diff --git a/examples/notebooks/Tutorial_contingency_analisys.ipynb b/examples/notebooks/Tutorial_contingency_analisys.ipynb
deleted file mode 100644
index 748b808..0000000
--- a/examples/notebooks/Tutorial_contingency_analisys.ipynb
+++ /dev/null
@@ -1,823 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Contingency Analysis in Transmission Grids\n",
-    "\n",
-    "Contingency analysis is a critical process in power system operations used to assess the impact of potential failures (e.g., line outages) on grid stability and reliability. It helps operators prepare for unexpected events by simulating scenarios such as N-1 or N-2 contingencies, where one or more components are removed from service. This analysis ensures that the grid can continue to operate within safe limits even under stressed conditions.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## Dataset Generation and Model Evaluation\n",
-    "\n",
-    "The dataset used in this study originates from the Texas transmission grid, which includes approximately 2,000 nodes. Using the contingency mode of the `gridfm-datakit`, we simulated N-2 contingencies by removing up to two transmission lines at a time. For each scenario, we first solved the optimal power flow (OPF) problem to determine the generation dispatch. Then, we applied the contingency by removing lines and re-solved the power flow to observe the resulting grid state.\n",
-    "\n",
-    "This process generated around 100,000 unique scenarios. Our model, **GridFMv0.1**, was fine-tuned on this dataset to predict power flow outcomes. For demonstration purposes, we selected a subsample of 10 scenarios. The `gridfm-datakit` also computed DC power flow results, enabling a comparison between GridFMv0.1 predictions and traditional DC power flow estimates, specifically in terms of line loading accuracy.\n",
-    "\n",
-    "All predictions are benchmarked against the ground truth obtained from AC power flow simulations. Additionally, we analyze bus voltage violations, which GridFM can predict but are not captured by the DC solver, highlighting GridFM’s enhanced capabilities in modeling grid behavior.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "\n",
-    "if \"google.colab\" in sys.modules:\n",
-    "    try:\n",
-    "        !git clone https://github.com/gridfm/gridfm-graphkit.git\n",
-    "        %cd /content/gridfm-graphkit\n",
-    "        !pip install .\n",
-    "        %cd examples/notebooks/\n",
-    "    except Exception as e:\n",
-    "        print(f\"Failed to start Google Collab setup, due to {e}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gridfm_graphkit.datasets.postprocessing import (\n",
-    "    compute_branch_currents_kA,\n",
-    "    compute_loading,\n",
-    ")\n",
-    "from gridfm_graphkit.datasets.postprocessing import create_admittance_matrix\n",
-    "from gridfm_graphkit.utils.utils import compute_cm_metrics\n",
-    "from gridfm_graphkit.utils.visualization import (\n",
-    "    plot_mass_correlation_density,\n",
-    "    plot_cm,\n",
-    "    plot_loading_predictions,\n",
-    "    plot_mass_correlation_density_voltage,\n",
-    ")\n",
-    "\n",
-    "import os\n",
-    "from tqdm import tqdm\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Data\n",
-    "\n",
-    "We load both the ground truth and predicted values of the power flow solution. The predictions are generated using the `gridfm-graphkit` CLI:\n",
-    "\n",
-    "```bash\n",
-    "gridfm-graphkit predict ...\n",
-    "```\n",
-    "\n",
-    "We then merge the datasets using `scenario` and `bus` as keys, allowing us to align the predicted and actual values for each grid state and bus."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "root_pred_folder = \"../data/contingency_texas/\"\n",
-    "prediction_dir = \"prediction_gridfm01\"\n",
-    "label_plot = \"GridFM_v0.1 Fine-tuned\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf_node_GT = pd.read_csv(os.path.join(root_pred_folder, \"pf_node_10_examples.csv\"))\n",
-    "pg_node_predicted = pd.read_csv(\n",
-    "    os.path.join(root_pred_folder, \"predictions_10_examples.csv\")\n",
-    ")\n",
-    "\n",
-    "branch_idx_removed = pd.read_csv(\"{}branch_idx_removed.csv\".format(root_pred_folder))\n",
-    "edge_params = pd.read_csv(\"{}edge_params.csv\".format(root_pred_folder))\n",
-    "bus_params = pd.read_csv(\"{}bus_params.csv\".format(root_pred_folder))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>scenario</th>\n",
-       "      <th>bus</th>\n",
-       "      <th>PD_pred</th>\n",
-       "      <th>QD_pred</th>\n",
-       "      <th>PG_pred</th>\n",
-       "      <th>QG_pred</th>\n",
-       "      <th>VM_pred</th>\n",
-       "      <th>VA_pred</th>\n",
-       "      <th>Pd</th>\n",
-       "      <th>Qd</th>\n",
-       "      <th>Pg</th>\n",
-       "      <th>Qg</th>\n",
-       "      <th>Vm</th>\n",
-       "      <th>Va</th>\n",
-       "      <th>PQ</th>\n",
-       "      <th>PV</th>\n",
-       "      <th>REF</th>\n",
-       "      <th>Vm_dc</th>\n",
-       "      <th>Va_dc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>6665</td>\n",
-       "      <td>0</td>\n",
-       "      <td>289.88303</td>\n",
-       "      <td>386.46190</td>\n",
-       "      <td>-269.17026</td>\n",
-       "      <td>95.973550</td>\n",
-       "      <td>0.973004</td>\n",
-       "      <td>-23.517038</td>\n",
-       "      <td>17.641483</td>\n",
-       "      <td>4.849515</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.973382</td>\n",
-       "      <td>-23.651456</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-18.540175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6665</td>\n",
-       "      <td>1</td>\n",
-       "      <td>399.95087</td>\n",
-       "      <td>111.63771</td>\n",
-       "      <td>380.59073</td>\n",
-       "      <td>-19.526617</td>\n",
-       "      <td>1.027900</td>\n",
-       "      <td>-16.083895</td>\n",
-       "      <td>12.862669</td>\n",
-       "      <td>3.603862</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.028113</td>\n",
-       "      <td>-16.356100</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-10.642648</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6665</td>\n",
-       "      <td>2</td>\n",
-       "      <td>418.41193</td>\n",
-       "      <td>336.66327</td>\n",
-       "      <td>372.41583</td>\n",
-       "      <td>-16.310265</td>\n",
-       "      <td>0.982477</td>\n",
-       "      <td>-15.997509</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.982483</td>\n",
-       "      <td>-16.219837</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-11.845012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>6665</td>\n",
-       "      <td>3</td>\n",
-       "      <td>541.56036</td>\n",
-       "      <td>361.17136</td>\n",
-       "      <td>260.12170</td>\n",
-       "      <td>-6.565100</td>\n",
-       "      <td>0.994687</td>\n",
-       "      <td>-17.817055</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>158.25</td>\n",
-       "      <td>-6.701038</td>\n",
-       "      <td>0.989999</td>\n",
-       "      <td>-18.047683</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.989999</td>\n",
-       "      <td>-13.627838</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>6665</td>\n",
-       "      <td>4</td>\n",
-       "      <td>535.40500</td>\n",
-       "      <td>315.90330</td>\n",
-       "      <td>378.09350</td>\n",
-       "      <td>10.435824</td>\n",
-       "      <td>1.005842</td>\n",
-       "      <td>-13.160706</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.005977</td>\n",
-       "      <td>-13.327186</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-7.870087</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   scenario  bus    PD_pred    QD_pred    PG_pred    QG_pred   VM_pred  \\\n",
-       "0      6665    0  289.88303  386.46190 -269.17026  95.973550  0.973004   \n",
-       "1      6665    1  399.95087  111.63771  380.59073 -19.526617  1.027900   \n",
-       "2      6665    2  418.41193  336.66327  372.41583 -16.310265  0.982477   \n",
-       "3      6665    3  541.56036  361.17136  260.12170  -6.565100  0.994687   \n",
-       "4      6665    4  535.40500  315.90330  378.09350  10.435824  1.005842   \n",
-       "\n",
-       "     VA_pred         Pd        Qd      Pg        Qg        Vm         Va   PQ  \\\n",
-       "0 -23.517038  17.641483  4.849515    0.00  0.000000  0.973382 -23.651456  1.0   \n",
-       "1 -16.083895  12.862669  3.603862    0.00  0.000000  1.028113 -16.356100  1.0   \n",
-       "2 -15.997509   0.000000  0.000000    0.00  0.000000  0.982483 -16.219837  1.0   \n",
-       "3 -17.817055   0.000000  0.000000  158.25 -6.701038  0.989999 -18.047683  0.0   \n",
-       "4 -13.160706   0.000000  0.000000    0.00  0.000000  1.005977 -13.327186  1.0   \n",
-       "\n",
-       "    PV  REF     Vm_dc      Va_dc  \n",
-       "0  0.0  0.0  1.000000 -18.540175  \n",
-       "1  0.0  0.0  1.000000 -10.642648  \n",
-       "2  0.0  0.0  1.000000 -11.845012  \n",
-       "3  1.0  0.0  0.989999 -13.627838  \n",
-       "4  0.0  0.0  1.000000  -7.870087  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pf_node = pg_node_predicted.merge(pf_node_GT, on=[\"scenario\", \"bus\"], how=\"left\")\n",
-    "pf_node.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Admittance matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sn_mva = 100\n",
-    "Yf, Yt, Vf_base_kV, Vt_base_kV = create_admittance_matrix(\n",
-    "    bus_params, edge_params, sn_mva\n",
-    ")\n",
-    "rate_a = edge_params[\"rate_a\"].values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Correct voltage predictions for GridFM and DC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf_node[\"Vm_pred_corrected\"] = pf_node[\"VM_pred\"]\n",
