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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](\"https://avatars1.githubusercontent.com/u/8135259?s=200\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visit us at - https://github.com/supercoderz\n",
"\n",
"pyeurofx\n",
"========\n",
"\n",
"pyeurofx is a library that allows you to fetch the close prices of various majot currencies vs the Euro.\n",
"The library provides simple functions that you can invoke and get the data as list of tuples or as pandas data frames.\n",
"\n",
"pyeurofx makes use of the following libraries - \n",
"- requests\n",
"- lxml\n",
"- pandas\n",
"\n",
"\n",
"The data is fetched from the following XML sources - \n",
"\n",
"- Historical data - 'http://www.ecb.europa.eu/stats/eurofxref/eurofxref-hist.xml'\n",
"- Daily data - 'http://www.ecb.europa.eu/stats/eurofxref/eurofxref-daily.xml'"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from eurofx import *"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Daily data\n",
"----------\n",
"\n",
"You can get the daily data or the historical data. Daily data will be the close price released at 3 PM CET on that day or the prevvious day depending on when you are running the call. Historical close prices will be the close prices for all the days since 1 Apr 1999\n",
"\n",
"The daily data is returned as a list of tuples - each tuple is a currency, date, rate pair."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"daily = get_daily_data()\n",
"print daily"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Historical data\n",
"---------------\n",
"\n",
"\n",
"Fetching all the historical data is just one function call - the data is returned just like daily data in a list of tuples."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"historical = get_historical_data()\n",
"print historical.__len__()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"124398\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first date of historical data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print historical[-1:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[('ZAR', datetime.date(1999, 1, 4), '6.9358')]\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"getting data as pandas data frame\n",
