diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index edaef1c72..e386b0858 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -11,9 +11,17 @@ repos:
   - repo: https://github.com/psf/black
     rev: 23.3.0
     hooks:
-      - id: black
+      - id: black-jupyter
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     rev: "v0.0.261"
     hooks:
       - id: ruff
         args: ["--fix"]
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.7.0
+    hooks:
+      - id: nbqa-black
+        additional_dependencies: [jupytext, black]
+      - id: nbqa
+        args: ["ruff", "--fix", "--ignore=E402,B018,F704"]
+        additional_dependencies: [jupytext, ruff]
diff --git a/docs/source/algorithms_and_examples.md b/docs/source/algorithms_and_examples.md
index 3c702784a..e5c532dc3 100644
--- a/docs/source/algorithms_and_examples.md
+++ b/docs/source/algorithms_and_examples.md
@@ -1,15 +1,13 @@
 ---
-kernelspec:
-  name: python3
-  display_name: python3
 jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: '0.13'
-    jupytext_version: 1.13.8
-execution:
-  timeout: 300
+    format_version: 0.13
+    jupytext_version: 1.14.5
+kernelspec:
+  display_name: python3
+  name: python3
 ---
 
 ```{include} ../../README.md
@@ -101,16 +99,17 @@ def plot_loss_interval(learner):
     return hv.Scatter((x, y)).opts(size=6, color="r")
 
 
-def plot(learner, npoints):
-    adaptive.runner.simple(learner, npoints_goal= npoints)
+def plot_interval(learner, npoints):
+    adaptive.runner.simple(learner, npoints_goal=npoints)
     return (learner.plot() * plot_loss_interval(learner))[:, -1.1:1.1]
 
 
 def get_hm(loss_per_interval, N=101):
     learner = adaptive.Learner1D(f, bounds=(-1, 1), loss_per_interval=loss_per_interval)
-    plots = {n: plot(learner, n) for n in range(N)}
+    plots = {n: plot_interval(learner, n) for n in range(N)}
     return hv.HoloMap(plots, kdims=["npoints"])
 
+
 plot_homo = get_hm(uniform_loss).relabel("homogeneous sampling")
 plot_adaptive = get_hm(default_loss).relabel("with adaptive")
 layout = plot_homo + plot_adaptive
@@ -122,7 +121,6 @@ layout.opts(toolbar=None)
 ```{code-cell} ipython3
 :tags: [hide-input]
 
-
 def ring(xy):
     import numpy as np
 
@@ -131,7 +129,7 @@ def ring(xy):
     return x + np.exp(-((x**2 + y**2 - 0.75**2) ** 2) / a**4)
 
 
-def plot(learner, npoints):
+def plot_compare(learner, npoints):
     adaptive.runner.simple(learner, npoints_goal=npoints)
     learner2 = adaptive.Learner2D(ring, bounds=learner.bounds)
     xs = ys = np.linspace(*learner.bounds[0], int(learner.npoints**0.5))
@@ -146,7 +144,7 @@ def plot(learner, npoints):
 
 
 learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])
-plots = {n: plot(learner, n) for n in range(4, 1010, 20)}
+plots = {n: plot_compare(learner, n) for n in range(4, 1010, 20)}
 hv.HoloMap(plots, kdims=["npoints"]).collate()
 ```
 
@@ -155,7 +153,6 @@ hv.HoloMap(plots, kdims=["npoints"]).collate()
 ```{code-cell} ipython3
 :tags: [hide-input]
 
-
 def g(n):
     import random
 
@@ -167,12 +164,12 @@ def g(n):
 learner = adaptive.AverageLearner(g, atol=None, rtol=0.01)
 
 
-def plot(learner, npoints):
+def plot_avg(learner, npoints):
     adaptive.runner.simple(learner, npoints_goal=npoints)
     return learner.plot().relabel(f"loss={learner.loss():.2f}")
 
