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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Investigation of different voltage levels on the Transmission Capacity using Italy and Germany as a case study\n",
+    "\n",
+    "This notebooks aims to investigate transmission capacity lines in `base.nc` from PyPSA-Earth workflow to improve accuracy of power flow modeling based on the conversation from [here](https://github.com/pypsa-meets-earth/documentation/pull/68). In another analysis done [here](https://github.com/pypsa-meets-earth/documentation/blob/main/notebooks/validation/osm_validation_using_at_and_mk.ipynb), it is observed that there might be some inconsistencies when translating power grid data from OSM-extracted dataset `base.csv` to parameters of the power flow model `base.nc`.\n",
+    "\n",
+    "For this analysis, Italy and Germany were chosen to be used as a case study to reveal a reason behind the high discrepancy in terms of line capacity between OSM-extracted data and ENTSOE data, as has been detected [here](https://github.com/pypsa-meets-earth/documentation/pull/68)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gbotemi/miniconda3/envs/pypsa-earth-updated/lib/python3.10/site-packages/pypsa/networkclustering.py:16: UserWarning: The namespace `pypsa.networkclustering` is deprecated and will be removed in PyPSA v0.24. Please use `pypsa.clustering.spatial instead`. \n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pypsa\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import geopandas as gpd\n",
+    "from shapely import wkt\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\", 1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading data files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook uses a prepared `base.nc` network model which should be loaded manually from [here](https://drive.google.com/drive/folders/18dV790r11hHKIwpbyDBaxMV4XbhBFQde?usp=drive_link). After extraction, the files will be into subfolder of the current working folder \"osm_data_validation_AT_MK_IT_DE\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There is a need to extract current (i_nom) of line_types that are used in pypsa-earth for rebasing from pypsa network\n",
+    "n = pypsa.Network()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:pypsa.io:Imported network elec.nc has buses, carriers, generators, lines, links, loads, storage_units, transformers\n"
+     ]
+    }
+   ],
+   "source": [
+    "# A clean entsoe network will be needed for the analysis\n",
+    "# Download the network from here: https://drive.google.com/file/d/16DHvFbNah9LblbXOjIbZ6H0cHmCaYH_U/view?usp=drive_link\n",
+    "entsoe_nc = pypsa.Network(\"elec.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Raw entsoe data is also needed, but needs to undergo preprocessing\n",
+    "\n",
+    "# Download raw entsoe data. Uncomment the next line to download the file\n",
+    "# !wget \"https://github.com/PyPSA/pypsa-eur/raw/master/data/entsoegridkit/lines.csv\" -O \"entsoe_lines_ref.csv\"\n",
+    "\n",
+    "# Load entsoe line.csv with pandas dataframe\n",
+    "entsoe_ref_csv = pd.read_csv(\"entsoe_lines_ref.csv\", delimiter=\",\", quotechar=\"'\")\n",
+    "\n",
+    "# Apply well known text(wkt) to format the geometry column properly\n",
+    "entsoe_ref_csv[\"geometry\"] = entsoe_ref_csv.geometry.apply(wkt.loads)\n",
+    "\n",
+    "# Load the data into a geopandas dataframe and format it using a compatible crs\n",
+    "entsoe_csv = gpd.GeoDataFrame(entsoe_ref_csv, geometry=\"geometry\", crs=\"EPSG:3035\")\n",
+    "\n",
+    "# Convert voltage from kV to V\n",
+    "entsoe_csv[\"voltage\"] = entsoe_csv.voltage * 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Networks paths\n",
+    "de_base_nc_file = f\"osm_data_validation_AT_MK_IT_DE/networks/DE/base.nc\"\n",
+    "it_base_nc_file = f\"osm_data_validation_AT_MK_IT_DE/networks/IT/base.nc\"\n",
+    "\n",
+    "# Resource files paths\n",
+    "de_base_csv_file = f\"osm_data_validation_AT_MK_IT_DE/resources/DE/base_network/all_lines_build_network.csv\"\n",
+    "it_base_csv_file = f\"osm_data_validation_AT_MK_IT_DE/resources/IT/base_network/all_lines_build_network.csv\"\n",
+    "\n",
+    "de_country_shape_file = (\n",
+    "    f\"osm_data_validation_AT_MK_IT_DE/resources/DE/shapes/country_shapes.geojson\"\n",
+    ")\n",
+    "it_country_shape_file = (\n",
+    "    f\"osm_data_validation_AT_MK_IT_DE/resources/IT/shapes/country_shapes.geojson\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:pypsa.io:Imported network base.nc has buses, lines, links, transformers\n",
+      "INFO:pypsa.io:Imported network base.nc has buses, lines, links, transformers\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Loading network files\n",
+    "de_base_nc = pypsa.Network(de_base_nc_file)\n",
+    "it_base_nc = pypsa.Network(it_base_nc_file)\n",
+    "\n",
+    "# Loading base csv file for IT and DE.\n",
+    "# Geometry length in osm_data needs to be reporojected to the same projection as entsoe data\n",
+    "de_base_csv = pd.read_csv(de_base_csv_file)\n",
+    "de_base_csv[\"geometry\"] = de_base_csv.geometry.apply(wkt.loads)\n",
+    "de_base_csv = gpd.GeoDataFrame(de_base_csv, geometry=\"geometry\", crs=\"EPSG:4326\")\n",
+    "de_base_csv[\"reprojected_length\"] = de_base_csv.to_crs(\"EPSG:3035\").length\n",
+    "\n",
+    "it_base_csv = pd.read_csv(it_base_csv_file)\n",
+    "it_base_csv[\"geometry\"] = it_base_csv.geometry.apply(wkt.loads)\n",
+    "it_base_csv = gpd.GeoDataFrame(it_base_csv, geometry=\"geometry\", crs=\"EPSG:4326\")\n",
+    "it_base_csv[\"reprojected_length\"] = it_base_csv.to_crs(\"EPSG:3035\").length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Country shape files will be used as a filter in entsoe data\n",
+    "\n",
+    "de_country_shape = gpd.read_file(de_country_shape_file)\n",
+    "it_country_shape = gpd.read_file(it_country_shape_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_i_nom_by_voltage(\n",
+    "    voltage_value: int, voltage_dict: dict, network: pypsa.Network\n",
+    ") -> int:\n",
+    "    \"\"\"\n",
+    "    Function returns the current corresponding to the voltage_value using line_types in pypsa network\n",
+    "    \"\"\"\n",
+    "\n",
+    "    line_type = max(voltage_dict.values())\n",
+    "\n",
+    "    for idx in sorted(voltage_dict.keys()):\n",
+    "        if (voltage_value) <= idx:\n",
+    "            line_type = voltage_dict.get(idx)\n",
+    "            break\n",
+    "\n",
+    "    return network.line_types.loc[line_type, \"i_nom\"]\n",
+    "\n",
+    "\n",
+    "def calculate_s_nom(\n",
+    "    df: gpd.GeoDataFrame, voltage_dict: dict, network: pypsa.Network\n",
+    ") -> gpd.GeoDataFrame:\n",
+    "    \"\"\"\n",
+    "    Function calculates the limit of the apparent power (s_nom) using the given formula defined from pypsa earth\n",
+    "    https://github.com/pypsa-meets-earth/pypsa-earth/blob/main/scripts/base_network.py#L353-L363\n",
+    "    s_nom(MVA) = sqrt(3) * v_nom(kV) * circuits * i_nom(kA)\n",
+    "    \"\"\"\n",
+    "\n",
+    "    df[\"voltage_kV\"] = df[\"voltage\"] / 1e3  # convert voltage to kV\n",
+    "    df[\"s_nom\"] = (\n",
+    "        (df[\"voltage\"].apply(lambda row: get_i_nom_by_voltage(row, voltage_dict, n)))\n",
+    "        * df[\"voltage_kV\"]\n",
+    "        * np.sqrt(3)\n",
+    "        * df[\"circuits\"]\n",
+    "    )\n",
+    "\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reproducing PyPSA Earth Workflow with Base csv file\n",
+    "\n",
+    "This section aims to reproduce the pypsa-earth workflow(base.nc) with osm-base-csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Accounting for voltage lower than 110kV\n",
+    "u_low = 110"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dictionary storing standard line types used in pypsa-earth workflow\n",
+    "voltage_dict = {\n",
+    "    u_low: \"243-AL1/39-ST1A 110.0\",\n",
+    "    220: \"Al/St 240/40 2-bundle 220.0\",\n",
+    "    300: \"Al/St 240/40 3-bundle 300.0\",\n",
+    "    380: \"Al/St 240/40 4-bundle 380.0\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate s_nom values\n",
+    "de_base_csv_s_nom = calculate_s_nom(de_base_csv, voltage_dict, network=n)\n",
+    "it_base_csv_s_nom = calculate_s_nom(it_base_csv, voltage_dict, network=n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the sum total for s_nom in base_csv and base_nc\n",
+    "sum_de_base_csv_snom = de_base_csv_s_nom.s_nom.sum()\n",
+    "sum_de_base_nc_snom = de_base_nc.lines.s_nom.sum()\n",
+    "\n",
+    "sum_it_base_csv_snom = it_base_csv_s_nom.s_nom.sum()\n",
