, key + 1, 1)
            else:
                idx = key.indices(self.max_idx() + 1)
            for i in range(*idx):
                self._call(const.SELECT_, self.ENTRY_, ctypes.c_long(i),
                           const.DELETE_, self.ENTRY_)

        def __iter__(self, current=0):
            """Iterates over all entries in this Attribute

            Returns data from one entry at a time until reaches the end.
            @note: Returned in entry-number order.
            """
            while current <= self.max_idx():
                if self.has_entry(current):
                    value = yield(self._get_entry(current))
                    if value != None:
                        current = value
                current += 1

        def __reversed__(self, current=None):
            """Iterates over all entries in this Attribute

            Returns data from one entry at a time, starting at end and going
            to beginning.
            @note: Returned in entry-number order.
            """
            if current is None:
                current = self.max_idx()
            while current >= 0:
                if self.has_entry(current):
                    value = yield(self._get_entry(current))
                    if value != None:
                        current = value
                current -= 1

        def __len__(self):
            """Number of Entries for this Attr. NOT same as max Entry number.

            @return: Number of Entries
            @rtype: int
            """
            count = ctypes.c_long(0)
            self._call(const.GET_, self.ATTR_NUMENTRIES_, ctypes.byref(count))
            return count.value

        def __repr__(self):
            """Returns representation of an attribute

            Cannot return anything that can be eval'd to create a copy of the
            attribtute, so just wrap the informal representation in angle brackets.
            @return: all the data in this attribute
            @rtype: str
            """
            return '<\n' + str(self) + '\n>'

        def __str__(self):
            """Returns a string representation of the attribute

            This is an 'informal' representation in that it cannot be evaluated
            directly to create an L{Attr}.

            @return: all the data in this attribute
            @rtype: str
            """
            if self._cdf_file._opened:
                return '\n'.join([str(item) for item in self])
            else:
                if isinstance(self._name, str):
                    return 'Attribute "{0}" in closed CDF {1}'.format(
                        self._name, self._cdf_file.pathname)
                else:
                    return 'Attribute "{0}" in closed CDF {1}'.format(
                        self._name.decode('ascii'),
                        self._cdf_file.pathname.decode('ascii'))

        def insert(self, index, data):
            """Insert an entry at a particular number

            Inserts entry at particular number while moving all subsequent
            entries to one entry number later. Does not close gaps.

            Parameters
            ==========
            index : int
                index where to put the new entry
            data :
                data for the new entry
            """
            max_entry = self.max_idx()
            if index > max_entry: #Easy case
                self[index] = data
                return
            for i in range(max_entry, index - 1, -1):
                if self.has_entry(i+1):
                    self.__delitem__(i+1)
                if self.has_entry(i):
                    self.new(self.__getitem__(i), type=self.type(i), number=i+1)
            self[index] = data

        def append(self, data):
            """Add an entry to end of attribute

            Puts entry after last defined entry (does not fill gaps)

            Parameters
            ==========
            data :
                data for the new entry
            """
            self[self.max_idx() + 1] = data

        def _call(self, *args, **kwargs):
            """Select this CDF and Attr and call the CDF internal interface

            @param args: Passed directly to the CDF library interface.
            @type args: various, see :py:mod:`ctypes`.
            @return: CDF status from the library
            @rtype: ctypes.c_long
            @note: Terminal NULL_ is automatically added to L{args}.
            @raise CDFError: if CDF library reports an error
            @raise CDFWarning: if CDF library reports a warning and interpreter
                               is set to error on warnings.
            """
            return self._cdf_file._call(
                const.SELECT_, const.ATTR_,
                ctypes.c_long(self._cdf_file.attr_num(self._name)[0]),
                *args, **kwargs)

        def _entry_len(self, number):
            """Number of elements in an Entry

            @param number: number of Entry
            @type number: int
            @return: number of elements
            @rtype: int
            """
            if not self.has_entry(number):
                raise IndexError('list index ' + str(number) + ' out of range.')
            count = ctypes.c_long(0)
            self._call(
                const.SELECT_, self.ENTRY_, ctypes.c_long(number),
                const.GET_, self.ENTRY_NUMELEMS_, ctypes.byref(count))
            return count.value

        def type(self, number, new_type=None):
            """Find or change the CDF type of a particular Entry number

            Parameters
            ==========
            number : int
                number of Entry to check or change

            Other Parameters
            ================
            new_type
                type to change this Entry to, from :mod:`~spacepy.pycdf.const`.
                Omit to only check type.

