from __future__ import division
# pylint: disable-msg=W0402

import re
import string
import sys
import tempfile
import warnings
import inspect
import os
import subprocess
import locale
import traceback

from datetime import datetime
from functools import wraps, partial
from contextlib import contextmanager
from distutils.version import LooseVersion

from numpy.random import randn, rand
import numpy as np

import pandas as pd
from pandas.core.dtypes.missing import array_equivalent
from pandas.core.dtypes.common import (
    is_datetimelike_v_numeric,
    is_datetimelike_v_object,
    is_number, is_bool,
    needs_i8_conversion,
    is_categorical_dtype,
    is_interval_dtype,
    is_sequence,
    is_list_like)
from pandas.io.formats.printing import pprint_thing
from pandas.core.algorithms import take_1d

import pandas.compat as compat
from pandas.compat import (
    filter, map, zip, range, unichr, lrange, lmap, lzip, u, callable, Counter,
    raise_with_traceback, httplib, is_platform_windows, is_platform_32bit,
    StringIO, PY3
)

from pandas.core.computation import expressions as expr

from pandas import (bdate_range, CategoricalIndex, Categorical, IntervalIndex,
                    DatetimeIndex, TimedeltaIndex, PeriodIndex, RangeIndex,
                    Index, MultiIndex,
                    Series, DataFrame, Panel, Panel4D)

from pandas._libs import testing as _testing
from pandas.io.common import urlopen
try:
    import pytest
    slow = pytest.mark.slow
except ImportError:
    # Should be ok to just ignore. If you actually need
    # slow then you'll hit an import error long before getting here.
    pass


N = 30
K = 4
_RAISE_NETWORK_ERROR_DEFAULT = False


# set testing_mode
_testing_mode_warnings = (DeprecationWarning, compat.ResourceWarning)


def set_testing_mode():
    # set the testing mode filters
    testing_mode = os.environ.get('PANDAS_TESTING_MODE', 'None')
    if 'deprecate' in testing_mode:
        warnings.simplefilter('always', _testing_mode_warnings)


def reset_testing_mode():
    # reset the testing mode filters
    testing_mode = os.environ.get('PANDAS_TESTING_MODE', 'None')
    if 'deprecate' in testing_mode:
        warnings.simplefilter('ignore', _testing_mode_warnings)


set_testing_mode()


def reset_display_options():
    """
    Reset the display options for printing and representing objects.
    """

    pd.reset_option('^display.', silent=True)


def round_trip_pickle(obj, path=None):
    """
    Pickle an object and then read it again.

    Parameters
    ----------
    obj : pandas object
        The object to pickle and then re-read.
    path : str, default None
        The path where the pickled object is written and then read.

    Returns
    -------
    round_trip_pickled_object : pandas object
        The original object that was pickled and then re-read.
    """

    if path is None:
        path = u('__%s__.pickle' % rands(10))
    with ensure_clean(path) as path:
        pd.to_pickle(obj, path)
        return pd.read_pickle(path)


def assert_almost_equal(left, right, check_exact=False,
                        check_dtype='equiv', check_less_precise=False,
                        **kwargs):
    """
    Check that the left and right objects are approximately equal.

    Parameters
    ----------
    left : object
    right : object
    check_exact : bool, default True
        Whether to compare number exactly.
    check_dtype: bool, default True
        check dtype if both a and b are the same type
    check_less_precise : bool or int, default False
        Specify comparison precision. Only used when check_exact is False.
        5 digits (False) or 3 digits (True) after decimal points are compared.
        If int, then specify the digits to compare
    """
    if isinstance(left, pd.Index):
        return assert_index_equal(left, right, check_exact=check_exact,
                                  exact=check_dtype,
                                  check_less_precise=check_less_precise,
                                  **kwargs)

    elif isinstance(left, pd.Series):
        return assert_series_equal(left, right, check_exact=check_exact,
                                   check_dtype=check_dtype,
                                   check_less_precise=check_less_precise,
                                   **kwargs)

    elif isinstance(left, pd.DataFrame):
        return assert_frame_equal(left, right, check_exact=check_exact,
                                  check_dtype=check_dtype,
                                  check_less_precise=check_less_precise,
                                  **kwargs)

    else:
        # other sequences
        if check_dtype:
            if is_number(left) and is_number(right):
                # do not compare numeric classes, like np.float64 and float
                pass
            elif is_bool(left) and is_bool(right):
                # do not compare bool classes, like np.bool_ and bool
                pass
            else:
                if (isinstance(left, np.ndarray) or
                        isinstance(right, np.ndarray)):
                    obj = 'numpy array'
                else:
                    obj = 'Input'
                assert_class_equal(left, right, obj=obj)
        return _testing.assert_almost_equal(
            left, right,
            check_dtype=check_dtype,
            check_less_precise=check_less_precise,
            **kwargs)


def _check_isinstance(left, right, cls):
    """
    Helper method for our assert_* methods that ensures that
    the two objects being compared have the right type before
    proceeding with the comparison.

    Parameters
    ----------
    left : The first object being compared.
    right : The second object being compared.
    cls : The class type to check against.

    Raises
    ------
    AssertionError : Either `left` or `right` is not an instance of `cls`.
    """

    err_msg = "{0} Expected type {1}, found {2} instead"
    cls_name = cls.__name__

    if not isinstance(left, cls):
        raise AssertionError(err_msg.format(cls_name, cls, type(left)))
    if not isinstance(right, cls):
        raise AssertionError(err_msg.format(cls_name, cls, type(right)))


def assert_dict_equal(left, right, compare_keys=True):

    _check_isinstance(left, right, dict)
    return _testing.assert_dict_equal(left, right, compare_keys=compare_keys)


def randbool(size=(), p=0.5):
    return rand(*size) <= p


RANDS_CHARS = np.array(list(string.ascii_letters + string.digits),
                       dtype=(np.str_, 1))
RANDU_CHARS = np.array(list(u("").join(map(unichr, lrange(1488, 1488 + 26))) +
                            string.digits), dtype=(np.unicode_, 1))


def rands_array(nchars, size, dtype='O'):
    """Generate an array of byte strings."""
    retval = (np.random.choice(RANDS_CHARS, size=nchars * np.prod(size))
              .view((np.str_, nchars)).reshape(size))
    if dtype is None:
        return retval
    else:
        return retval.astype(dtype)


def randu_array(nchars, size, dtype='O'):
    """Generate an array of unicode strings."""
    retval = (np.random.choice(RANDU_CHARS, size=nchars * np.prod(size))
              .view((np.unicode_, nchars)).reshape(size))
    if dtype is None:
        return retval
    else:
        return retval.astype(dtype)


def rands(nchars):
    """
    Generate one random byte string.

    See `rands_array` if you want to create an array of random strings.

    """
    return ''.join(np.random.choice(RANDS_CHARS, nchars))


def randu(nchars):
    """
    Generate one random unicode string.

    See `randu_array` if you want to create an array of random unicode strings.

    """
    return ''.join(np.random.choice(RANDU_CHARS, nchars))


def close(fignum=None):
    from matplotlib.pyplot import get_fignums, close as _close

    if fignum is None:
        for fignum in get_fignums():
            _close(fignum)
    else:
        _close(fignum)


def _skip_if_32bit():
    import pytest
    if is_platform_32bit():
        pytest.skip("skipping for 32 bit")


def _skip_module_if_no_mpl():
    import pytest

    mpl = pytest.importorskip("matplotlib")
    mpl.use("Agg", warn=False)


def _skip_if_no_mpl():
    try:
        import matplotlib as mpl
        mpl.use("Agg", warn=False)
    except ImportError:
        import pytest
        pytest.skip("matplotlib not installed")


def _skip_if_mpl_1_5():
    import matplotlib as mpl

    v = mpl.__version__
    if v > LooseVersion('1.4.3') or v[0] == '0':
        import pytest
        pytest.skip("matplotlib 1.5")
    else:
        mpl.use("Agg", warn=False)


def _skip_if_no_scipy():
    try:
        import scipy.stats  # noqa
    except ImportError:
        import pytest
        pytest.skip("no scipy.stats module")
    try:
        import scipy.interpolate  # noqa
    except ImportError:
        import pytest
        pytest.skip('scipy.interpolate missing')
    try:
        import scipy.sparse  # noqa
    except ImportError:
        import pytest
        pytest.skip('scipy.sparse missing')


def _check_if_lzma():
    try:
        return compat.import_lzma()
    except ImportError:
        return False


def _skip_if_no_lzma():
    import pytest
    return _check_if_lzma() or pytest.skip('need backports.lzma to run')


def _skip_if_no_xarray():
    try:
        import xarray
    except ImportError:
        import pytest
        pytest.skip("xarray not installed")

    v = xarray.__version__
    if v < LooseVersion('0.7.0'):
        import pytest
        pytest.skip("xarray not version is too low: {0}".format(v))


def _skip_if_no_pytz():
    try:
        import pytz  # noqa
    except ImportError:
        import pytest
        pytest.skip("pytz not installed")


def _skip_if_no_dateutil():
    try:
        import dateutil  # noqa
    except ImportError:
        import pytest
        pytest.skip("dateutil not installed")


def _skip_if_windows_python_3():
    if PY3 and is_platform_windows():
        import pytest
        pytest.skip("not used on python 3/win32")


def _skip_if_windows():
    if is_platform_windows():
        import pytest
        pytest.skip("Running on Windows")


def _skip_if_no_pathlib():
    try:
        from pathlib import Path  # noqa
    except ImportError:
        import pytest
        pytest.skip("pathlib not available")


def _skip_if_no_localpath():
    try:
        from py.path import local as LocalPath  # noqa
    except ImportError:
        import pytest
        pytest.skip("py.path not installed")


def _incompat_bottleneck_version(method):
    """ skip if we have bottleneck installed
    and its >= 1.0
    as we don't match the nansum/nanprod behavior for all-nan
    ops, see GH9422
    """
    if method not in ['sum', 'prod']:
        return False
    try:
        import bottleneck as bn
        return bn.__version__ >= LooseVersion('1.0')
    except ImportError:
        return False


def skip_if_no_ne(engine='numexpr'):
    from pandas.core.computation.expressions import (
        _USE_NUMEXPR,
        _NUMEXPR_INSTALLED)

    if engine == 'numexpr':
        if not _USE_NUMEXPR:
            import pytest
            pytest.skip("numexpr enabled->{enabled}, "
                        "installed->{installed}".format(
                            enabled=_USE_NUMEXPR,
                            installed=_NUMEXPR_INSTALLED))


def _skip_if_has_locale():
    import locale
    lang, _ = locale.getlocale()
    if lang is not None:
        import pytest
        pytest.skip("Specific locale is set {0}".format(lang))


def _skip_if_not_us_locale():
    import locale
    lang, _ = locale.getlocale()
    if lang != 'en_US':
        import pytest
        pytest.skip("Specific locale is set {0}".format(lang))


def _skip_if_no_mock():
    try:
        import mock  # noqa
    except ImportError:
        try:
            from unittest import mock  # noqa
        except ImportError:
            import nose
            raise nose.SkipTest("mock is not installed")


def _skip_if_no_ipython():
    try:
        import IPython  # noqa
    except ImportError:
        import nose
        raise nose.SkipTest("IPython not installed")

# -----------------------------------------------------------------------------
# locale utilities


def check_output(*popenargs, **kwargs):
    # shamelessly taken from Python 2.7 source
    r"""Run command with arguments and return its output as a byte string.

