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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Logistic regression: using general fit (for paper)\n",
    "\n",
    "Exploring different fitting techniques and using MLE asymptotic approx of parameter distribution to estimate model CI intervals.\n",
    "\n",
    "Using Logistic regression class and considering full data and trimmed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18cec5e",
   "metadata": {},
   "source": [
    "## Common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b351af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "import numdifftools as nd\n",
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# our libs\n",
    "import data_utils\n",
    "import logit_utils_gen\n",
    "\n",
    "np.set_printoptions(precision=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0de7f02-942b-42ff-874c-3c0e2ac1e0dc",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3373f87f",
   "metadata": {
    "tags": []
   },
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   "source": [
    "verbose = False\n",
    "\n",
    "results_path = \"./results\"\n",
    "if not os.path.exists(results_path): os.makedirs(results_path)\n",
    "\n",
    "data_path = \"../../data/\"\n",
    "suv_filename = os.path.join(data_path, \"suv_percentilesSLOthenUWM.mat\")\n",
    "flags_filename = os.path.join(data_path,\"flags_combined.mat\")\n",
    "normal_range_filename = os.path.join(data_path, \"normal_range.mat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "811d990d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Loading all data from mat files\n",
    "\"\"\"\n",
    "suv_dict = scipy.io.loadmat(suv_filename)\n",
    "flags_dict = scipy.io.loadmat(flags_filename)\n",
    "normal_range_dict = scipy.io.loadmat(normal_range_filename)"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "\"\"\"\n",
    "Data preparation\n",
    "\"\"\"\n",
    "# get data\n",
    "perc = 95\n",
    "organ = \"lung\"\n",
    "\n",
    "x0, y0 = data_utils.get_data(organ, perc, suv_dict, flags_dict)\n",
    "\n",
    "# values for trimmed dataset\n",
    "value_to_drop = 2.48    # target value\n",
    "tol = 1e-2              # tolerance: matches 2.481... too\n",
    "drop_mask = (np.abs(x0 - value_to_drop) <= tol)\n",
    "\n",
    "df_data = data_utils.prepare_data(x0, y0, drop_mask)\n",
    "df_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f27d9a03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['X', 'Y', 'scale', 'dataset'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e9bb7b-5a50-4cae-9af4-261c11453dd4",
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   "source": [
    "## Fit\n",
    "\n",
    "Ref: logistic regression documentation\n",
    "* https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
    "* https://stats.stackexchange.com/questions/186830/what-is-scikit-learns-logisticregression-minimizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "239ae789",
   "metadata": {
    "tags": []
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       "      <th></th>\n",
       "      <th>scale</th>\n",
       "      <th>dataset</th>\n",
       "      <th>beta</th>\n",
       "      <th>cost</th>\n",
       "      <th>cost_jac</th>\n",
       "      <th>|cost_jac|</th>\n",
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       "      <th>|nllf_jac|</th>\n",
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       "      <th>...</th>\n",
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       "      <th>0</th>\n",
       "      <td>log</td>\n",
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       "   scale dataset                                               beta      cost  \\\n",
       "0    log    FULL  [-40.70878922505933, 152.89575085005256, -186....  4.605007   \n",
       "1    log    TRIM  [-318.6183131682148, 1693.40495002855, -3000.7...  1.700810   \n",
       "2  plain    FULL  [-123.99287081095154, 163.79463422187158, -71....  4.782070   \n",
       "3  plain    TRIM  [-450.10674749899573, 727.8800281668483, -395....  2.480642   \n",
       "\n",
       "                                            cost_jac    |cost_jac|      nllf  \\\n",
       "0  [-2.4868437196394044e-10, 3.6003680332904804e-...  7.937674e-09  4.602970   \n",
       "1  [-7.761886179751551e-07, 1.7954575335350695e-0...  6.218827e-06  1.592281   \n",
       "2  [3.0976353080470293e-07, -1.8667052563097625e-...  1.074177e-05  4.766500   \n",
       "3  [1.650575872704516e-07, 6.102354581899746e-06,...  9.334844e-06  2.277101   \n",
       "\n",
       "                                            nllf_jac  |nllf_jac|  \\\n",
       "0  [8.141732976574669e-05, 3.6003387946217734e-09...    0.000090   \n",
       "1  [0.0006364604377184544, 1.7953841824532158e-06...    0.000660   \n",
       "2  [0.0002482955051527078, -1.8666987464091033e-0...    0.000249   \n",
       "3  [0.0009003785525852619, 9.85299595721717e-07, ...    0.000902   \n",
       "\n",
       "                                                 cov  ...        AIC  \\\n",
       "0  [[83.89825652628299, 5.4349239020498005e-08, -...  ...  17.205940   \n",
       "1  [[4027.1019634624868, 0.001332308933499051, 12...  ...  11.184562   \n",
       "2  [[356.44598574301244, -2.6549155531725276e-07,...  ...  17.533001   \n",
       "3  [[401741.88513256714, 1094.5066215667102, 7367...  ...  12.554202   \n",
       "\n",
       "         BIC         A      chi2  p-value(chi2)   n  k  dof  \\\n",
       "0  25.447712  0.948276  8.316716            1.0  58  4   54   \n",
       "1  19.356767  0.982456  2.348806            1.0  57  4   53   \n",
       "2  25.774773  0.965517  9.164985            1.0  58  4   54   \n",
       "3  20.726407  0.964912  3.848636            1.0  57  4   53   \n",
       "\n",
       "                                                pars  success  \n",
       "0  [-40.70878922505933, 1.4619334353444514e-08, 1...     True  \n",
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       "\n",
       "[4 rows x 25 columns]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining two-sided confidence intervals\n",
    "alpha = 0.05   # significance level\n",
    "probs = [alpha/2, 1 - alpha/2]\n",
    "\n",
    "# fits\n",
    "lg = logit_utils_gen.LogisticPolyRegression(3, mono = True, lam = (0, 1e-6))\n",
    "\n",
    "lst = []\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Fitting: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # fit\n",
    "    res_fit = lg.fit(X, Y, method=\"diff_evol\")\n",
    "    \n",
    "    # calc nllf and beta for fitted pars\n",
    "    res_nllf = lg.get_nllf(X, Y, res_fit['pars'], jac = True)\n",
    "\n",
    "    # cost and jacobian norm\n",
    "    cost = lg.get_cost(X, Y, res_fit['pars'], jac = True)\n",
    "    \n",
    "    # beta of fitted pars\n",
    "    beta = lg.get_beta(res_fit['pars'])\n",
    "    \n",
    "    # covariance matrix of pars\n",
    "    cov = lg.get_cov(X, Y, res_fit['pars'])\n",
    "    \n",
    "    # standard error of pars\n",
    "    pars_SE = lg.get_SE_pars_normal(cov)\n",
    "\n",
    "    # CI of params (assuming asymptotic distr of parameters) : Wald approximation\n",
    "    pars_CI = lg.get_pars_quantiles_normal(probs, res_fit['pars'], cov)\n",
    "    \n",
    "    # goodness of fit\n",
    "    res_gof = lg.goodness_of_fit(X, Y, res_fit['pars'])\n",
    "\n",
    "    lst.append({\"scale\": scale, \"dataset\": dataset, \"beta\": beta} | \n",
    "               {\"cost\": cost[0], \"cost_jac\": cost[1], \"|cost_jac|\": np.linalg.norm(cost[1])} |\n",
    "               {\"nllf\": res_nllf[0], \"nllf_jac\": res_nllf[1], \"|nllf_jac|\": np.linalg.norm(res_nllf[1])} | \n",
    "               {\"cov\": cov, \"cov_evals\": np.linalg.eigvals(cov)} |\n",
    "               {\"SE\": pars_SE, \"LCL\": pars_CI[0], \"UCL\": pars_CI[1]} |\n",
    "               res_gof |res_fit)\n",
    " \n",
    "df_fit = pd.DataFrame(lst)\n",
    "df_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa65982",
   "metadata": {},
   "source": [
    "## Checking stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2851e6d3",
   "metadata": {},
   "source": [
    "Problem: The entries in covariance matrices are quite large.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9de8d697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking: log, FULL\n",
      "Cost jacobian:\n"
     ]
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    "scales = df_data[\"scale\"].unique()\n",
    "datasets = df_data[\"dataset\"].unique()\n",
    "\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    print(f\"Checking: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # fit\n",
    "    res_fit = df_fit.loc[(df_fit['scale'] == scale) & (df_fit['dataset'] == dataset)].iloc[0]\n",
    "    \n",
    "    pars = res_fit['pars']\n",
    "    \n",
    "    print(\"Cost jacobian:\")\n",
    "    cost_fun = lambda p: lg.get_cost(X, Y, p, jac = False)\n",
    "    display(pd.DataFrame({'cost_jac': res_fit['cost_jac'], 'gradient': nd.Gradient(cost_fun)(pars)}).T)\n",
    "\n",
    "    print(\"nllf jacobian:\")\n",
    "    nllf_fun = lambda p: lg.get_nllf(X, Y, p, jac = False)\n",
    "    display(pd.DataFrame({'nllf_jac': res_fit['nllf_jac'], 'gradient': nd.Gradient(nllf_fun)(pars)}).T)\n",
    "\n",
    "    numH = nd.Hessian(cost_fun)(pars)\n",
    "    directH = lg.get_cost_hessian(X, Y, pars)\n",
    "    print(\"num hess:\")\n",
    "    display(pd.DataFrame(numH))\n",
    "    print(\"direct hess:\")\n",
    "    display(pd.DataFrame(directH))\n",
    "\n",
    "    print('cov via directH:') \n",
    "    display(pd.DataFrame(res_fit['cov']))\n",
    "    print('inv_hess cost:')\n",
    "    display(pd.DataFrame(np.linalg.pinv(numH)))\n",
    "\n",
    "    print('eigenvalues directH:') \n",
    "    display(pd.DataFrame(np.linalg.eigvals(directH)))\n",
    "    print('eigenvalues numH:')\n",
    "    display(pd.DataFrame(np.linalg.eigvals(numH)))\n",
    "\n",
    "    print(\"cond directH:\", np.linalg.cond(directH))\n",
    "    print(\"cond numH:\", np.linalg.cond(numH))\n",
    "\n",
    "    print(\"difference between hessians:\")\n",
    "    display(pd.DataFrame(directH - numH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ee83ebac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking: FULL\n",
      "Checking: TRIM\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fit_res(scale, dataset):\n",
    "    return df_fit.loc[(df_fit['scale'] == scale) & (df_fit['dataset'] == dataset)].iloc[0]\n",
    "\n",
    "def get_data(scale, dataset):\n",
    "    g = df_data.loc[(df_data['scale'] == scale) & (df_data['dataset'] == dataset)]\n",
    "    return g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "scales = df_data[\"scale\"].unique()\n",
    "datasets = df_data[\"dataset\"].unique()\n",
    "\n",
    "plot_labs = dict(zip(datasets, ['A', 'B']))\n",
    "\n",
    "fig, axs = plt.subplots(\n",
    "    ncols = len(datasets),\n",
    "    nrows = 1,\n",
    "    figsize = (5*len(datasets), 4),\n",
    "    layout = \"constrained\",\n",
    "    sharey=True)\n",
    "\n",
    "for ax, dataset in zip(axs, datasets):\n",
    "\n",
    "    print(f\"Checking: {dataset}\")\n",
    "    res_fit = get_fit_res(\"log\", dataset)\n",
    "    X, Y = get_data(\"log\", dataset)\n",
    "\n",
    "    pars = res_fit['pars']\n",
    "    #ax.set_title(f\"log, {dataset}\")\n",
    "    # label on plots\n",
    "    ax.text(0.05, 0.05, f\"{plot_labs[dataset]}\", fontsize=14, transform=ax.transAxes)\n",
    "    \n",
    "    for j in range(len(pars)):\n",
    "\n",
    "        x_tmp = np.linspace(-200, 200, 400)\n",
    "        y_tmp = np.zeros_like(x_tmp)\n",
    "        \n",
    "        for i, dp in enumerate(x_tmp):\n",
    "            pars_tmp = pars.copy()\n",
    "            pars_tmp[j] = pars_tmp[j] + dp\n",
    "            y_tmp[i] = lg.get_cost(X, Y, pars_tmp, jac = False)\n",
    "\n",
    "        ax.plot(x_tmp, y_tmp, label = f\"parameter {j}\")\n",
    "        ax.set_yscale(\"log\")\n",
    "        ax.set_xlabel(f\"parameter perturbation\")\n",
    "        ax.set_ylabel(\"cost\")\n",
    "\n",
    "\n",
    "for ax in axs.flat:  ax.label_outer()\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(0.975, 0.3), loc='center right')\n",
    "\n",
    "plt.savefig(os.path.join(results_path, \"logit_cost_min_paper.pdf\"), bbox_inches = \"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4070360",
   "metadata": {},
   "source": [
    "## Show fit results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9e51e07c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['scale', 'dataset', 'beta', 'cost', 'cost_jac', '|cost_jac|', 'nllf',\n",
       "       'nllf_jac', '|nllf_jac|', 'cov', 'cov_evals', 'SE', 'LCL', 'UCL', 'LLF',\n",
       "       'AIC', 'BIC', 'A', 'chi2', 'p-value(chi2)', 'n', 'k', 'dof', 'pars',\n",
       "       'success'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fit.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a0a518b",
   "metadata": {},
   "outputs": [
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         "name": "p-value(chi2)",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scale</th>\n",
       "      <th>dataset</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "      <th>A</th>\n",
       "      <th>n</th>\n",
       "      <th>k</th>\n",
       "      <th>|cost_jac|</th>\n",
       "      <th>dof</th>\n",
       "      <th>cost</th>\n",
       "      <th>chi2</th>\n",
       "      <th>p-value(chi2)</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>log</td>\n",
       "      <td>FULL</td>\n",
       "      <td>17.205940</td>\n",
       "      <td>25.447712</td>\n",
       "      <td>0.948276</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>11.184562</td>\n",
       "      <td>19.356767</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>17.533001</td>\n",
       "      <td>25.774773</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
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       "      <td>20.726407</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "   scale dataset        AIC        BIC         A   n  k    |cost_jac|  dof  \\\n",
