01.6_Revision.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b3d18b0-8523-430e-8bae-2545acfd2d89",
   "metadata": {},
   "source": [
    "# Revision"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "473aa78e-39d9-4767-a8e6-b3b7af5f6123",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2298698f-c3be-448d-833a-3ecc7259d3db",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%run nnn/ipynb_init.py\n",
    "%matplotlib inline\n",
    "\n",
    "from nnn.uv import *\n",
    "# from nnn import train_nn as tnn\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c34f2130-4f04-4c0d-9f50-dfcbd14c83af",
   "metadata": {},
   "outputs": [],
   "source": [
    "notebook_name = '01.6_Revision'\n",
    "figdir = os.path.join('./fig', notebook_name)\n",
    "figpath = lambda x: os.path.join('./fig', notebook_name, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "72e6ae07-42fb-46e4-9216-efc4611e34f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mydata = tnn.MyData()\n",
    "# mydata.load_everything()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f6fe4cb1-cf58-4606-999c-ff236700f188",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_1M = pd.read_csv('./data/models/processed/arr_v1_1M_n=27732.csv', index_col=0)\n",
    "data_split_dict = fileio.read_json('./data/models/raw/data_split.json')\n",
    "santa_lucia = fileio.read_santalucia_df('./data/literature/SantaLucia.tsv')\n",
    "\n",
    "arr = pd.read_csv('./data/models/raw/arr_v1_n=27732.csv', index_col=0)\n",
    "arr_adj = pd.read_csv('./data/models/processed/arr_v1_adjusted_n=27732.csv', index_col=0)\n",
    "agg_result_file='./data/uv_melt/uvmelt_agg_230901.csv'\n",
    "uv_df = pd.read_csv(agg_result_file, index_col=0).set_index('SEQID')\n",
    "annotation = pd.read_table('./data/annotation/NNNlib2b_annotation_20220519.tsv', index_col=0)\n",
    "\n",
    "ecl_oligo_df = pd.read_csv('./data/uv_melt/ECLTables/ECLOligos230502.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e8c461-f403-4c8f-9c3c-22c7607d9c67",
   "metadata": {},
   "source": [
    "## Reproducibility of array and UV melting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c82ade2f-5f27-4fdb-b679-b5022c58bf4e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eaa9e117-0f8a-4bdc-a742-40321e66eddc",
   "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\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78e766bd-640e-4735-bd68-222e278dfb97",
   "metadata": {},
   "outputs": [
    {
     "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"
    }
   ],
   "source": [
    "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_230907.csv'\n",
    "\n",
    "result_df = pd.read_csv(result_file, index_col=0)\n",
    "result_agg_df = 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=False)\n",
    "\n",
    "result_agg_df = result_agg_df[result_agg_df.SEQID.isin(hplc_clean_seqid)]\n",
    "\n",
    "arr = pd.read_csv('./data/models/raw/arr_v0_n=30680.csv', index_col=0)\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba45699e-3bc2-449f-80bc-9df71e879f32",
   "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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",
      "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'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f35aaa0-e068-4929-8e4b-0d269c6d5a6a",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Replicates on different days"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e1e423-3e59-4a02-9952-dcb585e14aba",
   "metadata": {},
   "source": [
    "Salt correct all to one concentration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a5683eaa-aa32-4597-a5d8-12dd1c377f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "uv_rep_df = result_df.query('rmse < 0.002 & dH_std < 10 & Tm_std < 5 & dH < 0 & dH > -100 & Tm > 0 & Tm < 120')\n",
    "uv_rep_df = uv_rep_df.set_index('SEQID').join(annotation[['RefSeq']])\n",
    "uv_rep_df['GC'] = uv_rep_df['RefSeq'].apply(util.get_GC_content)\n",
    "uv_rep_df['dG_37_adj'] = uv_rep_df.apply(lambda row: util.get_Na_adjusted_dG_37(\n",
    "    Tm=row.Tm, dH=row.dH, GC=row.GC, Na=0.075, from_Na=(row.Na_mM + 50)/1000), axis=1)\n",
    "uv_rep_df = uv_rep_df.groupby(['SEQID', 'curve_date'])[['dG_37_adj']].apply(np.mean).reset_index()\n",
    "uv_rep_df = uv_rep_df[uv_rep_df.duplicated(subset=['SEQID'], keep=False)]\n",
    "\n",
    "both_ids = [x for x in np.unique(uv_rep_df.SEQID) if not x.startswith('HP')]\n",
    "arr_rep_df = arraydata.data_all.loc[both_ids, ['dG_37-r%d'%x for x in [1,2,3]]]\n",
    "arr_rep_df = arr_rep_df.reset_index().melt(id_vars='SEQID').sort_values('SEQID')\n",
    "\n",
    "uv_rep_df = uv_rep_df.loc[uv_rep_df.SEQID.apply(lambda x: not x.startswith('HP'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f63ca64b-51c6-4548-a833-e031958d8a55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(3,3), sharex=True, sharey=True)\n",
    "\n",
    "sns.violinplot(data=uv_rep_df, y='SEQID', x='dG_37_adj', \n",
    "              linewidth=.5, color='gray',\n",
    "              ax=ax[0])\n",
    "sns.stripplot(data=uv_rep_df, y='SEQID', x='dG_37_adj', \n",
    "              marker='.', c='y', size=7, edgecolor='k', linewidth=.5,\n",
    "              alpha=.8, ax=ax[0])\n",
    "ax[0].set_title('UV melting')\n",
    "\n",
    "sns.violinplot(data=arr_rep_df, y='SEQID', x='value', \n",
    "              color='gray', linewidth=.5,\n",
    "              ax=ax[1])\n",
    "sns.stripplot(data=arr_rep_df, y='SEQID', x='value', \n",
    "              marker='.', c='cornflowerblue', size=7, edgecolor='k', linewidth=.5,\n",
    "              alpha=.8, ax=ax[1])\n",
    "ax[1].set_title('Array Melt')\n",
    "plt.xlabel('dG_37 (kcal/mol)')\n",
    "util.beutify_all_ax(ax, x_locator = 1, shrink=True, do_not_resize=True)\n",
    "util.save_fig(figpath('salt_corrected_UV_melt_vs_array_reproducibility_over_days_stripplot.pdf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6874924b-d471-4ddd-8fc0-420591d2a495",
   "metadata": {},
   "source": [
    "### Single UV curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ce6d742-7f89-4607-b442-5671354eb8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "uv_rep_df = result_df.query('Na_mM == 25').query('rmse < 0.002 & dH_std < 10 & Tm_std < 5 & dH < 0 & dH > -100 & Tm > 0 & Tm < 120')\n",
    "uv_rep_df = uv_rep_df[uv_rep_df.duplicated(subset=['SEQID'], keep=False)].sort_values('dG_37')\n",
    "\n",
    "uv_rep_df['cuvette'] = uv_rep_df.apply(lambda row: str(row.curve_date)+'_'+str(row.curve_num), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6e8efa7f-bd30-4e19-a4bf-095c9f2c3e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2,5))\n",
    "sns.violinplot(data=uv_rep_df, y='SEQID', x='dG_37', color='gray', ax=ax)\n",
    "sns.stripplot(data=uv_rep_df, y='SEQID', x='dG_37', hue='cuvette',\n",
    "              marker='.', size=10, edgecolor='k', linewidth=.5, palette='Set3',\n",
    "              alpha=.9, ax=ax)\n",
    "ax.get_legend().remove()\n",
    "util.beutify(ax, shrink=True, do_not_resize=True)\n",
    "util.save_fig(figpath('single_curve_by_cuvette_UV_melt_reproducibility_stripplot.pdf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1918016f-06a1-4577-bac2-bf66983db855",
   "metadata": {},
   "source": [
    "### Strand concentrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4951cdc5-1b29-4341-b8b1-57f48fa79377",
   "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>SEQID</th>\n",
       "      <th>curve_date</th>\n",
       "      <th>curve_num</th>\n",
       "      <th>dH_uv</th>\n",
       "      <th>dH_uv_std</th>\n",
       "      <th>n_curve</th>\n",
       "      <th>Tm_uv</th>\n",
       "      <th>Tm_uv_std</th>\n",
       "      <th>dG_37_uv</th>\n",
       "      <th>dG_37_uv_std</th>\n",
       "      <th>dS_uv</th>\n",
       "      <th>dS_uv_std</th>\n",
       "      <th>is_hairpin</th>\n",
       "      <th>Na_mM</th>\n",
       "      <th>conc_uM</th>\n",
       "      <th>Purification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HP018</td>\n",
       "      <td>23020411</td>\n",
       "      <td>6</td>\n",
       "      <td>-38.331193</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>62.145979</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.874700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.114320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HP014</td>\n",
       "      <td>23020411</td>\n",
       "      <td>2</td>\n",
       "      <td>-29.188951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>39.230761</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.208443</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.093440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HP015</td>\n",
       "      <td>23020411</td>\n",
       "      <td>3</td>\n",
       "      <td>-29.711657</td>\n",
       "      <td>0.396712</td>\n",
       "      <td>2</td>\n",
       "      <td>48.281266</td>\n",
       "      <td>6.199074</td>\n",
       "      <td>-1.042789</td>\n",
       "      <td>0.780775</td>\n",
       "      <td>-0.092435</td>\n",
       "      <td>0.002168</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HP016</td>\n",
       "      <td>23020411</td>\n",
       "      <td>4</td>\n",
       "      <td>-23.410537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>48.590226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.843331</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.072762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HP021</td>\n",
       "      <td>23020517</td>\n",
       "      <td>3</td>\n",
       "      <td>-27.307065</td>\n",
       "      <td>1.508616</td>\n",
       "      <td>2</td>\n",
       "      <td>35.965614</td>\n",
       "      <td>5.617608</td>\n",
       "      <td>0.091377</td>\n",
       "      <td>2.194315</td>\n",
       "      <td>-0.088339</td>\n",
       "      <td>0.005138</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HP023</td>\n",
       "      <td>23020517</td>\n",
       "      <td>5</td>\n",
       "      <td>-33.696133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>28.400352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.960950</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.111743</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HP019</td>\n",
       "      <td>23020517</td>\n",
       "      <td>1</td>\n",
       "      <td>-44.254994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>28.323514</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.273670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.146796</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>MMTM176</td>\n",
       "      <td>23020906</td>\n",
       "      <td>5</td>\n",
       "      <td>-33.259670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>60.404827</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.333760</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.099713</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>MMCNNN2336</td>\n",
       "      <td>23020906</td>\n",
       "      <td>2</td>\n",
       "      <td>-51.039304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>60.131877</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.542452</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.153142</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>TETNNN1696</td>\n",
       "      <td>23021722</td>\n",
       "      <td>6</td>\n",
       "      <td>-25.233867</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>44.488611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.594911</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.079442</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>TETNNN1570</td>\n",
       "      <td>23021815</td>\n",
       "      <td>4</td>\n",
       "      <td>-29.769729</td>\n",
       "      <td>1.759750</td>\n",
       "      <td>2</td>\n",
       "      <td>45.425193</td>\n",
       "      <td>0.652481</td>\n",
       "      <td>-0.787305</td>\n",
       "      <td>2.456691</td>\n",
       "      <td>-0.093446</td>\n",
       "      <td>0.005527</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BC0</td>\n",
       "      <td>23021815</td>\n",
       "      <td>5</td>\n",
       "      <td>-24.417998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>78.269157</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.867545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.069484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HP008</td>\n",
       "      <td>23022109</td>\n",
       "      <td>1</td>\n",
       "      <td>-26.467347</td>\n",
       "      <td>3.154696</td>\n",
       "      <td>2</td>\n",
       "      <td>45.013137</td>\n",
       "      <td>7.870171</td>\n",
       "      <td>-0.666597</td>\n",
       "      <td>4.451578</td>\n",
       "      <td>-0.083188</td>\n",
       "      <td>0.010127</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HP010</td>\n",
       "      <td>23022109</td>\n",
       "      <td>2</td>\n",
       "      <td>-32.813365</td>\n",
       "      <td>2.711408</td>\n",
       "      <td>2</td>\n",
       "      <td>43.466428</td>\n",
       "      <td>3.659162</td>\n",
       "      <td>-0.670165</td>\n",
       "      <td>3.813690</td>\n",
       "      <td>-0.103638</td>\n",
       "      <td>0.008647</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>MMTM1325</td>\n",
       "      <td>23030919</td>\n",
       "      <td>1</td>\n",
       "      <td>-24.756239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.678243</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.263326</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.075747</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>MMTM181</td>\n",
       "      <td>23030919</td>\n",
       "      <td>3</td>\n",
       "      <td>-40.946780</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>55.583944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.314798</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.124559</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>MMTM185</td>\n",
       "      <td>23030919</td>\n",
       "      <td>4</td>\n",
       "      <td>-35.834041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.166056</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.879313</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.109478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>MMTM189</td>\n",
       "      <td>23030919</td>\n",
       "      <td>5</td>\n",
       "      <td>-39.548141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>58.616453</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.576784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.119205</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>MMTM1917</td>\n",
       "      <td>23030919</td>\n",
       "      <td>6</td>\n",
       "      <td>-18.214123</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>56.966333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.101640</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.055175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>MMTM29</td>\n",
       "      <td>23031009</td>\n",
       "      <td>1</td>\n",
       "      <td>-22.067128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>56.024550</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.275363</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.067038</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>MMCNNN2337</td>\n",
       "      <td>23031102</td>\n",
       "      <td>6</td>\n",
       "      <td>-41.390610</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>64.463218</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.366928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.122598</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HP007</td>\n",
       "      <td>23031209</td>\n",
       "      <td>4</td>\n",
       "      <td>-39.669745</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>30.049076</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.909440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.130837</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HP005</td>\n",
       "      <td>23031209</td>\n",
       "      <td>2</td>\n",
       "      <td>-56.826476</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>49.014153</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.119174</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.176390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HP003</td>\n",
       "      <td>23031320</td>\n",
       "      <td>6</td>\n",
       "      <td>-35.130547</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>51.545151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.573720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.108195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>standard desalting</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SEQID  curve_date  curve_num      dH_uv  dH_uv_std  n_curve  \\\n",
       "14       HP018    23020411          6 -38.331193   0.000000        1   \n",
       "11       HP014    23020411          2 -29.188951   0.000000        1   \n",
       "12       HP015    23020411          3 -29.711657   0.396712        2   \n",
       "13       HP016    23020411          4 -23.410537   0.000000        1   \n",
       "16       HP021    23020517          3 -27.307065   1.508616        2   \n",
       "18       HP023    23020517          5 -33.696133   0.000000        1   \n",
       "15       HP019    23020517          1 -44.254994   0.000000        1   \n",
       "40     MMTM176    23020906          5 -33.259670   0.000000        1   \n",
       "36  MMCNNN2336    23020906          2 -51.039304   0.000000        1   \n",
       "54  TETNNN1696    23021722          6 -25.233867   0.000000        1   \n",
       "53  TETNNN1570    23021815          4 -29.769729   1.759750        2   \n",
       "0          BC0    23021815          5 -24.417998   0.000000        1   \n",
       "7        HP008    23022109          1 -26.467347   3.154696        2   \n",
       "10       HP010    23022109          2 -32.813365   2.711408        2   \n",
       "39    MMTM1325    23030919          1 -24.756239   0.000000        1   \n",
       "42     MMTM181    23030919          3 -40.946780   0.000000        1   \n",
       "43     MMTM185    23030919          4 -35.834041   0.000000        1   \n",
       "44     MMTM189    23030919          5 -39.548141   0.000000        1   \n",
       "45    MMTM1917    23030919          6 -18.214123   0.000000        1   \n",
       "46      MMTM29    23031009          1 -22.067128   0.000000        1   \n",
       "37  MMCNNN2337    23031102          6 -41.390610   0.000000        1   \n",
       "6        HP007    23031209          4 -39.669745   0.000000        1   \n",
       "5        HP005    23031209          2 -56.826476   0.000000        1   \n",
       "4        HP003    23031320          6 -35.130547   0.000000        1   \n",
       "\n",
       "        Tm_uv  Tm_uv_std  dG_37_uv  dG_37_uv_std     dS_uv  dS_uv_std  \\\n",
       "14  62.145979   0.000000 -2.874700      0.000000 -0.114320   0.000000   \n",
       "11  39.230761   0.000000 -0.208443      0.000000 -0.093440   0.000000   \n",
       "12  48.281266   6.199074 -1.042789      0.780775 -0.092435   0.002168   \n",
       "13  48.590226   0.000000 -0.843331      0.000000 -0.072762   0.000000   \n",
       "16  35.965614   5.617608  0.091377      2.194315 -0.088339   0.005138   \n",
       "18  28.400352   0.000000  0.960950      0.000000 -0.111743   0.000000   \n",
       "15  28.323514   0.000000  1.273670      0.000000 -0.146796   0.000000   \n",
       "40  60.404827   0.000000 -2.333760      0.000000 -0.099713   0.000000   \n",