-    "pf_node[\"Va_pred_corrected\"] = pf_node[\"VA_pred\"]\n",
-    "\n",
-    "pf_node.loc[pf_node.PV == 1, \"Vm_pred_corrected\"] = pf_node.loc[pf_node.PV == 1, \"Vm\"]\n",
-    "pf_node.loc[pf_node.REF == 1, \"Va_pred_corrected\"] = pf_node.loc[pf_node.REF == 1, \"Va\"]\n",
-    "\n",
-    "pf_node[\"Vm_dc_corrected\"] = pf_node[\"Vm_dc\"]\n",
-    "pf_node[\"Va_dc_corrected\"] = pf_node[\"Va_dc\"]\n",
-    "\n",
-    "pf_node.loc[pf_node.PV == 1, \"Vm_dc_corrected\"] = pf_node.loc[pf_node.PV == 1, \"Vm\"]\n",
-    "pf_node.loc[pf_node.REF == 1, \"Va_dc_corrected\"] = pf_node.loc[pf_node.REF == 1, \"Va\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Compute branch current and line loading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 10/10 [00:00<00:00, 497.50it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "loadings = []\n",
-    "loadings_pred = []\n",
-    "loadings_dc = []\n",
-    "\n",
-    "for scenario_idx in tqdm(pf_node.scenario.unique()):\n",
-    "    pf_node_scenario = pf_node[pf_node.scenario == scenario_idx]\n",
-    "    branch_idx_removed_scenario = (\n",
-    "        branch_idx_removed[branch_idx_removed.scenario == scenario_idx]\n",
-    "        .iloc[:, 1:]\n",
-    "        .values\n",
-    "    )\n",
-    "    # remove nan\n",
-    "    branch_idx_removed_scenario = branch_idx_removed_scenario[\n",
-    "        ~np.isnan(branch_idx_removed_scenario)\n",
-    "    ].astype(np.int32)\n",
-    "    V_true = pf_node_scenario[\"Vm\"].values * np.exp(\n",
-    "        1j * pf_node_scenario[\"Va\"].values * np.pi / 180\n",
-    "    )\n",
-    "    V_pred = pf_node_scenario[\"Vm_pred_corrected\"].values * np.exp(\n",
-    "        1j * pf_node_scenario[\"Va_pred_corrected\"].values * np.pi / 180\n",
-    "    )\n",
-    "    V_dc = pf_node_scenario[\"Vm_dc_corrected\"].values * np.exp(\n",
-    "        1j * pf_node_scenario[\"Va_dc_corrected\"].values * np.pi / 180\n",
-    "    )\n",
-    "    If_true, It_true = compute_branch_currents_kA(\n",
-    "        Yf, Yt, V_true, Vf_base_kV, Vt_base_kV, sn_mva\n",
-    "    )\n",
-    "    If_pred, It_pred = compute_branch_currents_kA(\n",
-    "        Yf, Yt, V_pred, Vf_base_kV, Vt_base_kV, sn_mva\n",
-    "    )\n",
-    "    If_dc, It_dc = compute_branch_currents_kA(\n",
-    "        Yf, Yt, V_dc, Vf_base_kV, Vt_base_kV, sn_mva\n",
-    "    )\n",
-    "\n",
-    "    loading_true = compute_loading(If_true, It_true, Vf_base_kV, Vt_base_kV, rate_a)\n",
-    "    loading_pred = compute_loading(If_pred, It_pred, Vf_base_kV, Vt_base_kV, rate_a)\n",
-    "    loading_dc = compute_loading(If_dc, It_dc, Vf_base_kV, Vt_base_kV, rate_a)\n",
-    "\n",
-    "    # remove the branches that are removed from loading\n",
-    "    loading_true[branch_idx_removed_scenario] = -1\n",
-    "    loading_pred[branch_idx_removed_scenario] = -1\n",
-    "    loading_dc[branch_idx_removed_scenario] = -1\n",
-    "\n",
-    "    loadings.append(loading_true)\n",
-    "    loadings_pred.append(loading_pred)\n",
-    "    loadings_dc.append(loading_dc)\n",
-    "\n",
-    "\n",
-    "loadings = np.array(loadings)\n",
-    "loadings_pred = np.array(loadings_pred)\n",
-    "loadings_dc = np.array(loadings_dc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "removed_lines = loadings == -1\n",
-    "removed_lines_pred = loadings_pred == -1\n",
-    "removed_lines_dc = loadings_dc == -1\n",
-    "\n",
-    "\n",
-    "# assert the same lines are removed\n",
-    "assert (removed_lines == removed_lines_pred).all()\n",
-    "assert (removed_lines == removed_lines_dc).all()\n",
-    "\n",
-    "# assert the same number of lines are removed\n",
-    "assert removed_lines.sum() == removed_lines_pred.sum()\n",
-    "assert removed_lines.sum() == removed_lines_dc.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "overloadings = loadings[not removed_lines] > 1.0\n",
-    "overloadings_pred = loadings_pred[not removed_lines] > 1.0\n",
-    "overloadings_dc = loadings_dc[not removed_lines] > 1.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Compute metrics for overloading classification for GridFM and DC PF\n",
-    "- Below are the results of GridFM for overloading classification (GNN version, not transformer, finetuned on PF for IEEE300, and later finetuned on Contingency Data)\n",
-    "- Ground truth is AC PF."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.991\n",
-      "Confusion Matrix:\n",
-      "TP: 4753, FP: 150, TN: 27126, FN: 154\n",
-      "GridFM\n",
-      "TPR: 0.969 (percentage of overloadings correctly predicted)\n",
-      "FPR: 0.005 (percentage of non-overloadings predicted as overloadings)\n",
-      "TNR: 0.99\n",
-      "FNR: 0.03\n"
-     ]
-    }
-   ],
-   "source": [
-    "TP_gridfm, FP_gridfm, TN_gridfm, FN_gridfm = compute_cm_metrics(\n",
-    "    overloadings, overloadings_pred, prediction_dir, label_plot\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Below are the results of DC PF for overloading classification\n",
-    "- Ground truth is AC PF.\n",
-    "- We use a threshold of 0.95 to make sure we identify all overloads"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.960\n",
-      "Confusion Matrix:\n",
-      "TP: 4113, FP: 505, TN: 26771, FN: 794\n",
-      "GridFM\n",
-      "TPR: 0.838 (percentage of overloadings correctly predicted)\n",
-      "FPR: 0.019 (percentage of non-overloadings predicted as overloadings)\n",
-      "TNR: 0.98\n",
-      "FNR: 0.16\n"
-     ]
-    }
-   ],
-   "source": [
-    "TP_dc, FP_dc, TN_dc, FN_dc = compute_cm_metrics(\n",
-    "    overloadings, overloadings_dc, \"DC\", label_plot\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Histogram of true line loadings\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(loadings[not removed_lines], bins=100)\n",
-    "plt.xlabel(\"Line Loadings\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "# log scale\n",
-    "plt.savefig(f\"loadings_histogram_{prediction_dir}.png\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predicted vs True line loading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 900x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Valid lines\n",
-    "valid_mask = not removed_lines\n",
-    "\n",
-    "# Extract valid values\n",
-    "true_vals = loadings[valid_mask]\n",
-    "gfm_vals = loadings_pred[valid_mask]\n",
-    "dc_vals = loadings_dc[valid_mask]\n",
-    "\n",
-    "plot_mass_correlation_density(true_vals, gfm_vals, prediction_dir, label_plot)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 900x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_mass_correlation_density(true_vals, dc_vals, \"DC\", \"DC Solver\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_cm(TN_gridfm, FP_gridfm, FN_gridfm, TP_gridfm, prediction_dir, label_plot)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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fmQ7W2idF+/roYQ6i4q+//vKEbH3d6nwy+hrUWjUtu/ahCj/nkX7BaxDU/lY6Qkmb5bS/is6/ox2odaSRPhYaYO/UGVu30cdDy6CPl3bY1Zo9DUYa6vfv32/26z3/j75G9bnQZip9b2kA00Aybdo0s72+Ru9mlm0NMhq09fAR+n5zBzpvejt6mAjtpK3vax2xqDNra6jTDvH6/tXHSfudAT4CPd4ciCmrVq1ytW/f3sxBo/Nl6LwjOqvwU0895VqwYEGE7XUOGp1JV+da0XlCdLbXpk2b3nbmYe85Vu4090lk89gonZejTZs2Zi4ULetDDz3kmjdvnt95WCZNmuRq3ry5mUMladKkZn4ZndvjvffeM3OqRHXmYZ0XRecccc9d89hjj7mWLl0a5fsTlXlibjePzZ34m8dGTZ061VWmTBlz3/U50sdix44dkT4nOhdRuXLlzOMa2czDkQl/33WeluzZs5tZdrdv3x5h+5EjR5rt9XVzJ97zrLhPep90RmW97zp7sfccSf7oPET6eDRs2NCVJUsW81zqPooVK2Zey1u2bLljOXR+pjlz5phZt8uWLevKmDGjmS8mZcqUrgceeMDMe+Q9V403nR9GH1u9TT3pZX9zxvibx8bb3r17zXxIuk3Lli1vW94///zT1aJFC3N/3e9TfT10797dtXXrVr8zD+P+FqJ/fKMOAABAcKKPDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgjfv2kAqhpTsHuggAYsGltcPlcsQDpwOwQJIopBZqbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKyRQBxiyZIl0bpe5cqVY7wsAAAgODkm2FStWlVCQkI8/7tcLp//I3Pjxo1YLhkAAAgWjgk2ixYt8vn/ypUr0q1bN7l48aI8//zzUrBgQbN827ZtMnLkSEmWLJm8//77ASotAABwohCXVo04UNeuXWXZsmWmiSpJkiQ+6zTsVKlSxTRDffTRR9Haf2jpzjFUUgBOcmntcLl8PdClABAbkiQI4s7DEyZMkNatW0cINSpp0qRm3fjx4wNSNgAA4EyODTYXLlyQQ4cORbpe12nNDQAAgOODTc2aNWXo0KEyffr0COumTZtm1uk2AAAAju9jc+DAAalevbr8+++/kjlzZsmXL59ZvmPHDjl48KDkzZtXfv31V8mWLVu09k8fG8BO9LEB7BXUfWyyZs0q69evlyFDhkixYsXkyJEj5lS0aFH5+OOPzbrohhoAAGAnx9bYxDZqbAA7UWMD2Cuoa2wAAACCdoI+fw4fPiyjRo2SNWvWyJkzZ+TmzZs+63Vm4l9++SVg5QMAAM7i2GCzYcMGc5iFS5cumVmHN27cKEWKFJHTp0+bjsXaeTh79uyBLiYAAHAQxzZFde/eXZInTy7bt2+XhQsXmmNH6RDvffv2yffffy+nTp2SwYMHB7qYAADAQRwbbJYvXy4vvPCC5MiRQ+LFu1VMd1NUs2bNpFWrVvLmm28GuJQAAMBJHBtsNMRkzJjRXE6VKpXEjx9fTp486VlfvHhxWb16dQBLCAAAnMaxwSZ37tyya9cuc1lrbPR/bZJyW7FihQk8AAAAjg82tWvXlilTpnj+79ixo3z99dfmMAo1atSQsWPHylNPPRXQMgIAAGdx7AR92jl4586dUqJECUmYMKHpPDxw4EBznChtlqpfv7707NlTEiVKFK39M0EfYCcm6APu7wn6HBtsYhvBBrATwQa4v4ONY+ex8Xb+/HkzzFvp3DU6DBwAACBo+tioVatWSbVq1SR16tTmQJh60st61O+//vor0MUDAAAO49gamz/++MPMPKx9aJ599lkpXLiwWb5161aZOHGiVK5cWRYvXizly5cPdFEBAIBDOLaPjY5+2r17tyxbtkwyZcrks+7IkSNSoUIFMwR8wYIF0do/fWwAO9HHBrBXUB/dW2tsdObh8KFG6cR9zz//vKxcuTIgZQMAAM7k2GCjk/Jdvx75z64bN254DrUAAACgHJsMHnnkEfnss89kz549Edbt3btXPv/8c9McBQAA4Pg+NmvXrjUdhLXWplGjRlKgQAGzXI/2PXPmTEmQIIEsXbpUSpYsGa3908cGsBN9bAB7BfU8NqVLlzb9bHr16iU//vijXLx40SxPmjSpPProozJgwAApUqRIoIsJAAAcxLE1NuGP9H3s2DFzOX369DHSt4YaG8BO1NgA9grqGhtvGmR0JBQAAEBQBJt+/frd9XVCQkLkrbfeipXyAACA4OOYpqjoNC9psNFh39FBUxRgJ5qiAHsFVVOU9qMBAACwbh6bK1eumJFQGzZsCHRRAABAEHFksNEDXzZr1kxWrFgR6KIAAIAg4shgo31n8ufPL8ePHw90UQAAQBBxZLBRPXv2lOHDh5uZhgEAAKLCMZ2Hw9Mjd6dNm1aKFSsmVatWlVy5ckloaGiEmp2hQ4cGrIwAAMBZHDPcOzrDvxnuDSA8hnsD9gqq4d7hMfwbAABY08cGAADgbjm2xsa7r82iRYvk6NGj8tJLL5nRUnqk723btkmBAgUkefLkgS4iAABwCMfW2Fy9elUaN24sFSpUkF69esmnn34q+/bt8/S/qV27Nh2HAQBAcNTY6MEtf/rpJ/niiy+kWrVqUrBgQc+6JEmSmAn8Zs6caUIPgkeW9GHSuFZpqVOxqBTMlVEypkspJ89clJXrd8qQMQtk1aY9fq+XM0ta6dahttR8uLBkTJtCTp+7JNt2HpavJi+V6QvXerb7qu/T0vrxh25bhr6f/ySDR841lyuVzS/zv+5y2+137jsmRR/v6/k/d7Z08lS98lKqcHYpUzi7ZMmQSvYcPCGF6vW5y0cDwGO1qsvBgwf8rnugXHkZNWZchB+933z9lcye9aMcPnxIwsLCpHKVatLplVfNSFpvBw7sl7