MAINT: Clean checks

arch/compat/numba.py

@@ -38,7 +38,7 @@ def wrapper(*args: Any, **kwargs: Any) -> Callable[..., Any]:
             return wrapper
 
         # Used as a decorator, e.g., @jit
-        def wrap(func):
+        def wrap(func: Callable[..., Any]) -> Callable[..., Any]:
             @functools.wraps(func)
             def wrapper(*args: Any, **kwargs: Any) -> Callable[..., Any]:
                 import warnings

arch/covariance/kernel.py

@@ -139,7 +139,7 @@ def one_sided_strict(self) -> Float64Array | DataFrame:
 
 
 @jit(nopython=True)
-def _cov_jit(df, k, num_weights, w, x):
+def _cov_jit(df: int, k: int, num_weights: int, w: np.ndarray, x: np.ndarray) -> np.ndarray:
     oss = np.zeros((k, k))
     for i in range(1, num_weights):
         oss += w[i] * (x[i:].T @ x[:-i]) / df

arch/unitroot/critical_values/simulation/engle_granger_simulation.py

@@ -9,6 +9,7 @@
 import os
 from random import shuffle
 import sys
+from typing import Sequence
 
 import colorama
 from joblib import Parallel, cpu_count, delayed
@@ -35,7 +36,7 @@
 def block(
     rg: Generator,
     trend: str,
-    sample_sizes: list[int],
+    sample_sizes: Sequence[int],
     cross_section_size: int,
     simulations: int,
     idx: int,

arch/unitroot/critical_values/simulation/phillips-ouliaris-simulation-process.py

@@ -229,23 +229,23 @@ def fit_pval_model(quantiles: pd.DataFrame | pd.Series) -> PvalueResult:
 plt.rc("figure", figsize=(16, 8))
 sns.set_style("darkgrid")
 pdf = matplotlib.backends.backend_pdf.PdfPages("output.pdf")
-for key in quantiles:
-    temp = quantiles[key]
+for quantile_key in quantiles:
+    temp = quantiles[quantile_key]
     y = temp.index.to_numpy()[:, None]
     x = temp.to_numpy()
-    stat = key[0]
+    stat = quantile_key[0]
     if stat in ("z_t", "z_a"):
         x = -1 * x
     if stat in ("p_u", "p_z"):
         y = 1 - y
     fig, ax = plt.subplots(1, 1)
     plt.plot(x, y)
-    plt.title(key)
+    plt.title(", ".join(quantile_key))
     pdf.savefig(fig)
     if stat in ("p_u", "p_z"):
         fig, ax = plt.subplots(1, 1)
         plt.plot(np.log(x), y)
-        plt.title(f"Log {key[0]}, {key[1]}")
+        plt.title(f"Log {quantile_key[0]}, {quantile_key[1]}")
         pdf.savefig(fig)
 pdf.close()
 

arch/unitroot/critical_values/simulation/phillips-ouliaris-simulation.py

@@ -314,7 +314,7 @@ def worker(
             )
             last_print_remaining = remaining
     results = results.quantile(QUANTILES)
-    results.to_hdf(full_path, "results")
+    results.to_hdf(full_path, key="results")
     if os.path.exists(temp_file_name(full_path)):
         try:
             os.unlink(temp_file_name(full_path))

arch/univariate/mean.py

@@ -309,7 +309,7 @@
 
         self._init_model()
 
-    def _scale_changed(self):
+    def _scale_changed(self) -> None:
         """
         Called when the scale has changed.  This allows the model
         to update any values that are affected by the scale changes,

doc/source/images/favicon.py

@@ -18,8 +18,8 @@
 price = data["Adj Close"]
 rets = 100 * price.resample("M").last().pct_change()
 
-l, u = rets.quantile([0.01, 0.99])
-bins = np.linspace(l, u, NBINS)
+lower, upper = rets.quantile([0.01, 0.99])
+bins = np.linspace(lower, upper, NBINS)
 fig = plt.figure(frameon=False)
 fig.set_size_inches(8, 8)
 ax = fig.add_subplot("111")

examples/bootstrap_examples.ipynb

@@ -20,7 +20,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn\n",
     "\n",
@@ -51,10 +50,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import arch.data.frenchdata\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
+    "import arch.data.frenchdata\n",
+    "\n",
     "ff = arch.data.frenchdata.load()"
    ]
   },
@@ -654,7 +654,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -668,7 +668,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

examples/multiple-comparison_examples.ipynb

@@ -20,7 +20,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn\n",
     "\n",
@@ -527,7 +526,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -541,7 +540,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

examples/unitroot_cointegration_examples.ipynb

@@ -24,7 +24,6 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn\n",
     "\n",
@@ -66,6 +65,7 @@
    ],
    "source": [
     "import numpy as np\n",
+    "\n",
     "from arch.data import crude\n",
     "\n",
     "data = crude.load()\n",
@@ -815,7 +815,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -829,7 +829,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

examples/univariate_forecasting_with_exogenous_variables.ipynb

@@ -980,7 +980,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

examples/univariate_volatility_modeling.ipynb

@@ -1349,7 +1349,8 @@
    ],
    "source": [
     "import numpy as np\n",
-    "from arch.univariate import GARCH, ConstantMean, SkewStudent\n",
+    "\n",
+    "from arch.univariate import ConstantMean, SkewStudent\n",
     "\n",
     "rs = np.random.RandomState([892380934, 189201902, 129129894, 9890437])\n",
     "# Save the initial state to reset later\n",
@@ -1483,7 +1484,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/univariate_volatility_scenarios.ipynb

@@ -29,6 +29,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
+    "\n",
     "from arch.univariate import GARCH, ConstantMean, Normal\n",
     "\n",
     "sns.set_style(\"darkgrid\")\n",
@@ -382,8 +383,6 @@
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
     "df = pd.concat(\n",
     "    [\n",
     "        forecasts.residual_variance.iloc[-1],\n",
@@ -530,7 +529,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

requirements-dev.txt

@@ -23,6 +23,7 @@ flake8
 mypy
 ruff
 pyupgrade>=3.4.0
+jupyterlab-code-formatter
 
 # Documentation
 ipython>=7