+    "sum_it_base_nc_snom = it_base_nc.lines.s_nom.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XzZs3TydPnvR6/PTp0/ruu+/UsmVLrytA+vv769FHH9X+/fu1fft2r3kyeg1kpE2bNl73W7duLen/Xztz5syRy+XSI488oosXL3pu+fPnV4UKFbRkyZIM11GzZk0FBwd7TgtesGCB6tatq7vuukvLly/XH3/8oaSkJO3cudPzWjl79qwWLlyoe++9V7ly5fJad5MmTXT27FnPKX9z5sxRuXLlVLFiRa92jRo1ksvlSlVjvXr1FB4e7rmfL18+5c2bN9P7rHXr1l6vtfj4eNWoUcOzzyTp1KlT6tu3r2666SYFBAQoICBAYWFhOn36tLZu3eppV61aNX355Zfq16+flixZojNnznit68cff9S2bds8z9PV++HgwYOpXhNpadq0qfz9/T33r36d7NixQ7t27dJjjz32p8Z2+uSTT1SzZk2FhYUpICBAgYGBGjt2rNe2psho/58+fVqrV6/Wfffd51VLeHi4mjdvnumasvO9XrFiRQUFBalz58768MMPvU5HTDFnzhzlzp1bzZs393qeKlasqPz582fqvXK1tN7fZ8+e9dTlq/fnn/msuv/++9Ot4dVXX1X79u31+uuva8SIEfLzy9q/KF9++aXq1avn+dxPS0afv5LUsWNHnTlzRtOmTfNMGzdunNxut+ezMSuyez9llV112m+KN954Q6tXr051y5cvX7atGwD+DnJMIHb69GlVqFBBb7/99p9eRo8ePfT+++9r6NCh2rZtm2bPnq1q1aplY5UAIE2bNk3t2rXT+++/r+rVqys6Olpt27bVoUOHsrysmJgYr/tut1uSPP9UHzlyRNLloOtqBQsW9DyeIjo6OlW7oKCgVNODgoIkXQ4truXXX3/1jKuSniNHjih//vypBg3OmzevAgICMqwxpZbM1phWhz9//vyeWiRp1apVuvPOOyVJY8aM0bfffqvVq1fr+eefl6RUoUVa+zct/fv319ChQ7Vy5Uo1btxYMTExql+/vtasWSNJOnbsmMws3efryhpTZPQauJaAgIBU81+9L3755ReZmfLly6fAwECv28qVK/Xbb79luJ7g4GCvcfIWLlyohg0bqm7durp06ZKWLVumBQsWSJLnH+4jR47o4sWLGjlyZKr1NmnSRJI86/7ll1+0cePGVO3Cw8NlZqlqvHqbU/ZbZvbZlfvo6mlXPjetW7fW22+/rU6dOmnevHlatWqVVq9erTx58nit59///rf69u2rWbNmqV69eoqOjtY999yjnTt3erZNkp555plU29elSxev/XAtGb1OUsa4yuj9mpYZM2aoVatWKlSokCZOnKgVK1Zo9erV6tixY5qfERnt/2PHjik5OTnd/ZxZ2fleL1GihL766ivlzZtXXbt2VYkSJVSiRAmv8f9++eUXHT9+XEFBQameq0OHDmXqebpaRs+br96ff+az6lqfkxMnTlShQoX00EMPZVhvWjLzXZPR568k3XLLLapatapn7MxLly5p4sSJatGiRZrfjxnJ7v2UVXv37pXb7U5Ve/HixVWlSpVUt8DAwGxbNwD8HeSYq0w2btw41RVUrnT+/Hm98MILmjRpko4fP65y5crpjTfeUN26dSVJW7du1ahRo7Rp06YbcolkAM4VGxur4cOHa/jw4dq3b58+++wz9evXT4cPH870QPWZlfLP1MGDB1P9s3DgwAHFxsZm6/qulidPnlSD918tJiZG3333nczMKxQ7fPiwLl68mO01pgQMV0oJI1P219SpUxUYGKg5c+Z4HaGS3tHH17oC2JUCAgLUu3dv9e7dW8ePH9dXX32l5557To0aNVJSUpKioqLk5+engwcPppo3ZaD87NwfFy9e1JEjR7z+6b56X8TGxsrlcmnZsmWef8avlNa0tNSvX18DBgzQqlWrtH//fjVs2FDh4eGqWrWqFixYoAMHDqhkyZKKi4uTJEVFRXmOoujatWuayyxWrJinxpCQEH3wwQdptsvu11Ba4fWhQ4c8++zEiROaM2eOBg4cqH79+nnanDt3TkePHvWaLzQ0VIMGDdKgQYP0yy+/eI4Wa968ubZt2+apvX///rrvvvvSrCc7+i158uSRpAzfr2mZOHGiihUrpmnTpnm9F/7MYODS5efe5XKlu58zK7vf67Vq1VKtWrV06dIlrVmzRiNHjlTPnj2VL18+PfTQQ54LW6T3OX7lUXHZxZfvz6x+Vl3rc3Lu3Ll68MEHVatWLS1cuFDx8fGZqjtFZr5rMvr8zZUrlySpQ4cO6tKli7Zu3aqffvpJBw8eVIcOHbJUT4rs3k9Z8fPPP2vt2rWqU6eOAgJyzL98AJCtcswRYhnp0KGDvv32W02dOlUbN27UAw88oLvuusvzC+zs2bNVvHhxzZkzR8WKFVPRokXVqVOnVB1XAMhORYoUUbdu3dSwYUOtW7cu25d/xx13SLr8D+uVVq9era1bt2bbpeHT07hxY+3YsUOLFi1Kt039+vV16tSpVP+AfvTRR57Hs9Pvv/+uzz77zGva5MmT5efnp9q1a0u6/A9JQECA1ylmZ86c0YQJE7Ktjty5c6tly5bq2rWrjh49qj179ig0NFS33nqrZsyY4XUUUXJysiZOnKjChQtn6tTMrJg0aZLX/cmTJ0uS5wejZs2aycz0888/p3lEQfny5T3zXusoqwYNGujixYt68cUXVbhwYZUuXdoz/auvvvKctpYiV65cqlevntavX6+EhIQ0150SajRr1ky7du1STExMmu2uvNpbdpgyZYrXqUh79+7V8uXLPfvM5XLJzFKFEe+//36qKxxeKV++fGrfvr0efvhhbd++XX/88YdKlSqlm2++Wd9//32a21alSpVsCVpKliypEiVK6IMPPshykOVyuRQUFOT1j/yhQ4fSvMpkZoSGhqpatWqaMWOG1xFmv//+u2bPnp3p5Vyv97q/v79uvfVWz9VOUz67mzVrpiNHjujSpUtpPk/X4wdXX70/s/uzKj4+3hPq1apVy9M/z6zGjRtr8eLFmTp9WEr78zfFww8/rODgYI0fP17jx49XoUKFPEcRZpUvPtOly6/hTp066eLFi3r22WezffkAkFM44ueCXbt2acqUKdq/f7/n8ORnnnlGc+fO1bhx4/Taa6/pp59+0t69e/XJJ5/oo48+0qVLl9SrVy+1bNnymv/IAUBWnDhxQvXq1VPr1q1VunRphYeHa/Xq1Zo7d266R3/8L0qVKqXOnTtr5MiR8vPzU+PGjbVnzx69+OKLiouLU69evbJ9nVfq2bOnpk2bphYtWqhfv36qVq2azpw5o6VLl6pZs2aqV6+e2rZtq3feeUft2rXTnj17VL58eX3zzTd67bXX1KRJk2uOP/ZnxMTE6KmnntK+fftUsmRJffHFFxozZoyeeuopFSlSRNLl8ZaGDRum1q1bq3Pnzjpy5IiGDh2a6aMt0tO8eXOVK1dOVapUUZ48ebR3714NHz5c8fHxuvnmmyVJQ4YMUcOGDVWvXj0988wzCgoK0rvvvqtNmzZpypQp2Xb0gHT5tNK33npLp06dUtWqVbV8+XK98soraty4sW6//XZJl8cX6ty5szp06KA1a9aodu3aCg0N1cGDB/XNN9+ofPnyeuqppyRJ5cuX14wZMzRq1ChVrlxZfn5+qlKliiSpcuXKioqK0vz5872OtmjQoIFefvllz99XGjFihG6//XbVqlVLTz31lIoWLarff/9dP/74o2bPnu35fu7Zs6emT5+u2rVrq1evXkpISFBycrL27dun+fPnq0+fPrr11luzbb8dPnxY9957rx5//HGdOHFCAwcOVHBwsPr37y9JioiIUO3atfXmm28qNjZWRYsW1dKlSzV27Fjlzp3ba1m33nqrmjVrpoSEBEVFRWnr1q2aMGGCqlev7jli5T//+Y8aN26sRo0aqX379ipUqJCOHj2qrVu3at26dfrkk08kXQ7mSpQooXbt2mns2LFZ3q533nlHzZs312233aZevXqpSJEi2rdvn+bNm5cqOL1Ss2bNNGPGDHXp0kUtW7ZUUlKSXn75ZRUoUCDLwUaKl19+WXfddZcaNmyoPn366NKlS3rjjTcUGhqa6R8rs/O9/t5772nRokVq2rSpihQporNnz3qOSEx53T700EOaNGmSmjRpoh49eqhatWoKDAzU/v37tXjxYrVo0UL33nvvn9of6fHl+zO7P6sKFCigpUuXqlGjRqpdu7YWLFigcuXKZWrewYMH68svv1Tt2rX13HPPqXz58jp+/Ljmzp2r3r17q3Tp0pn6/JUuh2X33nuvxo8fr+PHj+uZZ57J8phmV7ren+n79u3TypUrlZycrBMnTmj9+vX64IMPtHfvXr311ltphnk7d+70jMF4pcKFC/+p06YB4G/LJ0P5X2eSbObMmZ77H3/8sUmy0NBQr1tAQIC1atXKzC5fdUiS11XC1q5da5Js27ZtN3oTAORQZ8+etSeffNISEhIsIiLCQkJCrFSpUjZw4MAsXWI+5WpUV19VL+UKUosXL/ZMu3Tpkr3xxhtWsmRJCwwMtNjYWHvkkUdSXU2qTp06dsstt6RaV3x8vDVt2jTVdEnWtWvXDGs9duyY9ejRw4oUKWKBgYGWN29ea9q0qddn65EjR+zJJ5+0AgUKWEBAgMXHx1v//v3t7NmzGa4zvSttpeyLTz75JNU2LlmyxKpUqWJut9sKFChgzz33XKor+H3wwQdWqlQpc7vdVrx4cRsyZIiNHTvWJNnu3bsz3D8pj115lcm33nrLatSoYbGxsRYUFGRFihSxxx57zPbs2eM137Jly+yOO+6w0NBQCwkJsdtuu81mz57t1SYrr4G0tGvXzkJDQ23jxo1Wt25dCwkJsejoaHvqqae8ruJ55f649dZbPTWVKFHC2rZt63VV0qNHj1rLli0td+7c5nK5Ul0N8N577zVJNmnSJM+08+fPW2hoqPn5+dmxY8dSrXf37t3WsWNHK1SokAUGBlqePHmsRo0a9sorr3i1O3XqlL3wwgtWqlQpCwo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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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75ZZbbNOmTbZ+/XqrV6+etWjRwr3tevXq2fjx483MbOvWrVayZEnz8/OztLQ0MzPr06eP3Xfffe72JUqUsHnz5nkcE6f3ipnZRx99ZHFxcbZ7927bsmWLde7c2WrXrm3nzp0zM7NXXnnFoqKi7PPPP7e9e/famjVrbM6cOe79aNq0qXXu3Nk2btxou3fvtqFDh1p4eLh7vy+W1ResUaOGLVmyxHbt2mX33HOPRUdH25kzZ8zMbMuWLebv72+PPfaYbd261bZv326TJk2yX3/91b2epUuXmr+/v506dSrH7QDXMkI0oBBp0aKF1a1b12PaM888Y23btvWYlpyc7O5QZS1Xs2ZNy8zMdLd58sknrWbNmu7n0dHRduedd3qsp1u3btamTRuPaf/v//0/u+GGG8zMbNeuXSbJ1q9f756flJRkkvIVoo0YMcIqV65sp0+fznG/o6Ki3B2OLM8995w7YPnPf/5jYWFhduLECff8KVOm5ClEu3ifH3roIevTp4/HtDVr1piPj4+dPHnS4uLiLCQkJNfOcn5DtJyOj5nZE088YbfeeqvHtKy2L730knvamTNnrEKFCvbyyy/nup/9+vWzrl27mtn5zqkkS0hIyLFts2bN7MUXX/SYNmvWLIuMjMx1/VmWL19uPj4+7vfdxWrXrp1rGLd582ZzuVy2d+9eMzsfyJUvX94mT57s0W7ixIlWqVIlx1oAANeevPRn2rdvb4899pj7+aBBgzz++NKjR48c+wzFixe3c+fO2ddff22S3L9HTs6ePWvBwcG2ePHiXNtMmzbNqlev7lF3RkaGBQYG2vLly/O8Xkk2aNAgj3arV682SXb06FGP6S1atMj2x6mbbrrJHYLl5O6777YBAwaY2fnjNnToUCtVqpR99913dubMGStevLh9+umnZmY2cuTIbPs0efJk93HMquHifmvWfnz44Yf297//3WrUqGHJycke85944gmPQMzMs38VHx9vRYoUsf3797vnf/fddybJHaAOGTLEOnXqZGZmEyZMsHvuucfq1atnn3zyiZmZVatWzaZMmWJm/9cv/fzzzz226fReycmhQ4dMkn377bdmZvb444/brbfe6nGcsqxcudJCQkKyBVlVq1a1//znPzmuP6sv+M4772Tb96yQ8oEHHrCmTZvmuHyWb775Jl/vc+BawuWcQCHToEEDj+dff/21Vq9ereLFi7sfNWrUkCSP08kbNWrkcU+Jxo0b6/vvv9e5c+dyXXdSUlK2m9o3bdrUvVxSUpKKFi3qsVyNGjVUokSJfO3T1q1b1axZM/n6+mab9+uvvyo5OVmPPPKIxz4+//zz7v1LSkpSnTp1VKxYMY/9y4ucjufMmTM9ttWuXTtlZmZqz549atOmjaKjo1WlShU99NBDev/99/XHH3/ka3/z4uTJkwoICMhx3oX7lnX8L7xP2NSpU9WgQQOVLl1axYsX19tvv639+/dLksLCwtSzZ0+1a9dOnTt31sSJE5WSkuKx/2PHjvXY/0cffVQpKSmO+7l161ZVqFBB1apVy3H+wIED9fzzz6tp06YaPXq0tm3b5p4XGxurGjVquC8tSExM1KFDh3Tvvfd6rCMwMPCyHG8AwF+DU3/m0Ucf1dy5c3Xq1CmdOXNG77//vnr16uWxjpz6DMePH1dycrLq1Kmj1q1bq3bt2vrb3/6mt99+2+MyuEOHDqlv376qVq2aQkNDFRoaquPHj7t/Z/v27evxGyqd/2394YcfFBwc7J4eFhamU6dOufsyTuvNcnG/5VJiYmI8nkdGRurQoUO5tm/ZsqX7FhGJiYlq1aqVmjdvrsTERG3cuFEnT5509wuTkpLUuHFjj9eiadOmOn78uA4cOOBY7+DBg7Vu3TqtWbNGFSpUyPM+ZW07KipKUVFR7mk33HCDSpQo4e4PtWzZUmvWrFFmZqYSExPVsmVLtWzZUomJiUpNTdXu3bvVokULSef7XJJy7Hdd6r0ine9rd+vWTVWqVFFISIj7Nh1Zr1vPnj21detWVa9eXQMHDlR8fLx7XV9//bWOHz+u8PBwj/fMnj17PPrwObnwtc26LDXrtd26datat259yeUDAwMliT4VCqWi3i4AwJUVFBTk8TwzM1OdO3fWyy+/nK3thfd6+DPrNrNsN3O1C+6jlvXvS93w1cfHJ9tyF98IN+uHPCeZmZmSpLfffls333yzx7wiRYpkW3d+5XQ8//GPf2jgwIHZ2lasWFF+fn7avHmzEhISFB8fr1GjRunZZ5/Vxo0bVaJECfn4+GSr58/c+LdUqVL69ttv89w+6zX44IMPNHjwYL366qtq3LixgoOD9corr+irr75yt50xY4YGDhyoZcuWaf78+Xr66ae1YsUKNWrUSJmZmRozZozuvvvubNvILdTLcqnXUZJ69+6tdu3a6ZNPPlF8fLzGjRunV199VY8//rik8wMHzJkzR8OHD9ecOXPUrl07lSpVymMdv/32m0qXLp2nYwIAKHw6d+4sf39/LVy4UP7+/srIyFDXrl3ztKzL5VKRIkW0YsUKrV27VvHx8Zo0aZKeeuopffXVV6pcubJ69uypX3/9VRMmTFB0dLT8/f3VuHFj932yxo4dq2HDhnmsNzMzU/Xr18/xRu5Zv2lO681ycb/lUi7+46TL5XL3q3LSsmVLPfHEE/rhhx+0fft29/1pExMTdezYMdWvX1/BwcGSLt1HvHB6bvW2adNGc+fO1fLly/Xggw/meZ9y2/bF05s3b67ff/9dmzdv1po1a/Tcc88pKipKL774ourWrasyZcqoZs2aks7fc9XlcnmEpU6yttO5c2dFRUXp7bffVrly5ZSZmalatWq5X7d69eppz549+vTTT/XZZ5/p3nvv1W233aaPPvpImZmZioyM9Li3bRanP0hf+Npm1ZL12jr1x6Tz/SlJ9KlQKHEmGlDI1atXT999950qVaqk6667zuNxYcdl/fr1HsutX79e119/vTuIyskNN9ygL774wmPa2rVrVa1aNRUpUkQ1a9bU2bNntWnTJvf8Xbt2eQyznvXjfOHZThcOMiCd/2vamjVrcgybIiIiVL58ef3000/Z9i/rr3033HCDvvnmG/dfEnPa37zKOp4Xb+u6666Tn5+fpPNnf912223617/+pW3btmnv3r1atWqVe38v3Nf09HTt2bMn1+1lrfPCMwKl82dm7dy5M8eA8MJ9O3v2rL7++mv32Ydr1qxRkyZN1K9fP8XGxuq6667L8a+ZsbGxGjFihNauXatatWppzpw57v3ftWtXjvufFYjmJiYmRgcOHNDu3btzbRMVFaW+fftqwYIFGjp0qN5++233vG7duunbb7/V119/rY8++ijHTvX27dsVGxt7yToAANcup/5M0aJF1aNHD82YMUMzZszQ/fff73EmkaQc+wzFixd3nxHlcrnUtGlTjRkzRlu2bJGfn58WLlwo6fzv7MCBA9WhQwf3wEsXDuZUpkwZj99O6fxv6/fff59t3nXXXafQ0NA8rTc3ufUj/oxatWopPDxczz//vOrUqaOQkBC1aNFCiYmJSkhIcJ+5JZ3ve61du9ajn7J27VoFBwerfPnyjtu64447NGfOHPXu3Vvz5s3LV5033HCD9u/f7z4bTDo/yFZaWpo7GAsNDVXdunX1xhtvyOVy6YYbblCzZs20ZcsWLVmyxGNf/Pz8dMMNN2jHjh3ZtnWp98qRI0eUlJSkp59+Wq1bt1bNmjVzDOJCQkJ033336e2339b8+fMVFxen3377TfXq1VNqaqqKFi2a7X1x8R8R8yMmJkYrV668ZJvt27erQoUK/9N2gL8qQjSgkOvfv79+++03PfDAA9qwYYN++uknxcfHq1evXh4dquTkZA0ZMkS7du3S3LlzNWnSJD3xxBOXXPfQoUO1cuVKPffcc9q9e7feffddvfHGG+6/sFavXl233367Hn30UX311Vf6+uuv1bt3b4+/gAUGBqpRo0Z66aWXtGPHDn3++ed6+umnPbYzYMAApaen6/7779emTZv0/fffa9asWdq1a5ck6dlnn9W4ceM0ceJE7d69W99++61mzJih1157TdL58MXHx0ePPPKIduzYoaVLl2r8+PF/6ng++eSTWrdunfr376+tW7fq+++/18cff+w+W2rJkiV6/fXXtXXrVu3bt0/vvfeeMjMzVb16dUnSrbfeqlmzZmnNmjXavn27evToccmgskyZMgoMDNSyZcv0yy+/KC0tTZLUqlUrnThxQt999122ZSZPnqyFCxdq586d6t+/v44ePeq+VOW6667Tpk2btHz5cu3evVvPPPOMNm7c6F52z549GjFihNatW6d9+/YpPj5eu3fvdnc6R40apffee0/PPvusvvvuOyUlJbnPVnPSokULNW/eXF27dtWKFSvcf3ldtmyZJGnQoEFavny59uzZo82bN2vVqlXu7UrnRypt0qSJHnnkEZ09e1ZdunTJto01a9aobdu2jrUAAK5NeenP9O7dW6tWrdKnn36a7VJO6fyIiVl9hk8//VSjR4/WgAED5OPjo6+++kovvviiNm3apP3792vBggX69ddf3b9X1113nWbNmqWkpCR99dVXevDBBx3P/HnwwQdVqlQpdenSRWvWrNGePXuUmJioJ554wn3p459ZryRFR0fL5XJpyZIl+vXXX3X8+PG8HspsXC6XmjdvrtmzZ7tHxYyJidHp06e1cuVKj5Ey+/Xrp+TkZD3++OPauXOn/vvf/2r06NEaMmSI4x/dstx1112aNWuWHn74YX300Ud5rvO2225TTEyMHnzwQW3evFkbNmxQ9+7d1aJFC4/LR1u2bKnZs2erRYsWcrlcKlmypG644QbNnz8/26if7dq1y/aHY+nS75WskVbfeust/fDDD1q1apWGDBnisfy///1vzZs3Tzt37tTu3bv14YcfqmzZsipRooRuu+02NW7cWHfeeaeWL1+uvXv3au3atXr66afdf6DesGGDatSooZ9//jnPx2fEiBHauHGj+vXrp23btmnnzp2aMmWKRyhLfwqFmnduxQbAG1q0aGFPPPFEtum7d++2u+66y0qUKGGBgYFWo0YNGzRokPsmpi1atLB+/fpZ3759LSQkxEqWLGnDhw/3uMlpTjfENzs/6tANN9xgvr6+VrFiRXvllVc85qekpFjHjh3N39/fKlasaO+99162de3YscMaNWpkgYGBVrduXYuPj/cYWMDs/A1O27Zta8WKFbPg4GBr1qyZeyQtM7P333/f6tata35+flayZElr3ry5LViwwD1/3bp1VqdOHfPz87O6deu6R6tyGlggp33esGGDtWnTxooXL25BQUEWExNjL7zwgpmdH2SgRYsWVrJkSQsMDLSYmBj3yJdmZmlpaXbvvfdaSEiIRUVF2cyZMy85sICZ2dtvv21RUVHm4+PjcSPd+++/34YPH+5+nnUz2Tlz5tjNN99sfn5+VrNmTVu5cqW7zalTp6xnz54WGhpqJUqUsMcee8yGDx9uderUMTOz1NRUu/POOy0yMtL8/PwsOjraRo0a5XGD3GXLlrlHWQ0JCbGGDRt6jOR6KUeOHLGHH37YwsPDLSAgwGrVqmVLliwxM7MBAwZY1apVzd/f30qXLm0PPfSQe1TRLJMnTzZJ1r1792zrXrt2rZUoUcL++OOPPNUCALi25KU/k6VZs2bugZAu1KNHD+vSpYuNGjXKwsPDrXjx4ta7d2/3zd137Nhh7dq1s9KlS5u/v79Vq1bNJk2a5F5+8+bN1qBBA/P397frr7/ePvzww1z7ExdKSUmx7t27W6lSpczf39+qVKlijz76qHu0yLys9+L+Q5axY8da2bJlzeVyWY8ePdzH6uI+Y5cuXdzzczNp0iST5P7tzlquSJEi7lqzJCQk2E033WR+fn5WtmxZe/LJJ90jROZWQ077MX/+fAsICLC4uDgzcx5YwMxs3759dscdd1hQUJAFBwfb3/72N0tNTfVYZvHixSbJ3njjDfe0J554wiTZ9u3bPdomJSVZYGCge3RTM+f3ipnZihUrrGbNmubv728xMTGWkJDgsX9vvfWW1a1b14KCgiwkJMRat25tmzdvdi+fnp5ujz/+uJUrV858fX0tKirKHnzwQfegCVkDR+zZs8fM8jZgl9n516ZJkybm7+9vJUqUsHbt2rkHnzh58qSFhITYunXrsr02QGHgMvsfbgYEoFBo2bKl6tatqwkTJni7FOTDt99+q9tuu819M+LC7m9/+5tiY2M1cuRIb5cCAPCCvPZnzEw1atTQP/7xj2xnBvXs2VPHjh3TokWLLl+h+Eu699573be7uJZNnjxZ//3vfz0GOQAKEy7nBIBrVO3atfWvf/1Le/fu9XYpXpeRkaE6depo8ODB3i4FAHAVO3TokF577TX9/PPPevjhh71dDv5CXnnlFfeIqtcyX19fTZo0ydtlAF7D6JwAcA3r0aOHt0twe//99/WPf/wjx3nR0dE53r+toPj7++fpvmwAgMItIiJCpUqV0ltvvaWSJUt6uxz8hURHR7vvgXst69Onj7dLALyKyzkBAFfE77//rl9++SXHeb6+voqOjr7CFQEAAABA3hGiAQAAAAAAAA64JxoAAAAAAADggBANAAAAAAAAcECIBgAAAAAAADggRAMAAAAAAAAcEKIBAAAAAAAADgjRAAAAAAAAAAeEaAAAAAAAAIADQjQAAAAAAADAwf8HXZMFTJ8yw+QAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plots comparison for reproduced workflow vs pypsa-earth workflow\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.bar(\n",
