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@bthallplz
Created April 12, 2018 23:44
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The base for an interactive Econ Notebook in Jupyter
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1c2cd66dc6cf443da2c852323f787c3b",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"interactive(children=(IntSlider(value=3, description='b', max=9, min=-3), Button(description='Run Interact', style=ButtonStyle()), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use('seaborn-darkgrid')\n",
"\n",
"# Need to label axes\n",
"\n",
"def tweak_plot(b):\n",
"\n",
" ts_length = 50\n",
" t_values = [] # Empty list\n",
"\n",
" ya_values = [] # Empty list\n",
"\n",
" yb_values = [] # Empty list\n",
"\n",
" for t in range(ts_length):\n",
" t_values.append(t)\n",
"\n",
" # Baseline\n",
" ya = t * 2.5\n",
" \n",
" # growth y = growth k\n",
" \n",
" # Production function: yt = A * K^alpha * L^(1-alpha)\n",
" ya_values.append(ya)\n",
"\n",
" # Modified\n",
" yb = t * b # Using input from slider, `b`\n",
" yb_values.append(yb)\n",
"\n",
" fig, ax = plt.subplots()\n",
" \n",
" ax.plot(t_values, ya_values, color='red', linewidth=2, linestyle=\"--\", alpha=0.6) # First plot: unlabeled, smaller linewidth, alpha reduced\n",
" ax.annotate('baseline result',\n",
" xy=(ts_length, ya), xycoords='data',\n",
" xytext=(20, 20), textcoords='offset points', fontsize=16,\n",
" arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
" \n",
" ax.plot(t_values, yb_values, color='red', linewidth=4, linestyle=\"-\", label='growth') # Second plot\n",
" ax.annotate('your result',\n",
" xy=(ts_length, yb), xycoords='data',\n",
" xytext=(20, 20), textcoords='offset points', fontsize=16,\n",
" arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
" \n",
" ax.legend(loc='upper left')\n",
" \n",
" plt.show()\n",
"\n",
"interact_manual(tweak_plot, b=3);"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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ncNRRx3DHHXfRsKH1LnnTpkcye/bjNGjQkC++KL7gjBCiOjO2bqXW1ZdR++pLYxKxabORf+MgdnzwCaETS7MwlUglVSoZV5YNGzZwYvQvSdHF2Zs3Pw4ArX+gZctWALRo0ZL16zUnnXQya9d+x88/r+fooxV+fyGmabJjx469CbioPe1nZ9cnL6/4AjBCiGrJNHH/7wXqdjgV9xsLYsIhdSw5b75H3oR7IaP874YZhpFtGMbDhmFMLPeTiaSqUsnY98BDhI5RB96xlELHKHwPPLSfPUyibxBgs/3zR+Jw7LmdZLBn1nkkYmIYNg477HD++msLq1d/ywknnEiDBg359NOPOPLI+K8u2O3/rMdd0TPYhRDJZ/t9I7UvOo9at96ELeffk7JMp5O8EaPZuWQFoVanlntfDMPIMAxjDNarRWC9jiOqkSr1zDh4ekd2rtz3LdzyevXh4IMPYd26H+jcuQuffvpxTLxZs+Z89dUXdO3anW+++ZJjj20GQP36DVmx4gMmTJjM7t27