Nbqa black (#4168)

* first 5

* notebook 5-10

* notebooks 10-15

* notebooks 15-20

* notebooks 20-25

* notebooks 25-30

* notebooks 25-30

* rest of the notebooks

* 🔧

* fixup docs/source/notebooks/SMC2_gaussians.ipynb

* fixup docs/source/notebooks/normalizing_flows_overview.ipynb

Co-authored-by: Marco Gorelli <[email protected]>

Author: MarcoGorelli

.pre-commit-config.yaml

@@ -8,20 +8,25 @@ repos:
   rev: 0.3.0
   hooks:
     - id: nbqa
-      args: ['isort']
+      args: [black]
+      name: nbqa-black
+      alias: nbqa-black
+      additional_dependencies: [black]
+    - id: nbqa
+      args: [isort]
       name: nbqa-isort
       alias: nbqa-isort
-      additional_dependencies: ['isort']
+      additional_dependencies: [isort]
     - id: nbqa
-      args: ['pyupgrade']
+      args: [pyupgrade]
       name: nbqa-pyupgrade
       alias: nbqa-pyupgrade
-      additional_dependencies: ['pyupgrade']
+      additional_dependencies: [pyupgrade]
 - repo: https://github.com/asottile/pyupgrade
   rev: v2.7.2
   hooks:
     - id: pyupgrade
-      args: ['--py36-plus']
+      args: [--py36-plus]
 - repo: https://github.com/psf/black
   rev: 20.8b1
   hooks:

docs/source/notebooks/GP-MaunaLoa2.ipynb

@@ -68,7 +68,7 @@
     "%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\")"
    ]
   },
   {
@@ -1310,9 +1310,7 @@
     "    ηq = pm.HalfNormal(\"ηq\", sigma=5)\n",
     "\n",
     "    cov1 = η ** 2 * pm.gp.cov.ExpQuad(1, ℓ)\n",
-    "    cov2 = η ** 2 * pm.gp.cov.ExpQuad(1, ℓ) + ηq ** 2 * pm.gp.cov.Polynomial(\n",
-    "        1, x0, 2, c\n",
-    "    )\n",
+    "    cov2 = η ** 2 * pm.gp.cov.ExpQuad(1, ℓ) + ηq ** 2 * pm.gp.cov.Polynomial(1, x0, 2, c)\n",
     "\n",
     "    # construct changepoint cov\n",
     "    sc_cov1 = pm.gp.cov.ScaledCov(1, cov1, logistic, (-a, x0))\n",
@@ -1799,10 +1797,10 @@
    "outputs": [],
    "source": [
     "class CustomWhiteNoise(pm.gp.cov.Covariance):\n",
-    "    \"\"\" Custom White Noise covariance\n",
+    "    \"\"\"Custom White Noise covariance\n",
     "    - sigma1 is applied to the first n1 points in the data\n",
     "    - sigma2 is applied to the next n2 points in the data\n",
-    "    \n",
+    "\n",
     "    The total number of data points n = n1 + n2\n",
     "    \"\"\"\n",
     "\n",
@@ -1922,11 +1920,7 @@
     "    ℓp_decay = pm.Gamma(\"ℓp_decay\", alpha=40, beta=0.1)\n",
     "    ℓp_smooth = pm.Normal(\"ℓp_smooth \", mu=1.0, sigma=0.05)\n",
     "    period = 1 * 0.01  # we know the period is annual\n",
-    "    cov_p = (\n",
-    "        ηp ** 2\n",
-    "        * pm.gp.cov.Periodic(1, period, ℓp_smooth)\n",
-    "        * pm.gp.cov.ExpQuad(1, ℓp_decay)\n",
-    "    )\n",
+    "    cov_p = ηp ** 2 * pm.gp.cov.Periodic(1, period, ℓp_smooth) * pm.gp.cov.ExpQuad(1, ℓp_decay)\n",
     "    gp_p = pm.gp.Marginal(cov_func=cov_p)\n",
     "\n",
     "    gp = gp_c + gp_m + gp_s + gp_p\n",
@@ -1943,9 +1937,7 @@
     "    σ2 = pm.Gamma(\"σ2\", alpha=3, beta=50)\n",
     "    η_noise = pm.HalfNormal(\"η_noise\", sigma=1)\n",
     "    ℓ_noise = pm.Gamma(\"ℓ_noise\", alpha=2, beta=200)\n",
-    "    cov_noise = η_noise ** 2 * pm.gp.cov.Matern32(1, ℓ_noise) + CustomWhiteNoise(\n",
-    "        σ1, σ2, 111, 545\n",
-    "    )\n",
+    "    cov_noise = η_noise ** 2 * pm.gp.cov.Matern32(1, ℓ_noise) + CustomWhiteNoise(σ1, σ2, 111, 545)\n",
     "\n",
     "    y_ = gp.marginal_likelihood(\"y\", X=t_combined[:, None], y=y_n, noise=cov_noise)"
    ]
@@ -2141,9 +2133,7 @@
    ],
    "source": [
     "plt.figure(figsize=(12, 5))\n",
-    "plt.plot(\n",
-    "    tnew * 100, y_sd * ppc[\"fnew\"][0:200:5, :].T + y_mu, color=\"lightblue\", alpha=0.8\n",
-    ")\n",
+    "plt.plot(tnew * 100, y_sd * ppc[\"fnew\"][0:200:5, :].T + y_mu, color=\"lightblue\", alpha=0.8)\n",
     "plt.plot(\n",
     "    [-1000, -1001],\n",
     "    [-1000, -1001],\n",
@@ -2198,9 +2188,7 @@
    ],
    "source": [
     "plt.figure(figsize=(12, 5))\n",
-    "plt.plot(\n",
-    "    tnew * 100, y_sd * ppc[\"fnew\"][0:200:5, :].T + y_mu, color=\"lightblue\", alpha=0.8\n",
-    ")\n",
+    "plt.plot(tnew * 100, y_sd * ppc[\"fnew\"][0:200:5, :].T + y_mu, color=\"lightblue\", alpha=0.8)\n",
     "plt.plot(\n",
     "    [-1000, -1001],\n",
     "    [-1000, -1001],\n",
@@ -2325,9 +2313,7 @@
    "source": [
     "plt.figure(figsize=(12, 5))\n",
     "\n",
-    "plt.plot(\n",
-    "    tnew * 100, y_sd * ppc[\"fnew2\"][0:200:10, :].T + y_mu, color=\"lightblue\", alpha=0.8\n",
-    ")\n",
+    "plt.plot(tnew * 100, y_sd * ppc[\"fnew2\"][0:200:10, :].T + y_mu, color=\"lightblue\", alpha=0.8)\n",
     "plt.plot(\n",
     "    [-1000, -1001],\n",
     "    [-1000, -1001],\n",
@@ -2452,9 +2438,7 @@
    "source": [
     "plt.figure(figsize=(12, 5))\n",
     "\n",
-    "plt.plot(\n",
-    "    tnew * 100, y_sd * ppc[\"fnew3\"][0:200:10, :].T + y_mu, color=\"lightblue\", alpha=0.8\n",
-    ")\n",
+    "plt.plot(tnew * 100, y_sd * ppc[\"fnew3\"][0:200:10, :].T + y_mu, color=\"lightblue\", alpha=0.8)\n",
     "plt.plot(\n",
     "    [-1000, -1001],\n",
     "    [-1000, -1001],\n",