02.1_UV_melt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0161426b-24e5-48ad-b67a-cda34422eeed",
   "metadata": {},
   "source": [
    "Figure 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53815b85-4e77-40ea-9df8-1d7a02250c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%run nnn/ipynb_init.py\n",
    "%matplotlib inline\n",
    "\n",
    "from nnn.uv import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de914708-3130-4e1e-81c1-56ddb9b0a74d",
   "metadata": {},
   "source": [
    "# Prepare UV melting data for direct comparison\n",
    "\n",
    "Aggregate to SEQID level and salt adjust to array salt level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08e64c08-f494-4a3c-848b-625a8d575433",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "arr = pd.read_csv('./data/models/raw/arr_v1_n=27732.csv', index_col=0)\n",
    "uv_oligos = pd.read_csv('./data/uv_melt/ECLTables/ECLOligos230502.csv').set_index('OligoID')\n",
    "\n",
    "agg_result_file='./data/uv_melt/uvmelt_agg_230502.csv'\n",
    "uv_df = pd.read_csv(agg_result_file, index_col=0)\n",
    "uv_df = uv_df[uv_df.SEQID.isin(hplc_clean_seqid)]\n",
    "uv_df = uv_df.query('is_hairpin').sort_values(by=['conc_uM', 'n_curve', 'curve_date'])\n",
    "\n",
    "pname_uv = ['dG_37_uv', 'dH_uv', 'Tm_uv']\n",
    "agg_list = [uv_df.groupby(['SEQID', 'Na_mM'])[p].apply(np.nanmedian) for p in pname_uv]\n",
    "agg_df = pd.DataFrame(agg_list[0]).reset_index()\n",
    "\n",
    "for i,p in enumerate(pname_uv):\n",
    "    agg_df[p] = agg_list[i].values\n",
    "\n",
    "agg_df = agg_df.set_index('SEQID').join(arr).join(uv_oligos['sequence'])\n",
    "\n",
    "def adjust_uv_param(i, agg_df):    \n",
    "    row = agg_df.iloc[i,:]\n",
    "    if row.Na_mM > 25:\n",
    "        adjusted_dict = util.get_Na_adjusted_param(\n",
    "            Na=0.083, from_Na=(row.Na_mM + 50)/1000,\n",
    "            dH=row.dH_uv, Tm=row.Tm_uv, seq=row.RefSeq)\n",
    "        for p in ['dG_37', 'dH', 'Tm']:\n",
    "            row[p+'_uv'] = adjusted_dict[p]\n",
    "    return row\n",
    "        \n",
    "for i in range(len(agg_df)):\n",
    "    agg_df.iloc[i,:] = adjust_uv_param(i, agg_df)\n",
    "\n",
    "agg_df.drop(columns=['Na_mM'], inplace=True)\n",
    "# agg_df.rename(columns=dict(sequence='RefSeq'), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "67c81344-143a-425a-9365-ac6c1215dc10",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_df.to_csv('./data/models/processed/uv_hairpin_arr_salt_hplc_filtered.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f1102132-eaf4-467b-9bd2-8b9060233ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_result_file='./data/uv_melt/uvmelt_agg_230502.csv'\n",
    "uv_df = pd.read_csv(agg_result_file, index_col=0)\n",
    "\n",
    "pname_uv = ['dG_37_uv', 'dH_uv', 'Tm_uv']\n",
