01.0_DataPrep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "99bfd0a5-d7b3-4223-b617-4fb5926f02eb",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b615cf9-6f2f-4c30-b081-7875ed08ff44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import draw_rna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "59e4c435-22de-466a-bf1b-f41d3e5a6eea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'RiboGraphViz'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "File \u001b[0;32m~/workspace/nnn_paper/nnn/ipynb_init.py:13\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjson\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mos\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mpickle\u001b[39;00m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m defaultdict\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mRiboGraphViz\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RGV\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mRiboGraphViz\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LoopExtruder, StackExtruder\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mipynb\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdraw\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m draw_struct\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'RiboGraphViz'"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'nupack'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnnn/ipynb_init.py\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minline\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnupack\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'nupack'"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%run nnn/ipynb_init.py\n",
    "%matplotlib inline\n",
    "import nupack\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cf7c4e-d993-4191-a6b0-b73d536eadc8",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data prep"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226045a7-ec70-49ab-9b41-c750608392a8",
   "metadata": {},
   "source": [
    "## Clean up the variant annotation file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb9ccbc-ec29-4d9e-b284-3290f620a5f1",
   "metadata": {},
   "source": [
    "Clean up the annotation file:\n",
    "\n",
    "- Drop duplicated variants\n",
    "\n",
    "- Remove variants not really used for DNA experiment\n",
    "\n",
    "- Unify SEQID to upper case\n",
    "\n",
    "**Input:**\n",
    "\n",
    "- `./data/annotation/NNNlib2b_annotation_20220519.tsv`\n",
    "\n",
    "\n",
    "**Output:**\n",
    "\n",
    "- `./data/annotation/NNNlib2b_annotation_2024.tsv`\n",
    "\n",
    "- `./data/annotation/NNNlib2b_annotation_2024_duplicates_dropped.tsv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bf118ade-63be-4d3a-922f-0461e898c274",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# File names\n",
    "annotation_file = './data/annotation/NNNlib2b_annotation_20220519.tsv'\n",
    "new_annotation_file = './data/annotation/NNNlib2b_annotation_2024.tsv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "81244319-f41e-4817-bb9f-34378c7c6d30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['3mer', 'AC_mismatch_varpos', 'BaeControls', 'Centered',\n",
       "       'GT_mismatch_varpos', 'NN', 'NNN', 'NNN_3primebulge',\n",
       "       'NNN_5prime_bulge', 'NN_3primebulge', 'NN_5primebulge',\n",
       "       'PolyNTControls', 'RepeatControls', 'StemDangle_3p',\n",
       "       'StemDangle_53p', 'StemDangle_5p', 'SuperStem', 'VARloop',\n",
       "       'VariableLoops', 'WBcontrols', 'WC_5ntstem', 'WC_6ntstem',\n",
       "       'WC_7ntstem', 'ssfluor'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(annotation.ConstructType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "79c299dc-cf9c-402c-9cf7-16766df22486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series</th>\n",
       "      <th>ConstructType</th>\n",
       "      <th>bottomScaffold</th>\n",
       "      <th>topScaffold</th>\n",
       "      <th>RefSeq</th>\n",
       "      <th>dH_NUPACK</th>\n",
       "      <th>dS_NUPACK</th>\n",
       "      <th>Tm_NUPACK</th>\n",
       "      <th>dG_37_ensemble_NUPACK</th>\n",
       "      <th>TargetStruct</th>\n",
       "      <th>dG_37_NUPACK</th>\n",
       "      <th>dG_37_ensemble_150mM_NUPACK</th>\n",
       "      <th>dG_37_ensemble_75mM_NUPACK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEQID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WB0</th>\n",
       "      <td>External</td>\n",
       "      <td>WBcontrols</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATCAGTGAAAAATCTCTGAA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.418536</td>\n",
       "      <td>....................</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.960523</td>\n",
       "      <td>-1.653704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB1</th>\n",
       "      <td>External</td>\n",
       "      <td>WBcontrols</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATCAGTGAAAAATCAATGAA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.536109</td>\n",
       "      <td>....................</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.991338</td>\n",
       "      <td>-1.645522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB2</th>\n",
       "      <td>External</td>\n",
       "      <td>WBcontrols</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATCAGTGAAAAATCAGACAA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.519408</td>\n",
       "      <td>....................</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.641846</td>\n",
       "      <td>-0.559613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB3</th>\n",
       "      <td>External</td>\n",
       "      <td>WBcontrols</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATCAGTGAGAAATCACTGAA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.286549</td>\n",
       "      <td>....................</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.365937</td>\n",
       "      <td>-5.848357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB4</th>\n",
       "      <td>External</td>\n",
       "      <td>WBcontrols</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATCAGTGAGAAATCTCTGAA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.107837</td>\n",
       "      <td>....................</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.440235</td>\n",
       "      <td>-3.027912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB5</th>\n",
       "      <td>External</td>\n",
       "      <td>WBcontrols</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATCAGTGAGAAATCAATGAA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.411567</td>\n",
       "      <td>....................</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.721281</td>\n",
       "      <td>-3.292959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB6</th>\n",
       "      <td>External</td>\n",
       "      <td>WBcontrols</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATCAGTGAGAAATCACAGAA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.195017</td>\n",
       "      <td>....................</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.618224</td>\n",
       "      <td>-3.267303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Series ConstructType bottomScaffold topScaffold  \\\n",
       "SEQID                                                      \n",
       "WB0    External    WBcontrols            NaN         NaN   \n",
       "WB1    External    WBcontrols            NaN         NaN   \n",
       "WB2    External    WBcontrols            NaN         NaN   \n",
       "WB3    External    WBcontrols            NaN         NaN   \n",
       "WB4    External    WBcontrols            NaN         NaN   \n",
       "WB5    External    WBcontrols            NaN         NaN   \n",
       "WB6    External    WBcontrols            NaN         NaN   \n",
       "\n",
       "                     RefSeq  dH_NUPACK  dS_NUPACK  Tm_NUPACK  \\\n",
       "SEQID                                                          \n",
       "WB0    ATCAGTGAAAAATCTCTGAA        0.0       -0.0        NaN   \n",
       "WB1    ATCAGTGAAAAATCAATGAA        0.0       -0.0        NaN   \n",
       "WB2    ATCAGTGAAAAATCAGACAA        0.0       -0.0        NaN   \n",
       "WB3    ATCAGTGAGAAATCACTGAA        0.0       -0.0        NaN   \n",
       "WB4    ATCAGTGAGAAATCTCTGAA        0.0       -0.0        NaN   \n",
       "WB5    ATCAGTGAGAAATCAATGAA        0.0       -0.0        NaN   \n",
       "WB6    ATCAGTGAGAAATCACAGAA        0.0       -0.0        NaN   \n",
       "\n",
       "       dG_37_ensemble_NUPACK          TargetStruct  dG_37_NUPACK  \\\n",
       "SEQID                                                              \n",
       "WB0                -2.418536  ....................           0.0   \n",
       "WB1                -2.536109  ....................           0.0   \n",
       "WB2                -0.519408  ....................           0.0   \n",
       "WB3                -7.286549  ....................           0.0   \n",
       "WB4                -4.107837  ....................           0.0   \n",
       "WB5                -4.411567  ....................           0.0   \n",
       "WB6                -4.195017  ....................           0.0   \n",
       "\n",
       "       dG_37_ensemble_150mM_NUPACK  dG_37_ensemble_75mM_NUPACK  \n",
       "SEQID                                                           \n",
       "WB0                      -1.960523                   -1.653704  \n",
       "WB1                      -1.991338                   -1.645522  \n",
       "WB2                      -0.641846                   -0.559613  \n",
       "WB3                      -6.365937                   -5.848357  \n",
       "WB4                      -3.440235                   -3.027912  \n",
       "WB5                      -3.721281                   -3.292959  \n",
       "WB6                      -3.618224                   -3.267303  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotation.query('ConstructType == \"WBcontrols\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "92de6e37-f1af-492a-a34c-96a9010e827d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the 220519 annotation file\n",
    "annotation = pd.read_table('./data/annotation/NNNlib2b_annotation_20220519.tsv', index_col=0)\n",
    "\n",
    "# Unify all sequences to upper case\n",
    "annotation['RefSeq'] = annotation.RefSeq.apply(lambda x: x.upper())\n",
    "\n",
    "# Remove excessive (duplicated) VARloops\n",
    "# annotation = annotation.loc[[seqid for seqid in annotation.index if not seqid.startswith('VAR')]]\n",
    "\n",
    "# Remove pseudoknots and Pumillo RBP binding variants as they are for RNA and not really analyzed\n",
    "annotation = annotation.query('ConstructType != \"PUMcontrols\" & ConstructType != \"pseudoknots\" & ConstructType != \"pseudoknot_pt_muts\"')\n",
    "\n",
    "# Remove duplicated WBcontrols (only keep WB but not WB_controls...)\n",
    "annotation.rename(index={'WB_controls260831':'WB7'}, inplace=True)\n",
    "# annotation = annotation.loc[[seqid for seqid in annotation.index if (not seqid.startswith('WB')) or seqid.startswith('WB_')]]\n",
    "annotation = annotation.loc[[seqid for seqid in annotation.index if (not seqid.startswith('WB')) or (not '_controls' in seqid)]]\n",
    "\n",
    "# Save the new annotation file\n",
    "annotation.to_csv(new_annotation_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "147e91cf-f040-431f-82c6-bb1008db66f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ATCAGTGAAAAATCACTGAA'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotation.loc['WB_controls260831', 'RefSeq']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "91e1a3c1-6fbc-4791-9e42-04a9fa7376a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'ATCAGTGAAAAATCACTGAA' in annotation.RefSeq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "213a393f-648b-4d15-afa2-528b84bfa4e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotation.rename(index={'WB_controls260831':'WB7'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f48208bf-5d3e-417b-809a-e4f71eef9c7a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "~~~~~~~~~~Annotation file~~~~~~~~~~~\n",
      "\n",
      "\n",
      "============ TOTAL ============\n",
      "All: 41311\tUnique: 40234\n",
      "\n",
      "============ Bulges ============\n",
      "All: 11520\tUnique: 10880\n",
      "\n",
      "============ Control ============\n",
      "All: 595\tUnique: 594\n",
      "\n",
      "============ External ============\n",
      "All: 32\tUnique: 32\n",
      "\n",
      "============ MisMatches ============\n",
      "All: 18240\tUnique: 17856\n",
      "\n",
      "============ TETRAloop ============\n",
      "All: 6144\tUnique: 6144\n",
      "\n",
      "============ TRIloop ============\n",
      "All: 1536\tUnique: 1536\n",
      "\n",
      "============ VARloop ============\n",
      "All: 172\tUnique: 136\n",
      "\n",
      "============ WatsonCrick ============\n",
      "All: 3072\tUnique: 3072\n"
     ]
    }
   ],
   "source": [
    "# Print accounting numbers\n",
    "print('\\n~~~~~~~~~~Annotation file~~~~~~~~~~~\\n')\n",
    "print('\\n============', 'TOTAL', '============')\n",
    "print('All: %d\\tUnique: %d' %\n",
    "     (len(annotation), len(np.unique(annotation.RefSeq))))\n",
    "for seq_class in np.unique(annotation.Series):\n",
    "    print('\\n============', seq_class, '============')\n",
    "    print('All: %d\\tUnique: %d' % \n",
    "          (len(annotation.query(f'Series == \"{seq_class}\"')), len(np.unique(annotation.query(f'Series == \"{seq_class}\"').RefSeq))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d615df-dc51-4117-81a0-7d632736a3a6",
   "metadata": {},
   "source": [
    "The annotation file `NNNlib2b_annotation_2024_duplicates_dropped.tsv` was made by dropping duplicates in `NNNlib2b_annotation_2024.tsv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "52765578-caf2-4055-a8c5-29e7d6dde856",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotation_file = './data/annotation/NNNlib2b_annotation_2024.tsv'\n",
    "new_annotation_file = './data/annotation/NNNlib2b_annotation_2024_duplicates_dropped.tsv'\n",
    "annotation = fileio.read_annotation(annotation_file)\n",
    "annotation_unique = annotation.drop_duplicates(subset=['RefSeq'], keep='last')\n",
    "annotation_unique.to_csv(new_annotation_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8b2803c9-b84f-4b48-811f-0259bc830b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./data/annotation/NNNlib2b_annotation_20220519.tsv'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotation_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a680413a-e14b-4f4c-b2ce-e1a856c74000",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.43% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "64.31% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "71.46% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "Load from file ./data/fitted_variant/NNNlib2b_DNA_20211022_v6.CPvariant.gz\n",
      "86.43% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "Load from file ./data/fitted_variant/NNNlib2b_DNA_20211216_v9.CPvariant.gz\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "\n",
      "Combining dH\n",
      "\n",
      "Combining Tm\n",
      "\n",
      "Combining dG_37\n",
      "\n",
      "Combining dS\n",
      "\n",
      "Combining fmax\n",
      "\n",
      "Combining fmin\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 501.969x137.795 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "replicate_df = pd.read_table('./data/nnnlib2b_replicates.tsv')\n",
    "arraydata = ArrayData(replicate_df=replicate_df.iloc[:4,:],\n",
    "                      annotation_file=annotation_file,\n",
    "                      learn_error_adjust_from=('r1', 'r2'))\n",
    "arr = arraydata.data.join(arraydata.annotation)\n",
    "# arr_v0 = pd.read_csv('./data/models/raw/arr_v0_n=30872.csv', index_col=0)\n",
    "# arr_v1 = pd.read_csv('./data/models/raw/arr_v1_n=27732.csv', index_col=0)\n",
    "# arr_v1_1M = pd.read_csv('./data/models/processed/arr_v1_1M_n=27732.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6ce52f20-0c20-498f-9104-06128e05519a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dH</th>\n",
       "      <th>dH_se</th>\n",
       "      <th>dH_lb</th>\n",
       "      <th>dH_ub</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Tm_se</th>\n",
       "      <th>Tm_lb</th>\n",
       "      <th>Tm_ub</th>\n",
       "      <th>dG_37</th>\n",
       "      <th>dG_37_se</th>\n",
       "      <th>...</th>\n",
       "      <th>dG_37_ensemble_NUPACK</th>\n",
       "      <th>TargetStruct</th>\n",
       "      <th>dG_37_NUPACK</th>\n",
       "      <th>dG_37_ensemble_150mM_NUPACK</th>\n",
       "      <th>dG_37_ensemble_75mM_NUPACK</th>\n",
       "      <th>GC</th>\n",
       "      <th>Tm_NUPACK_salt_corrected</th>\n",
       "      <th>dG_37_NUPACK_salt_corrected</th>\n",
       "      <th>dH_NUPACK_salt_corrected</th>\n",
       "      <th>dS_NUPACK_salt_corrected</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEQID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BC0</th>\n",