+    "    [\"reproduced result(base_csv)\", \"pypsa-earth workflow(base.nc)\"],\n",
+    "    [sum_de_base_csv_snom, sum_de_base_nc_snom],\n",
+    ")\n",
+    "plt.title(f\"s_nom comparison between base.nc and base_network_csv for DE \")\n",
+    "plt.ylabel(\"MVA\")\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.bar(\n",
+    "    [\"reproduced result(base_csv)\", \"pypsa-earth workflow(base.nc)\"],\n",
+    "    [sum_it_base_csv_snom, sum_it_base_nc_snom],\n",
+    ")\n",
+    "plt.title(f\"s_nom comparison between base.nc and base_network_csv for IT \")\n",
+    "plt.ylabel(\"MVA\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Checking the difference between entsoe.nc(preprocessed) and entsoe.csv(raw)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "v_nom\n",
+       "220    3408\n",
+       "380    2887\n",
+       "300     397\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "entsoe_nc.lines.v_nom.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "voltage\n",
+       "220000    5812\n",
+       "380000    3595\n",
+       "300000     831\n",
+       "132000     633\n",
+       "500000     234\n",
+       "750000      46\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "entsoe_csv.voltage.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating a new column to easily identify DE and IT in entsoe_csv\n",
+    "entsoe_csv[f\"if_de\"] = entsoe_csv[\"geometry\"].apply(\n",
+    "    lambda row: row.within(de_country_shape[\"geometry\"][0])\n",
+    ")\n",
+    "entsoe_csv[f\"if_it\"] = entsoe_csv[\"geometry\"].apply(\n",
+    "    lambda row: row.within(it_country_shape[\"geometry\"][0])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge country data in bus into entsoe_nc lines\n",
+    "entsoe_nc_country = entsoe_nc.lines.merge(\n",
+    "    entsoe_nc.buses[\"country\"].reset_index(), how=\"left\", left_on=\"bus0\", right_on=\"Bus\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filtering for country data in entsoe_nc\n",
+    "entsoe_nc_filtered_de = entsoe_nc_country[entsoe_nc_country.country == \"DE\"]\n",
+    "entsoe_nc_filtered_it = entsoe_nc_country[entsoe_nc_country.country == \"IT\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Capacity Analysis for Germany and Italy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set voltage filterring threshold in V\n",
+    "voltage_threshold = 200000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Voltage levels for DE\n",
+      "[ 60000  63000  65000 110000 150000 155000 220000 225000 300000 320000\n",
+      " 380000 400000 525000 600000]\n",
+      "Voltage levels for IT\n",
+      "[ 60000  66000  70000 110000 120000 130000 132000 135000 150000 200000\n",
+      " 220000 225000 320000 380000 400000 500000]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Voltage levels in updated base_csv\n",
+    "print(\"Voltage levels for DE\")\n",
+    "print(np.sort(de_base_csv_s_nom.voltage.unique()))\n",
+    "print(\"Voltage levels for IT\")\n",
+    "print(np.sort(it_base_csv_s_nom.voltage.unique()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voltages present in the osm_files starts from 60kV because the threshold in the meta data is set to 51kV."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### s_nom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get sum total for s_nom for records above the set threshold\n",
+    "\n",
+    "sum_de_base_csv_snom_200kv = de_base_csv_s_nom[\n",
+    "    de_base_csv_s_nom.voltage > voltage_threshold\n",
+    "].s_nom.sum()\n",
+    "sum_it_base_csv_snom_200kv = it_base_csv_s_nom[\n",
+    "    it_base_csv_s_nom.voltage > voltage_threshold\n",
+    "].s_nom.sum()\n",
+    "\n",
+    "entsoe_s_nom = calculate_s_nom(\n",
+    "    entsoe_csv[entsoe_csv.voltage >= voltage_threshold], voltage_dict, n\n",
+    ")\n",
+    "sum_de_entsoe_snom = entsoe_s_nom[entsoe_s_nom[f\"if_de\"] == True].s_nom.sum()\n",
+    "sum_it_entsoe_snom = entsoe_s_nom[entsoe_s_nom[f\"if_it\"] == True].s_nom.sum()\n",
+    "\n",
+    "sum_entose_nc_de_snom = entsoe_nc_filtered_de.s_nom.sum()\n",
+    "sum_entose_nc_it_snom = entsoe_nc_filtered_it.s_nom.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plots comparison for s_nom between osm_base_network_csv, entsoe_csv and entsoe_nc\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.bar(\n",
+    "    [\"osm_base_csv\", \"entsoe_csv\", \"entsoe_nc\"],\n",
+    "    [sum_de_base_csv_snom_200kv, sum_de_entsoe_snom, sum_entose_nc_de_snom],\n",
+    ")\n",
+    "plt.title(\n",
+    "    f\"s_nom comparison between osm_base_csv, entsoe.nc and entsoe_csv for DE for voltage greater than 200kV\"\n",
+    ")\n",
+    "plt.ylabel(\"MVA\")\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.bar(\n",
+    "    [\"osm_base_csv\", \"entsoe_csv\", \"entsoe_nc\"],\n",
+    "    [sum_it_base_csv_snom_200kv, sum_it_entsoe_snom, sum_entose_nc_it_snom],\n",
+    ")\n",
+    "plt.title(\n",
+    "    f\"s_nom comparison between osm_base_csv, entsoe.nc and entsoe_csv for IT for voltage greater than 200kV\"\n",
+    ")\n",
+    "plt.ylabel(\"MVA\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### TWKm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analysis on the line capacity. TWKm is calculated as `s_nom * length`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Total sum of line capacity in base_csv for lines above the set threshold\n",
+    "de_base_csv_s_nom_200kv = de_base_csv_s_nom[\n",
+    "    de_base_csv_s_nom.voltage > voltage_threshold\n",
+    "]\n",
+    "de_twkm_base_csv = (\n",
+    "    de_base_csv_s_nom_200kv.s_nom * de_base_csv_s_nom_200kv.reprojected_length\n",
+    ").sum() / 1e3\n",
+    "\n",
+    "it_base_csv_s_nom_200kv = it_base_csv_s_nom[\n",
+    "    it_base_csv_s_nom.voltage > voltage_threshold\n",
+    "]\n",
+    "it_twkm_base_csv = (\n",
+    "    it_base_csv_s_nom_200kv.s_nom * it_base_csv_s_nom_200kv.reprojected_length\n",
+    ").sum() / 1e3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Total sum of line capacity in entsoe_csv\n",
+    "de_entsoe_snom_csv = entsoe_s_nom[entsoe_s_nom[f\"if_de\"] == True]\n",
+    "de_twkm_entsoe_csv = (\n",
+    "    (de_entsoe_snom_csv[\"length\"] / 1e3) * (de_entsoe_snom_csv[\"s_nom\"])\n",
+    ").sum()\n",
+    "\n",
+    "it_entsoe_snom_csv = entsoe_s_nom[entsoe_s_nom[f\"if_it\"] == True]\n",
+    "it_twkm_entsoe_csv = (\n",
+    "    (it_entsoe_snom_csv[\"length\"] / 1e3) * (it_entsoe_snom_csv[\"s_nom\"])\n",
+    ").sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Total sum of line capacity in entsoe_nc\n",
+    "de_twkm_entsoe_nc = (\n",
+    "    entsoe_nc_filtered_de[\"length\"] * entsoe_nc_filtered_de[\"s_nom\"]\n",
+    ").sum()\n",
+    "\n",
+    "it_twkm_entsoe_nc = (\n",
+    "    entsoe_nc_filtered_it[\"length\"] * entsoe_nc_filtered_it[\"s_nom\"]\n",
+    ").sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot comparison for line capacity between osm_base_csv, entsoe_csv and entsoe_nc\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.bar(\n",
+    "    [\"osm_base_csv\", \"entsoe_csv\", \"entsoe_nc\"],\n",
+    "    [de_twkm_base_csv, de_twkm_entsoe_csv, de_twkm_entsoe_nc],\n",
+    ")\n",
+    "plt.title(\n",
+    "    f\"TWKM comparison between osm_base_csv, entsoe_csv and entsoe.nc for DE for voltage greater than 200kV\"\n",
+    ")\n",
+    "plt.ylabel(\"TWKm\")\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.bar(\n",
+    "    [\"osm_base_csv\", \"entsoe_csv\", \"entsoe_nc\"],\n",
+    "    [it_twkm_base_csv, it_twkm_entsoe_csv, it_twkm_entsoe_nc],\n",
+    ")\n",
+    "plt.title(\n",
+    "    f\"TWKM comparison between osm_base_csv, entsoe_csv and entsoe.nc for IT for voltage greater than 200kV\"\n",
+    ")\n",
+    "plt.ylabel(\"TWKm\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Auxiliary s_nom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analysis done on s_nom after removing the effect of num_parallel or circuits from s_nom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Removing the effect of num_parallel from s_nom\n",
+    "entsoe_nc_filtered_de[\"s_nom_single\"] = (\n",
+    "    entsoe_nc_filtered_de[\"s_nom\"] / entsoe_nc_filtered_de[\"num_parallel\"]\n",
+    ")\n",
+    "entsoe_nc_filtered_it[\"s_nom_single\"] = (\n",
+    "    entsoe_nc_filtered_it[\"s_nom\"] / entsoe_nc_filtered_it[\"num_parallel\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Removing the effect of circuits from s_nom\n",
+    "de_base_csv_s_nom[\"s_nom_single\"] = (\n",
+    "    de_base_csv_s_nom[\"s_nom\"] / de_base_csv_s_nom[\"circuits\"]\n",
+    ")\n",
+    "it_base_csv_s_nom[\"s_nom_single\"] = (\n",
+    "    it_base_csv_s_nom[\"s_nom\"] / it_base_csv_s_nom[\"circuits\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Removing the effect of circuits from s_nom\n",
+    "entsoe_s_nom[\"s_nom_single\"] = entsoe_s_nom.apply(\n",