    If the exit code was non-zero it raises a CalledProcessError.  The
    CalledProcessError object will have the return code in the returncode
    attribute and output in the output attribute.

    The arguments are the same as for the Popen constructor.  Example:

    >>> check_output(["ls", "-l", "/dev/null"])
    'crw-rw-rw- 1 root root 1, 3 Oct 18  2007 /dev/null\n'

    The stdout argument is not allowed as it is used internally.
    To capture standard error in the result, use stderr=STDOUT.

    >>> check_output(["/bin/sh", "-c",
    ...               "ls -l non_existent_file ; exit 0"],
    ...              stderr=STDOUT)
    'ls: non_existent_file: No such file or directory\n'
    """
    if 'stdout' in kwargs:
        raise ValueError('stdout argument not allowed, it will be overridden.')
    process = subprocess.Popen(stdout=subprocess.PIPE, stderr=subprocess.PIPE,
                               *popenargs, **kwargs)
    output, unused_err = process.communicate()
    retcode = process.poll()
    if retcode:
        cmd = kwargs.get("args")
        if cmd is None:
            cmd = popenargs[0]
        raise subprocess.CalledProcessError(retcode, cmd, output=output)
    return output


def _default_locale_getter():
    try:
        raw_locales = check_output(['locale -a'], shell=True)
    except subprocess.CalledProcessError as e:
        raise type(e)("%s, the 'locale -a' command cannot be found on your "
                      "system" % e)
    return raw_locales


def get_locales(prefix=None, normalize=True,
                locale_getter=_default_locale_getter):
    """Get all the locales that are available on the system.

    Parameters
    ----------
    prefix : str
        If not ``None`` then return only those locales with the prefix
        provided. For example to get all English language locales (those that
        start with ``"en"``), pass ``prefix="en"``.
    normalize : bool
        Call ``locale.normalize`` on the resulting list of available locales.
        If ``True``, only locales that can be set without throwing an
        ``Exception`` are returned.
    locale_getter : callable
        The function to use to retrieve the current locales. This should return
        a string with each locale separated by a newline character.

    Returns
    -------
    locales : list of strings
        A list of locale strings that can be set with ``locale.setlocale()``.
        For example::

            locale.setlocale(locale.LC_ALL, locale_string)

    On error will return None (no locale available, e.g. Windows)

    """
    try:
        raw_locales = locale_getter()
    except:
        return None

    try:
        # raw_locales is "\n" separated list of locales
        # it may contain non-decodable parts, so split
        # extract what we can and then rejoin.
        raw_locales = raw_locales.split(b'\n')
        out_locales = []
        for x in raw_locales:
            if PY3:
                out_locales.append(str(
                    x, encoding=pd.options.display.encoding))
            else:
                out_locales.append(str(x))

    except TypeError:
        pass

    if prefix is None:
        return _valid_locales(out_locales, normalize)

    found = re.compile('%s.*' % prefix).findall('\n'.join(out_locales))
    return _valid_locales(found, normalize)


@contextmanager
def set_locale(new_locale, lc_var=locale.LC_ALL):
    """Context manager for temporarily setting a locale.

    Parameters
    ----------
    new_locale : str or tuple
        A string of the form <language_country>.<encoding>. For example to set
        the current locale to US English with a UTF8 encoding, you would pass
        "en_US.UTF-8".

    Notes
    -----
    This is useful when you want to run a particular block of code under a
    particular locale, without globally setting the locale. This probably isn't
    thread-safe.
    """
    current_locale = locale.getlocale()

    try:
        locale.setlocale(lc_var, new_locale)

        try:
            normalized_locale = locale.getlocale()
        except ValueError:
            yield new_locale
        else:
            if all(lc is not None for lc in normalized_locale):
                yield '.'.join(normalized_locale)
            else:
                yield new_locale
    finally:
        locale.setlocale(lc_var, current_locale)


def _can_set_locale(lc):
    """Check to see if we can set a locale without throwing an exception.

    Parameters
    ----------
    lc : str
        The locale to attempt to set.

    Returns
    -------
    isvalid : bool
        Whether the passed locale can be set
    """
    try:
        with set_locale(lc):
            pass
    except locale.Error:  # horrible name for a Exception subclass
        return False
    else:
        return True


def _valid_locales(locales, normalize):
    """Return a list of normalized locales that do not throw an ``Exception``
    when set.

    Parameters
    ----------
    locales : str
        A string where each locale is separated by a newline.
    normalize : bool
        Whether to call ``locale.normalize`` on each locale.

    Returns
    -------
    valid_locales : list
        A list of valid locales.
    """
    if normalize:
        normalizer = lambda x: locale.normalize(x.strip())
    else:
        normalizer = lambda x: x.strip()

    return list(filter(_can_set_locale, map(normalizer, locales)))

# -----------------------------------------------------------------------------
# Stdout / stderr decorators


def capture_stdout(f):
    """
    Decorator to capture stdout in a buffer so that it can be checked
    (or suppressed) during testing.

    Parameters
    ----------
    f : callable
        The test that is capturing stdout.

    Returns
    -------
    f : callable
        The decorated test ``f``, which captures stdout.

    Examples
    --------

    >>> from pandas.util.testing import capture_stdout
    >>>
    >>> import sys
    >>>
    >>> @capture_stdout
    ... def test_print_pass():
    ...     print("foo")
    ...     out = sys.stdout.getvalue()
    ...     assert out == "foo\n"
    >>>
    >>> @capture_stdout
    ... def test_print_fail():
    ...     print("foo")
    ...     out = sys.stdout.getvalue()
    ...     assert out == "bar\n"
    ...
    AssertionError: assert 'foo\n' == 'bar\n'
    """

    @wraps(f)
    def wrapper(*args, **kwargs):
        try:
            sys.stdout = StringIO()
            f(*args, **kwargs)
        finally:
            sys.stdout = sys.__stdout__

    return wrapper


def capture_stderr(f):
    """
    Decorator to capture stderr in a buffer so that it can be checked
    (or suppressed) during testing.

    Parameters
    ----------
    f : callable
        The test that is capturing stderr.

    Returns
    -------
    f : callable
        The decorated test ``f``, which captures stderr.

    Examples
    --------

    >>> from pandas.util.testing import capture_stderr
    >>>
    >>> import sys
    >>>
    >>> @capture_stderr
    ... def test_stderr_pass():
    ...     sys.stderr.write("foo")
    ...     out = sys.stderr.getvalue()
    ...     assert out == "foo\n"
    >>>
    >>> @capture_stderr
    ... def test_stderr_fail():
    ...     sys.stderr.write("foo")
    ...     out = sys.stderr.getvalue()
    ...     assert out == "bar\n"
    ...
    AssertionError: assert 'foo\n' == 'bar\n'
    """

    @wraps(f)
    def wrapper(*args, **kwargs):
        try:
            sys.stderr = StringIO()
            f(*args, **kwargs)
        finally:
            sys.stderr = sys.__stderr__

    return wrapper

# -----------------------------------------------------------------------------
# Console debugging tools


def debug(f, *args, **kwargs):
    from pdb import Pdb as OldPdb
    try:
        from IPython.core.debugger import Pdb
        kw = dict(color_scheme='Linux')
    except ImportError:
        Pdb = OldPdb
        kw = {}
    pdb = Pdb(**kw)
    return pdb.runcall(f, *args, **kwargs)


def pudebug(f, *args, **kwargs):
    import pudb
    return pudb.runcall(f, *args, **kwargs)


def set_trace():
    from IPython.core.debugger import Pdb
    try:
        Pdb(color_scheme='Linux').set_trace(sys._getframe().f_back)
    except:
        from pdb import Pdb as OldPdb
        OldPdb().set_trace(sys._getframe().f_back)

# -----------------------------------------------------------------------------
# contextmanager to ensure the file cleanup


@contextmanager
def ensure_clean(filename=None, return_filelike=False):
    """Gets a temporary path and agrees to remove on close.

