notebooks/07_protein_analysis_figures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "import concurrent.futures\n",
    "import io\n",
    "import itertools\n",
    "import math\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "import torch\n",
    "from IPython.display import SVG, Image, set_matplotlib_formats\n",
    "from kmbio import PDB\n",
    "from kmtools import structure_tools\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.patches import Patch\n",
    "from scipy import stats\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"max_columns\", 1000)\n",
    "pd.set_option(\"max_rows\", 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "set_matplotlib_formats(\"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/kimlab1/strokach/workspace/proteinsolver/notebooks/07_protein_analysis_figures')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOTEBOOK_NAME = \"07_protein_analysis_figures\"\n",
    "NOTEBOOK_PATH = Path(NOTEBOOK_NAME).resolve(strict=True)\n",
    "NOTEBOOK_PATH.mkdir(exist_ok=True)\n",
    "NOTEBOOK_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/kimlab1/database_data/datapkg_output_dir/proteinsolver')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if \"DATAPKG_OUTPUT_DIR\" in os.environ:\n",
    "    OUTPUT_DIR = Path(os.getenv(\"DATAPKG_OUTPUT_DIR\")).joinpath(\"proteinsolver\").resolve()\n",
    "else:\n",
    "    OUTPUT_DIR = NOTEBOOK_DIR.parent\n",
    "OUTPUT_DIR.mkdir(exist_ok=True)\n",
    "\n",
    "display(OUTPUT_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_diff(df, colname):\n",
    "    if f\"{colname}_wt\" not in df:\n",
    "        before_ = len(df)\n",
    "        ext = \".pdb\" if not df.iloc[0][\"name\"].endswith(\".pdb\") else \"\"\n",
    "        row_is_wt = (df[\"name\"] + ext) == df[\"my_wt\"]\n",
    "        assert set(df[row_is_wt][\"my_wt\"]) == set(df[\"my_wt\"])\n",
    "        df2 = df[row_is_wt][[\"my_wt\", colname]].rename(columns={colname: f\"{colname}_wt\"})\n",
    "        df = df.merge(df2, on=\"my_wt\")\n",
    "        assert before_ == len(df), (before_, len(df))\n",
    "    df[f\"{colname}_change\"] = df[f\"{colname}\"] - df[f\"{colname}_wt\"]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_conf_interval(r, num):\n",
    "    stderr = 1.0 / math.sqrt(num - 3)\n",
    "    delta = 1.96 * stderr\n",
    "    lower = math.tanh(math.atanh(r) - delta)\n",
    "    upper = math.tanh(math.atanh(r) + delta)\n",
    "    return r - lower, upper - r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_confidence_interval(\n",
    "    values1, values2, fn, num_iterations=10_000, show_progress=True, seed=42\n",
    "):\n",
    "    rng = np.random.default_rng(seed)\n",
    "    outputs = []\n",
    "    for _ in tqdm(range(num_iterations), disable=not show_progress):\n",
    "        index = rng.choice(len(values1), len(values1), replace=True)\n",
    "        while len(np.unique(index)) == 1:\n",
    "            index = rng.choice(len(values1), len(values1), replace=True)\n",
    "        values1_sample = values1[index]\n",
    "        values2_sample = values2[index]\n",
    "        output = fn(values1_sample, values2_sample)\n",
    "        outputs.append(output)\n",
    "    lower = np.quantile(outputs, 0.05)\n",
    "    upper = np.quantile(outputs, 0.95)\n",
    "    return lower, upper, outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_correlations(corrs_ref, corrs):\n",
    "    num_greater = ((corrs_ref.reshape(-1, 1) - corrs.reshape(1, -1)) > 0).sum()\n",
    "    num_any = len(corrs_ref) * len(corrs)\n",
    "    return num_greater / num_any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap_confidence_interval(\n",
    "#     np.array([1, 2, 3, 4, 5]),\n",
    "#     np.array([1, 2, 3, 5, 4]),\n",
    "#     fn=lambda v1, v2: stats.spearmanr(v1, v2)[0],\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_correlation_table(text, sep=\"|\"):\n",
    "    buf = io.StringIO()\n",
    "    buf.write(text)\n",
    "    buf.seek(0)\n",
    "    df = pd.read_csv(buf, sep=sep)\n",
    "    if \"N\" in df:\n",
    "        df[\"corr_lower_bound\"], df[\"corr_upper_bound\"] = list(\n",
    "            zip(*[get_conf_interval(corr, N) for corr, N in df[[\"corr\", \"N\"]].values])\n",
    "        )\n",
    "        del df[\"N\"]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_aa_sequence(structure_file):\n",
    "    structure = PDB.load(structure_file)\n",
    "    model_id = next(structure.models).id\n",
    "    chain_id = next(structure.chains).id\n",
    "    aa_sequence = structure_tools.extract_aa_sequence(structure, model_id, chain_id)\n",
    "    return aa_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_ddg_correlation(\n",
    "    my_wt, feature_column, target_column, df, return_corrs=False, show_progress=False,\n",
    "):\n",
    "    sign = 1 if feature_column in [\"network_score_change\", \"scan_sm_log_change\"] else -1\n",
    "    df_notnull = df.dropna(subset=[target_column, feature_column])\n",
    "    if len(df) > len(df_notnull):\n",
    "        print(\n",
    "            f\"Removing {len(df) - len(df_notnull)} rows with null values for columns: \"\n",
    "            f\"{[target_column, feature_column]}.\"\n",
    "        )\n",
    "        df = df_notnull\n",
    "    values_ref = df[target_column].values\n",
    "    values = sign * df[feature_column].values\n",
    "    corr = stats.spearmanr(values_ref, values)\n",
    "    lower, upper, corrs = bootstrap_confidence_interval(\n",
    "        values_ref,\n",
    "        values,\n",
    "        fn=lambda v1, v2: stats.spearmanr(v1, v2)[0],\n",
    "        show_progress=show_progress,\n",
    "    )\n",
    "    result = {\n",
    "        \"my_wt\": my_wt,\n",
    "        \"feature\": feature_column,\n",
    "        \"corr\": corr[0],\n",
    "        \"pvalue\": corr[1],\n",
    "        \"corr_lower_bound\": lower,\n",
    "        \"corr_upper_bound\": upper,\n",
    "    }\n",
    "    if return_corrs:\n",
    "        result[\"corrs\"] = corrs\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_stability_correlation(\n",
    "    library, domain, feature_column, gp, return_corrs=False, show_progress=False,\n",
    "):\n",
    "    if \"betanov\" in feature_column or \"talaris\" in feature_column:\n",
    "        values = (-gp[feature_column] / gp[\"sequence_length\"]).values\n",
    "    else:\n",
    "        values = gp[feature_column].values\n",
    "    values_ref = gp[[\"stabilityscore_t\", \"stabilityscore_c\"]].mean(axis=1).values\n",
    "    corr = stats.spearmanr(values_ref, values)\n",
    "    lower, upper, corrs = bootstrap_confidence_interval(\n",
    "        values_ref,\n",
    "        values,\n",
    "        fn=lambda v1, v2: stats.spearmanr(v1, v2)[0],\n",
    "        show_progress=show_progress,\n",
    "    )\n",
    "    result = {\n",
    "        \"library\": library,\n",
    "        \"domain\": domain,\n",
    "        \"feature\": feature_column,\n",
    "        \"corr\": corr[0],\n",
    "        \"pvalue\": corr[1],\n",
    "        \"corr_lower_bound\": lower,\n",
    "        \"corr_upper_bound\": upper,\n",
    "    }\n",
    "    if return_corrs:\n",
    "        result[\"corrs\"] = corrs\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_superior_to_ps_string(pvalue, cutoff):\n",
    "    if pd.isnull(pvalue):\n",
    "        return \"\"\n",
    "    elif pvalue < cutoff:\n",
    "        return f\"Superior (p = {pvalue:.4f})\"\n",
    "    elif pvalue > (1 - cutoff):\n",
    "        return f\"Inferior (p = {pvalue:.4f})\"\n",
    "    else:\n",
    "        return f\"No sig. diff. (p = {pvalue:.4f})\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rosetta scores - Protherm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKGELDKAIGRNTNGVITKDEAEKLFNQDVDAAVRGILRNAKLKPVYDSLDAVRRAALINMVFQMGETGVAGFTNSLRMLQQKRWDEAAVNLAKSRWYNQTPNRAKRVITTFRTGTWDAYK'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_aa_sequence(\n",
    "    \"/home/kimlab1/strokach/workspace/adjacency-net-v2/notebooks/01_protherm_dataset/structures/pdb107l.entA_0--G44S-wt.pdb\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pdb107l.entA_0--G44S-wt.pdb\n",
      "pdb160l.entA_0--A120M-wt.pdb\n",
      "ls: write error: Broken pipe\n"
     ]
    }
   ],
   "source": [
    "!ls /home/kimlab1/strokach/workspace/adjacency-net-v2/notebooks/01_protherm_dataset/structures/ | head -n 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/kimlab1/strokach/workspace/adjacency-net-v2/notebooks/01_protherm_dataset/structures')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STRUCTURES_PATH = Path(\n",
    "    \"/home/kimlab1/strokach/workspace/adjacency-net-v2/notebooks/01_protherm_dataset/structures/\"\n",
    ").resolve(strict=True)\n",
    "STRUCTURES_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "protherm_design_rue = pd.read_csv(NOTEBOOK_PATH.joinpath(\"protherm_design_wt_RUE.csv\"), index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average out energies of wild-type structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "protherm_design_rue[\"filename_prefix\"] = protherm_design_rue[\"filename\"].str.split(\"-\").str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "179\n"
     ]
    }
   ],
   "source": [
    "filename_prefix_to_reu_wt = {}\n",
    "for filename_prefix, gp in protherm_design_rue.groupby(\"filename_prefix\"):\n",
    "    filename_prefix_to_reu_wt[filename_prefix] = gp[\"RUE_wt\"].mean()\n",
    "print(len(filename_prefix_to_reu_wt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "protherm_design_rue[\"RUE_wt_2\"] = protherm_design_rue[\"filename_prefix\"].map(\n",
    "    filename_prefix_to_reu_wt\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "protherm_design_rue[\"rosetta_reu_change\"] = (\n",
    "    protherm_design_rue[\"RUE_mut\"] - protherm_design_rue[\"RUE_wt_2\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=-0.008007305189965822, pvalue=0.6372197700858309)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.spearmanr(protherm_design_rue[\"rosetta_reu_change\"], protherm_design_rue[\"ddg_exp\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalize by sequence length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    protherm_design_rue[\"sequence_length\"] = torch.load(\n",
    "        NOTEBOOK_PATH.joinpath(\"protherm-sequence-lengths.torch\")\n",
    "    )\n",
    "except FileNotFoundError:\n",
    "    sequence_lengths = [\n",
    "        len(get_aa_sequence(STRUCTURES_PATH.joinpath(s)))\n",
    "        for s in tqdm(protherm_design_rue[\"filename\"].values)\n",
    "    ]\n",
    "    torch.save(sequence_lengths, NOTEBOOK_PATH.joinpath(\"protherm-sequence-lengths.torch\"))\n",
    "    protherm_design_rue[\"sequence_length\"] = sequence_lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "protherm_design_rue[\"rosetta_reu_norm_change\"] = (\n",
    "    protherm_design_rue[\"rosetta_reu_change\"] / protherm_design_rue[\"sequence_length\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.08262914198402263, pvalue=1.0897621694155652e-06)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.spearmanr(protherm_design_rue[\"rosetta_reu_norm_change\"], protherm_design_rue[\"ddg_exp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>chain_id</th>\n",
       "      <th>mutation</th>\n",
       "      <th>ddg_exp</th>\n",
       "      <th>RUE_mut</th>\n",
       "      <th>wt_aa</th>\n",
       "      <th>pos</th>\n",
       "      <th>mut</th>\n",
       "      <th>RUE_wt</th>\n",
       "      <th>filename_prefix</th>\n",
       "      <th>RUE_wt_2</th>\n",
       "      <th>rosetta_reu_change</th>\n",
       "      <th>sequence_length</th>\n",
       "      <th>rosetta_reu_norm_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pdb107l.entA_0--G44S-wt.pdb</td>\n",
       "      <td>A</td>\n",
       "      <td>G44S</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-165.203</td>\n",
       "      <td>G</td>\n",
       "      <td>44</td>\n",
       "      <td>S</td>\n",
       "      <td>-342.526</td>\n",
       "      <td>pdb107l.entA_0</td>\n",
       "      <td>-342.526</td>\n",
       "      <td>177.323</td>\n",
       "      <td>162</td>\n",
       "      <td>1.094586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pdb160l.entA_0--A120M-wt.pdb</td>\n",
       "      <td>A</td>\n",
       "      <td>A120M</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-136.290</td>\n",
       "      <td>A</td>\n",
       "      <td>120</td>\n",
       "      <td>M</td>\n",
       "      <td>-351.957</td>\n",
       "      <td>pdb160l.entA_0</td>\n",
       "      <td>-351.957</td>\n",
       "      <td>215.667</td>\n",
       "      <td>162</td>\n",
       "      <td>1.331278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pdb161l.entA_0--A116N-wt.pdb</td>\n",
       "      <td>A</td>\n",
       "      <td>A116N</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-147.909</td>\n",
       "      <td>A</td>\n",
       "      <td>116</td>\n",
       "      <td>N</td>\n",
       "      <td>-360.530</td>\n",
       "      <td>pdb161l.entA_0</td>\n",
       "      <td>-360.530</td>\n",
       "      <td>212.621</td>\n",
       "      <td>162</td>\n",
       "      <td>1.312475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pdb162l.entA_0--A122Q-wt.pdb</td>\n",
       "      <td>A</td>\n",
       "      <td>A122Q</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-177.392</td>\n",
       "      <td>A</td>\n",
       "      <td>122</td>\n",
       "      <td>Q</td>\n",
       "      <td>-365.948</td>\n",
       "      <td>pdb162l.entA_0</td>\n",
       "      <td>-365.948</td>\n",
       "      <td>188.556</td>\n",
       "      <td>162</td>\n",
       "      <td>1.163926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pdb163l.entA_0--A123Q-wt.pdb</td>\n",
       "      <td>A</td>\n",
       "      <td>A123Q</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-170.658</td>\n",
       "      <td>A</td>\n",
       "      <td>123</td>\n",
       "      <td>Q</td>\n",
       "      <td>-345.064</td>\n",
       "      <td>pdb163l.entA_0</td>\n",
       "      <td>-345.064</td>\n",
       "      <td>174.406</td>\n",
       "      <td>162</td>\n",
       "      <td>1.076580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       filename chain_id mutation  ddg_exp  RUE_mut wt_aa  \\\n",
       "0   pdb107l.entA_0--G44S-wt.pdb        A     G44S    -0.53 -165.203     G   \n",
       "1  pdb160l.entA_0--A120M-wt.pdb        A    A120M    -0.20 -136.290     A   \n",
       "2  pdb161l.entA_0--A116N-wt.pdb        A    A116N     0.17 -147.909     A   \n",
       "3  pdb162l.entA_0--A122Q-wt.pdb        A    A122Q    -0.24 -177.392     A   \n",
       "4  pdb163l.entA_0--A123Q-wt.pdb        A    A123Q    -0.22 -170.658     A   \n",
       "\n",
       "   pos mut   RUE_wt filename_prefix  RUE_wt_2  rosetta_reu_change  \\\n",
       "0   44   S -342.526  pdb107l.entA_0  -342.526             177.323   \n",
       "1  120   M -351.957  pdb160l.entA_0  -351.957             215.667   \n",
       "2  116   N -360.530  pdb161l.entA_0  -360.530             212.621   \n",
       "3  122   Q -365.948  pdb162l.entA_0  -365.948             188.556   \n",
       "4  123   Q -345.064  pdb163l.entA_0  -345.064             174.406   \n",
       "\n",
       "   sequence_length  rosetta_reu_norm_change  \n",
       "0              162                 1.094586  \n",
       "1              162                 1.331278  \n",
       "2              162                 1.312475  \n",
       "3              162                 1.163926  \n",
       "4              162                 1.076580  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protherm_design_rue.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Protherm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "protherm_wresults_df = torch.load(\n",
    "    NOTEBOOK_PATH.parent.joinpath(\"protein_analysis\", \"protherm_wresults.torch\")\n",
    ")\n",
    "protherm_wresults_df[\"ddg_exp\"] = -protherm_wresults_df[\"ddg_exp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rosetta scores\n",
    "assert (protherm_wresults_df[\"mutation\"] == protherm_design_rue[\"mutation\"]).all()\n",
    "\n",
    "protherm_wresults_df[\"rosetta_reu_change\"] = protherm_design_rue[\"rosetta_reu_change\"].values\n",
    "protherm_wresults_df[\"rosetta_reu_norm_change\"] = protherm_design_rue[\n",
    "    \"rosetta_reu_norm_change\"\n",
    "].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "protherm_has_homologs = pq.read_table(\n",
    "    NOTEBOOK_PATH.parents[2].joinpath(\n",
    "        \"neurips19-graph-protein-design\",\n",
    "        \"notebooks\",\n",
    "        \"02_protherm_remote_subset\",\n",
    "        \"protherm-has-homologs.parquet\",\n",
    "    )\n",
    ").to_pandas(integer_object_nulls=True)\n",
    "\n",
    "protherm_wresults_df[\"has_homologs\"] = (\n",
    "    protherm_wresults_df.set_index([\"structure_id\", \"chain_id\"]).index\n",
    ").map(protherm_has_homologs.set_index([\"pdb_id\", \"pdb_chain\"])[\"has_homologs_35\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed to match 163 mutations.\n",
      "Missing 98 mutations.\n"
     ]
    }
   ],
   "source": [
    "# Ingraham network predictions\n",
    "ingraham_df = pq.read_table(\n",
    "    NOTEBOOK_PATH.parents[2].joinpath(\n",
    "        \"neurips19-graph-protein-design\",\n",
    "        \"notebooks\",\n",
    "        \"02_predict_mutation_effect\",\n",
    "        \"protherm_mutation.parquet\",\n",
    "    )\n",
    ").to_pandas(integer_object_nulls=True)\n",
    "\n",
    "ingraham_df[\"pdb_id\"], ingraham_df[\"pdb_chain\"] = list(zip(*ingraham_df[\"name\"].str.split(\"-\")))\n",
    "\n",
    "unique_mutations = set(tuple(t) for t in ingraham_df[[\"pdb_id\", \"pdb_chain\", \"mutation\"]].values)\n",
    "unique_known_mutations = set(\n",
    "    tuple(t) for t in protherm_wresults_df[[\"structure_id\", \"chain_id\", \"mutation\"]].values\n",
    ")\n",