+    "    lambda row: row[\"s_nom\"] / row[\"circuits\"], axis=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>s_nom</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>voltage</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>60000</th>\n",
+       "      <td>329.995649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63000</th>\n",
+       "      <td>656.897589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65000</th>\n",
+       "      <td>668.069317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110000</th>\n",
+       "      <td>1084.063977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150000</th>\n",
+       "      <td>670.303663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155000</th>\n",
+       "      <td>692.647118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220000</th>\n",
+       "      <td>3091.328058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225000</th>\n",
+       "      <td>3016.366481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300000</th>\n",
+       "      <td>1340.607325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320000</th>\n",
+       "      <td>2451.396252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380000</th>\n",
+       "      <td>5515.227978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400000</th>\n",
+       "      <td>893.738217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525000</th>\n",
+       "      <td>2346.062819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600000</th>\n",
+       "      <td>2681.214650</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               s_nom\n",
+       "voltage             \n",
+       "60000     329.995649\n",
+       "63000     656.897589\n",
+       "65000     668.069317\n",
+       "110000   1084.063977\n",
+       "150000    670.303663\n",
+       "155000    692.647118\n",
+       "220000   3091.328058\n",
+       "225000   3016.366481\n",
+       "300000   1340.607325\n",
+       "320000   2451.396252\n",
+       "380000   5515.227978\n",
+       "400000    893.738217\n",
+       "525000   2346.062819\n",
+       "600000   2681.214650"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "de_base_csv_s_nom.groupby([\"voltage\"])[[\"s_nom\"]].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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>voltage</th>\n",
+       "      <th>60000</th>\n",
+       "      <th>63000</th>\n",
+       "      <th>65000</th>\n",
+       "      <th>110000</th>\n",
+       "      <th>150000</th>\n",
+       "      <th>155000</th>\n",
+       "      <th>220000</th>\n",
+       "      <th>225000</th>\n",
+       "      <th>300000</th>\n",
+       "      <th>320000</th>\n",
+       "      <th>380000</th>\n",
+       "      <th>400000</th>\n",
+       "      <th>525000</th>\n",
+       "      <th>600000</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>s_nom_single</th>\n",
+       "      <td>268.121465</td>\n",
+       "      <td>281.527538</td>\n",
+       "      <td>290.464920</td>\n",
+       "      <td>491.556019</td>\n",
+       "      <td>670.303663</td>\n",
+       "      <td>692.647118</td>\n",
+       "      <td>983.112038</td>\n",
+       "      <td>1005.455494</td>\n",
+       "      <td>1340.607325</td>\n",
+       "      <td>1429.981147</td>\n",
+       "      <td>1698.102612</td>\n",
+       "      <td>1787.476433</td>\n",
+       "      <td>2346.062819</td>\n",
+       "      <td>2681.21465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>s_nom</th>\n",
+       "      <td>329.995649</td>\n",
+       "      <td>656.897589</td>\n",
+       "      <td>668.069317</td>\n",
+       "      <td>1084.063977</td>\n",
+       "      <td>670.303663</td>\n",
+       "      <td>692.647118</td>\n",
+       "      <td>3091.328058</td>\n",
+       "      <td>3016.366481</td>\n",
+       "      <td>1340.607325</td>\n",
+       "      <td>2451.396252</td>\n",
+       "      <td>5515.227978</td>\n",
+       "      <td>893.738217</td>\n",
+       "      <td>2346.062819</td>\n",
+       "      <td>2681.21465</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "voltage           60000       63000       65000        110000      150000  \\\n",
+       "s_nom_single  268.121465  281.527538  290.464920   491.556019  670.303663   \n",
+       "s_nom         329.995649  656.897589  668.069317  1084.063977  670.303663   \n",
+       "\n",
+       "voltage           155000       220000       225000       300000       320000  \\\n",
+       "s_nom_single  692.647118   983.112038  1005.455494  1340.607325  1429.981147   \n",
+       "s_nom         692.647118  3091.328058  3016.366481  1340.607325  2451.396252   \n",
+       "\n",
+       "voltage            380000       400000       525000      600000  \n",
+       "s_nom_single  1698.102612  1787.476433  2346.062819  2681.21465  \n",
+       "s_nom         5515.227978   893.738217  2346.062819  2681.21465  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Table comparing auxiliary s_nom with s_nom in base_csv for DE\n",
+    "s_nom_single_base_s_nom_de = (\n",
+    "    de_base_csv_s_nom.groupby([\"voltage\"])[[\"s_nom_single\"]]\n",
+    "    .mean()\n",
+    "    .merge(\n",
+    "        de_base_csv_s_nom.groupby([\"voltage\"])[[\"s_nom\"]].mean(),\n",
+    "        left_on=\"voltage\",\n",
+    "        right_on=\"voltage\",\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "s_nom_single_base_s_nom_de.plot(kind=\"bar\", subplots=False)\n",
+    "plt.ylabel(\"MVA\")\n",
+    "plt.title(\"s_nom vs s_nom_single comparison in osm base_csv for DE\")\n",
+    "\n",
+    "s_nom_single_base_s_nom_de.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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>voltage</th>\n",
+       "      <th>220000</th>\n",
+       "      <th>380000</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>s_nom_single</th>\n",
+       "      <td>983.112038</td>\n",
+       "      <td>1698.102612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>s_nom</th>\n",
+       "      <td>1907.129911</td>\n",
+       "      <td>3667.608233</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "voltage            220000       380000\n",
+       "s_nom_single   983.112038  1698.102612\n",
+       "s_nom         1907.129911  3667.608233"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Table comparing auxiliary s_nom with s_nom in entsoe_csv for DE\n",
+    "s_nom_single_entsoe_s_nom_de = (\n",
+    "    entsoe_s_nom[entsoe_s_nom[f\"if_de\"] == True]\n",
+    "    .groupby([\"voltage\"])[[\"s_nom_single\"]]\n",
+    "    .mean()\n",
+    "    .merge(\n",
+    "        entsoe_s_nom[entsoe_s_nom[f\"if_de\"] == True]\n",
+    "        .groupby([\"voltage\"])[[\"s_nom\"]]\n",
+    "        .mean(),\n",
+    "        left_on=\"voltage\",\n",
+    "        right_on=\"voltage\",\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "s_nom_single_entsoe_s_nom_de.plot(kind=\"bar\", subplots=False)\n",
+    "plt.ylabel(\"MVA\")\n",
+    "plt.title(\"s_nom vs s_nom_single comparison in entsoe_csv for DE\")\n",
+    "\n",
+    "s_nom_single_entsoe_s_nom_de.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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>voltage</th>\n",
+       "      <th>60000</th>\n",
+       "      <th>66000</th>\n",
+       "      <th>70000</th>\n",
+       "      <th>110000</th>\n",
+       "      <th>120000</th>\n",
+       "      <th>130000</th>\n",
+       "      <th>132000</th>\n",
+       "      <th>135000</th>\n",
+       "      <th>150000</th>\n",
+       "      <th>200000</th>\n",
+       "      <th>220000</th>\n",
+       "      <th>225000</th>\n",
+       "      <th>320000</th>\n",
+       "      <th>380000</th>\n",
+       "      <th>400000</th>\n",
+       "      <th>500000</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>s_nom_single</th>\n",
+       "      <td>268.121465</td>\n",
+       "      <td>294.933612</td>\n",
+       "      <td>312.808376</td>\n",
+       "      <td>491.556019</td>\n",
+       "      <td>536.242930</td>\n",
+       "      <td>580.929841</td>\n",
+       "      <td>589.867223</td>\n",
+       "      <td>603.273296</td>\n",
+       "      <td>670.303663</td>\n",
+       "      <td>893.738217</td>\n",
+       "      <td>983.112038</td>\n",
+       "      <td>1005.455494</td>\n",
+       "      <td>1429.981147</td>\n",
+       "      <td>1698.102612</td>\n",
+       "      <td>1787.476433</td>\n",
+       "      <td>2234.345542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>s_nom</th>\n",
+       "      <td>287.671989</td>\n",
+       "      <td>294.933612</td>\n",
+       "      <td>312.808376</td>\n",
+       "      <td>491.556019</td>\n",
+       "      <td>643.491516</td>\n",
+       "      <td>580.929841</td>\n",
+       "      <td>660.224880</td>\n",
+       "      <td>603.273296</td>\n",
+       "      <td>727.832135</td>\n",
+       "      <td>1340.607325</td>\n",
+       "      <td>1348.324342</td>\n",
+       "      <td>1005.455494</td>\n",
+       "      <td>2859.962293</td>\n",
+       "      <td>2023.271197</td>\n",
+       "      <td>1787.476433</td>\n",
+       "      <td>2234.345542</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "voltage           60000       66000       70000       110000      120000  \\\n",
+       "s_nom_single  268.121465  294.933612  312.808376  491.556019  536.242930   \n",
+       "s_nom         287.671989  294.933612  312.808376  491.556019  643.491516   \n",
+       "\n",
+       "voltage           130000      132000      135000      150000       200000  \\\n",
+       "s_nom_single  580.929841  589.867223  603.273296  670.303663   893.738217   \n",
+       "s_nom         580.929841  660.224880  603.273296  727.832135  1340.607325   \n",
+       "\n",
+       "voltage            220000       225000       320000       380000       400000  \\\n",
