pre-commit autoupdate (except jupytext)

.pre-commit-config.yaml

@@ -3,11 +3,11 @@ ci:
 
 repos:
 - repo: https://github.com/psf/black
-  rev: 23.7.0
+  rev: 24.2.0
   hooks:
     - id: black-jupyter
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.7.0
+  rev: 1.7.1
   hooks:
     - id: nbqa-isort
       additional_dependencies: [isort==5.6.4]

examples/howto/blackbox_external_likelihood_numpy.ipynb

@@ -164,7 +164,6 @@
    "source": [
     "# define a pytensor Op for our likelihood function\n",
     "class LogLike(pt.Op):\n",
-    "\n",
     "    \"\"\"\n",
     "    Specify what type of object will be passed and returned to the Op when it is\n",
     "    called. In our case we will be passing it a vector of values (the parameters\n",
@@ -446,7 +445,6 @@
     "\n",
     "\n",
     "class LogLikeGrad(pt.Op):\n",
-    "\n",
     "    \"\"\"\n",
     "    This Op will be called with a vector of values and also return a vector of\n",
     "    values - the gradients in each dimension.\n",

examples/howto/blackbox_external_likelihood_numpy.myst.md

@@ -119,7 +119,6 @@ So, what we actually need to do is create a {ref}`PyTensor Op <pytensor:creating
 ```{code-cell} ipython3
 # define a pytensor Op for our likelihood function
 class LogLike(pt.Op):
-
     """
     Specify what type of object will be passed and returned to the Op when it is
     called. In our case we will be passing it a vector of values (the parameters
@@ -294,7 +293,6 @@ class LogLikeWithGrad(pt.Op):
 
 
 class LogLikeGrad(pt.Op):
-
     """
     This Op will be called with a vector of values and also return a vector of
     values - the gradients in each dimension.

examples/ode_models/ODE_with_manual_gradients.ipynb

@@ -189,9 +189,9 @@
     "                ret = np.zeros(\n",
     "                    (self._n_states, self._n_odeparams + self._n_ivs)\n",
     "                )  # except the following entries\n",
-    "                ret[\n",
-    "                    0, 0\n",
-    "                ] = X  # \\frac{\\partial  [\\alpha X - \\beta XY]}{\\partial \\alpha}, and so on...\n",
+    "                ret[0, 0] = (\n",
+    "                    X  # \\frac{\\partial  [\\alpha X - \\beta XY]}{\\partial \\alpha}, and so on...\n",
+    "                )\n",
     "                ret[0, 1] = -X * Y\n",
     "                ret[1, 2] = -Y\n",
     "                ret[1, 3] = X * Y\n",

examples/ode_models/ODE_with_manual_gradients.myst.md

@@ -157,9 +157,9 @@ class LotkaVolterraModel:
                 ret = np.zeros(
                     (self._n_states, self._n_odeparams + self._n_ivs)
                 )  # except the following entries
-                ret[
-                    0, 0
-                ] = X  # \frac{\partial  [\alpha X - \beta XY]}{\partial \alpha}, and so on...
+                ret[0, 0] = (
+                    X  # \frac{\partial  [\alpha X - \beta XY]}{\partial \alpha}, and so on...
+                )
                 ret[0, 1] = -X * Y
                 ret[1, 2] = -Y
                 ret[1, 3] = X * Y

examples/samplers/samplers_mvnormal.py

@@ -6,7 +6,6 @@
 normalized effective sampling rates.
 """
 
-
 import time
 
 import arviz as az

scripts/rerun.py

@@ -14,6 +14,7 @@
 python scripts/rerun.py --fp_notebook=examples/case_studies/BEST.ipynb --commit_to=rerun-best --push_to=mine
 ```
 """
+
 import argparse
 import logging
 import pathlib

sphinxext/thumbnail_extractor.py

@@ -3,6 +3,7 @@
 
 Modified from the seaborn project, which modified the mpld3 project.
 """
+
 import base64
 import json
 import os