Introduce pre-commit hooks

.pre-commit-config.yaml

@@ -5,7 +5,9 @@ repos:
     rev: v4.6.0
     hooks:
       - id: trailing-whitespace
-        exclude: ^pineappl_cli/tests/
+        # `.rs` files are taken care of by `cargo fmt`
+        # `.github/workflows/release.yml` contains vital spaces
+        exclude: ^(.*\.rs|\.github/workflows/release.yml)$
       - id: end-of-file-fixer
       - id: check-merge-conflict
   - repo: https://github.com/astral-sh/ruff-pre-commit

pineappl_py/docs/source/advanced.ipynb

@@ -122,6 +122,7 @@
     "\n",
     "np.random.seed(1234567890)\n",
     "\n",
+    "\n",
     "def hadronic_ps_gen(\n",
     "    mmin: float, mmax: float\n",
     ") -> Tuple[float, float, float, float, float, float]:\n",
@@ -156,7 +157,7 @@
     "    r1 = np.random.uniform()\n",
     "    r2 = np.random.uniform()\n",
     "    r3 = np.random.uniform()\n",
-    "    \n",
+    "\n",
     "    # generate partonic x1 and x2\n",
     "    tau0 = smin / smax\n",
     "    tau = pow(tau0, r1)\n",
@@ -256,6 +257,7 @@
    "source": [
     "import pineappl\n",
     "\n",
+    "\n",
     "def fill_grid(grid: pineappl.grid.Grid, calls: int):\n",
     "    \"\"\"Fill grid with points.\"\"\"\n",
     "\n",
@@ -293,7 +295,7 @@
     "        weight = jacobian * photon_photon_matrix_element(s, u, t)\n",
     "        # set factorization and renormalization scale to (roughly) the Z-boson mass\n",
     "        q2 = 90.0 * 90.0\n",
-    "    \n",
+    "\n",
     "        # fill the interpolation grid\n",
     "        grid.fill(x1, x2, q2, 0, np.abs(yll), 0, weight)"
    ]
@@ -367,7 +369,7 @@
     "grid = generate_grid(1000000)\n",
     "\n",
     "# perform convolution with PDFs: this performs the x1 and x2 integrals\n",
-    "# of the partonic cross sections with the PDFs as given by our master \n",
+    "# of the partonic cross sections with the PDFs as given by our master\n",
     "# formula\n",
     "pdf = lhapdf.mkPDF(\"NNPDF31_nnlo_as_0118_luxqed\", 0)\n",
     "bins = grid.convolve_with_one(2212, pdf.xfxQ2, pdf.alphasQ2)"
@@ -411,8 +413,10 @@
     "nbins = np.append(bins, bins[-1])\n",
     "edges = np.arange(0.0, 2.4, 0.1)\n",
     "\n",
-    "ax.step(edges, nbins, where='post', color=\"C1\")\n",
-    "plt.fill_between(np.arange(0.0, 2.4, 0.1), nbins, step=\"post\", color=\"C1\", alpha=0.2)\n",
+    "ax.step(edges, nbins, where=\"post\", color=\"C1\")\n",
+    "plt.fill_between(\n",
+    "    np.arange(0.0, 2.4, 0.1), nbins, step=\"post\", color=\"C1\", alpha=0.2\n",
+    ")\n",
     "ax.set_xlabel(\"$|y_{\\ell\\ell}|$\")\n",
     "ax.set_ylabel(\"$\\mathrm{d} \\sigma / \\mathrm{d} |y_{\\ell\\ell}|$ [pb]\")\n",
     "ax.grid(True, alpha=0.5)\n",

