new_linear_combine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import savemat, loadmat\n",
    "import pandas as pd\n",
    "import pdb\n",
    "import json\n",
    "import numpy as np\n",
    "from numpy import median, mean\n",
    "from sklearn.linear_model import BayesianRidge, LinearRegression, RidgeCV, Ridge\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.model_selection import cross_val_score, LeaveOneOut\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "radius 1+2 linear model\n",
      "Mean squared error: 9.60\n",
      "Coefficient of determination: 0.9998\n"
     ]
    }
   ],
   "source": [
    "ac = loadmat('./data/Train_comb_NN_model.mat')\n",
    "\n",
    "y = ac['y']\n",
    "y = y.flatten()\n",
    "\n",
    "alphas = np.logspace(-6, 6, 200)\n",
    "\n",
    "\n",
    "Xrc = ac['X_comb_train']\n",
    "regr_rcombined = RidgeCV(alphas=alphas,fit_intercept= False).fit(Xrc, y)\n",
    "\n",
    "y_pred_rc = regr_rcombined.predict(Xrc)\n",
    "mse_rc = mean_squared_error(y, y_pred_rc)\n",
    "r2 = r2_score(y, y_pred_rc)\n",
    "\n",
    "\n",
    "print('radius 1+2 linear model')\n",
    "print('Mean squared error: %.2f'\n",
    "    % mse_rc)\n",
    "print('Coefficient of determination: %.4f'\n",
    "    % r2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.7,0.25,'$R^2$ = 0.9998')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpa8g7yr0zyYNJ7hhrOybJ9Unubs9Ht/YkuSzJziS3JTl1qLokSZMZcg/iXcCZi9ouBm6oqo3ADW0d4CxgY3tsBS4fsC5J0gQGC4iq+p/A1xY1bwK2t+XtwDlj7VfWyI3AUUlOHKo2SdLyhrwndc8JVbUHoKr2JDm+ta8D7h/rt6u17Vnh+iQdTF6/eBKjecd1K1vHGrVaDlL37l1d3Y7J1iRzSebm5+cHLkuSDl0rHRAPLEwdtecHW/su4KSxfuuB3b03qKptVTVbVbMzMzODFitJh7KVDogdwOa2vBm4Zqz9gnY202nAwwtTUZKk6RjsGESS9wJ/HzguyS7gzcBbgKuSbAHuA85t3a8FzgZ2Ao8ArxuqLknSZAYLiKo6fy+bzuj0LeDCoWqRJO271XKQWpK0yqz0aa6S9Ji9nYaqVcE9CElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpffpJY0LL8tvWa5ByFJ6jIgJEldTjFJOvR4r+qJGBCS9o3HFA4Zq2qKKcmZSb6QZGeSi6ddjyQdylbNHkSSw4A/BF4J7AI+nWRHVX1uupVJOmQ49fR9Vk1AAC8BdlbVPQBJ3gdsAgwIaRqcSjrkraaAWAfcP7a+C/g7U6pFq9W+/oa3Gn8jHLomf7DrAFlNAZFOWz2uU7IV2NpWv5XkC4NWdeAdB3xl2kUcQKtjPFf0/vnsd//pjGlfx7BvVsfndGCt3JiG/WzGrdSY/uYknVZTQOwCThpbXw/sXtypqrYB21aqqAMtyVxVzU67jgPlYBsPOKa1wjENbzWdxfRpYGOSk5McAZwH7JhyTZJ0yFo1exBV9WiSXwQ+DBwGvLOq7pxyWZJ0yFo1AQFQVdcC1067joGt2emxvTjYxgOOaa1wTANL1eOOA0uStKqOQUiSVhEDYiBJfjvJ55PcluTqJEeNbfv1djmRLyT5qbH2VX2pkSTnJrkzyf9NMjvWviHJXyW5tT3ePrbtxUlub2O6LMmKnS84ib2NqW1bk5/TuCS/keTLY5/N2WPbuuNb7dbS3/9Sktzb/m/cmmSutR2T5Pokd7fno6daZFX5GOAB/CRweFt+K/DWtvwC4LPAkcDJwBcZHZQ/rC0/Bzii9XnBtMexaEzPB54HfAKYHWvfANyxl9d8Cngpo++5/A/grGmPY8IxrdnPadH4fgP41U57d3zTrneC8aypv/9lxnIvcNyitv8EXNyWL174uTGth3sQA6mqj1TVo231Rkbf64DR5UPeV1Xfqao/B3YyuszI/7/USFV9F1i41MiqUVV3VdXEX0xMciLwzKr6sxr9i78SOGewAvfDEmNas5/ThPY2vtXuYPn735tNwPa2vJ0p/38xIFbGP2H02zP0Lymybon2teLkJJ9J8skkf6+1rWM0jgVraUwH0+f0i22q851jUxZrcRywduvuKeAjSW5uV4gAOKGq9gC05+OnVh2r7DTXtSbJR4G/0dl0SVVd0/pcAjwKvGfhZZ3+RT+sV/wUs0nG1LEHeHZVfTXJi4H/nuSFTHj5lKHt55hW9ec0bqnxAZcDv8moxt8E/jOjX1hWxWezH9Zq3T0vr6rdSY4Hrk/y+WkXtJgB8QRU1SuW2p5kM/Aq4Iw2xQJLX1Jk2UuNDG25Me3lNd8BvtOWb07yReBvMRrr+rGua2ZMrPLPadyk40vyX4A/basTXdpmFVqrdT9OVe1uzw8muZrR9NkDSU6sqj1tivbBadboFNNAkpwJvAl4dVU9MrZpB3BekiOTnAxsZHQgd81eaiTJTLufB0mew2hM97Rd5G8mOa2dvXQBsLff2Febg+Jzaj9kFrwGuKMt7218q92a+vvfmyRPS/KMhWVGJ7XcwWgsm1u3zUz7/8u0j+QfrA9GB/3uB25tj7ePbbuE0ZkYX2DsrB7gbOD/tG2XTHsMnTG9htFvcN8BHgA+3Nr/EXAnozNKbgH+4dhrZhn9w/8i8Ae0L2eulsfexrSWP6dF43s3cDtwG6MfPicuN77V/lhLf/9LjOE57f/LZ9v/nUta+7HADcDd7fmYadbpN6klSV1OMUmSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQtoHSV6TpJL80AR9fyrJ/0oy1677/64kx7VtT0nyu0n+MMlvDV+5tO8MCGnfnA/MMbrEw14lOZfRtf03V9UscAqjb8c+uXV5A/BHVXUhsGzYSNNgQEgTSvJ04MeBLYyCYm/9ngb8PvCPq+oegKr6XlX9+6pauPz5C4Hb2/WEHtnLW0lT5dVcpcmdA3y0qm5L8pdJTq2qWzr9zgY+W1V3LvFeVwHbGIXDfxygVukJMyCkyZ3P6Ic6jH7An8/o4oSLvZDHrppKksuA04FvVdVpAFX1IeBDg1YrPUFOMUkTSHIso+v1X9ea3g/8bLuM+WJ/Nb5SVb8E/Crff3c9adUzIKTJvBa4tkY3R6JG93H+C+DHklye5D+0260eDXwY+OkkPwjQQuSV9Pc2pFXLKSZpMucDfzvJvWNtxzLao/jxqvpUkqur6iHgoST/FrguyfeAv2Z05tO7V7po6YnwfhDSE5DkXcA/Z/TL1m9X1b+YbkXSgeMehPTEfBi4AvgG8Jkp1yIdUAaE9MQ8CdgNBLhyyrVIB5RTTJKkLs9ikiR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnr/wGtW+z44D+CbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(regr_rcombined.coef_[0:1730], bins=50, color = 'tomato')#for ridgeCV\n",
    "\n",
    "plt.xlabel('$\\Delta_g G^o$')\n",
    "plt.ylabel('Count')\n",
    "# plt.savefig('./figures/ridge_group_info_radius2_manual_correct_new_color.png')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# ax.scatter(y, predicted, color = 'burlywood')\n",
    "ax.scatter(y, y_pred_rc, color = 'tomato')\n",
    "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=1,)\n",
    "ax.set_xlabel('Measured $\\Delta_r G^o$')\n",
    "ax.set_ylabel('Predicted $\\Delta_r G^o$')\n",
    "plt.figtext(.7, .2, \"MSE = %.2f\" % mse_rc)\n",
    "plt.figtext(.7, .25, \"$R^2$ = %.4f\" % r2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross-validataion result : radius 1 + 2\n",
      "('median of cv is: ', 5.484989593117513)\n",
      "('mean of cv is: ', 16.25610650703227)\n"
     ]
    }
   ],
   "source": [
    "r1_d = loadmat('./data/dGPredictor_stereo.mat')\n",
    "r2_d = loadmat('./data/radius2_mat_data_modified_manual.mat')\n",
    "S = r1_d['train_S']\n",
    "\n",
    "Gr1 = r1_d['G']\n",
    "Gr2 = r2_d['G_inc_r2_compar']\n",
    "\n",
    "df_S = pd.DataFrame(r1_d['train_S'])\n",
    "df_S_unique = df_S.T.drop_duplicates().T\n",
    "unque_cols = df_S_unique.columns.values.tolist()\n",
    "S = S[:, unque_cols]\n",
    "\n",
    "b_list = json.load(open('./data/median_b_manual_correction_r2.json')) # it will be same for both radius, it just remove all the repeated data points from the training data\n",
    "b = np.asarray(b_list)\n",
    "b = np.reshape(b,(-1,1))\n",
    "\n",
    "STG1 = np.dot(S.T, Gr1)\n",
    "STG2 = np.dot(S.T, Gr2)\n",
    "\n",
    "\n",
    "X1 = STG1\n",
    "X2 = STG2\n",
    "yy = b\n",
    "yy = yy.flatten()\n",
    "\n",
    "\n",
    "## cross validation combined moiety model\n",
    "\n",