       "      <td>-35.454179</td>\n",
       "      <td>6.754350</td>\n",
       "      <td>-58.708445</td>\n",
       "      <td>-21.186396</td>\n",
       "      <td>67.019004</td>\n",
       "      <td>1.526445</td>\n",
       "      <td>63.666165</td>\n",
       "      <td>71.418702</td>\n",
       "      <td>-3.061226</td>\n",
       "      <td>0.496111</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.654079</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-6.41</td>\n",
       "      <td>-5.707661</td>\n",
       "      <td>-5.300504</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>73.069702</td>\n",
       "      <td>-5.021550</td>\n",
       "      <td>-48.2</td>\n",
       "      <td>-0.134741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC1</th>\n",
       "      <td>-29.405457</td>\n",
       "      <td>2.259464</td>\n",
       "      <td>-39.583422</td>\n",
       "      <td>-22.635332</td>\n",
       "      <td>43.745758</td>\n",
       "      <td>0.448672</td>\n",
       "      <td>42.451361</td>\n",
       "      <td>45.010831</td>\n",
       "      <td>-1.031010</td>\n",
       "      <td>0.063069</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.630936</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-5.38</td>\n",
       "      <td>-4.688133</td>\n",
       "      <td>-4.283077</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>66.346868</td>\n",
       "      <td>-3.898545</td>\n",
       "      <td>-45.1</td>\n",
       "      <td>-0.128067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC10</th>\n",
       "      <td>-13.860604</td>\n",
       "      <td>6.090826</td>\n",
       "      <td>-40.334697</td>\n",
       "      <td>-7.611236</td>\n",
       "      <td>19.970895</td>\n",
       "      <td>1.921306</td>\n",
       "      <td>15.557824</td>\n",
       "      <td>27.646350</td>\n",
       "      <td>0.784015</td>\n",
       "      <td>0.409934</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.594503</td>\n",
       "      <td>(((.((....)).)))</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.167094</td>\n",
       "      <td>-1.034802</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>27.562143</td>\n",
       "      <td>0.900750</td>\n",
       "      <td>-28.7</td>\n",
       "      <td>-0.092213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC11</th>\n",
       "      <td>-19.310492</td>\n",
       "      <td>6.175993</td>\n",
       "      <td>-40.411542</td>\n",
       "      <td>-11.712279</td>\n",
       "      <td>24.234945</td>\n",
       "      <td>1.821857</td>\n",
       "      <td>20.111575</td>\n",
       "      <td>29.321448</td>\n",
       "      <td>0.799597</td>\n",
       "      <td>0.400100</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.791497</td>\n",
       "      <td>(((.((....)).)))</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-2.876428</td>\n",
       "      <td>-2.557364</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>54.749507</td>\n",
       "      <td>-2.078628</td>\n",
       "      <td>-38.4</td>\n",
       "      <td>-0.113042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC15</th>\n",
       "      <td>-28.359914</td>\n",
       "      <td>2.648732</td>\n",
       "      <td>-38.883415</td>\n",
       "      <td>-21.838619</td>\n",
       "      <td>31.556180</td>\n",
       "      <td>0.680925</td>\n",
       "      <td>29.854361</td>\n",
       "      <td>33.653719</td>\n",
       "      <td>0.523107</td>\n",
       "      <td>0.098943</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.409790</td>\n",
       "      <td>((((.((....)).))))</td>\n",
       "      <td>-2.62</td>\n",
       "      <td>-2.471721</td>\n",
       "      <td>-2.176915</td>\n",
       "      <td>44.444444</td>\n",
       "      <td>51.193660</td>\n",
       "      <td>-1.501008</td>\n",
       "      <td>-34.3</td>\n",
       "      <td>-0.102144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257601</th>\n",
       "      <td>-26.345117</td>\n",
       "      <td>3.277633</td>\n",
       "      <td>-39.706480</td>\n",
       "      <td>-19.440711</td>\n",
       "      <td>40.492735</td>\n",
       "      <td>0.908829</td>\n",
       "      <td>38.219125</td>\n",
       "      <td>43.781862</td>\n",
       "      <td>-0.325330</td>\n",
       "      <td>0.071060</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.250290</td>\n",
       "      <td>((..............))</td>\n",
       "      <td>1.43</td>\n",
       "      <td>-4.222346</td>\n",
       "      <td>-3.852054</td>\n",
       "      <td>61.111111</td>\n",
       "      <td>6.646542</td>\n",
       "      <td>1.909319</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.061357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257621</th>\n",
       "      <td>-25.128278</td>\n",
       "      <td>2.998856</td>\n",
       "      <td>-37.745495</td>\n",
       "      <td>-18.452277</td>\n",
       "      <td>35.096619</td>\n",
       "      <td>0.880154</td>\n",
       "      <td>33.089326</td>\n",
       "      <td>38.058575</td>\n",
       "      <td>0.152467</td>\n",
       "      <td>0.084654</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.141857</td>\n",
       "      <td>((.................))</td>\n",
       "      <td>1.73</td>\n",
       "      <td>-4.110832</td>\n",
       "      <td>-3.739269</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>2.095381</td>\n",
       "      <td>2.231904</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.062325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257641</th>\n",
       "      <td>-29.589393</td>\n",
       "      <td>3.039279</td>\n",
       "      <td>-41.359873</td>\n",
       "      <td>-22.461319</td>\n",
       "      <td>27.963405</td>\n",
       "      <td>0.649335</td>\n",
       "      <td>26.181828</td>\n",
       "      <td>30.161514</td>\n",
       "      <td>0.869059</td>\n",
       "      <td>0.140237</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.942000</td>\n",
       "      <td>((....................))</td>\n",
       "      <td>1.93</td>\n",
       "      <td>-3.911455</td>\n",
       "      <td>-3.540350</td>\n",
       "      <td>54.166667</td>\n",
       "      <td>-0.882917</td>\n",
       "      <td>2.448843</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.062970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257661</th>\n",
       "      <td>-32.478784</td>\n",
       "      <td>7.639435</td>\n",
       "      <td>-58.155438</td>\n",
       "      <td>-16.919440</td>\n",
       "      <td>22.513432</td>\n",
       "      <td>1.281541</td>\n",
       "      <td>19.969923</td>\n",
       "      <td>26.418400</td>\n",
       "      <td>1.534949</td>\n",
       "      <td>0.555585</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.646560</td>\n",
       "      <td>((.......................))</td>\n",
       "      <td>2.23</td>\n",
       "      <td>-3.618833</td>\n",
       "      <td>-3.249542</td>\n",
       "      <td>51.851852</td>\n",
       "      <td>-5.070475</td>\n",
       "      <td>2.762018</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.063937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257971</th>\n",
       "      <td>-20.282780</td>\n",
       "      <td>2.263058</td>\n",
       "      <td>-27.980846</td>\n",
       "      <td>-14.657163</td>\n",
       "      <td>29.825036</td>\n",
       "      <td>1.529281</td>\n",
       "      <td>26.480286</td>\n",
       "      <td>36.072235</td>\n",
       "      <td>0.466027</td>\n",
       "      <td>0.138313</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.850790</td>\n",
       "      <td>((...........))</td>\n",
       "      <td>2.32</td>\n",
       "      <td>-2.948253</td>\n",
       "      <td>-2.634014</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>-16.789226</td>\n",
       "      <td>2.769604</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>-0.050040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31000 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      dH     dH_se      dH_lb      dH_ub         Tm     Tm_se  \\\n",
       "SEQID                                                                           \n",
       "BC0           -35.454179  6.754350 -58.708445 -21.186396  67.019004  1.526445   \n",
       "BC1           -29.405457  2.259464 -39.583422 -22.635332  43.745758  0.448672   \n",
       "BC10          -13.860604  6.090826 -40.334697  -7.611236  19.970895  1.921306   \n",
       "BC11          -19.310492  6.175993 -40.411542 -11.712279  24.234945  1.821857   \n",
       "BC15          -28.359914  2.648732 -38.883415 -21.838619  31.556180  0.680925   \n",
       "...                  ...       ...        ...        ...        ...       ...   \n",
       "varloop257601 -26.345117  3.277633 -39.706480 -19.440711  40.492735  0.908829   \n",
       "varloop257621 -25.128278  2.998856 -37.745495 -18.452277  35.096619  0.880154   \n",
       "varloop257641 -29.589393  3.039279 -41.359873 -22.461319  27.963405  0.649335   \n",
       "varloop257661 -32.478784  7.639435 -58.155438 -16.919440  22.513432  1.281541   \n",
       "varloop257971 -20.282780  2.263058 -27.980846 -14.657163  29.825036  1.529281   \n",
       "\n",
       "                   Tm_lb      Tm_ub     dG_37  dG_37_se  ...  \\\n",
       "SEQID                                                    ...   \n",
       "BC0            63.666165  71.418702 -3.061226  0.496111  ...   \n",
       "BC1            42.451361  45.010831 -1.031010  0.063069  ...   \n",
       "BC10           15.557824  27.646350  0.784015  0.409934  ...   \n",
       "BC11           20.111575  29.321448  0.799597  0.400100  ...   \n",
       "BC15           29.854361  33.653719  0.523107  0.098943  ...   \n",
       "...                  ...        ...       ...       ...  ...   \n",
       "varloop257601  38.219125  43.781862 -0.325330  0.071060  ...   \n",
       "varloop257621  33.089326  38.058575  0.152467  0.084654  ...   \n",
       "varloop257641  26.181828  30.161514  0.869059  0.140237  ...   \n",
       "varloop257661  19.969923  26.418400  1.534949  0.555585  ...   \n",
       "varloop257971  26.480286  36.072235  0.466027  0.138313  ...   \n",
       "\n",
       "               dG_37_ensemble_NUPACK                 TargetStruct  \\\n",
       "SEQID                                                               \n",
       "BC0                        -6.654079             ((((((....))))))   \n",
       "BC1                        -5.630936             ((((((....))))))   \n",
       "BC10                       -1.594503             (((.((....)).)))   \n",
       "BC11                       -3.791497             (((.((....)).)))   \n",
       "BC15                       -3.409790           ((((.((....)).))))   \n",
       "...                              ...                          ...   \n",
       "varloop257601              -5.250290           ((..............))   \n",
       "varloop257621              -5.141857        ((.................))   \n",
       "varloop257641              -4.942000     ((....................))   \n",
       "varloop257661              -4.646560  ((.......................))   \n",
       "varloop257971              -3.850790              ((...........))   \n",
       "\n",
       "               dG_37_NUPACK  dG_37_ensemble_150mM_NUPACK  \\\n",
       "SEQID                                                      \n",
       "BC0                   -6.41                    -5.707661   \n",
       "BC1                   -5.38                    -4.688133   \n",
       "BC10                  -0.10                    -1.167094   \n",
       "BC11                  -3.34                    -2.876428   \n",
       "BC15                  -2.62                    -2.471721   \n",
       "...                     ...                          ...   \n",
       "varloop257601          1.43                    -4.222346   \n",
       "varloop257621          1.73                    -4.110832   \n",
       "varloop257641          1.93                    -3.911455   \n",
       "varloop257661          2.23                    -3.618833   \n",
       "varloop257971          2.32                    -2.948253   \n",
       "\n",
       "               dG_37_ensemble_75mM_NUPACK         GC  \\\n",
       "SEQID                                                  \n",
       "BC0                             -5.300504  56.250000   \n",
       "BC1                             -4.283077  43.750000   \n",
       "BC10                            -1.034802  37.500000   \n",
       "BC11                            -2.557364  43.750000   \n",
       "BC15                            -2.176915  44.444444   \n",
       "...                                   ...        ...   \n",
       "varloop257601                   -3.852054  61.111111   \n",
       "varloop257621                   -3.739269  57.142857   \n",
       "varloop257641                   -3.540350  54.166667   \n",
       "varloop257661                   -3.249542  51.851852   \n",
       "varloop257971                   -2.634014  40.000000   \n",
       "\n",
       "               Tm_NUPACK_salt_corrected  dG_37_NUPACK_salt_corrected  \\\n",
       "SEQID                                                                  \n",
       "BC0                           73.069702                    -5.021550   \n",
       "BC1                           66.346868                    -3.898545   \n",
       "BC10                          27.562143                     0.900750   \n",
       "BC11                          54.749507                    -2.078628   \n",
       "BC15                          51.193660                    -1.501008   \n",
       "...                                 ...                          ...   \n",
       "varloop257601                  6.646542                     1.909319   \n",
       "varloop257621                  2.095381                     2.231904   \n",
       "varloop257641                 -0.882917                     2.448843   \n",
       "varloop257661                 -5.070475                     2.762018   \n",
       "varloop257971                -16.789226                     2.769604   \n",
       "\n",
       "               dH_NUPACK_salt_corrected  dS_NUPACK_salt_corrected  \n",
       "SEQID                                                              \n",
       "BC0                               -48.2                 -0.134741  \n",
       "BC1                               -45.1                 -0.128067  \n",
       "BC10                              -28.7                 -0.092213  \n",
       "BC11                              -38.4                 -0.113042  \n",
       "BC15                              -34.3                 -0.102144  \n",
       "...                                 ...                       ...  \n",
       "varloop257601                     -17.6                 -0.061357  \n",
       "varloop257621                     -17.6                 -0.062325  \n",
       "varloop257641                     -17.6                 -0.062970  \n",
       "varloop257661                     -17.6                 -0.063937  \n",
       "varloop257971                     -13.2                 -0.050040  \n",
       "\n",
       "[31000 rows x 42 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3b055b27-2913-4cd6-8bc1-3ff2af6dcaa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'WB3' in arr.index.tolist(), 'WB_controls260871' in arr.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a6bfe438-4b38-4c32-8cb1-9bbbd82355c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30751, 42)\n"
     ]
    }
   ],
   "source": [
    "excluded_construct_type = ['pseudoknot_pt_muts', 'PolyNTControls', 'RepeatControls', 'StemDangle_3p', 'StemDangle_53p', 'StemDangle_5p', 'PUMcontrols']\n",
    "arr_v0 = arr[arr.reset_index().ConstructType.apply(lambda s: s not in excluded_construct_type).values]\n",
    "arr_v0 = arr_v0.dropna(subset=['ConstructType', 'RefSeq','TargetStruct'])\n",
    "# arr_v0.rename(index={'WB3':'WB_controls260871'}, inplace=True)\n",
    "print(arr_v0.shape)\n",
    "arr_v0.to_csv(os.path.join('./data/models/raw', 'arr_v0_n=%d.csv' % arr_v0.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "10caa4c4-03ab-4f4b-9bd5-bb55d2be95cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Make the index in the data file compliant with the annotation file\n",
    "arr_v0.rename(index={'WB3':'WB_controls260871'}, inplace=True)\n",
    "arr_v1.rename(index={'WB3':'WB_controls260871'}, inplace=True)\n",
    "arr_v1_1M.rename(index={'WB3':'WB_controls260871'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17fa403e-612c-4507-ae72-1f27f383eddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotation.to_csv(new_annotation_file, sep='\\t')\n",
    "arr_v0.to_csv('./data/models/raw/arr_v0_n=30872.csv')\n",
    "arr_v1.to_csv('./data/models/raw/arr_v1_n=27732.csv')\n",
    "arr_v1_1M.to_csv('./data/models/processed/arr_v1_1M_n=27732.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b95cd353-38a1-4575-8149-446fb65a2ec9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Basic filtering\n",
    "\n",
    "QC filtering and removing control constructs that do not have hairpin melting behaviors.\n",
