nbqa black 5 notebooks (#4165)

Author: MarcoGorelli

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
     -   id: end-of-file-fixer
     -   id: check-toml
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 0.2.3
+  rev: 0.3.0
   hooks:
     - id: nbqa
       args: ['isort']

docs/source/notebooks/GLM-negative-binomial-regression.ipynb

@@ -383,9 +383,7 @@
     }
    ],
    "source": [
-    "g = sns.catplot(\n",
-    "    x=\"nsneeze\", row=\"nomeds\", col=\"alcohol\", data=df, kind=\"count\", aspect=1.5\n",
-    ")\n",
+    "g = sns.catplot(x=\"nsneeze\", row=\"nomeds\", col=\"alcohol\", data=df, kind=\"count\", aspect=1.5)\n",
     "\n",
     "# Make x-axis ticklabels less crowded\n",
     "ax = g.axes[1, 0]\n",
@@ -464,9 +462,7 @@
     "fml = \"nsneeze ~ alcohol + nomeds + alcohol:nomeds\"\n",
     "\n",
     "with pm.Model() as model:\n",
-    "    pm.glm.GLM.from_formula(\n",
-    "        formula=fml, data=df, family=pm.glm.families.NegativeBinomial()\n",
-    "    )\n",
+    "    pm.glm.GLM.from_formula(formula=fml, data=df, family=pm.glm.families.NegativeBinomial())\n",
     "    trace = pm.sample(1000, tune=1000, cores=2, return_inferencedata=True)"
    ]
   },

docs/source/notebooks/MLDA_gravity_surveying.ipynb

@@ -117,7 +117,7 @@
     "%config InlineBackend.figure_format = 'retina'\n",
     "RANDOM_SEED = 123446\n",
     "np.random.seed(RANDOM_SEED)\n",
-    "az.style.use('arviz-darkgrid')"
+    "az.style.use(\"arviz-darkgrid\")"
    ]
   },
   {
@@ -212,9 +212,7 @@
     "        \"\"\"\n",
     "        Find eigenvalues and eigenvectors using Arnoldi iteration.\n",
     "        \"\"\"\n",
-    "        eigvals, eigvecs = eigh(\n",
-    "            self.cov, eigvals=(self.n_points - self.mkl, self.n_points - 1)\n",
-    "        )\n",
+    "        eigvals, eigvecs = eigh(self.cov, eigvals=(self.n_points - self.mkl, self.n_points - 1))\n",
     "\n",
     "        order = np.flip(np.argsort(eigvals))\n",
     "        self.eigenvalues = eigvals[order]\n",
@@ -316,7 +314,7 @@
     "class Gravity:\n",
     "    \"\"\"\n",
     "    Gravity is a class that implements a simple gravity surveying problem,\n",
-    "    as described in Hansen, P. C. (2010). Discrete Inverse Problems: Insight and Algorithms. \n",
+    "    as described in Hansen, P. C. (2010). Discrete Inverse Problems: Insight and Algorithms.\n",
     "    Society for Industrial and Applied Mathematics.\n",
     "    It uses midpoint quadrature to evaluate a Fredholm integral of the first kind.\n",
     "    \"\"\"\n",
@@ -349,9 +347,7 @@
     "\n",
     "        # Create coordinate vectors.\n",
     "        self.T_coords = np.c_[TX.ravel(), TY.ravel(), np.zeros(self.n_quad ** 2)]\n",
-    "        self.S_coords = np.c_[\n",
-    "            SX.ravel(), SY.ravel(), self.depth * np.ones(self.n_data ** 2)\n",
-    "        ]\n",
+    "        self.S_coords = np.c_[SX.ravel(), SY.ravel(), self.depth * np.ones(self.n_data ** 2)]\n",
     "\n",
     "        # Set the quadrature weights.\n",
     "        self.w = 1 / self.n_quad ** 2\n",
@@ -485,9 +481,7 @@
     ")\n",
     "fig.colorbar(g, ax=axes[0])\n",
     "axes[1].set_title(\"Noisy Signal\")\n",
-    "d = axes[1].imshow(\n",
-    "    data.reshape(n_data, n_data), extent=(0, 1, 0, 1), origin=\"lower\", cmap=\"plasma\"\n",
-    ")\n",
+    "d = axes[1].imshow(data.reshape(n_data, n_data), extent=(0, 1, 0, 1), origin=\"lower\", cmap=\"plasma\")\n",
     "fig.colorbar(d, ax=axes[1])\n",
     "plt.show()"
    ]
@@ -531,9 +525,7 @@
     "\n",
     "        # Create coordinate vectors.\n",
     "        self.T_coords = np.c_[TX.ravel(), TY.ravel(), np.zeros(self.n_quad ** 2)]\n",
-    "        self.S_coords = np.c_[\n",
-    "            SX.ravel(), SY.ravel(), self.depth * np.ones(self.n_data ** 2)\n",
-    "        ]\n",
+    "        self.S_coords = np.c_[SX.ravel(), SY.ravel(), self.depth * np.ones(self.n_data ** 2)]\n",
     "\n",
     "        # Set the quadrature weights.\n",
     "        self.w = 1 / self.n_quad ** 2\n",

docs/source/notebooks/log-gaussian-cox-process.ipynb

@@ -70,7 +70,7 @@
     "%config InlineBackend.figure_format = 'retina'\n",
     "RANDOM_SEED = 8927\n",
     "np.random.seed(RANDOM_SEED)\n",
-    "az.style.use('arviz-darkgrid')"
+    "az.style.use(\"arviz-darkgrid\")"
    ]
   },
   {
@@ -588,9 +588,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "intensity_samples = np.asarray(\n",
-    "    [x for x in new_samples[\"intensity_new\"] if np.all(np.isfinite(x))]\n",
-    ")"
+    "intensity_samples = np.asarray([x for x in new_samples[\"intensity_new\"] if np.all(np.isfinite(x))])"
    ]
   },
   {

docs/source/notebooks/marginalized_gaussian_mixture_model.ipynb

@@ -42,7 +42,7 @@
     "%config InlineBackend.figure_format = 'retina'\n",
     "RANDOM_SEED = 8927\n",
     "np.random.seed(RANDOM_SEED)\n",
-    "az.style.use('arviz-darkgrid')"
+    "az.style.use(\"arviz-darkgrid\")"
    ]
   },
   {

docs/source/notebooks/sampling_compound_step.ipynb

@@ -127,9 +127,9 @@
    "source": [
     "n_ = theano.shared(np.asarray([10, 15]))\n",
     "with pm.Model() as m:\n",
-    "    p = pm.Beta('p', 1., 1.)\n",
-    "    ni = pm.Bernoulli('ni', .5)\n",
-    "    k = pm.Binomial('k', p=p, n=n_[ni], observed=4)\n",
+    "    p = pm.Beta(\"p\", 1.0, 1.0)\n",
+    "    ni = pm.Bernoulli(\"ni\", 0.5)\n",
+    "    k = pm.Binomial(\"k\", p=p, n=n_[ni], observed=4)\n",
     "    trace = pm.sample()"
    ]
   },