repo2docker/tests/stencila/basic/archive/py-jupyter/py-jupyter.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jupyter notebooks ([@perez2015project; @kluyver2016jupyter; @ragan2014jupyter]) are one of the most popular platforms for doing reproducible research. Stencila supports importing of Jupyter Notebook `.ipynb` files. This allows you to work with collegues to refine a document for final publication while still retaining the code cells, and thus reprodubility of your the work. In the future we also plan to support exporting to `.ipynb` files. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Markdown cells\n",
"\n",
"Most standard Markdown should be supported by the importer including inline `code`, headings etc (although the Stencila user interface do not currently support rendering of some elements e.g. math and lists).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Code cells\n",
"\n",
"Code cells in notebooks are imported without loss. Stencila's user interface currently differs from Jupyter in that code cells are executed on update while you are typing. This produces a very reactive user experience but is inappropriate for more compute intensive, longer running code cells. We are currently working on improving this to allowing users to decide to execute cells explicitly (e.g. using `Ctrl+Enter`)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello this is Python 3.5 and it is Tue Feb 13 10:56:10 2018'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys\n",
"import time\n",
"'Hello this is Python %s.%s and it is %s' % (sys.version_info[0], sys.version_info[1], time.strftime('%c'))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stencila also support Jupyter code cells that produce plots. The cell below produces a simple plot based on the example from [the Matplotlib website](https://matplotlib.org/examples/shapes_and_collections/scatter_demo.html). Try changing the code below (for example, the variable `N`)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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8Uken0zFn4S2s+90/MlHfw1dJwtmSJkzm+C6H4wkh0JoMOBwOFQRGEIfDwRtv\nvMumTacIC5tLSkr3bel6fQARESlERKRgt7fw0kvb2bPnGA8+eOegzyY3mszYnJ5et3O7vTjd+H2V\nvuFOBYERyuFwsG/fftat20plpQ2328z+kwUEpH8Ha3MwXquH8+erEJSg07vJSE8iOTmpy1w+QgiS\nJ1zPzv3Ps+DUKcaN63mVsItdM2c+lpdSOXi0nrGZwYQGm+CiFh273U1xWQutrkimTJsG3TT3SLcH\nnU59HUcKu93OH/7wHMePC5KTV6HV6vu8r8lkITX1OkpK8vnP/3ySn/zkQeLi4gatrmPHjefdHZKF\nObLH5saTRbWkZOWg1/f9XK4GfvmrE0JcB/yWjqykz0kpf9HFNnnAbwA9UCulXOSPY49GZ86c4emn\n/0x1tZ6IiGySk6MoKzuJMXI2waFJnbZ3ux0UFDRw+vR5Jk3OJKWLjmCNRos57ho2bt7nUxAQQpAz\naRotljQKy6zgsRJqEQig3Q4tdgOx8ROYNiYNbTcXeUdbOxqnWyWbGyG8Xi9PP/0KJ08aGDNmTr/6\ncYQQxMWNp7bWyP/+77M89tjfEhLSffPlQCQmJmKMGMvh0yVMG9v1LHWH080X+e0svXP0LXw04Bk6\nQggN8ASwHJgA3CmEyL5smxDg/4AbpJQTgdsGetzRSErJRx+t5z/+4zkcjrGkps4jODgagMLzZzBF\nTOtyP53OSEhIHKaAFA4dLGbPngO43K5O20UmjONQQQ11dXU+1Stv2ky85fXkzlpI5oQ8AiNmYArL\nIW7MNVwz91rSM7K6DQAAZYdOMm/CNPUkMEJs376TffsaSU7uXwC4WFRUOi0tY3jttbe7XKvCH4QQ\n3HLnA2w6ZWLP8Qrcbu8ln9c1tvPKxhJSpt1EVlbWoNRhOPPHNM2ZQKGUskRK6QL+Atx82TbfBN6W\nUpYDSCl9u8ooSCl5990PeOONXSQmLiUs7EL7a3NzDc2uQIzmnh+p9TojoWFjqKp2sWf3Adxu9yWf\nazRaCJ7KgYNHfKrbjGk5OPNLcdodhIaGkpCQQGJSElHR0Wh66fjzer007y9gfu5sn46pDI3GxkZe\nffVT4uPn+20kV0LCNHbvruLIEd++d76Iiori3of+mdPOyfzmvVLe/6KE9btKeOnTEl78wkn2gvu5\n/qZbRuXoNH/ceiUApRe9LqMjMFwsC9ALITYDQcDvpZSv+OHYo8aePXt57739pKQsQqe7tOPK4WhD\nGCP69AWT5d3NAAAgAElEQVQWCEJC4qmrr+Do0eNMy5mCuKgR3xgYSXVtkU91M5vNzMuezL7Pd5O9\ncqFPf0jn9x4lIyhy1E3QGal27NiN251CQMClTTd2ezNVVYXYne24vG4MWj1BgcFER2eh1/ecNVYI\nDaGhOXzwwRamTJkyaBfiyMhI7rn/Yerr6ykqKsLj8TAmJITMzMxR/RR6pc5cB+QAiwEzsEsIsUtK\neaarjR977LGv/52Xl0deXt4VqOLw1djYyIsvvk9s7JxOAQDA43GB6Pu4a4EgJDiOkpIi4uOrL8nm\nqdUbsdmcPtdx1XXXc+75P3F2617SF87s0x9y2dF83Dvyue/+7/t8POXKc7vdbNiwh6iojhQfUkoa\nGs5zruwE5U0NyPAJaEzJCJ0er8eJrKtAe+YNUmOSSEmYiMUS3W3ZYWFJFBXtpbS0lOTk5EE9j4iI\nCCIiIgb1GINty5YtbNmyxS9l+SMIlAMX/9YSv3zvYmVAnZTSDtiFENuAKUCvQUCBN9/8ALc7mcDA\n0C4/12r1INt8KlMIDYHmeA4dzufaZVFfDyH1uBwE9JD6oTsmk4mH7/k2f3r9JY7XbSBlwQyCoztP\nUgNob2qmZNdhAvKreOSeBwgN7fq8lOGloqKCtjYTEREheDwujp78nJJWO9rYWVjSstF0MULI42zj\nbO0xzhzexKTEdNLG5HZ5gyCEQIgkTp8+0ykIVFZWUlxcDEBqaqpKQU7nm+Of/exn/S7LH0FgH5Ah\nhEgBKoE7gDsv2+Z94A9CCC1gBGYBv/bDsa96jY2N7N59koSEFd1uYzSakY6zSNnzELhO+xkCsVp1\n1NTUfD1Ez9FeR3Rk/1LrWiwWfnDvg3yxczufvbKR4ogAQqdkYgoOQgiBo60d64kiREkdiyZPZ9ED\nt2CxWPp1rKHg9Xr55MP3kFJy3Y2rRtWsUugIAlKG4fW62X90HRUiitBJdyA03V9GtAYzIQmz8URN\n5EjBm7hc2xmbOa/L72lgYASnT59n6dKO121tbTz/4lscONaAMGQjkeDYxYwpkXz73jVqTomfDDgI\nSCk9QoiHgU+5MEQ0Xwjx3Y6P5dNSygIhxAbgKOABnpZSnhzosUeDvXv3IWUsWm33v6rg4GgsunYc\nbZWYgnxrWzcawzhzpoS4uDi8Xg80HWbG9Hv7XV+j0cjSRUtYtCCPgoICDuQfo6m9Gq+UxAQEcmPW\nbCbdMmlETsipqanhyOa3EUimzbxm1PVjlJVVo9OFcLxgCxVEEJp5U59vOrSGIELH38nJE69gDjhG\nUtLkTtuYzRGcP58PdATcJ558jVOlqaSMvwfx5QRG6b2Og6c/w/bka/zokQdGZUeuv/mlT0BK+Qkw\n9rL3/nTZ68eBx/1xvNHk0KFThIQk9riNEILM5HQOlh/yOQiYAizUN1Tg9rhpqChganYUkZFdN+P4\nQqvVMmHCBCZMmDDgsoaL6Ohopi5eg5SSmJhe0mRchRwOF3Z7K0UNdYRM+Z7PF2CNzoQlaw3HTj5P\nfPy4ThPMNBodTmfH0OXCwkLyzwpSxi+95DhCoyEpfRknTj7NmTNnyMzMHPiJjXJqJY9hzOv1cu5c\nGUFB4b1uGxeXiba9AJejyadjCAQCAy1NzbRW7mLZotz+Vveqp9FoWHHTalbefMuoawoC0Ot1VNac\nRUTldNn+36cyAsJwmpOoqencHej1etB/mX7k0OEC9OYLI4WcTifttnag46ZHFziFQ4cL+nkmysVU\nEBjGmpubcbk06HS9j/zR641MzRpPS/FbeNx2n44jpZ6iI+8zZ0IA2dnZve+gjEqRkcGUNpRjjpky\noHIMMTMoLD3ZaXJYe3sj8fEdeYTsDje6i4aWOl1OHM4Lo9Z0ehMOx6XzXJT+UUFgGHO73XT0pfdN\nUtIEJiaF0VT0Gm5nc5/2kV4PrRVbSAk6y31rb1PLPCrdcrvdeC3x6AwDW4woIDSVBrsNh6P1kvfb\n2+sYN65jZFBmejz21rNffxZkDiIs5MIoMnvrWdLTBi/f0Gii/uKHsY4mB9+m0memT2d6WixtZ5/D\nWr4Zl72xy+28bjvNtQdpKnyeGE0h37xt5RXN8a5ceXa7ne3bt1NZWdmv/S0WC1pTAG6P7/NILiaE\nQBgsuFwXnlillHi9ZaSnpwEwbdoUArWnaW4s67R/U2MpZt0ZcnKmDqgeSofRO01uBLBYLGg0Ljwe\nd4+jgy4mhCAleRLRUWMoKz/FmZIXadPFI03xaLQmvNKJcDWhaS0gOSaKMdOm0NSkH5UdnaPNnt27\n2Lf9cY4emsn3f/BvPu8vhCAuIRprWx0hwQMbGSU0OrzeC805zc1VxMfrSUvrCAKBgYH83UO38Kvf\nv461PofQyI6khtbakxg5zKM/uBWTqeeZyErfqCAwjOl0OpKTY2loqCckxLeLdECAhcyMGaSnTaWu\n7jw2mxWny4VOq8VgMBEVtRKDIRApJU1NewY1la8yPKSlZ3D08DQmTp7br/0DAgKIjQrG2tCAxxOJ\nVtv/Yb7Sbfs6nYSUkrq6gzz00KUpRzIzM/n5zx5g5859HDj8IW6Pm5lz4pg75w4SE3seMaf0nQoC\nw9zkyVm8//45n4PAVzQaHdHRad1+3txcQ2JipJp4MwokJSXxg7/7z37vHxMTg8n5OeOyZ3LiZCmh\noWmX5J3qK7e9CZ27DaOxo2+hquokkyYFMnNm55FpYWFhZGenU9Fcx76iIipLbWx87RSxAQGszJ1F\nzrRpKgX5AKkgMMzNnp3Lu+9ux+ud3GmRdn9obDzLmjXz/V6ucvWJjY1lXHIwxXoXERFgtVYRbPH9\nCbK15jDj4tLRavVYrRVotSe4776HOg1K8Hq9vPXhh6wvLiJwVi5JN61EZzB0PL2WlfP8/oN8vGcX\nj9zzLb/MbRmtVMfwIJBS4na7/ZIfPSYmhilTUqip8S2zZ1/YbC2YTI3k5HS9DsFgcToH1rGoDJ3r\n8nKxlR9g1qxpBAW10NRc3pHOoY+k142oPURSwgTq60uw2bbxox+t7XKJyQ8++YR1dTWk3n8vCTnT\n0H05y1wIQWhSIumrb6Jl7jU8/vKLtLa2dtpf6Rv1JOAHbreb/Px8Th85QuXp09SWl4HHixSCkKgo\nErKyGDNxIlOmTOnXo+sdd6zin//5dzgcCRiN/mm2kVJSUbGX73zn+iv6OF1WVsaBY0e4cfkKNRx1\nBBo/fjxRus9oqT7HvLm5HDx4jMqq0wSZk9Hre/8eNZftJMYcRG3tMaKjG/mHf3igy6yhjY2NfHT0\nMKnf/+7XF/+uxE2dwtmqKr7YtYsVy0bfqmD+IAZrNZ/+EkLI4Van7rjdbrZv3cqeDz4gsrmJbKOB\nWIuFaLMZrUaDlJL6dhtVrS2cbbdRZDAyedkylqxc6XMb/IYNG3n11V2kpi5ECA1WayVNTVUIoSUy\nMhmzOcyn8srLjzF2rIdHHvn+Fb0Yf/TRRl5/fTvTpiXy939/36jO4z5SVVRU8O+/fRXTuG8QEpNC\neVk5R44W4nIFYDRGYjQGoblsfotE0li6E4reY0pWNDfdNIebblrR7bDkdZ9+yrtuO6lLl/Ran/b6\nelpffp3HH/3RqP0+CSGQUvYrkdLo/B/zg8rKSt5+9lmCz57hzpgYIiPGdNpGCEGkOZBIcyATgVaH\nk13rP+b/du7kxu98x6fZuUuXLqa0tIJNmzbjslcTaipm0hhwuuH4WYEwzSQ9czEdq332rKIin8jI\nBh588OErfje+ZctxYmLu59ixv9LY2NhlM4AyvMXHx/Pjv7mNXz39JpXNecSnTSM2Lpbq6mqKispp\nbCxBSgMdlxeB19VGe+Vuop3H+Pt/+AbLli3pdT3hvYWnCV+5vE/1CYyIoMoSRGVlJUlJndfYVnqm\nngT64cyZM7z5+C9ZotUwITra50RapdYmPmxoYM79DzBnft87Zd1uN3/70P2Ea44wPzcbo6HjacLl\n9vDJ9ioa3deRPKb73D9ut4OysoMkJXl49NHvDUke/y++2MUrr2xi5swMvv3tb6gmoRGspqaGt97/\nlP0FlRA5lcjUHAKCwpBAS0sz1ppSms4fwNhWyPJ5k/nGLTf2+Qn4J7/9Nbrb1xDYx8Vfil/7Cz9e\nkEd6evoAzmjkUk8CV9D58+d565f/y61BZhJ7uZvpTlJoCHeZjLz+zNMYjEZmzLx8Nc6u1dfXk54o\nuG3pDI4ePYXdFoQ5KAK9zsCC6RG8vG4n3uRpaC7L7+7xuKmpOYvDcYYbb5zFDTd0/xg+2ObPv4b5\n868ZkmMr/hUdHc33H7ybhoYGdu7ez6adL1Db0o5EiwYP8dFhfHtVDtOnf8Pn5s8gYwDNbe19DgLe\n1jY1eayfVBDwgcPh4K0nn2SF0eh7AJASLnpiCDGZuD0+nleefZaU1NQ+NYtUVFSQlqAhOSmR6KhI\nzp0r5mxRCW63ASFMGLXtNDZWEBAQgtvtoLW1AaezEY2mjlmzxrF8+XcZM2aMj2etKD0LDw/nhpXX\ncsPKa/F6vbjdbvR6/YBy/c8dP57Xjh0nNLn35p3myioiXE414bGfVBDwwcaPPyaxqpLM1DF93sft\ncnHi2EGsDVWYg8KYOCUX05ejccIDA5hntfLuSy/x4KOP9vpHYzQaaW3vaCozmUyMG5dNZmYG9fX1\n1Nc3otnUgMFQiEajJzTURG5uMunpk8nKylJLOCpXhEaj8cuCQbnTp/Pn3/8O+/y5mIK7X+lOSknN\n7j3cMz1XNS32kwoCfdTa2sqRdev4XmKCT/udO1tIABVMnhpKebWVU/lHmJIz++vPc+JiOXz8OEVF\nRb22Z2ZkZPDhW4HUNbQTGd7xeK3T6YiJiaGyTrJ0xSzufeDvfT85RRlmgoKC+Ob8BbzwlzcZ883b\nMQZ1zlwqpaT0i+2k1TUwZ9WtQ1DLq4MKnX10YN8+xno9BOh9W0zDbmslPNiA0AjCQ0zY21su+VwI\nQU6AkT2ff95rWQaDgWXX38crH1STX1iL1ytxONzsOVTOp3s1XLvydp/qpij+JqWkvLzcLxMlF86b\nx9qJkyl/5nlKtmzF1tiIlBKPy0X1iROcffk1UgvP8cO131IZcAdAjQ7qoz/++7+zxNpIUqhvfQFV\nlRWcP72HhCgtNY1uQmImkpaRdck2TreH31dW8tMnn+rTOOfTp0/zxefvU37+JEKjZeyEOSxcfL3K\nBKoMuerqat5++2PWrv0GQV3cvfdHbW0tO/ft4/NjR2lpb0er0TApJYWlM2cxduzYUbnK2+UGMjpI\nBYE+cLvd/Px73+OHCXHo+/GFq62pwdpQh9kSTFx8Qpdt/8+XnGf1v/8HCQl9b27yeDxoNBq12LYy\nrNhstkGbhe71ejvWI1Df+UuoIaKDrLa2llDp7VcAAIiKjiYqOrrnbaSkqqrKpyCg7oCU4Wgw05Co\nzl//U/+jfeBwODAN8p1HgOg4jqIoypWkngT6QAjh4yKPvvNKdZejKNBxM5Sfn09TUxNGo5Hs7Gw1\nxHkQ+SUICCGuA35Lx5PFc1LKX3SzXS6wE7hdSvmOP459JVgsFpq9gxsGmoUgw08daYoyEkkp+XzL\nF7y9bhc2fQrSFIVwNyP+spUF09O447Yb1azgQTDgICA6MpY9ASwBKoB9Qoj3pZQFXWz3P8CGgR7z\nSgsLC8MVEECb04nZDxNhulItOxJzKcpotX7DJl7fWETi1O8Rbb4wCs/jWs6WE59S9/Sr/PBvvoXe\nx2HaSs/88SQwEyiUUpYACCH+AtwMFFy23Q+At4DuM5wNU0IIErKzKS7IZ0JMzx28/dHQbsMTFERY\nmG/poBXlatHQ0MCbnxwgNHstlTV1tDQVYGtvwev1otXqCDTH8dnBU0zduo2lfUgvrfSdP4JAAlB6\n0esyOgLD14QQ8cAqKeUiIUTfsqUNM9MXLWL3wYNMGISyD9fVMm3VLWrY2wjidrtxuVwYjUbVlzNA\nUkre+OtbFFQKQsUeosMgPsRAYIwejUaD2+Omrb0Sl8XML3/1CypL81l87WqfRtIp3btSHcO/BX58\n0eser3aPPfbY1//Oy8sjLy9vUCrli3HjxrEuLIzK5hbigi1+K9fmcnFUwvfmzPFbmcrgaGhoYNeu\nfWzceACr1YYQeqR0ERsbyooVM5kxIwez2TzU1RxR2tra+PDdP7P5s7fJyFxDaroFp92G1+NCI70Y\n9YGYjDqCAg1EhWdS2hLI+KijvP7MQabNuY1FS64dlUOlt2zZwpYtW/xS1oAniwkhZgOPSSmv+/L1\nTwB5ceewEOKrBXIFEAm0Ad+RUn7QRXnDbrLYV44cOcK2X/+Ke1PHoPXT3d/754oJXX0LK266qdtt\nXC4XBQUFtLa2Eh4eTmZmprr7vIKsVit//vMH7N17HiHGERk5mYCAsK8m6NDSUkVDwxH0+rMsXjyR\nW265XqUx6IOmpiZeevZxkixFbN1bwdnGCPTCgdnoQacBtxfaHFoMllgCosYSGZ+O9djLPPMvU2ht\nc/LeplJEyDxuv+v+Ubui2FeGdMawEEILnKKjY7gS2AvcKaXM72b7F4APuxsdNJyDgJSS1597DvPO\n7SxLSRlw882x6mp2hUfy0L/+a7edXSdOnOSp9z6iNSoBwiKguoIYWxMP33kbiYmJAzq+0ruqqioe\nf/xlrNYJxMVNR6vtvlPS5bJRXr6NzMwGfvjDe/2WNuFq5HA4+MOvH8PVsBONq5kYcxtOTShjMsZi\n0F+4s3c6PTQ02Thb3saeEy0khLn55T8uJshswOPx8vanxYiwJay5fe2obk4d8rQRXw4R/R0Xhoj+\njxDiu3Q8ETx92bbPAx+NxCAAHVPin//1r0k9V8TCpMR+f/FO1tTwuc7AvT/9KdHdzCYuLy/n3176\nMyE33YUl9kKu9PqzpxGb3ue/fvA9LBb/NU0pl7JarfzHf/wJm20u0dHj+rSPlJKysh1kZpbzyCMP\n+CWt8tXoid89zomdz7Fmnon5UyxoBGzYXYsuYhJGU+cFaLxeD3Xnj6ETTs7WWVh5bQ4Txsbgdnt5\n5u1i5ix/hClTpw7BmQwPQx4E/Gm4BwHoaMd89