    "\n",
    "Clean up data for model training.\n",
    "\n",
    "### arr_v0\n",
    "\n",
    "Only basic curve fitting QC, no 2 state filter or $T_m$ dynamic range filter. \n",
    "\n",
    "This version was not used in the paper, but was still kept for accounting purposes and for consistency with historical analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "106c288d-655c-42d5-8a50-c26e55854a95",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.43% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "64.31% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "71.46% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "Load from file ./data/fitted_variant/NNNlib2b_DNA_20211022_v6.CPvariant.gz\n",
      "86.43% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "Load from file ./data/fitted_variant/NNNlib2b_DNA_20211216_v9.CPvariant.gz\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "\n",
      "Combining dH\n",
      "\n",
      "Combining Tm\n",
      "\n",
      "Combining dG_37\n",
      "\n",
      "Combining dS\n",
      "\n",
      "Combining fmax\n",
      "\n",
      "Combining fmin\n",
      "(30751, 42)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 501.969x137.795 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annotation_file = './data/annotation/NNNlib2b_annotation_20220519.tsv'\n",
    "annotation = fileio.read_annotation(annotation_file)\n",
    "replicate_df = pd.read_table('./data/nnnlib2b_replicates.tsv')\n",
    "arraydata = ArrayData(replicate_df=replicate_df.iloc[:4,:],\n",
    "                      annotation_file=annotation_file,\n",
    "                      learn_error_adjust_from=('r1', 'r2'))\n",
    "arr = arraydata.data.join(arraydata.annotation)\n",
    "\n",
    "# removing control constructs that do not have hairpin melting behavior\n",
    "excluded_construct_type = ['pseudoknot_pt_muts', 'PolyNTControls', 'RepeatControls', 'StemDangle_3p', 'StemDangle_53p', 'StemDangle_5p', 'PUMcontrols']\n",
    "arr = arr[arr.reset_index().ConstructType.apply(lambda s: s not in excluded_construct_type).values]\n",
    "arr = arr.dropna(subset=['ConstructType', 'RefSeq','TargetStruct'])\n",
    "\n",
    "print(arr.shape)\n",
    "arr.to_csv(os.path.join('./data/models/raw', 'arr_v0_n=%d.csv' % arr.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c96d7b2-bf36-47e4-86ca-9c5fc12c3be5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### arr_v1\n",
    "\n",
    "With (relaxed) 2-state filter & variant-level Tm filter.\n",
    "\n",
    "**Input:**\n",
    "\n",
    "- `./data/nnnlib2b_replicates.tsv`\n",
    "\n",
    "- `./data/annotation/NNNlib2b_annotation_2024.tsv`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bd4efd0d-746f-417b-9e60-6521fb40bbc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.43% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n"
     ]
    }
   ],
   "source": [
    "df = fileio.read_fitted_variant(replicate_df.filename[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "38e32b85-d381-4efd-aa4b-32b548345cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['WB3']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in df.index if x.startswith('WB')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2d8ca063-5a4e-404d-9fea-874695515f41",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.43% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "64.31% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "71.46% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "86.43% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "64.31% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "71.46% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr_v1_n=27731.csv\n"
     ]
    }
   ],
   "source": [
    "annotation_file = './data/annotation/NNNlib2b_annotation_2024.tsv'\n",
    "annotation = fileio.read_annotation(annotation_file, sodium=0.083)\n",
    "replicate_df = pd.read_table('./data/nnnlib2b_replicates.tsv')\n",
    "\n",
    "arraydata = ArrayData(replicate_df=replicate_df.iloc[:4,:],\n",
    "                    annotation_file=annotation_file)\n",
    "\n",
    "# two-state criterion\n",
    "myfilter = \"dH_err_rel < 0.5 & Tm_err_abs < 10 & redchi < 2 & n_inlier > 8\"\n",
    "pass_df = arraydata.filter_two_state(min_rep_pass=1, force_recalculate=True, overwrite_dH=False, \n",
    "                                     inplace=True, myfilter=myfilter)\n",
    "\n",
    "# Tm in range filter\n",
    "variant_filter = 'Tm < 60 & Tm > 0'\n",
    "arr = arraydata.data.join(arraydata.annotation).query(variant_filter)\n",
    "\n",
    "# Make the index in the data file consistent with the annotation file\n",
    "# arr.rename(index={'WB3':'WB_controls260871'}, inplace=True)\n",
    "\n",
    "# Remove non-melting variants\n",
    "excluded_construct_type = ['pseudoknot_pt_muts', 'PolyNTControls', 'RepeatControls', 'StemDangle_3p', 'StemDangle_53p', 'StemDangle_5p', 'PUMcontrols']\n",
    "arr = arr[arr.reset_index().ConstructType.apply(lambda s: s not in excluded_construct_type).values]\n",
    "arr = arr.dropna(subset=['ConstructType', 'RefSeq','TargetStruct'])\n",
    "\n",
    "print('arr_v1_n=%d.csv' % arr.shape[0])\n",
    "arr.to_csv(os.path.join('./data/models/raw', 'arr_v1_n=%d.csv' % arr.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2e1888fb-fb03-472a-8f9d-0141b6b9bde1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'WB3' in arr.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "05ba6acd-df7c-40f3-bfda-fe4aa5dac778",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_v1 = pd.read_csv('./data/models/raw/arr_v1_n=27732.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a9b493c2-518d-4b7a-b0c3-d3a475a32a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>designed</th>\n",
       "      <th>fitted</th>\n",
       "      <th>passed2state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bulges</th>\n",
       "      <td>11520</td>\n",
       "      <td>9474</td>\n",
       "      <td>9408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>595</td>\n",
       "      <td>127</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>External</th>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MisMatches</th>\n",
       "      <td>18240</td>\n",
       "      <td>11366</td>\n",
       "      <td>11086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETRAloop</th>\n",
       "      <td>6144</td>\n",
       "      <td>5749</td>\n",
       "      <td>5465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRIloop</th>\n",
       "      <td>1536</td>\n",
       "      <td>1454</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VARloop</th>\n",
       "      <td>172</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WatsonCrick</th>\n",
       "      <td>3072</td>\n",
       "      <td>2680</td>\n",
       "      <td>2479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            designed fitted passed2state\n",
       "Bulges         11520   9474         9408\n",
       "Control          595    127          112\n",
       "External          31     20           20\n",
       "MisMatches     18240  11366        11086\n",
       "TETRAloop       6144   5749         5465\n",
       "TRIloop         1536   1454         1402\n",
       "VARloop          172      7            5\n",
       "WatsonCrick     3072   2680         2479"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arraydata.accounting_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10ca3335-d617-4c22-8469-082754d37eb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.87% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "64.31% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "71.46% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "75.87% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "64.31% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "71.46% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "28890/31101 (92.89%) variants meet the two-state criterion.\n",
      "27437/28890 (94.97%) variants met the Tm in range criterion.\n",
      "(27259, 45)\n"
     ]
    }
   ],
   "source": [
    "annotation_file = './data/annotation/NNNlib2b_annotation_20220519.tsv'\n",
    "annotation = fileio.read_annotation(annotation_file, sodium=0.083)\n",
    "replicate_df = pd.read_table('./data/nnnlib2b_replicates.tsv')\n",
    "\n",
    "arraydata = ArrayData(replicate_df=replicate_df.iloc[:4,:],\n",
    "                    annotation_file=annotation_file)\n",
    "\n",
    "myfilter = \"dH_err_rel < 0.5 & Tm_err_abs < 10 & redchi < 2 & n_inlier > 8\"\n",
    "pass_df = arraydata.filter_two_state(min_rep_pass=1, force_recalculate=True, overwrite_dH=False, \n",
    "                                     inplace=True, myfilter=myfilter, plot_fig=False)\n",
    "\n",
    "variant_filter = 'Tm < 60 & Tm > 0'\n",
    "arr = arraydata.data.join(arraydata.annotation)\n",
    "arr_in_range = arr.query(variant_filter)\n",
    "print('%d/%d (%.2f%%) variants met the Tm in range criterion.' % \n",
    "        (len(arr_in_range), len(arr), len(arr_in_range)/len(arr)*100))\n",
    "arr = arr_in_range\n",
    "\n",
    "excluded_construct_type = ['pseudoknot_pt_muts', 'PolyNTControls', 'RepeatControls', 'StemDangle_3p', 'StemDangle_53p', 'StemDangle_5p', 'PUMcontrols']\n",
    "arr = arr[arr.reset_index().ConstructType.apply(lambda s: s not in excluded_construct_type).values]\n",
    "arr = arr.dropna(subset=['ConstructType', 'RefSeq','TargetStruct'])\n",
    "\n",
    "print(arr.shape)\n",
    "arr.to_csv(os.path.join('./data/models/raw', 'arr_v1_n=%d.csv' % arr.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a3331a7-0d20-4b9d-9547-ad7ac1479287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'3mer': 1927,\n",
       " 'AC_mismatch_varpos': 2565,\n",
       " 'BaeControls': 17,\n",
       " 'Centered': 2557,\n",
       " 'GT_mismatch_varpos': 3432,\n",
       " 'NN': 1023,\n",
       " 'NNN': 4874,\n",
       " 'NNN_3primebulge': 4156,\n",
       " 'NNN_5prime_bulge': 4112,\n",
       " 'NN_3primebulge': 296,\n",
       " 'NN_5primebulge': 297,\n",
       " 'PUMcontrols': 24,\n",
       " 'PolyNTControls': 5,\n",
       " 'RepeatControls': 22,\n",
       " 'StemDangle_3p': 14,\n",
       " 'StemDangle_53p': 10,\n",
       " 'StemDangle_5p': 20,\n",
       " 'VARloop': 5,\n",
       " 'WBcontrols': 1,\n",
       " 'WC_5ntstem': 759,\n",
       " 'WC_6ntstem': 683,\n",
       " 'WC_7ntstem': 555,\n",
       " 'pseudoknot_pt_muts': 83}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctype, counts = np.unique(arr_in_range.ConstructType, return_counts=True)\n",
    "dict(zip(ctype, counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c59af8-fb07-460d-be4a-af5ed440c464",
   "metadata": {},
   "source": [
    "### arr_v0 with control data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e077ded-ea9c-4b7b-92ba-da33d04be8d0",
   "metadata": {},
   "source": [
    "Included in the paper supplementary for completeness. Not used for model training etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "345d3bce-35e1-4850-965d-145be1f4aecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.87% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "64.31% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "71.46% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "Load from file ./data/fitted_variant/NNNlib2b_DNA_20211022_v10.CPvariant.gz\n",
      "75.87% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "Load from file ./data/fitted_variant/NNNlib2b_DNA_20211216_v9.CPvariant.gz\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "\n",
      "Combining dH\n",
      "\n",
      "Combining Tm\n",
      "\n",
      "Combining dG_37\n",
      "\n",
      "Combining dS\n",
      "\n",
      "Combining fmax\n",
      "\n",
      "Combining fmin\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 501.969x137.795 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CwkKZTCZFRUXp5MmTngXAZ9a6lGexWGSz2SRJ/fr10zvvvKO8vDwVFxfr2Wef1Z/+9Kdzvt/NN9+sjRs36j//+Y/sdrtmz56tgwcPKi0tTRs3btS2bdtUVlamt99+WyaTSddee+05z3nLLbfo/fff188//6yysjLNmDFDTz31lCQpLS1N7777rvLz83Xs2DG98cYbcjgcslgsKi4u9hl5ufXWW/XOO+/o4MGDKiws1PTp03XzzTfLbDafs45wxMgNAKBWVMceNNXl9ttv19atW9WtWzfFx8frrrvuUmZmpn766SfFVQhJaWlpevXVV3X69Gk98MADysvL08CBA2Wz2dS1a1c9//zz53y/yy+/XM8995weeeQRHT9+XN27d9fEiRNltVr1xhtv6MUXX1ROTo6uuuoqzZs3TxaL5Zzn7N69u0aMGKH7779fJ06c0LXXXqsZM2ZIkgYPHqzDhw/rtttuk8vl0tChQ9W7d28VFhYqKSlJnTp10uLFiz3nuvPOO3X06FENHTpUNptNqampeu6554L8qoaPCHcI7+eaN2+e9u3bp1deeUVut1t/+tOftGzZMpWVlalbt26aPHly0ENiO3fuVIcOHbRjxw61b9++hio3pk/HjfPZgyKmcWM5iou92k1xcbrp15X7AOCP0+nUnj17dNlllykqKirU5SCMVPa9UZ1/v0MyLWW32zVz5kxPwpR+uVd/69at+uijj/Txxx+rpKRE06ZNC0V5AACgDgvJtNTLL7+s3NxcDRkyxDO/efLkST344INKSkqSJN11113685//XOk5jh496ve2vcpWgqP6mGJj/W6jXl1bnwMAUBUhCTcTJkxQUlKSZs+e7Vk9/sADD3j12bJliy6//PJKz7Fo0SLNmTPHp72kpKR6i4VfdWUbdQBA/ROScHNmdKYyS5cu1bp167RkyZJK+wwePFipqak+7VlZWRo0aFCVawQAAHVT2N0tNW/ePP3tb3/TO++8o4suuqjSfsnJyUpOTvZpP/NcDQBA6JzZjyXcn0GE2nfme6KyPYGqQ1iFm9dee00bNmzQP/7xD11yySWhLgcAcJ4iIyNlNpuVn5+vxo0b1+gfMtQdZWVlOnLkiGJiYhQZWXP3NIVNuFm8eLHWrVunDz/80GvLaABA3XTxxRdr//79On78eKhLQZiIiIhQo0aN/M68VKewCTfz589Xfn6+0tLSPG0XXXSRVq5cGcKqAADny2Kx6L/+67/kcrmYnoIiIiI8/2paSMPNhAkTPP+9du3aEFYCAKgpNTn9APjDdxwAADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADCUsHkqOGrX55MmyVFU5DmObtw4hNUAAFB9CDf1lKOoyCvcmGJjQ1gNAADVh2kpAABgKIQbAABgKIQbAABgKKy5QbUxxcb6LFQ2xcWp2/TpIawKAFDfEG5QrSouVAYAoLYxLQUAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAwlpOFm3rx5euaZZzzHixYtUvfu3dWhQwdNnjxZTqczhNUBAIC6KCThxm63a+bMmZoxY4an7dtvv9WsWbO0YMECbdy4Uf/5z3+0bNmyUJQHAADqsJCEm5dffllZWVkaMmSIp23NmjXq37+/LrnkEiUmJmrMmDFaunRpKMoDAAB1mCkUbzphwgQlJSVp9uzZys3NlSTt27dPPXr08PRp2bKl9u7dW+k5jh49qry8PJ/27Ozs6i8YAADUGSEJN0lJST5txcXFiomJ8RzHxsaquLi40nMsWrRIc+bM8WkvKSmpniIBAECdFJJw409MTIxKS0s9x8XFxYqLi6u0/+DBg5WamurTnpWVpUGDBtVIjQAAIPyFTbhp3bq19u3b5znet2+ffvOb31TaPzk5WcnJyT7t5QMSpM8nTZKjqMirLbpx4xBVAwBAzQubfW5+97vfaeXKldqzZ49OnDihefPmqV+/fqEuq85zFBX5/HNWCDsAABhJ2Izc/Pa3v9XEiRP1+9//XjabTQMGDNA999wT6rIAAEAdE9JwM2HCBK/jgQMHauDAgSGqBgAAGEHYTEsBAABUB8INAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwlLB5KjiMyRQbq88nTZKjqMi7PS5O3aZPD1FVAAAjI9ygxjmKinzCDQAANYVpKQAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYChhF24yMjLUr18/dejQQffcc4+ys7NDXRIAAKhDwircOJ1OTZw4Uc8995y2b9+uzp0764UXXgh1WQAAoA4Jq3Bz8uRJFRQUyOVySZIiIyMVExMT4qoAAEBdYgp1AeUlJibqjjvu0MiRIxUVFaWGDRvqgw8+8Nv36NGjysvL82lnGgsAgPotrMKNw+FQfHy8FixYoPbt22vu3LmaOHGili1bpoiICK++ixYt0pw5c3zOUVJSUlvlAgCAMBRW4WbDhg06ePCgunbtKkl6+OGH9d577+n7779XmzZtvPoOHjxYqampPufIysrSoEGDaqVeAAAQfsIq3Bw5ckROp9NzHBkZqaioKJnNZp++ycnJSk5O9mkvLS2t0RoBAEB4C6sFxV26dNG2bdv0+eefy+l0at68eUpOTlarVq1CXRoAAKgjwmrk5oorrtArr7yil156Sfn5+brqqqv0l7/8RVFRUaEuDQAA1BFhFW4k6dZbb9Wtt94a6jIAAEAdFVbTUgAAAFUVdiM3OH+fT5okR1GR5zi6ceMQVgMAQGgQbgzEUVTkFW5MsbEhrAYAgNBgWgoAABgK4QYAABgK4QYAABgK4QYAABhK0OEmNze3JuoAAACoFkGHm5tvvln33Xefli1bJpvNVhM1AQAAnLegbwX/7LPPtG7dOi1evFgvvfSSUlNTNWDAAHXv3l2Rkcxy1VfHjx9XTGSkHEVFshcWetrj/LRFRkYqOS4uFGUCAOqBoNNIo0aNdPfdd2vhwoVatWqVrrzySv3xj39U9+7dNXXqVGVnZ9dEnQhzLpdLTqdTTpdLDofD889fm8vlCnW5AAADO++hliNHjmjTpk3asGGD9u/fry5dushkMmn48OF68803q7NGAACAgAU9LbVo0SKtXr1au3btUvv27TVw4EClpaUpPj5ektSzZ0+NHj1aY8eOrfZiYRym2Fifx0WY4uLUbfr0EFYFADCCoMPN/Pnzddttt+n1119X8+bNfV5v2bKlJk2aVC3FIbQCXUdjNpvP6/wVHxcBAEB1CHpa6o477tDYsWN9gs2MGTMkSUlJSRo6dGj1VIeQCnQdjdPpDHWpAAB4BDRyk5eXp127dkmS5s6dq9atW8vtdnteP336tN577z099thjNVMlAABAgAIKNxdccIH++te/6sSJEyotLdXUqVO9Xo+OjmaNDQAACAsBhZvo6GgtXbpUkvTggw/qrbfeqtGiAAAAzlfAC4pPnjyphg0b6rXXXlNBQYHfPo0aNaqmsgAAAM5PwOGmZ8+e2rlzp7p06aKIiAiv19xutyIiIvTdd99Ve4EAAADBCDjcrFmzRpK0efPmGisGAACgqgK+FbxZs2aSpBYtWuiCCy5QixYtlJKSou3bt+vHH39UixYtaqxIAACAQAW9z82yZcvUo0cPSdL06dM1bdo0PfPMMzxyAQAAhIWgdyhesGCB5syZI4fDocWLF+tvf/ubmjRponvuuYfbwRGQyMhIFZw8KXthodfOx2arVatWrVJZWZmnzWq1qm/fvqEoEwBQRwUdbnJzc3X99ddr+/btslgsatu2rSSpsNwfKeBcXC6XZ5fjMyKdThUWFsput4ewMgBAXRd0uElOTtZXX32lZcuW6YYbbpAkrVq1ShdddFG1FwcAABCsoMPNo48+qvHjx6tBgwaaP3++MjIy9Pzzz2v27Nk1UR8AAEBQgg43qamp2rZtmyIjf1mLnJKSoq1btyo+Pr7aiwMAAAhW0OHG6XQqPT1d+/btk8vl8nrtoYceqrbCAAAAzkfQ4eaZZ57Rxx9/rGuuuUZms7kmagIAADhvQYebTz/9VAsXLtQll1xSE/UAAABUSdCb+EVHR3NnFAAACFtBh5tRo0bphRde0N69e1VQUOD1DwAAINSCnpaaOXOmiouL9dFHH3meDs5TwQEAQLgIOtysXr26JuoAAACoFkFPS7Vo0UJNmjTRt99+qw0bNigxMVEnT57kqeAAACAsBD1yk52drVGjRikuLk65ublKTU3VoEGDNGPGDB5wCAAAQi7okZsXX3xRo0eP1rp162QymdSyZUvNnDlTc+bMqYn6AAAAghJ0uPnuu+909913S5JnQXHv3r2Vk5NTvZUBAACch6DDTePGjfXDDz94tf34449KTk6utqIAAADOV9BrbkaNGqUHHnhAw4cPV1lZmd5//329++67GjVqVE3UBwAAEJSgw83AgQPVsGFDLV68WM2bN9emTZv08MMPq1+/fjVRHwAAQFCCDjeSdPPNN+vmm2+u7loAH1arVenp6bLZbAH15Y49AEBQ4SY/P18LFizQV199pdOnT6tZs2b67W9/q2HDhikxMbGmakQ9Z7PZdPr06VCXAQCoIwJeULxv3z4NGDBAu3btUq9evTRy5Ehdd911ysjI0B133FFtd0sdOnRIo0aN0nXXXaf+/fvrX//6V7WcFwAA1A8Bh5tp06ZpwIABeu+99zRmzBgNHDhQDz74oD744AP16dNHs2bNqnIxLpdL999/v2644QZt27ZNI0eO1COPPFLl8wIAgPoj4GmpzMxMTZ061e9r48aN0+23317lYnbu3KnIyEjdf//9kqQ77rhDl19+uVwulyIjg75rHQAA1EMBhxu73a4LLrjA72sJCQnVsiZi9+7dat26tZ5++mlt3rxZrVu31pQpU/wGm6NHjyovL8+nPTs7u8p1GNnx48cVExkpR1GR7IWFnva4Cm1mszlUJQIAUCUBh5szuxFXxu12V7mYU6dO6ZNPPtErr7yiyZMn64MPPtD48eO1bt06nz+2ixYt8vvIh5KSkirXYWQul0tOp1NOl0sOh8PTXrGNkTIAQF0VcLhxu93KysqqNMRUR7ixWCxq3bq1Z4pr+PDh+stf/qK9e/fq8ssv9+o7ePBgpaam+pwjKytLgwYNqnItAACgbgo43BQXF+uOO+6o9PVzjewEolWrVl7TW263Wy6Xy29wSk5O9vvIh9LS0irXAQAA6q6Aw83u3btrsg5J0vXXXy+Hw6EFCxZo2LBhevfdd5WYmOgzagMAAFCZsFpYERcXpwULFig9PV3XXXed1qxZo1mzZlXLqBAAAKgfzuvxCzXpsssu08KFC0NdBgAAqKPCauQGAACgqgg3AADAUAg3AADAUAg3AADAUAg3AADAUMLubinUbxUfs2Ey8S0KAAgOfzkQNsxWqyLXrJGr3C7VJQkJMg8YEMKqAAB1DeEGYcVus3k9rdxsNvNNCgAICmtuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAofDYHoQ9s9ksi8Xi026320NQDQAg3BFu6qDPJ02So6jIqy26ceMQVVOzzFarHEuWKKqgwKddaWkEHACAD8JNHeQoKvIJN6bY2BBVU/PshYVeTwoHAOBsWHMDAAAMhXADAAAMhXADAAAMhXADAAAMhQXFMAyr1ar09HTZbLaA+vbt27cWqgIA1DbCDQzFZrPp9OnToS4DABBCTEsBAABDIdwAAABDIdwAAABDIdwAAABDIdwAAABDIdwAAABDIdwAAABDYZ8b1Flms9nr2GTi2xkAQLhBHWW2WhW5Zo1c5TbsK0lIkHnAgBBWBQAIB4Qb1Fl2m032wkLPsdls5hsaAMCaGwAAYCyEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYChhG262b9+uNm3ahLoMAABQx4RluCkpKdFzzz0nt9sd6lIAAEAdE5bhZubMmerevXuoywAAAHVQ2O1W/69//Us7d+7Un/70J/3v//5vpf2OHj2qvLw8n/bs7OyaLA8AAIS5sAo3drtdzz//vN544w1FRUWdte+iRYs0Z84cn/aSkpKaKg8AANQBYRVuZs+erdTUVLVp00a5ubln7Tt48GClpqb6tGdlZWnQoEE1VSIAAAhzYRVuNm7cqLy8PL333nuexcQdO3bUypUr1bx5c6++ycnJSk5O9jlHaWlprdQKAADCU1iFm/Xr13v+Ozc3Vz169FBmZmYIKwIAAHVNWN4tBQAAcL7CauSmvKZNm+r7778PdRl1wvHjxxUTGSlHUZHshYWe9rgKbWazOVQlAgBQa8I23CBwLpdLTqdTTpdLDofD016xLTLS+AN1ZrNZFovFp91ut3sdW61Wpaeny2aznfOcVqtVffv2rbYaAQA1i3ADwzBbrXIsWaKoggKfdqWl+QQcm82m06dP12KFAIDaQLiBodgLC72m5gAA9Y/x5ykAAEC9QrgBAACGwrQU6oWKd4qZTHzrA4BR8Rsehme2WhW5Zo1c5RYPlyQkyDxgQAirAgDUFMIN6gW7zea10NhsNvPNDwAGxZobAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKOxjhnrLbDbLYrH4tNvtdq9jq9Wq9PR02Wy2c57TarWqb9++1VYjACB4hBvUS2arVY4lSxRVUODTrrQ0n4Bjs9l0utzjGwAA4Ytwg3rLXljo9UgGAIAxsOYGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCvvcABWYzWavY5OJHxMAqEv4rQ2UY7ZaFblmjVzldiMuSUiQecCAEFYFAAgG4QaowG6zee1cbDab+UEBgDqE39lh7vNJk+QoKvIcRzduHMJqAAAIf4SbMOcoKvIKN6bY2BBWAwBA+CPcANXIarUqPT1dNpst4P59+/at4aoAoH4h3ADVzGaz6XS5BckAgNrFPjcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQuBUcCIDZbJbFYjlnPx6yCQChx29i4BzMVqscS5YoqqDAqz0uKUnOkhKVVuEhm8Fs+seGfwAQGMINEAB7YaHXwzSlX0NPcXGVH7LJpn8AUL1YcwMAAAyFkRugmlW2Psdut4egGgCofwg3QDWqbH2O2WqV0tIIOABQC8JuWmrNmjXq27evOnTooKFDh+rHH38MdUlAUM6szyn/ryzAp4QDAKourMJNdna2pkyZounTp+urr75Sjx499NBDD4W6LAAAUIeE1bRUTk6O7r33Xl1zzTWSpKFDh2rGjBk6ffq0GjRo4NX36NGjysvL8zlHdnZ2rdQKBMtsNoe6BACoF8Iq3HTv3l3du3f3HG/ZskXNmzf3CTaStGjRIs2ZM8envaSkpEZrBM6H2WpV5Jo1cpW75dtstco0fHgIqwIAYwqrcFPed999p8mTJ+uVV17x+/rgwYOVmprq056VlaVBgwbVdHlA0Ow2m89eOcFgwz8ACExYhpuMjAz993//tx5//HH16dPHb5/k5GQlJyf7tJeWltZ0eUDIsOEfAJxb2IWb9PR0Pf3003rttdcqDTYAAACVCatw88MPP+jJJ5/U3Llz1bVr11CXAwAA6qCwCjf/+Mc/VFJSonHjxnm1r1+/XikpKSGqCgAA1CVhFW4mT56syZMnh7oMAABQh4XVJn4AAABVRbgBAACGElbTUgB+4e+p4sFgTxwA9RnhBggzFotFMevXez1s02y1KmbUKNntdq/gc7anjLMnDoD6inADhKGyCrsZm61WFb73nuynTinK6fS0KS3trAEHAOojwg0QQiaTSWaz2Ws0xmw2y+Wnb5nNpjKbTQ6Ho/YKBIA6iHADhIi/0RhJiklKUnFk1db6+wtNlY3w8LRyAEZDuAFCyN9ojNlqDfjj/QUTs9mskg8+8JnCMvfvr7KyMt+PX75cn65b52kzxcWp2/TpwX4qABA2CDdAHWW2WhW5Zo1cFRYNxyQl+YQms9UqcyV97TabHOVGjkyxsfp80iQ5ioq8+hJ6ANQVhBugDrNXWHgs/RJkIoLoGxkZqePHj8vl+mWlT1xkpBxFRX77bt68Wb169arWzwEAqhvhJkylp6fLbrfLnp/vdUtwxT88rJdAdXC5XJ5RHqfTKWe54zMinU4VVRjNAYBwRLgJUzabTaWlpYpyOr3+yFT8wxNZxYWnAAAYDeEGQMDi4uIC2vnYbDYrcuVKWcuNLLJmB0BtIdwACJjJZFJRUZFKS0s9bf5uMbdYLLKcOqXo2NjaLA8AJBFuAASoqrskcxcWgNpCuAEQsKrukuwoKvIJNwBQ3Qg3AGpEoLeYxyUna83o0azPAVBtCDcAqqSyXZKlwG4xdzqdchQVKTomxtPGFBaAqiDcADhvZ9sl2RHhbytB/4LZSHDVqlWyWCzq27dv1T8BAIZEuAFQJcHsknw2gW4kWFhYqOjo6KqUDMDg2AEOAAAYCiM3AOocq9Ua0GaCZ/oyhQXUL4QbAHWK2Wz2u5lgRYHsvQPAmAg3AOqMMwuYT5WUyOVyKcrpVFxSkpwlJSott6jZbLXK3L+/ysrK1LBhQ23evNnz0M+ysjLvc5rN0vLlshcWKjIyUgkJCZK4Mwuoywg3AOoUu80m96/hxuFwyGy1ylFc7LWo2Wy1yvzrXVynzGa5XC45nU6ZrVaVVthR2WKxKOrkSdkLC2WxWHTUZpPL5fLcmeU3DOmX52z16tWrdj5pAEEh3AAwJM9dXBaL151Y53Km75k7syoGoYgVK37Zpdls1kfvvCOXyyVLfLzMAwf6BKEzx6z7AWoX4QYAglDmJzRFxsTItWiRz9RYxVEiALWDcBOmzL8OpbtCXQhgMBV3VDabzdXyc+Z3v59f36viuh9/qjLKc+bcFUeOKmIECfUF4SZMVNxq3p6fr5jERBVHshURUF387agck5RUIz9n5d+r/LqfyhZAuypZAO1zXrNZZYsXe4KU2WxWWVmZImNiqn2kiNvtUVcRbsJExacll9lsiiz3rB0A1aPiCIvZaq20b1VHefyt+wlmAbS/IBSRlKSS06dVeuqUJMn567kjnc5z1hPM/kBJSUmy2Ww6XeHRGlU9L0EItYFwAwB+1OYojxRcEKqKswUWi8Xyf/XY7X4fino+5wVqG+EGACoRzChPXVI+xJxhNpsVtWqVyn4dfTllNssUFyfr737ndy0PC6URzgg3AGBg5YOMyWRSXFyc53b28mKSklRcfpTIYlFUbKxnuqw8s9Uq1dKdYIFOd0m/TKUVFRUxPQbCDQAYhb81QhVHY2KbNPGZ6pIqH5XydxdYxfcymUwym81+R4QqatiwobZs2aJT//u/Pue1xMdL/+//eUaKgln3I/0SWFgnBIlwAwCGUOkaIT+jMdX9XmdCU1xhodfiZ0k+i6LP9C2/KPoMp9OpyNJST7gJdt1PsFgnZFyEGwAwiNpcI+T1Xr+GJr97/VRcFH2WgOUvNAW77qeyEaTaXCMULiNC4VJHKBBuAABhw19oCnTdj8lkkv3DDxV18uQ5+wajpm6hr2nhUkcoEG4AAGGtsnU/fvsWFgbctzIVR35MJpNOnjzpExSqOkIULut+gg1vdWHRNuEGAFAnVVyPYzIF9yfN3xRWxUXYklSSkCDzgAFe/SwWi2LWr/e566wkIUFxd96p0tJSr/bKQo/dbg+obzABRPolhAQjmIXYNbFoOykpSV9//XVAtQaCcAMAqHP8LaAuadFCERER/vtXCEIxMTF+p7B8FmH/+rH+/liW+RlRimvUSI4lSxRVUOBVq79pMX/TaGebQgv2zrFwEEwQKikpqbb3JdwAAOqkitNVFUdRzqgsCPkLJ5Utwq44ynO2R3H4mxrzd9eXyWQKqq+/kabKRoT89a9PGy8SbgAAhhdoEPLHbLX6jMYE8ygOf+FK8j/SVFlfR4sWirDZAhoRslgsKvngA9lPnVLUr88cC3ZRdSB7FoUzwg0AAOdQcYQl2Nvs/S2KrixgVdY3mMXS9sJCldlscjgcQdUpVb6eyGy1yjR8eNDnCwXCDQAAdZS/KazKNj6srL3iFJbZbPY7ZVeZUO8r5E/YhZsdO3Zo8uTJOnDggNq1a6dp06apSZMmoS6rWqSnp/9ywZcv9/qmsSYnq+j4cU9bTe7ICQAwhsqmsGKSkuQIcLorLilJJQ6H1xTW2abc/AWhineXma1Wmfv3D+qxHCaTKei73c56vmo7UzUoKSnRww8/rMmTJ6tHjx569dVX9dprr2n69OmhLq1a2Gw2lZaWKurkSa9wExkTI6fL5Rk+jAxwHhcAUL9Vtiu0v3vGKu1bVuY1hVXpomqrVYXvvecbhCreXWa1yrxmjU6VlMjlcnn6VnwUR/m2UyUlKsvPD/rzr0xY/RXNyMhQSkqK+vTpI4vFookTJyo9PV1FRUWhLg0AgHqvzGbzWv9TVsnfZ/uv/Sr2tZc7Lt9WZrPJUVxcbXWG1cjNzz//rFatWnmOGzVqpLi4OO3fv19t2rTx6nv06FHl5eX5nCMrK0uS9N1339VorecjJyfnl2RcVqYy1//dRBhbUiKH3e5pMzkccjqdiqnQboS+Z/qbi4sD6nuquFhlpaWyu7xvujRC39iSEsnhUElZmdxud9B9/fWrD30ddrvcv37Pud3uSr9eZ9pMBu4rBf+z6u9nzwh9pdr9ua7sezQc+ob6Z9Xfz2kgfY/8OnJUXA0hJ6zCTVFRkaKjo73aYmNj/W7ss2jRIs2ZM8en/dSvT5m99957a6ZIAABQY7755hvdcMMNVTpHWIWb2NhYnxXWxcXFiouL8+k7ePBgpaam+rTv2rVLjz/+uKZNm6Yrr7yyxmrFuWVnZ2vSpEmaPn26LrnkklCXU69xLcIL1yN8cC3CR1ZWlh5//HFdc801VT5XWIWb1q1ba/Xq1Z7jgoIC2Ww2XXzxxT59k5OTlZyc7Pc8F1xwgdq1a6errrqqxmrFuUVHRysmJkZXXnkl1yLEuBbhhesRPrgW4SM6OloXXHCBEhISqnyusFpQ3KVLFx0+fFjr1q2T3W7XzJkzlZqaqpiYmFCXBgAA6oiwCjcxMTF688039dZbb6lz5846cOCAJk+eHOqyAABAHRJW01KSdO2112rFihWhLgMAANRRYTVyAwAAUFWGCzdJSUl66KGHlJSUFOpS6j2uRfjgWoQXrkf44FqEj+q8FhHu8ruHAQAA1HGGG7kBAAD1G+EGAAAYCuEGAAAYSp0NNzt27FD//v3Vtm1bjRw5UseOHfPpk5eXp5EjR6pdu3bq16+fdu3aFYJKjS+Qa3HGrl27lJaWVovV1S+BXIusrCwNGTJEHTp00O9+9ztt2rQpBJXWD4Fcj6+//lp33HGH2rVrp1tuuUWffvpp7RdaDwTze+rEiRO6/vrrtW3btlqssP4I5FqsWbNGV111ldq1a+f5d+LEicDfxF0HFRcXu6+//nr3hg0b3KWlpe4XXnjB/dhjj/n0+/3vf+9+7bXX3KWlpe7ly5e7b7rpJrfD4QhBxcYV6LVwu93u1atXuzt27Oju3bt3LVdZPwRyLRwOh/umm25yf/jhh26n0+nOyMhwt2/f3n3w4MEQVW1cgVwPp9Pp7tGjh3vFihVut9vt/uKLL9zXXnutu6ioKBQlG1Ywv6fcbrf70Ucfdbdp08b95Zdf1mKV9UOg12LGjBnumTNnnvf71MmRm4yMDKWkpKhPnz6yWCyaOHGi0tPTVVRU5OlTWFiorVu3aty4cbJYLLrtttvUoEEDffnllyGs3HgCuRbSLyl89uzZGjt2bIgqNb5ArsWxY8d09dVXa+DAgYqMjFSXLl3UsmVLfffddyGs3JgCuR6RkZFau3atBgwYIKfTqRMnTig+Pl5RUVEhrNx4Av09JUkff/yxCgsLdeGFF4agUuML9Fp8//33uuyyy877fepkuPn555/VqlUrz3GjRo0UFxen/fv3e9r279+vhIQENWjQwNPWqlUrZWdn12aphhfItZCkrl27eoYZUTMCuRYpKSmaPXu25zgnJ0fZ2dm6/PLLa7PUeiHQn424uDg5HA5de+21euyxxzRx4kRZLJZartbYAr0Wp06d0rRp0zRlypRarrD+CPRafP/991qyZIluuOEG9e/fX5988klQ7xN2j18IRFFRkaKjo73aYmNjVVJSctY+MTExXn1QdYFcC0lKTEyszbLqpUCvxRknT57UuHHjNHjwYF100UW1UWK9Esz1iIqK0s6dO7Vz5049+OCDuuaaawic1SjQazF16lQNGzZMTZs2rc3y6pVAroXdbtdFF12kgQMHKjU1VRkZGXrkkUe0bNkyr2B0NnVy5CY2NlZ2u92rrbi4WHFxcV59SktLvfqUlJR49UHVBXItUDuCuRY5OTm6++67dcUVV+jJJ5+srRLrlWCuR0REhCwWi7p06aJu3brpiy++qK0y64VArsXWrVu1f/9+3X333bVdXr0SyLWwWCz6+9//rrS0NFksFvXo0UOdOnUK6ueiToab1q1ba9++fZ7jgoIC2Ww2XXzxxZ62li1bqqCgQIWFhZ62n376Sb/5zW9qs1TDC+RaoHYEei327t2rwYMHKzU1VVOnTlVkZJ38NRD2ArkeJ06c0M033+z1P2J2u91rOh1VF8i12LBhg7KysnTdddepY8eOOnjwoB588EGtWrUqBBUbVyDX4siRI5o5c6bXx5WVlQU1XVsnf6t16dJFhw8f1rp162S32zVz5kylpqYqJibG0yc+Pl433HCDZs2aJbvdrpUrV6qgoEAdO3YMYeXGE8i1QO0I5FqUlpbqwQcf1ODBgzVp0qQQVmt8gVyPhIQENWzYUG+99ZacTqe2bNmir7/+WjfddFPoCjegQK7FSy+9pF27dikzM1OZmZm68MIL9dZbb6l///4hrNx4ArkWDRo00MKFC7VkyRK5XC5t3LhR33zzjXr16hX4G1Xllq5Q+te//uUeMGCAu23btu5Ro0a58/Pz3YcOHXK3bdvWfejQIbfb7XYfPXrUPXr0aHf79u3d/fv3d3/99dchrtqYArkWZ3z55ZfcCl6DznUt1qxZ477sssvcbdu29fq3Zs2aUJduSIH8bOzfv9993333uTt06OC+/fbb3Tt27Ahx1cYUzO8pt9vt7t27N7eC15BArkVmZqb79ttvd7dt29Z96623Bn0teHAmAAAwlDo5LQUAAFAZwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUU6gLAGAcv//973X11VdrwoQJcrvd+vDDD7V06VLt3btXTqdTrVu31sCBAzVkyBBFRESc83x79uzRSy+9pKysLEVHR+uWW27RH/7wB1ksFj3wwAPasWOHp6/b7VZxcbGmT5/O84CAeo5wA6BGPP7449q9e7eefvpptW3bVlFRUfr3v/+tp556Svn5+XrooYfO+vFut1vjx4/X7bffrvnz56ugoEAjR47Uu+++q9GjR+udd97x6j9t2jR9/fXXSktLq8lPC0AdQLgBcN62bdumV155Rfv371e3bt1UWFgoSdqyZYs+/fRTrVmzRikpKZ7+HTt21Ouvv65///vf5zx3RESEVq5cKYvFIkk6duyY7Ha7EhISfPpmZmbqgw8+0OrVq2U2m8957o8//lgzZszQkSNH1KxZM40YMUJ33nmnJOnHH3/UK6+8om+//VaJiYkaNWqUBg8eHNDXA0B4YM0NgPNy/PhxjR07VsOGDVNmZqbS0tKUmZkpSUpPT1fPnj29gs0Z7du313333RfQe8TGxioqKkoDBw7UbbfdpuTkZJ8pJ7fbrZdfflljx45Vs2bNznlOp9OpP/zhD3ruueeUmZmpJ598Ui+//LJOnjwpm82mkSNH6vrrr9c///lP/fnPf9bcuXO1adOmgOoFEB4INwDOyyeffKKUlBQNHDhQJpNJt956q9q3by9JnhGR8m644QZ17NhRHTp00DXXXKMTJ04E/F4LFy7UZ599ptLSUr3wwgter23ZskVHjx7V0KFDAzpXVFSUGjRooGXLlumrr75Sp06dtGPHDjVs2FBbtmxRXFycRo8eLbPZrDZt2mjo0KH68MMPA64VQOgRbgCcl7y8PDVt2tSr7cILL5QkNWnSRHl5eV6vffHFF8rMzNSKFStkt9vldrsDfq/o6GilpKRo/Pjx2rBhg9drS5Ys0e23367Y2NiAz/c///M/cjqdmjBhgjp37qzXX39ddrtdhw4d0sGDB9WxY0fPv7feeku5ubkBnxtA6LHmBsB5SUlJUU5OjlfbkSNHdPHFF6tPnz565plndPz4cSUmJp7X+Y8fP6677rpLCxcu9ExvlZWV6YILLvD0sdvt+uyzz/TBBx8EfN6ioiIdPnxYM2bMkNvt1q5du/TQQw/p8ssvV0pKitq0aaOlS5d6+ufn58vhcJzX5wAgNBi5AXBeevbsqYKCAr377rtyOBzatGmTvvrqK0lS79691a1bN40YMUIZGRkqKyuT0+nUtm3b9MQTT6hhw4aehcKVSUxMVLNmzfTGG2+ouLhYBw8e1KxZszRw4EBPn927dysqKkpt2rQJuG6n06lx48Zp5cqVkqTk5GRFRESoUaNGuummm5Sbm6vFixfL4XAoNzdXI0eO1Lx5887jKwQgVCLcwYwNA0A533zzjaZMmaIff/xR7dq1U3R0tNc+N8uWLdPSpUuVnZ2t0tJSXXjhherVq5eGDx+uxo0bn/P8R48e1csvv6wvv/xS8fHxuvPOO/Xggw8qKipKkrRu3Tr98Y9/1MaNG4Oq+7PPPtP06dN14MABWa1WDRkyxHNr+u7duzV16lR99913MplM6tu3r5566qlzhjEA4YNwAwAADIVpKQAAYCgsKAYQEps2bdLjjz9e6euTJk0K+Pbu8rKyss76cffcc89Z3xdA3ce0FAAAMBSmpQAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKH8f1dYmDUXiEx8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annotation_file = './data/annotation/NNNlib2b_annotation_20220519.tsv'\n",
    "replicate_df = pd.read_table('./data/nnnlib2b_replicates.tsv')\n",
    "arraydata = ArrayData(replicate_df=replicate_df.iloc[:4,:],\n",
    "                      annotation_file=annotation_file,\n",
    "                      learn_error_adjust_from=('r1', 'r2'))\n",
    "arr = arraydata.data.join(arraydata.annotation)\n",
    "arr_v0 = pd.read_csv('./data/models/raw/arr_v0_n=30872.csv', index_col=0)\n",
    "excluded_construct_type = ['pseudoknot_pt_muts', 'PUMcontrols']\n",
    "arr_v0 = arr[arr.reset_index().ConstructType.apply(lambda s: s not in excluded_construct_type).values]\n",
    "arr_v0 = arr_v0.dropna(subset=['ConstructType', 'RefSeq','TargetStruct'])\n",
    "arr_v0.rename(index={'WB3':'WB_controls260871'}, inplace=True)\n",
    "arr_v0.to_csv(os.path.join('./data/models/raw', 'arr_v0_n=%d.csv' % arr_v0.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "440c0368-31a6-4ad2-a657-ad414f82a3b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dH</th>\n",
       "      <th>dH_se</th>\n",
       "      <th>dH_lb</th>\n",
       "      <th>dH_ub</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Tm_se</th>\n",
       "      <th>Tm_lb</th>\n",
       "      <th>Tm_ub</th>\n",
       "      <th>dG_37</th>\n",
       "      <th>dG_37_se</th>\n",
       "      <th>...</th>\n",
       "      <th>dG_37_ensemble_NUPACK</th>\n",
       "      <th>TargetStruct</th>\n",
       "      <th>dG_37_NUPACK</th>\n",
       "      <th>dG_37_ensemble_150mM_NUPACK</th>\n",
       "      <th>dG_37_ensemble_75mM_NUPACK</th>\n",
       "      <th>GC</th>\n",
       "      <th>Tm_NUPACK_salt_corrected</th>\n",
       "      <th>dG_37_NUPACK_salt_corrected</th>\n",
       "      <th>dH_NUPACK_salt_corrected</th>\n",
       "      <th>dS_NUPACK_salt_corrected</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEQID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PNTC1</th>\n",
       "      <td>-7.648206</td>\n",
       "      <td>1.775741</td>\n",
       "      <td>-11.135930</td>\n",
       "      <td>-4.366764</td>\n",
       "      <td>78.793200</td>\n",
       "      <td>13.138525</td>\n",
       "      <td>63.323615</td>\n",
       "      <td>109.360781</td>\n",
       "      <td>-0.916186</td>\n",
       "      <td>0.231533</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>..</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PNTC4</th>\n",
       "      <td>-4.472448</td>\n",
       "      <td>1.203489</td>\n",
       "      <td>-6.921921</td>\n",
       "      <td>-2.403929</td>\n",
       "      <td>53.052762</td>\n",
       "      <td>14.177557</td>\n",
       "      <td>28.672130</td>\n",
       "      <td>79.441090</td>\n",
       "      <td>-0.210992</td>\n",
       "      <td>0.140216</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>.....</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PNTC43</th>\n",
       "      <td>-5.101692</td>\n",
       "      <td>1.067589</td>\n",
       "      <td>-7.686777</td>\n",
       "      <td>-3.759027</td>\n",
       "      <td>71.006778</td>\n",
       "      <td>15.914130</td>\n",
       "      <td>48.276700</td>\n",
       "      <td>97.913898</td>\n",
       "      <td>-0.504483</td>\n",
       "      <td>0.157935</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>....</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PNTC44</th>\n",
       "      <td>-6.250533</td>\n",
       "      <td>0.624976</td>\n",
       "      <td>-8.953442</td>\n",
       "      <td>-4.154810</td>\n",
       "      <td>50.960900</td>\n",
       "      <td>5.309646</td>\n",
       "      <td>31.978803</td>\n",
       "      <td>69.803305</td>\n",
       "      <td>-0.265505</td>\n",
       "      <td>0.088842</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>.....</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PNTC45</th>\n",
       "      <td>-6.045450</td>\n",
       "      <td>1.141377</td>\n",
       "      <td>-9.944842</td>\n",
       "      <td>-3.797804</td>\n",
       "      <td>37.862146</td>\n",
       "      <td>6.345553</td>\n",
       "      <td>20.704052</td>\n",
       "      <td>54.911182</td>\n",
       "      <td>-0.004883</td>\n",
       "      <td>0.143087</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>......</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD90</th>\n",
       "      <td>-4.981602</td>\n",
       "      <td>0.518320</td>\n",
       "      <td>-6.988471</td>\n",
       "      <td>-3.213197</td>\n",
       "      <td>47.802061</td>\n",
       "      <td>5.734932</td>\n",
       "      <td>29.095481</td>\n",
       "      <td>69.316136</td>\n",
       "      <td>-0.176236</td>\n",
       "      <td>0.081003</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.651917</td>\n",
       "      <td>((((((((....)))))))).....</td>\n",
       "      <td>-8.694564</td>\n",
       "      <td>-7.214151</td>\n",
       "      <td>-6.622648</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>68.039465</td>\n",
       "      <td>-6.276615</td>\n",
       "      <td>-68.993290</td>\n",
       "      <td>-0.194349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD92</th>\n",
       "      <td>-5.157644</td>\n",
       "      <td>1.560263</td>\n",
       "      <td>-9.438979</td>\n",
       "      <td>-3.247748</td>\n",
       "      <td>15.636943</td>\n",
       "      <td>8.524406</td>\n",
       "      <td>2.053734</td>\n",
       "      <td>34.787313</td>\n",
       "      <td>0.370471</td>\n",
       "      <td>0.248441</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.443914</td>\n",
       "      <td>......((((((((....))))))))</td>\n",
       "      <td>-8.513050</td>\n",
       "      <td>-7.085290</td>\n",
       "      <td>-6.500164</td>\n",
       "      <td>34.615385</td>\n",
       "      <td>68.319435</td>\n",
       "      <td>-6.130990</td>\n",
       "      <td>-66.844944</td>\n",
       "      <td>-0.188040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD93</th>\n",
       "      <td>-5.184335</td>\n",
       "      <td>0.634817</td>\n",
       "      <td>-8.579373</td>\n",
       "      <td>-3.507288</td>\n",
       "      <td>35.367721</td>\n",
       "      <td>5.354183</td>\n",
       "      <td>19.338231</td>\n",
       "      <td>55.024302</td>\n",
       "      <td>0.010671</td>\n",
       "      <td>0.100631</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.651996</td>\n",
       "      <td>((((((((....))))))))......</td>\n",
       "      <td>-8.694564</td>\n",
       "      <td>-7.214991</td>\n",
       "      <td>-6.623592</td>\n",
       "      <td>34.615385</td>\n",
       "      <td>67.871490</td>\n",
       "      <td>-6.245723</td>\n",
       "      <td>-68.993290</td>\n",
       "      <td>-0.194349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD96</th>\n",
       "      <td>-3.876066</td>\n",
       "      <td>0.793170</td>\n",
       "      <td>-7.247002</td>\n",
       "      <td>-2.141668</td>\n",
       "      <td>22.124843</td>\n",
       "      <td>8.359311</td>\n",
       "      <td>2.895484</td>\n",
       "      <td>53.436448</td>\n",
       "      <td>0.181243</td>\n",
       "      <td>0.128635</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.652037</td>\n",
       "      <td>((((((((....)))))))).......</td>\n",
       "      <td>-8.694564</td>\n",
       "      <td>-7.215408</td>\n",
       "      <td>-6.624057</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>67.716105</td>\n",
       "      <td>-6.217119</td>\n",
       "      <td>-68.993290</td>\n",
       "      <td>-0.194349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD99</th>\n",
