step1.2_submit_10-fold_blr_models.ipynb

{
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   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import gaussian_kde\n",
    "import pcntoolkit as ptk\n",
    "from pcntoolkit.normative import estimate, predict, evaluate\n",
    "from pcntoolkit.util.utils import compute_MSLL, create_design_matrix\n",
    "from pcntoolkit.util.utils import calibration_descriptives\n",
    "from pcntoolkit.model.hbr import bspline_fit, bspline_transform\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6c06aff",
   "metadata": {
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   "outputs": [],
   "source": [
    "projdir = '/project_cephfs/3022017.02/projects/stijdboe/make_results/10_folds_results/'\n",
    "textfiles = os.path.join(projdir,'textfiles')\n",
    "if not os.path.exists(textfiles):\n",
    "    os.mkdir(textfiles)\n",
    "data_dir = '/project_cephfs/3022017.02/projects/stijdboe/Data'\n",
    "folds_dir = os.path.join(data_dir,'10_folds_sexcov')\n",
    "python_path = '/project_cephfs/3022017.02/projects/stijdboe/stijn2/bin/python3.10'\n",
    "normative_path = '/project_cephfs/3022017.02/projects/stijdboe/PCNtoolkit/pcntoolkit/normative.py'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc4e0196",
   "metadata": {
    "id": "896842d7-8913-4137-9d86-4757c42bcf1b"
   },
   "source": [
    "### Fit the models\n",
    "\n",
    "Now we fit the models. This involves looping over the IDPs we have selected. We will use a module from PCNtoolkit to set up the design matrices, containing the covariates, fixed effects for site and nonlinear basis expansion. "
   ]
  },
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     "text": [
      "       site_id\n",
      "0           73\n",
      "2           73\n",
      "3           73\n",
      "4           73\n",
      "5           73\n",
      "...        ...\n",
      "29409       58\n",
      "29410       58\n",
      "29411       58\n",
      "29412       58\n",
      "29414       58\n",
      "\n",
      "[51900 rows x 1 columns]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
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   "source": [
    "warp =  'WarpSinArcsinh'\n",
    "# For each fold\n",
    "for i in range(10):\n",
    "\n",
    "    this_identifier = f\"fold_{i}_blr\"\n",
    "    fold_dir = os.path.join(folds_dir,f'fold_{i}')\n",
    "    processing_dir = os.path.join(projdir, this_identifier+'/')\n",
    "    if not os.path.exists(processing_dir):\n",
    "        os.mkdir(processing_dir)\n",
    "\n",
    "    log_dir = os.path.join(processing_dir, 'log')           #\n",
    "    if not os.path.isdir(log_dir):\n",
    "        os.mkdir(log_dir)\n",
    "\n",
    "    # The paths to the data\n",
    "    X_tr_path = os.path.join(fold_dir, 'X_train.pkl')\n",
    "    Y_tr_path = os.path.join(fold_dir, 'Y_train.pkl')\n",
    "    Z_tr_path = os.path.join(fold_dir, 'Z_train.pkl')\n",
    "    \n",
    "    X_te_path = os.path.join(fold_dir, 'X_test.pkl')\n",
    "    Y_te_path = os.path.join(fold_dir, 'Y_test.pkl')\n",
    "    Z_te_path = os.path.join(fold_dir, 'Z_test.pkl')\n",
    "    \n",
    "    with open(X_tr_path, 'rb') as file:\n",
    "        X_tr = pickle.load(file)\n",
    "    with open(Z_tr_path, 'rb') as file:\n",
    "        Z_tr = pickle.load(file)\n",
    "    with open(X_te_path, 'rb') as file:\n",
    "        X_te = pickle.load(file)\n",
    "    with open(Z_te_path, 'rb') as file:\n",
    "        Z_te = pickle.load(file)\n",
    "    print(Z_tr)\n",
    "    \n",
    "    \n",
    "    \n",
    "    Phi_tr = create_design_matrix(X_tr, site_ids = Z_tr['site_id'].to_numpy(), basis='bspline')\n",
    "    Phi_cov_tr = create_design_matrix(X_tr, basis='bspline')\n",
    "    Phi_te = create_design_matrix(X_te, site_ids = Z_te['site_id'].to_numpy(), basis='bspline')\n",
    "    Phi_cov_te = create_design_matrix(X_te, basis='bspline')\n",
    "\n",
    "    # Save as text files \n",
    "    Phi_tr_path = os.path.join(textfiles, f'Phi_tr_{i}.txt' )\n",
    "    Phi_te_path = os.path.join(textfiles, f'Phi_te_{i}.txt' )\n",
    "    np.savetxt(Phi_tr_path, Phi_tr)\n",
    "    np.savetxt(Phi_te_path, Phi_te)\n",
    "    \n",
    "    Phi_cov_tr_path = os.path.join(textfiles, f'Phi_cov_tr_{i}.txt' )\n",
    "    Phi_cov_te_path = os.path.join(textfiles, f'Phi_cov_te_{i}.txt' )\n",
    "    np.savetxt(Phi_cov_tr_path, Phi_cov_tr)\n",
    "    np.savetxt(Phi_cov_te_path, Phi_cov_te)\n",
    "    \n",
    "    \n",
    "    # Load response variables\n",
    "    with open(Y_tr_path, 'rb') as file:\n",
    "        Y_tr = pickle.load(file)\n",
    "    with open(Y_te_path, 'rb') as file:\n",
    "        Y_te = pickle.load(file)\n",
    "    \n",
    "    # Loop over features\n",
    "    for iff, feature in enumerate(Y_tr.columns):\n",