q1a0R62y++1Fk6dno5hu4JnMKxwWbixInSsWNHef755+XEiRMR1hcuXFimTJkSkLIh+jq2rCJvtKstO/Yek4Urt8nxU+clX4700qBqCXN6pucYmTp/jc91qj9YSCZ//Jy5PGfJJtm1/7ikSplUiufPItUeLOgTbGYt2iB7Dp70e9uvtqkhyZMmloUrtnqWaSAZ8OUcv9tXf7CgPFI6ryz4/b/tVYXSeaX3i3Xl+vUbsm3XEcmYNuU9PSbA/S5FihTSqnXbCMuzZMkaYVBJl84dZcXyZVKiZCmpUau27N2zR6ZPmyJ//PG7jPtusqRJkybCfgoWLCTVatT0G5xgH8cGG+1TU7x48UjXx48f3/S1QXD5a9MeqfXsJ7Js9b8RwsKcES/L0J5Pyo+LNsjVa7fG62bPlFq++6CDHDx6Ruq9OEz2HT7lc7348X1bU2ct3mBO4ZUunN2EkY1/H5C/Nv9XK7T30EkZOMJ/sGlYo5Q5HzPD99Aey9b8K1XafCgb/j4gl69ck1MrP77rxwHAf1KkSBmlmpMfZ84woeaxuvVl0Psfmik/1OTvJ8rAfu/I8E8/kbff6RfhegULFaZm5j7i2D422bNnNx2EI7N8+XLJly9fnJYJ927mr+sjhBq1fO0O+W3VP5ImLJkUy5/Fs/zNDnUkLEWovDxwUoRQo27ciNq0AG0bPmzOx/7we5S2f6BoTlOOddv2ybpt+33W7T5wQv7cuNuEGgBxZ/rUW7X0r7zW1RNqVLPmLSRb9uwy56dZcvny5QCWEE7g2Bqbp556SoYMGSJNmjQxo5+U+4U8cuRImTx5sgwePDjApURMunb91mSL2sTj1rhmadNc9duqv02ti/aJiRcSIuv/3i+L//xbojK/ZJLECeXJxx4wQeS72X9GqSzPNHrEnI+ZEbUgBCD6tN/MzBnT5dixo5IsWXIpWry4lChR0mebK1euyMYN6yVX7twRmqj0u+Ghhx+RqZO/ly2bN0mZsg/4rD967KhM+m6CnD9/TtKkTSvlyj0o2XPkiJP7hrjn2GCjnYJ1qHflypVNfxp94b722mty8uRJ2b9/v9StW9f8Dztok5P2aTl07Ixs+vegWZYra1pJmyqZrN68R4b1aiHPNq3oc521W/dJs1dHyIGjp2+770Y1S0mqFEll8ty/5NTZOzdfJk2SSJrVKSMXL12VSXNW3eM9A3Anx48fk7d79/BZVrRYcXnvgyGeALJv317TxyZHjlx+95Ej563le/bsjhBsVq5Ybk5u+n1St34D6f12X0maNGks3CMEkmODTaJEiWTu3LkyYcIEmTp1qjl0gib2EiVKyIABA6R169Y+VZEIXgkSxJNRA9qYmpVeQ3+Qmzdv1cJkSJPCnJcsmE0K5Mooz709Tn5avEFSJg+Vbh3qSIcmFUz/myptP7rt/p9peKv2ZXS4vjKR0VFbehvf/fSnnDl/6Z7vH4DIPdGosZQuU1by5S9gQsae3btl3Lej5acfZ8rzHZ6RqT/8aGpxzp87Z7ZPnsL/3GXJk91afv7cec+yJElC5fkXX5LqNWpKtuw5xHXzpmzdukWGDf3YjKq6fOmyDBk6LI7uKeR+DzZKg8vTTz9tTtGlYUhP3hInThwDpUNMPccj+7Y2TUyjpi2XibP/qyGJF+9WcE2QIL70+3iGjJ/1h/lfh3p3HjDRjIoqXyK3PFIqj6xYt9Pv/vNkTycVy+Q1I6m06Soqnvlff5wxUeyPAyD6dMi1t0KFC8vAQe+byxpupk2ZIm2eaRetfevw704v+07n8OBDD5sRVS2aNZJfFs6XrVs2S+EiRe/hHsBpHNt5uFu3brJ27X/DeKNr0KBBZp4D75MugzNCzYh3WkmLuuVM7Yh2EPZ25tx/tSU//bYxwvVnL9lkzssUibytvO0TD5sJHcfOjFpIyZ8zg1Qok0/+3XtUlq7+5y7uDYCY1LTZk+Z83dpb0z8kT5EiQo2Mt/MXzt+2RsdbaGio1G/whLm89n/7hz0cG2yGDRsmDzzwgDmEgk7Wt3FjxC+2qOjRo4ecOXPG56TLEPhQ455M7/uf/5Ln+oyL0BF45/7jno7E3iHHzb0sSZJEfm9Da3yebvCg2ce4mSvvqrYmqqOnAMSO1KlTm/NLl271i8uWLbv5kbJ3726/2+/dc2t5zv/1tbmTVO79X6S52TaODTY6j83o0aPNiKj3339fSpUqJUWLFpX+/fvL9u3bo7wfbXZKmTKlz4mmKGeEGg0dU+atlva9x3r61Xi7cvW6rNywy1wunCdThPWF/rds78GIEziqRysWNbMCz1+xVQ4eO3PHcumcOE/Vf1CuXbsh436MWhACEDs2brg1H1WWrFk9M84XK15Cdu/aFWGmYv1RtPL3FRIamlSKFC0Wxf2v99k/7BHPyTNRtmnTRmbPni1HjhyRr776SrJly2aCTZEiRUzQYbh38DY/aaiZNn+NtOvlP9S4jZyy1Jz3erGuJEr4X5cw7Uzc+vEH5ez5SzJ/+ZY71L5ErdNw3UrFJFO6lDJ3+WY5cuJWR0UAsWfXzh1y6dIlv8s/GfKhuVy3XgPP8ibNmpvzTz8e4lPDO2XyJNm/b58Z6aQByE07CvubEmLhgvkya+YPkjJlmFSsVDnG7xcCK8QVlYlAHEQPrzBu3Djp06ePnD9/3oyWio7Q0r4d1hA3er1Q18wAfO7CZfnsu8Vy3c8Ee7MWrTez+rpNeL+9NK5VRrbvOiwLf99qRizprMA6LPvZt76VST//FWEfOqLq37kD5MSZC5Lv0d5Rmshv6icvSL0qxaVJly/NoRsio0PQB73WyPO/Hjfq0pVrMsPr0A49Pp4hJ05fuONtIuZdWjtcLt+auBoO98Vnw2Tc2NFS5oFykiVzFtP3RYdrL12yRK5fvyYdnntBXnm1q2d7He7d6cXnPIdUKPtAOdm3d6/pBKw1L+MnTvE5pEKHZ1qbYeIlS5aSDBkzyc2bN2Trli2yds1qM/L2g48+karVIz+WFJwnSYIgHxXl7dq1a/Lzzz/L999/L7NmzTKhRmcnRnDJmeXWh06KZEmk+3OP+t1Gj9/kHWza9BgjK9fvMrMHd2hS8VYT1fpd8v438/zOYqy0RihhwvgyYdYfUQo1mdOHSZ0KReTg0dMyd9nm226bLDRxhANt6jGovJfp8acINsDtlSv/oOzcuUO2bd0qa1f/ZWYNTpUqtVSqXFmat3hKHqngO3eV9rEZOvwLcxBMHTE1/tsxEhaWSho1biqdX3k1wnGi6tV/XBYumCcb1q+X06cXm2CUIWNGadykmRlplTtP3ji+x5D7vcbm+vXrMn/+fBNm9EjeZ8+elcyZM0vTpk3lySeflEceuTU/SXRQYwPYiRobwF5BXWPToUMH+eGHH+TUqVOSLl06admypbRo0cLMRMzEfAAAIKiCjYaaRo0amZqZ6tWrm6N5AwAABGWw0ZFQCRI4tngAAMCBHJsc3KHmwoUL8ttvv8mePXvM/zlz5pQqVapIsmTJAlxCAADgNI4NNu7Zh3v37m1GQHn3cdY5bgYOHCidO9MBGAAABMEEfd9++6106dJFihUrJt99952sW7fOnCZOnCjFixc363Q+GwAAAMcP99aZhVOlSiW//PJLhI7DOilfjRo15PTp0ybsRAfDvQE7MdwbuL+Hezu2xkaPB9WsWTO/o6F0ma67m2NGAQAA+zk22ISFhcnu3f6P4qp0nR7QEgAAwPHBpl69eqbz8KRJkyKs05mIhw8fLg0a/HdwNAAAAMf2sTl27JgZ1q3NTZkyZZL8+fOb5f/8848cPnxYChUqZIaB66zE0UEfG8BO9LEB7BXUfWzSp08va9askSFDhphRUDphn5708scffyyrV6+OdqgBAAB2cmyNTWyjxgawEzU2gL2CusbG35G+lyxZImfOnAl0UQAAgEMFTbA5ceKEVKtWzTRBAQAABHWwUfdpqxkAALAx2ISEhAS6CAAAwMGCKthQYwMAAIL26N7hh3/v2rXLzGkDAAAQ1MEmXrx4kjNnzkAXAwAAOJijg83WrVtl9OjRsnPnTjl16lSEpijtc6NH/wYAAHB0sBk3bpy0a9dOEiZMKAULFpTUqVNH2IY+NwAAIChmHs6bN6+kSZNGfv7551g5dAIzDwN2YuZhwF5BPfPwwYMHpX379hwPCgAARJljg02JEiVMuAEAAAj6YKNH9R41apSsWLEi0EUBAABBwrGdh9977z0JCwuTSpUqSZEiRSRHjhwSP378CKOiZs6cGbAyAgAAZ3FssNmwYYMJLhpozp8/L1u2bImwDYdYAAAAQRFsdu/eHegiAACAIOPYPjYAAADW1Ni4/fbbbzJ79mzZs2eP+V8Pq1CvXj2pUqVKoIsGAAAcxrHB5urVq9KyZUv54YcfzAzDqVKlMstPnz4tH330kTRq1EgmTpxoZiYGAABwdFNU3759ZcaMGfL666/LoUOH5OTJk+Z0+PBheeONN2T69OnSr1+/QBcTAAA4iGMPqZA7d26pWrWqOQimP88884wsXrw42p2MOaQCYCcOqQDYK6gPqaC1NA8++GCk63Wd1t4AAAA4Pthky5bN1MjcrlOxbgMAAOD4YNO2bVuZPHmyvPjii7J9+3a5ceOG3Lx501zu2LGjTJkyxTRHAQAAOL6PjQaZDh06yLfffmtmGI4X71YG03CjRdbgo8eSci+/W/SxAexEHxvg/u5j49hg47Z+/XqZM2eO7N271zOPTd26dc3Rv+8FwQawE8EGuL+DjWPnsXErWbKkOQEAAARVsLnbWhhtotIaHQAAAMcFmzRp0kTpiN06zFs7EXN0bwAA4Nhgc7vh3e5A895778mIESMkfvz40rp16zgrGwAAcD5HBZvIHDlyRAYPHixfffWVXLt2TZ5++mnp1auX5M2bN9BFAwAADuLoYOOuofEONL1795Y8efIEumgAAMCBEjg10GgNzciRI02g0SYnDTR6/CgAAICgCDZ6fCh3oLl+/bq0adPGNDkRaAAAQNAFG+0zc+XKFSlVqpT07NnTBJpTp06ZU2TKlCkTp2UEAADO5aiZh70Pj3CnodxabN1GD70QHcw8DNiJmYcBewXdzMOjR48OdBEAAEAQc1Sw0QNbAgAARFf0Do0NAADgQAQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgjQT3cuUDBw7IkiVL5OjRo9KkSRPJli2b3LhxQ86cOSNhYWESP378mCspAABAbNTYuFwu6dq1q+TOnVtatWplLv/9999m3fnz5yVXrlwybNiw6OwaAAAgboPNBx98IEOHDpU33nhDFixYYIKOm9bUNG7cWKZNmxb9UgEAAMRVsBk5cqS0adNG3n33XSlVqlSE9SVKlPDU4AAAADg62Ozbt08eeeSRSNcnS5ZMzp49ey/lAgAAiJtgkyFDBhNuIrN69WrJkSNHdHYNAAAQt8FG+9B8+eWXsnPnTs+ykJAQcz5//nwZM2aMNGvWLPqlAgAAiIYQl3fP3yjS4dyVK1eWXbt2SaVKlWTu3LlSq1YtMyLq999/l9KlS5th4EmTJhWnCi3dOdBFABALLq0dLpevB7oUAGJDkgSxVGOjI59Wrlwp3bp1M3PZJEmSRH777Tc5ffq09OnTR5YuXeroUAMAAOwUrRobG1BjA9iJGhvAXrFWYwMAAGDNIRXat29/x220M/GoUaOis3sAAIC4Cza//vqrZxSUmx4j6tChQ+Y8ffr0Zi4bAAAAxweb3bt3+11+7do1GTFihHzyySfmUAsAAABxKUb72CRMmFA6d+4stWvXNucAAABxKVY6D5csWdLMYwMAAOD4pqg70WYop89jo0NCAdy/Q0IB2Clab/9+/fr5Xa4T9GlNzZo1a6R79+7iZBev3ZfT9wDWS5owRHYcvRToYgCIBXkzhMbOBH3x4vlvwUqdOrXkzZtXnn32WXnuuecijJxyEoINYCeCDXB/B5to1djcvHkzOlcDAABwVufhS5cuSdeuXWXWrFmxUyIAAIC4CjahoaFmrpojR45E9zYBAACcM9y7bNmysmnTppgvDQAAQFwHG51ZeNKkSfL111/L9escRhcAADhDlEdF6TDuwoULm+NAFS9eXE6cOGGaoxInTixZs2Y1TVQ+Ow4JkfXr14tTMSoKsBOjogB7xeioqGrVqsn48eOlZcuWkjZtWkmXLp0ULFjwXssIAAAQY6IcbLRix125s3jx4pgrAQAAgJOPFQUAAOD4YOPkmYQBAACi3HlYD6NwN8FGt3XyiCk6DwN2ovMwYK8YP6RCzZo1pUCBAvdSJgAAgFhzV8Gmbdu28tRTT8VeaQAAAO4BnYcBAIA1CDYAAMAaBBsAAHD/9bG5efNm7JYEAADgHlFjAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYI0E4gC5c+eWkJCQu7qObr9jx45YKxMAAAg+jgg2VapUiRBs/vrrL9m8ebMUKVJEChYsaJZt375dtmzZIsWKFZOyZcsGqLQAAMCpHBFsxowZ4/P/Dz/8YE4LFiyQGjVq+KzTZc2bN5f+/fvHcSkBAIDThbhcLpc4TIkSJaRhw4bSr18/v+vfeustE3w2btwY7du4eM1xdxtADEiaMER2HL0U6GIAiAV5M4QGZ+fhf/75R9KmTRvpel1H/xoAABAUwSZv3rwyevRoOX/+fIR1586dk2+++Uby5MkTkLIBAADnckQfm/AGDBggTZs2lUKFCskzzzwj+fLl89TkjB07Vo4cOSJTpkwJdDEBAIDDOLKPjZo/f7783//9n6xfv95nealSpWTQoEFSp06de9o/fWwAO9HHBri/+9g4Nti4HT58WPbs2WMu58yZUzJlyhQj+yXYAHYi2AD3d7BxZFOUNw0yMRVmAACA3RwdbPbv3y9r166VM2fOyM2bNyOsb9OmTUDKBQAAnMmRTVGXL1+Wtm3byrRp00yg0VmJ3cX0nqH4xo0b0b4NmqIAO9EUBdgraOex6dmzp0yfPl0GDhwoixcvNqFGR0Nph+LHHntMSpYsGaFTMQAAgCODzdSpU6Vdu3ZmVFTRokXNsqxZs0rNmjXlp59+klSpUslnn30W6GICAACHcWSwOXr0qJQvX95cDg29Ve104cIFz/omTZqYGh0AAADHB5uMGTPKiRMnzOWkSZNK6tSpzZG93c6ePWv64QAAADh+VNSDDz4oy5YtM01RqkGDBvLBBx9I5syZTWfijz/+WB566KFAFxMAADiMI2tsXnnlFXMsqCtXrpj/+/fvb/rVtG7d2oyWCgsLk08//TTQxQQAAA7jyOHe/mhNzcaNGyV+/PjmGFIJEtxbZRPDvQE7MdwbsJcVMw+7xYsXzwzzBgAACLpgo5PvjR8/XmbPnu1zrKj69etLq1atTM0NAACA45ui9BAKevTuVatWSYoUKUx/G7Vr1y4zIkqHgs+bN09SpkwZ7dugKQqwE01RgL2CdubhXr16yerVq2XYsGFy7NgxWbNmjTnp/DbDhw+Xv/76y2wDAADg+BobnWW4adOmMnTo0EhHTensxAcPHoz2bVBjA9iJGhvAXkFbY6OT8xUsWDDS9Toq6uTJk3FaJgAA4HyO7DycL18++fHHH+Wll17yu17X5c2bN87Lhbj34w/TpU/vnrfdpvyDD8mIUWM8/2/csF5GjRwh69euMYfiyJQ5izxat560f/Z5SZIkyR1vs3PH52X50iWSKFEi+WPNhhi5HwB8TZkwWkZ/eatWfsiX30qhoiU863b8s02W/jpf/t2+Vf79e5ucPXNKipcqK+8NG+V3X5cvX5I5P0yRf7ZvkR1/b5MD+/aYgyePnjxbMmbO6vc6477+XLZuWi/79+2Ws6dPS5LQUMmQKYtUq/WYPPZEU0mS5M41A3AmRwYbDTSdO3eWunXryquvvioFChQwy/WwCjox34IFC0xfG9ivYKHC8kLHTn7XLVwwX3b8+488XKGiZ9kvC+ZL9ze7mukBatSqLWnTpZP1a9fKyC8/l1V/rDQBSANLZKZPnSy/L18miRMnNh+MAGLe7p3/yvhvvjBh4vKliM2Gvy9dJJPHfyMJEiaUrNlzmmBzO2dOnZSvPxtiLmfIlFmSp0gp586eue11Zk2fJFmy5ZAy5R6WsFRp5NKlC7Jx7V8ycvhH8svcWfLhF2MJN0HKscFGOwoPHjzYjH7yljBhQnn77belY8eOASsf4jbY6Cm8a9euyvcTJ5iJGhs83tAs0+OHDez/jnYdk9HjvpMiRYuZ5RpQ3nu3v3w/8TsZ/+0YU3Pjz8ED+2XIB+/J022ekQXz58qJ48dj+d4B95/r16/JkIFvSZ58BU2wWDR/doRtKlWrJQ9VqCq58uaTs2fOyNMNa952nynDUsuAIV9I/oJFJEXKMHnr9Zdk9Z8rbnud8TMWSKLEiSMs/6B/L1OmBXNmSoPGLaJxDxFojuxjo9555x3Zv3+/TJgwQd59911z0su6rE+fPoEuHgJs0S+/yOnTp6VS5aqmVkatX7dWTp08KdVq1PCEGhUSEiKdXn7VXJ46eZLfmhhd9s5bvSRd+vTSsfMrcXhPgPvLpG+/lj27d8hr3d+R+PH8fwXlzJ1P8hUsLAkSJIzSPkOTJjU1LxpqospfqHGHKnVo/74o7wvO4sgaG7d06dJJixYkZkQ0Y9oUc96oSVPPMncNS9as2SJsnyJlSkmZMkwOHTwo+/ftk+w5cvisnzhhnKz+a5WMGjM+Sv1wANw97TPz/bej5OkOHSVHbmf2k1z1+1JznjNPvkAXBcEcbPbu3Rut6+UI9+WE+8PBgwfkzz9WSsaMmeSRipU8y1OlTmXODxzYH+E6586dk7P/a3Pfs2e3T7DR/4cN/VhatmotpcqUiZP7ANxvrl29Kh8N7C158heQpk89I07qxHzlymW5cP6cbNm4Tv7ZtsXU/tR4tH6gi4ZgDja5cuUyzQXROewC7j8/zphuDoraoGEjn0NrlCpdRpInT26aqbZt3SKFChfxrPti+H9Hgz937qznsu7n7Z7dJX269NLplVvNVQBi3rhRn8uB/Xvl068nOuqQONMmjpWzZ057/q9Wu550fr1XlJvB4DyOCDbffPNNtIIN7j8aRGb+MMO8Xho2auKzLmnSZNL1ze7Sr09vaduqhdSsVefWqKh1a2Xrls2SO3ce2bVrp8QL+a9df+zoUWZ4+FffjJXQUEZAALFBh1VPn/StPNXuRcnlsCaeST8tNucnTxyX9Wv+NEPQX3vhaRnw0ReSLkPGQBcPwRpsnnkm9qolr1y5Yk7edCivxIt8yC+c64/fV8jhQwel/EMPS9ZsEfvSaJ+b9BkyyNhvvpbFi34xQahIsWJmmPeYUSNNsEmTNo3Zds/uXfLlZ8OkeYuW8kC58gG4N4D9bly/Lh8NfEty5c0vzZ9uJ06VJm06qVarrmTNlkNeff5pGfnZR9Kj7/uBLhaCNdh4u3jxomTPnl26d+8ub7755j3vb9CgQdK3b1+fZTqqqlsvRlYFoxnTp5rzRo3/6zQcXsVKlc0pvN49upn5bQoVLmr+37ljh1y9qsPGvzMnf0oXK2TOl6z403RABnB3Ll26KAf33+pH+Xi1cn636fpiG3Pee+AQeaRydQmkAoWLmXlwdE4bBCfHBZukSZOauUmSJUsWI/vr0aOHdO3aNUKNDb1zgs/p06dk8a+/SFhYmFSveWtIZlStW7NGDh44IBUrVTFHjFdZsmaVhpEEpPlz55iavgZPNDL/J7zNpH4AIqfvndr1br2Pwtu0frUJPQ9VrGrmosmYOYsE2qWLF+XihfOSOu2taSQQfBwXbFSTJk3MQS51Er577XujIcY0PYXDQTCDz+xZP8q1a9ekbv3HI509+Pz586YDsbejR4