       "      <td>-3.174603</td>\n",
       "      <td>2.300422</td>\n",
       "      <td>-6.666587</td>\n",
       "      <td>-1.290672</td>\n",
       "      <td>2.579335</td>\n",
       "      <td>20.935993</td>\n",
       "      <td>-50.502632</td>\n",
       "      <td>29.457776</td>\n",
       "      <td>0.400426</td>\n",
       "      <td>0.286939</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.652072</td>\n",
       "      <td>((((((((....))))))))........</td>\n",
       "      <td>-8.694564</td>\n",
       "      <td>-7.215780</td>\n",
       "      <td>-6.624469</td>\n",
       "      <td>32.142857</td>\n",
       "      <td>67.571946</td>\n",
       "      <td>-6.190558</td>\n",
       "      <td>-68.993290</td>\n",
       "      <td>-0.194349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              dH     dH_se      dH_lb     dH_ub         Tm      Tm_se  \\\n",
       "SEQID                                                                   \n",
       "PNTC1  -7.648206  1.775741 -11.135930 -4.366764  78.793200  13.138525   \n",
       "PNTC4  -4.472448  1.203489  -6.921921 -2.403929  53.052762  14.177557   \n",
       "PNTC43 -5.101692  1.067589  -7.686777 -3.759027  71.006778  15.914130   \n",
       "PNTC44 -6.250533  0.624976  -8.953442 -4.154810  50.960900   5.309646   \n",
       "PNTC45 -6.045450  1.141377  -9.944842 -3.797804  37.862146   6.345553   \n",
       "...          ...       ...        ...       ...        ...        ...   \n",
       "SD90   -4.981602  0.518320  -6.988471 -3.213197  47.802061   5.734932   \n",
       "SD92   -5.157644  1.560263  -9.438979 -3.247748  15.636943   8.524406   \n",
       "SD93   -5.184335  0.634817  -8.579373 -3.507288  35.367721   5.354183   \n",
       "SD96   -3.876066  0.793170  -7.247002 -2.141668  22.124843   8.359311   \n",
       "SD99   -3.174603  2.300422  -6.666587 -1.290672   2.579335  20.935993   \n",
       "\n",
       "            Tm_lb       Tm_ub     dG_37  dG_37_se  ...  dG_37_ensemble_NUPACK  \\\n",
       "SEQID                                              ...                          \n",
       "PNTC1   63.323615  109.360781 -0.916186  0.231533  ...              -0.000000   \n",
       "PNTC4   28.672130   79.441090 -0.210992  0.140216  ...              -0.000000   \n",
       "PNTC43  48.276700   97.913898 -0.504483  0.157935  ...              -0.000000   \n",
       "PNTC44  31.978803   69.803305 -0.265505  0.088842  ...              -0.000000   \n",
       "PNTC45  20.704052   54.911182 -0.004883  0.143087  ...              -0.000000   \n",
       "...           ...         ...       ...       ...  ...                    ...   \n",
       "SD90    29.095481   69.316136 -0.176236  0.081003  ...              -8.651917   \n",
       "SD92     2.053734   34.787313  0.370471  0.248441  ...              -8.443914   \n",
       "SD93    19.338231   55.024302  0.010671  0.100631  ...              -8.651996   \n",
       "SD96     2.895484   53.436448  0.181243  0.128635  ...              -8.652037   \n",
       "SD99   -50.502632   29.457776  0.400426  0.286939  ...              -8.652072   \n",
       "\n",
       "                        TargetStruct  dG_37_NUPACK  \\\n",
       "SEQID                                                \n",
       "PNTC1                             ..      0.000000   \n",
       "PNTC4                          .....      0.000000   \n",
       "PNTC43                          ....      0.000000   \n",
       "PNTC44                         .....      0.000000   \n",
       "PNTC45                        ......      0.000000   \n",
       "...                              ...           ...   \n",
       "SD90       ((((((((....)))))))).....     -8.694564   \n",
       "SD92      ......((((((((....))))))))     -8.513050   \n",
       "SD93      ((((((((....))))))))......     -8.694564   \n",
       "SD96     ((((((((....)))))))).......     -8.694564   \n",
       "SD99    ((((((((....))))))))........     -8.694564   \n",
       "\n",
       "        dG_37_ensemble_150mM_NUPACK  dG_37_ensemble_75mM_NUPACK         GC  \\\n",
       "SEQID                                                                        \n",
       "PNTC1                     -0.000000                   -0.000000   0.000000   \n",
       "PNTC4                     -0.000000                   -0.000000   0.000000   \n",
       "PNTC43                    -0.000000                   -0.000000   0.000000   \n",
       "PNTC44                    -0.000000                   -0.000000   0.000000   \n",
       "PNTC45                    -0.000000                   -0.000000   0.000000   \n",
       "...                             ...                         ...        ...   \n",
       "SD90                      -7.214151                   -6.622648  36.000000   \n",
       "SD92                      -7.085290                   -6.500164  34.615385   \n",
       "SD93                      -7.214991                   -6.623592  34.615385   \n",
       "SD96                      -7.215408                   -6.624057  33.333333   \n",
       "SD99                      -7.215780                   -6.624469  32.142857   \n",
       "\n",
       "        Tm_NUPACK_salt_corrected  dG_37_NUPACK_salt_corrected  \\\n",
       "SEQID                                                           \n",
       "PNTC1                        NaN                          NaN   \n",
       "PNTC4                        NaN                          NaN   \n",
       "PNTC43                       NaN                          NaN   \n",
       "PNTC44                       NaN                          NaN   \n",
       "PNTC45                       NaN                          NaN   \n",
       "...                          ...                          ...   \n",
       "SD90                   68.039465                    -6.276615   \n",
       "SD92                   68.319435                    -6.130990   \n",
       "SD93                   67.871490                    -6.245723   \n",
       "SD96                   67.716105                    -6.217119   \n",
       "SD99                   67.571946                    -6.190558   \n",
       "\n",
       "        dH_NUPACK_salt_corrected  dS_NUPACK_salt_corrected  \n",
       "SEQID                                                       \n",
       "PNTC1                   0.000000                 -0.000000  \n",
       "PNTC4                   0.000000                 -0.000000  \n",
       "PNTC43                  0.000000                 -0.000000  \n",
       "PNTC44                  0.000000                 -0.000000  \n",
       "PNTC45                  0.000000                 -0.000000  \n",
       "...                          ...                       ...  \n",
       "SD90                  -68.993290                 -0.194349  \n",
       "SD92                  -66.844944                 -0.188040  \n",
       "SD93                  -68.993290                 -0.194349  \n",
       "SD96                  -68.993290                 -0.194349  \n",
       "SD99                  -68.993290                 -0.194349  \n",
       "\n",
       "[106 rows x 42 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_v0.query('Series == \"Control\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3478cd-b070-45b8-8cbc-dd86ea652e36",
   "metadata": {
    "tags": []
   },
   "source": [
    "### arr_p_unfold\n",
    "\n",
    "For sequence-to-melt-curve deep learning models. Control variants excluded in the arr file for structure energy models are included here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07f7a9c8-83c7-4a18-af80-1dbf6edaf37f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.43% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "64.31% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "71.46% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "Load from file ./data/fitted_variant/NNNlib2b_DNA_20211022_v6.CPvariant.gz\n",
      "86.43% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "Load from file ./data/fitted_variant/NNNlib2b_DNA_20211216_v9.CPvariant.gz\n",
      "78.88% variants passed the filter n_clusters > 5 & dG_37_se < 2 & Tm_se < 25 & dH_se < 25 & RMSE < 0.5\n",
      "\n",
      "Combining dH\n",
      "\n",
      "Combining Tm\n",
      "\n",
      "Combining dG_37\n",
      "\n",
      "Combining dS\n",
      "\n",
      "Combining fmax\n",
      "\n",
      "Combining fmin\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 501.969x137.795 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N/E9QWFiRnp/ymrAHAL5kMpm0bNkyrV+/XlFRUbr55pu9+rzU1FS99NJLmj17tqTLWwfVrl3b83pkZGSxn5eenq569eoVaBcZGamUlBTVr1/fcz4gIED16tXTmTNnir1e/uPU1FRt2LBBy5cv95yz2+3KycnRuXPnCsxfbdSokaSS14Ir/ARVgwYNlJOTo4yMDElSRL5ef5PJ5BmK81eEG1xRVZkACAClkZCQoH379mnbtm2qWbOmjh49qoSEhKt+3nXXXaennnpKPXv2lHR5gu7Fixc9rwcEBBT7eVFRUZ5FayXp2LFj2rlzp6Kjo3X8+HHPeZfLpdOnT6tu3br6/vvvi1wv/3FUVJSefPJJDR8+XNLlp4VTUlIUEhKi6OhonT171tN2586dcrvdCgsLK7a+6OhopaSkeI5PnTols9msmjVrXu1b4pcYlgIAVIqgsDAFWSwV91HCL+7iZGZmKigoSCaTSRcvXvRMAM7NzS3SNjg4WFlZWZKk3r1765133lFaWpqsVquef/55/fnPf77q+/Xq1Uvbtm3Tf//7X9ntdi1atEgnT55UXFyctm3bpr179yo3N1dvv/22goKC1LJly6te87777tP777+vn3/+Wbm5uYqPj9ezzz4rSYqLi9O7776r9PR0nTt3Tq+99pocDoeCg4NltVqL9Lz06dNH77zzjk6ePKnMzEzNnz9fvXr1ktlsvmod/oieGwBApfCnIe37779fu3btUpcuXRQeHq6HHnpIiYmJ+umnn2SxWAq0jYuL0yuvvKJLly7p8ccfV1pamgYMGKCsrCx16tRJL7744lXfr1mzZnrhhRf05JNP6vz58+ratasmTZqkGjVq6LXXXtNLL72klJQU3XbbbVq6dKmCg4Oves2uXbtq+PDheuyxx5SRkaGWLVt6NqQeNGiQTp8+rf79+8vlcmnIkCHq0aOHMjMzFRUVpfbt22vVqlWeaz344IM6e/ashgwZoqysLMXGxuqFF14o5XfVfwS4/f15rlI6ePCg2rZtqwMHDqhNmza+LsfndowbV2BYKbRu3WKHmoo7X9y5IItFdy9ZUvGFA6jSnE6njh49qptvvlkmk8nX5cCPlPRnozx/fzMsBQAADIVwAwAADIVwAwAADIVwAwAADIVwAwAod3nrsRjsmRWUg7w/EyWtCVQeeBQcAFDuAgMDZTablZ6errp161boLzJUHbm5uTpz5oxCQ0MVGFhx/SuEGwBAhbjxxht1/PhxnT9/3telwE8EBASoTp06io6OrtD3IdwAACpEcHCwfvvb38rlcjE8BQUEBHg+KhrhBgBQoSpy+AEoDn/iAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoRBuAACAoQT5ugBULUFhYdo9ebIcVmuBc13i431YFQAA/0O4Qak5rFY5srN9XQYAAMViWAoAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgKe0uhzNhMEwDgTwg3KBdspgkA8Bc+HZZaunSppk2b5jleuXKlunbtqrZt22rGjBlyOp0+rA4AAFRFPgk3drtdCxYsUHy+YYtvv/1WCxcu1PLly7Vt2zb997//1dq1a31RHgAAqMJ8Em5mzZqlpKQkDR482HNu48aN6tu3r5o2barIyEiNHj1aa9as8UV5AACgCvPJnJuJEycqKipKixYtUmpqqiTp2LFj6tatm6dNo0aN9OOPP5Z4jbNnzyotLa3I+eTk5PIvGAAAVBk+CTdRUVFFzlmtVoWGhnqOw8LCZM339E1hK1eu1OLFi4uct9ls5VMkAACokvzmaanQ0FDl5OR4jq1WqywWS4ntBw0apNjY2CLnk5KSNHDgwAqpEQAA+D+/CTdNmjTRsWPHPMfHjh3Tb37zmxLbR0dHKzo6usj5/AEJAABUP36zQvHvf/97rV+/XkePHlVGRoaWLl2q3r17+7osAABQxfhNz83vfvc7TZo0SX/84x+VlZWlfv366Q9/+IOvywIAAFWMT8PNxIkTCxwPGDBAAwYM8FE1AADACPxmWAoAAKA8EG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChBPm6AJSf3ZMny2G1eo5DIiJ8WA0AAL5BuDEQh9UqR3a25zgoLMyH1QAA4BsMSwEAAEMh3AAAAEMh3AAAAEMh3AAAAEMh3AAAAEMh3AAAAEMh3AAAAEMh3AAAAEMh3AAAAEMh3AAAAEMh3AAAAENhbylUiKCwsCIbeQaFhalLfLwPqwIAVAeEG1SYwht5AgBQGRiWAgAAhkK4AQAAhkK4AQAAhkK4AQAAhkK4AQAAhkK4AQAAhkK4AQAAhsI6N9Vcenq6XC6XLLq8Lo09M9PzmkWSKydHNUNCfFYfAAClRbip5lwul5xOp5xOp+f/8zidTrldLh9WBwBA6TEsBQAADIWeGwPLyMiQPSuryFBT3vBTUBC3HwBgPPx2MzCXy1XsUFPeucBAOu4AAMbDbzcAAGAohBsAAGAofhdu9uzZo969e6tt27b6wx/+oOTkZF+XBAAAqhC/CjdOp1OTJk3SCy+8oP3796tDhw6aPn26r8sCAABViF+Fm4sXL+rChQty/bq2SmBgoEJDQ31cFQAAqEr86mmpyMhIPfDAAxoxYoRMJpNq166tDz74oNi2Z8+eVVpaWpHzDGMBAFC9+VW4cTgcCg8P1/Lly9WmTRstWbJEkyZN0tq1axUQEFCg7cqVK7V48eIi17DZbJVVLgAA8EN+FW62bt2qkydPqlOnTpKkJ554Qu+9956+++47NW/evEDbQYMGKTY2tsg1kpKSNHDgwEqpFwAA+B+/CjdnzpwpsOBcYGCgTCaTzGZzkbbR0dGKjo4ucj4nJ6dCawQAAP7NryYUd+zYUXv37tXu3bvldDq1dOlSRUdHq3Hjxr4urdoKDAxUenq60tLSlJaWpvT09CLHAAD4E7/qubnllls0e/Zsvfzyy0pPT9dtt92mN954QyaTydelVWv5t3AobvdwAAD8iV+FG0nq06eP+vTp4+syAABAFeVXw1IAAABlRbgBAACGQrgBAACGQrgBAACGQrgBAACGQrgBAACGQrgBAACGQrgBAACGQrgBAACGQrgBAACG4nfbL8C4gsLCtHvyZDms1gLnusTH+7AqAIDREG5QqRxWqxzZ2b4uAwBgYAxLAQAAQyHcAAAAQyHcAAAAQyHcAAAAQyHcAAAAQyl1uElNTa2IOgAAAMpFqcNNr1699Oijj2rt2rXKysqqiJoAAACuWanXufniiy+0adMmrVq1Si+//LJiY2PVr18/de3aVYGBjHJVloSEBFnzLYZnNpvlOHdOub8GzqCgIAVHRPiqPAAAfKbUaaROnTp6+OGHtWLFCm3YsEG33nqrXn/9dXXt2lVz5sxRcnJyRdSJQqxWq7Kzswt8OJ1Oz4fL5fJ1iQAA+MQ1d7WcOXNG27dv19atW3X8+HF17NhRQUFBGjZsmN58883yrBEAAMBrpR6WWrlypRISEnTo0CG1adNGAwYMUFxcnMLDwyVJ3bt316hRozR27NhyLxYAAOBqSh1uli1bpv79++vVV19VgwYNirzeqFEjTZkypVyKAwAAKK1SD0s98MADGjt2bJFgE//rzs5RUVEaMmRI+VQHAABQSl713KSlpenQoUOSpCVLlqhJkyZyu92e1y9duqT33ntPkydPrpgqAQAAvORVuKlVq5b++te/KiMjQzk5OZozZ06B10NCQphjAwAA/IJX4SYkJERr1qyRJI0ZM0ZvvfVWhRYFAABwrbyeUHzx4kXVrl1bc+fO1YULF4ptU6dOnXIqCwAA4Np4HW66d++ugwcPqmPHjgoICCjwmtvtVkBAgA4fPlzuBQIAAJSG1+Fm48aNkqRPP/20wooBAAAoK68fBa9fv74kqWHDhqpVq5YaNmyomJgY7d+/Xz/88IMaNmxYYUUCAAB4q9SL+K1du1azZs3SwYMHNX/+fG3YsEEBAQEaMmQIT0xVQ4GBgcrIyJA9K0v2zEzPeYskh9Uqe2amAgMDVbduXd8VCQCoVkq9iN/y5cu1ePFiORwOrVq1Sm+88YY++OADrVixoiLqQxXgcrnkcrkKbNyZt3knm3gCACpbqXtuUlNT1blzZ+3fv1/BwcFq1aqVJCkz37/aAQAAfKXU4SY6Olr79u3T2rVrdeedd0qSNmzYoBtuuKHciwMAACitUoebp556SuPHj1fNmjW1bNky7dmzRy+++KIWLVpUEfUBAACUSqnDTWxsrPbu3avAwMvTdWJiYrRr1y6Fh4eXe3EAAAClVepw43Q6tWXLFh07dqzIRNEJEyaUW2EAAADXotThZtq0afrss8/UokULmc3miqgJAADgmpU63OzYsUMrVqxQ06ZNK6IeAACAMin1OjchISE8GQUAAPxWqcPNyJEjNX36dP3444+6cOFCgQ8AAABfK/Ww1IIFC2S1WvXRRx95dgdnV3AAAOAvSh1uEhISKqIOAACAclHqYamGDRvquuuu07fffqutW7cqMjJSFy9eZFdwAADgF0rdc5OcnKyRI0fKYrEoNTVVsbGxGjhwoOLj43XvvfdWRI0AAABeK3XPzUsvvaRRo0Zp06ZNCgoKUqNGjbRgwQItXry4XAo6deqURo4cqTvuuEN9+/bVv//973K5LgAAqB5KHW4OHz6shx9+WJI8E4p79OihlJSUMhfjcrn02GOP6c4779TevXs1YsQIPfnkk2W+LgAAqD5KPSxVt25dff/992revLnn3A8//KDo6OgyF3Pw4EEFBgbqsccekyQ98MADatasmVwul2cvKwAAgCspdbgZOXKkHn/8cQ0bNky5ubl6//339e6772rkyJFlLubIkSNq0qSJnnvuOX366adq0qSJZs6cWWywOXv2rNLS0oqcT05OLnMdAACg6ip1uBkwYIBq166tVatWqUGDBtq+fbueeOIJ9e7du8zF/PLLL/r88881e/ZszZgxQx988IHGjx+vTZs2FdnHauXKlcXO87HZbGWuA5UnKCxMuydPlsNqLXCuS3y8D6sCAFRlpQ43ktSrVy/16tWrvGtRcHCwmjRpovvvv1+SNGzYML3xxhv68ccf1axZswJtBw0apNjY2CLXSEpK0sCBA8u9NlQch9UqR3a2r8sAABhEqcJNenq6li9frn379unSpUuqX7++fve732no0KGKjIwsczGNGzfWpUuXPMdut1sul0tut7tI2+jo6GLn+eTk5JS5DgAAUHV5HW6OHTumIUOGqEmTJrrnnnsUERGh9PR07dixQx999JHef/99NWjQoEzFdO7cWQ6HQ8uXL9fQoUP17rvvKjIyskivDYyNoSoAQFl4HW7mzZunfv366emnny5wfsyYMZo9e7YWLlyouXPnlqkYi8Wi5cuXa/r06Vq4cKGaNGmihQsXeh45R/XBUBUA4Fp5HW4SExM1Z86cYl8bN26cZ55MWd18881asWJFuVwLAABUP14vHmO321WrVq1iX4uIiCgwVwYAAMBXvA43VxsaKm7SLwAAQGXzeljK7XYrKSmpxBBDuAEAAP7A63BjtVr1wAMPlPg6k34BAIA/8DrcHDlypCLrAAAAKBfsRgkAAAyFcAMAAAzlmvaWAkojMDBQ6enpCtXlxfnsmZme1yz634J9devW9VmNAADjINygUrhcLjmdTs9/8+Sdc7lcPqwOAGAkDEsBAABDIdwAAABDIdwAAABDIdwAAABDIdwAAABD4WkpVAlBYWHaPXmyHFZrgXNd4uN9WBUAwB8RblBl5K2HAwDAlTAsBQAADIVwAwAADIVwAwAADIVwAwAADIVwAwAADIVwAwAADIVHwasos9kss9lc4Jh9tQEAINxUWQEffyzTpUue47CoKFkD6YgDAIBwU0XlZmcrNyvLc2y3WHxYDQAA/oN/6gMAAEMh3AAAAEMh3AAAAEMh3AAAAENhQjGqrKCwMO2ePFkOq7XAuS7x8T6sCgDga4QbVGkOq1WO7GxflwEA8CMMSwEAAEMh3AAAAEMh3AAAAEMh3AAAAENhQjGqDZ6sAoDqgXCDaoMnqwCgeiDcwFCKW/tGkkIiInxUEQCgshFuYDjF9dAEhYX5qBoAQGVjQjEAADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUnpaCXwgMDFR6erpcLpcsuvzEkz0z0/O6RZIrJ0c1Q0J8ViMAoGog3MBvuFwuOZ1OOZ1Oz//ncTqdCpA8AUhSkRAUGBiohnXr+qByAIA/IdygSskfeooLQQAA+G242b9/v4YOHaojR474uhQYVHGrGbPfFABUfX4Zbmw2m1544QW53W5flwKDK7yaMYEHAKo+v3xaasGCBeratauvy0A1lRd4PB+F9qkCAPg3v+u5+fe//62DBw/qz3/+s/7v//6vxHZnz55VWlpakfPJyckVWR4AAPBzfhVu7Ha7XnzxRb322msymUxXbLty5UotXry4yHmbzVZR5QEAgCrAr8LNokWLFBsbq+bNmys1NfWKbQcNGqTY2Ngi55OSkjRw4MCKKhEAAPg5vwo327ZtU1pamt577z3PZOJ27dpp/fr1atCgQYG20dHRio6OLnKNnJycSqkVAAD4J78KN5s3b/b8f2pqqrp166bExEQfVgQAAKoav3xaCgAA4Fr5Vc9NfvXq1dN3333n6zJQhQQGBiojI0P2rKwC+1JJ/9uqwZGdrbql3KKBtW8AoGrx23ADXAuXy1Xslgx5WzXk7UtVWoUX+wMA+C+GpQAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKGwiB+qlcDAQKWnp8vlcnlWLc6/mrFFkisnRzVDQnxWIwCgbAg3qHbyVjDOW7U4/2rGTqdT7mtcxRgA4B8YlgIAAIZCz40fSkhIkDXfJo2FRUVFVWI1AABULYQbP2S1WpV9hU0abTZbJVaD4rBTOAD4L8INcI3YKRwA/BNzbgAAgKHQcwMUkv9xcUnFPjJeKyhI9qws2X75xXPOXKOGPv74Y+Xm5iosLEx9+vSp7NIBACLcAMXK/4h4cY+Mu1yuIucCnU7Zs7OVm5tb6fUCAP6HYSkAAGAo9NwA5chsNhf4LwCg8hFugHJitlgUuHGjXJcuyWEyacfWrTweDgA+QLgBylFuVpZys7LkMpnksFhYDwcAfIBwA1Qw1sMBgMrFhGIAAGAohBsAAGAohBsAAGAohBsAAGAohBsAAGAohBsAAGAoPAoOVIC8zTdDVXTTzbwNNoOCgthcEwAqAOEGqCB5G2uWtMFmrVq1lJCQIGu+Bf4KY3dxACg9wg3gA2azWUFBQbLZbJ5dxNlNHADKB+EGqGR5e1Bl22xyuVwyOZ0KrlFDiosj4ABAOSDcAD6Qm5Ul16/hJm/IqridxNldHABKj3AD+IH8O4rnqREVJVdOjnZs3VqgLRtvAsCVEW4AP5G3o3geu8Uit90uh9vtw6oAoOphnRsAAGAohBsAAGAohBsAAGAohBsAAGAohBsAAGAohBsAAGAohBsAAGAorHMD+LG83cVdLpfnXN6u4nlbNbC5JgAURLgB/Fxxu4q7c3M94YYtGgCgIMINUMUU3qrBYTJp965dbMkAAL8i3ABVUP6tGlwmkxxWq48rAgD/4XfhZuPGjVq4cKHOnTun5s2ba+bMmfrtb3/r67IAvxYUFqbdkycXCDlssAmguvKrp6WSk5M1c+ZMzZ8/X/v27VO3bt00YcIEX5cFVAkOq1WO7Oz/fdCbA6Ca8quem5SUFD3yyCNq0aKFJGnIkCGKj4/XpUuXVLNmTR9X5zuFJ4wGBfnVbYOPBQYGKiMjQ/asLNkzMz3n8z9VxRNVAKoTv/ot2bVrV3Xt2tVzvHPnTjVo0KDYYHP27FmlpaUVOZ+cnFyhNVY2s9mssM2bZf91foUk5darp4CAAB9WBX/jcrmKfarKnp3teaoKAKoLvwo3+R0+fFgzZszQ7Nmzi3195cqVWrx4cZHzNputokurdPZ8k0clMdwAAMAV+GW42bNnj/7f//t/mjp1qnr27Flsm0GDBik2NrbI+aSkJA0cOLCiSwQAAH7K78LNli1b9Nxzz2nu3LklBhtJio6OVnR0dJHzOTk5FVkeAADwc34Vbr7//ns988wzWrJkiTp16uTrcoAqL28yes2aNbV582bZbLZi5+Aw4RiAkfhVuPnnP/8pm82mcePGFTi/efNmxcTE+KgqoGrKv5Jxttksl8slU2io1Lcvk4wBGJpfhZsZM2ZoxowZvi4DMAzPSsa/hpvAkBCZ823dIEnBNWoo4IEHinwuiwICqKr8KtwAqHi5hZ6+k6SQYtrlLQoIAFWNX61QDAAAUFb03ABQeHi4Z8KxdHkisvXcuQI9PCHVeJVwAFUL4Qao5swWi2wrVsh26ZJnheOgqCi53e4CKx67XC5flQgApUK4AaDc7GzlZmV5wozdYvFxRQBw7Qg3ALwSGBiozZs361K+J60KY70cAP6AcAPAazabTdlXeIIqLCxMCQkJsl5h/zMCEICKRrgBUK6sVusVAxAAVDTCDYAyydviQZKCgoJkNpsLnMvDqsgAKgvhBsA1M5vNCtu8WfZfHxnPNpsVEhkpS3a2cgqtgqy4OAIOgEpBuAHgtcK9MmazWfb8Kx6bzTKFhha7CnKeq83LYU4OgLIi3ADwitlikXP1apl++cVzLiwqStZA7xY6zwtFQUFBBXYnpzcHQHkj3ADwWt56OHm8XQ+n2B3KnU6GqwBUCMINgEpReIfy/KsfA0B5YuNMAABgKPTcAPAr3iwEGBERIZvNxmKBAIpFuAHgd662EGBYWBiLBQIoEeEGgE8VXvAvKKj4H0vFLRYIAMUh3ADwmfxPUeXJrV1blgcfLPAEldlsVtCGDQUWCzSFhsrcq1eJT1qxzxVQfRFuAPhUkQX/atcufj0dq7XAYoEul+uq12boCqieCDcA/I636+mwhxWA4hBuAFRJZotF7kJDWqVdFPBqQ1c8lQVUTYQbAFXWlfaw8taVhq54KguomljEDwAAGAo9NwBQBjyVBfgfwg0AQ8m/+7jZbC5xPZzynHjszaKDBCCg8hBuABhGcbuP14qMlMNmU04xE48rE3N3gMpDuAFgKIV3Hw8MCZHTZivzxGMAVQfhBgAMhOEvgHADoJoym81F5uX4cgHA8lhzJyIiotyGvwhJqMoINwCqnby5Odk2m1wul0xOp4Jr1JC5b98CAaekTTwrSlnX3AkLC6uUWgB/R7gBUC3lZmXJ9Wu4cTqdMlssMheziae5X78in1uVdyj35smtiIiISqwIKH+EGz+Uv5vcbDbr6tsDAigPhVc8zjWbi6x0ajabFbZ5c6l2KPc35dEDxNYV8GeEGx8o6QeC2WyWOSFBjl9+kcnplPTrbsiBLCQN+ErhtXLMZrPs+UOQlzuUGxFbV8BfEW58oKS/8GazWaGZmcrNypLz13BT0m7IACqe2WKRc/VqmX75xXOuPP7BUZWGsYCqiHADAFeQm51dYKiqpH9weLsScuFhLenyooJBf/hDOVRbtXg7/8ebp8Su1IYhsuqHcAMAZWS2WOQuNBlZKnklZHsxu5nXvMp2EVVt4rK3vJn/U9Y2DJFVP4QbACgHhScjl4bZYpFtxQo5Ll3yzLerERVVYNuIqjhxGfAVwg0AVKDCiwWW9ARk3vBX/vl2BbaNqMYTlytDeW5ueqXrMPxVOQg3AFBBilsssKwTkosbmqInp3yU19AVQ2C+R7gBgApUeLHAsjwBWdzcnrx5PQScilceCyBWVg9Raa5jRIQbAKhCyjK3B2VXHgsg+lsPkREXYyTcAIABMXxVdVX2FhlGfNKMcAMAVVxekMmbuGyxWBS0YUORtXTMffv61U7oKFllbJEhVe4+Yler5dSpU+X2XoQbHyr8LysjrmEBoGLlTVp2Xbqk7F+fqAqLjJTVai0wfJW3MejVdkK/4nt5uWFo4SfEJEKUr1TGTvLehqjK7AEi3PhIcauUhkVFKTcgwIdVAaiKPPNwfg03gSEhJba72k7owTVqKHTYMDkcjiJ7auXvDcpbd8fSu3eB4JLXLttuLxCimPRsbJURokqDcONDhVcptVssItoAqEyFJygXt6Cg9OueWvl7g/IWFSwUjvLa5Q9Rl5tfe890cSs3E5RwJX4Xbg4cOKAZM2boxIkTat26tebNm6frrrvO12UBQLVReEFBqeQ9tQqHo+La5R86y1NSD1FhoaGhynrvPZl4/B2l4Ffhxmaz6YknntCMGTPUrVs3vfLKK5o7d67mz5/v69LKVd5fZNYaBVBdeNNDVHjLCUnKrVdPjkLzh0pytX25rhSiSmpTXICqqk+iVad5nX4Vbvbs2aOYmBj17NlTkjRp0iR17dpVL730kixlWPjKH+yePFmOXydbOc6duzzhrwyrlAJAVXfVLSckz8/NwgpPbrZYLLKvXFlgHmNeWMo/ibq4ACVJ7nr1FJidLdMvv3jOFZ5wXdL75G93tSBVlqBVljaF50zl1V1c71l5BzpfTDD3q3Dz888/q3Hjxp7jOnXqyGKx6Pjx42revHmBtmfPnlVaWlqRayQlJUmSDh8+XKG1lmTfvn3KyckpcM5kMsnx/fdy2GwymUyXJ/K53XLl5Cg3X7dviM2mAIdD9txcudxuz7nC7S5kZclhs8le6HPztzNJCs7OlqOY98hrZ5LkdrnkcruLfR9v6inN+5hLeA9XTo5cubkVVkt5v48396CivuaKugf+9OeuvN+nLPegLLWU9V7709+1a6mnQv+uBQYqcPVqT/BJM5kUXKuWbBcuKDdfGAoJDLxci93+v1p+PZdbKDRZgoIuv0++SbGleZ+8dmkmk9wul4Jr1ZLTbi8QzkJq19YFh0P27Gy5877m2rWLtPslIuLy9zV/LYXa5dXisNmKBMC8tmm/ft1ut1shtWsr5+LFInWb//a3AvUEhYbKdeedcjgckqScnBw5nU45Pv9cDpvN87n52+Xk5Mhutxf53SddDliBX36ptF//PLjd7iLvkScjI0OSrvjUlbf8KtxkZ2crpNAs/7CwMNnyfUPzrFy5UosXLy5y/pdfU/cjjzxSMUUCAGBkq1aVb7tSfu4333yjO++889qvLT8LN2FhYbLb7QXOWa3WYoekBg0apNjY2CLnDx06pKlTp2revHm69dZbK6xWXF1ycrKmTJmi+fPnq2nTpr4up1rjXvgX7of/4F74j6SkJE2dOlUtWrQo87X8Ktw0adJECQkJnuMLFy4oKytLN954Y5G20dHRio6OLvY6tWrVUuvWrXXbbbdVWK24upCQEIWGhurWW2/lXvgY98K/cD/8B/fCf4SEhKhWrVrlsmqyX81o7dixo06fPq1NmzbJbrdrwYIFio2NVWhoqK9LAwAAVYRfhZvQ0FC9+eabeuutt9ShQwedOHFCM2bM8HVZAACgCvGrYSlJatmypdatW+frMgAAQBXlVz03AAAAZWW4cBMVFaUJEyYoKirK16VUe9wL/8G98C/cD//BvfAf5XkvAtzufKs0AQAAVHGG67kBAADVG+EGAAAYCuEGAAAYSpUNNwcOHFDfvn3VqlUrjRgxQufOnSvSJi0tTSNGjFDr1q3Vu3dvHTp0yAeVGp839yLPoUOHFBcXV4nVVS/e3IukpCQNHjxYbdu21e9//3tt377dB5VWD97cj6+//loPPPCAWrdurfvuu087duyo/EKrgdL8nMrIyFDnzp21d+/eSqyw+vDmXmzcuFG33XabWrdu7fnI21jTK+4qyGq1ujt37uzeunWrOycnxz19+nT35MmTi7T74x//6J47d647JyfH/fHHH7vvvvtut8Ph8EHFxuXtvXC73e6EhAR3u3bt3D169KjkKqsHb+6Fw+Fw33333e4PP/zQ7XQ63Xv27HG3adPGffLkSR9VbVze3A+n0+nu1q2be926dW632+3+8ssv3S1btnRnZ2f7omTDKs3PKbfb7X7qqafczZs3d3/11VeVWGX14O29iI+Pdy9YsOCa36dK9tzs2bNHMTEx6tmzp4KDgzVp0iRt2bJF2fm2h8/MzNSuXbs0btw4BQcHq3///qpZs6a++uorH1ZuPN7cC+lyCl+0aJHGjh3ro0qNz5t7ce7cOd1+++0aMGCAAgMD1bFjRzVq1EiHDx/2YeXG5M39CAwM1CeffKJ+/frJ6XQqIyND4eHhMplMPqzceLz9OSVJn332mTIzM3X99df7oFLj8/ZefPfdd7r55puv+X2qZLj5+eef1bhxY89xnTp1ZLFYdPz4cc+548ePKyIiQjVr1vSca9y4sZKTkyuzVMPz5l5IUqdOnTzdjKgY3tyLmJgYLVq0yHOckpKi5ORkNWvWrDJLrRa8/bthsVjkcDjUsmVLTZ48WZMmTVJwcHAlV2ts3t6LX375RfPmzdPMmTMrucLqw9t78d1332n16tW688471bdvX33++eeleh+/237BG9nZ2QoJCSlwLiwsTDab7YptQkNDC7RB2XlzLyQpMjKyMsuqlry9F3kuXryocePGadCgQbrhhhsqo8RqpTT3w2Qy6eDBgzp48KDGjBmjFi1aEDjLkbf3Ys6cORo6dKjq1atXmeVVK97cC7vdrhtuuEEDBgxQbGys9uzZoyeffFJr164tEIyupEr23ISFhclutxc4Z7VaZbFYCrTJyckp0MZmsxVog7Lz5l6gcpTmXqSkpOjhhx/WLbfcomeeeaaySqxWSnM/AgICFBwcrI4dO6pLly768ssvK6vMasGbe7Fr1y4dP35cDz/8cGWXV614cy+Cg4P1j3/8Q3FxcQoODla3bt3Uvn37Uv29qJLhpkmTJjp27Jjn+MKFC8rKytKNN97oOdeoUSNduHBBmZmZnnM//fSTfvOb31RmqYbnzb1A5fD2Xvz4448aNGiQYmNjNWfOHAUGVskfA37Pm/uRkZGhXr16FfiHmN1uLzCcjrLz5l5s3bpVSUlJuuOOO9SuXTudPHlSY8aM0YYNG3xQsXF5cy/OnDmjBQsWFPi83NzcUg3XVsmfah07dtTp06e1adMm2e12LViwQLGxsQoNDfW0CQ8P15133qmFCxfKbrdr/fr1unDhgtq1a+fDyo3Hm3uByuHNvcjJydGYMWM0aNAgTZkyxYfVGp839yMiIkK1a9fWW2+9JafTqZ07d+rrr7/W3Xff7bvCDcibe/Hyyy/r0KFDSkxMVGJioq6//nq99dZb6tu3rw8rNx5v7kXNmjW1YsUKrV69Wi6XS9u2bdM333yje+65x/s3KssjXb7073//292vXz93q1at3CNHjnSnp6e7T5065W7VqpX71KlTbrfb7T579qx71KhR7jZt2rj79u3r/vrrr31ctTF5cy/yfPXVVzwKXoGudi82btzovvnmm92tWrUq8LFx40Zfl25I3vzdOH78uPvRRx91t23b1n3//fe7Dxw44OOqjak0P6fcbre7R48ePApeQby5F4mJie7777/f3apVK3efPn1KfS/YOBMAABhKlRyWAgAAKAnhBgAAGArhBgAAGArhBgAAGArhBgAAGArhBgAAGArhBgAAGArhBgAAGArhBgAAGEqQrwsAYBx//OMfdfvtt2vixIlyu9368MMPtWbNGv34449yOp1q0qSJBgwYoMGDBysgIOCq1zt69KhefvllJSUlKSQkRPfdd5/+9Kc/KTg4WI8//rgOHDjgaet2u2W1WjV//nz2AwKqOcINgAoxdepUHTlyRM8995xatWolk8mk//znP3r22WeVnp6uCRMmXPHz3W63xo8fr/vvv1/Lli3ThQsXNGLECL377rsaNWqU3nnnnQLt582bp6+//lpxcXEV+WUBqAIINwCu2d69ezV79mwdP35cXbp0UWZmpiRp586d2rFjhzZu3KiYmBhP+3bt2unVV1/Vf/7zn6teOyAgQOvXr1dwcLAk6dy5c7Lb7YqIiCjSNjExUR988IESEhJkNpuveu3PPvtM8fHxOnPmjOrXr6/hw4frwQcflCT98MMPmj17tr799ltFRkZq5MiRGjRokFffDwD+gTk3AK7J+fPnNXbsWA0dOlSJiYmKi4tTYmKiJGnLli3q3r17gWCTp02bNnr00Ue9eo+wsDCZTCYNGDBA/fv3V3R0dJEhJ7fbrVmzZmns2LGqX7/+Va/pdDr1pz/9SS+88IISExP1zDPPaNasWbp48aKysrI0YsQIde7cWf/617/0l7/8RUuWLNH27du9qheAfyDcALgmn3/+uWJiYjRgwAAFBQWpT58+atOmjSR5ekTyu/POO9WuXTu1bdtWLVq0UEZGhtfvtWLFCn3xxRfKycnR9OnTC7y2c+dOnT17VkOGDPHqWiaTSTVr1tTatWu1b98+tW/fXgcOHFDt2rW1c+dOWSwWjRo1SmazWc2bN9eQIUP04Ycfel0rAN8j3AC4JmlpaapXr16Bc9dff70k6brrrlNaWlqB17788kslJiZq3bp1stvtcrvdXr9XSEiIYmJiNH78eG3durXAa6tXr9b999+vsLAwr6/397//XU6nUxMnTlSHDh306quvym6369SpUzp58qTatWvn+XjrrbeUmprq9bUB+B5zbgBck5iYGKWkpBQ4d+bMGd14443q2bOnpk2bpvPnzysyMvKarn/+/Hk99NBDWrFihWd4Kzc3V7Vq1fK0sdvt+uKLL/TBBx94fd3s7GydPn1a8fHxcrvdOnTokCZMmKBmzZopJiZGzZs315o1azzt09PT5XA4rulrAOAb9NwAuCbdu3fXhQsX9O6778rhcGj79u3at2+fJKlHjx7q0qWLhg8frj179ig3N1dOp1N79+7V008/rdq1a3smCpckMjJS9evX12uvvSar1aqTJ09q4cKFGjBggKfNkSNHZDKZ1Lx5c6/rdjqdGjdunNavXy9Jio6OVkBAgOrUqaO7775bqampWrVqlRwOh1JTUzVixAgtXbr0Gr5DAHwlwF2avmEAyOebb77RzJkz9cMPP6h169YKCQkpsM7N2rVrtWbNGiUnJysnJ0fXX3+97rnnHg0bNkx169a96vXPnj2rWbNm6auvvlJ4eLgefPBBjRkzRiaTSZK0adMmvf7669q2bVup6v7iiy80f/58nThxQjVq1NDgwYM9j6YfOXJEc+bM0eHDhxUUFKR7771Xzz777FXDGAD/QbgBAACGwrAUAAAwFCYUA/CJ7du3a+rUqSW+PmXKFK8f784vKSnpip/3hz/84YrvC6DqY1gKAAAYCsNSAADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUP4/03jcDmBuj9wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annotation_file = './data/annotation/NNNlib2b_annotation_20220519.tsv'\n",
    "annotation = fileio.read_annotation(annotation_file)\n",
    "replicate_df = pd.read_table('./data/nnnlib2b_replicates.tsv')\n",
    "arraydata = ArrayData(replicate_df=replicate_df.iloc[:4,:],\n",
    "                      annotation_file=annotation_file,\n",
    "                      learn_error_adjust_from=('r1', 'r2'))\n",
    "\n",
    "arraydata.p_unfold.to_csv('./data/models/raw/arr_p_unfold_n=%d.csv'%len(arraydata.p_unfold))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ff1526d-7dd8-4d57-9064-c8790d67fa6b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Basic preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3733b43-7f33-46de-b86f-28120c6ee3f4",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Adjust offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e7a8dc3a-7d2c-4b0c-a5f0-b395f13ffd33",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = pd.read_csv('./data/models/raw/arr_v1_n=27732.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "af0ca66b-899d-4bd6-b09c-f97847fa56f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.0, 0.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Get the offset the violent way\n",
    "\"\"\"\n",
    "# copy pasted from command center for convenience\n",
    "# only one peak found by AnalyzePeaks\n",
    "hplc_clean_seqid = [\"WC68_stem_3p\", \"WC68_stem_5p\", \"WC1025_split_5p\", \\\n",
    "\"WC1025_split_3p\", \"WC1375_stem_5p\", \"WC1375_stem_3p\", \"DMM004_5p\", \\\n",
    "\"DMM004_3p\", \"DMM005_5p\", \"DMM005_3p\", \"DMM006_5p\", \"DMM007_5p\", \\\n",
    "\"DMM007_3p\", \"BC0\", \"BC1\", \"BC6\", \"BC7\", \"BC8\", \"BC9\", \"BC10\", \\\n",
    "\"BC11\", \"MMCNNN2328\", \"MMCNNN2331\", \"MMCNNN2334\", \"MMCNNN2335\", \\\n",
    "\"MMCNNN2336\", \"MMCNNN2337\", \"MMTM1325\", \"MMTM176\", \"MMTM178\", \\\n",
    "\"MMTM179\", \"MMTM181\", \"MMTM185\", \"MMTM189\", \"MMTM1917\", \"MMTM29\", \\\n",
    "\"MMTM397\", \"MMTM509\", \"MMTM877\", \"DMM003_3p_rc\", \"DMM004_3p_rc\", \\\n",
    "\"DMM005_3p_rc\", \"DMM006_3p_rc\", \"DMM007_3p_rc\", \"TETNN1003\", \\\n",
    "\"TETNN999\", \"TETNN963\", \"TETNN979\", \"TETNNN1696\", \"TETNNN2082\", \\\n",
    "\"TETNNN1570\", \"HP002\", \"HP003\", \"HP004\", \"HP005\", \"HP007\", \"HP008\", \\\n",
    "\"HP010\", \"HP011\", \"HP012\", \"HP014\", \"HP015\", \"HP016\", \"HP017\", \\\n",
    "\"HP018\", \"HP019\", \"HP021\", \"HP022\", \"HP023\", \"HP024\"]\n",
    "\n",
    "sample_sheet_file = './data/uv_melt/ECLTables/ECLSampleSheet230502.csv'\n",
    "datadir=\"./data/uv_melt/ECLExportAuto\"\n",
    "result_file='./data/uv_melt/uvmelt_230502_2.csv'\n",
    "agg_result_file='./data/uv_melt/uvmelt_agg_230502.csv'\n",
    "\n",
    "result_df = pd.read_csv(result_file, index_col=0)\n",
    "result_agg_df = uv.agg_fit_result(uvmelt_result_file=result_file,\n",
    "                               agg_result_file=agg_result_file,\n",
    "                               single_curve_qc_criteria='rmse < 0.002 & dH_std < 10 & Tm_std < 5 & dH < 0 & dH > -100 & Tm > 0 & Tm < 120',\n",
    "                               Tm_std_thresh=10, dH_std_thresh=15,\n",
    "                               sample_sheet_file=sample_sheet_file, \n",
    "                               clean=True, only_use_cooling=True)\n",
    "\n",
    "result_agg_df = result_agg_df[result_agg_df.SEQID.isin(hplc_clean_seqid)]\n",
    "\n",
    "vf = result_agg_df.query('is_hairpin & Na_mM == 25').sort_values(by=['conc_uM', 'n_curve', 'curve_date']).drop_duplicates(keep='last').merge(arr, how='left', on='SEQID')\n",
    "\n",
    "df_q = vf.dropna(subset=['Tm', 'dG_37'])#.query(plot_query)\n",
    "df_q.dG_37_uv_std = 0\n",
    "\n",
    "dTm = np.median(df_q.Tm_uv - df_q.Tm)\n",
    "df_q['Tm_adj'] = df_q.Tm + dTm\n",
    "df_q['dG_37_adj'] = util.get_dG(dH=df_q.dH, Tm=df_q.Tm_adj, celsius=37)\n",
    "\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.plot(df_q.Tm_uv, df_q.Tm_adj, '.')\n",
    "lim = [40,80]\n",
    "plt.xlim(lim)\n",
    "plt.ylim(lim)\n",
    "\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.plot(df_q.dG_37_uv, df_q.dG_37_adj, '.')\n",
    "lim = [-4,0]\n",
    "plt.xlim(lim)\n",
    "plt.ylim(lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e394ad5-5b7b-42d5-afe2-b84aa38cf16b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_offset(dH, Tm, dTm):\n",
    "    Tm_adj = Tm + dTm\n",
    "    dG_37_adj = util.get_dG(dH=dH, Tm=Tm_adj, celsius=37)\n",
    "    dS_adj = dH / (Tm_adj + 273.15)\n",
    "    return dict(dH=dH, dS=dS_adj, Tm=Tm_adj, dG_37=dG_37_adj)\n",
    "\n",
    "cols = ['dH_se', 'Tm_se', 'dG_37_se', 'dS_se', 'RefSeq', 'TargetStruct', 'Series']\n",
    "arr_adj = pd.DataFrame(adjust_offset(arr.dH, arr.Tm, dTm))\n",
    "arr_adj[cols] = arr[cols]\n",
    "\n",
    "arr_adj.to_csv(os.path.join('./data/models/processed', 'arr_v1_adjusted_n=%d.csv' % arr_adj.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "27eed232-d22a-4abc-a094-4e2319b1ecce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.348410006623254"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dTm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fb1176a6-578a-4b86-8143-569878f506f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.20 -> -0.92 kcal/mol, ddG = -0.71 kcal/mol\n"
     ]
    }
   ],
   "source": [
    "dG_median = util.get_dG(dH=np.median(arr.dH), Tm=np.median(arr.Tm), celsius=37)\n",
    "dG_adj_median = util.get_dG(dH=np.median(arr.dH), Tm=np.median(arr.Tm) + dTm, celsius=37)\n",
    "print('%.2f -> %.2f kcal/mol, ddG = %.2f kcal/mol' % (dG_median, dG_adj_median, dG_adj_median - dG_median))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4762823f-365c-43c3-a0dc-3deb3d1cfaf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168.76668749849256"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(arr.dH) * (1 - (37 + 273.15) / (np.median(arr.Tm)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b4fb6f2-a618-46a8-bf24-032684a1b8a2",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Salt correct array data to 1M Na+\n",
    "\n",
    "`arr_adj` -> `arr_1M`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4f85247-f61a-47b9-9786-c76849a7d84b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.61 s, sys: 24.8 ms, total: 5.64 s\n",
      "Wall time: 5.65 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "param_names = ['dH','dS','Tm', 'dG_37']\n",
    "arr_1M = pd.DataFrame(index=arr_adj.index, columns=param_names)\n",
    "\n",
    "for i, row in arr_adj.iterrows():\n",
    "    param_name_dict = util.get_Na_adjusted_param(Na=1.0,dH=row.dH, Tm=row.Tm, seq=row.RefSeq)\n",
    "    arr_1M.loc[i,:] = param_name_dict\n",
    "\n",
    "arr_1M[cols] = arr[cols]\n",
    "\n",
    "# remove dangles\n",
    "# they should have been removed already but just in case\n",
    "sd_mask = arr_1M.apply(lambda row: not row.name.startswith('SD'), axis=1)\n",
    "arr_1M = arr_1M[sd_mask.values]\n",
    "\n",
    "arr_1M.to_csv('./data/models/processed/arr_v1_1M_n=%d.csv'%arr_1M.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e0f3e8e-e7b9-4753-aeae-b131f096ca17",
   "metadata": {},
   "source": [
    "### Same for arr_v0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d3809ad7-1d57-40ad-b769-2f0051a97a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = pd.read_csv('./data/models/raw/arr_v0_n=30872.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9b3b855b-ea89-4460-809c-55295aaac921",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['dH_se', 'Tm_se', 'dG_37_se', 'dS_se', 'RefSeq', 'TargetStruct', 'Series']\n",
    "arr_adj = pd.DataFrame(adjust_offset(arr.dH, arr.Tm, dTm))\n",
    "arr_adj[cols] = arr[cols]\n",
    "\n",
    "arr_adj.to_csv(os.path.join('./data/models/processed', 'arr_v0_adjusted_n=%d.csv' % arr_adj.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "18321225-6694-4bb6-9439-f28ec210dd01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.22 s, sys: 60.9 ms, total: 6.28 s\n",
      "Wall time: 6.31 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "param_names = ['dH','dS','Tm', 'dG_37']\n",
    "arr_1M = pd.DataFrame(index=arr_adj.index, columns=param_names)\n",
    "\n",
    "for i, row in arr_adj.iterrows():\n",
    "    param_name_dict = util.get_Na_adjusted_param(Na=1.0,dH=row.dH, Tm=row.Tm, seq=row.RefSeq)\n",
    "    arr_1M.loc[i,:] = param_name_dict\n",
    "\n",
    "arr_1M[cols] = arr[cols]\n",
    "\n",
    "# remove dangles\n",
    "# they should have been removed already but just in case\n",
    "sd_mask = arr_1M.apply(lambda row: not row.name.startswith('SD'), axis=1)\n",
    "arr_1M = arr_1M[sd_mask.values]\n",
    "\n",
    "arr_1M.to_csv('./data/models/processed/arr_v0_1M_n=%d.csv'%arr_1M.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4833563c-f87b-4870-83ae-d10303b7b92d",
   "metadata": {},
   "source": [
    "## Rude preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337582f2-cc34-4407-8d41-8913cfa01b66",
   "metadata": {},
   "source": [
    "### Original to 1M Na+"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0260c6-4da0-423b-b53f-3ecb5a965ed6",
   "metadata": {},
   "source": [
    "`arr -> arr_1M`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ed2acc9-2629-4785-b51e-4c27270c25f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = pd.read_csv('./data/models/raw/arr_v1_n=27732.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f07ee2a-efc0-4374-9f9e-6700bd2b7da7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.67 s, sys: 27.4 ms, total: 5.69 s\n",
      "Wall time: 5.71 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "param_names = ['dH','dS','Tm', 'dG_37']\n",
    "arr_1M = pd.DataFrame(index=arr.index, columns=param_names)\n",
    "\n",
    "for i, row in arr.iterrows():\n",
    "    param_name_dict = util.get_Na_adjusted_param(Na=1.0,dH=row.dH, Tm=row.Tm, seq=row.RefSeq)\n",
    "    arr_1M.loc[i,:] = param_name_dict\n",
    "\n",
    "cols = ['dH_se', 'Tm_se', 'dG_37_se', 'dS_se', 'RefSeq', 'TargetStruct', 'Series']\n",
    "arr_1M[cols] = arr[cols]\n",
    "\n",
    "# remove dangles\n",
    "# they should have been removed already but just in case\n",
    "sd_mask = arr_1M.apply(lambda row: not row.name.startswith('SD'), axis=1)\n",
    "arr_1M = arr_1M[sd_mask.values]\n",
    "\n",
    "arr_1M.to_csv('./data/models/processed/arr_v2_1M_n=%d.csv'%arr_1M.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c48944-86d9-4d10-bd0a-8f40ac444974",
   "metadata": {},
   "source": [
    "### Adjust offset w.r.t. NUPACK (maybe slightly circular)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af0b579-b1b1-497d-bcdb-c3bbba94826a",
   "metadata": {},
   "source": [