    "print(f\"Failed to match {len(unique_mutations -unique_known_mutations)} mutations.\")\n",
    "print(f\"Missing {len(unique_known_mutations - unique_mutations)} mutations.\")\n",
    "\n",
    "protherm_wresults_df[\"ingraham_neglogp_change\"] = (\n",
    "    protherm_wresults_df.set_index([\"structure_id\", \"chain_id\", \"mutation\"]).index\n",
    ").map(ingraham_df.set_index([\"pdb_id\", \"pdb_chain\", \"mutation\"])[\"neglogp_change\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GAPF - Mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "MUTATION_METHOD = \"logproba.scan\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "stability_scores_for_mutations = torch.load(\n",
    "    NOTEBOOK_PATH.parent.joinpath(\n",
    "        \"06_global_analysis_of_protein_folding\",\n",
    "        f\"stability_scores_for_mutations.{MUTATION_METHOD}.torch\",\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rosetta energy function scores\n",
    "carles_rosetta_scores_df = pd.read_csv(NOTEBOOK_PATH.joinpath(\"GAPF_design_RUE_wt.csv\"))\n",
    "carles_rosetta_scores_df[\"rosetta_score_change\"] = (\n",
    "    carles_rosetta_scores_df[\"RUE\"] - carles_rosetta_scores_df[\"RUE_wt\"]\n",
    ")\n",
    "\n",
    "stability_scores_for_mutations[\"ssm2\"][\"rosetta_score_change\"] = (\n",
    "    stability_scores_for_mutations[\"ssm2\"]\n",
    "    .set_index([\"my_wt\", \"pos\", \"wt_aa\", \"mut\", \"sequence\"])\n",
    "    .index.map(\n",
    "        carles_rosetta_scores_df.set_index([\"my_wt\", \"pos\", \"wt_aa\", \"mut\", \"sequence\"])[\n",
    "            \"rosetta_score_change\"\n",
    "        ]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ingraham network predictions\n",
    "ingraham_df = pq.read_table(\n",
    "    NOTEBOOK_PATH.parents[2].joinpath(\n",
    "        \"neurips19-graph-protein-design\",\n",
    "        \"notebooks\",\n",
    "        \"02_predict_mutation_effect\",\n",
    "        \"rocklin_2017_mutation_ssm2.parquet\",\n",
    "    )\n",
    ").to_pandas(integer_object_nulls=True)\n",
    "\n",
    "(\n",
    "    stability_scores_for_mutations[\"ssm2\"][\"ingraham_neglogp_change\"]\n",
    ") = stability_scores_for_mutations[\"ssm2\"][\"sequence\"].map(\n",
    "    ingraham_df.set_index(\"mut_seq\")[\"neglogp_change\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_name(name):\n",
    "    if name.startswith(\"Pin1\"):\n",
    "        prot, mut = name.split(\"_\")\n",
    "        mut = f\"{mut[0]}{int(mut[1:-1]) - 1}{mut[-1]}\"\n",
    "        return f\"{prot}_{mut}\"\n",
    "    elif name.startswith(\"villin\"):\n",
    "        prot, mut = name.split(\"_\")\n",
    "        mut = f\"{mut[0]}{int(mut[1:-1]) - 5}{mut[-1]}\"\n",
    "        return f\"{prot}_{mut}\"\n",
    "    else:\n",
    "        return name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rosetta's ddg_monomer\n",
    "cartesian_ddg_df = pq.read_table(\n",
    "    OUTPUT_DIR.parent.joinpath(\"tkpod-rosetta-ddg\", \"rocklin_2017_ssm2_cartesian_ddg.parquet\")\n",
    ").to_pandas(integer_object_nulls=True)\n",
    "cartesian_ddg_df[\"name\"] = cartesian_ddg_df[\"name\"] + \"_\" + cartesian_ddg_df[\"mutation\"]\n",
    "cartesian_ddg_df[\"name\"] = cartesian_ddg_df[\"name\"].apply(adjust_name).values\n",
    "\n",
    "assert not set(cartesian_ddg_df[\"name\"]) - set(stability_scores_for_mutations[\"ssm2\"][\"name\"])\n",
    "\n",
    "stability_scores_for_mutations[\"ssm2\"][\"cartesian_ddg_change\"] = (\n",
    "    stability_scores_for_mutations[\"ssm2\"][\"name\"]\n",
    ").map(cartesian_ddg_df.set_index(\"name\")[\"dg_change\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rosetta's cartesian_ddg\n",
    "ddg_monomer_dg = pq.read_table(\n",
    "    OUTPUT_DIR.parent.joinpath(\"tkpod-rosetta-ddg\", \"rocklin_2017_ssm2_ddg_monomer.parquet\")\n",
    ").to_pandas(integer_object_nulls=True)\n",
    "ddg_monomer_dg[\"name\"] = ddg_monomer_dg[\"name\"] + \"_\" + ddg_monomer_dg[\"mutation\"]\n",
    "ddg_monomer_dg[\"name\"] = ddg_monomer_dg[\"name\"].apply(adjust_name)\n",
    "\n",
    "assert not set(ddg_monomer_dg[\"name\"]) - set(stability_scores_for_mutations[\"ssm2\"][\"name\"])\n",
    "\n",
    "stability_scores_for_mutations[\"ssm2\"][\"ddg_monomer_change\"] = (\n",
    "    stability_scores_for_mutations[\"ssm2\"][\"name\"]\n",
    ").map(ddg_monomer_dg.set_index(\"name\")[\"dg_change\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ec50_t</th>\n",
       "      <th>delta_ec50_t</th>\n",
       "      <th>ec50_95ci_lbound_t</th>\n",
       "      <th>ec50_95ci_ubound_t</th>\n",
       "      <th>ec50_95ci_t</th>\n",
       "      <th>ec50_pred_t</th>\n",
       "      <th>delta_pred_vs_wt_t</th>\n",
       "      <th>ec50_rise_t</th>\n",
       "      <th>stabilityscore_t</th>\n",
       "      <th>ec50_c</th>\n",
       "      <th>delta_ec50_c</th>\n",
       "      <th>ec50_95ci_lbound_c</th>\n",
       "      <th>ec50_95ci_ubound_c</th>\n",
       "      <th>ec50_95ci_c</th>\n",
       "      <th>ec50_pred_c</th>\n",
       "      <th>delta_pred_vs_wt_c</th>\n",
       "      <th>ec50_rise_c</th>\n",
       "      <th>stabilityscore_c</th>\n",
       "      <th>ec50_rise_c_adj</th>\n",
       "      <th>stabilityscore_c_adj</th>\n",
       "      <th>consensus_ec50_rise</th>\n",
       "      <th>consensus_stability_score</th>\n",
       "      <th>network_score</th>\n",
       "      <th>rosetta_score_change</th>\n",
       "      <th>ingraham_neglogp_change</th>\n",
       "      <th>cartesian_ddg_change</th>\n",
       "      <th>ddg_monomer_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.00000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>12851.000000</td>\n",
       "      <td>9913.000000</td>\n",
       "      <td>9900.00000</td>\n",
       "      <td>10674.000000</td>\n",
       "      <td>10674.000000</td>\n",
       "      <td>10674.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.510077</td>\n",
       "      <td>3.558901</td>\n",
       "      <td>-0.911788</td>\n",
       "      <td>3.419096</td>\n",
       "      <td>3.734309</td>\n",
       "      <td>0.315213</td>\n",
       "      <td>-1.257221</td>\n",
       "      <td>0.018486</td>\n",
       "      <td>4.81615</td>\n",
       "      <td>1.449826</td>\n",
       "      <td>2.572824</td>\n",
       "      <td>-0.589170</td>\n",
       "      <td>2.414310</td>\n",
       "      <td>2.774313</td>\n",
       "      <td>0.360003</td>\n",
       "      <td>-0.755856</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>3.328778</td>\n",
       "      <td>1.002042</td>\n",
       "      <td>3.330559</td>\n",
       "      <td>1.002575</td>\n",
       "      <td>3.500066</td>\n",
       "      <td>1.053593</td>\n",
       "      <td>-81.464641</td>\n",
       "      <td>60.61660</td>\n",
       "      <td>0.061666</td>\n",
       "      <td>4.626575</td>\n",
       "      <td>3.026326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.232652</td>\n",
       "      <td>1.529182</td>\n",
       "      <td>1.394367</td>\n",
       "      <td>1.550756</td>\n",
       "      <td>1.474168</td>\n",
       "      <td>0.255741</td>\n",
       "      <td>1.040099</td>\n",
       "      <td>0.349419</td>\n",
       "      <td>2.03009</td>\n",
       "      <td>0.611108</td>\n",
       "      <td>1.152953</td>\n",
       "      <td>0.860679</td>\n",
       "      <td>1.170877</td>\n",
       "      <td>1.118192</td>\n",
       "      <td>0.305776</td>\n",
       "      <td>0.787102</td>\n",
       "      <td>0.360246</td>\n",
       "      <td>1.357631</td>\n",
       "      <td>0.408728</td>\n",
       "      <td>1.354769</td>\n",
       "      <td>0.407864</td>\n",
       "      <td>1.322078</td>\n",
       "      <td>0.398012</td>\n",
       "      <td>16.119577</td>\n",
       "      <td>78.36315</td>\n",
       "      <td>0.049345</td>\n",
       "      <td>7.498591</td>\n",
       "      <td>9.581338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.070000</td>\n",
       "      <td>-5.730000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.770000</td>\n",
       "      <td>-2.570000</td>\n",
       "      <td>-2.06000</td>\n",
       "      <td>-0.620000</td>\n",
       "      <td>-0.790000</td>\n",
       "      <td>-4.580000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.820000</td>\n",
       "      <td>-2.160000</td>\n",
       "      <td>-0.880000</td>\n",
       "      <td>-0.270000</td>\n",
       "      <td>-0.880000</td>\n",
       "      <td>-0.270000</td>\n",
       "      <td>-0.870000</td>\n",
       "      <td>-0.260000</td>\n",
       "      <td>-132.226303</td>\n",
       "      <td>11.53400</td>\n",
       "      <td>-0.104663</td>\n",
       "      <td>-11.788000</td>\n",
       "      <td>-8.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>-1.730000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-1.760000</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>3.36000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>1.940000</td>\n",
       "      <td>-0.950000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-1.250000</td>\n",
       "      <td>-0.070000</td>\n",
       "      <td>2.460000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>2.470000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>-89.816566</td>\n",
       "      <td>21.30825</td>\n",
       "      <td>0.026294</td>\n",
       "      <td>0.405500</td>\n",
       "      <td>-0.401000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.510000</td>\n",
       "      <td>-0.130000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-1.160000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>5.28000</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>-0.320000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-0.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.490000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>-76.851799</td>\n",
       "      <td>25.94650</td>\n",
       "      <td>0.059815</td>\n",
       "      <td>2.402000</td>\n",
       "      <td>1.611500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-0.490000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>6.25000</td>\n",
       "      <td>1.880000</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>1.320000</td>\n",
       "      <td>-69.900856</td>\n",
       "      <td>86.10625</td>\n",
       "      <td>0.099222</td>\n",
       "      <td>6.178500</td>\n",
       "      <td>4.504750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>7.470000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>2.630000</td>\n",
       "      <td>8.79000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2.890000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>8.840000</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>8.840000</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>8.280000</td>\n",
       "      <td>2.490000</td>\n",
       "      <td>-54.083675</td>\n",
       "      <td>2097.36100</td>\n",
       "      <td>0.249719</td>\n",
       "      <td>99.926667</td>\n",
       "      <td>188.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                pos        ec50_t  delta_ec50_t  ec50_95ci_lbound_t  \\\n",
       "count  12851.000000  12851.000000  12851.000000        12851.000000   \n",
       "mean      21.510077      3.558901     -0.911788            3.419096   \n",
       "std       12.232652      1.529182      1.394367            1.550756   \n",
       "min        0.000000     -1.070000     -5.730000           -0.500000   \n",
       "25%       11.000000      2.540000     -1.730000            2.400000   \n",
       "50%       21.000000      4.510000     -0.130000            4.300000   \n",
       "75%       32.000000      4.660000      0.010000            4.600000   \n",
       "max       46.000000      7.470000      2.880000            6.100000   \n",
       "\n",
       "       ec50_95ci_ubound_t   ec50_95ci_t   ec50_pred_t  delta_pred_vs_wt_t  \\\n",
       "count        12851.000000  12851.000000  12851.000000        12851.000000   \n",
       "mean             3.734309      0.315213     -1.257221            0.018486   \n",
       "std              1.474168      0.255741      1.040099            0.349419   \n",
       "min             -0.400000      0.000000     -3.770000           -2.570000   \n",
       "25%              2.700000      0.200000     -1.760000           -0.050000   \n",
       "50%              4.700000      0.300000     -1.160000           -0.000000   \n",
       "75%              4.800000      0.400000     -0.490000            0.070000   \n",
       "max              6.500000      6.800000      1.670000            2.630000   \n",
       "\n",
       "       ec50_rise_t  stabilityscore_t        ec50_c  delta_ec50_c  \\\n",
       "count  12851.00000      12851.000000  12851.000000  12851.000000   \n",
       "mean       4.81615          1.449826      2.572824     -0.589170   \n",
       "std        2.03009          0.611108      1.152953      0.860679   \n",
       "min       -2.06000         -0.620000     -0.790000     -4.580000   \n",
       "25%        3.36000          1.010000      1.940000     -0.950000   \n",
       "50%        5.28000          1.590000      2.770000     -0.320000   \n",
       "75%        6.25000          1.880000      3.520000      0.000000   \n",
       "max        8.79000          2.650000      6.600000      2.890000   \n",
       "\n",
       "       ec50_95ci_lbound_c  ec50_95ci_ubound_c   ec50_95ci_c   ec50_pred_c  \\\n",
       "count        12851.000000        12851.000000  12851.000000  12851.000000   \n",
       "mean             2.414310            2.774313      0.360003     -0.755856   \n",
       "std              1.170877            1.118192      0.305776      0.787102   \n",
       "min             -0.500000           -0.500000      0.000000     -2.820000   \n",
       "25%              1.800000            2.200000      0.200000     -1.250000   \n",
       "50%              2.600000            3.000000      0.300000     -0.850000   \n",
       "75%              3.400000            3.700000      0.500000     -0.200000   \n",
       "max              6.500000            8.500000      9.000000      2.700000   \n",
       "\n",
       "       delta_pred_vs_wt_c   ec50_rise_c  stabilityscore_c  ec50_rise_c_adj  \\\n",
       "count        12851.000000  12851.000000      12851.000000     12851.000000   \n",
       "mean             0.006494      3.328778          1.002042         3.330559   \n",
       "std              0.360246      1.357631          0.408728         1.354769   \n",
       "min             -2.160000     -0.880000         -0.270000        -0.880000   \n",
       "25%             -0.070000      2.460000          0.740000         2.470000   \n",
       "50%              0.000000      3.310000          1.000000         3.310000   \n",
       "75%              0.060000      4.170000          1.260000         4.170000   \n",
       "max              2.640000      8.840000          2.660000         8.840000   \n",
       "\n",
       "       stabilityscore_c_adj  consensus_ec50_rise  consensus_stability_score  \\\n",
       "count          12851.000000         12851.000000               12851.000000   \n",
       "mean               1.002575             3.500066                   1.053593   \n",
       "std                0.407864             1.322078                   0.398012   \n",
       "min               -0.270000            -0.870000                  -0.260000   \n",
       "25%                0.740000             2.570000                   0.770000   \n",
       "50%                1.000000             3.490000                   1.050000   \n",
       "75%                1.260000             4.370000                   1.320000   \n",
       "max                2.660000             8.280000                   2.490000   \n",
       "\n",
       "       network_score  rosetta_score_change  ingraham_neglogp_change  \\\n",
       "count    9913.000000            9900.00000             10674.000000   \n",
       "mean      -81.464641              60.61660                 0.061666   \n",
       "std        16.119577              78.36315                 0.049345   \n",
       "min      -132.226303              11.53400                -0.104663   \n",
       "25%       -89.816566              21.30825                 0.026294   \n",
       "50%       -76.851799              25.94650                 0.059815   \n",
       "75%       -69.900856              86.10625                 0.099222   \n",
       "max       -54.083675            2097.36100                 0.249719   \n",
       "\n",
       "       cartesian_ddg_change  ddg_monomer_change  \n",
       "count          10674.000000        10674.000000  \n",
       "mean               4.626575            3.026326  \n",
       "std                7.498591            9.581338  \n",
       "min              -11.788000           -8.990000  \n",
       "25%                0.405500           -0.401000  \n",
       "50%                2.402000            1.611500  \n",
       "75%                6.178500            4.504750  \n",
       "max               99.926667          188.960000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stability_scores_for_mutations[\"ssm2\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Format `ssm2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "stability_scores_for_mutations[\"ssm2\"][\"consensus_stability_score2\"] = (\n",
    "    stability_scores_for_mutations[\"ssm2\"][\"stabilityscore_t\"]\n",
    "    + stability_scores_for_mutations[\"ssm2\"][\"stabilityscore_c\"]\n",
    ") / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in [\n",
    "    \"network_score\",\n",
    "    \"stabilityscore_t\",\n",
    "    \"stabilityscore_c\",\n",
    "    \"consensus_stability_score\",\n",
    "    \"consensus_stability_score2\",\n",
    "]:\n",
    "    stability_scores_for_mutations[\"ssm2\"] = add_diff(\n",
    "        stability_scores_for_mutations[\"ssm2\"], column\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sequence</th>\n",