"---------------------------------\n",
"\n",
"you can easily get the data as a pandas data frame by using the _df versions of the daily and historical commands\n",
"\n",
"Any date which has a missing data will show up as a NaN in pandas data frame."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"daily_df = get_daily_data_df()\n",
"print daily_df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" USD JPY BGN CZK DKK GBP HUF LTL \\\n",
"2014-07-25 1.3440 136.97 1.9558 27.482 7.4568 0.79115 308.06 3.4528 \n",
"\n",
" PLN RON ... ILS INR KRW MXN MYR \\\n",
"2014-07-25 4.1435 4.3973 ... 4.6004 80.7677 1380.11 17.4162 4.2655 \n",
"\n",
" NZD PHP SGD THB ZAR \n",
"2014-07-25 1.5728 58.199 1.6682 42.780 14.1489 \n",
"\n",
"[1 rows x 32 columns]\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"historical_df = get_historical_data_df()\n",
"print historical_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" USD JPY BGN CZK DKK GBP HUF LTL \\\n",
"2014-07-25 1.344 136.97 1.9558 27.482 7.4568 0.79115 308.06 3.4528 \n",
"2014-07-24 1.3472 136.97 1.9558 27.48 7.4573 0.79215 307.76 3.4528 \n",
"2014-07-23 1.3465 136.51 1.9558 27.454 7.4569 0.7908 307.15 3.4528 \n",
"2014-07-22 1.3481 136.93 1.9558 27.485 7.4567 0.7905 309.63 3.4528 \n",
"2014-07-21 1.3518 136.97 1.9558 27.468 7.4567 0.7915 309.56 3.4528 \n",
"\n",
" PLN RON ... ZAR LVL EEK SKK ISK CYP MTL \\\n",
"2014-07-25 4.1435 4.3973 ... 14.1489 NaN NaN NaN NaN NaN NaN \n",
"2014-07-24 4.1408 4.4068 ... 14.138 NaN NaN NaN NaN NaN NaN \n",
"2014-07-23 4.134 4.4228 ... 14.1759 NaN NaN NaN NaN NaN NaN \n",
"2014-07-22 4.1473 4.4396 ... 14.3199 NaN NaN NaN NaN NaN NaN \n",
"2014-07-21 4.1511 4.4445 ... 14.3647 NaN NaN NaN NaN NaN NaN \n",
"\n",
" SIT ROL TRL \n",
"2014-07-25 NaN NaN NaN \n",
"2014-07-24 NaN NaN NaN \n",
"2014-07-23 NaN NaN NaN \n",
"2014-07-22 NaN NaN NaN \n",
"2014-07-21 NaN NaN NaN \n",
"\n",
