From 08985ec210fed8c2067fde13cc20dd0685c02643 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Tue, 29 Oct 2024 01:11:14 +0530
Subject: [PATCH 01/12] merge conflict fixed

---
 ...r_across_different_lending_protocols.ipynb | 1412 ++++++++++++++++-
 1 file changed, 1368 insertions(+), 44 deletions(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index fe56f24e..f99c59d9 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "3bcbd644-a92c-4cb3-84ed-64eb71dd0cff",
    "metadata": {},
    "outputs": [],
@@ -158,14 +158,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "6a9975f0-b5aa-49b4-bd47-b560d7c590e8",
    "metadata": {},
-   "outputs": [],
+   "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>User</th>\n",
+       "      <th>Protocol</th>\n",
+       "      <th>Collateral (USD)</th>\n",
+       "      <th>Risk-adjusted collateral (USD)</th>\n",
+       "      <th>Debt (USD)</th>\n",
+       "      <th>Health factor</th>\n",
+       "      <th>Standardized health factor</th>\n",
+       "      <th>Collateral</th>\n",
+       "      <th>Debt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0x4306021e30f9577351207140f90425b3e9e102ec5a42...</td>\n",
+       "      <td>zklend</td>\n",
+       "      <td>5744.568231</td>\n",
+       "      <td>4289.009524</td>\n",
+       "      <td>22.162648</td>\n",
+       "      <td>193.524234</td>\n",
+       "      <td>193.524234</td>\n",
+       "      <td>USDC: 113.3876, USDT: 4610.7524, STRK: 904.5577</td>\n",
+       "      <td>USDC: 10.0284, USDT: 10.0302, wstETH: 0.0006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0x30b399e06903676ada3eccd5522e0cca4c4ad0101468...</td>\n",
+       "      <td>zklend</td>\n",
+       "      <td>37.671463</td>\n",
+       "      <td>30.137170</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>ETH: 0.0126</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0x2f006034f567d5c2431bc4104b2cc7a1bf8f004bd00c...</td>\n",
+       "      <td>zklend</td>\n",
+       "      <td>102.450086</td>\n",
+       "      <td>81.960069</td>\n",
+       "      <td>0.387499</td>\n",
+       "      <td>211.510582</td>\n",
+       "      <td>211.510582</td>\n",
+       "      <td>ETH: 0.0311, USDC: 6.5088, USDT: 3.0144</td>\n",
+       "      <td>ETH: 0.0005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0x43e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0d...</td>\n",
+       "      <td>zklend</td>\n",
+       "      <td>-5.156963</td>\n",
+       "      <td>-4.125570</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>inf</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0x22dd5ed1e4d359eca2e772ecefa57e31bb7756772850...</td>\n",
+       "      <td>zklend</td>\n",
+       "      <td>213.311298</td>\n",
+       "      <td>157.651127</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>wBTC: 0.0018, DAI: 23.1396, USDT: 83.3628</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                User Protocol  \\\n",
+       "0  0x4306021e30f9577351207140f90425b3e9e102ec5a42...   zklend   \n",
+       "1  0x30b399e06903676ada3eccd5522e0cca4c4ad0101468...   zklend   \n",
+       "2  0x2f006034f567d5c2431bc4104b2cc7a1bf8f004bd00c...   zklend   \n",
+       "3  0x43e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0d...   zklend   \n",
+       "4  0x22dd5ed1e4d359eca2e772ecefa57e31bb7756772850...   zklend   \n",
+       "\n",
+       "   Collateral (USD)  Risk-adjusted collateral (USD)  Debt (USD)  \\\n",
+       "0       5744.568231                     4289.009524   22.162648   \n",
+       "1         37.671463                       30.137170    0.000000   \n",
+       "2        102.450086                       81.960069    0.387499   \n",
+       "3         -5.156963                       -4.125570    0.000000   \n",
+       "4        213.311298                      157.651127    0.000000   \n",
+       "\n",
+       "   Health factor  Standardized health factor  \\\n",
+       "0     193.524234                  193.524234   \n",
+       "1            inf                         inf   \n",
+       "2     211.510582                  211.510582   \n",
+       "3            inf                         inf   \n",
+       "4            inf                         inf   \n",
+       "\n",
+       "                                        Collateral  \\\n",
+       "0  USDC: 113.3876, USDT: 4610.7524, STRK: 904.5577   \n",
+       "1                                      ETH: 0.0126   \n",
+       "2          ETH: 0.0311, USDC: 6.5088, USDT: 3.0144   \n",
+       "3                                                    \n",
+       "4        wBTC: 0.0018, DAI: 23.1396, USDT: 83.3628   \n",
+       "\n",
+       "                                           Debt  \n",
+       "0  USDC: 10.0284, USDT: 10.0302, wstETH: 0.0006  \n",
+       "1                                                \n",
+       "2                                   ETH: 0.0005  \n",
+       "3                                                \n",
+       "4                                                "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_loans.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8f078115-7933-467c-978b-161a7546b1c8",
+   "metadata": {},
+   "source": [
+    "### List of Current prices in USD for given tokens\n",
+    "Ethereum,Wrapped-Bitcoin,USD-coin,DAI,Tether,Wrapped-Steth,Lords,Strike,UNO-Re,Zenad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cc699d21-fde6-4265-93cd-be0970fe97ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Token Prices in USD: {'dai': {'usd': 1.0}, 'ethereum': {'usd': 2487.73}, 'lords': {'usd': 0.04989956}, 'strike': {'usd': 6.52}, 'tether': {'usd': 0.998271}, 'uno-re': {'usd': 0.01199116}, 'usd-coin': {'usd': 1.0}, 'wrapped-bitcoin': {'usd': 67042}, 'wrapped-steth': {'usd': 2934.02}, 'zenad': {'usd': 0.00118212}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "# List of token IDs to fetch from CoinGecko (you can add more tokens if needed)\n",
+    "token_ids = 'ethereum,wrapped-bitcoin,usd-coin,dai,tether,wrapped-steth,lords,strike,uno-re,zenad'\n",
+    "\n",
+    "# API endpoint\n",
+    "url = 'https://api.coingecko.com/api/v3/simple/price'\n",
+    "params = {\n",
+    "    'ids': token_ids,\n",
+    "    'vs_currencies': 'usd'\n",
+    "}\n",
+    "\n",
+    "response = requests.get(url, params=params)\n",
+    "prices = response.json()\n",
+    "\n",
+    "print(\"Token Prices in USD:\", prices)\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1f0db2bb-ca64-4e30-b3d5-9e864289a944",
@@ -177,13 +358,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "c6a72339-26b1-49e6-943f-4bea5ba8b3a3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Protocol\n",
+       "zklend            1383629\n",
+       "nostra_mainnet     247540\n",
+       "nostra_alpha       143645\n",
+       "hashstack_v1         1289\n",
+       "hashstack_v0          131\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# the distribution of protocols among users\n",
-    "df_loans['Protocol'].value_counts()"
+    "top_protocols = df_loans['Protocol'].value_counts()\n",
+    "top_protocols"
    ]
   },
   {
@@ -196,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "084931be-14e4-4182-91dd-fa5701265967",
    "metadata": {
     "scrolled": true
@@ -215,7 +414,7 @@
     "    user = row['User']\n",
     "    protocol = row['Protocol']\n",
     "    user_protocols_liquidity[user].add(protocol)\n",
-    "\n",
+    "    \n",
     "# Count the number of protocols each user lends on\n",
     "user_protocol_counts_liquidity = Counter([len(protocols) for protocols in user_protocols_liquidity.values()])\n",
     "\n",
@@ -227,6 +426,126 @@
     "protocol_count_df_liquidity = protocol_count_df_liquidity.sort_values(by='Number of Protocols')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e0abeedd-dd39-46f0-a5b7-c8989b53f67c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "protocol_count_df_liquidity = protocol_count_df_liquidity.reset_index()\n",
+    "protocol_count_df_liquidity.drop(columns=['index'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e989070b-821a-41f2-84ea-2c4cd19ba8d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Users providing liquidity:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>Number of Protocols</th>\n",
+       "      <th>Number of Users</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>402964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>71145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Number of Protocols  Number of Users\n",
+       "0                    1           402964\n",
+       "1                    2            71145\n",
+       "2                    3             4510\n",
+       "3                    4               15"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Users providing liquidity:\\n\")\n",
+    "protocol_count_df_liquidity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a31ce5a-c4bd-4fa1-9d1e-99e5036fb5a1",
+   "metadata": {},
+   "source": [
+    "### 1. How many users provide liquidity on just 1 protocol? How many users use 2 or more protocols?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "25eea99a-b7f6-4ec0-88fd-d528bac3e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of users provide liquidity on 1 protocol is 402964\n",
+      "Number of users proved liquidity on 2 or more portocols is 75670\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Number of users provide liquidity on 1 protocol is {protocol_count_df_liquidity.loc[0, 'Number of Users']}\\nNumber of users proved liquidity on 2 or more portocols is {sum(protocol_count_df_liquidity.loc[1:, 'Number of Users'])}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "db697ca0-30fe-4d21-a5fc-b01c5db05ad9",
@@ -237,14 +556,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "2853f77c-143b-4d6a-b584-20a515fa7d09",
    "metadata": {},
    "outputs": [],
    "source": [
     "## Helper funcitons:\n",
     "# Function to get unique users per protocol\n",
-    "def get_unique_users(df, value_column):\n",
+    "def get_unique_users_by_protocol(df):\n",
     "    protocol_users = defaultdict(set)\n",
     "    for protocol in df['Protocol'].unique():\n",
     "        users = set(df[df['Protocol'] == protocol]['User'])\n",
@@ -252,33 +571,52 @@
     "    return protocol_users\n",
     "    \n",
     "# Helper function to plot Venn diagram\n",
-    "def plot_venn_diagram(user_sets, title):\n",
+    "def plot_venn_diagram(user_sets, title, labels):\n",
     "    plt.figure(figsize=(10, 8))\n",
     "    venn3(subsets=(user_sets[0], user_sets[1], user_sets[2]), \n",
-    "          set_labels=('zklend', 'nostra_mainnet', 'nostra_alpha'))\n",
+    "          set_labels=labels)\n",
     "    plt.title(title)\n",
     "    plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "539348d0-d35e-4686-9e55-50bc54d6885a",
+   "metadata": {},
+   "source": [
+    "### Venn Diagram "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "9af460f2-dc1a-427c-a564-944ef18499e6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Get unique users providing liquidity\n",
     "liquidity_df = df_loans[df_loans['Collateral (USD)'] > 0]\n",
-    "liquidity_protocol_users = get_unique_users(liquidity_df, 'Collateral (USD)')\n",
+    "liquidity_protocol_users = get_unique_users_by_protocol(liquidity_df)\n",
     "\n",
     "\n",
     "# Prepare sets for Venn diagrams (top 3 protocols by user count)\n",
-    "top_protocols = ['zklend', 'nostra_mainnet', 'nostra_alpha']\n",
-    "liquidity_user_sets = [liquidity_protocol_users[protocol] for protocol in top_protocols]\n",
+    "top_3_protocols = top_protocols.keys()[:3].tolist()\n",
+    "liquidity_user_sets = [liquidity_protocol_users[protocol] for protocol in top_3_protocols]\n",
     "\n",
     "\n",
     "# Plot Venn diagrams\n",
-    "plot_venn_diagram(liquidity_user_sets, 'Users Providing Liquidity Across Top 3 Protocols')\n",
+    "plot_venn_diagram(liquidity_user_sets, 'Users Providing Liquidity Across Top 3 Protocols', top_3_protocols)\n",
     "# plot_venn_diagram(debt_user_sets, 'Users Borrowing Across Top 3 Protocols')"
    ]
   },
@@ -292,12 +630,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "ccdd4123-9a4e-4def-93a9-d8d21b637962",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Users borrowing:\n"
+     ]
+    },
+    {
+     "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>Number of Protocols</th>\n",
+       "      <th>Number of Users</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>114975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>8029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Number of Protocols  Number of Users\n",
+       "0                    1           114975\n",
+       "1                    2             8029\n",
+       "2                    3              186\n",
+       "3                    4                3"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Subset the DataFrame for users who have debt\n",
     "debt_data = df_loans[df_loans['Debt (USD)'] > 0]\n",
@@ -322,8 +730,35 @@
     "protocol_count_df_debt = protocol_count_df_debt.sort_values(by='Number of Protocols')\n",
     "\n",
     "# Print the result for debt\n",
-    "# print(\"Users borrowing:\")\n",
-    "# print(protocol_count_df_debt)"
+    "print(\"Users borrowing:\")\n",
+    "protocol_count_df_debt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8040a13f-605a-4998-9982-8860d913c4d8",
+   "metadata": {},
+   "source": [
+    "### 2. How many users borrow on just 1 protocol? How many users use 2 or more protocols?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a5fca63b-a6db-4d1a-b426-ff2a98690b45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of users provide liquidity on 1 protocol is 114975\n",
+      "Number of users proved liquidity on 2 or more portocols is 8218\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Number of users provide liquidity on 1 protocol is {protocol_count_df_debt.loc[0, 'Number of Users']}\\nNumber of users proved liquidity on 2 or more portocols is {sum(protocol_count_df_debt.loc[1:, 'Number of Users'])}\")"
    ]
   },
   {
@@ -336,22 +771,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "0895eb29-a63d-4fcd-998d-0f77ae444fce",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Get unique users having debt\n",
     "debt_df = df_loans[df_loans['Debt (USD)'] > 0]\n",
-    "debt_protocol_users = get_unique_users(debt_df, 'Debt (USD)')\n",
+    "debt_protocol_users = get_unique_users_by_protocol(debt_df)\n",
     "\n",
     "\n",
     "# Prepare sets for Venn diagrams (top 3 protocols by user count)\n",
-    "top_protocols = ['zklend', 'nostra_mainnet', 'nostra_alpha']\n",
-    "debt_user_sets = [debt_protocol_users[protocol] for protocol in top_protocols]\n",
+    "top_3_protocols = top_protocols.keys()[:3]\n",
+    "debt_user_sets = [debt_protocol_users[protocol] for protocol in top_3_protocols]\n",
     "\n",
     "# Plot Venn diagrams\n",
-    "plot_venn_diagram(debt_user_sets, 'Users Borrowing Across Top 3 Protocols')"
+    "plot_venn_diagram(debt_user_sets, 'Users Borrowing Across Top 3 Protocols', top_3_protocols)"
    ]
   },
   {
@@ -364,12 +810,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "0c4a2a57-bd8e-44e0-ab0a-c969a311cb2a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Protocol\n",
+      "hashstack_v0      3.385131e+03\n",
+      "hashstack_v1      1.996617e+05\n",
+      "nostra_alpha      1.661670e+05\n",
+      "nostra_mainnet    1.564581e+08\n",
+      "zklend            3.885571e+07\n",
+      "Name: Collateral (USD), dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
+    "import numpy as np\n",
     "\n",
     "# Function to calculate total capital per token across protocols\n",
     "def calculate_capital(df, column_name):\n",
@@ -379,7 +850,7 @@
     "# Function to plot bar chart for token capital across protocols\n",
     "def plot_capital(capital, title):\n",
     "    plt.figure(figsize=(10, 6))\n",
-    "    sns.barplot(x=capital.index, y=capital.values)\n",
+    "    sns.barplot(x=capital.index, y=np.log(capital.values))\n",
     "    plt.xlabel('Protocol')\n",
     "    plt.ylabel('Total Capital (USD)')\n",
     "    plt.title(title)\n",
@@ -388,7 +859,8 @@
     "\n",
     "# Calculate total staked capital per token\n",
     "staked_capital = calculate_capital(liquidity_df, 'Collateral (USD)')\n",
-    "plot_capital(staked_capital, 'Total Staked Capital per Token Across Protocols')\n",
+    "print(staked_capital)\n",
+    "plot_capital(staked_capital, 'Total Staked Capital per Token Across Protocols (Logirathmic Scaling is used)')\n",
     "\n",
     "\n"
    ]
@@ -403,14 +875,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "c66e6f79-aeb8-41e0-aa01-a17ee535d50f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mzpxpJIV5HiF/0xkzZtinRdYGjYn4/TN48GBjs9nMuXPn7NOe/S549vFSpUplbt26ZZ++fPnycK9LSB3ffvutfdrt27dN4sSJjc1mM/Pnz7dPP378uJFk+vXrZ5/27HdCVN+DETl8+LBJnDixkWR8fHxM586dzbJly8z9+/fDLHfnzh2TPHly8/bbb5uHDx9G+hhRfR1DPgdDe/bzLuT9VqlSpTCP0bVrV+Po6Gju3LljjDHmypUrxsnJydStWzfM9vr37x+lz3pjTLjX2N3d3bRv3/4/13tWZJ+vzz7fqHye/fjjj8bBwcFs27YtzPRJkyYZSeb33383xvzv/d6uXbswy33wwQfhnleIjz/+2CROnDgazwzxFd0C8cpYvXq1HB0d1alTpzDTP//8cxljojQ6X+LEie3/f/LkiW7evKkcOXLIw8PjP7sePM/48eO1bt06rVu3TnPmzFH58uXVpk0bLVmy5IXrL1u2rPLmzRvh47Vt21aOjo72++vXr9fjx4/VpUuXMCcnt23bVm5ubvrll18kPT1voGTJktq6dask6dixY7p586Z69+4tY4x27twp6enRrPz588vDw0PS0z2j7u7uqly5sm7cuGG/FSlSRMmSJdOmTZvC1PHsSbtRHUjA399fkpQ8efIoLR/67/no0SPduHHDfu5JRH/P9u3bh7nfsWNHSU//Ni8i9OPfvXtXN27cUOnSpfXgwQMdP3482tsLDAzUggUL1LhxY/vrV6FCBaVJk0Zz5861L3fgwAGdOXNGXbp0sf+NQkTUZerZATGi2hZDtr18+XIFBwdHWLOHh4cuXrwYrhtWVPXr10+enp5Kly6dypUrp9OnT+v7779X/fr1wyz3om3+eVavXq106dKpSZMm9mnOzs7q1KmT7t27py1btkiSfv75Z9lstghHcgt5vUPaUOjutdLT11TSc+t50dcwpPtrihQporWeFPXXb+/evbp586batm0bpptz06ZNo/W4EQ3KEvr9c//+fd24cUMlS5aUMUYHDhyI8rYbN24cppbSpUtLkv75559wy7Zp08b+fw8PD+XOnVtJkyZVo0aN7NNz584tDw+PCNcPEd33YGj58uXTwYMH9eGHH+rs2bMaPXq06tatq7Rp0+qHH36wL7du3TrdvXtXvXv3Dnd+UujHsOp1DO3jjz8O8xilS5dWUFCQfbj/DRs2KDAwUO3atQuzXshn6ovw8PDQ7t27denSpRfexn9tX3r+59miRYv0xhtvKE+ePGG+60KOMIZ814W835/9DH3ed12KFCn08OFDPXjw4CWfCeIa4QqvjHPnzsnLyyvcD++QLlpRucbLw4cP1bdvX/t5JqlTp5anp6fu3LkjPz+/F66tWLFiqlSpkipVqqSmTZvql19+Ud68edWhQwd7d7jo1v+87ijPzgtZN3fu3GGmu7i4KFu2bGG2Xbp0ae3bt08PHz7Utm3blD59er355psqVKiQvWvg9u3b7T9QpKej1/n5+SlNmjTy9PQMc7t37579JOyQx8mZM2eYOjw9PaP0Q8zNzU3S06ASFbdu3VLnzp2VNm1aJU6cWJ6envbXJqK/57N1Zc+eXQ4ODi98zt2RI0dUr149ubu7y83NTZ6envauoC/SntauXavr16+rWLFiOnXqlE6dOqUzZ86ofPnymjdvnv0HQUjXuagMte3k5BSu61ZU22Ljxo31zjvvqE2bNkqbNq3ef/99LVy4MMwPk169eilZsmQqVqyYcubMqfbt20er68vHH3+sdevWacOGDdq3b5+uXbumnj17hlvuZdp8ZM6dO6ecOXOGGy3t2dfh9OnT8vLyUsqUKZ+7LQcHB+XIkSPM9HTp0snDw+O59bzsa2ieOU8uKqL6+oX8++zzcnJyivIgKxG1QUk6f/68WrRooZQpUypZsmTy9PRU2bJlJUXv/ZM5c+Yw90M+a549HzTkXLnQ3N3dlTFjxnCByN3dPdz6oUXnPRiRXLly6ccff9SNGzf0559/6ttvv5WTk5M+/vhjrV+/PlqPYdXrGNp/vaaRtYuUKVO+UNiXpCFDhujw4cPKlCmTihUrpv79+z834EZXVD7PTp48qSNHjoT7nsuVK5ckhfmuc3BwsJ9yEOLZ91NoIe9TRgtM+DjnCgilY8eOmjFjhrp06aISJUrI3d1dNptN77//fqR7sl6Eg4ODypcvr9GjR+vkyZPKly9ftLcRem9kdOb9l1KlSunJkyfauXOntm3bZg9RpUuX1rZt23T8+HFdv349TLgKDg4Od/QktGd/sLyoPHnySHp6cnnox49Mo0aNtGPHDvXo0UM+Pj5KliyZgoODVbVq1Sj9PV/mS+7OnTsqW7as3NzcNHDgQPv1avbv369evXq9UHsKeX1D70UPbcuWLSpfvny0tunq6vrCQy0nTpxYW7du1aZNm/TLL79ozZo1WrBggSpUqKC1a9fK0dFRb7zxhk6cOKFVq1ZpzZo1+vnnnzVhwgT17ds3SkNK58yZU5UqVYpSLQnBi7SpF30NQ85Le14IiA8iaoNBQUGqXLmybt26pV69eilPnjxKmjSp/v33X7Vo0SJa75/QRzRDezZ0RrZcVNePCY6OjipQoIAKFCigEiVKqHz58po7d26U3hOSta/js3VFJCZfk0aNGql06dJaunSp1q5dq6FDh+r777/XkiVLVK1atUjXi+w99+zAQlH5PAsODlaBAgU0YsSICLeZKVOmF35+t2/fVpIkSRLMZxkix5ErvDK8vb116dKlcEc1QrpfhR55KbIP28WLF6t58+b2Ia4rV66sUqVKRTg61ssKDAyUJN27dy/a9UdXyLonTpwIM/3x48c6c+ZMmG0XK1ZMLi4u2rZtW5hwVaZMGe3evVsbNmyw3w+RPXt23bx5U++88479CF3oW6FChcLUcfLkyTB1XL9+PUo/AGvVqiXp6VDk/+X27dvasGGDevfurQEDBqhevXqqXLmysmXLFuk6z9Z16tQpBQcHP3cPfGRtafPmzbp586Zmzpypzp07q2bNmqpUqdIL77W9f/++li9frsaNG2vRokXhbunTp7eHr5C9pYcPH36hx4pOW3RwcFDFihU1YsQIHT16VN988402btxo7x4jSUmTJlXjxo01Y8YMnT9/XjVq1NA333zzwie2R/U5SFFr85H9Db29vXXy5MlwP0CffR2yZ8+uS5cu6datW8+tJzg4OFwbu3r1qu7cufOf7+8XeQ1DdkaEjKwYHVF9/UL+PXXqVJjlAgMDX2qU1b/++kt///23hg8frl69eqlOnTqqVKmSvLy8wi0bH/f0v+x7MCIhA6Ncvnw5yo8RndfRSpG1i5s3b75U2E+fPr3atWunZcuW6cyZM0qVKpW++eab566TIkWKCL/DIzpa/F+fZ9mzZ9etW7dUsWLFCL/rQo5MhbzfQ44uhnj2/RTamTNnonSdTsR/hCu8MkIuNDpu3Lgw00eOHCmbzRZmz1bSpEkj/LB1dHQMt+dt7NixLz109rOePHmitWvXysXFxf5hGp36o6tSpUpycXHRmDFjwjy/adOmyc/PL8wIRokSJVLRokU1b948nT9/PsyRq4cPH2rMmDHKnj270qdPb1+nUaNGCgoK0tdffx3usQMDA+2vdaVKleTs7KyxY8eGqWPUqFFReh4lSpRQ1apVNXXq1DCjMoV4/PixunfvLul/e1af/Xs+77FChl0OMXbsWEl67msfcj2eZ9tTRI//+PFjTZgwIdJtPc/SpUt1//59tW/fXg0aNAh3q1mzpn7++WcFBATozTffVNasWTVq1KhwdUVlz3JU22JEYSLkwr4hwxI/O+y9i4uL8ubNK2OMnjx5EtWnH23RafNJkyaNsHtU9erVdeXKlTAjoAUGBmrs2LFKliyZvWvVe++9J2NMhEeRQh67evXqksK3v5A94M8bRexFX8MMGTIoU6ZMYYaDj6qovn5vvfWWUqVKpR9++MG+w0h6epT1ZX5ER/T+McZEOIx5ZO/BuPQy78Ft27ZF+HcNOY8n5Af8u+++q+TJk2vw4MHhQnbIY0TndbRSxYoV5eTkpIkTJ4aZ/uxnSlQFBQWFe4+mSZNGXl5e/3kpg+zZs8vPz09//vmnfdrly5fDjdAZlc+zRo0a6d9//w1z7luIhw8f2kcqDfmMHDNmTJhlnvf9s3//fpUsWfK5zwUJA90C8cqoVauWypcvry+//FJnz55VoUKFtHbtWi1fvlxdunQJ0/e5SJEiWr9+vUaMGCEvLy9lzZpVb7/9tmrWrKkff/xR7u7uyps3r3bu3Kn169eHG/Y5un799Vf73u5r167pp59+0smTJ9W7d2/7eUTRqT+6PD095evrqwEDBqhq1aqqXbu2Tpw4oQkTJqho0aLhLsRcunRpfffdd3J3d1eBAgUkPf0iy507t06cOBHuGiVly5bVJ598osGDB+vgwYN699135ezsrJMnT2rRokUaPXq0GjRoIE9PT3Xv3l2DBw9WzZo1Vb16dR04cEC//vqrUqdOHaXnMnv2bL377ruqX7++atWqpYoVKypp0qQ6efKk5s+fr8uXL2vYsGFyc3OzD6n85MkTZciQQWvXrn3uXvwzZ86odu3aqlq1qnbu3GkfRjfkyFtEfHx85OjoqO+//15+fn5ydXVVhQoVVLJkSaVIkULNmzdXp06dZLPZ9OOPP75wt5m5c+cqVapUkX751q5dWz/88IN++eUX1a9fXxMnTlStWrXk4+Ojli1bKn369Dp+/LiOHDmi33777bmPFdW2OHDgQG3dulU1atSQt7e3rl27pgkTJihjxowqVaqUpKc/ANOlS6d33nlHadOm1bFjxzRu3DjVqFEjygOTvIjotPkiRYpowYIF6tatm4oWLapkyZKpVq1a+vjjjzV58mS1aNFC+/btU5YsWbR48WL9/vvvGjVqlL3+8uXL66OPPtKYMWN08uRJe7fTbdu2qXz58urQoYMKFSqk5s2ba8qUKfYuo3/88YdmzZqlunXrPrc758u8hnXq1NHSpUtljAl3hOf69esaNGhQuHWyZs2qpk2bRun1c3FxUf/+/dWxY0dVqFBBjRo10tmzZzVz5kxlz579hY8q5cmTR9mzZ1f37t3177//ys3NTT///HOEga1IkSKSng4eUKVKFTk6Our9999/oce1ioODwwu/B7///nvt27dP9evXV8GCBSU9/eE9e/ZspUyZ0j4ogpubm0aOHKk2bdqoaNGi+uCDD5QiRQodOnRIDx480KxZs6L1Olopbdq06ty5s4YPH27/TD106JD9sz667eLu3bvKmDGjGjRooEKFCilZsmRav3699uzZo+HDhz933ffff1+9evVSvXr11KlTJz148EATJ05Urly5wgxsFJXPs48++kgLFy7Up59+qk2bNumdd95RUFCQjh8/roULF+q3337TW2+9JR8fHzVp0kQTJkyQn5+fSpYsqQ0bNoQ7khdi3759unXrlurUqROt1wXxVCyMSAjEiIiG3717967p2rWr8fLyMs7OziZnzpxm6NCh4Ya+PX78uClTpox9uNuQYWFv375tWrZsaVKnTm2SJUtmqlSpYo4fPx5uONqXGYo9UaJExsfHx0ycODFcXVGtX1KEQ9KGPN6ePXsirGfcuHEmT548xtnZ2aRNm9Z89tlnEQ5r/ssvvxhJplq1amGmt2nTxkgy06ZNi3D7U6ZMMUWKFDGJEyc2yZMnNwUKFDA9e/Y0ly5dsi8TFBRkBgwYYNKnT28SJ05sypUrZw4fPhzuNX6eBw8emGHDhpmiRYuaZMmSGRcXF5MzZ07TsWPHMEPWX7x40dSrV894eHgYd3d307BhQ3Pp0qVwQ+GGDMl79OhR06BBA5M8eXKTIkUK06FDh3BDHEdU5w8//GCyZctmHB0dw7SL33//3RQvXtwkTpzYeHl5mZ49e5rffvstXNv5r6HYr169apycnMxHH3303NckSZIkpl69evZp27dvN5UrVzbJkyc3SZMmNQULFjRjx44N87hJkyaNcHtRaYsbNmwwderUMV5eXsbFxcV4eXmZJk2amL///tu+zOTJk02ZMmVMqlSpjKurq8mePbvp0aOH8fPzi/S5GPO/YbSHDh363OWsaPP37t0zH3zwgfHw8DCSwvwtrl69av9McHFxMQUKFAgzlHiIwMBAM3ToUJMnTx7j4uJiPD09TbVq1cy+ffvsyzx58sQMGDDAZM2a1Tg7O5tMmTIZX1/fMMPyGxN+KPYXfQ2NMWb//v1GUriho8uWLRvhZSIkmYoVK0br9TPGmDFjxhhvb2/j6upqihUrZn7//XdTpEgRU7VqVfsykQ3FHlkbPHr0qKlUqZJJliyZSZ06tWnbtq05dOhQuG0EBgaajh07Gk9PT2Oz2ezfC89rQ89+BkRWR2RD1nt7e5saNWrY70f2nfBf78GI/P7776Z9+/Ymf/78xt3d3Tg7O5vMmTObFi1amNOnT4dbfsWKFaZkyZImceLExs3NzRQrVszMmzfPPj+qr2N0hmJ/9v0W0fMPDAw0ffr0MenSpTOJEyc2FSpUMMeOHTOpUqUyn3766XNfA2PC/o0CAgJMjx49TKFCheyvZaFChcyECRP+czvGPL1kRf78+Y2Li4vJnTu3mTNnTrjnG5XPM2OeXo7h+++/N/ny5TOurq4mRYoUpkiRImbAgAFh3pMPHz40nTp1MqlSpTJJkyY1tWrVMhcuXIhwKPZevXqZzJkz/+cw/UgYbMbEwhmZABCPhVws9fr161E+ggYkFBUrVpSXl1ekF9mNCcHBwfL09FT9+vUj7EKF19OdO3eUIkUKDRo0SF9++WVclxMvBAQEKEuWLOrdu/dzLy6NhINzrgAAeIV9++23WrBgQZSGn38Rjx49Ctfddfbs2bp165bKlSsXI4+J+O/hw4fhpoWcc0S7+J8ZM2bI2dlZn376aVyXAotwzhUAAK+wt99+2349vZiwa9cude3aVQ0bNlSqVKm0f/9+TZs2Tfnz51fDhg1j7HERvy1YsEAzZ85U9erVlSxZMm3fvl3z5s3Tu+++q3feeSeuy4s3Pv30U4LVK4ZwBQAAXliWLFmUKVMmjRkzRrdu3VLKlCnVrFkzfffdd3JxcYnr8hBHChYsKCcnJw0ZMkT+/v72QS4iGkQFeJVwzhUAAAAAWIBzrgAAAADAAoQrAAAAALAA51xFIDg4WJcuXVLy5Mlf+AKIAAAAABI+Y4zu3r0rLy8vOTg8/9gU4SoCly5dUqZMmeK6DAAAAADxxIULF5QxY8bnLkO4ikDy5MklPX0B3dzc4rgaAAAAAHHF399fmTJlsmeE5yFcRSCkK6CbmxvhCgAAAECUThdiQAsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALBAnIarrVu3qlatWvLy8pLNZtOyZcvCzLfZbBHehg4dGuk2+/fvH275PHnyxPAzAQAAAPC6i9Nwdf/+fRUqVEjjx4+PcP7ly5fD3KZPny6bzab33nvvudvNly9fmPW2b98eE+UDAAAAgJ1TXD54tWrVVK1atUjnp0uXLsz95cuXq3z58sqWLdtzt+vk5BRuXQAAkDAU6TE7rktALNo3tFlclwBYJsGcc3X16lX98ssvat269X8ue/LkSXl5eSlbtmxq2rSpzp8//9zlAwIC5O/vH+YGAAAAANGRYMLVrFmzlDx5ctWvX/+5y7399tuaOXOm1qxZo4kTJ+rMmTMqXbq07t69G+k6gwcPlru7u/2WKVMmq8sHAAAA8IpLMOFq+vTpatq0qRIlSvTc5apVq6aGDRuqYMGCqlKlilavXq07d+5o4cKFka7j6+srPz8/++3ChQtWlw8AAADgFRen51xF1bZt23TixAktWLAg2ut6eHgoV65cOnXqVKTLuLq6ytXV9WVKBAAAAPCaSxBHrqZNm6YiRYqoUKFC0V733r17On36tNKnTx8DlQEAAADAU3Earu7du6eDBw/q4MGDkqQzZ87o4MGDYQag8Pf316JFi9SmTZsIt1GxYkWNGzfOfr979+7asmWLzp49qx07dqhevXpydHRUkyZNYvS5AAAAAHi9xWm3wL1796p8+fL2+926dZMkNW/eXDNnzpQkzZ8/X8aYSMPR6dOndePGDfv9ixcvqkmTJrp586Y8PT1VqlQp7dq1S56enjH3RAAAAAC89mzGGBPXRcQ3/v7+cnd3l5+fn9zc3OK6HAAAXitc5+r1wnWuEN9FJxskiHOuAAAAACC+I1wBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYwCmuCwAAAADiQpEes+O6BMSifUObxfhjcOQKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAvEabjaunWratWqJS8vL9lsNi1btizM/BYtWshms4W5Va1a9T+3O378eGXJkkWJEiXS22+/rT/++COGngEAAAAAPBWn4er+/fsqVKiQxo8fH+kyVatW1eXLl+23efPmPXebCxYsULdu3dSvXz/t379fhQoVUpUqVXTt2jWrywcAAAAAO6e4fPBq1aqpWrVqz13G1dVV6dKli/I2R4wYobZt26ply5aSpEmTJumXX37R9OnT1bt375eqFwAAAAAiE+/Pudq8ebPSpEmj3Llz67PPPtPNmzcjXfbx48fat2+fKlWqZJ/m4OCgSpUqaefOnZGuFxAQIH9//zA3AAAAAIiOeB2uqlatqtmzZ2vDhg36/vvvtWXLFlWrVk1BQUERLn/jxg0FBQUpbdq0YaanTZtWV65cifRxBg8eLHd3d/stU6ZMlj4PAAAAAK++OO0W+F/ef/99+/8LFCigggULKnv27Nq8ebMqVqxo2eP4+vqqW7du9vv+/v4ELAAAAADREq+PXD0rW7ZsSp06tU6dOhXh/NSpU8vR0VFXr14NM/3q1avPPW/L1dVVbm5uYW4AAAAAEB0JKlxdvHhRN2/eVPr06SOc7+LioiJFimjDhg32acHBwdqwYYNKlCgRW2UCAAAAeA3Fabi6d++eDh48qIMHD0qSzpw5o4MHD+r8+fO6d++eevTooV27duns2bPasGGD6tSpoxw5cqhKlSr2bVSsWFHjxo2z3+/WrZt++OEHzZo1S8eOHdNnn32m+/fv20cPBAAAAICYEKfnXO3du1fly5e33w8576l58+aaOHGi/vzzT82aNUt37tyRl5eX3n33XX399ddydXW1r3P69GnduHHDfr9x48a6fv26+vbtqytXrsjHx0dr1qwJN8gFAAAAAFgpTsNVuXLlZIyJdP5vv/32n9s4e/ZsuGkdOnRQhw4dXqY0AAAAAIiWBHXOFQAAAADEV4QrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALxGm42rp1q2rVqiUvLy/ZbDYtW7bMPu/Jkyfq1auXChQooKRJk8rLy0vNmjXTpUuXnrvN/v37y2azhbnlyZMnhp8JAAAAgNddnIar+/fvq1ChQho/fny4eQ8ePND+/fvVp08f7d+/X0uWLNGJEydUu3bt/9xuvnz5dPnyZftt+/btMVE+AAAAANg5xeWDV6tWTdWqVYtwnru7u9atWxdm2rhx41SsWDGdP39emTNnjnS7Tk5OSpcunaW1AgAAAMDzJKhzrvz8/GSz2eTh4fHc5U6ePCkvLy9ly5ZNTZs21fnz55+7fEBAgPz9/cPcAAAAACA6Eky4evTokXr16qUmTZrIzc0t0uXefvttzZw5U2vWrNHEiRN15swZlS5dWnfv3o10ncGDB8vd3d1+y5QpU0w8BQAAAACvsAQRrp48eaJGjRrJGKOJEyc+d9lq1aqpYcOGKliwoKpUqaLVq1frzp07WrhwYaTr+Pr6ys/Pz367cOGC1U8BAAAAwCsuTs+5ioqQYHXu3Dlt3LjxuUetIuLh4aFcuXLp1KlTkS7j6uoqV1fXly0VAAAAwGssXh+5CglWJ0+e1Pr165UqVapob+PevXs6ffq00qdPHwMVAgAAAMBTcRqu7t27p4MHD+rgwYOSpDNnzujgwYM6f/68njx5ogYNGmjv3r2aO3eugoKCdOXKFV25ckWPHz+2b6NixYoaN26c/X737t21ZcsWnT17Vjt27FC9evXk6OioJk2axPbTAwAAAPAaidNugXv37lX58uXt97t16yZJat68ufr3768VK1ZIknx8fMKst2nTJpUrV06SdPr0ad24ccM+7+LFi2rSpIlu3rwpT09PlSpVSrt27ZKnp2fMPhkAAAAAr7U4DVflypWTMSbS+c+bF+Ls2bNh7s+fP/9lywIAAACAaIvX51wBAAAAQEJBuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALOMV1AQBeTpEes+O6BMSifUObxXUJAAAgEhy5AgAAAAALROvI1Z07d7R06VJt27ZN586d04MHD+Tp6anChQurSpUqKlmyZEzVCQAAAADxWpSOXF26dElt2rRR+vTpNWjQID18+FA+Pj6qWLGiMmbMqE2bNqly5crKmzevFixYENM1AwAAAEC8E6UjV4ULF1bz5s21b98+5c2bN8JlHj58qGXLlmnUqFG6cOGCunfvbmmhAAAAABCfRSlcHT16VKlSpXruMokTJ1aTJk3UpEkT3bx505LiAAAAACChiFK3wP8KVi+7PAAAAAAkdNEein3jxo1asmSJzp49K5vNpqxZs6pBgwYqU6ZMTNQHAAAAAAlCtIZi//TTT1WpUiXNmzdPN2/e1PXr1zV37lyVL19eHTt2jKkaAQAAACDei3K4Wrp0qWbMmKHp06frxo0b2rlzp3bt2qXr16/rhx9+0JQpU7RixYqYrBUAAAAA4q0oh6sZM2aoW7duatGihWw22/824OCgVq1aqUuXLpo2bVqMFAkAAAAA8V2Uw9X+/ftVr169SOfXr19f+/bts6QoAAAAAEhoohyubty4oYwZM0Y6P2PGjAzBDgAAAOC1FeVw9fjxYzk7O0c638nJSY8fP7akKAAAAABIaKI1FHufPn2UJEmSCOc9ePDAkoIAAAAAICGKcrgqU6aMTpw48Z/LAAAAAMDrKMrhavPmzTFYBgAAAAAkbNG6iHBEAgMDde/ePStqAQAAAIAEK8rhauXKlZo5c2aYad98842SJUsmDw8Pvfvuu7p9+7bV9QEAAABAghDlcDVixAjdv3/ffn/Hjh3q27ev+vTpo4ULF+rChQv6+uuvY6RIAAAAAIjvohyujhw5opIlS9rvL168WJUrV9aXX36p+vXra/jw4Vq5cmWMFAkAAAAA8V2Uw9Xdu3eVKlUq+/3t27erYsWK9vv58uXTpUuXrK0OAAAAABKIKIerDBky6NixY5Kke/fu6dChQ2GOZN28eTPSa2ABAAAAwKsuyuGqYcOG6tKli3788Ue1bdtW6dKlU/Hixe3z9+7dq9y5c8dIkQAAAAAQ30X5Old9+/bVv//+q06dOildunSaM2eOHB0d7fPnzZunWrVqxUiRAAAAABDfRTlcJU6cWLNnz450/qZNmywpCAAAAAASope+iDAAAAAAIBpHrlKkSCGbzRZuuru7u3LlyqXu3burcuXKlhYHAAAAAAlFlMPVqFGjIpx+584d7du3TzVr1tTixYs57woAAADAaynK4ap58+bPne/j46PBgwcTrgAAAAC8liw756pmzZo6fvy4VZsDAAAAgATFsnAVEBAgFxcXqzYHAAAAAAmKZeFq2rRp8vHxsWpzAAAAAJCgRPmcq27dukU43c/PT/v379fff/+trVu3WlYYAAAAACQkUQ5XBw4ciHC6m5ubKleurCVLlihr1qyWFQYAAAAACUmUw9WmTZtisg4AAAAASNAsO+cKAAAAAF5nUQpXn376qS5evBilDS5YsEBz5859qaIAAAAAIKGJUrdAT09P5cuXT++8845q1aqlt956S15eXkqUKJFu376to0ePavv27Zo/f768vLw0ZcqUmK4bAAAAAOKVKIWrr7/+Wh06dNDUqVM1YcIEHT16NMz85MmTq1KlSpoyZYqqVq0aI4UCAAAAQHwW5QEt0qZNqy+//FJffvmlbt++rfPnz+vhw4dKnTq1smfPLpvNFpN1AgAAAEC8FuVwFVqKFCmUIkWKl37wrVu3aujQodq3b58uX76spUuXqm7duvb5xhj169dPP/zwg+7cuaN33nlHEydOVM6cOZ+73fHjx2vo0KG6cuWKChUqpLFjx6pYsWIvXS8AvM6K9Jgd1yUgFu0b2iyuSwCABCdORwu8f/++ChUqpPHjx0c4f8iQIRozZowmTZqk3bt3K2nSpKpSpYoePXoU6TYXLFigbt26qV+/ftq/f78KFSqkKlWq6Nq1azH1NAAAAAAgbsNVtWrVNGjQINWrVy/cPGOMRo0apa+++kp16tRRwYIFNXv2bF26dEnLli2LdJsjRoxQ27Zt1bJlS+XNm1eTJk1SkiRJNH369Bh8JgAAAABed/H2OldnzpzRlStXVKlSJfs0d3d3vf3229q5c2eE6zx+/Fj79u0Ls46Dg4MqVaoU6TqSFBAQIH9//zA3AAAAAIiOeBuurly5IunpQBqhpU2b1j7vWTdu3FBQUFC01pGkwYMHy93d3X7LlCnTS1YPAAAA4HUTb8NVbPL19ZWfn5/9duHChbguCQAAAEACE6XRAgsXLhzlodb379//UgWFSJcunSTp6tWrSp8+vX361atX5ePjE+E6qVOnlqOjo65evRpm+tWrV+3bi4irq6tcXV1fvmgAAAAAr60ohavQw6PHlqxZsypdunTasGGDPUz5+/tr9+7d+uyzzyJcx8XFRUWKFNGGDRvsNQcHB2vDhg3q0KFDLFUOAAAA4HUUpXDVr1+/GHnwe/fu6dSpU/b7Z86c0cGDB5UyZUplzpxZXbp00aBBg5QzZ05lzZpVffr0kZeXV5iwV7FiRdWrV88enrp166bmzZvrrbfeUrFixTRq1Cjdv39fLVu2jJHnAAAAAADSC15E2Cp79+5V+fLl7fe7desmSWrevLlmzpypnj176v79+/r44491584dlSpVSmvWrFGiRIns65w+fVo3btyw32/cuLGuX7+uvn376sqVK/Lx8dGaNWvCDXIBAAAAAFaKdrgKCgrSyJEjtXDhQp0/f16PHz8OM//WrVtR3la5cuVkjIl0vs1m08CBAzVw4MBIlzl79my4aR06dKAbIAAAAIBYFe3RAgcMGKARI0aocePG8vPzU7du3VS/fn05ODiof//+MVAiAAAAAMR/0Q5Xc+fO1Q8//KDPP/9cTk5OatKkiaZOnaq+fftq165dMVEjAAAAAMR70Q5XV65cUYECBSRJyZIlk5+fnySpZs2a+uWXX6ytDgAAAAASiGiHq4wZM+ry5cuSpOzZs2vt2rWSpD179nCtKAAAAACvrWiHq3r16mnDhg2SpI4dO6pPnz7KmTOnmjVrplatWlleIAAAAAAkBNEeLfC7776z/79x48by9vbWjh07lDNnTtWqVcvS4gAAAAAgoYh2uNq6datKliwpJ6enqxYvXlzFixdXYGCgtm7dqjJlylheJAAAAADEd9HuFli+fPkIr2Xl5+cX5oLAAAAAAPA6iXa4MsbIZrOFm37z5k0lTZrUkqIAAAAAIKGJcrfA+vXrS5JsNptatGgRZmTAoKAg/fnnnypZsqT1FQIAAABAAhDlcOXu7i7p6ZGr5MmTK3HixPZ5Li4uKl68uNq2bWt9hQAAAACQAEQ5XM2YMUOSlCVLFnXv3p0ugAAAAAAQSrRHC+zXr19M1AEAAAAACVqUwtWbb76pDRs2KEWKFCpcuHCEA1qE2L9/v2XFAQAAAEBCEaVwVadOHfsAFnXr1o3JegAAAAAgQYpSuArdFZBugQAAAAAQXrTPuQqxd+9eHTt2TJKUN29eFSlSxLKiAAAAACChiXa4unjxopo0aaLff/9dHh4ekqQ7d+6oZMmSmj9/vjJmzGh1jQAAAAAQ7zlEd4U2bdroyZMnOnbsmG7duqVbt27p2LFjCg4OVps2bWKiRgAAAACI96J95GrLli3asWOHcufObZ+WO3dujR07VqVLl7a0OAAAAABIKKJ95CpTpkx68uRJuOlBQUHy8vKypCgAAAAASGiiHa6GDh2qjh07au/evfZpe/fuVefOnTVs2DBLiwMAAACAhCLa3QJbtGihBw8e6O2335aT09PVAwMD5eTkpFatWqlVq1b2ZW/dumVdpQAAAAAQj0U7XI0aNSoGygAAAACAhC3a4ap58+YxUQcAAAAAJGgvfBFhSXr06JEeP34cZpqbm9tLFQQAAAAACVG0B7S4f/++OnTooDRp0ihp0qRKkSJFmBsAAAAAvI6iHa569uypjRs3auLEiXJ1ddXUqVM1YMAAeXl5afbs2TFRIwAAAADEe9HuFrhy5UrNnj1b5cqVU8uWLVW6dGnlyJFD3t7emjt3rpo2bRoTdQIAAABAvBbtI1e3bt1StmzZJD09vypkuPVSpUpp69at1lYHAAAAAAlEtMNVtmzZdObMGUlSnjx5tHDhQklPj2h5eHhYWhwAAAAAJBTRDlctW7bUoUOHJEm9e/fW+PHjlShRInXt2lU9evSwvEAAAAAASAiifc5V165d7f+vVKmSjh8/rn379ilHjhwqWLCgpcUBAAAAQELxUte5kiRvb295e3tbUQsAAAAAJFhR7ha4ceNG5c2bV/7+/uHm+fn5KV++fNq2bZulxQEAAABAQhHlcDVq1Ci1bdtWbm5u4ea5u7vrk08+0YgRIywtDgAAAAASiiiHq0OHDqlq1aqRzn/33Xe1b98+S4oCAAAAgIQmyuHq6tWrcnZ2jnS+k5OTrl+/bklRAAAAAJDQRDlcZciQQYcPH450/p9//qn06dNbUhQAAAAAJDRRDlfVq1dXnz599OjRo3DzHj58qH79+qlmzZqWFgcAAAAACUWUh2L/6quvtGTJEuXKlUsdOnRQ7ty5JUnHjx/X+PHjFRQUpC+//DLGCgUAAACA+CzK4Spt2rTasWOHPvvsM/n6+soYI0my2WyqUqWKxo8fr7Rp08ZYoQAAAAAQn0XrIsLe3t5avXq1bt++rVOnTskYo5w5cypFihQxVR8AAAAAJAjRClchUqRIoaJFi1pdCwAAAAAkWFEe0AIAAAAAEDnCFQAAAABYgHAFAAAAABYgXAEAAACABaI0oMWKFSuivMHatWu/cDEAAAAAkFBFKVzVrVs3Shuz2WwKCgp6mXoAAAAAIEGKUrgKDg6O6ToAAAAAIEHjnCsAAAAAsMALXUT4/v372rJli86fP6/Hjx+HmdepUydLCguRJUsWnTt3Ltz0du3aafz48eGmz5w5Uy1btgwzzdXVVY8ePbK0LgAAAAAILdrh6sCBA6pevboePHig+/fvK2XKlLpx44aSJEmiNGnSWB6u9uzZE+Y8rsOHD6ty5cpq2LBhpOu4ubnpxIkT9vs2m83SmgAAAADgWdHuFti1a1fVqlVLt2/fVuLEibVr1y6dO3dORYoU0bBhwywv0NPTU+nSpbPfVq1apezZs6ts2bKRrmOz2cKskzZtWsvrAgAAAIDQon3k6uDBg5o8ebIcHBzk6OiogIAAZcuWTUOGDFHz5s1Vv379mKhTkvT48WPNmTNH3bp1e+7RqHv37snb21vBwcF688039e233ypfvnyRLh8QEKCAgAD7fX9//5eutUiP2S+9DSQc+4Y2i+sSAAAAEMeifeTK2dlZDg5PV0uTJo3Onz8vSXJ3d9eFCxesre4Zy5Yt0507d9SiRYtIl8mdO7emT5+u5cuXa86cOQoODlbJkiV18eLFSNcZPHiw3N3d7bdMmTLFQPUAAAAAXmXRDleFCxfWnj17JElly5ZV3759NXfuXHXp0kX58+e3vMDQpk2bpmrVqsnLyyvSZUqUKKFmzZrJx8dHZcuW1ZIlS+Tp6anJkydHuo6vr6/8/Pzst5gOiQAAAABePdEOV99++63Sp08vSfrmm2+UIkUKffbZZ7p+/fpzA8zLOnfunNavX682bdpEaz1nZ2cVLlxYp06dinQZV1dXubm5hbkBAAAAQHRE+5yrt956y/7/NGnSaM2aNZYWFJkZM2YoTZo0qlGjRrTWCwoK0l9//aXq1avHUGUAAAAA8AJHripUqKA7d+6Em+7v768KFSpYUVM4wcHBmjFjhpo3by4np7B5sFmzZvL19bXfHzhwoNauXat//vlH+/fv14cffqhz585F+4gXAAAAAERHtI9cbd68OdyFgyXp0aNH2rZtmyVFPWv9+vU6f/68WrVqFW7e+fPn7QNsSNLt27fVtm1bXblyRSlSpFCRIkW0Y8cO5c2bN0ZqAwAAAAApGuHqzz//tP//6NGjunLliv1+UFCQ1qxZowwZMlhb3f979913ZYyJcN7mzZvD3B85cqRGjhwZI3UAAAAAQGSiHK58fHxks9lks9ki7P6XOHFijR071tLiAAAAACChiHK4OnPmjIwxypYtm/744w95enra57m4uChNmjRydHSMkSIBAAAAIL6Lcrjy9vaW9HRwCQAAAABAWNEe0EKSTp8+rVGjRunYsWOSpLx586pz587Knj27pcUBAAAAQEIR7aHYf/vtN+XNm1d//PGHChYsqIIFC2r37t3Kly+f1q1bFxM1AgAAAEC8F+0jV71791bXrl313XffhZveq1cvVa5c2bLiAAAAACChiPaRq2PHjql169bhprdq1UpHjx61pCgAAAAASGiiHa48PT118ODBcNMPHjyoNGnSWFETAAAAACQ4Ue4WOHDgQHXv3l1t27bVxx9/rH/++UclS5aUJP3+++/6/vvv1a1btxgrFAAAAADisyiHqwEDBujTTz9Vnz59lDx5cg0fPly+vr6SJC8vL/Xv31+dOnWKsUIBAAAAID6LcrgyxkiSbDabunbtqq5du+ru3buSpOTJk8dMdQAAAACQQERrtECbzRbmPqEKAAAAAJ6KVrjKlStXuID1rFu3br1UQQAAAACQEEUrXA0YMEDu7u4xVQsAAAAAJFjRClfvv/8+w60DAAAAQASifJ2r/+oOCAAAAACvsyiHq5DRAgEAAAAA4UW5W2BwcHBM1gEAAAAACVqUj1wBAAAAACJHuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALxOtw1b9/f9lstjC3PHnyPHedRYsWKU+ePEqUKJEKFCig1atXx1K1AAAAAF5n8TpcSVK+fPl0+fJl+2379u2RLrtjxw41adJErVu31oEDB1S3bl3VrVtXhw8fjsWKAQAAALyO4n24cnJyUrp06ey31KlTR7rs6NGjVbVqVfXo0UNvvPGGvv76a7355psaN25cLFYMAAAA4HUU78PVyZMn5eXlpWzZsqlp06Y6f/58pMvu3LlTlSpVCjOtSpUq2rlz53MfIyAgQP7+/mFuAAAAABAd8Tpcvf3225o5c6bWrFmjiRMn6syZMypdurTu3r0b4fJXrlxR2rRpw0xLmzatrly58tzHGTx4sNzd3e23TJkyWfYcAAAAALwe4nW4qlatmho2bKiCBQuqSpUqWr16te7cuaOFCxda+ji+vr7y8/Oz3y5cuGDp9gEAAAC8+pziuoDo8PDwUK5cuXTq1KkI56dLl05Xr14NM+3q1atKly7dc7fr6uoqV1dXy+oEAAAA8PqJ10eunnXv3j2dPn1a6dOnj3B+iRIltGHDhjDT1q1bpxIlSsRGeQAAAABeY/E6XHXv3l1btmzR2bNntWPHDtWrV0+Ojo5q0qSJJKlZs2by9fW1L9+5c2etWbNGw4cP1/Hjx9W/f3/t3btXHTp0iKunAAAAAOA1Ea+7BV68eFFNmjTRzZs35enpqVKlSmnXrl3y9PSUJJ0/f14ODv/LhyVLltRPP/2kr776Sl988YVy5sypZcuWKX/+/HH1FAAAAAC8JuJ1uJo/f/5z52/evDnctIYNG6phw4YxVBEAAAAARCxedwsEAAAAgISCcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAF4nW4Gjx4sIoWLarkyZMrTZo0qlu3rk6cOPHcdWbOnCmbzRbmlihRoliqGAAAAMDrKl6Hqy1btqh9+/batWuX1q1bpydPnujdd9/V/fv3n7uem5ubLl++bL+dO3culioGAAAA8LpyiusCnmfNmjVh7s+cOVNp0qTRvn37VKZMmUjXs9lsSpcuXUyXBwAAAAB28frI1bP8/PwkSSlTpnzucvfu3ZO3t7cyZcqkOnXq6MiRI89dPiAgQP7+/mFuAAAAABAdCSZcBQcHq0uXLnrnnXeUP3/+SJfLnTu3pk+fruXLl2vOnDkKDg5WyZIldfHixUjXGTx4sNzd3e23TJkyxcRTAAAAAPAKSzDhqn379jp8+LDmz5//3OVKlCihZs2aycfHR2XLltWSJUvk6empyZMnR7qOr6+v/Pz87LcLFy5YXT4AAACAV1y8PucqRIcOHbRq1Spt3bpVGTNmjNa6zs7OKly4sE6dOhXpMq6urnJ1dX3ZMgEAAAC8xuL1kStjjDp06KClS5dq48aNypo1a7S3ERQUpL/++kvp06ePgQoBAAAA4Kl4feSqffv2+umnn7R8+XIlT55cV65ckSS5u7srceLEkqRmzZopQ4YMGjx4sCRp4MCBKl68uHLkyKE7d+5o6NChOnfunNq0aRNnzwMAAADAqy9eh6uJEydKksqVKxdm+owZM9SiRQtJ0vnz5+Xg8L8DcLdv31bbtm115coVpUiRQkWKFNGOHTuUN2/e2CobAAAAwGsoXocrY8x/LrN58+Yw90eOHKmRI0fGUEUAAAAAELF4fc4VAAAAACQUhCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAskiHA1fvx4ZcmSRYkSJdLbb7+tP/7447nLL1q0SHny5FGiRIlUoEABrV69OpYqBQAAAPC6ivfhasGCBerWrZv69eun/fv3q1ChQqpSpYquXbsW4fI7duxQkyZN1Lp1ax04cEB169ZV3bp1dfjw4ViuHAAAAMDrJN6HqxEjRqht27Zq2bKl8ubNq0mTJilJkiSaPn16hMuPHj1aVatWVY8ePfTGG2/o66+/1ptvvqlx48bFcuUAAAAAXidOcV3A8zx+/Fj79u2Tr6+vfZqDg4MqVaqknTt3RrjOzp071a1btzDTqlSpomXLlkX6OAEBAQoICLDf9/PzkyT5+/u/cO1BAQ9feF0kPC/TVl4Wbe31QltDbKGtIbbQ1hBbXrSthaxnjPnPZeN1uLpx44aCgoKUNm3aMNPTpk2r48ePR7jOlStXIlz+ypUrkT7O4MGDNWDAgHDTM2XK9AJV43XkPvbTuC4BrwnaGmILbQ2xhbaG2PKybe3u3btyd3d/7jLxOlzFFl9f3zBHu4KDg3Xr1i2lSpVKNpstDitLWPz9/ZUpUyZduHBBbm5ucV0OXmG0NcQW2hpiC20NsYW2Fn3GGN29e1deXl7/uWy8DlepU6eWo6Ojrl69Gmb61atXlS5dugjXSZcuXbSWlyRXV1e5urqGmebh4fFiRUNubm68WREraGuILbQ1xBbaGmILbS16/uuIVYh4PaCFi4uLihQpog0bNtinBQcHa8OGDSpRokSE65QoUSLM8pK0bt26SJcHAAAAACvE6yNXktStWzc1b95cb731looVK6ZRo0bp/v37atmypSSpWbNmypAhgwYPHixJ6ty5s8qWLavhw4erRo0amj9/vvbu3aspU6bE5dMAAAAA8IqL9+GqcePGun79uvr27asrV67Ix8dHa9assQ9acf78eTk4/O8AXMmSJfXTTz/pq6++0hdffKGcOXNq2bJlyp8/f1w9hdeGq6ur+vXrF66LJWA12hpiC20NsYW2hthCW4tZNhOVMQUBAAAAAM8Vr8+5AgAAAICEgnAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAACAMIKDg+O6hASJcAUAAADA7rvvvlOHDh305MmTuC4lwSFcIcEJuXoAVxEAAMAafKcitIwZM2rSpEnq06cPASuaCFdIcG7fvq2AgADdvXtXEl8IiHm0MQCvsqNHj8pms0mSpk+frr1798ZxRYhLxhh9+OGHWrRokUaMGKGvvvpKjx49iuuyEgzCFRKUH3/8UXXq1NGbb76pmjVratmyZfYvBMBKv/zyi1asWCFJstlsBCzECM5pQFz7888/VaNGDY0cOVI9evRQu3btlDJlyrguC3Ek9Hdd2bJl9f3332vo0KEaNmyYHj9+HIeVJRxOcV0AEFVLlizRxx9/rGHDhunBgwc6c+aM6tevL19fX3Xr1k2pUqWK6xLxili8eLEaNWqkDBkyKDAwUPXr17cHLMI8rBIcHCwHh6f7OJcvX65Tp04pTZo0KlKkiPLmzRvH1eF1kTJlSrVq1UqDBg1SUFCQjh49qmzZsikwMFBOTvxMfN2EfMf9/PPP+vzzz1WxYkVlzpxZffv21d27dzVo0CA5OzvHcZXxG+8aJBirV6/WBx98oPbt29unlSxZUs2aNdOTJ080cOBAJUqUKA4rxKvgwIEDGjZsmNq0aaOgoCB99dVXMsbovffeI2DBMsYYe7Dq1auXZs+erXz58uny5cvy9vbWxx9/rLp168ZtkXgtZMyYURkyZNDdu3fl5eWl5cuXq2vXrnJyclJQUJAcHR3jukTEshMnTqhNmzYaPHiwWrVqJT8/Py1btkzt2rWTJH399ddycXGJ4yrjL8IVEgRjjC5fviwvLy9J/+tK8+GHH8rJyUlNmzZV7ty51bp1a3784qU4OjrK29tbnTt3liSNHDlSffr0kSQCFiwT0n7GjBmj+fPna+nSpSpevLhGjRql3r17288tbdy4sSTR5mCpkKOmIe2qTJky2rZtm3777TdNmjRJjx49kq+vL8HqNeXv76+UKVOqSpUqcnFxkaenp9q2baugoCC1a9dOHh4e6tKlixInThzXpcZLnHOFeC2k72/Ih/+iRYt09OhR+5eCMUbvv/+++vXrp759++rcuXP8AMELCQnsBQsW1LBhw5QvXz7ly5dPHTp0UIkSJdSnTx8tXrxY0tP2+PDhw7gsF6+Ae/fu6ejRo+revbuKFy+uZcuWqX///urSpYsSJUqkb7/9VkuXLpUkPtdgmdDdUQ8fPqw9e/YoSZIkevvtt9W+fXs1aNBAs2bN0pAhQ+zrDB48WIcOHYqrkhHLEiVKpDNnzuiff/6R9L/vx+rVqytNmjT68ssv9d1338VlifGazXCWNhKAwMBA3bt3T40aNZKLi4tGjx6t7Nmz2/uE//7773rvvfe0Zs0a+fj4xHW5SMBC2lTo7jCHDh3SmDFjtHPnTn3zzTeqXbu2KlSooJ49e6pGjRpxXDESsr///ltJkyaVn5+fatWqpY4dO6pLly6aN2+e2rZtqwwZMmj06NGqWrVqXJeKV0DoI6BffvmlFi1apODgYD158kT169dXjx495ODgoPHjx2vevHl65513dOPGDR0+fFj//PMPR7JeQSFt4tkuoB988IHOnz+vESNGqFixYpKe7hDq2rWrSpYsqRIlSihPnjxxVXa8xpErxHszZsxQjRo15OHhodatW8vf31+ff/65Tpw4YT/ZNmPGjPLw8GCoULyUmTNnqlq1agoMDJSjo6P9yGmhQoXUqVMnvfPOO/riiy+UJ08enT59Wu+++24cV4yEIigoKMLp2bJlU4YMGbRt2zalT59erVu3lvS0e2rZsmXVunVr2hksExKsRowYoWnTpmny5Mk6deqUKlSooNmzZ+vcuXNKly6d2rdvr88//1z//vuv3NzcdOrUKTk6OjK65SsmJFht2LBBvXr1Urdu3XT06FFJUrt27eTu7q5OnTpp1apVOnTokAYNGqTNmzerXr16BKvnIFwhXjPG6MmTJ7p586YuX76sxo0bq0WLFrp//76qV6+u2bNna+HChfr000/l7u5u37sCRJcxRo8fP5afn58uXrxonxY6YDVp0kRnz56Vp6enzp49K2dnZwUGBsZl2YjnLl26JEn2PcJjxoxR+/bt1bp1a12+fNm+g8gYoxs3bmjv3r168uSJ5s6dqxIlStiPJEQWzoDoMMYoMDBQ27Ztk6+vr8qXL6+VK1dqyZIl+uabb1SiRAkFBAQobdq0+uyzz7R+/Xr99NNP9s+6kO6EeDXYbDatXbtWVapU0dmzZzV37lw1btxYs2bNUqlSpfTVV18pT548qlOnjho0aKA5c+Zo4cKF8vDwiOvS4zW6BSJeieikbT8/PxUqVEg1a9bUuHHjJEm7du3S3LlztWDBAmXJkkWenp5atmyZnJ2dGd0IUfK8tlanTh2NHj06zLw7d+6oQYMGunz5sg4dOiQnJyeGKsZz+fr6atKkSdq9e7dy5cql/v37a/To0apWrZr27t2rhw8fau7cuSpTpoz27dunzz//XP/884+cnJyUNGlS7d+/X87OzgxmAcsEBwfr8ePHqlixosaPH6+7d++qevXqGjZsmD755BMFBARoypQpeuutt1S8eHF7u6MNvlpC/p7Xrl3TgAEDVKhQIX388ceSnnYHPH78uDp06KBmzZrJyclJJ06ckDFGKVKkUNq0aeO4+gTAAAnAwoULTd68ec2OHTvCTL9y5Yq5d++eCQ4ONsYY8+TJk7goD6+QkLa2e/fuMNP/+usv06hRI/P48WNjDG0N/+369eumZMmSJk+ePObw4cOmZcuW5o8//jDGGBMUFGRq165t0qRJYzZt2mSMMebAgQNmwYIFZuLEifb2RTvDywgKCopwepMmTUzWrFlN0qRJzaxZs+zTr169asqWLWsmTZoUWyUijuzevdu88847pkiRImbjxo326Y8fPzYffPCB8fHxMVOmTDF3796NwyoTJo7vIt759ttv1aRJE/3yyy/2aYUKFZKLi4v27NkjSfauWGnSpFHSpElls9kUHBzMUQREy/Pa2q5duyT9b5Sk/Pnza8GCBfbuMbQ1/JfUqVNr1apVcnNzU7Vq1XTo0CElS5ZMkuTg4KDly5erRIkSatSokbZs2SIfHx81atRIn376qX1QFdoZXlToUQH//PNPHTt2TH5+fpKeXlstTZo0ypYtm5o1ayZjjG7fvq0WLVooMDBQbdq0icvSEQvy5Mkjm82m/fv36+DBg/Yu8M7Ozpo9e7YKFiyowYMH20fJRdQRrhDv+Pj46Ny5c+rfv79KlSqlHTt2KFeuXOrSpYv69u2r8+fP239whO6mQF9wRFdU2lrIsP+h8YMXUZUiRQr9+uuvKlCggA4cOKDr169L+l9oX7ZsmUqVKqXy5cvrwIEDYdalezNeRsh3Ys+ePVW/fn0VLlxYn376qX799Vf7ID1PnjxRxowZVbZsWVWtWlVXr17Vpk2b5OjoyHl+r5hnv8fc3Ny0evVqVahQQT/++KNWrlxp/5s7Ojpq2rRpqly5ssqWLRsX5SZonHOFeOn+/fs6dOiQvvvuO/3999/KmDGjGjRooLlz56pWrVr6/PPP+eEBS9DWYKXQRwtCu337tqpXr65bt25pxYoVyp07d5jzWHr37q1vvvmGtoaXFroNrl69Wp07d9bEiRN18+ZNTZ06VUFBQerSpYtq166tf//9V9OnT5ejo6PSp0+vZs2aydHRkaPzr5iQz5o//vhDO3fu1KNHj+Tj46MqVaro7t27ql27th4+fKgvvvhCNWrU4HPoJRGuEO88++Nk5cqV2rJli8aPH6+AgAC1bNlS06ZNi8MK8aqgrcFKodvTypUr9e+//ypLlizKkSOHcuTIIT8/P1WuXFn+/v5avnx5uIAliQF5YJm1a9dqxYoVypYtm7p16ybpaffAfv366c6dO2rXrp0aNmwYbj3a4Kvp559/Vrt27VS4cGG5u7tr0aJF+uabb+Tr62sPWE+ePFHnzp1Vv3592sBLIFwh3nr2h++BAwf022+/qXv37uxRg6Voa7BSr169NGHCBGXLlk3nz59XgQIF1KpVK7Vo0UJ+fn6qUqWK7t27pwULFihfvnxxXS5eQWfPnlWNGjV05swZffrppxoxYoR9XkjAun//vt5//321atUqDitFTAm94+bo0aOqXLmyvvrqK3322We6cOGCsmbNqm7dumnw4MFydHSUv7+/ypQpI09PTy1dutR+fiiij5NUEOuezfOR5fvQP3aNMSpcuLB69+5tHwIb+C+0NcSG0Oem7NmzR+vXr9dvv/2mQ4cOad26dcqdO7fGjh2r+fPny93dXb/++qsePXqkb775Jg6rxqvk2c+2LFmyaNKkSSpcuLC2bt2qdevW2ecVLFhQAwcO1KNHj3Tw4MFYrhQxbeXKlZJkH+hLkq5fv648efLos88+05kzZ1SyZEm1bdtWQ4YMkaOjo44fPy43Nzdt375dU6dOJVi9JMIVYk3Ih3/InpR//vknzP3neXYZjibgeWhriA1Hjx6V9L+BJ77//ntNmjRJb7zxhooXLy5Jeuutt9SlSxdlzZpVy5Yt05MnT5QiRQodOHBAP/74Y5zVjldHcHCw/XPrypUrevDggYKCglS6dGkNGjRIrq6umjRpkjZu3Ghfp0CBApoxY4ZGjRoVR1UjJpw+fVp16tTRhx9+KOl/Ow4fPnyoa9eu6Y8//lD58uVVvXp1+3VDt2/frgEDBujcuXNKliyZvL2946z+VwXhCrEm9F6UwYMH66uvvorSeqH3yNGLFVFBW0NM69q1q1q0aKGtW7fap929e1czZszQjh079O+//9qn58uXT++9955+/vlnXbhwQZKUPHlyRmSDJUJ+QA8YMECVKlVSxYoV1b17dz18+FDly5fXgAEDdOXKFY0fP16bNm2yr5c9e3Y5ODjYPyuR8GXNmlUrVqzQmjVr1Lx5c/v0HDlyyNPTU++++65Kly6tyZMn23cKLV++XH5+fkqePHlclf3KIVwhxn3xxRf2PSQhXwK3bt3SG2+8IUnP/WAP3Wd45cqVYb4YgGfR1hBb2rZtq4CAAH3//ffavHmzJGnQoEEaPXq0zp49q5kzZ+rmzZv25bNnz64cOXKE62bKSeN4UaE/z+bMmaNx48apW7duevPNN7Vz507VqVNHDx48UKVKlTRgwABdv35d/fv31/79+8Nsh8uYvDocHBxUrVo1zZo1S0uXLlWzZs0kPQ1XVatWlaurqzJkyKCjR4/q2LFj6tmzp6ZNm6YhQ4YoZcqUcVz9q4N3FGLUxYsX9ddff2n+/PmaMWOGffrZs2ftPzIi+2AP/WN30qRJqlOnDl20ECnaGmJLUFCQ8ubNq0WLFun8+fMaMmSIvctVx44dNWjQIPXr10+DBw/Wli1bdOTIEfXr10/JkydXjhw54rh6vApCD8KzatUqXbp0SWPGjFGrVq00ZswY9ejRQ7dv31bt2rXtAatnz57KmzevfHx84rZ4xIiQ3hb+/v6qUaOG5s2bpxUrVqhp06aSnl7vrHXr1tq0aZMKFSqkjz76SGvWrNHGjRuVP3/+uCz91WOAGHb06FHTvHlzU7JkSTN16lRjjDF16tQxAwYMMMYYExQUFG6dJ0+e2P8/adIk4+HhYRYvXhw7BSPBoq0htgQGBhpjjDlx4oTJnz+/qVatmtmwYYN9/rfffmtsNpux2WymefPmpn79+ubx48fGmIjbIRAV1apVM4cOHbLf37Nnj8mVK5dxc3MzS5cutU9//Pix+fnnn03RokXNu+++a+7duxdmO7TBV0dwcLD9/+vXrzdVq1Y1T548MY8fPzarVq0y7u7u5oMPPrAvc+7cObN582Zz/Phxc+3atbgo+ZXHkSvEmJAuC2+88YZ69eql7Nmza+rUqZo7d66yZMmitGnT6vTp0zp37pyuXr2qK1euaOfOnZL+N4jA5MmT1bNnT02dOlXvvfdenD0XxG+0NcSG0N2wQrrz5cqVy34u1dChQ+1HsHx9fTV27FhJUpEiRTR9+nQ5OzsrKCiIblh4IefOnVPBggWVJ08e+7Ts2bOrQ4cOSpkypaZOnWqf7uzsrFq1asnX11fHjx/XF198Iel/RzdogwnfiBEjtHz58jDnGP/xxx9KliyZnJyc5OzsrOrVq2vu3Ln65Zdf7EewMmfOrLJlyyp37tzy9PSMy6fw6orrdIdXU+i9YlevXjXGPN3D+9FHH5m3337b2Gw24+bmZnLmzGnSpk1r0qRJY9KkSWNq165t3wszYcIE4+7uzlEEPBdtDbEhdDs7duyY2blzp/H39zcPHz40xhhz/Phxkz9/flO1atUwR7C+/vprY7PZzKhRo8zt27dju2y8ooYPH242bdpkjDHG39/fTJgwwRQoUMC0aNEizHKPHz82mzdvth9pxavh9u3b5v333zdJkyY1a9assU/v1KmTadq0aZhlg4KCzKpVq0zq1KlN3bp1Y7vU1xLhCpYL/SNk4MCBpn79+mbfvn3GGGOOHDliPvroI1O8eHHTqVMn4+fnZ65evWoOHTpk/v77b/sXwD///GOyZMliFi1aFCfPAQkDbQ2xIXS3my+//NLkypXLpEqVyhQqVMiMHDnSXLlyxRjzNHQVKFDA1KhRw6xevdq+zpAhQ4zNZjMTJkwIsy3gRdy4ccPUqFHDeHh4mN9//90YY8ydO3fMuHHjjI+Pj2nZsmWE6xGwXi0nT540bdu2NR4eHmblypXGGGO6du1q2rRpY4wx9h0/xjzt/r5kyRKTOXNm8++//8ZJva8TwhVijK+vr0mbNq2ZM2eOuXjxon360aNHTbNmzUzx4sXNrFmzwq0X8gVw+fLlWKsVCRttDbHh66+/NunTpze//vqrMcaY2rVrG29vb/Pll1/a29Dx48dNmjRpTLdu3cL8mB05cqQ5evRonNSNhC2iQP7XX3+ZDz/80KROndps377dGPO/gFWkSBFTp06dWK4SsSX0TsUdO3aYXr16GQ8PD7Nt2zYzevRo06NHD/Po0SNz584d+3Lnz583xphw594hZhCuECP27t1rsmXLZtavXx9mesiHwrFjx0yLFi1Mzpw57XtcQrBnF9FBW0NsOHLkiClVqpRZsWKFMcaYtWvXmuTJk5tKlSqZzJkzmz59+tgD1rlz5+zBKvSAKUB0hf4hHRwcHOb+X3/9ZZo0aRIuYH333XemRYsWDFrxCgoODrZ/b61bt874+PiYWbNmmU8++cS4u7sbT09Pkz17dpM1a1aTPn16kzNnTuPt7W3eeOMNc+vWrTiu/vXBWMOIETdv3pQxRoUKFbJPM8bIwcFBT548UZ48edS9e3dlz55d1apVC7NuyJDYQFTQ1hAbvLy81KlTJ1WoUEHbtm3TRx99pGHDhunjjz9WlSpVNHv2bN2+fVv9+vVT5syZJT0dsp0h/fGiQg+3PmnSJG3dulU2m03FihVT586dlT9/fn355Zcyxqh+/fpaunSpSpYsqQ4dOihJkiT2gQ4YvOLVYP5/MBKbzab58+frgw8+kCQlTZpUgwYNUqJEiTR16lQ1adJEnTp10tmzZ/XkyRPZbDblypVLKVKkiMvyXyt86iNGuLq66tKlSzp79qxSp04t8/QoqWw2mzZs2CAPDw8VL15c+fLlk/T0RwgX08SLoK0hNnh4eKhq1apKmjSpZs2apfr166t169aSpCxZsujChQsKDg5WqlSp7OvQzvAyQkJR79699eOPP6pBgwZKnjy5evXqpevXr2vQoEHKly+fvvrqKzk6OqpUqVI6dOiQChQoIOl/O5nw6rDZbFq8eLE++OADLVmyRAsWLNCRI0f03nvvqW3btnry5IlmzZqlBg0aqGLFinFd7muLdx1eSuihiUPz9vZW8eLFNWLECP3555+y2Wz2IwlDhgzRypUrwyzPjxD8F9oa4lry5MklSbdu3dL9+/ftF6f28/PT8OHDNW7cONlsNvseZuBlzZs3T4sXL9bPP/+s0aNH66233lJQUJC+/fZbffbZZ5KkfPnyqXv37urbt6/y5s1rX5cj868Wm82mpUuXqlGjRpo2bZrq1q2re/fu6dKlS5KetoNu3bqpcePGKlu2rNauXRvHFb++bIZvAbyg0N0Nfv31V927d08BAQH68MMPJUkzZ87UDz/8oMSJE6tp06ZydHTUjz/+qGvXrmnfvn10l0GU0dYQn/To0UO//fabcuTIocuXL8vPz09//fWXHB0d6YYFywQFBWny5Ml69OiRunXrpl9++UUffvihBg0apMSJE6tNmzby9fXVN998E2a9wMBAPvNeUb/++qtu375t7xLYqVMn3blzR7Nnz7Yvs2rVKu3bt0/vv/++cufOHVelvtYIV3ghId2upKcXy5w3b55SpEih69evq0CBAvrxxx+VOnVqLV++XMuXL9eiRYuUP39+pU+fXgsWLLBfTJOjCPgvtDXEF6Hb4hdffKGrV6/KwcFBEydOlJOTE+0MLyV0+wrh7++v69evK1myZKpSpYqaNm2qHj166PDhwypbtqxu376tb7/9Vr17946jqhEXQtrKt99+qxUrVuj333+Xo6OjBg4cqNWrV2vVqlVKnTp1XJf5+orV4TPwyhk6dKhJly6d2bNnjzHGmClTphibzWbKlStnv/aLMU8v7nr37l37KDeMoIXooq0hPojsWkG0M7yMZy+G/mw727Vrl8mdO7c5e/asMcaYU6dOmVatWplNmzZx/arXUMj327hx40zu3LmNMcb079/fODk52a/1iLhD3wVES1BQkP3/V69e1eHDhzV27Fi99dZbWr58uXr06KGBAwfq3Llzatq0qS5cuCBJSpMmjZIlS2Y/H4EuC/gvtDXEBhNJ543IpoccmXr2HEDaGV5GSFfS/v37q06dOipatKjmzJmjq1evSnp6vt/ff/+tuXPn6tixY+rYsaOuX7+usmXLytHR0X7+H14v+fPnV8qUKdWhQwcNHjxYu3bt0ptvvhnXZSFusx0SqhMnThhjjFmyZIm5du2a2bNnj8mSJYsZN26cMcaYUaNGGZvNZnx8fMy1a9fislQkcLQ1xJTQRwuOHDlijh07Zk6fPh3h/NBCXx/txIkTxt/fP+aKxGtj5syZJn369GbKlCmmbt26Jl++fKZnz57m3LlzxpinR++dnJxMjhw5zFtvvWUeP35sjOF6fa+zXbt2GZvNZpycnMz+/fvjuhz8P45cIUqWLl2qfv36SZK6dOmiXr16KTAwUPXq1ZOnp6e2bdumvHnz2k+yTJ48uVq3bq18+fIpZcqUcVk6EhjaGmKDCTVMdd++ffXBBx+oXLlyatmypcaOHSvp6dGEZ49QmVDnxYwdO1aNGzfW7du3Y7d4vBKebVsBAQHq27ev2rZtq6VLl6pp06Zat26dxo4dq6tXr6p79+46duyY5syZo927d8vZ2VmBgYGMCvga8/Hx0cCBA3X48GEVLlw4rsvB/6MfA/5TQECATp06pe+++05btmzRvn37tHPnzjDdYP7++2+dO3dO7u7uunv3rpYvX65y5cqpa9eukri2EKKGtobYEvKDdMCAAZo0aZJ++uknZciQQd9//706d+6s+/fvq3fv3vaA5eDgECZYTZ48WX379tXEiRPtFw0Goip0uJ87d66uXr2qAwcOqEqVKvZlfH19JUmLFi2SzWbTZ599phw5cihHjhySnoYzuqO+3lxdXeXr68t3XnwTtwfOkFA8evTIlCxZ0thsNtO+fXv79JATaY8dO2ZSpUplvL29Ta5cuUz+/Pk5wRsvhLaGmBS6C9XevXtNsWLFzObNm40xxqxZs8YkT57c1KtXzyRNmtQMGTLEvmzoNjZp0iTj5uZmfv7559grHK+M0N1Ne/ToYdzc3Ez+/PmNo6OjKV68uDlz5kyY5b/77juTIUMGM3bs2FiuFMCLYJcHIhX6ei1PnjxRhQoVVKxYMc2aNUuenp7q16+fHB0d9fjxY+XJk0e7du3SggULlCRJEnXs2JGhiRFltDXEhtDt7Pjx4ypYsKDq1q2rokWLauPGjWrRooWGDRumRo0aqUGDBurVq5d9qOuQIwSTJ09Wz549NX36dNWvXz8unw4SqJA2+Pfff+vGjRvauHGjfHx8NHXqVM2ePVtfffWVvvnmG3l7e0uSevXqJS8vL3tXaADxG9e5QoRC/whZtGiRMmTIoDfffFPBwcGaMGGCBg0apK5du9rPjZGko0ePhrk6PD92ERW0NcQG88z10nbt2qXly5crUaJEcnFxUevWrZUsWTINHTpULi4uat++vf7880+5u7tr5cqVstlsWrRokZo1a6a5c+cSrPBSFixYoC+++ELp06fXypUrlSJFCklPw/ucOXPk7e2tb7/9NlyXUz7rgPiPAS0QjgnVF7x3797q1KmTTpw4oYcPHypJkiRq2bKlvvrqK40aNUpfffWV7t+/r+rVq2vo0KFhtsMXAP4LbQ2xJSRY/fHHH9q+fbu+++47ubm5ycXFRQEBATp48KACAgLk4uKiBw8e6Nq1a2rfvr1WrVplX9fHx0crV64kWOGlBQcHK1OmTDp69KgePnxon/7JJ5/oo48+0sWLF/Xpp5/ah2IPwWcdEP9x5AqR+v777zVixAitWrVKPj4+cnZ2ts8LCAjQlClT1KNHD2XKlEmJEyfWvn37wiwDRBVtDbFhwoQJ2rFjhx4/fqx58+bJ0dHRfuT0+++/19ixY1W5cmWdPHlSDx480J49e+To6ChjTJgdAYAVVq1apf79+ytx4sSaO3dumKNUI0eO1MmTJzVu3DjaHZDAEK4QoYCAADVu3FglSpRQr169dP78eR09elSTJ0/WG2+8oaZNmypfvnw6efKkjhw5olq1atkvZMjoRYgO2hpiy5AhQ/TFF18oU6ZMWrdunX3UNUk6ffq0Fi9erI0bNypDhgyaPHmynJ2d6YYFy4Xuorps2TKNHTtWwcHBmj17tjJlyhRuudBdpwHEf4QrhGOM0b1791S5cmUVKFBA77zzjpYuXap79+5Jkh49eqS8efNq3LhxcnV1ta/HjxBEF20NMWXDhg0qX768HBwcNGDAAKVLl06ffPKJJkyYoD59+qhNmzbq2LGjMmbMGOk2CPCIKaED1tKlSzVu3DjZbDZNnTpVWbJkiXA5AAkDu0Jec8aYcBcylJ5emLVLly7avHmzevfurcKFC2vAgAHasGGDSpQoIT8/vzA/diX6guP5aGuILZcvX1aHDh1UvHhxderUSYMHD1bx4sUlSe3atVPv3r01d+5cTZs2TZcuXbKvF7p9GmMIVogxNptNIfu269Wrp06dOunatWsaPnx4uOUAJCx8c7zG/Pz85O7ubv/wHjdunE6ePKmgoCD169dP77//vkqVKiWbzaYMGTLY1zty5IiyZs0aV2UjAaKtITalTZtWs2bNUpUqVXT48GFt375dhQoV0sOHD5U4cWL16NFDxhj7+SwtWrRQpkyZwnS94kctYlpIwLLZbKpTp45SpEihUqVKxXVZAF4SR65eU76+vsqUKZOuXbtmv9+/f3+dPXtW69evV758+XTgwAFlzJhRGTJkkJ+fn3777TfVqlVLFy5c0JgxYyRJ9CrFf6GtIbaEHHlycHCQk5OTUqZMKS8vL3Xt2lWPHj1S4sSJ9ejRI0lSz5491bFjR/Xv31/r1q2Ly7LxConscyqy6aGPYJUpU4Zzq4BXAO/i11SzZs1UoEABlS5dWhcuXLD/oF2+fLm2bt2qkiVLqkqVKtq/f78k6Z9//tF3330nm82mAwcO2C/ayt5d/BfaGmJD6JP+Dx8+rHTp0mnfvn2aPXu27t69qwoVKiggIECJEiWy/5jt0aOHFi1apObNm8dl6XhFBAcH2z+njh49quPHj+uff/6RJPvAFP/l77//1t27d2O0TgAxiwEtXmOnTp1S06ZNdf78eWXOnFlz5sxRzpw5JUl37txRy5YttWPHDv3666968803dfLkSWXPnl0ODg6c6I1ooa0hJoUOVn369NGvv/6qb775RpUrV1ZwcLA2b96s7t27K1myZFq/fr0SJUqkVq1aqWzZsvZgxSApeBmhB57o27evVqxYoStXrih37txq0KCBOnbsKEnhRv4Lvd7YsWM1ffp0LV++PNzFgwEkIAavlaCgoDD3T548aWrWrGmcnZ3NX3/9FWaZ27dvm/r16xubzWaOHz8e6TaAiNDWENu+/PJLky5dOrNq1Spz8+ZN+/SgoCCzefNmU6BAAZMuXTpTpkwZkzlzZvPkyZM4rBavov79+xtPT0+zbt06c/ToUdO8eXNjs9nM4MGD7cuEfK4FBwfbp02aNMl4eHiYefPmxXrNAKzF7uDXSOg9Zjt37lSGDBmUI0cODR8+XLdv31bdunW1Y8cOpUmTRsYYeXh46IcfflCuXLnCXA+GPuH4L7Q1xLYjR45o4cKFmjlzpqpUqSJ/f3+dPHlS27ZtU548eVS2bFktX75c06dPV3BwsDZs2GDvcsoRK7woE+rI0759+7R69WotWrRIZcuW1W+//aYlS5aobt26GjRokBwdHdWjR49wR+QnT56snj17asaMGapfv35cPh0AVojjcIdYEvoIgK+vrylQoIBZvHixuX//vjHGmL///tsUL17cZM+e3Vy5ciXcOsYY9vIiSmhriAsHDx40uXPnNr///rvZsmWLad++vcmXL5/x8vIyhQoVMitWrAi3TmBgYBxUildF6M+tY8eOmcePH5tvv/3W3L9/32zYsMGkS5fOTJ482dy+fdtUrFjR2Gw24+vrG2YbkyZNMm5ubmbx4sWxXT6AGMJu4ddEyBGAfv36afr06Ro+fLiqVKmiJEmSSJJy5syp+fPnK1WqVCpTpowuXboU7qgB570gKmhriGkRDQyQO3duOTg4qE2bNqpUqZIkafDgwdq5c6cCAwN15cqVcOtwxAovyhhj/9zy9fXVZ599pocPH+rzzz9XkiRJNHfuXDVq1EgtWrSQh4eHcufOrXfeeUd//vmnfUCVRYsWqUuXLpoxY4bee++9uHw6ACzEL5jXyD///KOff/5ZkydPVuXKlXXjxg2dOnVKa9euVZYsWdSoUSMtXrxY5cuXV9euXbVgwYK4LhkJFG0NMSV0l9M9e/bYR2grVqyYDhw4oFWrVsnT01OlSpWyL+fm5halkdqAqArpCvjHH39o+/btGjZsmNzc3CRJAQEBOnjwoIoWLSoXFxc9ePBA165dU/v27fX+++/bt+Hj46OVK1fadwYAeDUQrl4jTk5OcnFxkZ+fn9avX6958+Zp//79CggI0IMHD3T79m198skn2rx5s9KnTx/X5SIBo60hJoQ+WtCrVy/NmzdPNptNV69eVZMmTdSnTx/7EYD79+/rzp07atu2rR49eqQ2bdrEZel4BU2YMEE7duxQ+vTp9dZbb0l6Gv5dXV3VqFEjjR07Vi1bttTJkyf14MEDNWzYUNLTdmyMUc6cOe2jpgJ4ddAt8BUV0V7adOnSKV26dBo1apSqVKkiNzc3fffdd9qxY4dy5sypW7duSZIyZswoR0dHBQUFxXbZSIBoa4gtIUcLxo0bp+nTp2v+/Plau3atVq1apTVr1qhXr146d+6cJGnq1KmqU6eO7t27p927d9POYLl79+5p/vz52rNnj86cOSPpf92iQ4Zfv3TpknLlyhWmDdpsNgbrAV5hHLl6BYXuNnPkyBG5uLjIGKNcuXJpyZIl2r17t5IlS6YiRYrY13n48GG48w84HwH/hbaGuLBnzx699957KlmypIwxyp07t3799VeVKVNGb7zxhgYOHKjmzZvLzc1NzZo1k6OjI9dLw0vZsGGDypcvLwcHBw0YMEDp0qVTz549lSxZMvXp00c//PCDOnbsqIwZM0qSsmfPrl69eqlXr172bdAGgdcDFxF+xZhQw8L2799fixcv1oMHD+Ti4iJfX1/7BTOlp3vdQvqBX758WXv37uWDH1FGW0NsCN3OJOnJkyeqVq2aMmbMqJkzZyo4OFiBgYFycXHRiBEjNGnSJO3cuVOpUqWyr8Nw63gZly9fVoUKFZQ8eXIVL15cU6ZM0e7du1WoUCFJ0tChQzV69Gi1bdtWbdu2lZeXl6SwO5+ebccAXl0cl37FhP6xO2HCBI0cOVK//fab3nrrLbVs2VKTJ0+2Lzt79mw1a9ZMjx8/1p49e+zXfAGigraGmBYyWIX0dJCUa9euydnZWc2aNdPixYu1YcMGOTg4yNnZWZLk6uqq1KlTK3ny5GG2Q7DCy0ibNq1mzZqlkydPaurUqdq+fbsKFSqkhw8fSpJ69OihTp06adq0aZo2bZouXLggKex1+ghWwOuDcPUK2rdvn7Zs2aL58+ercuXK+vvvv/XLL7+oRo0a+uyzz/TDDz9Iklq3bq1u3bpp7dq1cnZ2VmBgID9CEC20NcSkkB+nX3zxhWrXrq28efPau2K1atVK7du315o1axQcHCw/Pz+tWrVKGTJksIct4GWEnE/q4OAgJycnpUyZUl5eXuratasePXqkxIkT69GjR5Kknj17qmPHjurfv7/WrVsXl2UDiGN0C3wFPNvd4OLFi5o7d666dOmi33//XR9++KH69u2rjz76SPXq1dP69ev1/fffq0ePHvZ16DaDqKCtITaE7k61aNEide3aVePGjdOff/6pNWvWKHPmzCpevLj+/fdfjRw5UtmyZZOjo6NcXV21Z88eOTs70w0LLyV0Gzx8+LBSpkypJEmS6OjRo2rXrp2SJEmiTZs2ydXVNUxbW7JkierUqcNnHPAaI1wlcKF/qJ4+fVrJkiVT2rRp7V8MLVq0UJIkSTR69Gg5Ozvr008/1b59+5QoUSJt3bqVHx+IMtoaYtvWrVv1888/q1ChQmrVqpUkacWKFRo7dqxSpEihtm3bKk2aNPaBUxo3bszgFXhpoYNVnz599Ouvv+qbb75R5cqVFRwcrM2bN6t79+5KliyZ1q9fr0SJEqlVq1YqW7as/VxTdiIBry+6BSZQEydO1MGDB+0f3r6+vqpTp47y5cunnj17at++fZKkQ4cOKWnSpHJ2dtbDhw91/fp19e/fX9u2bZPNZhPZGv+Ftoa4cOXKFbVq1UozZ86Uv7+/fXrt2rXVqVMn3bx5UxMmTFBAQIA+/vhjffDBB/ahrglWeBkhweqrr77S1KlTNWDAABUtWtTePbBChQoaPXq0/P39lTVrVpUtW1YbNmxQ06ZN7dsgWAGvL45cJUBnzpxRmTJlVK1aNfXs2dPeTSGk28zq1avl5eWlr776Stu3b1f37t3VsmVLHTx4UE+ePNGePXvk6OhItxn8J9oa4tKff/6phg0bytvbW8OHD1eBAgXs81avXq1evXqpZs2aGjx4cBxWiVfRkSNHVK9ePY0dO1ZVqlSRv7+/rl69qm3btilPnjwqWbKkzpw5o+nTpys4OFgDBgywD9RDsAJeb4SrBOrgwYNq06aNSpcuLQcHB+XNm1etW7eWJK1atUrDhw9XihQp9P777+vGjRtasWKFMmTIoEmTJsnZ2ZkvAEQZbQ1x6dChQ2rZsqXeeustde7cWfny5bPP27Fjh95++23aFyx36NAhNW7cWNOnT1dgYKAWLlyozZs36/bt2/L09NTXX3+tWrVqhVmHzzoAEt0CEywfHx9NmTJF27dv14wZM3T37l37vJo1a6pbt27y9/fXwoULVahQIa1Zs0bTpk1jpDZEG20NcalQoUKaNm2a9u3bp9GjR+vo0aP2eSVLlrR3BQReVMiogKHlzp1bDg4OatOmjSpVqiRJGjx4sHbu3KnAwEBduXIl3Dp81gGQCFcJ2ptvvqnp06crRYoUWr16tf766y/7vFq1aqlr1646ceKEVq5caZ9ujOF8BEQbbQ1xqXDhwpo6daoOHjyofv366cyZM2Hm86MWLyr04BV79uzR7t279ccffyhRokQ6cOCAvv76a61fv15jxoxRrVq1lDlzZrm5uUUYyABAolvgK4FuM4gttDXEpT/++EOTJk3S1KlTw1ygFXgRoc8F7dWrl+bNmyebzaarV6+qSZMm6tOnj7JlyyZJun//vu7cuaO2bdvqypUr9vNJAeBZhKtXxIEDB9SmTRsVKVJEXbp0Ud68ecPMpy84rEJbQ1wK+UEc+ogD8DLGjRunAQMGaPny5UqVKpUuXLigjz76SKVKldKwYcPk7e2t0aNH68cff1SSJEm0YcMGzicFECnC1SvkwIED+uSTT+Tt7a0hQ4Yoa9ascV0SXlG0NcQlRp+ElZo3b67EiRNr0qRJ9rZ18OBBlSlTRl26dNHAgQN1584dLV26VM2aNeNaagCei91+r5DChQtr3LhxSp48uby9veO6HLzCaGuISwQrvKhn9yc/efJE//5fe3cXEtXWx3H8N5YzTo6i9OJkmCaCNINopBYVRSlkoNlF6IWkKWmEiYIR2NUUdVEQSA550Rt6VdgLmohiQy9gUIKiE4kYBcrJ0pKCBLUan4tD8yQ8PdVp1OP4/VzO2mvvtTebGX7zX3vtv/7SxMSEt31qakpJSUlyOBy6fv263r9/r7CwMBUWFvIuNQA/RbjyM6mpqbpy5YoCAgJ44BazinsNwELi8Xi8wfzly5caGRlRYGCg8vPzdfPmTblcLgUEBCgwMFCSZDKZtGLFCoWEhMzYD1MBAfw/hCs/ZDAYND09zfMImHXcawAWim/fUydOnNDevXtls9l0/PhxWSwWFRUVqbS0VK2trfJ4PPr48aOam5u1Zs0ab9gCgF9BXdtPMW0Gc4V7DcC/2feLnzQ0NKi+vl5Op1O9vb1qbW3V4OCgNm/erKysLGVmZio2NlZLliyRyWRSZ2en908kvusA/AoWtAAAAH7v0aNHunXrlhITE1VUVCRJampqUk1NjcLDw1VcXKxVq1bpyZMnslgsys3NZfEKAL+NcAUAAPzamzdvtG3bNo2OjurkyZOqqKjwtt29e1fV1dUKDQ1VVVWVUlNTvW0stw7gd/GgBAAA8GtWq1W3b9+W1WpVS0uL3G63ty0rK0uVlZV68eKF7ty5M6MfwQrA76JyBQAAFoWenh4VFhYqOTlZ5eXlstvt3rbHjx9r06ZNBCoAf4RwBQAAFo3u7m4dOnRIGzduVEVFhWw224x2pgIC+BOEKwAAsKh0d3fr8OHDio6O1rlz57Ru3br5HhIAP8EzVwAAYFHZsGGDnE6nQkJCFB0dPd/DAeBHqFwBAIBF6dv7q75/FxYA/AnCFQAAWLR4QTAAX+JvGgAAsGgRrAD4EuEKAAAAAHyAcAUAAAAAPkC4AgAAAAAfIFwBAAAAgA8QrgAAAADABwhXAADMI4fDoaSkpPkeBgDABwhXAIAF5+DBgzIYDDIYDDIajYqLi9OpU6f05cuXP9rnvn37fDdIAMCis3S+BwAAwD+RkZGha9euaXJyUi0tLSotLVVgYKCqqqpmbDc1NSWj0ThPowQALCZUrgAAC5LJZJLValV0dLSOHDmi9PR0NTU1eStQZ86cUWRkpOLj4yVJbrdbu3btktls1vLly1VSUqJPnz5J+ntqXl1dnRobG70VsQcPHvy03zdXr16V3W6XyWTS6tWrdfToUW/b4OCgsrOzZbFYFBoaqpycHL19+3ZuLhIAYE4RrgAAfsFsNmtqakqS5HK51N/fr/b2djU3N2t8fFy7d+9WeHi4Ojs71dDQoHv37nlD0LFjx5STk6OMjAwNDw9reHhYW7Zs+Wk/SaqtrVVpaalKSkrkdrvV1NSkuLg4SZLH41F2drbGxsb08OFDtbe36+XLl8rNzZ37CwQAmHVMCwQALGjT09NyuVxqa2tTWVmZRkdHFRwcrMuXL3unA166dEkTExOqr69XcHCwJMnpdCorK0tnz55VRESEzGazJicnZbVavfuuq6v7ab/Tp0+rsrJS5eXl3n4pKSmS/g55brdbr169UlRUlCSpvr5edrtdnZ2d3u0AAP6ByhUAYEFqbm6WxWJRUFCQ9uzZo9zcXDkcDklSQkLCjOes+vr6lJiY6A1IkrR161Z5PB719/f/8Bg/6zcyMqLXr18rLS3th/2joqK8wUqSbDabwsLC1NfX909PHQDwL0XlCgCwIO3cuVO1tbUyGo2KjIzU0qX//Un7PgzNJrPZPCfHAQAsDFSuAAALUnBwsOLi4rR27doZwep/Wb9+vXp6ejQ+Pu79rKOjQwEBAd4FL4xGo75+/fpb/UJCQhQTEyOXy/XD4w4NDWloaMj72fPnz/XhwwfZbLbfPmcAwL8b4QoA4Pfy8vIUFBSkgoICPXv2TPfv31dZWZkOHDigiIgISVJMTIx6e3vV39+vd+/e6fPnz7/Uz+Fw6Pz587pw4YIGBgbU1dWlmpoaSVJ6eroSEhKUl5enrq4uPX36VPn5+dqxY4eSk5Pn7XoAAGYH4QoA4PeWLVumtrY2jY2NKSUlRfv371daWpqcTqd3m+LiYsXHxys5OVkrV65UR0fHL/UrKChQdXW1Ll68KLvdrszMTA0MDEiSDAaDGhsbFR4eru3btys9PV2xsbG6cePGnF8DAMDsM0xPT0/P9yAAAAAAYKGjcgUAAAAAPkC4AgAAAAAfIFwBAAAAgA8QrgAAAADABwhXAAAAAOADhCsAAAAA8AHCFQAAAAD4AOEKAAAAAHyAcAUAAAAAPkC4AgAAAAAfIFwBAAAAgA/8BxNjhHT91VBeAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Calculate total borrowed capital per token\n",
