Merge pull request #462 from bashtage/optional-type-checking

MAINT: Make typing_extensions optional

Author: bashtage

arch/bootstrap/_samplers_python.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from arch.compat.numba import jit
 
 from arch.typing import NDArray

arch/bootstrap/base.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import copy
 from typing import (
     Any,
@@ -796,7 +798,7 @@ def _bca_acceleration(
         jk_params = _loo_jackknife(func, nobs, self._args, self._kwargs, extra_kwags)
         return _get_acceleration(jk_params)
 
-    def clone(self, *args: ArrayLike, **kwargs: ArrayLike) -> "IIDBootstrap":
+    def clone(self, *args: ArrayLike, **kwargs: ArrayLike) -> IIDBootstrap:
         """
         Clones the bootstrap using different data with a fresh RandomState.
 

arch/bootstrap/multiple_comparison.py

@@ -1,10 +1,23 @@
+from __future__ import annotations
+
 import sys
-from typing import Any, Dict, Hashable, List, Optional, Sequence, Tuple, Union, cast
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    Hashable,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+    cast,
+)
 
-if sys.version_info < (3, 8):
-    from typing_extensions import Literal
-else:
+if sys.version_info >= (3, 8):
     from typing import Literal
+elif TYPE_CHECKING:
+    from typing_extensions import Literal
 
 import numpy as np
 import pandas as pd

arch/covariance/kernel.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from typing import List, Optional
 

arch/typing.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import datetime as dt
 from typing import Callable, Hashable, Optional, Tuple, TypeVar, Union
 

arch/unitroot/_engle_granger.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import Optional
 
 import numpy as np
@@ -32,7 +34,7 @@ def engle_granger(
     lags: Optional[int] = None,
     max_lags: Optional[int] = None,
     method: str = "bic",
-) -> "EngleGrangerTestResults":
+) -> EngleGrangerTestResults:
     r"""
     Test for cointegration within a set of time series.
 

arch/unitroot/_phillips_ouliaris.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import Optional
 
 import numpy as np
@@ -42,7 +44,7 @@ def _po_ptests(
     kernel: str,
     bandwidth: Optional[int],
     force_int: bool,
-) -> "PhillipsOuliarisTestResults":
+) -> PhillipsOuliarisTestResults:
     nobs = z.shape[0]
     z_lead = z.iloc[1:]
     z_lag = add_trend(z.iloc[:-1], trend=trend)
@@ -94,7 +96,7 @@ def _po_ztests(
     kernel: str,
     bandwidth: Optional[int],
     force_int: bool,
-) -> "PhillipsOuliarisTestResults":
+) -> PhillipsOuliarisTestResults:
     # Za and Zt tests
     u = np.asarray(xsection.resid)[:, None]
     nobs = u.shape[0]
@@ -139,7 +141,7 @@ def phillips_ouliaris(
     kernel: str = "bartlett",
     bandwidth: Optional[int] = None,
     force_int: bool = False,
-) -> "PhillipsOuliarisTestResults":
+) -> PhillipsOuliarisTestResults:
     r"""
     Test for cointegration within a set of time series.
 

arch/unitroot/_shared.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import Any, Dict, NamedTuple, Optional, Tuple, Type
 
 import pandas as pd
@@ -219,7 +221,7 @@ def resid(self) -> pd.Series:
 
     def plot(
         self, axes: Optional["plt.Axes"] = None, title: Optional[str] = None
-    ) -> "plt.Figure":
+    ) -> plt.Figure:
         """
         Plot the cointegration residuals.
 

arch/unitroot/cointegration.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import List, Optional, Sequence, Tuple
 
 import numpy as np

arch/unitroot/critical_values/dfgls.py

@@ -5,6 +5,7 @@
 These have been computed using the methodology of MacKinnon (1994) and (2010)
 simulation. See dfgls_critival_values_simulation for implementation.
 """
+from __future__ import annotations
 
 from numpy import array
 

arch/unitroot/critical_values/dickey_fuller.py

@@ -6,6 +6,8 @@
 did not appear in the original paper and have been computed using an identical
 simulation.
 """
+from __future__ import annotations
+
 from typing import Dict
 
