Merge pull request #166 from bashtage/absolute-imports

COMPAT: Att absolute import

Author: bashtage

arch/__init__.py

@@ -1,5 +1,5 @@
-from .univariate.mean import arch_model
-from ._version import get_versions
+from arch.univariate.mean import arch_model
+from arch._version import get_versions
 
 __version__ = get_versions()['version']
 del get_versions

arch/bootstrap/__init__.py

@@ -1,6 +1,8 @@
-from .base import IIDBootstrap, CircularBlockBootstrap, MovingBlockBootstrap, \
+from __future__ import absolute_import
+
+from arch.bootstrap.base import IIDBootstrap, CircularBlockBootstrap, MovingBlockBootstrap, \
     StationaryBootstrap
-from .multiple_comparrison import SPA, RealityCheck, StepM, MCS
+from arch.bootstrap.multiple_comparrison import SPA, RealityCheck, StepM, MCS
 
 __all__ = ['IIDBootstrap', 'CircularBlockBootstrap', 'MovingBlockBootstrap',
            'StationaryBootstrap', 'SPA', 'RealityCheck', 'StepM', 'MCS',

arch/bootstrap/_samplers_python.py

@@ -1,5 +1,6 @@
 from __future__ import absolute_import, division
-from ..compat.numba import jit
+
+from arch.compat.numba import jit
 
 
 def stationary_bootstrap_sample_python(indices, u, p):

arch/bootstrap/base.py

@@ -1,6 +1,7 @@
 from __future__ import absolute_import, division
-from ..compat.python import iteritems, itervalues, add_metaclass, range
-from ..utility.array import DocStringInheritor
+
+from arch.compat.python import iteritems, itervalues, add_metaclass, range
+from arch.utility.array import DocStringInheritor
 
 import copy
 
@@ -13,9 +14,9 @@
            'MovingBlockBootstrap']
 
 try:
-    from ._samplers import stationary_bootstrap_sample
+    from arch.bootstrap._samplers import stationary_bootstrap_sample
 except ImportError:  # pragma: no cover
-    from ._samplers_python import stationary_bootstrap_sample
+    from arch.bootstrap._samplers_python import stationary_bootstrap_sample
 
 
 def _loo_jackknife(func, nobs, args, kwargs):

arch/bootstrap/multiple_comparrison.py

@@ -1,13 +1,13 @@
-from __future__ import division
-from ..compat.python import add_metaclass, iteritems
-from ..utility.array import DocStringInheritor, ensure2d
+from __future__ import absolute_import, division
+from arch.compat.python import add_metaclass, iteritems
 
 from collections import OrderedDict
 
 import numpy as np
 import pandas as pd
 
-from .base import StationaryBootstrap, CircularBlockBootstrap, \
+from arch.utility.array import DocStringInheritor, ensure2d
+from arch.bootstrap.base import StationaryBootstrap, CircularBlockBootstrap, \
     MovingBlockBootstrap
 
 __all__ = ['StepM', 'SPA', 'RealityCheck']

arch/bootstrap/tests/test_multiple_comparrison.py

@@ -1,4 +1,4 @@
-from __future__ import division
+from __future__ import absolute_import, division
 
 from unittest import TestCase
 

arch/unitroot/__init__.py

@@ -1,3 +1,5 @@
-from .unitroot import ADF, KPSS, DFGLS, VarianceRatio, PhillipsPerron
+from __future__ import absolute_import, division
+
+from arch.unitroot.unitroot import ADF, KPSS, DFGLS, VarianceRatio, PhillipsPerron
 