       "      <th>my_wt</th>\n",
       "      <th>pos</th>\n",
       "      <th>mut</th>\n",
       "      <th>wt_aa</th>\n",
       "      <th>ec50_t</th>\n",
       "      <th>delta_ec50_t</th>\n",
       "      <th>ec50_95ci_lbound_t</th>\n",
       "      <th>ec50_95ci_ubound_t</th>\n",
       "      <th>ec50_95ci_t</th>\n",
       "      <th>ec50_pred_t</th>\n",
       "      <th>delta_pred_vs_wt_t</th>\n",
       "      <th>ec50_rise_t</th>\n",
       "      <th>stabilityscore_t</th>\n",
       "      <th>ec50_c</th>\n",
       "      <th>delta_ec50_c</th>\n",
       "      <th>ec50_95ci_lbound_c</th>\n",
       "      <th>ec50_95ci_ubound_c</th>\n",
       "      <th>ec50_95ci_c</th>\n",
       "      <th>ec50_pred_c</th>\n",
       "      <th>delta_pred_vs_wt_c</th>\n",
       "      <th>ec50_rise_c</th>\n",
       "      <th>stabilityscore_c</th>\n",
       "      <th>ec50_rise_c_adj</th>\n",
       "      <th>stabilityscore_c_adj</th>\n",
       "      <th>consensus_ec50_rise</th>\n",
       "      <th>consensus_stability_score</th>\n",
       "      <th>library_name</th>\n",
       "      <th>network_score</th>\n",
       "      <th>rosetta_score_change</th>\n",
       "      <th>ingraham_neglogp_change</th>\n",
       "      <th>cartesian_ddg_change</th>\n",
       "      <th>ddg_monomer_change</th>\n",
       "      <th>consensus_stability_score2</th>\n",
       "      <th>network_score_wt</th>\n",
       "      <th>network_score_change</th>\n",
       "      <th>stabilityscore_t_wt</th>\n",
       "      <th>stabilityscore_t_change</th>\n",
       "      <th>stabilityscore_c_wt</th>\n",
       "      <th>stabilityscore_c_change</th>\n",
       "      <th>consensus_stability_score_wt</th>\n",
       "      <th>consensus_stability_score_change</th>\n",
       "      <th>consensus_stability_score2_wt</th>\n",
       "      <th>consensus_stability_score2_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EEHEE_rd3_0037.pdb</td>\n",
       "      <td>TTIKVNGQEYTVPLSPEQAAKAAKKRWPDYEVQIHGNTVKVTR</td>\n",
       "      <td>EEHEE_rd3_0037.pdb</td>\n",
       "      <td>0</td>\n",
       "      <td>na</td>\n",
       "      <td>wt</td>\n",
       "      <td>4.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.69</td>\n",
       "      <td>1.71</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.85</td>\n",
       "      <td>ssm2</td>\n",
       "      <td>-80.553299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.28</td>\n",
       "      <td>-80.553299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EEHEE_rd3_0037.pdb_A19D</td>\n",
       "      <td>TTIKVNGQEYTVPLSPEQDAKAAKKRWPDYEVQIHGNTVKVTR</td>\n",
       "      <td>EEHEE_rd3_0037.pdb</td>\n",
       "      <td>19</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-5.07</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.81</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.30</td>\n",
       "      <td>ssm2</td>\n",
       "      <td>-88.784714</td>\n",
       "      <td>51.13</td>\n",
       "      <td>0.109372</td>\n",
       "      <td>20.296</td>\n",
       "      <td>9.879</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-80.553299</td>\n",
       "      <td>-8.231415</td>\n",
       "      <td>1.71</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>1.28</td>\n",
       "      <td>-1.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name                                     sequence  \\\n",
       "0       EEHEE_rd3_0037.pdb  TTIKVNGQEYTVPLSPEQAAKAAKKRWPDYEVQIHGNTVKVTR   \n",
       "1  EEHEE_rd3_0037.pdb_A19D  TTIKVNGQEYTVPLSPEQDAKAAKKRWPDYEVQIHGNTVKVTR   \n",
       "\n",
       "                my_wt  pos mut wt_aa  ec50_t  delta_ec50_t  \\\n",
       "0  EEHEE_rd3_0037.pdb    0  na    wt    4.67          0.00   \n",
       "1  EEHEE_rd3_0037.pdb   19   D     A   -0.40         -5.07   \n",
       "\n",
       "   ec50_95ci_lbound_t  ec50_95ci_ubound_t  ec50_95ci_t  ec50_pred_t  \\\n",
       "0                 4.7                 4.7          0.0        -1.02   \n",
       "1                -0.5                -0.3          0.2        -0.87   \n",
       "\n",
       "   delta_pred_vs_wt_t  ec50_rise_t  stabilityscore_t  ec50_c  delta_ec50_c  \\\n",
       "0                0.00         5.69              1.71    3.33          0.00   \n",
       "1                0.15         0.47              0.14    1.81         -1.52   \n",
       "\n",
       "   ec50_95ci_lbound_c  ec50_95ci_ubound_c  ec50_95ci_c  ec50_pred_c  \\\n",
       "0                 3.3                 3.3          0.0         0.51   \n",
       "1                 1.7                 1.9          0.2         0.81   \n",
       "\n",
       "   delta_pred_vs_wt_c  ec50_rise_c  stabilityscore_c  ec50_rise_c_adj  \\\n",
       "0                 0.0         2.83              0.85             2.83   \n",
       "1                 0.3         1.00              0.30             1.00   \n",
       "\n",
       "   stabilityscore_c_adj  consensus_ec50_rise  consensus_stability_score  \\\n",
       "0                  0.85                 2.83                       0.85   \n",
       "1                  0.30                 0.99                       0.30   \n",
       "\n",
       "  library_name  network_score  rosetta_score_change  ingraham_neglogp_change  \\\n",
       "0         ssm2     -80.553299                   NaN                      NaN   \n",
       "1         ssm2     -88.784714                 51.13                 0.109372   \n",
       "\n",
       "   cartesian_ddg_change  ddg_monomer_change  consensus_stability_score2  \\\n",
       "0                   NaN                 NaN                        1.28   \n",
       "1                20.296               9.879                        0.22   \n",
       "\n",
       "   network_score_wt  network_score_change  stabilityscore_t_wt  \\\n",
       "0        -80.553299              0.000000                 1.71   \n",
       "1        -80.553299             -8.231415                 1.71   \n",
       "\n",
       "   stabilityscore_t_change  stabilityscore_c_wt  stabilityscore_c_change  \\\n",
       "0                     0.00                 0.85                     0.00   \n",
       "1                    -1.57                 0.85                    -0.55   \n",
       "\n",
       "   consensus_stability_score_wt  consensus_stability_score_change  \\\n",
       "0                          0.85                              0.00   \n",
       "1                          0.85                             -0.55   \n",
       "\n",
       "   consensus_stability_score2_wt  consensus_stability_score2_change  \n",
       "0                           1.28                               0.00  \n",
       "1                           1.28                              -1.06  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(stability_scores_for_mutations[\"ssm2\"].head(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_reu_df = stability_scores_for_mutations[\"ssm2\"][\n",
    "    stability_scores_for_mutations[\"ssm2\"][\"rosetta_score_change\"].isnull()\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2951"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missing_reu_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2934"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(missing_reu_df[\"pos\"] != 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2934"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(missing_reu_df[\"mut\"] != \"na\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Format `fig1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "stability_scores_for_mutations[\"fig1\"] = stability_scores_for_mutations[\"fig1\"].rename(\n",
    "    columns={\"deltaGunf thermal\": \"dg_thermal\", \"deltaGunf chemical\": \"dg_chemical\"}\n",
    ")\n",
    "\n",
    "stability_scores_for_mutations[\"fig1\"][\"consensus_stability_score\"] = (\n",
    "    stability_scores_for_mutations[\"fig1\"][\"stabilityscore_t\"]\n",
    "    + stability_scores_for_mutations[\"fig1\"][\"stabilityscore_c\"]\n",
    ") / 2\n",
    "\n",
    "stability_scores_for_mutations[\"fig1\"][\"consensus_dg\"] = (\n",
    "    stability_scores_for_mutations[\"fig1\"][\"dg_thermal\"]\n",
    "    + stability_scores_for_mutations[\"fig1\"][\"dg_chemical\"]\n",
    ") / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in [\n",
    "    \"network_score\",\n",
    "    \"stabilityscore_t\",\n",
    "    \"stabilityscore_c\",\n",
    "    \"consensus_stability_score\",\n",
    "    \"dg_thermal\",\n",
    "    \"dg_chemical\",\n",
    "    \"consensus_dg\",\n",
    "    \"Tm\",\n",
    "]:\n",
    "    stability_scores_for_mutations[\"fig1\"] = add_diff(\n",
    "        stability_scores_for_mutations[\"fig1\"], column\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GAPF - Stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "STABILITY_METHOD = \"logproba.incremental\"  # \"logproba.randmask50\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "stability_scores_for_designs = torch.load(\n",
    "    NOTEBOOK_PATH.parent.joinpath(\n",
    "        \"06_global_analysis_of_protein_folding\",\n",
    "        f\"stability_scores_for_selections.{STABILITY_METHOD}.torch\",\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1, 5):\n",
    "    df = pq.read_table(\n",
    "        NOTEBOOK_PATH.parents[2].joinpath(\n",
    "            \"neurips19-graph-protein-design\",\n",
    "            \"notebooks\",\n",
    "            \"02_predict_mutation_effect\",\n",
    "            f\"rocklin_2017_stability_rd{i}.parquet\",\n",
    "        )\n",
    "    ).to_pandas(integer_object_nulls=True)\n",
    "    assert not set(df[\"name\"]) - set(stability_scores_for_designs[f\"rd{i}\"][\"name\"])\n",
    "    stability_scores_for_designs[f\"rd{i}\"][\"structured_transformer_neglogp\"] = (\n",
    "        -stability_scores_for_designs[f\"rd{i}\"][\"name\"].map(df.set_index(\"name\")[\"neglogp\"]).values\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Protherm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename_wt</th>\n",
       "      <th>chain_id</th>\n",
       "      <th>mutation</th>\n",
       "      <th>cartesian_ddg_beta_nov15_cart_1</th>\n",
       "      <th>ddg_exp</th>\n",
       "      <th>cartesian_ddg_beta_nov16_cart_1</th>\n",
       "      <th>cartesian_ddg_score12_cart_1</th>\n",
       "      <th>cartesian_ddg_talaris2013_cart_1</th>\n",
       "      <th>cartesian_ddg_talaris2014_cart_1</th>\n",
       "      <th>ddg_monomer_soft_rep_design_1</th>\n",
       "      <th>local_filename_wt</th>\n",
       "      <th>structure_id</th>\n",
       "      <th>model_id</th>\n",
       "      <th>qseq</th>\n",
       "      <th>residue_idx_1_corrected</th>\n",
       "      <th>residue_idx_2_corrected</th>\n",
       "      <th>distances</th>\n",
       "      <th>mutation_matches_sequence</th>\n",
       "      <th>qseq_mutation</th>\n",
       "      <th>wt_wt</th>\n",
       "      <th>wt_mut</th>\n",
       "      <th>wt_sm_wt</th>\n",
       "      <th>wt_sm_mut</th>\n",
       "      <th>wt_sm_log_wt</th>\n",
       "      <th>wt_sm_log_mut</th>\n",
       "      <th>mut_wt</th>\n",
       "      <th>mut_mut</th>\n",
       "      <th>mut_sm_wt</th>\n",
       "      <th>mut_sm_mut</th>\n",
       "      <th>mut_sm_log_wt</th>\n",
       "      <th>mut_sm_log_mut</th>\n",
       "      <th>blank_wt</th>\n",
       "      <th>blank_mut</th>\n",
       "      <th>blank_sm_wt</th>\n",
       "      <th>blank_sm_mut</th>\n",
       "      <th>blank_sm_log_wt</th>\n",
       "      <th>blank_sm_log_mut</th>\n",
       "      <th>scan_wt</th>\n",
       "      <th>scan_mut</th>\n",
       "      <th>scan_sm_wt</th>\n",
       "      <th>scan_sm_mut</th>\n",
       "      <th>scan_sm_log_wt</th>\n",
       "      <th>scan_sm_log_mut</th>\n",
       "      <th>incremental_wt</th>\n",
       "      <th>incremental_mut</th>\n",
       "      <th>incremental_sm_wt</th>\n",
       "      <th>incremental_sm_mut</th>\n",
       "      <th>incremental_sm_log_wt</th>\n",
       "      <th>incremental_sm_log_mut</th>\n",
       "      <th>blank_change</th>\n",
       "      <th>blank_sm_change</th>\n",
       "      <th>wt_mut_change</th>\n",
       "      <th>wt_mut_sm_change</th>\n",
       "      <th>scan_change</th>\n",
       "      <th>scan_sm_change</th>\n",
       "      <th>scan_sm_log_change</th>\n",
       "      <th>incremental_change</th>\n",
       "      <th>incremental_sm_change</th>\n",
       "      <th>incremental_sm_log_change</th>\n",
       "      <th>rosetta_reu_change</th>\n",
       "      <th>rosetta_reu_norm_change</th>\n",
       "      <th>has_homologs</th>\n",
       "      <th>ingraham_neglogp_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/kimlab2/database_data/biological-data-wa...</td>\n",
       "      <td>A</td>\n",
       "      <td>G44S</td>\n",
       "      <td>-1.808667</td>\n",
       "      <td>0.53</td>\n",
       "      <td>-0.701</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-0.289667</td>\n",
       "      <td>-0.633667</td>\n",
       "      <td>-2.384</td>\n",
       "      <td>/home/kimlab1/database_data/datapkg/adjacency-...</td>\n",
       "      <td>107l</td>\n",
       "      <td>0</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKGEL...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 66, 70, 91...</td>\n",
       "      <td>[1.3463744749991646, 4.7287436401778065, 6.389...</td>\n",
       "      <td>True</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
       "      <td>24.131248</td>\n",
       "      <td>7.463029</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.768777e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-16.668221</td>\n",
       "      <td>-0.822239</td>\n",
       "      <td>18.555481</td>\n",
       "      <td>3.840270e-09</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>-19.377724</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.785366</td>\n",
       "      <td>0.187889</td>\n",
       "      <td>0.013420</td>\n",
       "      <td>0.035516</td>\n",
       "      <td>-4.311016</td>\n",
       "      <td>-3.337761</td>\n",
       "      <td>3.701211</td>\n",
       "      <td>3.713295</td>\n",
       "      <td>0.343849</td>\n",
       "      <td>0.344718</td>\n",
       "      <td>-1.652878</td>\n",
       "      <td>-1.642744</td>\n",
       "      <td>3.488405</td>\n",
       "      <td>3.494410</td>\n",
       "      <td>0.331307</td>\n",
       "      <td>0.331441</td>\n",
       "      <td>-1.689137</td>\n",
       "      <td>-1.683219</td>\n",
       "      <td>0.973255</td>\n",
       "      <td>0.022096</td>\n",
       "      <td>-5.575768</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.012084</td>\n",
       "      <td>0.000869</td>\n",
       "      <td>0.010134</td>\n",
       "      <td>0.006004</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.005919</td>\n",
       "      <td>177.323</td>\n",
       "      <td>1.094586</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.012789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/kimlab2/database_data/biological-data-wa...</td>\n",
       "      <td>A</td>\n",
       "      <td>A120M</td>\n",
       "      <td>2.617667</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.560</td>\n",
       "      <td>-0.069000</td>\n",
       "      <td>-0.188000</td>\n",
       "      <td>2.472</td>\n",
       "      <td>/home/kimlab1/database_data/datapkg/adjacency-...</td>\n",
       "      <td>160l</td>\n",
       "      <td>0</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 66, 70, 91, 92...</td>\n",
       "      <td>[1.345760255620924, 4.727237880524551, 6.42311...</td>\n",
       "      <td>True</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
       "      <td>18.279926</td>\n",
       "      <td>-7.932950</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>4.129451e-12</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-26.212877</td>\n",
       "      <td>-5.231137</td>\n",
       "      <td>20.824055</td>\n",
       "      <td>4.834716e-12</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>-26.055199</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>2.087064</td>\n",
       "      <td>-0.255827</td>\n",
       "      <td>0.264194</td>\n",
       "      <td>0.025376</td>\n",
       "      <td>-1.331073</td>\n",
       "      <td>-3.673964</td>\n",
       "      <td>3.762726</td>\n",
       "      <td>3.734712</td>\n",
       "      <td>0.334620</td>\n",
       "      <td>0.332865</td>\n",
       "      <td>-1.675424</td>\n",
       "      <td>-1.687917</td>\n",
       "      <td>3.623382</td>\n",
       "      <td>3.601132</td>\n",
       "      <td>0.329045</td>\n",
       "      <td>0.327471</td>\n",
       "      <td>-1.718992</td>\n",
       "      <td>-1.734681</td>\n",
       "      <td>-2.342890</td>\n",
       "      <td>-0.238818</td>\n",
       "      <td>2.544128</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.028014</td>\n",
       "      <td>-0.001755</td>\n",
       "      <td>-0.012493</td>\n",
       "      <td>-0.022250</td>\n",
       "      <td>-0.001574</td>\n",
       "      <td>-0.015689</td>\n",
       "      <td>215.667</td>\n",
       "      <td>1.331278</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.001481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         filename_wt chain_id mutation  \\\n",
       "0  /home/kimlab2/database_data/biological-data-wa...        A     G44S   \n",
       "1  /home/kimlab2/database_data/biological-data-wa...        A    A120M   \n",
       "\n",
       "   cartesian_ddg_beta_nov15_cart_1  ddg_exp  cartesian_ddg_beta_nov16_cart_1  \\\n",
       "0                        -1.808667     0.53                           -0.701   \n",
       "1                         2.617667     0.20                            0.354   \n",
       "\n",
       "   cartesian_ddg_score12_cart_1  cartesian_ddg_talaris2013_cart_1  \\\n",
       "0                         0.088                         -0.289667   \n",
