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

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

bench/plot.ipynb

@@ -5255,8 +5255,8 @@
     "print(\n",
     "    f\"\"\"\\\n",
     "benchmark results\n",
-    "  {data['datetime']}\n",
-    "  {data['machine_info']['cpu']['brand_raw']}\n",
+    "  {data[\"datetime\"]}\n",
+    "  {data[\"machine_info\"][\"cpu\"][\"brand_raw\"]}\n",
     "\"\"\"\n",
     ")\n",
     "\n",

doc/notebooks/automatic_differentiation.ipynb

@@ -1614,7 +1614,7 @@
     "y = -np.arange(len(times))\n",
     "plt.barh(y, x)\n",
     "for yi, k, v in zip(y, times, x):\n",
-    "    plt.text(v, yi, f\"{v/xmin:.1f}x\")\n",
+    "    plt.text(v, yi, f\"{v / xmin:.1f}x\")\n",
     "plt.yticks(y, times.keys())\n",
     "for loc in (\"top\", \"right\"):\n",
     "    plt.gca().spines[loc].set_visible(False)\n",
@@ -3886,7 +3886,7 @@
     "# check fit quality\n",
     "chi2 = m.fval\n",
     "ndof = len(data_y) - 3\n",
-    "plt.title(f\"$\\\\chi^2 / n_\\\\mathrm{{dof}} = {chi2:.2f} / {ndof} = {chi2/ndof:.2f}$\");"
+    "plt.title(f\"$\\\\chi^2 / n_\\\\mathrm{{dof}} = {chi2:.2f} / {ndof} = {chi2 / ndof:.2f}$\");"
    ]
   },
   {

doc/notebooks/binned_vs_unbinned.ipynb

@@ -10286,7 +10286,7 @@
     "y = [v[2] for v in results.values()]\n",
     "plt.plot(x, y, \"o\")\n",
     "for xi, yi in zip(x[1:], y[1:]):\n",
-    "    plt.text(xi, yi * 1.2, f\"{y[0]/yi:.0f}x\", ha=\"center\")\n",
+    "    plt.text(xi, yi * 1.2, f\"{y[0] / yi:.0f}x\", ha=\"center\")\n",
     "plt.xticks(x, [f\"{x}\" for x in results.keys()])\n",
     "plt.ylabel(\"time / sec\")\n",
     "plt.semilogy();"

doc/notebooks/numba.ipynb

@@ -2232,7 +2232,7 @@
    "source": [
     "for signature, code in norm_pdf.inspect_asm().items():\n",
     "    print(\n",
-    "        f\"signature: {signature}\\n{'-'*(len(str(signature)) + 11)}\\n{code[:1000]}\\n[...]\"\n",
+    "        f\"signature: {signature}\\n{'-' * (len(str(signature)) + 11)}\\n{code[:1000]}\\n[...]\"\n",
     "    )"
    ]
   },
@@ -2283,7 +2283,7 @@
     "from collections import Counter\n",
     "\n",
     "for signature, code in norm_pdf.inspect_asm().items():\n",
-    "    print(f\"signature: {signature}\\n{'-'*(len(str(signature)) + 11)}\")\n",
+    "    print(f\"signature: {signature}\\n{'-' * (len(str(signature)) + 11)}\")\n",
     "    instructions = []\n",
     "    for line in code.split(\"\\n\"):\n",
     "        instr = line.strip().split(\"\\t\")[0]\n",

