Rerunning precommit

Author: ckrapu

examples/gaussian_processes/GP-Kron.ipynb

@@ -504,7 +504,7 @@
     "    fnew = gp.conditional(\"fnew\", Xnew)\n",
     "\n",
     "with model:\n",
-    "    ppc = pm.sample_posterior_predictive(tr, 200, var_names=['fnew'])"
+    "    ppc = pm.sample_posterior_predictive(tr, 200, var_names=[\"fnew\"])"
    ]
   },
   {
@@ -540,9 +540,7 @@
     "    Xnew[:, 0], Xnew[:, 1], s=30, c=np.mean(ppc[\"fnew\"], axis=0), marker=\"s\", norm=norm, cmap=cmap\n",
     ")\n",
     "plt.ylabel(\"x2\"), plt.xlabel(\"x1\")\n",
-    "plt.title(\n",
-    "    \"observed data 'y' (circles) with mean of conditional, or predicted, samples (squares)\"\n",
-    ");"
+    "plt.title(\"observed data 'y' (circles) with mean of conditional, or predicted, samples (squares)\");"
    ]
   },
   {
@@ -573,11 +571,11 @@
     }
    ],
    "source": [
-    "fig, axs = plt.subplots(2,2, figsize=(24, 16))\n",
-    "axs      = axs.ravel()\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(24, 16))\n",
+    "axs = axs.ravel()\n",
     "\n",
     "for i, ax in enumerate(axs):\n",
-    "    ax.axis('off')   \n",
+    "    ax.axis(\"off\")\n",
     "    ax.scatter(X[:, 0], X[:, 1], s=20, c=y, marker=\"o\", norm=norm, cmap=cmap)\n",
     "    ax.scatter(Xnew[:, 0], Xnew[:, 1], s=20, c=ppc[\"fnew\"][i], marker=\"s\", norm=norm, cmap=cmap)\n",
     "    ax.set_title(f\"Sample {i+1}\", fontsize=24)"