       "1                         0.560                         -0.069000   \n",
       "\n",
       "   cartesian_ddg_talaris2014_cart_1  ddg_monomer_soft_rep_design_1  \\\n",
       "0                         -0.633667                         -2.384   \n",
       "1                         -0.188000                          2.472   \n",
       "\n",
       "                                   local_filename_wt structure_id  model_id  \\\n",
       "0  /home/kimlab1/database_data/datapkg/adjacency-...         107l         0   \n",
       "1  /home/kimlab1/database_data/datapkg/adjacency-...         160l         0   \n",
       "\n",
       "                                                qseq  \\\n",
       "0  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKGEL...   \n",
       "1  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...   \n",
       "\n",
       "                             residue_idx_1_corrected  \\\n",
       "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "\n",
       "                             residue_idx_2_corrected  \\\n",
       "0  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 66, 70, 91...   \n",
       "1  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 66, 70, 91, 92...   \n",
       "\n",
       "                                           distances  \\\n",
       "0  [1.3463744749991646, 4.7287436401778065, 6.389...   \n",
       "1  [1.345760255620924, 4.727237880524551, 6.42311...   \n",
       "\n",
       "   mutation_matches_sequence  \\\n",
       "0                       True   \n",
       "1                       True   \n",
       "\n",
       "                                       qseq_mutation      wt_wt    wt_mut  \\\n",
       "0  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...  24.131248  7.463029   \n",
       "1  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...  18.279926 -7.932950   \n",
       "\n",
       "   wt_sm_wt     wt_sm_mut  wt_sm_log_wt  wt_sm_log_mut    mut_wt    mut_mut  \\\n",
       "0  1.000000  5.768777e-08      0.000000     -16.668221 -0.822239  18.555481   \n",
       "1  0.999999  4.129451e-12     -0.000001     -26.212877 -5.231137  20.824055   \n",
       "\n",
       "      mut_sm_wt  mut_sm_mut  mut_sm_log_wt  mut_sm_log_mut  blank_wt  \\\n",
       "0  3.840270e-09    0.999997     -19.377724       -0.000003 -0.785366   \n",
       "1  4.834716e-12    0.999992     -26.055199       -0.000008  2.087064   \n",
       "\n",
       "   blank_mut  blank_sm_wt  blank_sm_mut  blank_sm_log_wt  blank_sm_log_mut  \\\n",
       "0   0.187889     0.013420      0.035516        -4.311016         -3.337761   \n",
       "1  -0.255827     0.264194      0.025376        -1.331073         -3.673964   \n",
       "\n",
       "    scan_wt  scan_mut  scan_sm_wt  scan_sm_mut  scan_sm_log_wt  \\\n",
       "0  3.701211  3.713295    0.343849     0.344718       -1.652878   \n",
       "1  3.762726  3.734712    0.334620     0.332865       -1.675424   \n",
       "\n",
       "   scan_sm_log_mut  incremental_wt  incremental_mut  incremental_sm_wt  \\\n",
       "0        -1.642744        3.488405         3.494410           0.331307   \n",
       "1        -1.687917        3.623382         3.601132           0.329045   \n",
       "\n",
       "   incremental_sm_mut  incremental_sm_log_wt  incremental_sm_log_mut  \\\n",
       "0            0.331441              -1.689137               -1.683219   \n",
       "1            0.327471              -1.718992               -1.734681   \n",
       "\n",
       "   blank_change  blank_sm_change  wt_mut_change  wt_mut_sm_change  \\\n",
       "0      0.973255         0.022096      -5.575768         -0.000003   \n",
       "1     -2.342890        -0.238818       2.544128         -0.000006   \n",
       "\n",
       "   scan_change  scan_sm_change  scan_sm_log_change  incremental_change  \\\n",
       "0     0.012084        0.000869            0.010134            0.006004   \n",
       "1    -0.028014       -0.001755           -0.012493           -0.022250   \n",
       "\n",
       "   incremental_sm_change  incremental_sm_log_change  rosetta_reu_change  \\\n",
       "0               0.000135                   0.005919             177.323   \n",
       "1              -0.001574                  -0.015689             215.667   \n",
       "\n",
       "   rosetta_reu_norm_change has_homologs  ingraham_neglogp_change  \n",
       "0                 1.094586         True                -0.012789  \n",
       "1                 1.331278         True                -0.001481  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protherm_wresults_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert protherm_wresults_df[\"rosetta_reu_change\"].notnull().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bc098af6b304e58965a6ace4efc1098",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=6.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 98 rows with null values for columns: ['ddg_exp', 'ingraham_neglogp_change'].\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d0668fed62d4d378888638f3dfbe0eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=6.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "feature_columns = [\n",
    "    \"scan_sm_log_change\",\n",
    "    \"ingraham_neglogp_change\",\n",
    "    \"rosetta_reu_change\",\n",
    "    \"rosetta_reu_norm_change\",\n",
    "    \"ddg_monomer_soft_rep_design_1\",\n",
    "    \"cartesian_ddg_beta_nov16_cart_1\",\n",
    "]\n",
    "\n",
    "results = []\n",
    "with concurrent.futures.ProcessPoolExecutor() as pool:\n",
    "    futures_list = []\n",
    "\n",
    "    futures = pool.map(\n",
    "        compute_ddg_correlation,\n",
    "        itertools.repeat(\"protherm\"),\n",
    "        feature_columns,\n",
    "        itertools.repeat(\"ddg_exp\"),\n",
    "        itertools.repeat(protherm_wresults_df),\n",
    "        itertools.repeat(True),\n",
    "    )\n",
    "    futures_list.append(futures)\n",
    "\n",
    "    df = protherm_wresults_df[~protherm_wresults_df[\"has_homologs\"].astype(np.bool)].dropna(\n",
    "        subset=feature_columns\n",
    "    )\n",
    "    futures = pool.map(\n",
    "        compute_ddg_correlation,\n",
    "        itertools.repeat(\"protherm_restricted\"),\n",
    "        feature_columns,\n",
    "        itertools.repeat(\"ddg_exp\"),\n",
    "        itertools.repeat(df),\n",
    "        itertools.repeat(True),\n",
    "    )\n",
    "    futures_list.append(futures)\n",
    "\n",
    "    for futures in futures_list:\n",
    "        rows = list(tqdm(futures, total=len(feature_columns)))\n",
    "        results.extend(rows)\n",
    "\n",
    "protherm_correlations_df = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confidence intervals and statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>my_wt</th>\n",
       "      <th>feature</th>\n",
       "      <th>corr</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>corr_lower_bound</th>\n",
       "      <th>corr_upper_bound</th>\n",
       "      <th>corrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>protherm</td>\n",
       "      <td>scan_sm_log_change</td>\n",
       "      <td>0.443801</td>\n",
       "      <td>1.783294e-167</td>\n",
       "      <td>0.418884</td>\n",
       "      <td>0.467848</td>\n",
       "      <td>[0.45250500910915964, 0.4539986028642947, 0.43...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>protherm</td>\n",
       "      <td>ingraham_neglogp_change</td>\n",
       "      <td>0.423651</td>\n",
       "      <td>5.097932e-147</td>\n",
       "      <td>0.398339</td>\n",
       "      <td>0.448537</td>\n",
       "      <td>[0.41180902391332747, 0.4324873983628168, 0.41...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>protherm</td>\n",
       "      <td>rosetta_reu_change</td>\n",
       "      <td>-0.008009</td>\n",
       "      <td>6.371405e-01</td>\n",
       "      <td>-0.036040</td>\n",
       "      <td>0.020069</td>\n",
       "      <td>[-0.0010261458224625469, 0.003809044271975604,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>protherm</td>\n",
       "      <td>rosetta_reu_norm_change</td>\n",
       "      <td>0.082635</td>\n",
       "      <td>1.087827e-06</td>\n",
       "      <td>0.055081</td>\n",
       "      <td>0.110619</td>\n",
       "      <td>[0.094509269061831, 0.10341561823282504, 0.083...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>protherm</td>\n",
       "      <td>ddg_monomer_soft_rep_design_1</td>\n",
       "      <td>0.316684</td>\n",
       "      <td>1.052233e-81</td>\n",
       "      <td>0.288679</td>\n",
       "      <td>0.343957</td>\n",
       "      <td>[0.34277119758684976, 0.35025710099312035, 0.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>protherm</td>\n",
       "      <td>cartesian_ddg_beta_nov16_cart_1</td>\n",
       "      <td>0.591317</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.569914</td>\n",
       "      <td>0.611849</td>\n",
       "      <td>[0.6150787021991209, 0.6045340186249214, 0.575...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>scan_sm_log_change</td>\n",
       "      <td>0.551231</td>\n",
       "      <td>1.286834e-15</td>\n",
       "      <td>0.460245</td>\n",
       "      <td>0.626613</td>\n",
       "      <td>[0.5474567884505825, 0.5128820581282577, 0.495...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>ingraham_neglogp_change</td>\n",
       "      <td>0.402468</td>\n",
       "      <td>2.338008e-08</td>\n",
       "      <td>0.292393</td>\n",
       "      <td>0.501931</td>\n",
       "      <td>[0.3437037317240288, 0.38480698238976907, 0.41...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>rosetta_reu_change</td>\n",
       "      <td>0.342982</td>\n",
       "      <td>2.601666e-06</td>\n",
       "      <td>0.221314</td>\n",
       "      <td>0.456911</td>\n",
       "      <td>[0.3077332765170531, 0.3341168543784393, 0.324...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>rosetta_reu_norm_change</td>\n",
       "      <td>0.426413</td>\n",
       "      <td>2.654239e-09</td>\n",
       "      <td>0.314821</td>\n",
       "      <td>0.526722</td>\n",
       "      <td>[0.3959240391392928, 0.4362734815150072, 0.481...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>ddg_monomer_soft_rep_design_1</td>\n",
       "      <td>0.334282</td>\n",
       "      <td>4.800736e-06</td>\n",
       "      <td>0.210696</td>\n",
       "      <td>0.450418</td>\n",
       "      <td>[0.28312321704227533, 0.29930466660101807, 0.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>cartesian_ddg_beta_nov16_cart_1</td>\n",
       "      <td>0.463448</td>\n",
       "      <td>6.447494e-11</td>\n",
       "      <td>0.351755</td>\n",
       "      <td>0.562951</td>\n",
       "      <td>[0.4629345372154813, 0.5370364074066614, 0.520...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  my_wt                          feature      corr  \\\n",
       "0              protherm               scan_sm_log_change  0.443801   \n",
       "1              protherm          ingraham_neglogp_change  0.423651   \n",
       "2              protherm               rosetta_reu_change -0.008009   \n",
       "3              protherm          rosetta_reu_norm_change  0.082635   \n",
       "4              protherm    ddg_monomer_soft_rep_design_1  0.316684   \n",
       "5              protherm  cartesian_ddg_beta_nov16_cart_1  0.591317   \n",
       "6   protherm_restricted               scan_sm_log_change  0.551231   \n",
       "7   protherm_restricted          ingraham_neglogp_change  0.402468   \n",
       "8   protherm_restricted               rosetta_reu_change  0.342982   \n",
       "9   protherm_restricted          rosetta_reu_norm_change  0.426413   \n",
       "10  protherm_restricted    ddg_monomer_soft_rep_design_1  0.334282   \n",
       "11  protherm_restricted  cartesian_ddg_beta_nov16_cart_1  0.463448   \n",
       "\n",
       "           pvalue  corr_lower_bound  corr_upper_bound  \\\n",
       "0   1.783294e-167          0.418884          0.467848   \n",
       "1   5.097932e-147          0.398339          0.448537   \n",
       "2    6.371405e-01         -0.036040          0.020069   \n",
       "3    1.087827e-06          0.055081          0.110619   \n",
       "4    1.052233e-81          0.288679          0.343957   \n",
       "5    0.000000e+00          0.569914          0.611849   \n",
       "6    1.286834e-15          0.460245          0.626613   \n",
       "7    2.338008e-08          0.292393          0.501931   \n",
       "8    2.601666e-06          0.221314          0.456911   \n",
       "9    2.654239e-09          0.314821          0.526722   \n",
       "10   4.800736e-06          0.210696          0.450418   \n",
       "11   6.447494e-11          0.351755          0.562951   \n",
       "\n",
       "                                                corrs  \n",
       "0   [0.45250500910915964, 0.4539986028642947, 0.43...  \n",
       "1   [0.41180902391332747, 0.4324873983628168, 0.41...  \n",
       "2   [-0.0010261458224625469, 0.003809044271975604,...  \n",
       "3   [0.094509269061831, 0.10341561823282504, 0.083...  \n",
       "4   [0.34277119758684976, 0.35025710099312035, 0.3...  \n",
       "5   [0.6150787021991209, 0.6045340186249214, 0.575...  \n",
       "6   [0.5474567884505825, 0.5128820581282577, 0.495...  \n",
       "7   [0.3437037317240288, 0.38480698238976907, 0.41...  \n",
       "8   [0.3077332765170531, 0.3341168543784393, 0.324...  \n",
       "9   [0.3959240391392928, 0.4362734815150072, 0.481...  \n",
       "10  [0.28312321704227533, 0.29930466660101807, 0.2...  \n",
       "11  [0.4629345372154813, 0.5370364074066614, 0.520...  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protherm_correlations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    \"scan_sm_log_change\",\n",
    "    \"ingraham_neglogp_change\",\n",
    "    \"rosetta_reu_norm_change\",\n",
    "    \"ddg_monomer_soft_rep_design_1\",\n",
    "    \"cartesian_ddg_beta_nov16_cart_1\",\n",
    "]\n",
    "\n",
    "columns_to_save = [\n",
    "    \"group\",\n",
    "    \"feature_name\",\n",
    "    \"corr\",\n",
    "    \"pvalue\",\n",
    "    \"corr_lower_bound\",\n",
    "    \"corr_upper_bound\",\n",
    "    \"superior_to_ps\",\n",
    "]\n",
    "\n",
    "LABELS = {\n",
    "    # ProteinSolver\n",
    "    \"scan_sm_log_change\": \"ProteinSolver\",\n",
    "    \"ingraham_neglogp_change\": \"Ingraham et al.\",\n",
    "    \"rosetta_reu_change\": \"Rosetta (score)\",\n",
    "    \"rosetta_reu_norm_change\": \"Rosetta (score)\",\n",
    "    \"ddg_monomer_soft_rep_design_1\": \"Rosetta (ddg_monomer)\",\n",
    "    \"cartesian_ddg_beta_nov15_cart_1\": \"Rosetta (cartesian_ddg)\",\n",
    "    \"cartesian_ddg_beta_nov16_cart_1\": \"Rosetta (cartesian_ddg)\",\n",
    "    \"cartesian_ddg_score12_cart_1\": \"Rosetta (cartesian_ddg)\",\n",
    "    \"cartesian_ddg_talaris2013_cart_1\": \"Rosetta (cartesian_ddg)\",\n",
    "    \"cartesian_ddg_talaris2014_cart_1\": \"Rosetta (cartesian_ddg)\",\n",
    "    # Rocklin 2017 mutations / stability\n",
    "    \"network_score\": \"ProteinSolver\",\n",
    "    \"structured_transformer_neglogp\": \"Ingraham et al.\",\n",
    "    \"betanov15_score\": \"Rosetta (score)\",\n",
    "    #\n",
    "    \"network_score_change\": \"ProteinSolver\",\n",
    "    \"ingraham_neglogp_change\": \"Ingraham et al.\",\n",
    "    \"rosetta_score_change\": \"Rosetta (score)\",\n",
    "    \"ddg_monomer_change\": \"Rosetta (ddg_monomer)\",\n",
    "    \"cartesian_ddg_change\": \"Rosetta (cartesian_ddg)\",\n",
    "}\n",
    "\n",
    "stat_dfs = []\n",
    "for group, df in protherm_correlations_df.rename(columns={\"my_wt\": \"group\"}).groupby(\"group\"):\n",
    "    ps_column = \"scan_sm_log_change\"\n",
    "\n",
    "    df_ps = df[df[\"feature\"] == ps_column]\n",
    "    assert len(df_ps) == 1\n",
    "    corrs_ref = np.array(df_ps.iloc[0][\"corrs\"])\n",
    "\n",
    "    df = df.copy()\n",
    "    df[\"superior_to_ps_pvalue\"] = np.nan\n",
    "    df.loc[df[\"feature\"] != ps_column, \"superior_to_ps_pvalue\"] = (\n",
    "        df[df[\"feature\"] != ps_column][\"corrs\"]\n",
    "    ).apply(lambda c: compare_correlations(corrs_ref, np.array(c)))\n",
    "\n",
    "    df[\"superior_to_ps\"] = df[\"superior_to_ps_pvalue\"].apply(\n",
    "        lambda pvalue: get_superior_to_ps_string(pvalue, cutoff=0.025 / (14 * 4))\n",
    "    )\n",
    "\n",
    "    df = df.set_index(\"feature\").reindex(columns, axis=0).reset_index()\n",
    "    df[\"feature_name\"] = df[\"feature\"].map(LABELS)\n",
    "\n",
    "    #     display(df[columns_to_save])\n",
    "    stat_dfs.append(df[columns_to_save])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>feature_name</th>\n",
       "      <th>corr</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>corr_lower_bound</th>\n",
       "      <th>corr_upper_bound</th>\n",
       "      <th>superior_to_ps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>protherm</td>\n",
       "      <td>ProteinSolver</td>\n",
       "      <td>0.443801</td>\n",
       "      <td>1.783294e-167</td>\n",
       "      <td>0.418884</td>\n",
       "      <td>0.467848</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>protherm</td>\n",
       "      <td>Ingraham et al.</td>\n",
       "      <td>0.423651</td>\n",
       "      <td>5.097932e-147</td>\n",
       "      <td>0.398339</td>\n",
       "      <td>0.448537</td>\n",
       "      <td>No sig. diff. (p = 0.8265)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>protherm</td>\n",
       "      <td>Rosetta (score)</td>\n",
       "      <td>0.082635</td>\n",
       "      <td>1.087827e-06</td>\n",
       "      <td>0.055081</td>\n",
       "      <td>0.110619</td>\n",
       "      <td>Inferior (p = 1.0000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>protherm</td>\n",
       "      <td>Rosetta (ddg_monomer)</td>\n",
       "      <td>0.316684</td>\n",