    "XX = np.concatenate((X1, X2), axis =1)\n",
    "\n",
    "alphas = np.logspace(-6, 6, 200)\n",
    "regr = RidgeCV(alphas=alphas,fit_intercept= False).fit(XX, yy)\n",
    "\n",
    "regr_cv = Ridge(alpha=regr.alpha_,fit_intercept=False)\n",
    "scores_cv = -cross_val_score(regr_cv, XX, yy, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n",
    "\n",
    "\n",
    "print('cross-validataion result : radius 1 + 2')\n",
    "print('median of cv is: ', median(scores_cv))\n",
    "print('mean of cv is: ', mean(scores_cv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test case KEGG dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "radius 1+2 linear model\n",
      "Mean squared error: 9.60\n",
      "Coefficient of determination: 0.9998\n"
     ]
    }
   ],
   "source": [
    "ac = loadmat('./data/Test_KEGG_comb_all_grp_NN_model.mat')\n",
    "\n",
    "y = ac['y']\n",
    "y = y.flatten()\n",
    "\n",
    "alphas = np.logspace(-6, 6, 200)\n",
    "\n",
    "Xrc = ac['X_comb_all']\n",
    "regr_rcombined = BayesianRidge(tol=1e-6, fit_intercept=False, compute_score=True).fit(Xrc, y)\n",
    "\n",
    "y_pred_rc = regr_rcombined.predict(Xrc)\n",
    "mse_rc = mean_squared_error(y, y_pred_rc)\n",
    "r2 = r2_score(y, y_pred_rc)\n",
    "\n",
    "\n",
    "print('radius 1+2 linear model')\n",
    "print('Mean squared error: %.2f'\n",
    "    % mse_rc)\n",
    "print('Coefficient of determination: %.4f'\n",
    "    % r2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = (pd.read_csv(\"./data/reaction_rule.csv\", index_col=0))\n",
    "df2 = (pd.read_csv(\"./data/reaction_rule_r2_py3_manual_modified.csv\", index_col=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule_vec1 = df.to_numpy().T\n",
    "rule_vec2 = df2.to_numpy().T\n",
    "\n",
    "m, n = rule_vec1.shape\n",
    "m2, n2 = rule_vec2.shape\n",
    "\n",
    "zeros1 = np.zeros((m, 44))\n",
    "zeros2 = np.zeros((m2, 44))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = rule_vec1\n",
    "X1 = np.concatenate((rule_vec1,zeros1),1)\n",
    "\n",
    "X2 = rule_vec2\n",
    "X2 = np.concatenate((rule_vec2,zeros2),1)\n",
    "\n",
    "X_comb_all = np.concatenate((X1, X2), axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ymean, ystd = regr_rcombined.predict(X_comb_all, return_std=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rxns = df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = {}\n",
    "result['reaction'] = rxns\n",
    "result['dG'] = ymean\n",
    "result['dG_std'] = ystd\n",
    "df_result = pd.DataFrame.from_dict(result)\n",
    "df_result.to_csv('./result/result_M1_2Linear_comb_model_Kegg_all.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis Section 2.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read groups that exist in thermodynamic data\n",
    "\n",
    "df_groups1 = pd.read_csv('./data/group_names_TECRDB.txt',header=None)\n",
    "groups1 = df_groups1[0].tolist()\n",
    "\n",
    "\n",
    "df_groups2 = pd.read_csv('./data/r2_grp_TECRDB_name.txt',header=None)\n",
    "groups2 = df_groups2[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "grps_all = groups1 + groups2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1435, 7053)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24881, 7053)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\new_env\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# frames = [df, df2]\n",
    "df_all = pd.concat([df, df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26316, 7053)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rule_1 = df_all.drop(grps_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rule_2 = df_rule_1.loc[:, (df_rule_1 != 0).any(axis=0)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1172, 2166)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rule_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.28966397277755"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(7053.0 -2166.0 )*100.0 /7053.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4887"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7053-2166 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# reactions with zero group changes using component contribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = regr_rcombined.coef_\n",
    "sigma = regr_rcombined.sigma_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "cov = [np.sqrt(sigma[i][i]) for i in range(len(sigma))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "info = dict()\n",
    "info['coef'] = coef\n",
    "info['cov'] = cov\n",
    "df_info = pd.DataFrame.from_dict(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info1 = df_info[:1435] # leaving 44 entries for the zeroes that were added in the data and only account for the moieties \n",
    "df_info2 = df_info[1479:26360]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info.shape\n",
    "grps_df1 = pd.read_csv('./data/group_names_all.txt',header=None)\n",
    "grps_df2 = pd.read_csv('./data/group_names_r2_py3_modified_manual.txt',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1479"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " len(grps_df1) + 44"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info_all = pd.concat([df_info1, df_info2])\n",
    "grps_info_all = pd.concat([grps_df1, grps_df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info1['group'] = grps_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info2 = df_info2.drop(columns=['group', 'groups']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "grps_df2.index = range(1479, 26360, 1)\n",
    "grps_df2;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info2['group'] = grps_df2\n",
    "# df_info2;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_TECRDB1 = df_info1[df_info1['group'].isin(groups1)]\n",
    "df_TECRDB2 = df_info2[df_info2['group'].isin(groups2)]\n",
    "\n",
    "df_TECRDB_all = pd.concat([df_TECRDB1, df_TECRDB2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,\"$\\\\Delta G^{o'}$\")"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_TECRDB_all = df_TECRDB_all.sort_values('coef')\n",
