def test_pickle():
    import tempfile
    from numpy.testing import assert_equal
    tmpdir = tempfile.mkdtemp(prefix='pickle')
    a = lrange(10)
    save_pickle(a, tmpdir+'/res.pkl')
    b = load_pickle(tmpdir+'/res.pkl')
    assert_equal(a, b)

    #cleanup, tested on Windows
    try:
        import os
        os.remove(tmpdir+'/res.pkl')
        os.rmdir(tmpdir)
    except (OSError, IOError):
        pass
    assert not os.path.exists(tmpdir)

    #test with file handle
    fh = BytesIO()
    save_pickle(a, fh)
    fh.seek(0,0)
    c = load_pickle(fh)
    fh.close()
    assert_equal(a,b)

 def test_pickle(self):
     fh = BytesIO()
     #test wrapped results load save pickle
     self.res.save(fh)
     fh.seek(0,0)
     res_unpickled = self.res.__class__.load(fh)
     assert_(type(res_unpickled) is type(self.res))

def test_stata_writer_pandas():
    buf = BytesIO()
    dta = macrodata.load_pandas().data
    dta4 = dta.copy()
    for col in ('year','quarter'):
        dta[col] = dta[col].astype(np.int64)
        dta4[col] = dta4[col].astype(np.int32)
    # dta is int64 'i8'  given to Stata writer
    with pytest.warns(FutureWarning):
        writer = StataWriter(buf, dta)

    with warnings.catch_warnings(record=True) as w:
        writer.write_file()
        assert len(w) == 0
    buf.seek(0)

    with pytest.warns(FutureWarning):
        dta2 = genfromdta(buf)

    dta5 = DataFrame.from_records(dta2)
    # dta2 is int32 'i4'  returned from Stata reader

    if dta5.dtypes[1] is np.dtype('int64'):
        ptesting.assert_frame_equal(dta.reset_index(), dta5)
    else:
        # don't check index because it has different size, int32 versus int64
        ptesting.assert_frame_equal(dta4, dta5[dta5.columns[1:]])

 def test_pickle(self):
     from statsmodels.compat.python import BytesIO
     fh = BytesIO()
     #test wrapped results load save pickle
     self.res1.save(fh)
     fh.seek(0,0)
     res_unpickled = self.res1.__class__.load(fh)
     assert_(type(res_unpickled) is type(self.res1))

 def test_pickle(self):
     fh = BytesIO()
     #test wrapped results load save pickle
     del self.res.model.data.orig_endog
     self.res.save(fh)
     fh.seek(0,0)
     res_unpickled = self.res.__class__.load(fh)
     assert type(res_unpickled) is type(self.res)  # noqa: E721

    def test_pickle_wrapper(self):

        fh = BytesIO()  # use cPickle with binary content

        # test unwrapped results load save pickle
        self.results._results.save(fh)
        fh.seek(0, 0)
        res_unpickled = self.results._results.__class__.load(fh)
        assert_(type(res_unpickled) is type(self.results._results))

        # test wrapped results load save
        fh.seek(0, 0)
        self.results.save(fh)
        fh.seek(0, 0)
        res_unpickled = self.results.__class__.load(fh)
        fh.close()
        # print type(res_unpickled)
        assert_(type(res_unpickled) is type(self.results))

        before = sorted(iterkeys(self.results.__dict__))
        after = sorted(iterkeys(res_unpickled.__dict__))
        assert_(before == after, msg='not equal %r and %r' % (before, after))

        before = sorted(iterkeys(self.results._results.__dict__))
        after = sorted(iterkeys(res_unpickled._results.__dict__))
        assert_(before == after, msg='not equal %r and %r' % (before, after))

        before = sorted(iterkeys(self.results.model.__dict__))
        after = sorted(iterkeys(res_unpickled.model.__dict__))
        assert_(before == after, msg='not equal %r and %r' % (before, after))

        before = sorted(iterkeys(self.results._cache))
        after = sorted(iterkeys(res_unpickled._cache))
        assert_(before == after, msg='not equal %r and %r' % (before, after))

def test_stata_writer_array():
    buf = BytesIO()
    dta = macrodata.load(as_pandas=False).data
    dta = DataFrame.from_records(dta)
    dta.columns = ["v%d" % i for i in range(1,15)]
    writer = StataWriter(buf, dta.values)
    writer.write_file()
    buf.seek(0)
    dta2 = genfromdta(buf)
    dta = dta.to_records(index=False)
    assert_array_equal(dta, dta2)

def test_stata_writer_structured():
    buf = BytesIO()
    dta = macrodata.load().data
    dtype = dta.dtype
    dta = dta.astype(np.dtype([('year', int),
                               ('quarter', int)] + dtype.descr[2:]))
    writer = StataWriter(buf, dta)
    writer.write_file()
    buf.seek(0)
    dta2 = genfromdta(buf)
    assert_array_equal(dta, dta2)

def test_stata_writer_structured():
    buf = BytesIO()
    dta = macrodata.load(as_pandas=False).data
    dtype = dta.dtype
    dt = [('year', int), ('quarter', int)] + dtype.descr[2:]
    if not PY3:  # Remove unicode
        dt = [(name.encode('ascii'), typ) for name, typ in dt]
    dta = dta.astype(np.dtype(dt))
    writer = StataWriter(buf, dta)
    writer.write_file()
    buf.seek(0)
    dta2 = genfromdta(buf)
    assert_array_equal(dta, dta2)

