BTC_analysis_timeBased.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b847886",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "268dd480",
   "metadata": {},
   "outputs": [],
   "source": [
    "parent_dir=\"/home/pranay5255/Desktop/Price_action_data_hourly/\"\n",
    "paFolder_files=os.listdir(parent_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "425b2848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2022', '2019', '2018', '2021', '2020', '2023']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paFolder_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4dd5fe10",
   "metadata": {},
   "outputs": [],
   "source": [
    "def TickerWiseDataFetch_PandasTransform(ticker,timeframe):\n",
    "    main_df=pd.DataFrame()\n",
    "\n",
    "    yearly_dict={}\n",
    "    parent_dir=\"/home/pranay5255/Desktop/Price_action_data_hourly/\"\n",
    "    paFolder_files=os.listdir(parent_dir)\n",
    "    for folder in paFolder_files:\n",
    "        yearly_dir=os.path.join(parent_dir,folder)\n",
    "        _files=os.listdir(yearly_dir)\n",
    "        for file in _files:\n",
    "            # print(file)\n",
    "            if ticker in file and timeframe in file:\n",
    "                yearly_dict[folder]=pd.read_csv(os.path.join(yearly_dir,file),index_col=[0]).reset_index()\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    for year in yearly_dict:\n",
    "        main_df=pd.concat([main_df,yearly_dict[year]],ignore_index=True)\n",
    "        main_df.sort_values(by='timestamp',inplace=True)\n",
    "        print(year)\n",
    "    \n",
    "\n",
    "    return(main_df)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "248e0cd5",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d96f1e25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022\n",
      "2019\n",
      "2018\n",
      "2021\n",
      "2020\n",
      "2023\n",
      "2022\n",
      "2019\n",
      "2018\n",
      "2021\n",
      "2020\n",
      "2023\n",
      "2022\n",
      "2021\n",
      "2023\n",
      "2022\n",
      "2021\n",
      "2023\n"
     ]
    }
   ],
   "source": [
    "BTC_data=TickerWiseDataFetch_PandasTransform(ticker='BTC',timeframe='1h')\n",
    "ETH_data=TickerWiseDataFetch_PandasTransform(ticker='ETH',timeframe='1h')\n",
    "AAVE_data=TickerWiseDataFetch_PandasTransform(ticker='AAVE',timeframe='1h')\n",
    "ATOM_data=TickerWiseDataFetch_PandasTransform(ticker='ATOM',timeframe='1h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73dafb95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0fb2fa95",
   "metadata": {},
   "outputs": [],
   "source": [
    "price_data={\"BTC\": BTC_data['close'].pct_change(),\n",
    "\"ETH\": ETH_data['close'].pct_change(),\n",
    "\"AAVE\": AAVE_data['close'].pct_change(),\n",
    "\"ATOM\": ATOM_data['close'].pct_change()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "538c07c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def create_correlation_heatmap(price_data):\n",
    "    df = pd.DataFrame(price_data)\n",
    "    correlation_matrix = df.corr()\n",
    "    plt.figure(figsize=(15,10))\n",
    "    sns.heatmap(correlation_matrix, annot=True)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "\n",
    "create_correlation_heatmap(price_data=price_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bc76175b",
   "metadata": {},
   "outputs": [],
   "source": [
    "BTC_data_test=BTC_data[BTC_data['timestamp'] >'2023-01-01 00:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7abfe258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43794    16551.47\n",
       "43795    16548.19\n",
       "43796    16533.04\n",
       "43797    16521.85\n",
       "43798    16530.71\n",
       "           ...   \n",
       "46356    29358.31\n",
       "46357    29464.43\n",
       "46358    29462.67\n",
       "46359    29495.28\n",
       "46360    29495.29\n",
       "Name: close, Length: 2567, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "BTC_data_test['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "48dcc55a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2566 8.26434918199403 1057.8366952952358\n",
      "[[ 0.53971014  0.52196745  0.48862108 ...  0.34036523  0.35971745\n",
      "   0.36830316]\n",
      " [ 0.56943231  0.55278041  0.52128545 ...  0.36039898  0.37841896\n",
      "   0.38635825]\n",
      " [ 0.60196423  0.58647872  0.55688541 ...  0.38084717  0.39728836\n",
      "   0.40439173]\n",
      " ...\n",
      " [73.72042877 73.75945406 73.79815378 ... 20.76485685 20.71616365\n",
      "  20.66742367]\n",
      " [74.25419599 74.28615332 74.31782547 ... 24.98315573 24.93007026\n",
      "  24.87694539]\n",
      " [93.76878878 93.79264227 93.81627931 ... 46.39844463 46.36155573\n",
      "  46.32465166]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pycwt as wavelet\n",
    "\n",
    "# Prepare the data\n",
    "# We will use the closing price for our analysis\n",
    "price = BTC_data_test['close'].values\n",
    "time = np.arange(len(price))\n",
    "\n",
    "# We will assume that we have one observation per day\n",
    "dt = 1.0\n",
    "\n",
    "# We subtract the mean and divide by the standard deviation to normalize the data\n",
    "std = price.std()  \n",
    "mean = price.mean()\n",
    "price = (price - mean) / std\n",
    "\n",
    "# The following parameters are defined for the wavelet analysis\n",
    "mother = wavelet.Morlet(6)  # Morlet mother wavelet with m=6\n",
    "s0 = 8 * dt  # Starting scale, in this case 2 days\n",
    "dj = 1 / 12  # Twelve sub-octaves per octaves\n",
    "J = 7 / dj  # Seven powers of two with dj sub-octaves\n",
    "alpha = 0.3  # Lag-1 autocorrelation for white noise\n",
    "\n",
    "# Perform the wavelet transform\n",
    "wave, scales, freqs, coi, fft, fftfreqs = wavelet.cwt(price, dt, dj, s0, J, mother)\n",
    "power = (np.abs(wave)) ** 2\n",
    "fft_power = np.abs(fft) ** 2\n",
    "period = 1 / freqs\n",
    "\n",
    "print(max(time), min(period), max(period))\n",
    "print(power)\n",
    "# Plot the results\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.imshow(power, extent=[0, max(time), min(period), max(period)], aspect='auto', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('BTC Price - Wavelet Power Spectrum')\n",
    "plt.xlabel('Time (hours)')\n",
    "plt.ylabel('Period (hours)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5872d278",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x=10783536690092973843715749\n",
    "exponent_gwei=1e-17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b3eafa65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85, 2567)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "578c41d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0000000000000007"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.power(x,exponent_gwei)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "08fb86b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assume you have a time series data\n",
    "data = BTC_data_test['close']\n",
    "\n",
    "# Detrend and normalize data\n",
    "mean = np.mean(data)\n",
    "std = np.std(data)\n",
    "data = (data - mean) / std\n",
    "\n",
    "# Apply FFT\n",
    "fft_result = np.fft.fft(data)\n",
    "\n",
    "# Calculate power spectrum\n",
    "power_spectrum = np.abs(fft_result)**2 / len(data)\n",
    "\n",
    "# Get the frequencies\n",
    "frequencies = np.fft.fftfreq(len(data))\n",
    "\n",
    "# Plot the power spectrum\n",
    "plt.plot(frequencies, power_spectrum)\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylabel('Power')\n",