            Returns
            =======
            out : int
                CDF variable type, see :mod:`~spacepy.pycdf.const`

            Notes
            =====
            If changing types, old and new must be equivalent, see CDF
            User's Guide section 2.5.5 pg. 57
            """
            if new_type != None:
                if not hasattr(new_type, 'value'):
                    new_type = ctypes.c_long(new_type)
                size = ctypes.c_long(self._entry_len(number))
                status = self._call(const.SELECT_, self.ENTRY_, ctypes.c_long(number),
                                    const.PUT_, self.ENTRY_DATASPEC_, new_type, size,
                                    ignore=(const.NO_SUCH_ENTRY,))
                if status == const.NO_SUCH_ENTRY:
                    raise IndexError('list index ' + str(number) + ' out of range.')
            cdftype = ctypes.c_long(0)
            status = self._call(const.SELECT_, self.ENTRY_, ctypes.c_long(number),
                                const.GET_, self.ENTRY_DATATYPE_, ctypes.byref(cdftype),
                                ignore=(const.NO_SUCH_ENTRY,))
            if status == const.NO_SUCH_ENTRY:
                raise IndexError('list index ' + str(number) + ' out of range.')
            return cdftype.value

        def has_entry(self, number):
            """Check if this attribute has a particular Entry number

            Parameters
            ==========
            number : int
                number of Entry to check or change

            Returns
            =======
            out : bool
                True if ``number`` is a valid entry number; False if not
            """
            status = self._call(const.CONFIRM_, self.ENTRY_EXISTENCE_,
                                ctypes.c_long(number),
                                ignore=(const.NO_SUCH_ENTRY, ))
            return not status == const.NO_SUCH_ENTRY

        def max_idx(self):
            """Maximum index of Entries for this Attr

            Returns
            =======
            out : int
                maximum Entry number
            """
            count = ctypes.c_long(0)
            self._call(const.GET_, self.ATTR_MAXENTRY_, ctypes.byref(count))
            return count.value

        def new(self, data, type=None, number=None):
            """Create a new Entry in this Attribute

            .. note:: If ``number`` is provided and an Entry with that number
                      already exists, it will be overwritten.

            Parameters
            ==========
            data
                data to put in the Entry

            Other Parameters
            ================
            type : int
                type of the new Entry, from :mod:`~spacepy.pycdf.const`
                (otherwise guessed from ``data``)
            number : int
                Entry number to write, default is lowest available number.
            """
            if number is None:
                number = 0
                while self.has_entry(number):
                    number += 1
            (dims, types, elements) = _Hyperslice.types(
                data, backward=self._cdf_file.backward)
            if type is None:
                #Guess based on other entries
                type = self._check_other_entries(types)
            if type is None and self.ENTRY_ == const.zENTRY_:
                #Try to match variable type
                    vartype = self._cdf_file[number].type()
                    if vartype in types:
                        type = vartype
            if type is None:
                type = types[0]
            elif hasattr(type, 'value'):
                type = type.value
            self._write_entry(number, data, type, dims, elements)

        def number(self):
            """Find the attribute number for this attribute

            Returns
            =======
            out : int
                attribute number
            """
            no = ctypes.c_long(0)
            self._cdf_file._call(const.GET_, const.ATTR_NUMBER_,
                                 self._name, ctypes.byref(no))
            return no.value

        def global_scope(self):
            """Determine scope of this attribute.

            Returns
            =======
            out : bool
                True if global (i.e. gAttr), False if zAttr
            """
            return self._cdf_file.attr_num(self._name)[1]

        def rename(self, new_name):
            """Rename this attribute

            Renaming a zAttribute renames it for *all* zVariables in this CDF!