       "36  60.131877   0.000000 -3.542452      0.000000 -0.153142   0.000000   \n",
       "54  44.488611   0.000000 -0.594911      0.000000 -0.079442   0.000000   \n",
       "53  45.425193   0.652481 -0.787305      2.456691 -0.093446   0.005527   \n",
       "0   78.269157   0.000000 -2.867545      0.000000 -0.069484   0.000000   \n",
       "7   45.013137   7.870171 -0.666597      4.451578 -0.083188   0.010127   \n",
       "10  43.466428   3.659162 -0.670165      3.813690 -0.103638   0.008647   \n",
       "39  53.678243   0.000000 -1.263326      0.000000 -0.075747   0.000000   \n",
       "42  55.583944   0.000000 -2.314798      0.000000 -0.124559   0.000000   \n",
       "43  54.166056   0.000000 -1.879313      0.000000 -0.109478   0.000000   \n",
       "44  58.616453   0.000000 -2.576784      0.000000 -0.119205   0.000000   \n",
       "45  56.966333   0.000000 -1.101640      0.000000 -0.055175   0.000000   \n",
       "46  56.024550   0.000000 -1.275363      0.000000 -0.067038   0.000000   \n",
       "37  64.463218   0.000000 -3.366928      0.000000 -0.122598   0.000000   \n",
       "6   30.049076   0.000000  0.909440      0.000000 -0.130837   0.000000   \n",
       "5   49.014153   0.000000 -2.119174      0.000000 -0.176390   0.000000   \n",
       "4   51.545151   0.000000 -1.573720      0.000000 -0.108195   0.000000   \n",
       "\n",
       "    is_hairpin  Na_mM  conc_uM        Purification  \n",
       "14        True   25.0      6.0  standard desalting  \n",
       "11        True   25.0      6.0  standard desalting  \n",
       "12        True   25.0      6.0  standard desalting  \n",
       "13        True   25.0      6.0  standard desalting  \n",
       "16        True   25.0      6.0  standard desalting  \n",
       "18        True   25.0      6.0  standard desalting  \n",
       "15        True   25.0      6.0  standard desalting  \n",
       "40        True   25.0      6.0  standard desalting  \n",
       "36        True   25.0      6.0  standard desalting  \n",
       "54        True   25.0      6.0  standard desalting  \n",
       "53        True   25.0      6.0  standard desalting  \n",
       "0         True   25.0      6.0  standard desalting  \n",
       "7         True   25.0      6.0  standard desalting  \n",
       "10        True   25.0      6.0  standard desalting  \n",
       "39        True   25.0      9.0  standard desalting  \n",
       "42        True   25.0      9.0  standard desalting  \n",
       "43        True   25.0      9.0  standard desalting  \n",
       "44        True   25.0      9.0  standard desalting  \n",
       "45        True   25.0      9.0  standard desalting  \n",
       "46        True   25.0      9.0  standard desalting  \n",
       "37        True   25.0      9.0  standard desalting  \n",
       "6         True   25.0      9.0  standard desalting  \n",
       "5         True   25.0      9.0  standard desalting  \n",
       "4         True   25.0      9.0  standard desalting  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_agg_df.sort_values('curve_date')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69fe95ab-892e-41a8-9097-826561dcfda6",
   "metadata": {},
   "source": [
    "## ssfluor controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "403ec8b3-7e6d-4525-9cb7-50af718b300a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = fileio.read_pickle('./data/series_merged/NNNlib2b_DNA_20211221.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "1999b9b7-f36c-4357-ab2c-54d1cc48b358",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(annotation['ConstructType'], on='SEQID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "282fdedb-222f-4ce3-a9d2-fc44485a660a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AC_mismatch_varpos', 'BaeControls', 'Centered',\n",
       "       'GT_mismatch_varpos', 'NNN', 'NNN_3primebulge', 'NNN_5prime_bulge',\n",
       "       'PUMcontrols', 'PolyNTControls', 'RepeatControls', 'SuperStem',\n",
       "       'WBcontrols', 'WC_5ntstem', 'WC_6ntstem', 'WC_7ntstem',\n",
       "       'pseudoknots'], dtype=object)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df.ConstructType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7105f538-43b3-4dce-9c6f-2456b17dc15c",
   "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",
       "       'PUMcontrols', 'PolyNTControls', 'RepeatControls', 'StemDangle_3p',\n",
       "       'StemDangle_53p', 'StemDangle_5p', 'VARloop', 'VariableLoops',\n",
       "       'WBcontrols', 'WC_5ntstem', 'WC_6ntstem', 'WC_7ntstem',\n",
       "       'pseudoknot_pt_muts'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(arraydata.curve['r1'].join(annotation['ConstructType']).ConstructType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "8a62b3ea-3513-464d-800b-9f13c7438ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/series/NNNlib2b_DNA_tile1_focused_green_2_600ms.CPseries.json.csv')\n",
    "df = df.merge(annotation['ConstructType'], on='SEQID')\n",
    "ssfluor_df = df.query('ConstructType == \"ssfluor\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "a04bc091-003f-4d45-a7f6-d74b65321c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "ssfluor_df['nuc'] = ssfluor_df.RefSeq.apply(lambda x: x[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "63d7bba0-cb29-42be-a5c9-931635d0b3d2",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'float' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:1567\u001b[0m, in \u001b[0;36mGroupBy.apply\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1564\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m rewrite_warning(\n\u001b[1;32m   1565\u001b[0m         old_msg, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, new_msg\n\u001b[1;32m   1566\u001b[0m     ) \u001b[38;5;28;01mif\u001b[39;00m is_np_func \u001b[38;5;28;01melse\u001b[39;00m nullcontext():\n\u001b[0;32m-> 1567\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_apply_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_selected_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1568\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   1569\u001b[0m     \u001b[38;5;66;03m# gh-20949\u001b[39;00m\n\u001b[1;32m   1570\u001b[0m     \u001b[38;5;66;03m# try again, with .apply acting as a filtering\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1574\u001b[0m     \u001b[38;5;66;03m# fails on *some* columns, e.g. a numeric operation\u001b[39;00m\n\u001b[1;32m   1575\u001b[0m     \u001b[38;5;66;03m# on a string grouper column\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:1629\u001b[0m, in \u001b[0;36mGroupBy._python_apply_general\u001b[0;34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[0m\n\u001b[1;32m   1601\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1602\u001b[0m \u001b[38;5;124;03mApply function f in python space\u001b[39;00m\n\u001b[1;32m   1603\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1627\u001b[0m \u001b[38;5;124;03m    data after applying f\u001b[39;00m\n\u001b[1;32m   1628\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1629\u001b[0m values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/core/groupby/ops.py:839\u001b[0m, in \u001b[0;36mBaseGrouper.apply\u001b[0;34m(self, f, data, axis)\u001b[0m\n\u001b[1;32m    838\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[0;32m--> 839\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    840\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mmedian\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/numpy/lib/function_base.py:3816\u001b[0m, in \u001b[0;36mmedian\u001b[0;34m(a, axis, out, overwrite_input, keepdims)\u001b[0m\n\u001b[1;32m   3736\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3737\u001b[0m \u001b[38;5;124;03mCompute the median along the specified axis.\u001b[39;00m\n\u001b[1;32m   3738\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3814\u001b[0m \n\u001b[1;32m   3815\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 3816\u001b[0m r, k \u001b[38;5;241m=\u001b[39m \u001b[43m_ureduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_median\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3817\u001b[0m \u001b[43m                \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3818\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keepdims:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/numpy/lib/function_base.py:3725\u001b[0m, in \u001b[0;36m_ureduce\u001b[0;34m(a, func, **kwargs)\u001b[0m\n\u001b[1;32m   3723\u001b[0m     keepdim \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m,) \u001b[38;5;241m*\u001b[39m a\u001b[38;5;241m.\u001b[39mndim\n\u001b[0;32m-> 3725\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r, keepdim\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/numpy/lib/function_base.py:3851\u001b[0m, in \u001b[0;36m_median\u001b[0;34m(a, axis, out, overwrite_input)\u001b[0m\n\u001b[1;32m   3850\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3851\u001b[0m     part \u001b[38;5;241m=\u001b[39m \u001b[43mpartition\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3853\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m part\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m ():\n\u001b[1;32m   3854\u001b[0m     \u001b[38;5;66;03m# make 0-D arrays work\u001b[39;00m\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mpartition\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/numpy/core/fromnumeric.py:758\u001b[0m, in \u001b[0;36mpartition\u001b[0;34m(a, kth, axis, kind, order)\u001b[0m\n\u001b[1;32m    757\u001b[0m     a \u001b[38;5;241m=\u001b[39m asanyarray(a)\u001b[38;5;241m.\u001b[39mcopy(order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mK\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 758\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpartition\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    759\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n",
      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'float' and 'str'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[254], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ssfluor_grouped_df \u001b[38;5;241m=\u001b[39m \u001b[43mssfluor_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnuc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:1588\u001b[0m, in \u001b[0;36mGroupBy.apply\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1580\u001b[0m             new_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   1581\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe operation \u001b[39m\u001b[38;5;132;01m{\u001b[39;00morig_func\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m failed on a column. If any error \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1582\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis raised, this will raise an exception in a future version \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1583\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof pandas. Drop these columns to avoid this warning.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1584\u001b[0m             )\n\u001b[1;32m   1585\u001b[0m             \u001b[38;5;28;01mwith\u001b[39;00m rewrite_warning(\n\u001b[1;32m   1586\u001b[0m                 old_msg, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, new_msg\n\u001b[1;32m   1587\u001b[0m             ) \u001b[38;5;28;01mif\u001b[39;00m is_np_func \u001b[38;5;28;01melse\u001b[39;00m nullcontext():\n\u001b[0;32m-> 1588\u001b[0m                 \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_apply_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_selected_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1590\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:1629\u001b[0m, in \u001b[0;36mGroupBy._python_apply_general\u001b[0;34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[0m\n\u001b[1;32m   1592\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m   1593\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_python_apply_general\u001b[39m(\n\u001b[1;32m   1594\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1599\u001b[0m     is_agg: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   1600\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[1;32m   1601\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1602\u001b[0m \u001b[38;5;124;03m    Apply function f in python space\u001b[39;00m\n\u001b[1;32m   1603\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1627\u001b[0m \u001b[38;5;124;03m        data after applying f\u001b[39;00m\n\u001b[1;32m   1628\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1629\u001b[0m     values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1630\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1631\u001b[0m         not_indexed_same \u001b[38;5;241m=\u001b[39m mutated \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmutated\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/core/groupby/ops.py:839\u001b[0m, in \u001b[0;36mBaseGrouper.apply\u001b[0;34m(self, f, data, axis)\u001b[0m\n\u001b[1;32m    837\u001b[0m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[1;32m    838\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[0;32m--> 839\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    840\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[1;32m    841\u001b[0m     mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mmedian\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/numpy/lib/function_base.py:3816\u001b[0m, in \u001b[0;36mmedian\u001b[0;34m(a, axis, out, overwrite_input, keepdims)\u001b[0m\n\u001b[1;32m   3734\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_median_dispatcher)\n\u001b[1;32m   3735\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmedian\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, overwrite_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m   3736\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3737\u001b[0m \u001b[38;5;124;03m    Compute the median along the specified axis.\u001b[39;00m\n\u001b[1;32m   3738\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3814\u001b[0m \n\u001b[1;32m   3815\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3816\u001b[0m     r, k \u001b[38;5;241m=\u001b[39m \u001b[43m_ureduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_median\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3817\u001b[0m \u001b[43m                    \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3818\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m keepdims:\n\u001b[1;32m   3819\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m r\u001b[38;5;241m.\u001b[39mreshape(k)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/numpy/lib/function_base.py:3725\u001b[0m, in \u001b[0;36m_ureduce\u001b[0;34m(a, func, **kwargs)\u001b[0m\n\u001b[1;32m   3722\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   3723\u001b[0m     keepdim \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m,) \u001b[38;5;241m*\u001b[39m a\u001b[38;5;241m.\u001b[39mndim\n\u001b[0;32m-> 3725\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r, keepdim\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/numpy/lib/function_base.py:3851\u001b[0m, in \u001b[0;36m_median\u001b[0;34m(a, axis, out, overwrite_input)\u001b[0m\n\u001b[1;32m   3849\u001b[0m         part \u001b[38;5;241m=\u001b[39m a\n\u001b[1;32m   3850\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3851\u001b[0m     part \u001b[38;5;241m=\u001b[39m \u001b[43mpartition\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3853\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m part\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m ():\n\u001b[1;32m   3854\u001b[0m     \u001b[38;5;66;03m# make 0-D arrays work\u001b[39;00m\n\u001b[1;32m   3855\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m part\u001b[38;5;241m.\u001b[39mitem()\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mpartition\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/numpy/core/fromnumeric.py:758\u001b[0m, in \u001b[0;36mpartition\u001b[0;34m(a, kth, axis, kind, order)\u001b[0m\n\u001b[1;32m    756\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    757\u001b[0m     a \u001b[38;5;241m=\u001b[39m asanyarray(a)\u001b[38;5;241m.\u001b[39mcopy(order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mK\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 758\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpartition\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    759\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n",
      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'float' and 'str'"
     ]
    }
   ],
   "source": [
    "ssfluor_grouped_df = ssfluor_df.groupby('nuc').apply(np.median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "f2f5ee58-fd8f-4824-8bfd-f92efe200477",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Green08_Quench'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Green08_Quench'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[253], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m cols \u001b[38;5;241m=\u001b[39m ssfluor_grouped_df\u001b[38;5;241m.\u001b[39mcolumns[\u001b[38;5;241m2\u001b[39m:]\n\u001b[0;32m----> 2\u001b[0m ssfluor_grouped_df[cols] \u001b[38;5;241m=\u001b[39m ssfluor_grouped_df[cols]\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m/\u001b[39m \u001b[43mssfluor_grouped_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGreen08_Quench\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m64\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/nnn/lib/python3.8/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Green08_Quench'"
     ]
    }
   ],
   "source": [
    "cols = ssfluor_grouped_df.columns[2:]\n",
    "ssfluor_grouped_df[cols] = ssfluor_grouped_df[cols].values / ssfluor_grouped_df['Green07_Cy3'].values.reshape(64,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "370fc096-fd4e-432c-8751-22fe4a84343a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ssfluor_grouped_df[cols].values.T)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "544ad00c-bf02-4d17-bea1-53851b527e9e",
   "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>clusterID</th>\n",
       "      <th>Green07_Cy3</th>\n",
       "      <th>Green09_Quench</th>\n",
       "      <th>Green12_20.0</th>\n",
       "      <th>Green13_22.5</th>\n",
       "      <th>Green14_25.0</th>\n",
       "      <th>Green15_27.5</th>\n",
       "      <th>Green16_30.0</th>\n",
       "      <th>Green17_32.5</th>\n",
       "      <th>Green18_35.0</th>\n",
       "      <th>...</th>\n",
       "      <th>Green25_52.5</th>\n",
       "      <th>Green26_55.0</th>\n",
       "      <th>Green27_57.5</th>\n",
       "      <th>Green28_60.0</th>\n",
       "      <th>Green29_20.0</th>\n",
       "      <th>RefSeq</th>\n",
       "      <th>SEQID</th>\n",
       "      <th>Series</th>\n",
       "      <th>ConstructType</th>\n",
       "      <th>nuc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:17841:2000</td>\n",
       "      <td>2010.605974</td>\n",
       "      <td>1789.877135</td>\n",
       "      <td>3693.822317</td>\n",
       "      <td>2988.802393</td>\n",
       "      <td>2787.492107</td>\n",
       "      <td>2785.247144</td>\n",
       "      <td>2295.730884</td>\n",
       "      <td>2141.064764</td>\n",
       "      <td>1975.032858</td>\n",
       "      <td>...</td>\n",
       "      <td>1334.303988</td>\n",
       "      <td>1078.358115</td>\n",
       "      <td>948.004514</td>\n",
       "      <td>698.390657</td>\n",
       "      <td>3020.470829</td>\n",
       "      <td>CCGAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC23</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>CCG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:19349:4761</td>\n",
       "      <td>1933.609098</td>\n",
       "      <td>1988.670794</td>\n",