    "plt.title('Power Spectrum')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d21f709b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fbe697f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80bea570",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8b401f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc491ea9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfaaf004",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7a42490",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc516c30",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'result'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/home/pranay5255/BTC_Analysis_EDA/BTC_analysis_timeBased.ipynb Cell 22\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/pranay5255/BTC_Analysis_EDA/BTC_analysis_timeBased.ipynb#X30sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     your_contract_abi \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39mload(f)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/pranay5255/BTC_Analysis_EDA/BTC_analysis_timeBased.ipynb#X30sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39m# Access and use the abi variable in your code\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/pranay5255/BTC_Analysis_EDA/BTC_analysis_timeBased.ipynb#X30sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mprint\u001b[39m(abi\u001b[39m.\u001b[39;49mresult)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'result'"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('../Desktop/maker_dao_abi.json', 'r') as f:\n",
    "    your_contract_abi = json.load(f)\n",
    "\n",
    "# Access and use the abi variable in your code\n",
    "print(abi.result)  # Print the contract ABI\n",
    "# ... Other code using the contract ABI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "76211d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"constant\":true,\"inputs\":[],\"name\":\"name\",\"outputs\":[{\"name\":\"\",\"type\":\"bytes32\"}],\"payable\":false,\"stateMutability\":\"view\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[],\"name\":\"stop\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"guy\",\"type\":\"address\"},{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"approve\",\"outputs\":[{\"name\":\"\",\"type\":\"bool\"}],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"owner_\",\"type\":\"address\"}],\"name\":\"setOwner\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":true,\"inputs\":[],\"name\":\"totalSupply\",\"outputs\":[{\"name\":\"\",\"type\":\"uint256\"}],\"payable\":false,\"stateMutability\":\"view\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"src\",\"type\":\"address\"},{\"name\":\"dst\",\"type\":\"address\"},{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"transferFrom\",\"outputs\":[{\"name\":\"\",\"type\":\"bool\"}],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":true,\"inputs\":[],\"name\":\"decimals\",\"outputs\":[{\"name\":\"\",\"type\":\"uint256\"}],\"payable\":false,\"stateMutability\":\"view\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"guy\",\"type\":\"address\"},{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"mint\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"burn\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"name_\",\"type\":\"bytes32\"}],\"name\":\"setName\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":true,\"inputs\":[{\"name\":\"src\",\"type\":\"address\"}],\"name\":\"balanceOf\",\"outputs\":[{\"name\":\"\",\"type\":\"uint256\"}],\"payable\":false,\"stateMutability\":\"view\",\"type\":\"function\"},{\"constant\":true,\"inputs\":[],\"name\":\"stopped\",\"outputs\":[{\"name\":\"\",\"type\":\"bool\"}],\"payable\":false,\"stateMutability\":\"view\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"authority_\",\"type\":\"address\"}],\"name\":\"setAuthority\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":true,\"inputs\":[],\"name\":\"owner\",\"outputs\":[{\"name\":\"\",\"type\":\"address\"}],\"payable\":false,\"stateMutability\":\"view\",\"type\":\"function\"},{\"constant\":true,\"inputs\":[],\"name\":\"symbol\",\"outputs\":[{\"name\":\"\",\"type\":\"bytes32\"}],\"payable\":false,\"stateMutability\":\"view\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"guy\",\"type\":\"address\"},{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"burn\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"mint\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"dst\",\"type\":\"address\"},{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"transfer\",\"outputs\":[{\"name\":\"\",\"type\":\"bool\"}],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"dst\",\"type\":\"address\"},{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"push\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"src\",\"type\":\"address\"},{\"name\":\"dst\",\"type\":\"address\"},{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"move\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[],\"name\":\"start\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":true,\"inputs\":[],\"name\":\"authority\",\"outputs\":[{\"name\":\"\",\"type\":\"address\"}],\"payable\":false,\"stateMutability\":\"view\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"guy\",\"type\":\"address\"}],\"name\":\"approve\",\"outputs\":[{\"name\":\"\",\"type\":\"bool\"}],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"constant\":true,\"inputs\":[{\"name\":\"src\",\"type\":\"address\"},{\"name\":\"guy\",\"type\":\"address\"}],\"name\":\"allowance\",\"outputs\":[{\"name\":\"\",\"type\":\"uint256\"}],\"payable\":false,\"stateMutability\":\"view\",\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"src\",\"type\":\"address\"},{\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"pull\",\"outputs\":[],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"name\":\"symbol_\",\"type\":\"bytes32\"}],\"payable\":false,\"stateMutability\":\"nonpayable\",\"type\":\"constructor\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"name\":\"guy\",\"type\":\"address\"},{\"indexed\":false,\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"Mint\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"name\":\"guy\",\"type\":\"address\"},{\"indexed\":false,\"name\":\"wad\",\"type\":\"uint256\"}],\"name\":\"Burn\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"name\":\"authority\",\"type\":\"address\"}],\"name\":\"LogSetAuthority\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"name\":\"owner\",\"type\":\"address\"}],\"name\":\"LogSetOwner\",\"type\":\"event\"},{\"anonymous\":true,\"inputs\":[{\"indexed\":true,\"name\":\"sig\",\"type\":\"bytes4\"},{\"indexed\":true,\"name\":\"guy\",\"type\":\"address\"},{\"indexed\":true,\"name\":\"foo\",\"type\":\"bytes32\"},{\"indexed\":true,\"name\":\"bar\",\"type\":\"bytes32\"},{\"indexed\":false,\"name\":\"wad\",\"type\":\"uint256\"},{\"indexed\":false,\"name\":\"fax\",\"type\":\"bytes\"}],\"name\":\"LogNote\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"name\":\"from\",\"type\":\"address\"},{\"indexed\":true,\"name\":\"to\",\"type\":\"address\"},{\"indexed\":false,\"name\":\"value\",\"type\":\"uint256\"}],\"name\":\"Transfer\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"name\":\"owner\",\"type\":\"address\"},{\"indexed\":true,\"name\":\"spender\",\"type\":\"address\"},{\"indexed\":false,\"name\":\"value\",\"type\":\"uint256\"}],\"name\":\"Approval\",\"type\":\"event\"}]'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abi['result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a21976d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PAAAAAEKO4AcAAAAAIUfwA4AezDNdAAAA0CMQ/AAAAAAg5Ah+ANCDWaYLAAAAegSCHwAAAACEHMEPAHowxvgBAIBUEPwAAAAAIOQIfgDQgzHGDwAApILgBwAAAAAhR/ADgB6MMX4AACAVBD8AAAAACDmCHwD0YIzxAwAAqSD4AQAAAEDIEfwAoAdjjB8AAEgFwQ8AAAAAQo7gBwA9GGP8AABAKgh+AAAAABByBD8A6MEY4wcAAFJB8AMAAACAkCP4AUAPxhg/AACQCoIfAAAAAIQcwQ8AejDG+AEAgFQQ/AAAAAAg5Ah+ANCDMcYPAACkguAHAAAAACFH8AOAHowxfgAAIBUEPwAAAAAIOYIfAPRgjPEDAACpIPgBAAAAQMgR/ACgB2OMHwAASAXBDwAAAABCjuAHAD0YY/wAAEAqCH4AAAAAEHIEPwDowRjjBwAAUkHwAwAAAICQI/gBQA/GGD8AAJAKgh8AAAAAhBzBDwB6MMb4AQCAVBD8AAAAACDk0hb8zOweM9tuZks7mH+mmVWY2cLg78cJ82aY2SozKzCza9NVRgDo6RjjBwAAUpHOFr/7JM3YzTKvufvRwd8NkmRm2ZL+LOl8SYdKuszMDk1jOQEAAAAg1NIW/Nz9VUlle/HQEyQVuPtad2+U9JCkizq1cAAQEozxAwAAqcj0GL+TzWyRmT1jZocF08ZJ2pSwTGEwrV1mdo2ZzTez+cXFxeksKwAAAAD0SJkMfu9ImuTuR0n6k6TH9+ZJ3P1Od5/u7tNHjhzZmeUDgG6PMX4AACAVGQt+7l7p7tXB7acl5ZrZCElFkiYkLDo+mAYAAAAA2AsZC35mNtrMLLh9QlCWUknzJB1oZlPMLE/SpZJmZqqcANCdMcYPAACkIiddT2xmD0o6U9IIMyuUdL2kXEly99slfVzSl82sWVKdpEvd3SU1m9lXJc2WlC3pHndflq5yAgAAAEDYpS34uftlu5l/q6RbO5j3tKSn01EuAAgTxvgBAIBUZPqsngAAAACANCP4AUAPxhg/AACQCoIfAAAAAIQcwQ8AejDG+AEAgFQQ/AAAAAAg5Ah+ANCDMcYPAACkguAHAAAAACFH8AOAHowxfgAAIBUEPwAAAAAIOYIfAPRgjPEDAACpIPgBAAAAQMgR/ACgB2OMHwAASAXBDwAAAABCjuAHAD0YY/wAAEAqCH4AAAAAEHIEPwDowRjjBwAAUkHwAwAAAICQI/gBQA/GGD8AAJAKgh8AAAAAhBzBDwB6MMb4AQCAVBD8AAAAACDkCH4A0IMxxg8AAKSC4AcAAAAAIUfwA4AejDF+AAAgFQQ/AAAAAAg5gh8A9GCM8QMAAKkg+AEAAABAyBH8AKAHY4wfAABIBcEPAAAAAEKO4AcAPRhj/AAAQCoIfgAAAAAQcgQ/AOjBGOMHAABSQfADAAAAgJAj+AFAD8YYPwAAkAqCHwAAAACEHMEPAHowxvgBAIBUEPwAAAAAIOQIfgDQgzHGDwAApILgBwAAAAAhR/ADgB6MMX4AACAVBD8AAAAACDmCHwD0YIzxAwAAqSD4AQAAAEDIEfwAoAdjjB8AAEgFwQ8AejC6egIAgFQQ/AAAAAAg5Ah+AAAAABByBD8A6MEY4wcAAFJB8AOAHowxfgAAIBUEPwAIAVr+AABAMgQ/AAgBWv4AAEAyBD8A6MFo6QMAAKkg+AFAD0ZLHwAASEXagp+Z3WNm281saQfzP21mi81siZnNMbOjEuatD6YvNLP56SojAIQFLX8AACCZdLb43SdpRpL56ySd4e5HSLpR0p1t5p/l7ke7+/Q0lQ8AQoOWPwAAkExOup7Y3V81s8lJ5s9JuDtX0vh0lQUAwoqWPgAAkIruMsbvaknPJNx3Sc+a2QIzuybZA83sGjObb2bzi4uL01pIAOhuaOkDAACpSFuLX6rM7CxFg9+pCZNPdfciMxsl6TkzW+nur7b3eHe/U0E30enTp3MMBKBXouUPAAAkk9EWPzM7UtJdki5y99LYdHcvCv5vl/SYpBMyU0IA6Bmo9QIAAMlkLPiZ2URJj0r6jLu/lzC9v5kNjN2WdK6kds8MCgC9HS19AAAgFWnr6mlmD0o6U9IIMyuUdL2kXEly99sl/VjScEm3mZkkNQdn8NxP0mPBtBxJ/3T3WekqJwD0ZLT0AQCAVKTzrJ6X7Wb+5yV9vp3payUdtesjAAAdoeUPAAAk013O6gkA2Ae0/AEAgGQIfgDQg9HSBwAAUkHwA4AejJY+AACQCoIfAIQALX8AACAZgh8AhAAtfwAAIBmCHwD0YLT0AQCAVBD8AKAHo6UPAACkguAHACFAyx8AAEiG4AcAIUDLHwAASIbgBwA9GC19AAAgFQQ/AOjBaOkDAACpIPgBQAjQ8gcAAJIh+AFACNDyBwAAkiH4AUAPRksfAABIBcEPAHowWvoAAEAqCH4AEAK0/AEAgGQIfgAQArT8AQCAZAh+ANCD0dIHAABSQfADgB6Mlj4AAJAKgh8AAAAAhBzBDwDCwGn7AwAAHSP4AUAPxhg/AACQipSCn5mdamZXBbdHmtmU9BYLAJAK2vkAAEAqdhv8zOx6Sf8n6bpgUq6kf6SzUAAAAACAzpNKi99HJV0oqUaS3H2zpIHpLBQAYM/Q8gcAAJJJJfg1ursrOK4ws/7pLRIAIFWM8QMAAKlIJfj9y8zukDTEzL4g6XlJf01vsQAAqaClDwAApCJndwu4+6/N7AOSKiVNk/Rjd38u7SUDAAAAAHSK3QY/SQqCHmEPALopLuMHAACS2W3wM7Mq7exNlKfoWT1r3H1QOgsGANg9xvgBAIBUpNLVM34GTzMzSRdJOimdhQIApIaGPgAAkIqULuAe41GPSzovPcUBAAAAAHS2VLp6XpxwN0vSdEn1aSsRAGCPOYP8AABAEqmc3OXDCbebJa1XtLsnACDDGOMHAABSkcoYv6u6oiAAgD1HOx8AAEhFh8HPzP6kJMcU7v71tJQIAAAAANCpkrX4ze+yUgAA9gktfwAAIJkOg5+7/60rCwIA2HOM8QMAAKlI5ayeIyX9n6RDJeXHprv72WksFwAgBbT0AQCAVKRyHb8HJK2QNEXSTxU9q+e8NJYJAAAAANCJUgl+w939bklN7v6Ku39OEq19ANCNcBk/AACQTCrX8WsK/m8xswskbZY0LH1FAgCkijF+AAAgFcku55Dr7k2SfmZmgyV9W9KfJA2S9M0uKh8AIAka+gAAQCqStfgVmdlMSQ9KqnT3pZLO6ppiAQAAAAA6S7IxfocoehKXH0raZGZ/MLOTuqZYAIA9QcsfAABIpsPg5+6l7n6Hu58l6QRJayX9zszWmNnPu6yEAIAOMcYPAACkIpWzesrdN0u6W9JfJFVJ+nw6CwUASA0tfQAAIBVJg5+Z5ZvZJ8zsUUkFil7G4VpJY7uicAAAAACAfZfsrJ7/lHSOpFcUvYj7p9y9vqsKBgBInXMhPwAAkESys3rOkvRFd6/qqsIAAPYMY/wAAEAqOgx+7n5/VxYEALDnaOcDAACpSOnkLgAAAACAnmt3J3fJMrNTuqowAAAAAIDOlzT4uXuLpD/v7ZOb2T1mtt3MlnYw38zsj2ZWYGaLzezYhHlXmNnq4O+KvS0DAIQZY/wAAEAqUunq+YKZfczM9ub44j5JM5LMP1/SgcHfNYpeJ1BmNkzS9ZJOVPTi8deb2dC9WD8AhBpj/AAAQCpSCX5flPRvSY1mVmlmVWZWmcqTu/urksqSLHKRpPs9aq6kIWY2RtJ5kp5z9zJ33yHpOSUPkADQq3E1BwAAkEyyyzlIktx9YBrXP07SpoT7hcG0jqbvwsyuUbS1UBMnTkxPKQGgm6KrJwAASMVuW/yCcXiXm9mPgvsTzOyE9BctNe5+p7tPd/fpI0eOzHRxAAAAAKDbSaWr522STpb0qeB+tfbhhC9tFEmakHB/fDCto+kAgAT08AQAAKlIJfid6O5fkVQvScGYu7xOWv9MSZ8NWhVPklTh7lskzZZ0rpkNDU7qcm4wDQDQDicCAgCAJHY7xk9Sk5llK6hYNrORklpSeXIze1DSmZJGmFmhomfqzJUkd79d0tOSPiipQFKtpKuCeWVmdqOkecFT3eDuyU4SAwC9EmP8AABAKlIJfn+U9JikUWb2c0kfl/TDVJ7c3S/bzXyX9JUO5t0j6Z5U1gMAAAAA6FiHwc/Mprj7Ond/wMwWSHq/opXLH3H3FV1WQgBAh+jgCQAAUpGsxe8/ko4zsxfc/f2SVnZRmQAAe4jr+AEAgGSSBb8sM/u+pIPM7FttZ7r7b9NXLABAKhjjBwAAUpHsrJ6XSoooGg4HtvMHAAAAAOgBOmzxc/dVkn5pZovd/ZkuLBMAIEX08AQAAKlI5Tp+B5nZoOBae3eb2Ttmdm7aSwYASBkBEAAAJJNK8Pucu1cqehH1YZI+I+nmtJYKAJASxvgBAIBUpBL8YscVH5R0v7svE8caAAAAANBjpBL8FpjZs4oGv9lmNlBSS3qLBQBIBV08AQBAKpJdziHmaklHS1rr7rVmNlzSVWktFQBgj3AdPwAAkMxug5+7t5jZNkmHmlkqQREA0EXodw8AAFKx2yBnZr+UdImk5Ype10+K9i56NY3lAgCkgIY+AACQilRa8D4iaZq7N6S5LAAAAACANEjl5C5rJeWmuyAAgL3ntP0BAIAkUmnxq5W00MxekBRv9XP3r6etVACAlDDGDwAApCKV4Dcz+AMAdDO08wEAgFSkclbPv3VFQQAAAAAA6ZHKWT0PlHSTpEMl5cemu/vUNJYLALAHuI4fAABIJpWTu9wr6S+SmiWdJel+Sf9IZ6EAAKlhjB8AAEhFKsGvr7u/IMncfYO7/0TSBektFgAgFTT0AQCAVKRycpcGM8uStNrMviqpSNKA9BYLAAAAANBZUmnx+19J/SR9XdJxki6XdEU6C4V9F2lx3f7KGtU2Nme6KAAAAAAyLGnwM7NsSZe4e7W7F7r7Ve7+MXef20Xlw1767+LNuvmZlbpl9qpMFwVAGjHGDwAApKLD4GdmOe4ekXRqF5YHnaS+KSJJqmmgxQ8IM8b4AQCAVCQb4/e2pGMlvWtmMyX9W1JNbKa7P5rmsgEAAAAAOkEqJ3fJl1Qq6WxFK5ct+E/wA4BuwrmQHwAASCJZ8BtlZt+StFQ7A18MRxgA0A0wxg8AAKQiWfDLVvSyDe0dVxD8AKAb4MsYAACkIlnw2+LuN3RZSQAAAAAAaZHscg70IAKAHoIhfgAAIJlkwe/9XVYKAMBeoYYOAACkosPg5+5lXVkQAMCeo6EPAACkIlmLHwAAAAAgBAh+ABACtPwBAIBkCH4A0IMxxg8AAKSC4AcAPRgtfQAAIBUEPwAAAAAIOYIfAIQA1/EDAADJEPwAoAdjjB8AAEgFwQ8AejAa+gAAQCoIfgAAAAAQcgQ/AAgBp+0PAAAkQfADgB6MMX4AACAVBD8A6MFo5wMAAKkg+AEAAABAyBH8ACAEuI4fAABIhuAHAD0YY/wAAEAqCH4A0IPR0AcAAFJB8AMAAACAkCP4AUAI0PIHAACSIfgBAAAAQMgR/ACgB+NsngAAIBUEPwAAAAAIubQGPzObYWarzKzAzK5tZ/7vzGxh8PeemZUnzIskzJuZznICQI9H0x8AAEgiJ11PbGbZkv4s6QOSCiXNM7OZ7r48toy7fzNh+a9JOibhKerc/eh0lQ8AAAAAeot0tvidIKnA3de6e6OkhyRdlGT5yyQ9mMbyAEAI0dIHAAB2L53Bb5ykTQn3C4NpuzCzSZKmSHoxYXK+mc03s7lm9pGOVmJm1wTLzS8uLu6EYgMAAABAuHSXk7tcKuk/7h5JmDbJ3adL+pSk35vZ/u090N3vdPfp7j595MiRXVFWAOh2aPcDAADJpDP4FUmakHB/fDCtPZeqTTdPdy8K/q+V9LJaj/8DAAAAAKQoncFvnqQDzWyKmeUpGu52OTunmR0saaikNxOmDTWzPsHtEZLeJ2l528cCQG/HyTwBAEAq0nZWT3dvNrOvSpotKVvSPe6+zMxukDTf3WMh8FJJD7m3Onw5RNIdZtaiaDi9OfFsoAAAAACA1KUt+EmSuz8t6ek2037c5v5P2nncHElHpLNsABAmtPwBAIBkusvJXQAAAAAAaULwA4AejJY+AACQCoIfAAAAAIQcwQ8AQsC5kh8AAEiC4AcAAAAAIUfwA4AejJY+AACQCoIfAAAAAIQcwQ8AQoCzewIAgGQIfgAAAAAQcgQ/AOjBaOkDAACpIPgBAAAAQMgR/AAgBGj4AwAAyRD8AAAAACDkCH4A0IPR0gcAAFJB8AMAAACAkCP4AUAIcHZPAACQDMEPAAAAAEKO4AcAPRgtfQAAIBUEPwAAAAAIOYIfAISAc35PAACQBMEPAAAAAEKO4AcAPRgtfQAAIBUEPwAAAAAIOYIfAIQBDX8AACAJgh8AAAAAhBzBDwB6Mlr6AABACgh+AAAAABByBD8ACAEa/gAAQDIEPwAAAAAIOYIfAPRgtPQBAIBUEPwAAAAAIOQIfgAQAu60/QEAgI4R/AAAAAAg5Ah+ANCD0dIHAABSQfADAAAAgJAj+AFACNDwBwAAkiH4AQAAAEDIEfwAoAejoQ8AAKSC4AcAAAAAIUfwCznG/QC9A7s6AABIhuAHAD0YlTsAACAVBL+QM8t0CQAAAABkGsEPAAAAAEKO4AcAIUCXTwAAkAzBDwB6MPIeAABIBcEPAAAAAEKO4AcAAAAAIUfwCynG+wC9i9PpEwAAJEHwA4AezKnlAQAAKSD4hRSHggAAAABiCH4AAAAAEHIEv5Ci9xfQu7DPAwCAZAh+IcWJHoDegT0dAACkIq3Bz8xmmNkqMysws2vbmX+lmRWb2cLg7/MJ864ws9XB3xXpLCcAAAAAhFlOup7YzLIl/VnSByQVSppnZjPdfXmbRR9296+2eewwSddLmq5ohfaC4LE70lXesKHbFwAAAICYdLb4nSCpwN3XunujpIckXZTiY8+T9Jy7lwVh7zlJM9JUTgAAAAAItXQGv3GSNiXcLwymtfUxM1tsZv8xswl7+FiZ2TVmNt/M5hcXF3dGuUOBBj+gl2BnBwAAKcj0yV2elDTZ3Y9UtFXvb3v6BO5+p7tPd/fpI0eO7PQCAgAAAEBPl87gVyRpQsL98cG0OHcvdfeG4O5dko5L9bHYDQb5AQAAAAikM/jNk3SgmU0xszxJl0qambiAmY1JuHuhpBXB7dmSzjWzoWY2VNK5wTSkiNgH9C5OZQ8AAEgibWf1dPdmM/uqooEtW9I97r7MzG6QNN/dZ0r6upldKKlZUpmkK4PHlpnZjYqGR0m6wd3L0lVWAOipuGYnAABIRdqCnyS5+9OSnm4z7ccJt6+TdF0Hj71H0j3pLF+YUfkPAAAAICbTJ3cBAAAAAKQZwS+kGO8D9C7s8QAAIBmCHwD0YNTxAACAVBD8QopjQQAAAAAxBL+QohUAAAAAQAzBDwBCgMoeAACQDMEvpDgGBHoHAh8AAEgFwQ8AAAAAQo7gF1JczgEAAABADMEPAELA6eANAACSIPgBQA9G4AMAAKkg+IUUPT0BAAAAxBD8AAAAACDkCH4hRfcvoHehlR8AACRD8AOAHozABwAAUkHwCykOBgEAAADEEPwAAAAAIOQIfiFFgx/Qu7DPAwCAZAh+IUVXT6B3YFcHAACpIPgBAAAAQMgR/EKKyzkAAAAAiCH4AUAI0L0bAAAkQ/ALKQ4Cgd6BfR0AAKSC4AcAAAAAIUfwAwAAAICQI/iFlNP/C+hl2OcBAEDHCH4A0KMR+AAAwO4R/EKKBj8AAAAAMQQ/AAAAAAg5gl9I0eAH9C608gMAgGQIfgDQgxH4AABAKgh+IcXBIAAAAIAYgl9IOZ09AQAAAAQIfgAQArTyAwCAZAh+IcVBINA7sKsDAIBUEPxCjgAIAAAAgOAXUuQ9AAAAADEEv5AjAAK9Ayd0AgAAyRD8wiro40lXTyDcnJ0cAACkgOAXUhwKAgAAAIgh+IUc3b8AAAAAEPxCit5fQO/CPg8AAJIh+IUdB4NAqLGLAwCAVBD8QirWxZODQgAAAAAEPwAAAAAIOYJfSMXG+3Cqd6B3YE8HAADJEPxCioNAoHegbgcAAKSC4BdyHBMCAAAAIPiFFK0AAAAAAGIIfiFHAAR6B/Z1AACQDMEvpLicA9A7sI8DAIBUEPwAAAAAIOTSGvzMbIaZrTKzAjO7tp353zKz5Wa22MxeMLNJCfMiZrYw+JuZznKGEpdzAAAAABDISdcTm1m2pD9L+oCkQknzzGymuy9PWOxdSdPdvdbMvizpV5IuCebVufvR6Spf2BH3gN7F2esBAEAS6WzxO0FSgbuvdfdGSQ9JuihxAXd/yd1rg7tzJY1PY3l6JQ4FgXCjVR8AAKQincFvnKRNCfcLg2kduVrSMwn3881svpnNNbOPdPQgM7smWG5+cXHxPhU4TDgYBAAAABCTtq6ee8LMLpc0XdIZCZMnuXuRmU2V9KKZLXH3NW0f6+53SrpTkqZPn07aaYstAgAAAPR66WzxK5I0IeH++GBaK2Z2jqQfSLrQ3Rti0929KPi/VtLLko5JY1lDJ9bgx7gfoJdgVwcAAEmkM/jNk3SgmU0xszxJl0pqdXZOMztG0h2Khr7tCdOHmlmf4PYISe+TlHhSGAAAAABAitLW1dPdm83sq5JmS8qWdI+7LzOzGyTNd/eZkm6RNEDSv81Mkja6+4WSDpF0h5m1KBpOb25zNlDsRqzyn6F+AAAAANI6xs/dn5b0dJtpP064fU4Hj5sj6Yh0li3sCHwAAAAAYtJ6AXdkHgEQ6B3Y1QEAQDIEv5DipC5A70DlDgAASAXBL+QIgAAAAAAIfiEVv5wDuQ8AAADo9Qh+ABACTi0PAABIguAXchwKAuFGd24AAJAKgl9IUfsPAAAAIIbgF3LkPwAAAAAEv5Dydm4BCJ/4iZwyWwwAANDNEfwAAAAAIOQIfiHF5RwAAAAAxBD8AAAAACDkCH4hFTvFOw1+QLjRug8AAFJB8AspDgIBAAAAxBD8Qo7r+QEAAAAg+IWUt/kPAAAAoPci+AFAD8Z4XgAAkAqCX0hxwgcAQG9U3xTR5Guf0gNvbch0UQCgWyH4AQCA0CiraZQk3fpiQYZLAgDdC8EvtOj+BQDoffjdA4D2EfxCii6eQO+ws1s3Oz2QyDJdAADoZgh+IcfBIIB0uOCPr+mLf5+f6WIAAIAU5WS6AEgP8h6AdFq2uVLLNldmuhjALlpa+AEEgPbQ4gcAAEKjhZpPAGgXwS+k4tf24vcPCDVv8x/o7Zpp8QOAdhH8AABAaEQIfgDQLoJfSMXP9Ec7AACgF4kFPzPO6wkAiQh+IUXcAwD0RrT4AUD7CH4hxxg/IOQY5Ae0QvADgPYR/EJq50WdM1sOAAC6Eid3AYD2EfwAAPukvimixYXlmS4GIInLOQBARwh+IRW/nAP9vwCk2fVPLNOFt76hzeV1mS4KoOZI7OQuGS4IAHQzBD8A6MG6QyXP8i2VkqTtVQ0ZKwMQwxi/8Iq0uJwWXWCvEfzCKiRj/N5cU6rTfvWi6hojcnfN+P2remrxlkwXq0s8+PZGfeL2OfH7v569St946N0MlghoX7+8bElSbUNzhkvS860rqdHxP38+3np61b1v685X12S4VD1LJA0/fD96fKl+MnNZ/P7n7punu15b2+nrQcfKahq1//ef1t/mrM/I+rdX1eukX7yg1duqJElLiyp08k0vaEdNY0bK05G/z92g9938opYWVWS6KOiGCH4h1cPzXtzPnlquTWV1KtherYbmFq3cWqVv/mthpovVJa57dInmrd8Rv3/rSwV6fOHmDJYIaF//PjmSpGqC3z57YO4GFVc1xCu4XlpVrF88vTLDpepZIi0tnf6cf5+7QfclBI4XV27Xz55a0enrQcfKgoD1tzc3ZGT9s5dt09bKet0bfA7+9OJqbamo15trSzNSno488W6RisrrtCLoiQEkIviFXE8PgLGKW7OEM7X19BcF9HAtbbrSxYJfTSPBr7MwPnvvRTo/96EbyM6KDtqsqGvKyPqbmqMfrNygHLGxpLnZ3etQOvbN0RThOwS76l6fVnSaeB/4Hr7fJxY/9qXb287YxngGJJOJS7e07UrXP+jqWVGbmQMyIFGsxS9dJ3dpW/ERRq+tLtbka5/SprLaTBclrilI9JUZCn7NwecqJwh6TS2x4Ne9ziIUO0ZqTkPLN3o+gh+6tVjoMZOagi+x8P/ktkatHbqbtifP6BsLfnW0+O0rzkS579Ld4lfXFEnvCrqBf80vlCS9s3HHbpbsOrHgl6nrNMZ+i3OCoBerjM7J6p6H0o3NBD/sqnt+WrHPPP4/HKEh0uLxL93e1gLWRL8ldDNtg19sl8xUF6wwcu9933WdJd0tHbWN4Q9+3fGzl+lK0HjXziDo7Qyi3es3OvbWZXp7oXsi+KFbi3VZaIq0JHT1zGSJuh7Br/tbUlihydc+pVtfXK3J1z6lNcXVmS5SWrWtcY8Fwd7QEpJultDkx4Hb3ol9Hk3paT6t6wXBrztqzvBvYSzgxcb0xX6bu1vLWiy0Z3p7oXsi+IVUJsb9pFNTxLtdrVpX4eCv+3tjTYkk6dfPvidJmrOm687yFm/d78oxfm2CX2zfbCD4dRoXlT57K93X8attCn+XZuuGfY4bM7w/tO3q2RjxVtO7i0hChTnQFsEvpLzN/55qZ5eFFjU29/RXs3fafnlTi9f9ZLU5Rgr7yR8SD6xbErph1zcT/PZV4keJA7e909nBr+3+XNPA5zwTmjPe1TM4z0CbFrXutp82BcdKTSH/HcLeIfihW9t5WuKWbvfl2lXavu7uVrsIKatN7Xi6WxwyLfH1RdzjB0D1Tb1zH00H98y3cPRUnX0B94Y2Xfno6pkZmT4GiHftjHib+91rP42Vq6mbdUFF90DwC6lYjVR3HKC9J9x3dqWgq2dUdxtPgF27RYX9kiP/Xbw5fjvS4vGa5Xq6enYqKnn2TnyMXyf1VmxIaMl2d9X2gutV7jyGyHBBEiTuD5no+RI7qU88WEW6Z5fKWEVFps5+GvOPuRv06DuFGS1DZ1i0qVy/mrUy08XoNAS/NCoqr9PFt72hl1Ztz3RReqzEFj+6ekY1RDi47m7aXsapK1v84gdoXdix+2dPrYjfbm5JbPHjs7nPgs9SizuVPHups7sEJrb4NUW8V53EqDuFmsSytG2F7Qq1wfsea0nrri1rjd2kJfKHjy/Vt/61KKNl6AwX/2WObnt5TWi+jwl+aZSTZXpnY7k2l9d1+brDMsYv9gKaI96tfoC6UtuDGFoBup+2LX6ZrmntSpGIxz+jdPXsPL21e/uvZ6/SnIKSfXqOzm5xT6zQaIy0qKo+/C1+se+0TIeHRIm9fjJRyVQdvO9Nbcb2dadtJCVcZqKblGvZ5gptKqvNdDH2Wuz7pKo+HJcrIvil0eC+uZK4ttW+iP18N/bSgyBp1x+VsNQ6hckuZ7nsReG8uaUl3tWzgZO77LNIQvex3ravu7tufalAn7rrrX16nub45Rw6R2LrUmNzS/w3PT83/IdQ3ak1qymh108mWvxi73tsjF9zNz2rZ2O8RbJ7lOuCP76u0371UqaLsddi122sbghHhU/4v7UyqE9OlvKys1RZl4EPS0gu5+CJ1/HrJl9iXa1t4O1tB4M9wS4nf+hFXcEiLZzcpTMljh/qbZVdnfX56eyu1g1NrYNfZRAA0nWdwO6kO7VmNSW0+GUi+MXe99h+2dBNr+PX2KYrKvZNdnDa7rC09BP80sjMNKhvjio7qXn4rF+/rE/cPielZWPjfXp6VIqVv72unjtqGjX9Z8/pnY07ur5gXag54q0OZPgy737atnTVdeHJHzJxHb9E0TF+tPjtib+8vEbTfviMquqbdNRPn9Vrq4vj82Ktp43NPb+yq6E5osnXPqU7XlmT0vKd1ZWqs4NfYkVOU6Ql/pvenUJRqm57uUCTr31qt2ElVuna0XLXPrJYU697qtPLl0xi62MmvmsqEoJfc0KLfHu/yVX1TZp87VO6/831u33ez903Tw+8taFTytjS4vEWb44VOkfsuo2ddSyfaQS/NBuUn9tpXT3XldRo3vpwh5y2Eq/j1/ZL7K11pSqpbtRtLxVkoGRdp+1rz0RNJ5Jre3BUG+LTvbc9U3D0rJ49p8XvhieX6/F3izJahl/OWqmG5hbN37BDFXVN+u1z78XnJZ44Ym8O3HbUNHZaOffV8s2VkqR731if0vKVnVSj3lHwc3e9tbZ0j892vaVi5zj9hoSunpEW36uQ+d62KpVWN+zx4zrDna+ulSRtq6xPutzOYRbtv76H5m1Si3feWLvCHbX66G1v6J9vbexwmcSx0w1d/F3j7q2CX22byoC2tlVG39+7X1+32+d9ceV2/eCxpZ1SzsTKiEwOOQjTJY1ys6NRiRY/pGRQ39x494B9kfhDlcqPlme6GaCTxAbVNkZadvkSiwWgvJz2P8bz1pfpI39+o8efabDt+Mbu1q2kt1hbXK373mj/R7y7dfWsqm9KqXYycd+ob4q0OhlApMXb3XfahrvEFr/uvq/VN0V0zxvr9I2HF2a6KJKkjaXREx7kZO3sMph44og9bVGatXSLjrnxOb3bDXpB1DdF9NHboj1UYuPdd6ezW/zaXs/v0XeKdMmdczVz0eb2Htah2PsktR7jJ+1dq8q5v3tV5/7u1T1+XGfon5cjSbs96VysAmJ3vzdFnXTyugUbdujdjeW66ZkVHS7TmMEK0LqmSDx4NjZ7q2s5ttcyH7vkx+7CV2dXEiZuo0y2SLf3W7CnFS5V9U2tKl3aWlJYscf78t6gqyf2yKC+uSnXYr61tlQLNpRpwYYdu+w0VQmDSnfU7vzRqW5o1qJN5R0+56LCCq0trlakJVrTmcwr7xVraVFFq2kbSmtUVF6ngu3V2t5ODeGcghItbGf9LS2uuUHNasH2Km2vSl67uDsVdU2av6Esfn9tcXW8ZjAnK0uzlm7VIwsKNWvpFjVFWvT2ujJ9/9ElWripXGuKqyVJiwvLdxmcO3dtqSItrjfX7KwFXlpUoUffKVRxVUN83W23S+w1zlq6RetKaiRFfyDnrS/bZbmObCit0aayWq0prk5a+9qccNZEqfWBRlOk/XXuqGnUii2VKZclndxdc9aUaO5uPn/tPS7xfWlojmj++jKt2FKp55dv2+3nOaa+KaInF21u9/Mb8+LKbVq5tePtVVXfpLN/84p+8uRyzSkoUU3C5+ittaW7BL3YZybxc7SjplGPvVuo55Zv05OLNqu+KSJ31+xlW1WwvXqXMs9fX6bK+ibNXLRZs5dt1YIN0YP5LRV1mrlos+56be0u3xN1jdHXetzPntexNzyn9cFn8/XVJVpcWK6G5ojueX2dnlu+Td9/bIkO/tGseEg49Zcv6hN3vKllmyu0Ykulrn1ksQ7+0Sw1NEe0bHOFlm2u0CMLCvVIm+syrdxSqY3BGduaW1wlSVoyahubW227xO21r92SdtQ07raW+YmFO1v6Xn2vWGU1jWqKtKiitkkNzZG96spTVtPY6rPzVsJ3yrqSmviBy+LCcj2yoFCPvbtz+8X20ezE4BfvpuWtura1JHltm8pqdccra/TfxVuC563a49eRqvnry1LqZhf7vErSoL458dtvrS3VGwUlaoq06JklW7ShNPoZrWuMtGrtWVK463due9rbv99aF/1uiLQ56F5bEt3PYt/ZMe9u3KE/PL9af5+7oVWo21xep0cWFOqFlTsvyfTetiq9t23n/toYdP2ctXSLquqb9NrqYj2yoFDPL9+mVVurtL6kptX6Yp//0jYts28UlCT9Ld+d7VX1en11iQq2V8d/D9u+Tkka0CcIfkkOqKWdvzOPvVvY6vdpcWG5ymt3ln3W0q3x25vKavVGQclehcHS6uhzVtU3dxgQWp/cZdfPYENzRM8u2xr/zpWkitrod3FxVYNmLd2qxYXl8X1pY2mt3lpbGj8+iX3vStFufYsLy3c+T8LnYl1JtVZu3bmPJYbQirrod/amsug2iLS4Xlq5vcPf4/I2DQNlNY2auWhz/FilcEetnlq8ZZfvx6VFFXrlvWK19fbanccD7YXOxuYWvb66JP4698a7G3dozpqdZ96NfXZfWrU93srfXqBNnPbmmujxbszWinq9+l6x1hbv3Lc+9pc5OvmmF1W4Y9czgr5RUKIP3/q6vv7gu3tU9tLqBs1ctFk1DdHP2fPLt+m9bdH3sinSoqcWb9EjCwq1ubxOCzeVa05BSbxiLrFLvhT9vD+yoFBbK/bt+Lar5ex+EeyLwX1z4wdfydQ0NOuSO+fG71987DhN22+g9h85QOccul+rL7K1xdUa1n+YJOl7/1mkp5ds1bs/+oCG9s+LL5N4QPjd/yzW2QeP0i2zV+nBL5ykk/cfvsv6i8rrdMU9b0uS1t98QXz6Gbe8HL/dNzdbK26cEb+/o6Yxfva1dTd9sNUp7R94e6N+9PhS/fWz0/WF++drUH6OFv/kvN1uh7ZitWp3vLK21fSzf/NK/Paba0tb1focNWFIqx/QyrpmVdU36cJb39A5h+ynu66YLikadK+4520dPHqgVm6t0h8vO0YXHjVW19w/X5sr6nXZCRN108VH6Mp739a7G8u1+ufnx5v8JemtdWX60j/e0YGjBui5b52hW2av1F9fW6dZ3zhNB48etNvXlrhtc7JMBb/4YPx+4g9f2y5fieH118+u0h2vrNXTXz9Nh47duc5P3PGmCrZX7/K+ZMJD8zbpukeXSJL+fvUJOu3AkSk97pF3ivSdfy+Kvy83Pb1S981Z32qZ5Tecp355yb/GnlhYpP97JLr+P1x6tC46epxmLd2q/y7erFs/dawKtlfpc/fN14A+OVr60/Y/o7e9vHOM0qfueksfOHQ//fWz0zVvfVmr/TamuKpBX7h/vrZU1OvTJ07Uzz96hG5/dU2rz/HNFx+hA/cbqC/+fYFGDOij+T88Jz7v2kcW6/GFm3XZCRP14Ns7D4bf+9n5+sFjS/VicCBaVtMY/3F3Sf9esEk/fmJZfPkH396oq0+bosvvju6n3z1vmm6ZvapVWd9eV6ZDxgxSSXWjSqobdcEfX281/1sPL9JTS7Z0uH2//MA7re7/evYqfebkSTps7OBdlv3QH1/X1sp6Lb9h5/eIu+uonz6r8w8frb9cflx8+o6aRtU3RzR6UL7MTHWNEfXNy47Pr2+KKDc7S9lZpqr6Jh1z43P68pn76/9mHNxqmSyzeK+AW2bv7FL52eD77sxpI/XyqmKdesAIvV5Q0ur7LxWfuH2O1hTXaN1NH9TrBSX6zN07v1Ni1t98ga66d94uB/vLgoOkusaI3t24Q0eOHxIPe9ur6lvVMDe1tKhPVrbac83fF3RJRc+Swgp9/PY39T9n7q/vJWzn9ry5ZmfFTJ+caLlffa84vt1PP2ikXn2vWIeNHaSnvn6aHn23UP9esDMUf/jW17XyxhnKz23/Ncc88k6hfvDYUn3k6LH6/aXHqLqhOT4kIuKumoZmffPhhfrymfvrzy/tOtbQ3fXZu9+OV66WVDXomx84SJL086dX6KnFrT/7bVuLP3n7mzIzrdhSqc+cNEl/n7vrWK2RA/to3g+i+3diKNpeWa9Rg/JVUt2gT3fwW5qqc3/3qsprW4eI6ZOG6j9fPqXVtH59otvzmw8vUl52ti44cky7zxdrLdpW2aAfP7FUd3xmuiqD39EDRg2IL3fL7FWaPmmoTpw6vNWZG//7tVP1i6dX6K4rpuutdWU6aL+BGjekb4flT6ww2lpZrzGDd1028XIOv5y1Uo+/u1m/+eRR8WlPLtqi7/x7kWYcNlq3fyb6XXLpX+dqxZZKXXjU2PhxwudPnaIffuhQnX7LzvKuv/kCffc/i/Xkos2ae9379fn752lpUaVW/WyG+uRkq2jHzvdtTXFN/HhJal0Ze8/r6/SHF1Zr8vB+8ddy1X3zlJedpfd+fv4urymxa7a769YXC3TPG+v0gw8eoi+cPlXf+fcizV1bptsvP04zDh8dX/ZDf4p+T7/+f2dp/NDourZV1uvz989vt1yzl23VuxvLVd8Uif+Ozrn2bI1N8p60p6XF4y35i358rppbWuKfXSl6UsNVPzu/3Ra/m59ZqRs/crg2ldXqsr9GfzcLfn6+crKz9IHfvRL/vlt/8wVqafF4BcuqrVXx1yhFf/cS11nfFNnt94QU/a37+9wNenLRZv34Q4fqpKnD9fn752tgfo6W/OQ8vbBim77yz+jv2QcO3U/PLd8mSRoYVJY8sXCzfvvJo+MVdT96YqleXlWs+646XqMH56e4BTMvrS1+ZjbDzFaZWYGZXdvO/D5m9nAw/y0zm5ww77pg+ioz2/PE0E1MHt5PhTtq1dAc0aayWr23rard2ovVbWr8H32nSDc9s1Kfv3++ymoatb1y55fiosIKffPhhfr7m+u1cGO5JOmFldtV1xjRlfe+rRm/f1UvrSrWOYeM0pnTRmr1tiot2xytPe2oe0diLUusZaZtrVvbVo11pTsDbUl16wOaVUENeKwmZW/GblQ3NO9yoCRFA2ii4qoG5edmafY3Tldutu1Sa7q9ql4bgq46ia1OG4Pyxw7Q1hXXqLaxWZuD2pvYNnk32MZtt12sJTH23sWWS/yB6Mh/FrRuNYl1IdlUVqu7X1/X6uBgbUmN5q7bWTO2MeF6OPF1tilbrAWpuLpBjc0tumX2Sv3i6RW7tGi0tLjWFld32iDwOWtK9M2HF7bqGpXYmlWYsG0q65vkHu1O2F7tbexxsffh7XW71lC2V6PdVmLrx/8+tFCS9KV/LNB/F29ReW2j1pVEy1rd0KyC7VVavW3X1pLEWm1p50Fte/uyJG0ordWW4HO0PvicrStuXdZ1CS0BJdUNeujtjSqvbdT2yno9vjB6gPLiym2tHvPetqpW739xVUP8vX/1veJWoS++joT1PvZukUYPytdNFx8Rn9biO1/Hh44cozs+c5xGD9r5I5YY+o4av2uYixk1sI+G98/TQ/M26YI/vt6qK1TM2pIa1TZGdPMzK1u9Bkl6ZunW+HdOcVWDTvzFCzr5phf14NubouH0x7Pirbw1Dc06+Eez9OMnlqqhOaI3CqLTY2P3Vmyp1Mk3vaCDfzRLV/9tnqTo/ttea+TLq6K1uK8H145L1t2wsr5JO2oa45/b+qaI1gTbd3NFffy9SQx9klRe26jSmkZ97ewDNHVE//j0JUFPgkWFFfrobXP0/UeXxL8L3igo1bf/vfPix4U76vSXl9fotpcLdPsra/Tiym1qaXE9PG/jLp/ZVMeP1TY2a+7a0pS6hrq7rntssSTFv0/b87UH39UpN72gW18q0KTh/XTClGHxUJW4D78atFbEvj/b7h9S6+86KXpgu76kRk8t3hLvlrx4U3QbFu6o03vbquK9M/KysxRpia7n2eXb4gerUusD4pLqRlU1NOvr7z9QA/rkaG3Cd0pimU6YPKxVWS48aqwuOGKMisrr4qE7ViFz1IQhrZYtrmpQwfZqXXrnm3phxc7WwznB90jid+Srq0u0viT6W1Td0JzyWKm2oe/iY8dp6eaKXa7lFhsbN2JAnh6a13o83YotlfrH3A0qrmpo1cVzbXGNymoa4997sfLGvg8uuXPuLscL19w/X3PWlOrtdWW66t55+sRfWp+YrqKuSSXVDfGAkLhvvryquFUL29aKet33xjqtStivlhZV6pF3CuXu+uMLq3XZnXM1L/h8vb2+TO6u/ywojL83iS3Qd72+rlVluhTdt58MguH60hotLYo+bkNprarqm/R6QYnMpP1H9ldb727coa0V9UHviMrgOVp/dhsjLVqxpbLVb+PLq7a3OibZUdsUP64o2F6tkuqGeAva+oTjrcRQtb5k5/Ml/h7l5WS1+px/59+LdPsra1pVnrbdvzpSVd8UbyXdmtD6u6GsZpfX2dAcrahub7hD7Dg0saI+9nyJlVwrtlRqU8JrWVdSo4ranWVYV9L6eDmVFreq+iZ98o434+/xmuLq+Lauqm9WRW1TPGhecMSYeOiTWve4S2z9XldSo2MnDtGJU3ZtTOnO0tbiZ2bZkv4s6QOSCiXNM7OZ7r48YbGrJe1w9wPM7FJJv5R0iZkdKulSSYdJGivpeTM7yN279wCSdhwwaoBaXLrtpTX6wwur49N//tHD9aEjx8bHPqxK0s3s2Buf0xHjdh5w3fjf6CZ87N0iDe0Xffx3/r1IA/rkxA9iJGn04HxNGTFAL6+KdmmSokGgPYk776V3zlXBz89XWe2uocvd47WRGxK+iDaU1mjkwD7x+7FWiMRuoImPTcXGDg4u9h/VX0uLKjVuSN/4Qe8p+4/QtNEDdd5ho+PdnWKKqxqUE1yHJXH1ReWtvyxqGpvjBzR9c7N3ObhZX1qrScN3fuknvv7E7mu7+zJ1d/3++ffanf4/D7yjJUUVrbqD/THhcxNd767P39HFUTeU1mppfUW8pnvCsH76zEmT4vP/8dYG/fiJZbrylMn6yYWHJS13Kn7//Gq9va5ME4b21bfOnSap9SDv7ZUNKtxRqx8+Hq0p++LpU/Xw/E3qk5OlV757Vqtau1gYjG3b9g5/1pXUaNLw/qqoa9KYQfmqb47IZMrPzZKZqbqhWf9opwY+8fGJ79c5v42Ou4m1+mwqq1VOtmldSY0uO2GCHnx7k6Sd1wfbllAhM3VE//hBY+KPXuz5N5bVKjvL4tvjjldbt2Jf++gS/fDxpTovoVY38fmlnbW8MVX1zfF9u+2Yl3FD+mpDaW2rA4aC7dW6ZPoEXTJ9QrwVdkdto+56LTp28ar3TdFxk4bqwbc3tvqBHzEgTyXVjRo5MF/Szi54+blZuvzESbrr9XXaXtWgsw8eFT/4Xb6lQsdN2nmwnNhS/d/Fm/XF06eqMdLS6qD3tpfX6CtnHaCFm8rjLQ7ff2yJDh49UFL0wPr4ycN03u+j79MDb23U8yu2xbfT4L65KtherfP/8Fq8gui11SWqb4rokjvflCQ9+IWTlJ1luvyut9odA7OhtFaHj9s14D69ZIv+J2jd/OzJk/R0m1bQn8xcpgkJtdKJYp+LI8cP0WNJTiwzc9FmHd0mNMR86I+vdzh2NMukw8cNih+sdvQ9HxMNri26+LY5Oyu+dtPStKSoIv78HYWR8tpGPblos44cP1ibK+r1qRMmaunmyngYS+yuH9PY3KL6pkj8QG/M4Px4pcm6khpNHNZP+bnZqm+K6AO/fSVeiXjZCRP02uqSeKXo/A07Wo2bO3L8YM3fsEOL2ukymlgZFNs/j54wWMdMHKI126tVWt2gYf3zWn2vThnRX28ndI879cAR+uT0CfrS3xdo1rJoxVDs9+iMg0Zq0aZynTVtpM6cNkrXz1ymm59ZqblryzQ3oSveNx5eqCkj+rfqdhdrSTKLfs+cOW2U7rny+F1eQ1OkRVfdO095OVn662ent5p39alTdNjYQXr0nSK9t61ah44dpIfnbdS00YNUUdeki48Zp9zsLD08f5MeeGuDPn3iJN0ye2X8d+KHj7c+0cjq7dU69sbndinDhUePi2/ftts5VoH6/Ipt8ftvrilVdUOzJg7rpw//6XU1RlqUl52ly0+apKVFlRo5sI+Kqxp03aNL9NrqYt326Wir3W+eXdWqNTjRlor6+MmR3gxCVFlNo675+4JWB+9tK0efWtx6bNj1CZVmiRV9n/rrWyqraVCLS8dMHKKcLItX9kjSoPwcrSmu0Uk3vaCBfXJahYS2zv/Da5Kkp79+mmobm3XlvfNazZ/+s+cU27WeXb5V/1qwKX6ahsTfqcSAlxiQYt1LpejvUWzsYWV9U7vj05YWVWi/Qfka0jdXlfVNGj+0nzaU1mi/QfnqH7Ryvb2uTJ+8401deNRY/eHSo+MVNrEytfddsKW8fpeKv4P2G6B3NpartLqh1XHhqb98SaMSjh0Tt1PMz55aoZ89tUKD8nPUNy97l9/FbZX1ckW/o84/fHSrnlkxq9pUxm0ordWYhFa6T989V8P799G4IX31sePG7dLLZerI/lpbHB2eM3ZIX0VaXEU76nTBEWNa9UTpCWxPB1um/MRmJ0v6ibufF9y/TpLc/aaEZWYHy7xpZjmStkoaKenaxGUTl0u2zunTp/v8+fOTLdLlVm6t1Izf7/wQ/+HSo+OtDrnZptGD85WbnaW17dR2tnXU+MG6+rSpenNNqUYPytfv2gkPkvTRY8bpsXeL9Ln3TdHpB43QlffOk9nO87yMGNBnl8fUNja36n89rH+e3L3VeMLoY/MUO+yta2xWTfCYgfk58e48UvSLprG5RVmm+BfZfoP6KNtM2dkW/Z9lGtw3VyXVje32B29sjrTbUnjBEWP01JIt+tYHDtKiTeV6YeV23fiRw+OB5lezVrbqmtcvL1tZQQhIfP1ta//75GSpT06WKuubdd5h+2n2sm0aMaBPq+VijzVTqxrDYf3zVFnXpOYWV9/c7PiXZvtcJdWNOnHKML2VUAMeO7iOaTs/Ji87S4OCCoPy2sZ4C8GhYwYp0uIqrWlsVeapI/qrcEedhvSLfrlPGz1IxZX1aox4/H3KzjJNHdFfrui1isYP7auNZXUaPbiPKuqa1NISLV9Reb0mDOurLeX16puXrYnD+mnFlkoN7psrV7QWre3nLNmYr0QjBuRp4rB+2lhWp3FD8lsdSBw1fnC7B3CttktOVryWer9BfTRmcN9oTWGbMRSxrn2SdNykoSqvbWz1Qy5JMw4bra2V9Vq4qVz98rJV2xjRvVcdr6sSfqjfd8BwrS2uiR+kHjZ2ULy2N+bUA0ZozpoSzTh8tJ5fsV3HTRwaPzhJtPSn5+mlldv1tWC8wiFjBmnV1kol/qZ+b8Y0/fnFgvg+J6ndA43pk4Zq/oYd8W6ibT9XsS5Ek699Kr6ttlU2aGi/XL3+f2erf58c3fDkct0TnMjmwFEDNOPw0frTiwW69PgJemLh5ngAefKrp+qQMQN1wA+ekSR96sSJ8XFaB4waoCyLdrVucY9/No+bNLRV7XuivOwsDe6XG9+3nvjK+3TRn9+Iz8/PzVK/vJx42I35yln769F3iuLvhSQ98PkTVVrTqK8/+G48THz+1Cn6wQWHxAPO5vI6PTRvU6vKlUH5OcrLydaYwfnx9RwyZqBWbKmSu2vi8H6tDt73xHPfPF3/+9BCLd9SqROmDNPb68p0+LhBGj2or46eMFi/fvY95WVnKS8nSz+98DB99JhxmrVsazxwnjVtpP5y+XFqirToltmrdP+bG9Q3N1vzfniOBvTJUUuL6wO/e0WbdtRpUP7OE6rkZVvrE2M0tezyuRnePy9p8Gtoiqi6sVlHjh+i9SU1Onj0QG0orVVziys/N0sHjx6oRYXRsVQPfuEkTZ88VDlZph8+vlT/XlCos6eN0gsrt7V7Ioy+udmqa4ro7INH6dZPHaNDfzy71fw+OVntnsxjSL9cnX/4aBXuqNNrq0tazfvnF07U5/82v8OTZwzpl6ucLIvvG89983TdN2e9Hnhro7JMGtIvT2U1jZo0vJ82lNbqshMm6JmlW+Mta7d+6hh96Mix+vNLBbt0n7798mP1pX+8owuOGKMrTpmsT94RPXQZPShf0ycP1QVHjFFjpCV+LLA7J0wepmH986InUmppUaTFtba4Jh5mYoFJkq44eZJ+etHh2l5Vr1NuelEThvVTXWOkVUXOladM1pfP3F8f+O0rqm9u0QEjB2hdSY2GD8jTd8+b1m65vnrWARo5sI8emrdJK7ZUamB+jv762em6tJ2u7hOG9dXHjh2v3z+/usP3TpK+/8GD9di7m+OtcondMSXp/MNHq64popdXFevI8YO1OPgNiB3jtOfUA0Zo9faqeDiIHS90hh9/6FCdftAIffPhRVpSVKGxg/N12LjBrQKmJI0dnB8PvjFnHDSy3TF5qRo1sI+OnThULldZTWOrM7yfesAIDe6Xq7XFNfFtee6h++nNNaU6fdpIzSko0Y7aJl17/sGaOKyfzj54lA7+0awO1zWgT45O3n+4WlpcBcXV8crmxPdgT33oyDGtKuWPnjAkHgAvPX6C8nKy9NWzD9AJP39BkvSxY8dr+IA8TRnRX4s2lWvskL7aUlGvSEuL/jW/dSVAXnZW/Ptt5MA+OnDUAPXNzVZTiyvbpCyzVuN0pegxXG5Wlgb1zdH7D95PD8+PVuqec8go3XXF8TryJ7NbHX+ef/hoPbN0a/BdN0h1jRGt2lalmy4+QpedMHGvtkm6mdkCd5/edno6x/iNk7Qp4X6hpBM7Wsbdm82sQtLwYPrcNo8d195KzOwaSddI0sSJ3W/jHzx6kP72uRP07LKtGjUwXxcdPU41DRFtragLujo0qjHSovftH609/PWzq1S4o1YnTR2uoycMideI5eVk6ZgJQzVxeD9deNRYSdEujEs3V+q7507TA29tUJ+cLE2fPEyXHj9BB+43QJ8+YZL69cnWjRcdptcLSrS4sELHThyqwf3aP8PakeMG68NHjdWfXiyIdwmMRFzZ2abG5hb1zc3e5Sxp0/YbqPLaJm1r5+Qt7lJ2VvQgo09ulpqaXZHgADD2V1rToGmjB7ZqLUw0rF+exg3tG6+ZmjZ6YLyV9INHjNYHjxit0w8aqY8fOz7+mO/NOFgHjBqgBRt2KNLiygr6YxfuqFNulmm/oJZnSN/gh7+mMf46Y+v85PQJGj0oP/jiMNUF3bpqGpo1enC+NpTW6syDRurk/Ydr2ebK+IFw4vMkk5+TratPm6J7Xl+nyrom9cnNUotHI3VdU0T98rJ1xcmT9ezybdpcXqe6xoiG9MvTURMG7xIGIxHX1sp6FZXXaXDfXB00eqBWb6vS8AF5GpSfq/e2VenTJ03UaQeO0KylW7WkqFKHjRsc3+bTJw3VuxvL4zVi00YPVMG2ap124AitKa7W1BEDlJNl2lpZr9MPGqHlmyt19IQhqgzOuHXS1OHaUlEnM9Mp+0/SsP55WrSpXGOG9FUk4lpSVKFJw/vpgFEDtLm8Xss2V+gzJ0/S5OH99ctZKzVhWD8dNGqgVm2r1NriGp1+4Ait3l6tUw8YoeED8hRpcW0ur9OJU4bpoqPH6bfPvacTpwzTjMNH64mFm3XY2EHq3ydbhTvqNHpwvtylRZvKVd/coqMmDNGFR43VjMNHa/bSrZq1bKs2lEYPWteW1MRPUvCRo8dqQ1mtNpRGazDXFFfLLPojUFRer6H9cnXK/sN1yfQJenV1sfYblK/6phZNHNZPkRbXgPwcnXPIfvrI0eNUXN2gOWtK1D8vR186Y3+V1jRq9bZqTR7eT186c38dOX6w+uXlaOGmHTpi/BANys/RgD45+vBRY7VoU7kWbNyhz586VeV10RP0HD1hqIp21Ol/zjxAIwb00ZOLNuuMg0bqn29tVHaW6cD8HB04aqDmbSjT5943RZcHFSBriqtV3xTR5vI6fesD49TirllLt+r9h4ySJP30wsN056trNSI4oLjp4iPiFRZXnjJZ/ftk65LjJ2j80Ohr7JeXo0+dMFHnHT5ab64p1RdPn6rhQbj/4ulTNX3yMA3Mz9H2ygZNGNZXbxSUaFj/PB0zYWi09cJM/fOydfGx4/WjJ5bq0DGDdMCoASrYXq39BvXR+w/ZT/e/uV7u0qptVTpo1EAdNWGI7rvqeD27fJuamluUG4zVG9w3V5dMn6B731inEQP66KtnH6ALjhirf769QQP65OrEqcP0vgNGqKK2SR88YrRKqhv12ZMn60tnTG0VbsYO6atvnnOg8nOzorXUTRHl5WSppcW1fEulDh49ULnZWVpfWqOh/XN19alTdMyEobr79XXxMR3bKut15SmTtaGsVs8tj3a/zMoyFWyrVlF5nfrkZumgUQN10OjomO2/XH6sXi8o0ZnTRum5ZVt10dHjNLR/nqrqo12NGpoj+tCRY/Xh4Hv+g0eM0b+/dLLum7NeV54yWfm52crPzdYNFx2uGYeP1qD83PgJO7KyTN85d5peK9gZgpojLdpR26SRA/vEW6qzLPqd/urqYlXXN+uUA4bH38tkDhk9UCMH5uvOV9eoucX1vgNGqE9ulrZX1qtwR53GD+2rE6YM07GThsRr3c84aKTmr9+htSXVmjZ6oKZPGqb1pTU6bOwgHT1hqOauLdXa4mptrWzQh48ao355Ofr5Rw/X4k0Vysk2jRqYr+qGJuXnZstdOn7KMBXuqNWyzZX6wCH76ayDR6lwR62+/a9FQVe8Adp/5ACdsv8I/f6So3Xby2tUuKNWB+03UIU76pRl0qTh/TV+aHRsU11TRNX1zZo0vL8+ftx4lVY3KisretKwbZX1+u550/SHF1brq2cfqEuOn6hnl23V8i2VOmtadD+6+tQpOmr8EJXXNeq2l9boiHGDddbBo3T5SRP1lbMO0OhB+XrkyyerpiGiw8YOarWdJwzrF29VHD+0n+54ZY22VNTr+g8fqltmr9JJU4drSVGFKuuatK6kRtlZppxsU06WaeyQfJ0wZZiag+76w/vn6YxpI/XNc6LjE0cNzNc3zjlQL60q1rhxfVVZ36T6poj2HzlAl50wUfsNytf9V5+of83fpIJt1Zoyor9+/YmjdOjYQeqfl6Oi8jqdduAIDeufp/LaJk0OuihffOw4vbmmVBOHR7+3rz3/YA3rn6cnF21Wdpbplo8fFf9tGZifq3c27FBpTYMG5edq1bYqbSit1QGjBuj4ycN0zen76/OnTo0fP4wc0Ef7jxygiromvbNxR7wb8EVHj9VXzzpAK7ZWaeHGcv3v+w9USXWDJg/vryVFFXJ3HRa00n/htKkqr23UrS8W6OJjx+u8w/ZTVUOztlfW6+T9h6tvbrRS59F3i3TomEEqrmrQ9RcepsfeKVRpTaOG9svT5vI6nTltlEYMyNPWynqdMHmYFhdW6LSDRqhPTrbuvnK6rn1kiW6++AhVNTQrN9t06gEj9dKq7Tp56nCdftAIFVc1av76Mp1ywAgVldfpwqPGqqUleumG5hZXn5wsDcjPUXFVg4b2y1NDc0TltU2qa4rokDGDott4WD+9sGKbhvbP02uri7WupCbea+nkqcM1dWR/ba9q0PqEk0jl5WTpsuMn6JAxg7SmuFort1Rq7JC+uvKUKfrc+6bExztfMn2CisrrdOjYQXpvW5WmjOivuWvLdPqBI7Rgww5tLI32dhnSL0/vO2CEFm0qV1PE9YXTpujsg/fT7OAkOsXVDZo0rJ8+dtx4LSms0KxlW9W/T45M0d46pTWNGj0oX7/82JH61AkT9d8lW7SksEL/c+b+euSdQl15ypRW55341ceOVH1zRJ89eXJ8Wttg9YXTpmr5lkqdf/gY3fvGOpVUN2h7VYP65WVr1dYqldc2qVxNyg2+yyMtrnFD+qqhuUWn7D9cnzpxop5fvk2NkRYdN2moLjp6nC4/aZLK6xp16JjouRIevOYk3fbyGuVkmYqrGvTZkyertjGiovI6DeiTo8bmFh02dpBOmtqzunlK6W3x+7ikGe7++eD+ZySd6O5fTVhmabBMYXB/jaLh8CeS5rr7P4Lpd0t6xt3/k2yd3bHFDwAAAAC6Skctfuk8uUuRpAkJ98cH09pdJujqOVhSaYqPBQAAAACkIJ3Bb56kA81sipnlKXqylpltlpkp6Yrg9sclvejRJsiZki4Nzvo5RdKBkt4WAAAAAGCPpW2MXzBm76uSZkvKlnSPuy8zsxskzXf3mZLulvR3MyuQVKZoOFSw3L8kLZfULOkrPfGMngAAAADQHaRtjF8mMMYPAAAAQG+WiTF+AAAAAIBugOAHAAAAACFH8AMAAACAkCP4AQAAAEDIEfwAAAAAIOQIfgAAAAAQcgQ/AAAAAAg5gh8AAAAAhBzBDwAAAABCjuAHAAAAACFH8AMAAACAkCP4AQAAAEDIEfwAAAAAIOQIfgAAAAAQcgQ/AAAAAAg5gh8AAAAAhBzBDwAAAABCjuAHAAAAACFH8AMAAACAkDN3z3QZOo2ZFUvakOlytGOEpJJMF6IXY/tnHu9B5vEeZBbbP/N4DzKP9yCz2P6Z11XvwSR3H9l2YqiCX3dlZvPdfXqmy9Fbsf0zj/cg83gPMovtn3m8B5nHe5BZbP/My/R7QFdPAAAAAAg5gh8AAAAAhBzBr2vcmekC9HJs/8zjPcg83oPMYvtnHu9B5vEeZBbbP/My+h4wxg8AAAAAQo4WPwAAAAAIOYIfAAAAAIQcwS+NzGyGma0yswIzuzbT5QkrM5tgZi+Z2XIzW2Zm/xtMH2Zmz5nZ6uD/0GC6mdkfg/dlsZkdm9lXEA5mlm1m75rZf4P7U8zsrWA7P2xmecH0PsH9gmD+5IwWPCTMbIiZ/cfMVprZCjM7mX2ga5nZN4PvoKVm9qCZ5bMfpJeZ3WNm281sacK0Pf7cm9kVwfKrzeyKTLyWnqiD7X9L8D202MweM7MhCfOuC7b/KjM7L2E6x0t7qb33IGHet83MzWxEcJ99IA06eg/M7GvBvrDMzH6VMD1j+wHBL03MLFvSnyWdL+lQSZeZ2aGZLVVoNUv6trsfKukkSV8JtvW1kl5w9wMlvRDcl6LvyYHB3zWS/tL1RQ6l/5W0IuH+LyX9zt0PkLRD0tXB9Ksl7Qim/y5YDvvuD5JmufvBko5S9L1gH+giZjZO0tclTXf3wyVlS7pU7Afpdp+kGW2m7dHn3syGSbpe0omSTpB0fSwsYrfu067b/zlJh7v7kZLek3SdJAW/y5dKOix4zG1BhSHHS/vmPu36HsjMJkg6V9LGhMnsA+lxn9q8B2Z2lqSLJB3l7odJ+nUwPaP7AcEvfU6QVODua929UdJDin4A0MncfYu7vxPcrlL0gHecotv7b8Fif5P0keD2RZLu96i5koaY2ZiuLXW4mNl4SRdIuiu4b5LOlvSfYJG22z/2vvxH0vuD5bGXzGywpNMl3S1J7t7o7uViH+hqOZL6mlmOpH6Stoj9IK3c/VVJZW0m7+nn/jxJz7l7mbvvUDS47HIgjV21t/3d/Vl3bw7uzpU0Prh9kaSH3L3B3ddJKlD0WInjpX3QwT4gRSuUvicp8SyO7ANp0MF78GVJN7t7Q7DM9mB6RvcDgl/6jJO0KeF+YTANaRR0lzpG0luS9nP3LcGsrZL2C27z3nS+3yv6A9MS3B8uqTzhxz9xG8e3fzC/Ilgee2+KpGJJ91q0u+1dZtZf7ANdxt2LFK3R3aho4KuQtEDsB5mwp5979of0+ZykZ4LbbP8uYmYXSSpy90VtZvEedJ2DJJ0WdOV/xcyOD6Zn9D0g+CE0zGyApEckfcPdKxPnefS6JVy7JA3M7EOStrv7gkyXpRfLkXSspL+4+zGSarSze5sk9oF0C7pFXaRoCB8rqb+oMc84PveZY2Y/UHQoxgOZLktvYmb9JH1f0o8zXZZeLkfSMEWHIH1X0r+6Q68Ogl/6FEmakHB/fDANaWBmuYqGvgfc/dFg8rZY97Xgf6yZnfemc71P0oVmtl7RrglnKzrebEjQ5U1qvY3j2z+YP1hSaVcWOIQKJRW6+1vB/f8oGgTZB7rOOZLWuXuxuzdJelTRfYP9oOvt6eee/aGTmdmVkj4k6dO+84LRbP+usb+iFVCLgt/l8ZLeMbPR4j3oSoWSHg261b6taI+oEcrwe0DwS595kg606Bnd8hQdyDkzw2UKpaAG5W5JK9z9twmzZkqKnZnqCklPJEz/bHB2q5MkVSR0C8Iecvfr3H28u09W9HP+ort/WtJLkj4eLNZ2+8fel48Hy1Mjvw/cfaukTWY2LZj0fknLxT7QlTZKOsnM+gXfSbH3gP2g6+3p5362pHPNbGjQcntuMA17wcxmKNr1/0J3r02YNVPSpRY9o+0URU8w8rY4XupU7r7E3Ue5++Tgd7lQ0rHB7wT7QNd5XNJZkmRmB0nKk1SiTO8H7s5fmv4kfVDRM1qtkfSDTJcnrH+STlW0K89iSQuDvw8qOl7mBUmrJT0vaViwvCl65qQ1kpYoeha+jL+OMPxJOlPSf4PbU4MvswJJ/5bUJ5ieH9wvCOZPzXS5w/An6WhJ84P94HFJQ9kHuvw9+KmklZKWSvq7pD7sB2nf5g8qOqaySdED3Kv35nOv6Fi0guDvqky/rp7y18H2L1B0rFLs9/j2hOV/EGz/VZLOT5jO8VInvgdt5q+XNCK4zT7QRe+BokHvH8HvwTuSzk5YPmP7gQUrAgAAAACEFF09AQAAACDkCH4AAAAAEHIEPwAAAAAIOYIfAAAAAIQcwQ8AAAAAQo7gBwDo0cxsuJktDP62mllRwv28Tl7XwcHzvmtm++/lc9xnZh/f/ZKtHrPezEbszfoAAJCknEwXAACAfeHupYpex1Bm9hNJ1e7+69h8M8tx9+ZOWt1HJP3H3X+W6gPMLNvdI520fgAA9gotfgCA0Ala1W43s7ck/crMTjCzN4OWujlmNi1Y7koze9TMZpnZajP7VTA9O3iOpWa2xMy+aWYflPQNSV82s5eC5S43s7eDVsA7zCw7mF5tZr8xs0WSTk5SzvVm9lMzeydYz8HB9OFm9qyZLTOzuxS98HLsMbus08yON7PFZpZvZv2Dxx2enq0LAOiJaPEDAITVeEmnuHvEzAZJOs3dm83sHEm/kPSxYLmjJR0jqUHSKjP7k6RRksa5++GSZGZD3L3czG5X0KJoZodIukTS+9y9ycxuk/RpSfdL6i/pLXf/dgrlLHH3Y83sfyR9R9LnJV0v6XV3v8HMLpB0dVCOdtfp7veb2UxJP5PUV9I/3H3pvmw8AEC4EPwAAGH174QuloMl/c3MDpTkknITlnvB3SskycyWS5okaZmkqUEIfErSs+08//slHSdpnplJ0cC1PZgXkfRIiuV8NPi/QNLFwe3TY7fd/Skz25HCOm+QNE9SvaSvp7huAEAvQfADAIRVTcLtGyW95O4fNbPJkl5OmNeQcDsiKcfdd5jZUZLOk/QlSZ+U9Lk2z2+S/ubu17Wz7vo9GNcXW39Eu/9dTrbO4ZIGKBpq89X69QMAejnG+AEAeoPBkoqC21fubuHgDJpZ7v6IpB9KOradxV6Q9HEzGxU8ZpiZTeqc4upVSZ8Knvd8SUNTWOcdkn4k6QFJv+ykcgAAQoIWPwBAb/ArRbt6/lDRrpu7M07SvWYWqyDdpYXN3ZcHz/dssFyTpK9I2tAJ5f2ppAfNbJmkOZI2JlunmZ0hqcnd/xmcYGaOmZ3t7i92QlkAACFg7p7pMgAAAAAA0oiungAAAAAQcgQ/AAAAAAg5gh8AAAAAhBzBDwAAAABCjuAHAAAAACFH8AMAAACAkCP4AQAAAEDI/T+KeyCwdQ57RQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import web3\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Initialize Web3 provider\n",
    "web3_provider = web3.Web3(web3.HTTPProvider('https://mainnet.infura.io/v3/8cfed8aad74b48f68c7a82bef639ec4d'))\n",
    "\n",
    "# Define protocol contract address\n",
    "protocol_contract_address = '0x9f8F72aA9304c8B593d555F12eF6589cC3A579A2'  \n",
    "# Replace with the actual contract address\n",
    "\n",
    "# Define start and end blocks for data retrieval\n",
    "start_block = 10000000\n",
    "end_block = 10010000\n",
    "\n",
    "# Retrieve blockchain data\n",
    "contract = web3_provider.eth.contract(address=protocol_contract_address, abi=your_contract_abi['result'])\n",
    "transfer_events = contract.events.Transfer.getLogs(fromBlock=start_block, toBlock=end_block)\n",
    "\n",
    "# Process and filter transfer events\n",
    "transfers = []\n",
    "for event in transfer_events:\n",
    "    # Extract relevant information from the event\n",
    "    transfer_data = {\n",
    "        'from': event['args']['from'],\n",
    "        'to': event['args']['to'],\n",
    "        'value': event['args']['value'],\n",
    "    }\n",
    "    transfers.append(transfer_data)\n",
    "\n",
    "# Plot transfer data\n",
    "x = range(len(transfers))\n",
    "y = [transfer['value'] for transfer in transfers]\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.xlabel('Transfer Index')\n",
    "plt.ylabel('Transfer Value')\n",
    "plt.title('Transfers for Protocol XYZ')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2fcb404d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Block number: -107420112\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "# Function to convert a timestamp to block number using Infura\n",
    "def timestamp_to_block_number(timestamp):\n",
    "    # Infura API endpoint\n",
    "    endpoint = \"https://mainnet.infura.io/v3/8cfed8aad74b48f68c7a82bef639ec4d\"\n",
    "\n",
    "    # Request block number from Infura\n",
    "    payload = {\n",
    "        \"jsonrpc\": \"2.0\",\n",
    "        \"method\": \"eth_blockNumber\",\n",
    "        \"params\": [],\n",
    "        \"id\": 1\n",
    "    }\n",
    "    response = requests.post(endpoint, json=payload).json()\n",