     "borrowed_capital = calculate_capital(debt_df, 'Debt (USD)')\n",
-    "plot_capital(borrowed_capital, 'Total Borrowed Capital Across Protocols')"
+    "plot_capital(borrowed_capital, 'Total Borrowed Capital Across Protocols (Logirathmic Scaling is used)')"
    ]
   },
   {
@@ -451,7 +934,74 @@
    "execution_count": 20,
    "id": "7ac76044-ee76-4807-b497-ad1541ec45a2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          Protocol   Token  Total Collateral (USD)\n",
+      "0           zklend    USDC            6.481915e+06\n",
+      "1           zklend    USDT            3.356178e+06\n",
+      "2           zklend    STRK            1.239352e+07\n",
+      "3           zklend     ETH            4.235030e+03\n",
+      "4           zklend    wBTC            3.604780e+01\n",
+      "5           zklend     DAI            7.715796e+04\n",
+      "6           zklend  wstETH            5.272330e+01\n",
+      "7     nostra_alpha    USDC            4.002109e+04\n",
+      "8     nostra_alpha     ETH            3.202590e+01\n",
+      "9     nostra_alpha    USDT            3.019192e+04\n",
+      "10  nostra_mainnet     ETH            1.928400e+04\n",
+      "11  nostra_mainnet    USDC            1.720035e+07\n",
+      "12  nostra_mainnet     DAI            7.468461e+04\n",
+      "13  nostra_mainnet    USDT            1.256802e+07\n",
+      "14  nostra_mainnet  wstETH            5.437920e+01\n",
+      "15  nostra_mainnet    STRK            5.948398e+07\n",
+      "16  nostra_mainnet    wBTC            2.448470e+01\n",
+      "17  nostra_mainnet   LORDS            1.882704e+06\n",
+      "18    hashstack_v0    USDC            1.130584e+03\n",
+      "19    hashstack_v0     ETH            6.064000e-01\n",
+      "20    hashstack_v0    USDT            2.268058e+02\n",
+      "21    hashstack_v0     DAI            9.689930e+01\n",
+      "22    hashstack_v0    wBTC            2.000000e-03\n",
+      "23    hashstack_v1    USDT            2.316824e+04\n",
+      "24    hashstack_v1    USDC            4.014684e+04\n",
+      "25    hashstack_v1     DAI            5.764160e+02\n",
+      "26    hashstack_v1     ETH            1.978970e+01\n",
+      "27    hashstack_v1    wBTC            2.710000e-02\n",
+      "          Protocol   Token  Total Debt (USD)\n",
+      "0           zklend     ETH      1.299392e+03\n",
+      "1           zklend    USDC      4.787483e+06\n",
+      "2           zklend    USDT      2.116141e+06\n",
+      "3           zklend  wstETH      2.938450e+01\n",
+      "4           zklend    wBTC      5.681700e+00\n",
+      "5           zklend     DAI      6.299988e+04\n",
+      "6           zklend    STRK      1.945519e+06\n",
+      "7     nostra_alpha    USDT      4.961984e+03\n",
+      "8     nostra_alpha    wBTC      7.600000e-03\n",
+      "9     nostra_alpha    USDC      8.623691e+03\n",
+      "10    nostra_alpha     ETH      3.410900e+00\n",
+      "11    nostra_alpha     DAI      1.949320e+03\n",
+      "12  nostra_mainnet    USDT      6.838741e+06\n",
+      "13  nostra_mainnet     ETH      6.265038e+03\n",
+      "14  nostra_mainnet    USDC      9.499169e+06\n",
+      "15  nostra_mainnet    STRK      6.207267e+06\n",
+      "16  nostra_mainnet  wstETH      3.458160e+01\n",
+      "17  nostra_mainnet    wBTC      1.179560e+01\n",
+      "18  nostra_mainnet   LORDS      4.322130e+04\n",
+      "19  nostra_mainnet     DAI      5.101548e+04\n",
+      "20    hashstack_v0     ETH      1.989000e-01\n",
+      "21    hashstack_v0    USDT      1.257607e+02\n",
+      "22    hashstack_v0    USDC      8.258461e+02\n",
+      "23    hashstack_v0     DAI      1.601284e+02\n",
+      "24    hashstack_v0    wBTC      3.500000e-03\n",
+      "25    hashstack_v1    USDT      3.371473e+04\n",
+      "26    hashstack_v1     ETH      1.637170e+01\n",
+      "27    hashstack_v1    USDC      5.081960e+04\n",
+      "28    hashstack_v1     DAI      6.368208e+02\n",
+      "29    hashstack_v1    wBTC      2.390000e-02\n"
+     ]
+    }
+   ],
    "source": [
     "# agregating the data\n",
     "# Convert the aggregated data to DataFrame for better readability\n",
@@ -459,17 +1009,674 @@
     "collateral_df = pd.DataFrame(collateral_list, columns=['Protocol', 'Token', 'Total Collateral (USD)'])\n",
     "\n",
     "debt_list = [(protocol, token, amount) for protocol, tokens in debt_amounts.items() for token, amount in tokens.items()]\n",
-    "debt_df = pd.DataFrame(debt_list, columns=['Protocol', 'Token', 'Total Debt (USD)'])"
+    "debt_df = pd.DataFrame(debt_list, columns=['Protocol', 'Token', 'Total Debt (USD)'])\n",
+    "print(collateral_df, debt_df, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b88d6fad-fb16-4514-9137-946cd2a5c443",
+   "metadata": {},
+   "source": [
+    "#### Coverting to USD denominater"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
+   "id": "48b48551-98a3-455f-a9cd-55f8257b5a17",
+   "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>Protocol</th>\n",
+       "      <th>Token</th>\n",
+       "      <th>Total Collateral (USD)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>6.481915e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>3.350375e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>STRK</td>\n",
+       "      <td>8.080574e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>1.053561e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>wBTC</td>\n",
+       "      <td>2.416717e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>DAI</td>\n",
+       "      <td>7.715796e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>wstETH</td>\n",
+       "      <td>1.546912e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>nostra_alpha</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>4.002109e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>nostra_alpha</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>7.967179e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>nostra_alpha</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>3.013972e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>4.797338e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>1.720035e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>DAI</td>\n",
+       "      <td>7.468461e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>1.254629e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>wstETH</td>\n",
+       "      <td>1.595497e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>STRK</td>\n",
+       "      <td>3.878356e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>wBTC</td>\n",
+       "      <td>1.641503e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>LORDS</td>\n",
+       "      <td>9.394610e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>1.130584e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>1.508559e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>2.264137e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>DAI</td>\n",
+       "      <td>9.689930e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>wBTC</td>\n",
+       "      <td>1.340840e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>2.312818e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>4.014684e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>DAI</td>\n",
+       "      <td>5.764160e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>4.923143e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>wBTC</td>\n",
+       "      <td>1.816838e+03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Protocol   Token  Total Collateral (USD)\n",
+       "0           zklend    USDC            6.481915e+06\n",
+       "1           zklend    USDT            3.350375e+06\n",
+       "2           zklend    STRK            8.080574e+07\n",
+       "3           zklend     ETH            1.053561e+07\n",
+       "4           zklend    wBTC            2.416717e+06\n",
+       "5           zklend     DAI            7.715796e+04\n",
+       "6           zklend  wstETH            1.546912e+05\n",
+       "7     nostra_alpha    USDC            4.002109e+04\n",
+       "8     nostra_alpha     ETH            7.967179e+04\n",
+       "9     nostra_alpha    USDT            3.013972e+04\n",
+       "10  nostra_mainnet     ETH            4.797338e+07\n",
+       "11  nostra_mainnet    USDC            1.720035e+07\n",
+       "12  nostra_mainnet     DAI            7.468461e+04\n",
+       "13  nostra_mainnet    USDT            1.254629e+07\n",
+       "14  nostra_mainnet  wstETH            1.595497e+05\n",
+       "15  nostra_mainnet    STRK            3.878356e+08\n",
+       "16  nostra_mainnet    wBTC            1.641503e+06\n",
+       "17  nostra_mainnet   LORDS            9.394610e+04\n",
+       "18    hashstack_v0    USDC            1.130584e+03\n",
+       "19    hashstack_v0     ETH            1.508559e+03\n",
+       "20    hashstack_v0    USDT            2.264137e+02\n",
+       "21    hashstack_v0     DAI            9.689930e+01\n",
+       "22    hashstack_v0    wBTC            1.340840e+02\n",
+       "23    hashstack_v1    USDT            2.312818e+04\n",
+       "24    hashstack_v1    USDC            4.014684e+04\n",
+       "25    hashstack_v1     DAI            5.764160e+02\n",
+       "26    hashstack_v1     ETH            4.923143e+04\n",
+       "27    hashstack_v1    wBTC            1.816838e+03"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "token_ids = []\n",
+    "for token in tokens:\n",
+    "    if token == 'DAI':\n",
+    "        token_ids.append('dai')\n",
+    "    elif token == 'ETH':\n",
+    "        token_ids.append('ethereum')\n",
+    "    elif token == 'USDC':\n",
+    "        token_ids.append('usd-coin')\n",
+    "    elif token == 'USDT':\n",
+    "        token_ids.append('tether')\n",
+    "    elif token == 'wBTC':\n",
+    "        token_ids.append('wrapped-bitcoin')\n",
+    "    elif token == 'LORDS':\n",
+    "        token_ids.append('lords')\n",
+    "    elif token == 'STRK':\n",
+    "        token_ids.append('strike')\n",
+    "    elif token == 'wstETH':\n",
+    "        token_ids.append('wrapped-steth')\n",
+    "    elif token == 'ZEND':\n",
+    "        token_ids.append('zenad')\n",
+    "    elif token == 'UNO':\n",
+    "        token_ids.append('uno-re')\n",
+    "#print(token_ids)\n",
+    "\n",
+    "# Total Collateral (USD)\n",
+    "for token, token_id in zip(tokens,token_ids):\n",
+    "    val = collateral_df[collateral_df['Token'] == token].loc[:, 'Total Collateral (USD)'] * prices[token_id]['usd']\n",
+    "    collateral_df.loc[collateral_df['Token'] == token, 'Total Collateral (USD)'] = val\n",
+    "collateral_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1dde9c83-bafc-4119-9a30-47edf2c816e5",
+   "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>Protocol</th>\n",
+       "      <th>Token</th>\n",
+       "      <th>Total Debt (USD)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>3.232536e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>4.787483e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>2.112483e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>wstETH</td>\n",
+       "      <td>8.621471e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>wBTC</td>\n",
+       "      <td>3.809125e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>DAI</td>\n",
+       "      <td>6.299988e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>zklend</td>\n",
+       "      <td>STRK</td>\n",
+       "      <td>1.268478e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>nostra_alpha</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>4.953404e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>nostra_alpha</td>\n",
+       "      <td>wBTC</td>\n",
+       "      <td>5.095192e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>nostra_alpha</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>8.623691e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>nostra_alpha</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>8.485398e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>nostra_alpha</td>\n",
+       "      <td>DAI</td>\n",
+       "      <td>1.949320e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>6.826917e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>1.558572e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>9.499169e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>STRK</td>\n",
+       "      <td>4.047138e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>wstETH</td>\n",
+       "      <td>1.014631e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>wBTC</td>\n",
+       "      <td>7.908006e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>LORDS</td>\n",
+       "      <td>2.156724e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>nostra_mainnet</td>\n",
+       "      <td>DAI</td>\n",
+       "      <td>5.101548e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>4.948095e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>1.255433e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>8.258461e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>DAI</td>\n",
+       "      <td>1.601284e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>hashstack_v0</td>\n",
+       "      <td>wBTC</td>\n",
+       "      <td>2.346470e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>USDT</td>\n",
+       "      <td>3.365644e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>ETH</td>\n",
+       "      <td>4.072837e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>USDC</td>\n",
+       "      <td>5.081960e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>DAI</td>\n",
+       "      <td>6.368208e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>hashstack_v1</td>\n",
+       "      <td>wBTC</td>\n",
+       "      <td>1.602304e+03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Protocol   Token  Total Debt (USD)\n",
+       "0           zklend     ETH      3.232536e+06\n",
+       "1           zklend    USDC      4.787483e+06\n",
+       "2           zklend    USDT      2.112483e+06\n",
+       "3           zklend  wstETH      8.621471e+04\n",
+       "4           zklend    wBTC      3.809125e+05\n",
+       "5           zklend     DAI      6.299988e+04\n",
+       "6           zklend    STRK      1.268478e+07\n",
+       "7     nostra_alpha    USDT      4.953404e+03\n",
+       "8     nostra_alpha    wBTC      5.095192e+02\n",
+       "9     nostra_alpha    USDC      8.623691e+03\n",
+       "10    nostra_alpha     ETH      8.485398e+03\n",
+       "11    nostra_alpha     DAI      1.949320e+03\n",
+       "12  nostra_mainnet    USDT      6.826917e+06\n",
+       "13  nostra_mainnet     ETH      1.558572e+07\n",
+       "14  nostra_mainnet    USDC      9.499169e+06\n",
+       "15  nostra_mainnet    STRK      4.047138e+07\n",
+       "16  nostra_mainnet  wstETH      1.014631e+05\n",
+       "17  nostra_mainnet    wBTC      7.908006e+05\n",
+       "18  nostra_mainnet   LORDS      2.156724e+03\n",
+       "19  nostra_mainnet     DAI      5.101548e+04\n",
+       "20    hashstack_v0     ETH      4.948095e+02\n",
+       "21    hashstack_v0    USDT      1.255433e+02\n",
+       "22    hashstack_v0    USDC      8.258461e+02\n",
+       "23    hashstack_v0     DAI      1.601284e+02\n",
+       "24    hashstack_v0    wBTC      2.346470e+02\n",
+       "25    hashstack_v1    USDT      3.365644e+04\n",
+       "26    hashstack_v1     ETH      4.072837e+04\n",
+       "27    hashstack_v1    USDC      5.081960e+04\n",
+       "28    hashstack_v1     DAI      6.368208e+02\n",
+       "29    hashstack_v1    wBTC      1.602304e+03"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Total Debt (USD)\n",
+    "for token, token_id in zip(tokens,token_ids):\n",
+    "    val = debt_df[debt_df['Token'] == token].loc[:, 'Total Debt (USD)'] * prices[token_id]['usd']\n",
+    "    debt_df.loc[debt_df['Token'] == token, 'Total Debt (USD)'] = val\n",
+    "debt_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdc71978-7b1c-421d-837a-96f61e54a051",
+   "metadata": {},
+   "source": [
+    "### Collateral data per protocol per token USD Equivalent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "id": "521fbd91-b7d6-4cfd-99aa-a203e63f3daa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Protocol        Token \n",
+       "hashstack_v0    DAI       9.689930e+01\n",
+       "                ETH       1.508559e+03\n",
+       "                USDC      1.130584e+03\n",
+       "                USDT      2.264137e+02\n",
+       "                wBTC      1.340840e+02\n",
+       "hashstack_v1    DAI       5.764160e+02\n",
+       "                ETH       4.923143e+04\n",
+       "                USDC      4.014684e+04\n",
+       "                USDT      2.312818e+04\n",
+       "                wBTC      1.816838e+03\n",
+       "nostra_alpha    ETH       7.967179e+04\n",
+       "                USDC      4.002109e+04\n",
+       "                USDT      3.013972e+04\n",
+       "nostra_mainnet  DAI       7.468461e+04\n",
+       "                ETH       4.797338e+07\n",
+       "                LORDS     9.394610e+04\n",
+       "                STRK      3.878356e+08\n",
+       "                USDC      1.720035e+07\n",
+       "                USDT      1.254629e+07\n",
+       "                wBTC      1.641503e+06\n",
+       "                wstETH    1.595497e+05\n",
+       "zklend          DAI       7.715796e+04\n",
+       "                ETH       1.053561e+07\n",
+       "                STRK      8.080574e+07\n",
+       "                USDC      6.481915e+06\n",
+       "                USDT      3.350375e+06\n",
+       "                wBTC      2.416717e+06\n",
+       "                wstETH    1.546912e+05\n",
+       "Name: Total Collateral (USD), dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "collateral_per_protocol_token = collateral_df.groupby(['Protocol','Token'])['Total Collateral (USD)'].sum()\n",
+    "collateral_per_protocol_token"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11ddb189-eef7-492a-b0eb-f54f295ab3ff",
+   "metadata": {},
+   "source": [
+    "### Debt data per protocol per token USD Equivalent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "bab1ec7c-8181-45e8-bbd6-f81f49920962",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Protocol        Token \n",
+       "hashstack_v0    DAI       1.601284e+02\n",
+       "                ETH       4.948095e+02\n",
+       "                USDC      8.258461e+02\n",
+       "                USDT      1.255433e+02\n",
+       "                wBTC      2.346470e+02\n",
+       "hashstack_v1    DAI       6.368208e+02\n",
+       "                ETH       4.072837e+04\n",
+       "                USDC      5.081960e+04\n",
+       "                USDT      3.365644e+04\n",
+       "                wBTC      1.602304e+03\n",
+       "nostra_alpha    DAI       1.949320e+03\n",
+       "                ETH       8.485398e+03\n",
+       "                USDC      8.623691e+03\n",
+       "                USDT      4.953404e+03\n",
+       "                wBTC      5.095192e+02\n",
+       "nostra_mainnet  DAI       5.101548e+04\n",
+       "                ETH       1.558572e+07\n",
+       "                LORDS     2.156724e+03\n",
+       "                STRK      4.047138e+07\n",
+       "                USDC      9.499169e+06\n",
+       "                USDT      6.826917e+06\n",
+       "                wBTC      7.908006e+05\n",
+       "                wstETH    1.014631e+05\n",
+       "zklend          DAI       6.299988e+04\n",
+       "                ETH       3.232536e+06\n",
+       "                STRK      1.268478e+07\n",
+       "                USDC      4.787483e+06\n",
+       "                USDT      2.112483e+06\n",
+       "                wBTC      3.809125e+05\n",
+       "                wstETH    8.621471e+04\n",
+       "Name: Total Debt (USD), dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "collateral_df.groupby(['Protocol','Token'])['Total Collateral (USD)'].sum()"
+    "debt_per_protocol_token = debt_df.groupby(['Protocol','Token'])['Total Debt (USD)'].sum()\n",
+    "debt_per_protocol_token"
    ]
   },
   {
@@ -482,10 +1689,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "d654007a-8ef0-4c33-ab35-50f65b030cfd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualization\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import seaborn as sns\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "collateral_df['Normalized Collateral (USD)'] = scaler.fit_transform(collateral_df[['Total Collateral (USD)']])\n",
+    "debt_df['Normalized Debt (USD)'] = scaler.fit_transform(debt_df[['Total Debt (USD)']])\n",
+    "\n",
+    "# Plotting collateral amounts\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.barplot(data=collateral_df, x='Protocol', y='Normalized Collateral (USD)', hue='Token')\n",
+    "plt.xlabel('Tokens')\n",
+    "plt.ylabel('Normalized Collateral (USD)')\n",
+    "plt.title('Normalized Total Collateral per Token and Protocol')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.legend(title='Protocol')\n",
+    "plt.show()\n",
+    "\n",
+    "# Plotting debt amounts\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.barplot(data=debt_df, x='Protocol', y='Normalized Debt (USD)', hue='Token')\n",
+    "plt.xlabel('Tokens')\n",
+    "plt.ylabel('Normalized Debt (USD)')\n",
+    "plt.title('Normalized Total Debt per Token and Protocol')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.legend(title='Protocol')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "8a03e0a2-e684-4924-a1ef-7028687d1fcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Visualization\n",
     "import matplotlib.pyplot as plt\n",
@@ -512,10 +1798,48 @@
     "plt.show()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e24113f0-360d-452f-9d31-f5dade9a68d4",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "In this analysis, we addressed several questions regarding user interactions with DeFi protocols in terms of liquidity provision, borrowing, and capital distribution. Here are the key findings:\n",
+    "\n",
+    "### 1. How many users provide liquidity on just 1 protocol? How many users use 2 or more protocols?\n",
+    "   - We found that the majority of users tend to provide liquidity on a **single protocol**, with a smaller subset of users participating in **multiple protocols**. This indicates a preference for consolidating liquidity on one platform, possibly due to familiarity, incentives, or protocol-specific advantages.\n",
+    "\n",
+    "### 2. How many users borrow on just 1 protocol? How many users use 2 or more protocols?\n",
+    "   - Similar to liquidity provision, most users **borrow from a single protocol**. However, a minority of users engage with multiple borrowing protocols, likely optimizing for different collateral types, interest rates, or loan conditions.\n",
+    "\n",
+    "### 3. Visualizing capital distribution across protocols if a user has 10k USD deposited in the pools.\n",
+    "   - We assumed that each user has deposited **10,000 USD** worth of capital and calculated how this capital is distributed across the protocols. The majority of users allocate most of their liquidity to a **single protocol**. This trend suggests that users tend to stake a significant portion of their capital in the protocol they trust most, with smaller amounts distributed across other platforms.\n",
+    "\n",
+    "### 4. Capital distribution on a per-token basis.\n",
+    "   - The analysis shows significant variation in capital distribution across different tokens and protocols. \n",
+    "     - **hashstack_v0**: Most of the capital is held in **USDC** (1130.58 USD) and **USDT** (226.81 USD), with smaller amounts in **DAI**, **ETH**, and **wBTC**.\n",
+    "     - **hashstack_v1**: The largest amounts are in **USDC** (40,146.84 USD) and **USDT** (23,168.24 USD), followed by **DAI**, **ETH**, and **wBTC**.\n",
+    "     - **nostra_alpha**: Significant collateral is in **USDC** (40,021.09 USD) and **USDT** (30,191.92 USD), with a moderate amount in **ETH**.\n",
+    "     - **nostra_mainnet**: A very large portion is allocated to **STRK** (59,483,980 USD) and **LORDS** (1,882,704 USD), with considerable amounts in **DAI** (74,684.61 USD), **USDC** (17,200,350 USD), and **USDT** (12,568,020 USD).\n",
+    "     - **zklend**: The largest holdings are in **STRK** (12,393,520 USD) and **USDC** (6,481,915 USD), with significant amounts in **DAI**, **ETH**, **USDT**, and **wBTC**.\n",
+    "\n",
+    "   - **Stablecoins** like **USDC** and **USDT** dominate capital distribution across several protocols, while protocols such as **nostra_mainnet** and **zklend** have substantial holdings in **STRK** and **LORDS**. The diversity in token preferences reflects protocol-specific strategies and user preferences, with stablecoins being the preferred choice for liquidity in most protocols.\n",
+    "\n",
+    "\n",
+    "### Additional Insights:\n",
+    "   - **Venn diagrams** illustrated the overlap between users who provide liquidity or borrow from multiple protocols. The diagrams highlight that while most users are active on just one protocol, there is a meaningful overlap of users across two or more platforms.\n",
+    "   - **Token-based visualization** further showcased how liquidity and borrowing vary significantly based on token type, offering insights into protocol token preferences.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "This analysis provides a clear understanding of how users interact with multiple DeFi protocols, how they distribute their capital, and which tokens and protocols are preferred. Further exploration could involve time-based analysis, exploring user behavior trends over time, or investigating protocol-specific incentives that drive user engagement across platforms.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28bd7cf4-a32b-44a6-9111-684bf7d43335",
+   "id": "a1d50ed4-3ec1-479f-9caf-fe2b49fd07c6",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -537,7 +1861,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,

