FMT: Run black

arch/bootstrap/__init__.py

@@ -1,6 +1,7 @@
 """
 Tools for implementing statistical bootstraps
 """
+
 from arch.bootstrap import _samplers_python
 from arch.bootstrap.base import (
     CircularBlockBootstrap,

arch/covariance/kernel.py

@@ -179,6 +179,7 @@ class CovarianceEstimator(ABC):
 
     where :math:`z=\frac{h}{H}, h=0, 1, \ldots, H` where H is the bandwidth.
     """
+
     _name = ""
 
     def __init__(

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 +6,7 @@
 did not appear in the original paper and have been computed using an identical
 simulation.
 """
+
 from __future__ import annotations
 
 from numpy import array, asarray, inf

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_z_critical_values_simulation.py

@@ -7,6 +7,7 @@
 
 Remote clusters can be used by modifying the call to Client.
 """
+
 import datetime
 from typing import Literal, cast
 

arch/unitroot/critical_values/simulation/adf_z_critical_values_simulation_joblib.py

@@ -1,6 +1,7 @@
 """
 Simulation of ADF z-test critical values.  Closely follows MacKinnon (2010).
 """
+
 from __future__ import annotations
 
 import argparse

arch/unitroot/critical_values/simulation/adf_z_critical_values_simulation_large_cluster.py

@@ -10,6 +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 annotations
 
 import datetime

arch/unitroot/critical_values/simulation/dfgls_critical_values_simulation.py

@@ -3,6 +3,7 @@
 to MacKinnon (2010).  Makes use of parallel_fun in statsmodels which works
 best when joblib is installed.
 """
+
 from __future__ import annotations
 
 import datetime

arch/unitroot/critical_values/simulation/engle_granger_simulation.py

@@ -1,6 +1,7 @@
 """
 Simulation for critical value production for Engle-Granger
 """
+
 import argparse
 import datetime as dt
 from functools import partial

arch/unitroot/critical_values/simulation/kpss_critical_values_simulation.py

@@ -2,6 +2,7 @@
 Calculates quantiles of the KPSS test statistic for both the constant
 and constant plus trend scenarios.
 """
+
 import os
 from typing import Optional, cast
 

arch/unitroot/critical_values/zivot_andrews.py

@@ -7,6 +7,7 @@
 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

arch/univariate/base.py

@@ -1,6 +1,7 @@
 """
 Core classes for ARCH models
 """
+
 from __future__ import annotations
 
 from abc import ABCMeta, abstractmethod

arch/univariate/mean.py

@@ -2,6 +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 annotations
 
 from collections.abc import Mapping, Sequence
@@ -240,6 +241,7 @@ class HARX(ARCHModel, metaclass=AbstractDocStringInheritor):
     where :math:`\bar{y}_{t-L_{i,0}:L_{i,1}}` is the average value of
     :math:`y_t` between :math:`t-L_{i,0}` and :math:`t - L_{i,1}`.
     """
+
     # To allow for addition mean parameters in simulation
     _extra_simulation_params: int = 0
 
@@ -1624,6 +1626,7 @@ class ARCHInMean(ARX):
 
     where :math:`f(\cdot)` is the function specified by ``form``.
     """
+
     # To account for GIM param
     _extra_simulation_params = 1
 

arch/univariate/recursions_python.py

@@ -4,6 +4,7 @@
 export ARCH_NO_BINARY=1
 python -m pip install .
 """
+
 from __future__ import annotations
 
 from arch.compat.numba import jit

arch/univariate/volatility.py

@@ -3,6 +3,7 @@
 inherit from :class:`VolatilityProcess` and provide the same methods with the
 same inputs.
 """
+
 from __future__ import annotations
 
 from abc import ABCMeta, abstractmethod

arch/utility/array.py

@@ -1,6 +1,7 @@
 """
 Utility functions that do not explicitly relate to Volatility modeling
 """
+
 from __future__ import annotations
 
 from arch.compat.pandas import is_datetime64_any_dtype
@@ -86,7 +87,7 @@ def ensure1d(
 
 
 def ensure2d(
-    x: (Sequence[float | int] | Sequence[Sequence[float | int]] | ArrayLike),
+    x: Sequence[float | int] | Sequence[Sequence[float | int]] | ArrayLike,
     name: str,
 ) -> DataFrame | NDArray:
     if isinstance(x, Series):

azure-pipelines.yml

@@ -14,6 +14,8 @@ variables:
   MPLBACKEND: agg
   PYTEST_PATTERN: "(not slow)"
   coverage: true
+  test.install: false
+  pip.pre: false
 
 jobs:
 - template: ci/azure/azure_template_posix.yml