03_CryptoCurrencyExtraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crypto currency extraction\n",
    "\n",
    "- Retrieve historical data of a crypto currency (Bitcoin)\n",
    "    - https://min-api.cryptocompare.com/data/histominute?fsym=BTC&tsym=USD&limit=10\n",
    "- Analyze it and store the wanted parameters\n",
    "- Plot the stored data\n",
    "\n",
    "## 1. Historical data retrievement\n",
    "\n",
    "The API used to retrieve historical data is **CryptoCompare** https://min-api.cryptocompare.com/. We chose this API because it provides many benefits for free :\n",
    "1. Retrieve historical data at minute interval up to 7 days. For older times, interval becomes hourly or daily. \n",
    "2. Many crypto currencies available TODO give a list\n",
    "3. Easy to use, no need to authenticate\n",
    "4. High rate limits : https://min-api.cryptocompare.com/stats/rate/limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTC\n"
     ]
    }
   ],
   "source": [
    "import urllib.request\n",
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np\n",
    "import datetime\n",
    "import time\n",
    "import os\n",
    "\n",
    "## API config\n",
    "BASE_URL         = \"https://min-api.cryptocompare.com/data/histominute\" # API base url\n",
    "CURRENCY         = \"USD\" # Available currency in the API\n",
    "APP_NAME         = \"HES_SO_master_crypto_analysis\" # API advises us to give an app name in requests\n",
    "\n",
    "### CHANGE THIS PARAMETER IF YOU WANT DIFFERENT CRYPTO \n",
    "# file = open(\"current_crypto.txt\", \"r\")\n",
    "# if file:\n",
    "#     CRYPTO_SYMBOL = file.read() \n",
    "# file.close()\n",
    "# if CRYPTO_SYMBOL is None:\n",
    "CRYPTO_SYMBOL    = 'BTC' # ZIL | BTC | NEXO # Crypto currency retrieved from API\n",
    "print(CRYPTO_SYMBOL)\n",
    "MAX_LIMIT        = 2000 # maximum data retrieved per API request\n",
    "\n",
    "## personal config\n",
    "FILE_PATH        = \"data/crypto/%s\"%(CRYPTO_SYMBOL) # Relative path to historical data\n",
    "SEP_CHAR         = '~' # character seperating dates from and to in filename\n",
    "ENVS             = ['CRYPTO', 'MOST_RECENT_FILE', 'MOST_RECENT_FILE_LINE_COUNT', 'MOST_RECENT_TS'] # Stored in var.csv\n",
    "MAX_ROW_PER_FILE = 10000 # Each file storing data has a maximum amount of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data_minutely(toTs, limit, crypto_symbol):\n",
    "    '''\n",
    "    Get data minutely\n",
    "    \n",
    "    --- params\n",
    "    @ toTs = the data to return until this time. set to -1 if you want to retrieve most recent data\n",
    "    @ limit = number of minutes to retrieve from 1 to 2000\n",
    "    @ crypto_symbol = the symbol of cryptocurrency (e.g. 'BTC' for bitcoin)\n",
    "    '''\n",
    "    contents = urllib.request.urlopen(\n",
    "        \"%s?fsym=%s&tsym=%s&limit=%s&toTs=%s&extraParams=%s\"%(BASE_URL,crypto_symbol,CURRENCY,limit,toTs,APP_NAME)\n",
    "    ).read()\n",
    "    json_string = contents.decode(\"utf-8\")\n",
    "    obj = json.loads(json_string)\n",
    "    df = pd.DataFrame.from_dict(obj['Data'])\n",
    "    if not df.empty:\n",
    "        return df.drop(['volumefrom', 'volumeto'], axis=1)\n",
    "    return df\n",
    "\n",
    "def get_var(key, crypto):\n",
    "    df_var = pd.read_csv(\"data/crypto/var_crypto.csv\", sep=',',\n",
    "                         dtype={'MOST_RECENT_TS': np.int32, 'MOST_RECENT_FILE_LINE_COUNT': np.int32})\n",
    "    return df_var[key].loc[df_var['CRYPTO'] == crypto].values[0]\n",
    "\n",
    "def update_var(key, value, crypto):\n",
    "    df_var = pd.read_csv(\"data/crypto/var_crypto.csv\", sep=',',\n",
    "                         dtype={'MOST_RECENT_TS': np.int32, 'MOST_RECENT_FILE_LINE_COUNT': np.int32})\n",
    "    df_var[key].loc[df_var['CRYPTO'] == crypto] = str(value)\n",
    "    df_var.to_csv(\"data/crypto/var_crypto.csv\", index=False)\n",
    "    \n",
    "def add_new_crypto(crypto):\n",
    "    df_var = pd.read_csv(\"data/crypto/var_crypto.csv\", sep=',',\n",
    "                         dtype={'MOST_RECENT_TS': np.int32, 'MOST_RECENT_FILE_LINE_COUNT': np.int32})\n",
    "    if df_var[ENVS[0]].loc[df_var['CRYPTO'] == crypto].empty:\n",