+YfjcamF96+b85anTivz79Bvjdzx8rV5jh45GtBxA1iRMnkVe7+68h14n6NNg0f7q9zyEVYtuhg/slefIUEea90ckDR3z6gWnCLvfQfzOaI7g4Mtjo3DU6+3C1atXkueeeM6Om/HXsLMPQ3PvKD+5mKK+5a8LT+Wjm/PSjlCpdVtKkSSNHDh82fW10VuK3+w2QwkVuNUMBcK59e3bJlPHfmMtXrt7qI7l/724ThNy69urvcx09pMLZ07cOvbB75z//W/ax57ujToPGUrREaXN507rVMvyjgVK0eGnJlCWrpEiZSk6dPC7rVv8hx48ekew580jb5zrH0b3FfRFsqlat6rm8dOmtyZLCzxKrNTkM975/bNq4Qf795x8pVryE5C8Q+ZHfS5YqLatXrZIlixfJ2bNnJVWqVFKhUhVp1+FZn+HfAJxLQ8bCubPCLTvhsyx8sFm2eIEcPXzIZ9ny3xZ6Lhcv/YAn2BQpXkoea9BENm1YIzv/3S7nz5+TpKFJJXuu3PJ4k5ZSv/GTHCcqiIW4HHikv7Fjx0Zpu7Zt20b7NmiKAuyUNGGI7Dga8cCKAIJf3gyhwRls4gLBBrATwQa4v4ONYw+C6Xbo0CFZv369XLhwIdBFAQAADufYYDNz5kwpVKiQZMuWzXQS/uOPP8zy48ePS+nSpWXGjBmBLiIAAHAYRwabWbNmSePGjc3RvXUyPe/WMl2WNWtWGTNmTEDLCAAAnMeRwaZfv35SuXJlWbZsmXTq1CnC+ocffljWrl0bkLIBAADncmSw2bRpkzRv3jzS9RkzZpSjR4/GaZkAAIDzOTLY6GEVbtdZeOfOnZI2bdo4LRMAAHA+RwYbnXFY57K5fv16hHWHDx+WkSNHSu3atQNSNgAA4FyODDYDBw6U/fv3S7ly5WTEiBFmluF58+ZJ7969pXjx4qYzsXYqBgAACIoJ+jZv3ixdunSRRYsW+YyK0sMtfPbZZ1K4cOF72j8T9AF2YoI+wF5WzDx86tQp+ffff83RVvPkySPp06ePkf0SbAA7EWwAewVtsNmyZYsUKRK7Bywk2AB2ItgA9graQyoUK1ZMSpQoIe+++66prQEAAIgKRwabL774wjQ5vf3221KwYEEpW7asfPDBB7Jnz55AFw0AADiYI5ui3I4cOSJTpkyRyZMny/Lly82y8uXLS4sWLaRZs2aSJUuWaO+bpijATjRFAfYK2j42/hw4cMATcv78808zBPzatWvR3h/BBrATwQawV9D2sfEnc+bMUrRoUTPMW2cm1lFSAAAA3hKIg2ll0uLFi+X777+XGTNmyPHjxyV16tSmKerJJ58MdPEAAIDDODLYLF261DQ5TZ061RzsMmXKlNKwYUMTZmrWrCkJEjiy2AAAIMAc2ccmXrx4kjx5cmnQoIEJM48++qgkSpQoRm+DPjaAnehjA9zffWwcWfWhnYTr169vDoJ59uxZuXr1aowHGwAAYB/HdR7evXu3/PLLL1KgQAHTBJUtWzYJCwuTHDlySKdOnWTXrl2BLiIAAHAoRzVFzZw5U1q3bi3nz5+XXLlymdmHU6RIIefOnZMNGzaY0JMsWTIZP368PPHEE/d0WzRFAXaiKQqwV1DNY6PHhypTpow50OWIESOkUqVKfjsVv/jii7Jz505ZvXr1PR1PimAD2IlgA9grqILN008/bYZ2a81MmjRpIt3u5MmTpianWrVqMm7cuGjfHsEGsBPBBrBXUE3Qt2jRIunQocNtQ43S9e3bt5dff/01zsoGAACCg2OCzYkTJ0y/mqjInTu32R4AAMCRwSZdunRRHvGk2+n2AAAAjgw2VatWlVGjRpk+NLej63U73R4AAMCRwaZnz56mealy5cqyYsUKv9vo8ipVqpjtevToEedlBAAAzuaYUVFq+vTp0qZNG7l06ZLpb1OyZEmfeWy0CSpJkiQyduxYadq06T3dFqOiADsxKgqwV1AN93bTOWref/99+emnn+TgwYOe5ZkzZzaHWXjzzTclX75893w7BBvATgQbwF5BGWy86XGitLZGa2308AoxiWAD2IlgA9graA+C6aZhJqYDDQAAsJdjOg8DAADcK4INAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AADAGgQbAABgDYINAACwBsEGAABYg2ADAACsQbABAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKwR4nK5XIEuBBBbrly5IoMGDZIePXpI4sSJA10cADGI9zf8IdjAamfPnpWwsDA5c+aMpEyZMtDFARCDeH/DH5qiAACANQg2AADAGgQbAABgDYINrKYdCvv06UPHQsBCvL/hD52HAQCANaixAQAA1iDYAAAAaxBsgGgKCQmRd955J85vd/Hixea29RzAf8aMGWPeG7t3747z237mmWckV65ccX67iIhggwgfCkmSJJEDBw5EWF+1alUpVqxYQMoGwFk2b94sTz/9tGTNmtV03s2SJYu0atXKLAcCiWADv9OUDx48ONDFAOBQ06dPlzJlysgvv/wi7dq1k88//1w6dOggixYtMstnzJgR6CLiPpYg0AWA85QqVUpGjhxpjr+iv8Lwn5s3b8rVq1dNrRZwP9qxY4e0bt1a8uTJI0uWLJH06dN71nXp0kUqVapk1m/YsMFsExcuXLggyZIli5PbgvNRY4MIevbsKTdu3Lhjrc3169elf//+kjdvXlMVre3Lel2t8fGmy+vXry/Lli2T8uXLm1CgH3jffvttlMt09OhR84swY8aM5volS5aUsWPHetZfu3ZN0qRJY349+juejF7njTfe8CzTMur8F/ny5TNlz549u3Tr1i1C2bVprnPnzjJhwgQpWrSo2Xbu3LmRlnPt2rXy2GOPmePWJE+eXGrUqCErV6702ebkyZOmLMWLFzfb6LZ6nfXr10fY3/79+6Vhw4bmQztDhgzy2muvRSgjEJc++OADuXjxonz11Vc+oUalS5dORowYYYLG+++/L1OnTjXvod9++y3CfnQ7Xbdp0ybPsm3btknTpk3Ne1nfsw888ID8+OOPfpvMdZ8vvfSSeV9ky5bttmXWGiX3+1d/rHXq1ElOnz7ts83SpUulWbNmkiNHDs9ngr7fLl26FGF/P/zwg2mW1zLqOTVUDqPz2ABq9OjROqeRa9WqVa727du7kiRJ4jpw4IBnfZUqVVxFixb1/N+2bVuzfdOmTV2fffaZq02bNub/hg0b+uw3Z86croIFC7oyZszo6tmzp2v48OGuMmXKuEJCQlybNm26Y7kuXrzoKly4sCthwoSu1157zfXpp5+6KlWqZG7rk08+8WynZU6VKpXrypUrPtcfO3as536pGzduuGrXru1KmjSp69VXX3WNGDHC1blzZ1eCBAlcTzzxhM919Xp62+nTp3f17dvX3M+1a9d61vXp08ezrd6XZMmSuTJnzuzq37+/a/Dgwa7cuXO7EidO7Fq5cqVnOy1H3rx5Xd27dze33a9fP1fWrFldYWFhPo+33u8CBQqY56Fbt27mvpYtW9ZVokQJc9uLFi2642MHxLQsWbK4cuXKddttdH22bNnMazh58uSul156KcI21apV8/k80fePvgeKFCnieu+998znROXKlc3nxPTp0yN8Tul2+pk0bNgw817zXrdr1y7P9voe1WU1a9Y02+p7PX78+K5y5cq5rl696tnu5ZdfdtWtW9f17rvvmvdlhw4dzHb6+eZt3rx5rnjx4rmKFSvmGjJkiKtXr16m3Hpf9LMOgUewgd9gs2PHDvNF/8orr/gNNuvWrTPbPvvssz77eOONN8zyX3/91bNM3+y6bMmSJZ5lR48eNV/4r7/++h3LpV/oev3x48d7lukH0sMPP2w+NM+ePev5wNHtZs2a5XN9/bDKkyeP5/9x48aZD6alS5f6bPfll1+a6y9fvtyzTP/XbTdv3hyhXOGDjQa6RIkSmcfO7eDBg64UKVKYD2i3y5cvm3DlTT+I9fHQkBP+fk+ePNmz7MKFC658+fIRbBAQp0+fNq+98D8Awnv88cfNdvrebNmypStDhgyu69eve9YfOnTIvK+8X+81atRwFS9e3Lw/3G7evOl65JFHXPnz54/wOVWxYkWfffoLNvo5o+9J/SHj/Z7T0KTbffPNN55lGsLCGzRokAlWe/bs8SwrVaqU+fGij4Xb/Pnzzf4INs5AUxT80qYibSfX6uZDhw5FWD9nzhxz3rVrV5/lr7/+ujmfPXu2z/IiRYqYtnc3rcIuWLCg7Ny5845l0dvKlCmTtGzZ0rMsYcKE8sorr8j58+c91dzVq1c3VeHff/+9Z7tTp07JggUL5Mknn/QsmzJlihQuXFgKFSokx48f95z0+ko7QHqrUqWKKf/taNPd/PnzTbORd7+CzJkzy1NPPWWa4bRJTGk1d7x48TzXO3HihGmS0sdjzZo1Pvdbr69V825JkyaV559//o6PGRAbzp07Z85TpEhx2+3c6/U1r+89bUr2np5Am6i0v5r7fanNs7/++qs0b97c3Ib7PanvjTp16sg///wTYaTmc889J/Hjx79tORYuXGj6xL366que95z7utoE7P05FRoa6rmsTWl6+4888oj++DdNzEo/C9etWydt27aVsLAwz/a1atW642cE4g7BBpHq3bu36Ufjr6/Nnj17zAeF9lHxpgEkVapUZr03bbcOL3Xq1CZ4uL/gDx8+7HPSDyT3beXPn9/ng0lpOHGvVwkSJJAmTZrIzJkzPf1QdPSG9r/xDjb6IalDUjVceZ8KFChg1uuHsLfcuXPf8bE6duyY6Xeg4SQ8Lad+iO/bt8/8r5c//vhjc5805GgY09vXzpZnzpzxeYz18dX+BN783QYQF9yBxR1wohKAHn30URMCvH9w6GUdpOB+z/37778mQLz11lsR3pfaFy6670v3Z0P490yiRInMDxDvz6m9e/eauWi0f4/+0NDb1h81yv2+dG+v793weF86B6OiECl94+s8FVpr0717d7/bhP/SjUxkv6zchyrTL/3wH1Rac6Jz59yNFi1amE6JP//8s6k9mTx5sqmZ0c7GbhostOPukCFD/O5DOw168/4lFxPeffdd8wHevn170/laP0g1tOmvSi0b4FQaULQWUUP47eh6nd9Ga0WUvhe1g6124j1y5IgsX77cvA/c3K977VSvNTT+hP8RFZPvS/1hpbUuWnP0f//3f+YzQzvsay2Rhh3el8GFYIM71tqMHz9e3nvvPZ/lOXPmNG92rf1w15wo/dDS0Qa6/m5oTY82GXlzhxHdl35Q6u1519roCAr3erfKlSubD179RVixYkVTvd2rVy+f/eooLh2BpCOWohrM7kR/3Wkz0fbt2yOs03Jqud2BSavhq1WrJqNGjfLZTh83rb1x0/ulI0Y0/HmX099tAHFFRzjqdBDavKrvsfB0dJHO/PvCCy94lmmNqY5i1Hlvtm7dal7T3rWo7uZbbWKuWbNmjJXV/dmg7xnvJmKtDd61a5fntjZu3Ch///23KWObNm0824X/THLvTz/3wuN96Rw0ReG2NARorY3WgmjzkFvdunXN+SeffOKzvbsWpF69end1OzpsUj9kvE/aVOW+Lb1t76psbSIbNmyYqTJ2VxcrDRDaJ2XWrFkybtw4s533B6jSdnz9JaYfzuHp0E5tX79bWiNVu3Zt0wzmPZ27Br3vvvvOfAG4f73qtu6aKu9+P+H7EOj9PnjwoAlCbu5htkCgvPnmm6a2RIOL9oHxpjUeL774ogn5up2bvp+1ZlLfw3rSaR+8a2h1yLbWzurnjL8+fdrUGx16u9rs9Omnn/q85/RHhTYvuT+n3DXK3tvo5aFDh/rsT380aROaBiDvZmMNQFu2bIlWGRHzqLHBHWmNh4YE/UWic0G4a1O0A51+yWpNg4aLP//807zhtdpZayRiinaW1Q88rRJevXq1mRdHv+y1OluDVfiOjBpkNPRo27w2OXnXKCntFK1NVPoBrM1dFSpUMFXRWrOiy+fNm2fmz7hbAwYMMB9wGmJ0fg3t86Pl1v4+OqeH9y/efv36mTl3tHOi/lrUeXLCT2amHRyHDx9ufkHq/dYPVX0e9EsDCBTtX6Lvcz18gr6/dH4pDSka6DUwaKfbiRMnmh9FbloT07hxY5k0aZL54fDhhx9G2O9nn31m3ju6T33t6/tBfxj8/vvvZj4nf/M8RaUmVSca7du3r+nr8/jjj5vPMW0SK1eunPnRprTpScurTWH6A0N/hEybNs3TB9DboEGDTCDSsmpzsoY5/bzRz0YdzAAHCPSwLDhzuHd47jlrvOeduHbtmpnbRedq0TlmsmfP7urRo4fPcE2lQyDr1asXYZ86fFxPUXHkyBFXu3btXOnSpTPDN3VYqJbXHx0iqmXR8g4YMMDvNjpcXOfK0Pujw6xTp05t5ojR+3PmzBnPdrqPTp06+d1H+OHeas2aNa46deqYYeg6T47O1bFixQqfbfTx0WHuOmQ0NDTUVaFCBdfvv//u9/HQYaY6dFb3pfe9S5currlz5zLcGwG3YcMGM5RbX8f6/s+UKZP5f+PGjX63X7BggXnd6vDpffv2+d1Gp0rQ+bB0X7pPnd+pfv36rqlTp0bpc8rfPDbu4d2FChUy+9T5tDp27Og6deqUzzZbtmwxc93oe1ffa88995xr/fr1Zn/hP2umTZtm5rfSzw6dT0fn2dHPSIZ7O0OI/gl0uAIAAIgJ9LEBAADWINgAAABrEGwAAIA1CDYAAMAaBBsAAGANgg0AALAGwQYAAFiDYAMAAKxBsAEAANYg2AAIWnrcMD2GmNvixYvNkdD13KllBBC7CDYAom3MmDEmSLhPepT2AgUKSOfOnc0BDIPFnDlz5J133gl0MQDEAI7uDeCe6dHK9QjPly9flmXLlskXX3xhwsKmTZvi9GjklStXlkuXLkmiRInu6npaVj26NOEGCH4EGwD37LHHHpMHHnjAXH722Wclbdq0MmTIEJk5c6a0bNkywvYXLlyQZMmSxXg54sWLZ2qNANy/aIoCEOOqV69uznft2mX6lyRPnlx27NghdevWlRQpUkirVq3M+ps3b8onn3wiRYsWNYEkY8aM8sILL8ipU6d89udyuWTAgAGSLVs2UwNUrVo12bx5c4TbjayPzR9//GFuO3Xq1CZQlShRQoYOHWrWafm0tkZ5N6u5xXQZAcQuamwAxDgNMUprbtT169elTp06UrFiRfnwww89zVMaELSfTrt27eSVV14xQWj48OGydu1aWb58uSRMmNBs9/bbb5vQoOFET2vWrJHatWvL1atX71iWBQsWSP369SVz5szSpUsXyZQpk2zdulV++ukn87+W4eDBg2a7cePGRbh+XJQRQAxyAUA0jR492qUfIwsXLnQdO3bMtW/fPtekSZNcadOmdYWGhrr279/vatu2rdmme/fuPtddunSpWT5hwgSf5XPnzvVZfvToUVeiRIlc9erVc928edOzXc+ePc12un+3RYsWmWV6rq5fv+7KnTu3K2fOnK5Tp0753I73vjp16mSuF15slBFA7KIpCsA9q1mzpqRPn16yZ88uLVq0ME1PM2bMkKxZs3q26dixo891pkyZImFhYVKrVi05fvy451S2bFlz/UWLFpntFi5caGo9Xn75ZZ8moldfffWO5dJaFa1h0W1TpUrls857X5GJizICiFk0RQG4Z9pHRYd5J0iQwPRBKViwoOnI66bLte+Jt3/++UfOnDkjGTJk8LvPo0ePmvM9e/aY8/z58/us1yClfWai0iRWrFixaN2vuCgjgJhFsAFwz8qXL+8ZFeVP4sSJfYKOu1OuBoYJEyb4vY6GgkALhjIC8EWwARAQefPmNU04FSpUkNDQ0Ei3y5kzp6f2JE+ePJ7lx44dizAyyd9tKJ1PR5vLIhNZs1RclBFAzKKPDYCAaN68udy4cUP69+8fYZ2Oojp9+rS5rIFERx4NGzbMDKl20yHYd1KmTBkzcaBu696fm/e+3HPqhN8mLsoIIGZRYwMgIKpUqWKGUg8aNEjWrVtnhkZrONBaD+20q/PMNG3a1DT3vPHGG2Y7HbatQ6m1U/DPP/8s6dKlu+1taPOXzoLcoEEDKVWqlBmyrcO+t23bZuaYmTdvntlOOwMrHc6tw9Ljx49vOkHHRRkBxLBYHnUF4D4Y7r1q1apIt9GhzsmSJYt0/VdffeUqW7asGR6eIkUKV/HixV3dunVzHTx40LPNjRs3XH379nVlzpzZbFe1alXXpk2bzDDu2w33dlu2bJmrVq1aZv9alhIlSriGDRvmWa/Dwl9++WVX+vTpXSEhIRGGfsdkGQHErhD9E9NhCQAAIBDoYwMAAKxBsAEAANYg2AAAAGsQbAAAgDUINgAAwBoEGwAAYA2CDQAAsAbBBgAAWINgAwAArEGwAQAA1iDYAAAAaxBsAACANQg2AABAbPH/X/PI8ueQNGMAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_cm(TN_dc, FP_dc, FN_dc, TP_dc, \"DC\", \"DC