[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

docs/examples/notebooks/learn/details_cls.ipynb

@@ -17,14 +17,14 @@
     "\n",
     "\n",
     "def gpdf(qq, muprime, sig, mu):\n",
-    "    cut = mu ** 2 / sig ** 2\n",
+    "    cut = mu**2 / sig**2\n",
     "\n",
     "    # 1/√(2π)\n",
     "    standard_pre = 1 / np.sqrt(2 * np.pi)\n",
     "\n",
     "    # compute the arg of the exponential\n",
     "    muprime_muhat1_minus_over_sig = (\n",
-    "        qq - (mu ** 2 / sig ** 2 - (2 * mu * muprime / sig ** 2))\n",
+    "        qq - (mu**2 / sig**2 - (2 * mu * muprime / sig**2))\n",
     "    ) / (2 * mu / sig)\n",
     "    muprime_muhat2_minus_over_sig = np.sqrt(qq) - (mu - muprime) / sig\n",
     "\n",
@@ -49,14 +49,14 @@
     "\n",
     "\n",
     "def testrail_parab(muhat, mu, sigma):\n",
-    "    return (muhat - mu) ** 2 / sigma ** 2\n",
+    "    return (muhat - mu) ** 2 / sigma**2\n",
     "\n",
     "\n",
     "def testrail_flat(muhat, mu, sigma):\n",
     "    return np.where(\n",
     "        muhat < 0,\n",
-    "        (muhat - mu) ** 2 / sigma ** 2 - muhat ** 2 / sigma ** 2,\n",
-    "        np.where(muhat < mu, (muhat - mu) ** 2 / sigma ** 2, 0.0),\n",
+    "        (muhat - mu) ** 2 / sigma**2 - muhat**2 / sigma**2,\n",
+    "        np.where(muhat < mu, (muhat - mu) ** 2 / sigma**2, 0.0),\n",
     "    )\n",
     "\n",
     "\n",
@@ -119,7 +119,7 @@
     "mumin, mumax = 1e-2, 1.2\n",
     "maxsigma = 4\n",
     "\n",
-    "qqspan = np.linspace(0, maxsigma ** 2, 10001)\n",
+    "qqspan = np.linspace(0, maxsigma**2, 10001)\n",
     "muspan = np.linspace(mumin, mumax, 31)\n",
     "muhatspan = np.linspace(muhatmin, muhatmax, 1001)\n",
     "test_stats = []\n",
@@ -150,7 +150,7 @@
     "        model.config.suggested_bounds(),\n",
     "        model.config.suggested_fixed(),\n",
     "    )\n",
-    "    sigma = np.sqrt(mu_test ** 2 / asimov_pllr_ts)\n",
+    "    sigma = np.sqrt(mu_test**2 / asimov_pllr_ts)\n",
     "\n",
     "    tstats = [ds.shift, db.shift, ts]\n",
     "    pvals = [cl_sb, cl_b, cl_s]\n",
@@ -405,7 +405,6 @@
     "    test_stats,\n",
     "    vals,\n",
     "):\n",
-    "\n",
     "    mutest_at_index = muspan[index]\n",
     "    means_at_index = -test_stats[index, [1, 0]]\n",
     "    ts_at_index = -test_stats[index, 2]\n",
@@ -433,7 +432,7 @@
     "    tsrail_parab = testrail_parab(muhatspan, mutest_at_index / sigma, sigma=1)\n",
     "    tsrail_flat = testrail_flat(muhatspan, mutest_at_index / sigma, sigma=1)\n",
     "\n",
-    "    tail_span_qq = np.linspace(obs_pllr_ts, maxsigma ** 2, 1001)\n",
+    "    tail_span_qq = np.linspace(obs_pllr_ts, maxsigma**2, 1001)\n",
     "\n",
     "    empirial_muhat_over_sigma = empirial_muhat / sigma\n",
     "\n",
@@ -451,13 +450,13 @@
     "\n",
     "    ax.plot(muhatspan, tsrail_parab, c='grey', linestyle='dashed')\n",
     "    ax.plot(muhatspan, tsrail_flat, c='grey')\n",
-    "    ax.vlines(0, 0, maxsigma ** 2, colors='green', linestyles='dashed')\n",
-    "    ax.vlines(means_at_index[1], 0, maxsigma ** 2, colors='red', linestyles='dashed')\n",
-    "    ax.vlines(ts_at_index, 0, maxsigma ** 2, colors='black', linestyles='dashed')\n",
+    "    ax.vlines(0, 0, maxsigma**2, colors='green', linestyles='dashed')\n",
+    "    ax.vlines(means_at_index[1], 0, maxsigma**2, colors='red', linestyles='dashed')\n",
+    "    ax.vlines(ts_at_index, 0, maxsigma**2, colors='black', linestyles='dashed')\n",
     "    ax.vlines(\n",
     "        empirial_muhat_over_sigma,\n",
     "        0,\n",
-    "        maxsigma ** 2,\n",
+    "        maxsigma**2,\n",
     "        colors='orange',\n",
     "        linestyles='dashed',\n",
     "    )\n",
@@ -473,7 +472,7 @@
     "    ax.scatter(ts_at_index, obs_pllr_ts, c='black')\n",
     "    ax.scatter(mutest_at_index / sigma, 0.0, c='grey')\n",
     "\n",
-    "    ax.set_ylim(-1, maxsigma ** 2)\n",
+    "    ax.set_ylim(-1, maxsigma**2)\n",
     "\n",
     "    ax = axarr['B']\n",
     "    ax.set_xlabel(r'$p(\\tilde{q}_\\mu)$')\n",
@@ -482,7 +481,7 @@
     "\n",
     "    ax.hist(\n",
     "        qmu_tilde_b,\n",
-    "        bins=np.linspace(0.0, maxsigma ** 2, 30),\n",
+    "        bins=np.linspace(0.0, maxsigma**2, 30),\n",
     "        density=True,\n",
     "        histtype='step',\n",
     "        edgecolor='green',\n",
@@ -491,7 +490,7 @@
     "    )\n",
     "    ax.hist(\n",
     "        qmu_tilde_s,\n",
-    "        bins=np.linspace(0.0, maxsigma ** 2, 30),\n",
+    "        bins=np.linspace(0.0, maxsigma**2, 30),\n",
     "        density=True,\n",
     "        histtype='step',\n",
     "        edgecolor='red',\n",
@@ -512,7 +511,7 @@
     "    ax.hlines(obs_pllr_ts, 0.0, 10.0, colors='black', linestyles='dashed')\n",
     "    ax.hlines(asimov_pllr_ts, 0.0, 10.0, colors='green', linestyles='dashed')\n",
     "    ax.set_xlim(1e-3, 1e1)\n",
-    "    ax.set_ylim(-1, maxsigma ** 2)\n",
+    "    ax.set_ylim(-1, maxsigma**2)\n",
     "\n",
     "    ax = axarr['F']\n",
     "\n",