    "agg_list = [uv_df.groupby(['SEQID', 'Na_mM', 'is_hairpin'])[p].apply(np.nanmedian) for p in pname_uv]\n",
    "agg_df = pd.DataFrame(agg_list[0]).reset_index()\n",
    "\n",
    "for i,p in enumerate(pname_uv):\n",
    "    agg_df[p] = agg_list[i].values\n",
    "\n",
    "agg_df = agg_df.set_index('SEQID').join(arr)\n",
    "        \n",
    "for i in range(len(agg_df)):\n",
    "    agg_df.iloc[i,:] = adjust_uv_param(i, agg_df)\n",
    "\n",
    "agg_df.drop(columns=['Na_mM'], inplace=True)\n",
    "\n",
    "agg_df.to_csv('./data/models/processed/uv_arr_salt_not_hplc_filtered.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "93f09007-4920-4472-b123-9d199e5b17ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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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0tDT0ej1jxozhrrvucnRItcgueGfPnuXNN98kOzubTz75hJ49e9oyLo9T151cax+fBrubWKKuAjrzzBmWhYfTKyyMxMTEJg8/bNq0icOHD7Nnzx5GjRrVpOcSnEd4eDgxMTGMHz+egICAJj2XyBH3FBQURHBwMI888gh32mEVeWPILnhqtZq//e1vBAUF8fLLL9syJo9T353c7u7dGzzFwBL1HQPUsl8/BoaENHkF1ddff81dd93F8uXLGTp0KFdvblIXXJPRaKSsrIyAgADi4+Np3769yBGhFtOK3aCgIPOqbmclq+DNmTMHgOPHj9OyZUvef/99cVqCFdVXiEqNRj6Nj+cvN4thXd1NLFHfMUDtfH2tkqSxsbEAzJ07F3D8EmSh8fR6PV999RX5+flMnz4dPz8/kSNCLfv27WPbtm3mo32c/fcpq+C9+uqrtYYxbty40eShDaHK7c6jGxgayrwLF8jTatncuXOTVmnG+fjwXvPm/H+5uaBQoKBpBbSmQ4cO0a1bNzp27IhKpeLgwYMsX76cqVOnWuX5BfvQarWsWbOGCxcu0LlzZzRW7OojcsQ9SJLEzp072bFjB2FhYYQ14X3JnmQVvI0bN1JaWkq7du1QqVScOHGC8PBwRo8ebev4PEKcj89t7+QCVSoC/fyaVOwALl68SOXGjQR17Uq5ry9bmlhAa5oxYwY//PADH330EQqFgv79+4s3MhdTWlrKypUryc3NpXfv3iQmJlr1U7vIEdcnSRLffvst+/btIzo6mokTJ+Lv7+/osGSRVfCSkpK4evUqBw4cwGAwMHbsWId0unZn06OjrXYnV5ezZ8/y2VdfccPPj+DAQMLUaiK8vTlYUmLV3pyDBg2qs6uF4Pxu3LjBF198wR9//MH999/PwIEDbTJEJXLEdUmSxKZNmzhy5AitWrVi/PjxVh0BsDXZi1aaNWvGkCFDbBmLx6t5J2dqBVZurBrsPFhSAjSueXRwcDBH77iDbyIjQa8Hvd7cp9NevTkF5+bj40NQUBA9e/akb9++jg5HcEIKhYLw8HDat2/PqFGjHNYEurFkFzzB/mq2AmtMgSopKSEwMJDw8HA+GzaMPL2+1mMa25uzJtPipurE4ibnZ8oRtVrNxIkTbdoZQ+SIa9JqtSgUCry9vRkwYABGo9GpOqjIZdEqzfPnz5v3V4gktT05J5HXxzSp/MsvvzBlyhSio6OJ9fUl1haB3mTKiTlz5oj8cBEXLlxg9erVPPDAA9x77702fxMTOeJ6SktLSUtLw9fXl4kTJ6JSqVyy2IHMgieS1DHkNmyuSZIkMjMz2bt3L82bN290X0zBvWVlZbFhwwaUSmWj+2IK7q24uJjU1FQKCwsZOHCgyxY6EzGk6WaMRiObN282TyqPGzfObicLf/3110DVatCNGzcClp1mLdjP0aNH2bhxI76+viQlJRHdyP6slhI54jr++OMPUlNTuX79OomJifTp08fRITWZRUOaBw4cMP+3uNOzHmstTjEYDKxfv56srCyHTCrHxcUB8Pzzzzv9BlRPZmoCHRQURHJyMuF1DJvbisgR13DlyhVSU1MpLy9nxIgRdOnSxdEhWYVFQ5qCbVhjcQpUnSwcGhpKly5dGD58uN1PFk5PT6ekpASNRoNCocBgMNC7d2+7xiA0LDQ0lKioKMaPH09wcLBdX1vkiGsICAggMDCQxx9/nPbt2zs6HKuRfYd39OhR4uPjUSgUnDlzhk2bNtk6No/RlMUpUHWysEajQaVS8fDDDwOOadm0YMEC3nnnHWbPng3A22+/bfcYhLpJkkRpaSkBAQG0bduW1q1bO2Q+RuSIczOt2A0ICCAlJcXl5+xqkn2Ht3DhQl566SUA3n33XZsG5WkauzgF/pxUbt68OSNHjnT4MFFZWRnbt29HkiQqKysdGotQxWAwsHnzZi5dusS0adMIDAx06BuZyBHndPjwYbZu3cqYMWNo166d2xU7sGDRip+fn/nTmOiy4hyqTyr37t3b4cUOqvqu7tmzB4BXXnnFwdEIOp2ODRs28NtvvxEfH+8UhzeLHHE+e/bsITMzk+DgYLd+f5dd8GbNmkVZWRkKhcIp/tJ4upycHFauXOl0k8oajYYHHnjA0WEIQEVFBatXr+bSpUt07dqV4cOHO8WndpEjzkOSJH788Ud++uknwsPDSU5OduttTBZtS/Dz87NVHIIFLl26xMqVKzEajYwdO5b4+HhHhyQ4mbKyMlJTU7ly5Qr33HMPCQkJTjECIDiXb775hv379xMTE8PEiRPd/j1edsH7/vvvGTx4MACrV69m/PjxNgvK3Zi2HdTU2KbNgYGBBAYGck9iIqWRkeZtDE19Xmt49dVX0el05hWiYoWvY2g0GgICAnjwwQe57777nKrYiRxxHmFhYdx5552MHTvWpZpAN1aDBS8/P58PP/yQEydOsH37dhQKBS1btrRHbG6j5rYDE0u3HZjOIAwNDWXmzJm8efEi825uYWjK81pTixYtePrppx3y2kJVjvj7+6NSqRg/frxTDGHWJHLEsXQ6HQaDAR8fH+655x569+7tlHliCw0WvMjISBYsWEBeXh6XL1/GaDQ61adFV2DadjDq+HEANnTsCFjWtHnPnj388MMPJCcn06JFC5RKpVWe19r27dtHVlaWubuL+PRuP7///jsrV66kb9++3H///U77JiZyxHEqKipYtWoVCoWC5ORk1Gq10+aJLcge0nz77bcZMGAAarUahUIhNotawLTtwPdmYvUIDJR9bc1J5eobhZvyvLayZMkSdu3aBcCAAQMcHI3nOH/+PGvWrMFoNNqtTVhjiRxxjBs3bpCWlkZeXh79+vWze2MKZyC7tLdv357hw4czYsQI0fvOTiRJIiMjg59++omYmBimTp1a5wqqEoOBM2VlZBYWOiDKW82bNw+dTkdFRQULFy50dDge4dSpU6xcudL8qb1du3aODum2RI7YX1FREcuXLycvL49BgwYxZMgQjxypk32HV1BQwBtvvGH+WgxD2JYkSXz99dccO3aMO+64g3HjxtU5qbwsN5fLWi0AiceO8Vl8PNMd+Anfx8eHhIQEAA4ePOiwODzFkSNH2LRpE35+fiQlJdG8eXNHh9QgkSP29ccff/Dll19SUlLC0KFD6dWrl6NDchjZBW/ChAkcOXKENm3aEBUVZcuYBKpag4WEhBAfH8+oUaNQq2v/qrIrKkjJyjJ/LQEzsrJICA0lrsYJCdZeKVqfNm3aMGfOHCRJolu3blZ7XqFuwcHBhIWFMW7cOMLCwhwdjiwiR+zLz88PPz8/Hn74YTp16uTocBxKdsFbvnw5QUFBDBgwgAULFvDJJ5/YMi6XV1eBKTcaUTcwjFBRUYFarUatVvPggw8iSVK9k8qny8sx1vieAThTXl6r4FlrpejtnDp1irZt29K2bVvAMf08PUH1vph33HEHzzzzjMssPBA5Yj+mVd1+fn5u2RezMWQXPH9/fzQaDSEhIXY9csZV1Vdgwuu4UzMxTSqHhIQwZswYlErlbd8Q2vn6ooRbip4KaFtHJxx7rOjMz8/n6tWr5q/1er1Y3GRlRqORjIwMTp8+zbRp0wgODnapNzKRI/Zx8uRJ0tPTefLJJ+nQoYNL5Ygtyf5TGDZsGHl5ecydO5cJEybYMia3MCMmhgM9e3Knjw93+viY/zu0ng8L1SeVY2NjZX3yjfPx4dNqXVaUwNL4+Fp3d1C1orNHYCC+SiW+SiU9AgPpERhoteHMwsJCCgsL2b59O/DnghvBegwGA+np6Rw4cICwsDC7HexrLSJH7OPgwYOsX78ejUZjl76YV7RaTpWW1vrnys21Bc5E9h1eeXk5LVu2RJIkdu/eTc+ePW0Zl8ura8uAbz2fsgoKCkhNTW3UpPL06GjmXbhAnlbL5s6dSXDQPE5oaChxcXF06tTJXLBff/11h8TijiorK1m/fj1nzpyhQ4cOPPnkk7XmdTMzM3nppZdYuHCheVGIMxE5Yns///wz27dvJyQkhOTkZJsXvCtaLY8cPco1vR69JFGk1xOiVqNWKAhVq/m2SxeaO1EHF9l3eBkZGeZtCSNGjLBlTG6rru0DOTk5LF++nNLSUkaOHNmoFVSBKhVt/fwcVuyg6vDZ3r17o9VqadasGaWlpWzZssVh8bgTrVZLWloaZ86coXv37nUuYlq2bBmJiYkcPXqUxMREli1b5qBo6ydyxLa+//57tm/fTkREBNOmTbPL3V2RXs81vR5vhQI1N9cpAN4KBdf0eor0epvHYAnZBS80NJTdu3ezf/9+9u/fb8uY3JJp+0AlVdsHluXmAlVzo35+fowfP97iFVS5Wi0HS0ooNxopNxo5WFLCwZIScm8zlGDrPXsVFRW0adOGBx54gGvXrtnkNTyNl5cX/v7+3HvvvQwbNqzWfEx2djYpKSlIkgRUDRXOmDGD7OxsR4TbIJEjthESEkJcXBxTp04l0M5NKDRKJd5KJQrAW6lE46RzhrKHNC9evEjfvn1RqVRiZZUFSgwGrmi1PFXf9oHgYGbOnNmoSeWaC2N63uyrWd/KS3vs2QsODubtt99GkiRCQ0Ot+tyeprS0FD8/P5RKJaNHj65/te7p0xiNt67XNRgMnDlzhri4OHuEahGRI9ZjMBjQ6XT4+PjQs2dPunfvLhao3IZFqzQrKyvd/vgIa6peYGoyALvOn2dcE1ZQmVZe1lTXyktL9uw1VmFhIcOGDaO8vBy9Xu/W52rZWn5+PqmpqXTr1o3BgwffNkdMp1NXL3oqlcq89N+ZiByxnsrKStatW0dFRQWTJk3C29tbFLsGyC54vXr14tq1a+YhCLGU+PZqFpiaFJJExyZ+ujUtjJHDkj17jbVjxw5ybw7VmsyaNcsqz+1JsrOzWblyJZWVlbI6p8TFxfHpp5+SkpKC0WhEqVSydOlSp7y7EzliHeXl5axatYrs7Gx69OhRZ2MKe9IajVQajUhApbHmO83tXdFq65zrC1Grrb7gRfbHAS8vL06ePIm3tzchISEWv9DVq1cZM2YMADt37uTVV19l7ty5GAwGi5/LFdRVYEwUksSiO+6gsx3bQJn27FVX3569xqr5RtYYnpYnNZ09e5Yvv/wSg8HAhAkT6Hhzv2RDpk+fTkZGBl27diUjI4Pp06fbJL7MzEy6du1KZmZmo64XOdJ0JSUlrFixguzsbPr3789jjz3msDu7ELWaULWaSklCD/gqleiBSkkiVK0mpI5CXH0bw09FRTx4+DCDjxxh4OHD9DxwgMFHjpBw9CiPHD1q9a0Nsj8WHDp0CH9/fx555BFefvllBg4caNELff7558TExACwefNm3n//fTZu3MiePXvo379/rcfn5+dTUFBQ6/tnz5616HUdIVerpcJorLUpHElCKUl83aEDw+3c79K0Z+8vN+86b7dnr7FGjBjBkiVLmDp1KkqlkhUrVlj8HJbkiSvnSF1OnjzJhg0b8Pb2ZsKECbRo0cKi6xMSEmy6HWHZsmU89dRTSJJEYmIin332WZ2F9XbbI+ydI+BeeVJUVMQXX3xBUVER3QcPJq57d7LKym55jC3ujOrTXKPh2y5dZN+h1dzGcFWnQydJKAAFoAPCuXWVpzV/Fovug69du8bevXuprKMnY00rVqxg9+7dAHTu3JkhQ4awevVqAHOnloiICP744486r1+7di2LFy+2JDynUV+XFY0k0drf3+7FzsTWe/ZiY2PR6/WEh4djMBhsnieunCN1CQgIQKvVsnXrVrp162ZxwbOl+laCJiQk3DJ02lBRtHeOgHvliY+PD76+vnTo35+XJIlrR486bP9bY4Yiq29jQJKokCRUVBU7SaFAIUnmVZ6VNrhjl13wHn/8cY4dO8aZM2dkbRadMmUKU6ZMAeDFF18kPz+fQ4cOkZmZab79LigoIDIyss7rx44dy6BBg2p9/+zZs7z44otyw3YI02KSkb/+yiWtFhXwQZs2LMrJcfgK10CVikAb7tlLSUlh6dKlKBQKnnnmmQYf35Q8ceUcqa60tBR/f3+2bdtmXr3Y2DsoW5GzElRuUbRnjoB98qSx81ByrzPliI+PD3/5y1/4rbyca0ePmgtHudFo0zujuuJuyoZz07YF052dQqFAslm0f5Jd8DZs2MBLL71EQECAxS/y3nvvATB79mwSEhLQaDS89tprGAwG3nrrrTqviYyMrDeBnV20RkOI0UhlSQlKLy/a+fvzbIsWLLHC/IWzO378ODqdjm7dunH27FmL7lIszRN75ogtiowkSSxatIi9e/cycOBAnn766SbfQdmKnJWgcrdH2DNHwPZ50tg3f7nXnTlzhvXr1/PYY4/RuXNnlEolBTodOqMR7xqHuCpvFkBbM92pKanK1TKjkWBJAoWCAp2O0+XlTtVhxUR2wVMqlcyfP9/8dWPOw3vnnXcAGDhwoMVzgK6kpKSEtLQ0dC1bonCSRtum0xvKb74hHSwpAax/PFBT53rB+fLEFkXGaDTy17/+lX/+859IksSqVatqPaaxd1BNlZmZydNPPw1UnU5uev6GVoLK3R7hbjlSc5hO7t2WnOsKz5whPT0db29v857FK1otKVlZ5Ot0KHQ6DFRtM8qprESlUBBup/ccgySRd/P1Aa7odKhuxvJUVhY7unWTXfQkqvK5Mas8LSF7aU/Xrl3p06cPffr04Z577rFZQK7u2rVrLF++nLNFRaj9/UGhMHdBKTca0dnh01ddlubk0PPAAc5XVHC+ooKeBw7Q88ABlubkWP21LJnrdXa26GKi1+v59NNPzcWuPpbcQVmLqUXZhQsXuHDhAo888oi5TVlDK0FNRdE0zHi77RHulCMmje02Ut91x48fZ8OGDfj7+zN16lTzn2ORXs91g8E8HFg9g4yA8WZO2bqps3TztRTVvjbFdL2BtmLVtzEowPz3QM4qz6aQ/WyxsbFA1RE227dv54knnrBqIO7AtFm4tLSUkoceMp+HZyowcPvjgWzJkk3qTWHpXK+zs3YXE51Ox5o1a9izZ0+dxU6hUJjPQGzsHVR9GhqWrVncTVJSUsx3eQ2tBJ0+fTpxcXG8/PLLvPvuu3U+1t1yxBa0Wi0/7ttHfLNmJCUl1dmRRgFQbQizeuEo0OkYd+KEXZo611yVcLt1CqZtDNf0evSAn1JJoEqFEghSq/ksPp6Im3eotlhtKvvd1/QXT6/Xk56ebtUg3IWvry8+Pj4kJiYS2qYN3x06BPx59tyo48cbPADWVizZpN4UTZnrdUbW7mKiUqnw8/Nj4MCBpKam1nreFStW8P7779dZLJqywVzOsGxdxR2qhl8tKfANFUV3yxFbUCqVREREMHX06Dr/nBRUzdcZqn0t3fwn6OaH6sYMs1pKglvmDE13bPWxdBuDtckueKYip1ar7TJJ7krKysrw8/MjMDCQp59+GtXNiWS9JJGn1VJQWUlCWFi9xwO5E2vM9ToTa3UxKSsrw9fXF6VSyYgRI1AoFBgMhlrPm5SURFJSUr3PI+cOqia5c391FXeo+p1as02Zu+WISWO7jVS/TntzKb6XlxejRo2q90OBSqGglY8PFUYjl7VaWmg0eCsUlBmNt9wlVV8Nac3l/iFqNUEqFXk3v64+tKrk5uKZejTXaBy2oEX2O3CHDh0YM2YMOTk5nD9/3pYx2Y3ptIGa/9zutIGajh8/zkcffWSeRzEVu5qnI7x2/rxFpxq4qmnTphEWFkZkZCTPPvuso8OxiqZ2Mbl69SqfffYZ27ZtAzCfZN/Y501ISODw4cOyV4vKnfszFffqQ1IKhYJPP/3Uqoti3C1HGtNtpOZ1OkkiQKfjRmkpWqORULWasAamG4yS9