CLN: notebooks black formatting (#4152)

Co-authored-by: Raisa Dzhamtyrova <[email protected]>

Author: raisadz

docs/source/notebooks/GLM-robust-with-outlier-detection.ipynb

@@ -467,9 +467,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dfhoggs = (dfhogg[[\"x\", \"y\"]] - dfhogg[[\"x\", \"y\"]].mean(0)) / (\n",
-    "    2 * dfhogg[[\"x\", \"y\"]].std(0)\n",
-    ")\n",
+    "dfhoggs = (dfhogg[[\"x\", \"y\"]] - dfhogg[[\"x\", \"y\"]].mean(0)) / (2 * dfhogg[[\"x\", \"y\"]].std(0))\n",
     "dfhoggs[\"sigma_x\"] = dfhogg[\"sigma_x\"] / (2 * dfhogg[\"x\"].std())\n",
     "dfhoggs[\"sigma_y\"] = dfhogg[\"sigma_y\"] / (2 * dfhogg[\"y\"].std())"
    ]
@@ -505,9 +503,7 @@
     "    marginal_kws={\"bins\": 12, \"kde\": True, \"kde_kws\": {\"cut\": 1}},\n",
     "    joint_kws={\"edgecolor\": \"w\", \"linewidth\": 1, \"s\": 80},\n",
     ")\n",
-    "_ = gd.ax_joint.errorbar(\n",
-    "    \"x\", \"y\", \"sigma_y\", \"sigma_x\", fmt=\"none\", ecolor=\"#4878d0\", data=dfhoggs\n",
-    ")\n",
+    "_ = gd.ax_joint.errorbar(\"x\", \"y\", \"sigma_y\", \"sigma_x\", fmt=\"none\", ecolor=\"#4878d0\", data=dfhoggs)\n",
     "_ = gd.fig.suptitle(\n",
     "    (\n",
     "        \"Quick view to confirm action of\\n\"\n",
@@ -639,9 +635,7 @@
     "    y_est = b0 + b1 * dfhoggs[\"x\"]\n",
     "\n",
     "    ## Define Normal likelihood\n",
-    "    likelihood = pm.Normal(\n",
-    "        \"likelihood\", mu=y_est, sigma=dfhoggs[\"sigma_y\"], observed=dfhoggs[\"y\"]\n",
-    "    )\n",
+    "    likelihood = pm.Normal(\"likelihood\", mu=y_est, sigma=dfhoggs[\"sigma_y\"], observed=dfhoggs[\"y\"])\n",
     "\n",
     "pm.model_to_graphviz(mdl_ols)"
    ]
@@ -1274,10 +1268,7 @@
     "\n",
     "gd = sns.JointGrid(x=\"b0_intercept\", y=\"b1_slope\", data=df_trc, height=8)\n",
     "_ = gd.fig.suptitle(\n",
-    "    (\n",
-    "        \"Posterior joint distributions\"\n",
-    "        + \"\\n(showing general movement from OLS to StudentT)\"\n",
-    "    ),\n",
+    "    (\"Posterior joint distributions\" + \"\\n(showing general movement from OLS to StudentT)\"),\n",
     "    y=1.05,\n",
     ")\n",
     "\n",
@@ -1297,11 +1288,9 @@
     "            grp[\"b0_intercept\"], grp[\"b1_slope\"], ax=gd.ax_joint, alpha=0.2, label=idx\n",
     "        )\n",
     "        _ = sns.kdeplot(grp[\"b0_intercept\"], grp[\"b1_slope\"], ax=gd.ax_joint, **kde_kws)\n",
+    "        _ = sns.distplot(grp[\"b0_intercept\"], bins=x_bin_edges, ax=gd.ax_marg_x, **dist_kws)\n",
     "        _ = sns.distplot(\n",
-    "            grp[\"b0_intercept\"], bins=x_bin_edges, ax=gd.ax_marg_x, **dist_kws\n",
-    "        )\n",
-    "        _ = sns.distplot(grp[\"b1_slope\"], vertical=True, bins=y_bin_edges,\n",
-    "            ax=gd.ax_marg_y, **dist_kws\n",
+    "            grp[\"b1_slope\"], vertical=True, bins=y_bin_edges, ax=gd.ax_marg_y, **dist_kws\n",
     "        )\n",
     "_ = gd.ax_joint.legend()"
    ]
@@ -1419,18 +1408,14 @@
     "    y_est_out = pm.Normal(\"y_est_out\", mu=0, sigma=10, testval=pm.floatX(0.0))  # (1, )\n",