 __all__ = ['ADF', 'KPSS', 'DFGLS', 'VarianceRatio', 'PhillipsPerron']

arch/unitroot/critical_values/dickey_fuller.py

@@ -6,7 +6,7 @@
 did not appear in the original paper and have been computed using an identical
 simulation.
 """
-from ...compat.python import iteritems
+from arch.compat.python import iteritems
 
 from numpy import asarray, inf
 

arch/unitroot/critical_values/simulation/adf_z_critical_values_simulation.py

@@ -7,7 +7,7 @@
 
 Remote clusters can be used by modifying the call to Client.
 """
-from __future__ import division, print_function
+from __future__ import absolute_import, division, print_function
 
 import datetime
 

arch/unitroot/critical_values/simulation/adf_z_critical_values_simulation_joblib.py

@@ -7,7 +7,7 @@
 
 Remote clusters can be used by modifying the call to Client.
 """
-from __future__ import division, print_function
+from __future__ import absolute_import, division, print_function
 
 from statsmodels.tools.parallel import parallel_func
 import datetime

arch/unitroot/critical_values/simulation/adf_z_critical_values_simulation_large_cluster.py

@@ -10,7 +10,7 @@
 This version has been optimized for execution on a large cluster and should
 scale well with 128 or more engines.
 """
-from __future__ import division, print_function
+from __future__ import absolute_import, division, print_function
 
 import datetime
 import time

arch/unitroot/critical_values/simulation/dfgls_critical_values_simulation.py

@@ -3,7 +3,7 @@
 to MacKinnon (2010).  Makes use of parallel_fun in statsmodels which works
 best when joblib is installed.
 """
-from __future__ import division, print_function
+from __future__ import absolute_import, division, print_function
 
 import datetime
 

arch/unitroot/critical_values/simulation/kpss_critical_values_simulation.py

@@ -2,7 +2,7 @@
 Calculates quantiles of the KPSS test statistic for both the constant
 and constant plus trend scenarios.
 """
-from __future__ import division, print_function
+from __future__ import absolute_import, division, print_function
 
 import os
 

arch/unitroot/unitroot.py

@@ -1,5 +1,5 @@
-from __future__ import division, absolute_import
-from ..compat.python import add_metaclass, range, lmap, long
+from __future__ import absolute_import, division
+from arch.compat.python import add_metaclass, range, lmap, long
 
 import warnings
 
@@ -14,19 +14,18 @@
 from statsmodels.iolib.summary import Summary
 from statsmodels.iolib.table import SimpleTable
 
-from ..utility import cov_nw
-from ..utility.exceptions import InvalidLengthWarning, invalid_length_doc
-from .critical_values.dickey_fuller import (adf_z_cv_approx, adf_z_large_p,
-                                            adf_z_max, adf_z_min,
-                                            adf_z_small_p, adf_z_star,
-                                            tau_2010, tau_large_p, tau_max,
-                                            tau_min, tau_small_p, tau_star)
-from .critical_values.kpss import kpss_critical_values
-from .critical_values.dfgls import (dfgls_large_p, dfgls_small_p,
-                                    dfgls_tau_max, dfgls_tau_min,
-                                    dfgls_tau_star, dfgls_cv_approx)
-from ..utility.array import ensure1d, DocStringInheritor
-from ..utility.timeseries import add_trend
+from arch.utility import cov_nw
+from arch.utility.exceptions import InvalidLengthWarning, invalid_length_doc
+from arch.unitroot.critical_values.dickey_fuller import (adf_z_cv_approx, adf_z_large_p, adf_z_max,
+                                                         adf_z_min, adf_z_small_p, adf_z_star,
+                                                         tau_2010, tau_large_p, tau_max,
+                                                         tau_min, tau_small_p, tau_star)
+from arch.unitroot.critical_values.kpss import kpss_critical_values
+from arch.unitroot.critical_values.dfgls import (dfgls_large_p, dfgls_small_p,
+                                                 dfgls_tau_max, dfgls_tau_min,
+                                                 dfgls_tau_star, dfgls_cv_approx)
+from arch.utility.array import ensure1d, DocStringInheritor
+from arch.utility.timeseries import add_trend
 