 
-plots = {n: plot(learner, n) for n in range(10, 10000, 200)}
+plots = {n: plot_avg(learner, n) for n in range(10, 10000, 200)}
 hv.HoloMap(plots, kdims=["npoints"])
 ```
 
@@ -181,7 +178,6 @@ hv.HoloMap(plots, kdims=["npoints"])
 ```{code-cell} ipython3
 :tags: [hide-input]
 
-
 def sphere(xyz):
     import numpy as np
 
diff --git a/docs/source/logo.md b/docs/source/logo.md
index c0baf5ddd..e7e553899 100644
--- a/docs/source/logo.md
+++ b/docs/source/logo.md
@@ -4,19 +4,16 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.1
+    jupytext_version: 1.14.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
   name: python3
-execution:
-  timeout: 300
 ---
 
 ```{code-cell} ipython3
 :tags: [remove-input]
 
-import os
 import functools
 import subprocess
 import tempfile
@@ -75,7 +72,7 @@ def remove_rounded_corners(fname):
 
 def learner_till(till, learner, data):
     new_learner = adaptive.Learner2D(None, bounds=learner.bounds)
-    new_learner.data = {k: v for k, v in data[:till]}
+    new_learner.data = dict(data[:till])
     for x, y in learner._bounds_points:
         # always include the bounds
         new_learner.tell((x, y), learner.data[x, y])
diff --git a/docs/source/tutorial/tutorial.BalancingLearner.md b/docs/source/tutorial/tutorial.BalancingLearner.md
index 5f43bdd64..b33a6f823 100644
--- a/docs/source/tutorial/tutorial.BalancingLearner.md
+++ b/docs/source/tutorial/tutorial.BalancingLearner.md
@@ -1,14 +1,15 @@
 ---
-kernelspec:
-  name: python3
-  display_name: python3
 jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: '0.13'
-    jupytext_version: 1.13.8
+    format_version: 0.13
+    jupytext_version: 1.14.5
+kernelspec:
+  display_name: python3
+  name: python3
 ---
+
 # Tutorial {class}`~adaptive.BalancingLearner`
 
 ```{note}
@@ -60,7 +61,10 @@ runner.live_info()
 ```
 
 ```{code-cell} ipython3
-plotter = lambda learner: hv.Overlay([L.plot() for L in learner.learners])
+def plotter(learner):
+    return hv.Overlay([L.plot() for L in learner.learners])
+
+
 runner.live_plot(plotter=plotter, update_interval=0.1)
 ```
 
@@ -83,7 +87,7 @@ combos = {
 }
 
 learner = adaptive.BalancingLearner.from_product(
-    jacobi, adaptive.Learner1D, dict(bounds=(0, 1)), combos
+    jacobi, adaptive.Learner1D, {"bounds": (0, 1)}, combos
 )
 
 runner = adaptive.BlockingRunner(learner, loss_goal=0.01)
diff --git a/docs/source/tutorial/tutorial.DataSaver.md b/docs/source/tutorial/tutorial.DataSaver.md
index 4d4e0efc4..1eb35e707 100644
--- a/docs/source/tutorial/tutorial.DataSaver.md
+++ b/docs/source/tutorial/tutorial.DataSaver.md
@@ -69,7 +69,7 @@ runner.live_info()
 ```
 
 ```{code-cell} ipython3
-runner.live_plot(plotter=lambda l: l.learner.plot(), update_interval=0.1)
+runner.live_plot(plotter=lambda lrn: lrn.learner.plot(), update_interval=0.1)
 ```
 