    "        new_line = pd.DataFrame([[crypto,-1,\"1\",0]], columns=ENVS)\n",
    "        df_var = df_var.append(new_line)\n",
    "        df_var.to_csv(\"data/crypto/var_crypto.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "add_new_crypto(CRYPTO_SYMBOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Data understanding\n",
    "\n",
    "The data returned contains the following information :\n",
    "\n",
    "- time : unix timestamp when data was recorded\n",
    "- open : The price at opening time of the interval\n",
    "- close : The price at close time of the interval\n",
    "- low : The lowest price observed during the interval\n",
    "- high : The higher price reached during the interval\n",
    "- volumefrom : TODO\n",
    "- volumeto : TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1565827680\n",
      "2019-08-14 23:58:00\n",
      "2019-08-15 00:08:00\n",
      "(11, 5)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10016.01</td>\n",
       "      <td>10030.09</td>\n",
       "      <td>10016.01</td>\n",
       "      <td>10030.06</td>\n",
       "      <td>1565827080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10031.89</td>\n",
       "      <td>10032.98</td>\n",
       "      <td>10015.47</td>\n",
       "      <td>10016.01</td>\n",
       "      <td>1565827140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10090.93</td>\n",
       "      <td>10091.99</td>\n",
       "      <td>10031.56</td>\n",
       "      <td>10031.89</td>\n",
       "      <td>1565827200</td>\n",
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      "text/plain": [
       "      close      high       low      open        time\n",
       "0  10016.01  10030.09  10016.01  10030.06  1565827080\n",
       "1  10031.89  10032.98  10015.47  10016.01  1565827140\n",
       "2  10090.93  10091.99  10031.56  10031.89  1565827200"
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   ],
   "source": [
    "df_histo = get_data_minutely(-1, 10, CRYPTO_SYMBOL)\n",
    "print(df_histo['time'].iloc[-1])\n",
    "print(datetime.datetime.utcfromtimestamp(int(df_histo['time'].iloc[0])).strftime('%Y-%m-%d %H:%M:%S'))\n",
    "print(datetime.datetime.utcfromtimestamp(int(df_histo['time'].iloc[-1])).strftime('%Y-%m-%d %H:%M:%S'))\n",
    "print(df_histo.shape)\n",
    "df_histo.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Collect and save data\n",
    "\n",
    "Data is collected from the API and then saved in csv files. Each csv file contains a maximum amount of rows. We split the data into multiple files if this max amount is reached.\n",
    "\n",
    "The API have limitations depending the time interval between two currencies. The more precise route for historical data let us retrieve currencies every minute but is limited to 7 days in the past. To retrieve more data back in the past, we must retrieve data hourly or daily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range= 2019-08-13 14:48:00 2019-08-15 00:08:00\n",
      "range= 2019-08-12 05:28:00 2019-08-13 14:48:00\n",
      "range= 2019-08-10 20:08:00 2019-08-12 05:28:00\n",
      "range= 2019-08-09 10:48:00 2019-08-10 20:08:00\n",
      "range= 2019-08-08 01:28:00 2019-08-09 10:48:00\n",
      "range= 2019-08-08 00:13:00 2019-08-08 01:28:00\n",
      "0\n",
      "df_to_fulfill (10000, 5)\n",
      "df_remaining (81, 5)\n",
      "done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "# Nummber of objects wished to be retrieved from API. \n",
    "# If using minutely, then it corresponds to the number of minutes\n",
    "total_wished = 60 * 24 * 7 # API LIMIT is 7 days minutely which is 60 * 24 * 7 minutes\n",
    "\n",
    "# Last unix timestamp to return data for\n",
    "toTs = -1\n",
    "\n",
    "# Init dataframe of historical data\n",
    "df_historical = pd.DataFrame()\n",
    "\n",
    "# Retrieve existing most recent file if any\n",
    "# If any file is present and it is not full, we'll use it to store future data\n",
    "last_ts = get_var(ENVS[3], CRYPTO_SYMBOL)\n",
    "last_file_line_cnt = 0\n",
    "last_file = None\n",
    "if last_ts > 0:\n",
    "    last_file = get_var(ENVS[1], CRYPTO_SYMBOL)\n",
    "    last_file_line_cnt = get_var(ENVS[2], CRYPTO_SYMBOL)\n",