       "      <td>4836.090933</td>\n",
       "      <td>4027.687372</td>\n",
       "      <td>3507.685470</td>\n",
       "      <td>2980.492760</td>\n",
       "      <td>2396.324513</td>\n",
       "      <td>2455.168893</td>\n",
       "      <td>2356.714182</td>\n",
       "      <td>...</td>\n",
       "      <td>1199.892283</td>\n",
       "      <td>1091.843277</td>\n",
       "      <td>979.510413</td>\n",
       "      <td>753.826541</td>\n",
       "      <td>4281.442912</td>\n",
       "      <td>CCGAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC23</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>CCG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:14566:5135</td>\n",
       "      <td>1607.582806</td>\n",
       "      <td>1578.999265</td>\n",
       "      <td>4153.517149</td>\n",
       "      <td>3704.715318</td>\n",
       "      <td>3259.436665</td>\n",
       "      <td>3160.263840</td>\n",
       "      <td>2977.654289</td>\n",
       "      <td>2639.966614</td>\n",
       "      <td>2080.786067</td>\n",
       "      <td>...</td>\n",
       "      <td>1287.058233</td>\n",
       "      <td>1076.411125</td>\n",
       "      <td>1207.690206</td>\n",
       "      <td>1275.752442</td>\n",
       "      <td>3691.872959</td>\n",
       "      <td>CCGAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC23</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>CCG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:11802:5455</td>\n",
       "      <td>1292.722517</td>\n",
       "      <td>1242.808864</td>\n",
       "      <td>2761.061015</td>\n",
       "      <td>2603.326584</td>\n",
       "      <td>1883.095072</td>\n",
       "      <td>2205.522753</td>\n",
       "      <td>1715.576859</td>\n",
       "      <td>1773.850313</td>\n",
       "      <td>1470.881750</td>\n",
       "      <td>...</td>\n",
       "      <td>741.315102</td>\n",
       "      <td>712.769550</td>\n",
       "      <td>716.127909</td>\n",
       "      <td>498.887114</td>\n",
       "      <td>2186.393645</td>\n",
       "      <td>CCGAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC23</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>CCG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:17437:5640</td>\n",
       "      <td>1123.858922</td>\n",
       "      <td>836.610373</td>\n",
       "      <td>2330.255414</td>\n",
       "      <td>2091.454883</td>\n",
       "      <td>1685.068670</td>\n",
       "      <td>1379.883577</td>\n",
       "      <td>1388.135245</td>\n",
       "      <td>1154.910976</td>\n",
       "      <td>917.177063</td>\n",
       "      <td>...</td>\n",
       "      <td>603.960885</td>\n",
       "      <td>499.842439</td>\n",
       "      <td>498.897797</td>\n",
       "      <td>354.970255</td>\n",
       "      <td>2005.251781</td>\n",
       "      <td>CCGAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC23</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>CCG</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>70232</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:5524:20313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGTAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC46</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>TGT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70233</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:8729:21543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGTAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC46</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>TGT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70234</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:20887:21628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGTAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC46</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>TGT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70235</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:22754:22538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGTAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC46</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>TGT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70236</th>\n",
       "      <td>M00653:160:000000000-K4R35:1:2101:11342:22796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGTAACAACAACAACAACAACAACAACAACAACAACAAC</td>\n",
       "      <td>SSFC46</td>\n",
       "      <td>Control</td>\n",
       "      <td>ssfluor</td>\n",
       "      <td>TGT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1782 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           clusterID  Green07_Cy3  \\\n",
       "817     M00653:160:000000000-K4R35:1:2101:17841:2000  2010.605974   \n",
       "818     M00653:160:000000000-K4R35:1:2101:19349:4761  1933.609098   \n",
       "819     M00653:160:000000000-K4R35:1:2101:14566:5135  1607.582806   \n",
       "820     M00653:160:000000000-K4R35:1:2101:11802:5455  1292.722517   \n",
       "821     M00653:160:000000000-K4R35:1:2101:17437:5640  1123.858922   \n",
       "...                                              ...          ...   \n",
       "70232   M00653:160:000000000-K4R35:1:2101:5524:20313          NaN   \n",
       "70233   M00653:160:000000000-K4R35:1:2101:8729:21543          NaN   \n",
       "70234  M00653:160:000000000-K4R35:1:2101:20887:21628          NaN   \n",
       "70235  M00653:160:000000000-K4R35:1:2101:22754:22538          NaN   \n",
       "70236  M00653:160:000000000-K4R35:1:2101:11342:22796          NaN   \n",
       "\n",
       "       Green09_Quench  Green12_20.0  Green13_22.5  Green14_25.0  Green15_27.5  \\\n",
       "817       1789.877135   3693.822317   2988.802393   2787.492107   2785.247144   \n",
       "818       1988.670794   4836.090933   4027.687372   3507.685470   2980.492760   \n",
       "819       1578.999265   4153.517149   3704.715318   3259.436665   3160.263840   \n",
       "820       1242.808864   2761.061015   2603.326584   1883.095072   2205.522753   \n",
       "821        836.610373   2330.255414   2091.454883   1685.068670   1379.883577   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "70232             NaN           NaN           NaN           NaN           NaN   \n",
       "70233             NaN           NaN           NaN           NaN           NaN   \n",
       "70234             NaN           NaN           NaN           NaN           NaN   \n",
       "70235             NaN           NaN           NaN           NaN           NaN   \n",
       "70236             NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "       Green16_30.0  Green17_32.5  Green18_35.0  ...  Green25_52.5  \\\n",
       "817     2295.730884   2141.064764   1975.032858  ...   1334.303988   \n",
       "818     2396.324513   2455.168893   2356.714182  ...   1199.892283   \n",
       "819     2977.654289   2639.966614   2080.786067  ...   1287.058233   \n",
       "820     1715.576859   1773.850313   1470.881750  ...    741.315102   \n",
       "821     1388.135245   1154.910976    917.177063  ...    603.960885   \n",
       "...             ...           ...           ...  ...           ...   \n",
       "70232           NaN           NaN           NaN  ...           NaN   \n",
       "70233           NaN           NaN           NaN  ...           NaN   \n",
       "70234           NaN           NaN           NaN  ...           NaN   \n",
       "70235           NaN           NaN           NaN  ...           NaN   \n",
       "70236           NaN           NaN           NaN  ...           NaN   \n",
       "\n",
       "       Green26_55.0  Green27_57.5  Green28_60.0  Green29_20.0  \\\n",
       "817     1078.358115    948.004514    698.390657   3020.470829   \n",
       "818     1091.843277    979.510413    753.826541   4281.442912   \n",
       "819     1076.411125   1207.690206   1275.752442   3691.872959   \n",
       "820      712.769550    716.127909    498.887114   2186.393645   \n",
       "821      499.842439    498.897797    354.970255   2005.251781   \n",
       "...             ...           ...           ...           ...   \n",
       "70232           NaN           NaN           NaN           NaN   \n",
       "70233           NaN           NaN           NaN           NaN   \n",
       "70234           NaN           NaN           NaN           NaN   \n",
       "70235           NaN           NaN           NaN           NaN   \n",
       "70236           NaN           NaN           NaN           NaN   \n",
       "\n",
       "                                        RefSeq   SEQID   Series  \\\n",
       "817    CCGAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC23  Control   \n",
       "818    CCGAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC23  Control   \n",
       "819    CCGAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC23  Control   \n",
       "820    CCGAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC23  Control   \n",
       "821    CCGAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC23  Control   \n",
       "...                                        ...     ...      ...   \n",
       "70232  TGTAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC46  Control   \n",
       "70233  TGTAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC46  Control   \n",
       "70234  TGTAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC46  Control   \n",
       "70235  TGTAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC46  Control   \n",
       "70236  TGTAACAACAACAACAACAACAACAACAACAACAACAAC  SSFC46  Control   \n",
       "\n",
       "       ConstructType  nuc  \n",
       "817          ssfluor  CCG  \n",
       "818          ssfluor  CCG  \n",
       "819          ssfluor  CCG  \n",
       "820          ssfluor  CCG  \n",
       "821          ssfluor  CCG  \n",
       "...              ...  ...  \n",
       "70232        ssfluor  TGT  \n",
       "70233        ssfluor  TGT  \n",
       "70234        ssfluor  TGT  \n",
       "70235        ssfluor  TGT  \n",
       "70236        ssfluor  TGT  \n",
       "\n",
       "[1782 rows x 26 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssfluor_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7d6b68-816e-497e-b6a8-581adce90b8a",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Clean up the annotation file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8810e32-e831-4282-88a5-4a82d3c3dd6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotation = pd.read_table('./data/annotation/NNNlib2b_annotation_20220519.tsv', index_col=0)\n",
    "new_annotation_file = './data/annotation/NNNlib2b_annotation_2024.tsv'\n",
    "new_annotation = pd.read_table('./data/annotation/NNNlib2b_annotation_2024.tsv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "cf0d1cbc-a288-4fb0-8366-e50a8edff7a7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "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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",
      "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",
    "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": 145,
   "id": "739d41bb-bf47-4117-a392-afa8f5cf8f40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30978, 42)\n"
     ]
    }
   ],
   "source": [
    "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",
    "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": 131,
   "id": "415ef886-9acd-4227-95bd-e07a17966adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unify all sequences to upper case\n",
    "annotation['RefSeq'] = annotation.RefSeq.apply(lambda x: x.upper())\n",
    "\n",
    "# Remove VARloops that are not really there\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 not really analyzed\n",
    "annotation = annotation.query('ConstructType != \"PUMcontrols\" & ConstructType != \"pseudoknots\" & ConstructType != \"pseudoknot_pt_muts\"')\n",
    "\n",
    "# Remove repetitive WBcontrols\n",
    "annotation = annotation.loc[[seqid for seqid in annotation.index if (not seqid.startswith('WB')) or seqid.startswith('WB_')]]\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": 132,
   "id": "f4309bdd-e16e-4b21-82a7-6ea665a7d42d",
   "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": "code",
   "execution_count": 144,
   "id": "b44aef8e-b3d3-46f7-b452-fee84552bfd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41171 40130\n",
      "31101 31101\n",
      "30978 30978\n",
      "27732 27732\n"
     ]
    }
   ],
   "source": [
    "for df in [annotation, arr, arr_v0, arr_v1]:\n",
    "    print(len(df), len(np.unique(df.RefSeq)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "3ecd760f-9704-4064-ae81-e6424b14e988",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Bulges\n",
      "11520 10880\n",
      "9708 9708\n",
      "9708 9708\n",
      "9097 9097\n",
      "\n",
      " Control\n",
      "595 594\n",
      "106 106\n",
      "106 106\n",
      "0 0\n",
      "\n",
      " External\n",
      "32 32\n",
      "143 143\n",
      "143 143\n",
      "19 19\n",
      "\n",
      " MisMatches\n",
      "18240 17856\n",
      "11433 11433\n",
      "11433 11433\n",
      "10725 10725\n",
      "\n",
      " TETRAloop\n",
      "6144 6144\n",
      "5652 5652\n",
      "5652 5652\n",
      "4691 4691\n",
      "\n",
      " TRIloop\n",
      "1536 1536\n",
      "1440 1440\n",
      "1440 1440\n",
      "1168 1168\n",
      "\n",
      " VARloop\n",
      "32 32\n",
      "17 17\n",
      "17 17\n",
      "5 5\n",
      "\n",
      " WatsonCrick\n",
      "3072 3072\n",
      "2602 2602\n",
      "2602 2602\n",
      "2027 2027\n"
     ]
    }
   ],
   "source": [
    "for seq_class in np.unique(annotation.Series):\n",
    "    print('\\n', seq_class)\n",
    "    for df in [annotation, arr, arr_v0, arr_v1]:\n",
    "        print(len(df.query(f'Series == \"{seq_class}\"')), len(np.unique(df.query(f'Series == \"{seq_class}\"').RefSeq)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "b550cfca-cda9-4ecf-9acd-e2e74a364431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BaeControls', 'PUMcontrols', 'WBcontrols', 'pseudoknot_pt_muts'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = arr_v0\n",
    "seq_class = 'External'\n",
    "np.unique(df.query(f'Series == \"{seq_class}\"').ConstructType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ec001d3-7c6f-491f-998e-c31d458aa90b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_df_diff(df1, df2):\n",
    "    # return pd.concat([df1, df2]).drop_duplicates(keep=False, ignore_index=True)\n",
    "    return pd.concat([df1.reset_index(), df2.reset_index()]).drop_duplicates(keep=False, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f00b8e2b-28aa-46b5-b9ee-7c8da927e8ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEQID</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>...</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VAR1</td>\n",
       "      <td>-12.902622</td>\n",
       "      <td>2.027848</td>\n",
       "      <td>-17.828146</td>\n",
       "      <td>-10.230760</td>\n",
       "      <td>54.279944</td>\n",
       "      <td>4.497456</td>\n",
       "      <td>44.930533</td>\n",
       "      <td>62.067388</td>\n",
       "      <td>-0.669630</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.041513</td>\n",
       "      <td>((....))</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.809242</td>\n",
       "      <td>-0.714050</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>45.186043</td>\n",
       "      <td>-0.411442</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-0.048880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VAR45</td>\n",
       "      <td>-34.666319</td>\n",
       "      <td>8.318789</td>\n",
       "      <td>-53.146571</td>\n",
       "      <td>-19.553041</td>\n",
       "      <td>60.230678</td>\n",
       "      <td>2.045054</td>\n",
       "      <td>55.476879</td>\n",
       "      <td>63.839446</td>\n",
       "      <td>-2.382943</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.215415</td>\n",
       "      <td>((........))</td>\n",
       "      <td>1.13</td>\n",
       "      <td>-4.454826</td>\n",
       "      <td>-4.157297</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>12.190441</td>\n",
       "      <td>1.530271</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VAR47</td>\n",
       "      <td>-26.212047</td>\n",
       "      <td>5.634624</td>\n",
       "      <td>-41.531596</td>\n",
       "      <td>-18.953544</td>\n",
       "      <td>52.552525</td>\n",
       "      <td>2.572008</td>\n",
       "      <td>46.545943</td>\n",
       "      <td>56.395255</td>\n",
       "      <td>-1.244589</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.318463</td>\n",
       "      <td>((.........))</td>\n",
       "      <td>1.13</td>\n",
       "      <td>-3.560380</td>\n",
       "      <td>-3.264435</td>\n",
       "      <td>69.230769</td>\n",
       "      <td>11.701521</td>\n",
       "      <td>1.563106</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VAR49</td>\n",
       "      <td>-24.250715</td>\n",
       "      <td>5.816776</td>\n",
       "      <td>-38.882539</td>\n",
       "      <td>-17.452559</td>\n",
       "      <td>45.657784</td>\n",
       "      <td>2.859158</td>\n",
       "      <td>40.891720</td>\n",
       "      <td>50.634368</td>\n",
       "      <td>-0.651440</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.631629</td>\n",
       "      <td>((..........))</td>\n",
       "      <td>1.13</td>\n",
       "      <td>-2.881807</td>\n",
       "      <td>-2.590738</td>\n",
       "      <td>64.285714</td>\n",
       "      <td>11.283778</td>\n",
       "      <td>1.591251</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VAR51</td>\n",
       "      <td>-22.633814</td>\n",
       "      <td>5.538407</td>\n",
       "      <td>-35.529610</td>\n",
       "      <td>-15.344050</td>\n",
       "      <td>40.147117</td>\n",
       "      <td>2.798879</td>\n",
       "      <td>36.665983</td>\n",
       "      <td>47.034102</td>\n",
       "      <td>-0.228376</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.338805</td>\n",
       "      <td>((...........))</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-2.595177</td>\n",
       "      <td>-2.307839</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>9.452036</td>\n",
       "      <td>1.715643</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>VAR53</td>\n",
       "      <td>-24.534543</td>\n",
       "      <td>6.217685</td>\n",
       "      <td>-43.185388</td>\n",
       "      <td>-17.942465</td>\n",
       "      <td>37.712841</td>\n",
       "      <td>1.296061</td>\n",
       "      <td>35.309428</td>\n",
       "      <td>40.037517</td>\n",
       "      <td>-0.053274</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.338056</td>\n",
       "      <td>((............))</td>\n",
       "      <td>1.33</td>\n",
       "      <td>-2.594212</td>\n",
       "      <td>-2.306773</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>7.687783</td>\n",
       "      <td>1.836986</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.061035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VAR55</td>\n",
       "      <td>-17.448293</td>\n",
       "      <td>5.104732</td>\n",
       "      <td>-34.974347</td>\n",
       "      <td>-12.099081</td>\n",
       "      <td>31.811261</td>\n",
       "      <td>4.243502</td>\n",
       "      <td>23.994224</td>\n",
       "      <td>41.047176</td>\n",
       "      <td>0.309509</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.335055</td>\n",
       "      <td>((.............))</td>\n",
       "      <td>1.43</td>\n",
       "      <td>-2.589680</td>\n",
       "      <td>-2.301588</td>\n",
       "      <td>52.941176</td>\n",
       "      <td>5.981257</td>\n",