 Now the `DataSavingLearner` will have an dictionary attribute `extra_data` that has `x` as key and the data that was returned by `learner.function` as values.
diff --git a/docs/source/tutorial/tutorial.IntegratorLearner.md b/docs/source/tutorial/tutorial.IntegratorLearner.md
index 8110512a9..50aaf2e5b 100644
--- a/docs/source/tutorial/tutorial.IntegratorLearner.md
+++ b/docs/source/tutorial/tutorial.IntegratorLearner.md
@@ -1,14 +1,15 @@
 ---
-kernelspec:
-  name: python3
-  display_name: python3
 jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: '0.13'
-    jupytext_version: 1.13.8
+    format_version: 0.13
+    jupytext_version: 1.14.5
+kernelspec:
+  display_name: python3
+  name: python3
 ---
+
 # Tutorial {class}`~adaptive.IntegratorLearner`
 
 ```{note}
@@ -60,9 +61,7 @@ learner = adaptive.IntegratorLearner(f24, bounds=(0, 3), tol=1e-8)
 # We use a SequentialExecutor, which runs the function to be learned in
 # *this* process only. This means we don't pay
 # the overhead of evaluating the function in another process.
-runner = adaptive.Runner(
-    learner, executor=SequentialExecutor()
-)
+runner = adaptive.Runner(learner, executor=SequentialExecutor())
 ```
 
 ```{code-cell} ipython3
diff --git a/docs/source/tutorial/tutorial.Learner1D.md b/docs/source/tutorial/tutorial.Learner1D.md
index db80e03ec..de40a83d1 100644
--- a/docs/source/tutorial/tutorial.Learner1D.md
+++ b/docs/source/tutorial/tutorial.Learner1D.md
@@ -1,14 +1,15 @@
 ---
-kernelspec:
-  name: python3
-  display_name: python3
 jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: '0.13'
-    jupytext_version: 1.13.8
+    format_version: 0.13
+    jupytext_version: 1.14.5
+kernelspec:
+  display_name: python3
+  name: python3
 ---
+
 (TutorialLearner1D)=
 # Tutorial {class}`~adaptive.Learner1D`
 
@@ -112,6 +113,8 @@ random.seed(0)
 offsets = [random.uniform(-0.8, 0.8) for _ in range(3)]
 
 # sharp peaks at random locations in the domain
+
+
 def f_levels(x, offsets=offsets):
     a = 0.01
     return np.array(
@@ -124,7 +127,9 @@ The `Learner1D` can be used for such functions:
 
 ```{code-cell} ipython3
 learner = adaptive.Learner1D(f_levels, bounds=(-1, 1))
-runner = adaptive.Runner(learner, loss_goal=0.01)  # continue until `learner.loss()<=0.01`
+runner = adaptive.Runner(
+    learner, loss_goal=0.01
+)  # continue until `learner.loss()<=0.01`
 ```
 
 ```{code-cell} ipython3
@@ -211,12 +216,14 @@ learner.to_numpy()
 ```
 
 If Pandas is installed (optional dependency), you can also run
+
 ```{code-cell} ipython3
 df = learner.to_dataframe()
 df
 ```
 
 and load that data into a new learner with
+
 ```{code-cell} ipython3
 new_learner = adaptive.Learner1D(learner.function, (-1, 1))  # create an empty learner
 new_learner.load_dataframe(df)  # load the pandas.DataFrame's data
diff --git a/docs/source/tutorial/tutorial.Learner2D.md b/docs/source/tutorial/tutorial.Learner2D.md
index d15446fe4..7d0130fc6 100644
--- a/docs/source/tutorial/tutorial.Learner2D.md
+++ b/docs/source/tutorial/tutorial.Learner2D.md
@@ -1,14 +1,15 @@
 ---
-kernelspec:
-  name: python3
-  display_name: python3
 jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: '0.13'
-    jupytext_version: 1.13.8
+    format_version: 0.13
+    jupytext_version: 1.14.5
+kernelspec:
+  display_name: python3
+  name: python3
 ---
+
 # Tutorial {class}`~adaptive.Learner2D`
 
 ```{note}
@@ -24,6 +25,7 @@ import holoviews as hv
 import numpy as np
 
 from functools import partial
+
 adaptive.notebook_extension()
 ```
 