    "mean = df_TECRDB_all['coef']\n",
    "std  = df_TECRDB_all['cov']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax.errorbar(range(0,len(df_TECRDB_all.index)), mean, yerr=3*std, markersize='1',fmt='o', ecolor='burlywood', capthick=1)#,alpha=0.4)\n",
    "ax.axhline(y=0,linewidth=1,color='k')\n",
    "\n",
    "ax.axhline(y=270,linewidth=1,color='grey')\n",
    "ax.axhline(y=-270,linewidth=1,color='grey')\n",
    "ax.fill_between(range(0,len(df_TECRDB_all.index)), -270, 270, alpha=0.2, color='white')\n",
    "plt.xlabel('reaction')\n",
    "plt.ylabel('$\\Delta G^{o\\'}$')\n",
    "# ax.errorbar(prediction.reaction, mean, yerr=std, fmt='-o') #fmt=None to plot bars only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_TECRDB_all['coef'], bins=50)\n",
    "# plt.xscale('log')\n",
    "plt.xlabel('$\\Delta_g G^o$')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>coef</th>\n",
       "      <th>cov</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>-5.459416e-13</td>\n",
       "      <td>23.594649</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17822</th>\n",
       "      <td>-5.459416e-13</td>\n",
       "      <td>23.594649</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25109</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ccc(cc)C(N)=O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3843</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.805414</td>\n",
       "      <td>CC(=C)C(C)(C)CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13024</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@@H](O)C[NH2+]C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15077</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@H](O)C(=O)NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21118</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>c[C@@H](C)[C@@H](CC)[C@H](C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15083</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@H](O)C(=O)[C@H](C)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15220</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@H](O)[C@@H](C)OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21865</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>c[nH+]cn(c)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12278</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@@H](C)CCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25337</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>cccc(C)s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14433</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@H](C)N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24698</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ccc(I)c(c)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13988</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@@](C)(O)CCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24756</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ccc(O)c(c)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5016</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CC(C)(C)S[C@H](C)N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11192</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>COCC(=C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14413</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@H](C)C[C@H](C)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4634</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CC(=O)[C@@H](O)C(O)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>33.265883</td>\n",
       "      <td>C[C@H](N)S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24776</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ccc(OC)c(c)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22374</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>cc(C)cc(c)I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@H](N)C(C)(C)SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12957</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@@H](O)C(OC)OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>33.290443</td>\n",
       "      <td>C[N+](C)(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4968</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CC(C)(C)COP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13087</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[C@@H](O)[C@@H](O)CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>27.733442</td>\n",
       "      <td>C=C\\CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12014</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CSCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25400</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ccccs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20632</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>cN[C@@H](C)[C@H](C)N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>32.276005</td>\n",
       "      <td>C=C(C)[C@](C)(CC)[C@@H](C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8033</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CCC=C(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10394</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CNC(=O)CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25480</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ccnc(c)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25514</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ccsc(c)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>33.283000</td>\n",