efnl/9K9e0/ASuqBQGlXaxNCVHmRCGlPPY534niM/LyYcLDlyeROn004emetPBmGYQeuBsZjrdLV1jTNn8r9xCLpqtSVcWU555zerF79NYMGXc+OHdv/dRULcO21N7J48VvceuuNvPXWG3vLm7VseTLbtv1FrVq1Oe64E/jii8/33s4+6aSTGTfudn755ecKH48QonzY1/9I1rndybx9ZEwiNtPT8Y2/h5y3llZUIm4OfARcDvQ1TfNCScTVlyz6AWzZspnffttAmzbtWLNmNXPnzuHBB0tairP81cjFEEpAxp1aqvS4g0E8sx/C88B9GIFATDjQvgO50x4m0vTIOAfvX0nGXXTRD8MwnFjLUA4F7gTmmKYZKfWJRZVSpW5TV5aMDC8vvfQCTz/9OKYJQ4bsr161ECKVOFZ/g3fIIJxrVsfEIpm1yBs3kcJLrwBb8m40rl27hmbNmv9rDguAYRgnYa0JvRVoZZrmxnjHi+pHkjGQmZlZpa6EhRBVQEEBGdPuJ332QxjhcEzYf3Z3fFOmEzm4cVJPu3XrVvr2PYdvv12Hx+MBwDAMN1at3RuwroqfTaCkoqjCJBkLIUQxjk8/IXPoQBw/xz6CjdSrh++eKfj7DWDvaxhJ9PTTT9Cnz3l7E/GXX64C+ApYD7QwTXNz0k8qKp0kYyGEiDJ8uWRMGk/63MfjxgvPOx/fpPsxDzqoXM5fWFjIM8/MZcGCt8nPz+f+++/h1VdfAquG8MtyNVxzyWxqIYQAXEvfpU6HNnETcbjRwex67iVyH32y3BIxwGuvvUKLFidht9vp0eMsNm36k+XLP8U0zZckEddskoyFECnN2LGdzIHXU/s/A7D/+UdMvODyq9m54jMC3XqUaz9M0+Sxx/6PVq1OpVevrlx55TU89thcdu/OwTCMCwzDSCvXDohKJbephRCpyTRxLZpP5ugR2P7eFhMONzmC3OmzCBZZIrc8LV++jC1bNjF37uP07duf5cuXMXXqvbhcboA+wLtAYYV0RlQ4uTIWQqQc29Yt1LryEmpfe0VMIjZtNvJvvpUdH3xSYYkYYMyY29i5cyd169YjHA7Tu3cf3n13Od988wOmaV5immb8QsiiRpArYyFE6jBN0v77PBl33YFt966YcKjZceTOmEUoupJe0oRC4Nj/P7dPP/0idevWo169esk9t6gWJBkLIVKCbcOvZI4YguvDZTEx0+kkf9ht5N8yFFyu5J00FMK1/H2cK1eQP2wkZmatfe569NHHJO+8otopUTJWSh0PLACma61nKaUOxVoFxgkEgUu11luUUpcAQ4AI8JjWem459VsIIUomHCb9iUfJmDwRIz922clgq1Oswg7RAjDJYvvjd9Kefxrbtr8BcKz+tkJve4vq5YDPjJVSGcBMYGmRzZOAOVrrM4DXgWHR/e4CugCdgNuUUnWT3mMhhCghu15HVq+z8d55e0wiNj0efBMnk/PGe0lPxABm7doYublEGjSg4MaBkojFfpXkytgPnAOMKrLtZv6Z1bcNOBloA6zSWu8CUEqtANoDi5LWWyGEKIlAAM/M6XgenIIRjC1lGujQidxpDxFpckTyzun34/zkI4IdzgC7HTOzFgU33UKk0cFQrBKcEMUdMBlrrUNASClVdFsegFLKDgzEWh2mIVZi3mML0Kh4e16vG4cjsf8x7XYbWVmehI6tzmTcqUXGXTbGF6uwX389xprvYmJm7dqEp0zFuPIqaiVrKUvTxPjqS4x58yAnB7NWOuaZZ1mxLLX/Y0ndn7f4t4QncEUT8XPA+1rrpdHnxUUZQMyKMT6fP9FTVu0Sa+VIxp1aZNwJys8nY+pk0h+ZiRGJrSjo794T35QHiTRsBLsKytDTf9g2b8L9+jzs0TWsI4ceSmHdhkRKMY4SllAsUz9F1VeW2dRPAeu11ndHP/8J9CoSbwx8Wob2hRCiRJwfr8Q7dBCOX3+JiUUOyib3vgcI9O6bvMIO+fm4330b50crIRLB9HgI9OxNsHXbpJZSFKkjoWQcvQoOaK3HFdn8GfCEUioLCGE9Lx5S9i4KIUR8Ru5uMiaMI/2ZJ+PGC8+/CN/EyZh1k/vuruO71ThXfAiGQbD96fi7nQMZGUk9h0gtB0zGSqlWwDSgCRBUSg0A6gOFSqkPort9r7W+WSk1GngH6/b03XsmcwkhRLK5lryDd8QQ7Jv+jImFGx+Cb+p0Al26Je18hi8X02vdLg6d2prgHxsJtmuf9HrGIjUZFV0IZNu23IRPKM/SUouMO7WUdNzG9u14x44ibd7LceMFV11L3tjx+11gozSM3N243lyEY/U35I+8HbNOct/YLOEz4+QXThZViqzAJYSoHkwT9/x5eO8YiW379phwqOmR+GbMJtj2tOScLxzG+dEKXO+8hVHoB7sN+4ZfCSU5GQsBkoyFENWAbfMmvKOG4V78VkzMtNspGDiYvOGjID09Keez/7Qe9+uvYtuyBYBQs2b4zz0Ps379pLQvRHGSjIUQVZdpkvb8M2SMH4std3dMOHTcCVZhhxYtk3ZK15J3cL1tJf1IvXr4+5xHuPlxyZuJLUQckoyFEFWS7ddfyBx+K66VH8bETJeL/OGjyB80BJzOpJ43dNwJOJctJdj5LAJnnJn09oWIR5KxEKJqCYdJn/MIGfdNxCiIXZwjeGobcmfMJpyMKkemiX3tGhzrvsff/wIwDCKNDibvzgmQllb29oUoIUnGQogqw/7D92QOHYjzqy9jYqYnA9/YcRRefX1SFtYw/vqLtAXzsK9bB0Do+BP/KRghiVhUMEnGQojKFwhgm/gAde6bHL+wwxmdyZ32MJHDDi/7ufx+XO+9Y9U1Dkcw09MInN0jOVfaQiRIkrEQolI5vvqCzKGDsP/wfUwsUjsL34R78V90SVImUDm++Qr3wvkYu6z1iIKt2xDo2XvvYh5CVBZJxkKIypGfT8b995D+2Oz4hR16nkvufdMwGzRI2iltmzZh7NpF+LDD8PcbkJwrbSGSQJKxEKLCOVd+aF0N/7YhJhbJrk/ufdMI9O5T9hPl5WH7exuRw5sAEDirK5H69Qm1OlVeVRJViiRjIUSFMXbvIuPuO0l/7um48cILL8Y34d6yLzkZieD87BNcb70BDgd5o8ZYk7LcbkKntC5b20KUA0nGQogK4Xrnbbwjh2DfsjkmFj7kUMxHHyW3dYcyn8e24VfSXn8V2x9/WG0feRRGYQGmzJAWVZgkYyFEuTL+/hvvmJGkvT4vJmYaBoVXX0femHHUPqQBlKFAhpG7G9cbC3F+scpqOysLf+8+1upccktaVHGSjIUQ5cM0cc97Ge/YUdh27IgJh446mtwHZxFq2y4p50p/cg62338Hh51Ap7MInNkF3O6yty1EBZBkLIRIOtuff+C9bSju996JiZl2OwWDhliFHcp66zgSsRYAMQz83c7B+fFK/H3OwzzooLK1K0QFk2QshEieSIS0Z58iY8Jd2Hy5MeHgCS3wzZhF6IQWZTqNsWM77oXzMb1e/AMuBCDcrDnhZs3L1K4QlUWSsRAiKey//IR32K24Pl4ZEzPdbvJG3k7BTbeUrfBCMIhr2RJc7y+BYAgzzY2/Ry/IyChDz4WofJKMhRBlEwqR/uhsMqbcg1FYGBMOtmlH7vRZhI86OvFzmCaONaut1bOiz59DLU/G3+tcScSiRpBkLIRImH3tGquwwzdfx8QiGV7yxo6n8Kpry1bYIRQi/anH9xZ0iBx8MP5+/Qk3PSrxNoWoYiQZCyFKz+/HM30qnocfxAiFYsKBM7uQO3UGkUMPK/u5HA5MTwamJ51A954E27VPStUmIaqSEiVjpdTxwAJgutZ6llLqUOA5wA5sBi7TWvuVUpcAQ4AI8JjWem459VsIUUkcX3xO5tBBOPS6mFikTh18E+/Df/5Fib/ba5o4vvqCSN16RI5oCoC/Tz/o008KOoga64C/XiqlMoCZwNIimycAs7XWHYANwNXR/e4CugCdgNuUUmVc004IUWXk5ZFx52iyenaNm4gLz+3HjhWr8F/wn4QTse3PP0if9RBpLz5P2muvWK8uAaY3UxKxqNFKcmXsB84BRhXZ1gm4Mfr9AmAYoIFVWutdAEqpFUB7YFGyOiuEqBzO5cvIHD4Y+8YNMbFw/Qb47n+QQM/eiZ8gLw/jrfl43v8ATBPT6yXQoZOsnCVSxgGTsdY6BISUUkU3Z2it/dHvtwCNgIbAtiL77NkuhKimjF05ZIwbQ/qLz8WNF1x8GXnjJ2Fm1UnsBJEIzk8/xvX2mxjhABgGgY5nEOjaHdLTy9BzIaqXRCdwmUW+N6Kfi/8KaxTbDwCv143DYU/opHa7jawsT0LHVmcy7tRSVcZtLFiA/dZBGJtjCzuYTZoQfuRRHGd1oXZZTpKXh+39xRAOYDQ7FseAC3A0akTlj77iVJWft6hciSbjPKVUuta6AGiMNYnrT6BXkX0aA58WP9Dn8xffVGJZWR5yyrCQfHUl404tlT1u46+/8N4xkrSFr8fETMOg4PqbyBt9p/V+bwL9NHbvwvRkgMMBGDjO6QsOB94O7cjZVVCmYhHVUUl+3tnZ8ry8pks0GS8B+gPPR78uBj4DnlBKZQEhrOfFQ5LRSSFEBTBN3C//F++do7Hl5MSEQ8cocqfPInRqm8TaD4VwrliO673FBLqcTfDMrtbmk0+x4vJ8WKSwAyZjpVQrYBrQBAgqpQYAlwBPK6VuAH4DntFaB5VSo4F3sG5P371nMpcQomqz/b6RzJFDrGUmizEdDvJvHUb+0JEJV0Gyr/sB9/x52LZZ00rsW7YQLFOPhahZSjKB60us2dPFdY2z76vAq2XvlhCiQkQipD31BBmTxmPL88WEgy1aWktZHn9CQs0b27fjXvAajrVrrNNlZ+Pv25/wsc3K0mshahxZgUuIFGX/aT2ZQwfh/OyTmJiZlkbebWMouHFg9Nlu6dk2b8Iz4wEIhTHdLgJduxPscEbC7QlRk8nfCiFSTTBI+iMzyZg6GcMfO6Ey0K49vgcfJnxkGQo7AJGGjQgf1gSzTh38PXtj1s4qU3tC1GSSjIVIIfbvVpM5ZCDO776NiUW8meTdNYHCy69KaO1n29YtuBbNx9+nP2Z2NkRnXpepZKIQKUKSsRCpoLAQz4NT8MycjhEOx4T9Xc7GN3UGkcaHlL7tggJc7y3GteJDa/nKtLcovPQKKyaJWIgSkWQsRA3n+OxTMocOxPHT+phYpG5dfJPux9//gtK/WmSaOL74HPebizByc8EwCLY7DX+PXgc+VgjxL5KMhaipfD4y7r2b9CfnYJgxi+FR2Pc8fPdMtW4pl5Jt6xbcL/0X+28bAAg3OQJ/v/5EDjm0rL0WIiVJMhaiBnIuW0rmiMHYf98YEws3bIRvynQC3c9JuH3TsGH/YyNmZib+XucSanWqLNohRBlIMhaiBjF27sA7bgxp/3shbrzgsivJu2tC6Wc2RyI4vvuW0IkngWFg1q9PwVXXET6iKaSlJaHnQqQ2ScZC1BCuRQvIHD0c27a/YmLhw5uQ++BM6z3fUrL/8hPu1+dh27SJwksvJ9SyldVms+Zl7rMQwiLJWIhqzti6lczbR+B+Y0FMzCpXr1iYAAAgAElEQVTscDN5o8dahR1K0+6uHNyLFuD4+iurrbp1MdOlupAQ5UGSsRDVlWnifulFvHfdHr+wgzqW3Bmzree5pREK4fxwGa4l72L4A+B0EDizC4HOXeRVJSHKiSRjIaoh28bfyBwxGNcH78fETKeT/MHDyR88PKHCDs5PPsL95hsAhE44Ef+5fTHr1itrl4UQ+yHJWIjqJBIhbe4cvJPuxsjPiwkHW55M7vTZhJsfV7p2g8G9V73Btqdh1z8Q7NiZ8DEqGb0WQhyAJGMhqgn7j9oq7LDqs5iYmZ5O3qixFNxwM9jtJW/U78e19D2cX3xO3vBR1nNlp5PCa29MYs+FEAciyViIqi4YxDP7ITwP3IcRCMSEA+07kDvtYSJNjyx5m6aJ49uvcS9agBF93uz4YS2hU1onq9dCiFKQZCxEFeZY/Q2ZgwfiWPtdTCySWYu88ZOsdaBLseCGbctm3K/Pwx5dHjNyyCEU9u1P5IimSeu3EKJ0JBkLURUVFJDxwH2k/9/D8Qs7dOuBb8p0Io0OLlWzzuXLcL+xECIRTI+HQM/eBFu3TahKkxAieSQZC1HFGCtXUOe663D8/FNMLFKvHr57p+Lv2z+h5ScjhxwCpkmw/en4u51T6nePhRDlQ5KxEFWE4cslY+I4HE89ETde2P8CfJPux6xX8teMbBt/w7FeEzjrbADCRx5N3h13yqtKQlQxkoyFqAJcS9/FO2II9j//iImFD26Mb+p0Al27l7g9w5eL681FOD+3Zl6HjjyaSJMjACQRC1EFSTIWohIZO7bjvfN20l75X9x4wRXXkHfX3ZiZtUrWYCSC86MVuN55C6OgEOw2Ah07E2nYKIm9FkIkW0LJWCnlBZ4F6gIu4G5gC/AIYAKrtdY3JauTQtQ4pol74et4bx+B7e+/Y8KhI5rimz6L4Gmnl7hJ+8/rcb/2KrYtWwAIH3sshX36Y9avn7RuCyHKR6JTKK8EtNa6EzAAeAiYAQzWWrcH6imleiSlh0LUMLYtm6l15SXUuu7KmERs2myEhw1n57KPS5WIARxffoFtyxYi9epRcNW1FFx7oyRiIaqJRG9T/w2cGP2+DrADOEJrvSq6bQHQBXi7bN0TogYxTdJefI6McWOw7d4VEw41O47ch2bj7XQ65OQfuL1gECMnBzM7G4DAOb0w69Uj0LGzFHQQopoxTNNM6ECl1GLgKKxk3BuYrbVuGY2dBVyjtb64+HEFBQHT4SjFcn1F2O02wuFIQsdWZzLuGuCXX7DffCO29+MXdojcMYbIyNvA5SrZuFevxvbqK2C3ExkzFhzVf/pHjfp5l0JJxu102kv/HpuoVhJ9ZnwpsFFr3V0p1QJ4FfAV2cXAenYcw+fzJ3JKALKyPOSU5IqhhpFxV2PhMOlPPErG5IkY+bFjCbY6xSrscGwzyA9Bfmi/4za2bcO9YB6OH34AINKwIQUbN9eIGdI14uedgJKMOzs7s4J6IypLor9OtwfeAdBafxud0FV09YDGwOYy9k2Ias2u15E5ZCDOL1fFxEyPh7zb76Tg2htLVtjB78e15F1cHy6DUBgzzU2g2zkE23coXWEIIUSVlGgy/gloA8xTSh0O5AIblFKna61XAucBM5PURyGql0AAz8zpeKZPjV/YoUMncqc9tPe93wMyTTyPzMT2++8ABE9tTaBn75K/7iSEqPISTcaPAXOVUsujbdyI9WrTY0opG/CZ1npJkvooRLXh+PpLMocMwvHD2phYpFZt8ibcS+F/Li3dUpaGQeC0Drg+XmEVdChpEhdCVBsJJWOttQ+4IE6oQ9m6I0Q1lZ9PxtTJpD8yEyMSOxnH36MXvvunlWzxjfx83K+/iun17l11K3Rqa0Kntk5oPWohRNVX/adgClHJnB+vxDt0EI5ff4mJRQ7Kxjd5Kv5z+x04kUYiOD//FNv7i3Fu34XpdhFo3xE8HknCQtRwkoyFSJCxexcZE8aR/uzcuPHCC/6Db8