    Parameters
    ----------
    filename : str (optional)
        if None, creates a temporary file which is then removed when out of
        scope. if passed, creates temporary file with filename as ending.
    return_filelike : bool (default False)
        if True, returns a file-like which is *always* cleaned. Necessary for
        savefig and other functions which want to append extensions.
    """
    filename = filename or ''
    fd = None

    if return_filelike:
        f = tempfile.TemporaryFile(suffix=filename)
        try:
            yield f
        finally:
            f.close()
    else:
        # don't generate tempfile if using a path with directory specified
        if len(os.path.dirname(filename)):
            raise ValueError("Can't pass a qualified name to ensure_clean()")

        try:
            fd, filename = tempfile.mkstemp(suffix=filename)
        except UnicodeEncodeError:
            import pytest
            pytest.skip('no unicode file names on this system')

        try:
            yield filename
        finally:
            try:
                os.close(fd)
            except Exception as e:
                print("Couldn't close file descriptor: %d (file: %s)" %
                      (fd, filename))
            try:
                if os.path.exists(filename):
                    os.remove(filename)
            except Exception as e:
                print("Exception on removing file: %s" % e)


def get_data_path(f=''):
    """Return the path of a data file, these are relative to the current test
    directory.
    """
    # get our callers file
    _, filename, _, _, _, _ = inspect.getouterframes(inspect.currentframe())[1]
    base_dir = os.path.abspath(os.path.dirname(filename))
    return os.path.join(base_dir, 'data', f)

# -----------------------------------------------------------------------------
# Comparators


def equalContents(arr1, arr2):
    """Checks if the set of unique elements of arr1 and arr2 are equivalent.
    """
    return frozenset(arr1) == frozenset(arr2)


def assert_index_equal(left, right, exact='equiv', check_names=True,
                       check_less_precise=False, check_exact=True,
                       check_categorical=True, obj='Index'):
    """Check that left and right Index are equal.

    Parameters
    ----------
    left : Index
    right : Index
    exact : bool / string {'equiv'}, default False
        Whether to check the Index class, dtype and inferred_type
        are identical. If 'equiv', then RangeIndex can be substituted for
        Int64Index as well.
    check_names : bool, default True
        Whether to check the names attribute.
    check_less_precise : bool or int, default False
        Specify comparison precision. Only used when check_exact is False.
        5 digits (False) or 3 digits (True) after decimal points are compared.
        If int, then specify the digits to compare
    check_exact : bool, default True
        Whether to compare number exactly.
    check_categorical : bool, default True
        Whether to compare internal Categorical exactly.
    obj : str, default 'Index'
        Specify object name being compared, internally used to show appropriate
        assertion message
    """

    def _check_types(l, r, obj='Index'):
        if exact:
            assert_class_equal(left, right, exact=exact, obj=obj)
            assert_attr_equal('dtype', l, r, obj=obj)
            # allow string-like to have different inferred_types
            if l.inferred_type in ('string', 'unicode'):
                assert r.inferred_type in ('string', 'unicode')
            else:
                assert_attr_equal('inferred_type', l, r, obj=obj)

    def _get_ilevel_values(index, level):
        # accept level number only
        unique = index.levels[level]
        labels = index.labels[level]
        filled = take_1d(unique.values, labels, fill_value=unique._na_value)
        values = unique._shallow_copy(filled, name=index.names[level])
        return values

    # instance validation
    _check_isinstance(left, right, Index)

    # class / dtype comparison
    _check_types(left, right, obj=obj)

    # level comparison
    if left.nlevels != right.nlevels:
        raise_assert_detail(obj, '{0} levels are different'.format(obj),
                            '{0}, {1}'.format(left.nlevels, left),
                            '{0}, {1}'.format(right.nlevels, right))

    # length comparison
    if len(left) != len(right):
        raise_assert_detail(obj, '{0} length are different'.format(obj),
                            '{0}, {1}'.format(len(left), left),
                            '{0}, {1}'.format(len(right), right))

    # MultiIndex special comparison for little-friendly error messages
    if left.nlevels > 1:
        for level in range(left.nlevels):
            # cannot use get_level_values here because it can change dtype
            llevel = _get_ilevel_values(left, level)
            rlevel = _get_ilevel_values(right, level)

            lobj = 'MultiIndex level [{0}]'.format(level)
            assert_index_equal(llevel, rlevel,
                               exact=exact, check_names=check_names,
                               check_less_precise=check_less_precise,
                               check_exact=check_exact, obj=lobj)
            # get_level_values may change dtype
            _check_types(left.levels[level], right.levels[level], obj=obj)

    if check_exact:
        if not left.equals(right):
            diff = np.sum((left.values != right.values)
                          .astype(int)) * 100.0 / len(left)
            msg = '{0} values are different ({1} %)'\
                .format(obj, np.round(diff, 5))
            raise_assert_detail(obj, msg, left, right)
    else:
        _testing.assert_almost_equal(left.values, right.values,
                                     check_less_precise=check_less_precise,
                                     check_dtype=exact,
                                     obj=obj, lobj=left, robj=right)

    # metadata comparison
    if check_names:
        assert_attr_equal('names', left, right, obj=obj)
    if isinstance(left, pd.PeriodIndex) or isinstance(right, pd.PeriodIndex):
        assert_attr_equal('freq', left, right, obj=obj)
    if (isinstance(left, pd.IntervalIndex) or
            isinstance(right, pd.IntervalIndex)):
        assert_attr_equal('closed', left, right, obj=obj)

    if check_categorical:
        if is_categorical_dtype(left) or is_categorical_dtype(right):
            assert_categorical_equal(left.values, right.values,
                                     obj='{0} category'.format(obj))


def assert_class_equal(left, right, exact=True, obj='Input'):
    """checks classes are equal."""

    def repr_class(x):
        if isinstance(x, Index):
            # return Index as it is to include values in the error message
            return x

        try:
            return x.__class__.__name__
        except AttributeError:
            return repr(type(x))

    if exact == 'equiv':
        if type(left) != type(right):
            # allow equivalence of Int64Index/RangeIndex
            types = set([type(left).__name__, type(right).__name__])
            if len(types - set(['Int64Index', 'RangeIndex'])):
                msg = '{0} classes are not equivalent'.format(obj)
                raise_assert_detail(obj, msg, repr_class(left),
                                    repr_class(right))
    elif exact:
        if type(left) != type(right):
            msg = '{0} classes are different'.format(obj)
            raise_assert_detail(obj, msg, repr_class(left),
                                repr_class(right))


def assert_attr_equal(attr, left, right, obj='Attributes'):
    """checks attributes are equal. Both objects must have attribute.

    Parameters
    ----------
    attr : str
        Attribute name being compared.
    left : object
    right : object
    obj : str, default 'Attributes'
        Specify object name being compared, internally used to show appropriate
        assertion message
    """

    left_attr = getattr(left, attr)
    right_attr = getattr(right, attr)

    if left_attr is right_attr:
        return True
    elif (is_number(left_attr) and np.isnan(left_attr) and
          is_number(right_attr) and np.isnan(right_attr)):
        # np.nan
        return True

    try:
        result = left_attr == right_attr
    except TypeError:
        # datetimetz on rhs may raise TypeError
        result = False
    if not isinstance(result, bool):
        result = result.all()

    if result:
        return True
    else:
        raise_assert_detail(obj, 'Attribute "{0}" are different'.format(attr),
                            left_attr, right_attr)


def assert_is_valid_plot_return_object(objs):
    import matplotlib.pyplot as plt
    if isinstance(objs, (pd.Series, np.ndarray)):
        for el in objs.ravel():
            msg = ('one of \'objs\' is not a matplotlib Axes instance, '
                   'type encountered {0!r}')
            assert isinstance(el, (plt.Axes, dict)), msg.format(
                el.__class__.__name__)
    else:
        assert isinstance(objs, (plt.Artist, tuple, dict)), \
            ('objs is neither an ndarray of Artist instances nor a '
             'single Artist instance, tuple, or dict, "objs" is a {0!r} '
             ''.format(objs.__class__.__name__))


def isiterable(obj):
    return hasattr(obj, '__iter__')


def is_sorted(seq):
    if isinstance(seq, (Index, Series)):
        seq = seq.values
    # sorting does not change precisions
    return assert_numpy_array_equal(seq, np.sort(np.array(seq)))


def assert_categorical_equal(left, right, check_dtype=True,
                             obj='Categorical', check_category_order=True):
    """Test that Categoricals are equivalent.

    Parameters
    ----------
    left, right : Categorical
        Categoricals to compare
    check_dtype : bool, default True
        Check that integer dtype of the codes are the same
    obj : str, default 'Categorical'
        Specify object name being compared, internally used to show appropriate
        assertion message
    check_category_order : bool, default True
        Whether the order of the categories should be compared, which
        implies identical integer codes.  If False, only the resulting
        values are compared.  The ordered attribute is
        checked regardless.
    """
    _check_isinstance(left, right, Categorical)

    if check_category_order:
        assert_index_equal(left.categories, right.categories,
                           obj='{0}.categories'.format(obj))
        assert_numpy_array_equal(left.codes, right.codes,
                                 check_dtype=check_dtype,
                                 obj='{0}.codes'.format(obj))
    else:
        assert_index_equal(left.categories.sort_values(),
                           right.categories.sort_values(),
                           obj='{0}.categories'.format(obj))
        assert_index_equal(left.categories.take(left.codes),
                           right.categories.take(right.codes),
                           obj='{0}.values'.format(obj))

    assert_attr_equal('ordered', left, right, obj=obj)


def raise_assert_detail(obj, message, left, right, diff=None):
    if isinstance(left, np.ndarray):
        left = pprint_thing(left)
    if isinstance(right, np.ndarray):
        right = pprint_thing(right)

    msg = """{0} are different

{1}
[left]:  {2}
[right]: {3}""".format(obj, message, left, right)

    if diff is not None:
        msg = msg + "\n[diff]: {diff}".format(diff=diff)

    raise AssertionError(msg)


def assert_numpy_array_equal(left, right, strict_nan=False,
                             check_dtype=True, err_msg=None,
                             obj='numpy array', check_same=None):
    """ Checks that 'np.ndarray' is equivalent

    Parameters
    ----------
    left : np.ndarray or iterable
    right : np.ndarray or iterable
    strict_nan : bool, default False
        If True, consider NaN and None to be different.
    check_dtype: bool, default True
        check dtype if both a and b are np.ndarray
    err_msg : str, default None
        If provided, used as assertion message
    obj : str, default 'numpy array'
        Specify object name being compared, internally used to show appropriate
        assertion message
    check_same : None|'copy'|'same', default None
        Ensure left and right refer/do not refer to the same memory area
    """

    # instance validation
    # Show a detailed error message when classes are different
    assert_class_equal(left, right, obj=obj)
    # both classes must be an np.ndarray
    _check_isinstance(left, right, np.ndarray)

    def _get_base(obj):
        return obj.base if getattr(obj, 'base', None) is not None else obj

    left_base = _get_base(left)
    right_base = _get_base(right)

    if check_same == 'same':
        if left_base is not right_base:
            msg = "%r is not %r" % (left_base, right_base)
            raise AssertionError(msg)
    elif check_same == 'copy':
        if left_base is right_base:
            msg = "%r is %r" % (left_base, right_base)
            raise AssertionError(msg)

    def _raise(left, right, err_msg):
        if err_msg is None:
            if left.shape != right.shape:
                raise_assert_detail(obj, '{0} shapes are different'
                                    .format(obj), left.shape, right.shape)

            diff = 0
            for l, r in zip(left, right):
                # count up differences
                if not array_equivalent(l, r, strict_nan=strict_nan):
                    diff += 1

            diff = diff * 100.0 / left.size
            msg = '{0} values are different ({1} %)'\
                .format(obj, np.round(diff, 5))
            raise_assert_detail(obj, msg, left, right)

        raise AssertionError(err_msg)

    # compare shape and values
    if not array_equivalent(left, right, strict_nan=strict_nan):
        _raise(left, right, err_msg)

    if check_dtype:
        if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
            assert_attr_equal('dtype', left, right, obj=obj)

    return True


# This could be refactored to use the NDFrame.equals method
def assert_series_equal(left, right, check_dtype=True,
                        check_index_type='equiv',
                        check_series_type=True,
                        check_less_precise=False,
                        check_names=True,
                        check_exact=False,
                        check_datetimelike_compat=False,
                        check_categorical=True,
                        obj='Series'):
    """Check that left and right Series are equal.