def test_plot_acf_kwargs():
    # Just test that it runs.
    fig = plt.figure()
    ax = fig.add_subplot(111)

    ar = np.r_[1., -0.9]
    ma = np.r_[1., 0.9]
    armaprocess = tsp.ArmaProcess(ar, ma)
    rs = np.random.RandomState(1234)
    acf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)

    buff = BytesIO()
    plot_acf(acf, ax=ax)
    fig.savefig(buff, format='rgba')
    plt.close(fig)

    buff_with_vlines = BytesIO()
    fig_with_vlines = plt.figure()
    ax = fig_with_vlines.add_subplot(111)
    vlines_kwargs = {'linestyles': 'dashdot'}
    plot_acf(acf, ax=ax, vlines_kwargs=vlines_kwargs)
    fig_with_vlines.savefig(buff_with_vlines, format='rgba')
    plt.close(fig_with_vlines)

    buff.seek(0)
    buff_with_vlines.seek(0)
    plain = buff.read()
    with_vlines = buff_with_vlines.read()

    assert_(with_vlines != plain)

def test_missing_roundtrip():
    buf = BytesIO()
    dta = np.array([(np.nan, np.inf, "")],
                      dtype=[("double_miss", float), ("float_miss", np.float32),
                              ("string_miss", "a1")])
    writer = StataWriter(buf, dta)
    writer.write_file()
    buf.seek(0)
    dta = genfromdta(buf, missing_flt=np.nan)
    assert_(isnull(dta[0][0]))
    assert_(isnull(dta[0][1]))
    assert_(dta[0][2] == asbytes(""))

    dta = genfromdta(os.path.join(curdir, "results/data_missing.dta"),
            missing_flt=-999)
    assert_(np.all([dta[0][i] == -999 for i in range(5)]))

def test_datetime_roundtrip():
    dta = np.array([(1, datetime(2010, 1, 1), 2),
                    (2, datetime(2010, 2, 1), 3),
                    (4, datetime(2010, 3, 1), 5)],
                    dtype=[('var1', float), ('var2', object), ('var3', float)])
    buf = BytesIO()

    with pytest.warns(FutureWarning):
        writer = StataWriter(buf, dta, {"var2" : "tm"})

    writer.write_file()
    buf.seek(0)

    with pytest.warns(FutureWarning):
        dta2 = genfromdta(buf)

    assert_equal(dta, dta2)

    dta = DataFrame.from_records(dta)
    buf = BytesIO()

    with pytest.warns(FutureWarning):
        writer = StataWriter(buf, dta, {"var2" : "tm"})

    writer.write_file()
    buf.seek(0)

    with pytest.warns(FutureWarning):
        dta2 = genfromdta(buf, pandas=True)

    ptesting.assert_frame_equal(dta, dta2.drop('index', axis=1))

def check_pickle(obj):
    fh = BytesIO()
    cPickle.dump(obj, fh, protocol=cPickle.HIGHEST_PROTOCOL)
    plen = fh.tell()
    fh.seek(0, 0)
    res = cPickle.load(fh)
    fh.close()
    return res, plen

def webuse(data, baseurl='http://www.stata-press.com/data/r11/', as_df=True):
    """
    Download and return an example dataset from Stata.

    Parameters
    ----------
    data : str
        Name of dataset to fetch.
    baseurl : str
        The base URL to the stata datasets.
    as_df : bool
        If True, returns a `pandas.DataFrame`

    Returns
    -------
    dta : Record Array
        A record array containing the Stata dataset.

    Examples
    --------
    >>> dta = webuse('auto')

    Notes
    -----
    Make sure baseurl has trailing forward slash. Doesn't do any
    error checking in response URLs.
    """
    # lazy imports
    from statsmodels.iolib import genfromdta

    url = urljoin(baseurl, data+'.dta')
    dta = urlopen(url)
    dta = BytesIO(dta.read())  # make it truly file-like
    if as_df:  # could make this faster if we don't process dta twice?
        return DataFrame.from_records(genfromdta(dta))
    else:
        return genfromdta(dta)

def test_stata_writer_pandas():
    buf = BytesIO()
    dta = macrodata.load().data
    dtype = dta.dtype
    #as of 0.9.0 pandas only supports i8 and f8
    dta = dta.astype(np.dtype([('year', 'i8'),
                               ('quarter', 'i8')] + dtype.descr[2:]))
    dta4 = dta.astype(np.dtype([('year', 'i4'),
                               ('quarter', 'i4')] + dtype.descr[2:]))
    dta = DataFrame.from_records(dta)
    dta4 = DataFrame.from_records(dta4)
    # dta is int64 'i8'  given to Stata writer
    writer = StataWriter(buf, dta)
    writer.write_file()
    buf.seek(0)
    dta2 = genfromdta(buf)
    dta5 = DataFrame.from_records(dta2)
    # dta2 is int32 'i4'  returned from Stata reader

    if dta5.dtypes[1] is np.dtype('int64'):
        ptesting.assert_frame_equal(dta.reset_index(), dta5)
    else:
        # don't check index because it has different size, int32 versus int64
        ptesting.assert_frame_equal(dta4, dta5[dta5.columns[1:]])