"[5 rows x 41 columns]\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plotting data\n",
"-------------\n",
"\n",
"Once you the pandas data frame, using matplot lib, you can easily plot data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(historical_df['USD'])\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
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FCyzAw3H8eBH725SKPQsOGD/e8ev++qvj5yDcE7cWe0t88YXlNAdyA5+Warue\nOYOtUs/eWUyZgnbJDdxpGUEi7JGuX4+ROlKxVzsySW3YeEhxMb4dSmFRRcJB+08+UdcG4eQ31rZo\ngTO+pZ0L9lYRFOT4ddWqXka4H41S7Js35wucW4O/f303jsHAPzCUBraEee31gBXo2L27fhZMHx/t\nC1QL3RmjRmErFXt3KJLN7uOKFfw2acZUPz/LvX9HkZvpbDDgG4UUNoNbDbEnPJdGKfYNcfSouPyf\nv7/8AC07hgmDHMuXq2tbQ7CKXLW1KLJnz+IHAENOLdXjVQu5EERpnh53mOl5RCa134OS+d+bNuHD\n/PnnsT6wmhQUWO/+CgkBePddPpGbLWhd/J1wHzxS7Hv0ENdJ9fbG6Awp5eWYdZD1rKRwnOUQSa1g\nk4SY2APwkSbCUEKt2L1b2/PrhfAB9fzzKL4AAMOHi48zGDDqa80a9a5dV4c1AoRF3BvC0tyAhkhN\ntf07ROPEI8VeytKl4oIfrDj5nDmuOfi4Zg3AsmW8G4AlctND7C3Ff4eHa3tdLXnySX6W8G23ifdV\nVfFRMmrBooHkZnJbolUr+5K3SbO+ArhWlS5CP0jsAaMiAPjZk6+9xu+TluRzBZKSMEMiE6H338e2\nulp7sWcT1dhkNIZWuZv0QCjm7EHP6NMH02dL0yVbQ00N73YTcuAAtgMGWH+utm0bTrctxz331N9G\ns3E9ExJ74CMtFi+uv48NQroi7K2D1SstLdVe7B9+GAeupRNzhAPV7uCzB+BTErA01QD1Q3Pvvx/d\nePa84X3zjfyEu++/x9aSe1COgABxYRNr8fGpH0QgVzOZaPy4SOZx58LimOX+MV09AdXChZhCAQBz\nt0+dqv015SKdmjRxzZKESuzZg4Pwcm6axYv5h1azZvYJZHGx/PaRI22fpBUQYHninxL/+7/17Th7\nlk/5THgOJPa3eOIJHHwcMUK8XZjt0hWRpi9wZq/NywsLunTs6DwbbKFJE8whL+XsWYAOHfh1b2+A\noiLbz8987AYD5tYJCcF1YUEca2nRwj6xF0adMU6eJLH3RMiNcws/PxyknTdPvN0VKhopYTRiz40l\nd/vsM+fa89lnANOnO9cGRxEKPQD+bdTU1B+naAhhoXAW7QNg30B6QACKtDRaaOBA8VwBa3Bl1ySh\nHST2t2A9+Bs3sBIVy0OiZvZBLfD2xgyOrhg11Fho1Qp75bY++IWFxIcN45ftEXuW654l/8vPx7kh\n27fLzwK2KfaCAAAgAElEQVQGwDeBu+4Sj0U1aeKaqagJ7SGxv8Wnn2L70Uf4T8QG6lw9yZd00E7a\nKyXUoajI9tm0wtKHgwbxy5mZtou9sP4xx+GgsjAvEseJcxZdv46un337sAPDcZgGYvVqcXpuwnMg\nsZfh0iUU+/HjMXe8KyNM1gUgDhsl1OWnn8T5crZtk68rwGB1egH4QWCOw3TcjhRDWbcO23Pn+HGA\npUvFdReEk/2Yi69rV/sHegn3h8T+FsLi4QDo1lm92vV7ysJSdgD65+rxFFgBcOGb1AMPWD/Qyb7H\nevvScpO2IExTfO0aPkiOHsV15jpiE7cAxMnPAgLqP7QIz4DE/hbJyeJ1d4kVl7oDlNI7E/bDZp1K\ne+Tx8fLHS8MdWToO9iYgl8++Ic6dq7/t6lXMfsrO++672Ardj0KxZ8n9pEnfiMYPib0A4YCauyAN\nc3SXh5S7wcR+/XqxL96S733hQvH6uXP49sjeFNevt92GsDD57UVFWHULgBdzodgLk6GxaB61i7EQ\nrg+JvQDhIJi78OijAFu3OtuKxg8T++nTxcnFpH8zVVVYGyE3F9cffBCLtQMAPPccf1ybNtrYmZiI\nLZtoByDOhcOiyygbpuehKPaTJk1aYTKZSqKjo2USwgJ8/fXXT8bExOT06tXr8D333PPb4cOHe2lj\npj6wXvGOHc61wxYMBj55lz29RcI6du3il1u25F0jX30lFtNhw9AvzpKcbdkiFnlHkea779ABoEsX\nfn39eixByGxKScE6uwyDAScOCucAEB4Cx3EWP7t27Uo4dOhQXFRU1BG5/Xv27Lm7vLy8JcdxkJaW\nNiQ+Pn6f3HF4GfcAgOOys51thW2cOYN2HzjgbEsaLwMH4j0G4LjUVP6eA3Dc66/zxzVvzm9/8UXc\nVlnJb2MfRxCep2tXjlu3rv75ATjOz0/5+4Trc0s7FXXa2o+i5y4hIWF3fn5+J0v777777r1sOT4+\nfn9hYWGoWg8hZ1FcbF+RCGfCJoTRxCrt+PlnvmJZZaV4AFbY6xcWwWHuNS3dgydPAjzyiPw+S7UW\nevasn8iOaPyoNkyzfPnyyQ899JDFUgmzZs36ezkxMRESmXPRxXA3oQfg/bAk9tohHPgeMwbdJYMH\nA+zdi9lGH3gAHwhCTpyo/90+fXBSlSPMmgXQvz8/UctS8jlLRdJbtXIvV6UnkZGRARla5QtvqOuf\nl5fXyZIbh322b99+X2RkZO6lS5cC5fYDvTNqitmMr+V//ulsSxo3L7/Mu0AefZTjBgzguCVLxG4R\noRvlxx/577JtQ4eqZw8Ax33yCS5/9119N86lS5a/R/+S7gGo6MZxOBrn8OHDvZ555pllmzdvHhEY\nGEjDPk6AhdG5emoHd+ftt/nl9esBdu5Uvud33skvb9uGbVqaevZcvswP/o4ZAzBling/K2EpZeNG\ngMBA9ewg3AOHxP7cuXMdHn300fVfffXVUxEREafUMoqwnT/+cJ/Uwu5KmzbipGKbNoknLAGgf37B\nAsz+KUxfMHCg+va0aiV2EUknBlpy73Tpgg8Ka9xJ1dU4k5xwfwycQkHKpKSktTt37hxQVlYWZDKZ\nSlJSUmaazWYjAEBycvKSKVOmfL5hw4ZRHTp0OAcAYDQazZmZmX3rXcRg4JSuQxDuBBPYP//ErJPP\nPovrdXUosGazvNB27ow98A8+0N42AMu1Zk+fBoiIwMRu8+dbPldNDT7QxoyhurXOwmAwAMdxqkyV\nVBR7tSCxJxoTY8agK6SmBnvw//oXbmfFyZ1ZQMYasS8uxrkZM2fiYK8l3n0Xj1E6F6Etaoo9zaAl\nCBtZt47PJCmsZNasmesU875wwfI+Fr21ahVATg5fAF1KYSG/TDNu9eXGDaxhrCYk9gThAE8+CXDo\nkLOt4Dl5Eh84wvECKUzsz54FiI3lC69LEb4lCOcPENqzfTtAUpK65ySxJwgHMBpdqxhIRETDSc6M\nRoA332z4XOztBYBy4OuNsLKZWpDYE4QHcvvt4vW//qp/jLCWgyPFVgjXgMSeIFSAuTkGD3auHdYS\nECBe37Ch/jHC8py//aa9TYS2kNgThAr4+WHESnq6sy2xDlYmkSGdLwCAqZrnzUO3UF6ePnYR/O9C\n7fQnJPYE4YFIxZ4N2gopLMQ3gC5dAN5/Xx+7CKxRDFDf1eYoJPYE4YEwsX/xRVyW9iLLyzEn/7Zt\nAKdobryusCR1zIWmFiT2BOGBMLF/4AGcJFZRwe/jOIBly3D5mWf0t83TCQnB9t571T0vzaAlCA/k\nwgUAkwl776wuLfsXNRjQdXPmDNa37dAB/cj0L6wP//M/mGp92jQALy+aQUsQhAOwnr0lv/CZM/xx\nLKUCib0+lJcDtG4tntSmBiT2BOGB+PpixaqICLFvWBpP7+8P8NZbOIBLE6v04coVzGiqNiT2BOGB\nGAyYFtvLC8MqWX77MWP4Y2bNwv0GAz4Qbt50iqkeR3k5iT1BEBrg58cL+bFj/PYvv+SXfXwwOReh\nLf/5D+bFad1a/XOT2BOEh8OEvKZGnDZh3TrxMdSz1x5WKEbtGHsAEnuC8HhYoRVpgfLYWH6ZxF4f\n9u7FVu0YewASe4IgbnHwoOV9vr4k9nqidiQOAIk9QRC3+PZby/uoZ68fY8dqc14Se4IgAAAgLMzy\nPhJ7/XjxRW3Oqyj2kyZNWmEymUqio6OPyO0/fvx497vvvnuvj4/PzQULFryqjYkEQWjNokUAUVG4\nfP48wKBB4v0k9vrg46NdMRxFsZ84ceLK9PT0IZb2t2nT5uKiRYtefO211z5U3zSCIPSiZUuAtDRc\nNpkAfv5ZvF8Yejl3LqZRINSF47Bovbe3NudXFPuEhITdgYGBly3tb9u2bWnv3r0PGo1GFymzTBCE\nPUiLmUjx9uYLtMyYAbB0qfY2eRpXr6Lgs+gotWmgWqV6zGIJNgAgMTEREhMT9bo0QRAN4OODrXAG\nrZCvvsLP5Mm4fuGCPnZ5Ch99xGoGZMCsWRmaXMMpYk8QhGvh6ytuG0I405ZwnFf/HvFMhFmzEv/e\nnpKSoto1KBqHIIi/e/a5ufL7pW4eSoqmDXfcod25VRF7tfItEwThHJjY//67/P6yMmw3b8a2ZUvM\neU+FyNWFFS7RAsXiJUlJSWt37tw5oKysLMhkMpWkpKTMNJvNRgCA5OTkJefPn2/fp0+fA1evXm3h\n5eVVFxAQcC03N7eHv79/hfA8VLyEIFybEycAunfHZUv/qj4+GC0CgFWUdu1SPt5TOXUK4J13ANau\nte74Rx4B2LQJl+PiAA4d4vcZDOoVL6FKVQRBwOnTmNsewLJ4G42YLE0K/WuL+eADgNdft+6+1NSI\n6/+uXw8wahS/rqbY6zZASxCE67NqleV9ckJP1Gf+/PrbPv0U4J//BGgqUNwPPxSPfRQVAQQHa2cX\n9ewJgoCqKoCXXgJYssTyMZaSc9XVaZO4y11h94Ldl9paFPlTpwDCw+sfx5CTSDV79hSNQxAENGum\nLPRKsMlWBMIKuHvdUtfSUmzZeIezILEnCMIqhPMgZ8zANjgY4OJFp5jjsgjdXVu3AuzZg8s9e/Lb\nnTEpjcSeIAirELod5sxBt0NICMCZM86zyRWpEMQiDh4MMHp0/WOOH+eXQ0P53r+WkNgTBGEVcn75\nzp3FpQwJftBV6J+XMmAAv1xYCBAUpK1NACT2BEFYiVy++9OnrY8n9wRqawGyszGqqWVL677z+OOa\nmvQ3FHpJEIRVfPYZuiWEs2aFE4AIgJ07sW3XDuDSJX57s2YAvXrx63Fx+GCIiAD44gt9bKOePUEQ\nVuHrC5CUBLB4Mb+NFTyhyGrk6FFs27UDyM/ntw8bBnDgAL8eFIR1Ab7/nk9VoTUk9gRB2M2KFdi+\n8opz7XAVtm7Ftm1b8faPPuKXly7F4jAdO+pnFwCJPUEQDtCqFbb79zvXDleBjWv4+4u3h4RgUZI3\n3gBIThYfqxck9gRB2E1tLbZ1dc61w5kcuVWhu6AAxzUAxGK/YwcKvZ+fOJVCQ9XB1IbEniAIuwkM\nxLZ/f+fa4SzKynDgtbYW0yEAAOzdK64jyyajCROeOQMSe4Ig7MZkAvj4Y89NklZZiW1FBR9f37o1\ntrt3ixOfaVVb1lpI7AmCcIiAAID/+z/PjMiZMgXbigqAiRNxmcXX9+8PYDbzxwrF/sUX9bFPCIk9\nQRAOwcINnZ3oyxn88gu2N24AdOuGyyaT/LFM7CdMwIej3pDYEwThENOmYVtS4lw7nAFLifDuu+ir\n//hjy8cWFWH7+efa2yUHiT1BEA7BCm4UFzvXDmcwbBi2rHZv8+YNf8dZA7Uk9gRBOMzddyuHX9bW\nYrm+xsbNm9jm5mLr5+c8WxpCUewnTZq0wmQylURHRx+xdMxLL730f127dj0ZExOTk5WVFae+iQRB\nuDp79wKMH4+ZMffurb+/tBTrsu7Yob9tWiLNa6PUs+/WDSAmRlt7lFAU+4kTJ65MT08fYml/amrq\nQ6dOnYo4efJk16