Solver\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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eNEs2OQ4dOmQ+G2699Vaz/+WXX8rtt99uMl36x68GbBrcZQ16Hn30UTPtiwZkvsEUwk/QB0Rdu3YVj8eTbXP+KlH6Q7Fjxw6Tpl2zZo35iwSFV9Agx5FbfZHvMR3KT2AEhL4LL7zQdKOpn3/+2XusIDTr9NJLL5k/ajXg0UBr4sSJsm3bNu85b7/9tvzxxx8yd+5cad68uTnn5ZdfljfeeCNbDanS6+gcdvo4hwZcWneq3X1Ks0Hjxo2TQYMGmeyQdtvp82bNZGltk2a+9JyGDRsW8B1CKAn6gAjBb+pHA0wtkVNfpKPefGe99jcnkgZFujwIgNCkf5xqt5JzuyglCjpVitYdaf2nZoB0Dcply5aZ+7UcQrNR2m3l0G417RLTmqOcaCZo/vz55g9lpde++eabTTZKffPNN/L444+bVe+dTYu/tVZKu/8c7dq1K/TrQmixYth9YYuqdfOXwnWVTyFzx50HZWEQhLVZi7Hjflkj8dEdsk3wCCB8aKCidULqggvOTK2hI8MKM6ilUqVKZt1K3bR+SLvI9KtmbgpDr6NB2qJFi8zgGO0ie+GFF7z3a/2RZomuv/76bI/VmiKHbxCG8BYEH6XBKdyLqgMpt2LsrMPvfReadW7TfQaEns8//9zU1dxwww1m/8orrzRdUs8++2yO5xdkxJhmnbTrTWuHVLNmzUxGx9lXq1evNtmepk2b5ngNDWo02NHM0DvvvGPO0wJqh97W7FKTJk2ybU4WCXYhQ4QSyRpl7Tb7tXJbF1oEoDC020m7tDRjrjU7WpysyxzpsHstTHYyKa+99popaNZRWffdd58JLrTQ+r333jMjg999991s19aBMLo498CBA81UJzqdysqVK808R2PHjvV2f+k5Wu+jRc779++Xe++91zwmtwE0+jhto45ku+222zLdpzVLep/WBd14440mCNKgS5d60swU7ENAFGyyzuuTw6yyOtS9U2P/y48Ei/xO+HgmU+R/fTkgrIXAzNEaANWpU8cshaQFy1rPo4XQGqD4ZlN0WPtXX31lgiUtRtbFsRs0aGCKoP0FGToJY3R0tOm+0gJtzQ45+6NHjzbn6ILdOopNR4Rp95fua2ZKF/fOjT5v9erVTSZI2+NLu+R03iStI3rmmWfMpJOalbrzzjsD8p4h9JTyFKUSzgL6A62TiumkYZUrVw7otbWg+OLK/TMva5GPgEgLl536HA2OCjsqzE1ZM0T6PiiW+EA40hFSOoea1tv41qcAKL6frYJ+ftNRGqJymlEaAAAUDl1mITTKLGvxsQZFKRGsSg8AQFEREOUyykw3J+UWLEPsVTAMsy+qrEXW+9LW/68bjVoiAEDJIyAKIaFYKwQAQCgIg1yDHWypGcpp2Q8AAIobGaIQkdNq8wAAIDDIEIUAW7JDDrJEAICSRkDkh44w01lTdRIwAAAQ3ugyc3GUmY606ph6RGSF/1mndeJFW4bW6/uRslGkQdUKIoePyAt/3mBmsc42eSUAAAFGQAQAFnURD289XGyh654tWLDArJfmpq5du0rr1q1l8uTJrrYDuaPLDEFHC8Y1MwYgOOjCrrqOmC7Wqssj6IKqnTt3lmnTpsnvv/8uoeqLL74wa6elpqYG5fVQssgQBSEnGAiFBVwBhLdt27aZ4Kdq1ary1FNPSYsWLaRcuXKyefNmmTFjhtSrV8+sbp+T06dPm0VTQ92pU6ckMjLS7WagmJEhAgD4NXz4cLPK/bp16+Smm26SZs2aSePGjc3K9osWLZI+ffp4z9XsiGaNNEA666yz5MknnzTH9dh5551ngoqmTZvKG2+84X2Ms8K9b7eWZlj0mGZcfDMvy5cvl3bt2pnV7i+55BKzir2vp59+2mSvKlWqJHfccYdZ+NMffd5u3bqZ29WqVTPXHzx4sLeLa+TIkXL//fdLzZo1pWfPnnm2M7frqYyMDHnooYekevXqEhUVZbrzEFwIiIJ8uL2ts1Pr69aCagDuOXjwoCxdutQMMNEAJyf6we9LP+ivu+46k0EaOnSofPTRR6a77e9//7t89913cvfdd8uQIUNkxYoVBW7PP/7xD3n++edNcKZBml7f8d5775nn1iyW3l+nTh2ZOtV/fVaDBg1k/vz55rYGVrt375YXX3zRe//rr79uArjVq1fL9OnT82xbfq6n7+GaNWvk2Weflccff1yWLVtW4PcAxYcuMwS9M4ERa5wBJS05OVk8Ho/J6vjSrImTfdFg6ZlnnvHed8stt5iAxzFgwACTKdFMkxozZox8/fXX8txzz3kzKvmlGafLLrvM3B43bpxcffXVph1a16QFy5oV0k098cQT8tlnn/nNEpUuXdpka9Q555xjugR9nX/++SZwcWgGKDd5Xa9ly5YyYcIE77Vffvllk/G64oorCvQeoPiQIQrieYiYjRpAMFq7dq3pOrrooovk5MmTme7TLi1fW7ZsMTVIvnRfjxeUBhUOzQCpffv2eZ+nQ4cOmc7v1KmTFFbbtrrYdOD4tt1pv9N2BAcyREG22r12k9ky7xCA4KajyrRLLGutjtYQqQoVKmR7jL+uNX8iIs78Xa6ZKN9i7Jz4Fmg7XXVam1Mcsr6OgrQzJ1mLy7X9xdV2FA4ZIgBAjmrUqGG6dLR75/jx44W6hhZhax2OL93XDLyqVauW+ao1N47CzBukz6P1Ob60ay43zsix9PT0PK+fn3YW5HoIPmSIAAB+aWGydnFpV5gWLWvXj2ZLEhMT5Ycffsiza+nBBx80o9PatGkjPXr0kP/+97/y4YcfmvoeJ8vUsWNHM0KsUaNGphvpkUceKXA7tXBba5W0ndret956S77//ntvNisn5557rsnUfPzxx9K7d2/TlrPPPjvHc/PTzoJcD8GHgAgAXBTsM0frcPmNGzea0VtxcXHy66+/mnmINMPzwAMPeIul/enbt68ZbaVF1Bq0aDAxe/ZsM7TdMWvWLFMMrcGVFnBrMfOVV15ZoHb2799ftm7daoa2ayH1DTfcIPfcc48sWbLE72N0DqXHHnvMFGhrIfjtt98uc+bM8Xt+Xu0s6PUQXEp5fDtEkY1TQ3TkyBGpXLlyQK8dN7uvXJPRJNMEjKaGiGJq49fKf/3l+cYN/8y01EGwf4gAvvQDevv27SYY0BFRAIr/Z6ugn9/UEAUZgiEAAEoeXWYICZoVSth60Kx8X+5MbSMAAAFDQISgVT9tvfd2ykaR+nqjcn83mwQACFN0mQXxxIzImWaKgFBEySYQvD9TBER+6KSMSUlJZmgpABSFMynf77//7nZTgLDy+/9+prJOfFkYdJkBQDHTda50bStnqQZdrT3roqgACpYZ0mBIf6b0Z0t/xoqKgAgASkBUVJT5yvpVQOBoMOT8bBUVAREAlADNCOmCnroSekHWwAKQM+0mC0RmyEFAhJCyIW2e200AikR/gQfylziAwKCoGgAAWI+AKIjosh0AAKDkERABAADrERABAADrERAhpA2cP9HtJgAAwgABEQAAsB7D7nNZy0y39PR0t5uCHBZ8TZj5wJkDVau42yAAQFggQ+QHa5kBAGAPMkRBImHbQUmJOOF2M0LGwojk/91q63JLAADhgAxR0H3AAwCAkkZABAAArEdABAAArEdABAAArEdABAAArEdAFCQjzFB4UzdNdbsJAIAQR0AEAACsR0CEsEK2CABQGEzMiLBAIAQAKAoyRAAAwHpkiBDyi72mbDxzu0HVCmdutB7uapsAAKGHDBEAALAeARHCRkrqCbO9sOwnt5sCAAgxBEQAAMB6BER+TJkyRWJiYiQ2NtbtpgAAgGJGQOTHiBEjJCkpSRITE91uCgAAKGYERAAAwHoERAAAwHoERAhLjDQDABQEAREAALAeARHCGmucAQDyg4AIYWdD2jy3mwAACDEERAAAwHos7oqwzRJN3VTD7WYAAEIEGSIAAGA9MkQIO/XT1p+5sb3Cma+th7vaHgBA8CMgctnCiGS3mwAAgPXoMgMAANYjQ4SwlZJ6wu0mAABCBBkiAABgPQIihD3WNQMA5IWACAAAWI+ACAAAWI+ACAAAWI+ACAAAWI+ACAAAWI+ACAAAWM+KgOi6666TatWqyY033uh2UwAAQBCyIiAaNWqUzJ071+1mAACAIGVFQNS1a1epVKmS282ASzakzXO7CQCAIOd6QLRq1Srp06eP1K1bV0qVKiULFizIds6UKVMkOjpaypcvLx06dJC1a9e60lYAABCeXA+Ijh8/Lq1atTJBT07mzZsnY8aMkQkTJsiGDRvMuT179pR9+/Z5z2ndurU0b94827Zr164SfCUAACBUub7afa9evczmz6RJk2TYsGEyZMgQsz99+nRZtGiRzJo1S8aNG2eObdq0KWDtOXnypNkcaWlpAbs2AAAITq5niHJz6tQpWb9+vfTo0cN7LCIiwuwnJCQUy3PGx8dLlSpVvFuDBg2K5XkAAEDwCOqA6MCBA5Keni61a9fOdFz39+zZk+/raADVr18/+eSTT6R+/fq5BlNxcXFy5MgR75aSklKk1wAAAIKf611mJeGzzz7L97nlypUzGwAAsEdQZ4hq1qwppUuXlr1792Y6rvtRUVGutQsAAISXoA6IIiMjpW3btrJ8+XLvsYyMDLPfqVOnYn1uHfUWExMjsbGxxfo8KH7109bL1I8GmE1WxJ/ZAAAIpoDo2LFjZpSYM1Js+/bt5vbOnTvNvg65f/XVV+X111+XLVu2yD333GOG6jujzorLiBEjJCkpSRITE4v1eVCypqZ+63YTAABByPUaonXr1km3bt28+xoAqUGDBsmcOXOkf//+sn//fhk/frwppNY5hxYvXpyt0BoAACBkAyJdVsPj8eR6zsiRI80GFFZK6gnztUHVCm43BQAQhFzvMgMAAHAbAZEfFFUDAGAPAiI/KKoGAMAeBEQAAMB6BEQAAMB6BESAj6mbprrdBACACwiIAACA9QiI/GCUmR1eWPaTDJw/0e1mAABcRkDkB6PMwlfCtoMmEAIAwEFABAAArEdABAAArEdABPhI2HrQ7SYAAFxAQAQrF3kFAMAXAZEfjDILfwUprNb5iZijCADCVxm3GxDMo8x0S0tLkypVqrjdHARYx50zvLf3RSS72hYAgPsIiGCdhRHJck1GE+9txTB8ALAbXWYAAMB6ZIiA/Nr+pcjhI5mPdYtzqzUAgAAiQwQAAKxHhggQkQ1p89xuAgDARWSIgHxgwkYACG9kiHKZh0i39PR0t5uCYuCMLsvViniZmvqtDK/aUuqnrZcUDYwOHZROjWuURBMBACWIDJEfrHZvLx2CzzB8ALALAREAALAeXWZAUayIz7zPMHwAsCdDtG3btsC3BAhRCdsOmg0AYFlA1KRJE+nWrZu8+eab8scffwS+VQAAAMEeEG3YsEFatmwpY8aMkaioKLn77rtl7dq1gW8dAABAsAZErVu3lhdffFF27dols2bNkt27d0uXLl2kefPmMmnSJNm/f3/gWwoE6SSN+RrCDwAI31FmZcqUkeuvv17ef/99eeaZZyQ5OVkeeOABadCggdx+++0mUAJCgc4z5Lt13Dkj1/N1fiJnAwBYHhCtW7dOhg8fLnXq1DGZIQ2Gtm7dKsuWLTPZo2uvvVZClU7KGBMTI7GxsW43BS6hUBoA7FGoYfca/MyePVt+/PFH6d27t8ydO9d8jYg4E181atRI5syZI9HR0RLKEzPqlpaWJlWqVHG7OQAAINgComnTpsnQoUNl8ODBJjuUk3POOUdmzpxZ1PYBwZk5qu52KwAArgdE2iXWsGFDb0bI4fF4JCUlxdwXGRkpgwYNClQ7gRLlFEo3kApuNwUAEKw1ROedd54cOHAg2/FDhw6Z7jIAAICwD4g0E5STY8eOSfny5YvaJgAAgODtMtOJGFWpUqVk/PjxUrFiRe996enpsmbNGjNHERDOXWnXZDSRlNQTZr9BVbrUAMC6gGjjxo3eDNHmzZtNnZBDb7dq1coMvQcAAAjbgGjFihXm65AhQ8xM1ZUrVy6udgFBK6eZqfXYxtTfZLgrLQIAuDLKTOcgAgAAsC4g0iU6dLJFzQrp7dx8+OGHgWgbAABAcAVEOluzFlM7twEAAKwLiHy7yWzoMtO1zHTT0XOwl44mSzh0sIir/gEAgl2hfs2fOHFCfv/9d+/+jh07ZPLkybJ06VIJF7qOWVJSkiQmJrrdFAAAEIwBka5irwu6qtTUVGnfvr08//zz5riucwbYwpmPCABgYUC0YcMGufTSS83tDz74QKKiokyWSIOkl156KdBtBAAACL6ASLvLKlWqZG5rN5mOOtOFXjt27GgCIwAAgLAPiJo0aSILFiwwK9svWbJErrzySnN83759TNYIAADsCIh0HTNdoiM6Olo6dOggnTp18maL2rRpE+g2AgAABN9M1TfeeKN06dJFdu/ebdYvc1x++eVy3XXXBbJ9AAAAwRkQKS2k1s2XjjYDAACwIiA6fvy4PP3007J8+XJTN5SRkZHp/m3btgWqfQAAAMEZEN15552ycuVKGThwoNSpU8e7pAcAAIA1AdGnn34qixYtks6dOwe+RUAoWxGf/Vi3ODdaAgAo7lFm1apVk+rVqxfmoQAAAOEREE2cONEMvfddzwwIVwsjkt1uAgAgGLvMdN2yrVu3Su3atc1cRGXLls22tAcAAEBYB0R9+/YNfEsAAABCKSCaMGFC4FsCAAAQSjVEKjU1VV577TWJi4uTQ4cOebvKfvvtNwkHU6ZMkZiYGImNjXW7KQAAIBgzRN9++6306NFDqlSpIr/88osMGzbMjDr78MMPZefOnTJ37lwJdSNGjDBbWlqaeZ0AACB8FSpDNGbMGBk8eLD8/PPPUr58ee/x3r17y6pVqwLZPgAAgOAMiBITE+Xuu+/OdrxevXqyZ8+eQLQLsNLUTVPdbgIAWKlQAVG5cuVMV1JWP/30k9SqVSsQ7QIAAAjugOiaa66Rxx9/XE6fPm32dS0zrR0aO3as3HDDDYFuIwAAQPAFRDox47Fjx0w26MSJE3LZZZdJkyZNpFKlSvLkk08GvpUAAADBNspMR10tW7ZMVq9eLd98840Jji6++GIz8gwAACDsA6KMjAyZM2eOGWKvQ+61u6xRo0YSFRUlHo/H7AM2Skk9IQmHDprbnRrXcLs5AIDiCog04NH6oU8++URatWolLVq0MMe2bNlihuFrkLRgwYKCXBIIfyviM+93i3OrJQCAQAREmhnSeYaWL18u3bp1y3Tf559/btY400kZb7/99oJcFggrCyOSpZOQIQKAsC2qfuedd+Thhx/OFgyp7t27y7hx4+Stt94KZPsAAACCKyDSJTuuuuoqv/f36tXLFFkDAACEbUCki7jWrl3b7/163+HDhwPRLgAAgOAMiNLT06VMGf9lR6VLl5Y///wzEO0CAAAI3lFmOppMl+7IycmTJwPVLgAAgOAMiAYNGpTnOYwwA3L3wrKfzNfRV1zgdlMAAIUJiGbPnl2Q0wEAAMJ3LTMAAIBwQkAEAACsR0AEFIOEbWfWNMvJhrR5Zsuptihhq//HAQCKDwERAACwHgERAACwXtgHRCkpKdK1a1eJiYmRli1byvvvv+92k4DsVsRLx50zpH7aenMbABDEw+5Dkc6sPXnyZGndurXs2bNH2rZtK71795azzjrL7aYBAIAgEfYZojp16phgSEVFRUnNmjXNmmxAcVgYkez9OjX12zwLrAEAwcH1gGjVqlXSp08fqVu3rpQqVUoWLFiQ7ZwpU6ZIdHS0lC9fXjp06CBr164t1HOtX7/erMfWoEGDALQcAACEC9cDouPHj0urVq1M0JOTefPmyZgxY2TChAmyYcMGc27Pnj1l37593nM0A9S8efNs265du7znaFZIlxWZMWNGibwuAAAQOlyvIerVq5fZ/Jk0aZIMGzZMhgwZYvanT58uixYtklmzZsm4cePMsU2bNuX6HLrobN++fc35l1xySZ7n+i5Sm5aWVsBXBAAAQo3rGaLcnDp1ynRz9ejRw3ssIiLC7CckJOTrGh6PRwYPHizdu3eXgQMH5nl+fHy8VKlSxbvRvQYAQPgL6oDowIEDpuandu3amY7rvo4Yy4/Vq1ebbjetTdKuNd02b97s9/y4uDg5cuSId9Nh+wAAILy53mVW3Lp06SIZGRn5Pr9cuXJmAwAA9gjqDJEOkS9durTs3bs303Hd1yH0QCjwHYLva+qmqa60BwAQYgFRZGSkmUhx+fLl3mOa7dH9Tp06Fetz66g3nd06Nja2WJ8HAAC4z/Uus2PHjkly8pnJ7NT27dvNqLHq1atLw4YNzZD7QYMGSbt27aR9+/Zm1mkdqu+MOisuI0aMMJuOMtPiagAAEL5cD4jWrVsn3bp18+5rAKQ0CJozZ470799f9u/fL+PHjzeF1FoUvXjx4myF1gAAACEbEOnCqzo0PjcjR440GxAOzAKuansFkcNH3G4OACDYa4iAcJGSeiLbmmZ6zMF6ZwDgLgIiPyiqBgDAHgREfmhBdVJSkiQmJrrdFAAAUMwIiAAAgPUIiAAAgPUIiAAAgPUIiAAAgPUIiPxglBmKY6g9w+sBIDgREPnBKDMAAOxBQAQAAKxHQAQAAKxHQAQAAKzn+uKugC0WRiTneHxq6reSEnFCrsloUuJtAgCcQYbID0aZAQBgDwIiPxhlBgCAPQiIAACA9QiIAACA9QiIgCDDbNYAUPIIiAAAgPUIiAAAgPUIiAAAgPUIiPxgHiIAAOxBQOQH8xABAGAPAiIAAGA9AiIgyNc6AwAUPwIiAABgPQIiAABgPQIiAABgvTJuNwBADlbEZz/WLc6NlgCAFcgQAQAA6xEQ+cHEjAAA2IOAyA8mZgQAwB4ERAAAwHoUVQOhisJrAAgYMkQAAMB6BEQAAMB6BEQAAMB6BEQAAMB6BEQAAMB6BEQAAMB6BEQAAMB6BEQAAMB6BER+sJYZAAD2ICDyg7XMAACwBwERAACwHgERAACwHou7Ai5K2HZQUiJOuN0MALAeGSIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiI/pkyZIjExMRIbG+t2UwAAQDEjIPJjxIgRkpSUJImJiW43BQAAFDMCIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIqAYLYxILtL9AICSQUAEAACsR0AEAACsR0AEAACsR0AEAACsV8btBgDIpxXxBT+nW1zhrpufxwFAGCFDBIShhG0H3W4CAIQUAiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGA9AiIAAGC9sA+IUlNTpV27dtK6dWtp3ry5vPrqq243CQAABJmwn6m6UqVKsmrVKqlYsaIcP37cBEXXX3+91KhRw+2mAQCAIBH2GaLSpUubYEidPHlSPB6P2QAAAIImINLsTZ8+faRu3bpSqlQpWbBgQbZzpkyZItHR0VK+fHnp0KGDrF27tsDdZq1atZL69evLgw8+KDVr1gzgKwAAAKHO9YBIu7E0WNGgJyfz5s2TMWPGyIQJE2TDhg3m3J49e8q+ffu85zj1QVm3Xbt2mfurVq0q33zzjWzfvl3efvtt2bt3r9/2aBYpLS0t0wYAAMKb6zVEvXr1Mps/kyZNkmHDhsmQIUPM/vTp02XRokUya9YsGTdunDm2adOmfD1X7dq1TUD15Zdfyo033pjjOfHx8fLYY48V6rUAAIDQ5HqGKDenTp2S9evXS48ePbzHIiIizH5CQkK+rqHZoKNHj5rbR44cMV10TZs29Xt+XFycOc/ZUlJSAvBKAABAMHM9Q5SbAwcOSHp6usns+NL9H374IV/X2LFjh9x1113eYup7771XWrRo4ff8cuXKmQ0AANgjqAOiQGjfvn2+u9SAsLMiPvuxbnFutAQAglpQd5npaDAdNp+1CFr3o6KiivW5tcg7JiZGYmNji/V5AACA+4I6IIqMjJS2bdvK8uXLvccyMjLMfqdOnYr1uUeMGCFJSUmSmJhYrM8DAADc53qX2bFjxyQ5Odm7r0PjtYurevXq0rBhQzPkftCgQWb5De3+mjx5shmq74w6AwAACPmAaN26ddKtWzfvvgZASoOgOXPmSP/+/WX//v0yfvx42bNnj5lzaPHixdkKrQEAAEI2IOratWueS2mMHDnSbAAAANbVELmJomoAAOxBQOQHRdUAANiDgAgAAFiPgAgAAFiPgAgAAFiPgAgAAFiPgMgPRpkBAGAPAiI/GGUGAIA9CIgAAID1CIgAAID1CIgAAID1CIgAAID1CIj8YJQZAAD2ICDyg1FmAADYg4AIAABYj4AIAABYj4AIAABYj4AIAABYj4AIAABYj4AIAABYj4DID+YhAgDAHgREfjAPEQAA9iAgAgAA1ivjdgMAlLAV8QU/p1tc4J4r67Xyc05hFdfrCFT73H4uFB3/X2HznpEhAgAA1iMgAgAA1iMgAgAA1iMgAgAA1iMgAgAA1iMg8oOJGQEAsAcBkR9MzAgAgD0IiAAAgPUIiAAAgPUIiAAAgPUIiAAAgPUIiAAAgPUIiAAAgPUIiAAAgPUIiAAAgPXKuN2AYOfxeMzXtLS0gF/75InTAb8mQt/xjJOSdvyPol3jRNGvkUlhv/9zakPWa+XnnMLKeu1AvY5i+H3gynOh6Pj/Ctr3zPncdj7H81LKk98zLfXrr79KgwYN3G4GAAAohJSUFKlfv36e5xEQ5SEjI0N27dollSpVklKlSgU0ctVAS/+jKleuLDbjvciM9+MvvBd/4b3IjPfjL7wXOb8X+rl99OhRqVu3rkRE5F0hRJdZHvRNzE9kWVj6zWv7N7CD9yIz3o+/8F78hfciM96Pv/BeZH8vqlSpIvlFUTUAALAeAREAALAeAZFLypUrJxMmTDBfbcd7kRnvx194L/7Ce5EZ78dfeC8C815QVA0AAKxHhggAAFiPgAgAAFiPgAgAAFiPgAgAAFiPgMglU6ZMkejoaClfvrx06NBB1q5dKzZatWqV9OnTx8wkqjOBL1iwQGwUHx8vsbGxZmbVc845R/r27Ss//vij2GratGnSsmVL7+RqnTp1kk8//dTtZgWFp59+2