       "      <td>1.052233e-81</td>\n",
       "      <td>0.288679</td>\n",
       "      <td>0.343957</td>\n",
       "      <td>Inferior (p = 1.0000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>protherm</td>\n",
       "      <td>Rosetta (cartesian_ddg)</td>\n",
       "      <td>0.591317</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.569914</td>\n",
       "      <td>0.611849</td>\n",
       "      <td>Superior (p = 0.0000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>ProteinSolver</td>\n",
       "      <td>0.551231</td>\n",
       "      <td>1.286834e-15</td>\n",
       "      <td>0.460245</td>\n",
       "      <td>0.626613</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>Ingraham et al.</td>\n",
       "      <td>0.402468</td>\n",
       "      <td>2.338008e-08</td>\n",
       "      <td>0.292393</td>\n",
       "      <td>0.501931</td>\n",
       "      <td>No sig. diff. (p = 0.9645)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>Rosetta (score)</td>\n",
       "      <td>0.426413</td>\n",
       "      <td>2.654239e-09</td>\n",
       "      <td>0.314821</td>\n",
       "      <td>0.526722</td>\n",
       "      <td>No sig. diff. (p = 0.9341)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>Rosetta (ddg_monomer)</td>\n",
       "      <td>0.334282</td>\n",
       "      <td>4.800736e-06</td>\n",
       "      <td>0.210696</td>\n",
       "      <td>0.450418</td>\n",
       "      <td>No sig. diff. (p = 0.9927)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>protherm_restricted</td>\n",
       "      <td>Rosetta (cartesian_ddg)</td>\n",
       "      <td>0.463448</td>\n",
       "      <td>6.447494e-11</td>\n",
       "      <td>0.351755</td>\n",
       "      <td>0.562951</td>\n",
       "      <td>No sig. diff. (p = 0.8535)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 group             feature_name      corr         pvalue  \\\n",
       "0             protherm            ProteinSolver  0.443801  1.783294e-167   \n",
       "1             protherm          Ingraham et al.  0.423651  5.097932e-147   \n",
       "2             protherm          Rosetta (score)  0.082635   1.087827e-06   \n",
       "3             protherm    Rosetta (ddg_monomer)  0.316684   1.052233e-81   \n",
       "4             protherm  Rosetta (cartesian_ddg)  0.591317   0.000000e+00   \n",
       "5  protherm_restricted            ProteinSolver  0.551231   1.286834e-15   \n",
       "6  protherm_restricted          Ingraham et al.  0.402468   2.338008e-08   \n",
       "7  protherm_restricted          Rosetta (score)  0.426413   2.654239e-09   \n",
       "8  protherm_restricted    Rosetta (ddg_monomer)  0.334282   4.800736e-06   \n",
       "9  protherm_restricted  Rosetta (cartesian_ddg)  0.463448   6.447494e-11   \n",
       "\n",
       "   corr_lower_bound  corr_upper_bound              superior_to_ps  \n",
       "0          0.418884          0.467848                              \n",
       "1          0.398339          0.448537  No sig. diff. (p = 0.8265)  \n",
       "2          0.055081          0.110619       Inferior (p = 1.0000)  \n",
       "3          0.288679          0.343957       Inferior (p = 1.0000)  \n",
       "4          0.569914          0.611849       Superior (p = 0.0000)  \n",
       "5          0.460245          0.626613                              \n",
       "6          0.292393          0.501931  No sig. diff. (p = 0.9645)  \n",
       "7          0.314821          0.526722  No sig. diff. (p = 0.9341)  \n",
       "8          0.210696          0.450418  No sig. diff. (p = 0.9927)  \n",
       "9          0.351755          0.562951  No sig. diff. (p = 0.8535)  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protherm_stats_df = pd.concat(stat_dfs, ignore_index=True, sort=False)\n",
    "\n",
    "protherm_stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "protherm_stats_df.to_csv(\n",
    "    NOTEBOOK_PATH.joinpath(\"protherm-mutation-stats.csv\"), sep=\"\\t\", index=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "protherm_correlations_df[\"label\"] = protherm_correlations_df[\"feature\"].map(LABELS)\n",
    "protherm_correlations_df[\"name\"] = \"Protherm\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
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       "<Figure size 72x216 with 1 Axes>"
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     "metadata": {
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   "source": [
    "features_to_compare = [\n",
    "    \"scan_sm_log_change\",\n",
    "    \"ingraham_neglogp_change\",\n",
    "    \"rosetta_reu_norm_change\",\n",
    "    \"ddg_monomer_soft_rep_design_1\",\n",
    "    \"cartesian_ddg_beta_nov16_cart_1\",\n",
    "]\n",
    "\n",
    "figl, axl = plt.subplots(figsize=(9.65, 0.35))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(1.0, 3))\n",
    "\n",
    "gp = (\n",
    "    protherm_correlations_df[protherm_correlations_df[\"my_wt\"] == \"protherm\"]\n",
    "    .set_index(\"feature\")\n",
    "    .loc[features_to_compare]\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "width = 0.2  # the width of the bars\n",
    "\n",
    "labels = gp[\"label\"].unique()\n",
    "offsets = np.linspace(-width / 2, width / 2, len(labels) + 2)\n",
    "for j, label in enumerate(labels):\n",
    "    #     print(label, offsets[j + 1], offsets)\n",
    "    gp_subset = gp[gp[\"label\"] == label]\n",
    "    x = np.arange(len(gp_subset))  # the label locations\n",
    "    x_subset = x + offsets[j + 1]\n",
    "    #     print(x_subset, gp_subset[\"corr\"].values)\n",
    "    rects1 = ax.errorbar(\n",
    "        x_subset,\n",
    "        gp_subset[\"corr\"].values,\n",
    "        fmt=\"o\",\n",
    "        alpha=0.6 if label not in [\"ProteinSolver\"] else 1.0,\n",
    "        #         width,\n",
    "        yerr=np.abs(\n",
    "            gp_subset[[\"corr_lower_bound\", \"corr_upper_bound\"]].values - gp_subset[[\"corr\"]].values\n",
    "        ).T,\n",
    "        label=label,\n",
    "    )\n",
    "\n",
    "ax.set_ylabel(\"Spearman's ρ\")\n",
    "\n",
    "ylim = np.array([-0.05, 0.75])\n",
    "ax.set_ylim(*ylim)\n",
    "ax.set_yticks(np.arange(*(ylim + 0.05) * 10) / 10)\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels([f\"{'Protherm':>9s}\"], rotation=\"vertical\")\n",
    "\n",
    "for tic in ax.yaxis.get_major_ticks():\n",
    "    tic.tick1line.set_visible(False)\n",
    "    tic.tick2line.set_visible(False)\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "ax.grid(axis=\"y\", linestyle=\":\")\n",
    "\n",
    "# ax.set_xlabel(group)\n",
    "ax.set_xlim(x[0] - 0.1, x[-1] + 0.1)\n",
    "ax.hlines(0, x[0] - 0.1, x[-1] + 0.1, linestyles=\"--\", linewidth=1)\n",
    "\n",
    "axl.legend(*ax.get_legend_handles_labels(), ncol=6, bbox_to_anchor=(1.133, 1.25))\n",
    "axl.axis(\"off\")\n",
    "\n",
    "fig.subplots_adjust(left=0.56, bottom=0.325, top=1, right=0.95)\n",
    "fig.savefig(NOTEBOOK_PATH.joinpath(\"protherm-correlations.pdf\"))\n",
    "fig.savefig(NOTEBOOK_PATH.joinpath(\"protherm-correlations.svg\"))\n",
    "fig.savefig(NOTEBOOK_PATH.joinpath(\"protherm-correlations.png\"), dpi=300)\n",
    "\n",
    "figl.savefig(NOTEBOOK_PATH.joinpath(\"legend.pdf\"))\n",
    "figl.savefig(NOTEBOOK_PATH.joinpath(\"legend.svg\"))\n",
    "figl.savefig(NOTEBOOK_PATH.joinpath(\"legend.png\"), dpi=300)\n",
    "\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
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       "    </g>\n",
       "    <g id=\"text_5\">\n",
       "     <!-- Rosetta (cartesian_ddg) -->\n",
       "     <g transform=\"translate(568.697448 14.865938)scale(0.1 -0.1)\">\n",
       "      <use xlink:href=\"#DejaVuSans-82\"/>\n",
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   ],
   "source": [
    "from IPython.display import SVG\n",
    "\n",
    "display(\n",
    "    SVG(\n",
    "        \"/home/kimlab1/strokach/workspace/proteinsolver/notebooks/07_protein_analysis_figures/legend.svg\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GAPF - Mutations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "774\n",
      "774\n",
      "774\n",
      "774\n",
      "0\n",
      "Skipping EHEE_0882.pdb!\n",
      "0\n",
      "Skipping EHEE_rd2_0005.pdb!\n",
      "0\n",
      "Skipping EHEE_rd3_0015.pdb!\n",
      "774\n",
      "774\n",
      "774\n",
      "774\n",
      "0\n",
      "Skipping HHH_0142.pdb!\n",
      "774\n",
      "774\n",
      "702\n",
      "828\n",
      "630\n"
     ]
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   "source": [
    "feature_columns = [\n",
    "    \"network_score_change\",\n",
    "    \"ingraham_neglogp_change\",\n",
    "    \"rosetta_score_change\",\n",
    "    \"ddg_monomer_change\",\n",
    "    \"cartesian_ddg_change\",\n",
    "]\n",
    "target_column = \"consensus_stability_score2_change\"\n",
    "\n",
    "results = []\n",
    "with concurrent.futures.ProcessPoolExecutor() as pool:\n",
    "    futures_list = []\n",
    "\n",
    "    input_dfs = []\n",
    "    for my_wt, gp in stability_scores_for_mutations[\"ssm2\"].groupby(\"my_wt\"):\n",
    "        df = gp[feature_columns + [target_column]].dropna()\n",
    "        print(len(df))\n",
    "        if df.empty:\n",
    "            print(f\"Skipping {my_wt}!\")\n",
    "            continue\n",
    "        input_dfs.append(df)\n",
    "\n",
    "        futures = pool.map(\n",
    "            compute_ddg_correlation,\n",
    "            itertools.repeat(my_wt),\n",
    "            feature_columns,\n",
    "            itertools.repeat(target_column),\n",
    "            itertools.repeat(df),\n",
    "            itertools.repeat(True),\n",
    "        )\n",
    "        futures_list.append(futures)\n",
    "\n",
    "    df = pd.concat(input_dfs, ignore_index=True, sort=False)\n",
    "    futures = pool.map(\n",
    "        compute_ddg_correlation,\n",
    "        itertools.repeat(\"all\"),\n",
    "        feature_columns,\n",
    "        itertools.repeat(target_column),\n",
    "        itertools.repeat(df),\n",
    "        itertools.repeat(True),\n",
    "    )\n",
    "    futures_list.append(futures)\n",
    "\n",
    "    for futures in futures_list:\n",
    "        rows = list(tqdm(futures, total=len(feature_columns)))\n",
    "        results.extend(rows)\n",
    "\n",
    "correlations_df = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_name(s):\n",
    "    s = s.replace(\".pdb\", \"\")\n",
    "    if \"_\" in s:\n",
    "        s1, s2 = s.split(\"_\", 1)\n",
    "        s2 = s2.replace(\"_\", \"-\")\n",
    "        return s1, f\"{s2:>9s}\"\n",
    "    else:\n",
    "        return \"Known\", f\"{s:>9s}\"\n",
    "\n",
    "\n",
    "correlations_df[\"group\"], correlations_df[\"name\"] = list(\n",
    "    zip(*correlations_df[\"my_wt\"].apply(to_name).values)\n",
    ")\n",
    "correlations_df = correlations_df.sort_values(\"my_wt\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confidence intervals and statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    \"network_score_change\",\n",
    "    \"ingraham_neglogp_change\",\n",
    "    \"rosetta_score_change\",\n",
    "    \"ddg_monomer_change\",\n",
    "    \"cartesian_ddg_change\",\n",
    "]\n",
    "\n",
    "columns_to_save = [\n",
    "    \"group\",\n",
    "    \"name\",\n",
    "    \"feature_name\",\n",
    "    \"corr\",\n",
    "    \"pvalue\",\n",
    "    \"corr_lower_bound\",\n",
    "    \"corr_upper_bound\",\n",
    "    \"superior_to_ps\",\n",
    "]\n",
    "\n",
    "stat_dfs = {}\n",
    "for (group, name), df in correlations_df[(correlations_df[\"my_wt\"] != \"all\")].groupby(\n",
    "    [\"group\", \"name\"]\n",
    "):\n",
    "    df_ps = df[df[\"feature\"] == \"network_score_change\"]\n",
    "    assert len(df_ps) == 1\n",
    "    corrs_ref = np.array(df_ps.iloc[0][\"corrs\"])\n",
    "\n",
    "    df = df.copy()\n",
    "    df[\"superior_to_ps_pvalue\"] = np.nan\n",
    "    df.loc[df[\"feature\"] != \"network_score_change\", \"superior_to_ps_pvalue\"] = (\n",
    "        df[df[\"feature\"] != \"network_score_change\"][\"corrs\"]\n",
    "    ).apply(lambda c: compare_correlations(corrs_ref, np.array(c)))\n",
    "\n",
    "    df[\"superior_to_ps\"] = df[\"superior_to_ps_pvalue\"].apply(\n",
    "        lambda pvalue: get_superior_to_ps_string(pvalue, cutoff=0.025 / (14 * 4))\n",
    "    )\n",
    "\n",
    "    df = df.set_index(\"feature\").reindex(columns, axis=0).reset_index()\n",
    "    df[\"feature_name\"] = df[\"feature\"].map(LABELS)\n",
    "\n",
    "    #     display(df[columns_to_save])\n",
    "    stat_dfs[(group, name)] = df[columns_to_save]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for group in reversed(sorted({k[0] for k in stat_dfs})):\n",
    "    names = sorted({k[1] for k in stat_dfs if k[0] == group})\n",
    "    for name in names:\n",
    "        df = stat_dfs[(group, name)]\n",
    "        dfs.append(df)\n",
    "mutation_stat_df = pd.concat(dfs, ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(mutation_stat_df) == (13 * 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "mutation_stat_df.to_csv(\n",
    "    NOTEBOOK_PATH.joinpath(\"rocklin-2017-mutation-stats.csv\"), sep=\"\\t\", index=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
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   "source": [
    "columns = [\n",
    "    \"network_score_change\",\n",
    "    \"ingraham_neglogp_change\",\n",
    "    \"rosetta_score_change\",\n",
    "    \"ddg_monomer_change\",\n",
    "    \"cartesian_ddg_change\",\n",
    "]\n",
    "\n",
    "fig, axs = plt.subplots(1, 4, figsize=(6, 3), gridspec_kw={\"width_ratios\": [3, 2, 4, 4]})\n",
    "\n",
    "for i, (group, gp) in enumerate(\n",
    "    sorted(\n",
    "        correlations_df[correlations_df[\"my_wt\"] != \"all\"].groupby(\"group\"),\n",
    "        key=lambda t: t[0],\n",
    "        reverse=True,\n",
    "    )\n",
    "):\n",
    "    ax = axs[i]\n",
    "    gp = gp.sort_values(\"name\")\n",
    "\n",
    "    width = 0.2  # the width of the bars\n",
    "    offsets = np.linspace(-width, width, len(columns))\n",
    "\n",
    "    x = None\n",
    "    x_labels = None\n",
    "    for column_idx, column in enumerate(columns):\n",
    "        gp_column = gp[gp[\"feature\"] == column]\n",
    "        if x is None:\n",
    "            x = np.arange(len(gp_column))\n",
    "            x_labels = gp_column[\"name\"].values\n",
    "        else:\n",
    "            assert (x_labels == gp_column[\"name\"].values).all()\n",
    "        rects = ax.errorbar(\n",
    "            x + offsets[column_idx],\n",
    "            gp_column[\"corr\"].values,\n",
    "            fmt=\"o\",# if column not in [\"network_score_change\"] else \"s\",\n",
    "            alpha=0.6 if column not in [\"network_score_change\"] else 1.0,\n",
    "            #         width,\n",
    "            yerr=np.abs(\n",
    "                (\n",
    "                    gp_column[[\"corr_lower_bound\", \"corr_upper_bound\"]].values\n",
    "                    - gp_column[[\"corr\"]].values\n",
    "                ).T\n",
    "            ),\n",
    "            label=LABELS[column],\n",
    "        )\n",
    "\n",
    "    ylim = np.array([-0.05, 0.75])\n",
    "    ax.set_ylim(*ylim)\n",
    "    ax.set_yticks(np.arange(*(ylim + 0.05) * 10) / 10)\n",
    "\n",
    "    ax.vlines(np.arange(0.5, len(x) - 0.5), *ylim, linestyles=\":\", linewidth=0.5)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(x_labels, rotation=\"vertical\")\n",
    "    ax.set_xlabel(group)\n",
    "    ax.set_xlim(x[0] - 0.5, x[-1] + 0.5)\n",
    "    ax.hlines(0, x[0] - 0.5, x[-1] + 0.5, linestyles=\"--\", linewidth=1)\n",
    "\n",
    "    if i == 0:\n",
    "        ax.set_ylabel(\"Spearman's ρ\")\n",
    "\n",
    "    if i > 0:\n",
    "        ax.set_yticklabels(\"\")\n",
    "\n",
    "    for tic in ax.yaxis.get_major_ticks():\n",
    "        tic.tick1line.set_visible(False)\n",
    "        tic.tick2line.set_visible(False)\n",
    "\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"left\"].set_visible(False)\n",
    "    ax.grid(axis=\"y\", linestyle=\":\")\n",
    "\n",
    "fig.subplots_adjust(left=0.09, bottom=0.325, top=1, right=0.98)\n",
    "fig.savefig(NOTEBOOK_PATH.joinpath(\"gapf-mutation-correlations.pdf\"))\n",
    "fig.savefig(NOTEBOOK_PATH.joinpath(\"gapf-mutation-correlations.svg\"))\n",
    "fig.savefig(NOTEBOOK_PATH.joinpath(\"gapf-mutation-correlations.png\"), dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
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    "### GAPF - Stability"
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   "source": [
    "#### Calculate correlations"
   ]
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   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
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    "feature_columns = [\n",
    "    \"network_score\",\n",
    "    \"structured_transformer_neglogp\",\n",
    "    \"talaris2013_score\",\n",
    "    \"betanov15_score\",\n",
    "]"
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   "execution_count": 164,
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       "      <td>1.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-78.022</td>\n",
       "      <td>-126.266</td>\n",
       "      <td>rd1</td>\n",
       "      <td>-1.901309</td>\n",
       "      <td>-1.901310</td>\n",
       "      <td>41</td>\n",
       "      <td>EEHEE</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.841325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EEHEE_rd1_0003.pdb</td>\n",
       "      <td>GSWRVHFRGETYTADTEDDAKQLAKDAGARRIESSNGEVRVEL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-73.631</td>\n",
       "      <td>-129.425</td>\n",
       "      <td>rd1</td>\n",
       "      <td>-1.465720</td>\n",
       "      <td>-1.465720</td>\n",
       "      <td>41</td>\n",