       "      <td>1.955818</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.061357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>VAR84</td>\n",
       "      <td>-22.166976</td>\n",
       "      <td>6.070583</td>\n",
       "      <td>-39.026358</td>\n",
       "      <td>-14.997992</td>\n",
       "      <td>42.683910</td>\n",
       "      <td>2.789499</td>\n",
       "      <td>40.159838</td>\n",
       "      <td>48.520071</td>\n",
       "      <td>-0.430147</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.360611</td>\n",
       "      <td>((........))</td>\n",
       "      <td>2.22</td>\n",
       "      <td>-1.923885</td>\n",
       "      <td>-1.698488</td>\n",
       "      <td>41.666667</td>\n",
       "      <td>-15.058104</td>\n",
       "      <td>2.662490</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>-0.049718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VAR86</td>\n",
       "      <td>-18.553340</td>\n",
       "      <td>5.103550</td>\n",
       "      <td>-30.925063</td>\n",
       "      <td>-12.559271</td>\n",
       "      <td>29.112833</td>\n",
       "      <td>3.654289</td>\n",
       "      <td>23.585440</td>\n",
       "      <td>36.385364</td>\n",
       "      <td>0.470431</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.528999</td>\n",
       "      <td>((.........))</td>\n",
       "      <td>2.22</td>\n",
       "      <td>-1.155698</td>\n",
       "      <td>-0.969601</td>\n",
       "      <td>38.461538</td>\n",
       "      <td>-15.280516</td>\n",
       "      <td>2.676171</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>-0.049718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SEQID         dH     dH_se      dH_lb      dH_ub         Tm     Tm_se  \\\n",
       "0   VAR1 -12.902622  2.027848 -17.828146 -10.230760  54.279944  4.497456   \n",
       "1  VAR45 -34.666319  8.318789 -53.146571 -19.553041  60.230678  2.045054   \n",
       "2  VAR47 -26.212047  5.634624 -41.531596 -18.953544  52.552525  2.572008   \n",
       "3  VAR49 -24.250715  5.816776 -38.882539 -17.452559  45.657784  2.859158   \n",
       "4  VAR51 -22.633814  5.538407 -35.529610 -15.344050  40.147117  2.798879   \n",
       "5  VAR53 -24.534543  6.217685 -43.185388 -17.942465  37.712841  1.296061   \n",
       "6  VAR55 -17.448293  5.104732 -34.974347 -12.099081  31.811261  4.243502   \n",
       "7  VAR84 -22.166976  6.070583 -39.026358 -14.997992  42.683910  2.789499   \n",
       "8  VAR86 -18.553340  5.103550 -30.925063 -12.559271  29.112833  3.654289   \n",
       "\n",
       "       Tm_lb      Tm_ub     dG_37  ...  dG_37_ensemble_NUPACK  \\\n",
       "0  44.930533  62.067388 -0.669630  ...              -1.041513   \n",
       "1  55.476879  63.839446 -2.382943  ...              -5.215415   \n",
       "2  46.545943  56.395255 -1.244589  ...              -4.318463   \n",
       "3  40.891720  50.634368 -0.651440  ...              -3.631629   \n",
       "4  36.665983  47.034102 -0.228376  ...              -3.338805   \n",
       "5  35.309428  40.037517 -0.053274  ...              -3.338056   \n",
       "6  23.994224  41.047176  0.309509  ...              -3.335055   \n",
       "7  40.159838  48.520071 -0.430147  ...              -2.360611   \n",
       "8  23.585440  36.385364  0.470431  ...              -1.528999   \n",
       "\n",
       "        TargetStruct  dG_37_NUPACK  dG_37_ensemble_150mM_NUPACK  \\\n",
       "0           ((....))         -0.84                    -0.809242   \n",
       "1       ((........))          1.13                    -4.454826   \n",
       "2      ((.........))          1.13                    -3.560380   \n",
       "3     ((..........))          1.13                    -2.881807   \n",
       "4    ((...........))          1.23                    -2.595177   \n",
       "5   ((............))          1.33                    -2.594212   \n",
       "6  ((.............))          1.43                    -2.589680   \n",
       "7       ((........))          2.22                    -1.923885   \n",
       "8      ((.........))          2.22                    -1.155698   \n",
       "\n",
       "   dG_37_ensemble_75mM_NUPACK         GC  Tm_NUPACK_salt_corrected  \\\n",
       "0                   -0.714050  62.500000                 45.186043   \n",
       "1                   -4.157297  75.000000                 12.190441   \n",
       "2                   -3.264435  69.230769                 11.701521   \n",
       "3                   -2.590738  64.285714                 11.283778   \n",
       "4                   -2.307839  60.000000                  9.452036   \n",
       "5                   -2.306773  56.250000                  7.687783   \n",
       "6                   -2.301588  52.941176                  5.981257   \n",
       "7                   -1.698488  41.666667                -15.058104   \n",
       "8                   -0.969601  38.461538                -15.280516   \n",
       "\n",
       "   dG_37_NUPACK_salt_corrected  dH_NUPACK_salt_corrected  \\\n",
       "0                    -0.411442                     -16.0   \n",
       "1                     1.530271                     -17.6   \n",
       "2                     1.563106                     -17.6   \n",
       "3                     1.591251                     -17.6   \n",
       "4                     1.715643                     -17.6   \n",
       "5                     1.836986                     -17.6   \n",
       "6                     1.955818                     -17.6   \n",
       "7                     2.662490                     -13.2   \n",
       "8                     2.676171                     -13.2   \n",
       "\n",
       "   dS_NUPACK_salt_corrected  \n",
       "0                 -0.048880  \n",
       "1                 -0.060390  \n",
       "2                 -0.060390  \n",
       "3                 -0.060390  \n",
       "4                 -0.060713  \n",
       "5                 -0.061035  \n",
       "6                 -0.061357  \n",
       "7                 -0.049718  \n",
       "8                 -0.049718  \n",
       "\n",
       "[9 rows x 43 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_v0_30872 = pd.read_csv('./data/models/raw/arr_v0_n=30872.csv', index_col=0)\n",
    "arr_v0_30863 = pd.read_csv('./data/models/raw/arr_v0_n=30863.csv', index_col=0)\n",
    "\n",
    "check_df_diff(arr_v0_30872, arr_v0_30863)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "897ba949-3972-4150-8ee5-de5500f51f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>BC16</th>\n",
       "      <td>-27.266132</td>\n",
       "      <td>5.913166</td>\n",
       "      <td>-51.558953</td>\n",
       "      <td>-14.639782</td>\n",
       "      <td>26.229147</td>\n",
       "      <td>1.780851</td>\n",
       "      <td>22.129733</td>\n",
       "      <td>33.527041</td>\n",
       "      <td>0.952244</td>\n",
       "      <td>0.370833</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.380627</td>\n",
       "      <td>((((.((....)).))))</td>\n",
       "      <td>-2.62</td>\n",
       "      <td>-2.410764</td>\n",
       "      <td>-2.101205</td>\n",
       "      <td>44.444444</td>\n",
       "      <td>50.821835</td>\n",
       "      <td>-1.484696</td>\n",
       "      <td>-34.8</td>\n",
       "      <td>-0.103756</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>varloop257581</th>\n",
       "      <td>-29.733194</td>\n",
       "      <td>2.724231</td>\n",
       "      <td>-39.369022</td>\n",
       "      <td>-22.523638</td>\n",
       "      <td>56.350862</td>\n",
       "      <td>1.252395</td>\n",
       "      <td>52.801659</td>\n",
       "      <td>60.852077</td>\n",
       "      <td>-1.717710</td>\n",
       "      <td>0.107837</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.224229</td>\n",
       "      <td>((...........))</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-5.185212</td>\n",
       "      <td>-4.809472</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>10.008263</td>\n",
       "      <td>1.677700</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060713</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>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>27732 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      dH     dH_se      dH_lb      dH_ub         Tm     Tm_se  \\\n",
       "SEQID                                                                           \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",
       "BC16          -27.266132  5.913166 -51.558953 -14.639782  26.229147  1.780851   \n",
       "...                  ...       ...        ...        ...        ...       ...   \n",
       "varloop257581 -29.733194  2.724231 -39.369022 -22.523638  56.350862  1.252395   \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",
       "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",
       "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",
       "BC16           22.129733  33.527041  0.952244  0.370833  ...   \n",
       "...                  ...        ...       ...       ...  ...   \n",
       "varloop257581  52.801659  60.852077 -1.717710  0.107837  ...   \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",
       "varloop257971  26.480286  36.072235  0.466027  0.138313  ...   \n",
       "\n",
       "               dG_37_ensemble_NUPACK              TargetStruct  dG_37_NUPACK  \\\n",
       "SEQID                                                                          \n",
       "BC1                        -5.630936          ((((((....))))))         -5.38   \n",
       "BC10                       -1.594503          (((.((....)).)))         -0.10   \n",
       "BC11                       -3.791497          (((.((....)).)))         -3.34   \n",
       "BC15                       -3.409790        ((((.((....)).))))         -2.62   \n",
       "BC16                       -3.380627        ((((.((....)).))))         -2.62   \n",
       "...                              ...                       ...           ...   \n",
       "varloop257581              -6.224229           ((...........))          1.23   \n",
       "varloop257601              -5.250290        ((..............))          1.43   \n",
       "varloop257621              -5.141857     ((.................))          1.73   \n",
       "varloop257641              -4.942000  ((....................))          1.93   \n",
       "varloop257971              -3.850790           ((...........))          2.32   \n",
       "\n",
       "               dG_37_ensemble_150mM_NUPACK  dG_37_ensemble_75mM_NUPACK  \\\n",
       "SEQID                                                                    \n",
       "BC1                              -4.688133                   -4.283077   \n",
       "BC10                             -1.167094                   -1.034802   \n",
       "BC11                             -2.876428                   -2.557364   \n",
       "BC15                             -2.471721                   -2.176915   \n",
       "BC16                             -2.410764                   -2.101205   \n",
       "...                                    ...                         ...   \n",
       "varloop257581                    -5.185212                   -4.809472   \n",
       "varloop257601                    -4.222346                   -3.852054   \n",
       "varloop257621                    -4.110832                   -3.739269   \n",
       "varloop257641                    -3.911455                   -3.540350   \n",
       "varloop257971                    -2.948253                   -2.634014   \n",
       "\n",
       "                      GC  Tm_NUPACK_salt_corrected  \\\n",
       "SEQID                                                \n",
       "BC1            43.750000                 66.346868   \n",
       "BC10           37.500000                 27.562143   \n",
       "BC11           43.750000                 54.749507   \n",
       "BC15           44.444444                 51.193660   \n",
       "BC16           44.444444                 50.821835   \n",
       "...                  ...                       ...   \n",
       "varloop257581  66.666667                 10.008263   \n",
       "varloop257601  61.111111                  6.646542   \n",
       "varloop257621  57.142857                  2.095381   \n",
       "varloop257641  54.166667                 -0.882917   \n",
       "varloop257971  40.000000                -16.789226   \n",
       "\n",
       "               dG_37_NUPACK_salt_corrected  dH_NUPACK_salt_corrected  \\\n",
       "SEQID                                                                  \n",
       "BC1                              -3.898545                     -45.1   \n",
       "BC10                              0.900750                     -28.7   \n",
       "BC11                             -2.078628                     -38.4   \n",
       "BC15                             -1.501008                     -34.3   \n",
       "BC16                             -1.484696                     -34.8   \n",
       "...                                    ...                       ...   \n",
       "varloop257581                     1.677700                     -17.6   \n",
       "varloop257601                     1.909319                     -17.6   \n",
       "varloop257621                     2.231904                     -17.6   \n",
       "varloop257641                     2.448843                     -17.6   \n",
       "varloop257971                     2.769604                     -13.2   \n",
       "\n",
       "               dS_NUPACK_salt_corrected  \n",
       "SEQID                                    \n",
       "BC1                           -0.128067  \n",
       "BC10                          -0.092213  \n",
       "BC11                          -0.113042  \n",
       "BC15                          -0.102144  \n",
       "BC16                          -0.103756  \n",
       "...                                 ...  \n",
       "varloop257581                 -0.060713  \n",
       "varloop257601                 -0.061357  \n",
       "varloop257621                 -0.062325  \n",
       "varloop257641                 -0.062970  \n",
       "varloop257971                 -0.050040  \n",
       "\n",
       "[27732 rows x 45 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_v1_27732"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b08e78ba-7744-4e15-9149-e39edde24033",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <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",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BC1</th>\n",
       "      <td>-25.430247</td>\n",
       "      <td>2.255642</td>\n",
       "      <td>-35.912486</td>\n",
       "      <td>-19.078323</td>\n",
       "      <td>50.396977</td>\n",
       "      <td>1.116339</td>\n",
       "      <td>46.229170</td>\n",
       "      <td>54.563746</td>\n",
       "      <td>-1.066146</td>\n",
       "      <td>0.062352</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>-15.188329</td>\n",
       "      <td>5.657807</td>\n",
       "      <td>-43.969039</td>\n",
       "      <td>-8.172837</td>\n",
       "      <td>21.158312</td>\n",
       "      <td>1.480272</td>\n",
       "      <td>17.214121</td>\n",
       "      <td>28.022211</td>\n",
       "      <td>0.832575</td>\n",
       "      <td>0.378496</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>-20.672912</td>\n",
       "      <td>5.729148</td>\n",
       "      <td>-43.248468</td>\n",
       "      <td>-12.068890</td>\n",
       "      <td>24.390480</td>\n",
       "      <td>1.597244</td>\n",
       "      <td>20.338410</td>\n",
       "      <td>30.358979</td>\n",
       "      <td>0.851254</td>\n",
       "      <td>0.371873</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>-27.385935</td>\n",
       "      <td>2.266459</td>\n",
       "      <td>-37.633981</td>\n",
       "      <td>-21.222365</td>\n",
       "      <td>32.007491</td>\n",
       "      <td>0.601332</td>\n",
       "      <td>30.141003</td>\n",
       "      <td>34.149926</td>\n",
       "      <td>0.432290</td>\n",
       "      <td>0.078125</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>BC16</th>\n",
       "      <td>-27.625318</td>\n",
       "      <td>3.846930</td>\n",
       "      <td>-46.248377</td>\n",
       "      <td>-18.655438</td>\n",
       "      <td>28.554980</td>\n",
       "      <td>0.869090</td>\n",
       "      <td>25.985170</td>\n",
       "      <td>31.637629</td>\n",
       "      <td>0.767593</td>\n",
       "      <td>0.157002</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.380627</td>\n",
       "      <td>((((.((....)).))))</td>\n",
       "      <td>-2.62</td>\n",
       "      <td>-2.410764</td>\n",
       "      <td>-2.101205</td>\n",
       "      <td>44.444444</td>\n",
       "      <td>50.821835</td>\n",
       "      <td>-1.484696</td>\n",
       "      <td>-34.8</td>\n",
       "      <td>-0.103756</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>varloop257581</th>\n",
       "      <td>-30.113339</td>\n",
       "      <td>2.465559</td>\n",
       "      <td>-40.039398</td>\n",
       "      <td>-22.022371</td>\n",
       "      <td>55.518578</td>\n",
       "      <td>1.084237</td>\n",
       "      <td>51.790940</td>\n",
       "      <td>59.821981</td>\n",
       "      <td>-1.695340</td>\n",
       "      <td>0.099419</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.224229</td>\n",
       "      <td>((...........))</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-5.185212</td>\n",
       "      <td>-4.809472</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>10.008263</td>\n",
       "      <td>1.677700</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>varloop257601</th>\n",
       "      <td>-26.500923</td>\n",
       "      <td>3.150377</td>\n",
       "      <td>-40.927297</td>\n",
       "      <td>-19.171595</td>\n",
       "      <td>40.540359</td>\n",
       "      <td>0.901226</td>\n",
       "      <td>38.257660</td>\n",
       "      <td>43.968515</td>\n",
       "      <td>-0.359340</td>\n",
       "      <td>0.066578</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>-25.428394</td>\n",
       "      <td>2.709762</td>\n",
       "      <td>-39.047699</td>\n",
       "      <td>-18.710171</td>\n",
       "      <td>35.505143</td>\n",
       "      <td>0.764797</td>\n",
       "      <td>33.462020</td>\n",
       "      <td>38.633627</td>\n",
       "      <td>0.112487</td>\n",
       "      <td>0.070860</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>-30.926836</td>\n",
       "      <td>2.333663</td>\n",
       "      <td>-41.108467</td>\n",
       "      <td>-24.312362</td>\n",
       "      <td>28.614761</td>\n",
       "      <td>0.525377</td>\n",
       "      <td>26.970169</td>\n",
       "      <td>30.808674</td>\n",
       "      <td>0.815457</td>\n",
       "      <td>0.100598</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>varloop257971</th>\n",
       "      <td>-21.752232</td>\n",
       "      <td>1.911159</td>\n",
       "      <td>-29.675803</td>\n",
       "      <td>-16.046139</td>\n",
       "      <td>31.122857</td>\n",
       "      <td>0.847426</td>\n",
       "      <td>29.139173</td>\n",
       "      <td>34.508730</td>\n",
       "      <td>0.453617</td>\n",
       "      <td>0.086073</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>27259 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      dH     dH_se      dH_lb      dH_ub         Tm     Tm_se  \\\n",
       "SEQID                                                                           \n",
       "BC1           -25.430247  2.255642 -35.912486 -19.078323  50.396977  1.116339   \n",
       "BC10          -15.188329  5.657807 -43.969039  -8.172837  21.158312  1.480272   \n",
       "BC11          -20.672912  5.729148 -43.248468 -12.068890  24.390480  1.597244   \n",
       "BC15          -27.385935  2.266459 -37.633981 -21.222365  32.007491  0.601332   \n",
       "BC16          -27.625318  3.846930 -46.248377 -18.655438  28.554980  0.869090   \n",
       "...                  ...       ...        ...        ...        ...       ...   \n",
       "varloop257581 -30.113339  2.465559 -40.039398 -22.022371  55.518578  1.084237   \n",
       "varloop257601 -26.500923  3.150377 -40.927297 -19.171595  40.540359  0.901226   \n",
       "varloop257621 -25.428394  2.709762 -39.047699 -18.710171  35.505143  0.764797   \n",
       "varloop257641 -30.926836  2.333663 -41.108467 -24.312362  28.614761  0.525377   \n",
       "varloop257971 -21.752232  1.911159 -29.675803 -16.046139  31.122857  0.847426   \n",
       "\n",
       "                   Tm_lb      Tm_ub     dG_37  dG_37_se  ...  \\\n",
       "SEQID                                                    ...   \n",
       "BC1            46.229170  54.563746 -1.066146  0.062352  ...   \n",
       "BC10           17.214121  28.022211  0.832575  0.378496  ...   \n",
       "BC11           20.338410  30.358979  0.851254  0.371873  ...   \n",
       "BC15           30.141003  34.149926  0.432290  0.078125  ...   \n",
       "BC16           25.985170  31.637629  0.767593  0.157002  ...   \n",
       "...                  ...        ...       ...       ...  ...   \n",
       "varloop257581  51.790940  59.821981 -1.695340  0.099419  ...   \n",
       "varloop257601  38.257660  43.968515 -0.359340  0.066578  ...   \n",
       "varloop257621  33.462020  38.633627  0.112487  0.070860  ...   \n",
       "varloop257641  26.970169  30.808674  0.815457  0.100598  ...   \n",
       "varloop257971  29.139173  34.508730  0.453617  0.086073  ...   \n",
       "\n",
       "               dG_37_ensemble_NUPACK              TargetStruct  dG_37_NUPACK  \\\n",
       "SEQID                                                                          \n",
       "BC1                        -5.630936          ((((((....))))))         -5.38   \n",
       "BC10                       -1.594503          (((.((....)).)))         -0.10   \n",
       "BC11                       -3.791497          (((.((....)).)))         -3.34   \n",
       "BC15                       -3.409790        ((((.((....)).))))         -2.62   \n",
       "BC16                       -3.380627        ((((.((....)).))))         -2.62   \n",
       "...                              ...                       ...           ...   \n",
       "varloop257581              -6.224229           ((...........))          1.23   \n",
       "varloop257601              -5.250290        ((..............))          1.43   \n",
       "varloop257621              -5.141857     ((.................))          1.73   \n",
       "varloop257641              -4.942000  ((....................))          1.93   \n",
       "varloop257971              -3.850790           ((...........))          2.32   \n",
       "\n",
       "               dG_37_ensemble_150mM_NUPACK  dG_37_ensemble_75mM_NUPACK  \\\n",
       "SEQID                                                                    \n",
       "BC1                              -4.688133                   -4.283077   \n",
       "BC10                             -1.167094                   -1.034802   \n",
       "BC11                             -2.876428                   -2.557364   \n",
       "BC15                             -2.471721                   -2.176915   \n",
       "BC16                             -2.410764                   -2.101205   \n",
       "...                                    ...                         ...   \n",
       "varloop257581                    -5.185212                   -4.809472   \n",
       "varloop257601                    -4.222346                   -3.852054   \n",
       "varloop257621                    -4.110832                   -3.739269   \n",
       "varloop257641                    -3.911455                   -3.540350   \n",
       "varloop257971                    -2.948253                   -2.634014   \n",
       "\n",
       "                      GC  Tm_NUPACK_salt_corrected  \\\n",
       "SEQID                                                \n",
       "BC1            43.750000                 66.346868   \n",
       "BC10           37.500000                 27.562143   \n",
       "BC11           43.750000                 54.749507   \n",
       "BC15           44.444444                 51.193660   \n",
       "BC16           44.444444                 50.821835   \n",
       "...                  ...                       ...   \n",
       "varloop257581  66.666667                 10.008263   \n",
       "varloop257601  61.111111                  6.646542   \n",
       "varloop257621  57.142857                  2.095381   \n",
       "varloop257641  54.166667                 -0.882917   \n",
       "varloop257971  40.000000                -16.789226   \n",
       "\n",
       "               dG_37_NUPACK_salt_corrected  dH_NUPACK_salt_corrected  \\\n",
       "SEQID                                                                  \n",
       "BC1                              -3.898545                     -45.1   \n",
       "BC10                              0.900750                     -28.7   \n",
       "BC11                             -2.078628                     -38.4   \n",
       "BC15                             -1.501008                     -34.3   \n",
       "BC16                             -1.484696                     -34.8   \n",
       "...                                    ...                       ...   \n",
       "varloop257581                     1.677700                     -17.6   \n",
       "varloop257601                     1.909319                     -17.6   \n",
       "varloop257621                     2.231904                     -17.6   \n",
       "varloop257641                     2.448843                     -17.6   \n",
       "varloop257971                     2.769604                     -13.2   \n",
       "\n",
       "               dS_NUPACK_salt_corrected  \n",
       "SEQID                                    \n",
       "BC1                           -0.128067  \n",
       "BC10                          -0.092213  \n",
       "BC11                          -0.113042  \n",
       "BC15                          -0.102144  \n",
       "BC16                          -0.103756  \n",
       "...                                 ...  \n",
       "varloop257581                 -0.060713  \n",
       "varloop257601                 -0.061357  \n",
       "varloop257621                 -0.062325  \n",
       "varloop257641                 -0.062970  \n",
       "varloop257971                 -0.050040  \n",
       "\n",
       "[27259 rows x 45 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_v1_27259"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0393a024-5b55-4249-a697-5c92a66f736d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEQID</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>...</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WB3</td>\n",
       "      <td>-14.050527</td>\n",
       "      <td>1.941307</td>\n",
       "      <td>-17.939506</td>\n",
       "      <td>-10.487480</td>\n",
       "      <td>36.816406</td>\n",
       "      <td>4.510599</td>\n",
       "      <td>31.583443</td>\n",
       "      <td>49.343604</td>\n",
       "      <td>0.007925</td>\n",
       "      <td>...</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",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WB_controls260831</td>\n",
       "      <td>-20.648566</td>\n",
       "      <td>2.469625</td>\n",
       "      <td>-28.488077</td>\n",
       "      <td>-14.492679</td>\n",
       "      <td>22.500901</td>\n",
       "      <td>0.768721</td>\n",
       "      <td>20.590386</td>\n",
       "      <td>25.067059</td>\n",
       "      <td>1.014242</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.900108</td>\n",
       "      <td>....................</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.564383</td>\n",
       "      <td>-4.089245</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WB3</td>\n",
       "      <td>-14.050527</td>\n",
       "      <td>1.941307</td>\n",
       "      <td>-17.939506</td>\n",
       "      <td>-10.487480</td>\n",
       "      <td>36.816406</td>\n",
       "      <td>4.510599</td>\n",
       "      <td>31.583443</td>\n",
       "      <td>49.343604</td>\n",
       "      <td>0.007925</td>\n",
       "      <td>...</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",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               SEQID         dH     dH_se      dH_lb      dH_ub         Tm  \\\n",
       "0                WB3 -14.050527  1.941307 -17.939506 -10.487480  36.816406   \n",
       "1  WB_controls260831 -20.648566  2.469625 -28.488077 -14.492679  22.500901   \n",
       "2                WB3 -14.050527  1.941307 -17.939506 -10.487480  36.816406   \n",
       "\n",
       "      Tm_se      Tm_lb      Tm_ub     dG_37  ...  dG_37_ensemble_NUPACK  \\\n",
       "0  4.510599  31.583443  49.343604  0.007925  ...              -7.286549   \n",
       "1  0.768721  20.590386  25.067059  1.014242  ...              -5.900108   \n",
       "2  4.510599  31.583443  49.343604  0.007925  ...              -7.286549   \n",
       "\n",
       "           TargetStruct  dG_37_NUPACK  dG_37_ensemble_150mM_NUPACK  \\\n",
       "0  ....................           0.0                    -6.365937   \n",
       "1  ....................           0.0                    -4.564383   \n",
       "2  ....................           0.0                    -6.365937   \n",
       "\n",
       "   dG_37_ensemble_75mM_NUPACK    GC  Tm_NUPACK_salt_corrected  \\\n",
       "0                   -5.848357   0.0                       NaN   \n",
       "1                   -4.089245  30.0                       NaN   \n",
       "2                   -5.848357  35.0                       NaN   \n",
       "\n",
       "   dG_37_NUPACK_salt_corrected  dH_NUPACK_salt_corrected  \\\n",
       "0                          NaN                       0.0   \n",
       "1                          NaN                       0.0   \n",
       "2                          NaN                       0.0   \n",
       "\n",
       "   dS_NUPACK_salt_corrected  \n",
       "0                      -0.0  \n",
       "1                      -0.0  \n",
       "2                      -0.0  \n",
       "\n",
       "[3 rows x 46 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('./data/models/raw/arr_v1_n=27732.csv', index_col=0)\n",
    "df2 = pd.read_csv('./data/models/raw/arr_v1_n=27731.csv', index_col=0)\n",
    "\n",
    "check_df_diff(df1, df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "82e9699c-c727-49cb-86fc-c70cc3d229d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'WB_controls260871' in df1.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f3fa66ab-cbd8-4ad1-81a4-61edf43c84ba",
   "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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-12.902622</td>\n",
       "      <td>2.027848</td>\n",
       "      <td>-17.828146</td>\n",
       "      <td>-10.230760</td>\n",
       "      <td>54.279944</td>\n",
       "      <td>4.497456</td>\n",
       "      <td>44.930533</td>\n",
       "      <td>62.067388</td>\n",
       "      <td>-0.669630</td>\n",
       "      <td>0.160434</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.041513</td>\n",
       "      <td>((....))</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.809242</td>\n",
       "      <td>-0.714050</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>45.186043</td>\n",
       "      <td>-0.411442</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-0.048880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-34.666319</td>\n",
       "      <td>8.318789</td>\n",
       "      <td>-53.146571</td>\n",
       "      <td>-19.553041</td>\n",
       "      <td>60.230678</td>\n",
       "      <td>2.045054</td>\n",
       "      <td>55.476879</td>\n",
       "      <td>63.839446</td>\n",
       "      <td>-2.382943</td>\n",
       "      <td>0.520578</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.215415</td>\n",
       "      <td>((........))</td>\n",
       "      <td>1.13</td>\n",
       "      <td>-4.454826</td>\n",
       "      <td>-4.157297</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>12.190441</td>\n",
       "      <td>1.530271</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-26.212047</td>\n",
       "      <td>5.634624</td>\n",
       "      <td>-41.531596</td>\n",
       "      <td>-18.953544</td>\n",
       "      <td>52.552525</td>\n",
       "      <td>2.572008</td>\n",
       "      <td>46.545943</td>\n",
       "      <td>56.395255</td>\n",
       "      <td>-1.244589</td>\n",
       "      <td>0.127425</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.318463</td>\n",
       "      <td>((.........))</td>\n",
       "      <td>1.13</td>\n",
       "      <td>-3.560380</td>\n",
       "      <td>-3.264435</td>\n",
       "      <td>69.230769</td>\n",
       "      <td>11.701521</td>\n",
       "      <td>1.563106</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-24.250715</td>\n",
       "      <td>5.816776</td>\n",
       "      <td>-38.882539</td>\n",
       "      <td>-17.452559</td>\n",
       "      <td>45.657784</td>\n",
       "      <td>2.859158</td>\n",
       "      <td>40.891720</td>\n",
       "      <td>50.634368</td>\n",
       "      <td>-0.651440</td>\n",
       "      <td>0.126573</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.631629</td>\n",
       "      <td>((..........))</td>\n",
       "      <td>1.13</td>\n",
       "      <td>-2.881807</td>\n",
       "      <td>-2.590738</td>\n",
       "      <td>64.285714</td>\n",
       "      <td>11.283778</td>\n",
       "      <td>1.591251</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-22.633814</td>\n",
       "      <td>5.538407</td>\n",
       "      <td>-35.529610</td>\n",
       "      <td>-15.344050</td>\n",
       "      <td>40.147117</td>\n",
       "      <td>2.798879</td>\n",
       "      <td>36.665983</td>\n",
       "      <td>47.034102</td>\n",
       "      <td>-0.228376</td>\n",
       "      <td>0.144761</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.338805</td>\n",
       "      <td>((...........))</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-2.595177</td>\n",
       "      <td>-2.307839</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>9.452036</td>\n",
       "      <td>1.715643</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.060713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-24.534543</td>\n",
       "      <td>6.217685</td>\n",
       "      <td>-43.185388</td>\n",
       "      <td>-17.942465</td>\n",
       "      <td>37.712841</td>\n",
       "      <td>1.296061</td>\n",
       "      <td>35.309428</td>\n",
       "      <td>40.037517</td>\n",
       "      <td>-0.053274</td>\n",
       "      <td>0.093988</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.338056</td>\n",
       "      <td>((............))</td>\n",
       "      <td>1.33</td>\n",
       "      <td>-2.594212</td>\n",
       "      <td>-2.306773</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>7.687783</td>\n",
       "      <td>1.836986</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.061035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-17.448293</td>\n",
       "      <td>5.104732</td>\n",
       "      <td>-34.974347</td>\n",
       "      <td>-12.099081</td>\n",
       "      <td>31.811261</td>\n",
       "      <td>4.243502</td>\n",
       "      <td>23.994224</td>\n",
       "      <td>41.047176</td>\n",
       "      <td>0.309509</td>\n",
       "      <td>0.376152</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.335055</td>\n",
       "      <td>((.............))</td>\n",
       "      <td>1.43</td>\n",
       "      <td>-2.589680</td>\n",
       "      <td>-2.301588</td>\n",
       "      <td>52.941176</td>\n",
       "      <td>5.981257</td>\n",
       "      <td>1.955818</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>-0.061357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-22.166976</td>\n",
       "      <td>6.070583</td>\n",
       "      <td>-39.026358</td>\n",
       "      <td>-14.997992</td>\n",
       "      <td>42.683910</td>\n",
       "      <td>2.789499</td>\n",
       "      <td>40.159838</td>\n",
       "      <td>48.520071</td>\n",
       "      <td>-0.430147</td>\n",
       "      <td>0.095633</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.360611</td>\n",
       "      <td>((........))</td>\n",
       "      <td>2.22</td>\n",
       "      <td>-1.923885</td>\n",
       "      <td>-1.698488</td>\n",
       "      <td>41.666667</td>\n",
       "      <td>-15.058104</td>\n",
       "      <td>2.662490</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>-0.049718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-18.553340</td>\n",
       "      <td>5.103550</td>\n",
       "      <td>-30.925063</td>\n",
       "      <td>-12.559271</td>\n",
       "      <td>29.112833</td>\n",
       "      <td>3.654289</td>\n",
       "      <td>23.585440</td>\n",
       "      <td>36.385364</td>\n",
       "      <td>0.470431</td>\n",
       "      <td>0.381929</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.528999</td>\n",
       "      <td>((.........))</td>\n",
       "      <td>2.22</td>\n",
       "      <td>-1.155698</td>\n",
       "      <td>-0.969601</td>\n",
       "      <td>38.461538</td>\n",
       "      <td>-15.280516</td>\n",
       "      <td>2.676171</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>-0.049718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          dH     dH_se      dH_lb      dH_ub         Tm     Tm_se      Tm_lb  \\\n",
       "0 -12.902622  2.027848 -17.828146 -10.230760  54.279944  4.497456  44.930533   \n",
       "1 -34.666319  8.318789 -53.146571 -19.553041  60.230678  2.045054  55.476879   \n",
       "2 -26.212047  5.634624 -41.531596 -18.953544  52.552525  2.572008  46.545943   \n",
       "3 -24.250715  5.816776 -38.882539 -17.452559  45.657784  2.859158  40.891720   \n",
       "4 -22.633814  5.538407 -35.529610 -15.344050  40.147117  2.798879  36.665983   \n",
       "5 -24.534543  6.217685 -43.185388 -17.942465  37.712841  1.296061  35.309428   \n",
       "6 -17.448293  5.104732 -34.974347 -12.099081  31.811261  4.243502  23.994224   \n",
       "7 -22.166976  6.070583 -39.026358 -14.997992  42.683910  2.789499  40.159838   \n",
       "8 -18.553340  5.103550 -30.925063 -12.559271  29.112833  3.654289  23.585440   \n",
       "\n",
       "       Tm_ub     dG_37  dG_37_se  ...  dG_37_ensemble_NUPACK  \\\n",
       "0  62.067388 -0.669630  0.160434  ...              -1.041513   \n",
       "1  63.839446 -2.382943  0.520578  ...              -5.215415   \n",
       "2  56.395255 -1.244589  0.127425  ...              -4.318463   \n",
       "3  50.634368 -0.651440  0.126573  ...              -3.631629   \n",
       "4  47.034102 -0.228376  0.144761  ...              -3.338805   \n",
       "5  40.037517 -0.053274  0.093988  ...              -3.338056   \n",
       "6  41.047176  0.309509  0.376152  ...              -3.335055   \n",
       "7  48.520071 -0.430147  0.095633  ...              -2.360611   \n",
       "8  36.385364  0.470431  0.381929  ...              -1.528999   \n",
       "\n",
       "        TargetStruct  dG_37_NUPACK  dG_37_ensemble_150mM_NUPACK  \\\n",