    "`arr_1M -> arr_1M_adj`\n",
    "\n",
    "Only use training data for adjustment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d27b8cc-0ce7-40a2-9faf-7bba176c02d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_1M = pd.read_csv('./data/models/processed/arr_v2_1M_n=27732.csv', index_col=0)\n",
    "data_split_dict = fileio.read_json('./data/models/raw/data_split.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "146ce507-144b-4f3a-9444-ddfc8275772c",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_1M_train = arr_1M.loc[[x for x in arr_1M.index if x in data_split_dict['train_ind']]]\n",
    "\n",
    "arr_1M_train = arr_1M_train.join(arr[['ConstructType', 'dG_37_NUPACK', 'Tm_NUPACK']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e00e8d6a-3aa0-4c9c-88a5-b6ac294ec4af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ddG_37', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr_1M_train['ddG_37'] = arr_1M_train.dG_37_NUPACK - arr_1M_train.dG_37\n",
    "sns.kdeplot(data=arr_1M_train, x='ddG_37', hue='Series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b549f4da-3faf-4f60-a7e7-f1830086aa4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='dTm', ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr_1M_train['dTm'] = arr_1M_train.Tm_NUPACK - arr_1M_train.Tm\n",
    "sns.kdeplot(data=arr_1M_train, x='dTm', hue='Series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "305ed679-143d-41f5-b5c7-7e51cb35b680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ddG_37', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=arr_1M_train.query('Series == \"WatsonCrick\"'), x='ddG_37', hue='ConstructType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "54523486-5bf1-4ffa-961f-d20f5a9fd345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series       ConstructType     \n",
       "Bulges       NNN_3primebulge       10.005800\n",
       "             NNN_5prime_bulge      12.147991\n",
       "             NN_3primebulge        22.231302\n",
       "             NN_5primebulge        22.455473\n",
       "External     BaeControls           20.294685\n",
       "             WBcontrols             0.000000\n",
       "MisMatches   3mer                  11.585653\n",
       "             AC_mismatch_varpos    26.345570\n",
       "             Centered              17.193788\n",
       "             GT_mismatch_varpos     6.527488\n",
       "TETRAloop    NN                    16.742231\n",
       "             NNN                   11.729666\n",
       "TRIloop      NN                    15.616951\n",
       "             NNN                   10.190561\n",
       "VARloop      VARloop              -35.681387\n",
       "WatsonCrick  WC_5ntstem            26.291553\n",
       "             WC_6ntstem            19.304034\n",
       "             WC_7ntstem            17.750857\n",
       "Name: dTm, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dTm_by_series = arr_1M_train.groupby(['Series', 'ConstructType'])['dTm'].apply(np.median).fillna(value=0)\n",
    "dTm_by_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ed2b7b69-bec7-4bd8-8a3d-5b1c5c411844",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_1M_train['Tm_adj'] = arr_1M_train.apply(lambda row: row.Tm + dTm_by_series[row['Series']][row['ConstructType']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f5a71923-2bd8-4bb4-b364-6ee2b5da916f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tm</th>\n",
       "      <th>Tm_adj</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEQID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BC1</th>\n",
       "      <td>54.750994</td>\n",
       "      <td>75.045680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC10</th>\n",
       "      <td>29.959820</td>\n",
       "      <td>50.254505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC11</th>\n",
       "      <td>33.906068</td>\n",
       "      <td>54.200753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC15</th>\n",
       "      <td>41.645712</td>\n",
       "      <td>61.940398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC16</th>\n",
       "      <td>35.963347</td>\n",
       "      <td>56.258033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257581</th>\n",
       "      <td>65.502814</td>\n",
       "      <td>29.821427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257601</th>\n",
       "      <td>49.375317</td>\n",
       "      <td>13.693931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257621</th>\n",
       "      <td>44.087860</td>\n",
       "      <td>8.406473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257641</th>\n",
       "      <td>36.835432</td>\n",
       "      <td>1.154045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257971</th>\n",
       "      <td>40.252870</td>\n",
       "      <td>4.571483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24979 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Tm     Tm_adj\n",
       "SEQID                              \n",
       "BC1            54.750994  75.045680\n",
       "BC10           29.959820  50.254505\n",
       "BC11           33.906068  54.200753\n",
       "BC15           41.645712  61.940398\n",
       "BC16           35.963347  56.258033\n",
       "...                  ...        ...\n",
       "varloop257581  65.502814  29.821427\n",
       "varloop257601  49.375317  13.693931\n",
       "varloop257621  44.087860   8.406473\n",
       "varloop257641  36.835432   1.154045\n",
       "varloop257971  40.252870   4.571483\n",
       "\n",
       "[24979 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1M_train[['Tm', 'Tm_adj']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4ab8932-a6b0-4f56-b195-f2a70609652d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recalculate(dH, Tm_adj):\n",
    "    dG_37_adj = util.get_dG(dH=dH, Tm=Tm_adj, celsius=37)\n",
    "    dS_adj = dH / (Tm_adj + 273.15)\n",
    "    return dict(dH=dH, dS=dS_adj, Tm=Tm_adj, dG_37=dG_37_adj)\n",
    "\n",
    "cols = ['dH_se', 'Tm_se', 'dG_37_se', 'dS_se', 'RefSeq', 'TargetStruct', 'Series']\n",
    "arr_adj = pd.DataFrame(recalculate(arr_1M_train.dH, arr_1M_train.Tm_adj))\n",
    "arr_adj[cols] = arr[cols]\n",
    "\n",
    "arr_adj.to_csv(os.path.join('./data/models/processed', 'arr_v2_adjusted_n=%d.csv' % arr_adj.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fccbee7c-2de6-4d49-8d7b-79abc72eddf3",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Update data split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe78dfa-2c04-4f2d-84c7-23cb4207a963",
   "metadata": {},
   "source": [
    "Remove the variants measured by UV melting from the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a95d0ebf-26cd-4115-98a9-834da518a1b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_result_file='./data/uv_melt/uvmelt_agg_230502.csv'\n",
    "uv_df = pd.read_csv(agg_result_file, index_col=1)\n",
    "arr_1M = pd.read_csv('./data/models/processed/arr_v1_1M_n=27732.csv', index_col=0)\n",
    "arr_p_unfold = pd.read_csv('./data/models/raw/arr_p_unfold_n=31000.csv', index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7b7c9a09-5a32-4397-8346-697a98f5b972",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train val  test\n",
      "24979 1315 1438\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "ind2split = list(set(arr_1M.index) - set(uv_df.index))\n",
    "series2split = arr_1M.loc[ind2split, 'Series']\n",
    "\n",
    "train_val_ind, test_ind = train_test_split(ind2split, test_size=.05,\n",
    "                                       random_state=42, stratify=series2split)\n",
    "train_ind, val_ind = train_test_split(arr_1M.loc[train_val_ind,:].index, test_size=.05,\n",
    "                                      random_state=43, stratify=arr_1M.loc[train_val_ind,:].Series)\n",
    "test_ind += list(set(uv_df.index) & set(arr_1M.index))\n",
    "n_train, n_val, n_test = len(train_ind), len(val_ind), len(test_ind)\n",
    "print('train','val ','test')\n",
    "print(n_train, n_val, n_test)\n",
    "\n",
    "split_dict = dict(train_ind=train_ind.tolist(),\n",
    "                  val_ind=val_ind.tolist(),\n",
    "                  test_ind=test_ind)\n",
    "with open('./data/models/raw/data_split.json', 'w') as fh:\n",
    "    json.dump(split_dict, fh, indent=4)\n",
    "\n",
    "additional_ind = list(set(arr_p_unfold.index) - set(arr_1M.index))\n",
    "\n",
    "additional_train_val_ind, additional_test_ind = train_test_split(additional_ind, test_size=.1,\n",
    "                                       random_state=44)\n",
    "additional_train_ind, additional_val_ind = train_test_split(arr_p_unfold.loc[additional_train_val_ind,:].index.tolist(), test_size=.1,\n",
    "                                      random_state=45)\n",
    "\n",
    "split_dict['train_ind'] += additional_train_ind\n",
    "split_dict['val_ind'] += additional_val_ind\n",
    "split_dict['test_ind'] += additional_test_ind\n",
    "\n",
    "fileio.write_json(split_dict, './data/models/raw/data_split_p_unfold.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64992417-68c4-4c32-a5a9-1a442e04f006",
   "metadata": {},
   "source": [
    "## Other data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6379b8e6-75ce-4e4b-9d4b-3041bd8702f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "ECL uv data cleaning\n",
    "Reduced to HP only as the reset are already in the array dataset.\n",
    "\"\"\"\n",
    "from nnn import train_nn as tnn\n",
    "\n",
    "mydata = tnn.MyData()\n",
    "mydata.load_everything()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "1e4b240a-d0e2-432c-95ec-a4a711ac3787",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val  test\n",
      "6 6\n"
     ]
    }
   ],
   "source": [
    "uv_agg_df = fileio.clean_uv_df(mydata.uv_df, mydata.ecl_oligo_df, mydata.annotation)\n",
    "uv_agg_df = uv_agg_df.reset_index(names=['SEQID'])\n",
    "uv_agg_df = uv_agg_df.loc[uv_agg_df.SEQID.apply(lambda x: x.startswith('HP'))]\n",
    "uv_agg_df = uv_agg_df.set_index('SEQID')\n",
    "uv_agg_df.to_csv('./data/models/raw/uv_n=%d.csv'%len(uv_agg_df))\n",
    "\n",
    "val_ind, test_ind = train_test_split(np.unique(uv_agg_df.index), test_size=.5,\n",
    "                                       random_state=42)\n",
    "\n",
    "print('val ','test')\n",
    "print(len(uv_agg_df.loc[val_ind]), len(uv_agg_df.loc[test_ind]))\n",
    "\n",
    "split_dict = dict(\n",
    "    train_ind=[],\n",
    "    val_ind=val_ind.tolist(),\n",
    "    test_ind=test_ind.tolist())\n",
    "fileio.write_json(split_dict, './data/models/raw/data_split_uv.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "c6970487-0001-4957-a2ff-0a786c94567a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sl_mm = pd.read_csv('./data/literature/SantaLucia_single_mismatch.csv', index_col=0)\n",
    "sl_mm['isWC'] = sl_mm.dG_37.apply(lambda x: x == 'WC')\n",
    "sl_wc = pd.read_csv('./data/literature/SantaLucia_full.tsv', sep='\\t', index_col=0)\n",
    "\n",
    "def add_wc(row):\n",
    "    if row.dG_37 == 'WC':\n",
    "        return float(sl_wc.loc[row.name, 'dG_37'])\n",
    "    else:\n",
    "        return float(row.dG_37)\n",
    "    \n",
    "sl_mm['dG_37'] = sl_mm.apply(add_wc, axis=1)\n",
    "sl_mm.to_csv('./data/literature/SantaLucia_mm_wc.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "9f1e6d6d-ffeb-461b-b73d-8b0081cea7ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val  test\n",
      "1387 1388\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Oliveira data split (OV)\n",
    "\"\"\"\n",
    "center_df = fileio.read_Oliveira_df('./data/literature/Oliveira_2020_mismatches.csv')\n",
    "\n",
    "val_ind, test_ind = train_test_split(center_df.index, test_size=.5,\n",
    "                                       random_state=42)\n",
    "\n",
    "n_val, n_test = len(val_ind), len(test_ind)\n",
    "print('val ','test')\n",
    "print(n_val, n_test)\n",
    "\n",
    "split_dict = dict(train_ind=[],\n",
    "                  val_ind=val_ind.tolist(),\n",
    "                  test_ind=test_ind.tolist())\n",
    "\n",
    "with open('./data/models/raw/data_split_Oliveira.json', 'w') as fh:\n",
    "    json.dump(split_dict, fh, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "e24ebb25-b0de-48b1-a9f8-57f0b25f1cd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train val  test\n",
      "0 174 174\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "348oligos data split (DP) (lit_uv)\n",
    "train:val:test = 0:1:1\n",
    "\"\"\"\n",
    "uv_df = pd.read_csv('./data/literature/compiled_DNA_Tm_348oligos.csv', index_col=0)\n",
    "\n",
    "val_ind, test_ind = train_test_split(uv_df.index, test_size=.5,\n",
    "                                       random_state=42)\n",
    "train_ind = []\n",
    "n_train, n_val, n_test = len(train_ind), len(val_ind), len(test_ind)\n",
    "print('train', 'val ','test')\n",
    "print(n_train, n_val, n_test)\n",
    "\n",
    "split_dict = dict(\n",
    "    train_ind=train_ind,\n",
    "    val_ind=val_ind.tolist(),\n",
    "    test_ind=test_ind.tolist())\n",
    "\n",
    "with open('./data/models/raw/data_split_348oligos.json', 'w') as fh:\n",
    "    json.dump(split_dict, fh, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d335ed31",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Combining things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "ab95d4a5-48d8-42ca-90b1-bd65e8aa1209",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[24979, 2882, 3006]\n"
     ]
    }
   ],
   "source": [
    "val_df, combined_split_dict = fileio.read_val_df('all')\n",
    "val_df.to_csv('./data/models/raw/combined_dataset.csv')\n",
    "fileio.write_json(combined_split_dict, './data/models/raw/combined_data_split.json')\n",
    "print([len(inds) for inds in combined_split_dict.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "b0e420f5-b1ad-4422-93eb-e434be6f3bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = fileio.load_val_df('./data/models/raw/combined_dataset.csv')\n",
    "\n",
    "### SOME TESTS ###\n",
    "# Check duplicates. Should be none.\n",
    "assert len(df.loc[(df.index.duplicated())]) == 0\n",
    "\n",
    "# Test indexing works \n",
    "for test_id in ['DP347', 'WC999', 'varloop257621', 'OV2679']:\n",
    "    ind = np.searchsorted(df.index, [test_id])\n",
    "    assert df.iloc[ind[0],:].name == test_id\n",
    "    \n",
    "### Save back ###\n",
    "df.fillna(0, inplace=True)\n",
    "df.to_csv('./data/models/raw/combined_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f057a079-a522-4d1f-8476-d107c43b291d",
   "metadata": {},
   "source": [
    "# Tetraloops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8cbc6e9e-4701-475f-a3b0-a5c4523de896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TETRAloop', 'VARloop', 'WatsonCrick'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_nnn_tetraloop(seq):\n",
    "    flag = True\n",
    "    \n",
    "    if len(seq) != 16:\n",
    "        flag = False\n",
    "        \n",
    "    if not seq.startswith('GATC'):\n",
    "        flag = False\n",
    "        \n",
    "    return flag\n",
    "\n",
    "my_df = mydata.annotation\n",
    "rescue_df = my_df[my_df.RefSeq.apply(is_nnn_tetraloop)]\n",
    "np.unique(my_df[my_df.RefSeq.apply(is_nnn_tetraloop)].Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d7a6a574-e6f5-4ae4-ab65-635a76e7ec19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series</th>\n",
       "      <th>ConstructType</th>\n",
       "      <th>bottomScaffold</th>\n",
       "      <th>topScaffold</th>\n",
       "      <th>RefSeq</th>\n",
       "      <th>dH_NUPACK</th>\n",
       "      <th>dS_NUPACK</th>\n",
       "      <th>Tm_NUPACK</th>\n",
       "      <th>dG_37_ensemble_NUPACK</th>\n",
       "      <th>TargetStruct</th>\n",
       "      <th>dG_37_NUPACK</th>\n",
       "      <th>dG_37_ensemble_150mM_NUPACK</th>\n",
       "      <th>dG_37_ensemble_75mM_NUPACK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEQID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WC337</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCAAGAAATTGATC</td>\n",
       "      <td>-43.8</td>\n",
       "      <td>-0.127906</td>\n",
       "      <td>69.289375</td>\n",
       "      <td>-4.408689</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-3.532474</td>\n",
       "      <td>-3.142028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC340</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCATGAAAATGATC</td>\n",
       "      <td>-42.3</td>\n",
       "      <td>-0.123456</td>\n",
       "      <td>69.481105</td>\n",
       "      <td>-4.289815</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-4.01</td>\n",
       "      <td>-3.414562</td>\n",
       "      <td>-3.024805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC343</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCAGGAAACTGATC</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-0.129582</td>\n",
       "      <td>74.119221</td>\n",
       "      <td>-5.077725</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.194838</td>\n",
       "      <td>-3.800274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC346</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCACGAAAGTGATC</td>\n",
       "      <td>-46.7</td>\n",
