update notebook

Author: MarcoGorelli

docs/source/notebooks/blackbox_external_likelihood.ipynb

@@ -51,7 +51,7 @@
     "import theano\n",
     "import theano.tensor as tt\n",
     "\n",
-    "print(\"Running on PyMC3 v{}\".format(pm.__version__))"
+    "print(f\"Running on PyMC3 v{pm.__version__}\")"
    ]
   },
   {
@@ -697,7 +697,7 @@
     "            color=colors[i],\n",
     "            hist_kwargs={\"density\": True},\n",
     "            **hist2dkwargs,\n",
-    "            truths=[mtrue, ctrue]\n",
+    "            truths=[mtrue, ctrue],\n",
     "        )\n",
     "    else:\n",
     "        corner.corner(\n",
@@ -729,15 +729,15 @@
     "test_grad_op_func = theano.function([var], test_grad_op(var))\n",
     "grad_vals = test_grad_op_func([mtrue, ctrue])\n",
     "\n",
-    "print('Gradient returned by \"LogLikeGrad\": {}'.format(grad_vals))\n",
+    "print(f'Gradient returned by \"LogLikeGrad\": {grad_vals}')\n",
     "\n",
     "# test the gradient called through LogLikeWithGrad\n",
     "test_gradded_op = LogLikeWithGrad(my_loglike, data, x, sigma)\n",
     "test_gradded_op_grad = tt.grad(test_gradded_op(var), var)\n",
     "test_gradded_op_grad_func = theano.function([var], test_gradded_op_grad)\n",
     "grad_vals_2 = test_gradded_op_grad_func([mtrue, ctrue])\n",
     "\n",
-    "print('Gradient returned by \"LogLikeWithGrad\": {}'.format(grad_vals_2))\n",
+    "print(f'Gradient returned by \"LogLikeWithGrad\": {grad_vals_2}')\n",
     "\n",
     "# test the gradient that PyMC3 uses for the Normal log likelihood\n",
     "test_model = pm.Model()\n",
@@ -751,7 +751,7 @@
     "    gradfunc.set_extra_values({\"m_interval__\": mtrue, \"c_interval__\": ctrue})\n",
     "    grad_vals_pymc3 = gradfunc(np.array([mtrue, ctrue]))[1]  # get dlogp values\n",
     "\n",
-    "print('Gradient returned by PyMC3 \"Normal\" distribution: {}'.format(grad_vals_pymc3))"
+    "print(f'Gradient returned by PyMC3 \"Normal\" distribution: {grad_vals_pymc3}')"
    ]
   },
   {