07_predict_af2_and_order.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict the states with AF2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boilerplate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/home/pleung/projects/bistable_bundle/r4/hinges\n",
      "dig59\n"
     ]
    },
    {
     "data": {
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       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                var nbb_unformatted_code = \"%load_ext nb_black\\n# python internal\\nimport collections\\nimport copy\\nimport gc\\nfrom glob import glob\\nimport h5py\\nimport itertools\\nimport os\\nimport random\\nimport re\\nimport socket\\nimport shutil\\nimport subprocess\\nimport sys\\n\\n# conda/pip\\nimport dask\\nimport graphviz\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np\\nimport scipy\\nimport seaborn as sns\\nfrom tqdm import tqdm\\n\\n# special packages on the DIGS\\nimport py3Dmol\\nimport pymol\\nimport pyrosetta\\n\\n# notebook magic\\n%matplotlib inline\\n%load_ext autoreload\\n%autoreload 2\\n\\nprint(os.getcwd())\\nprint(socket.gethostname())\";\n",
       "                var nbb_formatted_code = \"%load_ext nb_black\\n# python internal\\nimport collections\\nimport copy\\nimport gc\\nfrom glob import glob\\nimport h5py\\nimport itertools\\nimport os\\nimport random\\nimport re\\nimport socket\\nimport shutil\\nimport subprocess\\nimport sys\\n\\n# conda/pip\\nimport dask\\nimport graphviz\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np\\nimport scipy\\nimport seaborn as sns\\nfrom tqdm import tqdm\\n\\n# special packages on the DIGS\\nimport py3Dmol\\nimport pymol\\nimport pyrosetta\\n\\n# notebook magic\\n%matplotlib inline\\n%load_ext autoreload\\n%autoreload 2\\n\\nprint(os.getcwd())\\nprint(socket.gethostname())\";\n",
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "%load_ext nb_black\n",
    "# python internal\n",
    "import collections\n",
    "import copy\n",
    "import gc\n",
    "from glob import glob\n",
    "import h5py\n",
    "import itertools\n",
    "import os\n",
    "import random\n",
    "import re\n",
    "import socket\n",
    "import shutil\n",
    "import subprocess\n",
    "import sys\n",
    "\n",
    "# conda/pip\n",
    "import dask\n",
    "import graphviz\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "# special packages on the DIGS\n",
    "import py3Dmol\n",
    "import pymol\n",
    "import pyrosetta\n",
    "\n",
    "# notebook magic\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "print(os.getcwd())\n",
    "print(socket.gethostname())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get all decoys that had decent two state rosetta metrics\n",
    "Hacky function to load JSON-like data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HIS_burial_X</th>\n",
       "      <th>HIS_burial_Y</th>\n",
       "      <th>TRP_burial_X</th>\n",
       "      <th>TRP_burial_Y</th>\n",
       "      <th>X_nocys_path</th>\n",
       "      <th>X_path</th>\n",
       "      <th>X_seq</th>\n",
       "      <th>Y_nocys_path</th>\n",
       "      <th>Y_path</th>\n",
       "      <th>Y_seq</th>\n",
       "      <th>...</th>\n",
       "      <th>total_length</th>\n",
       "      <th>total_score</th>\n",
       "      <th>total_score_X</th>\n",
       "      <th>total_score_Y</th>\n",
       "      <th>vbuns_X</th>\n",
       "      <th>vbuns_Y</th>\n",
       "      <th>wnm_all_X</th>\n",
       "      <th>wnm_all_Y</th>\n",
       "      <th>wnm_hlx_X</th>\n",
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       "      <th>/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0000/2021.06.26.14.24.46.542335_1c3348aaff704bdaa73c6d67fa2db82a.pdb.bz2</th>\n",
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       "      <td>TKEEKERIERIEKEVRSPDPENIREAVRKAKELAEENDSEEVKRLL...</td>\n",
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       "      <td>TKEEKERIERIEKEVRSPDPENIREAVRKAKELAEENDSCEVKRLL...</td>\n",
       "      <td>...</td>\n",
       "      <td>156.0</td>\n",
       "      <td>-200.714910</td>\n",
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       "      <td>-490.601990</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>TKEEKERIERIEKEVRSPDPENIREAVRKAVELMRENPSEEVKRLV...</td>\n",
       "      <td>/mnt/home/pleung/projects/bistable_bundle/r4/h...</td>\n",
       "      <td>/mnt/home/pleung/projects/bistable_bundle/r4/h...</td>\n",
       "      <td>TKEEKERIERIEKEVRSPDPENIREAVRKAVELMRENPSCEVKRLV...</td>\n",
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       "      <td>156.0</td>\n",
       "      <td>15.583651</td>\n",
       "      <td>-518.669983</td>\n",
       "      <td>-505.704010</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.696476</td>\n",
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       "      <td>TIEEIAAELKKTIKEAAEAGADEEAVKDARKLVKEVEKAAKRGVDE...</td>\n",
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      "text/plain": [
       "                                                    HIS_burial_X  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    191.598892   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    191.742218   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.000000   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.000000   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.000000   \n",
       "\n",
       "                                                    HIS_burial_Y  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    161.116302   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    190.977066   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.000000   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.000000   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.000000   \n",
       "\n",
       "                                                    TRP_burial_X  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    235.250000   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    235.254822   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    221.614243   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    218.944855   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    191.111252   \n",
       "\n",
       "                                                    TRP_burial_Y  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    221.223358   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    219.012299   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    242.966599   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    230.889313   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    208.557068   \n",
       "\n",
       "                                                                                         X_nocys_path  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "\n",
       "                                                                                               X_path  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "\n",
       "                                                                                                X_seq  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  TKEEKERIERIEKEVRSPDPENIREAVRKAKELAEENDSEEVKRLL...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  TKEEKERIERIEKEVRSPDPENIREAVRKAVELMRENPSEEVKRLV...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  DSEEEQERIRRILKEARKSGTEESLEQAIEDVAQLAKKSQDPRVLE...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  TKEEKERIERIEKEVRSPDPENIREAVRKAWELVEENPSEEALRLL...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  TIEEIAAELKKTIKEAAEAGADEEAVKDARKLVKEVEKAAKRGVDE...   \n",
       "\n",
       "                                                                                         Y_nocys_path  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "\n",
       "                                                                                               Y_path  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  /mnt/home/pleung/projects/bistable_bundle/r4/h...   \n",
       "\n",
       "                                                                                                Y_seq  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  TKEEKERIERIEKEVRSPDPENIREAVRKAKELAEENDSCEVKRLL...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  TKEEKERIERIEKEVRSPDPENIREAVRKAVELMRENPSCEVKRLV...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  DSEEEQERIRRILKEARKSGTEESLEQAIEDVAQLAKKSQDPRVLE...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  TKEEKERIERIEKEVRSPDPENIREAVRKAWELVEENPSCEALRLL...   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  TIEEIAAELKKTIKEAAEAGADEEAVKDARKLVKEVEKAAKRGVDE...   \n",
       "\n",
       "                                                    ...  total_length  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  ...         156.0   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  ...         156.0   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  ...         160.0   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  ...         156.0   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  ...         184.0   \n",
       "\n",
       "                                                    total_score  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  -200.714910   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    15.583651   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  -205.345371   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...   -67.159011   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    14.682345   \n",
       "\n",
       "                                                    total_score_X  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -495.980011   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -518.669983   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -548.015991   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -514.296997   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -566.111023   \n",
       "\n",
       "                                                    total_score_Y  vbuns_X  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -490.601990      0.0   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -505.704010      0.0   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -559.239014      0.0   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -492.813995      0.0   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...    -566.744995      0.0   \n",
       "\n",
       "                                                    vbuns_Y  wnm_all_X  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.0   0.344967   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.0   0.349388   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.0   0.433880   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.0   0.353671   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...      0.0   0.687405   \n",
       "\n",
       "                                                   wnm_all_Y wnm_hlx_X  \\\n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  0.369241  0.121823   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  0.696476  0.120602   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  0.644063  0.134351   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  0.777966  0.124616   \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...  0.673628  0.122836   \n",
       "\n",
       "                                                    wnm_hlx_Y  \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...   0.108301  \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...   0.129675  \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...   0.108984  \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...   0.113692  \n",
       "/mnt/home/pleung/projects/bistable_bundle/r4/hi...   0.106946  \n",
       "\n",
       "[5 rows x 145 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"def read_scorefile(scores):\\n    import pandas as pd\\n\\n    scores = pd.read_json(scores, orient=\\\"records\\\", typ=\\\"frame\\\", lines=True)\\n    scores = scores.T\\n    mat = scores.values\\n    n = mat.shape[0]\\n    dicts = list(mat[range(n), range(n)])\\n    index = scores.index\\n    tabulated_scores = pd.DataFrame(dicts, index=index)\\n    return tabulated_scores\\n\\n\\noutput_path = os.path.join(os.getcwd(), \\\"06_cleanup\\\")\\nscores = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df = read_scorefile(scores)\\nscores_df.head()\";\n",
       "                var nbb_formatted_code = \"def read_scorefile(scores):\\n    import pandas as pd\\n\\n    scores = pd.read_json(scores, orient=\\\"records\\\", typ=\\\"frame\\\", lines=True)\\n    scores = scores.T\\n    mat = scores.values\\n    n = mat.shape[0]\\n    dicts = list(mat[range(n), range(n)])\\n    index = scores.index\\n    tabulated_scores = pd.DataFrame(dicts, index=index)\\n    return tabulated_scores\\n\\n\\noutput_path = os.path.join(os.getcwd(), \\\"06_cleanup\\\")\\nscores = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df = read_scorefile(scores)\\nscores_df.head()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def read_scorefile(scores):\n",
    "    import pandas as pd\n",
    "\n",
    "    scores = pd.read_json(scores, orient=\"records\", typ=\"frame\", lines=True)\n",
    "    scores = scores.T\n",
    "    mat = scores.values\n",
    "    n = mat.shape[0]\n",
    "    dicts = list(mat[range(n), range(n)])\n",
    "    index = scores.index\n",
    "    tabulated_scores = pd.DataFrame(dicts, index=index)\n",
    "    return tabulated_scores\n",
    "\n",
    "\n",
    "output_path = os.path.join(os.getcwd(), \"06_cleanup\")\n",
    "scores = os.path.join(output_path, \"scores.json\")\n",
    "scores_df = read_scorefile(scores)\n",
    "scores_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Refilter to remove accidentally introduced TRP\n",
    "Could use `if row[\"final_sequence\"].count(\"AAA\") < 2:` to remove polyALA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12617it [00:01, 10135.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before refiltering: 12617, after filtering: 12602\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"idces = []\\nfor i, row in tqdm(scores_df.iterrows()):\\n    if row[\\\"final_sequence\\\"].count(\\\"W\\\") == 1:\\n        idces.append(i)\\n\\none_trp = scores_df.loc[idces]\\nprint(f\\\"before refiltering: {len(scores_df)}, after filtering: {len(one_trp)}\\\")\";\n",
       "                var nbb_formatted_code = \"idces = []\\nfor i, row in tqdm(scores_df.iterrows()):\\n    if row[\\\"final_sequence\\\"].count(\\\"W\\\") == 1:\\n        idces.append(i)\\n\\none_trp = scores_df.loc[idces]\\nprint(f\\\"before refiltering: {len(scores_df)}, after filtering: {len(one_trp)}\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idces = []\n",
    "for i, row in tqdm(scores_df.iterrows()):\n",
    "    if row[\"final_sequence\"].count(\"W\") == 1:\n",
    "        idces.append(i)\n",
    "\n",
    "one_trp = scores_df.loc[idces]\n",
    "print(f\"before refiltering: {len(scores_df)}, after filtering: {len(one_trp)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a list of state Y's to fold with AF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"with open(os.path.join(os.getcwd(), \\\"06_cleanup\\\", \\\"1trp_refiltered_Y.list\\\"), \\\"w\\\") as f:\\n    for i in one_trp.Y_path.values:\\n        print(i, file=f)\";\n",
       "                var nbb_formatted_code = \"with open(os.path.join(os.getcwd(), \\\"06_cleanup\\\", \\\"1trp_refiltered_Y.list\\\"), \\\"w\\\") as f:\\n    for i in one_trp.Y_path.values:\\n        print(i, file=f)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(os.path.join(os.getcwd(), \"06_cleanup\", \"1trp_refiltered_Y.list\"), \"w\") as f:\n",
    "    for i in one_trp.Y_path.values:\n",
    "        print(i, file=f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use AF2 to predict state Y's\n",
    "if those have low RMSD and form disulfides, predict state X's,  \n",
    "if those have low RMSD and form disulfides, predict state U's,  \n",
    "then check RMSD to the X and Y state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12602it [00:08, 1506.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run the following commands\n",