diff --git a/docs/source/tutorial/tutorial.LearnerND.md b/docs/source/tutorial/tutorial.LearnerND.md
index c03f6b5c8..46f948708 100644
--- a/docs/source/tutorial/tutorial.LearnerND.md
+++ b/docs/source/tutorial/tutorial.LearnerND.md
@@ -1,16 +1,15 @@
 ---
-kernelspec:
-  name: python3
-  display_name: python3
 jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: '0.13'
-    jupytext_version: 1.13.8
-execution:
-  timeout: 300
+    format_version: 0.13
+    jupytext_version: 1.14.5
+kernelspec:
+  display_name: python3
+  name: python3
 ---
+
 # Tutorial {class}`~adaptive.LearnerND`
 
 ```{note}
@@ -111,6 +110,7 @@ You could use the following code as an example:
 ```{code-cell} ipython3
 import scipy
 
+
 def f(xyz):
     x, y, z = xyz
     return x**4 + y**4 + z**4 - (x**2 + y**2 + z**2) ** 2
diff --git a/docs/source/tutorial/tutorial.advanced-topics.md b/docs/source/tutorial/tutorial.advanced-topics.md
index 92d4cae43..2dfc6cf29 100644
--- a/docs/source/tutorial/tutorial.advanced-topics.md
+++ b/docs/source/tutorial/tutorial.advanced-topics.md
@@ -1,14 +1,15 @@
 ---
-kernelspec:
-  name: python3
-  display_name: python3
 jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: '0.13'
-    jupytext_version: 1.13.8
+    format_version: 0.13
+    jupytext_version: 1.14.5
+kernelspec:
+  display_name: python3
+  name: python3
 ---
+
 # Advanced Topics
 
 ```{note}
@@ -24,7 +25,6 @@ import adaptive
 adaptive.notebook_extension()
 
 import asyncio
-from functools import partial
 import random
 
 offset = random.uniform(-0.5, 0.5)
@@ -92,7 +92,7 @@ def slow_f(x):
 learner = adaptive.Learner1D(slow_f, bounds=[0, 1])
 runner = adaptive.Runner(learner, npoints_goal=100)
 runner.start_periodic_saving(
-    save_kwargs=dict(fname="data/periodic_example.p"), interval=6
+    save_kwargs={"fname": "data/periodic_example.p"}, interval=6
 )
 ```
 
@@ -168,9 +168,7 @@ If you want to enable determinism, want to continue using the non-blocking {clas
 from adaptive.runner import SequentialExecutor
 
 learner = adaptive.Learner1D(f, bounds=(-1, 1))
-runner = adaptive.Runner(
-    learner, executor=SequentialExecutor(), loss_goal=0.01
-)
+runner = adaptive.Runner(learner, executor=SequentialExecutor(), loss_goal=0.01)
 ```
 
 ```{code-cell} ipython3
@@ -275,6 +273,7 @@ If the runner stopped due to an exception then asking for the result will raise
 
 ```{code-cell} ipython3
 :tags: [raises-exception]
+
 runner.task.result()
 ```
 
@@ -380,6 +379,7 @@ a slow part `g` which can be reused by multiple inputs and shared across functio
 ```{code-cell} ipython3
 import time
 
+
 def f(x):
     """
     Integer part of `x` repeats and should be reused
@@ -407,9 +407,10 @@ from dask import delayed
 # Convert g and h to dask.Delayed objects
 g, h = delayed(g), delayed(h)
 
+
 @delayed
 def f(x, y):
-    return (x + y)**2
+    return (x + y) ** 2
 ```
 