doc/notebooks/weighted_histograms.ipynb

@@ -6927,13 +6927,13 @@
     "    lambd1,\n",
     "    bins=20,\n",
     "    alpha=0.5,\n",
-    "    label=f\"ExtendedBinnedNLL\\nvalid={np.mean(valid1)*100:.0f}%\\nmean = {np.mean(lambd1):.2f}\\nstd.dev. = {np.std(lambd1):.2f}\",\n",
+    "    label=f\"ExtendedBinnedNLL\\nvalid={np.mean(valid1) * 100:.0f}%\\nmean = {np.mean(lambd1):.2f}\\nstd.dev. = {np.std(lambd1):.2f}\",\n",
     ")\n",
     "plt.hist(\n",
     "    lambd2,\n",
     "    bins=20,\n",
     "    alpha=0.5,\n",
-    "    label=f\"BinnedNLL\\nvalid={np.mean(valid2)*100:.0f}%\\nmean = {np.mean(lambd2):.2f}\\nstd.dev. = {np.std(lambd2):.2f}\",\n",
+    "    label=f\"BinnedNLL\\nvalid={np.mean(valid2) * 100:.0f}%\\nmean = {np.mean(lambd2):.2f}\\nstd.dev. = {np.std(lambd2):.2f}\",\n",
     ")\n",
     "plt.legend()\n",
     "\n",
@@ -9187,15 +9187,15 @@
     "x = np.linspace(0, 100)\n",
     "plt.plot(x, chi2(bins - 2).pdf(x))\n",
     "plt.title(\n",
-    "    f\"ExtendedBinnedNLL minimum value\\nndf = {bins-2} mean = {np.mean(minval1):.2f} median = {np.median(minval1):.2f}\"\n",
+    "    f\"ExtendedBinnedNLL minimum value\\nndf = {bins - 2} mean = {np.mean(minval1):.2f} median = {np.median(minval1):.2f}\"\n",
     ")\n",
     "\n",
     "plt.sca(ax[1])\n",
     "plt.hist(minval2, bins=50, density=True)\n",
     "x = np.linspace(0, 100)\n",
     "plt.plot(x, chi2(bins - 2).pdf(x))\n",
     "plt.title(\n",
-    "    f\"BinnedNLL minimum value\\nndf = {bins-2} mean = {np.mean(minval2):.2f} median = {np.median(minval2):.2f}\"\n",
+    "    f\"BinnedNLL minimum value\\nndf = {bins - 2} mean = {np.mean(minval2):.2f} median = {np.median(minval2):.2f}\"\n",
     ");"
    ]
   },
@@ -11951,13 +11951,13 @@
     "    lambd1,\n",
     "    bins=20,\n",
     "    alpha=0.5,\n",
-    "    label=f\"ExtendedBinnedNLL\\nvalid={np.mean(valid1)*100:.0f}%\\nmean = {np.mean(lambd1):.2f}\\nstd.dev. = {np.std(lambd1):.3f}\",\n",
+    "    label=f\"ExtendedBinnedNLL\\nvalid={np.mean(valid1) * 100:.0f}%\\nmean = {np.mean(lambd1):.2f}\\nstd.dev. = {np.std(lambd1):.3f}\",\n",
     ")\n",
     "plt.hist(\n",
     "    lambd2,\n",
     "    bins=20,\n",
     "    alpha=0.5,\n",
-    "    label=f\"BinnedNLL\\nvalid={np.mean(valid2)*100:.0f}%\\nmean = {np.mean(lambd2):.2f}\\nstd.dev. = {np.std(lambd2):.3f}\",\n",
+    "    label=f\"BinnedNLL\\nvalid={np.mean(valid2) * 100:.0f}%\\nmean = {np.mean(lambd2):.2f}\\nstd.dev. = {np.std(lambd2):.3f}\",\n",
     ")\n",
     "plt.legend()\n",
     "\n",
@@ -14031,15 +14031,15 @@
     "x = np.linspace(0, 100)\n",
     "plt.plot(x, chi2(bins - 2).pdf(x))\n",
     "plt.title(\n",
-    "    f\"ExtendedBinnedNLL minimum value\\nndf = {bins-2} mean = {np.mean(minval1):.2f} median = {np.median(minval1):.2f}\"\n",
+    "    f\"ExtendedBinnedNLL minimum value\\nndf = {bins - 2} mean = {np.mean(minval1):.2f} median = {np.median(minval1):.2f}\"\n",
     ")\n",
     "\n",
     "plt.sca(ax[1])\n",
     "plt.hist(minval2, bins=50, density=True)\n",
     "x = np.linspace(0, 100)\n",
     "plt.plot(x, chi2(bins - 2).pdf(x))\n",
     "plt.title(\n",
-    "    f\"BinnedNLL minimum value\\nndf = {bins-2} mean = {np.mean(minval2):.2f} median = {np.median(minval2):.2f}\"\n",
+    "    f\"BinnedNLL minimum value\\nndf = {bins - 2} mean = {np.mean(minval2):.2f} median = {np.median(minval2):.2f}\"\n",
     ");"
    ]
   },

tests/test_cost.py

@@ -904,8 +904,7 @@ def cdf(r, mx, my, mz, sx, sy, sz):
         with pytest.raises(
             ValueError,
             match=(
-                'use_pdf="numerical" is not supported '
-                "for multidimensional histograms"
+                'use_pdf="numerical" is not supported for multidimensional histograms'
             ),
         ):
             BinnedNLL(n, xe, pdf, use_pdf=use_pdf)