def test_summary():
    dates = pd.date_range(start='1980-01-01', end='1984-01-01', freq='AS')
    endog = pd.Series([1,2,3,4,5], index=dates)
    mod = MLEModel(endog, **kwargs)
    res = mod.filter([])

    # Get the summary
    txt = str(res.summary())

    # Test res.summary when the model has dates
    assert_equal(re.search('Sample:\s+01-01-1980', txt) is not None, True)
    assert_equal(re.search('\s+- 01-01-1984', txt) is not None, True)

    # Test res.summary when `model_name` was not provided
    assert_equal(re.search('Model:\s+MLEModel', txt) is not None, True)

    # Smoke test that summary still works when diagnostic tests fail
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        res.filter_results._standardized_forecasts_error[:] = np.nan
        res.summary()
        res.filter_results._standardized_forecasts_error = 1
        res.summary()
        res.filter_results._standardized_forecasts_error = 'a'
        res.summary()

    def setup_class(cls, which='mixed', *args, **kwargs):
        # Data
        dta = datasets.macrodata.load_pandas().data
        dta.index = pd.date_range(start='1959-01-01', end='2009-7-01', freq='QS')
        obs = np.log(dta[['realgdp','realcons','realinv']]).diff().iloc[1:] * 400

        if which == 'all':
            obs.iloc[:50, :] = np.nan
            obs.iloc[119:130, :] = np.nan
        elif which == 'partial':
            obs.iloc[0:50, 0] = np.nan
            obs.iloc[119:130, 0] = np.nan
        elif which == 'mixed':
            obs.iloc[0:50, 0] = np.nan
            obs.iloc[19:70, 1] = np.nan
            obs.iloc[39:90, 2] = np.nan
            obs.iloc[119:130, 0] = np.nan
            obs.iloc[119:130, 2] = np.nan

        mod = cls.create_model(obs, **kwargs)
        cls.model = mod.ssm

        n_disturbance_variates = (
            (cls.model.k_endog + cls.model.k_posdef) * cls.model.nobs
        )
        np.random.seed(1234)
        dv = np.random.normal(size=n_disturbance_variates)
        isv = np.random.normal(size=cls.model.k_states)

        # Collapsed filtering, smoothing, and simulation smoothing
        cls.model.filter_collapsed = True
        cls.results_b = cls.model.smooth()
        cls.sim_b = cls.model.simulation_smoother()
        cls.sim_b.simulate(disturbance_variates=dv, initial_state_variates=isv)

        # Conventional filtering, smoothing, and simulation smoothing
        cls.model.filter_collapsed = False
        cls.results_a = cls.model.smooth()
        cls.sim_a = cls.model.simulation_smoother()
        cls.sim_a.simulate(disturbance_variates=dv, initial_state_variates=isv)

        # Create the model with augmented state space
        kwargs.pop('filter_collapsed', None)
        mod = MLEModel(obs, k_states=4, k_posdef=2, **kwargs)
        mod['design', :3, :2] = np.array([[-32.47143586, 17.33779024],
                                          [-7.40264169, 1.69279859],
                                          [-209.04702853, 125.2879374]])
        mod['obs_cov'] = np.diag(
            np.array([0.0622668, 1.95666886, 58.37473642]))
        mod['transition', :2, :2] = np.array([[0.29935707, 0.33289005],
                                              [-0.7639868, 1.2844237]])
        mod['transition', 2:, :2] = np.eye(2)
        mod['selection', :2, :2] = np.eye(2)
        mod['state_cov'] = np.array([[1.2, -0.25],
                                     [-0.25, 1.1]])

        mod.initialize_approximate_diffuse(1e6)
        cls.augmented_model = mod.ssm
        cls.augmented_results = mod.ssm.smooth()

    def setup_class(cls, which='none', **kwargs):
        # Results
        path = current_path + os.sep + 'results/results_smoothing_generalobscov_R.csv'
        cls.desired = pd.read_csv(path)