      "sbatch -a 1-$(cat '07_run_af2_state_Y_designs.cmds' | wc -l) '07_run_af2_state_Y_designs.sh'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"import os, stat, subprocess\\n\\n\\ndef create_tasks(selected):\\n    with open(selected, \\\"r\\\") as f:\\n        for file in f:\\n            tasks = {}\\n            path = file.rstrip()\\n            tasks[\\\"-s\\\"] = path\\n            yield tasks\\n\\n\\ndef file_len(file):\\n    \\\"\\\"\\\"https://stackoverflow.com/questions/845058/how-to-get-line-count-of-a-large-file-cheaply-in-python\\\"\\\"\\\"\\n    with open(file) as f:\\n        for i, l in enumerate(f):\\n            pass\\n    return i + 1\\n\\n\\n# cleanup old cmdslists\\nfor cmdslist in glob(os.path.join(os.getcwd(), \\\"07_run*.cmds\\\")):\\n    os.remove(cmdslist)\\n\\n\\ninfer = os.path.join(os.getcwd(), \\\"run_af2_design_pdb.py\\\")\\n\\njid = \\\"{SLURM_JOB_ID%;*}\\\"\\nsid = \\\"{SLURM_ARRAY_TASK_ID}p\\\"\\n\\nfor designs, mem, queue in [(\\\"state_Y\\\", \\\"10\\\", \\\"short\\\")]:\\n    targets = os.path.join(os.getcwd(), f\\\"06_cleanup/1trp_refiltered_Y.list\\\")\\n    for i, tasks in enumerate(tqdm(create_tasks(targets))):\\n        if os.path.isfile(tasks[\\\"-s\\\"].replace(\\\".pdb\\\", \\\".json\\\")):\\n            continue\\n        else:\\n            pass\\n        tasklist = f\\\"07_run_af2_{designs}_designs.cmds\\\"\\n        gpu = \\\"\\\"\\n        queue = queue\\n        core_mem = f\\\"#SBATCH --mem={mem}G \\\\n\\\"\\n        run_sh = \\\"\\\"\\\"#!/usr/bin/env bash \\\\n#SBATCH -J inference \\\\n#SBATCH -e /mnt/home/pleung/logs/slurm_logs/infer-%J.err \\\\n#SBATCH -o /mnt/home/pleung/logs/slurm_logs/infer-%J.out \\\\n#SBATCH -p {queue} \\\\n{gpu}{core_mem}\\\\nJOB_ID=${jid} \\\\nCMD=$(sed -n \\\"${sid}\\\" {tasklist}) \\\\necho \\\"${c}\\\" | bash\\\"\\\"\\\".format(\\n            queue=queue,\\n            gpu=gpu,\\n            core_mem=core_mem,\\n            jid=jid,\\n            sid=sid,\\n            tasklist=tasklist,\\n            c=\\\"{CMD}\\\",\\n        )\\n        shell = f\\\"07_run_af2_{designs}_designs.sh\\\"\\n        with open(shell, \\\"w+\\\") as f:\\n            print(run_sh, file=f)\\n        st = os.stat(shell)\\n        os.chmod(shell, st.st_mode | stat.S_IEXEC)\\n        with open(tasklist, \\\"a+\\\") as f:\\n            args_ = \\\" \\\".join([\\\" \\\".join([k, str(v)]) for k, v in tasks.items()])\\n            cmd = f\\\"{infer} {args_} --save_pdbs\\\"\\n            print(cmd, file=f)\\n\\n# Let's go\\nprint(\\\"Run the following commands\\\")\\nfor i in \\\"state_Y\\\".split():\\n    print(\\n        f\\\"sbatch -a 1-$(cat '07_run_af2_{i}_designs.cmds' | wc -l) '07_run_af2_{i}_designs.sh'\\\"\\n    )\";\n",
       "                var nbb_formatted_code = \"import os, stat, subprocess\\n\\n\\ndef create_tasks(selected):\\n    with open(selected, \\\"r\\\") as f:\\n        for file in f:\\n            tasks = {}\\n            path = file.rstrip()\\n            tasks[\\\"-s\\\"] = path\\n            yield tasks\\n\\n\\ndef file_len(file):\\n    \\\"\\\"\\\"https://stackoverflow.com/questions/845058/how-to-get-line-count-of-a-large-file-cheaply-in-python\\\"\\\"\\\"\\n    with open(file) as f:\\n        for i, l in enumerate(f):\\n            pass\\n    return i + 1\\n\\n\\n# cleanup old cmdslists\\nfor cmdslist in glob(os.path.join(os.getcwd(), \\\"07_run*.cmds\\\")):\\n    os.remove(cmdslist)\\n\\n\\ninfer = os.path.join(os.getcwd(), \\\"run_af2_design_pdb.py\\\")\\n\\njid = \\\"{SLURM_JOB_ID%;*}\\\"\\nsid = \\\"{SLURM_ARRAY_TASK_ID}p\\\"\\n\\nfor designs, mem, queue in [(\\\"state_Y\\\", \\\"10\\\", \\\"short\\\")]:\\n    targets = os.path.join(os.getcwd(), f\\\"06_cleanup/1trp_refiltered_Y.list\\\")\\n    for i, tasks in enumerate(tqdm(create_tasks(targets))):\\n        if os.path.isfile(tasks[\\\"-s\\\"].replace(\\\".pdb\\\", \\\".json\\\")):\\n            continue\\n        else:\\n            pass\\n        tasklist = f\\\"07_run_af2_{designs}_designs.cmds\\\"\\n        gpu = \\\"\\\"\\n        queue = queue\\n        core_mem = f\\\"#SBATCH --mem={mem}G \\\\n\\\"\\n        run_sh = \\\"\\\"\\\"#!/usr/bin/env bash \\\\n#SBATCH -J inference \\\\n#SBATCH -e /mnt/home/pleung/logs/slurm_logs/infer-%J.err \\\\n#SBATCH -o /mnt/home/pleung/logs/slurm_logs/infer-%J.out \\\\n#SBATCH -p {queue} \\\\n{gpu}{core_mem}\\\\nJOB_ID=${jid} \\\\nCMD=$(sed -n \\\"${sid}\\\" {tasklist}) \\\\necho \\\"${c}\\\" | bash\\\"\\\"\\\".format(\\n            queue=queue,\\n            gpu=gpu,\\n            core_mem=core_mem,\\n            jid=jid,\\n            sid=sid,\\n            tasklist=tasklist,\\n            c=\\\"{CMD}\\\",\\n        )\\n        shell = f\\\"07_run_af2_{designs}_designs.sh\\\"\\n        with open(shell, \\\"w+\\\") as f:\\n            print(run_sh, file=f)\\n        st = os.stat(shell)\\n        os.chmod(shell, st.st_mode | stat.S_IEXEC)\\n        with open(tasklist, \\\"a+\\\") as f:\\n            args_ = \\\" \\\".join([\\\" \\\".join([k, str(v)]) for k, v in tasks.items()])\\n            cmd = f\\\"{infer} {args_} --save_pdbs\\\"\\n            print(cmd, file=f)\\n\\n# Let's go\\nprint(\\\"Run the following commands\\\")\\nfor i in \\\"state_Y\\\".split():\\n    print(\\n        f\\\"sbatch -a 1-$(cat '07_run_af2_{i}_designs.cmds' | wc -l) '07_run_af2_{i}_designs.sh'\\\"\\n    )\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os, stat, subprocess\n",
    "\n",
    "\n",
    "def create_tasks(selected):\n",
    "    with open(selected, \"r\") as f:\n",
    "        for file in f:\n",
    "            tasks = {}\n",
    "            path = file.rstrip()\n",
    "            tasks[\"-s\"] = path\n",
    "            yield tasks\n",
    "\n",
    "\n",
    "def file_len(file):\n",
    "    \"\"\"https://stackoverflow.com/questions/845058/how-to-get-line-count-of-a-large-file-cheaply-in-python\"\"\"\n",
    "    with open(file) as f:\n",
    "        for i, l in enumerate(f):\n",
    "            pass\n",
    "    return i + 1\n",
    "\n",
    "\n",
    "# cleanup old cmdslists\n",
    "for cmdslist in glob(os.path.join(os.getcwd(), \"07_run*.cmds\")):\n",
    "    os.remove(cmdslist)\n",
    "\n",
    "\n",
    "infer = os.path.join(os.getcwd(), \"run_af2_design_pdb.py\")\n",
    "\n",
    "jid = \"{SLURM_JOB_ID%;*}\"\n",
    "sid = \"{SLURM_ARRAY_TASK_ID}p\"\n",
    "\n",
    "for designs, mem, queue in [(\"state_Y\", \"10\", \"short\")]:\n",
    "    targets = os.path.join(os.getcwd(), f\"06_cleanup/1trp_refiltered_Y.list\")\n",
    "    for i, tasks in enumerate(tqdm(create_tasks(targets))):\n",
    "        if os.path.isfile(tasks[\"-s\"].replace(\".pdb\", \".json\")):\n",
    "            continue\n",
    "        else:\n",
    "            pass\n",
    "        tasklist = f\"07_run_af2_{designs}_designs.cmds\"\n",
    "        gpu = \"\"\n",
    "        queue = queue\n",
    "        core_mem = f\"#SBATCH --mem={mem}G \\n\"\n",
    "        run_sh = \"\"\"#!/usr/bin/env bash \\n#SBATCH -J inference \\n#SBATCH -e /mnt/home/pleung/logs/slurm_logs/infer-%J.err \\n#SBATCH -o /mnt/home/pleung/logs/slurm_logs/infer-%J.out \\n#SBATCH -p {queue} \\n{gpu}{core_mem}\\nJOB_ID=${jid} \\nCMD=$(sed -n \"${sid}\" {tasklist}) \\necho \"${c}\" | bash\"\"\".format(\n",
    "            queue=queue,\n",
    "            gpu=gpu,\n",
    "            core_mem=core_mem,\n",
    "            jid=jid,\n",
    "            sid=sid,\n",
    "            tasklist=tasklist,\n",
    "            c=\"{CMD}\",\n",
    "        )\n",
    "        shell = f\"07_run_af2_{designs}_designs.sh\"\n",
    "        with open(shell, \"w+\") as f:\n",
    "            print(run_sh, file=f)\n",
    "        st = os.stat(shell)\n",
    "        os.chmod(shell, st.st_mode | stat.S_IEXEC)\n",
    "        with open(tasklist, \"a+\") as f:\n",
    "            args_ = \" \".join([\" \".join([k, str(v)]) for k, v in tasks.items()])\n",
    "            cmd = f\"{infer} {args_} --save_pdbs\"\n",
    "            print(cmd, file=f)\n",
    "\n",
    "# Let's go\n",
    "print(\"Run the following commands\")\n",
    "for i in \"state_Y\".split():\n",
    "    print(\n",
    "        f\"sbatch -a 1-$(cat '07_run_af2_{i}_designs.cmds' | wc -l) '07_run_af2_{i}_designs.sh'\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted batch job 33883768\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"!sbatch -a 1-$(cat '07_run_af2_state_Y_designs.cmds' | wc -l) '07_run_af2_state_Y_designs.sh'\";\n",
       "                var nbb_formatted_code = \"!sbatch -a 1-$(cat '07_run_af2_state_Y_designs.cmds' | wc -l) '07_run_af2_state_Y_designs.sh'\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!sbatch -a 1-$(cat '07_run_af2_state_Y_designs.cmds' | wc -l) '07_run_af2_state_Y_designs.sh'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now need to retrieve the JSONs containing scores for the AF2 inference runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading 12496 runs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12496/12496 [22:30<00:00,  9.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19min 37s, sys: 26.7 s, total: 20min 4s\n",
      "Wall time: 22min 37s\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"%%time\\nimport json, time\\n\\n\\noutput_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_paths = glob(os.path.join(output_path, \\\"*/*.json\\\"))\\nprint(f\\\"reading {len(output_paths)} runs\\\")\\ntime.sleep(0.5)\\nscores = {}\\nfor path in tqdm(output_paths):\\n    pdb = path.replace(\\\"json\\\", \\\"pdb\\\")\\n    key = f\\\"{pdb}\\\"\\n    with open(path, \\\"r\\\") as f:\\n        try:\\n            values_dict = json.load(f)\\n            values = {}\\n            for model, results in values_dict.items():\\n                results.pop(\\\"pae\\\") # pae is too big?\\n                for name, result in results.items():\\n                    values[name+\\\"_\\\"+model] = result\\n            del values_dict\\n        except:\\n            print(path)\\n    scores[key] = values\\nscores_df = pd.DataFrame(scores)\\nscores_df = scores_df.T\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_formatted_code = \"%%time\\nimport json, time\\n\\n\\noutput_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_paths = glob(os.path.join(output_path, \\\"*/*.json\\\"))\\nprint(f\\\"reading {len(output_paths)} runs\\\")\\ntime.sleep(0.5)\\nscores = {}\\nfor path in tqdm(output_paths):\\n    pdb = path.replace(\\\"json\\\", \\\"pdb\\\")\\n    key = f\\\"{pdb}\\\"\\n    with open(path, \\\"r\\\") as f:\\n        try:\\n            values_dict = json.load(f)\\n            values = {}\\n            for model, results in values_dict.items():\\n                results.pop(\\\"pae\\\") # pae is too big?\\n                for name, result in results.items():\\n                    values[name+\\\"_\\\"+model] = result\\n            del values_dict\\n        except:\\n            print(path)\\n    scores[key] = values\\nscores_df = pd.DataFrame(scores)\\nscores_df = scores_df.T\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "import json, time\n",
    "\n",
    "\n",
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_paths = glob(os.path.join(output_path, \"*/*.json\"))\n",
    "print(f\"reading {len(output_paths)} runs\")\n",
    "time.sleep(0.5)\n",
    "scores = {}\n",
    "for path in tqdm(output_paths):\n",
    "    pdb = path.replace(\"json\", \"pdb\")\n",
    "    key = f\"{pdb}\"\n",
    "    with open(path, \"r\") as f:\n",
    "        try:\n",
    "            values_dict = json.load(f)\n",
    "            values = {}\n",
    "            for model, results in values_dict.items():\n",
    "                results.pop(\"pae\") # pae is too big?\n",
    "                for name, result in results.items():\n",
    "                    values[name+\"_\"+model] = result\n",
    "            del values_dict\n",
    "        except:\n",
    "            print(path)\n",
    "    scores[key] = values\n",
    "scores_df = pd.DataFrame(scores)\n",
    "scores_df = scores_df.T\n",
    "output_file = os.path.join(output_path, \"scores.json\")\n",
    "scores_df.to_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add additional columns incorporating metadata from best decoy by plDDT and lowest RMSD decoy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  2.01it/s]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"def row2best_model(\\n    row: pd.Series,\\n) -> str:\\n    best_model = \\\"1\\\"\\n    for model in \\\"2345\\\":\\n        if (\\n            row[f\\\"average_plddts_model_{model}\\\"]\\n            > row[f\\\"average_plddts_model_{best_model}\\\"]\\n        ):\\n            best_model = model\\n        else:\\n            pass\\n    return best_model\\n\\n\\ndef row2best_metadata(row: pd.Series, **kwargs) -> float:\\n    best_model = row[\\\"best_model\\\"]\\n    best_average_metadata = row[f\\\"{key}_model_{best_model}\\\"]\\n    return best_average_metadata\\n\\n\\ndef row2lowest_rmsd_model(\\n    row: pd.Series,\\n) -> str:\\n    lowest_rmsd_model = \\\"1\\\"\\n    for model in \\\"2345\\\":\\n        if (\\n            row[f\\\"rmsd_to_input_model_{model}\\\"]\\n            < row[f\\\"rmsd_to_input_model_{lowest_rmsd_model}\\\"]\\n        ):\\n            lowest_rmsd_model = model\\n        else:\\n            pass\\n    return lowest_rmsd_model\\n\\n\\ndef row2lowest_rmsd__metadata(row: pd.Series, **kwargs) -> float:\\n    lowest_rmsd_model = row[\\\"lowest_rmsd_model\\\"]\\n    lowest_rmsd_metadata = row[f\\\"{key}_model_{lowest_rmsd_model}\\\"]\\n    return lowest_rmsd_metadata\\n\\n\\ndef row2plddt_rmsd_agree(row: pd.Series) -> bool:\\n    if row[\\\"best_model\\\"] == row[\\\"lowest_rmsd_model\\\"]:\\n        return True\\n    else:\\n        return False\\n\\n\\nscores_df[\\\"best_model\\\"] = scores_df.apply(row2best_model, axis=1)\\nscores_df[\\\"lowest_rmsd_model\\\"] = scores_df.apply(row2lowest_rmsd_model, axis=1)\\nfor key in tqdm(\\n    \\\"average_plddts plddt ptm rmsd_to_input average_DAN_plddts DAN_plddt pdb_path\\\".split()\\n):\\n    scores_df[f\\\"best_{key}\\\"] = scores_df.apply(\\n        row2best_metadata, kwargs={\\\"key\\\": key}, axis=1\\n    )\\n    scores_df[f\\\"lowest_rmsd_{key}\\\"] = scores_df.apply(\\n        row2lowest_rmsd__metadata, kwargs={\\\"key\\\": key}, axis=1\\n    )\\nscores_df[\\\"plddt_rmsd_agree\\\"] = scores_df.apply(row2plddt_rmsd_agree, axis=1)\";\n",
       "                var nbb_formatted_code = \"def row2best_model(\\n    row: pd.Series,\\n) -> str:\\n    best_model = \\\"1\\\"\\n    for model in \\\"2345\\\":\\n        if (\\n            row[f\\\"average_plddts_model_{model}\\\"]\\n            > row[f\\\"average_plddts_model_{best_model}\\\"]\\n        ):\\n            best_model = model\\n        else:\\n            pass\\n    return best_model\\n\\n\\ndef row2best_metadata(row: pd.Series, **kwargs) -> float:\\n    best_model = row[\\\"best_model\\\"]\\n    best_average_metadata = row[f\\\"{key}_model_{best_model}\\\"]\\n    return best_average_metadata\\n\\n\\ndef row2lowest_rmsd_model(\\n    row: pd.Series,\\n) -> str:\\n    lowest_rmsd_model = \\\"1\\\"\\n    for model in \\\"2345\\\":\\n        if (\\n            row[f\\\"rmsd_to_input_model_{model}\\\"]\\n            < row[f\\\"rmsd_to_input_model_{lowest_rmsd_model}\\\"]\\n        ):\\n            lowest_rmsd_model = model\\n        else:\\n            pass\\n    return lowest_rmsd_model\\n\\n\\ndef row2lowest_rmsd__metadata(row: pd.Series, **kwargs) -> float:\\n    lowest_rmsd_model = row[\\\"lowest_rmsd_model\\\"]\\n    lowest_rmsd_metadata = row[f\\\"{key}_model_{lowest_rmsd_model}\\\"]\\n    return lowest_rmsd_metadata\\n\\n\\ndef row2plddt_rmsd_agree(row: pd.Series) -> bool:\\n    if row[\\\"best_model\\\"] == row[\\\"lowest_rmsd_model\\\"]:\\n        return True\\n    else:\\n        return False\\n\\n\\nscores_df[\\\"best_model\\\"] = scores_df.apply(row2best_model, axis=1)\\nscores_df[\\\"lowest_rmsd_model\\\"] = scores_df.apply(row2lowest_rmsd_model, axis=1)\\nfor key in tqdm(\\n    \\\"average_plddts plddt ptm rmsd_to_input average_DAN_plddts DAN_plddt pdb_path\\\".split()\\n):\\n    scores_df[f\\\"best_{key}\\\"] = scores_df.apply(\\n        row2best_metadata, kwargs={\\\"key\\\": key}, axis=1\\n    )\\n    scores_df[f\\\"lowest_rmsd_{key}\\\"] = scores_df.apply(\\n        row2lowest_rmsd__metadata, kwargs={\\\"key\\\": key}, axis=1\\n    )\\nscores_df[\\\"plddt_rmsd_agree\\\"] = scores_df.apply(row2plddt_rmsd_agree, axis=1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def row2best_model(\n",
    "    row: pd.Series,\n",
    ") -> str:\n",
    "    best_model = \"1\"\n",
    "    for model in \"2345\":\n",
    "        if (\n",
    "            row[f\"average_plddts_model_{model}\"]\n",
    "            > row[f\"average_plddts_model_{best_model}\"]\n",
    "        ):\n",
    "            best_model = model\n",
    "        else:\n",
    "            pass\n",
    "    return best_model\n",
    "\n",
    "\n",
    "def row2best_metadata(row: pd.Series, **kwargs) -> float:\n",
    "    best_model = row[\"best_model\"]\n",
    "    best_average_metadata = row[f\"{key}_model_{best_model}\"]\n",
    "    return best_average_metadata\n",
    "\n",
    "\n",
    "def row2lowest_rmsd_model(\n",
    "    row: pd.Series,\n",
    ") -> str:\n",
    "    lowest_rmsd_model = \"1\"\n",
    "    for model in \"2345\":\n",
    "        if (\n",
    "            row[f\"rmsd_to_input_model_{model}\"]\n",
    "            < row[f\"rmsd_to_input_model_{lowest_rmsd_model}\"]\n",
    "        ):\n",
    "            lowest_rmsd_model = model\n",
    "        else:\n",
    "            pass\n",
    "    return lowest_rmsd_model\n",
    "\n",
    "\n",
    "def row2lowest_rmsd__metadata(row: pd.Series, **kwargs) -> float:\n",
    "    lowest_rmsd_model = row[\"lowest_rmsd_model\"]\n",
    "    lowest_rmsd_metadata = row[f\"{key}_model_{lowest_rmsd_model}\"]\n",
    "    return lowest_rmsd_metadata\n",
    "\n",
    "\n",
    "def row2plddt_rmsd_agree(row: pd.Series) -> bool:\n",
    "    if row[\"best_model\"] == row[\"lowest_rmsd_model\"]:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "\n",