       "0    log    FULL  17.205940  25.447712  0.948276  58  4  7.937674e-09   54   \n",
       "1    log    TRIM  11.184562  19.356767  0.982456  57  4  6.218827e-06   53   \n",
       "2  plain    FULL  17.533001  25.774773  0.965517  58  4  1.074177e-05   54   \n",
       "3  plain    TRIM  12.554202  20.726407  0.964912  57  4  9.334844e-06   53   \n",
       "\n",
       "       cost      chi2  p-value(chi2)  success  \n",
       "0  4.605007  8.316716            1.0     True  \n",
       "1  1.700810  2.348806            1.0     True  \n",
       "2  4.782070  9.164985            1.0     True  \n",
       "3  2.480642  3.848636            1.0     True  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "df_fit[['scale', 'dataset', 'AIC', 'BIC', 'A', 'n', 'k', '|cost_jac|', 'dof', 'cost','chi2', \"p-value(chi2)\", 'success']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96acc3ab",
   "metadata": {},
   "outputs": [
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       "      <td>log</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>3</td>\n",
       "      <td>1772.499797</td>\n",
       "      <td>41.151002</td>\n",
       "      <td>3.34217</td>\n",
       "      <td>34.600468</td>\n",
       "      <td>47.701535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>0</td>\n",
       "      <td>-123.992871</td>\n",
       "      <td>-123.992871</td>\n",
       "      <td>18.879777</td>\n",
       "      <td>-160.996554</td>\n",
       "      <td>-86.989188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>1</td>\n",
       "      <td>163.794634</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.320488</td>\n",
       "      <td>-2.588108</td>\n",
       "      <td>2.588108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>2</td>\n",
       "      <td>-71.448487</td>\n",
       "      <td>5.582686</td>\n",
       "      <td>0.473536</td>\n",
       "      <td>4.654573</td>\n",
       "      <td>6.510799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>3</td>\n",
       "      <td>10.388794</td>\n",
       "      <td>-12.798228</td>\n",
       "      <td>0.888694</td>\n",
       "      <td>-14.540036</td>\n",
       "      <td>-11.056419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>0</td>\n",
       "      <td>-450.106747</td>\n",
       "      <td>-450.106747</td>\n",
       "      <td>633.831117</td>\n",
       "      <td>-1692.392909</td>\n",
       "      <td>792.179414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>1</td>\n",
       "      <td>727.880028</td>\n",
       "      <td>2.558527</td>\n",
       "      <td>2.352904</td>\n",
       "      <td>-2.053081</td>\n",
       "      <td>7.170135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>2</td>\n",
       "      <td>-395.551807</td>\n",
       "      <td>-14.727709</td>\n",
       "      <td>11.775012</td>\n",
       "      <td>-37.806308</td>\n",
       "      <td>8.350891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>3</td>\n",
       "      <td>72.3018</td>\n",
       "      <td>26.857661</td>\n",
       "      <td>20.350077</td>\n",
       "      <td>-13.027757</td>\n",
       "      <td>66.74308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    scale dataset idx         beta        pars          SE          LCL  \\\n",
       "0     log    FULL   0   -40.708789  -40.708789    9.159599   -58.661274   \n",
       "1     log    FULL   1   152.895751         0.0    2.015071    -3.949467   \n",
       "2     log    FULL   2  -186.372091   15.072426    2.088266      10.9795   \n",
       "3     log    FULL   3    75.726012  -12.365102    1.282418   -14.878595   \n",
       "4     log    TRIM   0  -318.618313 -318.618313   63.459451  -442.996552   \n",
       "5     log    TRIM   1   1693.40495    0.000037    4.519607    -8.858231   \n",
       "6     log    TRIM   2 -3000.779864  -72.921186    6.604729   -85.866218   \n",
       "7     log    TRIM   3  1772.499797   41.151002     3.34217    34.600468   \n",
       "8   plain    FULL   0  -123.992871 -123.992871   18.879777  -160.996554   \n",
       "9   plain    FULL   1   163.794634        -0.0    1.320488    -2.588108   \n",
       "10  plain    FULL   2   -71.448487    5.582686    0.473536     4.654573   \n",
       "11  plain    FULL   3    10.388794  -12.798228    0.888694   -14.540036   \n",
       "12  plain    TRIM   0  -450.106747 -450.106747  633.831117 -1692.392909   \n",
       "13  plain    TRIM   1   727.880028    2.558527    2.352904    -2.053081   \n",
       "14  plain    TRIM   2  -395.551807  -14.727709   11.775012   -37.806308   \n",
       "15  plain    TRIM   3      72.3018   26.857661   20.350077   -13.027757   \n",
       "\n",
       "           UCL  \n",
       "0   -22.756305  \n",
       "1     3.949467  \n",
       "2    19.165353  \n",
       "3     -9.85161  \n",
       "4  -194.240074  \n",
       "5     8.858304  \n",
       "6   -59.976154  \n",
       "7    47.701535  \n",
       "8   -86.989188  \n",
       "9     2.588108  \n",
       "10    6.510799  \n",
       "11  -11.056419  \n",
       "12  792.179414  \n",
       "13    7.170135  \n",
       "14    8.350891  \n",
       "15    66.74308  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table of parameters and their CI (Wald approximation)\n",
    "df_pars = df_fit[[\"scale\", \"dataset\", \"beta\", \"pars\", \"SE\", \"LCL\", \"UCL\"]].copy()\n",
    "\n",
    "# add index column for exploding\n",
    "indices = df_pars[\"pars\"].apply(lambda x: list(range(len(x))))\n",
    "df_pars.insert(2, \"idx\", indices)\n",
    "\n",
    "# convert to long format by exploding the pars, SE, LCL, UCL, beta columns\n",
    "cols = [\"idx\", \"beta\", \"pars\", \"SE\", \"LCL\", \"UCL\"]\n",
    "df_pars_long = df_pars.explode(cols, ignore_index=True)\n",
    "df_pars_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7438f05d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yww8/UF1djVUFTURan9MJe1e61pNGeTcWOarC8iquWvgLq3bnE2yz8O/LR3BSny5HP1DkKAoLC5kwYQLg+rCfl5fHli1bqK6u5qyzzmr0rDgiDTqwDVYtdK2f/giY1W7W1gzD4NFPN/PqD7sAuP7kntx+ej+sFv1biO9RJtmKysrKWL16NXAwqY+Pj6dHjx7s2rWLOXPmeMbTi0gryt0B5fmu6ejih3g7GjmCvFI7l89bwcZ9RYQH+rHoqlEMS4rwdljiI6qrq/n222/rbI+NjeXyyy/3QkTic756AAwH9DsLUk7ydjSdjmEYzP58iyeRf+ScwVw2pruXoxJpPfqKqhXdd999GIZB3759iYiI8Gy/7rrrAHjppZe8FJlIJ7PnZ9cycThY/LwbizQou6iCC/+9nI37iugSYuPt68YokZcWFR0djWEYnse2bdu4++67ycvL46KLLmLevHneDlE6sl3fw9bPwGSB0x7ydjSdjmEYPL5kK//+bicAM/80SIm8+Dwl863otddeA2DatGm1tv/jH//AbDazZ88eUlNTvRGaSOfiLn6nLvbtVmllNVMX/ML27BLiwwJ45/qxDEjQ2EZpXX369OHRRx/l0UcfBeCOO+7wckTSYTmd8OW9rvURV0KXPt6NpxN68sttvPyt63P1Q38cxOVjU7wbkEgbUDLfSlasWEFOTg7gmlfeZDJ5Hn5+fjidTkAfHETahLv4XdJo78Yh9ap2OLnpP2vYlOlqkf+/68fSKybE22FJJ3LZZZcBkJeX5+VIpMPa8olrKjpbKJw8w9vRdDpvrUjnX9/sAOCByQO5YlyKdwMSaSMaM99K7r77bsA15U18fN1psBwOB6mpqXz22WdtHZpI51KaC7nbXetqmW93DMPg4U828fWWbPytZuZOGUFydJC3w5JOZvfu3d4OQTq6tW+6lqOugZAY78bSyfy8M5f7P9oAwPTT+nLliT28HJFI21Ey3wqcTiffffcdAB999JGncu7hQkNDKSkp4bXXXmPKlCltGKFIJ7L3F9eyS18IivJuLFLHvB928dry3ZhM8OxFwzg+OdLbIUkn9Le//Q1wFcITabLSHNjxlWt96CXejaWT2ZNXxg1vrKLaaTB5aFdu+n1vb4ck0qbUzb4VPPnkk1RXVxMeHt5gIg9w7rnnAvD444+3UWQinZC7+J262Lc7X27M4tHPNgNw9xkD+MPgBC9HJL7OPTWd+zF06FCCgoJYu3YtJpOJV155xdshSke04X1wVrvmlY/p6+1oOo2SymquWfQr+WVVHJcYzj/PH4LJZPJ2WCJtSi3zreCFF14A4M9//vMR95s1axavv/46mzZtIi8vj6gotRqKtLg97vnllcy3J7tzS5n+f+swDLhsTDLXjFe3SGl99U1NZ7VaGTRoEHPnzmXs2LFeikw6tHVvu5ZDLvJuHJ2I02lw6ztr2bq/mJhQf+ZOGUGgzeLtsETanJL5VpCWltao/bp164ZhGK0bjEhnVm2HjFWudSXz7UZltYMb31pNSWU1I1MieXDyILWmSKs66aSTdL+V1nFgG+xb7ZqObvD53o6m03j5u1T+t2k/NquZVy4fTnx4gLdDEvEKdbMXEd+VtR6qKyAwUtMEtSOPfbaFDRlFRAb58dzFx2O16FYkIh3UbzWt8n1OU+G7NrJ2TwFPfbkNgIf/OEi1VqRT0ycoEfFdew6Zkk4tv+3Ckg1ZLPwpDYAnLxhKQnigdwMSEWkupxN++z/X+pALvRtLJ1FcUcXN/1lDtdPgrCEJXDgyydshiXiVknkR8V2e4neakq492JNXxh3vrQPgut/15Pf947wckYjIMUj/CQr3gH8Y9DvD29F0Cvd/tJH0vDISIwKZde5xGqIlnZ6SeRHxTYZxSPG7Md6NRah2OLn57TUUVVRzfHIEt0/q5+2QRESOjbvw3cA/gZ96GbW2xav38sGaDMw1U5mGB/p5OyQRr1MyLyK+qXAPFGeC2eqaLki86t/f7WRNegGhAVaev/h4/DROXkQ6sqpy2PSRa32oqti3tt25pdz34QYA/n5qX0akaAYoEVAyLyK+au8vrmX8ELAFeTeWTm5LVhHPfOUqVvTg5EF0i9S/h4h0cFs/g8oiCE+G5HHejsanOZ0G/3h3HaV2B6NSopj2+97eDkmk3VAyLyK+KW+XaxnT37txdHL2aie3/d86qhwGEwfEcd4Jid4OSUTk2G3+r2t53J/BrI/TrWnBT2n8kpZPsM3CkxcMxWLWOHkRN737iIhvKtzjWkao0q03/eubHWzcV0REkB+zzhusYkUi0vE5HZD6jWu97x+8G4uP25VTypwvtgBw91kDSIpSzy6RQymZFxHfVJDuWoYrmfeW9XsLeeGbHQDM/NNgYkMDvByRiEgL2LcGKgogIBwSh3s7Gp/lcBrc/u46KqqcnNS7C5eMSvZ2SCLtjpJ5EfFNBe6Wed38vaGy2sFt767F4TQ487h4zh6S4O2QRERaxo6vXMueE8Bi9WoovmzBj7v4dXc+If5WZp+vaehE6qNkXkR8j2Gom72XvfhNKtv2lxAdbGPmn9S9XkR8yI6lrmWvU70bhw/beaCEOV9sBeDuMweocKpIA5TMi4jvKT0A1RWACcK6eTuaTmfb/mJeXObqXv/gHwcRHeLv5YhERFpIeT5k/Opa761kvjU4nQZ3vv8bldVOxvfpwsWj9KW8SEOUzIuI73F3sQ9NAKvNu7F0Mo6aD2Gu6vWx6l4vIr5l57dgOKFLPwjXl8Wt4Y0Vuz3V62efP0Q9u0SOQMm8iPiegt2upcbLt7nXl6exJr2AEH8rM89R93oR8TGpNV3s1SrfKjIKynn8c1f1+jvP6E9iRKCXIxJp35TMi4jv0Xh5r8goKOefNWMc7zyjPwnh+hAmIj7EMA6Ol1cy3+IMw+CeD9ZTancwonskl43u7u2QRNo9JfMi4nvc09KpZb7NGIbBvR+sp6zmQ9ilmkJIRHzNga1QlAHWAOh+orej8Tkfrs1g2dYD2KxmHv/zEMxm9ewSORol8yLie9xj5jXHfJv5eN0+vtl6AJvFzOzz9SFMRHyQu4t993Hgp55HLSmnpJKH/rsJgL+f2odeMSFejkikY1AyLyK+Ry3zbSq/1M7DNR/Cpv2+N71j9SFMRHyQpqRrNQ9+vJGCsioGJoRx3e96ejsckQ5DybyI+JZac8wrmW8Lj3y6mdxSO33jQvjryb28HY6ISMurKofdP7rWNV6+RS3ZkMUnv2ViMZt4/Pwh+FmUnog0lv63iIhvKc8He4lrXdMGtboftufw/uq9mEzw2HlDsFl1WxERH7T7R6iugNCuENPf29H4jIIyO/d9tAGA63/Xk+O6hXs5IpGORZ+6RMS3uLvYB8dqTGMrK7c7uPuD9QBMGdOd4d0jvRyRiEgr2fG1a9n7VNCUmy1m5iebOVBcSa+YYG4+tY+3wxHpcJTMi4hv0Xj5NvPM0m2k55WREB7A7X9QS5WI+LBd37qWvX7v3Th8yDdbsz09u/7556EE+Fm8HZJIh6NkXkR8i+aYbxMbMgp59ftdAMz802BC/K1ejkhEpJWU5cH+ja71lJO8G4uPKKqo4u7Frp5dV5/YQz27RJpJybyI+Ba1zLe6KoeTO977DYfT4KzjEpg4MM7bIYmItJ49KwADovtASKy3o/EJj322hczCCrpHB3Hb6f28HY5Ih6VkXkR8i+aYb3UvL0tlU2YREUF+PPjHQd4OR0Skde3+ybXsPs67cfiIb7Zm85+Vri/eHz9/CIE2da8XaS4l8yLiWzwt8929G4eP2ppVzHNfbwfgwcmDiAn193JEIiKtzJPMn+jdOHxAfqmdO977DYCp41IY0zPayxGJdGxK5kXEtxS6k3m1zLe0aoeT299bR5XDYOKAWP40rKu3QxIRaV2VJZC51rXefaxXQ+noDMPg7g/Wc6C4kt6xIcw4Q4VTRY6VknkR8R0Vha4HqJt9K3j1h138treQ0AArj5xzHCZNzyQivm7vL+Csdt1TVIvlmHywJoPPN2RhNZt4+oJhql4v0gKUzIuI73CPlw+MAv8Q78biY3Zkl/DU/7YBcN/ZA4kPD/ByRCIibUDj5VvE3vwyHvjINSPALRP7cFy3cC9HJOIblMyLiO9QJftWUeVwctu767BXO/ld3xj+Mrybt0MSEWkb6ctdSyXzzeZ0Gtz2f+sorqzmhOQI/npyL2+HJOIzlMyLiO/QHPOt4vml21m3p4CwACuzz1P3ehHpJKorXd3sQcXvjsFzX29nxa48gmwWnrpgGFaL0g+RlqL/TSLiO1TJvsX9kpbHv77ZAcCj5x5H14hAL0ckItJG9q2B6goIjoHo3t6OpkP6btsBnl3qmgFl5p8Gk9Il2MsRifgWJfMi4jvcybyK37WIoooqbn1nLU4Dzjs+kclDVb1eRDqR3T+6lsljQT2SmmxfQTl/f3sNhgEXj0rifA3REmlxSuZFxHd4utlrzHxLePCjjezNL6dbZCAP/WmQt8MREWlbml++2ezVTm58azX5ZVUM6hrGA5N1DxFpDUrmRcR3FGiO+Zby33X7WLwmA7MJnrlwGKEBft4OSUSk7TgdkL7Cta7id00267PNrEl31Vp56dLhmoZOpJUomRcR32AvhbJc17q62R+T1AMl3LV4PQDTTunNiJQoL0ckItLGstaDvRj8wyFOrcpNsXj1Xhb+lAbAUxcMIzk6yLsBifgwJfMi4hvcc8z7h0NghFdD6chKKqu5/vVVlFRWMyoliptO7ePtkERE2p67i33yaDCrVbmxftyRwx3v/QbA3yb0YuLAOC9HJOLblMyLiG/QtHTHzDAMbn93HTuyS4gL8+dflx6Pn6YQEpHOyF38Tl3sG21zZhF/fX0V1U6Ds4ck8I/T+3k7JBGfp09pIuIbCna7lip+12wvf7uTzzdk4Wcx8dJlw4kNDfB2SCIibc8wIH25az1ZyXxj7Cso58oFv1BcWc2oHlE88ZehmM2aAUCktbWLZP6FF14gJSWFgIAARo8ezcqVK4+4/zPPPEO/fv0IDAwkKSmJW2+9lYqKijaKVkTaJXc3e42Xb5bvtx9gzhdbAHjwj4M4ITnSyxGJiHhJ7g5XDRZrAHQ93tvRtHuF5VVcueAXsooq6B0bwtzLR6jgnUgb8Xoy/8477zB9+nQeeOABVq9ezdChQ5k0aRLZ2dn17v/WW28xY8YMHnjgATZv3sy8efN45513uPvuu9s4chFpV4r2uZbhid6NowPavr+YaW+twWnAhSOSuGSUejeISCeW/rNr2fUEsNq8G0s7V1BmZ8r8lWzdX0xsqD8LrxxJeJBmPxFpK15P5p966imuvfZarrzySgYOHMjLL79MUFAQ8+fPr3f/n376iRNPPJFLLrmElJQUTj/9dC6++OKjtuaLiI8rznQtQ7t6N44OZm9+GZfPW0lheRXHJ0fw0J8GYTKpa6SIdGJ7apL55NHejaOdyy6u4KJXfmbdngIig/xYcOVIukWqcr1IW/JqMm+321m1ahUTJ070bDObzUycOJHly5fXe8y4ceNYtWqVJ3nfuXMnn332GWeeeWaD16msrKSoqKjWQ0R8jLtlPizBu3F0ILkllUyZt9LTNXL+FSPVNVJale7H0iG455dPGuPdONqxjIJyLvz3z2zJcrXIv3P9WAZ1Dfd2WCKdjleT+ZycHBwOB3FxtaetiIuLIysrq95jLrnkEh5++GFOOukk/Pz86NWrFxMmTDhiN/vHHnuM8PBwzyMpSWNqRXyKYRzSMq9kvjFKKqu5cuEv7MwpJTEikNevHkVksLqTSuvS/VjavdJcyN3uWk8a5d1Y2qmdB0r4y0s/sSunlG6Rgbz717H0jQv1dlginZLXu9k31bJly5g1axYvvvgiq1evZvHixXz66afMnDmzwWPuuusuCgsLPY89e/a0YcQi0uoqCqGqzLUepm72R1Nud3D967/y295CooJtvHb1KBLCA70dlnQCuh9Lu7enplW+Sz8IivJuLO3Qkg1Z/OmFH9lXWEGvmGDe/etYukcHezsskU7L6s2Ld+nSBYvFwv79+2tt379/P/Hx8fUec99993H55ZdzzTXXAHDcccdRWlrKddddxz333IPZXPf7CX9/f/z9/Vv+BYhI++BulQ+IAD8lpUdSUGbn6kW/smp3PsE2CwuvHEmvmBBvhyWdhO7H0u55pqTTePlD2audzP58C/N/3AXA8ckRzJ0ygi4h+v8s4k1ebZm32WwMHz6cpUuXerY5nU6WLl3K2LFj6z2mrKysTsJusbjGeBqG0XrBikj75Rkvr1b5I9lXUM5fXl7Oqt35hAVYWXjVKIZ0i/B2WCIi7ccejZc/3N78Mv7y7+WeRP7a8T34v+vHKpEXaQe82jIPMH36dK644gpGjBjBqFGjeOaZZygtLeXKK68EYMqUKSQmJvLYY48BMHnyZJ566imOP/54Ro8ezY4dO7jvvvuYPHmyJ6kXkU5G4+WPavv+YqbMX0lmYQXxYQEsumoU/eI1xlFExKOqAvatca0nK5mvqHLw6vc7eXFZKmV2B2EBVp68YBinDYw7+sEi0ia8nsxfeOGFHDhwgPvvv5+srCyGDRvGkiVLPEXx0tPTa7XE33vvvZhMJu69914yMjKIiYlh8uTJPProo956CSLibUU1ybwq2ddr2dZs/v72WgrLq+gZE8zrV48mMULDEUREaslcCw47BHWBqJ7ejsZrDMPg0/WZPPbZFjIKygEY3j2SZy4cRlKUpp4TaU+8nswDTJs2jWnTptX73LJly2r9bLVaeeCBB3jggQfaIDIR6RCKMlxLzTFfS0WVg38u2erpGjksKYL5U0cSpar1IiJ1pbvnlx8DJpN3Y/GCymoHSzZksfCnNNakFwCQEB7AjDP688ehXTF1wt+JSHvXLpJ5EZFjUqyW+cNt31/MzW+vZXOmax7vqeNSmHFGf80jLyLSEM94+c5V/G5XTilvr0zn3VV7ySu1AxDoZ+GvJ/fiut/1JNCm+4ZIe6VkXkQ6PncBPLXMU253MP/HXTz/9XYqqpxEBduY8+chnDpAYxxFRBpkGAeTeR8fL59Xamd5ai4/pubw044c0nLLPM8lhAdw4cgkLhmVTGxYgBejFJHGUDIvIh2fWuapdjh5d9Venv7fNrKLKwEY36cLT/5lqD6QiYgcTe4OKMsFawAkDPV2NHU4nAZVDicOp4HTMHDP32QYrjHu1U6DaodBtdNJRZWDwvIqzyOn2M7OnFJ25ZSwK6eU/UWVtc5tNsHv+sZw6ejunNIvBqvFq5NdiUgTKJkXkY6t2g6lB1zrnbBlvtzu4PMNmby4LJUd2SUAdIsM5B+n9+OPQ7tiNmuMo4jIUbnHy3c9AaxtM+WaYRjkltpJzS4h9UApqQdKSM8ro6DMTkFZFQU1yXiVw0lLz77cNy6Ecb26cGLvLozuGUVYgF/LXkBE2oSSeRHp2EqyXEuzHwRFezeWNmIYBuv2FvJ/v+7hv2v3UVxZDUBkkB/Tft+Hy8Yk42/VGEcRkUbb4y5+17rj5QvLq/hpRw7fbT/Ad9tyPNXiW4LVbMJiNhHgZyE80M/ziAy20SM6iB4xwfToEkKP6GDCg5S8i/gCJfMi0rEVHTLHvNl3uwbuKyjn5525rNiZx/KduaTnHRzjmBQVyAXDk7jixBS1roiINEe6u/hdy4+XdzgNvt6SzcKfdvHzzjwczoPN7CaTqzdVr5gQesWEkNIlmOhgGxGBfoQH+REW4Ie/nxk/sxmrxYTVbK5zq7OYXEm8qs2LdD5K5kWkYyuuKX7XwcbLO5yusY0Op0FVtUFRhas7ZVFFFYVlVaTnlZGWW0ZaTim7ckrJKqqodby/1cwZg+O5YGQSY3pEqzu9iEhzleZC7nbXetKoFjttcUUV7/66l4U/pdX6ArZXTDDj+8Rwct8YRvWIIthfH8dFpHn07iEiHduhLfNtcbmKKtJySknLLWN3Tim788rIK7VTVJOIF5VXU1HtwOFwFSRyOA0cNYMdDfcSmjz+0WyC4xLDGdMzmjE9oxmREkmoWuFFRI6du4t9l34QFHXMpzMMg3d+2cOszzZTVOEaBhUe6MfFo5K5ZFQyydFBx3wNERFQMi8iHZ2nZT6xVU6fX2pnxa5cft6Zx887c9mSVdwq1wnwMxMWcHCMY9eIQFKig0jpEkz36GD6xoUoeRcRaQ3py13L7mOP+VQ7D5Rw1+L1rNiVB7ha4a86qQfnHp9IkE0fu0WkZeldRUQ6tqKWn5bOXu3kf5v285+V6fyYmlOnFT0m1J+U6CC6RwfTPSqI2DB/wgL8CAt0jW8MtJmxms1YzCasFhNmk4nDO8FbLe7xj64xkDar7473FxFp19yV7JObn8xXO5z8+7udPLt0O/ZqJ4F+Fm47vS9Tx6VoqjcRaTVK5kWkYytuuW72e/PLeH35bt5btZfcUrtne9+4EE/39lE9ougS0jbTFomISCuzl8G+ta715OYVvyuzV3PTW2tYuiUbcM3Z/ug5g0mKUnd6EWldSuZFpGMrcnezb/4c82X2al5elsq/v9tJZbUTgNhQfy4YkcSFI5P0gUxExFftWw3OKtcXwhHdm3x4bkklVy/6lbV7CvC3mnn03OM4/4REVZYXkTahZF5EOi7DOKaWecMw+HjdPmZ/voXMQle1+FE9orjmpB78vn+sukaKiPg693j55DGueeKacmhuGVcsWMmunFIigvyYd8UIhnc/9gJ6IiKNpWReRDqu8nyorpmyrYnJfE5JJTf/Zw0/peYCkBgRyL1nDeAPg+PVoiIi0lk0c7z8pn1FTJm/gpwSO4kRgSy6ahS9Y0NaIUARkYYpmReRjsvdKh8YBX4BjT5sQ0Yh1732K/sKKwj0s/C3Cb249nc9CfCztFKgIiLS7jgdsGela70J4+X3F1Vw1cJfyCmxMzAhjIVXjiQ2rPH3IBGRlqJkXkQ6Lk8l+8aPl/9obQZ3vPcbldVOenYJ5pUpI9SaIiLSGWVvgsoisIVC7KBGHVJR5eC6134lq6iC3rEh/Oe6MYQHatpQEfEOJfMi0nG555hvRBd7p9Pg8SVb+Pd3OwE4pV8Mz1x0vD6EiYh0Vu4u9t1GgOXoH4kNw+Af765j3d5Czxh53UNExJuUzItIx9XIOeYNw2DWZ5t59YddANx4Si+mn9YPi1lj40VEOi1P8bvGjZd/bukOPvktE6vZxMuXDad7dHArBicicnRK5kWk4/K0zB+5m/1L36Z6Evl/nj+EC0YmtXZkIiLS3qWvcC0bMV7+s/WZPP3VNgAeOWcwY3pGt2ZkIiKNonmXRKTj8swx33DL/H9WpvPPJVsBuPesAUrkRUQECvZA0V4wWVzd7I/gQHEldy1eD8DVJ/XgolHJbRGhiMhRKZkXkY7L3c2+gZb5z9dncs8Hrg9gf5vQi2vG92yryEREpD1zj5dPGAq2I3eXf+i/Gyksr2JQ1zDuOqN/GwQnItI4SuZFpOMqbrhlfnV6Pn9/ey1OAy4elcTtk/q1cXAiItJuNXK8/Ndb9vPJb5mYTTD7vCFYLfroLCLth96RRKRjqq6EslzX+mEt8yWV1dzy9lrsDienD4zjkXOOw2RSsTsREanhbpk/wnj50spq7vtwI+DqXn9ct/C2iExEpNGUzItIx1Rc08Xe4g9BUbWeeujjjaTnlZEYEcgTFwxV1XoRETmoPN81xzwcMZl/4sutZBSU0y0ykFtP69tGwYmINJ6SeRHpmDzj5ePhkFb3z9dn8u6qvZhM8NQFQwkL0BzAIiJyiD0rAQOiekJIbL27rN1TwMKf0gB49NzjCLJpAigRaX+UzItIx+QZL3+wi/3+ogruqil4d8PJvRitqYNERORwu39yLbuPq/dpp9Pgng/WYxhwzrCunNw3pg2DExFpPCXzItIxeVrmXcXvnE6Df7y7joKyKgYnhnHLRHWJFBGReuz+0bXsflK9T3+5KYuN+4oI9bdy39kD2zAwEZGmUTIvIh2Te8x8Tcv8myvT+X57DgF+Zp658HhsVr29iYjIYeylsG+Na72elnnDMHh26Q4App6YQnSIf1tGJyLSJPq0KyIdU1FNN/vQBIoqqnjqy60A3PmH/vSODfFiYCIi0m7t/QWc1RDWDSKS6zz91eZsNmcWEWyzcNWJPbwQoIhI4ymZF5GOqSjDtQzrysvLUskvq6JnTDCXj+nu3bhERKT9OnS8/GFTlhqGwXNLtwNwxbgUIoNtbR2diEiTKJkXkY4pfzcAB/wSmPfDLgBm/KE/Vove1kREpAFHKH73zdZs1mcUEmSzcM34nm0cmIhI0+lTr4h0PPYyKMkC4PnVVVRWOxmVEsVpA+O8HJiIiLRb1ZWubvYA3U+s9dShY+UvH9OdKLXKi0gHoGReRDqegnQAHH6hvP5bEQB3nzUA02FdJkVERDz2rYHqCgiOgS59aj313fYc1u0pIMDPrFZ5EekwlMyLSMdT4Opiv4dYDMPEWUMSGJYU4d2YRESkfUv7wbU8bLy8YRg8+9U2AC4d3Z2YUFWwF5GOQcm8iHQ8+WkAbK6Iws9i4s5J/b0bj4iItH+e8fK1u9iv3VPA6vQCbFYz1/9OrfIi0nEomReRjqcmmd9jxHL5mBSSo4O8G4+IiLRvjmrYs8K1fljxu3d+2QPA2UMSiA0LaOvIRESaTcm8iHQ4BftcUwftM8Vxw4ReXo5GRETavazfwF4CAeEQO9CzuaSymo/X7QPgopF1550XEWnPlMyLSIdTmpUKQGKPARrbKCIiR+fuYp88FswWz+ZPf9tHmd1Bzy7BjEyJ9FJwIiLNo2ReRDqUzIIyIiozAJgwZoSXoxERkQ6hgfnl3V3sLxiZpBlRRKTDUTIvIh3Ke9+vI9hUiRMTffoMPPoBIiLSuTmdkF63+N22/cWsTi/AajZx3gmJXgpORKT5lMyLSIdRbnewYvVqACoD48BPhYpEROQoDmyG8nzwC4aEoZ7N7lb5UwfEEhuq+4mIdDxK5kWkw/hgTQZRla5CRQExPbwcjYiIdAjuLvZJo8DiB0BltYPFq/cCcOHIJG9FJiJyTJTMi0iHYBgGC37cRTdTNgCmSCXzIiLSCGk/uJaHdLH/alM2+WVVxIcF8Ls+MV4KTETk2CiZF5EO4YcdOWzPLqGn9YBrQ2SKV+MREZEOwDBg94+u9ZSDyfzbv6QD8JcR3bBa9HFYRDomvXuJSIcw/4ddAAwPLXRtiOzuxWhERKRDyNkGpQfAGgCJwwHYm1/GDztyALhghLrYi0jHpWReRNq97KIKlm1ztci7u9mrZV5ERI7K3cW+20iw+gPw6W+ZGAaM7RlNUlSQF4MTETk2SuZFpN37b80Hr5FJIfiVuArgKZkXEZGj8nSxP8mz6ctN+wE487h4b0QkItJilMyLSLv30doMAC7ubwbD6eouGRLn5ahERKRdM4yDLfM1yXx2cQWr0/MBOG2gknkR6diOKZmvrKxsqThEROq180AJv+0txGI2cWpcmWtjRHcwmbwbmIiItG+5qVCyHyz+kDgCcFWxNwwYmhRBfLjmlheRjq1Jyfznn3/OFVdcQc+ePfHz8yMoKIiwsDBOPvlkHn30Ufbt29dacYpIJ/XxOtf7ykm9uxBeoS72IiLSSLvd4+VHgJ8rcf9yUxYApw9U7y4R6fgalcx/8MEH9O3bl6uuugqr1cqdd97J4sWL+eKLL3j11Vc5+eST+eqrr+jZsyd//etfOXDgQGvHLSKdgGEYfLTWlcCfc3xXyE9zPaFkXkREjuawLvbFFVX8tCMXgEmDlMyLSMfXqGT+n//8J08//TQZGRnMmzeP66+/nsmTJzNx4kQuuOACHn74Yb755htSU1OJiIjgjTfeaFIQL7zwAikpKQQEBDB69GhWrlx5xP0LCgq48cYbSUhIwN/fn759+/LZZ5816Zoi0v6tzyhkV04pAX5m19hGTzKvaelEROQIDAPSaorfdXfNL79s6wHsDic9Y4LpHRvqxeBERFqGtTE7LV++vFEnS0xMZPbs2U0K4J133mH69Om8/PLLjB49mmeeeYZJkyaxdetWYmNj6+xvt9s57bTTiI2N5b333iMxMZHdu3cTERHRpOuKSPvnbpWfOCCOEH8rFOx2PaGWeREROZL8XVC8D8x+rmnpOFjF/nQVvhMRH9GoZP5QFRUVBATUXzAkMzOThISEJp3vqaee4tprr+XKK68E4OWXX+bTTz9l/vz5zJgxo87+8+fPJy8vj59++gk/Pz8AUlJSmvYiRKTdczgN/lszXv6cYYmujepmLyIijeFulU8cDrYgKqsdfLMlG4DT1cVeRHxEk6vZn3DCCaxdu7bO9vfff58hQ4Y06Vx2u51Vq1YxceLEgwGZzUycOLHB3gAff/wxY8eO5cYbbyQuLo7Bgwcza9YsHA5Hg9eprKykqKio1kNE2refd+aSXVxJeKAfv+sbAxWFUO6aTogIdbMX6Yh0P5Y2c9h4+eWpuZRUVhMb6s+wbhHei0tEpAU1OZmfMGECY8aM4fHHHwegtLSUqVOncvnll3P33Xc36Vw5OTk4HA7i4mp/QxoXF0dWVla9x+zcuZP33nsPh8PBZ599xn333ceTTz7JI4880uB1HnvsMcLDwz2PpKSkJsUpIm3PPbf8mcclYLOaIb+mi31QF/AP8WJkItJcuh9Lm9ld0zKf4hov7+5if9rAOMxmTW0qIr6hyd3sX3zxRc466yyuueYaPvnkEzIzMwkJCWHlypUMHjy4NWKsxel0EhsbyyuvvILFYmH48OFkZGQwZ84cHnjggXqPueuuu5g+fbrn56KiIn2AEGnHKqsdfL7B9YXeOcO6ujaqi71Ih6f7sbSJ/N1QuAfMVkgajdNp8D/3ePlBGi8vIr6jyck8wBlnnMF5553HSy+9hNVq5b///W+zEvkuXbpgsVjYv39/re379+8nPr7+N9uEhAT8/PywWCyebQMGDCArKwu73Y7NZqtzjL+/P/7+/k2OT0S8Y8XOPIorXN0hR6ZEuTaqkr1Ih6f7sbQJdxf7rieALZg1u/M5UFxJqL+VsT2jvRubiEgLanI3+9TUVMaOHcsnn3zCF198wR133MEf//hH7rjjDqqqqpp0LpvNxvDhw1m6dKlnm9PpZOnSpYwdO7beY0488UR27NiB0+n0bNu2bRsJCQn1JvIi0vF8XVOk6Pf9Yw92h1QlexERaYzDuth/vcXVaDShf6xr2JaIiI9o8jvasGHD6NGjB+vWreO0007jkUce4ZtvvmHx4sWMGjWqyQFMnz6duXPnsmjRIjZv3swNN9xAaWmpp7r9lClTuOuuuzz733DDDeTl5fH3v/+dbdu28emnnzJr1ixuvPHGJl9bRNqnZVtdyfyEfodMT6lu9iIi0hi7a88v/+OOXABO7hvjrYhERFpFs8bMX3755bW2jRs3jjVr1nDLLbc0OYALL7yQAwcOcP/995OVlcWwYcNYsmSJpyheeno6ZvPB7xySkpL44osvuPXWWxkyZAiJiYn8/e9/584772zytUWk/dl5oIS03DL8LCZO6tPl4BPuZF6V7EVEpCFF+1z3C5MZkkZTVFHFb3sLABjbS13sRcS3NDmZPzyRdwsNDWXevHnNCmLatGlMmzat3ueWLVtWZ9vYsWP5+eefm3UtEWnfvtl6AIBRPaII8a95i7KXQd4u13p0by9FJiIi7d7un1zL+CEQEMbKTftxGpASHURiRKB3YxMRaWEaOCQi7co3NePlTzm0i33mWjAcEBIPYV29E5iIiLR/7mS+pov9T6muLvbjendp6AgRkQ5LybyItBulldWs2OX64HVK/0OS+b2/upbdRoBJ8wOLiEgDPMn8OAB+Ss0BYJy62IuID1IyLyLtxg87cqhyGHSPDqJnl+CDT2TUJPOJw70TmIiItH+luXBgs2s9eSy5JZVsySoGYIympBMRH6RkXkTaDXcV+1P6xWI6tAV+7yrXstsIL0QlIiIdQvpy1zKmPwRHs3ynq6dX//hQuoT4ezEwEZHWoWReRNoFwzD4Zour+F2tLvbFWVC0FzBB1+O9E5yIiLR/dbrY14yX76Xx8iLim5qVzJ911llkZmbWWRcRaa7NmcVkFVUQ6GdhdI+og0+4x8vHDgD/UO8EJyIi7d9h88sv9yTz6mIvIr6pWcn8d999R3l5eZ11EZHm+qami/2JvaMJ8LMcfELj5UVE5GgqiyHrN9d68lj2FZSzK6cUswlG9Yw68rEiIh2UutmLSLvwtXtKukO72EPtSvYiIiL12bMCDCdEpkB4oqeL/XHdIggL8PNubCIirUTJvIh4XX6pnTXp+QBMOHR+eacD9q1xrScqmRcRkQbUmV/eNSXdiepiLyI+TMm8iHjdj6k5OA3oFxdKYkTgwScObAV7CfgFu8bMi4iI1OeQ4neGYRwyXl7F70TEdymZFxGvW7EzD4Cxh7eguMfLdz0ezBZERETqqCqHjJopTJPHsiunlMzCCmwWM8O7R3o3NhGRVqRkXkS8bsUuVwvKmMOLFHnGy6v4nYiINCBjFTjsEBIPUT094+WPT44g0KYvgkXEdymZFxGvyi2pZNv+EgBG9Ti8Zb6mpUXj5UVEpCG7l7uW3ceBycTynepiLyKdQ7OS+e7du+Pn51dnXUSkqVbucnWx7xsXQlSw7eATlSWQvcm1rkr2IiLSEM/88uMAWLPbVVB1ZA91sRcR32ZtzkEbNmyod11EpKlW1CTzow9vlc9c65pmKLQrhHVt+8BERKT9c1TD3l9c68ljySqsYF9hBWYTDO0W4dXQRERam7rZi4hX/bzTPV7+sGTePV4+8YQ2jkhERDqM7E2uWU/8wyB2gGea037xYQT7N6vNSkSkw1AyLyJeU1BmZ+v+YgBG9Tis+J27kr262IuISEP2rnQtE4eD2cKaPQWAq/idiIivUzIvIl6zclcehgG9YoKJCfWv/eReFb8TEZGj2FOTzCeNBvC0zB+fFOGlgERE2o6SeRHxGs94+cO72B/YCsX7wGR2zTEvIiJSH08yP5Iqh5Pf9hYCcILmlxeRTkDJvIh4jXt++dGHd7H/6XnXsu8Z4B/SxlGJiEiHUHIA8ncBJkgcwZbMYiqrnYQH+tEjOtjb0YmItLpGJ/NfffXVEZ93Op088sgjxxyQiHQORRVVbNpXBBxW/K44C357x7V+4t+9EJmIiHQI7vHyMf0hMILVNV3shyVFYDabvBiYiEjbaHQyf+aZZzJt2jTKysrqPLdhwwZGjhzJSy+91KLBiYjv+jUtD6cBKdFBxIUFHHxixb/BYXeNf0we7b0ARUSkfduzwrVMGgUcMl5exe9EpJNo9Jwd33//PVOnTmXo0KEsXLiQE088EafTyaxZs5g5cybnn3/+UVvvRUTcft5Zz/zylcXwyzzX+ribvRCViIh0GHtq5pd3J/OeSvYaLy8uTqcTu93u7TCkg/Pz88NisXg7jHo1OpkfPXo0a9asYcaMGZxyyilcd911/Pzzz+zZs4f//Oc/nHfeea0Zp4j4mBU188uP7nnIePnVr0FlIUT3hn5neikyERFp96rtsG+1az1pNLkllezOdfUeHaZK9gLY7XZ27dqF0+n0dijiAyIiIoiPj8dkal9DeBqdzAMEBATw9NNPk52dzYsvvkhwcDC//vor/fr1a634RMQHlVRWs6FmvLynkr2jCpa/6FofdxOYVZ9TREQasH89VFdAYCRE92bN5mwAeseGEB7o5+XgxNsMwyAzMxOLxUJSUhJmfaaQZjIMg7KyMrKzXe8xCQkJXo6otiYl86mpqUydOpXt27fz8ssvs2DBAiZMmMDLL7/Mn/70p9aKUUR8zK9peTicBt0iA0mMCHRt3PgBFO2F4FgYcpF3AxQRkfbNPSVdt1FgMrFmj+aXl4Oqq6spKyuja9euBAUFeTsc6eACA12fVbOzs4mNjW1XXe4b/TXVv/71L4YOHUpsbCzr16/nuuuu48cff+SWW27hoosu4vLLL6egoKAVQxURX/FrmutDl2e8vGHAj8+61kdfD34BDRwpIiJCrfnlAdakFwAaLy8uDocDAJvN5uVIxFe4vxSqqqryciS1NTqZv//++/n3v//N+++/T0xMjOtgs5k777yTX3/9lc2bNzNo0KBWC1REfIe7BeWE7hFQuBfeuQz2bwC/YBh5tXeDExGR9s+TzI/G4TRY5yl+F+G1kKT9aW/jm6Xjaq9/S43uZr9x48YGxwgMGjSIFStWMGvWrBYLTER8k9Np8NueQsw4+X3B+/DCk2AvAbMVJj3iGv8oIiLSkMIM17Askxm6nsD27GJK7Q6CbRb6xoV6OzoRkTbT6GT+aIP9LRYL99133zEHJCI+KPM32PE/qCymqCCf+xy7GOK/i4Tl6a7nk0bD5GchdoB34xQRkfZvb02rfNwg8A9h9W7XvWRoUgQWc/tsPRNprgkTJjBs2DCeeeYZb4ci7ZBKO4pI63A6YPMnsOAs+Pd4WPow/PA0ERsWcoH1W/qb0iEgHM5+Bq5cokReREQaxzO//GgA1qTXFL9TF3sRli1bhslkarVaZiaTqc7jpJNOAiAtLQ2TycTatWvrHDdhwgRuueUWz88pKSkNfkFxpPMcKa6AgAB2795da/s555zD1KlTa23LysripptuomfPnvj7+5OUlMTkyZNZunRpo6/XXjSqZd5sNjdrnMADDzzA/fff3+TjRKSD2/o5LJkB+Wmun00W6HcGhCfxza5SfsmoZEif7vzhz9dCSKxXQxURkQ5mzwrXstsoANbUjJcflqRhWtKynE6DjIJySu3VBNusJEYEYlbvDxYsWMAf/vAHz8/tpdCgyWTi/vvvZ9GiRQ3uk5aWxoknnkhERARz5szhuOOOo6qqii+++IIbb7yRLVu2tGHEx65RyfyuXbuadfKIiIhmHSciHVhRJrx3FVSVQUAEjLgSRl4D4d0AmPPs92xyFPHS8BOUyIuISNNUVUDmOtd60ijK7NWkHigBYGhSuBcDE1+zI7uYLzbsJ/VACRXVDgKsFnrFhDBpcBy9Y1unNkNpaSk33HADixcvJjQ0lH/84x919nn99dd59tln2bp1K8HBwfz+97/nmWeeITY2lrS0NE455RQAIiNdX25dccUVLFy4kCVLlvDII4+wYcMGLBYLY8eO5dlnn6VXr15NjjMiIoL4+Phje7GtYNq0aTz11FPcfvvtDB48uN59/va3v2EymVi5ciXBwcGe7YMGDeKqq65qq1BbTKOS+e7du7d2HCLiK755xJXIdxsJUz4C28E3yjJ7NVv3FwMwTN0hRUSkqfZvBGcVBEVDZAqb0/MxDIgN9Sc2VNOaSsvYkV3Mgh/TyCu1kxAeQJAtkDJ7NRv2FbKvsJwrT0xplYT+9ttv59tvv+Wjjz4iNjaWu+++m9WrVzNs2DDPPlVVVcycOZN+/fqRnZ3N9OnTmTp1Kp999hlJSUm8//77nH/++WzdupWwsDDPHOmlpaVMnz6dIUOGUFJSwv3338+5557L2rVrMZtdI68nTJhASkoKCxcubPHX1hZOPPFEtm3bxowZM/jkk0/qPJ+Xl8eSJUt49NFHayXybh2xIbrRBfCys7OJjW24Fa26uprVq1czatSoFglMRDqgrA2w5k3X+qRZtRJ5gPV7C3E4DeLC/EkID/RCgCIi0qFlrnEtux4PJhMbMooAGNQ1zItBiS9xOg2+2LCfvFI7fWJDPEONQwP8CPG3sj27hC837qdnl5AW7XJfUlLCvHnzeOONNzj11FMBWLRoEd26dau136Gtxz179uS5555j5MiRlJSUEBISQlRUFACxsbG1ktPzzz+/1nnmz59PTEwMmzZt8rRiJycnH7XoOcDFF1+MxWLx/PzGG29wzjnnNOn1tpbHHnuMIUOG8P333zN+/Phaz+3YsQPDMOjfv7+Xomt5jS6Al5CQQHZ2tufn4447jj179nh+zs3NZezYsS0bnYh0HIYBX94LGDDwHEiq+8XeWs+4xoi2jExERHzFvppkPmEYABv3FQIwOFFd7KVlZBSUk3qghITwgDo1w0wmEwnhAezILiGjoLxFr5uamordbmf06NGebVFRUfTr16/WfqtWrWLy5MkkJycTGhrKySefDEB6evoRz799+3YuvvhievbsSVhYGCkpKXWOe+2113jssceOGuvTTz/N2rVrPY/TTjutsS+z1Q0cOJApU6YwY8aMOs8ZhuGFiFpXo1vmD3/xaWlpVFVVHXEfEelEdiyFnd+AxQYTH6x3l7UqUiQiIsdi31rXsuvxAIe0zCuZl5ZRaq+motpBkK3+HoSBNgv7iyootVe3cWSurvKTJk1i0qRJvPnmm8TExJCens6kSZOw2+1HPHby5Ml0796duXPn0rVrV5xOJ4MHDz7qcfWJj4+nd+/edbaHhbl6yBQWFtZ5rqCggPDwtvl/+tBDD9G3b18+/PDDWtv79OmDyWTqcEXujqRFp6ZrTsV7EfEBjuqaVnlg1HUQ1aPe3dzJvKYPEhGRJqsqh+zNrvWux1NZ7WB7tqsOi7rZS0sJtlkJsFooayBZL7c78LdaCLY1uk20UXr16oWfnx8rVqzwbMvPz2fbtm2en7ds2UJubi6zZ89m/Pjx9O/fv1bPaThYWd7hcHi25ebmsnXrVu69915OPfVUBgwYQH5+fovGD66eBF26dGHVqlW1thcVFbFjxw769u3b4tesT1JSEtOmTePuu++u9XuIiopi0qRJvPDCC5SWltY5rrWm82tNLftXKCKd05rX4cBmCIyE39WtvAqwv6iCzMIKzCY4Tt0hRUSkqbI2gOGA4BgI68r2fUVUOQzCA/3oFqk6LNIyEiMC6RUTwoZ9hYT4W2s1VhqGQWZhBcclhpMY0bJ/cyEhIVx99dXcfvvtREdHExsbyz333OMpTgeuMe02m43nn3+ev/71r2zYsIGZM2fWOk/37t0xmUx88sknnHnmmQQGBhIZGUl0dDSvvPIKCQkJpKen19sNfcqUKSQmJjaqq31Dpk+fzqxZs4iLi2PMmDHk5uYyc+ZMYmJiOO+882rtm5GRUWcu+UMLr2/durXO+QcNGoSfn99R47jrrruYO3cuu3bt4sILL/Rsf+GFFzjxxBMZNWoUDz/8MEOGDKG6upr//e9/vPTSS2zevLmJr9i7Gp3Mm0wmiouLCQgIwDAMTCYTJSUlFBW5uje5lyLSyTiqYVnNm/7Jd7oS+nqsSS8AoG9cKMH++h5RRESaKHOta+kpfuceLx+m3qHSYsxmE5MGx7GvsJzt2a6x84E2C+V2B5mFFUQF2zh9UFyrzDc/Z84cSkpKmDx5MqGhodx22221uqzHxMSwcOFC7r77bp577jlOOOEEnnjiCf74xz969klMTOShhx5ixowZXHnllUyZMoWFCxfy9ttvc/PNNzN48GD69evHc889x4QJE2pdPz09vdaXB81xxx13EBISwuOPP05qaipRUVGceOKJfPPNN57K+m5PPPEETzzxRK1tr7/+OieddBIAF110UZ3z79mzp05RwPpERUVx5513cvfdd9fa3rNnT1avXs2jjz7KbbfdRmZmJjExMQwfPpyXXnqpqS/X60xGIwe6m83mOt9M1ffzoV0Z2quioiLCw8MpLCz0jO0QkWbauwpe/T0EhMM/doDVVu9usz/fwsvfpnLxqCQeO29IGwcpIo3R1vdH3Y+lST78G6x90/XF8Sl3c9+HG3j9591c97ue3H3mAG9HJ+1IRUUFu3btokePHgQENG/KwkPnma+sdnWt7x0bwumDWm+eeWm/WuJvqikae39sdPPYN9980yKBiYiP2f2Da9n9xAYTeYC1e1xjs1TJXkREmuWwSvYbairZa7y8tIbesaH0nBBCRkE5pfZqgm1WEiMCW6VFXqS5Gp3Mu6c9OJK8vLxjCkZEOqC0H13L7ic2uIvDafDbXteHLlWyFxGRJrOXwYGaCtRdj8fhNNic6RriqWnppLWYzSaSooK8HYZIg1qkmv2XX37JBRdcQGJiYkucTkQ6CqcD0pe71lMaTua37S+mzO4gxN9K79iQNgpORER8RtZ6MJwQEg9hCew8UEJFlZNgm4Ue0cHejk5ExCuanczv3r2bBx54gJSUFP7yl79gNpt57bXXWjI2EWnvsn6DyiLwD4P4hsfBu6ekG9ItHIu6p4mISFO5u9i755ev6WI/ICFM3Z5FpNNqUklpu93O4sWLefXVV/nxxx+ZOHEie/fuZc2aNRx33HGtFaOItFfuLvbJY8FsaXC3dTXJvMbLi4hIs3gq2Q8DYEOGutiLiDS6Zf6mm26ia9euPPvss5x77rns3buX//73v5hMJiyWhj/Ei4gP212TzB+hiz3gGS8/pFtEKwckIiI+6bCW+Y0qfici0viW+Zdeeok777yTGTNmEBqq6RhEOj2nE3b/5FrvflKDu1VWO9i2vxiA47qpBUVERJqosgQObHWtJwzD6TTYWNMyP6ir7isi0nk1umX+9ddfZ+XKlSQkJHDhhRfyySefdIg55UWklezfABUFYAuBhKEN7rYtq4Rqp0FkkB9dw1t/Xk4REfExWesBA0K7Qmgce/LLKK6sxmYx0ydORVVFpPNqdDJ/8cUX87///Y/169fTv39/brzxRuLj43E6nWzatKk1YxSR9sjdxT55DFga7uSzPsPVFXJwYjgmk4oUiYhIE9XpYu9qle+fEIqfpUUmZhIR6ZCa/A7Yo0cPHnroIdLS0njjjTc4//zzueyyy+jWrRs333xza8QoIu1R2g+u5RHml4eDFYdVpEhERJrFU/yuppJ9hsbLS+cxYcIEbrnlFm+HIe1Us7/ONJlMTJo0if/7v/9j3759/OMf/+Dbb79tydhEpL06dLx8SsPj5QE2ulvmNa5RRESaw9MyPww42DKv8fIidS1btgyTyURBQUGrnN9kMtV5nHSS67NgWloaJpOJtWvX1jnu8C8lUlJSeOaZZ+q9xpHOczTXX389FouFd999t85zDz74YL3x9+/fv8nXaS9apG9SVFQUt9xyC+vWrWvW8S+88AIpKSkEBAQwevRoVq5c2ajj3n77bUwmE+ecc06zrisizXRgM5TngV+Qp6WkPlUOJ5uzXMXvBieqBUVERJqoshhytrvWE4ZhGIanZV49vqTVOZ2Qvxv2b3QtnU5vR9QuLFiwgMzMTM/j448/9nZIAJSVlfH2229zxx13MH/+/Hr3GTRoUK3YMzMz+eGHH9o40pbTqGR+9uzZlJeXN+qEK1as4NNPP210AO+88w7Tp0/ngQceYPXq1QwdOpRJkyaRnZ19xOPS0tL4xz/+wfjx4xt9LRFpIe755ZNGg8Wvwd227y/BXu0kNMBKclRQGwUnIiI+w138LqwbhMRwoLiS3FI7ZhP0j9fsStKKDmyFH56Cb2bBt/90LX946uDMCq2gtLSUKVOmEBISQkJCAk8++WSdfV5//XVGjBhBaGgo8fHxXHLJJZ68KS0tjVNOOQWAyMhITCYTU6dOBWDJkiWcdNJJREREEB0dzdlnn01qamqz4oyIiCA+Pt7ziIqKat4LbmHvvvsuAwcOZMaMGXz33Xfs2bOnzj5Wq7VW7PHx8XTp0sUL0baMRiXzmzZtIjk5mb/97W98/vnnHDhwwPNcdXU1v/32Gy+++CLjxo3jwgsvbNLUdU899RTXXnstV155JQMHDuTll18mKCiowW9TABwOB5deeikPPfQQPXv2bPS1RKSF7K75BvMo88t7xst3VfE7ERFphqwNrmXCEAA2Zbq62PeMCSHAz+KtqMTXHdgKP78Mmb9BUBRE93EtM39zbW+lhP7222/n22+/5aOPPuLLL79k2bJlrF69utY+VVVVzJw5k3Xr1vHhhx+SlpbmSdiTkpJ4//33Adi6dSuZmZk8++yzgOuLgunTp/Prr7+ydOlSzGYz5557Ls5DehtMmDDBc66OaN68eVx22WWEh4dzxhlnsHDhQm+H1OoaNc/8a6+9xrp16/jXv/7FJZdcQlFRERaLBX9/f8rKygA4/vjjueaaa5g6dSoBAY2bfsput7Nq1Sruuusuzzaz2czEiRNZvnx5g8c9/PDDxMbGcvXVV/P9998f9TqVlZVUVlZ6fi4qKmpUfCJSD8M42DJ/hPnlgUO6QqqLvYjofizNsH+9axk3CIAtNUO31CovrcbphM3/hbJciOkP7sYI/zCICYUDW2DLJ64E39xysymUlJQwb9483njjDU499VQAFi1aRLdu3Wrtd9VVV3nWe/bsyXPPPcfIkSMpKSkhJCTE00oeGxtLRESEZ9/zzz+/1nnmz59PTEwMmzZtYvDgwQAkJyeTkJBw1FgvvvhiLJaDX6a98cYbXh/2vH37dn7++WcWL14MwGWXXcb06dO59957azUorV+/npCQ2lNaXnbZZbz88sttGm9LaVQyDzB06FDmzp3Lv//9b9atW0d6ejrl5eV06dKFYcOGNat7Qk5ODg6Hg7i4uFrb4+Li2LJlS73H/PDDD8ybN69JBREee+wxHnrooSbHJyL1yNkOZTlgDYDEE464q8Y1