       "      <td>EEHEE</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.643996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EEHEE_rd1_0004.pdb</td>\n",
       "      <td>GSYTIEENGEKYTFRTRDEAEEWARRQGAQTVETRGTELRSRA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.26</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-79.304</td>\n",
       "      <td>-130.277</td>\n",
       "      <td>rd1</td>\n",
       "      <td>-1.953686</td>\n",
       "      <td>-1.953686</td>\n",
       "      <td>41</td>\n",
       "      <td>EEHEE</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.921230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EEHEE_rd1_0005.pdb</td>\n",
       "      <td>GSVELEDNGRRVEATSTQEARDRAKKEGATTWTESGTRIEVRG</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.56</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-71.705</td>\n",
       "      <td>-115.069</td>\n",
       "      <td>rd1</td>\n",
       "      <td>-1.957716</td>\n",
       "      <td>-1.957716</td>\n",
       "      <td>41</td>\n",
       "      <td>EEHEE</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.023995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name                                     sequence  \\\n",
       "0  EEHEE_rd1_0001.pdb  GSQEVNSGTQTYKNASPEEAERIARKAGATTWTEKGNKWEIRI   \n",
       "1  EEHEE_rd1_0002.pdb  GSYELEVGNYRYRADDPEQLKEEAKKVGARDVQTDGNNFKVRG   \n",
       "2  EEHEE_rd1_0003.pdb  GSWRVHFRGETYTADTEDDAKQLAKDAGARRIESSNGEVRVEL   \n",
       "3  EEHEE_rd1_0004.pdb  GSYTIEENGEKYTFRTRDEAEEWARRQGAQTVETRGTELRSRA   \n",
       "4  EEHEE_rd1_0005.pdb  GSVELEDNGRRVEATSTQEARDRAKKEGATTWTESGTRIEVRG   \n",
       "\n",
       "   assay_library  ec50_t  ec50_95ci_lbound_t  ec50_95ci_ubound_t  ec50_95ci_t  \\\n",
       "0            1.0    1.90                 1.8                 2.0          0.2   \n",
       "1            1.0    0.18                -0.5                 0.7          1.2   \n",
       "2            1.0    1.65                 1.6                 1.7          0.1   \n",
       "3            1.0    2.26                 2.2                 2.4          0.2   \n",
       "4            1.0    0.04                 0.0                 0.1          0.1   \n",
       "\n",
       "   ec50_pred_t  ec50_rise_t  stabilityscore_t  ec50_c  ec50_95ci_lbound_c  \\\n",
       "0         1.06         0.84              0.40    0.93                 0.7   \n",
       "1         0.48        -0.30             -0.14    1.08                 0.5   \n",
       "2         1.59         0.06              0.03    1.05                 1.0   \n",
       "3         1.51         0.74              0.35   -0.30                -0.5   \n",
       "4         0.95        -0.91             -0.44    0.10                 0.1   \n",
       "\n",
       "   ec50_95ci_ubound_c  ec50_95ci_c  ec50_pred_c  ec50_rise_c  \\\n",
       "0                 1.1          0.4         0.57         0.36   \n",
       "1                 1.6          1.1        -0.27         1.35   \n",
       "2                 1.1          0.1         0.91         0.13   \n",
       "3                 0.1          0.6         0.37        -0.67   \n",
       "4                 0.1          0.0         2.56        -2.46   \n",
       "\n",
       "   stabilityscore_c  stabilityscore  talaris2013_score  betanov15_score  \\\n",
       "0              0.17            0.17            -78.360         -132.050   \n",
       "1              0.65           -0.14            -78.022         -126.266   \n",
       "2              0.06            0.03            -73.631         -129.425   \n",
       "3             -0.32           -0.32            -79.304         -130.277   \n",
       "4             -1.18           -1.18            -71.705         -115.069   \n",
       "\n",
       "  library_name  network_score  network_score_wref  sequence_length domain  \\\n",
       "0          rd1      -1.987571           -1.987572               41  EEHEE   \n",
       "1          rd1      -1.901309           -1.901310               41  EEHEE   \n",
       "2          rd1      -1.465720           -1.465720               41  EEHEE   \n",
       "3          rd1      -1.953686           -1.953686               41  EEHEE   \n",
       "4          rd1      -1.957716           -1.957716               41  EEHEE   \n",
       "\n",
       "   identity_to_ref  structured_transformer_neglogp  \n",
       "0                1                       -2.078434  \n",
       "1                1                       -1.841325  \n",
       "2                1                       -1.643996  \n",
       "3                1                       -1.921230  \n",
       "4                1                       -2.023995  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stability_scores_for_designs[\"rd1\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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     "text": [
      "\n"
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    },
    {
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     "text": [
      "\n"
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    },
    {
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     "text": [
      "\n"
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   ],
   "source": [
    "correlations = []\n",
    "with concurrent.futures.ProcessPoolExecutor() as pool:\n",
    "    futures_list = []\n",
    "    for library in [\"rd1\", \"rd2\", \"rd3\", \"rd4\"]:\n",
    "        df = (\n",
    "            stability_scores_for_designs[library]\n",
    "            .dropna(\n",
    "                subset=[\n",
    "                    \"network_score\",\n",
    "                    \"talaris2013_score\",\n",
    "                    \"betanov15_score\",\n",
    "                    \"domain\",\n",
    "                    \"stabilityscore_t\",\n",
    "                    \"stabilityscore_c\",\n",
    "                ]\n",
    "            )\n",
    "            .copy()\n",
    "        )\n",
    "        for domain, gp in df.groupby(\"domain\"):\n",
    "            futures = pool.map(\n",
    "                compute_stability_correlation,\n",
    "                itertools.repeat(library),\n",
    "                itertools.repeat(domain),\n",
    "                feature_columns,\n",
    "                itertools.repeat(gp),\n",
    "                itertools.repeat(True),\n",
    "            )\n",
    "            futures_list.append(futures)\n",
    "    for futures in futures_list:\n",
    "        results = list(tqdm(futures, total=len(feature_columns)))\n",
    "        correlations.extend(results)\n",
    "\n",
    "stability_correlations_df = pd.DataFrame(correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confidence intervals and statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    \"network_score\",\n",
    "    \"structured_transformer_neglogp\",\n",
    "    \"betanov15_score\",\n",
    "]\n",
    "\n",
    "columns_to_save = [\n",
    "    \"library\",\n",
    "    \"domain\",\n",
    "    \"feature_name\",\n",
    "    \"corr\",\n",
    "    \"pvalue\",\n",
    "    \"corr_lower_bound\",\n",
    "    \"corr_upper_bound\",\n",
    "    \"superior_to_ps\",\n",
    "]\n",
    "\n",
    "stat_dfs = []\n",
    "for (library, domain), df in sorted(\n",
    "    stability_correlations_df[stability_correlations_df[\"feature\"].isin(columns)].groupby(\n",
    "        [\"library\", \"domain\"]\n",
    "    ),\n",
    "    key=lambda t: t[0],\n",
    "):\n",
    "    df_ps = df[df[\"feature\"] == \"network_score\"]\n",
    "    assert len(df_ps) == 1\n",
    "    corrs_ref = np.array(df_ps.iloc[0][\"corrs\"])\n",
    "\n",
    "    df = df.copy()\n",
    "    df[\"superior_to_ps_pvalue\"] = np.nan\n",
    "    df.loc[df[\"feature\"] != \"network_score\", \"superior_to_ps_pvalue\"] = (\n",
    "        df[df[\"feature\"] != \"network_score\"][\"corrs\"]\n",
    "    ).apply(lambda c: compare_correlations(corrs_ref, np.array(c)))\n",
    "\n",
    "    df[\"superior_to_ps\"] = df[\"superior_to_ps_pvalue\"].apply(\n",
    "        lambda pvalue: get_superior_to_ps_string(pvalue, cutoff=0.025 / (32))\n",
    "    )\n",
    "\n",
    "    df = df.set_index(\"feature\").reindex(columns, axis=0).reset_index()\n",
    "    df[\"feature_name\"] = df[\"feature\"].map(LABELS)\n",
    "\n",
    "    #     display(df[columns_to_save])\n",
    "    stat_dfs.append(df[columns_to_save])\n",
    "stability_stat_df = pd.concat(stat_dfs, ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(stability_stat_df) == (32 + 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "stability_stat_df.to_csv(\n",
    "    NOTEBOOK_PATH.joinpath(\"rocklin-2017-stability-stats.csv\"), sep=\"\\t\", index=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
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    "columns = [\n",
    "    \"network_score\",\n",
    "    \"structured_transformer_neglogp\",\n",
    "    \"betanov15_score\",\n",
    "]\n",
    "\n",
    "fig, axs = plt.subplots(1, 4, figsize=(5, 3))\n",
    "\n",
    "for i, (domain, gp) in enumerate(\n",
    "    sorted(stability_correlations_df.groupby(\"domain\"), key=lambda x: x[0], reverse=True)\n",
    "):\n",
    "    ax = axs[i]\n",
    "\n",
    "    gp = gp.sort_values([\"domain\", \"library\"])\n",
    "\n",
    "    width = 0.2  # the width of the bars\n",
    "    offsets = np.linspace(-width, width, len(columns))\n",
    "\n",
    "    x = None\n",
    "    x_labels = None\n",
    "    for column_idx, column in enumerate(columns):\n",
    "        gp_column = gp[gp[\"feature\"] == column]\n",
    "        if x is None:\n",
    "            x = np.arange(len(gp_column))\n",
    "            x_labels = gp_column[\"domain\"].values\n",
    "        else:\n",
    "            assert (x_labels == gp_column[\"domain\"].values).all()\n",
    "        rects = ax.errorbar(\n",
    "            x + offsets[column_idx],\n",
    "            gp_column[\"corr\"].values,\n",
    "            fmt=\"o\", # if column not in [\"network_score\"] else \"s\",\n",
    "            alpha=0.6 if column not in [\"network_score\"] else 1.0,\n",
    "            yerr=np.abs(\n",
    "                gp_column[[\"corr_lower_bound\", \"corr_upper_bound\"]].values\n",
    "                - gp_column[[\"corr\"]].values\n",
    "            ).T,\n",
    "            label=LABELS[column],\n",
    "        )\n",
    "\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels([\"{:>9s}\".format(f\"Round {i}\") for i in range(1, 5)], rotation=\"vertical\")\n",
    "    ax.set_xlabel(domain)\n",
    "    ax.set_xlim(x[0] - 0.5, x[-1] + 0.5)\n",
    "    ax.hlines(0, x[0] - 0.5, x[-1] + 0.5, linestyles=\"--\", linewidth=1)\n",
    "\n",
    "    if i == 0:\n",
    "        ax.set_ylabel(\"Spearman's ρ\")\n",
    "\n",
    "    ax.set_yticks([i / 10 for i in range(0, 7)])\n",
    "    if i > 0:\n",
    "        ax.set_yticklabels(\"\")\n",
    "\n",
    "    for tic in ax.yaxis.get_major_ticks():\n",
    "        tic.tick1line.set_visible(False)\n",
    "        tic.tick2line.set_visible(False)\n",
    "\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"left\"].set_visible(False)\n",
    "    ax.grid(axis=\"y\", linestyle=\":\")\n",
    "\n",
    "    ylim = (-0.05, 0.52)\n",
    "    ax.vlines(np.arange(0.5, len(x) - 0.5), *ylim, linestyles=\":\", linewidth=0.5)\n",
    "    ax.set_ylim(*ylim)\n",
    "\n",
    "\n",
    "fig.subplots_adjust(left=0.11, bottom=0.325, top=0.955, right=0.99)\n",
    "fig.savefig(NOTEBOOK_PATH.joinpath(\"gapf-design-correlations.pdf\"))\n",
    "fig.savefig(NOTEBOOK_PATH.joinpath(\"gapf-design-correlations.svg\"))\n",
    "fig.savefig(NOTEBOOK_PATH.joinpath(\"gapf-design-correlations.png\"), dpi=300)"
   ]
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  {
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  {
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   "execution_count": 187,
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   "outputs": [
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       "     </g>\n",
       "     <g id=\"text_1\">\n",
       "      <!--   Round 1 -->\n",
       "      <defs>\n",
       "       <path id=\"DejaVuSans-32\"/>\n",
       "       <path d=\"M 44.390625 34.1875  Q 47.5625 33.109375 50.5625 29.59375  Q 53.5625 26.078125 56.59375 19.921875  L 66.609375 0  L 56 0  L 46.6875 18.703125  Q 43.0625 26.03125 39.671875 28.421875  Q 36.28125 30.8125 30.421875 30.8125  L 19.671875 30.8125  L 19.671875 0  L 9.8125 0  L 9.8125 72.90625  L 32.078125 72.90625  Q 44.578125 72.90625 50.734375 67.671875  Q 56.890625 62.453125 56.890625 51.90625  Q 56.890625 45.015625 53.6875 40.46875  Q 50.484375 35.9375 44.390625 34.1875  z M 19.671875 64.796875  L 19.671875 38.921875  L 32.078125 38.921875  Q 39.203125 38.921875 42.84375 42.21875  Q 46.484375 45.515625 46.484375 51.90625  Q 46.484375 58.296875 42.84375 61.546875  Q 39.203125 64.796875 32.078125 64.796875  z \" id=\"DejaVuSans-82\"/>\n",
       "       <path d=\"M 30.609375 48.390625  Q 23.390625 48.390625 19.1875 42.75  Q 14.984375 37.109375 14.984375 27.296875  Q 14.984375 17.484375 19.15625 11.84375  Q 23.34375 6.203125 30.609375 6.203125  Q 37.796875 6.203125 41.984375 11.859375  Q 46.1875 17.53125 46.1875 27.296875  Q 46.1875 37.015625 41.984375 42.703125  Q 37.796875 48.390625 30.609375 48.390625  z M 30.609375 56  Q 42.328125 56 49.015625 48.375  Q 55.71875 40.765625 55.71875 27.296875  Q 55.71875 13.875 49.015625 6.21875  Q 42.328125 -1.421875 30.609375 -1.421875  Q 18.84375 -1.421875 12.171875 6.21875  Q 5.515625 13.875 5.515625 27.296875  Q 5.515625 40.765625 12.171875 48.375  Q 18.84375 56 30.609375 56  z \" id=\"DejaVuSans-111\"/>\n",
       "       <path d=\"M 8.5 21.578125  L 8.5 54.6875  L 17.484375 54.6875  L 17.484375 21.921875  Q 17.484375 14.15625 20.5 10.265625  Q 23.53125 6.390625 29.59375 6.390625  Q 36.859375 6.390625 41.078125 11.03125  Q 45.3125 15.671875 45.3125 23.6875  L 45.3125 54.6875  L 54.296875 54.6875  L 54.296875 0  L 45.3125 0  L 45.3125 8.40625  Q 42.046875 3.421875 37.71875 1  Q 33.40625 -1.421875 27.6875 -1.421875  Q 18.265625 -1.421875 13.375 4.4375  Q 8.5 10.296875 8.5 21.578125  z M 31.109375 56  z \" id=\"DejaVuSans-117\"/>\n",
       "       <path d=\"M 54.890625 33.015625  L 54.890625 0  L 45.90625 0  L 45.90625 32.71875  Q 45.90625 40.484375 42.875 44.328125  Q 39.84375 48.1875 33.796875 48.1875  Q 26.515625 48.1875 22.3125 43.546875  Q 18.109375 38.921875 18.109375 30.90625  L 18.109375 0  L 9.078125 0  L 9.078125 54.6875  L 18.109375 54.6875  L 18.109375 46.1875  Q 21.34375 51.125 25.703125 53.5625  Q 30.078125 56 35.796875 56  Q 45.21875 56 50.046875 50.171875  Q 54.890625 44.34375 54.890625 33.015625  z \" id=\"DejaVuSans-110\"/>\n",
       "       <path d=\"M 45.40625 46.390625  L 45.40625 75.984375  L 54.390625 75.984375  L 54.390625 0  L 45.40625 0  L 45.40625 8.203125  Q 42.578125 3.328125 38.25 0.953125  Q 33.9375 -1.421875 27.875 -1.421875  Q 17.96875 -1.421875 11.734375 6.484375  Q 5.515625 14.40625 5.515625 27.296875  Q 5.515625 40.1875 11.734375 48.09375  Q 17.96875 56 27.875 56  Q 33.9375 56 38.25 53.625  Q 42.578125 51.265625 45.40625 46.390625  z M 14.796875 27.296875  Q 14.796875 17.390625 18.875 11.75  Q 22.953125 6.109375 30.078125 6.109375  Q 37.203125 6.109375 41.296875 11.75  Q 45.40625 17.390625 45.40625 27.296875  Q 45.40625 37.203125 41.296875 42.84375  Q 37.203125 48.484375 30.078125 48.484375  Q 22.953125 48.484375 18.875 42.84375  Q 14.796875 37.203125 14.796875 27.296875  z \" id=\"DejaVuSans-100\"/>\n",
       "       <path d=\"M 12.40625 8.296875  L 28.515625 8.296875  L 28.515625 63.921875  L 10.984375 60.40625  L 10.984375 69.390625  L 28.421875 72.90625  L 38.28125 72.90625  L 38.28125 8.296875  L 54.390625 8.296875  L 54.390625 0  L 12.40625 0  z \" id=\"DejaVuSans-49\"/>\n",
       "      </defs>\n",
       "      <g transform=\"translate(50.968071 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"31.787109\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
       "       <use x=\"128.556641\" xlink:href=\"#DejaVuSans-111\"/>\n",
       "       <use x=\"189.738281\" xlink:href=\"#DejaVuSans-117\"/>\n",
       "       <use x=\"253.117188\" xlink:href=\"#DejaVuSans-110\"/>\n",
       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-49\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_2\">\n",
       "     <g id=\"line2d_2\">\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"65.426087\" xlink:href=\"#mbe445f4491\" y=\"145.8\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_2\">\n",
       "      <!--   Round 2 -->\n",
       "      <defs>\n",
       "       <path d=\"M 19.1875 8.296875  L 53.609375 8.296875  L 53.609375 0  L 7.328125 0  L 7.328125 8.296875  Q 12.9375 14.109375 22.625 23.890625  Q 32.328125 33.6875 34.8125 36.53125  Q 39.546875 41.84375 41.421875 45.53125  Q 43.3125 49.21875 43.3125 52.78125  Q 43.3125 58.59375 39.234375 62.25  Q 35.15625 65.921875 28.609375 65.921875  Q 23.96875 65.921875 18.8125 64.3125  Q 13.671875 62.703125 7.8125 59.421875  L 7.8125 69.390625  Q 13.765625 71.78125 18.9375 73  Q 24.125 74.21875 28.421875 74.21875  Q 39.75 74.21875 46.484375 68.546875  Q 53.21875 62.890625 53.21875 53.421875  Q 53.21875 48.921875 51.53125 44.890625  Q 49.859375 40.875 45.40625 35.40625  Q 44.1875 33.984375 37.640625 27.21875  Q 31.109375 20.453125 19.1875 8.296875  z \" id=\"DejaVuSans-50\"/>\n",
       "      </defs>\n",
       "      <g transform=\"translate(68.185462 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"31.787109\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
       "       <use x=\"128.556641\" xlink:href=\"#DejaVuSans-111\"/>\n",
       "       <use x=\"189.738281\" xlink:href=\"#DejaVuSans-117\"/>\n",
       "       <use x=\"253.117188\" xlink:href=\"#DejaVuSans-110\"/>\n",
       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-50\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_3\">\n",
       "     <g id=\"line2d_3\">\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"82.643478\" xlink:href=\"#mbe445f4491\" y=\"145.8\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_3\">\n",