vys3H///WKbRx991Lx23+3CCy8UW/32229y2223SY0aNaRChQrSokULWbdundgoOjo62/eGbiNGjMj3NQiIXDBv3jwZM2aMGRq4YcMGadWqlfTs2VP27dsntjl+/Lh5/Rog2mzlypXmB/frr7+WZcuWyenTp+XKK68074+NdHZ4/eBfv369+QXfvXt3ufbaa+X7778XmyUmJsorr7xigkVbXXTRRbJ7927v9n//939io8OHD0vnzp2lbNmy5o+FpKQkef7556VatWpi68/Gbp/vC/09qvr165f/i+iwe5Ss9u3be0aMGOHdT09P99StW9cTHx/vsZl+O3700UduNyMo7Nu3z7wfK1eudLspQaNatWqe1157zWOro0ePes4//3zPsmXLPJdddpln1KhRHttMmDDB06pVK7ebERTGjh3r6dKli9vNCFr683Heeed5MjIy8v0YMkQl7NSpU+av3h49emRaL033ExISXG0bgseRI0fM1+rVq4vt0tPT5d133zXZMu06s5VmEK+++upMvzts9PPPP5su9saNG8utt94qO3fuFBstXLhQ2rVrZzIg2s3epk0befXVV91uVtB8zr755psydOjQAi3KTkBUwg4cOGB+wdeuXTvTcd3fs2ePa+1C8MjIyDD1IZoOb968udhq8+bNcvbZZ5sZZ//f//t/8tFHH0lMTIzYSANC7V7XWjObab3lnDlzZPHixabObPv27XLppZeaFc1ts23bNvMenH/++bJkyRK555575L777pPXX39dbLdgwQJJTU2VwYMHF+hxrHYPBGEm4LvvvrO2NsLRtGlT2bRpk8mWffDBBzJo0CBTa2VbUJSSkiKjRo0yNRE6CMNmvXr18t7WOioNkM4991x577335I477hDb/nDSDNFTTz1l9jVDpL83pk+fbn5WbDZz5kzzvaKZxIIgQ1TCatasKaVLl5a9e/dmOq77UVFRrrULwWHkyJHy8ccfy4oVK0xhsc0iIyOlSZMm0rZtW5MZ0eL7F198UWyjXew64OLiiy+WMmXKmE0Dw5deesnc1oyzrapWrSoXXHCBJCcni23q1KmT7Y+DZs2aWduF6NixY4d89tlncuedd0pBERC58Etef8EvX748U6Sv+zbXR9hOa8o1GNJuoc8//1waNWrkdpOCjv6cnDx5Umxz+eWXm+5DzZY5m2YGtH5Gb+sfWLY6duyYbN261QQHttEu9axTc/z0008mY2az2bNnm5oqrbcrKLrMXKBD7jWlqb/U2rdvL5MnTzYFo0OGDBEbf6H5/nWnNQH6S16LiRs2bCg2dZO9/fbb8p///MfMReTUk1WpUsXML2KbuLg4k/LW7wGtD9H35osvvjC1ErbR74estWRnnXWWmXvGthqzBx54wMxbph/6u3btMlOXaEA4YMAAsc3o0aPlkksuMV1mN910k5nLbsaMGWaz+Y+m2bNnm89XzZ4WWLGOe4Nf//73vz0NGzb0REZGmmH4X3/9tcdGK1asMMPLs26DBg3y2CSn90C32bNne2w0dOhQz7nnnmt+PmrVquW5/PLLPUuXLnW7WUHD1mH3/fv399SpU8d8X9SrV8/sJycne2z13//+19O8eXNPuXLlPBdeeKFnxowZHpstWbLE/N788ccfC/X4UvpPcURqAAAAoYIaIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIgAAYD0CIgBB75dffpFSpUqZZV2ULuOh+6mpqRLs5syZYxYhBRDcCIgAFMjgwYOlb9++rrZB13DavXu3WeutuMyfP9+sk/Xbb7/leP/5559v1iUEEB4IiACEnMjISImKijJZouJyzTXXmAVUX3/99Wz3rVq1yixKfMcddxTb8wMoWQREAAJq5cqV0r59eylXrpzUqVNHxo0bJ3/++af3/sWLF0uXLl1MN5IGHH/7299k69atma6hK3e3adNGypcvL+3atZONGzdmuj9rl5nTLbVkyRJp1qyZnH322XLVVVeZLJJD23Dfffd5n3fs2LFmVWx/2a6yZcvKwIEDzbWzmjVrlnTo0EEuuugimTRpkrRo0cKsQN+gQQMZPny4HDt2rEAZtvvvv1+6du2aadXu+Ph4adSokVSoUEFatWolH3zwgff+w4cPy6233iq1atUy92u2Slf5BlB4BEQAAka7l3r37i2xsbHyzTffyLRp02TmzJnyxBNPeM85fvy46Wpat26dLF++XCIiIuS6664zQYDSYEKDpJiYGFm/fr08+uij8sADD+T53L///rs899xz8sYbb5gMzs6dOzM97plnnpG33nrLBA6rV6+WtLQ0WbBgQa7X1AzQzz//bK7n0PZpcOJkh7T9L730knz//fcmm/T555/LQw89JEWhwdDcuXNl+vTp5rqjR4+W2267zQSb6p///KckJSXJp59+Klu2bDHvc82aNYv0nID1/lr4HgDyNmjQIM+1116b430PP/ywp2nTpp6MjAzvsSlTpnjOPvtsT3p6eo6P2b9/v0d/FW3evNnsv/LKK54aNWp4Tpw44T1n2rRp5pyNGzea/RUrVpj9w4cPm/3Zs2eb/eTk5EzPW7t2be++3v7Xv/7l3f/zzz89DRs29PtaHB07djSv2TFz5kxPxYoVPWlpaTme//7775v2O7RtVapUyfX9GzVqlOeyyy4zt//44w9z/a+++irTOXfccYdnwIAB5nafPn08Q4YMybXdAAqGDBGAgNFsRadOnTLV9nTu3NlkVX799VezrxmXAQMGSOPGjaVy5coSHR1tjmtGx7lGy5YtTXeZQ6+Zl4oVK8p5553n3dfuun379pnbR44ckb1795quPIcWTLdt2zbP6w4dOtRkhI4ePertLuvXr59UqlTJ7H/22Wdy+eWXS7169cwx7WY7ePCgyVgVhtYm6WOvuOIK0/XnbJoxcroW77nnHnn33XeldevWJhv11VdfFeq5APyFgAhAierTp48cOnRIXn31VVmzZo3Z1KlTp4p0Xa358aVBmcejiaOiufnmm83X9957zwRz2t3mdJfpdADavacBnI5K0y6+KVOm5Pp6tIsta7tOnz7tve3UHy1atMhMM+Bs2kXm1BH16tVLduzYYbrSdu3aZQKy/HQrAvCPgAhAwGhBc0JCQqYPfA0gNHNSv359kzn58ccf5ZFHHjEf4nq+Fghnvca3334rf/zxh/fY119/XaR26fD82rVrS2JiovdYenq6bNiwIc/Hats1I6SZIa0/uuCCC+TSSy8192kApLVPzz//vHTs2NHcpwFKbrQQ2rfYWznzKymtndKCdM2YNWnSJNOmRdu+19Gi8DfffFMmT54sM2bMKNB7AiCzMln2ASBP2gXl+yGudOSWjrDSD+d7771XRo4caYKfCRMmmCJqzYxUq1bNnKcf3tqlpR/6OgrN1y233CL/+Mc/ZNiwYRIXF2eyMFosXVTaJi1W1sDiwgsvlH//+98mGMvP0H3NCGkQpN15OjrNodfS7I5eSzNfGvxpIXRuunfvLv/6179MF5h2BWpA891335lRdU4Aptkezf5osKUj8vT91mtrF6MGQePHjzfdfTrK7eTJk/Lxxx+bQBJAERSw5giA5bQoWH91ZN206Fd98cUXntjYWE9kZKQnKirKM3bsWM/p06e9j1+2bJmnWbNmnnLlynlatmxpztfHf/TRR95zEhISPK1atTLXaN26tWf+/Pl5FlX7Fi4rvZ7vrzhtw8iRIz2VK1f2VKtWzbSrX79+nptvvjlfr1uLxUuXLu3ZtWtXpuOTJk3y1KlTx1OhQgVPz549PXPnzs2zbePHjzdF3np89OjRpl1OUbXSovTJkyeb5yxbtqynVq1a5torV64090+cONG8h/qc1atXN0Xa27Zty9frAJCzUvpPUQIqAAhFmn3RrMpNN90kEydOdLs5AFxGlxkAK2gR8tKlS+Wyyy4z3Uwvv/yybN++3XTRAQBF1QCsoDVMOuu0ThqpUwFs3rzZDJmn9gaAossMAABYjwwRAACwHgERAACwHgERAACwHgERAACwHgERAACwHgERAACwHgERAACwHgERAAAQ2/1/r55+ytmWW90AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Histograms of loadings\n",
-    "plot_loading_predictions(\n",
-    "    loadings_pred[not removed_lines],\n",
-    "    loadings_dc[not removed_lines],\n",
-    "    loadings[not removed_lines],\n",
-    "    prediction_dir,\n",
-    "    label_plot,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create df from loadings\n",
-    "loadings_df = pd.DataFrame(loadings)\n",
-    "loadings_df.columns = [f\"branch_{i}\" for i in range(loadings_df.shape[1])]\n",
-    "\n",
-    "loadings_pred_df = pd.DataFrame(loadings_pred)\n",
-    "loadings_pred_df.columns = [f\"branch_{i}\" for i in range(loadings_pred_df.shape[1])]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loadings_df[\"scenario\"] = pf_node[\"scenario\"].unique()\n",
-    "loadings_pred_df[\"scenario\"] = pf_node[\"scenario\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# make bar plot of wrongly classified loadings for different bins\n",
-    "bins = np.arange(0, 2.2, 0.2)\n",
-    "mse_pred = []\n",
-    "mse_dc = []\n",
-    "for i in range(len(bins) - 1):\n",
-    "    idx_in_bins = (loadings[not removed_lines] > bins[i]) & (\n",
-    "        loadings[not removed_lines] < bins[i + 1]\n",
-    "    )\n",
-    "    mse_pred.append(\n",
-    "        np.mean(\n",
-    "            (\n",
-    "                loadings_pred[not removed_lines][idx_in_bins]\n",
-    "                - loadings[not removed_lines][idx_in_bins]\n",
-    "            )\n",
-    "            ** 2\n",
-    "        )\n",
-    "    )\n",
-    "    mse_dc.append(\n",
-    "        np.mean(\n",
-    "            (\n",
-    "                loadings_dc[not removed_lines][idx_in_bins]\n",
-    "                - loadings[not removed_lines][idx_in_bins]\n",
-    "            )\n",
-    "            ** 2\n",
-    "        )\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "# labels\n",
-    "labels = [f\"{bins[i]:.1f}-{bins[i + 1]:.1f}\" for i in range(len(bins) - 1)]\n",
-    "plt.bar(labels, mse_pred, label=label_plot, alpha=0.5)\n",
-    "plt.bar(labels, mse_dc, label=\"DC\", alpha=0.5)\n",
-    "plt.legend()\n",
-    "plt.xlabel(\"Loadings\")\n",
-    "plt.ylabel(\"MSE\")\n",
-    "# y log scale\n",
-    "plt.yscale(\"log\")\n",
-    "# rotate x labels\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.savefig(f\"loading_mse_{prediction_dir}.png\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Voltage violations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# merge bus_params[\"vmax\"] and bus_params[\"vmin\"] with pf_node on bus_idx\n",
-    "pf_node = pd.merge(pf_node, bus_params[[\"bus\", \"vmax\", \"vmin\"]], on=\"bus\", how=\"left\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3200x2400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_mass_correlation_density_voltage(pf_node, prediction_dir, label_plot)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/notebooks/Tutorial_contingency_analysis.ipynb b/examples/notebooks/Tutorial_contingency_analysis.ipynb
new file mode 100644
index 0000000..496085a
--- /dev/null
+++ b/examples/notebooks/Tutorial_contingency_analysis.ipynb
@@ -0,0 +1,450 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "01e097ef",
+   "metadata": {},
+   "source": [
+    "# Contingency Analysis in Transmission Grids\n",
+    "\n",
+    "Contingency analysis is a critical process in power system operations used to assess the impact of potential failures (e.g., line outages) on grid stability and reliability. It helps operators prepare for unexpected events by simulating scenarios such as N-1 or N-2 contingencies, where one or more components are removed from service. This analysis ensures that the grid can continue to operate within safe limits even under stressed conditions.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Dataset Generation and Model Evaluation\n",
+    "\n",
+    "The dataset used in this study originates from the Texas transmission grid, which includes approximately 2,000 nodes. Using the contingency mode of the `gridfm-datakit`, we simulated N-2 contingencies by removing up to two transmission lines at a time. For each scenario, we first solved the optimal power flow (OPF) problem to determine the generation dispatch. Then, we applied the contingency by removing lines and re-solved the power flow to observe the resulting grid state.\n",
+    "\n",
+    "This process generated around 100,000 unique scenarios. Our model, **GENCO**, was trained on this dataset to predict power flow outcomes. For demonstration purposes, we selected a subsample of 880 scenarios. The `gridfm-datakit` also computed DC power flow results, enabling a comparison between GENCO predictions and traditional DC power flow estimates, specifically in terms of line loading accuracy.\n",
+    "\n",
+    "All predictions are benchmarked against the ground truth obtained from AC power flow simulations. Additionally, we analyze bus voltage violations, which GENCO can predict but are not captured by the DC solver, highlighting GENCO’s enhanced capabilities in modeling grid behavior.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc1e73b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Only run this cell if running on Colab.\n",
+    "import sys\n",
+    "\n",
+    "if \"google.colab\" in sys.modules:\n",
+    "    try:\n",
+    "        #!git clone https://github.com/gridfm/gridfm-graphkit.git   CHANGE IF WE PUT THIS NOTEBOOK ON MAIN\n",
+    "        !git clone -b normalization_fixed_eval_predict https://github.com/gridfm/gridfm-graphkit.git\n",
+    "        !pip install ./gridfm-graphkit\n",
+    "    except Exception as e:\n",
+    "        print(f\"Failed to start Google Collab setup, due to {e}\")\n",
+    "\n",