pineappl_py/docs/source/introduction.ipynb

@@ -125,6 +125,7 @@
     "# We first need to load the PDF set with LHAPDF\n",
     "import lhapdf\n",
     "import numpy as np\n",
+    "\n",
     "# `Polars` is a better alternative to Pandas (written in Rust!)\n",
     "import polars as pl\n",
     "\n",
@@ -188,10 +189,12 @@
    ],
    "source": [
     "predictions = grid.convolve_with_one(2212, pdf.xfxQ2, pdf.alphasQ2)\n",
-    "df_preds = pl.DataFrame({\n",
-    "    \"bins\": range(predictions.size),\n",
-    "    \"predictions\": predictions,\n",
-    "})\n",
+    "df_preds = pl.DataFrame(\n",
+    "    {\n",
+    "        \"bins\": range(predictions.size),\n",
+    "        \"predictions\": predictions,\n",
+    "    }\n",
+    ")\n",
     "df_preds"
    ]
   },
@@ -231,17 +234,44 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "# Experimental central values as provided by HepData\n",
-    "data_central = np.array([\n",
-    "    1223.0, 3263.0, 4983.0, 6719.0, 8051.0, 8967.0, 9561.0, 9822.0, 9721.0, 9030.0, 7748.0, 6059.0, 4385.0, 2724.0, 1584.0, 749.0, 383.0, 11.0\n",
-    "])\n",
+    "data_central = np.array(\n",
+    "    [\n",
+    "        1223.0,\n",
+    "        3263.0,\n",
+    "        4983.0,\n",
+    "        6719.0,\n",
+    "        8051.0,\n",
+    "        8967.0,\n",
+    "        9561.0,\n",
+    "        9822.0,\n",
+    "        9721.0,\n",
+    "        9030.0,\n",
+    "        7748.0,\n",
+    "        6059.0,\n",
+    "        4385.0,\n",
+    "        2724.0,\n",
+    "        1584.0,\n",
+    "        749.0,\n",
+    "        383.0,\n",
+    "        11.0,\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "# Normalization for each bin. See Section below for more details.\n",
-    "bin_norm = np.array([0.125 for _ in range(predictions.size - 2)] + [0.250, 0.250])\n",
+    "bin_norm = np.array(\n",
+    "    [0.125 for _ in range(predictions.size - 2)] + [0.250, 0.250]\n",
+    ")\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(5.6, 3.9))\n",
     "# Factor of `1e3` takes into account the unit conversion into `fb`\n",
-    "ax.plot(df_preds[\"bins\"], 1e3 * bin_norm * df_preds[\"predictions\"], 's', markersize=8, label=\"theory\")\n",
-    "ax.plot(df_preds[\"bins\"], data_central, 'o', markersize=8, label=\"data\")\n",
+    "ax.plot(\n",
+    "    df_preds[\"bins\"],\n",
+    "    1e3 * bin_norm * df_preds[\"predictions\"],\n",
+    "    \"s\",\n",
+    "    markersize=8,\n",
+    "    label=\"theory\",\n",
+    ")\n",
+    "ax.plot(df_preds[\"bins\"], data_central, \"o\", markersize=8, label=\"data\")\n",
     "ax.grid(True, alpha=0.5)\n",
     "ax.set_yscale(\"log\")\n",
     "ax.set_xlabel(\"bins\")\n",
@@ -417,10 +447,7 @@
     "for idx, o in enumerate(grid.orders()):\n",
     "    orders.append(o.as_tuple())\n",
     "\n",
-    "df_orders = pl.DataFrame(\n",
-    "    np.array(orders),\n",
-    "    schema=[\"as\", \"a\", \"lf\", \"lr\"]\n",
-    ")\n",
+    "df_orders = pl.DataFrame(np.array(orders), schema=[\"as\", \"a\", \"lf\", \"lr\"])\n",
     "df_orders.with_row_index()"
    ]
   },
@@ -506,10 +533,18 @@
     "# an associated bin normalization.\n",
     "df = pl.DataFrame({})\n",
     "for bin_dim in range(bin_dims):\n",
-    "    df = pl.concat([df,pl.DataFrame({\n",
-    "        f\"dim {bin_dim} left\": grid.bin_left(bin_dim),\n",
-    "        f\"dim {bin_dim} right\": grid.bin_right(bin_dim),\n",
-    "    })],how=\"vertical\",)\n",
+    "    df = pl.concat(\n",
+    "        [\n",
+    "            df,\n",
+    "            pl.DataFrame(\n",
+    "                {\n",
+    "                    f\"dim {bin_dim} left\": grid.bin_left(bin_dim),\n",
+    "                    f\"dim {bin_dim} right\": grid.bin_right(bin_dim),\n",
+    "                }\n",
+    "            ),\n",
+    "        ],\n",
+    "        how=\"vertical\",\n",
+    "    )\n",
     "df"
    ]
   },
@@ -670,7 +705,9 @@
    ],
    "source": [
     "# Load our modified grids\n",
-    "grid_nrm = pineappl.grid.Grid.read(\"./LHCB_DY_8TEV_custom_normalizations.pineappl.lz4\")\n",
+    "grid_nrm = pineappl.grid.Grid.read(\n",
+    "    \"./LHCB_DY_8TEV_custom_normalizations.pineappl.lz4\"\n",
+    ")\n",
     "df_nbins = pl.DataFrame({\"bin normalization\": grid_nrm.bin_normalizations()})\n",
     "df_nbins.with_row_index()"
    ]