    Parameters
    ----------
    left : Series
    right : Series
    check_dtype : bool, default True
        Whether to check the Series dtype is identical.
    check_index_type : bool / string {'equiv'}, default 'equiv'
        Whether to check the Index class, dtype and inferred_type
        are identical.
    check_series_type : bool, default True
        Whether to check the Series class is identical.
    check_less_precise : bool or int, default False
        Specify comparison precision. Only used when check_exact is False.
        5 digits (False) or 3 digits (True) after decimal points are compared.
        If int, then specify the digits to compare
    check_exact : bool, default False
        Whether to compare number exactly.
    check_names : bool, default True
        Whether to check the Series and Index names attribute.
    check_datetimelike_compat : bool, default False
        Compare datetime-like which is comparable ignoring dtype.
    check_categorical : bool, default True
        Whether to compare internal Categorical exactly.
    obj : str, default 'Series'
        Specify object name being compared, internally used to show appropriate
        assertion message
    """

    # instance validation
    _check_isinstance(left, right, Series)

    if check_series_type:
        # ToDo: There are some tests using rhs is sparse
        # lhs is dense. Should use assert_class_equal in future
        assert isinstance(left, type(right))
        # assert_class_equal(left, right, obj=obj)

    # length comparison
    if len(left) != len(right):
        raise_assert_detail(obj, 'Series length are different',
                            '{0}, {1}'.format(len(left), left.index),
                            '{0}, {1}'.format(len(right), right.index))

    # index comparison
    assert_index_equal(left.index, right.index, exact=check_index_type,
                       check_names=check_names,
                       check_less_precise=check_less_precise,
                       check_exact=check_exact,
                       check_categorical=check_categorical,
                       obj='{0}.index'.format(obj))

    if check_dtype:
        assert_attr_equal('dtype', left, right)

    if check_exact:
        assert_numpy_array_equal(left.get_values(), right.get_values(),
                                 check_dtype=check_dtype,
                                 obj='{0}'.format(obj),)
    elif check_datetimelike_compat:
        # we want to check only if we have compat dtypes
        # e.g. integer and M|m are NOT compat, but we can simply check
        # the values in that case
        if (is_datetimelike_v_numeric(left, right) or
            is_datetimelike_v_object(left, right) or
            needs_i8_conversion(left) or
                needs_i8_conversion(right)):

            # datetimelike may have different objects (e.g. datetime.datetime
            # vs Timestamp) but will compare equal
            if not Index(left.values).equals(Index(right.values)):
                msg = '[datetimelike_compat=True] {0} is not equal to {1}.'
                raise AssertionError(msg.format(left.values, right.values))
        else:
            assert_numpy_array_equal(left.get_values(), right.get_values(),
                                     check_dtype=check_dtype)
    elif is_interval_dtype(left) or is_interval_dtype(right):
        # TODO: big hack here
        l = pd.IntervalIndex(left)
        r = pd.IntervalIndex(right)
        assert_index_equal(l, r, obj='{0}.index'.format(obj))

    else:
        _testing.assert_almost_equal(left.get_values(), right.get_values(),
                                     check_less_precise=check_less_precise,
                                     check_dtype=check_dtype,
                                     obj='{0}'.format(obj))

    # metadata comparison
    if check_names:
        assert_attr_equal('name', left, right, obj=obj)

    if check_categorical:
        if is_categorical_dtype(left) or is_categorical_dtype(right):
            assert_categorical_equal(left.values, right.values,
                                     obj='{0} category'.format(obj))


# This could be refactored to use the NDFrame.equals method
def assert_frame_equal(left, right, check_dtype=True,
                       check_index_type='equiv',
                       check_column_type='equiv',
                       check_frame_type=True,
                       check_less_precise=False,
                       check_names=True,
                       by_blocks=False,
                       check_exact=False,
                       check_datetimelike_compat=False,
                       check_categorical=True,
                       check_like=False,
                       obj='DataFrame'):
    """Check that left and right DataFrame are equal.

    Parameters
    ----------
    left : DataFrame
    right : DataFrame
    check_dtype : bool, default True
        Whether to check the DataFrame dtype is identical.
    check_index_type : bool / string {'equiv'}, default False
        Whether to check the Index class, dtype and inferred_type
        are identical.
    check_column_type : bool / string {'equiv'}, default False
        Whether to check the columns class, dtype and inferred_type
        are identical.
    check_frame_type : bool, default False
        Whether to check the DataFrame class is identical.
    check_less_precise : bool or int, default False
        Specify comparison precision. Only used when check_exact is False.
        5 digits (False) or 3 digits (True) after decimal points are compared.
        If int, then specify the digits to compare
    check_names : bool, default True
        Whether to check the Index names attribute.
    by_blocks : bool, default False
        Specify how to compare internal data. If False, compare by columns.
        If True, compare by blocks.
    check_exact : bool, default False
        Whether to compare number exactly.
    check_datetimelike_compat : bool, default False
        Compare datetime-like which is comparable ignoring dtype.
    check_categorical : bool, default True
        Whether to compare internal Categorical exactly.
    check_like : bool, default False
        If true, ignore the order of rows & columns
    obj : str, default 'DataFrame'
        Specify object name being compared, internally used to show appropriate
        assertion message
    """

    # instance validation
    _check_isinstance(left, right, DataFrame)

    if check_frame_type:
        # ToDo: There are some tests using rhs is SparseDataFrame
        # lhs is DataFrame. Should use assert_class_equal in future
        assert isinstance(left, type(right))
        # assert_class_equal(left, right, obj=obj)

    # shape comparison
    if left.shape != right.shape:
        raise_assert_detail(obj,
                            'DataFrame shape mismatch',
                            '({0}, {1})'.format(*left.shape),
                            '({0}, {1})'.format(*right.shape))

    if check_like:
        left, right = left.reindex_like(right), right

    # index comparison
    assert_index_equal(left.index, right.index, exact=check_index_type,
                       check_names=check_names,
                       check_less_precise=check_less_precise,
                       check_exact=check_exact,
                       check_categorical=check_categorical,
                       obj='{0}.index'.format(obj))

    # column comparison
    assert_index_equal(left.columns, right.columns, exact=check_column_type,
                       check_names=check_names,
                       check_less_precise=check_less_precise,
                       check_exact=check_exact,
                       check_categorical=check_categorical,
                       obj='{0}.columns'.format(obj))

    # compare by blocks
    if by_blocks:
        rblocks = right.blocks
        lblocks = left.blocks
        for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))):
            assert dtype in lblocks
            assert dtype in rblocks
            assert_frame_equal(lblocks[dtype], rblocks[dtype],
                               check_dtype=check_dtype, obj='DataFrame.blocks')

    # compare by columns
    else:
        for i, col in enumerate(left.columns):
            assert col in right
            lcol = left.iloc[:, i]
            rcol = right.iloc[:, i]
            assert_series_equal(
                lcol, rcol, check_dtype=check_dtype,
                check_index_type=check_index_type,
                check_less_precise=check_less_precise,
                check_exact=check_exact, check_names=check_names,
                check_datetimelike_compat=check_datetimelike_compat,
                check_categorical=check_categorical,
                obj='DataFrame.iloc[:, {0}]'.format(i))


def assert_panelnd_equal(left, right,
                         check_dtype=True,
                         check_panel_type=False,
                         check_less_precise=False,
                         assert_func=assert_frame_equal,
                         check_names=False,
                         by_blocks=False,
                         obj='Panel'):
    """Check that left and right Panels are equal.

    Parameters
    ----------
    left : Panel (or nd)
    right : Panel (or nd)
    check_dtype : bool, default True
        Whether to check the Panel dtype is identical.
    check_panel_type : bool, default False
        Whether to check the Panel class is identical.
    check_less_precise : bool or int, default False
        Specify comparison precision. Only used when check_exact is False.
        5 digits (False) or 3 digits (True) after decimal points are compared.
        If int, then specify the digits to compare
    assert_func : function for comparing data
    check_names : bool, default True
        Whether to check the Index names attribute.
    by_blocks : bool, default False
        Specify how to compare internal data. If False, compare by columns.
        If True, compare by blocks.
    obj : str, default 'Panel'
        Specify the object name being compared, internally used to show
        the appropriate assertion message.
    """

    if check_panel_type:
        assert_class_equal(left, right, obj=obj)

    for axis in left._AXIS_ORDERS:
        left_ind = getattr(left, axis)
        right_ind = getattr(right, axis)
        assert_index_equal(left_ind, right_ind, check_names=check_names)

    if by_blocks:
        rblocks = right.blocks
        lblocks = left.blocks
        for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))):
            assert dtype in lblocks
            assert dtype in rblocks
            array_equivalent(lblocks[dtype].values, rblocks[dtype].values)
    else:

        # can potentially be slow
        for i, item in enumerate(left._get_axis(0)):
            assert item in right, "non-matching item (right) '%s'" % item
            litem = left.iloc[i]
            ritem = right.iloc[i]
            assert_func(litem, ritem, check_less_precise=check_less_precise)

        for i, item in enumerate(right._get_axis(0)):
            assert item in left, "non-matching item (left) '%s'" % item


# TODO: strangely check_names fails in py3 ?
_panel_frame_equal = partial(assert_frame_equal, check_names=False)
assert_panel_equal = partial(assert_panelnd_equal,
                             assert_func=_panel_frame_equal)
assert_panel4d_equal = partial(assert_panelnd_equal,
                               assert_func=assert_panel_equal)


# -----------------------------------------------------------------------------
# Sparse


def assert_sp_array_equal(left, right, check_dtype=True):
    """Check that the left and right SparseArray are equal.