From 9ca144d4601c4d78fbba4a7ad0d48d63423e3c33 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Sun, 3 Nov 2024 20:25:42 +0530
Subject: [PATCH 02/12] dpendencies added with poetry add

---
 apps/legacy_app/poetry.lock    | 1546 +++++++++++++++++++++++++++++++-
 apps/legacy_app/pyproject.toml |    2 +
 2 files changed, 1513 insertions(+), 35 deletions(-)

diff --git a/apps/legacy_app/poetry.lock b/apps/legacy_app/poetry.lock
index 2f393bbb..26b95429 100644
--- a/apps/legacy_app/poetry.lock
+++ b/apps/legacy_app/poetry.lock
@@ -171,6 +171,28 @@ files = [
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
+[[package]]
+name = "anyio"
+version = "4.6.2.post1"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
+    {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+trio = ["trio (>=0.26.1)"]
+
 [[package]]
 name = "appdirs"
 version = "1.4.4"
@@ -182,6 +204,93 @@ files = [
     {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
 ]
 
+[[package]]
+name = "appnope"
+version = "0.1.4"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
+]
+
+[[package]]
+name = "argon2-cffi"
+version = "23.1.0"
+description = "Argon2 for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[[package]]
+name = "arrow"
+version = "1.3.0"
+description = "Better dates & times for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+types-python-dateutil = ">=2.8.10"
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+[package.extras]
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+
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 version = "3.8.1"
@@ -199,6 +308,38 @@ typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
 [package.extras]
 tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
 
+[[package]]
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+python-versions = "*"
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+    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
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+[package.dependencies]
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+
+[package.extras]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+
+[[package]]
+name = "async-lru"
+version = "2.0.4"
+description = "Simple LRU cache for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
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 name = "async-timeout"
 version = "4.0.3"
@@ -229,11 +370,25 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
+[[package]]
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+version = "2.16.0"
+description = "Internationalization utilities"
+optional = false
+python-versions = ">=3.8"
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+[package.extras]
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+
 [[package]]
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 version = "4.12.3"
 description = "Screen-scraping library"
-optional = true
+optional = false
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 files = [
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@@ -296,6 +451,23 @@ d = ["aiohttp (>=3.10)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
+[[package]]
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+version = "6.2.0"
+description = "An easy safelist-based HTML-sanitizing tool."
+optional = false
+python-versions = ">=3.9"
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+[package.dependencies]
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+
+[package.extras]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
+
 [[package]]
 name = "blinker"
 version = "1.8.2"
@@ -329,6 +501,85 @@ files = [
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 ]
 
+[[package]]
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+description = "Foreign Function Interface for Python calling C code."
+optional = false
+python-versions = ">=3.8"
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+[package.dependencies]
+pycparser = "*"
+
 [[package]]
 name = "cfgv"
 version = "3.4.0"
@@ -479,6 +730,23 @@ files = [
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+description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
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+[package.dependencies]
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+
+[package.extras]
+test = ["pytest"]
+
 [[package]]
 name = "contourpy"
 version = "1.3.0"
@@ -763,6 +1031,41 @@ files = [
 docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
 tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
+[[package]]
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+description = "An implementation of the Debug Adapter Protocol for Python"
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+python-versions = ">=3.8"
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+]
+
 [[package]]
 name = "decorator"
 version = "5.1.1"
@@ -774,6 +1077,17 @@ files = [
     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
 ]
 
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+description = "XML bomb protection for Python stdlib modules"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
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+    {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
+]
+
 [[package]]
 name = "dill"
 version = "0.3.9"
@@ -832,6 +1146,34 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
+name = "executing"
+version = "2.1.0"
+description = "Get the currently executing AST node of a frame, and other information"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "executing-2.1.0.tar.gz", hash = "sha256:8ea27ddd260da8150fa5a708269c4a10e76161e2496ec3e587da9e3c0fe4b9ab"},
+]
+
+[package.extras]
+tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
+
+[[package]]
+name = "fastjsonschema"
+version = "2.20.0"
+description = "Fastest Python implementation of JSON schema"
+optional = false
+python-versions = "*"
+files = [
+    {file = "fastjsonschema-2.20.0-py3-none-any.whl", hash = "sha256:5875f0b0fa7a0043a91e93a9b8f793bcbbba9691e7fd83dca95c28ba26d21f0a"},
+    {file = "fastjsonschema-2.20.0.tar.gz", hash = "sha256:3d48fc5300ee96f5d116f10fe6f28d938e6008f59a6a025c2649475b87f76a23"},
+]
+
+[package.extras]
+devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
+
 [[package]]
 name = "fastparquet"
 version = "2024.5.0"
@@ -971,6 +1313,17 @@ ufo = ["fs (>=2.2.0,<3)"]
 unicode = ["unicodedata2 (>=15.1.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
+[[package]]
+name = "fqdn"
+version = "1.5.1"
+description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
+optional = false
+python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
+files = [
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+    {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
+]
+
 [[package]]
 name = "frozendict"
 version = "2.4.6"
@@ -1395,6 +1748,17 @@ protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4
 [package.extras]
 grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
+[[package]]
+name = "h11"
+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+]
+
 [[package]]
 name = "html5lib"
 version = "1.1"
@@ -1416,6 +1780,52 @@ chardet = ["chardet (>=2.2)"]
 genshi = ["genshi"]
 lxml = ["lxml"]
 
+[[package]]
+name = "httpcore"
+version = "1.0.6"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "httpcore-1.0.6.tar.gz", hash = "sha256:73f6dbd6eb8c21bbf7ef8efad555481853f5f6acdeaff1edb0694289269ee17f"},
+]
+
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.13,<0.15"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<1.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.27.2"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0"},
+    {file = "httpx-0.27.2.tar.gz", hash = "sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = "==1.*"
+idna = "*"
+sniffio = "*"
+
+[package.extras]
+brotli = ["brotli", "brotlicffi"]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+zstd = ["zstandard (>=0.18.0)"]
+
 [[package]]
 name = "identify"
 version = "2.6.1"
@@ -1455,6 +1865,112 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "ipykernel"
+version = "6.29.5"
+description = "IPython Kernel for Jupyter"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
+[[package]]
+name = "isoduration"
+version = "20.11.0"
+description = "Operations with ISO 8601 durations"
+optional = false
+python-versions = ">=3.7"
+files = [
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+[package.dependencies]
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+
 [[package]]
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 version = "5.13.2"
@@ -1469,6 +1985,25 @@ files = [
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 colors = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "jedi"
+version = "0.19.1"
+description = "An autocompletion tool for Python that can be used for text editors."
+optional = false
+python-versions = ">=3.6"
+files = [
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+    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
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+[package.dependencies]
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+[package.extras]
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+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+
 [[package]]
 name = "jinja2"
 version = "3.1.4"
@@ -1486,6 +2021,28 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
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+version = "0.9.25"
+description = "A Python implementation of the JSON5 data format."
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
+]
+
+[[package]]
+name = "jsonpointer"
+version = "3.0.0"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
 [[package]]
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 version = "4.23.0"
@@ -1499,9 +2056,17 @@ files = [
 
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 attrs = ">=22.2.0"
+fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
 jsonschema-specifications = ">=2023.03.6"
 referencing = ">=0.28.4"
+rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
 rpds-py = ">=0.7.1"
+uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
 
 [package.extras]
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
@@ -1521,6 +2086,266 @@ files = [
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 referencing = ">=0.31.0"
 
+[[package]]
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+version = "1.1.1"
+description = "Jupyter metapackage. Install all the Jupyter components in one go."
+optional = false
+python-versions = "*"
+files = [
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+nbconvert = "*"
+notebook = "*"
+
+[[package]]
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+version = "8.6.3"
+description = "Jupyter protocol implementation and client libraries"
+optional = false
+python-versions = ">=3.8"
+files = [
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+description = "Jupyter terminal console"
+optional = false
+python-versions = ">=3.7"
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+    {file = "jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d"},
+]
+
+[[package]]
+name = "jupyterlab-server"
+version = "2.27.3"
+description = "A set of server components for JupyterLab and JupyterLab like applications."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyterlab_server-2.27.3-py3-none-any.whl", hash = "sha256:e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4"},
+    {file = "jupyterlab_server-2.27.3.tar.gz", hash = "sha256:eb36caca59e74471988f0ae25c77945610b887f777255aa21f8065def9e51ed4"},
+]
+
+[package.dependencies]
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+jinja2 = ">=3.0.3"
+json5 = ">=0.9.0"
+jsonschema = ">=4.18.0"
+jupyter-server = ">=1.21,<3"
+packaging = ">=21.3"
+requests = ">=2.31"
+
+[package.extras]
+docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
+openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+
+[[package]]
+name = "jupyterlab-widgets"
+version = "3.0.13"
+description = "Jupyter interactive widgets for JupyterLab"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jupyterlab_widgets-3.0.13-py3-none-any.whl", hash = "sha256:e3cda2c233ce144192f1e29914ad522b2f4c40e77214b0cc97377ca3d323db54"},
+    {file = "jupyterlab_widgets-3.0.13.tar.gz", hash = "sha256:a2966d385328c1942b683a8cd96b89b8dd82c8b8f81dda902bb2bc06d46f5bed"},
+]
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.7"
@@ -2020,6 +2845,20 @@ pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 setuptools_scm = ">=7"
 
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.7"
+description = "Inline Matplotlib backend for Jupyter"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"},
+    {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"},
+]
+
+[package.dependencies]
+traitlets = "*"
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -2031,6 +2870,17 @@ files = [
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]
 
+[[package]]
+name = "mistune"
+version = "3.0.2"
+description = "A sane and fast Markdown parser with useful plugins and renderers"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "mistune-3.0.2-py3-none-any.whl", hash = "sha256:71481854c30fdbc938963d3605b72501f5c10a9320ecd412c121c163a1c7d205"},
+    {file = "mistune-3.0.2.tar.gz", hash = "sha256:fc7f93ded930c92394ef2cb6f04a8aabab4117a91449e72dcc8dfa646a508be8"},
+]
+
 [[package]]
 name = "mpmath"
 version = "1.3.0"
@@ -2153,57 +3003,188 @@ files = [
 typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 
 [[package]]
-name = "multitasking"
-version = "0.0.11"
-description = "Non-blocking Python methods using decorators"
-optional = true
-python-versions = "*"
+name = "multitasking"
+version = "0.0.11"
+description = "Non-blocking Python methods using decorators"
+optional = true
+python-versions = "*"
+files = [
+    {file = "multitasking-0.0.11-py3-none-any.whl", hash = "sha256:1e5b37a5f8fc1e6cfaafd1a82b6b1cc6d2ed20037d3b89c25a84f499bd7b3dd4"},
+    {file = "multitasking-0.0.11.tar.gz", hash = "sha256:4d6bc3cc65f9b2dca72fb5a787850a88dae8f620c2b36ae9b55248e51bcd6026"},
+]
+
+[[package]]
+name = "mypy-extensions"
+version = "1.0.0"
+description = "Type system extensions for programs checked with the mypy type checker."
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
+    {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
+]
+
+[[package]]
+name = "narwhals"
+version = "1.11.1"
+description = "Extremely lightweight compatibility layer between dataframe libraries"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "narwhals-1.11.1-py3-none-any.whl", hash = "sha256:2bcf7c1649668f3b3ad1410c52f456da0a81b725a4c33f716062d0021354f049"},
+    {file = "narwhals-1.11.1.tar.gz", hash = "sha256:4cb7e144117622052782d2522a78785058c42a860ce27854f25ad9fa5d6fda3d"},
+]
+
+[package.extras]
+cudf = ["cudf (>=23.08.00)"]
+dask = ["dask[dataframe] (>=2024.7)"]
+modin = ["modin"]
+pandas = ["pandas (>=0.25.3)"]
+polars = ["polars (>=0.20.3)"]
+pyarrow = ["pyarrow (>=11.0.0)"]
+
+[[package]]
+name = "nbclient"
+version = "0.10.0"
+description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "nbclient-0.10.0-py3-none-any.whl", hash = "sha256:f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f"},
+    {file = "nbclient-0.10.0.tar.gz", hash = "sha256:4b3f1b7dba531e498449c4db4f53da339c91d449dc11e9af3a43b4eb5c5abb09"},
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+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+nbformat = ">=5.1"
+traitlets = ">=5.4"
+
+[package.extras]
+dev = ["pre-commit"]
+docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+
+[[package]]
+name = "nbconvert"
+version = "7.16.4"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "nbconvert-7.16.4-py3-none-any.whl", hash = "sha256:05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3"},
+    {file = "nbconvert-7.16.4.tar.gz", hash = "sha256:86ca91ba266b0a448dc96fa6c5b9d98affabde2867b363258703536807f9f7f4"},
+]
+
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+bleach = "!=5.0.0"
+defusedxml = "*"
+jinja2 = ">=3.0"
+jupyter-core = ">=4.7"
+jupyterlab-pygments = "*"
+markupsafe = ">=2.0"
+mistune = ">=2.0.3,<4"
+nbclient = ">=0.5.0"
+nbformat = ">=5.7"
+packaging = "*"
+pandocfilters = ">=1.4.1"
+pygments = ">=2.4.1"
+tinycss2 = "*"
+traitlets = ">=5.1"
+
+[package.extras]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
+docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
+qtpng = ["pyqtwebengine (>=5.15)"]
+serve = ["tornado (>=6.1)"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
+webpdf = ["playwright"]
+
+[[package]]
+name = "nbformat"
+version = "5.10.4"
+description = "The Jupyter Notebook format"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"},
+    {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"},
+]
+
+[package.dependencies]
+fastjsonschema = ">=2.15"
+jsonschema = ">=2.6"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+traitlets = ">=5.1"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["pep440", "pre-commit", "pytest", "testpath"]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.6.0"
+description = "Patch asyncio to allow nested event loops"
+optional = false
+python-versions = ">=3.5"
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+    {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
+    {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
 ]
 
 [[package]]
-name = "mypy-extensions"
-version = "1.0.0"
-description = "Type system extensions for programs checked with the mypy type checker."
+name = "nodeenv"
+version = "1.9.1"
+description = "Node.js virtual environment builder"
 optional = false
-python-versions = ">=3.5"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 files = [
-    {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
-    {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
+    {file = "nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9"},
+    {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
 ]
 
 [[package]]
-name = "narwhals"
-version = "1.11.1"
-description = "Extremely lightweight compatibility layer between dataframe libraries"
+name = "notebook"
+version = "7.2.2"
+description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "narwhals-1.11.1-py3-none-any.whl", hash = "sha256:2bcf7c1649668f3b3ad1410c52f456da0a81b725a4c33f716062d0021354f049"},
-    {file = "narwhals-1.11.1.tar.gz", hash = "sha256:4cb7e144117622052782d2522a78785058c42a860ce27854f25ad9fa5d6fda3d"},
+    {file = "notebook-7.2.2-py3-none-any.whl", hash = "sha256:c89264081f671bc02eec0ed470a627ed791b9156cad9285226b31611d3e9fe1c"},
+    {file = "notebook-7.2.2.tar.gz", hash = "sha256:2ef07d4220421623ad3fe88118d687bc0450055570cdd160814a59cf3a1c516e"},
 ]
 
+[package.dependencies]
+jupyter-server = ">=2.4.0,<3"
+jupyterlab = ">=4.2.0,<4.3"
+jupyterlab-server = ">=2.27.1,<3"
+notebook-shim = ">=0.2,<0.3"
+tornado = ">=6.2.0"
+
 [package.extras]
-cudf = ["cudf (>=23.08.00)"]
-dask = ["dask[dataframe] (>=2024.7)"]
-modin = ["modin"]
-pandas = ["pandas (>=0.25.3)"]
-polars = ["polars (>=0.20.3)"]
-pyarrow = ["pyarrow (>=11.0.0)"]
+dev = ["hatch", "pre-commit"]
+docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
 
 [[package]]
-name = "nodeenv"
-version = "1.9.1"
-description = "Node.js virtual environment builder"
+name = "notebook-shim"
+version = "0.2.4"
+description = "A shim layer for notebook traits and config"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+python-versions = ">=3.7"
 files = [
-    {file = "nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9"},
-    {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
+    {file = "notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef"},
+    {file = "notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb"},
 ]
 
+[package.dependencies]
+jupyter-server = ">=1.8,<3"
+
+[package.extras]
+test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"]
+
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -2265,6 +3246,17 @@ rsa = ["cryptography (>=3.0.0)"]
 signals = ["blinker (>=1.4.0)"]
 signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
+[[package]]
+name = "overrides"
+version = "7.7.0"
+description = "A decorator to automatically detect mismatch when overriding a method."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49"},
+    {file = "overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a"},
+]
+
 [[package]]
 name = "packaging"
 version = "24.1"
@@ -2362,6 +3354,32 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.9.2)"]
 
+[[package]]
+name = "pandocfilters"
+version = "1.5.1"
+description = "Utilities for writing pandoc filters in python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc"},
+    {file = "pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e"},
+]
+
+[[package]]
+name = "parso"
+version = "0.8.4"
+description = "A Python Parser"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18"},
+    {file = "parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d"},
+]
+
+[package.extras]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["docopt", "pytest"]
+
 [[package]]
 name = "pathspec"
 version = "0.12.1"
@@ -2383,6 +3401,20 @@ files = [
     {file = "peewee-3.17.7.tar.gz", hash = "sha256:6aefc700bd530fc6ac23fa19c9c5b47041751d92985b799169c8e318e97eabaa"},
 ]
 
+[[package]]
+name = "pexpect"
+version = "4.9.0"
+description = "Pexpect allows easy control of interactive console applications."
+optional = false
+python-versions = "*"
+files = [
+    {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"},
+    {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"},
+]
+
+[package.dependencies]
+ptyprocess = ">=0.5"
+
 [[package]]
 name = "pillow"
 version = "10.4.0"
@@ -2619,6 +3651,34 @@ nodeenv = ">=0.11.1"
 pyyaml = ">=5.1"
 virtualenv = ">=20.10.0"
 
+[[package]]
+name = "prometheus-client"
+version = "0.21.0"
+description = "Python client for the Prometheus monitoring system."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "prometheus_client-0.21.0-py3-none-any.whl", hash = "sha256:4fa6b4dd0ac16d58bb587c04b1caae65b8c5043e85f778f42f5f632f6af2e166"},
+    {file = "prometheus_client-0.21.0.tar.gz", hash = "sha256:96c83c606b71ff2b0a433c98889d275f51ffec6c5e267de37c7a2b5c9aa9233e"},
+]
+
+[package.extras]
+twisted = ["twisted"]
+
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.48"
+description = "Library for building powerful interactive command lines in Python"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "prompt_toolkit-3.0.48-py3-none-any.whl", hash = "sha256:f49a827f90062e411f1ce1f854f2aedb3c23353244f8108b89283587397ac10e"},
+    {file = "prompt_toolkit-3.0.48.tar.gz", hash = "sha256:d6623ab0477a80df74e646bdbc93621143f5caf104206aa29294d53de1a03d90"},
+]
+
+[package.dependencies]
+wcwidth = "*"
+
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 version = "0.35.1"
@@ -3327,6 +4600,31 @@ requests = ">=2.0.0"
 [package.extras]
 rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
 
+[[package]]
+name = "rfc3339-validator"
+version = "0.1.4"
+description = "A pure python RFC3339 validator"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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+[package.dependencies]
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+
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+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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@@ -3472,11 +4770,27 @@ files = [
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 pyasn1 = ">=0.1.3"
 
+[[package]]
+name = "send2trash"
+version = "1.8.3"
+description = "Send file to trash natively under Mac OS X, Windows and Linux"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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+win32 = ["pywin32"]
+
 [[package]]
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-optional = true
+optional = false
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+optional = false
+python-versions = ">=3.7"
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+
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+
 [[package]]
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@@ -3635,6 +4979,45 @@ files = [
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 test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
+[[package]]
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+python-versions = ">=3.8"
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+tornado = ">=6.1.0"
+
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+typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
+
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+optional = false
+python-versions = ">=3.8"
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+
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+test = ["pytest", "ruff"]
+
 [[package]]
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 version = "0.10.2"
@@ -3697,6 +5080,32 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "traitlets"
+version = "5.14.3"
+description = "Traitlets Python configuration system"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
+    {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
+]
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
+
+[[package]]
+name = "types-python-dateutil"
+version = "2.9.0.20241003"
+description = "Typing stubs for python-dateutil"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "types-python-dateutil-2.9.0.20241003.tar.gz", hash = "sha256:58cb85449b2a56d6684e41aeefb4c4280631246a0da1a719bdbe6f3fb0317446"},
+    {file = "types_python_dateutil-2.9.0.20241003-py3-none-any.whl", hash = "sha256:250e1d8e80e7bbc3a6c99b907762711d1a1cdd00e978ad39cb5940f6f0a87f3d"},
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -3734,6 +5143,20 @@ files = [
     {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
 ]
 
+[[package]]
+name = "uri-template"
+version = "1.3.0"
+description = "RFC 6570 URI Template Processor"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
+    {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
+]
+
+[package.extras]
+dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
+
 [[package]]
 name = "urllib3"
 version = "2.2.3"
@@ -3813,17 +5236,70 @@ files = [
 [package.extras]
 watchmedo = ["PyYAML (>=3.10)"]
 
+[[package]]
+name = "wcwidth"
+version = "0.2.13"
+description = "Measures the displayed width of unicode strings in a terminal"
+optional = false
+python-versions = "*"
+files = [
+    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
+    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
+]
+
+[[package]]
+name = "webcolors"
+version = "24.8.0"
+description = "A library for working with the color formats defined by HTML and CSS."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
+    {file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
+]
+
+[package.extras]
+docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
+tests = ["coverage[toml]"]
+
 [[package]]
 name = "webencodings"
 version = "0.5.1"
 description = "Character encoding aliases for legacy web content"
-optional = true
+optional = false
 python-versions = "*"
 files = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
 
+[[package]]
+name = "websocket-client"
+version = "1.8.0"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
+    {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.13"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71"},
+    {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.16.0"
@@ -3954,4 +5430,4 @@ liquidation = ["matplotlib", "tqdm", "yfinance"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "68d4ea92dc7bb8038417efaa4be20e2a641e6db8ee40f935b0e68d3eccbb5585"
+content-hash = "59215abe9cc5eb2f894a2c116afca96838761ed2f89e327b999c5938f77eee53"
diff --git a/apps/legacy_app/pyproject.toml b/apps/legacy_app/pyproject.toml
index fb37c6df..7eede42e 100644
--- a/apps/legacy_app/pyproject.toml
+++ b/apps/legacy_app/pyproject.toml
@@ -25,6 +25,8 @@ pydantic = "^2.9.2"
 pytest = "^8.3.3"
 python-dotenv = "^1.0.1"
 
+jupyter = "^1.1.1"
+notebook = "^7.2.2"
 [tool.poetry.group.dev.dependencies]
 black = "^24.8.0"
 isort = "^5.13.2"

From d550dfe2f0fc13d183e0b5258f2d0a6e40716563 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Sat, 9 Nov 2024 19:57:51 +0530
Subject: [PATCH 03/12] Update
 apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb

Co-authored-by: lukaspetrasek <47862369+lukaspetrasek@users.noreply.github.com>
---
 ...lyze_user_behavior_across_different_lending_protocols.ipynb | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index f99c59d9..a0f30c98 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -433,8 +433,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "protocol_count_df_liquidity = protocol_count_df_liquidity.reset_index()\n",
-    "protocol_count_df_liquidity.drop(columns=['index'], inplace=True)"
+    "protocol_count_df_liquidity = protocol_count_df_liquidity.reset_index(drop = True)"
    ]
   },
   {

From ff48b5753821dd4a03808e1fe9c57449b2654a52 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Sat, 9 Nov 2024 20:01:09 +0530
Subject: [PATCH 04/12] Update
 apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb

Co-authored-by: lukaspetrasek <47862369+lukaspetrasek@users.noreply.github.com>
---
 ...alyze_user_behavior_across_different_lending_protocols.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index a0f30c98..8af5a101 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -605,7 +605,7 @@
    ],
    "source": [
     "# Get unique users providing liquidity\n",
-    "liquidity_df = df_loans[df_loans['Collateral (USD)'] > 0]\n",
+    "liquidity_df = loans[loans['Collateral (USD)'] > 0]\n",
     "liquidity_protocol_users = get_unique_users_by_protocol(liquidity_df)\n",
     "\n",
     "\n",

From 8df2707fe314424227b33906b0787022ee720e14 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Sat, 9 Nov 2024 20:07:53 +0530
Subject: [PATCH 05/12] some changes

---
 .gitignore                                    |   2 +
 ...r_across_different_lending_protocols.ipynb | 286 ++++++++------
 apps/legacy_app/poetry.lock                   | 358 +++++++++++++++++-
 apps/legacy_app/pyproject.toml                |   5 +-
 4 files changed, 519 insertions(+), 132 deletions(-)

diff --git a/.gitignore b/.gitignore
index 2f1b0d1f..7475b8e1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,6 @@ __pycache__
 .ipynb_checkpoints
 .vscode
 .env
+.venv
+shell.nix
 storage_credentials.json
diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index 8af5a101..5bce1a49 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -60,18 +60,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "3bcbd644-a92c-4cb3-84ed-64eb71dd0cff",
    "metadata": {},
    "outputs": [],
    "source": [
     "# importing necessary libraries\n",
+    "import os\n",
+    "import sys\n",
     "import pandas as pd\n",
+    "root_dir = os.path.abspath(os.path.join(os.getcwd(), '..'))\n",
+    "sys.path.append(root_dir)\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "from matplotlib_venn import venn3\n",
     "from sqlalchemy import create_engine\n",
-    "import gcsfs"
+    "import gcsfs\n",
+    "import requests\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from collections import defaultdict, Counter\n",
+    "import re\n",
+    "import src.helpers\n",
+    "import src.main_chart\n",
+    "import src.persistent_state\n",
+    "import src.settings\n",
+    "import src.swap_amm\n",
+    "import src.utils\n",
+    "import math"
    ]
   },
   {
@@ -90,34 +107,6 @@
     "#### From Postgress"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "fb9b9d7d-1abc-498b-8358-7c12cb01b0f4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from sqlalchemy import create_engine\n",
-    "\n",
-    "# # List of protocols (table names in the PostgreSQL database)\n",
-    "# protocols = [\"zklend\", \"nostra_alpha\", \"nostra_mainnet\", \"hashstack_v0\", \"hashstack_v1\"]\n",
-    "\n",
-    "# # Database connection string\n",
-    "# db_connection_string = 'postgresql://username:password@hostname:port/database'\n",
-    "\n",
-    "# # Load data from PostgreSQL\n",
-    "# postgres_df_list = []\n",
-    "# engine = create_engine(db_connection_string)\n",
-    "\n",
-    "# for protocol in protocols:\n",
-    "#     df = pd.read_sql_table(protocol, con=engine)\n",
-    "#     df['Protocol'] = protocol\n",
-    "#     postgres_df_list.append(df)\n",
-    "\n",
-    "# # Combine all PostgreSQL DataFrames into one\n",
-    "# df_loans_postgres = pd.concat(postgres_df_list, ignore_index=True)a"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "c5820bcd-27a7-4a72-b2c9-8b81dda59110",
@@ -128,37 +117,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "bc9bd276-f6c0-4903-8f18-85e580de6f2d",
+   "execution_count": 8,
+   "id": "2624ecf7-a4de-4ed6-b075-4a7b1ea5a1c3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing zklend from Google Storage...\n",
+      "Processing nostra_alpha from Google Storage...\n",
+      "Processing nostra_mainnet from Google Storage...\n",
+      "Processing hashstack_v0 from Google Storage...\n",
+      "Processing hashstack_v1 from Google Storage...\n",
+      "Combined dataframe shape: (1776234, 9)\n"
+     ]
+    }
+   ],
    "source": [
-    "# Dictionary of Parquet URLs\n",
-    "parquet_urls = {\n",
-    "    \"zklend\": \"https://storage.googleapis.com/derisk-persistent-state/zklend_data/loans.parquet\",\n",
-    "    \"nostra_alpha\": \"https://storage.googleapis.com/derisk-persistent-state/nostra_alpha_data/loans.parquet\",\n",
-    "    \"nostra_mainnet\": \"https://storage.googleapis.com/derisk-persistent-state/nostra_mainnet_data/loans.parquet\",\n",
-    "    \"hashstack_v0\": \"https://storage.googleapis.com/derisk-persistent-state/hashstack_v0_data/loans.parquet\",\n",
-    "    \"hashstack_v1\": \"https://storage.googleapis.com/derisk-persistent-state/hashstack_v1_data/loans.parquet\",\n",
-    "}\n",
+    "def load_protocol_data(protocols: list[str]) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Load data from Google Storage for the specified protocols and combine them into a single DataFrame.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    protocols (list[str]): A list of protocol names to load data for.\n",
+    "    \n",
+    "    Returns:\n",
+    "    pd.DataFrame: A DataFrame containing the combined data from all specified protocols.\n",
+    "    \n",
+    "    \"\"\"    \n",
+    "    combined_protocols_df = pd.DataFrame()\n",
+    "    \n",
+    "    for protocol in protocols:\n",
+    "        # Read from google storage\n",
+    "        try:\n",
+    "            url = f\"https://storage.googleapis.com/derisk-persistent-state/{protocol}_data/loans.parquet\"      \n",
+    "            print(f\"Processing {protocol} from Google Storage...\")\n",
+    "            df_protocol = pd.read_parquet(url)\n",
+    "        except:\n",
+    "            #url = f\"https://storage.googleapis.com/derisk-persistent-state/{protocol}_data/loans.parquet\"      \n",
+    "            #print(f\"Processing {protocol} from Google Storage...\")\n",
+    "            #df_protocol = pd.read_parquet(url)\n",
+    "            print(\"Moving forward\")\n",
+    "        # Read from SQL DB\n",
+    "        ## Uncomment the following code block and comment the above three lines to load data from a local database\n",
+    "        # connection = src.db.establish_connection()\n",
+    "        # query = f\"SELECT * FROM {protocol}_data\" # Ensure table name is correct\n",
+    "        # print(f\"Processing {protocol} from local database...\")\n",
+    "        # df_protocol = pd.read_sql(query, con = connection)\n",
+    "        # connection.close()\n",
+    "        \n",
+    "        df_protocol['Protocol'] = protocol\n",
+    "        combined_protocols_df = pd.concat([combined_protocols_df, df_protocol], ignore_index=True)\n",
+    "    return combined_protocols_df\n",
     "\n",
-    "# Load data from GCS\n",
-    "gcs_df_list = []\n",
-    "for protocol, url in parquet_urls.items():\n",
-    "    fs = gcsfs.GCSFileSystem()\n",
-    "    gcs_path = url.replace('https://storage.googleapis.com/', '')\n",
-    "    with fs.open(gcs_path, 'rb') as f:\n",
-    "        df = pd.read_parquet(f, engine='pyarrow')\n",
-    "        df['Protocol'] = protocol\n",
-    "        gcs_df_list.append(df)\n",
+    "# List of protocols\n",
+    "PROTOCOLS = ['zklend', 'nostra_alpha', 'nostra_mainnet', 'hashstack_v0', 'hashstack_v1']\n",
     "\n",
-    "# Combine all GCS DataFrames into one\n",
-    "df_loans = pd.concat(gcs_df_list, ignore_index=True)"
+    "# Load the data\n",
+    "df_loans = load_protocol_data(PROTOCOLS)\n",
+    "print(f\"Combined dataframe shape: {df_loans.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6324abae-c1bb-4204-a919-fb52209722e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "User                              0\n",
+       "Protocol                          0\n",
+       "Collateral (USD)                  0\n",
+       "Risk-adjusted collateral (USD)    0\n",
+       "Debt (USD)                        0\n",
+       "Health factor                     0\n",
+       "Standardized health factor        0\n",
+       "Collateral                        0\n",
+       "Debt                              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_loans.isnull().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "6a9975f0-b5aa-49b4-bd47-b560d7c590e8",
    "metadata": {},
    "outputs": [
@@ -296,7 +349,7 @@
        "4                                                "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -314,6 +367,61 @@
     "Ethereum,Wrapped-Bitcoin,USD-coin,DAI,Tether,Wrapped-Steth,Lords,Strike,UNO-Re,Zenad"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5ba21cdc-3a84-429d-a1e5-2380046230f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fetch_prices(collateral_token: str) -> float:\n",
+    "    # Fetch underlying addresses and decimals\n",
+    "    collateral_token_underlying_address = (\n",
+    "        src.helpers.UNDERLYING_SYMBOLS_TO_UNDERLYING_ADDRESSES[collateral_token]\n",
+    "    )\n",
+    "    collateral_token_decimals = int(\n",
+    "        math.log10(src.settings.TOKEN_SETTINGS[collateral_token].decimal_factor)\n",
+    "    )\n",
+    "    underlying_addresses_to_decimals = {\n",
+    "        collateral_token_underlying_address: collateral_token_decimals\n",
+    "    }\n",
+    "\n",
+    "    # Fetch prices\n",
+    "    prices = src.helpers.get_prices(token_decimals=underlying_addresses_to_decimals)\n",
+    "    collateral_token_price = prices[collateral_token_underlying_address]\n",
+    "\n",
+    "    return collateral_token_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "938f1b8f-eb9c-4508-9fa0-35eca038b3d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[12], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m prices \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m token \u001b[38;5;129;01min\u001b[39;00m COLLATERAL_TOKENS:\n\u001b[0;32m----> 4\u001b[0m     price \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_prices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m     prices[token] \u001b[38;5;241m=\u001b[39m price\n\u001b[1;32m      6\u001b[0m prices\n",
+      "Cell \u001b[0;32mIn[11], line 14\u001b[0m, in \u001b[0;36mfetch_prices\u001b[0;34m(collateral_token)\u001b[0m\n\u001b[1;32m      9\u001b[0m underlying_addresses_to_decimals \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     10\u001b[0m     collateral_token_underlying_address: collateral_token_decimals\n\u001b[1;32m     11\u001b[0m }\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# Fetch prices\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m prices \u001b[38;5;241m=\u001b[39m \u001b[43msrc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhelpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_prices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken_decimals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munderlying_addresses_to_decimals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m collateral_token_price \u001b[38;5;241m=\u001b[39m prices[collateral_token_underlying_address]\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m collateral_token_price\n",
+      "File \u001b[0;32m~/odhack/derisk-research/apps/legacy_app/src/helpers.py:163\u001b[0m, in \u001b[0;36mget_prices\u001b[0;34m(token_decimals)\u001b[0m\n\u001b[1;32m    160\u001b[0m token_info \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mdict\u001b[39m(y) \u001b[38;5;28;01mfor\u001b[39;00m y \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;28mtuple\u001b[39m(x\u001b[38;5;241m.\u001b[39mitems()) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m token_info}]\n\u001b[1;32m    162\u001b[0m \u001b[38;5;66;03m# Perform sanity checks.\u001b[39;00m\n\u001b[0;32m--> 163\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(token_info) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m decimals \u001b[38;5;241m==\u001b[39m token_info[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdecimals\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    166\u001b[0m prices[token] \u001b[38;5;241m=\u001b[39m token_info[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcurrentPrice\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "COLLATERAL_TOKENS = [\"ETH\", \"WBTC\", \"STRK\", \"USDC\", \"USDT\", \"DAI\", \"DAI V2\"]\n",
+    "prices = {}\n",
+    "for token in COLLATERAL_TOKENS:\n",
+    "    price = fetch_prices(token)\n",
+    "    prices[token] = price\n",
+    "prices"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -329,8 +437,6 @@
     }
    ],
    "source": [
-    "import requests\n",
-    "\n",
     "# List of token IDs to fetch from CoinGecko (you can add more tokens if needed)\n",
     "token_ids = 'ethereum,wrapped-bitcoin,usd-coin,dai,tether,wrapped-steth,lords,strike,uno-re,zenad'\n",
     "\n",
@@ -402,8 +508,6 @@
    },
    "outputs": [],
    "source": [
-    "from collections import defaultdict, Counter\n",
-    "\n",
     "liquidity_data = df_loans[df_loans['Collateral (USD)'] > 0]\n",
     "\n",
     "# Initialize a dictionary to store users and their associated protocols for liquidity\n",
@@ -518,33 +622,6 @@
     "protocol_count_df_liquidity"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "7a31ce5a-c4bd-4fa1-9d1e-99e5036fb5a1",
-   "metadata": {},
-   "source": [
-    "### 1. How many users provide liquidity on just 1 protocol? How many users use 2 or more protocols?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "25eea99a-b7f6-4ec0-88fd-d528bac3e45c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of users provide liquidity on 1 protocol is 402964\n",
-      "Number of users proved liquidity on 2 or more portocols is 75670\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Number of users provide liquidity on 1 protocol is {protocol_count_df_liquidity.loc[0, 'Number of Users']}\\nNumber of users proved liquidity on 2 or more portocols is {sum(protocol_count_df_liquidity.loc[1:, 'Number of Users'])}\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "db697ca0-30fe-4d21-a5fc-b01c5db05ad9",
@@ -733,33 +810,6 @@
     "protocol_count_df_debt"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "8040a13f-605a-4998-9982-8860d913c4d8",
-   "metadata": {},
-   "source": [
-    "### 2. How many users borrow on just 1 protocol? How many users use 2 or more protocols?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "a5fca63b-a6db-4d1a-b426-ff2a98690b45",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of users provide liquidity on 1 protocol is 114975\n",
-      "Number of users proved liquidity on 2 or more portocols is 8218\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Number of users provide liquidity on 1 protocol is {protocol_count_df_debt.loc[0, 'Number of Users']}\\nNumber of users proved liquidity on 2 or more portocols is {sum(protocol_count_df_debt.loc[1:, 'Number of Users'])}\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "2bd81043-e6fc-44cf-b6d3-57c18656abfb",
@@ -838,9 +888,6 @@
     }
    ],
    "source": [
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "\n",
     "# Function to calculate total capital per token across protocols\n",
     "def calculate_capital(df, column_name):\n",
     "    capital_per_protocol = df.groupby('Protocol')[column_name].sum()\n",
@@ -910,7 +957,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import re\n",
     "# List of tokens\n",
     "tokens = [\"ETH\", \"wBTC\", \"USDC\", \"DAI\", \"USDT\", \"wstETH\", \"LORDS\", \"STRK\", \"UNO\", \"ZEND\"]\n",
     "\n",
@@ -1715,9 +1761,6 @@
    ],
    "source": [
     "# Visualization\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "import seaborn as sns\n",
     "\n",
     "scaler = StandardScaler()\n",
     "collateral_df['Normalized Collateral (USD)'] = scaler.fit_transform(collateral_df[['Total Collateral (USD)']])\n",
@@ -1773,9 +1816,6 @@
    ],
    "source": [
     "# Visualization\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
     "# Plotting collateral amounts\n",
     "plt.figure(figsize=(12, 8))\n",
     "sns.barplot(data=collateral_df, x='Protocol', y='Total Collateral (USD)', hue='Token')\n",
@@ -1860,7 +1900,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/apps/legacy_app/poetry.lock b/apps/legacy_app/poetry.lock
index 26b95429..1825e2be 100644
--- a/apps/legacy_app/poetry.lock
+++ b/apps/legacy_app/poetry.lock
@@ -751,7 +751,7 @@ test = ["pytest"]
 name = "contourpy"
 version = "1.3.0"
 description = "Python library for calculating contours of 2D quadrilateral grids"
-optional = true
+optional = false
 python-versions = ">=3.9"
 files = [
     {file = "contourpy-1.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:880ea32e5c774634f9fcd46504bf9f080a41ad855f4fef54f5380f5133d343c7"},
@@ -1020,7 +1020,7 @@ build = ["cmake (>=3.22.4)"]
 name = "cycler"
 version = "0.12.1"
 description = "Composable style cycles"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
     {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
@@ -1246,7 +1246,7 @@ typing = ["typing-extensions (>=4.12.2)"]
 name = "fonttools"
 version = "4.54.1"
 description = "Tools to manipulate font files"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
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@@ -1748,6 +1748,92 @@ protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4
 [package.extras]
 grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
+[[package]]
+name = "greenlet"
+version = "3.1.1"
+description = "Lightweight in-process concurrent programming"
+optional = false
+python-versions = ">=3.7"
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 version = "0.14.0"
@@ -2021,6 +2107,17 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
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 version = "1.4.7"
 description = "A fast implementation of the Cassowary constraint solver"
-optional = true
+optional = false
 python-versions = ">=3.8"
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 version = "3.8.0"
 description = "Python plotting package"
-optional = true
+optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
 traitlets = "*"
 
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 version = "0.1.2"
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 version = "3.2.0"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-optional = true
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+aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
+asyncio = ["greenlet (!=0.4.17)"]
+asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
+mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5,!=1.1.10)"]
+mssql = ["pyodbc"]
+mssql-pymssql = ["pymssql"]
+mssql-pyodbc = ["pyodbc"]
+mypy = ["mypy (>=0.910)"]
+mysql = ["mysqlclient (>=1.4.0)"]
+mysql-connector = ["mysql-connector-python"]
+oracle = ["cx_oracle (>=8)"]
+oracle-oracledb = ["oracledb (>=1.0.1)"]
+postgresql = ["psycopg2 (>=2.7)"]
+postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
+postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
+postgresql-psycopg = ["psycopg (>=3.0.7)"]
+postgresql-psycopg2binary = ["psycopg2-binary"]
+postgresql-psycopg2cffi = ["psycopg2cffi"]
+postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
+pymysql = ["pymysql"]
+sqlcipher = ["sqlcipher3_binary"]
+
 [[package]]
 name = "stack-data"
 version = "0.6.3"
@@ -5000,6 +5331,17 @@ docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
 typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.5.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"},
+    {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"},
+]
+
 [[package]]
 name = "tinycss2"
 version = "1.4.0"
@@ -5430,4 +5772,4 @@ liquidation = ["matplotlib", "tqdm", "yfinance"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "59215abe9cc5eb2f894a2c116afca96838761ed2f89e327b999c5938f77eee53"
+content-hash = "ee28fd4a5fae6000c414309e0cc5e22fb94639406da60daa581d30caf3d31ed0"
diff --git a/apps/legacy_app/pyproject.toml b/apps/legacy_app/pyproject.toml
index 7eede42e..0944f6ec 100644
--- a/apps/legacy_app/pyproject.toml
+++ b/apps/legacy_app/pyproject.toml
@@ -26,7 +26,10 @@ pytest = "^8.3.3"
 python-dotenv = "^1.0.1"
 
 jupyter = "^1.1.1"
-notebook = "^7.2.2"
+matplotlib-venn = "^1.1.1"
+sqlalchemy = "^2.0.36"
+seaborn = "^0.13.2"
+scikit-learn = "^1.5.2"
 [tool.poetry.group.dev.dependencies]
 black = "^24.8.0"
 isort = "^5.13.2"

From 15c13417af60fadd95a41b3eef365a79d64eea35 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Sat, 9 Nov 2024 21:33:29 +0530
Subject: [PATCH 06/12] requested changes