 
 __all__ = ['ADF', 'DFGLS', 'PhillipsPerron', 'KPSS', 'VarianceRatio',

arch/univariate/__init__.py

@@ -1,9 +1,9 @@
 from __future__ import absolute_import
 
-from .mean import HARX, ConstantMean, ZeroMean, ARX, arch_model, LS
-from .volatility import (GARCH, ARCH, HARCH, ConstantVariance, EWMAVariance,
-                         RiskMetrics2006, EGARCH, FixedVariance)
-from .distribution import Distribution, Normal, StudentsT, SkewStudent
+from arch.univariate.mean import HARX, ConstantMean, ZeroMean, ARX, arch_model, LS
+from arch.univariate.volatility import (GARCH, ARCH, HARCH, ConstantVariance, EWMAVariance,
+                                        RiskMetrics2006, EGARCH, FixedVariance)
+from arch.univariate.distribution import Distribution, Normal, StudentsT, SkewStudent
 
 __all__ = ['HARX', 'ConstantMean', 'ZeroMean', 'ARX', 'arch_model', 'LS',
            'GARCH', 'ARCH', 'HARCH', 'ConstantVariance',

arch/univariate/base.py

@@ -1,8 +1,8 @@
 """
 Core classes for ARCH models
 """
-from __future__ import division, absolute_import
-from ..compat.python import add_metaclass, range
+from __future__ import absolute_import, division
+from arch.compat.python import add_metaclass, range
 
 from copy import deepcopy
 from functools import partial
@@ -20,10 +20,10 @@
 from statsmodels.iolib.table import SimpleTable
 from statsmodels.tools.numdiff import approx_fprime, approx_hess
 
-from .distribution import Distribution, Normal
-from .volatility import VolatilityProcess, ConstantVariance
-from ..utility.array import ensure1d, DocStringInheritor
-from ..utility.exceptions import ConvergenceWarning, StartingValueWarning, \
+from arch.univariate.distribution import Distribution, Normal
+from arch.univariate.volatility import VolatilityProcess, ConstantVariance
+from arch.utility.array import ensure1d, DocStringInheritor
+from arch.utility.exceptions import ConvergenceWarning, StartingValueWarning, \
     convergence_warning, starting_value_warning
 
 __all__ = ['implicit_constant', 'ARCHModelResult', 'ARCHModel']

arch/univariate/distribution.py

@@ -3,16 +3,16 @@
 Distributions to use in ARCH models.  All distributions must inherit from
 :class:`Distribution` and provide the same methods with the same inputs.
 """
-from __future__ import division, absolute_import
-from ..compat.python import add_metaclass
+from __future__ import absolute_import, division
+from arch.compat.python import add_metaclass
 
 from numpy.random import standard_normal, standard_t
 from numpy import (empty, array, sqrt, log, exp, sign, pi, sum, asarray,
                    ones_like)
 from scipy.special import gammaln
 import scipy.stats as stats
 
-from ..utility.array import DocStringInheritor
+from arch.utility.array import DocStringInheritor
 
 
 __all__ = ['Distribution', 'Normal', 'StudentsT', 'SkewStudent']

arch/univariate/mean.py

@@ -2,7 +2,7 @@
 Mean models to use with ARCH processes.  All mean models must inherit from
 :class:`ARCHModel` and provide the same methods with the same inputs.
 """
-from __future__ import division, absolute_import
+from __future__ import absolute_import, division
 
 import copy
 from collections import OrderedDict
@@ -13,11 +13,11 @@
 from statsmodels.tools.decorators import cache_readonly
 from statsmodels.tsa.tsatools import lagmat
 
-from .base import ARCHModel, implicit_constant, ARCHModelResult, ARCHModelForecast
-from .distribution import Normal, StudentsT, SkewStudent
-from .volatility import ARCH, GARCH, HARCH, ConstantVariance, EGARCH
-from ..compat.python import range, iteritems
-from ..utility.array import ensure1d, parse_dataframe, cutoff_to_index
+from arch.univariate.base import ARCHModel, implicit_constant, ARCHModelResult, ARCHModelForecast
+from arch.univariate.distribution import Normal, StudentsT, SkewStudent
+from arch.univariate.volatility import ARCH, GARCH, HARCH, ConstantVariance, EGARCH
+from arch.compat.python import range, iteritems
+from arch.utility.array import ensure1d, parse_dataframe, cutoff_to_index
 