VLl/7zueee+0x9EwmCcAdYXvtjxywfw2LR3Z3ffsPoIxZ6ySgvt/yd48cxI6az\nUBT7hISE3YGBgZct7d+8efOIp59++gsAgPj4+P3l5eWtSkpKLIxFEwTRWOnRg1+WS+xVXY0tS6/g\n7vTvjxWomjUTb3/qKefYYw0OpTguKioKCQsLK2DroaGhhYWFhaEmk6neuPysWbP+Xk5MTIREYY0z\ngiDcmvBw3m/95JMA48aJ9y9ahK2vr752acmGDRhueuECZrkEEE+isoeMjAzIyMhw2DY5HM5nL618\nbjAYZKdWCMWeIIjGRUICwA8/1N/esydARgafHXP4cHwoREbqap4mNG2KlbratkWXDssT5AjSjnBK\nSorjJ72FQ9E4ISEhRQUFBX/nbissLAwNCQkpctwsgiDciaFD5bfn5gLk5PBuHACAXbv0sUlr5s0T\nFyRxdjqEhnBI7EeMGLF59erVEwAA9u3bd1erVq3K5Vw4BEE0bljs+LBhWF/12DGsrQqAOWPY7FIA\nTPPrzgjTQuhVeEQNFN04SUlJa3fu3DmgrKwsKCwsrCAlJWWm2Ww2AgAkJycveeihh1JTU1MfioiI\nONW8efPrK1eunKiP2QRBuBIspe+MGZj3RThgu3cvP+kIAODKFX1tUxthvhtHffR6YuB0yF5kMBg4\nPa5DEITzMJsxvLBPH+U8Oc2a8ZOR3JHr1/mH2x13iB9kamMwGOqNi9oLzaAlCEIVjEaA0FDLQh8a\niu3TT+tnkxYIe/aHDzvPDlshsScIQjWk5fiEsKRhS5fqY4tWCAeb27d3nh22QmJPEIRqsIHa0aPr\n73v5ZX1t0YL33hOnMNa7aLgjkM+eIAhNMEg8zXV1ONnq1CmAAwecY5OtFBVhsXAAzO/DJk8BAKSm\nAtx2m7b5bshnTxCEy+PjA9C3L042+uMPFP/hwwG6dnW2ZdaxfTuOM7B+6sMPi/cPHercxGa24kaB\nQwRBuBP5+Sj4Xl44kxYA12/ccKpZVjNwILZVVWi3MImbK6cytgSJPUEQmiBXnq95c6zX6uoIc/NX\nVuLs2MuClJDOqCHrKOTGIQhCN4KCAC5edM61DQa+qlZDCFMf3LgBcO6ceL87ReEwqGdPEIRu+Pvj\npCRncf687d+prASoqcHlI0cAAgNJ7AmCIBTx9wf4808sdNKli7OtkUcaOFhZiROp4uIAoqKcY5Ma\nkNgTBKEbLHFYeHh9UdWK6mqAX36x/njhDNlmzVDsq6utKybuypDYEwShG61b63/NrCyAhx6y/nhh\n9amWLdFnf/Om+4s9DdASBKEbBgMW+9CDykqc0XvkCL/N1pDJigqAr7/GcQYSe4IgCBt47z1stY63\n37YNB1a/+orfJi0Q3hCVlVhli8SeIAjCRthM1PJy27/LceJEZEqw4uY7d4q3nz6tfH6540jsCYIg\nbOS227CdPt32744eLfapKyF9KPTuja1wwpSUHTv45S5dAFatApgwgcSeIAjCblavtu34DRvwAwAw\nezZASQMFUKVuooMHMXRSyX0kfRB4e2O6BBJ7giAInXj0UX75nXcA1q9XPl7OP+/rqyz2TZsCdO8O\nkJeH697e+IZAYk8QBGEHS5fiBCupP90WZs5U3i8V9YQEZbGvrAT44APMdNmpE27z9sa3ic8/9wCx\nT09PH9K9e/fjXbt2PTlv3rx6deEvX74cOGrUqA0xMTE58fHx+48ePdpTG1MJgmgshIZiWGNionXH\nHz9ef1tpqfJ3hD37jz8G2LQJQy8tReQsWYI56oU1ZVmOnCtXxCGc7oii2NfW1jZ54YUXFqenpw/J\nzc3tsXbt2qRjx45FCo+ZM2fOm3fcccehnJycmNWrV0+YOnXqQm1NJgjC3WGRMtZQVAQQeUt1li/n\nt48bp/w9YQ9+yBDMaaPUs790CVthdkvh8tWr1tvsiiiKfWZmZt+IiIhTnTp1yjcajeaxY8d+s2nT\nppHCY44dOxZ533337QAA6Nat24n8/PxOpaWlOk2bIAjCHfFqwKewfTtOwPrpJ3FUDXOvAPDJyaQU\nFOD