 Next we define a computation using coroutines such that it reuses previously submitted tasks.
@@ -421,6 +422,7 @@ client = await Client(asynchronous=True)
 
 g_futures = {}
 
+
 async def f_parallel(x):
     # Get or sumbit the slow function future
     if (g_future := g_futures.get(int(x))) is None:
diff --git a/docs/source/tutorial/tutorial.custom_loss.md b/docs/source/tutorial/tutorial.custom_loss.md
index f76af484d..222dc6306 100644
--- a/docs/source/tutorial/tutorial.custom_loss.md
+++ b/docs/source/tutorial/tutorial.custom_loss.md
@@ -1,14 +1,15 @@
 ---
-kernelspec:
-  name: python3
-  display_name: python3
 jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: '0.13'
-    jupytext_version: 1.13.8
+    format_version: 0.13
+    jupytext_version: 1.14.5
+kernelspec:
+  display_name: python3
+  name: python3
 ---
+
 # Custom adaptive logic for 1D and 2D
 
 ```{note}
@@ -25,7 +26,6 @@ adaptive.notebook_extension()
 
 # Import modules that are used in multiple cells
 import numpy as np
-from functools import partial
 import holoviews as hv
 ```
 
@@ -79,9 +79,6 @@ learner.plot().select(y=(0, 10000))
 ```
 
 ```{code-cell} ipython3
-from adaptive.runner import SequentialExecutor
-
-
 def uniform_sampling_2d(ip):
     from adaptive.learner.learner2D import areas
 
@@ -99,7 +96,9 @@ learner = adaptive.Learner2D(
 )
 
 # this takes a while, so use the async Runner so we know *something* is happening
-runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.03 or l.npoints > 1000)
+runner = adaptive.Runner(
+    learner, goal=lambda lrn: lrn.loss() < 0.03 or lrn.npoints > 1000
+)
 ```
 