     "\n",
     "    # very weakly informative prior for additional variance for outliers\n",
-    "    sigma_y_out = pm.HalfNormal(\n",
-    "        \"sigma_y_out\", sigma=10, testval=pm.floatX(1.0)\n",
-    "    )  # (1, )\n",
+    "    sigma_y_out = pm.HalfNormal(\"sigma_y_out\", sigma=10, testval=pm.floatX(1.0))  # (1, )\n",
     "\n",
     "    # create in/outlier distributions to get a logp evaluated on the observed y\n",
     "    # this is not strictly a pymc3 likelihood, but behaves like one when we\n",
     "    # evaluate it within a Potential (which is minimised)\n",
     "    inlier_logp = pm.Normal.dist(mu=y_est_in, sigma=tsv_sigma_y).logp(tsv_y)\n",
     "\n",
-    "    outlier_logp = pm.Normal.dist(mu=y_est_out, sigma=tsv_sigma_y + sigma_y_out).logp(\n",
-    "        tsv_y\n",
-    "    )\n",
+    "    outlier_logp = pm.Normal.dist(mu=y_est_out, sigma=tsv_sigma_y + sigma_y_out).logp(tsv_y)\n",
     "\n",
     "    # frac_outliers only needs to span [0, .5]\n",
     "    # testval for is_outlier initialised in order to create class asymmetry\n",
@@ -1860,14 +1845,10 @@
     "dist_kws = dict(kde_kws=dict(cut=1), axlabel=False)\n",
     "\n",
     "for idx, grp in df_trc.groupby(\"model\"):\n",
-    "    _ = sns.scatterplot(\n",
-    "        grp[\"b0_intercept\"], grp[\"b1_slope\"], ax=gd.ax_joint, alpha=0.2, label=idx\n",
-    "    )\n",
+    "    _ = sns.scatterplot(grp[\"b0_intercept\"], grp[\"b1_slope\"], ax=gd.ax_joint, alpha=0.2, label=idx)\n",
     "    _ = sns.kdeplot(grp[\"b0_intercept\"], grp[\"b1_slope\"], ax=gd.ax_joint, **kde_kws)\n",
     "    _ = sns.distplot(grp[\"b0_intercept\"], **dist_kws, bins=x_bin_edges, ax=gd.ax_marg_x)\n",
-    "    _ = sns.distplot(\n",
-    "        grp[\"b1_slope\"], **dist_kws, vertical=True, bins=y_bin_edges, ax=gd.ax_marg_y\n",
-    "    )\n",
+    "    _ = sns.distplot(grp[\"b1_slope\"], **dist_kws, vertical=True, bins=y_bin_edges, ax=gd.ax_marg_y)\n",
     "_ = gd.ax_joint.legend()"
    ]
   },
@@ -1931,12 +1912,8 @@
     }
    ],
    "source": [
-    "df_outlier_results = pd.DataFrame.from_records(\n",
-    "    trc_hogg[\"is_outlier\"], columns=dfhoggs.index\n",
-    ")\n",
-    "dfm_outlier_results = pd.melt(\n",
-    "    df_outlier_results, var_name=\"datapoint_id\", value_name=\"is_outlier\"\n",
-    ")\n",
+    "df_outlier_results = pd.DataFrame.from_records(trc_hogg[\"is_outlier\"], columns=dfhoggs.index)\n",
+    "dfm_outlier_results = pd.melt(df_outlier_results, var_name=\"datapoint_id\", value_name=\"is_outlier\")\n",
     "\n",
     "gd = sns.catplot(\n",
     "    y=\"datapoint_id\",\n",
@@ -2032,9 +2009,7 @@
     }
    ],
    "source": [
-    "dfhoggs[\"annotate_for_investigation\"] = (\n",
-    "    np.quantile(trc_hogg[\"is_outlier\"], 0.75, axis=0) == 1\n",
-    ")\n",
+    "dfhoggs[\"annotate_for_investigation\"] = np.quantile(trc_hogg[\"is_outlier\"], 0.75, axis=0) == 1\n",
     "dfhoggs[\"annotate_for_investigation\"].value_counts()"
    ]
   },