       "      <td>CC(C)(C)S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9484</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CC[C@](C)(O)CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22972</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>cc([nH])N[C@@H](C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9786</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CCc(cc)sc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6234</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CC(N)C=O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9896</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CN(C)C(C(=O)[O-])=C(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22611</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>cc(N)n(c[nH+])[C@@H](C)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22696</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>cc(O)c(O)c(c)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6157</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CC(N)=C(CO)CS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>33.179985</td>\n",
       "      <td>c[C@@H](C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16397</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[N+](C)(C)CCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26255</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nc(N)[nH]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6013</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CC(C)[NH3+]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22613</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>cc(N)nc(C)n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16389</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C[N+](C)(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22612</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>cc(N)n(cn)[C@@H](C)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10378</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CNC(=O)C(C)[NH3+]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10029</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CN(C)[C@@H](C(=O)[O-])C(C)(C)S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25798</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>cnc(N)c(c)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>1.379678e-13</td>\n",
       "      <td>23.479002</td>\n",
       "      <td>[Fe]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17924</th>\n",
       "      <td>1.379678e-13</td>\n",
       "      <td>23.479002</td>\n",
       "      <td>[Fe]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17934</th>\n",
       "      <td>7.329150e-13</td>\n",
       "      <td>23.591897</td>\n",
       "      <td>[Mn]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>7.329150e-13</td>\n",
       "      <td>23.591897</td>\n",
       "      <td>[Mn]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               coef        cov                           group\n",
       "794   -5.459416e-13  23.594649                               S\n",
       "17822 -5.459416e-13  23.594649                               S\n",
       "25109  0.000000e+00   0.000000                   ccc(cc)C(N)=O\n",
       "3843   0.000000e+00   8.805414                 CC(=C)C(C)(C)CC\n",
       "13024  0.000000e+00   0.000000              C[C@@H](O)C[NH2+]C\n",
       "15077  0.000000e+00   0.000000                C[C@H](O)C(=O)NC\n",
       "21118  0.000000e+00   0.000000   c[C@@H](C)[C@@H](CC)[C@H](C)C\n",
       "15083  0.000000e+00   0.000000         C[C@H](O)C(=O)[C@H](C)O\n",
       "15220  0.000000e+00   0.000000            C[C@H](O)[C@@H](C)OC\n",
       "21865  0.000000e+00   0.000000                    c[nH+]cn(c)C\n",
       "12278  0.000000e+00   0.000000                   C[C@@H](C)CCC\n",
       "25337  0.000000e+00   0.000000                        cccc(C)s\n",
       "14433  0.000000e+00   0.000000                      C[C@H](C)N\n",
       "24698  0.000000e+00   0.000000                     ccc(I)c(c)O\n",
       "13988  0.000000e+00   0.000000                 C[C@@](C)(O)CCO\n",
       "24756  0.000000e+00   0.000000                     ccc(O)c(c)O\n",
       "5016   0.000000e+00   0.000000              CC(C)(C)S[C@H](C)N\n",
       "11192  0.000000e+00   0.000000                       COCC(=C)C\n",
       "14413  0.000000e+00   0.000000             C[C@H](C)C[C@H](C)O\n",
       "4634   0.000000e+00   0.000000            CC(=O)[C@@H](O)C(O)O\n",
       "473    0.000000e+00  33.265883                      C[C@H](N)S\n",
       "24776  0.000000e+00   0.000000                    ccc(OC)c(c)O\n",
       "22374  0.000000e+00   0.000000                     cc(C)cc(c)I\n",
       "14983  0.000000e+00   0.000000              C[C@H](N)C(C)(C)SC\n",
       "12957  0.000000e+00   0.000000               C[C@@H](O)C(OC)OC\n",
       "528    0.000000e+00  33.290443                    C[N+](C)(C)C\n",
       "4968   0.000000e+00   0.000000                     CC(C)(C)COP\n",
       "13087  0.000000e+00   0.000000           C[C@@H](O)[C@@H](O)CC\n",
       "3358   0.000000e+00  27.733442                          C=C\\CC\n",
       "12014  0.000000e+00   0.000000                            CSCC\n",
       "...             ...        ...                             ...\n",
       "25400  0.000000e+00   0.000000                           ccccs\n",
       "20632  0.000000e+00   0.000000            cN[C@@H](C)[C@H](C)N\n",
       "2349   0.000000e+00  32.276005     C=C(C)[C@](C)(CC)[C@@H](C)C\n",
       "8033   0.000000e+00   0.000000                       CCC=C(C)C\n",
       "10394  0.000000e+00   0.000000                       CNC(=O)CO\n",
       "25480  0.000000e+00   0.000000                        ccnc(c)C\n",
       "25514  0.000000e+00   0.000000                        ccsc(c)C\n",
       "162    0.000000e+00  33.283000                       CC(C)(C)S\n",
       "9484   0.000000e+00   0.000000                  CC[C@](C)(O)CC\n",
       "22972  0.000000e+00   0.000000             cc([nH])N[C@@H](C)C\n",
       "9786   0.000000e+00   0.000000                       CCc(cc)sc\n",
       "6234   0.000000e+00   0.000000                        CC(N)C=O\n",
       "9896   0.000000e+00   0.000000         CN(C)C(C(=O)[O-])=C(C)C\n",
       "22611  0.000000e+00   0.000000        cc(N)n(c[nH+])[C@@H](C)O\n",
       "22696  0.000000e+00   0.000000                  cc(O)c(O)c(c)O\n",
       "6157   0.000000e+00   0.000000                   CC(N)=C(CO)CS\n",