Y9/xHj8GCsSE7D48OjvdHvYUlZGYWQUax99tMek\nby/99S22hyaRkDOr02cln33MXdGBLF28qF/noPTuiSde5PDhaBITO///90RKSXHxetasieTGG1cM\nUu1GrvfefYePX/5//MvaCJJjL6SYqG1oY+cJG9rgNMyWUITQIJE42ttobyxhfLyTsamhVNQ5+evW\ndubOn8HMqUlU1rTw6gYnD//ofwc1ZcVwNpAgoBqW+8FsNvPAI4+Qcs9anq+uZXtpKS29pHxwuN0c\nrKjgufPn8SxZysM/+1mvWT8PF5UQkdH1YvSW9GwOnyvp9zkoPaurq+PAgXLi46f7vK8Qgri4uWzY\ncBCXyzUItRu5jh09yuZ3H+fRNUGXBACAqHAzC6cEEaUpoqnsIM2VJ2gqO4Kh/SSzM72MTQ3tGK4d\nZeS+5RZ2fLGfY/lVxEVbSItp4cjhQ0N0ViPb6O5NGQCtVsuipUuZOGUKu7dt49mNG4lxOYmRXqJM\nJvQaLR7ppdFupxJBhRCkXjOHW5cs6fMSj1oBjcVFBMcnYo68dPlJr9uFYRSOirhSdu7cixDjO63X\n3FcmUwhVVTEcO3aMnJwcP9duZGpubub9N55g8fhmMpLDu9wmNNjErEkm7A43DqcbnVZPYEBIp2bX\nUIuOOxeZeeWzg6QkLiF3Qhgf7FrP7GvUKDtfqSAwQFFRUdx4661ce8MNlJaWUl5WRmlxMW6HA41O\nR0RSEjMTE0lKSvKp/d7pdFJbcI4dnzyFJT6CnLtuJGnmhQXaW04cYvbkrp8SlIGRUvLppweIjr5r\nQOVYLJPYtOmACgJfWvfBX5kYW09aiB6drudGCJNRh8nY8+UpNsJAbrqNdZtOcPvN02i1nqetrU0N\n0/WRCgJ+YjQaycjIICMjwy/lnTp1CpcjkkgRjM0VwdG3PiRp5jW4HQ4q9m4nrb2BqVOn+OVYyqUc\nDgdtbV4iI31bQMjr9dDWVktraw0uVztOZxstLQVYrVZCQjrfzY4mdXV1lJ7awc25Jgx+TJY7b1Iw\nv3mnlAZrNnERgoqKCjIzM/13gFFABYFhSqfTodcL5k6bxP6j+6kvP0bp689AUwPzMlP5xre/pUae\nDBKn04kQfc9P43C0UH5+D9VnNmF0thGMxCQleq+HRtcZ/ufv6onNyGDBypVMmTJlVE5uOrB3Fznp\nGrxuG0aD/7oidTrBtFTYf6SU0KAAWlpaet9JuYQKAsPU2LFjWbIki82bNzIxS8+99/4rcXFxhIaG\nqvHng8xoNCKls9ftpJRUVR6l+NBrxLttzAqMwBxwIeWx2+3A621kWUoKZTU1rPvtb/li3Dju+Pa3\nR91SoGfy93BrbhiNtQ1+fyKakGrinb3lJKVm+GVt49FGBYFhSqPRsHbtndx22yr0ev2onxF5JRkM\nBkJCDLS31xMYGNHlNlJKzp7+hJaTH5BrjsJi7ryd09FKeEQgQgiSwsNJDAvjVHExf3jsMe770Y9I\nT08f7FMZFpxOJ9a6CqLCE2lpNOJye/xafnSYAau1mZA2L+kq1bTP1BDRYS4gIGBUBwCbzUZFRQVl\nZWU0NjZekTs9IQTXXTeT2toj3W5Tcm4bbSc+IDckEYuh645Ip6uK1NQL6cGFEGTHxZFrMvHC449T\nVVXl97oPR/X19UQEC7RaDZbgUFra/Vu+VisIt8DZMjtxcXG976BcYvReXZRhy2q1snv3PrZuPUpV\nVTMaTQhCaPB62wgM9JKTk0Fe3ixSU1MHrbN19uwZ/PWvf8DjmYdWe2nfS0tLJVXH3mZOcDz6boaQ\nulw2TCY7UVFRnT6LDQkh227n9Wef5Yf/+I9XfR+By+VCr+34PVksFs61SaSUfv3deT0eHJ4AlY69\nH1QQUIYNj8fD559v5Y03tuPxZBERcS3JyRF0rEfUwelsY/fuQrZufZsZMyK4++7Vg/KHHxoayty5\nGezYsYvk5IWXfHb22LuM1eox6rrpmJeS1tYSJk9O7DbRX0Z0NOdPn2b//v3MmuXbjOSRRqvV4vZ0\nPMGZzWYMpggarC1EhHVOD9Ff1Q02Ji5cOKpHYPWXag5ShgWbzcbvfvccr7xSSFTUnaSk5BEUFHVJ\nAAAwGMzEx09lzJi7OHo0in/5l//j3Llzg1KnO+64iYSEYioqDnz9XmtrNc6ak8SaO9/hAyAl1qYi\nEuIFqakp3ZYthGBceDjb1q276jszw8PDaWj1fn2eCcmZnK92+O28HQ43heWwIG+pX8obbVQQUIac\nx+Phj398hWPHLKSm3oTR2PukOiE0JCTkotVeyy9+8Srl5eV+r5fZbOaRR+4jLu4EJSWf43S2UVud\nTzyg6eKO0+2y02g9RXyci5zpk3pN9x0XEkJTaSm1tbV+r/twEhAQQKAlmnqrDYCYmBikLoaKmla/\nlL/vZCPRiZNVVt1+UkFAGXKffbaZI0ckycl5Pj/Oh4Ulo9Es4Omn3xyUPD1hYWH8+MffZflyHfX1\nL3G+8D1M0ovX40J6vXg8TtrbG7BaT+JyHWfixBBm5E7tU2e+EIJQoKKiwu/1Hm7GZE3jVHED8GUH\n+fipFFcJmlrsAyq3vKqFE6UGFi671R/VHJVUEFCGVGNjI2++uYuEhCX9bs+Njh5LcXEQO3bs8nPt\nOgQGBnLHHav57W9/xIRMiAiqx+44QmvrPpzOY4SGVDN7diLXLp9LZma6Twv+BHq91NfVDUq9h5MZ\ns+azv9CD19vRBBQYGMi4SXM4XuSk4csnBF9IKSmtbKa41ohVjGP23Dw/13j0UB3DypDatWsvXu9Y\njMaBTYCLiprJRx+tZ8GCeYO26lpAQACxsbHMio8n2E8pi7VC4PH4d9z8cJSQkEBQ9EQO5Z9m+oSO\nYZzh4eFMmLKA/ON7iWyykpoQ3GtOIQC7w82p4mbcmmgwJxGVPIn4+Phe91O6pp4ElCG1ZctRIiLG\nD7icoKBoGhoMlJaW+qFW3TMHB2P3Y7OTAzCPkhngN956D5uOeC5pAgoNDWXGrEV4DOnsPt5CYYmV\npmY7Ho/3kn1dLg/1je2cOGNlf76d0Ngc0sdOZ8cpHTfccveVPpWrinoSuIrY7XbKysqorKigua6j\ns9EcGkZcQgKJiYnDLrtiW1sbtbVtJCd3PSvXV1JGU1FRQUpK96NyBio5K4u6zz4jOjjYL+W1aDSj\nZoJTdHQ0c5bdzRufPc+3rk/GaOi4/Oj1erLHTcKemkllRTlnqstpa7Vi0Eo0GnB7wCN1WILDiYpL\nZmxMDG4PvLSuhLnLHuh2ZT6lb1QQuArU1dWxc/PnnNy6iVjpIl4jifryD6zF5WafV8O7HkH67HnM\nWbqMhISEIa5xh4aGBjSaUL+N7dbpwqmsHNz29YzsbNatX8/An13A5nTSqtMNm9/HlTB3/kKamxp5\n6eN3uPPaRCzmC6vymUwmUtPSSU1Lx+v14nA48Hq9aLVajEbj19+TljYHf/60nOTJtzJn3oKhOpWr\nhgoCI5jX62XHtm3s+utrzDZ6eTglhiBj1xOY7C43R47u4C87tzLhhtUsuW5Ft4vbXylerxch/Ddb\nVgiB2895aS43fvx43rJYaLLZCBlgv8Cp6mqmL1uG0YflSUc6IQQrbljFF5YQnvrgNa6dZmDy2OhO\nNwIajabTUpFSSo4UVLPxsJvZS+5j3gLfRpNVV1ezY9s2tFot8/PyCA/vemGb0UYFgRHK7Xbz1ssv\nYd+9he+mJxAS0HPiLJNex6yUBCY5XXy87m1eKjzN3d9/GNMQJtwKDAzE6/VfIhmXq53QUP8003RH\nr9ezePVqdr34IosHkLai1W6nRAhuWbLEzzUc/oQQLMhbTObYcXzw1otsP3GK/9/encdHVd6LH/88\ns2TfF7KRBQIJAUIACYtsARRBXGivtbjgQqtWa632am1/P3tL723rbV+9vdryq7bWrVZFW6u1Aoos\nYZXLsYcAAB3lSURBVFMwrIEQSEhISEKWSYasJJM5M8/vjwkIkmQmyWQmIc/79eL1mkmec+abwznz\nPedZs8cbyUiNuuzJ4IKWNguFJfXkFVsxhKaz+pH7iY2N7dNnNjY28vuf/5yYtjZsUnLos8946mc/\nG3JVpN6gksAwJKXkg/VvIfJ2sHriGPR96ZLoY+S2CSl8fKqAt/74Avc++pjX5q6JjIzEaGxH0zow\nGAaejIQwMXr04K+2tmDhQo7s3cvxsjIm9aNXima381lVFUvXrOl2bqGRIi4ujgcf/RHl5eXs35tL\n7oYD6O21RIcIDHrQbAJTsx2bLpCx6fO4aXUOSUlJ/Uq8JSUlBLW2ktnVXrTrzBkqKiqYMEGtzqeS\nwDCUf+QIpp1beCAjpU8J4AIhBMvGJfHm8UPszs1loZfuRnU6HdOmpXL48CliYycPaF+a1oFOV0NS\nUpKbouuZXq/n3ocfZt0vf4k4e5aMuDiXv5g6NY3dZ84wdskSFixc6HyDq5wQgpSUFFJS7kPKe2lq\naqKhoQFN0zAYDERGRrplVbbg4GCa7XY6NQ27lLRJ2aflXq9mqovoMGOxWNj8l1dZmRiNQe/8v09K\nianexNH8PPbn5XL48OdUV1djl3ZuSU1g33vrMZvNHoi8e4sXz6K9PX/A88jU1BQwf36GxxbcCQ8P\n57s//jHmxES2nz5Na0fvI1+llFSazWyqqGDCzTezavXqQRvPMFwJIQgLCyM1NZX09HRSU1MJC3NP\nx4Hx48cz69Zb+eTsWbbU1HDdqlUjqkG+N8IdkzgJIZYBz+FIKi9LKX/1ld/fCTzd9bYFeFhKebSH\nfcmrfUKtgfhi3z7K//Ii30h33g1Ss2kczd+P1KqJjzUSGGjEYrFRXdvJ+Y5QsqbOYVdVHXLp11m6\n4iYPRH8lu93Ob3/7EkVFScTHT+vXPjo6mqmvf4df/OJBj6/YpWkaudu2sf2DDwhtbyfe15fI4GAC\nfHyQUtJ4/jymlhYqbTYCEhP5+j33qDVwvaijowMhxFXXGC+EQErZr2w54CQgHNM8FgFLgLNAHrBK\nSnnikjKzgUIpZVNXwlgrpZzdw/5UEujFn/77F1x3vo6xUc6nTy4oOIyBMtLGXfk4XVHZTE19GGMn\nzuDl+g6e+p/nvTYNb319Pc888wIBASsICelbHbvN1klZ2fusWTOVRYu8V71isVjIz8+nuKCAMydP\n0trcjNDpiI6NJTkjg8lZWYwdO/aqmepY0zRqamqora2ls7MTvV5PVFQUcXFxV/TqUQbfQJKAO9oE\nZgLFUsryrmDWA7cCF5OAlHLvJeX3Auo5rB+sViumslKS0p0fvvaOdhrN5cyeEdLtF8/ohGDq6s3Q\n2Y7xfBtms5nISPcM2uqrqKgo/v3fV/HrX7+D1bqIyEjXll20WFqoqNjIzTcnkZPj3f7ivr6+ZGdn\nk52d7dU4Blt1dTV7c3Mp3JlLmGYlVoAvEg3IR1AnIXHaDGYuWUJaWtpVk/SuZu5IAgnApWP1K3Ek\nhp58G9jkhs8dcerq6ojU41JbQENDA1EREn0PZYUQxETrqDfVEGcMoaamxmtJABx1tj/5yT28+OK7\nnD5dTExMdo/r+2qahZqaAuAAa9YsYNGiBerLZpBZrVa2bNpEwYcfkG3U8WjsKAJ9rhyTotnsFJzI\nZ8v+fXwxcza33HEnoaGhXohYcZVHewcJIRYB9wPzeiu3du3ai69zcnLIyckZ1LiGC4vFgr+L33V2\nmx1nsxkbDDrs7VYC9JIOJw2bnpCUlMTatY+Rm7uLDRs+wGQKBmIwGsMQQofVeh6oQ4hq5s4dz4oV\nnm8DGIna2tp4Y93viSgq5KGk0QT49DzI0KDXkRUXy2S7nd1HDvCnoiLuevIpNcGbm+Xm5pKbm+uW\nfbmjTWA2jjr+ZV3vfwTIbhqHpwDvAcuklCW97E+1CfSgrKyM7b/5L+5PS3Ratr6+noqy3UybEtZj\nmaJTjfgGZnGwU0/Kmu8xbVr/GmYHg6ZplJeXU1VVRVVVA3a7JCwsgOTk0SQnJ6vufR5itVp5+bf/\nw5jSIpYkJ/b5ietEXT0b0bPmmf/w6pPm1c7bbQJ5wDghRDJQDawC7ri0gBAiCUcCWN1bAlB6Fx4e\nToPm2iLdEZERFBcF0NTcQWjIlQOxLBaNunod2ePiaThdx/QhNoTeYDCQmppKaqpr7QNXg5qaGkpK\nStA0jaioKNLT011anGYwbfvkE8KLClmSmtKvKrcJo6JoqjzL+6+9xponnlDdYoegAZ9hUkqbEOJR\nYDNfdhEtFEI85Pi1/BPwEyAC+INwnElWKWVv7QZKN0JCQpABwTR3WJxOE6ETOsanT6egcA/pqXYi\nIvwvXsTNLRZOFLWRlDIdg8FIrdXe52H4ivucO3eOV199m/z8SiAW0CNEE6GhnaxefQvXXDPdK3HV\n1taS/8E/+E7S6AG1ucxMiKPw2BEOHDhw1TecD0duGSfgTqo6qHf/fGc9kfu2MC/FtfVUzWYzpSXH\n0KxmAgMEFgto9kCSUyYSFxfPsbN17I8bz33ff2KQI1e609zczM9//jyNjfHExU3E0ePaobW1gbq6\nPTz66EpmzvT8l+eHf/sbQVs/ZmGy8+pHZ06bz/FxYBjf/ela1Yg/CLxdHaR4UPb8Bby77RNm2+wu\n9RKKiIggPGI+ra2tWCwWjAYjISEhF04a9jW2MeeeGzwQudKdjz/egskUQXLyldNmBAVFotMt5NVX\n3ycra4pHBzjZbDaObd/GI7Humas/JTwMe+kZqqurVSPxEKMq6IaZ+Ph4YmfNZUdZlcvbCATBQcFE\nRUZdNg/LwcoabKkT1SRag6C9vR2z2Uxzc3OPy0daLBa2bs0jLq7n1QkCAkJpbw/jyJEjgxVqt0wm\nE8FWC8FuSjxCCJJ0UFlZ6Zb9Ke6jngSGoZtuX8ULBUdJqTeTGtW/Bt3a5la2npfcd+/9qrHOTTRN\no7CwkB15