Tidied up code in examples-bayes-gLV

Removed some commented out code as no longer required

Author: ChaniaClare

Diff for: examples/gLV/examples-bayes-gLV.ipynb

@@ -247,7 +247,7 @@
     }
    ],
    "source": [
-    "# read in pickled simulated parameters, mu, M, epsilon\n",
+    "# read in pickled simulated parameters, mu, M, epsilon, created in examples-sim-gLV.ipynb\n",
     "num_species = 5\n",
     "with open(\"params-s5.pkl\", \"rb\") as f:\n",
     "    params = pickle.load(f)\n",
@@ -818,20 +818,6 @@
     "#inference.plot_posterior(idata)\n",
     "\n",
     "\n",
-    "\n",
-    "\n",
-    "#nX = num_species\n",
-    "#n_obs = times.shape[0] - 1\n",
-    "#noise_stddev = 0.1\n",
-    "\n",
-    "# Params for shrinkage on M_ij (non diagonal elements)\n",
-    "#DA = nX*nX - nX\n",
-    "#DA0 = 3     # expected number of non zero entries in M_ij\n",
-    "#N = n_obs - 2\n",
-    "\n",
-    "#inference = infergLVbayes(X, F, mu_prior, M_prior, DA=DA, DA0=DA0, N=N, noise_stddev=noise_stddev)\n",
-    "#idata = inference.run_bayes_gLV_shrinkage()\n",
-    "\n",
     "# print summary\n",
     "summary = az.summary(idata, var_names=[\"mu_hat\", \"M_ii_hat\", \"M_ij_hat\", \"M_hat\", \"sigma\"])\n",
     "print(summary[[\"mean\", \"sd\", \"r_hat\"]])\n",
@@ -862,7 +848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "c6d6c2df",
    "metadata": {
     "ExecuteTime": {
@@ -1108,20 +1094,6 @@
     "#inference.plot_posterior_pert(idata)\n",
     "\n",
     "\n",
-    "\n",
-    "\n",
-    "#nX = num_species\n",
-    "#n_obs = times.shape[0] - 1\n",
-    "#noise_stddev = 0.1\n",
-    "\n",
-    "# Params for shrinkage on M_ij (non diagonal elements)\n",
-    "#DA = nX*nX - nX\n",
-    "#DA0 = 3     # expected number of non zero entries in M_ij\n",
-    "#N = n_obs - 2\n",
-    "\n",
-    "#inference = infergLVbayes(X, F, mu_prior, M_prior, DA=DA, DA0=DA0, N=N, noise_stddev=noise_stddev, epsilon=epsilon)\n",
-    "#idata = inference.run_bayes_gLV_shrinkage_pert()\n",
-    "\n",
     "# print summary\n",
     "summary = az.summary(idata, var_names=[\"mu_hat\", \"M_ii_hat\", \"M_ij_hat\", \"M_hat\", \"epsilon_hat\", \"sigma\"])\n",
     "print(summary[[\"mean\", \"sd\", \"r_hat\"]])\n",