    "        batch_dir = os.path.join(processing_dir, f'batch_{iff+1}')\n",
    "        if not os.path.exists(batch_dir):\n",
    "            os.mkdir(batch_dir)\n",
    "        os.chdir(batch_dir)\n",
    "        \n",
    "        F_tr = Y_tr[feature].to_numpy()\n",
    "        F_tr_mean = np.mean(F_tr, axis = 0)\n",
    "        F_tr_sd = np.std(F_tr, axis = 0)\n",
    "        F_tr = (F_tr-F_tr_mean)/F_tr_sd\n",
    "        F_te = Y_te[feature].to_numpy()\n",
    "        F_te = (F_te-F_tr_mean)/F_tr_sd\n",
    "        \n",
    "        # Save as text files \n",
    "        Y_feature_tr_path = os.path.join(textfiles, f'Y_{feature}_tr_{i}.txt' )\n",
    "        Y_feature_te_path = os.path.join(textfiles, f'Y_{feature}_te_{i}.txt' )\n",
    "        np.savetxt(Y_feature_tr_path, F_tr)\n",
    "        np.savetxt(Y_feature_te_path, F_te)\n",
    "        \n",
    "        # print(np.sum(np.isinf(Phi_cov_te)))\n",
    "\n",
    "#         # Fit the model\n",
    "        estimate(\n",
    "                 Phi_tr_path,  \n",
    "                 Y_feature_tr_path,\n",
    "                 testcov=Phi_te_path, \n",
    "                 testresp=Y_feature_te_path,\n",
    "                 alg='blr', optimizer = 'powell', \n",
    "                 savemodel=True, warp=warp, warp_reparam=True)\n",
    "        # Fit the model\n",
    "        # estimate(\n",
    "        #          Phi_tr_path,  \n",
    "        #          Y_feature_tr_path,\n",
    "        #          testcov=Phi_te_path, \n",
    "        #          testresp=Y_feature_te_path,\n",
    "        #          alg='blr', optimizer = 'powell',\n",
    "        #          savemodel=True)\n",
    "\n",
    "\n"
   ]
  },
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   "execution_count": null,
   "id": "ff9afa3b",
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   "source": [
    "\n",
    "for idp_num, idp in enumerate(idp_ids): \n",
    "    print('Running IDP', idp_num, idp, ':')\n",
    "   \n",
    "    # set output dir \n",
    "    idp_dir = os.path.join(out_dir, idp)\n",
    "    os.makedirs(os.path.join(idp_dir), exist_ok=True)\n",
    "    os.chdir(idp_dir)\n",
    "    \n",
    "    # extract the response variables for training and test set\n",
    "    y_tr = df_tr[idp].to_numpy() \n",
    "    y_te = df_te[idp].to_numpy()\n",
    "    \n",
    "    # remove gross outliers and implausible values\n",
    "    yz_tr = (y_tr - np.mean(y_tr)) / np.std(y_tr)\n",
    "    yz_te = (y_te - np.mean(y_te)) / np.std(y_te)\n",
    "    nz_tr = np.bitwise_and(np.abs(yz_tr) < outlier_thresh, y_tr > 0)\n",
    "    nz_te = np.bitwise_and(np.abs(yz_te) < outlier_thresh, y_te > 0)\n",
    "    y_tr = y_tr[nz_tr]\n",
    "    y_te = y_te[nz_te]\n",
    "    \n",
    "    # write out the response variables for training and test\n",
    "    resp_file_tr = os.path.join(idp_dir, 'resp_tr.txt')\n",
    "    resp_file_te = os.path.join(idp_dir, 'resp_te.txt') \n",
    "    np.savetxt(resp_file_tr, y_tr)\n",
    "    np.savetxt(resp_file_te, y_te)\n",
    "        \n",
    "    # configure the design matrix\n",
    "    X_tr = create_design_matrix(df_tr[cols_cov].loc[nz_tr], \n",
    "                                site_ids = df_tr['site'].loc[nz_tr],\n",
    "                                basis = 'bspline', \n",
    "                                xmin = xmin, \n",
    "                                xmax = xmax)\n",
    "    X_te = create_design_matrix(df_te[cols_cov].loc[nz_te], \n",
    "                                site_ids = df_te['site'].loc[nz_te], \n",
    "                                all_sites=site_ids,\n",
    "                                basis = 'bspline', \n",
    "                                xmin = xmin, \n",
    "                                xmax = xmax)\n",
    "\n",
    "    # configure and save the covariates\n",
    "    cov_file_tr = os.path.join(idp_dir, 'cov_bspline_tr.txt')\n",
    "    cov_file_te = os.path.join(idp_dir, 'cov_bspline_te.txt')\n",
    "    np.savetxt(cov_file_tr, X_tr)\n",
    "    np.savetxt(cov_file_te, X_te)\n",
    "\n",
    "    if not force_refit and os.path.exists(os.path.join(idp_dir, 'Models', 'NM_0_0_estimate.pkl')):\n",
    "        print('Making predictions using a pre-existing model...')\n",
    "        suffix = 'predict'\n",
    "        \n",
    "        # Make prdictsion with test data\n",
    "        predict(cov_file_te, \n",
    "                alg='blr', \n",
    "                respfile=resp_file_te, \n",
    "                model_path=os.path.join(idp_dir,'Models'),\n",
    "                outputsuffix=suffix)\n",
    "    else:\n",
    "        print('Estimating the normative model...')\n",
    "        estimate(cov_file_tr, resp_file_tr, testresp=resp_file_te, \n",