            Parameters
            ==========
            new_name : str
                 the new name of the attribute
            """
            try:
                enc_name = new_name.encode('ascii')
            except AttributeError:
                enc_name = new_name
            if len(enc_name) > const.CDF_ATTR_NAME_LEN256:
                raise CDFError(const.BAD_ATTR_NAME)
            self._call(const.PUT_, const.ATTR_NAME_, enc_name)
            self._cdf_file.add_attr_to_cache(
                enc_name,
                *self._cdf_file.attr_num(self._name)) #still in cache
            del self._cdf_file._attr_info[self._name]
            self._name = enc_name

        def _get_entry(self, number):
            """Read an Entry associated with this L{Attr}

            @param number: number of Entry to return
            @type number: int
            @return: data from entry numbered L{number}
            @rtype: list or str
            """
            if not self.has_entry(number):
                raise IndexError('list index ' + str(number) + ' out of range.')
            #Make a big enough buffer
            length = self._entry_len(number)
            cdftype = self.type(number)
            if cdftype in (const.CDF_CHAR.value, const.CDF_UCHAR.value):
                buff = numpy.empty((), 'S{0}'.format(length), order='C')
            else:
                if not cdftype in lib.numpytypedict:
                    raise CDFError(const.BAD_DATA_TYPE)
                buff = numpy.empty((length,), lib.numpytypedict[cdftype],
                                   order='C')
            buff = numpy.require(buff, requirements=('C', 'A', 'W'))
            self._call(const.SELECT_, self.ENTRY_, ctypes.c_long(number),
                       const.GET_, self.ENTRY_DATA_,
                       buff.ctypes.data_as(ctypes.c_void_p))

            #decode
            if cdftype in (const.CDF_CHAR.value, const.CDF_UCHAR.value):
                if str == bytes or self._raw: #Py2k, leave as bytes
                    result = bytes(buff)
                else: #Py3k, make unicode
                    result = str(numpy.char.array(buff).decode())
            else:
                if not self._raw:
                    if cdftype == const.CDF_EPOCH.value:
                        result = lib.v_epoch_to_datetime(buff)
                    elif cdftype == const.CDF_EPOCH16.value:
                        result = lib.v_epoch16_to_datetime(buff)
                    elif cdftype == const.CDF_TIME_TT2000.value:
                        result = lib.v_tt2000_to_datetime(buff)
                    else:
                        result = buff
                else:
                    result = buff
                if length == 1:
                    result = result[0]

            return result

        def _write_entry(self, number, data, cdf_type, dims, elements):
            """Write an Entry to this Attr.

            @param number: number of Entry to write
            @type number: int
            @param data: data to write
            @param cdf_type: the CDF type to write, from :py:mod:`pycdf.const`
            @param dims: dimensions of L{data}
            @type dims: list
            @param elements: number of elements in L{data}, 1 unless it is a string
            @type elements: int
            """
            n_write = 1 if len(dims) == 0 else dims[0]
            if cdf_type in (const.CDF_CHAR.value, const.CDF_UCHAR.value):
                data = numpy.require(data, requirements=('C', 'A', 'W'),
                                     dtype=numpy.dtype('S' + str(elements)))
                n_write = elements
            elif cdf_type == const.CDF_EPOCH16.value:
                raw_in = True #Assume each element is pair of floats
                if not self._raw:
                    try:
                        data = lib.v_datetime_to_epoch16(data)
                        raw_in = False #Nope, not raw, was datetime
                    except AttributeError:
                        pass
                if raw_in: #Floats passed in, extra dim of (2,)
                    dims = dims[:-1]
                    if len(dims) == 0:
                        n_write = 1
                data = numpy.require(data, requirements=('C', 'A', 'W'),
                                     dtype=numpy.float64)
            elif cdf_type == const.CDF_EPOCH.value:
                if not self._raw:
                    try:
                        data = lib.v_datetime_to_epoch(data),
                    except AttributeError:
                        pass
                data = numpy.require(data, requirements=('C', 'A', 'W'),
                                     dtype=numpy.float64)
            elif cdf_type == const.CDF_TIME_TT2000.value:
                if not self._raw:
                    try:
                        data = lib.v_datetime_to_tt2000(data)
                    except AttributeError:
                        pass
                data = numpy.require(data, requirements=('C', 'A', 'W'),
                                     dtype=numpy.int64)
            elif cdf_type in lib.numpytypedict:
                data = numpy.require(data, requirements=('C', 'A', 'W'),
                                     dtype=lib.numpytypedict[cdf_type])
            else:
                raise CDFError(const.BAD_DATA_TYPE)
            self._call(const.SELECT_, self.ENTRY_, ctypes.c_long(number),
                       const.PUT_, self.ENTRY_DATA_, ctypes.c_long(cdf_type),
                       ctypes.c_long(n_write),
                       data.ctypes.data_as(ctypes.c_void_p))

        def _delete(self):
            """Delete this Attribute

            Also deletes all Entries associated with it.
            """
            self._call(const.DELETE_, const.ATTR_)
            self._cdf_file.clear_attr_from_cache(self._name)
            self._name = None

    class zAttr(Attr):
        """zAttribute for zVariables within a CDF.