+    "# If running on Colab you will be asked to restart the session. This is normal and you should do so.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e999efac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%cd gridfm-graphkit/examples/notebooks/\n",
+    "%pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5ae9b9a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from contingency_utils import *\n",
+    "from pathlib import Path\n",
+    "sys.path.append(\"../\")\n",
+    "\n",
+    "if 'google.colab' in sys.modules:\n",
+    "  import gdown\n",
+    "  from google.colab import output\n",
+    "  output.enable_custom_widget_manager()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f99fcb6",
+   "metadata": {},
+   "source": [
+    "## Load Data\n",
+    "\n",
+    "We load both the ground truth and predicted values of the power flow solution. The predictions are generated using the `gridfm-graphkit` CLI:\n",
+    "\n",
+    "```bash\n",
+    "gridfm-graphkit predict ...\n",
+    "```\n",
+    "\n",
+    "We then merge the datasets using `scenario` and `bus` as keys, allowing us to align the predicted and actual values for each grid state and bus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d4ee1161",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_pred_folder = \"../data/contingency/\"\n",
+    "prediction_label = \"predictionsv1\"\n",
+    "label_plot = \"GENCO\"\n",
+    "data_path = Path(\"../data/contingency/\")\n",
+    "data_path.mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "172672e7",
+   "metadata": {},
+   "source": [
+    "Download the sample data provided for this tutorial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6a18700",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if 'google.colab' in sys.modules:\n",
+    "  try:\n",
+    "    !gdown 19gOdmVHou4NTbIch7tjtjynQpQHg1sLG -O ../data/contingency/texas_nminus2_small.zip\n",
+    "  except:\n",
+    "    print(f\"Download failed via g.down!\")\n",
+    "    # full data path\n",
+    "    # https://drive.google.com/file/d/19gOdmVHou4NTbIch7tjtjynQpQHg1sLG/view?usp=sharing\n",
+    "\n",
+    "!unzip ../data/contingency/texas_nminus2_small.zip -d ../data/contingency/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6953a73a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = pd.read_parquet(os.path.join(root_pred_folder, \"filtered_preds_light2.parquet\"))\n",
+    "bus_data = pd.read_parquet(os.path.join(root_pred_folder, \"filtered_bus_data_light2.parquet\"))\n",
+    "branch_data = pd.read_parquet(os.path.join(root_pred_folder, \"filtered_branch_data_light2.parquet\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c688121f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create one df for GT and preds\n",
+    "pf_node = preds.merge(bus_data, on=[\"scenario\", \"bus\"], how=\"left\")\n",
+    "pf_node.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9676c904",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build GENCO/DC state variants used to compute comparable branch flows\n",
+    "pf_node[\"Vm_pred_corrected\"] = pf_node[\"Vm_pred\"].astype(np.float64)\n",
+    "pf_node[\"Va_pred_corrected\"] = pf_node[\"Va_pred\"]\n",
+    "pf_node[\"Vm_dc_corrected\"] = np.ones(len(pf_node), dtype=np.float64)\n",
+    "pf_node[\"Va_dc_corrected\"] = pf_node[\"Va_dc\"]\n",
+    "# Branch-power function expects degree inputs\n",
+    "pf_node['Va_pred_corrected'] = np.rad2deg(pf_node['Va_pred_corrected'])\n",
+    "pf_node"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4581c81d",
+   "metadata": {},
+   "source": [
+    "## Compute branch current and line loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2cad79c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute per-branch loading for AC ground truth, GENCO prediction, and DC baseline.\n",
+    "rated_branch_data = branch_data[branch_data[\"rate_a\"] > 0].copy()\n",
+    "# Stress-test: reducing thermal limits increases normalized loading values.\n",
+    "rate_a = rated_branch_data[\"rate_a\"].to_numpy(dtype=np.float64) / 1.25\n",
+    "\n",
+    "loadings_map = {}\n",
+    "for flag in (\"gt\", \"pred\", \"dc\"):\n",
+    "    pf, qf, pt, qt = compute_branch_powers_vectorized(\n",
+    "        rated_branch_data,\n",
+    "        pf_node,\n",
+    "        sn_mva=100.0,\n",
+    "        flag=flag,\n",
+    "    )\n",
+    "    s_from = np.hypot(pf, qf)\n",
+    "    s_to = np.hypot(pt, qt)\n",
+    "    loadings_map[flag] = (np.maximum(s_from, s_to) / rate_a).flatten()\n",
+    "\n",
+    "loadings = loadings_map[\"gt\"]\n",
+    "loadings_genco = loadings_map[\"pred\"]\n",
+    "loadings_dc = loadings_map[\"dc\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1493bde4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loadings = loadings.flatten()\n",
+    "loadings_pred = loadings_genco.flatten()\n",
+    "loadings_dc = loadings_dc.flatten()\n",
+    "\n",
+    "overloadings_mask = (loadings > 1.0)\n",
+    "overloadings_pred_mask = (loadings_pred > 0.988)\n",
+    "overloadings_dc_mask = (loadings_dc > 0.944)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4fa3d9f",
+   "metadata": {},
+   "source": [
+    "## Compute metrics for overloading classification for GridFM and DC PF\n",
+    "- Below are the results of GENCO for overloading classification (HGNS version)\n",
+    "- Ground truth is AC PF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7b8f1a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TP_genco, FP_genco, TN_genco, FN_genco = compute_cm_metrics(\n",
+    "    overloadings_mask, overloadings_pred_mask, prediction_label, label_plot\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2802d70",
+   "metadata": {},
+   "source": [
+    "- Below are the results of DC PF for overloading classification\n",
+    "- Ground truth is AC PF.\n",
+    "- We use a threshold of 0.95 to make sure we identify all overloads"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20046615",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TP_dc, FP_dc, TN_dc, FN_dc = compute_cm_metrics(\n",
+    "    overloadings_mask, overloadings_dc_mask, \"DC\", label_plot\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "278df625",
+   "metadata": {},
+   "source": [
+    "## Histogram of true line loadings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f05b288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(loadings, bins=100)\n",
+    "plt.xlabel(\"Line Loadings\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Line loadings\")\n",
+    "# log scale\n",
+    "plt.savefig(f\"loadings_histogram_{prediction_label}.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "533da292",
+   "metadata": {},
+   "source": [
+    "## Predicted vs True line loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cf5e1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_vals = loadings\n",
+    "gfm_vals = loadings_pred\n",
+    "dc_vals = loadings_dc\n",
+    "\n",
+    "plot_mass_correlation_density(true_vals, gfm_vals, prediction_label, label_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b600ca8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_mass_correlation_density(true_vals, dc_vals, \"DC\", \"DC Solver\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "129775d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_cm(TN_genco, FP_genco, FN_genco, TP_genco, prediction_label, label_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "129775d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_cm(TN_dc, FP_dc, FN_dc, TP_dc, \"DC\", \"DC Solver\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b721ed3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Histograms of loadings\n",
+    "plot_loading_predictions(\n",
+    "    loadings_pred,\n",
+    "    loadings_dc,\n",
+    "    loadings,\n",
+    "    prediction_label,\n",
+    "    label_plot,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12e67075",
+   "metadata": {},
+   "source": [
+    "## Voltage violations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a9a27c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_mass_correlation_density_voltage(pf_node, prediction_label, label_plot, vm_nominal=1.0,\n",
+    "    vm_dev_threshold=0.075)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5a9a27c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute CM for Vm and Vm predicted\n",
+    "vm_dev_true = np.abs(pf_node[\"Vm\"].to_numpy() - 1.0)\n",
+    "vm_dev_genco = np.abs(pf_node[\"Vm_pred_corrected\"].to_numpy() - 1.0)\n",
+    "vm_violation_true = vm_dev_true > 0.075\n",
+    "vm_pred_genco = vm_dev_genco > 0.071"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40016bdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TP_genco_vm, FP_genco_vm, TN_genco_vm, FN_genco_vm = compute_cm_metrics(\n",
+    "    vm_violation_true, vm_pred_genco, \"Vm violations\", label_plot\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40016bdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_cm(TN_genco_vm, FP_genco_vm, TP_genco_vm, TP_dc, \"VM\", \"Vm Violation\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d008e8b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/notebooks/contingency_utils.py b/examples/notebooks/contingency_utils.py
new file mode 100644
index 0000000..e14eed5
--- /dev/null
+++ b/examples/notebooks/contingency_utils.py
@@ -0,0 +1,348 @@
+import matplotlib.pyplot as plt
+from matplotlib.colors import LogNorm
+from scipy.stats import pearsonr
+import seaborn as sns
+import numpy as np
+import copy
+import pandas as pd
+from typing import Tuple, Union
+
+# Power flow utils
+def compute_branch_powers_vectorized(
+    branch_df: pd.DataFrame,
+    bus_df: pd.DataFrame,
+    sn_mva: float,
+    flag: str,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Compute branch power flows for all branches in a vectorized fashion.