+       "s_nom_single   983.112038  1005.455494  1429.981147  1698.102612  1787.476433   \n",
+       "s_nom         1348.324342  1005.455494  2859.962293  2023.271197  1787.476433   \n",
+       "\n",
+       "voltage            500000  \n",
+       "s_nom_single  2234.345542  \n",
+       "s_nom         2234.345542  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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GLy8vtG/fXmn3+eefY/z48YiLi0PLli3h4uKCrl274tSpUyUu/9++rihlvS5tbW1hY2NjMK2odVkw/VHr8kHNmjVDXFwccnNz0bt3b/j4+CA4OLjU506V5r1aWFgU6quHh8cjl3316lUAQPfu3WFpaWnw+OijjyAiuHXrVrGvv379+iNP3L158yY8PT0Lnc/i7u4OCwuLR27XBdtvabfrot63p6en0hcA+PXXX9GuXTsAwMKFC/HLL7/g0KFDmDx5MgAU+iNT1HZUlIkTJ2LWrFlITExEREQEXF1d0bp1axw+fBgAkJaWBhEpdrt8sI8FHvX5P66Hl1ewzEctr7Sf4+Ns70/6WRelLD//GjVqYNu2bXB3d8fQoUNRo0YN1KhRw+CcsKtXryI9PR1WVlaFvkOpqanFnq/zsLS0NOTl5T3y+1Sa39GIiAh4eXkp/7lNS0vDhg0b0Lt3b5ibm5eqPw961O9pfn4+0tLSDKaX9jtTnhiInpCbmxvmzJmDc+fO4fz585gxYwbWrVtnlLFHCn6oUlJSCs27cuUK3Nzcyrzmw/r27Yt79+5hzZo1xX6J7OzsMG3aNJw4cQKpqamYP38+EhMT0blz5xKX/W9f929UhHX5oC5dumD79u3Q6/XYtWsXfHx8EB0dXeiExH/D1dUVubm5hYJLaUJ7wXqYO3cuDh06VOSjpGBVpUqVQif8F9W/q1evFjpZ9dq1a8jNzS3zz6Ig5D2oYF0UbBdr1qyBpaUlNm3ahJ49eyI8PBwNGzYsdpmlHc/HwsICo0ePxm+//YZbt25h9erVuHjxItq3b4+7d+/C2dkZZmZmxW6XAMp92yytx/kcjbm9P0pZf/4vvvgiNm7cCL1ej8TERISFhWHUqFFYs2YNACgnuxf3/fnyyy9L1W8XFxeYm5s/8vtUmt/Rgj2OcXFxSE9Px6pVq5CdnY2+ffuWqi8Pe9TvqZmZGZydnQ2mV8QxsBiIypCfnx+GDRuGtm3b4rfffivz5bdq1QoAsGLFCoPphw4dQnJyMlq3bl3mNR8WFBSE0NBQLFmypFRfIg8PD/Tp0we9evXCyZMnC+02LevXlVZYWBi0Wm2hdXnp0iXs2LGjXNZlUaytrdG8eXN89NFHAIDff//9iZdZcGns2rVrDaYX/GCXpEmTJnBycsJff/2Fhg0bFvko+N95USIiIvD3338XGiPkQa1bt8bt27cRFxdnMP3bb79V5pelzMxMbNiwwWDaqlWrYGZmhmbNmgH458fawsLCIOhnZWVh+fLlZdYPJycndO/eHUOHDsWtW7dw7tw52NnZITQ0FOvWrTPYI5Ofn48VK1bAx8enXMd8eRz/5nM0xvb+KMb6/M3NzREaGoovvvgCAJS/AZGRkbh58yby8vKK/P4EBgaWqt9arRbNmzfH999/X+q9SiX9jhb853b16tVYunQpwsLCUKtWrVIt92GBgYGoWrUqVq1aZRCI79y5gx9//FG58qyi4zhET0Cv16Nly5aIjo5GrVq14ODggEOHDiE+Ph7dunUr83qBgYEYOHAg5s6dCzMzM0RERODcuXN4++234evri7feeqvMaxalX79+GDRoEK5cuYLw8PBCX+jQ0FBERkaibt26cHZ2RnJyMpYvX/7IL8W/fd2/4eTkhLfffhuTJk1C79690atXL9y8eRPTpk2DjY0NpkyZUqb1SvLOO+/g0qVLaN26NXx8fJCeno7PPvsMlpaWZTLOR4cOHdCkSROMGTMGGRkZaNCgAQ4cOKD8oXrwmP/D7O3tMXfuXMTGxuLWrVvo3r073N3dcf36dRw9ehTXr1/H/Pnzi339qFGjsHbtWnTp0gUTJkxAo0aNkJWVhd27dyMyMhItW7ZE79698cUXXyA2Nhbnzp1DSEgI9u3bh+nTp6Njx44lnn/0b7i6umLw4MG4cOECatasiS1btmDhwoUYPHiwcu5Mp06dMHv2bERHR2PgwIG4efMmZs2apRyO+rc6d+6M4OBgNGzYEFWqVMH58+cxZ84c+Pv7IyAgAAAwY8YMtG3bFi1btsTYsWNhZWWFL7/8EsePH8fq1asr5P+sAZT6czT29v4oZfn5f/XVV9ixYwc6deoEPz8/3Lt3TxmioOD9vvrqq1i5ciU6duyIkSNHolGjRrC0tMSlS5ewc+dOdOnSBS+99FKp+j579mw0bdoUoaGhmDBhAp599llcvXoVGzZswIIFC+Dg4FDq39FatWohLCwMM2bMwMWLF/H111//63VqZmaGjz/+GK+99hoiIyMxaNAgZGdnY+bMmUhPT1eGKKnwyuTUbJW6d++evPnmm1K3bl1xdHQUrVYrgYGBMmXKFLlz506pl1NwtcihQ4cMphecXb9z505lWl5ennz00UdSs2ZNsbS0FDc3N3n99dfl4sWLBq9t3ry51KlTp1Atf39/6dSpU6HpKOKy0+Lo9XrRarUCQBYuXFho/oQJE6Rhw4bi7Ows1tbW8swzz8hbb72lXJZfnH/7ugcVd0VCUVdpiYh88803UrduXbGyshKdTiddunRRLiMtUNxVDI+7jouzadMmiYiIkKpVq4qVlZW4u7tLx44dDS5VLekqsweHWxAp+tL5W7duSd++fcXJyUlsbW2lbdu2kpiYKADks88+K/G1IiK7d++WTp06iYuLi1haWkrVqlWlU6dOxV758aC0tDQZOXKk+Pn5iaWlpbi7u0unTp3kxIkTSpubN2/Km2++KV5eXmJhYSH+/v4yceJEuXfvnsGyitpOC9bNzJkzDaYXtS0UfGa7du2Shg0birW1tXh5ecmkSZMkJyfH4PWLFy+WwMBAZVucMWOGLFq0qND6Kenzfvgqs08++UTCw8PFzc1NrKysxM/PT/r37y/nzp0zeN3evXulVatWYmdnJ1qtVho3blzo6p/H+d0oSnFXmRX1Xpo3by7NmzcvcXkipfscS7O9F/c9Lu49F/ddKOp9lOXnf+DAAXnppZfE399frK2txdXVVZo3by4bNmwwWFZOTo7MmjVL6tWrJzY2NmJvby+1atWSQYMGyalTpx65Xh/0119/SY8ePcTV1VXZhvr06aOs48f5Hf36668FgGi1WtHr9aWqX9JVX3FxcRIaGio2NjZiZ2cnrVu3ll9++cWgTWk/q9LUEynbq8w0Ig8d8CUiVVi1ahVee+01/PLLLwgPDzd1d8pFixYtcOPGDRw/ftzUXSGiCoaHzIhUYPXq1bh8+TJCQkJgZmaGxMREzJw5E82aNVNNGCIiKgkDkRGJSKHh4B9mbm5eYc8JqEie1nWZm5tb4nwzM7MSz+EpKw4ODlizZg3ef/993LlzB15eXujTpw/ef/99o9cmopLl5+cjPz+/xDYWFvxzbWy8ysyIli1bVmjciYcfBYPmUcl27979yHX5b+4hZmyP6vODA1oaU2RkJA4fPoz09HTk5OTgwoUL+PzzzyvsndiNZdeuXTxcRhXOu++++8jfinPnzpm6m5WeSc8hmj9/PubPn6980HXq1ME777yjjJQpIpg2bRq+/vprpKWlKZc01qlTR1lGdnY2xo4di9WrVyMrKwutW7fGl19+aTB4VVpaGkaMGKFcahkVFYW5c+fCycnJqO/v5s2bOHv2bIltAgMD4eDgYNR+VAaZmZlF3pz1QdWrVy9yUDlTKhhwrzhubm6oVq1a+XSGiCqkK1euPPKGvHXr1i1xiAt6ciYNRBs3boS5uTmeffZZAP/sUZk5cyZ+//131KlTBx999BE++OADLF26FDVr1sT777+PPXv24OTJk0qIGDx4MDZu3IilS5fC1dUVY8aMwa1bt3DkyBFlDImIiAhcunRJuaxw4MCBqFatGjZu3GiaN05EREQVSoW7yszFxQUzZ85Ev3794O3tjVGjRmH8+PEA/tkb5OHhgY8++giDBg2CXq9HlSpVsHz5crzyyisA/knavr6+2LJlC9q3b4/k5GTUrl0biYmJCA0NBQBlNNETJ06UelAsIiIiqrwqzFlaeXl5+P7773Hnzh2EhYXh7NmzSE1NVe4nA/z/UU3379+PQYMG4ciRI8jJyTFo4+3tjeDgYOzfvx/t27fHgQMHoNPplDAEAI0bN4ZOp8P+/fuLDUTZ2dnIzs5Wnufn5+PWrVtwdXWtcCfuEhERUdFEBJmZmfD29i7xIhaTB6Jjx44hLCwM9+7dg729PdavX4/atWtj//79AArfiM/Dw0O563JqaiqsrKwK3SPFw8NDuTdNamoq3N3dC9V1d3cv8V5OM2bMwLRp057ovREREVHFcPHixRJvjmvyQBQYGIikpCSkp6fjxx9/RGxsrMGVVw/vjRGRR+6hebhNUe0ftZyJEydi9OjRynO9Xg8/Pz9cvHhRdVfmEBERPa0yMjLg6+v7yAuYTB6IrKyslJOqGzZsiEOHDuGzzz5TzhtKTU2Fl5eX0v7atWvKXiNPT0/cv38faWlpBnuJrl27pgw25+npWeTdja9fv17iXbqtra2LvG+Ro6MjAxEREdFT5lE7UyrcOEQiguzsbFSvXh2enp5ISEhQ5t2/fx+7d+9Wwk6DBg1gaWlp0CYlJQXHjx9X2oSFhUGv1+PXX39V2hw8eBB6vZ4j9BIREREAE+8hmjRpEiIiIuDr64vMzEysWbMGu3btQnx8PDQaDUaNGoXp06cjICAAAQEBmD59OmxtbREdHQ0A0Ol06N+/P8aMGQNXV1e4uLhg7NixCAkJUe40HBQUhA4dOmDAgAFYsGABgH8uu4+MjOQVZkRERATAxIHo6tWriImJQUpKCnQ6HerWrYv4+Hi0bdsWADBu3DhkZWVhyJAhysCMP//8s8FxwE8//RQWFhbo2bOnMjDj0qVLlTGIAGDlypUYMWKEcjVaVFQU5s2bV75vloiIiCqsCjcOUUWVkZEBnU4HvV5f4jlEeXl5yMnJKcee0dPK0tLSILgTEVHZK+3fb5OfVF1ZiAhSU1ORnp5u6q7QU8TJyQmenp4c24qIyMQYiMpIQRhyd3eHra0t/8BRiUQEd+/exbVr1wDA4EpKIiIqfwxEZSAvL08JQxXt5qJUcWm1WgD/DBPh7u7Ow2dERCZU4S67fxoVnDNka2tr4p7Q06Zgm+F5Z0REpsVAVIZ4mIweF7cZIqKKgYGIiIiIVI+BiFShRYsWGDVqVJkuc9euXdBoNLyykIioEuBJ1UZUbcLmcq137sNO5VrvabJu3TpYWlqauhtERFRBMRCRKri4uJi6C0REVIHxkJnK/fDDDwgJCYFWq4WrqyvatGmDO3fulPiaPn36oGvXrpg1axa8vLzg6uqKoUOHGlwplZaWht69e8PZ2Rm2traIiIjAqVOnlPlLly6Fk5MTNm3ahMDAQNja2qJ79+64c+cOli1bhmrVqsHZ2RnDhw9HXl5eqd7Ll19+iYCAANjY2MDDwwPdu3dX5j18yKxatWqYPn06+vXrBwcHB/j5+eHrr782WN7+/ftRv3592NjYoGHDhoiLi4NGo0FSUlKxfdi/fz+aNWsGrVYLX19fjBgx4pHrk4iITI97iFQsJSUFvXr1wscff4yXXnoJmZmZ2Lt3L0pzN5edO3fCy8sLO3fuxOnTp/HKK6+gfv36GDBgAIB/QtOpU6ewYcMGODo6Yvz48ejYsSP++usv5dDV3bt38fnnn2PNmjXIzMxEt27d0K1bNzg5OWHLli04c+YMXn75ZTRt2hSvvPJKif05fPgwRowYgeXLlyM8PBy3bt3C3r17S3zNJ598gvfeew+TJk3CDz/8gMGDB6NZs2aoVasWMjMz0blzZ3Ts2BGrVq3C+fPnH3kO0rFjx9C+fXu89957WLRoEa5fv45hw4Zh2LBhWLJkySPXKRE9JabqHjFfXz79oDLFQKRiKSkpyM3NRbdu3eDv7w8ACAkJKdVrnZ2dMW/ePJibm6NWrVro1KkTtm/fjgEDBihB6JdffkF4eDiAf26w6+vri7i4OPTo0QPAP2PvzJ8/HzVq1AAAdO/eHcuXL8fVq1dhb2+P2rVro2XLlti5c+cjA9GFCxdgZ2eHyMhIODg4wN/fH88991yJr+nYsSOGDBkCABg/fjw+/fRT7Nq1C7Vq1cLKlSuh0WiwcOFC2NjYoHbt2rh8+bIS+Ioyc+ZMREdHK8EpICAAn3/+OZo3b4758+fDxsamVOuWiIjKHw+ZqVi9evXQunVrhISEoEePHli4cCHS0tJK9do6deoYjKzs5eWl3IYiOTkZFhYWCA0NVea7uroiMDAQycnJyjRbW1slDAGAh4cHqlWrBnt7e4NpBcstSdu2beHv749nnnkGMTExWLlyJe7evVvia+rWrav8W6PRwNPTU6l18uRJ1K1b1yDENGrUqMTlHTlyBEuXLoW9vb3yaN++PfLz83H27NlHvgciIjIdBiIVMzc3R0JCAn766SfUrl0bc+fORWBgYKn+eD98xZZGo0F+fj4AFHvITUQMBiIsahklLbckDg4O+O2337B69Wp4eXnhnXfeQb169Uq8JP5R7+HhQRMfdSgxPz8fgwYNQlJSkvI4evQoTp06ZRD8iIio4mEgUjmNRoMmTZpg2rRp+P3332FlZYX169c/0TJr166N3NxcHDx4UJl28+ZN/P333wgKCnrSLhfLwsICbdq0wccff4w//vgD586dw44dO/7VsmrVqoU//vgD2dnZyrTDhw+X+Jrnn38ef/75J5599tlCDysrq3/VDyIiKh8MRCp28OBBTJ8+HYcPH8aFCxewbt06XL9+/YlDS0BAALp06YIBAwZg3759OHr0KF5//XVUrVoVXbp0KaPeG9q0aRM+//xzJCUl4fz58/j222+Rn5+PwMDAf7W86Oho5OfnY+DAgUhOTsbWrVsxa9YsAMXfbmP8+PE4cOAAhg4diqSkJOVcquHDh//r90VEROWDJ1UbUUUfKNHR0RF79uzBnDlzkJGRAX9/f3zyySeIiIh44mUvWbIEI0eORGRkJO7fv49mzZphy5YtRhsc0cnJCevWrcPUqVNx7949BAQEYPXq1ahTp86/Wp6joyM2btyIwYMHo379+ggJCcE777yD6OjoYk+Orlu3Lnbv3o3JkyfjxRdfhIigRo0ajzwhnIiITE8jpbnGmpCRkQGdTge9Xg9HR0eDeffu3cPZs2dRvXp1XklUia1cuRJ9+/aFXq+HVqstk2Vy2yF6CvGy+6dKSX+/H8Q9RETF+Pbbb/HMM8+gatWqOHr0KMaPH4+ePXuWWRgiIqKKg4GICnnwsveH/fTTT3jxxRfLsTf/2Lt3b4mH8m7fvl3mNVNTU/HOO+8gNTUVXl5e6NGjBz744IMyr0NERKbHQESFlHRriqpVq5ZfRx7QsGHDEvtlDOPGjcO4cePKtSYREZkGAxEV8uyzz5q6C4VotdoK2S8iIqoceNk9ERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHi+7N6ZHDe9e5vU4XDwREdG/wT1EREREpHoMRCr3ww8/ICQkBFqtFq6urmjTpg3u3LlT4mv69OmDrl27YtasWfDy8oKrqyuGDh2KnJwcpU1aWhp69+4NZ2dn2NraIiIiAqdOnVLmL126FE5OTti0aRMCAwNha2uL7t27486dO1i2bBmqVasGZ2dnDB8+HHl5eUZ7/0RERAAPmalaSkoKevXqhY8//hgvvfQSMjMzsXfvXojII1+7c+dOeHl5YefOnTh9+jReeeUV1K9fHwMGDADwT2g6deoUNmzYAEdHR4wfPx4dO3bEX3/9BUtLSwDA3bt38fnnn2PNmjXIzMxEt27d0K1bNzg5OWHLli04c+YMXn75ZTRt2hSvvPKKUdcFERGpGwORiqWkpCA3NxfdunWDv78/ACAkJKRUr3V2dsa8efNgbm6OWrVqoVOnTti+fTsGDBigBKFffvkF4eHhAICVK1fC19cXcXFx6NGjBwAgJycH8+fPR40aNQAA3bt3x/Lly3H16lXY29ujdu3aaNmyJXbu3MlARERERsVDZipWr149tG7dGiEhIejRowcWLlyItLS0Ur22Tp06MDc3V557eXnh2rVrAIDk5GRYWFggNDRUme/q6orAwEAkJycr02xtbZUwBAAeHh6oVq0a7O3tDaYVLJeIiMhYGIhUzNzcHAkJCfjpp59Qu3ZtzJ07F4GBgTh79uwjX1tw2KuARqNBfn4+ABR7yE1EoNFoSlxGScslIiIyFgYildNoNGjSpAmmTZuG33//HVZWVli/fv0TLbN27drIzc3FwYMHlWk3b97E33//jaCgoCftMhERUZnjOUQqdvDgQWzfvh3t2rWDu7s7Dh48iOvXrz9xaAkICECXLl0wYMAALFiwAA4ODpgwYQKqVq2KLl26lFHviYiIyg4DkTFV8IESHR0dsWfPHsyZMwcZGRnw9/fHJ598goiIiCde9pIlSzBy5EhERkbi/v37aNasGbZs2VLokBgREVFFoJHSXGNNyMjIgE6ng16vh6Ojo8G8e/fu4ezZs6hevTpsbGxM1EN6GnHbIXoKPeouBBX8P8NqU9Lf7wfxHCIiIiJSPQYiKsTe3r7Yx969e03dPSIiojLHc4iokKSkpGLnVa1atfw6QkREVE4YiKiQZ5991tRdICIiKlc8ZFaGeH46PS5uM0REFQMDURl48GalRI+jYJvhcARERKbFQ2ZlwNzcHE5OTso9t2xtbQ1uUUH0MBHB3bt3ce3aNTg5ORncF46IiMofA1EZ8fT0BADeiJQei5OTk7LtEBGR6TAQlRGNRgMvLy+4u7sjJyfH1N2hp4ClpSX3DBERVRAMRGXM3Nycf+SIiIieMjypmoiIiFSPgYiIiIhUz6SBaMaMGXjhhRfg4OAAd3d3dO3aFSdPnjRo06dPH2g0GoNH48aNDdpkZ2dj+PDhcHNzg52dHaKionDp0iWDNmlpaYiJiYFOp4NOp0NMTAzS09ON/RaJiIjoKWDSQLR7924MHToUiYmJSEhIQG5uLtq1a4c7d+4YtOvQoQNSUlKUx5YtWwzmjxo1CuvXr8eaNWuwb98+3L59G5GRkcjLy1PaREdHIykpCfHx8YiPj0dSUhJiYmLK5X0SERFRxWbSk6rj4+MNni9ZsgTu7u44cuQImjVrpky3trYu9tJkvV6PRYsWYfny5WjTpg0AYMWKFfD19cW2bdvQvn17JCcnIz4+HomJiQgNDQUALFy4EGFhYTh58iQCAwON9A6JiIjoaVChziHS6/UAABcXF4Ppu3btgru7O2rWrIkBAwYYjPVz5MgR5OTkoF27dso0b29vBAcHY//+/QCAAwcOQKfTKWEIABo3bgydTqe0eVh2djYyMjIMHkRERFQ5VZhAJCIYPXo0mjZtiuDgYGV6REQEVq5ciR07duCTTz7BoUOH0KpVK2RnZwMAUlNTYWVlBWdnZ4PleXh4IDU1VWnj7u5eqKa7u7vS5mEzZsxQzjfS6XTw9fUtq7dKREREFUyFGYdo2LBh+OOPP7Bv3z6D6a+88ory7+DgYDRs2BD+/v7YvHkzunXrVuzyRMTg9hlF3Urj4TYPmjhxIkaPHq08z8jIYCgiIiKqpCrEHqLhw4djw4YN2LlzJ3x8fEps6+XlBX9/f5w6dQrAP7fMuH//PtLS0gzaXbt2DR4eHkqbq1evFlrW9evXlTYPs7a2hqOjo8GDiIiIKieTBiIRwbBhw7Bu3Trs2LED1atXf+Rrbt68iYsXL8LLywsA0KBBA1haWiIhIUFpk5KSguPHjyM8PBwAEBYWBr1ej19//VVpc/DgQej1eqUNERERqZdJD5kNHToUq1atwn//+184ODgo5/PodDpotVrcvn0bU6dOxcsvvwwvLy+cO3cOkyZNgpubG1566SWlbf/+/TFmzBi4urrCxcUFY8eORUhIiHLVWVBQEDp06IABAwZgwYIFAICBAwciMjKSV5gRERGRaQPR/PnzAQAtWrQwmL5kyRL06dMH5ubmOHbsGL799lukp6fDy8sLLVu2xNq1a+Hg4KC0//TTT2FhYYGePXsiKysLrVu3xtKlSw3uKbZy5UqMGDFCuRotKioK8+bNM/6bJCIiogpPIyJi6k48DTIyMqDT6aDX63k+ERGRmk3VPWK+vnz6QaVS2r/fFeKkaiIiIiJTYiAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVszB1B4iIiIiqTdhc7LxzNtElv3iq/onrcw8RERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpnkkD0YwZM/DCCy/AwcEB7u7u6Nq1K06ePGnQRkQwdepUeHt7Q6vVokWLFvjzzz8N2mRnZ2P48OFwc3ODnZ0doqKicOnSJYM2aWlpiImJgU6ng06nQ0xMDNLT0439FomIiOgpYNJAtHv3bgwdOhSJiYlISEhAbm4u2rVrhzt37ihtPv74Y8yePRvz5s3DoUOH4OnpibZt2yIzM1NpM2rUKKxfvx5r1qzBvn37cPv2bURGRiIvL09pEx0djaSkJMTHxyM+Ph5JSUmIiYkp1/dLRET0RKbqSn7Qv2ZhyuLx8fEGz5csWQJ3d3ccOXIEzZo1g4hgzpw5mDx5Mrp16wYAWLZsGTw8PLBq1SoMGjQIer0eixYtwvLly9GmTRsAwIoVK+Dr64tt27ahffv2SE5ORnx8PBITExEaGgoAWLhwIcLCwnDy5EkEBgaW7xsnIiKiCqVCnUOk1+sBAC4uLgCAs2fPIjU1Fe3atVPaWFtbo3nz5ti/fz8A4MiRI8jJyTFo4+3tjeDgYKXNgQMHoNPplDAEAI0bN4ZOp1PaPCw7OxsZGRkGDyIiIqqcKkwgEhGMHj0aTZs2RXBwMAAgNTUVAODh4WHQ1sPDQ5mXmpoKKysrODs7l9jG3d29UE13d3elzcNmzJihnG+k0+ng6+v7ZG+QiIiIKqwKE4iGDRuGP/74A6tXry40T6PRGDwXkULTHvZwm6Lal7SciRMnQq/XK4+LFy+W5m0QERHRU6hCBKLhw4djw4YN2LlzJ3x8fJTpnp6eAFBoL861a9eUvUaenp64f/8+0tLSSmxz9erVQnWvX79eaO9TAWtrazg6Oho8iIiIqHIyaSASEQwbNgzr1q3Djh07UL16dYP51atXh6enJxISEpRp9+/fx+7duxEeHg4AaNCgASwtLQ3apKSk4Pjx40qbsLAw6PV6/Prrr0qbgwcPQq/XK22IiIhIvUx6ldnQoUOxatUq/Pe//4WDg4OyJ0in00Gr1UKj0WDUqFGYPn06AgICEBAQgOnTp8PW1hbR0dFK2/79+2PMmDFwdXWFi4sLxo4di5CQEOWqs6CgIHTo0AEDBgzAggULAAADBw5EZGQkrzAjIiIi0wai+fPnAwBatGhhMH3JkiXo06cPAGDcuHHIysrCkCFDkJaWhtDQUPz8889wcHBQ2n/66aewsLBAz549kZWVhdatW2Pp0qUwNzdX2qxcuRIjRoxQrkaLiorCvHnzjPsGiYiI6KmgERExdSeeBhkZGdDpdNDr9TyfiIhIzR41AOJUfeWsbWTVJmwudt45m+iSX1zC+y7t3+8KcVI1ERERkSkxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkepZmLoDREREVDFUm7C52HnnbKJLfvFUfRn3pnxxDxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHm/dQUREVIGUfPuMcuyIynAPEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqV6ZBaK0tDTMnTsX9evXL6tFEhEREZWLJ77b/bZt27Bo0SLExcXBzc0N3bp1K4t+EREREZWbfxWILly4gCVLlmDJkiW4ffs20tLS8N133+Hll18u6/4RERERGd1jHTL77rvv0K5dOwQFBeH48eP47LPPcOXKFZiZmSEoKMhYfSQiIiIyqsfaQxQdHY1x48bhxx9/hIODg7H6RERERFSuHmsPUb9+/fDll1+iQ4cO+Oqrr5CWlmasfhERERGVm8cKRF9//TVSUlIwcOBArF69Gl5eXujSpQtEBPn5+cbqIxEREZFRPfZl91qtFrGxsdi9ezeOHTuG2rVrw8PDA02aNEF0dDTWrVtnjH4SERERGc1jBaKkpCSD5wEBAZgxYwYuXryIFStW4O7du+jVq1epl7dnzx507twZ3t7e0Gg0iIuLM5jfp08faDQag0fjxo0N2mRnZ2P48OFwc3ODnZ0doqKicOnSJYM2aWlpiImJgU6ng06nQ0xMDNLT0x/nrRMREVEl9liB6Pnnn0eDBg0wf/586PX6/78QMzN07twZcXFxuHjxYqmXd+fOHdSrVw/z5s0rtk2HDh2QkpKiPLZs2WIwf9SoUVi/fj3WrFmDffv24fbt24iMjEReXp7SJjo6GklJSYiPj0d8fDySkpIQExPzGO+ciIiIKrPHusrsl19+weLFizFhwgSMGTMG3bp1Q//+/dGyZUuljbu7e6mXFxERgYiIiBLbWFtbw9PTs8h5er0eixYtwvLly9GmTRsAwIoVK+Dr64tt27ahffv2SE5ORnx8PBITExEaGgoAWLhwIcLCwnDy5EkEBgaWur9ERERUOT3WHqKwsDAsXLgQqampmD9/Pi5duoQ2bdqgRo0a+OCDDwodqioLu3btgru7O2rWrIkBAwbg2rVryrwjR44gJycH7dq1U6Z5e3sjODgY+/fvBwAcOHAAOp1OCUMA0LhxY+h0OqVNUbKzs5GRkWHwICIiosrpX93LrODE6l27duHvv/9Gr169sGDBAlSvXh0dO3Yss85FRERg5cqV2LFjBz755BMcOnQIrVq1QnZ2NgAgNTUVVlZWcHZ2Nnidh4cHUlNTlTZF7bVyd3dX2hRlxowZyjlHOp0Ovr6+Zfa+iIiIqGJ54nuZ1ahRAxMmTICvry8mTZqErVu3lkW/AACvvPKK8u/g4GA0bNgQ/v7+2Lx5c4n3TBMRaDQa5fmD/y6uzcMmTpyI0aNHK88zMjIYioiIiCqpJwpEu3fvxuLFi/Hjjz/C3NwcPXv2RP/+/cuqb4V4eXnB398fp06dAgB4enri/v37SEtLM9hLdO3aNYSHhyttrl69WmhZ169fh4eHR7G1rK2tYW1tXcbvgIiIiCqixz5kdvHiRbz33nuoUaMGWrZsif/973+YO3curly5goULFxa6LL4s3bx5ExcvXoSXlxcAoEGDBrC0tERCQoLSJiUlBcePH1cCUVhYGPR6PX799VelzcGDB6HX65U2REREpG6PtYeobdu22LlzJ6pUqYLevXujX79+T3SV1u3bt3H69Gnl+dmzZ5GUlAQXFxe4uLhg6tSpePnll+Hl5YVz585h0qRJcHNzw0svvQQA0Ol06N+/P8aMGQNXV1e4uLhg7NixCAkJUa46CwoKQocOHTBgwAAsWLAAADBw4EBERkbyCjMiIiIC8JiBSKvVYt26dejUqRPMzc2fuPjhw4cNLtkvOGcnNjYW8+fPx7Fjx/Dtt98iPT0dXl5eaNmyJdauXWtwY9lPP/0UFhYW6NmzJ7KystC6dWssXbrUoH8rV67EiBEjlKvRoqKiShz7iIiIiNTlsQKRq6sr4uLiCo0o/bDFixeXanktWrSAiBQ7vzQnaNvY2GDu3LmYO3dusW1cXFywYsWKUvWJiIiI1OexAtGyZcvg7++P5557rsQgQ0RERPQ0eaxA9Oabb2LNmjU4c+YM+vXrh9dffx0uLi7G6hsREZFJVJuwudh552zKsSNUbh7rKrMvv/wSKSkpGD9+PDZu3AhfX1/07NkTW7du5R4jIiIiemo99mX31tbW6NWrFxISEvDXX3+hTp06GDJkCPz9/XH79m1j9JGIiIjIqP7VrTsKaDQaaDQaiAjy8/PLqk9ERERE5eqxA1F2djZWr16Ntm3bIjAwEMeOHcO8efNw4cIF2NvbG6OPREREREb1WCdVDxkyBGvWrIGfnx/69u2LNWvWwNXV1Vh9IyIiIioXjxWIvvrqK/j5+aF69erYvXs3du/eXWS7devWlUnniIiIiMrDYwWi3r17l3iHeCIiIqKn0WMFoqVLlxqpG0RERESm80RXmRERERFVBgxEREREpHqPdciMiIgqmKm6R8zXV87aRGWMe4iIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9UwaiPbs2YPOnTvD29sbGo0GcXFxBvNFBFOnToW3tze0Wi1atGiBP//806BNdnY2hg8fDjc3N9jZ2SEqKgqXLl0yaJOWloaYmBjodDrodDrExMQgPT3dyO+OiIiInhYmDUR37txBvXr1MG/evCLnf/zxx5g9ezbmzZuHQ4cOwdPTE23btkVmZqbSZtSoUVi/fj3WrFmDffv24fbt24iMjEReXp7SJjo6GklJSYiPj0d8fDySkpIQExNj9PdHRERETwcLUxaPiIhAREREkfNEBHPmzMHkyZPRrVs3AMCyZcvg4eGBVatWYdCgQdDr9Vi0aBGWL1+ONm3aAABWrFgBX19fbNu2De3bt0dycjLi4+ORmJiI0NBQAMDChQsRFhaGkydPIjAwsHzeLBEREVVYFfYcorNnzyI1NRXt2rVTpllbW6N58+bYv38/AODIkSPIyckxaOPt7Y3g4GClzYEDB6DT6ZQwBACNGzeGTqdT2hQlOzsbGRkZBg8iIiKqnEy6h6gkqampAAAPDw+D6R4eHjh//rzSxsrKCs7OzoXaFLw+NTUV7u7uhZbv7u6utCnKjBkzMG3atCd6D0RE9O9Vm7C52HnnbKJLfvFUfRn3hiq7CruHqIBGozF4LiKFpj3s4TZFtX/UciZOnAi9Xq88Ll68+Jg9JyIioqdFhd1D5OnpCeCfPTxeXl7K9GvXril7jTw9PXH//n2kpaUZ7CW6du0awsPDlTZXr14ttPzr168X2vv0IGtra1hbW5fJeyEielqVvJemHDtCZGQVdg9R9erV4enpiYSEBGXa/fv3sXv3biXsNGjQAJaWlgZtUlJScPz4caVNWFgY9Ho9fv31V6XNwYMHodfrlTZERESkbibdQ3T79m2cPn1aeX727FkkJSXBxcUFfn5+GDVqFKZPn46AgAAEBARg+vTpsLW1RXT0P8eOdTod+vfvjzFjxsDV1RUuLi4YO3YsQkJClKvOgoKC0KFDBwwYMAALFiwAAAwcOBCRkZG8woyIiIgAmDgQHT58GC1btlSejx49GgAQGxuLpUuXYty4ccjKysKQIUOQlpaG0NBQ/Pzzz3BwcFBe8+mnn8LCwgI9e/ZEVlYWWrdujaVLl8Lc3Fxps3LlSowYMUK5Gi0qKqrYsY+IiIhIfUwaiFq0aAERKXa+RqPB1KlTMXXq1GLb2NjYYO7cuZg7d26xbVxcXLBixYon6SoRERFVYhX2HCIiIiKi8sJARERERKrHQERERESqx0BEREREqsdARERERKrHQERERESqV2Fv3UFERP/g7TOIjI97iIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9SxM3QEioqfeVN0j5uvLpx9E9K9xDxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeL7snIiqFahM2FzvvnE05doSIjIKBiIjKDsfjIaKnFA+ZERERkepxDxGRMXBPCRHRU4V7iIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1eFI1ET01Sh4LKLrkF/NEdiIqAfcQERERkepxDxERPRaO2ExElREDEdFTiIeOiIjKFg+ZERERkeoxEBEREZHqMRARERGR6jEQERERkerxpGqif4lXWxERVR4Veg/R1KlTodFoDB6enp7KfBHB1KlT4e3tDa1WixYtWuDPP/80WEZ2djaGDx8ONzc32NnZISoqCpcuXSrvt0JEREQVWIUORABQp04dpKSkKI9jx44p8z7++GPMnj0b8+bNw6FDh+Dp6Ym2bdsiMzNTaTNq1CisX78ea9aswb59+3D79m1ERkYiLy/PFG+HiIiIKqAKf8jMwsLCYK9QARHBnDlzMHnyZHTr1g0AsGzZMnh4eGDVqlUYNGgQ9Ho9Fi1ahOXLl6NNmzYAgBUrVsDX1xfbtm1D+/bty/W9EBERUcVU4fcQnTp1Ct7e3qhevTpeffVVnDlzBgBw9uxZpKamol27dkpba2trNG/eHPv37wcAHDlyBDk5OQZtvL29ERwcrLQpTnZ2NjIyMgweREREVDlV6EAUGhqKb7/9Flu3bsXChQuRmpqK8PBw3Lx5E6mpqQAADw8Pg9d4eHgo81JTU2FlZQVnZ+di2xRnxowZ0Ol0ysPX17cM3xkRERFVJBX6kFlERITy75CQEISFhaFGjRpYtmwZGjduDADQaDQGrxGRQtMeVpo2EydOxOjRo5XnGRkZDEUVEG9hQUREZaFC7yF6mJ2dHUJCQnDq1CnlvKKH9/Rcu3ZN2Wvk6emJ+/fvIy0trdg2xbG2toajo6PBg4iIiCqnpyoQZWdnIzk5GV5eXqhevTo8PT2RkJCgzL9//z52796N8PBwAECDBg1gaWlp0CYlJQXHjx9X2hARERFV6ENmY8eORefOneHn54dr167h/fffR0ZGBmJjY6HRaDBq1ChMnz4dAQEBCAgIwPTp02Fra4vo6H8Oleh0OvTv3x9jxoyBq6srXFxcMHbsWISEhChXnRERERFV6EB06dIl9OrVCzdu3ECVKlXQuHFjJCYmwt/fHwAwbtw4ZGVlYciQIUhLS0NoaCh+/vlnODg4KMv49NNPYWFhgZ49eyIrKwutW7fG0qVLYW5ubqq3RURERBVMhQ5Ea9asKXG+RqPB1KlTMXXq1GLb2NjYYO7cuZg7d24Z946IiIgqi6fqHCIiIiIiY2AgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1bMwdQfo6VdtwuZi552ziS75xVP1ZdwbIiKix8c9RERERKR63ENUSXAvDRER0b/HQFRepuoeMZ+hhIiIyFR4yIyIiIhUj3uIylDJh63KsSNERET0WLiHiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFRPVYHoyy+/RPXq1WFjY4MGDRpg7969pu4SERERVQCqCURr167FqFGjMHnyZPz+++948cUXERERgQsXLpi6a0RERGRiqglEs2fPRv/+/fHGG28gKCgIc+bMga+vL+bPn2/qrhEREZGJWZi6A+Xh/v37OHLkCCZMmGAwvV27dti/f3+Rr8nOzkZ2drbyXK/XAwAyMjKKrZOffbfYeRkaKbmTJSy3NFibtVmbtVmbtVm7qFn/zBN5xDJEBS5fviwA5JdffjGY/sEHH0jNmjWLfM2UKVMEAB988MEHH3zwUQkeFy9eLDErqGIPUQGNRmPwXEQKTSswceJEjB49Wnmen5+PW7duwdXVtdjXFCcjIwO+vr64ePEiHB0dH7/jT4C1WZu1WZu1WVvNtUUEmZmZ8Pb2LrGdKgKRm5sbzM3NkZqaajD92rVr8PDwKPI11tbWsLa2Npjm5OT0RP1wdHQs9w2JtVmbtVmbtVlb7bV1Ot0j26jipGorKys0aNAACQkJBtMTEhIQHh5uol4RERFRRaGKPUQAMHr0aMTExKBhw4YICwvD119/jQsXLuDNN980ddeIiIjIxFQTiF555RXcvHkT7777LlJSUhAcHIwtW7bA39/f6LWtra0xZcqUQofgygNrszZrszZrszZrP5pG5FHXoRERERFVbqo4h4iIiIioJAxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6qhmHqLzdvn0bR44cQWpqKjQaDTw8PNCgQQPY29tX6tpEZHznz583+H6Xx3hqaq+tVmpa5wxEZSw3NxdjxozBwoULce/ePVhZWUFEkJOTAxsbGwwcOBAzZ86EpaVlpapdoCKEMbX+YLN25a/96aefYvbs2bhy5QoKhpDTaDTw9vbGmDFjMGrUKNY2EjVtZ4BK17lQmRoxYoRUrVpV1qxZI2lpacr0tLQ0WbNmjfj6+srIkSMrXe2cnBwZMWKEaLVa0Wg0Ym1tLVZWVqLRaESr1crIkSPl/v37RqldYPbs2eLj4yNmZmai0WhEo9GImZmZ+Pj4yKeffsrarP1U13733XfF0dFRPvzwQ/n999/lypUrcvnyZfn999/lww8/FJ1OJ++99x5rlzG1bWci6l3nDERlzM3NTbZv317s/G3btombm1ulq23KMCai3h9s1lZPbR8fH1m/fn2x89etWyfe3t5lXlfNtdW4nYmod50zEJUxOzs7OXr0aLHzf//9d7Gzs6t0tU0ZxkTU+4PN2uqprdVq5a+//ip2/vHjx0Wr1ZZ5XTXXVuN2JqLedc6rzMpYy5YtMXr0aFy9erXQvKtXr2LcuHFo1apVpaudlZUFNze3Yue7uroiKyvLKLUB4ObNmwgMDCx2fs2aNZGWlsbarP3U1m7UqBE++OAD5ObmFpqXm5uL6dOno1GjRmVeV8211bidAepd57y5axm7ePEiOnbsiBMnTiA4OBgeHh7QaDRITU3F8ePHUbt2bWzevBk+Pj6Vqnbnzp2RlZWFlStXwsPDw2De1atXERMTAxsbG2zYsKHMawNAixYt4OPjg6VLl8LCwvBagdzcXMTGxuLy5cvYtWsXa7P2U1n72LFjaNeuHbKzs9G8eXOD7/eePXtgbW2NhIQE1KlTp0zrqrm2GrczQL3rnIHICPLz87F161YkJiYiNTUVAODp6YmwsDC0a9cOZmbG2zFnqtqmDGOAen+wWVtdtTMzM7FixYoiv9/R0dFwdHQs85pqrq3W7QxQ5zpnIKIyY8ogCKjzB5u11Vebyhe3s/JnqvfNQGQkp06dwv79+w3GUAgPD0dAQEClrk1ExvXwWF+enp54/vnnTTLoq1pqq5Xa1jkHZixjer0evXv3xsaNG6HT6eDu7g4RwfXr15GRkYHOnTvj22+/NUrCNWXtAqYOY2r9wWbtyl9brYO+VsQBZyvzdgaoeJ0b5do1FYuJiZGQkBBJTEwsNC8xMVHq1q0rvXv3rnS109PTJSoqSjQajTg5OUnNmjUlICBAnJycxMzMTLp06SJ6vd4otUVMOzAka7N2edRW66Cvah1w1pS11brOGYjKmE6nKzKQFDhw4IDodLpKV9uUYUxEvT/YrK2e2mod9FWtA86asrZa1zkDURnT6XRy