Ofd1c1/qxUKc7GzpeYaDZ/GxxPp5UWMtzfNTfshvbyI9vZG5aQHDMi+w9u5cydarZZ+/fpx4MABnnzySVvGZRf1bRCv77SBmg4cOMB//vMfAgICbmmCW1ej5rcsONXAla1Zs4aZM2ei1Wr54IMPePvttx0dklU0totJXl4eqamplJWV1bk03tbdUcCyYVnTHWTNVZrW5G450thhOtN1+eXlZGRkkJ2dTft27Rjcrx9h3t71XldpNOKnVHLdYEAvSfgolVRIEgajkXAvL0LUarucQ9fO15dIb2+u6fUoqJqPUygUGMEmC06sQXZEFRUVbNy4keeff978SdXVmRZyjDp+HPhzrq2hhRySJPHzzz/z/fffExoaSnJy8i2TyvX10Vzavj29qp1TZe0FI87AYDBQVFSE0WhEkiRycnLMbaA8zaVLl1i1ahV6vZ7Ro0fToUOHOh9n643klg7LJiQk2HQUxx1zpLHDdEEGA1vWr0ebk0Nir14kJibeti/mFa2WSadOUWIwoJMkSvR6AtVqVECASsWXd91Fc43GXPCa0tS5IY6ej2sM2QVv/vz56PV6jEZjvRs8XY1pIYdpbq2HzEMTt2/fzi+//EJUVBRJSUm1xtlNjZqrp5cKeLRZM6v2rnRGgYGBbNy4Eaha2Zuenu6R3fDPnj3LmjVrUCgUTJgwgdatW9f5OHttJG/M3J+tiBypUlJSQmpqKgUFBdx33308+OCDDXZiMu3d81UqURmN/CFJRCoUeCuVlBuNeN98L6u5GtI0zIqVl/s7cj6uMWT/1HPnzuX06dM0b96cK1eu8NVXX9kyLqfm7+9PixYtGD9+PL51nDZgj0bNzsoT37jq4uvrS0BAAKNGjTJv6anJXhvJTewxfCqHyJEq3t7eeHl58fDDD9OvXz+Lrm1oMYor3n3Zg+yCFxAQQEpKCtHR0WRkZNgyJqek1+sxGAxoNBruvfde7rnnHvMClbrYulGz4JxMK3ZjYmKYNWvWbXPE1U4qF6zDlCMajYZp06bdNkeawtXuvuxB9ipNHx8fwsLCWLJkCZcvX7ZlTE5Hq9WyatUq1qxZg/7mJyY5SRqoUtHWho2anc2///1v839fu3aNv/71rw6Mxr4kSWLnzp3885//NHftbyhHTItJqnPWk8qtxZNzBODixYt88sknHLq5R9dWxU6om+yC9z//8z/4+vry+uuv8+GHH9oyJqdSVlbGl19+yfnz5wkPDxcJehsBAQGsWLGCffv28fLLL7vFknM5JEli27Zt/PjjjwQFBeEjc+jaklZc7sJTcwTgt99+Iy0tDUmSGnWIdnU1F6Nobdh/0p3ILnj/+Mc/WLlyJZcuXWLu3Lm2jMlpXL9+nRUrVpCTk8N9993Ho48+6vDjfZzZuHHj8PPz45///Cf/93//59Z3KiZGo5HNmzezZ88eWrZsyeTJky3qIGKvk8qdhSfmCMCxY8dYu3YtXl5eTJ48mTsbuR2psXv+hCoWzeFpNBp69+7N5s2bbRmTU7h69SpffvklxcXFjZpU9kRz5swBqpbjv/TSS2g0GrfpolEXg8HAhg0bOHXqFO3atWP06NHmA0kt4SyLSezB03IEYP/+/WRkZBAUFERSUhIRERGNfi6xGKVpZBe8Dh06sG7dOp555hn69+9vy5icgkajwcvLi8cff5xu3bo5OhyXsGDBAnQ6HV5eXpSUlBAoc5uHq1IqlWg0Gjp37szjjz8uhrtl8LQcgaoVu+Hh4SQlJREcHNzk5xOLURpPdsEbOHAgcXFxGI1Gtx7WM62g8vf3v6UvpiXsdfacs/nXv/5FXFwcw4YNY/Xq1ej1embOnOnosKyuoqICb29vlEolw4cPR6FQuPXfCWvylByRJImKigp8fX3p1KkTHTp0EB+InIDsObw33niDixcv8vvvv/P777/bMia7KzEYOFNWxr+PHOHjjz8m6+b+ucYmqD3PnnMmWVlZDBs2DKg6UsZdeq5WV1xczLJly8jIyDAf4yOKnXyekCMGg4GNGzeyfPlyysvLAbEa01nIvsOLj4+nZ8+eeLtZOyxTk2eAp65eZWRUFJOaeGyJvc6eczY1c8P0l91dFBYWkpqaSnFxMT179hSFrhHcPUd0Oh0bNmzgt99+o3379qjFIhKnIvu3cfToUSorK82fVNyhW0LNJs8oFGxs3ZqPmrhvzl5nzzmbxx57jGeffZaWLVty+fJltzokODc3l7S0NMrLy3niiSfo2rWro0NySe6cI1qtltWrV3Px4kW6dOnC8OHDxZ2dk7HoDi8lJaXOVlquqq4mzwbgTHm5R7QBs7b777+f/v37U1xcTEhICDt27HB0SFZx8eJFVq1ahcFgYOzYscTHxzs6JJflrjlSVlZGWloaubm59OnTh0ceeUSMADgh2XN4RUVFvPnmm8yZM8e8tNjVmZo8V6cC2rpRUbe3wsJCVq1axfDhw2udt+aqfHx88PX1JSkpSRQ7K3DHHPHy8kKtVvPAAw+IYufEZN/hvfHGGzYMw76MRiM6nc6jmzzbwuzZswkICGDw4MEUFRWRkpLi6JCapLy8HF9fX6Kionj22WfF8JQVuGuOmDaUixxxbrLv8NyFXq9n3bp1rFy5Ep1Ox/ToaFpoNHgDGZ07Mz062tEhuqzOnTtTWFjIrl27KC4udnQ4TbJ3714WLVpEXl4eIFbZWYs75UhOTg6LFy9m//79gMgRVyC74L322mt888036HQ6W8ZjU1qtlpUrV5KVlUVoaKi5h6GnNXm2lYkTJ/LGG28wfPhwQkJCzKdmuxJJktixYwfffvstfn5+svtiCvK4Q44AXLhwgS+++ILKykqrbCYX7EP2kOa8efPYvXs3zz77LLGxsUyaNIlWrVrZMjarKi0tZeXKlWJS2YbmzJmDr68vMTExvPrqqy734UiSJL799lv27dtHdHQ0EydOxN/f39FhuRVXzxGo2ku4fv161Go1SUlJLvU+6Olk3+GtWLGCLVu28MADD/DUU0/x7rvv2jIuq7p+/TrLly8nNzdXTCrbkCRJvPbaa5w7dw6gUX0lHcVoNLJx40b27dtHq1atmDx5sih2NuDKOQJw5MgR1q5di0ajYfLkyaLYuRjZd3h9+/Zl6tSpANy4ccOlCp6XlxcqlYpHHnmEe+65x9HhuC3TYaamE7xdiUKhwMvLi/j4eEaOHOlyb8SuwpVzBKp67IaEhDBx4kTCxBSIy5Fd8L744gtOnz5NdHQ0ubm5fPXVV7aMyypMK6h8fX156qmnRNcDG1OpVOYtK6Z/O3snfK1Wi5eXF0qlkkcffRSg1qGsgvW4Yo5U74t511130bZtW/Fe4qIsOh4oJSWF6OhovvnmG1vGZBUXLlxgzZo1DBs2jI4dO4oEtYM+ffoAVXdLgYGB3HfffQ6O6PZKS0tJS0sjKiqKxx9/XBQ6O3C1HJEkiW+++YZz584xdepU/P39xXuJC5P9N9zHx4ewsDCWLFnCpUuXbBlTk2VlZZGWlgZg0WGcQtPExsYSGxtLTEwMZWVlTt2goKioiOXLl3PlyhVCQ0MdHY7HcKUcMRgMfP311+zfv5+goCBR6NyA7N9g69at6dWrFx07dnTq/SZHjhxh06ZN5s4Y0WJfnd2YPr2b/Pjjjw6K5PYKCgpIS0vj+vXrJCYm1opbsB1XyRGdTsf69es5ffo0d911FyNHjhQFzw3I/g1mZGRQWlpqPrDRGZu+7t27l2+//Zbg4GCSk5PFpLKd5VQ7/igvL4+ysjIHRlO3nJwc0tLS0Gq1PPnkk3Tu3NnRIXkUV8gRrVbLqlWruHTpEt26dWPYsGFiuNtNyC54Q4YM4c4777RlLE3m5eVl0cnCnnpQq62kp6cDVfMzQUFBvPPOOw6OqDYvLy+8vLwYMWIE7dq1c3Q4HscVckSlUqFUKunbty8PP/yw2MLkRmQXvOPHjzN06FCnmxOTJInKyko0Gg09evSgS5cusocelubkMO/iRfPXPQ8cAOD1Vq14w8mLuzOKjY01/7dCoWDHjh1OMxJQUVGBj48PERERPPvss2J4ykFcIUfUajUTJ05EpVKJYudmZP+tVyqVzJ8/3/y1MywlNhgMbNq0icLCQiZNmoRGo7HojcxTD2q1FdObmcFg4PPPPyc8PNwp3swOHz5MZmYmycnJxMTEiGLnQM6aIwUFBaSmptKvXz/69esncsRNyf6tJicnc+zYMSRJ4saNG7aMSRa9Xs/atWs5ffo08fHxjVpI46kHtdpKnz59+O233/jkk0+YNGmSUyw53717N9u2bSM4OBiN+F07nDPmSH5+Plu3bkWr1ZrXKAjuSXbB+/DDD4mOjqZjx44cPXrUohc5e/Ysy5cvR6lUkpycTG5uLpmZmSgUCubNm9eoYpWRkUFlZaWYVHYiixcv5sSJEzz//PMEBASQk5NDTEyM7OutnSf79+/n4sWLREREkJSURFBQkKU/kmBlzpYjAP/5z39o1qwZ48aNE/O6bk52lQgNDWXQoEEEBARY3B18w4YNREVFoVAoiI2NZfPmzcyfP59evXqxZ88ei4MGuHLlCn379mX48OGi2DmRu+++m23btpGenm5eoCCXtfPk0KFDxMbGMmXKFFHsnIgz5QhUzSUmJyeLYucBZN/hJSQkEBUVxcqVK4mIiGjw8StWrGD37t0A/Pe//2XHjh2cP3+eLVu2mPsURkRE8Mcff9R5fX5+PgUFBbW+f/LkSeDPDawnTpyQ+yMINvbggw/W+t7x48dve82WLVs4c+YMKpXK4jxpKEe8vb3p2bMn58+fb8yPI9iAvXMEGs6Trl27UlJS0mAcgnNr3bo1vr6+t32M7II3YMAAAP7+97/LevyUKVOYMmUKAC+++CJ+fn4EBQVhNBrNd2QFBQVERkbWef3atWtZvHhxvc+/bNkyli1bJjd8wYmlp6fTsWNHi/OkoRzZvHkzmzdvtlncgv00Nkeg4Tz5xz/+YZOYBfsy5cjtKCQ7tC0/duwYy5YtIyAggL/97W8cPnyYH374AYPBwFtvvVXnkGR9n8qOHDnCvHnzePvtt7nrrrtsHbrVnD17lhdffJH33nuPNm3aODoc2ewRt+mTmaV5InLEOThzjoDIE2dgzxy5LcnF/Prrr1L79u2lX3/91dGhWETEbT+uGLMkibjtTcRtP84Ss1jtIQiCIHgEUfAEQRAEjyAKniAIguARRMETBEEQPILLFbyIiAhmzZolay+gMxFx248rxgwibnsTcduPs8Rsl20JgiAIguBoLneHJwiCIAiNIQqeIAiC4BFc6tAnW3RKt4erV6/y9NNPs27dOnbu3OkSMe/fv581a9ZQWVnJc8895zJ/1q6aIyDyxF5EjtiXM+WIS93h2aJTuj18/vnn5iNQXCXmGzdusHDhQmbOnMnPP//sMnG7ao6AyBN7ETliX86UI05/h9fUUxccoXrMnTt3ZsiQIaxevRrAaWOGW+Pu1asX/fr1Iy0tjRdeeIGsrCzA+eN2lRwBkSf2JHLEvpw1R5y+4DX11AVHqBlzfn4+hw4dIjMz02ljhlvjLi4uZv78+cyaNYuwsDCXidtVcgREntiTyBH7ctYccaltCY3plO4sZs+ezTvvvMOOHTtcIuY5c+aQn59PaGgoDz30ED4+Pi4RtyvnCIg8sQeRI/blTDniUgVPEARBEBrLOT8SCIIgCIKViYInCIIgeARR8ARBEASPIAqeIAiC4BFEwRMEQRA8gih4NpSXl+foEAQnJ3JEkEPkiXV4fMFbtGgR2dnZAOzdu5f169czc+ZM8/9fvXq1xe1v0tPT2bt3Lx999BEAS5YssTiu7OxsFi1aVCu+9PR082MKCgpYv379LY9pyOzZswE4evQo//3vf2VdY/p5VqxYQXl5uYU/iesTOdIwT88REHkih6PzxOk7rdibSqWiXbt2nDt3jtatW3Po0CHGjRsHVCXOnDlziI+Pp7S0lOjoaDQaDWPGjGHRokUAdO/eHYCKigqOHz/O9u3buXDhAtnZ2fz973+nS5cuBAUFMWrUKP7xj38QFBREfn6++fpz587x2WefoVQqad68+W1jTUtLIyUlhc8//5wbN27w4osvMnfuXJYsWYJOp6NFixa0bt2azMxMrl69ygsvvGC+du/evQwaNIjk5OQ6f5758+fj5+fHgAEDzNc8+OCDbNiwgeTkZKv+mbsakSMiR+QQeeJ8eeLxd3gKhaLW1yNHjmTr1q1cuHCBtm3b3vKYTp06MXv2bLRaLc899xznzp1j69ataLVa/Pz8OHr0KAA+Pj7cfffdPPTQQ+ZrO3TowP/+7/9y8uRJMjIymDx5MnPmzMFgMJgfs379el5++WWmTZtm/t7ChQuZPXs2n3766S2xXrlyBX9/f6Cq7dBTTz1lbtczd+5c+vfvT0xMDImJicTExHDs2DHztRUVFWg0mnp/nkmTJvHmm2+ybds28zWtWrW65Tk8hcgRkSNyiDxx/jzx+Du8wMBA8vPziYuLIy8vj8DAQFq2bEleXh7/+c9/GDNmzC2P9/PzQ61W4+3tDVQltdFo5NFHH6Vv375kZmZSUVFh/n/V+fr6AqBWq9Hr9UiSVOdfkur9/QBeeukl4uLi2Lt3L7///rv5+9WP1UhJSWHdunWMGjUKU/Oc7Oxsdu7cyb333kunTp0wGo1A1REjzZo1q/fnqR5XzUY8arXnpYzIEZEjcog8cf488czMrGbYsGG88sor5gRasGABgPnoCjnNTYcOHcq8efNIT09n6NCh5iRt0aIFK1eurPeaBQsWEBIScsv3x44dy3vvvYeXlxcRERG3fd2IiAhu3LgBVA1/ZGVlUVlZSU5ODq+//jrt27cnPDycX375heLiYrp06QLArl27bhleqCu2d999F39/f4YOHUpRURFQNUTSqVOnBv883I3IkbpjEzlyK5EndcfmTHkiemk6yLlz51i/fj0Gg4E+ffrcMlwhV1FREd999x2jR4+2QYS1ffHFF0yYMMF8LIlgWyJHBDlEnsgnCp4gCILgETx+0YogCILgGUTBEwRBEDyCKHiCIAiCRxAFTxAEQfAIouAJgiAIHkEUPEEQBMEjiIInCIIgeARR8ARBEASPIAqeIAiC4BH+Hw5yCGmamEgNAAAAAElFTkSuQmCC",
      "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 501.969x137.795 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for export\n",
    "series = 'All'\n",
    "plot_query = 'n_curve > 0'\n",
    "df_q = agg_df.dropna(subset=['Tm', 'dG_37'])#.query(plot_query)\n",
    "df_q[['dG_37_uv_std', 'Tm_uv_std', 'dH_uv_std']] = 0\n",
    "\n",
    "def mae_norm(x, xerr, y):\n",
    "    epsilon = 1e-2\n",
    "    aerr = np.abs(x - y)\n",
    "    return np.mean(aerr / (xerr + epsilon))\n",
    "\n",
    "def plot_uv_comparison(df_q, param, lim, array_suffix=''):\n",
    "    unit_dict = dict(dH='kcal/mol', dS='kcal/mol', dG_37='kcal/mol', Tm='°C')\n",
    "    color_dict = dict(dH='c', Tm='cornflowerblue', dG_37='teal', dS='steelblue')\n",
    "    param_name_dict = dict(dH=r'$dH°$', dS=r'$dS°$', dG_37=r'$dG°_{37}$', Tm=r'$T_m$')\n",
    "    unit = unit_dict[param]\n",
    "    param_name = param_name_dict[param]\n",
    "    \n",
    "    kwargs = dict(fmt = '.', alpha=1, marker='o', markersize=3, capsize=2)\n",
    "    \n",
    "    fig, ax = plt.subplots(1, 3, figsize=(3*4.25*cm, 3.5*cm))\n",
    "    x = df_q[param + '_uv']\n",
    "    y = df_q[param+array_suffix]\n",
    "    xerr = df_q[param+'_uv_std'].values\n",
    "    ax[0].plot(lim, lim, '--', c='gray')\n",
    "    ax[0].errorbar(x, y,\n",
    "                   xerr=xerr, yerr=df_q[param+'_se'],\n",
    "                   color=color_dict[param], **kwargs)\n",
    "    ax[0].set_xlim(lim)\n",
    "    ax[0].set_ylim(lim)\n",
