Run pre-commit filters on all files

python-adaptive · Sep 19, 2022 · 202d22a · 202d22a
1 parent 09e4f35
commit 202d22a
Showing 1 changed file with 14 additions and 12 deletions.
diff --git 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",
@@ -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"
@@ -568,7 +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",
@@ -817,7 +819,7 @@
    "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",