8ODBYucnJiYaNRCZsrapwpiIen+wWVs9tdU66KtaB5w1ZW21rnMOzFjGOnfujAEDBuDw4cOF5h0+fBhvvvkmoqKiKl1t4J8b//2beWXBlANDsjZrl0dttQ76qtYBZ01ZW63rnHuIylhaWpp06NBBNBqNODs7S2BgoNSqVUucnZ3FzMxMIiIiDHYDVpbar7/+utStW1cOHTpUaN6hQ4ekfv36EhMTY5TaIiKRkZHSunVrSU1NLTQvNTVV2rZtK507d2Zt1n5qa1+4cEGCg4PFwsJC6tevL+3bt5cOHTpI/fr1xcLCQurWrSsXL14s87pqrq3G7UxEveucl90bSXJycpFjKNSqVatS1k5PT0evXr2wdetWODk5wd3dHRqNBlevXoVer0f79u2xatUqODk5GaW+WkcIZ2111VbjoK+mrK3W7QxQ5zpnIKIyZcogqMYfbNZWX20qX9zOyp+p3jcDkRGICLZt21ZoPJ4mTZqgdevWRj2fxpS1icj41Droq6nHOFMjta1zBqIydvnyZURGRuLYsWPK7j4RwbVr13D8+HHUq1cPGzZsQNWqVStVbaBihDG1/mCzduWvrdZBX9U+4Kwpaqt2nRvlzCQVi4qKklatWsmVK1cKzbty5Yq0atVKunTpUulqX7p0SerXry/m5uZSr149adeunbRt21bq1asn5ubm8vzzz8ulS5eMUlvEtANDsjZrl0dttQ76qtYBZ01ZW63rnIGojNnZ2UlSUlKx83/77TejjtdhqtqmDGMi6v3BZm311FbroK9qHXDWlLXVus4ZiMqYm5ub7Nixo9j527dvN+pAXqaqbcowJqLeH2zWVk9tUw+8qtbaatvOCmqrcZ1XzlPUTejVV19FbGwsfvjhB+j1emW6Xq/HDz/8gL59+yI6OrrS1dZqtbh161ax89PS0qDVao1Su4ApB4ZkbdY2dm21Dvqq5gFnTVVbtevcKDFLxbKzs+XNN98UKysrMTMzExsbG7GxsREzMzOxsrKSwYMHS3Z2dqWrPWzYMPH19ZXvv/9e0tPTlenp6eny/fffi5+fn4wYMcIotUVMOzAka7N2edRW66Cvah1w1pS11brOeZWZkWRkZODw4cPK0Oeenp5o0KCBUc/Kf7D2kSNHDMZvMHbt+/fvY+TIkVi8eDFyc3NhZWWlTLewsED//v0xZ84cZXpZM+XAkKzN2uU5CKnaBn0tcOLECRw4cEA1A86aejsD1DfILwMRlSlTBkFAvX8s1PaHqiLUNuU6p/Kl1u+2KZnifTMQGcGdO3ewatWqIsfj6dWrF+zs7IxS99KlS7CxsVFujLd371589dVXuHDhAvz9/TF06FCEhYUZpTYRGZ9w0FcAQFJSEk6dOgUvLy80adKEA84aQUX6vMsLA1EZ++uvv9C2bVvcvXsXzZs3Nxgccffu3bCzs8PPP/+M2rVrl3nt8PBwvP3224iIiMB///tfdOvWDZGRkQgKCsLff/+NTZs2Yd26dYiMjCzz2oDpgmABtf6xMPUPV35+fpFD6efn5+PSpUvw8/Mzav0C5flH0hTrXK2DvkZHR2PBggVwcHDA7du38fLLLyMhIQGWlpbIyclBgwYNkJCQYLRDR2r8bqt2kF+jnJmkYi1atJBXX321yJOXs7OzpVevXtKiRQuj1HZwcJCzZ8+KiEhoaKh8+OGHBvPnzp0rzz33nFFq//nnn+Lt7S1OTk7SpUsXGThwoAwYMEC6dOkiTk5OUrVqVfnzzz+NUlvEtANDqrW2Xq+XHj16iI2Njbi7u8s777wjubm5yvzU1FQxMzMzSu1evXpJRkaGiIhkZmZKu3btRKPRiJWVlWg0GmnYsKHRTvo01TpX66CvZmZmcvXqVRERGTt2rFSvXl2OHDkiIiLHjh2ToKAgeeutt4xSW63fbbUO8stAVMa0Wm2Jf/iPHTsmWq3WKLV1Op0cPXpURETc3d2Vfxc4ffq02NraGqW2KYOgiHr/WJiy9ogRI6RmzZry/fffy8KFC8Xf3186deqkbAOpqami0WiMUtuUfyRNtc7VOuirRqNRPus6derI2rVrDeZv3rxZAgICjFJbrd9ttQ7yy0BUxry9vSUuLq7Y+evXrxdvb2+j1I6KipIJEyaIiEj79u3ls88+M5i/cOFCo/1wmDIIiqj3j4Upa/v5+cnOnTuV5zdu3JDQ0FBp166d3Lt3z6h7iEz5R9JU61ytg75qNBq5du2a0o+Hf2fOnTsnNjY2Rqmt1u+2Wgf55cCMZWzAgAGIjY3FrFmzcPToUaSmpuLq1as4evQoZs2ahX79+mHQoEFGqf3hhx9i4cKFiI2NRdOmTTF58mTExMRg+vTpiI2NxbBhwzBp0iSj1HZ2dsapU6eKnX/69Gk4OzsbpTZg2oEh1Vr7xo0b8Pf3V567uroiISEBmZmZ6NixI+7evWuUugUKziO4evUqgoODDebVqVMHFy9eNEpdU61ztQ76CgBvv/02Ro8eDTMzM+WqowI3btyAvb29Ueqq9but2kF+jRKzVO7DDz8ULy8v0Wg0YmZmJmZmZqLRaMTLy0s++ugjo9Y+ffq0vPrqq+Lg4CAajUY0Go1YWlpKeHi4rF+/3mh1p0yZIjqdTmbOnClJSUmSkpIiqampkpSUJDNnzhRnZ2eZNm2a0eqbcmBItdYODAyUzZs3F5qemZkpYWFhUq9ePaPuIRo0aJC89dZb4u7uLtu3bzeYf/jwYaP9D9ZU61ytg742b95cWrRooTy++eYbg/nvvvuuNG/e3Ci11frdVusgvwxERnTmzBnZv3+/7N+/X86cOVOutfPz8yU1NVWuXLki9+/fL5eapgyCav1jYcraw4cPl+7duxc5LyMjQ0JDQ40WiEz5R9KU61zkn5PZt2/fLqtWrZJVq1bJjh07jHb376Jq79ixwyS1i/O///1PLl68aJRlq/W7XcAU25op3zcvu6cyd/bsWYPBtKpXr15utU0xSrdaa6elpeHKlSuoU6dOkfNv376NI0eOoHnz5kbrQ3HOnDkDKysr+Pj4GK2GKT9vKl9q+25XBKYY5JeBqIz9/vvvcHJyUkLAihUrMH/+fGVwxGHDhuHVV1+tdLWHDx+Onj174sUXXzTK8onI9GN9Xbp0CU5OToXO2cnJycGBAwfQrFmzSllbjUy9rZkCT6ouY/3798e5c+cAAN988w0GDhyIhg0bYvLkyXjhhRcwYMAALF68uNLV/uKLL9CiRQvUrFkTH330UaETH03t6tWrePfdd41a49KlS7h9+3ah6Tk5OdizZ4/R6t68eRM7d+5UTkS8ceMGPvroI7z77rtITk42Wl01135QTk4O4uLiMHPmTKxYsQJ37twxSp2//voLNWvWxLhx45CWlgY/Pz/4+PggLS0N//nPfxAYGIi//vrLKLVTUlLQqFEj+Pv7w8nJCbGxsQbb+q1bt9CyZctKV7uAqb7bGzduxJQpU3DgwAEAwI4dO9CxY0d06NABX3/9tdHqmnJbA/4JYwsXLkTfvn0RERGBjh07om/fvvjmm2+M9v0CwJOqy5qtra2cP39eRESee+45WbBggcH8lStXSu3atStdbY1GI9u2bZORI0eKm5ubWFpaSlRUlGzcuFHy8vKMUvNxJCUlGe18litXrsgLL7wgZmZmYm5uLr1795bMzExlvjEvPz948KDodDrlrtSHDx+W6tWrS0BAgDz77LOi1WqV8XlYu2yEhYUpgz5eu3ZNgoODxcrKSgICAsTGxkb8/PyMMnCcKcf66t27tzRu3FgOHTokCQkJ0rBhQ2nQoIHcunVLRIw75pQpa5vyuz1//nyxsLCQBg0aiKOjo6xYsUIcHBzkjTfekEGDBolWq5U5c+YYpbYptzVTDvLLQFTGXF1d5fDhwyLyz+CID4+ncPr0aaONx2PK2g+OC3P//n1Zu3attG/fXszNzcXb21smTZokp06dMkptEZGjR4+W+Fi7dq3RfrhM+YPdpk0beeONNyQjI0NmzpwpPj4+8sYbbyjz+/fvL127dmXtMvTgtj5gwACpX7++pKSkiMg/YzGFh4dLv379yryuKcf68vb2loMHDyrP7927J126dJH69evLzZs3jRoMTFnblN/toKAg+frrr0VEZMeOHWJjYyNffPGFMn/JkiUSFBRklNqm3NZMGcYYiMrY66+/Lv379xcRkR49esj//d//GcyfPn26hISEVLraD/6ReND58+dlypQp4u/vb7QfrYL6BVe1PfwomF4Zf7CdnZ3lr7/+EpF/gqiZmZlBX3777TepWrUqa5ehB7f1mjVryqZNmwzm79y5U6pVq1bmdU056KudnZ38/fffBtNycnKka9euUrduXfnjjz+Mto2bsrYpv9tarVbZ4y8iYmlpKceOHVOenz171mh3HjDltmbKMMZAVMYuX74s1apVk2bNmsno0aNFq9VK06ZNZcCAAdKsWTOxsrIqcuyWp712cYGoQH5+vvz8889GqS3yz8iqixYtknPnzhX52Lx5c6X8wbazs1PuXyciYm9vL//73/+U5+fPnzfqKL5qrP3gyMnu7u5FjpxsbW1d5nVNOdZXSEiI/PDDD4WmF2znfn5+RtvGTVnblN9tHx8f2bNnj4j889uu0WgMfr937dolPj4+Rqltym3NlGGMgcgI0tLSZPz48VK7dm2xsbERKysr8ff3l+joaDl06FClrF2tWjW5ceOG0Zb/KO3bt5f33nuv2PlJSUlG27Vtyh/sWrVqGQxKuGnTJrl7967yPDEx0Wg/mmqtrdFopGPHjvLSSy+Js7OzbNmyxWD+gQMHxMPDwyi1TTXW17hx46Rdu3ZFzsvJyZGoqCijbeOmrG3K7/bQoUMlICBA3n//fWnUqJHExsZKrVq15KeffpL4+HgJCQkxyqHZAqba1kwZxhiIqFJYt26dLF++vNj5t27dkqVLlxqldml+sI0VxqZOnSqrV68udv6kSZOkW7durF2G+vTpY/D47rvvDOaPHTtW2rdvb5TaBcp70NecnJwSB+TLzc2Vc+fOVbrapgxjt2/fljfeeEOCg4PlzTfflPv378vMmTPFyspKNBqNtGjRosS98mXlwW3twb2wxmSqMMZxiIieUG5uLu7evVvsgGF5eXm4dOmSwX2/ysvdu3dhbm4Oa2tr1i4nd+7cgbm5OWxsbMq9NpWtivjdvnfvHnJycuDg4FBuNQtYWVnh6NGjCAoKKpd65T3IL8chIlW4ePEi+vXrZ5RlW1hYlDh66pUrVzBt2jSj1H6UmzdvYvDgwaxdjm7duoUhQ4YYZdlZWVnYt29fkWPA3Lt3D99++61R6qq1toWFBS5fvowlS5bgxIkTAIATJ05g8ODB6NevH3bv3m3UMJScnIwlS5bg5MmTSu233noLI0eOxI4dO4xWd/To0UU+8vLy8OGHHyrPja169eqoVasWDh48iFmzZuGDDz4w2k2bAXAcIlIHY45DxNqsXR61T548Kf7+/sphhObNm8uVK1eU+ca84kmttX/66SexsrISFxcXsbGxkZ9++kmqVKkibdq0kdatW4uFhUWhGwtXhtoajUbq169vcL/AFi1aiEajkRdeeEFatGghLVu2NEptLy8v5XzUM2fOiJeXl3h6ekrbtm3Fx8dHdDqdJCcnG6W2hfGiFlH52bBhQ4nzz5w5w9qs/VTXHj9+PEJCQnD48GGkp6dj9OjRaNKkCXbt2gU/Pz+j1FR77XfffRf/+c9/8P7772PNmjWIjo7G4MGD8cEHHwAAJk+ejA8//BCtWrWqVLU/+OADLFy4EJ988onB8i0tLbF06VLUrl27zGsWSE1NRV5eHgBg0qRJCAwMxObNm2Fra4vs7Gx0794db7/9Nr7//vuyL26UmEVUzkoah+jB8YhYm7Wf1tru7u7yxx9/GEwbMmSI+Pn5yf/+9z+j7ilRa21HR0dlQNm8vDyxsLAwGAH92LFjRrui0JS1RUR+/fVXqVmzpowZM0bu378vIiIWFhZGGyW6wINDuFSvXr3QXjBjXkHKc4ioUvDy8sKPP/6I/Pz8Ih+//fYba7P2U107KysLFhaGO/W/+OILREVFoXnz5vj777+NUlfNtR9kZmYGGxsbODk5KdMcHByg1+srZe0XXngBR44cwfXr19GwYUMcO3YMGo3GaPUeVFAnOzsbHh4eBvM8PDxw/fp1o9RlIKJKoUGDBiX+IdJoNBAjXVDJ2qxdHrVr1aqFw4cPF5o+d+5cdOnSBVFRUWVeU+21q1WrhtOnTyvPDxw4YHCY7uLFi/Dy8qp0tQvY29tj2bJlmDhxItq2bascyjK21q1b4/nnn0dGRkahwHvhwgW4ubkZpS7PIaJK4T//+U+Jd0F+9tlnsXPnTtZm7ae29ksvvYTVq1cjJiam0Lx58+YhPz8fX331VZnXVXPtwYMHG4SA4OBgg/k//fSTUc7hMXXth7366qto2rQpjhw5YvQhBqZMmWLw3NbW1uD5xo0b8eKLLxqlNschIiIiItXjITMiIiJSPQYiIiIiUj0GIiIiIlI9BiIiqvQ0Gg3i4uJM3Q0iqsAYiIhIVc6dOweNRoOkpCRTd4WIKhAGIiIiIlI9BiIiqtAWLFiAqlWrIj8/32B6VFQUYmNjAQDz589HjRo1YGVlhcDAQCxfvrzY5VWvXh0A8Nxzz0Gj0aBFixYAgEOHDqFt27Zwc3ODTqdD8+bNCw3AeOLECTRt2hQ2NjaoXbs2tm3bVuhw3OXLl/HKK6/A2dkZrq6u6NKlC86dO/fkK4KIjIqBiIgqtB49euDGjRsGgx2mpaVh69ateO2117B+/XqMHDkSY8aMwfHjxzFo0CD07du32MERf/31VwDAtm3bkJKSgnXr1gEAMjMzERsbi7179yIxMREBAQHo2LEjMjMzAQD5+fno2rUrbG1tcfDgQXz99deYPHmywbLv3r2Lli1bwt7eHnv27MG+fftgb2+PDh064P79+8ZYPURUVoxyhzQiojIUFRUl/fr1U54vWLBAPD09JTc3V8LDw2XAgAEG7Xv06CEdO3ZUngOQ9evXi4jI2bNnBYD8/vvvJdbMzc0VBwcH2bhxo4iI/PTTT2JhYSEpKSlKm4SEBINlL1q0SAIDAyU/P19pk52dLVqtVrZu3fpv3joRlRPuISKiCu+1117Djz/+iOzsbADAypUr8eqrr8Lc3BzJyclo0qSJQfsmTZogOTn5sWpcu3YNb775JmrWrAmdTgedTofbt2/jwoULAICTJ0/C19cXnp6eymsaNWpksIwjR47g9OnTcHBwgL29Pezt7eHi4oJ79+7hf//7379560RUTngvMyKq8Dp37oz8/Hxs3rwZL7zwAvbu3YvZs2cr8x++C7eIPPadufv06YPr169jzpw58Pf3h7W1NcLCwpRDXaVZZn5+Pho0aICVK1cWmlelSpXH6g8RlS8GIiKq8LRaLbp164aVK1fi9OnTqFmzJho0aAAACAoKwr59+9C7d2+l/f79+xEUFFTksqysrACg0J279+7diy+//BIdO3YE8M/dxG/cuKHMr1WrFi5cuICrV6/Cw8MDwD8nYj/o+eefx9q1a+Hu7g5HR8cnfNdEVJ54yIyIngqvvfYaNm/ejMWLF+P1119Xpv/nP//B0qVL8dVXX+HUqVOYPXs21q1bh7Fjxxa5HHd3d2i1WsTHx+Pq1avQ6/UA/rlL/fLly5GcnIyDBw/itddeg1arVV7Xtm1b1KhRA7Gxsfjjjz/wyy+/KCdVF+w5eu211+Dm5oYuXbpg7969OHv2LHbv3o2RI0fi0qVLxlo1RFQGGIiI6KnQqlUruLi44OTJk4iOjlamd+3aFZ999hlmzpyJOnXqYMGCBViyZIlyOf3DLCws8Pnnn2PBggXw9vZGly5dAACLFy9GWloannvuOcTExGDEiBFwd3dXXmdubo64uDjcvn0bL7zwAt544w383//9HwDAxsYGAGBra4s9e/bAz88P3bp1Q1BQEPr164esrCzuMSKq4DQiIqbuBBHR0+iXX35B06ZNcfr0adSoUcPU3SGiJ8BARERUSuvXr4e9vT0CAgJw+vRpjBw5Es7Ozti3b5+pu0ZET4gnVRMRlVJmZibGjRuHixcvws3NDW3atMEnn3xi6m4RURngHiIiIiJSPZ5UTURERKrHQERERESqx0BEREREqsdARERERKrHQERERESqx0BEREREqsdARERERKrHQERERESqx0BEREREqvf/AI3gbG47QJdVAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Table comparing auxiliary s_nom with s_nom in base_csv for IT\n",
+    "s_nom_single_base_s_nom_it = (\n",
+    "    it_base_csv_s_nom.groupby([\"voltage\"])[[\"s_nom_single\"]]\n",
+    "    .mean()\n",
+    "    .merge(\n",
+    "        it_base_csv_s_nom.groupby([\"voltage\"])[[\"s_nom\"]].mean(),\n",
+    "        left_on=\"voltage\",\n",
+    "        right_on=\"voltage\",\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "s_nom_single_base_s_nom_it.plot(kind=\"bar\", subplots=False)\n",
+    "plt.ylabel(\"MVA\")\n",
+    "plt.title(\"s_nom vs s_nom_single comparison in osm base_csv for IT\")\n",
+    "\n",
+    "s_nom_single_base_s_nom_it.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>voltage</th>\n",
+       "      <th>220000</th>\n",
+       "      <th>380000</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>s_nom_single</th>\n",
+       "      <td>983.112038</td>\n",
+       "      <td>1698.102612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>s_nom</th>\n",
+       "      <td>1228.890048</td>\n",
+       "      <td>1928.709139</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "voltage            220000       380000\n",
+       "s_nom_single   983.112038  1698.102612\n",
+       "s_nom         1228.890048  1928.709139"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Table comparing auxiliary s_nom with s_nom in entsoe_csv for IT\n",
+    "s_nom_single_entsoe_s_nom_it = (\n",
+    "    entsoe_s_nom[entsoe_s_nom[f\"if_it\"] == True]\n",
+    "    .groupby([\"voltage\"])[[\"s_nom_single\"]]\n",
+    "    .mean()\n",
+    "    .merge(\n",
+    "        entsoe_s_nom[entsoe_s_nom[f\"if_it\"] == True]\n",