       "0           ((....))         -0.84                    -0.809242   \n",
       "1       ((........))          1.13                    -4.454826   \n",
       "2      ((.........))          1.13                    -3.560380   \n",
       "3     ((..........))          1.13                    -2.881807   \n",
       "4    ((...........))          1.23                    -2.595177   \n",
       "5   ((............))          1.33                    -2.594212   \n",
       "6  ((.............))          1.43                    -2.589680   \n",
       "7       ((........))          2.22                    -1.923885   \n",
       "8      ((.........))          2.22                    -1.155698   \n",
       "\n",
       "   dG_37_ensemble_75mM_NUPACK         GC  Tm_NUPACK_salt_corrected  \\\n",
       "0                   -0.714050  62.500000                 45.186043   \n",
       "1                   -4.157297  75.000000                 12.190441   \n",
       "2                   -3.264435  69.230769                 11.701521   \n",
       "3                   -2.590738  64.285714                 11.283778   \n",
       "4                   -2.307839  60.000000                  9.452036   \n",
       "5                   -2.306773  56.250000                  7.687783   \n",
       "6                   -2.301588  52.941176                  5.981257   \n",
       "7                   -1.698488  41.666667                -15.058104   \n",
       "8                   -0.969601  38.461538                -15.280516   \n",
       "\n",
       "   dG_37_NUPACK_salt_corrected  dH_NUPACK_salt_corrected  \\\n",
       "0                    -0.411442                     -16.0   \n",
       "1                     1.530271                     -17.6   \n",
       "2                     1.563106                     -17.6   \n",
       "3                     1.591251                     -17.6   \n",
       "4                     1.715643                     -17.6   \n",
       "5                     1.836986                     -17.6   \n",
       "6                     1.955818                     -17.6   \n",
       "7                     2.662490                     -13.2   \n",
       "8                     2.676171                     -13.2   \n",
       "\n",
       "   dS_NUPACK_salt_corrected  \n",
       "0                 -0.048880  \n",
       "1                 -0.060390  \n",
       "2                 -0.060390  \n",
       "3                 -0.060390  \n",
       "4                 -0.060713  \n",
       "5                 -0.061035  \n",
       "6                 -0.061357  \n",
       "7                 -0.049718  \n",
       "8                 -0.049718  \n",
       "\n",
       "[9 rows x 42 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([arr_v0_30863, arr_v0_30872]).drop_duplicates(keep=False, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "27b618ef-acd6-4174-b0e1-b387f6be9d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41311"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_annotation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c9a168e-2825-4b48-987d-1016ea299e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PUMcontrols', 'WBcontrols', 'pseudoknot_pt_muts', 'pseudoknots'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(pd.concat([annotation, new_annotation]).drop_duplicates(keep=False, ignore_index=True).ConstructType)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d62a3bd-827f-4df8-bd83-3911507c4119",
   "metadata": {},
   "source": [
    "## Response 1.5\n",
    "\n",
    "2368 unique sequences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "22c07f04-f1b4-4d01-89b7-f1d665f05dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotation_old = pd.read_table('./data/annotation/NNNlib2b_annotation_20220519.tsv', index_col=0)\n",
    "annotation_new = pd.read_table('./data/annotation/NNNlib2b_annotation_2024.tsv', index_col=0)\n",
    "annotation_unique = pd.read_table('./data/annotation/NNNlib2b_annotation_2024_duplicates_dropped.tsv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "67d1be76-2810-4264-8277-da97abd8e1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mm = annotation_unique.query('Series == \"MisMatches\" & ConstructType == \"AC_mismatch_varpos\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "e9fd2f49-bddc-41f1-a6b8-7b5ab246ee14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['3mer', 'AC_mismatch_varpos', 'Centered', 'GT_mismatch_varpos'],\n",
       "       dtype=object),\n",
       " array([7104, 3840, 3072, 3840]))"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(mm.ConstructType, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "57f098c1-9e62-4306-b90f-a8324b979819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['PolyNTControls', 'RepeatControls', 'StemDangle_3p',\n",
       "        'StemDangle_53p', 'StemDangle_5p', 'SuperStem', 'ssfluor'],\n",
       "       dtype=object),\n",
       " array([ 80, 285,  55,  50,  55,   5,  64]))"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = annotation_unique.query('Series == \"Control\"')\n",
    "np.unique(ct.ConstructType, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "17d5497d-6fce-455a-be3b-4ad6039cbc36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>SD0</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AGCGCGAAAGCGCA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>.((((....)))).</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD3</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAGCGCGAAAGCGCAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>..((((....))))..</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD6</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAGCGCGAAAGCGCAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>...((((....))))...</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD9</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAGCGCGAAAGCGCAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>....((((....))))....</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD12</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAGCGCGAAAGCGCAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>.....((((....)))).....</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD15</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAAGCGCGAAAGCGCAAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>......((((....))))......</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD18</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAAAGCGCGAAAGCGCAAAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>.......((((....)))).......</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD21</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAAAAGCGCGAAAGCGCAAAAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>........((((....))))........</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD24</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAAAAAGCGCGAAAGCGCAAAAAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>.........((((....)))).........</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD27</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>..........((((....))))..........</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD30</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>...........((((....))))...........</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD33</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>............((((....))))............</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD36</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>.............((((....)))).............</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD39</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCGCGCGC</td>\n",
       "      <td>AAAAAAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAAAAAA</td>\n",
       "      <td>-44.238234</td>\n",
       "      <td>-0.119968</td>\n",
       "      <td>95.598867</td>\n",
       "      <td>-6.660701</td>\n",
       "      <td>..............((((....))))..............</td>\n",
       "      <td>-6.997614</td>\n",
       "      <td>-6.151043</td>\n",
       "      <td>-5.837569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD44</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>ACGCGCGCGGAAACGCGCGCGA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>.((((((((....)))))))).</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD47</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>AACGCGCGCGGAAACGCGCGCGAA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>..((((((((....))))))))..</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD50</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>AAACGCGCGCGGAAACGCGCGCGAAA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>...((((((((....))))))))...</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD53</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>AAAACGCGCGCGGAAACGCGCGCGAAAA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>....((((((((....))))))))....</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD56</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>AAAAACGCGCGCGGAAACGCGCGCGAAAAA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>.....((((((((....)))))))).....</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD59</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>AAAAAACGCGCGCGGAAACGCGCGCGAAAAAA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>......((((((((....))))))))......</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD62</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>AAAAAAACGCGCGCGGAAACGCGCGCGAAAAAAA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>.......((((((((....)))))))).......</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD65</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>AAAAAAAACGCGCGCGGAAACGCGCGCGAAAAAAAA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>........((((((((....))))))))........</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD68</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>AAAAAAAAACGCGCGCGGAAACGCGCGCGAAAAAAAAA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>.........((((((((....)))))))).........</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD71</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CGCGCGCGCGCGCGCG</td>\n",
       "      <td>AAAAAAAAAACGCGCGCGGAAACGCGCGCGAAAAAAAAAA</td>\n",
       "      <td>-84.172503</td>\n",
       "      <td>-0.219334</td>\n",
       "      <td>110.614048</td>\n",
       "      <td>-15.883671</td>\n",
       "      <td>..........((((((((....))))))))..........</td>\n",
       "      <td>-16.122762</td>\n",
       "      <td>-14.400408</td>\n",
       "      <td>-13.769400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD76</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AGATCGATCGAAAGATCGATCA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.183883</td>\n",
       "      <td>.((((((((....)))))))).</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.846656</td>\n",
       "      <td>-7.231992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD79</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AAGATCGATCGAAAGATCGATCAA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.183948</td>\n",
       "      <td>..((((((((....))))))))..</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.846864</td>\n",
       "      <td>-7.232242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD82</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AAAGATCGATCGAAAGATCGATCAAA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.184049</td>\n",
       "      <td>...((((((((....))))))))...</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.847456</td>\n",
       "      <td>-7.232951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD85</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AAAAGATCGATCGAAAGATCGATCAAAA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.184191</td>\n",
       "      <td>....((((((((....))))))))....</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.848494</td>\n",
       "      <td>-7.234186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD88</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AAAAAGATCGATCGAAAGATCGATCAAAAA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.184333</td>\n",
       "      <td>.....((((((((....)))))))).....</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.849485</td>\n",
       "      <td>-7.235340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD91</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AAAAAAGATCGATCGAAAGATCGATCAAAAAA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.184399</td>\n",
       "      <td>......((((((((....))))))))......</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.849980</td>\n",
       "      <td>-7.235923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD94</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AAAAAAAGATCGATCGAAAGATCGATCAAAAAAA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.184441</td>\n",
       "      <td>.......((((((((....)))))))).......</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.850283</td>\n",
       "      <td>-7.236281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD97</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AAAAAAAAGATCGATCGAAAGATCGATCAAAAAAAA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.184479</td>\n",
       "      <td>........((((((((....))))))))........</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.850565</td>\n",
       "      <td>-7.236613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD100</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AAAAAAAAAGATCGATCGAAAGATCGATCAAAAAAAAA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.184516</td>\n",
       "      <td>.........((((((((....)))))))).........</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.850844</td>\n",
       "      <td>-7.236943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD103</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATCGATCGATCGATC</td>\n",
       "      <td>AAAAAAAAAAGATCGATCGAAAGATCGATCAAAAAAAAAA</td>\n",
       "      <td>-71.838234</td>\n",
       "      <td>-0.200962</td>\n",
       "      <td>84.322569</td>\n",
       "      <td>-9.184550</td>\n",
       "      <td>..........((((((((....))))))))..........</td>\n",
       "      <td>-9.477614</td>\n",
       "      <td>-7.851109</td>\n",
       "      <td>-7.237255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD108</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AGCCGCCCGGAAACGGGCGGCA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>.((((((((....)))))))).</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD111</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AAGCCGCCCGGAAACGGGCGGCAA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>..((((((((....))))))))..</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD114</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AAAGCCGCCCGGAAACGGGCGGCAAA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>...((((((((....))))))))...</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD117</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AAAAGCCGCCCGGAAACGGGCGGCAAAA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>....((((((((....))))))))....</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD120</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AAAAAGCCGCCCGGAAACGGGCGGCAAAAA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>.....((((((((....)))))))).....</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD123</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AAAAAAGCCGCCCGGAAACGGGCGGCAAAAAA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>......((((((((....))))))))......</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD126</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AAAAAAAGCCGCCCGGAAACGGGCGGCAAAAAAA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>.......((((((((....)))))))).......</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD129</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AAAAAAAAGCCGCCCGGAAACGGGCGGCAAAAAAAA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>........((((((((....))))))))........</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD132</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AAAAAAAAAGCCGCCCGGAAACGGGCGGCAAAAAAAAA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>.........((((((((....)))))))).........</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD135</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCCGCGGGCGGC</td>\n",
       "      <td>AAAAAAAAAAGCCGCCCGGAAACGGGCGGCAAAAAAAAAA</td>\n",
       "      <td>-77.738234</td>\n",
       "      <td>-0.203186</td>\n",
       "      <td>109.445922</td>\n",
       "      <td>-14.350751</td>\n",
       "      <td>..........((((((((....))))))))..........</td>\n",
       "      <td>-14.687614</td>\n",
       "      <td>-12.975957</td>\n",
       "      <td>-12.346489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD140</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCGGGCCCGGGCCCGGCGGC</td>\n",
       "      <td>AGCCGCCGGGCCCGAAAGGGCCCGGCGGCA</td>\n",
       "      <td>-112.638234</td>\n",
       "      <td>-0.290692</td>\n",
       "      <td>114.332683</td>\n",
       "      <td>-22.112585</td>\n",
       "      <td>.((((((((((((....)))))))))))).</td>\n",
       "      <td>-22.447614</td>\n",
       "      <td>-19.873521</td>\n",
       "      <td>-18.928361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD143</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCGGGCCCGGGCCCGGCGGC</td>\n",
       "      <td>AAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAA</td>\n",
       "      <td>-112.638234</td>\n",
       "      <td>-0.290692</td>\n",
       "      <td>114.332683</td>\n",
       "      <td>-22.112585</td>\n",
       "      <td>..((((((((((((....))))))))))))..</td>\n",
       "      <td>-22.447614</td>\n",
       "      <td>-19.873521</td>\n",
       "      <td>-18.928361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD146</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCGGGCCCGGGCCCGGCGGC</td>\n",
       "      <td>AAAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAAA</td>\n",
       "      <td>-112.638234</td>\n",
       "      <td>-0.290692</td>\n",
       "      <td>114.332683</td>\n",
       "      <td>-22.112585</td>\n",
       "      <td>...((((((((((((....))))))))))))...</td>\n",
       "      <td>-22.447614</td>\n",
       "      <td>-19.873521</td>\n",
       "      <td>-18.928361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD149</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCGGGCCCGGGCCCGGCGGC</td>\n",
       "      <td>AAAAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAAAA</td>\n",
       "      <td>-112.638234</td>\n",
       "      <td>-0.290692</td>\n",
       "      <td>114.332683</td>\n",
       "      <td>-22.112585</td>\n",
       "      <td>....((((((((((((....))))))))))))....</td>\n",
       "      <td>-22.447614</td>\n",
       "      <td>-19.873521</td>\n",
       "      <td>-18.928361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD152</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCGGGCCCGGGCCCGGCGGC</td>\n",
       "      <td>AAAAAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAAAAA</td>\n",
       "      <td>-112.638234</td>\n",
       "      <td>-0.290692</td>\n",
       "      <td>114.332683</td>\n",
       "      <td>-22.112585</td>\n",
       "      <td>.....((((((((((((....)))))))))))).....</td>\n",
       "      <td>-22.447614</td>\n",
       "      <td>-19.873521</td>\n",
       "      <td>-18.928361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD155</th>\n",
       "      <td>Control</td>\n",
