Remove warning filters when running PyMC sampling in Getting Started …

…tutorial
exoplanet-dev · Nov 2, 2023 · 525ed6c · 525ed6c
1 parent 06d182e
commit 525ed6c
Showing 1 changed file with 38 additions and 44 deletions.
diff --git a/docs/tutorials/first.ipynb b/docs/tutorials/first.ipynb
@@ -340,50 +340,44 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import warnings\n",
-    "\n",
-    "with warnings.catch_warnings():\n",
-    "    warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
-    "    warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
-    "\n",
-    "    import pymc as pm\n",
-    "    from celerite2.pymc import GaussianProcess, terms as pm_terms\n",
-    "\n",
-    "    with pm.Model() as model:\n",
-    "        mean = pm.Normal(\"mean\", mu=0.0, sigma=prior_sigma)\n",
-    "        log_jitter = pm.Normal(\"log_jitter\", mu=0.0, sigma=prior_sigma)\n",
-    "\n",
-    "        log_sigma1 = pm.Normal(\"log_sigma1\", mu=0.0, sigma=prior_sigma)\n",
-    "        log_rho1 = pm.Normal(\"log_rho1\", mu=0.0, sigma=prior_sigma)\n",
-    "        log_tau = pm.Normal(\"log_tau\", mu=0.0, sigma=prior_sigma)\n",
-    "        term1 = pm_terms.SHOTerm(\n",
-    "            sigma=pm.math.exp(log_sigma1),\n",
-    "            rho=pm.math.exp(log_rho1),\n",
-    "            tau=pm.math.exp(log_tau),\n",
-    "        )\n",
-    "\n",
-    "        log_sigma2 = pm.Normal(\"log_sigma2\", mu=0.0, sigma=prior_sigma)\n",
-    "        log_rho2 = pm.Normal(\"log_rho2\", mu=0.0, sigma=prior_sigma)\n",
-    "        term2 = pm_terms.SHOTerm(\n",
-    "            sigma=pm.math.exp(log_sigma2), rho=pm.math.exp(log_rho2), Q=0.25\n",
-    "        )\n",
-    "\n",
-    "        kernel = term1 + term2\n",
-    "        gp = GaussianProcess(kernel, mean=mean)\n",
-    "        gp.compute(t, diag=yerr**2 + pm.math.exp(log_jitter), quiet=True)\n",
-    "        gp.marginal(\"obs\", observed=y)\n",
-    "\n",
-    "        pm.Deterministic(\"psd\", kernel.get_psd(omega))\n",
-    "\n",
-    "        trace = pm.sample(\n",
-    "            tune=1000,\n",
-    "            draws=1000,\n",
-    "            target_accept=0.9,\n",
-    "            init=\"adapt_full\",\n",
-    "            cores=2,\n",
-    "            chains=2,\n",
-    "            random_seed=34923,\n",
-    "        )"
+    "import pymc as pm\n",
+    "from celerite2.pymc import GaussianProcess, terms as pm_terms\n",
+    "\n",
+    "with pm.Model() as model:\n",
+    "    mean = pm.Normal(\"mean\", mu=0.0, sigma=prior_sigma)\n",
+    "    log_jitter = pm.Normal(\"log_jitter\", mu=0.0, sigma=prior_sigma)\n",
+    "\n",
+    "    log_sigma1 = pm.Normal(\"log_sigma1\", mu=0.0, sigma=prior_sigma)\n",
+    "    log_rho1 = pm.Normal(\"log_rho1\", mu=0.0, sigma=prior_sigma)\n",
+    "    log_tau = pm.Normal(\"log_tau\", mu=0.0, sigma=prior_sigma)\n",
+    "    term1 = pm_terms.SHOTerm(\n",
+    "        sigma=pm.math.exp(log_sigma1),\n",
+    "        rho=pm.math.exp(log_rho1),\n",
+    "        tau=pm.math.exp(log_tau),\n",
+    "    )\n",
+    "\n",
+    "    log_sigma2 = pm.Normal(\"log_sigma2\", mu=0.0, sigma=prior_sigma)\n",
+    "    log_rho2 = pm.Normal(\"log_rho2\", mu=0.0, sigma=prior_sigma)\n",
+    "    term2 = pm_terms.SHOTerm(\n",
+    "        sigma=pm.math.exp(log_sigma2), rho=pm.math.exp(log_rho2), Q=0.25\n",
+    "    )\n",
+    "\n",
+    "    kernel = term1 + term2\n",
+    "    gp = GaussianProcess(kernel, mean=mean)\n",
+    "    gp.compute(t, diag=yerr**2 + pm.math.exp(log_jitter), quiet=True)\n",
+    "    gp.marginal(\"obs\", observed=y)\n",
+    "\n",
+    "    pm.Deterministic(\"psd\", kernel.get_psd(omega))\n",
+    "\n",
+    "    trace = pm.sample(\n",
+    "        tune=1000,\n",
+    "        draws=1000,\n",
+    "        target_accept=0.9,\n",
+    "        init=\"adapt_full\",\n",
+    "        cores=2,\n",
+    "        chains=2,\n",
+    "        random_seed=34923,\n",
+    "    )"
    ]
   },
   {