 ```{code-cell} ipython3
@@ -113,7 +112,10 @@ runner.live_info()
 ```
 
 ```{code-cell} ipython3
-plotter = lambda l: l.plot(tri_alpha=0.3).relabel("1 / (x^2 + y^2) in log scale")
+def plotter(lrn):
+    return lrn.plot(tri_alpha=0.3).relabel("1 / (x^2 + y^2) in log scale")
+
+
 runner.live_plot(update_interval=0.2, plotter=plotter)
 ```
 
diff --git a/example-notebook.ipynb b/example-notebook.ipynb
index d3a739056..af6fa1888 100644
--- a/example-notebook.ipynb
+++ b/example-notebook.ipynb
@@ -27,11 +27,12 @@
     "\n",
     "adaptive.notebook_extension()\n",
     "\n",
+    "import random\n",
+    "from functools import partial\n",
+    "\n",
     "# Import modules that are used in multiple cells\n",
     "import holoviews as hv\n",
-    "import numpy as np\n",
-    "from functools import partial\n",
-    "import random"
+    "import numpy as np"
    ]
   },
   {
@@ -60,15 +61,15 @@
     "\n",
     "\n",
     "def peak(x, offset=offset, wait=True):\n",
-    "    from time import sleep\n",
     "    from random import random\n",
+    "    from time import sleep\n",
     "\n",
     "    a = 0.01\n",
     "    if wait:\n",
     "        # we pretend that this is a slow function\n",
     "        sleep(random())\n",
     "\n",
-    "    return x + a**2 / (a**2 + (x - offset)**2)"
+    "    return x + a**2 / (a**2 + (x - offset) ** 2)"
    ]
   },
   {
@@ -173,16 +174,17 @@
    "outputs": [],
    "source": [
     "def ring(xy, wait=True):\n",
-    "    import numpy as np\n",
-    "    from time import sleep\n",
     "    from random import random\n",
+    "    from time import sleep\n",
+    "\n",
+    "    import numpy as np\n",
     "\n",
     "    if wait:\n",
     "        # we pretend that this is a slow function\n",
     "        sleep(random() / 10)\n",
     "    x, y = xy\n",
     "    a = 0.2\n",
-    "    return x + np.exp(-((x**2 + y**2 - 0.75**2)**2) / a**4)\n",
+    "    return x + np.exp(-((x**2 + y**2 - 0.75**2) ** 2) / a**4)\n",
     "\n",
     "\n",
     "learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])"
@@ -223,7 +225,7 @@
     "# Create a learner and add data on homogeneous grid, so that we can plot it\n",
     "learner2 = adaptive.Learner2D(ring, bounds=learner.bounds)\n",
     "n = int(learner.npoints**0.5)\n",
-    "xs, ys = [np.linspace(*bounds, n) for bounds in learner.bounds]\n",
+    "xs, ys = (np.linspace(*bounds, n) for bounds in learner.bounds)\n",
     "xys = list(itertools.product(xs, ys))\n",
     "zs = [ring(xy, wait=False) for xy in xys]\n",
     "learner2.tell_many(xys, zs)\n",
@@ -233,7 +235,7 @@
     "    + learner.plot().relabel(\"With adaptive\")\n",
     "    + learner2.plot(n, tri_alpha=0.4)\n",
     "    + learner.plot(tri_alpha=0.4)\n",
-    ").cols(2).opts({\"EdgePaths\": dict(color=\"w\")})"
+    ").cols(2).opts({\"EdgePaths\": {\"color\": \"w\"}})"
    ]
   },
   {
@@ -316,7 +318,7 @@
    "source": [
     "def noisy_peak(seed_x, sigma=0, peak_width=0.05, offset=-0.5):\n",
     "    seed, x = seed_x\n",
-    "    y = x**3 - x + 3 * peak_width**2 / (peak_width**2 + (x - offset)**2)\n",
+    "    y = x**3 - x + 3 * peak_width**2 / (peak_width**2 + (x - offset) ** 2)\n",
     "    rng = np.random.RandomState(int(seed))\n",
     "    noise = rng.normal(scale=sigma)\n",
     "    return y + noise"
@@ -373,8 +375,8 @@
     "learner = adaptive.AverageLearner1D(partial(noisy_peak, sigma=1), bounds=(-2, 2))\n",
     "\n",
     "\n",
-    "def goal(l):\n",
-    "    return l.nsamples >= 10_000 and l.min_samples_per_point >= 20\n",
+    "def goal(lrn):\n",
+    "    return lrn.nsamples >= 10_000 and lrn.min_samples_per_point >= 20\n",
     "\n",
     "\n",
     "runner = adaptive.Runner(learner, goal=goal)\n",
@@ -446,10 +448,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We initialize a learner again and pass the bounds and relative tolerance we want to reach. Then in the `Runner` we pass `goal=lambda l: l.done()` where `learner.done()` is `True` when the relative tolerance has been reached."
+    "We initialize a learner again and pass the bounds and relative tolerance we want to reach. Then in the `Runner` we pass `goal=lambda lrn: lrn.done()` where `learner.done()` is `True` when the relative tolerance has been reached."