       "      <td>StemDangle_53p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCCGCCGGGCCCGGGCCCGGCGGC</td>\n",
       "      <td>AAAAAAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAAAAAA</td>\n",
       "      <td>-112.638234</td>\n",
       "      <td>-0.290692</td>\n",
       "      <td>114.332683</td>\n",
       "      <td>-22.112585</td>\n",
       "      <td>......((((((((((((....))))))))))))......</td>\n",
       "      <td>-22.447614</td>\n",
       "      <td>-19.873521</td>\n",
       "      <td>-18.928361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Series   ConstructType bottomScaffold               topScaffold  \\\n",
       "SEQID                                                                     \n",
       "SD0    Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD3    Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD6    Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD9    Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD12   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD15   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD18   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD21   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD24   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD27   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD30   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD33   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD36   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD39   Control  StemDangle_53p            NaN                  GCGCGCGC   \n",
       "SD44   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD47   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD50   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD53   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD56   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD59   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD62   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD65   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD68   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD71   Control  StemDangle_53p            NaN          CGCGCGCGCGCGCGCG   \n",
       "SD76   Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD79   Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD82   Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD85   Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD88   Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD91   Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD94   Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD97   Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD100  Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD103  Control  StemDangle_53p            NaN          GATCGATCGATCGATC   \n",
       "SD108  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD111  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD114  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD117  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD120  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD123  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD126  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD129  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD132  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD135  Control  StemDangle_53p            NaN          GCCGCCCGCGGGCGGC   \n",
       "SD140  Control  StemDangle_53p            NaN  GCCGCCGGGCCCGGGCCCGGCGGC   \n",
       "SD143  Control  StemDangle_53p            NaN  GCCGCCGGGCCCGGGCCCGGCGGC   \n",
       "SD146  Control  StemDangle_53p            NaN  GCCGCCGGGCCCGGGCCCGGCGGC   \n",
       "SD149  Control  StemDangle_53p            NaN  GCCGCCGGGCCCGGGCCCGGCGGC   \n",
       "SD152  Control  StemDangle_53p            NaN  GCCGCCGGGCCCGGGCCCGGCGGC   \n",
       "SD155  Control  StemDangle_53p            NaN  GCCGCCGGGCCCGGGCCCGGCGGC   \n",
       "\n",
       "                                         RefSeq   dH_NUPACK  dS_NUPACK  \\\n",
       "SEQID                                                                    \n",
       "SD0                              AGCGCGAAAGCGCA  -44.238234  -0.119968   \n",
       "SD3                            AAGCGCGAAAGCGCAA  -44.238234  -0.119968   \n",
       "SD6                          AAAGCGCGAAAGCGCAAA  -44.238234  -0.119968   \n",
       "SD9                        AAAAGCGCGAAAGCGCAAAA  -44.238234  -0.119968   \n",
       "SD12                     AAAAAGCGCGAAAGCGCAAAAA  -44.238234  -0.119968   \n",
       "SD15                   AAAAAAGCGCGAAAGCGCAAAAAA  -44.238234  -0.119968   \n",
       "SD18                 AAAAAAAGCGCGAAAGCGCAAAAAAA  -44.238234  -0.119968   \n",
       "SD21               AAAAAAAAGCGCGAAAGCGCAAAAAAAA  -44.238234  -0.119968   \n",
       "SD24             AAAAAAAAAGCGCGAAAGCGCAAAAAAAAA  -44.238234  -0.119968   \n",
       "SD27           AAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAA  -44.238234  -0.119968   \n",
       "SD30         AAAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAAA  -44.238234  -0.119968   \n",
       "SD33       AAAAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAAAA  -44.238234  -0.119968   \n",
       "SD36     AAAAAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAAAAA  -44.238234  -0.119968   \n",
       "SD39   AAAAAAAAAAAAAAGCGCGAAAGCGCAAAAAAAAAAAAAA  -44.238234  -0.119968   \n",
       "SD44                     ACGCGCGCGGAAACGCGCGCGA  -84.172503  -0.219334   \n",
       "SD47                   AACGCGCGCGGAAACGCGCGCGAA  -84.172503  -0.219334   \n",
       "SD50                 AAACGCGCGCGGAAACGCGCGCGAAA  -84.172503  -0.219334   \n",
       "SD53               AAAACGCGCGCGGAAACGCGCGCGAAAA  -84.172503  -0.219334   \n",
       "SD56             AAAAACGCGCGCGGAAACGCGCGCGAAAAA  -84.172503  -0.219334   \n",
       "SD59           AAAAAACGCGCGCGGAAACGCGCGCGAAAAAA  -84.172503  -0.219334   \n",
       "SD62         AAAAAAACGCGCGCGGAAACGCGCGCGAAAAAAA  -84.172503  -0.219334   \n",
       "SD65       AAAAAAAACGCGCGCGGAAACGCGCGCGAAAAAAAA  -84.172503  -0.219334   \n",
       "SD68     AAAAAAAAACGCGCGCGGAAACGCGCGCGAAAAAAAAA  -84.172503  -0.219334   \n",
       "SD71   AAAAAAAAAACGCGCGCGGAAACGCGCGCGAAAAAAAAAA  -84.172503  -0.219334   \n",
       "SD76                     AGATCGATCGAAAGATCGATCA  -71.838234  -0.200962   \n",
       "SD79                   AAGATCGATCGAAAGATCGATCAA  -71.838234  -0.200962   \n",
       "SD82                 AAAGATCGATCGAAAGATCGATCAAA  -71.838234  -0.200962   \n",
       "SD85               AAAAGATCGATCGAAAGATCGATCAAAA  -71.838234  -0.200962   \n",
       "SD88             AAAAAGATCGATCGAAAGATCGATCAAAAA  -71.838234  -0.200962   \n",
       "SD91           AAAAAAGATCGATCGAAAGATCGATCAAAAAA  -71.838234  -0.200962   \n",
       "SD94         AAAAAAAGATCGATCGAAAGATCGATCAAAAAAA  -71.838234  -0.200962   \n",
       "SD97       AAAAAAAAGATCGATCGAAAGATCGATCAAAAAAAA  -71.838234  -0.200962   \n",
       "SD100    AAAAAAAAAGATCGATCGAAAGATCGATCAAAAAAAAA  -71.838234  -0.200962   \n",
       "SD103  AAAAAAAAAAGATCGATCGAAAGATCGATCAAAAAAAAAA  -71.838234  -0.200962   \n",
       "SD108                    AGCCGCCCGGAAACGGGCGGCA  -77.738234  -0.203186   \n",
       "SD111                  AAGCCGCCCGGAAACGGGCGGCAA  -77.738234  -0.203186   \n",
       "SD114                AAAGCCGCCCGGAAACGGGCGGCAAA  -77.738234  -0.203186   \n",
       "SD117              AAAAGCCGCCCGGAAACGGGCGGCAAAA  -77.738234  -0.203186   \n",
       "SD120            AAAAAGCCGCCCGGAAACGGGCGGCAAAAA  -77.738234  -0.203186   \n",
       "SD123          AAAAAAGCCGCCCGGAAACGGGCGGCAAAAAA  -77.738234  -0.203186   \n",
       "SD126        AAAAAAAGCCGCCCGGAAACGGGCGGCAAAAAAA  -77.738234  -0.203186   \n",
       "SD129      AAAAAAAAGCCGCCCGGAAACGGGCGGCAAAAAAAA  -77.738234  -0.203186   \n",
       "SD132    AAAAAAAAAGCCGCCCGGAAACGGGCGGCAAAAAAAAA  -77.738234  -0.203186   \n",
       "SD135  AAAAAAAAAAGCCGCCCGGAAACGGGCGGCAAAAAAAAAA  -77.738234  -0.203186   \n",
       "SD140            AGCCGCCGGGCCCGAAAGGGCCCGGCGGCA -112.638234  -0.290692   \n",
       "SD143          AAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAA -112.638234  -0.290692   \n",
       "SD146        AAAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAAA -112.638234  -0.290692   \n",
       "SD149      AAAAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAAAA -112.638234  -0.290692   \n",
       "SD152    AAAAAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAAAAA -112.638234  -0.290692   \n",
       "SD155  AAAAAAGCCGCCGGGCCCGAAAGGGCCCGGCGGCAAAAAA -112.638234  -0.290692   \n",
       "\n",
       "        Tm_NUPACK  dG_37_ensemble_NUPACK  \\\n",
       "SEQID                                      \n",
       "SD0     95.598867              -6.660701   \n",
       "SD3     95.598867              -6.660701   \n",
       "SD6     95.598867              -6.660701   \n",
       "SD9     95.598867              -6.660701   \n",
       "SD12    95.598867              -6.660701   \n",
       "SD15    95.598867              -6.660701   \n",
       "SD18    95.598867              -6.660701   \n",
       "SD21    95.598867              -6.660701   \n",
       "SD24    95.598867              -6.660701   \n",
       "SD27    95.598867              -6.660701   \n",
       "SD30    95.598867              -6.660701   \n",
       "SD33    95.598867              -6.660701   \n",
       "SD36    95.598867              -6.660701   \n",
       "SD39    95.598867              -6.660701   \n",
       "SD44   110.614048             -15.883671   \n",
       "SD47   110.614048             -15.883671   \n",
       "SD50   110.614048             -15.883671   \n",
       "SD53   110.614048             -15.883671   \n",
       "SD56   110.614048             -15.883671   \n",
       "SD59   110.614048             -15.883671   \n",
       "SD62   110.614048             -15.883671   \n",
       "SD65   110.614048             -15.883671   \n",
       "SD68   110.614048             -15.883671   \n",
       "SD71   110.614048             -15.883671   \n",
       "SD76    84.322569              -9.183883   \n",
       "SD79    84.322569              -9.183948   \n",
       "SD82    84.322569              -9.184049   \n",
       "SD85    84.322569              -9.184191   \n",
       "SD88    84.322569              -9.184333   \n",
       "SD91    84.322569              -9.184399   \n",
       "SD94    84.322569              -9.184441   \n",
       "SD97    84.322569              -9.184479   \n",
       "SD100   84.322569              -9.184516   \n",
       "SD103   84.322569              -9.184550   \n",
       "SD108  109.445922             -14.350751   \n",
       "SD111  109.445922             -14.350751   \n",
       "SD114  109.445922             -14.350751   \n",
       "SD117  109.445922             -14.350751   \n",
       "SD120  109.445922             -14.350751   \n",
       "SD123  109.445922             -14.350751   \n",
       "SD126  109.445922             -14.350751   \n",
       "SD129  109.445922             -14.350751   \n",
       "SD132  109.445922             -14.350751   \n",
       "SD135  109.445922             -14.350751   \n",
       "SD140  114.332683             -22.112585   \n",
       "SD143  114.332683             -22.112585   \n",
       "SD146  114.332683             -22.112585   \n",
       "SD149  114.332683             -22.112585   \n",
       "SD152  114.332683             -22.112585   \n",
       "SD155  114.332683             -22.112585   \n",
       "\n",
       "                                   TargetStruct  dG_37_NUPACK  \\\n",
       "SEQID                                                           \n",
       "SD0                              .((((....)))).     -6.997614   \n",
       "SD3                            ..((((....))))..     -6.997614   \n",
       "SD6                          ...((((....))))...     -6.997614   \n",
       "SD9                        ....((((....))))....     -6.997614   \n",
       "SD12                     .....((((....)))).....     -6.997614   \n",
       "SD15                   ......((((....))))......     -6.997614   \n",
       "SD18                 .......((((....)))).......     -6.997614   \n",
       "SD21               ........((((....))))........     -6.997614   \n",
       "SD24             .........((((....)))).........     -6.997614   \n",
       "SD27           ..........((((....))))..........     -6.997614   \n",
       "SD30         ...........((((....))))...........     -6.997614   \n",
       "SD33       ............((((....))))............     -6.997614   \n",
       "SD36     .............((((....)))).............     -6.997614   \n",
       "SD39   ..............((((....))))..............     -6.997614   \n",
       "SD44                     .((((((((....)))))))).    -16.122762   \n",
       "SD47                   ..((((((((....))))))))..    -16.122762   \n",
       "SD50                 ...((((((((....))))))))...    -16.122762   \n",
       "SD53               ....((((((((....))))))))....    -16.122762   \n",
       "SD56             .....((((((((....)))))))).....    -16.122762   \n",
       "SD59           ......((((((((....))))))))......    -16.122762   \n",
       "SD62         .......((((((((....)))))))).......    -16.122762   \n",
       "SD65       ........((((((((....))))))))........    -16.122762   \n",
       "SD68     .........((((((((....)))))))).........    -16.122762   \n",
       "SD71   ..........((((((((....))))))))..........    -16.122762   \n",
       "SD76                     .((((((((....)))))))).     -9.477614   \n",
       "SD79                   ..((((((((....))))))))..     -9.477614   \n",
       "SD82                 ...((((((((....))))))))...     -9.477614   \n",
       "SD85               ....((((((((....))))))))....     -9.477614   \n",
       "SD88             .....((((((((....)))))))).....     -9.477614   \n",
       "SD91           ......((((((((....))))))))......     -9.477614   \n",
       "SD94         .......((((((((....)))))))).......     -9.477614   \n",
       "SD97       ........((((((((....))))))))........     -9.477614   \n",
       "SD100    .........((((((((....)))))))).........     -9.477614   \n",
       "SD103  ..........((((((((....))))))))..........     -9.477614   \n",
       "SD108                    .((((((((....)))))))).    -14.687614   \n",
       "SD111                  ..((((((((....))))))))..    -14.687614   \n",
       "SD114                ...((((((((....))))))))...    -14.687614   \n",
       "SD117              ....((((((((....))))))))....    -14.687614   \n",
       "SD120            .....((((((((....)))))))).....    -14.687614   \n",
       "SD123          ......((((((((....))))))))......    -14.687614   \n",
       "SD126        .......((((((((....)))))))).......    -14.687614   \n",
       "SD129      ........((((((((....))))))))........    -14.687614   \n",
       "SD132    .........((((((((....)))))))).........    -14.687614   \n",
       "SD135  ..........((((((((....))))))))..........    -14.687614   \n",
       "SD140            .((((((((((((....)))))))))))).    -22.447614   \n",
       "SD143          ..((((((((((((....))))))))))))..    -22.447614   \n",
       "SD146        ...((((((((((((....))))))))))))...    -22.447614   \n",
       "SD149      ....((((((((((((....))))))))))))....    -22.447614   \n",
       "SD152    .....((((((((((((....)))))))))))).....    -22.447614   \n",
       "SD155  ......((((((((((((....))))))))))))......    -22.447614   \n",
       "\n",
       "       dG_37_ensemble_150mM_NUPACK  dG_37_ensemble_75mM_NUPACK  \n",
       "SEQID                                                           \n",
       "SD0                      -6.151043                   -5.837569  \n",
       "SD3                      -6.151043                   -5.837569  \n",
       "SD6                      -6.151043                   -5.837569  \n",
       "SD9                      -6.151043                   -5.837569  \n",
       "SD12                     -6.151043                   -5.837569  \n",
       "SD15                     -6.151043                   -5.837569  \n",
       "SD18                     -6.151043                   -5.837569  \n",
       "SD21                     -6.151043                   -5.837569  \n",
       "SD24                     -6.151043                   -5.837569  \n",
       "SD27                     -6.151043                   -5.837569  \n",
       "SD30                     -6.151043                   -5.837569  \n",
       "SD33                     -6.151043                   -5.837569  \n",
       "SD36                     -6.151043                   -5.837569  \n",
       "SD39                     -6.151043                   -5.837569  \n",
       "SD44                    -14.400408                  -13.769400  \n",
       "SD47                    -14.400408                  -13.769400  \n",
       "SD50                    -14.400408                  -13.769400  \n",
       "SD53                    -14.400408                  -13.769400  \n",
       "SD56                    -14.400408                  -13.769400  \n",
       "SD59                    -14.400408                  -13.769400  \n",
       "SD62                    -14.400408                  -13.769400  \n",
       "SD65                    -14.400408                  -13.769400  \n",
       "SD68                    -14.400408                  -13.769400  \n",
       "SD71                    -14.400408                  -13.769400  \n",
       "SD76                     -7.846656                   -7.231992  \n",
       "SD79                     -7.846864                   -7.232242  \n",
       "SD82                     -7.847456                   -7.232951  \n",
       "SD85                     -7.848494                   -7.234186  \n",
       "SD88                     -7.849485                   -7.235340  \n",
       "SD91                     -7.849980                   -7.235923  \n",
       "SD94                     -7.850283                   -7.236281  \n",
       "SD97                     -7.850565                   -7.236613  \n",
       "SD100                    -7.850844                   -7.236943  \n",
       "SD103                    -7.851109                   -7.237255  \n",
       "SD108                   -12.975957                  -12.346489  \n",
       "SD111                   -12.975957                  -12.346489  \n",
       "SD114                   -12.975957                  -12.346489  \n",
       "SD117                   -12.975957                  -12.346489  \n",
       "SD120                   -12.975957                  -12.346489  \n",
       "SD123                   -12.975957                  -12.346489  \n",
       "SD126                   -12.975957                  -12.346489  \n",
       "SD129                   -12.975957                  -12.346489  \n",
       "SD132                   -12.975957                  -12.346489  \n",
       "SD135                   -12.975957                  -12.346489  \n",
       "SD140                   -19.873521                  -18.928361  \n",
       "SD143                   -19.873521                  -18.928361  \n",
       "SD146                   -19.873521                  -18.928361  \n",
       "SD149                   -19.873521                  -18.928361  \n",
       "SD152                   -19.873521                  -18.928361  \n",
       "SD155                   -19.873521                  -18.928361  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotation.query('Series == \"Control\" & ConstructType == \"StemDangle_53p\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "0965df3a-3282-4c66-b105-0d4e05745961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 453.567x453.567 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row = mm.iloc[4000,:]\n",