OympOY0xwAdps6PX9MzLupY52bMv6yHT0NCAzeaPj09Ar/v18RlFWVklM2d6rhmtrq6O\nGDfX2sQYDNSUlYEH/w7FOZUEhqGgoCBuf+wHvPObZ1lpbyBtVN+63lU1NvN21TlufPQHamk+NzGZ\nTPzprT/THmYlcfYYrku/6eIU3a2NLRw/cIrtr+zk+qmLuOG6GxBCIITAbne+CI7dbvN4orZYLHS/\nPFH/+RkMdLa7b/0IxT3ULeAwlZyczB0//L98ZPNjQ3E5Fk1zuo1ms7O9tIK3znVy0+M/ZHJmpgci\nvfrV19fz/OvrCM+JY969i0iemHLZGg1BYcFMWTKNBY9ez46Kz/lgwz+RUhIdHU1QkKS9vanX/dts\ntaSne7arrMFgwPkZ1TdWuw2Dz9U1cdvVQCWBYSwxMZFHfvpf2OYv47niajYUl1Naf472TuvFMp2a\njXJzI1tKzvC/JyqozZzDQz/7JRMyMrwY+dVDSskr77xK3JJkxmb1/kXt6+/LnLsWsLdiP8eOHcNg\nMLBs2bVUV/c8lXZTUw3h4Z1kePj/Kzo6mjo3d9KrtXQyKtn57LeKZ6nqoEFgt9spKSlh2xf7KKyq\nwKrZCAsMZOHkKcyaMcOtc6n4+flxy+3fJGfZcg7m5bHj8EGqT5cgOi0IwGYwMio5meTs61gze44a\ntelmJSUlnDO0kDl1lkvlfXx9GL9kIlt3bCczM5MlSxaRn19EYeHnJCRMvdg+IKUdk6kMq/UoP/zh\n/R4fNBYTE4NZ6LBoGr5u+uwqdGSqnkFDjhon4GaNjY384a2/UqqX6DMzIDwUG3Y6m1toKzxF8Jmz\nrF6wmMULFw5af2kpJRaLBSklvr6+quF3EL369mu0ptkYf02ay9vY7Xa2/+5jnrj9e8THx9PR0cG/\n/rWJLVu+wGoNQQg9dnsTGRnxfPObN5Hspbvnd197lcS8z5g5euCT/ta0tLLeCk/86tfqfBwEapzA\nENHS0sKvX/0zlelj6EyIpKHFRKCuHYOPHvzAFppAeVwg/7npfSorK7n37rsHJQ4hRL8WkK+vr6ep\nyVE/HRISMqLXv3VVQelx5q9c2qdtdDodERmjKC0tJT4+Hj8/P77xja9x883LKS8vx2azERUV5fVG\n+5k5i/hgZy7TbDaMA1yHenetiRl336cSwBCkkoAbvbdpA4dCfbFHQuQojfGTUq446ePHxWBKDeX5\nZ19HJ+zcdcfdXr0wbDYbx44dI2/LxzSVniDKR4cQgnqLjeAxaWRft5zJkyd7fQ6bochms9GpWfHx\n63s/GmOAkfMdl/eU8fPzIz093V3hDVhKSgoJOUvYtns7N4zp/9NIYZ2J2tFJfH1er5MHK16irmw3\naWlp4c2tm9F/6wZSpyei03V/56TT6YkZm4B25/X8a8NHBIcGc+uKlV4ZSt/R0cH6l19CnshjXkwI\naVOT0OkccdjtklN1Z/n85d9yYNxU7njgOwQE9N6ffaTR6XTodXrsNhv6PiZJm9WGr3Ho95RZcdtt\nvHi8gFHVNUyL6/v8UtXNLWw838kdP3gQo7HnKagV71HPZm7y7rvv0JgawdjpY3tMAJeKzp5EZ0Qw\neeW7yc/P90CEl9M0jbdfepHo0gPcO2UME+KiLiYAAJ1OkBYbyT1ZY0ioPMpbf/wDVqu1lz16Vmtr\nK3l5eezcuZNDhw5hsVg8HoMQgsSY0dSW1fR526bT54bFWggBAQHc8+RT7PALJre8Apvd7vK2x2tN\nvGVu5ubHf0Bi4sDnH1IGh0oCbiClZOfBPURnjXW5ascnKAC70Ye0ucls37ulxy6Cg+XA/v0Yiw5w\nY0byZV/+XyWE4Ib0JILK8vli794ey3mKzWbjvX9s4PGn1vH/XqvklXet/O7PRTz+5HNs377L48dx\n4Yz5lOeV9mkbc00DxiYdaWmuNyZ7U1RUFA888xOqsq7h5VNlnDTVY7f3fJyrmpp5t7iU7aGR3PHM\nf3i8e6vSN6o6yA1Onz5Np+E8/gF9fLyXktHj4ynasZeqqipGj3ZtZlD4coqCvLwjFBWVYzI1IKUk\nPDyU8eOTmT59EllZWd1OOialJG/zRm5OjHSpGkoIwfzEKP7+6UaunTfPa7NASin565v/YOsejaS0\nxzEYv2z87mhv4pW31mO1aixdushjMWVOzuS9Le9jrq4nIi7KaXkpJSd3FpBzzfxh1UgaEhLC6u88\nzLF589mzcSObik6QpBfE6BwjgTW7nTrNTpWEzqhRzLj3W9x27bWqCmgYUEnADfYf+YK0GQkcrjgL\nTHVpm3bTOQL14BfgR+I1MRzMP+BSEpBSkp+fz+uv/4Nz53T4+iYQHDyJhATHSlsWy3ny8+vZt+9T\n/P3fZ9WqG5k3b+5lXzgVFRWIujMkTXW9sS8+LBi/8jOcPn2asWPHurydO1VWVrJ9TzUpEx5Bp7/8\n1PXzDyUx7S7efX8d116bTVBQkEdi8vHx4a6b7uDVt99gxuq5hEX3vJKblJLDnx4gojGYuSvneiQ+\ndxJCkJmZSWZmJiaTiaqqKmoqKznX1obBx4f4hARmxMcTHx8/rBLcSKeSgBucazGTnp3G8Q8PYWls\nwTfM+dKH5v3HWHhNMkIIQiKDMVc2ON1G0zTefPMdtm4tIDp6OsnJV955+vsH4+8fDIyhvr6Gn/70\nL4wa9QZ33bWSBQuuJSwsDLPZTLyfvk939EII4n0FZrPZa0lgz56DGAJmXJEALvDxDULTTeTQoSPM\nn++5L9lJEydxt3UVb762nrg5SYydNg6/QP+Lv5dSUltWTclnRYxqD2fNXffj080i7cNJdHS0owvx\nVNduepShSyUBN9BsGr7+QcyalsTOzXtIvG0popc7obbqenTHCpn08HUA6PQ6NFvvM7XYbDZefvkN\nPvvsLCkpS3ptfJZSUlxcyvHjdcC1VFeXUlz8L7KyDvHd7y5HpwNB3+vOdcIx0MlbKqrMBIZM6bWM\n0S+OmtpaD0X0palZU4mNiWXXvt3s+f02ApNDMAb5IDU7rWebCReh3Jx9A9OmTlNVJMqQopKAGwT4\nBdJx3sLMxZlU/3UHp97fQtyKhRj8Lq+Pl1LSfLqK5vc/4Zu3TCMwxNHlsuO8hUC/3qeEzs3dwZ49\n5YwZs+Cy1ae6U1dXR0GBmZCQGej1RqRMoanpC0ymKF54YRv33z+bxn7MDnbOCmM8VM3SncBAX7SG\n9l7L2KznCfD3zl12bGws37j1Nm5auoLS0lLa29sxGAxEzohk9OiBLdGoKINFJQE3mDAmg12Fmxkz\nMYlb717Izo0H2f/8azAxjYDUZHRGA5bGFjoPFxDW3sJdX5vG6HFfzqFSXVjHDek9V1/U19fz9tuf\nkJDgPAEAFBdX4es7Fr3ecccphCAkZDoVFVsJCJjKmTMmTMZgGlrPExnkWt//xvMdVAl/bh83zqXy\ng2HOrAy+OHKY6NjuF2GRUmK3HGHKlK95