       "1147   0.000000e+00  33.179985                     c[C@@H](C)C\n",
       "16397  0.000000e+00   0.000000                  C[N+](C)(C)CCO\n",
       "26255  0.000000e+00   0.000000                       nc(N)[nH]\n",
       "6013   0.000000e+00   0.000000                     CC(C)[NH3+]\n",
       "22613  0.000000e+00   0.000000                     cc(N)nc(C)n\n",
       "16389  0.000000e+00   0.000000                    C[N+](C)(C)C\n",
       "22612  0.000000e+00   0.000000            cc(N)n(cn)[C@@H](C)O\n",
       "10378  0.000000e+00   0.000000               CNC(=O)C(C)[NH3+]\n",
       "10029  0.000000e+00   0.000000  CN(C)[C@@H](C(=O)[O-])C(C)(C)S\n",
       "25798  0.000000e+00   0.000000                     cnc(N)c(c)C\n",
       "861    1.379678e-13  23.479002                            [Fe]\n",
       "17924  1.379678e-13  23.479002                            [Fe]\n",
       "17934  7.329150e-13  23.591897                            [Mn]\n",
       "872    7.329150e-13  23.591897                            [Mn]\n",
       "\n",
       "[155 rows x 3 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_TECRDB_all[(df_TECRDB_all['coef']<1e-3) & (df_TECRDB_all['coef']>-1e-3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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>coef</th>\n",
       "      <th>cov</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>-226.531632</td>\n",
       "      <td>23.591848</td>\n",
       "      <td>[Mg+2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17931</th>\n",
       "      <td>-226.531632</td>\n",
       "      <td>23.591848</td>\n",
       "      <td>[Mg+2]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             coef        cov   group\n",
       "869   -226.531632  23.591848  [Mg+2]\n",
       "17931 -226.531632  23.591848  [Mg+2]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_TECRDB_all[(df_TECRDB_all['coef']<-225)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analyze groups contribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09299612677498964"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_rcombined.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha_val = regr_rcombined.lambda_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.29056126498055"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev_dg_group = np.sqrt(1/alpha_val)\n",
    "std_dev_dg_group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# analyze zero group change"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## component contribution reactions with zero group changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "319"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc = pd.read_csv('./data/rule_cc.csv',index_col=0)\n",
    "all_zeros = df_cc.loc[:, (df_cc == 0).all()]\n",
    "rxns_zeros = all_zeros.columns.tolist()\n",
    "len(rxns_zeros)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = pd.read_excel('./data/optstoic_v3_complete_table.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "dframe = db[db['reaction_id'].isin(rxns_zeros)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\new_env\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "first_digit = []\n",
    "\n",
    "for a in dframe['EC_number'].tolist():\n",
    "#     print(a)\n",
    "    if '.' in str(a):\n",
    "        first_digit.append(a[0])\n",
    "    else:\n",
    "        first_digit.append(None)\n",
    "# first_digit = pd.DataFrame(first_digit).reset_index()\n",
    "\n",
    "dframe['EC_first_digit'] = first_digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FACOAE120</th>\n",
       "      <th>R00004</th>\n",
       "      <th>R00005</th>\n",
       "      <th>R00006</th>\n",
       "      <th>R00008</th>\n",
       "      <th>R00009</th>\n",
       "      <th>R00010</th>\n",
       "      <th>R00011</th>\n",
       "      <th>R00012</th>\n",
       "      <th>R00013</th>\n",
       "      <th>...</th>\n",
       "      <th>R11107</th>\n",
       "      <th>R11113</th>\n",
       "      <th>R11114</th>\n",
       "      <th>R11115</th>\n",
       "      <th>R11116</th>\n",
       "      <th>R11117</th>\n",
       "      <th>R11118</th>\n",
       "      <th>R11119</th>\n",
       "      <th>R11124</th>\n",
       "      <th>R11125</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BO</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>BrC(Br)Br</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "<p>5 rows × 7053 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           FACOAE120  R00004  R00005  R00006  R00008  R00009  R00010  R00011  \\\n",
       "BO               0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "Bc(c)c           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "BrC(Br)Br        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "C                0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "C#C              0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "           R00012  R00013  ...  R11107  R11113  R11114  R11115  R11116  \\\n",
       "BO            0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "Bc(c)c        0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "BrC(Br)Br     0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "C             0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "C#C           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "           R11117  R11118  R11119  R11124  R11125  \n",
       "BO            0.0     0.0     0.0     0.0     0.0  \n",
       "Bc(c)c        0.0     0.0     0.0     0.0     0.0  \n",
       "BrC(Br)Br     0.0     0.0     0.0     0.0     0.0  \n",
       "C             0.0     0.0     0.0     0.0     0.0  \n",
       "C#C           0.0     0.0     0.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 7053 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ac = df_all.loc[:, (df_all == 0).all()]\n",
    "rxns_zeros_ac = all_ac.columns.tolist()\n",