docs/source/notebooks/GP-MaunaLoa.ipynb

@@ -400,7 +400,7 @@
     "%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\")"
    ]
   },
   {
@@ -687,14 +687,10 @@
     "    left=pd.to_datetime(\"2003-12-15\"), fill_alpha=0.1, fill_color=\"firebrick\"\n",
     ")\n",
     "p.add_layout(predict_region)\n",
-    "ppm400 = Span(\n",
-    "    location=400, dimension=\"width\", line_color=\"red\", line_dash=\"dashed\", line_width=2\n",
-    ")\n",
+    "ppm400 = Span(location=400, dimension=\"width\", line_color=\"red\", line_dash=\"dashed\", line_width=2)\n",
     "p.add_layout(ppm400)\n",
     "\n",
-    "p.line(\n",
-    "    data_monthly.index, data_monthly[\"CO2\"], line_width=2, line_color=\"black\", alpha=0.5\n",
-    ")\n",
+    "p.line(data_monthly.index, data_monthly[\"CO2\"], line_width=2, line_color=\"black\", alpha=0.5)\n",
     "p.circle(data_monthly.index, data_monthly[\"CO2\"], line_color=\"black\", alpha=0.1, size=2)\n",
     "\n",
     "train_label = Label(\n",
@@ -1074,9 +1070,7 @@
     "    period = pm.Normal(\"period\", mu=1, sigma=0.05)\n",
     "    ℓ_psmooth = pm.Gamma(\"ℓ_psmooth \", alpha=4, beta=3)\n",
     "    cov_seasonal = (\n",
-    "        η_per ** 2\n",
-    "        * pm.gp.cov.Periodic(1, period, ℓ_psmooth)\n",
-    "        * pm.gp.cov.Matern52(1, ℓ_pdecay)\n",
+    "        η_per ** 2 * pm.gp.cov.Periodic(1, period, ℓ_psmooth) * pm.gp.cov.Matern52(1, ℓ_pdecay)\n",
     "    )\n",
     "    gp_seasonal = pm.gp.Marginal(cov_func=cov_seasonal)\n",
     "\n",
@@ -1204,25 +1198,19 @@
     "mu, var = gp_trend.predict(\n",
     "    tnew, point=mp, given={\"gp\": gp, \"X\": t, \"y\": y, \"noise\": cov_noise}, diag=True\n",
     ")\n",
-    "fit = fit.assign(\n",
-    "    mu_trend=mu * std_co2 + first_co2, sd_trend=np.sqrt(var * std_co2 ** 2)\n",
-    ")\n",
+    "fit = fit.assign(mu_trend=mu * std_co2 + first_co2, sd_trend=np.sqrt(var * std_co2 ** 2))\n",
     "\n",
     "print(\"Predicting with gp_medium ...\")\n",
     "mu, var = gp_medium.predict(\n",
     "    tnew, point=mp, given={\"gp\": gp, \"X\": t, \"y\": y, \"noise\": cov_noise}, diag=True\n",
     ")\n",
-    "fit = fit.assign(\n",
-    "    mu_medium=mu * std_co2 + first_co2, sd_medium=np.sqrt(var * std_co2 ** 2)\n",
-    ")\n",
+    "fit = fit.assign(mu_medium=mu * std_co2 + first_co2, sd_medium=np.sqrt(var * std_co2 ** 2))\n",
     "\n",
     "print(\"Predicting with gp_seasonal ...\")\n",
     "mu, var = gp_seasonal.predict(\n",
     "    tnew, point=mp, given={\"gp\": gp, \"X\": t, \"y\": y, \"noise\": cov_noise}, diag=True\n",
     ")\n",
-    "fit = fit.assign(\n",
-    "    mu_seasonal=mu * std_co2 + first_co2, sd_seasonal=np.sqrt(var * std_co2 ** 2)\n",
-    ")\n",
+    "fit = fit.assign(mu_seasonal=mu * std_co2 + first_co2, sd_seasonal=np.sqrt(var * std_co2 ** 2))\n",
     "print(\"Done\")"
    ]
   },
@@ -1346,9 +1334,7 @@
     ")\n",
     "\n",
     "# true value\n",
-    "p.circle(\n",
-    "    data_early.index, data_early[\"CO2\"], color=\"black\", legend_label=\"Observed data\"\n",
-    ")\n",
+    "p.circle(data_early.index, data_early[\"CO2\"], color=\"black\", legend_label=\"Observed data\")\n",
     "p.legend.location = \"top_left\"\n",
     "show(p)"
    ]
@@ -1439,9 +1425,7 @@
    "source": [
     "# plot several years\n",
     "\n",
-    "p = figure(\n",
-    "    title=\"Several years of the seasonal component\", plot_width=550, plot_height=350\n",
-    ")\n",
+    "p = figure(title=\"Several years of the seasonal component\", plot_width=550, plot_height=350)\n",
     "p.yaxis.axis_label = \"Δ CO2 [ppm]\"\n",
     "p.xaxis.axis_label = \"Month\"\n",
     "\n",
@@ -1596,9 +1580,7 @@
     "band_x = np.append(dates, dates[::-1])\n",
     "band_y = np.append(lower, upper[::-1])\n",
     "\n",
-    "p.line(\n",
-    "    dates, mu_pred_sc, line_width=2, line_color=\"firebrick\", legend_label=\"Total fit\"\n",
-    ")\n",
+    "p.line(dates, mu_pred_sc, line_width=2, line_color=\"firebrick\", legend_label=\"Total fit\")\n",
     "p.patch(band_x, band_y, color=\"firebrick\", alpha=0.6, line_color=\"white\")\n",
     "\n",
     "# some predictions\n",
@@ -1613,9 +1595,7 @@
     "# true value\n",
     "# p.line(data_later.index, data_later['CO2'],\n",
     "#       line_width=2, line_color=\"black\", legend=\"Observed data\")\n",
-    "p.circle(\n",
-    "    data_later.index, data_later[\"CO2\"], color=\"black\", legend_label=\"Observed data\"\n",
-    ")\n",
+    "p.circle(data_later.index, data_later[\"CO2\"], color=\"black\", legend_label=\"Observed data\")\n",
     "\n",
     "ppm400 = Span(\n",
     "    location=400,\n",