 __all__ = ['HARX', 'ConstantMean', 'ZeroMean', 'ARX', 'arch_model', 'LS']
 

arch/univariate/recursions_python.py

@@ -3,9 +3,9 @@
 testing and if it isn't possible to install the Cython version using
 python setup.py install --no-binary
 """
-from __future__ import division, absolute_import
-from ..compat.python import range
-from ..compat.numba import jit
+from __future__ import absolute_import, division
+from arch.compat.python import range
+from arch.compat.numba import jit
 
 from numpy import log
 import numpy as np

arch/univariate/tests/test_forecast.py

@@ -7,9 +7,9 @@
 from numpy.testing import assert_allclose
 from pandas.util.testing import assert_frame_equal
 
-from ..mean import _ar_to_impulse, _ar_forecast
-from ...univariate import arch_model
-from .test_variance_forecasting import preserved_state
+from arch.univariate.mean import _ar_to_impulse, _ar_forecast
+from arch.univariate import arch_model
+from arch.univariate.tests.test_variance_forecasting import preserved_state
 
 
 class TestForecasting(TestCase):

arch/univariate/volatility.py

@@ -3,22 +3,23 @@
 inherit from :class:`VolatilityProcess` and provide the same methods with the
 same inputs.
 """
-from __future__ import division, absolute_import
+from __future__ import absolute_import, division
 
 import itertools
 
 import numpy as np
 from numpy import (sqrt, ones, zeros, isscalar, sign, ones_like, arange, empty, abs, array, finfo,
                    float64, log, exp, floor)
 
-from .distribution import Normal
-from ..compat.python import add_metaclass, range
-from ..utility.array import ensure1d, DocStringInheritor
+from arch.univariate.distribution import Normal
+from arch.compat.python import add_metaclass, range
+from arch.utility.array import ensure1d, DocStringInheritor
 
 try:
-    from .recursions import garch_recursion, harch_recursion, egarch_recursion
+    from arch.univariate.recursions import garch_recursion, harch_recursion, egarch_recursion
 except ImportError:  # pragma: no cover
-    from .recursions_python import (garch_recursion, harch_recursion, egarch_recursion)
+    from arch.univariate.recursions_python import (garch_recursion, harch_recursion,
+                                                   egarch_recursion)
 
 __all__ = ['GARCH', 'ARCH', 'HARCH', 'ConstantVariance', 'EWMAVariance', 'RiskMetrics2006',
            'EGARCH', 'FixedVariance']

arch/utility/__init__.py

@@ -1,3 +1,3 @@
-from .cov import cov_nw
+from arch.utility.cov import cov_nw
 
 __all__ = ['cov_nw']

arch/utility/array.py

@@ -2,8 +2,8 @@
 Utility functions that do not explicitly relate to Volatility modeling
 """
 from __future__ import print_function, division, absolute_import
-from ..compat.pandas import is_datetime64_dtype
-from ..compat.python import long
+from arch.compat.pandas import is_datetime64_dtype
+from arch.compat.python import long
 
 import datetime as dt
 

arch/utility/cov.py

@@ -1,4 +1,4 @@
-from __future__ import division
+from __future__ import absolute_import, division
 
 
 def cov_nw(y, lags=0, demean=True, axis=0, ddof=0):

arch/utility/tests/test_cov.py

@@ -1,4 +1,4 @@
-from __future__ import division
+from __future__ import absolute_import, division
 
 from unittest import TestCase
 

arch/utility/tests/test_timeseries.py

@@ -1,4 +1,4 @@
-from __future__ import division
+from __future__ import absolute_import, division
 
 from unittest import TestCase
 import warnings