    "                 testcov=cov_file_te, alg='blr', optimizer = 'l-bfgs-b', \n",
    "                 savemodel=True, warp=warp, warp_reparam=True)\n",
    "        suffix = 'estimate'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd5803e",
   "metadata": {
    "id": "925f77cf-c873-4047-91ac-50b9571704fd"
   },
   "source": [
    "### Compute error metrics\n",
    "\n",
    "In this section we compute the following error metrics for all IDPs (all evaluated on the test set):\n",
    "\n",
    "- Negative log likelihood (NLL)\n",
    "- Explained variance (EV)\n",
    "- Mean standardized log loss (MSLL)\n",
    "- Bayesian information Criteria (BIC)\n",
    "- Skew and Kurtosis of the Z-distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c674ed7",
   "metadata": {
    "collapsed": true,
    "id": "2e9d7500-4f46-4ee1-9756-81758ae5b1d1",
    "jupyter": {
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   "outputs": [],
   "source": [
    "# initialise dataframe we will use to store quantitative metrics \n",
    "blr_metrics = pd.DataFrame(columns = ['eid', 'NLL', 'EV', 'MSLL', 'BIC','Skew','Kurtosis'])\n",
    "\n",
    "for idp_num, idp in enumerate(idp_ids): \n",
    "    idp_dir = os.path.join(out_dir, idp)\n",
    "    \n",
    "    # load the predictions and true data. We use a custom function that ensures 2d arrays\n",
    "    # equivalent to: y = np.loadtxt(filename); y = y[:, np.newaxis]\n",
    "    yhat_te = load_2d(os.path.join(idp_dir, 'yhat_' + suffix + '.txt'))\n",
    "    s2_te = load_2d(os.path.join(idp_dir, 'ys2_' + suffix + '.txt'))\n",
    "    y_te = load_2d(os.path.join(idp_dir, 'resp_te.txt'))\n",
    "    \n",
    "    with open(os.path.join(idp_dir,'Models', 'NM_0_0_estimate.pkl'), 'rb') as handle:\n",
    "        nm = pickle.load(handle) \n",
    "    \n",
    "    # compute error metrics\n",
    "    if warp is None:\n",
    "        metrics = evaluate(y_te, yhat_te)  \n",
    "        \n",
    "        # compute MSLL manually as a sanity check\n",
    "        y_tr_mean = np.array( [[np.mean(y_tr)]] )\n",
    "        y_tr_var = np.array( [[np.var(y_tr)]] )\n",
    "        MSLL = compute_MSLL(y_te, yhat_te, s2_te, y_tr_mean, y_tr_var)         \n",
    "    else:\n",
    "        warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1] \n",
    "        W = nm.blr.warp\n",
    "        \n",
    "        # warp predictions\n",
    "        med_te = W.warp_predictions(np.squeeze(yhat_te), np.squeeze(s2_te), warp_param)[0]\n",
    "        med_te = med_te[:, np.newaxis]\n",
    "       \n",
    "        # evaluation metrics\n",
    "        metrics = evaluate(y_te, med_te)\n",
    "        \n",
    "        # compute MSLL manually\n",
    "        y_te_w = W.f(y_te, warp_param)\n",
    "        y_tr_w = W.f(y_tr, warp_param)\n",
    "        y_tr_mean = np.array( [[np.mean(y_tr_w)]] )\n",
    "        y_tr_var = np.array( [[np.var(y_tr_w)]] )\n",
    "        MSLL = compute_MSLL(y_te_w, yhat_te, s2_te, y_tr_mean, y_tr_var)     \n",
    "    \n",
    "    Z = np.loadtxt(os.path.join(idp_dir, 'Z_' + suffix + '.txt'))\n",
    "    [skew, sdskew, kurtosis, sdkurtosis, semean, sesd] = calibration_descriptives(Z)\n",
    "    \n",
    "    BIC = len(nm.blr.hyp) * np.log(y_tr.shape[0]) + 2 * nm.neg_log_lik\n",
    "    \n",
    "    blr_metrics.loc[len(blr_metrics)] = [idp, nm.neg_log_lik, metrics['EXPV'][0], \n",
    "                                         MSLL[0], BIC, skew, kurtosis]\n",
    "    \n",
    "display(blr_metrics)\n",
    "\n",
    "blr_metrics.to_csv(os.path.join(out_dir,'blr_metrics.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2029a2d3",
   "metadata": {
    "collapsed": true,
    "id": "NCpzbIwGxVWj",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "blr_metrics['EV'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c1c56a4",
   "metadata": {
    "collapsed": true,
    "id": "Et7L-t9RJl75",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "blr_metrics['MSLL'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dab97bb",
   "metadata": {
    "collapsed": true,
    "id": "s25LW4QuJqfW",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "blr_metrics['EV'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d23544c4",
   "metadata": {
    "id": "mBhUMsojJu5J"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'blr_metrics' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [30]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mblr_metrics\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSLL\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mhist()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'blr_metrics' is not defined"
     ]
    }
   ],
   "source": [
    "blr_metrics['MSLL'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d66cde6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-8.94787519 -8.43212233 -7.97344021 ... -9.41712842 -9.20172603\n",