docs/source/notebooks/LKJ.ipynb

@@ -99,9 +99,7 @@
     "\n",
     "blue, _, red, *_ = sns.color_palette()\n",
     "\n",
-    "e = Ellipse(\n",
-    "    μ_actual, 2 * np.sqrt(5.991 * var[0]), 2 * np.sqrt(5.991 * var[1]), angle=angle\n",
-    ")\n",
+    "e = Ellipse(μ_actual, 2 * np.sqrt(5.991 * var[0]), 2 * np.sqrt(5.991 * var[1]), angle=angle)\n",
     "e.set_alpha(0.5)\n",
     "e.set_facecolor(blue)\n",
     "e.set_zorder(10)\n",
@@ -135,9 +133,7 @@
    "outputs": [],
    "source": [
     "with pm.Model() as m:\n",
-    "    packed_L = pm.LKJCholeskyCov(\n",
-    "        \"packed_L\", n=2, eta=2.0, sd_dist=pm.Exponential.dist(1.0)\n",
-    "    )"
+    "    packed_L = pm.LKJCholeskyCov(\"packed_L\", n=2, eta=2.0, sd_dist=pm.Exponential.dist(1.0))"
    ]
   },
   {
@@ -540,9 +536,7 @@
    ],
    "source": [
     "with model:\n",
-    "    trace = pm.sample(\n",
-    "        random_seed=RANDOM_SEED, init=\"adapt_diag\", return_inferencedata=True\n",
-    "    )\n",
+    "    trace = pm.sample(random_seed=RANDOM_SEED, init=\"adapt_diag\", return_inferencedata=True)\n",
     "az.summary(trace, var_names=[\"~chol\"], round_to=2)"
    ]
   },
@@ -664,9 +658,7 @@
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 6))\n",
     "\n",
-    "e = Ellipse(\n",
-    "    μ_actual, 2 * np.sqrt(5.991 * var[0]), 2 * np.sqrt(5.991 * var[1]), angle=angle\n",
-    ")\n",
+    "e = Ellipse(μ_actual, 2 * np.sqrt(5.991 * var[0]), 2 * np.sqrt(5.991 * var[1]), angle=angle)\n",
     "e.set_alpha(0.5)\n",
     "e.set_facecolor(blue)\n",
     "e.set_zorder(10)\n",