    Parameters
    ----------
    left : SparseArray
    right : SparseArray
    check_dtype : bool, default True
        Whether to check the data dtype is identical.
    """

    _check_isinstance(left, right, pd.SparseArray)

    assert_numpy_array_equal(left.sp_values, right.sp_values,
                             check_dtype=check_dtype)

    # SparseIndex comparison
    assert isinstance(left.sp_index, pd._libs.sparse.SparseIndex)
    assert isinstance(right.sp_index, pd._libs.sparse.SparseIndex)

    if not left.sp_index.equals(right.sp_index):
        raise_assert_detail('SparseArray.index', 'index are not equal',
                            left.sp_index, right.sp_index)

    assert_attr_equal('fill_value', left, right)
    if check_dtype:
        assert_attr_equal('dtype', left, right)
    assert_numpy_array_equal(left.values, right.values,
                             check_dtype=check_dtype)


def assert_sp_series_equal(left, right, check_dtype=True, exact_indices=True,
                           check_series_type=True, check_names=True,
                           obj='SparseSeries'):
    """Check that the left and right SparseSeries are equal.

    Parameters
    ----------
    left : SparseSeries
    right : SparseSeries
    check_dtype : bool, default True
        Whether to check the Series dtype is identical.
    exact_indices : bool, default True
    check_series_type : bool, default True
        Whether to check the SparseSeries class is identical.
    check_names : bool, default True
        Whether to check the SparseSeries name attribute.
    obj : str, default 'SparseSeries'
        Specify the object name being compared, internally used to show
        the appropriate assertion message.
    """
    _check_isinstance(left, right, pd.SparseSeries)

    if check_series_type:
        assert_class_equal(left, right, obj=obj)

    assert_index_equal(left.index, right.index,
                       obj='{0}.index'.format(obj))

    assert_sp_array_equal(left.block.values, right.block.values)

    if check_names:
        assert_attr_equal('name', left, right)
    if check_dtype:
        assert_attr_equal('dtype', left, right)

    assert_numpy_array_equal(left.values, right.values)


def assert_sp_frame_equal(left, right, check_dtype=True, exact_indices=True,
                          check_frame_type=True, obj='SparseDataFrame'):
    """Check that the left and right SparseDataFrame are equal.

    Parameters
    ----------
    left : SparseDataFrame
    right : SparseDataFrame
    check_dtype : bool, default True
        Whether to check the Series dtype is identical.
    exact_indices : bool, default True
        SparseSeries SparseIndex objects must be exactly the same,
        otherwise just compare dense representations.
    check_frame_type : bool, default True
        Whether to check the SparseDataFrame class is identical.
    obj : str, default 'SparseDataFrame'
        Specify the object name being compared, internally used to show
        the appropriate assertion message.
    """
    _check_isinstance(left, right, pd.SparseDataFrame)

    if check_frame_type:
        assert_class_equal(left, right, obj=obj)

    assert_index_equal(left.index, right.index,
                       obj='{0}.index'.format(obj))
    assert_index_equal(left.columns, right.columns,
                       obj='{0}.columns'.format(obj))

    for col, series in compat.iteritems(left):
        assert (col in right)
        # trade-off?

        if exact_indices:
            assert_sp_series_equal(series, right[col],
                                   check_dtype=check_dtype)
        else:
            assert_series_equal(series.to_dense(), right[col].to_dense(),
                                check_dtype=check_dtype)

    assert_attr_equal('default_fill_value', left, right, obj=obj)

    # do I care?
    # assert(left.default_kind == right.default_kind)

    for col in right:
        assert (col in left)


def assert_sp_list_equal(left, right):
    assert isinstance(left, pd.SparseList)
    assert isinstance(right, pd.SparseList)

    assert_sp_array_equal(left.to_array(), right.to_array())

# -----------------------------------------------------------------------------
# Others


def assert_contains_all(iterable, dic):
    for k in iterable:
        assert k in dic, "Did not contain item: '%r'" % k


def assert_copy(iter1, iter2, **eql_kwargs):
    """
    iter1, iter2: iterables that produce elements
    comparable with assert_almost_equal

    Checks that the elements are equal, but not
    the same object. (Does not check that items
    in sequences are also not the same object)
    """
    for elem1, elem2 in zip(iter1, iter2):
        assert_almost_equal(elem1, elem2, **eql_kwargs)
        assert elem1 is not elem2, ("Expected object %r and "
                                    "object %r to be different "
                                    "objects, were same."
                                    % (type(elem1), type(elem2)))


def getCols(k):
    return string.ascii_uppercase[:k]


def getArangeMat():
    return np.arange(N * K).reshape((N, K))


# make index
def makeStringIndex(k=10, name=None):
    return Index(rands_array(nchars=10, size=k), name=name)


def makeUnicodeIndex(k=10, name=None):
    return Index(randu_array(nchars=10, size=k), name=name)


def makeCategoricalIndex(k=10, n=3, name=None):
    """ make a length k index or n categories """
    x = rands_array(nchars=4, size=n)
    return CategoricalIndex(np.random.choice(x, k), name=name)


def makeIntervalIndex(k=10, name=None):
    """ make a length k IntervalIndex """
    x = np.linspace(0, 100, num=(k + 1))
    return IntervalIndex.from_breaks(x, name=name)


def makeBoolIndex(k=10, name=None):
    if k == 1:
        return Index([True], name=name)
    elif k == 2:
        return Index([False, True], name=name)
    return Index([False, True] + [False] * (k - 2), name=name)


def makeIntIndex(k=10, name=None):
    return Index(lrange(k), name=name)


def makeUIntIndex(k=10, name=None):
    return Index([2**63 + i for i in lrange(k)], name=name)


def makeRangeIndex(k=10, name=None):
    return RangeIndex(0, k, 1, name=name)


def makeFloatIndex(k=10, name=None):
    values = sorted(np.random.random_sample(k)) - np.random.random_sample(1)
    return Index(values * (10 ** np.random.randint(0, 9)), name=name)


def makeDateIndex(k=10, freq='B', name=None):
    dt = datetime(2000, 1, 1)
    dr = bdate_range(dt, periods=k, freq=freq, name=name)
    return DatetimeIndex(dr, name=name)


def makeTimedeltaIndex(k=10, freq='D', name=None):
    return TimedeltaIndex(start='1 day', periods=k, freq=freq, name=name)


def makePeriodIndex(k=10, name=None):
    dt = datetime(2000, 1, 1)
    dr = PeriodIndex(start=dt, periods=k, freq='B', name=name)
    return dr


def all_index_generator(k=10):
    """Generator which can be iterated over to get instances of all the various
    index classes.

    Parameters
    ----------
    k: length of each of the index instances
    """
    all_make_index_funcs = [makeIntIndex, makeFloatIndex, makeStringIndex,
                            makeUnicodeIndex, makeDateIndex, makePeriodIndex,
                            makeTimedeltaIndex, makeBoolIndex,
                            makeCategoricalIndex]
    for make_index_func in all_make_index_funcs:
        yield make_index_func(k=k)


def all_timeseries_index_generator(k=10):
    """Generator which can be iterated over to get instances of all the classes
    which represent time-seires.

    Parameters
    ----------
    k: length of each of the index instances
    """
    make_index_funcs = [makeDateIndex, makePeriodIndex, makeTimedeltaIndex]
    for make_index_func in make_index_funcs:
        yield make_index_func(k=k)


# make series
def makeFloatSeries(name=None):
    index = makeStringIndex(N)
    return Series(randn(N), index=index, name=name)


def makeStringSeries(name=None):
    index = makeStringIndex(N)
    return Series(randn(N), index=index, name=name)


def makeObjectSeries(name=None):
    dateIndex = makeDateIndex(N)
    dateIndex = Index(dateIndex, dtype=object)
    index = makeStringIndex(N)
    return Series(dateIndex, index=index, name=name)


def getSeriesData():
    index = makeStringIndex(N)
    return dict((c, Series(randn(N), index=index)) for c in getCols(K))


def makeTimeSeries(nper=None, freq='B', name=None):
    if nper is None:
        nper = N
    return Series(randn(nper), index=makeDateIndex(nper, freq=freq), name=name)


def makePeriodSeries(nper=None, name=None):
    if nper is None:
        nper = N
    return Series(randn(nper), index=makePeriodIndex(nper), name=name)


def getTimeSeriesData(nper=None, freq='B'):
    return dict((c, makeTimeSeries(nper, freq)) for c in getCols(K))


def getPeriodData(nper=None):
    return dict((c, makePeriodSeries(nper)) for c in getCols(K))


