re-running pre-commit

Rethinking_2/Chp_05.ipynb

@@ -490,8 +490,8 @@
     "\n",
     "x = np.linspace(-2, 2, 50)\n",
     "\n",
-    "prior_samp_a = az.extract(idata_5_1, group = \"prior\", var_names=\"a\").to_numpy()[:50]\n",
-    "prior_samp_bA = az.extract(idata_5_1, group = \"prior\", var_names=\"bA\").to_numpy()[:50]\n",
+    "prior_samp_a = az.extract(idata_5_1, group=\"prior\", var_names=\"a\").to_numpy()[:50]\n",
+    "prior_samp_bA = az.extract(idata_5_1, group=\"prior\", var_names=\"bA\").to_numpy()[:50]\n",
     "\n",
     "for a, ba in zip(prior_samp_a, prior_samp_bA):\n",
     "    y = a + ba * x\n",
@@ -2396,7 +2396,9 @@
    ],
    "source": [
     "mu = marriage_mu_counterfactual.T\n",
-    "sigma = az.extract(idata_5_3_A_marriage_ppc, group=\"posterior_predictive\", var_names=\"sigma_m\").to_numpy()\n",
+    "sigma = az.extract(\n",
+    "    idata_5_3_A_marriage_ppc, group=\"posterior_predictive\", var_names=\"sigma_m\"\n",
+    ").to_numpy()\n",
     "M_sim = stats.norm(loc=mu, scale=sigma).rvs()\n",
     "M_sim.shape"
    ]

Rethinking_2/Chp_08.ipynb

@@ -1951,11 +1951,11 @@
    "source": [
     "with m_8_4:\n",
     "    prior_8_4 = pm.sample_prior_predictive(var_names=[\"a\", \"bw\", \"bs\"])\n",
-    "    prior_8_4 = az.extract(prior_8_4, group = \"prior\")\n",
+    "    prior_8_4 = az.extract(prior_8_4, group=\"prior\")\n",
     "\n",
     "with m_8_5:\n",
     "    prior_8_5 = pm.sample_prior_predictive(var_names=[\"a\", \"bw\", \"bs\", \"bws\"])\n",
-    "    prior_8_5 = az.extract(prior_8_5, group = \"prior\")"
+    "    prior_8_5 = az.extract(prior_8_5, group=\"prior\")"
    ]
   },
   {

Rethinking_2/Chp_09.ipynb

@@ -23,9 +23,8 @@
     "import pandas as pd\n",
     "import pymc as pm\n",
     "\n",
-    "from scipy import stats\n",
     "from numpy.random import default_rng\n",
-    "\n",
+    "from scipy import stats\n",
     "\n",
     "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
     "pd.set_option(\"mode.chained_assignment\", None)"
@@ -1163,9 +1162,7 @@
    "source": [
     "# Figure 9.10\n",
     "with m_9_3:\n",
-    "    m_9_3_prior = az.extract(\n",
-    "        pm.sample_prior_predictive(var_names=[\"alpha\", \"sigma\"])[\"prior\"]\n",
-    "    )\n",
+    "    m_9_3_prior = az.extract(pm.sample_prior_predictive(var_names=[\"alpha\", \"sigma\"])[\"prior\"])\n",
     "    m_9_3_post = az.extract(idata_9_3, group=\"posterior\", var_names=[\"alpha\", \"sigma\"])\n",
     "\n",
     "_, axs = plt.subplots(1, 2, figsize=[8, 3.5], constrained_layout=True)\n",

Rethinking_2/Chp_12.ipynb

@@ -480,7 +480,9 @@
    "source": [
     "gender_ = \"female\"\n",
     "x_plot = np.linspace(0, 1, 100)\n",
-    "post_theta = az.extract(idata_12_1.sel(gender=gender_), group=\"posterior\", var_names=\"theta\").to_numpy()\n",
+    "post_theta = az.extract(\n",
+    "    idata_12_1.sel(gender=gender_), group=\"posterior\", var_names=\"theta\"\n",
+    ").to_numpy()\n",
     "\n",
     "# draw posterior mean beta distribution:\n",
     "pbar_mean = logistic(post_a.sel(gender=gender_)).mean()\n",

Rethinking_2/Chp_14.ipynb

@@ -834,9 +834,7 @@
     "ax.text(0, 0.8, \"prior\", horizontalalignment=\"center\")\n",
     "\n",
     "az.plot_kde(\n",
-    "    np.array(\n",
-    "        az.extract(post[\"Rho\"].sel(chol_cov_corr_dim_0=1, chol_cov_corr_dim_1=0)).to_array()\n",
-    "    ),\n",
+    "    np.array(az.extract(post[\"Rho\"].sel(chol_cov_corr_dim_0=1, chol_cov_corr_dim_1=0)).to_array()),\n",
     "    ax=ax,\n",
     "    plot_kwargs={\"color\": \"C0\"},\n",
     ")\n",

Rethinking_2/Chp_16.ipynb

@@ -920,7 +920,7 @@
     "    )\n",
     "\n",
     "    # Compute log ( p_s * Pr(y_i|s) )\n",
-    "    phi = pt.concatenate([p[None] for p in phi] , axis=0)\n",
+    "    phi = pt.concatenate([p[None] for p in phi], axis=0)\n",
     "    phi = pt.log(phi) + pt.log(p[:, None])\n",
     "\n",
     "    # Compute average log-probability of y_i\n",