Small update

Author: hannahbaumann

abfe_tutorial/abfe_analysis.ipynb

@@ -281,7 +281,7 @@
    "metadata": {},
    "source": [
     "### Methods to extract and manipulate the ABFE results\n",
-    "The next four methods allow you to extract ABFE results (extract_results_dict) and then manipulate them to get different types of results.\n",
+    "The next three methods allow you to extract ABFE results (extract_results_dict) and then manipulate them to get different types of results.\n",
     "\n",
     "These include:\n",
     "\n",
@@ -404,42 +404,6 @@
     "    return df"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "5e026eaf-2a95-4d4a-873d-b0d6e8d622c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _generate_raw(legs:dict) -> None:\n",
-    "    \"\"\"\n",
-    "    Write out all legs found and their DG values, or indicate that they have failed.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    legs : dict\n",
-    "        Dict of legs to write out.\n",
-    "    \"\"\"\n",
-    "    data = []\n",
-    "    for lig, results in sorted(legs.items()):\n",
-    "        for simtype, repeats in sorted(results.items()):\n",
-    "            if simtype != 'overall':\n",
-    "                for repeat in repeats:\n",
-    "                    m, u = format_estimate_uncertainty(repeat[0].m, repeat[1].m, unc_prec=2)\n",
-    "                    data.append((simtype, lig, m, u))\n",
-    "\n",
-    "    df = pd.DataFrame(\n",
-    "        data,\n",
-    "        columns=[\n",
-    "            \"leg\",\n",
-    "            \"ligand_i\",\n",
-    "            \"DG(i->j) (kcal/mol)\",\n",
-    "            \"uncertainty (kcal/mol)\",\n",
-    "        ],\n",
-    "    )\n",
-    "    return df"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "98a84e37-91c3-4f78-8a68-a54f8835f7d2",