      " -7.80355596]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe435160af0>]"
      ]
     },
     "execution_count": 9,
     "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": [
    "# with open('/project_cephfs/3022017.02/projects/stijdboe/make_results/10_folds_results/fold_0_blr/batch_1/Z_estimate.txt') as file:\n",
    "z = np.loadtxt('/project_cephfs/3022017.02/projects/stijdboe/make_results/10_folds_results/fold_0_blr_sexcov/batch_5/Z_estimate.txt')\n",
    "print(z)\n",
    "plt.scatter(np.sort(z), np.sort(np.random.randn(*z.shape)))\n",
    "plt.plot([-4,4],[-4,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "37dd3ef4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.96 0.02 0.15\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.9 -0.07 0.04\n"
     ]
    },
    {
     "data": {
      "image/png": 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f3txdyV9WF/PZRYUsO3Nc2Nc/r2AwF04dxv3/3sfV8/PJGZQU9m2YyGI9UsZEoY5B321ePx6XsK28nu3ldfj8fn7+8i5+/doeLpwyjCc+vzBsIQogKzWR2fnZJHpc1DS1kehx2UBzEzXavH6+88xWCnPS+OZFE/ttO9+8aBLN7T5+/c/d/bYNEzmsR8qYKBQ66HtnRT1ZKYnUNrXynWe3U17bzDXzR/OJuXmkJHbv8u2eCIQpC04m+jyzsYzio038+TOnkpwQ/v8bHcblDuLa+fk88u5BPrOwgLG5g/ptW8Z51iNlTBSqb24nJfiHoKHVR3ltM/e+vo+qhla+c8kUrjk1n2MtXoerNCZy+P3K79/Yx+QRGXxkYm6/b+/L504gyePipy/u7PdtGWdZkDImCoUO+t5WXstvX99LksfFl88Zz6kFg20AuDGdrNpbxe4jDSw7sxCR/p/jKTc9iVvOGseL2ypYvbeq37dnnGNBypgoVJiTRkVdM3ev2M4T68oYmpHEx+eOYtboLJravDS1eXs9H44xsejp9WVkJHtYMm3EgG3zpjPGMmZIKnc9vZWWTle7mthhQcqYKFTT1MY9/9rLu/urOX3sYD59ej6DkjzUNrfH5QBwEblIRHaKyB4RuaOL9y8Tkc0islFEikRkcch7xSKypeO9ga3cDISmNi8vbavg4ukj+nVsVGcpiW5+eMV09lc12sDzGGaDzY2JMjsq6rnhj2uoaWrjq+dN4JxJw4DAH4uOEBVPRMQN3AucD5QCa0XkOVXdHrLYP4HnVFVFZAbwODAp5P2zVdXOv8SoV987QmObj8tnjxrwbS8an8Mn5ubxuzf2ccmMkUwZ2fP7W5rIZj1SxkSRF7ce4mO/XU2b18+Prpj+foiCwLxO9c3tDlbnmPnAHlXdp6ptwKPAZaELqGqDqmrwaRqgmLjx6vbD5AxKZH7BYEe2f9fSyWSnJnDHU5vx+e1XL9ZYkDImCvj9yi9f2cUtD63nlGHp/PyTMxk9OPUDy8TxAPNRQEnI89Lgax8gIleIyA5gBfDZkLcUeFlE1onIsq42ICLLgqcEiyorK8NYuulvXp+f13ce4eyJQ3E5dCPhrNRE/uvSqWwurePPq2zG81hjQcqYCNfY6uULD6/j//65myvn5PG76+eQ4Bbe3XeUTSU11De3xfsA867+On7oY7+qPq2qk4DLgR+EvLVIVecAS4BbReTMLtouV9V5qjovN7f/L5034bPuQA31LV7OnTzU0ToumTGCs07J5f/+uZtauzdlTLEgZUwEO3i0iY/9djWvbD/Mdy6ZwpfOGcfzmw7x3qF6MlM8NLX5WHegmlavL+4GmIcoBUaHPM8Dyo+3sKq+AYwTkZzg8/Lg9yPA0wROFZoY8drOIyS4hcUTnA3AIsK3L55EQ6uX+17f62gtJrwsSBkToVbvqeKj977FobpmvnPJFNKT3PzsxZ3sqKinqdWH1w8JbmHqyCzSkxPiNUQBrAUmiEihiCQCVwPPhS4gIuMlOHmQiMwBEoGjIpImIunB19OAC4CtA1q96Ver9lQxJz+bQUnOX1s1aXgGl88axQNvH7BeqRhiQcqYCKOq/HV1Mdf/aQ2DUxO58+JJ5GensqOinhavj3avj8Y2LxV1zfgVqhtb43WQOQCq6gVuA14C3gMeV9VtInKLiNwSXOxKYKuIbCRwhd9VwcHnw4C3RGQTsAZYoaovDvhOmH5R29TGtvJ6Fo3PcbqU9y07cyzN7T4efveg06WYMHE+ohtj3tfq9fHdZ7bxWFEJ500eyo2LCvC4XJRUN+FXIWdQEq1eP42tXoakJVHf3I7X72f8sHSnS3eUqq4EVnZ67f6Qxz8BftJFu33AzH4v0Dji7b1HUYWF44Y4Xcr7Jo/I4IwJOfxldTHLzhxLgtv6M6Kd/QsaEyEqj7Vy7e/f5bGiEm47ezzLr59HeW0zb+6qZMWWcg5WN+BXRVU51uLF4xaqGltxu1zxOsjcmBNavfcoqYluZo7OcrqUD7jh9AIqj7Xy7512BWgssCBlTATYUlrHR+95i23lddxz7Wz+48KJlNQ0sa64moaWdoamJ+ERYe+RY+ASUhNdVB1rIcHt4vwpw+J5fJQxx/Xu/qPMKxgccb0+H5mYy5C0RP6+rtTpUkwY2Kk9Yxz27MYyvvn3zeQMSuLJLyxkUJKHx9Yc5PlNZRxtasMjLjJTPCQnuslMSaSyrplTC4cwKNnD+VOGM2aI9UYZ01ltUxu7Djdw6YyRTpfyIQluF5fPHsUDbxdT09hGdpp9EIpmkRXTjYkjPr/ykxd3cPujG5mZl8Wzty1iUJKHZ9aXUlrTREVdCy6/0u7z0eb1U1nXQpLHTXZaEmdNHMrH5462EGXMcaw/WAPAPIdmMz+Zy2eNot2nvPreYadLMX1kQcoYB9S3tHPzA0Xc9/perl2Qz0M3LSBnUBL/fK+C+hYvm0pqcQl4PB7SkhIQEcYOHYTH7eKMCbnxPGeUMd1SVFyDxyXMirDxUR2mjcpgZGYyL22zIBXt7NSeMQNsX2UDNz9QxIGjTdx9+TSuO20MtU1t/G1NKcvf2IfP56fdD4NTE6hubCM7zUOrV/FrMlUNLZwWQVcgGROpig7UMHVkBimJbqdL6ZKIcMHU4fxtzUGa2rykJtqf42hlPVLGDKDXdx7hsntXUdPUzkM3LeC608awpbSWbz2xiV+8spPaxja8PsXn93O0oQ23S2hs9YG6UL9yxoRcO51nzEm0ef1sKqll7pjIPK3X4YKpw2j1+nljV5XTpZg+sAhszABQVX7/5j5+/MIOJg7PYPn1cxk9OJWXtx7if1/aQVltEz4/JHqEFq8fjwd8PqW+pY0haYmcM3koboGlMyNv4KwxkWZbeR2tXj/zCrKdLuWETi0YTFqimzd3V3LRtOFOl2N6yYKUMf2spd3Ht5/awtMbylg6fQT/+4kZpCZ6WL2nkh+/sIOjja14faB+aFMlJdmN3wcut+L3K4PTEhmVlcJp44ZYb5Qx3VBUHBxoPiayg1SC28Xp43J4c7f1SEUzC1LG9KOKuhY+/2ARm0rr+Pr5p3DbOeMREbaU1vKdZ7ZSWtOE+sEPCOBTaG/3k5LoJi0pgRGZqXzx7HGceYqzd643JpoUHagmf3AqQzOSnS7lpM48JYdX3zvMgaON9kEpSlmQMqafrD9Yw+cfXEdTq5fl18/lgqmBrvvVeyr5739so7iqCQgEKD+ggFuhuV1JcCuCcOaEHGbkZTm1C8ZEHVVl3YEazpyQ63Qp3XJGsM43d1dZkIpSNtjcmH7wRFEJV//uHVIT3Tx966IPhKg7/r6J3RWN+OD9L1fwq+PxqKwUPru4gGtPG2PTHBjTAweONlHV0MbcCB8f1aFgSCrDM5JZs7/a6VJML1mPlDFh5PX5+Z+V7/HnVcUsHp/Dty6ayIOr9/P6jiPUtrTT0uqnvVMbDX4JkOyBT8wdzdcvnGQBypheKDrQMT4qsq/Y6yAizC3IpqjYglS0siBlTJjUNrVx2yMbeGtPFR+dOQK/3881v1tFQzt0zGTjC1leCAQoCJzaS0sSFo/NtRBlTB+sO1BNRrKHCUMHOV1Kt506JpsVmw9RVtvMqKwUp8sxPWRBypgw2HX4GDc/UER5bTOnDE1jxaZDHwhNvuO06whTbmDK8AxuO3eChShj+qCouIY5Y7JxucTpUrqt4zY2RcXVjJo1yuFqTE/ZGClj+ujlbRVcce8qapvaGZToYteRxuMGp1Ce4HHeBeQNSeG7l05jug0sN6bXapva2H2kgVMj9P56xzNpeDppie73p20w0aXPQUpERovIv0TkPRHZJiK3h6MwYyKdqvKbf+5m2YPryBucSnqSm5rm7kSogAQPpCQIBTkp/OiK6Rai+kBELhKRnSKyR0Tu6OL9y0Rks4hsFJEiEVnc3bYmeqwLjo+aG+HzR3XmcbuYMyb7/fFdJrqEo0fKC3xdVScDpwG3isiUMKzXmIjV1Obltkc28PNXdnH5rJHkpiVQWtvS7fYZSUJ6SiJz8rO4+/LpLBwfHZdqRyIRcQP3AkuAKcA1XRyD/gnMVNVZwGeBP/SgrYkSRQcCNyqeGYUfSuaNGcyOinrqWzpfjmIiXZ/HSKnqIeBQ8PExEXkPGAVs7+u6jYlEpTVN3PzAOnZW1LN0+nDe2HmI6mY9aTsBUj0wPT+LxeNzmZGXxYy8LBsT1XfzgT2qug9ARB4FLiPkGKSqDSHLp/H/x/mftK2JHuuKa5g6KjNib1R8IvMKslGF9Qdq+MhEm4A3moR1sLmIFACzgXe7eG8ZsAwgPz8/nJs1ZsC8u+8oNz1QREOLFwVWbKnoVrsxg5O56tQxLJ0xwibdC79RQEnI81JgQeeFROQK4EfAUGBpT9qayNfm9bOptJbrThvjdCm9Mmt0Fm6XUFRsQSrahC1Iicgg4EngK6pa3/l9VV0OLAeYN2/eyT++GxNhPvvnNby2s7LH7a6cNYKfXz2nHyoyQV1dnvWhY4yqPg08LSJnAj8AzutuW/sgGPm2dtyoOMrGR3VIS/IwdWQGRQdsPqloE5ar9kQkgUCIelhVnwrHOo2JFOsPVDPpP1f2KkR99dzxFqL6XykwOuR5HlB+vIVV9Q1gnIjkdLetqi5X1XmqOi8318azRaKOCS2jZUbzrswancXm0jp8futriCbhuGpPgD8C76nqL/pekjGR44FV+/nYfW/T4u35ge0LZxZy+/kT+6Eq08laYIKIFIpIInA18FzoAiIyPnisQkTmAInA0e60NdFhzf5qCnPSGJoe+TcqPp4ZeVk0tfnYV9lw8oVNxAjHqb1FwPXAFhHZGHztTlVdGYZ1G+OYHzy/lT+uOtCrtmdPGMK3LraLvwaCqnpF5DbgJQJzm/5JVbeJyC3B9+8HrgRuEJF2oBm4SlUV6LKtIzties3vV9YW13Dh1GFOl9InM/MyAdhUWseEYekOV2O6KxxX7b1F1+MMjIla1y1/m7f29XysQkaSm88ttp6ogRb84Lay02v3hzz+CfCT7rY10WX3kQbqmtujbiLOzsbmDiIt0c3m0lo+PjfP6XJMN9ktYowJ4fcrc3/wMjXN3h61y0nz8OQXF9sVecY4YE1wfNT8wugOUm6XMG1UJptK65wuxfSABSljgt7dV8VVyz80c8cJZSa7+d6lU7li7uiTL2yM6Rdr91czND2J/MGpTpfSZzNHZ/GXVcW0ef0keuwubtHAgpSJeweONnLVfaupaGjrdhs38K0lE1l21vj+K8wYc1Kqytriak4tHEzweoKoNiMvkzafn50Vx5geHDNlIpsFKRPXzvvZa+ypau5RmxHpCbx91wX9VJExpidKa5o5VNfC/CgfH9Wh4/Y2m0prLUhFCQtSJi4t/dXrbKto7HG7YYM8FqKMiSBrg+Ojon2geYe87BSyUxPYXFoLROcs7fHGgpSJO2PvWIG/F+1GZSSy6s7zw16PMab31hZXk57sYeLw2JguQESYkReYmNNEBxvJZuLGy1sPUdDLEDU3L91ClDERaM3+auaNycbtiv7xUR1m5mWy6/Axmtp6dvWwcYb1SJm4cOaPX+VgbWuv2j5y03wWjrfbghgTaY42tLK3spErY2zOpRl5WfgVtpXXx8wpy1hmQcrEvAl3rKC9F+0WjMnksS8sDns9xpjwWFtcA8CCKJ8/qrMZo4MznJfUWpCKAhakTEwruGNFj9tMHJrKS187ux+qMcaE09riapI8LqaPynK6lLAamp7MiMxkGycVJSxImZh05T1vsK70WI/a5Gcl8cYd5/VTRcaYcFtbXM2s0VkxOXHljLzM4JV7JtLF3m+fiXuFd6zocYhaPDbbQpQxUaSx1cu28vqovy3M8czIy6L4aBN1Tb0ZmGAGkvVImZjR27mhrpw1gp9fPacfKjLG9Jf1B2vw+TVmxxB1TMy5uayWMybYxS6RzIKUiQm9GQsFUPzjpWGuxBgzENbur8YlMGdMttOl9IvpowIDzjeX1lmQinB2as9Evd6EKDcWooyJZmuKq5k6MpNBSbHZH5CZmkDBkFQbJxUFLEiZqNabEFU4OJm9FqKMiVptXj8b42BqAJvhPDpYkDJR6fvPbulViPrlJ2bwr2+e2w8VGWMGytbyOlra/cwvjM3Teh1m5GVyqK6FI8danC7FnEBs9omamDbtuytoaOtZmySBnT+yXqhYJSIXAf9H4KztH1T1x53e/xTwreDTBuALqrop+F4xcAzwAV5VnTdQdZveKQreqHjumNjvkQLYUlrHuZOTnS3GHJcFKRM1Hnp7P//57PYet1t+3RwumDaiHyoykUBE3MC9wPlAKbBWRJ5T1dBflv3AWapaIyJLgOXAgpD3z1bVqgEr2vTJmv01FOakkZue5HQp/WraqAxcAptK6zh38jCnyzHHYUHKRIXe3ubFBpTHhfnAHlXdByAijwKXAe8HKVVdHbL8O0Bs3Zwtjvj9yroD1Zw/JfaDRWqihwlD023AeYSzMVImol15zxsU9CJEJWIhKo6MAkpCnpcGXzuezwEvhDxX4GURWSciy7pqICLLRKRIRIoqKyv7XLDpvX1VDdQ0tTMvxgead5iel8mW0jpU1elSzHFYkDIRq6AXM5QDjEhPYJeFqHgiXbzW5V8dETmbQJD6VsjLi1R1DrAEuFVEzvzQylSXq+o8VZ2Xm2tz+jip40bFsX7FXoeZeZkcbWyjrLbZ6VLMcdipPRORejvB5vO3LWJ6cICmiRulwOiQ53lAeeeFRGQG8Adgiaoe7XhdVcuD34+IyNMEThW+0a8Vm15bu7+anEGJFAxJdbqUAdEx4HxzaR152fGxz9HGeqRMRPnJyu19mqXcQlRcWgtMEJFCEUkErgaeC11ARPKBp4DrVXVXyOtpIpLe8Ri4ANg6YJWbHlt7oJp5YwYj0lVHZOyZNCKdBLfYfFIRzHqkTMTozbQGALlpbtZ+56LwF2Sigqp6ReQ24CUC0x/8SVW3icgtwffvB74LDAF+G/wD3DHNwTDg6eBrHuARVX3Rgd0w3VBR10JJdTOfWVjodCkDJsnjZtLwDBtwHsEsSJmIYPfKM32hqiuBlZ1euz/k8U3ATV202wfM7PcCTVgUHQjMH3VqQWxPxNnZjLxMnttYjt+vuFzx0RMXTezUnnFcb0LUpdOGWYgyJs4UFdeQmuhmyogMp0sZUDPzsjjW6qX4aKPTpZguWI+UcVRvQpQFKGPi05r91czOz8Ljjq8+gOl5mUBgwPnY3EEOV2M6i6/fRhNRehqi0hIsRBkTr+pb2tlRUR830x6EmjB0EMkJLjbZOKmIZD1SZsCt3lPJtX9Y06M2FqCMiW8bDtbi1/iZPyqUx+1i2shMu3IvQlmQMgPqynve6PEkmxaijDFFxdW4XcKs0VlOl+KI6XmZ/G3NQbw+f9yd2ox09q9hBszku3o+U7mFKGMMBMZHTR2ZQVpSfH7+n5mXRUu7n91HGpwuxXRiQcoMiII7VtDs6/7yLixEGWMC2rx+NpbUMm9M/J3W6zDj/QHntc4WYj7EgpTpdz0dVD4k1cU+C1HGmKCt5XW0ev3ML4yv+aNCFQxJIz3JY+OkIlB89pGaAfGlh4p4fuvhHrW5c8lElp01vp8qMsZEo6LiwEScc+O4R8rlEqbn2YDzSGRByvQLmx/KGBMuRcU1FAxJJTc9yelSHDUjL4s/vrWPVq+PJI/b6XJMUFhO7YnIRSKyU0T2iMgd4ViniV4Woowx4aKqrD9Yy5wx8Xtar8OMvEzafcqOQz27aMf0rz4HKRFxA/cCS4ApwDUiMqWv6zXRyUKUMSacSmuaqWpoZXa+BSkbcB6ZwtEjNR/Yo6r7VLUNeBS4LAzrNVFk9Z5KC1HGmLDbUFILwOw4nT8q1KisFIakJbLJxklFlHAEqVFAScjz0uBrHyAiy0SkSESKKisrw7BZEyn+75WdPZ6pHCxEGWNObsPBGpITXEwanu50KY4TCQw432JBKqKEI0hJF6/ph15QXa6q81R1Xm5ubhg2ayLBgrtf4pf/3NOjNhOHplqIMsZ0y/qDtczIi78bFR/PjLwsdh85RmOr1+lSTFA4rtorBUaHPM8DysOwXhPh7FSeMaY/tbT72F5ex2cXFzpdSsSYPToLv8Lm0jpOHzfE6XIM4emRWgtMEJFCEUkErgaeC8N6TQTraYhKS7AQZfrPya4cFpFPicjm4NdqEZnZ3bbGOdvK62n3KXNsoPn7Ou41uKGkxtlCzPv6HKRU1QvcBrwEvAc8rqrb+rpeE7l6GqImDk1l2w8sRJn+0c0rh/cDZ6nqDOAHwPIetDUO2XAwEBZsoPn/l52WyNicNNYfqHW6FBMUlgk5VXUlsDIc6zKRrach6u7LpnDd6dYtb/rV+1cOA4hIx5XD2zsWUNXVIcu/Q2AIQrfaGudsKKllVFYKQzOSnS4loszKz+KNXZWoKiJdDVM2A8lG75lu62mIKv7xUgtRZiB068rhEJ8DXuhJW7vq2BkbDtQwOz/L6TIizpz8bKoa2iitaXa6FIPdIsZ0w7TvrqChrWdtbDyUGUDdunIYQETOJhCkFvekraouJ3g6cN68eV2u24RXRV0L5XUtfM7GR31IR7hcf7CG0YNTnS3GWI+UObGCOyxEmYjXrSuHRWQG8AfgMlU92pO2ZuBtDA6mnmM9Uh8ycVg6qYluNhysdboUg/VImeP40kNFPL/1cI/bWYgyDnj/ymGgjMCVw9eGLiAi+cBTwPWquqsnbY0zNhysJdHtYsrIDKdLiTget4sZeZnvD8Y3zrIgZT6kN/NDgYUo4wxV9YpIx5XDbuBPqrpNRG4Jvn8/8F1gCPDb4OBcb3CC4C7bOrIj5gPWH6xh6qgMkjxup0uJSLPzs/n9G/toafeRnGA/IydZkDIfYCHKRKOurhwOBqiOxzcBN3W3rXFWu8/P5tI6PrVgjNOlRKzZo7Pw+pWtZXXMKxjsdDlxzYKUAeDMH7/KwdrWHrdzA3stRBljwmhnxTFavX5m2fio45odHIS/4WCtBSmHWZAyve6FunbeKH748VnhLcYYE/e2lQduyjt9VKbDlUSu3PQkRg9OYb2Nk3KcBak4Z6fyjDGRZktZHelJHsbYpf0nNHt0Nmv2VztdRtyz6Q/iWG9C1KiMRAtRxph+tbWsnikjM3C5bNbuE5mTn0VFfQuH6mxiTidZj1Qc+snK7dz3xv4et7MAZYzpb16fn/cO1XP9aTbQ/GQ6xkmtP1DL0hkpDlcTvyxIxZnCO1Z0PeXzCWQmw6bvWYgyxvS/PZUNtHr9TLPxUSc1eUQGSR4X6w7UsHTGCKfLiVsWpOLE0+tK+OoTm3vc7qvnjuf28yf2Q0XGGPNhW0oDA80tSJ1cosfF7Pws1hbbOCknWZCKA4t++Apl9T28zwt2Ks8YM/C2ldeTmuimMCfN6VKiwvzCIdzz2m6OtbSTnpzgdDlxyQabx7iCO1b0OESleixEGWOcsaWsjqkjM3DbQPNuWVA4GL/CugM2DYJTLEjFqPN+9lqvrsqbOXIQ2++2EGWMGXg+v7K9vJ6pI+20XnfNzs/C4xKbBsFBdmovBvV2bqjl183hgmk2YNEY44z9VQ00t/tsIs4eSE30MD0v04KUgyxIxZjehCi7zYsxJhJsKbOB5r0xv3Awf3prv93A2CF2ai9GXHXfW70KUb/8xAwLUcaYiLC1rJ7kBBfjcm2geU+cVjiEdp/a7WIcYj1SMcBu82KMiQVbyuqYPCIDj9s+4/fE3IJsRGDN/moWjstxupy4Y7+tUa43IWpQooUoY0xk8QcHmtv4qJ7LSE5gyogM3t1n46ScYD1SUWrB3S9xuMHb43ZXzhrBz6+e0w8VGWNM7xUfbaSh1cs0u2KvVxYUDuHhdw/YOCkHWI9UlFm9p5KCO1b0OETlZyVR/OOlFqJMTBKRi0Rkp4jsEZE7unh/koi8LSKtIvIfnd4rFpEtIrJRRIoGrmoTamt5PWADzXtr0fghtHr9Nk7KAdYjFUWW/up1tlU09rid9UKZWCYibuBe4HygFFgrIs+p6vaQxaqBLwOXH2c1Z6tqVb8Wak5oa1kdiR4XE4YNcrqUqDS/cDBul7B6z1EbJzXArEcqSkz9zopehag7l0y0EGVi3Xxgj6ruU9U24FHgstAFVPWIqq4F2p0o0Jzc1rI6Jg9PJ8EGmvdKenICM/IyWbXXPg8MNPuNjQKT71pBYw8P/yPSEyj+8VKWnTW+f4oyJnKMAkpCnpcGX+suBV4WkXUisiyslZluUVW2ltUx1U7r9cmicTlsLq3jWIt9XhhIFqQi2NPrSii4YwXNvp61O3vCEN6+64L+KcqYyNPVTdm0B+0XqeocYAlwq4ic+aENiCwTkSIRKaqsrOxtneY4SqqbqW/x2hV7fbRw/BB8frWr9waYBakIdeffN/LVJzb3qI0HeP62Rfz5c6f1T1HGRKZSYHTI8zygvLuNVbU8+P0I8DSBU4Wdl1muqvNUdV5ubm4fyzWdvT+juV2x1ydz8rNJ8rjs9N4As8HmEejCX/yLnUeaetTmziUT7TSeiVdrgQkiUgiUAVcD13anoYikAS5VPRZ8fAHw3/1WqenS1vI6EtzCKcNtoHlfJCe4ObVgMG/vPep0KXHFglQEufPvG3mkqKxHbdISYNsPbHJNE79U1SsitwEvEbh15J9UdZuI3BJ8/34RGQ4UARmAX0S+AkwBcoCnRQQCx8NHVPVFB3Yjrm0tq+OUYekkeWz+o75aOH4IP31xJ1UNreQMSnK6nLhgQSpCLPrhK5TVt/Wozdy8dJ687UPDOYyJO6q6EljZ6bX7Qx5XEDjl11k9MLN/qzMn0jHQ/MKpw50uJSYsGpcD7GT13qN8dOZIp8uJCzZGymFPrythwh0rehyibjw930KUMSbqldU2U9PUblfshcm0UZlkJHtYvcfGSQ0U65Fy0Of/soaXdvTsCqDMZOHf3zyPrNTEfqrKGGMGztaywIzmdsVeeLhdwmljh9iA8wFkPVIOWfqr13scohaMyWTT9y62EGWMiRlby+pwu4RJw9OdLiVmLBw3hJLqZkqqe3bRkukdC1IDbEtpLdO/27NZyhMEll83h8e+sLgfKzPGmIG3tbyOCUMH2Y12w2jR+MAtYlbZ6b0BYUFqAL289RCf+O0qjvVgONSl04ax+0dLuWDaiP4rzBhjHNAx0NxuVBxe44cOYmh6EqtsGoQB0acxUiLyv8ClQBuwF7hRVWvDUFdMeXpdCT9cuZ3KRm+326QlwO8/PZ+F423yP2NMbCqrbaaqoY2ZeRakwklEWDhuCG/tqUJVCU7vYfpJX3ukXgGmqeoMYBfw7b6XFFueXlfCfz67uUchak5eBqu+fb6FKGNMTNtUEpjRfNbobIcriT0Lx+dQ1dDGrsMNTpcS8/oUpFT1ZVXtSAjv0PU8LXHtl6/uorEHp/IunJTLU7edYQPKjTExb2NJDYkeFxNtoHnYdYyTesvGSfW7cI6R+izwwvHejMebfn7/2S0crGnp9vJfPXc8v/vMh27zZYwxMWlTSR1TR2aQ6LHhuuE2KiuFwpw0G3A+AE46RkpEXgW6mnL2LlV9NrjMXYAXePh461HV5cBygHnz5vXkzuxR6fvPbuHPbx/s1rL5mUk8uOx0xgxJ6+eqjDEmMnh9fraU1XH1/NEnX9j0yuLxOTy5vpQ2r9/Caj86aZBS1fNO9L6IfBq4BDhXVWM+IJ3IltJafvnKTt7aWUV3zualJghfOe8Uu9mwMSbu7D7SQHO7j1mjs5wuJWYtGp/Dg+8cYGNJLfMLBztdTszq61V7FwHfAs5S1bie+evlrYf47jNbqWjo3oCo/OxknvuSjYUyxsSnTSW1AMzMy3K0jlh2+rghuATe2l1pQaof9bWv7x4gHXhFRDaKyP0naxCLtpTWcvc/tnU7RGUmu/jxlTMsRBlj4tbGklqyUhMYMyTV6VJiVmZKAjNHZ/GmjZPqV33qkVLVuD8nVdvUxk9f2EFpbWu323zjwkk2tYExJq5tLKllZl6WzXHUzxaPz+Hef+2hrrmdzJQEp8uJSTb6rI/u+9du3t53FH83lk0QuHPJRK47vbDf6zLGmEjV1OZl1+FjzLTxUf1u8fgc/Apv2yzn/caCVB9sKa3lsbUleLsxxD4/O5l7PzXHBpYbY+Le5tI6/AqzRtuM5v1tdn42qYlumwahH/Xp1F48O3C0kW89uYnaFt8Jl5s2Ip07l062U3nGGBO0Zn81IjA33wZA97dEj4vTxg6xiTn7kfVI9cKBo4387IUd7Dp0/Kn3s1JcPHLTfP5x+5kWoozpZyJykYjsFJE9InJHF+9PEpG3RaRVRP6jJ21N+L27/yiThmeQmWpjdgbCovE57K9qpLQmri+u7zcWpHqotqmN5W/sZdW+Ko7XF+UB/uuSaRagjBkAIuIG7gWWAFOAa0RkSqfFqoEvAz/rRVsTRm1eP+sO1LDALscfMGdMCN4uZrf1SvUHC1I9UNvUxktbK/j3zkoaWry4gM7Xm7iBpTOHc8Vcm63XmAEyH9ijqvtUtQ14FLgsdAFVPaKqa4H2nrY14bW1vI6Wdr8FqQE0YegghmUk2TQI/cSCVA+8vfcoq/dU0tTWjgiIQMc4c3fwa8LwQXztgkkOVmlM3BkFlIQ8Lw2+Fra28Xiv0P7ScfXYqRakBoyIsGh8Dqv3VOHzx/UNSPqFBaluqm1q4+F3i1l/sIaWNh9eH3g10CPlARI8MCjFzdfOO8XumWfMwOpqIqLu/rXoVltVXa6q81R1Xm6unbLvizd2VTJlRAY5g5KcLiWunHVKLjVN7WwqrXW6lJhjQaobapvaeHp9Ce+VH6O13YsPwUfgh+cGxAXDM1O55axxXDBthMPVGhN3SoHQc+l5QPkAtDU91NDqZf3BGs48xcLoQDvrlFxcAv/accTpUmKOBalueHvvUV7cdhi/+jna5KPVqyR7hLQkF2nJbsblpnLLWWO5Zv4Yp0s1Jh6tBSaISKGIJAJXA88NQFvTQ+/uO0q7TzkzOPjZDJys1ETm5Gfzr50WpMLNgtRJ1Da18e9dR6hpaKW+1Y9fISPZTXZKAkluF+nJCVw6K4+Lpo2we+cZ4wBV9QK3AS8B7wGPq+o2EblFRG4BEJHhIlIKfA34TxEpFZGM47V1Zk9i3+s7K0lJcDO3INvpUuLS2ZOGsrWsniP1LU6XElNsQs4TqG1q46n1JazeU8XBmhYS3UJ+Tio+n9Lu85OalMCpY7L51IIxFqKMcZCqrgRWdnrt/pDHFQRO23WrrQk/VeWV7Yc585Qckjxup8uJS+dMGsr/vrSTf+08wlWn5jtdTsywINWF2qY2nt1YylPrSth1pInmdj9piW4ykj0kiJCR4uZYq5fRg1P5zOJCC1HGGHMSW8rqqKhv4RtTJjpdStyaNDydEZnJvLbDglQ4WZDq5MDRRv66uphXtx/myLFWWrx+0pPcpCS4yE5NoNXrp8WrjM9N50vnTWB6XpbTJRtjTMR7edth3C7hnElDnS4lbokIZ08ayrMbymj1+qxnMExsjFSI2qY2XtlewfoD1Ryqb6HV62dYeiLD0pNITfTgRynMSWPOmCzuunSKhShjjOkGVWXFlkMsKBxMdpr14Dvp7IlDaWzzsWZ/tdOlxAwLUiH2VzWytayezWX1CDAmJ5Xs1EQ8bhdJbqHd62dc7iCWTB9hc0UZY0w3bS6tY39VI5fNGul0KXHvjAk5pCa6eXFrhdOlxAwLUkHVja385MUdPLOxnES3i9HZKaQluvGr0u714/G4yM1IYXpeJjOsJ8oYY7rtmY1lJLpdXGTz7DkuOcHN2ROH8tK2CpvlPEzieoxUbVMb+6sa2VvZwH2v72VvZSPjc9MYmp7A/qrGwKzlbqHJ6yfB4+LiacNYPCHXBpcbY0w3tXp9PL+pnHMmDSUzJcHpcgywZPpwVmw5RFFxNQvGDnG6nKgXt0GqtqmNt3ZXsreykT+vKqauuZ2ZeZlMz8sg2e0hyeNh/9FG/D7lI5OGctWpo21MlDHG9NCLWyuoamjjmgV2lVikOHviUJI8Ll7YWmFBKgziNkhtLq3lrd2VPLupAr8qCwqyGTIoEbcIaUkeJgxNZ2xuGnPGDGbpDDuvb4wxvfHg2wcoGJLKGeNtNvNIkZbk4axTcnlxawXfvWQKLldXt5w03RWXY6RUlYfeOchjRWVkpSZwzan55A1OJcHjoq65nYKcNApz0yjMGcTIrBSnyzXGmKi0vbyeogM1XHfaGPtjHWGWTB9ORX0LG0pqnS4l6sVdkGr1+vjG3zfz8vbDTB6Rwe3nTGD80EGkJLppbvPT5vXR3NZOXXMbWakJFObY1XnGGNMbD75zgOQEF5+YO/rkC5sBde7kYSS4hZVbDjldStSLqyB1pL6Fq5e/w9/XlXL5rJFcMmM4CqQmuikYkkZmiofs1ESavX6mjMy0geXGGNNLdc3tPLOhjMtmjiIz1QaZR5qM5AQ+MnEoz28qt6v3+ihuxkhtKqll2YNF1Dd7ue9Tczh93BDe3F1JXXM79c0+khJcnHFKLmdYeDLGmD578O1imtt93LBwjNOlmOO4YvYoXtl+mFV7qjjzlFyny4lacRGknt5Qyree3MLQ9CSe+uJCJo/IAOCMCbnsr2qkvrmdjJTAaTwLUcYY0zdNbV7++NZ+zp6Yy9SRmU6XY47jnElDSU/28MyGMgtSfRDTQcrnV37y4g6Wv7GP08YO5refmsvgkNsTZKUmMjvfgpMxxoTTI+8epKapndvOGe90KeYEkhPcXDJjBM9uLOfuNi+piTEdCfpNzI6Rqmtq58a/rGX5G/u44fQxPPi5BR8IUcYYY8Kv1evj928GPrzOHTPY6XLMSVwxO4+mNh8vbbNbxvRWTMbPPUcauPmBIkprmvjRx6ZzzfwPTwTXMau5ndYzxpjweaKolMP1rfz8E7OcLsV0w7wx2eRlp/DU+jKumJ3ndDlRKeZ6pF7bcZgr7l1FfXM7j9x82nFD1IaDNbR5/WSnJtLm9bPhYA21TW0OVGyM6SsRuUhEdorIHhG5o4v3RUR+HXx/s4jMCXmvWES2iMhGESka2MpjS0u7j9+8tpu5Y7JZNN5mzI4GLpfwsTl5vLWnipLqJqfLiUoxE6RUlfte38vn/lpE/pBUnvvSYk4t6LpbeX9VI6mJHlITPYjI+4/3VzUOcNXGmL4SETdwL7AEmAJcIyJTOi22BJgQ/FoG3Nfp/bNVdZaqzuvvemPZX1cXc7i+lW9eOBERm4AzWlx96mgEeGTNQadLiUoxEaSa23zc/uhGfvLiDpZOH8Hfb1nIqBPMSF7f3E5KgvsDr6UkuKlvbu/vUo0x4Tcf2KOq+1S1DXgUuKzTMpcBD2jAO0CWiIwY6EJjWV1zO799fS8fmZhr92+LMiOzUjhv8jAeW1tCq9fndDlRJ+qDVHltM5/43Wqe31zONy6cyG+umU1KovuEbTJSEmhu/+AvS3O7jwy7M7kx0WgUUBLyvDT4WneXUeBlEVknIsv6rcoYt/yNvdQ1t/ONCyc6XYrphetPH0N1YxsvbrVB5z0V1UGqqLiaj96ziuKqJv5wwzxuPXt8t7qTC3PSaGrz0tTmRVXff2y3gzEmKnX1n77zVM0nWmaRqs4hcPrvVhE580MbEFkmIkUiUlRZWdm3amPQwaNN/OHN/Xx05kibNypKLRqXQ8GQVB58+4DTpUSdqA1Sj645yDW/f4dBSW6euXUh504e1u22gfmjskn0uKhpaiPR42J2frZdtWdMdCoFQm/mlgeUd3cZVe34fgR4msCpwg9Q1eWqOk9V5+Xm2sSFnf33P7bhdgnfvniS06WYXnK5hOtOG0PRgRq2ltU5XU5Uibog1e7z81/PbuWOp7Zw2tghPHvrYsYPTe/xejrC1FkTh1qIMia6rQUmiEihiCQCVwPPdVrmOeCG4NV7pwF1qnpIRNJEJB1ARNKAC4CtA1l8tHt1+2Fefe8It587gRGZxx+baiLfJ+aNJj3Jwz2v7XG6lKgSVfNI1TS28cWH1/P2vqPcfEYh37poEh53z7KgzR9lTGxRVa+I3Aa8BLiBP6nqNhG5Jfj+/cBK4GJgD9AE3BhsPgx4OjgkwAM8oqovDvAuRK3mNh/f/8c2xg8dxI2LCp0ux/RRZkoCNy4q4Nev7WFHRT2Thmc4XVJUCEuQEpH/AP4XyFXVqnCss7MdFfXc/EBRcKK3mVw5t+cTh3XMH5Wa6CE7NZHmdh8bDtZYj5QxUU5VVxIIS6Gv3R/yWIFbu2i3D5jZ7wXGqB+/8B4l1c08cvMCEj1Rd4LDdOGziwv506pifvPaHu69ds7JG5i+n9oTkdHA+UC/TUDx4tYKPvbb1bS2+3n886f3KkSBzR9ljDHh8u9dlfz17QPcuKiAheNynC7HhElWaiKfXjiGlVsOsfvwMafLiQrh+AjxS+CbfPgqmT7z+5VfvbqLWx5axynD0nn+S4uZNTqr1+uz+aOMMabvapva+MYTm5gwdBDfusgGmMeamxaPJTXBzY9e2EGgM9ecSJ+ClIh8FChT1U3dWLbHlw+v3HqIX726myvn5PHostMYlpHcl3LJSEmg8lgrOyvqWXegmp0V9VQea7X5o4wxppv8fuWrj22kurGNX141i+SEE8/bZ6JPdloiXznvFF7bcYSXth12upyId9IxUiLyKjC8i7fuAu4kcJXLSanqcmA5wLx587oVcZdOH0HyDW7OnTw0LLcbGJyWyBs7j5CZkkB6cgLHWtopq2ni8jl2o0ZjjOmOX/1zN//aWckPLpvKtFE2Z1SsunFRAU9tKON7z21j8YQcBiVF1bVpA+qkPVKqep6qTuv8BewDCoFNIlJMYF6W9SLSVejqFRHhvCnDwnbPpurGNqaNymJQcgLHWn0MSk5g2qgsqhvtZsXGGHMyL2+r4Nf/3M0n5uZx3WljnC7H9COP28UPr5jG4WMt/PzlnU6XE9F6HTFVdQswtON5MEzN66+r9sKhvrmd3PQkhoacIlRVaposSBljzInsOdLA1x7fxIy8TH5w+TS7KXEcmJ2fzXULxvDnVcWcMSGHcyZ1f+LreBJX16vaPfaMMabnapvaWPZgEUkeF/dfN9fGRcWRu5ZOZsqIDL7y6EZKqpucLicihS1IqWpBJPdGgd1jzxhjeqql3ceyB9ZRWt3MfdfNZWSWzV4eT5IT3Nx/3VwAbnloHc1tvpO0iD9x1SNl99gzxpju8/uVrz+xiTXF1fz8kzOZXzjY6ZKMA/KHpPLLq2bx3qF6bnloHW1ev9MlRZS4ClJg99gzxpju8PmVbz65mRWbD3HnxZO4dOZIp0syDjp38jB+9LHp/HtXJV95bANen4WpDnY9ozHGmA/w+vx8/YlNPLuxnK+edwrLzhzndEkmAlx1aj7HWrzcveI9VDfYPGJBcdcjZYwx5vhqGtv43F+LeHZjOd+4cCK3nzfB6ZJMBLnpjLF855IpvLitgquWv0PlsVanS3KcBSljjDEArDtQw9Jfv8nbe4/ywyumc+vZ450uyUSgzy0u5HfXzWVXxTEu+c2b/GvHEadLcpQFKWOMiXPltc187bGNXHnfalwu4ckvLOTaBflOl2Ui2AVTh/P3L5xOVkoiN/5lLV9/fBNH6lucLssRNkbKGGPikNfn59391Ty2toQXt1UAcMtZ4/ji2ePISLa59czJTR2ZyXNfWsSv/7mb3/17Hyu2lPPphQXctHgsuelJTpc3YCxIGWNMjGv3+dl9uIGtZXVsLa9jS1kd7x2qp6XdT0ayh2tOHc3NZ44lLzvV6VJNlEnyuPnGhZP45LzR/OrV3Sx/Yx9/ems/S6aN4Or5o1lQOAS3K7ZnwbcgZYwxMaTV62NXRQNbgqFpa1kdOyqOvT/3T1qim6kjM7l2/hjmjMnivMnD7Mor02djhqTxy6tm8aVzxvPgOwf4+7pSnttUTs6gJC6aNozF43NYUDiE7LTYm3Io5oNUbVMb+6saqW9uJyMlgcKcNJs7ypgYIyIXAf8HuIE/qOqPO70vwfcvBpqAz6jq+u60jWQt7T7eO1Qf6Gkqq2dreR27Dh+j3acApCd7mDYyk88sLGDqyAymjcqkcEgarhjvITDOGZs7iP+6dCrfvHASr+04woot5Ty1voyH3jmICEwensHs/CymjsxkysgMJg5LJyUxuoN8TAep2qY2NhysITXRQ3ZqIs3tPjYcrLGJOI2JISLiBu4FzgdKgbUi8pyqbg9ZbAkwIfi1ALgPWNDNtr22+/AxvH5FBAQJfofA/X5Dn8v7r3csR/B5m9dPTVM7tU1tHG1oY//RRvYeaWBfVSP7qxrx+QOhKSs1gemjMvnc4rFMH5XJtFEZ5A9OtZsLG0ekJLpZOmMES2eMoN3nZ3NpLav3HOXtfUd5blM5D7978P1lh6YnkZedwujBqWSnJpKW5CYtyUNqghuP20WCW/C4XHhCvid84LELt0tI8rhITnC//z05wU2yx4XH3b/X1cV0kNpf1UhqoofUxMBudnzfX9XI7HwLUsbEiPnAHlXdByAijwKXAaFh6DLgAVVV4B0RyRKREUBBN9r22jW/f4eqhrZwrOp9HpcwZkgq43IHcdHU4UwLhqZRWSkWmkxESnC7mDtmMHPHDOZL505AVSmtaWZbeR27DjdQUt1ESU0T6w7UUNfcTmOrl+Dng7DwuOQDASvJ48LlEn5/w7yw3Gs3poNUfXM72Z16nlIS3NQ0hffAZoxx1CigJOR5KYFep5MtM6qbbRGRZcAygPz87k8L8NOPz6C13Y8CqqBo8DsEMl2n10PeUwCFBI+QlZpIdmoig1MTGZGVTEI/f8I2pj+JCKMHpzJ6cCoXTfvw+6pKS7uf5nYfXp+fdr8GvvsUn19p9/nxhrzm9fvx+pRWr59Wr4+Wdh+tXj8t7T5a2kO+e320tgeW8auS5AnP/6OYDlIZKQk0t/ve74kCaG73kZFil/YaE0O66obp/Hn2eMt0py2quhxYDjBv3rxuf1Y+Z9Kw7i5qjAkSEVIS3VEzdiqmP9YU5qTR1Oalqc2Lqr7/OBxdecaYiFEKjA55ngeUd3OZ7rQ1xpjjiukglZWayOz8bBI9Lmqa2kj0uGyguTGxZy0wQUQKRSQRuBp4rtMyzwE3SMBpQJ2qHupmW2OMOa6YPrUHHWHKgpMxsUpVvSJyG/ASgSkM/qSq20TkluD79wMrCUx9sIfA9Ac3nqitA7thjIlSMR+kjDGxT1VXEghLoa/dH/JYgVu729YYY7orpk/tGWOMMcb0JwtSxhhjjDG9ZEHKGGOMMaaXLEgZY4wxxvSSdMyuO6AbFakEDnRz8Rygqh/LcVos75/tW/Tqj/0bo6q5YV7ngOvh8Qti/3cF4mMfwfYzlvR0H497/HIkSPWEiBSp6jyn6+gvsbx/tm/RK9b3byDFw88yHvYRbD9jSTj30U7tGWOMMcb0kgUpY4wxxpheioYgtdzpAvpZLO+f7Vv0ivX9G0jx8LOMh30E289YErZ9jPgxUsYYY4wxkSoaeqSMMcYYYyJSVAUpEfkPEVERyXG6lnARkf8VkR0isllEnhaRLKdr6isRuUhEdorIHhG5w+l6wklERovIv0TkPRHZJiK3O11TuImIW0Q2iMg/nK4l1sTiMaxDLB7LQsXycQ3i49gWKpzHuagJUiIyGjgfOOh0LWH2CjBNVWcAu4BvO1xPn4iIG7gXWAJMAa4RkSnOVhVWXuDrqjoZOA24Ncb2D+B24D2ni4g1MXwM6xBTx7JQcXBcg/g4toUK23EuaoIU8Evgm0BMDepS1ZdV1Rt8+g6Q52Q9YTAf2KOq+1S1DXgUuMzhmsJGVQ+p6vrg42ME/iOOcraq8BGRPGAp8Aena4lBMXkM6xCDx7JQMX1cg9g/toUK93EuKoKUiHwUKFPVTU7X0s8+C7zgdBF9NAooCXleSuz+ZywAZgPvOlxKOP2KwB97v8N1xJQ4OoZ1iIVjWai4Oa5BzB7bQv2KMB7nPOFYSTiIyKvA8C7eugu4E7hgYCsKnxPtm6o+G1zmLgJdqw8PZG39QLp4LeY+gYvIIOBJ4CuqWu90PeEgIpcAR1R1nYh8xOFyok4sH8M6xNmxLFRcHNcgNo9tofrjOBcxQUpVz+vqdRGZDhQCm0QEAt3F60VkvqpWDGCJvXa8fesgIp8GLgHO1eifj6IUGB3yPA8od6iWfiEiCQQONA+r6lNO1xNGi4CPisjFQDKQISIPqep1DtcVFWL5GNYhzo5loWL+uAYxfWwLFfbjXNTNIyUixcA8VY2JGyqKyEXAL4CzVLXS6Xr6SkQ8BAaanguUAWuBa1V1m6OFhYkE/hL+FahW1a84XE6/CX5S+w9VvcThUmJOrB3DOsTasSxUrB/XIH6ObaHCdZyLijFSMe4eIB14RUQ2isj9ThfUF8HBprcBLxEYrPh4LB1sCHyauR44J/jvtTH4ycaYeBdTx7JQcXBcAzu29VrU9UgZY4wxxkQK65EyxhhjjOklC1LGGGOMMb1kQcoYY4wxppcsSBljjDHG9JIFKWOMMcaYXrIgZYwxxhjTSxakjDHGGGN6yYKUMcYYY0wv/T+ZnLuINt61mgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.01 1.04 0.19 -0.01\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.02 1.11 0.98 1.37\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.02 0.96 0.03 0.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.96 -0.15 0.12\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import skew, kurtosis\n",
    "\n",
    "\n",
    "for i in range(6):\n",
    "    z = np.loadtxt(f'/project_cephfs/3022017.02/projects/stijdboe/make_results/10_folds_results/fold_0_blr/batch_{i+1}/Z_estimate.txt')\n",
    "    print(round(z.mean(),2), round(z.var(),2), round(skew(z),2), round(kurtosis(z),2))\n",
    "    fig, ax = plt.subplots(1,2, figsize= (10,4))\n",
    "    ax[0].scatter( np.sort(np.random.randn(*z.shape)),np.sort(z),alpha =0.2)\n",
    "    ax[0].plot([-4,4],[-4,4])\n",
    "    \n",
    "    dens = gaussian_kde(z)\n",
    "    ran = np.linspace(-4,4,200)\n",
    "    ax[1].plot(ran, dens(ran))\n",
    "    plt.suptitle(Y_tr.columns[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b81c7bea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Right-Cerebellum-White-Matter\n",
      "[-0.    1.    0.06  0.57]\n",
      "EstimatedTotalIntraCranialVol\n",
      "[-0.    1.   -0.04  0.71]\n",
      "Right-Lateral-Ventricle\n",
      "[-0.    1.    0.32  0.89]\n",
      "WM-hypointensities\n",
      "[-0.    1.    0.34  0.52]\n",
      "rh_S_interm_prim-Jensen_thickness\n",
      "[-0.    1.    0.03  0.31]\n",
      "Brain-Stem\n",
      "[-0.    0.99 -0.    0.51]\n"
     ]
    }
   ],
   "source": [
    "moments = np.zeros((10,6,4))\n",
    "import scipy\n",
    "for iff, feature in enumerate(Y_tr.columns):\n",
    "    for f in range(10):\n",
    "        z = np.loadtxt(f'/project_cephfs/3022017.02/projects/stijdboe/make_results/10_folds_results/fold_{f}_blr/batch_{iff+1}/Z_estimate.txt')\n",
    "        moments[f,iff,0] = np.mean(z)\n",
    "        moments[f,iff,1] = np.var(z)\n",
    "        moments[f,iff,2] = scipy.stats.skew(z)\n",
    "        moments[f,iff,3] = scipy.stats.kurtosis(z)\n",
    "    print(feature)\n",
    "    print(np.round(np.mean(moments[:,iff],axis=0),2))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3ffe6c51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.    1.    0.06  0.57]\n",
      " [-0.    1.   -0.04  0.71]\n",
      " [-0.    1.    0.32  0.89]\n",
      " [-0.    1.    0.34  0.52]\n",
      " [-0.    1.    0.03  0.31]\n",
      " [-0.    0.99 -0.    0.51]]\n"
     ]
    }
   ],
   "source": [
    "print(np.round(moments.mean(axis=0),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47d6e9c4",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "1_fit_normative_models.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}