       "      <!--   Round 3 -->\n",
       "      <defs>\n",
       "       <path d=\"M 40.578125 39.3125  Q 47.65625 37.796875 51.625 33  Q 55.609375 28.21875 55.609375 21.1875  Q 55.609375 10.40625 48.1875 4.484375  Q 40.765625 -1.421875 27.09375 -1.421875  Q 22.515625 -1.421875 17.65625 -0.515625  Q 12.796875 0.390625 7.625 2.203125  L 7.625 11.71875  Q 11.71875 9.328125 16.59375 8.109375  Q 21.484375 6.890625 26.8125 6.890625  Q 36.078125 6.890625 40.9375 10.546875  Q 45.796875 14.203125 45.796875 21.1875  Q 45.796875 27.640625 41.28125 31.265625  Q 36.765625 34.90625 28.71875 34.90625  L 20.21875 34.90625  L 20.21875 43.015625  L 29.109375 43.015625  Q 36.375 43.015625 40.234375 45.921875  Q 44.09375 48.828125 44.09375 54.296875  Q 44.09375 59.90625 40.109375 62.90625  Q 36.140625 65.921875 28.71875 65.921875  Q 24.65625 65.921875 20.015625 65.03125  Q 15.375 64.15625 9.8125 62.3125  L 9.8125 71.09375  Q 15.4375 72.65625 20.34375 73.4375  Q 25.25 74.21875 29.59375 74.21875  Q 40.828125 74.21875 47.359375 69.109375  Q 53.90625 64.015625 53.90625 55.328125  Q 53.90625 49.265625 50.4375 45.09375  Q 46.96875 40.921875 40.578125 39.3125  z \" id=\"DejaVuSans-51\"/>\n",
       "      </defs>\n",
       "      <g transform=\"translate(85.402853 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"31.787109\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
       "       <use x=\"128.556641\" xlink:href=\"#DejaVuSans-111\"/>\n",
       "       <use x=\"189.738281\" xlink:href=\"#DejaVuSans-117\"/>\n",
       "       <use x=\"253.117188\" xlink:href=\"#DejaVuSans-110\"/>\n",
       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-51\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_4\">\n",
       "     <g id=\"line2d_4\">\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"99.86087\" xlink:href=\"#mbe445f4491\" y=\"145.8\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_4\">\n",
       "      <!--   Round 4 -->\n",
       "      <defs>\n",
       "       <path d=\"M 37.796875 64.3125  L 12.890625 25.390625  L 37.796875 25.390625  z M 35.203125 72.90625  L 47.609375 72.90625  L 47.609375 25.390625  L 58.015625 25.390625  L 58.015625 17.1875  L 47.609375 17.1875  L 47.609375 0  L 37.796875 0  L 37.796875 17.1875  L 4.890625 17.1875  L 4.890625 26.703125  z \" id=\"DejaVuSans-52\"/>\n",
       "      </defs>\n",
       "      <g transform=\"translate(102.620245 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"31.787109\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
       "       <use x=\"128.556641\" xlink:href=\"#DejaVuSans-111\"/>\n",
       "       <use x=\"189.738281\" xlink:href=\"#DejaVuSans-117\"/>\n",
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       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-52\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"text_5\">\n",
       "     <!-- HHH -->\n",
       "     <defs>\n",
       "      <path d=\"M 9.8125 72.90625  L 19.671875 72.90625  L 19.671875 43.015625  L 55.515625 43.015625  L 55.515625 72.90625  L 65.375 72.90625  L 65.375 0  L 55.515625 0  L 55.515625 34.71875  L 19.671875 34.71875  L 19.671875 0  L 9.8125 0  z \" id=\"DejaVuSans-72\"/>\n",
       "     </defs>\n",
       "     <g transform=\"translate(62.754314 211.935937)scale(0.1 -0.1)\">\n",
       "      <use xlink:href=\"#DejaVuSans-72\"/>\n",
       "      <use x=\"75.195312\" xlink:href=\"#DejaVuSans-72\"/>\n",
       "      <use x=\"150.390625\" xlink:href=\"#DejaVuSans-72\"/>\n",
       "     </g>\n",
       "    </g>\n",
       "   </g>\n",
       "   <g id=\"matplotlib.axis_2\">\n",
       "    <g id=\"ytick_1\">\n",
       "     <g id=\"line2d_5\">\n",
       "      <path clip-path=\"url(#pb162fd0196)\" d=\"M 39.6 133.863158  L 108.469565 133.863158  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "     <g id=\"text_6\">\n",
       "      <!-- 0.0 -->\n",
       "      <defs>\n",
       "       <path d=\"M 31.78125 66.40625  Q 24.171875 66.40625 20.328125 58.90625  Q 16.5 51.421875 16.5 36.375  Q 16.5 21.390625 20.328125 13.890625  Q 24.171875 6.390625 31.78125 6.390625  Q 39.453125 6.390625 43.28125 13.890625  Q 47.125 21.390625 47.125 36.375  Q 47.125 51.421875 43.28125 58.90625  Q 39.453125 66.40625 31.78125 66.40625  z M 31.78125 74.21875  Q 44.046875 74.21875 50.515625 64.515625  Q 56.984375 54.828125 56.984375 36.375  Q 56.984375 17.96875 50.515625 8.265625  Q 44.046875 -1.421875 31.78125 -1.421875  Q 19.53125 -1.421875 13.0625 8.265625  Q 6.59375 17.96875 6.59375 36.375  Q 6.59375 54.828125 13.0625 64.515625  Q 19.53125 74.21875 31.78125 74.21875  z \" id=\"DejaVuSans-48\"/>\n",
       "       <path d=\"M 10.6875 12.40625  L 21 12.40625  L 21 0  L 10.6875 0  z \" id=\"DejaVuSans-46\"/>\n",
       "      </defs>\n",
       "      <g transform=\"translate(16.696875 137.662377)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-48\"/>\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\n",
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       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_2\">\n",
       "     <g id=\"line2d_6\">\n",
       "      <path clip-path=\"url(#pb162fd0196)\" d=\"M 39.6 109.989474  L 108.469565 109.989474  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "     <g id=\"text_7\">\n",
       "      <!-- 0.1 -->\n",
       "      <g transform=\"translate(16.696875 113.788692)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-48\"/>\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\n",
       "       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-49\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_3\">\n",
       "     <g id=\"line2d_7\">\n",
       "      <path clip-path=\"url(#pb162fd0196)\" d=\"M 39.6 86.115789  L 108.469565 86.115789  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "     <g id=\"text_8\">\n",
       "      <!-- 0.2 -->\n",
       "      <g transform=\"translate(16.696875 89.915008)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-48\"/>\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\n",
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       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_4\">\n",
       "     <g id=\"line2d_8\">\n",
       "      <path clip-path=\"url(#pb162fd0196)\" d=\"M 39.6 62.242105  L 108.469565 62.242105  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "     <g id=\"text_9\">\n",
       "      <!-- 0.3 -->\n",
       "      <g transform=\"translate(16.696875 66.041324)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-48\"/>\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\n",
       "       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-51\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_5\">\n",
       "     <g id=\"line2d_9\">\n",
       "      <path clip-path=\"url(#pb162fd0196)\" d=\"M 39.6 38.368421  L 108.469565 38.368421  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
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       "     <g id=\"text_10\">\n",
       "      <!-- 0.4 -->\n",
       "      <g transform=\"translate(16.696875 42.16764)scale(0.1 -0.1)\">\n",
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       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_6\">\n",
       "     <g id=\"line2d_10\">\n",
       "      <path clip-path=\"url(#pb162fd0196)\" d=\"M 39.6 14.494737  L 108.469565 14.494737  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "     <g id=\"text_11\">\n",
       "      <!-- 0.5 -->\n",
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       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
       "       <use x=\"128.556641\" xlink:href=\"#DejaVuSans-111\"/>\n",
       "       <use x=\"189.738281\" xlink:href=\"#DejaVuSans-117\"/>\n",
       "       <use x=\"253.117188\" xlink:href=\"#DejaVuSans-110\"/>\n",
       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-50\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_7\">\n",
       "     <g id=\"line2d_16\">\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"165.286957\" xlink:href=\"#mbe445f4491\" y=\"145.8\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_15\">\n",
       "      <!--   Round 3 -->\n",
       "      <g transform=\"translate(168.046332 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"31.787109\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
       "       <use x=\"128.556641\" xlink:href=\"#DejaVuSans-111\"/>\n",
       "       <use x=\"189.738281\" xlink:href=\"#DejaVuSans-117\"/>\n",
       "       <use x=\"253.117188\" xlink:href=\"#DejaVuSans-110\"/>\n",
       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-51\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_8\">\n",
       "     <g id=\"line2d_17\">\n",
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       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"182.504348\" xlink:href=\"#mbe445f4491\" y=\"145.8\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_16\">\n",
       "      <!--   Round 4 -->\n",
       "      <g transform=\"translate(185.263723 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"31.787109\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
       "       <use x=\"128.556641\" xlink:href=\"#DejaVuSans-111\"/>\n",
       "       <use x=\"189.738281\" xlink:href=\"#DejaVuSans-117\"/>\n",
       "       <use x=\"253.117188\" xlink:href=\"#DejaVuSans-110\"/>\n",
       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-52\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
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       "     <!-- HEEH -->\n",
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       "     </defs>\n",
       "     <g transform=\"translate(142.839198 211.935937)scale(0.1 -0.1)\">\n",
       "      <use xlink:href=\"#DejaVuSans-72\"/>\n",
       "      <use x=\"75.195312\" xlink:href=\"#DejaVuSans-69\"/>\n",
       "      <use x=\"138.378906\" xlink:href=\"#DejaVuSans-69\"/>\n",
       "      <use x=\"201.5625\" xlink:href=\"#DejaVuSans-72\"/>\n",
       "     </g>\n",
       "    </g>\n",
       "   </g>\n",
       "   <g id=\"matplotlib.axis_4\">\n",
       "    <g id=\"ytick_7\">\n",
       "     <g id=\"line2d_18\">\n",
       "      <path clip-path=\"url(#p18961687d2)\" d=\"M 122.243478 133.863158  L 191.113043 133.863158  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_8\">\n",
       "     <g id=\"line2d_19\">\n",
       "      <path clip-path=\"url(#p18961687d2)\" d=\"M 122.243478 109.989474  L 191.113043 109.989474  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_9\">\n",
       "     <g id=\"line2d_20\">\n",
       "      <path clip-path=\"url(#p18961687d2)\" d=\"M 122.243478 86.115789  L 191.113043 86.115789  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_10\">\n",
       "     <g id=\"line2d_21\">\n",
       "      <path clip-path=\"url(#p18961687d2)\" d=\"M 122.243478 62.242105  L 191.113043 62.242105  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_11\">\n",
       "     <g id=\"line2d_22\">\n",
       "      <path clip-path=\"url(#p18961687d2)\" d=\"M 122.243478 38.368421  L 191.113043 38.368421  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_12\">\n",
       "     <g id=\"line2d_23\">\n",
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       "    <path clip-path=\"url(#p18961687d2)\" d=\"M 144.626087 145.070716  L 144.626087 122.49477  \" style=\"fill:none;stroke:#1f77b4;stroke-width:1.5;\"/>\n",
       "    <path clip-path=\"url(#p18961687d2)\" d=\"M 161.843478 119.859648  L 161.843478 101.866079  \" style=\"fill:none;stroke:#1f77b4;stroke-width:1.5;\"/>\n",
       "    <path clip-path=\"url(#p18961687d2)\" d=\"M 179.06087 127.260256  L 179.06087 102.582197  \" style=\"fill:none;stroke:#1f77b4;stroke-width:1.5;\"/>\n",
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       "   <g id=\"LineCollection_7\">\n",
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       "    <path clip-path=\"url(#p18961687d2)\" d=\"M 148.069565 138.114165  L 148.069565 115.120426  \" style=\"fill:none;stroke:#ff7f0e;stroke-opacity:0.6;stroke-width:1.5;\"/>\n",
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       "   <g id=\"LineCollection_8\">\n",
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       "    <path clip-path=\"url(#p18961687d2)\" d=\"M 151.513043 141.233353  L 151.513043 118.631108  \" style=\"fill:none;stroke:#2ca02c;stroke-opacity:0.6;stroke-width:1.5;\"/>\n",
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       "    <path clip-path=\"url(#p18961687d2)\" d=\"M 185.947826 130.257113  L 185.947826 105.01776  \" style=\"fill:none;stroke:#2ca02c;stroke-opacity:0.6;stroke-width:1.5;\"/>\n",
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       "   <g id=\"LineCollection_9\">\n",
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       "   <g id=\"LineCollection_10\">\n",
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       "   <g id=\"line2d_24\">\n",
       "    <g clip-path=\"url(#p18961687d2)\">\n",
       "     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"127.408696\" xlink:href=\"#m6c81cb87e1\" y=\"129.603991\"/>\n",
       "     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"144.626087\" xlink:href=\"#m6c81cb87e1\" y=\"133.625604\"/>\n",
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       "   <g id=\"line2d_25\">\n",
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       "    </g>\n",
       "   </g>\n",
       "   <g id=\"line2d_26\">\n",
       "    <g clip-path=\"url(#p18961687d2)\">\n",
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       "     <use style=\"fill:#2ca02c;fill-opacity:0.6;stroke:#2ca02c;stroke-opacity:0.6;\" x=\"185.947826\" xlink:href=\"#mda10b0e675\" y=\"117.311453\"/>\n",
       "    </g>\n",
       "   </g>\n",
       "   <g id=\"patch_5\">\n",
       "    <path d=\"M 122.243478 145.8  L 191.113043 145.8  \" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n",
       "   </g>\n",
       "  </g>\n",
       "  <g id=\"axes_3\">\n",
       "   <g id=\"patch_6\">\n",
       "    <path d=\"M 204.886957 145.8  L 273.756522 145.8  L 273.756522 9.72  L 204.886957 9.72  z \" style=\"fill:#ffffff;\"/>\n",
       "   </g>\n",
       "   <g id=\"matplotlib.axis_5\">\n",
       "    <g id=\"xtick_9\">\n",
       "     <g id=\"line2d_27\">\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"213.495652\" xlink:href=\"#mbe445f4491\" y=\"145.8\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_18\">\n",
       "      <!--   Round 1 -->\n",
       "      <g transform=\"translate(216.255027 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"31.787109\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
       "       <use x=\"128.556641\" xlink:href=\"#DejaVuSans-111\"/>\n",
       "       <use x=\"189.738281\" xlink:href=\"#DejaVuSans-117\"/>\n",
       "       <use x=\"253.117188\" xlink:href=\"#DejaVuSans-110\"/>\n",
       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-49\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_10\">\n",
       "     <g id=\"line2d_28\">\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"230.713043\" xlink:href=\"#mbe445f4491\" y=\"145.8\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_19\">\n",
       "      <!--   Round 2 -->\n",
       "      <g transform=\"translate(233.472418 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
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       "       <use x=\"31.787109\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
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       "       <use x=\"189.738281\" xlink:href=\"#DejaVuSans-117\"/>\n",
       "       <use x=\"253.117188\" xlink:href=\"#DejaVuSans-110\"/>\n",
       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-50\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_11\">\n",
       "     <g id=\"line2d_29\">\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"247.930435\" xlink:href=\"#mbe445f4491\" y=\"145.8\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_20\">\n",
       "      <!--   Round 3 -->\n",
       "      <g transform=\"translate(250.68981 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
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       "       <use x=\"31.787109\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
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       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-51\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_12\">\n",
       "     <g id=\"line2d_30\">\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"265.147826\" xlink:href=\"#mbe445f4491\" y=\"145.8\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_21\">\n",
       "      <!--   Round 4 -->\n",
       "      <g transform=\"translate(267.907201 200.3375)rotate(-90)scale(0.1 -0.1)\">\n",
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       "       <use x=\"63.574219\" xlink:href=\"#DejaVuSans-82\"/>\n",
       "       <use x=\"128.556641\" xlink:href=\"#DejaVuSans-111\"/>\n",
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       "       <use x=\"316.496094\" xlink:href=\"#DejaVuSans-100\"/>\n",
       "       <use x=\"379.972656\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "       <use x=\"411.759766\" xlink:href=\"#DejaVuSans-52\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"text_22\">\n",
       "     <!-- EHEE -->\n",
       "     <g transform=\"translate(226.083458 211.935937)scale(0.1 -0.1)\">\n",
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       "      <use x=\"63.183594\" xlink:href=\"#DejaVuSans-72\"/>\n",
       "      <use x=\"138.378906\" xlink:href=\"#DejaVuSans-69\"/>\n",
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       "    </g>\n",
       "   </g>\n",
       "   <g id=\"matplotlib.axis_6\">\n",
       "    <g id=\"ytick_13\">\n",
       "     <g id=\"line2d_31\">\n",
       "      <path clip-path=\"url(#p929ab6a50b)\" d=\"M 204.886957 133.863158  L 273.756522 133.863158  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