isihdD+WJtu/0bV0J/M1LfMDEvQlsbSSwj2uzzrhiQcTeTeTCcIS4cA2136R3VvssqmpqdjtdkaPHu3ZFhUVRb9+/Wrtt2rVKh588EHWrVtHfn6+p2U9PT2dgQMHNnj+7du3c//997NixQpycnJqHedO5l977bVGxfr0008zceJEz8+N+QKgtc2fP59JkyZ5ctIzzzyTq6++mq+//trz5QhAv3796ozxDwvruO8njU7m3cxmM8cffzzHH99w0avWUlxczOWXX87cuXOb9OXBXXfdxfTp0z0/FxUVkZSU1Bohivi+A5tdy7hBYPVvcDeH0/B0h1QyLyKg+7E0kdMB2e57znGAWualDdhLoLoC/ILrf94WBMX7XPu1sdLSUiZNmsSkSZN48803iYmJIT09nUmTJmG324947OTJk+nevTtz586la9euOJ1OBg8efNTj6hMfH0/v3r3rbHcnxYWFhXWeKygoIDy8dT4POhwOFi1aRFZWFlartdb2+fPn10rmbTZbvbF3VI1O5h0OB0888QQff/wxdrudU089lQceeIDAwMBmX7xLly5YLBb2799fa/v+/fuJj4+vs39qaippaWlMnjzZs839rZLVamXr1q306tWrznH+/v74+zecdIhIE+TucC2jj/xGmHqghIoqJ8E2Cz2iG7ghikinovuxNEneLqgqA2sgRPWgstrBjmxXAqWWeWk1thBX78OqUlfX+sPZy1zP20LqPncMevXqhZ+fHytWrCA5ORmA/Px8tm3bxsknnwzAli1byM3NZfbs2Z4vQn/99dfa4dtsgCt3c8vNzWXr1q3MnTvXUzy8NSq4R0VF0aVLF1atWuWJGVxf3O7YsYO+ffu2+DUBPvvsM4qLi1mzZk2t7v8bNmzgyiuvpKCgoNaQA1/S6GR+1qxZPPjgg0ycOJHAwECeffZZsrOzj1io7mhsNhvDhw9n6dKlnnEWTqeTpUuXMm3atDr79+/fn/Xr19fadu+991JcXMyzzz6rb/dF2kJuTeXToyTz7i72g7qGYzar+J2IiDTR/prid7EDwGwhNauIaqdBWICVhPDG1WcSabLwJOjSx1XsLia0dld7w4CiDOg61LVfCwoJCeHqq6/m9ttvJzo6mtjYWO655x7Mh4zLT05Oxmaz8fzzz/PXv/6VDRs2MHPmzFrn6d69OyaTiU8++YQzzzyTwMBAIiMjiY6O5pVXXiEhIYH09HRmzJhRJ4YpU6aQmJjIY4891uzXMX36dGbNmkVcXBxjxowhNzeXmTNnEhMTw3nnnVdr34yMjDpDp7t3Pzh0YevWuoUGBw0ahJ9f7ZmU5s2bx1lnncXQoUNrbR84cCC33norb775JjfeeCPgKt6elZVVaz+TyVRn2HdH0ehk/rXXXuPFF1/k+uuvB+Crr77irLPO4tVXX631R9ZU06dP54orrmDEiBGMGjWKZ555htLSUq688kqg9h9VQECAZ0yHm/tblsO3i0gr8STzdXvBHGpDhquL/SAVvxMRkeZwj5ePd33G25Lluq/0TwjTDCnSesxmGDAZCjNcxe7CEl1d6+1lrkQ+OBr6n92ixe/c5syZQ0lJCZMnTyY0NJTbbrutVpf1mJgYFi5cyN13381zzz3HCSecwBNPPMEf//hHzz6JiYk89NBDzJgxgyuvvJIpU6awcOFC3n77bW6++WYGDx5Mv379eO6555gwYUKt66enpx9TXgdwxx13EBISwuOPP05qaipRUVGceOKJfPPNN3V6dD/xxBM88cQTtba9/vrrnHSSq8DyRRddVOf8e/bsqVUUcP/+/Xz66ae89dZbdfZ1V+yfN2+eJ5nfuHFjnTH+/v7+VFRUNO8Fe5nJMAyjMTv6+/uzY8eOWq3fAQEB7Nixo06Vxab617/+xZw5c8jKymLYsGE899xznuIPEyZMICUlpcGpBaZOnUpBQQEffvhho69XVFREeHg4hYWFHbrggYhX/LOXqwDedd9C12EN7nbBy8tZmZbHk38ZyvnDj+09QkTaRlvfH3U/liP6z8Ww9TM4458w+npmfbaZV77byRVju/PQn9SIIw2rqKhg165d9OjRo9GzbNVxYKurqn3OdtcYemsAxPR1JfIx/Y5+vPiUFvmbaoLG3h8b3TJfXV1dJ3A/Pz+qqqqaH2WNadOm1dutHmDZsmVHPLYzzB8o0m6U57sSeThiy7zTabCxZo7547qp+J2IiDSDu5t9TSX7zZkHW+ZFWl1MP9f0c4V7XMXubCGurvWt0CIv0lyNTuYNw2Dq1Km1CtdUVFTw17/+leDgg8Wt3HP7iYgPyt3pWobEg3/DlYTTcksptTsI8DPTs4uK34mISBNVFEJBumvdk8y7Ktmr+J20GbO5RaefE2lpjU7mr7jiijrbLrvsshYNRkTaubzGFb9bX1P8bmBCGFaLvsEWEZEm2r/JtQzrBoGRHCiuJKekEpMJ+sa1bBVxEZGOqtHJ/IIFC1ozDhHpCDzT0h25+N3GfZpfXkREjsFhXey31swvnxIdTJCt0R9fRUR8mprMRKTxGpnMu6elG9xVybyIiDSDO5k/vJJ9fMNDvEREOhsl8yLSeJ5kvuFu9oZhHJxjXtPSiYhIc7inpTtsvHz/eN1XRETclMyLSOMYxiFzzDeczGcUlFNUUY2fxUSfWLWgiIhIEzmdB8fMxx0HHKxkPyBB9xURETcl8yLSOCXZrqlZTGaITGlwN/d4+T6xodiseosREZEmyt8FVaWueb2jelLlcLIjuwRQJXsRkUPpk7aINI67i31EMlj9G9xtU00yP6irPnCJiEgzuLvYx/QHi5VdOaXYHU5C/K0kRgR6NzYRkXZEybyINI47mY9qXCX7gUrmRUSkOdzJfE3xO3cX+37xoZjNJm9FJdLqDMPguuuuIyoqCpPJxNq1a5kwYQK33HKLt0OTdkrJvIg0TiOK38HBD12DVMleRESawzMtnbuSvbv4ncbLi29bsmQJCxcu5JNPPiEzM5PBgwezePFiZs6c6dknJSWFZ5555qjnSklJwWQy1Xp069bN87zJZOLDDz+sc9zUqVM555xzPD8f7cuEhs4jbUMTdYpI4+TtdC2PkMznl9rJKCgHoL+KFImISHMcNsf8weJ36vElvi01NZWEhATGjRvn2RYVFdXs8z388MNce+21np8tFssxxSftj1rmRaRxGjHHvPsDV3JUEGEBfm0RlYiI+JKKIshPc627W+ZrpqVTJXvxZVOnTuWmm24iPT0dk8lESkoKULtlfMKECezevZtbb73V09p+JKGhocTHx3seMTExrfwqpK2pZV5Ejs7pOKRlvuFkfqOK34mIyLHI3uxahnaFoCjyS+1kFVUA0DdOybw0j2EYlFc5vHLtQD/LUZNugGeffZZevXrxyiuv8Msvv9Tbir548WKGDh3KddddV6vFXTovJfMicnSFe8BhB4sNwpMa3G1TTcv8QHWFFBGR5jisi717vHy3yEBC1eNLmqm8ysHA+7/wyrU3PTyJINvRU67w8HBCQ0OxWCzEx8fXu09UVBQWi8XT4n40d955J/fee6/n51mzZnHzzTc3Pnhp95TMi8jReSrZ9wRzw+OtNu4rBGBQopJ5ERFpBnfLfNxAALZkaby8SHPdfvvtTJ061fNzly5dvBeMtAol8yJydLlHL35XUeUg9UApAAMTVMleRESa4cAW1zJmAHDIeHlVspdjEOhnYdPDk7x2bW/p0qULvXvX/9ktNDSUwsLCOtsLCgoID9fnuI5CybyIHF0jit9tzSrG4TSIDrYRF+bfRoGJiIhPcbfMx/YHDrbM91fLvBwDk8nUqK7uHYHNZsPhOPbx//369WPVqlVcccUVnm0Oh4N169ZxzTXXHPP5pW34xl+1iLQuTzf7hpN5z3j5rmGNKvQiIiJSS8kBKMsBTNClHw6nwdb9mmNe5FApKSl89913XHTRRfj7+ze76/z06dO5+uqr6d+/P6eddhqlpaU8//zz5Ofn10nmDxw4wNq1a2ttS0hIIC4uDoBdu3bVeb5Pnz4EBwc3KzZpPCXzInJ0npb5hrvZu8fLD1QlexERaY4DNa3ykSlgCyI9p5SKKicBfma6RyspEAHX3PHXX389vXr1orKyEsMwmnWeiy++GMMweOqpp5gxYwZBQUEMHz6c7777zpOku7311lu89dZbtbbNnDnTU1xv+vTpdc7//fffc9JJJzUrNmk8JfMicmTVla5q9nDEZH7TPlWyFxGRY5BdM14+1j1e3nVf6RsXisWsHl/i+2655RbPnPJuy5Ytq/XzmDFjWLdu3VHPlZaWdtR9LrnkEi655JIj7nP49Q/X3C8TpGWYvR2AiLRz+WlgOMEWCiGx9e7icBpsrilSNKiriqaIiEgzZG9yLWNc4+U3Z6mLvYjIkSiZF5EjO7T4XQNj4dNySymvchDgZ6ZHF3WFFBGRZnBXso+tmZaupmW+f7x6fImI1EfJvIgcWX6aaxnVo8FdNu47+IFLXSFFRKTJDKOeSvY1LfMJapkXEamPknkRObLCva5leFKDu7jHyw9S8TsREWmO4iyoKACTGaL7UFJZTXpeGaCWeRGRhiiZF5Ejcxe/C+/W4C6qZC8iIsfEXck+qif4BbC1plU+LsyfqGCbFwMTEWm/lMyLyJEVZriWDSTzhmEc0jKv4nciItIMh1Wy3+opfqcviUVEGqJkXkSOzN3NPiyx3qeziyvJLbVjNkG/OI1rFBGRZvBUsq+Zli7LXYtF9xURkYYomReRhlVXQmm2a72BMfMbMlxd7HvHhhBos7RVZCIi4ks8lexrit9lqvidiMjRKJkXkYYV1XSxtwZAUFS9u2zIcLWeDFYXexERaQ7DOKSb/UAMw2BzlqalExE5GiXzItIwTyX7bg3OMb+hpvjdoEQl8yIi0gyFe8FeDGYrRPViX2EFxRXVWM0mesWEeDs6Ea+aMGECt9xyi7fDkHZKybyINOwoxe/g4LR0g1XJXkREmsPdxT66N1htbMl03Vd6x4Zgs+qjqkhTLFu2DJPJREFBQYued+HChZhMpiM+0tLSePDBBz0/WywWkpKSuO6668jLy6t1vpSUFJ555plaP5tMJt5+++061x40aBAmk4mFCxc2GJ/7un/9619rbV+7dq0ntkO9//77TJgwgfDwcEJCQhgyZAgPP/xwnTjbO71DikjDPMXv6k/m80rtZBSUA5qWTkREmim7Zlq6WHfxO3cle42XF+9yGk4ySjLYlr+NjJIMnIbT2yF5zYUXXkhmZqbnMXbsWK699tpa25KSXPWVBg0aRGZmJunp6SxYsIAlS5Zwww03HPUaSUlJLFiwoNa2n3/+maysLIKDg496fEBAAPPmzWP79u1H3O+ee+7hwgsvZOTIkXz++eds2LCBJ598knXr1vH6668f9TrtiZJ5EWlY0SHd7Ovhnl8+JTqI0AC/topKRER8iTuZjzksmU/Ql8TiPTsLdjJv/TxeWPMC/173b15Y8wLz1s9jZ8HOVrtmaWkpU6ZMISQkhISEBJ588sk6+7z++uuMGDGC0NBQ4uPjueSSS8jOdhUrTktL45RTTgEgMjISk8nE1KlTAViyZAknnXQSERERREdHc/bZZ5Oamtro2AIDA4mPj/c8bDYbQUFBtbZZLK5CyFarlfj4eBITE5k4cSJ/+ctf+N///nfUa1x66aV8++237Nmzx7Nt/vz5XHrppVit1qMe369fP0455RTuueeeBvdZuXIls2bN4sknn2TOnDmMGzeOlJQUTjvtNN5//32uuOKKRvw22g8l8yLSMM+Y+fqnpXMXv9N4eRERabYD7pZ5dyV7172ln1rmxUt2Fuzkzc1vsjlvMxH+EaSEpRDhH8HmvM28ufnNVkvob7/9dr799ls++ugjvvzyS5YtW8bq1atr7VNVVcXMmTNZt24dH374IWlpaZ6EPSkpiffffx+ArVu3kpmZybPPPgu4viiYPn06v/76K0uXLsVsNnPuuefidB7sbTBhwgTPuVpKWloaX3zxBTab7aj7xsXFMWnSJBYtWgRAWVkZ77zzDldddVWjrzd79mzef/99fv3113qff/PNNwkJCeFvf/tbvc9HREQ0+lrtwdG/4hCRzqvwyC3z7uJ3qmQvIiLN4nTCga2u9ZgBVFQ52JlTCsAAVbIXL3AaTpamLyW/Mp9e4b0w1RQADrGF0MuvF6mFqSxNX0pKeApmU8u1i5aUlDBv3jzeeOMNTj31VAAWLVpEt261P4Mdmtj27NmT5557jpEjR1JSUkJISAhRUa7Zh2JjY2slpueff36t88yfP5+YmBg2bdrE4MGDAUhOTiYhIeGYX8v69esJCQnB4XBQUVEBwFNPPdWoY6+66ipuu+027rnnHt577z169erFsGHDGn3tE044gQsuuIA777yTpUuX1nl++/bt9OzZEz8/3+hRqpZ5EamfYRySzNc/x7yn+F2iPnCJiEgzFOyGqjKw2CCqJzuyS3A4DSKC/IgL8/d2dNIJZZZmsqtwF/FB8Z5E3s1kMhEfFM+uwl1klma26HVTU1Ox2+2MHj3asy0qKop+/frV2m/VqlVMnjyZ5ORkQkNDOfnkkwFIT08/4vm3b9/OxRdfTM+ePQkLCyMlJaXOca+99hqPPfbYMb+Wfv36sXbtWn755RfuvPNOJk2axE033dSoY8866yxKSkr47rvvmD9/fpNa5d0eeeQRvv/+e7788ss6zxmG0eTztWdK5kWkfhWFYC9xrYfV7WZfXFHFrprWk0FqmRcRkeZwV7Lv0hcs1lrF7w5PpETaQmlVKZWOSgKtgfU+H2gNpNJRSWlVaRtH5uoqP2nSJMLCwnjzzTf55Zdf+OCDDwCw2+1HPHby5Mnk5eUxd+5cVqxYwYoVKxp1XHPYbDZ69+7N4MGDmT17NhaLhYceeqhRx1qtVi6//HIeeOABVqxYwaWXXtrk6/fq1Ytrr72WGTNm1Ene+/bty86dO6mqqmryedsjJfMiUr+immnpAqPAFlTnaXerfNfwAKKCjz4OSkREpA5P8TvXeHl3YdUBKn4nXhLsF4y/xZ/y6vJ6ny+vLsff4k+w39GrqzdFr1698PPz8yTZAPn5+Wzbts3z85YtW8jNzWX27NmMHz+e/v37e4rfubnHpjscDs+23Nxctm7dyr333supp57KgAEDyM/Pb9H4j+Tee+/liSeeYN++fY3a/6qrruLbb7/lT3/6E5GRkc265v3338+2bdvqTHV3ySWXUFJSwosvvljvcS09pV9rUzIvIvU7WvG7fSp+JyIixyi7dvG7je57i3p8iZckBCfQI7wHWWVZdVp1DcMgqyyLHuE9SAg+9rHlhwoJCeHqq6/m9ttv5+uvv2bDhg1MnToVs/lgupacnIzNZuP5559n586dfPzxx8ycObPWebp3747JZOKTTz7hwIEDlJSUEBkZSXR0NK+88go7duzg66+/Zvr06XVimDJlCnfddVeLvi6AsWPHMmTIEGbNmtWo/QcMGEBOTk6daeqaIi4ujunTp/Pcc8/V2j569GjuuOMObrvtNu644w6WL1/O7t27Wbp0KX/5y188xfc6CiXzIlK/wpppQRoYL78xQ8XvRETkGHkq2Q/C6TTY7Enm1TIv3mE2mTk1+VQi/SNJLUylxF6Cw+mgxF5CamEqkf6RnJp8aosWv3ObM2cO48ePZ/LkyUycOJGTTjqJ4cOHe56PiYlh4cKFvPvuuwwcOJDZs2fzxBNP1DpHYmIiDz30EDNmzCAuLo5p06ZhNpt5++23WbVqFYMHD+bWW29lzpw5da6fnp5OZmbL1gJwu/XWW3n11VdrTTt3JNHR0QQG1j/UobH+8Y9/EBISUmf7448/zltvvcWKFSuYNGkSgwYNYvr06QwZMqTDTU1nMnytCkAjFBUVER4eTmFhIWFhulmI1Ourh+CHp2DUdXBm3Tf8SU9/x9b9xcy7YgSnDojzQoAi0tLa+v6o+3En56iGWV3BUQk3r2W3EcvJc5Zhs5rZ+NAk/Cxqc5LmqaioYNeuXfTo0YOAgIBmnWNnwU6Wpi9lV+EuKh2V+Fv86RHeg1OTT6VnRM8Wjljau5b4m2qKxt4fNTWdiNTP3c2+nuJ35XYH27NdRYrUFVJERJolf5crkfcLgojubNy4H4B+caFK5MXrekb0JCU8hczSTEqrSgn2CyYhOKFVWuRFmkvJvIjUz10Ar5455rdkFeE0oEuITVMHiYhI82Rvci1j+oPZ7Cl+py720l6YTWYSQ+qvHSTSHuirJRGpn2fMfN1kfsMhBYo0dZCIiDSLp/jdAOCQ4ncqrCoi0ihK5kWkLqcDimqmD6knmfcUv0tU64mIiDSTu2X+8GReLfMiIo2iZF5E6irJBmc1mCwQEl/naU0dJCIix+yQlvnsogoOFFdiNsGAeCXzIiKNoWReROpyF78LTQBL7dIa9monW7Ncxe80LZ2IiDRLVQXkprrWYwd6viTuGRNCoM3ixcBERDoOJfMiUldRTTJfTxf7bfuLsTuchAZYSYo6tvk/RUSkk8rdDoYDAsIhNEHF70REmkHJvIjU5W6ZD69bwXXd3gIAhnRT8TsREWkmTxf7gWAyaby8iEgzKJkXkboKG26ZX5teAMCwpIi2i0dERHxLg8XvNHxLRKSxlMyLSF2eZD6pzlNr9xQAMCwpsg0DEhERn5K9xbWMHUhRRRXpeWWAWuZFDjdhwgRuueUWb4ch7ZSSeRGpy53Mh9XuZl9cUcWOAyWAWuZFROQYHNIyv6mmVT4xIpCIIJsXgxLp+JYtW4bJZKKgoKBFz7tw4UJMJtMRH2lpaTz44IOeny0WC0lJSVx33XXk5eXVOl9KSgrPPPNMrZ9NJhNvv/12nWsPGjQIk8nEwoULjxrn3r17sdlsDB48uN7nG4q9vut2BO0imX/hhRdISUkhICCA0aNHs3Llygb3nTt3LuPHjycyMpLIyEgmTpx4xP1FpBmKMlzLw7rZ/7a3EMOAbpGBxIT6eyEwERHp8CpLoGC3az1mgMbLS7tlOJ3Y92ZQsXUb9r0ZGE6nt0PymgsvvJDMzEzPY+zYsVx77bW1tiUluXp0Dho0iMzMTNLT01mwYAFLlizhhhtuOOo1kpKSWLBgQa1tP//8M1lZWQQHBzcqzoULF3LBBRdQVFTEihUr6t1nwYIFteLOzMzknHPOadT52xuvJ/PvvPMO06dP54EHHmD16tUMHTqUSZMmkZ2dXe/+y5Yt4+KLL+abb75h+fLlJCUlcfrpp5ORkdHGkYv4qKpyKD3gWj8smT/YxT6ibWMSERHfcWCraxkSB8HRbMxwV7LXeHlpPypTU8l9ZS4Hnn+OnBdf5MDzz5H7ylwqU1Nb7ZqlpaVMmTKFkJAQEhISePLJJ+vs8/rrrzNixAhCQ0OJj4/nkksu8eRNaWlpnHLKKQBERkZiMpmYOnUqAEuWLOGkk04iIiKC6Ohozj77bFKb8FoCAwOJj4/3PGw2G0FBQbW2WSyuaSWtVivx8fEkJiYyceJE/vKXv/C///3vqNe49NJL+fbbb9mzZ49n2/z587n00kuxWq1HONLFMAwWLFjA5ZdfziWXXMK8efPq3S8iIqJW3PHx8QQEBDTyN9G+eD2Zf+qpp7j22mu58sorGThwIC+//DJBQUHMnz+/3v3ffPNN/va3vzFs2DD69+/Pq6++itPpZOnSpQ1eo7KykqKioloPEWlA0T7X0i8IAmuPi1+j4ncicgx0PxbgCMXv1DIv7UNlaip5r79OxeZNWCIisfXogSUikorNm8h7/fVWS+hvv/12vv32Wz766CO+/PJLli1bxurVq2vtU1VVxcyZM1m3bh0ffvghaWlpnoQ9KSmJ999/H4CtW7eSmZnJs88+C7i+KJg+fTq//vorS5cuxWw2c+655+I8pLfBhAkTPOdqKWlpaXzxxRfYbEcfQhMXF8ekSZNYtGgRAGVlZbzzzjtcddVVjbrWN998Q1lZGRMnTuSyyy7j7bffprS09Jjib++O/hVHK7Lb7axatYq77rrLs81sNjNx4kSWL1/eqHOUlZVRVVVFVFRUg/s89thjPPTQQ8ccr0incOh4+UOmnjMMg7V78gE4PjnCC4GJSEen+7EAtaalq6hyeGqxDEpUMi/eZzidFP/vKxz5+dh69fZMw2sJCcHcqzf21B0Uf7UUW48emMwt1y5aUlLCvHnzeOONNzj11FMBWLRoEd261e4leWhi27NnT5577jlGjhxJSUkJISEhnpwoNjaWiIgIz77nn39+rfPMnz+fmJgYNm3a5BlfnpycTEJCwjG/lvXr1xMSEoLD4aCiogJwNeA2xlVXXcVtt93GPffcw3vvvUevXr0YNmxYo46dN28eF110ERaLhcGDB9OzZ0/efffdOl9QXHzxxZ5eBG6bNm0iOTm5UddpT7zaMp+Tk4PD4SAuLq7W9ri4OLKyshp1jjvvvJOuXbsyceLEBve56667KCws9DwO7bohIodpYFq6vfnl5JTYsZpN6gopIs2i+7EAB1vmY/qzNasYh9MgKthGfFjH7OYqvqVqXyaVu3ZijU/wJPJuJpMJa1w8lTtTqdqX2aLXTU1NxW63M3r0aM+2qKgo+vXrV2u/VatWMXnyZJKTkwkNDeXkk08GID09/Yjn3759OxdffDE9e/YkLCyMlJSUOse99tprPPbYY8f8Wvr168fatWv55ZdfuPPOO5k0aRI33XRTo44966yzKCkp4bvvvmP+/PmNbpUvKChg8eLFXHbZZZ5tl112Wb1d7Z9++mnWrl1b69G1a9fGvbh2xqst88dq9uzZvP322yxbtuyI4xz8/f3x91exLpFGaaD4nXu8/ICEMAL8LIiINJXuxwLUapk/tIv94YmTiDc4S0sxKioxBwbW+7w5KIjq7GycXui+XVpayqRJk5g0aRJvvvkmMTExpKenM2nSJOx2+xGPnTx5Mt27d2fu3Ll07doVp9PJ4MGDj3pcc9hsNnr37g248rWzzjqLhx56iJkzZx71WKvVyuWXX84DDzzAihUr+OCDDxp1zbfeeouKiopaX4YYhoHT6WTbtm307dvXsz0+Pt4TX0fn1Zb5Ll26YLFY2L9/f63t+/fvJz4+/ojHPvHEE8yePZsvv/ySIUOGtGaYIp1LQc03tCp+JyIiLa0sD0pqel/G9GPDPlfxu4EaLy/thDk4GFOAP87y8nqfd5aVYfK3YW5kdfXG6tWrF35+frUqsOfn57Nt2zbPz1u2bCE3N5fZs2czfvx4+vfvX6douHtsusPh8GzLzc1l69at3HvvvZx66qkMGDCA/Pz8Fo3/SO69916eeOIJ9u3b16j9r7rqKr799lv+9Kc/ERkZefQDcHWxv+2222q1tq9bt47x48c3WIvNF3g1mbfZbAwfPrxW8Tp3MbuxY8c2eNw///lPZs6cyZIlSxgxYkRbhCrSebinC4roXmuzknkRETlm7lb58GQICPMUVj1e9xZpJ/y6JuDfoyfVWZkYhlHrOcMwqN6fhX/PXvh1Pfax5YcKCQnh6quv5vbbb+frr79mw4YNTJ06FfMh4/KTk5Ox2Ww8//zz7Ny5k48//rhOa3f37t0xmUx88sknHDhwgJKSEiIjI4mOjuaVV15hx44dfP3110yfPr1ODFOmTKlVy6yljB07liFDhjBr1qxG7T9gwABycnLqTFPXkLVr17J69WquueYaBg8eXOtx8cUXs2jRIqqrqz37FxQUkJWVVevRUQvleb2a/fTp05k7dy6LFi1i8+bN3HDDDZSWlnLllVcCdf+oHn/8ce677z7mz59PSkqK5x+gpKTEWy9BxLfk1yTzkQeT+SqHkw01UwcNU/E7ERFprkMq2ZdUVrM1y9XN/oTkxrW+ibQ2k9lM6GkTsURGYk/dgaO4GMPhwFFcjD11B5bIKEInntqixe/c5syZw/jx45k8eTITJ07kpJNOYvjw4Z7nY2JiWLhwIe+++y4DBw5k9uzZPPHEE7XOkZiYyEMPPcSMGTOIi4tj2rRpmM1m3n77bVatWsXgwYO59dZbmTNnTp3rp6enk5nZsrUA3G699VZeffXVRtdKiY6OJrCBoQ6HmzdvHgMHDqR///51njv33HPJzs7ms88+82y78sorSUhIqPV4/vnnG/dC2hmTcfhXTl7wr3/9izlz5pCVlcWwYcN47rnnPOMdJkyYQEpKCgsXLgQgJSWF3bt31znHAw88wIMPPtio6xUVFREeHk5hYSFhYerWJeLhqIZHYsFwwK2bIDwRgPV7C5n8rx8IC7Cy9v7TMZs1rlHEF7X1/VH3407ok+nw6zw48RZ+7HETl766gsSIQH6c8XtvRyY+pKKigl27dtGjR49mzx9emZpK8f++onLXToxKOyZ/G/49exE68VT8e/Vq4YilvWuJv6mmaOz9sV0UwJs2bRrTpk2r97lly5bV+jktLa31AxLprIoyXIm82Q9CD3Yfc09JNzQpQom8iIg03yHF71btdt1bhndXq7y0P/69emHr0YOqfZk4S0sxBwfj1zWhVVrkRZqrXSTzItJOeMbLJ8EhN6s1NePlNaZRRESazemErPWu9bhBrF7tSuZP0PAtaadMZjO2boneDkOkQfpqSUQOyj9K8Tt94BIRkebK2wn2YrAG4OzSz1P8bnj3KO/GJSLSQSmZF5GD3NPSHVL8rrCsip0HXBU+h3aL8EJQIiLiEzLXupZxg9mZV0FheRUBfmb6J4R6NSwRkY5KybyIHFTPtHTr9hYA0D06iOgQfy8EJSIiPiFznWuZMJTVuwsAGNItAj+LPo6KiDSH3j1F5KB6pqX7NS0P0Hh5ERE5Ru6W+a7DVPxORKQFKJkXkYM8LfMpnk0/peYCMK5XFy8EJCIiPsEwarfMp7uL3ymZFxFpLiXzIuJSVQHFma71mpb50spqT/G7sb2ivRSYiIh0ePlpUFEIFhuFIb3Znl0CwPEqrCoi0mxK5kXEpXCPa+kXDEGuxP2XtDyqnQZJUYEkRQV5MTgREenQ3K3ysQNZs89VVDUlOoguqsUiItJsSuZFxOXQ8fImE3BIF/ue6mIvIiLHoFYX+wJAXexFDmcYBtdddx1RUVGYTCbWrl3LhAkTuOWWW7wdmrRTSuZFxKUgzbU8pJL9T6k5AIzrrS72IiJyDA4pfre6pvjdCSp+J1LLkiVLWLhwIZ988gmZmZkMHjyYxYsXM3PmTM8+KSkpPPPMMw2eY9myZZhMpiM+li1bxsKFCz0/m81mEhISuPDCC0lPT691vsO/TJgwYQImk4nZs2fXufZZZ52FyWTiwQcfPNZfhTSSknkRcTmskn1BmZ2N+4oAGNtTybyIiDTTIcXvHHFDPbVY1DIvUltqaioJCQmMGzeO+Ph4rFYrUVFRhIaGNvoc48aNIzMz0/O44IIL+MMf/lBr27hx4wAICwsjMzOTjIwM3n//fbZu3cpf/vKXo14jKSmJhQsX1tqWkZHB0qVLSUhIaNJrlmOjZF5EXA6bY/7nnXkYBvSODSE2LMCLgYmISIdWuBfKcsFsZTtJlFRWE2yz0C++8QmKiK+bOnUqN910E+np6ZhMJlJSUoDaLeMTJkxg9+7d3HrrrZ5W9cPZbDbi4+M9j8DAQPz9/Wtts9lsAJhMJuLj4z1fIFx99dWsXLmSoqKiI8Z69tlnk5OTw48//ujZtmjRIk4//XRiY2Nb5hcijaJkXkRcDmuZX+7uYq8q9iIicizc4+VjBrBqXzkAw5IjsJjrJiIircIwwF7qnYdhNCrEZ599locffphu3bqRmZnJL7/8UmefxYsX061bNx5++GFPK3tLyc7O5oMPPsBisWCxWI64r81m49JLL2XBggWebQsXLuSqq65qsXikcazeDkBE2glPy3wycOj88krmRUTkGLjHyycMZfXuAkBd7KWNVZXBrK7eufbd+8AWfNTdwsPDCQ0NxWKxEB8fX+8+UVFRWCwWQkNDG9ynKQoLCwkJCcEwDMrKygC4+eabCQ4+erxXXXUV48eP59lnn2XVqlUUFhZy9tlna7x8G1MyLyJQUQTlroJERHQnu7iC7dklmEwwuoeSeREROQY1LfNGwlB+/sb1RbGK34l4X2hoKKtXr6aqqorPP/+cN998k0cffbRRxw4dOpQ+ffrw3nvv8c0333D55ZdjtSq1bGv6jYsIFNRULg2MhIAwlm/JAGBgQhiRwTYvBiYiIh1eTTKfEdiXjIJSbFYzo3tEeTko6VT8glwt5N66djtlNpvp3bs3AAMGDCA1NZUbbriB119/vVHHX3XVVbzwwgts2rSJlStXtmao0gAl8yJSp/jdcnWxFxGRllCUCSX7wWTmy9wYoJTRPaIIsukjqLQhk6lRXd07ApvNhsPhaJVzz5gxg169enHrrbdywgknHHX/Sy65hH/84x8MHTqUgQMHtkpMcmQqgCcidYrfHRwv38VbEYmIiC9wF7/r0o+lqcUAnNJP1a5FmislJYXvvvuOjIwMcnJyWvTcSUlJnHvuudx///2N2j8yMpLMzEyWLl3aonFI4ymZF5FaLfN78spIzyvDYjYxUt0gRUTkWNQUv6uKO46Vu/IAmNAvxosBiXRsDz/8MGlpafTq1YuYmJb/v3Trrbfy6aefNrrbfERERKMK5knrUB8nEanVMr98p6tVfmi3cEL89RYhIiLHoKZlPtXamyqHQXJUED266IO/SH1uueUWz5zybsuWLav185gxY1i3bl2jz7lw4cJ6t0+dOpWpU6fW2T5mzBiMQ6bTO/z6h/98uLVr1zY6Njl2apkXkUNa5lNYtjUbUBd7ERFpAfvWAvBdcSIAp/SLwWTS/PIiIi1BybxIZ2cYnpb58pBEvt7iSuYnDTr2+UtFRKQTy9sFxfswzFbe2RMBwASNlxcRaTFK5kU6u7JcqCoF4JusACqqnCRHBTE4MczLgYmISIe261sAymNPILXIhM1qZkxPzZIiItJSlMyLdHbu8fKhCfx3o6s40VlDEtQNUkREjs1OVzK/OWAYAGN7RhNos3gxIBER36JkXqSzK0gDwBGe7Olif9ZxCV4MSEREOjyn09My/9/ifoCq2EvbO7SQm8ixaK9/S0rmRTq7mpb5faZYKqudpEQHMairutiLiMgx2L8BynIxbMG8k+UaJ6/55aWtWCyuHiB2u93LkYivKCsrA8DPz8/LkdSmeadEOruaSva/lYQD6mIvIiItoKZV/kD0SMqLLKREB5GiKemkjVitVoKCgjhw4AB+fn6YzWq/lOYxDIOysjKys7OJiIjwfFHUXiiZF+nsalrmf8hxfcg667iu3oxGRER8wc5lAKxkMKAq9tK2TCYTCQkJ7Nq1i927d3s7HPEBERERxMe3v5melMyLdGaGAVm/AbCluis9uwQzICHUy0GJiEiHVm2H3T8B8HZOL0Dj5aXt2Ww2+vTpo672csz8/PzaXYu8m5J5kc4sbyeU5VKFHxuNFK5XF3sRETlWe3+BqjLsAdH8UBBLqL9VU9KJV5jNZgICArwdhkir0QASkc5sz0oA1jtTsOPHWUNUxV5ERI5RTRf7ddahgIlzT0gkwK99tmqJiHRkSuZFOrO9rmR+lbMPvWKC6RenLvYiInKMapL59wt6A3DJ6GQvBiMi4ruUzIt0Znt+AWC1sw9nDemqLvYiInJsKoogYxUA31UP5vjkCPrHa7pTEZHWoGRepLOqLMbI3gjAGqMP5x6f6OWARESkw9v9IxgO9pgS2EcXLhmlVnkRkdaiZF6ks8pYjclwkmFEM3zwIHpo/l8RETlWNV3sv60aSGiAlbOHaLpTEZHWomRepJMq2PYjAGucfbhhQi8vRyMiIj5h57cA/OA8jvOOTyTQpsJ3IiKtRcm8SCeVtfF7AAqjj2dwYriXoxERkQ6vcC8c2IzTMLHcOZCLVfhORKRVKZkX6YSyC8uJK1oPwNBxp3s5GhER8QmrFgHwi9GPXsndVPhORKSVKZkX6YQ++Oo7Ik3FVGJj0AknejscERHp6KorMVYtAGBR9elcrMJ3IiKtTsm8SCdTWFbF7nXLACjvMhiT1d+7AYmISMe36SNMpQfINKJYbhujwnciIm1AybxIJ/Pa8jQGObcCEN5HrfIiInLsqpe/DMCb1ady46kDVPhORKQNKJkX6UT2FZQz9/udnGDeDoApaZSXIxIRkQ4vYxXWzFVUGlZ+jZ7MFeNSvB2RiEinoGRepJNwOA1ufWctjopi+pr3ujYqmRcRkWOU+/W/APjUOYbbzhuPn0UfL0VE2oLebUU6iRe/2cGKXXmMtu3CghPCkyE03tthiYhIB1ZdtJ+w1I8B2Nv7MkamRHk5IhGRzkPJvEgnsGp3Ps8sdXWtv21AoWtj0kgvRiQiIr5g7UfP4Uc1G+jFpeef5+1wREQ6FSXzIj6uqKKKv7+9BofT4E/DujKwpvgd3dTFXkREmm9PThHddvwHgKIhVxMdotlRRETakpJ5ER/mdBrcvXg9e/PLSYoK5NHTEzCl/+x6Ui3zIiLSTHvyyvjw5fuIN+VSYApnzNlXezskEZFOR8m8iI8qtzuY9p/VfPJbJhaziWcvOp6Qbx+CyiKIHQQJw7wdooiIdEB78sp46qWX+VvVIgBME+7EbAvwblAiIp2Q1dsBiEjL219UwbWv/cpvewvxs5j455+HcEL1b7DuLcAEk58Fs+YAFhGRptmTV8btL7/Pv+1zsJgMygZdRPjv/ubtsEREOiUl8yI+ZkNGIdcs+pWsogoig/z49+UjGNUtCF76o2uHkVeri72IiDTZxn2FTF/0HS9WPEq4uQx7wkiCzn0OTCZvhyYi0ikpmT9G1Q4HazN3kVteTHRgKMMSemC1tF2LZ0tfv6HzlZSX8/yKD0gv3k9yaBw3jT6XAJutzr5Ao7a1ZIyDY5PZkJ1OdmkBeeXFRAWGEhscQf/oRD7c+jP7inOID44iOSKOTQfScDidhPsH0yU4nNjgCAbHJrM2axer9m0hvSCb/IoCgmyBHB8/gAsHj8dqsbAqI5VV+7ZR7XBQVFmC1WKlW3gsfx54IgDvbPietVmbKLdXMiCmF6OTBjIisXe9r7Oh+HPLiwm0+LEsbS2bc7biAMYmDGVU0iAA1mRuZ1/xAZxOJ5jMdA2JJsQ/mLKqMgxMmJ1BrN5p5rst5VQ5IDHSjzvO7EqOfSe/vfsSQ/JSKfIL4z+hIxmcthmr2UJBZalX/m7r+z14I4aOzuk0yCgop9ReTbDNSmJEIGazPlRL59XU95XG7n/ofoEWP75LW8fekiz8LTZO6j6MyupqogJDiQ4Mw8CgsLLsqNd3nzO7tIADpYUUVZZiAoL9AiirLsfpdJJfXkJ+RSGBfoH0jEiksKKYlVm/YQYGdenHX4dP5r/bVvLtnpWYgDFdh2IxW9mYs73e+9GhryPCPxgDg+zSAtbu28aO/F2UVJfSI7QHof5B5FcUUlZVQURAGHa7hfW7wkjLCmC+3xP0smRSGtCFz4feiHXLGjZkp5JTnk+ANZC+0cnEh0YSGxzR6u/ruo+0PN1XRDoWk2EYhreDeOGFF5gzZw5ZWVkMHTqU559/nlGjGq60/e6773LfffeRlpZGnz59ePzxxznzzDMbfb2ioiLCw8MpLCwkLCys2XF/s3M9b/z2KenFaVQ57fiZbSSHpnDZkLM4pedxzT6vt67f0Pkqqyr4rfBbnOZCMBlgmDAZwYRbuhNoCfHsG2INw2SC4qoiz7ZQvzAMA0qqi1olxmrsOJxVOA0HlUYRTiox44/ZsFFNKYapCnBgmKowMDA7zWA2ADMmI5hAUzQOw46dQpymYjBVgQkwTGD4YTNiCbJGU1adSxX5GOYKwADDgskIws8IwWGqwmEqBJO9JkozJkcEvYNHcfPoy2u9zsPjd1BFtdOOxexHaVUudvN+TOYqz/6GASanFZPJioEDTNWu62PyXMtpj6QqbzxVRSeA4QeAJWgXwfGfYzJXkFJdxHtZ/9/enUdHUeV7AP/e6j2dnZCFfTXILmgyuAw4IKDowZl5T/EwDo+HzhsfjPJ0mMHzRhn1OATEXUY8PhSfOkRcUEcYNCIgwyA6AZ6EHQwgS4gBknQ6Sy/1e390p0lnId2hs3Ty/ZyTA6m+VXV/uVV36aq6dQgmAPNTUvF5TByUmKAhDnYtBXZjYpset439Hdr63OkMjhQ78GnBWRz9oQLVHi+sRgMGdo/FlOFpGJQa197ZoygVqfaxPfYXbr0Savq66UpqjsOtFdepiwUCDZrXAqXsUDDAABMsWjxijUlN7r92mwfLClDuOQVRTl8dD4FA99XwSgDoDQOtrf4FENGglL8bp8TXZgQlutgeTeh7Lb4tPoATjmNwekpRrZfBLVUQ5QAMrovbr9PEiNcG9/lr4Tr/Y9ygDuIh4xqM1r5DlVKYldoH+y02CFyA0v15Vb596nFINPbDFQnDWq1eZzsSeWxXiDqOUNvHdh/Mv/POO/jlL3+JFStWIDs7G8899xzeffddHDx4EKmpqQ3S/+Mf/8CPf/xjLF68GLfeeiv+8pe/YMmSJdi5cyeGDx8e0j4j0XnY9N0ePP3VSlS4y5Bs6Q6rIQbV3kqcr/kBsaYEPPSjOa3amER6/01tr7BiD1zaGUABmm6B72YOF3StBhCFWBmMgfHDUVJVhLOuAxAlSDNdie62dPxQVYSz7v1QopBmHoIUW3pE8+jyunCqah88ygHAAyAGRsTAIw7oWgUAQIMJgAQG80Dt4Njg6zgJIEoHlDfQ9bnYUVK+NURBEzNEc8HXy9F8HSbRIJqvQ6egfCuKBigdIoCmxyDFPBSPXj8fNw4Y0SD/NZ4anKrZDy+cgA7oxgoA+sW7FQWQulmq7VzpRngr+8JbNRDeyoHQq3oB8F2J0KzHYU7eCoP9EBQEI11VePhCCUa4XNhks+E3KWkQBUAJlJhgkCSkmgbBLTVtctwC7X/udAZHih14fdsxnHe6kJFgRYzZiEqXB2fKqpFsN2P2df3Y8aIWidbBfLj1Sqjp66ar8jpQgaO+9kJd7Dr5elEKSjQARiixwYQ4pJj6wNVI3Vq7zXM1Z1Dp/QG6cvjbITd8g3kVtP3ghiCYiP8Od/8APPA74GuP/Gk03QoFG2INabAbklDiPg6PlPnaSiWBdUQA3ZUGb8UQeCqGQK/qg2vUYfzWtAbZ2gEAQKVSeDilGz6PiQlum6T2awRfZpQ3AXGGHki2ZES8Xmc7EnlsV4g6llDbx3a/zf6ZZ57Bvffei9mzZwMAVqxYgXXr1uG1117DwoULG6R//vnnMXXqVCxYsAAA8MQTTyAvLw8vvfQSVqxY0SZ59ni9eOvbdahwl6GXvR+U8jWYdi0OMUY7TjqP4e1v1+OGvkNb5XavSO+/qe1ZdCs8qgRQOpSYAJh9KyhAQUGUoFJOwOu5EmWeEkAZoSBweM8hFelweEugYASUQrn3HFJVBuymyOQRAE5Vfgdd6QCMEOWCEoFBWeBRPwTyLMoD39UCDQo6BDqg6YAeA2gVgPL6QqrtDEHzXZVXuq+DA8D3ZUC1L3AxAxAopfuv+td2nATQzb6L+qJBKQ90rQbnXcfx5v99gnG9MvHWt+vgcPnyL6JwquY4RNdgQDpqUAS446BghK5bIF4roFshuhXiiYW4k6D7f8TdDZC6p64gxrYPCcmbEGcpRDdd8JOyKkytrEBvjweAr/P1ZFJ3f99OoOD70kFHBRzeEgyMHY5Tlcdb9bhtrBzb+tzpDHRd8GnBWZx3ujA4NRbK3wuPs5oQazHicHEFPtt7FgNSYnlrJHUJ4dYroaavrbcr3GVIt/TCXucGQKv94jfQaPi/zBXfIF+MABR0uFDuPYcB9mE4XXWxbgWAt75dh3J3Kbziga4qGzxvHjSQD95VA3UH8rV8g2p/W6ZbAQg8YgR0A6o8NlTrgMudBk1SYNANMHvsiHHFIsYdgxiPFRlSgSu14xii3sMQy/fopUoAADUAcuPjsDI+HucNhgaPyatAJg0ABKI54RE3HO6yiNbrbEcij+0KUfRq18G8y+VCfn4+Hn744cAyTdMwadIkbN++vdF1tm/fjgcffDBo2ZQpU/Dhhx82uZ+amhrU1NQEfi8vL7+sfO8+U4gTjmNItnSHUhqmHsrHv1RvrZfqY+i7lsLV6BYu35+bTRHe/pvfXrhrfxHCsrbMY3s5Dny/C9i9LDL5V4AyCxQESvn+NQC+zty5hskrlcIXMTF4Iz4ORSYTFDy+Tp+u+R+Z0FElF1DpcSLZ0h3HHYXYfaYQV/caFIncNlD/3AkKTWltkodod6q0Ckd/qEBGgjXQ4aqllEJGghVHiitwqrQKvZNj2imXRI2LdHsMBNcr1xw/il+Xr2skVXB7E0obil1LI1Bv17Z7vv2Htu+WU/VG/arecoV8GOp/WVA3sanhYjeAtbGxeCUxHmeNl+g2KvjvThN/G6OjWj+PJC0jovU625HIY7tCFL3adTBfUlICr9eLtLS0oOVpaWk4cOBAo+sUFRU1mr6oqKjJ/SxevBiPPfbY5WfY71yVA27dBavBV6EpEViUJ2LbJ2qpSqVQqRT+z2LBensstsRYUF37CrraJ2rqPlmjBCJeuMWNOGMCLtSU4FyVo9XyV//cqc9isLV6HqKd0+VBtceLGLOt0c9tZgPOllfD6WKdRB1PpNtjILheUQK2xy3ggoYKZYBTUzhvUDhkNuKQ2YyDZhMOmc2o0LTmNxJQe6uADoEHBmWAy1sVsXqd7UjksV0hil7tfpt9W3j44YeDruaXl5ejd+/eLd5eN1scTJoZ1d5K2LU4bOw3Cl96hgU+r/JUotLjxNyrf4ErU3tdVt4bs7/4JJb/8y3EGO2wGRs2ZuHuv6ntFVWeRpnsx8UJbSwAAPE/Lw/ogNJhR3/U6A5oync46eJBkqkXLrhPBi1Ltw6G3RQTkTw63ZUoqj4MAPCqikDfQRMbvFqp/1Z5+PMI31UCTQ+kU16L7xl4VW9yIfE/+658ExsF0TXfYwOA73l7zVtvXYPvmUnlfw4fOpTXjnhDT/ws8zb89cinsBtjEWOyodLtxOnqgzAoAzyohNdQHNabfSTwoyDKN4ivhgaBEdA8/mclVeAxAh//DpS6GJooKBhgUibUeKtg1MzoZmu9Z+Lqnzv1tUUeop3dbITVaECly4M4a8PLaFUuLyxGA+zmLlG9U5SJdHsMBNcru3r1x69dc4M+r9/ehNqGThs0CeuOfI4Yox1lrtKL7aHyT2gSuMJd55Z7rwVQGgwSCwBItw6GphDYPwAs/+db0GDAOXchdFXlm59Fc8HXXoUzjZE08n/x36av+5cZIMoLaC5ANwOIBWCALgpuwzl4NUGNUvBE9NVyF+/9VzDCK96I1utsRyKP7QpR9GrXszIlJQUGgwFnz54NWn727Fmkp6c3uk56enpY6QHAYrHAYrFcfob9Rmf0R5+4fjhadhAxRjtcFhNcFl/lJ6LjpPMMBiUPwfWjrm+V57W6ZfTDmye242jZQfSKSQq6zawl+29qezZLHxyt2Atdq4ESE5T4t6XcEOXyzw5vRWrMYPxQtR/VUgpAYNOSkRqbAUfFOVTpFwAo2LQkpMUlwam0iOQRsKBc2VAtpdChQ1QllNhhUVbUwANduXzP+Qdm2NUA/zPzCgYo3eSfmd7r63LUfWYe/mfmaztHgf7SxWfmoXxXHFC7LgDoFn8XRgDlgYgBRl1Dv25Dccd1t2NL2Xe+/FuTAasN5TiDar0UmopHjSpF0OR34ZLaafi8voF87a2OUvvFhOaf7M83Q5JS/lmHlQabSkKM0Y5TlccxKGFI4HWCraH+uVP/2D1f80Or5yHa9Uy0YWD3WBScLkOsxRh0S6SI4ExZNUb0TEDPxMavsBC1p0i3x0C9esXeDx7TxWO/sfYm1DZ0xvU/xdbyQhwtO4j0xN446vS3h4D/u9HaNqLOYF50QGJgQA2sWhK62xPxfdXxwP4B4M0T23G47ACqjbGokjJ/e+INTIDX2ER39Z+LD/kz3ehrumAGNCNikApNGVCln4coK0TVNL1uU5qakE8AQAfE4GtDxQCrSka1XonBEazX2Y5EHtsVougVzn1TEWc2mzF27Fhs3LgxsEzXdWzcuBHjxo1rdJ1x48YFpQeAvLy8JtO3BqPBgF+MnIZYUwJOOo/B6XbAo3vgdDtw0nkMsaYEzBx5S6tNvBLp/Te1vRqphlFSANF83+zDf+VA/ANWUYhBH2hGDQnGboB4IPAgztANOgRxhhTfgFc8iDckwyt6xPJY6XGim6UnNGgAPP4vGhS8UuOb8Ec0CLxQusE/eZ/un82+dsb5at+/ugkQQ507z3X/1WxffCLKN6GdbvWtq1yA8kJEoHQTAOVbV5T/Sw5fh8w3e7AFyea+uHvUrbCazQ3zb+oJQINbzkPzxKJ2lvwA8d0RL43cJNDwgozmv1ikAN0A3+Bd83WqpHaKYw+UKP8s/TogGjSJRZwhBacqj7f6cQu0/7nTGWiawpThaUi2m3G4uAKOajc8ug5HtRuHiyuQbDdj8rA0TlJEXUa49Uqo6evW20U1JxGj+vjaCwB1K2GpfVdKnc80mBBvSMbpquC6tXbf8aZEGJQRmsQEP/qEi7PC19lBk6R2IN/IU1S+ts7fHnmtMOhxMCkz4g0pMMAC6CZ/GxfGH7vOW1bqt03+2VwA/yv2lG6HUZkQF+F6ne1I5LFdIYpeHeLVdLNmzcIrr7yCrKwsPPfcc1izZg0OHDiAtLQ0/PKXv0TPnj2xePFiAL5X040fPx45OTmYNm0acnNz8ac//anNX00HBL/j1KO7YNTM6BvXHzNH3tLm75mPxP6b2l61u+qS75mvTRtn8v0tHe7yOssSICKo8JS3Sh7dEXnPvBsulDbynnkzzNK9xe+ZHxybhd9kNf2eeY/ugifU98zD6P+ioOF75mtfP+SLyQDA5LuTQvk6MjpcEFUJwOO/xV41eM98Wx63jf0d2vrc6Qzqvg+4xuO7BXJQaiwmD+P7gKnlovXVdED49Uqo6VvynnmrlnDJurWt3zM/ODYL4/uE/575Br/XPoom/i/KFRp5z7zB/575vshMGN5q9Trbkchju0LUcUTNe+YB4KWXXsJTTz2FoqIijB49Gi+88AKys7MBABMmTEC/fv2watWqQPp3330Xf/jDH3Ds2DEMHjwYS5cuxS233BLy/iLZefB4vdh9phDnqhzoZovD6Iz+bfptcKT339T2Kqqq8OKOtTjhOIs+cWn4TfZPYTWbG6QFENKySOZxeGofFBSfQLGzFOerHEi2xSHVnogh3Xriw4Nf4bSjBOn2ZPRJTMO+H47Bq+tIsNiRYk9Aqj0Rw1P7YHdRIfJPH8CJ0mJcqC5FjNmGq9KvxJ3Db4DRYED+qaPIP30IHq8X5TUVMBqM6JWQin8Zeh0A4J2CrdhdtA9Vrhpc2X0gsnsPxdU9BzUaZ1P5P1flgM1gwuZju7G/5CC8AMZljEJWb998DLvOHMZpxw/QdR1QGnrEdkOsxY5KdyUECgkWOxJtsSivqUSyLQ7JtjgoKJyrKkeJswylVeU4W3kB6bEpGJMxGEbNgNIaZ7sct439HdojD9FO1wWnSqvgdHlgNxvRM9HGKyd0WaJ5MA+EX6+Emr5uOpvBhC+P/R9OVhTBYjDj+r6jUePxINkWh262eAgEZTWVze6/dpvFzlL84CxDeY0TCoDdZEWlpwq6ruNCVQUuVJfBZrJhQGJPlFU78HXRt9AADEvJxK/H3oa/HvoaW77/GgrAj3qMgkEzYm/J4Ubbo7pxJFrsEAiKnaXYffoQjlwoRIXHif5x/RFnicGF6jJUuquRaI2HQflewKeUAWmxyUi0xSPJ397EGm0oKD6KkqoLsBptuKJbH6THJSHVntjq9Trbkchju0LUMUTVYL6ttXVnhYiIKBpE+2CeiIioMwi1fWzXZ+aJiIiIiIiIKHwczBMRERERERFFGQ7miYiIiIiIiKIMB/NEREREREREUYaDeSIiIiIiIqIow8E8ERERERERUZThYJ6IiIiIiIgoynAwT0RERERERBRljO2dgfYgIgCA8vLyds4JERFRx1HbLta2k62N7TEREVFDobbHXXIw73A4AAC9e/du55wQERF1PA6HAwkJCW2yH4DtMRERUWOaa4+VtNXX7x2Irus4ffo04uLioJSK2HbLy8vRu3dvfP/994iPj4/Ydjs6xt114u6KMQNdM+6uGDPAuE+cOAGlFHr06AFNa/0n8VqrPW4rXf146UpxM+auETPQNeNmzB0vZhGBw+Fotj3uklfmNU1Dr169Wm378fHxHfKgaG2Mu+voijEDXTPurhgz0HXjTkhIaNO4W7s9bitd9XjpinEz5q6jK8bNmDuWUO6Q4wR4RERERERERFGGg3kiIiIiIiKiKMPBfARZLBYsWrQIFoulvbPSphh314m7K8YMdM24u2LMAOPuanFfrq76d+uKcTPmrqMrxs2Yo1eXnACPiIiIiIiIKJrxyjwRERERERFRlOFgnoiIiIiIiCjKcDBPREREREREFGU4mCciIiIiIiKKMhzMh+n8+fOYOXMm4uPjkZiYiDlz5qCiouKS6X/zm98gMzMTNpsNffr0wf3334+ysrKgdEqpBj+5ubmtHU6Tli9fjn79+sFqtSI7Oxtff/31JdO/++67GDJkCKxWK0aMGIH169cHfS4iePTRR5GRkQGbzYZJkybh8OHDrRlC2MKJ+dVXX8UNN9yApKQkJCUlYdKkSQ3S/9u//VuDMp06dWprhxG2cOJetWpVg5isVmtQms5W1hMmTGj0/Jw2bVogTTSU9ZdffonbbrsNPXr0gFIKH374YbPrbN68GWPGjIHFYsGgQYOwatWqBmnCrSvaUrgxf/DBB7jpppvQvXt3xMfHY9y4cfj000+D0vzxj39sUNZDhgxpxSjCF27cmzdvbvQYLyoqCkrXkcu6tbTWedORtdbx05EtXrwY11xzDeLi4pCamorbb78dBw8ebHa95vo+HVlLYg6lD9DRvfzyyxg5ciTi4+MD9fzf/va3S64TzeUMhB9zZyjn+nJycqCUwvz58y+ZLhrLmoP5MM2cORN79+5FXl4ePvnkE3z55Zf41a9+1WT606dP4/Tp01i2bBkKCgqwatUqbNiwAXPmzGmQ9vXXX8eZM2cCP7fffnsrRtK0d955Bw8++CAWLVqEnTt3YtSoUZgyZQqKi4sbTf+Pf/wDd911F+bMmYNdu3bh9ttvx+23346CgoJAmqVLl+KFF17AihUrsGPHDtjtdkyZMgXV1dVtFdYlhRvz5s2bcdddd2HTpk3Yvn07evfujcmTJ+PUqVNB6aZOnRpUpqtXr26LcEIWbtwAEB8fHxTT8ePHgz7vbGX9wQcfBMVbUFAAg8GAf/3Xfw1K19HL2ul0YtSoUVi+fHlI6QsLCzFt2jTceOON2L17N+bPn4977rknaHDbkuOnLYUb85dffombbroJ69evR35+Pm688Ubcdttt2LVrV1C6YcOGBZX13//+99bIfouFG3etgwcPBsWVmpoa+Kyjl3VraY3zpqNrjeOno9uyZQvmzp2Lr776Cnl5eXC73Zg8eTKcTmeT64TS9+nIWhIz0HwfoKPr1asXcnJykJ+fj3/+85/4yU9+gunTp2Pv3r2Npo/2cgbCjxmI/nKu65tvvsErr7yCkSNHXjJd1Ja1UMj27dsnAOSbb74JLPvb3/4mSik5depUyNtZs2aNmM1mcbvdgWUAZO3atZHMbotlZWXJ3LlzA797vV7p0aOHLF68uNH0d9xxh0ybNi1oWXZ2tvzHf/yHiIjoui7p6eny1FNPBT4vLS0Vi8Uiq1evboUIwhduzPV5PB6Ji4uTN954I7Bs1qxZMn369EhnNaLCjfv111+XhISEJrfXFcr62Weflbi4OKmoqAgsi4ayriuU+uZ3v/udDBs2LGjZnXfeKVOmTAn8frl/y7bU0jp26NCh8thjjwV+X7RokYwaNSpyGWtlocS9adMmASAXLlxoMk00lXVridR5E00idfxEm+LiYgEgW7ZsaTJNc32faBNKzM31AaJVUlKS/M///E+jn3W2cq51qZg7Uzk7HA4ZPHiw5OXlyfjx4+WBBx5oMm20ljWvzIdh+/btSExMxNVXXx1YNmnSJGiahh07doS8nbKyMsTHx8NoNAYtnzt3LlJSUpCVlYXXXnsNIhKxvIfK5XIhPz8fkyZNCizTNA2TJk3C9u3bG11n+/btQekBYMqUKYH0hYWFKCoqCkqTkJCA7OzsJrfZlloSc32VlZVwu91ITk4OWr5582akpqYiMzMT9913H86dOxfRvF+OlsZdUVGBvn37onfv3g2+2e0KZb1y5UrMmDEDdrs9aHlHLuuWaO68jsTfsqPTdR0Oh6PBeX348GH06NEDAwYMwMyZM3HixIl2ymFkjR49GhkZGbjpppuwbdu2wPKuUNaR0tx505k1dfxEo9pHIeuf+3V1trIOJWbg0n2AaOP1epGbmwun04lx48Y1mqazlXMoMQOdp5znzp2LadOmNSjDxkRrWXMwH4aioqIGt40ZjUYkJyeH/GxYSUkJnnjiiQa35j/++ONYs2YN8vLy8POf/xz/+Z//iRdffDFieQ9VSUkJvF4v0tLSgpanpaU1GWNRUdEl09f+G84221JLYq7v97//PXr06BFUCUydOhX/+7//i40bN2LJkiXYsmULbr75Zni93ojmv6VaEndmZiZee+01fPTRR3jrrbeg6zquvfZanDx5EkDnL+uvv/4aBQUFuOeee4KWd/Sybommzuvy8nJUVVVF5Lzp6JYtW4aKigrccccdgWXZ2dmBx6VefvllFBYW4oYbboDD4WjHnF6ejIwMrFixAu+//z7ef/999O7dGxMmTMDOnTsBRKaO7CqaO286o+aOn2ij6zrmz5+P6667DsOHD28yXXN9n2gSaszN9QGixZ49exAbGwuLxYJf//rXWLt2LYYOHdpo2s5SzuHE3FnKOTc3Fzt37sTixYtDSh+tZW1sPknnt3DhQixZsuSSafbv33/Z+ykvL8e0adMwdOhQ/PGPfwz67JFHHgn8/6qrroLT6cRTTz2F+++//7L3S60rJycHubm52Lx5c9AEITNmzAj8f8SIERg5ciQGDhyIzZs3Y+LEie2R1cs2bty4oG9yr732Wlx55ZV45ZVX8MQTT7RjztrGypUrMWLECGRlZQUt74xl3dX95S9/wWOPPYaPPvoo6Evcm2++OfD/kSNHIjs7G3379sWaNWsanQslGmRmZiIzMzPw+7XXXoujR4/i2WefxZtvvtmOOaNo0NmOn7lz56KgoKDDzYXRmkKNubP0ATIzM7F7926UlZXhvffew6xZs7Bly5YmB7edQTgxd4Zy/v777/HAAw8gLy8v6ifvaw6vzAN46KGHsH///kv+DBgwAOnp6Q0m+/F4PDh//jzS09MvuQ+Hw4GpU6ciLi4Oa9euhclkumT67OxsnDx5EjU1NZcdXzhSUlJgMBhw9uzZoOVnz55tMsb09PRLpq/9N5xttqWWxFxr2bJlyMnJwWeffdbsxBoDBgxASkoKjhw5ctl5joTLibuWyWT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      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x axis labels for plots\n",
    "xlabs = {\"plain\": r\"$X$\", \"log\": r\"$\\log(X)$\"}\n",
    "\n",
    "# set index for easier access to fit parameters\n",
    "df_fit_index = df_fit.set_index([\"scale\", \"dataset\"])\n",
    "scales = df_data[\"scale\"].unique()\n",
    "plot_labs = dict(zip(scales[::-1], ['A', 'B']))\n",
    "\n",
    "#\n",
    "# plotting data and fits\n",
    "#\n",
    "\n",
    "fig, axs = plt.subplots(\n",
    "    ncols = len(scales), \n",
    "    figsize = (5*len(scales), 4),\n",
    "    layout = \"constrained\")\n",
    "\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Plotting: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # choose axis and set labels\n",
    "    ax = axs[0 if scale == \"log\" else 1]\n",
    "    ax.set_xlabel(xlabs[scale])\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # label on plots\n",