    "\n",
    "    if \"result\" in response:\n",
    "        latest_block_number = int(response[\"result\"], 16)\n",
    "\n",
    "        # Infura block time is approximately 13 seconds\n",
    "        block_time_interval = 13\n",
    "\n",
    "        # Calculate the target block number based on the timestamp\n",
    "        target_block_number = latest_block_number - (timestamp // block_time_interval)\n",
    "        return target_block_number\n",
    "    else:\n",
    "        print(\"Error: Unable to retrieve block number from Infura\")\n",
    "        return None\n",
    "\n",
    "# Example usage\n",
    "timestamp = 1622772000  # Example timestamp (June 4, 2021, 12:00:00 in Unix timestamp format)\n",
    "block_number = timestamp_to_block_number(timestamp)\n",
    "\n",
    "if block_number is not None:\n",
    "    print(\"Block number:\", block_number)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80796bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NaN\n",
       "1            NaN\n",
       "2            NaN\n",
       "3         323.62\n",
       "4         391.57\n",
       "           ...  \n",
       "185389     -3.51\n",
       "185390     17.33\n",
       "185391    -32.57\n",
       "185392    -40.77\n",
       "185393    -74.61\n",
       "Name: close, Length: 185394, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df['close'].diff(lag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "366194ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "\n",
    "# Define the API endpoint URL\n",
    "def get_all_protocols_onDefilllama():\n",
    "    api_url = \"https://api.llama.fi/protocols\"\n",
    "\n",
    "    # Send a GET request to the API endpoint\n",
    "    response = requests.get(api_url)\n",
    "\n",
    "    # Check if the request was successful\n",
    "    if response.status_code == 200:\n",
    "        # Extract the data from the response\n",
    "        data = response.json()\n",
    "\n",
    "        # Create a DataFrame from the data\n",
    "        df = pd.DataFrame(data)\n",
    "    else:\n",
    "        print(\"Error: Unable to fetch data from the API\")\n",
    "    return(df)\n",
    "\n",
    "def get_protocol_TVLbyname(protocol_name=\"uniswap\"):\n",
    "    api_url = \"https://api.llama.fi/tvl/\"+protocol_name\n",
    "    # print(api_url)\n",
    "\n",
    "    # Send a GET request to the API endpoint\n",
    "    response = requests.get(api_url)\n",
    "\n",
    "    # Check if the request was successful\n",
    "    if response.status_code == 200:\n",
    "        # Extract the data from the response\n",
    "        data = response.json()\n",
    "        # print(data)\n",
    "        # Create a DataFrame from the data\n",
    "        # df = pd.DataFrame(data)\n",
    "    else:\n",
    "        print(\"Error: Unable to fetch data from the API\")\n",
    "    return(\"TVL is {}\".format(data))\n",
    "\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e295ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TVL is 5431676257.576322'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_protocol_TVLbyname(protocol_name='aave')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91f26a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TVL is 4116348790.390712'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_protocol_TVLbyname(protocol_name='uniswap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f51959f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://api.llama.fi/tvl/uniswap\n",
      "4116348790.390712\n"
     ]
    }
   ],
   "source": [
    "api_url = \"https://api.llama.fi/tvl/\"+\"uniswap\"\n",
    "print(api_url)\n",
    "\n",
    "# Send a GET request to the API endpoint\n",
    "response = requests.get(api_url)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Extract the data from the response\n",
    "    data = response.json()\n",
    "    print(data)\n",
    "    # Create a DataFrame from the data\n",
    "    # df = pd.DataFrame(data)\n",
    "else:\n",
    "    print(\"Error: Unable to fetch data from the API\")\n",
    "\n",
    "return(\"TVL is {}\".format(data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc495124",
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>symbol</th>\n",
       "      <th>url</th>\n",
       "      <th>description</th>\n",
       "      <th>chain</th>\n",
       "      <th>logo</th>\n",
       "      <th>audits</th>\n",
       "      <th>audit_note</th>\n",
       "      <th>...</th>\n",
       "      <th>mcap</th>\n",
       "      <th>stablecoins</th>\n",
       "      <th>language</th>\n",
       "      <th>parentProtocol</th>\n",
       "      <th>staking</th>\n",
       "      <th>wrongLiquidity</th>\n",
       "      <th>pool2</th>\n",
       "      <th>assetToken</th>\n",
       "      <th>oraclesByChain</th>\n",
       "      <th>github</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2269</td>\n",
       "      <td>Binance CEX</td>\n",
       "      <td>None</td>\n",
       "      <td>-</td>\n",
       "      <td>https://www.binance.com</td>\n",
       "      <td>Binance is a cryptocurrency exchange which is ...</td>\n",
       "      <td>Multi-Chain</td>\n",
       "      <td>https://icons.llama.fi/binance-cex.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>182</td>\n",
       "      <td>Lido</td>\n",
       "      <td>0x5a98fcbea516cf06857215779fd812ca3bef1b32</td>\n",
       "      <td>LDO</td>\n",
       "      <td>https://lido.fi/</td>\n",
       "      <td>Liquidity for staked assets. Daily rewards, no...</td>\n",
       "      <td>Multi-Chain</td>\n",
       "      <td>https://icons.llama.fi/lido.png</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2.010541e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2272</td>\n",
       "      <td>OKX</td>\n",
       "      <td>None</td>\n",
       "      <td>-</td>\n",
       "      <td>https://www.okx.com</td>\n",
       "      <td>OKX, formerly known as OKEx, is a Seychelles-b...</td>\n",
       "      <td>Multi-Chain</td>\n",
       "      <td>https://icons.llama.fi/okx.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2275</td>\n",
       "      <td>Bitfinex</td>\n",
       "      <td>None</td>\n",
       "      <td>-</td>\n",
       "      <td>https://www.bitfinex.com</td>\n",
       "      <td>Bitfinex facilitates a graphical trading exper...</td>\n",
       "      <td>Multi-Chain</td>\n",
       "      <td>https://icons.llama.fi/bitfinex.png</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240</td>\n",
       "      <td>Polygon Bridge &amp; Staking</td>\n",
       "      <td>0x7d1afa7b718fb893db30a3abc0cfc608aacfebb0</td>\n",
       "      <td>MATIC</td>\n",
       "      <td>https://polygon.technology/</td>\n",
       "      <td>Ethereum sidechain\\r\\n</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>https://icons.llama.fi/polygon.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>8.315996e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>3033</td>\n",
       "      <td>FireBot</td>\n",
       "      <td>polygon:0xd125443f38a69d776177c2b9c041f462936f...</td>\n",
       "      <td>FBX</td>\n",
       "      <td>https://firebot.gg</td>\n",
       "      <td>Let AI manage your crypto and keep your portfo...</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>https://icons.llama.fi/firebot.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.480555e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>236619.468578</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>3035</td>\n",
       "      <td>Dexilla</td>\n",
       "      <td>None</td>\n",
       "      <td>-</td>\n",
       "      <td>https://dexilla.com/</td>\n",
       "      <td>Dexilla is a revolutionary order book DEX that...</td>\n",