    "scores_df[\"best_model\"] = scores_df.apply(row2best_model, axis=1)\n",
    "scores_df[\"lowest_rmsd_model\"] = scores_df.apply(row2lowest_rmsd_model, axis=1)\n",
    "for key in tqdm(\n",
    "    \"average_plddts plddt ptm rmsd_to_input average_DAN_plddts DAN_plddt pdb_path\".split()\n",
    "):\n",
    "    scores_df[f\"best_{key}\"] = scores_df.apply(\n",
    "        row2best_metadata, kwargs={\"key\": key}, axis=1\n",
    "    )\n",
    "    scores_df[f\"lowest_rmsd_{key}\"] = scores_df.apply(\n",
    "        row2lowest_rmsd__metadata, kwargs={\"key\": key}, axis=1\n",
    "    )\n",
    "scores_df[\"plddt_rmsd_agree\"] = scores_df.apply(row2plddt_rmsd_agree, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dump df as json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 25;\n",
       "                var nbb_unformatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_formatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_file = os.path.join(output_path, \"scores.json\")\n",
    "scores_df.to_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reload df from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 26;\n",
       "                var nbb_unformatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df = pd.read_json(output_file)\";\n",
       "                var nbb_formatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df = pd.read_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_file = os.path.join(output_path, \"scores.json\")\n",
    "scores_df = pd.read_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribute metadata collection to cluster with dask and collect results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyRosetta-4 2021 [Rosetta PyRosetta4.conda.linux.cxx11thread.serialization.CentOS.python38.Release 2021.27+release.7ce64884a77d606b7b667c363527acc846541030 2021-07-09T18:10:05] retrieved from: http://www.pyrosetta.org\n",
      "(C) Copyright Rosetta Commons Member Institutions. Created in JHU by Sergey Lyskov and PyRosetta Team.\n",
      "core.init: {0} Checking for fconfig files in pwd and ./rosetta/flags\n",
      "core.init: {0} Rosetta version: PyRosetta4.conda.linux.cxx11thread.serialization.CentOS.python38.Release r289 2021.27+release.7ce6488 7ce64884a77d606b7b667c363527acc846541030 http://www.pyrosetta.org 2021-07-09T18:10:05\n",
      "core.init: {0} command: PyRosetta -corrections:beta_nov16 true -database /home/pleung/.conda/envs/phil/lib/python3.8/site-packages/pyrosetta/database\n",
      "basic.random.init_random_generator: {0} 'RNG device' seed mode, using '/dev/urandom', seed=-549098848 seed_offset=0 real_seed=-549098848 thread_index=0\n",
      "basic.random.init_random_generator: {0} RandomGenerator:init: Normal mode, seed=-549098848 RG_type=mt19937\n",
      "run the following from your local terminal:\n",
      "ssh -L 8000:localhost:8787 pleung@dig107\n",
      "#!/usr/bin/env bash\n",
      "\n",
      "#SBATCH -J dask-worker\n",
      "#SBATCH -e /mnt/home/pleung/logs/slurm_logs/dask-worker-%J.err\n",
      "#SBATCH -o /mnt/home/pleung/logs/slurm_logs/dask-worker-%J.out\n",
      "#SBATCH -p long\n",
      "#SBATCH -n 1\n",
      "#SBATCH --cpus-per-task=1\n",
      "#SBATCH --mem=2G\n",
      "#SBATCH -t 23:30:00\n",
      "\n",
      "JOB_ID=${SLURM_JOB_ID%;*}\n",
      "\n",
      "/home/pleung/.conda/envs/phil/bin/python -m distributed.cli.dask_worker tcp://172.16.131.211:41085 --nthreads 1 --memory-limit 1.86GiB --name name --nanny --death-timeout 120 --local-directory $TMPDIR/dask --lifetime 23h --lifetime-stagger 4m\n",
      "\n",
      "<Client: 'tcp://172.16.131.211:41085' processes=0 threads=0, memory=0 B>\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 27;\n",
       "                var nbb_unformatted_code = \"from dask.distributed import Client\\nfrom dask_jobqueue import SLURMCluster\\nfrom glob import glob\\nimport logging\\nimport pwd\\n\\n\\ndef distribute_get_dslf_fa13(pdb:str) -> float:\\n    pyrosetta.init(\\\"-corrections:beta_nov16 true\\\")\\n    sfxn = pyrosetta.get_fa_scorefxn()\\n    pose = pyrosetta.io.pose_from_file(pdb)\\n    sfxn.score(pose)\\n    dslf_fa13 = pose.scores[\\\"dslf_fa13\\\"]\\n    return dslf_fa13\\n\\n\\npyrosetta.init(\\\"-corrections:beta_nov16 true\\\")\\n\\n\\nprint(\\\"run the following from your local terminal:\\\")\\nprint(\\n    f\\\"ssh -L 8000:localhost:8787 {pwd.getpwuid(os.getuid()).pw_name}@{socket.gethostname()}\\\"\\n)\\n\\nsubset = scores_df[scores_df[\\\"lowest_rmsd_rmsd_to_input\\\"] < 1.5]\\n\\nfutures = {}\\nresults = {}\\n\\nif __name__ == \\\"__main__\\\":\\n    # configure SLURM cluster as a context manager\\n    with SLURMCluster(\\n        cores=1,\\n        processes=1,\\n        job_cpu=1,\\n        memory=\\\"2GB\\\",\\n        queue=\\\"long\\\",\\n        walltime=\\\"23:30:00\\\",\\n        death_timeout=120,\\n        local_directory=\\\"$TMPDIR/dask\\\",\\n        log_directory=\\\"/mnt/home/pleung/logs/slurm_logs\\\",\\n        extra=[\\\"--lifetime\\\", \\\"23h\\\", \\\"--lifetime-stagger\\\", \\\"4m\\\"],\\n    ) as cluster:\\n        print(cluster.job_script())\\n        # scale between 1-200 workers,\\n        cluster.adapt(\\n            minimum=1,\\n            maximum=200,\\n            wait_count=400,  # Number of consecutive times that a worker should be suggested for removal it is removed\\n            interval=\\\"5s\\\",  # Time between checks\\n        )\\n        # setup a client to interact with the cluster as a context manager\\n        with Client(cluster) as client:\\n            print(client)\\n            for path in subset.index:\\n                future = client.submit(distribute_get_dslf_fa13, path)\\n                futures[path] = future\\n            for i, pending in futures.items():\\n                result = pending.result()\\n                results[i] = result\";\n",
       "                var nbb_formatted_code = \"from dask.distributed import Client\\nfrom dask_jobqueue import SLURMCluster\\nfrom glob import glob\\nimport logging\\nimport pwd\\n\\n\\ndef distribute_get_dslf_fa13(pdb: str) -> float:\\n    pyrosetta.init(\\\"-corrections:beta_nov16 true\\\")\\n    sfxn = pyrosetta.get_fa_scorefxn()\\n    pose = pyrosetta.io.pose_from_file(pdb)\\n    sfxn.score(pose)\\n    dslf_fa13 = pose.scores[\\\"dslf_fa13\\\"]\\n    return dslf_fa13\\n\\n\\npyrosetta.init(\\\"-corrections:beta_nov16 true\\\")\\n\\n\\nprint(\\\"run the following from your local terminal:\\\")\\nprint(\\n    f\\\"ssh -L 8000:localhost:8787 {pwd.getpwuid(os.getuid()).pw_name}@{socket.gethostname()}\\\"\\n)\\n\\nsubset = scores_df[scores_df[\\\"lowest_rmsd_rmsd_to_input\\\"] < 1.5]\\n\\nfutures = {}\\nresults = {}\\n\\nif __name__ == \\\"__main__\\\":\\n    # configure SLURM cluster as a context manager\\n    with SLURMCluster(\\n        cores=1,\\n        processes=1,\\n        job_cpu=1,\\n        memory=\\\"2GB\\\",\\n        queue=\\\"long\\\",\\n        walltime=\\\"23:30:00\\\",\\n        death_timeout=120,\\n        local_directory=\\\"$TMPDIR/dask\\\",\\n        log_directory=\\\"/mnt/home/pleung/logs/slurm_logs\\\",\\n        extra=[\\\"--lifetime\\\", \\\"23h\\\", \\\"--lifetime-stagger\\\", \\\"4m\\\"],\\n    ) as cluster:\\n        print(cluster.job_script())\\n        # scale between 1-200 workers,\\n        cluster.adapt(\\n            minimum=1,\\n            maximum=200,\\n            wait_count=400,  # Number of consecutive times that a worker should be suggested for removal it is removed\\n            interval=\\\"5s\\\",  # Time between checks\\n        )\\n        # setup a client to interact with the cluster as a context manager\\n        with Client(cluster) as client:\\n            print(client)\\n            for path in subset.index:\\n                future = client.submit(distribute_get_dslf_fa13, path)\\n                futures[path] = future\\n            for i, pending in futures.items():\\n                result = pending.result()\\n                results[i] = result\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "from dask_jobqueue import SLURMCluster\n",
    "from glob import glob\n",
    "import logging\n",
    "import pwd\n",
    "\n",
    "\n",
    "def distribute_get_dslf_fa13(pdb:str) -> float:\n",
    "    pyrosetta.init(\"-corrections:beta_nov16 true\")\n",
    "    sfxn = pyrosetta.get_fa_scorefxn()\n",
    "    pose = pyrosetta.io.pose_from_file(pdb)\n",
    "    sfxn.score(pose)\n",
    "    dslf_fa13 = pose.scores[\"dslf_fa13\"]\n",
    "    return dslf_fa13\n",
    "\n",
    "\n",
    "pyrosetta.init(\"-corrections:beta_nov16 true\")\n",
    "\n",
    "\n",
    "print(\"run the following from your local terminal:\")\n",
    "print(\n",
    "    f\"ssh -L 8000:localhost:8787 {pwd.getpwuid(os.getuid()).pw_name}@{socket.gethostname()}\"\n",
    ")\n",
    "\n",
    "subset = scores_df[scores_df[\"lowest_rmsd_rmsd_to_input\"] < 1.5]\n",
    "\n",
    "futures = {}\n",
    "results = {}\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # configure SLURM cluster as a context manager\n",
    "    with SLURMCluster(\n",
    "        cores=1,\n",
    "        processes=1,\n",
    "        job_cpu=1,\n",
    "        memory=\"2GB\",\n",
    "        queue=\"long\",\n",
    "        walltime=\"23:30:00\",\n",
    "        death_timeout=120,\n",
    "        local_directory=\"$TMPDIR/dask\",\n",
    "        log_directory=\"/mnt/home/pleung/logs/slurm_logs\",\n",
    "        extra=[\"--lifetime\", \"23h\", \"--lifetime-stagger\", \"4m\"],\n",
    "    ) as cluster:\n",
    "        print(cluster.job_script())\n",
    "        # scale between 1-200 workers,\n",
    "        cluster.adapt(\n",
    "            minimum=1,\n",
    "            maximum=200,\n",
    "            wait_count=400,  # Number of consecutive times that a worker should be suggested for removal it is removed\n",
    "            interval=\"5s\",  # Time between checks\n",
    "        )\n",
    "        # setup a client to interact with the cluster as a context manager\n",
    "        with Client(cluster) as client:\n",
    "            print(client)\n",
    "            for path in subset.index:\n",
    "                future = client.submit(distribute_get_dslf_fa13, path)\n",
    "                futures[path] = future\n",
    "            for i, pending in futures.items():\n",
    "                result = pending.result()\n",
    "                results[i] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"scores_df[\\\"dslf_fa13\\\"] = pd.Series(results)\";\n",
       "                var nbb_formatted_code = \"scores_df[\\\"dslf_fa13\\\"] = pd.Series(results)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_df[\"dslf_fa13\"] = pd.Series(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dump df as json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 29;\n",
       "                var nbb_unformatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_formatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_file = os.path.join(output_path, \"scores.json\")\n",
    "scores_df.to_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reload df from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df = pd.read_json(output_file)\";\n",
       "                var nbb_formatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores.json\\\")\\nscores_df = pd.read_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_file = os.path.join(output_path, \"scores.json\")\n",
    "scores_df = pd.read_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get state Y's that have low RMSD for at least one prediction and have formed disulfides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"good_state_Y = scores_df[scores_df[\\\"lowest_rmsd_rmsd_to_input\\\"] < 1.5]\\ngood_state_Y = good_state_Y[good_state_Y[\\\"dslf_fa13\\\"] < 0]\\ngood_state_Y[\\\"lowest_rmsd_model\\\"] = \\\"model_\\\" + good_state_Y[\\\"lowest_rmsd_model\\\"].astype(str)\";\n",
       "                var nbb_formatted_code = \"good_state_Y = scores_df[scores_df[\\\"lowest_rmsd_rmsd_to_input\\\"] < 1.5]\\ngood_state_Y = good_state_Y[good_state_Y[\\\"dslf_fa13\\\"] < 0]\\ngood_state_Y[\\\"lowest_rmsd_model\\\"] = \\\"model_\\\" + good_state_Y[\\\"lowest_rmsd_model\\\"].astype(\\n    str\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "good_state_Y = scores_df[scores_df[\"lowest_rmsd_rmsd_to_input\"] < 1.5]\n",
    "good_state_Y = good_state_Y[good_state_Y[\"dslf_fa13\"] < 0]\n",
    "good_state_Y[\"lowest_rmsd_model\"] = \"model_\" + good_state_Y[\"lowest_rmsd_model\"].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a list of state X's to fold with AF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"with open(os.path.join(os.getcwd(), \\\"06_cleanup\\\", \\\"good_state_Y.list\\\"), \\\"w\\\") as f:\\n    for i in good_state_Y.index:\\n        print(i.replace(\\\"Y\\\", \\\"X\\\"), file=f)\";\n",
       "                var nbb_formatted_code = \"with open(os.path.join(os.getcwd(), \\\"06_cleanup\\\", \\\"good_state_Y.list\\\"), \\\"w\\\") as f:\\n    for i in good_state_Y.index:\\n        print(i.replace(\\\"Y\\\", \\\"X\\\"), file=f)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(os.path.join(os.getcwd(), \"06_cleanup\", \"good_state_Y.list\"), \"w\") as f:\n",
    "    for i in good_state_Y.index:\n",
    "        print(i.replace(\"Y\", \"X\"), file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"sns.set(\\n    context=\\\"talk\\\",\\n    font_scale=1.5,  # make the font larger; default is pretty small\\n    style=\\\"ticks\\\",  # make the background white with black lines\\n    palette=\\\"colorblind\\\",  # a color palette that is colorblind friendly!\\n)\\nfig = plt.figure(figsize=(20, 20), tight_layout=True)\\n_ = sns.histplot(data=good_state_Y, x=\\\"lowest_rmsd_rmsd_to_input\\\", y=\\\"dslf_fa13\\\", hue=\\\"lowest_rmsd_model\\\")\";\n",
       "                var nbb_formatted_code = \"sns.set(\\n    context=\\\"talk\\\",\\n    font_scale=1.5,  # make the font larger; default is pretty small\\n    style=\\\"ticks\\\",  # make the background white with black lines\\n    palette=\\\"colorblind\\\",  # a color palette that is colorblind friendly!\\n)\\nfig = plt.figure(figsize=(20, 20), tight_layout=True)\\n_ = sns.histplot(\\n    data=good_state_Y,\\n    x=\\\"lowest_rmsd_rmsd_to_input\\\",\\n    y=\\\"dslf_fa13\\\",\\n    hue=\\\"lowest_rmsd_model\\\",\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(\n",
    "    context=\"talk\",\n",
    "    font_scale=1.5,  # make the font larger; default is pretty small\n",
    "    style=\"ticks\",  # make the background white with black lines\n",
    "    palette=\"colorblind\",  # a color palette that is colorblind friendly!\n",
    ")\n",
    "fig = plt.figure(figsize=(20, 20), tight_layout=True)\n",
    "_ = sns.histplot(data=good_state_Y, x=\"lowest_rmsd_rmsd_to_input\", y=\"dslf_fa13\", hue=\"lowest_rmsd_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"fig = plt.figure(figsize=(20, 20), tight_layout=True)\\n_ = sns.histplot(data=good_state_Y, x=\\\"lowest_rmsd_rmsd_to_input\\\", y=\\\"lowest_rmsd_average_plddts\\\", hue=\\\"lowest_rmsd_model\\\")\";\n",
       "                var nbb_formatted_code = \"fig = plt.figure(figsize=(20, 20), tight_layout=True)\\n_ = sns.histplot(\\n    data=good_state_Y,\\n    x=\\\"lowest_rmsd_rmsd_to_input\\\",\\n    y=\\\"lowest_rmsd_average_plddts\\\",\\n    hue=\\\"lowest_rmsd_model\\\",\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 20), tight_layout=True)\n",
    "_ = sns.histplot(data=good_state_Y, x=\"lowest_rmsd_rmsd_to_input\", y=\"lowest_rmsd_average_plddts\", hue=\"lowest_rmsd_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use AF2 to predict state X's\n",
    "if those have low RMSD and form disulfides, predict state U's,  \n",
    "then check RMSD to the X and Y state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1163it [00:01, 1029.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run the following commands\n",