# make frame
def makeTimeDataFrame(nper=None, freq='B'):
    data = getTimeSeriesData(nper, freq)
    return DataFrame(data)


def makeDataFrame():
    data = getSeriesData()
    return DataFrame(data)


def getMixedTypeDict():
    index = Index(['a', 'b', 'c', 'd', 'e'])

    data = {
        'A': [0., 1., 2., 3., 4.],
        'B': [0., 1., 0., 1., 0.],
        'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
        'D': bdate_range('1/1/2009', periods=5)
    }

    return index, data


def makeMixedDataFrame():
    return DataFrame(getMixedTypeDict()[1])


def makePeriodFrame(nper=None):
    data = getPeriodData(nper)
    return DataFrame(data)


def makePanel(nper=None):
    cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]]
    data = dict((c, makeTimeDataFrame(nper)) for c in cols)
    return Panel.fromDict(data)


def makePeriodPanel(nper=None):
    cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]]
    data = dict((c, makePeriodFrame(nper)) for c in cols)
    return Panel.fromDict(data)


def makePanel4D(nper=None):
    with warnings.catch_warnings(record=True):
        d = dict(l1=makePanel(nper), l2=makePanel(nper),
                 l3=makePanel(nper))
    return Panel4D(d)


def makeCustomIndex(nentries, nlevels, prefix='#', names=False, ndupe_l=None,
                    idx_type=None):
    """Create an index/multindex with given dimensions, levels, names, etc'

    nentries - number of entries in index
    nlevels - number of levels (> 1 produces multindex)
    prefix - a string prefix for labels
    names - (Optional), bool or list of strings. if True will use default
       names, if false will use no names, if a list is given, the name of
       each level in the index will be taken from the list.
    ndupe_l - (Optional), list of ints, the number of rows for which the
       label will repeated at the corresponding level, you can specify just
       the first few, the rest will use the default ndupe_l of 1.
       len(ndupe_l) <= nlevels.
    idx_type - "i"/"f"/"s"/"u"/"dt"/"p"/"td".
       If idx_type is not None, `idx_nlevels` must be 1.
       "i"/"f" creates an integer/float index,
       "s"/"u" creates a string/unicode index
       "dt" create a datetime index.
       "td" create a datetime index.

        if unspecified, string labels will be generated.
    """

    if ndupe_l is None:
        ndupe_l = [1] * nlevels
    assert (is_sequence(ndupe_l) and len(ndupe_l) <= nlevels)
    assert (names is None or names is False or
            names is True or len(names) is nlevels)
    assert idx_type is None or \
        (idx_type in ('i', 'f', 's', 'u', 'dt', 'p', 'td') and nlevels == 1)

    if names is True:
        # build default names
        names = [prefix + str(i) for i in range(nlevels)]
    if names is False:
        # pass None to index constructor for no name
        names = None

    # make singelton case uniform
    if isinstance(names, compat.string_types) and nlevels == 1:
        names = [names]

    # specific 1D index type requested?
    idx_func = dict(i=makeIntIndex, f=makeFloatIndex,
                    s=makeStringIndex, u=makeUnicodeIndex,
                    dt=makeDateIndex, td=makeTimedeltaIndex,
                    p=makePeriodIndex).get(idx_type)
    if idx_func:
        idx = idx_func(nentries)
        # but we need to fill in the name
        if names:
            idx.name = names[0]
        return idx
    elif idx_type is not None:
        raise ValueError('"%s" is not a legal value for `idx_type`, use  '
                         '"i"/"f"/"s"/"u"/"dt/"p"/"td".' % idx_type)

    if len(ndupe_l) < nlevels:
        ndupe_l.extend([1] * (nlevels - len(ndupe_l)))
    assert len(ndupe_l) == nlevels

    assert all([x > 0 for x in ndupe_l])

    tuples = []
    for i in range(nlevels):
        def keyfunc(x):
            import re
            numeric_tuple = re.sub("[^\d_]_?", "", x).split("_")
            return lmap(int, numeric_tuple)

        # build a list of lists to create the index from
        div_factor = nentries // ndupe_l[i] + 1
        cnt = Counter()
        for j in range(div_factor):
            label = prefix + '_l%d_g' % i + str(j)
            cnt[label] = ndupe_l[i]
        # cute Counter trick
        result = list(sorted(cnt.elements(), key=keyfunc))[:nentries]
        tuples.append(result)

    tuples = lzip(*tuples)

    # convert tuples to index
    if nentries == 1:
        index = Index(tuples[0], name=names[0])
    else:
        index = MultiIndex.from_tuples(tuples, names=names)
    return index


def makeCustomDataframe(nrows, ncols, c_idx_names=True, r_idx_names=True,
                        c_idx_nlevels=1, r_idx_nlevels=1, data_gen_f=None,
                        c_ndupe_l=None, r_ndupe_l=None, dtype=None,
                        c_idx_type=None, r_idx_type=None):
    """
   nrows,  ncols - number of data rows/cols
   c_idx_names, idx_names  - False/True/list of strings,  yields No names ,
        default names or  uses the provided names for the levels of the
        corresponding  index. You can provide a single string when
        c_idx_nlevels ==1.
   c_idx_nlevels - number of levels in columns index. > 1 will yield MultiIndex
   r_idx_nlevels - number of levels in rows index. > 1 will yield MultiIndex
   data_gen_f - a function f(row,col) which return the data value
        at that position, the default generator used yields values of the form
        "RxCy" based on position.
   c_ndupe_l, r_ndupe_l - list of integers, determines the number
        of duplicates for each label at a given level of the corresponding
        index. The default `None` value produces a multiplicity of 1 across
        all levels, i.e. a unique index. Will accept a partial list of length
        N < idx_nlevels, for just the first N levels. If ndupe doesn't divide
        nrows/ncol, the last label might have lower multiplicity.
   dtype - passed to the DataFrame constructor as is, in case you wish to
        have more control in conjuncion with a custom `data_gen_f`
   r_idx_type, c_idx_type -  "i"/"f"/"s"/"u"/"dt"/"td".
       If idx_type is not None, `idx_nlevels` must be 1.
       "i"/"f" creates an integer/float index,
       "s"/"u" creates a string/unicode index
       "dt" create a datetime index.
       "td" create a timedelta index.

        if unspecified, string labels will be generated.

    Examples:

    # 5 row, 3 columns, default names on both, single index on both axis
    >> makeCustomDataframe(5,3)

    # make the data a random int between 1 and 100
    >> mkdf(5,3,data_gen_f=lambda r,c:randint(1,100))

    # 2-level multiindex on rows with each label duplicated
    # twice on first level, default names on both axis, single
    # index on both axis
    >> a=makeCustomDataframe(5,3,r_idx_nlevels=2,r_ndupe_l=[2])

    # DatetimeIndex on row, index with unicode labels on columns
    # no names on either axis
    >> a=makeCustomDataframe(5,3,c_idx_names=False,r_idx_names=False,
                             r_idx_type="dt",c_idx_type="u")

    # 4-level multindex on rows with names provided, 2-level multindex
    # on columns with default labels and default names.
    >> a=makeCustomDataframe(5,3,r_idx_nlevels=4,
                             r_idx_names=["FEE","FI","FO","FAM"],
                             c_idx_nlevels=2)

    >> a=mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)
    """

    assert c_idx_nlevels > 0
    assert r_idx_nlevels > 0
    assert r_idx_type is None or \
        (r_idx_type in ('i', 'f', 's',
                        'u', 'dt', 'p', 'td') and r_idx_nlevels == 1)
    assert c_idx_type is None or \
        (c_idx_type in ('i', 'f', 's',
                        'u', 'dt', 'p', 'td') and c_idx_nlevels == 1)

    columns = makeCustomIndex(ncols, nlevels=c_idx_nlevels, prefix='C',
                              names=c_idx_names, ndupe_l=c_ndupe_l,
                              idx_type=c_idx_type)
    index = makeCustomIndex(nrows, nlevels=r_idx_nlevels, prefix='R',
                            names=r_idx_names, ndupe_l=r_ndupe_l,
                            idx_type=r_idx_type)

    # by default, generate data based on location
    if data_gen_f is None:
        data_gen_f = lambda r, c: "R%dC%d" % (r, c)

    data = [[data_gen_f(r, c) for c in range(ncols)] for r in range(nrows)]

    return DataFrame(data, index, columns, dtype=dtype)


def _create_missing_idx(nrows, ncols, density, random_state=None):
    if random_state is None:
        random_state = np.random
    else:
        random_state = np.random.RandomState(random_state)

    # below is cribbed from scipy.sparse
    size = int(np.round((1 - density) * nrows * ncols))
    # generate a few more to ensure unique values
    min_rows = 5
    fac = 1.02
    extra_size = min(size + min_rows, fac * size)

    def _gen_unique_rand(rng, _extra_size):
        ind = rng.rand(int(_extra_size))
        return np.unique(np.floor(ind * nrows * ncols))[:size]

    ind = _gen_unique_rand(random_state, extra_size)
    while ind.size < size:
        extra_size *= 1.05
        ind = _gen_unique_rand(random_state, extra_size)

    j = np.floor(ind * 1. / nrows).astype(int)
    i = (ind - j * nrows).astype(int)
    return i.tolist(), j.tolist()


def makeMissingCustomDataframe(nrows, ncols, density=.9, random_state=None,
                               c_idx_names=True, r_idx_names=True,
                               c_idx_nlevels=1, r_idx_nlevels=1,
                               data_gen_f=None,
                               c_ndupe_l=None, r_ndupe_l=None, dtype=None,
                               c_idx_type=None, r_idx_type=None):
    """
    Parameters
    ----------
    Density : float, optional
        Float in (0, 1) that gives the percentage of non-missing numbers in
        the DataFrame.
    random_state : {np.random.RandomState, int}, optional
        Random number generator or random seed.

    See makeCustomDataframe for descriptions of the rest of the parameters.
    """
    df = makeCustomDataframe(nrows, ncols, c_idx_names=c_idx_names,
                             r_idx_names=r_idx_names,
                             c_idx_nlevels=c_idx_nlevels,
                             r_idx_nlevels=r_idx_nlevels,
                             data_gen_f=data_gen_f,
                             c_ndupe_l=c_ndupe_l, r_ndupe_l=r_ndupe_l,
                             dtype=dtype, c_idx_type=c_idx_type,
                             r_idx_type=r_idx_type)

    i, j = _create_missing_idx(nrows, ncols, density, random_state)
    df.values[i, j] = np.nan
    return df


def makeMissingDataframe(density=.9, random_state=None):
    df = makeDataFrame()
    i, j = _create_missing_idx(*df.shape, density=density,
                               random_state=random_state)
    df.values[i, j] = np.nan
    return df


def add_nans(panel):
    I, J, N = panel.shape
    for i, item in enumerate(panel.items):
        dm = panel[item]
        for j, col in enumerate(dm.columns):
            dm[col][:i + j] = np.NaN
    return panel


def add_nans_panel4d(panel4d):
    for l, label in enumerate(panel4d.labels):
        panel = panel4d[label]
        add_nans(panel)
    return panel4d


class TestSubDict(dict):

    def __init__(self, *args, **kwargs):
        dict.__init__(self, *args, **kwargs)


# Dependency checker when running tests.
#
# Copied this from nipy/nipype
# Copyright of respective developers, License: BSD-3
def skip_if_no_package(pkg_name, min_version=None, max_version=None,
                       app='pandas', checker=LooseVersion):
    """Check that the min/max version of the required package is installed.