    "len(rxns_zeros_ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all1 = dframe[~dframe['reaction_id'].isin(rxns_zeros_ac)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(277, 28)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "319-277"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\new_env\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "tmp = []\n",
    "for i,r in enumerate(dframe['reaction_id'].tolist()):\n",
    "    if r in rxns_zeros_ac:\n",
    "        tmp.append(None)\n",
    "    else:\n",
    "        tmp.append(first_digit[i])\n",
    "\n",
    "dframe['EC_first_digit_dGPredictor'] =tmp "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x54ba3e08>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dframe[['EC_first_digit','EC_first_digit_dGPredictor']].apply(pd.Series.value_counts).plot(kind=\"bar\")\n",
    "ax.legend([\"component contribution\", \"resolved by dGPredictor\"], loc='best',prop={'size': 9})#,bbox_to_anchor=(1.0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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>EC_first_digit</th>\n",
       "      <th>EC_first_digit_dGPredictor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>146</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>126</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EC_first_digit  EC_first_digit_dGPredictor\n",
       "1              13                        11.0\n",
       "2             146                       128.0\n",
       "3               1                         NaN\n",
       "4               2                         NaN\n",
       "5             126                       110.0"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe[['EC_first_digit','EC_first_digit_dGPredictor']].apply(pd.Series.value_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "13+146+1+2+126"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 + 54+93"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "319-288"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "dframe.to_csv('./result/analyze_zero_group_change.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all2 = dframe[dframe['reaction_id'].isin(rxns_zeros_ac)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "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>reaction_id</th>\n",
       "      <th>description</th>\n",
       "      <th>EC_number</th>\n",
       "      <th>reaction_elemental_balance</th>\n",
       "      <th>new_reaction_enter_db</th>\n",
       "      <th>reaction_equation</th>\n",
       "      <th>reaction</th>\n",
       "      <th>reaction_formula</th>\n",
       "      <th>old_database_dG0prime (kcal/mol)</th>\n",
       "      <th>old_database_reaction_type</th>\n",
       "      <th>...</th>\n",
       "      <th>dG_cannot_estimate_by_component_contribution</th>\n",
       "      <th>pH_7.0_dG_prime_lo (kJ/mol)</th>\n",
       "      <th>pH_7.0_dG_prime_up (kJ/mol)</th>\n",
       "      <th>rxntype_curated</th>\n",
       "      <th>rxntype_from_dG</th>\n",
       "      <th>reaction_type</th>\n",
       "      <th>lower_bound (mmol/gDW h)</th>\n",
       "      <th>upper_bound (mmol/gDW h)</th>\n",
       "      <th>EC_first_digit</th>\n",
       "      <th>EC_first_digit_dGPredictor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>R00062</td>\n",
       "      <td>bilirubin-glucuronoside:bilirubin-glucuronosid...</td>\n",
       "      <td>2.4.1.95</td>\n",
       "      <td>All balanced</td>\n",
       "      <td>1</td>\n",
       "      <td>2 Bilirubin-glucuronoside &lt;=&gt; Bilirubin + Bili...</td>\n",
       "      <td>2 C03374 &lt;=&gt; C00486 + C05787</td>\n",
       "      <td>2 C39H44N4O12 &lt;=&gt; C33H36N4O6 + C45H52N4O18</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>R00112</td>\n",
       "      <td>NADPH:NAD+ oxidoreductase</td>\n",
       "      <td>1.6.1.1;1.6.1.2;1.6.1.3</td>\n",
       "      <td>All balanced</td>\n",
       "      <td>1</td>\n",
       "      <td>NADPH + NAD+ &lt;=&gt; NADP+ + NADH</td>\n",
       "      <td>C00005 + C00003 &lt;=&gt; C00006 + C00004</td>\n",
       "      <td>C21H30N7O17P3 + C21H28N7O14P2 &lt;=&gt; C21H29N7O17P...</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-58.056398</td>\n",
       "      <td>56.097440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>R00139</td>\n",
       "      <td>ATP:2'-deoxy-5-hydroxymethylcytidine-5'-diphos...</td>\n",
       "      <td>2.7.4.6</td>\n",
       "      <td>All balanced</td>\n",
       "      <td>1</td>\n",
       "      <td>2'-Deoxy-5-hydroxymethylcytidine-5'-diphosphat...</td>\n",
       "      <td>C11038 + C00002 &lt;=&gt; C11039 + C00008</td>\n",
       "      <td>C10H17N3O11P2 + C10H16N5O13P3 &lt;=&gt; C10H18N3O14P...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-54.412978</td>\n",
       "      <td>59.740860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>R00156</td>\n",
       "      <td>ATP:UDP phosphotransferase</td>\n",
       "      <td>2.7.4.6</td>\n",
       "      <td>All balanced</td>\n",
       "      <td>1</td>\n",
       "      <td>ATP + UDP &lt;=&gt; ADP + UTP</td>\n",
       "      <td>C00002 + C00015 &lt;=&gt; C00008 + C00075</td>\n",
       "      <td>C10H16N5O13P3 + C9H14N2O12P2 &lt;=&gt; C10H15N5O10P2...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-59.423639</td>\n",
       "      <td>54.730200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>R00330</td>\n",
       "      <td>ATP:GDP phosphotransferase</td>\n",
       "      <td>2.7.4.6</td>\n",
       "      <td>All balanced</td>\n",
       "      <td>1</td>\n",
       "      <td>ATP + GDP &lt;=&gt; ADP + GTP</td>\n",
       "      <td>C00002 + C00035 &lt;=&gt; C00008 + C00044</td>\n",
       "      <td>C10H16N5O13P3 + C10H15N5O11P2 &lt;=&gt; C10H15N5O10P...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-59.548720</td>\n",
       "      <td>54.605118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    reaction_id                                        description  \\\n",