      "sbatch -a 1-$(cat '07_run_af2_state_X_designs.cmds' | wc -l) '07_run_af2_state_X_designs.sh'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"import os, stat, subprocess\\n\\n\\ndef create_tasks(selected):\\n    with open(selected, \\\"r\\\") as f:\\n        for file in f:\\n            tasks = {}\\n            path = file.rstrip()\\n            tasks[\\\"-s\\\"] = path\\n            yield tasks\\n\\n\\ndef file_len(file):\\n    \\\"\\\"\\\"https://stackoverflow.com/questions/845058/how-to-get-line-count-of-a-large-file-cheaply-in-python\\\"\\\"\\\"\\n    with open(file) as f:\\n        for i, l in enumerate(f):\\n            pass\\n    return i + 1\\n\\n\\n# cleanup old cmdslists\\nfor cmdslist in glob(os.path.join(os.getcwd(), \\\"07_run*state_X*.cmds\\\")):\\n    os.remove(cmdslist)\\n\\n\\ninfer = os.path.join(os.getcwd(), \\\"run_af2_design_pdb.py\\\")\\n\\njid = \\\"{SLURM_JOB_ID%;*}\\\"\\nsid = \\\"{SLURM_ARRAY_TASK_ID}p\\\"\\n\\nfor designs, mem, queue in [(\\\"state_X\\\", \\\"12\\\", \\\"short\\\")]:\\n    targets = os.path.join(os.getcwd(), f\\\"06_cleanup/good_state_Y.list\\\")\\n    for i, tasks in enumerate(tqdm(create_tasks(targets))):\\n        if os.path.isfile(tasks[\\\"-s\\\"].replace(\\\".pdb\\\", \\\".json\\\")):\\n            continue\\n        else:\\n            pass\\n        tasklist = f\\\"07_run_af2_{designs}_designs.cmds\\\"\\n        gpu = \\\"\\\"\\n        queue = queue\\n        core_mem = f\\\"#SBATCH --mem={mem}G \\\\n\\\"\\n        run_sh = \\\"\\\"\\\"#!/usr/bin/env bash \\\\n#SBATCH -J inference \\\\n#SBATCH -e /mnt/home/pleung/logs/slurm_logs/infer-%J.err \\\\n#SBATCH -o /mnt/home/pleung/logs/slurm_logs/infer-%J.out \\\\n#SBATCH -p {queue} \\\\n{gpu}{core_mem}\\\\nJOB_ID=${jid} \\\\nCMD=$(sed -n \\\"${sid}\\\" {tasklist}) \\\\necho \\\"${c}\\\" | bash\\\"\\\"\\\".format(\\n            queue=queue,\\n            gpu=gpu,\\n            core_mem=core_mem,\\n            jid=jid,\\n            sid=sid,\\n            tasklist=tasklist,\\n            c=\\\"{CMD}\\\",\\n        )\\n        shell = f\\\"07_run_af2_{designs}_designs.sh\\\"\\n        with open(shell, \\\"w+\\\") as f:\\n            print(run_sh, file=f)\\n        st = os.stat(shell)\\n        os.chmod(shell, st.st_mode | stat.S_IEXEC)\\n        with open(tasklist, \\\"a+\\\") as f:\\n            args_ = \\\" \\\".join([\\\" \\\".join([k, str(v)]) for k, v in tasks.items()])\\n            cmd = f\\\"{infer} {args_} --save_pdbs\\\"\\n            print(cmd, file=f)\\n\\n# Let's go\\nprint(\\\"Run the following commands\\\")\\nfor i in \\\"state_X\\\".split():\\n    print(\\n        f\\\"sbatch -a 1-$(cat '07_run_af2_{i}_designs.cmds' | wc -l) '07_run_af2_{i}_designs.sh'\\\"\\n    )\";\n",
       "                var nbb_formatted_code = \"import os, stat, subprocess\\n\\n\\ndef create_tasks(selected):\\n    with open(selected, \\\"r\\\") as f:\\n        for file in f:\\n            tasks = {}\\n            path = file.rstrip()\\n            tasks[\\\"-s\\\"] = path\\n            yield tasks\\n\\n\\ndef file_len(file):\\n    \\\"\\\"\\\"https://stackoverflow.com/questions/845058/how-to-get-line-count-of-a-large-file-cheaply-in-python\\\"\\\"\\\"\\n    with open(file) as f:\\n        for i, l in enumerate(f):\\n            pass\\n    return i + 1\\n\\n\\n# cleanup old cmdslists\\nfor cmdslist in glob(os.path.join(os.getcwd(), \\\"07_run*state_X*.cmds\\\")):\\n    os.remove(cmdslist)\\n\\n\\ninfer = os.path.join(os.getcwd(), \\\"run_af2_design_pdb.py\\\")\\n\\njid = \\\"{SLURM_JOB_ID%;*}\\\"\\nsid = \\\"{SLURM_ARRAY_TASK_ID}p\\\"\\n\\nfor designs, mem, queue in [(\\\"state_X\\\", \\\"12\\\", \\\"short\\\")]:\\n    targets = os.path.join(os.getcwd(), f\\\"06_cleanup/good_state_Y.list\\\")\\n    for i, tasks in enumerate(tqdm(create_tasks(targets))):\\n        if os.path.isfile(tasks[\\\"-s\\\"].replace(\\\".pdb\\\", \\\".json\\\")):\\n            continue\\n        else:\\n            pass\\n        tasklist = f\\\"07_run_af2_{designs}_designs.cmds\\\"\\n        gpu = \\\"\\\"\\n        queue = queue\\n        core_mem = f\\\"#SBATCH --mem={mem}G \\\\n\\\"\\n        run_sh = \\\"\\\"\\\"#!/usr/bin/env bash \\\\n#SBATCH -J inference \\\\n#SBATCH -e /mnt/home/pleung/logs/slurm_logs/infer-%J.err \\\\n#SBATCH -o /mnt/home/pleung/logs/slurm_logs/infer-%J.out \\\\n#SBATCH -p {queue} \\\\n{gpu}{core_mem}\\\\nJOB_ID=${jid} \\\\nCMD=$(sed -n \\\"${sid}\\\" {tasklist}) \\\\necho \\\"${c}\\\" | bash\\\"\\\"\\\".format(\\n            queue=queue,\\n            gpu=gpu,\\n            core_mem=core_mem,\\n            jid=jid,\\n            sid=sid,\\n            tasklist=tasklist,\\n            c=\\\"{CMD}\\\",\\n        )\\n        shell = f\\\"07_run_af2_{designs}_designs.sh\\\"\\n        with open(shell, \\\"w+\\\") as f:\\n            print(run_sh, file=f)\\n        st = os.stat(shell)\\n        os.chmod(shell, st.st_mode | stat.S_IEXEC)\\n        with open(tasklist, \\\"a+\\\") as f:\\n            args_ = \\\" \\\".join([\\\" \\\".join([k, str(v)]) for k, v in tasks.items()])\\n            cmd = f\\\"{infer} {args_} --save_pdbs\\\"\\n            print(cmd, file=f)\\n\\n# Let's go\\nprint(\\\"Run the following commands\\\")\\nfor i in \\\"state_X\\\".split():\\n    print(\\n        f\\\"sbatch -a 1-$(cat '07_run_af2_{i}_designs.cmds' | wc -l) '07_run_af2_{i}_designs.sh'\\\"\\n    )\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os, stat, subprocess\n",
    "\n",
    "\n",
    "def create_tasks(selected):\n",
    "    with open(selected, \"r\") as f:\n",
    "        for file in f:\n",
    "            tasks = {}\n",
    "            path = file.rstrip()\n",
    "            tasks[\"-s\"] = path\n",
    "            yield tasks\n",
    "\n",
    "\n",
    "def file_len(file):\n",
    "    \"\"\"https://stackoverflow.com/questions/845058/how-to-get-line-count-of-a-large-file-cheaply-in-python\"\"\"\n",
    "    with open(file) as f:\n",
    "        for i, l in enumerate(f):\n",
    "            pass\n",
    "    return i + 1\n",
    "\n",
    "\n",
    "# cleanup old cmdslists\n",
    "for cmdslist in glob(os.path.join(os.getcwd(), \"07_run*state_X*.cmds\")):\n",
    "    os.remove(cmdslist)\n",
    "\n",
    "\n",
    "infer = os.path.join(os.getcwd(), \"run_af2_design_pdb.py\")\n",
    "\n",
    "jid = \"{SLURM_JOB_ID%;*}\"\n",
    "sid = \"{SLURM_ARRAY_TASK_ID}p\"\n",
    "\n",
    "for designs, mem, queue in [(\"state_X\", \"12\", \"short\")]:\n",
    "    targets = os.path.join(os.getcwd(), f\"06_cleanup/good_state_Y.list\")\n",
    "    for i, tasks in enumerate(tqdm(create_tasks(targets))):\n",
    "        if os.path.isfile(tasks[\"-s\"].replace(\".pdb\", \".json\")):\n",
    "            continue\n",
    "        else:\n",
    "            pass\n",
    "        tasklist = f\"07_run_af2_{designs}_designs.cmds\"\n",
    "        gpu = \"\"\n",
    "        queue = queue\n",
    "        core_mem = f\"#SBATCH --mem={mem}G \\n\"\n",
    "        run_sh = \"\"\"#!/usr/bin/env bash \\n#SBATCH -J inference \\n#SBATCH -e /mnt/home/pleung/logs/slurm_logs/infer-%J.err \\n#SBATCH -o /mnt/home/pleung/logs/slurm_logs/infer-%J.out \\n#SBATCH -p {queue} \\n{gpu}{core_mem}\\nJOB_ID=${jid} \\nCMD=$(sed -n \"${sid}\" {tasklist}) \\necho \"${c}\" | bash\"\"\".format(\n",
    "            queue=queue,\n",
    "            gpu=gpu,\n",
    "            core_mem=core_mem,\n",
    "            jid=jid,\n",
    "            sid=sid,\n",
    "            tasklist=tasklist,\n",
    "            c=\"{CMD}\",\n",
    "        )\n",
    "        shell = f\"07_run_af2_{designs}_designs.sh\"\n",
    "        with open(shell, \"w+\") as f:\n",
    "            print(run_sh, file=f)\n",
    "        st = os.stat(shell)\n",
    "        os.chmod(shell, st.st_mode | stat.S_IEXEC)\n",
    "        with open(tasklist, \"a+\") as f:\n",
    "            args_ = \" \".join([\" \".join([k, str(v)]) for k, v in tasks.items()])\n",
    "            cmd = f\"{infer} {args_} --save_pdbs\"\n",
    "            print(cmd, file=f)\n",
    "\n",
    "# Let's go\n",
    "print(\"Run the following commands\")\n",
    "for i in \"state_X\".split():\n",
    "    print(\n",
    "        f\"sbatch -a 1-$(cat '07_run_af2_{i}_designs.cmds' | wc -l) '07_run_af2_{i}_designs.sh'\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted batch job 34585972\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"!sbatch -a 1-$(cat '07_run_af2_state_X_designs.cmds' | wc -l) '07_run_af2_state_X_designs.sh'\";\n",
       "                var nbb_formatted_code = \"!sbatch -a 1-$(cat '07_run_af2_state_X_designs.cmds' | wc -l) '07_run_af2_state_X_designs.sh'\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!sbatch -a 1-$(cat '07_run_af2_state_X_designs.cmds' | wc -l) '07_run_af2_state_X_designs.sh'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now need to retrieve the JSONs containing scores for the AF2 inference runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading 1251 runs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 87/1251 [00:12<03:15,  5.96it/s]"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import json, time\n",
    "\n",
    "\n",
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_paths = glob(os.path.join(output_path, \"*/*X*.json\"))\n",
    "print(f\"reading {len(output_paths)} runs\")\n",
    "time.sleep(0.5)\n",
    "scores = {}\n",
    "for path in tqdm(output_paths):\n",
    "    pdb = path.replace(\"json\", \"pdb\")\n",
    "    key = f\"{pdb}\"\n",
    "    with open(path, \"r\") as f:\n",
    "        try:\n",
    "            values_dict = json.load(f)\n",
    "            values = {}\n",
    "            for model, results in values_dict.items():\n",
    "                results.pop(\"pae\") # pae is too big?\n",
    "                for name, result in results.items():\n",
    "                    values[name+\"_\"+model] = result\n",
    "            del values_dict\n",
    "        except:\n",
    "            print(path)\n",
    "    scores[key] = values\n",
    "scores_df = pd.DataFrame(scores)\n",
    "scores_df = scores_df.T\n",
    "output_file = os.path.join(output_path, \"scores_X.json\")\n",
    "scores_df.to_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add additional columns incorporating metadata from best decoy by plDDT and lowest RMSD decoy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def row2best_model(\n",
    "    row: pd.Series,\n",
    ") -> str:\n",
    "    best_model = \"1\"\n",
    "    for model in \"2345\":\n",
    "        if (\n",
    "            row[f\"average_plddts_model_{model}\"]\n",
    "            > row[f\"average_plddts_model_{best_model}\"]\n",
    "        ):\n",
    "            best_model = model\n",
    "        else:\n",
    "            pass\n",
    "    return best_model\n",
    "\n",
    "\n",
    "def row2best_metadata(row: pd.Series, **kwargs) -> float:\n",
    "    best_model = row[\"best_model\"]\n",
    "    best_average_metadata = row[f\"{key}_model_{best_model}\"]\n",
    "    return best_average_metadata\n",
    "\n",
    "\n",
    "def row2lowest_rmsd_model(\n",
    "    row: pd.Series,\n",
    ") -> str:\n",
    "    lowest_rmsd_model = \"1\"\n",
    "    for model in \"2345\":\n",
    "        if (\n",
    "            row[f\"rmsd_to_input_model_{model}\"]\n",
    "            < row[f\"rmsd_to_input_model_{lowest_rmsd_model}\"]\n",
    "        ):\n",
    "            lowest_rmsd_model = model\n",
    "        else:\n",
    "            pass\n",
    "    return lowest_rmsd_model\n",
    "\n",
    "\n",
    "def row2lowest_rmsd__metadata(row: pd.Series, **kwargs) -> float:\n",
    "    lowest_rmsd_model = row[\"lowest_rmsd_model\"]\n",
    "    lowest_rmsd_metadata = row[f\"{key}_model_{lowest_rmsd_model}\"]\n",
    "    return lowest_rmsd_metadata\n",
    "\n",
    "\n",
    "def row2plddt_rmsd_agree(row: pd.Series) -> bool:\n",
    "    if row[\"best_model\"] == row[\"lowest_rmsd_model\"]:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "\n",
    "scores_df[\"best_model\"] = scores_df.apply(row2best_model, axis=1)\n",
    "scores_df[\"lowest_rmsd_model\"] = scores_df.apply(row2lowest_rmsd_model, axis=1)\n",
    "for key in tqdm(\n",
    "    \"average_plddts plddt ptm rmsd_to_input average_DAN_plddts DAN_plddt pdb_path\".split()\n",
    "):\n",
    "    scores_df[f\"best_{key}\"] = scores_df.apply(\n",
    "        row2best_metadata, kwargs={\"key\": key}, axis=1\n",
    "    )\n",
    "    scores_df[f\"lowest_rmsd_{key}\"] = scores_df.apply(\n",
    "        row2lowest_rmsd__metadata, kwargs={\"key\": key}, axis=1\n",
    "    )\n",
    "scores_df[\"plddt_rmsd_agree\"] = scores_df.apply(row2plddt_rmsd_agree, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dump df as json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores_X.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_formatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores_X.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_file = os.path.join(output_path, \"scores_X.json\")\n",
    "scores_df.to_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reload df from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores_X.json\\\")\\nscores_df = pd.read_json(output_file)\";\n",
       "                var nbb_formatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores_X.json\\\")\\nscores_df = pd.read_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_file = os.path.join(output_path, \"scores_X.json\")\n",
    "scores_df = pd.read_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribute metadata collection to cluster with dask and collect results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyRosetta-4 2021 [Rosetta PyRosetta4.conda.linux.cxx11thread.serialization.CentOS.python38.Release 2021.27+release.7ce64884a77d606b7b667c363527acc846541030 2021-07-09T18:10:05] retrieved from: http://www.pyrosetta.org\n",
      "(C) Copyright Rosetta Commons Member Institutions. Created in JHU by Sergey Lyskov and PyRosetta Team.\n",
      "core.init: {0} Checking for fconfig files in pwd and ./rosetta/flags\n",
      "core.init: {0} Rosetta version: PyRosetta4.conda.linux.cxx11thread.serialization.CentOS.python38.Release r289 2021.27+release.7ce6488 7ce64884a77d606b7b667c363527acc846541030 http://www.pyrosetta.org 2021-07-09T18:10:05\n",
      "core.init: {0} command: PyRosetta -corrections:beta_nov16 true -database /home/pleung/.conda/envs/phil/lib/python3.8/site-packages/pyrosetta/database\n",
      "basic.random.init_random_generator: {0} 'RNG device' seed mode, using '/dev/urandom', seed=-1730518657 seed_offset=0 real_seed=-1730518657 thread_index=0\n",
      "basic.random.init_random_generator: {0} RandomGenerator:init: Normal mode, seed=-1730518657 RG_type=mt19937\n",
      "run the following from your local terminal:\n",
      "ssh -L 8000:localhost:8787 pleung@dig107\n",
      "#!/usr/bin/env bash\n",
      "\n",
      "#SBATCH -J dask-worker\n",
      "#SBATCH -e /mnt/home/pleung/logs/slurm_logs/dask-worker-%J.err\n",
      "#SBATCH -o /mnt/home/pleung/logs/slurm_logs/dask-worker-%J.out\n",
      "#SBATCH -p long\n",
      "#SBATCH -n 1\n",
      "#SBATCH --cpus-per-task=1\n",
      "#SBATCH --mem=2G\n",
      "#SBATCH -t 23:30:00\n",
      "\n",
      "JOB_ID=${SLURM_JOB_ID%;*}\n",
      "\n",
      "/home/pleung/.conda/envs/phil/bin/python -m distributed.cli.dask_worker tcp://172.16.131.211:44193 --nthreads 1 --memory-limit 1.86GiB --name name --nanny --death-timeout 120 --local-directory $TMPDIR/dask --lifetime 23h --lifetime-stagger 4m\n",
      "\n",
      "<Client: 'tcp://172.16.131.211:44193' processes=0 threads=0, memory=0 B>\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"from dask.distributed import Client\\nfrom dask_jobqueue import SLURMCluster\\nfrom glob import glob\\nimport logging\\nimport pwd\\n\\n\\ndef distribute_get_dslf_fa13(pdb:str) -> float:\\n    pyrosetta.init(\\\"-corrections:beta_nov16 true\\\")\\n    sfxn = pyrosetta.get_fa_scorefxn()\\n    pose = pyrosetta.io.pose_from_file(pdb)\\n    sfxn.score(pose)\\n    dslf_fa13 = pose.scores[\\\"dslf_fa13\\\"]\\n    return dslf_fa13\\n\\n\\npyrosetta.init(\\\"-corrections:beta_nov16 true\\\")\\n\\n\\nprint(\\\"run the following from your local terminal:\\\")\\nprint(\\n    f\\\"ssh -L 8000:localhost:8787 {pwd.getpwuid(os.getuid()).pw_name}@{socket.gethostname()}\\\"\\n)\\n\\nfutures = {}\\nresults = {}\\n\\nif __name__ == \\\"__main__\\\":\\n    # configure SLURM cluster as a context manager\\n    with SLURMCluster(\\n        cores=1,\\n        processes=1,\\n        job_cpu=1,\\n        memory=\\\"2GB\\\",\\n        queue=\\\"long\\\",\\n        walltime=\\\"23:30:00\\\",\\n        death_timeout=120,\\n        local_directory=\\\"$TMPDIR/dask\\\",\\n        log_directory=\\\"/mnt/home/pleung/logs/slurm_logs\\\",\\n        extra=[\\\"--lifetime\\\", \\\"23h\\\", \\\"--lifetime-stagger\\\", \\\"4m\\\"],\\n    ) as cluster:\\n        print(cluster.job_script())\\n        # scale between 1-200 workers,\\n        cluster.adapt(\\n            minimum=1,\\n            maximum=200,\\n            wait_count=400,  # Number of consecutive times that a worker should be suggested for removal it is removed\\n            interval=\\\"5s\\\",  # Time between checks\\n        )\\n        # setup a client to interact with the cluster as a context manager\\n        with Client(cluster) as client:\\n            print(client)\\n            for path in scores_df.index:\\n                future = client.submit(distribute_get_dslf_fa13, path)\\n                futures[path] = future\\n            for i, pending in futures.items():\\n                result = pending.result()\\n                results[i] = result\";\n",