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       "    </g>\n",
       "    <g id=\"ytick_14\">\n",
       "     <g id=\"line2d_32\">\n",
       "      <path clip-path=\"url(#p929ab6a50b)\" d=\"M 204.886957 109.989474  L 273.756522 109.989474  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
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       "    </g>\n",
       "    <g id=\"ytick_15\">\n",
       "     <g id=\"line2d_33\">\n",
       "      <path clip-path=\"url(#p929ab6a50b)\" d=\"M 204.886957 86.115789  L 273.756522 86.115789  \" style=\"fill:none;stroke:#b0b0b0;stroke-dasharray:0.8,1.32;stroke-dashoffset:0;stroke-width:0.8;\"/>\n",
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       "    </g>\n",
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    "display(\n",
    "    SVG(\n",
    "        \"/home/kimlab1/strokach/workspace/proteinsolver/notebooks/07_protein_analysis_figures/gapf-design-correlations.svg\"\n",
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   "source": [
    "## Overall assessment"
   ]
  },
  {
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   "source": [
    "### Mutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
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   "source": [
    "feature_columns = [\n",
    "    \"scan_sm_log_change\",\n",
    "    \"ingraham_neglogp_change\",\n",
    "    \"rosetta_reu_change\",\n",
    "    \"rosetta_reu_norm_change\",\n",
    "    \"ddg_monomer_soft_rep_design_1\",\n",
    "    \"cartesian_ddg_beta_nov16_cart_1\",\n",
    "]\n",
    "target_column = \"ddg_exp\"\n",
    "\n",
    "protherm_mutation_df = protherm_wresults_df[feature_columns + [target_column]].dropna()\n",
    "protherm_mutation_df = protherm_mutation_df.rename(\n",
    "    columns={\n",
    "        \"scan_sm_log_change\": \"proteinsolver\",\n",
    "        \"ingraham_neglogp_change\": \"ingraham_et_al\",\n",
    "        \"rosetta_reu_change\": \"rosetta_relax\",\n",
    "        #         \"rosetta_reu_norm_change\": \"rosetta_relax\",\n",
    "        \"ddg_monomer_soft_rep_design_1\": \"ddg_monomer\",\n",
    "        \"cartesian_ddg_beta_nov16_cart_1\": \"cartesian_ddg\",\n",
    "    }\n",
    ")"
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  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
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   "source": [
    "feature_columns = [\n",
    "    \"network_score_change\",\n",
    "    \"ingraham_neglogp_change\",\n",
    "    \"rosetta_score_change\",\n",
    "    \"ddg_monomer_change\",\n",
    "    \"cartesian_ddg_change\",\n",
    "]\n",
    "target_column = \"consensus_stability_score2_change\"\n",
    "\n",
    "rocklin_mutation_df = stability_scores_for_mutations[\"ssm2\"][\n",
    "    feature_columns + [target_column]\n",
    "].dropna()\n",
    "rocklin_mutation_df = rocklin_mutation_df.rename(\n",
    "    columns={\n",
    "        \"network_score_change\": \"proteinsolver\",\n",
    "        \"ingraham_neglogp_change\": \"ingraham_et_al\",\n",
    "        \"rosetta_score_change\": \"rosetta_relax\",\n",
    "        \"ddg_monomer_change\": \"ddg_monomer\",\n",
    "        \"cartesian_ddg_change\": \"cartesian_ddg\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
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      "text/plain": [
       "{'proteinsolver': 0.011931350221857429,\n",
       " 'ingraham_et_al': 0.033971901424229144,\n",
       " 'rosetta_relax': 63.403,\n",
       " 'ddg_monomer': 2880,\n",
       " 'cartesian_ddg': 2160}"
      ]
     },
     "execution_count": 422,
     "metadata": {},
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    }
   ],
   "source": [
    "mutation_timing = {\n",
    "    \"proteinsolver\": 0.05965675110928714 / 5,\n",
    "    #     \"proteinsolver\": 1.9114373258780688 / 5,\n",
    "    \"ingraham_et_al\": 0.16985950712114573 / 5,\n",
    "    \"rosetta_relax\": np.mean([36.827, 2 * 60 + 48.637, 55.437, 42.630, 13.484]),\n",
    "    \"ddg_monomer\": 48 * 60,\n",
    "    \"cartesian_ddg\": 36 * 60,\n",
    "}\n",
    "mutation_timing"
   ]
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  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_mutation_summary(df, target_column, mutation_timing):\n",
    "    results = []\n",
    "    for feature_column in mutation_timing:\n",
    "        sign = (\n",
    "            1\n",
    "            if feature_column in [\"proteinsolver\", \"network_score_change\", \"scan_sm_log_change\"]\n",
    "            else -1\n",
    "        )\n",
    "        df_notnull = df.dropna(subset=[target_column, feature_column])\n",
    "        if len(df) > len(df_notnull):\n",
    "            print(\n",
    "                f\"Removing {len(df) - len(df_notnull)} rows with null values for columns: \"\n",
    "                f\"{[target_column, feature_column]}.\"\n",
    "            )\n",
    "            df = df_notnull\n",
    "        values_ref = df[target_column].values\n",
    "        values = sign * df[feature_column].values\n",
    "        corr = stats.spearmanr(values_ref, values)\n",
    "        lower, upper, corrs = bootstrap_confidence_interval(\n",
    "            values_ref, values, fn=lambda v1, v2: stats.spearmanr(v1, v2)[0], show_progress=True,\n",
    "        )\n",
    "        results.append(\n",
    "            {\n",
    "                \"method\": feature_column,\n",
    "                \"corr\": corr[0],\n",
    "                \"pvalue\": corr[1],\n",
    "                \"corr_lower_bound\": lower,\n",
    "                \"corr_upper_bound\": upper,\n",
    "            }\n",
    "        )\n",
    "    result_df = pd.DataFrame(results)\n",
    "    result_df[\"runtime\"] = result_df[\"method\"].map(mutation_timing).values\n",
    "    return result_df"
   ]
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    "protherm_mutation_summary_df = generate_mutation_summary(\n",
    "    protherm_mutation_df, \"ddg_exp\", mutation_timing\n",
    ")"
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    "rocklin_mutation_summary_df = generate_mutation_summary(\n",
    "    rocklin_mutation_df, \"consensus_stability_score2_change\", mutation_timing\n",
    ")"
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    "# protherm_mutation_summary_df[\"runtime\"] = protherm_mutation_summary_df[\"method\"].map(mutation_timing).values\n",
    "# rocklin_mutation_summary_df[\"runtime\"] = rocklin_mutation_summary_df[\"method\"].map(mutation_timing).values"
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      "text/plain": [
       "<Figure size 324x216 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "cmap = plt.cm.get_cmap(\"tab10\")\n",
    "\n",
    "fg, ax = plt.subplots(figsize=(4.5, 3))\n",
    "ax.set_xscale(\"log\")\n",
    "\n",
    "\n",
    "df = protherm_mutation_summary_df\n",
    "for idx, method in enumerate(df[\"method\"].unique()):\n",
    "    df_chunk = df[df[\"method\"] == method]\n",
    "    rects = ax.errorbar(\n",
    "        df_chunk[\"runtime\"].values,\n",
    "        df_chunk[\"corr\"].values,\n",
    "        fmt=\"s\",\n",
    "        alpha=0.6 if method.lower() not in [\"proteinsolver\"] else 1.0,\n",
    "        yerr=np.abs(\n",
    "            df_chunk[[\"corr_lower_bound\", \"corr_upper_bound\"]].values - df_chunk[[\"corr\"]].values\n",
    "        ).T,\n",
    "        color=cmap(idx),\n",
    "        label=method,\n",
    "    )\n",
    "\n",
    "\n",
    "df = rocklin_mutation_summary_df\n",
    "for idx, method in enumerate(df[\"method\"].unique()):\n",
    "    df_chunk = df[df[\"method\"] == method]\n",
    "    rects = ax.errorbar(\n",
    "        df_chunk[\"runtime\"].values,\n",
    "        df_chunk[\"corr\"].values,\n",
    "        fmt=\"o\",\n",
    "        alpha=0.6 if method.lower() not in [\"proteinsolver\"] else 1.0,\n",
    "        yerr=np.abs(\n",
    "            df_chunk[[\"corr_lower_bound\", \"corr_upper_bound\"]].values - df_chunk[[\"corr\"]].values\n",
    "        ).T,\n",
    "        color=cmap(idx),\n",
    "        label=method,\n",
    "    )\n",
    "\n",
    "\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], marker=\"s\", color=\"k\", lw=1, alpha=0.6, label=\"Protherm\"),\n",
    "    Line2D([0], [0], marker=\"o\", color=\"k\", lw=1, alpha=0.6, label=\"Rocklin et al.\"),\n",
    "]\n",
    "ax.legend(handles=legend_elements, loc=\"lower left\")\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"Time required to evalate a single mutation (s)\")\n",
    "ax.set_ylabel(\"Spearman's ρ\")\n",
    "fg.subplots_adjust(left=0.16, bottom=0.15, top=0.97, right=0.9)\n",
    "fg.savefig(NOTEBOOK_PATH.joinpath(\"combined-mutation-evaluation.pdf\"))\n",
    "fg.savefig(NOTEBOOK_PATH.joinpath(\"combined-mutation-evaluation.svg\"))\n",
    "fg.savefig(NOTEBOOK_PATH.joinpath(\"combined-mutation-evaluation.png\"), dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for library in [\"rd1\", \"rd2\", \"rd3\", \"rd4\"]:\n",
    "    df = (\n",
    "        stability_scores_for_designs[library]\n",
    "        .dropna(\n",
    "            subset=[\n",
    "                \"network_score\",\n",
    "                \"talaris2013_score\",\n",
    "                \"betanov15_score\",\n",
    "                \"domain\",\n",
    "                \"stabilityscore_t\",\n",
    "                \"stabilityscore_c\",\n",
    "            ]\n",
    "        )\n",
    "        .copy()\n",
    "    )\n",
    "    df[\"stabilityscore_t\"] = df[\"stabilityscore_t\"] - df[\"stabilityscore_t\"].mean()\n",
    "    df[\"stabilityscore_c\"] = df[\"stabilityscore_c\"] - df[\"stabilityscore_c\"].mean()\n",
    "    dfs.append(df)\n",
    "\n",
    "protein_stability_df = pd.concat(dfs, ignore_index=True, sort=False)\n",
    "protein_stability_df = protein_stability_df.rename(\n",
    "    columns={\n",
    "        \"network_score\": \"proteinsolver\",\n",
    "        \"structured_transformer_neglogp\": \"ingraham_et_al\",\n",
    "        \"betanov15_score\": \"rosetta_design\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sequence</th>\n",
       "      <th>assay_library</th>\n",
       "      <th>ec50_t</th>\n",
       "      <th>ec50_95ci_lbound_t</th>\n",
       "      <th>ec50_95ci_ubound_t</th>\n",
       "      <th>ec50_95ci_t</th>\n",
       "      <th>ec50_pred_t</th>\n",
       "      <th>ec50_rise_t</th>\n",
       "      <th>stabilityscore_t</th>\n",
       "      <th>ec50_c</th>\n",
       "      <th>ec50_95ci_lbound_c</th>\n",
       "      <th>ec50_95ci_ubound_c</th>\n",
       "      <th>ec50_95ci_c</th>\n",
       "      <th>ec50_pred_c</th>\n",
       "      <th>ec50_rise_c</th>\n",
       "      <th>stabilityscore_c</th>\n",
       "      <th>stabilityscore</th>\n",
       "      <th>talaris2013_score</th>\n",
       "      <th>rosetta_design</th>\n",
       "      <th>library_name</th>\n",
       "      <th>proteinsolver</th>\n",
       "      <th>network_score_wref</th>\n",
       "      <th>sequence_length</th>\n",
       "      <th>domain</th>\n",
       "      <th>identity_to_ref</th>\n",
       "      <th>ingraham_et_al</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EEHEE_rd1_0001.pdb</td>\n",
       "      <td>GSQEVNSGTQTYKNASPEEAERIARKAGATTWTEKGNKWEIRI</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.0842</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.013841</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-78.360</td>\n",
       "      <td>-132.050</td>\n",
       "      <td>rd1</td>\n",
       "      <td>-1.987571</td>\n",
       "      <td>-1.987572</td>\n",
       "      <td>41.0</td>\n",
       "      <td>EEHEE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.078434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EEHEE_rd1_0002.pdb</td>\n",
       "      <td>GSYELEVGNYRYRADDPEQLKEEAKKVGARDVQTDGNNFKVRG</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.4558</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.466159</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-78.022</td>\n",
       "      <td>-126.266</td>\n",
       "      <td>rd1</td>\n",
       "      <td>-1.901309</td>\n",
       "      <td>-1.901310</td>\n",
       "      <td>41.0</td>\n",
       "      <td>EEHEE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.841325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                 name                                     sequence  \\\n",
       "0  EEHEE_rd1_0001.pdb  GSQEVNSGTQTYKNASPEEAERIARKAGATTWTEKGNKWEIRI   \n",
       "1  EEHEE_rd1_0002.pdb  GSYELEVGNYRYRADDPEQLKEEAKKVGARDVQTDGNNFKVRG   \n",
       "\n",
       "   assay_library  ec50_t  ec50_95ci_lbound_t  ec50_95ci_ubound_t  ec50_95ci_t  \\\n",
       "0            1.0    1.90                 1.8                 2.0          0.2   \n",
       "1            1.0    0.18                -0.5                 0.7          1.2   \n",
       "\n",
       "   ec50_pred_t  ec50_rise_t  stabilityscore_t  ec50_c  ec50_95ci_lbound_c  \\\n",
       "0         1.06         0.84            0.0842    0.93                 0.7   \n",
       "1         0.48        -0.30           -0.4558    1.08                 0.5   \n",
       "\n",
       "   ec50_95ci_ubound_c  ec50_95ci_c  ec50_pred_c  ec50_rise_c  \\\n",
       "0                 1.1          0.4         0.57         0.36   \n",
       "1                 1.6          1.1        -0.27         1.35   \n",
       "\n",
       "   stabilityscore_c  stabilityscore  talaris2013_score  rosetta_design  \\\n",
       "0         -0.013841            0.17            -78.360        -132.050   \n",
       "1          0.466159           -0.14            -78.022        -126.266   \n",
       "\n",
       "  library_name  proteinsolver  network_score_wref  sequence_length domain  \\\n",
       "0          rd1      -1.987571           -1.987572             41.0  EEHEE   \n",
       "1          rd1      -1.901309           -1.901310             41.0  EEHEE   \n",
       "\n",
       "   identity_to_ref  ingraham_et_al  \n",
       "0              1.0       -2.078434  \n",
       "1              1.0       -1.841325  "
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein_stability_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'proteinsolver': 0.4627381797414273, 'ingraham_et_al': 0.46474447120912377, 'rosetta_design': 2042.8}\n"
     ]
    }
   ],
   "source": [
    "design_timing = {\n",
    "    \"proteinsolver\": 0.4627381797414273,\n",
    "    \"ingraham_et_al\": 0.46474447120912377,\n",
    "    \"rosetta_design\": 2042.8,\n",
    "}\n",
    "print(design_timing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
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      "\n"
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      "\n"
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   "source": [
    "results = []\n",
    "for feature_column in [\n",
    "    \"proteinsolver\",\n",
    "    \"ingraham_et_al\",\n",
    "    \"rosetta_design\",\n",
    "]:\n",
    "    if any(query in feature_column for query in [\"betanov\", \"talaris\", \"rosetta\"]):\n",
    "        values = (\n",
    "            -protein_stability_df[feature_column] / protein_stability_df[\"sequence_length\"]\n",
    "        ).values\n",
    "    else:\n",
    "        values = protein_stability_df[feature_column].values\n",
    "    values_ref = protein_stability_df[[\"stabilityscore_t\", \"stabilityscore_c\"]].mean(axis=1).values\n",
    "    corr = stats.spearmanr(values_ref, values)\n",
    "    lower, upper, corrs = bootstrap_confidence_interval(\n",
    "        values_ref, values, fn=lambda v1, v2: stats.spearmanr(v1, v2)[0], show_progress=True,\n",
    "    )\n",
    "    results.append(\n",
    "        {\n",
    "            \"method\": feature_column,\n",
    "            \"corr\": corr[0],\n",
    "            \"pvalue\": corr[1],\n",
    "            \"corr_lower_bound\": lower,\n",
    "            \"corr_upper_bound\": upper,\n",
    "        }\n",
    "    )\n",
    "protein_stability_summary_df = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "protein_stability_summary_df[\"runtime\"] = (\n",
    "    protein_stability_summary_df[\"method\"].map(design_timing).values\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
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       "<Figure size 324x216 with 1 Axes>"
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   "source": [
    "cmap = plt.cm.get_cmap(\"tab10\")\n",
    "\n",
    "fg, ax = plt.subplots(figsize=(4.5, 3))\n",
    "ax.set_xscale(\"log\")\n",
    "\n",
    "\n",
    "df = protein_stability_summary_df\n",
    "for idx, method in enumerate(df[\"method\"].unique()):\n",
    "    df_chunk = df[df[\"method\"] == method]\n",
    "    rects = ax.errorbar(\n",
    "        df_chunk[\"runtime\"].values,\n",
    "        df_chunk[\"corr\"].values,\n",
    "        fmt=\"o\",\n",
    "        alpha=0.6 if method.lower() not in [\"proteinsolver\"] else 1.0,\n",
    "        yerr=np.abs(\n",
    "            df_chunk[[\"corr_lower_bound\", \"corr_upper_bound\"]].values - df_chunk[[\"corr\"]].values\n",
    "        ).T,\n",
    "        color=cmap(idx),\n",
    "        label=method,\n",
    "    )\n",
    "\n",
    "\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], marker=\"o\", color=\"k\", lw=1, alpha=0.6, label=\"Rocklin et al.\"),\n",
    "]\n",
    "ax.legend(handles=legend_elements, loc=\"lower right\")\n",
    "\n",
    "\n",
    "# ax.set_ylim(-0.01, 0.41)\n",
    "ax.set_xlabel(\"Time required to generate and evaluate a single design (s)\")\n",
    "ax.set_ylabel(\"Spearman's ρ\")\n",
    "fg.subplots_adjust(left=0.16, bottom=0.15, top=0.97, right=0.9)\n",
    "fg.savefig(NOTEBOOK_PATH.joinpath(\"combined-design-evaluation.pdf\"))\n",
    "fg.savefig(NOTEBOOK_PATH.joinpath(\"combined-design-evaluation.svg\"))\n",
    "fg.savefig(NOTEBOOK_PATH.joinpath(\"combined-design-evaluation.png\"), dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
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