       "      <td>zkSync Era</td>\n",
       "      <td>https://icons.llama.fi/dexilla.png</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2852</th>\n",
       "      <td>3038</td>\n",
       "      <td>Rainbow</td>\n",
       "      <td>None</td>\n",
       "      <td>-</td>\n",
       "      <td>https://rainbow.me</td>\n",
       "      <td>The fun, simple, &amp; secure way to explore Web3,...</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>https://icons.llama.fi/rainbow.png</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>3041</td>\n",
       "      <td>Houdini Swap</td>\n",
       "      <td>0x888cea2bbdd5d47a4032cf63668d7525c74af57a</td>\n",
       "      <td>POOF</td>\n",
       "      <td>https://houdiniswap.com/</td>\n",
       "      <td>Houdini Swap is an aggregator of non-custodial...</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>https://icons.llama.fi/houdini-swap.png</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.033068e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>3042</td>\n",
       "      <td>The Llamas</td>\n",
       "      <td>None</td>\n",
       "      <td>-</td>\n",
       "      <td>https://www.thellamas.io</td>\n",
       "      <td>Llament the time you've been in human form? Do...</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>https://icons.llama.fi/the-llamas.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2855 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                      name  \\\n",
       "0     2269               Binance CEX   \n",
       "1      182                      Lido   \n",
       "2     2272                       OKX   \n",
       "3     2275                  Bitfinex   \n",
       "4      240  Polygon Bridge & Staking   \n",
       "...    ...                       ...   \n",
       "2850  3033                   FireBot   \n",
       "2851  3035                   Dexilla   \n",
       "2852  3038                   Rainbow   \n",
       "2853  3041              Houdini Swap   \n",
       "2854  3042                The Llamas   \n",
       "\n",
       "                                                address symbol  \\\n",
       "0                                                  None      -   \n",
       "1            0x5a98fcbea516cf06857215779fd812ca3bef1b32    LDO   \n",
       "2                                                  None      -   \n",
       "3                                                  None      -   \n",
       "4            0x7d1afa7b718fb893db30a3abc0cfc608aacfebb0  MATIC   \n",
       "...                                                 ...    ...   \n",
       "2850  polygon:0xd125443f38a69d776177c2b9c041f462936f...    FBX   \n",
       "2851                                               None      -   \n",
       "2852                                               None      -   \n",
       "2853         0x888cea2bbdd5d47a4032cf63668d7525c74af57a   POOF   \n",
       "2854                                               None      -   \n",
       "\n",
       "                              url  \\\n",
       "0         https://www.binance.com   \n",
       "1                https://lido.fi/   \n",
       "2             https://www.okx.com   \n",
       "3        https://www.bitfinex.com   \n",
       "4     https://polygon.technology/   \n",
       "...                           ...   \n",
       "2850           https://firebot.gg   \n",
       "2851         https://dexilla.com/   \n",
       "2852           https://rainbow.me   \n",
       "2853     https://houdiniswap.com/   \n",
       "2854     https://www.thellamas.io   \n",
       "\n",
       "                                            description        chain  \\\n",
       "0     Binance is a cryptocurrency exchange which is ...  Multi-Chain   \n",
       "1     Liquidity for staked assets. Daily rewards, no...  Multi-Chain   \n",
       "2     OKX, formerly known as OKEx, is a Seychelles-b...  Multi-Chain   \n",
       "3     Bitfinex facilitates a graphical trading exper...  Multi-Chain   \n",
       "4                                Ethereum sidechain\\r\\n      Polygon   \n",
       "...                                                 ...          ...   \n",
       "2850  Let AI manage your crypto and keep your portfo...      Polygon   \n",
       "2851  Dexilla is a revolutionary order book DEX that...   zkSync Era   \n",
       "2852  The fun, simple, & secure way to explore Web3,...     Ethereum   \n",
       "2853  Houdini Swap is an aggregator of non-custodial...     Ethereum   \n",
       "2854  Llament the time you've been in human form? Do...     Ethereum   \n",
       "\n",
       "                                         logo audits audit_note  ...  \\\n",
       "0      https://icons.llama.fi/binance-cex.jpg      0       None  ...   \n",
       "1             https://icons.llama.fi/lido.png      2       None  ...   \n",
       "2              https://icons.llama.fi/okx.jpg      0       None  ...   \n",
       "3         https://icons.llama.fi/bitfinex.png      0       None  ...   \n",
       "4          https://icons.llama.fi/polygon.jpg      0       None  ...   \n",
       "...                                       ...    ...        ...  ...   \n",
       "2850       https://icons.llama.fi/firebot.jpg      2       None  ...   \n",
       "2851       https://icons.llama.fi/dexilla.png      0       None  ...   \n",
       "2852       https://icons.llama.fi/rainbow.png      0       None  ...   \n",
       "2853  https://icons.llama.fi/houdini-swap.png      0       None  ...   \n",
       "2854    https://icons.llama.fi/the-llamas.jpg      0       None  ...   \n",
       "\n",
       "              mcap stablecoins language parentProtocol       staking  \\\n",
       "0              NaN         NaN      NaN            NaN           NaN   \n",
       "1     2.010541e+09         NaN      NaN            NaN           NaN   \n",
       "2              NaN         NaN      NaN            NaN           NaN   \n",
       "3              NaN         NaN      NaN            NaN           NaN   \n",
       "4     8.315996e+09         NaN      NaN            NaN           NaN   \n",
       "...            ...         ...      ...            ...           ...   \n",
       "2850  0.000000e+00         NaN      NaN            NaN  5.480555e+06   \n",
       "2851           NaN         NaN      NaN            NaN           NaN   \n",
       "2852           NaN         NaN      NaN            NaN           NaN   \n",
       "2853  0.000000e+00         NaN      NaN            NaN  7.033068e+06   \n",
       "2854           NaN         NaN      NaN            NaN           NaN   \n",
       "\n",
       "     wrongLiquidity          pool2 assetToken  oraclesByChain github  \n",
       "0               NaN            NaN        NaN             NaN    NaN  \n",
       "1               NaN            NaN        NaN             NaN    NaN  \n",
       "2               NaN            NaN        NaN             NaN    NaN  \n",
       "3               NaN            NaN        NaN             NaN    NaN  \n",
       "4               NaN            NaN        NaN             NaN    NaN  \n",
       "...             ...            ...        ...             ...    ...  \n",
       "2850            NaN  236619.468578        NaN             NaN    NaN  \n",
       "2851            NaN            NaN        NaN             NaN    NaN  \n",
       "2852            NaN            NaN        NaN             NaN    NaN  \n",
       "2853            NaN            NaN        NaN             NaN    NaN  \n",
       "2854            NaN            NaN        NaN             NaN    NaN  \n",
       "\n",
       "[2855 rows x 41 columns]"
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