    "    ax[0].set_xlabel(f'UV melting {param} ({unit})', fontsize=tick_font_size)\n",
    "    ax[0].set_ylabel(f'array measurement {param} ({unit})', fontsize=tick_font_size)\n",
    "    # ax[0].set_title('Array Melt Measurement\\ncorr = %.4f, MAE = %.4f' %\n",
    "    #                 (pearsonr(x, y)[0], mae_norm(x, xerr, y)))\n",
    "    \n",
    "    x = df_q[param+'_uv'].values\n",
    "    y = df_q[param+'_NUPACK_salt_corrected'].values\n",
    "    ax[1].errorbar(x, y,\n",
    "                   xerr=df_q[param+'_uv_std'],\n",
    "                   color='k', **kwargs)\n",
    "    ax[1].plot(lim, lim, '--', c='gray')\n",
    "    ax[1].set_xlim(lim)\n",
    "    ax[1].set_ylim(lim)\n",
    "    ax[1].set_xlabel(f'UV melting {param} ({unit})', fontsize=tick_font_size)\n",
    "    ax[1].set_ylabel(f'NUPACK prediction {param} ({unit})', fontsize=tick_font_size)\n",
    "    # ax[1].set_title('NUPACK prediction\\ncorr = %.4f, MAE = %4f' %\n",
    "    #                 (pearsonr(x, y)[0], mae_norm(x, xerr, y)))\n",
    "\n",
    "    x = df_q[param+array_suffix].values\n",
    "    y = df_q[param+'_NUPACK_salt_corrected'].values\n",
    "    kwargs['marker'] = 's'\n",
    "    kwargs['markersize'] = 5\n",
    "    kwargs['alpha'] = 0.8\n",
    "    ax[2].errorbar(x, y,\n",
    "               xerr=df_q[param+'_uv_std'],\n",
    "               color=color_dict[param], **kwargs)\n",
    "    ax[2].plot(lim, lim, '--', c='gray')\n",
    "    ax[2].set_xlim(lim)\n",
    "    ax[2].set_ylim(lim)\n",
    "    ax[2].set_xlabel(f'UV melting {param} ({unit})', fontsize=tick_font_size)\n",
    "    ax[2].set_ylabel(f'NUPACK prediction {param} ({unit})', fontsize=tick_font_size)\n",
    "    # ax[2].set_title('Array Melt vs NUPACK prediction\\ncorr = %.4f, MAE = %.4f' %\n",
    "    #                 (pearsonr(x, y)[0], mae(x, y)))\n",
    "    for i in range(3):\n",
    "        ax[i].set_yticklabels(ax[i].get_yticklabels(), fontsize=tick_font_size)\n",
    "        ax[i].set_xticklabels(ax[i].get_xticklabels(), fontsize=tick_font_size)\n",
    "\n",
    "    plt.suptitle(param)\n",
    "    sns.despine()\n",
    "    # plt.show()\n",
    "    \n",
    "plot_uv_comparison(df_q, 'dG_37', lim = (-5.5, 1))\n",
    "# util.save_fig(f'./fig/Fig2/Fig2b/{series}_dG_37.pdf')\n",
    "plot_uv_comparison(df_q, 'dH', lim = (-60, -10))\n",
    "# util.save_fig(f'./fig/Fig2/Fig2b/{series}_dH.pdf')\n",
    "plot_uv_comparison(df_q, 'Tm', lim = (30, 90))\n",
    "# util.save_fig(f'./fig/Fig2/Fig2b/{series}_Tm.pdf')\n",
    "# plot_uv_comparison(df_q, 'dS', lim = (-.18, -.01))\n",
    "# util.save_fig(f'./fig/Fig2/Fig2b/{series}_dS.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98d40cc6-3ce3-4749-9d4c-0402cf624ab2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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