       "      <td>-0.134548</td>\n",
       "      <td>73.938545</td>\n",
       "      <td>-5.236008</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.352368</td>\n",
       "      <td>-3.957440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC349</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCTAGAAATAGATC</td>\n",
       "      <td>-42.4</td>\n",
       "      <td>-0.125294</td>\n",
       "      <td>65.253500</td>\n",
       "      <td>-3.828455</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-2.961107</td>\n",
       "      <td>-2.577143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC352</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCTTGAAAAAGATC</td>\n",
       "      <td>-42.3</td>\n",
       "      <td>-0.123618</td>\n",
       "      <td>69.034272</td>\n",
       "      <td>-4.238612</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>-3.363334</td>\n",
       "      <td>-2.973716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC355</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCTGGAAACAGATC</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-0.129582</td>\n",
       "      <td>74.119221</td>\n",
       "      <td>-5.076523</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.192852</td>\n",
       "      <td>-3.797847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC358</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCTCGAAAGAGATC</td>\n",
       "      <td>-45.8</td>\n",
       "      <td>-0.132645</td>\n",
       "      <td>72.131235</td>\n",
       "      <td>-4.928765</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>-4.047904</td>\n",
       "      <td>-3.654557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC361</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGAGAAATCGATC</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>-0.132355</td>\n",
       "      <td>75.910414</td>\n",
       "      <td>-5.419805</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-5.15</td>\n",
       "      <td>-4.535783</td>\n",
       "      <td>-4.140111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC364</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGTGAAAACGATC</td>\n",
       "      <td>-45.6</td>\n",
       "      <td>-0.129969</td>\n",
       "      <td>77.701898</td>\n",
       "      <td>-5.557778</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-5.29</td>\n",
       "      <td>-4.672591</td>\n",
       "      <td>-4.276283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC367</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGGGAAACCGATC</td>\n",
       "      <td>-47.3</td>\n",
       "      <td>-0.132871</td>\n",
       "      <td>82.833863</td>\n",
       "      <td>-6.351073</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-6.09</td>\n",
       "      <td>-5.462921</td>\n",
       "      <td>-5.065094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC370</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGCGAAAGCGATC</td>\n",
       "      <td>-50.2</td>\n",
       "      <td>-0.140932</td>\n",
       "      <td>83.050641</td>\n",
       "      <td>-6.749814</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-6.49</td>\n",
       "      <td>-5.861195</td>\n",
       "      <td>-5.463146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC373</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCCAGAAATGGATC</td>\n",
       "      <td>-43.9</td>\n",
       "      <td>-0.125520</td>\n",
       "      <td>76.595312</td>\n",
       "      <td>-5.238368</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.354610</td>\n",
       "      <td>-3.959321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC376</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCCTGAAAAGGATC</td>\n",
       "      <td>-42.4</td>\n",
       "      <td>-0.121232</td>\n",
       "      <td>76.593617</td>\n",
       "      <td>-5.071645</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-4.80</td>\n",
       "      <td>-4.189336</td>\n",
       "      <td>-3.794834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC379</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCCGGAAACGGATC</td>\n",
       "      <td>-47.3</td>\n",
       "      <td>-0.132871</td>\n",
       "      <td>82.833863</td>\n",
       "      <td>-6.350617</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-6.09</td>\n",
       "      <td>-5.462892</td>\n",
       "      <td>-5.065384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WC382</th>\n",
       "      <td>WatsonCrick</td>\n",
       "      <td>WC_6ntstem</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCCCGAAAGGGATC</td>\n",
       "      <td>-45.8</td>\n",
       "      <td>-0.129099</td>\n",
       "      <td>81.616983</td>\n",
       "      <td>-6.022005</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-5.76</td>\n",
       "      <td>-5.135609</td>\n",
       "      <td>-4.738918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Series ConstructType bottomScaffold topScaffold            RefSeq  \\\n",
       "SEQID                                                                           \n",
       "WC337  WatsonCrick    WC_6ntstem             GC         NaN  GATCAAGAAATTGATC   \n",
       "WC340  WatsonCrick    WC_6ntstem             GC         NaN  GATCATGAAAATGATC   \n",
       "WC343  WatsonCrick    WC_6ntstem             GC         NaN  GATCAGGAAACTGATC   \n",
       "WC346  WatsonCrick    WC_6ntstem             GC         NaN  GATCACGAAAGTGATC   \n",
       "WC349  WatsonCrick    WC_6ntstem             GC         NaN  GATCTAGAAATAGATC   \n",
       "WC352  WatsonCrick    WC_6ntstem             GC         NaN  GATCTTGAAAAAGATC   \n",
       "WC355  WatsonCrick    WC_6ntstem             GC         NaN  GATCTGGAAACAGATC   \n",
       "WC358  WatsonCrick    WC_6ntstem             GC         NaN  GATCTCGAAAGAGATC   \n",
       "WC361  WatsonCrick    WC_6ntstem             GC         NaN  GATCGAGAAATCGATC   \n",
       "WC364  WatsonCrick    WC_6ntstem             GC         NaN  GATCGTGAAAACGATC   \n",
       "WC367  WatsonCrick    WC_6ntstem             GC         NaN  GATCGGGAAACCGATC   \n",
       "WC370  WatsonCrick    WC_6ntstem             GC         NaN  GATCGCGAAAGCGATC   \n",
       "WC373  WatsonCrick    WC_6ntstem             GC         NaN  GATCCAGAAATGGATC   \n",
       "WC376  WatsonCrick    WC_6ntstem             GC         NaN  GATCCTGAAAAGGATC   \n",
       "WC379  WatsonCrick    WC_6ntstem             GC         NaN  GATCCGGAAACGGATC   \n",
       "WC382  WatsonCrick    WC_6ntstem             GC         NaN  GATCCCGAAAGGGATC   \n",
       "\n",
       "       dH_NUPACK  dS_NUPACK  Tm_NUPACK  dG_37_ensemble_NUPACK  \\\n",
       "SEQID                                                           \n",
       "WC337      -43.8  -0.127906  69.289375              -4.408689   \n",
       "WC340      -42.3  -0.123456  69.481105              -4.289815   \n",
       "WC343      -45.0  -0.129582  74.119221              -5.077725   \n",
       "WC346      -46.7  -0.134548  73.938545              -5.236008   \n",
       "WC349      -42.4  -0.125294  65.253500              -3.828455   \n",
       "WC352      -42.3  -0.123618  69.034272              -4.238612   \n",
       "WC355      -45.0  -0.129582  74.119221              -5.076523   \n",
       "WC358      -45.8  -0.132645  72.131235              -4.928765   \n",
       "WC361      -46.2  -0.132355  75.910414              -5.419805   \n",
       "WC364      -45.6  -0.129969  77.701898              -5.557778   \n",
       "WC367      -47.3  -0.132871  82.833863              -6.351073   \n",
       "WC370      -50.2  -0.140932  83.050641              -6.749814   \n",
       "WC373      -43.9  -0.125520  76.595312              -5.238368   \n",
       "WC376      -42.4  -0.121232  76.593617              -5.071645   \n",
       "WC379      -47.3  -0.132871  82.833863              -6.350617   \n",
       "WC382      -45.8  -0.129099  81.616983              -6.022005   \n",
       "\n",
       "           TargetStruct  dG_37_NUPACK  dG_37_ensemble_150mM_NUPACK  \\\n",
       "SEQID                                                                \n",
       "WC337  ((((((....))))))         -4.13                    -3.532474   \n",
       "WC340  ((((((....))))))         -4.01                    -3.414562   \n",
       "WC343  ((((((....))))))         -4.81                    -4.194838   \n",
       "WC346  ((((((....))))))         -4.97                    -4.352368   \n",
       "WC349  ((((((....))))))         -3.54                    -2.961107   \n",
       "WC352  ((((((....))))))         -3.96                    -3.363334   \n",
       "WC355  ((((((....))))))         -4.81                    -4.192852   \n",
       "WC358  ((((((....))))))         -4.66                    -4.047904   \n",
       "WC361  ((((((....))))))         -5.15                    -4.535783   \n",
       "WC364  ((((((....))))))         -5.29                    -4.672591   \n",
       "WC367  ((((((....))))))         -6.09                    -5.462921   \n",
       "WC370  ((((((....))))))         -6.49                    -5.861195   \n",
       "WC373  ((((((....))))))         -4.97                    -4.354610   \n",
       "WC376  ((((((....))))))         -4.80                    -4.189336   \n",
       "WC379  ((((((....))))))         -6.09                    -5.462892   \n",
       "WC382  ((((((....))))))         -5.76                    -5.135609   \n",
       "\n",
       "       dG_37_ensemble_75mM_NUPACK  \n",
       "SEQID                              \n",
       "WC337                   -3.142028  \n",
       "WC340                   -3.024805  \n",
       "WC343                   -3.800274  \n",
       "WC346                   -3.957440  \n",
       "WC349                   -2.577143  \n",
       "WC352                   -2.973716  \n",
       "WC355                   -3.797847  \n",
       "WC358                   -3.654557  \n",
       "WC361                   -4.140111  \n",
       "WC364                   -4.276283  \n",
       "WC367                   -5.065094  \n",
       "WC370                   -5.463146  \n",
       "WC373                   -3.959321  \n",
       "WC376                   -3.794834  \n",
       "WC379                   -5.065384  \n",
       "WC382                   -4.738918  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rescue_df.query('Series == \"WatsonCrick\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "85ea98ff-1e5b-4b30-bbff-80eba3fbcb4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(my_df.loc['TETNNN192', 'RefSeq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "589ca0c1-2d87-4847-92d7-6cb3d883dae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# either explicitly TETNNN or is in fact a TETNNN\n",
    "rescue_seqid = np.unique(list(rescue_df.index) + [x for x in mydata.annotation.index if x.startswith('TETNNN')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f9e2deb7-9f21-4bc4-9255-7cfba3ca7c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'WC337' in set(rescue_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "44087f02-811f-43f1-80fe-03f4e0731491",
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted_single_variant_fn_list = ['./data/fitted_single_cluster/NNNlib2b_DNA_%s.CPvariant' % x \n",
    "                                 for x in ['20211022', '20211216', '20211221']]\n",
    "\n",
    "with open(fitted_single_variant_fn_list[0], 'r') as fh:\n",
    "    for line in fh:\n",
    "        header = line\n",
    "        break\n",
    "\n",
    "outfile = './data/fitted_single_cluster/rescue.CPvariant'\n",
    "if os.path.isfile(outfile):\n",
    "    os.remove(outfile)\n",
    "with open(outfile, 'a') as outfh:\n",
    "    outfh.write(header)\n",
    "    for fn in fitted_single_variant_fn_list:\n",
    "        with open(fn, 'r') as fh:\n",
    "            for line in fh:\n",
    "                seqid = line.split('\\t')[0]\n",
    "                if seqid == 'WC337':\n",
    "                    print('found!')\n",
    "                if seqid in rescue_seqid:\n",
    "                    outfh.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ae0d260f-d5de-4668-9b80-6366fdb8c855",
   "metadata": {},
   "outputs": [],
   "source": [
    "rescue_sc_df = pd.read_csv(outfile, sep='\\t')\n",
    "\n",
    "# rescue_sc_df = rescue_sc_df.query(\"rsqr > 0.5 & fmin_init > -1 & fmin_init < 2 & fmax_init > 0 & fmax_init < 2 & dH_init > -400\")\n",
    "# rescue_sc_df = rescue_sc_df.set_index('SEQID').join(mydata.annotation[['RefSeq']])\n",
    "# rescue_sc_df = rescue_sc_df.groupby('SEQID').apply(np.mean)\n",
    "\n",
    "# rescue_df = rescue_sc_df[['dH_init', 'Tm_init']]\n",
    "# rescue_df.columns = ['dH', 'Tm']\n",
    "# rescue_df.Tm = rescue_df.Tm - 273.15\n",
    "# rescue_df['dG_37'] = util.get_dG(dH=rescue_df.dH, Tm=rescue_df.Tm, celsius=37)\n",
    "# rescue_df = rescue_df.join(mydata.annotation[['RefSeq']])\n",
    "# rescue_df['tetraloop'] = rescue_df.RefSeq.apply(lambda x: x[5:11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "99037413-d7bd-401d-853b-0199fa649425",
   "metadata": {},
   "outputs": [],
   "source": [
    "rescue_df['tetraloop'] = rescue_df.RefSeq.apply(lambda x: x[5:11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "57d966d3-764f-4473-98c8-303f929cdf2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'WC337' in rescue_sc_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0ab69624-dbcb-4728-af34-da8006df2336",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'CGAAAG' in rescue_df.tetraloop.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "627ebbec-2ab8-4e48-bcfc-054243a95cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dH</th>\n",
       "      <th>Tm</th>\n",
       "      <th>dG_37</th>\n",
       "      <th>RefSeq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEQID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TETNNN0</th>\n",
       "      <td>-70.637718</td>\n",
       "      <td>27.610380</td>\n",
       "      <td>2.205282</td>\n",
       "      <td>GATCAAAAAATTGATC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNNN1</th>\n",
       "      <td>-45.088408</td>\n",
       "      <td>31.642404</td>\n",
       "      <td>0.792557</td>\n",
       "      <td>GATCAAAAATTTGATC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNNN10</th>\n",
       "      <td>-42.006303</td>\n",
       "      <td>35.483767</td>\n",
       "      <td>0.206365</td>\n",
       "      <td>GATCAAAACCTTGATC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNNN100</th>\n",
       "      <td>-58.045905</td>\n",
       "      <td>41.061653</td>\n",
       "      <td>-0.750330</td>\n",
       "      <td>GATCAATCTATTGATC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNNN1000</th>\n",
       "      <td>-89.413999</td>\n",
       "      <td>55.742603</td>\n",
       "      <td>-5.095436</td>\n",
       "      <td>GATCACGCCAGTGATC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNNN995</th>\n",
       "      <td>-62.012451</td>\n",
       "      <td>53.217764</td>\n",
       "      <td>-3.081503</td>\n",
       "      <td>GATCACGCAGGTGATC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNNN996</th>\n",
       "      <td>-99.543678</td>\n",
       "      <td>56.210875</td>\n",
       "      <td>-5.806158</td>\n",
       "      <td>GATCACGCTAGTGATC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNNN997</th>\n",
       "      <td>-49.938996</td>\n",
       "      <td>49.262384</td>\n",
       "      <td>-1.899341</td>\n",
       "      <td>GATCACGCTTGTGATC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNNN998</th>\n",
       "      <td>-59.637756</td>\n",
       "      <td>50.924217</td>\n",
       "      <td>-2.562404</td>\n",
       "      <td>GATCACGCTCGTGATC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNNN999</th>\n",
       "      <td>-56.975954</td>\n",
       "      <td>49.202792</td>\n",
       "      <td>-2.156847</td>\n",
       "      <td>GATCACGCTGGTGATC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4080 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dH         Tm     dG_37            RefSeq\n",
       "SEQID                                                       \n",
       "TETNNN0    -70.637718  27.610380  2.205282  GATCAAAAAATTGATC\n",
       "TETNNN1    -45.088408  31.642404  0.792557  GATCAAAAATTTGATC\n",
       "TETNNN10   -42.006303  35.483767  0.206365  GATCAAAACCTTGATC\n",
       "TETNNN100  -58.045905  41.061653 -0.750330  GATCAATCTATTGATC\n",
       "TETNNN1000 -89.413999  55.742603 -5.095436  GATCACGCCAGTGATC\n",
       "...               ...        ...       ...               ...\n",
       "TETNNN995  -62.012451  53.217764 -3.081503  GATCACGCAGGTGATC\n",
       "TETNNN996  -99.543678  56.210875 -5.806158  GATCACGCTAGTGATC\n",
       "TETNNN997  -49.938996  49.262384 -1.899341  GATCACGCTTGTGATC\n",
       "TETNNN998  -59.637756  50.924217 -2.562404  GATCACGCTCGTGATC\n",
       "TETNNN999  -56.975954  49.202792 -2.156847  GATCACGCTGGTGATC\n",
       "\n",
       "[4080 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rescue_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d52f55-d87c-4445-a0e0-ad5c3c350d81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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