3jxwBGDAAt3Xvjq2PD86GlYNFCQkRhmJOnapss6ujmC6hqKgoJCwsrICth4aGFu7fvz9eeExM\nTEzO+vXrH+3fv/+vmZmZfc+ePduxsLAwtG3btqKXrFmzZv29nJiYCInWvr8RBNHoiIvjBz/leOYZ\nbA8cEEftJCTwy4cOyX+3Qwd+OSJCLNgGg3wPvawM4F//qr9d+N2mOiSXycjIgIyMDE3OrWi+wWBo\nMFXR9OnT506dOnVhXFxcVnR09JG4uLisJk2a1EqPE4o9QRCejbc3RrxYynzJBNnHB33tAFj/lRU0\nBxBXjrJEQYF4fc0a/Dz3nHj7d9/xy3Pn8su9ejV8DTWRdoRTUlJUO7ei2IeEhBQVFBSEsfWCgoKw\n0NDQQuExAQEB11asWDGJrXfu3DmvS5cuZ1SzkCCIRknr1thz5jhxcfKaGuxpAwDMmIG9/GXLxBWu\n4uIAjh2rf05pdsvkZPlrFxQAhIXJ75s2jV8W1pi1Z8avK6HoOevdu/fBkydPds3Pz+9UXV3t/e23\n3z4xYsSIzcJjrly50rK6utobAGDZsmXPDBgwYKe/v78bFB4jCMKZ+PigG2f/fvH2wkLxenY2tsIo\nmnbt5BOq/fabeN2Sn/3VV/nlM2d4G9assTyeEBEhv91dUBT7pk2b1ixevPiFwYMHb+nRo0fuE088\n8W1kZOSxJUuWJC9ZsiQZACA3N7dHdHT0ke7dux/fsmXL4IULF7r5MAZBEHry3nsAaWm86+bzz8X7\nDxwAuP9+fvYsAJY3lAuhlJYPFA7oAgB8+SW2wodCeDjAF18ADB4MkJRU/5wBAVhwpWNHq34cl8XA\n6VBBwGAwcHpchyAI92LhQhT70lKAN9/EZaFLh7FxI8DIW3GAe/eiiN92G0BuLh+WCVD/u1LZWbEC\nYPJkDP1kYZXsO489BvDf/6ryY6mGwWAAjuNk7ojtUPESgiCcRtu2/OSoOXMAfv5Z/rjbb+eX776b\nz2HTowcuGwzW1bZlMfaPPYatcBzA2gRr7gqlSyAIwmmMHi1eP3AA24wM7JXv24cpDCIjxccJ/eq/\n/oote2hMmoTx9Z98Uv967CHBQjIzM+vva6xQz54gCKfRrBn6xK9dE29ntWrj4/Ejx/nzAO3b8711\n1kv/+GOAFi3kvzNlCvr+q6rwgcFy6wPg4OzXX9v/s7g61LMnCMKpXL7MZ5lkMLFXol07bG/exNw1\n167hg8GS0ANguoT778cUDAA4Q9dTILEnCMKpNGmCPXQh1og9G1j19cUB1y++wEyaDREYiEnPAAD6\n9+eTsVma4NVYIDcOQRBOx8cHM1v6+mKIo7e3dd8zmQAefBCX09KsqzErLDp+9SofnnnXXTaZ7HaQ\n2BME4RJ068YvC9MiKCGsViWNsbeEMOqmtJSfLPWf/1j3fXeFxJ4gCJfi8mXbUiAL+eyzho8RvjUU\nF/PLAQH2XdNdIJ89QRAuhS1C36aNeL1fv4a/I3URHTxo/fXcGRJ7giDcFmleHbl8OVKYG+fuu7Gd\nPVtdm1wVcuMQBOG2sMHW7dtxsDU4uOHvsIyaLHJn5Mj6aRUaIyT2BEG4LSxEs39/6wd1Q0KwZQXE\nfX3Vt8sVITcOQRBuS8uWAO+/b73QA2BcPcfxVbJs+a47Q2JPEITb4uUFMH26fd+1VBKxsUJiTxCE\nR0JiTxAE4QFYM5jbmKDiJQRBeCTXr+OHJVRzRdQsXkJiTxAE4aKoKfbkxhGQkZHhbBOswh3sdAcb\nAchOtSE7XZcGxT49PX1I9+7dj3ft2vXkvHnz3pDuLysrCxoyZEh6bGxsdlRU1B+rVq36hyaW6oC7\n/AG4g53uYCMA2ak2ZKfroij2tbW1TV544YXF6enpQ3Jzc3usXbs26dixY6ICYYsXL34hLi4uKzs7\nOzYjIyPx1VdfXVBTU0OTtQiCIFwIRbH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"text": [
""
]
}
],
"prompt_number": 7
}
],
"metadata": {}
}
]
}