Enable sphinx-lint pre-commit hook

Author: Armavica

.pre-commit-config.yaml

@@ -20,6 +20,11 @@ repos:
   rev: 054bda51dbe278b3e86f27c890e3f3ac877d616c
   hooks:
     - id: validate-cff
+- repo: https://github.com/sphinx-contrib/sphinx-lint
+  rev: v1.0.0
+  hooks:
+    - id: sphinx-lint
+      args: ["."]
 - repo: https://github.com/lucianopaz/head_of_apache
   rev: "0.0.3"
   hooks:
@@ -31,7 +36,7 @@ repos:
         - --exclude=binder/
         - --exclude=versioneer.py
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.5.4
+  rev: v0.6.5
   hooks:
     - id: ruff
       args: ["--fix", "--output-format=full"]

docs/source/guides/Gaussian_Processes.rst

@@ -151,7 +151,7 @@ conditioned on.
 Calling the `prior` method will create a PyMC random variable that represents
 the latent function :math:`f(x) = \mathbf{f}`::
 
-	f = gp.prior("f", X)
+    f = gp.prior("f", X)
 
 :code:`f` is a random variable that can be used within a PyMC model like any
 other type of random variable.  The first argument is the name of the random
@@ -166,7 +166,7 @@ Usually at this point, inference is performed on the model.  The
 distribution over the latent function at arbitrary :math:`x_*` input points,
 :math:`f(x_*)`.  To construct the conditional distribution we write::
 
-	f_star = gp.conditional("f_star", X_star)
+    f_star = gp.conditional("f_star", X_star)
 
 Additive GPs
 ============

docs/source/learn/core_notebooks/GLM_linear.ipynb

@@ -67,9 +67,10 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pymc as pm\n",
     "import xarray as xr\n",
     "\n",
+    "import pymc as pm\n",
+    "\n",
     "from pymc import HalfCauchy, Model, Normal, sample\n",
     "\n",
     "print(f\"Running on PyMC v{pm.__version__}\")"
@@ -256,8 +257,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "\n",
     "try:\n",
     "    import bambi as bmb\n",
     "except ImportError:\n",

docs/source/learn/core_notebooks/Gaussian_Processes.rst

@@ -148,7 +148,7 @@ conditioned on.
 Calling the `prior` method will create a PyMC random variable that represents
 the latent function :math:`f(x) = \mathbf{f}`::
 
-	f = gp.prior("f", X)
+    f = gp.prior("f", X)
 
 :code:`f` is a random variable that can be used within a PyMC model like any
 other type of random variable.  The first argument is the name of the random
@@ -163,7 +163,7 @@ Usually at this point, inference is performed on the model.  The
 distribution over the latent function at arbitrary :math:`x_*` input points,
 :math:`f(x_*)`.  To construct the conditional distribution we write::
 
-	f_star = gp.conditional("f_star", X_star)
+    f_star = gp.conditional("f_star", X_star)
 
 .. _additive_gp:
 

docs/source/learn/core_notebooks/dimensionality.ipynb

@@ -37,9 +37,10 @@
    "source": [
     "from functools import partial\n",
     "\n",
-    "import pymc as pm\n",
     "import numpy as np\n",
-    "import pytensor.tensor as pt"
+    "import pytensor.tensor as pt\n",
+    "\n",
+    "import pymc as pm"
    ]
   },
   {

docs/source/learn/core_notebooks/model_comparison.ipynb

@@ -15,8 +15,8 @@
    ],
    "source": [
     "import arviz as az\n",
-    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
+    "\n",
     "import pymc as pm\n",
     "\n",
     "print(f\"Running on PyMC v{pm.__version__}\")"

docs/source/learn/core_notebooks/posterior_predictive.ipynb

@@ -36,11 +36,11 @@
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import pymc as pm\n",
     "import xarray as xr\n",
     "\n",
     "from scipy.special import expit as logistic\n",
     "\n",
+    "import pymc as pm\n",
     "\n",
     "print(f\"Running on PyMC v{pm.__version__}\")"
    ]

docs/source/learn/core_notebooks/pymc_overview.ipynb

@@ -34,12 +34,13 @@
    },
    "outputs": [],
    "source": [
-    "import pytensor\n",
-    "import pytensor.tensor as pt\n",
-    "import pymc as pm\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import scipy.stats"
+    "import pytensor\n",
+    "import pytensor.tensor as pt\n",
+    "import scipy.stats\n",
+    "\n",
+    "import pymc as pm"
    ]
   },
   {
@@ -1838,7 +1839,7 @@
      "text": [
       "\n",
       "mu_value -> -1.612085713764618\n",
-      "sigma_log_value -> -11.324403641427345 \n",
+      "sigma_log_value -> -11.324403641427345\n",
       "x_value -> 9.081061466795328\n",
       "\n"
      ]
@@ -1848,7 +1849,7 @@
     "print(\n",
     "    f\"\"\"\n",
     "mu_value -> {scipy.stats.norm.logpdf(x=0, loc=0, scale=2)}\n",
-    "sigma_log_value -> {- 10 + scipy.stats.halfnorm.logpdf(x=np.exp(-10), loc=0, scale=3)} \n",
+    "sigma_log_value -> {- 10 + scipy.stats.halfnorm.logpdf(x=np.exp(-10), loc=0, scale=3)}\n",
     "x_value -> {scipy.stats.norm.logpdf(x=0, loc=0, scale=np.exp(-10))}\n",
     "\"\"\"\n",
     ")"