        .. warning::
            Because zAttributes are shared across all variables in a CDF,
            directly manipulating them may have unexpected consequences.
            It is safest to operate on zEntries via :class:`zAttrList`.

        .. note::
            When accessing a zAttr, pyCDF exposes only the zEntry corresponding
            to the associated zVariable.

        See Also
        ========
        :class:`Attr`
        """
        ENTRY_ = const.zENTRY_
        ENTRY_DATA_ = const.zENTRY_DATA_
        SCOPE = const.VARIABLE_SCOPE
        ENTRY_EXISTENCE_ = const.zENTRY_EXISTENCE_
        ATTR_NUMENTRIES_ = const.ATTR_NUMzENTRIES_
        ATTR_MAXENTRY_ = const.ATTR_MAXzENTRY_
        ENTRY_NUMELEMS_ = const.zENTRY_NUMELEMS_
        ENTRY_DATATYPE_ = const.zENTRY_DATATYPE_
        ENTRY_DATASPEC_ = const.zENTRY_DATASPEC_

        def insert(self, index, data):
            """Insert entry at particular index number

            Since there can only be one zEntry per zAttr, this cannot be
            implemented.

            Raises
            ======
            NotImplementedError : always
            """
            raise NotImplementedError

        def append(self, index, data):
            """Add entry to end of attribute list

            Since there can only be one zEntry per zAttr, this cannot be
            implemented.

            Raises
            ======
            NotImplementedError : always
            """
            raise NotImplementedError

    class gAttr(Attr):
        """Global Attribute for a CDF

        Represents a CDF attribute, providing access to the gEntries in a format
        that looks like a Python list. General list information is available in
        the python docs:
        `1 <http://docs.python.org/tutorial/introduction.html#lists>`_,
        `2 <http://docs.python.org/tutorial/datastructures.html#more-on-lists>`_,
        `3 <http://docs.python.org/library/stdtypes.html#typesseq>`_.

        Normally accessed by providing a key to a :class:`gAttrList`:

            >>> attribute = cdffile.attrs['attribute_name']
            >>> first_gentry = attribute[0]

        Each element of the list is a single gEntry of the appropriate type.
        The index to the elements is the gEntry number.

        A gEntry may be either a single string or a 1D array of numerical type.
        Entries of numerical type (everything but CDF_CHAR and CDF_UCHAR)
        with a single element are returned as scalars; multiple-element entries
        are returned as a list. No provision is made for accessing below
        the entry level; the whole list is returned at once (but Python's
        slicing syntax can be used to extract individual items from that list.)

        Multi-dimensional slicing is *not* supported; an entry with multiple
        elements will have all elements returned (and can thus be sliced itself).
        Example:

            >>> first_three = attribute[5, 0:3] #will fail
            >>> first_three = attribute[5][0:3] #first three elements of 5th Entry

        gEntries are *not* necessarily contiguous; a gAttribute may have an
        entry 0 and entry 2 without an entry 1. :meth:`~Attr.len` will return the
        *number* of gEntries; use :meth:`~Attr.max_idx` to find the highest defined
        gEntry number and :meth:`~Attr.has_entry` to determine if a particular
        gEntry number exists. Iterating over all entries is also supported::

            >>> entrylist = [entry for entry in attribute]

        Deleting gEntries will leave a "hole":

            >>> attribute[0:3] = [1, 2, 3]
            >>> del attribute[1]
            >>> attribute.has_entry(1)
            False
            >>> attribute.has_entry(2)
            True
            >>> print attribute[0:3]
            [1, None, 3]

        Multi-element slices over nonexistent gEntries will return ``None`` where
        no entry exists. Single-element indices for nonexistent gEntries will
        raise ``IndexError``. Assigning ``None`` to a gEntry will delete it.

