[pre-commit.ci] pre-commit autoupdate (#150)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/PyCQA/isort: 5.11.4 → 5.12.0](PyCQA/isort@5.11.4...5.12.0)
- [github.com/psf/black: 22.12.0 → 23.1.0](psf/black@22.12.0...23.1.0)
- [github.com/hadialqattan/pycln: v2.1.2 → v2.1.3](hadialqattan/pycln@v2.1.2...v2.1.3)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -9,13 +9,13 @@ repos:
       - id: end-of-file-fixer
         exclude_types: [json, binary]
   - repo: https://github.com/PyCQA/isort
-    rev: "5.11.4"
+    rev: "5.12.0"
     hooks:
       - id: isort
         additional_dependencies: [toml]
         exclude: docs/tutorials
   - repo: https://github.com/psf/black
-    rev: "22.12.0"
+    rev: "23.1.0"
     hooks:
       - id: black-jupyter
   - repo: https://github.com/kynan/nbstripout
@@ -24,7 +24,7 @@ repos:
       - id: nbstripout
         exclude: docs/benchmarks.ipynb
   - repo: https://github.com/hadialqattan/pycln
-    rev: "v2.1.2"
+    rev: "v2.1.3"
     hooks:
       - id: pycln
   - repo: https://github.com/pre-commit/mirrors-mypy

docs/tutorials/intro.ipynb

@@ -232,6 +232,7 @@
     "gp = GaussianProcess(kernel, X, diag=1e-5, mean=2.0)\n",
     "y_const = gp.sample(jax.random.PRNGKey(4), shape=(5,))\n",
     "\n",
+    "\n",
     "# And a GP with a general mean function\n",
     "def mean_function(x):\n",
     "    return 5 * jax.numpy.sin(x)\n",

docs/tutorials/quickstart.ipynb

@@ -109,7 +109,6 @@
     "\n",
     "\n",
     "def build_gp(theta, X):\n",
-    "\n",
     "    # We want most of our parameters to be positive so we take the `exp` here\n",
     "    # Note that we're using `jnp` instead of `np`\n",
     "    amps = jnp.exp(theta[\"log_amps\"])\n",

tests/test_solvers/test_quasisep/test_general.py

@@ -14,7 +14,7 @@ def test_matmul():
     x2 = np.sort(random.uniform(2, 8, 75))
     kernel = quasisep.Matern52(sigma=1.5, scale=3.4)
 
-    for (x1, x2) in [(x1, x2), (x1, x1), (x2, x1)]:
+    for x1, x2 in [(x1, x2), (x1, x1), (x2, x1)]:
         y = np.sin(x2)[:, None]
         K = kernel(x1, x2)
         mat = kernel.to_general_qsm(x1, x2)