    "    ax.text(0.05, 0.9, f\"{plot_labs[scale]}\", fontsize=14, transform=ax.transAxes)\n",
    "\n",
    "    # data\n",
    "    for i, lab in enumerate([\"NC\", \"AE\"]): \n",
    "        ax.scatter(X[Y == i], Y[Y == i], label = f'data: {dataset} {lab}', alpha = 0.5)\n",
    "\n",
    "    # fitted curve\n",
    "    fit_pars = df_fit_index.loc[(scale, dataset), \"pars\"]\n",
    "    x_fit = np.linspace(X.min(), X.max(), 100)\n",
    "    y_fit = lg.model(x_fit, fit_pars)\n",
    "\n",
    "    ax.plot(x_fit, y_fit, label = f\"fit {dataset}\")\n",
    "\n",
    "for ax in axs.flat:  ax.label_outer()\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(0.975, 0.4), loc='center right')\n",
    "\n",
    "plt.savefig(os.path.join(results_path, \"logit_fit_paper.pdf\"), bbox_inches = \"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c493876",
   "metadata": {},
   "outputs": [
    {
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       "shape": {
        "columns": 4,
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      "text/html": [
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       "<style scoped>\n",
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      ],
      "text/plain": [
       "               0            1            2             3\n",
       "0  401741.885133  1094.506622  7367.104638 -12879.714703\n",
       "1    1094.506622     5.536159    19.445105    -35.174998\n",
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       "3  -12879.714703   -35.174998  -238.188330    414.125641"
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_fit_index.loc[(\"plain\", \"TRIM\"), \"cov\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "995a7c29",
   "metadata": {},
   "outputs": [
    {
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       "              0             1             2             3\n",
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     "execution_count": 14,
     "metadata": {},
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    }
   ],
   "source": [
    "pd.DataFrame(df_fit_index.loc[(\"plain\", \"FULL\"), \"cov\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bda6a88d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/horvat/Documents/work/medfiz/git/uncertainty_study/python/logistic/logit_utils_gen.py:676: RuntimeWarning: overflow encountered in square\n",
      "  scales = np.sqrt(np.diag(S@cov_pars@S.T))/(4*np.cosh(F/2)**2)\n",
      "/home/horvat/Documents/work/medfiz/git/uncertainty_study/python/logistic/logit_utils_gen.py:676: RuntimeWarning: overflow encountered in multiply\n",
      "  scales = np.sqrt(np.diag(S@cov_pars@S.T))/(4*np.cosh(F/2)**2)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# plotting data and fits + simple confidence intervals\n",
    "#\n",
    "scales = df_data[\"scale\"].unique()\n",
    "datasets = df_data[\"dataset\"].unique()\n",
    "\n",
    "fig, axs = plt.subplots(\n",
    "    ncols = len(scales), \n",
    "    nrows = len(datasets), \n",
    "    figsize = (5*len(scales), 4*len(datasets)),\n",
    "    layout = \"constrained\")\n",
    "\n",
    "plot_labs = dict(zip([(scale, dataset) for dataset in datasets for scale in scales[::-1]], ['A','B','C','D']))\n",
    "if verbose: print(plot_labs)\n",
    "\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Plotting: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # choose axis and set labels\n",
    "    ax = axs[0 if dataset == \"FULL\" else 1, 0 if scale == \"log\" else 1]\n",
    "    ax.set_xlabel(xlabs[scale])\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # label on plots    \n",
    "    #ax.text(0.05, 0.9, f\"{plot_labs[(scale, dataset)]}:{scale},{dataset}\", fontsize=14, transform=ax.transAxes)\n",
    "    ax.text(0.05, 0.9, f\"{plot_labs[(scale, dataset)]}\", fontsize=14, transform=ax.transAxes)\n",
    "\n",
    "    # data\n",
    "    for i, lab in enumerate([\"NC\", \"AE\"]): \n",
    "        ax.scatter(X[Y == i], Y[Y == i], label = f'data: {lab}', alpha = 0.5)\n",
    "    \n",
    "    # get fit parameters and covariance matrix for scale and dataset\n",
    "    fit_pars = df_fit_index.loc[(scale, dataset), \"pars\"]\n",
    "    fit_cov = df_fit_index.loc[(scale, dataset), \"cov\"]\n",
    "    \n",
    "    # plotting fitted curve\n",
    "    x_fit = np.linspace(X.min(), X.max(), 100)\n",
    "    y_fit = lg.model(x_fit, fit_pars)\n",
    "    ax.plot(x_fit, y_fit, label = f\"fit\")\n",
    "\n",
    "    # plotting simple confidence intervals of quantiles of the model at probs\n",
    "    for method, c in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        \n",
    "        # define quantile model\n",
    "        fname = f\"lg.get_model_quantiles_{method}\"\n",
    "        \n",
    "        # get result of model quantiles at probs\n",
    "        res = eval(fname)(x_fit, probs, fit_pars, fit_cov)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(x_fit, *res, alpha = 0.5, color = c, label = f\"CI: {method}\")\n",
    "\n",
    "for ax in axs.flat:  ax.label_outer()\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(0.975, 0.2), framealpha=0.5, loc='center right')\n",
    "\n",
    "plt.savefig(os.path.join(results_path, \"logit_fit_CI_simple_paper.pdf\"), bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67fa2c6c",
   "metadata": {},
   "source": [
    "##  Model CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0c70ea17",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# prepare boostrapped parameters\n",
    "#\n",
    "\n",
    "n_boots = 10000\n",
    "boots_pars_results = dict()\n",
    "\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Discussing: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "    \n",
    "    # plot quantiles of models at bootstapped parameters using different bootstrapping methods\n",
    "    for method in [\"normal\", \"nonparam_boots\", \"nonparam_stratified_boots\", \"parametric_boots\"]:\n",
    "    \n",
    "        if verbose: print(f\"\\tmodel CI: scale:{scale}, dataset:{dataset}, method:{method}\")\n",
    "\n",
    "        # generate boostrapped parameters\n",
    "        fname = f\"lg.get_{method}_pars\"\n",
    "        boots_pars_results[(scale, dataset, method)] = eval(fname)(X, Y, m = n_boots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5c5f86b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "LOADING_RESULT_FROM_FILE = False\n",
    "\n",
    "if LOADING_RESULT_FROM_FILE:\n",
    "    with open(os.path.join(results_path, \"boots_pars_results.pkl\"), \"rb\") as f:\n",
    "        boots_pars_results = pickle.load(f)\n",
    "else:\n",
    "    with open(os.path.join(results_path, \"boots_pars_results.pkl\"), \"wb\") as f:\n",
    "        pickle.dump(boots_pars_results, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "00b2819a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/horvat/Documents/work/medfiz/git/uncertainty_study/python/logistic/logit_utils_gen.py:676: RuntimeWarning: overflow encountered in square\n",
      "  scales = np.sqrt(np.diag(S@cov_pars@S.T))/(4*np.cosh(F/2)**2)\n",
      "/home/horvat/Documents/work/medfiz/git/uncertainty_study/python/logistic/logit_utils_gen.py:676: RuntimeWarning: overflow encountered in multiply\n",
      "  scales = np.sqrt(np.diag(S@cov_pars@S.T))/(4*np.cosh(F/2)**2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# plotting data and fits + full set of confidence intervals\n",
    "#\n",
    "\n",
    "scales = df_data[\"scale\"].unique()\n",
    "datasets = df_data[\"dataset\"].unique()\n",
    "\n",
    "fig, axs = plt.subplots(\n",
    "    ncols = len(scales), \n",
    "    nrows = len(datasets), \n",
    "    figsize = (5*len(scales), 4*len(datasets)),\n",
    "    layout = \"constrained\")\n",
    "\n",
    "plot_labs = dict(zip([(scale, dataset) for dataset in datasets for scale in scales[::-1]], ['A','B','C','D']))\n",
    "if verbose: print(plot_labs)\n",
    "\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Plotting: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # choose axis and set labels\n",
    "    ax = axs[0 if dataset == \"FULL\" else 1, 0 if scale == \"log\" else 1]\n",
    "    ax.set_xlabel(xlabs[scale])\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "    \n",
    "    #ax.text(0.05, 0.9, f\"{plot_labs[(scale, dataset)]}:{scale}, {dataset}\", transform=ax.transAxes)\n",
    "    ax.text(0.05, 0.9, f\"{plot_labs[(scale, dataset)]}\", fontsize=14, transform=ax.transAxes)\n",
    "\n",
    "    # plotting data\n",
    "    for i, lab in enumerate([\"NC\", \"AE\"]): \n",
    "        ax.scatter(X[Y == i], Y[Y == i], label = f'data: {lab}', alpha = 0.5)\n",
    "    \n",
    "    # get fit parameters and covariance matrix for scale and dataset\n",
    "    fit_pars = df_fit_index.loc[(scale, dataset), \"pars\"]\n",
    "    fit_cov = df_fit_index.loc[(scale, dataset), \"cov\"]\n",
    "    \n",
    "    # plotting fitted curve\n",
    "    x_fit = np.linspace(X.min(), X.max(), 100)\n",
    "    y_fit = lg.model(x_fit, fit_pars)\n",
    "    ax.plot(x_fit, y_fit, label = f\"fit\")\n",
    "\n",
    "    # plotting simple confidence intervals of quantiles of the model at probs\n",
    "    for method, c in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        \n",
    "        # define quantile model\n",
    "        fname = f\"lg.get_model_quantiles_{method}\"\n",
    "        \n",
    "        # get result of model quantiles at probs\n",
    "        res = eval(fname)(x_fit, probs, fit_pars, fit_cov)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(x_fit, *res, color = c, alpha = 0.5, label = f\"CI: {method}\")\n",
    "\n",
    "    # plot quantiles of models at bootstapped parameters using different bootstrapping methods\n",
    "    #for method, c in zip(\n",
    "    #    [\"normal\", \"nonparam_boots\", \"nonparam_stratified_boots\", \"parametric_boots\"],\n",
    "    #    [\"orange\", \"pink\", \"purple\", \"cyan\"]):\n",
    "\n",
    "    for method, c in zip([\"nonparam_boots\", \"parametric_boots\"], [ \"blue\", \"cyan\"]):\n",
    "    \n",
    "        if verbose: print(f\"\\tmodel CI: scale:{scale}, dataset:{dataset}, method:{method}\")\n",
    "\n",
    "        # generate boostrapped parameters\n",
    "        bpars = boots_pars_results[(scale, dataset, method)]\n",
    "\n",
    "        # quantiles\n",
    "        quant = np.quantile([lg.model(x_fit, p) for p in bpars], probs, axis = 0)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(x_fit, *quant, color = c, alpha = 0.5, label = f\"CI: {method}\")\n",
    "\n",
    "for ax in axs.flat:  ax.label_outer()\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(0.975, 0.2), framealpha=0.5, loc='center right')\n",
    "\n",
    "plt.savefig(os.path.join(results_path, \"logit_fit_CI_paper.pdf\"), bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce16edf",
   "metadata": {},
   "source": [
    "## Parameter CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "91a199e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['scale', 'dataset', 'idx', 'beta', 'pars', 'SE', 'LCL', 'UCL'], dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pars.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "18dce684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "dataset",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "idx",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "beta",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "pars",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "SE",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "LCL[Wald]",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "UCL[Wald]",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "LCL[normal]",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "UCL[normal]",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "LCL[nonparam_boots]",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "UCL[nonparam_boots]",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "LCL[nonparam_stratified_boots]",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "UCL[nonparam_stratified_boots]",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "LCL[parametric_boots]",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "UCL[parametric_boots]",
         "rawType": "object",
         "type": "unknown"
        }
       ],
       "ref": "5aa86ec5-0f3c-4a7d-bcdc-e337d999c5df",
       "rows": [
        [
         "0",
         "log",
         "FULL",
         "[0, 1, 2, 3]",
         "[ -40.70878922505933  152.89575085005256 -186.3720911720346\n   75.72601194980655]",
         "[-4.0708789225059327e+01  1.4619334353444514e-08  1.5072426342477831e+01\n -1.2365102136660763e+01]",
         "[9.15959914659386   2.0150709906287263 2.088266288029099\n 1.2824176277027601]",
         "[-58.66127366520711   -3.949466553304419  10.979499627811649\n -14.878594500097469]",
         "[-22.756304784911546    3.9494665825430864  19.165353057144014\n  -9.851609773224057 ]",
         "[-58.6133480936447    -3.880833730936736 -19.19235286398565\n   9.77580083713744 ]",
         "[-22.02815318054564     3.9980539635915586 -10.947109940224818\n  14.86378359018737  ]",
         "[-315.39552259921345    -5.381885628829679  -69.1443912212005\n  -22.488381247890427]",
         "[-5.9298151325213775e+00  1.0235109616374880e-05  2.9011453116681011e+01\n  3.8809258905922782e+01]",
         "[-313.02229578473276    -5.412649524141675  -68.19666259904605\n  -23.774849898980822]",
         "[-5.8991434931969060e+00  1.0761943985787224e-05  3.1543594294851854e+01\n  3.8151073020784580e+01]",
         "[-318.6267145553689     -6.84252684038771   -74.06006331855127\n  -34.976531177183055]",
         "[-6.4235126958446891e+00  4.5760502382159089e-06  4.5757947137024544e+01\n  4.1151647643548927e+01]"
        ],
        [
         "1",
         "log",
         "TRIM",
         "[0, 1, 2, 3]",
         "[ -318.6183131682148  1693.40495002855   -3000.7798640517635\n  1772.4997973961392]",
         "[-3.186183131682148e+02  3.667554092681235e-05 -7.292118616827635e+01\n  4.115100181073609e+01]",
         "[63.4594513328195    4.519607234877146  6.604729465850617\n  3.342170378081635]",