        When assigning to a gEntry, the type is chosen to match the data;
        subject to that constraint, it will try to match
        (in order):

            #. existing gEntry of the same number in this gAttribute
            #. other gEntries in this gAttribute
            #. data-matching constraints described in :meth:`CDF.new`.

        See Also
        ========
        :class:`Attr`
        """
        ENTRY_ = const.gENTRY_
        ENTRY_DATA_ = const.gENTRY_DATA_
        SCOPE = const.GLOBAL_SCOPE
        ENTRY_EXISTENCE_ = const.gENTRY_EXISTENCE_
        ATTR_NUMENTRIES_ = const.ATTR_NUMgENTRIES_
        ATTR_MAXENTRY_ = const.ATTR_MAXgENTRY_
        ENTRY_NUMELEMS_ = const.gENTRY_NUMELEMS_
        ENTRY_DATATYPE_ = const.gENTRY_DATATYPE_
        ENTRY_DATASPEC_ = const.gENTRY_DATASPEC_

    class AttrList(MutableMapping):
        """Object representing a list of attributes.

        .. warning::
            This class should not be used directly, but only via its
            subclasses, :class:`gAttrList` and :class:`zAttrList`.
            Methods listed here are safe to use from the subclasses.

        .. autosummary::

            ~AttrList.clone
            ~AttrList.copy
            ~AttrList.from_dict
            ~AttrList.new
            ~AttrList.rename

        .. automethod:: clone
        .. automethod:: copy
        .. automethod:: from_dict
        .. automethod:: new
        .. automethod:: rename
        """

        def __init__(self, cdf_file, special_entry=None):
            """Initialize the attribute collection

            @param cdf_file: CDF these attributes are in
            @type cdf_file: :py:class:`pycdf.CDF`
            @param special_entry: callable which returns a "special" entry number,
            used to limit results for zAttrs to those which match the zVar
            (i.e. the var number)
            @type special_entry: callable
            """
            self._cdf_file = cdf_file
            self.special_entry = special_entry

        def __getitem__(self, name):
            """Find an Attribute by name

            @param name: name of the Attribute to return
            @type name: str
            @return: attribute named L{name}
            @rtype: L{Attr}
            @raise KeyError: if there is no attribute named L{name}
            @raise CDFError: other errors in CDF library
            """
            try:
                attrib = self.AttrType(self._cdf_file, name)
            except CDFError:
                (t, v, tb) = sys.exc_info()
                if v.status == const.NO_SUCH_ATTR:
                    raise KeyError(name + ': ' + str(v))
                else:
                    raise
            if attrib.global_scope() != self.global_scope:
                raise KeyError(name + ': no ' + self.attr_name + ' by that name.')
            return attrib

        def __setitem__(self, name, data):
            """Create an Attribute or change its entries

            @param name: name of Attribute to change
            @type name: str
            @param data: Entries to populate this Attribute with.
                         Any existing Entries will be deleted!
                         Another C{Attr} may be specified, in which
                         case all its entries are copied.
            @type data: scalar, list, or L{Attr}
            """
            if isinstance(data, AttrList):
                if name in self:
                    del self[name]
                attr = self._get_or_create(name)
                for entryno in range(data.max_idx()):
                    if data.has_entry(entryno):
                        attr.new(data[entryno], data.type(entryno), entryno)
            else:
                attr = self._get_or_create(name)
                if isinstance(data, str_classes):
                    data = [data]
                else:
                    try:
                        junk = len(data)
                    except TypeError:
                        data = [data]
                attr[:] = data
                del attr[len(data):]

        def __delitem__(self, name):
            """Delete an Attribute (and all its entries)

            @param name: name of Attribute to delete
            @type name: str
            """
            try:
                attr = self.AttrType(self._cdf_file, name)
            except CDFError:
                (t, v, tb) = sys.exc_info()
                if v.status == const.NO_SUCH_ATTR:
                    raise KeyError(name + ': ' + str(v))
                else:
                    raise
                if attr.global_scope() != self.global_scope:
                    raise KeyError(name + ': not ' + self.attr_name)
            attr._delete()

        def __iter__(self, current=0):
            """Iterates over all Attr in this CDF or variable