       "39       R00062  bilirubin-glucuronoside:bilirubin-glucuronosid...   \n",
       "71       R00112                          NADPH:NAD+ oxidoreductase   \n",
       "91       R00139  ATP:2'-deoxy-5-hydroxymethylcytidine-5'-diphos...   \n",
       "99       R00156                         ATP:UDP phosphotransferase   \n",
       "217      R00330                         ATP:GDP phosphotransferase   \n",
       "\n",
       "                   EC_number reaction_elemental_balance  \\\n",
       "39                  2.4.1.95               All balanced   \n",
       "71   1.6.1.1;1.6.1.2;1.6.1.3               All balanced   \n",
       "91                   2.7.4.6               All balanced   \n",
       "99                   2.7.4.6               All balanced   \n",
       "217                  2.7.4.6               All balanced   \n",
       "\n",
       "     new_reaction_enter_db                                  reaction_equation  \\\n",
       "39                       1  2 Bilirubin-glucuronoside <=> Bilirubin + Bili...   \n",
       "71                       1                      NADPH + NAD+ <=> NADP+ + NADH   \n",
       "91                       1  2'-Deoxy-5-hydroxymethylcytidine-5'-diphosphat...   \n",
       "99                       1                            ATP + UDP <=> ADP + UTP   \n",
       "217                      1                            ATP + GDP <=> ADP + GTP   \n",
       "\n",
       "                                reaction  \\\n",
       "39          2 C03374 <=> C00486 + C05787   \n",
       "71   C00005 + C00003 <=> C00006 + C00004   \n",
       "91   C11038 + C00002 <=> C11039 + C00008   \n",
       "99   C00002 + C00015 <=> C00008 + C00075   \n",
       "217  C00002 + C00035 <=> C00008 + C00044   \n",
       "\n",
       "                                      reaction_formula  \\\n",
       "39          2 C39H44N4O12 <=> C33H36N4O6 + C45H52N4O18   \n",
       "71   C21H30N7O17P3 + C21H28N7O14P2 <=> C21H29N7O17P...   \n",
       "91   C10H17N3O11P2 + C10H16N5O13P3 <=> C10H18N3O14P...   \n",
       "99   C10H16N5O13P3 + C9H14N2O12P2 <=> C10H15N5O10P2...   \n",
       "217  C10H16N5O13P3 + C10H15N5O11P2 <=> C10H15N5O10P...   \n",
       "\n",
       "     old_database_dG0prime (kcal/mol)  old_database_reaction_type  ...  \\\n",
       "39                               -0.1                         1.0  ...   \n",
       "71                                1.7                         1.0  ...   \n",
       "91                                0.0                         1.0  ...   \n",
       "99                                0.0                         1.0  ...   \n",
       "217                               0.0                         1.0  ...   \n",
       "\n",
       "    dG_cannot_estimate_by_component_contribution pH_7.0_dG_prime_lo (kJ/mol)  \\\n",
       "39                                             1                    0.000000   \n",
       "71                                             0                  -58.056398   \n",
       "91                                             0                  -54.412978   \n",
       "99                                             0                  -59.423639   \n",
       "217                                            0                  -59.548720   \n",
       "\n",
       "    pH_7.0_dG_prime_up (kJ/mol) rxntype_curated  rxntype_from_dG  \\\n",
       "39                     0.000000             NaN                1   \n",
       "71                    56.097440             NaN                1   \n",
       "91                    59.740860             NaN                1   \n",
       "99                    54.730200             NaN                1   \n",
       "217                   54.605118             NaN                1   \n",
       "\n",
       "     reaction_type  lower_bound (mmol/gDW h)  upper_bound (mmol/gDW h)  \\\n",
       "39               1                     -1000                      1000   \n",
       "71               1                     -1000                      1000   \n",
       "91               1                     -1000                      1000   \n",
       "99               1                     -1000                      1000   \n",
       "217              1                     -1000                      1000   \n",
       "\n",
       "     EC_first_digit  EC_first_digit_dGPredictor  \n",
       "39                2                        None  \n",
       "71                1                        None  \n",
       "91                2                        None  \n",
       "99                2                        None  \n",
       "217               2                        None  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ymean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "zero_dg_rxn = np.where(ymean == 0)\n",
    "zero_dg_rxn = list(zero_dg_rxn)\n",
    "zero_dg_rxn = zero_dg_rxn[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "rxn_zero = []\n",
    "for i in zero_dg_rxn:\n",
    "    temp = i\n",
    "    rxn_zero.append(rxns[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R01857',\n",
       " 'R03181',\n",
       " 'R00722',\n",
       " 'R04538',\n",
       " 'R00576',\n",
       " 'R03154',\n",
       " 'R00119',\n",
       " 'R01109',\n",
       " 'R07832']"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rxn_zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "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>reaction_id</th>\n",
       "      <th>EC_first_digit</th>\n",