---
 ...r_across_different_lending_protocols.ipynb | 750 +++++++-----------
 1 file changed, 308 insertions(+), 442 deletions(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index 5bce1a49..f1b369b7 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "id": "3bcbd644-a92c-4cb3-84ed-64eb71dd0cff",
    "metadata": {},
    "outputs": [],
@@ -83,11 +83,6 @@
     "from collections import defaultdict, Counter\n",
     "import re\n",
     "import src.helpers\n",
-    "import src.main_chart\n",
-    "import src.persistent_state\n",
-    "import src.settings\n",
-    "import src.swap_amm\n",
-    "import src.utils\n",
     "import math"
    ]
   },
@@ -117,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "2624ecf7-a4de-4ed6-b075-4a7b1ea5a1c3",
    "metadata": {},
    "outputs": [
@@ -155,17 +150,7 @@
     "            print(f\"Processing {protocol} from Google Storage...\")\n",
     "            df_protocol = pd.read_parquet(url)\n",
     "        except:\n",
-    "            #url = f\"https://storage.googleapis.com/derisk-persistent-state/{protocol}_data/loans.parquet\"      \n",
-    "            #print(f\"Processing {protocol} from Google Storage...\")\n",
-    "            #df_protocol = pd.read_parquet(url)\n",
-    "            print(\"Moving forward\")\n",
-    "        # Read from SQL DB\n",
-    "        ## Uncomment the following code block and comment the above three lines to load data from a local database\n",
-    "        # connection = src.db.establish_connection()\n",
-    "        # query = f\"SELECT * FROM {protocol}_data\" # Ensure table name is correct\n",
-    "        # print(f\"Processing {protocol} from local database...\")\n",
-    "        # df_protocol = pd.read_sql(query, con = connection)\n",
-    "        # connection.close()\n",
+    "            print(\"Moving forward...\")\n",
     "        \n",
     "        df_protocol['Protocol'] = protocol\n",
     "        combined_protocols_df = pd.concat([combined_protocols_df, df_protocol], ignore_index=True)\n",
@@ -175,13 +160,13 @@
     "PROTOCOLS = ['zklend', 'nostra_alpha', 'nostra_mainnet', 'hashstack_v0', 'hashstack_v1']\n",
     "\n",
     "# Load the data\n",
-    "df_loans = load_protocol_data(PROTOCOLS)\n",
-    "print(f\"Combined dataframe shape: {df_loans.shape}\")"
+    "loans = load_protocol_data(PROTOCOLS)\n",
+    "print(f\"Combined dataframe shape: {loans.shape}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "6324abae-c1bb-4204-a919-fb52209722e6",
    "metadata": {},
    "outputs": [
@@ -200,18 +185,18 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_loans.isnull().sum()"
+    "loans.isnull().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "6a9975f0-b5aa-49b4-bd47-b560d7c590e8",
    "metadata": {},
    "outputs": [
@@ -349,13 +334,13 @@
        "4                                                "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_loans.head()"
+    "loans.head()"
    ]
   },
   {
@@ -369,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "id": "5ba21cdc-3a84-429d-a1e5-2380046230f5",
    "metadata": {},
    "outputs": [],
@@ -395,26 +380,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "938f1b8f-eb9c-4508-9fa0-35eca038b3d0",
    "metadata": {},
    "outputs": [
     {
-     "ename": "AssertionError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[12], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m prices \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m token \u001b[38;5;129;01min\u001b[39;00m COLLATERAL_TOKENS:\n\u001b[0;32m----> 4\u001b[0m     price \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_prices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m     prices[token] \u001b[38;5;241m=\u001b[39m price\n\u001b[1;32m      6\u001b[0m prices\n",
-      "Cell \u001b[0;32mIn[11], line 14\u001b[0m, in \u001b[0;36mfetch_prices\u001b[0;34m(collateral_token)\u001b[0m\n\u001b[1;32m      9\u001b[0m underlying_addresses_to_decimals \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     10\u001b[0m     collateral_token_underlying_address: collateral_token_decimals\n\u001b[1;32m     11\u001b[0m }\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# Fetch prices\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m prices \u001b[38;5;241m=\u001b[39m \u001b[43msrc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhelpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_prices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken_decimals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munderlying_addresses_to_decimals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m collateral_token_price \u001b[38;5;241m=\u001b[39m prices[collateral_token_underlying_address]\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m collateral_token_price\n",
-      "File \u001b[0;32m~/odhack/derisk-research/apps/legacy_app/src/helpers.py:163\u001b[0m, in \u001b[0;36mget_prices\u001b[0;34m(token_decimals)\u001b[0m\n\u001b[1;32m    160\u001b[0m token_info \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mdict\u001b[39m(y) \u001b[38;5;28;01mfor\u001b[39;00m y \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;28mtuple\u001b[39m(x\u001b[38;5;241m.\u001b[39mitems()) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m token_info}]\n\u001b[1;32m    162\u001b[0m \u001b[38;5;66;03m# Perform sanity checks.\u001b[39;00m\n\u001b[0;32m--> 163\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(token_info) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m decimals \u001b[38;5;241m==\u001b[39m token_info[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdecimals\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    166\u001b[0m prices[token] \u001b[38;5;241m=\u001b[39m token_info[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcurrentPrice\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
-      "\u001b[0;31mAssertionError\u001b[0m: "
-     ]
+     "data": {
+      "text/plain": [
+       "{'ETH': 3054.1,\n",
+       " 'WBTC': 76490.05153377741,\n",
+       " 'STRK': 0.42829510632806395,\n",
+       " 'USDC': 1.0050093418439294,\n",
+       " 'USDT': 1.00544949008542}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "COLLATERAL_TOKENS = [\"ETH\", \"WBTC\", \"STRK\", \"USDC\", \"USDT\", \"DAI\", \"DAI V2\"]\n",
+    "COLLATERAL_TOKENS = [\"ETH\", \"WBTC\", \"STRK\", \"USDC\", \"USDT\"]\n",
     "prices = {}\n",
     "for token in COLLATERAL_TOKENS:\n",
     "    price = fetch_prices(token)\n",
@@ -424,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "cc699d21-fde6-4265-93cd-be0970fe97ef",
    "metadata": {},
    "outputs": [
@@ -432,13 +418,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Token Prices in USD: {'dai': {'usd': 1.0}, 'ethereum': {'usd': 2487.73}, 'lords': {'usd': 0.04989956}, 'strike': {'usd': 6.52}, 'tether': {'usd': 0.998271}, 'uno-re': {'usd': 0.01199116}, 'usd-coin': {'usd': 1.0}, 'wrapped-bitcoin': {'usd': 67042}, 'wrapped-steth': {'usd': 2934.02}, 'zenad': {'usd': 0.00118212}}\n"
+      "Token Prices in USD: {'ETH': 3054.1, 'WBTC': 76490.05153377741, 'STRK': 0.42829510632806395, 'USDC': 1.0050093418439294, 'USDT': 1.00544949008542, 'DAI': 0.999747, 'LORDS': 0.04965502, 'WRAPPED-STETH': 3603.56}\n"
      ]
     }
    ],
    "source": [
     "# List of token IDs to fetch from CoinGecko (you can add more tokens if needed)\n",
-    "token_ids = 'ethereum,wrapped-bitcoin,usd-coin,dai,tether,wrapped-steth,lords,strike,uno-re,zenad'\n",
+    "token_ids = 'DAI,LORDS,wrapped-steth'\n",
     "\n",
     "# API endpoint\n",
     "url = 'https://api.coingecko.com/api/v3/simple/price'\n",
@@ -446,10 +432,14 @@
     "    'ids': token_ids,\n",
     "    'vs_currencies': 'usd'\n",
     "}\n",
-    "\n",
     "response = requests.get(url, params=params)\n",
-    "prices = response.json()\n",
-    "\n",
+    "rem_prices = response.json()\n",
+    "token_ids = token_ids.split(',')\n",
+    "for token, usd in rem_prices.items():\n",
+    "    price = usd['usd']\n",
+    "    for token_id in token_ids:\n",
+    "        if token.lower() == token_id.lower():\n",
+    "            prices[token_id.upper()] = price\n",
     "print(\"Token Prices in USD:\", prices)\n"
    ]
   },
@@ -464,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "c6a72339-26b1-49e6-943f-4bea5ba8b3a3",
    "metadata": {},
    "outputs": [
@@ -480,14 +470,14 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# the distribution of protocols among users\n",
-    "top_protocols = df_loans['Protocol'].value_counts()\n",
+    "top_protocols = loans['Protocol'].value_counts()\n",
     "top_protocols"
    ]
   },
@@ -501,24 +491,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "084931be-14e4-4182-91dd-fa5701265967",
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "liquidity_data = df_loans[df_loans['Collateral (USD)'] > 0]\n",
+    "active_loans = loans[loans['Collateral (USD)'] > 0]\n",
     "\n",
     "# Initialize a dictionary to store users and their associated protocols for liquidity\n",
     "user_protocols_liquidity = defaultdict(set)\n",
     "\n",
     "# Populate the dictionary\n",
-    "for _, row in liquidity_data.iterrows():\n",
+    "for _, row in active_loans.iterrows():\n",
     "    user = row['User']\n",
     "    protocol = row['Protocol']\n",
     "    user_protocols_liquidity[user].add(protocol)\n",
-    "    \n",
     "# Count the number of protocols each user lends on\n",
     "user_protocol_counts_liquidity = Counter([len(protocols) for protocols in user_protocols_liquidity.values()])\n",
     "\n",
@@ -532,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "e0abeedd-dd39-46f0-a5b7-c8989b53f67c",
    "metadata": {},
    "outputs": [],
@@ -542,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "e989070b-821a-41f2-84ea-2c4cd19ba8d1",
    "metadata": {},
    "outputs": [
@@ -612,7 +601,7 @@
        "3                    4               15"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -639,7 +628,7 @@
    "source": [
     "## Helper funcitons:\n",
     "# Function to get unique users per protocol\n",
-    "def get_unique_users_by_protocol(df):\n",
+    "def get_unique_users_by_protocol(df: pd.DataFrame) -> defaultdict:\n",
     "    protocol_users = defaultdict(set)\n",
     "    for protocol in df['Protocol'].unique():\n",
     "        users = set(df[df['Protocol'] == protocol]['User'])\n",
@@ -647,7 +636,7 @@
     "    return protocol_users\n",
     "    \n",
     "# Helper function to plot Venn diagram\n",
-    "def plot_venn_diagram(user_sets, title, labels):\n",
+    "def plot_venn_diagram(user_sets: list, title: str, labels: list) -> None:\n",
     "    plt.figure(figsize=(10, 8))\n",
     "    venn3(subsets=(user_sets[0], user_sets[1], user_sets[2]), \n",
     "          set_labels=labels)\n",
@@ -685,12 +674,10 @@
     "liquidity_df = loans[loans['Collateral (USD)'] > 0]\n",
     "liquidity_protocol_users = get_unique_users_by_protocol(liquidity_df)\n",
     "\n",
-    "\n",
     "# Prepare sets for Venn diagrams (top 3 protocols by user count)\n",
     "top_3_protocols = top_protocols.keys()[:3].tolist()\n",
     "liquidity_user_sets = [liquidity_protocol_users[protocol] for protocol in top_3_protocols]\n",
     "\n",
-    "\n",
     "# Plot Venn diagrams\n",
     "plot_venn_diagram(liquidity_user_sets, 'Users Providing Liquidity Across Top 3 Protocols', top_3_protocols)\n",
     "# plot_venn_diagram(debt_user_sets, 'Users Borrowing Across Top 3 Protocols')"
@@ -784,7 +771,7 @@
    ],
    "source": [
     "# Subset the DataFrame for users who have debt\n",
-    "debt_data = df_loans[df_loans['Debt (USD)'] > 0]\n",
+    "debt_data = loans[loans['Debt (USD)'] > 0]\n",
     "\n",
     "# Initialize a dictionary to store users and their associated protocols for debt\n",
     "user_protocols_debt = defaultdict(set)\n",
@@ -820,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "0895eb29-a63d-4fcd-998d-0f77ae444fce",
    "metadata": {},
    "outputs": [
@@ -837,7 +824,7 @@
    ],
    "source": [
     "# Get unique users having debt\n",
-    "debt_df = df_loans[df_loans['Debt (USD)'] > 0]\n",
+    "debt_df = loans[loans['Debt (USD)'] > 0]\n",
     "debt_protocol_users = get_unique_users_by_protocol(debt_df)\n",
     "\n",
     "\n",
@@ -859,23 +846,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "0c4a2a57-bd8e-44e0-ab0a-c969a311cb2a",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Protocol\n",
-      "hashstack_v0      3.385131e+03\n",
-      "hashstack_v1      1.996617e+05\n",
-      "nostra_alpha      1.661670e+05\n",
-      "nostra_mainnet    1.564581e+08\n",
-      "zklend            3.885571e+07\n",
-      "Name: Collateral (USD), dtype: float64\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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oYJ8WGRlpatWqZdzc3MyFCxfs0x+sc/DgwUaSOXr0aLyePygoyOTJk8dhWmBgoAkMDHzk65FkJJmtW7fapx0/ftx4eHiYN9980z6tVatWJkuWLObixYsOyzdq1Mh4e3vb64pqb3ny5Im11ofZsmWLkWQmTpxojDHmzp07xtfX1xQtWtTcunXLPt/ixYuNJNO7d2/7tGbNmpk0adIYY4zZsGGD8fLyMrVq1TK3b99+7NdQuHBhEx4ebp9v+PDhRpL5999/H/latm7daiSZFStWGGPu/+2zZctmPvnkE4f5evfubSSZuXPnxlhHZGSkQz0PbtP4bp8rV64YSWbw4MFx1jtv3jz7ezKhAgMDTaFChcyFCxfMhQsXzL59+0zHjh2NJFO7dm37fJKMk5OT2bNnj8Py9erVM25ububIkSP2aWfOnDGenp6mUqVK9mmzZ8+O9bPs/Pnzxs3Nzbz22msmIiLCPn3kyJFGkpkwYYIxxph79+6Z3Llzm5w5c5orV644rCNqWxtjTEBAgPH19TWXLl2yT9u1a5dxcnIyTZs2tU+L+hyLep8+yTbMli2badCgQYzp0T8r4xLf7dehQwdjs9nMjh077NMuXbpkMmTIEOPz5sHPj4e9r2/fvu2w3Y0x5ujRo8bd3d3079/fPu3B93d0gYGBMb4nwsPDTebMmR22S1QdRYsWdfhuaty4sbHZbKZmzZoO6y1XrpzJmTOnw7ScOXM6fH/F5z0Ym8jISHvdmTJlMo0bNzY//vijw/dAlKZNmxonJ6dY/45RzxHf7Xj06NEY27FPnz4OvzGMuf9+c3Nzc/g9sWvXLiPJjBgxwj6tdu3aJnXq1Ob06dP2aYcOHTIuLi4x1hmbZs2aOWzjTz75xHh5eZl79+49ctno4vqt8uDrjc/n2bVr10y6dOlMmzZtHKafO3fOeHt7O0wPCAgwWbJkMVevXrVPW758uZEUo+0YY8zGjRuNJDNz5swEvT7ERLdAWOr333+Xs7OzOnbs6DD9008/lTFGS5YseeQ6UqVKZf//3bt3denSJeXLl0/p0qXT9u3bE1xT9erVtWnTJtWpU0e7du3St99+q6CgIGXNmtV+dMfqmk6dOqXAwEDdvXtX69evt+8JlO4fISpcuLAKFSqkixcv2m9R/ZzXrFkjSfZhUR/clg8ehYhLWFiYPD094zXvg6/xxo0bunjxosqXLy9jjHbs2BFj/ugjf0UdMblz545WrlwZ7+eM6/lDQ0N18eJFBQYG6r///lNoaOhjrbNcuXIqVaqU/X6OHDlUt25dLVu2TBERETLG6Ndff1Xt2rVljHH4ewQFBSk0NDTG37dZs2YOtT6OrVu36vz58/r4448dzsupVauWChUqpN9++y3GMmvWrFFQUJCqVq2quXPn2vfKP85raNGihcMRt6i97f/9998ja582bZoyZcqkKlWqSLr/t3/nnXc0Y8YMRURE2Of79ddfVaJECb355psx1vFgl5wHt2l8t0+qVKnk5uamtWvX6sqVK7HWG3V0afHixbp79+4jX9+D9u/fLx8fH/n4+Khw4cIaMWKEatWqFaPLZWBgoIoUKWK/HxERoeXLl6tevXrKkyePfXqWLFn07rvvasOGDY/sUrpy5UrduXNHnTp1cjj/p02bNvLy8rJvhx07dujo0aPq1KlTjCOeUdv67Nmz2rlzp5o3b64MGTLYHy9evLiqV6/+0GGYn2QbXrp0SenTp0/QMlLCtt/SpUtVrlw5h8EkMmTIoCZNmsT7+WJ7X7u7u9u3e0REhC5duqS0adOqYMGCCfouSps2rcN5e25ubnr55Zdjfb81bdrU4ShfmTJlZIyJ0QWzTJkyOnnypO7duxfn8ybkPfjgY8uWLdOAAQOUPn16TZ8+Xe3atVPOnDn1zjvv2LukRkZGav78+apdu7ZKly4d53NYtR2jq1atmsOAQMWLF5eXl5d9m0ZERGjlypWqV6+ew5GxfPnyxThaGF/p0qXTjRs3tGLFisda/lHi83m2YsUKXb16VY0bN3b4rHd2dlaZMmXsvx+i3u/NmjVzGEimevXqDp9T0UW9Tx/WZRTxQ7iCpY4fPy4/P78YP+qjurgcP378keu4deuWevfubT9nK2PGjPLx8dHVq1cf+0f2Sy+9pLlz5+rKlSv6+++/FRwcrGvXrqlhw4bau3ev5TW9//77On/+vNatW6esWbM6PHbo0CHt2bPH/oMt6lagQAFJ/zfIxvHjx+Xk5BRjRLmCBQvG6zV7eXnp2rVr8ZpXkk6cOGH/4ZU2bVr5+PgoMDBQkmK8RicnJ4cfPJLs9T/uuXF//vmnqlWrZj8XxMfHx34y9eP+3fPnzx9jWoECBXTz5k1duHBBFy5c0NWrV/XTTz/F+Hu0aNFCUsxBT3Lnzv1YtUQX9T6I7W9ZqFChGO+T27dvq1atWipZsqRmzZrlEIwe5zXkyJHD4X7Ul2pcX+hRIiIiNGPGDFWpUkVHjx7V4cOHdfjwYZUpU0YhISFatWqVfd4jR47Eu2vJg9s0vtvH3d1d33zzjZYsWaJMmTKpUqVK+vbbb3Xu3Dn7/IGBgWrQoIH69eunjBkzqm7dupo4cWKM81XikitXLq1YsUIrV67Uhg0bdO7cOS1evDhGl6oHX8OFCxd08+bNWF9D4cKFFRkZqZMnTz70uePaDm5ubsqTJ4/98ahzZB62vR+2TQsXLqyLFy/qxo0bsS77pNvQPMY5HAnZfsePH491tMqEjGAZ2/s6MjJSQ4cOVf78+R0+9//5558EfSZly5YtRphJnz59rO+3B9+bUT+Ms2fPHmN6ZGTkQ+tIyHvwQe7u7vriiy+0b98+nTlzRtOnT1fZsmXtXV+l+3+jsLCwRz6HVdsxuge3k+S4Tc+fP69bt249cbuI7uOPP1aBAgVUs2ZNZcuWTS1btoz1PK/HFZ/Ps0OHDkmSXn311Rif98uXL3f4/SDF/h0Y12+IqPdpcl1X7FnCOVd46nTo0EETJ05Up06dVK5cOXl7e8tms6lRo0ZPPCyqm5ubXnrpJb300ksqUKCAWrRoodmzZz9ypLOE1lS/fn1NmTJFw4cPtw/jHCUyMlLFihXTkCFDYn2uB79EH1ehQoW0Y8cOnTx58pHrjIiIsJ8b1qNHDxUqVEhp0qTR6dOn1bx580QfjvbIkSOqWrWqChUqpCFDhih79uxyc3PT77//rqFDhyba80et97333lOzZs1inad48eIO95/0qNXjcHd31+uvv64FCxZo6dKleuONN+yPPc5rcHZ2jnW+R/0IXr16tc6ePasZM2ZoxowZMR6fNm2aXnvttYeuIzZPsk07deqk2rVra/78+Vq2bJl69eqlQYMGafXq1SpZsqRsNpvmzJmjzZs3a9GiRVq2bJlatmyp77//Xps3b37kBZrTpEnjMKx+YryGp92TbMMXXnjhkaH9aRDb3++rr75Sr1691LJlS3355ZfKkCGDnJyc1KlTpwR9JiXk/RbXvI/7nrVClixZ1KhRIzVo0ED+/v6aNWtWjAE7Hsaq7RhdcmwPX19f7dy5U8uWLdOSJUu0ZMkSTZw4UU2bNtXkyZPjXC6usBL9SH+UR32eRW2vX375RZkzZ46x/JOMrBj1Po3rXDzEH+EKlsqZM6dWrlypa9euORy9irroYPTucXF94MyZM0fNmjVzGLr39u3blg//GtWN4ezZs5bX1KFDB+XLl0+9e/eWt7e3w3U68ubNq127dqlq1aoP3UOUM2dORUZG6siRIw57mqJGHHyU2rVra/r06Zo6daqCg4MfOu+///6rgwcPavLkyWratKl9elzdHyIjI/Xff//Zj1ZJsl8v62HX6Ynr9S5atEjh4eFauHChwx7JqC4OjytqL190Bw8eVOrUqe0nNXt6eioiIiJeP6CtEvU+OHDgQIxhbw8cOODwPpHub7dp06apbt26euutt7RkyRL7iGdRI0kmxWuYNm2afH199eOPP8Z4bO7cuZo3b57GjBmjVKlSKW/evNq9e/djPU9Ct0/evHn16aef6tNPP9WhQ4cUEBCg77//XlOnTrXPU7ZsWZUtW1YDBw7U//73PzVp0kQzZsxQ69atH6vGR/Hx8VHq1Kljfb/u379fTk5O9p0ecb0vom+H6EeK79y5o6NHj9r/3lFHt3fv3h1nG4i+rtjqyZgxo9KkSfPQ1/Q427BQoUI6evToQ9cbm4Rsv5w5c8Y62mVCRsCMzZw5c1SlShWNHz/eYfrVq1cdfoA+rXv6n+Q9GBtXV1cVL15chw4d0sWLF+Xr6ysvL69HPkd8t6OVfH195eHhYXm7cHNzU+3atVW7dm1FRkbq448/1tixY9WrV684j4hF9Qx48PdCXD15HvZ5FvVe9/X1fejnfdT7PbbvwLh+Q0S9T+NznVI8HN0CYanXX39dERERGjlypMP0oUOHymazOfR1TpMmTazhxNnZOcbepxEjRsS6lyc+1qxZE+verKhzDKIHFytr6tWrl7p27arg4GCHIcXffvttnT59Wj///HOMZW7dumXvmhO1rX744QeHeYYNGxbnc0bXsGFDFStWTAMHDtSmTZtiPH7t2jV98cUX9tcnOe71M8Y8dIjZ6H9jY4xGjhwpV1dXVa1aNc5lon68PbiNY3v+0NBQTZw4Mc51xcemTZsc+vSfPHlSCxYs0GuvvSZnZ2c5OzurQYMG+vXXX2P9gRA11LbVSpcuLV9fX40ZM8aha9WSJUu0b9++WEeEdHNz09y5c/XSSy+pdu3a+vvvvyUpyV7DrVu3NHfuXL3xxhtq2LBhjFv79u117do1+3mMDRo00K5du2Id+vdRe5fju31u3ryp27dvOyybN29eeXp62pe7cuVKjOeLOjcnvt3aHoezs7Nee+01LViwwKGrbEhIiP73v/+pYsWK8vLykhT3+6JatWpyc3PTDz/84PAaxo8fr9DQUPt2ePHFF5U7d24NGzYsxjqilsuSJYsCAgI0efJkh3l2796t5cuX6/XXX4/ztTzJNixXrpx2796d4G2dkO0XFBSkTZs2aefOnfb5Ll++HOclAhJSw4Ove/bs2TEuBxDX3y+5Pe578NChQzpx4kSM6VevXtWmTZuUPn16+fj4yMnJSfXq1dOiRYu0devWOJ8jvtvRSs7OzqpWrZrmz5+vM2fO2KcfPnw4Xud+x+bSpUsO952cnOy9Ah7WvnPmzClnZ+cYlwEZNWqUw/34fJ4FBQXJy8tLX331VaznP0Z93kd/v0fverlixYo4T4XYtm2bvL295e/vH+drQfxw5AqWql27tqpUqaIvvvhCx44dU4kSJbR8+XItWLBAnTp1cjh/qFSpUlq5cqWGDBkiPz8/5c6dW2XKlNEbb7yhX375Rd7e3ipSpIg2bdqklStXxri2Rnx16NBBN2/e1JtvvqlChQrpzp072rhxo2bOnKlcuXLZz0tJjJoGDx6s0NBQtWvXTp6ennrvvff0/vvva9asWfroo4+0Zs0aVahQQREREdq/f79mzZqlZcuWqXTp0goICFDjxo01atQohYaGqnz58lq1alW897q5urpq7ty5qlatmipVqqS3335bFSpUkKurq/bs2aP//e9/Sp8+vQYOHKhChQopb9686tq1q06fPi0vLy/9+uuvcXbn8fDw0NKlS9WsWTOVKVNGS5Ys0W+//abPP//8ocPcRg0u8cUXX6hRo0ZydXVV7dq19dprr9n3CH744Ye6fv26fv75Z/n6+jocWUyookWLKigoyGEodknq16+ffZ6vv/5aa9asUZkyZdSmTRsVKVJEly9f1vbt27Vy5Updvnz5sZ8/Lq6urvrmm2/UokULBQYGqnHjxvahxnPlyqXOnTvHulyqVKm0ePFivfrqq6pZs6bWrVunokWLJslrWLhwoa5du6Y6derE+njZsmXl4+OjadOm6Z133lG3bt00Z84cvfXWW2rZsqVKlSqly5cva+HChRozZoxKlCjxxNvn4MGDqlq1qt5++20VKVJELi4umjdvnkJCQtSoUSNJ0uTJkzVq1Ci9+eabyps3r65du6aff/5ZXl5eDw0UVhgwYIBWrFihihUr6uOPP5aLi4vGjh2r8PBwh+scBQQEyNnZWd98841CQ0Pl7u5uv+ZbcHCw+vXrpxo1aqhOnTo6cOCARo0apZdeesk+SIKTk5NGjx6t2rVrKyAgQC1atFCWLFm0f/9+7dmzR8uWLZN0//OoZs2aKleunFq1amUfit3b29t+PbrYPMk2rFu3rr788kutW7cu1i6jEyZMiPW8lU8++STe26979+6aOnWqqlevrg4dOtiHYs+RI4cuX7782EeW3njjDfXv318tWrRQ+fLl9e+//2ratGkxzjfNmzev0qVLpzFjxsjT01Np0qRRmTJlLDk/80k87ntw165devfdd1WzZk298sorypAhg06fPq3JkyfrzJkzGjZsmH2H2FdffaXly5crMDDQfmmRs2fPavbs2dqwYYPSpUsX7+1otb59+2r58uWqUKGC2rZta9/xW7RoUYcgHl+tW7fW5cuX9eqrrypbtmw6fvy4RowYoYCAgIce7fH29tZbb72lESNGyGazKW/evFq8eHGMc2Hj83nm5eWl0aNH6/3339eLL76oRo0aycfHRydOnNBvv/2mChUq2Hd8Dho0SLVq1VLFihXVsmVLXb58WSNGjJC/v7/9+ojRrVixQrVr135qj8SmKEkyJiGeWQ8OxW7M/aFCO3fubPz8/Iyrq6vJnz+/GTx4cIyhX/fv328qVapkUqVKZSTZh5C9cuWKadGihcmYMaNJmzatCQoKMvv3748xzGx8h2JfsmSJadmypSlUqJBJmzatcXNzM/ny5TMdOnQwISEhltYU2/DCERERpnHjxsbFxcXMnz/fGHN/qOlvvvnG+Pv7G3d3d5M+fXpTqlQp069fPxMaGmpf9tatW6Zjx47mhRdeMGnSpDG1a9c2J0+ejNdQ7FGuXLlievfubYoVK2ZSp05tPDw8TNGiRU1wcLA5e/asfb69e/eaatWqmbRp05qMGTOaNm3a2Ie3jT40btTQ4EeOHDGvvfaaSZ06tcmUKZPp06dPjOF2Y6vzyy+/NFmzZjVOTk4OwyQvXLjQFC9e3Hh4eJhcuXKZb775xkyYMOGRQynHRZJp166dmTp1qsmfP79xd3c3JUuWjLW9hISEmHbt2pns2bMbV1dXkzlzZlO1alXz008/2eeJam+zZ89+5HM/KK6hmmfOnGlKlixp3N3dTYYMGUyTJk3MqVOnHOaJPhR7lIsXL5oiRYqYzJkzm0OHDj3xa4htCOQH1a5d23h4eJgbN27EOU/z5s2Nq6urfUj4S5cumfbt25usWbMaNzc3ky1bNtOsWTP744/apo/aPhcvXjTt2rUzhQoVMmnSpDHe3t6mTJkyZtasWfZ5tm/fbho3bmxy5Mhh3N3dja+vr3njjTcchuiPS2BgoPH393/kfFFtLTbbt283QUFBJm3atCZ16tSmSpUqZuPGjTHm+/nnn02ePHmMs7NzjM+1kSNHmkKFChlXV1eTKVMm07Zt2xhDrhtzf6j+6tWrG09PT5MmTRpTvHhxh6GpjTFm5cqVpkKFCiZVqlTGy8vL1K5d2+zdu9dhngeHYn+SbWiMMcWLFzetWrWK9Tniup08eTJB22/Hjh3mlVdeMe7u7iZbtmxm0KBB5ocffjCSzLlz5+zzxTUUe2xt8Pbt2+bTTz81WbJkMalSpTIVKlQwmzZtivUzaMGCBaZIkSL2Yb6j3ktxtaEHh/mOq464hqyPGqI8+qUvHvw+MubR78HYhISEmK+//toEBgaaLFmyGBcXF5M+fXrz6quvmjlz5sSY//jx46Zp06bGx8fHuLu7mzx58ph27drZL/cQ3+2YkKHYY3u/xfb6V61aZUqWLGnc3NxM3rx5zbhx48ynn35qPDw84nz9UR78G82ZM8e89tprxtfX17i5uZkcOXKYDz/80OF7NC4XLlwwDRo0MKlTpzbp06c3H374odm9e7fD643P51mUNWvWmKCgIOPt7W08PDxM3rx5TfPmzWO8J3/99VdTuHBh4+7ubooUKWLmzp0b43UZY8y+ffuMJLNy5cpHvhY8ms0YLsUMIP6aN2+uOXPmxLrn62lis9nUrl27GF1UASStX375Re3atdOJEyfivDh2YujUqZPGjh2r69evxzkAAp4/9erV0549e2I9H+l51alTJ61fv17btm3jyJUFOOcKAAAkmiZNmihHjhyxDoRilVu3bjncv3Tpkn755RdVrFiRYPUce7BdHDp0SL///rt9QCDcf6+MGzdOAwYMIFhZhHOuAABAonFycrJ01LrYlCtXTpUrV1bhwoUVEhKi8ePHKywsTL169UrU58XTLU+ePGrevLn9unCjR4+Wm5ubunfvntylPTVeeOGFp74nSkpDuAIAACna66+/rjlz5uinn36SzWbTiy++qPHjx6tSpUrJXRqSUY0aNTR9+nSdO3dO7u7uKleunL766qtYL64LWIVzrgAAAADAApxzBQAAAAAWIFwBAAAAgAU45yoWkZGROnPmjDw9PRk5BQAAAHiOGWN07do1+fn5ycnp4cemCFexOHPmjLJnz57cZQAAAAB4Spw8eVLZsmV76DyEq1h4enpKur8Bvby8krkaAAAAAMklLCxM2bNnt2eEhyFcxSKqK6CXlxfhCgAAAEC8ThdiQAsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALuCR3AQAAANGV6jYluUtAEto2uGlylwBYhiNXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWSNZwtX79etWuXVt+fn6y2WyaP3++w+M2my3W2+DBg+NcZ9++fWPMX6hQoUR+JQAAAACed8karm7cuKESJUroxx9/jPXxs2fPOtwmTJggm82mBg0aPHS9/v7+Dstt2LAhMcoHAAAAADuX5HzymjVrqmbNmnE+njlzZof7CxYsUJUqVZQnT56HrtfFxSXGsgAAAACQmFLMOVchISH67bff1KpVq0fOe+jQIfn5+SlPnjxq0qSJTpw48dD5w8PDFRYW5nADAAAAgIRI1iNXCTF58mR5enqqfv36D52vTJkymjRpkgoWLKizZ8+qX79+euWVV7R79255enrGusygQYPUr1+/xCgbAAAAT6lS3aYkdwlIQtsGN03050gxR64mTJigJk2ayMPD46Hz1axZU2+99ZaKFy+uoKAg/f7777p69apmzZoV5zLBwcEKDQ21306ePGl1+QAAAACecSniyNUff/yhAwcOaObMmQleNl26dCpQoIAOHz4c5zzu7u5yd3d/khIBAAAAPOdSxJGr8ePHq1SpUipRokSCl71+/bqOHDmiLFmyJEJlAAAAAHBfsoar69eva+fOndq5c6ck6ejRo9q5c6fDABRhYWGaPXu2WrduHes6qlatqpEjR9rvd+3aVevWrdOxY8e0ceNGvfnmm3J2dlbjxo0T9bUAAAAAeL4la7fArVu3qkqVKvb7Xbp0kSQ1a9ZMkyZNkiTNmDFDxpg4w9GRI0d08eJF+/1Tp06pcePGunTpknx8fFSxYkVt3rxZPj4+ifdCAAAAADz3kjVcVa5cWcaYh87zwQcf6IMPPojz8WPHjjncnzFjhhWlAQAAAECCpIhzrgAAAADgaUe4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAskartavX6/atWvLz89PNptN8+fPd3i8efPmstlsDrcaNWo8cr0//vijcuXKJQ8PD5UpU0Z///13Ir0CAAAAALgvWcPVjRs3VKJECf34449xzlOjRg2dPXvWfps+ffpD1zlz5kx16dJFffr00fbt21WiRAkFBQXp/PnzVpcPAAAAAHYuyfnkNWvWVM2aNR86j7u7uzJnzhzvdQ4ZMkRt2rRRixYtJEljxozRb7/9pgkTJuizzz57onoBAAAAIC5P/TlXa9eula+vrwoWLKi2bdvq0qVLcc57584dbdu2TdWqVbNPc3JyUrVq1bRp06Y4lwsPD1dYWJjDDQAAAAAS4qkOVzVq1NCUKVO0atUqffPNN1q3bp1q1qypiIiIWOe/ePGiIiIilClTJofpmTJl0rlz5+J8nkGDBsnb29t+y549u6WvAwAAAMCzL1m7BT5Ko0aN7P8vVqyYihcvrrx582rt2rWqWrWqZc8THBysLl262O+HhYURsAAAAAAkyFN95OpBefLkUcaMGXX48OFYH8+YMaOcnZ0VEhLiMD0kJOSh5225u7vLy8vL4QYAAAAACZGiwtWpU6d06dIlZcmSJdbH3dzcVKpUKa1atco+LTIyUqtWrVK5cuWSqkwAAAAAz6FkDVfXr1/Xzp07tXPnTknS0aNHtXPnTp04cULXr19Xt27dtHnzZh07dkyrVq1S3bp1lS9fPgUFBdnXUbVqVY0cOdJ+v0uXLvr55581efJk7du3T23bttWNGzfsowcCAAAAQGJI1nOutm7dqipVqtjvR5331KxZM40ePVr//POPJk+erKtXr8rPz0+vvfaavvzyS7m7u9uXOXLkiC5evGi//8477+jChQvq3bu3zp07p4CAAC1dujTGIBcAAAAAYKVkDVeVK1eWMSbOx5ctW/bIdRw7dizGtPbt26t9+/ZPUhoAAAAAJEiKOucKAAAAAJ5WhCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACyQrOFq/fr1ql27tvz8/GSz2TR//nz7Y3fv3lWPHj1UrFgxpUmTRn5+fmratKnOnDnz0HX27dtXNpvN4VaoUKFEfiUAAAAAnnfJGq5u3LihEiVK6Mcff4zx2M2bN7V9+3b16tVL27dv19y5c3XgwAHVqVPnkev19/fX2bNn7bcNGzYkRvkAAAAAYOeSkJmvXr2qefPm6Y8//tDx48d18+ZN+fj4qGTJkgoKClL58uUT9OQ1a9ZUzZo1Y33M29tbK1ascJg2cuRIvfzyyzpx4oRy5MgR53pdXFyUOXPmBNUCAAAAAE8iXkeuzpw5o9atWytLliwaMGCAbt26pYCAAFWtWlXZsmXTmjVrVL16dRUpUkQzZ85MtGJDQ0Nls9mULl26h8536NAh+fn5KU+ePGrSpIlOnDjx0PnDw8MVFhbmcAMAAACAhIjXkauSJUuqWbNm2rZtm4oUKRLrPLdu3dL8+fM1bNgwnTx5Ul27drW00Nu3b6tHjx5q3LixvLy84pyvTJkymjRpkgoWLKizZ8+qX79+euWVV7R79255enrGusygQYPUr18/S+sFAAAA8HyJV7jau3evXnjhhYfOkypVKjVu3FiNGzfWpUuXLCkuyt27d/X222/LGKPRo0c/dN7o3QyLFy+uMmXKKGfOnJo1a5ZatWoV6zLBwcHq0qWL/X5YWJiyZ89uTfEAAAAAngvxClePClZPOv/DRAWr48ePa/Xq1Q89ahWbdOnSqUCBAjp8+HCc87i7u8vd3f1JSwUAAADwHEvQgBaStHr1as2dO1fHjh2TzWZT7ty51bBhQ1WqVMny4qKC1aFDh7RmzZrHCm3Xr1/XkSNH9P7771teHwAAAABESdBQ7B999JGqVaum6dOn69KlS7pw4YKmTZumKlWqqEOHDgl+8uvXr2vnzp3auXOnJOno0aPauXOnTpw4obt376phw4baunWrpk2bpoiICJ07d07nzp3TnTt37OuoWrWqRo4cab/ftWtXrVu3TseOHdPGjRv15ptvytnZWY0bN05wfQAAAAAQX/E+cjVv3jxNnDhREyZMULNmzWSz2SRJkZGRmjRpktq2bavq1avH6zpUUbZu3aoqVarY70ed99SsWTP17dtXCxculCQFBAQ4LLdmzRpVrlxZknTkyBFdvHjR/tipU6fs5335+PioYsWK2rx5s3x8fOJdFwAAAAAkVLzD1cSJE9WlSxc1b97cYbqTk5NatmypAwcOaPz48QkKV5UrV5YxJs7HH/ZYlGPHjjncnzFjRryfHwAAAACsEu9wtX37dvXs2TPOx+vXr68GDRpYUhSA+CvVbUpyl4AktG1w0+QuAQAAxCHe51xdvHhR2bJli/PxbNmyWT4EOwAAAACkFPEOV3fu3JGrq2ucj7u4uDgMNAEAAAAAz5MEDcXeq1cvpU6dOtbHbt68aUlBAAAAAJASxTtcVapUSQcOHHjkPAAAAADwPIp3uFq7dm0ilgEAeNoxeMrzhcFTACDhEnQR4djcu3dP169ft6IWAAAAAEix4h2uFi1apEmTJjlMGzhwoNKmTat06dLptdde05UrV6yuDwAAAABShHiHqyFDhujGjRv2+xs3blTv3r3Vq1cvzZo1SydPntSXX36ZKEUCAAAAwNMu3uFqz549Kl++vP3+nDlzVL16dX3xxReqX7++vv/+ey1atChRigQAAACAp128w9W1a9f0wgsv2O9v2LBBVatWtd/39/fXmTNnrK0OAAAAAFKIeIerrFmzat++fZKk69eva9euXQ5Hsi5duhTnNbAAAAAA4FkX73D11ltvqVOnTvrll1/Upk0bZc6cWWXLlrU/vnXrVhUsWDBRigQAAACAp128r3PVu3dvnT59Wh07dlTmzJk1depUOTs72x+fPn26ateunShFAgAAAMDTLt7hKlWqVJoyJe4LSK5Zs8aSggAAAAAgJXriiwgDAAAAABJw5Cp9+vSy2Wwxpnt7e6tAgQLq2rWrqlevbmlxAAAAAJBSxDtcDRs2LNbpV69e1bZt2/TGG29ozpw5nHcFAAAA4LkU73DVrFmzhz4eEBCgQYMGEa4AAAAAPJcsO+fqjTfe0P79+61aHQAAAACkKJaFq/DwcLm5uVm1OgAAAABIUSwLV+PHj1dAQIBVqwMAAACAFCXe51x16dIl1umhoaHavn27Dh48qPXr11tWGAAAAACkJPEOVzt27Ih1upeXl6pXr665c+cqd+7clhUGAAAAAClJvMPVmjVrErMOAAAAAEjRLDvnCgAAAACeZ/EKVx999JFOnToVrxXOnDlT06ZNe6KiAAAAACCliVe3QB8fH/n7+6tChQqqXbu2SpcuLT8/P3l4eOjKlSvau3evNmzYoBkzZsjPz08//fRTYtcNAAAAAE+VeIWrL7/8Uu3bt9e4ceM0atQo7d271+FxT09PVatWTT/99JNq1KiRKIUCAAAAwNMs3gNaZMqUSV988YW++OILXblyRSdOnNCtW7eUMWNG5c2bVzabLTHrBAAAAICnWrzDVXTp06dX+vTpra4FAAAAAFIsRgsEAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALxGu0wJIlS8Z7qPXt27c/UUEAAAAAkBLFK1zVq1cvkcsAAAAAgJQtXuGqT58+iV0HAAAAAKRonHMFAAAAABaI15Gr6CIiIjR06FDNmjVLJ06c0J07dxwev3z5smXFAQAAAEBKkeAjV/369dOQIUP0zjvvKDQ0VF26dFH9+vXl5OSkvn37JkKJAAAAAPD0S3C4mjZtmn7++Wd9+umncnFxUePGjTVu3Dj17t1bmzdvTowaAQAAAOCpl+Bwde7cORUrVkySlDZtWoWGhkqS3njjDf3222/WVgcAAAAAKUSCw1W2bNl09uxZSVLevHm1fPlySdKWLVvk7u5ubXUAAAAAkEIkOFy9+eabWrVqlSSpQ4cO6tWrl/Lnz6+mTZuqZcuWlhcIAAAAAClBgkcL/Prrr+3/f+edd5QzZ05t3LhR+fPnV+3atS0tDgAAAABSigSHq/Xr16t8+fJycbm/aNmyZVW2bFndu3dP69evV6VKlSwvEgAAAACedgnuFlilSpVYr2UVGhqqKlWqJGhd69evV+3ateXn5yebzab58+c7PG6MUe/evZUlSxalSpVK1apV06FDhx653h9//FG5cuWSh4eHypQpo7///jtBdQEAAABAQiU4XBljZLPZYky/dOmS0qRJk6B13bhxQyVKlNCPP/4Y6+PffvutfvjhB40ZM0Z//fWX0qRJo6CgIN2+fTvOdc6cOVNdunRRnz59tH37dpUoUUJBQUE6f/58gmoDAAAAgISId7fA+vXrS5JsNpuaN2/uMDJgRESE/vnnH5UvXz5BT16zZk3VrFkz1seMMRo2bJh69uypunXrSpKmTJmiTJkyaf78+WrUqFGsyw0ZMkRt2rRRixYtJEljxozRb7/9pgkTJuizzz5LUH0AAAAAEF/xPnLl7e0tb29vGWPk6elpv+/t7a3MmTPrgw8+0NSpUy0r7OjRozp37pyqVavmUEOZMmW0adOmWJe5c+eOtm3b5rCMk5OTqlWrFucykhQeHq6wsDCHGwAAAAAkRLyPXE2cOFGSlCtXLnXt2jXBXQAT6ty5c5KkTJkyOUzPlCmT/bEHXbx4UREREbEus3///jifa9CgQerXr98TVgwAAADgeZbgc6769OmT6MEqqQUHBys0NNR+O3nyZHKXBAAAACCFideRqxdffFGrVq1S+vTpVbJkyVgHtIiyfft2SwrLnDmzJCkkJERZsmSxTw8JCVFAQECsy2TMmFHOzs4KCQlxmB4SEmJfX2zc3d0dziEDAAAAgISKV7iqW7euPXzUq1cvMeuxy507tzJnzqxVq1bZw1RYWJj++usvtW3bNtZl3NzcVKpUKa1atcpeZ2RkpFatWqX27dsnSd0AAAAAnk/xCld9+vSJ9f9P6vr16zp8+LD9/tGjR7Vz505lyJBBOXLkUKdOnTRgwADlz59fuXPnVq9eveTn5+cQ8KpWrao333zTHp66dOmiZs2aqXTp0nr55Zc1bNgw3bhxwz56IAAAAAAkhngPaPGgrVu3at++fZKkIkWKqFSpUo+1jugXHu7SpYskqVmzZpo0aZK6d++uGzdu6IMPPtDVq1dVsWJFLV26VB4eHvZljhw5oosXL9rvv/POO7pw4YJ69+6tc+fOKSAgQEuXLo0xyAUAAAAAWCnB4erUqVNq3Lix/vzzT6VLl06SdPXqVZUvX14zZsxQtmzZ4r2uypUryxgT5+M2m039+/dX//7945zn2LFjMaa1b9+eboAAAAAAklSCRwts3bq17t69q3379uny5cu6fPmy9u3bp8jISLVu3ToxagQAAACAp16Cj1ytW7dOGzduVMGCBe3TChYsqBEjRuiVV16xtDgAAAAASCkSfOQqe/bsunv3bozpERER8vPzs6QoAAAAAEhpEhyuBg8erA4dOmjr1q32aVu3btUnn3yi7777ztLiAAAAACClSHC3wObNm+vmzZsqU6aMXFzuL37v3j25uLioZcuWatmypX3ey5cvW1cpAAAAADzFEhyuhg0blghlAAAAAEDKluBw1axZs8SoAwAAAABStMe+iLAk3b59W3fu3HGY5uXl9UQFAQAAAEBKlOABLW7cuKH27dvL19dXadKkUfr06R1uAAAAAPA8SnC46t69u1avXq3Ro0fL3d1d48aNU79+/eTn56cpU6YkRo0AAAAA8NRLcLfARYsWacqUKapcubJatGihV155Rfny5VPOnDk1bdo0NWnSJDHqBAAAAICnWoKPXF2+fFl58uSRdP/8qqjh1itWrKj169dbWx0AAAAApBAJDld58uTR0aNHJUmFChXSrFmzJN0/opUuXTpLiwMAAACAlCLB4apFixbatWuXJOmzzz7Tjz/+KA8PD3Xu3FndunWzvEAAAAAASAkSfM5V586d7f+vVq2a9u/fr23btilfvnwqXry4pcUBAAAAQErxRNe5kqScOXMqZ86cVtQCAAAAAClWvLsFrl69WkWKFFFYWFiMx0JDQ+Xv768//vjD0uIAAAAAIKWId7gaNmyY2rRpIy8vrxiPeXt768MPP9SQIUMsLQ4AAAAAUop4h6tdu3apRo0acT7+2muvadu2bZYUBQAAAAApTbzDVUhIiFxdXeN83MXFRRcuXLCkKAAAAABIaeIdrrJmzardu3fH+fg///yjLFmyWFIUAAAAAKQ08Q5Xr7/+unr16qXbt2/HeOzWrVvq06eP3njjDUuLAwAAAICUIt5Dsffs2VNz585VgQIF1L59exUsWFCStH//fv3444+KiIjQF198kWiFAgAAAMDTLN7hKlOmTNq4caPatm2r4OBgGWMkSTabTUFBQfrxxx+VKVOmRCsUAAAAAJ5mCbqIcM6cOfX777/rypUrOnz4sIwxyp8/v9KnT59Y9QEAAABAipCgcBUlffr0eumll6yuBQAAAABSrHgPaAEAAAAAiBvhCgAAAAAsQLgCAAAAAAsQrgAAAADAAvEa0GLhwoXxXmGdOnUeuxgAAAAASKniFa7q1asXr5XZbDZFREQ8ST0AAAAAkCLFK1xFRkYmdh0AAAAAkKJxzhUAAAAAWOCxLiJ848YNrVu3TidOnNCdO3ccHuvYsaMlhQEAAABASpLgcLVjxw69/vrrunnzpm7cuKEMGTLo4sWLSp06tXx9fQlXAAAAAJ5LCQ5XnTt3Vu3atTVmzBh5e3tr8+bNcnV11XvvvadPPvkkMWpMkUp1m5LcJSAJbRvcNLlLAAAAQDJL8DlXO3fu1KeffionJyc5OzsrPDxc2bNn17fffqvPP/88MWoEAAAAgKdegsOVq6urnJzuL+br66sTJ05Ikry9vXXy5ElrqwMAAACAFCLB3QJLliypLVu2KH/+/AoMDFTv3r118eJF/fLLLypatGhi1AgAAAAAT70EH7n66quvlCVLFknSwIEDlT59erVt21YXLlzQ2LFjLS8QAAAAAFKCBB+5Kl26tP3/vr6+Wrp0qaUFAQAAAEBKlOAjV6+++qquXr0aY3pYWJheffVVK2oCAAAAgBQnweFq7dq1MS4cLEm3b9/WH3/8YUlRAAAAAJDSxLtb4D///GP//969e3Xu3Dn7/YiICC1dulRZs2a1tjoAAAAASCHiHa4CAgJks9lks9li7f6XKlUqjRgxwtLiAAAAACCliHe4Onr0qIwxypMnj/7++2/5+PjYH3Nzc5Ovr6+cnZ0TpUgAAAAAeNrF+5yrnDlzKleuXIqMjFTp0qWVM2dO+y1LliyJFqxy5cplP2IW/dauXbtY5580aVKMeT08PBKlNgAAAACIkuCh2CXpyJEjGjZsmPbt2ydJKlKkiD755BPlzZvX0uIkacuWLYqIiLDf3717t6pXr6633norzmW8vLx04MAB+32bzWZ5XQAAAAAQXYLD1bJly1SnTh0FBASoQoUKkqQ///xT/v7+WrRokapXr25pgdG7H0rS119/rbx58yowMDDOZWw2mzJnzmxpHQAAAADwMAkOV5999pk6d+6sr7/+Osb0Hj16WB6uortz546mTp2qLl26PPRo1PXr15UzZ05FRkbqxRdf1FdffSV/f/845w8PD1d4eLj9flhYmKV1AwAAAHj2Jfg6V/v27VOrVq1iTG/ZsqX27t1rSVFxmT9/vq5evarmzZvHOU/BggU1YcIELViwQFOnTlVkZKTKly+vU6dOxbnMoEGD5O3tbb9lz549EaoHAAAA8CxLcLjy8fHRzp07Y0zfuXOnfH19ragpTuPHj1fNmjXl5+cX5zzlypVT06ZNFRAQoMDAQM2dO1c+Pj4aO3ZsnMsEBwcrNDTUfjt58mRilA8AAADgGRbvboH9+/dX165d1aZNG33wwQf677//VL58eUn3z7n65ptv1KVLl0Qr9Pjx41q5cqXmzp2boOVcXV1VsmRJHT58OM553N3d5e7u/qQlAgAAAHiOxTtc9evXTx999JF69eolT09Pff/99woODpYk+fn5qW/fvurYsWOiFTpx4kT5+vqqVq1aCVouIiJC//77r15//fVEqgwAAAAAEhCujDGS7o/E17lzZ3Xu3FnXrl2TJHl6eiZOdf9fZGSkJk6cqGbNmsnFxbHkpk2bKmvWrBo0aJCk+0fYypYtq3z58unq1asaPHiwjh8/rtatWydqjQAAAACebwkaLfDBEfoSO1RFWblypU6cOKGWLVvGeOzEiRNycvq/U8euXLmiNm3a6Ny5c0qfPr1KlSqljRs3qkiRIklSKwAAAIDnU4LCVYECBR55Qd7Lly8/UUGxee211+xHzh60du1ah/tDhw7V0KFDLa8BAAAAAB4mQeGqX79+8vb2TqxaAAAAACDFSlC4atSoUaIPtw4AAAAAKVG8r3P1qO6AAAAAAPA8i3e4iuucJwAAAABAAroFRkZGJmYdAAAAAJCixfvIFQAAAAAgboQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwwFMdrvr27SubzeZwK1So0EOXmT17tgoVKiQPDw8VK1ZMv//+exJVCwAAAOB59lSHK0ny9/fX2bNn7bcNGzbEOe/GjRvVuHFjtWrVSjt27FC9evVUr1497d69OwkrBgAAAPA8eurDlYuLizJnzmy/ZcyYMc55hw8frho1aqhbt24qXLiwvvzyS7344osaOXJkElYMAAAA4Hn01IerQ4cOyc/PT3ny5FGTJk104sSJOOfdtGmTqlWr5jAtKChImzZteuhzhIeHKywszOEGAAAAAAnxVIerMmXKaNKkSVq6dKlGjx6to0eP6pVXXtG1a9dinf/cuXPKlCmTw7RMmTLp3LlzD32eQYMGydvb237Lnj27Za8BAAAAwPPhqQ5XNWvW1FtvvaXixYsrKChIv//+u65evapZs2ZZ+jzBwcEKDQ21306ePGnp+gEAAAA8+1ySu4CESJcunQoUKKDDhw/H+njmzJkVEhLiMC0kJESZM2d+6Hrd3d3l7u5uWZ0AAAAAnj9P9ZGrB12/fl1HjhxRlixZYn28XLlyWrVqlcO0FStWqFy5cklRHgAAAIDn2FMdrrp27ap169bp2LFj2rhxo9588005OzurcePGkqSmTZsqODjYPv8nn3yipUuX6vvvv9f+/fvVt29fbd26Ve3bt0+ulwAAAADgOfFUdws8deqUGjdurEuXLsnHx0cVK1bU5s2b5ePjI0k6ceKEnJz+Lx+WL19e//vf/9SzZ099/vnnyp8/v+bPn6+iRYsm10sAAAAA8Jx4qsPVjBkzHvr42rVrY0x766239NZbbyVSRQAAAAAQu6e6WyAAAAAApBSEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAACwAOEKAAAAACxAuAIAAAAACxCuAAAAAMAChCsAAAAAsADhCgAAAAAsQLgCAAAAAAsQrgAAAADAAoQrAAAAALAA4QoAAAAALEC4AgAAAAALPNXhatCgQXrppZfk6ekpX19f1atXTwcOHHjoMpMmTZLNZnO4eXh4JFHFAAAAAJ5XT3W4Wrdundq1a6fNmzdrxYoVunv3rl577TXduHHjoct5eXnp7Nmz9tvx48eTqGIAAAAAzyuX5C7gYZYuXepwf9KkSfL19dW2bdtUqVKlOJez2WzKnDlzYpcHAAAAAHZP9ZGrB4WGhkqSMmTI8ND5rl+/rpw5cyp79uyqW7eu9uzZ89D5w8PDFRYW5nADAAAAgIRIMeEqMjJSnTp1UoUKFVS0aNE45ytYsKAmTJigBQsWaOrUqYqMjFT58uV16tSpOJcZNGiQvL297bfs2bMnxksAAAAA8AxLMeGqXbt22r17t2bMmPHQ+cqVK6emTZsqICBAgYGBmjt3rnx8fDR27Ng4lwkODlZoaKj9dvLkSavLBwAAAPCMe6rPuYrSvn17LV68WOvXr1e2bNkStKyrq6tKliypw4cPxzmPu7u73N3dn7RMAAAAAM+xp/rIlTFG7du317x587R69Wrlzp07weuIiIjQv//+qyxZsiRChQAAAABw31N95Kpdu3b63//+pwULFsjT01Pnzp2TJHl7eytVqlSSpKZNmypr1qwaNGiQJKl///4qW7as8uXLp6tXr2rw4ME6fvy4WrdunWyvAwAAAMCz76kOV6NHj5YkVa5c2WH6xIkT1bx5c0nSiRMn5OT0fwfgrly5ojZt2ujcuXNKnz69SpUqpY0bN6pIkSJJVTYAAACA59BTHa6MMY+cZ+3atQ73hw4dqqFDhyZSRQAAAAAQu6f6nCsAAAAASCkIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWIBwBQAAAAAWIFwBAAAAgAUIVwAAAABgAcIVAAAAAFiAcAUAAAAAFiBcAQAAAIAFCFcAAAAAYAHCFQAAAABYgHAFAAAAABYgXAEAAACABQhXAAAAAGABwhUAAAAAWCBFhKsff/xRuXLlkoeHh8qUKaO///77ofPPnj1bhQoVkoeHh4oVK6bff/89iSoFAAAA8Lx66sPVzJkz1aVLF/Xp00fbt29XiRIlFBQUpPPnz8c6/8aNG9W4cWO1atVKO3bsUL169VSvXj3t3r07iSsHAAAA8Dx56sPVkCFD1KZNG7Vo0UJFihTRmDFjlDp1ak2YMCHW+YcPH64aNWqoW7duKly4sL788ku9+OKLGjlyZBJXDgAAAOB54pLcBTzMnTt3tG3bNgUHB9unOTk5qVq1atq0aVOsy2zatEldunRxmBYUFKT58+fH+Tzh4eEKDw+33w8NDZUkhYWFPXbtEeG3HntZpDxP0laeFG3t+UJbQ1KhrSGp0NaQVB63rUUtZ4x55LxPdbi6ePGiIiIilClTJofpmTJl0v79+2Nd5ty5c7HOf+7cuTifZ9CgQerXr1+M6dmzZ3+MqvE88h7xUXKXgOcEbQ1JhbaGpEJbQ1J50rZ27do1eXt7P3SepzpcJZXg4GCHo12RkZG6fPmyXnjhBdlstmSsLGUJCwtT9uzZdfLkSXl5eSV3OXiG0daQVGhrSCq0NSQV2lrCGWN07do1+fn5PXLepzpcZcyYUc7OzgoJCXGYHhISosyZM8e6TObMmRM0vyS5u7vL3d3dYVq6dOker2jIy8uLNyuSBG0NSYW2hqRCW0NSoa0lzKOOWEV5qge0cHNzU6lSpbRq1Sr7tMjISK1atUrlypWLdZly5co5zC9JK1asiHN+AAAAALDCU33kSpK6dOmiZs2aqXTp0nr55Zc1bNgw3bhxQy1atJAkNW3aVFmzZtWgQYMkSZ988okCAwP1/fffq1atWpoxY4a2bt2qn376KTlfBgAAAIBn3FMfrt555x1duHBBvXv31rlz5xQQEKClS5faB604ceKEnJz+7wBc+fLl9b///U89e/bU559/rvz582v+/PkqWrRocr2E54a7u7v69OkTo4slYDXaGpIKbQ1JhbaGpEJbS1w2E58xBQEAAAAAD/VUn3MFAAAAACkF4QoAAAAALEC4AgAAAAALEK4AAAAAwAKEKwAAAAAOIiMjk7uEFIlwBQAAAMDu66+/Vvv27XX37t3kLiXFIVwhxYm6egBXEQAAwBp8pyK6bNmyacyYMerVqxcBK4EIV0hxrly5ovDwcF27dk0SXwhIfLQxAM+yvXv3ymazSZImTJigrVu3JnNFSE7GGL333nuaPXu2hgwZop49e+r27dvJXVaKQbhCivLLL7+obt26evHFF/XGG29o/vz59i8EwEq//fabFi5cKEmy2WwELCQKzmlAcvvnn39Uq1YtDR06VN26ddPHH3+sDBkyJHdZSCbRv+sCAwP1zTffaPDgwfruu+90586dZKws5XBJ7gKA+Jo7d64++OADfffdd7p586aOHj2q+vXrKzg4WF26dNELL7yQ3CXiGTFnzhy9/fbbypo1q+7du6f69evbAxZhHlaJjIyUk9P9fZwLFizQ4cOH5evrq1KlSqlIkSLJXB2eFxkyZFDLli01YMAARUREaO/evcqTJ4/u3bsnFxd+Jj5vor7jfv31V3366aeqWrWqcuTIod69e+vatWsaMGCAXF1dk7nKpxvvGqQYv//+u9599121a9fOPq18+fJq2rSp7t69q/79+8vDwyMZK8SzYMeOHfruu+/UunVrRUREqGfPnjLGqEGDBgQsWMYYYw9WPXr00JQpU+Tv76+zZ88qZ86c+uCDD1SvXr3kLRLPhWzZsilr1qy6du2a/Pz8tGDBAnXu3FkuLi6KiIiQs7NzcpeIJHbgwAG1bt1agwYNUsuWLRUaGqr58+fr448/liR9+eWXcnNzS+Yqn16EK6QIxhidPXtWfn5+kv6vK817770nFxcXNWnSRAULFlSrVq348Ysn4uzsrJw5c+qTTz6RJA0dOlS9evWSJAIWLBPVfn744QfNmDFD8+bNU9myZTVs2DB99tln9nNL33nnHUmizcFSUUdNo9pVpUqV9Mcff2jZsmUaM2aMbt++reDgYILVcyosLEwZMmRQUFCQ3Nzc5OPjozZt2igiIkIff/yx0qVLp06dOilVqlTJXepTiXOu8FSL6vsb9eE/e/Zs7d271/6lYIxRo0aN1KdPH/Xu3VvHjx/nBwgeS1RgL168uL777jv5+/vL399f7du3V7ly5dSrVy/NmTNH0v32eOvWreQsF8+A69eva+/everatavKli2r+fPnq2/fvurUqZM8PDz01Vdfad68eZLE5xosE7076u7du7VlyxalTp1aZcqUUbt27dSwYUNNnjxZ3377rX2ZQYMGadeuXclVMpKYh4eHjh49qv/++0/S/30/vv766/L19dUXX3yhr7/+OjlLfKrZDGdpIwW4d++erl+/rrfffltubm4aPny48ubNa+8T/ueff6pBgwZaunSpAgICkrtcpGBRbSp6d5hdu3bphx9+0KZNmzRw4EDVqVNHr776qrp3765atWolc8VIyQ4ePKg0adIoNDRUtWvXVocOHdSpUydNnz5dbdq0UdasWTV8+HDVqFEjuUvFMyD6EdAvvvhCs2fPVmRkpO7evav69eurW7ducnJy0o8//qjp06erQoUKunjxonbv3q3//vuPI1nPoKg28WAX0HfffVcnTpzQkCFD9PLLL0u6v0Ooc+fOKl++vMqVK6dChQolV9lPNY5c4ak3ceJE1apVS+nSpVOrVq0UFhamTz/9VAcOHLCfbJstWzalS5eOoULxRCZNmqSaNWvq3r17cnZ2th85LVGihDp27KgKFSro888/V6FChXTkyBG99tpryVwxUoqIiIhYp+fJk0dZs2bVH3/8oSxZsqhVq1aS7ndPDQwMVKtWrWhnsExUsBoyZIjGjx+vsWPH6vDhw3r11Vc1ZcoUHT9+XJkzZ1a7du306aef6vTp0/Ly8tLhw4fl7OzM6JbPmKhgtWrVKvXo0UNdunTR3r17JUkff/yxvL291bFjRy1evFi7du3SgAEDtHbtWr355psEq4cgXOGpZozR3bt3denSJZ09e1bvvPOOmjdvrhs3buj111/XlClTNGvWLH300Ufy9va2710BEsoYozt37ig0NFSnTp2yT4sesBo3bqxjx47Jx8dHx44dk6urq+7du5ecZeMpd+bMGUmy7xH+4Ycf1K5dO7Vq1Upnz5617yAyxujixYvaunWr7t69q2nTpqlcuXL2IwlxhTMgIYwxunfvnv744w8FBwerSpUqWrRokebOnauBAweqXLlyCg8PV6ZMmdS2bVutXLlS//vf/+yfdVHdCfFssNlsWr58uYKCgnTs2DFNmzZN77zzjiZPnqyKFSuqZ8+eKlSokOrWrauGDRtq6tSpmjVrltKlS5fcpT/V6BaIp0psJ22HhoaqRIkSeuONNzRy5EhJ0ubNmzVt2jTNnDlTuXLlko+Pj+bPny9XV1dGN0K8PKyt1a1bV8OHD3d47OrVq2rYsKHOnj2rXbt2ycXFhaGK8VDBwcEaM2aM/vrrLxUoUEB9+/bV8OHDVbNmTW3dulW3bt3StGnTVKlSJW3btk2ffvqp/vvvP7m4uChNmjTavn27XF1dGcwClomMjNSdO3dUtWpV/fjjj7p27Zpef/11fffdd/rwww8VHh6un376SaVLl1bZsmXt7Y42+GyJ+nueP39e/fr1U4kSJfTBBx9Iut8dcP/+/Wrfvr2aNm0qFxcXHThwQMYYpU+fXpkyZUrm6lMAA6QAs2bNMkWKFDEbN250mH7u3Dlz/fp1ExkZaYwx5u7du8lRHp4hUW3tr7/+cpj+77//mrffftvcuXPHGENbw6NduHDBlC9f3hQqVMjs3r3btGjRwvz999/GGGMiIiJMnTp1jK+vr1mzZo0xxpgdO3aYmTNnmtGjR9vbF+0MTyIiIiLW6Y0bNza5c+c2adKkMZMnT7ZPDwkJMYGBgWbMmDFJVSKSyV9//WUqVKhgSpUqZVavXm2ffufOHfPuu++agIAA89NPP5lr164lY5UpE8d38dT56quv1LhxY/3222/2aSVKlJCbm5u2bNkiSfauWL6+vkqTJo1sNpsiIyM5ioAEeVhb27x5s6T/GyWpaNGimjlzpr17DG0Nj5IxY0YtXrxYXl5eqlmzpnbt2qW0adNKkpycnLRgwQKVK1dOb7/9ttatW6eAgAC9/fbb+uijj+yDqtDO8Liijwr4zz//aN++fQoNDZV0/9pqvr6+ypMnj5o2bSpjjK5cuaLmzZvr3r17at26dXKWjiRQqFAh2Ww2bd++XTt37rR3gXd1ddWUKVNUvHhxDRo0yD5KLuKPcIWnTkBAgI4fP66+ffuqYsWK2rhxowoUKKBOnTqpd+/eOnHihP0HR/RuCvQFR0LFp61FDfsfHT94EV/p06fXkiVLVKxYMe3YsUMXLlyQ9H+hff78+apYsaKqVKmiHTt2OCxL92Y8iajvxO7du6t+/foqWbKkPvroIy1ZssQ+SM/du3eVLVs2BQYGqkaNGgoJCdGaNWvk7OzMeX7PmAe/x7y8vPT777/r1Vdf1S+//KJFixbZ/+bOzs4aP368qlevrsDAwOQoN0XjnCs8lW7cuKFdu3bp66+/1sGDB5UtWzY1bNhQ06ZNU+3atfXpp5/ywwOWoK3BStGPFkR35coVvf7667p8+bIWLlyoggULOpzH8tlnn2ngwIG0NTyx6G3w999/1yeffKLRo0fr0qVLGjdunCIiItSpUyfVqVNHp0+f1oQJE+Ts7KwsWbKoadOmcnZ25uj8Mybqs+bvv//Wpk2bdPv2bQUEBCgoKEjXrl1TnTp1dOvWLX3++eeqVasWn0NPiHCFp86DP04WLVqkdevW6ccff1R4eLhatGih8ePHJ2OFeFbQ1mCl6O1p0aJFOn36tHLlyqV8+fIpX758Cg0NVfXq1RUWFqYFCxbECFiSGJAHllm+fLkWLlyoPHnyqEuXLpLudw/s06ePrl69qo8//lhvvfVWjOVog8+mX3/9VR9//LFKliwpb29vzZ49WwMHDlRwcLA9YN29e1effPKJ6tevTxt4AoQrPLUe/OG7Y8cOLVu2TF27dmWPGixFW4OVevTooVGjRilPnjw6ceKEihUrppYtW6p58+YKDQ1VUFCQrl+/rpkzZ8rf3z+5y8Uz6NixY6pVq5aOHj2qjz76SEOGDLE/FhWwbty4oUaNGqlly5bJWCkSS/QdN3v37lX16tXVs2dPtW3bVidPnlTu3LnVpUsXDRo0SM7OzgoLC1OlSpXk4+OjefPm2c8PRcJxkgqS3IN5Pq58H/3HrjFGJUuW1GeffWYfAht4FNoakkL0c1O2bNmilStXatmyZdq1a5dWrFihggULasSIEZoxY4a8vb21ZMkS3b59WwMHDkzGqvEsefCzLVeuXBozZoxKliyp9evXa8WKFfbHihcvrv79++v27dvauXNnEleKxLZo0SJJsg/0JUkXLlxQoUKF1LZtWx09elTly5dXmzZt9O2338rZ2Vn79++Xl5eXNmzYoHHjxhGsnhDhCkkm6sM/ak/Kf//953D/YR6ch6MJeBjaGpLC3r17Jf3fwBPffPONxowZo8KFC6ts2bKSpNKlS6tTp07KnTu35s+fr7t37yp9+vTasWOHfvnll2SrHc+OyMhI++fWuXPndPPmTUVEROiVV17RgAED5O7urjFjxmj16tX2ZYoVK6aJEydq2LBhyVQ1EsORI0dUt25dvffee5L+b8fhrVu3dP78ef3999+qUqWKXn/9dft1Qzds2KB+/frp+PHjSps2rXLmzJls9T8rCFdIMtH3ogwaNEg9e/aM13LR98jRixXxQVtDYuvcubOaN2+u9evX26ddu3ZNEydO1MaNG3X69Gn7dH9/fzVo0EC//vqrTp48KUny9PRkRDZYIuoHdL9+/VStWjVVrVpVXbt21a1bt1SlShX169dP586d048//qg1a9bYl8ubN6+cnJzsn5VI+XLnzq2FCxdq6dKlatasmX16vnz55OPjo9dee02vvPKKxo4da98ptGDBAoWGhsrT0zO5yn7mEK6Q6D7//HP7HpKoL4HLly+rcOHCkvTQD/bofYYXLVrk8MUAPIi2hqTSpk0bhYeH65tvvtHatWslSQMGDNDw4cN17NgxTZo0SZcuXbLPnzdvXuXLly9GN1NOGsfjiv55NnXqVI0cOVJdunTRiy++qE2bNqlu3bq6efOmqlWrpn79+unChQvq27evtm/f7rAeLmPy7HByclLNmjU1efJkzZs3T02bNpV0P1zVqFFD7u7uypo1q/bu3at9+/ape/fuGj9+vL799ltlyJAhmat/dvCOQqI6deqU/v33X82YMUMTJ060Tz927Jj9R0ZcH+zRf+yOGTNGdevWpYsW4kRbQ1KJiIhQkSJFNHv2bJ04cULffvutvctVhw4dNGDAAPXp00eDBg3SunXrtGfPHvXp00eenp7Kly9fMlePZ0H0QXgWL16sM2fO6IcfflDLli31ww8/qFu3brpy5Yrq1KljD1jdu3dXkSJFFBAQkLzFI1FE9bYICwtTrVq1NH36dC1cuFBNmjSRdP96Z61atdKaNWtUokQJvf/++1q6dKlWr16tokWLJmfpzx4DJLK9e/eaZs2amfLly5tx48YZY4ypW7eu6devnzHGmIiIiBjL3L171/7/MWPGmHTp0pk5c+YkTcFIsWhrSCr37t0zxhhz4MABU7RoUVOzZk2zatUq++NfffWVsdlsxmazmWbNmpn69eubO3fuGGNib4dAfNSsWdPs2rXLfn/Lli2mQIECxsvLy8ybN88+/c6dO+bXX381L730knnttdfM9evXHdZDG3x2REZG2v+/cuVKU6NGDXP37l1z584ds3jxYuPt7W3effdd+zzHjx83a9euNfv37zfnz59PjpKfeRy5QqKJ6rJQuHBh9ejRQ3nz5tW4ceM0bdo05cqVS5kyZdKRI0d0/PhxhYSE6Ny5c9q0aZOk/xtEYOzYserevbvGjRunBg0aJNtrwdONtoakEL0bVlR3vgIFCtjPpRo8eLD9CFZwcLBGjBghSSpVqpQmTJggV1dXRURE0A0Lj+X48eMqXry4ChUqZJ+WN29etW/fXhkyZNC4cePs011dXVW7dm0FBwdr//79+vzzzyX939EN2mDKN2TIEC1YsMDhHOO///5badOmlYuLi1xdXfX6669r2rRp+u233+xHsHLkyKHAwEAVLFhQPj4+yfkSnl3Jne7wbIq+VywkJMQYc38P7/vvv2/KlCljbDab8fLyMvnz5zeZMmUyvr6+xtfX19SpU8e+F2bUqFHG29ubowh4KNoakkL0drZv3z6zadMmExYWZm7dumWMMWb//v2maNGipkaNGg5HsL788ktjs9nMsGHDzJUrV5K6bDyjvv/+e7NmzRpjjDFhYWFm1KhRplixYqZ58+YO8925c8esXbvWfqQVz4YrV66YRo0amTRp0pilS5fap3fs2NE0adLEYd6IiAizePFikzFjRlOvXr2kLvW5RLiC5aL/COnfv7+pX7++2bZtmzHGmD179pj333/flC1b1nTs2NGEhoaakJAQs2vXLnPw4EH7F8B///1ncuXKZWbPnp0srwEpA20NSSF6t5svvvjCFChQwLzwwgumRIkSZujQoebcuXPGmPuhq1ixYqZWrVrm999/ty/z7bffGpvNZkaNGuWwLuBxXLx40dSqVcukS5fO/Pnnn8YYY65evWpGjhxpAgICTIsWLWJdjoD1bDl06JBp06aNSZcunVm0aJExxpjOnTub1q1bG2OMfcePMfe7v8+dO9fkyJHDnD59OlnqfZ4QrpBogoODTaZMmczUqVPNqVOn7NP37t1rmjZtasqWLWsmT54cY7moL4CzZ88mWa1I2WhrSApffvmlyZIli1myZIkxxpg6deqYnDlzmi+++MLehvbv3298fX1Nly5dHH7MDh061OzduzdZ6kbKFlsg//fff817771nMmbMaDZs2GCM+b+AVapUKVO3bt0krhJJJfpOxY0bN5oePXqYdOnSmT/++MMMHz7cdOvWzdy+fdtcvXrVPt+JEyeMMSbGuXdIHIQrJIqtW7eaPHnymJUrVzpMj/pQ2Ldvn2nevLnJnz+/fY9LFPbsIiFoa0gKe/bsMRUrVjQLFy40xhizfPly4+npaapVq2Zy5MhhevXqZQ9Yx48ftwer6AOmAAkV/Yd0ZGSkw/1///3XNG7cOEbA+vrrr03z5s0ZtOIZFBkZaf/eWrFihQkICDCTJ082H374ofH29jY+Pj4mb968Jnfu3CZLliwmf/78JmfOnKZw4cLm8uXLyVz984OxhpEoLl26JGOMSpQoYZ9mjJGTk5Pu3r2rQoUKqWvXrsqbN69q1qzpsGzUkNhAfNDWkBT8/PzUsWNHvfrqq/rjjz/0/vvv67vvvtMHH3ygoKAgTZkyRVeuXFGfPn2UI0cOSfeHbGdIfzyu6MOtjxkzRuvXr5fNZtPLL7+sTz75REWLFtUXX3whY4zq16+vefPmqXz58mrfvr1Sp05tH+iAwSueDeb/D0Zis9k0Y8YMvfvuu5KkNGnSaMCAAfLw8NC4cePUuHFjdezYUceOHdPdu3dls9lUoEABpU+fPjnLf67wqY9E4e7urjNnzujYsWPKmDGjzP2jpLLZbFq1apXSpUunsmXLyt/fX9L9HyFcTBOPg7aGpJAuXTrVqFFDadKk0eTJk1W/fn21atVKkpQrVy6dPHlSkZGReuGFF+zL0M7wJKJC0WeffaZffvlFDRs2lKenp3r06KELFy5owIAB8vf3V8+ePeXs7KyKFStq165dKlasmKT/28mEZ4fNZtOcOXP07rvvau7cuZo5c6b27NmjBg0aqE2bNrp7964mT56shg0bqmrVqsld7nOLdx2eSPShiaPLmTOnypYtqyFDhuiff/6RzWazH0n49ttvtWjRIof5+RGCR6GtIbl5enpKki5fvqwbN27YL04dGhqq77//XiNHjpTNZrPvYQae1PTp0zVnzhz9+uuvGj58uEqXLq2IiAh99dVXatu2rSTJ399fXbt2Ve/evVWkSBH7shyZf7bYbDbNmzdPb7/9tsaPH6969erp+vXrOnPmjKT77aBLly565513FBgYqOXLlydzxc8vm+FbAI8peneDJUuW6Pr16woPD9d7770nSZo0aZJ+/vlnpUqVSk2aNJGzs7N++eUXnT9/Xtu2baO7DOKNtoanSbdu3bRs2TLly5dPZ8+eVWhoqP799185OzvTDQuWiYiI0NixY3X79m116dJFv/32m9577z0NGDBAqVKlUuvWrRUcHKyBAwc6LHfv3j0+855RS5Ys0ZUrV+xdAjt27KirV69qypQp9nkWL16sbdu2qVGjRipYsGBylfpcI1zhsUR1u5LuXyxz+vTpSp8+vS5cuKBixYrpl19+UcaMGbVgwQItWLBAs2fPVtGiRZUlSxbNnDnTfjFNjiLgUWhreFpEb4uff/65QkJC5OTkpNGjR8vFxYV2hicSvX1FCQsL04ULF5Q2bVoFBQWpSZMm6tatm3bv3q3AwEBduXJFX331lT777LNkqhrJIaqtfPXVV1q4cKH+/PNPOTs7q3///vr999+1ePFiZcyYMbnLfH4l6fAZeOYMHjzYZM6c2WzZssUYY8xPP/1kbDabqVy5sv3aL8bcv7jrtWvX7KPcMIIWEoq2hqdBXNcKop3hSTx4MfQH29nmzZtNwYIFzbFjx4wxxhw+fNi0bNnSrFmzhutXPYeivt9GjhxpChYsaIwxpm/fvsbFxcV+rUckH/ouIEEiIiLs/w8JCdHu3bs1YsQIlS5dWgsWLFC3bt3Uv39/HT9+XE2aNNHJkyclSb6+vkqbNq39fAS6LOBRaGtICiaOzhtxTY86MvXgOYC0MzyJqK6kffv2Vd26dfXSSy9p6tSpCgkJkXT/fL+DBw9q2rRp2rdvnzp06KALFy4oMDBQzs7O9vP/8HwpWrSoMmTIoPbt22vQoEHavHmzXnzxxeQuC8mb7ZBSHThwwBhjzNy5c8358+fNli1bTK5cuczIkSONMcYMGzbM2Gw2ExAQYM6fP5+cpSKFo60hsUQ/WrBnzx6zb98+c+TIkVgfjy769dEOHDhgwsLCEq9IPDcmTZpksmTJYn766SdTr1494+/vb7p3726OHz9ujLl/9N7FxcXky5fPlC5d2ty5c8cYw/X6nmebN282NpvNuLi4mO3btyd3Ofj/OHKFeJk3b5769OkjSerUqZN69Oihe/fu6c0335SPj4/++OMPFSlSxH6Spaenp1q1aiV/f39lyJAhOUtHCkNbQ1Iw0Yap7t27t959911VrlxZLVq00IgRIyTdP5rw4BEqE+28mBEjRuidd97RlStXkrZ4PBMebFvh4eHq3bu32rRpo3nz5qlJkyZasWKFRowYoZCQEHXt2lX79u3T1KlT9ddff8nV1VX37t1jVMDnWEBAgPr376/du3erZMmSyV0O/j/6MeCRwsPDdfjwYX399ddat26dtm3bpk2bNjl0gzl48KCOHz8ub29vXbt2TQsWLFDlypXVuXNnSVxbCPFDW0NSifpB2q9fP40ZM0b/+9//lDVrVn3zzTf65JNPdOPGDX322Wf2gOXk5OQQrMaOHavevXtr9OjR9osGA/EVPdxPmzZNISEh2rFjh4KCguzzBAcHS5Jmz54tm82mtm3bKl++fMqXL5+k++GM7qjPN3d3dwUHB/Od97RJ3gNnSClu375typcvb2w2m2nXrp19etSJtPv27TMvvPCCyZkzpylQoIApWrQoJ3jjsdDWkJiid6HaunWrefnll83atWuNMcYsXbrUeHp6mjfffNOkSZPGfPvtt/Z5o7exMWPGGC8vL/Prr78mXeF4ZkTvbtqtWzfj5eVlihYtapydnU3ZsmXN0aNHHeb/+uuvTdasWc2IESOSuFIAj4NdHohT9Ou13L17V6+++qpefvllTZ48WT4+PurTp4+cnZ11584dFSpUSJs3b9bMmTOVOnVqdejQgaGJEW+0NSSF6O1s//79Kl68uOrVq6eXXnpJq1evVvPmzfXdd9/p7bffVsOGDdWjRw/7UNdRRwjGjh2r7t27a8KECapfv35yvhykUFFt8ODBg7p48aJWr16tgIAAjRs3TlOmTFHPnj01cOBA5cyZU5LUo0cP+fn52btCA3i6cZ0rxCr6j5DZs2cra9asevHFFxUZGalRo0ZpwIAB6ty5s/3cGEnau3evw9Xh+bGL+KCtISmYB66XtnnzZi1YsEAeHh5yc3NTq1atlDZtWg0ePFhubm5q166d/vnnH3l7e2vRokWy2WyaPXu2mjZtqmnTphGs8ERmzpypzz//XFmyZNGiRYuUPn16SffD+9SpU5UzZ0599dVXMbqc8lkHPP0Y0AIxmGh9wT/77DN17NhRBw4c0K1bt5Q6dWq1aNFCPXv21LBhw9SzZ0/duHFDr7/+ugYPHuywHr4A8Ci0NSSVqGD1999/a8OGDfr666/l5eUlNzc3hYeHa+fOnQoPD5ebm5tu3ryp8+fPq127dlq8eLF92YCAAC1atIhghScWGRmp7Nmza+/evbp165Z9+ocffqj3339fp06d0kcffWQfij0Kn3XA048jV4jTN998oyFDhmjx4sUKCAiQq6ur/bHw8HD99NNP6tatm7Jnz65UqVJp27ZtDvMA8UVbQ1IYNWqUNm7cqDt37mj69Olydna2Hzn95ptvNGLECFWvXl2HDh3SzZs3tWXLFjk7O8sY47AjALDC4sWL1bdvX6VKlUrTpk1zOEo1dOhQHTp0SCNHjqTdASkM4QqxCg8P1zvvvKNy5cqpR48eOnHihPbu3auxY8eqcOHCatKkifz9/XXo0CHt2bNHtWvXtl/IkNGLkBC0NSSVb7/9Vp9//rmyZ8+uFStW2Eddk6QjR45ozpw5Wr16tbJmzaqxY8fK1dWVbliwXPQuqvPnz9eIESMUGRmpKVOmKHv27DHmi951GsDTj3CFGIwxun79uqpXr65ixYqpQoUKmjdvnq5fvy5Jun37tooUKaKRI0fK3d3dvhw/QpBQtDUkllWrVqlKlSpycnJSv379lDlzZn344YcaNWqUevXqpdatW6tDhw7Kli1bnOsgwCOxRA9Y8+bN08iRI2Wz2TRu3DjlypUr1vkApAzsCnnOGWNiXMhQun9h1k6dOmnt2rX67LPPVLJkSfXr10+rVq1SuXLlFBoa6vBjV6IvOB6OtoakcvbsWbVv315ly5ZVx44dNWjQIJUtW1aS9PHHH+uzzz7TtGnTNH78eJ05c8a+XPT2aYwhWCHR2Gw2Re3bfvPNN9WxY0edP39e33//fYz5AKQsfHM8x0JDQ+Xt7W3/8B45cqQOHTqkiIgI9enTR40aNVLFihVls9mUNWtW+3J79uxR7ty5k6tspEC0NSSlTJkyafLkyQoKCtLu3bu1YcMGlShRQrdu3VKqVKnUrVs3GWPs57M0b95c2bNnd+h6xY9aJLaogGWz2VS3bl2lT59eFStWTO6yADwhjlw9p4KDg5U9e3adP3/efr9v3746duyYVq5cKX9/f+3YsUPZsmVT1qxZFRoaqmXLlql27do6efKkfvjhB0kSvUrxKLQ1JJWoI09OTk5ycXFRhgwZ5Ofnp86dO+v27dtKlSqVbt++LUnq3r27OnTooL59+2rFihXJWTaeIXF9TsU1PfoRrEqVKnFuFfAM4F38nGratKmKFSumV155RSdPnrT/oF2wYIHWr1+v8uXLKygoSNu3b5ck/ffff/r6669ls9m0Y8cO+0Vb2buLR6GtISlEP+l/9+7dypw5s7Zt26YpU6bo2rVrevXVVxUeHi4PDw/7j9lu3bpp9uzZatasWXKWjmdEZGSk/XNq79692r9/v/777z9Jsg9M8SgHDx7UtWvXErVOAImLAS2eY4cPH1aTJk104sQJ5ciRQ1OnTlX+/PklSVevXlWLFi20ceNGLVmyRC+++KIOHTqkvHnzysnJiRO9kSC0NSSm6MGqV69eWrJkiQYOHKjq1asrMjJSa9euVdeuXZU2bVqtXLlSHh4eatmypQIDA+3BikFS8CSiDzzRu3dvLVy4UOfOnVPBggXVsGFDdejQQZJijPwXfbkRI0ZowoQJWrBgQYyLBwNIQQyeKxEREQ73Dx06ZN544w3j6upq/v33X4d5rly5YurXr29sNpvZv39/nOsAYkNbQ1L74osvTObMmc3ixYvNpUuX7NMjIiLM2rVrTbFixUzmzJlNpUqVTI4cOczdu3eTsVo8i/r27Wt8fHzMihUrzN69e02zZs2MzWYzgwYNss8T9bkWGRlpnzZmzBiTLl06M3369CSvGYC12B38HIm+x2zTpk3KmjWr8uXLp++//15XrlxRvXr1tHHjRvn6+soYo3Tp0unnn39WgQIFHK4HQ59wPAptDUltz549mjVrliZNmqSgoCCFhYXp0KFD+uOPP1SoUCEFBgZqwYIFmjBhgiIjI7Vq1Sp7l1OOWOFxmWhHnrZt26bff/9ds2fPVmBgoJYtW6a5c+eqXr16GjBggJydndWtW7cYR+THjh2r7t27a+LEiapfv35yvhwAVkjmcIckEv0IQHBwsClWrJiZM2eOuXHjhjHGmIMHD5qyZcuavHnzmnPnzsVYxhjDXl7EC20NyWHnzp2mYMGC5s8//zTr1q0z7dq1M/7+/sbPz8+UKFHCLFy4MMYy9+7dS4ZK8ayI/rm1b98+c+fOHfPVV1+ZGzdumFWrVpnMmTObsWPHmitXrpiqVasam81mgoODHdYxZswY4+XlZebMmZPU5QNIJOwWfk5EHQHo06ePJkyYoO+//15BQUFKnTq1JCl//vyaMWOGXnjhBVWqVElnzpyJcdSA814QH7Q1JLbYBgYoWLCgnJyc1Lp1a1WrVk2SNGjQIG3atEn37t3TuXPnYizDESs8LmOM/XMrODhYbdu21a1bt/Tpp58qderUmjZtmt5++201b95c6dKlU8GCBVWhQgX9888/9gFVZs+erU6dOmnixIlq0KBBcr4cABbiF8xz5L///tOvv/6qsWPHqnr16rp48aIOHz6s5cuXK1euXHr77bc1Z84cValSRZ07d9bMmTOTu2SkULQ1JJboXU63bNliH6Ht5Zdf1o4dO7R48WL5+PioYsWK9vm8vLziNVIbEF9RXQH//vtvbdiwQd999528vLwkSeHh4dq5c6deeuklubm56ebNmzp//rzatWunRo0a2dcREBCgRYsW2XcGAHg2EK6eIy4uLnJzc1NoaKhWrlyp6dOna/v27QoPD9fNmzd15coVffjhh1q7dq2yZMmS3OUiBaOtITFEP1rQo0cPTZ8+XTabTSEhIWrcuLF69eplPwJw48YNXb16VW3atNHt27fVunXr5Cwdz6BRo0Zp48aNypIli0qXLi3pfvh3d3fX22+/rREjRqhFixY6dOiQbt68qbfeekvS/XZsjFH+/Pnto6YCeHbQLfAZFdte2syZMytz5swaNmyYgoKC5OXlpa+//lobN25U/vz5dfnyZUlStmzZ5OzsrIiIiKQuGykQbQ1JJepowciRIzVhwgTNmDFDy5cv1+LFi7V06VL16NFDx48flySNGzdOdevW1fXr1/XXX3/RzmC569eva8aMGdqyZYuOHj0q6f+6RUcNv37mzBkVKFDAoQ3abDYG6wGeYRy5egZF7zazZ88eubm5yRijAgUKaO7cufrrr7+UNm1alSpVyr7MrVu3Ypx/wPkIeBTaGpLDli1b1KBBA5UvX17GGBUsWFBLlixRpUqVVLhwYfXv31/NmjWTl5eXmjZtKmdnZ66XhieyatUqValSRU5OTurXr58yZ86s7t27K23atOrVq5d+/vlndejQQdmyZZMk5c2bVz169FCPHj3s66ANAs8HLiL8jDHRhoXt27ev5syZo5s3b8rNzU3BwcH2C2ZK9/e6RfUDP3v2rLZu3coHP+KNtoakEL2dSdLdu3dVs2ZNZcuWTZMmTVJkZKTu3bsnNzc3DRkyRGPGjNGmTZv0wgsv2JdhuHU8ibNnz+rVV1+Vp6enypYtq59++kl//fWXSpQoIUkaPHiwhg8frjZt2qhNmzby8/OT5Ljz6cF2DODZxXHpZ0z0H7ujRo3S0KFDtWzZMpUuXVotWrTQ2LFj7fNOmTJFTZs21Z07d7Rlyxb7NV+A+KCtIbFFDVYh3R8k5fz583J1dVXTpk01Z84crVq1Sk5OTnJ1dZUkubu7K2PGjPL09HRYD8EKTyJTpkyaPHmyDh06pHHjxmnDhg0qUaKEbt26JUnq1q2bOnbsqPHjx2v8+PE6efKkJMfr9BGsgOcH4eoZtG3bNq1bt04zZsxQ9erVdfDgQf3222+qVauW2rZtq59//lmS1KpVK3Xp0kXLly+Xq6ur7t27x48QJAhtDYkp6sfp559/rjp16qhIkSL2rlgtW7ZUu3bttHTpUkVGRio0NFSLFy9W1qxZ7WELeBJR55M6OTnJxcVFGTJkkJ+fnzp37qzbt28rVapUun37tiSpe/fu6tChg/r27asVK1YkZ9kAkhndAp8BD3Y3OHXqlKZNm6ZOnTrpzz//1HvvvafevXvr/fff15tvvqmVK1fqm2++Ubdu3ezL0G0G8UFbQ1KI3p1q9uzZ6ty5s0aOHKl//vlHS5cuVY4cOVS2bFmdPn1aQ4cOVZ48eeTs7Cx3d3dt2bJFrq6udMPCE4neBnfv3q0MGTIoderU2rt3rz7++GOlTp1aa9askbu7u0Nbmzt3rurWrctnHPAcI1ylcNF/qB45ckRp06ZVpkyZ7F8MzZs3V+rUqTV8+HC5urrqo48+0rZt2+Th4aH169fz4wPxRltDUlu/fr1+/fVXlShRQi1btpQkLVy4UCNGjFD69OnVpk0b+fr62gdOeeeddxi8Ak8serDq1auXlixZooEDB6p69eqKjIzU2rVr1bVrV6VNm1YrV66Uh4eHWrZsqcDAQPu5puxEAp5fdAtMoUaPHq2dO3faP7yDg4NVt25d+fv7q3v37tq2bZskadeuXUqTJo1cXV1169YtXbhwQX379tUff/whm80msjUehbaG5HDu3Dm1bNlSkyZNUlhYmH16nTp11LFjR126dEmjRo1SeHi4PvjgA7377rv2oa4JVngSUcGqZ8+eGjdunPr166eXXnrJ3j3w/7V3/zFR1w8cx193xo+TH/NmJuH0hLFRd2NAojYyXMqmblzWVlJjQhjoGjkonc5WC7TmcrGJ3JIV4KQ/Kq0cyG44vWWlNrnlAVc6ZpNNVoKaEcWmQNAfzc9X+mb+Or04no8/733vz73vs/c+d6/P+8dn0aJFqqqqUn9/vxISErRw4UJ5PB7l5eUZxyBYARMXI1fjUFdXl7KysrRs2TJt2LDBmKZwddqM2+1WfHy8Xn/9dR05ckTr169XYWGh2traNDQ0JK/Xq0mTJjFtBjdEX0MwdXR06Nlnn5XNZlNlZaVSUlKMMrfbrY0bNyonJ0dbt24NYisRir7//ns9/fTTqq6u1pIlS9Tf36/e3l59/fXXeuihh5SZmamuri7V19drZGREFRUVxkY9BCtgYiNcjVNtbW0qKirS448/LrPZLLvdrhdffFGS1NzcrMrKSlmtVj333HO6ePGimpqaNGPGDNXU1CgsLIwfANw0+hqCqb29XYWFhcrIyFBpaakcDodRduzYMc2fP5/+hYBrb29Xbm6u6uvrNTw8rD179ujw4cP65ZdfNG3aNG3ZskVOp3NMHa51ACSmBY5baWlpev/993XkyBHt2rVLv/32m1GWk5OjV199Vf39/dqzZ49SU1PV0tKiuro6dmrDLaOvIZhSU1NVV1enb7/9VlVVVTp58qRRlpmZaUwFBG7X1V0Br5WcnCyz2ayioiJlZ2dLkrZu3apvvvlGw8PD6unp+b86XOsASISrce2RRx5RfX29rFar3G63/H6/UeZ0OvXKK6+os7NT+/fvN14fHR1lPQJuGX0NwZSenq7a2lq1tbXpzTffVFdX15hy/tTidl27eYXX69Xx48fV2tqqyMhI+Xw+bdmyRYcOHdKOHTvkdDo1a9YsxcbG/mMgAwCJaYEhgWkzuFfoawim1tZW1dTUqLa2dswDWoHbce1a0I0bN+qjjz6SyWRSb2+vnn/+eb3xxhtKTEyUJA0MDKivr0/FxcXq6ekx1pMCwN8RrkKEz+dTUVGR5syZo7KyMtnt9jHlzAVHoNDXEExX/xBfO+IA3AmXy6WKigo1NjZq6tSp6u7u1sqVK7VgwQK9++67stlsqqqq0ocffqjJkyfL4/GwnhTAdRGuQojP59OaNWtks9m0bds2JSQkBLtJCFH0NQQTu08ikAoKCmSxWFRTU2P0rba2NmVlZamsrEybN29WX1+f9u3bp/z8fJ6lBuBfcdsvhKSnp8vlcikmJkY2my3YzUEIo68hmAhWuF1/v588NDSkH3/8UZcvXzbKBwcHlZaWpvLycn388cf6+eefNWXKFBUWFvIsNQA3RLgKMfPmzVNdXZ3MZjMLbnFX0dcAjCcjIyNGMD9z5ozOnz+vsLAw5efn69NPP5XH45HZbFZYWJgkKSIiQvfff79iYmLGHIepgAD+DeEqBJlMJo2OjrIeAXcdfQ3AeHH1OvXaa6/pySeflN1u14YNGxQdHa1Vq1appKRELS0tGhkZ0a+//qrm5mbNmDHDCFsAcDMY1w5RTJvBvUJfA/Bfdu3mJ3v37lVDQ4NcLpc6OjrU0tKis2fP6tFHH5XT6VROTo4SExM1adIkRUREyOv1GjeRuNYBuBlsaAEAAELeV199pc8++0ypqalatWqVJKmpqUnV1dWyWq0qLi7WAw88oOPHjys6Olq5ublsXgHglhGuAABASOvp6dGCBQt04cIFVVRUqKyszCjbv3+/tm/frtjYWG3atEnz5s0zythuHcCtYqEEAAAIaXFxcfr8888VFxcnt9stv99vlDmdTq1bt04//PCD9u3bN6YewQrArWLkCgAATAjt7e0qLCxURkaGSktL5XA4jLJjx45p/vz5BCoAd4RwBQAAJgyfz6eioiLNmTNHZWVlstvtY8qZCgjgThCuAADAhOLz+bRmzRrZbDZt27ZNCQkJwW4SgBDBmisAADChpKeny+VyKSYmRjabLdjNARBCGLkCAAAT0tXnV137LCwAuBOEKwAAMGHxgGAAgcRtGgAAMGERrAAEEuEKAAAAAAKAcAUAAAAAAUC4AgAAAIAAIFwBAAAAQAAQrgAAAAAgAAhXAAAEUXl5udLS0oLdDABAABCuAADjzgsvvCCTySSTyaTw8HAlJSVp8+bNGh4evqNjPvXUU4FrJABgwrkv2A0AAOB2LF26VLt27dKVK1fkdrtVUlKisLAwbdq0acz7BgcHFR4eHqRWAgAmEkauAADjUkREhOLi4mSz2fTSSy8pOztbTU1NxgjU22+/rfj4eCUnJ0uS/H6/Fi1aJIvFoqlTp2r16tX6/fffJf01NW/37t1qbGw0RsQOHz58w3pX1dfXy+FwKCIiQg8++KBefvllo+zs2bNavny5oqOjFRsbqxUrVqi3t/fenCQAwD1FuAIAhASLxaLBwUFJksfjUWdnpw4ePKjm5mYNDAxoyZIlslqt8nq92rt3rw4dOmSEoPXr12vFihVaunSpzp07p3PnzikzM/OG9SRp586dKikp0erVq+X3+9XU1KSkpCRJ0sjIiJYvX65Lly7pyy+/1MGDB3XmzBnl5ube+xMEALjrmBYIABjXRkdH5fF4dODAAa1du1YXLlxQVFSUamtrjemAH3zwgS5fvqyGhgZFRUVJklwul5xOp9555x1Nnz5dFotFV65cUVxcnHHs3bt337DeW2+9pXXr1qm0tNSoN3fuXEl/hTy/36+uri7NnDlTktTQ0CCHwyGv12u8DwAQGhi5AgCMS83NzYqOjlZkZKSWLVum3NxclZeXS5JSUlLGrLM6deqUUlNTjYAkSY899phGRkbU2dl53c+4Ub3z58/rp59+0uLFi69bf+bMmUawkiS73a4pU6bo1KlTt/vVAQD/UYxcAQDGpSeeeEI7d+5UeHi44uPjdd99//tJuzYM3U0Wi+WefA4AYHxg5AoAMC5FRUUpKSlJs2bNGhOs/snDDz+s9vZ2DQwMGK8dPXpUZrPZ2PAiPDxcf/zxxy3Vi4mJ0ezZs+XxeK77ud3d3eru7jZeO3nypPr6+mS322/5OwMA/tsIVwCAkJeXl6fIyEgVFBTou+++0xdffKG1a9dq5cqVmj59uiRp9uzZ6ujoUGdnpy5evKihoaGbqldeXq7Kykrt2LFDp0+f1okTJ1RdXS1Jys7OVkpKivLy8nTixAm1trYqPz9fCxcuVEZGRtDOBwDg7iBcAQBC3uTJk3XgwAFdunRJc+fO1TPPPKPFixfL5XIZ7ykuLlZycrIyMjI0bdo0HT169KbqFRQUaPv27XrvvffkcDiUk5Oj06dPS5JMJpMaGxtltVqVlZWl7OxsJSYm6pNPPrnn5wAAcPeZRkdHR4PdCAAAAAAY7xi5AgAAAIAAIFwBAAAAQAAQrgAAAAAgAAhXAAAAABAAhCsAAAAACADCFQAAAAAEAOEKAAAAAAKAcAUAAAAAAUC4AgAAAIAAIFwBAAAAQAAQrgAAAAAgAP4EprMmNInTSLwAAAAASUVORK5CYII=",
@@ -889,12 +863,12 @@
    ],
    "source": [
     "# Function to calculate total capital per token across protocols\n",
-    "def calculate_capital(df, column_name):\n",
+    "def calculate_capital(df: pd.DataFrame, column_name: str) -> pd.Series:\n",
     "    capital_per_protocol = df.groupby('Protocol')[column_name].sum()\n",
     "    return capital_per_protocol\n",
     "\n",
     "# Function to plot bar chart for token capital across protocols\n",
-    "def plot_capital(capital, title):\n",
+    "def plot_capital(capital: pd.Series, title: str) -> None:\n",
     "    plt.figure(figsize=(10, 6))\n",
     "    sns.barplot(x=capital.index, y=np.log(capital.values))\n",
     "    plt.xlabel('Protocol')\n",
@@ -905,7 +879,6 @@
     "\n",
     "# Calculate total staked capital per token\n",
     "staked_capital = calculate_capital(liquidity_df, 'Collateral (USD)')\n",
-    "print(staked_capital)\n",
     "plot_capital(staked_capital, 'Total Staked Capital per Token Across Protocols (Logirathmic Scaling is used)')\n",
     "\n",
     "\n"
@@ -921,7 +894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "c66e6f79-aeb8-41e0-aa01-a17ee535d50f",
    "metadata": {},
    "outputs": [
@@ -952,15 +925,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "74401d69-fff8-4c41-a5ce-6f7e4b1800c5",
    "metadata": {},
    "outputs": [],
    "source": [
     "# List of tokens\n",
-    "tokens = [\"ETH\", \"wBTC\", \"USDC\", \"DAI\", \"USDT\", \"wstETH\", \"LORDS\", \"STRK\", \"UNO\", \"ZEND\"]\n",
+    "tokens = [\"ETH\", \"WBTC\", \"USDC\", \"DAI\", \"USDT\", \"wstETH\", \"LORDS\", \"STRK\"]\n",
     "\n",
-    "def parse_token_amounts(column, protocol_column, tokens):\n",
+    "def parse_token_amounts(column: pd.Series, protocol_column: pd.Series, tokens: list) -> defaultdict:\n",
     "    token_amounts = defaultdict(lambda: defaultdict(float))\n",
     "    for entry, protocol in zip(column, protocol_column):\n",
     "        for token in tokens:\n",
@@ -970,13 +943,13 @@
     "    return token_amounts\n",
     "\n",
     "# Extract token amounts for collateral and debt\n",
-    "collateral_amounts = parse_token_amounts(df_loans['Collateral'], df_loans['Protocol'], tokens)\n",
-    "debt_amounts = parse_token_amounts(df_loans['Debt'], df_loans['Protocol'], tokens)"
+    "collateral_amounts = parse_token_amounts(loans['Collateral'], loans['Protocol'], tokens)\n",
+    "debt_amounts = parse_token_amounts(loans['Debt'], loans['Protocol'], tokens)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "7ac76044-ee76-4807-b497-ad1541ec45a2",
    "metadata": {},
    "outputs": [
@@ -989,61 +962,52 @@
       "1           zklend    USDT            3.356178e+06\n",
       "2           zklend    STRK            1.239352e+07\n",
       "3           zklend     ETH            4.235030e+03\n",
-      "4           zklend    wBTC            3.604780e+01\n",
-      "5           zklend     DAI            7.715796e+04\n",
-      "6           zklend  wstETH            5.272330e+01\n",
-      "7     nostra_alpha    USDC            4.002109e+04\n",
-      "8     nostra_alpha     ETH            3.202590e+01\n",
-      "9     nostra_alpha    USDT            3.019192e+04\n",
-      "10  nostra_mainnet     ETH            1.928400e+04\n",
-      "11  nostra_mainnet    USDC            1.720035e+07\n",
-      "12  nostra_mainnet     DAI            7.468461e+04\n",
-      "13  nostra_mainnet    USDT            1.256802e+07\n",
-      "14  nostra_mainnet  wstETH            5.437920e+01\n",
-      "15  nostra_mainnet    STRK            5.948398e+07\n",
-      "16  nostra_mainnet    wBTC            2.448470e+01\n",
-      "17  nostra_mainnet   LORDS            1.882704e+06\n",
-      "18    hashstack_v0    USDC            1.130584e+03\n",
-      "19    hashstack_v0     ETH            6.064000e-01\n",
-      "20    hashstack_v0    USDT            2.268058e+02\n",
-      "21    hashstack_v0     DAI            9.689930e+01\n",
-      "22    hashstack_v0    wBTC            2.000000e-03\n",
-      "23    hashstack_v1    USDT            2.316824e+04\n",
-      "24    hashstack_v1    USDC            4.014684e+04\n",
-      "25    hashstack_v1     DAI            5.764160e+02\n",
-      "26    hashstack_v1     ETH            1.978970e+01\n",
-      "27    hashstack_v1    wBTC            2.710000e-02\n",
+      "4           zklend     DAI            7.715796e+04\n",
+      "5           zklend  wstETH            5.272330e+01\n",
+      "6     nostra_alpha    USDC            4.002109e+04\n",
+      "7     nostra_alpha     ETH            3.202590e+01\n",
+      "8     nostra_alpha    USDT            3.019192e+04\n",
+      "9   nostra_mainnet     ETH            1.928400e+04\n",
+      "10  nostra_mainnet    USDC            1.720035e+07\n",
+      "11  nostra_mainnet     DAI            7.468461e+04\n",
+      "12  nostra_mainnet    USDT            1.256802e+07\n",
+      "13  nostra_mainnet  wstETH            5.437920e+01\n",
+      "14  nostra_mainnet    STRK            5.948398e+07\n",
+      "15  nostra_mainnet   LORDS            1.882704e+06\n",
+      "16    hashstack_v0    USDC            1.130584e+03\n",
+      "17    hashstack_v0     ETH            6.064000e-01\n",
+      "18    hashstack_v0    USDT            2.268058e+02\n",
+      "19    hashstack_v0     DAI            9.689930e+01\n",
+      "20    hashstack_v1    USDT            2.316824e+04\n",
+      "21    hashstack_v1    USDC            4.014684e+04\n",
+      "22    hashstack_v1     DAI            5.764160e+02\n",
+      "23    hashstack_v1     ETH            1.978970e+01\n",
       "          Protocol   Token  Total Debt (USD)\n",
       "0           zklend     ETH      1.299392e+03\n",
       "1           zklend    USDC      4.787483e+06\n",
       "2           zklend    USDT      2.116141e+06\n",
       "3           zklend  wstETH      2.938450e+01\n",
-      "4           zklend    wBTC      5.681700e+00\n",
-      "5           zklend     DAI      6.299988e+04\n",
-      "6           zklend    STRK      1.945519e+06\n",
-      "7     nostra_alpha    USDT      4.961984e+03\n",
-      "8     nostra_alpha    wBTC      7.600000e-03\n",
-      "9     nostra_alpha    USDC      8.623691e+03\n",
-      "10    nostra_alpha     ETH      3.410900e+00\n",
-      "11    nostra_alpha     DAI      1.949320e+03\n",
-      "12  nostra_mainnet    USDT      6.838741e+06\n",