            Returns name of one L{Attr} at a time until reaches the end.
            @note: Returned in number order.
            """
            count = ctypes.c_long(0)
            self._cdf_file._call(const.GET_, const.CDF_NUMATTRS_,
                                 ctypes.byref(count))
            while current < count.value:
                candidate = self.AttrType(self._cdf_file, current)
                if candidate.global_scope() == self.global_scope:
                    if self.special_entry is None or \
                            candidate.has_entry(self.special_entry()):
                        if str == bytes:
                            value = yield(candidate._name)
                        else:
                            value = yield(candidate._name.decode())
                        if value != None:
                            current = self[value].number()
                current += 1

        def __repr__(self):
            """Returns representation of attribute list

            Cannot return anything that can be eval'd to create a copy of the
            list, so just wrap the informal representation in angle brackets.
            @return: all the data in this list of attributes
            @rtype: str
            """
            return '<' + self.__class__.__name__ + ':\n' + str(self) + '\n>'

        def __str__(self):
            """Returns a string representation of the attribute list

            This is an 'informal' representation in that it cannot be evaluated
            directly to create an L{AttrList}.

            @return: all the data in this list of attributes
            @rtype: str
            """
            if self._cdf_file._opened:
                return '\n'.join([key + ': ' + (
                    ('\n' + ' ' * (len(key) + 2)).join(
                    [str(value[i]) + ' [' + lib.cdftypenames[value.type(i)] + ']'
                     for i in range(value.max_idx() + 1) if value.has_entry(i)])
                    if isinstance(value, Attr)
                    else str(value) +
                    ' [' + lib.cdftypenames[self.type(key)] + ']'
                    )
                    for (key, value) in sorted(self.items())])
            else:
                if isinstance(self._cdf_file.pathname, str):
                    return 'Attribute list in closed CDF {0}'.format(
                        self._cdf_file.pathname)
                else:
                    return 'Attribute list in closed CDF {0}'.format(
                        self._cdf_file.pathname.decode('ascii'))

        def clone(self, master, name=None, new_name=None):
            """
            Clones another attribute list, or one attribute from it, into this
            list.

            Parameters
            ==========
            master : AttrList
                the attribute list to copy from. This can be any dict-like object.

            Other Parameters
            ================
            name : str (optional)
                name of attribute to clone (default: clone entire list)
            new_name : str (optional)
                name of the new attribute, default ``name``
            """
            if name is None:
                self._clone_list(master)
            else:
                self._clone_attr(master, name, new_name)

        def copy(self):
            """
            Create a copy of this attribute list

            Returns
            =======
            out : dict
                copy of the entries for all attributes in this list
            """
            return dict((key, value[:] if isinstance(value, Attr) else value)
                        for (key, value) in self.items())

        def new(self, name, data=None, type=None):
            """
            Create a new Attr in this AttrList

            Parameters
            ==========
            name : str
                name of the new Attribute

            Other Parameters
            ================
            data
                data to put into the first entry in the new Attribute
            type
                CDF type of the first entry from :mod:`~spacepy.pycdf.const`.
                Only used if data are specified.

            Raises
            ======
            KeyError : if the name already exists in this list
            """
            if name in self:
                raise KeyError(name + ' already exists.')
            #A zAttr without an Entry in this zVar will be a "get" not "create"
            attr = self._get_or_create(name)
            if data is not None:
                if self.special_entry is None:
                    attr.new(data, type)
                else:
                    attr.new(data, type, self.special_entry())

        def rename(self, old_name, new_name):
            """
            Rename an attribute in this list

            Renaming a zAttribute renames it for *all* zVariables in this CDF!

            Parameters
            ==========
            old_name : str
                the current name of the attribute
            new_name : str
                the new name of the attribute
            """
            AttrList.__getitem__(self, old_name).rename(new_name)

        def from_dict(self, in_dict):
            """
            Fill this list of attributes from a dictionary

            .. deprecated:: 0.1.5
               Use :meth:`~spacepy.pycdf.AttrList.clone` instead; it supports
               cloning from dictionaries.