    "draw_struct(row.RefSeq, row.TargetStruct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "23c53ba5-2e6e-4143-adf8-46073ae8ec89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GC'], dtype=object)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(mm.topScaffold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "64070620-8bb5-4baf-ba6f-1d3bb24d136a",
   "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>MMTM1198</th>\n",
       "      <td>-6.525975</td>\n",
       "      <td>13.864264</td>\n",
       "      <td>-62.203652</td>\n",
       "      <td>-3.614248</td>\n",
       "      <td>5.010621</td>\n",
       "      <td>7.833929</td>\n",
       "      <td>-12.520447</td>\n",
       "      <td>16.454558</td>\n",
       "      <td>0.830124</td>\n",
       "      <td>0.909403</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.257128</td>\n",
       "      <td>((.(.((....)).).))</td>\n",
       "      <td>0.66</td>\n",
       "      <td>-1.829500</td>\n",
       "      <td>-1.681525</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>21.209771</td>\n",
       "      <td>1.255237</td>\n",
       "      <td>-23.4</td>\n",
       "      <td>-0.077575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BNNN829</th>\n",
       "      <td>-6.670409</td>\n",
       "      <td>17.764431</td>\n",
       "      <td>-61.789867</td>\n",
       "      <td>-2.629287</td>\n",
       "      <td>5.147361</td>\n",
       "      <td>16.073381</td>\n",
       "      <td>-25.945127</td>\n",
       "      <td>18.494065</td>\n",
       "      <td>0.901687</td>\n",
       "      <td>1.037849</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.659130</td>\n",
       "      <td>((((((....)))..)))</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-1.635081</td>\n",
       "      <td>-1.476777</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>24.408996</td>\n",
       "      <td>1.387909</td>\n",
       "      <td>-32.8</td>\n",
       "      <td>-0.106400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BNN5</th>\n",
       "      <td>-5.581248</td>\n",
       "      <td>5.637288</td>\n",
       "      <td>-23.973275</td>\n",
       "      <td>-3.124852</td>\n",
       "      <td>5.627890</td>\n",
       "      <td>7.585876</td>\n",
       "      <td>-10.027511</td>\n",
       "      <td>17.360302</td>\n",
       "      <td>0.687053</td>\n",
       "      <td>0.383884</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.608250</td>\n",
       "      <td>((((((....)))..)))</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-3.243060</td>\n",
       "      <td>-2.998979</td>\n",
       "      <td>55.555556</td>\n",
       "      <td>50.118161</td>\n",
       "      <td>-1.562323</td>\n",
       "      <td>-38.5</td>\n",
       "      <td>-0.115493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMTM1333</th>\n",
       "      <td>-4.770891</td>\n",
       "      <td>15.207920</td>\n",
       "      <td>-30.467659</td>\n",
       "      <td>-1.584761</td>\n",
       "      <td>6.533386</td>\n",
       "      <td>18.169268</td>\n",
       "      <td>-57.126136</td>\n",
       "      <td>21.612603</td>\n",
       "      <td>0.620601</td>\n",
       "      <td>0.937509</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.389820</td>\n",
       "      <td>((.(.((....)).).))</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-1.946775</td>\n",
       "      <td>-1.794594</td>\n",
       "      <td>61.111111</td>\n",
       "      <td>32.590414</td>\n",
       "      <td>0.405276</td>\n",
       "      <td>-28.1</td>\n",
       "      <td>-0.089441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BNNN2736</th>\n",
       "      <td>-6.583530</td>\n",
       "      <td>20.173226</td>\n",
       "      <td>-73.672916</td>\n",
       "      <td>-2.807356</td>\n",
       "      <td>6.899756</td>\n",
       "      <td>11.393933</td>\n",
       "      <td>-28.186285</td>\n",
       "      <td>17.520198</td>\n",
       "      <td>0.775904</td>\n",
       "      <td>1.286732</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.396152</td>\n",
       "      <td>(((..(((....))))))</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-2.194250</td>\n",
       "      <td>-1.951241</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>23.041716</td>\n",
       "      <td>1.555153</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>-0.107561</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>WC2143</th>\n",
       "      <td>-30.244754</td>\n",
       "      <td>4.872433</td>\n",
       "      <td>-45.170265</td>\n",
       "      <td>-17.797973</td>\n",
       "      <td>59.989437</td>\n",
       "      <td>1.299909</td>\n",
       "      <td>56.970374</td>\n",
       "      <td>64.509140</td>\n",
       "      <td>-2.081039</td>\n",
       "      <td>0.308051</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.375044</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-7.25</td>\n",
       "      <td>-6.285362</td>\n",
       "      <td>-5.848049</td>\n",
       "      <td>68.750000</td>\n",
       "      <td>82.868705</td>\n",
       "      <td>-6.081149</td>\n",
       "      <td>-47.2</td>\n",
       "      <td>-0.128809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TETNN1137</th>\n",
       "      <td>-31.201794</td>\n",
       "      <td>3.675341</td>\n",
       "      <td>-45.592443</td>\n",
       "      <td>-21.331188</td>\n",
       "      <td>59.990053</td>\n",
       "      <td>1.109808</td>\n",
       "      <td>57.318400</td>\n",
       "      <td>64.677109</td>\n",
       "      <td>-2.186963</td>\n",
       "      <td>0.219980</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.739760</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-6.62</td>\n",
       "      <td>-5.579642</td>\n",
       "      <td>-5.138995</td>\n",
       "      <td>68.750000</td>\n",
       "      <td>74.826412</td>\n",
       "      <td>-5.391716</td>\n",
       "      <td>-49.6</td>\n",
       "      <td>-0.138578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRINNN715</th>\n",
       "      <td>-32.982417</td>\n",
       "      <td>4.255016</td>\n",
       "      <td>-46.076076</td>\n",
       "      <td>-20.472890</td>\n",
       "      <td>59.990382</td>\n",
       "      <td>1.192257</td>\n",
       "      <td>57.132167</td>\n",
       "      <td>64.513128</td>\n",
       "      <td>-2.272676</td>\n",
       "      <td>0.260454</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.754183</td>\n",
       "      <td>((((((...))))))</td>\n",
       "      <td>-4.39</td>\n",
       "      <td>-3.906072</td>\n",
       "      <td>-3.526701</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>61.906504</td>\n",
       "      <td>-3.270750</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>-0.127712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMCNNN515</th>\n",
       "      <td>-30.648527</td>\n",
       "      <td>4.228927</td>\n",
       "      <td>-46.801700</td>\n",
       "      <td>-18.790881</td>\n",
       "      <td>59.990770</td>\n",
       "      <td>1.416999</td>\n",
       "      <td>55.803738</td>\n",
       "      <td>65.790856</td>\n",
       "      <td>-2.035477</td>\n",
       "      <td>0.226447</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.741096</td>\n",
       "      <td>(((.(((....))).)))</td>\n",
       "      <td>-6.48</td>\n",
       "      <td>-5.828306</td>\n",
       "      <td>-5.422696</td>\n",
       "      <td>72.222222</td>\n",
       "      <td>72.774808</td>\n",
       "      <td>-5.274312</td>\n",
       "      <td>-51.0</td>\n",
       "      <td>-0.143543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMCNNN2925</th>\n",
       "      <td>-37.487930</td>\n",
       "      <td>5.263815</td>\n",
       "      <td>-54.992766</td>\n",
       "      <td>-22.650684</td>\n",
       "      <td>59.999016</td>\n",
       "      <td>0.995777</td>\n",
       "      <td>57.313548</td>\n",
       "      <td>63.149693</td>\n",
       "      <td>-2.611466</td>\n",
       "      <td>0.339228</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.532669</td>\n",
       "      <td>(((.(((....))).)))</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-4.422503</td>\n",
       "      <td>-3.999738</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>71.009421</td>\n",
       "      <td>-4.318382</td>\n",
       "      <td>-43.7</td>\n",
       "      <td>-0.123392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27732 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dH      dH_se      dH_lb      dH_ub         Tm      Tm_se  \\\n",
       "SEQID                                                                          \n",
       "MMTM1198    -6.525975  13.864264 -62.203652  -3.614248   5.010621   7.833929   \n",
       "BNNN829     -6.670409  17.764431 -61.789867  -2.629287   5.147361  16.073381   \n",
       "BNN5        -5.581248   5.637288 -23.973275  -3.124852   5.627890   7.585876   \n",
       "MMTM1333    -4.770891  15.207920 -30.467659  -1.584761   6.533386  18.169268   \n",
       "BNNN2736    -6.583530  20.173226 -73.672916  -2.807356   6.899756  11.393933   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "WC2143     -30.244754   4.872433 -45.170265 -17.797973  59.989437   1.299909   \n",
       "TETNN1137  -31.201794   3.675341 -45.592443 -21.331188  59.990053   1.109808   \n",
       "TRINNN715  -32.982417   4.255016 -46.076076 -20.472890  59.990382   1.192257   \n",
       "MMCNNN515  -30.648527   4.228927 -46.801700 -18.790881  59.990770   1.416999   \n",
       "MMCNNN2925 -37.487930   5.263815 -54.992766 -22.650684  59.999016   0.995777   \n",
       "\n",
       "                Tm_lb      Tm_ub     dG_37  dG_37_se  ...  \\\n",
       "SEQID                                                 ...   \n",
       "MMTM1198   -12.520447  16.454558  0.830124  0.909403  ...   \n",
       "BNNN829    -25.945127  18.494065  0.901687  1.037849  ...   \n",
       "BNN5       -10.027511  17.360302  0.687053  0.383884  ...   \n",
       "MMTM1333   -57.126136  21.612603  0.620601  0.937509  ...   \n",
       "BNNN2736   -28.186285  17.520198  0.775904  1.286732  ...   \n",
       "...               ...        ...       ...       ...  ...   \n",
       "WC2143      56.970374  64.509140 -2.081039  0.308051  ...   \n",
       "TETNN1137   57.318400  64.677109 -2.186963  0.219980  ...   \n",
       "TRINNN715   57.132167  64.513128 -2.272676  0.260454  ...   \n",
       "MMCNNN515   55.803738  65.790856 -2.035477  0.226447  ...   \n",
       "MMCNNN2925  57.313548  63.149693 -2.611466  0.339228  ...   \n",
       "\n",
       "            dG_37_ensemble_NUPACK        TargetStruct  dG_37_NUPACK  \\\n",
       "SEQID                                                                 \n",
       "MMTM1198                -2.257128  ((.(.((....)).).))          0.66   \n",
       "BNNN829                 -1.659130  ((((((....)))..)))          0.20   \n",
       "BNN5                    -3.608250  ((((((....)))..)))         -2.68   \n",
       "MMTM1333                -2.389820  ((.(.((....)).).))         -0.36   \n",
       "BNNN2736                -2.396152  (((..(((....))))))          0.36   \n",
       "...                           ...                 ...           ...   \n",
       "WC2143                  -7.375044    ((((((....))))))         -7.25   \n",
       "TETNN1137               -6.739760    ((((((....))))))         -6.62   \n",
       "TRINNN715               -4.754183     ((((((...))))))         -4.39   \n",
       "MMCNNN515               -6.741096  (((.(((....))).)))         -6.48   \n",
       "MMCNNN2925              -5.532669  (((.(((....))).)))         -5.43   \n",
       "\n",
       "            dG_37_ensemble_150mM_NUPACK  dG_37_ensemble_75mM_NUPACK  \\\n",
       "SEQID                                                                 \n",
       "MMTM1198                      -1.829500                   -1.681525   \n",
       "BNNN829                       -1.635081                   -1.476777   \n",
       "BNN5                          -3.243060                   -2.998979   \n",
       "MMTM1333                      -1.946775                   -1.794594   \n",
       "BNNN2736                      -2.194250                   -1.951241   \n",
       "...                                 ...                         ...   \n",
       "WC2143                        -6.285362                   -5.848049   \n",
       "TETNN1137                     -5.579642                   -5.138995   \n",
       "TRINNN715                     -3.906072                   -3.526701   \n",
       "MMCNNN515                     -5.828306                   -5.422696   \n",
       "MMCNNN2925                    -4.422503                   -3.999738   \n",
       "\n",
       "                   GC  Tm_NUPACK_salt_corrected  dG_37_NUPACK_salt_corrected  \\\n",
       "SEQID                                                                          \n",
       "MMTM1198    66.666667                 21.209771                     1.255237   \n",
       "BNNN829     33.333333                 24.408996                     1.387909   \n",
       "BNN5        55.555556                 50.118161                    -1.562323   \n",
       "MMTM1333    61.111111                 32.590414                     0.405276   \n",
       "BNNN2736    33.333333                 23.041716                     1.555153   \n",
       "...               ...                       ...                          ...   \n",
       "WC2143      68.750000                 82.868705                    -6.081149   \n",
       "TETNN1137   68.750000                 74.826412                    -5.391716   \n",
       "TRINNN715   66.666667                 61.906504                    -3.270750   \n",
       "MMCNNN515   72.222222                 72.774808                    -5.274312   \n",
       "MMCNNN2925  66.666667                 71.009421                    -4.318382   \n",
       "\n",
       "            dH_NUPACK_salt_corrected  dS_NUPACK_salt_corrected  \n",
       "SEQID                                                           \n",
       "MMTM1198                       -23.4                 -0.077575  \n",
       "BNNN829                        -32.8                 -0.106400  \n",
       "BNN5                           -38.5                 -0.115493  \n",
       "MMTM1333                       -28.1                 -0.089441  \n",
       "BNNN2736                       -33.0                 -0.107561  \n",
       "...                              ...                       ...  \n",
       "WC2143                         -47.2                 -0.128809  \n",
       "TETNN1137                      -49.6                 -0.138578  \n",
       "TRINNN715                      -44.0                 -0.127712  \n",
       "MMCNNN515                      -51.0                 -0.143543  \n",
       "MMCNNN2925                     -43.7                 -0.123392  \n",
       "\n",
       "[27732 rows x 45 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_v1.sort_values('Tm').iloc[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "8188158e-2b13-4f65-a376-d9b2ccc40ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "eg_df = df.query('SEQID == \"MMCNNN2925\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "7aecb661-ec75-4716-a4e1-33a716545bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Green07_Cy3',\n",
       " 'Green09_Quench',\n",
       " 'Green12_20.0',\n",
       " 'Green13_22.5',\n",
       " 'Green14_25.0',\n",
       " 'Green15_27.5',\n",
       " 'Green16_30.0',\n",
       " 'Green17_32.5',\n",
       " 'Green18_35.0',\n",
       " 'Green19_37.5',\n",
       " 'Green20_40.0',\n",
       " 'Green21_42.5',\n",
       " 'Green22_45.0',\n",
       " 'Green23_47.5',\n",
       " 'Green24_50.0',\n",
       " 'Green25_52.5',\n",
       " 'Green26_55.0',\n",
       " 'Green27_57.5',\n",
       " 'Green28_60.0',\n",
       " 'Green29_20.0']"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in eg_df.columns if x.startswith('Green')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "786ceebf-8743-4c7e-8963-2e06e1cb4f2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nnn import train_nn as tnn\n",
    "mydata = tnn.MyData()\n",
    "mydata.load_everything()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "3847ff03-6a3b-40d6-a91c-63cb8c1edc92",
   "metadata": {},
   "outputs": [],
   "source": [
    "curve = pd.read_csv('data/models/raw/arr_p_unfold_n=31000.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "c94f4af7-bed3-4a40-92b8-39a600a319fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 226,
     "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": [
    "plt.plot(curve.loc[['MMCNNN2925', 'TETNN1137']].T)\n",
    "plt.ylim([0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "6dd53aaf-aeaa-40fb-b903-bbd119445d0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20519"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(arr_v1.RefSeq.apply(util.get_GC_content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "4219bebc-4f94-4674-bb63-2ba723e633f9",
   "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>WC1375</th>\n",
       "      <td>-32.515649</td>\n",
       "      <td>2.333734</td>\n",
       "      <td>-43.057576</td>\n",
       "      <td>-25.513897</td>\n",
       "      <td>52.272962</td>\n",
       "      <td>0.784343</td>\n",
       "      <td>49.734161</td>\n",
       "      <td>55.226463</td>\n",
       "      <td>-1.696434</td>\n",
       "      <td>0.085923</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.011584</td>\n",
       "      <td>((((((....))))))</td>\n",
       "      <td>-5.91</td>\n",
       "      <td>-4.983541</td>\n",
       "      <td>-4.573978</td>\n",
       "      <td>56.25</td>\n",
       "      <td>71.335661</td>\n",
       "      <td>-4.584924</td>\n",
       "      <td>-46.0</td>\n",
       "      <td>-0.12926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               dH     dH_se      dH_lb      dH_ub         Tm     Tm_se  \\\n",
       "SEQID                                                                    \n",
       "WC1375 -32.515649  2.333734 -43.057576 -25.513897  52.272962  0.784343   \n",
       "\n",
       "            Tm_lb      Tm_ub     dG_37  dG_37_se  ...  dG_37_ensemble_NUPACK  \\\n",
       "SEQID                                             ...                          \n",
       "WC1375  49.734161  55.226463 -1.696434  0.085923  ...              -6.011584   \n",
       "\n",
       "            TargetStruct  dG_37_NUPACK  dG_37_ensemble_150mM_NUPACK  \\\n",
       "SEQID                                                                 \n",
       "WC1375  ((((((....))))))         -5.91                    -4.983541   \n",
       "\n",
       "        dG_37_ensemble_75mM_NUPACK     GC  Tm_NUPACK_salt_corrected  \\\n",
       "SEQID                                                                 \n",
       "WC1375                   -4.573978  56.25                 71.335661   \n",
       "\n",
       "        dG_37_NUPACK_salt_corrected  dH_NUPACK_salt_corrected  \\\n",
       "SEQID                                                           \n",
       "WC1375                    -4.584924                     -46.0   \n",
       "\n",
       "        dS_NUPACK_salt_corrected  \n",
       "SEQID                             \n",
       "WC1375                  -0.12926  \n",
       "\n",
       "[1 rows x 45 columns]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_v1.query('RefSeq == \"GTCAGGGAAACCTGAC\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06c1b4c6-2789-4e51-b60c-f91e1c3996da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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