        # Data
        dta = datasets.macrodata.load_pandas().data
        dta.index = pd.date_range(start='1959-01-01', end='2009-7-01', freq='QS')
        obs = dta[['realgdp','realcons','realinv']].diff().iloc[1:]

        if which == 'all':
            obs.iloc[:50, :] = np.nan
            obs.iloc[119:130, :] = np.nan
        elif which == 'partial':
            obs.iloc[0:50, 0] = np.nan
            obs.iloc[119:130, 0] = np.nan
        elif which == 'mixed':
            obs.iloc[0:50, 0] = np.nan
            obs.iloc[19:70, 1] = np.nan
            obs.iloc[39:90, 2] = np.nan
            obs.iloc[119:130, 0] = np.nan
            obs.iloc[119:130, 2] = np.nan

        # Create the model
        mod = MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
        mod['design'] = np.eye(3)
        mod['obs_cov'] = np.array([[ 609.0746647855,    0.          ,    0.          ],
                                   [   0.          ,    1.8774916622,    0.          ],
                                   [   0.          ,    0.          ,  124.6768281675]])
        mod['transition'] = np.array([[-0.8110473405,  1.8005304445,  1.0215975772],
                                      [-1.9846632699,  2.4091302213,  1.9264449765],
                                      [ 0.9181658823, -0.2442384581, -0.6393462272]])
        mod['selection'] = np.eye(3)
        mod['state_cov'] = np.array([[ 1552.9758843938,   612.7185121905,   877.6157204992],
                                     [  612.7185121905,   467.8739411204,    70.608037339 ],
                                     [  877.6157204992,    70.608037339 ,   900.5440385836]])
        mod.initialize_approximate_diffuse(1e6)
        cls.model = mod.ssm

        # Conventional filtering, smoothing, and simulation smoothing
        cls.model.filter_conventional = True
        cls.conventional_results = cls.model.smooth()
        n_disturbance_variates = (
            (cls.model.k_endog + cls.model.k_posdef) * cls.model.nobs
        )
        cls.conventional_sim = cls.model.simulation_smoother(
            disturbance_variates=np.zeros(n_disturbance_variates),
            initial_state_variates=np.zeros(cls.model.k_states)
        )

        # Univariate filtering, smoothing, and simulation smoothing
        cls.model.filter_univariate = True
        cls.univariate_results = cls.model.smooth()
        cls.univariate_sim = cls.model.simulation_smoother(
            disturbance_variates=np.zeros(n_disturbance_variates),
            initial_state_variates=np.zeros(cls.model.k_states)
        )

    def setup_class(cls, which, dtype=float, alternate_timing=False, **kwargs):
        # Results
        path = os.path.join(current_path, 'results',
                            'results_smoothing_generalobscov_R.csv')
        cls.desired = pd.read_csv(path)

        # Data
        dta = datasets.macrodata.load_pandas().data
        dta.index = pd.date_range(start='1959-01-01',
                                  end='2009-7-01', freq='QS')
        obs = dta[['realgdp', 'realcons', 'realinv']].diff().iloc[1:]

        if which == 'all':
            obs.iloc[:50, :] = np.nan
            obs.iloc[119:130, :] = np.nan
        elif which == 'partial':
            obs.iloc[0:50, 0] = np.nan
            obs.iloc[119:130, 0] = np.nan
        elif which == 'mixed':
            obs.iloc[0:50, 0] = np.nan
            obs.iloc[19:70, 1] = np.nan
            obs.iloc[39:90, 2] = np.nan
            obs.iloc[119:130, 0] = np.nan
            obs.iloc[119:130, 2] = np.nan

        # Create the model
        mod = MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
        mod['design'] = np.eye(3)
        X = (np.arange(9) + 1).reshape((3, 3)) / 10.
        mod['obs_cov'] = np.dot(X, X.T)
        mod['transition'] = np.eye(3)
        mod['selection'] = np.eye(3)
        mod['state_cov'] = np.eye(3)
        mod.initialize_approximate_diffuse(1e6)
        cls.model = mod.ssm