            Parameters
            ==========
            in_dict : dict
                Attribute list is populated entirely from this dictionary;
                all existing attributes are deleted.
            """
            warnings.warn("from_dict is deprecated and will be removed. Use clone.",
                          DeprecationWarning)
            for k in in_dict:
                self[k] = in_dict[k]
            for k in list(self):
                if not k in in_dict:
                    del self[k]

        def _clone_attr(self, master, name, new_name=None):
            """Clones a single attribute from one in this list or another

            Copies data and types from the master attribute to the new one

            @param master: attribute list to copy attribute from
            @type master: L{AttrList}
            @param name: name of attribute to copy
            @type name: str
            @param new_name: name of the new attribute, default L{name}
            @type new_name: str
            """
            if new_name is None:
                new_name = name
            self[new_name] = master[name]

        def _clone_list(self, master):
            """Clones this attribute list from another

            @param master: the attribute list to copy from
            @type master: L{AttrList}
            """
            for name in master:
                self._clone_attr(master, name)
            for name in list(self): #Can't iterate over a list we're changing
                if not name in master:
                    del self[name]

        def _get_or_create(self, name):
            """Retrieve L{Attr} or create it if it doesn't exist

            @param name: name of the attribute to look up or create
            @type name: str
            @return: attribute with this name
            @rtype: L{Attr}
            """
            attr = None
            try:
                attr = self.AttrType(self._cdf_file, name)
            except CDFError:
                (t, v, tb) = sys.exc_info()
                if v.status != const.NO_SUCH_ATTR:
                    raise
            if attr is None:
                attr = self.AttrType(self._cdf_file, name, True)
            elif attr.global_scope() != self.global_scope:
                    raise KeyError(name + ': not ' + self.attr_name)
            return attr

    class gAttrList(AttrList):
        """
        Object representing *all* the gAttributes in a CDF.

        Normally accessed as an attribute of an open :class:`CDF`:

            >>> global_attribs = cdffile.attrs

        Appears as a dictionary: keys are attribute names; each value is an
        attribute represented by a :class:`gAttr` object. To access the global
        attribute TEXT:

            >>> text_attr = cdffile.attrs['TEXT']

        See Also
        ========
        :class:`AttrList`
        """
        AttrType = gAttr
        attr_name = 'gAttribute'
        global_scope = True

        def __len__(self):
            """
            Number of gAttributes in this CDF

            Returns
            =======
            out : int
                number of gAttributes in the CDF
            """
            count = ctypes.c_long(0)
            self._cdf_file._call(const.GET_, const.CDF_NUMgATTRS_,
                                 ctypes.byref(count))
            return count.value

    class zAttrList(AttrList):
        """Object representing *all* the zAttributes in a zVariable.

        Normally accessed as an attribute of a :class:`Var` in an open
        CDF:

            >>> epoch_attribs = cdffile['Epoch'].attrs

        Appears as a dictionary: keys are attribute names, values are
        the value of the zEntry associated with the appropriate zVariable.
        Each vAttribute in a CDF may only have a *single* entry associated
        with each variable. The entry may be a string, a single numerical value,
        or a series of numerical values. Entries with multiple values are returned
        as an entire list; direct access to the individual elements is not
        possible.

        Example: finding the first dependency of (ISTP-compliant) variable
        ``Flux``:

            >>> print cdffile['Flux'].attrs['DEPEND_0']

        zAttributes are shared among zVariables, one zEntry allowed per zVariable.
        (pyCDF hides this detail.) Deleting the last zEntry for a zAttribute will
        delete the underlying zAttribute.

        zEntries are created and destroyed by the usual dict methods on the
        zAttrlist:

            >>> epoch_attribs['new_entry'] = [1, 2, 4] #assign a list to new zEntry
            >>> del epoch_attribs['new_entry'] #delete the zEntry

        The type of the zEntry is guessed from data provided. The type is chosen to
        match the data; subject to that constraint, it will try to match
        (in order):

            #. existing zEntry corresponding to this zVar
            #. other zEntries in this zAttribute
            #. the type of this zVar
            #. data-matching constraints described in :py:meth:`CDF.new`