OLLL+fv7M2nSJK/GoCiuUknADaZmTePD3H/Q3taBf6Af\ni2/JZvbidk4cLOHM0YNomp2QQB8yFqcyelz8ZXeELY2ttJ7pJPO2nqd13rXrM+z2OHx9A12Kx2xu\nJSjo8gZKnc6I0ZiGyVRBSUkoc5YsY9+Wd7gxI8WlfeadqSVr8de8Wpc9efJkYiNyqas+yqi4y4+X\nlJLK0zvInBDYp15WijLSqSTgBv7+/mRPnMPxvUVcs8RRZx0Q5M/0BZOZ7mTb458VcW3W/B7rie12\nO1u2fE5MjOtD7n18DNhsneh0/pf9PDAwmZqavRgM05k1dx4v7dpGYlUdmQm9z1FTeNZEvj6cby9Y\n6HIMg8FoNPLEY3fym/99k7KTxYRFTcfXL4TzbfU0N+QxPrGJhx64R1W7KEofqH5cbnJdzvU0Humg\n9GiZy9ucPHCKjmJBzvye+7WbTCba23UuPwUAjBkzira27tYg1mG1WklLiyY4OJi7HnuSze3+bDtZ\nTpul84rS5y1WdhSfYUOLkTsee9KtU2D3V0xMDP/504d54M44ov02I1veICliN48/OJGnn3rAY11D\nFeVqoaaSdqPa2lpe/Os6YmeHkjErDYOh+x48mlXj2J4TmA918PA9j/Y6x//Ro0d57rmPSEyc43Ic\nFouFXbsO0tYWS1DQaPR6I5rWQUtLGUbjEZ599t+4/npHz6SmpiZyP9lE4e7tjDNYiDY42hDqrZIi\nqw8T5i1i4dJlhIeH9+1gKIriMQOZStotSUAIsQx4DseTxctSyl91U+Z3wHKgDbhPSnm4h30N2yQA\njvUE3vvobxSdLSRhWhRjpyQREOyolmlrbqf0cDnV+WYmJmXytRX/RnBw72MKDh48yLp1n5KU5Nqi\nJRd0dHRQVHSa8vJ67HY9BoOd1NQYfH3bWLVqAsuXL7usfHt7OwUFBTSZzQCEhIczadIkNWmcogwD\nXl1PQDi6q6wDlgBngTwhxD+llCcuKbMcSJVSjhdCzAJeBGYP9LOHorCwML519wOYzWb27d/LoXcO\n0NbeCkBQQDDXTJzJvQ/OJCys55Gll/Lx8UEI54uRf5Wfnx9TpmQwaZINTdMwGo3odDrKyg7i53fl\nVBL+/v7MmDGjz5+jKMrw5o6G4ZlAsZSyHEAIsR64FThxSZlbgb8ASCn3CSFChRAxUkrPj+rxkIiI\nCJYvvZHlS28c0H5GjRqFlK393l6v11+26LlO10ZsbN+nBFYU5erkjobhBKDikveVXT/rrUxVN2WU\nbkRFRREQAB0d/U8EF9hsGtCsulAqinLRkOwiunbt2ouvc3JyyMnJ8Vos3qbT6Vi6dC4ffFBEUtK0\nAe3LZCojOzvDaTuEoihDW25uLrm5uW7Z14AbhoUQs4G1UsplXe9/BMhLG4eFEC8C26WU73S9PwEs\n7K46aLg3DA+Gc+fO8fTTvyI8fG7X5HB9p2mdVFRsZe3a7zBmzBg3R6goijcNpGHYHdVBecA4IUSy\nEMIHWAV8+JUyHwL3wMWk0Xg1twe4W3h4OHffvYLq6jzs9r43EkspqajYz4oVM1UCUBTlMgNOAlJK\nG/AosBkoANZLKQuFEA8JIR7sKrMROC2EOAX8EXhkoJ870sybN5fFi9MoK9uNzeb6Mo9S2jlzZj+T\nJwezcuXNgxihoijDkRosNozYbDb+9rf32bhxP+HhWYSHx/VavrXVTF3dQWbPTmHNmrvx8/Prtbyi\nKMOT1weLuZNKAs6dPHmS11//O2fPdmAwxBESEt3VViCwWNpoaanHYqkmLEzjzjtvITt7hppPR1Gu\nYioJjEB2u51Tp05x6NBRTp4so66uASkhPDyE9PQUsrImMnHiRAyGIdkBTFEUN1JJQFEUZQTzdu8g\nRVEUZZhSSUBRFGUEU0lAURRlBFNJQFEUZQRTSUBRFGUEU0lAURRlBFNJQFEUZQRTSUBRFGUEU0lA\nURRlBFNJQFEUZQRTSUBRFGUEU0lAURRlBFNJQFEUZQRTSUBRFGUEU0lAURRlBFNJQFEUZQRTSUBR\nFGUEU0lAURRlBFNJQFEUZQQbUBIQQoQLITYLIU4KIT4RQoR2U2a0EGKbEKJACHFUCPHYQD5TURRF\ncZ+BPgn8CNgipUwHtgE/7qaMBvxASjkJmAN8VwgxYYCfOyTl5uZ6O4QBUfF7l4rfu4Z7/P010CRw\nK/B61+vXgZVfLSClrJFSHu563QoUAgkD/NwhabifRCp+71Lxe9dwj7+/BpoERkkpa8HxZQ+M6q2w\nECIFmArsG+DnKoqiKG5gcFZACPEpEHPpjwAJPNNNcdnLfoKAvwPf73oiUBRFUbxMSNnj97bzjYUo\nBHKklLVCiFhgu5Qyo5tyBuAjYJOU8nkn++x/QIqiKCOUlFL0ZzunTwJOfAjcB/wKuBf4Zw/lXgGO\nO0sA0P8/RFEURem7gT4JRADvAolAOXC7lLJRCBEHvCSlvEkIMRfYCRzFUV0kgf8jpfx4wNEriqIo\nAzKgJKAoiqIMb14dMTxcB5sJIZYJIU4IIYqEEE/3UOZ3QohiIcRhIcRUT8fYG2fxCyHuFEIc6fq3\nWwiR6Y04e+LK8e8qly2EsAohvu7J+Jxx8fzJEUIcEkIcE0Js93SMPXHh3AkRQnzYdd4fFULc54Uw\neySEeFkIUSuEyO+lzFC+dnuNv1/XrpTSa/9wtCX8sOv108B/d1MmFpja9ToIOAlM8GLMOuAUkAwY\ngcNfjQdYDmzoej0L2OvN49yP+GcDoV2vlw23+C8ptxVHh4SvezvuPh7/UKAASOh6H+XtuPsQ+4+B\nZy/EDTQABm/Hfkl883B0U8/v4fdD9tp1Mf4+X7venjtoOA42mwkUSynLpZRWYD2Ov+NStwJ/AZBS\n7gNChRAxDA1O45dS7pVSNnW93cvQGtznyvEH+B6OLsl1ngzOBa7EfyfwnpSyCkBKWe/hGHviSuwS\nCO56HQw0SCk1D8bYKynlbuBcL0WG8rXrNP7+XLveTgLDcbBZAlBxyftKrjzQXy1T1U0Zb3El/kt9\nG9g0qBH1jdP4hRDxwEop5Qs4xrUMJa4c/zQgQgixXQiRJ4RY7bHoeudK7OuAiUKIs8AR4Pseis1d\nhvK121cuXbsD7SLqlBpsNnwJIRYB9+N4BB1OnsNRvXjBUEsEzhiA6cBiIBD4XAjxuZTylHfDcskN\nwCEp5WIhRCrwqRBiirpmPasv1+6gJwEp5fU9/a6rgSNGfjnYrNtH967BZn8H3pBS9jQWwVOqgKRL\n3o/u+tlXyyQ6KeMtrsSPEGIK8CdgmZSyt8dnT3Ml/hnAeiGEwFEvvVwIYZVSfuihGHvjSvyVQL2U\nsgPoEELsBLJw1Md7kyux3w88CyClLBFCnAYmAPs9EuHADeVr1yV9vXa9XR10YbAZuGmwmQfkAeOE\nEMlCCB9gFY6/41IfAvcACCFmA40Xqr2GAKfxCyGSgPeA1VLKEi/E2Bun8Uspx3b9G4Pj5uGRIZIA\nwLXz55/APCGEXggRgKOBstDDcXbHldjLgesAuurS04BSj0bpnKDnp8OhfO1e0GP8/bp2vdzSHQFs\nwdHjZzMQ1vXzOOCjrtdzARuOngiHgIM4Mpw3417WFXMx8KOunz0EPHhJmXU47tyOANO9GW9f4wde\nwtGr42DXMf/C2zH39fhfUvYVhlDvoD6cP0/i6CGUD3zP2zH34dyJAz7pijsfuMPbMX8l/reAs4AF\nOIPjyWU4Xbu9xt+fa1cNFlMURRnBvF0dpCiKoniRSgKKoigjmEoCiqIoI5hKAoqiKCOYSgKKoigj\nmEoCiqIoI5hKAoqiKCOYSgKKoigj2P8HBuOPsAytenIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb7526ab588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"N = 50\n",