+    "        .groupby([\"voltage\"])[[\"s_nom\"]]\n",
+    "        .mean(),\n",
+    "        left_on=\"voltage\",\n",
+    "        right_on=\"voltage\",\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "s_nom_single_entsoe_s_nom_it.plot(kind=\"bar\", subplots=False)\n",
+    "plt.ylabel(\"MVA\")\n",
+    "plt.title(\"s_nom vs s_nom_single comparison in entsoe_csv for DE\")\n",
+    "\n",
+    "s_nom_single_entsoe_s_nom_it.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparison between base_nc and base_csv using updated threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The analysis performed here is done with another version of pypsa-earth runs with increased voltage threshold (210kV)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download and extract the updated version of the data from the google drive link below. The extracted will be placed into \"pypsa-earth-networks v2\"\n",
+    "# https://drive.google.com/drive/folders/17Gdwe15yRk2nptCtM-Xm06AJcFRmM-_x?usp=drive_link"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pypsa.io:Importing network from PyPSA version v0.23.0 while current version is v0.24.0. Read the release notes at https://pypsa.readthedocs.io/en/latest/release_notes.html to prepare your network for import.\n",
+      "INFO:pypsa.io:Imported network base.nc has buses, lines, links, transformers\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the updated osm_base network\n",
+    "base_nc_v2 = pypsa.Network(\"pypsa-earth-networks v2/networks/base.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load updated osm_base file\n",
+    "base_csv_v2 = pd.read_csv(\n",
+    "    \"pypsa-earth-networks v2/resources/base_network/all_lines_build_network.csv\"\n",
+    ")\n",
+    "base_csv_v2[\"geometry\"] = base_csv_v2.geometry.apply(wkt.loads)\n",
+    "base_csv_v2 = gpd.GeoDataFrame(base_csv_v2, geometry=\"geometry\", crs=\"EPSG:4326\")\n",
+    "base_csv_v2[\"reprojected_length\"] = base_csv_v2.to_crs(\"EPSG:3035\").length\n",
+    "base_csv_v2_snom = calculate_s_nom(base_csv_v2, voltage_dict, base_nc_v2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### s_nom analysis with updated threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Summation of s_nom in base_csv\n",
+    "sum_de_base_csv_v2_snom = base_csv_v2_snom[base_csv_v2_snom.country == \"DE\"].s_nom.sum()\n",
+    "sum_de_base_nc_v2_snom = base_nc_v2.lines[base_nc_v2.lines.country == \"DE\"].s_nom.sum()\n",
+    "\n",
+    "sum_it_base_csv_v2_snom = base_csv_v2_snom[base_csv_v2_snom.country == \"IT\"].s_nom.sum()\n",
+    "sum_it_base_nc_v2_snom = base_nc_v2.lines[base_nc_v2.lines.country == \"IT\"].s_nom.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirming the threshold of the voltage values in base_csv which is set to 210kV."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "voltage\n",
+       "380000    704\n",
+       "220000    460\n",
+       "400000     23\n",
+       "225000     23\n",
+       "320000     11\n",
+       "525000      3\n",
+       "450000      2\n",
+       "600000      2\n",
+       "300000      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_csv_v2_snom[base_csv_v2_snom.country == \"DE\"].voltage.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "voltage\n",
+       "220000    361\n",
+       "380000    293\n",
+       "500000      6\n",
+       "400000      3\n",
+       "320000      2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_csv_v2_snom[base_csv_v2_snom.country == \"IT\"].voltage.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "v_nom\n",
+       "380.0    724\n",
+       "220.0    483\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_nc_v2.lines[base_nc_v2.lines.country == \"DE\"].v_nom.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "v_nom\n",
+       "220.0    361\n",
+       "380.0    295\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_nc_v2.lines[base_nc_v2.lines.country == \"IT\"].v_nom.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(13, 5))\n",
+    "plt.bar(\n",
+    "    [\"base_csv\", \"base_nc\", \"entsoe_csv\"],\n",
+    "    [sum_de_base_csv_v2_snom, sum_de_base_nc_v2_snom, sum_de_entsoe_snom],\n",
+    ")\n",
+    "plt.title(\n",
+    "    f\"s_nom comparison between osm_base_csv, osm_base_nc and entsoe_csv with increased threshold for DE\"\n",
+    ")\n",
+    "plt.ylabel(\"MVA\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(13, 5))\n",
+    "plt.bar(\n",
+    "    [\"base_csv\", \"base_nc\", \"entsoe_csv\"],\n",
+    "    [sum_it_base_csv_v2_snom, sum_it_base_nc_v2_snom, sum_it_entsoe_snom],\n",
+    ")\n",
+    "plt.title(\n",
+    "    f\"s_nom comparison between osm_base_csv, osm_base_nc and entsoe_csv with increased threshold for IT\"\n",
+    ")\n",
+    "plt.ylabel(\"MVA\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### TWKm analysis with updated threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set voltage filtering threshold in V\n",
+    "voltage_threshold_v2 = 200000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "voltage levels for DE\n",
+      "voltage\n",
+      "380000    704\n",
+      "220000    460\n",
+      "400000     23\n",
+      "225000     23\n",
+      "320000     11\n",
+      "525000      3\n",
+      "450000      2\n",
+      "600000      2\n",
+      "300000      1\n",
+      "Name: count, dtype: int64\n",
+      "___________________________________________________________\n",
+      "voltage levels for IT\n",
+      "voltage\n",
+      "220000    361\n",
+      "380000    293\n",
+      "500000      6\n",
+      "400000      3\n",
+      "320000      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Voltage levels in updated base_csv\n",
+    "print(\"voltage levels for DE\")\n",
+    "print(base_csv_v2_snom[(base_csv_v2_snom.country == \"DE\")].voltage.value_counts())\n",
+    "print(\"___________________________________________________________\")\n",
+    "print(\"voltage levels for IT\")\n",
+    "print(base_csv_v2_snom[(base_csv_v2_snom.country == \"IT\")].voltage.value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The voltages in the updated osm_base_csv does not go below 200kV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Total sum of line capacity in base_csv for lines above the set threshold\n",
+    "de_base_csv_s_nom_v2 = base_csv_v2_snom[\n",
+    "    (base_csv_v2_snom.country == \"DE\")\n",
+    "    & (base_csv_v2_snom.voltage > voltage_threshold_v2)\n",
+    "]\n",
+    "de_twkm_base_csv_v2 = (\n",
+    "    de_base_csv_s_nom_v2.s_nom * de_base_csv_s_nom_v2.reprojected_length\n",
+    ").sum() / 1e3\n",
+    "\n",
+    "it_base_csv_s_nom_v2 = base_csv_v2_snom[\n",
+    "    (base_csv_v2_snom.country == \"IT\")\n",
+    "    & (base_csv_v2_snom.voltage > voltage_threshold_v2)\n",
+    "]\n",
+    "it_twkm_base_csv_v2 = (\n",
+    "    it_base_csv_s_nom_v2.s_nom * it_base_csv_s_nom_v2.reprojected_length\n",
+    ").sum() / 1e3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Total sum of line capacity in entsoe_csv\n",
+    "de_entsoe_snom_csv = entsoe_s_nom[entsoe_s_nom[f\"if_de\"] == True]\n",
+    "de_twkm_entsoe_csv = (\n",
+    "    (de_entsoe_snom_csv[\"length\"] / 1e3) * (de_entsoe_snom_csv[\"s_nom\"])\n",
+    ").sum()\n",
+    "\n",
+    "it_entsoe_snom_csv = entsoe_s_nom[entsoe_s_nom[f\"if_it\"] == True]\n",
+    "it_twkm_entsoe_csv = (\n",
+    "    (it_entsoe_snom_csv[\"length\"] / 1e3) * (it_entsoe_snom_csv[\"s_nom\"])\n",
+    ").sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Total sum of line capacity in entsoe_nc\n",
+    "de_twkm_entsoe_nc = (\n",
+    "    entsoe_nc_filtered_de[\"length\"] * entsoe_nc_filtered_de[\"s_nom\"]\n",
+    ").sum()\n",
+    "\n",
+    "it_twkm_entsoe_nc = (\n",
+    "    entsoe_nc_filtered_it[\"length\"] * entsoe_nc_filtered_it[\"s_nom\"]\n",
+    ").sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.bar(\n",
+    "    [\"osm_base_csv\", \"entsoe_csv\", \"entsoe_nc\"],\n",
+    "    [de_twkm_base_csv_v2, de_twkm_entsoe_csv, de_twkm_entsoe_nc],\n",
+    ")\n",
+    "plt.title(\n",
+    "    f\"TWKM comparison between osm_base_csv, entsoe_csv and entsoe.nc for DE with increased voltage\"\n",
+    ")\n",
+    "plt.ylabel(\"TWKm\")\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.bar(\n",
+    "    [\"osm_base_csv\", \"entsoe_csv\", \"entsoe_nc\"],\n",
+    "    [it_twkm_base_csv_v2, it_twkm_entsoe_csv, it_twkm_entsoe_nc],\n",
+    ")\n",
+    "plt.title(\n",
+    "    f\"TWKM comparison between osm_base_csv, entsoe_csv and entsoe.nc for IT with increased voltage\"\n",
+    ")\n",
+    "plt.ylabel(\"TWKm\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "This analysis aimed to explore transmission capacity lines, focusing on the discrepancy between OSM-derived data in PyPSA-Earth model and the reference ENTSOE dataset for Italy and Germany. Specifically, `base.nc` has been examined in part of power grid data assumed there.\n",
+    "\n",
+    "The quantitative analysis that was carried out in this notebook involved calculation of the Estimated Transmission Network Capacity (TWKm) and Limit of the Apparent Power(s_nom). The analysis included an independent reproducing of PyPSA-Earth workflow to build `base.nc` network model using base -csv's data. As a result, large discrepancies have been detected which prompted further investigation to estimate the sensitivity of the outputs to the threshold voltage values. It has been shown that the observed discrepancy with the 210kV threshold was more than the discrepancy with the 51kV threshold.\n",
+    "\n",
+    "The inconsistencies hint at a possible modelling flaws in the current modelling approach using in OSM data neccessitating further investidation and modelling.\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pypsa-earth",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}