 from numpy import array, asarray, inf

arch/unitroot/critical_values/engle_granger.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 
 eg_num_variables = np.arange(1, 13)

arch/unitroot/critical_values/kpss.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from numpy import asarray
 
 kpss_critical_values = {}

arch/unitroot/critical_values/phillips_ouliaris.py

@@ -10,6 +10,7 @@
 P-type statistics with trend ct based on 11,250,000 simulations
 P-type statistics with trend ctt based on 13,250,000 simulations
 """
+from __future__ import annotations
 
 from math import inf
 

arch/unitroot/critical_values/simulation/adf_simulation.py

@@ -1,15 +1,17 @@
+from __future__ import annotations
+
 import os
 import platform
 import sys
-from typing import Optional, Union
+from typing import TYPE_CHECKING, Optional, Union
 
 from numpy import arange, array, cumsum, dot, ones, vstack
 from numpy.linalg import pinv
 from numpy.random import Generator, RandomState
 
 if sys.version_info >= (3, 8):
     from typing import Literal
-else:
+elif TYPE_CHECKING:
     from typing_extensions import Literal
 
 # Storage Location

arch/unitroot/critical_values/simulation/adf_z_critical_values_simulation_joblib.py

@@ -1,15 +1,17 @@
 """
 Simulation of ADF z-test critical values.  Closely follows MacKinnon (2010).
 """
+from __future__ import annotations
+
 import argparse
 import os
 import random
 import sys
-from typing import cast
+from typing import TYPE_CHECKING, cast
 
 if sys.version_info >= (3, 8):
     from typing import Literal
-else:
+elif TYPE_CHECKING:
     from typing_extensions import Literal
 
 from adf_simulation import (

arch/unitroot/critical_values/simulation/adf_z_critical_values_simulation_large_cluster.py

@@ -10,10 +10,12 @@
 This version has been optimized for execution on a large cluster and should
 scale well with 128 or more engines.
 """
+from __future__ import annotations
+
 import datetime
 import sys
 import time
-from typing import cast
+from typing import TYPE_CHECKING, cast
 
 from ipyparallel import Client, DirectView
 from numpy import array, nan, ndarray, percentile, savez
@@ -22,7 +24,7 @@
 
 if sys.version_info >= (3, 8):
     from typing import Literal
-else:
+elif TYPE_CHECKING:
     from typing_extensions import Literal
 
 # Time in seconds to sleep before checking if ready

arch/unitroot/critical_values/simulation/dfgls_critical_values_simulation.py

@@ -3,13 +3,15 @@
 to MacKinnon (2010).  Makes use of parallel_fun in statsmodels which works
 best when joblib is installed.
 """
+from __future__ import annotations
+
 import datetime
 import sys
-from typing import Optional, cast
+from typing import TYPE_CHECKING, Optional, cast
 
 if sys.version_info >= (3, 8):
     from typing import Literal
-else:
+elif TYPE_CHECKING:
     from typing_extensions import Literal
 
 import numpy as np

arch/unitroot/critical_values/zivot_andrews.py

@@ -7,6 +7,8 @@
 The p-values are generated through Monte Carlo simulation using 100,000
 replications and 2000 data points.
 """
+from __future__ import annotations
+
 from numpy import array
 
 # constant-only model

arch/unitroot/unitroot.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from abc import ABCMeta, abstractmethod
 from typing import Dict, List, Optional, Sequence, Tuple, Union
 import warnings
@@ -25,6 +27,7 @@
     isnan,
     log,
     nan,
+    ndarray,
     ones,
     pi,
     polyval,
@@ -237,8 +240,8 @@ def _autolag_ols_low_memory(
     m = rhs.shape[1]
     xpx = empty((m + maxlag, m + maxlag)) * nan
     xpy = empty((m + maxlag, 1)) * nan
-    assert isinstance(xpx, NDArray)
-    assert isinstance(xpy, NDArray)
+    assert isinstance(xpx, ndarray)
+    assert isinstance(xpy, ndarray)
     xpy[:m] = rhs.T @ lhs
     xpx[:m, :m] = rhs.T @ rhs
     for i in range(maxlag):

arch/univariate/__init__.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from arch.univariate import recursions_python
 from arch.univariate.distribution import (
     Distribution,