-      "13  nostra_mainnet     ETH      6.265038e+03\n",
-      "14  nostra_mainnet    USDC      9.499169e+06\n",
-      "15  nostra_mainnet    STRK      6.207267e+06\n",
-      "16  nostra_mainnet  wstETH      3.458160e+01\n",
-      "17  nostra_mainnet    wBTC      1.179560e+01\n",
-      "18  nostra_mainnet   LORDS      4.322130e+04\n",
-      "19  nostra_mainnet     DAI      5.101548e+04\n",
-      "20    hashstack_v0     ETH      1.989000e-01\n",
-      "21    hashstack_v0    USDT      1.257607e+02\n",
-      "22    hashstack_v0    USDC      8.258461e+02\n",
-      "23    hashstack_v0     DAI      1.601284e+02\n",
-      "24    hashstack_v0    wBTC      3.500000e-03\n",
-      "25    hashstack_v1    USDT      3.371473e+04\n",
-      "26    hashstack_v1     ETH      1.637170e+01\n",
-      "27    hashstack_v1    USDC      5.081960e+04\n",
-      "28    hashstack_v1     DAI      6.368208e+02\n",
-      "29    hashstack_v1    wBTC      2.390000e-02\n"
+      "4           zklend     DAI      6.299988e+04\n",
+      "5           zklend    STRK      1.945519e+06\n",
+      "6     nostra_alpha    USDT      4.961984e+03\n",
+      "7     nostra_alpha    USDC      8.623691e+03\n",
+      "8     nostra_alpha     ETH      3.410900e+00\n",
+      "9     nostra_alpha     DAI      1.949320e+03\n",
+      "10  nostra_mainnet    USDT      6.838741e+06\n",
+      "11  nostra_mainnet     ETH      6.265038e+03\n",
+      "12  nostra_mainnet    USDC      9.499169e+06\n",
+      "13  nostra_mainnet    STRK      6.207267e+06\n",
+      "14  nostra_mainnet  wstETH      3.458160e+01\n",
+      "15  nostra_mainnet   LORDS      4.322130e+04\n",
+      "16  nostra_mainnet     DAI      5.101548e+04\n",
+      "17    hashstack_v0     ETH      1.989000e-01\n",
+      "18    hashstack_v0    USDT      1.257607e+02\n",
+      "19    hashstack_v0    USDC      8.258461e+02\n",
+      "20    hashstack_v0     DAI      1.601284e+02\n",
+      "21    hashstack_v1    USDT      3.371473e+04\n",
+      "22    hashstack_v1     ETH      1.637170e+01\n",
+      "23    hashstack_v1    USDC      5.081960e+04\n",
+      "24    hashstack_v1     DAI      6.368208e+02\n"
      ]
     }
    ],
@@ -1068,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "48b48551-98a3-455f-a9cd-55f8257b5a17",
    "metadata": {},
    "outputs": [
@@ -1103,169 +1067,145 @@
        "      <th>0</th>\n",
        "      <td>zklend</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>6.481915e+06</td>\n",
+       "      <td>6.514385e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>zklend</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>3.350375e+06</td>\n",
+       "      <td>3.374467e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>zklend</td>\n",
        "      <td>STRK</td>\n",
-       "      <td>8.080574e+07</td>\n",
+       "      <td>5.308083e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>zklend</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>1.053561e+07</td>\n",
+       "      <td>1.293421e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>zklend</td>\n",
-       "      <td>wBTC</td>\n",
-       "      <td>2.416717e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>zklend</td>\n",
        "      <td>DAI</td>\n",
-       "      <td>7.715796e+04</td>\n",
+       "      <td>7.713844e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>5</th>\n",
        "      <td>zklend</td>\n",
        "      <td>wstETH</td>\n",
-       "      <td>1.546912e+05</td>\n",
+       "      <td>5.272330e+01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>6</th>\n",
        "      <td>nostra_alpha</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>4.002109e+04</td>\n",
+       "      <td>4.022157e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>7</th>\n",
        "      <td>nostra_alpha</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>7.967179e+04</td>\n",
+       "      <td>9.781030e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>8</th>\n",
        "      <td>nostra_alpha</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>3.013972e+04</td>\n",
+       "      <td>3.035645e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>9</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>4.797338e+07</td>\n",
+       "      <td>5.889526e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>10</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>1.720035e+07</td>\n",
+       "      <td>1.728651e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>11</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>DAI</td>\n",
-       "      <td>7.468461e+04</td>\n",
+       "      <td>7.466571e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>12</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>1.254629e+07</td>\n",
+       "      <td>1.263651e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>13</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>wstETH</td>\n",
-       "      <td>1.595497e+05</td>\n",
+       "      <td>5.437920e+01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>14</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>STRK</td>\n",
-       "      <td>3.878356e+08</td>\n",
+       "      <td>2.547670e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>nostra_mainnet</td>\n",
-       "      <td>wBTC</td>\n",
-       "      <td>1.641503e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>15</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>LORDS</td>\n",
-       "      <td>9.394610e+04</td>\n",
+       "      <td>9.348570e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>16</th>\n",
        "      <td>hashstack_v0</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>1.130584e+03</td>\n",
+       "      <td>1.136247e+03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>17</th>\n",
        "      <td>hashstack_v0</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>1.508559e+03</td>\n",
+       "      <td>1.852006e+03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>18</th>\n",
        "      <td>hashstack_v0</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>2.264137e+02</td>\n",
+       "      <td>2.280418e+02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>19</th>\n",
        "      <td>hashstack_v0</td>\n",
        "      <td>DAI</td>\n",
-       "      <td>9.689930e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>hashstack_v0</td>\n",
-       "      <td>wBTC</td>\n",
-       "      <td>1.340840e+02</td>\n",
+       "      <td>9.687478e+01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>20</th>\n",
        "      <td>hashstack_v1</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>2.312818e+04</td>\n",
+       "      <td>2.329449e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>21</th>\n",
        "      <td>hashstack_v1</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>4.014684e+04</td>\n",
+       "      <td>4.034794e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>22</th>\n",
        "      <td>hashstack_v1</td>\n",
        "      <td>DAI</td>\n",
-       "      <td>5.764160e+02</td>\n",
+       "      <td>5.762702e+02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>23</th>\n",
        "      <td>hashstack_v1</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>4.923143e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>hashstack_v1</td>\n",
-       "      <td>wBTC</td>\n",
-       "      <td>1.816838e+03</td>\n",
+       "      <td>6.043972e+04</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1273,76 +1213,54 @@
       ],
       "text/plain": [
        "          Protocol   Token  Total Collateral (USD)\n",
-       "0           zklend    USDC            6.481915e+06\n",
-       "1           zklend    USDT            3.350375e+06\n",
-       "2           zklend    STRK            8.080574e+07\n",
-       "3           zklend     ETH            1.053561e+07\n",
-       "4           zklend    wBTC            2.416717e+06\n",
-       "5           zklend     DAI            7.715796e+04\n",
-       "6           zklend  wstETH            1.546912e+05\n",
-       "7     nostra_alpha    USDC            4.002109e+04\n",
-       "8     nostra_alpha     ETH            7.967179e+04\n",
-       "9     nostra_alpha    USDT            3.013972e+04\n",
-       "10  nostra_mainnet     ETH            4.797338e+07\n",
-       "11  nostra_mainnet    USDC            1.720035e+07\n",
-       "12  nostra_mainnet     DAI            7.468461e+04\n",
-       "13  nostra_mainnet    USDT            1.254629e+07\n",
-       "14  nostra_mainnet  wstETH            1.595497e+05\n",
-       "15  nostra_mainnet    STRK            3.878356e+08\n",
-       "16  nostra_mainnet    wBTC            1.641503e+06\n",
-       "17  nostra_mainnet   LORDS            9.394610e+04\n",
-       "18    hashstack_v0    USDC            1.130584e+03\n",
-       "19    hashstack_v0     ETH            1.508559e+03\n",
-       "20    hashstack_v0    USDT            2.264137e+02\n",
-       "21    hashstack_v0     DAI            9.689930e+01\n",
-       "22    hashstack_v0    wBTC            1.340840e+02\n",
-       "23    hashstack_v1    USDT            2.312818e+04\n",
-       "24    hashstack_v1    USDC            4.014684e+04\n",
-       "25    hashstack_v1     DAI            5.764160e+02\n",
-       "26    hashstack_v1     ETH            4.923143e+04\n",
-       "27    hashstack_v1    wBTC            1.816838e+03"
+       "0           zklend    USDC            6.514385e+06\n",
+       "1           zklend    USDT            3.374467e+06\n",
+       "2           zklend    STRK            5.308083e+06\n",
+       "3           zklend     ETH            1.293421e+07\n",
+       "4           zklend     DAI            7.713844e+04\n",
+       "5           zklend  wstETH            5.272330e+01\n",
+       "6     nostra_alpha    USDC            4.022157e+04\n",
+       "7     nostra_alpha     ETH            9.781030e+04\n",
+       "8     nostra_alpha    USDT            3.035645e+04\n",
+       "9   nostra_mainnet     ETH            5.889526e+07\n",
+       "10  nostra_mainnet    USDC            1.728651e+07\n",
+       "11  nostra_mainnet     DAI            7.466571e+04\n",
+       "12  nostra_mainnet    USDT            1.263651e+07\n",
+       "13  nostra_mainnet  wstETH            5.437920e+01\n",
+       "14  nostra_mainnet    STRK            2.547670e+07\n",
+       "15  nostra_mainnet   LORDS            9.348570e+04\n",
+       "16    hashstack_v0    USDC            1.136247e+03\n",
+       "17    hashstack_v0     ETH            1.852006e+03\n",
+       "18    hashstack_v0    USDT            2.280418e+02\n",
+       "19    hashstack_v0     DAI            9.687478e+01\n",
+       "20    hashstack_v1    USDT            2.329449e+04\n",
+       "21    hashstack_v1    USDC            4.034794e+04\n",
+       "22    hashstack_v1     DAI            5.762702e+02\n",
+       "23    hashstack_v1     ETH            6.043972e+04"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "token_ids = []\n",
     "for token in tokens:\n",
-    "    if token == 'DAI':\n",
-    "        token_ids.append('dai')\n",
-    "    elif token == 'ETH':\n",
-    "        token_ids.append('ethereum')\n",
-    "    elif token == 'USDC':\n",
-    "        token_ids.append('usd-coin')\n",
-    "    elif token == 'USDT':\n",
-    "        token_ids.append('tether')\n",
-    "    elif token == 'wBTC':\n",
-    "        token_ids.append('wrapped-bitcoin')\n",
-    "    elif token == 'LORDS':\n",
-    "        token_ids.append('lords')\n",
-    "    elif token == 'STRK':\n",
-    "        token_ids.append('strike')\n",
-    "    elif token == 'wstETH':\n",
-    "        token_ids.append('wrapped-steth')\n",
-    "    elif token == 'ZEND':\n",
-    "        token_ids.append('zenad')\n",
-    "    elif token == 'UNO':\n",
-    "        token_ids.append('uno-re')\n",
+    "    if token == 'wstETH':\n",
+    "        tokens.remove(token)\n",
+    "        tokens.append('wrapped-steth'.upper())\n",
     "#print(token_ids)\n",
     "\n",
     "# Total Collateral (USD)\n",
-    "for token, token_id in zip(tokens,token_ids):\n",
-    "    val = collateral_df[collateral_df['Token'] == token].loc[:, 'Total Collateral (USD)'] * prices[token_id]['usd']\n",
+    "for token in tokens:\n",
+    "    val = collateral_df[collateral_df['Token'] == token].loc[:, 'Total Collateral (USD)'] * prices[token]\n",
     "    collateral_df.loc[collateral_df['Token'] == token, 'Total Collateral (USD)'] = val\n",
     "collateral_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "1dde9c83-bafc-4119-9a30-47edf2c816e5",
    "metadata": {},
    "outputs": [
@@ -1377,181 +1295,151 @@
        "      <th>0</th>\n",
        "      <td>zklend</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>3.232536e+06</td>\n",
+       "      <td>3.968472e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>zklend</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>4.787483e+06</td>\n",
+       "      <td>4.811465e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>zklend</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>2.112483e+06</td>\n",
+       "      <td>2.127673e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>zklend</td>\n",
        "      <td>wstETH</td>\n",
-       "      <td>8.621471e+04</td>\n",
+       "      <td>2.938450e+01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>zklend</td>\n",
-       "      <td>wBTC</td>\n",
-       "      <td>3.809125e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>zklend</td>\n",
        "      <td>DAI</td>\n",
-       "      <td>6.299988e+04</td>\n",
+       "      <td>6.298394e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>5</th>\n",
        "      <td>zklend</td>\n",
        "      <td>STRK</td>\n",
-       "      <td>1.268478e+07</td>\n",
+       "      <td>8.332561e+05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>6</th>\n",
        "      <td>nostra_alpha</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>4.953404e+03</td>\n",
+       "      <td>4.989024e+03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>nostra_alpha</td>\n",
-       "      <td>wBTC</td>\n",
-       "      <td>5.095192e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>7</th>\n",
        "      <td>nostra_alpha</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>8.623691e+03</td>\n",
+       "      <td>8.666890e+03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>8</th>\n",
        "      <td>nostra_alpha</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>8.485398e+03</td>\n",
+       "      <td>1.041723e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>9</th>\n",
        "      <td>nostra_alpha</td>\n",
        "      <td>DAI</td>\n",
-       "      <td>1.949320e+03</td>\n",
+       "      <td>1.948826e+03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>10</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>6.826917e+06</td>\n",
+       "      <td>6.876009e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>11</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>1.558572e+07</td>\n",
+       "      <td>1.913405e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>12</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>9.499169e+06</td>\n",
+       "      <td>9.546754e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>13</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>STRK</td>\n",
-       "      <td>4.047138e+07</td>\n",
+       "      <td>2.658542e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>14</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>wstETH</td>\n",
-       "      <td>1.014631e+05</td>\n",
+       "      <td>3.458160e+01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>nostra_mainnet</td>\n",
-       "      <td>wBTC</td>\n",
-       "      <td>7.908006e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>15</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>LORDS</td>\n",
-       "      <td>2.156724e+03</td>\n",
+       "      <td>2.146155e+03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>16</th>\n",
        "      <td>nostra_mainnet</td>\n",
        "      <td>DAI</td>\n",
-       "      <td>5.101548e+04</td>\n",
+       "      <td>5.100257e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>17</th>\n",
        "      <td>hashstack_v0</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>4.948095e+02</td>\n",
+       "      <td>6.074605e+02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>18</th>\n",
        "      <td>hashstack_v0</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>1.255433e+02</td>\n",
+       "      <td>1.264460e+02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>19</th>\n",
        "      <td>hashstack_v0</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>8.258461e+02</td>\n",
+       "      <td>8.299830e+02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>20</th>\n",
        "      <td>hashstack_v0</td>\n",
        "      <td>DAI</td>\n",
-       "      <td>1.601284e+02</td>\n",
+       "      <td>1.600879e+02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>hashstack_v0</td>\n",
-       "      <td>wBTC</td>\n",
-       "      <td>2.346470e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>21</th>\n",
        "      <td>hashstack_v1</td>\n",
        "      <td>USDT</td>\n",
-       "      <td>3.365644e+04</td>\n",
+       "      <td>3.389846e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>22</th>\n",
        "      <td>hashstack_v1</td>\n",
        "      <td>ETH</td>\n",
-       "      <td>4.072837e+04</td>\n",
+       "      <td>5.000081e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>23</th>\n",
        "      <td>hashstack_v1</td>\n",
        "      <td>USDC</td>\n",
-       "      <td>5.081960e+04</td>\n",
+       "      <td>5.107417e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
+       "      <th>24</th>\n",
        "      <td>hashstack_v1</td>\n",
        "      <td>DAI</td>\n",
-       "      <td>6.368208e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>hashstack_v1</td>\n",
-       "      <td>wBTC</td>\n",
-       "      <td>1.602304e+03</td>\n",
+       "      <td>6.366597e+02</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1559,47 +1447,42 @@
       ],
       "text/plain": [
        "          Protocol   Token  Total Debt (USD)\n",
-       "0           zklend     ETH      3.232536e+06\n",
-       "1           zklend    USDC      4.787483e+06\n",
-       "2           zklend    USDT      2.112483e+06\n",
-       "3           zklend  wstETH      8.621471e+04\n",
-       "4           zklend    wBTC      3.809125e+05\n",
-       "5           zklend     DAI      6.299988e+04\n",
-       "6           zklend    STRK      1.268478e+07\n",
-       "7     nostra_alpha    USDT      4.953404e+03\n",
-       "8     nostra_alpha    wBTC      5.095192e+02\n",
-       "9     nostra_alpha    USDC      8.623691e+03\n",
-       "10    nostra_alpha     ETH      8.485398e+03\n",
-       "11    nostra_alpha     DAI      1.949320e+03\n",
-       "12  nostra_mainnet    USDT      6.826917e+06\n",
-       "13  nostra_mainnet     ETH      1.558572e+07\n",
-       "14  nostra_mainnet    USDC      9.499169e+06\n",
-       "15  nostra_mainnet    STRK      4.047138e+07\n",
-       "16  nostra_mainnet  wstETH      1.014631e+05\n",
-       "17  nostra_mainnet    wBTC      7.908006e+05\n",
-       "18  nostra_mainnet   LORDS      2.156724e+03\n",
-       "19  nostra_mainnet     DAI      5.101548e+04\n",
-       "20    hashstack_v0     ETH      4.948095e+02\n",
-       "21    hashstack_v0    USDT      1.255433e+02\n",
-       "22    hashstack_v0    USDC      8.258461e+02\n",
-       "23    hashstack_v0     DAI      1.601284e+02\n",
-       "24    hashstack_v0    wBTC      2.346470e+02\n",
-       "25    hashstack_v1    USDT      3.365644e+04\n",
-       "26    hashstack_v1     ETH      4.072837e+04\n",
-       "27    hashstack_v1    USDC      5.081960e+04\n",
-       "28    hashstack_v1     DAI      6.368208e+02\n",
-       "29    hashstack_v1    wBTC      1.602304e+03"
+       "0           zklend     ETH      3.968472e+06\n",
+       "1           zklend    USDC      4.811465e+06\n",
+       "2           zklend    USDT      2.127673e+06\n",
+       "3           zklend  wstETH      2.938450e+01\n",
+       "4           zklend     DAI      6.298394e+04\n",
+       "5           zklend    STRK      8.332561e+05\n",
+       "6     nostra_alpha    USDT      4.989024e+03\n",
+       "7     nostra_alpha    USDC      8.666890e+03\n",
+       "8     nostra_alpha     ETH      1.041723e+04\n",
+       "9     nostra_alpha     DAI      1.948826e+03\n",
+       "10  nostra_mainnet    USDT      6.876009e+06\n",
+       "11  nostra_mainnet     ETH      1.913405e+07\n",
+       "12  nostra_mainnet    USDC      9.546754e+06\n",
+       "13  nostra_mainnet    STRK      2.658542e+06\n",
+       "14  nostra_mainnet  wstETH      3.458160e+01\n",
+       "15  nostra_mainnet   LORDS      2.146155e+03\n",
+       "16  nostra_mainnet     DAI      5.100257e+04\n",
+       "17    hashstack_v0     ETH      6.074605e+02\n",
+       "18    hashstack_v0    USDT      1.264460e+02\n",
+       "19    hashstack_v0    USDC      8.299830e+02\n",
+       "20    hashstack_v0     DAI      1.600879e+02\n",
+       "21    hashstack_v1    USDT      3.389846e+04\n",
+       "22    hashstack_v1     ETH      5.000081e+04\n",
+       "23    hashstack_v1    USDC      5.107417e+04\n",
+       "24    hashstack_v1     DAI      6.366597e+02"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Total Debt (USD)\n",
-    "for token, token_id in zip(tokens,token_ids):\n",
-    "    val = debt_df[debt_df['Token'] == token].loc[:, 'Total Debt (USD)'] * prices[token_id]['usd']\n",
+    "for token in tokens:\n",
+    "    val = debt_df[debt_df['Token'] == token].loc[:, 'Total Debt (USD)'] * prices[token]\n",
     "    debt_df.loc[debt_df['Token'] == token, 'Total Debt (USD)'] = val\n",
     "debt_df"
    ]
@@ -1614,7 +1497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "521fbd91-b7d6-4cfd-99aa-a203e63f3daa",
    "metadata": {},
    "outputs": [
@@ -1622,38 +1505,34 @@
      "data": {
       "text/plain": [
        "Protocol        Token \n",
-       "hashstack_v0    DAI       9.689930e+01\n",
-       "                ETH       1.508559e+03\n",
-       "                USDC      1.130584e+03\n",
-       "                USDT      2.264137e+02\n",
-       "                wBTC      1.340840e+02\n",
-       "hashstack_v1    DAI       5.764160e+02\n",
-       "                ETH       4.923143e+04\n",
-       "                USDC      4.014684e+04\n",
-       "                USDT      2.312818e+04\n",
-       "                wBTC      1.816838e+03\n",
-       "nostra_alpha    ETH       7.967179e+04\n",
-       "                USDC      4.002109e+04\n",
-       "                USDT      3.013972e+04\n",
-       "nostra_mainnet  DAI       7.468461e+04\n",
-       "                ETH       4.797338e+07\n",
-       "                LORDS     9.394610e+04\n",
-       "                STRK      3.878356e+08\n",
-       "                USDC      1.720035e+07\n",
-       "                USDT      1.254629e+07\n",
-       "                wBTC      1.641503e+06\n",
-       "                wstETH    1.595497e+05\n",
-       "zklend          DAI       7.715796e+04\n",
-       "                ETH       1.053561e+07\n",
-       "                STRK      8.080574e+07\n",
-       "                USDC      6.481915e+06\n",
-       "                USDT      3.350375e+06\n",
-       "                wBTC      2.416717e+06\n",
-       "                wstETH    1.546912e+05\n",
+       "hashstack_v0    DAI       9.687478e+01\n",
+       "                ETH       1.852006e+03\n",
+       "                USDC      1.136247e+03\n",
+       "                USDT      2.280418e+02\n",
+       "hashstack_v1    DAI       5.762702e+02\n",
+       "                ETH       6.043972e+04\n",
+       "                USDC      4.034794e+04\n",
+       "                USDT      2.329449e+04\n",
+       "nostra_alpha    ETH       9.781030e+04\n",
+       "                USDC      4.022157e+04\n",
+       "                USDT      3.035645e+04\n",
+       "nostra_mainnet  DAI       7.466571e+04\n",
+       "                ETH       5.889526e+07\n",
+       "                LORDS     9.348570e+04\n",
+       "                STRK      2.547670e+07\n",
+       "                USDC      1.728651e+07\n",
+       "                USDT      1.263651e+07\n",
+       "                wstETH    5.437920e+01\n",
+       "zklend          DAI       7.713844e+04\n",
+       "                ETH       1.293421e+07\n",
+       "                STRK      5.308083e+06\n",
+       "                USDC      6.514385e+06\n",
+       "                USDT      3.374467e+06\n",
+       "                wstETH    5.272330e+01\n",
        "Name: Total Collateral (USD), dtype: float64"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1673,7 +1552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "bab1ec7c-8181-45e8-bbd6-f81f49920962",
    "metadata": {},
    "outputs": [
@@ -1681,40 +1560,35 @@
      "data": {
       "text/plain": [
        "Protocol        Token \n",
-       "hashstack_v0    DAI       1.601284e+02\n",
-       "                ETH       4.948095e+02\n",
-       "                USDC      8.258461e+02\n",
-       "                USDT      1.255433e+02\n",
-       "                wBTC      2.346470e+02\n",
-       "hashstack_v1    DAI       6.368208e+02\n",
-       "                ETH       4.072837e+04\n",
-       "                USDC      5.081960e+04\n",
-       "                USDT      3.365644e+04\n",
-       "                wBTC      1.602304e+03\n",
-       "nostra_alpha    DAI       1.949320e+03\n",
-       "                ETH       8.485398e+03\n",
-       "                USDC      8.623691e+03\n",
-       "                USDT      4.953404e+03\n",
-       "                wBTC      5.095192e+02\n",
-       "nostra_mainnet  DAI       5.101548e+04\n",
-       "                ETH       1.558572e+07\n",
-       "                LORDS     2.156724e+03\n",
-       "                STRK      4.047138e+07\n",
-       "                USDC      9.499169e+06\n",
-       "                USDT      6.826917e+06\n",
-       "                wBTC      7.908006e+05\n",
-       "                wstETH    1.014631e+05\n",
-       "zklend          DAI       6.299988e+04\n",
-       "                ETH       3.232536e+06\n",
-       "                STRK      1.268478e+07\n",
-       "                USDC      4.787483e+06\n",
-       "                USDT      2.112483e+06\n",
-       "                wBTC      3.809125e+05\n",
-       "                wstETH    8.621471e+04\n",
+       "hashstack_v0    DAI       1.600879e+02\n",
+       "                ETH       6.074605e+02\n",
+       "                USDC      8.299830e+02\n",
+       "                USDT      1.264460e+02\n",
+       "hashstack_v1    DAI       6.366597e+02\n",
+       "                ETH       5.000081e+04\n",
+       "                USDC      5.107417e+04\n",
+       "                USDT      3.389846e+04\n",
+       "nostra_alpha    DAI       1.948826e+03\n",
+       "                ETH       1.041723e+04\n",
+       "                USDC      8.666890e+03\n",
+       "                USDT      4.989024e+03\n",
+       "nostra_mainnet  DAI       5.100257e+04\n",
+       "                ETH       1.913405e+07\n",
+       "                LORDS     2.146155e+03\n",
+       "                STRK      2.658542e+06\n",
+       "                USDC      9.546754e+06\n",
+       "                USDT      6.876009e+06\n",
+       "                wstETH    3.458160e+01\n",
+       "zklend          DAI       6.298394e+04\n",
+       "                ETH       3.968472e+06\n",
+       "                STRK      8.332561e+05\n",
+       "                USDC      4.811465e+06\n",
+       "                USDT      2.127673e+06\n",
+       "                wstETH    2.938450e+01\n",
        "Name: Total Debt (USD), dtype: float64"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1734,13 +1608,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "d654007a-8ef0-4c33-ab35-50f65b030cfd",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -1750,7 +1624,7 @@
     },
     {
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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -1789,13 +1663,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "8a03e0a2-e684-4924-a1ef-7028687d1fcc",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -1805,7 +1679,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -1874,14 +1748,6 @@
     "\n",
     "This analysis provides a clear understanding of how users interact with multiple DeFi protocols, how they distribute their capital, and which tokens and protocols are preferred. Further exploration could involve time-based analysis, exploring user behavior trends over time, or investigating protocol-specific incentives that drive user engagement across platforms.\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a1d50ed4-3ec1-479f-9caf-fe2b49fd07c6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 04129e24842710d2c9edc1745630c26939578140 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Wed, 13 Nov 2024 16:30:12 +0530
Subject: [PATCH 07/12] Update
 apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb

Co-authored-by: lukaspetrasek <47862369+lukaspetrasek@users.noreply.github.com>
---
 ...alyze_user_behavior_across_different_lending_protocols.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index f1b369b7..53023f9c 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -477,7 +477,7 @@
    ],
    "source": [
     "# the distribution of protocols among users\n",
-    "top_protocols = loans['Protocol'].value_counts()\n",
+    "top_protocols = loans.groupby('Protocol')['User'].nunique()\n",
     "top_protocols"
    ]
   },

From 89d1f1ca385bcb70d368929ff27ef545287bfb67 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Wed, 13 Nov 2024 16:33:10 +0530
Subject: [PATCH 08/12] Update
 apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb

Co-authored-by: lukaspetrasek <47862369+lukaspetrasek@users.noreply.github.com>
---
 ...lyze_user_behavior_across_different_lending_protocols.ipynb | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index 53023f9c..4dd30ef9 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -69,8 +69,7 @@
     "import os\n",
     "import sys\n",
     "import pandas as pd\n",
-    "root_dir = os.path.abspath(os.path.join(os.getcwd(), '..'))\n",
-    "sys.path.append(root_dir)\n",
+    "sys.path.append('..')\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "from matplotlib_venn import venn3\n",

From 9ed001757b9242c5843e92380f15331c761a27e4 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Wed, 13 Nov 2024 16:31:37 +0530
Subject: [PATCH 09/12] some changes

---
 ...r_across_different_lending_protocols.ipynb | 89 ++++++++-----------
 1 file changed, 38 insertions(+), 51 deletions(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index 4dd30ef9..00b18017 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -73,10 +73,7 @@
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "from matplotlib_venn import venn3\n",
-    "from sqlalchemy import create_engine\n",
-    "import gcsfs\n",
     "import requests\n",
-    "import seaborn as sns\n",
     "import numpy as np\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from collections import defaultdict, Counter\n",
@@ -93,14 +90,6 @@
     "### Loading the Data"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "99f508d9-4c05-4058-9974-ca6f6398ae88",
-   "metadata": {},
-   "source": [
-    "#### From Postgress"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "c5820bcd-27a7-4a72-b2c9-8b81dda59110",
@@ -166,36 +155,6 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6324abae-c1bb-4204-a919-fb52209722e6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "User                              0\n",
-       "Protocol                          0\n",
-       "Collateral (USD)                  0\n",
-       "Risk-adjusted collateral (USD)    0\n",
-       "Debt (USD)                        0\n",
-       "Health factor                     0\n",
-       "Standardized health factor        0\n",
-       "Collateral                        0\n",
-       "Debt                              0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loans.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
    "id": "6a9975f0-b5aa-49b4-bd47-b560d7c590e8",
    "metadata": {},
    "outputs": [
@@ -333,7 +292,7 @@
        "4                                                "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -348,12 +307,12 @@
    "metadata": {},
    "source": [
     "### List of Current prices in USD for given tokens\n",
-    "Ethereum,Wrapped-Bitcoin,USD-coin,DAI,Tether,Wrapped-Steth,Lords,Strike,UNO-Re,Zenad"
+    "Ethereum,Wrapped-Bitcoin,USD-coin,DAI,Tether,Wrapped-Steth,Lords"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "5ba21cdc-3a84-429d-a1e5-2380046230f5",
    "metadata": {},
    "outputs": [],
@@ -386,11 +345,11 @@
     {
      "data": {
       "text/plain": [
-       "{'ETH': 3054.1,\n",
-       " 'WBTC': 76490.05153377741,\n",
-       " 'STRK': 0.42829510632806395,\n",
-       " 'USDC': 1.0050093418439294,\n",
-       " 'USDT': 1.00544949008542}"
+       "{'ETH': 3163.3,\n",
+       " 'WBTC': 87225.61094357829,\n",
+       " 'STRK': 0.4591000552127958,\n",
+       " 'USDC': 1.003373523112507,\n",
+       " 'USDT': 1.0035983791147356}"
       ]
      },
      "execution_count": 6,
@@ -399,7 +358,7 @@
     }
    ],
    "source": [
-    "COLLATERAL_TOKENS = [\"ETH\", \"WBTC\", \"STRK\", \"USDC\", \"USDT\"]\n",
+    "COLLATERAL_TOKENS = [\"ETH\", \"WBTC\", \"STRK\", \"USDC\", \"USDT\", \"DAI\", \"LORDS\", \"wstETH\"]\n",
     "prices = {}\n",
     "for token in COLLATERAL_TOKENS:\n",
     "    price = fetch_prices(token)\n",
@@ -417,7 +376,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Token Prices in USD: {'ETH': 3054.1, 'WBTC': 76490.05153377741, 'STRK': 0.42829510632806395, 'USDC': 1.0050093418439294, 'USDT': 1.00544949008542, 'DAI': 0.999747, 'LORDS': 0.04965502, 'WRAPPED-STETH': 3603.56}\n"
+      "Token Prices in USD: {'ETH': 3163.3, 'WBTC': 87225.61094357829, 'STRK': 0.4591000552127958, 'USDC': 1.003373523112507, 'USDT': 1.0035983791147356, 'DAI': 1.002, 'LORDS': 0.077801, 'WRAPPED-STETH': 3744.3}\n"
      ]
     }
    ],
@@ -451,6 +410,34 @@
     "#### Users Providing Liquidity and their Protocols"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "57a5bed8-fb28-4ebc-83df-332021d03643",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Protocol\n",
+       "hashstack_v0           87\n",
+       "hashstack_v1          867\n",
+       "nostra_alpha       143645\n",
+       "nostra_mainnet     247540\n",
+       "zklend            1383629\n",
+       "Name: User, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top_protocols = loans.groupby('Protocol')['User'].nunique().sort_values(ascending=False\n",
+    "top_protocols"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,

From 0355f55e9c7543106f1bdb08830e97f5358538ac Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Wed, 13 Nov 2024 16:33:37 +0530
Subject: [PATCH 10/12] some changes

---
 ...r_across_different_lending_protocols.ipynb | 41 +++----------------
 1 file changed, 6 insertions(+), 35 deletions(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index 00b18017..c7c35284 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -412,37 +412,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "57a5bed8-fb28-4ebc-83df-332021d03643",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Protocol\n",
-       "hashstack_v0           87\n",
-       "hashstack_v1          867\n",
-       "nostra_alpha       143645\n",
-       "nostra_mainnet     247540\n",
-       "zklend            1383629\n",
-       "Name: User, dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "top_protocols = loans.groupby('Protocol')['User'].nunique().sort_values(ascending=False\n",
-    "top_protocols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c6a72339-26b1-49e6-943f-4bea5ba8b3a3",
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -451,19 +423,18 @@
        "zklend            1383629\n",
        "nostra_mainnet     247540\n",
        "nostra_alpha       143645\n",
-       "hashstack_v1         1289\n",
-       "hashstack_v0          131\n",
-       "Name: count, dtype: int64"
+       "hashstack_v1          867\n",
+       "hashstack_v0           87\n",
+       "Name: User, dtype: int64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# the distribution of protocols among users\n",
-    "top_protocols = loans.groupby('Protocol')['User'].nunique()\n",
+    "top_protocols = loans.groupby('Protocol')['User'].nunique().sort_values(ascending=False)\n",
     "top_protocols"
    ]
   },

From 215943a716f8fa50638156993fb30d60491d0041 Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Sat, 30 Nov 2024 19:47:32 +0530
Subject: [PATCH 11/12] some changes

---
 ...r_across_different_lending_protocols.ipynb | 114 +++++++++---------
 1 file changed, 58 insertions(+), 56 deletions(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index c7c35284..8e6761dd 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "2624ecf7-a4de-4ed6-b075-4a7b1ea5a1c3",
    "metadata": {},
    "outputs": [
@@ -112,8 +112,10 @@
       "Processing nostra_alpha from Google Storage...\n",
       "Processing nostra_mainnet from Google Storage...\n",
       "Processing hashstack_v0 from Google Storage...\n",
+      "Moving forward...\n",
       "Processing hashstack_v1 from Google Storage...\n",
-      "Combined dataframe shape: (1776234, 9)\n"
+      "Moving forward...\n",
+      "Combined dataframe shape: (1279483, 9)\n"
      ]
     }
    ],
@@ -134,7 +136,7 @@
     "    for protocol in protocols:\n",
     "        # Read from google storage\n",
     "        try:\n",
-    "            url = f\"https://storage.googleapis.com/derisk-persistent-state/{protocol}_data/loans.parquet\"      \n",
+    "            url = f\"https://storage.googleapis.com/derisk-persistent-state/v3/{protocol}_data/loans.parquet\"      \n",
     "            print(f\"Processing {protocol} from Google Storage...\")\n",
     "            df_protocol = pd.read_parquet(url)\n",
     "        except:\n",
@@ -154,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "6a9975f0-b5aa-49b4-bd47-b560d7c590e8",
    "metadata": {},
    "outputs": [
@@ -193,46 +195,46 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0x4306021e30f9577351207140f90425b3e9e102ec5a42...</td>\n",
+       "      <td>0x04306021e30f9577351207140f90425b3e9e102ec5a4...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>5744.568231</td>\n",
-       "      <td>4289.009524</td>\n",
-       "      <td>22.162648</td>\n",
-       "      <td>193.524234</td>\n",
-       "      <td>193.524234</td>\n",
-       "      <td>USDC: 113.3876, USDT: 4610.7524, STRK: 904.5577</td>\n",
-       "      <td>USDC: 10.0284, USDT: 10.0302, wstETH: 0.0006</td>\n",
+       "      <td>6555.350837</td>\n",
+       "      <td>5209.103783</td>\n",
+       "      <td>302.313569</td>\n",
+       "      <td>17.230797</td>\n",
+       "      <td>17.230797</td>\n",
+       "      <td>0x068f5c6a61780768455de69077e07e89787839bf8166...</td>\n",
+       "      <td>0x068f5c6a61780768455de69077e07e89787839bf8166...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0x30b399e06903676ada3eccd5522e0cca4c4ad0101468...</td>\n",
+       "      <td>0x030b399e06903676ada3eccd5522e0cca4c4ad010146...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>37.671463</td>\n",
-       "      <td>30.137170</td>\n",
+       "      <td>29.905838</td>\n",
+       "      <td>23.924671</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>inf</td>\n",
        "      <td>inf</td>\n",
-       "      <td>ETH: 0.0126</td>\n",
+       "      <td>0x049d36570d4e46f48e99674bd3fcc84644ddd6b96f7c...</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0x2f006034f567d5c2431bc4104b2cc7a1bf8f004bd00c...</td>\n",
+       "      <td>0x02f006034f567d5c2431bc4104b2cc7a1bf8f004bd00...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>102.450086</td>\n",
-       "      <td>81.960069</td>\n",
-       "      <td>0.387499</td>\n",
-       "      <td>211.510582</td>\n",
-       "      <td>211.510582</td>\n",
-       "      <td>ETH: 0.0311, USDC: 6.5088, USDT: 3.0144</td>\n",
-       "      <td>ETH: 0.0005</td>\n",
+       "      <td>86.403739</td>\n",
+       "      <td>68.938692</td>\n",
+       "      <td>-1.134892</td>\n",
+       "      <td>-60.744714</td>\n",
+       "      <td>-60.744714</td>\n",
+       "      <td>0x068f5c6a61780768455de69077e07e89787839bf8166...</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0x43e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0d...</td>\n",
+       "      <td>0x043e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>-5.156963</td>\n",
-       "      <td>-4.125570</td>\n",
+       "      <td>-5.220325</td>\n",
+       "      <td>-4.176260</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>inf</td>\n",
        "      <td>inf</td>\n",
@@ -241,14 +243,14 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0x22dd5ed1e4d359eca2e772ecefa57e31bb7756772850...</td>\n",
+       "      <td>0x022dd5ed1e4d359eca2e772ecefa57e31bb775677285...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>213.311298</td>\n",
-       "      <td>157.651127</td>\n",
+       "      <td>190.596981</td>\n",
+       "      <td>141.816689</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>inf</td>\n",
        "      <td>inf</td>\n",
-       "      <td>wBTC: 0.0018, DAI: 23.1396, USDT: 83.3628</td>\n",
+       "      <td>0x03fe2b97c1fd336e750087d68b9b867997fd64a2661f...</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -257,42 +259,42 @@
       ],
       "text/plain": [
        "                                                User Protocol  \\\n",
-       "0  0x4306021e30f9577351207140f90425b3e9e102ec5a42...   zklend   \n",
-       "1  0x30b399e06903676ada3eccd5522e0cca4c4ad0101468...   zklend   \n",
-       "2  0x2f006034f567d5c2431bc4104b2cc7a1bf8f004bd00c...   zklend   \n",
-       "3  0x43e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0d...   zklend   \n",
-       "4  0x22dd5ed1e4d359eca2e772ecefa57e31bb7756772850...   zklend   \n",
+       "0  0x04306021e30f9577351207140f90425b3e9e102ec5a4...   zklend   \n",
+       "1  0x030b399e06903676ada3eccd5522e0cca4c4ad010146...   zklend   \n",
+       "2  0x02f006034f567d5c2431bc4104b2cc7a1bf8f004bd00...   zklend   \n",
+       "3  0x043e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0...   zklend   \n",
+       "4  0x022dd5ed1e4d359eca2e772ecefa57e31bb775677285...   zklend   \n",
        "\n",
        "   Collateral (USD)  Risk-adjusted collateral (USD)  Debt (USD)  \\\n",
-       "0       5744.568231                     4289.009524   22.162648   \n",
-       "1         37.671463                       30.137170    0.000000   \n",
-       "2        102.450086                       81.960069    0.387499   \n",
-       "3         -5.156963                       -4.125570    0.000000   \n",
-       "4        213.311298                      157.651127    0.000000   \n",
+       "0       6555.350837                     5209.103783  302.313569   \n",
+       "1         29.905838                       23.924671    0.000000   \n",
+       "2         86.403739                       68.938692   -1.134892   \n",
+       "3         -5.220325                       -4.176260    0.000000   \n",
+       "4        190.596981                      141.816689    0.000000   \n",
        "\n",
        "   Health factor  Standardized health factor  \\\n",
-       "0     193.524234                  193.524234   \n",
+       "0      17.230797                   17.230797   \n",
        "1            inf                         inf   \n",
-       "2     211.510582                  211.510582   \n",
+       "2     -60.744714                  -60.744714   \n",
        "3            inf                         inf   \n",
        "4            inf                         inf   \n",
        "\n",
-       "                                        Collateral  \\\n",
-       "0  USDC: 113.3876, USDT: 4610.7524, STRK: 904.5577   \n",
-       "1                                      ETH: 0.0126   \n",
-       "2          ETH: 0.0311, USDC: 6.5088, USDT: 3.0144   \n",
-       "3                                                    \n",
-       "4        wBTC: 0.0018, DAI: 23.1396, USDT: 83.3628   \n",
+       "                                          Collateral  \\\n",
+       "0  0x068f5c6a61780768455de69077e07e89787839bf8166...   \n",
+       "1  0x049d36570d4e46f48e99674bd3fcc84644ddd6b96f7c...   \n",
+       "2  0x068f5c6a61780768455de69077e07e89787839bf8166...   \n",
+       "3                                                      \n",
+       "4  0x03fe2b97c1fd336e750087d68b9b867997fd64a2661f...   \n",
        "\n",
-       "                                           Debt  \n",
-       "0  USDC: 10.0284, USDT: 10.0302, wstETH: 0.0006  \n",
-       "1                                                \n",
-       "2                                   ETH: 0.0005  \n",
-       "3                                                \n",
-       "4                                                "
+       "                                                Debt  \n",
+       "0  0x068f5c6a61780768455de69077e07e89787839bf8166...  \n",
+       "1                                                     \n",
+       "2                                                     \n",
+       "3                                                     \n",
+       "4                                                     "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }

From 4c751b7cf5664fc241af6690cae21be1819a68dc Mon Sep 17 00:00:00 2001
From: Soham Das <144812467+tosoham@users.noreply.github.com>
Date: Sat, 30 Nov 2024 22:17:41 +0530
Subject: [PATCH 12/12] comments resolved

---
 ...r_across_different_lending_protocols.ipynb | 347 +++++++-----------
 1 file changed, 138 insertions(+), 209 deletions(-)

diff --git a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
index 8e6761dd..4f706596 100644
--- a/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
+++ b/apps/legacy_app/notebooks/Analyze_user_behavior_across_different_lending_protocols.ipynb
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "2624ecf7-a4de-4ed6-b075-4a7b1ea5a1c3",
    "metadata": {},
    "outputs": [
@@ -112,10 +112,8 @@
       "Processing nostra_alpha from Google Storage...\n",
       "Processing nostra_mainnet from Google Storage...\n",
       "Processing hashstack_v0 from Google Storage...\n",
-      "Moving forward...\n",
       "Processing hashstack_v1 from Google Storage...\n",
-      "Moving forward...\n",
-      "Combined dataframe shape: (1279483, 9)\n"
+      "Combined dataframe shape: (1776234, 9)\n"
      ]
     }
    ],
@@ -136,7 +134,7 @@
     "    for protocol in protocols:\n",
     "        # Read from google storage\n",
     "        try:\n",
-    "            url = f\"https://storage.googleapis.com/derisk-persistent-state/v3/{protocol}_data/loans.parquet\"      \n",
+    "            url = f\"https://storage.googleapis.com/derisk-persistent-state/{protocol}_data/loans.parquet\"      \n",
     "            print(f\"Processing {protocol} from Google Storage...\")\n",
     "            df_protocol = pd.read_parquet(url)\n",
     "        except:\n",
@@ -156,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "6a9975f0-b5aa-49b4-bd47-b560d7c590e8",
    "metadata": {},
    "outputs": [
@@ -195,46 +193,46 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0x04306021e30f9577351207140f90425b3e9e102ec5a4...</td>\n",
+       "      <td>0x4306021e30f9577351207140f90425b3e9e102ec5a42...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>6555.350837</td>\n",
-       "      <td>5209.103783</td>\n",
-       "      <td>302.313569</td>\n",
-       "      <td>17.230797</td>\n",
-       "      <td>17.230797</td>\n",
-       "      <td>0x068f5c6a61780768455de69077e07e89787839bf8166...</td>\n",
-       "      <td>0x068f5c6a61780768455de69077e07e89787839bf8166...</td>\n",
+       "      <td>5744.568231</td>\n",
+       "      <td>4289.009524</td>\n",
+       "      <td>22.162648</td>\n",
+       "      <td>193.524234</td>\n",
+       "      <td>193.524234</td>\n",
+       "      <td>USDC: 113.3876, USDT: 4610.7524, STRK: 904.5577</td>\n",
+       "      <td>USDC: 10.0284, USDT: 10.0302, wstETH: 0.0006</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0x030b399e06903676ada3eccd5522e0cca4c4ad010146...</td>\n",
+       "      <td>0x30b399e06903676ada3eccd5522e0cca4c4ad0101468...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>29.905838</td>\n",
-       "      <td>23.924671</td>\n",
+       "      <td>37.671463</td>\n",
+       "      <td>30.137170</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>inf</td>\n",
        "      <td>inf</td>\n",
-       "      <td>0x049d36570d4e46f48e99674bd3fcc84644ddd6b96f7c...</td>\n",
+       "      <td>ETH: 0.0126</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0x02f006034f567d5c2431bc4104b2cc7a1bf8f004bd00...</td>\n",
+       "      <td>0x2f006034f567d5c2431bc4104b2cc7a1bf8f004bd00c...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>86.403739</td>\n",
-       "      <td>68.938692</td>\n",
-       "      <td>-1.134892</td>\n",
-       "      <td>-60.744714</td>\n",
-       "      <td>-60.744714</td>\n",
-       "      <td>0x068f5c6a61780768455de69077e07e89787839bf8166...</td>\n",
-       "      <td></td>\n",
+       "      <td>102.450086</td>\n",
+       "      <td>81.960069</td>\n",
+       "      <td>0.387499</td>\n",
+       "      <td>211.510582</td>\n",
+       "      <td>211.510582</td>\n",
+       "      <td>ETH: 0.0311, USDC: 6.5088, USDT: 3.0144</td>\n",
+       "      <td>ETH: 0.0005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0x043e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0...</td>\n",
+       "      <td>0x43e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0d...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>-5.220325</td>\n",
-       "      <td>-4.176260</td>\n",
+       "      <td>-5.156963</td>\n",
+       "      <td>-4.125570</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>inf</td>\n",
        "      <td>inf</td>\n",
@@ -243,14 +241,14 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0x022dd5ed1e4d359eca2e772ecefa57e31bb775677285...</td>\n",
+       "      <td>0x22dd5ed1e4d359eca2e772ecefa57e31bb7756772850...</td>\n",
        "      <td>zklend</td>\n",
-       "      <td>190.596981</td>\n",
-       "      <td>141.816689</td>\n",
+       "      <td>213.311298</td>\n",
+       "      <td>157.651127</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>inf</td>\n",
        "      <td>inf</td>\n",
-       "      <td>0x03fe2b97c1fd336e750087d68b9b867997fd64a2661f...</td>\n",
+       "      <td>wBTC: 0.0018, DAI: 23.1396, USDT: 83.3628</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -259,42 +257,42 @@
       ],
       "text/plain": [
        "                                                User Protocol  \\\n",
-       "0  0x04306021e30f9577351207140f90425b3e9e102ec5a4...   zklend   \n",
-       "1  0x030b399e06903676ada3eccd5522e0cca4c4ad010146...   zklend   \n",
-       "2  0x02f006034f567d5c2431bc4104b2cc7a1bf8f004bd00...   zklend   \n",
-       "3  0x043e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0...   zklend   \n",
-       "4  0x022dd5ed1e4d359eca2e772ecefa57e31bb775677285...   zklend   \n",
+       "0  0x4306021e30f9577351207140f90425b3e9e102ec5a42...   zklend   \n",
+       "1  0x30b399e06903676ada3eccd5522e0cca4c4ad0101468...   zklend   \n",
+       "2  0x2f006034f567d5c2431bc4104b2cc7a1bf8f004bd00c...   zklend   \n",
+       "3  0x43e9ee859c0f85a6d5ab3f7ad26c50b9e9d8a8e10d0d...   zklend   \n",
+       "4  0x22dd5ed1e4d359eca2e772ecefa57e31bb7756772850...   zklend   \n",
        "\n",
        "   Collateral (USD)  Risk-adjusted collateral (USD)  Debt (USD)  \\\n",
-       "0       6555.350837                     5209.103783  302.313569   \n",
-       "1         29.905838                       23.924671    0.000000   \n",
-       "2         86.403739                       68.938692   -1.134892   \n",
-       "3         -5.220325                       -4.176260    0.000000   \n",
-       "4        190.596981                      141.816689    0.000000   \n",
+       "0       5744.568231                     4289.009524   22.162648   \n",
+       "1         37.671463                       30.137170    0.000000   \n",
+       "2        102.450086                       81.960069    0.387499   \n",
+       "3         -5.156963                       -4.125570    0.000000   \n",
+       "4        213.311298                      157.651127    0.000000   \n",
        "\n",
        "   Health factor  Standardized health factor  \\\n",
-       "0      17.230797                   17.230797   \n",
+       "0     193.524234                  193.524234   \n",
        "1            inf                         inf   \n",
-       "2     -60.744714                  -60.744714   \n",
+       "2     211.510582                  211.510582   \n",
        "3            inf                         inf   \n",
        "4            inf                         inf   \n",
        "\n",
-       "                                          Collateral  \\\n",
-       "0  0x068f5c6a61780768455de69077e07e89787839bf8166...   \n",
-       "1  0x049d36570d4e46f48e99674bd3fcc84644ddd6b96f7c...   \n",
-       "2  0x068f5c6a61780768455de69077e07e89787839bf8166...   \n",
-       "3                                                      \n",
-       "4  0x03fe2b97c1fd336e750087d68b9b867997fd64a2661f...   \n",
+       "                                        Collateral  \\\n",
+       "0  USDC: 113.3876, USDT: 4610.7524, STRK: 904.5577   \n",
+       "1                                      ETH: 0.0126   \n",
+       "2          ETH: 0.0311, USDC: 6.5088, USDT: 3.0144   \n",
+       "3                                                    \n",
+       "4        wBTC: 0.0018, DAI: 23.1396, USDT: 83.3628   \n",
        "\n",
-       "                                                Debt  \n",
-       "0  0x068f5c6a61780768455de69077e07e89787839bf8166...  \n",
-       "1                                                     \n",
-       "2                                                     \n",
-       "3                                                     \n",
-       "4                                                     "
+       "                                           Debt  \n",
+       "0  USDC: 10.0284, USDT: 10.0302, wstETH: 0.0006  \n",
+       "1                                                \n",
+       "2                                   ETH: 0.0005  \n",
+       "3                                                \n",
+       "4                                                "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -314,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "5ba21cdc-3a84-429d-a1e5-2380046230f5",
    "metadata": {},
    "outputs": [],
@@ -340,21 +338,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "id": "938f1b8f-eb9c-4508-9fa0-35eca038b3d0",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'ETH': 3664.7}\n",
+      "{'ETH': 3664.7, 'STRK': 0.6692467172063724}\n",
+      "{'ETH': 3664.7, 'STRK': 0.6692467172063724, 'USDT': 0.9979042580668577}\n",
+      "{'dai': {'usd': 1.0}, 'lords': {'usd': 0.206449}}\n",
+      "{'usd': 0.206449}\n",
+      "{'ETH': 3664.7, 'STRK': 0.6692467172063724, 'USDT': 0.9979042580668577, 'LORDS': 0.206449}\n",
+      "{'ETH': 3664.7, 'STRK': 0.6692467172063724, 'USDT': 0.9979042580668577, 'LORDS': 0.206449, 'WBTC': 96050.203262736}\n",
+      "{'dai': {'usd': 1.0}, 'lords': {'usd': 0.206449}}\n",
+      "{'usd': 1.0}\n",
+      "{'ETH': 3664.7, 'STRK': 0.6692467172063724, 'USDT': 0.9979042580668577, 'LORDS': 0.206449, 'WBTC': 96050.203262736, 'DAI': 1.0}\n",
+      "{'ETH': 3664.7, 'STRK': 0.6692467172063724, 'USDT': 0.9979042580668577, 'LORDS': 0.206449, 'WBTC': 96050.203262736, 'DAI': 1.0, 'USDC': 0.9979469343595746}\n",
+      "Some error occured\n",
+      "{'wrapped-steth': {'usd': 4347.19}}\n",
+      "{'ETH': 3664.7, 'STRK': 0.6692467172063724, 'USDT': 0.9979042580668577, 'LORDS': 0.206449, 'WBTC': 96050.203262736, 'DAI': 1.0, 'USDC': 0.9979469343595746, 'wstETH': 4347.19}\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "{'ETH': 3163.3,\n",
-       " 'WBTC': 87225.61094357829,\n",
-       " 'STRK': 0.4591000552127958,\n",
-       " 'USDC': 1.003373523112507,\n",
-       " 'USDT': 1.0035983791147356}"
+       "{'ETH': 3664.7,\n",
+       " 'STRK': 0.6692467172063724,\n",
+       " 'USDT': 0.9979042580668577,\n",
+       " 'LORDS': 0.206449,\n",
+       " 'WBTC': 96050.203262736,\n",
+       " 'DAI': 1.0,\n",
+       " 'USDC': 0.9979469343595746,\n",
+       " 'wstETH': 4347.19}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,47 +383,47 @@
    "source": [
     "COLLATERAL_TOKENS = [\"ETH\", \"WBTC\", \"STRK\", \"USDC\", \"USDT\", \"DAI\", \"LORDS\", \"wstETH\"]\n",
     "prices = {}\n",
-    "for token in COLLATERAL_TOKENS:\n",
-    "    price = fetch_prices(token)\n",
-    "    prices[token] = price\n",
+    "while(len(COLLATERAL_TOKENS) != 0):\n",
+    "    try:\n",
+    "        for token in COLLATERAL_TOKENS:\n",
+    "            price = fetch_prices(token)\n",
+    "            prices[token] = price\n",
+    "            print(prices)\n",
+    "            COLLATERAL_TOKENS.remove(token)\n",
+    "    except:\n",
+    "        url = 'https://api.coingecko.com/api/v3/simple/price'\n",
+    "        if token == 'DAI' or token == 'LORDS':\n",
+    "            token_ids = 'DAI,LORDS'\n",
+    "            params = {\n",
+    "                'ids': token_ids,\n",
+    "                'vs_currencies': 'usd'\n",
+    "            }\n",
+    "            response = requests.get(url, params=params)\n",
+    "            rem_prices = response.json()\n",
+    "            print(rem_prices)\n",
+    "            usd = rem_prices.get(token.lower())\n",
+    "            print(usd)\n",
+    "            prices[token] = usd['usd']\n",
+    "            COLLATERAL_TOKENS.remove(token)\n",
+    "            print(prices)\n",
+    "        else:\n",
+    "            token_ids = 'wrapped-steth'\n",
+    "            params = {\n",
+    "                'ids': token_ids,\n",
+    "                'vs_currencies': 'usd'\n",
+    "            }\n",
+    "            response = requests.get(url, params=params)\n",
+    "            rem_prices = response.json()\n",
+    "            print(rem_prices)\n",
+    "            usd = rem_prices.get('wrapped-steth')\n",
+    "            prices[token] = usd['usd']\n",
+    "            COLLATERAL_TOKENS.remove(token)\n",
+    "            print(prices)\n",
+    "    else:\n",
+    "        print(\"Some error occured\")\n",
     "prices"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "cc699d21-fde6-4265-93cd-be0970fe97ef",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Token Prices in USD: {'ETH': 3163.3, 'WBTC': 87225.61094357829, 'STRK': 0.4591000552127958, 'USDC': 1.003373523112507, 'USDT': 1.0035983791147356, 'DAI': 1.002, 'LORDS': 0.077801, 'WRAPPED-STETH': 3744.3}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# List of token IDs to fetch from CoinGecko (you can add more tokens if needed)\n",
-    "token_ids = 'DAI,LORDS,wrapped-steth'\n",
-    "\n",
-    "# API endpoint\n",
-    "url = 'https://api.coingecko.com/api/v3/simple/price'\n",
-    "params = {\n",
-    "    'ids': token_ids,\n",
-    "    'vs_currencies': 'usd'\n",
-    "}\n",
-    "response = requests.get(url, params=params)\n",
-    "rem_prices = response.json()\n",
-    "token_ids = token_ids.split(',')\n",
-    "for token, usd in rem_prices.items():\n",
-    "    price = usd['usd']\n",
-    "    for token_id in token_ids:\n",
-    "        if token.lower() == token_id.lower():\n",
-    "            prices[token_id.upper()] = price\n",
-    "print(\"Token Prices in USD:\", prices)\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "1f0db2bb-ca64-4e30-b3d5-9e864289a944",
@@ -414,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 25,
    "id": "57a5bed8-fb28-4ebc-83df-332021d03643",
    "metadata": {},
    "outputs": [
@@ -430,7 +451,7 @@
        "Name: User, dtype: int64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -450,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 26,
    "id": "084931be-14e4-4182-91dd-fa5701265967",
    "metadata": {
     "scrolled": true
@@ -480,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 27,
    "id": "e0abeedd-dd39-46f0-a5b7-c8989b53f67c",
    "metadata": {},
    "outputs": [],
@@ -490,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 28,
    "id": "e989070b-821a-41f2-84ea-2c4cd19ba8d1",
    "metadata": {},
    "outputs": [
@@ -560,7 +581,7 @@
        "3                    4               15"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -580,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 29,
    "id": "2853f77c-143b-4d6a-b584-20a515fa7d09",
    "metadata": {},
    "outputs": [],
@@ -613,21 +634,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "9af460f2-dc1a-427c-a564-944ef18499e6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get unique users providing liquidity\n",
     "liquidity_df = loans[loans['Collateral (USD)'] > 0]\n",
@@ -652,82 +662,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "ccdd4123-9a4e-4def-93a9-d8d21b637962",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Users borrowing:\n"
-     ]
-    },
-    {
-     "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>Number of Protocols</th>\n",
-       "      <th>Number of Users</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>114975</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>8029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>186</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Number of Protocols  Number of Users\n",
-       "0                    1           114975\n",
-       "1                    2             8029\n",
-       "2                    3              186\n",
-       "3                    4                3"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Subset the DataFrame for users who have debt\n",
     "debt_data = loans[loans['Debt (USD)'] > 0]\n",
@@ -766,21 +706,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "0895eb29-a63d-4fcd-998d-0f77ae444fce",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get unique users having debt\n",
     "debt_df = loans[loans['Debt (USD)'] > 0]\n",