edit titles to distinguish the two different versions

Author: OriolAbril

examples/case_studies/blackbox_external_likelihood.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Using a \"black box\" likelihood function"
+    "# Using a \"black box\" likelihood function (Cython)"
    ]
   },
   {
@@ -832,7 +832,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/case_studies/blackbox_external_likelihood_no_cython.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Using a \"black box\" likelihood function"
+    "# Using a \"black box\" likelihood function (numpy)"
    ]
   },
   {
@@ -79,11 +79,12 @@
    "source": [
     "def my_model(theta, x):\n",
     "    m, c = theta\n",
-    "    return m*x + c\n",
+    "    return m * x + c\n",
+    "\n",
     "\n",
     "def my_loglike(theta, x, data, sigma):\n",
-    "    model= my_model(theta, x)\n",
-    "    return -(0.5/sigma**2)*np.sum((data - model)**2)"
+    "    model = my_model(theta, x)\n",
+    "    return -(0.5 / sigma ** 2) * np.sum((data - model) ** 2)"
    ]
   },
   {
@@ -327,7 +328,7 @@
     "        the gradient of that function\n",
     "    x, data, sigma:\n",
     "        Observed variables as we have been using so far\n",
-    "    \n",
+    "\n",
     "\n",
     "    Returns\n",
     "    -------\n",
@@ -336,10 +337,10 @@
     "    \"\"\"\n",
     "\n",
     "    grads = np.empty(2)\n",
-    "    aux_vect = (data - my_model(theta, x)) # /(2*sigma**2)\n",
-    "    grads[0] = np.sum(aux_vect*x)\n",
+    "    aux_vect = data - my_model(theta, x)  # /(2*sigma**2)\n",
+    "    grads[0] = np.sum(aux_vect * x)\n",
     "    grads[1] = np.sum(aux_vect)\n",
-    "    \n",
+    "\n",
     "    return grads"
    ]
   },
@@ -673,10 +674,10 @@
    ],
    "source": [
     "_, axes = plt.subplots(3, 2, sharex=True, sharey=True)\n",
-    "az.plot_autocorr(idata_mh, combined=True, ax=axes[0,:]);\n",
-    "az.plot_autocorr(idata_grad, combined=True, ax=axes[1,:]);\n",
-    "az.plot_autocorr(idata, combined=True, ax=axes[2,:]);\n",
-    "axes[2,0].set_xlim(right=40);"
+    "az.plot_autocorr(idata_mh, combined=True, ax=axes[0, :])\n",
+    "az.plot_autocorr(idata_grad, combined=True, ax=axes[1, :])\n",
+    "az.plot_autocorr(idata, combined=True, ax=axes[2, :])\n",
+    "axes[2, 0].set_xlim(right=40);"
    ]
   },
   {
@@ -711,11 +712,11 @@
    "source": [
     "pair_kwargs = dict(\n",
     "    kind=\"kde\",\n",
-    "    marginals=True, \n",
-    "    reference_values={\"m\": mtrue, \"c\": ctrue}, \n",
+    "    marginals=True,\n",
+    "    reference_values={\"m\": mtrue, \"c\": ctrue},\n",
     "    kde_kwargs={\"contourf_kwargs\": {\"alpha\": 0}, \"contour_kwargs\": {\"colors\": \"C0\"}},\n",
     "    reference_values_kwargs={\"color\": \"k\", \"ms\": 15, \"marker\": \"d\"},\n",
-    "    marginal_kwargs={\"color\": \"C0\"}\n",
+    "    marginal_kwargs={\"color\": \"C0\"},\n",
     ")\n",
     "ax = az.plot_pair(idata_mh, **pair_kwargs)\n",
     "pair_kwargs[\"kde_kwargs\"][\"contour_kwargs\"][\"colors\"] = \"C1\"\n",

examples/table_of_contents_tutorials.js

@@ -16,6 +16,7 @@ Gallery.contents = {
     "samplers/DEMetropolisZ_tune_drop_fraction": "Deep dives",
     "case_studies/factor_analysis": "Deep dives",
     "case_studies/blackbox_external_likelihood": "How-To",
+    "case_studies/blackbox_external_likelihood_no_cython": "How-To",
     "pymc3_howto/profiling": "How-To",
     "pymc3_howto/howto_debugging": "How-To",
     "diagnostics_and_criticism/model_averaging": "How-To",