        # Conventional filtering, smoothing, and simulation smoothing
        cls.model.filter_conventional = True
        cls.conventional_results = cls.model.smooth()
        n_disturbance_variates = (
            (cls.model.k_endog + cls.model.k_posdef) * cls.model.nobs
        )
        cls.conventional_sim = cls.model.simulation_smoother(
            disturbance_variates=np.zeros(n_disturbance_variates),
            initial_state_variates=np.zeros(cls.model.k_states)
        )

        # Univariate filtering, smoothing, and simulation smoothing
        cls.model.filter_univariate = True
        cls.univariate_results = cls.model.smooth()
        cls.univariate_sim = cls.model.simulation_smoother(
            disturbance_variates=np.zeros(n_disturbance_variates),
            initial_state_variates=np.zeros(cls.model.k_states)
        )

def test_params():
    mod = MLEModel([1,2], **kwargs)

    # By default start_params raises NotImplementedError
    assert_raises(NotImplementedError, lambda: mod.start_params)
    # But param names are by default an empty array
    assert_equal(mod.param_names, [])

    # We can set them in the object if we want
    mod._start_params = [1]
    mod._param_names = ['a']

    assert_equal(mod.start_params, [1])
    assert_equal(mod.param_names, ['a'])

def test_summary():
    dates = pd.date_range(start='1980-01-01', end='1984-01-01', freq='AS')
    endog = pd.TimeSeries([1,2,3,4,5], index=dates)
    mod = MLEModel(endog, **kwargs)
    res = mod.filter([])

    # Get the summary
    txt = str(res.summary())

    # Test res.summary when the model has dates
    assert_equal(re.search('Sample:\s+01-01-1980', txt) is not None, True)
    assert_equal(re.search('\s+- 01-01-1984', txt) is not None, True)

    # Test res.summary when `model_name` was not provided
    assert_equal(re.search('Model:\s+MLEModel', txt) is not None, True)

 def create_model(cls, obs, **kwargs):
     # Create the model with typical state space
     mod = MLEModel(obs, k_states=2, k_posdef=2, **kwargs)
     mod['design'] = np.array([[-32.47143586, 17.33779024],
                               [-7.40264169, 1.69279859],
                               [-209.04702853, 125.2879374]])
     mod['obs_cov'] = np.diag(
         np.array([0.0622668, 1.95666886, 58.37473642]))
     mod['transition'] = np.array([[0.29935707, 0.33289005],
                                   [-0.7639868, 1.2844237]])
     mod['selection'] = np.eye(2)
     mod['state_cov'] = np.array([[1.2, -0.25],
                                  [-0.25, 1.1]])
     mod.initialize_approximate_diffuse(1e6)
     return mod

def test_forecast():
    # Numpy
    mod = MLEModel([1,2], **kwargs)
    res = mod.filter([])
    forecast = res.forecast(steps=10)
    assert_allclose(forecast, np.ones((10,)) * 2)
    assert_allclose(res.get_forecast(steps=10).predicted_mean, forecast)

    # Pandas
    index = pd.date_range('1960-01-01', periods=2, freq='MS')
    mod = MLEModel(pd.Series([1,2], index=index), **kwargs)
    res = mod.filter([])
    assert_allclose(res.forecast(steps=10), np.ones((10,)) * 2)
    assert_allclose(res.forecast(steps='1960-12-01'), np.ones((10,)) * 2)
    assert_allclose(res.get_forecast(steps=10).predicted_mean, np.ones((10,)) * 2)

def test_filter():
    endog = np.array([1., 2.])
    mod = MLEModel(endog, **kwargs)

    # Test return of ssm object
    res = mod.filter([], return_ssm=True)
    assert_equal(isinstance(res, kalman_filter.FilterResults), True)

    # Test return of full results object
    res = mod.filter([])
    assert_equal(isinstance(res, MLEResultsWrapper), True)
    assert_equal(res.cov_type, 'opg')