       "                var nbb_formatted_code = \"from dask.distributed import Client\\nfrom dask_jobqueue import SLURMCluster\\nfrom glob import glob\\nimport logging\\nimport pwd\\n\\n\\ndef distribute_get_dslf_fa13(pdb: str) -> float:\\n    pyrosetta.init(\\\"-corrections:beta_nov16 true\\\")\\n    sfxn = pyrosetta.get_fa_scorefxn()\\n    pose = pyrosetta.io.pose_from_file(pdb)\\n    sfxn.score(pose)\\n    dslf_fa13 = pose.scores[\\\"dslf_fa13\\\"]\\n    return dslf_fa13\\n\\n\\npyrosetta.init(\\\"-corrections:beta_nov16 true\\\")\\n\\n\\nprint(\\\"run the following from your local terminal:\\\")\\nprint(\\n    f\\\"ssh -L 8000:localhost:8787 {pwd.getpwuid(os.getuid()).pw_name}@{socket.gethostname()}\\\"\\n)\\n\\nfutures = {}\\nresults = {}\\n\\nif __name__ == \\\"__main__\\\":\\n    # configure SLURM cluster as a context manager\\n    with SLURMCluster(\\n        cores=1,\\n        processes=1,\\n        job_cpu=1,\\n        memory=\\\"2GB\\\",\\n        queue=\\\"long\\\",\\n        walltime=\\\"23:30:00\\\",\\n        death_timeout=120,\\n        local_directory=\\\"$TMPDIR/dask\\\",\\n        log_directory=\\\"/mnt/home/pleung/logs/slurm_logs\\\",\\n        extra=[\\\"--lifetime\\\", \\\"23h\\\", \\\"--lifetime-stagger\\\", \\\"4m\\\"],\\n    ) as cluster:\\n        print(cluster.job_script())\\n        # scale between 1-200 workers,\\n        cluster.adapt(\\n            minimum=1,\\n            maximum=200,\\n            wait_count=400,  # Number of consecutive times that a worker should be suggested for removal it is removed\\n            interval=\\\"5s\\\",  # Time between checks\\n        )\\n        # setup a client to interact with the cluster as a context manager\\n        with Client(cluster) as client:\\n            print(client)\\n            for path in scores_df.index:\\n                future = client.submit(distribute_get_dslf_fa13, path)\\n                futures[path] = future\\n            for i, pending in futures.items():\\n                result = pending.result()\\n                results[i] = result\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "from dask_jobqueue import SLURMCluster\n",
    "from glob import glob\n",
    "import logging\n",
    "import pwd\n",
    "\n",
    "\n",
    "def distribute_get_dslf_fa13(pdb:str) -> float:\n",
    "    pyrosetta.init(\"-corrections:beta_nov16 true\")\n",
    "    sfxn = pyrosetta.get_fa_scorefxn()\n",
    "    pose = pyrosetta.io.pose_from_file(pdb)\n",
    "    sfxn.score(pose)\n",
    "    dslf_fa13 = pose.scores[\"dslf_fa13\"]\n",
    "    return dslf_fa13\n",
    "\n",
    "\n",
    "pyrosetta.init(\"-corrections:beta_nov16 true\")\n",
    "\n",
    "\n",
    "print(\"run the following from your local terminal:\")\n",
    "print(\n",
    "    f\"ssh -L 8000:localhost:8787 {pwd.getpwuid(os.getuid()).pw_name}@{socket.gethostname()}\"\n",
    ")\n",
    "\n",
    "futures = {}\n",
    "results = {}\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # configure SLURM cluster as a context manager\n",
    "    with SLURMCluster(\n",
    "        cores=1,\n",
    "        processes=1,\n",
    "        job_cpu=1,\n",
    "        memory=\"2GB\",\n",
    "        queue=\"long\",\n",
    "        walltime=\"23:30:00\",\n",
    "        death_timeout=120,\n",
    "        local_directory=\"$TMPDIR/dask\",\n",
    "        log_directory=\"/mnt/home/pleung/logs/slurm_logs\",\n",
    "        extra=[\"--lifetime\", \"23h\", \"--lifetime-stagger\", \"4m\"],\n",
    "    ) as cluster:\n",
    "        print(cluster.job_script())\n",
    "        # scale between 1-200 workers,\n",
    "        cluster.adapt(\n",
    "            minimum=1,\n",
    "            maximum=200,\n",
    "            wait_count=400,  # Number of consecutive times that a worker should be suggested for removal it is removed\n",
    "            interval=\"5s\",  # Time between checks\n",
    "        )\n",
    "        # setup a client to interact with the cluster as a context manager\n",
    "        with Client(cluster) as client:\n",
    "            print(client)\n",
    "            for path in scores_df.index:\n",
    "                future = client.submit(distribute_get_dslf_fa13, path)\n",
    "                futures[path] = future\n",
    "            for i, pending in futures.items():\n",
    "                result = pending.result()\n",
    "                results[i] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"scores_df[\\\"dslf_fa13\\\"] = pd.Series(results)\";\n",
       "                var nbb_formatted_code = \"scores_df[\\\"dslf_fa13\\\"] = pd.Series(results)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_df[\"dslf_fa13\"] = pd.Series(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dump df as json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores_X.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_formatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores_X.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_file = os.path.join(output_path, \"scores_X.json\")\n",
    "scores_df.to_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reload df from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores_X.json\\\")\\nscores_df = pd.read_json(output_file)\";\n",
       "                var nbb_formatted_code = \"output_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_file = os.path.join(output_path, \\\"scores_X.json\\\")\\nscores_df = pd.read_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_file = os.path.join(output_path, \"scores_X.json\")\n",
    "scores_df = pd.read_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get state X's that have low RMSD for at least one prediction and have formed disulfides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"good_state_X = scores_df[scores_df[\\\"lowest_rmsd_rmsd_to_input\\\"] < 1.5]\\ngood_state_X = good_state_X[good_state_X[\\\"dslf_fa13\\\"] < 0]\\ngood_state_X[\\\"lowest_rmsd_model\\\"] = \\\"model_\\\" + good_state_X[\\\"lowest_rmsd_model\\\"].astype(str)\";\n",
       "                var nbb_formatted_code = \"good_state_X = scores_df[scores_df[\\\"lowest_rmsd_rmsd_to_input\\\"] < 1.5]\\ngood_state_X = good_state_X[good_state_X[\\\"dslf_fa13\\\"] < 0]\\ngood_state_X[\\\"lowest_rmsd_model\\\"] = \\\"model_\\\" + good_state_X[\\\"lowest_rmsd_model\\\"].astype(\\n    str\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "good_state_X = scores_df[scores_df[\"lowest_rmsd_rmsd_to_input\"] < 1.5]\n",
    "good_state_X = good_state_X[good_state_X[\"dslf_fa13\"] < 0]\n",
    "good_state_X[\"lowest_rmsd_model\"] = \"model_\" + good_state_X[\"lowest_rmsd_model\"].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a list of state U's to fold with AF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"with open(os.path.join(os.getcwd(), \\\"06_cleanup\\\", \\\"good_state_X.list\\\"), \\\"w\\\") as f:\\n    for i in good_state_X.index:\\n        print(i.replace(\\\"X\\\", \\\"X_nocys\\\"), file=f)\\n        print(i.replace(\\\"X\\\", \\\"Y_nocys\\\"), file=f)\";\n",
       "                var nbb_formatted_code = \"with open(os.path.join(os.getcwd(), \\\"06_cleanup\\\", \\\"good_state_X.list\\\"), \\\"w\\\") as f:\\n    for i in good_state_X.index:\\n        print(i.replace(\\\"X\\\", \\\"X_nocys\\\"), file=f)\\n        print(i.replace(\\\"X\\\", \\\"Y_nocys\\\"), file=f)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(os.path.join(os.getcwd(), \"06_cleanup\", \"good_state_X.list\"), \"w\") as f:\n",
    "    for i in good_state_X.index:\n",
    "        print(i.replace(\"X\", \"X_nocys\"), file=f)\n",
    "        print(i.replace(\"X\", \"Y_nocys\"), file=f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use AF2 to predict state U's\n",
    "then check RMSD to the X and Y state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, stat, subprocess\n",
    "\n",
    "\n",
    "def create_tasks(selected):\n",
    "    with open(selected, \"r\") as f:\n",
    "        for file in f:\n",
    "            tasks = {}\n",
    "            path = file.rstrip()\n",
    "            tasks[\"-s\"] = path\n",
    "            yield tasks\n",
    "\n",
    "\n",
    "def file_len(file):\n",
    "    \"\"\"https://stackoverflow.com/questions/845058/how-to-get-line-count-of-a-large-file-cheaply-in-python\"\"\"\n",
    "    with open(file) as f:\n",
    "        for i, l in enumerate(f):\n",
    "            pass\n",
    "    return i + 1\n",
    "\n",
    "\n",
    "# cleanup old cmdslists\n",
    "for cmdslist in glob(os.path.join(os.getcwd(), \"07_run*state_U*.cmds\")):\n",
    "    os.remove(cmdslist)\n",
    "\n",
    "\n",
    "infer = os.path.join(os.getcwd(), \"run_af2_design_pdb.py\")\n",
    "\n",
    "jid = \"{SLURM_JOB_ID%;*}\"\n",
    "sid = \"{SLURM_ARRAY_TASK_ID}p\"\n",
    "\n",
    "for designs, mem, queue in [(\"state_U\", \"12\", \"medium\")]:\n",
    "    targets = os.path.join(os.getcwd(), f\"06_cleanup/good_state_X.list\")\n",
    "    for i, tasks in enumerate(tqdm(create_tasks(targets))):\n",
    "        if os.path.isfile(tasks[\"-s\"].replace(\".pdb\", \".json\")):\n",
    "            continue\n",
    "        else:\n",
    "            pass\n",
    "        tasklist = f\"07_run_af2_{designs}_designs.cmds\"\n",
    "        gpu = \"\"\n",
    "        queue = queue\n",
    "        core_mem = f\"#SBATCH --mem={mem}G \\n\"\n",
    "        run_sh = \"\"\"#!/usr/bin/env bash \\n#SBATCH -J inference \\n#SBATCH -e /mnt/home/pleung/logs/slurm_logs/infer-%J.err \\n#SBATCH -o /mnt/home/pleung/logs/slurm_logs/infer-%J.out \\n#SBATCH -p {queue} \\n{gpu}{core_mem}\\nJOB_ID=${jid} \\nCMD=$(sed -n \"${sid}\" {tasklist}) \\necho \"${c}\" | bash\"\"\".format(\n",
    "            queue=queue,\n",
    "            gpu=gpu,\n",
    "            core_mem=core_mem,\n",
    "            jid=jid,\n",
    "            sid=sid,\n",
    "            tasklist=tasklist,\n",
    "            c=\"{CMD}\",\n",
    "        )\n",
    "        shell = f\"07_run_af2_{designs}_designs.sh\"\n",
    "        with open(shell, \"w+\") as f:\n",
    "            print(run_sh, file=f)\n",
    "        st = os.stat(shell)\n",
    "        os.chmod(shell, st.st_mode | stat.S_IEXEC)\n",
    "        with open(tasklist, \"a+\") as f:\n",
    "            args_ = \" \".join([\" \".join([k, str(v)]) for k, v in tasks.items()])\n",
    "            cmd = f\"{infer} {args_} --save_pdbs\"\n",
    "            print(cmd, file=f)\n",
    "\n",
    "# Let's go\n",
    "print(\"Run the following commands\")\n",
    "for i in \"state_U\".split():\n",
    "    print(\n",
    "        f\"sbatch -a 1-$(cat '07_run_af2_{i}_designs.cmds' | wc -l) '07_run_af2_{i}_designs.sh'\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted batch job 34792561\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"!sbatch -a 1-$(cat '07_run_af2_state_U_designs.cmds' | wc -l) '07_run_af2_state_U_designs.sh'\";\n",
       "                var nbb_formatted_code = \"!sbatch -a 1-$(cat '07_run_af2_state_U_designs.cmds' | wc -l) '07_run_af2_state_U_designs.sh'\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!sbatch -a 1-$(cat '07_run_af2_state_U_designs.cmds' | wc -l) '07_run_af2_state_U_designs.sh'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now need to retrieve the JSONs containing scores for the AF2 inference runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading 176 runs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 176/176 [00:15<00:00, 11.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.9 s, sys: 716 ms, total: 12.7 s\n",
      "Wall time: 18.5 s\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"%%time\\nimport json, time\\n\\n\\noutput_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_paths = glob(os.path.join(output_path, \\\"*/*nocys*.json\\\"))\\nprint(f\\\"reading {len(output_paths)} runs\\\")\\ntime.sleep(0.5)\\nscores = {}\\nfor path in tqdm(output_paths):\\n    pdb = path.replace(\\\"json\\\", \\\"pdb\\\")\\n    key = f\\\"{pdb}\\\"\\n    with open(path, \\\"r\\\") as f:\\n        try:\\n            values_dict = json.load(f)\\n            values = {}\\n            for model, results in values_dict.items():\\n                results.pop(\\\"pae\\\") # pae is too big?\\n                for name, result in results.items():\\n                    values[name+\\\"_\\\"+model] = result\\n            del values_dict\\n        except:\\n            print(path)\\n    scores[key] = values\\nscores_df = pd.DataFrame(scores)\\nscores_df = scores_df.T\\noutput_file = os.path.join(output_path, \\\"scores_U.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_formatted_code = \"%%time\\nimport json, time\\n\\n\\noutput_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\noutput_paths = glob(os.path.join(output_path, \\\"*/*nocys*.json\\\"))\\nprint(f\\\"reading {len(output_paths)} runs\\\")\\ntime.sleep(0.5)\\nscores = {}\\nfor path in tqdm(output_paths):\\n    pdb = path.replace(\\\"json\\\", \\\"pdb\\\")\\n    key = f\\\"{pdb}\\\"\\n    with open(path, \\\"r\\\") as f:\\n        try:\\n            values_dict = json.load(f)\\n            values = {}\\n            for model, results in values_dict.items():\\n                results.pop(\\\"pae\\\") # pae is too big?\\n                for name, result in results.items():\\n                    values[name+\\\"_\\\"+model] = result\\n            del values_dict\\n        except:\\n            print(path)\\n    scores[key] = values\\nscores_df = pd.DataFrame(scores)\\nscores_df = scores_df.T\\noutput_file = os.path.join(output_path, \\\"scores_U.json\\\")\\nscores_df.to_json(output_file)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "import json, time\n",
    "\n",
    "\n",
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "output_paths = glob(os.path.join(output_path, \"*/*nocys*.json\"))\n",
    "print(f\"reading {len(output_paths)} runs\")\n",
    "time.sleep(0.5)\n",
    "scores = {}\n",
    "for path in tqdm(output_paths):\n",
    "    pdb = path.replace(\"json\", \"pdb\")\n",
    "    key = f\"{pdb}\"\n",
    "    with open(path, \"r\") as f:\n",
    "        try:\n",
    "            values_dict = json.load(f)\n",
    "            values = {}\n",
    "            for model, results in values_dict.items():\n",
    "                results.pop(\"pae\") # pae is too big?\n",
    "                for name, result in results.items():\n",
    "                    values[name+\"_\"+model] = result\n",
    "            del values_dict\n",
    "        except:\n",
    "            print(path)\n",
    "    scores[key] = values\n",
    "scores_df = pd.DataFrame(scores)\n",
    "scores_df = scores_df.T\n",
    "output_file = os.path.join(output_path, \"scores_U.json\")\n",
    "scores_df.to_json(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add additional columns incorporating metadata from best decoy by plDDT and lowest RMSD decoy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:00<00:00, 125.57it/s]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"def row2best_model(\\n    row: pd.Series,\\n) -> str:\\n    best_model = \\\"1\\\"\\n    for model in \\\"2345\\\":\\n        if (\\n            row[f\\\"average_plddts_model_{model}\\\"]\\n            > row[f\\\"average_plddts_model_{best_model}\\\"]\\n        ):\\n            best_model = model\\n        else:\\n            pass\\n    return best_model\\n\\n\\ndef row2best_metadata(row: pd.Series, **kwargs) -> float:\\n    best_model = row[\\\"best_model\\\"]\\n    best_average_metadata = row[f\\\"{key}_model_{best_model}\\\"]\\n    return best_average_metadata\\n\\n\\ndef row2lowest_rmsd_model(\\n    row: pd.Series,\\n) -> str:\\n    lowest_rmsd_model = \\\"1\\\"\\n    for model in \\\"2345\\\":\\n        if (\\n            row[f\\\"rmsd_to_input_model_{model}\\\"]\\n            < row[f\\\"rmsd_to_input_model_{lowest_rmsd_model}\\\"]\\n        ):\\n            lowest_rmsd_model = model\\n        else:\\n            pass\\n    return lowest_rmsd_model\\n\\n\\ndef row2lowest_rmsd__metadata(row: pd.Series, **kwargs) -> float:\\n    lowest_rmsd_model = row[\\\"lowest_rmsd_model\\\"]\\n    lowest_rmsd_metadata = row[f\\\"{key}_model_{lowest_rmsd_model}\\\"]\\n    return lowest_rmsd_metadata\\n\\n\\ndef row2plddt_rmsd_agree(row: pd.Series) -> bool:\\n    if row[\\\"best_model\\\"] == row[\\\"lowest_rmsd_model\\\"]:\\n        return True\\n    else:\\n        return False\\n\\n\\nscores_df[\\\"best_model\\\"] = scores_df.apply(row2best_model, axis=1)\\nscores_df[\\\"lowest_rmsd_model\\\"] = scores_df.apply(row2lowest_rmsd_model, axis=1)\\nfor key in tqdm(\\n    \\\"average_plddts plddt ptm rmsd_to_input average_DAN_plddts DAN_plddt pdb_path\\\".split()\\n):\\n    scores_df[f\\\"best_{key}\\\"] = scores_df.apply(\\n        row2best_metadata, kwargs={\\\"key\\\": key}, axis=1\\n    )\\n    scores_df[f\\\"lowest_rmsd_{key}\\\"] = scores_df.apply(\\n        row2lowest_rmsd__metadata, kwargs={\\\"key\\\": key}, axis=1\\n    )\\nscores_df[\\\"plddt_rmsd_agree\\\"] = scores_df.apply(row2plddt_rmsd_agree, axis=1)\";\n",
       "                var nbb_formatted_code = \"def row2best_model(\\n    row: pd.Series,\\n) -> str:\\n    best_model = \\\"1\\\"\\n    for model in \\\"2345\\\":\\n        if (\\n            row[f\\\"average_plddts_model_{model}\\\"]\\n            > row[f\\\"average_plddts_model_{best_model}\\\"]\\n        ):\\n            best_model = model\\n        else:\\n            pass\\n    return best_model\\n\\n\\ndef row2best_metadata(row: pd.Series, **kwargs) -> float:\\n    best_model = row[\\\"best_model\\\"]\\n    best_average_metadata = row[f\\\"{key}_model_{best_model}\\\"]\\n    return best_average_metadata\\n\\n\\ndef row2lowest_rmsd_model(\\n    row: pd.Series,\\n) -> str:\\n    lowest_rmsd_model = \\\"1\\\"\\n    for model in \\\"2345\\\":\\n        if (\\n            row[f\\\"rmsd_to_input_model_{model}\\\"]\\n            < row[f\\\"rmsd_to_input_model_{lowest_rmsd_model}\\\"]\\n        ):\\n            lowest_rmsd_model = model\\n        else:\\n            pass\\n    return lowest_rmsd_model\\n\\n\\ndef row2lowest_rmsd__metadata(row: pd.Series, **kwargs) -> float:\\n    lowest_rmsd_model = row[\\\"lowest_rmsd_model\\\"]\\n    lowest_rmsd_metadata = row[f\\\"{key}_model_{lowest_rmsd_model}\\\"]\\n    return lowest_rmsd_metadata\\n\\n\\ndef row2plddt_rmsd_agree(row: pd.Series) -> bool:\\n    if row[\\\"best_model\\\"] == row[\\\"lowest_rmsd_model\\\"]:\\n        return True\\n    else:\\n        return False\\n\\n\\nscores_df[\\\"best_model\\\"] = scores_df.apply(row2best_model, axis=1)\\nscores_df[\\\"lowest_rmsd_model\\\"] = scores_df.apply(row2lowest_rmsd_model, axis=1)\\nfor key in tqdm(\\n    \\\"average_plddts plddt ptm rmsd_to_input average_DAN_plddts DAN_plddt pdb_path\\\".split()\\n):\\n    scores_df[f\\\"best_{key}\\\"] = scores_df.apply(\\n        row2best_metadata, kwargs={\\\"key\\\": key}, axis=1\\n    )\\n    scores_df[f\\\"lowest_rmsd_{key}\\\"] = scores_df.apply(\\n        row2lowest_rmsd__metadata, kwargs={\\\"key\\\": key}, axis=1\\n    )\\nscores_df[\\\"plddt_rmsd_agree\\\"] = scores_df.apply(row2plddt_rmsd_agree, axis=1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def row2best_model(\n",