        See Also
        ========
        :class:`AttrList`

        """
        AttrType = zAttr
        attr_name = 'zAttribute'
        global_scope = False

        def __init__(self, zvar):
            """Initialize the attribute collection

            @param zvar: zVariable these attributes are in
            @param zvar: :py:class:`pycdf.Var`
            """
            super(zAttrList, self).__init__(zvar.cdf_file, zvar._num)
            self._zvar = zvar

        def __getitem__(self, name):
            """Find an zEntry by name

            @param name: name of the zAttribute to return
            @type name: str
            @return: attribute named L{name}
            @rtype: L{zAttr}
            @raise KeyError: if there is no attribute named L{name} associated
                             with this zVariable
            @raise CDFError: other errors in CDF library
            """
            attrib = super(zAttrList, self).__getitem__(name)
            zvar_num = self._zvar._num()
            if attrib.has_entry(zvar_num):
                attrib._raw = self._zvar._raw
                return attrib[zvar_num]
            else:
                raise KeyError(name + ': no such attribute for variable ' +
                               self._zvar.name())

        def __delitem__(self, name):
            """Delete an zEntry by name

            @param name: name of the zEntry to delete
            @type name: str
            @raise KeyError: if there is no attribute named L{name} associated
                             with this zVariable
            @raise CDFError: other errors in CDF library
            @note: If this is the only remaining entry, the Attribute will be
                   deleted.
            """
            attrib = super(zAttrList, self).__getitem__(name)
            zvar_num = self._zvar._num()
            if not attrib.has_entry(zvar_num):
                raise KeyError(str(name) + ': no such attribute for variable ' +
                               str(self._zvar._name))
            del attrib[zvar_num]
            if len(attrib) == 0:
                attrib._delete()

        def __setitem__(self, name, data):
            """Sets a zEntry by name

            The type of the zEntry is guessed from L{data}. The type is chosen to
            match the data; subject to that constraint, it will try to match
            (in order):
            1. existing zEntry corresponding to this zVar
            2. other zEntries in this zAttribute
            3. the type of this zVar
            4. data-matching constraints described in L{_Hyperslice.types}

            @param name: name of zAttribute; zEntry for this zVariable will be set
                         in zAttribute by this name
            @type name: str
            @raise CDFError: errors in CDF library
            @raise ValueError: if unable to find a valid CDF type matching L{data},
                               or if L{data} is the wrong dimensions.
            """
            try:
                attr = super(zAttrList, self).__getitem__(name)
            except KeyError:
                attr = zAttr(self._cdf_file, name, True)
            attr._raw = self._zvar._raw
            attr[self._zvar._num()] = data

        def __len__(self):
            """Number of zAttributes in this variable

            @return: number of zAttributes in the CDF
                     which have entries for this variable.
            @rtype: int
            """
            length = 0
            count = ctypes.c_long(0)
            self._cdf_file._call(const.GET_, const.CDF_NUMATTRS_,
                                 ctypes.byref(count))
            current = 0
            while current < count.value:
                candidate = zAttr(self._cdf_file, current)
                if not candidate.global_scope():
                    if candidate.has_entry(self._zvar._num()):
                        length += 1
                current += 1
            return length

        def type(self, name, new_type=None):
            """Find or change the CDF type of a zEntry in this zVar

            @param name: name of the zAttr to check or change
            @type name: str
            @param new_type: type to change it to, see :py:mod:`pycdf.const`
            @type new_type: ctypes.c_long
            @return: CDF variable type, see :py:mod:`pycdf.const`
            @rtype: int
            @note: If changing types, old and new must be equivalent, see CDF
                   User's Guide section 2.5.5 pg. 57
            """
            attrib = super(zAttrList, self).__getitem__(name)
            zvar_num = self._zvar._num()
            if not attrib.has_entry(zvar_num):
                raise KeyError(name + ': no such attribute for variable ' +
                               self._zvar.name())
            return attrib.type(zvar_num, new_type)

        def _clone_attr(self, master, name, new_name=None):
            """Clones a single attribute from one in this list or another

            Copies data and types from the master attribute to the new one

            @param master: attribute list to copy attribute from
            @type master: L{zAttrList}
            @param name: name of attribute to copy
            @type name: str
            @param new_name: name of the new attribute, default L{name}
            @type new_name: str
            """
            if new_name is None:
                new_name = name
            if new_name in self:
                del self[new_name]
            self.new(new_name, master[name],
                     master.type(name) if hasattr(master, 'type') else None)