    ]
   },
   {
@@ -515,9 +518,9 @@
     "random.seed(0)\n",
     "offsets = [random.uniform(-0.8, 0.8) for _ in range(3)]\n",
     "\n",
+    "\n",
     "# sharp peaks at random locations in the domain\n",
     "def f_levels(x, offsets=offsets):\n",
-    "    a = 0.01\n",
     "    return np.array([offset + peak(x, offset, wait=False) for offset in offsets])"
    ]
   },
@@ -567,7 +570,7 @@
     "def sphere(xyz):\n",
     "    x, y, z = xyz\n",
     "    a = 0.4\n",
-    "    return x + z**2 + np.exp(-((x**2 + y**2 + z**2 - 0.75**2)**2) / a**4)\n",
+    "    return x + z**2 + np.exp(-((x**2 + y**2 + z**2 - 0.75**2) ** 2) / a**4)\n",
     "\n",
     "\n",
     "learner = adaptive.LearnerND(sphere, bounds=[(-1, 1), (-1, 1), (-1, 1)])\n",
@@ -612,7 +615,7 @@
    "source": [
     "def plot_cut(x1, x2, directions, learner=learner):\n",
     "    cut_mapping = {\"xyz\".index(d): x for d, x in zip(directions, [x1, x2])}\n",
-    "    return learner.plot_slice(cut_mapping).opts({\"Path\": dict(framewise=True)})\n",
+    "    return learner.plot_slice(cut_mapping).opts({\"Path\": {\"framewise\": True}})\n",
     "\n",
     "\n",
     "dm = hv.DynamicMap(plot_cut, kdims=[\"v1\", \"v2\", \"directions\"])\n",
@@ -709,7 +712,7 @@
     "\n",
     "def plot_logz(learner):\n",
     "    p = learner.plot(tri_alpha=0.3).relabel(\"1 / (x^2 + y^2) in log scale\")\n",
-    "    return p.opts({\"Image\": dict(logz=True), \"EdgePaths\": dict(color=\"w\")})\n",
+    "    return p.opts({\"Image\": {\"logz\": True}, \"EdgePaths\": {\"color\": \"w\"}})\n",
     "\n",
     "\n",
     "learner = adaptive.Learner2D(\n",
@@ -818,7 +821,7 @@
    "source": [
     "def h(x, offset=0):\n",
     "    a = 0.01\n",
-    "    return x + a**2 / (a**2 + (x - offset)**2)\n",
+    "    return x + a**2 / (a**2 + (x - offset) ** 2)\n",
     "\n",
     "\n",
     "learners = [\n",
@@ -837,7 +840,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter = lambda learner: hv.Overlay([L.plot() for L in learner.learners])\n",
+    "def plotter(learner):\n",
+    "    return hv.Overlay([lrn.plot() for lrn in learner.learners])\n",
+    "\n",
+    "\n",
     "runner.live_plot(plotter=plotter, update_interval=0.1)"
    ]
   },
@@ -868,7 +874,7 @@
     "}\n",
     "\n",
     "learner = adaptive.BalancingLearner.from_product(\n",
-    "    jacobi, adaptive.Learner1D, dict(bounds=(0, 1)), combos\n",
+    "    jacobi, adaptive.Learner1D, {\"bounds\": (0, 1)}, combos\n",
     ")\n",
     "\n",
     "runner = adaptive.BlockingRunner(learner, loss_goal=0.01)\n",
@@ -946,7 +952,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "runner.live_plot(plotter=lambda l: l.learner.plot(), update_interval=0.1)"
+    "runner.live_plot(plotter=lambda lrn: lrn.learner.plot(), update_interval=0.1)"
    ]
   },
   {
@@ -1243,7 +1249,7 @@
     "runner = adaptive.Runner(learner, npoints_goal=100)\n",
     "\n",
     "runner.start_periodic_saving(\n",
-    "    save_kwargs=dict(fname=\"data/periodic_example.p\"), interval=6\n",
+    "    save_kwargs={\"fname\": \"data/periodic_example.p\"}, interval=6\n",
     ")\n",
     "\n",
     "runner.live_info()"
@@ -1394,9 +1400,7 @@
     "\n",
     "learner = adaptive.Learner1D(peak, bounds=(-1, 1))\n",
     "\n",
-    "runner = adaptive.Runner(\n",
-    "    learner, executor=SequentialExecutor(), loss_goal=0.002\n",
-    ")\n",
+    "runner = adaptive.Runner(learner, executor=SequentialExecutor(), loss_goal=0.002)\n",
     "runner.live_info()\n",
     "runner.live_plot(update_interval=0.1)"
    ]
@@ -1573,7 +1577,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "learner.plot().Scatter.I.opts(style=dict(size=6)) * reconstructed_learner.plot()"
+    "learner.plot().Scatter.I.opts(style={\"size\": 6}) * reconstructed_learner.plot()"
    ]
   },
   {
diff --git a/readthedocs.yml b/readthedocs.yml
index 280e95ee7..23fab10c6 100644
--- a/readthedocs.yml
+++ b/readthedocs.yml
@@ -1,7 +1,7 @@
 version: 2
 
 build:
-    os: "ubuntu-20.04"
+    os: "ubuntu-22.04"
     tools:
         python: "mambaforge-4.10"