    "    row: pd.Series,\n",
    ") -> str:\n",
    "    best_model = \"1\"\n",
    "    for model in \"2345\":\n",
    "        if (\n",
    "            row[f\"average_plddts_model_{model}\"]\n",
    "            > row[f\"average_plddts_model_{best_model}\"]\n",
    "        ):\n",
    "            best_model = model\n",
    "        else:\n",
    "            pass\n",
    "    return best_model\n",
    "\n",
    "\n",
    "def row2best_metadata(row: pd.Series, **kwargs) -> float:\n",
    "    best_model = row[\"best_model\"]\n",
    "    best_average_metadata = row[f\"{key}_model_{best_model}\"]\n",
    "    return best_average_metadata\n",
    "\n",
    "\n",
    "def row2lowest_rmsd_model(\n",
    "    row: pd.Series,\n",
    ") -> str:\n",
    "    lowest_rmsd_model = \"1\"\n",
    "    for model in \"2345\":\n",
    "        if (\n",
    "            row[f\"rmsd_to_input_model_{model}\"]\n",
    "            < row[f\"rmsd_to_input_model_{lowest_rmsd_model}\"]\n",
    "        ):\n",
    "            lowest_rmsd_model = model\n",
    "        else:\n",
    "            pass\n",
    "    return lowest_rmsd_model\n",
    "\n",
    "\n",
    "def row2lowest_rmsd__metadata(row: pd.Series, **kwargs) -> float:\n",
    "    lowest_rmsd_model = row[\"lowest_rmsd_model\"]\n",
    "    lowest_rmsd_metadata = row[f\"{key}_model_{lowest_rmsd_model}\"]\n",
    "    return lowest_rmsd_metadata\n",
    "\n",
    "\n",
    "def row2plddt_rmsd_agree(row: pd.Series) -> bool:\n",
    "    if row[\"best_model\"] == row[\"lowest_rmsd_model\"]:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "\n",
    "scores_df[\"best_model\"] = scores_df.apply(row2best_model, axis=1)\n",
    "scores_df[\"lowest_rmsd_model\"] = scores_df.apply(row2lowest_rmsd_model, axis=1)\n",
    "for key in tqdm(\n",
    "    \"average_plddts plddt ptm rmsd_to_input average_DAN_plddts DAN_plddt pdb_path\".split()\n",
    "):\n",
    "    scores_df[f\"best_{key}\"] = scores_df.apply(\n",
    "        row2best_metadata, kwargs={\"key\": key}, axis=1\n",
    "    )\n",
    "    scores_df[f\"lowest_rmsd_{key}\"] = scores_df.apply(\n",
    "        row2lowest_rmsd__metadata, kwargs={\"key\": key}, axis=1\n",
    "    )\n",
    "scores_df[\"plddt_rmsd_agree\"] = scores_df.apply(row2plddt_rmsd_agree, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate dfs to combine base scores with AF2 results for the state U's that pass < 1.5 RMSD to either X or Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"best_u = scores_df[scores_df[\\\"best_rmsd_to_input\\\"] < 1.5]\\n# make index slices of the Y and X dfs from U, \\nbest_y_idx = [x.replace(\\\"X_nocys\\\", \\\"Y\\\", 1).replace(\\\"_nocys\\\", \\\"\\\") for x in best_u.index]\\nbest_x_idx = [x.replace(\\\"Y\\\", \\\"X\\\", 1) for x in best_y_idx]\\noutput_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\nbest_y = pd.read_json(os.path.join(output_path, \\\"scores.json\\\")).loc[best_y_idx]\\nbest_x = pd.read_json(os.path.join(output_path, \\\"scores_X.json\\\")).loc[best_x_idx]\\n# add X, Y and U suffix to df columns for X, Y, and U dfs repectively. \\nto_concatenate = {\\\"X\\\": best_x, \\\"Y\\\": best_y, \\\"U\\\": best_u}\\nprocessed = {}\\nfor suffix, df in to_concatenate.items():\\n    df = df.add_suffix(f\\\"_{suffix}\\\")\\n    # get rid of _model_* columns, we only care about best_* and lowest_rmsd_*\\n    columns_to_keep = [col for col in df.columns if (\\\"best_\\\" in col or \\\"lowest_rmsd_\\\" in col)]\\n    df = df[columns_to_keep]\\n    processed[suffix] = df\\nscores_df = read_scorefile(\\\"06_cleanup/scores.json\\\")\\n# map to infer an index slice from the base scores df \\nY_path2idx = dict(zip(list(scores_df.Y_path.values), list(scores_df.index)))\\nbase_df_indices = []\\nfor Y_path in processed[\\\"Y\\\"].index:\\n    index = Y_path2idx[Y_path]\\n    base_df_indices.append(index)\\n# slice out the final filtered results using the inferred slice map\\nfinal_scores_df = scores_df.loc[base_df_indices]\";\n",
       "                var nbb_formatted_code = \"best_u = scores_df[scores_df[\\\"best_rmsd_to_input\\\"] < 1.5]\\n# make index slices of the Y and X dfs from U,\\nbest_y_idx = [x.replace(\\\"X_nocys\\\", \\\"Y\\\", 1).replace(\\\"_nocys\\\", \\\"\\\") for x in best_u.index]\\nbest_x_idx = [x.replace(\\\"Y\\\", \\\"X\\\", 1) for x in best_y_idx]\\noutput_path = os.path.join(os.getcwd(), \\\"06_states\\\")\\nbest_y = pd.read_json(os.path.join(output_path, \\\"scores.json\\\")).loc[best_y_idx]\\nbest_x = pd.read_json(os.path.join(output_path, \\\"scores_X.json\\\")).loc[best_x_idx]\\n# add X, Y and U suffix to df columns for X, Y, and U dfs repectively.\\nto_concatenate = {\\\"X\\\": best_x, \\\"Y\\\": best_y, \\\"U\\\": best_u}\\nprocessed = {}\\nfor suffix, df in to_concatenate.items():\\n    df = df.add_suffix(f\\\"_{suffix}\\\")\\n    # get rid of _model_* columns, we only care about best_* and lowest_rmsd_*\\n    columns_to_keep = [\\n        col for col in df.columns if (\\\"best_\\\" in col or \\\"lowest_rmsd_\\\" in col)\\n    ]\\n    df = df[columns_to_keep]\\n    processed[suffix] = df\\nscores_df = read_scorefile(\\\"06_cleanup/scores.json\\\")\\n# map to infer an index slice from the base scores df\\nY_path2idx = dict(zip(list(scores_df.Y_path.values), list(scores_df.index)))\\nbase_df_indices = []\\nfor Y_path in processed[\\\"Y\\\"].index:\\n    index = Y_path2idx[Y_path]\\n    base_df_indices.append(index)\\n# slice out the final filtered results using the inferred slice map\\nfinal_scores_df = scores_df.loc[base_df_indices]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_u = scores_df[scores_df[\"best_rmsd_to_input\"] < 1.5]\n",
    "# make index slices of the Y and X dfs from U, \n",
    "best_y_idx = [x.replace(\"X_nocys\", \"Y\", 1).replace(\"_nocys\", \"\") for x in best_u.index]\n",
    "best_x_idx = [x.replace(\"Y\", \"X\", 1) for x in best_y_idx]\n",
    "output_path = os.path.join(os.getcwd(), \"06_states\")\n",
    "best_y = pd.read_json(os.path.join(output_path, \"scores.json\")).loc[best_y_idx]\n",
    "best_x = pd.read_json(os.path.join(output_path, \"scores_X.json\")).loc[best_x_idx]\n",
    "# add X, Y and U suffix to df columns for X, Y, and U dfs repectively. \n",
    "to_concatenate = {\"X\": best_x, \"Y\": best_y, \"U\": best_u}\n",
    "processed = {}\n",
    "for suffix, df in to_concatenate.items():\n",
    "    df = df.add_suffix(f\"_{suffix}\")\n",
    "    # get rid of _model_* columns, we only care about best_* and lowest_rmsd_*\n",
    "    columns_to_keep = [col for col in df.columns if (\"best_\" in col or \"lowest_rmsd_\" in col)]\n",
    "    df = df[columns_to_keep]\n",
    "    processed[suffix] = df\n",
    "scores_df = read_scorefile(\"06_cleanup/scores.json\")\n",
    "# map to infer an index slice from the base scores df \n",
    "Y_path2idx = dict(zip(list(scores_df.Y_path.values), list(scores_df.index)))\n",
    "base_df_indices = []\n",
    "for Y_path in processed[\"Y\"].index:\n",
    "    index = Y_path2idx[Y_path]\n",
    "    base_df_indices.append(index)\n",
    "# slice out the final filtered results using the inferred slice map\n",
    "final_scores_df = scores_df.loc[base_df_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"def row2base_df_idx(row: pd.Series) -> str:\\n    name = row.name\\n    name_prefix = name.split(\\\"_X\\\")[0].split(\\\"_Y\\\")[0]\\n    # get original index from mapping made in prev cell\\n    base_df_idx = index = Y_path2idx[name_prefix+\\\"_Y.pdb\\\"]\\n    return base_df_idx\\n\\n# reindex best X, Y, U\\nreindexed = [final_scores_df]\\n\\nfor _, df in processed.items():\\n    df[\\\"old_index\\\"] = df.apply(row2base_df_idx, axis=1)\\n    df.set_index(\\\"old_index\\\", inplace=True)\\n    reindexed.append(df)\\n\\n# concat the dfs\\nfinal_scores_df = pd.concat(reindexed, axis=1)\\n# save as final scores df then figure out subset of states/scaffolds to order after manual selection?\";\n",
       "                var nbb_formatted_code = \"def row2base_df_idx(row: pd.Series) -> str:\\n    name = row.name\\n    name_prefix = name.split(\\\"_X\\\")[0].split(\\\"_Y\\\")[0]\\n    # get original index from mapping made in prev cell\\n    base_df_idx = index = Y_path2idx[name_prefix + \\\"_Y.pdb\\\"]\\n    return base_df_idx\\n\\n\\n# reindex best X, Y, U\\nreindexed = [final_scores_df]\\n\\nfor _, df in processed.items():\\n    df[\\\"old_index\\\"] = df.apply(row2base_df_idx, axis=1)\\n    df.set_index(\\\"old_index\\\", inplace=True)\\n    reindexed.append(df)\\n\\n# concat the dfs\\nfinal_scores_df = pd.concat(reindexed, axis=1)\\n# save as final scores df then figure out subset of states/scaffolds to order after manual selection?\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def row2base_df_idx(row: pd.Series) -> str:\n",
    "    name = row.name\n",
    "    name_prefix = name.split(\"_X\")[0].split(\"_Y\")[0]\n",
    "    # get original index from mapping made in prev cell\n",
    "    base_df_idx = index = Y_path2idx[name_prefix+\"_Y.pdb\"]\n",
    "    return base_df_idx\n",
    "\n",
    "# reindex best X, Y, U\n",
    "reindexed = [final_scores_df]\n",
    "\n",
    "for _, df in processed.items():\n",
    "    df[\"old_index\"] = df.apply(row2base_df_idx, axis=1)\n",
    "    df.set_index(\"old_index\", inplace=True)\n",
    "    reindexed.append(df)\n",
    "\n",
    "# concat the dfs\n",
    "final_scores_df = pd.concat(reindexed, axis=1)\n",
    "# save as final scores df then figure out subset of states/scaffolds to order after manual selection?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add state info and rmsd between state X and state Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyRosetta-4 2021 [Rosetta PyRosetta4.conda.linux.cxx11thread.serialization.CentOS.python38.Release 2021.27+release.7ce64884a77d606b7b667c363527acc846541030 2021-07-09T18:10:05] retrieved from: http://www.pyrosetta.org\n",
      "(C) Copyright Rosetta Commons Member Institutions. Created in JHU by Sergey Lyskov and PyRosetta Team.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40/40 [00:00<00:00, 6195.20it/s]\n",
      "100%|██████████| 40/40 [01:26<00:00,  2.17s/it]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"from pyrosetta.rosetta.core.pose import Pose\\nimport time\\nfrom tqdm import tqdm\\n\\npyrosetta.init(\\\"-mute all\\\")\\ntqdm.pandas()\\n\\ndef row2state(row: pd.Series) -> str:\\n    state = (\\n        row[\\\"parent\\\"]\\n        + \\\"_p_\\\"\\n        + str(int(row[\\\"pivot_helix\\\"]))\\n        + \\\"_s_\\\"\\n        + str(int(row[\\\"shift\\\"]))\\n    )\\n    return state\\n\\ndef row2rmsd_xy(row: pd.Series) -> float:\\n    \\n    def get_rmsd(design: Pose, prediction: Pose) -> float:\\n        \\\"\\\"\\\"Calculate Ca-RMSD of prediction to design\\\"\\\"\\\"\\n        rmsd_calc = pyrosetta.rosetta.core.simple_metrics.metrics.RMSDMetric()\\n        rmsd_calc.set_rmsd_type(pyrosetta.rosetta.core.scoring.rmsd_atoms(3))\\n        rmsd_calc.set_run_superimpose(True)\\n        rmsd_calc.set_comparison_pose(design)\\n        rmsd = float(rmsd_calc.calculate(prediction))\\n        return rmsd\\n    \\n    x_path = row[\\\"X_path\\\"]\\n    y_path = row[\\\"Y_path\\\"]\\n    x, y = pyrosetta.io.pose_from_file(x_path), pyrosetta.io.pose_from_file(y_path)\\n    rmsd_xy = get_rmsd(x, y)\\n    return rmsd_xy\\n    \\n    \\ntime.sleep(0.5)\\nfinal_scores_df[\\\"state\\\"] = final_scores_df.progress_apply(row2state, axis=1)\\nfinal_scores_df[\\\"rmsd_xy\\\"] = final_scores_df.progress_apply(row2rmsd_xy, axis=1)\";\n",
       "                var nbb_formatted_code = \"from pyrosetta.rosetta.core.pose import Pose\\nimport time\\nfrom tqdm import tqdm\\n\\npyrosetta.init(\\\"-mute all\\\")\\ntqdm.pandas()\\n\\n\\ndef row2state(row: pd.Series) -> str:\\n    state = (\\n        row[\\\"parent\\\"]\\n        + \\\"_p_\\\"\\n        + str(int(row[\\\"pivot_helix\\\"]))\\n        + \\\"_s_\\\"\\n        + str(int(row[\\\"shift\\\"]))\\n    )\\n    return state\\n\\n\\ndef row2rmsd_xy(row: pd.Series) -> float:\\n    def get_rmsd(design: Pose, prediction: Pose) -> float:\\n        \\\"\\\"\\\"Calculate Ca-RMSD of prediction to design\\\"\\\"\\\"\\n        rmsd_calc = pyrosetta.rosetta.core.simple_metrics.metrics.RMSDMetric()\\n        rmsd_calc.set_rmsd_type(pyrosetta.rosetta.core.scoring.rmsd_atoms(3))\\n        rmsd_calc.set_run_superimpose(True)\\n        rmsd_calc.set_comparison_pose(design)\\n        rmsd = float(rmsd_calc.calculate(prediction))\\n        return rmsd\\n\\n    x_path = row[\\\"X_path\\\"]\\n    y_path = row[\\\"Y_path\\\"]\\n    x, y = pyrosetta.io.pose_from_file(x_path), pyrosetta.io.pose_from_file(y_path)\\n    rmsd_xy = get_rmsd(x, y)\\n    return rmsd_xy\\n\\n\\ntime.sleep(0.5)\\nfinal_scores_df[\\\"state\\\"] = final_scores_df.progress_apply(row2state, axis=1)\\nfinal_scores_df[\\\"rmsd_xy\\\"] = final_scores_df.progress_apply(row2rmsd_xy, axis=1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyrosetta.rosetta.core.pose import Pose\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "\n",
    "pyrosetta.init(\"-mute all\")\n",
    "tqdm.pandas()\n",
    "\n",
    "def row2state(row: pd.Series) -> str:\n",
    "    state = (\n",
    "        row[\"parent\"]\n",
    "        + \"_p_\"\n",
    "        + str(int(row[\"pivot_helix\"]))\n",
    "        + \"_s_\"\n",
    "        + str(int(row[\"shift\"]))\n",
    "    )\n",
    "    return state\n",
    "\n",
    "def row2rmsd_xy(row: pd.Series) -> float:\n",
    "    \n",
    "    def get_rmsd(design: Pose, prediction: Pose) -> float:\n",
    "        \"\"\"Calculate Ca-RMSD of prediction to design\"\"\"\n",
    "        rmsd_calc = pyrosetta.rosetta.core.simple_metrics.metrics.RMSDMetric()\n",
    "        rmsd_calc.set_rmsd_type(pyrosetta.rosetta.core.scoring.rmsd_atoms(3))\n",
    "        rmsd_calc.set_run_superimpose(True)\n",
    "        rmsd_calc.set_comparison_pose(design)\n",
    "        rmsd = float(rmsd_calc.calculate(prediction))\n",
    "        return rmsd\n",
    "    \n",
    "    x_path = row[\"X_path\"]\n",
    "    y_path = row[\"Y_path\"]\n",
    "    x, y = pyrosetta.io.pose_from_file(x_path), pyrosetta.io.pose_from_file(y_path)\n",
    "    rmsd_xy = get_rmsd(x, y)\n",
    "    return rmsd_xy\n",
    "    \n",
    "    \n",
    "time.sleep(0.5)\n",
    "final_scores_df[\"state\"] = final_scores_df.progress_apply(row2state, axis=1)\n",
    "final_scores_df[\"rmsd_xy\"] = final_scores_df.progress_apply(row2rmsd_xy, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next time should probably remove state pairs that have less than 3 RMSD between states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "parent\n",
       "DHR07_nocys    2.942777\n",
       "DHR10          9.440462\n",
       "DHR21          3.089594\n",
       "DHR26_nocys    3.826754\n",
       "DHR54          7.365664\n",
       "hDHR10_5CWG    3.453502\n",
       "hDHR9_HFuse    2.414947\n",
       "Name: rmsd_xy, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"final_scores_df.groupby(\\\"parent\\\")[\\\"rmsd_xy\\\"].mean()\";\n",
       "                var nbb_formatted_code = \"final_scores_df.groupby(\\\"parent\\\")[\\\"rmsd_xy\\\"].mean()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_scores_df.groupby(\"parent\")[\"rmsd_xy\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"output_path = os.path.join(os.getcwd(), \\\"07_to_order\\\")\\nos.makedirs(output_path, exist_ok=True)\";\n",
       "                var nbb_formatted_code = \"output_path = os.path.join(os.getcwd(), \\\"07_to_order\\\")\\nos.makedirs(output_path, exist_ok=True)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_path = os.path.join(os.getcwd(), \"07_to_order\")\n",
    "os.makedirs(output_path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mv: cannot stat '*.cmds': No such file or directory\n",
      "mv: cannot stat '*.sh': No such file or directory\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"!mv *.cmds *.sh 07_to_order\";\n",