         "[-442.9965522592134     -8.858230729084942  -85.86621804897403\n   34.60046823949947 ]",
         "[-194.24007407721626     8.858304080166793  -59.97615428757867\n   47.70153538197271 ]",
         "[-442.413435690136      -8.840910632215914   60.12752165257121\n  -47.68174179854637 ]",
         "[-189.77353434511247     8.782460447027841   85.99414400403779\n  -34.47450440300598 ]",
         "[-360.040401188123     -13.565687430905436  -75.40053611866169\n  -37.40782274138315 ]",
         "[-1.1310259340488017e+01  2.6052782573579581e-05  6.7198661954516709e+01\n  4.2530232577245165e+01]",
         "[-357.9095296999922    -13.434971285005846  -74.96337089865733\n  -40.20195965605087 ]",
         "[-1.1227028264328574e+01  2.8735888981371008e-05  7.1618666107916340e+01\n  4.2196670396257346e+01]",
         "[-1.2694885082911394e+02 -5.2866702374455121e+00 -5.2807838768844718e+01\n -1.7397860658263173e-04]",
         "[-1.4172703961864348e+01 -5.0666106220880649e-10  1.0009744813954666e-04\n  1.0264210290976191e+01]"
        ],
        [
         "2",
         "plain",
         "FULL",
         "[0, 1, 2, 3]",
         "[-123.99287081095154  163.79463422187158  -71.44848714314551\n   10.38879435681899]",
         "[-1.2399287081095154e+02 -3.2549503295759507e-08  5.5826860085855596e+00\n -1.2798227776605305e+01]",
         "[18.879777163489308   1.3204875400985643  0.4735356467079985\n  0.888694223608501 ]",
         "[-160.9965540875324     -2.58810805317658     4.654573195642\n  -14.540036448146752]",
         "[-86.98918753437073     2.5881079880775735   6.51079882152912\n -11.056419105063858 ]",
         "[-160.78195713023533     -2.5431031196809517    4.64640531229026\n  -14.51819485526843  ]",
         "[-85.70776274990214     2.6199250325390695   6.519855044136494\n -11.010004801559509 ]",
         "[-653.4546087765481     -2.680367594105309  -17.4179505831331\n  -29.15253000035244 ]",
         "[-1.1724191395755772e+01  6.3122639572363926e-06  1.6103667731144213e+01\n  3.1540561542319061e+01]",
         "[-625.866106987995      -2.768088840481631  -16.96320022712164\n  -30.29450752118477 ]",
         "[-1.1576249349159095e+01  5.7120259330401642e-06  1.6398748548858084e+01\n  3.0856804685721499e+01]",
         "[-519.9181692980177     -3.828433856483589  -17.478053485149314\n  -21.125973650354737]",
         "[-1.3741762947553546e+01  3.3383531707073148e-05  1.1155344901939584e+01\n  2.9302530568580575e+01]"
        ],
        [
         "3",
         "plain",
         "TRIM",
         "[0, 1, 2, 3]",
         "[-450.10674749899573  727.8800281668483  -395.55180657932544\n   72.30179973452464]",
         "[-450.10674749899573      2.5585274930890147  -14.727708552370729\n   26.857661201861116 ]",
         "[633.8311172012361      2.3529044626711726  11.775012205107716\n  20.350077163086553 ]",
         "[-1692.3929094942048       -2.0530805128100527   -37.806308391901425\n   -13.027757120399574 ]",
         "[792.179414496213      7.170135498988081   8.350891287159957\n  66.7430795241218  ]",
         "[-1686.2130220922372       -2.0938244787955878   -37.72156593673228\n   -14.74776762886781  ]",
         "[836.1917630675644      7.1760574695412815   9.144342206149423\n  66.70943845550282  ]",
         "[-732.6157759520047      -3.5689686354337042  -18.82898133636525\n  -32.514578439793894 ]",
         "[-2.0128549489680175e+01  1.6978531783484566e-05  1.8062388906131293e+01\n  3.4031801898213480e+01]",
         "[-701.7050817340362     -3.710997763583124  -17.92989548086037\n  -33.50246564672744 ]",
         "[-2.0880607074306813e+01  1.5241416263632105e-05  1.8618402196767928e+01\n  3.2220792591519434e+01]",
         "[-543.4651247601868      -3.9805170362105287  -17.79778970594239\n  -21.288354062823313 ]",
         "[-3.0306069506729404e+01  2.4348035458824826e-06  1.5911320064604043e+01\n  3.0691028623974987e+01]"
        ]
       ],
       "shape": {
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        "rows": 4
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      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>scale</th>\n",
       "      <th>dataset</th>\n",
       "      <th>idx</th>\n",
       "      <th>beta</th>\n",
       "      <th>pars</th>\n",
       "      <th>SE</th>\n",
       "      <th>LCL[Wald]</th>\n",
       "      <th>UCL[Wald]</th>\n",
       "      <th>LCL[normal]</th>\n",
       "      <th>UCL[normal]</th>\n",
       "      <th>LCL[nonparam_boots]</th>\n",
       "      <th>UCL[nonparam_boots]</th>\n",
       "      <th>LCL[nonparam_stratified_boots]</th>\n",
       "      <th>UCL[nonparam_stratified_boots]</th>\n",
       "      <th>LCL[parametric_boots]</th>\n",
       "      <th>UCL[parametric_boots]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>log</td>\n",
       "      <td>FULL</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[-40.70878922505933, 152.89575085005256, -186....</td>\n",
       "      <td>[-40.70878922505933, 1.4619334353444514e-08, 1...</td>\n",
       "      <td>[9.15959914659386, 2.0150709906287263, 2.08826...</td>\n",
       "      <td>[-58.66127366520711, -3.949466553304419, 10.97...</td>\n",
       "      <td>[-22.756304784911546, 3.9494665825430864, 19.1...</td>\n",
       "      <td>[-58.6133480936447, -3.880833730936736, -19.19...</td>\n",
       "      <td>[-22.02815318054564, 3.9980539635915586, -10.9...</td>\n",
       "      <td>[-315.39552259921345, -5.381885628829679, -69....</td>\n",
       "      <td>[-5.9298151325213775, 1.023510961637488e-05, 2...</td>\n",
       "      <td>[-313.02229578473276, -5.412649524141675, -68....</td>\n",
       "      <td>[-5.899143493196906, 1.0761943985787224e-05, 3...</td>\n",
       "      <td>[-318.6267145553689, -6.84252684038771, -74.06...</td>\n",
       "      <td>[-6.423512695844689, 4.576050238215909e-06, 45...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[-318.6183131682148, 1693.40495002855, -3000.7...</td>\n",
       "      <td>[-318.6183131682148, 3.667554092681235e-05, -7...</td>\n",
       "      <td>[63.4594513328195, 4.519607234877146, 6.604729...</td>\n",
       "      <td>[-442.9965522592134, -8.858230729084942, -85.8...</td>\n",
       "      <td>[-194.24007407721626, 8.858304080166793, -59.9...</td>\n",
       "      <td>[-442.413435690136, -8.840910632215914, 60.127...</td>\n",
       "      <td>[-189.77353434511247, 8.782460447027841, 85.99...</td>\n",
       "      <td>[-360.040401188123, -13.565687430905436, -75.4...</td>\n",
       "      <td>[-11.310259340488017, 2.605278257357958e-05, 6...</td>\n",
       "      <td>[-357.9095296999922, -13.434971285005846, -74....</td>\n",
       "      <td>[-11.227028264328574, 2.8735888981371008e-05, ...</td>\n",
       "      <td>[-126.94885082911394, -5.286670237445512, -52....</td>\n",
       "      <td>[-14.172703961864348, -5.066610622088065e-10, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[-123.99287081095154, 163.79463422187158, -71....</td>\n",
       "      <td>[-123.99287081095154, -3.254950329575951e-08, ...</td>\n",
       "      <td>[18.879777163489308, 1.3204875400985643, 0.473...</td>\n",
       "      <td>[-160.9965540875324, -2.58810805317658, 4.6545...</td>\n",
       "      <td>[-86.98918753437073, 2.5881079880775735, 6.510...</td>\n",
       "      <td>[-160.78195713023533, -2.5431031196809517, 4.6...</td>\n",
       "      <td>[-85.70776274990214, 2.6199250325390695, 6.519...</td>\n",
       "      <td>[-653.4546087765481, -2.680367594105309, -17.4...</td>\n",
       "      <td>[-11.724191395755772, 6.312263957236393e-06, 1...</td>\n",
       "      <td>[-625.866106987995, -2.768088840481631, -16.96...</td>\n",
       "      <td>[-11.576249349159095, 5.712025933040164e-06, 1...</td>\n",
       "      <td>[-519.9181692980177, -3.828433856483589, -17.4...</td>\n",
       "      <td>[-13.741762947553546, 3.338353170707315e-05, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[-450.10674749899573, 727.8800281668483, -395....</td>\n",
       "      <td>[-450.10674749899573, 2.5585274930890147, -14....</td>\n",
       "      <td>[633.8311172012361, 2.3529044626711726, 11.775...</td>\n",
       "      <td>[-1692.3929094942048, -2.0530805128100527, -37...</td>\n",
       "      <td>[792.179414496213, 7.170135498988081, 8.350891...</td>\n",
       "      <td>[-1686.2130220922372, -2.0938244787955878, -37...</td>\n",
       "      <td>[836.1917630675644, 7.1760574695412815, 9.1443...</td>\n",
       "      <td>[-732.6157759520047, -3.5689686354337042, -18....</td>\n",
       "      <td>[-20.128549489680175, 1.6978531783484566e-05, ...</td>\n",
       "      <td>[-701.7050817340362, -3.710997763583124, -17.9...</td>\n",
       "      <td>[-20.880607074306813, 1.5241416263632105e-05, ...</td>\n",
       "      <td>[-543.4651247601868, -3.9805170362105287, -17....</td>\n",
       "      <td>[-30.306069506729404, 2.4348035458824826e-06, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scale dataset           idx  \\\n",
       "0    log    FULL  [0, 1, 2, 3]   \n",
       "1    log    TRIM  [0, 1, 2, 3]   \n",
       "2  plain    FULL  [0, 1, 2, 3]   \n",
       "3  plain    TRIM  [0, 1, 2, 3]   \n",
       "\n",
       "                                                beta  \\\n",
       "0  [-40.70878922505933, 152.89575085005256, -186....   \n",
       "1  [-318.6183131682148, 1693.40495002855, -3000.7...   \n",
       "2  [-123.99287081095154, 163.79463422187158, -71....   \n",
       "3  [-450.10674749899573, 727.8800281668483, -395....   \n",
       "\n",
       "                                                pars  \\\n",
       "0  [-40.70878922505933, 1.4619334353444514e-08, 1...   \n",
       "1  [-318.6183131682148, 3.667554092681235e-05, -7...   \n",
       "2  [-123.99287081095154, -3.254950329575951e-08, ...   \n",
       "3  [-450.10674749899573, 2.5585274930890147, -14....   \n",
       "\n",
       "                                                  SE  \\\n",
       "0  [9.15959914659386, 2.0150709906287263, 2.08826...   \n",
       "1  [63.4594513328195, 4.519607234877146, 6.604729...   \n",
       "2  [18.879777163489308, 1.3204875400985643, 0.473...   \n",
       "3  [633.8311172012361, 2.3529044626711726, 11.775...   \n",
       "\n",
       "                                           LCL[Wald]  \\\n",
       "0  [-58.66127366520711, -3.949466553304419, 10.97...   \n",
       "1  [-442.9965522592134, -8.858230729084942, -85.8...   \n",
       "2  [-160.9965540875324, -2.58810805317658, 4.6545...   \n",
       "3  [-1692.3929094942048, -2.0530805128100527, -37...   \n",
       "\n",
       "                                           UCL[Wald]  \\\n",
       "0  [-22.756304784911546, 3.9494665825430864, 19.1...   \n",
       "1  [-194.24007407721626, 8.858304080166793, -59.9...   \n",
       "2  [-86.98918753437073, 2.5881079880775735, 6.510...   \n",
       "3  [792.179414496213, 7.170135498988081, 8.350891...   \n",
       "\n",
       "                                         LCL[normal]  \\\n",
       "0  [-58.6133480936447, -3.880833730936736, -19.19...   \n",
       "1  [-442.413435690136, -8.840910632215914, 60.127...   \n",
       "2  [-160.78195713023533, -2.5431031196809517, 4.6...   \n",
       "3  [-1686.2130220922372, -2.0938244787955878, -37...   \n",
       "\n",
       "                                         UCL[normal]  \\\n",
       "0  [-22.02815318054564, 3.9980539635915586, -10.9...   \n",
       "1  [-189.77353434511247, 8.782460447027841, 85.99...   \n",
       "2  [-85.70776274990214, 2.6199250325390695, 6.519...   \n",
       "3  [836.1917630675644, 7.1760574695412815, 9.1443...   \n",
       "\n",
       "                                 LCL[nonparam_boots]  \\\n",
       "0  [-315.39552259921345, -5.381885628829679, -69....   \n",
       "1  [-360.040401188123, -13.565687430905436, -75.4...   \n",
       "2  [-653.4546087765481, -2.680367594105309, -17.4...   \n",
       "3  [-732.6157759520047, -3.5689686354337042, -18....   \n",
       "\n",
       "                                 UCL[nonparam_boots]  \\\n",
       "0  [-5.9298151325213775, 1.023510961637488e-05, 2...   \n",
       "1  [-11.310259340488017, 2.605278257357958e-05, 6...   \n",
       "2  [-11.724191395755772, 6.312263957236393e-06, 1...   \n",
       "3  [-20.128549489680175, 1.6978531783484566e-05, ...   \n",
       "\n",
       "                      LCL[nonparam_stratified_boots]  \\\n",
       "0  [-313.02229578473276, -5.412649524141675, -68....   \n",
       "1  [-357.9095296999922, -13.434971285005846, -74....   \n",
       "2  [-625.866106987995, -2.768088840481631, -16.96...   \n",
       "3  [-701.7050817340362, -3.710997763583124, -17.9...   \n",
       "\n",
       "                      UCL[nonparam_stratified_boots]  \\\n",
       "0  [-5.899143493196906, 1.0761943985787224e-05, 3...   \n",
       "1  [-11.227028264328574, 2.8735888981371008e-05, ...   \n",
       "2  [-11.576249349159095, 5.712025933040164e-06, 1...   \n",
       "3  [-20.880607074306813, 1.5241416263632105e-05, ...   \n",
       "\n",
       "                               LCL[parametric_boots]  \\\n",
       "0  [-318.6267145553689, -6.84252684038771, -74.06...   \n",
       "1  [-126.94885082911394, -5.286670237445512, -52....   \n",
       "2  [-519.9181692980177, -3.828433856483589, -17.4...   \n",
       "3  [-543.4651247601868, -3.9805170362105287, -17....   \n",
       "\n",
       "                               UCL[parametric_boots]  \n",
       "0  [-6.423512695844689, 4.576050238215909e-06, 45...  \n",
       "1  [-14.172703961864348, -5.066610622088065e-10, ...  \n",
       "2  [-13.741762947553546, 3.338353170707315e-05, 1...  \n",
       "3  [-30.306069506729404, 2.4348035458824826e-06, ...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_res = dict()\n",
    "for (scale, dataset), _ in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Data: {scale}, {dataset}\")\n",
    "\n",
    "    # plot quantiles of models at bootstapped parameters\n",
    "    for method in [\"normal\", \"nonparam_boots\", \"nonparam_stratified_boots\", \"parametric_boots\"]:\n",
    "    \n",
    "        if verbose: print(f\"\\tmodel CI: scale:{scale}, dataset:{dataset}, method:{method}\")\n",