"N = min(N, 1000) # Prevent generation of too many numbers :)\n",
"x = np.random.rand(N)\n",
"y = np.random.rand(N)\n",
"colors = np.random.rand(N)\n",
"area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii\n",
"\n",
"plt.scatter(x, y, s=area, c=colors, alpha=0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are currently working on supporting [Jupyter's magic commands](http://ipython.readthedocs.io/en/stable/interactive/magics.html) in Stencila via a bridge to Jupyter kernels."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Metadata\n",
"\n",
"To add some metadata about the document (such as authors, title, abstract and so on), In Jupyter, select `Edit -> Edit Notebook metadata` from the top menu. Add the title and abstract as JSON strings and authors and organisations metadata as [JSON arrays](https://www.w3schools.com/js/js_json_arrays.asp). Author `affiliation` identifiers (like `university-of-earth` below) must be unique and preferably use only lowercase characters and no spaces.\n",
" \n",
"For example,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
" \"authors\": [\n",
" {\n",
" \"given-names\": \"Your first name goes here\",\n",
" \"surname\": \"Your last name goes here\",\n",
" \"email\": \"your.email@your-organisation\",\n",
" \"corresponding\": \"yes / no\",\n",
" \"affiliation\": \"university-of-earth\"\n",
" }\n",
" ],\n",
" \n",
" \"organisations\": [ \n",
" {\n",
" \"university-of-earth\": {\n",
" \"institution\": \"Your organisation name\",\n",
" \"city\": \"Your city\",\n",
" \"country\": \"Your country\" \n",
" }\n",
" ],\n",
"\n",
" \"title\": \"Your title goes here\",\n",
" \"abstract\": \"This is a paper about lots of different interesting things\",\n",
" \n",
" ```\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Citations and references \n",
"\n",
"Stencila supports Pandoc style citations and reference lists within Jupyter notebook Markdown cells. Add a `bibliography` entry to the notebook's metadata which points to a file containing your list of references e.g.\n",
"\n",
"```json\n",
"\"bibliography\": \"my-bibliography.bibtex\"\n",
"```\n",
"\n",
"Then, within Markdown cells, you can insert citations inside square brackets and separated by semicolons. Each citation is represented using the `@` symbol followed by the citation identifier from the bibliography database e.g.\n",
"\n",
"```json\n",
"[@perez2015project; @kluyver2016jupyter]\n",
"```\n",
"\n",
"The [cite2c](https://github.com/takluyver/cite2c) Jupyter extension allows for easier, \"cite-while-you-write\" insertion of citations from a Zotero library. We're hoping to support conversion of cite2cstyle citations/references in the [future](https://github.com/stencila/convert/issues/14).\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"authors": [
{
"given-names": "Aleksandra",
"surname": "Pawlik"
}
],
"bibliography": "bibliography.bibtex",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
},
"title": "Jupyter and Stencila"
},
"nbformat": 4,
"nbformat_minor": 2
}