    # Test return of full results object, specific covariance type
    res = mod.filter([], cov_type='oim')
    assert_equal(isinstance(res, MLEResultsWrapper), True)
    assert_equal(res.cov_type, 'oim')

def test_diagnostics_nile_eviews():
    # Test the diagnostic tests using the Nile dataset. Results are from 
    # "Fitting State Space Models with EViews" (Van den Bossche 2011,
    # Journal of Statistical Software).
    # For parameter values, see Figure 2
    # For Ljung-Box and Jarque-Bera statistics and p-values, see Figure 5
    # The Heteroskedasticity statistic is not provided in this paper.
    niledata = nile.data.load_pandas().data
    niledata.index = pd.date_range('1871-01-01', '1970-01-01', freq='AS')

    mod = MLEModel(niledata['volume'], k_states=1,
        initialization='approximate_diffuse', initial_variance=1e15,
        loglikelihood_burn=1)
    mod.ssm['design', 0, 0] = 1
    mod.ssm['obs_cov', 0, 0] = np.exp(9.600350)
    mod.ssm['transition', 0, 0] = 1
    mod.ssm['selection', 0, 0] = 1
    mod.ssm['state_cov', 0, 0] = np.exp(7.348705)
    res = mod.filter([])

    # Test Ljung-Box
    # Note: only 3 digits provided in the reference paper
    actual = res.test_serial_correlation(method='ljungbox', lags=10)[0, :, -1]
    assert_allclose(actual, [13.117, 0.217], atol=1e-3)

    # Test Jarque-Bera
    actual = res.test_normality(method='jarquebera')[0, :2]
    assert_allclose(actual, [0.041686, 0.979373], atol=1e-5)

def test_diagnostics_nile_durbinkoopman():
    # Test the diagnostic tests using the Nile dataset. Results are from 
    # Durbin and Koopman (2012); parameter values reported on page 37; test
    # statistics on page 40
    niledata = nile.data.load_pandas().data
    niledata.index = pd.date_range('1871-01-01', '1970-01-01', freq='AS')

    mod = MLEModel(niledata['volume'], k_states=1,
        initialization='approximate_diffuse', initial_variance=1e15,
        loglikelihood_burn=1)
    mod.ssm['design', 0, 0] = 1
    mod.ssm['obs_cov', 0, 0] = 15099.
    mod.ssm['transition', 0, 0] = 1
    mod.ssm['selection', 0, 0] = 1
    mod.ssm['state_cov', 0, 0] = 1469.1
    res = mod.filter([])

    # Test Ljung-Box
    # Note: only 3 digits provided in the reference paper
    actual = res.test_serial_correlation(method='ljungbox', lags=9)[0, 0, -1]
    assert_allclose(actual, [8.84], atol=1e-2)

    # Test Jarque-Bera
    # Note: The book reports 0.09 for Kurtosis, because it is reporting the
    # statistic less the mean of the Kurtosis distribution (which is 3).
    norm = res.test_normality(method='jarquebera')[0]
    actual = [norm[0], norm[2], norm[3]]
    assert_allclose(actual, [0.05, -0.03, 3.09], atol=1e-2)

    # Test Heteroskedasticity
    # Note: only 2 digits provided in the book
    actual = res.test_heteroskedasticity(method='breakvar')[0, 0]
    assert_allclose(actual, [0.61], atol=1e-2)

def test_predict():
    dates = pd.date_range(start='1980-01-01', end='1981-01-01', freq='AS')
    endog = pd.TimeSeries([1,2], index=dates)
    mod = MLEModel(endog, **kwargs)
    res = mod.filter([])

    # Test that predict with start=None, end=None does prediction with full
    # dataset
    assert_equal(res.predict().shape, (mod.k_endog, mod.nobs))

    # Test a string value to the dynamic option
    assert_allclose(res.predict(dynamic='1981-01-01'), res.predict())

    # Test an invalid date string value to the dynamic option
    assert_raises(ValueError, res.predict, dynamic='1982-01-01')