    "\n",
    "        # get boostrapped parameters\n",
    "        bpars = boots_pars_results[(scale, dataset, method)]\n",
    "    \n",
    "        # quantiles\n",
    "        quant = np.quantile(bpars, probs, axis = 0)\n",
    "        \n",
    "        if (scale, dataset) not in dt_res: dt_res[(scale, dataset)] = dict()\n",
    "        for lab, q in zip([\"LCL\", \"UCL\"], quant):\n",
    "            dt_res[(scale, dataset)][f\"{lab}[{method}]\"] = q\n",
    "\n",
    "\n",
    "# Joining results of bootstrapped pars quantiles with Wald CI of pars\n",
    "if verbose: print(f\"pars CI: scale:{scales}, dateset:{datasets}, method:Wald/delta\")\n",
    "\n",
    "method = \"Wald\"\n",
    "df_pars_CI_wald = df_pars.rename(columns={\"LCL\": f\"LCL[{method}]\", \"UCL\": f\"UCL[{method}]\"})\n",
    "df_pars_CI_wald = df_pars_CI_wald.set_index([\"scale\", \"dataset\"])\n",
    "\n",
    "df_res_CI_other = pd.DataFrame(dt_res).T\n",
    "df_res_CI_other.index.names = ['scale', 'dataset']\n",
    "\n",
    "df_pars_CI = df_pars_CI_wald.join(df_res_CI_other)\n",
    "df_pars_CI.reset_index(inplace=True)\n",
    "df_pars_CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e76c0167",
   "metadata": {},
   "outputs": [
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       "      <td>-71.448487</td>\n",
       "      <td>5.582686</td>\n",
       "      <td>0.473536</td>\n",
       "      <td>4.654573</td>\n",
       "      <td>6.510799</td>\n",
       "      <td>4.646405</td>\n",
       "      <td>6.519855</td>\n",
       "      <td>-17.417951</td>\n",
       "      <td>16.103668</td>\n",
       "      <td>-16.9632</td>\n",
       "      <td>16.398749</td>\n",
       "      <td>-17.478053</td>\n",
       "      <td>11.155345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>3</td>\n",
       "      <td>10.388794</td>\n",
       "      <td>-12.798228</td>\n",
       "      <td>0.888694</td>\n",
       "      <td>-14.540036</td>\n",
       "      <td>-11.056419</td>\n",
       "      <td>-14.518195</td>\n",
       "      <td>-11.010005</td>\n",
       "      <td>-29.15253</td>\n",
       "      <td>31.540562</td>\n",
       "      <td>-30.294508</td>\n",
       "      <td>30.856805</td>\n",
       "      <td>-21.125974</td>\n",
       "      <td>29.302531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>0</td>\n",
       "      <td>-450.106747</td>\n",
       "      <td>-450.106747</td>\n",
       "      <td>633.831117</td>\n",
       "      <td>-1692.392909</td>\n",
       "      <td>792.179414</td>\n",
       "      <td>-1686.213022</td>\n",
       "      <td>836.191763</td>\n",
       "      <td>-732.615776</td>\n",
       "      <td>-20.128549</td>\n",
       "      <td>-701.705082</td>\n",
       "      <td>-20.880607</td>\n",
       "      <td>-543.465125</td>\n",
       "      <td>-30.30607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>1</td>\n",
       "      <td>727.880028</td>\n",
       "      <td>2.558527</td>\n",
       "      <td>2.352904</td>\n",
       "      <td>-2.053081</td>\n",
       "      <td>7.170135</td>\n",
       "      <td>-2.093824</td>\n",
       "      <td>7.176057</td>\n",
       "      <td>-3.568969</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>-3.710998</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>-3.980517</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>2</td>\n",
       "      <td>-395.551807</td>\n",
       "      <td>-14.727709</td>\n",
       "      <td>11.775012</td>\n",
       "      <td>-37.806308</td>\n",
       "      <td>8.350891</td>\n",
       "      <td>-37.721566</td>\n",
       "      <td>9.144342</td>\n",
       "      <td>-18.828981</td>\n",
       "      <td>18.062389</td>\n",
       "      <td>-17.929895</td>\n",
       "      <td>18.618402</td>\n",
       "      <td>-17.79779</td>\n",
       "      <td>15.91132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>3</td>\n",
       "      <td>72.3018</td>\n",
       "      <td>26.857661</td>\n",
       "      <td>20.350077</td>\n",
       "      <td>-13.027757</td>\n",
       "      <td>66.74308</td>\n",
       "      <td>-14.747768</td>\n",
       "      <td>66.709438</td>\n",
       "      <td>-32.514578</td>\n",
       "      <td>34.031802</td>\n",
       "      <td>-33.502466</td>\n",
       "      <td>32.220793</td>\n",
       "      <td>-21.288354</td>\n",
       "      <td>30.691029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    scale dataset idx         beta        pars          SE    LCL[Wald]  \\\n",
       "0     log    FULL   0   -40.708789  -40.708789    9.159599   -58.661274   \n",
       "1     log    FULL   1   152.895751         0.0    2.015071    -3.949467   \n",
       "2     log    FULL   2  -186.372091   15.072426    2.088266      10.9795   \n",
       "3     log    FULL   3    75.726012  -12.365102    1.282418   -14.878595   \n",
       "4     log    TRIM   0  -318.618313 -318.618313   63.459451  -442.996552   \n",
       "5     log    TRIM   1   1693.40495    0.000037    4.519607    -8.858231   \n",
       "6     log    TRIM   2 -3000.779864  -72.921186    6.604729   -85.866218   \n",
       "7     log    TRIM   3  1772.499797   41.151002     3.34217    34.600468   \n",
       "8   plain    FULL   0  -123.992871 -123.992871   18.879777  -160.996554   \n",
       "9   plain    FULL   1   163.794634        -0.0    1.320488    -2.588108   \n",
       "10  plain    FULL   2   -71.448487    5.582686    0.473536     4.654573   \n",
       "11  plain    FULL   3    10.388794  -12.798228    0.888694   -14.540036   \n",
       "12  plain    TRIM   0  -450.106747 -450.106747  633.831117 -1692.392909   \n",
       "13  plain    TRIM   1   727.880028    2.558527    2.352904    -2.053081   \n",
       "14  plain    TRIM   2  -395.551807  -14.727709   11.775012   -37.806308   \n",
       "15  plain    TRIM   3      72.3018   26.857661   20.350077   -13.027757   \n",
       "\n",
       "     UCL[Wald]  LCL[normal] UCL[normal] LCL[nonparam_boots]  \\\n",
       "0   -22.756305   -58.613348  -22.028153         -315.395523   \n",
       "1     3.949467    -3.880834    3.998054           -5.381886   \n",
       "2    19.165353   -19.192353   -10.94711          -69.144391   \n",
       "3     -9.85161     9.775801   14.863784          -22.488381   \n",
       "4  -194.240074  -442.413436 -189.773534         -360.040401   \n",
       "5     8.858304    -8.840911     8.78246          -13.565687   \n",
       "6   -59.976154    60.127522   85.994144          -75.400536   \n",
       "7    47.701535   -47.681742  -34.474504          -37.407823   \n",
       "8   -86.989188  -160.781957  -85.707763         -653.454609   \n",
       "9     2.588108    -2.543103    2.619925           -2.680368   \n",
       "10    6.510799     4.646405    6.519855          -17.417951   \n",
       "11  -11.056419   -14.518195  -11.010005           -29.15253   \n",
       "12  792.179414 -1686.213022  836.191763         -732.615776   \n",
       "13    7.170135    -2.093824    7.176057           -3.568969   \n",
       "14    8.350891   -37.721566    9.144342          -18.828981   \n",
       "15    66.74308   -14.747768   66.709438          -32.514578   \n",
       "\n",
       "   UCL[nonparam_boots] LCL[nonparam_stratified_boots]  \\\n",
       "0            -5.929815                    -313.022296   \n",
       "1              0.00001                       -5.41265   \n",
       "2            29.011453                     -68.196663   \n",
       "3            38.809259                      -23.77485   \n",
       "4           -11.310259                     -357.90953   \n",
       "5             0.000026                     -13.434971   \n",
       "6            67.198662                     -74.963371   \n",
       "7            42.530233                      -40.20196   \n",
       "8           -11.724191                    -625.866107   \n",
       "9             0.000006                      -2.768089   \n",
       "10           16.103668                       -16.9632   \n",
       "11           31.540562                     -30.294508   \n",
       "12          -20.128549                    -701.705082   \n",
       "13            0.000017                      -3.710998   \n",
       "14           18.062389                     -17.929895   \n",
       "15           34.031802                     -33.502466   \n",
       "\n",
       "   UCL[nonparam_stratified_boots] LCL[parametric_boots] UCL[parametric_boots]  \n",
       "0                       -5.899143           -318.626715             -6.423513  \n",
       "1                        0.000011             -6.842527              0.000005  \n",
       "2                       31.543594            -74.060063             45.757947  \n",
       "3                       38.151073            -34.976531             41.151648  \n",
       "4                      -11.227028           -126.948851            -14.172704  \n",
       "5                        0.000029              -5.28667                  -0.0  \n",
       "6                       71.618666            -52.807839                0.0001  \n",
       "7                        42.19667             -0.000174              10.26421  \n",
       "8                      -11.576249           -519.918169            -13.741763  \n",
       "9                        0.000006             -3.828434              0.000033  \n",
       "10                      16.398749            -17.478053             11.155345  \n",
       "11                      30.856805            -21.125974             29.302531  \n",
       "12                     -20.880607           -543.465125             -30.30607  \n",
       "13                       0.000015             -3.980517              0.000002  \n",
       "14                      18.618402             -17.79779              15.91132  \n",
       "15                      32.220793            -21.288354             30.691029  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to long format by exploding the pars, SE, LCL, UCL, beta columns\n",
    "cols_CI = df_pars_CI.columns[df_pars_CI.columns.str.startswith((\"LCL\", \"UCL\"))].tolist()\n",
    "cols = [\"idx\", \"beta\", \"pars\", \"SE\"] + cols_CI\n",
    "\n",
    "df_pars_CI_long = df_pars_CI.explode(cols, ignore_index=True)\n",
    "df_pars_CI_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4084084d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllllllllllllll}\n",
      "\\toprule\n",
      " & scale & dataset & idx & beta & pars & SE & LCL[Wald] & UCL[Wald] & LCL[normal] & UCL[normal] & LCL[nonparam_boots] & UCL[nonparam_boots] & LCL[nonparam_stratified_boots] & UCL[nonparam_stratified_boots] & LCL[parametric_boots] & UCL[parametric_boots] \\\\\n",
      "\\midrule\n",
      "0 & log & FULL & 0 & -40.708789 & -40.708789 & 9.159599 & -58.661274 & -22.756305 & -58.613348 & -22.028153 & -315.395523 & -5.929815 & -313.022296 & -5.899143 & -318.626715 & -6.423513 \\\\\n",
      "1 & log & FULL & 1 & 152.895751 & 0.000000 & 2.015071 & -3.949467 & 3.949467 & -3.880834 & 3.998054 & -5.381886 & 0.000010 & -5.412650 & 0.000011 & -6.842527 & 0.000005 \\\\\n",
      "2 & log & FULL & 2 & -186.372091 & 15.072426 & 2.088266 & 10.979500 & 19.165353 & -19.192353 & -10.947110 & -69.144391 & 29.011453 & -68.196663 & 31.543594 & -74.060063 & 45.757947 \\\\\n",
      "3 & log & FULL & 3 & 75.726012 & -12.365102 & 1.282418 & -14.878595 & -9.851610 & 9.775801 & 14.863784 & -22.488381 & 38.809259 & -23.774850 & 38.151073 & -34.976531 & 41.151648 \\\\\n",
      "4 & log & TRIM & 0 & -318.618313 & -318.618313 & 63.459451 & -442.996552 & -194.240074 & -442.413436 & -189.773534 & -360.040401 & -11.310259 & -357.909530 & -11.227028 & -126.948851 & -14.172704 \\\\\n",
      "5 & log & TRIM & 1 & 1693.404950 & 0.000037 & 4.519607 & -8.858231 & 8.858304 & -8.840911 & 8.782460 & -13.565687 & 0.000026 & -13.434971 & 0.000029 & -5.286670 & -0.000000 \\\\\n",
      "6 & log & TRIM & 2 & -3000.779864 & -72.921186 & 6.604729 & -85.866218 & -59.976154 & 60.127522 & 85.994144 & -75.400536 & 67.198662 & -74.963371 & 71.618666 & -52.807839 & 0.000100 \\\\\n",
      "7 & log & TRIM & 3 & 1772.499797 & 41.151002 & 3.342170 & 34.600468 & 47.701535 & -47.681742 & -34.474504 & -37.407823 & 42.530233 & -40.201960 & 42.196670 & -0.000174 & 10.264210 \\\\\n",
      "8 & plain & FULL & 0 & -123.992871 & -123.992871 & 18.879777 & -160.996554 & -86.989188 & -160.781957 & -85.707763 & -653.454609 & -11.724191 & -625.866107 & -11.576249 & -519.918169 & -13.741763 \\\\\n",
      "9 & plain & FULL & 1 & 163.794634 & -0.000000 & 1.320488 & -2.588108 & 2.588108 & -2.543103 & 2.619925 & -2.680368 & 0.000006 & -2.768089 & 0.000006 & -3.828434 & 0.000033 \\\\\n",
      "10 & plain & FULL & 2 & -71.448487 & 5.582686 & 0.473536 & 4.654573 & 6.510799 & 4.646405 & 6.519855 & -17.417951 & 16.103668 & -16.963200 & 16.398749 & -17.478053 & 11.155345 \\\\\n",
      "11 & plain & FULL & 3 & 10.388794 & -12.798228 & 0.888694 & -14.540036 & -11.056419 & -14.518195 & -11.010005 & -29.152530 & 31.540562 & -30.294508 & 30.856805 & -21.125974 & 29.302531 \\\\\n",
      "12 & plain & TRIM & 0 & -450.106747 & -450.106747 & 633.831117 & -1692.392909 & 792.179414 & -1686.213022 & 836.191763 & -732.615776 & -20.128549 & -701.705082 & -20.880607 & -543.465125 & -30.306070 \\\\\n",
      "13 & plain & TRIM & 1 & 727.880028 & 2.558527 & 2.352904 & -2.053081 & 7.170135 & -2.093824 & 7.176057 & -3.568969 & 0.000017 & -3.710998 & 0.000015 & -3.980517 & 0.000002 \\\\\n",
      "14 & plain & TRIM & 2 & -395.551807 & -14.727709 & 11.775012 & -37.806308 & 8.350891 & -37.721566 & 9.144342 & -18.828981 & 18.062389 & -17.929895 & 18.618402 & -17.797790 & 15.911320 \\\\\n",
      "15 & plain & TRIM & 3 & 72.301800 & 26.857661 & 20.350077 & -13.027757 & 66.743080 & -14.747768 & 66.709438 & -32.514578 & 34.031802 & -33.502466 & 32.220793 & -21.288354 & 30.691029 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# to copy and past in latex\n",
    "print(df_pars_CI_long.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d60267f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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