       "                var nbb_formatted_code = \"!mv *.cmds *.sh 07_to_order\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!mv *.cmds *.sh 07_to_order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove state pairs that have less than 3 RMSD between states then make pymol commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"pymol_df = final_scores_df[final_scores_df[\\\"rmsd_xy\\\"] > 3.0]\\nprint(len(pymol_df))\\n# sort by state\\nsorted_df = pymol_df.sort_values(\\\"state\\\")\\n\\n\\nwith open(os.path.join(output_path, \\\"pymol.cmds\\\"), \\\"w\\\") as f:\\n    for i in sorted_df.index:\\n        print(\\\"#\\\"*80, file=f)\\n        print(f\\\"# {sorted_df.loc[i]['state']} : \\\", file=f)\\n        print(\\\"# \\\" + i, file=f)\\n        for key in [\\\"X_path\\\", \\\"Y_path\\\", \\\"best_pdb_path_X\\\", \\\"lowest_rmsd_pdb_path_X\\\", \\\"best_pdb_path_Y\\\", \\\"lowest_rmsd_pdb_path_Y\\\", \\\"best_pdb_path_U\\\", \\\"lowest_rmsd_pdb_path_U\\\"]:\\n            print(f\\\"load {sorted_df.loc[i][key]}, {key.replace('path', '', 1)}\\\", file=f)\\n        print(\\\"super_all('X_')\\\\n@~/.pymolrc\\\", file=f)\\n        print(\\\"sele resn trp\\\", file=f)\\n        print(\\\"color hotpink, sele and sc.\\\", file=f)\\n        print(\\\"color atomic, sele and (not elem C)\\\", file=f)\\n        print(\\\"show sticks, sele and sc. ; hide sticks, hydro and (elem C extend 1)\\\", file=f)\\n        print(\\\"sele resn cys\\\", file=f)\\n        print(\\\"color tv_yellow, sele and sc.\\\", file=f)\\n        print(\\\"color atomic, sele and (not elem C)\\\", file=f)\\n        print(\\\"show sticks, sele and sc. ; hide sticks, hydro and (elem C extend 1)\\\", file=f)\\n        print(\\\"make_sc_hb\\\", file=f)\\n        print(\\\"cmd.set('surface_color','white'),cmd.set('surface_cavity_mode',1,'',0), cmd.show('surface','all'), cmd.set('transparency',0.5,'',0)\\\", file=f)\\n        print(\\\"\\\"\\\"cmd.show('lines',\\\"((byres (all))&(sc.|(n. CA|n. N&r. PRO)))\\\"), cmd.hide('lines',\\\"(hydro and (elem C extend 1))\\\"), cmd.color('atomic','(not elem C)')\\\"\\\"\\\", file=f)\";\n",
       "                var nbb_formatted_code = \"pymol_df = final_scores_df[final_scores_df[\\\"rmsd_xy\\\"] > 3.0]\\nprint(len(pymol_df))\\n# sort by state\\nsorted_df = pymol_df.sort_values(\\\"state\\\")\\n\\n\\nwith open(os.path.join(output_path, \\\"pymol.cmds\\\"), \\\"w\\\") as f:\\n    for i in sorted_df.index:\\n        print(\\\"#\\\" * 80, file=f)\\n        print(f\\\"# {sorted_df.loc[i]['state']} : \\\", file=f)\\n        print(\\\"# \\\" + i, file=f)\\n        for key in [\\n            \\\"X_path\\\",\\n            \\\"Y_path\\\",\\n            \\\"best_pdb_path_X\\\",\\n            \\\"lowest_rmsd_pdb_path_X\\\",\\n            \\\"best_pdb_path_Y\\\",\\n            \\\"lowest_rmsd_pdb_path_Y\\\",\\n            \\\"best_pdb_path_U\\\",\\n            \\\"lowest_rmsd_pdb_path_U\\\",\\n        ]:\\n            print(f\\\"load {sorted_df.loc[i][key]}, {key.replace('path', '', 1)}\\\", file=f)\\n        print(\\\"super_all('X_')\\\\n@~/.pymolrc\\\", file=f)\\n        print(\\\"sele resn trp\\\", file=f)\\n        print(\\\"color hotpink, sele and sc.\\\", file=f)\\n        print(\\\"color atomic, sele and (not elem C)\\\", file=f)\\n        print(\\n            \\\"show sticks, sele and sc. ; hide sticks, hydro and (elem C extend 1)\\\",\\n            file=f,\\n        )\\n        print(\\\"sele resn cys\\\", file=f)\\n        print(\\\"color tv_yellow, sele and sc.\\\", file=f)\\n        print(\\\"color atomic, sele and (not elem C)\\\", file=f)\\n        print(\\n            \\\"show sticks, sele and sc. ; hide sticks, hydro and (elem C extend 1)\\\",\\n            file=f,\\n        )\\n        print(\\\"make_sc_hb\\\", file=f)\\n        print(\\n            \\\"cmd.set('surface_color','white'),cmd.set('surface_cavity_mode',1,'',0), cmd.show('surface','all'), cmd.set('transparency',0.5,'',0)\\\",\\n            file=f,\\n        )\\n        print(\\n            \\\"\\\"\\\"cmd.show('lines',\\\"((byres (all))&(sc.|(n. CA|n. N&r. PRO)))\\\"), cmd.hide('lines',\\\"(hydro and (elem C extend 1))\\\"), cmd.color('atomic','(not elem C)')\\\"\\\"\\\",\\n            file=f,\\n        )\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pymol_df = final_scores_df[final_scores_df[\"rmsd_xy\"] > 3.0]\n",
    "print(len(pymol_df))\n",
    "# sort by state\n",
    "sorted_df = pymol_df.sort_values(\"state\")\n",
    "\n",
    "\n",
    "with open(os.path.join(output_path, \"pymol.cmds\"), \"w\") as f:\n",
    "    for i in sorted_df.index:\n",
    "        print(\"#\"*80, file=f)\n",
    "        print(f\"# {sorted_df.loc[i]['state']} : \", file=f)\n",
    "        print(\"# \" + i, file=f)\n",
    "        for key in [\"X_path\", \"Y_path\", \"best_pdb_path_X\", \"lowest_rmsd_pdb_path_X\", \"best_pdb_path_Y\", \"lowest_rmsd_pdb_path_Y\", \"best_pdb_path_U\", \"lowest_rmsd_pdb_path_U\"]:\n",
    "            print(f\"load {sorted_df.loc[i][key]}, {key.replace('path', '', 1)}\", file=f)\n",
    "        print(\"super_all('X_')\\n@~/.pymolrc\", file=f)\n",
    "        print(\"sele resn trp\", file=f)\n",
    "        print(\"color hotpink, sele and sc.\", file=f)\n",
    "        print(\"color atomic, sele and (not elem C)\", file=f)\n",
    "        print(\"show sticks, sele and sc. ; hide sticks, hydro and (elem C extend 1)\", file=f)\n",
    "        print(\"sele resn cys\", file=f)\n",
    "        print(\"color tv_yellow, sele and sc.\", file=f)\n",
    "        print(\"color atomic, sele and (not elem C)\", file=f)\n",
    "        print(\"show sticks, sele and sc. ; hide sticks, hydro and (elem C extend 1)\", file=f)\n",
    "        print(\"make_sc_hb\", file=f)\n",
    "        print(\"cmd.set('surface_color','white'),cmd.set('surface_cavity_mode',1,'',0), cmd.show('surface','all'), cmd.set('transparency',0.5,'',0)\", file=f)\n",
    "        print(\"\"\"cmd.show('lines',\"((byres (all))&(sc.|(n. CA|n. N&r. PRO)))\"), cmd.hide('lines',\"(hydro and (elem C extend 1))\"), cmd.color('atomic','(not elem C)')\"\"\", file=f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the ones that looked good in pymol and save final metadata\n",
    "zero pad names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"picks = [\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0010/2021.06.26.14.24.46.542335_1629c8aab5af4ad78f14a1bd48028545.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0008/2021.06.26.14.24.46.542335_4bb5b4f2ddae4f64b398fbe12dd46c61.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0000/2021.06.26.14.24.46.542335_fdecc1006bdb4e64a1ddb33edf5e48c8.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0011/2021.06.26.14.24.46.542335_8d6070b513a9449c81721a655fd76122.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0009/2021.06.26.14.24.46.542335_36d7f530f46844a185d83041dda397bc.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0002/2021.06.26.14.24.46.542335_083091f886264c118d93d738d34dfb34.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0006/2021.06.26.14.24.46.542335_2979bf092e29423d921058ebd04c7f4d.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0009/2021.06.26.14.24.46.542335_d64048355cb74945b295d9ed6f6a3776.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0008/2021.06.26.14.24.46.542335_74dd060543fd497e91789af4bf1eeb11.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0000/2021.06.26.14.24.46.542335_c3bc332de1e740dc84b21e8c999b9499.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0006/2021.06.26.14.24.46.542335_8637eeba59094d54af0d460ba03c3427.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0005/2021.06.26.14.24.46.542335_fdece923bd264840bc4c315b101451f4.pdb.bz2\\\",\\n]\\nto_order_df = final_scores_df.loc[picks]\\nto_order_df.index.name = \\\"abspath\\\"\\nnames = [f\\\"sc_{(str(i)).zfill(3)}\\\" for i in range(1, 13)]\\nto_order_df[\\\"ordered_name\\\"] = names\\nto_order_df.reset_index(inplace=True)\\nto_order_df.set_index(\\\"ordered_name\\\", inplace=True)\\nto_order_df.to_json(os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"final_scores.json\\\"))\";\n",
       "                var nbb_formatted_code = \"picks = [\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0010/2021.06.26.14.24.46.542335_1629c8aab5af4ad78f14a1bd48028545.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0008/2021.06.26.14.24.46.542335_4bb5b4f2ddae4f64b398fbe12dd46c61.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0000/2021.06.26.14.24.46.542335_fdecc1006bdb4e64a1ddb33edf5e48c8.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0011/2021.06.26.14.24.46.542335_8d6070b513a9449c81721a655fd76122.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0009/2021.06.26.14.24.46.542335_36d7f530f46844a185d83041dda397bc.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0002/2021.06.26.14.24.46.542335_083091f886264c118d93d738d34dfb34.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0006/2021.06.26.14.24.46.542335_2979bf092e29423d921058ebd04c7f4d.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0009/2021.06.26.14.24.46.542335_d64048355cb74945b295d9ed6f6a3776.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0008/2021.06.26.14.24.46.542335_74dd060543fd497e91789af4bf1eeb11.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0000/2021.06.26.14.24.46.542335_c3bc332de1e740dc84b21e8c999b9499.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0006/2021.06.26.14.24.46.542335_8637eeba59094d54af0d460ba03c3427.pdb.bz2\\\",\\n    \\\"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0005/2021.06.26.14.24.46.542335_fdece923bd264840bc4c315b101451f4.pdb.bz2\\\",\\n]\\nto_order_df = final_scores_df.loc[picks]\\nto_order_df.index.name = \\\"abspath\\\"\\nnames = [f\\\"sc_{(str(i)).zfill(3)}\\\" for i in range(1, 13)]\\nto_order_df[\\\"ordered_name\\\"] = names\\nto_order_df.reset_index(inplace=True)\\nto_order_df.set_index(\\\"ordered_name\\\", inplace=True)\\nto_order_df.to_json(os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"final_scores.json\\\"))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "picks = [\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0010/2021.06.26.14.24.46.542335_1629c8aab5af4ad78f14a1bd48028545.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0008/2021.06.26.14.24.46.542335_4bb5b4f2ddae4f64b398fbe12dd46c61.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0000/2021.06.26.14.24.46.542335_fdecc1006bdb4e64a1ddb33edf5e48c8.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0011/2021.06.26.14.24.46.542335_8d6070b513a9449c81721a655fd76122.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0009/2021.06.26.14.24.46.542335_36d7f530f46844a185d83041dda397bc.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0002/2021.06.26.14.24.46.542335_083091f886264c118d93d738d34dfb34.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0006/2021.06.26.14.24.46.542335_2979bf092e29423d921058ebd04c7f4d.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0009/2021.06.26.14.24.46.542335_d64048355cb74945b295d9ed6f6a3776.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0008/2021.06.26.14.24.46.542335_74dd060543fd497e91789af4bf1eeb11.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0000/2021.06.26.14.24.46.542335_c3bc332de1e740dc84b21e8c999b9499.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0006/2021.06.26.14.24.46.542335_8637eeba59094d54af0d460ba03c3427.pdb.bz2\",\n",
    "    \"/mnt/home/pleung/projects/bistable_bundle/r4/hinges/06_cleanup/decoys/0005/2021.06.26.14.24.46.542335_fdece923bd264840bc4c315b101451f4.pdb.bz2\",\n",
    "]\n",
    "to_order_df = final_scores_df.loc[picks]\n",
    "to_order_df.index.name = \"abspath\"\n",
    "names = [f\"sc_{(str(i)).zfill(3)}\" for i in range(1, 13)]\n",
    "to_order_df[\"ordered_name\"] = names\n",
    "to_order_df.reset_index(inplace=True)\n",
    "to_order_df.set_index(\"ordered_name\", inplace=True)\n",
    "to_order_df.to_json(os.path.join(os.getcwd(), \"07_to_order\", \"final_scores.json\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reload df for reverse translating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"to_order_df = pd.read_json(\\n    os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"final_scores.json\\\"), dtype=\\\"float\\\"\\n)\";\n",
       "                var nbb_formatted_code = \"to_order_df = pd.read_json(\\n    os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"final_scores.json\\\"), dtype=\\\"float\\\"\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_order_df = pd.read_json(\n",
    "    os.path.join(os.getcwd(), \"07_to_order\", \"final_scores.json\"), dtype=\"float\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make functions to domesticate sequences uses Ryan's `domesticator`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"def cmd_no_stderr(command, wait=True):\\n    \\\"\\\"\\\"@nrbennet @bcov @pleung\\\"\\\"\\\"\\n    the_command = subprocess.Popen(\\n        command,\\n        shell=True,\\n        stdout=subprocess.PIPE,\\n        stderr=subprocess.PIPE,\\n        universal_newlines=True,\\n    )\\n    if not wait:\\n        return\\n    the_stuff = the_command.communicate()\\n    return str(the_stuff[0])\\n\\n\\ndef capture_1shot_domesticator(stdout: str) -> str:\\n    \\\"\\\"\\\"split input into lines.\\n    loop once through discarding lines up to ones including >.\\n    return joined output\\\"\\\"\\\"\\n    sequence = []\\n    append = False\\n    for line in stdout.splitlines():\\n        if append:\\n            sequence.append(line)\\n        else:\\n            pass\\n        if \\\">unknown_seq1\\\" in line:\\n            append = True\\n        else:\\n            pass\\n    to_return = \\\"\\\".join(sequence)\\n    return to_return\\n\\n\\ndef generate_potential_kmers(seq: str) -> list:\\n    from itertools import combinations\\n\\n    # len k is 8\\n    k = 8\\n    kmers = [\\n        seq[x:y]\\n        for x, y in combinations(range(len(seq) + 1), r=2)\\n        if len(seq[x:y]) == k\\n    ]\\n\\n    return kmers\";\n",
       "                var nbb_formatted_code = \"def cmd_no_stderr(command, wait=True):\\n    \\\"\\\"\\\"@nrbennet @bcov @pleung\\\"\\\"\\\"\\n    the_command = subprocess.Popen(\\n        command,\\n        shell=True,\\n        stdout=subprocess.PIPE,\\n        stderr=subprocess.PIPE,\\n        universal_newlines=True,\\n    )\\n    if not wait:\\n        return\\n    the_stuff = the_command.communicate()\\n    return str(the_stuff[0])\\n\\n\\ndef capture_1shot_domesticator(stdout: str) -> str:\\n    \\\"\\\"\\\"split input into lines.\\n    loop once through discarding lines up to ones including >.\\n    return joined output\\\"\\\"\\\"\\n    sequence = []\\n    append = False\\n    for line in stdout.splitlines():\\n        if append:\\n            sequence.append(line)\\n        else:\\n            pass\\n        if \\\">unknown_seq1\\\" in line:\\n            append = True\\n        else:\\n            pass\\n    to_return = \\\"\\\".join(sequence)\\n    return to_return\\n\\n\\ndef generate_potential_kmers(seq: str) -> list:\\n    from itertools import combinations\\n\\n    # len k is 8\\n    k = 8\\n    kmers = [\\n        seq[x:y]\\n        for x, y in combinations(range(len(seq) + 1), r=2)\\n        if len(seq[x:y]) == k\\n    ]\\n\\n    return kmers\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cmd_no_stderr(command, wait=True):\n",
    "    \"\"\"@nrbennet @bcov @pleung\"\"\"\n",
    "    the_command = subprocess.Popen(\n",
    "        command,\n",
    "        shell=True,\n",
    "        stdout=subprocess.PIPE,\n",
    "        stderr=subprocess.PIPE,\n",
    "        universal_newlines=True,\n",
    "    )\n",
    "    if not wait:\n",
    "        return\n",
    "    the_stuff = the_command.communicate()\n",
    "    return str(the_stuff[0])\n",
    "\n",
    "\n",
    "def capture_1shot_domesticator(stdout: str) -> str:\n",
    "    \"\"\"split input into lines.\n",
    "    loop once through discarding lines up to ones including >.\n",
    "    return joined output\"\"\"\n",
    "    sequence = []\n",
    "    append = False\n",
    "    for line in stdout.splitlines():\n",
    "        if append:\n",
    "            sequence.append(line)\n",
    "        else:\n",
    "            pass\n",
    "        if \">unknown_seq1\" in line:\n",
    "            append = True\n",
    "        else:\n",
    "            pass\n",
    "    to_return = \"\".join(sequence)\n",
    "    return to_return\n",
    "\n",
    "\n",
    "def generate_potential_kmers(seq: str) -> list:\n",
    "    from itertools import combinations\n",
    "\n",
    "    # len k is 8\n",
    "    k = 8\n",
    "    kmers = [\n",
    "        seq[x:y]\n",
    "        for x, y in combinations(range(len(seq) + 1), r=2)\n",
    "        if len(seq[x:y]) == k\n",
    "    ]\n",
    "\n",
    "    return kmers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reverse translate designs to order\n",
    "add only scarless GS overhangs for GGA. These will go in either a regular pet29b+ GGA vector or a SNAC GGA vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12it [00:55,  4.59s/it]\n",
      "12it [00:53,  4.45s/it]\n",