+
+    Args:
+        branch_df: DataFrame with branch data including Yff, Yft, Ytf, Ytt admittances
+        bus_df: DataFrame with bus data including Vm and Va (or Va_dc for DC mode)
+        dc: If True, use DC power flow (Va_dc, Vm=1.0), else use AC (Va, Vm)
+        sn_mva: System base power in MVA used to scale complex power results
+
+    Returns:
+        Tuple of (pf, qf, pt, qt) power flow arrays in MW/MVAR
+    """
+    scenarios = branch_df["scenario"].to_numpy(dtype=int)
+    from_bus = branch_df["from_bus"].to_numpy(dtype=int)
+    to_bus = branch_df["to_bus"].to_numpy(dtype=int)
+
+    idx_from = pd.MultiIndex.from_arrays(
+        [scenarios, from_bus],
+        names=["scenario", "bus"],
+    )
+    idx_to = pd.MultiIndex.from_arrays([scenarios, to_bus], names=["scenario", "bus"])
+
+    bus_df_indexed = bus_df.set_index(["scenario", "bus"]).copy()
+    if flag=='gt':
+        Va = np.radians(bus_df_indexed["Va"])
+        Vm = bus_df_indexed["Vm"]
+
+    elif flag=='pred':
+        Va = np.radians(bus_df_indexed["Va_pred_corrected"])
+        Vm = bus_df_indexed["Vm_pred_corrected"]
+
+    else:
+        Va = np.radians(bus_df_indexed["Va_dc_corrected"])
+        Vm = bus_df_indexed["Vm_dc_corrected"]
+
+    bus_df_indexed["V"] = Vm * (np.cos(Va) + 1j * np.sin(Va))
+    Vf = bus_df_indexed["V"].loc[idx_from].to_numpy(dtype=np.complex128)
+    Vt = bus_df_indexed["V"].loc[idx_to].to_numpy(dtype=np.complex128)
+
+    Yff = branch_df["Yff_r"].to_numpy(dtype=np.float64) + 1j * branch_df[
+        "Yff_i"
+    ].to_numpy(dtype=np.float64)
+    Yft = branch_df["Yft_r"].to_numpy(dtype=np.float64) + 1j * branch_df[
+        "Yft_i"
+    ].to_numpy(dtype=np.float64)
+    Ytf = branch_df["Ytf_r"].to_numpy(dtype=np.float64) + 1j * branch_df[
+        "Ytf_i"
+    ].to_numpy(dtype=np.float64)
+    Ytt = branch_df["Ytt_r"].to_numpy(dtype=np.float64) + 1j * branch_df[
+        "Ytt_i"
+    ].to_numpy(dtype=np.float64)
+
+    If = Yff * Vf + Yft * Vt
+    It = Ytt * Vt + Ytf * Vf
+
+    Sf = Vf * np.conj(If) * sn_mva
+    St = Vt * np.conj(It) * sn_mva
+
+    pf = np.real(Sf)
+    qf = np.imag(Sf)
+    pt = np.real(St)
+    qt = np.imag(St)
+
+    return pf, qf, pt, qt
+
+# Plotting utils
+def compute_cm_metrics(y_test, y_pred, model_name, label_plot):
+    """
+    Compute confusion matrix (TP,FP,TN,FN) for predicted overleads along with their respective rates and accuracy metric.
+
+    Parameters:
+    - y_pred: predicted overloads
+    - y_test: ground truth overloads
+    - prediction_dir:
+    - label_plot:
+    """
+
+    TP = (y_test & y_pred).sum()
+    FP = ((~y_test) & y_pred).sum()
+    TN = ((~y_test) & (~y_pred)).sum()
+    FN = (y_test & (~y_pred)).sum()
+
+    # accuracy
+    accuracy = (TP + TN) / (TP + FP + TN + FN)
+    print(f"Accuracy: {accuracy:.3f}")
+
+    TPR = TP / (TP + FN)
+    FPR = FP / (FP + TN)
+    TNR = TN / (TN + FP)
+    FNR = FN / (FN + TP)
+    # TODO change text to fit both overloadings and voltage violations
+    print("Confusion Matrix:")
+    print(f"TP: {TP}, FP: {FP}, TN: {TN}, FN: {FN}")
+    print(
+        f"GENCO\nTPR: {TPR:.3f} (percentage of overloadings correctly predicted)\nFPR: {FPR:.3f} (percentage of non-overloadings predicted as overloadings)\nTNR: {TNR:.2f}\nFNR: {FNR:.2f}",
+    )
+    with open(f"metrics_overloading_{model_name}.txt", "w") as f:
+        f.write(f"Accuracy: {accuracy:.3f}\n")
+        f.write("Confusion Matrix:\n")
+        f.write(f"TP: {TP}, FP: {FP}, TN: {TN}, FN: {FN}\n")
+        f.write(f"{label_plot} Metrics:\n")
+        f.write(f"TPR: {TPR:.5f} (percentage of overloadings correctly predicted)\n")
+        f.write(
+            f"FPR: {FPR:.5f} (percentage of non-overloadings predicted as overloadings)\n",
+        )
+        f.write(f"TNR: {TNR:.5f}\n")
+        f.write(f"FNR: {FNR:.5f}\n")
+    return TP, FP, TN, FN
+
+
+def plot_mass_correlation_density(
+    true_vals,
+    gfm_vals,
+    model_name,
+    label_plot,
+    x_max=2,
+    y_max=3,
+):
+    """
+    TODO docstring
+
+    """
+    # TODO check if these parameters need to be passed by func or default behavior
+    vmin = 1
+    x_min = 0
+    y_min = 0
+    bin_width = 0.01  # consistent bin width for both plots
+
+    # Generate consistent bins
+    x_bins = np.arange(x_min, x_max + bin_width, bin_width)
+    y_bins = np.arange(y_min, y_max + bin_width, bin_width)
+
+    # estimate vmax on mean count of elements across bins
+    counts, _, _ = np.histogram2d(true_vals, gfm_vals, bins=[x_bins, y_bins])
+
+    counts[counts == 0] = np.nan
+    means = np.nanmean(counts)
+    std = np.nanstd(counts)
+    vmax = means + 3 * std
+
+    # Pearson correlations
+    corr_gfm, _ = pearsonr(true_vals, gfm_vals)
+
+    # Create figure with shared x-axis
+    fig, ax1 = plt.subplots(figsize=(9, 7))
+
+    # --- GENCO Mass Correlation ---
+    h1 = ax1.hist2d(
+        true_vals,
+        gfm_vals,
+        bins=[x_bins, y_bins],
+        norm=LogNorm(vmin=vmin, vmax=vmax),
+        cmap="inferno",
+    )
+    ax1.axvline(1, color="black", linestyle="--", linewidth=2.0)
+    ax1.axhline(1, color="black", linestyle="--", linewidth=2.0)
+    ax1.plot([0, 5], [0, 5], "k--", linewidth=0.5)
+    ax1.set_xlabel("True Loadings", fontsize=12)
+    ax1.set_ylabel("Predicted Loadings", fontsize=12)
+    ax1.set_title(label_plot, fontsize=14)
+    ax1.text(
+        x_max - 1.5,
+        0.93,
+        f"r = {corr_gfm:.5f}",
+        transform=ax1.transAxes,
+        fontsize=13,
+        weight="bold",
+    )
+
+    # Colorbar
+    cbar = fig.colorbar(h1[3], ax=ax1, pad=0.02)
+    cbar.set_label("Number of samples", fontsize=10)
+
+    # Style adjustments
+    ax1.set_xlim(x_min, x_max)
+    ax1.set_ylim(y_min, y_max)
+    ax1.grid(True, linewidth=0.3)
+    ax1.tick_params(axis="both", labelsize=10)
+
+    plt.tight_layout()
+    plt.savefig(f"mass_correlation_density_{model_name}.png", bbox_inches="tight")
+    plt.show()
+
+def plot_cm(TN, FP, FN, TP, model_name, label_plot):
+    """
+    TODO docstring
+    """
+    cm = np.array([[TN, FP], [FN, TP]])
+
+    cm_labels = ["No violation", "Violation"]
+
+    fig_cm, ax_cm = plt.subplots(figsize=(6, 6))
+
+    sns.heatmap(
+        cm,
+        annot=True,
+        fmt="d",
+        cbar=False,
+        square=True,
+        linewidths=0.5,
+        cmap="Blues",
+        xticklabels=cm_labels,
+        yticklabels=cm_labels,
+        ax=ax_cm,
+        annot_kws={"size": 14},
+    )
+
+    ax_cm.set_xlabel("Predicted", fontsize=12)
+    ax_cm.set_ylabel("True", fontsize=12)
+    ax_cm.set_title(f"Confusion Matrix {label_plot}", fontsize=14)
+    ax_cm.tick_params(axis="both", labelsize=12)
+
+    plt.tight_layout()
+    plt.savefig(f"confusion_matrix_overload_{model_name}.png", bbox_inches="tight")
+    plt.show()
+
+def plot_loading_predictions(
+    loadings_pred,
+    loadings_dc,
+    loadings_gt,
+    prediction_dir,
+    label_plot,
+):
+    """
+    TODO docstrings
+    """
+    plt.hist(
+        loadings_pred,
+        alpha=0.5,
+        label=label_plot,
+        density=True,
+        bins=100,
+    )
+    plt.hist(loadings_dc, alpha=0.5, label="DC Solver", density=True, bins=100)
+    plt.hist(loadings_gt, alpha=0.5, label="Ground truth", density=True, bins=100)
+
+    plt.xlabel("Loading Values")
+    plt.ylabel("Density")
+    plt.yscale("log")
+    plt.legend()
+
+    plt.savefig(f"distribution_loading_predictions_{prediction_dir}.png")
+    plt.show()
+
+def plot_mass_correlation_density_voltage(
+    pf_node,
+    prediction_dir,
+    label_plot,
+    x_min=0.85,
+    y_min=0.85,
+    x_max=1.15,
+    y_max=1.15,
+    vm_nominal=1.0,
+    vm_dev_threshold=0.05,
+):
+    """
+    TODO docstrings
+    TODO refactor if we pass by parameters a few more plot deets we can use plot_mass_correlation_density for both
+
+    """
+    # Get the global min and max for color scaling (avoid log(0) by setting min to at least 1)
+    vmin = 1
+    bin_width = 0.001  # consistent bin width for both plots
+
+    # Generate consistent bins
+    x_bins = np.arange(x_min, x_max + bin_width, bin_width)
+    y_bins = np.arange(y_min, y_max + bin_width, bin_width)
+
+    # estimate vmax on mean count of elements across bins
+    counts, _, _ = np.histogram2d(
+        pf_node["Vm"],
+        pf_node["Vm_pred_corrected"],
+        bins=[x_bins, y_bins],
+    )
+
+    counts[counts == 0] = np.nan
+    means = np.nanmean(counts)
+    std = np.nanstd(counts)
+    vmax = means + 3 * std
+
+    # Pearson correlations
+    corr_vm, _ = pearsonr(pf_node["Vm"], pf_node["Vm_pred_corrected"])
+
+    # Create figure with shared x-axis
+    fig, ax1 = plt.subplots(figsize=(8, 6), dpi=400)
+
+    # --- GENCO Mass Correlation ---
+    h1 = ax1.hist2d(
+        pf_node["Vm"],
+        pf_node["Vm_pred_corrected"],
+        bins=[x_bins, y_bins],
+        norm=LogNorm(vmin=vmin, vmax=vmax),
+        cmap="inferno",
+    )
+    vm_lower_limit = vm_nominal - vm_dev_threshold
+    vm_upper_limit = vm_nominal + vm_dev_threshold
+    ax1.axvline(vm_lower_limit, color="black", linestyle="--", linewidth=2.0)
+    ax1.axhline(vm_lower_limit, color="black", linestyle="--", linewidth=2.0)
+    ax1.axvline(vm_upper_limit, color="black", linestyle="--", linewidth=2.0)
+    ax1.axhline(vm_upper_limit, color="black", linestyle="--", linewidth=2.0)
+
+    ax1.plot([0, 5], [0, 5], "k--", linewidth=0.5)
+    ax1.set_xlabel("True Voltage Magnitude", fontsize=12)
+    ax1.set_ylabel("Predicted Voltage magnitude", fontsize=12)
+    ax1.set_title(label_plot, fontsize=14)
+    ax1.text(
+        0.5,
+        0.95,
+        f"r = {corr_vm:.5f}",
+        transform=ax1.transAxes,
+        fontsize=13,
+        weight="bold",
+        ha="center",
+        va="top",
+    )
+
+    # Colorbar
+    cbar = fig.colorbar(h1[3], ax=ax1, pad=0.02)
+    cbar.set_label("Number of samples", fontsize=10)
+
+    # Style adjustments
+    ax1.set_xlim(x_min, x_max)
+    ax1.set_ylim(y_min, y_max)
+    ax1.grid(True, linewidth=0.3)
+    ax1.tick_params(axis="both", labelsize=10)
+
+    plt.tight_layout()
+    plt.savefig(
+        f"mass_correlation_density_voltage_{prediction_dir}.png",
+        bbox_inches="tight",
+    )
+    plt.show()
+