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

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

Author: pre-commit-ci[bot]

docs/explanation/2023_07_presentation.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "7fb27b941602401d91542211134fc71a",
    "metadata": {
     "notebookRunGroups": {
      "groupValue": "1"
@@ -457,6 +458,7 @@
    "outputs": [],
    "source": [
     "from __future__ import annotations\n",
+    "\n",
     "from egglog import *\n",
     "\n",
     "egraph = EGraph()\n",
@@ -465,16 +467,13 @@
     "# 1. Describe domain with types & functions\n",
     "@egraph.class_\n",
     "class Num(Expr):\n",
-    "    def __init__(self, i: i64Like) -> None:\n",
-    "        ...\n",
+    "    def __init__(self, i: i64Like) -> None: ...\n",
     "\n",
-    "    def __add__(self, other: Num) -> Num:\n",
-    "        ...\n",
+    "    def __add__(self, other: Num) -> Num: ...\n",
     "\n",
     "\n",
     "@egraph.function(cost=20)\n",
-    "def fib(x: i64Like) -> Num:\n",
-    "    ..."
+    "def fib(x: i64Like) -> Num: ..."
    ]
   },
   {
@@ -1406,16 +1405,17 @@
    "outputs": [],
    "source": [
     "import timeit\n",
+    "\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
     "\n",
     "stmts = {\n",
     "    \"original\": \"run_lda(X_np, y_np)\",\n",
     "    \"numba\": \"numba_fn(X_np, y_np)\",\n",
     "}\n",
-    "df = pd.DataFrame.from_dict(\n",
-    "    {name: timeit.repeat(stmt, globals=globals(), number=1, repeat=10) for name, stmt in stmts.items()}\n",
-    ")\n",
+    "df = pd.DataFrame.from_dict({\n",
+    "    name: timeit.repeat(stmt, globals=globals(), number=1, repeat=10) for name, stmt in stmts.items()\n",
+    "})\n",
     "df_melt = pd.melt(df, var_name=\"function\", value_name=\"time\")"
    ]
   },

docs/explanation/2023_11_09_portland_state.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "7fb27b941602401d91542211134fc71a",
    "metadata": {},
    "source": [
     "```{post} 2023-11-04\n",
@@ -12,6 +13,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "acae54e37e7d407bbb7b55eff062a284",
    "metadata": {
     "tags": [
      "hide-input"
@@ -122,26 +124,23 @@
    "outputs": [],
    "source": [
     "from __future__ import annotations\n",
+    "\n",
     "from egglog import *\n",
     "\n",
     "egraph = EGraph()\n",
     "\n",
     "\n",
     "@egraph.class_\n",
     "class NDArray(Expr):\n",
-    "    def __init__(self, i: i64Like) -> None:\n",
-    "        ...\n",
+    "    def __init__(self, i: i64Like) -> None: ...\n",
     "\n",
-    "    def __add__(self, other: NDArray) -> NDArray:\n",
-    "        ...\n",
+    "    def __add__(self, other: NDArray) -> NDArray: ...\n",
     "\n",
-    "    def __mul__(self, other: NDArray) -> NDArray:\n",
-    "        ...\n",
+    "    def __mul__(self, other: NDArray) -> NDArray: ...\n",
     "\n",
     "\n",
     "@egraph.function(cost=2)\n",
-    "def arange(i: i64Like) -> NDArray:\n",
-    "    ...\n",
+    "def arange(i: i64Like) -> NDArray: ...\n",
     "\n",
     "\n",
     "# Register rewrite rule that asserts for all values x of type NDArray\n",
@@ -6754,18 +6753,16 @@
    "outputs": [],
    "source": [
     "import timeit\n",
+    "\n",
     "import pandas as pd\n",
     "\n",
     "stmts = {\n",
     "    \"original\": \"run_lda(X_np, y_np)\",\n",
     "    \"numba\": \"numba_fn(X_np, y_np)\",\n",
     "}\n",
-    "df = pd.DataFrame.from_dict(\n",
-    "    {\n",
-    "        name: timeit.repeat(stmt, globals=globals(), number=1, repeat=10)\n",
-    "        for name, stmt in stmts.items()\n",
-    "    }\n",
-    ")"
+    "df = pd.DataFrame.from_dict({\n",
+    "    name: timeit.repeat(stmt, globals=globals(), number=1, repeat=10) for name, stmt in stmts.items()\n",
+    "})"
    ]
   },
   {
@@ -6828,6 +6825,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9a63283cbaf04dbcab1f6479b197f3a8",
    "metadata": {},
    "source": []
   }