      "12it [00:50,  4.18s/it]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"prefix = \\\"atactacggtctcaagga\\\"  # for scarless cloning\\nsuffix = \\\"GGttcccgagaccgtaatgc\\\"  # for scarless cloning\\nwith open(os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"sc_dna.list\\\"), \\\"w+\\\") as bicis:\\n    for state, seq in {\\\"X\\\": \\\"X_seq\\\", \\\"Y\\\": \\\"Y_seq\\\", \\\"U\\\": \\\"final_sequence\\\"}.items(): \\n        for index, row in tqdm(to_order_df.iterrows()):\\n            state_seq = row[seq]\\n            fully_domesticated = capture_1shot_domesticator(\\n                cmd_no_stderr(\\n                    f\\\"./domesticator.py {state_seq} --avoid_restriction_sites BsaI XhoI NdeI --avoid_patterns AGGAGG GCTGGTGG ATCTGTT GGRGGT --avoid_kmers 8 --avoid_kmers_boost 10 --species e_coli\\\"\\n                )\\n            )\\n            bicis.write(f\\\"{index}_{state}\\\" + \\\"\\\\t\\\" + prefix + fully_domesticated + suffix + \\\"\\\\n\\\")\";\n",
       "                var nbb_formatted_code = \"prefix = \\\"atactacggtctcaagga\\\"  # for scarless cloning\\nsuffix = \\\"GGttcccgagaccgtaatgc\\\"  # for scarless cloning\\nwith open(os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"sc_dna.list\\\"), \\\"w+\\\") as bicis:\\n    for state, seq in {\\\"X\\\": \\\"X_seq\\\", \\\"Y\\\": \\\"Y_seq\\\", \\\"U\\\": \\\"final_sequence\\\"}.items():\\n        for index, row in tqdm(to_order_df.iterrows()):\\n            state_seq = row[seq]\\n            fully_domesticated = capture_1shot_domesticator(\\n                cmd_no_stderr(\\n                    f\\\"./domesticator.py {state_seq} --avoid_restriction_sites BsaI XhoI NdeI --avoid_patterns AGGAGG GCTGGTGG ATCTGTT GGRGGT --avoid_kmers 8 --avoid_kmers_boost 10 --species e_coli\\\"\\n                )\\n            )\\n            bicis.write(\\n                f\\\"{index}_{state}\\\" + \\\"\\\\t\\\" + prefix + fully_domesticated + suffix + \\\"\\\\n\\\"\\n            )\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prefix = \"atactacggtctcaagga\"  # for scarless cloning\n",
    "suffix = \"GGttcccgagaccgtaatgc\"  # for scarless cloning\n",
    "with open(os.path.join(os.getcwd(), \"07_to_order\", \"sc_dna.list\"), \"w+\") as bicis:\n",
    "    for state, seq in {\"X\": \"X_seq\", \"Y\": \"Y_seq\", \"U\": \"final_sequence\"}.items(): \n",
    "        for index, row in tqdm(to_order_df.iterrows()):\n",
    "            state_seq = row[seq]\n",
    "            fully_domesticated = capture_1shot_domesticator(\n",
    "                cmd_no_stderr(\n",
    "                    f\"./domesticator.py {state_seq} --avoid_restriction_sites BsaI XhoI NdeI --avoid_patterns AGGAGG GCTGGTGG ATCTGTT GGRGGT --avoid_kmers 8 --avoid_kmers_boost 10 --species e_coli\"\n",
    "                )\n",
    "            )\n",
    "            bicis.write(f\"{index}_{state}\" + \"\\t\" + prefix + fully_domesticated + suffix + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reverse translate designs to order for Cullen's DEER designs on FF74\n",
    "add only scarless GS overhangs for GGA. These will go in a SNAC GGA vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24it [02:08,  5.35s/it]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"cu_deer_df = pd.read_json(\\\"/home/cdemakis/to_order/hinges/092821_cu_deer/seqs.json\\\")\\nprefix = \\\"atactacggtctcaagga\\\"  # for scarless cloning\\nsuffix = \\\"GGttcccgagaccgtaatgc\\\"  # for scarless cloning\\nwith open(os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"cu_deer_dna.list\\\"), \\\"w+\\\") as bicis:\\n    for index, row in tqdm(cu_deer_df.iterrows()):\\n        chA = row[\\\"chA_seq\\\"]\\n        fully_domesticated = capture_1shot_domesticator(\\n            cmd_no_stderr(\\n                f\\\"./domesticator.py {chA} --avoid_restriction_sites BsaI XhoI NdeI --avoid_patterns AGGAGG GCTGGTGG ATCTGTT GGRGGT --avoid_kmers 8 --avoid_kmers_boost 10 --species e_coli\\\"\\n            )\\n        )\\n        bicis.write(f\\\"deer_{str(index).zfill(3)}\\\" + \\\"\\\\t\\\" + prefix + fully_domesticated + suffix + \\\"\\\\n\\\")\";\n",
       "                var nbb_formatted_code = \"cu_deer_df = pd.read_json(\\\"/home/cdemakis/to_order/hinges/092821_cu_deer/seqs.json\\\")\\nprefix = \\\"atactacggtctcaagga\\\"  # for scarless cloning\\nsuffix = \\\"GGttcccgagaccgtaatgc\\\"  # for scarless cloning\\nwith open(os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"cu_deer_dna.list\\\"), \\\"w+\\\") as bicis:\\n    for index, row in tqdm(cu_deer_df.iterrows()):\\n        chA = row[\\\"chA_seq\\\"]\\n        fully_domesticated = capture_1shot_domesticator(\\n            cmd_no_stderr(\\n                f\\\"./domesticator.py {chA} --avoid_restriction_sites BsaI XhoI NdeI --avoid_patterns AGGAGG GCTGGTGG ATCTGTT GGRGGT --avoid_kmers 8 --avoid_kmers_boost 10 --species e_coli\\\"\\n            )\\n        )\\n        bicis.write(\\n            f\\\"deer_{str(index).zfill(3)}\\\"\\n            + \\\"\\\\t\\\"\\n            + prefix\\n            + fully_domesticated\\n            + suffix\\n            + \\\"\\\\n\\\"\\n        )\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cu_deer_df = pd.read_json(\"/home/cdemakis/to_order/hinges/092821_cu_deer/seqs.json\")\n",
    "prefix = \"atactacggtctcaagga\"  # for scarless cloning\n",
    "suffix = \"GGttcccgagaccgtaatgc\"  # for scarless cloning\n",
    "with open(os.path.join(os.getcwd(), \"07_to_order\", \"cu_deer_dna.list\"), \"w+\") as bicis:\n",
    "    for index, row in tqdm(cu_deer_df.iterrows()):\n",
    "        chA = row[\"chA_seq\"]\n",
    "        fully_domesticated = capture_1shot_domesticator(\n",
    "            cmd_no_stderr(\n",
    "                f\"./domesticator.py {chA} --avoid_restriction_sites BsaI XhoI NdeI --avoid_patterns AGGAGG GCTGGTGG ATCTGTT GGRGGT --avoid_kmers 8 --avoid_kmers_boost 10 --species e_coli\"\n",
    "            )\n",
    "        )\n",
    "        bicis.write(f\"deer_{str(index).zfill(3)}\" + \"\\t\" + prefix + fully_domesticated + suffix + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reverse translate designs to order for Cullen's Crispy Snippies\n",
    "add intra-CDS RBS site between seqs, scarless GS overhanges for GGA and N terminal `GSGSGS` for crispy snippies. These will go in LM671"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "96it [07:50,  4.90s/it]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"crispy_snippy_df = pd.read_json(\\\"/home/cdemakis/to_order/hinges/092821_enz_hinges/eblock.json\\\")\\ncrispy_snippy_df.index.name = \\\"abspath\\\"\\nnames = [f\\\"csnp_{(str(i)).zfill(3)}\\\" for i in range(1, 97)]\\ncrispy_snippy_df[\\\"ordered_name\\\"] = names\\ncrispy_snippy_df.reset_index(inplace=True)\\ncrispy_snippy_df.set_index(\\\"ordered_name\\\", inplace=True)\\nprefix = \\\"atactacggtctcaagga\\\"  # for scarless cloning\\nirbs = \\\"agcggcggcagctagtaaagaaggagatatcatatgagcggcggcagc\\\"  # for bicstronic expression and normalization\\nsuffix = \\\"GGttcccgagaccgtaatgc\\\"  # for scarless cloning\\nwith open(os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"crispy_snippy_dna.list\\\"), \\\"w+\\\") as bicis:\\n    for index, row in tqdm(crispy_snippy_df.iterrows()):\\n        chA = row[\\\"chA_seq\\\"]\\n        chB = row[\\\"chB_seq\\\"]\\n        len_chB_dna = 3 * len(chB)\\n        fully_domesticated = capture_1shot_domesticator(\\n            cmd_no_stderr(\\n                f\\\"./domesticator.py {'GSGSGS'+chB+chA} --avoid_restriction_sites BsaI XhoI NdeI --avoid_patterns AGGAGG GCTGGTGG ATCTGTT GGRGGT --avoid_kmers 8 --avoid_kmers_boost 10 --species e_coli\\\"\\n            )\\n        )\\n        chB_dna = fully_domesticated[:len_chB_dna]\\n        chA_dna = fully_domesticated[len_chB_dna:]\\n        bicis.write(index + \\\"\\\\t\\\" + prefix + chB_dna + irbs + chA_dna + suffix + \\\"\\\\n\\\")\";\n",
       "                var nbb_formatted_code = \"crispy_snippy_df = pd.read_json(\\n    \\\"/home/cdemakis/to_order/hinges/092821_enz_hinges/eblock.json\\\"\\n)\\ncrispy_snippy_df.index.name = \\\"abspath\\\"\\nnames = [f\\\"csnp_{(str(i)).zfill(3)}\\\" for i in range(1, 97)]\\ncrispy_snippy_df[\\\"ordered_name\\\"] = names\\ncrispy_snippy_df.reset_index(inplace=True)\\ncrispy_snippy_df.set_index(\\\"ordered_name\\\", inplace=True)\\nprefix = \\\"atactacggtctcaagga\\\"  # for scarless cloning\\nirbs = \\\"agcggcggcagctagtaaagaaggagatatcatatgagcggcggcagc\\\"  # for bicstronic expression and normalization\\nsuffix = \\\"GGttcccgagaccgtaatgc\\\"  # for scarless cloning\\nwith open(\\n    os.path.join(os.getcwd(), \\\"07_to_order\\\", \\\"crispy_snippy_dna.list\\\"), \\\"w+\\\"\\n) as bicis:\\n    for index, row in tqdm(crispy_snippy_df.iterrows()):\\n        chA = row[\\\"chA_seq\\\"]\\n        chB = row[\\\"chB_seq\\\"]\\n        len_chB_dna = 3 * len(chB)\\n        fully_domesticated = capture_1shot_domesticator(\\n            cmd_no_stderr(\\n                f\\\"./domesticator.py {'GSGSGS'+chB+chA} --avoid_restriction_sites BsaI XhoI NdeI --avoid_patterns AGGAGG GCTGGTGG ATCTGTT GGRGGT --avoid_kmers 8 --avoid_kmers_boost 10 --species e_coli\\\"\\n            )\\n        )\\n        chB_dna = fully_domesticated[:len_chB_dna]\\n        chA_dna = fully_domesticated[len_chB_dna:]\\n        bicis.write(index + \\\"\\\\t\\\" + prefix + chB_dna + irbs + chA_dna + suffix + \\\"\\\\n\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crispy_snippy_df = pd.read_json(\"/home/cdemakis/to_order/hinges/092821_enz_hinges/eblock.json\")\n",
    "crispy_snippy_df.index.name = \"abspath\"\n",
    "names = [f\"csnp_{(str(i)).zfill(3)}\" for i in range(1, 97)]\n",
    "crispy_snippy_df[\"ordered_name\"] = names\n",
    "crispy_snippy_df.reset_index(inplace=True)\n",
    "crispy_snippy_df.set_index(\"ordered_name\", inplace=True)\n",
    "prefix = \"atactacggtctcaagga\"  # for scarless cloning\n",
    "irbs = \"agcggcggcagctagtaaagaaggagatatcatatgagcggcggcagc\"  # for bicstronic expression and normalization\n",
    "suffix = \"GGttcccgagaccgtaatgc\"  # for scarless cloning\n",
    "with open(os.path.join(os.getcwd(), \"07_to_order\", \"crispy_snippy_dna.list\"), \"w+\") as bicis:\n",
    "    for index, row in tqdm(crispy_snippy_df.iterrows()):\n",
    "        chA = row[\"chA_seq\"]\n",
    "        chB = row[\"chB_seq\"]\n",
    "        len_chB_dna = 3 * len(chB)\n",
    "        fully_domesticated = capture_1shot_domesticator(\n",
    "            cmd_no_stderr(\n",
    "                f\"./domesticator.py {'GSGSGS'+chB+chA} --avoid_restriction_sites BsaI XhoI NdeI --avoid_patterns AGGAGG GCTGGTGG ATCTGTT GGRGGT --avoid_kmers 8 --avoid_kmers_boost 10 --species e_coli\"\n",
    "            )\n",
    "        )\n",
    "        chB_dna = fully_domesticated[:len_chB_dna]\n",
    "        chA_dna = fully_domesticated[len_chB_dna:]\n",
    "        bicis.write(index + \"\\t\" + prefix + chB_dna + irbs + chA_dna + suffix + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unused blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"def rho(x, y, ax=None, **kwargs):\\n    \\\"\\\"\\\"Plot the correlation coefficient in the top left hand corner of a plot.\\n    https://stackoverflow.com/questions/50832204/show-correlation-values-in-pairplot-using-seaborn-in-python/50835066\\n    \\\"\\\"\\\"\\n    import scipy\\n\\n    r, _ = scipy.stats.pearsonr(x, y)\\n    ax = ax or plt.gca()\\n    # Unicode for lowercase rho (\\u03c1)\\n    rho = \\\"\\\\u03C1\\\"\\n    ax.annotate(f\\\"{rho} = {r:.2f}\\\", xy=(0.1, 0.9), xycoords=ax.transAxes)\\n\\n\\ndef plot_unity(xdata, ydata, **kwargs):\\n    \\\"\\\"\\\"https://stackoverflow.com/questions/48122019/how-can-i-plot-identity-lines-on-a-seaborn-pairplot\\\"\\\"\\\"\\n    xmin, ymin = (xdata.min(), ydata.min())\\n    xmax, ymax = (xdata.max(), ydata.max())\\n    xpoints = np.linspace(xmin, xmax, 100)\\n    ypoints = np.linspace(ymin, ymax, 100)\\n    plt.gca().plot(\\n        xpoints, ypoints, color=\\\"k\\\", marker=None, linestyle=\\\"--\\\", linewidth=4.0\\n    )\\n\\n\\ndef plot_unity_0(xdata, ydata, **kwargs):\\n    \\\"\\\"\\\"https://stackoverflow.com/questions/48122019/how-can-i-plot-identity-lines-on-a-seaborn-pairplot\\\"\\\"\\\"\\n    #     xmin, ymin = (xdata.min(), ydata.min())\\n    xmin, ymin = 0, 0\\n    xmax, ymax = (xdata.max(), ydata.max())\\n    xpoints = np.linspace(xmin, xmax, 100)\\n    ypoints = np.linspace(ymin, ymax, 100)\\n    plt.gca().plot(\\n        xpoints, ypoints, color=\\\"k\\\", marker=None, linestyle=\\\"--\\\", linewidth=4.0\\n    )\\n\\n\\nsns.set(\\n    context=\\\"talk\\\",\\n    font_scale=1.5,  # make the font larger; default is pretty small\\n    style=\\\"ticks\\\",  # make the background white with black lines\\n    palette=\\\"colorblind\\\",  # a color palette that is colorblind friendly!\\n)\\nfigs_dir = os.path.join(os.getcwd(), \\\"figs\\\")\\n\\nos.makedirs(figs_dir, exist_ok=True)\";\n",
       "                var nbb_formatted_code = \"def rho(x, y, ax=None, **kwargs):\\n    \\\"\\\"\\\"Plot the correlation coefficient in the top left hand corner of a plot.\\n    https://stackoverflow.com/questions/50832204/show-correlation-values-in-pairplot-using-seaborn-in-python/50835066\\n    \\\"\\\"\\\"\\n    import scipy\\n\\n    r, _ = scipy.stats.pearsonr(x, y)\\n    ax = ax or plt.gca()\\n    # Unicode for lowercase rho (\\u03c1)\\n    rho = \\\"\\\\u03C1\\\"\\n    ax.annotate(f\\\"{rho} = {r:.2f}\\\", xy=(0.1, 0.9), xycoords=ax.transAxes)\\n\\n\\ndef plot_unity(xdata, ydata, **kwargs):\\n    \\\"\\\"\\\"https://stackoverflow.com/questions/48122019/how-can-i-plot-identity-lines-on-a-seaborn-pairplot\\\"\\\"\\\"\\n    xmin, ymin = (xdata.min(), ydata.min())\\n    xmax, ymax = (xdata.max(), ydata.max())\\n    xpoints = np.linspace(xmin, xmax, 100)\\n    ypoints = np.linspace(ymin, ymax, 100)\\n    plt.gca().plot(\\n        xpoints, ypoints, color=\\\"k\\\", marker=None, linestyle=\\\"--\\\", linewidth=4.0\\n    )\\n\\n\\ndef plot_unity_0(xdata, ydata, **kwargs):\\n    \\\"\\\"\\\"https://stackoverflow.com/questions/48122019/how-can-i-plot-identity-lines-on-a-seaborn-pairplot\\\"\\\"\\\"\\n    #     xmin, ymin = (xdata.min(), ydata.min())\\n    xmin, ymin = 0, 0\\n    xmax, ymax = (xdata.max(), ydata.max())\\n    xpoints = np.linspace(xmin, xmax, 100)\\n    ypoints = np.linspace(ymin, ymax, 100)\\n    plt.gca().plot(\\n        xpoints, ypoints, color=\\\"k\\\", marker=None, linestyle=\\\"--\\\", linewidth=4.0\\n    )\\n\\n\\nsns.set(\\n    context=\\\"talk\\\",\\n    font_scale=1.5,  # make the font larger; default is pretty small\\n    style=\\\"ticks\\\",  # make the background white with black lines\\n    palette=\\\"colorblind\\\",  # a color palette that is colorblind friendly!\\n)\\nfigs_dir = os.path.join(os.getcwd(), \\\"figs\\\")\\n\\nos.makedirs(figs_dir, exist_ok=True)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rho(x, y, ax=None, **kwargs):\n",
    "    \"\"\"Plot the correlation coefficient in the top left hand corner of a plot.\n",
    "    https://stackoverflow.com/questions/50832204/show-correlation-values-in-pairplot-using-seaborn-in-python/50835066\n",
    "    \"\"\"\n",
    "    import scipy\n",
    "\n",
    "    r, _ = scipy.stats.pearsonr(x, y)\n",
    "    ax = ax or plt.gca()\n",
    "    # Unicode for lowercase rho (ρ)\n",
    "    rho = \"\\u03C1\"\n",
    "    ax.annotate(f\"{rho} = {r:.2f}\", xy=(0.1, 0.9), xycoords=ax.transAxes)\n",
    "\n",
    "\n",
    "def plot_unity(xdata, ydata, **kwargs):\n",
    "    \"\"\"https://stackoverflow.com/questions/48122019/how-can-i-plot-identity-lines-on-a-seaborn-pairplot\"\"\"\n",
    "    xmin, ymin = (xdata.min(), ydata.min())\n",
    "    xmax, ymax = (xdata.max(), ydata.max())\n",
    "    xpoints = np.linspace(xmin, xmax, 100)\n",
    "    ypoints = np.linspace(ymin, ymax, 100)\n",
    "    plt.gca().plot(\n",
    "        xpoints, ypoints, color=\"k\", marker=None, linestyle=\"--\", linewidth=4.0\n",
    "    )\n",
    "\n",
    "\n",
    "def plot_unity_0(xdata, ydata, **kwargs):\n",
    "    \"\"\"https://stackoverflow.com/questions/48122019/how-can-i-plot-identity-lines-on-a-seaborn-pairplot\"\"\"\n",
    "    #     xmin, ymin = (xdata.min(), ydata.min())\n",
    "    xmin, ymin = 0, 0\n",
    "    xmax, ymax = (xdata.max(), ydata.max())\n",
    "    xpoints = np.linspace(xmin, xmax, 100)\n",
    "    ypoints = np.linspace(ymin, ymax, 100)\n",
    "    plt.gca().plot(\n",
    "        xpoints, ypoints, color=\"k\", marker=None, linestyle=\"--\", linewidth=4.0\n",
    "    )\n",
    "\n",
    "\n",
    "sns.set(\n",
    "    context=\"talk\",\n",
    "    font_scale=1.5,  # make the font larger; default is pretty small\n",
    "    style=\"ticks\",  # make the background white with black lines\n",
    "    palette=\"colorblind\",  # a color palette that is colorblind friendly!\n",
    ")\n",
    "figs_dir = os.path.join(os.getcwd(), \"figs\")\n",
    "\n",
    "os.makedirs(figs_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1925416133162612"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"sum(list(scores_df.plddt_rmsd_agree.values))/len(scores_df)\";\n",
       "                var nbb_formatted_code = \"sum(list(scores_df.plddt_rmsd_agree.values)) / len(scores_df)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum(list(scores_df.plddt_rmsd_agree.values))/len(scores_df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "phil (3.8.2)",
   "language": "python",
   "name": "phil"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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 "nbformat": 4,
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