    If the package check fails, the test is automatically skipped.

    Parameters
    ----------
    pkg_name : string
        Name of the required package.
    min_version : string, optional
        Minimal version number for required package.
    max_version : string, optional
        Max version number for required package.
    app : string, optional
        Application that is performing the check. For instance, the
        name of the tutorial being executed that depends on specific
        packages.
    checker : object, optional
        The class that will perform the version checking. Default is
        distutils.version.LooseVersion.

    Examples
    --------
    package_check('numpy', '1.3')

    """

    import pytest
    if app:
        msg = '%s requires %s' % (app, pkg_name)
    else:
        msg = 'module requires %s' % pkg_name
    if min_version:
        msg += ' with version >= %s' % (min_version,)
    if max_version:
        msg += ' with version < %s' % (max_version,)
    try:
        mod = __import__(pkg_name)
    except ImportError:
        mod = None
    try:
        have_version = mod.__version__
    except AttributeError:
        pytest.skip('Cannot find version for %s' % pkg_name)
    if min_version and checker(have_version) < checker(min_version):
        pytest.skip(msg)
    if max_version and checker(have_version) >= checker(max_version):
        pytest.skip(msg)


def optional_args(decorator):
    """allows a decorator to take optional positional and keyword arguments.
    Assumes that taking a single, callable, positional argument means that
    it is decorating a function, i.e. something like this::

        @my_decorator
        def function(): pass

    Calls decorator with decorator(f, *args, **kwargs)"""

    @wraps(decorator)
    def wrapper(*args, **kwargs):
        def dec(f):
            return decorator(f, *args, **kwargs)

        is_decorating = not kwargs and len(args) == 1 and callable(args[0])
        if is_decorating:
            f = args[0]
            args = []
            return dec(f)
        else:
            return dec

    return wrapper


# skip tests on exceptions with this message
_network_error_messages = (
    # 'urlopen error timed out',
    # 'timeout: timed out',
    # 'socket.timeout: timed out',
    'timed out',
    'Server Hangup',
    'HTTP Error 503: Service Unavailable',
    '502: Proxy Error',
    'HTTP Error 502: internal error',
    'HTTP Error 502',
    'HTTP Error 503',
    'HTTP Error 403',
    'HTTP Error 400',
    'Temporary failure in name resolution',
    'Name or service not known',
    'Connection refused',
    'certificate verify',
)

# or this e.errno/e.reason.errno
_network_errno_vals = (
    101,  # Network is unreachable
    111,  # Connection refused
    110,  # Connection timed out
    104,  # Connection reset Error
    54,   # Connection reset by peer
    60,   # urllib.error.URLError: [Errno 60] Connection timed out
)

# Both of the above shouldn't mask real issues such as 404's
# or refused connections (changed DNS).
# But some tests (test_data yahoo) contact incredibly flakey
# servers.

# and conditionally raise on these exception types
_network_error_classes = (IOError, httplib.HTTPException)

if sys.version_info >= (3, 3):
    _network_error_classes += (TimeoutError,)  # noqa


def can_connect(url, error_classes=_network_error_classes):
    """Try to connect to the given url. True if succeeds, False if IOError
    raised

    Parameters
    ----------
    url : basestring
        The URL to try to connect to

    Returns
    -------
    connectable : bool
        Return True if no IOError (unable to connect) or URLError (bad url) was
        raised
    """
    try:
        with urlopen(url):
            pass
    except error_classes:
        return False
    else:
        return True


@optional_args
def network(t, url="http://www.google.com",
            raise_on_error=_RAISE_NETWORK_ERROR_DEFAULT,
            check_before_test=False,
            error_classes=_network_error_classes,
            skip_errnos=_network_errno_vals,
            _skip_on_messages=_network_error_messages,
            ):
    """
    Label a test as requiring network connection and, if an error is
    encountered, only raise if it does not find a network connection.

    In comparison to ``network``, this assumes an added contract to your test:
    you must assert that, under normal conditions, your test will ONLY fail if
    it does not have network connectivity.

    You can call this in 3 ways: as a standard decorator, with keyword
    arguments, or with a positional argument that is the url to check.

    Parameters
    ----------
    t : callable
        The test requiring network connectivity.
    url : path
        The url to test via ``pandas.io.common.urlopen`` to check
        for connectivity. Defaults to 'http://www.google.com'.
    raise_on_error : bool
        If True, never catches errors.
    check_before_test : bool
        If True, checks connectivity before running the test case.
    error_classes : tuple or Exception
        error classes to ignore. If not in ``error_classes``, raises the error.
        defaults to IOError. Be careful about changing the error classes here.
    skip_errnos : iterable of int
        Any exception that has .errno or .reason.erno set to one
        of these values will be skipped with an appropriate
        message.
    _skip_on_messages: iterable of string
        any exception e for which one of the strings is
        a substring of str(e) will be skipped with an appropriate
        message. Intended to supress errors where an errno isn't available.

    Notes
    -----
    * ``raise_on_error`` supercedes ``check_before_test``

    Returns
    -------
    t : callable
        The decorated test ``t``, with checks for connectivity errors.

    Example
    -------

    Tests decorated with @network will fail if it's possible to make a network
    connection to another URL (defaults to google.com)::

      >>> from pandas.util.testing import network
      >>> from pandas.io.common import urlopen
      >>> @network
      ... def test_network():
      ...     with urlopen("rabbit://bonanza.com"):
      ...         pass
      Traceback
         ...
      URLError: <urlopen error unknown url type: rabit>

      You can specify alternative URLs::

        >>> @network("http://www.yahoo.com")
        ... def test_something_with_yahoo():
        ...    raise IOError("Failure Message")
        >>> test_something_with_yahoo()
        Traceback (most recent call last):
            ...
        IOError: Failure Message

    If you set check_before_test, it will check the url first and not run the
    test on failure::

        >>> @network("failing://url.blaher", check_before_test=True)
        ... def test_something():
        ...     print("I ran!")
        ...     raise ValueError("Failure")
        >>> test_something()
        Traceback (most recent call last):
            ...

    Errors not related to networking will always be raised.
    """
    from pytest import skip
    t.network = True

    @wraps(t)
    def wrapper(*args, **kwargs):
        if check_before_test and not raise_on_error:
            if not can_connect(url, error_classes):
                skip()
        try:
            return t(*args, **kwargs)
        except Exception as e:
            errno = getattr(e, 'errno', None)
            if not errno and hasattr(errno, "reason"):
                errno = getattr(e.reason, 'errno', None)

            if errno in skip_errnos:
                skip("Skipping test due to known errno"
                     " and error %s" % e)

            try:
                e_str = traceback.format_exc(e)
            except:
                e_str = str(e)

            if any([m.lower() in e_str.lower() for m in _skip_on_messages]):
                skip("Skipping test because exception "
                     "message is known and error %s" % e)

            if not isinstance(e, error_classes):
                raise

            if raise_on_error or can_connect(url, error_classes):
                raise
            else:
                skip("Skipping test due to lack of connectivity"
                     " and error %s" % e)

    return wrapper


with_connectivity_check = network


class SimpleMock(object):

    """
    Poor man's mocking object

    Note: only works for new-style classes, assumes  __getattribute__ exists.

    >>> a = type("Duck",(),{})
    >>> a.attr1,a.attr2 ="fizz","buzz"
    >>> b = SimpleMock(a,"attr1","bar")
    >>> b.attr1 == "bar" and b.attr2 == "buzz"
    True
    >>> a.attr1 == "fizz" and a.attr2 == "buzz"
    True
    """

    def __init__(self, obj, *args, **kwds):
        assert(len(args) % 2 == 0)
        attrs = kwds.get("attrs", {})
        for k, v in zip(args[::2], args[1::2]):
            # dict comprehensions break 2.6
            attrs[k] = v
        self.attrs = attrs
        self.obj = obj

    def __getattribute__(self, name):
        attrs = object.__getattribute__(self, "attrs")
        obj = object.__getattribute__(self, "obj")
        return attrs.get(name, type(obj).__getattribute__(obj, name))


@contextmanager
def stdin_encoding(encoding=None):
    """
    Context manager for running bits of code while emulating an arbitrary
    stdin encoding.

    >>> import sys
    >>> _encoding = sys.stdin.encoding
    >>> with stdin_encoding('AES'): sys.stdin.encoding
    'AES'
    >>> sys.stdin.encoding==_encoding
    True

    """
    import sys

    _stdin = sys.stdin
    sys.stdin = SimpleMock(sys.stdin, "encoding", encoding)
    yield
    sys.stdin = _stdin


def assert_raises_regex(_exception, _regexp, _callable=None,
                        *args, **kwargs):
    """
    Check that the specified Exception is raised and that the error message
    matches a given regular expression pattern. This may be a regular
    expression object or a string containing a regular expression suitable
    for use by `re.search()`.

    This is a port of the `assertRaisesRegexp` function from unittest in
    Python 2.7. However, with our migration to `pytest`, please refrain
    from using this. Instead, use the following paradigm:

    with pytest.raises(_exception) as exc_info:
       func(*args, **kwargs)
    exc_info.matches(reg_exp)

    Examples
    --------
    >>> assert_raises_regex(ValueError, 'invalid literal for.*XYZ', int, 'XYZ')
    >>> import re
    >>> assert_raises_regex(ValueError, re.compile('literal'), int, 'XYZ')

    If an exception of a different type is raised, it bubbles up.