    "    now = int(time.time())\n",
    "    total_wished = int((now - last_ts)/60) # Get data we didn't retrieve from now until last time\n",
    "\n",
    "# Retrieve data for the 7 past days until last_ts\n",
    "while(total_wished > 0):\n",
    "    limit = min(total_wished, MAX_LIMIT)\n",
    "    df = get_data_minutely(toTs, limit, CRYPTO_SYMBOL)\n",
    "    #if df.empty:\n",
    "    #    print(df.shape)\n",
    "    #    break\n",
    "    df_historical = df_historical.append(df)\n",
    "    min_time = df['time'].iloc[0]\n",
    "    max_time = df['time'].iloc[-1]\n",
    "    print('range=', datetime.datetime.utcfromtimestamp(min_time).strftime('%Y-%m-%d %H:%M:%S'),\n",
    "         datetime.datetime.utcfromtimestamp(max_time).strftime('%Y-%m-%d %H:%M:%S'))\n",
    "    toTs = min_time\n",
    "    total_wished = total_wished - df.shape[0]\n",
    "\n",
    "# Sort data in ascending time\n",
    "if not df_historical.empty:\n",
    "\n",
    "    df_historical = df_historical.sort_values(by=['time'])\n",
    "\n",
    "    print(last_file_line_cnt)\n",
    "    ## Save historical data to csv\n",
    "    available_lines = MAX_ROW_PER_FILE - last_file_line_cnt\n",
    "    df_to_fulfill = df_historical.head(available_lines)\n",
    "    df_remaining  = df_historical.tail(df_historical.shape[0] - df_to_fulfill.shape[0])\n",
    "\n",
    "    print('df_to_fulfill', df_to_fulfill.shape)\n",
    "    print('df_remaining', df_remaining.shape)\n",
    "\n",
    "    # Fulfill last file if any\n",
    "    if not df_to_fulfill.empty:\n",
    "        from_str = datetime.datetime.utcfromtimestamp(df_to_fulfill['time'].iloc[0]).strftime('%Y-%m-%d %H-%M-%S')\n",
    "        max_time = df_to_fulfill['time'].iloc[-1]\n",
    "        to_str = datetime.datetime.utcfromtimestamp(max_time).strftime('%Y-%m-%d %H-%M-%S')\n",
    "        if last_file is None:\n",
    "            last_file = f\"{FILE_PATH}/{from_str}{SEP_CHAR}{to_str}.csv\"\n",
    "            df_to_fulfill.to_csv(last_file, mode='w', header=True, index=False)\n",
    "        else:\n",
    "            df_to_fulfill.to_csv(last_file, mode='a', header=False, index=False)\n",
    "            filename = f\"{last_file.split(SEP_CHAR)[0]}{SEP_CHAR}{to_str}.csv\"\n",
    "            os.rename(last_file, filename)\n",
    "        last_file_line_cnt = last_file_line_cnt + df_to_fulfill.shape[0]\n",
    "        \n",
    "    # Split remaining dataframe into chunk of MAX_ROW_PER_FILE rows max\n",
    "    list_df_remaining = [df_remaining[i:i+MAX_ROW_PER_FILE] for i in range(0, df_remaining.shape[0], MAX_ROW_PER_FILE)]\n",
    "    for df_remain in list_df_remaining:\n",
    "        from_str = datetime.datetime.utcfromtimestamp(df_remain['time'].iloc[0]).strftime('%Y-%m-%d %H-%M-%S')\n",
    "        max_time = df_remain['time'].iloc[-1]\n",
    "        to_str = datetime.datetime.utcfromtimestamp(max_time).strftime('%Y-%m-%d %H-%M-%S')\n",
    "        filename = f\"{FILE_PATH}/{from_str}{SEP_CHAR}{to_str}.csv\"\n",
    "        df_remain.to_csv(filename, index = False)\n",
    "        last_file_line_cnt = df_remain.shape[0]\n",
    "\n",
    "    # Update env variables\n",
    "    update_var(ENVS[3], max_time, CRYPTO_SYMBOL)\n",
    "    update_var(ENVS[1], filename, CRYPTO_SYMBOL)\n",
    "    update_var(ENVS[2], last_file_line_cnt, CRYPTO_SYMBOL)\n",
    "\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Plot crypto currency time series data\n",
    "\n",
    "Read and plot retrieved data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import init_notebook_mode, iplot\n",
    "import plotly.graph_objs as go\n",
    "init_notebook_mode(connected=True) # plotly offline mode\n",
    "\n",
    "import glob\n",
    "\n",
    "MAX_ROW = 500 # Plot the MAX_ROW most recent points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Read data from csv files\n",
    "\n",
    "For this example we'll collect max 1000 points (the most recent ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_most_recent_data_from_csv(folder, n_rows):\n",
    "    '''\n",
    "    @ folder : relative path where the data is contained\n",
    "    @ n_rows : the number of rows to retrieve up to the most recent ones\n",
    "    \n",