K9JZrpbNvwK2nz51nPhd0Owkcehb9ffysRCyFqPEnGQiTA9d5iq7DD5k0xsXDjQ/A9MGNvpaT9CgZxz3sZ56rPATCzsjAvvpCCps3kaliIFCLJWIhSMP7+G+/YUaS99krceMHV15E3djymt4TvhToc2HJ2gsNO4IwzCZzVFWeDOiVbgUsIUWNIMhaiJEwT9/x5eO8YiW379phwqOmR+GbMJtj2tAM2ZV//I5FatTEbNADDoLD/hdYpostaCiFSjyRjIQ7AtnkT3tuG4n4ndql1026nYOBg8oaPgvT0/bZj7NiOe9ECHKu/JXz00RTcMBAMQ5KwEEKSsRD7ZJqkPf8MGePHYsvdHRMOHXcCuTNmEWrRcv/tBIO4PliKa+l7EAyBy0n46GPANOW5sBACkGQsRFy2X38hc/ituFZ+GBMzXS7yR4wmf+Dg/VdHMk3sa9fgXvj63lvboZNa4u/dBzOrTnl1XQhRDUkyFqKocJj0OY+Qcd9EjIKCmHDwlNbkzphN+Bh1wKaM3N2kP/80BENEGjbEf94AwkceXQ6dFkJUd5KMhYiy//A9mUMH4vzqy5iY6cnAN3YchVddt//CDIWF4HZbz4Jr1cbfvSfY7VZBB5utHHsvhKjOJBkLEQjgmfEAnoemYQSDseEzOpM77WEihx2+7zZME8dXX+B+YyH+Hr0ItW4DQLDTmeXVayFEDSLJWKQ0x1dfkDlkII51P8TEIllZ+CZMxn/hxfudaGX78w/cr8/DHl0O07Fm9d5kLIQQJSHJWKSm/Hwy7ptE+pz/i1/YoVcfcic/YL0LvC95ebgXv4nzk4/BNDG9Xvw9z7UKOgghRClIMhYpx7liOZnDbsH+24aYWCS7Prn3TSPQu89+27Bt+pP0R2Zh5OeDzUawQ0f8XbvLWtJCiIRIMhYpw9iVQ8bdd5L+/DNx44UXXYLv7nsw69Q9YFuRBg0xMzOJNG6Mv2//kpVGFEKIfZBkLFKCa/FbeG8bin3L5phY+NDDyH3gIYKdz9rn8cbuXbjeeZtAj57WutN2OwU334KZ4ZWFO4QQZSbJWNRoxrZteMeMJG3+azEx0zAouOZ68u4YB15v/AZCIZwrluNa8g5GoR8A//kXWceXtBiEEEIcgCRjUTOZJu55L+MdOwrbjh0x4dDRx5D74CxCbdruswm7Xof79VexbdtmHXPc8QTO7FpuXRZCpC5JxqLGsf35B96RQ3AveTcmZtrt5N86lPyht0FaWtzjjR3bcS94Hcea7wCIZB+Ev+8Awsc2K9d+CyFSlyRjUXNEIqQ9M5eMieOw+XJjwsETWlhLWZ5w4n6bMXbvxrHmO0y3i0CXbgQ7dgKH/FURQpSfhP+FUUpdAtwGhIA7ge+A5wA7sBm4TGvtT0YnhTgQ+y8/4R16C65PPoqJmW43eSNvp+DmW+MnVdPE9tsGIk2OACDS5Aj8519IqFlzzNpZ5d11IYQgocVylVL1gHHA6UAvoC8wAZitte4AbACuTlIfhdi3UIj0mTOo0+m0uIk42KYdO5d9TMGtw+ImYtvWLaTP+T88M2dgX//jP8e1PU0SsRCiwiR6ZdwFWKK1zgVygeuVUr8CN0bjC4BhwCNl76IQ8dnXfEfm0EE4v/06JhbJ8JJ3590UXnlN/AINhYW43luM68PlEIlgetIx8nwV0GshhIiVaDJuAhhKqZeAg4HxQEaR29JbgLirIHi9bhyO/VS92Q+73UZWVuqtcCTjLsbvx3bvPdimTsEIhWLCkW7dCM/6P9IOP5yYKVqmifH5Zxivvwa7c8FlxzytI2afPjj39XpTBZOfd2pJ1XGLf0s0GRvAIUA/4HBgGWAWi5txjsPnS/wxclaWh5yc/ISPr65k3P9wrPqMzKGDsP+oY/aP1KljFXa44D/WQhxx/sycH7yPe9ECAMKHN8Hfrz+RQw+zZj5UkT9j+XmnlpKMOztb3mmv6RJNxluBj7XWIeBnpVQuEFJKpWutC4DGWJO4hEgOn4+M+yaS/vijGGbs73mFfc7Dd88UzPr1Y481zb2rZAVbt8X5xecEOp1JqNWpsnqWEKJKSLTa+bvAmUopm1LqIMALLAH6R+P9gcVJ6J8QOD94n7qd2uGZ80hMIg7Xb8Cup18k9/GnYxNxJILzoxV4HpoGe+oUezzkDx9F6JTWkoiFEFVGQlfGWus/lVKvAu8DHuAWYBXwrFLqBuA3IP5q/EKU1M6deIcOI/3F5+KGCy65nLxxEzGz6sTE7L/8hPv1edg2bQLA8e3XVgIGScJCiCon4feMtdaPAY8V2yxrBYqkcL25CMftw3Fu2RITCx/WhNxpDxE8o3NMzNiVg/uNhTi++hIAs25d/Of2JXT8/hf6EEKIyiTLCokqxfjrLzJvH4F70fyYmGkYFFx/E3mj74SMjJi449NPcC98DcMfAKeDQOcuBDqfBS5XRXRdCCESJslYVA2mifvl/+K9czS2nJyYcEgdS+70Wf/cao4nzY3hDxA64UT85/bFrFuvHDsshBDJI8lYVDrb7xvJHDEY17KlMTHT4SD/1mHkDx0Jbve/Ysb27dh//Xlvgg61aEl+Vp29y1oKIUR1IclYVJ5IhLSnniBj0nhscVa/ipzcipxpMwkfd/y/A4EArqXv4fpgKZgmeYc1sWZSG4YkYiFEtSTJWFQK+0/rraUsP/skJmampZF32xjco0cS9gWKBEwcq7/BvXA+RvRWdvCUUyE9filEIYSoLiQZi4oVDJL+yEwypk7G8MeuxhZo1x7f9JmEmx6F2+EArGRs27IZ9/x52NevByDSuDGF/QYQOaJpRfZeCCHKhSRjUWEc332Ld8ggnN99GxOLeDPJu2sChZdfFbewg+vtN7GvX4/p8RA4pxfBNu3iF4AQQohqSJKxKH+FhXgenIJn5nSMcDgm7O9yNr6pM4g0PuSfjaYJPh97Fonz9+6LWbs2/m7nxH2tSQghqjNJxqJcOT77lMxhg3AUqRW8R6RuXXyT7sff/4J/rYpl2/gb7vnzsHnccM3NYBiYBx2E/7zzK7LrQghRYSQZi/Lh85Fx792kPzknfmGHfv3xTZqCmZ29d5vhy8X15iKcqz63royz62Ls3CHvCwshajxJxiLpnMuWkjliMPbfN8bEwg0b4ZsynUD3c/7ZGIng/HglrnfewsgvALuNQMczcQzoi1kYqcCeCyFE5ZBkLJLG2LkD77gxpP3vhbjxgkuvsAo71M76Z6Npkj77YewbfgUgfOyxFPbpj1m/Pp60NChMvfq2QojUI8lYJIVr0QIyRw/Htu2vmFj48CbkPjiTYIczYg80DELHHY+Ruxv/uf2sBT6kqpIQIsVIMhZlYtu6Be/oEbjfXBgTM202Cm4YSN6oMeDxWBtDIVzL3yfirUWoTVsAgh07WYna6azIrgshRJUhyVgkxjRx/+8FvHfdgW1XnMIOzZpbhR1OPmXvNvv3a3EvfA3btr8xPemEWpwEaWngkP8NhRCpTf4VFKVm+22DVdhh+bKYmOl0kj9kBPmDh+8tXWhs24Z74es4vl8LQKRBA/z9BliJWAghhCRjUQrhMOlz55Bxz90Y+bETq4IntyJ3+mzCzZpHNwRxvfcOruXvQyiMmeYmcHYPgqd3BLu9gjsvhBBVlyRjUSL2HzWZQwbi/OLzmJiZnk7e7XdScN1N/06ydjuOdd9DKEzw1NYEevbGzKxVgb0WQojqQZKx2L9gEM+sGXim3Y8RCMSEA6d3JHfaw3sLNtg2b8L0eKzXl2w2Cs+/CMJhKW0ohBD7IclY7JPj26/JHDwQx/drYmKRzFrkjZ9E4aVXWK8i5efjfvdtnB+tJHRSSwovudza79DDKrrbQghR7ZQpGSul0oG1wARgKfAcYAc2A5dprWNr5Imqr6CAjAfuI/3/Ho5f2KFbD3xTphNpdLBVY/jTT3C/tQgjL89aR9rjsZazlPeFhRCiRMp6ZTwW2B79fgIwW2v9ilJqCnA18EgZ2xcVzPnJR3iHDsLxy88xschBB+G7dyr+PueBYWD7bYNVY3ijtexluOmR+Pv1J3Jw44ruthBCVGsJJ2Ol1LFAc+DN6KZOwI3R7xcAw5BkXG0YubvJmDSe9KeeiBsv7H8Bvkn3Y9azijYYOTvxzHoIIhGrtGHvPoROOlmuhoUQIgFluTKeBgwCroh+zihyW3oL0KgsHRMVx7X0XbwjhmD/84+YWLjRwfimTidwdg+I/FO0wcyqQ/D0DpgOJ4EuZ4PbXZFdFkKIGiWhZKyUuhz4RGv9q1Jqz+aidfKMYp/38nrdOByJvWNqt9vIyvIkdGx1Vm7j3r4d+4jh2F54Pm44fP0NRO6djKdWLTzr1mF75WUi/c6D44+3drjiUgDK6yciP+/UIuMWqSzRK+OeQFOlVC/gEMAP5Cml0rXWBUBjrElcMXy+xOd0ZWV5yMlJvSo+SR+3aeJe+Dre20dg+/vvmHDoiKb4ps8ieNrpGNt34H7qWRyrvwUg/PY7FBzSNHl92Q/5eacWGfe+ZWdnVlBvRGVJKBlrrS/c871SajywATgN6A88H/26uOzdE8lm27IZ723DcC9+MyZm2mwU3HQLeSNvB6cT15J3cC15F4IhcFm3owMdO1dCr4UQomZL5nvG44BnlVI3AL8BzySxbVFWpknai8+RMW4Mtt27YsKhZseR+9BsQiedjG3LZtLmPo5tuzVRPnRSS/y9+2Bm1anoXgshREooczLWWo8v8rFrWdsTyWfb8CuZw2/FtWJ5TMx0Oskfdhv5twzdW9ghklUHIxgk0rAh/n4DCB91dEV3WQghUoqswFWThcOkP/4IGfdNil/YodWp5M6YTfjwJrg+WErgjDOtWdFpaeTfOAgzOxtstorvtxBCpBhJxjWUfd0PZA4diPPLL2JipsdjFXa45gYcq78hY8q9GLt2QShM4Jxe1j4NGlR0l4UQImVJMq5pAgE8Dz+IZ/pUjGAwNtyxM7nTHgKHg/RHZ2P/9RcAwocdRuj4Eyq6t0IIIZBkXKM4vv6SzCEDcfzwfUwsUqs2eRPupfDcflZBh48/AtPE9Hrxn9ObUOs2snqWEEJUEknGNUF+Phn330P6Y7MxiqyStYe/Ry98908j0rAR9rVrcH60Emw2gh064u/aHTyy4IAQQlQmScbVnPOjFWQOHYR9w68xschB2eTe9wDB0zti1rXWlA43P45Al7MJtTyZSENZsVQIIaoCmSpbTRm7d+EdPpisfj3jJuLCC/7DzsXvY+TlkTF5ErZNf0YPNAj06CmJWAghqhC5Mq6GXO++jXfkUOybN8XEwo0PwXf/g5hut3XbutAPDju2P/6Q0oZCCFFFSTKuRoy//8Y79jbSXns1brzg6usouOgS3O8uxrZ1KwCh5sfh73Me5kEHVWRXhRBClIIk4+rANHG/9greMbftXaKyqFDTI/HNmA2BAOnPWauQRrIPwt+nP+FmzSu6t0IIIUpJknEVZ9v0J/YrR1DrrTiFHex2Cm6+lbwRoyE9HSNnJ66l7xE4oxPBjp3BIT9eIYSoDuRf66oqEiHt+WfIuPtObLm7Y8LB408gf+AQbL5cawlLwMyqQ97Y8eB0VnBnhRBClIUk4yrI9svPVmGHj1bExEyXy1o3ul49XB+vBMDx7deEWraydpBELIQQ1Y4k46okFCL9sf8j4/5JGIWFMeFgq1Mp7DcAx/ofYedOTE86ge49CbVoWQmdFUIIkSySjKsI+/drrcIOX38VEzMzMii48hqImDj0OjAMgm3bEejRE9ObWQm9FUIIkUySjCub34/noWl4HpoWv7BDpzMx5swh8N0PuOe/RvjwJvj79Sdy6GGV0FkhhBDlQZJxJXJ8uYrMoYNwrPshJhapnUX+oMEU3DqMrDoZBGsdhFmrFqETT5KCDkIIUcNIMq4MeXlk3DeJ9Dn/h2GaMeFAu/aEjjoa219bMXJ3Q50MsNnk2bAQQtRQkowrmHPFcjKH3YL9tw0xsUjdegQ6dsLMrIUBhA89DIKhCu+jEEKIiiXJuIIYu3LIuPtO0p9/Jm482LotoSZNwe3GrFMH/7l9CZ3QQm5JCyFECpBkXAFci9/Ce9tQ7Fs2x8TChx5GoPNZYAIOO4Ezu1qfXa6K76gQQohKkXAyVkpNATpE25gMrAKeA+zAZuAyrbU/GZ2sroxt2/COGUna/NdiYqZhUHj1dfjGjMe2Yzuupe/hP7cfZr16ldBTIYQQlSmhesZKqc7A8VrrdkB3YAYwAZitte4AbACuTlYnqx3TxP3K/6h7+ilxE3E4O5uCmwbhm/wAeL1EDjucwquulUQshBApKqFkDHwInB/9fieQAXQCFka3LQC6lKln1ZTtzz+odcn51Bp4PbadO/8VM202gieeRKBrdwgEMHJ27qMVIYQQqSSh29Ra6zCQF/14LfAW0K3IbektQKN4x3q9bhwOeyKnxW63kZXlSejYcheJYHt8DrbbR2P4fDFhs359Iu1Px1avHs7WrTH79qN2VlaJmq7S4y5HMu7UIuMWqaxME7iUUn2Aa4CzgR+LhAysKUkxfL7EHyNnZXnIyclP+PjyYv95Pd6ht2D/9OOYmOlwEDqhBaHmxxM59FAK+w0gckRTK1jCsVTVcZc3GXdqkXHvW3a2LHtb05VlAlc3YAzQXWu9SymVp5RK11oXAI2xJnHVbKEQ6Y/MImPqvfELO7RpR2HvPth//53AOb0ItmkHtkSfDAghhKipEkrGSqnawFSgi9Z6R3TzEqA/8Hz06+Kk9LCKsq/5jswhA3Gu/iYmZqZ78I2bSOGV10AgAOEwZGRUQi+FEEJUB4leGV8IHAS8rJTas+0K4Aml1A3Ab0D81S2qO78fz/QpeB6ejhGKXR0rfHBjAt16UHjZldZVcFpaxfdRCCFEtZLoBK45wJw4oa5l607V5lj1mVXY4UcdEzNdboKntiZ04kn4z+0LDllPRQghRMlIxigJn4+M+yaS/vijcQs7hA5vQrDNaQS69SDQtZtcDQshhCgVScYH4PzgfTJHDMa+8beYWCQjg+ApbQh2PZvCPv0xGzSohB4KIYSo7iQZ74ORs5OM8WNJf/G5uPGCSy6n8OLLIc1N6PgTpaCDEEKIhEkyjsP15iK8o4Zh/2trTCxyUDa7H32SYMdOFd8xIYQQNZIk4yKMrVvJvGMk7kXzY2ImED62OQWXX0WwwxkV3zkhhBA1liRjsAo7vPxfvHeOxpaTExOO1M7C37M3BTffSvgYFacBIYQQInEpn4xtv28kc+QQXO8viYmZhkGo5cnkjbidYOezwJ7YmtpCCCHE/qRuMo5ESHvqCTImjceWF1vYIXzIodYkretvwsysVfH9E0IIkTJSMhnbf1pP5tBBOD/7JCZmpqWRN2osBdfdCC5XJfROCCFEqkmtZBwMkv7ITDKmTsbwx1aPCh3RlF3/nUek6ZGV0DkhhBCpKmWSseO7b/EOGYTzu29jYqbTSeH5F+GbeB9kSqkyIYQQFavmJ+PCQjKm3U/6rBkY4XBMOHS0InfWo4RatqqEzgkhhBA1PBk7Pv2EzGGDcPy0PiZmpqWRN3g4BUNHSo1hIYQQlapGJmPDl0vGpPGkz308btx/dndyp0zHPLhxxXZMCCGEiKPGJWPn+++ROWII9j9+j4mFGx2Mb8p0At16VELPhBBCiPhqTDI2dmzHe9cdpL3837jxwt598U2fiVmrdgX3TAghhNi/6p+MTRPXGwvIHDUc29/bYsLhBg3InfkYwU5nVkLnhBBCiAOr1snYtnUL3lHDcb+1KCZmGgYFV11H3l0TwOOphN4JIYQQJVM9k7Fpkvbf58kYNwbbrtjCDqGmR5L7f48TOvmUSuicEEIIUTrVLhnbfttA5vDBuD5cFhMznU7yh44k/9ZhspSlEEKIaiPpyVgpNR1oi1UCeLDWelVSGg6Hsc18mLpjx2IU5MeEgye3Inf6bMLNmifldEIIIURFSWoyVkqdARyttW6nlGoOPAW0KWu7tl9/odbA67F/8XlMzHSnkTfmLgquu0lKHAohhKiWkr301FnAfACt9fdAHaVU2eoPFhZS+6LzcMZJxIH2Hdjx4acU3DhIErEQQohqK9m3qRsCXxb5vDW6bfeeDV6vG4ejFIlz4984fv3lX5vMWrUIT5mKcdXV1DKMMnW4OrDbbWRlpd6McBl3apFxi1SW7GRcPDMaWM+O9/L5YksX7pe3Llmt2+L8/FMA/N3PwXf/g0QaHQy7CsrQ1eojK8tDTk7sc/KaTsadWmTc+5adLdXkarpkJ+M/sa6E9zgY2FKmFm02cl5diOv9JXiaHcXuJgpS4GpYCCFE6kj2M+N3gQEASqmWwCatdW6ZW01LI3BOL2h5siRiIYQQNU5Sk7HW+mPgS6XUx8BMYGAy2xdCCCFqoqS/Z6y1Hp3sNoUQQoiaLNm3qYUQQghRSpKMhRBCiEomyVgIIYSoZJKMhRBCiEpmmKZ54L2EEEIIUW7kylgIIYSoZJKMhRBCiEomyVgIIYSoZElf9KM8KKWmA22xik4M1lqvquQulSul1PHAAmC61nqWUupQ4DnADmwGLtNal7LiRtWnlJoCdMD6/3IysIoaPm6llAd4GmgApAETgW+p4ePeQymVDqwFJgBLqeHjVkq1wvq7/VN003fAFGr4uMWBVfkrY6XUGcDRWut2wLXArEruUrlSSmVgLSW6tMjmCcBsrXUHYANwdSV0rVwppToDx0d/zt2BGaTAuIHewBf/3979vNgYxXEcf1uIiTArv8vuKztJSWKKmobJLMaPhTL5kY0fG0vkx0o0/gISsVAWTPnZrGxYEDb0mY0UEwuELORHFudMPUnd1Z3HPc/nVbfOOXdzPt3b832ec89zH0nrgG3AOZqRe8JR4ENuNyH3TOC6pJ78OkgzclsL/30xBtYDNwAkvQC6I2JWvVNqq+/ARmC8MtYDjOT2TWDDJM9pMjwAtub2J2AGDcgt6ZqkM7m7GFDFpP8AAAH2SURBVHhDA3IDRMRSYBlwKw/1UH7ufz0LsYfyc1sLnbBMPQ94Uum/z2Nf6plOe0n6CfyMiOrwjMqy1Ttg/qRPrM0k/QK+5e5e4DbQW3ruCfnhKouAfmC0IbmHgQPAUO4X/z0nXRmviYg7pBPO4zQjt7XQCVfGfz8zcQrpt+MmqeYtOn9EDAB7SAfpxuSWtBrYDFyhAbkjYifwUNKrynDxuUn7AU5J6iOddF4CplbeLzW3tdAJxfgt6Up4wgLS2WOTfMsbXQAWkjZ5FCcieoEjQJ+kzzQgd0SsyBv0kPSMtFpVfG5gEzAQEY9IRekYDcgt6aWkkdweIx3L5pSe21rrhGJ8H9gCEBHLgXFJX+ud0qQbBQZzexC4W+Nc2iIiZgNngX5JH/Nw8bmBtcBhgIiYS1rGLD63pO2SVkpaBZwn7SIvPndE7I6IQ7k9j7SL/iKF57bWOuLvMCPiNOmg9RvYL+l5zVNqm3zrwzCwBPhBWhnYQbr9ZTrwGtgl6UdNU2yLiNgHnADGKsNDpAN1ybm7gAukzVtdwEngMXCZgnNXRcQJ0i7iexSeOyK6gaukk65ppM/7KYXnttY6ohibmZmVrBOWqc3MzIrmYmxmZlYzF2MzM7OauRibmZnVzMXYzMysZi7GZmZmNXMxNjMzq5mLsZmZWc3+AIT9h7SDZfkIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5954728358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# To output an example plot\n",
"tweak_plot(2.75)"
]
}
],
"metadata": {
"kernel_info": {
"name": "python3"
},
"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": "3.6.4"
},
"nteract": {
"version": "0.8.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
channels:
- conda-forge
dependencies:
- appmode
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