    >>> assert_raises_regex(TypeError, 'literal', int, 'XYZ')
    Traceback (most recent call last):
        ...
    ValueError: invalid literal for int() with base 10: 'XYZ'
    >>> dct = dict()
    >>> assert_raises_regex(KeyError, 'pear', dct.__getitem__, 'apple')
    Traceback (most recent call last):
        ...
    AssertionError: "pear" does not match "'apple'"

    You can also use this in a with statement.
    >>> with assert_raises_regex(TypeError, 'unsupported operand type\(s\)'):
    ...     1 + {}
    >>> with assert_raises_regex(TypeError, 'banana'):
    ...     'apple'[0] = 'b'
    Traceback (most recent call last):
        ...
    AssertionError: "banana" does not match "'str' object does not support \
item assignment"
    """
    manager = _AssertRaisesContextmanager(exception=_exception, regexp=_regexp)
    if _callable is not None:
        with manager:
            _callable(*args, **kwargs)
    else:
        return manager


class _AssertRaisesContextmanager(object):
    """
    Context manager behind `assert_raises_regex`.
    """

    def __init__(self, exception, regexp=None):
        """
        Initialize an _AssertRaisesContextManager instance.

        Parameters
        ----------
        exception : class
            The expected Exception class.
        regexp : str, default None
            The regex to compare against the Exception message.
        """

        self.exception = exception

        if regexp is not None and not hasattr(regexp, "search"):
            regexp = re.compile(regexp, re.DOTALL)

        self.regexp = regexp

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, trace_back):
        expected = self.exception

        if not exc_type:
            exp_name = getattr(expected, "__name__", str(expected))
            raise AssertionError("{0} not raised.".format(exp_name))

        return self.exception_matches(exc_type, exc_value, trace_back)

    def exception_matches(self, exc_type, exc_value, trace_back):
        """
        Check that the Exception raised matches the expected Exception
        and expected error message regular expression.

        Parameters
        ----------
        exc_type : class
            The type of Exception raised.
        exc_value : Exception
            The instance of `exc_type` raised.
        trace_back : stack trace object
            The traceback object associated with `exc_value`.

        Returns
        -------
        is_matched : bool
            Whether or not the Exception raised matches the expected
            Exception class and expected error message regular expression.

        Raises
        ------
        AssertionError : The error message provided does not match
                         the expected error message regular expression.
        """

        if issubclass(exc_type, self.exception):
            if self.regexp is not None:
                val = str(exc_value)

                if not self.regexp.search(val):
                    e = AssertionError('"%s" does not match "%s"' %
                                       (self.regexp.pattern, str(val)))
                    raise_with_traceback(e, trace_back)

            return True
        else:
            # Failed, so allow Exception to bubble up.
            return False


@contextmanager
def assert_produces_warning(expected_warning=Warning, filter_level="always",
                            clear=None, check_stacklevel=True):
    """
    Context manager for running code that expects to raise (or not raise)
    warnings.  Checks that code raises the expected warning and only the
    expected warning. Pass ``False`` or ``None`` to check that it does *not*
    raise a warning. Defaults to ``exception.Warning``, baseclass of all
    Warnings. (basically a wrapper around ``warnings.catch_warnings``).

    >>> import warnings
    >>> with assert_produces_warning():
    ...     warnings.warn(UserWarning())
    ...
    >>> with assert_produces_warning(False):
    ...     warnings.warn(RuntimeWarning())
    ...
    Traceback (most recent call last):
        ...
    AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
    >>> with assert_produces_warning(UserWarning):
    ...     warnings.warn(RuntimeWarning())
    Traceback (most recent call last):
        ...
    AssertionError: Did not see expected warning of class 'UserWarning'.

    ..warn:: This is *not* thread-safe.
    """
    with warnings.catch_warnings(record=True) as w:

        if clear is not None:
            # make sure that we are clearning these warnings
            # if they have happened before
            # to guarantee that we will catch them
            if not is_list_like(clear):
                clear = [clear]
            for m in clear:
                try:
                    m.__warningregistry__.clear()
                except:
                    pass

        saw_warning = False
        warnings.simplefilter(filter_level)
        yield w
        extra_warnings = []

        for actual_warning in w:
            if (expected_warning and issubclass(actual_warning.category,
                                                expected_warning)):
                saw_warning = True

                if check_stacklevel and issubclass(actual_warning.category,
                                                   (FutureWarning,
                                                    DeprecationWarning)):
                    from inspect import getframeinfo, stack
                    caller = getframeinfo(stack()[2][0])
                    msg = ("Warning not set with correct stacklevel. "
                           "File where warning is raised: {0} != {1}. "
                           "Warning message: {2}".format(
                               actual_warning.filename, caller.filename,
                               actual_warning.message))
                    assert actual_warning.filename == caller.filename, msg
            else:
                extra_warnings.append(actual_warning.category.__name__)
        if expected_warning:
            assert saw_warning, ("Did not see expected warning of class %r."
                                 % expected_warning.__name__)
        assert not extra_warnings, ("Caused unexpected warning(s): %r."
                                    % extra_warnings)


class RNGContext(object):
    """
    Context manager to set the numpy random number generator speed. Returns
    to the original value upon exiting the context manager.

    Parameters
    ----------
    seed : int
        Seed for numpy.random.seed

    Examples
    --------

    with RNGContext(42):
        np.random.randn()
    """

    def __init__(self, seed):
        self.seed = seed

    def __enter__(self):

        self.start_state = np.random.get_state()
        np.random.seed(self.seed)

    def __exit__(self, exc_type, exc_value, traceback):

        np.random.set_state(self.start_state)


@contextmanager
def use_numexpr(use, min_elements=expr._MIN_ELEMENTS):
    olduse = expr._USE_NUMEXPR
    oldmin = expr._MIN_ELEMENTS
    expr.set_use_numexpr(use)
    expr._MIN_ELEMENTS = min_elements
    yield
    expr._MIN_ELEMENTS = oldmin
    expr.set_use_numexpr(olduse)


def test_parallel(num_threads=2, kwargs_list=None):
    """Decorator to run the same function multiple times in parallel.

    Parameters
    ----------
    num_threads : int, optional
        The number of times the function is run in parallel.
    kwargs_list : list of dicts, optional
        The list of kwargs to update original
        function kwargs on different threads.
    Notes
    -----
    This decorator does not pass the return value of the decorated function.

    Original from scikit-image:

    https://github.com/scikit-image/scikit-image/pull/1519

    """

    assert num_threads > 0
    has_kwargs_list = kwargs_list is not None
    if has_kwargs_list:
        assert len(kwargs_list) == num_threads
    import threading

    def wrapper(func):
        @wraps(func)
        def inner(*args, **kwargs):
            if has_kwargs_list:
                update_kwargs = lambda i: dict(kwargs, **kwargs_list[i])
            else:
                update_kwargs = lambda i: kwargs
            threads = []
            for i in range(num_threads):
                updated_kwargs = update_kwargs(i)
                thread = threading.Thread(target=func, args=args,
                                          kwargs=updated_kwargs)
                threads.append(thread)
            for thread in threads:
                thread.start()
            for thread in threads:
                thread.join()
        return inner
    return wrapper


class SubclassedSeries(Series):
    _metadata = ['testattr', 'name']

    @property
    def _constructor(self):
        return SubclassedSeries

    @property
    def _constructor_expanddim(self):
        return SubclassedDataFrame


class SubclassedDataFrame(DataFrame):
    _metadata = ['testattr']

    @property
    def _constructor(self):
        return SubclassedDataFrame

    @property
    def _constructor_sliced(self):
        return SubclassedSeries


class SubclassedSparseSeries(pd.SparseSeries):
    _metadata = ['testattr']

    @property
    def _constructor(self):
        return SubclassedSparseSeries

    @property
    def _constructor_expanddim(self):
        return SubclassedSparseDataFrame


class SubclassedSparseDataFrame(pd.SparseDataFrame):
    _metadata = ['testattr']

    @property
    def _constructor(self):
        return SubclassedSparseDataFrame

    @property
    def _constructor_sliced(self):
        return SubclassedSparseSeries


class SubclassedCategorical(Categorical):

    @property
    def _constructor(self):
        return SubclassedCategorical


@contextmanager
def patch(ob, attr, value):
    """Temporarily patch an attribute of an object.

    Parameters
    ----------
    ob : any
        The object to patch. This must support attribute assignment for `attr`.
    attr : str
        The name of the attribute to patch.
    value : any
        The temporary attribute to assign.

    Examples
    --------
    >>> class C(object):
    ...     attribute = 'original'
    ...
    >>> C.attribute
    'original'
    >>> with patch(C, 'attribute', 'patched'):
    ...     in_context = C.attribute
    ...
    >>> in_context
    'patched'
    >>> C.attribute  # the value is reset when the context manager exists
    'original'

    Correctly replaces attribute when the manager exits with an exception.
    >>> with patch(C, 'attribute', 'patched'):
    ...     in_context = C.attribute
    ...     raise ValueError()
    Traceback (most recent call last):
       ...
    ValueError
    >>> in_context
    'patched'
    >>> C.attribute
    'original'
    """
    noattr = object()  # mark that the attribute never existed
    old = getattr(ob, attr, noattr)
    setattr(ob, attr, value)
    try:
        yield
    finally:
        if old is noattr:
            delattr(ob, attr)
        else:
            setattr(ob, attr, old)


@contextmanager
def set_timezone(tz):
    """Context manager for temporarily setting a timezone.

    Parameters
    ----------
    tz : str
        A string representing a valid timezone.

    Examples
    --------

    >>> from datetime import datetime
    >>> from dateutil.tz import tzlocal
    >>> tzlocal().tzname(datetime.now())
    'IST'

    >>> with set_timezone('US/Eastern'):
    ...     tzlocal().tzname(datetime.now())
    ...
    'EDT'
    """
    if is_platform_windows():
        import pytest
        pytest.skip("timezone setting not supported on windows")

    import os
    import time

    def setTZ(tz):
        if tz is None:
            try:
                del os.environ['TZ']
            except:
                pass
        else:
            os.environ['TZ'] = tz
            time.tzset()

    orig_tz = os.environ.get('TZ')
    setTZ(tz)
    try:
        yield
    finally:
        setTZ(orig_tz)