    "    Return a dataframe containing n_rows of the most recent data retrieved\n",
    "    '''\n",
    "    files =  glob.glob(f\"{folder}/*.csv\")\n",
    "    files = sorted(files)\n",
    "    df = pd.DataFrame()\n",
    "    for file in reversed(files):\n",
    "        print(file)\n",
    "        df = df.append(pd.read_csv(file))\n",
    "        if df.shape[0] > n_rows:\n",
    "            break\n",
    "    return df.sort_values(by=['time']).tail(n_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/crypto/BTC/2019-08-14 22-48-00~2019-08-15 00-08-00.csv\n",
      "data/crypto/BTC/2019-08-08 00-13-00~2019-08-14 22-47-00.csv\n",
      "df shape (500, 5)\n",
      "from 1565797740 to 1565827680\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        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>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9581</th>\n",
       "      <td>10500.27</td>\n",
       "      <td>10503.50</td>\n",
       "      <td>10496.66</td>\n",
       "      <td>10496.66</td>\n",
       "      <td>2019-08-14 15:49:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9582</th>\n",
       "      <td>10497.03</td>\n",
       "      <td>10500.30</td>\n",
       "      <td>10495.13</td>\n",
       "      <td>10500.27</td>\n",
       "      <td>2019-08-14 15:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9583</th>\n",
       "      <td>10496.26</td>\n",
       "      <td>10497.03</td>\n",
       "      <td>10493.28</td>\n",
       "      <td>10497.03</td>\n",
       "      <td>2019-08-14 15:51:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9584</th>\n",
       "      <td>10495.98</td>\n",
       "      <td>10496.36</td>\n",
       "      <td>10494.28</td>\n",
       "      <td>10496.26</td>\n",
       "      <td>2019-08-14 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9585</th>\n",
       "      <td>10495.28</td>\n",
       "      <td>10496.34</td>\n",
       "      <td>10494.69</td>\n",
       "      <td>10495.98</td>\n",
       "      <td>2019-08-14 15:53:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         close      high       low      open                 time\n",
       "9581  10500.27  10503.50  10496.66  10496.66  2019-08-14 15:49:00\n",
       "9582  10497.03  10500.30  10495.13  10500.27  2019-08-14 15:50:00\n",
       "9583  10496.26  10497.03  10493.28  10497.03  2019-08-14 15:51:00\n",
       "9584  10495.98  10496.36  10494.28  10496.26  2019-08-14 15:52:00\n",
       "9585  10495.28  10496.34  10494.69  10495.98  2019-08-14 15:53:00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read MAX_POINTS from the last file(s)\n",
    "df_hist_minutes = get_most_recent_data_from_csv(FILE_PATH, MAX_ROW)\n",
    "print('df shape', df_hist_minutes.shape)\n",
    "from_str = df_hist_minutes['time'].iloc[0]\n",
    "to_str = df_hist_minutes['time'].iloc[-1]\n",
    "# from_str = datetime.datetime.utcfromtimestamp(df_hist_minutes['time'].iloc[0]).strftime('%Y-%m-%d %H-%M-%S')\n",
    "# to_str = datetime.datetime.utcfromtimestamp(df_hist_minutes['time'].iloc[-1]).strftime('%Y-%m-%d %H-%M-%S')\n",
    "print('from', from_str, 'to', to_str)\n",
    "df = df_hist_minutes\n",
    "df['time'] = df_hist_minutes['time'].apply(lambda x: datetime.datetime.utcfromtimestamp(x).strftime('%Y-%m-%d %H:%M:%S'))\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Plot candlestick like graph\n",
    "\n",
    "Plot only the most recent points to see if evereything did well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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       "                        {\"title\": {\"text\": \"BTC currency\"}, \"yaxis\": {\"title\": {\"text\": \"Price USD\"}}},\n",
       "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace = go.Candlestick(x=df.time,\n",
    "                       open=df.open,\n",
    "                       high=df.high,\n",
    "                       low=df.low,\n",
    "                       close=df.close)\n",
    "data = [trace]\n",
    "layout = {\n",
    "    'title': f\"{CRYPTO_SYMBOL} currency\",\n",
    "    'yaxis': {'title': 'Price USD'},\n",
    "}\n",
    "fig = dict(data=data, layout=layout)\n",
    "iplot(fig)"
   ]
  }
 ],
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