    # Test predict with full results
    assert_equal(isinstance(res.predict(full_results=True),
                            kalman_filter.FilterResults), True)

def test_predict():
    dates = pd.date_range(start='1980-01-01', end='1981-01-01', freq='AS')
    endog = pd.TimeSeries([1,2], index=dates)
    mod = MLEModel(endog, **kwargs)
    res = mod.filter([])

    # Test that predict with start=None, end=None does prediction with full
    # dataset
    predict = res.predict()
    assert_equal(predict.shape, (mod.nobs,))
    assert_allclose(res.get_prediction().predicted_mean, predict)

    # Test a string value to the dynamic option
    assert_allclose(res.predict(dynamic='1981-01-01'), res.predict())

    # Test an invalid date string value to the dynamic option
    assert_raises(ValueError, res.predict, dynamic='1982-01-01')

    # Test for passing a string to predict when dates are not set
    mod = MLEModel([1,2], **kwargs)
    res = mod.filter([])
    assert_raises(ValueError, res.predict, dynamic='string')

def test_from_formula():
    assert_raises(NotImplementedError, lambda: MLEModel.from_formula(1,2,3))

def test_transform():
    # The transforms in MLEModel are noops
    mod = MLEModel([1,2], **kwargs)

    # Test direct transform, untransform
    assert_allclose(mod.transform_params([2, 3]), [2, 3])
    assert_allclose(mod.untransform_params([2, 3]), [2, 3])    

    # Smoke test for transformation in `filter`, `update`, `loglike`,
    # `loglikeobs`
    mod.filter([], transformed=False)
    mod.update([], transformed=False)
    mod.loglike([], transformed=False)
    mod.loglikeobs([], transformed=False)

    # Note that mod is an SARIMAX instance, and the two parameters are
    # variances
    mod, _ = get_dummy_mod(fit=False)

    # Test direct transform, untransform
    assert_allclose(mod.transform_params([2, 3]), [4, 9])
    assert_allclose(mod.untransform_params([4, 9]), [2, 3])

    # Test transformation in `filter`
    res = mod.filter([2, 3], transformed=True)
    assert_allclose(res.params, [2, 3])

    res = mod.filter([2, 3], transformed=False)
    assert_allclose(res.params, [4, 9])

def test_forecast():
    mod = MLEModel([1,2], **kwargs)
    res = mod.filter([])
    assert_allclose(res.forecast(steps=10), [[2]*10])

def test_numpy_endog():
    # Test various types of numpy endog inputs

    # Check behavior of the link maintained between passed `endog` and
    # `mod.endog` arrays
    endog = np.array([1., 2.])
    mod = MLEModel(endog, **kwargs)
    assert_equal(mod.endog.base is not mod.data.orig_endog, True)
    assert_equal(mod.endog.base is not endog, True)
    assert_equal(mod.data.orig_endog.base is not endog, True)
    endog[0] = 2
    # there is no link to mod.endog
    assert_equal(mod.endog, np.r_[1, 2].reshape(2,1))
    # there remains a link to mod.data.orig_endog
    assert_equal(mod.data.orig_endog, endog)

    # Check behavior with different memory layouts / shapes

    # Example  (failure): 0-dim array
    endog = np.array(1.)
    # raises error due to len(endog) failing in Statsmodels base classes
    assert_raises(TypeError, check_endog, endog, **kwargs)

    # Example : 1-dim array, both C- and F-contiguous, length 2
    endog = np.array([1.,2.])
    assert_equal(endog.ndim, 1)
    assert_equal(endog.flags['C_CONTIGUOUS'], True)
    assert_equal(endog.flags['F_CONTIGUOUS'], True)
    assert_equal(endog.shape, (2,))
    mod = check_endog(endog, **kwargs)
    mod.filter([])

    # Example : 2-dim array, C-contiguous, long-shaped: (nobs, k_endog)
    endog = np.array([1., 2.]).reshape(2, 1)
    assert_equal(endog.ndim, 2)
    assert_equal(endog.flags['C_CONTIGUOUS'], True)
    assert_equal(endog.flags['F_CONTIGUOUS'], False)
    assert_equal(endog.shape, (2, 1))
    mod = check_endog(endog, **kwargs)
    mod.filter([])