       "      <th>EC_first_digit_dGPredictor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>R00062</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>R00112</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>R00139</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>R00156</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>R00330</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>R00363</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>R00570</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>R00616</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>R00647</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>R00722</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>R01137</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>R01378</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>R01571</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>R01857</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>R01989</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>R02093</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>R02303</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>R02326</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1561</th>\n",
       "      <td>R02331</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2083</th>\n",
       "      <td>R03128</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>R03181</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>R03342</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>R03530</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>R03868</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2546</th>\n",
       "      <td>R03892</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>R03925</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2586</th>\n",
       "      <td>R03966</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2673</th>\n",
       "      <td>R04134</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2810</th>\n",
       "      <td>R04379</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2860</th>\n",
       "      <td>R04482</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2888</th>\n",
       "      <td>R04538</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2945</th>\n",
       "      <td>R04686</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3364</th>\n",
       "      <td>R05389</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4465</th>\n",
       "      <td>R07248</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4545</th>\n",
       "      <td>R07393</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4817</th>\n",
       "      <td>R07839</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5308</th>\n",
       "      <td>R08591</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6004</th>\n",
       "      <td>R09641</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6048</th>\n",
       "      <td>R09705</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6876</th>\n",
       "      <td>R10774</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7043</th>\n",
       "      <td>R11034</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7067</th>\n",
       "      <td>R11065</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     reaction_id EC_first_digit EC_first_digit_dGPredictor\n",
       "39        R00062              2                       None\n",
       "71        R00112              1                       None\n",
       "91        R00139              2                       None\n",
       "99        R00156              2                       None\n",
       "217       R00330              2                       None\n",
       "244       R00363              5                       None\n",
       "373       R00570              2                       None\n",
       "399       R00616              2                       None\n",
       "407       R00647              1                       None\n",
       "473       R00722              2                       None\n",
       "781       R01137              2                       None\n",
       "946       R01378              5                       None\n",
       "1077      R01571              2                       None\n",
       "1259      R01857              2                       None\n",
       "1360      R01989              2                       None\n",
       "1407      R02093              2                       None\n",
       "1542      R02303              2                       None\n",
       "1556      R02326              2                       None\n",
       "1561      R02331              2                       None\n",
       "2083      R03128           None                       None\n",
       "2110      R03181              5                       None\n",
       "2223      R03342              5                       None\n",
       "2327      R03530              2                       None\n",
       "2529      R03868              5                       None\n",
       "2546      R03892           None                       None\n",
       "2560      R03925              5                       None\n",
       "2586      R03966              5                       None\n",
       "2673      R04134              5                       None\n",
       "2810      R04379              5                       None\n",
       "2860      R04482              5                       None\n",
       "2888      R04538              5                       None\n",
       "2945      R04686              5                       None\n",
       "3364      R05389              5                       None\n",
       "4465      R07248              2                       None\n",
       "4545      R07393              3                       None\n",
       "4817      R07839              5                       None\n",
       "5308      R08591           None                       None\n",
       "6004      R09641              2                       None\n",
       "6048      R09705              5                       None\n",
       "6876      R10774              4                       None\n",
       "7043      R11034              4                       None\n",
       "7067      R11065              5                       None"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all2[['reaction_id', 'EC_first_digit','EC_first_digit_dGPredictor']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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