    # Example : 2-dim array, C-contiguous, wide-shaped: (k_endog, nobs)
    endog = np.array([1., 2.]).reshape(1, 2)
    assert_equal(endog.ndim, 2)
    assert_equal(endog.flags['C_CONTIGUOUS'], True)
    assert_equal(endog.flags['F_CONTIGUOUS'], False)
    assert_equal(endog.shape, (1, 2))
    # raises error because arrays are always interpreted as
    # (nobs, k_endog), which means that k_endog=2 is incompatibile with shape
    # of design matrix (1, 1)
    assert_raises(ValueError, check_endog, endog, **kwargs)

    # Example : 2-dim array, F-contiguous, long-shaped (nobs, k_endog)
    endog = np.array([1., 2.]).reshape(1, 2).transpose()
    assert_equal(endog.ndim, 2)
    assert_equal(endog.flags['C_CONTIGUOUS'], False)
    assert_equal(endog.flags['F_CONTIGUOUS'], True)
    assert_equal(endog.shape, (2, 1))
    mod = check_endog(endog, **kwargs)
    mod.filter([])

    # Example : 2-dim array, F-contiguous, wide-shaped (k_endog, nobs)
    endog = np.array([1., 2.]).reshape(2, 1).transpose()
    assert_equal(endog.ndim, 2)
    assert_equal(endog.flags['C_CONTIGUOUS'], False)
    assert_equal(endog.flags['F_CONTIGUOUS'], True)
    assert_equal(endog.shape, (1, 2))
    # raises error because arrays are always interpreted as
    # (nobs, k_endog), which means that k_endog=2 is incompatibile with shape
    # of design matrix (1, 1)
    assert_raises(ValueError, check_endog, endog, **kwargs)

    # Example  (failure): 3-dim array
    endog = np.array([1., 2.]).reshape(2, 1, 1)
    # raises error due to direct ndim check in Statsmodels base classes
    assert_raises(ValueError, check_endog, endog, **kwargs)

    # Example : np.array with 2 columns
    # Update kwargs for k_endog=2
    kwargs2 = {
        'k_states': 1, 'design': [[1], [0.]], 'obs_cov': [[1, 0], [0, 1]],
        'transition': [[1]], 'selection': [[1]], 'state_cov': [[1]],
        'initialization': 'approximate_diffuse'
    }
    endog = np.array([[1., 2.], [3., 4.]])
    mod = check_endog(endog, k_endog=2, **kwargs2)
    mod.filter([])

def test_basic_endog():
    # Test various types of basic python endog inputs (e.g. lists, scalars...)

    # Check cannot call with non-array-like
    # fails due to checks in Statsmodels base classes
    assert_raises(ValueError, MLEModel, endog=1, k_states=1)
    assert_raises(ValueError, MLEModel, endog='a', k_states=1)
    assert_raises(ValueError, MLEModel, endog=True, k_states=1)

    # Check behavior with different types
    mod = MLEModel([1], **kwargs)
    res = mod.filter([])
    assert_equal(res.filter_results.endog, [[1]])

    mod = MLEModel([1.], **kwargs)
    res = mod.filter([])
    assert_equal(res.filter_results.endog, [[1]])

    mod = MLEModel([True], **kwargs)
    res = mod.filter([])
    assert_equal(res.filter_results.endog, [[1]])

    mod = MLEModel(['a'], **kwargs)
    # raises error due to inability coerce string to numeric
    assert_raises(ValueError, mod.filter, [])

    # Check that a different iterable tpyes give the expected result
    endog = [1.,2.]
    mod = check_endog(endog, **kwargs)
    mod.filter([])

    endog = [[1.],[2.]]
    mod = check_endog(endog, **kwargs)
    mod.filter([])

    endog = (1.,2.)
    mod = check_endog(endog, **kwargs)
    mod.filter([])